53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
"""Build heat demand time series."""
|
|
|
|
import geopandas as gpd
|
|
import atlite
|
|
import pandas as pd
|
|
import xarray as xr
|
|
import numpy as np
|
|
|
|
if __name__ == '__main__':
|
|
if 'snakemake' not in globals():
|
|
from helper import mock_snakemake
|
|
snakemake = mock_snakemake(
|
|
'build_heat_demands',
|
|
weather_year='1969',
|
|
simpl='',
|
|
clusters=37,
|
|
)
|
|
|
|
cutout_name = snakemake.input.cutout
|
|
year = snakemake.wildcards.weather_year
|
|
|
|
if year:
|
|
snapshots = dict(start=year, end=str(int(year)+1), closed="left")
|
|
cutout_name = cutout_name.format(weather_year=year)
|
|
else:
|
|
snapshots = snakemake.config['snapshots']
|
|
|
|
drop_leap_day = snakemake.config["atlite"].get("drop_leap_day", False)
|
|
time = pd.date_range(freq='h', **snapshots)
|
|
daily = pd.date_range(freq='D', **snapshots)
|
|
if drop_leap_day:
|
|
time = time[~((time.month == 2) & (time.day == 29))]
|
|
daily = daily[~((daily.month == 2) & (daily.day == 29))]
|
|
|
|
cutout = atlite.Cutout(cutout_name).sel(time=time)
|
|
|
|
clustered_regions = gpd.read_file(
|
|
snakemake.input.regions_onshore).set_index('name').buffer(0).squeeze()
|
|
|
|
I = cutout.indicatormatrix(clustered_regions)
|
|
|
|
for area in ["rural", "urban", "total"]:
|
|
|
|
pop_layout = xr.open_dataarray(snakemake.input[f'pop_layout_{area}'])
|
|
|
|
stacked_pop = pop_layout.stack(spatial=('y', 'x'))
|
|
M = I.T.dot(np.diag(I.dot(stacked_pop)))
|
|
|
|
heat_demand = cutout.heat_demand(
|
|
matrix=M.T, index=clustered_regions.index).sel(time=daily)
|
|
|
|
heat_demand.to_netcdf(snakemake.output[f"heat_demand_{area}"])
|