Upgrade Group Tree module package

backend_py/primary/primary/routers/flow_network/router.py

@@ -45,7 +45,7 @@ async def get_realization_flow_network(
         summary_access=summary_access,
         realization=realization,
         summary_frequency=summary_frequency,
-        node_types=unique_node_types,
+        selected_node_types=unique_node_types,
         flow_network_mode=NetworkModeOptions.SINGLE_REAL,
     )
 
@@ -57,7 +57,7 @@ async def get_realization_flow_network(
         dated_networks,
         edge_metadata,
         node_metadata,
-    ) = await network_assembler.create_dated_networks_and_metadata_lists()
+    ) = network_assembler.create_dated_networks_and_metadata_lists()
     create_data_time_ms = timer.lap_ms()
 
     LOGGER.info(

backend_py/primary/primary/services/flow_network_assembler/_assembler_performance_times.py

@@ -0,0 +1,42 @@
+import logging
+from dataclasses import dataclass
+
+LOGGER = logging.getLogger(__name__)
+
+
+@dataclass
+# Dataclass needs to save a bunch of timestamps. Many attributes is okay here, as splitting it would be more cumbersome
+# pylint: disable-next=too-many-instance-attributes
+class PerformanceTimes:
+    """Simple utility class to store performance timer results for different internal method calls"""
+
+    init_sumo_data: int = 0
+    init_summary_vector_list: int = 0
+    fetch_grouptree_df: int = 0
+    init_grouptree_df_model: int = 0
+    create_filtered_dataframe: int = 0
+    init_summary_vector_data_table: int = 0
+    create_node_classifications: int = 0
+    create_network_summary_vectors_info: int = 0
+
+    # Unused for logging for now, but available if needed
+    build_and_verify_vectors_of_interest: int = 0
+    create_well_node_classifications: int = 0
+
+    def log_sumo_download_times(self) -> None:
+        # Log download from Sumo times
+        LOGGER.info(
+            f"Total time to fetch data from Sumo: {self.init_sumo_data + self.init_summary_vector_data_table}ms, "
+            f"Get summary vector list in: {self.init_summary_vector_list}ms, "
+            f"Get group tree table in: {self.fetch_grouptree_df}ms, "
+            f"Get summary vectors in: {self.init_summary_vector_data_table}ms"
+        )
+
+    def log_structure_init_times(self) -> None:
+        # Log initialization of data structures times
+        LOGGER.info(
+            f"Initialize GroupTreeModel in: {self.init_grouptree_df_model}ms, "
+            f"Create filtered dataframe in: {self.create_filtered_dataframe}ms, "
+            f"Create node classifications in: {self.create_node_classifications}ms, "
+            f"Create group tree summary vectors info in: {self.create_network_summary_vectors_info}ms"
+        )

backend_py/primary/primary/services/flow_network_assembler/_utils.py

@@ -1,4 +1,6 @@
 import logging
+import pandas as pd
+import numpy as np
 
 from primary.services.sumo_access.group_tree_types import TreeType
 from .flow_network_types import (
@@ -9,11 +11,18 @@
     NodeSummaryVectorsInfo,
     EdgeOrNode,
     DataType,
+    NodeType,
 )
 
 
 LOGGER = logging.getLogger(__name__)
 
+NODE_TYPE_ENUM_TO_STRING_MAPPING = {
+    NodeType.INJ: "Injector",
+    NodeType.PROD: "Producer",
+    NodeType.OTHER: "Other",
+}
+
 
 FIELD_DATATYPE_VECTOR_MAP = {
     DataType.OILRATE: "FOPR",
@@ -255,3 +264,33 @@ def _compute_node_datatypes_for_name_and_keyword(
             datatypes.append(DataType.GASINJRATE)
 
     return datatypes
+
+
+def is_valid_node_type(node_classification: NodeClassification, valid_node_types: set[NodeType]) -> bool:
+    """Returns True if the node classification is a valid node type"""
+    if node_classification.IS_PROD and NodeType.PROD in valid_node_types:
+        return True
+    if node_classification.IS_INJ and NodeType.INJ in valid_node_types:
+        return True
+    if node_classification.IS_OTHER and NodeType.OTHER in valid_node_types:
+        return True
+    return False
+
+
+def create_edge_label_list_from_vfp_table_column(vfp_table_column: pd.Series) -> list[str]:
+    """
+    Creates an edge label list based on the column named "VFP_TABLE".
+
+    If the VFP_TABLE column is not present, the function will raise a ValueError.
+    """
+    if vfp_table_column.empty:
+        raise ValueError("VFP_TABLE column is empty.")
+
+    edge_labels: list[str] = []
+    for vfp_nb in vfp_table_column:
+        if vfp_nb in [None, 9999] or np.isnan(vfp_nb):
+            edge_labels.append("")
+        else:
+            edge_labels.append(f"VFP {int(vfp_nb)}")
+
+    return edge_labels