Add an overall climate change index

[DSSG-DFKI-Testuser]

Add an overall climate change index that takes as input the information from various weather features and calculates for a certain location for a certain point in time the climate change index. To illustrate this, here a simple example implementation:

import pandas as pd
import pytest
from sklearn.preprocessing import MinMaxScaler

def create_climate_change_index(
    df: pd.DataFrame, cols_and_weights_to_calculate_index: dict
) -> pd.DataFrame:
    # Normalize each column
    for key in cols_and_weights_to_calculate_index:
        scaler = MinMaxScaler()
        df[f"{key}_transformed"] = scaler.fit_transform(df[key].values.reshape(-1, 1))

    # Calculate the climate change index
    df["climate_change_index"] = 0
    for key in cols_and_weights_to_calculate_index:
        df["climate_change_index"] += (
            df[f"{key}_transformed"] * cols_and_weights_to_calculate_index[key]
        )

    return df

@pytest.fixture(scope="session")
def example_survey_weather_data():
    example_df = pd.DataFrame(
        {
            "lat": [2.75, 2.91, 3.07, 3.24],
            "lon": [4.25, 4.25, 4.25, 4.25],
            "date": ["2009-01-01", "2009-01-01", "2009-01-01", "2009-01-01"],
            "precipitation": [0.001, 0.002, 0.003, 0.004],
            "temperature": [34, 42, 28, 35],
            "heavy_rainfall_ranking": [0.2, 0.4, 0.6, 0.8],
            "heatwave_ranking": [0.4, 0.2, 0.6, 0.1],
            "spi": [-2.1, 1.2, 0.3, -0.4],
        }
    )

    return example_df


@pytest.fixture(scope="session")
def example_climate_indicator_weights_dict():
    cols_and_weights_to_calculate_index = {
        "precipitation": 0.2,
        "temperature": 0.3,
        "heavy_rainfall_ranking": 0.1,
        "heatwave_ranking": 0.1,
        "spi": 0.3,
    }

    return cols_and_weights_to_calculate_index