{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. matplotlib简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在数据分析中,数据的可视化是非常重要的一部分,将数据直观的显示不仅方便于理解,更有助于后续的数据清洗等一系列操作。\n",
    "\n",
    "matplotlib是一个用于图表的桌面绘图包,本文将对matplotlib的基本操作进行介绍。\n",
    "\n",
    "[matplotlib官网](https://matplotlib.org/)\n",
    "\n",
    "[matplotlib绘图样例](https://matplotlib.org/gallery/index.html),样例是我们学习matplotlib最好的资源"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python中引入matplotlib的惯例\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11db0cc50>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制简单的直线\n",
    "import numpy as np\n",
    "data = np.arange(10)\n",
    "plt.plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 图片与子图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib所绘制的图位于图片(Figure)对象中,在图片中添加一个或多个子图进行绘画。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11e1e0e10>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(2,2,1)#最多可以放四个子图的,且当前子图位于第一个\n",
    "ax2 = fig.add_subplot(2,2,2)#当前子图位于第二个\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "plt.plot([1.5,3.5,-2,1.6])#直接使用的话会在最后一个子图上进行绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "plt.plot([1.5,3.5,-2,1.6])\n",
    "plt.subplots_adjust(wspace=0, hspace=0)#使用subplots_adjust调整子图之间的距离,wspace与hspace分别代表宽度和高度的百分比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.2 颜色,标记和线的类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib的主函数plot可以接收指定的参数来绘制不同类型的图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11d1dc0f0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "data = np.arange(10)\n",
    "plt.plot(data, linestyle='--', color='r')#可以通过十六进制颜色代码的方式指定任何颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11d6ddac8>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.random.randn(30).cumsum(), color='#FFB6C1', linestyle='--', marker='o')\n",
    "#marker可以将凸显实际的数据点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 刻度,标签和图例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在matplotlib中我们也可以为图片添加必要的属性以方便理解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11d98a048>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.plot(np.random.randn(1000).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11d56f2e8>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#改变x轴刻度\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ticks = ax.set_xticks([0,250,500,750,1000])\n",
    "labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],\n",
    "                           rotation=30, fontsize='small')#设置x坐标的刻度,rotation可以设置刻度的旋转角度\n",
    "ax.set_title(\"My title\")#为图表设置标题\n",
    "ax.set_xlabel(\"x-label\")#为x轴设置标记\n",
    "ax.plot(np.random.randn(1000).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11e633320>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ajLagQ1tQpGVC8XY5M1mXjGdkBE5VQyAQiim/rBgbO7/lr4fMEXjm3DtAtWrVmDt37nnvKVu2LFOnTvWpflkJaOcezEvA80pOS8bNZgsFI6uNBcLWeVZhbCywCOi0TChWL+REoFU1BAqhnPLLirGxwCKgnXuwdeQrCNlVNZjNFgqOsbGzOGlj4iebTttJQf/mgHTuoV69kBOBVtXgzxgbyx4nbCwqKoojR46ElIMXEY4cOUJUVFS+xwjInPv27fD88/DDD7q5UdGiuprhvfdCM6LyMGOG7qmR07UxeI+xsexxwsYqVarE3r17sWP3Nn8iKiqKSpUq5fv9Aenco6P1pGli4tke06F6q5yVzLfOhQvraxQRYa5NXjE2ljN221hkZCTVqlWzZvAgJiCdO+imQACdOukJL/eqXgNnF3EcOgTTpsGCBU5rFJgYG8sZY2P+j8otj6WUigIWAIXRXwbfichbWc4pDHyJ3jv1CHCviOy80LiNGjWS2NjYPCucXfkjmHKszJhrVDDM9csdc42cQym1QkQa5XaeNxOqKUBrEakH1AfaKaWaZDmnO3BMRK4APgSG5lVhb8mpNM0fV8o5RdbmSaZkLW8YG8sdY2P+jzfb7ImIJLh/jXQfWcP9O4CJ7sffATcrpZTPtMyEv5ampWWkcSTxCIlpiY7P6puyyILhrzaWmpFKcrp/bGJqbMz/8aoUUikVrpRaBRwEfhORrN/PlwJ7AEQkHTgBlPGlov5WmnY06Sifr/yctIw0AAYvGkzZYWUpNqgYpYaWosWEFgz4awCnUnzbxtNbTFlk3vE3Gzt4+iCjY0fTcXJHKr5fkcIDCzN82XAAjiUdo/XE1gxeOJitR7farxzGxvwdr5y7iGSISH2gEnCdUqpOllOyi9LPC1+VUj2VUrFKqdi8ljXl1KfZ7tWo+07uo/fs3lT6oBLdZ3Vn0W4969buinYMv3U4Q24ewgPXPEBSWhIfLvuQwhG6NeiJ5BP2KUngNXvyB/ypQVhCagJXjLiC3j/1ZsOhDbSp3oYBrQbQvEpzAA4lHuJ48nFe/fNVanxcg9u+uY2/9/1tq47GxvKHr7cAzBFvdvTIfABvAc9nee4X4Ab34wjgMO7J2pyO/OzE1KuX3hkmKkr/7N07z0Pkm7SMNBm0YJAUfaeoFBpQSHrM7CH/xv0rLpcrx/ecTD4pIiIp6SlyxYgrpOu3XWX/qf12qSwiZ69ZeLjekca9mYwhB2rVOru7kt02lpKeIpP+m3Tm94mrJsqaA2suaGO7j++Wt+e/LeXeLSfEIBsPbbRD1XMwNpY3evcumG3h5U5M3jjzckAp9+MiwELg9iznPAmMdj/uBnyb27j5ce6dO4v06SOyapX+aefWZompiXLVyKuky9Qusv3o9jy9NyktSd6e/7YUHlBYyr5bVn7c9KNFWp6PP2yTFgjktD1bWJg98jcd3iT1Pq0nxCBrD6zN8/tPJp8854sh/lS8L9W7IMbGvMNXWwD60rnXBVYC/wFrgTfdz/cHOrkfRwHTgK3A30D13Mb19R6qVrF492I5nXpaRESOJh4t0FjrDq6TBqMbCDFI//n9LxiR+Yq4OJH//U+kaFH93y5SRKR8eZHVqy0XHVCsXClSrpy+PqCv1/33i8Tb4CN/2vyTlBxcUsq+W1ZmbpxZ4PGW710uRQYWkQ+WfGBszI/wlY1569y9qZb5T0QaiEhdEakjIv3dz78pIrPcj5NFpKuIXCEi14nI9nxmifyKb9d9S8sJLen/l95CvXSR0gUar1a5Wix+bDEP1H2AudvmkpKR4gs1L4ipavAOpypkJqyaQMfJHbm89OWs6LmCTld2KvCYV5e9mvY12tPv13489+tzuMSV+5sKgLEx77Ddxrz5BrDi8PfIfeKqiaJilNz0+U1yLOmYT8d2uVxn7gYSUxMlLSPNp+NnpXPns/lQc+t8LjndKoeH25P2++a/b+TWr26VUymnfDpuhitD+v7cV4hBeszsIRmuDJ+OnxVjYznjaxvDV2kZqw5/du5T1kyRsLfDpM2Xbc44YStITU+VFl+0kIe+f8jyD1/WW+ewMJEuXexJO/gzcXH6A+bJG9uVjok7GXfmsVWpE5fLJa//8boQg0xeM9kSGZkxNpY9vrYxb517QLb8tZKE1AT6zu3LTZVvYma3mRSNLGqZrMjwSNpUb8OXq7/kjT/fsEwOnL9NmssVetukZYcTW+jN2TKHy0dcfqaM1qL1fiil6N+qPzO7zeTe2vdaIiMzxsayx6ltGnPtLWMV+e0tYwebj2ymXNFyBc6xe4OI8MTsJxj771i+vPNLHqz3oGWyPB+4rIRqP5Cc+qOEhZ3dTs/XrDu4jhvG30D10tVZ9Ngiihcqbo2gbNh+bDtbj26l7eVtLZNhbOxcrLAxX/aWCQlOJJ9gzIoxiAg1y9S0xbGDjq5GdRhFy6ot6fFjDxbvXmyZLLNN2rnYvYXeodOH6Di5I0Uji/LjfT/a6tgB+s7pS+epnVkZv9IyGcbGzsXJbRqNcwdc4uK+6ffx5M9PsuHwBtvlR4ZHMv2e6bSs2pJSUaUsk2OqGs7FzuqFdFc6d317F3Gn4pjZbSaXXXSZ74XkwtiOY7m4yMV0mtKJg6cPWiLD2Ni5ONmnyDh34IOlHzBn6xxGtBtBrXK1HNHh4iIX88sDv1C7fG3Auj0jTT8QZ3rIiAg3XnYj4zqN4/pK11sjJBeiS0Qzq9ssDp0+xKMzHzU2ZiF+0afIm1lXKw5/qZaJ3Rcrkf0jpcvULrYs+MiNtIw06TGzhwz8a6BlMkJ90YndFTL+YFeZGbFshBCDjP93vGUyQr1yxkobw1TL5E66K537Z9zPJcUvYWzHsZZVLeSFcBXO6bTTvDX/LZbuWWqJjFC/dbazeuFY0jFu/PxGS+dS8spT1z3FiHYjLK2gCfXKGacqZDIT8tUyf2z/g0LhhWhWpZnTqpzhRPIJ6n9WH4BVT6zioqiLfC6jSxeYNSv7Gftgrmyws0JGRLjnu3v4YeMPLO2+lEYVcy1wsJ3EtEQUiiKRvs+XhGrljNU2ZqplciEpTVvXzdVv9ivHDnBR1EVM6jKJ3Sd28/LvL1siI1TbtdpZvTBt/TS+W/8dA1oN8FvH3mhMI2Lmx1gyfqhWzjhZIZOZkHTuCakJ1Pm0Dh8t+8hpVXKk6WVNeeb6Z/hm7TccOp233vfeEoq3znZVLxxNOsrTc56mYXRDnm/6vG8H9xFFI4vS9LKmvL/0fUvKI0M1/ecvO3mFpHN/48832H5su19GU5kZ0GoAa3qvoVyxcpbJGDNGO3XP7eK6dcFZ1WB39cK4f8dxJPEI4zqNIyIswvcCfMSwW4ZRtmhZevzYg3RXus/HD6XKGb+okMlEyDn35XuX89Hyj+jTqA83Vr7RaXUuSLFCxah8UWVEhFX7V1kiI1Rune3eZen5ps+ztPtS6leo7/vBfUjpIqX5uP3H/Bv/75kt/HxJduk/Y2P2EFLOPS0jjcd/fJyKJSoyuM1gp9XxmmFLhtF4bGPWHVzn87FD5dbZruqF5PRkDiQcIEyF0fjSxr4d3CLurnU3na7sxOzNsy2pfTc25ow+IeXc/4n7h01HNjGyw0hKFi7ptDpe81iDxyhRqAR95/a15MMX7LfOUVH6b1m/Xv+ekaE/gJ995ntZ7y15j5ojaxJ3Ks73g1uEUoqJd07kj4f+sKwc2NiY/eTq3JVSlyml5imlNiil1imlnsnmnJZKqRNKqVXu401r1C0YTS9ryra+27jjyjucViVPlC1algGtBvDnjj+ZscH393fZ3ToD1KgRHJUz97rLuSPcqW+rqhf2nNjDoIWDuKX6LVQsUdG3g1tMqahShIeFcyTxCGsPrvX5+MbGHCC3VU5ANHCt+3EJYDNQK8s5LYHZ3qya8hx2r1Bdd3CdrfJ8TVpGmlzzyTVS5cMqkpiaaImMYNtsIadNEsCaja+7fddNogZGyY5jO3w/uA24XC65bux1cvXIqyU1PdUSGcbGCg4+3GYvXkT+dT8+BWwALrXii8Yq/o3/lzqf1GHsirFOq5JvIsIi+KjdR7jExbZj2yyR0batjqSiovTvYWGBPfGVdYIrPBw6dICHH/Z99cKCXQuYsnYKLzZ9kaqlqvp2cJtQSvFas9fYcHgDo/4ZZYkMY2M24s03gOcAqgK7gZJZnm8JHAFWA3OA2jm8vycQC8RWrlzZmq+1LLhcLrnp85uk3LvlfL5dnhOkpKdYOn6vXrofRuaIxKoIxA5q1ZIzW5qFhVn3t7w17y2p/GFlS3fusgOXyyW3fnWrXDT4IjmQcMASGcbGCga+3mYPKA6sALpk81pJoLj7cQdgS27j2ZWWmb5+uhCDjIkdY4s8O0hKS5KfNv9kydjBshdmTrfLYWHWyQyG4EFEZMOhDRL+drg8+dOTloxvbKxgeOvcvaqWUUpFAtOBSSJy3oyeiJwUkQT345+BSKVU2TzfRviYdFc6r/zxCrXK1eLRBo86rY7PeG/Je9z+ze2W1L4HS1sCu5aAJ6UlnSlRtbIXv51cVfYqejbsyaHEQ7gkm+YwBcTYmD14Uy2jgPHABhH5IIdzKrjPQyl1nXvcI75UND9sOryJo0lHGdR6kF+vEswrT133FKWLlOal31+yZPxgaEtg1xLwj5Z/RN3RddlyZItvB3aYj9t/zNS7pxKmrKmWNjZmPd54vBuBB4E1SilPqPgqUBlAREYDdwO9lVLpQBLQzX374Ci1y9dmxzM7KBZZzGlVfEqpqFK83ux1+v3aj9+2/cYtl9/icxmetgQePG0JChfOvuOdv5BdRz6PA/H1BNfhxMMMXjSY22rcRo0yNXw7uMOEh4UDOkBKd6Wf2UTGlwSqjUVFQUrKuc9ZZWMFwpvcjRWH1Tn31ftXS1pGmqUynCQ5LVmqDq8qDUY3kAxXhs/Hz7rZgidH+vDDPhflU+LiRGrUOJv/tHIjjmfnPCthb4cFfJltTqRlpMml718qzT5vZsmGI4FqYw89pPWMiLDexrKDUN6s40jiEZp90Yxn5z7rtCqWUTiiMO+0fofI8EhLukZ6bpsTE/XvnsZiEyf676rCqCioWBG2ZMqQJCbClCm+v1XecWwHo/4ZxWP1H3Nsa0ariQiL4PXmr7Nw90Jmb57t8/EDzcY8jcG+/FL/nu7us5aY6F/pGA9B6dwHLxpMQmoCvRr1cloVS+lWpxvLui/jkuKXWDL+gQO6Xrd9+7Mr78B/VxV6Vgl6VtBHRWld27b1vax/4v6hWKFixLSM8f3gfkT3Bt2pcXEN3pj3hiWTq4FkY9u3a708+FVNezYE3U5Mu47voubImvzvmv/xxR1f+Hx8f+Tg6YNsP7adJpWaWDJ+RIR/79iU0843AL17wyefWCM3MS2RopFFcz8xwJn03yQe+P4Bvr37W7rW7mqJjEC1sfDwsxG8XYTsTkxv//U2CsXbLd92WhXb6PZdN+797l5S0lNyPzkf+PuqwqwlaVZHVBsPb0REQsKxg75DvDb6Wnaf2G2ZjECxMc9KVCvvCn1FUDn31IxUYuNi6d2oN5Uvquy0Orbx0o0vsfvEbsavHG/J+D//DDffDKmp2qhdLv9q2Zq1JE0EqlSBCRN830d7+7HtXPPpNZb0PvdXwsPC+bvH3zzX9DnLZASKjblcWr/UVGjTRuvtrwSVcy8UXoiVT6xkYOuBTqtiK20vb8tNlW/inYXvnNkb1tf4Y8tWJ3a+GbRwEOEqnHvr3GuNAD/FUxq5YNcC0jLSLJFhbMy3BI1zP3j6IKdSThEeFk6xQsFV154bSikGthpI3Kk4RsdaE+r4Y8vWrBNcVu98s/3YdiasmsATDZ8IuJa+vmDhroW0mNCCL1d/acn4xsZ8S9BMqD7ywyP8seMPtvXdRqHwQj4bN5Bo+1Vbalxcg1G3WdPRD3Ke+LJ74Ul2C0nA2gmu7jO7883ab9jedzvRJaKtEeLHiAhNxjdhf8J+Nj+1mcIRhS2RE8o25g0hNaG69ehWvv7va+6pdU/IOnaA2f+bbaljh/MnvjyUKmXfbWp8PFx0kX5sR9kj6B4yv2z7hScaPhGSjh30HeKAVgMsnd+B0LUxn+PNSicrDl+uUH34+4elyMAiEn/KpiVifs7HV9u0AAAgAElEQVS6g+vkRPIJy8bv1Sv7bnh2dPVzYnMED6dTTwdN58f84mmhXfH9ipKUlmSZnFC1MW8gVFaoeqL23o16U6G4ny0Rc4Adx3ZwzafX8PHyjy2T4Vl4EpaN9Xg2QbYCT3OmrChl7UKS48nHSXelUzSyaNB0fswvnug9LSON9YfWWyYn1GzMErz5BrDi8FXkPnTRUBO1Z6HjNx2l9JDSlkbvIiIPPnhuVKOU/nnJJb7vsxEXJ1K+/LlyPIfVvUge+eERqfNJnaDuVZRXrNrqMSuhYmN5gVCJ3F+88UU2PLnBRO2ZeKvFWxxLPsaI5SMslZOQALUzNQv0zM0fOKD7hviqfK1IEd0z5uDBc+WAln/ypG/kZMfWo1v5avVXtKnWJqjaRheUIpFFSHels+nwJkvlhIKNWUVAO/fjyccBqFKqisOa+BcNKzakY82OfLD0A04kn7BMzowZULOmtbfPud0m16xpbUnawAUDiQyP5KWbrOmdH8h0n9WdlhNbWra2AkLDxqwiYJ37liNbqPh+Raavn+60Kn5JTMsYktOTWb7P2vXbM2bolaD333/u854Kg4JUOGRXseDhoYesWYGaGTOfc2Eeq/8Y+xP2M2bFGEvlBLONWYk3OzFdppSap5TaoJRap5R6JptzlFJqhFJqq1LqP6XUtdaoe5aBC/Uq1Jsq32S1qIDk2uhriXsujraX21O3ldvtc1gYrF4NLVpc+IMYHw9Nmujznb5N/nzl5xQKL8SLN75ovbAApEXVFrSq2oohi4dYGr17yM3GlIIbbsjd0fuTjVlKbkl5IBq41v24BLAZqJXlnA7AHEABTYDluY1bkAnVzYc3S9jbYfLcL8/le4xQweVyya7ju2yR1bmznngKC8u5lEwpkehokVWrRK6/XqRJk3MfP/zwhd/78MNajh1kuDJkVfwqe4QFKPN3zBdikA+XfmiLPG9s7KGHRJo3Dwwbyw94OaGa5yoXYCZwS5bnPgPuy/T7JiD6QuMUxLk/OONBKTKwiOw/tT/fY4QKr/7+qpQaUsrW+uysFQ65HbVre3eenRULyWnJ9gkLcFpNaCWtJ7a2VWYw2Fh+8da552n6XylVFWgAZE3kXgrsyfT7Xvdz8XkZ3xsOnT7E1HVTefq6py3bpMJrUlJg4UKIjdVNKE6e1NPu0dFQvz60aqX7hDrI3bXuZtCiQYxYPoI3W7xpi0zP7fO6dd6d7815dt4mbz6ymRs/v5HJd02mTfU29gjNiaQk+OsvWLECdu3SF6FoUZ1PaNBA29jFFzuq4pS7p1CmSBlbZQa6jdmB185dKVUcmA48KyJZL4HK5i2S9QmlVE+gJ0DlyvlryVuuWDnW9Vnn7GKSNWtg+HCYNg1OndLPlS+vZ2aSk3VSLz1dJ/VatoSnn4aOHXVTCptpEN2AO668gw+WfkDf6/vact1mzIAuXXR+/eBB+O67/I/Vtav+foyPt29ia+CCgZxOPU3dS+raIzA7VqzQNjZjxtl96CpUOLsvXXy8bsASHq57z/btC+3aZV9SYjHli5UH4FTKKSLCIigSaX0Lx0C3MVvwJrwHIoFfgH45vG5rWsYxdu/WO/p6dsV97DGR2bNFjh4997zkZJHly0XeeEOkShV9fv36IvPmOaG1/Bv3rxCDxMyLsV12584iffqItG4tUrq0d7fGl18ucs89ItWq2Z/73Hhoo4S9HSbP//K8vYI9bNsm0qWLvhAlSuh1+HPnihw/fu55SUkiS5aIvPKKSMWK+vzrrtPPOUDcyTi5eOjF8v6S922X3bmztpV77hGpXt3/bayg4KucOzoq/xIYfoFzbuPcCdW/cxs3oJy7yyXyxRf6wxYVJfLqqyJHjnj33rQ0ka+/FqlcWV/uXr1ETp2yVN3suHPKnXLlx1dKhivDdtkeMjv6EiVELr1UHxER+qc/fNgemPGAFBlYRA4kHLBXsMslMmqUDhqKFRPp3/98h54TKSki48drJ6+USL9+2vnbTOuJraX8sPJyOvW07bI9BIKNpWekF+j9vnTuN6FTLP8Bq9xHB6AX0EvOfgGMArYBa4BGuY0bMM49MVFPv4NIixYi27fnf5znntMfvtq1RbZs8amauRF3Ms7RD10gsPv4bmei9lOnRO6+W9vYrbeK7NmT/3H69NHjXHutyC57qqQ8LNi5QIjBkeg9UFi6Z6lUHV61QFVYPnPuVh0B4dwPH9b1UyASEyOS4YOo97ffRC6+WOcoFi0q+Hh5JC0jzba+IIHIkt1L7I3a4+NF6tXTtX3DhukIvqDMmqXD1vLlRVasKPh4eeDmiTdL+WHlJSElwVa5gUK7r9tJ2XfLyqmU/N+9e+vcA3aFquXExUHz5rBqFUyfDm+95ZvJqjZt4J9/9AzOLbfA3LkFH9NLElITuHrU1QxdPNQ2mYGC/szADZfdcGaC0HJ27oQbb4StW+Gnn+D5589fJpkfOnbUO0tHRekJ/QULCj6ml8S0jOHg6YP8uPlH22QGCn/v+5u5W+fy3A3PUbxQcesFevMNYMXh15H7/v0iV10lUry4dZOgBw6INGggUriwjuZtosvULlJycEk5mng095NDiEd/eFT6ze1nn8Ddu0WqVhUpVUpk2TJrZOzZI3L11TqHb+Ndoln4lT0dJnWQi4deLCeTTxZoHEzknk9OnYJbb4Xdu/XW5i1bWiOnfHn47TfdlahTJx3N28BbLd7iZMpJhi8bbou8QGDj4Y1MXD3Rvq6PR4/qO7ijR+HXX+H6662RU6kS/PGHrolv316X8NpAvQr1AEhOt3FPPD9n1f5V/LzlZ5674TlKFC5hj1BvvgGsOPwyck9PF+nYUSQ8XGTOHHtk7t+vI7jo6PxPpOWRu6beZaL3TNw//X4p+k5ROZhw0Hphqam6lKNQIZEFC6yXJ6LvEqKjdVnuAXvmEz75+xOp9EElk3t3k+HKkOnrp/tkjwVM5J4PXnkFfvwRPvpILwixg0su0TITEnQEf/q05SLfbPEmJ1NOMmHVBMtl+TvrD63nmzXf8FTjpyhXzOLVxCLw1FPw558wZgw0a2atPA+XXQYzZ+oOW507Z7/rs4+pV6Eee0/u5dPYTy2XFQiEqTC6XN2FkoVL2ifUm28AKw6/i9w//1xXxfTp44z8n37SFROdO/umKicXFu1a5GjNu79w77R7pfig4nLo9CHrhQ0frm3spZesl5UdU6dq+Q8+6JuqnFxo+1VbKfduuZCP3u+ddq8MXzrcZ+NhSiHzwKpV+ja5TRu96MgpPvxQ/0vefdc2kQVdUBHobD+6Xb7f8L31ghYv1um+O+6w5cs7R95+W9vYp59aLmrx7sVCDPLuIvvs2d9YuGuhEIMMWzzMZ2Ma5+4tp0/rioLoaJFDNkRvF8LlErnrLr2c7p9/LBc3ff10qfJhFTmS6OVqW0P+OH5cz6tUrer9qlOryMgQadtWr7Ret85ycW2/aisV3qsgqemplsvyR1pOaCkV3qvg0wWE3jp3k3Pv1w82boSvvoKyZZ3VRSkYO1Z3lbzvPp2Ht5CaZWqy68QuPlz6oaVy/JG/9/1Nh0kd2HNiT+4nFwQR6N0b9uyBb745u+2PU4SFwcSJUKIEdOuW/f5yPmT4rcNZ+OhCIsMjLZXjj/y540/m75zPqze9StHIovYr4M03gBWHX0TuM2bom5cXX3Rak3P56y/dpuCRRywX1fXbrlJiUImQi97bftVWygwtU+Ca41yZOFHb2IAB1srJKz/9pPXq29c2kS4b8vz+RNPxTaXSB5UkKc23fX4waZlc2L9ftwFo1Eg3XvI3Xn9d/3umT7dUzJoDa0TFKHntj9csleNPeHqg+DIPmi27dumFcM2b6zJbf6NvX21jv/5qqZiktCS5/ZvbZeiioZbK8TeW710uP2/+2efjeuvclT7Xfho1aiSxsbGOyAZ02mPGDN1e4OqrndMjJ9LS4Lrr9IaQGzboXYAt4t7v7mXOljns7bfX3lItBxARWk1sxaYjm9jWd5t1t8sicPvtMH++3imialVr5BSE5GSoV0/b2po1UKyYZaLaT2pPbFwsO57ZYc/S+yBGKbVCRBrldl5o5tx/+gmmTIHXX/dPxw4QGQnjxumdCF60doPmga0G8ssDvwS9YwedB/1r11/W50GnTNErnN95xz8dO+jeM2PGwI4duneShcS0iOFw4mFG/j3SUjn+wKxNs+g+szsnkk84q4g34b0Vh2NpmZMnRS67TLfd9cd0TFaef17fOs+fb4u4YM+Lnkg+IYMWDPJ5HvQcDh8WKVdOpHFj/0zHZKVnT73GIjbWUjEdJnWQ0kNKB/XK6LSMNKk1qpZc+fGVkpZhTVk1plomB157Dfbu1VFxoUJOa5M7b78N1arB449bWtkgIvSd05e+c/paJsMfKFm4JK80e4WoiCjrhDz3HBw7pm3Mga0V88zQoXqldI8eOkVjEYNaD+J48nHeX/q+ZTKcZsKqCaw/tJ7BNw+2r1dRDoSWc//3Xxg5Ep58Epo0cVob7yhaFD77DLZs0R9Ci1BKISJ8Gvspmw5vskyOU6RmpNJxckfm75xvraAFC3Sp4QsvQF0H92DNC6VK6c/FqlX6p0XUq1CPKXdP4fmmz1smw0lOp57mrflv0fSyptx51Z1Oq+PVTkyfAweBtTm83hI4wdldmt705pbB9rSMyyXStKnewMDphST5oWtXvfBk507LRBxIOCDFBxWXzlMCbFNJLxixbIQQg8zZYmFDuLQ0kbp1ddrvdIDteuVy6V2gSpa0pblYMKb/3lnwjhCDLNplbXtlfLjNXnPg2lyc+2xvhGU+bHfuX3+t/9zx4+2V6yt27hQpUkQ7eQvpP7+/LQZqJyeST0jZd8tK64mtrXUqn3yibezbb62TYSUbNujV0d27Wypm3cF1Un90/aDr+777+G75ePnHlsvxmXPXY1E1oJ37qVN68+BGjZzt61FQPH1BrNpAREQSUhKkwnsVpPXE1pbJsJtXf39ViEFi91k4YXjkiF430bKlLU25LMOzz6+Fk6tHE49K6SGlpe1XbYMygrcau537EWA1MAeo7c2Ytjr3V17Rf+qSJfbJtILERN2Tu25dSxuc/b7td9lxbIdl49vJzmM7pfCAwvLAjAesFfTkk7riZPVqa+VYzfHjOnV5ww2Wfkl9uPRDIQb5cdOPlsmwizUH1sjNE2+W7Ue32yLPTudeEijuftwB2HKBcXoCsUBs5cqV7bgOIjt26I6PDz5ojzyr+e47saurn8vlsqycyy7SM9Jl7Iqxsu/kPuuErF2rHfuTT1onw0487a8nTbJMRGp6qlw98mq5/KPLJTkt2TI5VuNyuaT1xNZSekhpOXz6sC0ybXPu2Zy7Eyib23m2Re4PPKAnIm3a5chyXC6RZs1ELrlEp5ssIjU9Vdp93U5e/f1Vy2RYjW23/LffLnLRRc53FfUVGRl6f9+qVUWSrXO8v2z9RYhBPv3H+kDFKqavny7EICOXj7RNprfOvcClkEqpCkrpLduVUtehyyuPFHRcn7B6NUyaBM88o/eTDAaUgnff1bvqvG9dvXBkeCRli5blvaXvse3oNsvkWEVaRhrNJzRn8prJ1gr66y+YPRteftn5rqK+IixMl93u3AmffGKZmLaXt2VWt1n0uLaHZTKsJCktiX6/9OOa8tfwRKMnnFbnfHLz/sBkIB5IA/YC3YFeQC/3608B69A592VAU2++VWyJ3Nu317vLHw3CFXF33aV3td+/3zIR+07uk2LvFJNOkztZJsMqPDndmRtnWifE5dKrUCtV0vMhwUbbtnqS+Ngxy0X5st+5Xby/5H0hBpm3Y56tcgn5rpDz5onduxrZyqZNumytd29LxQxdNFSIQWasn2GpHF+y6/guKT6ouLT7up21qRnPtnWff26dDCdZuVJXzli8LeDK+JVSflh5+W3bb5bK8TWnU0/LV6u/sl2ut849OLtCiugVqHFxsHkzFClijRynefJJvXp1/XqoWdMSEWkZaTQe25hC4YVY3mM57gyc3yIidJzckXk757G291qqla5mjaDUVKhVS9vWqlWB0WYgPzz0EEybpj9Hl11miYjk9GTqflqXDMlgTe81zmxskQdc4iIlPYUikc74ldDuCjljBvz9t+7LEqyOHeDNN3VnvzfftExEZHgk393zHb89+JvfO3aAZXuX8dOWnxjYaqB1jh1gwgTYtg2GDAlexw4wYAC4XPqnRURFRDG241i2H9tOzPwYy+T4ivH/jueaT68h7lSc06pcGG/CeysOy9IyaWkiNWuK1Krl7GbXdvHqqzo1YEN9dUp6im21vAVh/o751m78nZIiUrmyyPXXB/aCJW95+mm9uffWrZaKeXzW4xL2dpisiFthqZyCEHcyTi4afJG0mtDKsQVYhGxXyM8/17eQgwZBhLNd2Wzh+ef1vpwW9+MG6DqtK+0mtSMxLdFyWflhx7EdALSo2oLwMAuj6S++gN27ISZGVy8FO6+8ojuo9u9vqZh3b3mX8sXKM3XtVEvl5BcRoc/PfUhOT+az2z/z/ztZb74BrDgsidyTk0UuvdTy1XV+h6ctwT//WCrmj+1/iIpR8sSPT1gqJz9MXjNZIvpHyIKdC6wVlJKiG4M1aRJaNvb883qh1oYNloqJOxnnty0JxsSOsWd7xlwgJCP38eNh3z4dYfj7t6ovefZZuPhiS3PvAK2rteaFpi/w2YrP+H7D95bKygs7j+/kidlP0LhiY2647AZrhX3+OezZEzpRu4cXX9Ttp2NiLBUTXSIapRSbDm9i8e7FlsrKCyLCjI0zaFO9Df1u6Oe0Ot7hzTeAFYfPI3dP1H7jjaEVUXkYMkRH74sXWyomJT1FGo1pJBcPvVj2nHB+1W9aRpo0Hd9USg4uaf18QHKyjtpD7c7Qw2uv2TK/43K5pPGYxlLhvQpyMOGgpbLyQnpGuhxLsr7mPzcIucjdE7WHWkTl4amnoHx5eOMNS8UUCi/EN12+4coyV/pF7r3/X/1ZsmcJo28bbW11DOhceyhG7R6ee86W+R2lFOM6jeNY0jEenfkoLnFZKi83xv07jgMJBwgPC6dUlHUb1fua4HDuycl6AvWmm+Dmm53WxhmKFdMTX3/+CfPmWSqqRpkaLH5sMTXL1EQHEs4RFRHFY/Uf475r7rNWUEqK3uz6hhvgllusleWvlC6tHfwPP4BVa1Tc1L2kLu+1fY+ftvzEm/OsTTdeiAmrJvD4j4/z0fKPHNMh33gT3ltx+DQtM3Kkvl38/XffjRmIJCXpvvVNm9qSNkhOS5Zu33WTT/7+xHJZWclwne3Lb8sEnGcjjl9/tV6WP3PihG5J0K6d5aJcLpf0mNlDiEFmb5ptubysLNi5QCL7R8rNE2+W1PRU2+XnBCGTlklOhsGDddTeurXT2jhLVBS8/josWQK//Wa5uIiwCE6mnOTpOU/z+/bfLZfnIe5UHA0+a8DCXQsBrC9JS0nRd4ZNm0KbNtbK8ndKloSXXoK5c2HpUktFKaUYddso3m3zLm2q23vdtx/bTuepnalWuhrTuk4jMjzSVvm+IPCde6jn2rPy2GN6mXhMjG7DYCHhYeFMvmsyV5e7mru/vZvV+1dbKg/gePJxbv/mdrYf285FURdZLg/QNrZ3r7ExD08+qTtgvv225aIKhRfihRtfoHBEYQ6ePsjmI5stlwnwwm8v4BIXs++bTekipW2R6XO8Ce+tOHySlvGkIW66KTSrF3Li0091CmHuXFvE7Ty2Uyp9UEnKvltW1h5Ya5mck8knpcm4JhLZP1J+3vyzZXLOIdSrsHLi3XfF7t3NWk1oJRXfryhbj1i7UlZE5GDCQVm6Z6nlcvIDIZGWGTdONwd7+20TUWXm0Udti94BqpSqwp8P/UnpqNKWVdAcTz5O6y9b88++f/i267e0r9HeEjnnYe4Ms6dPHyhXzvK698yMaD+ClPQUmn3RjDUH1vh8/LhTcfT7pR9pGWmUK1aOJpWa+FyGrXjzDWDFUeDI3RO1N2tmIqrsGD3a1uhdRM7Zkm/1ft/WQqdlpMn90++3d8/NpCQTtV+IYcPEjrUVmVl7YK1UfL+ilBpSSn7d6rvJ7VXxq6TKh1Wk+KDiPrddX0PQ93P/+GOt/h9/FGycYMWzTN6B5lbfrftOiEFe++O1AlcZTFs3TXYd3+UjzfKIqcK6MAkJIuXKidxyi61idxzbIdd8co00GN2gwA3iXC6XTFg5QYq+U1Quff9Sid0X6yMtrcNnzh34HDhIzhtkK2AEsBX4D7jWG8EFcu4mavcOT/Q+Z46tYpPSkuSxHx4TYpBrP7tWlu1Zlucx9pzYI92+6ybEII/PetwCLXPBE7Wb+ZwL44neFy2yVeyplFNnVkgfSDggK+NX5mucl397WYhBmn/RXOJPxftSRcvwpXNvDlx7AefeAZjjdvJNgOXeCC6Qcx8xwkTt3uBwa9oZ62fIJcMuEWKQl3972av37Dq+S5775TkpMrCIFB5QWPrP739Ousc2zJ2hdyQkiJQvb3v0npm+P/cVFaPk/un3e9UueMuRLRJ3Mk5ERBbtWiTDFg+ztkW0j/FpWgaoegHn/hlwX6bfNwHRuY2Zb+eelCQSHS3SvLmJqLzhs8/0v/lnm6pLsnAy+aS8/sfrMnnNZBHRVQh9ZveRsSvGyo+bfpQ5W+bI16u/lsOnD4uIyMC/Bkr42+Hyv+n/c653vLkzzBvvvedI9O7haOJRef6X56X4oOJCDHLlx1fK4IWDz7z+69Zf5ctVX8oLv74gDT9rKMQg/zf3/xzR1Rd469y92mZPKVUVmC0idbJ5bTYwREQWuX//A3hJRM5bn6yU6gn0BKhcuXLDXbt25Sr7PMaNg8cf18vsW7XK+/tDjdRUvQXfJZfAsmWOV3zM3TqXrtO6kpCacM7zs7rNouOVHdmfsJ/UjFQqX1TZIQ2BkSPh6afhjz/MwjhvSEyEatWgbl1bFs/lxPHk40xeM5kZG2dQ5aIqjOs0DoByw8pxOPEwhcIL0TC6IXdedScP1n2Q6BLRjulaELzdZs8Xzv0nYHAW5/6iiKy40Jj53kM1LU2vjrv9dscdVcAwdiz07Ak//QQdOjitDS5xsev4Lg4nHibNlUbpqNJUL12dwhGFnVZNr3i+/HK44gqYP9/YmLd88IHuO7NwoV4t7kesjF9J0ciiVCtdjULhhZxWp8DY6dw/A+aLyGT375uAliISf6ExLd0g23Aunui9fHlYvtw4rAvx8cfQt6+5M8wriYlQvTrUqQO/29eKIiBZuVJfp8j8tTSwc4PsWcBDStMEOJGbYzfYTKFCuufMP//AnDlOa+O/ePoUNW8OLVs6rU1gUbSo7jnzxx86ejdkz7Fj2raeecZyUbk6d6XUZGApcKVSaq9SqrtSqpdSqpf7lJ+B7ehSyLFAH8u0NeSfhx6CqlV1L24v7tZCkjFjID7erHjOL088oed2bFy1GnAMHw4nT0KvXrmfW0C8SstYgUnLOIBnMnr2bLjtNqe18S+SknSuvWZNnWs35I8PP4R+/eCvv/QdkOEsx4/rAOvmm2H69HwPY2daxhAoPPywNi6bes4EFGPH6qjdRJ0Fo1cvqFDBlo6RAcdHH8GJE5bvdezBOPdQIjJS595jY3XljEGTlKRz7S1bmlx7QSlSROfe//wTFixwWhv/4fhxnZLp3Bnq1bNFpHHuocZDD+maZBO9n2XMGNi/30TtvuKJJ3T0bq7nWUaM0A7epqgdjHMPPTzR+4oVJnoHXcI3ZIgue2zRwmltgoMiReDll/Vevn/95bQ2znP8uJ6LuOMOqF/fNrHGuYciDz6oa5JN9A6ffqqjdpMj9i09e0J0tIneQTv248d1pZqNGOceimSO3mfPdlob5zh1SkftbdtCs2ZOaxNceKL3+fNDu/royBHt3O++Gxo0sFW0ce6hygMPmOj944/h8GEYMMBpTYKTxx830fuwYZCQ4Mg1MM49VPFE7//+Cz/+6LQ29nP8uP7gdewI113ntDbBiSd6/+uv0IzeDxzQAcT//ge1a9su3jj3UObBB/XCnVCM3j150P79ndYkuAnl3PuQIZCSYnuu3YNx7qFMRISO3leuhFmznNbGPg4f1s79rrtsrV4ISaKi4JVXdPQ+b57T2tjHvn16sv6hh6BGDUdUMM491HnggdCL3j15UFMhYw+PPw4VK4aWjb3zDrhctta1Z8U491AnIgLeeANWrQqN6H3/fp0Hve8+R/KgIYknel+wIDRy7zt36j5O3bvrdh8OYRqHGSA9Ha66CooV0ymasCD+zn/mGRg1Ctav103CDPbg2QSlWjXdEjiYu24++ihMngxbt0KlSj4f3jQOM3hPRISeWPzvP5g0yWltrGPbNp0Hfewx49jtJipKpygWLw7uO8Q1a2DiRHjqKUsce14wkbtB43JB48Z6snHTJv1hDDa6ddNln1u26BywwV7S0/UOREppJxgR4bRGvue222DJEh1IXHyxJSJM5G7IG2Fh8O67sHu33iA62PjnH5g6VfcaN47dGSIidHngxo3w+edOa+N75s2Dn3+GV1+1zLHnBW/3UG0HfASEA+NEZEiW1x8BhgH73E+NFJFxFxrTRO5+Svv2sGyZpZGH7YhA69awbp3Og5Ys6bRGoYuIbvWwbZv+XxQr5rRGvsHlguuv1wuXNm+29M7XZ5G7UiocGAW0B2oB9ymlamVz6lQRqe8+LujYDX7M0KF6Q4HBg53WxHfMmaOrNN580zh2p1FK3yHu3w8ffOC0Nr7j22/1PgkDBvhNStObtMx1wFYR2S4iqcAU4A5r1TI4Rt26euHFiBE6sgp00tLg+efhiiv0akmD8zRtCl266EBi377cz/d3kpJ0m4W6dfW6ET/BG+d+KbAn0+973c9l5S6l1H9Kqe+UUpdlN5BSqqdSKlYpFXvo0KF8qGuwhUGDoFAheO45pzUpOKNGwYYNOkosVMhpbQje7OgAAA3bSURBVAwehg3TE6wvv+y0JgVn2DDYtUtvoxce7rQ2Z/DGuWdXkJo1Uf8jUFVE6gK/AxOzG0hExohIIxFpVK5cubxparCPihV1W4JZs+CXX5zWJv8cPKj7erRrB7ff7rQ2hsxUr67vqL7+WpdHBiq7dukUZteufrdFozfOfS+QORKvBMRlPkFEjohIivvXsUBD36hncIxnn9WpjGef1amNQOTVV/VOSx9+GNyLZgKVV16BSy+Fvn0hI8NpbfLHCy9o2xo2zGlNzsMb5/4PUEMpVU0pVQjoBpyzCkEpFZ3p107ABt+paHCEwoW1U9y4MTBLI2Njdbld37569a3B/yhWTE+u/vsvfPGF09rknfnzYdo0vSF4lSpOa3Me3pZCdgCGo0shPxeRd5RS/YFYEZmllBqMdurpwFGgt4hsvNCYphQyABDRizIWLdJ560uzm2rxQzIy9KTdzp26LO2ii5zWyJATItC8ubavjRuhbFmnNfKO1FS49lq9m9eGDVC0qG2ifbqISUR+FpGaInK5iLzjfu5NEZnlfvyKiNQWkXoi0io3x24IEJTSTbbS0+HJJwOno9/IkfD33/rOwzh2/0Yp+OQTXX7br5/T2njP0KF63cSoUbY69rxgVqgaLszll+vWuDNnwowZTmuTOzt3wmuv6cVY993ntDYGb7jmGl0189VXgTGBv2EDDBwI997r1xP1preMIXfS0/VWdPHxupti6dJOa5Q9ItChg+46uG6dX+ZBDTmQnKw3TklJgbVr/Xflqsul00jr12snf8kltqtgessYfEdEhO5PfeiQrg7wVyZPhrlzdZ2+ceyBRVQUjB2r77zeeMNpbXJmzBhduvnBB4449rxgnLvBO669Vi9qGj8eZs92Wpvz2bNHt1m9/no9P2AIPJo1g169YPhw/9zUY8sWXZt/883w8MNOa5MrJi1j8J6UFO089+3Tvd+jo3N/jx1kZOgPXGys3lHqiiuc1siQXxISoGFDOH0aVq+GMmWc1kiTmqorsLZv17bvYK92k5Yx+J7ChXXq4/RpHbm4XE5rpBkyRG/APGqUceyBTvHi8M03enXx44/7T4XWG2/AihU6PenwJhzeYpy7IW9cfbW+bf7tN//o6rd0qW4xcN99uuGZIfBp2FDPm3z/vc5xO83vv+vFVj176oZnAYJJyxjyjgjcfbfuPfP779CihTN6HDigq3jCwnQ6xtS0Bw8ul+4JtHCh3li7cWNn9Ni9W8u++GIduftBTbtJyxisQyk9sXrFFTqS2bbNfh2Sk+HOO/W2gNOnG8cebISF6aZiFSrAHXfA3r3265CQAB07alubPt0vHHteMM7dkD9KldL7kYJeyHHihH2yRaB7d71j1Fdf6UoeQ/BRvry2sVOnoFMnPddjFxkZcP/9uub+22+hVnb7E/k3xrkb8s8VV+iIZutWuOce+7pHvvOOnnR7552AyoEa8kGdOjBliq6cefBB+7pHvvKKTjsOHw633mqPTB9jnLuhYLRsCaNHw6+/6kgnPd1aeR9/rCsXHnxQfwANwc9tt8H77+sJ1scft75Ka9Ag3cK3Tx+9diJAiXBaAUMQ0L07HD+uF3iEhcGXX1qz69HHH+sWvnfeqXP+pkd76PDss3DsGPTvr//vY8b4ftcjEb3xxmuv6UBlxIiAtjHj3A2+4bnndET14os6//7tt1CihG/GdrkgJkZvPnznnfo2PTLSN2MbAoeYGP2zf38dTHz1le8mOTMydHAyfLh27BMm+NWWeflCRBw5GjZsKIYgZNw4kfBwkVq1RDZvLvh4J0+KdOkiAiKPPSaSllbwMQ2BzfDhIkqJNGggsnNnwcc7elSkfXttY337imRkFHxMC0Hvo5Grj/Uq566UaqeU2qSU2qqUOm9HW6VUYaXUVPfry5VSVX38HWQIFLp31/n3AwegQQOdj89vjnTBAqhXD374QS+YGjdONzEzhDbPPKOraLZtg7p1dRowv+t1fv1Vtxz+/Xdtqx99pFOLQUCuf4VSKhwYBbQHagH3KaWy1gV1B46JyBXAh8BQXytqCCBat9aLim64AXr31j/nz/f+A7h1K/zvf3pxlFK6tcD//V9A5z8NPua222DlSu3cH35Y28rSpd6/f8MGuOsuXQlTogQsWQJPPGGdvg7gzVfUdcBWEdkuIqnAFOCOLOfcAUx0P/4OuFkp80kMaSpV0hsvTJigF6C0agWNGukIfP36c0vaRPQ5X32lF43UrKkrI15/XZfA3XSTY3+GwY+pXl0HDaNH6y36mjaFJk30ROimTefeMYrArl16r9Z27XTd+i+/6Hmcf//Vthlk5Np+QCl1N9BORHq4f38QuF5Ensp0zlr3OXvdv29zn3M4p3FN+4EQIjFRO+5PP9XOGqBIEShXTqdZjhw5uwiqYkV49FHdttdfuk4a/J+EBO24R4/WwQPoydZy5XSa5cgROHlSP1+5sk4f9u6tXw8wvG0/4E0CM7sIPOs3gjfnoJTqCfQEqFy5sheiDUFB0aL6lveJJ3SedNEiWLNGtw5IS9M7O111lW4n3LBh0OQ8DTZSvDg8/bQ+Nm3SG2qsXaudekaG7g1z9dU6sq9fPyRSfN44973AZZl+rwTE5XDOXqVUBHARcDTrQCIyBhgDOnLPj8KGAOfyy/VhMFjFlVfqI8TxJkT6B6ihlKqmlCoEdANmZTlnFuDZmuRu4E/JLd9jMBgMBsvINXIXkXSl1FPAL0A48LmIrFNK9UfXW84CxgNfKaW2oiP2blYqbTAYDIYL41XRsIj8DPyc5bk3Mz1OBrr6VjWDwWAw5Bczc2UwGAxBiHHuBoPBEIQY524wGAxBiHHuBoPBEIQY524wGAxBSK7tBywTrNQhYFc+314WyLG1gcP4q25Gr7zhr3qB/+pm9Mob+dWriojk2jfBMedeEJRSsd70VnACf9XN6JU3/FUv8F/djF55w2q9TFrGYDAYghDj3A0GgyEICVTnPsZpBS6Av+pm9Mob/qoX+K9uRq+8YaleAZlzNxgMBsOFCdTI3WAwGAwXwO+ce0E241ZKveJ+fpNS6lab9eqnlFqvlPpPKfWHUqpKptcylFKr3EfWdslW6/WIUupQJvk9Mr32sFJqi/t4OOt7bdDtw0x6bVZKHc/0miXXTCn1uVLqoHv3sOxeV0qpEW6d/1NKXZvpNauvV2663e/W6T+l1BKlVL1Mr+1USq1xXy+fbnHmhV4tlVInMv2/3sz02gVtwGK9Xsik01q3TV3sfs3K63WZUmqeUmqDUmqdUuqZbM6x3s5ExG8OdEvhbUB1oBCwGqiV5Zw+wGj3427AVPfjWu7zCwPV3OOE26hXK6Co+3Fvj17u3xMcvF6PACOzee/FwHb3z9Lux6Xt1C3L+U+j20lbfc2aA9cCa3N4vQMwB727WBNguR3Xy0vdmnpkojesX57ptZ1AWYeuWUtgdkFtwNd6ZTm3I3qfCTuuVzRwrftxCWBzNp9Ly+3M3yL3gmzGfQcwRURSRGQHsNU9ni16icg8EUl0/7oMvWOV1XhzvXLiVuA3ETkqIseA34B2Dup2HzDZh/KzRUQWkM0uYZm4A/hSNMuAUkqpaKy/XrnqJiJL3LLBPhvz5prlREHs09d62WJfACISLyL/uh+fAjYAl2Y5zXI78zfnfimwJ9Pvezn/opw5R0TSgRNAGS/fa6VememO/lb2EKWUilVKLVNK3ekjnfKi113uW7/vlFKeLROtvF55Gt+dwqoG/JnpaauuWW7kpLfV1yuvZLUxAX5VSq1Qeq9iu7lBKbVaKTVHKVXb/ZxfXDOlVFG0g5ye6WlbrpfSaeMGwPIsL1luZ15t1mEjBdmM26tNuvOJ12MrpR4AGgEtMj1dWUTilFLVgT+VUmtEZJtNev0ITBaRFKVUL/RdT2sv32u1bh66Ad+JSEam56y6ZrnhhH3lCaVUK7RzvynT0ze6r1d54Del1EZ3ZGsH/6KXxCcopToAPwA18J9r1hFYLCKZo3zLr5dSqjj6C+VZETmZ9eVs3uJTO/O3yD0vm3Gjzt2M25v3WqkXSqk2wGtAJxFJ8TwvInHun9uB+ehvclv0EpEjmXQZCzT09r1W65aJbmS5ZbbwmuVGTnpbfb28QilVFxgH3CEiRzzPZ7peB4Hv8V1KMldE5KSIJLgf/wxEKqXK4ifXjAvblyXXSykViXbsk0RkRjanWG9nVkwoFGAiIgI9gVCNsxMwtbOc8yTnTqh+635cm3MnVLfjuwlVb/RqgJ48qpHl+dJAYffjssAWfDSp5KVe0ZkedwaWydmJmx1u/Uq7H19s5//Sfd6V6MktZcc1c49ZlZwnB2/j3Imuv+24Xl7qVhk9l9Q0y/PFgBKZHi8B2tmoVwXP/w/tJHe7r59XNmCVXu7XPcFfMbuul/tv/xIYfoFzLLcznxqmjy5MB/Ts8jbgNfdz/dHRMEAUMM1t5H8D1TO99zX3+zYB7W3W63fgALDKfcxyP98UWOM27DVAd5v1Ggysc8ufB1yV6b2Pua/jVuBRu/+X7t9jgCFZ3mfZNUNHcPFAGjpK6g70Anq5X1fAKLfOa4BGNl6v3HQbBxzLZGOx7ueru6/Vavf/+jWb9Xoqk439f/t2bAIwCEQB9AbLBJki46bORCm0uCJlIuHzXq0genzh0LPa5fNUA6vWNcccNR5a9Hlf79dWo5VytbPaV9eZH6oAgf7WcwfgBcIdIJBwBwgk3AECCXeAQMIdIJBwBwgk3AEC3SXeQIejAoVrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#添加图例\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "t1 = np.arange(0.0, 2.0, 0.01)\n",
    "s1 = 1 + np.sin(2 * np.pi * t1)\n",
    "t2 = np.arange(0.0, 2.0, 0.01)\n",
    "s2 = 2 + np.sin(2 * np.pi * t2)\n",
    "t3 = np.arange(0.0, 2.0, 0.01)\n",
    "s3 = 3 + np.sin(2 * np.pi * t3)\n",
    "ax.plot(t1,s1,'r',label='one')\n",
    "ax.plot(t2,s2,'g--',label='two')\n",
    "ax.plot(t3,s3,'b*',label='three')\n",
    "ax.legend(loc='best')#best会在图片的合适位置自动添加图例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.4 将图片保存到文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.savefig('figpath.svg')#参数为文件要保存的路径"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 使用pandas和seaborn绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x116c9cc90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.collections import PatchCollection\n",
    "from matplotlib.patches import Circle, Wedge, Polygon, Ellipse\n",
    "color = r'#39CF18'\n",
    "fig, ax = plt.subplots(figsize=[8,8])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "ax.set_fc(color)\n",
    "plt.ylim([0,40])\n",
    "plt.xlim([0,40])\n",
    "es1 = Ellipse([15,24], width=21, height=18, facecolor='white', zorder=1)\n",
    "es2 = Ellipse([26,16], width=18, height=15, facecolor='white',linewidth=5,edgecolor=color,zorder=1)\n",
    "c1 = Circle([11,27], radius=1.3, facecolor=color, zorder=2)\n",
    "c2 = Circle([19,27], radius=1.3, facecolor=color, zorder=2)\n",
    "c3 = Circle([23,18], radius=1, facecolor=color, zorder=2)\n",
    "c4 = Circle([29,18], radius=1, facecolor=color, zorder=2)\n",
    "ax.add_artist(es1)\n",
    "ax.add_artist(es2)\n",
    "ax.add_artist(c1)\n",
    "ax.add_artist(c2)\n",
    "ax.add_artist(c3)\n",
    "ax.add_artist(c4)\n",
    "# patches.extend([c1,c2,c3,c4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x116daa4d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circle1 = plt.Circle((0, 0), 0.2, color='r')\n",
    "circle2 = plt.Circle((0.5, 0.5), 0.2, color='blue')\n",
    "circle3 = plt.Circle((1, 1), 0.2, color='g', clip_on=False)\n",
    "\n",
    "fig, ax = plt.subplots() # note we must use plt.subplots, not plt.subplot\n",
    "# (or if you have an existing figure)\n",
    "# fig = plt.gcf()\n",
    "# ax = fig.gca()\n",
    "\n",
    "ax.add_artist(circle1)\n",
    "ax.add_artist(circle2)\n",
    "ax.add_artist(circle3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}