Allow a list of consolidations to be unwound

for update_or_create_hierarchy_from_dataframe function

Currently update_or_create_hierarchy_from_dataframe() allows complete unwinding of hierarchy, but not specific unwinding for consolidations.

The code change would allow a list of elements to be specified a parameter so selective unwinding is allowed.

This change also replace the param unwind with unwind_all, used for same purpose. Function is updated to be backward compatible so, using unwind would map the value to unwind_all and continue unwinding the entire hierarchy when True.

Author: Mr-SabyasachiBose

TM1py/Services/HierarchyService.py

@@ -427,8 +427,10 @@ def update_or_create_hierarchy_from_dataframe(
             verify_unique_elements: bool = False,
             verify_edges: bool = True,
             element_type_column: str = 'ElementType',
-            unwind: bool = False,
-            update_attribute_types: bool = False):
+            unwind_all: bool = False,
+            unwind_consolidation: list = None,
+            update_attribute_types: bool = False,
+            **kwargs):
         """ Update or Create a hierarchy based on a dataframe, while never deleting existing elements.
 
         :param dimension_name:
@@ -455,8 +457,10 @@ def update_or_create_hierarchy_from_dataframe(
             Abort early if element names are not unique
         :param verify_edges:
             Abort early if edges contain a circular reference
-        :param unwind: bool
+        :param unwind_all: bool
             Unwind hierarch before creating new edges
+        :param unwind_consolidation: list
+            Unwind a list specific consolidations in hierarch before creating new edges, if unwind_all is true, this list is ignored
         :param update_attribute_types: bool
             If True, function will delete and recreate attributes when a type change is requested.
             By default, function will not delete attributes.
@@ -485,6 +489,15 @@ def update_or_create_hierarchy_from_dataframe(
         if len(alias_columns) > 0:
             self._validate_alias_uniqueness(df=df[[element_column, *alias_columns]])
 
+        # backward compatibility for unwind, the value for unwind would be assinged to unwind_all. expected type is bool
+        if "unwind" in kwargs:
+            unwind_all = kwargs["unwind"]
+
+        # verify unwind_consolidation is a list
+        if unwind_consolidation is not None:
+            if not isinstance(unwind_consolidation, list):
+                raise ValueError(f"Inconsistent Type for 'unwind_consolidation': expected type-> list, current type-> '{type(unwind_consolidation)}'")
+
         # identify and sort level columns
         level_columns = []
         level_weight_columns = []
@@ -634,8 +647,23 @@ def update_or_create_hierarchy_from_dataframe(
                 sum_numeric_duplicates=False,
                 use_blob=True)
 
-        if unwind:
-            self.remove_all_edges(dimension_name, hierarchy_name)
+        if unwind_all:
+            self.remove_all_edges(dimension_name=dimension_name, hierarchy_name=hierarchy_name)
+        else:
+            if unwind_consolidation is not None:
+                all_edges = CaseAndSpaceInsensitiveTuplesDict()
+                for elem in unwind_consolidation:
+                    if self.elements.exists(dimension_name=dimension_name, hierarchy_name=hierarchy_name, element_name=elem):
+                        temp_edges = self.elements.get_edges_under_consolidation(
+                                            dimension_name=dimension_name, 
+                                            hierarchy_name=hierarchy_name, 
+                                            consolidation=elem)
+                        all_edges.join(temp_edges)
+                self.elements.delete_edges(
+                            dimension_name=dimension_name, 
+                            hierarchy_name=hierarchy_name,
+                            edges=all_edges,
+                            use_ti=self.is_admin)
 
         edges = CaseAndSpaceInsensitiveTuplesDict()
         for element_name, *record in df[[element_column, *level_columns, *level_weight_columns]].itertuples(

TM1py/Utils/Utils.py

@@ -1175,6 +1175,14 @@ def __eq__(self, other):
     def copy(self):
         return CaseAndSpaceInsensitiveTuplesDict(self._store.values())
 
+    # Join two dictionaries together
+    def join(self, other):
+        if isinstance(other, CaseAndSpaceInsensitiveTuplesDict):
+            for key,value in other.items():
+                self._store[tuple([lower_and_drop_spaces(item) for item in key])] = (key, value)
+        else:
+             raise ValueError("Other object is not of type CaseAndSpaceInsensitiveTuplesDict")
+
     def __repr__(self):
         return str(dict(self.items()))