pypsa-eur/scripts/build_clustered_population_layouts.py

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

"""
Build clustered population layouts.
"""

import atlite
import geopandas as gpd
import pandas as pd
import xarray as xr

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

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

    cutout = atlite.Cutout(snakemake.input.cutout)

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

    I = cutout.indicatormatrix(clustered_regions)

    pop = {}
    for item in ["total", "urban", "rural"]:
        pop_layout = xr.open_dataarray(snakemake.input[f"pop_layout_{item}"])
        pop[item] = I.dot(pop_layout.stack(spatial=("y", "x")))

    pop = pd.DataFrame(pop, index=clustered_regions.index)

    pop["ct"] = pop.index.str[:2]
    country_population = pop.total.groupby(pop.ct).sum()
    pop["fraction"] = pop.total / pop.ct.map(country_population)

    pop.to_csv(snakemake.output.clustered_pop_layout)