Allow running shares without other uncertainties.

Also, allow for a MC seed for reproducibility.

pathways/lca.py

@@ -493,6 +493,7 @@ def _calculate_year(args: tuple):
         shares,
         uncertain_parameters,
         remove_uncertainty,
+        seed
     ) = args
 
     print(f"------ Calculating LCA results for {year}...")
@@ -603,6 +604,7 @@ def _calculate_year(args: tuple):
                 bw_datapackage,
             ],
             use_distributions=True if use_distributions > 0 else False,
+            seed_override=seed
         )
 
         with CustomFilter("(almost) singular matrix"):

pathways/pathways.py

@@ -314,6 +314,7 @@ def calculate(
         use_distributions: int = 0,
         subshares: bool = False,
         remove_uncertainty: bool = False,
+        seed: int = 0,
         multiprocessing: bool = True,
     ) -> None:
         """
@@ -345,6 +346,8 @@ def calculate(
         :type subshares: bool, default is False
         :param remove_uncertainty: Boolean. If True, remove uncertainty from inventory exchanges.
         :type remove_uncertainty: bool, default is False
+        :param seed: Integer. Seed for random number generator.
+        :type seed: int, default is 0
         """
 
         self.scenarios = harmonize_units(self.scenarios, variables)
@@ -430,6 +433,7 @@ def calculate(
             shares = generate_samples(
                 years=self.scenarios.coords["year"].values.tolist(),
                 iterations=use_distributions,
+                seed=seed,
             )
 
         # Iterate over each combination of model, scenario, and year
@@ -461,6 +465,7 @@ def calculate(
                         shares,
                         uncertain_parameters,
                         remove_uncertainty,
+                        seed
                     )
                     for year in years
                 ]

pathways/stats.py

@@ -330,7 +330,11 @@ def log_mc_parameters_to_excel(
 
         indices = tehnosphere_indices[region]
 
-        df_technosphere_indices = create_mapping_sheet(indices=indices)
+        if indices:
+            df_technosphere_indices = create_mapping_sheet(indices=indices)
+            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(
@@ -339,9 +343,7 @@ def log_mc_parameters_to_excel(
         df_technology_shares.to_excel(
             writer, sheet_name="Technology shares", index=False
         )
-        df_technosphere_indices.to_excel(
-            writer, sheet_name="Indices mapping", index=False
-        )
+
 
         print(f"Monte Carlo parameters added to: {export_path.resolve()}")
 

pathways/subshares.py

@@ -268,7 +268,7 @@ def default_dict_factory():
     return defaultdict(dict)
 
 
-def load_and_normalize_shares(ranges: dict, iterations: int) -> dict:
+def load_and_normalize_shares(ranges: dict, iterations: int, seed: int) -> dict:
     """
     Load and normalize shares for parameters to sum to 1 while respecting their specified ranges.
     :param ranges: A dictionary with categories, technologies and market shares data.
@@ -282,7 +282,10 @@ def load_and_normalize_shares(ranges: dict, iterations: int) -> dict:
         for technology, params in technologies.items():
             for y, share in params["share"].items():
                 uncertainty_base = UncertaintyBase.from_dicts(share)
-                random_generator = MCRandomNumberGenerator(uncertainty_base)
+                random_generator = MCRandomNumberGenerator(
+                    params=uncertainty_base,
+                    seed=seed
+                )
                 shares[technology_group][y][technology] = np.squeeze(
                     np.array([random_generator.next() for _ in range(iterations)])
                 )
@@ -375,7 +378,7 @@ def interpolate_for_year(
         shares[technology_group][target_year][technology] = f(target_year)
 
 
-def generate_samples(years: list, iterations: int = 10) -> dict:
+def generate_samples(years: list, iterations: int = 10, seed: int = None) -> dict:
     """
     Generates and adjusts randomly selected shares for parameters to sum to 1
     while respecting their specified ranges, and interpolates missing years.
@@ -385,6 +388,6 @@ def generate_samples(years: list, iterations: int = 10) -> dict:
     :return: A dict with adjusted and interpolated shares for each technology and year.
     """
     ranges = load_subshares()
-    shares = load_and_normalize_shares(ranges, iterations)
+    shares = load_and_normalize_shares(ranges, iterations, seed)
     interpolate_shares(shares, years)
     return shares