from datetime import datetime import requests import os import joblib import pandas as pd import json def get_weather_csv(): return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki?unitGroup=metric&include=days&key=FYYH5HKD9558HBXD2D6KWXDGH&contentType=csv').csv() def get_weather_json_quick(date): return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/next7days?unitGroup=metric&key=J7TT2WGMUNNHD8JBEDXAJJXB2&contentType=json').json() def get_air_quality_data(): AIR_QUALITY_API_KEY = os.getenv('AIR_QUALITY_API_KEY') json = get_air_json(AIR_QUALITY_API_KEY) iaqi = json['iaqi'] forecast = json['forecast']['daily'] return [ json['aqi'], # AQI json['time']['s'][:10], # Date iaqi['h']['v'], iaqi['p']['v'], iaqi['pm10']['v'], iaqi['t']['v'], forecast['o3'][0]['avg'], forecast['o3'][0]['max'], forecast['o3'][0]['min'], forecast['pm10'][0]['avg'], forecast['pm10'][0]['max'], forecast['pm10'][0]['min'], forecast['pm25'][0]['avg'], forecast['pm25'][0]['max'], forecast['pm25'][0]['min'], forecast['uvi'][0]['avg'], forecast['uvi'][0]['avg'], forecast['uvi'][0]['avg'] ] def get_weather_data(json): #WEATHER_API_KEY = os.getenv('WEATHER_API_KEY') #csv = get_weather_csv() data = json['days'][0] print("data parsed sccessfully") #return [ # #json['address'].capitalize(), # data['datetime'], # data['feelslikemax'], # data['feelslikemin'], # data['feelslike'], # data['dew'], # data['humidity'], # data['precip'], # data['precipprob'], # data['precipcover'], # data['snow'], # data['snowdepth'], # data['windgust'], # data['windspeed'], # data['winddir'], # data['pressure'], # data['cloudcover'], # data['visibility'], # data['solarradiation'], # data['solarenergy'], # data['uvindex'], # data['conditions'] #] return data def get_weather_df(data): col_names = [ 'name', 'datetime', 'tempmax', 'tempmin', 'temp', 'feelslikemax', 'feelslikemin', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob', 'precipcover', 'snow', 'snowdepth', 'windgust', 'windspeed', 'winddir', 'sealevelpressure', 'cloudcover', 'visibility', 'solarradiation', 'solarenergy', 'uvindex', 'conditions' ] new_data = pd.DataFrame( data, columns=col_names ) new_data.datetime = new_data.datetime.apply(timestamp_2_time1) #new_data.rename(columes={'pressure':'sealevelpressure'}) return new_data def timestamp_2_time1(x): dt_obj = datetime.strptime(str(x), '%Y-%m-%d') dt_obj = dt_obj.timestamp() * 1000 return int(dt_obj) def timestamp_2_time(x): dt_obj = datetime.strptime(str(x), '%m/%d/%Y') dt_obj = dt_obj.timestamp() * 1000 return int(dt_obj)