This repository houses conversion pipelines for the IBL data releases, including the Brain Wide Map project.
git clone https:/github.com/catalystneuro/IBL-to-nwb
cd IBL-to-nwb
pip install -e .
for the exact environment used for the initial conversion, see src/ibl_to_nwb/_environments.
It is recommended to follow a similar approach for future conversions to leave a record of provenance.
NeuroConv has two primarily classes for handling conversions.
An Interface reads a single data stream (such as DLC pose estimation) and creates one or more neurodata objects, adding them to an in-memory pynwb.NWBFile object via the .add_to_nwbfile method. Before that it can also fetch and set local metadata: dict values for use or modification.
The Converter orchestrates the conversion by combining multiple interfaces, and can also be used to add additional metadata to the NWB file. It is responsible for creating the NWB file saved to disk.
Occasionally, a sub-Converter, such as the IBLSpikeGLXConverter, will be used to handle the conversion of multiple data streams that is more complex than a single interface can handle; though these behave like other Interfaces with respect to the main orchestrating Converter.
Anywhere you see handwritten text in the NWB files that is meant to be human-readable, it is likely that it was copied from the public Google IBL documents and written in the .yaml files found in src/ibl_to_nwb/_metadata.
Occasionally, especially if a portion of the text is pulled from source data, these values might be overwritten in the .add_to_nwbfile protocol of an interface, so always be sure to check that as well.
Open the script src/ibl_to_nwb/_scripts/convert_brainwide_map_raw_only.py.
Change any values at the top as needed, such as the session_id (equivalent to the 'eid' of ONE).
Then run the script.
Open the script src/ibl_to_nwb/_scripts/convert_brainwide_map_processed_only.py.
Change any values at the top as needed, such as the session_id (equivalent to the 'eid' of ONE).
Then run the script.
Set the environment variable DANDI_API_KEY, obtainable from clicking on your initials in the top right of https://dandiarchive.org/dandiset.
In an fresh environment, install the DANDI CLI:
pip install dandi
Download a shell of the dandiset:
dandi download DANDI:000409 --download dandiset.yaml
All outputs from the conversion scripts should be pre-organized, so we can just directly move all the sub- folders from the conversion output directory into the Dandiset folder. This should appear like:
|- 000409
| |- sub-CSH-ZAR-001
| |- |- sub-CSH-ZAR-001_ses-3e7..._desc-processed_behavior+ecephys.nwb
| |- |- sub-CSH-ZAR-001_ses-3e7..._desc-raw_ecephys+image.nwb
| |- |- ...
| |- ...
From a working directory of 000409, you can either scan for validations directly with:
dandi validate .
Of course, all assets ought to be valid, so you could also just directly upload the data to DANDI (this will also run validation as it iterates through the files):
dandi upload