New function that creates tree from list of qualified names.

src/_gettsim/interface.py

@@ -2,7 +2,6 @@
 import functools
 import inspect
 import warnings
-from functools import reduce
 from typing import Any, Literal, get_args
 
 import dags
@@ -38,7 +37,7 @@
 )
 from _gettsim.shared import (
     KeyErrorMessage,
-    create_dict_from_list,
+    create_tree_from_list_of_qualified_names,
     format_errors_and_warnings,
     format_list_linewise,
     get_by_path,
@@ -102,8 +101,8 @@ def compute_taxes_and_transfers(  # noqa: PLR0913
         data=data,
     )
     functions_not_overridden, functions_overridden = partition_tree_by_reference_tree(
-        tree_to_split=all_functions,
-        other_tree=data,
+        target_tree=all_functions,
+        reference_tree=data,
     )
     data = _convert_data_to_correct_types(data, functions_overridden)
 
@@ -195,7 +194,7 @@ def build_targets_tree(targets: NestedTargetDict | list[str] | str) -> NestedTar
 
     if isinstance(targets, list):
         # Build targets tree from list of strings
-        targets_tree = _build_targets_tree_from_list(targets)
+        targets_tree = create_tree_from_list_of_qualified_names(targets)
     elif isinstance(targets, dict) and all_leafs_none:
         # Input is already the correct targets tree
         targets_tree = targets
@@ -210,28 +209,6 @@ def build_targets_tree(targets: NestedTargetDict | list[str] | str) -> NestedTar
     return targets_tree
 
 
-def _build_targets_tree_from_list(targets: list[str]) -> NestedTargetDict:
-    """Build a tree from a list of targets.
-
-    Parameters
-    ----------
-    targets : list[str]
-        List of strings with names of functions whose output is actually needed by the
-        user.
-        Example: ["a__b__c", "a__b__d", "a__e"]
-
-    Returns
-    -------
-    tree : dict
-        Dictionary representing the tree.
-        Example: {"a": {"b": {"c": None, "d": None}, "e": None}}
-
-    """
-    paths = [create_dict_from_list(get_path_from_qualified_name(el)) for el in targets]
-    targets_tree = reduce(lambda x, y: merge_nested_dicts(x, y), paths, {})
-    return targets_tree
-
-
 def _build_targets_tree_from_dict(targets: dict[str, dict | str]) -> NestedTargetDict:
     """Build a tree from a dictionary of targets.
 
@@ -257,7 +234,7 @@ def _build_targets_tree_from_dict(targets: dict[str, dict | str]) -> NestedTarge
         elif isinstance(v, str):
             targets[k] = {v: None}
         elif isinstance(v, list):
-            targets[k] = _build_targets_tree_from_list(v)
+            targets[k] = create_tree_from_list_of_qualified_names(v)
 
     return targets
 

src/_gettsim/shared.py

@@ -35,6 +35,28 @@ def format_list_linewise(list_):
     ).format(formatted_list=formatted_list)
 
 
+def create_tree_from_list_of_qualified_names(qualified_names: list[str]) -> dict:
+    """Create a tree from a list of qualified names.
+
+    Parameters
+    ----------
+    qualified_names : list[str]
+        List of qualified names.
+        Example: ["a__b__c", "a__b__d", "a__e"]
+
+    Returns
+    -------
+    dict
+        Tree with qualified names as keys.
+        Example: {"a": {"b": {"c": None, "d": None}, "e": None}}
+    """
+    paths = [
+        create_dict_from_list(get_path_from_qualified_name(el))
+        for el in qualified_names
+    ]
+    return functools.reduce(lambda x, y: merge_nested_dicts(x, y), paths, {})
+
+
 def tree_update(
     tree: dict[str, Any], path: list[str], value: Any = None
 ) -> dict[str, Any]:

src/_gettsim_tests/test_namespaces.py

@@ -2,6 +2,7 @@
 
 import pandas as pd
 
+from _gettsim.aggregation import AggregateByGroupSpec, AggregateByPIDSpec
 from _gettsim.interface import compute_taxes_and_transfers
 from _gettsim.policy_environment import PolicyEnvironment
 from _gettsim_tests.test_data.namespaces.module1 import FUNCTIONS_MODULE1
@@ -27,20 +28,20 @@
 
 AGGREGATION_BY_GROUP_SPEC = {
     "module1": {
-        "group_mean_bg": {
-            "source_col": "f",
-            "aggr": "sum",
-        },
+        "group_mean_bg": AggregateByGroupSpec(
+            source_col="f",
+            aggr="sum",
+        ),
     },
 }
 
 AGGREGATION_BY_PID_SPEC = {
     "module2": {
-        "p_id_aggregation_target": {
-            "p_id_to_aggregate_by": "groupings__some_foreign_keys",
-            "source_col": "g_hh",
-            "aggr": "sum",
-        },
+        "p_id_aggregation_target": AggregateByPIDSpec(
+            p_id_to_aggregate_by="groupings__some_foreign_keys",
+            source_col="g_hh",
+            aggr="sum",
+        ),
     },
 }