Moves simple is_decision_node method into utilities and reuses it in …

…another utility. Adds test.

improver/categorical/decision_tree.py

@@ -53,6 +53,7 @@
     day_night_map,
     expand_nested_lists,
     get_parameter_names,
+    is_decision_node,
     is_variable,
     update_daynight,
     update_tree_thresholds,
@@ -164,24 +165,6 @@ def __repr__(self) -> str:
         """Represent the configured plugin instance as a string."""
         return "<ApplyDecisionTree start_node={}>".format(self.start_node)
 
-    @staticmethod
-    def _is_decision_node(key: str, query: Dict[str, Dict[str, Union[str, List]]]) -> bool:
-        """
-        Determine whether a given node is a decision node.
-        The meta node has a key of "meta", leaf nodes have a query key of "leaf", everything
-        else is a decision node.
-
-        Args:
-            key:
-                Decision name ("meta" indicates a non-decision node)
-            query:
-                Dict where key "leaf" indicates a non-decision node
-
-        Returns:
-            True if query represents a decision node
-        """
-        return key != "meta" and "leaf" not in query.keys()
-
     def prepare_input_cubes(
         self, cubes: CubeList
     ) -> Tuple[CubeList, Optional[List[str]]]:
@@ -215,7 +198,7 @@ def prepare_input_cubes(
         optional_node_data_missing = []
         missing_data = []
         for key, query in self.queries.items():
-            if not self._is_decision_node(key, query):
+            if not is_decision_node(key, query):
                 continue
             diagnostics = get_parameter_names(
                 expand_nested_lists(query, "diagnostic_fields")

improver/categorical/utilities.py

@@ -205,6 +205,24 @@ def update_daynight(cube: Cube, day_night: Dict) -> Cube:
     return cube_day_night
 
 
+def is_decision_node(key: str, query: Dict[str, Any]) -> bool:
+    """
+    Determine whether a given node is a decision node.
+    The meta node has a key of "meta", leaf nodes have a query key of "leaf", everything
+    else is a decision node.
+
+    Args:
+        key:
+            Decision name ("meta" indicates a non-decision node)
+        query:
+            Dict where key "leaf" indicates a non-decision node
+
+    Returns:
+        True if query represents a decision node
+    """
+    return key != "meta" and "leaf" not in query.keys()
+
+
 def interrogate_decision_tree(decision_tree: Dict[str, Dict[str, Any]]) -> str:
     """
     Obtain a list of necessary inputs from the decision tree as it is currently
@@ -223,8 +241,8 @@ def interrogate_decision_tree(decision_tree: Dict[str, Dict[str, Any]]) -> str:
     """
     # Diagnostic names and threshold values.
     requirements = {}
-    for query in decision_tree.values():
-        if "diagnostic_fields" not in query.keys():
+    for key, query in decision_tree.items():
+        if not is_decision_node(key, query):
             continue
         diagnostics = get_parameter_names(
             expand_nested_lists(query, "diagnostic_fields")

improver_tests/categorical/test_utilities.py

@@ -49,6 +49,7 @@
     expand_nested_lists,
     get_parameter_names,
     interrogate_decision_tree,
+    is_decision_node,
     update_daynight,
     update_tree_thresholds,
 )
@@ -718,5 +719,15 @@ def test_day_night_map():
     assert expected == result
 
 
+@pytest.mark.parametrize(
+    "name, node, expected",
+    (("anything", {}, True), ("meta", {}, False), ("a_leaf", {"leaf": 0}, False)),
+)
+def test_is_decision_node(name, node, expected):
+    """Tests that we can correctly distinguish between decision nodes and other nodes"""
+    result = is_decision_node(name, node)
+    assert result == expected
+
+
 if __name__ == "__main__":
     unittest.main()