(feature) Add activities_name_to_include in lca conf

appabuild/config/lca.py

@@ -25,6 +25,10 @@ class Model(BaseModel):
         name: name of the yaml file corresponding to the impact model (do not include file extension).
         path: output folder for saving impact model.
         compile: if True, precompute the symbolic expressions needed by Appa Run and store them in the impact model.
+        activities_name_to_include: list of activities name regexes to use as an
+        alternative to the include_in_tree activity flag. Any activity whose name matches
+        one of the regex in the list will be included in the impact model. This disables
+        the include_in_tree activity flag.
         metadata: information about the impact model, meant to help the user of it to better understand the LCA leading to the impact model.
         parameters: information about all free parameters needed by the functional unit of the impact model.
     """
@@ -33,6 +37,7 @@ class Model(BaseModel):
     path: Optional[str] = "."
     compile: bool
     metadata: Optional[ModelMetadata] = None
+    activities_name_to_include: Optional[List[str]] = None
     parameters: Optional[List[ImpactModelParam]] = []
 
     @field_validator("parameters", mode="before")

appabuild/database/databases.py

@@ -13,9 +13,9 @@
 
 import bw2data as bd
 import bw2io as bi
+import lca_algebraic as lcaa
 import yaml
 from apparun.parameters import ImpactModelParams
-from lca_algebraic import resetParams, setForeground
 from lxml.etree import XMLSyntaxError
 from pydantic_core import ValidationError
 
@@ -48,7 +48,7 @@ def execute_at_startup(self) -> None:
         already present in Brightway project.
         :return:
         """
-        resetParams(self.name)
+        return
 
     @abc.abstractmethod
     def import_in_project(self) -> None:
@@ -284,7 +284,7 @@ def find_activities_on_disk(self) -> None:
 
     def execute_at_startup(self):
         if self.name in bd.databases:
-            resetParams(self.name)
+            lcaa.resetParams()
             del bd.databases[self.name]
 
         self.find_activities_on_disk()
@@ -323,4 +323,4 @@ def import_in_project(self) -> None:
             activity.to_bw_format() for activity in self.context.activities
         ]
         bw_database.write(dict(to_write_activities))
-        setForeground(self.name)
+        lcaa.setForeground(self.name)

appabuild/database/user_database_elements.py

@@ -399,7 +399,6 @@ def from_serialized_activity(
                 ]
             )
             > 0
-            and serialized_activity.include_in_tree
         ):
             amount_of_copies = len(
                 [

appabuild/model/builder.py

@@ -8,6 +8,7 @@
 import itertools
 import logging
 import os
+import re
 from typing import List, Optional, Set, Tuple
 
 import bw2data as bd
@@ -72,6 +73,7 @@ def __init__(
         functional_unit: str,
         methods: list[str],
         output_path: str,
+        activities_name_to_include: Optional[List[str]] = None,
         metadata: Optional[ModelMetadata] = ModelMetadata(),
         parameters: Optional[ImpactModelParams] = None,
     ):
@@ -84,6 +86,10 @@ def __init__(
         :param metadata: information about the LCA behind the impact model.
             Should contain, or link to all information necessary for the end user's
             proper understanding of the impact model.
+        :param activities_name_to_include: list of activities name regexes to use as an
+        alternative to the include_in_tree activity flag. Any activity whose name matches
+        one of the regex in the list will be included in the impact model. This disables
+        the include_in_tree activity flag.
         :param parameters: an ImpactModelParam object will have to be created for each
         parameter used in all used datasets. See ImpactModelParam attributes to know
         required fields.
@@ -92,6 +98,7 @@ def __init__(
         self.functional_unit = functional_unit
         self.parameters = parameters
         self.methods = methods
+        self.activities_name_to_include = activities_name_to_include
         self.metadata = metadata
         self.output_path = output_path
         self.bw_user_database = bd.Database(self.user_database_name)
@@ -113,6 +120,7 @@ def from_yaml(lca_config_path: str) -> ImpactModelBuilder:
                 lca_config.model.path,
                 lca_config.model.name + ".yaml",
             ),
+            lca_config.model.activities_name_to_include,
             lca_config.model.metadata,
             ImpactModelParams.from_list(lca_config.model.parameters),
         )
@@ -122,7 +130,8 @@ def build_impact_model(
         self, foreground_database: Optional[ForegroundDatabase] = None
     ) -> ImpactModel:
         """
-        Build an Impact Model, the model is a represented as a tree with the functional unit as its root
+        Build an Impact Model, the model is a represented as a tree with the functional
+        unit as its root.
         :param foreground_database: database containing the functional unit
         :return: built impact model.
         """
@@ -259,7 +268,17 @@ def build_tree_node(self, tree_node: ImpactTreeNode):
                     e = f"Found recursive activity: {sub_act.get('name')}"
                     logger.exception(e)
                     raise ForegroundDatabaseError(e)
-                if sub_act.get("include_in_tree"):
+                if self.activities_name_to_include is not None:
+                    regexes = [
+                        re.compile(activity_name_to_include)
+                        for activity_name_to_include in self.activities_name_to_include
+                    ]
+                    sub_act_to_include = any(
+                        regex.match(sub_act.get("name")) for regex in regexes
+                    )
+                else:
+                    sub_act_to_include = sub_act.get("include_in_tree")
+                if sub_act_to_include:
                     amount = _getAmountOrFormula(exch)
                     child_tree_node = tree_node.new_child(
                         name=sub_act["name"],

samples/datasets/user_database/nvidia_ai_gpu/nvidia_gpu_die_manufacturing.yaml

@@ -6,6 +6,7 @@ amount: 1
 parameters:
 - cuda_core
 - architecture
+include_in_tree: True
 comment: "NVIDIA GPU die for Pascal and Maxwell architectures. Number of CUDA cores is used to estimate die area and technology node. Information for modelisation can be found on techpowerup.com (https://www.techpowerup.com/gpu-specs/nvidia-gp108.g808 for example). We assume dies are manufactured in Taiwan. We assume (arbitrarily) a defect density of ?"
 exchanges:
 - database: user_database

tests/data/cmd_build/nvidia_ai_gpu_chip_lca_conf_no_include_in_tree.yaml

@@ -0,0 +1,66 @@
+scope:
+  fu:
+    name: 'nvidia_ai_gpu_chip'
+    database: "user_database"
+  methods:
+    - "EFV3_CLIMATE_CHANGE"
+model:
+  path: "."
+  name: "nvidia_ai_gpu_chip"
+  compile: True
+  activities_name_to_include:
+    - "nvidia_ai_gpu_chip"
+    - ".*_manufacturing_?[0-9]*$" #the _?[0-9]* is important here as some activities downstream the switch in nvidia_gpu_die_manufacturing will be duplicated with a "_{number_of_duplicate}" suffix
+  metadata:
+    author:
+      name: Maxime PERALTA
+      organization: CEA
+      mail: maxime.peralta@cea.fr
+    reviewer:
+      name: Mathias TORCASO
+      organization: CEA
+      mail:
+    report:
+      link: https://appalca.github.io/
+      description: "A mock example of Appa LCA's impact model corresponding to a fictive AI chip accelerator based on NVIDIA GPU."
+      date: 07/10/2025
+      version: "1"
+      license: proprietary
+      appabuild_version: "0.3.6"
+  parameters:
+    - name: cuda_core
+      type: float
+      default:
+        architecture:
+          Maxwell: 1344
+          Pascal: 1280
+      min: 256
+      max: 4096
+    - name: architecture
+      type: enum
+      default: Maxwell
+      weights:
+        Maxwell: 1
+        Pascal: 1
+    - name: usage_location
+      type: enum
+      default: FR
+      weights:
+        FR: 1
+        EU: 1
+    - name: energy_per_inference
+      type: float
+      default:
+        architecture: #we model energy by inference using a model of the TDP function of the number of cuda cores. We suppose that TDP power corresponds to running inferences at 90fps.
+          Maxwell: "0.0878*cuda_core*1000/(90*3600)"
+          Pascal: "0.0679*cuda_core*1000/(90*3600)"
+      pm_perc: 0.2
+    - name: lifespan
+      type: float
+      default: 2.0
+      pm: 1
+    - name: inference_per_day
+      type: float
+      default: "30*3600*8" #30fps 8 hours a day
+      min: 0
+      max: 86400000

tests/end_to_end/test_cmd_build.py

@@ -1,5 +1,5 @@
 """
-Test that the command appabuild lca build works correctly with a simple example.
+Test that the command appabuild lca build works correctly with a simple example using CLI.
 """
 
 import os

tests/end_to_end/test_python_build.py

@@ -0,0 +1,73 @@
+"""
+Test that the command appabuild lca build works correctly with a simple example using
+Python API.
+"""
+
+import os
+
+import pytest
+import yaml
+from apparun.impact_model import ImpactModel
+from typer.testing import CliRunner
+
+from appabuild.cli.lca import build
+from tests import DATA_DIR
+
+runner = CliRunner()
+
+
+def test_build_with_include_in_tree():
+    appaconf_file = os.path.join(DATA_DIR, "cmd_build", "appalca_conf_wo_ei.yaml")
+    conf_file = os.path.join(DATA_DIR, "cmd_build", "nvidia_ai_gpu_chip_lca_conf.yaml")
+    expected_file = os.path.join(
+        DATA_DIR, "cmd_build", "nvidia_ai_gpu_chip_expected.yaml"
+    )
+    expected_scores_file = os.path.join(DATA_DIR, "cmd_build", "expected_scores.yaml")
+    build(appaconf_file, conf_file)
+
+    # Check the generated impact model is the same as expected
+    with open(expected_file, "r") as stream:
+        expected = yaml.safe_load(stream)
+
+    with open("nvidia_ai_gpu_chip.yaml", "r") as stream:
+        value = yaml.safe_load(stream)
+
+    assert expected == value, "result file not the same as expected file "
+
+    # Check that the generated impact model can be run by Appa Run
+    model = ImpactModel.from_yaml("nvidia_ai_gpu_chip.yaml")
+    scores = model.get_nodes_scores()
+    scores = {
+        score.name: score.lcia_scores.scores["EFV3_CLIMATE_CHANGE"][0]
+        for score in scores
+    }
+    with open(expected_scores_file, "r") as stream:
+        expected_scores = yaml.safe_load(stream)
+
+    assert scores == pytest.approx(expected_scores)
+    os.remove("nvidia_ai_gpu_chip.yaml")
+
+
+def test_build_wo_include_in_tree():
+    appaconf_file = os.path.join(DATA_DIR, "cmd_build", "appalca_conf_wo_ei.yaml")
+    conf_file = os.path.join(
+        DATA_DIR, "cmd_build", "nvidia_ai_gpu_chip_lca_conf_no_include_in_tree.yaml"
+    )
+    expected_scores_file = os.path.join(DATA_DIR, "cmd_build", "expected_scores.yaml")
+    build(appaconf_file, conf_file)
+
+    model = ImpactModel.from_yaml("nvidia_ai_gpu_chip.yaml")
+    assert len(model.tree.unnested_descendants) == 11
+
+    # Check that the generated impact model can be run by Appa Run
+    scores = model.get_nodes_scores()
+    scores = {
+        score.name: score.lcia_scores.scores["EFV3_CLIMATE_CHANGE"][0]
+        for score in scores
+    }
+    with open(expected_scores_file, "r") as stream:
+        expected_scores = yaml.safe_load(stream)
+
+    assert scores["nvidia_ai_gpu_chip"] == pytest.approx(
+        expected_scores["nvidia_ai_gpu_chip"]
+    )