functions.py

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)