{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Axes\n",
    "\n",
    "This example shows various axes command to position matplotlib axes.\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 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 依次创建 4 个重叠小轴域，仅展示坐标与文字，用于演示 axes() 的绝对定位效果\n",
    "\n",
    "# 在图窗左下角 10 %、10 % 处放置一个占图窗半宽半高的子图\n",
    "plt.axes((0.1, 0.1, 0.5, 0.5))\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(\n",
    "    0.1, 0.1, \"axes((0.1, 0.1, 0.5, 0.5))\", ha=\"left\", va=\"center\", size=16, alpha=0.5\n",
    ")\n",
    "\n",
    "# 在图窗左下角 20 %、20 % 处放置一个占图窗半宽半高的子图\n",
    "plt.axes((0.2, 0.2, 0.5, 0.5))\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(\n",
    "    0.1, 0.1, \"axes((0.2, 0.2, 0.5, 0.5))\", ha=\"left\", va=\"center\", size=16, alpha=0.5\n",
    ")\n",
    "\n",
    "# 在图窗左下角 30 %、30 % 处放置一个占图窗半宽半高的子图\n",
    "plt.axes((0.3, 0.3, 0.5, 0.5))\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(\n",
    "    0.1, 0.1, \"axes((0.3, 0.3, 0.5, 0.5))\", ha=\"left\", va=\"center\", size=16, alpha=0.5\n",
    ")\n",
    "\n",
    "# 在图窗左下角 40 %、40 % 处放置一个占图窗半宽半高的子图\n",
    "plt.axes((0.4, 0.4, 0.5, 0.5))\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.text(\n",
    "    0.1, 0.1, \"axes((0.4, 0.4, 0.5, 0.5))\", ha=\"left\", va=\"center\", size=16, alpha=0.5\n",
    ")\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
}