Merge remote-tracking branch 'origin/mc' into mc

# Conflicts:
#	pathways/lca.py

dev/timing.py

@@ -5,16 +5,18 @@
 
 p.calculate(
     methods=[
-        'EF v3.1 EN15804 - climate change - global warming potential (GWP100)',
-        'EF v3.1 EN15804 - ecotoxicity: freshwater - comparative toxic unit for ecosystems (CTUe)',
+        "EF v3.1 EN15804 - climate change - global warming potential (GWP100)",
+        "EF v3.1 EN15804 - ecotoxicity: freshwater - comparative toxic unit for ecosystems (CTUe)",
+    ],
+    regions=[
+        "CH",
     ],
-    regions=["CH",],
     scenarios=p.scenarios.pathway.values.tolist(),
     years=[
         2050,
     ],
     variables=[v for v in p.scenarios.coords["variables"].values if v.startswith("FE")],
     use_distributions=20,
     subshares=True,
-    multiprocessing=False
+    multiprocessing=False,
 )

pathways/pathways.py

@@ -367,7 +367,9 @@ def calculate(
                         results.update(
                             {
                                 (model, scenario, year): result
-                                for year, result in zip(years, p.map(_calculate_year, args))
+                                for year, result in zip(
+                                    years, p.map(_calculate_year, args)
+                                )
                             }
                         )
                 else:

pathways/stats.py

@@ -7,7 +7,7 @@
 import numpy as np
 import pandas as pd
 import statsmodels.api as sm
-from openpyxl import load_workbook, Workbook
+from openpyxl import Workbook, load_workbook
 from SALib.analyze import delta
 
 
@@ -313,8 +313,14 @@ def run_GSA_OLS(methods: list, export_path: Path):
     X_base = data.drop(columns=["Iteration", "Year"] + methods)
     X_base = sm.add_constant(X_base)
     corr_matrix = X_base.corr().abs()
-    upper_triangle = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
-    high_correlation = [column for column in upper_triangle.columns if any(upper_triangle[column] > 0.95)]
+    upper_triangle = corr_matrix.where(
+        np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)
+    )
+    high_correlation = [
+        column
+        for column in upper_triangle.columns
+        if any(upper_triangle[column] > 0.95)
+    ]
 
     if high_correlation:
         print(f"OLS: High multicollinearity detected in columns: {high_correlation}")

setup.py

@@ -55,7 +55,7 @@ def package_files(directory):
         "platformdirs",
         "fs",
         "statsmodels",
-        "SALib"
+        "SALib",
     ],
     url="https://github.com/polca/premise",
     description="Scenario-level LCA of energy systems and transition pathways",