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44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
[pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
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# -*- coding: utf-8 -*-
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"""
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Build clustered population layouts.
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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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if __name__ == "__main__":
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if "snakemake" not in globals():
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from helper import mock_snakemake
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[pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
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snakemake = mock_snakemake(
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"build_clustered_population_layouts",
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simpl="",
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clusters=48,
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)
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cutout = atlite.Cutout(snakemake.config["atlite"]["cutout"])
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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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[pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
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)
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I = cutout.indicatormatrix(clustered_regions)
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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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