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from datetime import datetime
import requests
import os
import joblib
import pandas as pd
import numpy as np
import json
from dotenv import load_dotenv
load_dotenv()
def get_weather_json(date, WEATHER_API_KEY):
return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/london/{date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json()
def get_weather_data(date):
WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
json = get_weather_json(date, WEATHER_API_KEY)
data = json['days'][0]
return [
json['address'].capitalize(),
data['datetime'],
data['tempmax'],
data['tempmin'],
data['temp'],
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']
]
def get_weather_df(data):
col_names = [
'city',
'date',
'tempmax',
'tempmin',
'temp',
'feelslikemax',
'feelslikemin',
'feelslike',
'dew',
'humidity',
'precip',
'precipprob',
'precipcover',
'snow',
'snowdepth',
'windgust',
'windspeed',
'winddir',
'pressure',
'cloudcover',
'visibility',
'solarradiation',
'solarenergy',
'uvindex',
'conditions'
]
new_data = pd.DataFrame(
data,
columns=col_names
)
new_data.date = new_data.date.apply(timestamp_2_time)
return new_data
def timestamp_2_time(x):
dt_obj = datetime.strptime(str(x), '%Y-%m-%d')
dt_obj = dt_obj.timestamp() * 1000
return int(dt_obj)
def encoder_range(temps):
boundary_list = np.array([0, 50, 100, 150, 200, 300])
redf = np.logical_not(temps<=boundary_list)
hift = np.concatenate((np.roll(redf, -1)[:, :-1], np.full((temps.shape[0], 1), False)), axis = 1)
cat = np.nonzero(np.not_equal(redf,hift))
air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
level = [air_pollution_level[el] for el in cat[1]]
return level
def get_aplevel(temps:np.ndarray) -> list:
boundary_list = np.array([0, 50, 100, 150, 200, 300]) # assert temps.shape == [x, 1]
redf = np.logical_not(temps<=boundary_list) # temps.shape[0] x boundary_list.shape[0] ndarray
hift = np.concatenate((np.roll(redf, -1)[:, :-1], np.full((temps.shape[0], 1), False)), axis = 1)
cat = np.nonzero(np.not_equal(redf,hift))
air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
level = [air_pollution_level[el] for el in cat[1]]
return level |