Antirrhinum Phase 1 Script.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the jupyter Notebook which will do the final data-analysis. It will do the tasks necessary for Phase 1:\n",
    "\n",
    "## 1) Give all Trimble readings (ID_track) a score. According to GPS_classification_15.11.txt\n",
    "\n",
    "## 2) Pick out the best reading (ID_track) to use where multiple readings are available for the same plant ID.\n",
    "\n",
    "### Input: The data tables for every year from Phase 0/ November/ Version 3\n",
    "\n",
    "### Output: \n",
    "1) The original data tables with the score for each ID_track\n",
    "\n",
    "2) A data table that containts the best reading (ID_track) for every Sample\n",
    "\n",
    "\n",
    "@ Author: Harald Ringbauer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cPickle as pickle\n",
    "import itertools\n",
    "import simplekml\n",
    "import math\n",
    "from pyproj import Proj  # Package for doing GPS-calculations and Projections\n",
    "from collections import Counter\n",
    "\n",
    "# Prepare the Longitude/Latitude to Easting/Northing transformer\n",
    "my_proj = Proj(proj='utm', \n",
    "               zone=\"31T\", ellps='WGS84', units='m')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A few global Variable names.\n",
    "# String of the Names of all Input Files:\n",
    "file_names = [\"Antspec2009_IDtrack_DF.csv\",\"Antspec2010_IDtrack_DF.csv\",\"Antspec2011_IDtrack_DF.csv\",\n",
    "              \"Antspec2012_IDtrack_DF.csv\",\"Antspec2013_IDtrack_DF.csv\",\"Antspec2014_IDtrack_DF.csv\"] \n",
    "\n",
    "output_file_name = \"Antspec_compl.csv\"\n",
    "id_track_decision_output_file_name = \"ID_track_decision.csv\"\n",
    "\n",
    "input_folder =  \"./Input_Data/\"    # Name of the Folder used for Input Files\n",
    "output_folder = \"./Output_Data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading ./Input_Data/Antspec2009_IDtrack_DF.csv\n",
      "Shape of Data: \n",
      "(1925, 27)\n",
      "\n",
      "Loading ./Input_Data/Antspec2010_IDtrack_DF.csv\n",
      "Shape of Data: \n",
      "(3119, 27)\n",
      "\n",
      "Loading ./Input_Data/Antspec2011_IDtrack_DF.csv\n",
      "Shape of Data: \n",
      "(2569, 27)\n",
      "\n",
      "Loading ./Input_Data/Antspec2012_IDtrack_DF.csv\n",
      "Shape of Data: \n",
      "(3741, 27)\n",
      "\n",
      "Loading ./Input_Data/Antspec2013_IDtrack_DF.csv\n",
      "Shape of Data: \n",
      "(5758, 27)\n",
      "\n",
      "Loading ./Input_Data/Antspec2014_IDtrack_DF.csv\n",
      "Shape of Data: \n",
      "(8715, 27)\n",
      "\n",
      "6 Files successfully loaded!\n",
      "All headers equal!\n",
      "\n",
      "Data Format: \n",
      "Population                object\n",
      "Location                  object\n",
      "VisitID                    int64\n",
      "PlantID                   object\n",
      "IDtrack                   object\n",
      "PhenoCat                 float64\n",
      "Date                      object\n",
      "year                       int64\n",
      "ThisYearLabel              int64\n",
      "Latitude                 float64\n",
      "Longitude                float64\n",
      "Altitude                 float64\n",
      "CorrectedLatitude        float64\n",
      "CorrectedLongitude       float64\n",
      "CorrectedAltitude        float64\n",
      "Easting                  float64\n",
      "Northing                 float64\n",
      "DistEastofCentre         float64\n",
      "DistNorthOfCentre        float64\n",
      "HorzErr                  float64\n",
      "VertErr                  float64\n",
      "GPS                        int64\n",
      "Comments                 float64\n",
      "Alive                     object\n",
      "Perennial                float64\n",
      "FloweringStage            object\n",
      "LocationManualUpdater    float64\n",
      "dtype: object\n",
      "\n",
      "Merging all the data!\n",
      "Nr. of all individuals: 25821\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Location</th>\n",
       "      <th>VisitID</th>\n",
       "      <th>PlantID</th>\n",
       "      <th>IDtrack</th>\n",
       "      <th>PhenoCat</th>\n",
       "      <th>Date</th>\n",
       "      <th>year</th>\n",
       "      <th>ThisYearLabel</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>DistEastofCentre</th>\n",
       "      <th>DistNorthOfCentre</th>\n",
       "      <th>HorzErr</th>\n",
       "      <th>VertErr</th>\n",
       "      <th>GPS</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Alive</th>\n",
       "      <th>Perennial</th>\n",
       "      <th>FloweringStage</th>\n",
       "      <th>LocationManualUpdater</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>J0001</td>\n",
       "      <td>1_J0001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321497</td>\n",
       "      <td>...</td>\n",
       "      <td>-232.275681</td>\n",
       "      <td>-131.158978</td>\n",
       "      <td>5.95776</td>\n",
       "      <td>9.13216</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>J0002</td>\n",
       "      <td>2_J0002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321479</td>\n",
       "      <td>...</td>\n",
       "      <td>-233.310219</td>\n",
       "      <td>-133.051083</td>\n",
       "      <td>5.92379</td>\n",
       "      <td>8.82668</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>J0003</td>\n",
       "      <td>3_J0003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321529</td>\n",
       "      <td>...</td>\n",
       "      <td>-241.008310</td>\n",
       "      <td>-127.430149</td>\n",
       "      <td>7.18771</td>\n",
       "      <td>9.08730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Population Location  VisitID PlantID  IDtrack PhenoCat      Date  year  \\\n",
       "0         HZ      Unk   2022.0   J0001  1_J0001      NaN  16/06/09  2009   \n",
       "1         HZ      Unk   2023.0   J0002  2_J0002      NaN  16/06/09  2009   \n",
       "2         HZ      Unk   2024.0   J0003  3_J0003      NaN  16/06/09  2009   \n",
       "\n",
       "   ThisYearLabel   Latitude          ...            DistEastofCentre  \\\n",
       "0            1.0  42.321497          ...                 -232.275681   \n",
       "1            1.0  42.321479          ...                 -233.310219   \n",
       "2            1.0  42.321529          ...                 -241.008310   \n",
       "\n",
       "   DistNorthOfCentre  HorzErr  VertErr  GPS  Comments  Alive  Perennial  \\\n",
       "0        -131.158978  5.95776  9.13216  1.0       NaN  Alive        NaN   \n",
       "1        -133.051083  5.92379  8.82668  1.0       NaN  Alive        NaN   \n",
       "2        -127.430149  7.18771  9.08730  1.0       NaN  Alive        NaN   \n",
       "\n",
       "   FloweringStage  LocationManualUpdater  \n",
       "0             NaN                    NaN  \n",
       "1             NaN                    NaN  \n",
       "2             NaN                    NaN  \n",
       "\n",
       "[3 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tables = []   # List into which all data_tables will go.\n",
    "all_data = []      # Data Frame for all the Data\n",
    "headers = []       # List into which all headers will go.\n",
    "\n",
    "\n",
    "for file_name in file_names:\n",
    "    path = input_folder + file_name\n",
    "    print(\"\\nLoading \" + path)\n",
    "    data = np.genfromtxt(path, delimiter = '$', dtype=None)\n",
    "    print(\"Shape of Data: \")\n",
    "    print(data.shape)\n",
    "    df = pd.read_csv(input_folder + file_name, delimiter=\"$\")     # Create Panda Data Frame\n",
    "    data_tables.append(df)                                        # Append Panda Data \n",
    "    headers.append(list(df))\n",
    "    \n",
    "print(\"\\n%i Files successfully loaded!\" % len(data_tables))      # Quick Status Update\n",
    "\n",
    "# Quickly check whether all headers are the same:\n",
    "for i in range(1,len(headers)):\n",
    "    equal=np.array_equal(headers[i-1], headers[i])\n",
    "    if equal == False:\n",
    "        raise ValueError(\"Not all headers are equal!\")\n",
    "print(\"All headers equal!\")\n",
    "\n",
    "print(\"\\nData Format: \")\n",
    "df = data_tables[0]\n",
    "print(df.dtypes)\n",
    "\n",
    "# Concatenate all the data into a single data Frame\n",
    "print(\"\\nMerging all the data!\")\n",
    "all_data=pd.concat(data_tables, axis=0, ignore_index=1) # Concatenate all Data; Ignore Indexing\n",
    "print(\"Nr. of all individuals: %i\" % all_data.shape[0])\n",
    "all_data.reset_index(drop=1)\n",
    "all_data.reset_index()\n",
    "all_data.head(3)\n",
    "#all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr. of NaNs in uncorrected: 1792\n",
      "Nr. of NaNs in corrected: 1466\n",
      "Nr. of NaNs in both: 1466\n",
      "Nr. Entries where GPS Entries are different: 17956 out of 25821\n",
      "Nr. of Entries with manual positon update:  404 out of 25821\n",
      "Nr. of Entries with Death or Label Only:  3225 out of 25821\n",
      "\n",
      "Plants from the same year 20911:\n",
      "Plants from the same year according to label: 20817\n",
      "\n",
      "How often do Track_Scores appear: \n",
      "3    15611\n",
      "2     5881\n",
      "0     3923\n",
      "1      406\n",
      "Name: Track_Score, dtype: int64\n",
      "Data Saved!\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th>Population</th>\n",
       "      <th>Location</th>\n",
       "      <th>VisitID</th>\n",
       "      <th>PlantID</th>\n",
       "      <th>IDtrack</th>\n",
       "      <th>PhenoCat</th>\n",
       "      <th>Date</th>\n",
       "      <th>year</th>\n",
       "      <th>ThisYearLabel</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>DistNorthOfCentre</th>\n",
       "      <th>HorzErr</th>\n",
       "      <th>VertErr</th>\n",
       "      <th>GPS</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Alive</th>\n",
       "      <th>Perennial</th>\n",
       "      <th>FloweringStage</th>\n",
       "      <th>LocationManualUpdater</th>\n",
       "      <th>Track_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>J0004</td>\n",
       "      <td>4_J0004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321466</td>\n",
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       "      <td>-134.625329</td>\n",
       "      <td>7.96771</td>\n",
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       "      <td>2026.0</td>\n",
       "      <td>J0005</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>J0006</td>\n",
       "      <td>6_J0006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2028.0</td>\n",
       "      <td>J0007</td>\n",
       "      <td>7_J0007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321452</td>\n",
       "      <td>...</td>\n",
       "      <td>-136.158144</td>\n",
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       "      <td>10.02140</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2029.0</td>\n",
       "      <td>J0008</td>\n",
       "      <td>8_J0008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321443</td>\n",
       "      <td>...</td>\n",
       "      <td>-137.199351</td>\n",
       "      <td>6.01057</td>\n",
       "      <td>8.57548</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>J0009</td>\n",
       "      <td>9_J0009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321464</td>\n",
       "      <td>...</td>\n",
       "      <td>-134.760391</td>\n",
       "      <td>6.24966</td>\n",
       "      <td>8.67587</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HZ</td>\n",
       "      <td>Unk</td>\n",
       "      <td>2031.0</td>\n",
       "      <td>J0010</td>\n",
       "      <td>10_J0010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16/06/09</td>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.321470</td>\n",
       "      <td>...</td>\n",
       "      <td>-134.153190</td>\n",
       "      <td>6.58789</td>\n",
       "      <td>10.76770</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Population Location  VisitID PlantID   IDtrack PhenoCat      Date  year  \\\n",
       "0         HZ      Unk   2022.0   J0001   1_J0001      NaN  16/06/09  2009   \n",
       "1         HZ      Unk   2023.0   J0002   2_J0002      NaN  16/06/09  2009   \n",
       "2         HZ      Unk   2024.0   J0003   3_J0003      NaN  16/06/09  2009   \n",
       "3         HZ      Unk   2025.0   J0004   4_J0004      NaN  16/06/09  2009   \n",
       "4         HZ      Unk   2026.0   J0005   5_J0005      NaN  16/06/09  2009   \n",
       "5         HZ      Unk   2027.0   J0006   6_J0006      NaN  16/06/09  2009   \n",
       "6         HZ      Unk   2028.0   J0007   7_J0007      NaN  16/06/09  2009   \n",
       "7         HZ      Unk   2029.0   J0008   8_J0008      NaN  16/06/09  2009   \n",
       "8         HZ      Unk   2030.0   J0009   9_J0009      NaN  16/06/09  2009   \n",
       "9         HZ      Unk   2031.0   J0010  10_J0010      NaN  16/06/09  2009   \n",
       "\n",
       "   ThisYearLabel   Latitude     ...       DistNorthOfCentre  HorzErr  \\\n",
       "0            1.0  42.321497     ...             -131.158978  5.95776   \n",
       "1            1.0  42.321479     ...             -133.051083  5.92379   \n",
       "2            1.0  42.321529     ...             -127.430149  7.18771   \n",
       "3            1.0  42.321466     ...             -134.625329  7.96771   \n",
       "4            1.0  42.321478     ...             -133.306468  6.40977   \n",
       "5            1.0  42.321452     ...             -135.910147  5.98568   \n",
       "6            1.0  42.321452     ...             -136.158144  6.25380   \n",
       "7            1.0  42.321443     ...             -137.199351  6.01057   \n",
       "8            1.0  42.321464     ...             -134.760391  6.24966   \n",
       "9            1.0  42.321470     ...             -134.153190  6.58789   \n",
       "\n",
       "    VertErr  GPS  Comments  Alive  Perennial  FloweringStage  \\\n",
       "0   9.13216  1.0       NaN  Alive        NaN             NaN   \n",
       "1   8.82668  1.0       NaN  Alive        NaN             NaN   \n",
       "2   9.08730  1.0       NaN  Alive        NaN             NaN   \n",
       "3  10.32340  1.0       NaN  Alive        NaN             NaN   \n",
       "4   8.79145  1.0       NaN  Alive        NaN             NaN   \n",
       "5  10.72680  1.0       NaN  Alive        NaN             NaN   \n",
       "6  10.02140  1.0       NaN  Alive        NaN             NaN   \n",
       "7   8.57548  1.0       NaN  Alive        NaN             NaN   \n",
       "8   8.67587  1.0       NaN  Alive        NaN             NaN   \n",
       "9  10.76770  1.0       NaN  Alive        NaN             NaN   \n",
       "\n",
       "   LocationManualUpdater  Track_Score  \n",
       "0                    NaN            2  \n",
       "1                    NaN            3  \n",
       "2                    NaN            2  \n",
       "3                    NaN            2  \n",
       "4                    NaN            2  \n",
       "5                    NaN            2  \n",
       "6                    NaN            2  \n",
       "7                    NaN            2  \n",
       "8                    NaN            2  \n",
       "9                    NaN            2  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Now do the ID - Track Score for Every single individual:\n",
    "all_data['Track_Score']=-1 # Set the Default Track Score to 0!\n",
    "un_corr_df = all_data.loc[:,['Latitude','Longitude','Altitude']]                          # The uncorrected Data\n",
    "corr_df = all_data.loc[:,['CorrectedLatitude','CorrectedLongitude','CorrectedAltitude']]  # The corrected Data                                  # The corrected Data\n",
    "\n",
    "\n",
    "un_corr_df.Longitude = un_corr_df.Longitude.round(8)  # Cut significants digits (due to weird bug)\n",
    "corr_df.CorrectedLongitude = corr_df.CorrectedLongitude.round(8) # Cut significant digits (due to weird bug)\n",
    "\n",
    "\n",
    "# Extract the indices where Nan\n",
    "nan_corr = corr_df.isnull().any(axis=1)   # Get all indices were at least one entry is not 0.\n",
    "nan_uncorr = un_corr_df.isnull().any(axis=1)   # Get all indices were at least one entry is not 0.\n",
    "\n",
    "# Some logical magic to find out where one entry is \n",
    "both_nan = nan_corr & nan_uncorr\n",
    "one_nan = ~nan_corr & nan_uncorr   # Corrected Value is not NaN, but uncorrected is!\n",
    "non_nan = ~nan_corr & ~nan_uncorr  # Both are not NaN\n",
    "\n",
    "\n",
    "# Give some Output\n",
    "print(\"Nr. of NaNs in uncorrected: %i\" % np.sum(list(nan_uncorr)))\n",
    "print(\"Nr. of NaNs in corrected: %i\" % np.sum(list(nan_corr)))\n",
    "print(\"Nr. of NaNs in both: %i\" % np.sum(list(both_nan)))\n",
    "\n",
    "# Extract where the two Position-Frames differ:\n",
    "diff_pos = (un_corr_df.values != corr_df.values).any(axis=1)  # Numpy Array where GPS positions different\n",
    "same_pos = np.logical_not(diff_pos)\n",
    "print(\"Nr. Entries where GPS Entries are different: %i out of %i\" % (np.sum(list(diff_pos)), corr_df.shape[0]))\n",
    "\n",
    "bd = non_nan & diff_pos\n",
    "bs = non_nan & same_pos\n",
    "\n",
    "# Extract where someone did a manual Update:\n",
    "manual_updater = ~all_data.LocationManualUpdater.isnull()   # Extract all entries where there is a manual Updater\n",
    "print(\"Nr. of Entries with manual positon update:  %i out of %i\" % (np.sum(list(manual_updater)), corr_df.shape[0]))\n",
    "\n",
    "# Extract where death or label only:\n",
    "plant_stat_wrong = (all_data.Alive == \"Label only\") | (all_data.Alive == \"Dead\")\n",
    "print(\"Nr. of Entries with Death or Label Only:  %i out of %i\" % (np.sum(list(plant_stat_wrong)), corr_df.shape[0]))\n",
    "\n",
    "# Do the classification per year\n",
    "first_chars = all_data.PlantID.str[0].values # First Character for every year\n",
    "years = all_data.year.values      # Year \n",
    "#print(set(first_char))  # The C comes from Cadi\n",
    "#print(set(year))        # The year\n",
    "\n",
    "char = ['J', 'K'  , 'L' ,'M' , 'P', 'S', 'C']  # Start with creating a dictionary:\n",
    "year = [2009, 2010, 2011, 2012, 2013, 2014, 2014]\n",
    "d = dict(zip(char, year))\n",
    "\n",
    "transf_year = np.array([d[i] for i in first_chars])  # Put first chars through dictionary\n",
    "\n",
    "same_year = (transf_year == years)\n",
    "same_year[first_chars=='C']= 1  # Set all Cadi Plants to this year\n",
    "\n",
    "print(\"\\nPlants from the same year %i:\" %np.sum(same_year))\n",
    "print(\"Plants from the same year according to label: %i\" %np.sum(all_data.ThisYearLabel))\n",
    "\n",
    "diff = (same_year != all_data.ThisYearLabel.values)\n",
    "#print(all_data.loc[diff,'PlantID']) # Print Differences between inferred difference and ThisYearLabel.values\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "############################ Actually set the entries\n",
    "#print(np.sum(list(one_nan)))\n",
    "all_data.loc[both_nan,'Track_Score'] = 0  # Set the lowest Score\n",
    "all_data.loc[one_nan,'Track_Score'] = 1   # Set Score = 1: Corrected is not NaN, but Uncorrected is!\n",
    "all_data.loc[bs,'Track_Score'] = 2        # Both Entries exist - but are the same\n",
    "all_data.loc[bd,'Track_Score'] = 3        # High score: Both GPS entries exist and are different\n",
    "\n",
    "all_data.loc[manual_updater, 'Track_Score'] = 1   # Set all entries where there was an manual Updater to 1.\n",
    "all_data.loc[plant_stat_wrong, 'Track_Score'] = 0 # Set all entries where the plant status is Dead/Alive to 0.\n",
    "\n",
    "print(\"\\nHow often do Track_Scores appear: \")\n",
    "all_values = all_data.Track_Score.value_counts()\n",
    "print(all_values)\n",
    "\n",
    "all_data.to_csv(output_folder + output_file_name, sep = '$')\n",
    "print(\"Data Saved!\")\n",
    "all_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 of Phase 1.\n",
    "Script for the ID Track decision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded\n",
      "\n",
      "Shape of Data: \n",
      "(25821, 29)\n",
      "J1548    8\n",
      "L1359    8\n",
      "J1251    8\n",
      "P0056    8\n",
      "M0121    7\n",
      "Name: PlantID, dtype: int64\n",
      "Nr of unique Labels: 18215\n",
      "Nr of total Labels: 25821\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlantID</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>ID_Track_Decision</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CADI0001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CADI0002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CADI0003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CADI0004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CADI0005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CADI0006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CADI0007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CADI0008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CADI0009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CADI0010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlantID  Latitude  Longitude  Altitude ID_Track_Decision  Score\n",
       "0  CADI0001         0          0         0               NaN    -10\n",
       "1  CADI0002         0          0         0               NaN    -10\n",
       "2  CADI0003         0          0         0               NaN    -10\n",
       "3  CADI0004         0          0         0               NaN    -10\n",
       "4  CADI0005         0          0         0               NaN    -10\n",
       "5  CADI0006         0          0         0               NaN    -10\n",
       "6  CADI0007         0          0         0               NaN    -10\n",
       "7  CADI0008         0          0         0               NaN    -10\n",
       "8  CADI0009         0          0         0               NaN    -10\n",
       "9  CADI0010         0          0         0               NaN    -10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First load the Data Frame\n",
    "df = pd.read_csv(output_folder + output_file_name, sep=\"$\")     # Create Panda Data Frame of all merged individuals\n",
    "print(\"Successfully loaded\\n\")\n",
    "print(\"Shape of Data: \")\n",
    "print(df.shape)\n",
    "\n",
    "# First do some statistics to get an overview over the Problem:\n",
    "counts=df['PlantID'].value_counts()\n",
    "max_counts = max(counts)\n",
    "print(counts[:5])\n",
    "bins = np.arange(-0.5,max_counts+0.5,1)\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(counts, bins=bins)\n",
    "plt.xlim([0,max_counts+1])\n",
    "plt.xlabel(\"Nr of Appearance of Label\")\n",
    "#plt.show()\n",
    "\n",
    "# Now extract unique ID-Labels:\n",
    "unique_labels=df.PlantID.unique()\n",
    "unique_labels=np.sort(unique_labels)   # Sort; so that everything is easy to find.\n",
    "print(\"Nr of unique Labels: %i\" % len(unique_labels))\n",
    "print(\"Nr of total Labels: %i\" % len(df))\n",
    "\n",
    "# Create a Data Frame into which finally the values are filled in; only unique Labels\n",
    "dic={'PlantID': unique_labels, 'Latitude': 0, 'Longitude': 0, 'Altitude': 0, 'ID_Track Decision': 'None', 'Score': -10}\n",
    "# At the end; there should be no -10 at the Score Column!!\n",
    "final_decision = pd.DataFrame(dic, columns=['PlantID','Latitude','Longitude', 'Altitude', 'ID_Track_Decision', 'Score'])\n",
    "final_decision.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, '22734_S2801']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now write functions to set the various Values of the Data Frame:\n",
    "# First, Pairwise GPS-Distance\n",
    "# Whole date frame is supposed to be df (for lookup)\n",
    "\n",
    "def GPS_Distance(coords1, coords2):\n",
    "    '''Calculates pairwise GPS distance.\n",
    "    coords: Arrays with Latitude, Longitude, Altitude '''\n",
    "    coords1, coords2 = np.array(coords1), np.array(coords2) # Make sure that things are arrays\n",
    "    \n",
    "    # Check whether everything is a float as should be:\n",
    "    assert(all(isinstance(item, float) for item in coords1))\n",
    "    assert(all(isinstance(item, float) for item in coords2))\n",
    "    \n",
    "    \n",
    "    lat1, lat2 = coords1[0], coords2[0]  # Load Latitude\n",
    "    lon1, lon2 = coords1[1], coords2[1]  # Load Longitude\n",
    "    alt1, alt2 = coords1[2], coords2[2]  # Load Altitude\n",
    "    \n",
    "    \n",
    "    x1, y1 = my_proj(lon1, lat1)  # Transform to Meters-Coordinates\n",
    "    x2, y2 = my_proj(lon2, lat2)  # Transform to Meters-Coordinates\n",
    "    \n",
    "    dist_eucl = np.linalg.norm([x1 - x2, y1 - y2, alt1 - alt2])  # Calculate the pairwise distance\n",
    "    return dist_eucl\n",
    "\n",
    "def look_up_plantID(plant_ID, lookup_df, col_name=\"PlantID\"):\n",
    "    '''Function that looks up plant_ID in dataframe - and returns a\n",
    "    list of all indices where the plants actually appear'''\n",
    "    lookup_list = lookup_df[col_name].values\n",
    "    \n",
    "    assert(isinstance(plant_ID, str))  # To make sure plantID is actually String\n",
    "    rel_indices = np.where(lookup_list == plant_ID)[0]   \n",
    "    \n",
    "    return rel_indices\n",
    "\n",
    "def set_single_plant_score(track_score):\n",
    "    '''Sets the score for a single Plant.\n",
    "    Simply takes track_score as input and return the corresponding values'''\n",
    "\n",
    "    #######################################\n",
    "    # Set Score 5/8: Single GPS entry with track_score 3\n",
    "    if track_score == 3:\n",
    "        return 5\n",
    "                \n",
    "    ########################################\n",
    "    # Set Score 4/7: Single GPS entry with track_score 2\n",
    "    elif track_score == 2:\n",
    "        return 4\n",
    "        \n",
    "    ########################################\n",
    "    # Set Score 3/6: Single GPS entry with track_score 1\n",
    "    elif track_score == 1:\n",
    "        return 3\n",
    "    \n",
    "    ########################################\n",
    "    # Set Score 0: Single GPS entry with track_score 0\n",
    "    elif track_score == 0:\n",
    "        return 0\n",
    "    \n",
    "    else:\n",
    "        print(\"Error!\")\n",
    "        return -1\n",
    "    \n",
    "    \n",
    "def set_score(plantID, df):\n",
    "    '''Function to set the score for plant ID using the Data-Table df\n",
    "    Return final score (0-10) and IT_Track Decision'''\n",
    "    assert(isinstance(plantID, str))  # To make sure plantID is actually String\n",
    "    rel_indices = look_up_plantID(plantID, df)\n",
    "    \n",
    "    # In case Index is not there:\n",
    "    if len(rel_indices) == 0:        \n",
    "        print(\"Index not found!\")\n",
    "        print(\"Put all your shit in bag. Put it in a shit museum. I don't care! But get it together!\")\n",
    "        return [-1, track_id]  # -1 is the error code\n",
    "    \n",
    "    # First find and extract ALL matching entries\n",
    "    matching_plants = df.iloc[rel_indices,:]    # Matching plants with the right PlantID; extract them.\n",
    "    coords_matching = matching_plants.loc[:,['CorrectedLatitude','CorrectedLongitude','CorrectedAltitude']] \n",
    "    scores = matching_plants.Track_Score      # Extract the Scores of all matching Plants\n",
    "    id_track = matching_plants.IDtrack        # Extract the IDTrack\n",
    "    \n",
    "    ############################################\n",
    "    # Set Score 0: All GPS Entries NaN\n",
    "    if np.sum(scores) == 0:  # If only Values with Track Score 0\n",
    "        return [0, id_track.iloc[0]]   # Return Score 0!\n",
    "    \n",
    "    ############################################\n",
    "    good_scores = np.where(scores>=1)[0]                      # Get the indices of actually valuable Scores\n",
    "    coords_matching = coords_matching.iloc[good_scores, :]    # Extract only the useful Entries (ID_Track 1/2/3)\n",
    "    scores = scores.iloc[good_scores]                      \n",
    "    id_track = id_track.iloc[good_scores]                  \n",
    "    \n",
    "    nr_entries = len(good_scores)  # Calculate the Nr of Plants with reasonable scores (that include GPS information)\n",
    "    \n",
    "    if nr_entries == 1:                           # In case there is only a single useful Entry\n",
    "        return [set_single_plant_score(scores.iloc[0]), id_track.iloc[0]]  # Set single Plant Score\n",
    "\n",
    "    \n",
    "    #######################################################################################\n",
    "    # Now we are in the case of MULTIPLE readings with track_score >= 1\n",
    "    # First filter out the fishy ones; i.e. where there are geographic mismatches\n",
    "    # between score 1,2 and 3 plants\n",
    "    assert(nr_entries>1)\n",
    "    \n",
    "    # First calculate all pairwise distances:\n",
    "    loc_dist_list = []  # Empty Local Distance List\n",
    "    \n",
    "    for (i, j) in itertools.combinations(range(nr_entries), r=2): # Works\n",
    "        coords1 = coords_matching.iloc[i,:]                                 # Ectract GPS-Coordinate 1\n",
    "        coords2 = coords_matching.iloc[j,:]                                 # Extract GPS-Coordinate 2\n",
    "        dist = GPS_Distance(coords1, coords2)                               # Calculate Distance between Entries\n",
    "        loc_dist_list.append(dist)\n",
    "        \n",
    "    max_dist = max(loc_dist_list)   # Calculate the maximum of pairwise distances\n",
    "    \n",
    "    max_score = max(scores)        # Max Score\n",
    "    highest_id = id_track.iloc[np.where(scores==max_score)[0][0]] # Get the best ID-Track for returning\n",
    "    \n",
    "    ########################################\n",
    "    # Set Score to 1: Serious Mismatch between multiple Score 2 & 3 plants!\n",
    "    if max_dist > 100:\n",
    "        # Maybe do a function here that tries to resolve things if possible\n",
    "        return [1, highest_id]\n",
    "      \n",
    "    ########################################\n",
    "    # Set Score to 2: Serious Mismatch (50-100m) between Score 2 & 3 plants!\n",
    "    if max_dist > 50:\n",
    "        return [2, highest_id]\n",
    "    \n",
    "    ###########################################################################################\n",
    "    # Now we are in the situation with no SERIOUS mismatches (>50m) but multiple useful entries\n",
    "    assert(len(scores)>1)\n",
    "    \n",
    "    max_score = max(scores)        # Max Score. Only use Max-Score Individuals for decision!  \n",
    "    \n",
    "    ########################################\n",
    "    # Set Score to 6: Multiple matching entries (<50m), and highest score 1 or 2\n",
    "    if (max_score==1) or (max_score==2):\n",
    "        return [1, highest_id]\n",
    "        \n",
    "    # Now we are in the case where highest ranking entry has score 3:\n",
    "    assert(max_score==3) # We should only be here if the max. score is 3\n",
    "    \n",
    "    #Extract the Score 3 ID-Tracks:\n",
    "    ind_score3 = np.where(scores==3)[0] # Indices where the maximum scores can be found\n",
    "    matching_plants = matching_plants.iloc[ind_score3,:]\n",
    "    coords_matching.iloc[ind_score3,:]\n",
    "    scores = scores.iloc[ind_score3]                      \n",
    "    id_track = id_track.iloc[ind_score3]\n",
    "\n",
    "\n",
    "    ########################################\n",
    "    # Set Score to 7: Multiple Matching Entries (<50m) with ID_Track Score 1,2,3. Only one Track_Score 3 \n",
    "    if len(ind_score3)==1 and (max_score==3):    # In case there is only one individual with the max. score\n",
    "        return [7, id_track.iloc[0]]\n",
    "    \n",
    "    # Now we are in the case of multiple entries with high quality entry (Track_Score = 3):\n",
    "    # Split this up in case according to the maximum distance\n",
    "    assert(len(ind_score3)>1)\n",
    "    \n",
    "    loc_dist_list = []\n",
    "    \n",
    "    for (i, j) in itertools.combinations(range(len(matching_plants)), r=2): # Works\n",
    "        coords1 = coords_matching.iloc[i,:]     # Ectract GPS-Coordinate 1\n",
    "        coords2 = coords_matching.iloc[j,:]     # Extract GPS-Coordinate 2\n",
    "        dist = GPS_Distance(coords1, coords2)   # Calculate Distance between Entries\n",
    "        loc_dist_list.append(dist)\n",
    "\n",
    "        max_dist = max(loc_dist_list)\n",
    "\n",
    "        if max_dist < 1.0:\n",
    "            ############################################\n",
    "            # Set Score 10\n",
    "            # Multiple Plants with ID_Track Score 3 recorded\n",
    "            # All GPS Entries are within 1 m\n",
    "            return [10, id_track.iloc[0]]\n",
    "\n",
    "        if max_dist < 5.0:\n",
    "            ############################################\n",
    "            # Set Score 9\n",
    "            # Multiple Plants with ID_Track Score 3 recorded\n",
    "            # All GPS Entries are within 5 m\n",
    "            return [9, id_track.iloc[0]]\n",
    "\n",
    "        if max_dist < 50.0:\n",
    "            ############################################\n",
    "            # Set Score 8\n",
    "            # Multiple Plants with ID_Track Score 3 recorded\n",
    "            # All GPS Entries are within 50 m\n",
    "            return [8, id_track.iloc[0]]\n",
    "                    \n",
    "    print(\"WUBBA LUBBA DUB DUB \\nSomething is seriously wrong!\")\n",
    "    return ([-1, id_track_iloc[0]])\n",
    "\n",
    "    ############################################\n",
    "    # Set Score of Plants to 2: Best Guess \n",
    "    # GPS Reading, but plant is likely mislabeld. Could be accurate, but resolution\n",
    "    # Speculative\n",
    "     \n",
    "#set_score(\"J1548\")\n",
    "set_score(\"S2801\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing ID-Track Decisions...\n",
      "Done. \n",
      "Extracting the right coordinates...\n",
      "Done. \n",
      "Saving...\n",
      "Done. \n"
     ]
    }
   ],
   "source": [
    "# Code that looks up the ID_Track Decision for every single plant:\n",
    "# Assumes df is the full Data-Frame\n",
    "# Assumes final_decision is the data_frame into which one writes in the end:\n",
    "#score_decision = [ for i in]\n",
    "print(\"Doing ID-Track Decisions...\")\n",
    "final_dec = np.array([set_score(i, df) for i in final_decision.PlantID])  # Takes about 30 secs to run\n",
    "print(\"Done. \")\n",
    "\n",
    "score_vec, id_track_dec = final_dec[:, 0], final_dec[:, 1]     # Extract score and id_track\n",
    "final_decision.Score = score_vec                               # Set the Score \n",
    "final_decision.ID_Track_Decision = id_track_dec                # Set the ID-Track\n",
    "\n",
    "# Write a function that finds Latitude/Longitude/Altitude:\n",
    "def geo_info(iD_Track, df):\n",
    "    '''Finds the Latitude, Longitude, Altitude for a given iD_Track in Dataframe df'''\n",
    "    index = np.where(df.IDtrack.values == iD_Track)[0]\n",
    "    assert(len(index)==1)  # Requires that the length of the Index List is one. If more we have a Problem\n",
    "    coords_corr = df.loc[index[0], ['CorrectedLatitude','CorrectedLongitude','CorrectedAltitude']]\n",
    "    return coords_corr.values\n",
    "\n",
    "print(\"Extracting the right coordinates...\")\n",
    "coordinates = np.array([geo_info(i, df) for i in final_decision.ID_Track_Decision])\n",
    "print(\"Done. \")\n",
    "final_decision.loc[:,['Latitude','Longitude','Altitude']]=coordinates\n",
    "final_decision.head(10)\n",
    "\n",
    "# Write a quick script to set 6681 to 6700 to Final Score 0 (this was unwanted Duplicates!!)\n",
    "for i in range(6681,6701):     # Remember the Python Indexing\n",
    "    plantID='S'+str(i)\n",
    "    index=np.where(final_decision.PlantID==plantID)[0][0]\n",
    "    final_decision.loc[index,'Score']=0\n",
    "print(\"Saving...\")\n",
    "\n",
    "\n",
    "final_decision.to_csv(output_folder + id_track_decision_output_file_name, sep = '$')\n",
    "print(\"Done. \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'5': 12028, '4': 2620, '7': 1528, '1': 601, '8': 361, '3': 338, '9': 311, '0': 237, '2': 119, '10': 72})\n",
      "Total length of data: 18215\n"
     ]
    }
   ],
   "source": [
    "#print([i for i in final_dec])\n",
    "# Do Statistics on the Score-Vector\n",
    "print(Counter(score_vec))  # Print Counter how often everything was scored!\n",
    "final_decision.head(10)\n",
    "print(\"Total length of data: %i\" %len(final_decision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Google-Maps Extractor successfully loaded!\n",
      "Creating .kml\n",
      "final_scores.kml successfully created! \n"
     ]
    }
   ],
   "source": [
    "# Extract GPS-Coordinates for Map:\n",
    "# Not Necessary for correction; it is just a nice toy\n",
    "# Assume data-frame is final_decision\n",
    "# \n",
    "google_map_file_name =  \"final_scores.kml\"\n",
    "######################################################## Style Files\n",
    "style = simplekml.StyleMap()  # Create a style map for highlight style and normal style FOR UNCORRECTED\n",
    "style.normalstyle.labelstyle.scale = 0\n",
    "style.normalstyle.iconstyle.icon.href = \"http://maps.google.com/mapfiles/kml/paddle/wht-blank.png\"\n",
    "style.highlightstyle.labelstyle.scale = 1\n",
    "style.highlightstyle.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/pushpin/blue-pushpin.png'\n",
    "\n",
    "style2 = simplekml.StyleMap()  # Create a style map for highlight style and normal style FOR UNCORRECTED\n",
    "style2.normalstyle.labelstyle.scale = 0\n",
    "style2.normalstyle.iconstyle.icon.href = \"http://maps.google.com/mapfiles/kml/paddle/ylw-blank.png\"\n",
    "style2.highlightstyle.labelstyle.scale = 1\n",
    "style2.highlightstyle.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/pushpin/blue-pushpin.png'\n",
    "\n",
    "style3 = simplekml.StyleMap()  # Create a style map for highlight style and normal style FOR UNCORRECTED\n",
    "style3.normalstyle.labelstyle.scale = 0\n",
    "style3.normalstyle.iconstyle.icon.href = \"http://maps.google.com/mapfiles/kml/paddle/orange-blank.png\"\n",
    "style3.highlightstyle.labelstyle.scale = 1\n",
    "style3.highlightstyle.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/pushpin/blue-pushpin.png'\n",
    "\n",
    "style4 = simplekml.StyleMap()  # Create a style map for highlight style and normal style FOR UNCORRECTED\n",
    "style4.normalstyle.labelstyle.scale = 0\n",
    "style4.normalstyle.iconstyle.icon.href = \"http://maps.google.com/mapfiles/kml/paddle/grn-blank.png\"\n",
    "style4.highlightstyle.labelstyle.scale = 1\n",
    "style4.highlightstyle.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/pushpin/blue-pushpin.png'\n",
    "############################################################\n",
    "def set_score_style(score):\n",
    "    '''Short function that sets the style for every score'''\n",
    "    score=int(score)\n",
    "\n",
    "    if score<=2:     # GPS Mismatch\n",
    "        return style\n",
    "\n",
    "    if score <= 5:  # Single GPS Entry\n",
    "        return style2\n",
    "\n",
    "    if score <= 8:  # Multiple GPS Entries\n",
    "        return style3\n",
    "\n",
    "    if score > 8:    # Great Entry\n",
    "        return style4\n",
    "\n",
    "\n",
    "    print(\"This was not supposed to happen!\")\n",
    "    return 0\n",
    "\n",
    "class KML_extractor(object):\n",
    "    '''\n",
    "    Class containing various methods to export to google Earth.\n",
    "    '''\n",
    "    kml = 0\n",
    "\n",
    "    def __init__(self):\n",
    "        '''\n",
    "        Constructor.\n",
    "        '''\n",
    "        print(\"Google-Maps Extractor successfully loaded!\")\n",
    "        # my_proj = Proj(proj='utm',zone=\"31T\",ellps='WGS84',units='m')   # Prepare the Longitude/Latidude to Easting/Northing transformation\n",
    "\n",
    "\n",
    "    def create_kml(self, names, coords, scores, output_path):\n",
    "        '''Method to extract  Google Earth Data'''\n",
    "        kml = simplekml.Kml()  # Load the KML Creater\n",
    "        \n",
    "\n",
    "       \n",
    "        for i in range(len(names)):\n",
    "            if math.isnan(coords[i, 0]):   # Check for nans\n",
    "                continue  # Stop if GPS-data marked missing \n",
    "            # Coords: Longitude - Latitude - Altitude    \n",
    "            pnt = kml.newpoint(name=names[i], coords=[tuple(coords[i, :])]) \n",
    "            pnt.stylemap = set_score_style(scores[i])\n",
    "            pnt.comment = str(scores[i])\n",
    "            pnt.altitudemode = 'absolute'\n",
    "            pnt.extrude = 1\n",
    "\n",
    "        kml.save(output_path)\n",
    "\n",
    "        \n",
    "kml_extractor = KML_extractor() # Create the KML Extractor\n",
    " \n",
    "coords = final_decision.loc[:, ['Longitude','Latitude','Altitude']].values   # Notice that Longitude and Latitude are swapped\n",
    "#good_inds = ~coords.isnull().any(axis=1) # Extract only values with actual coordinates\n",
    "\n",
    "names = final_decision.PlantID.values\n",
    "scores = final_decision.Score.values\n",
    "\n",
    "print(\"Creating .kml\")\n",
    "\n",
    "kml_extractor.create_kml(names, coords, scores, output_folder + google_map_file_name)\n",
    "print(google_map_file_name + \" successfully created! \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Area\n",
    "Created to play around with and try out stuff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Small Script that calculates all pairwise Distances of all plants and plots and gives information about their distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "image/png": 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gMUN8+9vf7nYVJEkyWEiSpOYYLCRJUmMMFpIkqTEGC0mS1BiDhSRJaozBQpIk\nNcZgIUmSGmOwkCRJjTFYSJKkxhgsJElSYwwWM9CKFSu2ud67oEqSJorBYgb61a9+tdV1/f393gVV\nkjRhDBYz0J133snpp5/O0qVLH7ZuYGBgy11QFy9ebLiQJDXKYDEDrV69mjPOOGPEYNHq7/7uv9tz\nIUlqlMFiB7ZhwwOsW7eWgYGBbldFkjRDGCwkSVJjDBaSJKkxBosZaM2aNcD2TzuVJKlpBosZaO3a\ntQDOnZAkTTqDhbSDWrp06VZPS5aksTJYzEBDQyHStixdunRUpyVLUicMFjPQpZcuABwKkSRNPoPF\njLQZgO9+97uNfxv1PiOSpG0xWMxgV155JTfffPOoy29vzH2q3Wekr6+v21WYEmyHh9gWFdvhIbbF\n5OtqsEjy9iRLkjyQ5OdJDu5mfWaiToZDtjfm3nqfkakwzOIbRmUqtMNUmQg6FdpiKrAdHmJbTL6u\nBYskrwc+AXwQOAi4CViQZM9u1WlHMNK1Lbb1YTA09NHf3z+s3IoVK7Y7LDJVPmymutZ2Gvr3jTfe\nOK3azomgkoZ0s8diLnBBKeXCUsrtwMnAWuBNXazTjHPRRRdt+ZC68cYbOeecc+o1qX/uxKtffRwL\nFy582LZDQx8HHHAgBxzwDF7zmuO2rLv99tu3Oywy9GFz8803j+pDslvzN2688UYOO+wwTj755K58\nMLZ+KA/9+5ZbbpmQD+rxhr2m/o+mYujs7+9n4cKFU2KYb6yG2nXdunWN7G+6/n8vXbqU9773vbz3\nve9trC00el0JFkkeCfQAVwwtK6UU4EfAYd2o00y1YMECrrrqKs444wx6e3u59NJL6zWl/rmZjRsf\n5Mgjj9ryRz/0c/Hixaxbt5b169exfv0DPPjg+i37Xb169aiHRQYGBoZ9SI70ZjXa+RvbesMY6c1r\nNG+Mt9xyCz//+c+54IILOnrjmwoTWfv7+3nggQe2Waa9R2QsgWXoQ3fo/2jhwoX84he/GPWHxUi9\nMuMJTk1/UPX393PAAQfyghccztOffiBz584dUxDudmAa+v9dv3799gtvx2j/Jrf299y6bGu/d9v6\nGxrP39fSpUuZN28e8+bNa6Qt1Jmdu/S6ewKPAJa3LV8OHDhC+UcD3HbbbRNcralt5cqVY9hqZ/75\nn08Fql6GqqeitJXZzIYND/D1r38deASvetVrePe7/5HzzrugrdxODJ1x0jop9H3vex+vfe1r2WOP\nPVi5ciVMBxZpAAAKTUlEQVRXXHEFRx11FKtWrQLg7LPPBuDSSy/l6quv5tRTT6OUwqmn/hM33HDD\nlrLr1lVXDD3ppJNG3N8ee+zBTTfdxLx58wDYf//9ednLXgbA4Ycfzg9/+EOuvPJKHvnIR7Lffvsx\nMDDAqaeexqZNmzjyyBfyile8gj322GNYe15xxRU87nGP27Ls0ksv5Y477hh21K11ALjiiis46KCD\nOPPM/0MphTPP/Ah77jnyCF57/QF+97vfceGFF/LGN76RpzzlKSxZsgSAb3zjG1x33XUAXHPNNcPq\nM9J+fvOb33DGGR9l8+YNfOQjH9nSlu3HePHFF29plyRb/s8OOeSQrR5zq6F2fPDBDWzYUIWYF7/4\nb9m4cT2bNm0ctt+t7WvJkiWcccYZW8p+5jOfGdVrb03r/vbbb78ty++6664xjakvWbKE9euroLph\nwybOPvts9txzz2H7bjXUJu3//0P1+ulPf7rld3gyDf0u3XPPPeOeW7BkyZItf5MXXXTRw9pi5cqV\nfP/73+eqq64myZZ2GKlthurV+v+9tTbc1rqR/g7aDf3OD7n33ntZtGjRuNpiumv57Hz0ZLxeqo6C\nyZXkCcBdwGGllGtblp8FvLCU8oK28icAX5ncWkqSNKOcWEr56kS/SLd6LAaATcDebcv34uG9GAAL\ngBOB3wEOmEmSNHqPBp5C9Vk64brSYwGQ5OfAtaWUd9XPA/QDnyyl/GtXKiVJksalWz0WAPOALyW5\nHriO6iyRxwJf7GKdJEnSOHQtWJRSvlFfs+JDVEMiNwLHlFIefqEFSZI0LXRtKESSJM083itEkiQ1\nxmAhSZIaM2WCRZK/SPK5JL9NsjbJr5KcXl+ls7Xcc5L8tL5x2e+T/NMI+/rvSW6ry9yU5KWTdyQT\nZ6bftC3JqUmuS3JfkuVJvpPkgLYyuyQ5L8lAktVJLk6yV1uZfZNckmRNkmVJzkoyZX7XO1W3y+Yk\n81qW7RDtkOSJSS6qj3Nt/fc8p63Mh5LcXa//YZL929bvkeQrSQaTrKzfZ3ad3CMZnyQ7Jflwy/vj\nr5N8YIRyM64tkhye5HtJ7qr/Dl45QplxH/doPlu6aVvtkGTnJB9PcnOS++syX0p1zajWfUxOO5RS\npsQDOAb4PHAU1fm2xwLLgLNayjwOWAp8CZgNvA5YA7y5pcxhwIPAe6iu4nkGsB54ZrePcZzt83qq\na3i8EXgGcAFwL7Bnt+vW4DH+APgf9f/ts4F/p7p2yWNayny6XvYiqpvXXQP8R8v6nYDFVOdrP7v+\nvboH+Ei3j2+MbXIw8FvgBmDejtQOwO7AEuBzVLcA+Avgb4CntpQ5pf47eAXwl8C/Ab8BHtVS5lJg\nEfB84AXAL4Evd/v4OmyL99f/fy8Bngz8HXAf8I6Z3hb1MX8IeDXV9Y9e2bZ+3MfNKD5buv3YVjsA\nu9V/68cBTwcOAX4OXNe2j0lph6431nYa8n3Ar1uev5Xq4lo7tyz7GHBry/OvAd9r289C4PxuH884\n2+LnwDktzwP8AfjnbtdtAo95T6priL+wfr4bVUh8TUuZA+syh9TPX0oVLPdsKfMWYGXr7810eAB/\nAtwB/DVwJXWw2FHaATgTuGo7Ze4G5rY83w14AHhd/Xx23S4HtZQ5BtgI7NPtY+ygLb4PfLZt2cXA\nhTtSW9T1bw8W4z7u0Xy2TKXHSO0wQpnnUwWQJ012O0z1btHdqZLokEOBn5ZSNrYsWwAcmGRW/fww\nqpuZ0VZm2t7cLDvuTdt2p7qxydDvQA/VKdKt7XAH1YXVhtrhUGBxKaX1zmgLgFnAsya6wg07D/h+\nKeXHbcufz47RDq8A/jPJN+qhsUVJ3jy0MslTgX0Y3g73AdcyvB1WllJuaNnvj6h+r/5qog+gQdcA\nRyV5OkCS5wL/jaqXb0driy0aPO7RfLZMN0Pvn6vq55PWDlM2WNRjZO8A/r+Wxfsw8o3LhtZtq8w+\nTF/bumnbdD6urUoS4GzgZ6WUW+vF+wAb6jeOVq3tMJrfkSkvyfHA84BTR1i9NztGO+xH9Q3qDuBo\nqveCTyZ5Q71+H6o3xW39XexDNYSwRSllE1VYnS7tAFXvzdeB25NsAK4Hzi6lfK1evyO1Raumjnsm\n/L1skWQXqt+Zr5ZS7q8XT1o7TPgFspJ8jGoMbGsKMLuU8suWbf6caizo66WUL2zvJRj5lp3tZWbi\nBTtm6nEBnA88E3jhKMqOth2mRVsleRJVqPrbUsqDnWzKDGoHqi8+15VSTquf35TkWVRh48vb2G40\n7TDd/nZeD5wAHA/cShU6z0lydynlom1sNxPbYjSaOO7UP6dV2yTZGfgmVb3fNppNaLgdJqPH4v9Q\nTTbc2mM21eQ0oJoFDvyY6pvqW9r2tYyRb1zWmli3Vmakm5tNF53etG1aS3Iu8DLgyFLK3S2rlgGP\nSrJb2yat7TDS///Q8+nSVj3A44HrkzyY5EGqSZrvqr+tLgd22QHaYSlwW9uy26gmL0J1jGHbfxfL\n6udbJHkEsAfTpx0AzgI+Vkr5ZinlllLKV4D5PNSjtSO1RavxHveyljIj7QOmUdu0hIp9gaNbeitg\nEtthwoNFKeWPpZRfbuexEbb0VFwJ/AJ40wi7WwgcUTfGkKOBO0opgy1ljmrb7m/r5dNS/a31elqO\nqx4qOIpq7HXGqEPFq4AXl1L621ZfTzXRqLUdDqD6oBlqh4XAs1NdLn7I0cAg1Te96eBHVGdyPA94\nbv34T6pv6UP/fpCZ3w5XU01KbXUg8HuAUsoSqjfC1nbYjWq8uLUddk9yUMs+jqL6MLp2Yqo9IR7L\nw78xbqZ+D9/B2mKLBo77upYy2/tsmdJaQsV+wFGllJVtRSavHbo9u7Vl5ukTgF8BPwSeSJWa9gb2\nbimzG9UM4C9RdZO/Hrgf+J8tZQ4DNvDQ6aanU52mOd1PN30d1Uzn1tNN/wg8vtt1a/AYz6c6a+Hw\n1v9/4NFtZZYAR1J9s7+ah59meRPVUNpzqGY9Lwc+3O3jG2fbbDkrZEdpB6pJquupvpU/jWooYDVw\nfEuZf67/Dl5BFcb+rX4faT3V8AdUYexgqgmPdwAXdfv4OmyL/0s1OfdlVKfdvoZqvPyjM70tgF2p\nAvXzqMLUu+vn+zZ13Izis6Xbj221A9UcvO9She5nt71/PnKy26HrjdVyQH9P1d3f+tgMbGor92zg\nKmBt/Yf2vhH2dRxwO9UH8c1UNzfr+jE20EZvo7p2wQNUyfL53a5Tw8e3eYTfgU3AG1vK7AJ8imp4\naDVVQt+rbT/7Ul0D436qD9OPAzt1+/jG2TY/Zniw2CHageqD9Ob67/0W4E0jlDm9fjNcSzWDff+2\n9btT9fYMUgXXzwKP7faxddgOu1LdEXoJ1XUFfkV1jZ6d28rNuLagGgYc6b3hC00e92g+W6ZqO1CF\nzfZ1Q8+PmOx28CZkkiSpMVP2dFNJkjT9GCwkSVJjDBaSJKkxBgtJktQYg4UkSWqMwUKSJDXGYCFJ\nkhpjsJAkSY0xWEiSpMYYLCRJUmMMFpIkqTH/P6vYeHC5nq4cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x699b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sJElSMQYLSZJUjMFiElq7du2g870KqiRptBgsJqHf/va3A87r6uryKqiSpFFj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\noejMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5a7b790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZIZIkTX8TcSjkkcDXgYcBq4CfAgeXUv5Uz18AbALOAXYGfgC8ZQLqscPo7+/v\ndhUkSQImZvDmiCMpSynrgLfWD43bThx//Lt50YtexD777NPtykiSdnDeK2Ta28zGjevstZAkTQkG\nC0mS1BiDhSRJaozBQpIkNcZgIUmSGmOwkCRJjTFYSJKkxhgsZpC+vj76+vq6XQ1J0g7MYDFDLF++\nnAMOmMMBB8wxXEiSusZgMUOsWbOGtWvvYe3ae7xYliSpawwWM8R//dd/dbsKkiQZLGYKg4UkaSow\nWEiSpMYYLCRJUmMMFpIkqTEGC0mS1BiDhSRJaozBYgZatWpVt6sgSdpBGSxmIC+QJUnqFoOFJElq\njMFCkiQ1xmCxA/IuqJKkiWKwmIFWr1497Ly+vj7vgipJmjAGixlozZo1w87r7+/3LqiSpAljsJAk\nSY0xWEiSpMYYLCRJUmMMFpIkqTEGixlozZo1nvEhSeoKg8UMtHDhKey772O58soru10VSdIOxmAx\nA5WyiU2bNvKxj32M5cuXD1tuLDcr8+JakqSRGCxmsG9/+9tcd911w87v9DoWXlxLkrQ9BosZrsmL\nYE21i2v19vZ2uwpTwlRoh+XLl3PiiSeO2EM2GaZCW0wFtsNWtsXk62qwSPKWJMuS3JvkZ0me1s36\n7AjGcvhjrCZ6ZzNTPjDG205ToR2WL1/OSSedZLCYImyHrWyLyde1YJHk5cCngA8ABwHXAhck2b1b\ndZqJ2nsWWp+vXr2aBQsWjDjIc3BMRV9f3312GtsbbzG4s7nuuuumxLfZ4XT72/ZU2SlLUhO62WOx\nAPhsKeXMUspNwBuBe4DXdbFOM865557LL37xiyF3nL/73e845ZRTeeYzD2XJkiX3WXZwTMX++x/A\n/vs/nn/4h5dsmbd8+fJRj7fo7+8f1Y6zWwNDd6Qd+3hDlIN3p7bBv+/atWsbWV9Tf+/JDu/Lly/n\nHe94B+94xzsaawuNXleCRZL7A/OAiwanlVIK8CPgkG7Uaaa65JJLWLx48ZYdZ2uPxeLFi4HNbNiw\ngcMPn79N7wTA9ddfz9q197Bu3VrWrbuX9eu3vkHXrFkz5vEWQ31YjXZg6EgfGEN9eE3kjnAq7GT7\n+vq49957RyzT2i5jDVF9fX0sWbJky99oyZIlXHnllaPeWTS9Y5nIHdVo1z3U33+q9H6tW7du3Osa\n7XtyuPdz67Th/u9Geg+N5/21fPlyFi5cyMKFCxtpC3VmVpded3fgfsDKtukrgQOGKP9AgBtvvHGC\nqzW1jXTE6Os0AAAKaUlEQVQ79OHN4l3vOgGojjWeeurpW+ZcddVV9W+bWb/+Xk477TROP/2zbNiw\njre97a2cfvpn29Z1P2ATABdeeOGWqccffzwveclL2G233Vi9ejUXXXQR8+fP33KX1VNOOQWA888/\nn8suu4wTTngfpRROOOGdXHPNNVvKrl17DwCvec1rhlzfbrvtxrXXXsvChQsB2G+//Tj66KMBOPTQ\nQ7nwwgu55JJLuP/978++++5Lf38/J5zwPjZt2sThhz+L5z//+ey2227btOdFF13E4x73uC31u/nm\nm4ds98E6AFx00UUcdNBBfPzjn6SUwsc//mF2333oI3jt9Yeqp+jMM8/kVa96FY9+9KNZtmwZAGef\nfTYf+chHtik70np+85vfcNJJH2Xz5vV8+MMf3tKW7dt4zjnnbGmXJFv+ZkcccQRXXXXVkK/XarAd\nN2xYz/r1VYh5znOOYOPGdWzatHGb9Q7XhsuWLeOkk07a8rcZans60b6+QX/84x/HfUx9uHW3GmyT\n9r//4LKXXnrplv/hyTT4v3T77bc30g6D78mzzjrrPm2xevVqvve977F48WUk2dIOQ7XNYL1a/z+G\na8OR5o3m/2bwf37QHXfcwdKlS8fVFtNdy77zgZPxeqk6CiZXkocDfwQOKaX8vGX6ycCzSil/21b+\nFcDXJreWkiTNKMeVUr4+0S/SrR6Lfqqvvnu2Td+D+/ZiAFwAHAf8DvCAmSRJo/dA4NFU+9IJ15Ue\nC4AkPwN+Xkp5W/08QB9wainl37pSKUmSNC7d6rEAWAh8JcnVwBVUZ4k8GPhyF+skSZLGoWvBopRy\ndn3Nig9SHRL5BXBUKWXyruAkSZIa1bVDIZIkaebxXiGSJKkxBgtJktSYKRMskjwqyeeT/DbJPUn+\nJ8mJ9VU6W8s9Ocml9Y3Lfp/knUOs66VJbqzLXJvkuZO3JRNnpt+0LckJSa5IcmeSlUm+nWT/tjI7\nJzk9SX+Su5Kck2SPtjJ7J/l+kruTrEhycpIp87/eqbpdNidZ2DJth2iHJI9Icla9nffU7+e5bWU+\nmOS2ev6FSfZrm79bkq8lGUiyuv6cecjkbsn4JNkpyYdaPh9/neS9Q5SbcW2R5NAk303yx/p98IIh\nyox7u0ezb+mmkdohyawkn0hyXZI/12W+kuqaUa3rmJx2KKVMiQdwFPAFYD7V+bbHACuAk1vKPBRY\nDnwFmAO8DLgbeH1LmUOADcDbqa7ieRKwDnhCt7dxnO3zcqpreLwKeDzwWeAOYPdu163BbTwP+P/r\nv+2BwH9TXbvkQS1l/r2e9myqm9ddDvykZf5OwPVU52sfWP9f3Q58uNvbN8Y2eRrwW+AaYOGO1A7A\nrsAy4PNUtwB4FPB3wGNayry7fh88H3gS8B3gN8ADWsqcDywFngr8LXAL8NVub1+HbfGv9d/v74F9\ngH8A7gT+caa3Rb3NHwT+F9X1j17QNn/c280o9i3dfozUDsAu9Xv9xcDjgKcDPwOuaFvHpLRD1xtr\nOw15PPDrludvorq41qyWaR8Dbmh5/p/Ad9vWswQ4o9vbM862+Bnw6ZbnAf4AvKvbdZvAbd4d2Ex1\nNdbBN8864EUtZQ6oyzy9fv5cqmC5e0uZNwCrW/9vpsMD+AvgZuD/Ay6hDhY7SjsAHwcWb6fMbcCC\nlue7APcCL6ufz6nb5aCWMkcBG4G9ur2NHbTF94DPtU07BzhzR2qLuv7twWLc2z2afctUegzVDkOU\neSpVAHnkZLfDVO8W3ZUqiQ46GLi0lLKxZdoFwAFJZtfPD6G6mRltZabtzc2y4960bVegsPV/YB7V\nKdKt7XAz1YXVBtvhYOD6UkrrndEuAGYDT5zoCjfsdOB7pZSL26Y/lR2jHZ4PXJXk7PrQ2NIkrx+c\nmeQxwF5s2w53Aj9n23ZYXUq5pmW9P6L6v3rGRG9Agy4H5id5HECSpwDPpOrl29HaYosGt3s0+5bp\nZvDzc039fNLaYcoGi/oY2T8C/7dl8l4MfeOywXkjldmL6Wukm7ZN5+0aVpIApwA/LaXcUE/eC1hf\nf3C0am2H0fyPTHlJjgX+BjhhiNl7smO0w75U36BuBo6k+iw4Nckr6/l7UX0ojvS+2IvqEMIWpZRN\nVGF1urQDVL033wBuSrIeuBo4pZTyn/X8HaktWjW13TPh/bJFkp2p/me+Xkr5cz150tphwi+QleRj\nVMfAhlOAOaWUW1qW+WuqY0HfKKV8cXsvUT9GuiDH9uZPVzN1uwDOAJ4APGsUZUfbDtOirZI8kipU\nHVFK2dDJosygdqD64nNFKeV99fNrkzyRKmx8dYTlRtMO0+2983LgFcCxwA1UofPTSW4rpZw1wnIz\nsS1Go4ntTv1zWrVNklnAN6nq/ebRLELD7TAZPRafpBpsONxjDtXgNKAaBQ5cTPVN9Q1t61rB0Dcu\na02sw5UZ6uZm00WnN22b1pJ8BjgaOLyUclvLrBXAA5Ls0rZIazsM9fcffD5d2moe8FfA1Uk2JNlA\nNUjzbfW31ZXAzjtAOywHbmybdiPV4EWotjGM/L5YUT/fIsn9gN2YPu0AcDLwsVLKN0spvyqlfA1Y\nxNYerR2pLVqNd7tXtJQZah0wjdqmJVTsDRzZ0lsBk9gOEx4sSil/KqXcsp3HRtjSU3EJcCXwuiFW\ntwQ4rG6MQUcCN5dSBlrKzG9b7oh6+rRUf2u9mpbtqg8VzKc69jpj1KHihcBzSil9bbOvphpo1NoO\n+1PtaAbbYQlwYKrLxQ86Ehig+qY3HfyI6kyOvwGeUj+uovqWPvj7BmZ+O1xGNSi11QHA7wFKKcuo\nPghb22EXquPFre2wa5KDWtYxn2pn9POJqfaEeDD3/ca4mfozfAdriy0a2O4rWspsb98ypbWEin2B\n+aWU1W1FJq8duj26tWXk6cOB/wEuBB5BlZr2BPZsKbML1Qjgr1B1k78c+DPwv1vKHAKsZ+vppidS\nnaY53U83fRnVSOfW003/BPxVt+vW4DaeQXXWwqGtf3/ggW1llgGHU32zv4z7nmZ5LdWhtCdTjXpe\nCXyo29s3zrbZclbIjtIOVINU11F9K38s1aGAu4BjW8q8q34fPJ8qjH2n/hxpPdXwPKow9jSqAY83\nA2d1e/s6bIsvUQ3OPZrqtNsXUR0v/+hMbwvgIVSB+m+owtQ/18/3bmq7GcW+pduPkdqBagzeuVSh\n+8C2z8/7T3Y7dL2xWjbo1VTd/a2PzcCmtnIHAouBe+o32vFDrOvFwE1UO+LrqG5u1vVtbKCN3kx1\n7YJ7qZLlU7tdp4a3b/MQ/wObgFe1lNkZOI3q8NBdVAl9j7b17E11DYw/U+1MPwHs1O3tG2fbXMy2\nwWKHaAeqHel19fv9V8DrhihzYv1heA/VCPb92ubvStXbM0AVXD8HPLjb29ZhOzyE6o7Qy6iuK/A/\nVNfomdVWbsa1BdVhwKE+G77Y5HaPZt8yVduBKmy2zxt8fthkt4M3IZMkSY2ZsqebSpKk6cdgIUmS\nGmOwkCRJjTFYSJKkxhgsJElSYwwWkiSpMQYLSZLUGIOFJElqjMFCkiQ1xmAhSZIaY7CQJEmN+X9X\n+WeSV4nw9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x982ca50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Individuals with multiple valueable Readings: 2490\n",
      "Number of Maximum Distances >500m: 180\n",
      "Number of Maximum Distances >100m: 312\n",
      "Number of Maximum Distances >50m: 412\n",
      "Number of Maximum Distances >10m: 924\n",
      "\n",
      "Total Number of Individuals with valuable readings where automatic resolution might be possible\n",
      "Number of Maximum Distances >500m: 13\n",
      "Number of Maximum Distances >100m: 24\n",
      "Number of Maximum Distances >50m: 46\n",
      "Number of Maximum Distances >10m: 126\n"
     ]
    }
   ],
   "source": [
    "# df is the full merged data frame\n",
    "df.head(10)\n",
    "counter = 0\n",
    "\n",
    "len(df)\n",
    "\n",
    "dist_list = []   # List where all the distances enter.\n",
    "max_list = []    # List where all maximums enter.\n",
    "min_list = []    # List where all minimums enter.\n",
    "\n",
    "for plant_id in df.PlantID.iloc[:]: # Iterate over all Plant IDs\n",
    "    inds=look_up_plantID(plant_id,df)\n",
    "    matching_plants = df.iloc[inds,:]\n",
    "    matching_plants = matching_plants[matching_plants.Track_Score>=3] # Extract plants with track_score at least 3\n",
    "    \n",
    "    \n",
    "    #matching_plants_awesome = matching_plants[matching_plants.Track_Score==3]\n",
    "    \n",
    "    \n",
    "    coords_matching = matching_plants.loc[:,['CorrectedLatitude','CorrectedLongitude','CorrectedAltitude']]\n",
    "    \n",
    "    loc_dist_list=[]\n",
    "\n",
    "    \n",
    "    # Calculate all pairwise distances:\n",
    "    # Iterate over all pairs of Individuals:\n",
    "    for (i, j) in itertools.combinations(range(len(matching_plants)), r=2): # Works\n",
    "        coords1 = coords_matching.iloc[i,:]\n",
    "        coords2 = coords_matching.iloc[j,:]\n",
    "        dist = GPS_Distance(coords1, coords2)\n",
    "        loc_dist_list.append(dist)\n",
    "\n",
    "    dist_list += loc_dist_list\n",
    "    \n",
    "    if len(loc_dist_list)>0:\n",
    "        #if np.max(loc_dist_list)>=50:  # Print Plants which are at least 50 m away from each other\n",
    "            #print(plant_id)\n",
    "        max_list.append(np.max(loc_dist_list))\n",
    "        min_list.append(np.min(loc_dist_list))\n",
    "        \n",
    "                \n",
    "    if counter % 500 == 0:\n",
    "        print(\"Doing Step: %i \" %counter)\n",
    "    counter += 1\n",
    "\n",
    "bins = np.linspace(-0.5, 1000.5, 1001)\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(dist_list, range=[0,1000], bins=bins)\n",
    "plt.title(\"All lengths\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(max_list, range=[0,1000], bins=bins)\n",
    "plt.title(\"Maximum Distances\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(min_list, range=[0,1000], bins=bins)\n",
    "plt.title(\"Minimum Distances\")\n",
    "plt.show()\n",
    "\n",
    "max_dists=np.array(max_list)\n",
    "min_dists=np.array(min_list)\n",
    "\n",
    "print(\"Total Number of Individuals with multiple valueable Readings: %i\" % len(max_dists))\n",
    "print(\"Number of Maximum Distances >500m: %i\" % np.sum(max_dists >= 500))\n",
    "print(\"Number of Maximum Distances >100m: %i\" % np.sum(max_dists >= 100))\n",
    "print(\"Number of Maximum Distances >50m: %i\" % np.sum(max_dists >= 50))\n",
    "print(\"Number of Maximum Distances >10m: %i\" % np.sum(max_dists >= 10))\n",
    "\n",
    "# Some Statistics where resolution might be possible:\n",
    "print(\"\\nTotal Number of Individuals with valuable readings where automatic resolution might be possible\")\n",
    "print(\"Number of Maximum Distances >500m: %i\" % np.sum((max_dists >= 500) * (min_dists <=10) ) )\n",
    "print(\"Number of Maximum Distances >100m: %i\" % np.sum((max_dists >= 100) * (min_dists <=10) ) )\n",
    "print(\"Number of Maximum Distances >50m: %i\" % np.sum((max_dists >= 50)* (min_dists <=10) ) )\n",
    "print(\"Number of Maximum Distances >10m: %i\" % np.sum((max_dists >= 10) * (min_dists <=10) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>Ea</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A          B    C  D     Ea   Fe\n",
       "0  1.1111 2013-01-02  1.3  1   test  foo\n",
       "1  1.1111 2013-01-02  1.3  2  train  foo\n",
       "2  1.1111 2013-01-02  1.3  3   test  foo\n",
       "3  1.1111 2013-01-02  1.3  4  train  foo"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A test data frame created for playing around:\n",
    "test_df = pd.DataFrame({ 'A' : 1.1111, 'B' : pd.Timestamp('20130102'), 'C' : pd.Series(1.3,index=list(range(4)),dtype='float32'),\n",
    "                                      'D' : np.array([1,2,3,4],dtype='int32'),\n",
    "                                      'Ea' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                                      'Fe' : 'foo' })\n",
    "test_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>Ea</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>5</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1111</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A          B    C  D     Ea   Fe\n",
       "0  1.1111 2013-01-02  1.3  1   test  foo\n",
       "1  1.1111 2013-01-02  1.3  2  train  foo\n",
       "2  1.1111 2013-01-02  1.3  5   test  foo\n",
       "3  1.1111 2013-01-02  1.3  4  train  foo"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.iloc[2,3] = 5\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>Ea</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D     Ea   Fe\n",
       "2  1.0 2013-01-02  1.0  3   test  foo\n",
       "3  1.0 2013-01-02  1.0  4  train  foo"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df[test_df.D>2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.DataFrame({ 'A' : 1., 'B' : pd.Timestamp('20130102'), 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                                      'D' : np.array([3] * 4,dtype='int32'),\n",
    "                                      'Ea' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                                      'Fe' : 'foo' })\n",
    "test_df1 = pd.DataFrame({ 'A' : 1, 'B' : pd.Timestamp('20130102'), 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                                      'D' : np.array([3] * 4,dtype='int32'),\n",
    "                                      'Ea' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                                      'Fe' : 'foo' })\n",
    "test_df1.iloc[2,3]=2.0\n",
    "test_df1\n",
    "\n",
    "a=(test_df.values!=test_df1.values).any(axis=1)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test2\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([1,2,4])\n",
    "a == b\n",
    "print(\"Test2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    4\n",
       "2    2\n",
       "1    1\n",
       "Name: Test, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame({\"Test\": [1,2,2,3,3,3,3]})\n",
    "a[\"Test\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "scores = df.Track_Score\n",
    "scores1 = scores[1400:1410]\n",
    "print(len(scores1))"
   ]
  }
 ],
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