Format entire code base based on new code guidelines

Following calliope-project#285.

scripts/biofuels/allocate.py

@@ -1,4 +1,5 @@
 """Take national potentials, allocate to regions based on feedstocks and proxies, and sum over feedstocks."""
+
 from enum import Enum
 
 import pandas as pd
@@ -10,6 +11,7 @@
 
 class GlobCover(Enum):
     """Original categories taken from GlobCover 2009 land cover."""
+
     POST_FLOODING = "lc_11"
     RAINFED_CROPLANDS = "lc_14"
     MOSAIC_CROPLAND = "lc_20"
@@ -25,88 +27,112 @@ class GlobCover(Enum):
     CLOSED_TO_OPEN_SHRUBLAND = "lc_130"
     CLOSED_TO_OPEN_HERBS = "lc_140"
     SPARSE_VEGETATION = "lc_150"
-    CLOSED_TO_OPEN_REGULARLY_FLOODED_FOREST = "lc_160" # doesn't exist in Europe
-    CLOSED_REGULARLY_FLOODED_FOREST = "lc_170" # doesn't exist in Europe
-    CLOSED_TO_OPEN_REGULARLY_FLOODED_GRASSLAND = "lc_180" # roughly 2.3% of land in Europe
+    CLOSED_TO_OPEN_REGULARLY_FLOODED_FOREST = "lc_160"  # doesn't exist in Europe
+    CLOSED_REGULARLY_FLOODED_FOREST = "lc_170"  # doesn't exist in Europe
+    CLOSED_TO_OPEN_REGULARLY_FLOODED_GRASSLAND = (
+        "lc_180"  # roughly 2.3% of land in Europe
+    )
     ARTIFICAL_SURFACES_AND_URBAN_AREAS = "lc_190"
     BARE_AREAS = "lc_200"
     WATER_BODIES = "lc_210"
     PERMANENT_SNOW = "lc_220"
     NO_DATA = "lc_230"
 
 
-FARM = [GlobCover.POST_FLOODING.value, GlobCover.RAINFED_CROPLANDS.value,
-        GlobCover.MOSAIC_CROPLAND.value, GlobCover.MOSAIC_VEGETATION.value]
-FOREST = [GlobCover.CLOSED_TO_OPEN_BROADLEAVED_FOREST.value, GlobCover.CLOSED_BROADLEAVED_FOREST.value,
-          GlobCover.OPEN_BROADLEAVED_FOREST.value, GlobCover.CLOSED_NEEDLELEAVED_FOREST.value,
-          GlobCover.OPEN_NEEDLELEAVED_FOREST.value, GlobCover.CLOSED_TO_OPEN_MIXED_FOREST.value,
-          GlobCover.MOSAIC_FOREST.value, GlobCover.CLOSED_TO_OPEN_REGULARLY_FLOODED_FOREST.value,
-          GlobCover.CLOSED_REGULARLY_FLOODED_FOREST.value]
+FARM = [
+    GlobCover.POST_FLOODING.value,
+    GlobCover.RAINFED_CROPLANDS.value,
+    GlobCover.MOSAIC_CROPLAND.value,
+    GlobCover.MOSAIC_VEGETATION.value,
+]
+FOREST = [
+    GlobCover.CLOSED_TO_OPEN_BROADLEAVED_FOREST.value,
+    GlobCover.CLOSED_BROADLEAVED_FOREST.value,
+    GlobCover.OPEN_BROADLEAVED_FOREST.value,
+    GlobCover.CLOSED_NEEDLELEAVED_FOREST.value,
+    GlobCover.OPEN_NEEDLELEAVED_FOREST.value,
+    GlobCover.CLOSED_TO_OPEN_MIXED_FOREST.value,
+    GlobCover.MOSAIC_FOREST.value,
+    GlobCover.CLOSED_TO_OPEN_REGULARLY_FLOODED_FOREST.value,
+    GlobCover.CLOSED_REGULARLY_FLOODED_FOREST.value,
+]
 
 
-def biofuel_potential(path_to_national_potentials, path_to_national_costs, path_to_units, path_to_land_cover,
-                      path_to_population, scenario, potential_year, cost_year, proxies, paths_to_output):
+def biofuel_potential(
+    path_to_national_potentials,
+    path_to_national_costs,
+    path_to_units,
+    path_to_land_cover,
+    path_to_population,
+    scenario,
+    potential_year,
+    cost_year,
+    proxies,
+    paths_to_output,
+):
     """Take national potentials from JRC report and allocate to regions based on proxies."""
     national_potentials = (
-        pd
-        .read_csv(path_to_national_potentials, index_col=["year", "scenario", "country_code", "feedstock"])["value"]
+        pd.read_csv(
+            path_to_national_potentials,
+            index_col=["year", "scenario", "country_code", "feedstock"],
+        )["value"]
         .mul(PJ_TO_MWH)
         .xs((potential_year, scenario), level=("year", "scenario"))
     )
     national_costs = (
-        pd
-        .read_csv(path_to_national_costs, index_col=["year", "scenario", "country_code", "feedstock"])["value"]
+        pd.read_csv(
+            path_to_national_costs,
+            index_col=["year", "scenario", "country_code", "feedstock"],
+        )["value"]
         .div(GJ_TO_MWH)
         .xs((potential_year, scenario), level=("year", "scenario"))
     )
     units = pd.read_csv(path_to_units).set_index("id")
-    if (len(units.index) == 1) and (units.index[0] == "EUR"): # special case for continental level
+    if (len(units.index) == 1) and (
+        units.index[0] == "EUR"
+    ):  # special case for continental level
         cost = (
-            national_costs
-            .mul(national_potentials)
-            .div(national_potentials.sum())
-            .sum()
+            national_costs.mul(national_potentials).div(national_potentials.sum()).sum()
+        )
+        national_costs = pd.Series([cost], index=["EUR"]).rename_axis(
+            index="country_code"
         )
-        national_costs = pd.Series([cost], index=["EUR"]).rename_axis(index="country_code")
         national_potentials = (
-            national_potentials
-            .rename(lambda x: "EUR", level="country_code")
+            national_potentials.rename(lambda x: "EUR", level="country_code")
             .groupby(level=["country_code", "feedstock"])
             .sum()
         )
 
     total_potential = allocate_potentials(
         national_potentials=national_potentials,
         units=units,
-        population=pd.read_csv(path_to_population, index_col=0).reindex(index=units.index)["population_sum"],
-        land_cover=pd.read_csv(path_to_land_cover, index_col=0).reindex(index=units.index),
-        proxies=proxies
+        population=pd.read_csv(path_to_population, index_col=0).reindex(
+            index=units.index
+        )["population_sum"],
+        land_cover=pd.read_csv(path_to_land_cover, index_col=0).reindex(
+            index=units.index
+        ),
+        proxies=proxies,
     )
     total_potential.to_csv(paths_to_output.potentials, index=True, header=True)
     weighted_cost = (
-        national_costs
-        .mul(national_potentials)
-        .div(national_potentials.sum())
-        .sum()
+        national_costs.mul(national_potentials).div(national_potentials.sum()).sum()
     )
     with open(paths_to_output.costs, "w") as f_cost:
         f_cost.write(str(weighted_cost))
 
 
 def allocate_potentials(national_potentials, units, population, land_cover, proxies):
-    regional_potentials = (
-        pd.merge(
-            national_potentials.reset_index(),
-            units["country_code"].reset_index(),
-            on="country_code"
-        )
-        .pivot(index="id", columns="feedstock", values="value")
-    )
+    regional_potentials = pd.merge(
+        national_potentials.reset_index(),
+        units["country_code"].reset_index(),
+        on="country_code",
+    ).pivot(index="id", columns="feedstock", values="value")
     shares = (
         pd.concat(
             [population, land_cover[FOREST].sum(axis=1), land_cover[FARM].sum(axis=1)],
             axis=1,
-            keys=['population', 'forest', "farmland"]
+            keys=["population", "forest", "farmland"],
         )
         .groupby(units.country_code)
         .transform(lambda x: x / x.sum())
@@ -127,5 +153,5 @@ def allocate_potentials(national_potentials, units, population, land_cover, prox
         potential_year=snakemake.params.potential_year,
         cost_year=snakemake.params.cost_year,
         proxies=snakemake.params.proxies,
-        paths_to_output=snakemake.output
+        paths_to_output=snakemake.output,
     )

scripts/biofuels/extract.py

@@ -1,5 +1,4 @@
 import pandas as pd
-
 from eurocalliopelib import utils
 
 SHEET_NAME_POTENTIALS = "ENER - NUTS0 EnergyCom"
@@ -12,35 +11,37 @@
 COL_NAME_VALUE = "Value"
 COL_NAME_VALUE2 = "NUTS0 Energy Commodity Cost "
 
-SCENARIO_NAME_MAP = {
-    "ENS_Low": "low",
-    "ENS_Med": "medium",
-    "ENS_High": "high"
-}
+SCENARIO_NAME_MAP = {"ENS_Low": "low", "ENS_Med": "medium", "ENS_High": "high"}
 
 COL_NAME_MAP = {
     COL_NAME_COUNTRY: "country_code",
     COL_NAME_FEEDSTOCK: "feedstock",
     COL_NAME_SCENARIO: "scenario",
     COL_NAME_YEAR: "year",
     COL_NAME_VALUE: "value",
-    COL_NAME_VALUE2: "value"
+    COL_NAME_VALUE2: "value",
 }
 
 MISSING_COST_VALUE = (2030, "high", "NOR", "municipal-waste")
 REPLACEMENT_COST_VALUE = (2030, "medium", "NOR", "municipal-waste")
 
 MISSING_POTENTIAL_VALUE = (2010, "medium", "BIH", "sludge")
-REPLACEMENT_POTENTIAL_VALUE = (2010, "high", "BIH", "sludge") # 0 PJ
+REPLACEMENT_POTENTIAL_VALUE = (2010, "high", "BIH", "sludge")  # 0 PJ
 
 
 def extract_biofuels(path_to_raw_data, feedstocks, paths_to_outputs):
-    potentials = read_and_filter_and_map_names(path_to_raw_data, SHEET_NAME_POTENTIALS, feedstocks)
-    potentials = replace(potentials, MISSING_POTENTIAL_VALUE, REPLACEMENT_POTENTIAL_VALUE)
+    potentials = read_and_filter_and_map_names(
+        path_to_raw_data, SHEET_NAME_POTENTIALS, feedstocks
+    )
+    potentials = replace(
+        potentials, MISSING_POTENTIAL_VALUE, REPLACEMENT_POTENTIAL_VALUE
+    )
     assert potentials["value"].isna().sum() == 0
     potentials.to_csv(paths_to_outputs.potentials, index=True, header=True)
 
-    costs = read_and_filter_and_map_names(path_to_raw_data, SHEET_NAME_COSTS, feedstocks)
+    costs = read_and_filter_and_map_names(
+        path_to_raw_data, SHEET_NAME_COSTS, feedstocks
+    )
     costs = replace(costs, MISSING_COST_VALUE, REPLACEMENT_COST_VALUE)
     assert costs["value"].isna().sum() == 0
     costs.to_csv(paths_to_outputs.costs, index=True, header=True)
@@ -60,7 +61,7 @@ def read_and_filter_and_map_names(path_to_raw_data, sheet_name, feedstocks):
     df[COL_NAME_FEEDSTOCK] = df[COL_NAME_FEEDSTOCK].map(feedstocks)
     df[COL_NAME_COUNTRY] = df[COL_NAME_COUNTRY].map(utils.eu_country_code_to_iso3)
     df.rename(columns=COL_NAME_MAP, inplace=True)
-    df["value"] = pd.to_numeric(df.value, errors='coerce')
+    df["value"] = pd.to_numeric(df.value, errors="coerce")
     return df
 
 
@@ -77,5 +78,5 @@ def replace(df, index_to_replace, index_replace_from):
     extract_biofuels(
         path_to_raw_data=snakemake.input.potentials_and_costs,
         feedstocks=snakemake.params.feedstocks,
-        paths_to_outputs=snakemake.output
+        paths_to_outputs=snakemake.output,
     )

scripts/biofuels/template_bio.py

@@ -1,22 +1,26 @@
 import pandas as pd
-
 from eurocalliopelib.template import parametrise_template
 
 
 def construct_techs_and_locations(
-    path_to_template, path_to_output, path_to_locations, path_to_biofuel_costs,
-    biofuel_efficiency, scaling_factors
+    path_to_template,
+    path_to_output,
+    path_to_locations,
+    path_to_biofuel_costs,
+    biofuel_efficiency,
+    scaling_factors,
 ):
-    with open(path_to_biofuel_costs, "r") as f_biofuel_costs:
+    with open(path_to_biofuel_costs) as f_biofuel_costs:
         biofuel_fuel_cost = float(f_biofuel_costs.readline())
     locations = pd.read_csv(path_to_locations, index_col=0)
 
     return parametrise_template(
-        path_to_template, path_to_output,
+        path_to_template,
+        path_to_output,
         biofuel_fuel_cost=biofuel_fuel_cost,
         biofuel_efficiency=biofuel_efficiency,
         scaling_factors=scaling_factors,
-        locations=locations
+        locations=locations,
     )
 
 
@@ -27,5 +31,5 @@ def construct_techs_and_locations(
         path_to_biofuel_costs=snakemake.input.biofuel_cost,
         biofuel_efficiency=snakemake.params.biofuel_efficiency,
         scaling_factors=snakemake.params.scaling_factors,
-        path_to_output=snakemake.output[0]
+        path_to_output=snakemake.output[0],
     )