diff --git a/src/hdf5_vis.py b/src/hdf5_vis.py
index 4bcef73..9ca2511 100644
--- a/src/hdf5_vis.py
+++ b/src/hdf5_vis.py
@@ -67,156 +67,3 @@ def display_group_hierarchy_on_a_treemap(filename: str):
     #pio.write_image(fig,file_name + ".png",width=800,height=600,format='png')
 
 #
-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"
-
-
-
-