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(
        file_path="raw_dataset_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().

Now run the entire conversion with:

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

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.