NWBConverter#

In neurophysiology, it is common to use multiple different acquisition or preprocessing systems with different proprietary formats in the same session. For instance, in a given extracellular electrophysiology experiment, you might have raw and processed data. The NWBConverter class streamlines this conversion process. This single NWBConverter object is responsible for combining those multiple read/write operations. Here is an example definition of a NWBConverter:

from neuroconv import NWBConverter,
from neuroconv.datainterfaces import (
    SpikeGLXRecordingInterface,
    PhySortingInterface
)

class ExampleNWBConverter(NWBConverter):
    data_interface_classes = dict(
        SpikeGLXRecording=SpikeGLXRecordingInterface,
        PhySorting=PhySortingInterface
    )

NWBConverter classes define a data_interface_classes dictionary, a class attribute that specifies all of the DataInterface classes used by this converter. Then you just need to input source_data, which specifies the input data to each DataInterface. The keys to this dictionary must match the keys of``data_interface_classes``.

source_data = dict(
    SpikeGLXRecording=dict(
        folder_path="raw_dataset_folder_path"
    ),
    PhySorting=dict(
        folder_path="sorted_dataset_path"
    )
)

example_nwb_converter = ExampleNWBConverter(source_data)

This creates an NWBConverter. To fetch metadata across all of the interfaces and merge them together, call:

metadata = converter.get_metadata()

The metadata can then be manually modified with any additional user-input, just like DataInterface objects:

metadata["NWBFile"]["session_description"] = "NeuroConv tutorial."
metadata["NWBFile"]["experimenter"] = "My name"
metadata["Subject"]["subject_id"] = "ID of experimental subject"

The final metadata dictionary should follow the form defined by NWBConverter.get_metadata_schema().

You can also specify conversion options for each data interface to control how the data is written to the NWB file:

conversion_options = dict(
    SpikeGLXRecording=dict(
        # Control memory usage with iterator options
        iterator_opts=dict(
            buffer_gb=1.0,  # Amount of memory to use
            chunk_mb=10.0,  # Size of chunks for writing
            display_progress=True  # Show progress bar
        ),
        # Write as raw (acquisition) processed, or LFP data
        write_as="raw",
        # For testing with a small subset of data
        stub_test=False,
        # For manual control of timestamps
        always_write_timestamps=False
    ),
    PhySorting=dict(
        # For testing with a small subset of data
        stub_test=False,
        #  write in processing module to write multiple units table
        write_as="processing"
        # Set the name of the units table in the processing module
        units_name = "MyUnitsTable",
    )
)

Note that they keys of the dictionary must match the keys of data_interface_classes. The values are dictionaries that specify the conversion options for each data interface. The available options depend on the specific data interface being used. You can find them by looking at the interface add_to_nwbfile method.

Now run the entire conversion with:

converter.run_conversion(
    metadata=metadata,
    nwbfile_path="my_nwbfile.nwb",
    conversion_options=conversion_options
)

Like DataInterface objects, NWBConverter objects can output an in-memory NWBFile object by calling NWBConverter.create_nwbfile(). This can be useful for debugging, for adding metadata to the file, or for further processing.

Though this example was only for two data streams (recording and spike-sorted data), it can easily extend to any number of sources, including video of a subject, extracted position estimates, stimuli, or any other data source.

NWBConverter#

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