013b705ee4
* Cluster first: build renewable profiles and add all assets after clustering * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * correction: pass landfall_lengths through functions * assign landfall_lenghts correctly * remove parameter add_land_use_constraint * fix network_dict * calculate distance to shoreline, remove underwater_fraction * adjust simplification parameter to exclude Crete from offshore wind connections * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove unused geth2015 hydro capacities * removing remaining traces of {simpl} wildcard * add release notes and update workflow graphics * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: lisazeyen <lisa.zeyen@web.de>
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Aggregate all rastered cutout data to base regions Voronoi cells.
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"""
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import logging
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import atlite
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import geopandas as gpd
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from _helpers import get_snapshots, set_scenario_config
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from atlite.aggregate import aggregate_matrix
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from dask.distributed import Client
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logger = logging.getLogger(__name__)
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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("build_hac_features")
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set_scenario_config(snakemake)
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params = snakemake.params
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nprocesses = int(snakemake.threads)
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if nprocesses > 1:
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client = Client(n_workers=nprocesses, threads_per_worker=1)
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else:
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client = None
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time = get_snapshots(params.snapshots, params.drop_leap_day)
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cutout = atlite.Cutout(snakemake.input.cutout).sel(time=time)
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regions = gpd.read_file(snakemake.input.regions).set_index("name")
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I = cutout.indicatormatrix(regions) # noqa: E741
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ds = cutout.data[params.features].map(
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aggregate_matrix, matrix=I, index=regions.index
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)
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ds = ds.load(scheduler=client)
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ds.to_netcdf(snakemake.output[0])
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