Python54-matplotlib画子图.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 功能需求\n",
    "在同一个界面中画出一组进行比较。\n",
    "matplotlib 中提供的 subplot 可以很好的解决这个问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib下, 一个 Figure 对象可以包含多个子图(Axes), 可以使用 subplot() 快速绘制, 其调用形式如下 :\n",
    "\n",
    "```subplot(numRows, numCols, plotNum)```\n",
    "\n",
    "图表的整个绘图区域被分成 ```numRows ```行和``` numCols``` 列\n",
    "\n",
    "然后按照从左到右，从上到下的顺序对每个子区域进行编号，左上的子区域的编号为1\n",
    "\n",
    "plotNum 参数指定创建的 Axes 对象所在的区域"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果 `numRows ＝ 2, numCols ＝ 3`, 那整个绘制图表样式为 2X3 的图片区域, 用坐标表示为\n",
    "```\n",
    "(1, 1), (1, 2), (1, 3)\n",
    "(2, 1), (2, 2), (2, 3)```\n",
    "\n",
    "这时, 当 plotNum ＝ 3 时, 表示的坐标为(1, 3), 即第一行第三列的子图\n",
    "\n",
    "如果` numRows`, `numCols` 和 `plotNum` 这三个数都小于 10 的话, 可以把它们缩写为一个整数, 例如 subplot(323) 和 subplot(3,2,3) 是相同的.\n",
    "\n",
    "subplot在 plotNum 指定的区域中创建一个轴对象. 如果新创建的轴和之前创建的轴重叠的话，之前的轴将被删除."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 将界面分为四个子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    for i,color in enumerate(\"rgby\"):\n",
    "        plt.subplot(221+i,facecolor=color)\n",
    "    plt.show()\n",
    "# (2,2,1),(2,2,2)\n",
    "# (2,2,3),(2,2,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.将界面分为三个子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 分成2x2，占用第一个，即第一行第一列的子图\n",
    "plt.subplot(221)\n",
    "# 分成2x2，占用第二个，即第一行第二列的子图\n",
    "plt.subplot(222)\n",
    "# 分成2x1，占用第二个，即第二行\n",
    "plt.subplot(212)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将整个表按照 2*2 划分 \n",
    "前两个简单, 分别是 (2, 2, 1) 和 (2, 2, 2)\n",
    "\n",
    "但是第三个图呢, 他占用了 (2, 2, 3) 和 (2, 2, 4)\n",
    "\n",
    "显示需要对其重新划分, 按照 2 * 1 划分\n",
    "有：\n",
    "\n",
    "`\n",
    "（2,1,1）\n",
    "（2,1,2）`\n",
    "\n",
    "第三个图占用了 (2, 1, 2) 的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一行第一列\n",
    "plt.subplot(211)\n",
    "plt.subplot(212)\n",
    "# 第二行第一列\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.加上函数图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fun(x):\n",
    "    y=np.exp(-x)*np.cos(2*np.pi*x)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
       "       2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
       "       3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# arange(start,end,step) 生成数字序列\n",
    "x1=np.arange(0,5,0.1)\n",
    "x2=np.arange(0,5,0.02)\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c6c9839cf8>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TrRulosK/2frG2FnQZWXe2wvkt0xPt5PlvAn1bN/eur8Csde2rR3zcXpNYKJhhi/YA+JwtI97neC8PNu5yHOtE6wLxSshpVMnO9krkN7j66/D/PnelQ30YiPivRC77VVV2QuAv8TEeJ9ILdB5E2lpcO21npO61SbQi82VV9oxiqYimfwgOsS/e3e7LJwDqW9nzrRZb6+ecojHSq6gHzX+Ol0nWAk5xgQuIHfd1XRqBzeBiv/evdaV85//eFc+0Nj7pUttr8zb76enW5eWvxkB1q6F777zvvyoUUHptTtBdIi/+4QNsPdfu7d/P7dzBc/TmT0ApGAXpdB1gpWQE0hvVcT7AVGALl3sZK/9+/2z5/4PJid7V37gQPjVr/wPL/32W3jqKe/F/Npr7XcaW4GrKe6/H844w/vyzz/v/YWwISZOtBfvIBAdKTH79LFLuAUY63/33bZ335v1XMd/eIzrWMxofsrrTOcyBrCGGF0oWAk1d93lv1gVF9vEad4OGAaaJt3XAco+fQITR7c9bydu9ewZWGpkb8NKnWLJksBSX3ggOnr+J5xg1ycNMPmRu1d/Fc8CMA3r41nKscRTzk1x/9Z1gpXQc9ppcOqp/n3Xn1DBQNKk+xOdUlEBBw74b8+XjJf79tmLjb+ZS325iwJ4+20brVVQ4LutkhJb3yBdbKJC/N1++pgY++zvoKzt1AsX8wrzGM927I++mUzeaXUBV1U+yS+nHAjIhqL4zK5dNl7cnzEtX0IT3TzzjHXF+IP7YuNLCoX+/eG66/yz58ucArC/4fXX2/xDobBXVWUvNP647fw5dj4Q8eLv9tNPzzuBP8g9AUXlTJsGHduU8TKX8Di/BKz781e/gsfNr+gg+zmLdzXyRwkt8+ZZ368/4cxjx9q5Asce6/13li+362T4M/N25Ei7YIovy06mpfk/XldV5Xld4vqkpNheoj/2Dh/2bU4BBJZ7LIgx/hAF4u/206exg4GuicP+RuVMngz/fiqBpzP/xlvmfDIz4cknbS6oOYfHsp00LuS1gGwois+4//z+9B4TEmzP2pcUxunpdpyguNh3e6eealcD84Xu3f0X//feg1mzvC/vXsrSH3sxMfDuu76lkgnk2LVuDWee6Xnd5QCIePF3++m3kk46W4/Y7iuTs5exaWUJVVV2TejJk+2+qojlMqZzKw8EbEOJLppazChgAolme/tt78M83QTaWy0t9d1eKLPydu/unxjHx9t1grOzvf9OWpodRPenfSNH2oubk2sr1yLixd8dfFNf/P0KyiktheOPhz/9qUEbczmFTfQ6YrvScqm1mNEZwCDgUmOMs8suuf3n/gjI9OnwyCO+fScQ8R850nf/fffuNsLI1+Uct2+3PeOFC337nr+TQjdssD1/X8Ze4uPtWgpB8tsHQsSL/7Rp9o52G93pzjZASEzEv6icJUvs7MQTT2zQBsAFvMZVPOO/DSXaCP5iRu7lHP3tifvqM3anPvZ1gNmXjJe1GTfO/pmaWoylPps3wwcf+O6eeuIJm8DOV955xy4V6etFatYsuOYa3+39/OdHaJGTRHycvzvXzpe/PZb3dp1F/56l/PHvbfzLwbNwob1FGzu2QRt33w2X5r3EyNhljH/iKiZP9jP2WokmGlrMaJTjVl591b/49K1b4aSTfPtOv37+pZMoLPQt46WbUaPsw1f8HRBNTfXdltteq1Z2Ilwo2LTJ//kdXhDxPX+w4vxY4UX8VN5gzWb/hH/mTPj43lxWyQCyhnc6IpJn8mR7LM5/fCI9KjczOWeNI3WvXwcnQlaVkOLNYkYYY6YaY3KNMbmFhYW+W5kwwffJPlVV1jUSKpeDv+mHq6rsxWb37tDYW7UK7rzTrjbmC+67KF8F+Y9/tBdUX/Hnrs0HokL8A8UdLtr70HJ+YKjnUM6JE+3zhx86ZjsrqybVuCaSiziaXMwI/FyoqDY//AAvvujbd3bvtnn5/RGQG26wIZu+4G9c+oED9q7G1zz727bZCWm+/p6bN9uIpHXrfPuev7N7Y2OtLV/WSHCn5VDx9wL3TD8/Fmpwh4texnTux57wjYZyZmWxv3s2C+78MOAeek0uIeE83mSenMz3DCWHpdV1mDJF7wKaOU0uZuQIr7xil/LzJSFZ167WP/2LX/hub/Vq3/3i2dnwz3/6Hp2SlGRTF/s6ptG2rR1g9jXjpb8D2r7O7q1vz5c1EvbvtwIQRPGPeJ9/NV26WJ+jH75Kd8jmIo5rcHttZs6EvQWnc1zFQqCKvLwYpk61n/nqbrIXHeExruc6/stqBrCMEXyLXbO1DSUcIrH6LsAfG0pwEZEKY4x7MaNY4FkRWeG4oe7drfAXFnqfmx98i++vb8/XKJp+/eDWW323ZYx/4Zd33+3fZBt/Y+/feMO/1Mq1Q3W9DRGsqLB/el8m5/lI9PT8ExJsJkE/IiIyMuBovuFCXiWesjrb63P33fDbin+SwzLE9fP5O+Fr82b4A3/lOv7Lg9zMUH5gCjOpIJ7ubGUlgxjHvIBsKMFHRN4Xkf4i0kdEghMD5k9vdf58uPlm/yZruWPvfYnAWbvWDoz5QxDW5GiUzp2tXvhqb/Bgm4XUV/w5dsnJNiopiNE+0SP+4PfkjWnT4Iq4mbzA5VQSC9BoKOfmzVDZwA2TLxO+3H5+ESgnnqf5BbfyABXUJKcqIZEDtGMW59GXtT7baMp2TIw9v5KTbccrLs4+q4upmeJPb/Wzz+D//s8Kna+kp1sftS+DsL/5DVxwge+2wL+JXmPH2iUnfcV9p+HLwHtBAfz3v/5FQWVkWP+tL1FGhw/7v+aAt4hIs3yMGDFCfOaMM0SOOcb374nI1qETZXn80WKMSGamyIwZDZfLzBQBkT/yZ5nPSWIl3G73hhkzRBITpfp79R/G1LzuSZ7sorN8y1HSmhKJjW28Xk3ZdNe79v6hyvWoW4fERP/sRBpArkTKuZ2fbw/O4497/51rrxXp2tV3WyIiH3wgMmqUyMaN3n9n2DCRc87xz968eSIvveR9+ZIS+3tMm+afvZIS38rPm2ftzZvnnz1fufdekdhY3+sp3p/XjvT8m5reboxJMMa84vp8sTEmywm7RzBpEpx9tl9f7b5nOYMvHlInrUNDuCd8ldGKk1lAKjt8mvDlHlw+i3f5CbOoHRWYmWknZM6YYW1sIYMpzGAY3/MIN1JZ6XsEUO0FagAGyCqG87VtM9sQYthAL57lSs7gfWKoDHiguam7C/e22p8HMnheO2Iqau9g0tIgN9cuxu4tgUSLnH46LFrkW9K0QOyNG+d728D/MFZfEs/VthfIAKwv641s22YHwn2tpy94c4Xw9MAOcq0HegOtgO+AQfXKXAc87np9CfBKU/v1q3fkL3v32qv6ffd5VXzGDJGz0nJFQH7TZbpPvWRjRGIpl/X0kmUMF0NldY+8vo3YWFfnhjtlLuMkgUM+3WWI1PT4k9gvz3ClVBAjLzBFQKQ9++Qv/F5e4ULZSwcRkDX0k2NZfMSdiKe7IXd9G7u7SGW7HMW3cgILpDUltoyr3Y3d+XTpYh9Q8zu4txlT9/O69ry/gyGSev7+cMwx9m44FJSW2h/9nnv8+35xsciXX4rs2+dd+QULrL2PP/bP3uzZIlde6X35+++39oqK/LN38skip5ziffnzzhMZNMgvU96e106I/3HAR7Xe3wncWa/MR8BxrtdxwC7AeNqv33+Q0lKR8nLfvvP55/anePdd779TWWnV57LLfDKVmSlyIa+IgExilke3kVvUYimXGCrqCJsvYpzNSlnFACknVh7gZkmm4AihbEWpXMgr8gXHSSrbrQ02Vot1U8JcX4BH8ZXM5FLZQUqdDzLYJCDyO+6TQrrIV4ySZ7hSbuYBmcgHEkdZoy6x+o8EDkkmG2UUX8kkZsk5vF392e+4T67nUY8XyogT/7fe8s0fl50tcvXV/tmqqBAZPlzkoYe8K79hg/3hn3nGP3sLF/om5i++aMuvWOGfvX/8w35//37vyt94o0j79v7ZEhE5/3zfxHzkSJFTT/XLlLfntROhnt5Mb68uIzY0bj/QxXURcI6PPrK3q199BaNHe/+9446zvh5v1x0F66c49VT45BOrN17O+pv2N2HAzx/gx6p+zOZcoPHB5YwM665xDzCnsZ27mcYtPEheXkKj4Z9uV09JCYxkMXM4hRISOYU5LODkButVRgKvcRGvcVH1tulcRj/W8ii/ZgZT2CyZQN0xwMpK6MQeTpIFnMIcnuIavuNoktnFOObzMaexiNHsII39dGAHNkzxG4bzOhcwgDWcxXtcxf8oJ4622LwpN/EQpzCH/XSghEQSOMwB2vErbIbK2ZzDObxbpw0rGMQ7rt90PPPYQ2f+s/mGJo5IBPHMM3bU39t431WrfM+X4yY21g5urvFyJnuXLvDyyzbu3h98HdBOSrIr+LkXgPeV2uGX7ds3XX7r1sBmSnfvbtdl8Jb8/IBXJmwSb64Qnh7AhcDTtd5fBjxar8wKoEet9+uBLg3sayqQC+RmZGT4fsn7+mt7NX/jDd+/6w+zZ4vcfrvXgzIzZohckGp7ONeZ/zTZg68/OHwW74iATGdytdukfs+2trsIRNpwUJ7mKklni1c9+NqPE1ggH3Fq9YaVZMuVPCMg0oG98hIXyxr6VX9eTFu5hBcFxHWncuRgcmOPLhTWcTdN405ZzLGyhn6yhXRZR29ZyNjqz3/O/+Qu/iZX8bScwXsynGWSxrYj9htVPf9ABnD9YdgwkbPPDo2tQAdwfWX+fGtv7lzvyu/da+9u/OXee629gwe9L//2236Z8va8dkL8m4/bZ+dO26RHHvHte/ffL/Lyyz6bc7tWmooQcpdNTBSZyAfyJaOlDQe9iqqp7b4BkTuZJgLyMDdWi2t9/3ca2+S/XCvtKGpQDL292LgfWWyQ33GfzOZsuYkHqy8qa+kjs5gkd3CvnMACieew12IfikfU+fzvucc2rLS06bLffy9y4YUiq1b5Z0tE5MwzvY+eW7VK5NNPRaqq/LfXqZPI9df7/31fWLPG/pbTp4fG3nPPWXtr1wbdVCjFPw7YAPSiZsB3cL0y11N3wPfVpvbr1x+kslIkPl7kjju8/05VlVVOH32jbqGM53B1j9WT2NQWcG97pg1/v0oe5CYRkFe4sI7Ax1Ahk5kuhXSRElrLBD7x2VbjA7e+PZwcuPXFnttGUxdjkQgU/6efto3btKnpsq/YcSX5/nv/bInY/0Ramndlb7rJ/gECEf/Bg+1ApzdMnChy1VX+2youFklKEnniiabLVlSI3HWXyKJF/tv74QerS1u3Nl32wAFbrqLCL1MhE39rizOBH13unLtd2/4CnOt63Rp4DVgHLAF6N7VPv/8gGRm+DcLu2GF/hocf9smMWyD/xJ+knFjpQqFHgTXGulHacPAI0fKGur3yKrmN+2UdvaUVpQIiD3Cz5NFTBGQJOTKAVT71ghuz6cuFwNvIoKbs1b8oeLpoeHPX1RgRJ/7vv29/iC+/bLrsQw/Zsrt3+2dLxF5sLrrIO0G/6CKRfv38tyUiMmeOyNKl3pXt2dPnYAu/2brV/pb/+U9o7L35prW3bJlfXw+p+Afj4fcf5OGHRV591fvyc+bYn+GTT3wy4xa64SwTAbmKpz2K+TE9dkoJreURbvCr5y9ypAuodiTOLCbJG5wnP+W1I8Io/Z0c1pDtxoTZXwEOJxEn/iUl1rVZWdl02VtuEWndOrCeuC+MGSMyblxobFVWisTFidx5Z2jsLVliT3I/ffAiYo/D3r3eXYwfecTa27nTL1PentfRld4B7BRzLxdYnjkT7rlwOQDHXjnEp0lB7rw/3zCcjWRxAa/X2V6f53L+TQKH+Tc10Se+rgbmXlMgM9O+L6VmAsh5vMVPeZM3uIDahzUxEZ5/PvCEcG7bVVWwa5d9iNj8UyKeJ8YpDtGmjc1c601ysfx8GwkTxMVA6hBoNAzYk+i115pOa1BQYE+8QO39/e82dXVTuCOQArWXnu7dH37rVrv8oy/Rh34QfeJfWmrX2mwCdzhk3N4CdpJCbn6qT7Nna5Z2NMxkMqfxMf1abzni2M6cCYMyDpD+1r/5IH4Su7sMwBgr4E8+6Z9g1l5WsiHc//dAbChUAar1AAAgAElEQVTNlGnTbHbJpmjTBoYMCczWypU2LfTbb3suV1XljPi//z5cdFHTOXecEuNVq+yavE3hzucTiD13PiFv8he5f0t/Moj6gje3B+F4+H1r7OVMvNruk9oTi/xxw2SxUSoxsuzSfx7xeWKiyK3YCSXH8YVjeXMacsME4v9uiRBpbh8Re4CnTPH/+76wfbv9Uzz2mOdylZV2ouSPPwZmb9Ys8crXvWKFyKWXiqxcGZi922+3ASJNucbuvtu6mbxxt3nixBNFTjih6XInn2zdaH7i7XkdfT1/99W5ickitTNk1s6m6UvmTLcrZKNk8eHflvHTL26uk6PGncfnBD7jPc7kK453LDVzQ26YpvISKVFAKFMfd+1qJ3s1ZS8mBsaM8W+pwtp4m/p40CC7qpk/6ZXr2ysvt38eT/z1r3ZmY6A9cW+P3Y032lTcQSbqxH/OSnsCTRi4zWNyr4wMSCefDzid0XxVZ7uvzJwJF947nE2bY4iXw+Tl1SzJCDCJt7mUl6rLO5GaWWmheOM62L0bjj/eulECITbWJpRryt66dTYboT/rBtTG21m+FRWB2XFTe5avJ4zxbhZwU7iPnYjncuedB+efH7i9Jogq8Z85E255yB7Q7mz1uA7utGkwMuF7TucjYrEDTL4OwLpx9/DP5w3yyGQo3yMC5zCbHmwBDMXUnDz+XGAUBfBO/LdssSlODh1yxl5TYjxnju3tFBUFZistzQptU+27+mpnUh9kZtr9NJVt83e/g5de8lzGG847Dx54wHPKjdJSWLzYLuMYZKJK/O++G9aX2t5DOvaEbczNMnky/OE8G+mzksEBDY7WLAM5mkpi+ZyxzGECs5nEg9xSp6y/FxhFAawYHzpkT+zGcGpAFOwA7CmneC6zdat1ifiyWElDxMXZBWh+9aum7XXoEJgtgJwcWLHCcz4iEfjPf2DJksDtjRkD111n76gaY906m5fsww8Dt9cEUSX+mzfDQdpxI//iE06ts70hhscvh/R09kingHzl7p78NtIZwxd8yOkks4t7uZOf83x1OY2+UQLmpptsT9VTuJcT0Slubr0VbrvNc5n8fNtrj3MgT+SYMU2vUZyf70zbvKGoCA4edMZeWRn88IPnMQYnL9xNEFXi7xbhR7mRrxlxxPYjWL488HA46oZe5pHFxbzK0XzH3dxbHYufmamDsYoDtGrVdOx+Xp4V4m7dnLF56JDn2Pu8vJrJJ4Hy1Vc2e2ljiNjenFP2LrwQ7ryz8c/dA3dO2Nu+HY46Ct56KzT2miCqxN8twunkV69W5dHN0q2b7WkEyOTJtkfvPl71/5vq6lEc48AB6/N+773Gy3TpYtONO9ETf+stewKvWNF4mY0bfVvxyxOvvWajXRobFC0osC4vp+zl53t26WzcaJ+dsJeebl0+7n02Zi8+PrAVw7wkqsTfLcL/ans3szivaTfLe+/BH/7gmO1Nm+w5O326vRAEOplLUY6gTRs7Zfvzzxsvc8stgUf6uHG7HzZtarzMZ5/Z2bJOkJVlxb2xiV7GwO2322gmJ+jVy7MY799vL369egVuKy7OuiGaEv+MDM/jAg7hxGIuzYrJk4Hl3eGBbWxaXxmSH7GhOqjYK0EhNtaKgycxdhJ3j9eTYPm7oEpDuEV240abyqI+KSlw333O2nv1VRs+2tCd0uWX20gmJ+15+i1/9zt7dxMCoqrnX02fPvZgbtnSeJn77rP+t/Ly0NVLUZzAk4CUlFgxfu45Z2wlJ0Pbto3bW73a9vp37nTGnlv8G7u47doV+HyC+vYqK2sGyRvCGOdyJDUl/sccY1cjDAHRKf7umYY//th4mWXL7Ch+fHzjZRSlOeJJQPLyahKDOYEx1l5jYvzll3DXXfa/5ARN3WncdZft3DnFkCEwcaKNxGmIK6+Ef/3LOXvXXgtPP93wZ6WldszD26UsAyTq3D4A9O9vn9euhdNOa7jMd9/B0UeHrk6K4hT9+9sZp2VlNvqnNm7RdMJH7ebXv4Z27Rr+bONGG+Pfs6czttq1s7HujbmSNm50tm2eYupF4PXXnZnd6+bYYxv/bP16O6/ixRfh0kuds9kI0dnzT0uzi0mffXbDnx84YE+wYcNCWy9FcYLbbrMdm/rCD8ER/6lT4Wc/a/izjRut8Dt5B92nDyQkNG7PybZ5YvduqxVO2ispgQ8+qAnprE0wjp0HolP8jYGLL24wVnbmTDi/3w8gwtX/PtqnHP6K0uzZsAFatw58tm1tKipgzZqG00Vs2OC8WH3wAfz5zw3XY/Nm5+2dfLJ179THnRreSXtFRXDmmTB7dmjseSA6xR/sQNQrr9TZ5M7hv35HItOZwoeFx/iUw19RmgXl5XDGGfDEE0d+lp1thczJXPBz59r9NhQPv3594Nk167N4MfzlL0emsFi3zrbdaXutW1s3cH1WrrTPTtpLTbWpKdz7rs2KFdCpU8NRTkEgesX/xRftrWqtgRx3ArbvGcblTGcrPRxLsawoISM+3qYJaCjW/5prbC4aJxk82D43NNFr61bnYvxr2xOxHbjadO4Mjz4KJ5zgvL1VqxqexTxoEPTu7ZwtY6y9hn7LFSvsZyFafS0g8TfGdDbGfGKMWet67tRIuUpjzLeuRwP3O0EgO9tmz6t1Arlz/HRjGyBHbFeUiKEhAamosBEjTpOebgc9GxKsuDhnkqzVxp1ypb69lBS77KLTbpHBg+3vVj/C6IorbB2cmCld396KFUfOYn7xRXj8cWdteSDQnv8dwFwR6QfMdb1viEMicrTrcW6ANr1jhCu3z7Jl1ZsyMiCRg2wmg7u4t852RYkoGuqt5ubamPw5c5y1ZYztAdcX4zfegOuvbzxM0l/69rV3N/XtLV3qOUbeXzzd2QSDwYNhz54j50ZkZNTUJQQEKv6ToDpt5fPATwLcn3P062d7K0uXVm+aNg1OTFhCHJV8zTGA5t1RIpSGeqsrVti7XSfdFLXt1RfHDz6wcekNRR0FQny8vXOvL45XXgm/+Y2ztsBe2K66qm420aIiGzDy8svO27v4YjvGUHuB9hUr4MEH7UUhRAQq/qkish3A9dzYSEVrY0yuMWaRMabRC4QxZqqrXG5hU4s4N8HMl2L48vAIlv53afWKXpMnw33nfAHAYkZr3h0lchk+3Eap1I7AWbHC5v5xKulZbaZOhaeeqrsQycqVweup5ubC//5X87683E7aDIa9pCSbSXTUqJptq1ZZf3CbNs7bS0uz2QVqu5PmzLHps0OYcaBJZ5YxZg7QUIJtX4ZJM0RkmzGmNzDPGPODiKyvX0hEngSeBMjJyWlirbPGcUf1pB1+ij10Zp9rRS+AyQe/hMGD2bO8weEJRYkMjjkG5s+vu23ZMhg61NlIHzf1FzwpL7e912uucd4WHHk38cMP1maw5uaI2FnM7vEEt7s4WPbefNPeXVxxRY291NSQRfqAFz1/ETlFRIY08Hgb2GmM6Qbgem4wI5GIbHM9bwA+BYY71oIGcEf1bKAP+7AiX1ICf7mrFBYsgJNOCqZ5RQkdbn/74cM2FNOBFOWN8sUX9v8D8O239k/lVHbN+mzdCueeWzN+4Y5sCpa9hx+27jK3x+Hzz23K92Dl1X/hhbr+5s8/t8cuRJE+ELjbZzbwc9frnwNv1y9gjOlkjElwvU4GxgANBLk6R030jnA79/FzngNg02bD9Yn/4/30IPVWlBaFMeZCY8wKY0yVMSYn5BX4xz9sT7G01Eb6/P3vdnGSYHHTTTa3DljfdGpq8C42nTrZtAvu1AsLFtiZxMGKznDf2SxYYO8CFi6EsWODJ8Zjx9p5C/n5VrA2brTbQomI+P0AumCjfNa6nju7tucAT7teHw/8AHznev6FN/seMWKE+Etmpog9giJfcJx8zxCBquptiYkiM2b4vXslSgByJbDzfyAwAHs3m+Pt9wI5t+vw4Yf2hJ4925n9NcWf/yxijMiOHfZ9ZWVw7U2cKNKnj0hVlbX52WfBs1VeLtKli8jkySIHD4rcdJPIW28Fz96qVfbYPfqoyJtvisTHi6xZ48iuvT2vAxL/YD4C+YPMmGEFHkSu4mkRkL9yt9zLHZLIAQF7gVBaNoGKv/sRNvE/fFgkOVlk6FCRRx4RKS11Zr+NsWKF/VNNnixSVhZcWyIizzxj7T37bPBtiYhcc41I69YiGzaExt7QoSLZ2SKHDons3+/Yblu0+IvYC0BmpkgCh+Q7hoqA7KSrJFMgYDswSssm4sVfROThh6X6lnbZMuf22xiXXGJtjR1re+TB5PBhkf79ba/YfbcRTNavF0lIEHnuueDbEhGZNUukVy+RAwcc3a2353V0pnSmZjWtrKzWjM+bxzm8wxxOYRddAZ3YpXiHp2g3sUEP3u5nKjAVIMPJk+/GG+1AZXq6jQAKNs89ZxOTjRsX/MHJVq3sIPO77zaeUtpJeveGb74J3SppP/mJDddt2zY09uph7IWi+ZGTkyO5ubkB78cd9lk7R1Riosb3K2CMWSYiAQ/UGmM+BW4VEa9OWKfObUVpCG/P6+hN7ObCvai7LqiuKIpSQ9S6fWqjC6orwcAYcx7wKNAVeM8Y862ITAxztRTFK5qt28cYUwg0sNwNAMnArhBWJ9REc/uaU9syRaRrqI3quR217WsubfPqvG624u8JY0yuE77a5ko0ty+a2+YE0f77RHP7Iq1tUe/zVxRFUY5ExV9RFKUFEqni/2S4KxBkorl90dw2J4j23yea2xdRbYtIn7+iKIoSGJHa81cURVECQMVfURSlBRJx4m+MOd0Ys8YYs84Y09iC8RGHMaanMWa+MWaVK0d8EBYrDT/GmFhjzDfGmHfDXZfmRLSe19Ayzu1IPK8jSvyNMbHAY8AZwCDgUmPMoPDWyjEqgFtEZCAwGrg+itpWm98Aq8JdieZElJ/X0DLO7Yg7ryNK/IGRwDoR2SAiZcDLwKQw18kRRGS7iHztel2MPZHSw1srZzHG9ADOAp4Od12aGVF7XkP0n9uRel5HmvinA1tqvc8nik4iN8aYLOw6x4vDWxPHeRj4HVAV7oo0M1rEeQ1Re25H5HkdaeLfUALxqIpVNca0A94AfisiReGuj1MYY84GCkRkWbjr0gyJ+vMaovPcjuTzOtLEPx/oWet9D2BbmOriOMaYeOyfY6aIvBnu+jjMGOBcY8wmrFtjvDFmRnir1GyI6vMaovrcjtjzOqImeRlj4oAfgQnAVmAp8DMRWRHWijmAMcYAzwN7ROS34a5PMDHGnIxd/OTscNelORDN5zW0nHM70s7riOr5i0gFcAPwEXbQ6NVo+YNgexCXYXsO37oeZ4a7UkrwifLzGvTcbpZEVM9fURRFcYaI6vkriqIozqDiryiK0gJR8VcURWmBNNsF3JOTkyUrKyvc1VAURYkoli1btsubNXybrfhnZWWRm5sb7mooiqJEFMaYPG/KqdtHURSlBaLiryiK0ozYuOsgizbsDrqdZuv2URRFaSlUVFYxd3UBMxbl8dnaXfRNaccnN52InRwdHFT8FUVRwkRBcSmvLNnCi0s2s31/Kd06tOaWU/tz8bE9gyr8oOKvKIoSUkSExRv3MH1RHh8t30FFlXBCv2T+fO5gJmSnEBcbGm+84+JvjHkWcKc5HeLa9mfgGqDQVewuEXnfaduKoijNleLScmZ9s5XpX+WxtuAAHdrEc8XxWUwenUmv5LYhr08wev7PAf8GXqi3/f9E5IEg2FMURWm2rNxWxIzFebz1zVZKyio5qkcH/nHBUZxzVHfatIoNW70cF38RWeharUdRFKVFcriikg+X72D6V3nk5u0lIS6Gc4d1Z8roTIb17Bju6gGh9fnfYIy5HMjFLua8t34BY8xUYCpARkZGCKumKIoSOFv2lPDiks28unQLuw+WkdUlkd+fNZALRvSgY2KrcFevDkFJ6ezq+b9by+efCuzCLk33V6CbiFzlaR85OTmiM3wVRWnuVFUJC9YWMuOrPOatKcAApwxM5bLjMhnTJ5mYmOBG7dTHGLNMRHKaKheSnr+I7HS/NsY8BbwbCruKoijBYs/BMl7N3cLMxXls2XOI5HYJ3DCuL5eOzKB7xzbhrl6ThET8jTHdRGS76+15wPJQ2FUURXESEeGbLfuY8VUe7/6wnbKKKkb16sztp2dz2qA0WsVFTtKEYIR6vgScDCQbY/KBPwEnG2OOxrp9NgHXOm1XURQlWJSUVfD2t9uY/lUeK7cX0S4hjkuO7cmU0Zn0T00Kd/X8IhjRPpc2sPkZp+0oiqIEm3UFB5ixKI83vs6nuLSC7LQkpp03hElHp9MuIbLnyEZ27RVFURymvLKKT1buZMaiPL5cv5v4WMOZQ7tx2ehMRmR2CnrahVCh4q8oigLs2F/KS0s289KSzRQUHya9Yxt+d/oALsrpSXK7hHBXz3FU/BVFabGICF+t3830RXl8vHInVSKc1L8rfx+dyckDUogNcZhmKFHxVxSlxbH/UDlvLMtnxuI8NhQepFNiPFeP7cXPRmWQ2SX0eXbCgYq/oigthuVb9zNjUR5vfbuV0vIqhmd05KGLhnHm0G60jg9fnp1woOKvKEpUU1peyXvfb2f6ojy+3bKPNvGxnDc8ncmjMhmS3iHc1QsbKv6KokQlebsP8uLizbyau4W9JeX07tqWP50ziPOP6UGHNvHhrl7YUfFXFCVqqKwS5q8uYPqiPBauLSTGGCYOTmXKqEyO69MlasI0nUDFX1GUiGfXgcO8snQLLy7ezNZ9h0htn8BvJvTjkmMzSOvQOtzVa5ao+CuKEpGICLl5e5n+VR4fLN9OeaUwpm8Xfn/WQE4ZlEp8iJZDjFRU/BVFiSgOHK7grW+2MmNRHqt3FJPUOo4pozOZPCqTvintwl29iEHFX1GUiGDNjmJmLMpj1jdbOXC4gsHd23Pf+UM59+juJLZSKfMV/cUURWm2lFVU8dGKHUxflMeSjXtoFRfD2UfZPDtH9+yoA7gBoOKvKEqzY+u+Q7y0eDMvL93CrgOHyeicyJ1nZHNhTk86t21eyyFGKir+iqI0C6qqhM/X7WL6ojzmrtqJABOyU5gyOpMT+3UN+XKI0Y6Kv6IoYWVfSRmv5eYzc3Eem3aX0KVtK355Uh8uHZlBz86J4a5e1KLiryhKWPhuyz6mL8rjne+2cbiiimOzOnHTqf05fUgaCXEtK89OOFDxVxQlZBwqq+Sd77cxY1Ee3+fvp22rWC4Y0YMpozMZ2K19uKvXolDxVxQl6GwoPMDMxZt5LXcLRaUV9E9tx18nDeYnw9NJaq15dsKBir+iKEGhorKKuasLmLEoj8/W7iIuxnDG0G5MGZXByF6dNUwzzKj4K4riKAVFpby8dAsvLdnM9v2ldO/QmltP689Fx/YkJUnz7DQXHBd/Y8yzwNlAgYgMcW3rDLwCZAGbgItEZK/TthVFCQ8iwuKNe5i+KI+Plu+goko4sX9X7jl3MOOzU4jTPDvNjmD0/J8D/g28UGvbHcBcEbnPGHOH6/3tQbCtKEoIKSotZ9bXNs/O2oIDdGgTz5VjsvjZqEx6JbeM5RAjFcfFX0QWGmOy6m2eBJzsev088Ckq/ooSsazcVsSMxXm89c1WSsoqGdazI/+84CjOGda9xS2HGKmEyuefKiLbAURkuzEmpaFCxpipwFSAjIyMEFVNURRvOFxRyQc/2Dw7y/L2khAXw6SjuzNldCZH9egY7uopPtKsBnxF5EngSYCcnBwJc3UURQG27CnhxSWbeXXpFnYfLKNXclt+f9ZALhjRg46JmmcnUgmV+O80xnRz9fq7AQUhsqsoih9UVgkLfyxkxqI85q0pwACnDkrlstFZHN+ni+bZiQJCJf6zgZ8D97me3w6RXUVRfGDPwTJezd3CzMV5bNlziK5JCfx6XF8uGZlB945twl09xUGCEer5EnZwN9kYkw/8CSv6rxpjfgFsBi502q6iKP4hIny9eR8zFuXx3g/bKauoYnTvztxx+kBOG6zLIUYrwYj2ubSRjyY4bUtRFP/ZdeAwH63YwcxFm1m5vYikhDguPbYnU0Zn0i81KdzVU4JMsxrwVRQleIgIK7YVMW91AXNXF/B9/j5EIDstiXvPG8qko7vTNkEloaWgR1pRopiSsgo+X7uLeasLmL+mgJ1FhzEGhvXoyM2n9GdcdgqDu7fXPDstEBV/RYkytuwpqe7dL9qwm7KKKpIS4jixf1fGZadw8oCuJLdLCHc1lTCj4q8oEU5FZRXL8vYyb00B81YVsLbgAAC9k9ty+ehMxmenkJPVmVZxOnCr1KDirygRyN6DZSz4sZC5qwtYsKaAotIK4mMNo3p14ZKRGYzPTtHcOopHVPwVJQIQEdbsLGbuqgLmry7g6817qRJIbteKiYPTmDAwhTF9k3VhFMVrVPwVpZlSWl7JV+t3M3f1TuavLmTrvkMADE3vwA3j+zEhO4Wh6R10tq3iFyr+itKM2L7/EPNWW9/9F+t3UVpeRWKrWMb2TebX4/syLjuF1Pa6IIoSOCr+ihJGKquEb7fsY97qncxbXciq7UUA9OzchkuOtb77Ub07kxCnaZIVZ1HxV5QQs/9QOQt/LGT+6gI+/bGQPQfLiI0x5GR24q4zsxmfnUKfru009l4JKir+ihJkRIT1hQddvfsClm7aS2WV0CkxnpMHpDA+O4UT+3WlQ6IO1iqhQ8VfUYLA4YpKFm/YY/33qwvYvKcEsKkUfnlSb8Znp3B0z07E6mCtEiZU/BXFIQqKSpm/xor9Z2t3UVJWSUJcDGP6JjP1xN6My04hXdMiK80EFX9F8ZOqKuGHrfure/c/bN0PQPcOrTn/mHTGZ6dwXO9k2rTSwVql+aHiryg+cOBwBZ+vLXQJfiG7DhwmxsDwjE7cNnEAEwamMCA1SQdrlWaPir+iNMGmXQere/eLN+6mvFJo3zqOkwakMD67Kyf1T6FzW13LVoksVPwVpR7llVUs3bSH+a7MmBsKDwLQN6UdV43pxbjsFEZkdtIVrpSIRsVfUYDdBw7z6Rrrzln4YyHFhytoFRvDqN6dXZkxU8nokhjuaiqKY6j4Ky0SEWHl9iLmrSpg3poCvt1iV7VKSUrgrKO6MS47hbF9k3VlKyVq0TNbaTEcKqvki3W7mLvaZsbcUVQKwLCeHfnthP5MGJjCoG7tNVGa0iJQ8Veimvy9JdW++y/X21Wt2raKrbOqVUqSJkpTWh4hFX9jzCagGKgEKkQkJ5T2leinorKKb7bsq857v2ZnMQBZXRKZMiqTCQNTOFZXtVKUsPT8x4nIrjDYVaKUfSV2Vat5qwv4dE0h+w+VExdjGNmrM7/PGcj47BR6d20X7moqSrNC3T5KxCEirC04UN27z83bQ5VAl7atOGVgKhMGpjC2XzLtdVUrRWmUUIu/AB8bYwR4QkSerP2hMWYqMBUgIyMjxFVTmjOl5ZUs2rCbeasLmLuqoHpVq8Hd23P9uL6Mz05hWI+OOlirKF4SavEfIyLbjDEpwCfGmNUistD9oeti8CRATk6OhLhuSjNjx/7S6pm1X6zbxaHyStrExzKmbzI3jO/LuAEppHXQwVpF8YeQir+IbHM9FxhjZgEjgYWev6W0FCqrhO/y99nonFUFrHStatWjUxsuzOnB+OwURvfuQut4TZSmKIESMvE3xrQFYkSk2PX6NOAvobKvNE+KSsv57MddrsHaAnYfLCPGQE5mZ+44w65q1S9FV7VSFKcJZc8/FZjl+hPHAS+KyIchtK80EzYUHqj23S/dtIeKKqFjYjwn9e/K+OwUTurflY6JmihNUYJJyMRfRDYAw0JlT2keVFUJm/eUsHpHEUs27mXe6p1s2m1XtRqQmsQ1J9pVrYb37EicJkpTlJChoZ6KY+w5WMbqHUWs3l7Mmh3FrN5RxI87D3CovBKAVnExHN+nC78YazNj9uikidIUJVyo+Cs+U1peybqCA6zeUcyaHUWs3lHM6h3FFBYfri7TuW0rstOSuGRkTwamtWdAWhID0pJ0sFZRmgkq/kqjVFUJW/cdYtX2ItuT31nM6u1FbNpdQmWVjcRNiIuhX2o7TuzXlYHdkqpFvmu7BB2kVZRmjIq/AtgUCbYnX+zqyRfx445iDpZVVpfJ6JzIgLQkzhrajQGu3nxWl0T11StKBKLi38I4XFHJ+oKDrNnpcte4/PPu9MYAHRPjGZCaxAUjepDdzYp8/9Qk2mlue0WJGvTfHKWICNv2l7J6e1GtHn0RGwoPUuFy2bSKjaFPSjuO69OFbJe7JjutPant1WWjKNGOin8UUFRaXu2uWeOOttlZTHFpRXWZ9I5tyE5L4tRBqQxIa092WhK9ktvqOrSK0kJR8Y8gyiur2FB4kNU7imqJfXF1kjOApNZxZKcl8ZOj0109+ST6pyVphktFUeqg4t8MERF2FJXWuGtcrpv1hQcor7Qum7gYQ5+u7cjJ6sTktAyyXS6bbh1aq8tGUZQmUfEPMwcOV7Cmlk/eLfj7D5VXl+nWoTXZaUmcPCDFiny3JHont9PVqBRF8RsV/xBRUVnFpt0Hq8V91fZi1uwsYsueGpdNu4Q4+qe246yjulX35AekJtEhUV02iqI4i4q/w4gIhcWHa0Te5Z9fW3CAsooqAGJjDL2S2zKsR0cuzulZPQDbo1MbddkoihISVPwDoKSsgh93HmDNjiLbk99ho2z2HCyrLpOSlEB2t/aM6ZvMgFTrsunTtZ2mOVAUJayo+HtBZZWQt/ugqydvwynX7Cgmb08J4lpvrE18LAPSkjhtUGp1vHx2WhKd2mpqYkVRmh8q/vXYdeBwTYqD7UWs2VnMjzuLKS23LpsYA1nJbRnUvT3nDe9BdjcbTtmzU6KuH6soSsTQYsW/tLyStTsP1ImwWb2jmF0HajJTJrdLIDsticmjMhmQlsTAtPb0S1WXjaIokU/Ui39VlbBlb0lNHhtXTptNuw7iynJAQlwMA9KSGDegK+9RN04AAAS8SURBVNnd2lenOkhulxDeyiuKogSJqBP/otJy3liWX92T/3FnMSWuzJTGQKYrM+XZR3VnoEvkM7u0JVZdNoqitCCiTvylCu55ZyWdEuPJTmvPRTk9XXnm29M/tR2JraKuyYqiKD4TdUrYITGepXefQnK7VhozryiK0ghRJ/4AXZPUV68oiuIJTQ6jKIrSAlHxVxRFaYEYcU9RbWYYYwqBvAB2kQzscqg64SRa2gHaluZKtLQlWtoBgbUlU0S6NlWo2Yp/oBhjckUkJ9z1CJRoaQdoW5or0dKWaGkHhKYt6vZRFEVpgaj4K4qitECiWfyfDHcFHCJa2gHaluZKtLQlWtoBIWhL1Pr8FUVRlMaJ5p6/oiiK0ggRLf7GmGeNMQXGmOWNfG6MMY8YY9YZY743xhwT6jp6ixdtOdkYs98Y863r8cdQ19EbjDE9jTHzjTGrjDErjDG/aaBMRBwXL9vS7I+LMaa1MWaJMeY7VzvuaaBMgjHmFdcxWWyMyQp9TZvGy7ZcYYwprHVMrg5HXb3FGBNrjPnGGPNuA58F77iISMQ+gBOBY4DljXx+JvABYIDRwOJw1zmAtpwMvBvuenrRjm7AMa7XScCPwKBIPC5etqXZHxfX79zO9ToeWAyMrlfmOuBx1+tLgFfCXe8A2nIF8O9w19WHNt0MvNjQeRTM4xLRPX8RWQjs8VBkEvCCWBYBHY0x3UJTO9/woi0RgYhsF5GvXa+LgVVAer1iEXFcvGxLs8f1Ox9wvY13PeoP9k0Cnne9fh2YYJphZkQv2xIxGGN6AGcBTzdSJGjHJaLF3wvSgS213ucTgX/eWhznut39wBgzONyVaQrXLepwbO+sNhF3XDy0BSLguLhcC98CBcAnItLoMRGRCmA/0CW0tfQOL9oC8FOXS/F1Y0zPEFfRFx4GfgdUNfJ50I5LtIt/Q1fISO0lfI2dtj0MeBR4K8z18Ygxph3wBvBbESmq/3EDX2m2x6WJtkTEcRGRShE5GugBjDTGDKlXJGKOiRdteQfIEpGjgDnU9JybFcaYs4ECEVnmqVgD2xw5LtEu/vlA7at+D2BbmOoSECJS5L7dFZH3gXhjTHKYq9Ugxph4rFjOFJE3GygSMcelqbZE0nEBEJF9wKfA6fU+qj4mxpg4oAPN3A3ZWFtEZLeIuBfjfgoYEeKqecsY4FxjzCbgZWC8MWZGvTJBOy7RLv6zgctd0SWjgf0isj3clfIHY0ya29dnjBmJPXa7w1urI3HV8RlglYg81EixiDgu3rQlEo6LMaarMaaj63Ub4BRgdb1is4Gfu15fAMwT1yhjc8KbttQbPzoXO1bT7BCRO0Wkh4hkYQdz54nIlHrFgnZcInoxF2PMS9hoi2RjTD7wJ+wAECLyOPA+NrJkHVACXBmemjaNF225APiVMaYCOARc0hz/nNjezGXADy6/LMBdQAZE3HHxpi2RcFy6Ac8bY2KxF6dXReRdY8xfgFwRmY29yE03xqzD9iwvCV91PeJNW240xpwLVGDbckXYausHoTouOsNXURSlBRLtbh9FURSlAVT8FUVRWiAq/oqiKC0QFX9FUZQWiIq/oihKC0TFX1EUpQWi4q8oitICUfFXFEVpgfw/lkkBfj/0VusAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure()，这个是创建一幅图，可以没有参数，\n",
    "#但是，也可以有参数，可以直接看源码的参数说明，figsize=(width,height) #这个指定图的大小，单位好像是inch，\n",
    "plt.figure(12)\n",
    "plt.subplot(221)\n",
    "plt.plot(x1,fun(x1),'bo',x2,fun(x2),'r--')\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(x2,np.cos(2*np.pi*x2),'r--')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot([1,2,3,4],[1,4,9,16])"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}