80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
from django.http import HttpResponse, JsonResponse
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from datetime import datetime
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import pandas as pd
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import statsmodels.api as sm
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stations = ['Kamppi (M)', 'Rautatientori - itä']
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station_dict = {}
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for station in stations:
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departure_data = pd.read_csv('datasets/' + station + '_hourly_aggregate.csv')
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return_data = pd.read_csv('datasets/' + station + '_return_hourly_aggregate.csv')
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departure_data['Departure'] = pd.to_datetime(departure_data['Departure'], format='mixed')
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return_data['Return'] = pd.to_datetime(return_data['Return'], format='mixed')
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departure_data.set_index(departure_data['Departure'], inplace=True)
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return_data.set_index(return_data['Return'], inplace=True)
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departure_data['trip'] = pd.to_numeric(departure_data['trip'], errors='coerce')
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return_data['trip'] = pd.to_numeric(return_data['trip'], errors='coerce')
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departure_data = departure_data.dropna(axis=1)
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return_data = return_data.dropna(axis=1)
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departure_mod = sm.tsa.statespace.SARIMAX(departure_data['trip'], order=(1, 1, 1), seasonal_order=(0, 1, 0, 24), freq='h').fit(disp=False, low_memory=True)
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return_mod = sm.tsa.statespace.SARIMAX(return_data['trip'], order=(1, 1, 1), seasonal_order=(0, 1, 0, 24), freq='h').fit(disp=False, low_memory=True)
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station_dict[station] = {}
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station_dict[station]['departure_mod'] = departure_mod
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station_dict[station]['return_mod'] = return_mod
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def index(request):
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return HttpResponse('You\'re at the app index.')
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def predict(request):
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if request.method == 'GET':
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current = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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timestamp = request.GET.get('timestamp', current)
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station = request.GET.get('station', 'Kamppi (M)')
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ts = timestamp
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defaultBikeCount = 28
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departingCount = -1
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if departure_data.index.max() >= pd.to_datetime(timestamp):
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ls = departure_data[departure_data['Departure'] == ts]['trip'].tolist()
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if len(ls) != 0:
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departingCount = ls[0]
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else:
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pass
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else:
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departure_mod = station_dict[station]['departure_mod']
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departForecast = departure_mod.forecast(timestamp)
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departingCount = round(departForecast[-1])
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returningCount = -1
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if return_data.index.max() >= pd.to_datetime(timestamp):
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ls = return_data[return_data['Return'] == ts]['trip'].tolist()
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if len(ls) != 0:
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returningCount = ls[0]
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else:
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pass
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else:
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return_mod = station_dict[station]['return_mod']
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returnForecast = return_mod.forecast(timestamp)
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returningCount = round(returnForecast[-1])
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bikeAtStationCount = defaultBikeCount - departingCount + returningCount
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result = {
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'timestamp': timestamp,
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'station': station,
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'departingCount': departingCount,
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'returningCount': returningCount,
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'bikeAtStationCount': bikeAtStationCount,
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'increasing': returningCount > departingCount
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}
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return JsonResponse(result) |