Exemplo n.º 1
0
    def barPlot(self, saveVar):
        bikesByStation = {}
        bikesForDict = []
        stationsForDict = []

        #Loading Station Data to be used in slicing the citibike availability data
        stationData = fullStationDataLoad()
        zipStations = stationData['id'][stationData["postalCode"] ==
                                        self.inputzip].tolist()
        #Loading citibike availability data
        citidf = citiBikeDataLoad()
        citiStations = citidf.columns.values
        citiStations = citiStations.astype(int)
        zipStations = list(set(zipStations).intersection(citiStations))
        if len(zipStations) == 0:
            print "There are no bike stations in this zip code"
        else:
            #Storing plot values and labels
            for stations in zipStations:
                bikesForDict.append(
                    int(citidf.loc[self.day, self.hour,
                                   self.month].ix[str(stations)]))
                stationsForDict.append(
                    str(stationData.loc[str(
                        stations)].ix['stationName']).strip())
            #Storing plot values and labels into dictionary for easy use
            bikesByStation = dict(zip(stationsForDict, bikesForDict))

            #Plotting bikes by station using a barchart
            plt.figure(figsize=(14, 14))
            width = 1
            plt.bar(range(len(bikesByStation)),
                    bikesByStation.values(),
                    width,
                    color='mediumpurple',
                    align='center')
            plt.xticks(range(len(bikesByStation)),
                       bikesByStation.keys(),
                       rotation=70,
                       fontsize=12,
                       ha="right")
            plt.xlim([-1, len(bikesByStation)])
            plt.title('Predicted Number of Bikes For ' + self.day +
                      's in Month ' + str(self.month) + " at " +
                      str(self.hour) + ' In ZipCode ' + str(self.inputzip),
                      fontsize=12)
            plt.xlabel('Station', fontsize=12)
            plt.ylabel('Predicted Number of Bikes', fontsize=12)
            plt.show()
            if saveVar == "Y":
                #Saves plot depending on user input
                plt.savefig('PlotOfCitiBikesAvailableOn' + self.day +
                            'sMonth' + str(self.month) + 'Hour' +
                            str(self.hour) + 'Zip' + str(self.inputzip) +
                            '.pdf')
            else:
                pass
Exemplo n.º 2
0
 def barPlot(self, saveVar):
     bikesByStation={}
     bikesForDict=[]
     stationsForDict=[]
     
     #Loading Station Data to be used in slicing the citibike availability data
     stationData=fullStationDataLoad()
     zipStations=stationData['id'][stationData["postalCode"]==self.inputzip].tolist()
     #Loading citibike availability data
     citidf=citiBikeDataLoad()
     citiStations = citidf.columns.values
     citiStations = citiStations.astype(int)
     zipStations = list(set(zipStations).intersection(citiStations))
     if len(zipStations)==0:
         print "There are no bike stations in this zip code"
     else:
         #Storing plot values and labels
         for stations in zipStations:
             bikesForDict.append(int(citidf.loc[self.day, self.hour, self.month].ix[str(stations)]))
             stationsForDict.append(str(stationData.loc[str(stations)].ix['stationName']).strip())
         #Storing plot values and labels into dictionary for easy use
         bikesByStation=dict(zip(stationsForDict, bikesForDict))
         
         #Plotting bikes by station using a barchart
         plt.figure(figsize=(14,14))
         width = 1
         plt.bar(range(len(bikesByStation)), bikesByStation.values(), width, color='mediumpurple', align='center')
         plt.xticks(range(len(bikesByStation)), bikesByStation.keys(), rotation=70, fontsize=12, ha="right")
         plt.xlim([-1,len(bikesByStation)])
         plt.title('Predicted Number of Bikes For '+self.day+'s in Month '+str(self.month)+" at "+str(self.hour)+' In ZipCode '+str(self.inputzip), fontsize=12)
         plt.xlabel('Station', fontsize=12)
         plt.ylabel('Predicted Number of Bikes', fontsize=12)
         plt.show()    
         if saveVar == "Y":
             #Saves plot depending on user input
             plt.savefig('PlotOfCitiBikesAvailableOn'+self.day+'sMonth'+str(self.month)+'Hour'+str(self.hour)+'Zip'+str(self.inputzip)+'.pdf')
         else:
             pass
Exemplo n.º 3
0
    def basemapPlot(self, saveVar):
        #Upper and lower bounds for borough's latitudes and longitudes
        latsAndLongs={}
        latsAndLongs["ManhattanLowerLat"]=40.698545
        latsAndLongs["ManhattanUpperLat"]=40.800038
        latsAndLongs["ManhattanLowerLong"]=-74.022824
        latsAndLongs["ManhattanUpperLong"]=-73.904194
        latsAndLongs["BrooklynLowerLat"]=40.677046
        latsAndLongs["BrooklynUpperLat"]=40.740347
        latsAndLongs["BrooklynLowerLong"]=-74.010918
        latsAndLongs["BrooklynUpperLong"]=-73.921764
        latsAndLongs["QueensLowerLat"]=40.737877
        latsAndLongs["QueensUpperLat"]=40.756525
        latsAndLongs["QueensLowerLong"]=-73.962982
        latsAndLongs["QueensUpperLong"]=-73.929904
        
        #Loading borough information to be used in generating correct basemap
        boroughdf=boroughDataLoad()
        boroughdf=boroughdf.set_index('ZipCode')['Borough'].to_dict()
        boro=boroughdf[self.inputzip]
        
        #Loading Station Data to be used in slicing the citibike availability data
        stationData=fullStationDataLoad()
        boroStations=stationData['id'][stationData["Borough"]==boro].tolist()
        boroLats=stationData[['id','latitude']][stationData["Borough"]==boro]
        boroLons=stationData[['id','longitude']][stationData["Borough"]==boro]
        
        #Loading the citibike availability data and storing the relevent information into lists
        citidf=citiBikeDataLoad()
        citiStations = citidf.columns.values
        citiStations = citiStations.astype(int)
        boroStations = list(set(boroStations).intersection(citiStations))
        if len(boroStations)==0:
            print "There are no bike stations in this area"
        else:
            #Plotting the basemap
            plt.figure(figsize=(14,14))
            m = Basemap(resolution='h',
                        projection='mill',
                        llcrnrlat = latsAndLongs[boro+"LowerLat"],
                        llcrnrlon = latsAndLongs[boro+"LowerLong"],
                        urcrnrlat = latsAndLongs[boro+"UpperLat"],
                        urcrnrlon = latsAndLongs[boro+"UpperLong"],
                        area_thresh = 0.1)
            m.drawcountries(linewidth=0.5)
            m.drawcoastlines(linewidth=0.5)
            m.drawstates(linewidth=0.5)
            m.fillcontinents(color = 'lightgray')
            
            #Storing data in lists to be plotted
            bikes=[]
            lats=[]
            lons=[]
            for stations in boroStations:
                bikes.append(int(citidf.loc[self.day, self.hour, self.month].ix[str(stations)]))
                lats.append(float(boroLats['latitude'][stationData["id"]==stations]))
                lons.append(float(boroLons['longitude'][stationData["id"]==stations]))
            
            #Plotting the stations on top of the basemap
            minMarkerSize = .4
            for bikes, lats, lons in zip(bikes, lats, lons):
                x,y = m(lons, lats)
                msize = bikes * minMarkerSize
                m.plot(x, y, 'bo', markersize=msize)
            plt.title('Predicted Number of Bikes In '+str(boro)+' on '+self.day+'s in Month '+str(self.month)+" at "+str(self.hour))

            if saveVar=="Y":
                #Saves plot depending on user input
                plt.savefig('MapOfCitiBikeAvailabilityFor'+self.day+'sInMonth'+str(self.month)+'atHour'+str(self.hour)+'.pdf')
            else:
                pass
            return m
Exemplo n.º 4
0
    def basemapPlot(self, saveVar):
        #Upper and lower bounds for borough's latitudes and longitudes
        latsAndLongs = {}
        latsAndLongs["ManhattanLowerLat"] = 40.698545
        latsAndLongs["ManhattanUpperLat"] = 40.800038
        latsAndLongs["ManhattanLowerLong"] = -74.022824
        latsAndLongs["ManhattanUpperLong"] = -73.904194
        latsAndLongs["BrooklynLowerLat"] = 40.677046
        latsAndLongs["BrooklynUpperLat"] = 40.740347
        latsAndLongs["BrooklynLowerLong"] = -74.010918
        latsAndLongs["BrooklynUpperLong"] = -73.921764
        latsAndLongs["QueensLowerLat"] = 40.737877
        latsAndLongs["QueensUpperLat"] = 40.756525
        latsAndLongs["QueensLowerLong"] = -73.962982
        latsAndLongs["QueensUpperLong"] = -73.929904

        #Loading borough information to be used in generating correct basemap
        boroughdf = boroughDataLoad()
        boroughdf = boroughdf.set_index('ZipCode')['Borough'].to_dict()
        boro = boroughdf[self.inputzip]

        #Loading Station Data to be used in slicing the citibike availability data
        stationData = fullStationDataLoad()
        boroStations = stationData['id'][stationData["Borough"] ==
                                         boro].tolist()
        boroLats = stationData[['id',
                                'latitude']][stationData["Borough"] == boro]
        boroLons = stationData[['id',
                                'longitude']][stationData["Borough"] == boro]

        #Loading the citibike availability data and storing the relevent information into lists
        citidf = citiBikeDataLoad()
        citiStations = citidf.columns.values
        citiStations = citiStations.astype(int)
        boroStations = list(set(boroStations).intersection(citiStations))
        if len(boroStations) == 0:
            print "There are no bike stations in this area"
        else:
            #Plotting the basemap
            plt.figure(figsize=(14, 14))
            m = Basemap(resolution='h',
                        projection='mill',
                        llcrnrlat=latsAndLongs[boro + "LowerLat"],
                        llcrnrlon=latsAndLongs[boro + "LowerLong"],
                        urcrnrlat=latsAndLongs[boro + "UpperLat"],
                        urcrnrlon=latsAndLongs[boro + "UpperLong"],
                        area_thresh=0.1)
            m.drawcountries(linewidth=0.5)
            m.drawcoastlines(linewidth=0.5)
            m.drawstates(linewidth=0.5)
            m.fillcontinents(color='lightgray')

            #Storing data in lists to be plotted
            bikes = []
            lats = []
            lons = []
            for stations in boroStations:
                bikes.append(
                    int(citidf.loc[self.day, self.hour,
                                   self.month].ix[str(stations)]))
                lats.append(
                    float(boroLats['latitude'][stationData["id"] == stations]))
                lons.append(
                    float(
                        boroLons['longitude'][stationData["id"] == stations]))

            #Plotting the stations on top of the basemap
            minMarkerSize = .4
            for bikes, lats, lons in zip(bikes, lats, lons):
                x, y = m(lons, lats)
                msize = bikes * minMarkerSize
                m.plot(x, y, 'bo', markersize=msize)
            plt.title('Predicted Number of Bikes In ' + str(boro) + ' on ' +
                      self.day + 's in Month ' + str(self.month) + " at " +
                      str(self.hour))

            if saveVar == "Y":
                #Saves plot depending on user input
                plt.savefig('MapOfCitiBikeAvailabilityFor' + self.day +
                            'sInMonth' + str(self.month) + 'atHour' +
                            str(self.hour) + '.pdf')
            else:
                pass
            return m