Commented out metadata info about group members for a given group. This is to...

Commented out metadata info about group members for a given group. This is to simplify yaml or json representation of the metadata.

src/hdf5_ops.py

@@ -459,50 +459,51 @@ def get_parent_child_relationships(file: h5py.File):
 
 def __print_metadata__(name, obj, folder_depth, yaml_dict): 
 
-    # TODO: should we enable deeper folders ?
-    if len(obj.name.split('/')) <= folder_depth:
+    """
+    Extracts metadata from HDF5 groups and datasets and organizes them into a dictionary with compact representation.
+    
+    Parameters:
+    -----------
+        name (str): Name of the HDF5 object being inspected.
+        obj (h5py.Group or h5py.Dataset): The HDF5 object (Group or Dataset).
+        folder_depth (int): Maximum depth of folders to explore.
+        yaml_dict (dict): Dictionary to populate with metadata.
+    """
+    # Process only objects within the specified folder depth
+    if len(obj.name.split('/')) <= folder_depth: # and ".h5" not in obj.name:
         name_to_list = obj.name.split('/')
-        name_head = name_to_list[-1]
-
-        if isinstance(obj,h5py.Group):
-            #print('name:', obj.name)
-            #print('attributes:', dict(obj.attrs))
-            #attr_dict = {}
-            group_dict = {}
+        name_head = name_to_list[-1] if not name_to_list[-1]=='' else obj.name
 
-            # Convert attribute dict to a YAML/JSON serializable dict
+        if isinstance(obj, h5py.Group):  # Handle groups
+            # Convert attributes to a YAML/JSON serializable format
             attr_dict = {key: utils.to_serializable_dtype(val) for key, val in obj.attrs.items()}
 
-            #for key, value in obj.attrs.items():
-                #print (key, value.dtype)
-            #    if key == 'Layout':
-            #        print(value)
+            # Initialize the group dictionary
+            group_dict = {"name": name_head, "attributes": attr_dict}
 
-            #    if not key in ['file_list','filtered_file_list']:
+            # Handle group members compactly
+            #subgroups = [member_name for member_name in obj if isinstance(obj[member_name], h5py.Group)]
+            #datasets = [member_name for member_name in obj if isinstance(obj[member_name], h5py.Dataset)]
 
-            #        value =  make_dtype_yaml_compatible(value)
-
-            #        attr_dict[key] = {'rename_as' : key,
-            #                          'value' : value
-            #                          }
-            
-            #group_dict[obj.name] = {'name': obj.name, 'attributes': attr_dict}
-            group_dict = {"name": name_head, "attributes": attr_dict, "datasets":{}}
-            #group_dict[obj.name]["name"] = obj.name
-            #group_dict[obj.name]["attributes"] =  attr_dict
-            #group_dict[obj.name]["datasets"] =  {}
-            #print(name)
+            # Summarize groups and datasets
+            #group_dict["content_summary"] = {
+            #    "group_count": len(subgroups),
+            #    "group_preview": subgroups[:3] + (["..."] if len(subgroups) > 3 else []),
+            #    "dataset_count": len(datasets),
+            #    "dataset_preview": datasets[:3] + (["..."] if len(datasets) > 3 else [])
+            #}
 
             yaml_dict[obj.name] = group_dict
-        elif isinstance(obj, h5py.Dataset):   
-            # Convert attribute dict to a YAML/JSON serializable dict
-            attr_dict = {key: utils.to_serializable_dtype(val) for key, val in obj.attrs.items()}         
-            parent_name = '/'.join(name_to_list[:-1])
-            yaml_dict[parent_name]["datasets"][name_head] = {"rename_as": name_head ,"attributes": attr_dict}
-            #print(yaml.dump(group_dict,sort_keys=False))
 
-        #elif len(obj.name.split('/')) == 3:
-        #    print(yaml.dump())
+        elif isinstance(obj, h5py.Dataset):  # Handle datasets
+            # Convert attributes to a YAML/JSON serializable format
+            attr_dict = {key: utils.to_serializable_dtype(val) for key, val in obj.attrs.items()}
+
+            dataset_dict = {"name": name_head, "attributes": attr_dict}
+
+            yaml_dict[obj.name] = dataset_dict
+
+
 
 def serialize_metadata(input_filename_path, folder_depth: int = 4, output_format: str = 'yaml') -> str:
     """
@@ -537,12 +538,13 @@ def serialize_metadata(input_filename_path, folder_depth: int = 4, output_format
     # Open the HDF5 file and extract metadata
     with h5py.File(input_filename_path, 'r') as f:
         # Convert attribute dict to a YAML/JSON serializable dict
-        attrs_dict = {key: utils.to_serializable_dtype(val) for key, val in f.attrs.items()} 
-        yaml_dict[f.name] = {
-            "name": f.name,
-            "attributes": attrs_dict,
-            "datasets": {}
-        }
+        #attrs_dict = {key: utils.to_serializable_dtype(val) for key, val in f.attrs.items()} 
+        #yaml_dict[f.name] = {
+        #    "name": f.name,
+        #    "attributes": attrs_dict,
+        #    "datasets": {}
+        #}
+        __print_metadata__(f.name, f, folder_depth, yaml_dict)
         # Traverse HDF5 file hierarchy and add datasets
         f.visititems(lambda name, obj: __print_metadata__(name, obj, folder_depth, yaml_dict))