diff --git a/src/hdf5_lib.py b/src/hdf5_lib.py
index 8867be9..07e12ce 100644
--- a/src/hdf5_lib.py
+++ b/src/hdf5_lib.py
@@ -477,6 +477,62 @@ def create_hdf5_file_from_filesystem_path(output_filename : str,
 
     return output_filename #, output_yml_filename_path
 
+import os
+#import src.hdf5_lib as h5lib
+import src.g5505_utils as utils
+import h5py
+
+def save_processed_dataframe_to_hdf5(df, annotator, src_hdf5_path, script_date, script_name):
+    """
+    Save processed dataframe columns with annotations to an HDF5 file.
+
+    Parameters:
+        df (pd.DataFrame): DataFrame containing processed time series.
+        annotator (): Annotator object with get_metadata method.
+        hdf5_path (str): Path to the source HDF5 file.
+        script_date (str): Date of the data generation script.
+        script_name (str): Name of the data generation script.
+    """
+    # Convert datetime columns to string
+    datetime_cols = df.select_dtypes(include=['datetime64']).columns
+    df[datetime_cols] = df[datetime_cols].applymap(str)
+
+    # Convert dataframe to structured array
+    icad_data_table = utils.dataframe_to_np_structured_array(df)
+
+    # Get metadata
+    metadata_dict = annotator.get_metadata()
+
+    # Prepare high-level attributes
+    high_level_attributes = {
+        'parent_files': metadata_dict['parent_files'],
+        **metadata_dict['metadata']['sample'],
+        **metadata_dict['metadata']['environment'],
+        **metadata_dict['metadata']['instruments']
+    }
+
+    # Prepare data level attributes
+    data_level_attributes = metadata_dict['metadata']['datasets']
+
+    # Generate output filename
+    parent_file_name = os.path.split(src_hdf5_path)[1]
+    output_filename = f'data_products/processed/fig_{script_date}_{parent_file_name}'
+
+    # Prepare file dictionary
+    file_dict = {
+        'name': script_name,
+        'attributes_dict': high_level_attributes,
+        'datasets': [{
+            'name': "data_table",
+            'data': icad_data_table,
+            'shape': icad_data_table.shape,
+            'attributes': data_level_attributes
+        }]
+    }
+
+    # Write to HDF5
+    with h5py.File(output_filename, 'w') as h5file:
+        transfer_file_dict_to_hdf5(h5file, '/', file_dict)
 
 
 def create_hdf5_file_from_dataframe(ofilename, input_data, approach : str, group_by_funcs : list, extract_attrs_func = None):