Calibration and testing

Device calibration

The metadata provides a generic class for storing Calibration metadata. In a generic calibration there is some set of known inputs and a paired set of measured output values.

Common calibrations include measuring power output (e.g. for lasers) with PowerCalibration and measuring volume outputs (e.g. for lick spouts controlled by a solenoid) with VolumeCalibration, but you can define any generic calibration using the base class.

Calibrations have an option to include fit parameters, if your calibration fit is not available in the FitType options please request that we add it by opening an issue.

Testing

Use subject_id="calibration" to mark data assets as test assets. When a “phantom” or other calibration object is used during testing please provide details about that object in a CalibrationObject. Before uploading calibration assets you need to ensure that your asset and metadata are compatible with the processing pipelines that will be run downstream.

Note that test assets that are not intended to be kept long-term should be immediately (or as soon as feasible) marked as archived on Code Ocean. Archived assets are deleted when unused for 30 days.

Manual calibration metadata

If the processing pipeline that will run on your test data asset requires certain fields in the subject or procedures metadata to be set you need to create an actual subject.json and procedures.json and upload these alongside your data asset.

from aind_data_schema.core.subject import Subject
from aind_data_schema.core.procedures import Procedures
from aind_data_schema.components.subjects import CalibrationObject
from aind_data_schema.components.devices import Device
from aind_data_schema_models.organizations import Organization

subject_id = "calibration"
 
subject = Subject(
    subject_id=subject_id,
    subject_details=CalibrationObject(
        empty=True,
        description="FIP calibration",
    ),
)

procedures = Procedures(subject_id=subject_id)
 
subject.write_standard_file()
procedures.write_standard_file()

Automated calibration metadata

If the processing pipeline that will run on your data asset does not read the subject and/or procedures metadata you can follow the instructions below to create empty subject and procedures files.

Note that this automation is only available if your job_type runs aind-metadata-mapper>=1.3.0. Please include as much detail as possible about your CalibrationObject in the upload settings.

from aind_data_schema.components.subjects import CalibrationObject
from aind_data_schema_models.modalities import Modality
from aind_metadata_mapper.gather_metadata import GatherMetadataJob
from aind_metadata_mapper.models import JobSettings, DataDescriptionSettings, SubjectSettings

job_settings = JobSettings(
    output_dir="/path/to/output",
    subject_id="calibration",
    data_description_settings=DataDescriptionSettings(
        project_name="<project-name>",
        modalities=[Modality.ECEPHYS],
    ),
    subject_settings=SubjectSettings(
        calibration_object=CalibrationObject(
            description="Neuropixels dummy probe",
            empty=False,
        )
    ),
)

job = GatherMetadataJob(job_settings=job_settings)
job.run_job()