swc-python-hands-on-code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!!!!!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World!!!!!\") #shift-enter or run button above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print(\"hi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*5+4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg = 60.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg_text = \"weight in kilograms\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.0\n"
     ]
    }
   ],
   "source": [
    "print(weight_kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kilograms 60.0\n"
     ]
    }
   ],
   "source": [
    "print(weight_kg_text, weight_kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in pounds: 132.0\n"
     ]
    }
   ],
   "source": [
    "print(\"weight in pounds:\", 2.2 * weight_kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg = 65.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight in kg is now: 65.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Weight in kg is now:\", weight_kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_in_pounds = 2.2 * weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kilograms 65.0 and in pounds: 143.0\n"
     ]
    }
   ],
   "source": [
    "print(weight_kg_text, weight_kg, 'and in pounds:', weight_in_pounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg = 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kilograms 100.0 pounds in still 143.0\n"
     ]
    }
   ],
   "source": [
    "print(weight_kg_text, weight_kg, 'pounds in still', weight_in_pounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/faustino/Desktop/Software-Carpentry-Workshop/swc-python_repository\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'loadtext'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-d50d9e5276f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'data/inflamation-01.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'loadtext'"
     ]
    }
   ],
   "source": [
    "numpy.loadtext(fname='data/inflamation-01.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'loadtext'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-7d68429fdad9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'data/inflamation-01.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'loadtext'"
     ]
    }
   ],
   "source": [
    "numpy.loadtext(fname='data/inflamation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'loadtext'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-9c7218916c17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'data/inflammation-01.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'loadtext'"
     ]
    }
   ],
   "source": [
    "numpy.loadtext(fname='data/inflammation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "data/inflamation-01.csv not found.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-0f4647528b1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'data/inflamation-01.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows)\u001b[0m\n\u001b[1;32m    953\u001b[0m             \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_is_string_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 955\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    956\u001b[0m             \u001b[0mfencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'encoding'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'latin1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataSource\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnewline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m    622\u001b[0m                                       encoding=encoding, newline=newline)\n\u001b[1;32m    623\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s not found.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: data/inflamation-01.csv not found."
     ]
    }
   ],
   "source": [
    "numpy.loadtxt(fname='data/inflamation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 3., 0., 0.],\n",
       "       [0., 1., 2., ..., 1., 0., 1.],\n",
       "       [0., 1., 1., ..., 2., 1., 1.],\n",
       "       ...,\n",
       "       [0., 1., 1., ..., 1., 1., 1.],\n",
       "       [0., 0., 0., ..., 0., 2., 0.],\n",
       "       [0., 0., 1., ..., 1., 1., 0.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1. ... 3. 0. 0.]\n",
      " [0. 1. 2. ... 1. 0. 1.]\n",
      " [0. 1. 1. ... 2. 1. 1.]\n",
      " ...\n",
      " [0. 1. 1. ... 1. 1. 1.]\n",
      " [0. 0. 0. ... 0. 2. 0.]\n",
      " [0. 0. 1. ... 1. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(data.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 40)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first value in data 0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"first value in data\", data[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "middle value in data 13.0\n"
     ]
    }
   ],
   "source": [
    "print(\"middle value in data\", data[30,20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1. 3. 1. 2. 4. 7. 8. 3.]\n",
      " [0. 1. 2. 1. 2. 1. 3. 2. 2. 6.]\n",
      " [0. 1. 1. 3. 3. 2. 6. 2. 5. 9.]\n",
      " [0. 0. 2. 0. 4. 2. 2. 1. 6. 7.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[0:4, 0:10]) #Pull first 4 rows and first 10 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1. 2. 2. 4. 2. 1. 6. 4.]\n",
      " [0. 0. 2. 2. 4. 2. 2. 5. 5. 8.]\n",
      " [0. 0. 1. 2. 3. 1. 2. 3. 5. 3.]\n",
      " [0. 0. 0. 3. 1. 5. 6. 5. 5. 8.]\n",
      " [0. 1. 1. 2. 1. 3. 5. 3. 5. 8.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[5:10, 0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "small = data[:3, 36:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 3., 0., 0.],\n",
       "       [1., 1., 0., 1.],\n",
       "       [2., 2., 1., 1.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "doubledata = data *2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 2., ..., 6., 0., 0.],\n",
       "       [0., 2., 4., ..., 2., 0., 2.],\n",
       "       [0., 2., 2., ..., 4., 2., 2.],\n",
       "       ...,\n",
       "       [0., 2., 2., ..., 2., 2., 2.],\n",
       "       [0., 0., 0., ..., 0., 4., 0.],\n",
       "       [0., 0., 2., ..., 2., 2., 0.]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doubledata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doubledata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 3., 0., 0.],\n",
       "       [1., 1., 0., 1.],\n",
       "       [2., 2., 1., 1.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "small = doubledata[:3, 36:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4., 6., 0., 0.],\n",
       "       [2., 2., 0., 2.],\n",
       "       [4., 4., 2., 2.]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "tripledata = doubledata + data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 3., ..., 9., 0., 0.],\n",
       "       [0., 3., 6., ..., 3., 0., 3.],\n",
       "       [0., 3., 3., ..., 6., 3., 3.],\n",
       "       ...,\n",
       "       [0., 3., 3., ..., 3., 3., 3.],\n",
       "       [0., 0., 0., ..., 0., 6., 0.],\n",
       "       [0., 0., 3., ..., 3., 3., 0.]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tripledata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tripledata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6., 9., 0., 0.],\n",
       "       [3., 3., 0., 3.],\n",
       "       [6., 6., 3., 3.]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tripledata[:3, 36:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.14875\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data)) #Simple statistical mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<built-in function ctime>\n"
     ]
    }
   ],
   "source": [
    "print(time.ctime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri May 31 13:49:06 2019\n"
     ]
    }
   ],
   "source": [
    "print(time.ctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data) #Assign the results of the 3 calculations to the the 3 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximmum inflammation:  20.0\n",
      "minimum inflammation:  0.0\n",
      "standard deviation:  4.613833197118566\n"
     ]
    }
   ],
   "source": [
    "print('maximmum inflammation: ', maxval)\n",
    "print('minimum inflammation: ', minval)\n",
    "print('standard deviation: ', stdval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pull function calls and help\n"
     ]
    }
   ],
   "source": [
    "print(\"Pull function calls and help.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.deg2rad?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_0 = data[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  1.,  3.,  1.,  2.,  4.,  7.,  8.,  3.,  3.,  3., 10.,\n",
       "        5.,  7.,  4.,  7.,  7., 12., 18.,  6., 13., 11., 11.,  7.,  7.,\n",
       "        4.,  6.,  8.,  8.,  4.,  4.,  5.,  7.,  3.,  4.,  2.,  3.,  0.,\n",
       "        0.])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_10 = data[10, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  0.,  0.,  4.,  3.,  3.,  5.,  5.,  4.,  5.,  8.,  7.,\n",
       "       10., 13.,  3.,  7., 13., 15., 18.,  8., 15., 15., 16., 11., 14.,\n",
       "       12.,  4., 10., 10.,  4.,  3.,  4.,  5.,  5.,  3.,  3.,  2.,  2.,\n",
       "        1.])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.45"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.mean(patient_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy.max(patient_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'numpy' has no attribute 'mac'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-18fd6c8be116>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmac\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatient_0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'mac'"
     ]
    }
   ],
   "source": [
    "numpy.mac(patient_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.max(patient_0) #Spell right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.min(data[: ,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.45 , 5.425, 6.1  , 5.9  , 5.55 , 6.225, 5.975, 6.65 , 6.625,\n",
       "       6.525, 6.775, 5.8  , 6.225, 5.75 , 5.225, 6.3  , 6.55 , 5.7  ,\n",
       "       5.85 , 6.55 , 5.775, 5.825, 6.175, 6.1  , 5.8  , 6.425, 6.05 ,\n",
       "       6.025, 6.175, 6.55 , 6.175, 6.35 , 6.725, 6.125, 7.075, 5.725,\n",
       "       5.925, 6.15 , 6.075, 5.75 , 5.975, 5.725, 6.3  , 5.9  , 6.75 ,\n",
       "       5.925, 7.225, 6.15 , 5.95 , 6.275, 5.7  , 6.1  , 6.825, 5.975,\n",
       "       6.725, 5.7  , 6.25 , 6.4  , 7.05 , 5.9  ])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean(axis=1) #Averaging across row 1 by column 0 - 39"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.45      ,  1.11666667,  1.75      ,  2.43333333,\n",
       "        3.15      ,  3.8       ,  3.88333333,  5.23333333,  5.51666667,\n",
       "        5.95      ,  5.9       ,  8.35      ,  7.73333333,  8.36666667,\n",
       "        9.5       ,  9.58333333, 10.63333333, 11.56666667, 12.35      ,\n",
       "       13.25      , 11.96666667, 11.03333333, 10.16666667, 10.        ,\n",
       "        8.66666667,  9.15      ,  7.25      ,  7.33333333,  6.58333333,\n",
       "        6.06666667,  5.95      ,  5.11666667,  3.6       ,  3.3       ,\n",
       "        3.56666667,  2.48333333,  1.5       ,  1.13333333,  0.56666667])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean(axis=0) #Averaging across row 0 by column 0 - 39"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60,)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean(axis=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean(axis=0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "mass = 47.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = 122"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "mass = mass *2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = age - 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-89-3878c9b4ab65>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-89-3878c9b4ab65>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"Mass is equal to :\" mass, \"Age is equla to: \", age)\u001b[0m\n\u001b[0m                                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "print(\"Mass is equal to :\" ,mass, \" and Age is equal to: \", age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.0\n"
     ]
    }
   ],
   "source": [
    "print(mass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n"
     ]
    }
   ],
   "source": [
    "print(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mass is equal to : 95.0 Age is equal to:  102\n"
     ]
    }
   ],
   "source": [
    "print(\"Mass is equal to :\" ,mass, \"Age is equal to: \", age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mass is equal to : 95.0  and Age is equal to:  102\n"
     ]
    }
   ],
   "source": [
    "print(\"Mass is equal to :\" ,mass, \" and Age is equal to: \", age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "element = 'oxygen'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first three characters oxy\n"
     ]
    }
   ],
   "source": [
    "print('first three characters', element[0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last three characters: gen\n"
     ]
    }
   ],
   "source": [
    "print('last three characters:', element[3:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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": [
    "image = matplotlib.pyplot.imshow(data)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l6TCXPR8BYJSwVcse50C+mzyuBzprZVmhi78HmdHOxS3zmisJm3e/ZKk75yYDOAXAWhxCHxrD6G8K3tjOuTSApwB8y3u/p6fjxeOsuZLR7xRk7nPOlSKzqX/lvT/oHy6oD433fhGARUAmS12uSZMeu9TV61MGDZvp5E/ZTipKOfFFLXHenyfMfTE1pke9oIs8MtK9vGXhdLXWOo/+AYtiNhzdV745ZJ1wds2BRh0hd/Vlq3Pj3y2ao9Y4E1256in7XkoBlh7sNm+vCp99+WZdGCiviKfIZBpNUkS+Jn9/cRk0vaUQq4gD8HMA73jvfySWrA+NUbQUcsU+F8B/AfBn59yb2b/dhUzfmSezPWnqAPAtk2EMGIVYRVZDl6qTWB8aoygpmrBV1lnS3MdmJe4hOEzouY5huhgi9zWXsLtdmqR66iuzc1ZwRR9984dqbbzoschcu+A3ai6Lwl85aaNa+7B6pJo/Xydc8xReyv3SZfb79rn62KmPBo1bzhlHFObLvW0kzdN0JS1pSuV7HVkZasgb+nPn+lux4a8FYi51I5HYxjYSiW1sI5EUbbXVOLi5koTdsxxaKV3E712ubbhHN4R75LgUMgCoWBedeuWX6XuCcV8JmeiyrzoAnH9ySJXf3q71LWety5QuWempp9fc9orOaO8SPdurVunvv/4S7fKXlaK4uhMXvB+2kvz8Au7VKeHmWS7GpW7VVo0jGtvYRiIpGnNfHOxSZ+RPYnzJGY2UHgCwZ2r4GZ76qPa311+oz6Hum8FINenHaVrTLZ73Cilw/kVvqzXpjr92gXbevrdvrJr/ESEScHctybipWkLMFJnyTQ06ui/dEI5leQEcjShYesT1xWRYbki4KE6JzGi3DBrDCNjGNhKJbWwjkRRN4fe4SlBxmS2A7t0os9CBfJewzLbhwu+dwp1ce5vO3h5UG20WlRnhADB+ida75303hJs+87gON/22cLHf/5O/U2stM3VWzDThuh9OWTHuYt2gaOVbwaWtHfPADhVWq9/nyNV63izCWGWILZBfVD/uXkiGRbDeZh1tRSkNIwLb2EYiKRpzX14yr/hZO5S+gCwv2EQl+6bsmap/dqtWiZ/azTpybQfVn57yQ5Es+129VrZCv6aMyuOsndW7p+bGeyu13JEJuQBw5+Tnc+Pvf+VStXb66PfV/Im2U3PjirXa/NgyM5j0TnhQrw1uji4wxEm4ndO4lFE0Un6w55hbTktpYuY+wxDYxjYSiW1sI5EMaHSf5FAi/bjwu4wq4zVpCmQ4M2fbZUFzT16q9W3j6Vpzy94xsjcMAEy/SbvNZZFKzsyZXV0beX4bmkiLigi+m2/RmTiP3fQFNZdZRhyxV704zPl98T2A1P0TV3SoNc5kku55jrKMK5gTV3WA771+v+tnFt1nHLnYxjYSiW1sI5EUjcaOq/7DLnW2a8te6lxkkbPUZXZ3+sNozdhMNlqZMQPofpEMZ5A3zQmakm3Tsrj7pBJt/175V33dufGxG3NjLm7JulWen8ymAXRvdX5frHfjsvw5y0hW3WL/gTw/dqmzXVsWtGSXumXQGEc0trGNRFI0LnVO2pTmv04yyx0K/HNZ1tr9cQCwfW6IVhtUoV3NpS9qCSH71XBhx/YqHfU28s1gRrz5lqVqbdGu0Fdm3cfHqrUq6hf5na8+nRs/tkKb90ru1mZNJxKBu+6tUGsVouqtzAQCgK42nUEz7uUgq7hgJcshKT/YzCrNfUMpCpBraav+Q+ZSN4xAIdVWhzrnXnXObcj2oLkn+/fjnHNrsz1onnDOxdfdNYx+pJAr9j4An/PezwAwE8DFzrmzACwE8K/ZHjTNAK779E7TMA6NQqqtegAHfaWl2f88gM8B+Pvs3x8B8D0A/34oL15on8c4l2uGoMFZ78bB4a9THw269N0bOVtba2x/X9C/I6G18C4qEAlR7P1fH9ZZMpdf/XJuzAVyuHfMxtbgmm+5QL/EOaTH5WPbJmjzozRzcnH51A59rds5K+hoDjPgz08WJ5KauifY3FfSHDR4bwtUFqSxnXODs7WxmwAsB1ALoMV7f9A4WY9MwyXDKAoK2tje+/3e+5kAJgI4A8BJ3R3W3WOtB40xEBySVcR734JM27uzAIx0zh2UMhMBdNtnznu/yHt/mvf+tFJEe+sMoy/p0aXunBsLoNN73+KcOwrAH5C5cbwGwFPe+8XOuZ8CeMt7/29xz3UoLvW4zHR2qUtXOFc2Ys0tM6v3UPUkWaBR2rQBYMQU7dodK/qTu+9ouyxXjfrr+PA6JW1RzSGA5df8XzX/7vZLIo/lfu1sO1drU7Q2loUmGZk6B+hUu57uX6R255DWuPBh/q45VUxSqEu9EAdNJYBHnHODkbnCP+m9f845txHAYufcPwN4A5kGTIZRFBRiFXkLmaal/PetyOhtwyg6BtSlHmfuyzfpRdM2IfxEdhyCuW/Mev3znaoL/vYDKW3u45bTkhkP6Oi5Slpf9afQT2fyOR+oNVkv+7xl39bnU6uz6FPbg1TiTBw220lZtW+GliJtldGFJ7lIT7ohuMlZ7nABHWn+6yBznzT/sSxh6SFlaZ65z1zqxpGMbWwjkdjGNhJJ8RSlpFBGqa3Y5crZGRIulMh9xNurgjlrL4nhctFmkTNdWMPK6Ne6H+t4Tg4FlSY+dpsPao+upiQ1NQDsnBXmB9q0/q4md/ekBSFzv52z3UVxd66cNWoTV936KDdumqU/MDYNDt8QxpzRI/U36+a4gqS9xa7YRiKxjW0kEtvYRiIpmtSwQ6m2yv0EZfWnMuoVxDbTVF3QuPy80hU+ZINawr4ZOlVs/JIQ9zJpge6BXr9cu7tlShcXd19xx3258fU1V6m1O7/0vJpfv/YfcuO7Tlmm1p6cor3MdXcJ3U99KGWR+PphOu0u3/0e7M8j89o4RodjcKixtmPTs/SBpmbsim0kEtvYRiIpGinChVGcMPH1VGBF9hvkIjhxxXa4IKMs+MIypZH6H6bqwrGy3wsATKTs7fsbgvzgiMKv33pbbnz87VrSyAI5AHDlZasRBRd+f213kFUda7Tc6BQtp7XRML8QvKSJWnRzlrqUFM3U6rusLRzLxUD1J63h6gWFYldsI5HYxjYSiW1sI5EUTdhqXo9AGbZKa3kFD4V+Yz2+75RqNZcFLKWmBnSGiHxOQOtSPnbkm9qNX7abcjuF6/nqC9aopd/VhL6PE4a2qLXUDB2juWTp7Nx4HuntJzaequajpocwWy7m3ik+Ppnd0x3yffZU+J0/a4nMbOL7F74PkiHLVgnKMAS2sY1EUjTmvjjPI/9UxXkeGS7qMnRbGHNRFx0ZqP/Ns9dNSpWds0iKtEZXeztuyEdqLr2AH36im0NfOkmb/46bGh775HbtaZz0HxwlGGSD7K3TE1woqEw4blum6ffF5j4Jm12lvIgrQgkAXsgPazltGALb2EYisY1tJJKi0dhxLnXOWP+EzEqsuSWso5tmhX/LXWTCG14Txj1lgZe0heeZe7bu67i6Yoo+v3R4rgdqzlNrpUtG5cb33KtLs1z0yP9Qc3m+nGHf+S1tB0s9FPQ6F/uRr8kZR1XLdqh5XHHJvGzzmCi9uLJM7tTPqPl+kbVu5j7DENjGNhKJbWwjkRSNxmYtJS2krN2G0DzOjs1FKo+uDPbxqmUNkY/bv1bbzmtv0+sTlwa78cbNOmNmCFVMmnFF6JfObnOI5+UilOzClg2U9q7S5ch13osuqjl8mQ4TlcXcx6zXj2OfgdTRvLaTQlPLN0YXEpVhED31Uo+tBFUgBV+xs8Xf33DOPZedWw8ao2g5FClyK4B3xNx60BhFS0FSxDk3EcAXANwL4B+dcw590IMmzl0q+5BwdB//PMVFjnEkoCygwz+lHNGn0RF7zSJBVprWAKCjVbu3tyycnhuvvES/xvknb8qNrxmrI/++feLfqPneV47JjbvmagPa8SltEm2tCNF9LSntUi9rDF+7LMID6KJBzHuUQcNhBrGFRMX3wNKDi1JKoRJXuDSOQq/YPwbwHQTpOxrWg8YoYgrp8/hFAE3e+9fln7s51HrQGEVDIVLkXABfcs5dikze5XBkruAjnXMl2at2bA8aAIuATKuOPjlrw+iBQjoa3AngTgBwzp0P4J+89193zi0B8FUAi5HpR/PbwzkR1lJxPWgGkcaOC4lsvEE3OJOZMMc/pM19788LbnzOLJn6Q21Qq78wmNBKyTXPmejuYmHi26F16uwRwY9fXbpHrZW16ucdtSmY/7ZepX9sWx4+Rs0hQgJkL3cgv1CnhO9R5LyU+ueMWaM/a75nUa8pwljztDgVpZSZ6QMRtno7MjeSW5DR3NaDxigaDslB471fiUw7POtBYxQ15lI3EkmPfR77Eu7zGGejlNWe4vo6AtrtyzZurhrVeGawie+t1O9d2mXZviubFQFA87Rgqz79ij+rNa4MJYvIz66uVWuyXyOHyl45/XU1X/7DkKXOqVfS3Q7o3pO1t2mNPag22LjZFt00R7u7ZU9I+Z6B/OZKElmdCwBGPxfs9VyRIC4tkCtBFdrn0a7YRiKxjW0kkqLpQRMnS7gIDiiDRpoGuRgiZ4h0DgtjzrKWPVUOpPRPcqpOF2vsSAezHUuPcS/rj3XnLBEJ+IKOBFxyb2gzvbVTmwK/v+1SRMGZ58OfmqTmey8P0mnIhuj+jCwvuChlzTfCB3biT7R5j7NrZCYTR1zKYjpcOYCR8sMyaAxDYBvbSCS2sY1EUjRFKeNgkx3D5kAJ9zGUx9ZfwLkRQZeyG7qZerTLvuIy9BQAVqd1lvpcYeLbuF5rbNk/nbX5wu89qOb/q+363Pj4kz9Ua1Vn6/e5ems4h85a3Qde6uq4wpwZwmfCLnPurd5WGcIXqpbF6HEq/F7KRSnplqo32BXbSCS2sY1EUjSex7z62ALuWcIZGDKij2s0c58ZGd3H5j6ZAMtMfVQXmZaSRtbcBvJNjC0zg+fx62f8p1qT3sSR136g1ra9oiP2OiqCCTKuVTUADK8J1yyONjxqR/T1rGKdjpmXXl42DfKxUY8DtHeYv888c66Ao/vM82gc0djGNhKJbWwjkRSNS72ENHZcBk1eD5oRQVez3j12iTY7bZkfTFbShQ4Ak5cGzcimQOlaBrRO5TbNrKNlfxgu7n7DXaEd9UWpLWrtgrf+Sc0f+nzI5fjHv8xTazPG6fe5MhXc/KlabbqU2njXifoehDNzxr0c5tzLhj9rmeXPhUL3x/Tt7G2WTBx2xTYSiW1sI5HYxjYSSdG41POKDwqN3VPYqtR6Y9ZEZ54DwPEPBS266Zbxaq1ibbCPd1Tof/OyehIAlJ0bQi9PJ337x3vOUfPZoke6zJgBgFWzQtHzBdBw1s73Tw5hrKyppQsd0CEBozZF26aljR0AqhdTKK8og8QZR+kGfSxnzUikr4F9FmyRl+t5+8LCVo0jGdvYRiIpGnNfXKQf/3Sxu1YminIBSzZRyQg1rg0tkQm4AACSIuU/DibH7Xfr1+T6z3EJuyUi8u7AFL3WVql7LtbXhnNvelNnzFx5w8tq/syG0MpaFtAEdA+akW9qIdA2QcsNmdQs3fRAvvQo36gL/khkRk1efWwy7XKRyt5gV2wjkdjGNhKJbWwjkRRNDxpGFi5k9zprNKnf2E3OBRjlYznTu6xNuuq1iYz1b8d64Yq+V2drs5tamsl+duYv1dqL03WPQ8njjeequcrUOZmOXaGPnSzc5qUv6q+58cwQVsufj+yDCQCTl4Z7DXahs2lVhqPqT0B/7j1lqSOuB42Z+4wjmUJbdWwD0ApgP4Au7/1pzrlRAJ4AMBnANgBf8973fTSLYfSCQ7liX+C9nymyF+4A8FK2udJL2blhFAUFpYZlr9inee93ir+9C+B8732Dc64SwErv/Qlxz3MoqWFx/f04a12GsXJfR866lrZWDkVVr5HS2pNTsWR/8t215ZFrgLYbMzIdbO/9uo0P30uw/V7C2lj2iKz7pn4v6dXaPi5h97u8X2CX+nHPRNut4z53rirAIRP9WZTSA/iDc+5159wN2b9VeO8bACD7/3HdPdB60BgDQaFWkXO999udc+MALHfOberxEVmsB40xEBS0sb3327P/b3LOPY3buAsBAAAHjklEQVRMJ4NG51ylkCJNfXli8ueIzX2cQROXAc29G+VP5FE7dDa5LB6Tl6WzW7vYS3eHj27GA/Eu4NWXBLc517z+3aLg+n7jgX9Ta/Pr5qi5jOAbsoHlhL5maNOcNlVKEx9nnnMFgM5hQYpwVn+cpOAAibg+QSwteb03FNIOL+WcG3ZwDOBvAbwN4FlkmioBfdBcyTD6kkKu2BUAns4040UJgMe898ucc+sAPOmcuw5AHYB5Mc9hGP1KIe3wtgKY0c3fPwbw+fxHGMbAUzRhq6BM5RLhVmUtx2YwWfCQzX2lZDWUan3nLG3uk6/Dr8H4+8ITc+F3LlI5LB00Lru+j/9KXW583HPXq7X/OWepmr9SG8JfOTOe6WgI4QIHGvXnt2PeJ5Fr+SZQqatJj1PP+PSIENog73sYrtYVZ+7Lq3dlLnXjSMY2tpFIbGMbiaRowlZLqJ+2dKkPIfc6azSph2UfRyC/ALmsBDV5qe6VKMNYB1Voey67octE73J2ua5EdJ/HEupHXpUK763qZP0+Fy24Qs1lDanOGdqdnXpIV5jaMS80SSqhwu8j14c594vkkFsJ93ZnH4H8HjgkIra5Ukyl3d5iV2wjkdjGNhJJ0RTMORxk1BtHp7GLXbqFufDk8JogE/ZAm8G4l03Td6PNgak1+jU70+H8vvPVp9Xae/vGRj4PbnpbTaVLPTouMZ+utJYQLdPC+2yaw4WB9LEySjAuuhAgiRFTWYClZVxRyt7uEbtiG4nENraRSGxjG4mkaFzq7DqVoYwy5BHID61smxBCUcvIhc66UGaxywKVgNbjLTP1v/l3b9TmvhPuCea0+gvjzVXzLludGz9815fVWvv8ltz40kkb1dqWhdPVfIgIMe2APp9S0tGDZIUpWpOam4ttxmXFdIzQobwcviBDG+JCEjhM1UHPrRKUYURgG9tIJEVr7mP5IeHsFlmUsuHqk9Qam/+qRNSZaoNMVC/WP6VcLEZm4nC/w7N/9Kqa/+rVs3LjaTfrVtHnjX4/N1738bFq7Wg6tvkPoRDlt6/9jVp77KYvqPnIa8P7/Kidij4uC5KLvYkMe3I1+nuQRSpTdZ/QscF8GvfdAoDvAzOwXbGNRGIb20gktrGNRFI00X2MjA7jaD7uIfjxF0M0HWtq1salu4Pe5MLlMlqNH8dRcCV3R2vY5+u0mU6a1Lamx6i1n01dnBsvWTpbrbErfO4Vf86NFyy7XJ/PBTpqsOutiYhClrKJy+IHgE7hu09t1+fDnx9/RupYYXYdSmtxxZF6i12xjURiG9tIJLaxjURSNC51Js7GLQu9M+xubz9dZ4R0DAt2WS6y2JUOOpWrRAHUN1wUkGydp3X97OpaNd+yPmS3bE9rhfn1W28L5z5Nv2Zqhk7JvmbsmvAaF9eotSe36zqN214JGT78POVLg/2Zi98fSGn7vS7G2dN1MHxGZT2EuEp4H8hsKi5KWSh2xTYSiW1sI5EUjbmPpYf8eWJ5MXSbdslK1zgX12Fkcq9M7AWAqY+28uE5uJDMoIogPwZRsuz2Cu2Grr8kmNTKx+voueYdwb3N5rSFn1mi5vNfui435qg8lhsdFUFSlFJGT8eIUJSS21oP3/CRmsuW3eWbdZ1tju6ToQ4c3SeL4uyn6D7+7vvC/GdXbCOR2MY2EoltbCORFNSDps9ezLmPALwPYAyAnT0c3p/Y+cRTTOdzrPc+JrU/Q79u7NyLOvdaIQ1y+gs7n3iK7XwKwaSIkUhsYxuJZKA29qIBet0o7HziKbbz6ZEB0diG8WljUsRIJP26sZ1zFzvn3nXObXHODUjvdefcL5xzTc65t8XfRjnnljvnarL/75tqmYWdzzHOuRXOuXecc39xzt06kOfknBvqnHvVObchez73ZP9+nHNubfZ8nnDOlfX0XAOK975f/kOm2FMtgGpk4hs3AJjeX68vzmMugFkA3hZ/uw/AHdnxHQAW9uP5VAKYlR0PA7AZwPSBOicADkA6Oy4FsBbAWQCeBHBV9u8/BfDf+vu7O6T30Y9f4NkAfi/mdwK4c0DeNDCZNva7ACrFRnt3wL6QTCPYi4rhnAAcDWA9gDORcdCUdPddFuN//SlFJgD4QMzrs38rBiq89w0AkP0/d9/oF5xzkwGcgsxVcsDOyTk32Dn3JjJtxJcj80vb4r0/GLJXTN9dt/TnxuaUFIAbgB/BOOfSAJ4C8C3vfXRlyH7Ae7/fez8TwEQAZwA4qbvD+vesDo3+3Nj1AI4R84kADr8bfN/Q6JyrBIDs/5v688Wdc6XIbOpfee8P1i4b0HMCAO99C4CVyGjskc65g0HgxfTddUt/bux1AKZm767LAFwF4Nl+fP04ngVwTXZ8DTI6t19wmSb1Pwfwjvf+RwN9Ts65sc65kdnxUQAuBPAOgBUAvtrf59Nr+vlm5FJk7vprAdw9EDcVAB4H0ACgE5lfkesAjAbwEoCa7P9H9eP5zEbmZ/0tAG9m/7t0oM4JwMkA3siez9sA/nf279UAXgWwBcASAEMG4vsr9D/zPBqJxDyPRiKxjW0kEtvYRiKxjW0kEtvYRiKxjW0kEtvYRiKxjW0kkv8PCTzocSsisZsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = matplotlib.pyplot.imshow(data)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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": [
    "ave_inflammation = numpy.mean(data, axis=0)\n",
    "ave_plot = matplotlib.pyplot.plot(ave_inflammation)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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": [
    "#Chart the maximum daily value\n",
    "maxplot = matplotlib.pyplot.plot(numpy.max(data, axis=0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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": [
    "#Chart the minimum daily value\n",
    "minplot = matplotlib.pyplot.plot(numpy.min(data, axis=0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-105-2264fe45bdc1>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-105-2264fe45bdc1>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    axes1 = fig.add_subplot(1.3.1) #Number of rows, Number of Columns, Number of Position\u001b[0m\n\u001b[0m                                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "axes1 = fig.add_subplot(1,3,1) #Number of rows, Number of Columns, Number of Position\n",
    "axes2 = fig.add_subplot(1,3,2)\n",
    "axes3 = fig.add_subplot(1,3,3)\n",
    "\n",
    "axes1.set_ylabel(\"average\")\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel(\"max\")\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "\n",
    "axes3.set_ylabel(\"min\")\n",
    "axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "fig.tigght_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Figure' object has no attribute 'tigght_layout'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-106-df9b977c082b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0maxes3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtigght_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Figure' object has no attribute 'tigght_layout'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "axes1 = fig.add_subplot(1,3,1) #Number of rows, Number of Columns, Number of Position\n",
    "axes2 = fig.add_subplot(1,3,2)\n",
    "axes3 = fig.add_subplot(1,3,3)\n",
    "\n",
    "axes1.set_ylabel(\"average\")\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel(\"max\")\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "\n",
    "axes3.set_ylabel(\"min\")\n",
    "axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "fig.tigght_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "axes1 = fig.add_subplot(1,3,1) #Number of rows, Number of Columns, Number of Position\n",
    "axes2 = fig.add_subplot(1,3,2)\n",
    "axes3 = fig.add_subplot(1,3,3)\n",
    "\n",
    "axes1.set_ylabel(\"average\")\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel(\"max\")\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "\n",
    "axes3.set_ylabel(\"min\")\n",
    "axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MEkLEuZDldBLRYwBuBNDBzMu9t/0zgJsAOAGcAnA3M/eFagwisJ0N7QCAa8eZdJqMBiwuSJWVTiHCpKauCSYj4aaL/HNYxnP14jykJSagtq4JV4zpvSuEmD7fKmWudXrba3OsFrjcjL5zI8hKmd7EVYip4l510UDP8TT1D094fKBzUs7YlVjPDSz5o/oY7Zeo5w8WGdXHO6XGmcdHlfj0TepOvIT6LL8xrrz/iBI3HVqsxL+8easSV1Sr72vLEtTPYfm//+WEYzLfrea+rs1Tcx93WZf5jTFnm5oH2lWp5rLqeaYtm9S/+3rOZtNV6u8R/Xs5sFJ9b59W364OKEBOp/4YwXKCA/28TEYoVzofB7BJu20ngOXMfBGA4wC+HcLnF+N4uaEdy4rSUJKZPO4xSwvT0NA6AGa5OivEbHKNuvHswWZcvTgPmVN4k5poMuKmlUV48Wgb7A5X8BOEEEF12j1vxnIuIKcTkF6dQggRskknM+8C0KPd9jIz+94NvQMgeIUMMaM6bMOoO9OLjUsLJjxuWVEa+oZG0KJd/RBChNauE53osjsnVUBIt6WyBMMjbrxwuDUEIxMi/nTZPNtrp7vS6TtPigkJIeJdOHM6/xzAjvHuJKJ7iGgfEe3r7OycxWHFtlfe6wAzsHFZ4K21PkuLPEvrh5skr1OI2VRT14zMZBOuWpQ35XMryzIwNycFtVLFVogZ4Wt3Mt3qtTmy0imEEADC1KeTiL4LwAXgV+Mdw8xbAWwFgKqqKtnjOUNePtqG0qwkLC6YuJff8uI0JJoMeKexG5uWT7wqKoSYGf3nRrCzoR13ri2FOWHq1wSJCFtWF+NnO4/jbM8QSrPG30IvhAiu0+ZAqiUBiSZj8IMD8E1WZaVTzKbRXrU3JFWoeYJ6H0a9BycAOK3q3yC9z6au6Vo1D3DONvU59N6gjXeoj+9YOaI+YLtaMdqZ75828tbO5UpssKrThVNO9eLt3a98QYnz3lCnQbd+8w0l1vt+Zm1T80r3Fqmf0/yN/o06++pKlVjv29mFTCV2p6ifZ9Yx9fEyjqvxSLr6dbIXqr+rnNZCJc5s8K/Xovf21HM29Z+n6Zr1lU4iugueAkN/xpIwOKvsDhfePNmNjUsLQDRxs3lLghFry7Pw1imphCnEbPnDoVY4XW5Ur5l+5sFt3oq3zxxonqlhCRG3Oi+gRycApFoSYE4wyEqnECLuzeqkk4g2AbgfwM3MPDSbzy2AXcc74Rx1j9sqRXfZvBwcb7ejwyZ5nULMhtq6JszPs2JF8cSV4yZSkpmMj1VkofZAsxQCE+ICddocyJlmPifg2X2Qa7XIpFMIEfdCNukkoqcAvA1gERE1EdEXADwEIBXATiI6SET/HarnF/5ePtqGrBQz1szJDH4wgHXzPdsG3j7VHeRIEYmI6DEi6iCiI2Nu+633tXeQiE4T0cFxzj1NRIe9x+2bvVHHr9Ndg9j3YS+qK0uC7kQIprqyBB90DaLujHSkEuJCdF3gSifg2WLbKdtrhRBxLmQ5ncx8Z4CbfxGq5xMTGxl145VjHdi0rAAJxslda1hWlI60xAS8ebILt6yafJN6ETEeh+dCz//4bmDmT/k+JqKfAZioUtRVzCz7q2dJ7YFmEAG3rp58b87xbF5RiP/77FHU1DVN+iKTEMJfp82BKxZc2KQzx2pBU69s7hKhY8yc+Pc86Tl72v16D04Afv0cg/Xp1HWtU3MJB4vUi6kJVvU1UbBNfZ0N3q3mEZq2+ffpHLldaZIB55tqjuUHDrVndcoptffnT76vrn090blOiT+1dL8S782fo8Q97TlK3FFT5jfG/Ab1bVaTNsYELQ/VmaKen3haXfhxpk+8W9HaqvYWTX1NTQJ1rK7wO0fP8fXr0xmtOZ0iPPY09sA27MLGZZMvCmQ0EC6dl403T3bLNr0oFKhtkQ95ltI+CeCpWR2UCMjtZtTWNWH9/BwUpidd8ONZLQnYtLwAz9e3YHhkNPgJQgg/wyOjsA27kGOdfL/cQHJTLeiyO2doVEIIEZ1k0hkn/vheOxJNBqyfnxP84DHWzc9Bc985nOmRq7Qx5nIA7cx8Ypz7GcDLRLSfiO6Z6IGkvdGF23u6B02957ClcuZ2FGypLMbAsAuvvNcxY48pRDzpusB2KT65VjN6Bh0YdcvFWyFE/JJJZ5x461QX1pZnIck8tbLvl83zTFLfPCl5nTHmTky8yrmOmSsBbAbwFSK6YrwDmXkrM1cxc1Vubu54h4kJ1NY1I8VsxMensBMhmMvm5aAgLVF6dgoxTb7iPzOR0+lmoHtQ8jrj3Ti1FrKIaCcRnfD+LzkRIiaFpU+nmF0dtmEcb7djS+XU2zDMy01BfpoFb57qwqcv8d+rLqIPESUA2AJgzXjHMHOL9/8OInoawMUAds3OCOPLOeco/nC4FZtXFCLZPHO/ko0Gwq2ri/HzNxrRabvwYihCxBvfltgLqV4LjOnVaXMiLzUxyNEixj0OrdYCgG8BeIWZf0xE3/LG91/Ik/j1VdRi19Xqn39Tv3+XgqnmcObvVS+qONPVv2eDUBc9rLvVPtItV6h9QPUa7oH6S7a/qOZHmjepCyTNwxlK/NA9ag7nfVu/rMRrbzs84flrsz9U4uOn1LzVvGr/Pp2n1qs7DMseVb9Op29S80wNg+rXSe9vqn9f9D6dOr0Ha6D8XbfWx1Xv25lQUa7Eeh/PwIlc/mSlMw74qs+umze1rbWAp9z7unk5ePtUN9yyNShWXAvgGDMHXAIjohQiSvV9DGAjgCOBjhUX7uWGNtgdLlRP46JQMNWVxRh1M7bXtwQ/WAihmKmVTt+kVSrYinFqLdwC4Anvx08AuHVWByXELJFJZxx482QX0pNMWFqUNq3zL5ufg55BJ95r87/KJCLXOG2LAOAOaFtriaiIiF7whvkAdhNRPYB3AfyBmV+crXHHm5q6ZhRnJOGSuf6V+S7UgvxUXFSSjpr9ssVWiKny5XRmp8zUSqdMOkVA+czcCgDe//MCHTS2fsII5GdJRB/ZXhvjmBlvnuzGpRXZMBqm1/vP16/zrZPdWFY0/ab1YnaN07YIzPz5ALe1ALje+3EjgJUhHZwAALQPDGP3iU785Yb5MEzz9RlMdWUJHth+FO+1DmBJ4fQuPAkRjzptDmQkm2BOuLDr87LSKWYCM28FsBUA0ihLtp6JqCOTzhj3YfcQmvvO4ctX+vflmazC9CRU5KTgzVNd+NIV038cIYTqmQPNcDNmtGqt7qaVRfjB8w2orWvCd29YGrLnESLWdNocyL3AfE4ASLEkINlsPL9dVwhNOxEVMnMrERUCCHnJcT2HM1BeoN4f0n6Juugwkqoen6m2g0RafbsS9y5U8wb7Vo0occkO7eLOLjWfsula/0KYxRvVHEo9x7JB6+159+WLldis9cgsTuxT4mtTjyrx/d+/V4kTNp9T4qGH/P+Wl/W7lFj/2hftUqdi9kL182xfq/4OKvmjer6eO2vWns8vtzZAbq5+Du9XP+9gfWAnS7bXxrg3T3UB8GyRvRCXzc/Gux/0wOlyBz9YCBEUM6OmrgmVZRmoyJ24QMOFyEox46rFeXjmYAtco/L6FWKyuuyOCy4i5OPp1SmTThHQdgB3eT++C8CzYRyLECEjk84Y99bJbhSkJaIiJ+WCHmfdvBwMOUdR39QX/GAhRFBHWwamXVV6qqorS9Bpc+CNk10hfy4hYkWnfeaqPudYLbLSKcartfBjANcR0QkA13ljIWKObK+NYW43461TXbhqcR6ILixf7NJ52SAC3jjh6fcphLgwNXVNMBsNuPGiwuAHX6CrFuciI9mE2rpmXLUoYI0KIYSmawZbDeVaLWjs8m9VIOLLeLUWAFwzqwMRIgxk0hnD3msbQO/QyLRapegyks1YWZKBXcc78Y3rFgY/QQgxrpFRN7YfbMG1S/OQkWwO+fNZEoy4eWURfrv3LAaGR5CWaAp+khBxbNDhwqBzdMa21+akmrHnA1npFKGh9+UM1lfRoeVwBurT2bVOvSCadUz9+bUXq3+79FzEjkr9gqqaP7nokSElfv9etW9n3hvqFMWxUj0eAFw/zFfi7z38nBI/XHClEm/IU9uH1Xeoeaa/bVD7lz5Tf7kSm7Q81rJH1c+5Z7F/3qn9dvXrZjilFvTLq1PTXsw29etkbVXv1/Nv9a97vtaH05mupu/o+ZuAf/7u6JplajxDOZ6yvTaGvXXS25/zAvM5fa5cmIv6pj70Djpn5PGEiFevv9+J7kEntqwO/dZany2VJXC43HjhUOusPacQ0cqXfzlzK52J6B0awYjkVQsh4pRMOmPYm6e6UJGbgoIAVcmmY8OiXDBD8sKEuEC1B5qQnWLGlYtyZ+05V5akoyI3BbV1zbP2nEJEK9+kM8c6MzsRfJPXbrtctBVCxCeZdMYop8uNdz/omZGttT4XlWQgI9mE19/vnLHHFCLe9A058ceGDty8qggm4+z9CiYiVFeW4N3TPTjT7b9NSQjxEV/Rn5krJGRWHlcIIeKN5HTGqPqmPgw5R7FufvaMPabRQLh8QS5eP94Jt5tD1sxeiFj2/KFWOEfdqJ6FqrW6W1cX419efh+1B5rw9WslNzuWEJERwD4Azcx8Y7jHE+06vSuSM7a91vs40jZFzAZX4+kJ77ccaFRid0WR3zHZzx9T4u4bF/sdM5a1dVSL1fud1okvsuo5nCO39yjxvL/3n7Kc+Iy6k+/hE2oO52C9Wviy+KYG7UnV8K365er589ReohW/UbfH63mtet4rAPSt0moo5Ks5lW23q88x72dqrPfV1Pt2mmzqw+s5n/rX3aym9wLwz/lF5sQdL/Qc4smSlc4Y9ebJLhABl1bM3Eon4Mnr7LI70NA6MKOPK0S8qKlrwqL8VCwrSgt+8AwrzkjCpRXZqK1rBjMHP0FEk68BeC/cg4gVnTYHiICsGSr05StIJCudQoh4JSudMWpPYw+WFaUhPXlmq1RescAziX39eCeWF6fP6GMLEesaO+04cKYP37l+8QW3MZqu6soSfHNbPfae7sXFc6X9USwgohIANwD4IYBvhHk4MaHT5kBWshkJM7QF3rfS+Z2nD+Pvnj0y6fOuWJiLn3+uakbGIIQQ4SSTzhg0MurGgbO9uPPishl/7Ly0RCwtTMPrxzvxlavmz/jjCxHLauuaYSDgllXFYRvDpuUF+Ltnj6C2rkkmnbHj3wD8LYDU8Q4gonsA3AMAZWUz/7ch1nTZZ65HJwAkmoz4SfUKNHYNTvqcd051453G7uAHCiFEFJBJZwxqaBnA8IgbVXNC84Zyw6JcbN3VKP3+hJgCt5vx9IFmrF+Qi/y0makoPR0plgRsWl6APxxqxfdvXoZEk39fMRE9iOhGAB3MvJ+INox3HDNvBbAVAKqqqmRvdRCdtpmddALAp9ZObbL/8Ksn8c8vvY/hkVF5nYoJ6X0TKVPdieaXs6cxNLb43eZYXaHEeo6nbYNaFyCtvl2JR7W8QD1L8MRn1GtkaSfU3T/6XiA9t9FzjroT4SubXlfirdtuU+Jtz61XYve8c0qculLNI3Wd0vtRqjmdPZvV862P+r9O9VxVZ6r6mZlt6v16nmpSm/o5ztmmfq8GVqq9SvV8XVO/mq+r53wCAX5etL6cet/XYDnD45Gczhi097TnRVNVPr3mrcFcuTAXLjef7wMqhAjunQ+60dx3DtWV4Vvl9KmuLIHN4cLOhvbgB4tItw7AzUR0GsBvAFxNRE+Gd0jRr8vuQK51ZiedU+V7fik+JISIBTLpjEH7P+xFaVZSyFZTKudkwmpJwOvHpXWKEJNVW9cMqyUBG5cWhHsouLQiG0Xpiaipawr3UMQFYuZvM3MJM5cDuAPAn5j5M2EeVlRjZnTaHMiZ4ZXOqcpJlTYrQojYIZPOGMPM2Hu6F2tDtLUWAExGA9bNz8br73dIBUwhJmHI6cKOw624YUUhkszh3yZnMBBuXV2MXcc70WEbDvdwhIgoNocLDpc7AlY6PReOZdIphIgFktMZY870DKHL7kBVeWgLhFy5MA8vHW3HyQ47FuSPW7tCCAHgpaNtGHSOYksEbK312VJZgv987RSePdCCL11REfwEEfGY+TUAr4V5GFGvyzvJm+mczqkmQsD2AAAgAElEQVT6qLenM6zjENFHz+HU+yoGywENRO/lqfd/HC5X+8L3LFZfP3ofz6Jd6qKFvVB9vpRfZihxyxX+Fd/TTqjxg49vUeKHvv/fSnzKqTbm/Onv1ZxPpz1ZiQ0rh5S48Q6tjondoN3vN0QsnHdWifseL1XijsvVvp1Lf6zuIjz2VXV31Mm71S9UXp2aZ6r3UzXb1ftTztj9xqh/70JVdUJWOmPM3tOeXyyhyuf0uXJRLgCg9oD0+xMimJr9zSjJTMLaEF8Mmor5eVasLM2QLbZCaHwrizlhXunMtsr2WiFE7AjZpJOIHiOiDiI6Mua2LCLaSUQnvP+HdmYUh/ad7kF6kgnzc/2rfM2k4owkXLskD//12in8xZN16JZCB0IE1Np/Dm+e6sKWyhIYDOHpzTmeT1QW41ibDQ0tA+EeihARw7eyGO6VTpPRgMxkkxQSEkLEhFCudD4OYJN227cAvMLMCwC84o3FDNr3YS/WzMmclTe3j3y2Ct/evBh/OtaBjQ/uwktH20L+nGLyxrnw830iaiaig95/149z7iYiep+IThKRvE4vwNMHmsEMbFkdOVtrfW68qAgmI8lqpxBjdHrznHO8K43hlGO1yEqnECImhGzSycy7APRoN98C4Anvx08AuDVUzx+PegedONlhD/nWWh+jgXDvlfPw3FfXoyA9Eff+7348/OrJWXluMSmPw//CDwA8yMyrvP9e0O8kIiOAhwFsBrAUwJ1EtDSkI41RzIzaumZUzclEeY7epSz8MlPMuHpxHp492AzXqDv4CULEgU67A0YDITM5/JPO3FQLOmWlUwgRA2a7kFA+M7cCADO3ElHeeAcS0T0A7gGAsrKpNVSOV/s/9OZzhrBybSCLClLxzFfW4c9+vgfPHGjGV66aP6vPLwJj5l1EVD6NUy8GcJKZGwGAiH4DzwWjhpkbXXw41NSPkx12/Oi2FeEeyriqK0vw0tF27DrRiasX5wc/QYgY12VzIsdqjojt8LmpFhw82xfuYYgIpxcKCkYvHDSa6X9R1F6sXnQxp6tTBmfqxK+P/D1qMSNj76AS6wVy9KJAHZXqupg7RS24AwD29Wr19YJt6pb4L+35nBIbTiUpccpKdW3smVW/UM8/oVYG+uTqfUr8+HduUeLBu/1fq80vq3OYwctHlLhkh/p56l+XBLv6dTZpsb1QrYivF2zSCwcZGlv8xojVajFBV+NpdQwV5UqsF6LyW2IcR8QWEmLmrcxcxcxVubm54R5OVNj7YQ/MRgMuKglehWymmYwGXDw3C41dgxgeGQ1+ggin+4jokHf7baBl8WIAY8utNXlvC4iI7iGifUS0r7NTereOVVvXBHOCATdcVBj84DDZsCgPWSlm1NQ1h3soQkSETrsj7EWEfGR7rRAiVsz2pLOdiAoBwPt/xyw/f0zbd7oXK0rSkWgKTx/AJYVpGHUzTrT7l2MWEeO/AMwDsApAK4CfBTgm0OXLcUsUywWiwJwuN7bXt+C6pflITzIFPyFMzAkG3LyyCDsb2tF/biT4CULEuE6bI+xFhHxyUy0Yco5i0OG/yiOEENFktied2wHc5f34LgDPzvLzx6zhkVEcbupH1ZzwFQReUujp1/leq1TCjFTM3M7Mo8zsBvBzeLbS6poAjG0kVQIgwH4MMZFX3+9A79AIqiOoN+d4tlQWw+ly4w+HWsM9FCHCrsvuQG6ErHT6xiEVbIUQ0S5kOZ1E9BSADQByiKgJwAMAfgzgd0T0BQBnANwequePN4eb++EcdaMqjH0A52SnIMlkRINMOiMWERX68qoB3AbgSIDD9gJYQERzATQDuAPAp2dpiDGjtq4JOVYzrlgQ+au/K4rTsSDPitq6Jnz6EsmhF/GLmdFldyAnQlY6fePotDkwJzvyipGJ6OCXg6cJlOdnTU9UYlO/mj/ptKYpcc9i9TWTdUwbg5bTachXH89cp+Zb2tefU+KKR/138SV8t0uJOwrVv1+W+mQlLt54Rok7Byd+TfU9XqrEP12oxu7b1TFmbfN/D57ZrF4w6l2n7gZ0WtVz0k6om82KXlS/N3r+be9S9fugG9G+j4mZ/il4+vdW39rGvWp+7lRziH1CNulk5jvHueuaUD1nPNt32vMDsCaMK51GA2FRQSqOtcmkMxKMc+FnAxGtgud3ymkA93qPLQLwKDNfz8wuIroPwEsAjAAeY+ajYfgUolbvoBN/OtaBz11ajgRjxKbOn0dE2FJZgp+8eAynuwYjstKuELOh/9wIRkZZVjqFEGKGzXb1WjHD7A4X/tjQjm37zmJebgqyUsJb4n1JYRpeONwKZgZR+Cv/xbNxLvz8IsBtYOYWANePiV8A4NdORUzOc4daMDLKqK4sCfdQJu221cX46UvHUHugGd+4bmG4hyNEWPiK9kTOSqfnb7oUExJCRLvIvwQvAtp3ugd/8eR+rPnBTnz9twdxbmQU37huUbiHhaWFqeg/N4JWbaleiHhSU9eMxQWpWFo08baXSFKQnoj183NQW9cEt3vculFCxDTf5C5SVjqzUywwkEw6hRDRT1Y6o9Com3H343thNhpwx9pS3LSyCJVlmRHRU2xJoedN9nutAyjKSApytBCx52SHHfVn+/C9G5aEeyhTVl1Zgq//9iD2nu7BJRXZ4R6OELOu07uNNVKq1xoNhKwUCzrtznAPRUQRva+inpOns23w391i7lcrJus5mc5UNTdwJHXiMbVsKlJil12tlt6nDUHPx2y8w7+6+oYU9fP65Fdrlfgf37hJfYz2HCW27laf4xr7fer9Wi/SjOPqBVlnq3q+tdn/4lDv19UczpRfZiixvVB9DrNNfY6udWrLNf3rNP+XagHAYN9rBMjp1L+3oaqVLSudUehkhx22YRe+e8MS/P0ty1FVnhURE04AWDxm0ilEPKqta4LRQLh5VVHwgyPMxmX5SDEbUVPXFO6hCBEWkbbSCQA5VrOsdAohop5MOqPQgTOeokGrSjOCHDn7rJYElGUl471WW7iHIsSsc7sZTx9oxhULcpCXmhj8hAiTbE7A9SsK8cLhNpxzjoZ7OELMuk67A2ajAWlJkbMRLDfVcn4FVgghopVMOqPQwbN9SE8yYW6EVphcUpgqK50iLr3d2I3W/mFsiaICQrotlSWwO1x4uaEt3EMRYtZ12ZzITbVEVCG83FQLumSlUwgR5SLnUp6YtINn+7CqNCOi/iiOtaQwDS83tGPI6UKyWX7ERPyoqWtCqiUB1y3ND/dQpu2SuVkozkhCTV0zbllVHO7hCDGrOu0O5FjDWwVel2v1rHRKVXgxnmB9OPW+inrHS3O/fw6/3rtRp/eP1HM29fPz96ixfb1JiVMK1MWK3ja1EF9mgf9iRsPDy5X4pH2pEpsr1bW12y9+R4m3nVqvxK+sf0iJty69VIl/9e7H1DGfUj+Hpqv8f3eUp6j5ko23q/VOXHb1Mczt6vvmjOOYMNZzPrOfD5LTGcBwufr9N2m9QKH1cfX7eeuZ3PPISmeUGXS4cLzdFpFba32WFKaBGXi/Td1ie6Z7CC8dldUTEZsGHS68eKQNN64sRKLJv4l1tDAYCFsqi7H7RCfaB6QKtYgvXTZHxBQR8slNtcDpcsPmCFV5DyGECD2ZdEaZQ039cDOwqiyCJ50FnqtTx8ZMOpkZ39x2EPf9ug6uUXe4hiZEyLx4pA1DztGo3lrrc9vqYrgZeOZAc7iHIsSs8qx0Rtak0zceKSYkhIhmMumMMgfP9gEAVpVE7qSzJDMJVkuCkte5+2QX9p7uxcgoo01WT0QMqqlrQllWMqrmTLzNKRpU5FpRWZaBmromMEvPThEfRt2MbntkrnQCMumMdkRUSkSvEtF7RHSUiL7mvT2LiHYS0Qnv/9H/R0SIACThLsocONOL8uxkZKZEVs7JWAYDYXHBR8WEmBkP7jwOo4Ew6mac7TmHkszkII8iRPRo7juHtxu78bVrFsRMztWWyhJ875kjONoygOXF/n29hIg1vUNOuDlyenT6+MbTJRVso50LwDeZuY6IUgHsJ6KdAD4P4BVm/jERfQvAtwDcfyFPpPdqpDXL1AO0HL1ARtLVCuwG7Zwz96i9qOdsm/gxR/U8QY3NruY6GgbVNJWVef6PX3+7dsO2LCXMq1N31r1ed5kSP/Ivj0w4pm3PqTmfeVo+Zcflau/QBKt/L9GOmjIlztL6cGY2aLmsS9MmvF/vqannY7or1Nxa/fs2sNK/5kSwnqx6b09X42m/x5gMWemMIsyMg2f7sLos8i+CLSlMw7FWG5gZrx/vRN2ZPnzx8rkAgLO9Q2EenRAz65kDzWAGtqyO/q21PjddVASz0YDf75eenSI++FYSZXutCAVmbmXmOu/HNgDvASgGcAuAJ7yHPQHg1vCMUIjQkklnFGntH0aHzRHRRYR8lhSmweZwoan3HB784wkUZyTha9csgIGAph6ZdIrYwcyoqWvCxeVZKMuOnRX89GQTrl2ah+31LRiRPGwRB3yTukhb6cxIMiHBQDLpjCFEVA5gNYA9APKZuRXwTEwB5I1zzj1EtI+I9o1AfhZE9JFJZxQ5n88ZFZPOVADAf752EvVn+/DVq+cj2ZyAwvQknO09F+bRCTFz6pv60dg5iOo1sddeZMvqEvQMOvH6+53hHooQIefbvpobYSudBgMhx2qR7bUxgoisAGoAfJ2ZJ93UnJm3MnMVM1eZEFk/o0JMhuR0RpGDZ/tgTjBgSWFa8IPDbFFBKoiAp949i9KsJFSv8Ww7LMlMwllZ6RQxpGZ/EywJBmxeURj84Chz5aJcZKeYUVPXhGujuPeoEJNxfntthK10AkBOqllWOmMAEZngmXD+iplrvTe3E1EhM7cSUSGAjqk+rt6HM6GifOJxaDl6gXpy6nl9XTcuVuKSP6p5ox/ervXpVLvmIeuY+vObtWPi2iR9C9X45E+W+h1ju117TWxWFzUs9eruo8zjo0r8pT2fU2J3u5rHeudNu5X4D1svV+KF81qVuLE9x2+M1lb1OfX8ST2HU/+8B4vU71XmcasSO63a+mGx+nXN1nI69ecH/L//es5msJ+nyZKVzihy4EwvlhWlwZwQ+d+2ZHMC5mZ7ksa/evUCmIyeMZdmJUtOp4gZDtconjvUgo8vK0Baoin4CVHGZDTg5lVFeOW9DvQNOcM9HCFCqsvuQJLJiBRz5PXZzbVa0GWX12A0I0+VuV8AeI+Z/3XMXdsB3OX9+C4Az8722ISYDZE/exEAgJFRNw4390fF1lqfteVZmJ9nxZbVH207LM1MRvuAA8MjoxOcKUR0ePVYB/qGRrClMva21vpUV5bAOerGc4dagx8sRBTrtDmQk2qOyArUOVaLrHRGv3UAPgvgaiI66P13PYAfA7iOiE4AuM4bCxFzZHttlHi/zYbhEXdUVK71+cfblsM1ykgwfnRtozTLUxK7ue8c5uVaxztViKhQU9eM3FQL1s/331ITK5YVpWFRfipq9jfhsx+bE+7hCBEynXZHxOVz+uSmenI63W6GwRB5k2IRHDPvBjDeN++a2RyLEOEgk84o4SsitDqKVjpNRgNM2i6l0izP/vqzPUMy6RRRrdvuwKvHOvDn6+cqF1ZiDRFhS2Ux/mnHMZzqtMvrVsSsLpsT5TmRWYE6N9UCl5vRf24kovt0i8ig5+TpfTr13o7OdP/pQKqWC+hMVXM22y+ZuH9z0YsT94fs0fIvyx5V3zB2VaopK8n3Nfs9R8FDE+8y+vyPapX44RNXKvGnyhqUuLlCfY/9whk1j9Ss9djUczgNp9ReowDQcoV6zoIn1fzJTC2f0pmqfl31XFhd70L1QpneL1XPxTXb/avRWw6o5+h7EfW+r9MVu++UYszBs33ITjGjJNP/BzqalGZ6J51SwVZEuefqW+ByM6orY6c353huXV0MAwFP1/n/0RciVnTaHRHXo9PnfK9OqWArhIhSMumMEgfO9GJVaUZE5ppMRV6qBeYEg/TqnAVE9BgRdRDRkTG3/TMRHSOiQ0T0NBEFXDonotNEdNibc7Jv9kYdPWrqmj1bTwtSwz2UkMtPS8T6Bbl4+kAz3G4OfoIQUWZk1I2eQWfE9ej08Y1L8jqFENFKJp0R4GSHHR/70Ss42WELeH9r/zmc6hxE5Zzoyeccj8FAKMlIkgq2s+NxAJu023YCWM7MFwE4DuDbE5x/FTOvYuaqEI0vah1vt+Fwc39crHL6VFcWo7nvHN75oDvcQxFixvUMeirDRvpKp/TqFEJEK8npjABHmvvRNjCM/3n7Q/zDLcv97q/Z3wQAuOmiIr/7olFxZhLO9sj22lBj5l1EVK7d9vKY8B0An5jNMcWKmromGA2Em1fFxmtyMjYuLYDVkoDaumZcNi92CyeJ+ORbQZSVThGNjJlBFiW0/Ez9p9xU4f+3zK3dpudodq1Te1Pn7xlQ4pN3q/cnt6o79fQemu1r1ecvf079We+cl+I3Ri5U80Dzqs8o8T++cZMSb7jomBLv7VaL43UOqs+R8kt1M9jg3T1KbH1RzY3Vnx8ATr9VqsQnPqPujirape4e6ls1oj5HqzpVSzljV+LM4+r9ev5l9vNq7Fhd4TdG/TbLgUYl1vu6QusLO1my0hkBOmyeJOKnDzTjnFNN33W7Gb/b14RLK7JRlh2ZBQ6mSnp1Row/B7BjnPsYwMtEtJ+I7pnoQYjoHiLaR0T7Ojs7Z3yQkWbUzXjmQDM2LMyN2FWRUEgyG3HDikLsONyKIad/c2khopkvVzJSJ51piQkwJxgkp1MIEbXCMukkor8moqNEdISIniKixHCMI1K0D3j+iNiGXfjDYbUX3jsfdONMzxA+tbY00KlRqTQzGX1DI7ANjwQ/WIQEEX0XgAvAr8Y5ZB0zVwLYDOArRHTFeI/FzFuZuYqZq3Jzc0Mw2sjy5skutA84UL0mfrbW+mypLMagcxQvHW0L91CEmFHnVzoj9EISESFXenUKIaLYrE86iagYwF8BqGLm5QCMAO6Y7XFEkvaBYZRnJ6MiNwVPvasuzf9u71mkJiZg0/KCMI1u5vl6dTZJBduwIKK7ANwI4M+YOWBVGGZu8f7fAeBpABfP3ggjW21dE9ISE3D14rxwD2XWrS3PQmlWEmr2SxXbSEJEiUT0LhHVey/o/n24xxRtfJO5SN69kJMqk04hRPQKV05nAoAkIhoBkAygJcjxMa3D5kBeWiKuW5KPH77wHo6327AwPxX950aw40gbPllVikS94WUUO982pWcISwrTwjya+EJEmwDcD+BKZg64x5mIUgAYmNnm/XgjgH+YxWFGLNvwCF482obqypKYek1OlsFAuG11Cf7jTyfQ2n8OhenR3cIphjgAXM3MdiIyAdhNRDuY+Z1wDyxadNkdSLUkIMkcua/rXKsFzX1ysVZcOL8cvQAGy9SezP4ZlRPTczgHFqj9IRc8qRbPbLpWHdOZL6rpZmX/5t8j+swX1bcxQ1r+5IbrjijxyZ+ofTc//6NnlfgDh7pb63VcpsT9p7TcWe1zWpXi38+yo7VMia2t6jl6jqZnWjQ+/fvitE5t/VDP1wxkVMvZTNB+XvxyiNVU13HN+qSTmZuJ6F8AnAFwDsDLWnGTuNMxMIwVJRnYUlmMn750DE+9ewYP3LQM2w82w+Fy45NVsbO1FvDkdALSqzPUiOgpABsA5BBRE4AH4KlWawGw09t+5x1m/jIRFQF4lJmvB5AP4Gnv/QkAfs3ML4bhU4g4O460YXjEjS1xVLVWV11ZjP/3ygk8c6AFf7FhXriHIwB4dyz43rmYvP/iurfNsbaB8xVpJ+N4uw05EZrP6ZObasb+D3vw1qmuSZ9jSTBgVWkmjIbobrcmhIh+sz7pJKJMALcAmAugD8A2IvoMMz+pHXcPgHsAoKyszO9xYgUze1Y6Uy3Itlrw8WUFqK1rxv2bFuN3+5qwpDANy4tjazUwM9mEFLMRZ6VXZ0gx850Bbv7FOMe2ALje+3EjgJUhHFrUqtnfhPLsZFSWBWxvGhfmZKegak4mauqa8OUrK6K+d3CsICIjgP0A5gN4mJn3aPfHxd9UwFOcb/O/v4HAyQPju3xBZFdlLs1KRu/QCD798z3BDx7joU+vxo0xUv1eCBG9wrG99loAHzBzJwAQUS2AywAok05m3gpgKwBUVVXF7BVbu8OFIeco8tM8V1jvvLgMzx9qxYM7j+Nwcz++f9PSmHtTR0QozUpGk1SwFVHkbM8Q9nzQg29etzDmXpNTVb2mBN+uPYzDzf24qCR+J+CRhJlHAawiogx4diosZ+YjY+6Pi7+pANDSNwxm4P5Ni7F6CheIFuanBj8ojL6wfi7Wlmdh1D25b5/T5cbnHntX6icIISJCOCadZwB8jIiS4dleew2AfWEYR0To8BYFyEv1FPC9tCIbc7KT8ciuRpiNBtyyqjicwwuZksxkWekUUeWZA57iObeujs3X5FRcv6IQD2w/ipr9TTLpjDDM3EdErwHYBOBIkMNjkq/Yzrr52TH182lJMGJtedaUzkk2G6X4UBzwy8GrKFdivXfjaGbwDM20+nYlbtmkrpZnHVN/rkbS1UYU+XvU5xxJVfMCT33TpMSGUxNfTGm6yux3W8E2NT/yygd2K/EzT12uxH/9o1olfug/tiix3iNz4X1q0by0GnWXCG3qVuLXDi32G6P+G6jtdv31qH4dMnarce9C9ejM42quqzNVvQgeLF93uDzb7zY9z1PP2fT7+YmWPp3eLT+/B1AH4LB3DFtnexyRon3A06Mzz7vSaTDQ+fYoG5flIzPF/0UWC0qzknC2dwjjFE8VIqIwM2oPNONjFVnnc5LjWXqSCRuX5mN7fQucLnfwE0RIEVGud4UTRJQEz46iYxOfFbu67JFfiXa25Fgt578eQggRTmHp08nMDzDzYmZezsyfZea4/Y3ouwKZn/bRFaJPVpViZUk6vnh5RbiGFXKlmckYco5OqdCDEOFSd6YPH3QNxnUBIV11ZQl6h0bw6vsd4R6KAAoBvEpEhwDsBbCTmZ8P85jCxvd3NdsamxdtpyJX2qyEBBFVENFzRNRFRB1E9CwRxe6bNiFmwKQnnUQ0h4iu9X6cRESRnfwQJc6vdI6pmpdjteDZ+9ZjVWnsbAvSSQVbEU1q6pqQaDLg+hWF4R5KxLh8QQ5yrBbU1jWFeyhxj5kPMfNqZr7IezE3rlscddkdyEg2wZIQue1PZkuuVSadIfJrAL8DUACgCMA2AE+FdURCRLhJTTqJ6EvwbIl9xHtTCYBnQjWoeNIx4ECy2QirJVwtU8OjNMvT30/yOkWkGx4ZxfP1Ldi0rCDuXqcTSTAacOuqIvzpWAd6ZceCiCCdNodsrfXKSTXL9trQIGb+X2Z2ef89iThvUyREMJN9B/UVABcD2AMAzHyCiPJCNqo40u5tlxJv1TBLM30rnTLpFJHtT8c6MDDskq21AWypLMGjuz/Ac4da8LlLy8M9HCEAeCaduTLpBADkWhPROzSCkVE3TMawZFTFqleJ6FsAfgPPZPNTAP5ARFkAwMw94RycXvhFLy6jF/1JPK0WxAlELww0WGZVYnuhurNgYIFaEKdol1oAB8fV12jb7erOt3k/U4v6dDzgvzPO/Ko6hhfOLFVik009fnf/AiUeKlSvEyRY1ef8dvkLSvxP1dcr8drsD5X4t/Y1fmPM36O+z+1bpdaFWPSIer+xt0WJ9QJO5n6XEo8snNqODr1oEAC4K9Tn4P1H1TFphYX0GJP8aZ/spNPBzE7fxIiIEiBXdGZE+8Aw8tISgx8YY1IsCchKMeNsj2yvFZGtZn8T8tMsWDc/snv4hcPSojQsKUxDTV2zTDpFxOiyO7AihqrWXoicVE9ea7fdiYL0+HuvEUKf8v5/r3b7n8Pz/ljyO4XQTPay1+tE9B0ASUR0HTx7158L3bDiR6d3pTMelWQmSa9OEdG67A68drwTt64uhtEQX7sRJqu6shj1Z/twssMe7qEIAUBWOsfyfR0kr3NmMfPcCf7JhFOIACY76fwWgE54WpzcC+AFAN8L1aDiSfvAsFK5Np6Uent1ut2Mfad78P3tR/HJ/35b/jiKiLH9YAtG3Yxq2Vo7rptXFcFoICkoJCLCkNOFQecocuP0Yq7O93XotA+HeSSxh4iWE9Eniehzvn/hHpMQkWxS22uZ2Q3g595/YobYHS4MOUfjd6UzKwkvHm3D+p/8CS39wzAnGOB0ubG9vgVfWD833MMTAjV1TVhRnI6F+VKsezx5qYm4YkEOnj7QjG9uXCQrwiKsumyeolY50i4FwEe9Sn1fFzEziOgBABsALIVnIWYzgN0A/ieMwxqXnuNp0eKBDQv9zkl97bgSD67MV+K0+nYtVs/POpatxKdvUnM8y59T8yct9WquI//0jBKbHi/1G2PZj44osf3h5Urcs3ni3XSXXaeeX9+h5jb+9c++rMT3fbVWiX/9lRuUuCDdf1r1/r3q513xG7W3ddO1ar6tyabGet5pltaBOfO4mis7mpmixMbeQSW2Bfhe63mipjXLlJi0x9B/niZrstVrDxPRIe3fG0T0IBFlB38EEYivXUq8rnSuKctEgoGwpDAND35qJfZ/71osLkjFjsOt4R6aEDjWNoCjLQOoriwO91AiXvWaErT2D+OdxuDFKIQIJd+Knqx0eny00ik7iGbYJwBcA6CNme8GsBKA/NAJMYHJFhLaAWAUnr5EAHCH9/8BAI8DuGlmhxUfOgY8fwTidaVz47ICHPvBJqVy7+blhfi3V47H9bZjERlq65qRYCDctLIo+MFx7tol+UhNTEDN/iYpuCTCqvP8Smd8/l3VJZqMSLUkSNrKzDvHzG4ichFRGoAOSPEgISY02ZzOdcz8bWY+7P33XQAbmPknAMpDN7zY1mHzXJGNx+q1PnqrmOtXFIAZeOloW5hGFJmIyO+HhIjk3X2IuEbdePpAM65anIdsefMaVKLJiBsvKj0cJzAAACAASURBVMKOI20YdLiCnyBEiPhW9OL1Ym4guakWWemcefuIKAOetLP9AOoAvBveIQkR2Sa70mklokuYeQ8AENHFAHzNceQdxjSdX+lMkz+OPgvyUzEvNwU7DrdJCwbVXiL6EjO/AwBEVA3gnwD4b84XF2z3yS502hyytXYKqiuL8dS7Z7DjSBs+sUYKL4nw6LI5QARkpUhOp09OqkVWOmcYM/+l98P/JqIXAaQx86FwjmksvS+nTs/703P6AKD7xsVKnNkwoMR+/SNtau6htVnNIy7apd7fvlZ975u/V/0Z7bCVqQMKUFqhQcvh7Lhc/Tz0Sc7fF+1Q4rIEtc/na7nqWty99WpHnMe/c4sSm7UpkN6rFAAWzmtW4ua16uelf96mfrXoV/sl6veyZ7H6dRvRvi4paiosBrRc3EDfa713p1/fTi2Hc7S31+8xJmOyk84vAniMiKwACJ5ttV8kohR43viKaWgfGEaSd+uL+Mj1Kwrx8Ksn0W13yCrTRz4Nz2vwNQBFALIBXB3WEcWw2rpmZCSbcNXivHAPJWqsmZOJOdnJqK1rkkmnCJtOuwNZyWYkGCe7kSv25VoteK9tIPiBYkqI6CJ4dvsleOP5zFw74UlCxLFJ/VZm5r3MvALAKgCrmPkiZn6XmQeZ+XehHWLsarc5kJdm8dtiGu82LS+Am4GXG9qDHxwnmPkwgB8C+DKAqwDcx8zSoyIEBoZH8NLRNtx0UREsCf5XLUVgRIQtq0vwdmM3mvvOhXs4UYuIthDRCSLqJ6IBIrIRkcwYJqnT5pAiQppcWemccUT0GIDHAFTDU9fkJgA3hnVQQkS4SS+xEdENAJYBSPRNkpj5H0I0rrjQMTCM/NT4zeccz9LCNJRlJWPHkTbceXFZ8BPiABH9AsA8ABfBs6X2OSJ6iJkfDu/IYs+Ow61wuNyoltW6KdtSWYwH/3gczxxoxleumh/u4USrnwK4iZnfC/dAolGX3SFFhDQ5VjNswy4Mj4wi0SQX0mbIx5h5abgHIUQ0mdSkk4j+G0AyPCssj8JTKloSpi9Qh82BZUVp4R5GxCEibF5RgF+88QH6h0aQnmwKflLsOwLgi8zMAD4goo8B+Ncwjykm1exvRkVuClaWTJwTI/yVZiXj4rlZqNnfhL/cME92cUxPu0w4p6/T5kB5eUrwA+OIb+W3y+5ASWZykKPFJL1NREuZuSHcAwnE1XhaiY2ZmUo8Un7h3Q71XEKzbeLjW65Q/x4Y8tUemqY/qn078/eouY29S/3fLw8WqY+ZcVB9v3jfV59T4q09lyrx3u45SlyUouYu/u0nnlbiX7+q9uVM+K66I48G/X/3uH6o5lTmQ911cOaLap9Nl119jea9oebC9mmVPPLq1L6fg2VqnqreT1XP8QSARC0H2NDYosR6Dqf+84Qev4cMaLJJD5cx8+cA9DLz3wO4FIB/l1YxJR0Dw8iTlc6ANi8vhMvN2PmebLEFAGZ+0Dvh9MX9zPyFYOcR0WNE1EFER8bclkVEO71b+HYSUeY4597lPeYEEd01M59JZDvTPYR3T/egurJEJkzTVF1ZjMauQRw82xfuoUSrfUT0WyK607vVdgsRbQn3oKIBM6PLLttrded7dcoW25n0BDwTz/e9vesPE1HEFBISIhJNdtLpu9wwRERFAEYAzA3NkOKD3eHCoHMU+VK5NqCVJekoSk/EjsOt4R5KRCCiBUT0eyJqIKJG379JnPo4gE3abd8C8AozLwDwijfWny8LwAMALgFwMYAHxpucxpLaA00gAm5bLVVrp2vzikJYEgyoqZOU42lKAzAEYCMkV2xK7A4XhkfcyLFK5dqxfNuNu+zOIEeKKXgMwGfh+fvqe41Kz3ohJjDZnM7nvP2I/hmeXkQMT28iMU0dA74enTLpDISIsGl5IZ5850PYhkeQmhj3W2x/Cc8k8EF4trnfDU8l6Qkx8y4iKtduvgXABu/HTwB4DcD92jEfB7CTmXsAgIh2wvPH9anpDD4aMDNq65px2bxsFGUkhXs4USst0YSPLyvAc/Wt+Lsbl0oxpili5rvDPYZo5VvJk5VOlax0hsQZZt4+lRO8/bZ3AbDA8/7798z8ABHNBfAbAFnwvMf+LDPLFQIRc4JOOonIAM+qSB+AGiJ6HkAiM/cHOVVMoN3bo1MKCY3v+hUFeOzND/CHQ624QwoKJTHzK0REzPwhgO8T0RvwTESnKp+ZWwGAmVuJKFBfkGIAZ8fETd7b/BDRPQDuAYCysuj9Pu37sBdneobwtWsWhHsoUW9LZTG217fgT+91YPOKwnAPJyoQ0d8y80+J6D/gubCrYOa/CsOwoopvJU8KCamyU2TSGQLHiOjXAJ4DPkrSC9IyxQHgama2E5EJwG4i2gHgGwAeZObfeGuofAHAf01lMH45dho9J0/vBWnsHQxwlpr3GSinciKJp7uVeMGTar7j+/fq+cVqTif/VJ1mZMB/2tHTnqPeUK8+5oOPq5kJt975hhJ3ajmYHTXqe5gGm9oHtO8q9eku03JA9fMBwF48cf9Sd7s6D0hpUzehdlWqOZvlz6lfJ/3r3LVO/ZsbKIczGL1PZ0KvmvOp9+2crKDba5nZDeBnY2KHTDgvXIdNVjqDWTMnE8uK0rB1VyNG3X7vweLNsPcC0Akiuo+IbgMQyiaSgVZRA34TmHkrM1cxc1Vubm4IhxRatXVNSDYbsWl5QbiHEvXWz89BXqoFNXXNwQ8WPr7iQfvG+SeCkJXOwMwJBmQkm9Bll0nnDEqCZxI56W3w7GH3hibvP4an5/bvvbc/AeDWUAxYiHCbbE7ny0RUTVJZY8Z0eFc689JkpXM8RIS/2DAPjV2DePFIW7iHE25fh6eC9F8BWAPgMwA+N83HaieiQgDw/t8R4JgmqMXCSgC0BDguJgyPjOL5+lZsWl6AFMukO0mJcSQYDbh1dTFee78D3fJGd1KY2VdmsQHAbQD+GsDfeP/9n3CNK5r4JlW5stLpJ9cqvTpn2KPMfPfYfwB+EewkIjIS0UF4/u7uBHAKQB8zu7yHTLiriIj2EdG+Ecj3UkSfyU46vwFgGwCnNKueGe0Dw0g0GZAqb3AntHl5IebmpOA/XzuJMcVb4xED+F8A2wFUwdOrc7p51dsB+KrR3gXg2QDHvARgIxFlegsIbfTeFpN2NrTD5nChulJ6c86U6soSuNyM7fUxe60iVJ6EJ4d7CzwrJ1KgZJI6bQ4YDYTMZCkkpMuxWtApF4Bm0n9M8jYFM48y8yp4LuReDGBJoMPGOff8riIT5MKKiD6TmvH8//buPL7J68ob+O9YlmRseQPLxgsGDGYNiw1ZISSQpAWyEUimydslTdOheSczk7SZ6T7dm07ad9rONGmntGmS6aTNZoeQjWaDELKQYBlDWMJijJFXGW+SjSXbOu8fkhPfR7Yl27IfLef7+fDBV3oe6VjytXWfe889zJwa/CgxGs1ON3LSkqQsQxCGBMJX1hThm+WHsOdEC66YF73LN8fpcfhmPA4B8AY59mNE9Ff4Ng3KIiI7fDmg/w7gKSK6E0AtgFv8x64EcBczf5mZW4noxwA+8D/UjwY2FYpFZTY78tKTcGnR+GuXCZ/501OxOC8N5bY63LFKNjsfBcdoNygRPg6nG9NSTEhIkL+rWtZUM6rsUsZovIjoUgCXAbAS0dcG3ZUGIORd05i5nYh2A7gEQAYRJfpnO8e0qkibs5lYNEuNW5Ojp9UzRN1OT7o6RMg8op1rUnM8Palqvzv2T2qqSt4edSyd0KUe3/z9PqWt/bRHXw+snW2+Ws3hPD9d/XiU6FKf4+nnVyvtV2//hdL+fvaGgOcY7MhDao7nB64l6gFDjJbal6vfl+ll9XXVvi4uzTYIBa+pBVC1ubWufPUEbc6o9n3U1u0Mhbbu61iFNNNJPp8jon/zt2cQ0UVhiSBONXX2IFvyTkJyU2k+pqcl4be7Tuodip4czLyDmU8z85mBf8FOYubbmDmXmY3MXMDMDzPzOWa+ipmL/f+3+o/dz8xfHnTun5h5rv/fIxP5zemp2dmDPccd2FSSLx9Ww2xLaQEO1XXgeFOQquFisO8T0R+lTufotbjcsonQMLJkeW24mABY4Ju0SR30rxPAzSOdSERWfyUIENEUAFfDl8u9a9C5w60+EiLqhbq287fwza6sA/BjAC4ADwG4cILiinkOpxsL80a3E1i8Mica8OXLZ+MnLx5FxZk2rJgZ8+Uih/J9IvojfHU1Q90pT4Rgx4F6eBnYLEtrw+6G5Xm4/6WjKLPZ8a0NQ60iE0O4A8AC+DYZGbhszwCkrwfhcLllE6FhWFPN6Pb0o8vdJ3nr48DMbwJ4k4geDeXCr0YugMeIyADfpM9TzPwCER0B8AQR/QRAJULIDRUiGoX6m+diZi4lokoAYOY2Ihpz0oT/Ss8fAVwA3x/TLzHzu2N9vGjU1NmDK+bH7VLRUbvtokI8uOskfrf7JP54e1xe65APohPkmQo7ls3IwNxsi96hxJwsixlXzrdie2Udvv7pBTDITHIoljHzkuCHCa0WpxvzciQbaCgDg/EWl1sGneHRTUS/ALAYwMc7QjLzuuFOYOaDAEqGuL0avvxOIWJaqL95ev1XZhjwLRHAKPLKhvCfAHYy883+wau2WE9Mc7n70OXpR47sXBuyFHMivnjZLPz6tRPYV30OF8df7p18EJ0AR+o7cazRiR/duFjvUGLW5tICvHa0GW+fbMGa+M3JHo33iGgRMx/RO5BowsxwyPLaYWVZfPMEDqcbM6elBDlahOBxAE/Ct9HXXfAti3XoFUywOp1D1+EcmStfk7M5yjqdWTb1ImNKrZpm4U1RP/o7XVNGfLxlD9UG3KatAr3nXfVv+azLzirt46fUM67Y+VU1xlNGtV2v5lu2bjivtLU1NrX5mQDgXqbW1XTljjzkaV+uHm9pUC+Id+Wpr2vm8X6lra3b6dHU6Ryqbqc2z5MrDittbY5wQJ3OEHf8CHX32v8C8CyAbCL6KYC9AO4P8VwFEaUBWAP/8gFm9jBzXGW3N3f6a3TKMqBR+eJls5BlMeMz297Dlx79ABVnYnZfm6G8R0SL9A4i1pTZ7DAaCNcvHXmTBTF2Vy3MRlpSIsptdr1DiRarARwgoo+I6CARHSKig3oHFek6zveit59lee0wBs90irCYxswPA+hl5jeZ+UvwbQokhBhGqLvXPk5EFQCugq9o/CZmPhrktOEUwXc16BEiWgagAsA9zKxchiGirQC2AkBhYeEYnyoy1LR04ZUjjViYm4Yl+elo9ifzy0zn6GQkm/D6fVfgz+/W4E9v12DL797FxbOn4qc3LYmHpZGrAdxORKfhy+kk+GpNL9U3rOjV1+/FcwfqsG5BNjJTpMTCRDEnGnD9sjyU2exw9vQiNckY/KT4tl7vAKLRwGBqYEZPqAZql8pmQmEzMB3VQETXwrfjrGwMIMQIQhp0EtF/AniSmR8K03OWAvgnZt7nf+xvAvi3wQcx8zYA2wBg5cqVUVugkZlx75MHcODsJ5O5Gcm+D105aXJFdrTSpxjxj+uK8aXVs/HE+2fxq9eO4+c7j2HbF1bqHdpEkw+iYbbnhAMtLo9sIDQJtqwowOP7avHyh434u5Uz9A4noo1hcxIBfHwxV2Y6hzY1xQQiGXSG0U+IKB3AffDV50wD8NWRTxEivoWa02kD8F0imgffMtsnmXn/GJ/TDsDOzPv87WfgG3TGpB1V9Thwth0/vGEx5mZbcNDegYP2dnR7+jFjalylsoZVsikRX1o9G6ccLjxbWQd3Xz/MiSGXyIo68kE0/MpsdchMNmLt/Gy9Q4l5JTMyMDsrBeU2uww6xYRocflq00naytASDQmYlmKCw+UJfrAIiplf8H/ZAWCtnrEAQ9TpzFRrWmpz8Lyaup1dhcFXi2nrcFoa1FzCrjz1M1i3JuEyU5OlnmhRcxe1+ZHaQlu1v54XEFPtl9UYtHU5HV1q/nJC18ifE7U5nC2latvrUlfqFD2vfg+F9x8PeMyuZvW1Zs02Njn71Pdm6jH1dUiqUVOFm0vVF9aVq35PaVXq85s61Dqh2pxPIPDnI2gO5xiFlNPJzI8x80b4dtc6DuABIjoxlidk5kYAZ4lovv+mqwDE5IYJPb39+PnOj7A4Lw2fv2QmVs3Nwv+9cg5+97kVeOxLF8X0IGmyrFuQjW5PP/ZVx1V+pxinjvO9ePVIE25YlgdTYqip7WKsiAibS/LxXnUrzrZ26x2OiEEDM3iykdDwpFZn+BDRPCJ6nYg+9LeXEtF39Y5LiEg22k9bc+Er2zALwLFxPO8/AXjcvznCcoxxU6JI9/De06hrP4/vXrtIis5PkMvmZMGcmIA3jjXrHYqIIi8ebICnz4stK2Rp7WS5qTQfALC9sk7nSEQscjjdMBoI6VMkZ3g41lSzbCQUPn8A8C34czv95VBu1TUiISJcSINOIhqY2fwRgA8BrGDm68f6pMx8gJlXMvNSZt7EzG3Bz4ouzc4e/HbXSXxqUQ4unRN35T0mzRSTAZfOmYZdHzWDOWpTf8UkK7fZMTfbgiX56cEPFmFRkJmMS4qmoryyTvqqCLsWf7kUIrnAOxyZ6QyrZGZ+X3Nb35BHCiEAhJ7TeRrAZfDtPGsGsJSIwMx7JiyyKPerV4/D0+/FtzYu1DuUmLduQTa+99xhVLd0YY415nexFeNU09KF/Wfa8I31C+QD6iTbUlqAf33mIGy17Vgxc+S6ckKMhsPplk2EgrCmmuFwucHM8rtv/FqIaA4+qV9/M4AGfUP6hDYHjzQ5nr3pat5g6u7AXETnlWoOpcml3q+t7ZhSq+ZPap/DfrUag1mTe+hepqZeTH9a7c+F9wdm4tlfvUBpf/3mZ5X2g7/ZrLR3ffPnSvvvT6iT09+64SX1/n1fUNrfLtmptJ+ao25iWfvtwLxT3Ku+cLRezam0p6oTU11z1DzRgpfVupoZAW/VyBdxjR09SnvoOp2aRwxTDqdWqMtr+wG8AWAngB8C+BuAH0xIRDHgaEMnnvzgLL5w6SzMzpIizBNtYCOYXbLEVoSgvLIORMCmEqnNOdk2LMnFFKMBZVKzU4RZi8v9cVkQMTSrxQxPnxdOt0zIhcHdAH4PYAER1QG4F8Bd+oYkRGQLddD5zwAuBHCGmdcCKIGv1qbQ6HL34V+erkLaFCP+eV2x3uHEhRlTk1GcbZG8ThGU18sot9mxem4WctOn6B1O3LGYE7H+gul4oaoePb39wU8QIkQOp1s2EQoiK9VXw1SW2IZFHYBHAPwUwBMAXgVwu64RCRHhQh109jBzDwAQkZmZjwGYH+ScuNPvZdzzRCWONnTiV3+3HOnJsqHBZFm3IBvvn26Fs6c3+MEibn1Q0wp723ls9m9qIybf5tJ8dPb04fWjcpEoHIhoBhHtIqKjRHSYiO7RO6bJ5vUyznV5ZHltEFaLb7mjDDrD4jkA18O3kVA9ABeALl0jEiLChZrTaSeiDADbAbxKRG3wdTIxyI9fOILXjjbjx5suwNoFUvtvMq1dkI3f76nG3hMt2LAkN/gJIi6V2+qQYjLg04un6x1K3LpsThampyWh3GbHtUulr4ZBH4D7mNlGRKkAKojoVWaOyVJkQ2nr9qDfy8iymPQOJaINzHTKDrZhUcDM6/UOYjjaup2kqctprqxW2tq6nQDgsajzUpY6tcZrf+bIOZxNF6oXgQpeU/MEA47X1K9MqVWP331wQUCMBTav0n6wQc3h7CxW7//sPfcp7bnfUH9NfuUvX1Han7l+b8BzDnbhNLWE+v6OwM0JPW+rOZu9FjUHUzs9Nf/3I5cVa96UprSzNa+BNh+zbZX6d9bkUo8HgJ5ZaoxJAUeo+qprghwxtJAGncx8k//LHxDRLgDp8OV3Cr8/7T2NR9+pwZdXz8bnL5mpdzhxZ8XMTKQmJeKNY80y6BRDOu/px4uHGrBhSS6STaFebxPhZkggbCrJxx/eqpbNX8KAmRvg38CEmZ1EdBRAPqK4/vWR+k5sPxD6Lsft3b4VLtbUYB+V4ttAzusT759F1dn2kM9bMTMT6y+Qv6sa7xDREmY+pHcgQkSLUX/yYuY3JyKQaPbakSb8+MUj+PTiHHxbdqvVhdGQgDXzrNj1kQNeL0tdVBHglSONcLn7ZGltBLh5RT7++81T2FFVjztXz9Y7nJhBRLPg23Nh3xD3bQWwFQAKCwsnNa7R+sNb1dh+oA5TjIaQz8mymLAoLy34gXEsM9mExXlpsNW2wVYbWqU6T58Xz1c1yKDTj4gOwbddaCKAO4ioGoAbAAFgZl6qZ3xCRDK53B8Gv3z1OIqzLfj1Z0pksKOjqxZk48WDDfiwvgNLCzL0DkdEmDJbHfIzpuCS2VI3V29zs1OxtCAdZRV2GXSGCRFZAJQBuJeZO7X3M/M2ANsAYOXKlRFdKLXZ2YPlMzLw7D+s0juUmJKQQHjxny8f1TkP7DyGP+yplou5n7hO7wCEiFahbiQkhuHp8+JEsxPrFuRgiin0q7Ii/K6YZwURZBdbEaCpswd7TziwuTRfPjhFiM0l+TjS0ImjDQHjIzFKRGSEb8D5ODOX6x3PeLU4PVL+JEJYLWb0eRkd52WTPgBg5jMj/dM7PiEimcx0jlN1iwu9/YyFual6hxL3plnMuCAvHfuqW/UOJaIR0XwATw66qQjA95j514OOuRK+3flO+28qZ+YfTVqQYba9sg5eBm4qkaW1keKG5fn4yYtHUW6z4zvXLtI7nKhFRATgYQBHmfmXescTDg6XGytmZeodhgCQ5c+5drjcyEyRjZoilSFT7S+UqdnQpk2zsa7m/q5CS8Bjajec0W4+5C4pUtqufPXnQ7txUNsidfm79vG1G+xoj884EHjB2NSh2RQrV538uW3t20r7xRPqTH9+kprbnLJM/fz49POrlfYtmo2FnjyyQmlPXRRYis3oVNu9mpf6/PTAjX0G074OBbvUDZ086epQTvu+aKXUugJu027qpN0kyqD5+dH+vCHEj90y0zlOA1fpF+ZKLkkkmD89FScdgR1KfIKZP2Lm5cy8HMAKAN0Anh3i0LcGjovmASczo8xmR2lhBoqsgX9YhT6mppiwdkE2th+oR1//yH90xYhWAfg8gHVEdMD/b6PeQY1Vb78XrV0y0xkpBt4HKbMihBgvGXSO07EGJ0yGBMzOSgl+sJhwc7MtcDjd6OiWpUAhugrAqVheFnS4vhPHm1zYXFqgdyhCY0tpARxON9462aJ3KFGLmfcyMzHz0kEXiV7SO66xau3yXcWXXY0jw8D7IGVWhBDjJYPOcTra6ERxjgVGg7yUkWCufybrpMMZ5EjhdyuAvw5z36VEVEVELxPR4uEegIi2EtF+ItrvcDgmJspxKLPZYTIk4PqlgXXIhL7WLrAiI9mIclud3qGICDEwo5YlM50RQWY6hRDhIjmd43S0oRNriq16hyH8inP8g85mF1bMnKpzNJGNiEwAbgDwrSHutgGYycwu/1K97QCKh3qcSN4Vs7ffix0H6nH1omykJ2tLMAu9mRMNuH5pHp7afxadPb1IS5L3KN45/DNqMtMZGdKmJMJkSPj4fRHRgdvUfEptjqc2Zy919/GAx3BeOW/E50iqOadpq/d3LstR2p5UbU6mOlmToQlBm/PZUjpETqdz5Dzj2Wb1QjitV2Ou61ErHWwsVMsbzy5Wz3+qfqXSLvyjdgNRD7Rqrh/d37WPvpKstE1N6v3t89TvOds2cnqKpU6NSZufCQAJ1fVKu79NLanEmhxO7f2hkum5cWhxueFwumUToQhSkJkMU2ICTjZLXmcINgCwMXOT9g5m7mRml//rlwAYiShrsgMcrzc/cuBclwebS2RpbaTasqIA7j4vXjrYoHcoIgIMzKhly6AzIhARrKlmmekUQoybDDrH4aNG3xJO2UQochgSCEVZKTghg85Q3IZhltYS0XT/rpggoovg+11xbqhjI1mZzY5pKSZcMV9WI0SqZQXpKLKmoMxm1zsUEQFkeW3kybKYZNAphBg3GXSOw8DOtQumy0xnJJmbbZGZziCIKBnANQDKB912FxHd5W/eDOBDIqoC8F8AbmXmiFo6G0x7twevH23GDcvzJOc6ghERtpQW4IOaNpw5F7jsR8SXFpcbFnOi1L2OINZUM1pcgcsGhRBiNCSncxyONjiRnWrGNLkiG1GKs1Px4qEGnPf0yweXYTBzN4Bpmtv+e9DXDwJ4cLLjCqfnDzbA0+/FFtm1NuLdVJKP//fKRyi31eGr14ycRyRim8PpRpZF6kFGkiyLGQfOdgQ/UEQMbc4dFakb6Wnz+nqGqO04VJ7nYNqczeZS9eJun6buZtoJ9fzWDeeVtrdJrRWZ6FIfb82lHwbEsDdnjtJOtaiP+dCJK5S28Wl1r48f/vRhpX3NY/+qtLXfQ5ZNzSvtvVctUJnyiJojCgDpc9T3QhuDNtc1b2ej0ta+zlppVWqGlDafVyuU2QNaoe4d2V9xWGlLnU4dHGvsxAJZWhtx5mZbwAycknqdca3cZsf8nFQszpM+GunyMqbg0qJpeLayDlE2oS7CrMXllk2EIow11YzWLjf6vdI3hRBjJ4POMert9+JEkwsLZWltxJmb/ckOtiI+VTtcqKxtx5YV+fCnpooIt6W0ALWt3dh/Zmy74onY4HDKoDPSWFPN8PInNVSFEGIsZNA5RqdbuuDp98omQhFoVlYyDAkkg844Vm6rQwIBm5bn6x2KCNH6C6Yj2WRAWYVsKBTPfMtrZdAZSbKkVqcQIgwkp3OMPt5ESMqlRBxzogEzpybLoDNOeb2MZyvrcHmxFdlpScFPEBEhxZyI9RdMx4sHG/CDGxYjySj52PHG3dePzp4+WGXQGVEGZp5bpFZn1NDm3GkrOWrz/sxD5AEGq9OZUqt+xkrOVWuB5u0cuQxW/z61Vuip+9T7C55X/wYcOX5BwGOY89SVTMtuRX/t6wAAIABJREFUOqW085Pa1RM0z/H9+g3qc+5SZ/MTv6PmS3bvUS9k92riqV8TuLIqbaeyfQZaStV3I8umHq+toarN2dTe37IqV2lnHlHv1+oqtATcZuroU9rGjh6lnVg0S2kHyxsdjsx0jtHRBieMBkJRVuCbJ/Q3J9uCk5LTGZfeO30Ode3nsblUZjmjzZbSAjjdfXj1SEDpWBEHBnZIleW1kcUqM51CiDCQQecYHWvsxNzsVJgS5SWMRHOzLahp6UJvv/b6noh15bY6WMyJ+NSi6XqHIkbp0qJpyEtPkpqdcapFanRGpCyZ6RRChIGMmMboWINTNhGKYMXZFvR5Wer+xZluTx9ePtSAa5fkSrmcKJSQQNhUko89xx1odvYEP0HElIGZNJnpjCwpJgOmGA0y0xkmRGQgokoiesHfnk1E+4joBBE9SURSM0jEJN1yOonIAGA/gDpmvk6vOMaircuDxs4e2UQogg3ewXZutlwciBd/O9yILk+/LK2NYptLC/Db3aew40A9vnx5YO04Ebsc/pm0LBl0RhQiQlaq6eP3R4zbPQCOAhj4EPkAgF8x8xNE9N8A7gTwu9E8oLYup1Zim5pviUy1PVSOnjZnszdd3SNBmxtoaehX2tpcQ5Mr2Moz9eer7V71+YeqgelxqheXTz6wSGnv3qA+55VLjynt261vK+2vLliitLvfmaG0+9aoZYPmpqgTG86cKQExtqcYlbapSR16tZSqj5l5JOAhFKc3qWOPDE05VW0N1gBD5HRqczhZU5dTzfgcok5niPSc6RzodFHnaKNsIhTp5lilbEo8KquoQ0HmFFw4a2rwg0VEmpttwbIZGXhGdrGNO58sr5WJnkhjtZhleW0YEFEBgGsB/NHfJgDrADzjP+QxAJv0iU6IiaXLoFPb6aLNsQYnAGDBdJnpjFQp5kTkpSfJoDOONHScx9unWrC5tAAJCVKbM5rdXJqPY41OHKnv1DsUMYkcLjfSpxhhTpSl8ZHGmmqW5bXh8WsAX8cnG8pOA9DOzAOTSXYAQy7VIaKtRLSfiPb3Qt4LEX30munUdroAgzuXw+GYvMhCcLShE1kWs+SdRLi5Oak4IYPOuPFsZR2Ygc0lsrQ22l23NA9GA8mGQnGmxeWWWc4IlWUxf7y7sBgbIroOQDMzVwy+eYhDeYjbwMzbmHklM680Qj5/iugz6TmdgzsdEV053HHMvA3ANgBYuXLlkB1wsnm9jMqzbdh3uhULZWltxJtrteD90+fg9bLMfMU4Zka5rQ4rZmZiVtbINapE5MtMMWHdgmw8d6AO39qwAIkG2fMuHjicbrmYG6GsqWa0dnnQ2++FUfrjWK0CcAMRbQSQBF9O568BZBBRon+2swBA/XifSJtzp63tqJUwRE6nNjcwoVoNq2nrQqXda9HkOz6i1uk8c0ue0j4/XZ13Kv4Pteql/Wq1vqXREjgU6CxWH4PWa+pyNqorElenn1DaRUZ1NY3JqT7H1GPqhZbqW9Wf/fZH1ZxPbDgfEGPGATWnU5v7qqXNndW2jS7182zW2+r7os2l1bLUBV48CsgD1dTl7KuuUdrBcoiHo8dvjoFOVwPgCQDriOh/dYgjZMebnPjpi0ew+oE3sOV376Kxswc3ryjQOywRxNxsC3p6vahrD/wlIGLLoboOnGx2YUup9MtYsaW0AC0uD/aciKyVLmLiOJxuKZcSoQbel3My2zlmzPwtZi5g5lkAbgXwBjN/FsAuADf7D7sdwHM6hSjEhJr0Qecwne5zkx1HqFpcbtzw4F48+k4NFuam4VefWYaK716NG5fLEr5IV5zj30zIIUtsY11ZhR2mxARcu3TkK3wielw5PxuZyUaU2er0DkVMkhaXR2Y6I5RVanVOpG8A+BoRnYQvx/NhneMRYkLoVjIlWmyvrENPrxcv33O5lEiJMnMHdrBtcmHt/GydoxETxdPnxY6qelyzKAfpU4zBTxBRwZSYgBuX5+Mv79eio7sX6cny3say855+uNx9MuiMUAPvi2wmFB7MvBvAbv/X1QAu0jMeISaDroPOwZ0uEjEznqmwY9mMDBlwRqHMFBOmpZhkB9sYt+ujZrR192KL1OaMOZtL8/HoOzV48VAD/s/FhXqHIybQwAyaLK+NTFb/+yK1OiPTaOsmavMEPVfOCzgmrappxMcoeE3NA226WK39efIOdeVRxnE1X/L8dPXxtHU/tRbd/WHAbbsPLlDaTpdaJ1Nbl3Pb/Tcp7X/X5GB+5r69SvvV/1ittIueUJeXJ37nrNLO/LqmHiqAU/d1K+3uU2qM2jqb9g3qItSCl0delNq5LEdpa+uheizq+ebK6sAHCVK3NTFIjmeoJBt8BIfrO3Gs0Sn5m1FsTrYFVfZ29HuH3ouqp7dfyjJEuXKbHVkWE9YUW/UORYTZkvx0FGdbZBfbONDsn0GTmc7INHAxQGY6hRBjJYPOETxTYYfJkIAbluYFP1hEpL9bOQPHGp34+c5jAfe5+/rx9/+zH9f95i00d/boEJ0Yr7YuD9441owbl+fLDqcxiIiwubQAFWfaUNPSFfwEEbUGBjNWmemMSFNMBqSaE2XQKYQYM/mUNgx3Xz+2H6jDNYtzJJcoit28ogCfv2Qmfr+nGs9WfjJb0u9l3PdUFd460QIvAxVnxrb9s9DX8wfr0dvPsmttDLupJB9EQHmlbCgUywaW18pMZ+TKSjXLRkJCiDGTjYSG8cbRZrR39+IWWVob9b53/SIcb3LiG2WHUJRlwdKCdPxgx2G8cLAB//rp+fjP10+g4kwbNiyJn51P/SWLnAD6AfQx80rN/QTgPwFsBNAN4IvMbJvsOIMps9VhwfRULMqTnOtYNT09CavnZqHcZse9VxVLzd0Y5XC6QQRMTTHpHYoYhtVilpnOCKWtmxgsxzMgr6+kKPAxNbU92zT1Hz2p6u/iXk35+mybmlvoyjUobW9Kn9JOqVVzHz0W9e+6Nn8TALLfUocxLaXqcxx5+QKl/fRPf6G0q3vV5/hZzcaA5xis5np1EiqtTN1roHtTYCqXuUp9nTKPq3U62+apMc//vfo6nPic+sIu+I1al1Ob05m6W00SdWrydd1DvNdJNecCbhtMm8MZ8PPVOuLpH5OZzmE8U2FHTpoZl0ueWNQzGhLw28+WwmoxY+uf9+PHLxzFn987g6+sKcLda+diaX46bLVxOdO5lpmXawecfhsAFPv/bQXwu0mNLAQnm12oOtsuOddxYEtpAext5/FBTYh/2UTUaXG5kZlsglGWyUesrFSTzHQKIcZMfrsPodnZg93HHbippAAGuaoeE6ZZzPjDF1ai83wf/vT2adyyogDf3OC7arZiZiY+rOtET29/kEeJKzcC+B/2eQ9ABhFF1FRwuc0OQwLhhuWScx3rPrU4Bykmg2woFMMcTrfkc0Y4mekUQoyHDDqH8FxlPfq9LDMoMWZRXhp+//kV+Icr5+Bnm5fAt4IUKJ2ZCU+/F4frO4I8QkxhAK8QUQURbR3i/nwAg/cCt/tvC0BEW4loPxHtdzgcExBqIK+X8WxlHdYUZyE7NSn4CSKqJZsSsXFJLl461IjzHrk4FIscLrfkc0Y4a6oZnT19coFWCDEmktOpwcx4uuIsSgozMDd75JpBIvqsmWfFmnnqkunSQt/a9IozbVgxc6oeYelhFTPXE1E2gFeJ6Bgz7xl0/1BT/EPWnWHmbQC2AcDKlSuHrk0TZu9Wn0NDRw++vXHhZDydiACbSwvwdIUdrxxpxI3LpSZrrGlxuTGzMFnvMMQIBsqmnOvyID9jSpCjxWQabZ1Ob1HwFULaWp5aOfvUC/Xa4+1rtfnZ6seDjANqfmTbIjW/sitP/RiirbkJAHstc5T2mqJTSvuITc3pvGLnV5W2Nif0gR/8Xmn/m+vvlfbcpeqGdnmXqq/B3mo1HgDo1dTl1OZwauudal8H7evWosmt1b5Orlz1c1HezpFzQAEAs6YpTaMmn9cwRGnPsZCZTo3D9Z043uSS3TDjiDXVjJnTkmE70653KJOGmev9/zcDeBbARZpD7ABmDGoXAKhHhCiz2ZGalIhrFg3xy1PEpItnT0V+xhSU2WQX21jDzL7ltTLTGdEG3h9ZYiuEGAsZdGo8f7AeiQmEa+NoJ1Phm+2sqG0D86RM1OmKiFKIKHXgawCfAvCh5rAdAL5APpcA6GDmhkkOdUhd7j7s/LAR1y3NRZLREPwEERMSEgibS/Ox94QDTVJXN6Z0efrR0+uVQWeEG3h/WmTQKYQYAxl0DsLMeKGqAauLs5Ap27bHldKZmXA43bC3ndc7lMmQA2AvEVUBeB/Ai8y8k4juIqK7/Me8BKAawEkAfwDwD/qEGmjnh43o9vTLaoQ4dFNJPrwMbJeanTFlYOYsSzYSimgD749DdrAVQoyB5HQOUnm2HXXt5/HVa+YFP1jElBWD8jpnTI3tvCJmrgawbIjb/3vQ1wzg7smMK1RlNjtmTkvGipmjy2ER0a/IakFpYQbKbHZsXVP08WZgIroNDDplpjOyTbP4LsbL8trIR5npStvQ1qW0e7Q5fB2Bq0cSqrUZNWp9R/vV6nP0WtSVYto6nfVr1N/XncXq8cX/61Taljo1R/RIvZqfCQAWTa3Q3S61ludn79urtF/9j9VKO+OLZ5X2V/7yFaXt2aDWEk04qF7sPgm1nXYicC6vs1h9HYwuNWbt65jzgdq/TC51qNY2T30O7fEBz6/J4Uyrago4RvvzEPjeq7R1YUMlM52DvFDVAJMhAZ9aLHli8Wb+9FSkmAzxWq8zatS1n8e71eewuaRABhxxanNpAY43uXC4vlPvUESYDNR+lJnOyGZONCB9ilFqdQohxkQGnX5eL+PFQ/VYM8+KtCRj8BNETDEkEEoKM1FxRgadkWx7ZR2Ygc2lsntpvLp+aR5MhgQ8UyE1O2OFzHRGD2uq1OoUQoyNDDr9PqhpRVOnG9cvkw2E4lVpYQaONnSiy90X/GAx6ZgZZTY7Lpo9NeaXQIvhpScbcfWibOyoqkdvvzf4CXGAiP5ERM1EpN0QLCq0uNwwJBAyk2UvhUhntZhlplMIMSYy6PR74WADkowJuHqhLK2NV6UzM+FloOps/JROiSYHzraj2tGFLTLLGfc2lxSgtcuD3R859A4lUjwKYL3eQYyVw+nG1BQTDAmyZD7SZclMpxBijGQjIQB9/V68/GED1i3IRopZXpJ4VeLfTMhW24bL5mbpHI3QKrfVwZyYgI1SzijuXTHfimkpJpRV2KVWKwBm3kNEs/SOAwDcff2445EP0DyKgUljR4+sXogSVosZta3duPqXb4Z8jsmQgF99ZjnmT0+dwMjim3Zjl0TNRkL9mSkjnt9VaAm4zZSubhzkyldXIsx8Wt1s5uQd6t9mV65a0mzW8+rvBPta9fFOfE79+ZjSqM6Ldc3pDYjxsxe9p7SfPLJCadf1ZCjtrd9+Vmlfk3JSaa89+C9K+5GrHlbaXzt8i9Jelq2+BrtT1I2MACDllJqyp934p3WBmlZQfav6fWe/pbaN6n5LAe+LyaWuAErdfVxp9xflBcRorqxWjxnjRkHByAgLwHvVrWhxeXD90sA3QsSP9ClGzMuxSF5nBHL39WNHVT0+vXg6UiXnOu4ZDQm4YXkeHn+vFu3dHmTIssygiGgrgK0AUFhYOGHPc7a1G++cOofSwgzkpk8J6Zz5Oaly8SBKbC7NR7OzB6GWtPb0e/HqkSa8X9Mqg04h4pwMOgG8cLAeKSYD1i7I1jsUobMVMzPx0qFGeL2MBFnqFTF2HWtGx/lebFkhtTmFz5bSAjzydg2eP9iAz18yU+9wIh4zbwOwDQBWrlwZ4pBh9AZmOP/l0/Nx2RxZMRJrLshPx4P/pzTk4/v6vSj+7suyJFcIITmdnj4vdh5uxNWLcpBkNAQ/QcS0ksJMdJzvxSmHS+9QxCDPVNQhO9WMVXOmBT9YxIXFeWmYn5OKcpvsYhtJWlweAEC27EQrACQaEjAtxSSbDwkhZKZz70kH2rt7cZ0srRUALp49FQCw73QrinNkKVAkOOdyY/dHzfjS6tlINMT9dTLhR0TYsiIf9790DKccLsyxBuYkick3MKMlNTfFgCyLbD400QyZmUqb2zrUAzQ5ndocPpSo+ZtAYK5g1tsNSvvMLern5rmPqPmNx/5putLO2dejtD056t9zU5M6JDGtOqe0L9TkTwLAmz+8TGmv/sYRpX3koQuU9p7SxUr7fs3j5e1RF4H8bOlGpa3N4dxbPUdpZxwITP+Zekz92fekq99n+3I1V7XoCTUn06XZO7E7V43R0qAe77GM/DkpoTrwdSRNDrB2Ck57f8DPV+uIT/nJc4d2WOzaXlmPjGQjrphn1TsUEQEKpyZjeloS3qs+F/xgMSl2VNWjz8vYUipLa4Vq0/J8JBDwrK1O71B0RUR/BfAugPlEZCeiO/WKxeF0w2ggpE+R3GvhI7U9hRBAnA86Xe4+vHKkERuX5MKUGNcvhfAjIlxSNBX7TreCQ90pQUyocludbymlbEIhNLLTknB5sRXPVtbB643f/srMtzFzLjMbmbmAmR8OftbEaHG5kWUxg0hy4oWP1PYUQgBxPuh85XAjenq9uKlE6v6JT1xcNA0OpxvVLV16hxL3jjc5caiuA5tlllMMY3NpPuraz+O907I6IRI4nG5YJZ9TDDJQ21Mu5AoR3+I6p3P7gXoUZE7BisLM4AeLuHFJkW+zmveqz0memM7KbHYYEgg3LpecazG0Ty2aDos5EeW2OtktNQI4nG7kpifpHYaIIFaLGe4+L5zuPqRJyasJoa2rqM3x1NLm6GnzDAFg2gvH1Bs052jrRbasUut0ZtlGDAGJFk3dTU1OZ+av1c9f9d9Rnx8ATB19Slubw9m64bz6nKfUMk7eOer9rly1XrD9lPo9NR9Qy019ZutbSnt71eUBMbbdq25MaXx6qtLOOKBmULry1YszLaVqO+2EOl+ozeHMPNIZEMNgzivnBdymfR2Nmhxgrjg84mOGatJnOoloBhHtIqKjRHSYiO6Z7BgAoNnZg70nHLhxeZ6UxhCKWdOSkZNmxnvVIWZGiwnR72Vsr6zDlfOssimJGNYUkwHXLsnFy4ca0O3pC36CmFADy2uFGJCV6tuQpkXyOoWIa3osr+0DcB8zLwRwCYC7iWjRZAfxQlUDvOzbiEKIwYgIF8+ehn3V52Q5kI7ePtmCpk631OYUQW0uzUeXpx9/O9yodyhxzetlnOvyyPJaobBafDPfspmQEPFt0gedzNzAzDb/104ARwFM+shv+wHf5iRSFkMM5ZKiaWh2unFa8jp1U26zIy0pEesWZOsdiohwF86aihlTp6CsIr53sdVbW7cH/V5GlsUU/GARNwYuQgzUcBVCxCddczqJaBaAEgD7hrhvK4CtAFBYWKi9e1xOOVw4aO/Ad69dGNbHFbHjkiLfmvv3qltRJHmdk87Z04udhxuxubQASUZtxSghVAkJhJtKCvCbN06goeM8ctOnBD9JhJ3Dv0OpNVVyOsUnBi5COJw9QY4UE8XQpl5A79fk7Glz+oDA3D9Xrvq32NLQP+Jj1Fyv5u+aXNrPUurMtza/0mPTrJj4aU5AjK0L1GO0NSz/cPH/KO3XFql1OrX+2rRKaV+5VJPXulRz/C71+FkfBM7mG19Th1pNF6spfdrXsblUnQ+c9bya+xqsfmrPrGlKW7vuZKj3OqkmyEZ8RbOUZtTV6SQiC4AyAPcyc0DWKzNvY+aVzLzSag1vDc3nKutABFy/TDYnEUObnZUCa6oZ+2RHTF28/KFvZ2mpzSlCtaU0H8y+2stCHwPLJ2V5rRgsM9kEQwJ9fFFCCBGfdBl0EpERvgHn48xcPpnPzczYfqAeq+ZkISdNrsaKofnqdU7DezGY1xnKZl5EdCURdRDRAf+/701mjGUVdszOSkFpYcZkPq2IYjOnpWDlzEyU2ewx12ejxUAtRlleKwZLSCBkWUxoccryWiHimR671xKAhwEcZeZfTvbz22rbUdvajU1Sm1MEcfHsqWjqdKPmXLfeoYRbqJt5vcXMy/3/fjRZwZ1t7ca+063YXJIvBebFqGxZUYCTzS4cqusIfrAIO5npFMPJsphlphMAEdUQ0SH/xdz9/tumEtGrRHTC/7/U8RMxSY+czlUAPg/gEBEd8N/2bWZ+aTKe/JmKs5hiNODTiwPXhgsx2EC9zn3V5zA7KyXI0dGDmRsANPi/dhLRwGZeR3QNzG97pW8zGLkwJEZr45JcfH/HYZRV2LG0QGbJJ5vD6YY5MQEWc1yXABdDsKaaZffaT6xl5pZB7W8CeJ2Z/52Ivulvf2M0D6ity6mtw6nNwUvQtI1FgelmnnQ1BzNnn3pO26I0pZ1Sq83ZVXM669eoF5G9LvX+hC41Z7TrDrX2aP2pwLF4+hw1BSpDUwPzGz/4inr/F88q7e4H1c8ZszrU/MmTe0YurpFYGvzC+Kn71O/Tsle9X5srW7BL7SfB8lYzj6ifT40d6vtw7roFmuMD63gG5IFWVitt7c+Tti5sqPTYvXYvMxMzLx00izIpA85uTx+er2rAxiW5SJUCxSKIOdYUZFnMeK86dvM6R9rMC8ClRFRFRC8T0cjZ92HCzCivrMMlRVMxY2py8BOEGCR9ihGfWpSDHVX18PR59Q4n7rS4fOVSZIWC0MqymD9efi0C3AjgMf/XjwHYpGMsQkwY3TYS0sNLhxrhcvfhMxfO0DsUEQWICBcXTcV71a0xmSMWZDMvG4CZzLwMwG8AbB/hcbYS0X4i2u9wOMYVk622HadburBZNhASY7SltABt3b3Y9VGz3qHEHYfTLUtrxZCsqb5BZyz+LR0lBvAKEVX4qzQAQI5/BdLASqQh64QN/lvbCxnAi+gTV4POpz44i9lZKbhwliyXF6G5pGgaGjt7UNsaW3mdwTbzYuZOZnb5v34JgJGIsoZ6rHDuNF1msyPJmICNS3LH9Tgifl1enIUsixnlNrveocSdFpcbWRYZdIpAVosZvf2MjvO9wQ+ObauYuRTABvj2U1gT6omD/9YaAwphCBH54ibxotrhwvs1rfj6+vmy9EeE7PK5WUgg4Pd7qnH/TUv0DicsQtnMi4imA2hiZiaii+C7QDWh64x7evvxQlU91i+eLjlhYswSDQnYtDwPj71bg7YuDzJTZCfVyeJwulE6Uy7qikBZ/hlwh9ONjOT47ZPMXO//v5mIngVwEYAmIspl5gYiygUQ9mUaATl5mjqdXYWB9cjTqpqUtjbvz+RSUxi0OZ5TGtXP2gWvqTmh2uc0afIpjR3q54BlDx0OiFFr7wa1RvNnFlUo7Re3Xa60Kx/6rdK+o1a9f2/1HKVtrtKm/agz99oamj5q/VFtXc62eWpOpzYns1ezeiTbpr7uvelqJQ5tPqb2N7K2ZisAJFSrpca8mhxf7f1jFTcznU/tt8OQQLhZlu2JUZiVlYIvX16Ev+yrxd4TLcFPiA4Dm3mtG1QSZSMR3UVEd/mPuRnAh0RUBeC/ANzKE7wu6o1jzejs6cOWFdJHxfhsWVGA3n7G8welZudk6ev3orXbIzOdYkhWyyeDznhFRClElDrwNYBPAfgQwA4At/sPux3Ac/pEKMTEiovphL5+L8psdqydb0W21OYUo/S1a+bhtaNN+EbZQfztq2uifhaOmfcCGHG6n5kfBPDg5ETkU1Zhx/S0JFw2Z8hVvEKEbGFuGhbmpqGswo4vXDpL73DiQmuXB8xSLkUMzZrqmwGK87IpOQCe9a+2SwTwF2beSUQfAHiKiO4EUAvgFh1jFGLCxMVM5+6PHHA43fi7lbKBkBi9JKMBv7h5GRo6zuP+l47qHU5ManG5sfu4AzeW5MGQIMvfxfhtKc1Hlb0DJ5udeocSF5oHanTKTKcYgtXiu+AfzzOdzFzNzMv8/xYz80/9t59j5quYudj/f6vesQoxEaJ7yiZET+4/iyyLGWsXDLkhmBBBrZiZiS9fXoRte6qx8YJcrC6W2bhweu5APfq9LMvfRdjcuDwfP3v5GMpsdfjG+gXBTxDjMlAOY2BGS4jB0qYkwmRIQIvLo3coMSmgbqKmnVg0S2lr8wBNHX0Bj9m5TK1nn1LrUtpGNUUT2mrmLaWpo37Owfjn6hPsPhj4e/zKpceUdqpFzZ/8665VSnvullqlPfuFv1fa3738eaX9zqkLlHbXnJE3wvI0BJZj9Dap33fjLT0j3n/ic+rrBmjLf2lyQJ1q25I+T2lrc3OH4i4pUtrB6nSqzwggxMskMT/T2ezswRvHmrGlNB9GQ8x/u2ICfe2aeSiypuAbZQfhco/8y1KMTrnNjqUF6SjO0f6yFWJsrKlmXDHPiu2Vdej3xn2Zhgnn+HimU1JYRCAiQpbFFNcznULEu5gfhT1r833guEWW1opxGlhmW99xHtvePKV3ODHjWGMnDtd3YnNJvt6hiBizuTQfDR09eK96QjdeFvgkVy9LZjrFMKyp5njP6RQirsX0oJOZUWazo6QwA3OzA7eDFmK0VszMxFULsvH4vlq4+/qDnyCCKrfVITGBcMNyGXSK8Lp6YQ5SkxJRViE1Oydai9ODFJMByaa4yNoRY2BNNaNFZjqFiFsx/dfhcH0njje58JNNFwQ/WIgQffGy2Xjt6D68UNUg5T3Gqa/fi2cr67B2QTamSj1FEWZJRgOuW5qH7ZV1+PGmPqRE+c7TkczhcsvOtWJEWRYzquwdwQ8U46bN4eQ29XU3a9ranD4gMOey6WI1ry9vp1qS6uQduUp71vNq/m7N9Wq+Y0KOmtto2avWwDQ9qq5QHGpXlt1Q8zwTLWrOZaJL3ZgwL0X9vvOWqu1t99+ktDM0z9e7rFNppzyiHtF4S3dAjImn1NqhGTa1balTX6fWBSP/HjU51XQRbb1U7fumfe+Heq+TajSrgTQ5nOES0zOd5bY6mAwJuG5pbvCDhQjRqrnTUJw8yS8qAAAPNklEQVRtwSPvnMYEl66MeXtPtsDhdGOLbCAkJsiW0nyc7+3Hyx826h1KTHM4e6RGpxiRNdWMcy635FgLEadidtDZ2+/Fjqo6rFuQjYxkmUER4UNE+OKqWfiwrhMVZ9qCnyCGVW6rQ0ayEWsXWPUORcSoFTMzMXNaMsptssR2IrW4PDLTKUaUZTHDy0Bbt+xgK0Q8itlB51snHGhxebC5VPLERPjdVJKPtKREPPJ2jd6hRK3Onl787XAjbliWB3NiwAbcQoQFEWFzSQHerT6HuvbzwU8QY+JwumWmU4xo4KKE7GArRHyK2QSXMlsdMpONuHK+1OYU4ZdsSsRtFxXij3tPo779PPIypgQ/SShePtQAd58Xm2VprZhgm0vz8avXjmN7ZR3uXjtX73BijruvHx3ne2WmU4xo4OejRXawDTtDZmbYH9OTrg4Rph5T37eeWdOUdrZNzS20r1VXGaadUPMrO6GWV8rZp+YeNn8/eGm6lLfVGHotasxfv/lZpX3aHWRV1d0fKs291XOU9liKuvVZ1OXk7fPU16H5cnX+z9SkHl+wS10ZoH1ftILlZw51vjbHN6Duq8ZYf95icqaz43wvXj3ShBuW5cGUGJPfoogAn7tkJpgZf37vjN6hRKWyijrMsaZgWcHEJKwLMWDG1GRcNHsqymx2ycOeAOdcvg9FMugUIxmYCZeZTiHiU0yOyF4+1ACPzKCICTZjajKuWZSDv75fi55eKZ8yGmdbu/F+TSs2lxaAiIKfIMQ4bSnNR7WjC4frO4MfLEZlYBAhy2vFSGR5rRDxLSYHneU23wzKUplBERPsjlWz0d7di+cO1OkdSlQps9lB5MuNFWIyXLs0D8/dvQqL89L0DiXmDCyXlJlOMZIUkwFTjAZZXitEnIq5nM7ac74ZlH/99HyZQRET7uLZU7FiZibau3uDHywAAMyMclsdLpszTXJhxaSxmBOxbIa26poIh09mOmWneDE8IkJWqklmOieANgdPuzWftyhPvb+tS2kbO9SamQDgylcv0Jk0JVa1uYGuXPVZ5z6i1vHU5oC2L1fnvT76ilqnc/4P1ZqX9quDTyTdcv1epf3ot29U2l13tCvtjYVHlPbJBxYpbfM89XvyQI3RqMnXTDgV+JnGqzlGm+NpalJfx9nbR16N40m3KO2UWpfS7lyWoz5+R/DcWO3PB0Ftc8XhoI8RipgbdJb5t8XfJDMoYhIQEZ6561K5wDEK+8+0oba1G/dcVax3KEKIMJDltSJUVosZDpnpFCIuxdTy2o7uXjz6Tg3WLchGvsygiEkiA87RKbfZkWwyYP0F0/UORQgRBi0uN9KSEpFklNJHYmRZFjNanFKnU4h4FFODzt/uPonOnl7866fn6x2KEGIIPb39eKGqAesvmI4Uc8wttBAiLjlcbmRJPqcIgTVVZjqFiFcxM+isaz+PR96pweaSAizMlY0ihIhErx5pgtPdh5tlZ2khYkaL0wOrLK0VIbCmmtHW7UFvvzf4wUKImBIzUw3/8cpHAICvfWqezpEIIYZTbrMjLz0JlxRNC36wECIqOFxuLJJdgUUIsixmMAOtXR7kpCXpHU7MMGRmjny/ZuMgra5CS8Bt0144prQbbluotKceU2es86qalLZ2QxutoifUDW5c+epGZG2L1N8pOR8EzpBf+sv3lfbj71+itOf9o1pZ4Ippal31D87NVNrJmuPbXilU2l/9YrnS/svd1yrtjC+qrwEAOLpSlDbvVD//mJwj145uujjYBkrqe+exqPOJKbXaTaIC+12wnw8O8vMVKl1mOoloPRF9REQnieib4328I/WdeLayDndcNktyOYUIQbA+SERmInrSf/8+Ipo13udsdvZgz4kWbCrJR0KC5MEKES7h/ps6Wg6nW2Y6RUikVqcQ8WvSB51EZADwEIANABYBuI2IFo181sge2HkMaUlG/MOVc8MRohAxLcQ+eCeANmaeC+BXAB4Y7/PuOFCPfi9jsyytFSJsJuJv6mic9/TD5e6TGp0iJB8POiWvU4i4o8fy2osAnGTmagAgoicA3AjgyIhnDeOdky1487gD3964AOnJxjCGKUTMCqUP3gjgB/6vnwHwIBERM4+8DmQEz1TYsWxGBuZmBy7jEUKMWVj/prZ2edDlDl7XbUCDv76fzHSKUAz8nBxvdGKudfi/BWZjArJTZfmtELFEj0FnPoCzg9p2ABeP5YGYGf++8xjy0pPwhUtnhSM2IeJBKH3w42OYuY+IOgBMA9Aylic8Ut+JY41O/PjGxWM5XQgxvLD9TQWAX/ztI/z1/dpRnzc9XQYIIjhrqhmGBMLPXj6Gn718bNjjLi/Owp/vHPOPsdDgtg6l7S4pUtqpu48HnHPuugVKW5vDqc3BNHaouYva3EKTS908Snu+pU4tpZP4HTU/UpsbCQAv1aqLOkxN6rCm2pKltP9Q/ITSfvr51Uq7z6JeV19z0yGlff/OTWqMa9VUob6DwVdyabPfta+LNpe1N1U9PqVejVH7OmtfRy1PeuDQT/vbW/vzEi56DDqHSuYKmD0hoq0AtgJAYWFhwAn+Y/DDGxajs6dP6oMJEbpQ+mBI/RQIra/OmDoFP9u8BOsXS21OIcIsbH9TAeDmFQVYMXN0m0Ykmwy4bI5sDiaCSzIa8PiXL4a97fyIx2XLcm0hYo4eg047gBmD2gUA6rUHMfM2ANsAYOXKlcMu6SspDM+OSkLEkVD64MAxdiJKBJAOoHWoBwulr6YmGXHbRcN/0BVCjFlY/6aumJk56kGnEKMhu5cLEZ/02L32AwDFRDSbiEwAbgWwQ4c4hIhXofTBHQBu9399M4A3xpPPKYSYMPI3VQghRMSb9JlOf37YPwL4GwADgD8x8+HJjkOIeDVcHySiHwHYz8w7ADwM4M9EdBK+Gc5b9YtYCDEc+ZsqhOhvaxvx/mB1PJ1XBq9xb+xQ6z12XagugfakqvUku3PV69R9FjUTYEqjNjNAzfE0PZivxnhL4I7Hq4tOKe2TtgylXW9RsxU/e899Sts4T40hZZm6oOt269vq860/obSfql+ptGvemQEt7WNmPq9uoFVzvboJqjdF3cgtoUubPhhsvlDzOg6RwxmM9ucpsWiW0u6rrhn1YwL6LK8FM78E4CU9nlsIMXQfZObvDfq6B8Atkx2XEGL05G+qEEKISKfH8lohhBBCCCGEEHFCBp1CCCGEEEIIISaMLstrhRBCCCGEEOGnzeHU5uhp8zOTaroCHqNzWY7S7g1Sizdvp7pp9sk7cpV28f86Rzz/xOfUgpQJOWoOZ8KpKQHn1OeoeaT2DWrNy8zpnUq7rVHdOVlb8/KBxU8r7Ttev1Npa+uAavM1PTlqPiYAGN9Wn9OT3q+08/aoMaRVOZT2mVvylHbmcfX8lFqX0u4qVHNGTR1qTObK6oAY+4vU59D+/ISrbqfMdAohhBBCCCGEmDAy6BRCCCGEEEIIMWFk0CmEEEIIIYQQYsJQNNR7JyIHgDMjHJIFoGWSwhkriTF8oiFObYwzmdmqVzCTRfrqpJEYw2dwnNJPfaLhvYuGGIHoiDMaY4yLvjqcQX04Gt+7SCQxjk9I/TEqBp3BENF+Zl4Z/Ej9SIzhEw1xRkOMeoiG10ViDI9oiBGInjgnUzS8JtEQIxAdcUqM0SsaXheJMTyiIcZgZHmtEEIIIYQQQogJI4NOIYQQQgghhBATJlYGndv0DiAEEmP4REOc0RCjHqLhdZEYwyMaYgSiJ87JFA2vSTTECERHnBJj9IqG10ViDI9oiHFEMZHTKYQQQgghhBAiMsXKTKcQQgghhBBCiAgkg04hhBBCCCGEEBMmqgedRLSeiD4iopNE9E294xlARH8iomYi+nDQbVOJ6FUiOuH/P1PnGGcQ0S4iOkpEh4nonkiLk4iSiOh9Iqryx/hD/+2ziWifP8YnicikV4yDYjUQUSURvRCpMepJ+uqY44v4fuqPR/pqjIjEvhrp/dQfT8T3VemnsUX66phjlL6qk6gddBKRAcBDADYAWATgNiJapG9UH3sUwHrNbd8E8DozFwN43d/WUx+A+5h5IYBLANztf/0iKU43gHXMvAzAcgDriegSAA8A+JU/xjYAd+oY44B7ABwd1I7EGHUhfXVcoqGfAtJXY0IE99VHEdn9FIiOvir9NEZIXx0X6as6idpBJ4CLAJxk5mpm9gB4AsCNOscEAGDmPQBaNTffCOAx/9ePAdg0qUFpMHMDM9v8Xzvh++WejwiKk31c/qbR/48BrAPwjP923V9LIioAcC2AP/rbhAiLUWfSV8coGvopIH01hkRkX430fgpER1+VfhpTpK+OkfRV/UTzoDMfwNlBbbv/tkiVw8wNgO8HHkC2zvF8jIhmASgBsA8RFqd/ic0BAM0AXgVwCkA7M/f5D4mE9/3XAL4OwOtvT0Pkxagn6athEMn9FJC+GiOiqa9GXB8YEMl9VfppzJC+GgbSVydXNA86aYjbpP7LKBGRBUAZgHuZuVPveLSYuZ+ZlwMogO/K3sKhDpvcqD5BRNcBaGbmisE3D3FoPP9syusxTpHeTwHpqzFCXo9xivS+Kv00ZshrMk7SVydfNA867QBmDGoXAKjXKZZQNBFRLgD4/2/WOR4QkRG+Dvc4M5f7b464OAGAmdsB7IZv/X0GESX679L7fV8F4AYiqoFvecs6+K7SRlKMepO+Og7R1E8B6atRLpr6asT1gWjqq9JPo5701XGQvqqPaB50fgCg2L+TkwnArQB26BzTSHYAuN3/9e0AntMxloEciYcBHGXmXw66K2LiJCIrEWX4v54C4Gr41t7vAnCz/zBdY2TmbzFzATPPgu9n8A1m/iwiKMYIIH11jKKhnwLSV2NINPXVSOsDEd9XpZ/GFOmrYyR9VUfMHLX/AGwEcBy+dc7f0TueQXH9FUADgF74rkbdCV9OwusATvj/n6pzjKvhm5Y/COCA/9/GSIoTwFIAlf4YPwTwPf/tRQDeB3ASwNMAzHq/5/64rgTwQiTHqONrI311bPFFfD/1xyl9NUb+RWJfjfR+6o8x4vuq9NPY+id9dcwxSl/V6R/5vwkhhBBCCCGEECLsonl5rRBCCCGEEEKICCeDTiGEEEIIIYQQE0YGnUIIIYQQQgghJowMOoUQQgghhBBCTBgZdAohhBBCCCGEmDAy6BRCCCGEEEIIMWFk0CmEEEIIIYQQYsL8f4GF57BAXbbeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = matplotlib.pyplot.figure(figsize=(13.0, 4.0))\n",
    "axes1 = fig.add_subplot(1,4,1) #Number of rows, Number of Columns, Number of Position\n",
    "axes2 = fig.add_subplot(1,4,2)\n",
    "axes3 = fig.add_subplot(1,4,3)\n",
    "axes4 = fig.add_subplot(1,4,4)\n",
    "\n",
    "axes1.set_ylabel(\"average\")\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel(\"max\")\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "\n",
    "axes3.set_ylabel(\"min\")\n",
    "axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "axes4.set_ylabel(\"heatmap\")\n",
    "axes4 = matplotlib.pyplot.imshow(data)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"lead\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l\n",
      "e\n",
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "print(word[0])\n",
    "print(word[1])\n",
    "print(word[2])\n",
    "print(word[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O\n",
      "x\n",
      "y\n",
      "g\n",
      "e\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "word = \"Oxygen\"\n",
    "for char in word:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are: 5 vowels\n"
     ]
    }
   ],
   "source": [
    "length = 0\n",
    "for vowel in 'aeiou':\n",
    "    length = length + 1\n",
    "print(\"There are:\", length, \"vowels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are: 1 vowels\n",
      "There are: 2 vowels\n",
      "There are: 3 vowels\n",
      "There are: 4 vowels\n",
      "There are: 5 vowels\n"
     ]
    }
   ],
   "source": [
    "#Running count inside the loop\n",
    "length = 0\n",
    "for vowel in 'aeiou':\n",
    "    length = length + 1\n",
    "    print(\"There are:\", length, \"vowels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inside the loop, letter is a\n",
      "Inside the loop, letter is b\n",
      "Inside the loop, letter is c\n",
      "after the loop, letter is:  c\n"
     ]
    }
   ],
   "source": [
    "letter = \"z\"\n",
    "for letter in \"abc\":\n",
    "    print(\"Inside the loop, letter is\",letter)\n",
    "print(\"after the loop, letter is: \", letter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(len('aeiou'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,4):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not greater\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "#Conditional Statements\n",
    "num = 37\n",
    "\n",
    "if num > 100:\n",
    "    print('greater')\n",
    "else:\n",
    "    print('not greater')\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minima add up to zero!\n"
     ]
    }
   ],
   "source": [
    "#Truth tests and logical comparisions. \n",
    "data = numpy.loadtxt(fname='data/inflammation-03.csv', delimiter=',')\n",
    "\n",
    "max_inflammation_0 = numpy.max(data, axis=0)[0]\n",
    "max_inflammation_20 = numpy.max(data, axis=0)[20]\n",
    "\n",
    "if max_inflammation_0 == 0 and max_inflammation_20 == 20:\n",
    "    print('Suspicious looking maxima')\n",
    "elif numpy.sum(numpy.min(data, axis=0)) == 0:\n",
    "    print('Minima add up to zero!')\n",
    "else:\n",
    "    print('Seems ok!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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": [
    "image = matplotlib.pyplot.imshow(data)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = matplotlib.pyplot.figure(figsize=(13.0, 4.0))\n",
    "axes1 = fig.add_subplot(1,4,1) #Number of rows, Number of Columns, Number of Position\n",
    "axes2 = fig.add_subplot(1,4,2)\n",
    "axes3 = fig.add_subplot(1,4,3)\n",
    "axes4 = fig.add_subplot(1,4,4)\n",
    "\n",
    "axes1.set_ylabel(\"average\")\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel(\"max\")\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "\n",
    "axes3.set_ylabel(\"min\")\n",
    "axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "axes4.set_ylabel(\"heatmap\")\n",
    "axes4 = matplotlib.pyplot.imshow(data)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating functions for resusable code with arguments\n",
    "\n",
    "def fahr_to_celsius(temp): \n",
    "    #common command in functions is a return command\n",
    "    return((temp - 32) * (5/9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now use the fahr_to_celsius function\n",
    "fahr_to_celsius(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature converted to:  0.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Temperature converted to: \", fahr_to_celsius(32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "freezing point of water:  0.0\n",
      "boiling point of water:  100.0\n"
     ]
    }
   ],
   "source": [
    "print(\"freezing point of water: \", fahr_to_celsius(32))\n",
    "print(\"boiling point of water: \", fahr_to_celsius(212))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The rest of the code below is an attempt to write functions and call the functions. -Grace Faustino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to plot a set of graph\n",
    "def function_plot_graph(plot_data):\n",
    "    fig = matplotlib.pyplot.figure(figsize=(13.0, 4.0))\n",
    "    axes1 = fig.add_subplot(1,4,1) #Number of rows, Number of Columns, Number of Position\n",
    "    axes2 = fig.add_subplot(1,4,2)\n",
    "    axes3 = fig.add_subplot(1,4,3)\n",
    "    axes4 = fig.add_subplot(1,4,4)\n",
    "\n",
    "    axes1.set_ylabel(\"average\")\n",
    "    axes1.plot(numpy.mean(plot_data, axis=0))\n",
    "\n",
    "    axes2.set_ylabel(\"max\")\n",
    "    axes2.plot(numpy.max(plot_data, axis=0))\n",
    "\n",
    "    axes3.set_ylabel(\"min\")\n",
    "    axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "    axes4.set_ylabel(\"heatmap\")\n",
    "    axes4 = matplotlib.pyplot.imshow(plot_data)\n",
    "\n",
    "\n",
    "    fig.tight_layout()\n",
    "\n",
    "    matplotlib.pyplot.show()\n",
    "    return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-164-55a3c31e807d>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-164-55a3c31e807d>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    data = numpy.loadtxt(fname='data/inflammation-03.csv', delimiter=',')g\u001b[0m\n\u001b[0m                                                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#Function to read a collection of data files\n",
    "def function_load_data(dataset):\n",
    "    data = numpy.loadtxt(fname='data/inflammation-03.csv', delimiter=',')g\n",
    "    return(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to check for suspicious data and return a true or false flag\n",
    "def function_check_data_sets(dataset):\n",
    "    #Truth tests and logical comparisions. \n",
    "    \n",
    "    max_inflammation_0 = numpy.max(data, axis=0)[0]\n",
    "    max_inflammation_20 = numpy.max(data, axis=0)[20]\n",
    "\n",
    "    if max_inflammation_0 == 0 and max_inflammation_20 == 20:\n",
    "        print('Suspicious looking maxima')\n",
    "    elif numpy.sum(numpy.min(data, axis=0)) == 0:\n",
    "        print('Minima add up to zero!')\n",
    "    else:\n",
    "        print('Seems ok!')\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DO ALL- \n",
    "\n",
    "#Call function to load data\n",
    "\n",
    "\n",
    "#Check for suspicious code.If data is good, then graph\n",
    "\n",
    "#Call Function and plot Graph\n",
    "function_plot_graph(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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