import datetime as dt
from airflow.utils.log.logging_mixin import LoggingMixin

import pandas as pd


__all__ = ["Interpolator"]


class Interpolator(LoggingMixin):

    @staticmethod
    def interpolate_series(
        start_date: dt.datetime,
        end_date: dt.datetime,
        dates: list[dt.datetime],
        values: list[int],
        historical_date: dt.datetime | None = None,
        historical_value: int | None = None,
    ) -> list[int]:
        series = pd.Series(data=values, index=pd.DatetimeIndex(dates))

        if dates[0] > start_date and historical_date is not None and historical_value is not None:
            # if there is something to fill at tail, and we have historical value
            interpolation_start_date = historical_date
            historical_series = pd.Series(data=(historical_value,), index=pd.DatetimeIndex((historical_date,)))
            series = pd.concat((historical_series, series))
        else:
            interpolation_start_date = dates[0]

        series = series.reindex(pd.date_range(interpolation_start_date, end_date, freq="D"))

        # fill head with last known value
        series = series.interpolate(method="ffill", limit_area="outside")
        # interpolate everything else
        series = series.interpolate(method="time", limit_area="inside")
        # take only integer part
        series = series.astype("ulonglong")
        # Take only dates in the range
        # https://github.com/python/mypy/issues/2410
        series = series[start_date:]  # type: ignore

        return series.tolist()