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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 transform(df): | |
df.loc[df["windgust"].isna(),'windgust'] = df['windspeed'] | |
df['snow'].fillna(0,inplace=True) | |
df['snowdepth'].fillna(0, inplace=True) | |
df['pressure'].fillna(df['pressure'].mean(), inplace=True) | |
return df | |
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 |