Apply ruff format 0.1.11

message_ix_models/model/material/data_aluminum.py

@@ -527,69 +527,74 @@ def gen_mock_demand_aluminum(scenario: message_ix.Scenario) -> pd.DataFrame:
 
     return demand2020_al
 
-def gen_data_alu_trade(scenario: message_ix.Scenario):
 
+def gen_data_alu_trade(scenario: message_ix.Scenario):
     results = defaultdict(list)
 
     data_trade = pd.read_csv(
-    package_data_path("material", "aluminum", "aluminum_trade_data.csv"))
+        package_data_path("material", "aluminum", "aluminum_trade_data.csv")
+    )
 
     data_trade.drop_duplicates()
 
     s_info = ScenarioInfo(scenario)
 
     modelyears = s_info.Y
     yv_ya = s_info.yv_ya
-    year_all =yv_ya["year_vtg"].unique()
+    year_all = yv_ya["year_vtg"].unique()
 
-    data_trade = data_trade[data_trade['Year'].isin(year_all)]
+    data_trade = data_trade[data_trade["Year"].isin(year_all)]
 
     # Divide R12_WEU as 0.7 WEU, 0.3 EEU.
-    data_trade.loc[(data_trade['Region']=='Europe'), 'Value'] *= 0.7
-    data_trade.loc[(data_trade['Region']=='Europe'), 'Region'] = 'West Europe'
+    data_trade.loc[(data_trade["Region"] == "Europe"), "Value"] *= 0.7
+    data_trade.loc[(data_trade["Region"] == "Europe"), "Region"] = "West Europe"
 
-    data_trade_eeu = data_trade[data_trade['Region'] == 'West Europe']
-    data_trade_eeu['Value'] *= (0.3 / 0.7)
-    data_trade_eeu['Region'] = 'East Europe'
+    data_trade_eeu = data_trade[data_trade["Region"] == "West Europe"]
+    data_trade_eeu["Value"] *= 0.3 / 0.7
+    data_trade_eeu["Region"] = "East Europe"
 
     data_trade = pd.concat([data_trade, data_trade_eeu])
 
     # Sum Japan and Oceania as PAO
 
-    condition = ((data_trade['Region'] == 'Japan') | (data_trade['Region'] == 'Oceania'))
+    condition = (data_trade["Region"] == "Japan") | (data_trade["Region"] == "Oceania")
     data_trade_pao = data_trade.loc[condition]
-    data_trade_pao = data_trade_pao.groupby(['Variable', 'Year'])['Value'].sum().reset_index()
+    data_trade_pao = (
+        data_trade_pao.groupby(["Variable", "Year"])["Value"].sum().reset_index()
+    )
 
-    data_trade_pao['Region'] = 'Pacific OECD'
+    data_trade_pao["Region"] = "Pacific OECD"
     data_trade = pd.concat([data_trade, data_trade_pao])
-    condition_updated = ((data_trade['Region'] == 'Japan') | (data_trade['Region'] == 'Oceania'))
+    condition_updated = (data_trade["Region"] == "Japan") | (
+        data_trade["Region"] == "Oceania"
+    )
     data_trade = data_trade.drop(data_trade[condition_updated].index)
 
     data_trade.reset_index(drop=True, inplace=True)
 
     # Divide Other Asia 50-50 to SAS and PAS
 
-    data_trade.loc[(data_trade['Region']=='Other Asia'), 'Value'] *= 0.5
-    data_trade.loc[(data_trade['Region']=='Other Asia'), 'Region'] = 'South Asia'
+    data_trade.loc[(data_trade["Region"] == "Other Asia"), "Value"] *= 0.5
+    data_trade.loc[(data_trade["Region"] == "Other Asia"), "Region"] = "South Asia"
 
-    data_trade_pas = data_trade[data_trade['Region'] == 'South Asia']
-    data_trade_pas['Region'] = 'Other Pacific Asia'
+    data_trade_pas = data_trade[data_trade["Region"] == "South Asia"]
+    data_trade_pas["Region"] = "Other Pacific Asia"
 
     data_trade = pd.concat([data_trade, data_trade_pas])
 
     # Divide Other Producing Regions 50-50s as Africa and FSU
 
-    data_trade.loc[(data_trade['Region']=='Other Producers'), 'Value'] *= 0.5
-    data_trade.loc[(data_trade['Region']=='Other Producers'), 'Region'] = 'Africa'
+    data_trade.loc[(data_trade["Region"] == "Other Producers"), "Value"] *= 0.5
+    data_trade.loc[(data_trade["Region"] == "Other Producers"), "Region"] = "Africa"
 
-    data_trade_fsu = data_trade[data_trade['Region'] == 'Africa']
-    data_trade_fsu['Region'] = 'Former Soviet Union'
+    data_trade_fsu = data_trade[data_trade["Region"] == "Africa"]
+    data_trade_fsu["Region"] = "Former Soviet Union"
 
     data_trade = pd.concat([data_trade, data_trade_fsu])
 
     # Drop non-producers
 
-    condition = data_trade['Region'] == 'Non Producers'
+    condition = data_trade["Region"] == "Non Producers"
     data_trade = data_trade.drop(data_trade[condition].index)
 
     s_info = ScenarioInfo(scenario)
@@ -601,99 +606,111 @@ def gen_data_alu_trade(scenario: message_ix.Scenario):
         region_tag = "R11_"
         china_mapping = "R11_CPA"
 
-    region_mapping =     {
-                          "China": china_mapping,
-                          "West Europe": region_tag + "WEU",
-                          "East Europe": region_tag + "EEU",
-                          "Pacific OECD": region_tag + "PAO",
-                          "South Asia": region_tag + "SAS",
-                          "Other Pacific Asia": region_tag + "PAS",
-                          "Africa": region_tag + "AFR",
-                          "Former Soviet Union": region_tag + "FSU",
-                          "Middle East": region_tag + "MEA",
-                          "North America": region_tag + "NAM",
-                          "South America": region_tag + "LAM"
-                         }
+    region_mapping = {
+        "China": china_mapping,
+        "West Europe": region_tag + "WEU",
+        "East Europe": region_tag + "EEU",
+        "Pacific OECD": region_tag + "PAO",
+        "South Asia": region_tag + "SAS",
+        "Other Pacific Asia": region_tag + "PAS",
+        "Africa": region_tag + "AFR",
+        "Former Soviet Union": region_tag + "FSU",
+        "Middle East": region_tag + "MEA",
+        "North America": region_tag + "NAM",
+        "South America": region_tag + "LAM",
+    }
 
     # Add the data as historical_activity
 
     data_trade = data_trade.replace(region_mapping)
-    data_trade.rename(columns= {"Region":"node_loc",
-                                "Year": "year_act",
-                                "Value": "value"}, inplace = True)
+    data_trade.rename(
+        columns={"Region": "node_loc", "Year": "year_act", "Value": "value"},
+        inplace=True,
+    )
 
     # Trade is at the product level.
     # For imports this corresponds to: USE|Inputs|Imports
 
-    data_import = data_trade[data_trade["Variable"]=="USE|Inputs|Imports"]
-    data_import_hist = data_import[data_import['year_act']<=2015]
+    data_import = data_trade[data_trade["Variable"] == "USE|Inputs|Imports"]
+    data_import_hist = data_import[data_import["year_act"] <= 2015]
     data_import_hist["technology"] = "import_aluminum"
-    data_import_hist["mode"]= "M1"
-    data_import_hist["time"]= "year"
-    data_import_hist["unit"]= "-"
-    data_import_hist.drop(['Variable'], axis=1, inplace = True)
+    data_import_hist["mode"] = "M1"
+    data_import_hist["time"] = "year"
+    data_import_hist["unit"] = "-"
+    data_import_hist.drop(["Variable"], axis=1, inplace=True)
     data_import_hist.reset_index(drop=True)
 
     # For exports this corresponds to: MANUFACTURING|Outputs|Exports
 
-    data_export = data_trade[data_trade["Variable"]=="MANUFACTURING|Outputs|Exports"]
-    data_export_hist = data_export[data_export['year_act']<=2015]
+    data_export = data_trade[data_trade["Variable"] == "MANUFACTURING|Outputs|Exports"]
+    data_export_hist = data_export[data_export["year_act"] <= 2015]
     data_export_hist["technology"] = "export_aluminum"
-    data_export_hist["mode"]= "M1"
-    data_export_hist["time"]= "year"
-    data_export_hist["unit"]= "-"
-    data_export_hist.drop(['Variable'], axis=1, inplace = True)
+    data_export_hist["mode"] = "M1"
+    data_export_hist["time"] = "year"
+    data_export_hist["unit"] = "-"
+    data_export_hist.drop(["Variable"], axis=1, inplace=True)
     data_export_hist.reset_index(drop=True)
 
-    results['historical_activity'].append(data_export_hist)
-    results['historical_activity'].append(data_import_hist)
+    results["historical_activity"].append(data_export_hist)
+    results["historical_activity"].append(data_import_hist)
 
     # Add data as historical_new_capacity for export
 
-    for r in data_export_hist['node_loc'].unique():
-        df_hist_cap = data_export_hist[data_export_hist['node_loc'] == r]
-        df_hist_cap = df_hist_cap.sort_values(by='year_act')
-        df_hist_cap['value_difference'] = df_hist_cap['value'].diff()
-        df_hist_cap['value_difference'] = df_hist_cap['value_difference'].fillna(df_hist_cap['value'])
-        df_hist_cap['historical_new_capacity'] = df_hist_cap['value_difference'] / 5
+    for r in data_export_hist["node_loc"].unique():
+        df_hist_cap = data_export_hist[data_export_hist["node_loc"] == r]
+        df_hist_cap = df_hist_cap.sort_values(by="year_act")
+        df_hist_cap["value_difference"] = df_hist_cap["value"].diff()
+        df_hist_cap["value_difference"] = df_hist_cap["value_difference"].fillna(
+            df_hist_cap["value"]
+        )
+        df_hist_cap["historical_new_capacity"] = df_hist_cap["value_difference"] / 5
 
-        df_hist_cap = df_hist_cap.drop(columns=['mode', 'time', 'value',
-                                    'value_difference'], axis = 1)
-        df_hist_cap.rename(columns= {"historical_new_capacity":"value",
-                                     "year_act": "year_vtg"}, inplace = True)
+        df_hist_cap = df_hist_cap.drop(
+            columns=["mode", "time", "value", "value_difference"], axis=1
+        )
+        df_hist_cap.rename(
+            columns={"historical_new_capacity": "value", "year_act": "year_vtg"},
+            inplace=True,
+        )
 
-        df_hist_cap['value'] = df_hist_cap['value'].apply(lambda x: 0 if x < 0 else x)
-        df_hist_cap["unit"]= "-"
-        results['historical_new_capacity'].append(df_hist_cap)
+        df_hist_cap["value"] = df_hist_cap["value"].apply(lambda x: 0 if x < 0 else x)
+        df_hist_cap["unit"] = "-"
+        results["historical_new_capacity"].append(df_hist_cap)
 
     # For China fixing 2020 and 2025 values
 
-    import_chn = data_import[(data_import['year_act']==2020)
-                            & (data_import['node_loc']=="R12_CHN")]
+    import_chn = data_import[
+        (data_import["year_act"] == 2020) & (data_import["node_loc"] == "R12_CHN")
+    ]
 
-    export_chn = data_export[(data_export['year_act']==2020)
-                            & (data_export['node_loc']=="R12_CHN")]
+    export_chn = data_export[
+        (data_export["year_act"] == 2020) & (data_export["node_loc"] == "R12_CHN")
+    ]
 
     # Merge the DataFrames on 'node_loc' and 'year'
-    merged_df = pd.merge(import_chn, export_chn, on=['node_loc', 'year_act'],
-                                            suffixes=('_import', '_export'))
+    merged_df = pd.merge(
+        import_chn,
+        export_chn,
+        on=["node_loc", "year_act"],
+        suffixes=("_import", "_export"),
+    )
 
     # Subtract the 'value_import' from 'value_export' to get net export value
-    merged_df['value'] = merged_df['value_export'] - merged_df['value_import']
+    merged_df["value"] = merged_df["value_export"] - merged_df["value_import"]
 
     # Select relevant columns for the final DataFrame
-    bound_act_net_export_chn = merged_df[['node_loc', 'year_act', 'value']]
+    bound_act_net_export_chn = merged_df[["node_loc", "year_act", "value"]]
 
     bound_act_net_export_chn["technology"] = "export_aluminum"
-    bound_act_net_export_chn["mode"]= "M1"
-    bound_act_net_export_chn["time"]= "year"
-    bound_act_net_export_chn["unit"]= "-"
+    bound_act_net_export_chn["mode"] = "M1"
+    bound_act_net_export_chn["time"] = "year"
+    bound_act_net_export_chn["unit"] = "-"
 
     bound_act_net_export_chn_2025 = bound_act_net_export_chn.replace({2020: 2025})
 
-    results['bound_activity_up'].append(bound_act_net_export_chn)
-    results['bound_activity_lo'].append(bound_act_net_export_chn)
-    results['bound_activity_up'].append(bound_act_net_export_chn_2025)
-    results['bound_activity_lo'].append(bound_act_net_export_chn_2025)
+    results["bound_activity_up"].append(bound_act_net_export_chn)
+    results["bound_activity_lo"].append(bound_act_net_export_chn)
+    results["bound_activity_up"].append(bound_act_net_export_chn_2025)
+    results["bound_activity_lo"].append(bound_act_net_export_chn_2025)
 
     return {par_name: pd.concat(dfs) for par_name, dfs in results.items()}

message_ix_models/model/material/data_cement.py

@@ -4,7 +4,11 @@
 from message_ix import make_df
 
 from message_ix_models import ScenarioInfo
-from message_ix_models.model.material.data_util import read_sector_data, read_timeseries, calculate_ini_new_cap
+from message_ix_models.model.material.data_util import (
+    read_sector_data,
+    read_timeseries,
+    calculate_ini_new_cap,
+)
 from message_ix_models.model.material.material_demand import material_demand_calc
 from message_ix_models.model.material.util import get_ssp_from_context, read_config
 from message_ix_models.util import (
@@ -14,6 +18,7 @@
     same_node,
 )
 
+
 def gen_mock_demand_cement(scenario):
     s_info = ScenarioInfo(scenario)
     nodes = s_info.N
@@ -167,7 +172,7 @@ def gen_data_cement(scenario, dry_run=False):
 
     # Techno-economic assumptions
     # TEMP: now add cement sector as well
-    data_cement = read_sector_data(scenario, "cement", "Global_cement_MESSAGE.xlsx" )
+    data_cement = read_sector_data(scenario, "cement", "Global_cement_MESSAGE.xlsx")
     # Special treatment for time-dependent Parameters
     data_cement_ts = read_timeseries(scenario, "cement", "Global_cement_MESSAGE.xlsx")
     tec_ts = set(data_cement_ts.technology)  # set of tecs with var_cost
@@ -345,7 +350,9 @@ def gen_data_cement(scenario, dry_run=False):
 
     # Create external demand param
     parname = "demand"
-    df_demand = material_demand_calc.derive_demand("cement", scenario, old_gdp=False, ssp=ssp)
+    df_demand = material_demand_calc.derive_demand(
+        "cement", scenario, old_gdp=False, ssp=ssp
+    )
     results[parname].append(df_demand)
 
     # Add CCS as addon
@@ -354,12 +361,12 @@ def gen_data_cement(scenario, dry_run=False):
     technology_1 = ["clinker_dry_cement"]
     df_1 = make_df(
         parname, mode="M1", type_addon="dry_ccs_cement", value=1, unit="-", **common
-    ).pipe(broadcast, node=nodes, technology= technology_1)
+    ).pipe(broadcast, node=nodes, technology=technology_1)
 
     technology_2 = ["clinker_wet_cement"]
     df_2 = make_df(
         parname, mode="M1", type_addon="wet_ccs_cement", value=1, unit="-", **common
-    ).pipe(broadcast, node=nodes, technology= technology_2)
+    ).pipe(broadcast, node=nodes, technology=technology_2)
 
     results[parname].append(df_1)
     results[parname].append(df_2)
@@ -370,12 +377,14 @@ def gen_data_cement(scenario, dry_run=False):
     results["initial_new_capacity_up"] = pd.concat(
         [
             calculate_ini_new_cap(
-                df_demand=df_demand.copy(deep=True), technology="clinker_dry_ccs_cement",
-                material = "cement"
+                df_demand=df_demand.copy(deep=True),
+                technology="clinker_dry_ccs_cement",
+                material="cement",
             ),
             calculate_ini_new_cap(
-                df_demand=df_demand.copy(deep=True), technology="clinker_wet_ccs_cement",
-                material = "cement"
+                df_demand=df_demand.copy(deep=True),
+                technology="clinker_wet_ccs_cement",
+                material="cement",
             ),
         ]
     )