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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/{date}/{date}?unitGroup=metric&include=days&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)
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