from datetime import timedelta
import polars as pl


def transform_prices(df: pl.DataFrame) -> pl.DataFrame:
    return df.with_columns(
        [
            pl.col("timestamp")
            .cast(pl.Datetime("ms"))
            .dt.replace_time_zone("UTC")
            .alias("timestamp")
        ]
    )


def transform_weather(df: pl.DataFrame) -> pl.DataFrame:
    return df.with_columns(
        [
            pl.col("timestamp")
            .str.to_datetime(
                format="%Y-%m-%dT%H:%M:%S%z",
                time_zone="UTC",
            )
            .cast(pl.Datetime("ms", time_zone="UTC"))
            .alias("timestamp")
        ]
    )


def join_dataframes(df_prices: pl.DataFrame, df_weather: pl.DataFrame) -> pl.DataFrame:
    return df_prices.join(df_weather, on="timestamp", how="inner").select(
        [
            pl.col("timestamp"),
            pl.col("price"),
            pl.col(
                [
                    "temperature",
                    "wind_speed",
                    "solar",
                    "sunshine",
                    "cloud_cover",
                    "precipitation",
                ]
            ),
        ]
    )


#
#
# def join_dataframes(df_prices: pl.DataFrame, df_weather: pl.DataFrame) -> pl.DataFrame:
#     return (
#         df_prices.sort("timestamp")  # ← Pflicht!
#         .join_asof(
#             df_weather.sort("timestamp"),  # ← Pflicht!
#             on="timestamp",
#             strategy="nearest",  # ← nearest statt backward!
#             tolerance=timedelta(hours=1),
#         )
#         .select(
#             [
#                 pl.col("timestamp"),
#                 pl.col("price"),
#                 pl.col(
#                     [
#                         "temperature",
#                         "wind_speed",
#                         "solar",
#                         "sunshine",
#                         "cloud_cover",
#                         "precipitation",
#                     ]
#                 ),
#             ]
#         )
#     )