dima/workflow_metadata_annotation.ipynb

Metadata Annotation Process

In this notebook, we will go through a simple metadata annotation process. This involves the following steps:

1. Define an HDF5 file.
2. Create a YAML representation of the HDF5 file.
3. Edit and augment the YAML with metadata.
4. Update the original file based on the edited YAML.

Import libraries and modules

• Excecute (or Run) the Cell below

In [ ]:

import src.metadata_review_lib as metadata_annotation
import src.hdf5_vis as hdf5_vis
import os

Step 1: Define an HDF5 file

• Set up the string variable hdf5_file_path with the path to the HDF5 file of interest.
• Excecute Cell.

In [ ]:

hdf5_file_path = "output_files/kinetic_flowtube_study_2023-06-29_LuciaI.h5"

Step 2: Create a YAML Representation of the File

We now convert HDF5 file structure and existing metadata into a YAML format. This will be used to add and edit metadata attributes.

• Excecute Cell.

In [ ]:

yaml_file_path = hdf5_vis.take_yml_snapshot_of_hdf5_file(hdf5_file_path)

if os.path.exists(yaml_file_path):
    print(f'The YAML file representation {yaml_file_path} of the HDF5 file {hdf5_file_path} was created successfully.')

Step 3: Edit and Augment YAML with Metadata

We can now manually edit the YAML file to add metadata.

• (Optional) automate your metadata annotation process by creating a program that takes the YAMl file and returns the modified version of it.
• Excecute Cell.

In [ ]:

def metadata_annotation_process(yaml_file_path):

    # Include metadata annotation logic, e.g., load yaml file and modify its content accordingly

    print(f'Ensure your edits to {yaml_file_path} have been properly incorporated and saved.')

    return yaml_file_path

yaml_file_path = metadata_annotation_process(yaml_file_path)

Step 4: Update the Original File Based on the Edited YAML

Lastly, we will update the original file with the metadata from the YAML file.

• Excecute Cell.

In [ ]:

metadata_annotation.update_hdf5_file_with_review(hdf5_file_path,yaml_file_path)