apply automated formatting

rules/build_sector.smk

@@ -710,8 +710,6 @@ rule build_transport_demand:
         "../scripts/build_transport_demand.py"
 
 
-
-
 rule build_district_heat_share:
     params:
         sector=config["sector"],
@@ -719,7 +717,8 @@ rule build_district_heat_share:
         district_heat_share=RESOURCES + "district_heat_share.csv",
         clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
     output:
-        district_heat_share=RESOURCES + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
+        district_heat_share=RESOURCES
+        + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
     threads: 1
     resources:
         mem_mb=1000,
@@ -782,8 +781,10 @@ rule prepare_sector_network:
         simplified_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}.csv",
         industrial_demand=RESOURCES
         + "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
-        hourly_heat_demand_total=RESOURCES + "hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc",
-	district_heat_share=RESOURCES + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
+        hourly_heat_demand_total=RESOURCES
+        + "hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc",
+        district_heat_share=RESOURCES
+        + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
         temp_soil_total=RESOURCES + "temp_soil_total_elec_s{simpl}_{clusters}.nc",
         temp_soil_rural=RESOURCES + "temp_soil_rural_elec_s{simpl}_{clusters}.nc",
         temp_soil_urban=RESOURCES + "temp_soil_urban_elec_s{simpl}_{clusters}.nc",

rules/solve_myopic.smk

@@ -11,8 +11,10 @@ rule build_existing_heating_distribution:
     input:
         existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
         clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
-        clustered_pop_energy_layout=RESOURCES + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
-        district_heat_share=RESOURCES + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
+        clustered_pop_energy_layout=RESOURCES
+        + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
+        district_heat_share=RESOURCES
+        + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
     output:
         existing_heating_distribution=RESOURCES
         + "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",

scripts/add_existing_baseyear.py

@@ -409,15 +409,13 @@ def add_heating_capacities_installed_before_baseyear(
     # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
     # TODO start from original file
 
-    existing_heating = pd.read_csv(snakemake.input.existing_heating_distribution,
-                                   header=[0,1],
-                                   index_col=0)
-
+    existing_heating = pd.read_csv(
+        snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
+    )
 
     techs = existing_heating.columns.get_level_values(1).unique()
 
     for name in existing_heating.columns.get_level_values(0).unique():
-
         name_type = "central" if name == "urban central" else "decentral"
 
         nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
@@ -451,7 +449,9 @@ def add_heating_capacities_installed_before_baseyear(
                 efficiency=efficiency,
                 capital_cost=costs.at[costs_name, "efficiency"]
                 * costs.at[costs_name, "fixed"],
-                p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")] * ratio / costs.at[costs_name, "efficiency"],
+                p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")]
+                * ratio
+                / costs.at[costs_name, "efficiency"],
                 build_year=int(grouping_year),
                 lifetime=costs.at[costs_name, "lifetime"],
             )
@@ -513,10 +513,11 @@ def add_heating_capacities_installed_before_baseyear(
                 efficiency2=costs.at["oil", "CO2 intensity"],
                 capital_cost=costs.at["decentral oil boiler", "efficiency"]
                 * costs.at["decentral oil boiler", "fixed"],
-                p_nom= (
+                p_nom=(
                     existing_heating.loc[nodes, (name, "oil boiler")]
                     * ratio
-                    / costs.at["decentral oil boiler", "efficiency"]),
+                    / costs.at["decentral oil boiler", "efficiency"]
+                ),
                 build_year=int(grouping_year),
                 lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
             )

scripts/build_district_heat_share.py

@@ -6,12 +6,10 @@
 Build district heat shares at each node, depending on investment year.
 """
 
-import pandas as pd
-
-from prepare_sector_network import get
-
 import logging
 
+import pandas as pd
+from prepare_sector_network import get
 
 logger = logging.getLogger(__name__)
 
@@ -29,11 +27,11 @@ if __name__ == "__main__":
 
     investment_year = int(snakemake.wildcards.planning_horizons[-4:])
 
-    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
-                             index_col=0)
+    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
 
-    district_heat_share = pd.read_csv(snakemake.input.district_heat_share,
-                                      index_col=0).squeeze()
+    district_heat_share = pd.read_csv(
+        snakemake.input.district_heat_share, index_col=0
+    ).squeeze()
 
     # make ct-based share nodal
     district_heat_share = district_heat_share.loc[pop_layout.ct]
@@ -62,17 +60,22 @@ if __name__ == "__main__":
 
     # difference of max potential and today's share of district heating
     diff = (urban_fraction * central_fraction) - dist_fraction_node
-    progress = get(snakemake.config["sector"]["district_heating"]["progress"], investment_year)
+    progress = get(
+        snakemake.config["sector"]["district_heating"]["progress"], investment_year
+    )
     dist_fraction_node += diff * progress
     logger.info(
         f"Increase district heating share by a progress factor of {progress:.2%} "
         f"resulting in new average share of {dist_fraction_node.mean():.2%}"
     )
 
-    df = pd.DataFrame({
-        "original district heat share": district_heat_share,
-        "district fraction of node": dist_fraction_node,
-        "urban fraction": urban_fraction
-    }, dtype=float)
+    df = pd.DataFrame(
+        {
+            "original district heat share": district_heat_share,
+            "district fraction of node": dist_fraction_node,
+            "urban fraction": urban_fraction,
+        },
+        dtype=float,
+    )
 
     df.to_csv(snakemake.output.district_heat_share)

scripts/build_energy_totals.py

@@ -569,21 +569,24 @@ def build_energy_totals(countries, eurostat, swiss, idees):
 
 
 def build_district_heat_share(countries, idees):
-
     # district heating share
-    district_heat = idees[
-        ["derived heat residential", "derived heat services"]
-    ].sum(axis=1)
-    total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(axis=1)
+    district_heat = idees[["derived heat residential", "derived heat services"]].sum(
+        axis=1
+    )
+    total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(
+        axis=1
+    )
 
-    district_heat_share = district_heat/total_heat
+    district_heat_share = district_heat / total_heat
 
     district_heat_share = district_heat_share.reindex(countries)
 
     # Missing district heating share
-    dh_share = pd.read_csv(
-        snakemake.input.district_heat_share, index_col=0, usecols=[0, 1]
-    ).div(100).squeeze()
+    dh_share = (
+        pd.read_csv(snakemake.input.district_heat_share, index_col=0, usecols=[0, 1])
+        .div(100)
+        .squeeze()
+    )
     # make conservative assumption and take minimum from both data sets
     district_heat_share = pd.concat(
         [district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1

scripts/build_existing_heating_distribution.py

@@ -6,9 +6,9 @@
 Builds table of existing heat generation capacities for initial planning
 horizon.
 """
-import pandas as pd
-import numpy as np
 import country_converter as coco
+import numpy as np
+import pandas as pd
 
 cc = coco.CountryConverter()
 
@@ -16,9 +16,9 @@ cc = coco.CountryConverter()
 def build_existing_heating():
     # retrieve existing heating capacities
 
-    existing_heating = pd.read_csv(snakemake.input.existing_heating,
-                                   index_col=0,
-                                   header=0)
+    existing_heating = pd.read_csv(
+        snakemake.input.existing_heating, index_col=0, header=0
+    )
 
     # data for Albania, Montenegro and Macedonia not included in database
     existing_heating.loc["Albania"] = np.nan
@@ -33,24 +33,25 @@ def build_existing_heating():
     existing_heating.index = cc.convert(existing_heating.index, to="iso2")
 
     # coal and oil boilers are assimilated to oil boilers
-    existing_heating["oil boiler"] = existing_heating["oil boiler"] + existing_heating["coal boiler"]
+    existing_heating["oil boiler"] = (
+        existing_heating["oil boiler"] + existing_heating["coal boiler"]
+    )
     existing_heating.drop(["coal boiler"], axis=1, inplace=True)
 
     # distribute technologies to nodes by population
-    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
-                             index_col=0)
+    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
 
     nodal_heating = existing_heating.loc[pop_layout.ct]
     nodal_heating.index = pop_layout.index
     nodal_heating = nodal_heating.multiply(pop_layout.fraction, axis=0)
 
-    district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
-                                     index_col=0)
+    district_heat_info = pd.read_csv(snakemake.input.district_heat_share, index_col=0)
     dist_fraction = district_heat_info["district fraction of node"]
     urban_fraction = district_heat_info["urban fraction"]
 
-    energy_layout = pd.read_csv(snakemake.input.clustered_pop_energy_layout,
-                                index_col=0)
+    energy_layout = pd.read_csv(
+        snakemake.input.clustered_pop_energy_layout, index_col=0
+    )
 
     uses = ["space", "water"]
     sectors = ["residential", "services"]
@@ -58,39 +59,51 @@ def build_existing_heating():
     nodal_sectoral_totals = pd.DataFrame(dtype=float)
 
     for sector in sectors:
-        nodal_sectoral_totals[sector] = energy_layout[[f"total {sector} {use}" for use in uses]].sum(axis=1)
-
-    nodal_sectoral_fraction = nodal_sectoral_totals.div(nodal_sectoral_totals.sum(axis=1),
-                                                        axis=0)
+        nodal_sectoral_totals[sector] = energy_layout[
+            [f"total {sector} {use}" for use in uses]
+        ].sum(axis=1)
 
+    nodal_sectoral_fraction = nodal_sectoral_totals.div(
+        nodal_sectoral_totals.sum(axis=1), axis=0
+    )
 
     nodal_heat_name_fraction = pd.DataFrame(dtype=float)
 
     nodal_heat_name_fraction["urban central"] = dist_fraction
 
     for sector in sectors:
+        nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[
+            sector
+        ] * (1 - urban_fraction)
+        nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[
+            sector
+        ] * (urban_fraction - dist_fraction)
 
-        nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[sector]*(1 - urban_fraction)
-        nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[sector]*(urban_fraction - dist_fraction)
+    nodal_heat_name_tech = pd.concat(
+        {
+            name: nodal_heating.multiply(nodal_heat_name_fraction[name], axis=0)
+            for name in nodal_heat_name_fraction.columns
+        },
+        axis=1,
+        names=["heat name", "technology"],
+    )
 
-
-    nodal_heat_name_tech = pd.concat({name : nodal_heating .multiply(nodal_heat_name_fraction[name],
-                                                                     axis=0) for name in nodal_heat_name_fraction.columns},
-                                     axis=1,
-                                     names=["heat name","technology"])
-
-
-    #move all ground HPs to rural, all air to urban
+    # move all ground HPs to rural, all air to urban
 
     for sector in sectors:
-        nodal_heat_name_tech[(f"{sector} rural","ground heat pump")] += (nodal_heat_name_tech[("urban central","ground heat pump")]*nodal_sectoral_fraction[sector]
-                                                                         + nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")])
-        nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")] = 0.
+        nodal_heat_name_tech[(f"{sector} rural", "ground heat pump")] += (
+            nodal_heat_name_tech[("urban central", "ground heat pump")]
+            * nodal_sectoral_fraction[sector]
+            + nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")]
+        )
+        nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")] = 0.0
 
-        nodal_heat_name_tech[(f"{sector} urban decentral","air heat pump")] += nodal_heat_name_tech[(f"{sector} rural","air heat pump")]
-        nodal_heat_name_tech[(f"{sector} rural","air heat pump")] = 0.
+        nodal_heat_name_tech[
+            (f"{sector} urban decentral", "air heat pump")
+        ] += nodal_heat_name_tech[(f"{sector} rural", "air heat pump")]
+        nodal_heat_name_tech[(f"{sector} rural", "air heat pump")] = 0.0
 
-    nodal_heat_name_tech[("urban central","ground heat pump")] = 0.
+    nodal_heat_name_tech[("urban central", "ground heat pump")] = 0.0
 
     nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)
 

scripts/build_hourly_heat_demand.py

@@ -48,15 +48,15 @@ if __name__ == "__main__":
         )
 
         if use == "space":
-            heat_demand[f"{sector} {use}"] = daily_space_heat_demand * intraday_year_profile
+            heat_demand[f"{sector} {use}"] = (
+                daily_space_heat_demand * intraday_year_profile
+            )
         else:
             heat_demand[f"{sector} {use}"] = intraday_year_profile
 
-    heat_demand = pd.concat(heat_demand,
-                            axis=1,
-                            names = ["sector use", "node"])
+    heat_demand = pd.concat(heat_demand, axis=1, names=["sector use", "node"])
 
-    heat_demand.index.name="snapshots"
+    heat_demand.index.name = "snapshots"
 
     ds = heat_demand.stack().to_xarray()
 

scripts/prepare_sector_network.py

@@ -1639,8 +1639,11 @@ def add_land_transport(n, costs):
 
 
 def build_heat_demand(n):
-
-    heat_demand_shape = xr.open_dataset(snakemake.input.hourly_heat_demand_total).to_dataframe().unstack(level=1)
+    heat_demand_shape = (
+        xr.open_dataset(snakemake.input.hourly_heat_demand_total)
+        .to_dataframe()
+        .unstack(level=1)
+    )
 
     sectors = ["residential", "services"]
     uses = ["water", "space"]
@@ -1648,7 +1651,6 @@ def build_heat_demand(n):
     heat_demand = {}
     electric_heat_supply = {}
     for sector, use in product(sectors, uses):
-
         name = f"{sector} {use}"
 
         heat_demand[name] = (
@@ -1678,8 +1680,7 @@ def add_heat(n, costs):
 
     heat_demand = build_heat_demand(n)
 
-    district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
-                                     index_col=0)
+    district_heat_info = pd.read_csv(snakemake.input.district_heat_share, index_col=0)
     dist_fraction = district_heat_info["district fraction of node"]
     urban_fraction = district_heat_info["urban fraction"]
 
@@ -1718,7 +1719,6 @@ def add_heat(n, costs):
         # 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
         solar_thermal = options["solar_cf_correction"] * solar_thermal / 1e3
 
-
     for name in heat_systems:
         name_type = "central" if name == "urban central" else "decentral"