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CodeExamples/GaussianNoisePlot.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0758d75a",
+   "metadata": {},
+   "source": [
+    "# Gaussian Noise Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0193150",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "   Plot a histogram of Gaussian noise\n",
+    "\"\"\"\n",
+    "\n",
+    "import math\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt     # Import the plot package as plt\n",
+    "\n",
+    "def main():\n",
+    "\n",
+    "    point = 10000                        # Number of points\n",
+    "    gauss_data = []                      # list to hold points\n",
+    "    mean = 20.0                          # mean of noise\n",
+    "    sd = 2.0                             # standard deviation of noise\n",
+    "\n",
+    "\n",
+    "    while point >= 0:                   # Fill list with gauss randoms\n",
+    "        gauss_data.append(random.gauss(mean,sd))\n",
+    "        point -= 1\n",
+    "\n",
+    "    #            Plot histogram with 40 bins and range of mean +/- 3sd\n",
+    "    plt.hist(gauss_data, bins = 40, range = [mean - 3*sd , mean + 3*sd])\n",
+    "    plt.title(\"Histogram of Gaussian Noise\")\n",
+    "    plt.show()\n",
+    "\n",
+    "main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}