49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
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import geopandas as gpd
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import atlite
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import pandas as pd
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import xarray as xr
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import scipy as sp
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if 'snakemake' not in globals():
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from vresutils import Dict
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import yaml
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snakemake = Dict()
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with open('config.yaml') as f:
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snakemake.config = yaml.load(f)
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snakemake.input = Dict()
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snakemake.output = Dict()
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time = pd.date_range(freq='m', **snakemake.config['snapshots'])
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params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]]))
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cutout = atlite.Cutout(snakemake.config['renewable']['onwind']['cutout'],
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cutout_dir=snakemake.config['atlite']['cutout_dir'],
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**params)
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clustered_busregions_as_geopd = gpd.read_file(snakemake.input.regions_onshore).set_index('name', drop=True)
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clustered_busregions = pd.Series(clustered_busregions_as_geopd.geometry, index=clustered_busregions_as_geopd.index)
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I = cutout.indicatormatrix(clustered_busregions)
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for item in ["total","rural","urban"]:
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pop_layout = xr.open_dataarray(snakemake.input['pop_layout_'+item])
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M = I.T.dot(sp.diag(I.dot(pop_layout.stack(spatial=('y', 'x')))))
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nonzero_sum = M.sum(axis=0, keepdims=True)
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nonzero_sum[nonzero_sum == 0.] = 1.
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M_tilde = M/nonzero_sum
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temp_air = cutout.temperature(matrix=M_tilde.T,index=clustered_busregions.index)
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temp_air.to_netcdf(snakemake.output["temp_air_"+item])
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temp_soil = cutout.soil_temperature(matrix=M_tilde.T,index=clustered_busregions.index)
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temp_soil.to_netcdf(snakemake.output["temp_soil_"+item])
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