{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第5章 Matplotlibを使いこなそう\n",
    "\n",
    "### 5-10: show()関数とオブジェクト指向スタイル"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ee8303c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト5.10.1：show()の利用\n",
    "x = [1, 2, 3]\n",
    "y = [3, 6, 9]\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'module://ipykernel.pylab.backend_inline'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams[\"backend\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TkAgg'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib tk\n",
    "rcParams['backend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'module://ipykernel.pylab.backend_inline'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.get_backend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ee86b35c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0c91N+yf/n43/u9TobOAJ4K20fyjyMHAMuG1AuU4Az9Beh3+nvWneBv1dr1WsFbRfpn57yVi/1+qVtE8rXAjMdr4n0z2yNbq/Vpmp8b21ilyD2lurWS9ofn8dA54G7qf99V8C/HaPXIPprs5huySpEMNwqkSSVIPFLUmFsbglqTAWtyQVxuKWpMJY3JJUGItbkgpjcUtSYf4XrAMBzwlM500AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ee87e9908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト5.10.2：複数のフィギュアを出力\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib._pylab_helpers import Gcf\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [3, 6, 9]\n",
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot(111)\n",
    "fig2 = plt.figure()\n",
    "ax2 = fig2.add_subplot(111)\n",
    "ax1.bar(x, y)\n",
    "ax2.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fig1:  [<matplotlib.backend_bases.FigureManagerBase object at 0x0000023EE88540B8>]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ee881f080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト5.10.3：fig1を生成\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib._pylab_helpers import Gcf\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [3, 6, 9]\n",
    "fig1 = plt.figure()\n",
    "ax1 = fig1.add_subplot(111)\n",
    "ax1.bar(x, y)\n",
    "print(\"fig1: \", Gcf.get_all_fig_managers())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fig1 & fig2:  [<matplotlib.backend_bases.FigureManagerBase object at 0x0000023EE88FF5F8>]\n"
     ]
    },
    {
     "data": {
      "image/png": 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0c91N+yf/n43/u9TobOAJ4K20fyjyMHAMuG1AuU4Az9Beh3+nvWneBv1dr1WsFbRfpn57yVi/1+qVtE8rXAjMdr4n0z2yNbq/Vpmp8b21ilyD2lurWS9ofn8dA54G7qf99V8C/HaPXIPprs5huySpEMNwqkSSVIPFLUmFsbglqTAWtyQVxuKWpMJY3JJUGItbkgpjcUtSYf4XrAMBzwlM500AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ee8955860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト5.10.5：fig2を生成\n",
    "fig2 = plt.figure()\n",
    "ax2 = fig2.add_subplot(111)\n",
    "ax2.scatter(x, y)\n",
    "print(\"fig1 & fig2: \", Gcf.get_all_fig_managers())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after show():  []\n"
     ]
    }
   ],
   "source": [
    "# リスト5.10.7：show()実行\n",
    "plt.show()\n",
    "print(\"after show(): \", Gcf.get_all_fig_managers())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23ee89d7b38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ee874fac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト5.10.9：MATLAB-styleでグラフを描画する\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [3, 6, 9]\n",
    "plt.bar(x, y)\n",
    "plt.title(\"Sample Bar Chart\")\n",
    "plt.xlabel(\"X\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23ee8a3d6a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト5.10.10：OOP-styleでグラフを描画する\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [3, 6, 9]\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(x, y)\n",
    "ax.set_title(\"Sample Bar Chart\")\n",
    "ax.set_xlabel(\"X\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}