Well33.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Well33\n",
    "\n",
    "Spencer Harris  \n",
    "Data Analysis  \n",
    "Fogland Point, Tiverton, Rhode Island, US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The data is pressure/temperature/conductivity data collected in a domestic well at 30 minute intervals. There is also another data set that is composed of barometric pressure for compensation. The exact source of that data is from In-Situ AquaTroll 200s that I installed on Fogland Point, RI. The format of the data is CSV. Also the is Percipitation data being loaded in from a Wunderground Personal Weater Station nearby to compare against the water level data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/sbhgeo/Documents/Fogland/Notebooks/Transducer_Data/Fogland'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd\n",
    "#os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# name/number of the well that we are looking at in this notebook\n",
    "subject_well = '33'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pulling in the hard measurements (water levels retrieved with water level sounder) and well TOC elevations\n",
    "man_measures = pd.read_csv('../data/All_WLs.csv', delimiter=',')\n",
    "# pulling in the transducer data\n",
    "barofile = '../data/troll/2017-08-17_16-05-25_fogland-point_Log_baro081717.csv'\n",
    "well33file = '../data/troll/2017-08-16_18-09-51_fogland-point_Log_3rodway081617.csv'\n",
    "baro = pd.read_csv(barofile, skiprows=25)\n",
    "well = pd.read_csv(well33file, skiprows=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4122, 3) (4162, 6)\n"
     ]
    }
   ],
   "source": [
    "#check for size of data\n",
    "print (baro.shape, well.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>Pressure (psi) (535311)</th>\n",
       "      <th>Temperature (°C) (535311)</th>\n",
       "      <th>Actual Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Specific Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Salinity (PSU) (535311)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2017-08-17 09:09:51.491</td>\n",
       "      <td>23.84580</td>\n",
       "      <td>11.68027</td>\n",
       "      <td>12279.40</td>\n",
       "      <td>16469.31</td>\n",
       "      <td>9.633535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2017-08-17 09:39:51.491</td>\n",
       "      <td>23.83716</td>\n",
       "      <td>11.68035</td>\n",
       "      <td>12277.87</td>\n",
       "      <td>16467.22</td>\n",
       "      <td>9.632214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2017-08-17 10:09:51.491</td>\n",
       "      <td>23.83613</td>\n",
       "      <td>11.68035</td>\n",
       "      <td>12277.95</td>\n",
       "      <td>16467.33</td>\n",
       "      <td>9.632283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2017-08-17 10:39:51.491</td>\n",
       "      <td>23.82495</td>\n",
       "      <td>11.68072</td>\n",
       "      <td>12283.24</td>\n",
       "      <td>16474.27</td>\n",
       "      <td>9.636679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2017-08-17 11:09:51.491</td>\n",
       "      <td>23.81416</td>\n",
       "      <td>11.68089</td>\n",
       "      <td>12281.03</td>\n",
       "      <td>16471.22</td>\n",
       "      <td>9.634759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2017-08-17 11:39:51.491</td>\n",
       "      <td>23.80641</td>\n",
       "      <td>11.68125</td>\n",
       "      <td>12282.49</td>\n",
       "      <td>16473.04</td>\n",
       "      <td>9.635916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2017-08-17 12:09:51.491</td>\n",
       "      <td>23.80050</td>\n",
       "      <td>11.68135</td>\n",
       "      <td>12286.92</td>\n",
       "      <td>16478.93</td>\n",
       "      <td>9.639647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2017-08-17 12:39:51.491</td>\n",
       "      <td>23.79322</td>\n",
       "      <td>11.68353</td>\n",
       "      <td>12286.72</td>\n",
       "      <td>16477.74</td>\n",
       "      <td>9.638950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2017-08-17 13:09:51.491</td>\n",
       "      <td>23.78602</td>\n",
       "      <td>11.68135</td>\n",
       "      <td>12285.50</td>\n",
       "      <td>16477.02</td>\n",
       "      <td>9.638439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2017-08-17 13:39:51.491</td>\n",
       "      <td>23.78289</td>\n",
       "      <td>11.68243</td>\n",
       "      <td>12287.19</td>\n",
       "      <td>16478.83</td>\n",
       "      <td>9.639610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2017-08-17 14:09:51.491</td>\n",
       "      <td>23.77623</td>\n",
       "      <td>11.68208</td>\n",
       "      <td>12288.60</td>\n",
       "      <td>16480.88</td>\n",
       "      <td>9.640894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2017-08-17 14:39:51.491</td>\n",
       "      <td>23.77856</td>\n",
       "      <td>11.68271</td>\n",
       "      <td>12287.30</td>\n",
       "      <td>16478.87</td>\n",
       "      <td>9.639639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2017-08-17 15:09:51.491</td>\n",
       "      <td>23.78413</td>\n",
       "      <td>11.68271</td>\n",
       "      <td>12289.55</td>\n",
       "      <td>16481.88</td>\n",
       "      <td>9.641546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2017-08-17 15:39:51.491</td>\n",
       "      <td>23.79322</td>\n",
       "      <td>11.68253</td>\n",
       "      <td>12292.09</td>\n",
       "      <td>16485.37</td>\n",
       "      <td>9.643744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2017-08-17 16:09:51.491</td>\n",
       "      <td>23.79852</td>\n",
       "      <td>11.68326</td>\n",
       "      <td>12289.84</td>\n",
       "      <td>16482.04</td>\n",
       "      <td>9.641663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2017-08-17 16:39:51.491</td>\n",
       "      <td>23.80490</td>\n",
       "      <td>11.68326</td>\n",
       "      <td>12296.10</td>\n",
       "      <td>16490.44</td>\n",
       "      <td>9.646975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017-08-17 17:09:51.491</td>\n",
       "      <td>23.80490</td>\n",
       "      <td>11.68399</td>\n",
       "      <td>12296.17</td>\n",
       "      <td>16490.23</td>\n",
       "      <td>9.646855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-08-17 17:39:51.491</td>\n",
       "      <td>23.81243</td>\n",
       "      <td>11.68389</td>\n",
       "      <td>12296.43</td>\n",
       "      <td>16490.62</td>\n",
       "      <td>9.647099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2017-08-17 18:09:51.491</td>\n",
       "      <td>23.81383</td>\n",
       "      <td>11.68408</td>\n",
       "      <td>12294.12</td>\n",
       "      <td>16487.44</td>\n",
       "      <td>9.645092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2017-08-17 18:39:51.491</td>\n",
       "      <td>23.81577</td>\n",
       "      <td>11.68471</td>\n",
       "      <td>12293.79</td>\n",
       "      <td>16486.73</td>\n",
       "      <td>9.644667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2017-08-17 19:09:51.491</td>\n",
       "      <td>23.81753</td>\n",
       "      <td>11.68497</td>\n",
       "      <td>12296.61</td>\n",
       "      <td>16490.39</td>\n",
       "      <td>9.646984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2017-08-17 19:39:51.491</td>\n",
       "      <td>23.82495</td>\n",
       "      <td>11.68516</td>\n",
       "      <td>12296.36</td>\n",
       "      <td>16489.99</td>\n",
       "      <td>9.646731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2017-08-17 20:09:51.491</td>\n",
       "      <td>23.82407</td>\n",
       "      <td>11.68516</td>\n",
       "      <td>12298.66</td>\n",
       "      <td>16493.06</td>\n",
       "      <td>9.648679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2017-08-17 20:39:51.491</td>\n",
       "      <td>23.82604</td>\n",
       "      <td>11.68562</td>\n",
       "      <td>12296.50</td>\n",
       "      <td>16489.98</td>\n",
       "      <td>9.646741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2017-08-17 21:09:51.491</td>\n",
       "      <td>23.82150</td>\n",
       "      <td>11.68543</td>\n",
       "      <td>12297.12</td>\n",
       "      <td>16490.89</td>\n",
       "      <td>9.647309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2017-08-17 21:39:51.491</td>\n",
       "      <td>23.82398</td>\n",
       "      <td>11.68616</td>\n",
       "      <td>12298.44</td>\n",
       "      <td>16492.36</td>\n",
       "      <td>9.648256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2017-08-17 22:09:51.491</td>\n",
       "      <td>23.81800</td>\n",
       "      <td>11.68580</td>\n",
       "      <td>12297.95</td>\n",
       "      <td>16491.84</td>\n",
       "      <td>9.647923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2017-08-17 22:39:51.491</td>\n",
       "      <td>23.81092</td>\n",
       "      <td>11.68643</td>\n",
       "      <td>12298.31</td>\n",
       "      <td>16492.06</td>\n",
       "      <td>9.648075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2017-08-17 23:09:51.491</td>\n",
       "      <td>23.81429</td>\n",
       "      <td>11.68725</td>\n",
       "      <td>12298.56</td>\n",
       "      <td>16492.06</td>\n",
       "      <td>9.648094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2017-08-17 23:39:51.491</td>\n",
       "      <td>23.80202</td>\n",
       "      <td>11.68707</td>\n",
       "      <td>12299.35</td>\n",
       "      <td>16493.19</td>\n",
       "      <td>9.648807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date Time  Pressure (psi) (535311)  \\\n",
       "30  2017-08-17 09:09:51.491                 23.84580   \n",
       "31  2017-08-17 09:39:51.491                 23.83716   \n",
       "32  2017-08-17 10:09:51.491                 23.83613   \n",
       "33  2017-08-17 10:39:51.491                 23.82495   \n",
       "34  2017-08-17 11:09:51.491                 23.81416   \n",
       "35  2017-08-17 11:39:51.491                 23.80641   \n",
       "36  2017-08-17 12:09:51.491                 23.80050   \n",
       "37  2017-08-17 12:39:51.491                 23.79322   \n",
       "38  2017-08-17 13:09:51.491                 23.78602   \n",
       "39  2017-08-17 13:39:51.491                 23.78289   \n",
       "40  2017-08-17 14:09:51.491                 23.77623   \n",
       "41  2017-08-17 14:39:51.491                 23.77856   \n",
       "42  2017-08-17 15:09:51.491                 23.78413   \n",
       "43  2017-08-17 15:39:51.491                 23.79322   \n",
       "44  2017-08-17 16:09:51.491                 23.79852   \n",
       "45  2017-08-17 16:39:51.491                 23.80490   \n",
       "46  2017-08-17 17:09:51.491                 23.80490   \n",
       "47  2017-08-17 17:39:51.491                 23.81243   \n",
       "48  2017-08-17 18:09:51.491                 23.81383   \n",
       "49  2017-08-17 18:39:51.491                 23.81577   \n",
       "50  2017-08-17 19:09:51.491                 23.81753   \n",
       "51  2017-08-17 19:39:51.491                 23.82495   \n",
       "52  2017-08-17 20:09:51.491                 23.82407   \n",
       "53  2017-08-17 20:39:51.491                 23.82604   \n",
       "54  2017-08-17 21:09:51.491                 23.82150   \n",
       "55  2017-08-17 21:39:51.491                 23.82398   \n",
       "56  2017-08-17 22:09:51.491                 23.81800   \n",
       "57  2017-08-17 22:39:51.491                 23.81092   \n",
       "58  2017-08-17 23:09:51.491                 23.81429   \n",
       "59  2017-08-17 23:39:51.491                 23.80202   \n",
       "\n",
       "    Temperature (°C) (535311)  Actual Conductivity (µS/cm) (535311)  \\\n",
       "30                   11.68027                              12279.40   \n",
       "31                   11.68035                              12277.87   \n",
       "32                   11.68035                              12277.95   \n",
       "33                   11.68072                              12283.24   \n",
       "34                   11.68089                              12281.03   \n",
       "35                   11.68125                              12282.49   \n",
       "36                   11.68135                              12286.92   \n",
       "37                   11.68353                              12286.72   \n",
       "38                   11.68135                              12285.50   \n",
       "39                   11.68243                              12287.19   \n",
       "40                   11.68208                              12288.60   \n",
       "41                   11.68271                              12287.30   \n",
       "42                   11.68271                              12289.55   \n",
       "43                   11.68253                              12292.09   \n",
       "44                   11.68326                              12289.84   \n",
       "45                   11.68326                              12296.10   \n",
       "46                   11.68399                              12296.17   \n",
       "47                   11.68389                              12296.43   \n",
       "48                   11.68408                              12294.12   \n",
       "49                   11.68471                              12293.79   \n",
       "50                   11.68497                              12296.61   \n",
       "51                   11.68516                              12296.36   \n",
       "52                   11.68516                              12298.66   \n",
       "53                   11.68562                              12296.50   \n",
       "54                   11.68543                              12297.12   \n",
       "55                   11.68616                              12298.44   \n",
       "56                   11.68580                              12297.95   \n",
       "57                   11.68643                              12298.31   \n",
       "58                   11.68725                              12298.56   \n",
       "59                   11.68707                              12299.35   \n",
       "\n",
       "    Specific Conductivity (µS/cm) (535311)  Salinity (PSU) (535311)  \n",
       "30                                16469.31                 9.633535  \n",
       "31                                16467.22                 9.632214  \n",
       "32                                16467.33                 9.632283  \n",
       "33                                16474.27                 9.636679  \n",
       "34                                16471.22                 9.634759  \n",
       "35                                16473.04                 9.635916  \n",
       "36                                16478.93                 9.639647  \n",
       "37                                16477.74                 9.638950  \n",
       "38                                16477.02                 9.638439  \n",
       "39                                16478.83                 9.639610  \n",
       "40                                16480.88                 9.640894  \n",
       "41                                16478.87                 9.639639  \n",
       "42                                16481.88                 9.641546  \n",
       "43                                16485.37                 9.643744  \n",
       "44                                16482.04                 9.641663  \n",
       "45                                16490.44                 9.646975  \n",
       "46                                16490.23                 9.646855  \n",
       "47                                16490.62                 9.647099  \n",
       "48                                16487.44                 9.645092  \n",
       "49                                16486.73                 9.644667  \n",
       "50                                16490.39                 9.646984  \n",
       "51                                16489.99                 9.646731  \n",
       "52                                16493.06                 9.648679  \n",
       "53                                16489.98                 9.646741  \n",
       "54                                16490.89                 9.647309  \n",
       "55                                16492.36                 9.648256  \n",
       "56                                16491.84                 9.647923  \n",
       "57                                16492.06                 9.648075  \n",
       "58                                16492.06                 9.648094  \n",
       "59                                16493.19                 9.648807  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well[30:60]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the two data frames are of different sizes we need to inspect them and try to line/match them up temporally the best we can. This shouldn't be too hard because both of the files have one measurement every 30 minutes. If the two files' temporal resolution was different then it would be more difficult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>Pressure (psi) (525373)</th>\n",
       "      <th>Temperature (°C) (525373)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-17 16:05:25.278</td>\n",
       "      <td>14.71757</td>\n",
       "      <td>20.58728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-17 16:35:25.278</td>\n",
       "      <td>14.71992</td>\n",
       "      <td>19.49414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-17 17:05:25.278</td>\n",
       "      <td>14.72049</td>\n",
       "      <td>18.43887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-17 17:35:25.278</td>\n",
       "      <td>14.71942</td>\n",
       "      <td>18.11523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-17 18:05:25.278</td>\n",
       "      <td>14.71662</td>\n",
       "      <td>18.02957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-08-17 18:35:25.278</td>\n",
       "      <td>14.72028</td>\n",
       "      <td>18.01942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-08-17 19:05:25.278</td>\n",
       "      <td>14.71957</td>\n",
       "      <td>17.98892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-08-17 19:35:25.278</td>\n",
       "      <td>14.72536</td>\n",
       "      <td>18.00272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-08-17 20:05:25.278</td>\n",
       "      <td>14.73132</td>\n",
       "      <td>18.00127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-08-17 20:35:25.278</td>\n",
       "      <td>14.72902</td>\n",
       "      <td>17.99982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date Time  Pressure (psi) (525373)  Temperature (°C) (525373)\n",
       "0  2017-08-17 16:05:25.278                 14.71757                   20.58728\n",
       "1  2017-08-17 16:35:25.278                 14.71992                   19.49414\n",
       "2  2017-08-17 17:05:25.278                 14.72049                   18.43887\n",
       "3  2017-08-17 17:35:25.278                 14.71942                   18.11523\n",
       "4  2017-08-17 18:05:25.278                 14.71662                   18.02957\n",
       "5  2017-08-17 18:35:25.278                 14.72028                   18.01942\n",
       "6  2017-08-17 19:05:25.278                 14.71957                   17.98892\n",
       "7  2017-08-17 19:35:25.278                 14.72536                   18.00272\n",
       "8  2017-08-17 20:05:25.278                 14.73132                   18.00127\n",
       "9  2017-08-17 20:35:25.278                 14.72902                   17.99982"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baro[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So if we inspect the data we note that the 44th row/measurement of `well33` actually matches up with the 0th row/measurement of `baro`  \n",
    "If we shift `well33` by 44 rows the two should match up quite nicely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#matching up our readings/indicies\n",
    "well = well.shift(-44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>Pressure (psi) (535311)</th>\n",
       "      <th>Temperature (°C) (535311)</th>\n",
       "      <th>Actual Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Specific Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Salinity (PSU) (535311)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-17 16:09:51.491</td>\n",
       "      <td>23.79852</td>\n",
       "      <td>11.68326</td>\n",
       "      <td>12289.84</td>\n",
       "      <td>16482.04</td>\n",
       "      <td>9.641663</td>\n",
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       "      <th>1</th>\n",
       "      <td>2017-08-17 16:39:51.491</td>\n",
       "      <td>23.80490</td>\n",
       "      <td>11.68326</td>\n",
       "      <td>12296.10</td>\n",
       "      <td>16490.44</td>\n",
       "      <td>9.646975</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-17 17:09:51.491</td>\n",
       "      <td>23.80490</td>\n",
       "      <td>11.68399</td>\n",
       "      <td>12296.17</td>\n",
       "      <td>16490.23</td>\n",
       "      <td>9.646855</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-17 17:39:51.491</td>\n",
       "      <td>23.81243</td>\n",
       "      <td>11.68389</td>\n",
       "      <td>12296.43</td>\n",
       "      <td>16490.62</td>\n",
       "      <td>9.647099</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-17 18:09:51.491</td>\n",
       "      <td>23.81383</td>\n",
       "      <td>11.68408</td>\n",
       "      <td>12294.12</td>\n",
       "      <td>16487.44</td>\n",
       "      <td>9.645092</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-08-17 18:39:51.491</td>\n",
       "      <td>23.81577</td>\n",
       "      <td>11.68471</td>\n",
       "      <td>12293.79</td>\n",
       "      <td>16486.73</td>\n",
       "      <td>9.644667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-08-17 19:09:51.491</td>\n",
       "      <td>23.81753</td>\n",
       "      <td>11.68497</td>\n",
       "      <td>12296.61</td>\n",
       "      <td>16490.39</td>\n",
       "      <td>9.646984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-08-17 19:39:51.491</td>\n",
       "      <td>23.82495</td>\n",
       "      <td>11.68516</td>\n",
       "      <td>12296.36</td>\n",
       "      <td>16489.99</td>\n",
       "      <td>9.646731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-08-17 20:09:51.491</td>\n",
       "      <td>23.82407</td>\n",
       "      <td>11.68516</td>\n",
       "      <td>12298.66</td>\n",
       "      <td>16493.06</td>\n",
       "      <td>9.648679</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-08-17 20:39:51.491</td>\n",
       "      <td>23.82604</td>\n",
       "      <td>11.68562</td>\n",
       "      <td>12296.50</td>\n",
       "      <td>16489.98</td>\n",
       "      <td>9.646741</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date Time  Pressure (psi) (535311)  \\\n",
       "0  2017-08-17 16:09:51.491                 23.79852   \n",
       "1  2017-08-17 16:39:51.491                 23.80490   \n",
       "2  2017-08-17 17:09:51.491                 23.80490   \n",
       "3  2017-08-17 17:39:51.491                 23.81243   \n",
       "4  2017-08-17 18:09:51.491                 23.81383   \n",
       "5  2017-08-17 18:39:51.491                 23.81577   \n",
       "6  2017-08-17 19:09:51.491                 23.81753   \n",
       "7  2017-08-17 19:39:51.491                 23.82495   \n",
       "8  2017-08-17 20:09:51.491                 23.82407   \n",
       "9  2017-08-17 20:39:51.491                 23.82604   \n",
       "\n",
       "   Temperature (°C) (535311)  Actual Conductivity (µS/cm) (535311)  \\\n",
       "0                   11.68326                              12289.84   \n",
       "1                   11.68326                              12296.10   \n",
       "2                   11.68399                              12296.17   \n",
       "3                   11.68389                              12296.43   \n",
       "4                   11.68408                              12294.12   \n",
       "5                   11.68471                              12293.79   \n",
       "6                   11.68497                              12296.61   \n",
       "7                   11.68516                              12296.36   \n",
       "8                   11.68516                              12298.66   \n",
       "9                   11.68562                              12296.50   \n",
       "\n",
       "   Specific Conductivity (µS/cm) (535311)  Salinity (PSU) (535311)  \n",
       "0                                16482.04                 9.641663  \n",
       "1                                16490.44                 9.646975  \n",
       "2                                16490.23                 9.646855  \n",
       "3                                16490.62                 9.647099  \n",
       "4                                16487.44                 9.645092  \n",
       "5                                16486.73                 9.644667  \n",
       "6                                16490.39                 9.646984  \n",
       "7                                16489.99                 9.646731  \n",
       "8                                16493.06                 9.648679  \n",
       "9                                16489.98                 9.646741  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date Time', 'Pressure (psi) (535311)', 'Temperature (°C) (535311)',\n",
      "       'Actual Conductivity (µS/cm) (535311)',\n",
      "       'Specific Conductivity (µS/cm) (535311)', 'Salinity (PSU) (535311)'],\n",
      "      dtype='object')\n",
      "Index(['Date Time', 'Pressure (psi) (525373)', 'Temperature (°C) (525373)'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(well.columns)\n",
    "print(baro.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**\"manual\" barometric compensation** derived from [In-Situ Inc.](https://in-situ.com/wp-content/uploads/2014/11/Manual-Level-Mode-Correction-for-Non-Vented-Sensors.pdf)  \n",
    "\n",
    "Basic idea: Depth is the amount of water over the pressure sensor plus barometric pressure.  \n",
    "\n",
    "Water level can be calculated in meters using the following equation: D = (0.703073 X (Pm-Bm)) / SG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "well['compd_(psi)']  = well['Pressure (psi) (535311)'].sub(baro['Pressure (psi) (525373)'].squeeze())\n",
    "well['baro_reading'] = baro['Pressure (psi) (525373)']\n",
    "Lr = man_measures\n",
    "# Lr = reference level\n",
    "well['Transducer_level_ft'] = (well['compd_(psi)'] * 2.30666) \n",
    "well['water_level_ft_LMSL'] = (Lr.iloc[0][' TOC Elevation (LSML)']) - (well['compd_(psi)'] * 2.30666)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below the date/time is being converted into a format that Pandas can read and then it is being assigned as the index for the data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "well['Datetime'] = pd.to_datetime(well['Date Time'], errors='coerce')\n",
    "well = well.set_index('Datetime')\n",
    "well = well.drop('Date Time', 1)\n",
    "\n",
    "baro['Datetime'] = pd.to_datetime(baro['Date Time'], errors='coerce')\n",
    "baro = baro.set_index('Datetime')\n",
    "baro= baro.drop('Date Time', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure (psi) (535311)</th>\n",
       "      <th>Temperature (°C) (535311)</th>\n",
       "      <th>Actual Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Specific Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Salinity (PSU) (535311)</th>\n",
       "      <th>compd_(psi)</th>\n",
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       "      <th>Transducer_level_ft</th>\n",
       "      <th>water_level_ft_LMSL</th>\n",
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       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-17 16:09:51.491</th>\n",
       "      <td>23.79852</td>\n",
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       "      <td>12289.84</td>\n",
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       "      <td>9.08095</td>\n",
       "      <td>14.71757</td>\n",
       "      <td>20.946664</td>\n",
       "      <td>0.585236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 16:39:51.491</th>\n",
       "      <td>23.80490</td>\n",
       "      <td>11.68326</td>\n",
       "      <td>12296.10</td>\n",
       "      <td>16490.44</td>\n",
       "      <td>9.646975</td>\n",
       "      <td>9.08498</td>\n",
       "      <td>14.71992</td>\n",
       "      <td>20.955960</td>\n",
       "      <td>0.575940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 17:09:51.491</th>\n",
       "      <td>23.80490</td>\n",
       "      <td>11.68399</td>\n",
       "      <td>12296.17</td>\n",
       "      <td>16490.23</td>\n",
       "      <td>9.646855</td>\n",
       "      <td>9.08441</td>\n",
       "      <td>14.72049</td>\n",
       "      <td>20.954645</td>\n",
       "      <td>0.577255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 17:39:51.491</th>\n",
       "      <td>23.81243</td>\n",
       "      <td>11.68389</td>\n",
       "      <td>12296.43</td>\n",
       "      <td>16490.62</td>\n",
       "      <td>9.647099</td>\n",
       "      <td>9.09301</td>\n",
       "      <td>14.71942</td>\n",
       "      <td>20.974482</td>\n",
       "      <td>0.557418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 18:09:51.491</th>\n",
       "      <td>23.81383</td>\n",
       "      <td>11.68408</td>\n",
       "      <td>12294.12</td>\n",
       "      <td>16487.44</td>\n",
       "      <td>9.645092</td>\n",
       "      <td>9.09721</td>\n",
       "      <td>14.71662</td>\n",
       "      <td>20.984170</td>\n",
       "      <td>0.547730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 18:39:51.491</th>\n",
       "      <td>23.81577</td>\n",
       "      <td>11.68471</td>\n",
       "      <td>12293.79</td>\n",
       "      <td>16486.73</td>\n",
       "      <td>9.644667</td>\n",
       "      <td>9.09549</td>\n",
       "      <td>14.72028</td>\n",
       "      <td>20.980203</td>\n",
       "      <td>0.551697</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 19:09:51.491</th>\n",
       "      <td>23.81753</td>\n",
       "      <td>11.68497</td>\n",
       "      <td>12296.61</td>\n",
       "      <td>16490.39</td>\n",
       "      <td>9.646984</td>\n",
       "      <td>9.09796</td>\n",
       "      <td>14.71957</td>\n",
       "      <td>20.985900</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 19:39:51.491</th>\n",
       "      <td>23.82495</td>\n",
       "      <td>11.68516</td>\n",
       "      <td>12296.36</td>\n",
       "      <td>16489.99</td>\n",
       "      <td>9.646731</td>\n",
       "      <td>9.09959</td>\n",
       "      <td>14.72536</td>\n",
       "      <td>20.989660</td>\n",
       "      <td>0.542240</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 20:09:51.491</th>\n",
       "      <td>23.82407</td>\n",
       "      <td>11.68516</td>\n",
       "      <td>12298.66</td>\n",
       "      <td>16493.06</td>\n",
       "      <td>9.648679</td>\n",
       "      <td>9.09275</td>\n",
       "      <td>14.73132</td>\n",
       "      <td>20.973883</td>\n",
       "      <td>0.558017</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 20:39:51.491</th>\n",
       "      <td>23.82604</td>\n",
       "      <td>11.68562</td>\n",
       "      <td>12296.50</td>\n",
       "      <td>16489.98</td>\n",
       "      <td>9.646741</td>\n",
       "      <td>9.09702</td>\n",
       "      <td>14.72902</td>\n",
       "      <td>20.983732</td>\n",
       "      <td>0.548168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pressure (psi) (535311)  Temperature (°C) (535311)  \\\n",
       "Datetime                                                                      \n",
       "2017-08-17 16:09:51.491                 23.79852                   11.68326   \n",
       "2017-08-17 16:39:51.491                 23.80490                   11.68326   \n",
       "2017-08-17 17:09:51.491                 23.80490                   11.68399   \n",
       "2017-08-17 17:39:51.491                 23.81243                   11.68389   \n",
       "2017-08-17 18:09:51.491                 23.81383                   11.68408   \n",
       "2017-08-17 18:39:51.491                 23.81577                   11.68471   \n",
       "2017-08-17 19:09:51.491                 23.81753                   11.68497   \n",
       "2017-08-17 19:39:51.491                 23.82495                   11.68516   \n",
       "2017-08-17 20:09:51.491                 23.82407                   11.68516   \n",
       "2017-08-17 20:39:51.491                 23.82604                   11.68562   \n",
       "\n",
       "                         Actual Conductivity (µS/cm) (535311)  \\\n",
       "Datetime                                                        \n",
       "2017-08-17 16:09:51.491                              12289.84   \n",
       "2017-08-17 16:39:51.491                              12296.10   \n",
       "2017-08-17 17:09:51.491                              12296.17   \n",
       "2017-08-17 17:39:51.491                              12296.43   \n",
       "2017-08-17 18:09:51.491                              12294.12   \n",
       "2017-08-17 18:39:51.491                              12293.79   \n",
       "2017-08-17 19:09:51.491                              12296.61   \n",
       "2017-08-17 19:39:51.491                              12296.36   \n",
       "2017-08-17 20:09:51.491                              12298.66   \n",
       "2017-08-17 20:39:51.491                              12296.50   \n",
       "\n",
       "                         Specific Conductivity (µS/cm) (535311)  \\\n",
       "Datetime                                                          \n",
       "2017-08-17 16:09:51.491                                16482.04   \n",
       "2017-08-17 16:39:51.491                                16490.44   \n",
       "2017-08-17 17:09:51.491                                16490.23   \n",
       "2017-08-17 17:39:51.491                                16490.62   \n",
       "2017-08-17 18:09:51.491                                16487.44   \n",
       "2017-08-17 18:39:51.491                                16486.73   \n",
       "2017-08-17 19:09:51.491                                16490.39   \n",
       "2017-08-17 19:39:51.491                                16489.99   \n",
       "2017-08-17 20:09:51.491                                16493.06   \n",
       "2017-08-17 20:39:51.491                                16489.98   \n",
       "\n",
       "                         Salinity (PSU) (535311)  compd_(psi)  baro_reading  \\\n",
       "Datetime                                                                      \n",
       "2017-08-17 16:09:51.491                 9.641663      9.08095      14.71757   \n",
       "2017-08-17 16:39:51.491                 9.646975      9.08498      14.71992   \n",
       "2017-08-17 17:09:51.491                 9.646855      9.08441      14.72049   \n",
       "2017-08-17 17:39:51.491                 9.647099      9.09301      14.71942   \n",
       "2017-08-17 18:09:51.491                 9.645092      9.09721      14.71662   \n",
       "2017-08-17 18:39:51.491                 9.644667      9.09549      14.72028   \n",
       "2017-08-17 19:09:51.491                 9.646984      9.09796      14.71957   \n",
       "2017-08-17 19:39:51.491                 9.646731      9.09959      14.72536   \n",
       "2017-08-17 20:09:51.491                 9.648679      9.09275      14.73132   \n",
       "2017-08-17 20:39:51.491                 9.646741      9.09702      14.72902   \n",
       "\n",
       "                         Transducer_level_ft  water_level_ft_LMSL  \n",
       "Datetime                                                           \n",
       "2017-08-17 16:09:51.491            20.946664             0.585236  \n",
       "2017-08-17 16:39:51.491            20.955960             0.575940  \n",
       "2017-08-17 17:09:51.491            20.954645             0.577255  \n",
       "2017-08-17 17:39:51.491            20.974482             0.557418  \n",
       "2017-08-17 18:09:51.491            20.984170             0.547730  \n",
       "2017-08-17 18:39:51.491            20.980203             0.551697  \n",
       "2017-08-17 19:09:51.491            20.985900             0.546000  \n",
       "2017-08-17 19:39:51.491            20.989660             0.542240  \n",
       "2017-08-17 20:09:51.491            20.973883             0.558017  \n",
       "2017-08-17 20:39:51.491            20.983732             0.548168  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['well', 'lat ', 'lon', 'feet below TOC', ' TOC Elevation (LSML)',\n",
       "       'Water level above LMSL'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man_measures['Datetime'] = pd.to_datetime(man_measures['Date Time'])\n",
    "man_measures = man_measures.set_index('Datetime')\n",
    "man_measures= man_measures.drop(('Date Time'), 1)\n",
    "man_measures= man_measures.drop(('date'), 1)\n",
    "man_measures= man_measures.drop(('time'), 1)\n",
    "# combine the above three lines into 1 using a list\n",
    "man_measures.columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we have selected out the manual reading that pertain to the `subject_well` we are currently looking at  \n",
    "\n",
    "The next level of efficency here would be to have the script automatically select where the transducer data matches with (is within 29 minutes) of a manual level.  \n",
    "\n",
    "But we are just going to look above and pick which row corresponds with the first few transducer readings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>feet below TOC</th>\n",
       "      <th>TOC Elevation (LSML)</th>\n",
       "      <th>Water level above LMSL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-17 15:20:00</th>\n",
       "      <td>33</td>\n",
       "      <td>41.561014</td>\n",
       "      <td>-71.219101</td>\n",
       "      <td>10.44</td>\n",
       "      <td>12.129</td>\n",
       "      <td>1.689</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-16 15:20:00</th>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-16 19:29:00</th>\n",
       "      <td>33</td>\n",
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       "      <td>0.989</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-18 08:55:00</th>\n",
       "      <td>33</td>\n",
       "      <td>41.561014</td>\n",
       "      <td>-71.219101</td>\n",
       "      <td>11.15</td>\n",
       "      <td>12.129</td>\n",
       "      <td>0.979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-11 09:10:00</th>\n",
       "      <td>33</td>\n",
       "      <td>41.561014</td>\n",
       "      <td>-71.219101</td>\n",
       "      <td>10.27</td>\n",
       "      <td>12.129</td>\n",
       "      <td>1.859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    well       lat         lon  feet below TOC  \\\n",
       "Datetime                                                         \n",
       "2017-06-17 15:20:00   33  41.561014 -71.219101           10.44   \n",
       "2017-08-16 15:20:00   33  41.561014 -71.219101           11.20   \n",
       "2017-08-16 19:29:00   33  41.561014 -71.219101           11.14   \n",
       "2017-08-18 08:55:00   33  41.561014 -71.219101           11.15   \n",
       "2017-11-11 09:10:00   33  41.561014 -71.219101           10.27   \n",
       "\n",
       "                      TOC Elevation (LSML)  Water level above LMSL  \n",
       "Datetime                                                            \n",
       "2017-06-17 15:20:00                 12.129                   1.689  \n",
       "2017-08-16 15:20:00                 12.129                   0.929  \n",
       "2017-08-16 19:29:00                 12.129                   0.989  \n",
       "2017-08-18 08:55:00                 12.129                   0.979  \n",
       "2017-11-11 09:10:00                 12.129                   1.859  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_measures = man_measures.loc[man_measures['well'].str.contains(subject_well)==True, : ]\n",
    "well_measures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### specific to this well\n",
    "In this case (subject_well = '56') we are picking row 2 which has a Datetime index of `2017-08-17 17:39:00` which matches up nicely with the transducer's 3rd reading, seen on row 2 of the `well56` dataframe, which has a Datetime index of `2017-08-17 17:34:14.116`   \n",
    "\n",
    "We will use the manual measurement to come up with our transducer's setting in the well (`man_measures`'s **feet below TOC** + `well56`'s **Transducer_level_ft**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.129660269400006"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transducer_set = well_measures.iloc[2]['feet below TOC'] + well.iloc[7]['Transducer_level_ft']\n",
    "transducer_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure (psi) (535311)</th>\n",
       "      <th>Temperature (°C) (535311)</th>\n",
       "      <th>Actual Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Specific Conductivity (µS/cm) (535311)</th>\n",
       "      <th>Salinity (PSU) (535311)</th>\n",
       "      <th>compd_(psi)</th>\n",
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       "      <th>GW_elevation_ft_(LSML)</th>\n",
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       "      <th>Datetime</th>\n",
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       "      <td>12294.12</td>\n",
       "      <td>16487.44</td>\n",
       "      <td>9.645092</td>\n",
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       "      <td>0.547730</td>\n",
       "      <td>11.145490</td>\n",
       "      <td>0.983510</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 18:39:51.491</th>\n",
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       "      <th>2017-08-17 20:39:51.491</th>\n",
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       "      <th>2017-08-17 21:39:51.491</th>\n",
       "      <td>23.82398</td>\n",
       "      <td>11.68616</td>\n",
       "      <td>12298.44</td>\n",
       "      <td>16492.36</td>\n",
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       "      <td>14.72709</td>\n",
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       "      <td>0.548468</td>\n",
       "      <td>11.146228</td>\n",
       "      <td>0.982772</td>\n",
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       "    <tr>\n",
       "      <th>2017-08-17 22:09:51.491</th>\n",
       "      <td>23.81800</td>\n",
       "      <td>11.68580</td>\n",
       "      <td>12297.95</td>\n",
       "      <td>16491.84</td>\n",
       "      <td>9.647923</td>\n",
       "      <td>9.09198</td>\n",
       "      <td>14.72602</td>\n",
       "      <td>20.972107</td>\n",
       "      <td>0.559793</td>\n",
       "      <td>11.157554</td>\n",
       "      <td>0.971446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 22:39:51.491</th>\n",
       "      <td>23.81092</td>\n",
       "      <td>11.68643</td>\n",
       "      <td>12298.31</td>\n",
       "      <td>16492.06</td>\n",
       "      <td>9.648075</td>\n",
       "      <td>9.08500</td>\n",
       "      <td>14.72592</td>\n",
       "      <td>20.956006</td>\n",
       "      <td>0.575894</td>\n",
       "      <td>11.173654</td>\n",
       "      <td>0.955346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 23:09:51.491</th>\n",
       "      <td>23.81429</td>\n",
       "      <td>11.68725</td>\n",
       "      <td>12298.56</td>\n",
       "      <td>16492.06</td>\n",
       "      <td>9.648094</td>\n",
       "      <td>9.08685</td>\n",
       "      <td>14.72744</td>\n",
       "      <td>20.960273</td>\n",
       "      <td>0.571627</td>\n",
       "      <td>11.169387</td>\n",
       "      <td>0.959613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-17 23:39:51.491</th>\n",
       "      <td>23.80202</td>\n",
       "      <td>11.68707</td>\n",
       "      <td>12299.35</td>\n",
       "      <td>16493.19</td>\n",
       "      <td>9.648807</td>\n",
       "      <td>9.07695</td>\n",
       "      <td>14.72507</td>\n",
       "      <td>20.937437</td>\n",
       "      <td>0.594463</td>\n",
       "      <td>11.192223</td>\n",
       "      <td>0.936777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 00:09:51.491</th>\n",
       "      <td>23.80037</td>\n",
       "      <td>11.68716</td>\n",
       "      <td>12299.69</td>\n",
       "      <td>16493.61</td>\n",
       "      <td>9.649071</td>\n",
       "      <td>9.08071</td>\n",
       "      <td>14.71966</td>\n",
       "      <td>20.946111</td>\n",
       "      <td>0.585789</td>\n",
       "      <td>11.183550</td>\n",
       "      <td>0.945450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 00:39:51.491</th>\n",
       "      <td>23.79368</td>\n",
       "      <td>11.68780</td>\n",
       "      <td>12300.14</td>\n",
       "      <td>16493.95</td>\n",
       "      <td>9.649302</td>\n",
       "      <td>9.07408</td>\n",
       "      <td>14.71960</td>\n",
       "      <td>20.930817</td>\n",
       "      <td>0.601083</td>\n",
       "      <td>11.198843</td>\n",
       "      <td>0.930157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 01:09:51.491</th>\n",
       "      <td>23.78696</td>\n",
       "      <td>11.68806</td>\n",
       "      <td>12298.68</td>\n",
       "      <td>16491.86</td>\n",
       "      <td>9.647991</td>\n",
       "      <td>9.06933</td>\n",
       "      <td>14.71763</td>\n",
       "      <td>20.919861</td>\n",
       "      <td>0.612039</td>\n",
       "      <td>11.209800</td>\n",
       "      <td>0.919200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 01:39:51.491</th>\n",
       "      <td>23.78413</td>\n",
       "      <td>11.68826</td>\n",
       "      <td>12298.41</td>\n",
       "      <td>16491.43</td>\n",
       "      <td>9.647721</td>\n",
       "      <td>9.06969</td>\n",
       "      <td>14.71444</td>\n",
       "      <td>20.920691</td>\n",
       "      <td>0.611209</td>\n",
       "      <td>11.208969</td>\n",
       "      <td>0.920031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 02:09:51.491</th>\n",
       "      <td>23.77468</td>\n",
       "      <td>11.68879</td>\n",
       "      <td>12299.68</td>\n",
       "      <td>16492.90</td>\n",
       "      <td>9.648666</td>\n",
       "      <td>9.06470</td>\n",
       "      <td>14.70998</td>\n",
       "      <td>20.909181</td>\n",
       "      <td>0.622719</td>\n",
       "      <td>11.220479</td>\n",
       "      <td>0.908521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 02:39:51.491</th>\n",
       "      <td>23.77864</td>\n",
       "      <td>11.68870</td>\n",
       "      <td>12300.51</td>\n",
       "      <td>16494.05</td>\n",
       "      <td>9.649391</td>\n",
       "      <td>9.07051</td>\n",
       "      <td>14.70813</td>\n",
       "      <td>20.922583</td>\n",
       "      <td>0.609317</td>\n",
       "      <td>11.207078</td>\n",
       "      <td>0.921922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 03:09:51.491</th>\n",
       "      <td>23.77468</td>\n",
       "      <td>11.68870</td>\n",
       "      <td>12302.39</td>\n",
       "      <td>16496.58</td>\n",
       "      <td>9.650990</td>\n",
       "      <td>9.06783</td>\n",
       "      <td>14.70685</td>\n",
       "      <td>20.916401</td>\n",
       "      <td>0.615499</td>\n",
       "      <td>11.213260</td>\n",
       "      <td>0.915740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 03:39:51.491</th>\n",
       "      <td>23.77351</td>\n",
       "      <td>11.68896</td>\n",
       "      <td>12299.23</td>\n",
       "      <td>16492.22</td>\n",
       "      <td>9.648243</td>\n",
       "      <td>9.07035</td>\n",
       "      <td>14.70316</td>\n",
       "      <td>20.922214</td>\n",
       "      <td>0.609686</td>\n",
       "      <td>11.207447</td>\n",
       "      <td>0.921553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 04:09:51.491</th>\n",
       "      <td>23.78491</td>\n",
       "      <td>11.68943</td>\n",
       "      <td>12302.51</td>\n",
       "      <td>16496.43</td>\n",
       "      <td>9.650915</td>\n",
       "      <td>9.08458</td>\n",
       "      <td>14.70033</td>\n",
       "      <td>20.955037</td>\n",
       "      <td>0.576863</td>\n",
       "      <td>11.174623</td>\n",
       "      <td>0.954377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 04:39:51.491</th>\n",
       "      <td>23.78976</td>\n",
       "      <td>11.69006</td>\n",
       "      <td>12298.97</td>\n",
       "      <td>16491.41</td>\n",
       "      <td>9.647758</td>\n",
       "      <td>9.08581</td>\n",
       "      <td>14.70395</td>\n",
       "      <td>20.957874</td>\n",
       "      <td>0.574026</td>\n",
       "      <td>11.171786</td>\n",
       "      <td>0.957214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 05:09:51.491</th>\n",
       "      <td>23.80038</td>\n",
       "      <td>11.68979</td>\n",
       "      <td>12301.29</td>\n",
       "      <td>16494.64</td>\n",
       "      <td>9.649793</td>\n",
       "      <td>9.09408</td>\n",
       "      <td>14.70630</td>\n",
       "      <td>20.976951</td>\n",
       "      <td>0.554949</td>\n",
       "      <td>11.152710</td>\n",
       "      <td>0.976290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 05:39:51.491</th>\n",
       "      <td>23.80037</td>\n",
       "      <td>11.69024</td>\n",
       "      <td>12299.32</td>\n",
       "      <td>16491.81</td>\n",
       "      <td>9.648009</td>\n",
       "      <td>9.09542</td>\n",
       "      <td>14.70495</td>\n",
       "      <td>20.980041</td>\n",
       "      <td>0.551859</td>\n",
       "      <td>11.149619</td>\n",
       "      <td>0.979381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 06:09:51.491</th>\n",
       "      <td>23.80038</td>\n",
       "      <td>11.69041</td>\n",
       "      <td>12298.14</td>\n",
       "      <td>16490.16</td>\n",
       "      <td>9.646971</td>\n",
       "      <td>9.09463</td>\n",
       "      <td>14.70575</td>\n",
       "      <td>20.978219</td>\n",
       "      <td>0.553681</td>\n",
       "      <td>11.151441</td>\n",
       "      <td>0.977559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 06:39:51.491</th>\n",
       "      <td>23.80912</td>\n",
       "      <td>11.69097</td>\n",
       "      <td>12298.81</td>\n",
       "      <td>16490.82</td>\n",
       "      <td>9.647404</td>\n",
       "      <td>9.10008</td>\n",
       "      <td>14.70904</td>\n",
       "      <td>20.990791</td>\n",
       "      <td>0.541109</td>\n",
       "      <td>11.138870</td>\n",
       "      <td>0.990130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4162 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Pressure (psi) (535311)  Temperature (°C) (535311)  \\\n",
       "Datetime                                                                      \n",
       "2017-08-17 16:09:51.491                 23.79852                   11.68326   \n",
       "2017-08-17 16:39:51.491                 23.80490                   11.68326   \n",
       "2017-08-17 17:09:51.491                 23.80490                   11.68399   \n",
       "2017-08-17 17:39:51.491                 23.81243                   11.68389   \n",
       "2017-08-17 18:09:51.491                 23.81383                   11.68408   \n",
       "2017-08-17 18:39:51.491                 23.81577                   11.68471   \n",
       "2017-08-17 19:09:51.491                 23.81753                   11.68497   \n",
       "2017-08-17 19:39:51.491                 23.82495                   11.68516   \n",
       "2017-08-17 20:09:51.491                 23.82407                   11.68516   \n",
       "2017-08-17 20:39:51.491                 23.82604                   11.68562   \n",
       "2017-08-17 21:09:51.491                 23.82150                   11.68543   \n",
       "2017-08-17 21:39:51.491                 23.82398                   11.68616   \n",
       "2017-08-17 22:09:51.491                 23.81800                   11.68580   \n",
       "2017-08-17 22:39:51.491                 23.81092                   11.68643   \n",
       "2017-08-17 23:09:51.491                 23.81429                   11.68725   \n",
       "2017-08-17 23:39:51.491                 23.80202                   11.68707   \n",
       "2017-08-18 00:09:51.491                 23.80037                   11.68716   \n",
       "2017-08-18 00:39:51.491                 23.79368                   11.68780   \n",
       "2017-08-18 01:09:51.491                 23.78696                   11.68806   \n",
       "2017-08-18 01:39:51.491                 23.78413                   11.68826   \n",
       "2017-08-18 02:09:51.491                 23.77468                   11.68879   \n",
       "2017-08-18 02:39:51.491                 23.77864                   11.68870   \n",
       "2017-08-18 03:09:51.491                 23.77468                   11.68870   \n",
       "2017-08-18 03:39:51.491                 23.77351                   11.68896   \n",
       "2017-08-18 04:09:51.491                 23.78491                   11.68943   \n",
       "2017-08-18 04:39:51.491                 23.78976                   11.69006   \n",
       "2017-08-18 05:09:51.491                 23.80038                   11.68979   \n",
       "2017-08-18 05:39:51.491                 23.80037                   11.69024   \n",
       "2017-08-18 06:09:51.491                 23.80038                   11.69041   \n",
       "2017-08-18 06:39:51.491                 23.80912                   11.69097   \n",
       "...                                          ...                        ...   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "NaT                                          NaN                        NaN   \n",
       "\n",
       "                         Actual Conductivity (µS/cm) (535311)  \\\n",
       "Datetime                                                        \n",
       "2017-08-17 16:09:51.491                              12289.84   \n",
       "2017-08-17 16:39:51.491                              12296.10   \n",
       "2017-08-17 17:09:51.491                              12296.17   \n",
       "2017-08-17 17:39:51.491                              12296.43   \n",
       "2017-08-17 18:09:51.491                              12294.12   \n",
       "2017-08-17 18:39:51.491                              12293.79   \n",
       "2017-08-17 19:09:51.491                              12296.61   \n",
       "2017-08-17 19:39:51.491                              12296.36   \n",
       "2017-08-17 20:09:51.491                              12298.66   \n",
       "2017-08-17 20:39:51.491                              12296.50   \n",
       "2017-08-17 21:09:51.491                              12297.12   \n",
       "2017-08-17 21:39:51.491                              12298.44   \n",
       "2017-08-17 22:09:51.491                              12297.95   \n",
       "2017-08-17 22:39:51.491                              12298.31   \n",
       "2017-08-17 23:09:51.491                              12298.56   \n",
       "2017-08-17 23:39:51.491                              12299.35   \n",
       "2017-08-18 00:09:51.491                              12299.69   \n",
       "2017-08-18 00:39:51.491                              12300.14   \n",
       "2017-08-18 01:09:51.491                              12298.68   \n",
       "2017-08-18 01:39:51.491                              12298.41   \n",
       "2017-08-18 02:09:51.491                              12299.68   \n",
       "2017-08-18 02:39:51.491                              12300.51   \n",
       "2017-08-18 03:09:51.491                              12302.39   \n",
       "2017-08-18 03:39:51.491                              12299.23   \n",
       "2017-08-18 04:09:51.491                              12302.51   \n",
       "2017-08-18 04:39:51.491                              12298.97   \n",
       "2017-08-18 05:09:51.491                              12301.29   \n",
       "2017-08-18 05:39:51.491                              12299.32   \n",
       "2017-08-18 06:09:51.491                              12298.14   \n",
       "2017-08-18 06:39:51.491                              12298.81   \n",
       "...                                                       ...   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "NaT                                                       NaN   \n",
       "\n",
       "                         Specific Conductivity (µS/cm) (535311)  \\\n",
       "Datetime                                                          \n",
       "2017-08-17 16:09:51.491                                16482.04   \n",
       "2017-08-17 16:39:51.491                                16490.44   \n",
       "2017-08-17 17:09:51.491                                16490.23   \n",
       "2017-08-17 17:39:51.491                                16490.62   \n",
       "2017-08-17 18:09:51.491                                16487.44   \n",
       "2017-08-17 18:39:51.491                                16486.73   \n",
       "2017-08-17 19:09:51.491                                16490.39   \n",
       "2017-08-17 19:39:51.491                                16489.99   \n",
       "2017-08-17 20:09:51.491                                16493.06   \n",
       "2017-08-17 20:39:51.491                                16489.98   \n",
       "2017-08-17 21:09:51.491                                16490.89   \n",
       "2017-08-17 21:39:51.491                                16492.36   \n",
       "2017-08-17 22:09:51.491                                16491.84   \n",
       "2017-08-17 22:39:51.491                                16492.06   \n",
       "2017-08-17 23:09:51.491                                16492.06   \n",
       "2017-08-17 23:39:51.491                                16493.19   \n",
       "2017-08-18 00:09:51.491                                16493.61   \n",
       "2017-08-18 00:39:51.491                                16493.95   \n",
       "2017-08-18 01:09:51.491                                16491.86   \n",
       "2017-08-18 01:39:51.491                                16491.43   \n",
       "2017-08-18 02:09:51.491                                16492.90   \n",
       "2017-08-18 02:39:51.491                                16494.05   \n",
       "2017-08-18 03:09:51.491                                16496.58   \n",
       "2017-08-18 03:39:51.491                                16492.22   \n",
       "2017-08-18 04:09:51.491                                16496.43   \n",
       "2017-08-18 04:39:51.491                                16491.41   \n",
       "2017-08-18 05:09:51.491                                16494.64   \n",
       "2017-08-18 05:39:51.491                                16491.81   \n",
       "2017-08-18 06:09:51.491                                16490.16   \n",
       "2017-08-18 06:39:51.491                                16490.82   \n",
       "...                                                         ...   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "NaT                                                         NaN   \n",
       "\n",
       "                         Salinity (PSU) (535311)  compd_(psi)  baro_reading  \\\n",
       "Datetime                                                                      \n",
       "2017-08-17 16:09:51.491                 9.641663      9.08095      14.71757   \n",
       "2017-08-17 16:39:51.491                 9.646975      9.08498      14.71992   \n",
       "2017-08-17 17:09:51.491                 9.646855      9.08441      14.72049   \n",
       "2017-08-17 17:39:51.491                 9.647099      9.09301      14.71942   \n",
       "2017-08-17 18:09:51.491                 9.645092      9.09721      14.71662   \n",
       "2017-08-17 18:39:51.491                 9.644667      9.09549      14.72028   \n",
       "2017-08-17 19:09:51.491                 9.646984      9.09796      14.71957   \n",
       "2017-08-17 19:39:51.491                 9.646731      9.09959      14.72536   \n",
       "2017-08-17 20:09:51.491                 9.648679      9.09275      14.73132   \n",
       "2017-08-17 20:39:51.491                 9.646741      9.09702      14.72902   \n",
       "2017-08-17 21:09:51.491                 9.647309      9.09561      14.72589   \n",
       "2017-08-17 21:39:51.491                 9.648256      9.09689      14.72709   \n",
       "2017-08-17 22:09:51.491                 9.647923      9.09198      14.72602   \n",
       "2017-08-17 22:39:51.491                 9.648075      9.08500      14.72592   \n",
       "2017-08-17 23:09:51.491                 9.648094      9.08685      14.72744   \n",
       "2017-08-17 23:39:51.491                 9.648807      9.07695      14.72507   \n",
       "2017-08-18 00:09:51.491                 9.649071      9.08071      14.71966   \n",
       "2017-08-18 00:39:51.491                 9.649302      9.07408      14.71960   \n",
       "2017-08-18 01:09:51.491                 9.647991      9.06933      14.71763   \n",
       "2017-08-18 01:39:51.491                 9.647721      9.06969      14.71444   \n",
       "2017-08-18 02:09:51.491                 9.648666      9.06470      14.70998   \n",
       "2017-08-18 02:39:51.491                 9.649391      9.07051      14.70813   \n",
       "2017-08-18 03:09:51.491                 9.650990      9.06783      14.70685   \n",
       "2017-08-18 03:39:51.491                 9.648243      9.07035      14.70316   \n",
       "2017-08-18 04:09:51.491                 9.650915      9.08458      14.70033   \n",
       "2017-08-18 04:39:51.491                 9.647758      9.08581      14.70395   \n",
       "2017-08-18 05:09:51.491                 9.649793      9.09408      14.70630   \n",
       "2017-08-18 05:39:51.491                 9.648009      9.09542      14.70495   \n",
       "2017-08-18 06:09:51.491                 9.646971      9.09463      14.70575   \n",
       "2017-08-18 06:39:51.491                 9.647404      9.10008      14.70904   \n",
       "...                                          ...          ...           ...   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "NaT                                          NaN          NaN           NaN   \n",
       "\n",
       "                         Transducer_level_ft  water_level_ft_LMSL  \\\n",
       "Datetime                                                            \n",
       "2017-08-17 16:09:51.491            20.946664             0.585236   \n",
       "2017-08-17 16:39:51.491            20.955960             0.575940   \n",
       "2017-08-17 17:09:51.491            20.954645             0.577255   \n",
       "2017-08-17 17:39:51.491            20.974482             0.557418   \n",
       "2017-08-17 18:09:51.491            20.984170             0.547730   \n",
       "2017-08-17 18:39:51.491            20.980203             0.551697   \n",
       "2017-08-17 19:09:51.491            20.985900             0.546000   \n",
       "2017-08-17 19:39:51.491            20.989660             0.542240   \n",
       "2017-08-17 20:09:51.491            20.973883             0.558017   \n",
       "2017-08-17 20:39:51.491            20.983732             0.548168   \n",
       "2017-08-17 21:09:51.491            20.980480             0.551420   \n",
       "2017-08-17 21:39:51.491            20.983432             0.548468   \n",
       "2017-08-17 22:09:51.491            20.972107             0.559793   \n",
       "2017-08-17 22:39:51.491            20.956006             0.575894   \n",
       "2017-08-17 23:09:51.491            20.960273             0.571627   \n",
       "2017-08-17 23:39:51.491            20.937437             0.594463   \n",
       "2017-08-18 00:09:51.491            20.946111             0.585789   \n",
       "2017-08-18 00:39:51.491            20.930817             0.601083   \n",
       "2017-08-18 01:09:51.491            20.919861             0.612039   \n",
       "2017-08-18 01:39:51.491            20.920691             0.611209   \n",
       "2017-08-18 02:09:51.491            20.909181             0.622719   \n",
       "2017-08-18 02:39:51.491            20.922583             0.609317   \n",
       "2017-08-18 03:09:51.491            20.916401             0.615499   \n",
       "2017-08-18 03:39:51.491            20.922214             0.609686   \n",
       "2017-08-18 04:09:51.491            20.955037             0.576863   \n",
       "2017-08-18 04:39:51.491            20.957874             0.574026   \n",
       "2017-08-18 05:09:51.491            20.976951             0.554949   \n",
       "2017-08-18 05:39:51.491            20.980041             0.551859   \n",
       "2017-08-18 06:09:51.491            20.978219             0.553681   \n",
       "2017-08-18 06:39:51.491            20.990791             0.541109   \n",
       "...                                      ...                  ...   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "NaT                                      NaN                  NaN   \n",
       "\n",
       "                         calculated_depth  GW_elevation_ft_(LSML)  \n",
       "Datetime                                                           \n",
       "2017-08-17 16:09:51.491         11.182996                0.946004  \n",
       "2017-08-17 16:39:51.491         11.173700                0.955300  \n",
       "2017-08-17 17:09:51.491         11.175015                0.953985  \n",
       "2017-08-17 17:39:51.491         11.155178                0.973822  \n",
       "2017-08-17 18:09:51.491         11.145490                0.983510  \n",
       "2017-08-17 18:39:51.491         11.149457                0.979543  \n",
       "2017-08-17 19:09:51.491         11.143760                0.985240  \n",
       "2017-08-17 19:39:51.491         11.140000                0.989000  \n",
       "2017-08-17 20:09:51.491         11.155778                0.973222  \n",
       "2017-08-17 20:39:51.491         11.145928                0.983072  \n",
       "2017-08-17 21:09:51.491         11.149181                0.979819  \n",
       "2017-08-17 21:39:51.491         11.146228                0.982772  \n",
       "2017-08-17 22:09:51.491         11.157554                0.971446  \n",
       "2017-08-17 22:39:51.491         11.173654                0.955346  \n",
       "2017-08-17 23:09:51.491         11.169387                0.959613  \n",
       "2017-08-17 23:39:51.491         11.192223                0.936777  \n",
       "2017-08-18 00:09:51.491         11.183550                0.945450  \n",
       "2017-08-18 00:39:51.491         11.198843                0.930157  \n",
       "2017-08-18 01:09:51.491         11.209800                0.919200  \n",
       "2017-08-18 01:39:51.491         11.208969                0.920031  \n",
       "2017-08-18 02:09:51.491         11.220479                0.908521  \n",
       "2017-08-18 02:39:51.491         11.207078                0.921922  \n",
       "2017-08-18 03:09:51.491         11.213260                0.915740  \n",
       "2017-08-18 03:39:51.491         11.207447                0.921553  \n",
       "2017-08-18 04:09:51.491         11.174623                0.954377  \n",
       "2017-08-18 04:39:51.491         11.171786                0.957214  \n",
       "2017-08-18 05:09:51.491         11.152710                0.976290  \n",
       "2017-08-18 05:39:51.491         11.149619                0.979381  \n",
       "2017-08-18 06:09:51.491         11.151441                0.977559  \n",
       "2017-08-18 06:39:51.491         11.138870                0.990130  \n",
       "...                                   ...                     ...  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "NaT                                   NaN                     NaN  \n",
       "\n",
       "[4162 rows x 11 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well['calculated_depth'] = transducer_set - well['Transducer_level_ft'] \n",
    "well['GW_elevation_ft_(LSML)'] = well_measures.iloc[2][' TOC Elevation (LSML)'] - well['calculated_depth']\n",
    "well[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.cbook as cbook\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 15)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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k+d5NEzPm2blr7Xt9Iylez+rdraWurq4cPXr0OjseAAAAbzzDzrsz29V2za8+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\nEaAkkIiIiIiIiIhIEfD/6R1ERniz83kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d3a4ab0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(well['GW_elevation_ft_(LSML)'])\n",
    "fig.autofmt_xdate()\n",
    "plt.ylim(0,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ugqkRTK0xJpMkSZIkSVILujwES4kXgIuAu4E7gWlAbbPbjq8/PxTYB/h9BJu2Mt/klBiZ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\nmCRJkiRJknrPIpgkSZIkSZJ67+c+LoX+7slSjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d37d723c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(well['Salinity (PSU) (535311)'])\n",
    "ax1.set_xlabel('Date')\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax1.set_ylabel('Salinity (PSU)', color='b')\n",
    "ax1.tick_params('y', colors='b')\n",
    "plt.ylim(9.5,10)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "s2 = well['Temperature (°C) (535311)']\n",
    "ax2.plot(s2, 'r.')\n",
    "ax2.set_ylabel('Temperature (°C)', color='r')\n",
    "ax2.tick_params('y', colors='r')\n",
    "plt.ylim(11,14)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching Precipitation from Wunderground\n",
    "modified from https://www.shanelynn.ie/analysis-of-weather-data-using-pandas-python-and-seaborn/  \n",
    "\n",
    "we are going to use this https://www.wunderground.com/personal-weather-station/dashboard?ID=KRIPORTS37#history/s20171019/e20171118/mmonth weather station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from dateutil import parser, rrule\n",
    "from datetime import datetime, time, date\n",
    "import time\n",
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getRainfallData(station, day, month, year):\n",
    "    \"\"\"\n",
    "    Function to return a data frame of minute-level weather data for a single Wunderground PWS station.\n",
    "    \n",
    "    Args:\n",
    "        station (string): Station code from the Wunderground website\n",
    "        day (int): Day of month for which data is requested\n",
    "        month (int): Month for which data is requested\n",
    "        year (int): Year for which data is requested\n",
    "    \n",
    "    Returns:\n",
    "        Pandas Dataframe with weather data for specified station and date.\n",
    "    \"\"\"\n",
    "    url = \"http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID={station}&day={day}&month={month}&year={year}&graphspan=day&format=1\"\n",
    "    full_url = url.format(station=station, day=day, month=month, year=year)\n",
    "    # Request data from wunderground data\n",
    "    response = requests.get(full_url, headers={'User-agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'})\n",
    "    data = response.text\n",
    "    # remove the excess <br> from the text data\n",
    "    data = data.replace('<br>', '')\n",
    "    # Convert to pandas dataframe (fails if issues with weather station)\n",
    "    try:\n",
    "        dataframe = pd.read_csv(io.StringIO(data), index_col=False)\n",
    "        dataframe['station'] = station\n",
    "    except Exception as e:\n",
    "        print(\"Issue with date: {}-{}-{} for station {}\".format(day,month,year, station))\n",
    "        return None\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate a list of all of the dates we want data for\n",
    "start_date = \"2017-08-01\"\n",
    "end_date = \"2017-11-17\"\n",
    "start = parser.parse(start_date)\n",
    "end = parser.parse(end_date)\n",
    "dates = list(rrule.rrule(rrule.DAILY, dtstart=start, until=end))\n",
    "\n",
    "# Create a list of stations here to download data for\n",
    "stations = [\"KRIPORTS37\"]\n",
    "# Set a backoff time in seconds if a request fails\n",
    "backoff_time = 10\n",
    "data = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on KRIPORTS37\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Length of values does not match length of index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-c7c0be1496a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mstation\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Working on {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstation\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;31m# Print period status update messages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Fogland/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2329\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2330\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2331\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2333\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Fogland/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2396\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2397\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2398\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Fogland/conda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value, broadcast)\u001b[0m\n\u001b[1;32m   2566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2567\u001b[0m             \u001b[0;31m# turn me into an ndarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2568\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sanitize_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2569\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2570\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\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[0;32m~/Documents/Fogland/conda/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_sanitize_index\u001b[0;34m(data, index, copy)\u001b[0m\n\u001b[1;32m   2877\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2878\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2879\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Length of values does not match length of '\u001b[0m \u001b[0;34m'index'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2881\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPeriodIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
     ]
    }
   ],
   "source": [
    "# Gather data for each station in turn and save to CSV.\n",
    "for station in stations:\n",
    "    print(\"Working on {}\".format(station))\n",
    "    data[station] = []\n",
    "    for date in dates:\n",
    "        # Print period status update messages\n",
    "        if date.day % 10 == 0:\n",
    "            print(\"Working on date: {} for station {}\".format(date, station))\n",
    "        done = False\n",
    "        while done == False:\n",
    "            try:\n",
    "                weather_data = getRainfallData(station, date.day, date.month, date.year)\n",
    "                done = True\n",
    "            except ConnectionError as e:\n",
    "                # May get rate limited by Wunderground.com, backoff if so.\n",
    "                print(\"Got connection error on {}\".format(date))\n",
    "                print(\"Will retry in {} seconds\".format(backoff_time))\n",
    "                time.sleep(10)\n",
    "        # Add each processed date to the overall data\n",
    "        data[station].append(weather_data)\n",
    "    # Finally combine all of the individual days and output to CSV for analysis.\n",
    "    pd.concat(data[station]).to_csv(\"./{}_weather.csv\".format(station))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Time', 'TemperatureF', 'DewpointF', 'PressureIn',\n",
       "       'WindDirection', 'WindDirectionDegrees', 'WindSpeedMPH',\n",
       "       'WindSpeedGustMPH', 'Humidity', 'HourlyPrecipIn', 'Conditions',\n",
       "       'Clouds', 'dailyrainin', 'SolarRadiationWatts/m^2', 'SoftwareType',\n",
       "       'DateUTC', 'station'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station = 'KRIPORTS37' # Sandy Point Farm, across the channel from Fogland Point\n",
    "data_raw = pd.read_csv('./{}_weather.csv'.format(station))\n",
    "data_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Give the variables some friendlier names and convert types as necessary.\n",
    "data_raw['temp'] = data_raw['TemperatureF'].astype(float)\n",
    "data_raw['rain'] = data_raw['HourlyPrecipIn'].astype(float)\n",
    "data_raw['date'] = data_raw['DateUTC'].apply(parser.parse)\n",
    "data_raw['humidity'] = data_raw['Humidity'].astype(float)\n",
    "data_raw['wind_direction'] = data_raw['WindDirectionDegrees']\n",
    "data_raw['wind'] = data_raw['WindSpeedMPH']\n",
    " \n",
    "# Extract out only the data we need.\n",
    "data = data_raw.loc[:, ['date', 'station', 'temp', 'rain', 'humidity', 'wind']]\n",
    "data = data[(data['date'] >= datetime(2017,8,2)) & (data['date'] <= datetime(2017,11,16))]\n",
    " \n",
    "# There's an issue with some stations that record rainfall ~-2500 where data is missing.\n",
    "if (data['rain'] < -500).sum() > 10:\n",
    "    print(\"There's more than 10 messed up days for {}\".format(station))\n",
    "    \n",
    "# remove the bad samples\n",
    "data = data[data['rain'] > -500]\n",
    " \n",
    "# Assign the \"day\" to every date entry\n",
    "data['day'] = data['date'].apply(lambda x: x.date())\n",
    " \n",
    "# Get the time, day, and hour of each timestamp in the dataset\n",
    "data['time_of_day'] = data['date'].apply(lambda x: x.time())\n",
    "data['day_of_week'] = data['date'].apply(lambda x: x.weekday())    \n",
    "data['hour_of_day'] = data['time_of_day'].apply(lambda x: x.hour)\n",
    "# Mark the month for each entry so we can look at monthly patterns\n",
    "data['month'] = data['date'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this data look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#! cat KRIPORTS37_weather.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Time</th>\n",
       "      <th>TemperatureF</th>\n",
       "      <th>DewpointF</th>\n",
       "      <th>PressureIn</th>\n",
       "      <th>WindDirection</th>\n",
       "      <th>WindDirectionDegrees</th>\n",
       "      <th>WindSpeedMPH</th>\n",
       "      <th>WindSpeedGustMPH</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>SolarRadiationWatts/m^2</th>\n",
       "      <th>SoftwareType</th>\n",
       "      <th>DateUTC</th>\n",
       "      <th>station</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain</th>\n",
       "      <th>date</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>2017-08-01 00:00:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.02</td>\n",
       "      <td>South</td>\n",
       "      <td>185</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:00:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>185</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:05:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2017-08-01 00:05:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.01</td>\n",
       "      <td>South</td>\n",
       "      <td>185</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:05:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:05:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>185</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:10:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2017-08-01 00:10:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.02</td>\n",
       "      <td>South</td>\n",
       "      <td>185</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:10:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:10:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>185</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:16:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2017-08-01 00:16:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.02</td>\n",
       "      <td>South</td>\n",
       "      <td>181</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:16:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:16:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>181</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:21:00</th>\n",
       "      <td>4</td>\n",
       "      <td>2017-08-01 00:21:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.02</td>\n",
       "      <td>South</td>\n",
       "      <td>182</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:21:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:21:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>182</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:22:00</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-08-01 00:22:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.01</td>\n",
       "      <td>South</td>\n",
       "      <td>182</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:22:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:22:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>182</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:27:00</th>\n",
       "      <td>6</td>\n",
       "      <td>2017-08-01 00:27:00</td>\n",
       "      <td>66.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>30.01</td>\n",
       "      <td>South</td>\n",
       "      <td>187</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:27:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:27:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>187</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:32:00</th>\n",
       "      <td>7</td>\n",
       "      <td>2017-08-01 00:32:00</td>\n",
       "      <td>66.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>30.02</td>\n",
       "      <td>South</td>\n",
       "      <td>182</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:32:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:32:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>182</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:37:00</th>\n",
       "      <td>8</td>\n",
       "      <td>2017-08-01 00:37:00</td>\n",
       "      <td>66.4</td>\n",
       "      <td>8.2</td>\n",
       "      <td>30.01</td>\n",
       "      <td>South</td>\n",
       "      <td>182</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:37:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:37:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>182</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:42:00</th>\n",
       "      <td>9</td>\n",
       "      <td>2017-08-01 00:42:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>South</td>\n",
       "      <td>182</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:42:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:42:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>182</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:48:00</th>\n",
       "      <td>10</td>\n",
       "      <td>2017-08-01 00:48:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>South</td>\n",
       "      <td>181</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:48:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:48:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>181</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:53:00</th>\n",
       "      <td>11</td>\n",
       "      <td>2017-08-01 00:53:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>South</td>\n",
       "      <td>181</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:53:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:53:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>181</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 00:58:00</th>\n",
       "      <td>12</td>\n",
       "      <td>2017-08-01 00:58:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.01</td>\n",
       "      <td>South</td>\n",
       "      <td>180</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 04:58:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 04:58:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>180</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:03:00</th>\n",
       "      <td>13</td>\n",
       "      <td>2017-08-01 01:03:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>South</td>\n",
       "      <td>180</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:03:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:03:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>180</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:08:00</th>\n",
       "      <td>14</td>\n",
       "      <td>2017-08-01 01:08:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.01</td>\n",
       "      <td>SSW</td>\n",
       "      <td>195</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:08:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:08:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:13:00</th>\n",
       "      <td>15</td>\n",
       "      <td>2017-08-01 01:13:00</td>\n",
       "      <td>66.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>30.01</td>\n",
       "      <td>SSW</td>\n",
       "      <td>195</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:13:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:13:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:18:00</th>\n",
       "      <td>16</td>\n",
       "      <td>2017-08-01 01:18:00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>195</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:18:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:18:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:24:00</th>\n",
       "      <td>17</td>\n",
       "      <td>2017-08-01 01:24:00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>30.01</td>\n",
       "      <td>SSW</td>\n",
       "      <td>195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:24:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:24:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:29:00</th>\n",
       "      <td>18</td>\n",
       "      <td>2017-08-01 01:29:00</td>\n",
       "      <td>65.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.01</td>\n",
       "      <td>SSW</td>\n",
       "      <td>195</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:29:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:29:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:34:00</th>\n",
       "      <td>19</td>\n",
       "      <td>2017-08-01 01:34:00</td>\n",
       "      <td>65.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>195</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:34:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:34:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:40:00</th>\n",
       "      <td>20</td>\n",
       "      <td>2017-08-01 01:40:00</td>\n",
       "      <td>65.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>195</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:40:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:40:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>195</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:45:00</th>\n",
       "      <td>21</td>\n",
       "      <td>2017-08-01 01:45:00</td>\n",
       "      <td>65.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>194</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:45:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:45:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>194</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:50:00</th>\n",
       "      <td>22</td>\n",
       "      <td>2017-08-01 01:50:00</td>\n",
       "      <td>65.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>207</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:50:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:50:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>207</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 01:55:00</th>\n",
       "      <td>23</td>\n",
       "      <td>2017-08-01 01:55:00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>205</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 05:55:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 05:55:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>205</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 02:00:00</th>\n",
       "      <td>24</td>\n",
       "      <td>2017-08-01 02:00:00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>202</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 06:00:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 06:00:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>202</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 02:05:00</th>\n",
       "      <td>25</td>\n",
       "      <td>2017-08-01 02:05:00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>202</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 06:05:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 06:05:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>202</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 02:10:00</th>\n",
       "      <td>26</td>\n",
       "      <td>2017-08-01 02:10:00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>202</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 06:10:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 06:10:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>202</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 02:15:00</th>\n",
       "      <td>27</td>\n",
       "      <td>2017-08-01 02:15:00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>201</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 06:15:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 06:15:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>201</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 02:21:00</th>\n",
       "      <td>28</td>\n",
       "      <td>2017-08-01 02:21:00</td>\n",
       "      <td>65.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>201</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 06:21:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 06:21:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>201</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 02:26:00</th>\n",
       "      <td>29</td>\n",
       "      <td>2017-08-01 02:26:00</td>\n",
       "      <td>65.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-08-01 06:26:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>65.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-01 06:26:00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>201</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:37:00</th>\n",
       "      <td>256</td>\n",
       "      <td>2017-11-17 21:37:00</td>\n",
       "      <td>34.7</td>\n",
       "      <td>26.6</td>\n",
       "      <td>29.99</td>\n",
       "      <td>ENE</td>\n",
       "      <td>60</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 02:37:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 02:37:00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:42:00</th>\n",
       "      <td>257</td>\n",
       "      <td>2017-11-17 21:42:00</td>\n",
       "      <td>34.7</td>\n",
       "      <td>26.6</td>\n",
       "      <td>29.99</td>\n",
       "      <td>ENE</td>\n",
       "      <td>70</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 02:42:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 02:42:00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>70</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:47:00</th>\n",
       "      <td>258</td>\n",
       "      <td>2017-11-17 21:47:00</td>\n",
       "      <td>34.7</td>\n",
       "      <td>26.6</td>\n",
       "      <td>29.99</td>\n",
       "      <td>ENE</td>\n",
       "      <td>78</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 02:47:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 02:47:00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:52:00</th>\n",
       "      <td>259</td>\n",
       "      <td>2017-11-17 21:52:00</td>\n",
       "      <td>34.3</td>\n",
       "      <td>26.6</td>\n",
       "      <td>29.99</td>\n",
       "      <td>East</td>\n",
       "      <td>82</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 02:52:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 02:52:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 21:57:00</th>\n",
       "      <td>260</td>\n",
       "      <td>2017-11-17 21:57:00</td>\n",
       "      <td>34.3</td>\n",
       "      <td>26.6</td>\n",
       "      <td>29.99</td>\n",
       "      <td>ENE</td>\n",
       "      <td>65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 02:57:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 02:57:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>65</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:02:00</th>\n",
       "      <td>261</td>\n",
       "      <td>2017-11-17 22:02:00</td>\n",
       "      <td>34.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>29.99</td>\n",
       "      <td>East</td>\n",
       "      <td>98</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:02:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:02:00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>98</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:07:00</th>\n",
       "      <td>262</td>\n",
       "      <td>2017-11-17 22:07:00</td>\n",
       "      <td>34.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>29.99</td>\n",
       "      <td>ENE</td>\n",
       "      <td>60</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:07:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:07:00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:16:00</th>\n",
       "      <td>263</td>\n",
       "      <td>2017-11-17 22:16:00</td>\n",
       "      <td>34.5</td>\n",
       "      <td>25.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>WSW</td>\n",
       "      <td>256</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:16:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:16:00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>256</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:21:00</th>\n",
       "      <td>264</td>\n",
       "      <td>2017-11-17 22:21:00</td>\n",
       "      <td>34.3</td>\n",
       "      <td>25.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>ESE</td>\n",
       "      <td>115</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:21:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:21:00</td>\n",
       "      <td>71.0</td>\n",
       "      <td>115</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:25:00</th>\n",
       "      <td>265</td>\n",
       "      <td>2017-11-17 22:25:00</td>\n",
       "      <td>34.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>30.00</td>\n",
       "      <td>NE</td>\n",
       "      <td>43</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:25:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:25:00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>43</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:26:00</th>\n",
       "      <td>266</td>\n",
       "      <td>2017-11-17 22:26:00</td>\n",
       "      <td>34.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>30.00</td>\n",
       "      <td>ENE</td>\n",
       "      <td>67</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:26:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:26:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>67</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:32:00</th>\n",
       "      <td>267</td>\n",
       "      <td>2017-11-17 22:32:00</td>\n",
       "      <td>34.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>197</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:32:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:32:00</td>\n",
       "      <td>75.0</td>\n",
       "      <td>197</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:33:00</th>\n",
       "      <td>268</td>\n",
       "      <td>2017-11-17 22:33:00</td>\n",
       "      <td>33.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.99</td>\n",
       "      <td>SSW</td>\n",
       "      <td>197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:33:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:33:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>197</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:35:00</th>\n",
       "      <td>269</td>\n",
       "      <td>2017-11-17 22:35:00</td>\n",
       "      <td>33.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.99</td>\n",
       "      <td>SSW</td>\n",
       "      <td>193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:35:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:35:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>193</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:40:00</th>\n",
       "      <td>270</td>\n",
       "      <td>2017-11-17 22:40:00</td>\n",
       "      <td>33.3</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:40:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:40:00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>193</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:45:00</th>\n",
       "      <td>271</td>\n",
       "      <td>2017-11-17 22:45:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:45:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:45:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:50:00</th>\n",
       "      <td>272</td>\n",
       "      <td>2017-11-17 22:50:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:50:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:50:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 22:55:00</th>\n",
       "      <td>273</td>\n",
       "      <td>2017-11-17 22:55:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 03:55:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 03:55:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:00:00</th>\n",
       "      <td>274</td>\n",
       "      <td>2017-11-17 23:00:00</td>\n",
       "      <td>32.7</td>\n",
       "      <td>28.8</td>\n",
       "      <td>30.00</td>\n",
       "      <td>South</td>\n",
       "      <td>188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:00:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:00:00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>188</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:05:00</th>\n",
       "      <td>275</td>\n",
       "      <td>2017-11-17 23:05:00</td>\n",
       "      <td>32.7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>South</td>\n",
       "      <td>186</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>83</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:05:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:05:00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>186</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:10:00</th>\n",
       "      <td>276</td>\n",
       "      <td>2017-11-17 23:10:00</td>\n",
       "      <td>32.7</td>\n",
       "      <td>28.4</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SE</td>\n",
       "      <td>128</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:10:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:10:00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>128</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:16:00</th>\n",
       "      <td>277</td>\n",
       "      <td>2017-11-17 23:16:00</td>\n",
       "      <td>32.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>South</td>\n",
       "      <td>187</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>83</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:16:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:16:00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>187</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:21:00</th>\n",
       "      <td>278</td>\n",
       "      <td>2017-11-17 23:21:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SSW</td>\n",
       "      <td>207</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:21:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:21:00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>207</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:26:00</th>\n",
       "      <td>279</td>\n",
       "      <td>2017-11-17 23:26:00</td>\n",
       "      <td>33.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.99</td>\n",
       "      <td>SSW</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:26:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:26:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>192</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:31:00</th>\n",
       "      <td>280</td>\n",
       "      <td>2017-11-17 23:31:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>ENE</td>\n",
       "      <td>76</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:31:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:31:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:36:00</th>\n",
       "      <td>281</td>\n",
       "      <td>2017-11-17 23:36:00</td>\n",
       "      <td>32.7</td>\n",
       "      <td>27.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SE</td>\n",
       "      <td>126</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:36:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:36:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>126</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:41:00</th>\n",
       "      <td>282</td>\n",
       "      <td>2017-11-17 23:41:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>WSW</td>\n",
       "      <td>251</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:41:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:41:00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>251</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:46:00</th>\n",
       "      <td>283</td>\n",
       "      <td>2017-11-17 23:46:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>West</td>\n",
       "      <td>268</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:46:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:46:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>268</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:51:00</th>\n",
       "      <td>284</td>\n",
       "      <td>2017-11-17 23:51:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>27.9</td>\n",
       "      <td>30.00</td>\n",
       "      <td>SW</td>\n",
       "      <td>216</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:51:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:51:00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17 23:56:00</th>\n",
       "      <td>285</td>\n",
       "      <td>2017-11-17 23:56:00</td>\n",
       "      <td>32.9</td>\n",
       "      <td>28.2</td>\n",
       "      <td>30.01</td>\n",
       "      <td>SSW</td>\n",
       "      <td>205</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>83</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WeatherSmart%V1.7.0</td>\n",
       "      <td>2017-11-18 04:56:00</td>\n",
       "      <td>KRIPORTS37</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-11-18 04:56:00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>205</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30626 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Unnamed: 0                 Time  TemperatureF  DewpointF  \\\n",
       "Datetime                                                                        \n",
       "2017-08-01 00:00:00           0  2017-08-01 00:00:00          66.2        8.1   \n",
       "2017-08-01 00:05:00           1  2017-08-01 00:05:00          66.2        8.1   \n",
       "2017-08-01 00:10:00           2  2017-08-01 00:10:00          66.2        8.1   \n",
       "2017-08-01 00:16:00           3  2017-08-01 00:16:00          66.2        8.1   \n",
       "2017-08-01 00:21:00           4  2017-08-01 00:21:00          66.2        8.1   \n",
       "2017-08-01 00:22:00           5  2017-08-01 00:22:00          66.2        8.1   \n",
       "2017-08-01 00:27:00           6  2017-08-01 00:27:00          66.4        8.2   \n",
       "2017-08-01 00:32:00           7  2017-08-01 00:32:00          66.4        8.2   \n",
       "2017-08-01 00:37:00           8  2017-08-01 00:37:00          66.4        8.2   \n",
       "2017-08-01 00:42:00           9  2017-08-01 00:42:00          66.2        8.1   \n",
       "2017-08-01 00:48:00          10  2017-08-01 00:48:00          66.2        8.1   \n",
       "2017-08-01 00:53:00          11  2017-08-01 00:53:00          66.2        8.1   \n",
       "2017-08-01 00:58:00          12  2017-08-01 00:58:00          66.2        8.1   \n",
       "2017-08-01 01:03:00          13  2017-08-01 01:03:00          66.2        8.1   \n",
       "2017-08-01 01:08:00          14  2017-08-01 01:08:00          66.2        8.1   \n",
       "2017-08-01 01:13:00          15  2017-08-01 01:13:00          66.2        8.1   \n",
       "2017-08-01 01:18:00          16  2017-08-01 01:18:00          66.0        7.9   \n",
       "2017-08-01 01:24:00          17  2017-08-01 01:24:00          66.0        7.9   \n",
       "2017-08-01 01:29:00          18  2017-08-01 01:29:00          65.8        7.7   \n",
       "2017-08-01 01:34:00          19  2017-08-01 01:34:00          65.8        7.7   \n",
       "2017-08-01 01:40:00          20  2017-08-01 01:40:00          65.8        7.7   \n",
       "2017-08-01 01:45:00          21  2017-08-01 01:45:00          65.8        7.7   \n",
       "2017-08-01 01:50:00          22  2017-08-01 01:50:00          65.8        7.7   \n",
       "2017-08-01 01:55:00          23  2017-08-01 01:55:00          66.0        7.9   \n",
       "2017-08-01 02:00:00          24  2017-08-01 02:00:00          66.0        7.9   \n",
       "2017-08-01 02:05:00          25  2017-08-01 02:05:00          66.0        7.9   \n",
       "2017-08-01 02:10:00          26  2017-08-01 02:10:00          66.0        7.9   \n",
       "2017-08-01 02:15:00          27  2017-08-01 02:15:00          66.0        7.9   \n",
       "2017-08-01 02:21:00          28  2017-08-01 02:21:00          65.8        7.7   \n",
       "2017-08-01 02:26:00          29  2017-08-01 02:26:00          65.8        7.7   \n",
       "...                         ...                  ...           ...        ...   \n",
       "2017-11-17 21:37:00         256  2017-11-17 21:37:00          34.7       26.6   \n",
       "2017-11-17 21:42:00         257  2017-11-17 21:42:00          34.7       26.6   \n",
       "2017-11-17 21:47:00         258  2017-11-17 21:47:00          34.7       26.6   \n",
       "2017-11-17 21:52:00         259  2017-11-17 21:52:00          34.3       26.6   \n",
       "2017-11-17 21:57:00         260  2017-11-17 21:57:00          34.3       26.6   \n",
       "2017-11-17 22:02:00         261  2017-11-17 22:02:00          34.3       26.2   \n",
       "2017-11-17 22:07:00         262  2017-11-17 22:07:00          34.3       26.2   \n",
       "2017-11-17 22:16:00         263  2017-11-17 22:16:00          34.5       25.7   \n",
       "2017-11-17 22:21:00         264  2017-11-17 22:21:00          34.3       25.9   \n",
       "2017-11-17 22:25:00         265  2017-11-17 22:25:00          34.3       26.2   \n",
       "2017-11-17 22:26:00         266  2017-11-17 22:26:00          34.2       26.4   \n",
       "2017-11-17 22:32:00         267  2017-11-17 22:32:00          34.0       26.8   \n",
       "2017-11-17 22:33:00         268  2017-11-17 22:33:00          33.8       27.0   \n",
       "2017-11-17 22:35:00         269  2017-11-17 22:35:00          33.8       27.0   \n",
       "2017-11-17 22:40:00         270  2017-11-17 22:40:00          33.3       27.5   \n",
       "2017-11-17 22:45:00         271  2017-11-17 22:45:00          32.9       27.9   \n",
       "2017-11-17 22:50:00         272  2017-11-17 22:50:00          32.9       27.9   \n",
       "2017-11-17 22:55:00         273  2017-11-17 22:55:00          32.9       27.9   \n",
       "2017-11-17 23:00:00         274  2017-11-17 23:00:00          32.7       28.8   \n",
       "2017-11-17 23:05:00         275  2017-11-17 23:05:00          32.7       28.0   \n",
       "2017-11-17 23:10:00         276  2017-11-17 23:10:00          32.7       28.4   \n",
       "2017-11-17 23:16:00         277  2017-11-17 23:16:00          32.4       27.7   \n",
       "2017-11-17 23:21:00         278  2017-11-17 23:21:00          32.9       27.1   \n",
       "2017-11-17 23:26:00         279  2017-11-17 23:26:00          33.3       28.0   \n",
       "2017-11-17 23:31:00         280  2017-11-17 23:31:00          32.9       27.9   \n",
       "2017-11-17 23:36:00         281  2017-11-17 23:36:00          32.7       27.7   \n",
       "2017-11-17 23:41:00         282  2017-11-17 23:41:00          32.9       27.7   \n",
       "2017-11-17 23:46:00         283  2017-11-17 23:46:00          32.9       27.9   \n",
       "2017-11-17 23:51:00         284  2017-11-17 23:51:00          32.9       27.9   \n",
       "2017-11-17 23:56:00         285  2017-11-17 23:56:00          32.9       28.2   \n",
       "\n",
       "                     PressureIn WindDirection  WindDirectionDegrees  \\\n",
       "Datetime                                                              \n",
       "2017-08-01 00:00:00       30.02         South                   185   \n",
       "2017-08-01 00:05:00       30.01         South                   185   \n",
       "2017-08-01 00:10:00       30.02         South                   185   \n",
       "2017-08-01 00:16:00       30.02         South                   181   \n",
       "2017-08-01 00:21:00       30.02         South                   182   \n",
       "2017-08-01 00:22:00       30.01         South                   182   \n",
       "2017-08-01 00:27:00       30.01         South                   187   \n",
       "2017-08-01 00:32:00       30.02         South                   182   \n",
       "2017-08-01 00:37:00       30.01         South                   182   \n",
       "2017-08-01 00:42:00       30.00         South                   182   \n",
       "2017-08-01 00:48:00       30.00         South                   181   \n",
       "2017-08-01 00:53:00       30.00         South                   181   \n",
       "2017-08-01 00:58:00       30.01         South                   180   \n",
       "2017-08-01 01:03:00       30.00         South                   180   \n",
       "2017-08-01 01:08:00       30.01           SSW                   195   \n",
       "2017-08-01 01:13:00       30.01           SSW                   195   \n",
       "2017-08-01 01:18:00       30.00           SSW                   195   \n",
       "2017-08-01 01:24:00       30.01           SSW                   195   \n",
       "2017-08-01 01:29:00       30.01           SSW                   195   \n",
       "2017-08-01 01:34:00       30.00           SSW                   195   \n",
       "2017-08-01 01:40:00       30.00           SSW                   195   \n",
       "2017-08-01 01:45:00       30.00           SSW                   194   \n",
       "2017-08-01 01:50:00       30.00           SSW                   207   \n",
       "2017-08-01 01:55:00       30.00           SSW                   205   \n",
       "2017-08-01 02:00:00       30.00           SSW                   202   \n",
       "2017-08-01 02:05:00       30.00           SSW                   202   \n",
       "2017-08-01 02:10:00       30.00           SSW                   202   \n",
       "2017-08-01 02:15:00       30.00           SSW                   201   \n",
       "2017-08-01 02:21:00       30.00           SSW                   201   \n",
       "2017-08-01 02:26:00       30.00           SSW                   201   \n",
       "...                         ...           ...                   ...   \n",
       "2017-11-17 21:37:00       29.99           ENE                    60   \n",
       "2017-11-17 21:42:00       29.99           ENE                    70   \n",
       "2017-11-17 21:47:00       29.99           ENE                    78   \n",
       "2017-11-17 21:52:00       29.99          East                    82   \n",
       "2017-11-17 21:57:00       29.99           ENE                    65   \n",
       "2017-11-17 22:02:00       29.99          East                    98   \n",
       "2017-11-17 22:07:00       29.99           ENE                    60   \n",
       "2017-11-17 22:16:00       30.00           WSW                   256   \n",
       "2017-11-17 22:21:00       30.00           ESE                   115   \n",
       "2017-11-17 22:25:00       30.00            NE                    43   \n",
       "2017-11-17 22:26:00       30.00           ENE                    67   \n",
       "2017-11-17 22:32:00       30.00           SSW                   197   \n",
       "2017-11-17 22:33:00       29.99           SSW                   197   \n",
       "2017-11-17 22:35:00       29.99           SSW                   193   \n",
       "2017-11-17 22:40:00       30.00           SSW                   193   \n",
       "2017-11-17 22:45:00       30.00           SSW                   192   \n",
       "2017-11-17 22:50:00       30.00           SSW                   192   \n",
       "2017-11-17 22:55:00       30.00           SSW                   192   \n",
       "2017-11-17 23:00:00       30.00         South                   188   \n",
       "2017-11-17 23:05:00       30.00         South                   186   \n",
       "2017-11-17 23:10:00       30.00            SE                   128   \n",
       "2017-11-17 23:16:00       30.00         South                   187   \n",
       "2017-11-17 23:21:00       30.00           SSW                   207   \n",
       "2017-11-17 23:26:00       29.99           SSW                   192   \n",
       "2017-11-17 23:31:00       30.00           ENE                    76   \n",
       "2017-11-17 23:36:00       30.00            SE                   126   \n",
       "2017-11-17 23:41:00       30.00           WSW                   251   \n",
       "2017-11-17 23:46:00       30.00          West                   268   \n",
       "2017-11-17 23:51:00       30.00            SW                   216   \n",
       "2017-11-17 23:56:00       30.01           SSW                   205   \n",
       "\n",
       "                     WindSpeedMPH  WindSpeedGustMPH  Humidity  ...   \\\n",
       "Datetime                                                       ...    \n",
       "2017-08-01 00:00:00           0.9               1.1        10  ...    \n",
       "2017-08-01 00:05:00           1.6               2.2        10  ...    \n",
       "2017-08-01 00:10:00           1.1               1.1        10  ...    \n",
       "2017-08-01 00:16:00           0.9               1.1        10  ...    \n",
       "2017-08-01 00:21:00           1.6               2.2        10  ...    \n",
       "2017-08-01 00:22:00           2.2               3.4        10  ...    \n",
       "2017-08-01 00:27:00           2.2               3.4        10  ...    \n",
       "2017-08-01 00:32:00           1.3               2.2        10  ...    \n",
       "2017-08-01 00:37:00           1.8               2.2        10  ...    \n",
       "2017-08-01 00:42:00           1.8               2.2        10  ...    \n",
       "2017-08-01 00:48:00           1.6               2.2        10  ...    \n",
       "2017-08-01 00:53:00           0.9               1.1        10  ...    \n",
       "2017-08-01 00:58:00           2.2               3.4        10  ...    \n",
       "2017-08-01 01:03:00           1.1               1.1        10  ...    \n",
       "2017-08-01 01:08:00           1.8               2.2        10  ...    \n",
       "2017-08-01 01:13:00           0.7               1.1        10  ...    \n",
       "2017-08-01 01:18:00           0.9               1.1        10  ...    \n",
       "2017-08-01 01:24:00           0.0               0.0        10  ...    \n",
       "2017-08-01 01:29:00           0.2               1.1        10  ...    \n",
       "2017-08-01 01:34:00           0.7               1.1        10  ...    \n",
       "2017-08-01 01:40:00           0.9               1.1        10  ...    \n",
       "2017-08-01 01:45:00           1.3               2.2        10  ...    \n",
       "2017-08-01 01:50:00           2.2               3.4        10  ...    \n",
       "2017-08-01 01:55:00           1.3               2.2        10  ...    \n",
       "2017-08-01 02:00:00           1.6               2.2        10  ...    \n",
       "2017-08-01 02:05:00           1.3               2.2        10  ...    \n",
       "2017-08-01 02:10:00           1.1               1.1        10  ...    \n",
       "2017-08-01 02:15:00           0.2               1.1        10  ...    \n",
       "2017-08-01 02:21:00           0.7               1.1        10  ...    \n",
       "2017-08-01 02:26:00           0.0               0.0        10  ...    \n",
       "...                           ...               ...       ...  ...    \n",
       "2017-11-17 21:37:00           1.8               2.2        72  ...    \n",
       "2017-11-17 21:42:00           2.5               3.4        72  ...    \n",
       "2017-11-17 21:47:00           1.8               2.2        72  ...    \n",
       "2017-11-17 21:52:00           1.8               2.2        73  ...    \n",
       "2017-11-17 21:57:00           0.0               0.0        73  ...    \n",
       "2017-11-17 22:02:00           2.0               2.2        72  ...    \n",
       "2017-11-17 22:07:00           0.7               1.1        72  ...    \n",
       "2017-11-17 22:16:00           1.8               2.2        70  ...    \n",
       "2017-11-17 22:21:00           1.8               2.2        71  ...    \n",
       "2017-11-17 22:25:00           0.9               1.1        72  ...    \n",
       "2017-11-17 22:26:00           0.7               1.1        73  ...    \n",
       "2017-11-17 22:32:00           1.1               2.2        75  ...    \n",
       "2017-11-17 22:33:00           0.0               0.0        76  ...    \n",
       "2017-11-17 22:35:00           0.0               0.0        76  ...    \n",
       "2017-11-17 22:40:00           0.0               0.0        79  ...    \n",
       "2017-11-17 22:45:00           0.0               0.0        82  ...    \n",
       "2017-11-17 22:50:00           0.0               0.0        82  ...    \n",
       "2017-11-17 22:55:00           0.0               0.0        82  ...    \n",
       "2017-11-17 23:00:00           0.0               0.0        85  ...    \n",
       "2017-11-17 23:05:00           0.2               1.1        83  ...    \n",
       "2017-11-17 23:10:00           0.4               1.1        84  ...    \n",
       "2017-11-17 23:16:00           1.1               1.1        83  ...    \n",
       "2017-11-17 23:21:00           0.7               1.1        79  ...    \n",
       "2017-11-17 23:26:00           0.0               0.0        81  ...    \n",
       "2017-11-17 23:31:00           0.2               1.1        82  ...    \n",
       "2017-11-17 23:36:00           0.7               1.1        82  ...    \n",
       "2017-11-17 23:41:00           1.3               2.2        81  ...    \n",
       "2017-11-17 23:46:00           0.4               1.1        82  ...    \n",
       "2017-11-17 23:51:00           0.7               1.1        82  ...    \n",
       "2017-11-17 23:56:00           1.8               2.2        83  ...    \n",
       "\n",
       "                     SolarRadiationWatts/m^2         SoftwareType  \\\n",
       "Datetime                                                            \n",
       "2017-08-01 00:00:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:05:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:10:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:16:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:21:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:22:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:27:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:32:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:37:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:42:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:48:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:53:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 00:58:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:03:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:08:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:13:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:18:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:24:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:29:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:34:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:40:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:45:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:50:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 01:55:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 02:00:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 02:05:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 02:10:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 02:15:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 02:21:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-08-01 02:26:00                      0.0  WeatherSmart%V1.7.0   \n",
       "...                                      ...                  ...   \n",
       "2017-11-17 21:37:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 21:42:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 21:47:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 21:52:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 21:57:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:02:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:07:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:16:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:21:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:25:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:26:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:32:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:33:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:35:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:40:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:45:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:50:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 22:55:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:00:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:05:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:10:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:16:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:21:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:26:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:31:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:36:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:41:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:46:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:51:00                      0.0  WeatherSmart%V1.7.0   \n",
       "2017-11-17 23:56:00                      0.0  WeatherSmart%V1.7.0   \n",
       "\n",
       "                                 DateUTC     station  temp rain  \\\n",
       "Datetime                                                          \n",
       "2017-08-01 00:00:00  2017-08-01 04:00:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:05:00  2017-08-01 04:05:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:10:00  2017-08-01 04:10:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:16:00  2017-08-01 04:16:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:21:00  2017-08-01 04:21:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:22:00  2017-08-01 04:22:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:27:00  2017-08-01 04:27:00  KRIPORTS37  66.4  0.0   \n",
       "2017-08-01 00:32:00  2017-08-01 04:32:00  KRIPORTS37  66.4  0.0   \n",
       "2017-08-01 00:37:00  2017-08-01 04:37:00  KRIPORTS37  66.4  0.0   \n",
       "2017-08-01 00:42:00  2017-08-01 04:42:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:48:00  2017-08-01 04:48:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:53:00  2017-08-01 04:53:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 00:58:00  2017-08-01 04:58:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 01:03:00  2017-08-01 05:03:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 01:08:00  2017-08-01 05:08:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 01:13:00  2017-08-01 05:13:00  KRIPORTS37  66.2  0.0   \n",
       "2017-08-01 01:18:00  2017-08-01 05:18:00  KRIPORTS37  66.0  0.0   \n",
       "2017-08-01 01:24:00  2017-08-01 05:24:00  KRIPORTS37  66.0  0.0   \n",
       "2017-08-01 01:29:00  2017-08-01 05:29:00  KRIPORTS37  65.8  0.0   \n",
       "2017-08-01 01:34:00  2017-08-01 05:34:00  KRIPORTS37  65.8  0.0   \n",
       "2017-08-01 01:40:00  2017-08-01 05:40:00  KRIPORTS37  65.8  0.0   \n",
       "2017-08-01 01:45:00  2017-08-01 05:45:00  KRIPORTS37  65.8  0.0   \n",
       "2017-08-01 01:50:00  2017-08-01 05:50:00  KRIPORTS37  65.8  0.0   \n",
       "2017-08-01 01:55:00  2017-08-01 05:55:00  KRIPORTS37  66.0  0.0   \n",
       "2017-08-01 02:00:00  2017-08-01 06:00:00  KRIPORTS37  66.0  0.0   \n",
       "2017-08-01 02:05:00  2017-08-01 06:05:00  KRIPORTS37  66.0  0.0   \n",
       "2017-08-01 02:10:00  2017-08-01 06:10:00  KRIPORTS37  66.0  0.0   \n",
       "2017-08-01 02:15:00  2017-08-01 06:15:00  KRIPORTS37  66.0  0.0   \n",
       "2017-08-01 02:21:00  2017-08-01 06:21:00  KRIPORTS37  65.8  0.0   \n",
       "2017-08-01 02:26:00  2017-08-01 06:26:00  KRIPORTS37  65.8  0.0   \n",
       "...                                  ...         ...   ...  ...   \n",
       "2017-11-17 21:37:00  2017-11-18 02:37:00  KRIPORTS37  34.7  0.0   \n",
       "2017-11-17 21:42:00  2017-11-18 02:42:00  KRIPORTS37  34.7  0.0   \n",
       "2017-11-17 21:47:00  2017-11-18 02:47:00  KRIPORTS37  34.7  0.0   \n",
       "2017-11-17 21:52:00  2017-11-18 02:52:00  KRIPORTS37  34.3  0.0   \n",
       "2017-11-17 21:57:00  2017-11-18 02:57:00  KRIPORTS37  34.3  0.0   \n",
       "2017-11-17 22:02:00  2017-11-18 03:02:00  KRIPORTS37  34.3  0.0   \n",
       "2017-11-17 22:07:00  2017-11-18 03:07:00  KRIPORTS37  34.3  0.0   \n",
       "2017-11-17 22:16:00  2017-11-18 03:16:00  KRIPORTS37  34.5  0.0   \n",
       "2017-11-17 22:21:00  2017-11-18 03:21:00  KRIPORTS37  34.3  0.0   \n",
       "2017-11-17 22:25:00  2017-11-18 03:25:00  KRIPORTS37  34.3  0.0   \n",
       "2017-11-17 22:26:00  2017-11-18 03:26:00  KRIPORTS37  34.2  0.0   \n",
       "2017-11-17 22:32:00  2017-11-18 03:32:00  KRIPORTS37  34.0  0.0   \n",
       "2017-11-17 22:33:00  2017-11-18 03:33:00  KRIPORTS37  33.8  0.0   \n",
       "2017-11-17 22:35:00  2017-11-18 03:35:00  KRIPORTS37  33.8  0.0   \n",
       "2017-11-17 22:40:00  2017-11-18 03:40:00  KRIPORTS37  33.3  0.0   \n",
       "2017-11-17 22:45:00  2017-11-18 03:45:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 22:50:00  2017-11-18 03:50:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 22:55:00  2017-11-18 03:55:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 23:00:00  2017-11-18 04:00:00  KRIPORTS37  32.7  0.0   \n",
       "2017-11-17 23:05:00  2017-11-18 04:05:00  KRIPORTS37  32.7  0.0   \n",
       "2017-11-17 23:10:00  2017-11-18 04:10:00  KRIPORTS37  32.7  0.0   \n",
       "2017-11-17 23:16:00  2017-11-18 04:16:00  KRIPORTS37  32.4  0.0   \n",
       "2017-11-17 23:21:00  2017-11-18 04:21:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 23:26:00  2017-11-18 04:26:00  KRIPORTS37  33.3  0.0   \n",
       "2017-11-17 23:31:00  2017-11-18 04:31:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 23:36:00  2017-11-18 04:36:00  KRIPORTS37  32.7  0.0   \n",
       "2017-11-17 23:41:00  2017-11-18 04:41:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 23:46:00  2017-11-18 04:46:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 23:51:00  2017-11-18 04:51:00  KRIPORTS37  32.9  0.0   \n",
       "2017-11-17 23:56:00  2017-11-18 04:56:00  KRIPORTS37  32.9  0.0   \n",
       "\n",
       "                                   date humidity  wind_direction  wind  \n",
       "Datetime                                                                \n",
       "2017-08-01 00:00:00 2017-08-01 04:00:00     10.0             185   0.9  \n",
       "2017-08-01 00:05:00 2017-08-01 04:05:00     10.0             185   1.6  \n",
       "2017-08-01 00:10:00 2017-08-01 04:10:00     10.0             185   1.1  \n",
       "2017-08-01 00:16:00 2017-08-01 04:16:00     10.0             181   0.9  \n",
       "2017-08-01 00:21:00 2017-08-01 04:21:00     10.0             182   1.6  \n",
       "2017-08-01 00:22:00 2017-08-01 04:22:00     10.0             182   2.2  \n",
       "2017-08-01 00:27:00 2017-08-01 04:27:00     10.0             187   2.2  \n",
       "2017-08-01 00:32:00 2017-08-01 04:32:00     10.0             182   1.3  \n",
       "2017-08-01 00:37:00 2017-08-01 04:37:00     10.0             182   1.8  \n",
       "2017-08-01 00:42:00 2017-08-01 04:42:00     10.0             182   1.8  \n",
       "2017-08-01 00:48:00 2017-08-01 04:48:00     10.0             181   1.6  \n",
       "2017-08-01 00:53:00 2017-08-01 04:53:00     10.0             181   0.9  \n",
       "2017-08-01 00:58:00 2017-08-01 04:58:00     10.0             180   2.2  \n",
       "2017-08-01 01:03:00 2017-08-01 05:03:00     10.0             180   1.1  \n",
       "2017-08-01 01:08:00 2017-08-01 05:08:00     10.0             195   1.8  \n",
       "2017-08-01 01:13:00 2017-08-01 05:13:00     10.0             195   0.7  \n",
       "2017-08-01 01:18:00 2017-08-01 05:18:00     10.0             195   0.9  \n",
       "2017-08-01 01:24:00 2017-08-01 05:24:00     10.0             195   0.0  \n",
       "2017-08-01 01:29:00 2017-08-01 05:29:00     10.0             195   0.2  \n",
       "2017-08-01 01:34:00 2017-08-01 05:34:00     10.0             195   0.7  \n",
       "2017-08-01 01:40:00 2017-08-01 05:40:00     10.0             195   0.9  \n",
       "2017-08-01 01:45:00 2017-08-01 05:45:00     10.0             194   1.3  \n",
       "2017-08-01 01:50:00 2017-08-01 05:50:00     10.0             207   2.2  \n",
       "2017-08-01 01:55:00 2017-08-01 05:55:00     10.0             205   1.3  \n",
       "2017-08-01 02:00:00 2017-08-01 06:00:00     10.0             202   1.6  \n",
       "2017-08-01 02:05:00 2017-08-01 06:05:00     10.0             202   1.3  \n",
       "2017-08-01 02:10:00 2017-08-01 06:10:00     10.0             202   1.1  \n",
       "2017-08-01 02:15:00 2017-08-01 06:15:00     10.0             201   0.2  \n",
       "2017-08-01 02:21:00 2017-08-01 06:21:00     10.0             201   0.7  \n",
       "2017-08-01 02:26:00 2017-08-01 06:26:00     10.0             201   0.0  \n",
       "...                                 ...      ...             ...   ...  \n",
       "2017-11-17 21:37:00 2017-11-18 02:37:00     72.0              60   1.8  \n",
       "2017-11-17 21:42:00 2017-11-18 02:42:00     72.0              70   2.5  \n",
       "2017-11-17 21:47:00 2017-11-18 02:47:00     72.0              78   1.8  \n",
       "2017-11-17 21:52:00 2017-11-18 02:52:00     73.0              82   1.8  \n",
       "2017-11-17 21:57:00 2017-11-18 02:57:00     73.0              65   0.0  \n",
       "2017-11-17 22:02:00 2017-11-18 03:02:00     72.0              98   2.0  \n",
       "2017-11-17 22:07:00 2017-11-18 03:07:00     72.0              60   0.7  \n",
       "2017-11-17 22:16:00 2017-11-18 03:16:00     70.0             256   1.8  \n",
       "2017-11-17 22:21:00 2017-11-18 03:21:00     71.0             115   1.8  \n",
       "2017-11-17 22:25:00 2017-11-18 03:25:00     72.0              43   0.9  \n",
       "2017-11-17 22:26:00 2017-11-18 03:26:00     73.0              67   0.7  \n",
       "2017-11-17 22:32:00 2017-11-18 03:32:00     75.0             197   1.1  \n",
       "2017-11-17 22:33:00 2017-11-18 03:33:00     76.0             197   0.0  \n",
       "2017-11-17 22:35:00 2017-11-18 03:35:00     76.0             193   0.0  \n",
       "2017-11-17 22:40:00 2017-11-18 03:40:00     79.0             193   0.0  \n",
       "2017-11-17 22:45:00 2017-11-18 03:45:00     82.0             192   0.0  \n",
       "2017-11-17 22:50:00 2017-11-18 03:50:00     82.0             192   0.0  \n",
       "2017-11-17 22:55:00 2017-11-18 03:55:00     82.0             192   0.0  \n",
       "2017-11-17 23:00:00 2017-11-18 04:00:00     85.0             188   0.0  \n",
       "2017-11-17 23:05:00 2017-11-18 04:05:00     83.0             186   0.2  \n",
       "2017-11-17 23:10:00 2017-11-18 04:10:00     84.0             128   0.4  \n",
       "2017-11-17 23:16:00 2017-11-18 04:16:00     83.0             187   1.1  \n",
       "2017-11-17 23:21:00 2017-11-18 04:21:00     79.0             207   0.7  \n",
       "2017-11-17 23:26:00 2017-11-18 04:26:00     81.0             192   0.0  \n",
       "2017-11-17 23:31:00 2017-11-18 04:31:00     82.0              76   0.2  \n",
       "2017-11-17 23:36:00 2017-11-18 04:36:00     82.0             126   0.7  \n",
       "2017-11-17 23:41:00 2017-11-18 04:41:00     81.0             251   1.3  \n",
       "2017-11-17 23:46:00 2017-11-18 04:46:00     82.0             268   0.4  \n",
       "2017-11-17 23:51:00 2017-11-18 04:51:00     82.0             216   0.7  \n",
       "2017-11-17 23:56:00 2017-11-18 04:56:00     83.0             205   1.8  \n",
       "\n",
       "[30626 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw['Datetime'] = pd.to_datetime(data_raw['Time'])\n",
    "data_raw = data_raw.set_index('Datetime')\n",
    "data_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TemperatureF', 'DewpointF', 'PressureIn', 'WindDirection',\n",
       "       'WindDirectionDegrees', 'WindSpeedMPH', 'WindSpeedGustMPH', 'Humidity',\n",
       "       'HourlyPrecipIn', 'Conditions', 'Clouds', 'dailyrainin',\n",
       "       'SolarRadiationWatts/m^2', 'SoftwareType', 'DateUTC', 'station', 'temp',\n",
       "       'rain', 'date', 'humidity', 'wind_direction', 'wind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw = data_raw.drop(('Time'), 1)\n",
    "data_raw = data_raw.drop(('Unnamed: 0'), 1)\n",
    "data_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736557.0, 736649.0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Ammz04Iys588bmXNWt4R3o169k93PL7c7OpLHr04uPyT597HJUuska+40a9vX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\nIr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d3abd7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(data_raw['rain'])\n",
    "\n",
    "ax2 = fig.add_subplot(1,1,1)\n",
    "ax2.plot(well['GW_elevation_ft_(LSML)'])\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.ylim([-1,15])\n",
    "plt.xlim(['2017-08-16','2017-11-16'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./   .git/\t\t  KRIPORTS37_weather.csv  Well55a.ipynb\r\n",
      "../  .ipynb_checkpoints/  Well33.ipynb\t\t  Well56.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls -aF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Lets try and see if tidal patterns have any effect on our transducer data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/sbhgeo/Documents/Fogland/Notebooks/Transducer_Data/Fogland'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fetching data from NOAA/CO-OPS via the [Data API](https://tidesandcurrents.noaa.gov/api/) \n",
    "specifically I am getting data from station [8452660](https://tidesandcurrents.noaa.gov/waterlevels.html?id=8452660&units=standard&bdate=20170501&edate=20171120&timezone=LST/LDT&datum=STND&interval=h&action=data)  \n",
    "Note that we dont have the *Verified* (`tydl_wl_data`) data for November...seems like the data is updated monthly? we will have to use the predicted values from `tydl_pred_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tidal_water_levels = 'https://tidesandcurrents.noaa.gov/api/datagetter?product=hourly_height&application=NOS.COOPS.TAC.WL&begin_date=20170501&end_date=20171120&datum=STND&station=8452660&time_zone=lst_ldt&units=english&format=csv'\n",
    "tidal_predictions = 'https://tidesandcurrents.noaa.gov/api/datagetter?product=predictions&application=NOS.COOPS.TAC.WL&begin_date=20170501&end_date=20171120&datum=STND&station=8452660&time_zone=lst_ldt&units=english&interval=h&format=csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tydl_wl_data = pd.read_csv(tidal_water_levels, delimiter=',')\n",
    "tydl_pred_data = pd.read_csv(tidal_predictions, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date Time', ' Water Level', ' Sigma', ' I', ' L '], dtype='object')\n",
      "Index(['Date Time', ' Prediction'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(tydl_wl_data.columns)\n",
    "print(tydl_pred_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tydl_wl_data['Datetime'] = pd.to_datetime(tydl_wl_data['Date Time'])\n",
    "tydl_wl_data = tydl_wl_data.set_index('Datetime')\n",
    "tydl_wl_data.rename(columns={' Water Level': 'Water Level'}, inplace=True)\n",
    "tydl_wl_data = tydl_wl_data.drop(('Date Time'), 1)\n",
    "\n",
    "tydl_pred_data['Datetime'] = pd.to_datetime(tydl_pred_data['Date Time'])\n",
    "tydl_pred_data = tydl_pred_data.set_index('Datetime')\n",
    "tydl_pred_data.rename(columns={' Prediction': 'Prediction'}, inplace=True)\n",
    "tydl_pred_data = tydl_pred_data.drop(('Date Time'), 1)\n",
    "\n",
    "#write a for-loop for the above?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736557.0, 736649.0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69HismOQ7F71T3vFv7zD/PfX8LtXKSJwNWbhgoYiIbNmb3g6+7/L3yQlXnVD/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Dx3/wDAzJ4CvgG839ITNmxNN5p2XSxt9Ue5+tkpnPW5fYsUjZSaJ5qcsH7pk3ts95hXZ6xg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\ndbGIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d36e712e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(data_raw['rain'])\n",
    "\n",
    "ax2 = fig.add_subplot(1,1,1)\n",
    "ax2.plot(well['GW_elevation_ft_(LSML)'])\n",
    "\n",
    "ax3 = fig.add_subplot(1,1,1)\n",
    "ax3.plot(tydl_pred_data['Prediction'])\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.ylim([-1,15])\n",
    "plt.xlim(['2017-08-16','2017-11-16'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736557.0, 736649.0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1MoH0M2MHYK5RKi57NNG/BUEgzJdRzgTKs4u7kWNDo86gyOy142c3m6U/jPt/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ssj4kYnIO6aKS2xWDkUrn5wdkNLqrFG4znaeGv8Y6AJVDk5DsW/hrq5rENV8qFOQEfX3/gOFIqe\n4jeNoieLHYs5c217RN/hY00YmrMnfF1pINA9VsYHtaiWASGBCw+qIYZLoDPnoMKhvQBV1JnQbIgu\nbK6EK7GKDKD0sH2wdZ7uWJWO0PPsy8GQKfjg7oMu6OFyMJx/mqeGQPOCNy9zvMwvuTtYwQTSLItl\nAlEJ2IdPNRPIZa724+V6OfOpxjp1BxMm0G7cdc4MzqXZSs0KukB2O/YdbTgbwvbBZewqx7YdC7bB\n1SyLbeDyAr6/NQCzOZFmvEec7JzbXmkCOTCryyQWjqq+cxsILBdNIC7H5YwWBxXzMscSS8uqVzaW\nz38tkHXsrKbjY549Z4/Ulmqgpzo+fKyKnYB3iP26zNnogyC2y5oZVI2JBPbQWElJhtGX7zr9kOpZ\nRfTzkJlheh8MgncZ59GXgz1NT1bAvJXWbnxp7Tv7d8pAgP9flXBxsNOVg1FWXfXcwBR2yQSU4uo6\ngpILfq6audeW9i64f5qerB2/NqeruWPBAALUO+avAQsVpFVbcy0Zpba08qfsHMSaIIGLilqmfRbP\nBoRvVLUctoDDIskEZT2POdhJzA9TwtoYkuKXthIRWffWysEcCFQxUtq9FEykzbhJJUN8TxUTyLHJ\n1oDoiCsscWXHrpQ9VEEQn0ODYtvAYSkSnQRiuvfhmAYRht0sJSpubddgspUYyTlUtJnPsRt3LfHR\ngUDQKxNNICsMLez4aZmaQP2aJtA3Hr9x+oOIPRXoHnHxSxlQwT53m7u+HOy8VqD9NT8TLgeblime\npqeOCaTz5+uPJy0iLvFl/6hiAkX0Wm1trpvx7uyyC8h5riVwSGxTRJ/Yr8gDFbtGY1BsixCCQBEb\n8vaqFK2TSCAwy/nRsNdqe5ufZXwgTT6oXeLrgo1DVYZ273urcjDWBCLbB9+3JQYkRtqO266C5Jrv\n68CpVLVk4hdXAZCeoyGsqH99y+czLwdri23R1hhggAOBLJV0hWLKTnqVXbCT2S0shnHTHCZxjLgk\ngP9/yyTH5GzZvDnrWHQZZwGUOGiqsnyOfcHZPGfI1GloC57JwGE7HyviNKEsCMRZaqMJpC1QO2Ho\ntygH08CFhetuaREPA/OweUgLHmvyqDA0Fh8NRHA+1+qeO8ykcrAVhyLiNOYYtOJONx07YF4Xr6wc\npk5AXAGago3TATRUysPj14mtp+ChcNLXxmIHTFHHAb2PShi6MuARJ3vibJZbWNhmdU69YckwjZWP\nrZldd72Y65zhbEwgmgsctHXHWjKzRgMBzZqpLe2uy4B/DNAkJ0uyVw64cSydim7taMpaDqaglXXk\nrgS/WHv0mezGXcfk4CBvWqiT0eKfFeZ2xchr48GUg3WJl6nWBHqZXqxeGcpelVX5Mr3EdtjGq+2r\ndWHoYy6HUBAoCUNrORh1R0waRMenNtf4WOjMiHOnkl9KOvH1aJKhA9TNc3w6PtlnBRukZUkafKqj\nrqXk6ViF3XDrekTvh4CyXtmzBg4ZYegKcK3sdQsedH4U2Wi+DxfsMICgTBxdd5It1Yz3yhpm2TtV\nYxATGEaslIPRXOd7WAseWtl2cSz1aTh5xz62snduYY830AzM1eWQzoEMttr4Zq9vYOZ2AWg1R0wC\n1L0r5+vg3Br84n6qTovWlsL+Sgl485tgr8/7VIxv2F48x85en+0ybB6D4+ojWCbQ8cQEQrmtYw0o\nW523adfEiEvjFbajOD/suOq23W3vWryF88Afj7jEYsy2wUdjRIC3DAKxtuYqCCRdGyt/NaJnN2Mt\njvCl5K2pRJGkj8j+UWVLsU8XuxHjkuPM5jONm+Q3pOPINVl7Tb5DuibyZ5wf7WJZ9rN4XLfrGrP2\nosYP+tyd74ljWntpgJ+0zy6XcSrQXREUeGyp7ws7oviC+tFYX6xdNBIJuG+uxGn+9dAnDm/5fGZM\noFYONl8WIqcJ1EAgU5fnEPM2SIo6u4qlU5aDYWERpNAFCG1BL4CmTnxaDEOXaRt9VtsGLppxLlDV\njqZL18uLLYIT+0xMrTgHFVUGLN3HPJ20UJCpkGM5fSGm0boyMSc+XYmZcitkF6C8f/9+hyBvN6d7\nYM0ILMTKBGrlCJv7C2ot5QXv70UYmsonNkMum6nKwVQUVgPD3bhL1wVacRO0K7Jja0CeAriqZ9OB\nQAbldk4ZZ0NSyY4Dh2h+loGLZoOrQEe0ZKrypi5QH/vyCc4KOse2ZZyN0bfBxg1Z4spR1UwFGEL8\nW9gs7Kdlui67UF1XBZolG68L3uLfLzOUnJ3TxdMFk7xIVo4OtjnnqOmoCWPNOWysnxSRA9Yhhq6b\nUFpfjK6UA+GrucDsMz73WvaatX+SjSWtIL7Ow3yIaZmaaDO2L8vS9B+cyD7EQTWoGGJoIMnz3Gti\ncGdILgermEDv3b1nRZ5xPAY9l1i69vS4R9hLPjb27co3iGnlyiy1RBd/J0Bd/BDYUleG0vk6s2c5\nsJ3jY3QsDtGtcoxk+DpdImz2STXoIg7DkOaUloN12yl51oShC9CTu80kVpH4J45dVyYyCv+vPXfj\n/1X2Gvvxc5+XuQTs4OtEXMaUslCrdcGxiuA3daLcK0ygkqVjAr30vDTIlfGYGKXFeqxrgp6j1Oi7\n4V2xX7G6hhXgm31WBuDbj73Ivp6jATSqfUbrN0sO8LnnZe5bxE8XTaBqTWD/mrXVIrwmED5Od02B\n9ogLkKIgENt+Z5exVmE/+OMRlzgp4gJC4KNjHr/91tO3uutVBmpE3WAlseskBh2HsV0bvxM88zIZ\nRWOOAReU+bCd43Vdwe7GLJzznGpAkwDwEZEE/lVbSMvEANzqNUWskCOMpANsBl/TvFw6NuIaOqC9\nKAdLFQA015klo6xnq71j/HPe59VWyjhXACXYa2VbKkCtvnol8O/wBY23+Nk3n3joS6pdDHHL59MH\ngbQrBjmqXVZy9ppA1WIbQRn6scg460Sbp1T6VJZXySJVZs2IvqxOVtUhRlv34TeMGquIGK5NnakW\nmEpwhO062DnYYITWZc7ZAHTBLzLIyiKZDnZAV4LGeI4ukE0g0AoTSIEjvFsVjHOBS+vctX8/lYMp\nEwjn4YWYFzz8m8oLhMar3cGwXQUAI/KCxQtZt9iLE9A6NFAJAwy7K/Ox5WC0PaLPzHVZVNX5MkCp\nZeO44EiMvhvXjsJrtY0YgDIZDCf2G9F3QuF9FFjlfVxGSR3b9kxGLzLtMomVo7pWDobrdWLOTuuK\nQZVSuK4AgTpNIGIjOnvt2E6caePzV+Cfso34Gbdg2ZSu4Lfboe9YVJaDuWPNGWhKmSsua5PsDY6l\n53ABa9te2NIqcHE0d24FrzY2dQIUUBmBS9s+X7LgFQjkwPy7zV2zG8pSvNvcpeuC7XvYPrRSgVaq\nRUygVA52fGpzjZkRykLq2J4bCsjlHG/DBEIp8GbYrHclKlgOOncccM6AUsVUxv/5mFXXJdh4Zgjx\nmOvmwdD7OsflmEoSuuxuUQ62Bnq6ZBS27cZd508hqKl8NnfvlR5cBVI4wIGZC+zY8zvUEvcyqLml\nHEzOj+vqbDzbX9on+d4rumQuoFoFYsh/YGaEG0Mu8G7yCRCGlnWvE4wW0N4yfpSdpHPk2jyk+1Pf\ntyqt7fym8z4Ve7tai5lV7lrE32/vuzLklhjdXRJ0YO1wORg/K/44kf339u8lcDEil4Px9qocDAkD\nZQJpi3h88Oz4/8f5onOGseCapby7e7cla/FBRzW9R7DrlKmsiX0e7/yurjGVOT7TY6n/ydsb49H4\nOvA3ErN5MCAB/37sba9LkOE+VoWhi3Kw9DzIz8G/mpRQnzyV4jq/cNx25WCdTyx2ER/HEFJB9y5Z\nYua0iu/r2qp2w5XROzYXfsP+XwLAhsv60q2hhkxyy+dTB4EqGmkVtCFwqUCgbp/l2C1EvI8z+rb0\n6YqTzvWYTLurMloRdWYD16UUuqrLhlWAl+CootZZoGnsn4kdhIss9IbuXL2PrmZZFykBjpClTovt\n/BJDDH0XhhVNoOfpuWk5KHuoLaquHOzu/Tgux5T1bZpApBnBC3EquzpmYVJcf8QFxEFbeVwXl3Xo\nmHfAD+9TloMJdREOS0R0TtlxPtqFUNlcXdC/4jCtGbhpmZJoaTU/1zLnjgLJWcwObDHUdwTqVYlG\npeODAEWfFfaxTKAr5WDOznWZRGF/KWvAloOdr1dZaWxLecwp40btMpe0XGNVpkyFBi549mPvZL0N\neILvnUOjIvtt+3xhFHC3Onbk1ijP6R6XuhzsGm3czhEuTxQnQLPXrh1wxHqQ57rNtO1GEwh2rjGB\nlrzdldYeZi8+/TQ9xX6zb2Bh69ii5WDn+0K2+dXuVdtHy27f3b3bZ5y3xASaPAikopHI8uFZ8PNV\nEAjfO9Yq1rBbA0O2G/zceV13wfItgLqC8+X8HHxbYw1ocF0OVEkllkUgstbQIl3XIoLuMp+xn4IE\nADsq1kl6JmrjTSDS2b/l4pci083naNc15/lZMYFuWVs1s752LGWywW4Mw5DmojIFWWS1gd2GvZOe\nV5HVx/ZWinaFseYSJlW3TtXo6/wNTToZ3xdrWMUwvtZ0wTGByqTTeawqqNOVg82XDrT8bpO0wHhh\nocNuPmwebmICoRwMfsMaa4ABddWqTJ1xyS9Vhib862mZ2nmejydwCOMafjKXwjEIxH9H9OVJ2Ofb\nz99O5444MYEioitHW2PXdUnLlfnJc90lo7bjRfeS/ZNK51V9X2bQMKCurEr2W8DocY0udD6rfedt\n+nw7e12Ug3XspPli9yMK7UUhWiigzL8FJqDvY41xo0C9/o0yTi2fq0Cg7bg96azSuKrYQ1WM1Nhc\nhc3CGsb7sLaSY065uPyWz6evCVS0QHXBRstsGGHohsIW9E8eVNq2L6J33l05QqPxiwOtQQVnnF3Z\nTFtAQCmUYAMUQRssF1ntCO9kITjSLBCzMhJoxcEvBxsr2UoXHHEGOU266ZACRnUOdNLMyxzH5RJk\n6gTEwuK0fpReG3FamNpiIF0DmtAe//68KDWWDhYpZgKRZgQvxE57B9fL19UyK9JWHgCUC6gej4+t\nA1kCs+YLcBTRZ4J24y5lY5Cdj8jC4nyPakha0K/Bp5SD3eSU6diSbEhF/9QWqGtMIAZ2q+1LLNl5\nH2qgybF3GrNQzsELi6OLrgFKLuvqbNO1crCWcVYnh7sq0CKF4ye9CJnrLnvtWEW4d7fd3uPsSz4Q\nAOJ+XNDIdtmVlWgw6UBt1hZx2S5X8mudpjmDWcw0aA4QzSt+Jgp2K2uBn2NiRogt1bLFtQQLl4Mp\noO5axDeR53MJV3PqRcyZz4FscMeSoc4x+D8fS+0yM4EAosKGwhZqN8en6SmVg6ldbJ3JzseZ5kvJ\nDq5LS3Na+dr0nL53+iyp+6TxXdy7dcBfsnODmTvYLuBQciLFD1EbxIF66h4jPsVawkvteITvXtqC\nI3MOC6AaP4RtA69hHKi7Z5LsjPhgJagxFt3BxkuHQBcYurnugBsEFeojVEygNX+ZS1r4t8lnrJ6X\nY9AU7B1+Xqrvoevxmi1tTDazvuC63L3jWVXvSueIBVAR3BeJH41TFDDT+7gF4MM5VBPIJRo5WIaP\nB7u4xImh93h8bPemc6TTBFqOXhi6CJCt+L4poU0sHwGOmPHDupd327tmm5k5z8LQ+OD68VG9Njzv\nbz335WAK2uMeK3YdYg4Xh3VzfTlaO8d+EyeW2D/pfIS5LjG3TCBmG41jA5oqJpBekyU6SJypQGzH\ntixY/hXQxAyWDrQXMEvjBz3/ZiRG+w3MSY7LOXbCMStQuSrhqmIkt091XZx4qeyGi5GY/HILYeWW\nz2fHBFKEfbPzWWJkyEXQKdGiXFafBpsO3Ihs9C3YsgiNfzkhhaBIW/qy6FvoRNDMBk+Qa0wg3ueW\nzHIlGM3HaoCZlB3w4sz31wXemlke911WhxlCeEYRPZuhy8AZx/owHVpddHK4UVq26yl3LGaK3y3L\n0sohuHNWxCWz0TLFyAabcrDDfMiaQIQOsyaQloPpOVQTA+3blfHjwKGX6aXRE4FE0+EAACAASURB\nVPn5cTkYg1No5xyRHQ1cXwPfxDkAQ4ifbwsqCg0YDfqneUr6ABG96r9Dv11pogYuthyiyOpYkIDn\ns3GShxjaM2FQmQMa7DPE0DGB+H3wudt1DZm9yKUY+kw0cNAFx2oCAbhRGq3YUmUCNVsqDpPLxrRM\nrWmTGRHdPbZjjT6YTJk2pRcPuatWCkLUcZhz10bezoC6LtAaFEcQ5Vm0UNqYkznSiSLSuEaSgc+d\nEhajAQrGnk5elYSuBcXIXjvwwrWIr8RMlfGobEsHAmmAoJpoCk69Ob5pdl+DhyQMTeW7ECDF/TcA\n/gwUaUCjrB6+HmVbaplY0y1kuzz3LZ2xr7NBZbCxkuVroPbY61ZZJpAE0Y4tnDK1AgTz9WLudHOK\nARqXpBoye7Kd29j4xug0z4rnrRvvLqmW7EzBjuoSd2PdSMAFhs43Y59mN/jmJ926BzCiKPmtQIe0\nhlKAxOCUG1tlwsSA9hG9aHNbjyUBqz4jj1MGznX9dtIN6f1SgK2AehVQpfEgzGM9VtMulTjFJZ2a\nULdJWjq20RoTyL2nBLRvsu95v71v+o7pHNosZTp05WBYd1qbdrKf2mkxwnetZTaE6ghVnRbvN/cX\nMP9KOZiCQCrWC50jZiQ1JpCA9tjP6ZCi6xP7FBHGLotMQYQkqQxwzudim5kSXsYnTqDO6H0gLWN3\nVSL4XrXdEgmhEKXuuoMJcJPm7XxpZtSxfZyfJcCx1TK8sk5qOZgru2q+ryZGC/ZO82MLxhjmdCoh\nK9hDHTgkoJVLAlbrN2MSDFbyGqr3eMvnB0cTyCFipJXhJmZENpZNhEqyZg7xVIeiy0TP2Znh40dc\n6iv5WF2pwFxMKHFAWiBgyiQ64GZZFyRrE92wMjoRx6JMAs+30kix2RDqWLHK/KgyFXMGh5zz1dhG\nMh4qJz3iXA4mNNbjfBIHdaDKm+ObJFzH+g8Yc1zexZpAXCe6xgRSEKjVeIMJtL3rak65JEtBINfN\nhynoCQQ6g0YRfTkYByjqzKwFFZ0+ALPPGJAk7QmX9SjLac5jMZXs0ALCgQvT5atuJA4MgNBnuiYB\nsyoa7S1OJ/5uDiwF/Qz2WAddmUDCrnOMwy4jqhkUARawX1sIGVQmB4Q7CVUB66q+xlBkajUjS5k2\nB+irE8IBq6VbmyBTAXUXsALkW5YliRyqFgqPUz4GAD48S/097kWdL77ea4wm7k7D29WZSqB9IQz9\nPD1n1p8EIpq9TuzFQhPoZhBorruDoWxYtSQgMv3u/t14mV/aO3w6EhOIxnVbdzYPsR/3l+40XD5b\nAOqlJpAT2C6YVs2JFH8DAaO2PU8l2EYzrGWvha1R+Tr8DK+xNTR44G3VnFKWjgKr49jrWLRzOH/K\n3AcHR2wbuvJHeSYOQO2eyQ3sEgXBXWkt76M+rgMFu/W7YHuyjbdsT3Ms3DuuwWXcWVsJ9+iSkxrs\n8LsaYugYuOoLON9QfUbYRge043qT7adz2GYto9k+C8CoTKC1cjBTWruWeOn0ekQPU20AzqO+HAv2\nH+ZDx3bk6+Uk63bYxtP0lAT28Rz5uSqTst3fCqiCDpARWW8HAtDaGReaQK4U2DGBcK346PFwT8ry\nj7iUg6kf7XRj1qQCHFOZ5zr7mQqc2+SV2szZawIp2F0BSsw24pjVdQfjOagkBL2miLok0x0LYtUW\n/HJgFrNhHKjB9pr2cYz2yl7yO9yNuVNdY4FKErsxhAQIxt8uBoWfp/61srQ7AGzoGb5dzGx8SQYr\nNS7/gQeB1uituqglKicHpZPUzgq1t2WcTUa/DBA06yBBCH6byg4KqlrlfKnR5clcOVMuKObrarpD\nfKzRAGBDL+II56BiR6HDhzqkagAispZEN2mcOJ6wd6oShmqxVYc7om4RryCQ0ms5G/LJ4ZN4tXvV\nsXd4keJ65qYJdGb8aHAEfR++LnyHRUoBMFdagcBfs+0to1+wA1AOxtl2FoZWkMSBbxxsuHN0Bu5s\nqFtXA50HsugweNvGiYzFCN9ZQBk/icFigqbKuawW+ogVp3PsneSSGi5zpwIcdB6kbKU4ixXw5wIX\n2A0diwkEorHFGRwei45xo45yJwxNYJrN+pINYluesv3GceBxqhk4/i0yZOMwJkZXSbemY7H9dckE\nXS/wLEtGqQnM7LgmwI7vnQEEHitu7iTWgDA8pmVqzkzHYDGgMuxcVQ52v7nvbP/z9Nx1tGnbN6cO\nhfux76gIIIaZkwgG4LCploQCSgg2IjLDjcH5u+2lTJZLuzpNIAGBNNDC82VNFcxRtdfNlq7YOcfW\ngJ1LQSYCF8PWYKaKBiIV4IHrUi3DND/JF7AMvmXygYs6veqzOXB+qZNqHOy44MGyQE3Jb3sm2zqR\n4WzpLeVgCk7Ztu4C3HBpUMSK+P9KGYEDp/C8XMlvS/yYRKvzBSJ6cea0TvN6LAFrB9CYIOgwHzJj\nwmk+0b3zGGJmRLsux0AVX0Dfe1XW0UprRfKgAcHyDF0ZfcksJL1Gvr9mFze5i9zT8amNj44VDOBo\ns4+H7UM8Hh/j6fiUOl2pT85sGscEcjpqL9NLV1qLa3Z2GQzN1h1suoBA2iI+4vIe8NHjgQnkugVb\nJtB0sEBBE4YejVRABay6BBIz35xPYZLuGusxQO6YPXosxwQaBtMdTJjNTBzQOPMwH2xitJrP6hsh\nccbXiuei8UOLWUWGoZJo2IybGIYhA6Ur5WAA0yqfvEpaWk2g6eJrdWvuSmJUtW81Xrd2cYUd74BH\nF3fc8vn0y8E2dTmYBqVwKDRwUNptB1JoNkIWj4jeSVdaqAoc4jg4phVtno8pmOwm4ZADU0ZiK7DH\ngkPEBLIlb0NfDsaLkZaDOUYBByjWwMm74u5grhxMKZDcno/PzYuXK0vab3oBvpf5JTbD5jK2iCI4\nLVNXDqbaEzh2xCmz8Gr76qJXYcrBOBvN2RXWKmqAzvauKy/A4q1zgUVWNaCqNIyqbj5dOdj8ko4T\nYcrBKHBh1ltjnhQGbr/ZdxR/Hj/O6UzBtSyc2I5rYsdaF6MGWl0Bh3BMx+LAIuFYd3hOLnBxYCg7\nzy5TAHFOC1qRE6lBCJ+3AoGYabUdtyftAAkyNUDghYXHFt+jKxFJoINm1UX8WoNJlNa2Z18Eei6o\nSDbeZJWckwWAEd9bfbWxGENkf5k+XVF1FbADywz3n7J8FMjqs3IltFV5DNh9a8kHtpmt7OpcrqRO\njhOGrkoYms3a+lLVqhwMzr0DqJuDd75vBoFcOdh+3HdCo8+TlIMJQxNMpKYtBHs57jutopfpJcZh\n7DKDbGN3m10cpz4wVHvdWJXib3Q204AkLpBVX0e36zlwvRG9Yw+73LHuVrLX3bydfUlCSkaRjoWC\n43yOVrKzBjSRbUiB+tAnONL6UrBZ1Q8BUMrPF2xAtx4mlm3hpFu2SGHLtYyA/WUdW1jfbMC6AjC6\n59VYWAowCpBoATDyATXJ0enqGcBOEwMqrqsB9s1glhsPAm5eY2C5Y1XJSZ3ryoLvNIHIx8RcX5al\n2ayH7UNmAh2f2vjoQESaCw+7h3hzeNMkEPBhQCnCAz98vQB7lHUDcAggEvxu1n1j+5tYoMeLHVf/\nmt8DPmqbGxOIGB4K2us7cc1oOlBQEixrDGqeI5ogvJZYwr8V43GNqazdvhg0sswh8YkZHLqVhKB+\nqfpfGhdzUgv7VewoC+Ybe82Aks7PqkW8I5PgmB0xpQCHcCxlx0dcgOsqDuu0kPk5noFrjstdso+r\ngHhN4iSD+gK3fD4zTaAys0EBAme0nHGNCDsQ2ws3dCkFIxCcIUucgiaagBE5G1xSoYvsGJ9fF69x\nvAQViZ0wbpvau7uuhOrSZKsWyLIWVZlAArJdOwfeScuSGOOqBq5qa8w6C/reua0xn7t1IhCxMBW0\nU4CGHX6Mw08On8Tr3etLNpgyxY0JRIEI6qxf7V5Z7Ym78c6WgzmGkIqsdpkKlHbNeRFeC9q24zbp\nHjGjyZV9AYyNqJkGbl6pUXLZ4DR+yMFbrQFe8nh3gK8Dh5yzVpW1gUZbgRrYpwOHJLjGPk44LpX/\nOGBMSgU0u8nnxXvqqKfEBOoYP8ulo41ux/F5zOm7srT4AhRUgeJ0j5J5ZaA/bZ9zi/gK7FnLtPG4\nYwFo52S5MhgFXNj2V6B9xTTFfvysOJB1Io6OmZbGA62hAMf53MpA0O1WtHkWWwqw52zPOk0gYgIp\n66UsBzteREDvthkEgt3lY705vGlZ86618BkE78rEqAsMj2vWHWIAip+VY8uxVlATjJauTwraN0aT\nKQergN2IXjsq4jIe7NqqjM5lPcDtOmGdGUwYvxo8uI57mLc63nVOuRIGXis0s5zOsQI0tUBgqNmT\nOE7ScTTZ1fRM5vxMXAkXnPG15Bm/w1SmZrLRDrgpmUArbE+c37I9hxoEckAMByIK3KTnZexcAhIV\nYCwCbw6CuvdeBKAu8Lb6gCbbrgkhtb9rFQt6LNjf6n3oPGRQh7czEMLjoelObu6Tpt/j9NiO0cpj\nzuPjy1/7ckScbNOr7at4PD6mBCC+w3kjBARy3cF2fXewpLmpCVACgapysGT7bxCGvhsvTKBlWeKj\n54/sNUV4eYhUDmbmYZd4KeIXBe07eyaAC9tStf3wdRDrMeiJbWvsSceQdAB80+sxyS4bZ5LPbytO\njP/lWP44B/ZzPm4Vs1q/kJKWamNdCRf7mGrHIy7+ql6X7TR29tu6qqUll7Bq9/MKzLfxtyYGOFlN\nz1H9OYeV3PL5zDWBOLPRLaq02FaaQC6LqgtkyrZLKYgeq6LxR5xeUkJh1THSQEAmG5BbVnPHeR0V\nuqrFb2jg2LOK1PisZdrKxZYyWiXbSNk4Z2e8AoeaMTmf+5bW5t1iewZi1Blu4sjipKuORQcCcRcc\nCThaF4KzzsRxOV6E66hDDUCgd/bvdNo7+K3Wrz4eH1NWh7vjDDFcygiUCYTSCtPqvkKgwQRygZbL\nIDv6siLTfA4sLMqkcCCtRfiXqWPcRPQGEdfb6VMJnTyxTgqWjgMiUnZebdP53ts5BIjQcrDKUWQG\nny54bVEtShv4vAy48n3wYqBBPO7xZk0gmusJZCOwxZ0jwnQHYxskDrHaIN6HgUSrCTT6TJsLWHGP\nyQlapHMXvZPNsMmCm7MvB9NzYB9lCGE/57CV9fDiLMIBucoE0qBYtA6S/RONkHmZrT3phKGXDA45\n4JrBebbXsH/Yz3Uu5GO9Ob5pv1fGD87hysFSi3gF58+BSAcCjb47GOtxaJkYynHZR+F7X2MC8dx1\nAAIzHhVY5gCFz4055ewc7j2iEIYu5vo11jMD5KsllkNRRjBcZ2knAIqcYauNYHwgBqHVxne6dvJM\nksM/Zw05Z0uTk35Di3hnlyMMCLRcP9bqddFYScHD4MEp3Edjbp7frwMxHajNAOPaWsnHwrndmMM7\ncUEjxnxXDmYSQvqs9FhV502UfvKz4lJ9PYcro0dzDgXrONHY/NIz4yfiIr7P22HjME8O8yHeHN7E\nv/qDfxURp7IglIMx0BLRs2qY/cMlVLC1rjvYYTrE631mNKWSWyP+b4Whi3KwShj6ZXqJj14+6hK+\n/DfYo9dAoGk+d4bcGPaFjBNNqkXk8d4l/CnRGOETSK201jCBGqhD7EmNQV35mG7HNrfdxZm4bych\ngmOV5WCcOCOgB/s5ckSy1+J3p3fFSTUCdSrQvN1LUdY7xNCVtuI6cP6ObVkA8Jth0zde4XVk6DGJ\nFHdUwCODhfwclWlqNHlv+Xz6IFBBqbY01tnTuFLgYtBAdXo5YHSDmhcvRltbgEDgFCaiQ/A42HBl\nX5q9vgWgGWI4bY+8PSI6FDiiNz5V4I3zVwshOzOVMDS2L8uSQAp2Gg7TIbabPsBWYWh1mAD2dLTb\nTe8MIxNTMWs0e81BUFcOdgaBeIHkLHHEZQwDBNoO20unMcmqoLyB2ThYvN314veKWjP7QxlC3M0H\nTpe2iGdgjB2N9BwnT19WII/HVstcDTl4cFlBdsYdZbRC/h3o2WU3BAjRRQrXux09u64KdHBeBYjb\ndnWSCWhymQJ97hVwvTZvVRhaGQsYP3wctqW8DwefiQlUMBA0q83n4MV7HEab8Wk6N2KDXIY+ZT30\neUng0s5R1FIz4OLKwdSWckkLtjH45zLI7XolM9jOPWaBa7axLkPEwUMTXizm537cX9isNE4A0PK5\nFbhBqZsDxxUE6jSBzgECyl7ZNrGd4+0KAsFWwv5F5LGYNIGUCSTlYHysNWHou+1dAseTkL5oAnG7\ndzxvPtZukzWM+J1sR+lsupLlS/ZB7Zm8Q+yr5d8RBJ4I4NoBHsKuc5llbL8VoJnnOc2pTvsM4K0p\nCeiYynMub0pMIAKIO2dYyptSxtkwTd0zqQAHPIOqTJf9PwXBG4CwphsjdnaVCaTHWnzXRoDH2M8l\nYG9lcHOiUxOEyfdV5qZhjwPMq4KgbszJe3TrNF8T9tFybnd/1Rxxvq/6FfDNNE7pGNTkfzr2NmwG\nniGeCTcf4W5bLH4fcYmFUCKFz8P2Id4c36TjR0QHdleaQM3277OswjSfEnjKBGJGu3YHezrmFvFP\nx6eWYIF/fUs52PP0HN96orbwwo6P6P1+NLWoyn+Sjyux0FVgVcaPApKsv9NVfQh5wOnSOv+z+WYc\ny64wgTT+1JhR75tBGOf7bsdtNzcTqWCekh+J/TRJ1a5rLQEqgBne183lYKMpB6PtvA8zCy32MPS2\nl8EhPlYCj01yh+21riOuoqhV6BgmkGMk3/L5gWACsVYGf8fOTAUCJYeCFxxDxePBXjnvjLbyA8dx\ndELht3qslO2X8pEOHFrJdnHZDG9v12vKCBzd2gW/moVP9GGHONKEZoc0OV9Kp1yOaTtnQyJ8hwQc\nSycgMt4260qLqoJATYD5vA9asif9h/P1fHL8JF5vL+Vgh+mQAJWIvhzs1e5VJ1TGlNiIHIigtae7\n3qZjUWh1VAwhZ0hwzbvNSV9jmqcGmOE9quZS5ZA6gIadXi0ZSk56AapEnOaA2872gXUWdBFuGjvG\nBmzHbW5Ju+QMuQY0wzCUrIwq86iLJD/DDhyiEkudn9WcqhYWzKkhhu49RWQnkp+XZiqUXsvb+Tk6\ncKoCgcrnNfaMn46N6Bg0Q0+F1np4Lg90TExc6zheShKSjR+yXWb7h223BMW4Bsf46cbv0NtYBrk4\niOdERjcPqVGCBkdOoyRlliEouhyT866gMsCe1XIwKX1C9xq14xUTiDPhrK/G3cFge3UfzjhP85R0\nhxiI4Xu8395fBKbFXqZznOeVzp2OHXAjE8jW+zMItJIx1DXBJUUwtpx/ElG3/VWwm+etA2g2Q19G\nwOPd2mXJRitIENF3QrUaGgwQizNc2R8HoGrQX9nf5H8WmXsurWVbnsp0N76jl5ayVyCQBqxaangt\nYOV70ZI3TZhwgKQyBRE9AMfvxLHzq6SMngPndtIN7Z24OVJodXBApUyHLsg8H7OVNJuxpfOt8kuV\nscs2E0lO3q7C0PgO9vph82BbxOMDW4Px9sUf/WJEnNj2jwfDBBKwW1u8t7/P36swdOvCdQaBXMmt\nloMBnGcwvzVqGU052M4zgZ6n5/ijpz86Pathm65X9UavAQW8tjr/8xYQyIHdriwy2Q3xddT+sS6t\nA3UwfltliQDwLKsScfJ9rpX7Jj02k1Bj35fLOx2Yz4kzrBNVbFrZ8ZYIM3bGEUBc57dVMgn5U5q0\nxD7OxlpMgpmmV9YR50d3wKO7d4D5ZP94fVHixC2fTxUEGqIHeo7zsVerZ8fo/JJY5FTr57WEoQEh\n+P1yeUgdXX+esqO85ImJ7dimDAQ+bwp+DTiFyeYco0osbPUcgy+TcOUmaVDROdJ9uKCJjU+RMeTW\nvh0YIawF3h5Rd79oIBBNZiwgXSnaudYZQTy+qzra4NyJknve1rqDETjEixr2izgtYNAQwvdKrU2Z\nbWoF/7B9aMCRiubp7/Fctptt7Df7ZJTWhKFB224U2vmldeyJyA409uV3dVyOLfjlwKUEC03wyUAM\n3j2YIhGRmEAOLEz6VALsWlCFHELNCuK6OjbgvI6wq9FPWW0TaKVgo7INxfzsbBY7JqA+i3aAgkMR\n0dNSRRMoldzSolqWgxlgDLaD2UNDDG3c6fNyDnED5sZ+8eSA6hrjkW2s2rk1jbNq/DJwg2Phmtlm\nHudjq32vQL7EZHMg13jpSGJBtuUCArngiN87ry/d9vM1sSYFl8dopy8+R9ME2r+22105GATw10Cg\n/ebSpp1Zinebu1QOBoce3+s+DMx/9HLSiXj/7v2IuIzrZVkSOH+3ufMt4rVM45xB16w2M4E0y4fM\ntpbCVcFn5dMkwFkCXLY1HUNIgHY+V1f6JOW41kk3x2KfwmWv3yobbcokGkBsMqLM0rYg9Gh014zD\n3Z7JVjSBZDzwc5/mKYvDExjrAJIqsYW1la9Lywi6LlVGE0hZqLYU+Jom0FCPLf4t20YGjjiZ4Owy\nAkpOyuAcVqvSJFiqZKquoakcDOwoDaiq7fLcdR42lhn5TYn1Z8ph1RfAdiSwGgg0X0r12f42TSBJ\nHHJ3sIhLohHXAL8XTCAGmfD7iFxCi3tUOz7EUMoqNIaQMIHut/cdo35apqZftx238Xx87hKmzG5a\nE4YGCPTDr3+4YwJtx21X5oN7Un0WFoGv2Bdd7DQLGGqSejzeE1CpCX9NhMl27KfnaPGkAcdL20vM\n9dUSNQFi1f/Sczv/JCXOyL+u4nW1MWxjk69OgLZWFAE0tOxbw8LcjLWsgWMPMTju1vW1xGha15ds\ne3UfxgScr65xCn6vWMktn08XBDoHvRFSjoAFSlA0OMpd4FI47+y0JHCIJib+rR6sZrqwHdusaKjL\ngtFipJOtcoz4t/M8++1cJ+qEF4fMgNCgjc8LB6QxIGhQcVbyWjkY1wB3A1rKPTiQjSBNIGeQxTGB\nqJxOQM5wcDb6WjmYo/g/Hh7j9e510uthJy4i61K8Obxpx7fdwc4LW2off/R6FQwCWYfCaQIVwtAA\n33BuHCO1iKfnC90jDdSb87Ppy8E0cHEgIs81Lm1ghpuOKz5HN9cN9b5i/ZVBk2YFiWK6VkpZdVvg\n3wJocmCHC9Q7dh3ZH76PJFo/ZeBcz43t6fxz1gTiYAO/dfRalCW5zEZEdN/xAu2yR9V12eyYs9d8\nrKEvD7TBpAD6HPymRZXO3crBiOmgZb0R2dGpsna8JrAzw9uxzTkHzEB1TmQF9iQ7TvMTQQWD9sf5\nmDp96XtqHW22ov9wvDj8aptg59T54uDlfnPfjs2A+m7cpTbApSbQ2f6xTtuHzx9GRAaBljiJA/Na\nxedOLeJNdzC3VuBf2F8O5nDeignkgk9+V84O8HiPuICbaucqlg6uoyp16RJIDA6NvV12jipno6sM\neRU8dGUS53m79qwqWnwCe03ZvTrpa5pAmnRCYKg2K7FsDUgB3Us9d2LfylqJMk8dc650pQWsJhBh\nG18FKF15ww221K49NE6ZoangiT5HXl+rRAqOrywk3AczPdOxlKE+e78U/+43+7TmakKI74MTcXxu\nzPVWAo2kKflgbB+SH0uBKZeD8foNpiU+8BnZv42ILAxN5WCsrxNxSZq+t3+v09hJHb1ER62BQ9p9\nd9ync7S14nzN0GRjmxxxiQn4Oeg1MxPoR17/SN+Q4Nz5kq/zGrsugT1ia7o5VSQnO2BV/JNSZN/E\neskPGfrGFS6edLaXtX8YoEklagq4zkUi4ZwwBYA5L3MClBKwuuTKGfyrybZhGDriAvv8/D4Qs0bk\naon2bjc9EwgxqCZkOgBefN/tsO3Wb05aVuAQH+OWcjAXK7g4pQMeaYyq38/26dpne/Mvv08ft0A5\nCmTExZlJE3NTO0zM+HGOBjsODJKw854cEMkGH5djjMtlUroJgpfBi1FydArD0OmULF6nhCdBGlQ0\n2ZwOCw82rUXFtfG5m6bL6GmI7DQw4LHEkn4LMEInMww/MrzKKnIZl5fppQkt83bOLG+HbQJVIk6L\nDTss2oUL512WpYmQMhOIdYpwvIgLEwiLFmecn6an2I+nbE9EXw72w69+uG1vGZSj18SIoOBhs001\n34fpEPu79fpyDmrY0XCBehLHW6bOsY7wYGw6lo4fATzUuWyL2nhhl7AzbAFfWvD4nbMQZZcVpIxA\nBC0sRcZFg6DuPswi2QV5JiBnUEPBHgfC4F8+FmcM+Z0zOIPnAT0ZXqT4PMwe6phAEgS5zI4COvje\n2T/H0rHZ/hUGDY6VwD/a3hwdtvEuQybXy06TPkfo5rRz0xzRJEMCRJeilSzNHQ6kK8AsPcMxsy+0\n4x/P6VRKCU0gKgfj8bDGBAJw1Eprz9srJtBhPsRxOV46b1BHreNyTEyg7xy+ExER33r+Vvypd/5U\nOx46wDweH8vuYCgHYyYQQKAfuvuhiIgE3iS9CmIhsZ3TjpEtmBN2XQX2cADv7PhaUBrRj5+ICyMP\nzx3ss924i5iz0wfb6Bx7ZmNbsHvsg+vKD3HnKLPR4vSuJcKUCb5aDlaAAXZtEwYqf+danmMf+DrL\nssQwDN2941gMbrpShcYqOq956XqH3i63YMuA8Fq2jbFhj7VIUsaAQBUTSG2N+owcFLssdXrv4s9p\noKlBYPWukPG2QOKwsWt+lVzqgDwurVgL7ulZMRMogUBT7trI/jL7YJy4U2HoBM6LmLQtB5sOXXkV\nC0Oz3o4yG2Hj37t7LzFrwLZUu1h1DeP1hcvBmu3dXgSgn6anlEjA55/8j/8kfv2bvx76YVAJ68bn\n7j8XX/vO19pvWrMUmQf4VyUo1P5FZLvM62S1frukHo93+CJ8DrWlLQY0do6FoRW8SSDB1lRwmAQS\n1g/8dhiGpCeo8yORJihexvHZj2Xfln0mfF/5RuxDdyCQAEe4H8fUY98T1+GAG97Ox7jGBMI5nC1T\nVlEHtLvt6hPPUpFB1+XANOf38zVf+3xm5WC62EVE950iYi5w4cWgczRMtfvVBAAAIABJREFUCQG+\nt6CKUO44AMM2R5G2mWWTBesmLYMqQqtGPSgmGlN+nZNVAU26ePFz6rI6FdNKsiHNwFFmIyJKw+uy\nVmV3MMrI6gSEEB4bkrZ9exEBbQHKeTHS9p1tkeSFbTrEcT7GtEwJBHKLqnYHQ3Z8P+5jieWkr3G8\n6PtEnIwT7pkX74oJ1Gn/UDbaUY6VRXeYLzohvBCngJEcwpbpYs2Reero59iOc3EJDBs4BmgqJllE\nzkg4x17nYVqkGIgwtdddOdjis9fJuBZlBDZ7PfSBQJk5mrOT7DLkCYCiRQ3PMbGgNj6z65hAmtnA\nMfCvsoqWZUm2tFq88K8bDw6gds9lmqcYRyrh4oxPUQbIz17LDlK2n8ZExVhjh4btH9tYHItL9Bi0\nX8t2oXSknZuB0jGve9X6clyObb8ExtLcTWCPAQV5PazAHgZ0HAiENvC8/Xl6bjaDbZMCTQiiAWAz\nswfX8+Hzh/HB3QftGpDtXmLphKG524y2iP/w6QQC4VgMgnOW2moCbUwr+Dl3B2tzZzo0XyOtL9x4\noGgR71gkDrDrnEiznSn++K4qr3LBdQJ8h57x6BxVZISrgAb34cRMOWhiXcQSaBp7G+v8P34mrFfB\nLI4KAFvtDmbu3QHX3JHOrq1i/5Rdovpuju3J++hcxHxvOl9m7SnH0CAlhRoEki1n5n6VvXe+txNU\nTkGxrCMq3YBzYHuXuR8LRpPx1Uu20ZLfewUwOu0fBWmPy6mjrD7D5/lSks/rd1UOBr93HMakV9aV\ng53tr8oXPOwe4s2hLwdTxiNAoHf373bMmvtNBnR4v9YdTASmH7YPSXeI152IaPb3cXpM2yMi/vT7\nfzr+0n/9l0I/CVQ62+739hm0avZa5kEpQSHVBxHZLvO8TXFrETdGGECdgBsrv+EYcUXQr82J2naq\nEuFjafyJY/O1Yp9bmEBsS/BdxSpS30jjdQZ0HMDmmPbYxs0YeP3uwJ6lFoZm9lcXQwx9Z3K8E9wH\nd03k2KkEp1S+hUCoDqCW9UW1H63/JyDmLZ/PrBzMOarqeKLWrQuoONgY+mCjLGGostcF64UHKLZV\nEwrHcmUHzHRwAU3SmJBygWt0XFdyxkwndfD0ep3uBk80DWjw/Hh7KgcztHF2PK8ZZAZc2GDgPHeb\nuxb0YwImJtBomEDnQIRpt7heduw5COISKi0HgxMPYWjWBMK9MaATkVkWvHhzmZiCQCmrRIGIloNp\nVhDXjW2cPYF+Et4jGxhcZwIQFpN5pNI9Z5TUiONdVQGuBRY4WB68c+lKuJTBElGLQbouURWAarPa\nKwGKY7Y4J5ltUwJblt5mKbCK721GgN6hG+/H+SQU3tgEQRR0Ah3gADnNGuzj3rsFjoY+s4SF2LHM\nGEisAKU1RkFZ7kf78OLpRJ4TfdoB6stFK8idQx0zXnfUYeNjacCagEcZW2lOS4bIlYmxk55YMlLa\nxe8ONlZLs9HtJaK3cTgHO1msbxERrUPXsizx4dOH8cH9B2n7J4dPIuKSLBiG4cQSonKwu/EulYlp\nOVgqSaAOjJUmEHSlWit4EmDm32o5rK5tTRhatd0KkLiaUxGXNbcCqCOuU/mrwEXnZzenDECDOdVp\nRsyeCcT34RIDbt4qEzz5bGRLFSDpysHIEe98oPO/q93BDF0/gc2UdEIgW5WpWXDevPfDfEi+mb53\nt+bjuTALVJ/X2rESE2ip2TgJWOYMvVlDU+JwNF3sikTKLfa6W/OHrBPXnqMJqBRo6kC2YdutuW27\nMoHQoXW86HFwUKr3p4wfsMedMDT8Uk4aRkR85/CdWGJJ7Bkci5mIEeeSW9GDxLVFZAbPbtzFw+Yh\nAdeNCTRm0KhpAu1y1zC28Wx7VSdzjQlUffR4u3HXWKP4tITp4MvBOk2gJb/ziH6cuLjGJSfVD9EY\nsGRPGhurpbXlOdgnHnq/JYFGBTiEf6/dt/ONjvOxBPPVN1Lw1iZ4l54YUoHQKiPSQFoGe+ap868j\nzvjC2Ld1Vx+T94H9c+t3pc3qjuXW765UVQBJjYV4fatkIG75fOZMIM54dANuLlq4FcFGCzI1oJLF\nVgO9NNEcVY1ektUEMsCRo5K6SYvr5UmLc1WOka1FldprB4DxYrQsSwqOFKFtZW1D3+a1lcdQEBIh\nGWRmpBjnq+sOJrXw2IcHMxx4zcw9TU+tFpSd7tTtZjSZWq4bng4pcHHC0JxFwQL2jcdvxOcfPt/O\njXvoQKAxl4Mxc6kFTmfNI93Oz3G/2WcRPDCBjPFp5WDnf0GfbcLQZ0MyL3NZ9sWtk8dhTNlKnYc8\nD7D9WhDtgAUNdtJcp3HtgBC2AZ0RLYxrNW9TJtwADlVG32VQlflRlWvqGG22cciAHTvDzEzA73ks\npnIEqpPncpa0z5kVh/NyEMaLGp6zA27YBumiit8uy9Jn+6+9EwHhbbA8ip2bPUMTSQbeDrBSs10K\n0Djxw5TtkjGn2xOYT9erNtaduxtzMh5s0E+Zq4rxc40JBJFn3u6A62VZbMnZcT4mAArnej4+N80K\nZgItsTSRZ854M3gD+8dMoG8/fzsiIn7o/lQOpracxac7TSAqjeTuYA0cGrfWd0naHiuaQA0kNoHv\ntUCdA18HLGiiyAE3LeFVNI/g7RoQR/Rgz2r2mq436ViMpqvM2IMBWEfUN4Lzjv1KUEPvw4EB5320\nOxhswziMluLvgjlmJF/T/jnMh/Tc8a8GFbo9BRWSwYbP6Pxl9vOqAAXPBDaT7bUDyBWcT4kf9cnH\nbbKxuC4HNKUxJMEOJ2bd+OXf8z7VOu3OMcRgGfXbkdhJlJxEIo4Bcu4oq11HWQAfNvO4HGOJxbeI\np3GF751drJhAuK7H46PtDoZuuQCb2PbhehXQwTOMuPjw2hSF/ejn6TkeDyfbz6W9z9PzRfNo8xYg\n0BnUQslZSoyiM6MygQgcs/Nz05fgtHlg5ppLTjKTrCyH1USY2tIrnaAVnHfzpvJPmGzANhnH4yYu\nJQhM943vXJzJJWfON2JARxOjDIzrs2rfEROIk9Xdmrtc9Hp4O2yWs5e4Jj4HvmNMgn0BxPHOL1Wm\nYALyCiakruvs3+v6plgFg9TXPp96i3gL6JwHApBbLdPo9DVWaFE4RwWQ4PtrwVnK1NJLstmxeWoB\nTQKaBOzp1Nzn2gGal7m19OPtXVabnDJ05tFAgJ8JjqWTswq8K2YE3x+DKm6xZYSUdSwiLqVV7R1S\nFpXfIQKLu+1dl5l7PDw2VXgtr4o4lz1QsOo0gZgJxJlw6OjgmvC5397Hh08fxkcvH8WPvP6R9D1n\nnPFBORiCMCx4DFpB8ygiUr0rnkvVctgJQ3NWCdf1nZfvpP87I56CCspouUBWGXlKb8X2iB7scdkC\nBo54zDEgmTIxBiRomUeZU9M8xRJLDqIlaOruY/HjXUFdPhbmDq6Z56dlNJGTDL2IiMyu0+deMRCO\ni88IpC5GVK7pGG74ju0cH+ta4LIWyOqCV4kJtuflbCmDfKPJtA3ZLnOGqh2rOAfO75wpZuOwUzgt\nU2LRdedYMqDEwVHnTNF8A2NCnzvWHdXNauOEnFu2QXx/FUCjoHnEZQ6sgUBsy/AdU/yTjSWgHed6\nni/sHS3h+sPHP4yIi+4EjsnipNoi/qOXj2IzbC4dxUYqIaBnwmCSzjfu9IgW0LhHtte8vUsyFPa6\nzCSOtU+zGTbJPiRfRwDnrlzJONfs8CsQbVmV4thDY8zZUsyZFFxLkqorORt6uwwb70CuZEsL/8Ql\nAV3ZKd5VEt+f+ySg2nhdJ1kDK9lrU1KNwB/3ju81qMD2NUCpC1h1+3JsvnUJMNLzUqYB/1b94io4\nrAJW9xwdwJgYPybYUT8E2zj5gHPZNX/OoJXzN9bWXGUbKYCgPqMmeLgByHE+Jr8X23EsZphhPwDd\nDJwgYNUEEo75yeETKwzdutYeT8nU3bizmkC4pk4A+mznGhPoQOVg5BPbUuDjc2pIcO2zHbcxxHAC\nj87PRa9Xm6V0JUMSkCvLK6L2567FSKmUaNz284DssoI9VqbAMeIEUFpjMHNiibttcUMWlLY7cWYH\n8nN5O76zNmM5prkZYWxGEWcqCKRdbvGdxo2wf9iO5wUfM9kGZZpqqdboy8GcPbkG2gOcWl2TxM6x\n7QVpg58Xn0OTv3z/1z7fMwg0DMNmGIZfHYbh/7762zih6EMMNpumi1ejt5pFzVHCNKvkAq2Inm7N\nDr8d7FwORiVcPEE0G8yDOgnfjn3pCgtDJyTW1YlKGYELZG3AKqAVO3j43mUM2SlMi+14aWtsy03O\nJUBwlNWZaIbaTAC8I9DGASAssaSgAvs8To/J+cI5tNtNlxHQcjAS4EvZY2FMRJwW3698+ysREQ0E\nasDRXINAh/kk1MsCklxC0QKqTV9G0FBuk/XQ7AI7LbiOxgQixySiB3tc0F+VPXDgazWlRrMgUGaZ\nFyPnZCVgQcoI1gBXDR4qhkU7lsmQdxRwYaO4crDGfBvz+yidBgFbmNXD707Lvqr3wU4yzssZMGa+\nKXiRQCATnHGZGAMbOL4CHmqDNKuegkwBdnmx56yHBpPONjHwiP1cGQGfQ9k4bEtxLAeou2BOHTbY\n9kRfJjaDHac8D6U8D9flstp87ufj80WjhO4vtYinjFYChwgsjIgmmN8AKLKxrRyM7N/bMIFeppeu\nhEtBoPfu3gt87rYX+j/WFy4Hg1g/RPlTOSwFYHfbu9M4nKfuupTZw2ynViY257IOXtsiLvMNx2GG\nhbMbbOfUbmjZigIefKzKgb85uXMlSaVALP+WRdVd4NIypcqEHPtuZpiH6jsgG4trVnYUkgx6f84u\na7lSBTjwb13QpiUtfI+3loMpQMN22SU6u+s6gycOHOLnhZIltWduPFg2mQlENAGg750z29rtxo1T\nm+hchG0kbHcFMfm6kn0/J4QSa9Xdh7CNOBnF50ZJdbXmtne7XEAgZuk49k6ymdNF7gC2FvZShaGP\niz93xMn/u6YJ1Jg1Ug7GJbSqCQRtzQYmERMI7/dlemk2lvXdnqant2ICcTkwmPP7zT6tz5wwjYjO\np1LdGLeup+Sd8U8YRHQizIi31oDVBHYbZuE0E6A+GkBdQfs5A/A4lk2QGSIAx79VGSWqc/B7XH9a\nK5g0QYBVRE6yJj9rBYjld6i2TEv9MA8rxk8Ch5bcMZffLa6VbS9+w7as8r35erGNz1/5vuwjuES5\nA8cTAC/r2y2f7wcT6H+KiN+85YfDMLTWcsnoFvV/SovihdBlBBSF7Sa5yaysZZy7QHauy8F48kcU\n9fCC3CatoCKjlmo7kYEjEMiWiQ0Z0MG9u4CGjcatVNmkhzTntrsaHIHi2jm9Z0Pdul8Y5LQFR/OU\nShXUKWRNoJIJxE46UXW5PCbpWJwnIZfGcBbl/bv347e+9VsREfEjr84gEAXYCgLdbe/SQtiul4IE\n5xzg8zLXwtC7zc4+R+0OBhCIgYWIyOVglNGaltwdLKIfW05nS4MNvEN10nEOdYZxDi4XaucwWZJU\nLkSALy9SLmhig+yCCox3LGxVJoa3aQDG76Na8PAu2BlVMXJlB1RZB2X1HOdjAjE566HOItOnOTi7\nFRTUxZufLwey2KZ2Edu79zvk8gK2QU7Mr+q2hX+rTBSOw0C7y6gxqO3KGdOatEx2jjBgxmPFPpM5\n063bOc7X8zJlQVEkEri8lBkQj8fHGGLIJQxzZgJtxixUy2xLZRsxcB1xsX8R0bGHNBDYb/bxdHyK\nj18+joiTyCeuISLim4/fjIiTWCk+rAmELDUARnRshE4bzoHn9HR86ro8Pk/PnV1mrba1ls4JuD47\n1hxspIQTUdad3XibObUWuMAuO+bFWmYZdqtjEUuGPAE35hyOCTQvcy47EB9Ik2q4Lp7PnKCrADP8\nfjtuU0DD98f3zMFLx2Y1gDq/k5bIWOYukHXvSkuG1mwpl3hWx6rAHrednxee4WE+NB+TE25s5xig\n5neC60pjjoJiHfPJLot/3wXFS132ley4JBmQAEi6Q5JMneYpzcOIFXbC6HX4OPDmhCIHrJYdet6H\nfUO8W0008jt8Ol7sOMZXBQJxIxMcC/ZOy8EYtI+4lDPuxwsLMiJ3reXv2H/g7WD8tH3OrMo3h9N2\nlIOB0ckJg1s+99v7eDw+tiSHglldeV4BoFpwSJLSHJCncS32gZP3ERebeQsjDrqBbjvHdDg320zV\nH+REFI6VmEA3Ehe6uen8meJYiTSh5xgvGqFr8xnPVoE8tv28FldgSztepQk0bLpzqO+Lfbjap2OG\nLdfXabedk9WcKK8qMto5hsw0bfGAlDPe8vmeQKBhGP6riPjvIuJ/eZv9NHApMy5z1gTiF26zJ4s8\n8KXfHtGzhxx44pz0akIx4slOuqUCmgycikwz3TrVdpLDotd7XLLzBZZOcvBWysHS85LgKJXIjf0z\n4RbxHGxY4zpT4MICxUKd1taatlThnHE5zsdSYwf7sDOuNFZcjwrXIRBw5WAf3H3QzoN276kTgnQH\ne7V9FY/Hx04YNTGBpEwMyDDKWnbjLmVplmXpOkokJ1LowAi02JnB821GZqBs6UzbDejAAUq3gGhw\nveRuBEkrSJgUcMq67bpQMBvQBUe0SLlAoGLXuXmQStGW/hyaQe4ySmpnDFNPA3t+7vx8ExNIgiBX\n2sVzCuL8h+nQvVum3iqgjntxgIeCPbguCC5WQH+yiyYDx46DBYdG332IgzMOQvQ5ajYPx1FGE37L\nrIE1YGxtHbl67pmAJsM2YnDKgYI8fhmg4bkOZx8JGZxDu7fwu0V3MGxnm6XaE4f5cLFz0lFM7d/9\n5j6WWOJbT9+KiIvIKI4JEAjgEPZhQWcWQH2enuPN8U3r2MjHQndEDk6wna8X32EOJg0P6UjCXX5w\nj0lzjpibPHe0pK/yaTQhU7F3IvpsbWfnyAaxf6LH6hJIYs8cCM6gDo/RFNgbprIF1GUdUSCC7Uli\n9sl6VDnifCwN7ivNj/R8F5EpICYZM+/4HBE9cKM2Xhk/zl+tGLtcLuACJ7VnsCmaQS7B+bl/j+oz\nVnYOx9exomUPlW2sAJoq8cNZcOyTAq35il9h5ltKvBDQxAnF9G4licNzOoFA53Wy0vEBCMRC+hGX\ncjBoYWIfXe/5WBGREpMKnrAmkHYHU0CH99MuiBCYxvOAyH/HAt3exdPx7ZhAERcQqDGBpNV90wTS\n4F4AVJsoN4xkrTrR0hwtzY4gprJhQmI/ZWg6XVgWbe5KaAX0nJYL65iZ9hXZgGNZPYdeU5Wc02NV\nSUMHQoO5iO/Vh8a7wrEAUjsyBwAdPN8EAlWaQIIvcKIR95eAJvLBdJ8E2ivj+3xdm6HvaJhYtuRf\nq6A8233s186x9N3BmA157fO9MoH+TkT8VETM134YcSoHi+jLsdiBjegX2845UIdJaKGtfEMDLVoI\nGeGvGAUdXX+WspIiOxaRnXSeIGwYEquoQFUdotucrDFrAvF9tOtyzqIrBysWwnHMJXKaqWAQSJlA\nnClQmhrQen0fnMXg6+XSLj6Hakywls7z8bk5sGwYXqaXNpF5AdGMxMP20lozIhKVFqKjEZE62kRc\nMuG8qD1sH04Ll5wDZWK4Ll64lzghz53QqAQVriQhMYFUGFo1gQSwa8yEwmFqxmfKrcp5/HAAHyHO\npZQXdAbuPBZ1AUkZO1mEdUFXu7GWOWfwQoMKXrx0Ed4MfQlDK9sbcsli6cDSPExjdMWJVC0Sx+BT\noBS/xXeH+UZNoALwqALWBNzweBDH4bhkCrGry05ANC3Q7JS17LUrXZn7NqhsM3UdibjYX3aMcD1V\nMKnPRFkWuHd+VxqQ47cJFDRlyC5AYSYQ3x+XavEzeZ6eE3MSz0K1GRQE4g6MLaA59ppADAKhFFe3\nN8YNyr6eTmVf7+xPINAqE0jKwZrQ8zmoUCYQa18woMNMIHRsRAkZi6OmIIgYQirUiOfI7ACUaKj4\nfnuHYjdwrFsYQtjWUfYX31lUQW13LJ0fuE7nh9gAfsngrS1tMMkrZj0r66TZDJOF10C9Ag/w3N2x\n7Pso3i2fZ60cjG1TBdxwYsu9X+cfKcsMv2W/tMtSGzuH5Jkma3j8lgBNoW3H59DSFcf4aWCljF+9\nLh5DKdDS5O85iMfvufmJ03Zr/v2QAQE3Dx1YuCxL8nHXAD4ep2yD8M4rVi6Sk21cnQP/kglkkpbM\nYE9/KwhEmkDKNmetNIxbTqbux0vDEl4rIiLuxrt4mS+aQFwOxq3eb2UCPWwfTl3Fzna5YwKtNEvB\nc6pA4iqph+81eRWRfR1NRnV+3tB3SoYvq+eel+vdwRiAcgzmOXLVh0s4VXOtKlFT3/44ky8n/qIm\nGlM8sGQpFmuTz3P3MB+Sj4f36Jh6CgLhmrW0Cz5mBwIVPrkyZvHu+B3iXjlGc1gFjwf2JdMYNbET\nl6S2dXoyrNVPoxxsGIb/PiK+vizLv77yux8fhuFXhmH4lel4eYiJ1WOyxEy96jIeski033MgUGQK\n8C9PWpvBqJhAQHRFtFmBJkWBWbS5msx6jgocspOWgopKzNQFxS7bXjlZHNyzIePFwJUSga7Pwou8\nsCgKjG3OYYLIM86hINBukxk/LqudwBYqB9NM+Dv7d+Ljw8ed8x4R8bn7z52OO2xb9pqDIF7occyn\n6WlVe0IXexyrCiquAQUAaJom0IuUg3EgKyy6iMx+SKr0lBUE2JGyqMa5TDTsYefHomQYOxBInE4G\ndTWzrGBPWpwNqMzz1mlopMWWsx4FfVhBTwZCtOwA29ix7YQlNSt5Q+kItjnGT6Ksr7CHUtvLxQBK\nQtd34BADOgnwMBk4BySqveY6eWyr3m9lM102D+fl55gYa8aWIpjic5TrC5dMLj0wlubh4LW5eI5w\nRp3fB7Zr2QHOoawenGMtKO1AIGIbMWiE6wH1//XutWUCNXHQ83U0sOcsAA2b9s2nb8Y4jB2o83x8\nbvOau32hrTxK1yIu4x7C0E1k9XzdT8cnG7iga05VpqvzENs4AEwllm6cuLkjmUT7e6aNEyOF/SZ1\n4NWeVYF6B3aPWV+tAoewjyvj1FIFBXrwe75eZJ27DPksmkDL1AJyBSI4UFd23bXni+tVUEXBCy4H\n4zmC3+K5D8OQ2lbjulJ3veK9K+ChzJMEHmtQM/f2F/s4u1yxx3H+qqxZfXIcD79VHxeNGthfxfeV\nJmXll+J6XVkb1lYc2yWX2niQMry25nIHRhqndp2c15lAL9NLp/1YATfQV2PGz9327iIMzSDQmY3T\ndf2k5GXSBKJzRGRNIBZahh5cxGmssjA+rpkZQsxcwjmTJtC5HKwlRqe3LAfb3MfT8akrB+PEADSM\nOCnMfhC/D/cOq0SRteMmQQj7q+AQSi8jxJ5RQrEJ9s85maqsZ4A6yV4iPossIxIhLOn5Sov4+dgB\nEfhXfXtlXLONZaYcnhW+0+SgsyXY9zAfOjZ0RyYhP5ZBEKwjDhxiP6tj6QyFrIwBjtaYQFVyB9sS\nAD9loBLXqYny7bBt1T7q/2GfWz/fCxPov4mI/2EYht+OiH8QEf/tMAz/m/5oWZb/eVmWP7csy5/b\n7QodC6NsXWWip+W0eKjz5eoSdeFMQWaVVSqcd/z2/yfvXWO2S6/ysLX3c3gP33k84xl7PJ4ZH7Cx\nydhgDDUhDvEB21BjKCXYpSFROZiAK6WKcmj4kx9pKiVREiQTkSZyqQqVIoFIrFKJoJIqEFIEUiur\nFbEbCPF55jt/7/s+5713fzzPde9rXfe69/t84/F842T/+d5vP8+zj/e97rWuda1rFdk7EdAUAEoq\ndIfj87GckPQ5QFOkkcJGJqrJdkGTBKa8OOsz0UmLY7lysMgZDmqvmanCTgMmE+uHDJWDhaAKCdQp\n7dZsu6ghG8HlYErpvji5aKer02xBN+uZQEeTPnus3cEiJtDZ+szMzJ1/1aySsxiVVkQaRqVsU2R8\n8DnKwfi541hsfByAIEDBPnMnBIEKbJy2zedOkQlUWChKWZLo3M640m84Sx2VVymzxSzObiYQSDLO\nTddnMbUMBPfIi0TULYRBcM6ssJOjTn00D7F4ZkygUR6gaPZcM5YO7BH2JI8TBnSwjwH1kFFQD9u5\niPHjMstt2WZm2Tyx5bzY4ryRExAt0FkyQcDNrB6+xJbjTNvueFG5C/TV9Fm5cjB6h8wQwv2t2213\nxGk9TXOBnSzVXcN+trGsCcRZ3yF7rQLQF6YXsv0XJxfTNZn1gqLJLhOzByAQl4Md1LvOYe0yaxGP\n58SluGY7thHpDvE9Ym5qq2c8RycMTfuTDRrl79CVHRSA3VGV08kdsNH5bixDor4K3pr1fkiUreRA\n3YHg5H/hN+zrlICbSN8nE4YWJ90FIkGplj4r3o/zRgFYqYSMQQLsY/+IAxotJ+R3EoGFDLqyjedS\nQ8c8kYDVBSKiL6QBirJb8Fm0XjAQ7dbQKPlSlbXPFKxUUDICMRnUZkY9+4wR+4LHb4m5WWLH4z5d\ngiWYn9E71Mx96DeJb4busK6bY5BcYV8S4Ay2g1EPAmUt4gcAJTNfVovS8KwcrPYt4k9Xp4mJORlN\nXGdGXA8LQ8838+Tf4vNls7TZepYYGWa9RMJis7DKKnedQxuDR1wOBrtbqjLgZ+wAhICtVirTdSU7\ngT/HSSclApj5McoMHp4fzASKNFuVqQKwm/1YtqVNtyUhVFU1KAztEqDdJrP7eB7qMyEpjfuLdCe1\nXAk+TQm0ZxAIdjGKsUu+L4M9m3aTGJJu/w7QzkB+Wd+4rJafO/+G1xHFN6LrcrF/UD0zqSeuZFzH\nnNqzKBm17/a8QaCu6/7brute1XXdU2b2YTP7ja7r/st9fqsInjKBXDkCvaTIgLuFUNgMyBBh8LjF\nPmItSGZjqPYRrB58V4MKBXsYbDmvrATXVhTyqnMnK6ICKpvCAWZt7+DxM+HFGfvZMETgGzvjPDgV\npGCnocQEUgOOeyu1Lx7S2OHuODwBZ5tZykawk661yZenl+10fZq0Zo7XAAAgAElEQVRaXvJCjFbG\n1qVdPaC06w7GTKCjyXbhAhCDRfVgdOAcwkhkFdetGkZRRwk2Plgcz+0OJs4MG/doPERAEzuqDHiU\nnC/nrBFDCL9xLI7AuXRgQB1nBSMwQPfjX2cDOPMopUfYj2ehx8IzCTNKgTCqBnNwFCNhaH6GCYQZ\nyDrg+PuWg7H9jZzbpg3mdO3Bdn5eUfYotNcS6EW139FzjAIX57gUHP4SqxK/0SAE+9XBS/ch5ziP\n6YBMuIL2HGArwIj3ocxNBFTJIaVynvlm3peD0Tx02mMEKquOGY+t+YaEoemdl8rBAHYzEygC2mGb\nbi5u2tH4KH0X+6/PrrtSMLMe7OHkg1lfDjbfzIvlYAxaMQikTCCcA5m2kjB0xKhiu5zmFGlwRfZM\ny48Y7ImAaD6WA2gCWn40d6JyKdjlklaQWe47MIOOWUi4hgho11J57GcwgEt5FFCPABrMkWh/CTDD\ncyz5fwoEM3gMJx3Xm5VSsjZioUw3gUBky1lH7bzSp7AcrO71plzZombiO8945GccvffSustBGPvL\nKTBlVnuhfAS+L48JxwLt8nVatfDS9da5v+zuo839Cpd8KYFvbc4EcmvrqC8B33QxE4gTBpkwtKyr\nyWbt/FL2PQ9Hh8mXY5ul67rqlZmZPXrhUeONu32hOy10J7tuyzQ4XZ8mtjtKu8y8HhKXgykTCB0g\nkXxF0vRocpRAe2jU7bMdjY+2TKBdkoNlGPDMNOHFn8OnYXuC/REjWcdclkCiMcdJp6gTNNtM9ic5\nSV+q7lBAScu72q7N/BnY64ggEMWTpWSmsjPdfbd+TYgSZNr1Dj5QCOaTH4n3wpgAjuHerbBhGAQp\nMYEAaClwwrZJz4H9mpgoMjfbTTgWeZwMshR34F+UaORnHK17+24vRHewvbdUby+UatwAT2YeVLhh\nl00L6E/8YIcc6NLgUbQ10myIFhYtFcCxSgBNVlYi18uDEM/M6V7wAsnXqwNBymBcyYOwo+BgpvsI\njA+3weOFkJ2ZyABEiDm6WuFcpaw27s1pAtG5NbPME9AxgRgEWs9cdxqcV2uTL04v2snqxO4s71hd\n1S4YefrK02ZmdrI+Sfu0Ow4vhLi+G4tt2QOCFNRYDwkDOiZQIeOs98jPN3UHWw1oAhWCfs484t/S\nAhlm7OgcmnE2K3RXqnzGxS3CUSBQeeAG+4ospMixpeuNgGCtvS4dC9eli4TLhFfjbGEZ12PnoOOd\n6zOJgJvSgoPjR2NLS774N+rcDmV2SufX/brYa7aU96tzUmLWaAlX9E6yYwmYxgF5Ola3yfa3bT9O\nJ1XfjU/L2nANUQkDB4ARUKqAUkp+tAVNoDYo16z6jFrEBGraxpabpdNQM9uKCyp7Ee9w2Sxt3a5T\nFjljAkmLeJSDjeuxTUfTZAdRDsv3ht/emN9IgYaZB4c4e43fAOwx86VlCCoA8vOzgZ0tdQdjez0d\nTbdso8Lcwe+ils4chO3DBNLSRAV7nO0NgBunHSVgAPbjN46N0/ryAjjwGdjNQAyBt+d1m2FASTvd\nnJe84ix1GBwJQMNlV3wN6kBrWboCIREYwO/WOe9k4zWB5LK7zXDZYOSj8HvXQHZcj52oOwf+zNIp\nlZaFz4tLaylpGiZMWBMoAM0i+6v+Mr+TLNAksMdpX8j1albdAeea+Cn4AnofpfmmDCEci0XgnQ0I\nmLxN55OZeMaRL4fxAr0yB6oQUM/2Oh1rp3mJd8clYOhkiw1sHDPfHQz3MVvPrLMu+b4qmI99zBA6\n25w5JhCXg3GZLr5ze3nbCVaftx2OD222mbnyNbMelOJxwoLV6uOqD6YscTToUYbmUEDuWCRBwr+u\n61Tl0LR9wovP7XTU6p4JxLbUzAMu8KMjLUMV6zczr8lIYBbWBMwD2EvHWioANzh+lBDJGJq7+amA\nEuYnjoHf8nPnON4lSwKB/XTNddAivi0IQwvjR8Eh56sHOILiCxFw7Y41EDPDR8hwD1p7onIwvs/z\nthcEBOq67v/ouu4/Pe97EIbOAgcJQlbtyjkg0SKsi9e6XccPtst1IUqBy3mLGk8czdRGwUaU2ajr\nOnO+2Jlyx6qGNYEUuWUHAMdCAMblVQwoaRlM9HwjZgQbGZf5pEEYshnABCp1tTqHOo1272bbSaPO\nlzrpIQhETKC66lsLR+VgJ6sTu7u8a1cPrrqShGceecZ00+5gqglkZnZj5oVOkYmJ7gPPw2WbuGQn\n6AIRObGpO9gOsFKdEAYdSuLepexYaU5Fc4fH1nlsDQYc3Fjk+VnlzloJoIlo6RGwULwPWiB5rvN9\npFItWtDTIqVBXhTM0biG2C9AYDz3LOgvBA5RUJoWqVGhHAxjTlgL4aJeYC1Ah2pcS+cYer64x9J4\nCMHurmd1RsyaUlARgtoBwKi/iQKayDYWtatoPHJ5FZ4H2+ti4BKAQ1papuWafE1t14Zzfd7MM+2x\nTbtxoDm/28Re3On18Jhj6r8ygQB0DzFuEtgzv5lEoc18J0YFgaCvBt0h1quIWsTz+bXTjVmsCXQ4\nOkxlvXwMDioizSUO7lm8l+21AucZiCgshwgEd+M9Kic8BzwplaQ6lg6zNehYXNqAEoPQB6o841GB\n2Ig5lM5Bvg58F753zcKXmC2leVvM4BZKgfE8onVk08YNKtJ1Fcqxhpi8Q4B65PBnCZOBY/FvomRq\nZEvTetWJHtNQErIwHktJmZCdX3kmejFZs0uY8rE4aHQg/9B4ENZU8qeadTgeouQZ+74azJV017IS\nLujcbLY2y2kCkY1Wxg3W7qgVvFnfyRbb8eQ4sTYXzcKxMVftKjGOOGnJrdjNtn7v4fgwJVFPVide\nyH/n456uTh04hLnyh3f+0F5+/HLbd2MmEDr/8vWcZzMn9SRj3Zl5Hyxa17EelnwH9V3YxjJwY9bH\nbmxLda4xeKPj3czr0mK9V59CG12cB9qz3xLa8S6vkHGAEsfrNAejZAnHgOoDKQgUPXf2PZmgkIE9\nu/gb9r3rurSf9UnZXuKanB3t/H79TXR+jWf1WFqCGPn36t+6Z9+Wtd323V5UJhC2LNASAT4GdPSB\nR1lXfKYIXvpN5EAHL487YWkAaOadgFHtS7ii8ipVWsfnbqJVI1erqU7WeZpAEU3PlZaVwKwCJTYa\nbGx8tGNG27ZbEeKdA+kMgGZD6N61RXw0afh6uX07v1uUaiHDoCBQWA5GTCCzPoN8a3HLLkwupEX2\n0vSSna5P7fbytl05uGK8HYwO7Ke/9aftH77nH6Z9nJFYtatME8jM7Pr8upn5VsgITswsFFm9LyYQ\nZf+YZl5ZlZhAmtFS54Q7W2XMD9GScKUKEYDKTIrA8GlWMB0rCNRdVpBAFS0vwG9KzqhSJvfNbmZA\nk2Rimq7XixiiFfN9OCaQLJDs7Ckwtg8TJx0rYjNELeIFYJzUO22uABQslqNV3unFc2anDO+PywAd\ns2bIyery0rJMtLlQgojzO9p2waGJzp2CCvrNps31H5gGzo6ZW98EeNRzuLLMNi7LbNom08XB2FKx\nTXayFptFDl7smJAcVGBs3VvdM7O+cxf2t12baQXh/rhNO4653PRduLDhOp6dPWuvufKatJ+ZPJcP\nPAh0PDm2+Xoethy+s7hj63adBSG4R24Rj+taNasw277YFJhAA5pADiitRRg6KHvl8ROW9XYiLElg\nM/btw37ANbANUmFSBNFRORg7l+o7YMxlPlAdl4Mp4KpZ6pKv4+y1zs8SLT7wabLgfuTnlD4rBW/x\nXVcOJvONn5cGKDhWpAlUAvRLoBX2aYIQ2lUoF41+E63TPE7dWtl6nzU9x6EkpDAjSgx1LqcxGwZ7\n3FwoMITMcq3BQX+jwAADoK7n5rUiLP0MWLaY66V3rjYA/8L2KtgT/Q324tn6zNlYBoQePn7YeDue\nHG/ZPl2XmDXcmQw+I2w/dwdbN+utL1eP7Gh0lOaAgkAoL7sxv2EPHz3szm1m9pnbn7EnLj1h+25o\nEa/C0MwE4oRXxgQaFcrBRpOiLTXr/dKIrWHWJxtRpuXKxIgpg2PyfvZbonmgPgXOq6AO/s8VJCrK\nj2ttul4rCMdTTSC+bzwXBeBdG/rKg+Mp8aFM8N3cyWKRzoMaeDYRc3IymqR4uQS2mPU2k33P9Hx3\niQyOGx2bi44VkUx4bPH5GZxymm+tBx7VLkb+fVQBxT6ui6UFUN9ne0mUg2nZyrrxi5pzkgPBNXzm\nAj0aiOqcIFjGxAlrr1tfvoFzOHCKGTdaXlVJCUOEtnZ5Vruo5h7UcKpjpIuwZrVZfT46VknfIgn2\nReVg3cZlRJ0BGAhYua2xc3Jk0uDekGU4GB+45566coz6crAEAm3yVpxmOxCIggw4/M/OnrVHjh5J\n+y9NL9nZ+sxuLW7ZtYO+JTy2D7/xw/Ztj39b+j/uU7V3zHqQ6vrsuo3rsdOlgGiemYVZ9ai8gDWB\nIlYGO7dVVdnB6CA5FExFNsuBgqGsWclRZUcqCvp5firtNgr6SwF8NN6jLInLPJZYJwQocZa6BARn\nGYHa25kwaCrMT71eV4a3M+xM404gbSDUHQUImA/LZpktIBgnEcCH45QYeZrhzECoSCuozYWh+Z04\nR7nARsRz2bSbLJjU4IwduSjb5UDwCKyUYJnPzb/BO4mYHJw558AMzzcsg5ExxHNEadVYR7LxUMVl\nB/wMFRzHdZ7LBNo59siigv2Q9NWoHJZLsgCGz5t5JhrKdvXHn/nx9Dezf1QT6Hh8bKt2lZiNXA72\n2ZPPmpnZKy68In2fy8G4KQDuFSxQBaccOE82E3MgYgI5oHTky8HUd8HcjYCmZJua81lpbDcilqKz\nZwF7shR08xwJARrJFIc+EJcRFUBdLWGIstSwy027BdojANVlREX7Bs8Vx+JA4LxEBr/bEtsccyGB\nrsRQKtpSDWoKMgVuXd+jTA33H927PpfInxsqd1bGD5c3sG3SZ6+Jn9AXCMaKshMiYEzHbzp3F8yR\nYE2A74tnqewdfe7sk5/HBFIfd99yMPwbgUCwp0fjI6ejczg6tHW7zvxYZgLx32ZbWzrbzBzTPsVh\n7Tr5smCBHowO0j2zjwJgxiwAgeotOHVjfsNdF+xt0zX3BQKVhKE5iGd/I2kFFTSBmPXM62QGHtcx\na4v95ZCp3HrGj1mfuAz9VWYCBWwcB6AKC0mTWqlMDGVlApLi/7imUsLAxaxyDo1/dS3kOJbnoWMC\nFfwvvK9SMtMsZ4EyvoBzRewd55MHYM+oGmUlZ/gu37viCEN2XJPVjCNEzO4EEg+UIJb8kH23FxcE\nKpSDRUwgnhxMey2VMLgJVY/CyawPNgI8nPMe6EVETpaCQ/g8AnuAAJt5hlAkDK2OnJ6D0WGHGssi\nfB6Yla43QL91sdVnAvQyCzba3LllZ2bVxppAq3YVBn+8eHIJYNQdDMdijQuezNzyEsdcNkt7bvac\no8uCrfOF0y9kTKBoU+0dZgIh8Hhu/pxdmlxKizeEobHYqmC1G/OjiVugozbi/BwdJXg0sTvLO2Zm\ndmW6vRdGszlDNURPVIcpAod4TkcOobLooqCf51qUWdZzJzFRDo4EDNX9JUCJwZ4Ssw/72AYo3Zi/\n65xLKUfQ68K75feH3+hi4BYcAv6g5TPbzIq6JlnXMAIpVLMr3WOTL+oOFIx0cdq8tMLRe5mFVQBV\n8Cwd5VnAoXHty17DgHVX1hedO/2mYOdKgGgWZBbAeV3foqCY3y+zoyJadbQfcydpqFF3RFxPxN5B\nllpBoE27SfYMwAz2R7YX98dAOzOBFGx56spT6e/XXX1d+vvC5EKaW1oOht+joxiXg2FjEGhcbUGM\ns82ZLZtlKm1gTaCIoRSJT/M6wqwiB6hTQMc2Vsc1xruyA4rM2ACcV8YYJ7w4EMDnzteJWIq134/r\nZFsaBdGRT1FKeJXETFkrQ4Mm2OWIEcfPJAERtL4MMj8ar9kQskskS41j8bkTCBRocDE4zoGLsvg0\nU1wC1CNwKgL6S/vxm3Xn7TVfb7pHYc0yoO+YUwzEBGycEqs9ApqczypJDh0P2bvipCmNx9I7xG8c\nE4iSEtH+qHSOmUBRwIqxlezJOC4H48TdpJ70INAo1wTifXyuz5983h45jkEg3S5MLthsPXMgZpI1\naFYpARAxgZgJiRKtruvsdH3qQCCc4/r8ursuTgY8fvHx4jXqdjg6TN0noxbxnAyb1H27cAXs1M45\n3zBa73fAauR3m51TSh7Y/ozBLPZaE0UKzuucirR/dL+CF/g/XxOeCQPHTq9LfSOy/VwNAn9G/dj0\nGSf8BZx2wOWO1bhscwAV15qV9RIIAuBKARK1M7zmgs3lbG+hHKzkF5qJ9m0ANE3qyfP2MXF+9kPU\nXu+zPTAQiBc73DAHuErJwncj9oOZ0JQF+cdiqw82Aocicboog6FOlmaWXXcwBnv26IzhRBEt76LE\n2R4WC+NAJ11vQcdHrzfpEwwBY+SYsDFZNsvcyTindIUHrk7AqObdgUB07qw7GNE/tUU8zsHZYLN+\nAXtu9pyrTcZC9uWzL/fdwAY2XDcy1JEm0PXZdad9gQUdAE1iAklAjuOfWw7WbpIobEQbHtfjFARF\n7ea53K5ET4wc5ZKOhRq4RJV9HkygEttIM8s4L2djsd8Z3a4X/4vYdQ68IPArsjOlBT3VnXeb8D4i\nhxuBJLdMxfgd1OTp/OJ5NDmy2XoWAudDgPoQE4j347NSgFLKhuAZhmWAXcwQ4vcYZUNcADhg5zRg\nDR22NmcUOBtfDdtMR5/uAhCoWTu7XBpDOFb0rnAfGTNrNw9hLwFs8DOMysE27aZYDpYxgXb7uQ28\nu792bWebvk27Mm70HNjYXtZVnWxmxgTagUsAgXB+Pi6DQGBC4vsc0JjFmkCq1XZei3gGexgo5Xde\nZAIVynoBanNAY5bbzBJLJxOGFhtUKlePysG4g1Oi/uucCuZaVK6ugAMHIvA3sqCpjoEmTfZF8/a8\nxFZk44uMmy5/7igHg74gvyv4QTrX0TzCLG7sUNK4OI8JVPJxdT3Gb/R5FQEatmfkZ0YsVC35TQFV\nxJwPGBBNJ12RCGgKSw3bslA43sVg0qmS+0BSupqE9+dYXsIuwbPN7HW97aSWgfMAgcQGmG3tE0Cg\nqISW7R3v//zJ510yk+2qbsfjY/v9W79vH/+/P57Ok9jm7SrpBSX5grrXBFq3veTA4Xin07aZWdu1\niTmE337p7Es238ydf8339LKjlxWvUTd9FlwOFtlM7hqGfQq0m+3Kwejd6jhJjBTVOmXGLnU3znyK\nnTYrPnM2s8oZ6o48UI2ts64IbiLWi5hAEdsS545sr1meGGAgJGMCyVxzurBNn9THfeAzXj+VVMC/\nmdQTW3d50ptt6VA5GIArBYFK7B0lbShwM659Z8YIaGeyQ2TfQ+ZUFDPv/Lks+Tvq15dSg4p9twdW\nDuYWNQF0Vs3Kl10NGVcKtjgLpoPtYHSQzo+AKloMhjIupSDTOV80aaMsmKKtDLaYeTouL5AOHGJA\niei4WVa7lVbEQbDBNarRgFbtgChz7uhotAgPtZPWLmDJ+eL95LAkuur40I2HKBudnPRCi/hIOHS+\nmdv12XW3SDFYw0FFaUvlYGAC0SKNQOb6/HrYBef24raZBQFVE5eDcVChHSjwfQaBcM5rB9fcPDAT\nQIkyWqUAF0CTZqmbLhf7dM54AEhGwfU+rLTIKXPXK4sqnolzOtuCjkWUDa5zrQw+ls4dDnTwOS8s\nvNhmv5EsCT5n5hvGCWwWWDK8eII2HYE9UTmYjrkIuCmVqeF3jt1XEPrEvoxuXXl9hOg3/ByVsYZ3\nyIwCHg9mVEbVrd0xSk5IxFxSx34oqGD7m4FsgTaXriOwjVmXDTDDmt424Bo27SYDL5S1kEBzcmay\ncrDKawIpCHQeEygxbtAdLABbzMw++b2ftF/+nl823WCnonIws23nMLO+1BbXXle1yzibbUH4BALt\nbKHTBGoWLrN+OD60tmtTEJTEpOutvgVKQVTIH46vUtZL40QBeDMfRGvJLc7hGIQMqhB4MmSDSmD3\ned1LnZ6WMJKz/So0GmSpGVTAb1UXkfcP0eKj5ENYDkZ+nitxF+aH+n86P9kuor02C/nzu0pMIG4j\n3qxcCYoGCRoI6LtygYgkCM8D+lFaHAHtIQgUvEdOrLlSFLKlpfdeOkckFh4lZh1rK/ArOHAa1Z7J\nFgVtUTzC5W5OGJrGXALhtYum+GYJtC+UAGqAi+/cW/oSfrPeHmY6lbuE4qpd7c0Ewnj9J5/+J2Ym\n5WC7sl6zgjC0dFqcb+bpetlmX5peSu+Qy8FYkiGSWyhtr7jY++IKAmXJ7boXgAbDY1SPbFLlDCG1\nmSEQHVQ4qO+gzE1NLKVjFeYBA+cl8MbMUqLBrPcjz+sOpuBQxATCHAxL0YJyN7UZGq8zsMm/ibRs\n8EwcE6ie2KbZZKC5rq0RCIPOm5yQ4Rif499UTih2hm1v6R6xX3+jfjSvuZVVPiFeSLq72EKbaO30\nZzPf4aUOArnsdesN+Lgae+p0Hatns9Nr5ietsgDWjR+ImiUJAY8uDlzCUpDWg0M4VhQIuAChy4Eb\n1f5J3TeigPUcFFgnVEl7AucqZpaDLJQzfIGOT+ld4VjLNu7cxUg6T3JkUKb11DFYFpuFVVa5Y4VM\noLoPMDftxmsCjQ7sy2dftk23CZlAZmZPXD6/ZrnUit3M7Nphv8jxOfAdgEBYbDWzbGZZt5mSPosa\nSzNLTKarhz2jybEAmmWaf1GwoYYsZFi0TeaMOme4yQNfFgdlcCpiimjnGnZGsc8xCAvZzSgjyYy8\nKBsSsXdGtQ+ONNOEc+ix+D5KFFOeB2a986Vd5Eq2zKyv9y+xh6LucmY5EygB7bIfz4v1Ndhhihxr\nDkIiUIUBmqhUQTPRfCx2shKjgN4VjlXKBuN9OScgCgClVKHEaMLxlAm0alfOLpfK2qLrZfq0A/IE\naGcNNb422A3WUMN+bRGPAKXIBJLuXKwJdGd5JwUpQ93BzMyevvK0fd21rzPdcA9sO81yJhCOB9D+\n4uSiGwtmW1BHRflxXdAq4vbF+AwAWKmUstT1JFrDslKm2geGGpDDnuo4UcBZxy6+mzEs2oLWSsEP\nYbtcYlviN5FmIftTmgjDOdiO8zPhQAf/btpNtl/9jYwNKAkAPgeepbLrsD9iyXA2uASs8rk0MHTj\nREp+p/U0relDTKAh26S/0eDB/aYLknoS/GbrWOeBNvjEaIjgjtV5If+oTGxcj7PxPvSu3LkHmMea\nXHIBq56DmUAC9gz6vp0P2hwQHGkCtWVNIF2L8RlsHPuiLzvcsmYUOGGg6KHDh9LfQ0ygu6u77v9c\nXsVJJ9jY6agfoyw5cDQ+srZr7dbiVna9nExlcIrXAb7e87Y3XHuDv17SBNJ5AAABn0fAHwJy9uci\ntnDJb+LfcCykmpQMdmuX0sheR6XvamfxOdYQtbEJ0IkA+LZvQZ+uKWJhaswazLXIZsDGadIQzyTS\nBIp8OTz3CEDFeUrlYCxYzbbPzI+HEhOIxadL9mSo4kVL3jh+UTvu7AnJOmi3Xj4XQNqvGSYQSpsQ\nOCRBJRokk9HEVs0qTbQsOCo4TJqlcWh9MBBLCyeyES5rVvtFDd/dR2MHn0fZMTYMEdLLqG4ovCg1\nmWoAMKF1cdZJi88ilDtjAo1yxyii9UXvigPG5YbEpCUDF3VcYTYXO9YoL2CWWWddWnBdxqUj7R3u\nDjbuBUWZRsuU1ldferWdt43rLfvk3jqv4+asDZ8jMYGWOybQZFgYmrM0pRazqjFh1oNA7Dhw0MYt\nyR3KHbQ13sfp5GDDrDdwMPqaFXRZEqXKBllBHtclIcpS5jHs6hcE8BkIRAshfsNBSAQ0bdrNYMkZ\nnH3ov+A3GiCg/bU6ZZmzzyDQ5DgxgSqr3AISMoGoBFEzK6XrYtDVMYFkIRzK0GdA4nkAjWgFaQcc\n/CbSL4H9VaaBGytdHAAik8jC1CGgvruP9N7JoTCz1NGQgXaz3gGKyu3OK+nTeahgIa4BTkPYIn6z\nSKARH+t0fWrjepwBj4mVJjZr1azs9uJ2cuzrqt6KgxaYQKUNa8VbHn6L2w/bfWNxw8z6ufDyo3Kb\n4aPxkd2Yb79/YdoD7aNqlLLXTncDINBSQCDoEwTBnFmu56Ud93Bes57NUDoW7G/kLLoMcuSfnKMJ\nhKAU1xb5FBrwp3OorwN73eYAjQNDoyRVF5SDDdjlom2Q4EGZHwysMqBfCh70WWEf+01aDsbjh9/V\ncrN0cwfHYr/JbPv+udSmFLCq3WD2eClg1ecC+xu9Xze2COjCc9b3i3dcArVVyH+IAaH3UdKu0qTM\nUImKY+1H64v4nw6kjcaJrNNmW98s8n2d39SVW8RrwtRsa3PAdnQg0K50igN4PqaZObb5EBMIyUds\nzLRfNauMeYwyWbNdOSyVg5ltuzzq9fK1MBOI/1agf2hjHTlmLq2aVeYHwdcx8wCfAqt7lVhCE0jL\nwWi8cwJAk5CRjxvNg8H4sPV2FvsV6Iatw/xkxotZ360X14hzROWzWcyqcabMKRc/SNJQk8IhE0iu\n67zEiyYnOc50SWEFgYS8ENn+tBYrs3APAB7XppUaEdtRj6X3omB+Ihzs4lllfOO+99keiCYQHKko\ncMEL58ynDir+TcosS/bILcKy2Krh5cGOoI33R3XGo2pY3DYrB6Pscdv22THNtruFkEXELBCGFiZS\nKUMegkMFACxi7/BiCzozawSovg/OrcdC4ALNmrA1apMbZGSpM4e7WWeCnuyMczkYxhyy2tx2GF0V\nzHymgjMYT15+0vbZJvUkZALxNT524bH0tzKBtByMs0qcbYqM4qTy1GJ2CgBCsbYRO4QlIA9lNi7w\nDdBv7I/GlVleSqQMGrdASjlYGFQw6Mn0cwJQ3YIXADRFkdPA8VMNAnw36kDGTKDQsNded2OvLPGO\nCaQaWCVbZrYdc7P1LB0rAaWjSQgWOuAxKBXQrDaf38wzgWyiZEcAACAASURBVHS/2rkSgMG2KXTM\nOgoqBFDSMbRp++CIz18qCTDL21a79yjOCWxjCAJFWZ3dufAOQ2BMzoFjZUEFwJ5NzAxTJhDOoeVN\noCMz0M73t+m2TCAWs4cdL5WD3VzctKZrnM4DNCPuBwR65uFnzMzT//l8t+a3rLIq3TtsN+aW/iZl\nqSe+fTz02CLdDYDzSUdt559ErZ7NcseX1ypluIHNUGI3w3ZkQb+AEaF/smMRV1YV2Yil/ThWmFhi\npogcK9zfDttr1jLEefhYapcjcMgsZz2rbchsVkDXd0ygQLOL7QlAJcwdZQLxu5o38wx0VbDQzJfa\n8DVzlnrfgLVUDsbnj0AzPpYyXfGc9Z3oOl1iQSnQjvOWACh8HrLz61FK9ik4hO9ysmbQ9xX2Lz/3\nlKwJmLFq4yMbwOWB0ZqLNQ/7eQ1jwIXF8Uv6OUUQaLf+vOllb8p+o1qXR+OjXhi6XdlsM7NxNe6D\nz3qaYqR10zeEwfj+wukXsmtkQIj9a54v+zReSfdDc/lwfOgYthFTsDR3XDVKkHAqJVjwTjJ91N1Y\nTAl0afYTresM2g8ygTg+pHvE3CkxgbIO0ZIwHfK7+ZlE2m6lZ6WJMwY2mRXs9JOEXcfXBVs2lCzZ\ntGUhfTzfJH/RBXo9Ahyl8RPYxQjoMsuZ9ngvUTJh3a5DH1PHXGbHK2/H1Z9ToGufbXz+V164jSnu\n7qHS5JiOpq7UhXVQIsScgwpkibOsOtXM4XylQNaxZAInPaLiOZZOwPjRumh2QNL+2gM6+G46Vhsz\nI3D+pm1sPBZHThDoCLRS6mskXsnXG51bJxOeOwcNOBZQ2866PnChRbikp6CdvrBfgwp8ttgsbNNt\nei2HHY0V9H5eVDn77TSBaCHdd5Ga1tOwOxhvDAId1L0m0LgeZwGjloMp+o1zmlnSKImYQAC0+NzK\nBMp0nYIMRinwxf7MSWdGXkD/hB1w56hiMIDp2VFwlK53ZwOczhZnyAvzGf+G5U0CDqnYpepVMPil\nds7dRxdknHf10lfG/ZiDQ5iBQFI6p+VgNxc3s254k3oLiM43cxtX43y+7einCbwlHQtd1DMni1h/\nJYo93l/kbEQLITsI2jbVrA82eAxpVktZWDwe+FjKguK1hym86d47H5DjXNH4VRAoLF1p1vk52p7u\njO9mjtG4n7u8n22sWe808Hhg51bZDJt2qwnEjjzWz5IwNLLBTPE/HB2m8auCpqXtH33nP0pjgTcu\nB2MWKGx3lAXjc7Ie3OH4MASB8H2UY5RAILWZELAsAatmPnhgJlDYKSWwD279VjHTADxxQTQnlsix\nD30Kcvi1jIC/C1A7YggNlYOBwRJlo5WpF2XO3bNqN6F/kgIwDnaEReKA/pZslmp2STaYr2vReBCI\nOxYx65md9Kh5w7JZJh0LDQT0ehXQcc9FkgMl4CjzcVsPnvA6hufpzsH2rwDMRWOLE638rtTHZX8Z\na7uZX/Pd+OUSlUJSJvOjKcHDaxiv06WkCDOB1lWvPRMxZhnMV192tpklJjY2Xss5IZlsdGducyDQ\n1DOBPvG+T4Qlt3/z2/+mffBXPpiamRyODvv7aHL/mlk33OEX3wEIxLaf/Wi2vfjercWtZF/ud1NN\noCiIZrCnxK7Lxi6XYJPPyu88Yl+smpwJpEAMjqXJSWYWRro8OAfHh9iPeDlkNhfmoF5TXdW27gLt\nOkmAZiQEAYE4wcnzxqxfV8/WZ9Z2bahlo9eFZj+Zdp7EQonpPiJ7SdUzbPu6rnMADTd8UNJGuhey\npbwWR6VaOL8KQ+N62T64cxRaxPN6z58r4UDt9T7bg9EEUhCGy8FqXw7GTk7TNSFIYdYvhMqGgSZQ\nlL0uZXA1aKureptR4yBX6gIjOi4zfkLHqD2/wxHOFQkyKoVOO43hWIpqsiOnEz1Svh9yQIoski4X\nu9PAJWqNyjo+HIBxqQI71iUaNmhy+Gxab1tbRsJ1/PuHjx5Of6sg6T7bZDSxL8++bGZ5lgUbt0LG\nvd5Z3nElaq47GDOBRn3JRUSPZEeDnQKUQvC5VaQSx+EMZ0TndEaJkPzOur4Tn2oNdE3oWKtgdHSO\naDFyGljMBGrLjDgcq0Stxb8lmnmUJWZGHJ8jypzzdUWZRwZI2MEy60sFwPxg9kXTNRkrw6wvB1NH\nDvNttp45HZRRNbLKqmSbI8dIF3XYUu3KgbGoAU1qex6BfIWSD6f51MYtSotAYkFjQoO5EtijYJ4G\nk9HCjTGkpRjs8PP/VaPKzTewioI1bN2ui0ygTBhamEDaoQvHisrBTlYnHgSqC8LQu3Hx7NkWBGKK\n/+H40ObrecbcHNqOJ8ch+A47+dz8OZ9lPhpmAmHjAIkZQvwd/H19ttUR4o6KPN8yh7Qpg0CRPdu0\nQcthocyrj8C+U8mecRAdMX7UBpXYwhH7Qf0TMKij/REbOgXqrdcyNOt9nQykrQsMIUkmhGyNJl/D\nIoaHY4SUmEABgNB2bRKGxobxs9gs3JjXoEJBIF3v8ZtU0lJiAgkQjd9EY86sBzCi4CHZ5WridCTx\nXPZlAg0BTQzORyydEMSM/HucI9A24hIZDX75WWXs3yr2N+6XCaSCuGyX9bnP1rNMuyf5Y1Y58OSJ\nS0+Ymdk7XvkO930eSwq2vP2xt4e29GVHL7OPfP1H0v+VWVMEgdpVkhAws6Qx94WTL1hd1U52gO2t\nbv/0Q//U/tmH/lnx8/M2dLOtq9qVg/H67eZOVK4esLwikoCyMqJysIj5FrJuAsYj679izIfC0Dsg\nhhOdWEO09BLJyZCN1+a+Rmh72SceSEpUto0bhuYBxolq7el9qC8XJbdLzHXV3sE9qB3vrPMai10P\nFqr2jku6S3WSAjRubAUxD2I0TfpEydwE5hd8SS0H4+vadxuf/5UXbtNyMEXQzHIm0KTuVff5RZQM\nciQYzQJm+KwUyEZBiPuMAjpoHJW0Jxy9jAPAgVrmsBysFlHE+wx+IwOQdcepPMqtbWzx/KPsdcQi\n2bQba6s2O9ZsM8uz1HQOV/ZFQEhpf8Zy2H2WaHK7oAa/RY21YwLtPrtycMW98yFBvdI2qScps/Gq\nS69yn33Ty7/JPnX9U/bGh97Yn3vclx3wws1Bxf0KQ0edJqBpxJRgZgJpyeSo6ksVFKQNjdXuvaMM\nJXQ6CRXfRxMoAmlTlqQAoEZ6LqX27W7BEyAY13xuMCWLLZ5JtEjofaRSuCpfVHlOmW3HaNM1yehr\nUJFAz7EPRGbrWciWi1h00NtKTKCg9jprXV+PHfiXBSj3C8IXNBtwrE23scPas1s0AOT3xcEGn0Pf\nu9MVCHTUtHwCv43KYxJwo07kbh5BUPm8zDk/KwVj4ZAm7R8uD+z6cjBmjJlZyFIc12Obb+aOnYn9\n63YdgkAAEc367lw4B5hAHAgcjA/s3uqeddbtDQKVNg5mGGi/dnjN3vXEu+zDb/xw9hsHAlFm+uLk\not2YbQHyqDzhi2dfNLOc7YR7V+0fJDlSpn+UO5FsM2fdrJjhRHCflTi1nkUdlbc6kKLNg2jVWslo\n+VpKXg/4J9VocD+ONxoHWe3WC0OjHXEGnJdsQxXPHTenugAgLtx7JDaswQ6fNwX3m4VPLO3G0ryZ\nZw0qcPxls3RzGmMJvksEqK+bdWLCRaxKThwuu2VugzjxEzC4My0der743LFmC3bcPXsBaJQJFGn3\nRQkTZvyU2Mo6tnCOCOSK1vyICYSgPCxdKbSIL3XRLM31IRDo4vSiY8q89upr7de+/9eyjrUM4CsI\nNLQxawcCz2Zbv3C2nmVNVMy26+TJ6iSxLzHeP3/6ebt6cNUBu7jOn3rrT2XnvnZ47b70gHTDeZEk\n03frSimbAuBL4wcMbwYRVdNUYyQVoVfGGPzPIf+Ez+/mRzB3mq7JEv4JJCDiglkO9jDbSH2NoWuK\nmECR7UUJMpKJq9a3iMc6fHe5FSRXHzMqi5yMJltGZQEEUv8PMYquhzo/9fwJOCoxgXiuE1tXn/15\nTCAkebEfOqwM2rMfvdgsMjteakLEa8K+24sLAgkTSBE0s75UgRXYR/UuQx2gsIo6Rm13WcAMv+ms\ny1DHLLMhGaqSKBeDQxromeUTxy1qwaRluh/+jWjY+pvwWO0wVZYdjZLoWcoCtXmAnSiQahADRhUm\nmk5mZhu5dvMEhESOFDSB2HnHJAHFPzGBducCvZ9BIPw+Yu587K0fs7e+/K3Z/tKG87ziwiuyRf0f\nf+c/tqZrHOU3MYEWd+zxi49n96GlkdwJISoj4OfLIMKff8uft3e/+t32hof6zgpqFFUPJNXbSy3z\nUIYRLIfzOicM0VKHgmvcOwvuspMe0VWjzDkWKdwHC+dFAcK4HicASgWQ0/0F5WAlAWT8Jgo2ogwK\n3g0WzyT4u3vuUXCP7mBagoNFaraZOfaZWU+9XTfrrFMd7lEDKjNLjBS9xwjAgE0Ja+67AJzncoEC\nHTkDEmuv/cPMxijjrKVa0TuJykpSlq8aO/2HEARSJlCgKZA9XwC7QSJj3a577R8RGi0xgVLmiAKG\nST1JmTnHGNvZk9PVqSuTxTmUCYSgEcwatqeHo8Okr/OVgkDHk2N74tIT9rmTz7lz1FVtP/Ounwl/\nk0DTapyVTXz69qez68L68KXTL9nx+DhjjEFbLmrrzoD6EBOI361ZWRMoAyN2Yyt1tGHWcxskd4Ig\nWpmNJZ9Ck1QOoJFzKHCTMYEEUMI5lDFRKi1j3ygCaKKMvibC8FmpxB3njXQOS4nDtmtt3szt4VHP\nIoa9BROIyzXxzKNyMDOzs9WZO4cLdrpelxGgfQTEMHgcPS+s+RGDm9ddfD8q60MgHbHg8ZsSQMMg\nEH9fWQhFYWgGK7u4iQv78ed1xt20PZvVBZNtbscze73Tc0nvqcvXvATOF3RNZpuZs/tm/VrOviq2\nV158ZbYP0gJm9wcCMTv84uRiir2G5BZWzcruLO8koB/f+fzJ57NOuo9deMx++yO//byY9aUN9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pnwyRZtSmo2nPzKrzZ1gC5U7Xp2F5QBIWF6FwPCcFgbAp8Nh27ZZJpsLiBRCIr/GFAIFec+U1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d37da21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(data_raw['rain'])\n",
    "\n",
    "ax2 = fig.add_subplot(1,1,1)\n",
    "ax2.plot(well['GW_elevation_ft_(LSML)'])\n",
    "\n",
    "ax3 = fig.add_subplot(1,1,1)\n",
    "ax3.plot(tydl_wl_data['Water Level'])\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.ylim([-1,15])\n",
    "plt.xlim(['2017-08-16','2017-11-16'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Stripping off index and irrelevant data before analysis continues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prep4analysis(originaldataframe):\n",
    "    new = (originaldataframe)\n",
    "    new = new.reset_index()\n",
    "    new = new.drop('Datetime', axis =1)\n",
    "    new = new[3:-50] #removing first and last 3 readings\n",
    "    new_array = np.array(new)\n",
    "    new_array = np.reshape(new_array, new_array.shape[0])\n",
    "    return new, new_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "wl = (well['GW_elevation_ft_(LSML)'])\n",
    "wl, wl_a = prep4analysis(wl)\n",
    "\n",
    "tmp = (well['Temperature (°C) (535311)'])\n",
    "tmp, tmp_a = prep4analysis(tmp)\n",
    "\n",
    "sal = (well['Salinity (PSU) (535311)'])\n",
    "sal, sal_a = prep4analysis(sal)\n",
    "\n",
    "tydl = (tydl_wl_data['Water Level'])\n",
    "tydl, tydl_a = prep4analysis(tydl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4109,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wl_a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# FFT analysis with periodogram and welchs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Periodogram_loglog_plot(x,fs, color):\n",
    "    f, Pxx_den = signal.periodogram(x,fs)\n",
    "    plt.loglog(f, Pxx_den, color)\n",
    "    plt.xlabel('Frequency Hz')\n",
    "    plt.ylabel('PSD [V**2/Hz]')\n",
    "    plt.title('Fig D\\nperiodogram method, power spectral density');\n",
    "    \n",
    "def Welchs_loglog_plot(x,fs, color):\n",
    "    f, Pxx_den = signal.welch(x, fs, nperseg=1024)\n",
    "    plt.loglog(f, Pxx_den, color)\n",
    "    plt.xlabel('Frequency Hz')\n",
    "    plt.ylabel('PSD [V**2/Hz]')\n",
    "    plt.title('Fig D\\nWelch\\'s method, power spectral density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736557.0, 736649.0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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58+aV62/UqFEUFhYyf/58UlJSKvW10a5CO39yD7WUlJRgx47ukiRJkqTDx5w5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\nG9KZ9GPqUVRcwqgpS3n+qwUsWrcNgLS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d22f169b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(data_raw['rain'], 'b', label= 'Precipitation in Inches\\nWeather Undergroud PWS: KRIPORTS37')\n",
    "ax1.legend()\n",
    "\n",
    "ax2 = fig.add_subplot(1,1,1)\n",
    "ax2.plot(well['GW_elevation_ft_(LSML)'][36:-3], label= 'Water level in Feet (LMSL)')\n",
    "ax2.legend()\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.ylim([-15,15])\n",
    "plt.xlim(['2017-08-16','2017-11-16'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure A.** Well 55a Water level data, Feet (LMSL), and Precipitation, Inches, from adjacent Weather Undergroud Personal Weather Station, PWS: KRIPORTS37.  \n",
    "\n",
    "This shows the relationship between the precipitation events (*dark blue*) and the water level (*light blue*) in well 55a. As seen in the figure, most precipitation events result in a slightly delayed increase in water level in well 55a."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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McRNNgM211v6tgXNLE14vMMbMMsbkWWv3JifahqkSTERERERERNJSzeqQLSwE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rzsllvghx+MLLGsrMj71vffi/XGa9s2I1g3erSR3WZ5800YO9aYlgCFiDOpCSaEiOBUrfVApdTP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\nhhuAa69t2/Z7C4cPt/z4zp1EcMrOBi64gCwrK2veDTKS404O7SIJAMck1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dc125Zzzjkk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\nIpiIiIiIiIiIiCS8/w98J6kjkueJzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d22d56320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(well['Salinity (PSU) (535311)'][:-50], 'r', label='Salinity (PSU)')\n",
    "ax1.set_xlabel('Date')\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax1.set_ylabel('Salinity (PSU)', color='r')\n",
    "ax1.tick_params('y', colors='r')\n",
    "ax1_legend = ax1.legend(loc =1)\n",
    "plt.gca().add_artist(ax1_legend)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "s2 = well['Temperature (°C) (535311)'][:-50]\n",
    "ax2.plot(s2, 'k', label= 'Temperature (°C)')\n",
    "ax2.set_ylabel('Temperature (°C)', color='k')\n",
    "ax2.tick_params('y', colors='r')\n",
    "plt.ylim(11.5,13.2)\n",
    "ax2.legend(loc=2)\n",
    "\n",
    "#water levels\n",
    "ax3 = ax1.twinx()\n",
    "s3 = well['GW_elevation_ft_(LSML)'][3:-50]\n",
    "ax3.plot(s3, 'C0', label= 'Water level in Feet (LMSL)')\n",
    "ax2.tick_params('y', colors='C0')\n",
    "ax3.legend(loc=3)\n",
    "\n",
    "#Precipitation\n",
    "ax4 = ax1.twinx()\n",
    "s4 = data_raw['rain'][3:-3]\n",
    "ax4.plot(s4, 'b', label='Precipitation in Inches\\nWeather Undergroud PWS: KRIPORTS37')\n",
    "ax2.tick_params('y', colors='b')\n",
    "ax4.legend(loc=4)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "fig.autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure B.** Well 55a Temperature °C, Salinity PSU, Water level data Feet (LMSL), and Precipitation Inches  \n",
    "\n",
    "This Figure shows how water levels are affected by precipitation and how precipitation and water levels are affecting Salinity. Looking at the precipitation event that occured around 2017-09-06 we see that there was very little effect on the Salinity and a minor effect on water levels.  \n",
    "\n",
    "Looking at the the *pumping* event around 2017-09-16 we can see that this pumping event directly and immediately affects Salinity concentrations in well 55a. The *pumping* event also causes a temporary increase in temperature. This is interesting because the other *pumping* event at around 2017-10-20 results in the same temporary increase in temperature but an opposite effect, a decrease, in salinity.  \n",
    "\n",
    "The large precipiation event around 2017-10-31 lowers the salinity concentration. \n",
    "\n",
    "interesting ideas: (well is poorly mixed as seen in the profiles that were plotted a few months ago, how does this play a role in this data? can it explain why we are seeing opposing situations in regards to pumping and salinity concentrations?  \n",
    "\n",
    "Note the Salinity range here... not much of a range, more data would be great to watch the salinity concentration decrease significantly, assuming it still will\n",
    "\n",
    "**personal notes** *This graph needs to be cleaned up significantly (legend, move water level ticks from right Y-axis to left, increase in X-axis ticks) check use of effect vs. affect*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZvCNqvt+s3sKcZTUzV9e6LTtL+5NcYrxG4lw1y22UzvXD94+9YzmP/DjmbN0c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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d238e76a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, Pxx_den = signal.periodogram(wl_a, 0.5)\n",
    "plt.loglog(f, Pxx_den)\n",
    "plt.xlabel('Frequency Hz')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.title('Fig D\\nperiodogram method, power spectral density');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'b' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-a33aa98c6cc9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPeriodogram_loglog_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwl_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
     ]
    }
   ],
   "source": [
    "Periodogram_loglog_plot(wl_a, 0.5, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X/iLgauAfbu1dCmwDagGvFld25wXgB+x0OfWAnsAjItLL6Uz4H9DP7ZABwGRjTKaI9MF2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Gqz3Q+Y4OUeo4DWfjxzso/7NgT6989uefP6Xr5e58t53s5xd9yf/cHw2RWkzy03Pjv53PLnswvU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\nM1tjZn8yswtDxw6Y2VN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d22847668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(14,7))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "Periodogram_loglog_plot(wl_a, 0.5, 'b') #fs of 0.5 for 1/2 hr sample rate\n",
    "plt.subplot(2, 2, 1)\n",
    "Welchs_loglog_plot(tydl_a, 1, 'g') #fs of 1 for 1 hr sample rate\n",
    "plt.ylim(10e-8, 100)\n",
    "plt.title('Periodogram method, Power Spectral Density\\nWater Levels')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "Periodogram_loglog_plot(tmp_a, 0.5, 'k') #fs of 0.5 for 1/2 hr sample rate\n",
    "plt.subplot(2, 2, 2)\n",
    "Welchs_loglog_plot(tydl_a, 1, 'g') #fs of 1 for 1 hr sample rate\n",
    "plt.ylim(10e-8, 100)\n",
    "plt.title('Periodogram method, Power Spectral Density\\nTemperatures')\n",
    "\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "Periodogram_loglog_plot(sal_a, 0.5, 'r') #fs of 0.5 for 1/2 hr sample rate\n",
    "plt.subplot(2, 2, 3)\n",
    "Welchs_loglog_plot(tydl_a, 1, 'g') #fs of 1 for 1 hr sample rate\n",
    "plt.ylim(10e-8, 100)\n",
    "plt.title('Periodogram method, Power Spectral Density\\nSalinity')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "Periodogram_loglog_plot(tydl_a, 1, 'c') #fs of 1 for 1 hr sample rate\n",
    "plt.subplot(2, 2, 4)\n",
    "Welchs_loglog_plot(tydl_a, 1, 'g') #fs of 1 for 1 hr sample rate\n",
    "plt.ylim(10e-8, 100)\n",
    "plt.title('Periodogram method, Power Spectral Density\\nTides')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure C.** Power Spectral Density (PSD) graphs for Water Levels, Temperatures, Salinity, and Tides at well 55a. The green line superimposed on the data is a Welch's Method PSD for local tidal data to visually check if there is any correlation. Lack of defined peaks in the collected data suggestes that there the is no periodicity in any of the data collected at well 55a. \n",
    "\n",
    "Well 55a is approximately XX feet deep with the bottom of the well at approximately -XX feet LMSL. If there was tidal influence we would see a peak develop at a frequency corresponding to tidal periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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