def processCommuteData():
frames = []
print ' Started Processing Commute Data'
fileName = "Master-Commute_Data-2015"
entries = OrderedDict()
towns = getMATowns()
""" Need to seperate list of towns into chunks of 100 """
while len(towns) > 0:
origins = ''
for each in towns[:100]:
origins += each+', MA|'
origins = origins[:-1]
commuteData = getCommuteData(origins)
df = parseCommuteData(commuteData)
frames.append(df)
towns = towns[100:]
entries['Commute-Data'] = pd.concat(frames, ignore_index=True)
populateMaster(os.path.join(commuteDataLocation, fileName+ext), entries)
print ' Done Processing Commute Data\n'
else:
currMin = str(currMin)
#
# # Add a leading 0 when hours is < 10
# if currHour < 10:
# currHour = '0'+str(currHour)
# else:
# currHour = str(currHour)
currTime = currHour+':'+currMin
if currTime in morningTimes or currTime in afternoonTimes:
print 'Processing for time ', currTime
date = str(datetime.datetime.now().date())
if currTime in morningTimes:
commuteData = getCommuteData(towns, 'now', work)
for row in range(len(commuteData["rows"])):
origin = str(commuteData['origin_addresses'][row]).split(',')[0]
destination = commuteData['destination_addresses'][0]
duration = convertToMin(str(commuteData['rows'][row]['elements'][0]['duration']['text']))
duration_in_traffic = convertToMin(str(commuteData['rows'][row]['elements'][0]['duration_in_traffic']['text']))
distance = str(commuteData['rows'][row]['elements'][0]['distance']['text']).split(' ')[0]
data[origin]['date'] = date
data[origin]['day'] = weekDay
data[origin]['times'][currTime]['dur'] = duration_in_traffic
data[origin]['times'][currTime]['dist'] = distance
if duration_in_traffic > data[origin]['max']:
data[origin]['max'] = duration_in_traffic
if duration_in_traffic < data[origin]['min']:
