Black reformating

polca · Mar 11, 2024 · 7b907bb · 7b907bb
1 parent 32a85ff
commit 7b907bb
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Showing 3 changed files with 36 additions and 23 deletions.
diff --git a/dev/timing.py b/dev/timing.py
@@ -9,11 +9,15 @@
     p.calculate(
         methods=[
             "RELICS - metals extraction - Lithium",
-            "RELICS - metals extraction - Molybdenum"
+            "RELICS - metals extraction - Molybdenum",
+        ],
+        regions=[
+            "WEU",
         ],
-        regions=["WEU",],
         scenarios=[scenario],
-        years=[2020, ],
+        years=[
+            2020,
+        ],
         variables=[
             v
             for v in p.scenarios.coords["variables"].values

diff --git a/pathways/pathways.py b/pathways/pathways.py
@@ -189,9 +189,7 @@ def fetch_inventories_locations(A_index: Dict[str, Tuple[str, str, str]]) -> Lis
     :return: List of locations.
     """
 
-    return list(set([
-        act[3] for act in A_index
-    ]))
+    return list(set([act[3] for act in A_index]))
 
 
 def generate_A_indices(A_index, reverse_classifications, lca_results_coords):
@@ -212,7 +210,6 @@ def generate_A_indices(A_index, reverse_classifications, lca_results_coords):
     return np.swapaxes(acts_idx, 0, 1)
 
 
-
 def process_region(data: Tuple) -> Union[None, Dict[str, Any]]:
     global impact_categories
     (
@@ -252,13 +249,22 @@ def process_region(data: Tuple) -> Union[None, Dict[str, Any]]:
     if lcia_matrix is not None:
         impact_categories = lca_results_coords["impact_category"].values
         target = np.zeros(
-            (len(act_categories), len(list(vars_idx)), len(locations), len(impact_categories))
+            (
+                len(act_categories),
+                len(list(vars_idx)),
+                len(locations),
+                len(impact_categories),
+            )
         )
     else:
         if flows is not None:
-            target = np.zeros((len(act_categories), len(list(vars_idx)), len(locations), len(flows)))
+            target = np.zeros(
+                (len(act_categories), len(list(vars_idx)), len(locations), len(flows))
+            )
         else:
-            target = np.zeros((len(act_categories), len(list(vars_idx)), len(locations), len(B_index)))
+            target = np.zeros(
+                (len(act_categories), len(list(vars_idx)), len(locations), len(B_index))
+            )
 
     for v, variable in enumerate(variables):
         idx, dataset = vars_idx[variable]["idx"], vars_idx[variable]["dataset"]
@@ -432,11 +438,13 @@ def create_dict_for_specific_model(row, model):
                 # Create the dictionary structure for this row for the specific model
                 dict_structure = {
                     key: {
-                        "dataset": [{
-                            "name": row["dataset name"],
-                            "reference product": row["dataset reference product"],
-                            "unit": row["unit"],
-                        }],
+                        "dataset": [
+                            {
+                                "name": row["dataset name"],
+                                "reference product": row["dataset reference product"],
+                                "unit": row["unit"],
+                            }
+                        ],
                         "scenario variable": row[model],
                     }
                 }
@@ -654,7 +662,7 @@ def calculate(
                     )
 
                     # Remove contribution from activities in other activities
-                    #A = remove_double_counting(A, vars_info)
+                    # A = remove_double_counting(A, vars_info)
 
                     # check unclassified activities
                     check_unclassified_activities(A_index, self.classifications)

diff --git a/pathways/utils.py b/pathways/utils.py
@@ -230,7 +230,9 @@ def create_lca_results_array(
 
 
 def display_results(
-    lca_results: Union[xr.DataArray, None], cutoff: float = 0.001, interpolate: bool = False
+    lca_results: Union[xr.DataArray, None],
+    cutoff: float = 0.001,
+    interpolate: bool = False,
 ) -> xr.DataArray:
     if lca_results is None:
         raise ValueError("No results to display")
@@ -246,22 +248,21 @@ def display_results(
 
     # Step 2: Aggregate values below the cutoff across the 'act_category' dimension
     # Summing all values below the cutoff for each combination of other dimensions
-    below_cutoff = lca_results.where(lca_results <= cutoff).sum(dim='act_category')
+    below_cutoff = lca_results.where(lca_results <= cutoff).sum(dim="act_category")
 
     # Since summing removes the 'act_category', we need to add it back
     # Create a new coordinate for 'act_category' that includes 'other'
-    new_act_category = np.append(lca_results.act_category.values, 'other')
+    new_act_category = np.append(lca_results.act_category.values, "other")
 
     # Create a new DataArray for below-cutoff values with 'act_category' as 'other'
     # This involves broadcasting below_cutoff to match the original array's dimensions but with 'act_category' replaced
-    other_data = below_cutoff.expand_dims({'act_category': ['other']}, axis=0)
+    other_data = below_cutoff.expand_dims({"act_category": ["other"]}, axis=0)
 
     # Step 3: Combine the above-cutoff data with the new 'other' data
     # Concatenate along the 'act_category' dimension
-    combined = xr.concat([above_cutoff, other_data], dim='act_category')
+    combined = xr.concat([above_cutoff, other_data], dim="act_category")
 
     # Ensure the 'act_category' coordinate is updated to include 'other'
-    combined = combined.assign_coords({'act_category': new_act_category})
+    combined = combined.assign_coords({"act_category": new_act_category})
 
     return combined
-