Merge branch 'main' of https://github.com/brightway-lca/brightway2-calc

Author: cmutel

CHANGES.md

@@ -1,5 +1,9 @@
 # `bw2calc` Changelog
 
+## 2.0.DEV23 (2024-09-18)
+
+* Allow `MethodConfig` as an input to `MultiLCA`
+
 ## 2.0.DEV22 (2024-09-18)
 
 * Add `MethodConfig` to `__init__.py` for better UX

bw2calc/__init__.py

@@ -9,7 +9,7 @@
     "MultiLCA",
 ]
 
-__version__ = "2.0.DEV22"
+__version__ = "2.0.DEV23"
 
 
 import platform

bw2calc/multi_lca.py

@@ -46,8 +46,8 @@ class MultiLCA(LCABase):
     ----------
     demands : dict[str, dict[int, float]]
         The demands for which the LCA will be calculated. The keys identify functional unit sets.
-    method_config : dict
-        Dictionary satisfying the `MethodConfig` specification.
+    method_config : dict | MethodConfig
+        Dictionary satisfying the `MethodConfig` specification or `MethodConfig` instance.
     data_objs : list[bw_processing.Datapackage]
         List of `bw_processing.Datapackage` objects. Should include data for all needed matrices.
     remapping_dicts : dict[str, dict]
@@ -80,7 +80,7 @@ class MultiLCA(LCABase):
     def __init__(
         self,
         demands: dict[str, dict[int, float]],
-        method_config: dict,
+        method_config: Union[dict, MethodConfig],
         data_objs: Iterable[Union[Path, AbstractFileSystem, bwp.DatapackageBase]],
         remapping_dicts: Optional[Iterable[dict]] = None,
         log_config: Optional[dict] = None,
@@ -91,6 +91,10 @@ def __init__(
     ):
         # Validation checks
         DemandsValidator(demands=demands)
+        if isinstance(method_config, MethodConfig):
+            method_config = {
+                key: value for key, value in method_config.model_dump().items() if value is not None
+            }
         MethodConfig(**method_config)
 
         self.demands = demands

tests/multi_lca.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pytest
 
-from bw2calc import MultiLCA
+from bw2calc import MethodConfig, MultiLCA
 from bw2calc.utils import get_datapackage
 
 # Technosphere
@@ -47,6 +47,16 @@ def config():
     }
 
 
+@pytest.fixture
+def method_config():
+    return MethodConfig(
+        impact_categories=[
+            ("first", "category"),
+            ("second", "category"),
+        ]
+    )
+
+
 @pytest.fixture
 def func_units():
     return {
@@ -127,6 +137,16 @@ def test_single_demand(dps, config):
     assert mlca.scores[(("second", "category"), "γ")] == 3 * 10
 
 
+def test_single_demand_method_config(dps, method_config):
+    single_func_unit = {"γ": {100: 1}}
+    mlca = MultiLCA(demands=single_func_unit, method_config=method_config, data_objs=dps)
+    mlca.lci()
+    mlca.lcia()
+
+    assert mlca.scores[(("first", "category"), "γ")] == 8 + 3
+    assert mlca.scores[(("second", "category"), "γ")] == 3 * 10
+
+
 def test_normalization(dps, func_units):
     config = {
         "impact_categories": [
@@ -528,7 +548,7 @@ def test_monte_carlo_multiple_iterations_selective_use_in_list_comprehension(dps
         assert np.unique(lst).shape == (10,)
 
 
-def test_bug_108(dps, config, func_units):
+def test_bug_108(dps, func_units):
     # https://github.com/brightway-lca/brightway2-calc/issues/108
     config = {
         "impact_categories": [
@@ -574,3 +594,39 @@ def test_bug_108(dps, config, func_units):
         == 3 * (3 * 10 + 1 * 10) * 84
     )
     assert mlca.scores[(("w", "1"), ("n", "1"), ("first", "category"), "γ")] == 3 * 42
+
+
+def test_pass_full_methodconfig(dps, func_units):
+    method_config = MethodConfig(
+        impact_categories=[
+            ("first", "category"),
+            ("second", "category"),
+        ],
+        normalizations={
+            ("n", "1"): [("first", "category")],
+            ("n", "2"): [("second", "category")],
+        },
+        weightings={
+            ("w", "1"): [("n", "1")],
+            ("w", "2"): [("n", "2")],
+        },
+    )
+
+    dps.append(
+        get_datapackage(fixture_dir / "multi_lca_simple_normalization.zip"),
+    )
+    dps.append(
+        get_datapackage(fixture_dir / "multi_lca_simple_weighting.zip"),
+    )
+
+    mlca = MultiLCA(demands=func_units, method_config=method_config, data_objs=dps)
+    mlca.lci()
+    mlca.lcia()
+    mlca.normalize()
+    mlca.weight()
+
+    assert (
+        mlca.scores[(("w", "2"), ("n", "2"), ("second", "category"), "ζ")]
+        == 3 * (3 * 10 + 1 * 10) * 84
+    )
+    assert mlca.scores[(("w", "1"), ("n", "1"), ("first", "category"), "γ")] == 3 * 42