Black reformating

pathways/lca.py

@@ -4,11 +4,11 @@
 """
 
 import logging
+import pickle
 import uuid
 from collections import defaultdict
 from pathlib import Path
 from typing import Any, Dict, List, Tuple
-import pickle
 
 import bw2calc as bc
 import bw_processing as bwp
@@ -25,9 +25,9 @@
 from .lcia import fill_characterization_factors_matrices
 from .stats import (
     create_mapping_sheet,
-    log_uncertainty_values,
     log_results,
     log_subshares,
+    log_uncertainty_values,
     run_GSA_delta,
     run_GSA_OLS,
 )
@@ -410,7 +410,10 @@ def process_region(data: Tuple) -> dict[str, ndarray[Any, dtype[Any]] | list[int
 
         # Save the technosphere indices to disk
         id_technosphere_indices = uuid.uuid4()
-        pickle.dump(lca.technosphere_indices, open(DIR_CACHED_DB / f"{id_technosphere_indices}.pkl", "wb"))
+        pickle.dump(
+            lca.technosphere_indices,
+            open(DIR_CACHED_DB / f"{id_technosphere_indices}.pkl", "wb"),
+        )
 
     # Save the characterization vectors to disk
     id_results_array = uuid.uuid4()

pathways/pathways.py

@@ -5,6 +5,7 @@
 """
 
 import logging
+import pickle
 from collections import defaultdict
 from multiprocessing import Pool, cpu_count
 from typing import Any, List, Optional
@@ -14,17 +15,16 @@
 import pyprind
 import xarray as xr
 import yaml
-import pickle
 
 from .data_validation import validate_datapackage
 from .filesystem_constants import DATA_DIR, DIR_CACHED_DB, STATS_DIR, USER_LOGS_DIR
 from .lca import _calculate_year, get_lca_matrices
 from .lcia import get_lcia_method_names
 from .stats import (
     create_mapping_sheet,
-    log_uncertainty_values,
     log_results,
     log_subshares,
+    log_uncertainty_values,
     run_GSA_delta,
 )
 from .subshares import generate_samples
@@ -519,7 +519,9 @@ def _fill_in_result_array(
                             ]
                         )
 
-                        uncertainty_indices = uncertainty_parameters[data["uncertainty_params"]]
+                        uncertainty_indices = uncertainty_parameters[
+                            data["uncertainty_params"]
+                        ]
                         uncertainty_vals = uncertainty_values[data["uncertainty_vals"]]
 
                         df_uncertainty_values = pd.concat(
@@ -553,20 +555,34 @@ def _fill_in_result_array(
 
                     indices = tehnosphere_indices[data["technosphere_indices"]]
                     # only keep indices which are also present in uncertainty_indices
-                    indices = {k: v for k, v in indices.items() if v in set(uncertainty_indices.flatten().tolist())}
+                    indices = {
+                        k: v
+                        for k, v in indices.items()
+                        if v in set(uncertainty_indices.flatten().tolist())
+                    }
                     df_technosphere_indices = create_mapping_sheet(indices=indices)
 
-                    df_sum_impacts.to_excel(writer, sheet_name="Total impacts", index=False)
-                    df_uncertainty_values.to_excel(writer, sheet_name="Monte Carlo values", index=False)
-                    df_technology_shares.to_excel(writer, sheet_name="Technology shares", index=False)
-                    df_technosphere_indices.to_excel(writer, sheet_name="Indices mapping", index=False)
+                    df_sum_impacts.to_excel(
+                        writer, sheet_name="Total impacts", index=False
+                    )
+                    df_uncertainty_values.to_excel(
+                        writer, sheet_name="Monte Carlo values", index=False
+                    )
+                    df_technology_shares.to_excel(
+                        writer, sheet_name="Technology shares", index=False
+                    )
+                    df_technosphere_indices.to_excel(
+                        writer, sheet_name="Indices mapping", index=False
+                    )
 
                     df_GSA = run_GSA_delta(
                         total_impacts=df_sum_impacts,
                         uncertainty_values=df_uncertainty_values,
-                        technology_shares=df_technology_shares
+                        technology_shares=df_technology_shares,
+                    )
+                    df_GSA.to_excel(
+                        writer, sheet_name="Global Sensitivity Analysis", index=False
                     )
-                    df_GSA.to_excel(writer, sheet_name="Global Sensitivity Analysis", index=False)
 
                     print(f"Statistical analysis: {export_path.resolve()}")
 

pathways/stats.py

@@ -98,7 +98,8 @@ def log_double_accounting(
 
 
 def log_subshares(
-    shares: dict, region: str,
+    shares: dict,
+    region: str,
 ) -> pd.DataFrame:
     """
     Create a pandas DataFrame where the keys of shares are the columns
@@ -113,9 +114,9 @@ def log_subshares(
 
 
 def log_uncertainty_values(
-        region: str,
-        uncertainty_indices: np.array,
-        uncertainty_values: np.array,
+    region: str,
+    uncertainty_indices: np.array,
+    uncertainty_values: np.array,
 ) -> pd.DataFrame:
     """
     Create a pandas DataFrame with the region and uncertainty indices as columns,
@@ -160,21 +161,21 @@ def log_results(
     return df[["iteration", "region"] + methods]
 
 
-def create_mapping_sheet(
-    indices: dict
-) -> pd.DataFrame:
+def create_mapping_sheet(indices: dict) -> pd.DataFrame:
     """
     Create a mapping sheet for the activities with uncertainties.
     """
 
     # Converting the dictionary into a pandas DataFrame
-    df = pd.DataFrame(indices.items(), columns=['Index', 'Value'])
+    df = pd.DataFrame(indices.items(), columns=["Index", "Value"])
 
     # Split the 'Index' column into four separate columns
-    df[['Name', 'Product', 'Unit', 'Region']] = pd.DataFrame(df['Index'].tolist(), index=df.index)
+    df[["Name", "Product", "Unit", "Region"]] = pd.DataFrame(
+        df["Index"].tolist(), index=df.index
+    )
 
     # Drop the now unnecessary 'Index' column
-    df.drop(columns=['Index'], inplace=True)
+    df.drop(columns=["Index"], inplace=True)
 
     return df
 
@@ -274,8 +275,12 @@ def run_GSA_delta(
     # merge uncertainty_values and technology_shares
     # based on "iteration" and "region" columns
 
-    df_parameters = uncertainty_values.merge(technology_shares, on=["iteration", "region"])
-    parameters = [param for param in df_parameters.columns if param not in ["iteration", "region"]]
+    df_parameters = uncertainty_values.merge(
+        technology_shares, on=["iteration", "region"]
+    )
+    parameters = [
+        param for param in df_parameters.columns if param not in ["iteration", "region"]
+    ]
 
     problem = {
         "num_vars": len(parameters),