import requests import os import pandas as pd import datetime import numpy as np from sklearn.preprocessing import OrdinalEncoder from dotenv import load_dotenv load_dotenv() ## TODO: write function to display the color coding of the categoies both in the df and as a guide. #sg like: def color_aq(val): color = 'green' if val else 'red' return f'background-color: {color}' # but better def get_air_quality_data(station_name): AIR_QUALITY_API_KEY = os.getenv('AIR_QUALITY_API_KEY') request_value = f'https://api.waqi.info/feed/{station_name}/?token={AIR_QUALITY_API_KEY}' answer = requests.get(request_value).json()["data"] forecast = answer['forecast']['daily'] return [ answer["time"]["s"][:10], # Date int(forecast['pm25'][0]['avg']), # avg predicted pm25 int(forecast['pm10'][0]['avg']), # avg predicted pm10 max(int(forecast['pm25'][0]['avg']), int(forecast['pm10'][0]['avg'])) # avg predicted aqi ] def get_air_quality_df(data): col_names = [ 'date', 'pm25', 'pm10', 'aqi' ] new_data = pd.DataFrame( data ).T new_data.columns = col_names new_data['pm25'] = pd.to_numeric(new_data['pm25']) new_data['pm10'] = pd.to_numeric(new_data['pm10']) new_data['aqi'] = pd.to_numeric(new_data['aqi']) return new_data def get_weather_data_daily(city): WEATHER_API_KEY = os.getenv('WEATHER_API_KEY') answer = requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{city}/today?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json() data = answer['days'][0] return [ answer['address'].lower(), 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_data_weekly(city: str, start_date: datetime) -> pd.DataFrame: WEATHER_API_KEY = os.getenv('WEATHER_API_KEY') end_date = f"{start_date + datetime.timedelta(days=6):%Y-%m-%d}" answer = requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{city}/{start_date}/{end_date}?unitGroup=metric&include=days&key={WEATHER_API_KEY}&contentType=json').json() weather_data = answer['days'] final_df = pd.DataFrame() for i in range(7): data = weather_data[i] list_of_data = [ answer['address'].lower(), 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'] ] weather_df = get_weather_df(list_of_data) final_df = pd.concat([final_df, weather_df]) return final_df def get_weather_df(data): col_names = [ 'name', '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 ).T new_data.columns = col_names for col in col_names: if col not in ['name', 'date', 'conditions']: new_data[col] = pd.to_numeric(new_data[col]) return new_data def data_encoder(X): X.drop(columns=['date', 'name'], inplace=True) X['conditions'] = OrdinalEncoder().fit_transform(X[['conditions']]) return X 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