{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i6yO30PVE8fQ"
   },
   "source": [
    "# matplotlib tutorial (1)  nitta@tsuda.ac.jp\n",
    "\n",
    "matplotlib is a very flexible system, and there are many ways to write it to achive a certain function. This is a useful feature at first glance, but it seems to be one of the reasons why matplotlib is confusing for beginners.\n",
    "\n",
    "Therefore, at least for beginners, we recommend that you follow the rules in this article and start using matplotlib.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b8QgF5H0M8rg"
   },
   "source": [
    "<h3><font color=\"red\"><b>Rule [1]:</b>  First, create the required number of coordinate systems (Axes) at once.</font></h3>\n",
    "\n",
    "Firt, get the required number of coordinate systems (Axes) <code>ax</code> with the following code.<br />\n",
    "<code><font color=\"blue\">fig, ax = plt.subplots(<i>rows</i>, <i>cols</i>, figsize=(<i>width</i> * <i>cols</i>, <i>height</i> * <i>rows</i>))</font></code> <br />\n",
    "\n",
    "Parameters:<br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; rows : number of rows of the coordinate system<br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; cols : number of columns of the coordinate system<br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; width : width of each coordinate system  ($\\times 100$ px)<br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; height : height of each coordinate system ($\\times 100$ px)<br />\n",
    "Returns: <br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; fig : Figure<br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp; ax : Axes <br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1 element &nbsp;&nbsp; if rows == 1 and cols ==1 <br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1-dimensional array &nbsp;&nbsp; else if rows == 1 and cols ==1 <br />\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2-dimensional array &nbsp;&nbsp; else if rows &gt; 1 and cols &gt; 1 <br />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XvfRP8lUCEWb"
   },
   "source": [
    "<h3><font color=\"red\"><b>Rul [2]:</b>  Drawing is done for each coordinate system (Axes). That is, use the drawing functions and axis setting functions of the Axes object.</font></h3>\n",
    "\n",
    "<ul>\n",
    "<li>Axes.plot()</li>\n",
    "<li>Axes.scatter()</li>\n",
    "<li>Axes.text()</li>\n",
    "<li>Axes.set_title()</li>\n",
    "<li>Axes.set_xlabel()</li>\n",
    "<li>Axes.set_ylabel()</li>\n",
    "<li>Axes.set_xticks()</li>\n",
    "<li>Axes.set_yticks()</li>\n",
    "<li>Axes.set_xticklabels()</li>\n",
    "<li>Axes.set_yticklabels()</li>\n",
    "<li>Axes.set_xlim()</li>\n",
    "<li>Axes.set_ylim()</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4TgD53DZP_p"
   },
   "source": [
    "<h3><font color=\"red\"><b>Rule [3]:</b> To display the contents drawn in each coordinate system (Axes) on the screen, use <code>plt.show()</code> only once at the end. </font></h3>\n",
    "\n",
    "To display the drawing contents on the screen, do it for <code>plt</code>, not for each coordinate system.<br />\n",
    "<code>plt.show()</code> <br />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bh1TuBkJcJiN"
   },
   "source": [
    "<h3><font color=\"red\"><b>Rule [4]:</b> To save the contents drawn in each coordinate system (Axes) to a file, specify the resolution and use <code>plt.savefig()</code>.</font></h3>\n",
    "\n",
    "To save the drawing contents to a file, do it for <code>plt</code> instead of each coordinate system.\n",
    "The default resolution is low, so specify a resolution such as <code>dpi=6400</code>.<br />\n",
    "<code>plt.savefig(<i>filepath</i>,dpi=<i>dpi</i>)</code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x4OlLfueFGO2"
   },
   "source": [
    "# Chapter 1: Understanding the Coordinate System (Axes Object)\n",
    "\n",
    "## 1-1: When ax is one coordinate system\n",
    "\n",
    "If <code>rows == 1 and cols == 1</code> in the function call of <code>plt.subplots()</code>, one coordinate system (Axes object) is returned to <code>ax</code>, so drawing commands are issued for this <code>ax</code>. \n",
    "\n",
    "<code><font color=\"blue\">fig, ax = plt.subplots(<b>1</b>, <b>1</b>, ...)</font></code> <br />\n",
    "<br /><img src=\"https://nw.tsuda.ac.jp/lec/python/matplotlib/tutorial/fig/subplots_1_1.png\"><br />\n",
    "\n",
    "Finally, <code>plt.show()</code> draws it inline in the jupyter notebook page.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "executionInfo": {
     "elapsed": 592,
     "status": "ok",
     "timestamp": 1648474514444,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "IKDLzi6RE59P",
    "outputId": "c2bc000f-202f-46a0-dc3c-eac80638877a"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample code 1-1\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n",
    "xs = [ 1, 2, 3, 4, 5 ]\n",
    "ys = [ 2, 5, 3, 7, 4 ]\n",
    "\n",
    "ax.plot(xs, ys)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n9L9uk7kJoyA"
   },
   "source": [
    "THe first line, '% matplotlib inline', is the instruction you specify when displaying the graph in the current page of jupyter notebook, which specifies \"display inline in the web page\".\n",
    "\n",
    "Draw a broken line graph with <code>ax.plot(an_array_of_x_coordinates, an_array_of_y_coordinates)</code>.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hAfNGGvaWsJ8"
   },
   "source": [
    "## 1-2: When <code>ax</code> is a 1-dimensional array of coordinate systems\n",
    "\n",
    "if only one of <i>rows</i> or <i>cols</i> is 1 in the function call of <code>plt.subplots()</code>, 1-dimensional array of Axes objects is returned and assigned to <code>ax</code>.\n",
    "\n",
    "\n",
    "if <code>rows==1 and cols &gt; 1</code>, horizontally aligned coordinate systems are returned (sample code 1-2-1)。\n",
    "\n",
    "<code><font color=\"blue\">fig, ax = plt.subplots(<b>1</b>, <b>2</b>, ...)</font></code> <br />\n",
    "<br /><img src=\"https://nw.tsuda.ac.jp/lec/python/matplotlib/tutorial/fig/subplots_1_2.png\"><br />\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "executionInfo": {
     "elapsed": 505,
     "status": "ok",
     "timestamp": 1648474514945,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "FpyPIeArJa-W",
    "outputId": "8a966e23-e700-40cc-ba21-2fdd4b389afe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wxBjNyk0xndLvHA5LD87P1dGGNr1YUm06BwAAwG8YmAD63fHTHdpeVq9b89PldIT/w33OZf64oZqckaTHth5RZ3eP6RwAAAC/YGAC6Hcv7KyWbUsL8yPv9thPWZalFQW5qjndofUfeUznAAAA+AUDE0C/8vlsbSz26OrsIcoYHGc6x6hrspM1c9Rg/eqtI+rwchUTAACEPgYmgH71fnmjqps6tGhGpukU4yzL0r/dkKf6lk794YNjpnMAAAAuGQMTQL9aX1Qld6xLBeOGmk4JCjOyBmt2boqe3F6uM2e7TOcAAABcEgYmgH7T1OZV4YGTunlqmmJcTtM5QWN5Qa5Ot3fpt+8eNZ0CAABwSRiYAPrNy7tq5O3xReR7X57PpPQk3TB+qH79zlE1tXlN5wAAAFw0BiaAfmHbtjYUeTQ53a3Lhieazgk6ywvy1Obt1pNvl5tOAQAAuGgMTAD9Yk91s0pPtvBwn6+QOzRB3548Qn94/5jqWs6azgEAALgoDEwA/WJDUZViXU4tmDzcdErQWjYvV109tp54i6uYAAAgNDEwAQRcW2e3/rL7uL45abgSYlymc4JWVnK8bp2eruc+rFLN6Q7TOQAAAH3GwAQQcK/vO6E2bw8P9+mF++bmSJIe23LYcAkAAEDfMTABBNzGIo9Gp8Qrf+Qg0ylBLy0pVt+7PFMvlFTraEOb6RwAAIA+YWACCKgjdS0qrmzSovwMWZZlOick3H39GLmcltZsKTOdAgAA0CcMTAABtbG4WlEOS7dMSzedEjJSE2L0w6tG6S97jqu0tsV0DgAAQK8xMAEEjLfbpxd3VmvuZalKSYg2nRNS7pw9WgMHROnhzaWmUwAAAHqNgQkgYN48dFKNbV4t5r0v+ywpboB+fO0obTpwUnurT5vOAQAA6BUGJoCAWV/k0bDEGM3KTTGdEpJ+fM0oDYpzaVUhP4sJAABCAwMTQEAcP92ht8vqdWt+upwOHu5zMRJiXLpz9hi9XVavj46eMp0DAABwQQxMAAHxws5q+Wzp1um89+Wl+Kcrs5SSEK1Vm0pl27bpHAAAgPNiYALwO5/P1sZij67OHqLMIXGmc0Ja7ACn7r0+Wx8dO6V3DjeYzgEAADgvBiYAv3u/vFHVTR1axMN9/GLxzAylJcVqVSFXMQEAQHBjYALwu/VFVXLHulQwbqjplLAQHeXUT+bmaG91szYfPGk6BwAA4CsxMAH4VVObV4UHTurmqWmKcTlN54SNW6alaVRyvB7eXCafj6uYAAAgODEwAfjVy7tq5O3xadEMHu7jT1FOh5bNy9Gh2ha9uve46RwAAIBzYmAC8BvbtrWhyKPJ6W5dNjzRdE7YWTBphMYOS9CaLYfV3eMznQMAAPAPGJgA/GZPdbNKT7ZoIVcvA8LhsPTg/FwdbWjTSyU1pnMAAAD+AQMTgN9sKPIo1uXUjZNHmE4JW/PHDdXkdLce3XpYnd09pnMAAAC+gIEJwC/avd16dc9xfWPicCXEuEznhC3LsrS8IE81pzu0/iOP6RwAAIAvYGAC8IvX955Qa2e3Fs/k9thAuzYnWTNHDdav3jqiDi9XMQEAQPBgYALwiw1FHo1OiVf+yEGmU8KeZVlaUZCn+pZO/fGDY6ZzAAAAPsPABHDJjtS1qriySYvyM2RZlumciDBz1GDNyk3R2u3lajnbZToHAABAEgMTgB9sLPYoymHplmnpplMiyoqCXJ1u79Jv3j1qOgUAAEASAxPAJfJ2+/RSSbXmXpaqlIRo0zkRZVJ6km4YP1S/eeeomtq8pnMAAAAYmAAuzZuHTqqh1avFMzJNp0SkB+fnqdXbraferjCdAgAAwMAEcGnWF3k0LDFGs3JTTKdEpLxhCbpx8gj9/v2jqms5azoHAABEOAYmgIt2/HSH3i6r16356XI6eLiPKcvm5aqrx9YTb5WbTgEAABGOgQngor2ws1o+W7p1Ou99adKo5Hh9d1q6nvuwSjWnO0znAACACMbABHBRfD5bG4s9ujp7iDKHxJnOiXj3z8uRJD2+9bDhEgAAEMkYmAAuyvvljapu6tDCfK5eBoO0pFh97/JMPb+zWkcb2kznAACACMXABHBRNhR75I516Ybxw0yn4BN3Xz9GLqelR7eUmU4BAAARioEJoM+a2rzatL9WN09NU4zLaToHn0hNiNEPrsrSn/ccV2lti+kcAAAQgRiYAPrsld018vb4tGgGt8cGmztnjdHAAVF6eHOp6RQAABCBGJgA+sS2bW0o8mhSuluXDU80nYMvGRQ/QD++dpQ2HTipfdXNpnMAAECE6dXAtCzrmGVZ+yzL2m1ZVnGgowAEr73VzTpU28LVyyD242tGKSnOpVWFXMUMZ5ybw9u6feuUtSZLjp85lLUmS+v2rTOdBAC9EtWHz73etu2GgJUACAnrizyKcTm0YPII0yn4CgkxLt05e4z+378dUtGxU5qRNdh0EgKHc3MYWrdvnZa+ulTtXe2SpMrmSi19dakkacnEJSbTAOCCuEUWkqT9Nc1atalU3T0+0ykIYu3ebr2657i+OXGEEmNcpnNwHj+4MkspCdH65aZS2bZtOgdAH6zcuvKzcfmp9q52rdy60lARAPRebwemLanQsqydlmUtPdcnWJa11LKsYsuyiuvr6/1XiIDz+WyteH6PfvXWEf2SW+pwHq/vPaHWzm4tnsntscEudoBT916frY+OntK7R7jAFaY4N4epquaqPn0cAIJJbwfmNbZtT5P0dUn3WJY168ufYNv207Zt59u2nZ+SkuLXSATW6/tO6FBti8YNT9RT2yv0xv5a00kIUhuLPRqdEq/8kYNMp6AXFs/MUFpSrFZxFTNccW4OU5nuzD59HACCSa8Gpm3bNZ/8b52klyXNDGQU+k93j0+PbClT7tCBeunuqzQ5I0krnt+jivpW02kIMkfqWlV0rEmL8jNkWZbpHPRCdJRT98/N1p7qZm0+eNJ0DvyMc3P4emjuQ4pzxX3hY3GuOD009yFDRQDQexccmJZlxVuWlfDp70sqkLQ/0GHoHy/vqlFFfZsenJ+nGJdTTyyZpgFRDt357E61e7tN5yGIbCz2KMph6ZZp6aZT0AffmZauUcnxenhzmXw+rmKGC87N4W3JxCV6esHTGukeKUuWRrpH6ukFT/OAHwAhoTdXMIdKeteyrD2SPpL0um3bbwQ2C/3B2+3To1sPa2KaWzeMHypJSkuK1WOLp+pIXav+/cV93FYHSX//e+WlkmrNvSxVKQnRpnPQB1FOh5bNy9Gh2ha9tu+E6Rz4D+fmMLdk4hIdW3ZMvp/6dGzZMcYlgJBxwYFp23aFbduTP/ltvG3b3J8RJjYUVam6qUPLC3K/cMvjNTnJWl6Qp7/sOa4/vH/MXCCCxpuHTqqh1ct7X4aoBZNGKG9ogtZsLuNJ0WGCczMAIFjxNiUR6mxXjx5/84hmZA3S7Nx/fPDDXbPHaN5lqfrF6x9rZ+UpA4UIJhuKPBqWGKNZOTwkJBQ5HJYeLMhVRUObXiqpMZ0DAADCGAMzQj3zQaXqWjq1oiDvnA9scTgsrV44RWmDYnX3uhLVt3QaqEQwONHcoe1l9fru9HRFOflXRqgqGDdUk9LdenTrYXV295jOAQAAYYrvFiNQa2e31m4v17U5ybp89JCv/Dx3rEtrl0xXc0eX7vtTCbfWRagXiqvls6WF+dweG8osy9LygjzVnO7QhiKP6RwAABCmGJgR6HfvHtWpNq+WF+Rd8HPHjUjUQzdN1I6KU/plYWk/1CGY+Hy2NhR7dNWYIcocEnfhL0BQm5WTrJlZg/X4m0fU4eUqJgAA8D8GZoRpbu/S0+9UaP64oZqSkdSrr/nO9HQtuTxTT22v0Bv7awNciGDyQUWjqps6eLhPmLAsSytuyFN9S6ee2XHMdA4AAAhDDMwI89Tb5Wrt7Nbygtw+fd3/XTBOkzOStOL5Paqobw1QHYLN+iKP3LEu3TB+mOkU+MnMUYM1KzdFa7eVq+Vsl+kcAAAQZhiYEaS+pVO/e++YvjVphMYOS+zT10ZHOfXEkmlyOS3d9WyJ2r3dAapEsGhq82rT/lrdPDVNMS6n6Rz40fL5uWpq79Jv3z1mOgUAAIQZBmYEWbutXN4enx6Yl3NRX5+WFKvHbpuqsroW/cdL+2Tbtp8LEUxe2V0jb4+P22PD0OSMJBWMG6pfv1Oh0+1e0zkAACCMMDAjxInmDj37YaW+My1No1MGXvTrXJuTouXzc/Xn3cf1xw8q/ViIYGLbtjYUeTQp3a3LhvftajdCw4MFuWr1duuptytMpwAAgDDCwIwQj209Itu2dd+ci7t6+Xl3X5eteZel6uevHdTOylN+qEOw2VvdrEO1LVy9DGNjhyVqwaQR+v17x1TXctZ0DgAACBMMzAhQ2dim54s9um1mpjIGX/pbTTgcllYvnKIRSbG6e12J6ls6/VCJYLK+yKMYl0MLJo8wnYIAemB+rrw9Pj3xVrnpFAAAECYYmBHg0S2HFeW0dO/12X57TXesS0/ePl2n27t0359K1N3j89trw6x2b7de3XNc35w4QokxLtM5CKBRyfH67rR0PfdhlY6f7jCdAwAAwgADM8wdPtmil3fX6AdXZik1Mcavrz1uRKL+++aJ2lFxSr8sLPXra8Oc1/eeUGtnN7fHRoj7P3no1+NvHjZcAgAAwgEDM8w9vLlM8QOidMfsMQF5/e9MT9eSyzP11PYKvbG/NiDHQP/aWOzR6OR4zcgaZDoF/SAtKVa3zczQxuJqHWtoM50DAABCHAMzjO2vadbf9tfqn68ZpcHxAwJ2nP+7YJwmZyRpxfN7VFHfGrDjIPCO1LWq6FiTFs7IkGVZpnPQT+6Zky2X09KaLWWmUwAAQIhjYIax1YWlcse69C/XjgrocaKjnHpiyTS5nJbuerZE7d7ugB4PgfN8sUdRDku3TEsznYJ+lJoQox9claU/7zmuspMtpnMAAEAIY2CGqZ2Vp/RWab3umD26Xx7UkpYUq8dum6qyuhb9x0v7ZNt2wI8J/+rq8enFkmrNGZuq1AT//rwugt+ds8YofkCUHi7kKiYAALh4DMwwtWpTmZIHRuuHV2X12zGvzUnR8vm5+vPu4/rjB5X9dlz4x9aP69TQ6tXimTzcJxINih+gH18zSm8cqNW+6mbTOQAAIEQxMMPQe0ca9EFFo+65foziBkT167Hvvi5b8y5L1S9eP6idlU39emxcmg1FVRqaGK1ZOSmmU2DIv1w7SklxLq3ezFOhAQDAxWFghhnbtvXLTaUa4Y7R9y7P7PfjOxyWVi+couHuWN2zrkQNrZ393oC+O9Hcoe1l9bp1eoainPxrIVIlxLh05+wx2lZar+Jjp0znAACAEMR3kmFm68d12u05rfvm5ig6ymmkwR3r0trbp6mp3av7ntul7h6fkQ703gvF1fLZ0sJ8bo+NdP905UglD4zWLzeV8rPUAACgzxiYYcTns7V6c5lGDonTd6enG20ZP8Kth26eqA8qGvXLQm63C2Y+n62NOz26aswQZQ6JM50Dw+IGROne68fow6On9O6RBtM5AAAgxDAww8hf95/QxyfO6IF5uXIFwW2O352eru9dnqmntlfojf21pnPwFT6oaJTnVIcWzeDqJf7utsszNcIdo1WFZVzFBAAAfWJ+hcAvunt8enhzmXJSB2rB5BGmcz7z0wXjNDndrRXP71FFfavpHJzD+iKP3LEu3TB+mOkUBInoKKfun5ujPZ7T2vJxnekcAAAQQhiYYeKV3cdVUd+m5QW5cjos0zmfiY5y6onbp8vltHTXsyVq93abTsLnNLV5tWl/rW6emqYYl5mf2UVw+s70dGUNidPqwlL5fFzFBAAAvcPADAPebp/WbCnTxDR3UF6FSkuK1WO3TVVZXYv+46V93HIXRF7ZXSNvj4+H++AfuJwOPTA/V4dqW/T6vhOmcwAAQIhgYIaBDcUeVTd1aHlBriwreK5eft61OSlaPj9Xf959XH/8oNJ0DvT3t7TZUOTRpHS3xo1INJ2DILRg0gjlDU3QI5vLeBo0AADoFQZmiDvb1aNfvXlY+SMHaXZuiumc87r7umzNuyxVv3j9oHZWNpnOiXh7q5t1qLaFq5f4Sg6HpQfm56qioU0v7Xn0ai4AACAASURBVKoxnQMAAEIAAzPEPbujUifPdGrFDXlBe/XyUw6HpdULp2i4O1b3rCtRQ2un6aSItqHYoxiXQzdOCZ6HQiH43DB+qCalu/XolsPq7O4xnQMAAIIcAzOEtXZ264lt5bo2J1lXjB5iOqdX3LEurb19mpravbrvuV3cdmdIu7dbf9l9XN+YOFyJMS7TOQhilmVpeUGeak53aGORx3QOAAAIcgzMEPa7d4/qVJtXywvyTKf0yfgRbj1080R9UNGoVYVlpnMi0l/31aq1s1uLZ2SaTkEImJWTrBlZg/T4m0fU4eUqJgAA+GoMzBDV3N6lp9+p0LzLhmpKRpLpnD777vR0fe/yTD25vVybDtSazok4G4qqNDo5XjOyBplOQQiwLEsrCvJU19KpZ3YcM50DAACCGAMzRD39TrlaO7u1vCDXdMpF++mCcZqc7taKjXtUUd9qOidilNe3quhYkxbOyAj6n9tF8Lh89BBdm5Ostdv+/u8eAACAc2FghqCG1k797r1j+takEbpseOi+vUR0lFNP3D5dUU5Ldz1bonYv37T2h41FHjkdlm6ZlmY6BSFmRUGemtq79Nt3j5pOAQAAQYqBGYKeeKtcZ7t6tGxejumUS5aWFKtHF09VWV2L/uOlfbJt23RSWOvq8enFkmrNHZuq1IQY0zkIMZMzkjR/3FD979sVOt3uNZ0DAACCEAMzxJxo7tCzH1bqO9PSNSZloOkcv5iVm6IH5+Xqz7uP648fVJrOCWtbP65TQ6tXi2fy3pe4OMsLctXq7dZTb1eYTgEAAEGIgRliHn/ziGzb1v1zQ//q5efdc3225o5N1S9eP6idlU2mc8LWhqIqDU2M1qycFNMpCFFjhyVqwaQR+v17x1TfwnvZAgCAL2JghpCqxnZtLPJo8YxMZQyOM53jVw6HpYcXTtFwd6zuWVeihla+cfW3E80d2l5Wr1unZyjKyT/6uHjL5uXI2+PTE9uOmE4BAABBhu8yQ8iarWVyOizdOyfbdEpAuONcWnv7NDW1e3Xfc7vU3eMznRRWXiiuls+WFuZzeywuzeiUgfrOtDSt21Gl46c7TOcAAIAgwsAMEUfqWvTKrhr94KosDU0M34ezjB/h1kM3T9QHFY1aVVhmOids+Hy2Nu706KoxQ5Q5JLyufsOM++fmyJatx9/kKiYAAPj/MTBDxMObyxTrcurO2WNMpwTcd6en63uXZ+rJ7eXadKDWdE5Y+KCiUZ5THVo0g6uX8I/0QXH63sxMPV/sUWVjm+kcAAAQJBiYIWB/TbP+uq9WP75mlAbHDzCd0y9+umCcJqe7tWLjHh1t4JvXS7WhyCN3rEs3jB9mOgVh5J7rsxXltLRmy2HTKQAAIEgwMEPAw5vL5I516V9mjTad0m+io5x64vbpinJauvOZnWr3dptOClmn271640CtbpoyQjEup+kchJHUxBj94MosvbK7RmUnW0znAACAIMDADHI7K5v05qE63TF7tBJjXKZz+lVaUqweXTxVZXUt+s+X9sm2bdNJIemVXTXydvu0aEam6RSEoTtnj1H8gCg9spmfmQYAAAzMoLdqU6mSBw7QD6/KMp1ixKzcFD04L1ev7D6uZ3ZUms4JObZta32RRxPT3Bo3ItF0DsLQoPgB+vE1o/S3/bXaX9NsOgcAABjGwAxi7x9p0AcVjbr7umzFDYgynWPMPddna+7YVP38tYPaWdlkOiek7Ktp1qHaFh7ug4D68bWj5I51aVVhqekUAABgGAMzSNm2rV8Wlmq4O0bfuzyyb210OCw9vHCKhrtjdc+6EjW0dppOChnrizyKcTl045QRplMQxhJjXLpz9hhtK61X8bFTpnMAAIBBDMwg9eahOu2qOq375+bwYBZJ7jiX1t4+TU3tXt333C519/hMJwW9dm+3Xt19XN+YODzifn4X/e8HV41U8sBorSos5eelAQCIYAzMIOTz2VpdWKaRQ+L03enppnOCxvgRbv3ipgn6oKJRqwp5oMiF/HVfrVo6u7WYh/ugH8QNiNI914/RjopTeu9Io+kcAABgCAMzCP1tf60OnjijZfNy5HLyf9Hn3ZqfodtmZurJ7eXadKDWdE5Q21jk0ejkeM3IGmQ6BRHie5dnaoQ7Rr/kKiYAABGr1+vFsiynZVm7LMt6LZBBka7HZ+vhzaXKSR2oGyenmc4JSj9dME6T0t1asXGPjja0mc4JSuX1rfro2CktnJEhy7JM5yBCREc5df/cHO3xnNbWj+tM50QEzs0AgN5Yt2+dstZkyfEzh7LWZGndvnUBO1ZfLo/9RNLHgQrB3728q0bl9W16cH6unA6GwbnEuJx6Ysk0RTkt3fnMTrV7u00nBZ2NRR45HZZumcZ/pED/+s70dGUNidOqwlL5fFzF7AecmwEA57Vu3zotfXWpKpsrZctWZXOllr66NGAjs1cD07KsdEnflPTrgFRAkuTt9unRrWWakJaor00YZjonqKUPitOji6eqrK5F//nSPm7H+5yuHp9eLKnW3LGpSk2IMZ2DCONyOrRsXq4O1bbo9X0nTOeENc7NAIDeWLl1pdq72r/wsfaudq3cujIgx+vtFcw1kv6PpK98dKdlWUstyyq2LKu4vr7eL3GRZmOxR55THVpekMdtjb0wKzdFD87L1Su7j+uZHZWmc4LG1o/r1NDq5b0vYcyCySOUO3SgnvuwynRKuOPcDAC4oKrmc5+Pv+rjl+qCA9OyrG9JqrNte+f5Ps+27adt2863bTs/JSXFb4GR4mxXjx5/87DyRw7Sdbn89eute67P1tyxqfr5awdVUtVkOicobCz2aGhitGbz9xEMcTosPfX9fP3uRzNMp4Qtzs0AgN7KdJ/7HQW+6uOXqjdXMK+WdKNlWcckrZc0x7KsZwNSE8Ge3VGpk2c6uXrZRw6HpYcXTtFwd6zufrZEDa2dppOMqm0+q22ldfru9HRF8QRiGDQqOZ738A0szs0AgF55aO5DinPFfeFjca44PTT3oYAc74Lfgdq2/R+2bafbtp0labGkN23bvj0gNRGqrbNba7eV65rsZF05ZojpnJDjjnNp7e3T1NTu1X3P7VJ3z1feLRb2Xtjpkc+WFuZzeywQzjg3AwB6a8nEJXp6wdMa6R4pS5ZGukfq6QVPa8nEJQE5XlRAXhV98rv3jqqxzavlBbmmU0LW+BFu/eKmCfq3F/ZqVWGZ/v3rY00n9Tufz9aGYo+uHD1EI4fEm84BAABAkFgycUnABuWX9ekeOtu2t9m2/a1AxUSi5vYuPfV2heZdlqqpmYNM54S0W/MzdNvMTD25vVybDtSazul3Oyoa5TnVocUzuXoJRBLOzQCAYMIPaRn2v+9UqOVstx6cn2c6JSz8dME4TUp3a8XGPTra0GY6p1+tL/IoMSZKN4znLW4AAABgBgPToIbWTv32vaP61qThGjci0XROWIhxOfXEkmlyOi3d+cxOtXu7TSf1i9PtXr1xoFY3T03jwSoAAAAwhoFp0Npt5Trb1aMH5vOzl/6UPihOjy2eqrK6Fv3nS/tk27bppIB7ZVeNvN0+LZoRmMdNAwAAAL3BwDSktvmsntlRqVumpWtMykDTOWFnVm6KHpiXq1d2H9czOypN5wSUbdtaX+TRxDQ3V8IBAABgFAPTkMffPCzbtvWTuTmmU8LWvddna87YVP38tYMqqWoynRMw+2qadai2RYtm8HAfAAAAmMXANKCqsV0bijxaPCNTGYPjLvwFuCgOh6VHFk7RMHeM7n62RA2tnaaTAmJ9kUcxLodunDLCdAoAAAAiHAPTgDVby+R0WLp3TrbplLDnjnNp7ZLpamr36v4/7VJ3j890kl+1e7v16u7j+sbE4UqMcZnOAQAAQIRjYPazI3UtemVXjf7pypEamhhjOiciTEhz6xc3TdD75Y1avbnMdI5f/XVfrVo6u7Uon9tjAQAAYB4Ds589svmwYl1O3XUdVy/70635GbptZqbWbitX4YFa0zl+s7HIo1HJ8Zo5arDpFAAAAICB2Z/21zTr9X0n9ONrRmlw/ADTORHnpwvGaVK6W8s37tHRhjbTOZesvL5VHx07pYX5GbIsy3QOAAAAwMDsT49sLlNiTJR+fO1o0ykRKcbl1BNLpsnptHTnMzvV7u02nXRJNhZ75HRY+s70NNMpAAAAgCQGZr8pqWrS1kN1umP2GLljeRiLKemD4vTY4qkqq2vRf760T7Ztm066KF09Pr24s1pzxqYqNYGf5QUAAEBwYGD2k1WbSpU8cIB+dHWW6ZSINys3RQ/My9Uru4/rmR2VpnMuypuH6tTQ6tVi3vsSAAAAQYSB2Q/eP9Kg98sbddd12YobEGU6B5LuvT5bc8am6uevHVRJVZPpnD7bUORRakK0ZuemmE4BAAAAPsPADDDbtrWqsFTD3TFacnmm6Rx8wuGw9MjCKRrmjtHdz5aoobXTdFKv1Taf1bbSOt2an64oJ/8IAwAAIHjw3WmAvVVap5Kq07pvTo5iXE7TOfgcd5xLa5dMV1O7V/f/aZe6e3ymk3rlhZ0e+WxpIe99CQAAgCDDwAwgn8/Wqk1lyhwcp1vz003n4BwmpLn185sm6P3yRq3eXGY654J8Plsbi6t15eghGjkk3nQOAAAA8AUMzAB640CtDp44o2XzcuTiVsagtTA/Q7fNzNDabeUqPFBrOue8dlQ0qupUuxbP5OolAAAAgg+rJ0B6fLYe3lymnNSB+vYU3qcw2P10wXhNTHNr+cY9OtrQZjrnK60v8igxJko3jB9mOgUAAAD4BwzMAHllV42O1LXqwfm5cjos0zm4gBiXU2tvnyan09Jdz+5Uu7fbdNI/ON3u1RsHanXz1DR+nhcAAABBiYEZAN5un9ZsLdP4EYlcaQoh6YPi9NjiqSo92aKVL++Xbdumk77glV018nb7tJD3vgQAAECQYmAGwPM7PfKc6tCKgjw5uHoZUmblpuiBebl6eVeNnt1RaTrnM7Zta32RRxPT3Bo/wm06BwAAADgnBqafne3q0eNbj2j6yEG6Li/FdA4uwr3XZ2vO2FT912sHVVLVZDpHkrSvplmHalu4egkAAICgxsD0s2d3VKr2zFmtKMiTZXH1MhQ5HJYeWThFw9wxuvvZEjW0dppO0oYij2JcDt04eYTpFAAAAOArMTD9qK2zW2u3levq7CG6cswQ0zm4BO44l9Yuma6mdq/u/9Mudff4jLV0eHv0l93H9Y0Jw+WOdRnrAAAAAC6EgelHv3//mBrbvFpRkGc6BX4wIc2tn980Qe+XN2r15jJjHX/dd0Itnd1axO2xAAAACHIMTD9p7ujSU9vLNe+yVE3NHGQ6B36yMD9Dt83M0Npt5So8UGukYUORR6OS4zVz1GAjxwcAAAB6i4HpJ//7doXOnO3WA/NzTafAz366YLwmprm1fOMeHW1o69djV9S36qNjp7QwP4Of6QUAAEDQY2D6QWNrp3773lF9c9Jw3kIiDMW4nFp7+zQ5nZbuenan2r3d/XbsDcUeOR2WvjM9rd+OCQAAAFwsBqYfrN1WrrNdPXpgHlcvw1X6oDg9uniqSk+2aOXL+2XbdsCP2dXj04s7azRnbKpSE2ICfjwAAADgUjEwL1Ft81n9cUelbpmWruzUgaZzEECzc1O0bG6uXt5Vo2d3VAb8eG8eqlNDa6cW83AfAAAAhAgG5iX61VuHZdu2fjI3x3QK+sF9c7J1fV6K/uu1gyqpagrosTYUeZSaEK3ZuSkBPQ4AAADgLwzMS+A51a71H3m0aEaGMgbHmc5BP3A4LD2yaIqGuWN0z7oSNbZ2BuQ4tc1nta20TrfmpyvKyT+mAAAACA1853oJ1mw5LKfD0n1zuHoZSZLiBmjtkuk61ebVfX/ape4en9+P8cJOj3z2398mBQAAAAgVDMyLdKSuRS/vqtb3rxipoYk8gCXSTEhz6+c3TdD75Y1avbnMr6/t89naWFytK0cP0cgh8X59bQAAACCQGJgX6ZEthxXrcuqu68aYToEhC/MzdNvMDK3dVq7CA7V+e90dFY2qOtWuRTzcBwAAACGGgXkRDhxv1ut7T+ifrxmlIQOjTefAoJ8uGK+JaW4t37hHRxva/PKaG4o9SoyJ0tcmDPPL6wEAAAD9hYF5ER4uLFNiTJT+5drRplNgWIzLqbW3T5PTaemuZ3eq3dt9Sa/X3N6lv+2v1U1T0xTjcvqpEgAAAOgfDMw+Kqlq0tZDdbpj9hi5Y12mcxAE0gfF6dHFU1V6skUrX94v27Yv+rVe2V0jb7eP22MBAAAQkhiYfbS6sFTJAwfoh1dlmU5BEJmdm6Jlc3P18q4aPbuj8qJew7ZtrS/yaEJaosaPcPu5EAAAAAg8BmYfvF/eoPeONOqu67IVHx1lOgdB5r452bo+L0X/9dpBlVQ19fnr99ec0ccnzmjRjMwA1AEAAACBx8DsJdu2tWpTqYYlxmjJ5QwA/COHw9Iji6ZoaGKM7llXosbWzj59/fqiKkVHOXTj5BEBKgQAAAACi4HZS9tK61VSdVr3zc3m4Sv4SklxA/Tk7dPV2ObV/et3qcfXu5/H7PD26C+7j+ubE4fzs70AAAAIWQzMXvD5bK0qLFXm4DgtzOfhKzi/CWlu/eLbE/TekUatLizt1df8dd8JtXR283AfAAAAhDQGZi+8caBWB46f0bJ5OXI5+UuGC1s4I0OLZ2ToiW3lKjxQe8HP31Ds0ajkeM0cNbgf6gAAAIDAYC1dQI/P1sOby5SdOlDfnpJmOgch5P+5cbwmprm1fOMeHW1o+8rPq6hv1UdHT2lhfoYsy+rHQgAAAMC/GJgX8OfdNTpS16oH5+fK6eCbf/RejMupJ5ZMk9Np6a5nd6rd233Oz9tQ7JHTYek70/kPGAAAAAhtDMzz6Orxac2Wwxo/IlFfGz/MdA5CUMbgOD26eKpKT7Zo5cv7ZdtffOhPV49PL+6s0ZyxqUpNiDFUCQAAAPgHA/M8NhZ7VHWqXcsLcuXg6iUu0uzcFC2bm6uXd9Xo2R2VX/i1Nw/VqaG1U4t4eBQAAADCAAPzK5zt6tHjW49oWmaSrs9LNZ2DEHffnGxdn5ei/3rtoEqqmj77+MYij1ITonVdXorBOgAAAMA/LjgwLcuKsSzrI8uy9liWdcCyrJ/1R5hp6z6sUu2Zs1pxQx4PXsElczgsPbJoioYmxuiedSVqbO1UbfNZvVVap+9OT1cUTycG0AeRem4GAAS/3nxX2ylpjm3bkyVNkfQ1y7KuCGyWWW2d3XrirSO6OnuIrhqTbDoHYSIpboCevH26Gtu8un/9Lm0s9shni/dWBXAxIu7cDATSun3rlLUmS46fOZS1Jkvr9q0znQSErKgLfYL996eStH7yh65PfrO/+itC3+/fP6bGNq+WF+SZTkGYmZDm1i++PUH/58W92lFxSleMHqys5HjTWQBCTCSem4FAWbdvnZa+ulTtXe2SpMrmSi19dakkacnEJSbTgJDUq/vyLMtyWpa1W1KdpM22bX8Y2Cxzmju69NT2cs0dm6ppmYNM5yAMLZyRocUzMtTjs7V4RqbpHAAhKpLOzUAgrdy68rNx+an2rnat3LrSUBEQ2no1MG3b7rFte4qkdEkzLcua8OXPsSxrqWVZxZZlFdfX1/u7s9/8+p0KnTnbrQcLck2nIIz97Nvj9fsfzdCNk0eYTgEQoiLp3AwEUlVzVZ8+DuD8+vRkEdu2T0t6S9LXzvFrT9u2nW/bdn5KSmg+EbOxtVO/ffeovjlxuMaPcJvOQRiLjnLqurxU3v4GwCUL93MzEGiZ7nPfTfRVHwdwfr15imyKZVlJn/x+rKT5kg4FOsyEJ7eXq6OrRw/M5+olACB4RdK5GQi0h+Y+pDhX3Bc+FueK00NzHzJUBIS2Cz7kR9JwSX+wLMupvw/SjbZtvxbYrP538sxZ/fGDSt08NV3ZqQNN5wAAcD4RcW4G+sOnD/JZuXWlqpqrlOnO1ENzH+IBP8BF6s1TZPdKmtoPLUY9/uZh9fhsLZuXYzoFAIDzipRzM9BflkxcwqAE/IR3d5fkOdWuDUUeLZqRoYzBcRf+AgAAAADAP2BgSnp062E5LEv3zeHqJQAAAABcrIgfmEfqWvVSSbW+f8VIDXPHmM4BAAAAgJAV8QNzzZYyxbicuvO6MaZTAAAAACCkRfTAPHj8jF7be0L/fPUoJQ+MNp0DAAAAACEtogfmw5tLlRgTpX+dNdp0CgAAAACEvIgdmCVVTdrycZ3umD1G7liX6RwAAAAACHkROzAfLizTkPgB+uFVWaZTAAAAACAsROTA/KC8Ue8eadBd141RfHSU6RwAAAAACAsRNzBt29aqwlINS4zR7VeMNJ0DAAAAAGEj4gbmtrJ67axs0r1zshXjcprOAQAAAICwEVED07ZtrS4sVcbgWC3MzzCdAwAAAABhJaIG5hv7a7W/5oyWzc3VgKiI+lMHAAAAgICLmJXV47O1enOZxqTE66apaaZzAAAAACDsRMzA/MueGh2pa9WD8/PkdFimcwAAAAAg7ETEwOzq8emRzYc1bniivj5hmOkcAAAAAAhLETEwny+uVtWpdq24IVcOrl4CAAAAQECE/cA829Wjx988rKmZSbo+L9V0DgAAAACErbAfmM99WKUTzWf1bwV5siyuXgIAAABAoIT1wGz3duuJbUd01Zghuio72XQOAAAAAIS1sB6Yv3vvmBpavVpekGc6BQAAAADCXtgOzOaOLj21vVxzxqZq+shBpnMAAAAAIOyF7cD8zTsVOnO2W8sLck2nAAAAAEBECMuB2djaqd+8e1TfnDhc40e4TecAAAAAQEQIy4H51NsV6ujq0QPzc0ynAAAAAEDECLuBefLMWf3h/WO6aWqaslMTTOcAAAAAQMQIu4H5qzePqMdna9lcfvYSAAAAAPpTWA1Mz6l2rS+q0qIZGcocEmc6BwAAAAAiSlgNzMe2HpZlWbp3TrbpFAAAAACIOGEzMMvrW/ViSbW+f8VIDXfHms4BAAAAgIgTNgPzkc1linE5ddd1Y0ynAAAAAEBECouB+fGJM3pt7wn96OosJQ+MNp0DAAAAABEpLAbm6sIyJcREaem1XL0EAAAAAFNCfmDuqmrSlo9P6o5Zo+WOc5nOAQAAAICIFfIDc3VhmQbHD9CPrh5lOgUAAAAAIlpID8wPyhv17pEG3X3dGMVHR5nOAQAAAICIFrID07ZtrS4s1dDEaN1+xUjTOQAAAAAQ8UJ2YG4rq1dxZZPum5OjGJfTdA4AAAAARLyQHJifXr1MHxSrhfkZpnMAAAAAAArRgbnpQK3215zRsnm5GhAVkn8KAAAAABB2Qm6d9fhsrS4s05iUeN08Nc10DgAAAADgEyE3MP+yp0aH61r14Pw8OR2W6RwAAAAAwCdCamB29fi0ZsthXTY8UV+fMMx0DgAAAADgc0JqYL6ws1qVje1aUZArB1cvAQAAACCohMzAPNvVo8e2HtbUzCTNGZtqOgcAAAAA8CUhMzD/9FGVTjSf1YqCPFkWVy8BAAAAINiExMBs93brf946oitHD9HV2cmmcwAAAAAA5xASA/P37x9TQ6tXK27IM50CAAAAAPgKQT8wmzu69NT2Cs0Zm6rpIweZzgEAAAAAfIWgH5i/efeomju69OD8XNMpAAAAAIDzCOqBearNq9+8U6FvTBymCWlu0zkAAAAAgPO44MC0LCvDsqy3LMs6aFnWAcuyftIfYZL05PZydXT1cPUSAIDPMXFuXrdvnbLWZMnxM4ey1mRp3b51gT4kACAERfXic7olLbdtu8SyrARJOy3L2mzb9sFAhtWdOas/vH9MN01JU3ZqQiAPBQBAqOnXc/O6feu09NWlau9qlyRVNldq6atLJUlLJi4JxCEBACHqglcwbds+Ydt2ySe/3yLpY0lpgQ771VtH1OOztWweVy8BAPi8/j43r9y68rNx+an2rnat3LoyUIcEAISoPv0MpmVZWZKmSvrwHL+21LKsYsuyiuvr6y85bGB0lP7pyixlDom75NcCACBc9ce5uaq5qk8fBwBErl4PTMuyBkp6UdIy27bPfPnXbdt+2rbtfNu2/7927i7UsrqM4/j3hx6qSRmhGWpwnPEyLHuZRCYmRBoKe1Ev8kKYLIOIXqCiCyEvjIK5laguZNDASsswjWkYI0EhushQs8YywgsjRRgzGosisJ4u9hqbObP3OWucff5rLf1+YDNr77VY58ezZq/nPPusvS7ZunXrGQe74Yo3c9OVF53xfiRJeqVq1Zt3bN5xWq9Lkl69eg2YSVaYNbA7quqejY0kSZLW07I379+7n00rJ19RtGllE/v37t/IHytJmqA+d5ENcBvwRFXdvPGRJEnSWlr35n0X7+PAlQfYuXknIezcvJMDVx7wBj+SpFP0uYvsHuA64EiSx7rXbqyqwxsXS5IkraF5b9538T4HSknSutYdMKvqF0AaZJEkST3YmyVJY3Vad5GVJEmSJGkRB0xJkiRJ0lI4YEqSJEmSlsIBU5IkSZK0FA6YkiRJkqSlcMCUJEmSJC2FA6YkSZIkaSkcMCVJkiRJS+GAKUmSJElailTV8neaPAf8aQm72gL8ZQn7ac3cbZm7valmN3dby8q9s6q2LmE/r2r2ZnM3Zu72pprd3G1teG/ekAFzWZI8XFWXDJ3jdJm7LXO3N9Xs5m5rqrm1tqkeV3O3Ze72pprd3G21yO0lspIkSZKkpXDAlCRJkiQtxdgHzANDB3iZzN2WudubanZztzXV3FrbVI+rudsyd3tTzW7utjY896i/gylJkiRJmo6x/wVTkiRJkjQRgw+YSb6d5GiSxxesT5JvJHkyyW+T7GqdcZ4euS9PcizJY93jptYZ50lyQZIHk/w+ye+SfGHONqOrec/co6t5ktcm+VWS33S5vzpnm9ckuaur90NJLmyf9JRMfXJfn+S5E+r9ySGyzpPkrCS/TnJozrrR1fu4DCsqcQAABENJREFUdXKPud5PJTnS5Xp4zvrRnVO0NntzW/bmtuzNw7A3tzVob66qQR/AZcAu4PEF6z8I3AcE2A08NHTmnrkvBw4NnXNOrm3Arm75XOCPwEVjr3nP3KOreVfDc7rlFeAhYPeqbT4L3NItXwvcNZHc1wPfGjrrgvxfAu6c9/9hjPXumXvM9X4K2LLG+tGdU3yse0ztzW1z25vb5rY3D5Pf3tw292C9efC/YFbVz4G/rrHJ1cB3auaXwHlJtrVJt1iP3KNUVc9W1aPd8t+BJ4DzV202upr3zD06XQ3/0T1d6R6rv/h8NXB7t3w3sDdJGkWcq2fuUUqyHfgQcOuCTUZXb+iVe8pGd07R2uzNbdmb27I3t2dvHqUNO6cMPmD2cD7w5xOeP80ETl6dd3eXMdyX5C1Dh1mtu/zgncw+ATvRqGu+Rm4YYc27SyseA44C91fVwnpX1YvAMeANbVOeqkdugI90l1XcneSCxhEX+TpwA/DfBetHWW/Wzw3jrDfMfsH5WZJHknxqzvpRn1P0skz5mI6uT5zI3tyGvbk5e3N7g/XmKQyYU/UosLOq3g58E/jxwHlOkuQc4EfAF6vqhaHz9LVO7lHWvKr+U1XvALYDlyZ569CZ+uiR+yfAhVX1NuB+/v/J42CSfBg4WlWPDJ3ldPTMPbp6n+A9VbUL+ADwuSSXDR1IWmCUfeI4e3M79uZ27M2DGaw3T2HAfAY48dOA7d1ro1ZVLxy/jKGqDgMrSbYMHAuAJCvMGsEdVXXPnE1GWfP1co+55gBV9TfgQeCKVateqneSs4HNwPNt0y22KHdVPV9V/+6e3gq8q3W2OfYAVyV5CvgB8N4k31u1zRjrvW7ukdYbgKp6pvv3KHAvcOmqTUZ5TtEZmeQxHXOfsDcPw97chL15AEP25ikMmAeBj3V3OtoNHKuqZ4cOtZ4kbzp+7XiSS5nVeug3Cl2m24AnqurmBZuNruZ9co+x5km2JjmvW34d8D7gD6s2Owh8vFu+Bnigqgb9TkWf3Kuu07+K2XdvBlVVX66q7VV1IbObBDxQVR9dtdno6t0n9xjrDZDk9UnOPb4MvB9YfQfP0Z1TdMYmeUzH2Ce6LPbmhuzNbdmb2xu6N5+9jJ2ciSTfZ3aHsS1Jnga+wuxLy1TVLcBhZnc5ehL4J/CJYZKerEfua4DPJHkR+Bdw7dBvlM4e4DrgSHcNP8CNwA4Ydc375B5jzbcBtyc5i1lT/WFVHUryNeDhqjrIrDl/N8mTzG5Oce1wcV/SJ/fnk1wFvMgs9/WDpV3HBOo910Tq/Ubg3u73x7OBO6vqp0k+DaM+p2gN9ubm7M1t2ZtHYAL1nmsi9R60N2f497gkSZIk6ZVgCpfISpIkSZImwAFTkiRJkrQUDpiSJEmSpKVwwJQkSZIkLYUDpiRJkiRpKRwwJUmSJElL4YApSZIkSVoKB0xJkiRJ0lL8D2kwTE2GOe6qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample code 1-2-1\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(8*2,6))\n",
    "xs = [ 1, 2, 3, 4, 5 ]\n",
    "ys = [ 2, 5, 3, 7, 4 ]\n",
    "\n",
    "ax[0].plot(xs, ys)\n",
    "ax[1].scatter(xs, ys, c='green')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nJvlnPSisRLn"
   },
   "source": [
    "If <code>rows &gt; 1 and cols == 0</code>, vertically aligned coordinate systems are returned (sample code 1-2-2)。\n",
    "\n",
    "<code><font color=\"blue\">fig, ax = plt.subplots(<b>2</b>, <b>1</b>, ...)</font></code> <br />\n",
    "<br /><img src=\"https://nw.tsuda.ac.jp/lec/python/matplotlib/tutorial/fig/subplots_2_1.png\"><br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 700
    },
    "executionInfo": {
     "elapsed": 760,
     "status": "ok",
     "timestamp": 1648474515703,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "cHA1VlsPX3ux",
    "outputId": "f62e77a3-5b9c-409a-c41e-490d97e0b8f1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample code 1-2-2\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, figsize=(8, 6*2))\n",
    "xs = [ 1, 2, 3, 4, 5 ]\n",
    "ys = [ 2, 5, 3, 7, 4 ]\n",
    "\n",
    "ax[0].plot(xs, ys)\n",
    "ax[1].scatter(xs, ys, c='green')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "boBkMGYGeOsc"
   },
   "source": [
    "## 1-3: When <ax> is a 2-Dimensional Array of Coordinate Systems\n",
    "\n",
    "\n",
    "if <code>rows &gt; 1 and cols &gt; 1</code> in the function call <code>plt.suplots()</code>, 2-dimensional array of coordinate systems (Axes) and assigned to <code>ax</code>.\n",
    "\n",
    "\n",
    "<code><font color=\"blue\">fig, ax = plt.subplots(<b>2</b>, <b>2</b>, ...)</font></code> <br />\n",
    "<br /><img src=\"https://nw.tsuda.ac.jp/lec/python/matplotlib/tutorial/fig/subplots_2_2.png\"><br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 700
    },
    "executionInfo": {
     "elapsed": 1049,
     "status": "ok",
     "timestamp": 1648474516748,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "eDs-Jb9dYN2n",
    "outputId": "3942acd4-bffd-4c22-ad10-634dc6b0a365"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample code 1-3\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize=(8*2, 6*2))\n",
    "xs = [ 1, 2, 3, 4, 5 ]\n",
    "ys = [ 2, 5, 3, 7, 4 ]\n",
    "ys2 = [ 1, 4, 9, 16, 25 ]\n",
    "\n",
    "ax[0][0].plot(xs, ys)\n",
    "ax[0][1].plot(xs, ys2, c='red')\n",
    "ax[1][0].scatter(xs, ys, c='green')\n",
    "ax[1][1].scatter(xs, ys2, c='orange')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BwhMLJFF7xaX"
   },
   "source": [
    "## 1-4: Display a string at the bottom of each coordinate system (Axes)\n",
    "\n",
    "The spacing between Axes objects is set with <code>plt.subplots_adjust()</code>.\n",
    "The vertical direction is set by the <code>hspace</code> parameter, and the horizontal direction is set by the <code>wspace</code> parameter.\n",
    "\n",
    "In order to secure a space for displaying characters under the coordinate system, it is necessary to expand the size of the entire display area in the vertical direction.\n",
    "Therefore, in the call to <code><font color=\"blue\">plt.suplots(..., figsize=(<i>width</i>, <font color=\"red\"><i>height</i></font>))</font></code>, increase the <code><font color=\"red\"><i>height</i></font></code> spacified in the argument <code>figsize</code> parameter.\n",
    "\n",
    "Also, call <code><font color=\"blue\">plt.suplots_adjust(hspace=<i>height</i>)</font></code> to arrange the coordinate systems verticall apart.\n",
    "\n",
    "The drawing of the character string is performed by the <code><font color=\"blue\">text(<i>x</i>, <i>y</i>, <i>str</i>, ha=..., transform=...)</font></code> function for the corrdinate system.\n",
    "It is <font color=\"red\">important to set the <code>transform</code> parameter to <code>transAxes</code> for each coordinate system</font>.\n",
    "The character position is specified by <i>x</i>, <i>y</i>, and <i>ha</i>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 764
    },
    "executionInfo": {
     "elapsed": 963,
     "status": "ok",
     "timestamp": 1648474517708,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "2sX27Hp2l3_L",
    "outputId": "dc2f4949-ad18-4be2-9bd7-adbc7117751b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x907.2 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample code 1-4\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "VSKIP=0.3    # vertical space between Axes\n",
    "rows = 2\n",
    "cols = 2\n",
    "\n",
    "fig, ax = plt.subplots(rows, cols, figsize=(8*cols, (6+VSKIP)*rows))\n",
    "plt.subplots_adjust(hspace = VSKIP)   # make space vertically\n",
    "for idx in range(rows * cols):\n",
    "    row = idx // cols\n",
    "    col = idx % cols\n",
    "    ax[row][col].text(0.5, -0.15, f'Fig {idx}', fontsize=16, ha='center', transform=ax[row][col].transAxes)\n",
    "\n",
    "xs = [ 1, 2, 3, 4, 5 ]\n",
    "ys = [ 2, 5, 3, 7, 4 ]\n",
    "ys2 = [ 1, 4, 9, 16, 25 ]\n",
    "\n",
    "ax[0][0].plot(xs, ys)\n",
    "ax[0][1].plot(xs, ys2, c='red')\n",
    "ax[1][0].scatter(xs, ys, c='green')\n",
    "ax[1][1].scatter(xs, ys2, c='orange')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nsq0u-MSk68N"
   },
   "source": [
    "## 1-5: Save an image (Figure) that summarizes the contents drawn in each coordinate system (Axes) to a file\n",
    "\n",
    "Specify the path (<code>path</code>) and resolution (<code>dpi</code>) of the image file to be saved, and save the drawing contents to the file.\n",
    "The format of the image file to be saved is specified by the extension of the file name.\n",
    "In sample code 1-5, it is saved in png format.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IG3ZjqmTfMqI"
   },
   "outputs": [],
   "source": [
    "# sample code 1-5\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(2, 2,figsize=(8*2, 6*2))\n",
    "xs = [ 1, 2, 3, 4, 5 ]\n",
    "ys = [ 2, 5, 3, 7, 4 ]\n",
    "ys2 = [ 1, 4, 9, 16, 25 ]\n",
    "\n",
    "ax[0][0].plot(xs, ys)\n",
    "ax[0][1].plot(xs, ys2, c='red')\n",
    "ax[1][0].scatter(xs, ys, c='green')\n",
    "ax[1][1].scatter(xs, ys2, c='orange')\n",
    "\n",
    "plt.savefig('./sample-1-4.png', dpi=600)\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1648474530674,
     "user": {
      "displayName": "Yoshihisa Nitta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64",
      "userId": "15888006800030996813"
     },
     "user_tz": -540
    },
    "id": "Gc3A2HKolM47",
    "outputId": "6056cf35-9a2f-4023-b8c6-07777fedb9e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 root root 572527 Mar 28 13:35 ./sample-1-4.png\n"
     ]
    }
   ],
   "source": [
    "# ファイルを確認する。\n",
    "import os\n",
    "if os.name == 'nt':\n",
    "    LS = 'dir'\n",
    "    LS_R = 'dir /s'\n",
    "else:\n",
    "    LS = 'ls -l'\n",
    "    LS_R = 'ls -lR'\n",
    "\n",
    "\n",
    "!{LS} ./sample-1-4.png"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOuO07bG5wswF4fb4BdLdy5",
   "collapsed_sections": [],
   "name": "matplotlib_tutorial_01_en.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
}