Spaces:
Runtime error
Runtime error
from datetime import datetime | |
import requests | |
import os | |
import joblib | |
import pandas as pd | |
import json | |
def get_weather_json_quick(date): | |
return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/{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) | |