Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "cdict = {'red'  :  ((0., 0., 0.), (0.2, 1., 1.), (0.6, 1., 1.), (1., 0., 0.)),\n",
    "         'green':  ((0., 0., 0.), (0.2, 0., 0.), (0.6, 1., 1.), (1., 1., 1.)),\n",
    "         'blue' :  ((0., 0., 0.), (0.2, 0., 0.), (0.6, 0., 0.), (1., 0., 0.))}\n",
    "\n",
    "my_cmap = colors.LinearSegmentedColormap('my_colormap', cdict, 1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INDEX</th>\n",
       "      <th>PROBE_ID</th>\n",
       "      <th>SAMPLE_DATE</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "      <th>HEADING</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>PROBE_DATA_PROVIDER</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>LOCAL_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10189</th>\n",
       "      <td>78826</td>\n",
       "      <td>18346307</td>\n",
       "      <td>2018-06-01 00:00:00</td>\n",
       "      <td>33.797273</td>\n",
       "      <td>-118.311998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FLEET51</td>\n",
       "      <td>378548.653320</td>\n",
       "      <td>3.740452e+06</td>\n",
       "      <td>2018-05-31 17:00:00 -0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69650</th>\n",
       "      <td>548192</td>\n",
       "      <td>19843587</td>\n",
       "      <td>2018-06-01 00:00:00</td>\n",
       "      <td>33.758240</td>\n",
       "      <td>-118.249032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FLEET51</td>\n",
       "      <td>384325.334413</td>\n",
       "      <td>3.736051e+06</td>\n",
       "      <td>2018-05-31 17:00:00 -0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26713</th>\n",
       "      <td>211747</td>\n",
       "      <td>E44B51E5-6E8E-41EA-AF76-FEAF1C6332C3</td>\n",
       "      <td>2018-06-01 00:00:00</td>\n",
       "      <td>33.804461</td>\n",
       "      <td>-118.081242</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>399919.154479</td>\n",
       "      <td>3.741001e+06</td>\n",
       "      <td>2018-05-31 17:00:00 -0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69653</th>\n",
       "      <td>548206</td>\n",
       "      <td>18323210</td>\n",
       "      <td>2018-06-01 00:00:00</td>\n",
       "      <td>33.807804</td>\n",
       "      <td>-118.282102</td>\n",
       "      <td>88.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>FLEET51</td>\n",
       "      <td>381330.868727</td>\n",
       "      <td>3.741585e+06</td>\n",
       "      <td>2018-05-31 17:00:00 -0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30927</th>\n",
       "      <td>244268</td>\n",
       "      <td>780FC5B5-B253-4F07-AEF8-C3DB2848A8D7</td>\n",
       "      <td>2018-06-01 00:00:00</td>\n",
       "      <td>33.810444</td>\n",
       "      <td>-118.078986</td>\n",
       "      <td>271.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>CONSUMER14</td>\n",
       "      <td>400134.933687</td>\n",
       "      <td>3.741662e+06</td>\n",
       "      <td>2018-05-31 17:00:00 -0700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        INDEX                              PROBE_ID          SAMPLE_DATE  \\\n",
       "10189   78826                              18346307  2018-06-01 00:00:00   \n",
       "69650  548192                              19843587  2018-06-01 00:00:00   \n",
       "26713  211747  E44B51E5-6E8E-41EA-AF76-FEAF1C6332C3  2018-06-01 00:00:00   \n",
       "69653  548206                              18323210  2018-06-01 00:00:00   \n",
       "30927  244268  780FC5B5-B253-4F07-AEF8-C3DB2848A8D7  2018-06-01 00:00:00   \n",
       "\n",
       "             LAT        LONG  HEADING  SPEED PROBE_DATA_PROVIDER  \\\n",
       "10189  33.797273 -118.311998      0.0    0.0             FLEET51   \n",
       "69650  33.758240 -118.249032      0.0    0.0             FLEET51   \n",
       "26713  33.804461 -118.081242      6.0   90.0          CONSUMER14   \n",
       "69653  33.807804 -118.282102     88.0   61.0             FLEET51   \n",
       "30927  33.810444 -118.078986    271.0   36.0          CONSUMER14   \n",
       "\n",
       "                   X             Y                 LOCAL_TIME  \n",
       "10189  378548.653320  3.740452e+06  2018-05-31 17:00:00 -0700  \n",
       "69650  384325.334413  3.736051e+06  2018-05-31 17:00:00 -0700  \n",
       "26713  399919.154479  3.741001e+06  2018-05-31 17:00:00 -0700  \n",
       "69653  381330.868727  3.741585e+06  2018-05-31 17:00:00 -0700  \n",
       "30927  400134.933687  3.741662e+06  2018-05-31 17:00:00 -0700  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('crop_data.csv')\n",
    "data.sort_values(by='SAMPLE_DATE', inplace=True, ascending=True) # This now sorts in date order\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   4223\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4224\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_searchsorted_monotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4225\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_searchsorted_monotonic\u001b[0;34m(self, label, side)\u001b[0m\n\u001b[1;32m   4182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'index must be monotonic increasing or decreasing'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: index must be monotonic increasing or decreasing",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-1148c1afc056>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# data.loc[1:,'LOCAL_TIME'] -\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'LOCAL_TIME'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1470\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1471\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1473\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    868\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 870\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    871\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1025\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0msection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m                 \u001b[0;31m# This is an elided recursive call to iloc/loc/etc'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1029\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'not applicable'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1478\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\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[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1864\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1865\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1866\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1867\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1868\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m   1509\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1510\u001b[0m         indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,\n\u001b[0;32m-> 1511\u001b[0;31m                                        slice_obj.step, kind=self.name)\n\u001b[0m\u001b[1;32m   1512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   4088\u001b[0m         \"\"\"\n\u001b[1;32m   4089\u001b[0m         start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[0;32m-> 4090\u001b[0;31m                                                  kind=kind)\n\u001b[0m\u001b[1;32m   4091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4092\u001b[0m         \u001b[0;31m# return a slice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   4295\u001b[0m         \u001b[0mend_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4296\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4297\u001b[0;31m             \u001b[0mend_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'right'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4298\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend_slice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4299\u001b[0m             \u001b[0mend_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   4225\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4226\u001b[0m                 \u001b[0;31m# raise the original KeyError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4227\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4229\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslc\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[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   4219\u001b[0m         \u001b[0;31m# we need to look up the label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4220\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4221\u001b[0;31m             \u001b[0mslc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_loc_only_exact_matches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4222\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4223\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_loc_only_exact_matches\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4188\u001b[0m         \u001b[0mget_slice_bound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4189\u001b[0m         \"\"\"\n\u001b[0;32m-> 4190\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3063\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3064\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3065\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3067\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -1"
     ]
    }
   ],
   "source": [
    "# data.loc[1:,'LOCAL_TIME'] - \n",
    "data.loc[:-1,'LOCAL_TIME']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique ID count 21\n",
      "unique Probe_ID count 23979\n",
      "unique time 86102\n",
      "raw data shape (1989370, 11)\n"
     ]
    }
   ],
   "source": [
    "print('unique ID count',len(data.PROBE_DATA_PROVIDER.unique()))\n",
    "print('unique Probe_ID count',len(data.PROBE_ID.unique()))\n",
    "print('unique time',len(data.LOCAL_TIME.unique()))\n",
    "print('raw data shape',data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw data shape (40, 11)\n"
     ]
    }
   ],
   "source": [
    "t1_series = data[data.LOCAL_TIME == '2018-06-01 16:49:26 -0700' ]\n",
    "t1_series.head()\n",
    "print('raw data shape',t1_series.shape)\n",
    "\n",
    "# t2_series = data[data.LOCAL_TIME.isin'2018-06-01 16:49:26 -0700' ]\n",
    "# print('raw data shape',t1_series.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw data shape (148, 11)\n",
      "raw data shape (357286, 11)\n"
     ]
    }
   ],
   "source": [
    "ID_series = data[data.PROBE_ID == '24046157' ]\n",
    "ID_series.head()\n",
    "print('raw data shape',ID_series.shape)\n",
    "\n",
    "fleet_series = data[data.PROBE_DATA_PROVIDER == 'FLEET51' ]\n",
    "\n",
    "print('raw data shape',fleet_series.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to set specific regions of interest\n",
    "intersection_center = data[data['edge_id'].isin(intersection_center_edges)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pos_heatmap(data,title):\n",
    "    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(14, 10))\n",
    "\n",
    "    scatter = ax.scatter(data['X'].values,\n",
    "                               data['Y'].values)\n",
    "    scatter_heat = ax.scatter(data['X'].values,\n",
    "                               data['Y'].values,\n",
    "                               c=data['SPEED'], s=45, alpha=0.9,cmap=my_cmap)\n",
    "    cbar = fig.colorbar(scatter_heat)\n",
    "    cbar.set_label(\"Speed (m/s)\",fontsize=20)\n",
    "    cbar.ax.tick_params(labelsize=18)\n",
    "    plt.title(title,fontsize=25)\n",
    "    plt.axis('off')\n",
    "    plt.show()   \n",
    "\n",
    "def route_map(data,title):\n",
    "    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(14, 10))\n",
    "\n",
    "    scatter = ax.plot(data['X'].values,\n",
    "                               data['Y'].values)\n",
    "#     scatter_time = ax.plot(data['X'].values,\n",
    "#                                data['Y'].values,\n",
    "#                                c=data.iloc[1:,'LOCAL_TIME'] - data.iloc[:-1,'LOCAL_TIME'], s=45, alpha=0.9,cmap=my_cmap)\n",
    "#     cbar = fig.colorbar(scatter_heat)\n",
    "#     cbar.set_label(\"Speed (m/s)\",fontsize=20)\n",
    "#     cbar.ax.tick_params(labelsize=18)\n",
    "    plt.title(title,fontsize=25)\n",
    "    plt.axis('off')\n",
    "    plt.show() \n",
    "    \n",
    "def mean_speed(data,title):\n",
    "    plt.figure(figsize = (14,10))\n",
    "    sns.lineplot(x=data['time'],y=data['speed'])\n",
    "    plt.title(title,fontsize=25)\n",
    "    plt.xlabel('Time (s)',fontsize=20)\n",
    "    plt.ylabel('Mean Speed (m/s)',fontsize=20)\n",
    "    plt.xticks(fontsize=16)\n",
    "    plt.yticks(fontsize=16)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = route_map(ID_series,'Heatmap of Speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pos_heatmap(ID_series,'Heatmap of Speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#http://polygons.openstreetmap.fr/get_geojson.py?id=396479&params=0\n",
    "#https://nominatim.openstreetmap.org/details.php?place_id=198792806\n",
    "import geopandas as gpd\n",
    "df_places = gpd.read_file('LA.geojson')\n",
    "df_places.head()\n",
    "df_places.plot(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#simulate the trips (statistical applciation)\n",
    "#estimate how many people are on the beach"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}