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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot] 2024-03-14 13:34:15 +00:00
parent 2023924572
commit ad9da61187
3 changed files with 3 additions and 5 deletions
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2
doc/configtables/load.csv
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@ -4,4 +4,4 @@ time_shift_for_large_gaps,string,string,"Periods which are used for copying time
manual_adjustments,bool,"{true, false}","Whether to adjust the load data manually according to the function in :func:`manual_adjustment`."
scaling_factor,--,float,"Global correction factor for the load time series."
fixed_year,--,Year or False,"To specify a fixed year for the load time series that deviates from the snapshots' year"
supplement_missing_data_artificially,bool,"{true, false}","Whether to supplement missing data for selected time period should be supplemented by artificial data from https://zenodo.org/record/7070438/files/demand_hourly.csv."
supplement_missing_data_artificially,bool,"{true, false}","Whether to supplement missing data for selected time period should be supplemented by artificial data from https://zenodo.org/record/7070438/files/demand_hourly.csv."

1 Unit Values Description
4 manual_adjustments bool {true, false} Whether to adjust the load data manually according to the function in :func:`manual_adjustment`.
5 scaling_factor -- float Global correction factor for the load time series.
6 fixed_year -- Year or False To specify a fixed year for the load time series that deviates from the snapshots' year
7 supplement_missing_data_artificially bool {true, false} Whether to supplement missing data for selected time period should be supplemented by artificial data from https://zenodo.org/record/7070438/files/demand_hourly.csv.

4
scripts/build_hydro_profile.py
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@ -73,9 +73,7 @@ cc = coco.CountryConverter()
def get_eia_annual_hydro_generation(fn, countries, capacities=False):
# in billion kWh/a = TWh/a
df = pd.read_csv(
fn, skiprows=2, index_col=1, na_values=[" ", "--"]
).iloc[1:, 1:]
df = pd.read_csv(fn, skiprows=2, index_col=1, na_values=[" ", "--"]).iloc[1:, 1:]
df.index = df.index.str.strip()
df.columns = df.columns.astype(int)

2
scripts/build_population_weighted_energy_totals.py
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@ -15,7 +15,7 @@ if __name__ == "__main__":
snakemake = mock_snakemake(
"build_population_weighted_energy_totals",
kind='energy',
kind="energy",
simpl="",
clusters=60,
)