20170715_AnAOB_oxygen_inhibition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis\n",
    "> @author: chaimdemulder  \n",
    "> @copyright: (c) 2015, Chaïm De Mulder\n",
    "\n",
    "This notebook describes the analysis of data obtained during experiments to establish the effect of dissolved oxygen on Anoxic Ammonia Oxidising Bacteria (AnAOB). For a detailed description of the experiments, the reader is referred to the paper describing the results of these experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load necessary packages and code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import scipy.integrate as integrate\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt   #plotten in python\n",
    "\n",
    "from os import listdir\n",
    "from datetime import timedelta\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataAnalysisFcns.py loaded\n",
      "DataReadingFcns.py loaded\n",
      "TimeConversionFcns.py loaded\n"
     ]
    }
   ],
   "source": [
    "%run Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCRIPT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File  RB-2016-02-24-17-21-01.csv  has 0 header lines, adding data to dataframe with columns Index(['TheTime', 'B_DO', 'B_K', 'B_LevelControl_in', 'B_LevelControl_out',\n",
      "       'B_MFC', 'B_NH4', 'B_pH', 'B_Pump_acid', 'B_Pump_base',\n",
      "       'B_Pump_effluent', 'B_Pump_effluent_speed', 'B_Pump_influent',\n",
      "       'B_Pump_influent_speed', 'B_Stirrer', 'B_T_DO', 'B_T_ISE',\n",
      "       'B_Valve_air', 'B_Valve_N2', 'B_slopeNH4'],\n",
      "      dtype='object')\n",
      "File  RB-2016-02-24-18-40-19.csv  has 0 header lines, adding data to dataframe with columns Index(['TheTime', 'B_DO', 'B_K', 'B_LevelControl_in', 'B_LevelControl_out',\n",
      "       'B_MFC', 'B_NH4', 'B_pH', 'B_Pump_acid', 'B_Pump_base',\n",
      "       'B_Pump_effluent', 'B_Pump_effluent_speed', 'B_Pump_influent',\n",
      "       'B_Pump_influent_speed', 'B_Stirrer', 'B_T_DO', 'B_T_ISE',\n",
      "       'B_Valve_air', 'B_Valve_N2', 'B_slopeNH4'],\n",
      "      dtype='object')\n",
      "File  RB-2016-02-24-20-00-03.csv  has 0 header lines, adding data to dataframe with columns Index(['TheTime', 'B_DO', 'B_K', 'B_LevelControl_in', 'B_LevelControl_out',\n",
      "       'B_MFC', 'B_NH4', 'B_pH', 'B_Pump_acid', 'B_Pump_base',\n",
      "       'B_Pump_effluent', 'B_Pump_effluent_speed', 'B_Pump_influent',\n",
      "       'B_Pump_influent_speed', 'B_Stirrer', 'B_T_DO', 'B_T_ISE',\n",
      "       'B_Valve_air', 'B_Valve_N2', 'B_slopeNH4'],\n",
      "      dtype='object')\n",
      "File  RB-2016-02-24-21-21-41.csv  has 0 header lines, adding data to dataframe with columns Index(['TheTime', 'B_DO', 'B_K', 'B_LevelControl_in', 'B_LevelControl_out',\n",
      "       'B_MFC', 'B_NH4', 'B_pH', 'B_Pump_acid', 'B_Pump_base',\n",
      "       'B_Pump_effluent', 'B_Pump_effluent_speed', 'B_Pump_influent',\n",
      "       'B_Pump_influent_speed', 'B_Stirrer', 'B_T_DO', 'B_T_ISE',\n",
      "       'B_Valve_air', 'B_Valve_N2', 'B_slopeNH4'],\n",
      "      dtype='object')\n",
      "File  slopes.csv  has 0 header lines, adding data to dataframe with columns Index(['B_DO', 'B_K', 'B_LevelControl_in', 'B_LevelControl_out', 'B_MFC',\n",
      "       'B_NH4', 'B_Pump_acid', 'B_Pump_base', 'B_Pump_effluent',\n",
      "       'B_Pump_effluent_speed', 'B_Pump_influent', 'B_Pump_influent_speed',\n",
      "       'B_Stirrer', 'B_T_DO', 'B_T_ISE', 'B_Valve_N2', 'B_Valve_air', 'B_pH',\n",
      "       'B_slopeNH4', 'TheTime', 'Unnamed: 0', 'mean', 'std'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "data = join_dir_files('./data',ext='csv',comment='#')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x115200ac8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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K3aiUmq6UGgOsKrUcHmIbY7OY9HRTqZGxppwPm6Pvjg30pWwkIicCXwc+qpTamfOW6e6V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\nYrEEnP8P3whcR3aOnSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11547dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.set_zorder(ax2.get_zorder()+1) # put ax1 in front of ax2 \n",
    "ax1.patch.set_visible(False) # hide the 'canvas' \n",
    "\n",
    "ax1.plot(data['TheTime'],data['B_NH4'],label='NH4')\n",
    "ax1.plot(data['TheTime'],data['B_slopeNH4']*3600*24,label='Slope')\n",
    "ax1.set_ylim(0,150)\n",
    "\n",
    "ax1.axhline(25, color='k', linestyle='--')#to define upper limit of slope extraction\n",
    "ax1.axhline(15, color='k', linestyle='--')#to define lower limit of slope extraction\n",
    "major_ticks = np.arange(0,150,10)                                              \n",
    "minor_ticks = np.arange(0,150,5)                                               \n",
    "                  \n",
    "ax1.set_yticks(major_ticks)                                                       \n",
    "ax1.set_yticks(minor_ticks, minor=True) \n",
    " \n",
    "plt.legend(loc=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0.0007)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ls+qmh14LPCbpdmA3cFPa/n5gjaTjwCngP0bEoXHprZmZjVmu004jYiOlMCjftq5sOYBV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11570bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax2 = plt.subplots()\n",
    "ax2.plot(data['TheTime'],data['B_DO'],label='DO',color='r')\n",
    "ax2.set_ylim(0,2)\n",
    "ax2.legend(loc=2)\n",
    "ax2.set_ylim(0,0.0007)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the arguments of the extract_slopes function\n",
    "path = './data'\n",
    "file_extension = 'csv'\n",
    "time_column = 'TheTime'\n",
    "data_column = 'B_NH4'\n",
    "filter_function = moving_slope_filter\n",
    "cutoff_value = 1000\n",
    "comment = '#'\n",
    "slope_direction_down = True\n",
    "lower_data_limit = 15\n",
    "upper_data_limit = 25\n",
    "time_unit = 'd'\n",
    "delta_t = timedelta(minutes=10)\n",
    "schrikkeljaar = True\n",
    "plot_parameters = [True,(-1000,-750,-500,-250,-200,-150,-100,-50,0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The extract slopes function (and all the other functions it uses) can be found in the Functions.py file, loaded at the beginning of this notebook. For a quick explanation of the function arguments, put the cursor within the brackets of the extract_slopes function and hit shift+tab.  \n",
    "The figures generated here are also saved in a new folder 'figures' in the same folder as this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 4 files...\n",
      "Creating log-file at ./data/log.md\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chaimdemulder/Documents/Work/github/2017_AnammoxInhibitionPaper/Functions.py:428: MatplotlibDeprecationWarning: The hold function was deprecated in version 2.0.\n",
      "  fig_hist.hold(True)\n",
      "/Users/chaimdemulder/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113356898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133097b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113255be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113134d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d71f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Gw3QYHcfCrcecjiWS51To4pMKBPjxQqc6zB7UipKFC/Dk1EQGz0ji0G+aCSO+S4UuPq1+\nhWJ8PziS4XfVZuHWY7QbGcc/f9imb5mKT1Khi88rEODHsLtqseSFNtzboDwfxu+lzYglfLX2oKY4\nik9RoUu+Ub54QUY+2JA5QyKpVaYIL83cqCmO4lNU6JLv1CtfjC+ebM7/dL2d+F0n6DwmngVbNMVR\nvF+WhW6MqWSMWWKM2WqM2WKMGea6vaQxZqExZpfrZ4m8jyuSO/z8DE9EVWfOkEjKFgvmqWnreOHr\nnzibon3r4r2ys4WeCjxvra0LNAcGGWPqAi8Di621tYDFrt9FvErtDFMcv006ROcxCazSERzFS2VZ\n6NbaI9baJNf1s8A2oALQDZjiWmwK0D2vQorkpWtTHL9+piWB/obeH63ijXnbSLmS5nQ0kd/ld+1D\nN8ZUBRoDq4Ey1tprp445CpTJ1WQibta0SgnmDo3ikYjKfBi/l27vLtfx1sWrZLvQjTEhwEwgxlp7\nJuN9Nn3u1w3nfxljnjLGJBpjEpOTk28prEheKxwUwD963MEnj9/JmZQrPPThKp6cmsie5HNORxPJ\nksnOPFxjTCAwB4i11o5y3bYDaGOtPWKMKQcstdbWudnzhIeH28TExFyILZL3Uq6k8fGyfUxcuoeL\nV9Lo07wKL3aqQ+GgAKejST5jjFlnrQ3ParnszHIxwMfAtmtl7vI90M91vR8wOydBRTxVcKA/g9rW\nZOmLbegdUYkpK/fTdVwCSQd+czqayA1lZ5dLK6AP0M4Ys8F16QK8CXQwxuwC7nL9LuJzQkOCeL37\nHXz+ZHOupFkemLiCd2J3cDlVp70Tz5KtXS65RbtcxNudTbnC3/61lW/WHaJe+aKMeagRtcoUcTqW\n+Lhc2+UiIv9WJDiQd3o15IM+TTlyOoWu45cxcekeTXEUj6BCF8mBTvXKEhsTTZvaYbw1fzvtR8Yx\ne8MvXL2qg32Jc1ToIjkUViSID/uGM/2JZhQrGMiwLzbQ/b3lbDp02ulokk+p0EVuUauaocwZEsnI\nXg05diaFHu8tZ9ziXaSm6UNTcS8Vukgu8PMz9GxakQUxrenaoByjFu7kgfdXsldfSBI3UqGL5KJi\nhQIZ+3Bj3n2kMftOnKfLuASmrdyvE2mIW6jQRfLAPQ3KExsTTUS1Urwyewv9PlnLkdMXnY4lPk6F\nLpJHyhYLZsrjd/Jat3qs3fcrHUfF88WaA9palzyjQhfJQ8YY+rSoyvyYKOpVKMrL326i7+Q1OtiX\n5AkVuogbVClVmBlPNOf17vVZf+AUHUfH88qszZw4p/OZSu5RoYu4iZ+f4dHmVVj6YhseiajMjDUH\naDNiKe/H7dEUR8kVKnQRNwsNCeK17vWJjYmmefVSvPnDdnp9sJJ9J847HU28nApdxCE1S4cwqV84\n43s3Zm/yebqMTWDaqp/1oankmApdxGH3Nkyf4hhetQSvzNrMY5+s5diZFKdjiRdSoYt4gLLFgpna\nP4K/d6vH6n0n6TQmnnmbjmT9QJEMVOgiHsIYQ98WVZk7NIoqpQozcHoSL8/cyIXLqU5HEy+hQhfx\nMDXCQvjmmRYMbFODLxMPcu/4ZWw5rCM4StZU6CIeKNDfj5c638ZnA5pxNiWV7hOWM2HJbtJ0vHW5\nCRW6iAdrVTOU+THRdKhbhhGxO3jwg5Xs1/RGyYQKXcTDlSxcgAmPNGHMQ43YeewsncfGMylhr7bW\n5b+o0EW8gDGG7o0rsHB4a1rVCOX1udvo9f4KHRNG/oMKXcSLlC0WzKR+4Yx5qBF7T5yn67gEpqzY\nr3OZCqBCF/E617bWF7gOHfC/32+h3ydrOHpaX0bK71ToIl6qdNFgPnnsTl7vXp/E/b/RcXQcszf8\n4nQscZAKXcSLGZN+BMd5w6KoUTqEYV9sYPCMJE5duOx0NHGACl3EB1QLLczXT7fghY61mb/5KJ3G\nxBO3M9npWOJmKnQRHxHg78fgdrWYNagVRYMD6Td5Da/O3szFy2lORxM3UaGL+Jj6FYrxryGRDIis\nxtSVP9N1XAIbDp5yOpa4gQpdxAcFB/rzyj11mfFkM1KupNFz4gpGLdypLyP5OBW6iA9rWSOU+cOj\n6daoPOMW72LI50lcStUuGF+lQhfxcUWDAxn1YCP+p+vtzNt0lAGfJnL6whWnY0keUKGL5BNPRFVn\nZK+GrNx7kugRS/h42T4up+rk1L4ky0I3xkw2xhw3xmzOcFsjY8wqY8wGY0yiMSYib2OKSG7o2bQi\nc4ZE0qBiMV6bs5W7RsUxb9MRncfUR2RnC/1ToPN1t70N/M1a2wh41fW7iHiB28sVZdqAZkzpH0HB\nQH8GTk9i8Iz1/HZeX0bydlkWurU2Hvj1+puBoq7rxYDDuZxLRPJY69phzBsWxUud67Bga/qXkZbu\nOO50LLkFOd2HHgOMMMYcBN4B/pTZgsaYp1y7ZRKTk/XNNRFP4u9nGNimJrMGtaJ4oUAe/3QtkxL2\naheMl8ppoT8LDLfWVgKGAx9ntqC19kNrbbi1NjwsLCyHqxORvFSvfDG+HxxJ53pleX3uNl6dvUUf\nmHqhnBZ6P+Bb1/WvAX0oKuLlggP9mfBIE56Ors60VT/TYbQ+MPU2OS30w0Br1/V2wK7ciSMiTvLz\nM/ypy+3/8YFpz4krSDrwm9PRJBtMVn99jTGfA22AUOAY8L/ADmAsEACkAAOtteuyWll4eLhNTEy8\nxcgi4g5pVy0z1x3inQU7SD53iSejqvNch9oEB/o7HS3fMcass9aGZ7mcO99OqdBFvM/5S6m8MW8b\n01cfoE6ZIox6qCH1yhdzOla+kt1C1zdFReSmCgcF8I8ed/DJY3fy64XL9JiwgsnL9mnfugdSoYtI\ntrS9rTSxMdFE1Qrl73O2MmBKIifOXXI6lmSgQheRbCtZuACT+oXz13vrsmzXCdqOWMr7cXtIuaIj\nOHoCFbqI/C7GGB5rVY15wyJpVr0kb/6wnfYj45i1/hfthnGYCl1EcqRm6SJM6ncnM55sRonCgcR8\nuYHHPlnLsTMpTkfLt1ToInJLWtYI5ftBkbzWrR6r952k05h45mzU4Z2coEIXkVvm52fo06Iq84ZG\nUbVUYQbPWM/A6ev0oambqdBFJNdUDwvhm2da8FLnOizaepwOo+KYvUH71t1FhS4iuSrA34+BbWoy\nb1gkVUMLM+yLDTz7WRK/6njreU6FLiJ5ombpInzzTEtevvs2Fm8/RsfR8fy4/ZjTsXyaCl1E8oy/\nn+GZ1jWYPSiS0JAC9P80kT99u4nzl1KdjuaTVOgikufqli/K7MGteDq6Ol+sPcDdYxNI3H/9idDk\nVqnQRcQtggL8+VOX2/nyqRZctZYHP1jJ2/O360QauUiFLiJuFVGtJPNjounVtBLvLd1D9wnL2XH0\nrNOxfIIKXUTcLiQogLceaMBHfcM5fjaFe99dxqSEvVy9qumNt0KFLiKO6VC3DPNjomlTO4zX526j\n90erOPjrBadjeS0Vuog4KjQkiA/6NGXEAw3YcvgMHUfHM2rhTs2EyQEVuog4zhhDr/BKzI+Jov3t\npRm3eBetRyxl+uqfSU3Th6bZpUIXEY9RsUQh3n2kCd8NbEm10EL85bvN9Jy4gj3J55yO5hVU6CLi\ncRpXLsFXT7dgXO/G/PzrBbqOS2Dqyv06JkwWVOgi4pGMMdzXsDyxMdE0r16KV2dvoe/kNRw9reOt\nZ0aFLiIerUzRYD557E5e716fxP2/0XF0+hEc5b+p0EXE4xljeLR5FeYNi6JG6RCGfbGBwTOSOHVB\nR3DMSIUuIl6jWmhhvn66BS90rM38zUfpNCaeuJ3JTsfyGCp0EfEqAf5+DG5Xi1mDWlE0OJB+k9fw\nyqzNXLyc5nQ0x6nQRcQr1a9QjH8NiWRAZDWmrfqZruMS2HDwlNOxHKVCFxGvFRzozyv31GXGk81I\nuZJGz4kreHv+dlKu5M+tdRW6iHi9ljVCmT88mvsbV+C9pXu4Z/wykg785nQst1Ohi4hPKBocyIhe\nDZnSP4ILl1LpOXEF//xhG5dS88/WugpdRHxK69phxA6P5uE7K/FB3F66vbuc7UfPOB3LLVToIuJz\nigQH8s/7G/Bxv3BOnLvMfeOX8/GyfT5/6IAsC90YM9kYc9wYs/m624cYY7YbY7YYY97Ou4giIjnT\n/vYyxMZEEV07lNfmbGXAlEROnrvkdKw8k50t9E+BzhlvMMa0BboBDa219YB3cj+aiMitKxUSxEd9\nw/nbffVYtvsEnccmsGTHcadj5YksC91aGw9cf3ruZ4E3rbWXXMv45uiIiE8wxtCvZVVmDWxFyUIF\nePyTtfz5u00+dxKNnO5Drw1EGWNWG2PijDF35mYoEZG8ULd8UWYPbsVT0dX5fM0BuoxLYN3PvjO9\nMaeFHgCUBJoDLwJfGWPMjRY0xjxljEk0xiQmJ+uYCyLirOBAf/7c5XY+f7I5qWmWXu+vYETsdi6n\nev+ZkXJa6IeAb226NcBVIPRGC1prP7TWhltrw8PCwnKaU0QkVzWvXor5MVHc36QiE5bsofuE5Ww5\nfNrpWLckp4U+C2gLYIypDRQATuRWKBERdygSHMg7vRryYZ+mHD97iW7vLmfUgh1eu7WenWmLnwMr\ngTrGmEPGmAHAZKC6ayrjF0A/6+sTPEXEZ3WsV5ZFz0VzX8PyjPtxN/e9u4yth73vy0jGnT0cHh5u\nExMT3bY+EZHf68ftx/jjzE2cunCZ4R1q83R0Dfz9bvgRodsYY9ZZa8OzWk7fFBURyaDdbWWIjYmm\nQ90yvD1/Bw99sJIDJy84HStbVOgiItcpWbgAEx5pwuiHGrLj2Fk6j43n8zUHPP7QASp0EZEbMMbQ\no3FFYmOiaVSpOH/6dhNPTEnk+NkUp6NlSoUuInIT5YsX5LMBzXjlnrok7D5B5zEJzN98xOlYN6RC\nFxHJgp+fYUBkNeYOiaR88WCe+SyJ577awJmUK05H+w8qdBGRbKpVpgjfDWzF0HY1mb3hMHePSWDl\nnpNOx/p/KnQRkd8h0N+P5zrW4ZtnWlAgwI9HJq3i9TlbPeI8pip0EZEcaFy5BHOHRvJosypMWraP\n+95dxuZfnD10gApdRCSHChUI4LXu9ZnSP4JTF67Q473lzFh9wLE8KnQRkVvUunYYsTHRtKoZyp+/\n28SEJbsdmbOuQhcRyQUlChfgo77h9GhcgRGxOxj+5QaOnL7o1gwBbl2biIgPC/T3Y2SvhlQqWYj3\nl+5h/pajPBlVnadb1yAkKO/rVlvoIiK5yM/P8FyH2ix+vjUd65Zl/I+7aTNiCSv25P0RxlXoIiJ5\noFLJQozr3ZhZg1pxe7miVA8NyfN1apeLiEgealSpONMGNHPLurSFLiLiI1ToIiI+QoUuIuIjVOgi\nIj5ChS4i4iNU6CIiPkKFLiLiI1ToIiI+wrjziGDGmGTg5ywWCwXy/juyuceb8npTVlDevOZNeb0p\nK+R+3irW2rCsFnJroWeHMSbRWhvudI7s8qa83pQVlDeveVNeb8oKzuXVLhcRER+hQhcR8RGeWOgf\nOh3gd/KmvN6UFZQ3r3lTXm/KCg7l9bh96CIikjOeuIUuIiI5Ya393RegErAE2ApsAYZluK8ksBDY\n5fpZIpPnmA+cAuZcd3sCsMF1OQzMukmOosAh4N0Mt7UDkoDNwBTSj/nueF4gLcNy39/g/nHAOU8Z\n38zyOjC+7V3r2wAsA2pm8vh+rnXsAvplNr6enNWDx/aGj3fgtXtLeT1xfIFGwErXujcCD91sfLO6\n5LTQywFNXNeLADuBuq7f3wZedl1/GXgrk+doD9yb2YvEtcxMoO9N7h8LzMBV6KS/4zgI1Hb9/ndg\ngCfkvdn/ECAcmJbhH4VH5nVifF3Pdbvr+kDg0xs8tiSw1/WzhOt6iQz3///4empWTx3brF5L7nzt\n3kpeTx1foDZQy3W9PHAEKJ7Z+GZ1ydEuF2vtEWttkuv6WWAbUMF1dzfS//rh+tk9k+dYDJzNbB3G\nmKKk/0Wdlcn9TYEywIIMN5cCLltrd7p+Xwj09IS8N3mcPzACeCnDujw1rxPja0l/JwZQjPR3Fdfr\nBCy01v5qrf3Nlasz/Pf4enBWTx3bTB/vwGv3VvJ65Phaa3daa3e5rh8GjgNhcOPxzcot70M3xlQF\nGgOrXTfbfOr+AAAC+ElEQVSVsdYecV0/Snrp5kR3YLG19oxrPeHGmEmu637ASOCF6x5zAggwxlyb\n0P8A6W+pHM3rEmyMSTLGrDLGZHxhDCZ9l8YRbsDD8joxvk8A84wxh4A+wJs3yFuB9K2vaw7x73+U\nmY6vh2X11LG9GXe/dm8lr8ePrzEmAigA7HHddNPxvZFbKnRjTAjpb9tjrhVDRjb9PYPN4dP3Bj7P\n8FyJ1tonXL8OBOZZaw/dYH0PA6ONMWtI/6uZ5gF5If2ru02AR4AxxpgaxpjyQC9g/I2e0NPyOjS+\nw4Eu1tqKwCfAqEzy/pebja+nZfWlsfXEvJ4+vsaYcqTvWnncWns1q/HNTI4L3RgTSPoATLfWfpvh\nrmOucNdCHs/Bc4cCEcDcTBZpAQw2xuwH3gH6GmPeBLDWrrTWRllrI4B40vdjOZ0Xa+0vrp97gaWk\nbwk0BmoCu13/LYWMMbs9OK9bx9cYEwY0tNZe22L6Emh5g0V/4T+3tiq6brvh+HpoVk8d28y49bWb\nC3k9dnxduz/nAn+x1q5y3Zzp+N5MjgrdGGOAj4Ft1tpR1939Pemf4uP6OTsHq3iA9A8YUm50p7X2\nD9baytbaqqTvdplqrX3Zla2062cQ8EfgfafzGmNKuPJcK9NWwFZr7VxrbVlrbVXXf8sFa21NT83r\n+t2d4/sbUMwYU9v1ewfS93FeLxbo6MpdAugIxN5ofIFanpgVPHZsb8iB1+4t5QXPHF9jTAHgO9I7\n7Jtrt2c2vlmu1Wbjk9PrL0Ak6W8/NvLvqW1dXPeVAhaTPtVnEVAyk+dIAJKBi6TvR+yU4b6lQOfr\nlg8HJt3geR7jP6ctjnAN3A7S3z45npf0v8ybgJ9cPwdkso5znp7X3eML9MiQZSlQ/UavB6A/sNt1\neTyz8fXkrB48tpm+ltz52r3VvJ44vsCjwJUM694ANMpsfLO66JuiIiI+Qt8UFRHxESp0EREfoUIX\nEfERKnQRER+hQhcR8REqdBERH6FCFxHxESp0EREf8X/xCwaX2m7M3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11344d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112c496d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112380f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d69dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dfe630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7IpwXb6/Klz2akSsijIc/WMjfJ63h+Kmcc6IWlb6IyHkaVCjCd33jeKxZDB/O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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a7f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113254cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11336b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11497d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1155ef358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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4fc637pOsIhARuUaZN79pzsPNqvDh/K10eHseq3alOx0rz1QEIiJeEB4azCud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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b34198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112915128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e366a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156396a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141f5c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ytksdjiRfSJ0/JsH7549RqYuI3IAxhva1SrCwn2v+mE83M2jOJk6e896xdpW6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\nIn5EpS4i4kdU6iIifuT/Ae9bntY7gIePAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157f6668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b62cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slopes,figure,axes = extract_slopes(path,time_column,data_column,filter_function,cutoff_value,file_extension,\n",
    "                                    comment,slope_direction_down,[lower_data_limit,upper_data_limit],time_unit,\n",
    "                                    delta_t,schrikkeljaar,plot_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the calculated slope values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "slopes.to_csv('./slopes.csv', sep='\\t')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "138px",
    "width": "252px"
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   "navigate_menu": true,
   "number_sections": true,
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   "threshold": "3",
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   "toc_section_display": "block",
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   "widenNotebook": false
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}