Black reformating

polca · Sep 7, 2023 · 6017d34 · 6017d34
1 parent 1654ac0
commit 6017d34
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Showing 2 changed files with 15 additions and 13 deletions.
diff --git a/premise/data_collection.py b/premise/data_collection.py
@@ -593,17 +593,16 @@ def __init__(
             self.fertilizer_use = (
                 fertilizer
                 / crops_production.sel(
-                variables=fertilizer.coords["variables"].values,
-                region=fertilizer.coords["region"].values,
-                year=fertilizer.coords["year"].values,
-            )
+                    variables=fertilizer.coords["variables"].values,
+                    region=fertilizer.coords["region"].values,
+                    year=fertilizer.coords["year"].values,
+                )
             ) * np.where(crops_production > 0, 1, np.nan)
             self.fertilizer_use /= self.fertilizer_use.sel(year=2020)
             self.fertilizer_use = self.fertilizer_use.interpolate_na(
                 dim="year", method="linear", fill_value="extrapolate"
             )
 
-
         self.trsp_cars = get_vehicle_fleet_composition(self.model, vehicle_type="car")
         self.trsp_trucks = get_vehicle_fleet_composition(
             self.model, vehicle_type="truck"
@@ -807,8 +806,6 @@ def __fetch_market_data(
                     .sum(dim="variables")
                 )
 
-
-
         # back-fill nans
         market_data = market_data.bfill(dim="year")
         # fill NaNs with zeros
@@ -1081,8 +1078,7 @@ def __get_iam_production_volumes(
 
         if available_vars:
             data_to_return = data.loc[
-                :, [v for v in flatten(input_vars.values())
-                    if v in available_vars], :
+                :, [v for v in flatten(input_vars.values()) if v in available_vars], :
             ]
 
         else:

diff --git a/premise/fuels.py b/premise/fuels.py
@@ -1788,10 +1788,16 @@ def should_adjust_land_use_change_emissions(
         )
 
     def adjust_fertilizer_use(self, dataset: dict, crop_type: str) -> dict:
-
-        scaling_factor = self.iam_data.fertilizer_use.sel(
-            region=dataset["location"] if dataset["location"] in self.regions else self.ecoinvent_to_iam_loc[dataset["location"]], variables=crop_type
-        ).interp(year=self.year).values.item(0)
+        scaling_factor = (
+            self.iam_data.fertilizer_use.sel(
+                region=dataset["location"]
+                if dataset["location"] in self.regions
+                else self.ecoinvent_to_iam_loc[dataset["location"]],
+                variables=crop_type,
+            )
+            .interp(year=self.year)
+            .values.item(0)
+        )
 
         if np.isnan(scaling_factor):
             scaling_factor = 1.0