updates to notebook

Author: j-demayo

python1_2025_workshop.ipynb

@@ -299,16 +299,6 @@
     "- The print() function is used to display the result of type(greeting) on the screen."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f5e8aa5d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ç"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -716,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "id": "5af768f4",
    "metadata": {},
    "outputs": [],
@@ -753,7 +743,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.loadtxt(fname=\"inflammation-01.csv\", delimiter=\",\")"
+    "np.loadtxt(fname=\"data/inflammation-01.csv\", delimiter=\",\")"
    ]
   },
   {
@@ -784,7 +774,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = np.loadtxt(fname=\"inflammation-01.csv\", delimiter=\",\")"
+    "data = np.loadtxt(fname=\"data/inflammation-01.csv\", delimiter=\",\")"
    ]
   },
   {
@@ -909,6 +899,14 @@
     "print(\"First value in data:\", data[0,0])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "92bd2173",
+   "metadata": {},
+   "source": [
+    "Let's look at a slice of the data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1091,9 +1089,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.imshow(data, aspect='1', cmap='viridis')\n",
+    "plt.imshow(data, aspect='1', cmap='Spectral_r')\n",
     "# aspect='1' This specifies the aspect ratio of the plot,\n",
-    "# cmap='viridis' This specifies the colormap to use, viridis is a sequencial colormap. \n",
+    "# cmap='Spectral_r' This specifies the colormap to use, viridis, and Spectral_r are sequencial colormaps. \n",
     "# 'viridis' is a popular perceptually-uniform colormap that \n",
     "# ranges from dark blue through green to yellow. It's colorblind-friendly and works well for scientific visualization\n",
     "plt.colorbar(label=\"Inflammation Level\")  # Adds a color legend\n",
@@ -1108,7 +1106,9 @@
    "id": "3edcfe88",
    "metadata": {},
    "source": [
-    "Storing our average inflammations each day in a variable and then calling that variable in a matplotlib plot and show set of functions.\n"
+    "Storing our average inflammations each day in a variable and then calling that variable in a matplotlib plot and show set of functions.\n",
+    "\n",
+    "`plt.plot()` is a function from the matplotlib.pyplot module in Python, used to create line plots. It is one of the most commonly used plotting functions in Matplotlib.\n"
    ]
   },
   {
@@ -1118,11 +1118,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Calculate the average inflammation per day across all patients\n",
     "ave_inflammation = np.mean(data, axis=0)\n",
+    "\n",
+    "# Plot the average inflammation\n",
     "plt.plot(ave_inflammation)\n",
+    "\n",
+    "# Display the plot\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "896b5926",
+   "metadata": {},
+   "source": [
+    "Here we can see the generated line plot representation of the average inflammation per day across all patients"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "390a3c8b",
@@ -1148,7 +1161,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(sorted(glob.glob(\"inflammation*.csv\")))"
+    "print(sorted(glob.glob(\"data/inflammation*.csv\")))"
    ]
   },
   {
@@ -1166,10 +1179,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filenames = sorted(glob.glob(\"inflammation*.csv\"))\n",
+    "filenames = sorted(glob.glob(\"data/inflammation*.csv\"))\n",
     "\n",
     "for filename in filenames:\n",
     "    print(filename)\n",
+    "    # Use NumPy's loadtxt function to load the CSV data from the file specified by 'filename'.\n",
+    "    # The 'fname' parameter receives the filename and 'delimiter' is set to a comma since the file is CSV-formatted.\n",
     "    data = np.loadtxt(fname=filename, delimiter=\",\")\n",
     "    \n",
     "    ave_inflammation = np.mean(data, axis=0)\n",
@@ -1250,7 +1265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "id": "ec2af987",
    "metadata": {},
    "outputs": [],