{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Grid\n",
    "\n",
    "Displaying a grid on the axes in matploblib.\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 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import ticker\n",
    "\n",
    "ax = plt.axes((0.025, 0.025, 0.95, 0.95))\n",
    "\n",
    "ax.set_xlim(0, 4)\n",
    "ax.set_ylim(0, 3)\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))\n",
    "ax.xaxis.set_minor_locator(ticker.MultipleLocator(0.1))\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(1.0))\n",
    "ax.yaxis.set_minor_locator(ticker.MultipleLocator(0.1))\n",
    "ax.grid(which=\"major\", axis=\"x\", linewidth=0.75, linestyle=\"-\", color=\"0.75\")\n",
    "ax.grid(which=\"minor\", axis=\"x\", linewidth=0.25, linestyle=\"-\", color=\"0.75\")\n",
    "ax.grid(which=\"major\", axis=\"y\", linewidth=0.75, linestyle=\"-\", color=\"0.75\")\n",
    "ax.grid(which=\"minor\", axis=\"y\", linewidth=0.25, linestyle=\"-\", color=\"0.75\")\n",
    "ax.set_xticklabels([])\n",
    "ax.set_yticklabels([])\n",
    "\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
}