hdmf

import os from hdmf.spec import GroupSpec, DatasetSpec, NamespaceBuilder from hdmf.common import DynamicTable, VectorData from hdmf.backends.hdf5 import HDF5IO # 1. Define a custom data type specification my_dataset_spec = DatasetSpec(name='my_data', doc='An example dataset', dtype='float32') my_group_spec = GroupSpec(name='MyTypeContainer', doc='A custom data type container', datasets=[my_dataset_spec]) # 2. Create a namespace for your specification namespace_builder = NamespaceBuilder( doc='My Custom HDMF Extension', name='my_extension', full_name='My Custom Extension', version='0.1.0', auto_detect_namespace=True ) # In a real scenario, you would save this to a YAML file and load it # For quickstart, we'll demonstrate using built-in common types # 3. Work with common HDMF data types, e.g., DynamicTable table = DynamicTable(name='example_table', description='An example table of items') table.add_column('item_name', 'Name of the item', dtype='text') table.add_column('quantity', 'Quantity of the item', dtype='int') table.add_row(item_name='Apple', quantity=10) table.add_row(item_name='Banana', quantity=5) # 4. Save to HDF5 file file_name = 'my_hdmf_data.h5' with HDF5IO(file_name, 'w') as io: io.write(table) print(f"DynamicTable saved to {file_name}") # 5. Read from HDF5 file with HDF5IO(file_name, 'r') as io: read_table = io.read() print("\nRead DynamicTable:") print(read_table) # Clean up os.remove(file_name)