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build_temperatur_profiles: parallelize

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Fabian Neumann 2022-12-28 12:20:34 +01:00
parent 99343b1221
commit 097d054f06
2 changed files with 26 additions and 24 deletions
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15
Snakefile
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@ -177,19 +177,14 @@ rule build_heat_demands:
rule build_temperature_profiles:
input:
pop_layout_total="resources/pop_layout_total.nc",
pop_layout_urban="resources/pop_layout_urban.nc",
pop_layout_rural="resources/pop_layout_rural.nc",
pop_layout="resources/pop_layout_{scope}.nc",
regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson")
output:
temp_soil_total="resources/temp_soil_total_elec_s{simpl}_{clusters}.nc",
temp_soil_rural="resources/temp_soil_rural_elec_s{simpl}_{clusters}.nc",
temp_soil_urban="resources/temp_soil_urban_elec_s{simpl}_{clusters}.nc",
temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
temp_air_rural="resources/temp_air_rural_elec_s{simpl}_{clusters}.nc",
temp_air_urban="resources/temp_air_urban_elec_s{simpl}_{clusters}.nc"
temp_soil="resources/temp_soil_{scope}_elec_s{simpl}_{clusters}.nc",
temp_air="resources/temp_air_{scope}_elec_s{simpl}_{clusters}.nc",
resources: mem_mb=20000
benchmark: "benchmarks/build_temperature_profiles/s{simpl}_{clusters}"
threads: 8
benchmark: "benchmarks/build_temperature_profiles/{scope}_s{simpl}_{clusters}"
script: "scripts/build_temperature_profiles.py"

21
scripts/build_temperature_profiles.py
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@ -5,6 +5,7 @@ import atlite
import pandas as pd
import xarray as xr
import numpy as np
from dask.distributed import Client, LocalCluster
if __name__ == '__main__':
if 'snakemake' not in globals():
@ -15,6 +16,10 @@ if __name__ == '__main__':
clusters=48,
)
nprocesses = int(snakemake.threads)
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
time = pd.date_range(freq='h', **snakemake.config['snapshots'])
cutout_config = snakemake.config['atlite']['cutout']
cutout = atlite.Cutout(cutout_config).sel(time=time)
@ -24,9 +29,7 @@ if __name__ == '__main__':
I = cutout.indicatormatrix(clustered_regions)
for area in ["total", "rural", "urban"]:
pop_layout = xr.open_dataarray(snakemake.input[f'pop_layout_{area}'])
pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
stacked_pop = pop_layout.stack(spatial=('y', 'x'))
M = I.T.dot(np.diag(I.dot(stacked_pop)))
@ -36,11 +39,15 @@ if __name__ == '__main__':
M_tilde = M / nonzero_sum
temp_air = cutout.temperature(
matrix=M_tilde.T, index=clustered_regions.index)
matrix=M_tilde.T, index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False)
temp_air.to_netcdf(snakemake.output[f"temp_air_{area}"])
temp_air.to_netcdf(snakemake.output.temp_air)
temp_soil = cutout.soil_temperature(
matrix=M_tilde.T, index=clustered_regions.index)
matrix=M_tilde.T, index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False)
temp_soil.to_netcdf(snakemake.output[f"temp_soil_{area}"])
temp_soil.to_netcdf(snakemake.output.temp_soil)