Merge remote-tracking branch 'origin/master'

carculator_utils/background_systems.py

@@ -4,7 +4,6 @@
 countries over time.
 """
 
-
 from typing import Dict, List
 
 import numpy as np

carculator_utils/energy_consumption.py

@@ -548,10 +548,8 @@ def motive_energy_per_km(
                 fuel_cell_system_efficiency == 0, 1, fuel_cell_system_efficiency
             )
 
-            _t = (
-                lambda x: x.T
-                if x.shape[-4:] != motive_energy_at_wheels.shape[-4:]
-                else x
+            _t = lambda x: (
+                x.T if x.shape[-4:] != motive_energy_at_wheels.shape[-4:] else x
             )
 
             motive_energy = motive_energy_at_wheels / (

carculator_utils/hot_emissions.py

@@ -284,9 +284,11 @@ def get_hot_emissions(
         if self.non_exhaust is not None:
             non_exhaust = self.non_exhaust.sel(
                 powertrain=[
-                    MAP_PWT[pt]
-                    if MAP_PWT[pt] in self.non_exhaust.powertrain.values
-                    else "BEV"
+                    (
+                        MAP_PWT[pt]
+                        if MAP_PWT[pt] in self.non_exhaust.powertrain.values
+                        else "BEV"
+                    )
                     for pt in emissions.powertrain.values
                 ],
                 euro_class=euro_class,
@@ -401,9 +403,11 @@ def get_hot_emissions(
 
         engine_wear = self.engine_wear.sel(
             powertrain=[
-                MAP_PWT[pt]
-                if MAP_PWT[pt] in self.engine_wear.powertrain.values
-                else "BEV"
+                (
+                    MAP_PWT[pt]
+                    if MAP_PWT[pt] in self.engine_wear.powertrain.values
+                    else "BEV"
+                )
                 for pt in emissions.powertrain.values
             ]
         )

carculator_utils/inventory.py

@@ -721,9 +721,11 @@ def get_B_matrix(self) -> xr.DataArray:
         return xr.DataArray(
             B,
             coords=[
-                [2005, 2010, 2020, 2030, 2040, 2050]
-                if self.scenario != "static"
-                else [2020],
+                (
+                    [2005, 2010, 2020, 2030, 2040, 2050]
+                    if self.scenario != "static"
+                    else [2020]
+                ),
                 np.asarray(list(self.impact_categories.keys()), dtype="object"),
                 np.asarray(list(self.inputs.keys()), dtype="object"),
             ],
@@ -807,26 +809,28 @@ def define_electricity_mix_for_fuel_prep(self) -> np.ndarray:
                 country = self.vm.country
 
             mix = [
-                self.bs.electricity_mix.sel(
-                    country=country,
-                    variable=self.electricity_technologies,
-                )
-                .interp(
-                    year=use_year[y],
-                    kwargs={"fill_value": "extrapolate"},
-                )
-                .mean(axis=0)
-                .values
-                if use_year[y][-1] <= 2050
-                else self.bs.electricity_mix.sel(
-                    country=country,
-                    variable=self.electricity_technologies,
-                )
-                .interp(
-                    year=np.arange(year, 2051), kwargs={"fill_value": "extrapolate"}
+                (
+                    self.bs.electricity_mix.sel(
+                        country=country,
+                        variable=self.electricity_technologies,
+                    )
+                    .interp(
+                        year=use_year[y],
+                        kwargs={"fill_value": "extrapolate"},
+                    )
+                    .mean(axis=0)
+                    .values
+                    if use_year[y][-1] <= 2050
+                    else self.bs.electricity_mix.sel(
+                        country=country,
+                        variable=self.electricity_technologies,
+                    )
+                    .interp(
+                        year=np.arange(year, 2051), kwargs={"fill_value": "extrapolate"}
+                    )
+                    .mean(axis=0)
+                    .values
                 )
-                .mean(axis=0)
-                .values
                 for y, year in enumerate(self.scope["year"])
             ]
 

carculator_utils/model.py

@@ -28,7 +28,6 @@ def load_default_specs_for_fuels():
 
 
 class VehicleModel:
-
     """
     This class represents the entirety of the vehicles considered,
     with useful attributes, such as an array that stores
@@ -1065,11 +1064,15 @@ def check_fuel_blend(self, fuel_blend: dict) -> dict:
             secondary = specs.get(
                 "secondary",
                 {
-                    "type": default_specs[fuel]["secondary"]
-                    if default_specs[fuel]["secondary"] != primary["type"]
-                    else [
-                        f for f in default_specs[fuel]["all"] if f != primary["type"]
-                    ][0],
+                    "type": (
+                        default_specs[fuel]["secondary"]
+                        if default_specs[fuel]["secondary"] != primary["type"]
+                        else [
+                            f
+                            for f in default_specs[fuel]["all"]
+                            if f != primary["type"]
+                        ][0]
+                    ),
                     "share": np.array([1]) - primary["share"],
                 },
             )
@@ -1462,9 +1465,9 @@ def set_noise_emissions(self) -> None:
         ) as stream:
             list_noise_emissions = yaml.safe_load(stream)
 
-        self.array.loc[
-            dict(parameter=list_noise_emissions)
-        ] = nem.get_sound_power_per_compartment()
+        self.array.loc[dict(parameter=list_noise_emissions)] = (
+            nem.get_sound_power_per_compartment()
+        )
 
     def calculate_cost_impacts(self, sensitivity=False) -> xr.DataArray:
         """

carculator_utils/particulates_emissions.py

@@ -2,6 +2,7 @@
 particulates_emissions.py contains ParticulatesEmissionsModel which calculated the amount of
 abrasion particles emitted, given a driving_cycles.
 """
+
 from typing import Union
 
 import numpy as np