initial upload

scientific-computing-2/auto_examples_jupyter_3/plot_normal_distribution.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Normal distribution: histogram and PDF\n\nExplore the normal distribution: a histogram built from samples and the\nPDF (probability density function).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\ndist = sp.stats.norm(loc=0, scale=1)  # standard normal distribution\nsample = dist.rvs(size=100000)  # \"random variate sample\"\nplt.hist(\n    sample,\n    bins=51,  # group the observations into 50 bins\n    density=True,  # normalize the frequencies\n    label=\"normalized histogram\",\n)\n\nx = np.linspace(-5, 5)  # possible values of the random variable\nplt.plot(x, dist.pdf(x), label=\"PDF\")\nplt.legend()\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}