def predict_all():
'''get hourly prediction data on the given date,
basically just repeat the same prediction process 24 fimes'''
u = connectMysql()
v = getForecast()
start_station = request.args.get("start_station")
destination_station = request.args.get("destination_station")
day_of_travel = request.args.get("day_of_travel")
hour_of_travel = request.args.get("hour_of_travel")
print(start_station, destination_station, day_of_travel, hour_of_travel)
for i in range(len(u)):
if start_station == u[i][2]:
start_station_number = u[i][1]
start_banking = u[i][6]
start_bike_stands = u[i][8]
if destination_station == u[i][2]:
end_station_number = u[i][1]
end_banking = u[i][6]
end_bike_stands = u[i][8]
day = pd.to_datetime(day_of_travel)
day = day.weekday() + 1
weather_info = []
result_1 = {}
result_2 = {}
# hour changes from 0 to 23 and the else arguments are the same
for clock in range(24):
for i in range(len(v)):
date = (v[i][0].split(" "))[0]
hour = int(v[i][1])
if int(hour_of_travel) == 23:
if (str(pd.to_datetime(day_of_travel) + datetime.timedelta(days=1)).split(" ")[0] == str(
date)) and hour == 0:
weather_info = v[i]
break
elif str(date) == str(day_of_travel):
if abs(int(clock) - hour) <= 1.5:
weather_info = v[i]
break
if len(weather_info) == 0:
weather_info = v[i]
temp = weather_info[2]
feels_like = weather_info[3]
temp_min = weather_info[4]
temp_max = weather_info[5]
pressure = weather_info[6]
humidity = weather_info[7]
wind_speed = weather_info[8]
main_describe = weather_info[9]
if start_banking == 'False':
start_banking = 0
else:
start_banking = 1
if end_banking == 'False':
end_banking = 0
else:
end_banking = 1
if main_describe == "Clouds":
main_describe = 0
elif main_describe == "Drizzle":
main_describe = 1
elif main_describe == "Rain":
main_describe = 2
else:
main_describe = 3
start_arr = [float(day), float(hour), float(start_station_number), float(start_bike_stands),
float(start_banking), float(main_describe), float(temp), float(feels_like), float(temp_min),
float(temp_max), float(wind_speed), float(pressure), float(humidity)]
end_arr = [float(day), float(hour), float(end_station_number), float(end_bike_stands), float(end_banking),
float(main_describe), float(temp), float(feels_like), float(temp_min), float(temp_max),
float(wind_speed), float(pressure), float(humidity)]
result_1[clock] = ML.predict_available_bike(start_arr)
result_2[clock] = ML.predict_available_stands(end_arr)
final = {
"24hour_bikes": result_1
, "24hour_stands": result_2
}
return json.dumps(final)
def pred():
'''get daily prediction data'''
# connect database to access real-time data
u = connectMysql()
v = getForecast()
# get data from frontend
start_station = request.args.get("start_station")
destination_station = request.args.get("destination_station")
day_of_travel = request.args.get("day_of_travel")
hour_of_travel = request.args.get("hour_of_travel")
# print(start_station,destination_station,day_of_travel,hour_of_travel)
# get backend data of the corresponding stations
for i in range(len(u)):
if start_station == u[i][2]:
start_station_number = u[i][1]
start_banking = u[i][6]
start_bike_stands = u[i][8]
if destination_station == u[i][2]:
end_station_number = u[i][1]
end_banking = u[i][6]
end_bike_stands = u[i][8]
# get weather forecast information according to the user's input
day = pd.to_datetime(day_of_travel)
day = day.weekday() + 1
weather_info = []
for i in range(len(v)):
date = (v[i][0].split(" "))[0]
hour = int(v[i][1])
if int(hour_of_travel) == 23:
if (str(pd.to_datetime(day_of_travel) + datetime.timedelta(days=1)).split(" ")[0] == str(
date)) and hour == 0:
weather_info = v[i]
break
elif str(date) == str(day_of_travel):
if abs(int(hour_of_travel) - hour) <= 1.5:
weather_info = v[i]
break
if len(weather_info) == 0:
weather_info = v[-1]
temp = weather_info[2]
feels_like = weather_info[3]
temp_min = weather_info[4]
temp_max = weather_info[5]
pressure = weather_info[6]
humidity = weather_info[7]
wind_speed = weather_info[8]
main_describe = weather_info[9]
# encode categorical features
if start_banking == 'False':
start_banking = 0
else:
start_banking = 1
if end_banking == 'False':
end_banking = 0
else:
end_banking = 1
if main_describe == "Clouds":
main_describe = 0
elif main_describe == "Drizzle":
main_describe = 1
elif main_describe == "Rain":
main_describe = 2
else:
main_describe = 3
# get historical data to print the first and second charts
s, d = getHistoricalData(start_station_number, end_station_number, hour_of_travel)
x_axis = []
y_axis_bike = []
y_axis_stands = []
for i in range(7):
x_axis.append(s[i][1].split(' ')[0])
y_axis_bike.append(s[i][11])
y_axis_stands.append(s[i][10])
# invoke prediction program and feed all parameters
start_arr = [float(day), float(hour_of_travel), float(start_station_number), float(start_bike_stands),
float(start_banking), float(main_describe), float(temp), float(feels_like), float(temp_min),
float(temp_max), float(wind_speed), float(pressure), float(humidity)]
end_arr = [float(day), float(hour_of_travel), float(end_station_number), float(end_bike_stands), float(end_banking),
float(main_describe), float(temp), float(feels_like), float(temp_min), float(temp_max),
float(wind_speed), float(pressure), float(humidity)]
result_1 = ML.predict_available_bike(start_arr)
result_2 = ML.predict_available_stands(end_arr)
final = {
"bike_available": result_1
, "stands_available": result_2
, "x_axis": x_axis
, "y_axis_bike": y_axis_bike
, "y_axis_stands": y_axis_stands
, "description": main_describe
}
return json.dumps(final)
