pypsa-eur/scripts/build_artificial_load_data.py

# SPDX-FileCopyrightText: 2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT

"""This rule downloads the load data"""

import logging
logger = logging.getLogger(__name__)
from _helpers import configure_logging

import pandas as pd

if __name__ == "__main__":

    if 'snakemake' not in globals():
        from _helpers import mock_snakemake
        snakemake = mock_snakemake('build_artificial_load_data', weather_year='')

    configure_logging(snakemake)

    weather_year = snakemake.wildcards.weather_year
    if weather_year:
        snapshots = dict(
            start=weather_year,
            end=str(int(weather_year)+1),
            inclusive="left"
        )
    else:
        snapshots = snakemake.config['snapshots']
    snapshots = pd.date_range(freq='h', **snapshots)

    fixed_year = snakemake.config["load"].get("fixed_year", False)
    years = slice(str(fixed_year), str(fixed_year)) if fixed_year else slice(snapshots[0], snapshots[-1])
    countries = snakemake.config['countries']

    load = pd.read_csv(
        snakemake.input[0],
        index_col=0,
        parse_dates=True
    ).loc[snapshots, countries]

    assert not load.isna().any().any(), 'Load data contains nans.'

    if fixed_year:
        load.index = load.index.map(lambda t: t.replace(year=snapshots.year[0]))

    load.to_csv(snakemake.output[0])