Merge remote-tracking branch 'origin/subshares_romain' into subshares…

…_romain

# Conflicts:
#	pathways/stats.py

pathways/lca.py

@@ -14,7 +14,9 @@
 import bw_processing as bwp
 import numpy as np
 import pyprind
-from bw2calc.monte_carlo import MonteCarloLCA ### Dev version coming: removed `MonteCarloLCA` (normal LCA class can do Monte Carlo) and added `IterativeLCA` (different solving strategy)
+from bw2calc.monte_carlo import (  # ## Dev version coming: removed `MonteCarloLCA` (normal LCA class can do Monte Carlo) and added `IterativeLCA` (different solving strategy)
+    MonteCarloLCA,
+)
 from bw2calc.utils import get_datapackage
 from bw_processing import Datapackage
 from numpy import dtype, ndarray
@@ -24,6 +26,13 @@
 
 from .filesystem_constants import DIR_CACHED_DB
 from .lcia import fill_characterization_factors_matrices
+from .stats import (
+    create_mapping_sheet,
+    log_intensities_to_excel,
+    log_results_to_excel,
+    log_subshares_to_excel,
+    run_stats_analysis,
+)
 from .subshares import (
     adjust_matrix_based_on_shares,
     find_technology_indices,
@@ -37,14 +46,6 @@
     read_indices_csv,
 )
 
-from .stats import (
-    log_subshares_to_excel,
-    log_intensities_to_excel,
-    log_results_to_excel,
-    create_mapping_sheet,
-    run_stats_analysis,
-)
-
 # disable warnings
 warnings.filterwarnings("ignore")
 
@@ -172,14 +173,16 @@ def select_filepath(keyword: str, fps):
     return dp, technosphere_inds, biosphere_inds, uncertain_parameters
 
 
-def find_uncertain_parameters(distributions_array: np.ndarray, indices_array: np.ndarray) -> list[tuple[int, int]]:
+def find_uncertain_parameters(
+    distributions_array: np.ndarray, indices_array: np.ndarray
+) -> list[tuple[int, int]]:
     """
     Find the uncertain parameters in the distributions array. They will be used for the stats report
     :param distributions_array:
     :param indices_array:
     :return:
     """
-    uncertain_indices = np.where(distributions_array['uncertainty_type'] != 0)[0]
+    uncertain_indices = np.where(distributions_array["uncertainty_type"] != 0)[0]
     uncertain_parameters = [tuple(indices_array[idx]) for idx in uncertain_indices]
 
     return uncertain_parameters
@@ -295,12 +298,16 @@ def process_region(data: Tuple) -> dict[str, ndarray[Any, dtype[Any]] | list[int
                 matrix_result = (characterization_matrix @ lca.inventory).toarray()
                 temp_results.append(matrix_result)
                 for i in range(len(uncertain_parameters)):
-                        param_key = f'{uncertain_parameters[i][0]}_to_{uncertain_parameters[i][1]}'
-                        param_keys.add(param_key)
-                        if param_key not in params:
-                            params[param_key] = []
-                        value = - lca.technosphere_matrix[uncertain_parameters[i][0], uncertain_parameters[i][1]]
-                        params[param_key].append(value)
+                    param_key = (
+                        f"{uncertain_parameters[i][0]}_to_{uncertain_parameters[i][1]}"
+                    )
+                    param_keys.add(param_key)
+                    if param_key not in params:
+                        params[param_key] = []
+                    value = -lca.technosphere_matrix[
+                        uncertain_parameters[i][0], uncertain_parameters[i][1]
+                    ]
+                    params[param_key].append(value)
 
             results = np.array(temp_results)
             for idx, method in enumerate(methods):
@@ -313,7 +320,6 @@ def process_region(data: Tuple) -> dict[str, ndarray[Any, dtype[Any]] | list[int
 
             log_intensities_to_excel(model, scenario, year, params)
 
-
         d.append(characterized_inventory)
 
         if debug:
@@ -387,9 +393,12 @@ def _calculate_year(args: tuple):
 
     # Try to load LCA matrices for the given model, scenario, and year
     try:
-        bw_datapackage, technosphere_indices, biosphere_indices, uncertain_parameters = get_lca_matrices(
-            filepaths, model, scenario, year
-        )
+        (
+            bw_datapackage,
+            technosphere_indices,
+            biosphere_indices,
+            uncertain_parameters,
+        ) = get_lca_matrices(filepaths, model, scenario, year)
 
     except FileNotFoundError:
         # If LCA matrices can't be loaded, skip to the next iteration
@@ -461,7 +470,14 @@ def _calculate_year(args: tuple):
             logging.info("Calculating LCA results with subshares.")
             shares_indices = find_technology_indices(regions, technosphere_indices, geo)
             correlated_arrays = adjust_matrix_based_on_shares(
-                filepaths, lca, shares_indices, shares, use_distributions, model, scenario, year
+                filepaths,
+                lca,
+                shares_indices,
+                shares,
+                use_distributions,
+                model,
+                scenario,
+                year,
             )
             bw_correlated = get_subshares_matrix(correlated_arrays)
 

pathways/lcia.py

@@ -24,11 +24,11 @@ def get_lcia_method_names():
 
 def format_lcia_method_exchanges(method):
     """
-    Format LCIA method data to fit such structure:
-    (name, unit, type, category, subcategory, amount, uncertainty type, uncertainty amount)
--
-    :param method: LCIA method
-    :return: list of tuples
+        Format LCIA method data to fit such structure:
+        (name, unit, type, category, subcategory, amount, uncertainty type, uncertainty amount)
+    -
+        :param method: LCIA method
+        :return: list of tuples
     """
 
     return {

pathways/pathways.py

@@ -431,7 +431,6 @@ def calculate(
 
         self._fill_in_result_array(results)
 
-
     def _fill_in_result_array(self, results: dict):
 
         # Assuming DIR_CACHED_DB, results, and self.lca_results are already defined

pathways/subshares.py

@@ -4,8 +4,8 @@
 import bw2calc
 import bw_processing
 import bw_processing as bwp
-import pandas as pd
 import numpy as np
+import pandas as pd
 import yaml
 from bw_processing import Datapackage
 from premise.geomap import Geomap
@@ -15,7 +15,6 @@
 from pathways.filesystem_constants import DATA_DIR
 from pathways.utils import get_activity_indices
 
-
 SUBSHARES = DATA_DIR / "technologies_shares.yaml"
 
 logging.basicConfig(

pathways/utils.py

@@ -102,6 +102,7 @@ def load_units_conversion() -> dict:
 
     return data
 
+
 def read_indices_csv(file_path: Path) -> dict[tuple[str, str, str, str], int]:
     """
     Reads a CSV file and returns its contents as a dictionary.
@@ -328,6 +329,7 @@ def clean_cache_directory():
 #
 #     return scenario_data
 
+
 def resize_scenario_data(
     scenario_data: xr.DataArray,
     model: List[str],