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

for more information, see https://pre-commit.ci
2024-03-04 16:16:26 +00:00 · 2024-03-04 16:16:26 +00:00 · 9607eb0e6b
commit 9607eb0e6b
parent 1ff8321c5e
4 changed files with 102 additions and 46 deletions
--- a/scripts/build_clustered_population_layouts.py
+++ b/scripts/build_clustered_population_layouts.py
@ -25,7 +25,7 @@ if __name__ == "__main__":
        )

    set_scenario_config(snakemake)
-    
+
    cutout_name = snakemake.input.cutout
    year = snakemake.wildcards.weather_year
    if year:
--- a/scripts/build_energy_totals.py
+++ b/scripts/build_energy_totals.py
@ -35,6 +35,7 @@ def reverse(dictionary):
    """
    return {v: k for k, v in dictionary.items()}

+
 non_EU = ["NO", "CH", "ME", "MK", "RS", "BA", "AL"]

 idees_rename = {"GR": "EL", "GB": "UK"}
@ -72,7 +73,10 @@ to_ipcc = {
 def eurostat_per_country(country):

    country_fn = idees_rename.get(country, country)
-    fn = snakemake.input.eurostat + f"/{country_fn}-Energy-balance-sheets-June-2021-edition.xlsb"
+    fn = (
+        snakemake.input.eurostat
+        + f"/{country_fn}-Energy-balance-sheets-June-2021-edition.xlsb"
+    )

    df = pd.read_excel(
        fn,
@ -93,27 +97,31 @@ def build_eurostat(countries, year=None):
    """

    nprocesses = snakemake.threads
-    tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries),
-                    desc='Build from eurostat database')
+    tqdm_kwargs = dict(
+        ascii=False,
+        unit=" country",
+        total=len(countries),
+        desc="Build from eurostat database",
+    )
    with mp.Pool(processes=nprocesses) as pool:
        dfs = list(tqdm(pool.imap(eurostat_per_country, countries), **tqdm_kwargs))

-    index_names = ['country', 'year', 'lvl1', 'lvl2', 'lvl3']
+    index_names = ["country", "year", "lvl1", "lvl2", "lvl3"]
    df = pd.concat(dfs, keys=countries, names=index_names)

-    df.dropna(how='all', axis=0, inplace=True)
-    df.dropna(how='all', axis=1, inplace=True)
-    df = df[df.index.get_level_values('lvl1') != 'ktoe']
+    df.dropna(how="all", axis=0, inplace=True)
+    df.dropna(how="all", axis=1, inplace=True)
+    df = df[df.index.get_level_values("lvl1") != "ktoe"]

    i = df.index.to_frame(index=False)
-    i.loc[i.lvl2 == 'Primary production', ['lvl1', 'lvl3']] = 'Main'
-    i.loc[i.lvl2 == 'Gross electricity production', 'lvl1'] = "Gross production"
+    i.loc[i.lvl2 == "Primary production", ["lvl1", "lvl3"]] = "Main"
+    i.loc[i.lvl2 == "Gross electricity production", "lvl1"] = "Gross production"
    i.ffill(inplace=True)
    df.index = pd.MultiIndex.from_frame(i)

    df.drop(list(range(1990, 2020)), axis=1, inplace=True)
    df.drop("Unnamed: 7", axis=1, inplace=True)
-    df.fillna(0., inplace=True)
+    df.fillna(0.0, inplace=True)

    # convert ktoe/a to TWh/a
    df *= 11.63 / 1e3
@ -121,7 +129,7 @@ def build_eurostat(countries, year=None):
    df.index = df.index.set_levels(df.index.levels[1].astype(int), level=1)

    if year:
-        df = df.xs(year, level='year')
+        df = df.xs(year, level="year")

    return df

@ -133,12 +141,12 @@ def build_swiss(year=None):

    fn = snakemake.input.swiss

-    df = pd.read_csv(fn, index_col=[0,1]).stack().unstack('item')
+    df = pd.read_csv(fn, index_col=[0, 1]).stack().unstack("item")
    df.index.names = ["country", "year"]
    df.index = df.index.set_levels(df.index.levels[1].astype(int), level=1)

    if year:
-        df = df.xs(year, level='year')
+        df = df.xs(year, level="year")

    # convert PJ/a to TWh/a
    df /= 3.6
@ -356,14 +364,14 @@ def build_idees(countries, year=None):
        unit=" country",
        total=len(countries),
        desc="Build from IDEES database",
-        disable=disable_progress
+        disable=disable_progress,
    )

    with mute_print():
        with mp.Pool(processes=nprocesses) as pool:
            totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))

-    totals = pd.concat(totals_list, keys=countries, names=['country', 'year'])
+    totals = pd.concat(totals_list, keys=countries, names=["country", "year"])

    # convert ktoe to TWh
    exclude = totals.columns.str.fullmatch("passenger cars")
@ -377,23 +385,19 @@ def build_idees(countries, year=None):

 def build_energy_totals(countries, eurostat, swiss, idees):

-    eurostat_fuels = dict(
-        electricity="Electricity",
-        total="Total"
-    )
+    eurostat_fuels = dict(electricity="Electricity", total="Total")

    eurostat_sectors = dict(
        residential="Households",
        services="Commercial & public services",
        road="Road",
-        rail="Rail"
+        rail="Rail",
    )

    to_drop = ["passenger cars", "passenger car efficiency"]

    new_index = pd.MultiIndex.from_product(
-        [countries, eurostat.index.levels[1]],
-        names=["country", "year"]
+        [countries, eurostat.index.levels[1]], names=["country", "year"]
    )

    df = idees.reindex(new_index).drop(to_drop, axis=1)
@ -404,7 +408,7 @@ def build_energy_totals(countries, eurostat, swiss, idees):
    # add international navigation

    slicer = idx[in_eurostat, :, :, "International maritime bunkers", :]
-    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
+    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0, 1]).sum()
    df.loc[in_eurostat, "total international navigation"] = fill_values

    # add swiss energy data
@ -414,11 +418,14 @@ def build_energy_totals(countries, eurostat, swiss, idees):
    # get values for missing countries based on Eurostat EnergyBalances
    # divide cooking/space/water according to averages in EU28

-    to_fill = df.index[df["total residential"].isna() & df.index.get_level_values('country').isin(eurostat_countries)]
+    to_fill = df.index[
+        df["total residential"].isna()
+        & df.index.get_level_values("country").isin(eurostat_countries)
+    ]
    uses = ["space", "cooking", "water"]

-    c = to_fill.get_level_values('country')
-    y = to_fill.get_level_values('year')
+    c = to_fill.get_level_values("country")
+    y = to_fill.get_level_values("year")

    for sector in ["residential", "services", "road", "rail"]:

@ -427,7 +434,9 @@ def build_energy_totals(countries, eurostat, swiss, idees):
        for fuel in ["electricity", "total"]:

            slicer = idx[c, y, :, :, eurostat_sectors[sector]]
-            fill_values = eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0,1]).sum()
+            fill_values = (
+                eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0, 1]).sum()
+            )
            df.loc[to_fill, f"{fuel} {sector}"] = fill_values

    for sector in ["residential", "services"]:
@ -488,7 +497,9 @@ def build_energy_totals(countries, eurostat, swiss, idees):
                    no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"]
                )
                fraction = nonelectric_use.div(nonelectric).mean()
-                df.loc["NO", f"total {sector} {use}"] = (total_heating * fraction).values
+                df.loc["NO", f"total {sector} {use}"] = (
+                    total_heating * fraction
+                ).values
                df.loc["NO", f"electricity {sector} {use}"] = (
                    total_heating * fraction * elec_fraction
                ).values
@ -496,17 +507,17 @@ def build_energy_totals(countries, eurostat, swiss, idees):
    # Missing aviation

    slicer = idx[c, y, :, :, "Domestic aviation"]
-    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
+    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0, 1]).sum()
    df.loc[to_fill, "total domestic aviation"] = fill_values

    slicer = idx[c, y, :, "International aviation", :]
-    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
+    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0, 1]).sum()
    df.loc[to_fill, "total international aviation"] = fill_values

    # missing domestic navigation

    slicer = idx[c, y, :, :, "Domestic navigation"]
-    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
+    fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0, 1]).sum()
    df.loc[to_fill, "total domestic navigation"] = fill_values

    # split road traffic for non-IDEES
@ -653,13 +664,15 @@ def build_eurostat_co2(countries, eurostat=None, year=1990):
    if eurostat is None:
        df = build_eurostat(countries, year)
    else:
-        df = eurostat.xs(year, level='year')
+        df = eurostat.xs(year, level="year")

    specific_emissions = pd.Series(index=df.columns, dtype=float)

    # emissions in tCO2_equiv per MWh_th
    specific_emissions["Solid fossil fuels"] = 0.36  # Approximates coal
-    specific_emissions["Oil and petroleum products"] = 0.285  # Average of distillate and residue
+    specific_emissions["Oil and petroleum products"] = (
+        0.285  # Average of distillate and residue
+    )
    specific_emissions["Natural gas"] = 0.2  # For natural gas

    # oil values from https://www.eia.gov/tools/faqs/faq.cfm?id=74&t=11
@ -675,19 +688,60 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):

    for ct in pd.Index(countries).intersection(["BA", "RS", "AL", "ME", "MK"]):
        mappings = {
-            "electricity": (ct, "Transformation input", "Electricity & heat generation", "Main"),
-            "residential non-elec": (ct, "Final energy consumption", "Other sectors", "Households"),
-            "services non-elec": (ct, "Final energy consumption", "Other sectors", "Commercial & public services"),
-            "road non-elec": (ct, "Final energy consumption", "Transport sector", "Road"),
-            "rail non-elec": (ct, "Final energy consumption", "Transport sector", "Rail"),
-            "domestic navigation": (ct, "Final energy consumption", "Transport sector", "Domestic navigation"),
+            "electricity": (
+                ct,
+                "Transformation input",
+                "Electricity & heat generation",
+                "Main",
+            ),
+            "residential non-elec": (
+                ct,
+                "Final energy consumption",
+                "Other sectors",
+                "Households",
+            ),
+            "services non-elec": (
+                ct,
+                "Final energy consumption",
+                "Other sectors",
+                "Commercial & public services",
+            ),
+            "road non-elec": (
+                ct,
+                "Final energy consumption",
+                "Transport sector",
+                "Road",
+            ),
+            "rail non-elec": (
+                ct,
+                "Final energy consumption",
+                "Transport sector",
+                "Rail",
+            ),
+            "domestic navigation": (
+                ct,
+                "Final energy consumption",
+                "Transport sector",
+                "Domestic navigation",
+            ),
            "international navigation": (ct, "Main", "International maritime bunkers"),
-            "domestic aviation": (ct, "Final energy consumption", "Transport sector", "Domestic aviation"),
+            "domestic aviation": (
+                ct,
+                "Final energy consumption",
+                "Transport sector",
+                "Domestic aviation",
+            ),
            "international aviation": (ct, "Main", "International aviation"),
            # does not include industrial process emissions or fuel processing/refining
-            "industrial non-elec": (ct, "Final energy consumption", "Industry sector", "Non-energy use in industry sector"),
+            "industrial non-elec": (
+                ct,
+                "Final energy consumption",
+                "Industry sector",
+                "Non-energy use in industry sector",
+            ),
            # does not include non-energy emissions
-            "agriculture": (eurostat_co2.index.get_level_values(0) == ct) & eurostat_co2.index.isin(["Agriculture & forestry", "Fishing"], level=3),
+            "agriculture": (eurostat_co2.index.get_level_values(0) == ct)
+            & eurostat_co2.index.isin(["Agriculture & forestry", "Fishing"], level=3),
        }

        for i, mi in mappings.items():
@ -771,6 +825,6 @@ if __name__ == "__main__":
    co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
    co2.to_csv(snakemake.output.co2_name)

-    idees_transport = idees.xs(data_year, level='year')
+    idees_transport = idees.xs(data_year, level="year")
    transport = build_transport_data(countries, population, idees_transport)
    transport.to_csv(snakemake.output.transport_name)
--- a/scripts/build_hydro_profile.py
+++ b/scripts/build_hydro_profile.py
@ -65,8 +65,8 @@ import atlite
 import country_converter as coco
 import geopandas as gpd
 import pandas as pd
-from numpy.polynomial import Polynomial
 from _helpers import configure_logging, set_scenario_config
+from numpy.polynomial import Polynomial

 cc = coco.CountryConverter()

--- a/scripts/cluster_gas_network.py
+++ b/scripts/cluster_gas_network.py
@ -105,7 +105,9 @@ if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

-        snakemake = mock_snakemake("cluster_gas_network", weather_year="", simpl="", clusters="37")
+        snakemake = mock_snakemake(
+            "cluster_gas_network", weather_year="", simpl="", clusters="37"
+        )
    configure_logging(snakemake)
    set_scenario_config(snakemake)