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. An example of how to define a NWBConverter would be

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 object that can aggregate and distribute across the data interfaces. 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:

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 converter.get_metadata_schema(). Now run the entire conversion with:

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

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.

The sections below describe source schema and metadata schema in more detail through another example for two data streams (ophys and ecephys data).