# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT

"""
Build COP time series for air- or ground-sourced heat pumps.
"""

import xarray as xr


def coefficient_of_performance(delta_T, source="air"):
    """
    COP is function of temp difference source to sink.

    The quadratic regression is based on Staffell et al. (2012)
    https://doi.org/10.1039/C2EE22653G.
    """
    if source == "air":
        return 6.81 - 0.121 * delta_T + 0.000630 * delta_T**2
    elif source == "soil":
        return 8.77 - 0.150 * delta_T + 0.000734 * delta_T**2
    else:
        raise NotImplementedError("'source' must be one of  ['air', 'soil']")


if __name__ == "__main__":
    if "snakemake" not in globals():
        from helper import mock_snakemake

        snakemake = mock_snakemake(
            "build_cop_profiles",
            simpl="",
            clusters=48,
        )

    for area in ["total", "urban", "rural"]:
        for source in ["air", "soil"]:
            source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_{area}"])

            delta_T = snakemake.config["sector"]["heat_pump_sink_T"] - source_T

            cop = coefficient_of_performance(delta_T, source)

            cop.to_netcdf(snakemake.output[f"cop_{source}_{area}"])