diff --git a/src/hdf5_ops.py b/src/hdf5_ops.py
index 133f0f3..883fc07 100644
--- a/src/hdf5_ops.py
+++ b/src/hdf5_ops.py
@@ -10,6 +10,7 @@ import numpy as np
 import utils.g5505_utils as utils
 import logging
 import datetime
+import yaml
 
 class HDF5DataOpsManager():
     def __init__(self, file_path, mode = 'r+') -> None:
@@ -227,4 +228,154 @@ def get_parent_child_relationships(file: h5py.File):
 
     file.visititems(node_visitor)
 
-    return nodes, parent, values 
\ No newline at end of file
+    return nodes, parent, values 
+
+
+def to_serializable_dtype(value):
+
+    """Transform value's dtype into  YAML/JSON compatible dtype
+
+    Parameters
+    ----------
+    value : _type_
+        _description_
+
+    Returns
+    -------
+    _type_
+        _description_
+    """
+    try:
+        if isinstance(value, np.generic):
+            if np.issubdtype(value.dtype, np.bytes_):
+                value = value.decode('utf-8')
+            elif np.issubdtype(value.dtype, np.unicode_):
+                value = str(value)
+            elif np.issubdtype(value.dtype, np.number):
+                value = float(value)
+            else:
+                print('Yaml-compatible data-type was not found. Value has been set to NaN.')
+                value = np.nan    
+        elif isinstance(value, np.ndarray):  
+            # Handling structured array types (with fields)
+            if value.dtype.names:
+                value = {field: to_serializable_dtype(value[field]) for field in value.dtype.names}
+            else:
+                # Handling regular array NumPy types                         
+                if np.issubdtype(value.dtype, np.bytes_):
+                    value = [item.decode('utf-8') for item in value] if len(value) > 1 else value[0].decode('utf-8')
+                elif np.issubdtype(value.dtype, np.unicode_):
+                    value = [str(item) for item in value] if len(value) > 1 else str(value[0])
+                elif np.issubdtype(value.dtype, np.integer):
+                    value = [int(item) for item in value] if len(value) > 1 else int(value[0])
+                elif np.issubdtype(value.dtype, np.floating):
+                    value = [float(item) for item in value] if len(value) > 1 else float(value[0])
+                else:
+                    print('Yaml-compatible data-type was not found. Value has been set to NaN.')
+                    value = np.nan
+
+    except Exception as e:
+        print(f'Error converting value: {e}. Value has been set to NaN.')
+        value = np.nan
+
+    return value
+
+def is_structured_array(attr_val):
+    if isinstance(attr_val,np.ndarray):
+        return True if attr_val.dtype.names is not None else False
+    else: 
+        return False
+
+def construct_attributes_dict(attrs_obj):
+
+    attr_dict = {}
+    for key, value in attrs_obj.items():
+        attr_dict[key] = {}
+        if not key in ['file_list','filtered_file_list']:
+            
+            if is_structured_array(value):
+               #for subattr in value.dtype.names:
+                   #attr_dict[key][subattr] = make_dtype_yaml_compatible(value[subattr])  
+                attr_dict[key] = to_serializable_dtype(value)             
+            else:
+                attr_dict[key] = {"rename_as" : key,
+                              "value" : to_serializable_dtype(value)
+                             }
+
+            #if isinstance(value,str):
+            #    value.replace('\\','\\\\')
+
+
+    return attr_dict
+
+def print_metadata(name, obj, folder_depth, yaml_dict): 
+
+    # TODO: should we enable deeper folders ?
+    if len(obj.name.split('/')) <= folder_depth:
+        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 = {}
+
+            attr_dict = construct_attributes_dict(obj.attrs)
+
+            #for key, value in obj.attrs.items():
+                #print (key, value.dtype)
+            #    if key == 'Layout':
+            #        print(value)
+
+            #    if not key in ['file_list','filtered_file_list']:
+
+            #        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)
+
+            yaml_dict[obj.name] = group_dict
+        elif isinstance(obj, h5py.Dataset):            
+            parent_name = '/'.join(name_to_list[:-1])
+            yaml_dict[parent_name]["datasets"][name_head] = {"rename_as": name_head ,"attributes": construct_attributes_dict(obj.attrs)}
+            #print(yaml.dump(group_dict,sort_keys=False))
+
+        #elif len(obj.name.split('/')) == 3:
+        #    print(yaml.dump())
+
+def take_yml_snapshot_of_hdf5_file(input_filename_path,folder_depth: int = 4):
+
+    yaml_dict = {}
+
+    output_filename_tail, ext = os.path.splitext(input_filename_path)
+
+    with h5py.File(input_filename_path,'r') as f:
+
+        attrs_dict = construct_attributes_dict(f.attrs)
+        yaml_dict[f.name] =  {"name": f.name, "attributes": attrs_dict, "datasets":{}}
+        f.visititems(lambda name, obj: print_metadata(name,obj,folder_depth,yaml_dict))
+
+    #with open(output_filename_tail+".json","w") as yaml_file:
+    #    json_obj = json.dumps(yaml_dict,indent=4,sort_keys=False,)
+    #    yaml_file.write(json_obj)
+
+    with open(output_filename_tail+".yaml","w") as yaml_file:
+        yaml_output = yaml.dump(yaml_dict,sort_keys=False)
+        #for key in yaml_dict:
+        #    yaml_output = yaml.dump(yaml_dict[key],sort_keys=False)
+        yaml_file.write(yaml_output )
+
+    return output_filename_tail+".yaml"
+
+
+
+