{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<__main__.Fib at 0x104a7a1d0>, 0, 10]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Fib():\n",
    "    def __init__(self):\n",
    "        self.prev = 0\n",
    "        self.curr = 1\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return self\n",
    "    \n",
    "    def __next__(self):\n",
    "        value = self.curr\n",
    "        self.curr += self.prev\n",
    "        self.prev = value\n",
    "        return value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "foo\n"
     ]
    }
   ],
   "source": [
    "def foo():\n",
    "    print('foo')\n",
    "    \n",
    "def bar(func):\n",
    "    func()\n",
    "    \n",
    "bar(foo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def square(a):\n",
    "    \"Return the square of a\"\n",
    "    return a ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "square??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "square?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.linalg?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2\n",
      "2 1\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "b = 2\n",
    "print (a, b)\n",
    "tmp = a\n",
    "a = b\n",
    "b = tmp\n",
    "print (a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2\n",
      "2 1\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "b = 2\n",
    "print (a, b)\n",
    "a, b = b, a\n",
    "print (a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] [1, 2, 3]\n",
      "[1, 99, 3] [1, 99, 3]\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = a\n",
    "print (a, b)\n",
    "b[1] = 99\n",
    "print (a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] [1, 2, 3]\n",
      "[1, 2, 3] [1, 99, 3]\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = a.copy()\n",
    "print (a, b)\n",
    "b[1] = 99\n",
    "print (a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] [1, 2, 3]\n",
      "[1, 2, 3] [1, 99, 3]\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = a[:]\n",
    "print (a, b)\n",
    "b[1] = 99\n",
    "print (a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 10)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'utf-8'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.getdefaultencoding()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10, dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.25,  0.5 ,  0.75,  1.  ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+000,   0.00000000e+000,   2.27314137e-314])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'random' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-75ab22471409>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'random' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.arange(8)+1).reshape(2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(lambda x: x * x, [x for x in range(10)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    4\n",
      "b    5\n",
      "c    6\n",
      "Name: 1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [[1,2,3],[4,5,6]]\n",
    "index = [0,1]\n",
    "columns=['a','b','c']\n",
    "df = pd.DataFrame(data=data, index=index, columns=columns)\n",
    "print (df.loc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b  c\n",
      "0  1  2  3\n",
      "1  4  5  6\n"
     ]
    }
   ],
   "source": [
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "Name: d, dtype: int64\n",
      "   a  b  c\n",
      "d  1  2  3\n",
      "e  4  5  6\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [[1,2,3],[4,5,6]]\n",
    "index = ['d','e']\n",
    "columns=['a','b','c']\n",
    "df = pd.DataFrame(data=data, index=index, columns=columns)\n",
    "print (df.loc['d'])\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#更改全局字体大小或者类型\n",
    "from matplotlib import rcParams\n",
    "rcParams.update({'font.size': 18, 'font.family': 'serif'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#将文中的字体全用latex渲染\n",
    "matplotlib.rcParams.update({'font.size': 18, 'text.usetex': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WJVWbxx+HAw+EbbaBZ56B//1fePRR2H9/w4SkXquKNRUpJRdBSL3x5z/DOefA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\nOuUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a1f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WJVWbxx+HAw+EbbaBZ56B//1fePRR2H9/w4SkXquKNRUpJRdBSL3x5z/DOefA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\nOuUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115520a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "\n",
    "from numpy import *\n",
    "x = linspace(0, 5, 10)\n",
    "y = x ** 2\n",
    "\n",
    "figure()\n",
    "plot(x, y, 'r')\n",
    "xlabel('x')\n",
    "ylabel('y')\n",
    "title('title')\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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e6zuJpIhuL3ZjS9UWii8oJiczx3cckYhQT4Ekh48/hkaN4PrrfSeRFPD0v5/m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\nREREBFBRICIiImH/D5/9UqiRqbufAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130aed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot(1,2,1)\n",
    "plot(x, y, 'r--')\n",
    "subplot(1,2,2)\n",
    "plot(y, x, 'g*-');\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6OYW1ZhhIJEnVySmsNcVAIkmqPk5hrTkGEklS9XEKa81xUaskqbo4hbUmVUQgiYi9IuLG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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c5f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n",
    "x = linspace(0, 5, 10)\n",
    "y = x ** 2\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i4mvUhdVrVJD4oPfee8/tCAFn77G9/OvHfzF+1fjUBmYjWo1QAzMRyZq6sHqV\nChIJaOcKkTErxxBiQuh3cz/63txXDcxE5MLUhdXrVJBIQMpYiAyoP4CnGj5F8YLF3Y4mIr5OXVhd\noYJEAooKERG5JOrC6hoVJBIQVIiIyCVTF1ZXaTVfHliwYEGWr02cONGLSYLP3mN7ee6H56g0uhLj\nVo1jQP0BbO2/lddbvq5iRESyL20X1rlz1YXVBSpI8kDr1q155plnOHPmTOqxffv20a5dO55//nkX\nkwUuFSIikmfSdmH96it1YXWJpmzywIIFC+jevTs//PAD06ZNY8uWLTz00ENcd911rF692u14AWXv\nsb38e/m/GRM7BmOMpmZE5NKoC6vPUEGSB2655RZWr15Nnz59CA8PJzk5mddee41nn31WHT/zSMZC\npH/9/ipEROTSqQurz1BBkkc2bNjAqlWrKF++PDt37uSPP/7g+PHjFCpUyO1ofk2FiIh4jLqw+hSt\nIckDI0aMoGHDhtx2222sXbuW2NhYfv75Z2688UaWL1/udjy/tPfYXp6PeZ7KoyszduVY+tfvrzUi\nIpJ31IXV52iEJA+MHj2aL7/8kjZt2gBQq1YtYmNjefHFF2nWrNlFd+mV/9GIiIh4nLqw+iQVJHng\n119/pUSJEumOhYaG8tZbb3HnnXe6lMq/qBAREa9QF1afpYIkD2QsRtJq2rSpF5P4n6Onj/L64tf5\nv9j/UyEiIp6lLqw+TWtIfNDw4cOpV68eRYoUoXTp0nTq1IkNGza4HSvPzd00l1rjajH6p9FaIyIi\nnqUurD5PBYkPWrJkCU888QQ//fQTMTExnDlzhttvv50TJ064HS1P7Du+j+5fdKf1x62pWrwqa/uu\nVSEiIp6jLqx+QVM2PmjOnDnpnk+ZMoVSpUoRFxdHo0aNXEp16ay1TF87nf7f9ScpOYnJHSbTo3YP\n9WoREc9J24U1JkZdWH2YChI/cOjQIYwxFCtWzO0oufZ34t889s1jzNk4hy7Xd2F069GUKVzG7Vgi\nEsjUhdWvqCDxcdZaBgwYQKNGjajph5V9sk1m3MpxvDDvBYpcUYRZXWfR/rr2bscSkWCgLqx+RQWJ\nj+vbty/r169n2bJlbkfJsd/2/kbvr3rz47Yf6RPRhxGtRhCWP8ztWCISDNSF1e+oIPFh/fr1Y86c\nOSxZsoSyZcte9PyBAwcSFpb+F35UVBRRUVGeipip02dPM2LpCF5f8jqVilZiUc9FNKnYxKsZRHxR\ndHQ00dH32JaGAAAgAElEQVTR6Y4lJia6lCaAqQurX1JB4qP69evHrFmzWLRoEVdffXW23jNq1CjC\nw8M9nOzCVmxfQe/Zvflj/x88e8uzvNz0ZfJflt/VTCK+IrN/IMTHxxMREeFSogCkLqx+SwWJD+rb\nty/R0dHMnj2bQoUKkZCQAEBYWBj58/vmL/ejp48yaP4g3vnpHcLLhrPq4VXULlPb7VgiEkzUhdWv\nqSDxQRMmTMAYQ7NmzdIdnzx5Mt27d3cn1AXM3TSXR79+lD3H9vDWbW/Rv0F/LgvRf1oi4kXqwur3\n9FvDByUnJ7sdIVv2Hd/HU3Of4sNfPqRl5ZbM6z6Pa4td63YsEQk26sIaEFSQSI5lbHA2qf0ket7U\nUw3ORMT70nZhXbpUXVj9mAoSyZFtidvo800f5mycwz017+GdNu+owZmIuENdWAOKChLJtpU7VtJ2\nWlsuz3c5X977JR2qq9GQiLhEXVgDjgoSyZbvNn1H5xmdqV26Nl9FfaWN8ETEXerCGnC0269c1NQ1\nU2kX3Y6WlVsS0z1GxYiIuEtdWAOSTxQkxpjGxpjZxpgdxphkY0z7DK9PTjme9jEnq8+TvGGtZeSy\nkfT4sgc9avfg83s/p2BoQbdjiUgwUxfWgOUTBQlQCFgN9AVsFud8C5QGyqQ8vNsPPcgk22QGzh3I\nczHP8XKTl/lvu/+qt4iIuEtdWAOaT/yGsdZ+B3wHYLK+d/SUtXav91IFr1NJp+jxZQ9mrJvBuLbj\neOzmx9yOJCLBTl1YA55PFCTZ1MwYkwAcBOYDg6y1B1zOFHAOnzpMp086sezvZczsMpO7atzldiQR\nCXZbt6oLaxDwl4LkW+AzYAtwLTAcmGOMaWitzWqKR3Jo15FdtJ3Wli0Ht/D9A99rh14Rcd/GjdCy\nJYSGqgtrgPOLgsRaOyPN03XGmF+BP4FmwAJXQgWYDfs3EPlRJKfPnmZJryXcUPoGtyOJSLBbv94p\nRooWdRqflSvndiLxIL8oSDKy1m4xxuwDqnCBgmTgwIGEhYWlO5bZ9t/BLnZHLHdMu4MSBUuwqOci\nrg672u1IIhLsVq+G226Df/wDfvgBSpVyO5FkEB0dTXR0dLpjiYmJuf48vyxIjDHlgeLArgudN2rU\nKMLDw70Tyk99u/Fb7v70bjU8ExHfERvrrBmpUgXmzoVixdxOJJnI7B/48fHxRERE5OrzfOK2X2NM\nIWNMbWPMTSmHrkl5XiHltZHGmPrGmIrGmJbAl8AGYK57qT1nyZIltG/fnnLlyhESEsLs2bM98nWm\nrplK++ntaVG5hRqeiYhvWLoUWrVy9qWJiVExEkR8oiAB6gI/A3E4fUj+DcQDQ4CzwI3ALOAP4L/A\nSqCJtfaMK2k97NixY9x0002MGzfOIzvoWmt5c+mbqQ3Pvrj3CzU8ExH3zZvnjIzUreuMjGSYcpfA\n5hNTNtbaRVy4OGrtrSy+oHXr1rRu7XzLnriJ6I0lbzBowSAGNR7E0OZDPVL0iIjkyJw5cNdd0Ly5\ns1mebu0NOr4yQiJeErczjsELB/NS45d4rcVrKkZExH2ffw4dO0Lr1vDllypGgpQKkiByKukUPWf1\n5IbSNzC46WC344iIOF1Xu3RxRkc+/RSuuMLtROISn5iyEe8YtngYv+/7nVUPryI0X6jbcUQk2E2a\nBL17Q48e8N57agcf5FSQBJAL9V2J2xnH8KXDeaXpK9QuU9ulhCLBLa/7Nvi1sWOhXz947DEYMwZC\nNGAf7FSQBJCs+q6knap5odELLiQTEcj7vg1+69//hqefhoEDnT9rLZuggsQnHTt2jE2bNqXeYbN5\n82bWrFlDsWLFqFChQo4/T1M1IuITrIVhw+CVV+Cll+C111SMSCoVJD5o1apVNG/eHGMMxhj++c9/\nAtCjRw8mTZqUo8/SVI2I+ARrnSJk+HCnKHnpJbcTiY9RQeKDmjZtSnJy8iV/jqZqRMQnWOtMz4we\n7UzRPPWU24nEB6kgCWCaqhER1yUnQ9++MHEijBvnLGIVyYQKkgClqRoRcV1SEjz0EHz0EUyeDD17\nup1IfJgKkgCkqRoRcd2ZM9CtG3z2GXz8MXTt6nYi8XEqSAKQpmpExFWnTjndV7/9FmbOdNrCi1yE\nCpIA8/OunzVVIyLuOX4cOnWCxYth1ixo08btROInVJAEmPd/fp9yRcppqkZEvO/IEWjfHmJj4Ztv\noEULtxOJH1FBEmBWbF9Bs0rNNFUjIt516BC0bQtr18L338Ott7qdSPyMNg8IICeSTrAmYQ0Nyzd0\nO4qIBJP9+6FlS/j9d5g3T8WI5IpGSALIb3t/Iyk5SQWJiHhPQgLcdhvs3g0LFkBtrV2T3FFBEkB+\n2f0LhS8vTK1StdyOIiLBYMcOZ2Tk8GFYtAhq1HA7kfgxFSQB5Jc9v1CvXD3yheRzO4qIBLqtW51i\nJCnJuaOmShW3E4mf0xqSAPJLwi80KNfA7RgiEug2boQmTZydelWMSB5RQRJADp44SMMKWj8iIh60\nfr1TjBQq5EzTVKzodiIJECpIfNjYsWOpXLkyBQoUoEGDBqxcufKi72lQ3j9HSKKjo92OcEmU313+\nnt9vrF4NTZtCqVJOMVKunNuJJICoIPFRn3zyCf/85z8ZMmQIP//8M7Vr1yYyMpJ9+/Zl+Z4KYRUo\nUbCEF1PmHX//haL87vL3/H4hNhaaN4dKlZy7aUqVcjuRBBgVJD5q1KhRPProo3Tv3p3q1aszYcIE\nChYsyKRJk7J8z42lb/RiQhEJGkuXQqtWULMmxMRAsWJuJ5IAlOOCxBjzgTGmiSfCiOPMmTPExcXR\nsmXL1GPGGFq1asXy5cuzfJ8KEhHJc/PmQWQk1K0Lc+dCWJjbiSRA5WaEJAyIMcZsNMa8aIzRJGIe\n27dvH2fPnqV06dLpjpcuXZrdu3dn+b4bS6kgEZE8NGcO3HGHs4j1m2+gcGG3E0kAy3EfEmttR2NM\nSeABoAcwxBgTA7wPzLLWnsnjjHIRJ06cAODMnjPEx8e7nCZ3EhMT/TY7KL/b/Dn/b7/9Bvzv/8c+\n4/PPoWtXZ3+aTz6BK65wO5EEuFw1RrPW7gX+A/zHGBMO9AI+BI4aYz4CxllrN+ZdzOBSokQJ8uXL\nR0JCQrrjCQkJlClT5rzzt27dCkD3B7p7I57HREREuB3hkii/u/w9/9atW7nVV/aAmTYNuneHu++G\nDz+EUG3WKZ53SZ1ajTFlgdtSHmeBOcANwHpjzLPW2lGXHjH4hIaGEhERwbx582jfvj0A1lrmzZvH\nk08+ed75kZGRfPTRR1SqVIkCBQp4O66IXIITJ06wdetWIiMj3Y7imDQJeveGHj3gvfcgnzo/i3fk\nuCAxxoQC7XFGRW4HfgHeBqZZaw+nnNMJmASoIMmlp556ip49exIREUG9evUYNWoUx48fp2fPnued\nW6JECe6//37vhxSRPOEzIyNjx0K/fvDYYzBmDIToRkzxntyMkOzCWQwbDdSz1q7O5JwFwKFLCRbs\nunTpwr59+3jllVdISEjgpptuYu7cuZQsWdLtaCISiP79b3j6aRg40PmzMW4nkiCTm4JkIPCptfZk\nVidYaw8BlXOdSgDo27cvffv2dTuGiAQya2HYMHjlFXjpJXjtNRUj4orc3GXzoSeCiIiIl1nrFCHD\nhztFyUsvuZ1IgtglLWoVERE/Za0zPTN6tDNF89RTbieSIKcVSwEgN5vw+YIhQ4YQEhKS7lGzZk23\nY2VpyZIltG/fnnLlyhESEsLs2bPPO+eVV17hH//4BwULFuS2225j06ZNLiTN3MXy9+rV67y/j7Zt\n27qUNr3hw4dTr149ihQpQunSpenUqRMbNmw47zxfvf7Zye/V65+c7CxcHT0axo1TMSI+QQWJn8vN\nJny+pFatWiQkJLB79252797N0qVL3Y6UpWPHjnHTTTcxbtw4TCZz7G+++SZjxozh3XffJTY2lkKF\nChEZGcnp06ddSHu+i+UHaNOmTbq/D1/ZtG7JkiU88cQT/PTTT8TExHDmzBluv/32dM3EfPn6Zyc/\neOn6JyVBr17w3//C5MlOYSLiC6y1AfcAwgEbFxdnA139+vXtk08+mfo8OTnZlitXzr755psupsqe\nV1991dapU8ftGLlijLGzZs1Kd6xs2bL2P//5T+rzxMREmz9/fvvJJ594O95FZZa/Z8+etlOnTi4l\nypm9e/daY4xdsmRJ6jF/uv6Z5ffK9T992touXazNl8/a6GjPfi0JSnFxcRawQLjN4e9ujZD4sdxu\nwudLNm7cSLly5bj22mvp1q0b27ZtcztSrmzZsoXdu3en+7soUqQI9evX95u/C4CFCxdSunRpqlev\nTt++fTlw4IDbkTJ16NAhjDEUS9l11t+uf8b853j0+p865XRe/eILmDnTaQsv4kNUkPix3G7C5ysa\nNGjAlClTmDt3LhMmTGDLli00adKEY8eOuR0tx3bv3o0xxm//LsCZLpg6dSrz589n5MiRLFq0iLZt\n254bdfQZ1loGDBhAo0aNUtcc+dP1zyw/ePj6Hz8O7dvD99/DrFnQseOlf6ZIHtNdNuKatK2ya9Wq\nRb169ahYsSIzZsygV69eLiYLTl26dEn98/XXX88NN9zAtddey8KFC2nevLmLydLr27cv69evZ9my\nZW5HyZWs8nvs+h854hQjsbHOjr0tWuT+s0Q8SCMkfiynm/D5urCwMKpVq+Yzd0bkRJkyZbDWBszf\nBUDlypUpUaKET/199OvXjzlz5rBw4ULKli2betxfrn9W+TOTJ9c/MREiIyEuzhkdUTEiPkwFiR9L\nuwnfOTZlE75bbrnFxWS5c/ToUTZt2nTRH9S+qHLlypQpUybd38Xhw4f56aef/PLvAmD79u3s37/f\nZ/4++vXrx6xZs1iwYAFXX311utf84fpfKH9mLvn6798PLVvC77/DvHngK/vliGRBUzZ+Lieb8Pma\nZ555hnbt2lGxYkV27NjB4MGDCQ0NJSoqyu1omTp27BibNm1KndPfvHkza9asoVixYlSoUIEBAwYw\nbNgwqlSpQqVKlXj55ZcpX748HTp0cDm540L5ixUrxpAhQ+jcuTNlypRh06ZNPPfcc1SrVs0ndqHt\n27cv0dHRzJ49m0KFCqWOhISFhZE/f34An77+F8t/7NixvL3+CQlw222wezcsWAC1a+fxdyTiATm9\nLccfHgTRbb/WWjt27FhbsWJFmz9/ftugQQO7cuVKtyNlS9euXW25cuVs/vz5bYUKFWxUVJTdvHmz\n27GytHDhQmuMsSEhIekevXr1Sj1n8ODBtmzZsrZAgQL29ttvtxs3bnQxcXoXyn/ixAkbGRlpS5cu\nba+44gpbuXJl26dPH7tnzx63Y1trbaa5Q0JC7AcffJDuPF+9/hfLn6fXf9s2a6+7ztqyZa1dvz6P\nvxORC7uU236N9bEV9HnBGBMOxMXFxREeHu52HBER7/jxR7jrLrjiCmeapkoVtxNJkImPjyciIgIg\nwlobn5P3ag2JiEggmDQJmjeHqlWdO2pUjIifUUEiIuLPkpKgf3946CHo2dMZGcnQj0XEH2hRq4iI\nv9q/H7p0gcWLnU3ytC+N+DEVJCIi/mjtWujQAQ4fhpgYaNrU7UQil0RTNiIi/uaLL6BBAyhcGFau\nVDEiAUEFiYiIv0hOhqFDnTtp2rRx7qqpVMntVCJ5QlM2IiL+4OhRZ9HqZ5/Ba6/BSy+BMW6nEskz\nKkhERHzdli3ODr2bN8OXXzprR0QCjE9M2RhjGhtjZhtjdhhjko0x7TM5Z6gxZqcx5rgx5gdjjG6y\nF5HAt2AB3HwzHDsGK1aoGJGA5RMFCVAIWA30xWk5m44x5jmgH/AIUA84Bsw1xlzuzZAiIl5jLYwd\n6+xJU6eO0+zs+uvdTiXiMT4xZWOt/Q74DsCYTCdF+wOvWWu/TjmnO5AAdARmeCuniIhXnD4Njz8O\n770HAwbAW2/BZT7x41rEY3xlhCRLxpjKQBkgdV9xa+1h4CegoVu5RDztww8/pESJEpw5cybd8Y4d\nO9KjRw+XUonHJSRAixYwdarTDn7UKBUjEhR8viDBKUYszohIWgkpr4kEpHvuuYfk5GRmz56demzv\n3r3MmTOHhx56yMVk4jFxcVC3Lvz5JyxaBL16uZ1IxGv8oSARCUr58+cnKiqKyZMnpx778MMPqVix\nIk2aNHExmXhEdDQ0agRly8KqVU7jM5Eg4g/jgLsBA5Qm/ShJaeDnC71x4MCBhIWFpTsWFRVFVFRU\nXmcU8YiHH36YevXqsWvXLsqWLcsHH3xAL/2rObCcPQuDBsGIEfDAA/Duu5A/v9upRC4qOjqa6Ojo\ndMcSExNz/XnG2vNuanGVMSYZ6GitnZ3m2E7gLWvtqJTnRXCKk+7W2k8z+YxwIC4uLo7w8HAvJRfx\njLp163LPPfdw2223Ub9+fbZu3Uq5cuXcjiV5ITER7rsPvvsORo6Ep55SszPxa/Hx8URERABEWGvj\nc/JenxghMcYUAqrgjIQAXGOMqQ0csNZuA94GBhljNgFbgdeA7cAsF+KKeFXv3r15++232b59O61a\ntVIxEig2bID27WH3bpgzByIj3U4k4ipfWUNSF2f6JQ5nAeu/gXhgCIC1diTwf8BEnLtrCgBtrLWn\nXUkr4kX33Xcf27dv57333tNi1kDx3XdQr54zGhIbq2JEBB8pSKy1i6y1IdbafBkeD6Y551Vr7T+s\ntQWttZHW2k1uZhbxliJFitC5c2cKFy5MB3Xp9G/WOj1F7rjDWcC6YgVUq+Z2KhGf4BMFiYhc2I4d\nO+jWrRuhoaFuR5HcOnECuneHZ5+F556DWbMgw6J7kWDmE2tIRCRzhw4dYsGCBSxatIjx48e7HUdy\na/t26NQJ1q2D6dPh3nvdTiTic1SQiPiwOnXqcOjQIUaOHEnVqlXdjiO58eOPcNddcPnlsHQp6M4/\nkUypIBHxYVu2bHE7glyKSZPgscecBawzZ0Lp0m4nEvFZWkMiIpLXkpKgf3946CHo2RPmzVMxInIR\nGiEREclL+/dDly6weDGMGwd9+qjZmUg2qCAREckra9dChw5w+DDExEDTpm4nEvEbmrIREckLX3zh\nbIhXuDCsXKliRCSHVJCIiFyK5GQYOtS5k6ZNG+eumkqV3E4l4nc0ZSMikltHjzqLVj/7zClKBg3S\nehGRXFJBIiKSG1u2QMeOsHkzfPmls3ZERHJNUzYiIjm1YAHcfLMzQrJ8uYoRkTyggkREJLushbFj\n4bbboE4dZ/FqrVpupxIJCCpIRESy4/RpePRR6NcPnngCvv0WihVzO5VIwNAaEhGRi0lIgM6dnRGR\nSZOgVy+3E4kEHBUkIiIXEhfnLF5NSoKFC6FhQ7cTiQQkTdmIiGQlOhoaNYKyZWHVKhUjIh6kgkRE\nJKOzZ+GFF+C+++Duu2HRIihXzu1UIgFNUzYiImklJjqFyHffwb/+BU89pWZnIl6ggkRE5JwNG6B9\ne9i9G+bMgchItxOJBA1N2YiIgDMiUq+eMxoSG6tiRMTLVJCISHCzFt56C+64w1nAumIFVKvmdiqR\noKOCRESC14kT0L07PPssPPcczJoFYWFupxIJSlpDIiLBac0a6NHDWTcSHQ1du7qdSCSoaYRERILL\nmTMwdCjUrevc3vvjjypGRHyARkhEJHisWQM9e8Kvvzp9RgYNgiuucDuViKAREhEJBmlHRZKS4Kef\n4LXXVIyI+BCNkIhIYNOoiIhf0AiJiAQmjYqI+BWNkIhI4NGoiIjf0QiJiAQOjYqI+C2NkIhIYNCo\niIhf0wiJiPg3jYqIBASNkIiI/9KoiEjA0AiJiPgfjYqIBByNkIiIf9GoiEhA0giJiPgHjYqIBDSN\nkIiI79OoiEjA0wiJiPgujYqIBA2NkIiIb9KoiEhQ0QiJiPgWjYqIBCWNkIiI79CoiEjQ0giJiLhP\noyIiQU8jJCLiLo2KiAgaIRERt2hURETS0AiJiHifRkVEJAONkIiI92hURESyoBESEfEOjYqIyAVo\nhEREPEujIiKSDRohERHP0aiIiGSTRkhEJO9pVEREckgjJCKStzQqIiK5oBESEckbGhURkUvgFwWJ\nMWawMSY5w2O927lEJMWaNVCvnlOQPP88rFoFERFupxIRP+JPUzZrgZaASXme5GIWEQFnVGT4cGck\npHp1Z1REhYiI5II/FSRJ1tq9bocQkRRaKyIiecgvpmxSVDXG7DDG/GmM+cgYU8HtQCJBSWtFRMQD\n/KUgWQH0BCKBPkBlYLExppCboUSCjtaKiIiH+MWUjbV2bpqna40xscBfQBdgsjupRILIiRMwciQM\nG6a1IiLiEX5RkGRkrU00xmwAqlzovIEDBxIWFpbuWFRUFFFRUZ6MJxI4Tp+G//4XXn8d9u51RkW0\nVkREgOjoaKKjo9MdS0xMzPXnGWvtpWbyOmNMYeBv4BVr7ZhMXg8H4uLi4ggPD/d6PhG/l5QEH34I\nQ4bA339Dt24weDBce63byUTEh8XHxxPhjJ5GWGvjc/Jev1hDYox5yxjTxBhT0RhzC/AFcAaIvshb\nRSQnkpPhk0+gVi148EG4+WZYuxamTlUxIiIe5RcFCVAemAb8DkwH9gINrLX7XU0lEiishdmzoU4d\n6NrVKT7i4uDTT6FmTbfTiUgQ8Is1JNZaLfoQ8QRrYd48eOkliI2FZs1g2TK45Ra3k4lIkPGXERIR\nyWvLlkHz5nDbbWAMxMTA/PkqRkTEFSpIRIJNXBy0bQuNGsGhQ85UzfLl0LKlU5iIiLhABYlIsFi3\nDjp3djqsbt7sLF6Nj4d27VSIiIjrVJCIBLpNm+CBB+CGG5wCZPJk586ZLl0gRD8CRMQ36KeRSKDa\ntg0eecTprDp/PowdC3/84WyId5lfrGcXkSCin0oigSYhAYYPh/HjoUgRePNN6NsXChRwO5mISJZU\nkIgEigMH4K234J13IDQUXn4Z+veHK690O5mIyEWpIBHxd0eOwNtvw7/+5bR8798fnn4aihVzO5mI\nSLapIBHxVydOOOtCRoyAo0fhscfghRegVCm3k4mI5JgKEhF/k3EH3gcfdHbgrVDB7WQiIrmmu2xE\n/EVSknPLbrVq8MQT0KoV/P47TJyoYkRE/J5GSER8XXKys8nd4MHObbudO8OcOdr0TkQCikZIRHxV\nVjvwzpypYkREAo4KEhFfY62z0V2DBtChg3O3zNKl8M03EB7udjoREY9QQSLiS7LagffWW91OJiLi\nUSpIRHxB2h14Dx7UDrwiEnRUkIi4KbMdeH/+WTvwikjQUUEi4gbtwCsiko5+8ol4U9odeOfNgzFj\ntAOviAjqQyLiHdqBV0TkglSQiHhS2h14L7vMafE+YIB24BURyUAFiYgnaAdeEZEcUUEikpf++AOm\nTnX2lzm3A+/zz0Pp0m4nExHxaSpIRC7VgQPO7boffAA//QRFi8L998Nzz2nTOxGRbFJBIpIbZ87A\nd985RchXX8HZs9CmDcyY4fQQyZ/f7YQiIn5FBYlITqxe7RQh06bBnj1QuzaMGAH33adpGRGRS6CC\nRORidu+Gjz921ob88otTeHTrBt27OwWJiIhcMhUkIpk5edLZT+aDD2DuXMiXz9l59403IDJSTcxE\nRPKYfqqKnGOts6Hd1KnOItVDh6BhQ6eb6r33wlVXuZ1QRCRgqSAR+esv+PBDpxDZuBGuvhoef9yZ\nkqlWze10IiJBQQWJBKejR2HmTGdKZuFCKFQI7r7b6R/StKk2uBMR8TIVJBI8kpNhwQKnCPnsMzhx\nApo3d57fdRcULux2QhGRoKWCRALfH384RcdHHzm77VatCi++CA884EzPiIiI61SQSGDKrHtq167Q\nowfUrw/GuJ1QRETSUEEigeNc99SpU51bdtU9VUTEb6ggEf+n7qkiIn5PBYn4J3VPFREJKCpIxH+o\ne6qISMDST3DxbeqeKiISFFSQiG9S91QRkaCigkR8h7qniogELRUk4i51TxUREVSQiLcdPQqxsc66\nkOXLYcUK2L9f3VNFRIKcChLxHGud9R8rVvyvAPn1V2dUJCzM6Zjarx+0bq3uqSIiQU4FieSdrEY/\nAGrUcO6O6dfP+d8aNbQmREREUqkgkdw5N/pxrvDIavSjYUPnz0WLup1YRER8mAoSyR6NfoiIiAep\nIJHzpR39OFd8aPRDREQ8SAWJaPRDRERcp4Ik2GQc/Vi+HNau1eiHiIi4SgVJoDtyBFauvPDoxxNP\naPRDRERcpYIkkGj0Q0RE/JQKEn+m0Q8REQkQfvUbyhjzuDFmizHmhDFmhTHmZrczeUVyMhw4AOvX\nO3u89OkDtWs7IxwtW8Jbb8GZM87ox3ffwcGDzrnvvw+9e8P113u9GImOjvbq15P0dP3dpevvLl1/\n/+Q3IyTGmHuBfwOPALHAQGCuMaaatXafq+Fy4sQJZxQjJ4+DB53pmHP8YPQjOjqaqKgot2MELV1/\nd+n6u0vX3z/5TUGCU4BMtNZOBTDG9AHuAB4ERno9TXKyUygcOJCz4uLEifM/KyQEihWD4sX/96he\nPf3z4sWhRIn/jYyIiIgEEL8oSIwxoUAE8Ma5Y9Zaa4yJARpe8hfIi1GLcwoWPL+IuO6684uLtI+w\nMJ8b4RAREfEmvyhIgBJAPiAhw/EE4Los37VkiXOXiSdHLTKekz9/Hn7bIiIiwcFfCpKcyg/w24AB\nKc/yO6MQ5x5Fi0LVqlC37v+ep30tLAwKF87+qEVSEiQkOA8BIDExkfj4eLdjBC1df3fp+rtL1989\nv/3227k/5vhf58ZmNu3gY1KmbI4Dna21s9McnwKEWWs7ZTj/PuBjr4YUERGRc+631k7LyRv8YoTE\nWnvGGBMHtARmAxhjTMrzdzJ5y1zgfmArcNJLMUVERIJdfqASzu/hHPGLERIAY0wXYArQh//d9ns3\nUN1au9fFaCIiInKJ/GKEBMBaO8MYUwIYCpQGVgORKkZERET8n9+MkIiIiEjgUvMLERERcZ0KEhER\nETdUZkoAAATMSURBVHFdQBYkQbsJn8uMMY2NMbONMTuMMcnGmPZuZwomxpgXjDGxxpjDxpgEY8wX\nxphqbucKFsaYPsaYNcaYxJTHj8aY1m7nCkbGmOdTfgb9x+0swcIYMzjlmqd9rM/JZwRcQZJmE77B\nQB1gDc4mfCVcDRYcCuEsNu4LaHGS9zUG/g+oD7QCQoHvjTEFXE0VPLYBzwHhOFtdzAdmGWNquJoq\nyKT8A/QRnJ/94l1rcW46KZPyaJSTNwfcolZjzArgJ2tt/5TnBucHxTvWWu9vwhekjDHJQMe0jezE\nu1KK8D1AE2vtUrfzBCNjzH7gaWvtZLezBANjTGEgDngMeBn42Vr7lLupgoMxZjDQwVobntvPCKgR\nkjSb8M07d8w6FVfebMIn4l+K4oxUHXA7SLAxxoQYY7oCBYHlbucJImOBr6y1890OEqSqpkzZ/2mM\n+cgYUyEnb/abPiTZlLtN+EQCTMrI4NvAUmttjuZxJfeMMbVwCpD8wBGgk7X2d3dTBYeUAvAmoK7b\nWYLUCqAn8AdQFngVWGyMqWWtPZadDwi0gkREHOOAmsCtbgcJMr8DtYEwnE7SU40xTVSUeJYxpjxO\nAd7KWnvG7TzByFqbtlX8WmNMLPAX0AXI1pRloBUk+4CzOItq0ioN7PZ+HBHvM8aMAdoCja21u9zO\nE0ystUnA5pSn/9/e/YNcVYBxHP/+hFCUpjLwz5K8o/lCpWiI4pDgUEFbEIEuNYTSELi6uAguQnOG\n6Kjg0BLkEA4iCRFFjjY0lMEbvEI4PA3nvqBN73Luc7z3+4G7HLiXh8vl8jvPec55HiQ5BJxjmGnQ\neN4CdgI/zrqDMHTLjyX5HNhaizYwOXFVtZbkIbCy2fcs1AzJLBlvLOEDnlvCd7erLmleZmHkA+BE\nVT3qrkdsAbZ2F7EEvgPeYLhkszp73QeuAauGkfmbDRivAJs+KVq0DgnAZeDr2XbgjSV82xkW82lE\nSXYw/AA3zlD2JVkF/q6q3/sqWw5JvgI+At4H1pNsdArXqsqt1yNLchH4FngEvMywcfw4cLKzrmUw\nm1F4blYqyTrwuKp+7alquSS5BNxmuEyzB7gAPAVubPYzFi6QuISv1dvA9wx3dhTD82AArgJnuopa\nIp8xfO93/nf8NPDN3KtZPq8x/NZ3AWvAT8BJ7/hoY1dkvvYC14FXgD+BH4DDVfV4sx+wcM8hkSRJ\nL56FmiGRJEkvJgOJJElqZyCRJEntDCSSJKmdgUSSJLUzkEiSpHYGEkmS1M5AIkmS2hlIJElSOwOJ\nJElqZyCRJEntDCSSJiHJq0n+SHL+mWPvJPk3yYnO2iSNz+V6kiYjySngFnAEeMiwrftmVX3ZWpik\n0RlIJE1KkivAu8B9YD9wsKqe9lYlaWwGEkmTkmQb8DOwF3izqn5pLknSHDhDImlqVoDdDP9PrzfX\nImlO7JBImowkLwH3gAfAb8AXwP6q+qu1MEmjM5BImowkl4APgQPAE+AO8E9VvddZl6TxeclG0iQk\nOQ6cBT6uqvUazpY+AY4m+bS3Okljs0MiSZLa2SGRJEntDCSSJKmdgUSSJLUzkEiSpHYGEkmS1M5A\nIkmS2hlIJElSOwOJJElqZyCRJEntDCSSJKmdgUSSJLUzkEiSpHb/AQlH2chun1QqAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c0f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n",
    "x = linspace(0, 5, 10)\n",
    "y = x ** 2\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title')\n",
    "\n",
    "# insert\n",
    "axes2.plot(y, x, 'g')\n",
    "axes2.set_xlabel('y')\n",
    "axes2.set_ylabel('x')\n",
    "axes2.set_title('insert title');\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
