|
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215 |
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# matplotlib\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. pyplot\n",
- "matplotlib.pyplot是一组命令风格的函数,它们使matplotlib的工作方式类似于MATLAB。每个pyplot函数都对图形进行一些更改:例如,创建图形,在图形中创建绘图区域,在绘图区域中绘制一些线,用标签装饰绘图,等等。在matplotlib。pyplot各种状态保存在函数调用,所以它跟踪当前图和绘图区域,和绘图功能是针对当前轴(请注意“axes”,在大多数地方的文档是指轴图的一部分,而不是严格的数学术语多个轴)。 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAnUUlEQVR4nO3dd3hUddrG8e8TCKH3gLTQexExgG3tBStiX3ddyyquW9zyKiAWLKx9i65rwVXXvroEFCn2hg0FVhJ670gCARJK6jzvHzNqlgUywUxOJnN/ritXZs6cmfP8mDD3nPYcc3dERCRxJQVdgIiIBEtBICKS4BQEIiIJTkEgIpLgFAQiIgmudtAFVFTLli29U6dOQZchIhJX5syZs8XdU/f1WNwFQadOnZg9e3bQZYiIxBUzW7O/x7RpSEQkwSkIREQSnIJARCTBKQhERBKcgkBEJMHFPAjMrJaZ/cfMpu7jsRQze8XMlpvZLDPrFOt6RETiUXZeARc98TnZ+QWV/tpVsUbwW2DRfh77ObDN3bsBfwHuq4J6RETizsPvLeOr1bk8/O6ySn/tmJ5HYGbtgTOBPwJ/2Mcsw4HbI7cnAo+Ymbl6Y4uIANDzlhkUloS+u//CrLW8MGstKbWTWDL+9EpZRqzXCP4KjAJC+3m8HbAOwN1LgB1Ai71nMrORZjbbzGbn5OTEqFQRkernyZ+l06Te99/Z6yYnMXxgW2aOPqHSlhGzIDCzs4Bsd5/zQ1/L3Se4e7q7p6em7vMMaRGRGqWguJQH3lrMlf/8ioLiEAak1E6isCREo5TatGpUt9KWFctNQ0cD55jZGUBdoLGZveDuPy0zzwagA7DezGoDTYCtMaxJRKTam706l1EZmazM2cWFh7dn664i2jatx6VD0njpy7XkVPIO45gFgbvfBNwEYGbHAzfsFQIAU4DLgc+BC4D3tX9ARBLVzsISHnhzMc99sYa2Terx3FVDOLbHf28FGX9uv0pfbpU3nTOzO4HZ7j4FeAp43syWA7nAJVVdj4hIdfDR0hzGTspi4449XH5kJ248rScNUqrmI7pKluLuHwIfRm7fVmZ6AXBhVdQgIlIdbd9dxF1TF5Exdz1dUxvw72uPJL1T8yqtIe7aUIuI1BQzsjZx6+sL2L67iF+f0I1fn9iNusm1qrwOBYGISBXLzivgttcX8OaCb+jXrjHPXjWYvm2bBFaPgkBEpIq4O/+es57xUxdSUBJi9LBeXPOjztSuFWzbNwWBiEgVWJe7m7GTs5i5bAtDOjXn3vP70yW1YdBlAQoCEZGYKg05z32+mgfeWoIBdw3vy0+GdiQpyYIu7TsKAhGRGFmenc/ojCzmrNnG8T1T+eOI/rRrWi/osv6HgkBEpJIVl4Z44qMVPPzecuqn1OIvFx/KuQPbYVZ91gLKUhCIiFSirPU7uHHiPBZ/k8+ZA9pwxzl9adkwJeiyDkhBICJSCQqKS/nru8t4cuZKWjSowxOXHc5pfQ8JuqyoKAhERH6gWSu3MmZSFqu27OLi9A6MPbM3TeolB11W1BQEIiIHKb+gmPvfXMLzX6yhQ/N6vHj1UI7u1jLosipMQSAichA+WJzNzZOz2JRXwM+P6cz/ndqD+nXi8yM1PqsWEQlI7q4i7pq6kMn/2UD3Vg3JuO4oBqU1C7qsH0RBICISBXdnWtYmxr2+gB17irn+pO786oSupNSu+iZxlU1BICJSjs15Bdzy2nzeWbiZAe2b8MLVQ+ndpnHQZVUaBYGIyH64O6/OXsf4aYsoKgkx9oxeXHV08E3iKpuCQERkH9Zu3c2YSZl8tmIrQzs3577zB9CpZYOgy4oJBYGISBmlIeeZT1fx4NtLqJ2UxN0j+nPJ4A7VqklcZVMQiIhELN2cz6iJmXy9bjsn9mrFH0f0o02T6tckrrIpCEQk4RWVhHjswxU88sEyGtVN5qFLBnLOoW2rbZO4yqYgEJGENm/ddkZnZLL4m3yGD2zLbWf1oUU1bxJX2RQEIpKQ9hSV8pd3l/KPmStp1agu//hZOif3aR10WYFQEIhIwvl8xVbGTMpkzdbdXDo0jTGn96Jx3fhpElfZFAQikjDyCoq5Z/piXv5yLR1b1Oela4ZyVNf4axJX2WIWBGZWF/gYSIksZ6K7j9trniuAB4ANkUmPuPs/YlWTiCSu9xZt5ubJ88nOL2DksV34/ck9qFcn/ttDVIZYrhEUAie6+04zSwY+MbMZ7v7FXvO94u6/jmEdIpLAtu4s5I43FjJl3kZ6HdKIJy47nEM7NA26rGolZkHg7g7sjNxNjvx4rJYnIlKWuzNl3kbueGMh+QXF/P7kHlx3fFfq1K5Z7SEqQ0z3EZhZLWAO0A34u7vP2sds55vZscBS4Pfuvm4frzMSGAmQlpYWw4pFpCbYtGMPt0yez3uLsxnYoSn3XzCAHq0bBV1WtWXhL+4xXohZU2Ay8Bt3n19megtgp7sXmtm1wMXufuKBXis9Pd1nz54d03pFJD6FQs7LX63lnumLKQmFuOHUnlx5dGdq1eD2ENEysznunr6vx6rkqCF3325mHwDDgPllpm8tM9s/gPuroh4RqXlWb9nFmEmZfLEyl6O6tuDe8waQ1qJ+0GXFhVgeNZQKFEdCoB5wCnDfXvO0cfdNkbvnAItiVY+I1EwlpSGe/nQVf3p7KXVqJ3Hf+f25KL1DwrSHqAyxXCNoAzwb2U+QBLzq7lPN7E5gtrtPAa43s3OAEiAXuCKG9YhIDbP4mzxGT8xk3vodnNKnNePP7UfrxnWDLivuVMk+gsqkfQQiUlhSyt8/WMGjHyynSb1k7hjelzP7t9FawAEEvo9ARKSyzF27jdETM1mWvZMRh7XjtrP60KxBnaDLimsKAhGJC7uLSvjT20t5+tNVHNK4Ls9cMZgTerUKuqwaQUEgItXep8u3MGZSJuty93DZER0ZNawnjRK4SVxlUxCISLW1Y08x90xfxL++Wkfnlg14ZeQRDO3SIuiyahwFgYhUS28v+IZbXpvP1l1F/OK4rvzu5O7UTVaTuFhQEIhItZKTX8jtbyxgWuYmerdpzFOXD6Z/+yZBl1WjKQhEpFpwd177egN3vLGQ3YWl3HBqD649rivJtdQkLtYUBCISuA3b93Dz5Cw+XJLDoLRwk7hurdQkrqooCEQkMKGQ8+KsNdw7YzEhh3Fn9+FnR3ZSk7gqpiAQkUCszNnJmIwsvlydy4+6t+TuEf3p0FxN4oKgIBCRKlVSGuLJmav4y7tLqVs7iQcuGMAFh7dXe4gAKQhEpMos3JjHqIx5zN+Qx2l9W3PX8H60UpO4wCkIRCTmCopLeeT95Tz+0Qqa1q/DYz8ZxOn92wRdlkQoCEQkpuasyWXUxExW5Ozi/EHtufWs3jStryZx1YmCQERiYldhCQ+8tYRnP19N2yb1ePaqIRzXIzXosmQfFAQiUuk+XprDTZOy2LhjDz87oiM3DutFwxR93FRXemdEpNLs2F3MXdMWMnHOerqkNuDVa49kcKfmQZcl5VAQiEileHP+Jm59fQG5u4r45fFduf4kNYmLFwoCEflBsvMLGPf6AmbM/4a+bRvzzBWD6ddOTeLiiYJARA6Ku5MxdwN3TV3InuJSRg3ryTU/6qImcXFIQSAiFbYudzdjJ2cxc9kWBndqxr3nD6BrasOgy5KDpCAQkaiFQs5zn6/m/reWYMCdw/vy06EdSVKTuLimIBCRqCzP3smYjExmr9nGsT1SuXtEP9o3U5O4mkBBICIHVFwaYsLHK3no3WXUT6nFny48lPMGtVOTuBokZkFgZnWBj4GUyHImuvu4veZJAZ4DDge2Ahe7++pY1SQi5cvOK+DXL/+HRy49jOy8QkZNzGThpjzO7N+G28/pS2qjlKBLlEpWbhCY2YXAm+6eb2a3AIOA8e4+t5ynFgInuvtOM0sGPjGzGe7+RZl5fg5sc/duZnYJcB9w8cENRUQqw8PvLeOr1blc+fRXLN6cT/MGdXj8p4czrN8hQZcmMRLNGsGt7v5vMzsGOBl4AHgMGHqgJ7m7Azsjd5MjP77XbMOB2yO3JwKPmJlFnisiVajnLTMoLAl9d3/BpjwAduwpVgjUcNEc8Fsa+X0mMMHdpwFRtQ40s1pm9jWQDbzj7rP2mqUdsA7A3UuAHUCLfbzOSDObbWazc3Jyolm0iFTQm787ls4tv9/5W6dWEsMHtuWT0ScEWJVUhWiCYIOZPUF4k830yHb9qM4YcfdSdx8ItAeGmFm/gynS3Se4e7q7p6emqnuhSGX7cEk2P/3HLFZt2Q1ASu0kikMhGqXUplUjXTimpotm09BFwDDgQXffbmZtgBsrspDI8z6IvM78Mg9tADoA682sNtCE8E5jEakC23YVcde0hUyau4FurRoytHNzurduxKVD0njpy7Xk5BcEXaJUgQMGgZnVAua6e69vp7n7JmBTeS9sZqlAcSQE6gGnEN4ZXNYU4HLgc+AC4H3tHxCJPXdnetY3jJsyn+27i7n+xG786sRupNT+vknc+HMPagVe4tABg8DdS81siZmlufvaCr52G+DZSJgkAa+6+1QzuxOY7e5TgKeA581sOZALXHIQYxCRCsjOK+CW1+bz9sLN9G/XhOeuGkqfto2DLksCFM2moWbAAjP7Etj17UR3P+dAT3L3TOCwfUy/rcztAuDCqKsVkYPm7vx79nrumraQopIQN53ei58f05naahKX8KI6fDTmVYhITK3L3c1Nk7L4ZPkWhnRuzr3n9aeLmsRJRLlB4O4fmVlHoLu7v2tm9QFdbUIkDpSGnGc/W80Dby2hVpIx/tx+XDokTU3i5L9Ec2bxNcBIoDnQlfCx/48DJ8W2NBH5IZZtzmd0RiZz127nhJ6p/HFEf9o2rRd0WVINRbNp6FfAEGAWgLsvM7NWMa1KRA5acWmIxz9cwd/eX06DlFr89eKBDB/YVk3iZL+iCYJCdy/69o8ocry/DvEUqYay1u/gxonzWPxNPmcf2pZxZ/ehZUM1iZMDiyYIPjKzsUA9MzsF+CXwRmzLEpGKKCgu5S/vLuXJj1eS2iiFJ3+Wzil9WgddlsSJaIJgDOEuoVnAtcB04B+xLEpEovfFyq2Mychk9dbd/HhIB8ac3psm9ZKDLkviSDRHDYXM7FnC+wgcWKKzf0WCl19QzL0zFvPirLWkNa/PS1cP5ahuLYMuS+JQNEcNnUn4KKEVgAGdzexad58R6+JEZN8+WJzN2MlZbM4r4OpjOvOHU3tQv44uOCgHJ5q/nD8BJ7j7cgAz6wpMAxQEIlUsd1cRd76xgNe+3kiP1g159CdHcVhas6DLkjgXTRDkfxsCESuB/BjVIyL74O5MzdzE7VMWkFdQzG9P6s6vTuhGndpqDyE/3H6DwMzOi9ycbWbTgVcJ7yO4EPiqCmoTEWBzXgE3T57Pu4s2c2j7Jtx3wVB6HaImcVJ5DrRGcHaZ25uB4yK3cwCdnigSY+7OK1+t44/TF1FcGuLmM3pz1TGdqaX2EFLJ9hsE7n5lVRYiIt9bs3UXN03K4rMVWzmiS3PuPW8AnVo2CLosqaGiOWqoM/AboFPZ+ctrQy0iFVcacp75dBUPvr2E5KQk7h7Rn0sGd1CTOImpaHYWv0b4AjJvAKGYViOSwJZ8E24S9/W67ZzUqxXjR/SjTRNthZXYiyYICtz94ZhXIpKgikpCPPrhcv7+wXIa1U3m4R8fxtkD2qhJnFSZaILgITMbB7wNFH470d3nxqwqkQQxb912Rk3MZMnmfIYPbMu4s/vSvEGdoMuSBBNNEPQHLgNO5PtNQx65LyIHYU9RKX9+ZwlPfbKKVo3q8tTl6ZzUW03iJBjRBMGFQBd3L4p1MSKJ4LMVW7hpUhZrtu7m0qFpjDm9F43rqkmcBCeaIJgPNAWyY1uKSM2WV1DMPdMX8/KXa+nYoj4vX3MER3ZtEXRZIlEFQVNgsZl9xX/vI9DhoyJRenfhZm5+LYuc/EJGHtuF35/cg3p1dOlvqR6iCYJxMa9CpIbaurOQO95YyJR5G+l1SCMmXJbOoR2aBl2WyH+J5noEH1VFISI1ibszZd5Gbp+ygJ2FJfzhlB784riuahIn1VI0Zxbn8/01iusAycAudz9g1ysz6wA8B7SOPH+Cuz+01zzHA68DqyKTJrn7nRWoX6Ta2bRjD7dMns97i7MZ2KEp918wgB6tGwVdlsh+RbNG8N1fsIXPcBkOHBHFa5cA/+fuc82sETDHzN5x94V7zTfT3c+qSNEi1VEo5Lz81Vrumb6Y0pBz61l9uOKoTmoSJ9VehS5pFLlE5WuRE8zGlDPvJmBT5Ha+mS0C2gF7B4FI3Fu1ZRdjMjKZtSqXo7u14J4RA0hrUT/oskSiEs2mofPK3E0C0oGCiizEzDoBhxG+7vHejjSzecBG4AZ3X7CP548ERgKkpaVVZNEiMVVSGuLpT1fxp7eXUqd2Eved35+L0juoPYTElWjWCMpel6AEWE1481BUzKwhkAH8zt3z9np4LtDR3Xea2RmEG9x13/s13H0CMAEgPT3d935cJAiLNuUxOiOTzPU7OKVPa8af24/WjesGXZZIhUWzj+Cgr0tgZsmEQ+BFd5+0j9fOK3N7upk9amYt3X3LwS5TJNYKS0r5+wcrePSD5TSpl8wjlx7Gmf3VJE7iVzSbhlKBa/jf6xFcVc7zjHD76kXu/uf9zHMIsNnd3cyGEN70tDXq6kWq2Ny12xg9MZNl2Ts577B23HpWH5qpSZzEuWg2Db0OzATeBUor8NpHE25Wl2VmX0emjQXSANz9ceAC4DozKwH2AJdEdkiLVCu7i0p48K2lPPPZKto0rsszVw7mhJ6tgi5LpFJEEwT13X10RV/Y3T8BDriu7O6PAI9U9LVFqtKny7cwZlIm63L3cNkRHRk1rCeN1CROapBogmCqmZ3h7tNjXo1INbJjTzF3T1vEK7PX0bllA14ZeQRDu6hJnNQ80QTBb4GxZlYIFBP+lu/lnVksEs/eXvANt7w2n627ivjFcV353cndqZusJnFSM1XozGKRmi4nv5Db31jAtMxN9G7TmKcuH0z/9k2CLkskpip0ZrFITeXuTP7PBu6cupDdhaXccGoPrj2uK8m11CROaj4FgSS8Ddv3cPPkLD5cksOgtHCTuG6ttCIsiUNBIAkrFHJenLWGe2csxoHbz+7DZUeqSZwknqiCwMyOAbq7+zORE8wauvuq8p4nUl2tzNnJmIwsvlydy4+6t+TuEf3p0FxN4iQxRXNm8TjCjeZ6As8Qvh7BC4RPGBOJKyWlIZ6cuYq/vLuUurWTeOCCAVxweHu1h5CEFs0awQjCnUPnArj7xsj1BUTiyoKNOxidkcn8DXmc1rc1dw3vRys1iROJKgiKIr2AHMDMGsS4JpFKVVBcyt/eX8bjH62kWf06PPaTQZzev03QZYlUG9EEwatm9gTQ1MyuAa4CnoxtWSKVY86aXEZNzGRFzi7OH9SeW8/qTdP6ahInUlY0J5Q9aGanAHmE9xPc5u7vxLwykR9gV2EJD7y1hGc/X03bJvV49qohHNcjNeiyRKqlqI4acvd3zGzWt/ObWXN3z41pZSIH6eOlOdw0KYuNO/bwsyM6cuOwXjRM0ZHSIvsTzVFD1wJ3EL48ZYhIryGgS2xLE6mYHbuLuWvaQibOWU+X1Aa8eu2RDO7UPOiyRKq9aL4m3QD001XDpDp7c/4mbn19Abm7ivjl8V25/iQ1iROJVjRBsALYHetCRA5Gdn4B415fwIz539CnTWOeuWIw/dqpSZxIRUQTBDcBn0X2ERR+O9Hdr49ZVSLlcHcmzlnP+GmL2FNcyo2n9WTksV3UJE7kIEQTBE8A7wNZhPcRiARqXe5uxk7OYuayLaR3bMa95w+gW6uGQZclEreiCYJkd/9DzCsRKUco5Dz3+Wruf2sJBtw5vC8/HdqRJDWJE/lBogmCGWY2EniD/940pMNHpcosz97JmIxMZq/ZxrE9Url7RD/aN1OTOJHKEE0Q/Djy+6Yy03T4qFSJ4tIQEz5eyUPvLqNenVr86cJDOW9QOzWJE6lE0ZxZ3LkqChHZ2/wNOxg1MZOFm/I4o/8h3HFOP1IbpQRdlkiNE80JZcnAdcCxkUkfAk+4e3EM65IEVlBcykPvLWPCxytp3qAOj/90EMP6qUmcSKxEs2noMcLXIHg0cv+yyLSrY1WUJK6vVucyemImK7fs4sLD23PLmX1oUj856LJEarRogmCwux9a5v77ZjavvCeZWQfgOaA14X0KE9z9ob3mMeAh4AzCJ61d4e5zoy1e4l92XgG/fvk/3H/+AJ7+dBXPfb6G9s3q8fzPh/Cj7moSJ1IVogmCUjPr6u4rAMysC1AaxfNKgP9z97mRC9nMMbN33H1hmXlOB7pHfoYSXtMYWqERSFx7+L1lfLUqlzMensme4lKuPLoTN5zakwZqEidSZaL533Yj8IGZrSTccK4jcGV5T3L3TcCmyO18M1sEtAPKBsFw4Dl3d+ALM2tqZm0iz5UarOctMygs+f78xN1F4e8WL81ay7iz+wZVlkhCKvd8fHd/j/A39uuB3wA93f2DiizEzDoRvtzlrL0eagesK3N/fWTa3s8faWazzWx2Tk5ORRYt1ZC7M+7svtSp/f0hoHWTkxg+sC0zR58QYGUiiancIDCzC4E67p4JnAO8bGaDol2AmTUEMoDfuXvewRTp7hPcPd3d01NTtd04nmXnFXDt83MYOzmLhnWSMSCldhKFJSEapdSmVSNdQ1ikqkWzaehWd/+3mR0DnAQ8SJTb8iOHnmYAL7r7pH3MsgHoUOZ++8g0qWHcnX/PXs9d0xZSVBJizOm9mLtmG60a1+XSIWm89OVacvILgi5TJCFFtbM48vtM4El3n2Zm48t7UuSIoKeARe7+5/3MNgX4tZn9i3Cw7ND+gZpn7dZwk7hPlm9hSOfm3Htef7qk/neTuPHn9guoOhGJJgg2RC5efwpwn5mlEMUmJeBowuccZJnZ15FpY4E0AHd/HJhO+NDR5YQPHy13J7TEj9KQ88/PVvPgW0uolWSMP7cflw5JU5M4kWommiC4CBgGPOju282sDeEjiQ7I3T8hfJTRgeZx4FfRFCrxZdnmfEZlZPKftds5vmcqd4/oT9um9YIuS0T2IZpeQ7uBSWXuf3dYqMjeikpCPP7RCh55fzkNUmrx14sHMnxgWzWJE6nGdNaOVJrM9dsZNTGTxd/kc9aANtx+Tl9aNlSTOJHqTkEgP1hBcSl/eWcpT85cScuGKUy47HBO7XtI0GWJSJQUBPKDfLFyK2MyMlm9dTc/HtKBMaf3pkk9NYkTiScKAjko+QXF3DtjMS/OWkta8/q8dPVQjurWMuiyROQgKAikwt5fvJmbJ89nc14BVx/TmT+c2oP6dfSnJBKv9L9Xopa7q4g731jAa19vpHurhjx63VEcltYs6LJE5AdSEEi53J03Mjdx+5QF5O0p5rcndeeXJ3QlpXatoEsTkUqgIJAD+mZHAbe8Np93F23m0PZNuO+aofQ6pHHQZYlIJVIQyD65O//6ah13T1tEcSjEzWf05qpjOlNL7SFEahwFgfyPNVt3MSYji89XbuWILs2597wBdGrZIOiyRCRGFATyndKQ88ynq3jw7SUkJyVx94j+XDK4g5rEidRwCgIBYMk34SZx89Zt56RerRg/oh9tmqhJnEgiUBAkuKKSEI9+uJy/f7CcRnWTeeiSgZxzqJrEiSQSBUEC+3rddkZPzGTJ5nyGD2zLbWf1oYWaxIkkHAVBAtpTVMqf3l7C05+uolWjujx1eTon9W4ddFkiEhAFQYL5bMUWxmRksTZ3N5cOTWPM6b1oXFdN4kQSmYIgQeQVFHPP9EW8/OU6Oraoz8vXHMGRXVsEXZaIVAMKggTw7sLN3PxaFjn5hYw8tgu/P7kH9eqoPYSIhCkIarCtOwu5/Y2FvDFvI70OacSEy9I5tEPToMsSkWpGQVADuTuvf72RO95YwM7CEv5wSg9+cVxX6tROCro0EamGFAQ1zMbte7jltfm8vzibgR2acv8FA+jRulHQZYlINaYgqCFCIeelL9dy74zFlIacW8/qwxVHdVKTOBEpl4KgBli1ZRdjMjKZtSqXo7u14J4RA0hrUT/oskQkTsQsCMzsaeAsINvd++3j8eOB14FVkUmT3P3OWNVTE5WUhnjqk1X8+Z2l1KmdxH3n9+ei9A5qDyEiFRLLNYJ/Ao8Azx1gnpnuflYMa6ixFm7MY3RGJlkbdnBKn9aMP7cfrRvXDbosEYlDMQsCd//YzDrF6vUTVWFJKY+8v5zHPlxB0/rJ/P3SQZzR/xCtBYjIQQt6H8GRZjYP2Ajc4O4L9jWTmY0ERgKkpaVVYXnVy5w12xidkcny7J2cd1g7bj2rD80a1Am6LBGJc0EGwVygo7vvNLMzgNeA7vua0d0nABMA0tPTvcoqrCZ2F5XwwFtL+Odnq2nTuC7PXDmYE3q2CrosEakhAgsCd88rc3u6mT1qZi3dfUtQNVVHnyzbwphJmazftofLjujIqGE9aaQmcSJSiQILAjM7BNjs7m5mQ4AkYGtQ9VQ3O/YU88dpC3l19no6t2zAKyOPYGgXNYkTkcoXy8NHXwaOB1qa2XpgHJAM4O6PAxcA15lZCbAHuMTdE26zz768teAbbn1tPlt3FXHd8V357UndqZusJnEiEhuxPGrox+U8/gjhw0slIie/kNunLGBa1iZ6t2nMU5cPpn/7JkGXJSI1XNBHDQnhJnGT5m7gzqkL2VNUyo2n9WTksV1IrqUmcSISewqCgG3Yvoexk7L4aGkOg9LCTeK6tVKTOBGpOgqCgIRCzguz1nDfjMU4cPvZfbjsSDWJE5GqpyAIwIqcnYzJyOSr1dv4UfeW3D2iPx2aq0mciARDQVCFiktDPDlzJX99dxl1ayfxwAUDuODw9moPISKBUhBUkfkbdjA6I5MFG/MY1vcQ7jy3L60aqUmciARPQRBjBcWl/O39ZTz+0Uqa1a/DYz8ZxOn92wRdlojIdxQEMTR7dS6jMjJZmbOL8we159azetO0vprEiUj1oiCIgV2F4SZxz36+mrZN6vHsVUM4rkdq0GWJiOyTgqCSfbQ0h7GTsti4Yw+XH9mJG0/rSYMU/TOLSPWlT6hKsn13EXdNXUTG3PV0SW3Av689kvROzYMuS0SkXAqCSjAjaxO3vr6AbbuL+NUJXfnNiWoSJyLxQ0HwA2TnFXDb6wt4c8E39G3bmGevGkzftmoSJyLxRUFwENydiXPWc9fUhRSUhBg1rCfX/EhN4kQkPikIKmhd7m7GTs5i5rItDO7UjHvPH0DX1IZBlyUictAUBFEqDTnPf76a+99aggF3De/LT4Z2JElN4kQkzikIorA8O5/RGVnMWbON43qk8scR/WjfTE3iRKRmUBAcQHFpiCc+WsHD7y2nfkot/nzRoYw4rJ2axIlIjaIg2I/5G3Zw48RMFm3K48z+bbj9nL6kNkoJuiwRkUqnINhLQXEpf313GU/OXEnzBnV4/KeHM6zfIUGXJSISMwqCMr5clcuYjExWbtnFxekdGHtGb5rUTw66LBGRmFIQAPkFxdz/5hKe/2IN7ZvV44WfD+WY7i2DLktEpEokfBB8sCSbmydlsSmvgKuO7swNp/Wgfp2E/2cRkQSSsJ9423YVcdfUhUz6zwa6tWrIxF8cxeEdmwVdlohIlYtZTwQze9rMss1s/n4eNzN72MyWm1mmmQ2KVS0Q7gt00ROfk51XwNTMjZz854+YMm8j15/YjWnXH6MQEJGEFcs1gn8CjwDP7efx04HukZ+hwGOR3zHx8HvL+Gp1Luc99hnrt+2hf7smvHD1UHq3aRyrRYqIxIWYBYG7f2xmnQ4wy3DgOXd34Asza2pmbdx9U2XW0fOWGRSWhL67v37bHgCWbs5XCIiIEMNNQ1FoB6wrc399ZNr/MLORZjbbzGbn5ORUaCEzR53AOQPbklI7PNSU2kkMH9iWmaNPOMiyRURqlrjom+zuE9w93d3TU1Mrdu3fVo3r0iilNkWlIVJqJ1FUGqJRSm1aNaobo2pFROJLkEcNbQA6lLnfPjKt0m3ZWchPhnbk0iFpvPTlWnLyC2KxGBGRuBRkEEwBfm1m/yK8k3hHZe8f+NYTl6V/d3v8uf1isQgRkbgVsyAws5eB44GWZrYeGAckA7j748B04AxgObAbuDJWtYiIyP7F8qihH5fzuAO/itXyRUQkOnGxs1hERGJHQSAikuAUBCIiCU5BICKS4Cy8zzZ+mFkOsOYgn94S2FKJ5QRJY6measpYaso4QGP5Vkd33+cZuXEXBD+Emc129/Ty56z+NJbqqaaMpaaMAzSWaGjTkIhIglMQiIgkuEQLgglBF1CJNJbqqaaMpaaMAzSWciXUPgIREflfibZGICIie1EQiIgkuBoZBGY2zMyWmNlyMxuzj8dTzOyVyOOzyrmkZqCiGMsVZpZjZl9Hfq4Oos7ymNnTZpZtZvP387iZ2cORcWaa2aCqrjFaUYzleDPbUeY9ua2qa4yGmXUwsw/MbKGZLTCz3+5jnrh4X6IcS7y8L3XN7EszmxcZyx37mKdyP8PcvUb9ALWAFUAXoA4wD+iz1zy/BB6P3L4EeCXoun/AWK4AHgm61ijGciwwCJi/n8fPAGYABhwBzAq65h8wluOBqUHXGcU42gCDIrcbAUv38fcVF+9LlGOJl/fFgIaR28nALOCIveap1M+wmrhGMARY7u4r3b0I+BcwfK95hgPPRm5PBE4yM6vCGqMVzVjigrt/DOQeYJbhwHMe9gXQ1MzaVE11FRPFWOKCu29y97mR2/nAIv73uuFx8b5EOZa4EPm33hm5mxz52fuonkr9DKuJQdAOWFfm/nr+9w/iu3ncvQTYAbSokuoqJpqxAJwfWW2faGYd9vF4PIh2rPHiyMiq/Qwz6xt0MeWJbFo4jPC3z7Li7n05wFggTt4XM6tlZl8D2cA77r7f96UyPsNqYhAkmjeATu4+AHiH778lSHDmEu7rcijwN+C1YMs5MDNrCGQAv3P3vKDr+SHKGUvcvC/uXuruAwlfy32ImcX0Grs1MQg2AGW/FbePTNvnPGZWG2gCbK2S6iqm3LG4+1Z3L4zc/QdweBXVVtmied/igrvnfbtq7+7TgWQzaxlwWftkZsmEPzhfdPdJ+5glbt6X8sYST+/Lt9x9O/ABMGyvhyr1M6wmBsFXQHcz62xmdQjvSJmy1zxTgMsjty8A3vfIXpdqptyx7LW99hzC20bj0RTgZ5GjVI4Adrj7pqCLOhhmdsi322vNbAjh/2fV7otGpMangEXu/uf9zBYX70s0Y4mj9yXVzJpGbtcDTgEW7zVbpX6GxeyaxUFx9xIz+zXwFuGjbp529wVmdicw292nEP6Ded7MlhPe6XdJcBXvX5Rjud7MzgFKCI/lisAKPgAze5nwURstzWw9MI7wTjDc/XFgOuEjVJYDu4Erg6m0fFGM5QLgOjMrAfYAl1TTLxpHA5cBWZHt0QBjgTSIu/clmrHEy/vSBnjWzGoRDqtX3X1qLD/D1GJCRCTB1cRNQyIiUgEKAhGRBKcgEBFJcAoCEZEEpyAQEUlwCgIRkQSnIBARSXD/D281bMIB/rbDAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 这一行配置matplotlib以显示嵌入在notebook中的图形,\n",
- "# 而不是为每个图打开一个新窗口。稍后会详细介绍。\n",
- "# 如果你是使用旧版本的IPython,尝试使用'%pylab inline'\n",
- "%matplotlib inline\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "plt.plot([1,2,3,4], '-*')\n",
- "plt.ylabel('some numbers')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "lines_to_next_cell": 2
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f1b287602e8>]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfCklEQVR4nO3dd3hVZbr+8e9D7zVBAkkMgoBUlYSmjm1U7A2xDDb0MDi9iTiOR8+MjmXKceacUYZRxq5Uu4xl7DMKCSChC9J2aAmEEggkJPv5/ZF4fkwGTNk72Xvt3J/r4nKXhet5WcmdlbXf513m7oiISPA0iXUBIiJSNwpwEZGAUoCLiASUAlxEJKAU4CIiAdWsIXeWlJTkGRkZDblLEZHAW7hw4Q53T676eoMGeEZGBjk5OQ25SxGRwDOzjUd6XZdQREQCSgEuIhJQCnARkYBSgIuIBJQCXEQkoKoNcDObbmb5ZrasyuvfN7NVZrbczB6uvxJFRORIanIG/iQw5vAXzOxM4FJgqLsPBH4b/dJEROTrVBvg7v4RUFjl5duAB929pHKb/HqoTUQk8PaXlHHvq8vZe/BQ1P/fdb0G3hc4zczmm9mHZpZ1tA3NbKKZ5ZhZTkFBQR13JyISPDv3lXDdXz7jmc82krOh6nlw5Ooa4M2ALsBI4HZgppnZkTZ092nununumcnJ/9YJKiKSkEKFxVw19VNWbSviz+OHcVb/Y6K+j7q20ucBc73idj4LzCwMJAE6xRaRRm/Vtr3cOH0BB0rLee7WEWRmdKmX/dT1DPxl4EwAM+sLtAB2RKkmEZHAWrC+kKumfgrArEmj6y28oQZn4Gb2AnAGkGRmecA9wHRgeuXUwlLgRtfNNUWkkXtnxXa+9/wienZuzdMThpPauU297q/aAHf3a4/y1vgo1yIiElgzsjdx59ylDE7txF9vyqJL2xb1vs8GXU5WRCTRuDuPfvAlv3lrNd/om8xj3zqZti0bJloV4CIidRQOO796YwV//ccGLj2xB78ZO5QWzRpuhRIFuIhIHZSWhfnZrCW8umQLE07pxS8uPIEmTY44m7reKMBFRGppf0kZk55dyMdrdnDHmP5MOv04jtIKU68U4CIitbBzXwkTnsxm2Za9PDx2COMy02JWiwJcRKSG8nYVc8MTC9i8+wB/Hj+Mbw6IfndlbSjARURqoKG6K2tDAS4iUo3sDYXc8mQ2rVs0Zdak0fTr3j7WJQEKcBGRr9XQ3ZW1oQAXETmKmdkhpszNbdDuytpQgIuIVBHL7sraiL+KRERiKNbdlbWhABcRqVRaFub22Ut45fPYdVfWhgJcRIR/7a6cPKYft53eOybdlbWhABeRRu+r7sqlm/fw8JVDGJcVu+7K2lCAi0ij9i/dlddnck6MuytrQwEuIo3W4d2Vz946gqw46K6sjWo/WjWz6WaWX3n7tKrv/dTM3MyS6qc8EZH6kb2hkHGV966cOWlU4MIbanZT4yeBMVVfNLM04FxgU5RrEhGpV++s2M74x+eT1L4lc24bTf/uHWJdUp1UG+Du/hFQeIS3/huYDOhmxiISGDOzQ0x6diH9u7dn9qTRcdUaX1t1ugZuZpcCm919SbxPsxERgYruysc+/JKH/7aa045PYur4YXHZXVkbta7ezNoAP6fi8klNtp8ITARIT0+v7e5ERCIWDjv3vbGS6f9YzyVDe/Dbq+K3u7I26jKC3kAvYImZbQBSgUVm1v1IG7v7NHfPdPfM5OTkulcqIlIHpWVhfjzzc6b/Yz03n5LBI1efmBDhDXU4A3f3pUC3r55Xhnimu++IYl0iIhHbX1LGbc8t4qMvCgLTXVkbNZlG+ALwKdDPzPLM7Jb6L0tEJDKF+0u57vH5fLKmgIevHMJ3zuiTUOENNTgDd/drq3k/I2rViIhEQd6uYm6YvoDNu4LXXVkbwf4IVkSkitXbirhh+nwOlJbzzC0jGN4reA06NaUAF5GEcfi9K2dOGhXYBp2aUoCLSEJ4d8V2vvv8Inp2as1TE4aT1iW4DTo1pQAXkcCbmRPizrlLGdSjA9NvyqJru5axLqlBKMBFJLASsbuyNhrPSEUkoSRqd2VtKMBFJHAOv3flzadkcPeFA+L63pX1RQEuIoGS6N2VtaEAF5HAKNxfys1PZrM0bzcPXTmYq7Ma9wJ5CnARCYTG0l1ZGwpwEYl7X3VXFjeC7sraUICLSFz7qruyVfOmzGoE3ZW1oQAXkbjVGLsra0MBLiJx6avuyoE9OvDXRtRdWRsKcBGJK+7O1A/X8dDfVjXK7sra0L+KiMSNcNi5/82VPPFJ4+2urA0FuIjEhdKyMJNnL+Hlz7dw0+gM/vOixtldWRsKcBGJucO7K28/rx/fOaPxdlfWRk3uiTndzPLNbNlhr/3GzFaZWa6ZvWRmneq1ShFJWIffu/KhKwfz3TMT796V9aUmF5eeBMZUee0dYJC7DwG+AO6Mcl0i0gjk7Spm7NR/smrrXqaOH9boW+Nrq9oAd/ePgMIqr73t7mWVTz8DUuuhNhFJYKu3FTH2sU8pKCrhmVtGcO7A7rEuKXCi8fHuBGDe0d40s4lmlmNmOQUFBVHYnYgEXc6GQq6a+k/C7syaNEqt8XUUUYCb2V1AGfDc0bZx92nununumcnJyZHsTkQSwLsrtvOtx+eT1K4lc24brdb4CNR5FoqZ3QRcBJzt7h61ikQkYam7MrrqFOBmNgaYDJzu7sXRLUlEEk3V7srHxg+jnborI1btv6CZvQCcASSZWR5wDxWzTloC71RO9/nM3SfVY50iElCHd1dePLQHv1N3ZdRUG+Dufu0RXn6iHmoRkQSj7sr6pd9hRKReqLuy/inARSTqDr935YNXDOaa4WrQqQ8KcBGJqsPvXTl1/DA16NQjBbiIRM0X24u44YkF7C8t070rG4ACXESiImdDIRMq710589ujOCFFDTr1TQEuIhH7+8rtfOe5RfTo1Jqnde/KBqMAF5GIzMoJMUXdlTGhABeROnF3/vzROh6ct4pT+yQx9Xp1VzY0/WuLSK2Fw86v31zJ4+qujCkFuIjUyqHyMJNn5/LS4s3qrowxBbiI1FhxaRm3PbuID9VdGRcU4CJSI+qujD8KcBGp1ubdB7j+ifnk7TrAY+OHcZ66K+OCAlxEvta/dFdOGM6I47rGuiSppAAXkaNauLGQCU/m0KJZE3VXxiEFuIgc0d9Xbue7zy8ipaO6K+OVAlxE/s1X3ZUDUjrw15uzSFJ3ZVyqdua9mU03s3wzW3bYa13M7B0zW1P53871W6aINISKe1d+ye2zcxl1XFdemDhS4R3HatI69SQwpsprU4C/u/vxwN8rn4tIgIXDzv1vrOTBeau4aEgK02/KUmt8nKs2wN39I6CwysuXAk9VPn4KuCy6ZYlIQzpUHuans5bw+CfruWl0Bn+85iS1xgdAXX+8HuPuWysfbwOOOdqGZjYRmAiQnq6J/yLx5vDuyp+d25fvntlH3ZUBEfGPWHd3wL/m/WnununumcnJyZHuTkSiaNf+Uq77y3w+XlPAA1cM5ntnHa/wDpC6noFvN7MUd99qZilAfjSLEpH6t3n3AW54Yj4hdVcGVl3PwF8Fbqx8fCPwSnTKEZGG8MX2Iq589J/kF5XwzIThCu+Aqsk0wheAT4F+ZpZnZrcADwLnmNka4JuVz0UkABZuLOSqqZ9S7s7Mb49Sa3yAVXsJxd2vPcpbZ0e5FhGpZ++tqrh3ZfcOrXjmlhHqrgw4TfIUaSRmL8zjjjm56q5MIApwkUbgzx9+yQO6d2XC0VEUSWDhsPPAvJX85eP1XDQkhd+NG0rLZk1jXZZEiQJcJEEdfu/KG0cdyz0XD9S9KxOMAlwkAam7snFQgIskmF2V967MzdvNA1cM5lrduzJhKcBFEsjh3ZWPfmsYYwapQSeRKcBFEoC7Mysnj1+9sQIcnp4wnJFq0El4CnCRgAsVFnPn3KV8snYHw3t14aErh9ArqW2sy5IGoAAXCajysPPUPzfwm7dW07SJcd9lg7hueLpmmjQiCnCRAFqzvYjJc3JZvGk3Z/RL5teXD6ZHp9axLksamAJcJEBKy8JM/fBL/ve9tbRt2ZRHrj6RS0/soSmCjZQCXCQgcvN2M3l2Lqu2FXHx0B7cc/EArWfSyCnAReLcwUPl/Pc7X/CXj9eR3L4lf7khk3MGHPUuhtKIKMBF4thn63YyZU4uG3YWc+3wNKacfwIdWzePdVkSJxTgInGo6OAhHpy3iufmbyK9Sxuev3UEo/skxbosiTMKcJE4896q7dz10jK27z3Iraf24ifn9qVNC32ryr/TV4VInCjcX8ovX1vOy59v4fhu7Xj0ttGclN451mVJHIsowM3sx8CtgANLgZvd/WA0ChNpLNyd13K3cu+ryyk6eIgfnn083zmzt9btlmrVOcDNrCfwA2CAux8ws5nANcCTUapNJOFt23OQX7y8jHdXbmdoakceGjuC/t07xLosCYhIL6E0A1qb2SGgDbAl8pJEEp+782J2iF+/sZJD4TB3XXACE07tRVO1wUst1DnA3X2zmf0W2AQcAN5297erbmdmE4GJAOnpWpdYZOPO/UyZs5RP1+1k5HFdePCKIWRo8SmpgyZ1/Ytm1hm4FOgF9ADamtn4qtu5+zR3z3T3zOTk5LpXKhJw5WHn8Y/Xcd4jH7Fs8x5+fflgnr91pMJb6iySSyjfBNa7ewGAmc0FRgPPRqMwkUSyelvF4lNLQrs5u3837rt8ECkdtfiURCaSAN8EjDSzNlRcQjkbyIlKVSIJorQszKMfrOVP76+lfavm/PHak7h4SIoWn5KoiOQa+Hwzmw0sAsqAxcC0aBUmEnSfh3Zzx+xcVm8v4tITe3DPxQPp0rZFrMuSBBLRLBR3vwe4J0q1iCSEA6Xl/O7t1Uz/x3q6tW/FEzdmcvYJWnxKok+dmCJR9M8vdzBlzlI2FRZz3Yh0ppzfnw6ttPiU1A8FuEgU7D14iAfeXMkLC0Ic27UNL/zHSEb11k2FpX4pwEUi9O6K7dz18lIKikqY+I3j+PE3+9K6hdrgpf4pwEXqaOe+Eu59bQWvLdlC/+7tmXZ9JkPTOsW6LGlEFOAiteTuvLpkC/e+upx9JWX85Jy+TDq9Ny2a1bkvTqROFOAitbBl9wF+8fIy3luVz4lpnXh47BD6HtM+1mVJI6UAF6mBcNh5fsEmHpy3ivKwc/dFA7hpdIYWn5KYUoCLVGP9jv1MmZPL/PWFnNKnKw9cPoT0rm1iXZaIAlzkaMrKwzzxyXp+/84XtGjWhIeuHMy4zDS1wUvcUICLHMHKrXu5Y04uuXl7OGfAMdx32SCO6dAq1mWJ/AsFuMhhSsrK+dN7a3n0gy/p1KY5f7ruZC4Y3F1n3RKXFOAilRZu3MUdc3JZm7+PK07qyd0XDaCzFp+SOKYAl0avuLSM37y1mif/uYGUDq34681ZnNmvW6zLEqmWAlwatU/W7GDK3Fzydh3g+pHHMnlMP9pr8SkJCAW4NEp7Dhzi/jdWMDMnj15JbZkxcSQjjtPiUxIsCnBpdN5avo27X17Gzv2lTDq9Nz/65vG0aq7FpyR4FODSaBQUlXDvq8t5Y+lWTkjpwBM3ZjE4tWOsyxKps4gC3Mw6AY8DgwAHJrj7p1GoSyRq3J2XFm/ml6+voLiknNvP68fEbxxH86ZafEqCLdIz8D8Af3P3sWbWAlB/scSVzbsP8PO5S/nwiwJOTq9YfKpPNy0+JYmhzgFuZh2BbwA3Abh7KVAanbJEIhMOO8/O38hD81bhwL0XD+D6UVp8ShJLJGfgvYAC4K9mNhRYCPzQ3fcfvpGZTQQmAqSnp0ewO5Ga+bJgH1Pm5JK9YRenHZ/Ery8fTFoX/XIoiSeSi4DNgJOBx9z9JGA/MKXqRu4+zd0z3T0zOTk5gt2JfL2y8jCPfrCW8//wMau3FfGbsUN4esJwhbckrEjOwPOAPHefX/l8NkcIcJGGsHzLHu6Yk8uyzXsZM7A7v7xsIN3aa/EpSWx1DnB332ZmITPr5+6rgbOBFdErTaR6Bw+V8z/vrWHqh+vo3KYFj33rZM4fnBLrskQaRKSzUL4PPFc5A2UdcHPkJYnUTM6GQibPyWVdwX6uPDmVuy86gU5ttPiUNB4RBbi7fw5kRqcUkZrZX1Kx+NRTn26gR8fWPDVhOKf31ecr0vioE1MC5aMvCrhz7lK27DnAjaMy+Nl5/WjXUl/G0jjpK18CYXdxKfe9sZLZC/M4Lrkts749isyMLrEuSySmFOAS9+Yt3crdryxnV3Ep3z2zN98/S4tPiYACXOJY/t6D/Ocry/nb8m0M7NGBpyZkMbCHFp8S+YoCXOKOuzN7YR6/en0FB8vCTB7Tj/84TYtPiVSlAJe4Eios5ucvLeXjNTvIyujMg1cOoXdyu1iXJRKXFOASF8Jh5+lPN/DwW6sx4FeXDuRbI46liRafEjkqBbjE3Nr8Iu6Ys5SFG3dxet9k7r98EKmdtX6JSHUU4BIzh8rDTPtoHX94dw1tWjbl9+OGcvlJPTHTWbdITSjAJSaWbd7D7bNzWbl1LxcOTuHeSwaS3L5lrMsSCRQFuDSog4fKeeTdNfzl43V0aduCqeOHMWZQ91iXJRJICnBpMAvWFzJlTi7rduxnXGYqd10wgI5tmse6LJHAUoBLvdtXUsZD81bxzGcbSe3cmmdvGcGpxyfFuiyRwFOAS716f3U+d81dyta9B5lwSi9+dl5f2rTQl51INOg7SerFrv2l/Or1FcxdvJk+3doxe9Johh3bOdZliSQUBbhElbvzxtKt3PPKcvYcOMQPzurDd8/qQ8tmWnxKJNoU4BI12/ce5O6Xl/H2iu0M7tmRZ24ZwYAeHWJdlkjCijjAzawpkANsdveLIi9JgsbdmZkT4r43VlJaFubO8/tzy6m9aKbFp0TqVTTOwH8IrAR0qtUIbdpZzJ0v5fKPtTsZ3qsLD105hF5JbWNdlkijEFGAm1kqcCFwP/CTqFQkgVBcWsbz8zfxu7e/oGkT477LBnHd8HQtPiXSgCI9A38EmAy0P9oGZjYRmAiQnp4e4e4kltydJXl7mJEd4rUlW9hXUsaZ/ZK5//LB9OjUOtbliTQ6dQ5wM7sIyHf3hWZ2xtG2c/dpwDSAzMxMr+v+JHZ2F5fy0uLNzMgOsWpbEa2aN+HCwT24OiuNrIzOWnxKJEYiOQM/BbjEzC4AWgEdzOxZdx8fndIklsJh59N1O3kxO8Rby7dRWhZmSGpH7r98EBcP7UGHVmqBF4m1Oge4u98J3AlQeQb+M4V38G3bc5DZC0PMyAkRKjxAh1bNuG54OuMy0zQlUCTOaB64cKg8zHur8pmRHeKD1fmEHUb37srPzu3HeQO76w7wInEqKgHu7h8AH0Tj/yUNZ13BPmbkhJizcDM79pXQrX1LbjujN+My0zi2q6YCisQ7nYE3MgdKy5m3bCsvZodYsL6Qpk2Ms/p345qsNE7vm6zmG5EAUYA3Ess27+HF7E28sngLRSVlHNu1DZPH9GPsyal069Aq1uWJSB0owBPYnuJDvLKkYvrf8i17admsCRcMTmFcZhojj+ui6X8iAacATzDuzvz1hczIDvHm0q2UlIUZkNKBX146kEuH9tQdcEQSiAI8QeTvPcjsRXnMzA6xYWcx7Vs246rMVK7JSmdQz46xLk9E6oECPMDKysN8sLqAGTkh3luVT3nYGd6rC98/63guGJxC6xaa/ieSyBTgAbRx535m5oSYlZNHflEJSe1acOtpvRiXmUbv5HaxLk9EGogCPCAOHirnreXbeHFBiE/X7aSJwRn9unF1Vhpn9e9Gc03/E2l0FOBxbsWWvczMCfHS4s3sOXCItC6t+ek5fRmbmUpKR60AKNKYKcDjUNHBQ7y6ZAszskPk5u2hRdMmnDeoO9dkpTHquK5ac1tEAAV43HB3cjbu4sUFFdP/Dhwqp98x7bnn4gFcdmJPOrdtEesSRSTOKMBjbMe+EuYuyuPF7BDrCvbTtkVTLjupB1dnpTM0taOabUTkqBTgMVAedj5aU8CMBSHeXbmdsrAz7NjOPDy2NxcOTqFtSx0WEamekqIBhQqLmZUTYtbCPLbuOUiXti24+ZQMrs5Ko0+3o96VTkTkiBTg9aykrJx3VmxnRnaIT9buAOC045O5+6IBfPOEY2jRTNP/RKRuFOD1ZPW2ImZkh3hpcR67ig/Rs1Nrfnj28VyVmUZP3QBYRKJAAR5F+0rKeH3JFmbkhFi8aTfNmxrnDujOuKw0Tu2TRFNN/xORKFKAR8jdWRzazYwFIV7L3UJxaTl9urXjFxeewOUn9aRru5axLlFEElSdA9zM0oCngWMAB6a5+x+iVVi8K9xfytxFeczIDrEmfx+tmzfl4qEpXJ2VzsnpnTT9T0TqXSRn4GXAT919kZm1Bxaa2TvuviJKtcWdcNj5ZO0OZuSEeHv5Ng6VOyemdeKBKwZz0ZAU2rfSWtsi0nDqHODuvhXYWvm4yMxWAj2BhAvwLbsPMCsnj5k5ITbvPkCnNs0ZP/JYrs5Ko3/3DrEuT0QaqahcAzezDOAkYP4R3psITARIT0+Pxu4aRGlZmL+v3M6L2SE+WlOAO5zaJ4kp5/fnnAHH0Kq51toWkdiKOMDNrB0wB/iRu++t+r67TwOmAWRmZnqk+6tva/P3MTMnxJyFeezcX0r3Dq343pl9GJeZRlqXNrEuT0Tk/0QU4GbWnIrwfs7d50anpIZXXFrGG7lbmZEdImfjLpo1Mc4+oRvXZKXzjb7Jmv4nInEpklkoBjwBrHT330evpIbh7uTm7eHF7BCvLdnCvpIyjktqy53n9+eKk1NJbq/pfyIS3yI5Az8FuB5YamafV772c3d/M+Kq6tHu4lJeXryZF7NDrNpWRKvmTbhgcArXZKWTldFZ0/9EJDAimYXyCRCItAuHnc/W7eTF7BB/W76N0rIwg3t25L7LBnHJiT3ooOl/IhJACd2JuW3PQWYvDDEzJ49NhcV0aNWMa7PSGJeVxsAeHWNdnohIRBIuwA+Vh3l/VT4zskO8vzqfsMPI47rwk3P6MmZQd03/E5GEkTABvn7HfmZkh5i9MI8d+0pIbt+SSaf3ZlxmGhlJbWNdnohI1AU6wA+UljNvWcX0v/nrC2naxDizXzeuzkrjzH7JNGuqtbZFJHEFMsCXbd7DjOwQL3++maKDZRzbtQ23n9ePscNSOaZDq1iXJyLSIAIT4HsOHOLVzyum/y3fspcWzZpwwaDuXJ2VzoheXWiiZhsRaWQCEeB//Psa/vT+WkrKwpyQ0oH/umQgl53Yk45tNP1PRBqvQAR4j06tGTsslWuy0hnUs4OabURECEiAjx2WythhqbEuQ0QkrmiahohIQCnARUQCSgEuIhJQCnARkYBSgIuIBJQCXEQkoBTgIiIBpQAXEQkoc2+4G8WbWQGwsY5/PQnYEcVyYkljiT+JMg7QWOJVJGM51t2Tq77YoAEeCTPLcffMWNcRDRpL/EmUcYDGEq/qYyy6hCIiElAKcBGRgApSgE+LdQFRpLHEn0QZB2gs8SrqYwnMNXAREflXQToDFxGRwyjARUQCKq4C3Mymm1m+mS07yvtmZn80s7VmlmtmJzd0jTVVg7GcYWZ7zOzzyj//2dA11oSZpZnZ+2a2wsyWm9kPj7BNII5LDccSlOPSyswWmNmSyrH81xG2aWlmMyqPy3wzy4hBqdWq4VhuMrOCw47LrbGotSbMrKmZLTaz14/wXnSPibvHzR/gG8DJwLKjvH8BMA8wYCQwP9Y1RzCWM4DXY11nDcaRApxc+bg98AUwIIjHpYZjCcpxMaBd5ePmwHxgZJVtvgNMrXx8DTAj1nVHMJabgP+Nda01HM9PgOeP9HUU7WMSV2fg7v4RUPg1m1wKPO0VPgM6mVlKw1RXOzUYSyC4+1Z3X1T5uAhYCfSsslkgjksNxxIIlf/W+yqfNq/8U3VGwqXAU5WPZwNnWxzeULaGYwkEM0sFLgQeP8omUT0mcRXgNdATCB32PI+AfgNWGlX5a+M8MxsY62KqU/nr3klUnCEdLnDH5WvGAgE5LpW/qn8O5APvuPtRj4u7lwF7gK4NWmQN1WAsAFdWXqKbbWZpDVthjT0CTAbCR3k/qsckaAGeSBZRsb7BUOB/gJdjW87XM7N2wBzgR+6+N9b1RKKasQTmuLh7ubufCKQCw81sUIxLqrMajOU1IMPdhwDv8P/PYuOGmV0E5Lv7wobaZ9ACfDNw+E/e1MrXAsfd9371a6O7vwk0N7OkGJd1RGbWnIrAe87d5x5hk8Acl+rGEqTj8hV33w28D4yp8tb/HRczawZ0BHY2aHG1dLSxuPtOdy+pfPo4MKyBS6uJU4BLzGwD8CJwlpk9W2WbqB6ToAX4q8ANlbMeRgJ73H1rrIuqCzPr/tW1LzMbTsWxiLtvrsoanwBWuvvvj7JZII5LTcYSoOOSbGadKh+3Bs4BVlXZ7FXgxsrHY4H3vPLTs3hSk7FU+UzlEio+v4gr7n6nu6e6ewYVH1C+5+7jq2wW1WPSrK5/sT6Y2QtUzAJIMrM84B4qPtDA3acCb1Ix42EtUAzcHJtKq1eDsYwFbjOzMuAAcE08fnNRcVZxPbC08holwM+BdAjccanJWIJyXFKAp8ysKRU/ZGa6++tm9ksgx91fpeKH1TNmtpaKD9SviV25X6smY/mBmV0ClFExlptiVm0t1ecxUSu9iEhABe0SioiIVFKAi4gElAJcRCSgFOAiIgGlABcRCSgFuIhIQCnARUQC6v8BG07ePfh3ZTkAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "对于每一对x、y参数,都有一个可选的第三个参数,它是表示图形的颜色和线条类型的格式字符串。格式字符串的字母和符号来自MATLAB,您可以将一个彩色字符串与一个行样式字符串连接起来。默认的格式字符串是' b- ',它是一条纯蓝色的线。例如,要用红色圆圈绘制上面的图形,您需要这样来设置:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "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",
- "plt.plot([1,2,3,4], [1,4,9,16], 'r.-')\n",
- "plt.axis([0, 6, 0, 20])\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "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 numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# 以200ms间隔均匀采样时间\n",
- "t = np.arange(0., 5., 0.2)\n",
- "\n",
- "# 红色的破折号,蓝色的正方形和绿色的三角形\n",
- "plt.plot(t, t, 'r--', \\\n",
- " t, t**2, 'bs', \\\n",
- " t, t**3, 'g^')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### [控制线条属性](https://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)\n",
- "\n",
- "线条有很多属性,你可以设置:线宽,折线样式,反锯齿,等等;看到matplotlib.lines.Line2D。设置行属性有几种方法\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1.1 处理多个图形和轴\n",
- "\n",
- "MATLAB和pyplot都有当前图和当前轴的概念。所有绘图命令都适用于当前轴。函数gca()返回当前轴(matplotlib.axes)。而gcf()返回当前的图(matplotlib.figure)。图实例)。通常,您不必担心这个,因为它都是在后台处理的。下面是创建两个次要情节的脚本。"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABBAklEQVR4nO2dd3iUVdbAfzeNBAiKdCkBFRtWzIcJIAoKNlbFih+o2BDdXXd1FQvurg111+7ufgqyCgpK01VBxUVBegsKiDSRIlgA6SSEEHK+P86MKaZN5m2Tub/nmWcy77xz77l5Z8577rnnnGtEBIvFYrHUfhL8FsBisVgs3mAVvsViscQJVuFbLBZLnGAVvsViscQJVuFbLBZLnJDktwAV0bhxY2nbtq3fYlgsFktMsXjx4p9FpEl57wVW4bdt25acnBy/xbBYLJaYwhizsaL3HHHpGGNeM8ZsNcYsr+B9Y4x5yRiz1hizzBjT0Yl+y2PMGGjbFhIS9HnMGLd6slgsltjCKR/+SOCCSt6/EGgfegwEXnao31KMGQMDB8LGjbmIwMaN+toqfYvFYnFI4YvITGBHJadcCrwhynzgcGNMCyf6LsmQIZCX9y1wPPA2AHl5etxisVjiHa+idFoCm0q83hw6VgpjzEBjTI4xJmfbtm0Rd/LddwBtgHbAzcCSEsctFoslvglUWKaIDBeRTBHJbNKk3EXmSmnTBiAZmAAcDtwCSOi4xWKxxDdeKfzvgdYlXrcKHXOUoUOhbl2AZsATwGJSUv7D0KFO92SxWCyxh1cK/wPg+lC0ThawW0R+dLqTfv1g+HDIyADoT1LS8TRt+lf+939tRVCLxWJxKizzbWAecJwxZrMx5mZjzCBjzKDQKR8B64C1wKvAHU70Wx79+sGGDSCSxCuv3MPmzcuZO3euW91ZLBZLzGCCWg8/MzNTok28ys3NpUWLFvTp04dRo0Y5JJnFYrEEF2PMYhHJLO+9QC3aOk29evXo378/48ePZ/fu3X6LY7FYLL5SqxU+wPXXX09+fj6TJk3yWxSLxWLxlVqv8Dt16kSrVq2YMGGC36JYLBaLr9R6hZ+QkMAVV1zBJ598wp49e/wWx2KxWHyj1it8gCuvvJIDBw7w8ccf+y2KxWKx+EZcKPzs7GyOOOIIq/AtFktcExcKPzExkWOP7cWbb07BmCJbNtliscQlcaHwx4yBL764kKKiLcASWzbZYrHEJXGh8IcMgYKC80Ov1K1jyyZbLJZ4Iy4UvpZHbgacAnxe5rjFYrHEB3Gh8IvLI58NzAUKyhy3WCyW2k9cKPzisslnA3lADnXrYssmWyyWuCIuFH64bHKrVt0AOPzwGQwfrsctFoslXogLhQ+q3DdtakKHDh0488wZVtlbLJa4I24Ufpizzz6b2bNnc/DgQb9FsVgsFk+JS4Wfm5vLF1984bcoFovF4ilxp/C7dVM//owZM3yWxGKxWLwl7hR+8+bNOe6446zCt1gscUfcKXxQt86sWbM4dOiQ36JYLBaLZzi1ifkFxpjVxpi1xpj7y3l/gDFmmzFmSehxixP91pQuXbqwd+9eVq5c6acYFovF4ilRK3xjTCLwL+BC4ETgWmPMieWcOk5ETgs9RkTbbzRkZWUBMH/+fD/FsFgsFk9xwsLvBKwVkXUiUgCMBS51oF3XaN++PQ0bNrQK32KxxBVOKPyWwKYSrzeHjpXlCmPMMmPMRGNM6/IaMsYMNMbkGGNytm3b5oBo5WOMISsryyp8i8USV3i1aDsJaCsipwBTgVHlnSQiw0UkU0QymzRp4qpAWVlZrFixgt27d7vaj8VisQQFJxT+90BJi71V6NgviMh2ETkQejkCOMOBfqMiKysLEWHRokV+i2KxWCye4ITCXwS0N8a0M8akAH2BD0qeYIxpUeLlJYDv4TGdOnUC7MKtxWKJH6JW+CJSCPwO+ARV5ONF5GtjzKPGmEtCp91pjPnaGLMUuBMYEG2/0XL44Ydzwgkn1AqFP2YMtG0LCQnY/XotFkuFJDnRiIh8BHxU5thfSvz9APCAE305SXZ2Nu+//z4igjHGb3FqxJgxuj9vXp6+Du/XC7b8s8ViKU1cZtqGycrKYvv27Xz77bd+i1ItSlryLVosplu3AdxwQxvy8hoCWcAzwB67X6/FYimXuFf4EBt+/LAlv3FjESL389NPnZg16z0OHeoMXAsY4F7gBOBTu1+vxWL5FXGt8E888UTq168fEwp/yBDIyzsEXA/8DbgJ2Ehi4ljg/4B5ocdhwPk0bOhrMrPFYgkgca3wExMT6dSpU0wofLXYHwDGAEOB4cBhHDoU3q8X1K2ziISEXuzYcSuvv/66L7JaLJZgEtcKH6B+/SwWL16KMXmBjnBp1Ogd4GngDuBB1IUDGRm6X29GBhgDGRn1eO219+nVqxe33HIrzZpNtdE7FosFcChKJ1YZMwamTMkCCoEv2LixayAjXLZu3cqBA4NISDiDoqIXfjlety4MHaqylpY3hYMHJzJ1ajZbt/YHlrJxY/NAjs1isXhHXFv4Q4ZAQcGZoVfq1glihMs999zDgQN7GDp0JBkZySFLXi37ipT344+nIzIO2AtcBxQFcmwWi8U74lrhq1+8KXAUuuBZ8ngwWLBgAW+++SZ33303999/Ehs2QFERbNhQuaWuY+gAvAR8ii70BmtsFovFW+Ja4bdpE/4rC1hQznF/ERHuvfdemjVrxoMPPhjRZ4vHcDNwNfBXYEVgxmaxWLwnrhX+0KHhCJcstN7b5l/84kHgs88+Y9asWfz5z38mPT09os8Wj80A/wQakJAwkMceK3JBUovFEgvEtcLv10/94M2bawJW48bzK/WLe4mI8PDDD9OyZUtuuSXyHSHDY9PonSY0avQMRUVz2L/fxudbLPFKXCt8UMW4ceOp1KlThxtumB8IZT9mDLRoMZc5c+aQl3cfEyfWqVE7/frxi89/27YbOOeccxg8eDA//fSTswL7gC0YZ7FETtwrfICUlBQ6duzIggULqj7ZZcIlFLZseRZoyM6dNzFwYPQKzRjDsGHD2L9/P4MHD3ZEVi8oT7EXl5kAkeKCcVbpWyxVICKBfJxxxhniJXfddZekpqZKQUGBp/2WJSNDBNYKGIEHRVWaHneCBx98UABp1mymGKPtjh7tTNtOM3q0SN26IlAoMEPgIUlIuEgSE08ROE7gDIG+As8LLJQ2bYpk9GgdU9DHZrG4BZAjFehVa+GHyMrKIj8/n2XLlvkqh4ZNvopOvu4oczx6jjrqQYxpzZYtv0WkMNDW8QMP5JGX9zTQDjgbeJKios0cOpQBnAo0AeYAdwGd+O6747jxxqFs3PiTtfwtlnKwCj9EUCpntm5dALwO9KbkXvBOhVM+9lg9RF4AvkKLrgUv2UxEeO+999i06URgMNAeGAvsBJaiG6qNAz4GvgN+QP9nLTl48CH0BnE38FPgxmax+IlV+CFat25NixYtfFf4l176PrAVuO2XY06GiupMoQ/QC/gzsKXEcf8I++qN2U39+lfRp08fkpPrA9OBz4BrAA1NbdSoZME4gBbUrTsgdO7q0LkvAUcDT7FxY4Fn43CCihakIz1usfyKinw9fj+89uGLiPTp00eOOeYYz/stybnnniuNG2dImzaFrvihdY1ABFYLJAvc4OgaQU0o9tV/JdBeIFGSk5+SgQMLQseLH3Xr6vnl+eqLxyYCawQuFUCSko6V++77JHC+/fLGUPy/KD3mQYOKJC3tgMChUsdvv73884MwPos/UIkP3xHlDFyAmlZrgfvLeb8OOgdfi6a0tq2qTT8U/t/+9jcBZNu2bZ73LSLyzTffCCCPP/64a32UVij3CyB16sz2VUGoov5YoK5Ac4GZv9yEIlmELU9Z1qnzkRx22DECCFwhsCkQSrE8WdPS9kh6+icCfxPoJ5At0EYgNSR/+FFXoKVAthhzrcADAiMFlgkU1Oh/5xUVyeT2cT/xWlZXFT6QCHyLFqRJQZ2sJ5Y55w7gldDffYFxVbXrh8KfMWOGADJ58mTP+xYRue+++yQxMVF++OEHV/sptob3SWJia2nT5lQ5ePCgq31WBrwlkCRwmsAPvyhAYyJvq7wfUZs2+wUeCynO+gLPChz0dVaj//8igUUCjwqcFfofhJV6a4EeAtcJ3CPw59AY/iLwJ4EbQ+8fVeZzdQQyBW6XlJSRAit+mRVUNjtymkhmL7ffLqHZy/cCqwSWSWpqjvTpM0fq1Jku8LnAfIEvJTV1hfTrt07S0n4Wjd6q3mynsjG7ebOpbMxuzczcVvjZwCclXj8APFDmnE+A7NDfScDPgKmsXT8U/r59+yQxMVEeeughz/suLCyUli1bym9+8xtP+50wYYIA8o9//MPTfsO88soroiGoZwvsKvUDcEohGxNu81uBi0KK8RSBOb4ovxde2CDwuGhoKaHxnyFwn8B/BbaX+j+ASGKi/OpY8fHCkGIfE7oZnBO6sYVvAg0EzhV4QNLT35O0tB/ETaX4ayV3QFJTvwnNXl4WGCxwpUCWwNECh5WQNdLH4aGbXqZAL9Ew3d8J/FXgJYExkp7+iaSm5gisF9gjUFRqzJEo5MqOp6UVhtr/QWCtpKYulQYN5gpMFXhP1LB5VeAFMWaoaNj1PQJ3Cdwp8FupX3+Q3HrrrfLEE0/U+PtWmcI3+n7NMcZcCVwgIreEXl8HnCkivytxzvLQOZtDr78NnfNzRe1mZmZKTk5OVLLVhI4dO9KoUSOmTp3qab+fffYZ5513HuPHj+eqq67yrF8RoVevXixatIg1a9bQtGlTz/oeMWIEt956K6eddjGrV09k//7UX96rW7fy8s+R0LathmgqArwH3AlsJjHxFg4degpo5Hi/UJwklpe3B5gIvAHMCL17Nlq6+lKgMaAL0vv3a+RUmLp14YYbYNSo6h/X7TBXox7UhaHHMnTvB4BWwJlAJ6ATDRueQH5+U/bvN6XaGT5c/9YxVNR3IRoptZGUlI0kJm5g//71wLrQYzNQsoZTCtAWaI1Wq22Mhtg2QRfm64QeKaGHAPnAgdBjP7AH2IFGbu0IPbaXeN7564tRqv9GJCc3IiHhMA4cCPeX+kvfxiQhcij0/zoUehQCB4FcYF/oueTf+yvpszwSQv0llno0b55IZmYmkyZNirA9xRizWEQyy32zojtBdR/AlcCIEq+vA/5Z5pzlQKsSr78FGpfT1kAgB8hp06ZNje9w0XD77bdLenq6FBYWetrvgAEDpEGDBpKXl+dpvyIiK1eulOTkZBkwYIDrfRW7k0YKGDnllAskPz/fVUu7fH/5XklNvUcgMWRhPhayzmo+syg7hlGjDkrTph8JXCvFfvj2Ao/J4Yevj9j9EMnx0gvY4UeewBzRRLVrQ5ZxSWs5VeDY0GzgCoEBkp5+pzRocG/ICv29wKDQZ3uJzkrahP6HZS3vFgJdBPqLuqFGiibPbZKSC89Vz15qevygwFaBlQKzBd4X+LfA30VnUrcI9BF1i3URnSGcHBp/G4EjQ8/tBI4ROF6gg6jbsYvA+QKXC1wvcLvAvQIPCzwtOot5Q2Ci6NrUTIHFIVm+E9guCQn5oi690mNwYlaLdelUn1GjRgkgy5cv96zP3NxcSU9Pl5tvvtmzPssyePBgAaR587muuTeKFe9oUTfGeZKWlufJwlp5SlFdPcslHM0DjUQXsjfUSOnq2A6FFNvtAk1C7R4hcIeoH1p/5Ma460uvyFXRqFFZRblN4CNR98efpNjVcoJAK9GbYR1RF1HD0JiOFugkcKHoGsMQgeECn4j64PPKVciNGjnnPonk+K/HXKxcy78xOncTinTMseDDT0Lnbe0oXrTtUOac31J60XZ8Ve36pfBXr14tgAwbNsyzPseOHSuATJ8+3bM+yzJixF4xplXoh57r6BcwjP64xgkkiPqZcx2zamouT/ixUOCykGwJkpBwrsALouGdhyr8kaalFcnzz6+XRo1GCwwQtWwRSBO4WhIS3hXIL1fZuE0ki4hOKsWKlJxTs5dIj1c0Zqd9+E6NOVpcVfjaPhcBa0KumiGhY48Cl4T+TgUmoGGZC4GjqmrTL4VfVFQkzZo1k379+nnWZ+/evaVVq1Zy6NAhz/osi/6o/xtSVne4oph0ipso0FVg7y991CQaxwnK+7Gnpm6UtLQhoRtf2D1RX+DM0A2hv+g0vnfoWIMS5zUUtZDfLjW+oMXJu60UvYoEinbMVb0XqyGhrit8Nx5+KXwRkauvvlpatWolRUVFrve1detWSUpKkvvuu8/1viqjOJLlTyHlNclRZfzee++Jhg9mS9hX7qW1WxEVu3pE4BuBYaJRH91FfbztRH27p4n6uu8QeEWaN18kJcMES44tKIqgKpxUihb/sAo/Qv75z38KIOvWrXOtj+KFtX8IIE8++ZVrfVWH4ml7vsCpAo0FvnNEGU+ePFmSk5Pl6KM7SVpa6dBLv63d8ojUhRFWdEGz5C3xiVX4EbJs2TIBZOTIka60X1o5nClwqu/KobRMKwUaiDGnyYgR+2rUVtjqa9z4LUlMTJaOHTvKzp07Y8IirGmyTCyMzVL7sQo/Qg4dOiRHHHGE3HTTTa60X2xBrgm5T5723bUhUlphNW36kRiTID179pT8/PyI2ihWis8JIAkJ3WTYsJ2uye0G1oVhiVWswq8Bl156qWuF1Ip9xH8RDU/c7OviZUW89tprAsjJJ58vrVvvrZaS05vZXtHUf0Rjlff7fjOzWOKFyhS+LY9cAfXqdWPt2rUY84PjJWe1tr0Ao4FzCde9d6rmvVPceOON3HLLCL76aiqbNnVFZHWpTUXKK8u7ceNiIBMYCQwBxgOpvpdftlgsWAu/PEaPFqlTZ1HIQn3b8QU4bX9OqP2RgV7gU4v9I9GkpFTRyozfl4m1LhL4QhITrw/NWI4UmBaYSByLJZ7AWviRMWQIHDhwGlrXYybg7K5Q/fpB166jMSYNuJyMDGfrtziJWuYXovl0VwBPAq3Zvr0jeXmXAxejdVE6cujQBFJT7yYtbTnQ/Zc2nNzAxWKx1Byr8MtBlVwS0BXdbank8egpKCjgyy/H0bfvZYiks2FDMJU9lHQztURdUGvQnbIaA98AP6JFuF4BvufAgWd49dWGZGSAMQT6ZmaxxBtJfgsQRNq0CVdX7Inum7oRyHDMx/7xxx+zY8cO+vfv70yDLjJ0aNlKie2pW/dh0tJg+/Zfn9+mjSp3q+AtluBhLfxyGDo0vGdqr9CRqY66JUaPHk2TJk3o2bOnMw26SL9+aqGXtdhffLHsvrLWdWOxBB1r4ZdD2Dp98MET+e67I6lbdyrDh9/iiNW6a9cuJk2axG233UZycnL0DXpAZRb7kCHq6mrTRpW9tewtluBiLfwK6NcPNm40DBjQizp1pnLNNYVVf6gaTJw4kQMHDsSEO6cq+vWDDRugqIhAr0NYLBbFKvwquPjii9m5cyfz5s1zpL3Ro0dz7LHHkplZ/oY0FovF4hZW4VdBr169SE5O5oMPPoi6rW+++YYZM2Zw/fXXY4yp+gMWi8XiIFbhV0GDBg3o3r27Iwp/+PDhJCYmctNNNzkgmcVisUSGVfjV4JJLLmHNmjWsWrWqxm3k5+fz+uuvc9lll9GiRQsHpbNYLJbqYRV+NejTpw/GGMaNGxfxZ8P1ZtLS3mH79u20bz/IeQEtFoulGliFXw2OPPJIzj77bN5++20tMVpNxozRpCVN4noFOIYXX+zhaCE2i8ViqS5W4VeTa6+9ltWrV7NkyZJqf2bIkHCG6nJgNnAb+/cnOFaTx2KxWCIhKoVvjDnCGDPVGPNN6LlhBecdMsYsCT2iX/30gSuuuIKUlBRGjhxZ7c8U1955Ht3HfUCZ4xaLxeId0Vr49wOfiUh7tMrY/RWct19ETgs9LomyT19o1KgRV155JaNGjSKvuLBMpWjtnc3Am8DNaMGx4NW9t1gs8UG0Cv9SYFTo71HAZVG2F2gGDRrE7t27GTt2bLXOHzoUkpKeAYqAewBbb8ZisfhHtAq/mYj8GPr7J6BZBeelGmNyjDHzjTGXVdSYMWZg6Lycbdu2RSma83Tt2pXWrU9m0KBnMeZQlTthdemyAXiZevVuwJi2tlSwxWLxlSqLpxljPgWal/NWqaVHERFjTEUhLBki8r0x5ihgmjHmKxH5tuxJIjIcGA6QmZlZ/XAYj3jrLcOWLQ9x8OA1wEQ2bryGgQP1vfKU+JAhQ0hKSmDVqkdo1cpTUS0Wi+VXVGnhi8h5InJSOY/3gS3GmBYAoeetFbTxfeh5HfA5cLpjI/CQIUOgoOBK4ETgL0B+hTthTZkyhbfeeot77rmHVlbbWyyWABCtS+cD4IbQ3zcA75c9wRjT0BhTJ/R3Y6ALsCLKfn1Bo2sS0KibNcCjvxwvuaF369bb6N9/ICeccAJDbAymxWIJCNEq/KeAnsaYb4DzQq8xxmQaY0aEzjkByDHGLAWmA0+JSEwq/OLoml7ATcDfgP9wxBHFCVYieWzefBnbt2+lb99RpKam+iavxWKxlMREkjnqJZmZmZKTk+O3GKUIZ85qVOY+dAvExdSt+xh5ef2A1cAfga+BcWRkXMWGDX5Ja7FY4hFjzGIRKbf+us20jYDS2/3Vp1Wrj+jY8ULy8u4HWqOTnC3AFOAqm2BlsVgChd3iMEJKb/fXEHifFi3m89NPS4EmwAWAbvZqE6wsFkuQsBa+AzzzTBZ1694GXE5Y2dsEK4vFEjSswneA0q4ebIKVxWIJJNal4xClXT0Wi8USPAIbpWOM2QZsjKKJxsDPDokTK9gxxwd2zPFBTcecISJNynsjsAo/WowxORWFJtVW7JjjAzvm+MCNMVsfvsViscQJVuFbLBZLnFCbFf5wvwXwATvm+MCOOT5wfMy11odvsVgsltLUZgvfYrFYLCWwCt9isVjihFqn8I0xFxhjVhtj1hpjKtpUvVZhjHnNGLPVGLPcb1m8wBjT2hgz3RizwhjztTHmD37L5AXGmFRjzEJjzNLQuB/xWyYvMMYkGmO+NMZM9lsWrzDGbDDGfGWMWWKMcaxscK3y4RtjEtGdSXoCm4FFwLWxWn+/uhhjuqH1mt8QkZP8lsdtQrurtRCRL4wx6cBi4LI4uM4GqCci+4wxycBs4A8iMt9n0VzFGHM3kAk0EJHefsvjBcaYDUCmiDiabFbbLPxOwFoRWSciBcBY4FKfZXIdEZkJ7PBbDq8QkR9F5IvQ33uBlUBLf6VyH1H2hV4mhx61x2IrB2NMK+BiYERV51qqprYp/JbAphKvNxMHiiCeMca0RfdIXuCzKJ4Qcm8sQfePnioitX3cLwCDgSKf5fAaAf5rjFlsjBnoVKO1TeFb4ghjTH3gHeCPIrLHb3m8QEQOichpQCugkzGm1rrwjDG9ga0isthvWXygq4h0BC4Efhty20ZNbVP436NbT4VpFTpmqWWEfNjvAGNE5F2/5fEaEdmF7hF9gc+iuEkX4JKQP3ss0MMYM9pfkbxBRL4PPW8F/oO6q6Omtin8RUB7Y0w7Y0wK0Bf4wGeZLA4TWrz8N7BSRJ7zWx6vMMY0McYcHvo7DQ1OWOWrUC4iIg+ISCsRaYv+lqeJSH+fxXIdY0y9UDACxph6QC/AkQi8WqXwRaQQ+B3wCbqQN15EvvZXKvcxxrwNzAOOM8ZsNsbc7LdMLtMFuA61+JaEHhf5LZQHtACmG2OWocbNVBGJm1DFOKIZMNsYsxRYCHwoIlOcaLhWhWVaLBaLpWJqlYVvsVgsloqxCt9isVjiBKvwLRaLJU4I7CbmjRs3lrZt2/othsViscQUixcv/rmiPW09U/jGmNeAcCJFlckibdu2JSfHsZpBFovFEhcYYzZW9J6XLp2R1O4kEYvFYgk0nil8Twt85ebCTz950lUgOHgQ5s+HjRXe2GsfRUUQbyHFIrB8OSxdGl9jX7sWFi2CQ4f8liTmCdSirTFmoDEmxxiTs23btpo39MEH0KIFXH895Oc7J2AQmTJFx5qdDW3bwoUXwq5dfkvlLosWQbt2sGyZvl6/Hr6PgwoaDzwAJ58Mp50Gxx8PK1f6LZG7bNkCZ50F7dtDp06werXfErlPXp6rN/NAKXwRGS4imSKS2aRJuWsO1ePMM+Gee+DNN+E3v6m9lsGKFTq+Vq1g/Hh4/HG18gsL/ZbMPRYsgLPPBmMgIUF/HNdcA1lZtX9W99e/wuuvw7//Dbt3Q+fOtVfpb9+uRswXX8Czz8LEiXDCCfpeQYG/srlFfr5+tx97zL0+RMSzB9AWWF6dc8844wyJmuHDRUDkiSeibyuozJghsmdP8euCAv9kcZu9e0WOPlokI0Pkp5+Kjy9eLJKaKnLRRSJFRb6J5woHD4rcfbfIpk2lj69fL9K4schf/uKLWK5zww0iyckic+eWPv7aayLt24vs3OmHVO5y112qr955J6pmgBypSAdX9IYbD88VflGRyNVXi6SliWzbFn17QeKHHyp+b/dukWuuEZk50zt5vOBPfxIxpvxxvfSSfp3HjPFeLjd54QUd19tv//q9zZu9l8cr1q8vf8wLFogkJorcfrvnIrnKokV6ne+4I+qmKlP4ntXSCRX4OgdoDGwB/ioi/67o/MzMTHEkLHPrVtizB445Jvq2gsKSJZCZqW6cyy//9ft5eXD00ernnT7dc/Fc4447dDo/opzNj4qK4PTTYf9+dXUlBTbFpPrk5sJRR0GHDjBtWsXnffstNGkCDRp4J5tbiKi7rjLuuEO/A2vW6LpVbeCii9RduX591NfRGLNYRDLLfbOiO4HfD0cs/LLUlun+ZZeJHHZY5dPaF19Ui2HaNK+k8obKruH774v07y+yY4d38rjJM8/oNZw1q+JzvvtOJClJ5NFHvZPLTSZMELnwwspn5Js2iaSkiNxyi3dyucnmzSJ164o89ZQjzREECz9SHLPwQa2Gyy9Xa+nZZ51p0y82bNBxDBlS+eJOfj60bq1RDu/G+P4g+/apFXvqqX5L4h1FRTpLy8iAzz+v/NyLLtJZ38aNkJzshXTucdZZuvi+ahUkJlZ83qBB8MYbGsmTnu6dfG6xdSvUq6ePKKnMwg9UlI5rGAOpqfDaazrlj2VefVXHM7CKbS5TU+GmmzRE9YcfvJHNLcaM0VDEpUurd/6XX+oNIpbJy1Mj5e67qz739tvhxx9h0iT35XKTr7+G2bPhttsqV/agIapffBH7yj4cQdi0qSPKviriQ+GDWgS7dqnfO1Y5dAhGjoSLL1brvSoGDoQ//EHDF2OZYcPUuj/llKrP3bcPunaFp55yXy43qV9fZ6OXXFL1uRddpN+HYcPcl8tNhg2DlBQYMKDqczMydI0q1nn+eQ0jz831pLsY1wQR0K2bLtyOjuEtMRMTISdHvyTV4eijVWk0b+6uXG6yYoVa7DffXPViHqiivOoqmDABDhxwXz432LVLF2mrmz+SmKhKcvr02E26KyyEsWPh0kuhcePqfWbDBujbV78fscro0fq99sC6h3hS+MZogs60aeovi1VatFBFXl0KC2HqVFi3zj2Z3GTcOJ2hXHVV9T/Tt68mJn3yiXtyucm778K550amyH7/e9i8GQ4/3DWxXKWgAO66S2fi1aVBA3jnHXj7bffkcpPVq9VN2bevZ13Gj8IH6N9fp/qxuLBVUAB9+sDMmZF9btcuLbfw6quuiOU6kyZp9mEks5Rzz4VGjfRmEYuMG6cL82ecUf3PNGmifuBYpW5d9cv36FH9zxxxBPTqpW7agAafVMr48WqIRmLMREl8Kfzjj4d774WGDf2WJHI+/RTeew/27o3sc40bw3nnxe6PYubMyG9Wycm64Dl1auyV1di+HT77DK6+unourJJ88YVe61hbpC8s1FlNpN9t0Fn7xo2wcKHzcrnNhAnQpQu0bOlZl/Gl8EGTsMaN0yl/LDFpkvqnzzsv8s9edpm6dFatclws16lfPzIXVpihQ9XHW1W0R9CYMkVvUn36RP7ZlBS9WXz4ofNyucm8eXDFFfDf/0b+2d691eU3ebLzcrmJCPzud1rzy0PiT+EvX64+sylT/Jak+ojoF7pXL6hTJ/LPX3yxPsfaj2LQII1KqglNmqibINb49FNo1kwzqSOlQweNXom16zx5ss7KevaM/LNHHAHXXafXO5YIh1Zfeqmn3cafwj/zTPXvxtKPYulSXZDr3btmn2/dWsMaI/X/+8nPP8Pw4dHV+A9HfcSSK+vVV2HWrJqF0hqj35FPP42tfJPJk3WdpqYlBUaOhDvvdFQk1/noI19cb/Gn8BMTNW75o49ix7+7d6/WA7/oopq38eGHugYQK0yZooo6PDupCXv2aOLZihXOyeU2SUla/72m9O6tSVtVZecGhfXr9fpEc51BNwGKZg8NL8nN1TWmZ57xvOv4U/igrpEdO6qfuek3Z52lhZWaNat5Gy1bxpY/+9NPdSbWsWPN2+jVS58/+8wZmdxm2DD16UYzI+nWDbp3j51rHZ51nn9+dO2ceqr6xGOBOXM0RyTaMdeA+FT43bvr87x5/spRHQoLnZue33sv/P3vzrTlJiKaRNS9e3RZwm3b6s5YsVIx9M031Z0TaXROSerW1VyT8M0u6Fx/vW7iEm3WbGamXudYcN9Nn64zua5dPe86PhV+y5Y6lbzjDr8lqZqZMzWZZu7c6NtasiQ2Mo1zc+HEE6NzYYXp0UPdG0F33+3bp7O4SOLQq2ovFrb3NEaVfTQ3OdD/27ZtWo8n6Eybpju0eZRdW5L4VPig1l+0XzIvmD5dldVJJ0XfVo8e8NVXwc80rl8fPv4Ybrwx+rYuukhdYjt3Rt+Wm8yZo7M5JxT+0qWaa/Lxx9G35SZr1ug1/uab6NsKz9or2zcgCOzdC4sXF8vrMfGr8DdsgH799J8fZKZNg//5H2c2twgrk6Av6DkZYXL55bpwW936LH4xbZqGJnbpEn1bJ5ygMflBd2VNnaoRNk5sVpORodnJQVf46enw3Xe+eRfiV+HXrw9vvRXseit792oGoVPT/DPO0C9ckH8UIvrDfeABZ9vds8fZ9pwmJUUrYzqRO5CSorOaIF9nUPkyMnSdxQmefVbXqYLOkUf6VtAwfhV+48a6sh/kH8Xs2TrNd2r6l5SkKftBLi2xYoVugBFNaGJZHn1U120OHnSuTad57DGYONG59nr0UH/2li3OtekkRUU6A3HKmAHNKHdihuQmf/wjvP++b93Hr8IHVaRz5gR3cev44+HJJ6FzZ+faHDFC2wwq4Ruwk4qgQwddxFy0yLk2naSgwPk2w0ZCUN06S5fquoqT1xnUTRTUBMNt2+DFF31dWI5vhd+jhyr7+fP9lqR82rWD++93vkSAiCbnBJHp03VB3cnNqc85R5+DOpsbPFgX5Z0MKezYEZ57TjPLg8jPP+sszunFyz/+MbgGzYwZ+uzTgi3Eu8Lv1g1OPlmtv6Cxb58uNrqxoUXHjlo/PWiIqHUWVtBO0aiRuu/CP7igMWOGJtU5GTWWmKj15Z3yjztNz54apeN0pcju3TWXobDQ2XadYMYMNd5qUifJIeJb4R92GCxbVvMaNW4yf77WgVmwwPm2W7VyJq7faQoL1d9+/fXOt92li/4vgxaPv2+ffgeddNuF2bVL1wWCtguWiHsJUl26aB7HV1+50340zJ+vJVJ83I8jvhV+mKKi4GXohd1MbkzJO3fWUsnbtzvfdjQkJ2u4mhtT3v791cURNMtv0SL9/mVnO9/2V1/p5hpB82mvX6+btXz0kfNth2+cQTNoior0+92tm69iWIX/6adaYjVoFsG8eZpt6saWdeFIhqCVlli0SPMj3CA7G265pWblpd0kfGPPynK+7cxMVTJBU37z5qkP342NP9q00bDHoG2IkpCg1+GRR/wVw9feg8BRR+lmKEH6UYioInDD6gNVBElJwRozwK23ao1wt/j22+BZu2eeCQ89pEaH06Sl6XpN0K7zvHlaVsCJ7PGyGKPj/fe/nW+7FmAVfrt2umA2Z47fkhSzZo1W83RL4detC088UbPds9xi716dZbk1ZoD77oMbbnCv/ZrQo4fG4LtF5846c3Ij9LOmzJunvmy3KnpmZDiTvesk110HAwb4LYVV+BijLo4gWUHt22sC0mWXudfHvfc6HwMdDTk57vmyw3TurC6joOz5unOn3uTcXEju3FlDj7/80r0+IiE3V2Pw3bzOO3fCb3+r7togIKL5AUVFfktiFT6gP4p16zTDMwgkJGg9lEaN3OujsFA3vf7xR/f6iITweoKbcePhtYug3NwnT4ZTTnE3Eef882HtWrWog0B+voaLRrvhSWXUrw+vvx6cvX03btSMZzfWaSLEKnyACy6ABx/0W4piHn64Zhs6R8KWLVpbZ9w4d/upLvPmaWaxm2UfTj8dUlODo/Dnz9faRh06uNdHerpuAh+UyrCNGsHTT7sThhomOVlvcEG5zmFjxs1ZTTWxCh/0Bzd0qG8FjUqxd6/6dN3+srZsqb7OoPwoXn4Z3njD3T5SUrTyaFDWa9z2ZYeZNk13gwpC6PGaNbrbk9t07qwz2CBklM+bp+tmJ5/styRW4f9CXp5+Qfxm4UL3fdlhOndW5RcERdCqlSpjt3n5ZV+LV/1Cbq4mXHlxnVetgn/9y72Q1+oiolU8b7vN/b66dFG3ZRDqJ516qq4pBGAh2Sr8MA8/rD8+L6yPyvDClx2mc2ddwNy0yf2+KmPWLHjpJW+K2HXoEIyZXE6OLtZ6dWMH//Mu1q/XzXe8+G5nZUHr1hrt5jc33xyYrUWtwg+Tna2ha35b+fPm6YKtGwlXZQkrG78Vwdtvayy6FynnIlo3/YMP3O+rMjp21EVFL/Y1PekkjXv3+zqHk8y8uMk1aqQbjfTp435flbFrV6D2YrAKP0xQlN/PP7u7oFWSU07RxWEn9o6Nhnnz1Opz25cNunj58sswapT7fVVGerr+353YyawqkpJ0rcDv77abCVdB5dVXNRAhCDMNrMIvpnlzLcnr949iwQJ45RVv+kpO1qqF6ene9Fce4eJhXkYwZGfrdfZr7UIEnnnG27ronTvrDNbP4nHhRWqvfNmffqqlFr791pv+ymPePNUrbmRS1wCr8EuSlRWM2vheLu6sXq31PfzaBCZcPMzLGOWsLM0/+O477/osybp1mvg2e7Z3fT72mN5YvZhFVcQLL8Bf/+pdf02b6vqUX0aciPYdgHDMMJ4qfGPMBcaY1caYtcaY+73su1oMHgwTJvhn+T30kPfp1ytX6oK1X2sX33yjSshLhe+3+86PuOwgxOF37Qpnn+1dfx06aBKWX0bcd99pMmc8KnxjTCLwL+BC4ETgWmPMiV71Xy1OP10Vj18/jkmTvE/791v5DRyoxeu8nPKecoruheBXdNL8+aqI3Ey4Ko/bb4ebbvK2zzAzZ2pmsZfGVGKiv2sXAUq4CuOlhd8JWCsi60SkABgLXOph/9Vj0iR9eM3evbB8ufdfjmbNtICcn2sX9ep5219Sku4veu+93vYbxquEq7Lk5moNej9msM8/ryUVvDamsrO1dk9urrf9ghqP//ynGhgBwUuF3xIoaVJtDh37BWPMQGNMjjEmZ9u2bR6KVoK//U0rSXqNmxthVEV2tj/T3nXrdNE4J8f7vv3adaigQLNN/airkpWlJTW8TsDy05d9wQWa6OVHxm3btoFJuAoTqEVbERkuIpkiktmkSRN/hMjOVn+21wlYXiZclSU7WysMen2TnTNHIylSUrztF3Tt4pxz3NlCsjJSUnSnscGDve0X/HPfbdigNxo/FH7Xrppl7LU+yc/X/BK/DNcK8FLhfw+0LvG6VehYsAgnYHldTrZZM+jb193iYRVx883qR/f6RzFvnvvFwyqiUSPdVHrWLO/7TknRNQSvOflkfxKwwv35VS3y0CGtWOklixfD//5vcGpVhfBS4S8C2htj2hljUoC+gM/pjuXglxV0yy1qEfhBWpo/087wps5+hAo2baq7nXl9nR94QKtF+kFSkn7Pjj/e234XL9YbjV/Fw267Tb9nXq5dBHDBFjxU+CJSCPwO+ARYCYwXEQ8zT6pJixZaRXLpUu/6zM+Hgwe96688/vEPbyM4vCweVhFZWd4mYInAa695m3BVlhdeUL+ylzz9tI7ZL192ZqbW8Fm/3rs+581Tg6JpU+/6rAae+vBF5CMROVZEjhaRoV72HRELF+oGCl4xfrym2K9b512fZdm8GUaP9i4B6+ef1YfuZVx2WbKzvU3AChcP83sjjPx8jQrzioQENaL8ImxUeBWYEMCEqzCBWrQNDE2behs+Nm+e+nXbtvWuz7JkZeksw6u1i4wMXbD1c1/dbt00imPfPm/6C8I0f/duXT94+WVv+vviC8218LMia4cO3q5dbNqkhoTfN/ZysAq/PLZvhxtvdH/XqTDz52t0ToKPl8PrtQu/XVig8dEff+zdovH8+ap4/FikDnPYYVo22Ctr97PPtIBYaqo3/ZWH18XjWrfWyKRrr/WmvwiwCr880tNh7Fj45BP3+/KjeFh5eFk8TkQt/D//2f2+qoNXSTlpabqXq99x2V4Wj5s3D445xvsIsLIMGQJPPulNX8bo99vNPalriFX45ZGSovu9eqH8FizwL+GqLJdequGhbvPttzrlbdPG/b6q4pln9IfpxdrF3/8ejD2Es7O1xovboYoiGpboVbnvyjj3XE3y84IhQ+C997zpK0Kswq+I7GwNJ3M7AatdO61kGAR/3wsvaCq424T3lA2CIjjmGL3Gixe7209RkbvtR4JXi5jr1mnCVRCuM+jevm4bcbm5mq2/cKG7/dQQq/ArIivLmwSso47SKple7HBVXQoL3W1/7lz1JZ9wgrv9VAevlN+TT8Jxx/m/hSZoPPzf/66zWDf58Uc1aIKi8G+7zf2tBsNbV3bp4m4/NcQq/IrIztYf6O7d7vVRVKSLhm72EQkimpTjdtr/nDn6//VzkTqMV8Xj5sxR332dOu72Ux2SkrRwXPv27vbTtata+X4lXJXFi7WL8Ow1CC7acgjALy6gHHkkrFoF55/vXh8rVug2d++/714fkWAMNG7srvITUUvr1lvd6yNS3FYERUXaflAsXdDaSe+/D/v3+y2Jd2Rnu188bu5cnbkGZIersliFXxVeWANBUgRuF48zBn7/e7j8cnfarwkDBuhCm1t+9pUrdTPrIE3zZ82Cyy5zb+1i1y5o2VKTCoNCeJ3MTYMmNxfOOsu99qPEKvzKeP99DSf73qUab3PnapLX0Ue7035NcLt43Ndfa1ZvkOjZE+64w72aPuECWkG6sbut/BYs0M18gmTphovHuVkhdfp075LaaoBV+JXRvLkmYbn1o5gzR5VAELafC+N2Atadd2r4Z9DYsME9a/e447R+jds+80gIF49za7F67lxdo/Gj3HdFJCXpNX7mGXf7CcLaVAUEV7IgcPrpusjmhvLbskXj0YNk9YEWj7v/fujY0fm2CwvVugramAGuv16tfDfo1k3DXYN0Ywd31y7mzNFM5vR059uOhuOOc2/zmz/+UUsiBxir8CsjnIDlhhXUuLFW5OzXz/m2o+XJJ90parZsmfo4g+TLDpOdrW4spxOw9u3TxX8/thWsCreKx4Vv7EG8zj/8oIrZDZfllCne1WWqIVbhV0V2tsbWOq0IEhPVAjrySGfbdYKiIlXOO3c62+7s2focRAs/O1vr+zi93eKnn2rURsA2wgDg6qt1Qbl166rPjYT9+7Xufu/ezrbrBMnJ8OKLzpdN2bYNVq8O5ne7BFbhV8Vll8Ef/uD8npiPPqqZf0Hk66/h1FPhA4f3p5k+Xf3GQSipUJazzlKXy/TpzrY7bZrW0Pmf/3G2XSdo0kTzLpz2Oaen66blF1zgbLtO0KSJFq9z+jp//rk+d+/ubLsOYxV+VXTtqtl5TkYb7NgBDz9cHJYZNDp0UJeT0zekF1+EN95wtk2naNQITjvN+TFPm6Y3Ez/27a0On38O99zjbJurVrmfrR0NPXrobLOgwLk2p03TG53b2ctRYhV+dSgogCVLnGtvxgz16fbo4VybTpKQoJbK9OnO+p7btAmmXzfMiBEwZoxz7W3ZorOloF5nUNfds886V0jt4EGdzdx9tzPtuUH37jpjd7LezSmn6KK/35VQq8Aq/OrwyCP6JXZqQWbaNKhbN5jT/DDdu+tGDt9+60x7kybBK68Ec/EyTMeOzq6phKf5QVb4YdmccnHk5OjvpFs3Z9pzg7PP1mi0n35yrs3bb4ennnKuPZewCr86dO+uU9TwomO0BH2aD8WKwCkXx//9H7z0UvBCE8vy8svObSZ//vnw7rsa3htUOnRQv7ZT1znczjnnONOeGxxxhCZTXnmlM+39+GPgo3PCWIVfHTp31tV9J34Uubk6nQyy1Qdw7LFa2K1v3+jbOnhQU/mDPmbQNYZ//MOZtg4/HPr0CfY03xi9LtOmOTP7mjZNF/wbN46+LTcJGx5OjPnBBzWpLsiz1xBW4VeHunU1bM8JhV+vnm5mHWQfJ+gP4oILdHP1aFm0SG90saDwe/RQ3260m3z/8IPWRf/hB2fkcpPu3fU7vn17dO3k52sgQixc5+XLNWIs2t+0iLYRtIz5CrAKv7r06KFFxZyKTQ+y1Rfmp5/g8cej9+NPm6Y/BjeSuZymRw+tZz5rVnTtTJ2qGcs//+yMXG5y662wZk30VnlSks4Kg1QJtSIyMnSN6rPPomtn3TpNXIuFmxxW4Vef66/X6Jr69aNrp2tXeO45Z2RymwMHdN/ZDz+Mrp2NGzXkMYB7fP6Kzp11bSVaRTBtmo73pJOckctNwnH40bokkpJ0thCEjW2qIj1dNzZ34jqDVfi1jnbtdKE1mjoca9bolNetWh5Ok5GhvskpU6Jr59VXvdkf2AnS0nQmsnVrzdsoKtJMznPPDXQhrVIMG6bbPUYTP//88zoLjhV69tSoomhmYVOmaBno4493Ti4XiZFvY0BYskS3I6ypJRS2lIOYcl4RvXurFZObG107Qdjpqbp8+CG8+WbNP794scbg/+Y3zsnkNo0bq3uipiUgfvxR16WiNQ68pHdvvTlHI/Mjj6hBEwP+e7AKPzK+/BKGDtWiZzVh8mQNg2vXzlm53KR3b3Xt1HTq+9vfwnXXOSuT24RnYDW9sa9YoTOFIJYWqIhevXTckybV7PMffaTPsWTMdOwIv/tddGWrTzoJLrzQOZlcxir8SLjoIr2T1+RHsXs3zJwZWz8I0DWHZs1qti1cURFMnKiLoLHGoEE1r9t/ww0a8RL00MSSpKdr7HxNFf7kyVqELSj711aHhAQNwa1pzf6339ZxxxBW4UdCs2a6U9A770T+2QMHNBvPqWQPr0hJ0R2q7rwz8s/Onq2+8EsucV4ut2nQQCNOduyI7HPhWUFamvMyuc0ll2jFxxUrIvvcvn26ZnHJJTHj2ijF8uU67kgoKoLBg2H4cHdkcgmr8CPl6qvVpRPpF6RpU800zcx0Ry43CYeQRrqgN26cKr5Ym9WAXufCQnjvvcg+9/TTWi8oFjcHv+IKrRVfr15kn/v6a12jueYaV8RylQMH9HpFWhZh3jw1hK6+2h25XMIq/Ei56iqdqq9ZU/3P7N6tcd1ubZLtNiLF+75Wl8JCdef07h19KKsfnHGGJuaMGxfZ58aO1bHHooXfooVG2mRkRPa5M8/UnI0gF8ariDp1tAT6f/6jyr+6jBunn42x2atV+JHSsqV+uSOJwJg4UYtJubVnqtsYo+6siROrvxHMgQO6YDtwoLuyuYUxarF+9plG3FSHVat0YT8WLd0wIlpI7Ztvqnf+wYP6mTp1YicEtSzXXKNG2ccfV+/8gwdhwgRd03MiE91DYvQK+Uxiolrre/ZU7/zhwzUZJRbdOWFuuEGzjN99t3rn16sHf/kLnHeeu3K5yYABum9BdYvcvfqqur8Cvq9ppezapYqsusmB//d/GoPu9O5oXtKzJzRvrtevOmzYoDe4AQPclMoVrMKvCSK6eFsdF8eXX2ptlkGDYnNBK8y558LRR2uJ46rYskUtICc3mPCDY4/VvIuGDas+Nz8fRo5U90Dz5m5L5h4NG6pfesyYqitAiuj3oWHD6v2Pgkpysm7JOGuWWvpV0b69lhu5+GL3ZXMYq/BrgjGalj1hQtU1tf/1L0hNjb1Y9LIkJKh7ZtYs3TSjMoYNU6VRk1DOoFFYqNe5OruTPf44/OlP7svkNrfdpsXjqtqdbPp0dWPddps3crnJ3XdrbZ3DDqv8vG3b9OaemKiPGMNIQEt6ZmZmSo7TG0o7yTffqJvm97/Xha7yyM/X6W7v3vDPf3ornxvs2KGW36236k2sPHbtKi5D4fSeuH5QUADHHacL9QsXxvYsrbqI6ALspk2wdm35WdIiWoJi7Vq1dmNxkbo8ior0mlf0/e7fX78HK1YEtgCiMWaxiJTrP7YWfk1p314Lqr38soZnlUdqKqxcqZZfbeCII/QGl5pacRbqc8+p0n/kEU9Fc42UFC0gl5NT8Q1s/Hh1bcRigll5GAOPPqqLk6tWlX/OwoU62xsypPYo+wMHdOb+5z+X//7XX8NbbwV/j4PKEBHXH8BVwNdAEZBZnc+cccYZEnjWrRNJSxO5995fv7d8uUhenvcyecEHH4ice65Ifn7p40uWiKSkiFxzjT9yuUVBgcgJJ4i0bCmyY0fp9zZvFmnYUKRzZ5GiIn/kc4Oiosq/v0VFIqNG/fo7EOsMGCCSmCiycGHp4wUFIp066bXets0f2aoJkCMV6FWvLPzlwOXATI/684Z27dTKeeKJ0sdXrdLolFj321eEMRqueMcdpZOx8vJ0O7/a4L4qSXKy+rO3bCld6333bg3pO3BAF2xrk7vHGLXcCwt1FhvOOD5wQIsIGqMz3FgqilcdnntO9zW+9lqtcw/q5rnrLp3VDBsWWyUzylLRncCNB/A5tcnCL8nmzWrZ/v73IocfLtK0qciyZX5L5R4PPSQCIllZInffLZKbq8drk5VblpdfFhk/Xv9+7z2RY44RSUoSGTvWX7nc5PPPddbWtq1e59NOEznsMJGff/ZbMveYO1fH2KSJyNKlIgcPipx/vo4/BqASCz9QCh8YCOQAOW3atHHxX+ICb74p0qaNSJ06IhdfrO6e2s6IESLHHadT4Nmz/ZbGW8aN0yn+Z5/5LYn7zJolkp0tkpAgcvLJxTe92szy5SI9exa78A4ciBljpjKF71iUjjHmU6C8AOQhIvJ+6JzPgXtEpMrwm8BH6ZSHiC7cxeqCTk0pLIy/Mccj9jrHBJVF6Th29UQkhlMqHcKY+PxBxOOY4xF7nWMeG5ZpsVgscYInCt8Y08cYsxnIBj40xnziRb8Wi8ViKSawmbbGmG3AxiiaaAxEsTtxTGLHHB/YMccHNR1zhog0Ke+NwCr8aDHG5FS0cFFbsWOOD+yY4wM3xmx9+BaLxRInWIVvsVgscUJtVvixtbuwM9gxxwd2zPGB42OutT58i8VisZSmNlv4FovFYimBVfgWi8USJ9Q6hW+MucAYs9oYs9YYc7/f8niBMeY1Y8xWY8xyv2XxAmNMa2PMdGPMCmPM18aYP/gtkxcYY1KNMQuNMUtD464lu8xUjjEm0RjzpTFmst+yeIUxZoMx5itjzBJjjGNFxWqVD98YkwisAXoCm4FFwLUissJXwVzGGNMN2Ae8ISIn+S2P2xhjWgAtROQLY0w6sBi4LA6uswHqicg+Y0wyMBv4g4jM91k0VzHG3A1kAg1EpLff8niBMWYDWlnY0WSz2mbhdwLWisg6ESkAxgKX+iyT64jITGCH33J4hYj8KCJfhP7eC6wEWvorlfuEqt/uC71MDj1qj8VWDsaYVsDFwAi/ZakN1DaF3xLYVOL1ZuJAEcQzxpi2wOnAAp9F8YSQe2MJsBWYKiK1fdwvAIPR7VHjCQH+a4xZbIwZ6FSjtU3hW+IIY0x94B3gjyKyx295vEBEDonIaUAroJMxpta68IwxvYGtIrLYb1l8oKuIdAQuBH4bcttGTW1T+N8DrUu8bhU6ZqllhHzY7wBjRORdv+XxGhHZBUwHLvBZFDfpAlwS8mePBXoYY0b7K5I3iMj3oeetwH9Qd3XU1DaFvwhob4xpZ4xJAfoCH/gsk8VhQouX/wZWishzfsvjFcaYJsaYw0N/p6HBCat8FcpFROQBEWklIm3R3/I0Eenvs1iuY4ypFwpGwBhTD+gFOBKBV6sUvogUAr8DPkEX8saLyNf+SuU+xpi3gXnAccaYzcaYm/2WyWW6ANehFt+S0OMiv4XygBbAdGPMMtS4mSoicROqGEc0A2YbY5YCC4EPRWSKEw3XqrBMi8VisVRMrbLwLRaLxVIxVuFbLBZLnGAVvsViscQJVuFbLBZLnGAVvsViscQJVuFbLBZLnGAVvsViscQJ/w/MYhGEpX6s/AAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "def f(t):\n",
- " return np.exp(-t) * np.cos(2*np.pi*t)\n",
- "\n",
- "t1 = np.arange(0.0, 5.0, 0.1)\n",
- "t2 = np.arange(0.0, 5.0, 0.02)\n",
- "\n",
- "plt.figure(1)\n",
- "plt.subplot(2,1,1)\n",
- "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
- "\n",
- "plt.subplot(2,1,2)\n",
- "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Image "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
|