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>
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build population layouts for all clustered model regions as total as well as
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split by urban and rural population.
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"""
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import atlite
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import geopandas as gpd
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import pandas as pd
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import xarray as xr
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from _helpers import set_scenario_config
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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_clustered_population_layouts", clusters=48)
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set_scenario_config(snakemake)
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cutout = atlite.Cutout(snakemake.input.cutout)
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clustered_regions = (
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gpd.read_file(snakemake.input.regions_onshore).set_index("name").buffer(0)
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)
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I = cutout.indicatormatrix(clustered_regions) # noqa: E741
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pop = {}
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for item in ["total", "urban", "rural"]:
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pop_layout = xr.open_dataarray(snakemake.input[f"pop_layout_{item}"])
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pop[item] = I.dot(pop_layout.stack(spatial=("y", "x")))
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pop = pd.DataFrame(pop, index=clustered_regions.index)
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pop["ct"] = pop.index.str[:2]
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country_population = pop.total.groupby(pop.ct).sum()
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pop["fraction"] = pop.total / pop.ct.map(country_population)
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pop.to_csv(snakemake.output.clustered_pop_layout)
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