get_weather.ipynb

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  {
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   },
   "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": 3,
   "metadata": {},
   "outputs": [
    {
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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",
       "      <th>LAT</th>\n",
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       "      <th>BEGIN</th>\n",
       "      <th>END</th>\n",
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       "    <tr>\n",
       "      <th>station_id</th>\n",
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       "      <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>2014-07-11</td>\n",
       "      <td>2017-08-22</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "            USAF   WBAN STATION NAME CTRY STATE ICAO  LAT  LON  ELEV(M)  \\\n",
       "station_id                                                                \n",
       "702699999   7026  99999   WXPOD 7026   AF   NaN  NaN  0.0  0.0   7026.0   \n",
       "707099999   7070  99999   WXPOD 7070   AF   NaN  NaN  0.0  0.0   7070.0   \n",
       "840399999   8403  99999         XM10  NaN   NaN  NaN  NaN  NaN      NaN   \n",
       "841199999   8411  99999         XM20  NaN   NaN  NaN  NaN  NaN      NaN   \n",
       "841499999   8414  99999         XM18  NaN   NaN  NaN  NaN  NaN      NaN   \n",
       "\n",
       "                BEGIN        END  \n",
       "station_id                        \n",
       "702699999  2014-07-11 2017-08-22  \n",
       "707099999  2014-09-23 2015-09-26  \n",
       "840399999  2014-01-01 2014-04-12  \n",
       "841199999  2014-01-02 2016-02-17  \n",
       "841499999  2014-01-01 2016-02-17  "
      ]
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   "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()"
   ]
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  {
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   "execution_count": 4,
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    {
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       "      <th>USAF</th>\n",
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       "      <td>UT</td>\n",
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       "      <td>US</td>\n",
       "      <td>UT</td>\n",
       "      <td>KKNB</td>\n",
       "      <td>37.011</td>\n",
       "      <td>-112.531</td>\n",
       "      <td>1483.8</td>\n",
       "      <td>2013-08-13</td>\n",
       "      <td>2017-08-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               USAF   WBAN                  STATION NAME CTRY STATE  ICAO  \\\n",
       "station_id                                                                  \n",
       "69133499999  691334  99999           HILL/WENDOVER R GWC   US    UT  KQSL   \n",
       "69430099999  694300  99999           EAGLE RANGE       &   US    UT   NaN   \n",
       "72056599999  720565  99999  HEBER CITY MUNICIPAL AIRPORT   US    UT  K36U   \n",
       "72056724180  720567  24180          BRIGHAM CITY AIRPORT   US    UT  KBMC   \n",
       "72056999999  720569  99999                    KANAB MUNI   US    UT  KKNB   \n",
       "\n",
       "                LAT      LON  ELEV(M)      BEGIN        END  \n",
       "station_id                                                   \n",
       "69133499999  41.050 -113.067   1299.0 1991-05-21 2003-08-16  \n",
       "69430099999  41.050 -113.067   1292.0 1980-04-23 1991-06-26  \n",
       "72056599999  40.482 -111.429   1718.2 2010-04-12 2018-01-30  \n",
       "72056724180  41.552 -112.062   1288.1 2014-07-24 2018-01-31  \n",
       "72056999999  37.011 -112.531   1483.8 2013-08-13 2017-08-19  "
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "utah_stations = stations[stations.STATE == 'UT']\n",
    "utah_stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dani9510\\AppData\\Local\\ESRI\\conda\\envs\\venv\\lib\\site-packages\\scipy\\spatial\\_plotutils.py:20: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\n",
      "  was_held = ax.ishold()\n"
     ]
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       "      <th>USAF</th>\n",
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       "      <td>KPUC</td>\n",
       "      <td>39.609</td>\n",
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       "      <td>38.371</td>\n",
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       "      <td>724750</td>\n",
       "      <td>99999</td>\n",
       "      <td>MILFORD MUNICIPAL</td>\n",
       "      <td>US</td>\n",
       "      <td>UT</td>\n",
       "      <td>KMLF</td>\n",
       "      <td>38.417</td>\n",
       "      <td>-113.017</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>2018-01-15</td>\n",
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      ],
      "text/plain": [
       "               USAF   WBAN           STATION NAME CTRY STATE  ICAO     LAT  \\\n",
       "station_id                                                                   \n",
       "72470093141  724700  93141  CARBON COUNTY AIRPORT   US    UT  KPUC  39.609   \n",
       "72472099999  724720  99999               BLANDING   US    UT   NaN  37.617   \n",
       "72472393025  724723  93025          BLANDING MUNI   US    UT  K4BL  37.583   \n",
       "72473523170  724735  23170             HANKSVILLE   US    UT  KHVE  38.371   \n",
       "72475099999  724750  99999      MILFORD MUNICIPAL   US    UT  KMLF  38.417   \n",
       "\n",
       "                 LON  ELEV(M)      BEGIN        END  \n",
       "station_id                                           \n",
       "72470093141 -110.755   1804.7 2006-01-01 2018-01-31  \n",
       "72472099999 -109.467   1840.0 1973-01-01 2018-01-29  \n",
       "72472393025 -109.483   1789.0 1990-02-08 2010-06-21  \n",
       "72473523170 -110.715   1354.5 1973-01-01 2018-01-31  \n",
       "72475099999 -113.017   1536.0 1973-01-01 2018-01-15  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
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U1KlTh/WmlZSUsGfPHmRkZGD69OkF3yYy5Ny5c2jXrh369++PoKCgKpfhGzly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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19df7e69da0>"
      ]
     },
     "metadata": {},
     "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(2010,1,1)\n",
    "valid_stations = utah_stations[(utah_stations.BEGIN < start) & (utah_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.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['72470093141', '72472099999', '72472393025', '72473523170', '72475099999', '72475423186', '72475593129', '72475623159', '72477693075', '72479599999', '72479694128', '72479723176', '72570594030', '72572024127', '72572424174', '72575024126', '72575399999', '72575524101', '72581024193', '74003024103', '9999993081', '9999994133', '9999994134', '9999994138', '99999923162', '99999953149']\n"
     ]
    }
   ],
   "source": [
    "stations = valid_stations.index.values.tolist()\n",
    "print(stations)\n",
    "valid_stations.to_csv('utah_stations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'https://www.ncdc.noaa.gov/access-data-service/api/v1/data'\n",
    "\n",
    "params = {\n",
    "    'dataset': 'global-hourly',\n",
    "    'startDate': '2018-02-02T00:00:00',\n",
    "    'endDate': '2018-02-22T17:00:00',\n",
    "    'dataTypes':'AA1,AA1,AA2,AA3,TMP,VIS,WND,AJ1,AT1,AT2,AT3,AT4,AT5,AT6,AT7,AT8',\n",
    "    'stations': stations,\n",
    "    'format': 'json'\n",
    "    \n",
    "}\n",
    "\n",
    "params['stations'] = ','.join(stations)\n",
    "res = requests.get(url,params=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DATE': '2018-02-21T06:35:00',\n",
       " 'QUALITY_CONTROL': 'V020',\n",
       " 'REPORT_TYPE': 'FM-15',\n",
       " 'SOURCE': '6',\n",
       " 'STATION': '99999923162',\n",
       " 'TMP': '-0150,5',\n",
       " 'VIS': '016093,5,N,5',\n",
       " 'WND': '999,9,C,0000,5'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.json()[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Station: 72056599999\n",
      "39021\n",
      "Station: 72056724180\n",
      "90980\n",
      "Station: 72056999999\n",
      "3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dani9510\\AppData\\Local\\ESRI\\conda\\envs\\venv\\lib\\site-packages\\pandas\\core\\frame.py:5186: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  other = other.loc[:, self.columns]\n",
      "C:\\Users\\dani9510\\AppData\\Local\\ESRI\\conda\\envs\\venv\\lib\\site-packages\\pandas\\core\\indexing.py:1367: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return self._getitem_tuple(key)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Station: 72057299999\n",
      "66886\n",
      "Station: 72092299999\n",
      "23556\n",
      "Station: 72470093141\n",
      "82095\n",
      "Station: 72472099999\n",
      "6351\n",
      "Station: 72472393025\n",
      "3\n",
      "Station: 72473323170\n",
      "47098\n",
      "Station: 72473523170\n",
      "84478\n",
      "Station: 72475023176\n",
      "16485\n",
      "Station: 72475099999\n",
      "42168\n",
      "Station: 72475423186\n",
      "164113\n",
      "Station: 72475593129\n",
      "84393\n",
      "Station: 72475623159\n",
      "92246\n",
      "Station: 72477693075\n",
      "80793\n",
      "Station: 72479599999\n",
      "6760\n",
      "Station: 72479694128\n",
      "92932\n",
      "Station: 72479723176\n",
      "98930\n",
      "Station: 72570594030\n",
      "84736\n",
      "Station: 72572024127\n",
      "107223\n",
      "Station: 72572424174\n",
      "125742\n",
      "Station: 72575024126\n",
      "86423\n",
      "Station: 72575399999\n",
      "24930\n",
      "Station: 72575524101\n",
      "117983\n",
      "Station: 72581024193\n",
      "186734\n",
      "Station: 74003024103\n",
      "7227\n",
      "Station: 999999480\n",
      "0\n",
      "Empty for station 999999480\n",
      "Station: 999999481\n",
      "0\n",
      "Empty for station 999999481\n",
      "Station: 9999993081\n",
      "0\n",
      "Empty for station 9999993081\n",
      "Station: 9999994133\n",
      "0\n",
      "Empty for station 9999994133\n",
      "Station: 9999994134\n",
      "0\n",
      "Empty for station 9999994134\n",
      "Station: 9999994138\n",
      "0\n",
      "Empty for station 9999994138\n",
      "Station: 9999994143\n",
      "0\n",
      "Empty for station 9999994143\n",
      "Station: 99999923162\n",
      "86596\n",
      "Station: 99999953004\n",
      "401174\n",
      "Station: 99999953010\n",
      "400885\n",
      "Station: 99999953012\n",
      "400559\n",
      "Station: 99999953013\n",
      "403774\n",
      "Station: 99999953014\n",
      "399291\n",
      "Station: 99999953149\n",
      "818771\n",
      "Station: 99999953165\n",
      "404640\n",
      "Station: 99999953166\n",
      "226611\n",
      "Station: 99999953167\n",
      "407337\n",
      "Station: 99999953171\n",
      "404328\n"
     ]
    }
   ],
   "source": [
    "url = 'https://www.ncdc.noaa.gov/access-data-service/api/v1/data'\n",
    "\n",
    "params = {\n",
    "    'dataset': 'global-hourly',\n",
    "    'startDate': '2010-01-01T00:00:00',\n",
    "    'endDate': '2018-02-01T00:00:00',\n",
    "    'dataTypes':'AA1,AA1,AA2,AA3,TMP,VIS,WND,AJ1,AT1,AT2,AT3,AT4,AT5,AT6,AT7,AT8',\n",
    "    'stations': stations,\n",
    "    'format': 'json'\n",
    "    \n",
    "}\n",
    "\n",
    "weather_data = pd.DataFrame()\n",
    "\n",
    "for station in stations:\n",
    "    params['stations'] = station\n",
    "    print('Station:',station)\n",
    "    res = requests.get(url,params=params)\n",
    "    js = res.json()\n",
    "    #print(js)\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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather_data.head()\n",
    "weather_data.to_csv('utah_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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