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

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rules/build_sector.smk

@@ -443,7 +443,8 @@ rule build_industry_sector_ratios_intermediate:
         industrial_production_per_country=RESOURCES
         + "industrial_production_per_country.csv",
     output:
-        industry_sector_ratios=RESOURCES + "industry_sector_ratios_{planning_horizons}.csv",
+        industry_sector_ratios=RESOURCES
+        + "industry_sector_ratios_{planning_horizons}.csv",
     threads: 1
     resources:
         mem_mb=1000,
@@ -559,7 +560,8 @@ rule build_industrial_production_per_node:
 
 rule build_industrial_energy_demand_per_node:
     input:
-        industry_sector_ratios=RESOURCES + "industry_sector_ratios_{planning_horizons}.csv",
+        industry_sector_ratios=RESOURCES
+        + "industry_sector_ratios_{planning_horizons}.csv",
         industrial_production_per_node=RESOURCES
         + "industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
         industrial_energy_demand_per_node_today=RESOURCES

scripts/build_industrial_energy_demand_per_country_today.py

@@ -96,19 +96,35 @@ def industrial_energy_demand_per_country(country, year, jrc_dir):
 
 def separate_basic_chemicals(demand, production):
 
-    ammonia = pd.DataFrame({"hydrogen": production["Ammonia"] * params["MWh_H2_per_tNH3_electrolysis"],
+    ammonia = pd.DataFrame(
-                            "electricity" : production["Ammonia"] * params["MWh_elec_per_tNH3_electrolysis"]}).T
+        {
-    chlorine = pd.DataFrame({"hydrogen": production["Chlorine"] * params["MWh_H2_per_tCl"],
+            "hydrogen": production["Ammonia"] * params["MWh_H2_per_tNH3_electrolysis"],
-                             "electricity" : production["Chlorine"] * params["MWh_elec_per_tCl"]}).T
+            "electricity": production["Ammonia"]
-    methanol = pd.DataFrame({"gas": production["Methanol"] * params["MWh_CH4_per_tMeOH"],
+            * params["MWh_elec_per_tNH3_electrolysis"],
-                             "electricity" : production["Methanol"] * params["MWh_elec_per_tMeOH"]}).T
+        }
+    ).T
+    chlorine = pd.DataFrame(
+        {
+            "hydrogen": production["Chlorine"] * params["MWh_H2_per_tCl"],
+            "electricity": production["Chlorine"] * params["MWh_elec_per_tCl"],
+        }
+    ).T
+    methanol = pd.DataFrame(
+        {
+            "gas": production["Methanol"] * params["MWh_CH4_per_tMeOH"],
+            "electricity": production["Methanol"] * params["MWh_elec_per_tMeOH"],
+        }
+    ).T
 
     demand["Ammonia"] = ammonia.unstack().reindex(index=demand.index, fill_value=0.0)
     demand["Chlorine"] = chlorine.unstack().reindex(index=demand.index, fill_value=0.0)
     demand["Methanol"] = methanol.unstack().reindex(index=demand.index, fill_value=0.0)
 
     demand["HVC"] = (
-        demand["Basic chemicals"] - demand["Ammonia"] - demand["Methanol"] - demand["Chlorine"]
+        demand["Basic chemicals"]
+        - demand["Ammonia"]
+        - demand["Methanol"]
+        - demand["Chlorine"]
     )
 
     demand.drop(columns="Basic chemicals", inplace=True)
@@ -166,8 +182,10 @@ if __name__ == "__main__":
     demand = industrial_energy_demand(countries.intersection(eu28), year)
 
     # output in MtMaterial/a
-    production = pd.read_csv(snakemake.input.industrial_production_per_country,
+    production = (
-                             index_col=0) / 1e3
+        pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
+        / 1e3
+    )
 
     demand = separate_basic_chemicals(demand, production)
 

scripts/build_industrial_energy_demand_per_node.py

@@ -21,9 +21,7 @@ if __name__ == "__main__":
 
     # import ratios
     fn = snakemake.input.industry_sector_ratios
-    sector_ratios = pd.read_csv(fn,
+    sector_ratios = pd.read_csv(fn, header=[0, 1], index_col=0)
-                                header=[0,1],
-                                index_col=0)
 
     # material demand per node and industry (Mton/a)
     fn = snakemake.input.industrial_production_per_node
@@ -33,14 +31,19 @@ if __name__ == "__main__":
     fn = snakemake.input.industrial_energy_demand_per_node_today
     nodal_today = pd.read_csv(fn, index_col=0)
 
-    nodal_sector_ratios = pd.concat({node: sector_ratios[node[:2]] for node in nodal_production.index},
+    nodal_sector_ratios = pd.concat(
-                                    axis=1)
+        {node: sector_ratios[node[:2]] for node in nodal_production.index}, axis=1
+    )
 
     nodal_production_stacked = nodal_production.stack()
-    nodal_production_stacked.index.names = [None,None]
+    nodal_production_stacked.index.names = [None, None]
 
     # final energy consumption per node and industry (TWh/a)
-    nodal_df = (nodal_sector_ratios.multiply(nodal_production_stacked)).T.groupby(level=0).sum()
+    nodal_df = (
+        (nodal_sector_ratios.multiply(nodal_production_stacked))
+        .T.groupby(level=0)
+        .sum()
+    )
 
     rename_sectors = {
         "elec": "electricity",

scripts/build_industrial_production_per_country.py

@@ -261,7 +261,11 @@ def separate_basic_chemicals(demand, year):
     demand["Basic chemicals"].clip(lower=0.0, inplace=True)
 
     # assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
-    distribution_key = demand["Basic chemicals"] / params["basic_chemicals_without_NH3_production_today"] / 1e3
+    distribution_key = (
+        demand["Basic chemicals"]
+        / params["basic_chemicals_without_NH3_production_today"]
+        / 1e3
+    )
     demand["HVC"] = params["HVC_production_today"] * 1e3 * distribution_key
     demand["Chlorine"] = params["chlorine_production_today"] * 1e3 * distribution_key
     demand["Methanol"] = params["methanol_production_today"] * 1e3 * distribution_key

scripts/build_industry_sector_ratios_intermediate.py

@@ -4,39 +4,45 @@
 # SPDX-License-Identifier: MIT
 """
 Build specific energy consumption by carrier and industries and by country,
-that interpolates between the current average energy consumption (from 2015-2020)
+that interpolates between the current average energy consumption (from
-and the ideal future best-in-class consumption.
+2015-2020) and the ideal future best-in-class consumption.
 """
 
 import pandas as pd
-
 from prepare_sector_network import get
 
+
 def build_industry_sector_ratios_intermediate():
 
     # in TWh/a
-    demand = pd.read_csv(snakemake.input.industrial_energy_demand_per_country_today,
+    demand = pd.read_csv(
-                         header=[0,1],
+        snakemake.input.industrial_energy_demand_per_country_today,
-                         index_col=0)
+        header=[0, 1],
+        index_col=0,
+    )
 
     # in Mt/a
-    production = pd.read_csv(snakemake.input.industrial_production_per_country,
+    production = (
-                             index_col=0) / 1e3
+        pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
+        / 1e3
+    )
     production = production.unstack().swaplevel()
 
     # in MWh/t
-    future_sector_ratios = pd.read_csv(snakemake.input.industry_sector_ratios,
+    future_sector_ratios = pd.read_csv(
-                                       index_col=0)
+        snakemake.input.industry_sector_ratios, index_col=0
+    )
 
-    production.index.names = [None,None]
+    production.index.names = [None, None]
 
     today_sector_ratios = demand.div(production, axis=1)
 
-    today_sector_ratios.drop(columns=today_sector_ratios.columns[today_sector_ratios.isna().all()],
+    today_sector_ratios.drop(
-                             inplace=True)
+        columns=today_sector_ratios.columns[today_sector_ratios.isna().all()],
+        inplace=True,
+    )
 
-    rename = pd.Series(today_sector_ratios.index,
+    rename = pd.Series(today_sector_ratios.index, today_sector_ratios.index)
-                       today_sector_ratios.index)
     rename["waste"] = "biomass"
     rename["electricity"] = "elec"
     rename["solid"] = "coke"
@@ -44,9 +50,7 @@ def build_industry_sector_ratios_intermediate():
     rename["other"] = "biomass"
     rename["liquid"] = "naphtha"
 
-    today_sector_ratios.rename(rename,
+    today_sector_ratios.rename(rename, inplace=True)
-                               inplace=True)
-
 
     fraction_future = get(params["sector_ratios_fraction_future"], year)
 
@@ -56,14 +60,22 @@ def build_industry_sector_ratios_intermediate():
 
         intermediate_sector_ratio = future_sector_ratios.copy()
 
-        intermediate_sector_ratio.loc[today_sector_ratios[ct].index,today_sector_ratios[ct].columns] = (fraction_future*intermediate_sector_ratio.loc[today_sector_ratios[ct].index,today_sector_ratios[ct].columns]
+        intermediate_sector_ratio.loc[
-                                                                                                        + (1 - fraction_future)*today_sector_ratios[ct])
+            today_sector_ratios[ct].index, today_sector_ratios[ct].columns
+        ] = (
+            fraction_future
+            * intermediate_sector_ratio.loc[
+                today_sector_ratios[ct].index, today_sector_ratios[ct].columns
+            ]
+            + (1 - fraction_future) * today_sector_ratios[ct]
+        )
         intermediate_sector_ratios[ct] = intermediate_sector_ratio
 
     intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1)
 
     intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios)
 
+
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake