functions.py

import requests
import os
import joblib
import pandas as pd
import datetime
import numpy as np
from sklearn.preprocessing import OrdinalEncoder
from dotenv import load_dotenv
load_dotenv(override=True)


def decode_features(df, feature_view):
    """Decodes features in the input DataFrame using corresponding Hopsworks Feature Store transformation functions"""
    df_res = df.copy()

    import inspect


    td_transformation_functions = feature_view._batch_scoring_server._transformation_functions

    res = {}
    for feature_name in td_transformation_functions:
        if feature_name in df_res.columns:
            td_transformation_function = td_transformation_functions[feature_name]
            sig, foobar_locals = inspect.signature(td_transformation_function.transformation_fn), locals()
            param_dict = dict([(param.name, param.default) for param in sig.parameters.values() if param.default != inspect._empty])
            if td_transformation_function.name == "min_max_scaler":
                df_res[feature_name] = df_res[feature_name].map(
                    lambda x: x * (param_dict["max_value"] - param_dict["min_value"]) + param_dict["min_value"])

            elif td_transformation_function.name == "standard_scaler":
                df_res[feature_name] = df_res[feature_name].map(
                    lambda x: x * param_dict['std_dev'] + param_dict["mean"])
            elif td_transformation_function.name == "label_encoder":
                dictionary = param_dict['value_to_index']
                dictionary_ = {v: k for k, v in dictionary.items()}
                df_res[feature_name] = df_res[feature_name].map(
                    lambda x: dictionary_[x])
    return df_res


def get_model(project, model_name, evaluation_metric, sort_metrics_by):
    """Retrieve desired model or download it from the Hopsworks Model Registry.
    In second case, it will be physically downloaded to this directory"""
    TARGET_FILE = "model.pkl"
    list_of_files = [os.path.join(dirpath,filename) for dirpath, _, filenames \
                     in os.walk('.') for filename in filenames if filename == TARGET_FILE]

    if list_of_files:
        model_path = list_of_files[0]
        model = joblib.load(model_path)
    else:
        if not os.path.exists(TARGET_FILE):
            mr = project.get_model_registry()
            # get best model based on custom metrics
            model = mr.get_best_model(model_name,
                                      evaluation_metric,
                                      sort_metrics_by)
            model_dir = model.download()
            model = joblib.load(model_dir + "/model.pkl")

    return model


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'])

    print(new_data)
    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