Taiwan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#postgres\n",
    "\n",
    "#These are header files AND useful functions :)\n",
    "import re;\n",
    "from datetime import datetime, timedelta\n",
    "from math import sin, cos, sqrt, atan2, radians\n",
    "import pandas as pd\n",
    "import math\n",
    "import geopandas as gpd\n",
    "import descartes\n",
    "import shapely\n",
    "from shapely.geometry import Point, Polygon\n",
    "from scipy import interpolate\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tnrange, tqdm_notebook\n",
    "from time import sleep\n",
    "from sqlalchemy import func, literal, Sequence\n",
    "import requests\n",
    "from xml.etree import ElementTree\n",
    "import numpy as np\n",
    "import folium\n",
    "\n",
    "%matplotlib inline\n",
    "#dtype={'stationId': str, 'utc_time':Timestamp, 'PM2.5': float, 'PM10': float, 'NO2':float, 'CO':float, 'O3':float, 'SO2':float }\n",
    "\n",
    "PAPER = 1;\n",
    "INTERP = 2;\n",
    "STD = 3;\n",
    "def computeDist(lat1, lon1, lat2, lon2, to_radians=True, earth_radius=6371):\n",
    "    \"\"\"\n",
    "    slightly modified version: of http://stackoverflow.com/a/29546836/2901002\n",
    "\n",
    "    Calculate the great circle distance between two points\n",
    "    on the earth (specified in decimal degrees or in radians)\n",
    "\n",
    "    All (lat, lon) coordinates must have numeric dtypes and be of equal length.\n",
    "\n",
    "    \"\"\"\n",
    "    if to_radians:\n",
    "        lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])\n",
    "\n",
    "    a = np.sin((lat2-lat1)/2.0)**2 + \\\n",
    "        np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2\n",
    "\n",
    "    return earth_radius * 2 * np.arcsin(np.sqrt(a))       \n",
    "\n",
    "def distance(lat1,lon1,lat2,lon2):\n",
    "    #lat1, lon1 = origin\n",
    "    #lat2, lon2 = destination\n",
    "    radius = 6371 # km\n",
    "\n",
    "    dlat = math.radians(lat2-lat1)\n",
    "    dlon = math.radians(lon2-lon1)\n",
    "    a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \\\n",
    "        * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)\n",
    "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n",
    "    d = radius * c\n",
    "\n",
    "    return d\n",
    "\n",
    "#The input of the following function: AQI DataFrame WITHIN ONE TIMESLICE \n",
    "def interpPlot(geo_df):\n",
    "    fig,ax = plt.subplots(figsize = (8,8));\n",
    "    divider = geo_df.shape[0]//4*3;\n",
    "    print(divider);\n",
    "    geo_df[geo_df.index < divider].plot(ax = ax, markersize = 20, color = \"black\", marker = \"o\", label=\"data used for interp\");\n",
    "    geo_df[geo_df.index >= divider].plot(ax = ax, markersize = 20, color = \"red\", marker = \"o\", label=\"data used for testing\");\n",
    "    plt.legend(prop={'size':15})\n",
    "    \n",
    "# The input should be the same as the interpPlot function.\n",
    "def getF(df):\n",
    "    latlist = df[['lat_proj']].values.flatten();\n",
    "    longlist = df[['lon_proj']].values.flatten();\n",
    "    pmlist = df[['PM2.5']].values.flatten();\n",
    "    f = interpolate.interp2d(latlist,longlist,pmlist,kind=\"linear\");\n",
    "    return f;\n",
    "def inverseMethod(data,lat,long,ignoreMe = False):\n",
    "    p = 2.0; # this is power parameter. i/d^p.\n",
    "    numerator = 0.0;\n",
    "    denominator = 0.0;\n",
    "    for index, row in data.iterrows():\n",
    "        currDist = computeDist(lat,long,row['latitude'],row['longitude']);\n",
    "        if currDist == 0:\n",
    "            if ignoreMe:\n",
    "                continue;\n",
    "            else:  \n",
    "                return row['PM2.5'];\n",
    "        wi_x = 1/(currDist**p);\n",
    "        numerator += (wi_x*row['PM2.5']);\n",
    "        denominator += (wi_x);\n",
    "    return (numerator/denominator);\n",
    "        \n",
    "    \n",
    "def interpolation(data):\n",
    "    divider = data.shape[0]//4*3;\n",
    "    print(divider)\n",
    "    interpData = data[data.index < divider];\n",
    "    testingData = data[data.index >= divider];\n",
    "    #lats = interpData[['latitude']].values.flatten();\n",
    "    #longs = interpData[['longitude']].values.flatten();\n",
    "    #pms = interpData[['PM2.5']].values.flatten();\n",
    "    \n",
    "    predictedPM = list();\n",
    "    for index, row in testingData.iterrows():\n",
    "        predictValue = inverseMethod(interpData,row['latitude'],row['longitude']);\n",
    "        predictedPM.append(predictValue);\n",
    "    \n",
    "    testingData['predicted_PM2.5'] = predictedPM;\n",
    "    predictedList = testingData[['predicted_PM2.5']].values.flatten().tolist();\n",
    "    actualList = testingData[['PM2.5']].values.flatten().tolist();\n",
    "    res = {'Predicted':predictedList,'Actual':actualList};\n",
    "    result = pd.DataFrame(res);\n",
    "    ax = plt.gca()\n",
    "    result.plot(kind='line',y='Actual',color='olive',ax=ax)\n",
    "    result.plot(kind='line',y='Predicted',color='red',ax=ax)\n",
    "    plt.legend();\n",
    "    plt.show();\n",
    "    return testingData;\n",
    "    \n",
    "def getTimeList(data):\n",
    "    timeSet = set();\n",
    "    for index,row in data.iterrows():\n",
    "        timeSet.add(row['utc_time']);\n",
    "    return list(timeSet);\n",
    "def truncateTime(data):\n",
    "    newTime = list();\n",
    "    timeList = data[['utc_time']].values.flatten();\n",
    "    for time in timeList:\n",
    "        time = time[:-3];\n",
    "        newTime.append(time);\n",
    "    data['utc_time'] = newTime;\n",
    "def convertTimeType(df):\n",
    "    print(\"now fetching times...\");\n",
    "    timeList = df[['utc_time']].values.flatten()\n",
    "\n",
    "    print(\"now converting time...\")\n",
    "    timeList = pd.to_datetime(timeList);\n",
    "    print(\"now adding this list...\");\n",
    "    df['utc_time'] = timeList;\n",
    "\n",
    "# Return value structure: {station: list of neighbours}\n",
    "#tqdm_notebook(df.iterrows(), desc='neighboursmap', total=df.shape[0]):\n",
    "def getNeighboursMap(df,threshold):\n",
    "    neighbourMap = {};\n",
    "    for index,row in tqdm_notebook(df.iterrows(), desc='neighboursmap', total=df.shape[0]):\n",
    "        currStation = row['stationId'];\n",
    "        neighbourMap[currStation] = list();\n",
    "        lat1 = row['latitude'];\n",
    "        long1 = row['longitude'];\n",
    "        for ind2, row2 in df.iterrows():\n",
    "            if computeDist(lat1,long1,row2['latitude'],row2['longitude']) <= threshold:\n",
    "                neighbourMap[currStation].append(row2['stationId']);\n",
    "    \n",
    "    \n",
    "    return neighbourMap;   \n",
    "\n",
    "# Return value of the following function: {time: {station: AQI}}\n",
    "\n",
    "    \n",
    "#tqdm_notebook(stations.iterrows(), desc='station2Data', total=stations.shape[0]):\n",
    "def getStation2DataMap(stations,record):\n",
    "    dataMap = {};\n",
    "    for index,row in tqdm_notebook(stations.iterrows(), desc='station2Data', total=stations.shape[0]):\n",
    "        currStation = row['stationId'];\n",
    "        mapped = record[record.stationId == currStation];\n",
    "        if mapped.empty:\n",
    "            continue;\n",
    "        mapped.sort_values(by=['utc_time'])\n",
    "        dataMap[currStation] = mapped;\n",
    "    return dataMap;\n",
    "def getTime2AQIMap(data):\n",
    "    timeMap = {};\n",
    "    #for index,row in tqdm_notebook(data.iterrows(), desc='time2AQI', total=data.shape[0]):\n",
    "    for index,row in tqdm_notebook(data.iterrows(), desc='time2AQI', total=data.shape[0]):\n",
    "        currTime = row['utc_time'];\n",
    "        currStation = row['stationId'];\n",
    "        currPM = row['PM2.5'];\n",
    "        if not (currTime in timeMap):\n",
    "            timeMap[currTime] = {};\n",
    "        \n",
    "        timeMap[currTime][currStation] = currPM;\n",
    "    return timeMap;\n",
    "\n",
    "# The following function returns: map {station: [list of anomaly time stamps]}\n",
    "# NOTE: data and useInterp needs to be set to true/false simultaneously! \n",
    "def Spatial(neighbourMap,timeMap, df = None,method = PAPER):\n",
    "    anomalyMap = {};\n",
    "    counter = 0;\n",
    "    #tqdm_notebook(data, desc='2nd loop'):\n",
    "    for time in tqdm_notebook(timeMap, desc='Outer Loop'):\n",
    "        counter+=1;\n",
    "        data = timeMap[time];\n",
    "        timeSlice = df[df.utc_time == time];\n",
    "        other = None;\n",
    "        stddev = 0.0;\n",
    "        f = None;\n",
    "        if not method == PAPER:\n",
    "            other = timeSlice[[\"PM2.5\"]].values.flatten();\n",
    "            stddev = np.nanstd(other);\n",
    "        if method == INTERP: \n",
    "            f = getF(timeSlice);\n",
    "        for station in data:\n",
    "            isAnomaly = False;\n",
    "            neighbours = neighbourMap[station];\n",
    "            neighbourData = list();\n",
    "            for s in neighbours:\n",
    "                if s in data:\n",
    "                    neighbourData.append(data[s]);\n",
    "            if len(neighbourData) <= 3:\n",
    "                continue;\n",
    "            neighbourData = np.asarray(neighbourData);\n",
    "\n",
    "            currPM = data[station];\n",
    "            \n",
    "            # IF we decide to use METHOD PRESENTED IN PAPER:\n",
    "            if method == PAPER:\n",
    "                lowerQuat = np.nanpercentile(neighbourData,25);\n",
    "                higherQuat = np.nanpercentile(neighbourData,75);\n",
    "                if np.isnan(lowerQuat) or np.isnan(higherQuat):\n",
    "                    continue;\n",
    "                qualifier1 = (currPM < (lowerQuat - 1.5*(higherQuat - lowerQuat)));\n",
    "                qualifier2 = (currPM > (higherQuat + 1.5*(higherQuat - lowerQuat)));\n",
    "                if qualifier1 or qualifier2:\n",
    "                    isAnomaly = True;\n",
    "                    \n",
    "            elif method == INTERP:# IF we decide to use INVERSE INTERPOLATION METHOD:\n",
    "                if np.isnan(stddev):\n",
    "                    continue;\n",
    "                thisData = timeSlice[timeSlice.stationId == station].reset_index(drop=True);\n",
    "                thisLat = thisData.loc[0,'latitude'];\n",
    "                thisLong = thisData.loc[0,'longitude']; \n",
    "                timeSlice = timeSlice[timeSlice.stationId != station];\n",
    "                #inverseMethod(data,lat,long,ignoreMe = False):\n",
    "                interpResult = f(thisLat,thisLong)[0];\n",
    "\n",
    "                diff = abs(interpResult - currPM);\n",
    "                if diff >= 1.5*stddev:\n",
    "                    isAnomaly = True;\n",
    "            elif method == STD:\n",
    "                stddev = np.nanstd(neighbourData);\n",
    "                if np.isnan(stddev):\n",
    "                    continue;\n",
    "                avg = np.nanmean(neighbourData);\n",
    "                if np.isnan(avg):\n",
    "                    continue;\n",
    "                diff = abs(currPM-avg);\n",
    "                \n",
    "                if diff >= 1.5*stddev:\n",
    "                    #print(\"neighbourAvg: \"+(str)(avg)+\" actual: \"+(str)(currPM));\n",
    "                    isAnomaly = True;\n",
    "            else:\n",
    "                isAnomaly = False;\n",
    "            if isAnomaly:\n",
    "                if not (station in anomalyMap):\n",
    "                    anomalyMap[station] = list();\n",
    "                anomalyMap[station].append(time);\n",
    "    \n",
    "    \n",
    "    return anomalyMap;\n",
    "    \n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "def plotStationResult(station,SpatialResult,neighbourMap,timeMap):\n",
    "    selfData = list();\n",
    "    neighbourMean = list();\n",
    "    neighbourMedian = list();\n",
    "    TimeList = SpatialResult[station];\n",
    "    TimeList.sort();\n",
    "    shortenedTime = list();\n",
    "    start = 100;\n",
    "    end = 120; #len(TimeList);\n",
    "    for i in range(start,end,1):\n",
    "        time = TimeList[i];\n",
    "        shortenedTime.append(time);\n",
    "        selfResult = timeMap[time][station];\n",
    "        neighbourResult = list();\n",
    "        for neighbour in neighbourMap[station]:\n",
    "            if neighbour in timeMap[time]:\n",
    "                neighbourResult.append(timeMap[time][neighbour]);\n",
    "        neighbourResult = np.asarray(neighbourResult);\n",
    "        # now begin pushing values:\n",
    "        selfData.append(selfResult);\n",
    "        neighbourMean.append(np.nanmean(neighbourResult));\n",
    "        neighbourMedian.append(np.nanmedian(neighbourResult));\n",
    "    # now wrap everything into a result map: \n",
    "    res = {\"utc_time\":shortenedTime,\"selfData\":selfData,\"neighbourMean\":neighbourMean,\"neighbourMedian\":neighbourMedian};\n",
    "    result = pd.DataFrame(res);\n",
    "    # now, PLOT WORK!!! \n",
    "    ax = plt.gca();\n",
    "    \n",
    "    result.plot(kind='line', x='utc_time',y='neighbourMean',color='red',marker='.',ax=ax);\n",
    "    result.plot(kind='line',x='utc_time',y='neighbourMedian',color='green',marker='.',ax=ax);\n",
    "    result.plot(kind='line',x='utc_time',y='selfData',color='black',marker='.',ax=ax);\n",
    "    plt.xticks( rotation='45')\n",
    "    plt.show();\n",
    "def plotFrequency(freqMap, total):\n",
    "    stationList = list(freqMap.keys());\n",
    "    percentList = [i/total for i in freqMap.values()]\n",
    "    print(type(stationList))\n",
    "    toPlot = pd.DataFrame({\"stations\":stationList,\"anomaly ratio\":percentList});\n",
    "    \n",
    "    ax = toPlot.plot.bar(x='stations', y='anomaly ratio', rot=0);\n",
    "    vals = ax.get_yticks()\n",
    "    ax.set_yticklabels(['{:,.2%}'.format(x) for x in vals])\n",
    "    plt.xticks( rotation='45')\n",
    "    return toPlot;\n",
    "#for index,row in tqdm_notebook(record.iterrows(), desc='North stage 1/2', total=record.shape[0]):\n",
    "def getNorthStations(stations,record):\n",
    "    cover = set();\n",
    "    for index,row in tqdm_notebook(record.iterrows(), desc='North stage 1/2', total=record.shape[0]):\n",
    "        cover.add(row[\"stationId\"]);\n",
    "\n",
    "    longlist = list();\n",
    "    latlist = list();\n",
    "    devlist = list();\n",
    "    \n",
    "    #tqdm_notebook(stations.iterrows(), desc='North stage 2/2', total=stations.shape[0]):\n",
    "    for index,row in tqdm_notebook(stations.iterrows(), desc='North stage 2/2', total=stations.shape[0]):\n",
    "        if row[\"stationId\"] in cover:\n",
    "            devlist.append(row[\"stationId\"]);\n",
    "            longlist.append(row[\"longitude\"]);\n",
    "            latlist.append(row[\"latitude\"]);\n",
    "    dic = {\"stationId\":devlist,\"longitude\":longlist,\"latitude\":latlist};\n",
    "    return pd.DataFrame(dic);\n",
    "\n",
    "def getFrequency(SpatialResult):\n",
    "    frequencyMap = {};\n",
    "    for station in SpatialResult: \n",
    "        frequencyMap[station] = len(SpatialResult[station]);\n",
    "\n",
    "    return frequencyMap;\n",
    "\n",
    "#THE FOLLOWING IS USED TO CONVERT COORDINATES: \n",
    "\n",
    "# Postgres connection\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "from sqlalchemy.orm import sessionmaker\n",
    "\n",
    "connection_string = 'postgresql+psycopg2://{usr}:{pwd}@jonsnow.usc.edu/air_quality_dev' \\\n",
    "    .format(usr='yijun', pwd='m\\\\tC7;cc')\n",
    "\n",
    "engine = create_engine(connection_string, echo=False)\n",
    "Session = sessionmaker(bind=engine, expire_on_commit=False)\n",
    "Base = declarative_base()\n",
    "session = Session()\n",
    "inEPSG = 4326\n",
    "outEPSG = 3826\n",
    "# utilize Postgis for projection\n",
    "\n",
    "\n",
    "def query_projection(x, y):\n",
    "    result = session.query(func.ST_X(func.ST_Transform(func.ST_SetSRID(func.ST_MakePoint(x, y), inEPSG), outEPSG)),\n",
    "                            func.ST_Y(func.ST_Transform(func.ST_SetSRID(func.ST_MakePoint(x, y), inEPSG), outEPSG)))\n",
    "    return result[0][0], result[0][1]\n",
    "\n",
    "\n",
    "def get_new_proj_coord(df):\n",
    "    new_lon, new_lat = [], []\n",
    "    #tqdm_notebook(df.iterrows(), desc='Outer Loop'):\n",
    "    for _, row in tqdm_notebook(df.iterrows(), desc='Outer Loop', total=df.shape[0]):\n",
    "        lon_proj, lat_proj = query_projection(row['longitude'], row['latitude'])\n",
    "        new_lon.append(lon_proj)\n",
    "        new_lat.append(lat_proj)\n",
    "    #print(\"Length of new_lon: \"+(str)(new_lon));\n",
    "    df['lon_proj'] = new_lon\n",
    "    df['lat_proj'] = new_lat\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_Time     object\n",
       "PM2.5        float64\n",
       "device_id     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record = pd.read_csv('taiwan_data.csv',delimiter = ',');\n",
    "stations = pd.read_csv('taiwan_device.csv',delimiter = ',')\n",
    "record = record[record.area == 'north'].reset_index(drop=True)\n",
    "\n",
    "record = record[[\"Date_Time\",\"PM2.5\",\"device_id\"]]\n",
    "record.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations.head()\n",
    "latList = list();\n",
    "longList = list();\n",
    "\n",
    "for index, row in stations.iterrows():\n",
    "    s = row['st_astext'];\n",
    "    p = re.compile(r\"[-+]?\\d*\\.\\d+|\\d+\")\n",
    "    floats = [float(i) for i in p.findall(s)]\n",
    "    #print(floats)\n",
    "    longList.append(floats[0]);\n",
    "    latList.append(floats[1]);\n",
    "stations['longitude'] = longList;\n",
    "stations['latitude'] = latList;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>stationId</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>001F023A0011</td>\n",
       "      <td>121.564</td>\n",
       "      <td>25.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001F023A0011</td>\n",
       "      <td>121.582</td>\n",
       "      <td>25.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0050FC819505</td>\n",
       "      <td>139.103</td>\n",
       "      <td>35.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00E04C870000</td>\n",
       "      <td>121.526</td>\n",
       "      <td>25.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00E04C870000</td>\n",
       "      <td>121.526</td>\n",
       "      <td>25.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      stationId  longitude  latitude\n",
       "0  001F023A0011    121.564    25.081\n",
       "1  001F023A0011    121.582    25.062\n",
       "2  0050FC819505    139.103    35.976\n",
       "3  00E04C870000    121.526    25.029\n",
       "4  00E04C870000    121.526    25.030"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = stations[[\"device_id\",\"longitude\",\"latitude\"]]\n",
    "stations = stations.rename(index=str, columns={\"device_id\": \"stationId\"})\n",
    "stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>utc_time</th>\n",
       "      <th>PM2.5</th>\n",
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       "      <th>2612983</th>\n",
       "      <td>2018-06-08 15:00:00-07</td>\n",
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       "                       utc_time     PM2.5     stationId\n",
       "2612981  2018-06-08 13:00:00-07  1.045455  74DA38EB138A\n",
       "2612982  2018-06-08 14:00:00-07  1.000000  74DA38EB138A\n",
       "2612983  2018-06-08 15:00:00-07  1.062500  74DA38EB138A\n",
       "2612984  2018-06-08 16:00:00-07  1.166667  74DA38EB138A\n",
       "2612985  2018-06-08 17:00:00-07  1.000000  74DA38EB138A"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record = record.rename(index=str, columns={\"device_id\":\"stationId\",\"Date_Time\":\"utc_time\"})\n",
    "record.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now fetching times...\n",
      "now converting time...\n",
      "now adding this list...\n"
     ]
    },
    {
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       "      <th>stationId</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <td>121.548</td>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-08 15:00:00</td>\n",
       "      <td>21.75</td>\n",
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       "      <th>3</th>\n",
       "      <td>2018-01-08 16:00:00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08 17:00:00</td>\n",
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      ],
      "text/plain": [
       "             utc_time  PM2.5     stationId  longitude  latitude\n",
       "0 2018-01-08 12:00:00  14.00  28C2DDDD400A    121.548    25.049\n",
       "1 2018-01-08 13:00:00  13.00  28C2DDDD400A    121.548    25.049\n",
       "2 2018-01-08 15:00:00  21.75  28C2DDDD400A    121.548    25.049\n",
       "3 2018-01-08 16:00:00  19.00  28C2DDDD400A    121.548    25.049\n",
       "4 2018-01-08 17:00:00   7.00  28C2DDDD400A    121.548    25.049"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NorthTaiwan = pd.merge(record,stations,on=\"stationId\")\n",
    "truncateTime(NorthTaiwan)\n",
    "convertTimeType(NorthTaiwan)\n",
    "NorthTaiwan.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>utc_time</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>stationId</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <td>2018-01-08 12:00:00</td>\n",
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       "      <th>1</th>\n",
       "      <td>2018-01-08 13:00:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-08 15:00:00</td>\n",
       "      <td>21.75</td>\n",
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       "      <td>2018-01-08 16:00:00</td>\n",
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       "      <td>28C2DDDD400A</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08 17:00:00</td>\n",
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      ],
      "text/plain": [
       "             utc_time  PM2.5     stationId  longitude  latitude\n",
       "0 2018-01-08 12:00:00  14.00  28C2DDDD400A    121.548    25.049\n",
       "1 2018-01-08 13:00:00  13.00  28C2DDDD400A    121.548    25.049\n",
       "2 2018-01-08 15:00:00  21.75  28C2DDDD400A    121.548    25.049\n",
       "3 2018-01-08 16:00:00  19.00  28C2DDDD400A    121.548    25.049\n",
       "4 2018-01-08 17:00:00   7.00  28C2DDDD400A    121.548    25.049"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NorthTaiwan.sort_values(by=['utc_time'])\n",
    "NorthTaiwan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb42a1a5a2834ad5862da892f20008ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='North stage 1/2', max=2612986, style=ProgressStyle(descriptio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d89b4b648b044d4a3e482ee0a83a750",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='North stage 2/2', max=7512, style=ProgressStyle(description_w…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "330bff9910d14e5cbc0957e6f5c29701",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='neighboursmap', max=2612986, style=ProgressStyle(description_…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-738398ac5a9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mstations_North\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetNorthStations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNorthTaiwan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mneighbourMap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetNeighboursMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNorthTaiwan\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mtime2AQIMap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetTime2AQIMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNorthTaiwan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-d65eee0079f7>\u001b[0m in \u001b[0;36mgetNeighboursMap\u001b[0;34m(df, threshold)\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0mlat1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0mlong1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mind2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcomputeDist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlat1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlong1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m                 \u001b[0mneighbourMap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrStation\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'stationId'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36miterrows\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    844\u001b[0m         \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    264\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m         \u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, axes, copy, dtype, fastpath)\u001b[0m\n\u001b[1;32m    138\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_is_copy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_item_cache'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "stations_North = getNorthStations(stations,NorthTaiwan)\n",
    "neighbourMap = getNeighboursMap(NorthTaiwan,3);\n",
    "time2AQIMap = getTime2AQIMap(NorthTaiwan);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91bdc64063444a5e862fa1acf8a9fff1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='neighboursmap', max=458, style=ProgressStyle(description_widt…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da93aa16fece48b18c8ca7ac48600d1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='time2AQI', max=2612986, style=ProgressStyle(description_width…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "089a06b8cedf40d18a15a7b52f706a5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Outer Loop', max=8690, style=ProgressStyle(description_width=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "neighbourMap = getNeighboursMap(stations_North,3);\n",
    "time2AQIMap = getTime2AQIMap(NorthTaiwan);\n",
    "#Mar_1_2.head()\n",
    "SpatialResult = Spatial(neighbourMap,time2AQIMap,NorthTaiwan,PAPER);\n",
    "SpatialResult\n",
    "station = \"74DA38C7D468\"\n",
    "plotStationResult(station,SpatialResult,neighbourMap,time2AQIMap);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "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>stations</th>\n",
       "      <th>anomaly ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74DA38B05108</td>\n",
       "      <td>0.286644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74DA388FF680</td>\n",
       "      <td>0.272717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74DA388FF5BE</td>\n",
       "      <td>0.246119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74DA38E69B3C</td>\n",
       "      <td>0.240411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74DA38AF47AA</td>\n",
       "      <td>0.229795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       stations  anomaly ratio\n",
       "0  74DA38B05108       0.286644\n",
       "1  74DA388FF680       0.272717\n",
       "2  74DA388FF5BE       0.246119\n",
       "3  74DA38E69B3C       0.240411\n",
       "4  74DA38AF47AA       0.229795"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#station = \"74DA38C7D468\"\n",
    "#plotStationResult(station,SpatialResult,neighbourMap,time2AQIMap);\n",
    "print(SpatialResult)\n",
    "def getTemporalMap(data):\n",
    "    #utc_time\tPM2.5\tstationId\tlongitude\tlatitude\n",
    "    tempMap = {};\n",
    "    for index,row in tqdm_notebook(data.iterrows(), desc='Outer Loop'):\n",
    "        if not row[\"utc_time\"] in tempMap:\n",
    "            tempMap[row[\"utc_time\"]] = {};\n",
    "        tempMap[row[\"utc_time\"]][row[\"stationId\"]] = row[\"PM2.5\"];\n",
    "    return tempMap;\n",
    "def runTemporal(tempMap):\n",
    "    anomalyMap = {};\n",
    "    #\n",
    "    for time in tqdm_notebook(tempMap, desc='Outer Loop'):\n",
    "        currMap = tempMap[time];\n",
    "        stationList = [];\n",
    "        currList = [];\n",
    "        lastList = [];\n",
    "        for station in currMap:\n",
    "            currAQI = currMap[station];\n",
    "            timebefore1 = time-timedelta(hours=1)\n",
    "            timebefore2 = time-timedelta(hours=2)\n",
    "            prevtime = 0;\n",
    "            if timebefore1 in tempMap and station in tempMap[timebefore1]:\n",
    "                prevtime = timebefore1;\n",
    "            elif timebefore2 in tempMap and station in tempMap[timebefore2]:\n",
    "                prevtime = timebefore2;\n",
    "            else:\n",
    "                continue;\n",
    "            prevAQI = tempMap[prevtime][station]\n",
    "            if abs(currAQI - prevAQI) >= currAQI/3:\n",
    "                stationList.append(station);\n",
    "                currList.append(currAQI);\n",
    "                lastList.append(prevAQI);\n",
    "        wrapUp = {\"stations\":stationList,\"currSliceValue\":currList,\"lastSliceValue\":lastList};\n",
    "        anomalyMap[time] = pd.DataFrame(wrapUp);\n",
    "    return anomalyMap;\n",
    "                    \n",
    "def temporalPlot(temporalRes):\n",
    "    ax = plt.gca();\n",
    "    temporalRes.plot(kind='line', x='stations',y='currSliceValue',color='red',marker='.',ax=ax);\n",
    "    temporalRes.plot(kind='line',x='stations',y='lastSliceValue',color='green',marker='.',ax=ax);\n",
    "    \n",
    "    plt.show();\n",
    "#full_data = getStation2DataMap(stations,Mar_1_2)\n",
    "#temporalRes = runTemporal(Mar_1_2_data)\n",
    "#tempMap = getTemporalMap(NorthTaiwan);\n",
    "\n",
    "\n",
    "#tempResult = runTemporal(tempMap)\n",
    "#tempResult\n",
    "\n",
    "#highFreqMap ={'74DA388FF680': 5314,'74DA38C7D468': 3174,'74DA388FF576': 2937,'74DA3895C2F4': 2887,'74DA38B0514E': 2365 };\n",
    "\n",
    "#plotFrequency(highFreqMap,8760)\n",
    "\n",
    "#tempFreq = {};\n",
    "#for time in tqdm_notebook(tempResult):\n",
    "#    df = tempResult[time];\n",
    "#    for index, row in df.iterrows():\n",
    "#        if not row[\"stations\"] in tempFreq:\n",
    "#            tempFreq[row[\"stations\"]] =0\n",
    "#        tempFreq[row[\"stations\"]] = tempFreq[row[\"stations\"]]+1;\n",
    "#tempFreq\n",
    "\n",
    "tempP = {'74DA38B05108': 2511,'74DA388FF680': 2389,'74DA388FF5BE': 2156,'74DA38E69B3C': 2106,'74DA38AF47AA': 2013}\n",
    "plotFrequency(tempP,8760)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'74DA388FF680': 5314, '74DA38E69D82': 3881, '74DA388FF5BE': 3645, '74DA38C7D468': 3174, '74DA388FF576': 2937, '74DA3895C2F4': 2887, '74DA38C7CFE2': 2548, '74DA388FF484': 2464, '74DA38B0514E': 2365, '74DA38B05108': 2277, '74DA388FF4AE': 2222, '74DA38E2B4EC': 2081, '74DA38B052DC': 1788, '28C2DDDD43FC': 1775, '74DA38C7D48C': 1757, '74DA3895C32A': 1730, '74DA38E2B62E': 1726, '74DA388FF604': 1724, '74DA38B05360': 1641, '74DA388FF6F2': 1448}\n",
      "<class 'list'>\n"
     ]
    },
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      "text/plain": [
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     "execution_count": 110,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(tempResult)\n",
    "#demoTime = t1+timedelta(hours=1)\n",
    "#demoTime\n",
    "#temporalPlot(tempResult[demoTime])\n",
    "from functools import cmp_to_key\n",
    "\n",
    "#This function plots the top 20 stations of the result. \n",
    "#input: frequencyMap is sth like {station: number_of_anomalous_times}\n",
    "def analyzeSpatial(frequencyMap,stations):\n",
    "    itemList = list(frequencyMap.items())\n",
    "    itemList\n",
    "    itemList.sort(key=cmp_to_key(lambda a,b: a[1]-b[1]),reverse=True)\n",
    "    toPlot = {};\n",
    "    for item in itemList[:20]:\n",
    "        #toPlot[item[0]] = itemList[item[1]]\n",
    "        toPlot[item[0]] = item[1]\n",
    "    print(toPlot)\n",
    "    plotFrequency(toPlot,8760)\n",
    "    latList = list();\n",
    "    longList = list();\n",
    "    stationList = list();\n",
    "    for device in toPlot:\n",
    "        stationList.append(device);\n",
    "        onlyOne = stations[stations.stationId == device].reset_index(drop=True)\n",
    "        latList.append(onlyOne.loc[0,\"latitude\"]);\n",
    "        longList.append(onlyOne.loc[0,\"longitude\"]);\n",
    "    info = {\"latitude\":latList,\"longitude\":longList,\"stationId\":stationList}\n",
    "    info = pd.DataFrame(info);\n",
    "    \n",
    "    locations = info[['latitude', 'longitude']]\n",
    "    locationlist = locations.values.tolist()\n",
    "    map = folium.Map(location=[24.964, 121.221], zoom_start=12)\n",
    "    for point in range(0, len(locationlist)):\n",
    "        folium.Marker(locationlist[point], popup=info['stationId'][point]).add_to(map)\n",
    "\n",
    "    \n",
    "    return map;\n",
    "analyzeSpatial(frequencyMap,stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a04a90453fb44deeafa5e064cce16d5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Outer Loop', max=8493, style=ProgressStyle(description_width=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-107-7a0dbe07f378>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMar_1_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_new_proj_coord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMar_1_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#station = \"74DA38C7D468\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#plotStationResult(station,SpatialResult,neighbourMap,time2AQIMap);\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-37-be60b6a8798b>\u001b[0m in \u001b[0;36mget_new_proj_coord\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;31m#tqdm_notebook(df.iterrows(), desc='Outer Loop'):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Outer Loop'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m         \u001b[0mlon_proj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat_proj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    363\u001b[0m         \u001b[0mnew_lon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlon_proj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0mnew_lat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlat_proj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-37-be60b6a8798b>\u001b[0m in \u001b[0;36mquery_projection\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m    353\u001b[0m     result = session.query(func.ST_X(func.ST_Transform(func.ST_SetSRID(func.ST_MakePoint(x, y), inEPSG), outEPSG)),\n\u001b[1;32m    354\u001b[0m                             func.ST_Y(func.ST_Transform(func.ST_SetSRID(func.ST_MakePoint(x, y), inEPSG), outEPSG)))\n\u001b[0;32m--> 355\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/orm/query.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   3010\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3011\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3012\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3014\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0m_generative\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_no_statement_condition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/orm/query.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   3005\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3006\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3007\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3008\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3009\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/orm/query.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3315\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_autoflush\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_populate_existing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3316\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_autoflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3317\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_and_instances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3319\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__str__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/orm/query.py\u001b[0m in \u001b[0;36m_execute_and_instances\u001b[0;34m(self, querycontext)\u001b[0m\n\u001b[1;32m   3340\u001b[0m         )\n\u001b[1;32m   3341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3342\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquerycontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3343\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minstances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquerycontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquerycontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, object_, *multiparams, **params)\u001b[0m\n\u001b[1;32m    986\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mObjectNotExecutableError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 988\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_execute_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/sql/elements.py\u001b[0m in \u001b[0;36m_execute_on_connection\u001b[0;34m(self, connection, multiparams, params)\u001b[0m\n\u001b[1;32m    285\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_execute_on_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupports_execution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_clauseelement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mObjectNotExecutableError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_clauseelement\u001b[0;34m(self, elem, multiparams, params)\u001b[0m\n\u001b[1;32m   1105\u001b[0m             \u001b[0mdistilled_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m             \u001b[0mcompiled_sql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1107\u001b[0;31m             \u001b[0mdistilled_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1108\u001b[0m         )\n\u001b[1;32m   1109\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m   1246\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1247\u001b[0m             self._handle_dbapi_exception(\n\u001b[0;32m-> 1248\u001b[0;31m                 \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1249\u001b[0m             )\n\u001b[1;32m   1250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1466\u001b[0m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_cause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqlalchemy_exception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1467\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1468\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1470\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb, cause)\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m   1242\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1243\u001b[0m                     self.dialect.do_execute(\n\u001b[0;32m-> 1244\u001b[0;31m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1245\u001b[0m                     )\n\u001b[1;32m   1246\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/encodings/utf_8.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(input, errors)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mencode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutf_8_encode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'strict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutf_8_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "Mar_1_2 = get_new_proj_coord(Mar_1_2);\n",
    "\n",
    "#station = \"74DA38C7D468\"\n",
    "#plotStationResult(station,SpatialResult,neighbourMap,time2AQIMap);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e516a7733b1e4561b9f35dcef9a898c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Outer Loop', max=24, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=16,28 m=355 fp=39647.080432 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=31,14 m=346 fp=46853.650545 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=33,14 m=349 fp=52314.236103 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=26,17 m=351 fp=54365.184147 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=22,20 m=346 fp=42344.237883 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=33,14 m=353 fp=60761.549835 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=33,14 m=354 fp=65326.034502 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=20,22 m=357 fp=50736.899294 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=20,22 m=357 fp=15624.581062 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=16,28 m=356 fp=12899.024992 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=23,19 m=355 fp=13989.878836 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=21,21 m=353 fp=11230.788464 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=19,23 m=356 fp=7703.337125 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=20,22 m=359 fp=7671.608850 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=21,21 m=358 fp=7961.364459 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=32,14 m=352 fp=14269.083960 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=20,22 m=351 fp=30659.636077 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=21,21 m=355 fp=10568.241073 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=21,21 m=357 fp=11719.000498 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=21,21 m=353 fp=12444.947648 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=22,20 m=355 fp=16804.172345 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=33,14 m=358 fp=19469.696932 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=33,14 m=351 fp=19509.580845 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n",
      "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/_fitpack_impl.py:976: RuntimeWarning: No more knots can be added because the number of B-spline\n",
      "coefficients already exceeds the number of data points m.\n",
      "Probable causes: either s or m too small. (fp>s)\n",
      "\tkx,ky=1,1 nx,ny=19,23 m=356 fp=27982.685991 s=0.000000\n",
      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'28C2DDDD400A': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 04:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 07:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00')],\n",
       " '28C2DDDD4180': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 07:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 19:00:00'),\n",
       "  Timestamp('2018-03-01 20:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD41B2': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00')],\n",
       " '28C2DDDD4233': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD4239': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 04:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 19:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD423C': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00')],\n",
       " '28C2DDDD4246': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 04:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 20:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD436E': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD4372': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 04:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 07:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 19:00:00'),\n",
       "  Timestamp('2018-03-01 20:00:00'),\n",
       "  Timestamp('2018-03-01 21:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD4378': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD43FC': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00')],\n",
       " '28C2DDDD4568': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 04:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 07:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 19:00:00'),\n",
       "  Timestamp('2018-03-01 20:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD4571': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 07:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '28C2DDDD4579': [Timestamp('2018-03-01 00:00:00'),\n",
       "  Timestamp('2018-03-01 01:00:00'),\n",
       "  Timestamp('2018-03-01 02:00:00'),\n",
       "  Timestamp('2018-03-01 03:00:00'),\n",
       "  Timestamp('2018-03-01 04:00:00'),\n",
       "  Timestamp('2018-03-01 05:00:00'),\n",
       "  Timestamp('2018-03-01 06:00:00'),\n",
       "  Timestamp('2018-03-01 07:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
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       "  Timestamp('2018-03-01 23:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '74DA38C7D152': [Timestamp('2018-03-01 07:00:00'),\n",
       "  Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 22:00:00'),\n",
       "  Timestamp('2018-03-01 23:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '74DA388FF528': [Timestamp('2018-03-01 09:00:00'),\n",
       "  Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 19:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '74DA3895DFE8': [Timestamp('2018-03-01 10:00:00'),\n",
       "  Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 20:00:00'),\n",
       "  Timestamp('2018-03-01 22:00:00'),\n",
       "  Timestamp('2018-03-01 23:00:00'),\n",
       "  Timestamp('2018-03-01 08:00:00')],\n",
       " '74DA38C7D31A': [Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00')],\n",
       " '74DA38C7D31C': [Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 19:00:00')],\n",
       " '74DA38E2B4C0': [Timestamp('2018-03-01 11:00:00'),\n",
       "  Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00'),\n",
       "  Timestamp('2018-03-01 19:00:00')],\n",
       " '74DA388FF380': [Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00')],\n",
       " '74DA38B0526A': [Timestamp('2018-03-01 12:00:00'),\n",
       "  Timestamp('2018-03-01 13:00:00'),\n",
       "  Timestamp('2018-03-01 14:00:00'),\n",
       "  Timestamp('2018-03-01 15:00:00')],\n",
       " '28C2DDDD459F': [Timestamp('2018-03-01 15:00:00'),\n",
       "  Timestamp('2018-03-01 16:00:00'),\n",
       "  Timestamp('2018-03-01 17:00:00'),\n",
       "  Timestamp('2018-03-01 18:00:00')]}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpResult = Spatial(neighbourMap,time2AQIMap,Mar_1_2,INTERP);\n",
    "interpResult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "station = \"28C2DDDD4239\"\n",
    "plotStationResult(station,interpResult,neighbourMap,time2AQIMap);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>stationId</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>001F023A0011</td>\n",
       "      <td>121.564</td>\n",
       "      <td>25.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001F023A0011</td>\n",
       "      <td>121.582</td>\n",
       "      <td>25.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0050FC819505</td>\n",
       "      <td>139.103</td>\n",
       "      <td>35.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00E04C870000</td>\n",
       "      <td>121.526</td>\n",
       "      <td>25.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00E04C870000</td>\n",
       "      <td>121.526</td>\n",
       "      <td>25.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      stationId  longitude  latitude\n",
       "0  001F023A0011    121.564    25.081\n",
       "1  001F023A0011    121.582    25.062\n",
       "2  0050FC819505    139.103    35.976\n",
       "3  00E04C870000    121.526    25.029\n",
       "4  00E04C870000    121.526    25.030"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_counters = stations.head().copy()\n",
    "df_counters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[35.976, 139.103]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "locations = df_counters[['latitude', 'longitude']]\n",
    "locationlist = locations.values.tolist()\n",
    "len(locationlist)\n",
    "locationlist[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "map = folium.Map(location=[35.976, 139.103], zoom_start=12)\n",
    "for point in range(0, len(locationlist)):\n",
    "    folium.Marker(locationlist[point], popup=df_counters['stationId'][point]).add_to(map)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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