SLEAP data conversion

Install NeuroConv with the additional dependencies necessary for reading SLEAP data.

pip install "neuroconv[sleap]"

Convert SLEAP pose estimation data to NWB using SLEAPInterface.

>>> from datetime import datetime
>>> from zoneinfo import ZoneInfo
>>> from pathlib import Path
>>> from neuroconv.datainterfaces import SLEAPInterface
>>>
>>> # Change the file_path so it points to the slp file in your system
>>> file_path = BEHAVIOR_DATA_PATH / "sleap" / "predictions_1.2.7_provenance_and_tracking.slp"
>>> interface = SLEAPInterface(file_path=file_path, verbose=False)
>>>
>>> # Extract what metadata we can from the source files
>>> metadata = interface.get_metadata()
>>> # session_start_time is required for conversion. If it cannot be inferred
>>> # automatically from the source files you must supply one.
>>> session_start_time = datetime(2020, 1, 1, 12, 30, 0, tzinfo=ZoneInfo("US/Pacific"))
>>> metadata["NWBFile"].update(session_start_time=session_start_time)
>>> # Add subject information (required for DANDI upload)
>>> metadata["Subject"] = dict(subject_id="subject1", species="Mus musculus", sex="M", age="P30D")
>>>
>>> # Choose a path for saving the nwb file and run the conversion
>>> nwbfile_path = f"{path_to_save_nwbfile}"  # This should be something like: "saved_file.nwb"
>>> interface.run_conversion(nwbfile_path=nwbfile_path, metadata=metadata)

NeuroConv aims to automatically add all the metadata annotations that are present in the source format. It is often the case that crucial information is not available there, such as the anatomical location, the meaning of the values, or a semantically meaningful description of the data. Follow the pose estimation how-to for a modality-relevant guide to adding this extra metadata, which makes the data more useful for future users and for the community as a whole. Its section on templates starts from scratch, and the reference template lists every element the metadata accepts.