{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3cTtnEob7hrd"
   },
   "source": [
    "# matplotlib 入門 (15) nitta@tsuda.ac.jp\n",
    "\n",
    "# 15章: 図全体 (Figure) と座標系 (Axes) の関係を理解する\n",
    "\n",
    "figure を作成するのと同時に Axes を必要な個数だけ作成してしまう書き方を薦めるが、変わった Axes を作成したり、Axesを変わった配置にしたい場合は、まず figure() で、必要な大きさの Figure を作成する。\n",
    "\n",
    "<pre>\n",
    "matplotlib.figure.Figure(*args, **kwargs)\n",
    "  [Parameters]\n",
    "    figsize=(6.4, 4.8) :  (width, height) dimension in inches\n",
    "    dpi=100 : dots per inch\n",
    "  [Returns]\n",
    "    Artist: the added artist\n",
    "</pre>\n",
    "\n",
    "それから、<code>figure.add_axes(rect=[left, bottom, width, height])</code> で好きな位置に Axes を配置していく。\n",
    "\n",
    "<pre>\n",
    "matplotlib.figure.Figure.add_axes(self, *args, **kwargs)\n",
    "  [Parameters]\n",
    "    rect: [left, bottom, width, height]\n",
    "    projection: projection types \n",
    "    sharex, sharey: share x or y axis\n",
    "    label: label for the returned axes\n",
    "  [Returns]\n",
    "    axes\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4IJkWBhBEyKF"
   },
   "source": [
    "<p>\n",
    "下の実行例では、最初の Axes の追加では <code>[left, bottom, width, height] = [0.1, 0.1, 0.8, 0.8]</code>\n",
    "と指定しているので、図中の青の Axes の配置となる。\n",
    "\n",
    "2番目の Axes の追加では <code>[left, bottom, width, height] = [0.6, 0.2, 0.4, 0.3]</code> と指定しているので、図中の緑の Axes の配置となる。\n",
    "</p>\n",
    "\n",
    "<img src=\"http://nw.tsuda.ac.jp/lec/python/matplotlib/tutorial/fig/add_axes.png\" width=\"640\" /><br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uA9qMNlH8QL1"
   },
   "outputs": [],
   "source": [
    "is_colab = 'google.colab' in str(get_ipython())   # for Google Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 278
    },
    "executionInfo": {
     "elapsed": 1036,
     "status": "ok",
     "timestamp": 1652572735493,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "RmqkSqU47KvB",
    "outputId": "04a755ea-9c46-4e2a-bb4c-aa931ba8efe2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(-2, 3, 10)\n",
    "y1 = x\n",
    "y2 = x ** 2\n",
    "\n",
    "fig = plt.figure(figsize=(5, 4))\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax.scatter(x, y1)\n",
    "\n",
    "ax2 = fig.add_axes([0.6, 0.2, 0.4, 0.3])\n",
    "ax2.scatter(x, y2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w6jsPKsK8Z0t"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNe9RmwBLfwdBHkYrzhaiIj",
   "collapsed_sections": [],
   "name": "matplotlib_tutorial_15.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}