preprocessing.curate_data.curate_weather

preprocessing.curate_data.curate_weather(weather_df, data, forecast_horizon)

Checks if the weather dataframe has the correct shape.

Parameters

Name	Type	Description	Default
weather_df	pd.DataFrame	DataFrame containing weather information.	required
data	pd.DataFrame	The main dataset.	required
forecast_horizon	int	The forecast horizon in hours.	required

Examples

import pandas as pd
from spotforecast2_safe.preprocessing.curate_data import curate_weather

FORECAST_HORIZON = 24
n_data = 48
data = pd.DataFrame(
    {"load": range(n_data)},
    index=pd.date_range("2023-01-01", periods=n_data, freq="h", tz="UTC"),
)
weather_df = pd.DataFrame(
    {"temp": range(n_data + FORECAST_HORIZON)},
    index=pd.date_range(
        "2023-01-01", periods=n_data + FORECAST_HORIZON, freq="h", tz="UTC"
    ),
)
curate_weather(weather_df, data, forecast_horizon=FORECAST_HORIZON)
assert weather_df.shape[0] == data.shape[0] + FORECAST_HORIZON
print("weather_df shape is correct:", weather_df.shape[0] == data.shape[0] + FORECAST_HORIZON)

weather_df shape is correct: True

Notes

If the weather dataframe does not have the expected number of rows, a diagnostic message describing the mismatch is logged via print and the function returns None without raising.