{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Temporary Test Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randn(100)\n",
    "y = np.random.randn(100) + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9c27be1c90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}