Colabで日本語表示.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Colabで日本語表示",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNL48DoiF0ztxQWP+/awp0V",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nagae/ICL_B_2021/blob/main/Colab%E3%81%A7%E6%97%A5%E6%9C%AC%E8%AA%9E%E8%A1%A8%E7%A4%BA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pm1ul4JGeOPr"
      },
      "source": [
        "# Colabで日本語表示\n",
        "`matplotlib` で軸のラベルやタイトルに日本語を使おうとして\n",
        "```python\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot([1, 2, 3, 4])\n",
        "plt.title(u\"->日本語のタイトルです<-\")\n",
        "plt.xlabel(u\"x軸です.\")\n",
        "plt.savefig('jp_font_tofu.png',dpi=100)\n",
        "plt.show()\n",
        "```\n",
        "などとすると，下記のように日本語文字が**豆腐**になってしまう．\n",
        "\n",
        "<img alt=\"tofu\" src=\"https://i.imgur.com/Z2OgSlT.png\" width=\"300\">\n",
        "\n",
        "これを回避するには，[`japanize-matplotlib` モジュール](https://pypi.org/project/japanize-matplotlib/1.1.2/)をインストールすればいい．仮想マシンに勝手にモジュールをインストールしていいものなんだろうかとは思うが，できてしまうのだから活用しない手は無い．\n",
        "\n",
        "モジュールをインストールするためには，コードセル内で下記のコマンドを実行する．`!` というのは Python コマンドではなく，仮想マシン上の**システムコマンド**であることを意味している．`pip install` は Python のモジュールをインストールするためのコマンドである．\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYigj-64-uRX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b15b527a-8548-4b6c-ece7-b5317d4bf933"
      },
      "source": [
        "!pip install japanize-matplotlib"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting japanize-matplotlib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/aa/85/08a4b7fe8987582d99d9bb7ad0ff1ec75439359a7f9690a0dbf2dbf98b15/japanize-matplotlib-1.1.3.tar.gz (4.1MB)\n",
            "\u001b[K     |████████████████████████████████| 4.1MB 4.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from japanize-matplotlib) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (1.19.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->japanize-matplotlib) (1.15.0)\n",
            "Building wheels for collected packages: japanize-matplotlib\n",
            "  Building wheel for japanize-matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for japanize-matplotlib: filename=japanize_matplotlib-1.1.3-cp37-none-any.whl size=4120276 sha256=96ed3018aa657389547d6f1de0a22f16faae03189707b63eb39a29d64fe64feb\n",
            "  Stored in directory: /root/.cache/pip/wheels/b7/d9/a2/f907d50b32a2d2008ce5d691d30fb6569c2c93eefcfde55202\n",
            "Successfully built japanize-matplotlib\n",
            "Installing collected packages: japanize-matplotlib\n",
            "Successfully installed japanize-matplotlib-1.1.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siMUXylvl_3d"
      },
      "source": [
        "こうしてインストールされた `japanize-matplotlib` モジュールをインポートすれば，それ以降の日本語がきれいに表示され，PDFとして保存することもできる．"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wS02CzJs1h9H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "2124d84f-440b-4aab-97ff-65b01f0dd5d4"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import japanize_matplotlib\n",
        "plt.plot([1, 2, 3, 4], label=\"ほげほげ\")\n",
        "plt.title(u\"->日本語のタイトルです<-\")\n",
        "plt.xlabel(u\"x軸です.\")\n",
        "plt.legend()\n",
        "plt.savefig('jp_font.png',dpi=100)\n",
        "plt.savefig('jp_font.pdf')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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ZpMuqcApzEYkbHy3dwu3T0ti4K48rT+jIn37RjXpR2hgr3KrGKEUkrm3fm8+EtxYz7dv1dGlRnynXHk//9o0jXValUpiLSMxyd95O28Rdb6azI6eAG4d14YZhXahdI/obY4WbwlxEYtKWXXnc8UY6cxZvpk+bhrx85SB6tk6MdFkRozAXkZji7ry+IJMJsxaTv7+IW0/vzm9P7EiNGGuMFW4KcxGJGeuyc7h1WhqfrdjKwI5NmDiqLx2b1Yt0WVGhTGFuZmOAc4E6wLfAte6eH7J8EtAdyAs+9Ki7vxneUkWkqioscl76Yg0Pv7uU6tWMe8/tza8GJsd0Y6xwKzXMzawZ0BA4wd3dzCYD5wCvh6yWDAx197zitiEicriWb97NmKmpfPvDDk45qjn3jexD60Z1Il1W1Ck1zN19K3A7gJnVBxKB9INWawQ8bWadgFRgjLvnHLwtMxsNjAZITk4+sspFJK7l7y/i6Y9X8uSHK6hXuzp//WU/zunXOm4aY4VbmY8YmNmrwGrgIyDjoMULgHHufjKQBYwrbhvu/qy7p7h7SvPmzQ+zZBGJd6mZOzj7yc949L1lnNY7iff+OIRz46jDYUUo8wFQd7/EzOoC/wQuAyaFLBsdsurrwN/CVaCIVB15BYU89t4ynvt0Fc0b1Oa5S1P4ec+WkS4rJpRlzrwfcLS7v+TuOWa2jMC0yoHldYDbgAnBg6KnA99UVMEiEp++XLWNsVNTWbMth4sHtuPWM3qQmBC/jbHCrSx75kuB68zs90AukAncGzwQ+qC7f2dmW4H5ZrYTWA9cU2EVi0hc2Z1XwIPvZPDqVz+Q3KQu/3fVII7vEv+NscLN3D0iL5ySkuILFiyIyGuLSHT4MGMzt09PZ/N/GmMdRZ1aVe9U/LIys4XunlLcMp00JCKVLntvPvfMXMQb322gW8v6/OOS4zkmuWo1xgo3hbmIVBp3Z2bqRsa/uYjdeQX84dSuXD+0C7VqVO1T8cNBYS4ilWLTzkBjrPeXbObodo14aFRfjkpqEOmy4obCXEQqlLsz+et13D9rCQVFRdxxZg+uOKEj1XUqflgpzEWkwqzdtpexU9OYt2obgzs15cFRfWjfVI2xKoLCXETCrrDIefHz1TwyZyk1q1XjgfP6cNGAdjqDswIpzEUkrJZuCjTG+n7dDk7t0YJ7z+1DUsOESJcV9xTmIhIW+fuL+PtHK/jH3BUkJtTkbxcfw4i+rbQ3XkkU5iJyxL5bt4MxU75n2eY9nNuvNXee1Ysm9WpFuqwqRWEuIoctN7+Qv8xZygufr6ZlYgIvXJ7CsO5qjBUJCnMROSxfrNzK2Klp/JCdwyWDkhl7encaqDFWxCjMRaRcduUV8MDbS/jX/HV0aFqXyaOP47hOTSNdVpWnMBeRMntv8WbueCONrN37uGZIJ246tRsJNdUYKxoozEWkVFv37GP8m4t4K3Uj3ZMa8NylKfRt26j0J0qlUZiLSIncnRnfbeDumYvYu6+QP/28G9cM6azGWFFIYS4ixdqwI5c73kjnw4wtHJMcaIzVtaUaY0UrhbmI/EhRkfN/83/gwXcyKCxy7hzRk8uO76DGWFFOYS4i/7F6615umZrK/NXZnNilGQ+c14d2TepGuiwpA4W5iLC/sIjnP1vNY+8to1aNajw0qi8XpLTVqfgxRGEuUsUt3rCLW6amkrZ+J7/o2ZIJ5/amZaIaY8UahblIFbVvfyFPfriCp+aupFHdmvz9V8dyRp8k7Y3HKIW5SBW0cO12bpmayootezjv2DaMO7MnjdUYK6YpzEWqkL379vPInKVM+mINrRvWYdIVAxh6VItIlyVhoDAXqSI+XZ7FrdPSyNyey6WD2zNmeHfq11YExIsyfSfNbAxwLlAH+Ba41t3zQ5ZfCNwMVAfmuvufKqBWETkMO3MKuO/txby2IJNOzerx2jWDGdixSaTLkjArNczNrBnQEDjB3d3MJgPnAK8Hl7cHJgADgV3AZDMb5e5TK65sESmL2embGDcjney9+Vw3tDP/87OuaowVp0oNc3ffCtwOYGb1gUQgPWSV4cBUd98ZXOcZ4ApAYS4SIVm7A42xZqVtpGerRF68fAC92zSMdFlSgco8YWZmrwK/AB4CMkIWNQU2hdzfCBR7RMXMRgOjAZKTk8tbq4iUwt2Z9s167nlrMbn5hfz5tKMYfXInalZXY6x4V+Ywd/dLzKwu8E/gMmBScNFmoGPIqknBx4rbxrPAswApKSl+GPWKSAkyt+dw2/R0PlmWRf/2jZk4qi9dWtSPdFlSScoyZ94PONrdX3L3HDNbBoQ2Mn4beN/MJrr7buBK4I2KKVdEDlZU5Lzy1VomvpOBA3ef3YvfHNeeamqMVaWUZc98KXCdmf0eyAUygXuDB0IfdPfvzOx+4BMzywc+1cFPkcqxMmsPY6em8vWa7ZzUtRn3j1RjrKqqLAdAc4Frill0Ucg6rwKvhrEuETmEgsIinvt0FX99fzl1albnkQuOZtSxbXQqfhWmMwZEYkz6+p3cMjWVRRt2cXrvJO4+pxctGqgxVlWnMBeJEXkFhfztw+U8/fEqGtetxVOXHMvpfVpFuiyJEgpzkRiwYE02Y6amsiprLxf0b8sdZ/akYd2akS5LoojCXCSK7dm3n4dnZ/Dyl2tp3bAOL185kJO7NY90WRKFFOYiUerjZVncNi2NDTtzuWxwB/582lHUU2MsKYF+MkSizI6cfCa8tYSp32TSuXk9Xr9mMCkd1BhLDk1hLhJF3knbyLgZi9iek8/vTunC74Z1UWMsKROFuUgU2LIrjztnLGL2ok30ap3IS1cOoFdrNcaSslOYi0SQu/P6wkzufWsxefuLuGV4d64+qSM11BhLyklhLhIh67JzuG16Gp8u38rADk14cFQfOjVXYyw5PApzkUpWWOS8PG8ND7+7FAMmnNOLSwapMZYcGYW5SCVasWU3t0xNY+Ha7Qzp1pz7z+tDm0Z1Il2WxAGFuUglKCgs4pmPV/LEByuoW7s6j154NCOPUWMsCR+FuUgFS8vcyZipqSzZuIsz+7Zi/Fm9aN6gdqTLkjijMBepIHkFhfz1/eU89+kqmtSrxTO/6c9pvZIiXZbEKYW5SAX4atU2xk5LY/XWvfwypR23ndFDjbGkQinMRcJod14BD81eyj+/XEu7JnV49apBnNClWaTLkipAYS4SJh8t3cLt09LYuCuPK0/oyM2ndaNuLb3FpHLoJ03kCG3fm8+EtxYz7dv1dG1Rn6nXHc+xyY0jXZZUMQpzkcPk7sxK28hdMxaxM7eAG4d14YZhXahdQ42xpPIpzEUOw+ZdedzxRjrvLd5MnzYNeeWqQfRolRjpsqQKU5iLlIO789qCddw7awn5+4u47YzuXHmCGmNJ5CnMRcroh205jJ2WyhcrtzGoYxMmjupLh2b1Il2WCKAwFylVYZEz6Ys1PPLuUqpXM+4b2ZuLBySrMZZEFYW5yCEs27ybMVNS+W7dDoZ1b8F9I3vTqqEaY0n0KVOYm9mFwE3AfmAjcLm754Qsn3vQU8a4+/xwFSlS2fL3F/HU3JU8+dFy6teuweMX9ePso1urMZZErVLD3MyaAGOAk9w918weBq4CnghZrba7D66gGkUq1ffrdnDL1FQyNu3mrKNbM/6snjStr8ZYEt1KDXN3zzazE909L+Q5uQeWm1kNoJGZvQYkAR8D4929sCIKFqkoufmFPPb+Mp7/dBXNG9TmuUtT+HnPlpEuS6RMyjTN4u55ZpYATARqAy+ELK4PzAVuBXYBzxLYc3/m4O2Y2WhgNEBycvKR1C0SVvNWbuPWaams2ZbDxQOTufWM7iQmqDGWxA5z99JXMmsLPAc84e7vlLLuGcAod//todZLSUnxBQsWlKdWkbDblVfAg+9k8H9f/UD7pnV54Lw+HN9ZjbEkOpnZQndPKW5ZWebME4BJwBXuvq6Y5UnAlcADHvjNMBz45ogqFqkEHyzZzO3T09myO4+rT+rIH39+FHVq6VR8iU1lmWY5FegB/DPkSP6HwDDgImAzgamWb8xsD/AdgakWkai0bc8+7p65mDe/38BRLRvw9G/6069do0iXJXJEyjTNUhE0zSKVzd158/sN3D1zMbvzCrjhlC5cP7QLtWroVHyJDUc0zSISDzbuzOWO6el8kLGFo9s14qFRfTkqqUGkyxIJG4W5xLWiImfy1+t44O0lFBQVcceZPbjihI5U16n4EmcU5hK31mzdy9hpqXy5KpvBnZry4Kg+tG+qxlgSnxTmEnf2Fxbx4udr+Mt7S6lZrRoPnteHXw5op1PxJa4pzCWuZGzaxS1TUvk+cyen9mjBvef2IalhQqTLEqlwCnOJC/v2F/L3j1byj49W0LBOTf528TGM6NtKe+NSZSjMJeZ9+8N2bpmayrLNezi3X2vuPKsXTerVinRZIpVKYS4xKyd/P3+Zs4wXPl9NUmICL1yewrDuaowlVZPCXGLSFyu2MnZaGj9k53DJoGTGnt6dBmqMJVWYwlxiys7cAh54ewmTv15Hh6Z1mTz6OI7r1DTSZYlEnMJcYsacRZu44410tu7ZxzVDOnHTqd1IqKnGWCKgMJcYsHXPPsa/uYi3UjfSPakBz1+WQt+2aowlEkphLlHL3Xnju/XcPXMxOfsK+dPPu3HNkM5qjCVSDIW5RKUNO3K5fXoaHy3N4pjkQGOsri3VGEukJApziSpFRc6r839g4jsZFBY5d47oyWXHd1BjLJFSKMwlaqzK2sPYaWnMX53NCV2a8sDIviQ3rRvpskRigsJcIm5/YRHPf7aax95bRq0a1XhoVF8uSGmrU/FFykFhLhG1eMMuxkz9nvT1u/hFz5ZMOLc3LRPVGEukvBTmEhH79hfy5IcreGruShrVrcnff3UsZ/RJ0t64yGFSmEulW7g20BhrxZY9nHdsG8ad2ZPGaowlckQU5lJp9u7bzyNzljLpizW0SkzgxSsGcMpRLSJdlkhcUJhLpfh0eRa3Tksjc3sulw5uz5jh3alfWz9+IuGid5NUqJ05Bdw7azGvL8ykY7N6vHbNYAZ2bBLpskTijsJcKszs9E2Mm5FO9t58rhvamf/5WVc1xhKpIApzCbstu/MY/+Yi3k7bRM9Wibx4+QB6t2kY6bJE4lqZwtzMLgRuAvYDG4HL3T0nZPmNwK+BWsAr7v5IBdQqUc7dmfbNeu55azG5+YX8+bSjGH1yJ2pWV2MskYpWapibWRNgDHCSu+ea2cPAVcATweUnABcDJwaf8qGZzXX3BRVUs0ShzO053DY9nU+WZdG/fWMmjupLlxb1I12WSJVRapi7e7aZnejueSHPyQ1ZZQTworvnA5jZC8A5gMK8Cigqcl75ai0T38nAgfFn9eTSwR2opsZYIpWqTNMs7p5nZgnARKA28ELI4qbAvJD7G4FBxW3HzEYDowGSk5MPp16JIiuz9nDLlFQWrN3OSV2bcf/IPrRrosZYIpFQ1jnztsBzwBPu/s5BizcDoWd+JAUf+wl3fxZ4FiAlJcXLXa1EhYLCIp79ZBWPf7CchBrVePj8vpzfX42xRCKpLHPmCcAk4Ap3X1fMKjOAR83sJaAIuAy4OZxFSvRIX7+TW6amsmjDLk7vncTd5/SiRQM1xhKJtLLsmZ8K9AD+GbLn9SEwDLjI3ReY2ZvAfAKfdpmsg5/xJ6+gkCc+WM4zn6yicd1aPHXJsZzep1WkyxKRIHOPzGxHSkqKL1igzI8FC9ZkM2ZqKquy9nJ+/7bccWYPGtVVYyyRymZmC909pbhlOmlISrRn334enp3By1+upXXDOrx85UBO7tY80mWJSDEU5lKsj5dlcdu0NDbszOWywR3482lHUU+NsUSilt6d8iM7cvK5563FTPtmPZ2b1+P1awaT0kGNsUSincJc/uPttI3cOSOd7TkF/O6ULvxuWBc1xhKJEQpzYcuuPO6csYjZizbRq3UiL105kF6t1RhLJJYozKswd+f1hZnc+9Zi8vYXccvw7lx9UkdqqDGWSMxRmFdR67JzuHVaGp+t2MqADo15cFRfOjdXYyyRWKUwr2IKi5yX563hodlLqWYw4ZxeXDKovRpjicQ4hXkVsmLLbsZMSeWbH3YwpFtz7j+vD20a1Yl0WSISBtEyMf0AAA10SURBVArzKqCgsIhnPl7JEx+soG7t6jx64dGMPKaNGmOJxBGFeZxLy9zJn6d8T8am3ZzZtxXjz+pF8wa1I12WiISZwjxO5RUU8tj7y3j+09U0qVeLZ37Tn9N6JUW6LBGpIArzOPTVqm2MnZbG6q17+WVKO247owcN69aMdFkiUoEU5nFkd14BE2dn8MqXP9CuSR1e+e0gTuzaLNJliUglUJjHiY8ytnD79DQ27srjyhM6cvNp3ahbS99ekapC7/YYl703nwlvLWb6t+vp2qI+U687nmOTG0e6LBGpZArzGOXuzErbyF0zFrEzt4Abh3XhhmFdqF1DjbFEqiKFeQzavCuPO95I573Fm+nTpiGvXDWIHq0SI12WiESQwjyGuDv//nod9729hPz9Rdx6end+e6IaY4mIwjxm/LAth7HTUvli5TYGdWzCg6P60rFZvUiXJSJRQmEe5QqLnBc/X80jc5ZSo1o17hvZm4sHJKsxloj8iMI8ii3bHGiM9d26HQzr3oL7RvamVUM1xhKRn1KYR6H8/UU8NXclT360nPq1a/D4Rf04++jWaowlIiVSmEeZ79ftYMyUVJZu3s1ZR7dm/Fk9aVpfjbFE5NAU5lEiN7+QR99byv9+tprmDWrz3KUp/Lxny0iXJSIxotQwN7PzgQuB49w9uZjlk4DuQF7woUfd/c1wFhnv5q3cxthpqazdlsPFA5O59YzuJCaoMZaIlF1Z9syzgOuB9BKWJwND3T2vhOVSgl15BTzwdgb/mv8D7ZvW5f+uHsTxndUYS0TKr9Qwd/ePgUMdfGsEPG1mnYBUYIy754Stwjj1wZLN3D49nS2787j6pI788edHUaeWTsUXkcMTjlMHFwDj3P1kAnvx40pa0cxGm9kCM1uQlZUVhpeOPdv27OPGf33Lb19aQMM6NZl2/QncfmZPBbmIHJEjPgDq7qND7r4O/O0Q6z4LPAuQkpLiR/rascTdefP7Ddw9czG78wr4w6lduX5oF2rV0Kn4InLkjijMzawOcBswwd3zgdOBb8JRWDzZuDOXO6an80HGFo5u14iHRvXlqKQGkS5LROLIYYW5mU0GHnT378xsKzDfzHYC64FrwllgLCsqcv719Q888HYG+4uKuOPMHlxxQkeq61R8EQmzMoe5uyeFfH1RyNePA4+Hua6Yt2brXsZOS+XLVdkM7tSUB0f1oX1TNcYSkYqhk4bCbH9hES98vpq/zFlGrerVePC8PvxyQDudii8iFUphHkYZm3Zxy5RUvs/cyak9WnDvuX1IapgQ6bJEpApQmIfBvv2F/P2jlfzjoxU0rFOTv118DCP6ttLeuIhUGoX5Efrmh+3cMiWV5Vv2MPKYNowb0ZMm9WpFuiwRqWIU5ocpJ38/f5mzjBc+X01SYgIvXJ7CsO5qjCUikaEwPwyfr9jK2GmprMvO5dfHJXPL8O40UGMsEYkghXk57Mwt4IG3lzD563V0aFqXyaOP47hOTSNdloiIwrys5izaxB1vpLN1zz6uGdKJm07tRkJN9VMRkeigMC/F1j37GP/mIt5K3Uj3pAY8f1kKfds2inRZIiI/ojAvgbvzxnfruXvmYnL2FfKnn3fj2qGdqVldjbFEJPoozIuxfkcut09PY+7SLI5JDjTG6tpSjbFEJHopzEMUFTmvzv+BB99eQpHDnSN6ctnxHdQYS0SinsI8aFXWHsZOTWP+mmxO7NKMB87rQ7smdSNdlohImVT5MN9fWMTzn63msfeWUbtGNR46vy8X9G+rU/FFJKZU6TBfvGEXY6Z+T/r6XZzWqyUTzulNi0Q1xhKR2FMlwzyvoJAnP1zB0x+vpFHdmvzjkmM5vXeS9sZFJGZVuTBfuDabMVNSWZm1l/OObcO4M3vSWI2xRCTGVZkw37tvPw+/u5SX5q2hdcM6TLpiAEOPahHpskREwqJKhPmny7O4dVoamdtzuXRwe8YM70792lVi6CJSRcR1ou3MKeDeWYt5fWEmnZrV47VrBjOwY5NIlyUiEnZxG+az0zcybsYisvfmc/3Qztz4s65qjCUicSvuwnzL7jzumrGId9I30bNVIi9ePoDebRpGuiwRkQoVN2Hu7kz9Zj0T3lpMbkEhfz7tKEaf3EmNsUSkSoiLMM/cnsNt09P5ZFkW/ds3ZuKovnRpUT/SZYmIVJqYDvOiIuefX65l4uwMAO4+uxe/Oa491dQYS0SqmFLD3MzOBy4EjnP35GKWXwjcDFQH5rr7n8JeZTFWbNnD2KmpLFi7nZO7Nef+kb1p21iNsUSkairLnnkWcD2QfvACM2sPTAAGAruAyWY2yt2nhrXKEAWFRTz7ySoef385dWpV55ELjmbUsW10Kr6IVGmlhrm7fwyUFJbDganuvjO4zjPAFUCFhPnOnAIufu5LFm/cxRl9khh/di9aNFBjLBGRI50zbwpsCrm/ESjxHHkzGw2MBkhO/smMTakS69SgT5uG3PizLgzv3arczxcRiVdHGuabgY4h95OCjxXL3Z8FngVISUnx8r6YmTHx/L7lfZqISNw70g9hvw2MNLMDF8i8EphxhNsUEZFyOqwwN7PJZtbP3TcC9wOfmNlXwOaKPPgpIiLFK/M0i7snhXx9UcjXrwKvhrkuEREpB53rLiISBxTmIiJxQGEuIhIHFOYiInFAYS4iEgfMvdzn7oTnhc2ygLWH+fRmwNYwlhNJ8TKWeBkHaCzRKF7GAUc2lvbu3ry4BREL8yNhZgvcPSXSdYRDvIwlXsYBGks0ipdxQMWNRdMsIiJxQGEuIhIHYjXMn410AWEUL2OJl3GAxhKN4mUcUEFjick5cxER+bFY3TMXEZEQCnMRkTgQ1WFuZhea2XwzW2hmfylm+Y3B5d+Z2c2RqLEsyjCOuQfdBkaizrIws/PN7DUz+6GE5Ycca7QowzgmmdmXId+Tsyu7xvII/r/PM7NPg+Oqe9DyWHqvHGocsfReGWNmX5jZt2b2gpnVOmh5eN8r7h6VN6A9sBRoCBjwb2BUyPITgHlAreDtMyAl0nWXdxzBdeZFus5yjGcIgZMeNh3OWKPldqhxBJd/CCREus4yjqUJsACoE7z/MHBjyPJYea8cchzBx2LivRL82bqP/x6XnAxcELI87O+VaN4z/8/Foj0w+meAc0OWjwBedPd8d88HXgDOiUCdpTnkOMysBtAouBfyiZlNMLPqkSq2NO7+sbuXdPZaad+zqFHKOAAaAU8HvydPHryHGE3cPRs40d1zgw/VAHJDVomJ90pp44il94q7b3X3293dzaw+kAikh6wS9vdKNId5aReLLtfFpCOotDrrA3MJXOh6KNAKuKqSagu3WPmelMUCYJy7nwxkAeMiXM8huXuemSWY2eNAHQKBfUDMfF9KGUfMvVfM7FVgNfARkBGyKOzfk2gO8838eHAHXyy6tOXR4pB1uvsOd78u+G8RMA2I2nnAUsTK96RU7j7a3dcF775OlH9PzKwtMB2Y7e7XunthyOKY+b4cahyx+F5x90sITKkcB1wWsijs35NoDvPSLhY9A7jUzGoG/9S6DHizkmssi0OOw8ySzOw2M7PgQ8OBbyq5xnCJiwt8m1md4J/wBw5YnU4Uf0/MLAGYBIx293eKWSUm3iuljSOW3itm1s/MLgNw9xxgGYGpuwPC/l6J2jD3Ei4WHTyCneTuCwj8QM4HvgRmBh+LKqWNg8Bv4/rAN2b2KYGDITF1tlu8XOA7ZBy5BLrazTezj4H+wD2Rre6QTgV6AP8M+ZTHnbH2XqGUcRBb75WlwPFmtiBYawfguYp8r+gMUBGROBC1e+YiIlJ2CnMRkTigMBcRiQMKcxGROKAwl7hkATXM7NXgv9WCjw83s8vL8PzaZtbJzFoeYp3PwliyyBGpEekCRCrI8QQ+H94XGE/g42zdgG5Ag+BH3Z4CqgPXAW2BBsA+oB/QhkB/lqeBzWaWCJzIf/ub1AZamtkNwa+z3X1SZQ1O5GAKc4lnpxFoeDQMWELg8+L/InDmXVegMYHTqOcBGe6+IRjOm4B7CYR6w+C2ahM4Sy8XyAN2Bf9NAwqD90UiRmEuccHMxhLotndXsK/HNwS6A/YC5vDfve5dQF2CDZzcfR+BPXDMrDdwgrv/ysy6Euj7scfMPnL3LIJ9QszsNmAQsNPdP6nEYYqUSGEu8eIh4G0z+x8CHerWE2hm1ApYQWCvuj+B5k31COyVP3/QNkYSaFWKuy83s5nAJ8HAByAY8v2A3wMxc3arxD+FucQFdy8ys3sI7I13JDC9cjaB08OTgEeBhQR6fyQB3YvZTE3+O62Cu79RzDpFBH4h7A++jkhU0KdZJC6YWU0C/VOuJBDc7QkE+5bgv83LsJkpwFgzO7qkFdx9JfAx8Dg/bmkqElHqzSJxwcweAta4+z/M7DFgqbs/bWavuPuvg+tsJHDAsxaBqZZT3H3NQds5CbgL6AQ85O5PF/NatYC/Ax+7+ysHLRsODHf3P4R9kCKHoDCXuHZQmId+fS/w/MFhHvK8akDtkKveHHi8OoE9+Dfc/aUKLV6kHBTmUmWYWU13L4h0HSIVQWEuIhIHdABURCQOKMxFROKAwlxEJA4ozEVE4oDCXEQkDvw/FGPavcuyoTwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}