# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Build heat demand time series using heating degree day (HDD) approximation.
"""

import atlite
import geopandas as gpd
import numpy as np
import xarray as xr
from _helpers import get_snapshots, set_scenario_config
from dask.distributed import Client, LocalCluster

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

        snakemake = mock_snakemake(
            "build_daily_heat_demands",
            scope="total",
            simpl="",
            clusters=48,
        )
    set_scenario_config(snakemake)

    nprocesses = int(snakemake.threads)
    cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
    client = Client(cluster, asynchronous=True)

    cutout_name = snakemake.input.cutout

    time = get_snapshots(snakemake.params.snapshots, snakemake.params.drop_leap_day)
    daily = get_snapshots(
        snakemake.params.snapshots,
        snakemake.params.drop_leap_day,
        freq="D",
    )

    cutout = atlite.Cutout(cutout_name).sel(time=time)

    clustered_regions = (
        gpd.read_file(snakemake.input.regions_onshore).set_index("name").buffer(0)
    )

    I = cutout.indicatormatrix(clustered_regions)  # noqa: E741

    pop_layout = xr.open_dataarray(snakemake.input.pop_layout)

    stacked_pop = pop_layout.stack(spatial=("y", "x"))
    M = I.T.dot(np.diag(I.dot(stacked_pop)))

    heat_demand = cutout.heat_demand(
        matrix=M.T,
        index=clustered_regions.index,
        dask_kwargs=dict(scheduler=client),
        show_progress=False,
    ).sel(time=daily)

    heat_demand.to_netcdf(snakemake.output.heat_demand)