{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Imshow elaborate\n",
    "\n",
    "An example demoing imshow and styling the figure.\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 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def f(x, y):\n",
    "    return (1 - x / 2 + x**5 + y**3) * np.exp(-(x**2) - y**2)\n",
    "\n",
    "\n",
    "n = 10\n",
    "x = np.linspace(-3, 3, int(3.5 * n))\n",
    "y = np.linspace(-3, 3, int(3.0 * n))\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = f(X, Y)\n",
    "\n",
    "plt.axes((0.025, 0.025, 0.95, 0.95))\n",
    "plt.imshow(Z, interpolation=\"nearest\", cmap=\"bone\", origin=\"lower\")\n",
    "plt.colorbar(shrink=0.92)\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])\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
}