SC_Python_Programming.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an introduction to Python notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n",
      "hello again\n"
     ]
    }
   ],
   "source": [
    "print(\"hello\")\n",
    "print(\"hello again\")\n",
    "# shift-enter runs this cell\n",
    "# this is a comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable   Type    Data/Info\n",
      "----------------------------\n",
      "a          int     3\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg = 55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55\n"
     ]
    }
   ],
   "source": [
    "print(weight_kg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_lb=2.2*weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in pounds 121.00000000000001\n"
     ]
    }
   ],
   "source": [
    "print(\"weight in pounds\", 2.2*weight_kg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "weight_lb=2.2*weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kg 55\n",
      "weight in pounds 121.00000000000001\n"
     ]
    }
   ],
   "source": [
    "print(\"weight in kg\",weight_kg)\n",
    "print(\"weight in pounds\",weight_lb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight in kg 100\n",
      "weight in pounds 121.00000000000001\n"
     ]
    }
   ],
   "source": [
    "print(\"weight in kg\",weight_kg)\n",
    "print(\"weight in pounds\",weight_lb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SC_Python_Programming.ipynb  inflammation-09.csv\r\n",
      "inflammation-01.csv          inflammation-10.csv\r\n",
      "inflammation-02.csv          inflammation-11.csv\r\n",
      "inflammation-03.csv          inflammation-12.csv\r\n",
      "inflammation-04.csv          small-01.csv\r\n",
      "inflammation-05.csv          small-02.csv\r\n",
      "inflammation-06.csv          small-03.csv\r\n",
      "inflammation-07.csv          testnotebook.ipynb\r\n",
      "inflammation-08.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/pawelpomorski/Desktop/data'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.loadtxt(fname=\"inflammation-01.csv\",delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=numpy.loadtxt(fname=\"inflammation-01.csv\",delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 40)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(data.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(data[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n"
     ]
    }
   ],
   "source": [
    "print(data[0][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.0\n"
     ]
    }
   ],
   "source": [
    "print(data[30][20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.  3.  3. 10.  5.  7.  4.  7.  7.\n",
      " 12. 18.  6. 13. 11. 11.  7.  7.  4.  6.  8.  8.  4.  4.  5.  7.  3.  4.\n",
      "  2.  3.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(data[0][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "datarow=data[0][:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.  3.  3. 10.  5.  7.  4.  7.  7.\n",
      " 12. 18.  6. 13. 11. 11.  7.  7.  4.  6.  8.  8.  4.  4.  5.  7.  3.  4.\n",
      "  2.  3.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(datarow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datarow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.  3. 10.  5.  7.  4.  7.  7. 12. 18.  6. 13. 11. 11.  7.  7.  4.  6.\n",
      "  8.  8.  4.  4.  5.  7.  3.  4.  2.  3.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(data[0][10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.  3.  3. 10.  5.  7.  4.  7.  7.\n",
      "  12. 18.  6. 13. 11. 11.  7.  7.  4.  6.  8.  8.  4.  4.  5.  7.  3.  4.\n",
      "   2.  3.  0.  0.]\n",
      " [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6. 10. 11.  5.  9.  4.  4.  7. 16.\n",
      "   8.  6. 18.  4. 12.  5. 12.  7. 11.  5. 11.  3.  3.  5.  4.  4.  5.  5.\n",
      "   1.  1.  0.  1.]\n",
      " [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.  5.  7.  4.  5.  4. 15.  5. 11.\n",
      "   9. 10. 19. 14. 12. 17.  7. 12. 11.  7.  4.  2. 10.  5.  4.  2.  2.  3.\n",
      "   2.  2.  1.  1.]\n",
      " [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7. 10.  7.  9. 13.  8.  8. 15. 10.\n",
      "  10.  7. 17.  4.  4.  7.  6. 15.  6.  4.  9. 11.  3.  5.  6.  3.  3.  4.\n",
      "   2.  3.  2.  1.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[0:4][0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.  3.  3. 10.  5.  7.  4.  7.  7.\n",
      "  12. 18.  6. 13. 11. 11.  7.  7.  4.  6.  8.  8.  4.  4.  5.  7.  3.  4.\n",
      "   2.  3.  0.  0.]\n",
      " [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6. 10. 11.  5.  9.  4.  4.  7. 16.\n",
      "   8.  6. 18.  4. 12.  5. 12.  7. 11.  5. 11.  3.  3.  5.  4.  4.  5.  5.\n",
      "   1.  1.  0.  1.]\n",
      " [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.  5.  7.  4.  5.  4. 15.  5. 11.\n",
      "   9. 10. 19. 14. 12. 17.  7. 12. 11.  7.  4.  2. 10.  5.  4.  2.  2.  3.\n",
      "   2.  2.  1.  1.]\n",
      " [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7. 10.  7.  9. 13.  8.  8. 15. 10.\n",
      "  10.  7. 17.  4.  4.  7.  6. 15.  6.  4.  9. 11.  3.  5.  6.  3.  3.  4.\n",
      "   2.  3.  2.  1.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[:4][:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  1.  0.  3.  2.  5.  4.  8.  2.  9.  3.  3. 10. 12.  9. 14. 11.\n",
      " 13.  8.  6. 18. 11.  9. 13. 11.  8.  5.  5.  2.  8.  5.  3.  5.  4.  1.\n",
      "  3.  1.  1.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(data[-1][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[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.]]\n"
     ]
    }
   ],
   "source": [
    "print(2*data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[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.]]\n"
     ]
    }
   ],
   "source": [
    "print(data+data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1. ... 9. 0. 0.]\n",
      " [0. 1. 4. ... 1. 0. 1.]\n",
      " [0. 1. 1. ... 4. 1. 1.]\n",
      " ...\n",
      " [0. 1. 1. ... 1. 1. 1.]\n",
      " [0. 0. 0. ... 0. 4. 0.]\n",
      " [0. 0. 1. ... 1. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(data*data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datarow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 2. ... 6. 0. 0.]\n",
      " [0. 1. 3. ... 4. 0. 1.]\n",
      " [0. 1. 2. ... 5. 1. 1.]\n",
      " ...\n",
      " [0. 1. 2. ... 4. 1. 1.]\n",
      " [0. 0. 1. ... 3. 2. 0.]\n",
      " [0. 0. 2. ... 4. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(data+datarow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "datahalf=datarow[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datahalf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data+datahalf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.14875\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Mar  4 14:09:09 2018\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "print(time.ctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.0 0.0 4.613833197118566\n"
     ]
    }
   ],
   "source": [
    "maxval,minval,stval=numpy.max(data),numpy.min(data),numpy.std(data)\n",
    "print(maxval,minval,stval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.0 0.0 4.613833197118566\n"
     ]
    }
   ],
   "source": [
    "maxval=numpy.max(data)\n",
    "minval=numpy.min(data)\n",
    "stval=numpy.std(data)\n",
    "\n",
    "print(maxval,minval,stval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.0 0.0 4.613833197118566\n"
     ]
    }
   ],
   "source": [
    "print(data.max() ,data.min() , data.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__array__',\n",
       " '__array_finalize__',\n",
       " '__array_interface__',\n",
       " '__array_prepare__',\n",
       " '__array_priority__',\n",
       " '__array_struct__',\n",
       " '__array_ufunc__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__complex__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__ifloordiv__',\n",
       " '__ilshift__',\n",
       " '__imatmul__',\n",
       " '__imod__',\n",
       " '__imul__',\n",
       " '__index__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__ipow__',\n",
       " '__irshift__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lshift__',\n",
       " '__lt__',\n",
       " '__matmul__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__or__',\n",
       " '__pos__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdivmod__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rfloordiv__',\n",
       " '__rlshift__',\n",
       " '__rmatmul__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__rpow__',\n",
       " '__rrshift__',\n",
       " '__rshift__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__xor__',\n",
       " 'all',\n",
       " 'any',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'astype',\n",
       " 'base',\n",
       " 'byteswap',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'compress',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'copy',\n",
       " 'ctypes',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'data',\n",
       " 'diagonal',\n",
       " 'dot',\n",
       " 'dtype',\n",
       " 'dump',\n",
       " 'dumps',\n",
       " 'fill',\n",
       " 'flags',\n",
       " 'flat',\n",
       " 'flatten',\n",
       " 'getfield',\n",
       " 'imag',\n",
       " 'item',\n",
       " 'itemset',\n",
       " 'itemsize',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'min',\n",
       " 'nbytes',\n",
       " 'ndim',\n",
       " 'newbyteorder',\n",
       " 'nonzero',\n",
       " 'partition',\n",
       " 'prod',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'ravel',\n",
       " 'real',\n",
       " 'repeat',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'round',\n",
       " 'searchsorted',\n",
       " 'setfield',\n",
       " 'setflags',\n",
       " 'shape',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'squeeze',\n",
       " 'std',\n",
       " 'strides',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'tobytes',\n",
       " 'tofile',\n",
       " 'tolist',\n",
       " 'tostring',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'var',\n",
       " 'view']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function max:\n",
      "\n",
      "max(...) method of numpy.ndarray instance\n",
      "    a.max(axis=None, out=None, keepdims=False)\n",
      "    \n",
      "    Return the maximum along a given axis.\n",
      "    \n",
      "    Refer to `numpy.amax` for full documentation.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    numpy.amax : equivalent function\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(data.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_0 = data[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.  3.  3. 10.  5.  7.  4.  7.  7.\n",
      " 12. 18.  6. 13. 11. 11.  7.  7.  4.  6.  8.  8.  4.  4.  5.  7.  3.  4.\n",
      "  2.  3.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(patient_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.0\n"
     ]
    }
   ],
   "source": [
    "print(patient_0.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.45\n"
     ]
    }
   ],
   "source": [
    "print(patient_0.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation for patient 2 is  19.0\n"
     ]
    }
   ],
   "source": [
    "print(\"maximum inflammation for patient 2 is \",numpy.max(data[2,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation for all patients [18. 18. 19. 17. 17. 18. 17. 20. 17. 18. 18. 18. 17. 16. 17. 18. 19. 19.\n",
      " 17. 19. 19. 16. 17. 15. 17. 17. 18. 17. 20. 17. 16. 19. 15. 15. 19. 17.\n",
      " 16. 17. 19. 16. 18. 19. 16. 19. 18. 16. 19. 15. 16. 18. 14. 20. 17. 15.\n",
      " 17. 16. 17. 19. 18. 18.]\n"
     ]
    }
   ],
   "source": [
    "print(\"maximum inflammation for all patients\",numpy.max(data,axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation for all patients [18. 18. 19. 17. 17. 18. 17. 20. 17. 18. 18. 18. 17. 16. 17. 18. 19. 19.\n",
      " 17. 19. 19. 16. 17. 15. 17. 17. 18. 17. 20. 17. 16. 19. 15. 15. 19. 17.\n",
      " 16. 17. 19. 16. 18. 19. 16. 19. 18. 16. 19. 15. 16. 18. 14. 20. 17. 15.\n",
      " 17. 16. 17. 19. 18. 18.]\n"
     ]
    }
   ],
   "source": [
    "print(\"maximum inflammation for all patients\",data.max(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation per day [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 19. 18. 17. 16. 15. 14. 13. 12. 11. 10.  9.  8.  7.  6.  5.\n",
      "  4.  3.  2.  1.]\n"
     ]
    }
   ],
   "source": [
    "print(\"maximum inflammation per day\",data.max(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_inflammation_per_patient=data.max(axis=1)\n",
    "max_inflammation_per_patient.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_inflammation_per_day=data.max(axis=0)\n",
    "max_inflammation_per_day.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b383c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot\n",
    "image = matplotlib.pyplot.imshow(data)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ave_inflammation = numpy.mean(data,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ave_inflammation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aca7828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ave_plot = matplotlib.pyplot.plot(ave_inflammation)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11acd6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_plot = matplotlib.pyplot.plot(numpy.max(data,axis=0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAHEJJREFUeJzt3XtsXOl5HvDnnQvvMyNRpCTOUFxKK+1K4jBeO/Ql3iK1N4Gz3rh20xqNjSQw0KBC0xi10QSuFwEKG2gRtEXTFIWRRrVdG7k5duxF003q2LDXtR04u5H2NmdE7Wp3tReeIUVxJZ0ZXobkzLz9Y+ZwuVpehhTP+c7heX4AQXI8OvP6LPnwzPd+5/tEVUFEROERM10AERHtDIObiChkGNxERCHD4CYiChkGNxFRyDC4iYhChsFNRBQyDG4iopBhcBMRhUzCi4MODAzo6OioF4cmItqXLl68OKeqg+0815PgHh0dxYULF7w4NBHRviQir7T7XA6VEBGFDIObiChkGNxERCHD4CYiChkGNxFRyLQ1q0REXgZQAVAHUFPVCS+LIiKize1kOuD7VXXOs0qIiKgtnszjJtoLSyt1fPlvr2J5te7Zazz0U0M4fTTt2fGJvNBucCuA74iIAvhDVT1/+xNE5ByAcwAwMjKydxVSZH3n0gz+8988BwAQ2fvjqwKXpiv44ic48kfh0m5w36+qJRE5DOC7InJZVX+4/gmtMD8PABMTE9yBmO5YYcpBZyKG4ud/AYn43vfRP/21p/B3L93Y8+MSea2t3wZVLbU+zwJ4BMC7vCyKCACskoPTQ2lPQhsA8rkMZspVXK8se3J8Iq9s+xshIr0iknK/BvABAJbXhVG0NRqKol3GeM678ed8LgOg+QeCKEzauZQ5AuDHIvIMgCcA/JWqftvbsijqXr2xiMpyDeOtcPXC2Wzzj0LRZnBTuGw7xq2qLwF4mw+1EK0ptMJ0LOtdcKe7kjg+0Lv2WkRhwTsnKZCskoOOeAz3HEl5+jr5XAaWXfb0NYj2GoObAsmyHdx7NIWOhLc/ovlsGvatJdxcWPH0dYj2EoObAkdVYdnlteahl8bZoKQQYnBT4EzdXIKztIq8hzNKXO4YOse5KUwY3BQ4VitEvZxR4sr0JDHS34Mix7kpRBjcFDgF20EiJp43Jl35XJpX3BQqDG4KnILt4J4jKXQl4768Xj6Xwas3FuEsrvryekR3isFNgaKqKJbKvgyTuPKtce4iG5QUEgxuCpSSU8WNhRVfGpMu3vpOYcPgpkBxG5N+TAV09fd2IHegGwU2KCkkGNwUKJbtIB4TnBnyd3ODfC699keDKOgY3BQolu3g1OE+3xqTrnw2g6tzC6hU2aCk4GNwU2CoKgp22dOFpTaTH3YblBwuoeBjcFNgzFaWMTe/7Oka3JtxZ5ZwuITCgMFNgVGY8r8x6RpMdeJouovBTaHA4KbAsEoOYvLGBgd+y+fSsDhUQiHA4KbAsGwHdw/2oaej3T2s91Y+l8GL1+exsFwz8vpE7WJwU2D4tZTrZvLZDFSByWledVOwMbgpEK5XljFTrhoN7vFhLvFK4cDgpkBYu2PS0Pg2ABxJd2Ew1cmtzCjwGNwUCG5wjxm84gaafzg4s4SCjsFNgVCwHZwY6EVfp5nGpGs8l8GV2QqWVupG6yDaCoObAqFYMtuYdI3lMmgoMDnD4RIKLgY3GXdjYQX2rSVfl3LdjLsOeJHDJRRgDG4yzsRSrpsZynShv7eDM0so0BjcZJwbkiYWl7qdiCCfy3BmCQUag5uMK5Yc3HWoB5nupOlSADRnljx/rYLqKhuUFEwMbjKuYDuBGCZxjecyqDUUz1+rmC6FaEMMbjLKWVzFazeW1pZVDQL3jwjHuSmoGNxklLtBr5+7um9n+GA3Mt1J3ohDgdV2cItIXESeEpFHvSyIomXtjkmDt7rfrtmgTLNBSYG1kyvuTwGY9KoQiqaC7WD4YDcO9naYLuVN8rkMnpupYKXWMF0K0Vu0FdwiMgzgFwF80dtyKGos2wnU+LYrn81gpd5gg5ICqd2FIX4fwGcApDyshSKmXF3Fy68v4qM/PWy6lLdwx9w/8oW/RVzEk9d4/+lB/OGvTXhybNrftg1uEfkQgFlVvSgi79vieecAnAOAkZGRPSuQ9q9iaww5SFMBXXcd6sHnPzyGmXLVk+NffOUmvn95Fsu1OjoTcU9eg/avdq647wfwYRF5CEAXgLSI/LGq/ur6J6nqeQDnAWBiYkL3vFLad4ql4NzqfjsRwSfeO+rZ8R99toQnrt7AlWvzgfz/T8G27Ri3qj6sqsOqOgrgYwC+f3toE+1GwXYwlOnCQF+n6VJ8N8654nQHOI+bjLECdsekn0b6e5DqSnCuOO3KjoJbVX+gqh/yqhiKjvnlGl6aWwjkjBI/iAjy2QyDm3aFV9xkxOR0GarA+HBwbrzx2/hwBpMzFazWOVecdobBTUYUpoLbmPTLWDaNlVoDV67Nmy6FQobBTUZYJQeHU504nOoyXYoxboPSXa+FqF0MbjLCsp1ALSxlwuih5ubIHOemnWJwk+8WV2p4YXYeYxEP7lhMcDab5pRA2jEGN/lucrqChgZrKVdT8tkMJqfLqLFBSTvA4CbfvbE5cHRnlLjGh9Oorjbw4vUF06VQiDC4yXeW7WCgrwNH09FtTLrceewc56adYHCT7wq2g7FsBuLRqnthcmKwDz0dcY5z044wuMlX1dU6rszOc3y7JR4TnB1Kry24RdQOBjf56vJMBfWGcnx7nXwug2KpjHqDi2pSexjc5Ks3GpO84nblcxksrtRxdY4NSmoPg5t8ZdkODvQkkTvQbbqUwHDffbBBSe1icJOvCq07JtmYfMPJwT50JmJsUFLbGNzkm+VaHc9fq2Asoku5biYRj+HMUJpX3NQ2Bjf55vmZeazWlTNKNjDealA22KCkNjC4yTdWiXdMbiafS2N+uYZXbiyaLoVCgMFNvinYDlJdCYz095guJXDy3IOSdoDBTb4p2g7yvGNyQ/ccSaEjHkORwU1tYHCTL1brDUzOVDA+zPHtjSTjMZweSvGKm9rC4CZfXLk2j5VaA2NZjm9vJp9rbh6sygYlbY3BTb5wp7pxRsnm8tkMytUaXruxZLoUCjgGN/miYDvo60xg9FCv6VICa5wNSmoTg5t8YZUcnM2mEYuxMbmZe472IRkXbh5M22Jwk+dq9QYmp8trmwbQxjoTcdxzJMU7KGlbDG7y3IvXF1BdbWB8mI3J7eSzbFDS9hjc5Dl3zJZX3NvLD2dwc3EV9i02KGlzDG7ynGU76E7GcWKwz3Qpgec2KC27bLgSCjIGN3nOspuNyTgbk9s6fTSFeEw4zk1bYnCTp+oNxaXpMudvt6krGcepw32cWUJbYnCTp67OzWNxpc47JneAd1DSdrYNbhHpEpEnROQZESmKyOf9KIz2B7cxyTVK2jeey2BufgUz5arpUiig2rniXgbwgKq+DcB9AB4Ukfd4WxbtF5ZdRmcihpNsTLbtjT0o2aCkjSW2e4I236/Nt75Ntj74Hm6fqK7W8c0np1BdbXhy/P/3/HWcGUojEeeoXLvODKURE+BrT7yKVz3aWCGb6cIHx4c8OTZ5b9vgBgARiQO4COAkgC+o6uMbPOccgHMAMDIyspc1koe+bc3gdx6xPH2NT77/pKfH3296OhKYuKsf37s8i+9dnvXsdX7y8AMYynR7dnzyTlvBrap1APeJyAEAj4hIXlWt255zHsB5AJiYmOAVeUg8O+WgKxnDTz77c56sIyICpDrb+jGjdf70X7wbCyt1T45dmHLwq196HIUph8EdUjv6jVLVWyLyAwAPAvD2Mo18YdkOzg6lcbC3w3QptE4iHkOm25vhpZ++6yBi0vxv/4Gxo568BnmrnVklg60rbYhIN4CfB3DZ68LIe42Golhy1vY7pGjo7ojj5OE+WCU2P8OqnSvuIQBfbY1zxwB8XVUf9bYs8sPV1xewsFJncEdQPpfBj67MmS6DdqmdWSXPAni7D7WQzywu/hRZ+WwG33rSxmy5isPpLtPl0A5xjlaEWbaDjkQMp45wjnXUuDdEcbedcGJwR5hll3FmKI0k51hHztmhNER4k09Y8Tc2olQVVslBnmuIRFJvZwInBnp5xR1SDO6IevXGIirVGlfti7DxXAZFrkIYSgzuiFrblYbBHVn5XAbTThVz88umS6EdYnBHlGWXkYwL7jmSMl0KGZJf222HV91hw+COKMt2cO/RFDoS/BGIqrNZdxVCBnfY8Lc2gtzGJMe3oy3dlcTxgV7OLAkhBncETd1cwq3FVYzxxpvIG8umObMkhBjcEeTOJOAVN43nMrBvLeHmworpUmgHGNwRVLAdJGKCe4+yMRl1aw1KTgsMFQZ3BFl2GaeOpNCVjJsuhQxz16nhOHe4MLgjRlVh2bxjkpoyPUkc6+/mzJKQYXBHzLRTxesLK9x1ndaM5zJsUIYMgzti3Csrzigh11g2g1dvLMJZXDVdCrWJwR0xlu0gJs3V4YiAN2YXcd2S8GBwR4xVKuPk4T50d7AxSU2cWRI+DO6IKdjcY5LerL+3A7kD3ShwZkloMLgjZLZcxfXKMrcqo7cYy6ZRZIMyNBjcEeLOHOCMErrdeC6Dl+YWUKmyQRkGDO4IsewyhI1J2kC+9cf8UonDJWHA4I6Qgu3gxEAvejsTpkuhgHGHzzifOxwY3BFS5FKutInBVCeOprtQ5BV3KDC4I2JufhnTTpUzSmhT+RyXeA0LBndEWNxjkraRz2Xw4vV5LK7UTJdC22BwR4Qb3Ge5uBRtIp/NQJUNyjBgcEeEZZdxfKAX6a6k6VIooNxpolwpMPgY3BFRsB2M8WqbtnA41YmBvk7eQRkCDO4IuLmwAvvWEmeU0JZEBOO5NBebCgEGdwS4iwexMUnbyecyuDI7j+pq3XQptIVtg1tEjonIYyIyKSJFEfmUH4XR3nGneHGNEtpOPpdBvaG4NM3hkiBr54q7BuC3VPUMgPcA+E0ROettWbSXinYZx/q7kelhY5K25r4r44JTwbZtcKvqtKo+2fq6AmASQM7rwmjvFGzeMUntyWa60N/bwRtxAm5Hi1aIyCiAtwN43Itiour/PFPCv/+rS2ioN8e/XlnGL7/zmDcHp31FRDCWTeORp2w89tx1T16jOxnHV//5u3B8oNeT40dB28EtIn0Avgng06r6lgEwETkH4BwAjIyM7FmBUfB/rWms1Bp4MD/kyfE74oJ/+o5hT45N+8+//rlTGD7Y48mxV+sN/MXFKfzoynUG9x1oK7hFJIlmaP+Jqn5ro+eo6nkA5wFgYmLCo2vH/algO3jv3QP43X8ybroUIrxztB/vHO335Niqiscuz6IwxaGYO9HOrBIB8CUAk6r6e96XFC3O4ipeu7GEsRxvjqH9T0QwlsvA4m31d6SdWSX3A/g1AA+IyNOtj4c8risy3DnWbB5SVIzn0rhyrcK54ndg26ESVf0xAPGhlkiyOMeaIiafzaDWUDw3U8Hbjh0wXU4o8c5Jwwq2g9yBbhzs7TBdCpEv3LninHK4ewxuw4qlMvIc36YIGT7YjQM9Sa6JcgcY3AaVq6u4OrfA8W2KFBFBPpvhFfcdYHAb5C5Yz8WfKGryuQyem6lgpdYwXUooMbgN4nZiFFX5XBqrdcXz1yqmSwklBrdBBdvBUKYLA32dpksh8tU4G5R3hMFtkGU7GOM0QIqgkf4epLoS3CZtlxjchswv1/ASG5MUUW6DksG9OwxuQyany1AFpwJSZOVzaUzOVLBaZ4NypxjchriL7PCKm6Iqn8tgpdbAlWvzpksJHQa3IVbJwWCqE4fTXaZLITLCvWixeCPOjjG4DbG4Kw1F3OihXvR1skG5GwxuA5ZW6nhhdh75LMe3KbpiMcHZbJrBvQsMbgMuTZfRUN54Q5TPZnBpuowaG5Q7wuA2gHdMEjWND6dRXW3gxesLpksJFQa3AZbt4FBvB4YybExStLnr0HO4ZGcY3AYUbAdjuQyau8IRRdeJwT50J+O89X2HGNw+q67WcWV2HuO88YYI8VaDkmtz7wyD22eXZyqoN5RblRG1jOcyKJbKqDfUdCmhweD2GRuTRG+Wz2WwuFLH1Tk2KNvF4PaZZTvIdCcxfLDbdClEgeCu18MGZfsY3D6zSs07JtmYJGo6OdiHzkSMwb0DDG4fLdfqeG6mwmESonUS8RjODKU5s2QHGNw+en5mHqt15VKuRLdxG5QNNijbwuD2kbsKGheXInqzfC6N+eUaXrmxaLqUUGBw+6hgO0h1JTDS32O6FKJAyXMPyh1hcPuoaDvIZ9mYJLrdqcMpdMRjKDK428Lg9slqvYHJmQrHt4k20JGI4fRQilfcbWJw++TKtXms1BqcUUK0iXyuuXmwKhuU22Fw+4R3TBJtLZ/NoFyt4bUbS6ZLCTwGt0+skoPejjiOH+o1XQpRIHEPyvZtG9wi8mURmRURy4+C9quC7WAsm0EsxsYk0UbuOdqHZFw4zt2Gdq64vwLgQY/r2Ndq9QYmp8scJiHaQmcijnuOpHjrexsS2z1BVX8oIqPel2LW1bkFPPnKTU+O/frCMqqrDc4oIdpGPpvBt4sz+ObFKU+OH4sBD9x7BJmepCfH98u2wd0uETkH4BwAjIyM7NVhffPb33gGFz0KbgCICTBxV79nxyfaD959oh9/fuE1/NY3nvHsNf7V++7GZx487dnx/bBnwa2q5wGcB4CJiYlQzeep1RuwbAcff9cx/MY/POnJa/R2xnGor9OTYxPtF7/09hzec+IQanVvIuTcH13As1PhH4rZs+AOsxeuz2O51sC7jx/CyCHejk5kiogge8C7tervO3YA3y7OQFVDfQczpwMCKExxjjVRFORzGdxaXMXUzXDPFW9nOuCfAfgJgHtFZEpEft37svxVLJXR0xHH8QHOsSbaz9yLs7BvTtzOrJKP+1GISc051mnEOceaaF87fTSFeKw5V/zB/JDpcnYt8kMl9YbiUqmMMe66TrTvdSXjOHW4D5ZdNl3KHYl8cL90fR5Lq3VubkAUEeP7YDGryAe3uy4CG5NE0ZDPZfD6wgpmylXTpexa5IO7MFVGVzKGuwfZmCSKgrXddkI8nzvywW2VHJwdSiMRj/ypIIqEs0NpxASwSuEd5450WjVajUkOkxBFR3dHHCcP94V6MatIB/fLry9gfrnG4CaKGHe3nbCKdHC76/7mORWQKFLy2QxmK8uYDWmDMtLBXSyV0ZGI4dSRPtOlEJGPxofDvdtOpIO7MOXgzNEUkmxMEkXK2aE0RJqzysIosomlqrBKDse3iSKotzOBEwO9vOIOm1dvLKJSZWOSKKrC3KCMbHC7axXwVneiaBrPZTDtVDE3v2y6lB2LbHAXbAfJuLAxSRRR7sJyYbzqjmxwW7aDe4+m0JmImy6FiAwYa23ezeAOibXGJOdvE0VWuiuJ0UM9oVziNZLBPXVzCbcWV9mYJIq4fC6zdiNemEQyuItcypWI0MwA+9YSbi6smC5lRyIZ3AXbQTwmOH00ZboUIjLInVUWtvnckQxuyy7j1OE+dCXZmCSKsrGs26AM1zh35IJbVWHZDudvExEO9HTgWH936GaWRC64Z8pVvL6wwvFtIgLQXCmQQyUB525XxOAmIqCZBa+8vghnadV0KW2LXHBbpTJi0lwdjIjIHTYthuiqO3rBbTs4ebgP3R1sTBLRG+++wzTOHcng5jAJEbn6ezuQO9AdqpklkQru2XIVs5Vl3upORG8ylk3zijuo3M6xu20RERHQHOd+aW4BlWo4GpSRCu7CVBnCxiQR3cYdPr1UCsdwSaSC2yo5ODHQi97OhOlSiChA1hqU+ym4ReRBEXlORF4Qkc96XZRX2Jgkoo0MpjpxJN0ZmnHubYNbROIAvgDggwDOAvi4iJz1urC9Nje/jGmnylvdiWhD4yHag7KdK+53AXhBVV9S1RUAXwPwEW/L2nvuf5Axzighog2MZTN48fo8FldqpkvZVjuDvTkAr637fgrAu70o5h/99x+julr34tBrt7O62xUREa03nsugocAH/9uP0BHfXfvvYE8Hvv4vf2aPK3urdoJbNnhM3/IkkXMAzgHAyMjIroq5e7AXK/XGrv5tO04fTSPdlfTs+EQUXu89eQj/bGIY88u7v+L2K19E9S0Z/OYniPwMgM+p6i+0vn8YAFT1dzf7NxMTE3rhwoW9rJOIaF8TkYuqOtHOc9t5P/D3AE6JyHER6QDwMQB/eScFEhHR7m07VKKqNRH5JIC/ARAH8GVVLXpeGRERbaitO1FU9a8B/LXHtRARURsideckEdF+wOAmIgoZBjcRUcgwuImIQobBTUQUMtvegLOrg4pcB/DKLv/5AIC5PSxnL7G23WFtu8Padiestd2lqoPtHMST4L4TInKh3buH/Mbadoe17Q5r250o1MahEiKikGFwExGFTBCD+7zpArbA2naHte0Oa9udfV9b4Ma4iYhoa0G84iYioi0EJriDvCGxiLwsIgUReVpEjC80LiJfFpFZEbHWPdYvIt8VkSutzwcDVNvnRMRunb+nReQhA3UdE5HHRGRSRIoi8qnW48bP2xa1BeG8dYnIEyLyTKu2z7cePy4ij7fO25+3lnwOSm1fEZGr687bfX7Xtq7GuIg8JSKPtr7fm/OmqsY/0Fwu9kUAJwB0AHgGwFnTda2r72UAA6brWFfPzwJ4BwBr3WP/CcBnW19/FsB/DFBtnwPw24bP2RCAd7S+TgF4Hs3Nr42fty1qC8J5EwB9ra+TAB4H8B4AXwfwsdbj/wPAbwSotq8A+KjJ87auxn8D4E8BPNr6fk/OW1CuuPfFhsR+UdUfArhx28MfAfDV1tdfBfCPfS2qZZPajFPVaVV9svV1BcAkmvupGj9vW9RmnDbNt75Ntj4UwAMA/qL1uKnztlltgSAiwwB+EcAXW98L9ui8BSW4N9qQOBA/uC0K4DsicrG1t2YQHVHVaaAZBAAOG67ndp8UkWdbQylGhnFcIjIK4O1oXqEF6rzdVhsQgPPWerv/NIBZAN9F893xLVV1N2c09vt6e22q6p63/9A6b/9VRDpN1Abg9wF8BoC7ke4h7NF5C0pwt7UhsUH3q+o7AHwQwG+KyM+aLihk/gDA3QDuAzAN4L+YKkRE+gB8E8CnVbVsqo6NbFBbIM6bqtZV9T4Aw2i+Oz6z0dP8rar1orfVJiJ5AA8DOA3gnQD6Afxbv+sSkQ8BmFXVi+sf3uCpuzpvQQnuKQDH1n0/DKBkqJa3UNVS6/MsgEfQ/OENmmsiMgQArc+zhutZo6rXWr9gDQD/E4bOn4gk0QzGP1HVb7UeDsR526i2oJw3l6reAvADNMeRD4iIu4OW8d/XdbU92Bp6UlVdBvC/YOa83Q/gwyLyMppDvw+geQW+J+ctKMEd2A2JRaRXRFLu1wA+AMDa+l8Z8ZcAPtH6+hMA/rfBWt7EDcaWX4KB89caX/wSgElV/b11/5Px87ZZbQE5b4MicqD1dTeAn0dzDP4xAB9tPc3Ueduotsvr/hALmmPIvp83VX1YVYdVdRTNPPu+qv4K9uq8me66ruu+PoRmN/1FAL9jup51dZ1Ac5bLMwCKQagNwJ+h+dZ5Fc13K7+O5vjZ9wBcaX3uD1BtfwSgAOBZNINyyEBd/wDNt6XPAni69fFQEM7bFrUF4bz9FICnWjVYAP5d6/ETAJ4A8AKAbwDoDFBt32+dNwvAH6M188TUB4D34Y1ZJXty3njnJBFRyARlqISIiNrE4CYiChkGNxFRyDC4iYhChsFNRBQyDG4iopBhcBMRhQyDm4goZP4/wP15vn6XgdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11adfe208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_plot = matplotlib.pyplot.plot(numpy.min(data,axis=0))\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aec5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
    "\n",
    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "\n",
    "axes1 = fig.add_subplot(1, 3, 1)\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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SC_Python_Programming.ipynb  inflammation-09.csv\r\n",
      "inflammation-01.csv          inflammation-10.csv\r\n",
      "inflammation-02.csv          inflammation-11.csv\r\n",
      "inflammation-03.csv          inflammation-12.csv\r\n",
      "inflammation-04.csv          small-01.csv\r\n",
      "inflammation-05.csv          small-02.csv\r\n",
      "inflammation-06.csv          small-03.csv\r\n",
      "inflammation-07.csv          testnotebook.ipynb\r\n",
      "inflammation-08.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "element = \"oxygen\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'oxyg'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getnewargs__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'capitalize',\n",
       " 'casefold',\n",
       " 'center',\n",
       " 'count',\n",
       " 'encode',\n",
       " 'endswith',\n",
       " 'expandtabs',\n",
       " 'find',\n",
       " 'format',\n",
       " 'format_map',\n",
       " 'index',\n",
       " 'isalnum',\n",
       " 'isalpha',\n",
       " 'isdecimal',\n",
       " 'isdigit',\n",
       " 'isidentifier',\n",
       " 'islower',\n",
       " 'isnumeric',\n",
       " 'isprintable',\n",
       " 'isspace',\n",
       " 'istitle',\n",
       " 'isupper',\n",
       " 'join',\n",
       " 'ljust',\n",
       " 'lower',\n",
       " 'lstrip',\n",
       " 'maketrans',\n",
       " 'partition',\n",
       " 'replace',\n",
       " 'rfind',\n",
       " 'rindex',\n",
       " 'rjust',\n",
       " 'rpartition',\n",
       " 'rsplit',\n",
       " 'rstrip',\n",
       " 'split',\n",
       " 'splitlines',\n",
       " 'startswith',\n",
       " 'strip',\n",
       " 'swapcase',\n",
       " 'title',\n",
       " 'translate',\n",
       " 'upper',\n",
       " 'zfill']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Oxygen'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OXYGEN'"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element.swapcase()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1=\"hello\"\n",
    "s2=\"world\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'helloworld'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1+s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3=s1+s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "helloworld\n"
     ]
    }
   ],
   "source": [
    "print(s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'helloworld'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_string=\"3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'33'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*number_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number=int(number_string)\n",
    "2*number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=str(2*number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number a is equal to 2\n"
     ]
    }
   ],
   "source": [
    "a=2\n",
    "b=\"Number a is equal to \" + str(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oxygen\n"
     ]
    }
   ],
   "source": [
    "print(element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n\n"
     ]
    }
   ],
   "source": [
    "print(element[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yg\n"
     ]
    }
   ],
   "source": [
    "print(element[2:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xyge\n"
     ]
    }
   ],
   "source": [
    "print(element[1:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hopper Grace\n"
     ]
    }
   ],
   "source": [
    "first, second = 'Grace', 'Hopper'\n",
    "third, fourth = second, first\n",
    "print(third, fourth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"lead\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lead\n"
     ]
    }
   ],
   "source": [
    "print(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "word=\"tin\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t\n",
      "i\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "print(word[0])\n",
    "print(word[1])\n",
    "print(word[2])\n",
    "#print(word[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "word=\"lead\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l\n",
      "e\n",
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for char in word:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "2\n",
      "7\n",
      "9\n",
      "8\n",
      "3\n",
      "4\n",
      "6\n",
      "2\n",
      "9\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "a=52798346298\n",
    "for char in str(a):\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "word=\"lead\"\n",
    "print(len(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l\n",
      "e\n",
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(word)):\n",
    "    print(word[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for i in range(2,len(word)):\n",
    "    print(word[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "---------\n",
      "3\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(word)):\n",
    "    print(i)\n",
    "print(\"---------\")\n",
    "print(i)\n",
    "print(word[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "mystring=\"Newton\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N\n",
      "e\n",
      "w\n",
      "t\n",
      "o\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(mystring)):\n",
    "    print(mystring[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index is 5\n",
      "n\n",
      "index is 4\n",
      "o\n",
      "index is 3\n",
      "t\n",
      "index is 2\n",
      "w\n",
      "index is 1\n",
      "e\n",
      "index is 0\n",
      "N\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(mystring)):\n",
    "    print(\"index is\",len(mystring)-i-1)\n",
    "    print(mystring[len(mystring)-i-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n\n",
      "no\n",
      "not\n",
      "notw\n",
      "notwe\n",
      "notweN\n"
     ]
    }
   ],
   "source": [
    "rev_mystring=\"\"\n",
    "for i in range(len(mystring)):\n",
    "    rev_mystring=rev_mystring+mystring[len(mystring)-i-1]\n",
    "    print(rev_mystring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n\n",
      "no\n",
      "not\n",
      "notw\n",
      "notwe\n",
      "notweN\n"
     ]
    }
   ],
   "source": [
    "rev_mystring=\"\"\n",
    "for i in range(len(mystring)-1,-1,-1):\n",
    "    rev_mystring=rev_mystring+mystring[i]\n",
    "    print(rev_mystring)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "odds = [1,3,5,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 5, 7]\n"
     ]
    }
   ],
   "source": [
    "print(odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first and last element of list 1 7\n"
     ]
    }
   ],
   "source": [
    "print(\"first and last element of list\", odds[0],odds[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first and second element of list [1, 3]\n"
     ]
    }
   ],
   "source": [
    "print(\"first and second element of list\", odds[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "for number in odds:\n",
    "    print(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "names=[\"Curie\",\"Darwin\",\"Turing\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Einstein', 'Darwin', 'Turing']\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(names)\n",
    "print(type(names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curie\n"
     ]
    }
   ],
   "source": [
    "print(names[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "names[0]=\"Einstein\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Einstein', 'Darwin', 'Turing']\n"
     ]
    }
   ],
   "source": [
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E\n"
     ]
    }
   ],
   "source": [
    "print(names[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "mystring=\"Test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T\n"
     ]
    }
   ],
   "source": [
    "print(mystring[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mystring[0]=\"B\"\n",
    "# this will not work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytuple=(1,3,5,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 5, 7)\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "print(mytuple)\n",
    "print(type(mytuple))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mytuple[0]=5\n",
    "# this will not work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist=mylist+[\"TEST1\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TEST1', 'TEST1', 'TEST1']\n"
     ]
    }
   ],
   "source": [
    "print(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TEST1', 'TEST1', 'TEST1', 'TEST1', 'TEST1', 'TEST1', 'TEST1', 'TEST1', 'TEST1']\n"
     ]
    }
   ],
   "source": [
    "print(3*mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[  [\"pepper\",\"zucchini\",\"onion\"] , [\"cabbage\",\"lettuce\",\"garlic\"] ,[\"apple\",\"pear\",\"banana\"]                                     ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pepper', 'zucchini', 'onion']\n"
     ]
    }
   ],
   "source": [
    "print(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cabbage', 'lettuce', 'garlic']\n"
     ]
    }
   ],
   "source": [
    "print(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pepper\n"
     ]
    }
   ],
   "source": [
    "print(x[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zucchini\n"
     ]
    }
   ],
   "source": [
    "print(x[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'append',\n",
       " 'clear',\n",
       " 'copy',\n",
       " 'count',\n",
       " 'extend',\n",
       " 'index',\n",
       " 'insert',\n",
       " 'pop',\n",
       " 'remove',\n",
       " 'reverse',\n",
       " 'sort']"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(x.reverse())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['apple', 'pear', 'banana'], ['cabbage', 'lettuce', 'garlic'], ['pepper', 'zucchini', 'onion']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.append([\"pineapple\",\"mango\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function insert:\n",
      "\n",
      "insert(...) method of builtins.list instance\n",
      "    L.insert(index, object) -- insert object before index\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(x.insert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.insert(0,[\"blueberry\",\"blackberry\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['blueberry', 'blackberry'], ['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "del x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x\n",
    "# y is not really a copy of x, it's just a different name for x (or contents of it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[0]=\"DIFFERENT\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DIFFERENT', ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DIFFERENT', ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=x.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DIFFERENT', ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "z[0]=\"something else\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['something else', ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DIFFERENT', ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana'], ['pineapple', 'mango']]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['h', 'e', 'l', 'l', 'o']\n"
     ]
    }
   ],
   "source": [
    "#Use a for-loop to convert the string “hello” into a list of letters:\n",
    "#[\"h\", \"e\", \"l\", \"l\", \"o\"]\n",
    "#Hint: You can create an empty list like this:\n",
    "#my_list = []\n",
    "\n",
    "hstring=\"hello\"\n",
    "\n",
    "my_list=[]\n",
    "\n",
    "for char in hstring:\n",
    "    my_list.append(char)\n",
    "print(my_list)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Data from Multiple Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['inflammation-01.csv', 'inflammation-02.csv', 'inflammation-03.csv', 'inflammation-04.csv', 'inflammation-05.csv', 'inflammation-06.csv', 'inflammation-07.csv', 'inflammation-08.csv', 'inflammation-09.csv', 'inflammation-10.csv', 'inflammation-11.csv', 'inflammation-12.csv']\n"
     ]
    }
   ],
   "source": [
    "print(glob.glob(\"inflammation*.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SC_Python_Programming.ipynb  inflammation-09.csv\r\n",
      "inflammation-01.csv          inflammation-10.csv\r\n",
      "inflammation-02.csv          inflammation-11.csv\r\n",
      "inflammation-03.csv          inflammation-12.csv\r\n",
      "inflammation-04.csv          small-01.csv\r\n",
      "inflammation-05.csv          small-02.csv\r\n",
      "inflammation-06.csv          small-03.csv\r\n",
      "inflammation-07.csv          testnotebook.ipynb\r\n",
      "inflammation-08.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames=glob.glob(\"inflammation*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(type(filenames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7d5828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b54a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D3U5VFwILAUpLS+2gSC9trPV2aLr366WMOMCBu54kJsCowQMO+P2JBVm8fd1cmtt7l6Z3zp/eta5RIRa2CbKqviUiJftdfQZwnO/rB4E3sAlyWDW3d3LZA0v5YHM9t311CmdPL4zo439pTB7ZnmT+uWKLTZCN6UFHVze3LPKGglwQ5lCQYA1MS+bqE0bz02dX8/raHTZ23WUOcLqIzAfSgIEi8pCqXuRwXTHFf5DumNGDD3jgrq96M+H2KxmcYQl8IRbp97yHqupWAN/nyDT5jFONbZ1cev9Slmyu5zfnTY345BggJSmB+ZPyefmT7TS19W6vljHx4NGlVWyMYChIsCw8xJ1U9ceqWqiqJcD5wGs2OQ698rom8rMO3I0i0opzM6w9XIhFz1/j/YjIFSJSJiJltbW1TpfjCp1d3azbvpd/rqjhlkVrOOuP/2ZZ5S5+e/40zpha4FhdZ04toKWjixdWbXOsBmOikT8UZNbIyIWCBCswPOSxMgsPMSZQZV1zj2l4TinJ9VCzq4X2TnsxGyqR7mKxXUTyVXWriOQDOw50Q9sf1TvvbtzJZQ8spbXDOzhSkhIYOzSTuy88kpOOGOZobaXFgxiVl8GPn/qYNVv38L3jR5PlcX6fpfm8Axys/V/gW4D/VeqNqrrImQpjjz8U5IEFkQ0FCdZJE4YysySHO19exxlTCxiQao2P3ERV38C7ldGEWHldMyeMi54XtUU5HroVana3MLKHlD7Te5FeQX4WuMT39SXAMxF+/Ji1aOVWEkX4zXlTWfz9Y/nkZyfzr+8e4/jkGCAhQXj0itl8dXohf/n3Zr502+v85d+brc9j9HkAmNfD9Xeq6lTfh02OQ8TJUJBgiQg3LvCHh2x0uhxjokJTWyc7G9soHhxFK8i+SXGFbbMImbBNkEXkEeA9YKyIVIvI5cAvgRNFZD1wou+yCYF3N9Yxc2QOZ04rYMzQzIh2qgjGkMw0fnHWZJ7/3n8wcXgWP/vXJ5zxh3/bJDmKqOpbQL3TdcQLp0NBgjV1RDanT7HwEGP8/Af0inOiZ6W22Hewz1L4QidssyhVvUBV81U12Xdg4D5VrVPVE1R1tO+zPRmHwLaGVjbVNnH0YYOdLuWQxucP5G+Xz+T/zpzIJ1v38P6mOqdLMod2lYh8LCL3i0iPiS92ZqB3/KEgl80Z6WgoSLCuPXks3d1w20vrnC7FGMf5V2mjaQ9yXmYq6cmJdlAvhKJrmdH0yXubdgJw1GG5DlcSHBHhnOmFZKQksmilHdyLcncDhwFTga3A7T3dSFUXqmqpqpbm5eVFsj7X8YeC5GSk8J25zoaCBMsfHvLUimpW1TQ4XY4xjqrwtVOLpgmyiFCc66HSVpBDxibIMeDdDXVkpSczIf/A6TzRJi05kePHD2Xx6m3WQiqKqep2Ve1S1W7gz8BMp2tyO38oyDVfHs3AKAgFCdZ35h5Odnoytyxag6qdmzbxq6KuidyMlKgI9QlUnOuxFeQQsgmyy6kq726s46hRuSQkRN8p+IOZP3EYdU3tLCm3nTbRytdtxu8rQO/zT80+nf5QkMHREwoSrKx0b3jIuxvreGOtbaMx8auirpmiKFo99ivOzaCqvoWubnsBGwo2QXa5qvoWana3cPTh7theEei4sUNIT07kBdtmERUOcLD21yKyUkQ+BuYC33e0SJd7xBcKcsMp46IqFCRYX5tVzMjBGdy8aI2982PiVkVdMyW50XNAz68410N7Vzfb9rQ6XUpMcN9faPM572707j8+2iX7jwOlpyQyd1weL6zaZq94o8ABDtZerKqTVHWyqp7uT8I0vecPBZk5MocTJ7gzujklKYHr51l4iIlfbZ1dbGloiar9x37+Sbu1egsNmyC73Hub6sjLTOWwvAFOl9Inp0zMZ2djG2W2zcLEOH8oyE8WjI/KUJBgnXzEUGaUDOLOl9fRaPHxJs5U1begGl0H9PyKrNVbSNkE2cUC9x+79Ql37rghpCYlWAy1iWn+UJAzpw5ncmG20+X0i4hw04IJ7Gxs509vWHiIiS+V9f4Wb9G3xWJ4djrJiWIT5BCxCbKLbaxtpHZvmyu3V/gNSE3iS2PyeGHVVrptm4WJUW4JBQmWPzzk3ncsPMTEl/Kd/pCQ6FtBTkwQRgzy2BaLELEJsou9u9EbsuGGgJCDWTA5n+172lhRtcvpUowJucBQkMJB0fek2lcWHmLiUWV9M5mpSeRkpDhdSo+Kcz22ghwiNkF2sXc31FGQnc6InOhP4jqY48cNISUxwUJDTMzxh4IM8iS7JhQkWBYeYuJReV0TRbmeqN3WWJybQUVdk/UqDwGbILtUd7fy3qY6jj7MvfuP/TLTkjl2zGBeWGnbLExs+SwUZIyrQkGC9Z25h5Nl4SEmjkRrize/4lwPTe1d7Gxsd7oU17MJskt9snUPDS0drux/3JNTJuazpaGVFVW7nS7FmJAIDAX52ix3hYIEy8JDTDzp7Oqmeld0hoT4+btr+A8Tmr6zCbJLvefbf3zUKHfvP/b78oShDExL4tp/fMSuJnvla9zvUZeHggTrQl94yC0WHmJi3NaGVjq6lJKoniB7V7f9hwlN38XuX+0Y99b6WkblZTAsK83pUkIiKz2ZP3+9lOr6Fr751zJaO7qcLsmYPtvb2sGdLg8FCZY/PGS9hYeYGOc//FaUE71bLAoHpSMCFfU2Qe4vmyC70LKKet5ev5OvTC1wupSQmjUqlzvPm8ryyl1c/egKS9czruUPBblpvrtDQYJl4SEmHpT72qeVDI7eFeTUpESGZ6Vbq7cQsAmyy6gqNz+/hiGZqVz+HyOdLifkFkzO578XTOCl1dv532dX28Ef4zr+UJAzpg5nygh3h4IEKzA85J43LTzExKbK+mZSkxIYmhnd79yWDLZWb6HgyARZRL4vIqtFZJWIPCIi0f3bFkVeWr2N5ZW7+cGJY/CkJDldTlhcdsxIrjh2FH97v4I/WlKXcZnbFntDQa6NkVCQYPnDQ/78toWHmNhUvrOJohwPCQnR/a5QUU6GrSCHQMQnyCJSAHwPKFXViUAicH6k64hmm3c28djSyi+0POvo6uZXL65l9JABfHV6oUPVRcYN88Zx5tTh3PrSWv74xganyzEmKKtqGng6BkNBguUPD7l9sYWHmNhTWd8clRHT+yvJ9bCruYOGlg6nS3E1p7ZYJAHpIpIEeIAtDtURlX7/6nquf3Il//nQMpoC9vM9uqSSzTu9p+KTYvhUPEBCgnDbOVM4Y+pwfv3iWn7/6nqnSzLmoPzbn7LTYy8UJFj+8JAnl1ezeouFh5jYoaqU1zXta6MWzfa1erNtFv0S8VmWqtYAtwGVwFagQVUX7387EblCRMpEpKy2Nr76a66saWDowFReXbOdc/70HlsbWmhs6+Q3r6xn1sgcjh83xOkSIyIpMYE7zp3KWdMKuP3lddz58jrbk2yi1muf7uC9TXUxGwoSLH94yM3PW3iIiR079rbR2tEd1S3e/Pa1erNtFv3ixBaLQcAZwEhgOJAhIhftfztVXaiqpapampeXF+kyHdPc3snG2kbOm1HEfZfOoLK+mTP+8G9ufGoldU3t/DhOTsX7JSYIt54zhXOmF/LbV9fbW7cmKvlDQUbGcChIsCw8xFkikiYiS0TkI99Zn585XVMs2NfizQVbLIpy/GEhtoLcH0FPkEWkWES+7Ps6XUQy+/iYXwY2q2qtqnYATwFH9/FnxZw1W/fQrTBx+EDmjh3Ck/91NClJCTz70RZOnZzP1Dg5FR8oMUH41dmTOa90BH94fYO9dWuiTryEggTrwlnFlOR6LDzEGW3A8ao6BZgKzBOR2Q7X5Hr7Wry5YAU5IzWJvMxUynfaCnJ/BNUGQUS+BVwB5ACHAYXAn4AT+vCYlcBsEfEALb6fUdaHnxOTVtXsAWBSYRYAY4dl8s8r53DfO5u59OgSBytzVkKC8O3jDuOxsipWb9nDEcOznC7JGODzoSAnxXgoSLBSkhK44ZTxfPuhZTxWVsWFs4qdLiluqHdfS6PvYrLvw/a6BHh8aRWLVm3t1X0q65pJTBCGZ6eHqarQKs7x8OqnO7j0L0t6db/D8wbwk1MnhKkqdwm2T9iVwEzgAwBVXS8ifdoIq6ofiMg/gOVAJ7ACWNiXnxWLVtY0MHhACsMGftb5bvCAVK6fN87BqqJDUY6H1KQE1m3b63QpxuzjDwW5P862Px1KYHjIGVMLGJAam20po5GIJALLgMOBu1T1gx5ucwXehS+KiuJrW9C972xix942inOCXw3OTEviollFrnmH6JzSQv7+QSW7mtqDvs/OxnbeWFvLd48fTZYnfs9R+AX7F6tNVdv9f/x93Sf6/IpUVX8K/LSv949lq2oaOGJ4lj3R9iAxQTh8yADW7Wg89I2NiYB4DAUJlohw4/zxfOWP73LPmxv54Unx1RfaSaraBUwVkWzgaRGZqKqr9rvNQnyLU6WlpXGzwtzdrVTWN3Px7GJuWhC7K6XnzSjivBm9e+Hz0upt/OffllFR38Rkj/09C/al0JsiciPe1mwnAk8A/wpfWfGptaOL9TsamVRg2wcOZOzQTFtBNlHDHwryI5v89Wha0SBOs/AQx6jqbuANYJ7DpUQNfzcKNxy2izR/e7hyaw8HBD9BvgGoBVYC/wksAn4SrqLi1Zqte+jqViYWDHS6lKg1Zlgm2/a0WgN04zh/KMg35pQwohdv1cab63zhIbe9ZB1oIkFE8nwrx4hIOt6D8Z86W1X0qHDRYbtI29f9wtrDAUFOkFW1W1X/rKrnqOpXfV/HzVsykbKqxtudYaKtIB/Q2KHe5inrt9sqsnFOYCjIlXMPd7qcqDYix8Olc0p4akX1vr9xJqzygddF5GNgKfCyqj7ncE1Rw9+urTjHVpD350lJYkhmqq0g+wQ1QRaRlSLy8X4fb4vInSKSG+4i48Wqmj0M8iRT4JJTsk4YPXQAAGttgmwcZKEgvXOlLzzklkUWHhJuqvqxqk5T1cmqOlFV/5/TNUWTivomkhKE4dlph75xHCrJzbAEPp9gt1i8ADwPXOj7+BfwFrANeCAslcWhlTUNTCywA3oHU5CdTkZKou1DNo6xUJDes/AQEy3K65opHJROkku6UURaUa7HEvh8gv0NmaOqP1bVlb6Pm4DjVPVXQEn4yosfbZ1drNu+17ZXHIKIMGZYpq0gH4SIfGFpREQGO1FLLLJQkL7xh4fcbOEhxkEVdU37opjNF5Xketixt43m9k6nS3FcsH/dB4jILP8FEZkJDPBdtP+KIbB22146u9U6WARh7NBM1m+3Vm8HsTQwOUtEzgbedbCemLG3tYPfvLKOmSUWCtJb3vCQcWzY0cjjZdVOl2PikKpSUde8r1uD+SJ/dw+LqQ5+gvxN4F4R2Swi5cC9wLdEJAP4RbiKiycr/Qf0LCHukEYPzaSuqZ2djW1OlxKtvgb8XkRuFZGHgW8BxztcU0y4581N7Gxs56YFFgrSFycfMYzS4kHc8fI6GttsbcVE1q7mDva2dtoK8kH4u3uU77QJcrBdLJaq6iS8ue5TfZv/l6hqk6o+Ht4S48Oqmj0MTEtiRI4d0DsUfycL24fcM1VdCdwMfBuYC1ylqrZk109bdrfw57c3WShIP4gINy0Yz87GNu55c6PT5UQ9ETlLRNaLSIOI7BGRvSKyx+m63Mrf4q03CXrxxt/do7Le9iEHnf0pIguAI4A0/8qJnY4NnVV2QC9oY4Z91sni6MNta+3+ROQ+4DBgMjAG+JeI/EFV73K2MnezUJDQCAwP+dqsIvKzbFHgIH4NnKaqa5wuJBb4W7yVDLYJ8oFkeZLJ9iRbqzeCb/P2J+A84LuAAOcAxWGsK660d3azdtte238cpLwBqWR7klln+5APZBUwV1U3q+pLwGzgSIdrcjULBQktf3jI7YstPOQQttvkOHQq6poRgcJBNoYPpthavQHB70E+WlW/DuxS1Z8BRwEjwldWfFm3fS/tXd3WwSJIIsKYoZmss04WPVLVOwODfFS1QVUvP9T9ROR+EdkhIqsCrssRkZd9b/O+LCKDwlV3tAoMBfnOcRYKEgr+8JAnl1ezeouFhxxEmYg8JiIX+LZbnCUiZzldlFtV1DWRPzCNtORt9MDyAAAgAElEQVREp0uJasU51uoNgp8gt/o+N4vIcKADGBmekuKPJej13tihmazbttdCB3ogIqNF5B8i8omIbPJ/BHHXB4B5+113A/Cqqo4GXvVdjiuBoSBZ6RYKEipXHmfhIUEYCDQDJwGn+T5OdbQiF6uob7YDekEoyfWwZXcL7Z3x3Y4x2Anyv3zZ7rcCy4Fy4JFwFRVvVm1pIDM1yQ4O9MKYYZnsbetk257WQ984/vwFuBtvC8a5wF+Bvx3qTqr6FlC/39VnAA/6vn4QODN0ZUa/zq5ufvHCpxYKEgZZHm94yL831PHGOgsP6YmqfqOHj8ucrsutvD2Q7Xn2UIpyM+hWqN4V39ssDjlBFpEEvCtIu1X1Sbx7j8ep6v+Evbo4sbJmD0cUDCQhwQ7oBWvMEN9BPetk0ZN0VX0VEFWtUNX/pe9t3oaq6lYA3+chPd1IRK4QkTIRKautjZ3JzqNLq9iwo5Hr51koSDj4w0Nued7CQwKJyHW+z78Xkd/t/+F0fW7U2NbJzsZ2W0EOgr/VW0Wc90I+5F98Ve0Gbg+43KaqtmksRBqaO1izZQ9TCq1tVG+M8bd6s33IPWn1vbBdLyJXichXOMDENlRUdaGqlqpqaV5eXjgfKmICQ0FOPsJCQcLBHx6y3sJD9uc/mFd2gA/TS/tavNkK8iEV+SfIO+N7H3KwSyKLReRsCVEPMhHJ9u2R/FRE1ojIUaH4uW703MottHd1c+rk4U6X4iqDMlIYkpnK2m3WyaIH1wAe4HvAdOAi4Ot9/FnbRSQfwPd5R0gqdAELBYmMk48YxowSCw8JpKr/8n35CfAV4PvAtb6PHzlVl5v5W7zZBPnQ8gak4klJjPtWb8FOkH8APAG0h6hZ+W+BF1V1HDCFz14tx50nl1UzZugAJhYMdLoU1xkzNJP1O2wFuQeKd8/xs0Ap3l7If+7jz3oWuMT39SXAM/2uzgW2NlgoSKSICDfO94aHLLTwkP09hPdMwVl4D+edivegnumlzybItsXiUESEohxP3MdNBxUUoqqZoXpAERkIHAtc6vvZ7UB7qH6+m2ze2cTyyt3ccMo4W6HqgzFDM/n7kgq6u3Xf/u3m9k7qm9rjvc/lw3hXmlYCQW/sFJFHgOOAwSJSDfwU+CXwuIhcDlTi7YEe8259yUJBIskfHrLw7U18bVYxw7LSnC4pWtSq6rNOFxELKuqaGDwghQGpQeejxbWS3AzWxfkCVLBBISIiF4nIf/sujxCRmX18zFFALfAXEVkhIveKyBde0sXqoZ9ATy+vRgTOnFrgdCmuNHbYAFo7uqna1YyqsmjlVo6/7U2Ov+1NPqza7XR5TqpV1Wd9QSEV/o9D3UlVL1DVfFVNVtVCVb1PVetU9QRVHe37vH+Xi5hjoSDO8IeH3LZ4rdOlRJOf+p4jrQ9yP1XUNVNk4zloxbkequtb6OqO3xaMwW6x+CPecJCv+S43An2NrU3Cm+p1t6pOA5roobdqLB76CdTdrTy1ooZjDh9sqyV95D+o9/In27nkL0v5zsPLyclIIS8zlf96aBk7G9scrtAx9qTaRxYK4hwLD+nRN4CpePuTWx/kfqioa6LEtlcErTg3g/aubrY2tDhdimOCnSDPUtUr8QWGqOouIKWPj1kNVKvqB77L/yAOY3CXlNdTvauFs48sdLoU1xrtmyD//Pk1rKjYxf+eNoFnr5rDPRdPp76pnav+vjxeW0fZk2ofvb7WQkGcdOVcCw/ZzxTfQtEl1ge571o7uti6p9X2H/eCv9VbPEdOBztB7hCRRLyHfxCRPHqxtzGQqm4DqkTEv7nvBLwndePKU8uryUhJ5CRrH9VnA1KTWDA5n7OmFfDqD7/EpXNGkpSYwMSCLH5x1iTe31TPL1/41OkynWBPqn3Q2dXNLYssFMRJWekWHrKf90VkgtNFuF31rmZUrYNFb/hbvcVzJ4tgd6v/DngaGCIiNwNfBX7Sj8f9LvCwiKQAm/CueMWNlvYuFq3cximT8vGk2IGB/rjraz2/+XDWkYV8VLWbe9/ZzOQR2Zw+Ja7a6L0vIhNUNe5eePaHPxTknounWyiIgy6cVcyD75Zzy/Nr+I/DB5MU3/8vjgEuEZHNQBsggKrqZGfLchdr8dZ7+VnppCQmUFEfv72Qg+1i8bCILMO72ivAmara59Zsqvoh3vZTcWnxJ9tobOu07RVhdtOCCXyydQ/X/+NjRg8ZwPj8uGmlZ0+qvRQYCnLSBHtXx0n+8JBvP7Scx8uq4301f57TBcSCcmvx1muJCUJhTjoVO+N3BTnYLha/BXJU9S5V/UN/JscGnlxeQ0F2OrNG5jhdSkxLSUrgrguPJDMtiW8/tIyG5g6nS4qUecBo4CQ+239svVMPwkJBoouFh3gFdqHpTUca83kVdU1kpiUxyGPnCnqjJDeD8rr4XUEO9r2r5cBPRGSDiNwqInG7+tsbqspjSyv50RMf8dNnVvGrFz/lN6+s4531tXxlWsG+3r0mfIZkpnH3RdPZsruFax5bQXcctKyxJ9XesVCQ6GPhIX3na8P6ui+ldrWIXO10TU6rqGumONdjL357yR8WEq8HZoPdYvEg8KCI5ABnA78SkSJVHR3W6lyspb2LG59eydMrahg8IIWOLqWprZPObiUlKYGvTrftFZEyvXgQ/3PaEfz3P1fxm1fX84MTxzhdkokiFgoSnQLDQy6YVUR+VrrTJblFJ/BDVV0uIpnAMhF5OZ7PJFTUNXFEQZbTZbhOSa6H5vYuahvbGJIZf+1oe3tC7HBgHFBCHHaeCFZVfTPffmgZn2zdww9PHMOVcw/ft1rc3tlNtyppyYkOVxlfLppVxMdVu/ndq+uZVJDFibbP1PBZKMgVx46yUJAodN3JY3lp1TZuX7yO286Z4nQ5rqCqW4Gtvq/3isgaoIAYec5uaOmgvTP4JlrdqlTvamH+pPwwVhWb/Hu2V1Y3MLkw+NV3EcjNSHH9in1QE2QR+RXeLPiNwGPA/6lqXEeVHcg763fy3UeW09mt3H/JDOaOG/K576ckxfWJbMeICP935kQ+3baXHzz2Ic9cNYdReQOcLss4SFW5ZZGFgkQzf3jIn9/exDfmlHDEcFsF7A0RKQGmAR8c/JbusGRzPefe816f7jtysB3Q661Red7/Zpc/WNbr+143b6zr/64Gu4K8GTgab0x0KjBZRFDVt8JWmQtV1DVx6V+WMCovg3suLrUBGWXSkhP508XTOe337/A/z6zmoW/Ocrok46DX1+7g3Y11/PS0CRYKEsWunHs4j5dVccuiNTx0+SzXr0pFiogMAJ4ErlHVPT18/wrgCoCiInd0Cvm42rsu95MF40ntxbuwqYkJnDo5rlp9hkRxbgZ3X3gkO5vae3W/u17bwMdV7k/DDHaC3AW8BhQCHwKzgfeA48NUlys9sqQKBf562SyLj45SBdnpnH1kAQ++V0FrR5dtdYlTgaEgF84qdroccxBZ6cl87/jR/L/nPuGNdbXMHTvk0HeKcyKSjHdy/LCqPtXTbVR1IbAQoLS01BWnsCrqmslMS+LyY0baC6UIOaUPW1Pe+HRHTHS/CPb9/u8BM4AKVZ2L9y0bizkK0NHVzT+WVTN37BCbHEe5WSNzae/sZkWl7RKKV/5QkOvnjbNtTy5w0exiSnI93PL8mniNjw+aeGeO9wFrVPUOp+sJpYr6ZkpyM2xyHOWKczNiovtFsM8MraraCiAiqar6KWBHvgO8umY7OxvbuGDmCKdLMYcwY2QOIvDB5jqnSzEOCAwFOdmi3l3BHx6yfkcjj5dVO11OtJsDXAwcLyIf+j7mO11UKFTUNe2LQDbRqzig+4WbBbvFolpEsoF/Ai+LyC5gS/jKcp9HllSRn5XGl8bkOV2KOYSs9GSOGD6Q9zfZBDke+UNB7r3EQkHc5OQjhlFa7A0POX3qcAak9rYJU3xQ1XfwpmfGlI6ubqp3tXDqZOtGEe38kd4Vdc2ubg8X1Aqyqn5FVXer6v8C/4337Zszw1mYm1Tvauat9bWcUzqCpER7u9YNZo3MZUXlblo7upwuxUSQPxTk9CnDmWqhIK4iIty0wMJD4tWW3S10dSvFOXb4Pdr528NV1Lk7prrXszlVfVNVn1XV3h1rjGGPL60C4NxSC/9wi9mjcmnr7OajKtuHHE9ue2kdqnDtybZDzI2mFQ3i1Mn5LHx7E9saWp0ux0RQuW+yVWxbLKJeQXY6iQlChcsP6tlyZz91dnXzeFk1x47Oo3CQDVy3mFni3Yf8/qZ6p0sxEbKqpoGnVlTzjTklFgriYtfPG0d3N9y2eK3TpZgIqvRNtvyrkyZ6pSQlMDw7Lf5WkM3nvbmulm17Wrlgpjv6SBqvLE8y44cNtIN6ceJzoSBz3d28Pt75w0OeXF7N6i3u77VqglNe10xacgJDMlOdLsUEoSQ3w1aQ490jS6oYPCCVE8Zbb063mTUqh2UVu2jrtH3Isc4fCnL1CaMtFCQGXHnc4WSlJ3PLojWubyVlglNR10xRjoeEhJg7fxiTinI8VNTbCnKfiEiiiKwQkeecqqG/tjW08tqn2zmntJBkO5znOv59yB9X2ypULAsMBfmahYLEhCyPNzzk3xvqeGOdteSPBxV1Tba9wkVKcjPY3dxBQ3OH06X0mZOzuquBNQ4+fr89XlZFt8L5M6z3sRvN8vVDfn+jbbOIZY+VWShILLpodjHFFh4SF7q7lcr6ZkrsgJ5r7Gv1Vu/ebRaOPFuISCGwALjXiccPhY6ubh7+oIJjx+TZq1qXyvakMHZoJu/bPuSY1djWyZ0vWyhILEpJSuCGed7wkCeWWXhILNu+t5W2zm6K7LnWNfzzonIXH9RzajnlN8B1gGtf9r+4ahvb97Rx6dH2lq2bzR6Vy7KKXbR3uvZX0RzEPW9uZGdjOzcusFCQWDRvojc85PbF62hq63S6HBMm/m4ItoLsHkW+TkGVLj6oF/EJsoicCuxQ1WWHuN0VIlImImW1tdG3x+zBd8spzvVw3Bg7nOdms0fl0NrRzcfV1g851lgoSOwLDA+5x8JDYpa/G4KFhLhHekoiQwem2gpyL80BTheRcuBRvHnxD+1/I1VdqKqlqlqalxdd8c2rahooq9jF148qsRO1LjdzZC4AH2y2fsix5raX1tHdbaEgsc7CQ2JfeV0zSQnC8Gz3xhbHo2KXt3qL+ARZVX+sqoWqWgKcD7ymqhdFuo7+eODdcjwpiZxjyXmul5Ph24e8yfYhxxILBYkv/vCQ2y08JCZV1jVTOCidJOsW5SrFOR5Xh4XYb1sv1TW28exHWzjryAIGplk/1Vgwe1QOZeW72LHXVp9igYWCxB9/eMg/llfzyZY9TpdjQqzcWry5UsngDHbsbaO53Z3nAxydIKvqG6p6qpM19NajS6to7+zmkqNKnC7FhMgpk/Jp7exizi9f4+pHV7CsYpeFD7jYG2trLRQkDll4SGxSVSrrmve1DTPuse+gnksDQ2wFuRc6u7p56P0Kjjl8MKOHZjpdjgmR2aNyefUHX+Ki2cW8tmYHZ9/9Lqf94R0e/qCChhb3NjmPR51d3dy8aI2FgsQhf3jIOxt2WnhIDKlvamdvW6etILtQib/V206bIMe8xZ9sZ2tDK5ccXeJ0KSbERuUN4KenHcH7N57Az8+cSEenctPTq5hx8yt895EVvLmulq7u+F2VEpFyEVkpIh+KSJnT9RyIhYLENwsPiT3+uGJr8eY+Rbn+FWR3HtSzZ5AgtXV2cc+bGxmRk87x46y1W6zKSE3iotnFvHjNf/DsVXM4f8YI3lpXyyX3L+Hsu9+lO44nycBcVZ2qqqVOF9ITfyjIjJJBFgoSpyw8JPbsa/FmE2TXyUpPZpAn2bWt3myCHITWji7+82/L+Ki6gR+dNJZEa+0W80SEyYXZ/L8zJrLkphO49uSxfFi127pdRDF/KMhNCyZYKEgcs/CQ2FJR14wIFA6yCbIbFedmUGkT5NjU2tHFt/5axpvravnV2ZM4Y2qB0yWZCEtNSuTyY0YyMC2JR5dWOV2OUxRYLCLLROSK/b/pdLCPPxTkNAsFiXsWHhJbKuqayR+YRlpyotOlmD4ozvVQ7tJeyDZBPoiW9i4ue2Ap72zYya/Pnsx5M4qcLsk4JC05ka9MK+DF1dvY3dzudDlOmKOqRwKnAFeKyLGB33Q62McfCnKdhYIYLDwkllRYizdXK87NYMvuFto73XcmwCbIB9DU1sk3HljC+5vquOPcKZxTOsLpkozDzp0xgvbObv65osbpUiJOVbf4Pu8AngZmOlvRZywUxPTEwkNiQ4W1eHO14hwP3QrVu9y3zcImyD1obOvk0r8sYcnmeu48bypfmWaJeQaOGJ7FxIKBPFZWHVd9VkUkQ0Qy/V8DJwGrnK3Kyx8KkmWhIGY/I3I8XHJ0sYWHuNje1g7qmtptBdnFSgZ7X9y4MVHPJsj72dvawdfv+4Dllbv53QXTbM+x+ZzzZhSxZuseVtXE1RPuUOAdEfkIWAI8r6ovOlwTYKEg5uCumjvawkNczD+pshVk9yrK8b64qXDhPmSbIAdoaOngovuW8HF1A3+4YBqnTh7udEkmypw+ZTipSQk8urTS6VIiRlU3qeoU38cRqnqz0zWBNxTkFl8oyIUWCmJ6EBge8mYchYeIyP0iskNEouKdnr6yCbL7DR6QQkZKoitbvdkE2Wd3czsX3fsBn2xp4I8XHskpk/KdLslEoaz0ZBZMyufZD7fQ0t7ldDlx7bGyKtZbKIg5hH3hIYviKjzkAWCe00X0V0W9vweybbFwKxGhKDfDlXHTSU4XEC1+9MTHrN22l3suns7x4yxkwBzYuTNG8NSKGhat3MrZ021/uhMsFMQEyx8e8l8PL+eJZdVcMDP2uxGp6lsiUuJ0HYHeWlfLypqGXt3nlTXbGTwghQGpNlVxs5JcD2UVu7jr9Q29ut/gASmcWzrCsb729luH9xT8K2u286OTxtjk2BzSrJE5lOR6eKysyibIDvGHgvz566UWCmIOyR8ecsfL6zh9ynAybMKFr5/5FQBFReF/0fD9xz6krqn3LTLnTxoWhmpMJM0oyeGFVdu49aXed5SZUZLDqLwBYajq0OyvBPCH1zaQmZbE148ucboU4wIiwrkzRvDrF9eyqbbRscEbr/yhIKdPGc60okFOl2NcwB8e8pU/vsvCtzbx/RPHOF2S41R1IbAQoLS0NKwnGPf4ulFce/JYvvkfI3t135RE2z7ldpcdM5KLZhejBP9r9mHlbs5b+D7ldU2OPcfG/W/euu17eXH1Nr5xdAkD0+wUvAnOV48sJDFBeLys2ulS4s7ti72hINdaKIjphX3hIW9tYvseCw+JJH/U8GF5GaQmJfbqw94hig0pSQm9+v9++BDvpNjJ9nBxP0H+w2sbyEhJ5Btzeveq1sS3IQPTmDt2CE8ur6Yjfg7+OG71lgaeXG6hIKZvrp83jq5utfCQCPNHDftbfhlzKDkZ3r3ncTVBFpERIvK6iKwRkdUicnWka/DbVNvIcx9v4aKjihmUkeJUGcalzpsxgtq9bbz+6Q6nS4kLqsrNz1soiOk7f3jIE8uqWbM1dnuZi8gjwHvAWBGpFpHLnazH2rWZ3hIRinM9jvZPdmIFuRP4oaqOB2YDV4rIBAfq4O43NpKcmMA3jxnlxMMbl5s7No+8zFQeL6tyupS4YKEgJhSumjuagWne8JBYpaoXqGq+qiaraqGq3udkPRV1TQwekGqHI02veCfIcbSCrKpbVXW57+u9wBog4nF1VfXNPL2ihgtmFpGXmRrphzcxICkxga9OL+S1T3fYnsYw84eClOR6LBTE9EuWJ5nvnTCat9fHV3iIk8rrmimx1WPTS8W5GVTtaqar25kUTEf3IPv6NE4DPujhe1eISJmIlNXWhv6P2J/e3EiCCP/5JVs9Nn13bukIuhX+scwO64WTPxTkhlPGWyiI6beL/eEhz69x7Mk3nlTWNVNkE2TTS8U5Hjq6lC27Wxx5fMeeaURkAPAkcI2qfmEzmKouVNVSVS3Ny8sL6WPvamrnibJqzp5eSH5Wekh/tokvIwdnMGtkDo+XVaFqT7ThYKEgJtT84SFrt+/lCdsiFVatHV1s29NKiaXhmV7yJyg6tc3CkQmyiCTjnRw/rKpPRfrxX1q9jfaubi6cFfuJSib8zpsxgoq6Zt7fVO90KTHJHwpy4/zx1vLJhMy8icOYXjyI219eR1Nbp9PlxCx/xLAd0DO95f+d8UeOR5oTXSwEuA9Yo6p3RPrxAZ5fuZXiXA9HDB/oxMObGHPKxHwy05LssF4Y+ENBTrNQEBNi/vCQ2r1tLHxrk9PlxKzynd7JTbGtIJteGjYwjZSkhLhaQZ4DXAwcLyIf+j7mR+rBdzW18+7GOuZPyrfVKBMS6SmJnDF1OItWbqWhpcPpcmKKPxTkOgsFMWFwZNEgFlh4SFj5V5DtkJ7prYQEoTjHuVZvTnSxeEdVRVUnq+pU38eiSD3+S6u30dWtLJiUH6mHNHHgvNIi2jq7efbDGqdLiRkWCmIi4QZfeMgdi9c5XUpMKq9rYmBaEtkeyxowvedkq7e4Ow5u2ytMOEwsGMjEgoHc89YmWtq7nC7H9VSVWxZZKIgJP394yOPLqmI6PMQpFXXNlAy27RWmb4pzM6ioa3bkEHxcTZDrbXuFCRMR4cb546ne1cIf39jgdDmu98baWv69wUJBTGTEQ3iIUyrqmimyd4BMHxXnemjp6KJ2b1vEHzuuJsiLbXuFCaOjDxvMmVOHc8+bm9hY2+h0Oa5loSAm0gLDQ95Ya9HxodLR1U3N7hZr8Wb6zH+4s9yBbRZxNUG27RUm3G5cMJ7U5AR++sxq64vcR4+XVftCQcZZKIiJGH94yC8WfWrhISFSs6uFrm61kBDTZ8W+dx+cOKgXN88+/u0VC2x7hQmjIZlpXHvyWN7ZsJPnPt7qdDmu09jWyR37QkGGOV2OiSMWHhJ65b5Jja0gm74qGJROYoI4clAvbibI/u4V8217hQmzC2cVM6kgi/977hP2tlrbt97whoK0WSiIcYSFh4SWhYSY/kpOTKAgO52Kepsgh82ilVspse0VJgISE4SfnzmR2sY27njZWkcFy0JBjNMsPCS0ync2k5acwJDMVKdLMS7mbfVmWyzCwrpXmEibMiKbC2cV8eC75SwttwjqYFgoiIkGFh4SOpX1TRTnZNjzrukXp3ohx8UE+S//3mzbK0zEXT9vHCNyPFz9yAp2N7c7XU5U84eCXGqhICYKXH/yODq7u7l98VqnS3G18rpm215h+q0kN4OGlo6IP4/G/AT5rtc38PvXNnDG1OG2vcJEVGZaMr+/YBq1jW1c94+PravFAQSGglxpoSAmChTlerjkqBKeWFZt4SF91N2tVNZbSIjpP3+rt0ivIsf0BPm3r6zn1pfWcubU4dx+zhR7m8dE3OTCbK6fN47Fn2znr+9VOF1OVLJQEBONvnu8hYf0x7Y9rbR3dltIiOk3/7sQ5RHehxyTE2RV5Y7Fa7nzlXWcfWQht587laTEmPynGhe4/JiRHD9uCDc/v4ZVNQ1OlxNVLBTERKssTzJXnzCaQZ4UWjssPr63/Kt91uLN9Jf/RValrSD3352vrOd3r23gvNIR3PrVySQm2MqxcY6IcNs5UxiUkcx3H1lBo7WP2sdCQUw0+8acEn53wTTSkhOdLsV1/F0HbA+y6a+05ESGDUyLeJpezD0jvbByK797dT3nlhbyi7MmkWCTYxMFcjJS+O3506ioa+K3r1jrN7BQEBP9bFte35XXNZOcKORnpTldiokBTrR6S4roo4XZ5p1NXPePj5k6Ipufn2mTYxNdZo/K5Y8XHsnRhw92upSosNAXCvLnr0+3iYgxMaayvonCQR7b3mhCojjXw+trayP6mDHzm9va0cV/PbSMxEThrguPtLdrTVSaNzGfgWl2EG1bQysLLRTEmJhVvtNavJnQKc7NoHZvW0QTLh2ZRYrIPBFZKyIbROSGUPzMnz6zmk+37eXO86ZSkJ0eih9pjPEJ9Zi9bfFaCwUxJkzC8RzbG6reFm/F1sHChIj/xVZlBCOnIz5BFpFE4C7gFGACcIGITOjPz/zHsmoeK6viqrmHM3fskFCUaYzxCfWYtVAQY8InHM+xvVXX1E5jW+e+/rXG9FfJvl7IkduH7MQe5JnABlXdBCAijwJnAJ/05Yd9um0PP/nnSo4alcv3TxwTwjKNMT4hG7OfCwU5zkJBjAmDkD7HPlFWxV2vb+jVfTq6vKFItsXChEqR73fppqdX8csXPj3obX9x1mSOOiy334/pxAS5AKgKuFwNzNr/RiJyBXAFQFFR0QF/mCc5iaMPG8wvz55k7dyMCY9Djtlgx2tXtzKlMJtTJuaT5bG92MaEQUifYwdnpjJlRHavizg2JY9Zo/o/STEGYGBaMj84cQwbaxsPfdv00ExtnZgg9zSL/UIGr6ouBBYClJaWHjCjtyjXw/2XzghddcaY/R1yzAY7XpMSE7hu3rjQVmeMCRTS59i5Y4fY1kUTFb53wuiIPp4Th/SqgREBlwuBLQ7UYYwJjo1ZY9zDxqsxIeDEBHkpMFpERopICnA+8KwDdRhjgmNj1hj3sPFqTAhEfIuFqnaKyFXAS0AicL+qro50HcaY4NiYNcY9bLwaExqOJOmp6iJgkROPbYzpPRuzxriHjVdj+s/i5owxxhhjjAlgE2RjjDHGGGMCiOoBu7tEDRGpBSoOcbPBwM4IlBMKbqoVrN5w27/eYlXNc6qY/orB8QpWb7i5vd5YH7Nu//8T7aze8OrTeHXFBDkYIlKmqqVO1xEMN9UKVm+4ua3eUHDbv9nqDS+rN7q57d9r9YZXvNRrWyyMMcYYY4wJYBNkY4wxxhhjAsTSBHmh0wX0gptqBas33NxWbyi47d9s9YaX1Rvd3PbvtXrDKy7qjZk9yMYYY4wxxoRCLBoLmFIAAAPJSURBVK0gG2OMMcYY0282QTbGGGOMMSaA6yfIIjJPRNaKyAYRucHpevYnIveLyA4RWRVwXY6IvCwi632fBzlZYyARGSEir4vIGhFZLSJX+66PyppFJE1ElojIR756f+a7fqSIfOCr9zERSXG6Vj8RSRSRFSLynO9y1NYaatE+XsFdY9bGa2TYmI3eMeum8Qo2ZiMhVOPV1RNkEUkE7gJOASYAF4jIBGer+oIHgHn7XXcD8KqqjgZe9V2OFp3AD1V1PDAbuNL33zRaa24DjlfVKcBUYJ6IzAZ+Bdzpq3cXcLmDNe7vamBNwOVorjVkXDJewV1j1sZrZNiYjd4x+wDuGa9gYzYSQjNeVdW1H8BRwEsBl38M/NjpunqoswRYFXB5LZDv+zofWOt0jQep/RngRDfUDHiA5cAsvKk5ST39njhcYyHeP37HA88BEq21huHf7orx6qvNlWPWxmtY6rQx+9nlqByzbh2vvvpszIa2xpCNV1evIAMFQFXA5WrfddFuqKpuBfB9HuJwPT0SkRJgGvABUVyz7+2UD4EdwMvARmC3qnb6bhJNvxe/Aa4Dun2Xc4neWkPNreMVovj338/Ga9jYmP2MW/6tUfv7H8jGbFiEbLy6fYIsPVxnfetCQEQGAE8C16jqHqfrORhV7VLVqXhfOc4Exvd0s8hW9UUiciqwQ1WXBV7dw00drzVM4unfGlE2XsPDxmxc/VsjysZs6IV6vCaFpCrnVAMjAi4XAlscqqU3totIvqpuFZF8vK/KooaIJOMduA+r6lO+q6O6ZgBV3S0ib+Dd15UtIkm+V43R8nsxBzhdROYDacBAvK92o7HWcHDreIUo/v238RpWNmbdOWaj+vffxmzYhHS8un0FeSkw2ndCMQU4H3jW4ZqC8Sxwie/rS/DuQYoKIiLAfcAaVb0j4FtRWbOI5IlItu/rdODLeDfnvw581XezqKhXVX+sqoWqWoL3d/U1Vb2QKKw1TNw6XiF6f/9tvIaRjVnXjtmo/P0HG7PhFPLx6vSG6hBsyJ4PrMO7J+Ymp+vpob5HgK1AB95X45fj3RPzKrDe9znH6ToD6j0G79sPHwMf+j7mR2vNwGRgha/eVcD/+K4fBSwBNgBPAKlO17pf3ccBz7mh1hD/u6N6vPpqdM2YtfEa0dptzEbhmHXTePXVa2M2MnX3e7xa1LQxxhhjjDEB3L7FwhhjjDHGmJCyCbIxxhhjjDEBbIJsjDHGGGNMAJsgG2OMMcYYE8AmyMYYY4wxxgSwCbIxxhhjjDEBbIJsjDHGGGNMgP8P4OD8c54ahXEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b54c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "filenames=glob.glob(\"inflammation*.csv\")\n",
    "\n",
    "filenames=filenames[0:4]\n",
    "\n",
    "for f in filenames:\n",
    "\n",
    "    data = numpy.loadtxt(fname=f, delimiter=',')\n",
    "\n",
    "    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "\n",
    "    axes1 = fig.add_subplot(1, 3, 1)   \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()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the difference between the average of the first dataset and the average of the second dataset, i.e., the difference between the leftmost plot of the first two figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0ec2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "filenames=glob.glob(\"inflammation*.csv\")\n",
    "\n",
    "data0=numpy.loadtxt(fname=filenames[0], delimiter=',')\n",
    "data1=numpy.loadtxt(fname=filenames[1], delimiter=',')\n",
    "\n",
    "diff = data1.mean(axis=0)-data0.mean(axis=0)\n",
    "\n",
    "fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "matplotlib.pyplot.plot(diff)\n",
    "fig.tight_layout()\n",
    "matplotlib.pyplot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making choices - if else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not greater\n",
      "all done\n"
     ]
    }
   ],
   "source": [
    "num =37\n",
    "if num > 100:\n",
    "    print(\"greater\")\n",
    "else:\n",
    "    print(\"not greater\")\n",
    "    \n",
    "print(\"all done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exactly equal\n",
      "all done\n"
     ]
    }
   ],
   "source": [
    "num =100\n",
    "if num > 100:\n",
    "    print(\"greater\")\n",
    "elif num==100:\n",
    "    print(\"exactly equal\")\n",
    "else:\n",
    "    print(\"not greater and not equal\")\n",
    "    \n",
    "print(\"all done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=num==100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "print(type(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = 1 == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1 >2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2>1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1>2) or (2>1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-01.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b18c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-02.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b3e1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minima add up to zero in  inflammation-03.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b57ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-04.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba5e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-05.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7b1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-06.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b56c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-07.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af778d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minima add up to zero in  inflammation-08.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b64f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-09.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b83a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-10.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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2H+CP74c3FCRUIsKdcyw8JF6ISLKIrBCRfztdi/lCcXUdfbunk56a3Ob3rdVb7LAJchwJ9j+e4pIJcp7Xw9ZdB2zPozEB97+5DiX8oSChah0esn2PhYe43PeBIqeLMIcqraklt439x0F52R627j5Akz0vOi5iWyxM9C3ZVM2ovt3IzkxzupSQ5GZ7aPYp2/fUHzy0Z0yiCoaCfPuMoREJBQnVj88dyeurd/DrN9fx4OUTHavDdJyIDATOB+4GfuhwOXFJVflgYxX76tvXy39TZe0xzxbkeT20+JRnl5bhbcdzeXKScNrwHDLS2l6ZNu1nE+Q40dDcQmFJDVecHNmWUOGUm/1FSxubIJtE1joU5OYIhYKEKhgeMnfRZr4xYzDjBkR3q4cJi98AtwHdjnYDEbkJuAkgN9c9zxuxYs22vVz7xCcduu+ovt2P+r3R/fzf+89/rG73z73rwjFcP2Nwh2oyR7IJcpxYWb6H+iYfU12yvQI4+DaT7UM2ie6dtf5QkP++eGxMHI67ZeYwXigs5+7Xivj7jVOieljQdI6IXABUqOoyETnjaLdT1bnAXICCggJLiGmnTZX7Afjj1wratcCTnARDenU96vfHDcji/dtmUtfYvjS9rzy6mI0V+9t1H3NsNkGOE++srSA5SZg6JNvpUkLWt3s6aclJlFgvZJPAmlp83DPPHwpyZYRDQULVPT2V7581nLteXcO76yo4c1Qfp0syoZsBXCQic4B0oLuI/E1Vr3G4rrgSPEh3yvBeRz1w11EdeUc1v1emLTaFmR3SiwOqyhurdzBtiJceHnfsPwb/nqmB2RkWN20S2rNLy9gUxVCQUFl4iDup6h2qOlBV84ErgHdschx+xdW19Ms6ejeKaMvzZlp7uDCLnb/GpsPW7dzHlqpaZo3r63Qp7Wat3kwiC4aCTBkcvVCQULUOD3mu0MJDjGmttLquzTQ8p+QHukI1NtuL2XCxCXIceH3VDkTg3LHuexs0L9tDaXUdqrYFzmki8icRqRCR1a2u+4WIbBWRTwMfc5ysMd4EQ0F+fn50Q0FCde6YPkzOz+ah+evZ39C+0/rGeaq6UFUvcLqOeFRcXUde9pFpeE7JzfbgU9i629ozhotNkOPAG6t3cHJeNr27pTtdSrsNyvawr6GZ3XVNTpdi4ElgVhvXP6SqEwMf86JcU9xyMhQkVCLCz84PhodscrocY2JCbUMzVfsbyOsVQyvIvYJdoWybRbjYBNnlNlfuZ93Ofa7cXgH+fVPwRT69cY6qLgJqnK4jUTgdChKqiYN6cNEJFh5iTFDwgF4srSDnBQ72WQpf+NgE2eVeX70DwLUT5NyDg9pe9caw74jIysAWjJ5t3UBEbhKRQhEprKysjHZ9rhMMBfnGjMGOhoKE6ifnjcTng/vfXO90KcY4Lvh8FUt7kHO6dSEjNdkO6oWRTZBd7o3VO5g4qAf9XfAk25bgBLnMVpBj1SPAUGAisB14oK0bqepcVS1Q1YKcnJxo1uc6wVCQ7Mw0bp7pbChIqILhIS+vKGf11j1Ol2OMo4LveMbSBFlEyPN6rCtUGNkE2cXKaupYtXUPs126egyQkZZM725d7G2hGKWqO1W1RVV9wOPAZKdrcrtgKMitZw+newyEgoTq5pnD6JGRyj3ziuxQrUloJdW1eDPTYiLUp7U8r8dWkMPIJsgu9kZge8Xscf0crqRzrNVb7BKR1r9clwLtzz81BzUHQ0F6xU4oSKiyMvzhIYs3VbNwnW2jMYmrpLruYBJsLMnzZlJWc4AWn72ADQebILvY66u3M6Zf95gcqO2R67UJciwQkWeAJcBIESkXkRuA/xWRVSKyEpgJ/MDRIl3umUAoyO2zR8VUKEiorpqSx+Bemdw9r8jCQ0zCKqmuI98bOwf0gvK8HhpbfOzYW+90KXHBfX+hDQA79tSzvHS3q7dXBOVme9ixt576pvZlz5vwUtUrVbWfqqYGkrieUNVrVXW8qk5Q1YtUdbvTdbpVMBRk8uBszhnjvp7lAGkpSfx0loWHmMTV0NzCtj0HYmr/cVBw0m6H3sPDJsgu9eaawPaK8e6fIOd5PahC+a5DW0ipqu11NHHji1CQ0TEZChKq88b24eT8nhYeYhJSWc0BVGPrgF5QrrV6CytHJsgi8gMRWSMiq0XkGRFxX8KFg2obmvn7x6UM692VYb27OV1Op+UGekmW1hz6qve2F1dyzRMf234q43rBUJBLJvZnwsAeTpfTKSLCneePoWp/I48utPAQk1iCz1N5MbjFon+PDFKTxSbIYRL1CbKIDAC+BxSo6jggGbgi2nW4VXOLj1v+vpwNFfv42ZxRTpcTFm296i0sruGFZeV8uLGapxYXO1SZMeHhllCQUAXDQ/74gYWHmMRSXBUMCYm9FeTkJGFQT49tsQgTp7ZYpAAZIpICeIBtDtXhKqrKz/+xmoXrKvl/l4znzFHu3Md4uF5d0/CkJR88qKeq3Pv6Wnp368Ipw3rxwFvr2Gb58salWoeCDOwZe0+qHWXhISYRldbU0a1LCtmZaU6X0qY8r8dWkMMk6hNkVd0K3A+U4g8e2KOqb0W7Djf63YKNPLu0jO+eOYyrprirRdSxiIi/1VtgUM//fCfLSnZx69kjuPey8bSocteraxyu0pj2C4aC9PSkuiYUJFQWHmISUXF1LbleT8yeI8jzZlJSXWvnd8LAiS0WPYGLgcFAfyBTRK5p43YWXdvK84VlPPT2er504kB+eM4Ip8sJu2Av5OYWH796Yy1DcjK5vGAgg7I9/ODsEcz/fOfBvs/GuMUXoSAjXBUKEqqbZw4jy8JDTAKJ1RZvQXleD7WNLVTtb3S6FNdzYovF2cAWVa1U1SbgZWD64Tey6NovLN5YxR0vr+LU4b2470vjY/aVa2cEJ8jPF5azqbKW284bRUqgT+w3ThnMqL7d+MWra9hX3+RwpcaEpnUoSDy949OahYeYRNLc4qN8V2yGhAQFu2scfujdtJ8TE+RSYKqIeMQ/0zsLKHKgDlcoq6nj5r8vZ0ivTP5w9YmuDBcIRZ7XQ0Ozj/teL+LE3B6cN/aL/dWpyUnc96UJ7NxXz/1vrnOwSmNC96zLQ0FCdXUgPOQeCw8xcW77nnqaWpT8mJ4g+1e3g4cJTcc5sQf5Y+BFYDmwKlDD3GjX4Qa1Dc3c+JdCfD7l8a8VxFzuezjlBgb13vpmbp99ZJ/YiYN68LWpefzloxI2Ve53okRjQravvomHXB4KEqpgeMgGCw8xcS54+C3YmjQWDeyZgQiUWDptpzmyrKGqd6nqKFUdF0jqanCijlimqvz4hc9Yv3Mf/3fVieT3it0BGQ7BVm9nj+7N5MHZbd7m22cMQ/WLkBRjYlUwFOTOOe4OBQmVhYeYRFAcaJ+W3yt2V5C7pCTTPyvDWr2FQfy+7+dy//fORl5fvYM7Zo/mtBHxvwc73+vhzjmj+e+Lxx31Nn2z0hk/IIu3P98ZxcqMaZ9gKMjFE/tzwiB3h4KEqnV4yGPvWXiIiU+lNXV0SUmiT7fYzjbL72Wt3sLBJsgx6N11FTwwfz2XThrAN08d7HQ5USEi3HjaEPr3yDjm7c4e3YcVZbup2m9vOpjYdP9b/lCQn8RJKEioguEhj79v4SEmPhVX1ZKb7SEpKbbfFcrNzrQV5DCwCXIMeuTdTeR5Pdx7WXx2rOiMs8f0RtXfPsuYWLN66x5eicNQkFAFw0MeeMvCQ0z8Ka2pi8mI6cPlez3sqmtizwHr+tQZNkGOMaXVdXxSXMPlBYNIT012upyYM6Zfd/pnpds2CxNzVJW7XyuiR0b8hYKEKhge8tLyctZss/AQEz9UleLq2oNt1GLZwVZvts2iU2yCHGNeWbEVgEsmDXC4ktgkIpw1ug/vb6iivqnF6XKMOeidtRUs2Vwdt6EgoQqGh9z9moWHmPhRsa+B+iZfTLd4CzrY6s22WXSKTZBjiKry8opypg3xMuA4e3ET2dlj+nCgqYXFm6qcLsUY4ItQkMFxHAoSKgsPcZaIpIvIJyLymYisEZFfOl1TPDjY4s0FWyyCXaFKrdVbp4Q8QRaRPBE5O/B1hoh0i1xZiWl56S5Kquu47ERbPT6WqUOyyUxLZv7ntg/ZxIZECQUJ1dVT8sj3eiw8xBkNwJmqegIwEZglIlMdrsn1DrZ4c8EKcmaXFHK6daG4ylaQOyMllBuJyI3ATUA2MBQYCDyKPwXPhMlLy7eSnprE7PH9nC4lpnVJSeb0kTksKNqJzzcu5k8Um/jWOhTk3DgPBQlVWkoSt88ezbf+toznCsu4ekqe0yUlDPXvawmmKaUGPmyvSyvPLy1j3urt7bpPaXUdyUly3E5LsSIv28OCtRV8/c+ftOt+w3K68vMLxkSoKncJaYIM3AJMBj4GUNUNItI7YlUloPqmFv792TZmje1L1y6h/m9JXGeP7sO8VTtYvW0PEwYmRq9ZE5uCoSB/SpBQkFC1Dg+5eOIA+7sWRSKSDCwDhgEPBxJsD7/NTfgXvsjNTaxtQX/8YDMV+xrIyw59NbhbegrXTMl1zTtEXykYyN8/LmVXbWPI96na38jCdZV898zhZHkS9xxFUKh/sRpUtTH4x19EUrBXpGH1ztoK9tY3c9mJA50uxRVmjuxNksDbn++0CbJxTCKGgoRKRPjZnNFc+ofFPPbeJn50bmL1hXaSqrYAE0WkB/CKiIxT1dWH3WYuMBegoKAgYZ7PfT6ltKaOa6fmcef58btS+tWTc/nqye174fPmmh38x1+XUVJTywSP/T0L9aXQeyLyMyBDRM4BXgD+FbmyEs/Ly8vp3a0LM4b1croUV+iZmUZBfjbzi2wfsnFOMBTkxzb5a9Ok3J5caOEhjlHV3cBCYJbDpcSMYDcKNxy2i7Zge7hiaw8HhD5Bvh2oBFYB/wHMA34eqaISTdX+Bhauq+TSSQNItv20ITtndB+Ktu+lfJcNZhN9wVCQ62fkM6gdb9UmmtsC4SH3v2nhIdEgIjmBlWNEJAM4G1jrbFWxo8RFh+2i7WD3C2sPB4Q4QVZVn6o+rqpfUdUvB75OmLdkIu1fn22j2ae2vaKdzg4ciJpvoSEmylqHgtwyc5jT5cS0Qdkevj4jn5dXlLN6q4WHREE/4F0RWQksBear6r8drilmBNu15WXbCvLhPGkp9O7WxVaQA0KaIIvIKhFZedjH+yLykIh4I11kvGpq8fHu2gqeWlzMuAHdGdnXOue1x+BemZwwMIs/LNzEnjqL1DTRY6Eg7XNLIDzknnkWHhJpqrpSVSep6gRVHaeq/+10TbGkpKaWlCShf490p0uJSfneTEvgCwh1i8XrwGvA1YGPfwGLgB3AkxGpLI59VrabX7y6hqn3LOD6J5ey+0ATPzh7hNNludLdl46npraRe+YVOV2KSRAWCtJ+Fh5iYkVxdR0De2aQ4pJuFNGW6/VYAl9AqF0sZqjqjFaXV4nIh6o6Q0SuiURh8Wrhugq+/uelpKUkcc7oPlwyaQCnj8ghLcUGa0eMG5DFjacO4dH3NnHhCf05ZbgdchSRdFWtP+y6Xqpq0YNhEAwFeezak1zT8ikWXD0lj6cWF3P3vCJOHd7LJijGESXVtQejmM2R8r0eXtzXQF1jM560xG7NGOpfqK4iMiV4QUQmA10DF5vDXlUcm7dqO93SU1h659k8fPWJnDOmj02OO+nWs4czuFcmt7+8krpG+3UElrZOzhKRLwGLHawnbuyrb+I3b69ncr6FgrSXPzxkFBsr9vN8YbnT5ZgEpKqUVNcd7NZgjhTs7mEx1aFPkL8J/FFEtohIMfBH4EYRyQTujVRx8UZVWbS+ilOG9SIrw/Ythkt6ajL3XTae8l0H7KS831XA70Xk1yLyNHAjcKbDNcWFx97bTNX+Ru4830JBOuK8sX0pyOvJg/PXs7/BXsya6NpV18S++mZbQT6GYHeP4iqbIIfaxWKpqo7Hn+s+MbD5/xNVrVXV5yNbYvzYULGfHXvrOX1EjtOlxJ0pQ7xcMzWXPy/ewvLSXU6X4yhVXQXcDXwLmAl8R1Vtya6Ttu0+wOPvb7ZQkE4QEe48fzRV+xt47L1NTpcT80TkMhHZICJ7RGSviOwTkb1O1+VWwRZv7UnQSzTB7h6lNbYPOeQJxVutAAAgAElEQVT39kXkfPw9kL8nIv8lIv/V0QcVkR4i8qKIrBWRIhGZ1tGf5SaL1vsPp5xmE+SI+OmsUfTtns4dL61K6JPyIvIEcCswAbge+JeI3OJsVe5noSDhYeEh7fK/wEWqmqWq3VW1m6p2d7ootwq2eMvvZRPko8nypNLDk2qt3gi9zdujwFeB7wICfAXI68Tj/hZ4Q1VHAScACdGC4L31lQzr3ZX+PTKcLiUudUtP5eaZw1i3cx+bqxL61e9qYKaqblHVN4GpwIkO1+RqFgoSXsHwkAfesi1Rx7FTVRPi+TEaSqrrEIGBPW0MH0uetXoDQl9Bnq6qXwN2qeovgWnAoI48oIh0B04DngBQ1cZAHGZcO9DYwsdbajhtuK0eR9KpgajuxZuqHa7EOar6UOsgH1Xdo6o3HO9+IvInEakQkdWtrssWkfmBt3nni0jPSNUdq1qHgtx8hoWChEMwPOSl5eWs2WbhIcdQKCLPiciVge0Wl4nIZU4X5VYl1bX0655Oemqy06XEtLxsa/UGoU+Qgy2j6kSkP9AEDO7gYw7BH1v9ZxFZISJ/DBz2O4SI3CQihSJSWFnp/r6ZH2+pprHZx+kjbYIcSXleD/2z0lm8MXE7monI8MAWps9FZHPwI4S7PgnMOuy624EFqjocWBC4nFBah4LY4drwueUMCw8JQXegDjgXuDDwcYGjFblYSU2dHdALQb7Xw7bdB2hs9jldiqNCnSD/K5Dt/mtgOVAMPNPBx0zB/3bvI6o6CailjSddVZ2rqgWqWpCT4/5J5aL1VXRJSWLK4GynS4lrIsL0Yb1Ysrkany9hn3T/DDyCvwXjTOAvwF+PdydVXQTUHHb1xcBTga+fAi4JX5mxr7nFx72vr7VQkAjI8vjDQz7cWM3C9e5fBIkEVb2+jY9vOF2XW/l7INv2iuPJ9WbiUyjfldjbLI47QRaRJPwrSLtV9SX8e49HqWpHD+mVA+Wq+nHg8oskwP7IRRsqmTw4297aiYLpQ73srmvi8+0Je9g7Q1UXAKKqJar6Czre5q2Pqm4HCHzu3daN4u0dn6Bnl5axsWI/P501ykJBIuDqKXnkez3c81oRzS2JvVrVmojcFvj8exH53eEfTtfnRvsbmqna32gryCEItnorSfBeyMf9i6+qPuCBVpcbVLXDm8ZUdQdQJiLBo+BnAZ939Oe5wdbdB9hYsd/au0XJ9KH+fchLEncfcn3ghe0GEfmOiFzKUSa24RJv7/jAoaEg5421UJBICIaHbLDwkMMFD+YVHuXDtNPBFm+2gnxcucEJcmIfdg95i8VbIvIlCV9n/O8CT4vISvy9le8J08+NSdbeLbr6ZqUzJCeTDzcl7D7kWwEP8D3gJOAa4Gsd/Fk7RaQfQOBzRVgqdAELBYmO88b25eR8Cw9pTVX/Ffjyc+BS4AfATwIfP3aqLjcLtnizCfLx5XTtgictOeFbvYU6Qf4h8ALQGI5m5ar6aWC1aYKqXqKqcZ3ssGh9Jf2y0hneu+vxb2zCYsbQXnyypYamxHzbVvHvOX4VKABGAI938Ge9ClwX+Po64J+drs4Ftu+xUJBoERF+NscfHjLXwkMO9zf8Zwouw3847wL8B/VMO30xQbYtFscjIuRmexI+bjrUJL1uqpqkqqnWrLx9mlt8fLCxitOG59gqVBRNH+qlrrGFz8rivoNgW57G/6T6JdrxpCoizwBLgJEiUi4iNwD3AeeIyAbgnMDluPfrNy0UJJqC4SFz39/Mjj31x79D4qhU1VcDPc1Lgh9OF+VGJdW19OqaRtcuKU6X4gr53syEb/UW0m9KYGvF1cBgVf0fERkE9FPVTyJaXRz4rHw3++qbbXtFlE0d4kXE3w+5ID/hOodUquqr7b2Tql55lG+d1cl6XCUYCnLTaUMsFCSKbjtvJG+u3sH9b63j/q+c4HQ5seIuEfkj/haLDcErVfVl50pyp5LqOnJtPIcsz+vhnbUVtPiU5KTEXNwLdYvFH/CHg1wVuLwfeDgiFcWZd9ZWkCRwSiDAwkRHz8w0xvTrzoeJ2Q/5rkB/cQsXaCcLBXGOhYe06Xr853RmYX2QO6WkupZ8214RsjxvJo0tvoSOgw/1vYYpqnqiiKwAUNVdIpIWwbpcTVVZsqmaRxdtZtH6SmYM85LlsYCBaJsxrBdPfljMgcYWMtISqr3e9cAoIBUIbsJWwFadjuPddf5QkF9eNNZCQRxwy8xhPF9Yxj3zivjbDVNsWxqcoKrjnS7C7eqbWti+t972H7dDsNVbaXVdwkZzhzpBbhKRZPxPsohIDl888ZpW5n++k9+/s4GV5Xvo1bULPzlvJNdMzXO6rIQ0baiXuYs2U1hSw6mJFfFtT6od0Nzi4555FgripKwMf3jIL//1OQvXVzJzZES7E7rBRyIyRlXjuhVqpJXvqkPVOli0R7DVW3F1HdMT9M20ULdY/A54BegtIncDHxDnrdk6Ys22Pdz4l0L21Tdz72Xj+eCnM7ll5jBbiXLI5PxsUpKExYnXD/kjERnjdBFuEwwFuX22hYI4ycJDDnEK8KmIrBORlSKyKtAe1bSDtXhrv35ZGaQlJ1FSk7gH9UJaQVbVp0VkGf7DOgJcoqpFx7lbwnlp2VbSkpN45ebp9PDYDhSnZXZJYeKgHiwO7ENubvHx2qrt/OmDLYwbkMXdl8btIuspwHUisgX/wR4BVFUnOFtW7GodCnLuGAsFcVIwPORbf1vO84Xlib6aP8vpAuJBsbV4a7fkJGFgdgYlVYnb6i3ULha/BZ5TVTuYdxRNLT5e/WwrZ43ubZPjGDJ9WC/+750NPL5oM3/5qJiymgNkpCazZttefnjOCLxduzhdYiTYk2o7BUNBnrjOQkFiQevwkIsm9k/Y1lzW0i08Sqpr6ZaeQk87C9Quid7qLdT3EZcDPxeRjSLyaxEpiGRRbvT+hkqq9jdy2YkDnS7FtDJ9qBefwt3zisjp2oW5157ES9+eTrNPeW3VdqfLi4jW/VKtd+rxWShI7LHwkI4TkUEi8q6IFInIGhH5vtM1Oa2kuo48r8de/LZTMCxEVZ0uxRGhbrF4CnhKRLLxhw/8SkRyVXV4RKtzkZeWbyU7M43Trd9xTDk5P5ufnz+aEwb14ORW/ZBH9e3GKyu28rVp+c4VZ2KChYLEptbhIVdOyaVfVobTJblFM/AjVV0uIt2AZSIyP5EP+pVU1zJ2QJbTZbhOvtdDXWMLlfsb6N0t3elyoq6971sNw98+Kh9/RrwB9hxoYv7nO7lqci5pKXa4J5YkJwnfPHXIEddfOmkA976+li1VtQzuZfvSEpWFgsS2YHjIA2+tt/CQEKnqdmB74Ot9IlIEDCBOnrP3HGiisTn0w5s+Vcp3HWDO+H4RrCo+Bfdsryrfw4SBoa++i4A3M831K/ah7kH+Ff4s+E3Ac8D/qGpCZvi2Zd6q7TQ2+7jsxAFOl2JCdPHEAdz3xlr+sWIrPzhnhNPlGAeoKvfMs1CQWBYMD3n8/c1cPyOfsf1tFbA9RCQfmAR87Gwl4fHJlhouf2xJh+5rCyHtNyTH/9/shqcK233f22aNdP3f1VBXkLcA04EhQBdggoigqosiVpmLvLy8nGG9uzLe3sJxjb5Z6Uwf6uUfn27l1rOHu/6Vrmm/d9dVsHhTNXddOMZaMcYwCw/pGBHpCrwE3Kqqe9v4/k3ATQC5ue7oFLKy3L8u9/PzR9MlNfTwpy7JSVwwoX+kyopbed5MHrn6RKpqG9t1v4ff2cjKMvenYYY6QW4B3gEGAp8CU4ElwJkRqss1SqprWVq8i9tmjbQ/3C5zycQB/OTFlawo282JuT2dLsdEUetQkKunWJBPLMvKSOV7Zw7nv/9t4SGhEpFU/JPjp1W1zQRNVZ0LzAUoKChwxSmskuo6uqWncMMpg+35Nkpmd2BrysK1FXHR/SLUDbPfA04GSlR1Jv63bCojVpWLvLJiKyL+yZZxl1nj+pKemsQry7c6XYqJsmAoyE9njbJzAy5wzVQLDwmV+GeOTwBFqvqg0/WEU0lNHfneTJscx7g8b2ZcdL8I9ZmhXlXrAUSki6quBRL+yLeq8vLyrUwf6qV/Dzth7Tbd0lM5Z0xf/r1yW7sOfRh3ax0Kct5YCwVxg2B4yIaK/TxfWO50ObFuBnAtcKaIfBr4mON0UeFQUl17MALZxK68Vt0v3CzUCXK5iPQA/gHMF5F/AtsiV5Y7LCvZRWlNHZdNst7HbnXppP7sqmti0Xp7QyRRBENBfna+hYK4yXlj+1KQ5w8P2d/Q7HQ5MUtVP1BVUdUJqjox8DHP6bo6q6nFR/muA+TbBDnmBSO9gxHfbhXSBFlVL1XV3ar6C+A/8b99c0kkC4t1Pp/y4Pz1dO2SwqxxfZ0ux3TQqcNz8Gam8coK22aRCIKhIBed0J+JFgriKiLCnedbeEii2rb7AC0+JS/bulHEumB7uISYILemqu+p6quq2r5jjYcRkWQRWSEi/+7Mz3HK05+UsnhTNT+bM5rMBI1BjQepyUlceEJ/5hftZM+BJqfLMRF2/5vrUYWfnJfwO8RcaVJuTy6Y0I+5729mx556p8sxUVQcmGzl2QpyzBvQI4PkJKHE5Qf1nDyd8n2gyMHH77DS6jrunVfEqcN7ceXkQU6XYzrpyycNpLHZx0vLbG9jPFu9dQ8vryjn+hn5FgriYj+dNQqfD+5/a53TpZgoKg1MtoKrkyZ2paUk0b9HeuKtIIeDiAwEzgf+6MTjd4bPp/zkxc9IEuG+L02wPYxxYNyALE7K68lflhTj87n71K1p2yGhIDPd3bw+0QXDQ15aXs6abe7vtWpCU1xdR3pqEr27dXG6FBOCfG+mrSB30G+A24Cjtg4QkZtEpFBECisrY+cA1V+WFPPxlhr+84LRDLDOFXHja9PyKK6uY9GG2PldM+ETDAX5/lnDLRQkDtxyxjCyMlK5Z16R61tJmdCUVNeRm+0hKckWpdwgN9tDSY2tILeLiFwAVKjqsmPdTlXnqmqBqhbk5OREqbpjK66q5VdvrOP0ETlcXmBbK+LJ7HH9yOnWhacWFztdigmz1qEgV1koSFzI8vjDQz7cWM1C60CTEEqqa217hYvkezPZXdfEnjr3nu1xYgV5BnCRiBQDz+Lv1fg3B+potzteXkVKsnDfl8bb1oo4k5aSxFWTc1m4vpLiKne/LWQO9VyhhYLEo2um5pFn4SEJwedTSmvqrMWbixxs9Vbj3ufTqD9bqOodqjpQVfOBK4B3VPWaaNfRXivLd7Nks/8t2n5ZtrUiHl01JZdkEf72UYnTpZgw2d/QzEPzLRQkHqWlJHH7LH94yAt2wDau7dxXT0Ozj1xbQXaN4Gp/sYsP6tlySoj+9lEJGanJfMW2VsStPt3TmT2+H88XllHXaEEE8eCx9zZZKEgcmzXOHx7ywFvrqbXwkLgV7IZgK8jukRvoFFTq4oN6jk6QVXWhql7gZA2h2FPXxD8/3cYlk/rbAZ84d920PPbWN/OPFQkfFOl6FgoS/1qHhzxm4SFxK9gNwUJC3CMjLZk+3bvYCnK8e2FZGQ3NPq6Zagd84t1JeT0Z0687f1lSbKfjXe7+N9fj81koSLyz8JD4V1xdR0qS0L9HutOlmHbIc3mrN5sgH4fPpzz9cSkn5fVkbP8sp8sxESYifH16Pmt37OPjLTVOl2M6yEJBEkswPOQBCw+JS6XVdQzsmUFKsk1Z3CQv2+PqsBD7bTuODzdVsaWqlmtt9ThhXDSxP726pvGzV1a5ukVNorJQkMQTDA95cXk5n2/b63Q5JsyKrcWbK+X3yqRiX4Nrz/TYBPk4/rqkhOzMNGaP7+t0KSZK0lOTefiqEymrqePmvy+jyVpIucrCdZUWCpKALDwkPqkqpdV1B9uGGfc4eFDPpYEhNkE+hm27D/B20U6+evIguqQkO12OiaIpQ7zce9kEPtxYzX/9c4094bpEc4uPu+cVWShIAgqGh3ywscrCQ+JITW0j+xqabQXZhfKDrd6qbIIcd575pBQFrpqc63QpxgFfPmkgN58xlGc+KeWJD7Y4XY6jRKRYRFaJyKciUuh0PUdjoSCJzcJD4k8wrthavLlPrje4guzOg3r2DHIUjc0+nvmkjDNH9rZDPgnsx+eOZPa4vtw9r4i3P9/pdDlOm6mqE1W1wOlC2hIMBTk5v6eFgiQoCw+JPwdbvNkE2XWyMlLp6Ul1bas3myAfxT9WbKVqfwPXTLO3aRNZUpLw4OUTGT8gix88/ym76xqdLskcRTAU5M7zx1goSAKz8JD4UlJdhwgM7GkTZDfK82ZSahPk+FHf1MJDb6/nhEE9OGNEjtPlGIdlpCXz6y+fwP6GZh5J3DACBd4SkWUictPh3xSRm0SkUEQKKyujv/8zGApyoYWCJDwLD4kvJdV19OueTnqqnQNyozyvh2KX9kK2CXIbnlxczPY99dw+a5StRBkARvbtxiUTB/Dkh8Xs3JuQYQQzVPVEYDZwi4ic1vqbqjpXVQtUtSAnJ/ovKoOhILdZKIjBwkPiSYm1eHO1PG8m23YfoLHZfWcCbIJ8mN11jfzh3Y3MHJnDtKFep8sxMeQHZ4+gxaf8/p0NTpcSdaq6LfC5AngFmOxsRV+wUBDTFgsPiQ8l1uLN1fKyPfgUyne5b5uFTZAP88jCTexraOa2WaOcLsXEmFyvhysn5/LsJ2Wujs9sLxHJFJFuwa+Bc4HVzlblFwwFybJQEHOYQdkerpueZ+EhLravvonq2kZbQXax/F7+FzduTNSzCXIrW3cf4M+Li7ls0kBG9+vudDkmBn33zGGkJAsPzV/vdCnR1Af4QEQ+Az4BXlPVNxyuCbBQEHNs35k53MJDXCw4qbIVZPfKzfa/uHHjopJNkFsJTnp+eO4Ihysxsap393S+Pn0w//xsG2t3JMaqlKpuVtUTAh9jVfVup2sCfyjIPYFQkKstFMS0oXV4yHsJFB4iIn8SkQoRiYl3ejrKJsju16trGplpya5s9WYT5IC1O/by0vJyvj49nwE9Mpwux8Swb58+lK5dUrj/zYRaRY45zxWWscFCQcxxHAwPmZdQ4SFPArOcLqKzSmqCPZBti4VbiQi53kxXxk2nOF1ArPj9Oxvp1iWFm88Y6nQpJsZleVL51ulD+fWb61hRuotJuT2dLinhWCiICVUwPOTbTy/nhWXlXJkAyaiqukhE8p2uo7VF6ytZtXVPu+7zdtFOenVNo2sXm6q4Wb7XQ2HJLh5+d2O77teraxqXFwxyrJuY/dYBtQ3NvP35Tq44eRA9PGlOl2Nc4OvT85m7aDNzF23mkWtOcrqchBMMBXn8awXWitEcVzA85MH567nohP5k2oSLQD/zmwBycyP/ouEHz31KdW37g5bmjO8bgWpMNJ2cn83rq3fw6zfb31Hm5PxshuR0jUBVx2d/JYB31lbQ0Ozj/An9nS7FuERmlxSumZrLHxZusj6dURYMBbnohP62em9CEgwPufQPi5m7aDM/OMfOmajqXGAuQEFBQURPMO4NdKP4yXkj+eapg9t137Rk2z7ldt84ZTDXTM1DCf3X7NPS3Xx17kcUV9c6NkGO+m+eiAwSkXdFpEhE1ojI96Ndw+FeW7md3t26UJBnT7YmdNdNyyc1KYknPtjidCkJ5YG3/KEgP7FQENMOB8NDFm1O1LAfxwSjhofmZNIlJbldH/YOUXxIS0lq1//3Yb39k2In28M58dKsGfiRqo4GpuJP5RrjQB2Afy/ju+sqmDO+H0lJNhBN6Hp3T+eSSf15vrCMXR1469C035pte3hpuYWCmI756axRtPjUwkOiLBg1HGz5ZczxZGf6954n1ARZVber6vLA1/uAImBAtOsIWlC0k4ZmHxdM6OdUCcbFvnnqEOqbfPztoxKnS4l7qsrdr1koiOm4YHjIC8vKKdoev20aReQZYAkwUkTKReQGJ+uxdm2mvUSEPK/H0f7Jjm7uCZyynQR83Mb3bhKRQhEprKyMXP/K11Zup2/3dE60vYymA0b06cbMkTk8taSY+qYWp8uJaxYKYsLhOzOH0z3dHx4Sr1T1SlXtp6qpqjpQVZ9wsp6S6lp6de1ihyNNu/gnyAm0ghwkIl2Bl4BbVfWIl/KqOldVC1S1ICcnJyI17KtvYuH6SmaP72vbK0yH3XjaEKr2N/KPFVudLiVuBUNB8r0eCwUxnZLlSeV7Zw3n/Q2JFR7ipOLqOvJt9di0U543k7JddbT4nEnBdGSCLCKp+CfHT6vqy07UALCgqIJG215hOmnaEC/jBnTn8fc343NoIMe7YCjI7bNHWyiI6bRrg+EhrxU59uSbSEqr68i1CbJpp7xsD00tyrbdBxx5fCe6WAjwBFCkqg9G+/Fb+/fK7fTLSmfSINteYTpORLjx1CFsqqzl3XUVTpcTdywUxIRbMDxk3c59vFBY5nQ5ca2+qYUde+vJt1aYpp2C7VOd2mbhxFLMDOBa4EwR+TTwMSfaReytb2LR+krrXmHCYs74fvTPSuf372xE1VakwikYCvKzOaOt5ZMJm1nj+nJSXk8emL+e2oZmp8uJW8GIYTugZ9or+DsTjByPNie6WHygqqKqE1R1YuBjXrTrePvznTS2+DjftleYMEhNTuLWs0fwadlu5q3a4XQ5cSMYCnKhhYKYMAuGh1Tua2Duos1OlxO3iqv8kxsLUzLt1bd7OmkpSQm1ghwTXlu5nQE9Mpg0qIfTpZg48aWTBjKqbzfue6OIhmbraBEOwVCQ2ywUxETAibk9Od/CQyIquIJsh/RMeyUlCXnZzrV6S8gJ8saKfSzaUMnscX3tLVsTNslJws/mjKas5gB/XWJ9kTvLQkFMNNweCA958K31TpcSl4qra+menkIPT5rTpRgXcrLVW8JNkPccaOLGvywjKyOVG9qZCW/M8Zw2IofTR+TwuwUb2F1n6XodparcM89CQUzkBcNDnl9WFtfhIU4pqa4jv5dtrzAdk+fNpKS6zpGzPQk1QW7xKd97ZgXlu+p45JqT6JeV4XRJJg79bM5o9jc087sFG50uxbUWrqvkw40WCmKiIxHCQ5xSUl1Hrr0DZDooz+vhQFMLlfsaov7YCTVB/t831/Le+kp+edE4Ts7PdrocE6dG9u3G5QWD+OtHxQcPqJjQWSiIibbW4SELrVVj2DS1+Ni6+4C1eDMdFjzcWezANouEmSD/89OtPPbeZq6ZmstVU3KdLsfEuR+eM4LU5CT+9821TpfiOs8XlgdCQUZZKIiJmmB4yL3z1lp4SJhs3XWAFp9aSIjpsLzAuw9OHNRLiGef1Vv3cNuLK5k8OJv/umCs0+WYBNC7ezr/cdpQ5q3awWdlu50uxzX2NzTz4MFQkL5Ol2MSiIWHhF9xYFJjK8imowb0zCA5SRw5qBf3E+TddY1862/L8Gam8YerT7QVKRM13zglnx6eVH67YIPTpbiGPxSkwUJBjCMsPCS8LCTEdFZqchIDemRQUmMT5LDy+ZTvP/spFXsb+MM1J9GraxenSzIJpFt6KjeeOoR31lbYKnIILBTEOM3CQ8KruKqO9NQkenez517Tcf5Wb7bFIqx+u2AD762v5K6LxjDRAkGMA742Lc9WkUNkoSAmFlh4SPiU1tSSl51p7waZTnGqF3LcTpDfXVvB797ZwJdOHMhVk+1QnnFG61XkleW2inw0wVCQr1soiIkBPz1vFM0+Hw+8tc7pUlytuLrOtleYTsv3ZrLnQFPUswXicoJcVlPHrc99yui+3bn70nH26tU46uAq8tu2ityW1qEgt1goiIkBuV4P103L54Vl5RYe0kE+n1JaYyEhpvOCrd6ivYocdxNkn0/5zjMrUFUeveYk0lOTnS7JJLjgKvICW0Vuk4WCmFj03TMtPKQzduytp7HZZyEhptOC70IUR3kfctxNkP+9ajufle3mrgvHWu9FEzO+Ni2PrAxbRT6chYKYWJXlSeX7Zw2npyeN+qYWp8txneBqn7V4M50VfJFVGuUV5JSoPlqENTb7uP/NdYzu151LJw1wuhxjDvKvIg/m/rfWs7J8NxMG2qFR+CIU5NFrrAWjiT3Xz8i3LXodFOw6YHuQTWelpybTt3t61NP04uoZ6e8fl1BaU8fts0eRlGR/1ExsuW56PlkZqby7ttLpUmKChYKYWGeT444rrq4jNVnol5XudCkmDjjR6i1uVpD31Tfxu3c2Mn2ol9OG93K6HGOO0C09lQU/Ot36cQfMDYSCPP61k2wiYkycKa2pZWBPDynJcbUOZxyS5/Xw7rroLi7FzW/u44s2U1PbyO2zR9mTrYlZNjn227GnnrkWCmJM3CqushZvJnzyvJlU7muIasKlIxNkEZklIutEZKOI3N7Zn1exr57H39/CBRP62d5OYyIg3GP2/rfWWSiIMRES7vHaXqr+Fm951sHChEnwxVZpFCOnoz5BFpFk4GFgNjAGuFJExnTmZ/727Q00tfj48bn2ZGtMuIV7zFooiDGRE4nn2Paqrm1kf0Pzwf61xnRW/sFeyNHbh+zEHuTJwEZV3QwgIs8CFwOfd+SHba7cz7NLy7hmSq41JDcmMsI2Zg8JBTnDQkGMiYCwPse+UFjGw+9ubNd9mloUsA4WJnyCbXvvfGU1972+9pi3vfeyCUwb6u30YzoxQR4AlLW6XA5MOfxGInITcBNAbu7Ro6J9qpwxIofvnjU8zGUaYwKOO2ZDHa8tPuWEgT2YPa4fWR4LBTEmAsL6HNurWxdOGNT+rYunpeUwZUjnJynGAHRPT+WH54xgU+X+4982IzxTWycmyG2doNMjrlCdC8wFKCgoOOL7QcN6d+OJr58cvuqMMYc77pgNdbymJCdx26xR4a3OGNNaWJ9jZ47szcyRvcNXnTEd9L0oL4Q6cUivHBjU6vJAYJsDdRhjQmNj1hj3sPFqTBg4MUFeCgwXkcEikgZcAbzqQB3GmNDYmDXGPWy8GhMGUd9ioarNIpHhFm0AAATBSURBVPId4E0gGfiTqq6Jdh3GmNDYmDXGPWy8GhMejiTpqeo8YJ4Tj22MaT8bs8a4h41XYzovbpL0jDHGGGOMCQebIBtjjDHGGNOKqB61u0vMEJFKoOQ4N+sFVEWhnHBwU61g9Uba4fXmqWqOU8V0VhyOV7B6I83t9cb7mHX7/59YZ/VGVofGqysmyKEQkUJVLXC6jlC4qVaweiPNbfWGg9v+zVZvZFm9sc1t/16rN7ISpV7bYmGMMcYYY0wrNkE2xhhjjDGmlXiaIM91uoB2cFOtYPVGmtvqDQe3/Zut3siyemOb2/69Vm9kJUS9cbMH2RhjjDHGmHCIpxVkY4wxxhhjOs0myMYYY4wxxrTi+gmyiMwSkXUislFEbne6nsOJyJ9EpEJEVre6LltE5ovIhsDnnk7W2JqIDBKRd0WkSETWiMj3A9fHZM0iki4in4jIZ4F6fxm4frCIfByo9zkRSXO61iARSRaRFSLy78DlmK013GJ9vIK7xqyN1+iwMRu7Y9ZN4xVszEZDuMarqyfIIpIMPAzMBsYAV4rIGGerOsKTwKzDrrsdWKCqw4EFgcuxohn4kaqOBqYCtwT+m8ZqzQ3Amap6AjARmCUiU4FfAQ8F6t0F3OBgjYf7PlDU6nIs1xo2Lhmv4K4xa+M1OmzMxu6YfRL3jFewMRsN4RmvquraD2Aa8Gary3cAdzhdVxt15gOrW11eB/QLfN0PWOd0jceo/Z/AOW6oGfAAy4Ep+FNzUtr6PXG4xoH4//idCfwbkFitNQL/dleM10BtrhyzNl4jUqeN2S8ux+SYdet4DdRnYza8NYZtvLp6BRkYAJS1ulweuC7W9VHV7QCBz70drqdNIpIPTAI+JoZrDryd8ilQAcwHNgG7VbU5cJNY+r34DXAb4Atc9hK7tYabW8crxPDvf5CN14ixMfsFt/xbY/b3vzUbsxERtvHq9gmytHGd9a0LAxHpCrwE3Kqqe52u51hUtUVVJ+J/5TgZGN3WzaJb1ZFE5AKgQlWXtb66jZs6XmuEJNK/NapsvEaGjdmE+rdGlY3Z8Av3eE0JS1XOKQcGtbo8ENjmUC3tsVNE+qnqdhHph/9VWcwQkVT8A/dpVX05cHVM1wygqrtFZCH+fV09RCQl8KoxVn4vZgAXicgcIB3ojv/VbizWGgluHa8Qw7//Nl4jysasO8dsTP/+25iNmLCOV7evIC8FhgdOKKYBVwCvOlxTKF4Frgt8fR3+PUgxQUQEeAIoUtUHW30rJmsWkRwR6RH4OgM4G//m/HeBLwduFhP1quodqjpQVfPx/66+o6pXE4O1RohbxyvE7u+/jdcIsjHr2jEbk7//YGM2ksI+Xp3eUB2GDdlzgPX498Tc6XQ9bdT3DLAdaML/avwG/HtiFgAbAp+zna6zVb2n4H/7YSXwaeBjTqzWDEwAVgTqXQ38V+D6IcAnwEbgBaCL07UeVvcZwL/dUGuY/90xPV4DNbpmzNp4jWrtNmZjcMy6abwG6rUxG526Oz1eLWraGGOMMcaYVty+xcIYY4wxxpiwsgmyMcYYY4wxrdgE2RhjjDHGmFZsgmyMMcYYY0wrNkE2xhhjjDGmFZsgG2OMMcYY04pNkI0xxhhjjGnl/wNoAwBzzYx1RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b8a8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minima add up to zero in  inflammation-11.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b25c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-12.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11afa8e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "filenames=glob.glob(\"inflammation*.csv\")\n",
    "\n",
    "filenames=filenames[0:12]\n",
    "\n",
    "for f in filenames:\n",
    "\n",
    "    data = numpy.loadtxt(fname=f, delimiter=',')\n",
    "\n",
    "# check for errors\n",
    "    \n",
    "    if (numpy.max(data,axis=0)[0] == 0  and  numpy.max(data,axis=0)[20] == 20):\n",
    "        print(\"suspicious looking maxima in \",f)\n",
    "    elif  ( numpy.sum( numpy.min(data,axis=0) ) == 0 ):\n",
    "        print(\"minima add up to zero in \",f)\n",
    "    else:\n",
    "        print(\"file seems ok \",f)\n",
    "    \n",
    "    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "\n",
    "    axes1 = fig.add_subplot(1, 3, 1)   \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": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "x=1\n",
    "x=x+1\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "long_variable_name_here=1\n",
    "long_variable_name_here=long_variable_name_here+1\n",
    "print(long_variable_name_here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "long_variable_name_here=1\n",
    "long_variable_name_here+=1\n",
    "print(long_variable_name_here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fahr_to_kelvin(temp):\n",
    "    return (( temp-32) * (5/9)) + 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "273.15\n"
     ]
    }
   ],
   "source": [
    "result=fahr_to_kelvin(32)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "freezing point of water 273.15\n",
      "boiling point of water 373.15\n"
     ]
    }
   ],
   "source": [
    "print(\"freezing point of water\",fahr_to_kelvin(32))\n",
    "print(\"boiling point of water\", fahr_to_kelvin(212))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# same function more less compact\n",
    "def fahr_to_kelvin(temp):\n",
    "    result_in_kelvin=(( temp-32) * (5.0/9.0)) + 273.15\n",
    "    return result_in_kelvin\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "freezing point of water 273.15\n",
      "boiling point of water 373.15\n"
     ]
    }
   ],
   "source": [
    "print(\"freezing point of water\",fahr_to_kelvin(32))\n",
    "print(\"boiling point of water\", fahr_to_kelvin(212))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "273.15\n"
     ]
    }
   ],
   "source": [
    "temp_in_kelv=fahr_to_kelvin(32)\n",
    "print(temp_in_kelv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5555555555555556"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5/9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5//9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kelvin_to_celsius(temp_k):\n",
    "    return temp_k-273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(kelvin_to_celsius(273.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kelvin_to_celsius ( fahr_to_kelvin(212) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fahr_to_celsius(temp_f):\n",
    "    return kelvin_to_celsius ( fahr_to_kelvin(temp_f) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahr_to_celsius(212)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-01.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b240b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-02.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b008400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minima add up to zero in  inflammation-03.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11afa1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-04.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aeeb588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-05.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b6dbcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-06.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7d5ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-07.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0b6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minima add up to zero in  inflammation-08.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b39cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-09.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b37bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-10.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11afd5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minima add up to zero in  inflammation-11.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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8yPcBnwEeAh6z13DXWq+jmzx5fo5Hz7QeF+2Qsce+Rl3NOiP9IcC7eHVOwrEmHmSH268Y5uJCjpNTjfmoF+eznJ3NAHgqyNmC1dzT4EGOBFnMFZtO7VKUzUzFg1xjsbCOoXprUlVtbn6hC9VUmxOXGo/jxVyRdL7ESDxMsWxYzNXuw5ngV1+ED8VCnp5lhwvzWeYyBR1JrSjKhqYrKRbGmPcaY64yxlxrjPkpY0yuG+voFou5IvPZQkeFZCUaKlTb+Q4w51G8OjFv0TYn1lv3DgJw/3PTDc89dGqm8rVXEV5t7mls0jMGFnLqQ1aUeioe5CUoyF4TMd3sGooBcHqmsUB27BVXb7V8xfVFr5fFAlpHzwGVKEe1UymKspHRSXpdYCFbtArJDhranBOlu+ANB/zEQn5PlSeVLxHy+wgFWv/T7huNM9QX4j6PAtmxV4B3c49zYqz3IDdrOHK4MJdtULHcvP/LT/LTH7+/5boVpVfxUpCbFcjVXoLWF7pDfSFiIb/npLwLc5bucJXdeFd/LDsXsvUXuslosGXMm7NWHQqkKMpGRgvkLuB0j89mvBvg3GQK3k13A9Egsx4nqEy+WHMCboaIcOueIe4/0dgB/9Cp2Urck5fFopWCDM1PnG/56Pf54xbDDZ6+sMBR7Z5XNiheCrJzzNRfVFbTaForyCLCzsEYp6czDc85CvLB8QTQeDeoYrHwUJAXWliltEBWFGUzoAVyF3D8hq18fg4VBTlYW/QOxEKexWsqXx0q0o5b9w5xejrD+bnqyTVXLPHYmTledOUIAZ94WyzsW6xeHmTA8/asMYYzM2nOzjYqXQ6z6ULl4kFZP4jIx0Xkoog87nrsfSJyVkQetj9e08019gLpfJFI0IffJ5XHIkE/oYCvoUBe7NCDDLBzKMYZD4uF06DnKMj17xcL2SJ9IX/NesC6E9TKKuUc39qQqyjKRkYL5C6wmLNOLF4KcD1pDw8ywEAs6Flgp/Pe42m9uHXvEFDrQ37i3Dz5Uplbdg/a+2ilINcrT47FovHEupgrUiiZlhcFs5k8i3lt8luH/BXwKo/HP2iMudH++Moar6nnSOWLNeqxg9c0PceO4bV9PTuHopyaTjekVEzMZ0lEAmwbiAIwk6ovkAsN6rGzHmhulVIFWVGUzYAWyGtMoVQmW7CmZjXLGHaT8fAgg10ge5ygmo2n9eLqrQn6w4GaAvkh2398865BBmIhZlJeCrLjQe5cQZ62T86tTqqz6QLGwGJeVeT1hDHm20CjWV1ZEulcydP+lPAY0V5Jo+nALrVzMEY6X6ocYw4T81nGkxGS0SAiXhaLYk3Em0MzXzRAqWwqvRM6jlpRlI2MFshrjNtC0EkXuBPzVq8gJ6PeUUyZfKnlcAE3fp9waM9gbYF8aobtA1G2JCIMxoKePun5Jt3vztADr9/LOXl7Nf0BFEvlyom3k+ZFZV3wThF51LZgDHptoCPjqyzmlqAg5zpXkKtJFrU+5AvzOcYSEfw+IRFpvBu0mCs2HMPQ3BcNde9fepwqirKB0QJ5jXGnOCzFgxwJNCrIc5nG8P9UvtiRb9Hh+XuHOHpxkalFq+P9oZOz3LJ70N6HdxE+nykQCfoI160pHgog4n3idArjZr+z+2fUh9wTfATYB9wInAf+yGsjHRlfJZ0veR6bXgVyKl9CpLH3wIuddoFcn2QxMZdlLBEBYDAWbFCQ55ehILsfU4uFoigbGS2Q1xj3rdROPMiZfJFo0I+vrpFmIBqkUDKVAtohnS917EEGuM32IT9wYoZzsxkuzGe5eZc1fMA6qTYqvnOZxil6AD6f0B8ONFGQrcdyxXLFNuLGvZ8Fbf5Z9xhjJowxJWNMGfgocGu317TeSeWLnqkUiWiwwbefzhWJeRz3XuwYtDzGp10FcqlsmFzMMW4XyAOxUGPMW7bgqSBrgawoiqIF8prjVkc7VZC9hn4M2HaG+iI7lSt27EEGuG77AOGAj/ufm67kH99sK8iDsRAz6UKDSj2fLTT4jx2aDRlwNwh52Tbcfwu1WKx/RGSr69sfAx5vtq1ikc4tTUHu9EK3LxxgJB6qSbKYWsxRKhvGEmHAutj1GhTS36RgB+9eAvdjOihEUZSNTOdSo7IiOMWfCMx1koOcL3neZh2IWeOmZ9N5tttd6pXtl1AghwI+bt41yP0npjAYIkEfV29NVPaRL5bJFGp9zfOZYkOChUMiEvRWkF3q1Wy6wNZktOZ5999CJ/GtL0Tk74EXAyMicgZ4L/BiEbkRMMAJ4D92bYE9QqsUC2eypqMYp/NLu9DdMRirsVg4EW9Vi0WIoxcXa36mmYLsRL+1UpC9inpFUZSNhCrIa4zjQR7rj3R0gmnmWxywVR63KmSMaXoSbsWte4d48tw83z4yyfU7Bgj6rf8Wg7ZK3ehdbKUgBzxj3twKspdto1ZB1hPvesIY8xZjzFZjTNAYs8MY8zF7RPx1xpjrjTGvM8ac7/Y61zvpfMnbYhEJNqS3pHLFjpttwfIhu4eFTMxbPQXjSZfFwnUMOmk6Xh5kEWlaADuP7RyKaoG8jhCRV4nIMyJyTETe4/F8WEQ+ZT9/n4jssR9/uYg8KCKP2Z9futZrV5T1ihbIa4yjju4YjHZksbDU21YKcvU1csUyZdNZNJSb2/YOUTbw7GSKm3dVwwicfdTnp8438SCDrSA3iXkL2OqYVzyUWiyUjU4q5z3lsuL5dR0DqVyJviUcx7uGopybzVAsWRGSjQpykFS+RL5oPb/YJInGIRHxvtB17g7tHIzpcbpOEBE/8GHg1cA1wFtE5Jq6zd4OzBhj9gMfBH7PfvwS8FpjzHXA24C/WZtVK8r6RwvkNcZRR3cOxTps0mvnQa4Wr6klREO5uWnXYKV4dRIsavbh0f3uDAWpx2o48i6QnTgqr997NlNABHyiCrLSGxRKZf7uvlOUOhhsUyyVyRXLnsem14j2dH6JCvJgjGLZcH7OKown5rL4fcJI3PIgD/RVLVlQvQj1UpChuYViLlPA7xO2JpsryNlCiU/ef0oH/qwdtwLHjDHHjTF54JPA6+u2eT3wCfvrzwB3iogYY35gjDlnP/4EEBGR8JqsWlHWOVogrzGL2SIBn7AlEWbOowGunnTB+0SZ9LBYpJsMFWlHNOTn+h1JAG6yEyzA8i1CrSXCGNNSQbb8lI3K0nQ6z96RvobXc5hL50lGg/RHghrzpvQE3zl2iV//3GPcd3yq7bapJhMxwTWB0nVhmMovTUHeWclCtnzIE/NZRuPhyhjperuUs694k0bARIsCORkNkowGWcwVK4q1m289fZH3fPYxHj831/H6lctiO3Da9f0Z+zHPbYwxRWAOGK7b5ieAHxhjcl470UxzZbOhBfIaY02vCjAQDZEvlSuDQJqRbtKkFwn6iQR9daqT9VpLVZAB3njLTl597XhFcYLqSdU9YCBTKFEsm0qBXk8i4n3inEnl2ToQsdbsYbGYSRcYiAaJhwN661bpCZzjwlFtW1EZHe1RkHqNdk43GSrSjJ2DVoF8xvYhX5jPVhIsoPFi1znGmjbbNkmjmc9aDbpOUe91rE7amer1vQvKquGVBVivvLTcRkSeh2W7aNpsq5nmymZDC+Q1ZjFXJB4JVOwL7RpdWqVSDERDNcVryj4JL9WDDPDvbtvFR956S+3rV06q7mgn+8TaokkPageilMqG2UyBoViIgWhjHitYFotkLER/JKATupSewDkWJhbaF8ipXHMF2St3ONWkoa8ZWwesiXluBdnxH4PbLuUUyNa+WlksvKxSbgW5fs0OlxatfWgM3JpxBtjp+n4HcK7ZNiISAJLY4+NFZAfwOeCnjTHPrvpqFaVH0AJ5jVnIFugPBz1TKLxolmIB1knP/fOZy1CQvQgFfPSF/DUFraMqtWrSA2oafOYyBYyBwb5Qw5or26TzDESDtgKtJ1Zl/eMUhxNLUZBbeJDdx0x6iRMxg34fW5ORStTbxHyukmABbgXZWrNzAdusSS9pDy9pyEDPWAk2rQvkXNPnlFXhAeCAiOwVkRDwZuDuum3uxmrCA3gj8E1jjBGRAeDLwK8ZY+5dsxUrSg+gBfIas5C1FORkkwa4ejKFFgpyXbHpNOkt5cTajvpx044q1KpJD2r9lNN2CsaQUyA3adIbiAWJR9RiofQGTgHoJEa0oqIge9zdiYcC+KT6evlimULJLElBBstmcXo6TbZQYi5TqFGQm1ksmqdYBMnbUXBunAK51TCRKS2Q1xTbU/xO4KvAU8CnjTFPiMhvi8jr7M0+BgyLyDHg3YATBfdOYD/w30TkYftjyxr/CoqyLtFBIWvMYq7IeCLiUmCaDwsplQ35YplY0PufaSAa4vilavh/ukUj0HIZ7AvW2DjaK8jWWt0nR+ekPNQXalizw6ztQQY4dlELZGX941wsOpnDrXAUZK+mOJ9PaprinG2XehzvGorxzWcucmGuNuINrEbccMBXudh1LBbxFgoyWMex+wK9E4vFlGOx0DSaNcMY8xXgK3WP/XfX11ngTR4/9zvA76z6AhWlB1EFeY2pNOl55BjX0+5E2aAgt2gEWi7OuGmH9h7kxoYjR0EejIXsgrv2dy6VDfPZqgd5USfpKT1AxWLRiYJcuXhtMYEyW2t/WKpVaudQlMmFHCemUgCMuwpksI/lVFVBDgV8hAPe7y1JD4XYGPs4bVcgp9SDrChK76MF8hpTadJrcYJxcDzFkSYFctK2Kzg+wcwqKMiWxcJLQe7cYjHjslgko5Zlw+1tXMjaHuWYFfNmfa8Zqsr6xjl2Ly7k2mYhp52it0kDbbJGQW5ux2iFE/X20MkZgJoUC7AuqKsxb83HxUPVQuV+f8oUShRKhkQk6Nlr4KAeZEVRNgJaIK8hxhirSS8SJBbyE/RLy2EhlROlR8wbWBaLfLHqE6x2yq+kghysU5Bbd787J133iXPKpSAPxIIN8XbO6w/ErJi3QsmQKzbmqyrKesJJWymVDVOp1jaLdgqyu0Be7sAfp0C+/8Q0AGPJRgXZudhdzBWbHsPOeqB2up+zvmQ0SCToI+T3NRTB2UKp4m/2Kp4VRVF6BS2Q15Cc3XwTDwcQkYqa2ox2nuL6aXrpfJFwwFcZDrASDMRCzGcLFYVsPlskGvQTCnj/14mHrYajegU5GvQTDfld2crV552T9kA0VC2w1b+orHPmM4VKk9vEXJsCuU0DbSIaqFx8LreXwMlCfvj0LLGQn/46q9VgX9DVpFdo2qAH3hYLp+BNRoOIiOcwkWnXWHpVkBVF6WW6UiCLyICIfEZEnhaRp0TkBd1Yx1pTH86fjAZaNuk5KmuzFIv6YjOVL66o/9jZhzHVk53Vxd58H86Js8aDnM4zZI+6TUYbp/M5KnrStlgAOk1PWffMZQpcOdYPtE+ySOUtz2/Q7/2WaynI1v/5ioK8xGN5JB4iGvSTLZQZS0QQqb1QdifSLGSLTafoQbUJ113kOl87x38iGmi4kHUa9OLhgBbIiqL0NN1SkP8U+CdjzFXADVjRNBsep/nG6Ryvj1CrJ9P2tmxto1+rzOTlUh8PNZ9tPmbawWo4qha4M6k8g31B+/U8bt06Fgt7kh54T+hSlPVCsVRmMVfkyrE40L5RL50r0dfi2HQuKo0x1YmYSyyQRYSdQ1Gg0X8M1rHn9Cy0U5ATHj0SbouF87m+Ee+SbTW5YrRPC2RFUXqaNS+QRSQBvAgrlxFjTN4YM7vW6+gGjiraH7ZOMAMetyjddJJiAVWLQjq38gVysm4f85li0wQLB/ftYoDpdKFSaHtN56tYLOwUC9ACWVnfOP8/943G8Un7AjmVL7bsDXByh3PFcjWNZhnHsmOzqE+wAOti10qMKdppOs2PY79P6A8HanzE8x4Fcv3716UFu0Ae6WMhW6DcpnlRURRlvdINBfkKYBL4SxH5gYj8hYj0dWEda0599miyyVQ5B8diEWnWpFfxIFctFivZoAcuBTnldL8XWna/Q21klfWzVYtFvW/a+rqajFGxWOg0PWUd4xSGg7EQo/3hSvZwM9K5UtMEC6jNHU5Xhoos/Vh2GvXGmhTIYF2QOnGTraj3GFcsFvYxmog0KshOQ+7ekThlA4t5vdBVFKU36bhAFpHdIvIy++uoiPTn3gy7AAAgAElEQVQvc58B4GbgI8aYm4AU1ak+7v29Q0QOi8jhycnJZe5qfbGQqx0WMBANtVGQ2zTpeVgsWp2El4NjiahYLOxJWq1IRGpPrDOpfOXk7BQCtU16VtEd8PsqJ+15VZCVdYxzAZiMBhlLRJhYaJdi0bo/wF0gOwpytMmFcStaFsi2zWkqlW+bYgEtCuQWCvLUYo5I0Bp7DbVWKkVRlF6iowJZRH4O+Azwv+2HdgCfX+Y+zwBnjDH32d9/BqtgrsEYc5cx5pAx5tDo6Ogyd7W+qDbpVU8wi7kihZJ3pFm7AjkS9BEK+FwpFqUVV5DrB5rMZTrwIEert2bzxTILuSLDtoIcCfqJBv012cqz6XxlP2qxUNYLj5+d44/vOeL5nLtYHEtEmGinIOdLLWPb3AN2UjkrKWY5aTQ7By0P8niysUB2jrGzMxmAhpSLepJ1TXjz2QL94UBlXcmo1WvgziyfWswzEg+3HEVdLhved/cTPH1hfim/mqIoyprSqYL888AdwDyAMeYosKx57caYC8BpETloP3Qn8ORyXqvXWKyzWDh2g2YTp7JtUixExPIxVxTk4op7kBMR64Q4k87bk7SKLVMsrJ+pWiwc5XnQLpChcQLgbKZQ+VtUm/RUeVK6yxcePsuHvnG00gvgxt2wNp6ItE+xyLU+NmsV5OXfCbp17xCvvnacW/cONTzn3MU5NZ0GaG+xqLNQzNXdPUpEA5TKppLxDHAplWc4Hm45ae/8fJa/+u4J/tvnH9eBQIqirFs6LZBzxpiK5CciAeBy3tl+AfhbEXkUuBH43ct4rZ7BUUUrFos6D3E96XwRv08INYmGAif83xkwsPIKslOEz2YKpPMlSmXTVkFORoOk8yUKpXIlF3WopkAO1TXpFSon1IDfRyzk15g3pes4kWXOZzfuTOCxRJi5TKFyQevFUiwW6dzyewkGYiE+8tZbGIl7p1gAnK4UyO2PY3eBW2+v8iqCLy3kGOkLVXOUvUZR25P2Hjgxw78c2Rj2OUVRNh6dFsj/IiK/DkRF5OXA/w98cbk7NcY8bNsnrjfGvMEYM7Pc1+olFnPWIA9nyIaXH9dNOl8iFvQ35Jm6scZNVweFLKfzvR2W4puvjplum2JhPb+QLVbGTDvqFTjpHbUDBQZcz8fDAbVYKF1n0i7knNHJbtyZwI7ft1WSRbuEmeoESktBXuk7QdY+gvikcwW5PsZtPlMk6bp75DVtbyqVYzgeqtxlajWKOhTw8UdfO6IqsqIo65JOC+T3YCVPPAb8R+ArwG+u1qJ6nSMTC55d7Qt1jTFOUdhsWEgmXyLS5kQ5ELXsCuWyIVMoLavzvR2DsRAzqULlZNeJBxmsk/10ulFBHuwLNkzSG3AV3f2RAAuaYqF0mVYK8lymQNAvRIP+it+3VZJFOwW5mjtcJJ1vPcRjufh8QjIa7NxiEQ2Ssu8EWWur7T9wvnYunI0xTC22t1hcsv+e/+nF+3js7BxffeLCZf5miqIoK09HBbIxpmyM+agx5k3GmDfaX+tlfxPe8deH+cA/Ns4+qY9W6khBblcg237ebLGEMUsfT9sJliXCrSC39y6CdeKsKMh97luzVYtFuWxsBdldIAdVQVa6zpQ99ML57GY+W6iMXK4oyE2SLEplQ7ZQbnlsBv0++kJ+y4OcW50LXbAuds/bhXwnCjJUbRJzmaoVChqHicxnihTLhpF4uDJy3qtAdi44fvaHrmDfaB9/+LUjlVH2iqIo64VOUyweE5FH6z7+VUQ+KCLDq73IXsIYw7nZLKftTnE3i3XTqwZaqCxgFcjtop4GYiFmM3lSdnbqalgsBu0i3DlRtleQnRNrkelUNS+2umbLYmGMYSFXpGyoOfH2R9RioXQXRw2FquLpxq2mVgrkJgpyujL4o71iO58trJpVCqxjzylG28e81UYuOhcFDvUqsWNJGYmHqiPnPZptpxZzxEJ+4uEA7375QY5dXOQLD5+9zN9MURRlZenUYvGPwJeBf29/fBH4NnAB+KtVWVmPMpcpkC+VvS0W2dpbp4k2CnK20F5BTkaDZAvlSlrESjfpgZVAMbMUD7JbQU7n6Y8ECLoaDQdjQQolq/vdiXtzF9BWgawWC6V7OGooeHuQ3Q1riUiAaNDfNMmi09HRTlPcajTbOtQfZ+3WA9Z7WqFUJp0v1aVY1CrMTvPdcF+48vPeFgvLpwzw6mvHed62BH/y9aPki95xl4qiKN2g0wL5DmPMrxljHrM/fgN4sTHm94A9q7e83uOifZv1wny24bbhYq62QPb7hEQk0EJBbt/N7lgTzs1aivVKDwpx9pErlpmYt363tpP0bOVpLlNg2jVFr/J60epEL+fioMZiEbbyoZWVRUQawnFFZKQba1nvTLqKYu8Ui6qaatkswk2b9FL2/+V2x6YzYMfyK6+Wgmwde36ftL075S6Q3bF2Dv3hACKuAtm2UznFb7MCeSqVr6Rs+HzCr7ziIKem03zuB2cu51dTFEVZUTotkOMicpvzjYjcCsTtb7WScXHRLiJLZdOgPFke5Fr1dSAWqhma4SadLzXNQHZwFKFzs9bJOboaCrK9j5NTVnNPxwpyxlKQ6wvkZKyqnDsRd7UeZLVYrBIPiMjtzjci8hPAd7u4nnXLlLtA9vAg12cCjyUiTQvk6sCfDiwW9qjp1VOQrTX3RwIt03Gg9jiu2Ktc/Qc+n9Afrl7gVxRku0D2GkUNlmXFUZkBXnxwlMFYkEfPzC3311IURVlxOn0X/lng4yISBwRrYMjPikgf8D9Xa3G9yORi9SR5fi5bM/J1oc6DDJbK0iwHOVPowIMcrVOQV6NJz97HqekUsZC/xi7hRSxkTQGbzxaYWsxXxs46DLqm8zkXB8moK+YtEiCdL1EslQm02ZeyJP4d1nH8z8A2YBh4aVdXtE5x1NBdQzFvBTlbG3k2lojwg9PeaZUVBbkDu9QjZ/LkS+VV8yA7A3s6SclopyCDdbHrPHdpMY8IDLnGyp+fa+zFmFrMccOOZOV7EWG0P+xpZVEURekWHRXIxpgHgOtEJAmIMWbW9fSnV2VlPYqjIANcmMvAzgHAavpZzBUbCuSBmPdtSOgsxSJZZ7FYDeVpwKUgt2vQA+uEZ2WoFplJ57lmW6Lu9ZwBKfnK716fYgGWJcWdj6xcHsaYx0Tk/cDfAAvAi4wxel/bA0cNvXKsn4dPz9Y8Z4xpSHQYT0aYeCKHMaZBmU3ZTXrtkikS0UClSFytFIuBioLc/jhOdFIg2+OmwfIWD8ZClYvaRDTIXF0OcrlsmE7lKyqzw3Bf2PNCZKMhIj8O/B7WJFqxP4wxJtHyBxVFWXM6fhcWkR8BngdEnBOAMea3V2ldPcvFhRw+gbKp2h7AKnbLplG5SUaDnPVIvAArB7mdxcIpIM+uogfZiWg7N5th/5Z4m60tHG+1pwfZPknPpAsVD3J9igVYlhQtkFcOEfkYsA+4HrgS+KKI/Jkx5sPdXdn6w1FDD4zF+ebTE5TKBr/Pet9LeUyUHEtEyBfLzKYLNWPVgY4TZpLRIE54ZnyVPMjO3Zt2DXoAkaCfUMDHfLZQKYLrL5Ad3zRYXu1h1++eiAYaUizmMgWKZVNjsQAY6Q/z+NlNYbH4feC1xpjGHFBFUdYVnca8/S/gJ7FGRAvwJmD3Kq6rZ5lcyLFjMEYo4Kvpaneazho9yN4WC2PswR8dDAoBOGffymxXUC8H56RaNu0j3hwS0SAT81lyxXJN5zy4J3DlmUnniYdrUy4SrgJZWVEeB15ijHnOGPNV4Hbg5i6vaV3iqKFj/WHKhpo+gXkPNXUsYRV8XkkW6Q4VZPfrrZYH2bk4bddo617TfBsFuVIgp3I1ynAyGiRfLNeM4Hb83CP9tQXycF9os1gsJrQ4VpTeoFOD5wuNMT8NzBhjfgt4AbBz9ZbVu1xcyDKWCLM1GakE8gOV2LJ4vcUiajXplesSL/KlMqWyaXuitDzBUomVa5e1uhzc9od2DXqV7SLBSlPfUF/tz4QDfmIhP7PpAnPp2iEhAPGwM6pao95WEmPMB90Dfowxc8aYt7f7ORH5uIhcFJHHXY8Nicg9InLU/jy4WuvuBo4a6hRyjicZ8CwWx1uMm+5UQXZffK5WikVVQe7sOHYK4GqTnofFwqUgO+kUznNQm/PuZEqP1KnsI/EQC9kiuWKJDc5hEfmUiLxFRH7c+bjcFxWRV4nIMyJyTETe4/F82N7vMRG5T0T2uJ77NfvxZ0TklZe7FkXZKHRaIDvv+mkR2QYUgL2rs6Te5uJCjtF+q0C+4GpQcdTQfg+LRdnAYr5WLc3Yne/tmvQsv2+IQsl0tP1ycApa6Fx5SkQDFTWtXkF2HpuxUyzqC2Tn9q9Gva0sInJARD4jIk+KyHHno4Mf/SvgVXWPvQf4hjHmAPAN+/sNg6OGOlYAt7o551EsjrUokCsKcpuL17VQkJ1jsdNR1olIgPlMkflMgVDAR6Tu/SURdTfp5WoKZHcKhoPjMx6O1ynI9vebwIecANLAK4DX2h8/ejkvKCJ+4MPAq4FrgLeIyDV1m70dS+DaD3wQyweNvd2bseyTrwL+3H49Rdn0dPou/EURGQD+AHgIMMBHV21VPczkQo4XHRglHPBz/3PTlccrBXJ9ikXMsRsUahQkJxqqE8vEQCzIJXs6lc/XOrppuQzGQqTzmSUpyA71HmRwlKk8s+l8JRfZoV8tFqvFXwLvxTpBvgT4GSzLVEuMMd92K042rwdebH/9CeCfgf+6MsvsPlOLea7elmDEtgy4Czcvi8UWx2Ix12gTSOVLhPw+QoHWekTSdaG4GneCwGWxaDMuvrKmaJBLi/mGpkT387limQXbp+z2IHspyI7ForFJr/p33jYQXcJv1FsYY35mFV72VuCYMeY4gIh8Euv4fNK1zeuB99lffwb4M7GaiV4PfNIYkwOeE5Fj9ut9b7mL+a0vPsGT5+aX++OKctlcsy3Be1/7vMt+nbbvkiLiw1KKZoF/EJEvARFjzKboqFgK2UKJhWyR0f4w0ZCfifks5bLB55OKGtposaieRNyelWp2avsC2ck27WTb5TIQC3J2NrMkD7JDfdOS9ViwoiBvrTshxisFslosVpioMeYbIiLGmJPA+0TkX7GK5qUyZow5D2CMOS8iW7w2EpF3AO8A2LVr13LXveZcWswx0heqKJueCrLrWAgH/Az1hZhY8FCQc0ViHVgm3K/XyfbLIRL08z9//DpecMVwR9sno0GenUw1LZCd4/y5SymgVhn2tFjYTcz1d5UcK8slj8zpjYCI/BdjzO+LyP+HJTDVYIz5xct4+e3Aadf3Z4Dbmm1jjCmKyBxWzON24Pt1P7vdaye9eiwrynJpWyAbY8oi8kdYvmPsK82N+S52mTgRb6P9YfojAYplw6VUji39ERazzZr0qpnAbpzGlk4sE06G8GrdloXqCa1T5cltxRjysFgMREM8PTdveZDrTrzVUdWqIK8wWfuC96iIvBM4ixU3tWoYY+4C7gI4dOhQQ2GwHskVS8xni4zEwwxEg/h9UqMgN2tYG0tEmPAYMZ/KlzpShN2vt1oKMsBbbu28uElEg3aKRcHTXuU8dnzSKZBDNT8L1CRZXLJTbfx1d7pG+ja8xcJpzDuMR4F8mXjdBarfR7NtOvlZ68EOj+WVUO4UZT3Q6bvw1+ypW591N/kotThDQrb0hyue4AtzWbb0RyoniXrvnzsT2E2n07fcr7HaCjIsLcUCwCeNhQRYt5Nnm3iQwwEfQb+oB3nleRcQA34R+B9YNoufXuZrTYjIVls93gpcXKE1dp3pVNUn6/MJQ32hmml689kiIo12qbFE2FNBTuWKHR2bNR7kVVKQl4rThDebLrClLnnCeR7g+OQigHeTXtrtQc41RLxBtbCe2qBJFsaYL9pfPgn8OrCH6vnXAH99GS9/htqm+R3AuSbbnBGRAJAEpjv8WUXZlHRaIL8b6ANKIpJBw809cRTkLf0RyvZ1xLnZLNfvqDaceeUgQ6OC7DT2dORBjq5+gVxVkDvvfnd+zssXPRgLVpIB6j3IIkJ/JKgWi5XHYA0J2Q04/5AfxcpFXip3A28DPmB//sJKLHA9UG0ks/5fWhFktR7keDjQ8P96PBHh8bON3stUvtTR4I9I0LowLJQMsVVotl0OThPxudkMBzwy0J3j/FnbYjHiVpDtCwj3sJCpxcYhIWC9d0WCvs0Q9fZ/gF8FHgPKK/SaDwAHRGQv1l2hN2NNzXTjHK/fA94IfNMYY0TkbuDvROSPsaZrHgDuX6F1KUpP0+kkvf7VXshG4OJC1WLhDNNykiwWskX67BHMbrx8elBNseik6HUU2L5Vmr4FVZ9zxwqyvZ2X/xhqi+JkrPE14+GANumtPH/LMk7OIvL3WA15IyJyBsuz/AHg0yLyduAUVjb6hsAp0pxibyQebvAge90VGUtEmErlKJTKNbne6Vyxo9HRzgTKxVxx3YxYd47jmXTB8+I4UVGQGz3IAb+PvpC/1mKxmOO6HQMNryMim2Wa3qQx5u6VfEHbU/xO4KuAH/i4MeYJEflt4LC9v48Bf2M34U1jFdHY230aS9kuAj9vjNnwWXuK0gkdVVR2t+u/B/YaY/6HiOwEthpj9ErTxeRCDr9PKh3ZIb+P83bs02K26Jk9Gglaykl9gZzuMOYNIBlzPMirabFYogfZ3s7Lfwy1RbFXDFx/JFDxbSsrxrJOzsaYtzR56s7LXM+6xCnSHLvAcDzEqVPpyvPzLQpkY6z3AXcSQypf6ngiZMI1TW894C6Km6VYADx3aZFwwNdwIeAeJAKN0/bcjPSHuZTa8AXye0XkL7CiEStXXcaYz17OixpjvgJ8pe6x/+76OkuTi1hjzPuB91/O/hVlI9Kp5PjnWIrTS7G8i4tYuYvPX6V19SQXF7KMxKuWgvFkpDLAYyFXaEiwcHCGhbhJF5agINsnqdVs7Nk9HCPgk8pAhHZUFWRvxdldFNd7kMEqkFVBXnFW5eS80XDUYkcNHYmHa7yxc5mC552U8WR1mp67QE7nix0P/khEghRKK3Xn/fJJtimQnb9DtlBm+0AUkdo7ZO6c5GyhxELOSvnxYqQv5DmJcIPxM8BVWBYn5x/aAHoMKso6o9OK6jZjzM0i8gMAY8yMiHQmiWwinCEhDuPJCOdn7QI5W2wazj9gN6y5yS4hB9kpNldjzLTDS6/awr3veSlbOi2Q7ZOpVwYy1BbF9SkWYE3TOzOTbnhcuSz05NwBU6l8jRo6HA+RypfI5EtEQ37mMgX2jTb6cZ1hIRfqkixSuVLH9qehvhD54vopkN13jLwuCkIBH9Ggn0yhVOM/rv58ddJepfmxyXvCcDzE4+c2fHroDcaY67q9CEVR2tNpgVywp+sYABEZZeUaDDYMkwu5ykkSYGsywkOnZgCrSa++690hUXcbEpaXYrGaHmQRqfnd2lFRkJvcWh50FcheHuSEKsirgZ6cO8CZCOeooZUIslSOHaEY81lvi8We4T4Anr24WPN4Ot+ZBxngV195sNKgux5w/57NGnST0SCZQqlhOp7z3Olp60K32RQ9h+G45UE2xjQo0RuI74vINcaYJ9tvqihKN+m0ovoQ8Dlgi4i8H6sL9jdXbVU9ysWFHNdtT1a+H09GmJjLUS4bFrJFtia9C8yBaJBT07VqabpQJBTwNTT1eZFcgxSLpRIN+fmN11zNS67yjtlNupv0PE68/ZFAQ8zb5EKOzzx4hljITzIaJBkNsnUgwlXjGqbSIXpy7oCpxXyNGuqkLlxazLNjMGZZLDy8+H3hADuHojw9sVB5rFw2pPOljjPKr966vv4vt7NYOI9fmM96KsPJaJAnXKOooXGKnsNIPEyxbJjPFD0vmjcI/wZ4m4g8h2VzchKhlpMkoyjKKtJpisXfisiDWE05ArzBGPNUmx/bVJTKhqnFWovFtmSUfKnMdDpvNemFvd/0B2JBHj3TmGLRacE7HA8RCfrY0t+5wrsW/NyLrmj6nLuoDwcaf8/+iNXN71aTPvHdE/zZt441bPv1d/8w+z0iqJQG9OTcAVP2cB8HR/GcWsyRK5bIFspNi8WDYwmOXKgWyBm7l6BTD/J6oy8UwCdQNs0bdJ3HvZThRCRYGfhTSQfxyEGGamrIpVRuIxfIr+r2AhRF6YxOUyz+FPiUMebDK7Vj27JxGDhrjPnRlXrdbjGVylE21ITpj9uK8fnZLAvZFk16sRAz6dpbi+l8qaMEC7BsGF971w9X9tcLhAI+4uGA53QusMZNl2z1zbGO3P/cNNfvSPKX/+H5zGUKPHZ2jl/65MMcnVjQArkz9OTcAZcW8lztuisxUhlikWfezvRtWiCPx/nWMxfJFUuEA35S9l2Q1ZxyuZr4fEIiavVItFKQAU8PshNbVyyVK7nnI/1NPMiuaXr7Rldi9esPe8S7oig9QKdhmw8Bvykix0TkD0Tk0Ars+5eojt/seapjpms9yABnZ9Ok8qWmHuQrRvrIFcucmKraLJyGoE7ZNRwjFFgf2amdkowGKxF19Th/K8dmkS2UePjMLLdfMcxwPMwVo/GKfePktDbzdYIx5qTXR7fXtZ4wxjCVytWooU7hdimVq/QKNPPjHhxPUCqbSi5wKt/bCjJUC+BmBXKiUiB7KMi2ujyfLTK1mCMa9De9WKhaWTb8sBBFUXqAjioqY8wnjDGvAW4FjgC/JyJHl7tTEdkB/AjwF8t9jfXGpGtIiIOj6B6zm3aapVjcsnsQgMMnpiuPZQqdWyx6leF4iKEmMXBOZrQzTe/RM3Pki2Vu3TNU2SYRCTLUF+LklBbIysowny1SKJkaNTQa8tMX8jO1mG9fII9ZM5WesW0Wva4gg3Wc+aR5jKTTkOvlLXaK6vlMoekUPYeNPm5aUZTeYqnv2vuxYqL2YE3eWS5/AvwXoOmEPhF5B/AOgF27dl3GrtYGp0B2WyxG+sIE/cJRu0BuNoVu32icRCTAQ6dmeNOhnYDV+R4L9u5JtRPe+9prCPm9LwL6w1XlCeD+56YAOLRnsGa7XUMxTk2nVnGVymZiqjJFr1YNHban6TlT4ZqpqXtH+gj4hGfsRj0njWY1M8pXm2Q0SCIa9BwZ7zwPVaXd67m5TIHJxVzTBAuwhgqJUDPWW1EUpVt0pCCLiKMY/zbwOHCLMea1y9mhiPwocNEY82Cr7YwxdxljDhljDo2Orn9D2sUFK/vUrSD7fFY02pEJW0FuYrHw+YSbdw/y4MmZymNLtVj0IrfsHuK6HUnP5yoWC7tAvu+5aa4a72+YSLZ7OKYKsrJiXKpEkdX+PxuOh2wPsq0gN7nYDQV87BuNVxr1UnZkWy9bLEbiIU/7ROX5/jAisCXhZbGoFshTi3lGmmQggzWaejAWYiqlCrKiKN2nU1njOeCFwBVAGLheRDDGfHsZ+7wDeJ2IvAaIAAkR+T/GmLcu47XWDRcXciQiASJ1jXVbkxEeOWOF3zezWAAc2j3IHz4zyZw9xjadL7FtoHdPqpdL1WJhNfg8eHKGN96yo2G73UMxvvjIOfLFcs95sJX1h6Mg16uhI/Ewp6fTFYtFMwUZ4Mrxfh6yL3bTOceD3LsK8q++6qrKhYEXP3HzdvaPxj2L6IrFIltgKlUbg+nFcF+okpesKIrSTTqtKErAN4F/An4L+CrwvuXs0Bjza8aYHcaYPcCbgW/2enEMlsXCa8rceDJamYzVrEkP4Gbbh/wDe7BIehMoyK1w/lYL2QJPnJsnnS9x696hhu12DfdRNujUPWVFuOQkLdQpyCPxEFMpl4LcJPIM4Krxfs7OZljIFioKci/3E2wfiLbMZ46FArxg37Dnc06BPJtu70EGS6nXJj1FUdYDnRbIvwg8HzhpjHkJcBMwuWqr6kEuLuQY9VBQtrmi11oVyDfsGMDvk4rNIlPoPOZtIxKvFMhF7n/Oal50N+g57B6OAZpkoawMjoJcPyJ9uC/MdCrPTLpAJOjzzO52uNJu1DsysUjabtLrZQ/y5eBYUU7PpCmWTUsPMlSn6SmKonSbTgvkrDEmCyAiYWPM08DBy925MeafezEDeXIhhzGm5rGLC1lPD954TYHc/LZsXzjA1Vv7qwXyEgaFbETioQAisJArct9z0+wd6fNU6HcPWQXyKfUhKyvA1GKewViQgL/2rXE4HqJUNpyaTre0V4ClIAMcmVioxLzFetiDfDlEgj5Cfl8l9s4rK9nNqN0MqSiK0m06LZDPiMgA8HngHhH5AnBu9Za1fvn+8Slu/5/f4K++e6LymDHGslj0NxbI7vHSrTzIALfsGuTh07MUSmVLQd6kqhNYjYvxUID5TIEHTkx7qsdgNUVGg35t1FNWhEtNkhacx45PLrYtkLcPRImF/DxzYYFUrkjAJ4T8m9MfL2INGjk+aTUqeyVduBnuCzGfLVZsaYqiKN2i0xzkHzPGzBpj3gf8N+BjwBtWc2Hrkflsgf/86UcolQ1/8/2TFRV5IVckWyjXJFg4bE1GAfBJex/izbsHSedLPHx6Fuht3+JKEI8EePDkDHOZgqf/GKwT8O5hjXpTVoapxTzDHkkLjvJ5ajrdNMHCwecTrhzr55kLC6TtO0HOhMzNSCIa4JRtgWo2Rc/BuRCZTqnNQlGU7rJkWcMY8y/GmLuNMZvuHey37n6S83MZ3nr7Lo5Ppjhs2yGcKXpb+hstAI6CHA8H2p4kD9kq6b8evQRogdwfCfDYWSsBpFmBDFYWsirISjtm03meODdX8+HkGjtcSuUY8bjQdRIaCiXTVkEGa2DIkQlLQe7lBIuVIBkNUihZYkJbBbnJNL10vliZqqkoirIWbO537iXwT49f4B8eOsMvvHQ//++L9/H5H5zjk/ef5vl7hjyHhDgMx8MEfNLSf+ywLRlhPBHhO0et/sfN3KQHVc/2tmSEHYPRptvtHo7xL0cmKZdN02EGivKm//W9ytAeh6u3JvjSL/wb/OOoBcAAAB0MSURBVPb/m2ZZvW5VuZMC+crxfj51+DQnp9Ob/kLX+XuJwGCs9d9upEmB/M6/+wHn57I1/1aKoiiryeY0xi2RiwtZfv1zj3Hd9iS/eOcBYqEAr7txG19+7Bzz2YLnkBAHvz0spFWChYOIcMvuwUpu8maOeYNq6sfz9w61VN93DfeRK5a5uKDNPYo3Z2bSHL24yE/dvpv//VO38L9/6hZ++WVX8tT5eb70qNVOkS+WmcsUPD3IA7EQTl3WbMy0G6dR79Ezs217DzY6jiVlMBZqaH6sx1Hq3UkW2UKJ7xy7xFPn5/niI5uy9UVRlC6gBXIbjDG85x8eI5Ur8sGfvIGg/Qb/5ufvJFsoc/fD51wKcqPFAmDnUJTBWGvvncPNuwcpla3bkZtdeXIKi1b2CqgmWZyYUh+y4s13n7VGlb/19t288nnjvPJ54/zCS/dz1Xg/H7znCIVSueJ79crq9fukEv3WSYHsRL1lC2Vim7jZFtyjqNu/BzoXJ+5peg+dnCFfLNMX8vPBr1v/VoqiKKuNFshtuO+5ab759EV+9ZUH2b+lv/L4dduTXL01waceOM3kQo5QwNd0eMDv/th1vP/Hru1of7fYA0MAosHNfWJ1LBa3tSuQhzXqTWnNd49dYiQe5sqxeOUxn0/4lVcc5MRUmn948Ezltn6zscrO451YLEb7w5WCsJfHTK8Ezt+r1bhqh76Qn3DAV6Mg3/vsJfw+4Xd//DpOTqX5zINnVm2tiqIoDlogt+GeJycIBXy85dZdNY+LCG9+/k4eOzvHvxyZZDQebmoDuGI0zhWjcc/n6rlma4KwPTJ5syvI125P8LxtCfa1+dttG4ji9wknPZIsHjw5Q0qbezY1xhjufXaKF+4bbjhG77x6CzfuHOBPv3GUc7MZoHlWr6MsJzqwS0FVRd7sCrIjHLSbogfW++pIPMyky4N877EpbtiR5HU3bOOmXQN86BtHyRZKq7ZeRVEU0AK5JcYYvv7UBHfsG/bsRH/DjdsJBXw8fWHBc0jIcggFfNywcwDQAvnf37abL//iD7VN/wj6fWwfiDYkWTx9YZ6f+Mh3efsnHtBc1U3Ms5OLTC7keKHHOGQR4VdfeZDzc1k+/K1jQPOkBefxThRkgIO2D1kV5M4VZLAKaUdBns8WePTMLHfsH7H+rV5h/Vv97X2nVm29vYSIDInIPSJy1P482GS7t9nbHBWRt9mPxUTkyyLytIg8ISIfWNvVK8r6RgvkFhy7uMjJqTQvu2bM8/lkLMhrrh0HvBMslotjs9jsTXpLwcpCri2Qv/TIeUTg+8en+c3PP9Yw/VDZHNx7zPIf37F/xPP5O/aP8IIrhivNsc2UTufxpRbIm11BXooHGaxC2vEg3398mrKBF+6z/u1euH+EO/YP8+ffOqZ3hizeA3zDGHMA+Ib9fQ0iMgS8F7gNuBV4r6uQ/kNjzFXATcAdIvLqtVm2oqx/tEBuwT1PTQBw51XeBTLATz7fsl54JVgsl397aCdvvX1XZciI0p7dw7VZyMYYvvToOe7YN8IvvHQ/nz58hru+fbyLK1S6xXefvcSOwSg77WZOL37llQcB6w5Os9QJRwHtpEkPqhaLvk1+oeukWHjlS3sx3FdVkO999hLhgI+bdg1Unv+VVxxkKpXnL+99buUX23u8HviE/fUn8B7g9UrgHmPMtDFmBrgHeJUxJm2M+RaAPdfgIWDHGqxZUXoCLZBb8PUnJ7h+R5LxpHc6BcDtVwzxplt28Iprxldsv3tH+vidN1yneZ9LYPdQH3OZArNp68T6xLl5Tkyl+ZHrt/LLL7uSH7l+Kx/4p6f56hMXurxSZS0plQ3fe3aKO/Z5q8cOt+we5BXXjLFnONa8l2Ckj5Dfx1ii+fuBm4Pj/fSF/Gwd2NwXutsHo4jAnuG+jrYfjoeZWsxjjPVv9/w9Q0RcmfA37RrkxQdH+evvnVytJfcSY8aY8wD25y0e22wHTru+P2M/VkFEBoDXYqnQnojIO0TksIgcnpycvOyFK8p6Z3Pf+2vB5EKOH5ye5ZdfdmXL7USEP3jTDWu0KqUZu+wki5NTaQZiIb706HkCPuFVzxvH5xP+6E03cGYmw7s++TCf/U8v5OqtiS6vWFkLrGl5RV64v9F/XM+H3nIT6Xzz5q9XPm+c7/zXl1Ti3toRDwf45199SdvhGBud3cN9fP/X7uz4wmIkHiJfKvPcpRRPX1jgv7xqW8M2d+wb4Z+fmWRqMeeZW72REJGvA14KzG90+hIej1X8ZiISAP4e+JAxpultNmPMXcBdAIcOHVK/mrLhUQW5Cd96+iLGwMuubm6vUNYPTtTbyel01V6xf4RBu5iJBP189KdvwSfwd9rgs2lw8o9f4NGgV08k6G9Z/Pp8wpYOizyH0f5w2+EYm4FOi2OoWlm++Mh5oOo/duP4u5+ZWFiB1a1vjDEvM8Zc6/HxBWBCRLYC2J8verzEGWCn6/sdgHviyl3AUWPMn6zW76AovYi+czfhnqcm2D4Q5eqt/e03VrrOriEnCznFo2fmODOT4Ueu31qzzZb+CAfH+zl6ceOfVBWLe49d4sqxeNMhPsr6w2mGvPuRs/RHAly7rfFuj1MgH7mw6Y/lu4G32V+/DfiCxzZfBV4hIoN2c94r7McQkd8BksC71mCtitJTaIHsQbZQ4l+PTvKyq7e0jRhT1gexUIDR/jAnp9J86dFzBP3CKz184Qe29HPs4mIXVqisNflimQdOTHsqkMr6xYnTe3YyxW17hz0V+C39YQZiwU2hILfhA8DLReQo8HL7e0TkkIj8BYAxZhr4H8AD9sdvG2OmRWQHlk3jGuAhEXlYRH62G7+EoqxH1IPswb3HLpEtlJvGuynrk91DVpLFvcfSvOjAKEkP7+eBsTifOnya6VS+Yy+p0hwROQEsACWgaIw51N0VVfnBqRmyhbJn/rGyfnEParmjiXdcRLhyrJ9nNrmCbIyZAu70ePww8LOu7z8OfLxumzN4+5MVRUEVZE++/tQE8XCA2/bqibWX2DUc48FTM5ybyzbYKxz2b7Gm8qmKvKK8xBhz43oqjgHufXYKn8BtV+hx3EsM9rkL5Obq/1Xj/RyZWNR8c0VRVgUtkOsolw1ff+oiP3xwlFBA/zy9xO6hPkplQyjg4+VN1P8Ddjat+pA3Pt979hLX7RjoeLCHsj4I+n0MxoKMxMMc2NJ8zPyVY/0s5oqctUeEK4qirCRaAdbx2Nk5JhdyvFzTK3qOPSNWo96LrxylP+JdFG1LRugL+Tk6oQryCmGAr4nIgyLyjvonu5WdWiobHjk9x217h9Zsn8rKcWCsn1c+b6xlD8hVTqOe+pAVRVkF1INcxxPn5gE4tMdzpL2yjnEml73+xu1NtxER9m+Jq8Vi5bjDGHNORLYA94jI08aYbztPdis79fxchnypzN6RzoZTKOuLv/vZ29pu49wNevrCAi9tMe1UURRlOaiCXMeZmTQBn+iY5x7k6q0Jvv7uF/Ga61pPNdy/RaPeVgpjzDn780Xgc8Ct3V2Rxelp67b7zsHm46WV9UvA72ubH52MBtmajGjUm6Ioq4IWyHWcnsmwbSCqY557lP1b+ttG8x0YizMxn2M+W1ijVW1MRKRPRPqdr7HyVR/v7qosTs+kgWo+trIxOTjez9NaICuKsgqseYEsIjtF5Fsi8pSIPCEiv7TWa2jFmZk0OwZVPd7IHNAki5ViDPiOiDwC3A982RjzT11eEwCnp9P4BLYO6ICQjczBsX6OT6YolMrdXoqiKBuMbijIReA/G2OuBm4Hfl5ErunCOjw5PZ3R27IbnANbLO/iMW3UuyyMMceNMTfYH88zxry/22tyOD2dZmsySlDHPG9oDo73ky+VOTmV6vZSFEXZYKz52cMYc94Y85D99QLwFNC8q2oNyRZKXFrMqYK8wdk+GCUc8KkPeQNzeiaj9opNwJWuRj1FUZSVpKvyiojsAW4C7vN4bs3joc7M2I09emLd0Ph9wr7ROEfVYrFhOT2dZueQXuhudPZvieMTtFFPUZQVp2sFsojEgX8A3mWMma9/3hhzlzHmkDHm0Ojo6JqsyWnsUQV543NgLK5ZyBuUbKHExYWcWqU2AZGgnz0jfaogK4qy4nSlQBaRIFZx/LfGmM92Yw1eqIK8eTiwJc7Z2QypXLHbS1FWmDNOgsWwHsebAWvktBbIiqKsLN1IsRDgY8BTxpg/Xuv9t+LMdJpQwMdoPNztpSirzH67Ue/ZSVWRNxpOBvIOVZA3BVeO9XNyOk06rxe7iqKsHN1QkO8A/m979x4c1Xmfcfz70+p+AQlJgEAIgSQH4xtgjEFcHLBJMNMpjusmdtMEt8447titO24ntduZpk08k2Ti2z8ZT2jjJDNNW9eNXTOuHUxcfAFsbAzGBhuBEBZac9WNmwTo8vaPPRIrJAE2i845u89nZmd13j2zPKz02333Pe95z7eAJWb2gXdb7kOOQaJtnZQX5pCmNZCTXs242FJvmmaRfPa1xkaQNQc5NUwbX4BzWrZRRBJrxC817ZxbDwSyB9rU1sFEzT9OCZPH5JIRMZ2ol4SaWjvIztCRoFTRt5JF3cHjXFte6HMaEUkWWiQ0TrStU/OPU0R6JI2pJfnUa6m3pNPU1sGkotwLXlFRksPk4jyy0tOo04l6IpJAKddBfqehhSfX7hrUfvJ0N60nz2gFixRSPU5LvSWjfa36optKImlGzbh86nSinogk0IhPsfDbz9/Yw7q6I9xdW0lRXmZ/e/8KFjqxJ2XUjM3n5Y8OcKqrh+yMiN9x5HPo6unlmfV7+ebcyeRnnX0bc84Rbe1gTmWRj+lkpF0xroC1Hx/in1bv6G8rzsvk/sXVOqdERL6QlBpBPtPdy6a9rQBsbWob8FhTq9ZATjU1Y2Mn9/StZNHd08sLW6NsrG/2OZlcyLqdh/nRKzt5YUt0QPvRzi6On+7WCHKKueXKcaSnGc9vifL8lijPbW7i8bW7Br3Pi4hcrJQaQd66r42OMz0AvN/YxpJp4/of61s7VR+sqaNvJYtdh46zt/kkT7y6i4bmk5QX5fDW9xZrDmuAbdzT0n//rXmV/e1nV7BQHaeS5deUsfyasv7ttpNnmPXoWjbWt3D95DE+JhORsEqpEeQN9c2kGUwpyWNLY/uAx5raOsnJiFAcN+1CkltlcR6RNOPvn9/OA/++lfSIcfvMiUTbOmls6fA7npzHxj2xUf63G1ro7XX97X1rIGuqVGorystketkoNuzR0SAR+WJSqoP8Vn0z100qZFFNCdui7XT39PY/Fm3roLwoR6OGKSQzPY2ZkwopKcjkyW9cxysPLuIvb64B4K3dR3xOJ8M5fPwUuw6dYNr4Ato7uvj4wNkr1Te1aQ1kiamtKmZLYzud3lFDEZHPI2U6yMdOdbGtqZ0F1SXMmlxEx5kedsYtC9TU2qn5xyno2e/O442/XczXZpYTSTMqi3NjUyx2a+QpqN72plc8tPQK4OxoMsSmWBTlZlCQneFLNgmO2uoSzvT0srmx1e8oIhJCKdNBfmdPC72OWAe5InaG+9Z9Z0/giI0g67Bsqomk2YCz3M2MhTWlvL2nha64IwwSHBvrWxiVnc7NV46jqjSPDfUt/Y81tXZo/rEAMKdyDOlp1j9fPRmZ2RgzW2tmu737IZdvMbOV3j67zWzlEI+vNrPtlz+xSHikTAd5fX0zORkRZlYUUV6UQ2lBFlv2xeYhH+3s4tipbh2WFQAW1pRw/HQ325raL7yzjLiNDc3MnVpMJM2YX13Cu3tbOdMd+zKji/1In7ysdGZMKkz2VWkeBl5zztUAr3nbA5jZGOD7wI3AHOD78R1pM7sd0ILwIudIqQ7yjVPHkJmehpkxq6KQ9xtjI8h9K1hoBFkgNncxzdA0iwBqau2gqbWT2qpiIPa76uzqYVu0nZ5eR9S7ip4IxKZZfPTZUY52dvkd5XJZAfza+/nXwG1D7PNVYK1zrtU51wasBZYBmFk+8BDw6AhkFQmVlOgg72/vpOHISRZUl/S3XT+5iH2tHTSfOK2LhMgAhbmZXFNeqBP1AmiDNxo436vluVOLMYu1Hzp2iq4epyNB0q+2qpheB5saknaaxTjn3AEA737sEPtMBJritqNeG8APgceBCy7bY2b3mtlmM9t85IjeGyX5pUQHeb33obqg5mwHuW8e8pbGNl0kRAZZVFPCB03tyTzyFEob97QwtiCL6rGxNawLczO5esJoNta39NdxhaZYiGdmRSHZGWmhnodsZr83s+1D3FZc7FMM0ebMbAZQ7Zx74WKexDm3yjk32zk3u7S09KLzi4RVSnSQN9Q3U5KfxZfGFfS3XT1xNBkR4/19bUTbOsnPSqcwV2e+S8zCmlJ63dkVE8R/zjk27mmhtqp4wHKMtdXFbG1qY9eh2Ko0OhIkfbLSI9xQOab/yEMYOeducc5dPcTtReCQmZUBePeHh3iKKDApbrsc2A/MA643s0+B9cAVZvb65fy/iIRJ0neQe3sdG+qbWVA98EM1OyPCVRNGs7WxXWsgyyAzKwrJy4xomkWA7Dp0guYTp6mtKhnQXltVQleP4/mtn2EGEwp1JEjOqq0qYffhExw+fsrvKJfDaqBvVYqVwItD7LMG+IqZFXkn530FWOOce9o5N8E5VwksAHY55748AplFQiHpO8h1h47TfOIMC2oGHxKaVVHEtmg7e5tP6gQ9GSAjksa8qmKdqBcgfesd11YXD2i/obKIjIixdV87E0bnkJme9G9r8jnM9/5ekvRo0I+BpWa2G1jqbWNms83sXwGcc63E5hq/591+4LWJyHkk/SfJ+t19J/UUD3ps1uRCTnf3sufISc0/lkEW1pSyr7WDxpaTfkcRYEN9C5OLcwd9mc3NTGemd06B6ljOddWE0YzKTg/1NIvhOOdanHM3O+dqvPtWr32zc+47cfs945yr9m6/HOJ5PnXOXT2S2UWCLuk7yL/bcZCasfmUjR78wXn95LNrqmvtVDlX30mdGkX2X3dPL5saWvqXdztXX7vqWM4VSTPmTi1mQ30Lzjm/44hISCR1B/nDaDvvN7Zx55yKIR8vG51D2ehsQCNPMtjUkjwmFuZoHnIAbIse5fjp7kHzj/v0LfumFSxkKPOrS/isvZNPWy64mpmICJDkHeRfbviU/Kx0vj67fNh9ZnmjyDrzXc5lZiyeVsrrdUfY397pd5yU9i9vNpCflc6iIc4lAJgxqZA/nVvBrVePH+FkEgZLp48jI2KserPB7ygiEhJJ20E+fOwUL324nzuuL6cge/jl226qKSU/K52KYnWQZbDvLqrCOXhy7S6/o6Ssj6JH+d2Og9yzYAqjh1mKMSOSxqO3XUNN3FKOIn0mFOZw15wKntvcpHMKROSiJG0H+d/eaaS713F3beV59/vj2eW8/cgS8rPSRyaYhMqkMbl8e95kfrslys6Dx/yOk5Iee7WOwtwMvrNwit9RJMQeWFxNesR46ve7/Y4iIiGQlB3kU109/GbTPm6eNpbKkrzz7mtm5x1hFrl/cTV5Wen85JWdfkdJOe/ubeWNXUf4i5uqVKdyScaOymZlbSX/88Fn1B087nccEQm4pOwgr962n5aTZ/jz+RpxkktXlJfJ/YurWVd3JFnXUg0k5xyPramjtCCLb8+r9DuOJIH7FlWRn5nOE2vr/I4iIgHnSwfZzJaZWZ2Z1ZvZw4l8buccz6zfy5fGFTBvmCWhRD6vu2srKRudzY9f+URLRcW5nLX85u5m3v20lb9aUk1OZiSRTy0pqigvk3sWTmHNjkN8GG33O46IBNiId5DNLAL8DLgVmA7cZWbTE/X87zS0svPgcf5sfqUuHS0Jk50R4aGlV7AtepT//eiA33EC4XLWsnOOx1+to7woh2/cMPQyjSJfxD0LplCUm8Fjr+rEWxEZnh9nps0B6p1zDQBm9p/ACuDjL/Jkf/T0RlpOnO7fbuvooig3g9tmTkxEVpF+t88q5xfr9/J3//0hj60ZeIj2v+6bx9iCbJ+S+SZhtfz063t49r19/dvdvY5oWyc/veNaXTpaEqogO4P7bqriR6/s5KafriN+GOXeRVX8yY36QiYi/nSQJwJNcdtR4MZzdzKze4F7ASoqhn/Dml42imOnuga0ffWq8WRn6JCsJFYkzXjqzhmserOBnt6B0ywy0lKyE3fBWr7YOp5QmM11kwoHtC27ajxf0xdduQxW1lbyWXsnRzsHfnaMLcjyKZGIBI0fHeSh5j0MmtTpnFsFrAKYPXv2sJM+f3ibLh8vI2fa+FE88fUZfscIigvW8sXW8YoZE1kxQ51hGRnZGRF+sEKfHSIyPD+GvaLApLjtcmC/DzlE5NKolkVEJCn50UF+D6gxsylmlgncCaz2IYeIXBrVsoiIJKURn2LhnOs2sweANUAEeMY5t2Okc4jIpVEti4hIsvLl+srOuZeBl/34t0UkcVTLIiKSjFLy1HsRERERkeGogywiIiIiEsfCcNlcMzsCNJ5nlxKgeYTifFHKmBhhzDjZOVfqV5igUB2PqDDkDGNG1TKq5RGkjInxheo4FB3kCzGzzc652X7nOB9lTAxlTF5heN3CkBHCkVMZk1cYXjdlTIxkzqgpFiIiIiIicdRBFhERERGJkywd5FV+B7gIypgYypi8wvC6hSEjhCOnMiavMLxuypgYSZsxKeYgi4iIiIgkSrKMIIuIiIiIJIQ6yCIiIiIicULdQTazZWZWZ2b1Zvaw33n6mNkzZnbYzLbHtY0xs7Vmttu7L/I54yQzW2dmn5jZDjN7MGg5zSzbzN41s21exn/22qeY2SYv47NmlulXxrisETPbamYvBTVjkAWxllXHCcuoOk4RQaxjCH4th6GOvTyhqOVE1XFoO8hmFgF+BtwKTAfuMrPp/qbq9ytg2TltDwOvOedqgNe8bT91A3/jnLsSmAvc771+Qcp5GljinLsOmAEsM7O5wE+AJ72MbcA9Pmbs8yDwSdx2EDMGUoBr+VeojhNBdZwCAlzHEPxaDkMdQ3hqOTF17JwL5Q2YB6yJ234EeMTvXHF5KoHtcdt1QJn3cxlQ53fGc/K+CCwNak4gF9gC3EjsijjpQ/0d+JStnNib1xLgJcCCljHItyDXsuo44flUx0l6C3Ide3lCU8tBr2MvTyBrOZF1HNoRZGAi0BS3HfXagmqcc+4AgHc/1uc8/cysEpgJbCJgOb1DJR8Ah4G1wB6g3TnX7e0ShN/7U8D3gF5vu5jgZQyyMNVyoOojnur4kqmOL02Y6hgCViN9glzHEIpaTlgdh7mDbEO0ac26z8nM8oHfAn/tnDvmd55zOed6nHMziH0rnANcOdRuI5vqLDP7A+Cwc+79+OYhdtXf5vD0el0i1fGlUR0nhF6vSxT0OoZg13Ki6zg9Ian8EQUmxW2XA/t9ynIxDplZmXPugJmVEfv25SszyyBWjL9xzj3vNQcuJ4Bzrt3MXic2P6vQzNK9b4R+/97nA39oZsuBbGAUsW+wQcoYdGGq5cDVh+o4IVTHly5MdQwBq5Ew1TEEtpYTWsdhHkF+D6jxzk7MBO4EVvuc6XxWAyu9n1cSm2PkGzMz4BfAJ865J+IeCkxOMys1s0Lv5xzgFmIT79cBd3i7+ZrROfeIc67cOVdJ7G/w/5xz3yRAGUMgTLUcmPoA1XGiqI4TIkx1DMGqkcDXMQS/lhNex35NpE7QZOzlwC5ic2D+we88cbn+AzgAdBH7Vn0PsXkwrwG7vfsxPmdcQOwww4fAB95teZByAtcCW72M24F/9NqnAu8C9cBzQJbfv3Mv15eBl4KcMai3INay6jhhGVXHKXILYh17uQJdy2GoYy9naGo5EXWsS02LiIiIiMQJ8xQLEREREZGEUwdZRERERCSOOsgiIiIiInHUQRYRERERiaMOsoiIiIhIHHWQRURERETiqIMsIiIiIhLn/wHvZ0x28SoZTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1f76a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspicious looking maxima in  inflammation-12.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b18cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def detect_problems(f):\n",
    "\n",
    "    \"\"\"\n",
    "     this function will detect suspicious features in data\n",
    " in particular, triangular maxima and minima which are all zero\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "\n",
    "\n",
    "    data = numpy.loadtxt(fname=f, delimiter=',')\n",
    "\n",
    "    if (numpy.max(data,axis=0)[0] == 0  and  numpy.max(data,axis=0)[20] == 20):\n",
    "        print(\"suspicious looking maxima in \",f)\n",
    "    elif  ( numpy.sum( numpy.min(data,axis=0) ) == 0 ):\n",
    "        print(\"minima add up to zero in \",f) # comment\n",
    "    else:\n",
    "        print(\"file seems ok \",f)\n",
    "     \n",
    "def analyze(f):    \n",
    "    \"\"\"\n",
    "    this function will plot average, minimum and maximum \n",
    "    \n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    data = numpy.loadtxt(fname=f , delimiter=',')\n",
    "\n",
    "\n",
    "    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))\n",
    "\n",
    "    axes1 = fig.add_subplot(1, 3, 1)   \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()\n",
    "\n",
    "import glob\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "filenames=glob.glob(\"inflammation*.csv\")\n",
    "\n",
    "filenames=filenames[0:12]\n",
    "\n",
    "for f in filenames:\n",
    "\n",
    "    detect_problems(f)\n",
    "    analyze(f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function analyze in module __main__:\n",
      "\n",
      "analyze(f)\n",
      "    this function will plot average, minimum and maximum\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(analyze)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function detect_problems in module __main__:\n",
      "\n",
      "detect_problems(f)\n",
      "        this function will detect suspicious features in data\n",
      "    in particular, triangular maxima and minima which are all zero\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(detect_problems)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function loadtxt in module numpy.lib.npyio:\n",
      "\n",
      "loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes')\n",
      "    Load data from a text file.\n",
      "    \n",
      "    Each row in the text file must have the same number of values.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    fname : file, str, or pathlib.Path\n",
      "        File, filename, or generator to read.  If the filename extension is\n",
      "        ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n",
      "        generators should return byte strings for Python 3k.\n",
      "    dtype : data-type, optional\n",
      "        Data-type of the resulting array; default: float.  If this is a\n",
      "        structured data-type, the resulting array will be 1-dimensional, and\n",
      "        each row will be interpreted as an element of the array.  In this\n",
      "        case, the number of columns used must match the number of fields in\n",
      "        the data-type.\n",
      "    comments : str or sequence of str, optional\n",
      "        The characters or list of characters used to indicate the start of a\n",
      "        comment. For backwards compatibility, byte strings will be decoded as\n",
      "        'latin1'. The default is '#'.\n",
      "    delimiter : str, optional\n",
      "        The string used to separate values. For backwards compatibility, byte\n",
      "        strings will be decoded as 'latin1'. The default is whitespace.\n",
      "    converters : dict, optional\n",
      "        A dictionary mapping column number to a function that will convert\n",
      "        that column to a float.  E.g., if column 0 is a date string:\n",
      "        ``converters = {0: datestr2num}``.  Converters can also be used to\n",
      "        provide a default value for missing data (but see also `genfromtxt`):\n",
      "        ``converters = {3: lambda s: float(s.strip() or 0)}``.  Default: None.\n",
      "    skiprows : int, optional\n",
      "        Skip the first `skiprows` lines; default: 0.\n",
      "    usecols : int or sequence, optional\n",
      "        Which columns to read, with 0 being the first. For example,\n",
      "        usecols = (1,4,5) will extract the 2nd, 5th and 6th columns.\n",
      "        The default, None, results in all columns being read.\n",
      "    \n",
      "        .. versionchanged:: 1.11.0\n",
      "            When a single column has to be read it is possible to use\n",
      "            an integer instead of a tuple. E.g ``usecols = 3`` reads the\n",
      "            fourth column the same way as `usecols = (3,)`` would.\n",
      "    unpack : bool, optional\n",
      "        If True, the returned array is transposed, so that arguments may be\n",
      "        unpacked using ``x, y, z = loadtxt(...)``.  When used with a structured\n",
      "        data-type, arrays are returned for each field.  Default is False.\n",
      "    ndmin : int, optional\n",
      "        The returned array will have at least `ndmin` dimensions.\n",
      "        Otherwise mono-dimensional axes will be squeezed.\n",
      "        Legal values: 0 (default), 1 or 2.\n",
      "    \n",
      "        .. versionadded:: 1.6.0\n",
      "    encoding : str, optional\n",
      "        Encoding used to decode the inputfile. Does not apply to input streams.\n",
      "        The special value 'bytes' enables backward compatibility workarounds\n",
      "        that ensures you receive byte arrays as results if possible and passes\n",
      "        latin1 encoded strings to converters. Override this value to receive\n",
      "        unicode arrays and pass strings as input to converters.  If set to None\n",
      "        the system default is used. The default value is 'bytes'.\n",
      "    \n",
      "        .. versionadded:: 1.14.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : ndarray\n",
      "        Data read from the text file.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    load, fromstring, fromregex\n",
      "    genfromtxt : Load data with missing values handled as specified.\n",
      "    scipy.io.loadmat : reads MATLAB data files\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    This function aims to be a fast reader for simply formatted files.  The\n",
      "    `genfromtxt` function provides more sophisticated handling of, e.g.,\n",
      "    lines with missing values.\n",
      "    \n",
      "    .. versionadded:: 1.10.0\n",
      "    \n",
      "    The strings produced by the Python float.hex method can be used as\n",
      "    input for floats.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> from io import StringIO   # StringIO behaves like a file object\n",
      "    >>> c = StringIO(\"0 1\\n2 3\")\n",
      "    >>> np.loadtxt(c)\n",
      "    array([[ 0.,  1.],\n",
      "           [ 2.,  3.]])\n",
      "    \n",
      "    >>> d = StringIO(\"M 21 72\\nF 35 58\")\n",
      "    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),\n",
      "    ...                      'formats': ('S1', 'i4', 'f4')})\n",
      "    array([('M', 21, 72.0), ('F', 35, 58.0)],\n",
      "          dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])\n",
      "    \n",
      "    >>> c = StringIO(\"1,0,2\\n3,0,4\")\n",
      "    >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)\n",
      "    >>> x\n",
      "    array([ 1.,  3.])\n",
      "    >>> y\n",
      "    array([ 2.,  4.])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kelvin_to_celsius(273.15)\n",
    "data = numpy.loadtxt(fname=f, delimiter=',')\n",
    "help(numpy.loadtxt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'Desktop'\n",
      "/Users/pawelpomorski/Desktop/data\n"
     ]
    }
   ],
   "source": [
    "cd Desktop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/pawelpomorski/Desktop\n"
     ]
    }
   ],
   "source": [
    "cd ~/Desktop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/pawelpomorski/Desktop/data\n"
     ]
    }
   ],
   "source": [
    "cd -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/pawelpomorski/Desktop/data'"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kelvin_to_celsius(temp_k):\n",
    "    assert(temp_k > 0)\n",
    "        \n",
    "    return temp_k-273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-265-52c1da7b8d52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkelvin_to_celsius\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-264-fe517559890f>\u001b[0m in \u001b[0;36mkelvin_to_celsius\u001b[0;34m(temp_k)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mkelvin_to_celsius\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_k\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32massert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_k\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtemp_k\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m273.15\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "kelvin_to_celsius(-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "def divide(x):\n",
    "    assert (x != 0)\n",
    "    return 1/x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divide(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-272-85b1dd09ee6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdivide\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-271-b59ef11a6c68>\u001b[0m in \u001b[0;36mdivide\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdivide\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "divide(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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