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
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
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
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