Address review comments.

python/lsst/analysis/tools/tasks/gatherResourceUsage.py

@@ -39,8 +39,8 @@
 
 import numpy as np
 import pandas as pd
-from lsst.daf.butler import Butler, DatasetRef
-from lsst.daf.butler.core.utils import globToRegex
+from lsst.daf.butler import Butler, DatasetRef, DatasetType
+from lsst.daf.butler.utils import globToRegex
 from lsst.pex.config import Field, ListField
 from lsst.pipe.base import (
     Instrument,
@@ -568,9 +568,9 @@ def __init__(
         # Start by querying for metadata datasets, since we'll need to know
         # which dataset types exist in the input collections in order to
         # build the pipeline.
-        base_dataset_type_filter = re.compile(r"\w+_metadata")
         input_dataset_types: Any
         if not dataset_type_names:
+            base_dataset_type_filter = re.compile(r"\w+_metadata")
             input_dataset_types = base_dataset_type_filter
         else:
             input_dataset_types = [globToRegex(expr) for expr in dataset_type_names]
@@ -586,25 +586,11 @@ def __init__(
             input_metadata_dataset_type = results.parentDatasetType
             refs_for_type = set(results)
             if refs_for_type:
-                if (m := re.fullmatch(r"^(\w+)_metadata$", input_metadata_dataset_type.name)) is None:
-                    continue
-                elif "gatherResourceUsage" in input_metadata_dataset_type.name:
-                    continue
-                else:
-                    input_task_label = m.group(1)
-                gather_task_label = f"{input_task_label}_gatherResourceUsage"
+                gather_task_label, gather_dataset_type_name = self._add_gather_task(
+                    pipeline_graph, input_metadata_dataset_type
+                )
                 metadata_refs[gather_task_label] = refs_for_type
-                gather_dataset_type_name = f"{input_task_label}_resource_usage"
-                gather_config = GatherResourceUsageConfig()
-                gather_config.dimensions = input_metadata_dataset_type.dimensions.names
-                gather_config.connections.input_metadata = input_metadata_dataset_type.name
-                gather_config.connections.output_table = gather_dataset_type_name
                 consolidate_config.input_names.append(gather_dataset_type_name)
-                pipeline_graph.add_task(
-                    label=gather_task_label,
-                    task_class=GatherResourceUsageTask,
-                    config=gather_config,
-                )
         pipeline_graph.add_task(
             task_class=ConsolidateResourceUsageTask,
             config=consolidate_config,
@@ -631,6 +617,48 @@ def __init__(
                 gather_inputs_for_task.append(dataset_key)
             self.gather_inputs[gather_task_label] = gather_inputs_for_task
 
+    @classmethod
+    def _add_gather_task(
+        cls, pipeline_graph: PipelineGraph, input_metadata_dataset_type: DatasetType
+    ) -> tuple[str, str]:
+        """Add a single configuration of `GatherResourceUsageTask` to a
+        pipeline graph.
+
+        Parameters
+        ----------
+        pipeline_graph : `lsst.pipe.base.PipelineGraph`
+            Pipeline graph to modify in-place.
+        input_metadata_dataset_type : `lsst.daf.butler.DatasetType`
+            Dataset type for the task's input dataset, which is the metadata
+            output of the task whose resource usage information is being
+            extracted.
+
+        Returns
+        -------
+        gather_task_label : `str`
+            Label of the new task in the pipeline.
+        gather_dataset_type_name : `str
+            Name of the task's output table dataset type.
+        """
+        if (m := re.fullmatch(r"^(\w+)_metadata$", input_metadata_dataset_type.name)) is None:
+            return
+        elif "gatherResourceUsage" in input_metadata_dataset_type.name:
+            return
+        else:
+            input_task_label = m.group(1)
+        gather_task_label = f"{input_task_label}_gatherResourceUsage"
+        gather_dataset_type_name = f"{input_task_label}_resource_usage"
+        gather_config = GatherResourceUsageConfig()
+        gather_config.dimensions = input_metadata_dataset_type.dimensions.names
+        gather_config.connections.input_metadata = input_metadata_dataset_type.name
+        gather_config.connections.output_table = gather_dataset_type_name
+        pipeline_graph.add_task(
+            label=gather_task_label,
+            task_class=GatherResourceUsageTask,
+            config=gather_config,
+        )
+        return gather_task_label, gather_dataset_type_name
+
     def process_subgraph(self, subgraph: PipelineGraph) -> QuantumGraphSkeleton:
         skeleton = QuantumGraphSkeleton(subgraph.tasks.keys())
         consolidate_inputs = []
@@ -689,10 +717,7 @@ def make_argument_parser(cls) -> argparse.ArgumentParser:
             "--dataset-types",
             type=str,
             action="extend",
-            help=(
-                "Glob-style patterns for input metadata dataset types.  If a pattern matches a "
-                "non-metadata dataset type, non-metadata matches will be ignored."
-            ),
+            help="Glob-style patterns for input metadata dataset types.",
         )
         parser.add_argument(
             "--where",