Get_weather-NJ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial import Voronoi,voronoi_plot_2d\n",
    "from shapely.geometry import Polygon\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USAF</th>\n",
       "      <th>WBAN</th>\n",
       "      <th>STATION NAME</th>\n",
       "      <th>CTRY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ICAO</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>ELEV(M)</th>\n",
       "      <th>BEGIN</th>\n",
       "      <th>END</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>700599999</th>\n",
       "      <td>7005</td>\n",
       "      <td>99999</td>\n",
       "      <td>CWOS 07005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-01-27</td>\n",
       "      <td>2012-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701199999</th>\n",
       "      <td>7011</td>\n",
       "      <td>99999</td>\n",
       "      <td>CWOS 07011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-10-25</td>\n",
       "      <td>2012-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701899999</th>\n",
       "      <td>7018</td>\n",
       "      <td>99999</td>\n",
       "      <td>WXPOD 7018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7018.0</td>\n",
       "      <td>2011-03-09</td>\n",
       "      <td>2013-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702599999</th>\n",
       "      <td>7025</td>\n",
       "      <td>99999</td>\n",
       "      <td>CWOS 07025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-01-27</td>\n",
       "      <td>2012-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702699999</th>\n",
       "      <td>7026</td>\n",
       "      <td>99999</td>\n",
       "      <td>WXPOD 7026</td>\n",
       "      <td>AF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7026.0</td>\n",
       "      <td>2012-07-13</td>\n",
       "      <td>2014-11-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            USAF   WBAN STATION NAME CTRY STATE ICAO  LAT  LON  ELEV(M)  \\\n",
       "station_id                                                                \n",
       "700599999   7005  99999   CWOS 07005  NaN   NaN  NaN  NaN  NaN      NaN   \n",
       "701199999   7011  99999   CWOS 07011  NaN   NaN  NaN  NaN  NaN      NaN   \n",
       "701899999   7018  99999   WXPOD 7018  NaN   NaN  NaN  0.0  0.0   7018.0   \n",
       "702599999   7025  99999   CWOS 07025  NaN   NaN  NaN  NaN  NaN      NaN   \n",
       "702699999   7026  99999   WXPOD 7026   AF   NaN  NaN  0.0  0.0   7026.0   \n",
       "\n",
       "                BEGIN        END  \n",
       "station_id                        \n",
       "700599999  2012-01-27 2012-01-27  \n",
       "701199999  2011-10-25 2012-11-29  \n",
       "701899999  2011-03-09 2013-07-30  \n",
       "702599999  2012-01-27 2012-01-27  \n",
       "702699999  2012-07-13 2014-11-20  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = pd.read_csv('isd-history.csv',parse_dates=['BEGIN','END'])\n",
    "stations['station_id'] = stations.apply(lambda x: str(x['USAF'])+str(x['WBAN']), axis=1)\n",
    "stations = stations.set_index('station_id')\n",
    "stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USAF</th>\n",
       "      <th>WBAN</th>\n",
       "      <th>STATION NAME</th>\n",
       "      <th>CTRY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ICAO</th>\n",
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       "      <th>station_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>720407462</th>\n",
       "      <td>720407</td>\n",
       "      <td>462</td>\n",
       "      <td>OCEAN COUNTY AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KMJX</td>\n",
       "      <td>39.928</td>\n",
       "      <td>-74.292</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2015-02-24</td>\n",
       "      <td>2015-09-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72040799999</th>\n",
       "      <td>720407</td>\n",
       "      <td>99999</td>\n",
       "      <td>ROBERT J MILLER AIRPARK</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KMJX</td>\n",
       "      <td>39.933</td>\n",
       "      <td>-74.300</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2007-11-14</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720581178</th>\n",
       "      <td>720581</td>\n",
       "      <td>178</td>\n",
       "      <td>LINDEN AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KLDJ</td>\n",
       "      <td>40.617</td>\n",
       "      <td>-74.250</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2015-06-02</td>\n",
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       "    <tr>\n",
       "      <th>72058199999</th>\n",
       "      <td>720581</td>\n",
       "      <td>99999</td>\n",
       "      <td>LINDEN</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KLDJ</td>\n",
       "      <td>40.617</td>\n",
       "      <td>-74.245</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2008-07-21</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72224754785</th>\n",
       "      <td>722247</td>\n",
       "      <td>54785</td>\n",
       "      <td>SOMERSET AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KSMQ</td>\n",
       "      <td>40.624</td>\n",
       "      <td>-74.669</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999914734</th>\n",
       "      <td>999999</td>\n",
       "      <td>14734</td>\n",
       "      <td>NEWARK INTERNATIONAL ARPT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KEWR</td>\n",
       "      <td>40.683</td>\n",
       "      <td>-74.169</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>1972-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999914780</th>\n",
       "      <td>999999</td>\n",
       "      <td>14780</td>\n",
       "      <td>LAKEHURST NAS</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KNEL</td>\n",
       "      <td>40.033</td>\n",
       "      <td>-74.350</td>\n",
       "      <td>31.4</td>\n",
       "      <td>1945-02-01</td>\n",
       "      <td>1972-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999914792</th>\n",
       "      <td>999999</td>\n",
       "      <td>14792</td>\n",
       "      <td>TRENTON MERCER COUNTY AP</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KTTN</td>\n",
       "      <td>40.277</td>\n",
       "      <td>-74.816</td>\n",
       "      <td>64.9</td>\n",
       "      <td>1945-02-20</td>\n",
       "      <td>1945-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999993730</th>\n",
       "      <td>999999</td>\n",
       "      <td>93730</td>\n",
       "      <td>ATLANTIC CITY INTL A</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KACY</td>\n",
       "      <td>39.449</td>\n",
       "      <td>-74.567</td>\n",
       "      <td>20.4</td>\n",
       "      <td>1965-01-01</td>\n",
       "      <td>1974-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999994741</th>\n",
       "      <td>999999</td>\n",
       "      <td>94741</td>\n",
       "      <td>TETERBORO AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KTEB</td>\n",
       "      <td>40.850</td>\n",
       "      <td>-74.061</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1947-01-01</td>\n",
       "      <td>1961-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "               USAF   WBAN               STATION NAME CTRY STATE  ICAO  \\\n",
       "station_id                                                               \n",
       "720407462    720407    462       OCEAN COUNTY AIRPORT   US    NJ  KMJX   \n",
       "72040799999  720407  99999    ROBERT J MILLER AIRPARK   US    NJ  KMJX   \n",
       "720581178    720581    178             LINDEN AIRPORT   US    NJ  KLDJ   \n",
       "72058199999  720581  99999                     LINDEN   US    NJ  KLDJ   \n",
       "72224754785  722247  54785           SOMERSET AIRPORT   US    NJ  KSMQ   \n",
       "...             ...    ...                        ...  ...   ...   ...   \n",
       "99999914734  999999  14734  NEWARK INTERNATIONAL ARPT   US    NJ  KEWR   \n",
       "99999914780  999999  14780              LAKEHURST NAS   US    NJ  KNEL   \n",
       "99999914792  999999  14792   TRENTON MERCER COUNTY AP   US    NJ  KTTN   \n",
       "99999993730  999999  93730       ATLANTIC CITY INTL A   US    NJ  KACY   \n",
       "99999994741  999999  94741          TETERBORO AIRPORT   US    NJ  KTEB   \n",
       "\n",
       "                LAT     LON  ELEV(M)      BEGIN        END  \n",
       "station_id                                                  \n",
       "720407462    39.928 -74.292     25.0 2015-02-24 2015-09-10  \n",
       "72040799999  39.933 -74.300     25.0 2007-11-14 2015-11-21  \n",
       "720581178    40.617 -74.250      7.0 2013-01-01 2015-06-02  \n",
       "72058199999  40.617 -74.245      7.0 2008-07-21 2015-11-21  \n",
       "72224754785  40.624 -74.669     32.0 2006-01-01 2015-11-22  \n",
       "...             ...     ...      ...        ...        ...  \n",
       "99999914734  40.683 -74.169      9.1 1948-01-01 1972-12-31  \n",
       "99999914780  40.033 -74.350     31.4 1945-02-01 1972-12-31  \n",
       "99999914792  40.277 -74.816     64.9 1945-02-20 1945-10-01  \n",
       "99999993730  39.449 -74.567     20.4 1965-01-01 1974-02-28  \n",
       "99999994741  40.850 -74.061      2.1 1947-01-01 1961-10-01  \n",
       "\n",
       "[63 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nj_stations = stations[stations.STATE == 'NJ']\n",
    "nj_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>72040799999</th>\n",
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       "      <td>99999</td>\n",
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       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
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       "      <td>2007-11-14</td>\n",
       "      <td>2015-11-21</td>\n",
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       "    <tr>\n",
       "      <th>72058199999</th>\n",
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       "      <td>2008-07-21</td>\n",
       "      <td>2015-11-21</td>\n",
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       "    <tr>\n",
       "      <th>72224754785</th>\n",
       "      <td>722247</td>\n",
       "      <td>54785</td>\n",
       "      <td>SOMERSET AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KSMQ</td>\n",
       "      <td>40.624</td>\n",
       "      <td>-74.669</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72407093730</th>\n",
       "      <td>724070</td>\n",
       "      <td>93730</td>\n",
       "      <td>ATLANTIC CITY INTERNATIONAL AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KACY</td>\n",
       "      <td>39.449</td>\n",
       "      <td>-74.567</td>\n",
       "      <td>18.3</td>\n",
       "      <td>1947-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72407493780</th>\n",
       "      <td>724074</td>\n",
       "      <td>93780</td>\n",
       "      <td>SOUTH JERSEY REGIONAL ARPT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KVAY</td>\n",
       "      <td>39.949</td>\n",
       "      <td>-74.842</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72407513735</th>\n",
       "      <td>724075</td>\n",
       "      <td>13735</td>\n",
       "      <td>MILLVILLE MUNICIPAL ARPT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KMIV</td>\n",
       "      <td>39.367</td>\n",
       "      <td>-75.067</td>\n",
       "      <td>21.3</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72407754779</th>\n",
       "      <td>724077</td>\n",
       "      <td>54779</td>\n",
       "      <td>AEROFLEX-ANDOVER AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>K12N</td>\n",
       "      <td>41.009</td>\n",
       "      <td>-74.737</td>\n",
       "      <td>177.7</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72408454760</th>\n",
       "      <td>724084</td>\n",
       "      <td>54760</td>\n",
       "      <td>MONMOUTH EXECUTIVE AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KBLM</td>\n",
       "      <td>40.183</td>\n",
       "      <td>-74.133</td>\n",
       "      <td>48.5</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409014780</th>\n",
       "      <td>724090</td>\n",
       "      <td>14780</td>\n",
       "      <td>NAES/MAXFIELD FIELD</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KNEL</td>\n",
       "      <td>40.033</td>\n",
       "      <td>-74.350</td>\n",
       "      <td>30.8</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409454743</th>\n",
       "      <td>724094</td>\n",
       "      <td>54743</td>\n",
       "      <td>ESSEX COUNTY AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KCDW</td>\n",
       "      <td>40.876</td>\n",
       "      <td>-74.283</td>\n",
       "      <td>52.7</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409514792</th>\n",
       "      <td>724095</td>\n",
       "      <td>14792</td>\n",
       "      <td>TRENTON MERCER AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KTTN</td>\n",
       "      <td>40.277</td>\n",
       "      <td>-74.816</td>\n",
       "      <td>56.1</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409614706</th>\n",
       "      <td>724096</td>\n",
       "      <td>14706</td>\n",
       "      <td>MCGUIRE AFB AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KWRI</td>\n",
       "      <td>40.017</td>\n",
       "      <td>-74.600</td>\n",
       "      <td>39.9</td>\n",
       "      <td>1942-08-01</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72409754738</th>\n",
       "      <td>724097</td>\n",
       "      <td>54738</td>\n",
       "      <td>MORRISTOWN MUNICIPAL AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KMMU</td>\n",
       "      <td>40.800</td>\n",
       "      <td>-74.417</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72502014734</th>\n",
       "      <td>725020</td>\n",
       "      <td>14734</td>\n",
       "      <td>NEWARK LIBERTY INTERNATIONAL AP</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KEWR</td>\n",
       "      <td>40.683</td>\n",
       "      <td>-74.169</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72502594741</th>\n",
       "      <td>725025</td>\n",
       "      <td>94741</td>\n",
       "      <td>TETERBORO AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KTEB</td>\n",
       "      <td>40.850</td>\n",
       "      <td>-74.061</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74000154793</th>\n",
       "      <td>740001</td>\n",
       "      <td>54793</td>\n",
       "      <td>SUSSEX AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KFWN</td>\n",
       "      <td>41.200</td>\n",
       "      <td>-74.623</td>\n",
       "      <td>128.3</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7459663726</th>\n",
       "      <td>745966</td>\n",
       "      <td>3726</td>\n",
       "      <td>CAPE MAY COUNTY AIRPORT</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>KWWD</td>\n",
       "      <td>39.008</td>\n",
       "      <td>-74.908</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99727099999</th>\n",
       "      <td>997270</td>\n",
       "      <td>99999</td>\n",
       "      <td>ATLANTIC CITY</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.350</td>\n",
       "      <td>-74.420</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99727699999</th>\n",
       "      <td>997276</td>\n",
       "      <td>99999</td>\n",
       "      <td>CAPE MAY</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.970</td>\n",
       "      <td>-74.970</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>2013-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99728999999</th>\n",
       "      <td>997289</td>\n",
       "      <td>99999</td>\n",
       "      <td>SANDY HOOK</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.470</td>\n",
       "      <td>-74.000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99768799999</th>\n",
       "      <td>997687</td>\n",
       "      <td>99999</td>\n",
       "      <td>BURLINGTON DEL RIVER</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.080</td>\n",
       "      <td>-74.880</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2006-07-10</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99774399999</th>\n",
       "      <td>997743</td>\n",
       "      <td>99999</td>\n",
       "      <td>ROBINS REEF</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.650</td>\n",
       "      <td>-74.067</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2006-10-24</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99777999999</th>\n",
       "      <td>997779</td>\n",
       "      <td>99999</td>\n",
       "      <td>SHIP JOHN SHOAL</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.300</td>\n",
       "      <td>-75.367</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2006-11-21</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99800299999</th>\n",
       "      <td>998002</td>\n",
       "      <td>99999</td>\n",
       "      <td>JACQUES COUSTEAU RESERVE</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.533</td>\n",
       "      <td>-74.450</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2007-10-16</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99822799999</th>\n",
       "      <td>998227</td>\n",
       "      <td>99999</td>\n",
       "      <td>ATLANTIC CITY</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.350</td>\n",
       "      <td>-74.433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009-07-01</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99834299999</th>\n",
       "      <td>998342</td>\n",
       "      <td>99999</td>\n",
       "      <td>TACONY/PALMYRA BRIDGE</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.012</td>\n",
       "      <td>-75.042</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2009-10-07</td>\n",
       "      <td>2015-02-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999913724</th>\n",
       "      <td>999999</td>\n",
       "      <td>13724</td>\n",
       "      <td>ATLANTIC CITY MARINA</td>\n",
       "      <td>US</td>\n",
       "      <td>NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.379</td>\n",
       "      <td>-74.424</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>2015-11-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               USAF   WBAN                         STATION NAME CTRY STATE  \\\n",
       "station_id                                                                   \n",
       "72040799999  720407  99999              ROBERT J MILLER AIRPARK   US    NJ   \n",
       "72058199999  720581  99999                               LINDEN   US    NJ   \n",
       "72224754785  722247  54785                     SOMERSET AIRPORT   US    NJ   \n",
       "72407093730  724070  93730  ATLANTIC CITY INTERNATIONAL AIRPORT   US    NJ   \n",
       "72407493780  724074  93780           SOUTH JERSEY REGIONAL ARPT   US    NJ   \n",
       "72407513735  724075  13735             MILLVILLE MUNICIPAL ARPT   US    NJ   \n",
       "72407754779  724077  54779             AEROFLEX-ANDOVER AIRPORT   US    NJ   \n",
       "72408454760  724084  54760           MONMOUTH EXECUTIVE AIRPORT   US    NJ   \n",
       "72409014780  724090  14780                  NAES/MAXFIELD FIELD   US    NJ   \n",
       "72409454743  724094  54743                 ESSEX COUNTY AIRPORT   US    NJ   \n",
       "72409514792  724095  14792               TRENTON MERCER AIRPORT   US    NJ   \n",
       "72409614706  724096  14706                  MCGUIRE AFB AIRPORT   US    NJ   \n",
       "72409754738  724097  54738         MORRISTOWN MUNICIPAL AIRPORT   US    NJ   \n",
       "72502014734  725020  14734      NEWARK LIBERTY INTERNATIONAL AP   US    NJ   \n",
       "72502594741  725025  94741                    TETERBORO AIRPORT   US    NJ   \n",
       "74000154793  740001  54793                       SUSSEX AIRPORT   US    NJ   \n",
       "7459663726   745966   3726              CAPE MAY COUNTY AIRPORT   US    NJ   \n",
       "99727099999  997270  99999                        ATLANTIC CITY   US    NJ   \n",
       "99727699999  997276  99999                             CAPE MAY   US    NJ   \n",
       "99728999999  997289  99999                           SANDY HOOK   US    NJ   \n",
       "99768799999  997687  99999                 BURLINGTON DEL RIVER   US    NJ   \n",
       "99774399999  997743  99999                          ROBINS REEF   US    NJ   \n",
       "99777999999  997779  99999                      SHIP JOHN SHOAL   US    NJ   \n",
       "99800299999  998002  99999             JACQUES COUSTEAU RESERVE   US    NJ   \n",
       "99822799999  998227  99999                        ATLANTIC CITY   US    NJ   \n",
       "99834299999  998342  99999                TACONY/PALMYRA BRIDGE   US    NJ   \n",
       "99999913724  999999  13724                 ATLANTIC CITY MARINA   US    NJ   \n",
       "\n",
       "             ICAO     LAT     LON  ELEV(M)      BEGIN        END  \n",
       "station_id                                                        \n",
       "72040799999  KMJX  39.933 -74.300     25.0 2007-11-14 2015-11-21  \n",
       "72058199999  KLDJ  40.617 -74.245      7.0 2008-07-21 2015-11-21  \n",
       "72224754785  KSMQ  40.624 -74.669     32.0 2006-01-01 2015-11-22  \n",
       "72407093730  KACY  39.449 -74.567     18.3 1947-01-01 2015-11-22  \n",
       "72407493780  KVAY  39.949 -74.842     16.2 2006-01-01 2015-11-21  \n",
       "72407513735  KMIV  39.367 -75.067     21.3 1973-01-01 2015-11-22  \n",
       "72407754779  K12N  41.009 -74.737    177.7 2006-01-01 2015-11-22  \n",
       "72408454760  KBLM  40.183 -74.133     48.5 2006-01-01 2015-11-22  \n",
       "72409014780  KNEL  40.033 -74.350     30.8 1973-01-01 2015-11-22  \n",
       "72409454743  KCDW  40.876 -74.283     52.7 2006-01-01 2015-11-22  \n",
       "72409514792  KTTN  40.277 -74.816     56.1 1973-01-01 2015-11-22  \n",
       "72409614706  KWRI  40.017 -74.600     39.9 1942-08-01 2015-11-21  \n",
       "72409754738  KMMU  40.800 -74.417     57.0 2006-01-01 2015-11-22  \n",
       "72502014734  KEWR  40.683 -74.169      2.1 1973-01-01 2015-11-22  \n",
       "72502594741  KTEB  40.850 -74.061      2.7 1973-01-01 2015-11-22  \n",
       "74000154793  KFWN  41.200 -74.623    128.3 2006-01-01 2015-11-22  \n",
       "7459663726   KWWD  39.008 -74.908      7.0 2006-01-01 2015-11-21  \n",
       "99727099999   NaN  39.350 -74.420     10.0 2005-01-04 2015-11-21  \n",
       "99727699999   NaN  38.970 -74.970     10.0 2005-01-04 2013-04-30  \n",
       "99728999999   NaN  40.470 -74.000     10.0 2005-01-04 2015-11-21  \n",
       "99768799999   NaN  40.080 -74.880      9.0 2006-07-10 2015-11-21  \n",
       "99774399999   NaN  40.650 -74.067     72.0 2006-10-24 2015-11-21  \n",
       "99777999999   NaN  39.300 -75.367      3.0 2006-11-21 2015-11-21  \n",
       "99800299999   NaN  39.533 -74.450     12.0 2007-10-16 2015-11-21  \n",
       "99822799999   NaN  39.350 -74.433      0.0 2009-07-01 2015-11-21  \n",
       "99834299999   NaN  40.012 -75.042     20.0 2009-10-07 2015-02-24  \n",
       "99999913724   NaN  39.379 -74.424      3.1 1948-01-01 2015-11-21  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's get stations that are valid for all of our time range. Not perfect, but I'd rather not deal with missing data for now\n",
    "start = pd.Timestamp(2013,1,1)\n",
    "end = pd.Timestamp(2018,12,31)\n",
    "valid_stations = nj_stations[(nj_stations.BEGIN < start) & (nj_stations.END > start)]\n",
    "plt.figure()\n",
    "\n",
    "lons = valid_stations.LON.values\n",
    "lats = valid_stations.LAT.values\n",
    "plt.plot(lons, lats,'ko')\n",
    "\n",
    "\n",
    "vor = Voronoi(np.vstack((lons,lats)).T)\n",
    "voronoi_plot_2d(vor,ax=plt.gca())\n",
    "plt.gca().set_aspect(1)\n",
    "valid_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['72040799999', '72058199999', '72224754785', '72407093730', '72407493780', '72407513735', '72407754779', '72408454760', '72409014780', '72409454743', '72409514792', '72409614706', '72409754738', '72502014734', '72502594741', '74000154793', '7459663726', '99727099999', '99727699999', '99728999999', '99768799999', '99774399999', '99777999999', '99800299999', '99822799999', '99834299999', '99999913724']\n"
     ]
    }
   ],
   "source": [
    "stations = valid_stations.index.values.tolist()\n",
    "print(stations)\n",
    "valid_stations.to_csv('nj_stations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Station: 72040799999\n",
      "2\n",
      "Empty for station 72040799999\n",
      "Station: 72058199999\n",
      "2\n",
      "Empty for station 72058199999\n",
      "Station: 72224754785\n",
      "2\n",
      "Empty for station 72224754785\n",
      "Station: 72407093730\n",
      "2\n",
      "Empty for station 72407093730\n",
      "Station: 72407493780\n",
      "2\n",
      "Empty for station 72407493780\n",
      "Station: 72407513735\n",
      "2\n",
      "Empty for station 72407513735\n",
      "Station: 72407754779\n",
      "2\n",
      "Empty for station 72407754779\n",
      "Station: 72408454760\n",
      "2\n",
      "Empty for station 72408454760\n",
      "Station: 72409014780\n",
      "2\n",
      "Empty for station 72409014780\n",
      "Station: 72409454743\n",
      "2\n",
      "Empty for station 72409454743\n",
      "Station: 72409514792\n",
      "2\n",
      "Empty for station 72409514792\n",
      "Station: 72409614706\n",
      "2\n",
      "Empty for station 72409614706\n",
      "Station: 72409754738\n",
      "2\n",
      "Empty for station 72409754738\n",
      "Station: 72502014734\n",
      "2\n",
      "Empty for station 72502014734\n",
      "Station: 72502594741\n",
      "2\n",
      "Empty for station 72502594741\n",
      "Station: 74000154793\n",
      "2\n",
      "Empty for station 74000154793\n",
      "Station: 7459663726\n",
      "2\n",
      "Empty for station 7459663726\n",
      "Station: 99727099999\n",
      "2\n",
      "Empty for station 99727099999\n",
      "Station: 99727699999\n",
      "2\n",
      "Empty for station 99727699999\n",
      "Station: 99728999999\n",
      "2\n",
      "Empty for station 99728999999\n",
      "Station: 99768799999\n",
      "2\n",
      "Empty for station 99768799999\n",
      "Station: 99774399999\n",
      "2\n",
      "Empty for station 99774399999\n",
      "Station: 99777999999\n",
      "2\n",
      "Empty for station 99777999999\n",
      "Station: 99800299999\n",
      "2\n",
      "Empty for station 99800299999\n",
      "Station: 99822799999\n",
      "2\n",
      "Empty for station 99822799999\n",
      "Station: 99834299999\n",
      "2\n",
      "Empty for station 99834299999\n",
      "Station: 99999913724\n",
      "2\n",
      "Empty for station 99999913724\n"
     ]
    }
   ],
   "source": [
    "#url = 'https://www.ncdc.noaa.gov/access-data-service/api/v1/dataset'\n",
    "\n",
    "# url = 'https://www.ncdc.noaa.gov/cdo-web/api/v2/dataset'\n",
    "\n",
    "# params = {\n",
    "#     'datasetid': 'global-hourly',\n",
    "#     'startdate': '2013-01-01',\n",
    "#     'enddate': '2018-12-31',\n",
    "#     'datatypeid':'GHCND',\n",
    "#     'stationid': stations,\n",
    "#     'format': 'json'\n",
    "    \n",
    "# }\n",
    "\n",
    "\n",
    "url = 'https://www.ncdc.noaa.gov/cdo-web/api/v2/data?datasetid=PRECIP_15&stationid=COOP:010008&units=metric&startdate=2010-01-01&enddate=2018-12-31'\n",
    "\n",
    "\n",
    "weather_data = pd.DataFrame()\n",
    "\n",
    "for station in stations:\n",
    "    params['stationid'] = station\n",
    "    print('Station:',station)\n",
    "    res = requests.get(url,params=params)\n",
    "    js = res.json()\n",
    "    print(len(js))\n",
    "    try:\n",
    "        weather_data = weather_data.append(js)\n",
    "    except:\n",
    "        print (\"Empty for station\",station)\n",
    "    time.sleep(0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data.head()\n",
    "weather_data.to_csv('nj_weather_2010-2018_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def parseWindDir(x):\n",
    "    # Wind direction, deg or 999\n",
    "    d = x.split(',')[0]\n",
    "    if d == '999':\n",
    "        return np.nan\n",
    "    return float(d)\n",
    "\n",
    "\n",
    "def parseWindSpeed(x):\n",
    "    s = x.split(',')[3]\n",
    "    if s == '9999':\n",
    "        return 0.0\n",
    "    return float(s) / 10.0\n",
    "\n",
    "def parseVisibility(x):\n",
    "    v = x.split(',')[0]\n",
    "    if v == '999999':\n",
    "        return 16093.0\n",
    "    return float(v)\n",
    "\n",
    "def parseTemp(x):\n",
    "    t = x.split(',')[0]\n",
    "    if t == '+9999':\n",
    "        return np.nan\n",
    "    return float(t) / 10.0\n",
    "\n",
    "def parseSnowDepth(x):\n",
    "    try:\n",
    "        t = x.split(',')[0]\n",
    "    except:\n",
    "        return 0.0\n",
    "\n",
    "    if t == '9999':\n",
    "        return 0.0\n",
    "    return float(t) \n",
    "\n",
    "def parsePrecip(x):\n",
    "    try:\n",
    "        p = x.split(',')[1]\n",
    "        return float(p) / 10.0\n",
    "    except:\n",
    "        return 0.0\n",
    "\n",
    "weather_lut = {\n",
    "    '01': 'fog',\n",
    "    '02': 'fog',\n",
    "    '03': 'thunder',\n",
    "    '04': 'sleet/hail',\n",
    "    '05': 'hail',\n",
    "    '06': 'glaze',\n",
    "    '07': 'dust',\n",
    "    '08': 'smoke',\n",
    "    '09': 'blowing_snow',\n",
    "    '10': 'tornado',\n",
    "    '11': 'winds',\n",
    "    '12': 'spray',\n",
    "    '13': 'mist',\n",
    "    '14': 'drizzle',\n",
    "    '15': 'freezing_drizzle',\n",
    "    '16': 'rain',\n",
    "    '17': 'freezing_rain',\n",
    "    '18': 'snow',\n",
    "    '19': 'unknown_precipitation',\n",
    "    '21': 'ground_fog',\n",
    "    '22': 'ice_fog'\n",
    "}\n",
    "def parseWeatherType(x):\n",
    "    try:\n",
    "        p = x.split(',')[1]\n",
    "    except:\n",
    "        return x\n",
    "    return weather_lut[p]\n",
    "        \n",
    "    \n",
    "weather_data['DATE'] = pd.to_datetime(weather_data[\"DATE\"])\n",
    "weather_data['wind_dir'] = weather_data.WND.apply(parseWindDir)\n",
    "weather_data['wind_speed'] = weather_data.WND.apply(parseWindSpeed)\n",
    "weather_data['visibility'] = weather_data.VIS.apply(parseVisibility)\n",
    "weather_data['temperature'] = weather_data.TMP.apply(parseTemp)\n",
    "weather_data['precip_01'] = weather_data.AA1.apply(parsePrecip)\n",
    "weather_data['precip_02'] = weather_data.AA2.apply(parsePrecip)\n",
    "weather_data['precip_03'] = weather_data.AA3.apply(parsePrecip)\n",
    "weather_data['precip_depth'] = weather_data[['precip_01','precip_02','precip_03']].max(axis=1)\n",
    "weather_data['snow_depth'] = weather_data.AJ1.apply(parseSnowDepth)\n",
    "weather_data['AT1'] = weather_data.AT1.apply(parseWeatherType)\n",
    "weather_data['AT2'] = weather_data.AT2.apply(parseWeatherType)\n",
    "weather_data['AT3'] = weather_data.AT3.apply(parseWeatherType)\n",
    "weather_data['AT4'] = weather_data.AT4.apply(parseWeatherType)\n",
    "weather_data['AT5'] = weather_data.AT5.apply(parseWeatherType)\n",
    "weather_data['AT6'] = weather_data.AT6.apply(parseWeatherType)\n",
    "weather_data['AT7'] = weather_data.AT7.apply(parseWeatherType)\n",
    "weather_data['AT8'] = weather_data.AT8.apply(parseWeatherType)\n",
    "\n",
    "weather_types = weather_data[['AT1','AT2','AT3','AT4','AT5','AT6','AT7','AT8']]\n",
    "\n",
    "weather_data['snowing'] = weather_types.apply(lambda x: x.isin(['snow'])).any(axis=1)\n",
    "weather_data['raining'] = weather_types.apply(lambda x: x.isin(['rain','freezing_rain','drizzle','freezing_drizzle'])).any(axis=1)\n",
    "weather_data['foggy'] = weather_types.apply(lambda x: x.isin(['fog','ground_fog','ice_fog'])).any(axis=1)\n",
    "weather_data['thunderstorm'] = weather_types.apply(lambda x: x.str.contains('thunder')).any(axis=1)\n",
    "weather_data['hailing'] = weather_types.apply(lambda x: x.isin(['sleet/hail','hail'])).any(axis=1)\n",
    "weather_data['icy'] = weather_types.apply(lambda x: x.isin(['glaze','freezing_rain,freezing_drizzle','ice_fog','snow'])).any(axis=1)\n",
    "weather_data['station_id'] = weather_data.STATION\n",
    "\n",
    "# Drop parsed columns\n",
    "weather_data = weather_data.drop(['WND','VIS','AA1','AA2','AA3','precip_01','precip_02','precip_03','AJ1'],axis=1)\n",
    "weather_data = weather_data.drop(['TMP','AT1','AT2','AT3','AT4','AT5','AT6','AT7','AT8','SOURCE','REPORT_TYPE','QUALITY_CONTROL','STATION'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>DATE</th>\n",
       "      <th>wind_dir</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>visibility</th>\n",
       "      <th>temperature</th>\n",
       "      <th>precip_depth</th>\n",
       "      <th>snow_depth</th>\n",
       "      <th>snowing</th>\n",
       "      <th>raining</th>\n",
       "      <th>foggy</th>\n",
       "      <th>thunderstorm</th>\n",
       "      <th>hailing</th>\n",
       "      <th>icy</th>\n",
       "      <th>station_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-04-12 18:35:00</td>\n",
       "      <td>150.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72056599999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-04-12 19:00:00</td>\n",
       "      <td>140.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72056599999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-04-12 19:14:00</td>\n",
       "      <td>180.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72056599999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-04-12 19:35:00</td>\n",
       "      <td>180.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72056599999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-04-12 19:54:00</td>\n",
       "      <td>180.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72056599999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DATE  wind_dir  wind_speed  visibility  temperature  \\\n",
       "0 2010-04-12 18:35:00     150.0        10.3     16093.0         17.0   \n",
       "1 2010-04-12 19:00:00     140.0         8.7     16093.0         16.0   \n",
       "2 2010-04-12 19:14:00     180.0         5.7     16093.0         16.0   \n",
       "3 2010-04-12 19:35:00     180.0         5.1     16093.0         15.0   \n",
       "4 2010-04-12 19:54:00     180.0         3.6     16093.0         16.0   \n",
       "\n",
       "   precip_depth  snow_depth  snowing  raining  foggy  thunderstorm  hailing  \\\n",
       "0           0.0         0.0    False    False  False         False    False   \n",
       "1           0.0         0.0    False    False  False         False    False   \n",
       "2           0.0         0.0    False    False  False         False    False   \n",
       "3           0.0         0.0    False    False  False         False    False   \n",
       "4           0.0         0.0    False    False  False         False    False   \n",
       "\n",
       "     icy   station_id  \n",
       "0  False  72056599999  \n",
       "1  False  72056599999  \n",
       "2  False  72056599999  \n",
       "3  False  72056599999  \n",
       "4  False  72056599999  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbb954c940>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/+qRDHPd7Vfjm5eWhFL7Lfj8N2Cp6UGvHLuFZkLP9/bJjceng7oHkcIJnlXt9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\n/w9ecGRU4uTwjgfFCsh16QBAMw+ZZ2U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db83848b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db83d64278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db83848630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/nttvv52JE44iTgGl8WMfddRRTJ48mdGjR3PQQQdRUVFBn97J3xsNhQ14rPSs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\nDdGzZTNNZZqe6Ve7htp+hyOCu5GFWj2Ljm3IosE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbb5c85f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = weather_data.set_index('DATE').temperature.resample('1d').mean()\n",
    "ts.plot()\n",
    "ts = weather_data.set_index('DATE').snow_depth.resample('1d').mean()\n",
    "plt.figure()\n",
    "ts.plot()\n",
    "ts = weather_data.set_index('DATE')[['icy','raining','foggy','hailing','snowing']].resample('1d').sum()\n",
    "plt.figure()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dani9510\\AppData\\Local\\ESRI\\conda\\envs\\venv\\lib\\site-packages\\numpy\\lib\\arraysetops.py:395: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>DATE</th>\n",
       "      <th>wind_dir</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>visibility</th>\n",
       "      <th>temperature</th>\n",
       "      <th>precip_depth</th>\n",
       "      <th>snow_depth</th>\n",
       "      <th>snowing</th>\n",
       "      <th>raining</th>\n",
       "      <th>foggy</th>\n",
       "      <th>thunderstorm</th>\n",
       "      <th>hailing</th>\n",
       "      <th>icy</th>\n",
       "      <th>station_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01 00:53:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72470093141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01 01:53:00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72470093141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01 02:53:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72470093141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01 03:53:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72470093141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01 04:53:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>16093.0</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>72470093141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE  wind_dir  wind_speed  visibility  temperature  \\\n",
       "0  2010-01-01 00:53:00      40.0         2.1     16093.0        -11.7   \n",
       "1  2010-01-01 01:53:00      30.0         2.6     16093.0        -11.7   \n",
       "2  2010-01-01 02:53:00      40.0         2.1     16093.0        -12.2   \n",
       "3  2010-01-01 03:53:00      40.0         2.6     16093.0        -11.7   \n",
       "4  2010-01-01 04:53:00      40.0         2.1     16093.0         -9.4   \n",
       "\n",
       "   precip_depth  snow_depth  snowing  raining  foggy  thunderstorm  hailing  \\\n",
       "0           0.0         0.0    False    False  False         False    False   \n",
       "1           0.0         0.0    False    False  False         False    False   \n",
       "2           0.0         0.0    False    False  False         False    False   \n",
       "3           0.0         0.0    False    False  False         False    False   \n",
       "4           0.0         0.0    False    False  False         False    False   \n",
       "\n",
       "     icy   station_id  \n",
       "0  False  72470093141  \n",
       "1  False  72470093141  \n",
       "2  False  72470093141  \n",
       "3  False  72470093141  \n",
       "4  False  72470093141  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#weather_data.to_csv('utah_weather_2010-2018.csv')\n",
    "weather_data = pd.read_csv('utah_weather_2010-2018.csv',index_col=0)\n",
    "weather_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_data['timestamp'] = pd.to_datetime(weather_data.DATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_index = pd.DatetimeIndex(weather_data['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aggs = {\n",
    "    'snowing': 'any',\n",
    "    'raining': 'any',\n",
    "    'foggy': 'any',\n",
    "    'icy': 'any',\n",
    "    'hailing': 'any',\n",
    "    'thunderstorm':'any',\n",
    "    'wind_speed': 'mean',\n",
    "    'visibility': 'mean',\n",
    "    'temperature': 'mean',\n",
    "    'precip_depth':'mean',\n",
    "    'snow_depth':'mean'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dani9510\\AppData\\Local\\ESRI\\conda\\envs\\venv\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: \n",
      ".resample() is now a deferred operation\n",
      "You called rolling(...) on this deferred object which materialized it into a dataframe\n",
      "by implicitly taking the mean.  Use .resample(...).mean() instead\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "resamp = pd.DataFrame()\n",
    "for _id in station_ids:\n",
    "    idx = weather_data.station_id == _id\n",
    "    ti = time_index[idx]\n",
    "\n",
    "    wdfi = weather_data[idx].set_index(ti)\n",
    "    floating = wdfi[['visibility','temperature','wind_speed','precip_depth','snow_depth']]\n",
    "    binaries = wdfi[['snowing','raining','foggy','icy','hailing','thunderstorm']]\n",
    "    b = binaries.resample('1h').rolling(24).apply(lambda x: x.any())\n",
    "    f = floating.resample('1h').agg({\n",
    "        'wind_speed': 'mean',\n",
    "        'visibility': 'mean',\n",
    "        'temperature': 'mean',\n",
    "        'precip_depth':'mean',\n",
    "        'snow_depth':'mean'\n",
    "    })\n",
    "\n",
    "    temp = pd.concat((f,b),axis=1)\n",
    "    temp['station_id'] = _id\n",
    "    resamp = resamp.append(temp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in ['visibility','temperature','wind_speed','precip_depth','snow_depth']:\n",
    "    resamp.loc[pd.isna(resamp[f]),f] = np.nanmedian(resamp[f])\n",
    "for f in ['snowing','raining','foggy','icy','hailing','thunderstorm']:\n",
    "    resamp.loc[pd.isna(resamp[f]),f] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "resamp.reset_index().set_index(['timestamp','station_id']).to_csv('utah_weather_2010-2018_grouped.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1218.8045225143433\n"
     ]
    }
   ],
   "source": [
    "if False:\n",
    "    t = time.time()\n",
    "    resamp = weather_data.set_index(['station_id',time_index.floor('1h')]).groupby(level=[0,1]).agg(aggs)\n",
    "    print (time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if False: resamp.to_csv('utah_weather_2010-2018_grouped.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samps = resamp.reset_index().set_index('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#samps[samps.snowing == True]\n",
    "samps['2016-01-06T06:00:00'].to_csv('test_hour.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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