{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Comparing 2 sets of samples from Gaussians\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generates 2 sets of observations\n",
    "rng = np.random.default_rng(27446968)\n",
    "samples1 = rng.normal(0, size=1000)\n",
    "samples2 = rng.normal(1, size=1000)\n",
    "\n",
    "# Compute a histogram of the sample\n",
    "bins = np.linspace(-4, 4, 30)\n",
    "histogram1, bins = np.histogram(samples1, bins=bins, density=True)\n",
    "histogram2, bins = np.histogram(samples2, bins=bins, density=True)\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.hist(samples1, bins=bins, density=True, label=\"Samples 1\")  # type: ignore[arg-type]\n",
    "plt.hist(samples2, bins=bins, density=True, label=\"Samples 2\")  # type: ignore[arg-type]\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}