Merged content into README.md

Author: slott56

README 2.md

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 # Python-Object-Oriented-Programming---4th-edition
 Code Repository for Python Object-Oriented Programming - 4th edition, Published by Packt
+
+The case study relies on a number of external packages. 
+It's often best to start with a tool like `conda`
+to build virtual environments and download packages. 
+This can also be done with other virtual environment managers.
+
+The following steps will help you build a working Python environment. Later, we'll add the additional
+packages used by the case study.
+
+1. Get **miniconda**. Find it here: https://docs.conda.io/en/latest/miniconda.html.
+
+2. Install **miniconda**. Find the instructions here: https://conda.io/projects/conda/en/latest/user-guide/install/index.html.
+   For the most part, the instructions can be summarized as "double-click the installer."
+   There are potential complications, so it can help to read through the the instructions.
+
+3. Use the **conda** tool to create a virtual environment that has Python. We'll add the required packages for this case study.
+
+   ```sh
+   % conda create -n CaseStudy python=3.9
+   % conda activate CaseStudy
+   ```
+
+4. Now that this is available, you can run Python. Try the following:
+
+    ```sh
+    % python
+    >>> print("Hello, world!")
+    Hello, world!
+    >>> exit()
+   ```
+   
+## Installing Other Components
+
+Now that you have a working Python environment, we can add some of packages we'll be using.
+
+```sh
+    % conda install bs4 pytest pillow
+```
+
+This will ask if you want to proceed. The answer is "y", for "yes." It will then download
+and install the four packages listed above, plus the packages they depend on.
+
+Not every package can be installed by **conda**, so PIP is sometimes needed. Specifically,
+we want to automate our testing with the `tox` tool, which isn't easily installed by **conda**.
+
+```sh
+    % python -m pip install tox
+```
+
+The file `environment.yml` has the exported environment used to produce this example.
+
+Once PIP has been run, **conda** can lose track of the extra installations. To make
+changes, it's often helpful to create a new conda environment with the packages available
+from **conda**, and then add PIP packages.
+
+Your versions may be slightly newer than the ones used by the author.
+
+## Multiple Python Version Setup
+
+The full test suite requires multiple versions of Python and the tox utility.
+
+There is some complexity when using Windows.
+See https://tox.readthedocs.io/en/latest/developers.html?highlight=windows#multiple-python-versions-on-windows
+
+The easiest way to do this is to create an additional conda environment,
+
+```sh
+    conda create --name=tox-py38 python=3.8
+```
+
+This environment will have the needed Python run-time.
+
+For Windows, only, edit the `python3.8.bat` file to point to
+this environment's executables. Generally, the name supplied will
+be correct.
+
+## Running the Test Suite
+
+The test suite requires tox and Python 3.8 (see above for additional installs.)
+
+Use the following command to run all of the tests.
+
+```sh
+    % make test
+```
+
+To run tests for a specific chapter, you can change the
+working director and run `tox`. Here's an exception for
+Chapter 2.
+
+```sh
+    % (cd ch_02; tox)
+```
+
+This will change to the chapter 2 directory, `ch_02`,
+and run `tox` in that directory.
+
+For Windows, use `cmd`:
+
+```cmd
+    C:\path\to\repo> cmd /c "CHDIR ch_02&&tox"
+```
+
+
+## An Integrated Development Environment
+
+You can edit Python code with any text editor. It can be easier to use
+a sophisticated IDE, but some developers are happy with simple text editors.
+There's no "best" IDE for Python. While the author uses PyCharm and Komodo Edit, some people
+prefer VS Code, or Spyder.
+
+Good UML diagrams can be created with the http://draw.io diagram editor. This creates
+`drawio` text files that can saved as part of a project. It can export PNG files for publication.
+This is very easy to install and use.
+
+Another  choice is to use plantuml. See https://plantuml.com. This can be incorporated
+into a markdown processing plug-in used by the PyCharm IDE.
+The plugin depends on **graphviz**, making the installation fairly complex.
+
+- Add the Markdown tool to PyCharm.
+
+- In the preferences for Markdown, install and enable PlantUML.
+
+- Use **conda** to install `graphviz` as well as installing the `plantuml-markdown` tools.
+  The `markdown_py` application can create an HTML draft of a Markdown doc. 
+  It needs to be installed separately, if this is needed.
+
+- Update the OS environment settings to set the `GRAPHVIZ_DOT` environment variable to name the conda virtual environment.
+  where `graphviz` was installed.
+  The macOS and Linux users should update their `~/.zshrc` or `~/.bashrc` file, depending on which shell is in use.
+  Windows users should set their system environment variables.
+
+- It may be necessary to create a `plantuml` shell script in `/usr/local/bin`. 
+  See https://github.com/mikitex70/plantuml-markdown for details on installation.
+
+The `plantuml` can be used to tranform UML files to PNG images.
+
+The Project Structure
+=====================
+
+Each chapter's code is in a separate directory, `ch_01`, `ch_02`, etc.
+
+Within the chapter, there's some combination of `docs`, `src`, and `tests` folders.
+There will also be a `pyproject.toml` file with parameters used to control tools
+like **tox**.
+
+Chapters and Case Study Content
+===============================
+
+1.  Object-Oriented Design.
+    Creating the 4+1 views of the problem domain.
+
+2.  Objects in Python.
+    Core data model of samples and training data.
+
+3.  When Objects Are Alike.
+    Algorithm Alternatives for k-NN -- euclidean, manhattan, chebyshev, minkowski
+
+4.  Expecting the Unexpected.
+    Central authentication and authorization for a web service.
+
+5.  When to Use Object-Oriented Programming.
+    Input validations for training data as well as requests.
+    Properties. Context Managers.
+
+6.  Abstract Base Classes (abc’s) and Operator Overloading.
+    Filtering and subsetting the training data to create test sets. Shuffling. Sorting. Random Selection. Filters.
+
+7.  Python Data Structures.
+    The ``@dataclass`` definitions and ``NamedTuple`` implementation choices.
+
+8.  Functional Techniques.
+    The essential k-NN algorithm as a functional design.
+    Computing test results for different K values and distance algorithms.
+
+9.  Strings and Serialization.
+    JSON serialization and deserialization of training data, requests and responses.
+
+10. The Iterator Pattern.
+    Revisiting the k-NN design to permit future flexibility.
+
+11. Common Design Patterns. (No case study.)
+
+12. Advanced Design Patterns. (No case study.)
+
+13. Testing Object-Oriented Programs.
+    Using Test-Driven Develoment on a small ciphering algorithm.
+    
+14. Concurrency.
+    Compressing image files using Run-Length Encoding.