62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
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# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build solar thermal collector time series.
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"""
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import atlite
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import geopandas as gpd
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import numpy as np
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import pandas as pd
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import xarray as xr
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from dask.distributed import Client, LocalCluster
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake(
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"build_solar_thermal_profiles",
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simpl="",
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clusters=48,
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)
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nprocesses = int(snakemake.threads)
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cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
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client = Client(cluster, asynchronous=True)
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config = snakemake.config["solar_thermal"]
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time = pd.date_range(freq="h", **snakemake.config["snapshots"])
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cutout = atlite.Cutout(snakemake.input.cutout).sel(time=time)
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clustered_regions = (
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gpd.read_file(snakemake.input.regions_onshore)
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.set_index("name")
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.buffer(0)
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.squeeze()
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)
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I = cutout.indicatormatrix(clustered_regions)
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pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
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stacked_pop = pop_layout.stack(spatial=("y", "x"))
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M = I.T.dot(np.diag(I.dot(stacked_pop)))
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nonzero_sum = M.sum(axis=0, keepdims=True)
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nonzero_sum[nonzero_sum == 0.0] = 1.0
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M_tilde = M / nonzero_sum
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solar_thermal = cutout.solar_thermal(
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**config,
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matrix=M_tilde.T,
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index=clustered_regions.index,
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dask_kwargs=dict(scheduler=client),
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show_progress=False
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)
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solar_thermal.to_netcdf(snakemake.output.solar_thermal)
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