def main():
ns_builder = NWBNamespaceBuilder(doc='data type for holding power or phase spectra for a signal',
name='ndx-spectrum',
version='0.2.2',
author='Ben Dichter',
contact='*****@*****.**')
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('TimeSeries', namespace='core')
ns_builder.include_type('DynamicTableRegion', namespace='core')
Spectrum = NWBGroupSpec(
neurodata_type_def='Spectrum',
neurodata_type_inc='NWBDataInterface',
doc='type for storing power or phase of spectrum')
for data_name in ('power', 'phase'):
Spectrum.add_dataset(
name=data_name,
doc='spectrum values',
dims=(('frequency',), ('frequency', 'channel')),
shape=((None,), (None, None)),
dtype='float',
quantity='?')
Spectrum.add_dataset(name='frequencies',
doc='frequencies of spectrum',
dims=('frequency',),
shape=(None,),
dtype='float')
Spectrum.add_link(target_type='TimeSeries',
doc='timeseries that this spectrum describes',
quantity='?',
name='source_timeseries')
Spectrum.add_dataset(name='electrodes',
doc='the electrodes that this series was generated from',
neurodata_type_inc='DynamicTableRegion',
quantity='?')
new_data_types = [Spectrum]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
ns_builder = NWBNamespaceBuilder(
doc="Stimulus images",
name=f"""{NAMESPACE}""",
version="""0.1.0""",
author="""Allen Institute for Brain Science""",
contact="""*****@*****.**"""
)
ns_builder.include_type('ImageSeries', namespace='core')
ns_builder.include_type('TimeSeries', namespace='core')
ns_builder.include_type('NWBDataInterface', namespace='core')
stimulus_template_spec = NWBGroupSpec(
neurodata_type_def='StimulusTemplate',
neurodata_type_inc='ImageSeries',
doc='Note: image names in control_description are referenced by '
'stimulus/presentation table as well as intervals '
'\n'
'Each image shown to the animals is warped to account for '
'distance and eye position relative to the monitor. This '
'extension stores the warped images that were shown to the animal '
'as well as an unwarped version of each image in which a mask has '
'been applied such that only the pixels visible after warping are '
'included',
datasets=[
NWBDatasetSpec(
name='unwarped',
dtype='float',
doc='Original image with mask applied such that only the '
'pixels visible after warping are included',
shape=(None, None, None)
)
]
)
new_data_types = [stimulus_template_spec]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__)))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc=
"""An NWB extension to describe the detailed genotype of an experimental subject""",
name="""ndx-genotype""",
version="""0.1.0""",
author=list(
map(str.strip,
"""Ryan Ly, Oliver Ruebel, Pam Baker, Lydia Ng""".split(','))),
contact=list(map(str.strip, """*****@*****.**""".split(','))))
ns_builder.include_type('Subject', namespace='core')
ns_builder.include_type('NWBFile', namespace='core')
ns_builder.include_type('NWBContainer', namespace='core')
ns_builder.include_type('DynamicTable', namespace='core')
ns_builder.include_type('VectorData', namespace='core')
ns_builder.include_type('Data', namespace='core')
ns_builder.include_type('Container', namespace='core')
genotypes_table_spec = NWBGroupSpec(
neurodata_type_def='GenotypesTable',
neurodata_type_inc='DynamicTable',
doc='A table to hold structured genotype information.',
attributes=[
NWBAttributeSpec(
name='process',
doc=
'Description of the process or assay used to determine the genotype, e.g., PCR.',
dtype='text',
required=False,
),
NWBAttributeSpec(
name='process_url',
doc=
('URL to online document that provides further details of the protocol used, e.g., '
'https://dx.doi.org/10.17504/protocols.io.yjifuke'),
dtype='text',
required=False,
),
NWBAttributeSpec(
name='assembly',
doc=
'Description of the assembly of the reference genome, e.g., GRCm38.p6.',
dtype='text',
required=False,
),
NWBAttributeSpec(
name='annotation',
doc=('Description of the annotation of the reference genome, '
'e.g., NCBI Mus musculus Annotation Release 108.'),
dtype='text',
required=False,
),
],
datasets=[
NWBDatasetSpec(
name='locus_symbol',
neurodata_type_inc='VectorData',
doc='Symbol/name of the locus, e.g., Rorb.',
dtype='text',
),
NWBDatasetSpec(
name='locus_type',
neurodata_type_inc='VectorData',
doc=
'Type of the locus, e.g., Gene, Transgene, Unclassified other.',
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='allele1_symbol',
neurodata_type_inc='VectorData',
doc=('Symbol/name of the first allele, e.g., Rorb-IRES2-Cre. '
'"wt" should be used to represent wild-type.'),
dtype='text',
),
NWBDatasetSpec(
name='allele1_type',
neurodata_type_inc='VectorData',
doc=
('Type of the first allele, e.g., Targeted (Recombinase), '
'Transgenic (Null/knockout, Transactivator), Targeted (Conditional ready, Inducible, Reporter).'
'"Wild Type" should be used to represent wild-type. Allele types can be found at: '
'http://www.informatics.jax.org/userhelp/ALLELE_phenotypic_categories_help.shtml#method'
),
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='allele2_symbol',
neurodata_type_inc='VectorData',
doc=('Smybol/name of the second allele, e.g., Rorb-IRES2-Cre. '
'"wt" should be used to represent wild-type.'),
dtype='text',
),
NWBDatasetSpec(
name='allele2_type',
neurodata_type_inc='VectorData',
doc=
('Type of the second allele, e.g., Targeted (Recombinase), '
'Transgenic (Null/knockout, Transactivator), Targeted (Conditional ready, Inducible, Reporter).'
'"Wild Type" should be used to represent wild-type. Allele types can be found at: '
'http://www.informatics.jax.org/userhelp/ALLELE_phenotypic_categories_help.shtml#method'
),
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='allele3_symbol',
neurodata_type_inc='VectorData',
doc=('Symbol/name of the third allele, e.g., Rorb-IRES2-Cre. '
'"wt" should be used to represent wild-type.'),
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='allele3_type',
neurodata_type_inc='VectorData',
doc=
('Type of the third allele, e.g., Targeted (Recombinase), '
'Transgenic (Null/knockout, Transactivator), Targeted (Conditional ready, Inducible, Reporter).'
'"Wild Type" should be used to represent wild-type. Allele types can be found at: '
'http://www.informatics.jax.org/userhelp/ALLELE_phenotypic_categories_help.shtml#method'
),
dtype='text',
quantity='?',
),
],
)
genotype_subject_spec = NWBGroupSpec(
neurodata_type_def='GenotypeSubject',
neurodata_type_inc='Subject',
doc=
('An enhanced Subject type that has an additional field for a genotype table. '
'NOTE: If this proposal for extension '
'to NWB gets merged with the core schema, then this type would be removed and the'
'Subject specification updated instead.'),
groups=[
NWBGroupSpec(
name='genotypes_table',
neurodata_type_inc='GenotypeTable',
doc='Structured genotype information for the subject.',
quantity='?',
),
],
)
genotype_nwbfile_spec = NWBGroupSpec(
neurodata_type_def='GenotypeNWBFile',
neurodata_type_inc='NWBFile',
doc=
('Extension of the NWBFile class to allow 1) placing the new GenotypeSubject type '
'in /general/subject in the NWBFile and 2) placing the new ontologies group containing an '
'ontology table and ontology map. NOTE: If this proposal for extension '
'to NWB gets merged with the core schema, then this type would be removed and the '
'NWBFile specification updated instead. The ontologies types will be incorporated from HDMF '
'when they are finalized.'),
groups=[
NWBGroupSpec(
name='general', # override existing general group
doc='Expanded definition of general from NWBFile.',
groups=[
NWBGroupSpec(
name='subject', # override existing subject type
neurodata_type_inc='GenotypeSubject',
doc=
'Subject information with structured genotype information.',
quantity='?',
),
],
),
NWBGroupSpec(
name='.ontologies',
doc='Information about ontological terms used in this file.',
quantity='?',
datasets=[
NWBDatasetSpec(
name='objects',
neurodata_type_inc='OntologyTable',
doc='The objects that conform to an ontology.',
),
NWBDatasetSpec(
name='terms',
neurodata_type_inc='OntologyMap',
doc='The ontological terms that get used in this file.',
),
],
),
],
)
ontology_table_spec = NWBDatasetSpec(
neurodata_type_def='OntologyTable',
doc=
('A table for identifying which objects in a file contain values that correspond to ontology terms or '
'centrally registered IDs (CRIDs)'),
dtype=[
NWBDtypeSpec(name='id',
dtype='uint64',
doc='The unique identifier in this table.'),
NWBDtypeSpec(
name='object_id',
dtype='text',
doc='The UUID for the object that uses this ontology term.'),
NWBDtypeSpec(
name='field',
dtype='text',
doc=
'The field from the object (specified by object_id) that uses this ontological term.'
),
NWBDtypeSpec(
name='item',
dtype='uint64',
doc='An index into the OntologyMap that contains the term.'),
],
shape=[None],
)
ontology_map_spec = NWBDatasetSpec(
neurodata_type_def='OntologyMap',
doc=
('A table for mapping user terms (i.e., keys) to ontology terms / registry symbols / '
'centrally registered IDs (CRIDs)'),
dtype=[
NWBDtypeSpec(name='id',
dtype='uint64',
doc='The unique identifier in this table.'),
NWBDtypeSpec(
name='key',
dtype='text',
doc=
'The user key that maps to the ontology term / registry symbol.'
),
NWBDtypeSpec(
name='ontology',
dtype='text',
doc='The ontology/registry that the term/symbol comes from.'),
NWBDtypeSpec(
name='uri',
dtype='text',
doc=
'The unique resource identifier for the ontology term / registry symbol.'
),
],
shape=[None],
)
new_data_types = [
genotypes_table_spec, genotype_subject_spec, genotype_nwbfile_spec,
ontology_table_spec, ontology_map_spec
]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
ns_builder = NWBNamespaceBuilder(
doc='type for storing metadata for Tank lab',
name='ndx-tank-metadata',
version='0.1.0',
author=['Szonja Weigl', 'Luiz Tauffer', 'Ben Dichter'],
contact=['*****@*****.**'])
ns_builder.include_type('LabMetaData', namespace='core')
ns_builder.include_type('DynamicTable', namespace='core')
LabMetaDataExtension = NWBGroupSpec(
doc='type for storing metadata for Tank lab',
neurodata_type_def='LabMetaDataExtension',
neurodata_type_inc='LabMetaData',
)
LabMetaDataExtension.add_attribute(
name='experiment_name',
doc='name of experiment run',
dtype='text',
)
LabMetaDataExtension.add_attribute(
name='world_file_name',
doc='name of world file run',
dtype='text',
)
LabMetaDataExtension.add_attribute(
name='protocol_name',
doc='name of protocol run',
dtype='text',
)
LabMetaDataExtension.add_attribute(
name='stimulus_bank_path',
doc='path of stimulus bank file',
dtype='text',
)
LabMetaDataExtension.add_attribute(
name='commit_id',
doc='Commit id for session run',
dtype='text',
)
LabMetaDataExtension.add_attribute(
name='location',
doc='Name of rig where session was run',
dtype='text',
)
LabMetaDataExtension.add_attribute(
name='session_performance',
doc='Performance of correct responses in %',
dtype='float',
required=False)
LabMetaDataExtension.add_attribute(
name='session_end_time',
doc='Datetime when session ended',
dtype='text', # temporary solution until datetime is fixed
)
LabMetaDataExtension.add_attribute(
name='num_trials',
doc='Number of trials during the session',
dtype='int',
)
RigExtension = NWBGroupSpec(
doc='type for storing rig information',
neurodata_type_def='RigExtension',
neurodata_type_inc='LabMetaData',
)
rig_attr = [('rig', 'text'), ('simulationMode', 'int'), ('hasDAQ', 'int'),
('hasSyncComm', 'int'), ('minIterationDT', 'float'),
('arduinoPort', 'text'), ('sensorDotsPerRev', 'float'),
('ballCircumference', 'float'), ('toroidXFormP1', 'float'),
('toroidXFormP2', 'float'), ('colorAdjustment', 'float'),
('soundAdjustment', 'float'), ('nidaqDevice', 'int'),
('nidaqPort', 'int'), ('nidaqLines', 'int'),
('syncClockChannel', 'int'), ('syncDataChannel', 'int'),
('rewardChannel', 'int'), ('rewardSize', 'float'),
('rewardDuration', 'float'), ('laserChannel', 'int'),
('rightPuffChannel', 'int'), ('leftPuffChannel', 'int'),
('webcam_name', 'text')]
for attr in rig_attr:
if attr[0] in ['sensorDotsPerRev', 'colorAdjustment', 'nidaqLines']:
RigExtension.add_attribute(name=attr[0],
doc='rig information',
dtype=attr[1],
shape=(None, ),
required=False)
else:
RigExtension.add_attribute(name=attr[0],
doc='rig information',
dtype=attr[1],
required=False)
LabMetaDataExtension.add_group(
name='rig',
neurodata_type_inc='RigExtension',
doc='type for storing rig information',
)
MazeExtension = NWBGroupSpec(
doc='type for storing maze information',
neurodata_type_def='MazeExtension',
neurodata_type_inc='DynamicTable',
)
LabMetaDataExtension.add_group(
name='mazes',
neurodata_type_inc='MazeExtension',
doc='type for storing maze information',
)
# export the extension to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder,
[LabMetaDataExtension, RigExtension, MazeExtension],
output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc="""NovelaNeurotechnologies Namespaces""",
name="""ndx-franklab-novela""",
version="""0.0.011.36""",
author=list(map(str.strip, """NovelaDevops""".split(','))),
contact=list(map(str.strip, """*****@*****.**""".split(','))))
# # as in which namespace they are found
# this is similar to specifying the Python modules that need to be imported
# to use your new data types
ns_builder.include_type('ElectrodeGroup', namespace='core')
ns_builder.include_type('Device', namespace='core')
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('ImageSeries', namespace='core')
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb
# for more information
shanks_electrode = NWBGroupSpec(
neurodata_type_def='ShanksElectrode',
neurodata_type_inc='NWBDataInterface',
doc='electrode in the probe',
attributes=[
NWBAttributeSpec(name='name',
doc='name of the electrode',
dtype='text'),
NWBAttributeSpec(name='rel_x',
doc='the rel_x value of the electrode',
dtype='float'),
NWBAttributeSpec(name='rel_y',
doc='the rel_y value of the electrode',
dtype='float'),
NWBAttributeSpec(name='rel_z',
doc='the rel_z value of the electrode',
dtype='float'),
])
shanks = NWBGroupSpec(neurodata_type_def='Shank',
neurodata_type_inc='NWBDataInterface',
doc='shank in the probe',
groups=[
NWBGroupSpec(
neurodata_type_inc='ShanksElectrode',
doc='electrode in the probe',
quantity='*')
],
attributes=[
NWBAttributeSpec(name='name',
doc='name of the shank',
dtype='text'),
])
probe = NWBGroupSpec(
doc='A custom Probes interface',
neurodata_type_def='Probe',
neurodata_type_inc='Device',
groups=[
NWBGroupSpec(neurodata_type_inc='Shank',
doc='shank in the probe',
quantity='*')
],
attributes=[
NWBAttributeSpec(name='id',
doc='unique id of the probe',
dtype='int'),
NWBAttributeSpec(name='probe_type',
doc='type of the probe',
dtype='text'),
NWBAttributeSpec(name='units',
doc='units in probe, acceptable values um or mm',
dtype='text'),
NWBAttributeSpec(name='probe_description',
doc='description of the probe',
dtype='text'),
NWBAttributeSpec(name='contact_side_numbering',
doc='is contact_side_numbering enabled',
dtype='bool'),
NWBAttributeSpec(name='contact_size',
doc='value of contact size in float',
dtype='float'),
])
data_acq_device = NWBGroupSpec(doc='A custom Device interface',
neurodata_type_def='DataAcqDevice',
neurodata_type_inc='Device',
attributes=[
NWBAttributeSpec(name='system',
doc='system of device',
dtype='text'),
NWBAttributeSpec(name='amplifier',
doc='amplifier',
dtype='text'),
NWBAttributeSpec(name='adc_circuit',
doc='adc_circuit',
dtype='text'),
])
camera_device = NWBGroupSpec(neurodata_type_def='CameraDevice',
neurodata_type_inc='Device',
doc='A custom Device interface',
attributes=[
NWBAttributeSpec(name='meters_per_pixel',
doc='meters per pixel',
dtype='float'),
NWBAttributeSpec(name='camera_name',
doc='name of the camera',
dtype='text'),
NWBAttributeSpec(
name='model',
doc='model of this camera device',
dtype='text'),
NWBAttributeSpec(name='lens',
doc='lens info',
dtype='text')
])
associated_files = NWBGroupSpec(
neurodata_type_def='AssociatedFiles',
neurodata_type_inc='NWBDataInterface',
doc='content of files linked with nwb',
attributes=[
NWBAttributeSpec(name='description',
doc='description of file',
dtype='text'),
NWBAttributeSpec(name='content',
doc='content of file',
dtype='text'),
NWBAttributeSpec(name='task_epochs',
doc='epochs this task belongs to',
dtype='text')
])
nwb_image_series = NWBGroupSpec(
neurodata_type_def='NwbImageSeries',
neurodata_type_inc='ImageSeries',
doc='Extension of ImageSeries object in NWB',
groups=[
NWBGroupSpec(neurodata_type_inc='Device',
doc='devices used to record video',
quantity='*')
],
)
header_device = NWBGroupSpec(
doc='metadata from global configuration from header',
neurodata_type_def='HeaderDevice',
neurodata_type_inc='Device',
attributes=[
NWBAttributeSpec(name='headstage_serial',
doc='headstage_serial from global configuration',
dtype='text'),
NWBAttributeSpec(
name='headstage_smart_ref_on',
doc='headstage_smart_ref_on from global configuration',
dtype='text'),
NWBAttributeSpec(name='realtime_mode',
doc='realtime_mode from global configuration',
dtype='text'),
NWBAttributeSpec(
name='headstage_auto_settle_on',
doc='headstage_auto_settle_on from global configuration',
dtype='text'),
NWBAttributeSpec(
name='timestamp_at_creation',
doc='timestamp_at_creation from global configuration',
dtype='text'),
NWBAttributeSpec(
name='controller_firmware_version',
doc='conntroller_firmware_version from global configuration',
dtype='text'),
NWBAttributeSpec(name='controller_serial',
doc='controller_serial from global configuration',
dtype='text'),
NWBAttributeSpec(
name='save_displayed_chan_only',
doc='save_displayed_chan_only from global configuration',
dtype='text'),
NWBAttributeSpec(
name='headstage_firmware_version',
doc='headstage_firmware_version from global configuration',
dtype='text'),
NWBAttributeSpec(
name='qt_version',
doc='qt_version_version from global configuration',
dtype='text'),
NWBAttributeSpec(
name='compile_date',
doc='compile_date_version from global configuration',
dtype='text'),
NWBAttributeSpec(
name='compile_time',
doc='compile_time_version from global configuration',
dtype='text'),
NWBAttributeSpec(
name='file_prefix',
doc='file_prefix_version from global configuration',
dtype='text'),
NWBAttributeSpec(
name='headstage_gyro_sensor_on',
doc=
'headstage_gyro_sensor_on_version from global configuration',
dtype='text'),
NWBAttributeSpec(
name='headstage_mag_sensor_on',
doc='headstage_mag_sensor_on_version from global configuration',
dtype='text'),
NWBAttributeSpec(
name='trodes_version',
doc='trodes_versionversion from global configuration',
dtype='text'),
NWBAttributeSpec(
name='headstage_accel_sensor_on',
doc='headstage_accel_sensor_on from global configuration',
dtype='text'),
NWBAttributeSpec(name='commit_head',
doc='commit_head from global configuration',
dtype='text'),
NWBAttributeSpec(
name='system_time_at_creation',
doc='system_time_at_creation from global configuration',
dtype='text'),
NWBAttributeSpec(name='file_path',
doc='file_path from global configuration',
dtype='text'),
])
nwb_electrode_group = NWBGroupSpec(
neurodata_type_def='NwbElectrodeGroup',
neurodata_type_inc='ElectrodeGroup',
doc='Custom nwb ElectrodeGroup',
attributes=[
NWBAttributeSpec(name='targeted_location',
doc='predicted location',
dtype='text'),
NWBAttributeSpec(name='targeted_x',
doc='predicted x coordinates',
dtype='float'),
NWBAttributeSpec(name='targeted_y',
doc='predicted y coordinates',
dtype='float'),
NWBAttributeSpec(name='targeted_z',
doc='predicted z coordinates',
dtype='float'),
NWBAttributeSpec(
name='units',
doc='units of fields, acceptable values: um or mm',
dtype='text'),
])
new_data_types = [
shanks_electrode, shanks, probe, data_acq_device, camera_device,
nwb_image_series, header_device, associated_files, nwb_electrode_group
]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc='An NWB:N extension for storing bipolar schema',
name='ndx-bipolar-scheme',
version='0.3.0',
author=list(
map(str.strip, 'Ben Dichter,Armin Najarpour,Ryan Ly'.split(','))),
contact=list(map(str.strip,
'*****@*****.**'.split(','))))
for type_name in ('LabMetaData', 'DynamicTableRegion', 'DynamicTable',
'VectorIndex'):
ns_builder.include_type(type_name, namespace='core')
ecephys_ext = NWBGroupSpec(
doc=
'Group that holds proposed extracellular electrophysiology extensions.',
neurodata_type_def='EcephysExt',
neurodata_type_inc='LabMetaData',
default_name='ecephys_ext',
groups=[
NWBGroupSpec(name='bipolar_scheme_table',
neurodata_type_inc='BipolarSchemeTable',
doc='Bipolar referencing scheme used',
quantity='?')
])
bipolar_scheme = NWBGroupSpec(
doc='Table that holds information about the bipolar scheme used',
neurodata_type_def='BipolarSchemeTable',
neurodata_type_inc='DynamicTable',
default_name='bipolar_scheme')
bipolar_scheme.add_dataset(name='anodes',
neurodata_type_inc='DynamicTableRegion',
doc='references the electrodes table',
dims=('num_electrodes', ),
shape=(None, ),
dtype='int')
bipolar_scheme.add_dataset(name='cathodes',
neurodata_type_inc='DynamicTableRegion',
doc='references the electrodes table',
dims=('num_electrodes', ),
shape=(None, ),
dtype='int')
bipolar_scheme.add_dataset(name='anodes_index',
neurodata_type_inc='VectorIndex',
doc='Indices for the anode table',
dims=('num_electrode_grp', ),
shape=(None, ))
bipolar_scheme.add_dataset(name='cathodes_index',
neurodata_type_inc='VectorIndex',
doc='Indices for the cathode table',
dims=('num_electrode_grp', ),
shape=(None, ))
new_data_types = [ecephys_ext, bipolar_scheme]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
ns_builder = NWBNamespaceBuilder(doc='type for storing time-varying FRET data',
name='ndx-fret',
version='0.2.1',
author=['Luiz Tauffer', 'Ben Dichter'],
contact=['*****@*****.**', '*****@*****.**'])
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('ImageSeries', namespace='core')
ns_builder.include_type('OpticalChannel', namespace='core')
ns_builder.include_type('Device', namespace='core')
# Define FRETSeries, type that stores Donor/Acceptor specific information
FRETSeries = NWBGroupSpec(
neurodata_type_def='FRETSeries',
neurodata_type_inc='ImageSeries',
doc='Donor/Acceptor specific information.',
)
FRETSeries.add_attribute(
name='fluorophore',
doc='Fluorophore name.',
dtype='text',
shape=None,
)
FRETSeries.add_group(
name='optical_channel',
doc='Group storing channel specific data',
neurodata_type_inc='OpticalChannel',
)
FRETSeries.add_link(
name='device',
doc='The device that was used to record.',
target_type='Device',
)
# Defines FRET, DataInterface that holds metadata and data for FRET experiments
FRET = NWBGroupSpec(
doc='type for storing time-varying FRET data',
neurodata_type_def='FRET',
neurodata_type_inc='NWBDataInterface',
)
FRET.add_attribute(
name='excitation_lambda',
doc='Excitation wavelength in nm.',
dtype='float',
shape=None,
)
FRET.add_attribute(
name='location',
doc='Location of imaging field.',
dtype='text',
shape=None,
required=False,
)
FRET.add_group(
name='donor',
doc='Group storing donor data',
neurodata_type_inc='FRETSeries',
)
FRET.add_group(
name='acceptor',
doc='Group storing acceptor data',
neurodata_type_inc='FRETSeries',
)
new_data_types = [FRET, FRETSeries]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
ns_builder = NWBNamespaceBuilder(doc='describe a maze of arbitrary shape',
name='ndx-maze',
version='0.1.0',
author='Ben Dichter',
contact='*****@*****.**')
node = NWBGroupSpec(
neurodata_type_def='Node',
neurodata_type_inc='NWBDataInterface',
doc=
"Abstract representation for any kind of node in the topological graph We won't actually"
" implement abstract nodes. Rather this is a parent group from which our more specific "
"types of nodes will inherit. Note that NWB specifications have inheritance. The quantity "
"'*' means that we can have any number (0 or more) nodes.",
quantity='*',
attributes=[
NWBAttributeSpec('name', 'the name of this node', 'text'),
NWBAttributeSpec(name='help',
doc='help doc',
dtype='text',
value='Apparatus Node')
])
edge = NWBGroupSpec(
neurodata_type_def='Edge',
neurodata_type_inc='NWBDataInterface',
doc=
"Edges between any two nodes in the graph. An edge's only dataset is the name (string) of "
"the two nodes that the edge connects Note that we don't actually include the nodes "
"themselves, just their names, in an edge.",
quantity='*',
datasets=[
NWBDatasetSpec(doc='names of the nodes this edge connects',
name='edge_nodes',
dtype='text',
dims=['first_node_name|second_node_name'],
shape=[2])
],
attributes=[
NWBAttributeSpec(name='help',
doc='help doc',
dtype='text',
value='Apparatus Edge')
])
point_node = NWBGroupSpec(
neurodata_type_def='PointNode',
neurodata_type_inc='Node',
doc=
'A node that represents a single 2D point in space (e.g. reward well, novel object'
' location)',
quantity='*',
datasets=[
NWBDatasetSpec(doc='x/y coordinate of this 2D point',
name='coords',
dtype='float',
dims=['num_coords', 'x_vals|y_vals'],
shape=[1, 2])
],
attributes=[
NWBAttributeSpec(name='help',
doc='help doc',
dtype='text',
value='Apparatus Point')
])
segment_node = NWBGroupSpec(
neurodata_type_def='SegmentNode',
neurodata_type_inc='Node',
doc=
'A node that represents a linear segment in 2D space, defined by its start and end'
' points (e.g. a single arm of W-track maze)',
quantity='*',
datasets=[
NWBDatasetSpec(
doc=
'x/y coordinates of the start and end points of this segment',
name='coords',
dtype='float',
dims=['num_coords', 'x_vals|y_vals'],
shape=[None, 2])
],
attributes=[
NWBAttributeSpec(name='help',
doc='help doc',
dtype='text',
value='Apparatus Segment')
])
polygon_node = NWBGroupSpec(
neurodata_type_def='PolygonNode',
neurodata_type_inc='Node',
doc=
'A node that represents a polygon area (e.g. open field, sleep box). A polygon is'
' defined by its external vertices and, optionally, by any interior points of '
'interest (e.g. interior wells, objects)',
quantity='*',
datasets=[
NWBDatasetSpec(
doc='x/y coordinates of the exterior points of this polygon',
name='coords',
dtype='float',
dims=['num_coords', 'x_vals|y_vals'],
shape=[None, 2]),
NWBDatasetSpec(
doc='x/y coordinates of interior points inside this polygon',
name='interior_coords',
dtype='float',
quantity='?',
dims=['num_coords', 'x_vals|y_vals'],
shape=[None, 2])
],
attributes=[
NWBAttributeSpec(name='help',
doc='help doc',
dtype='text',
value='Apparatus Polygon')
])
environment = NWBGroupSpec(neurodata_type_def='Environment',
neurodata_type_inc='NWBDataInterface',
default_name='Environment',
doc='a graph of nodes and edges',
quantity='*',
groups=[
NWBGroupSpec(neurodata_type_inc='Node',
doc='nodes in the graph',
quantity='*'),
NWBGroupSpec(neurodata_type_inc='Edge',
doc='edges in the graph',
quantity='*')
],
attributes=[
NWBAttributeSpec(name='help',
doc='help doc',
dtype='text',
value='Environment')
])
environments = NWBGroupSpec(neurodata_type_def='Environments',
neurodata_type_inc='LabMetaData',
default_name='environments',
doc='holds environments',
quantity='?',
groups=[
NWBGroupSpec(
neurodata_type_inc='Environment',
doc='holds structure of environment',
quantity='*')
],
attributes=[
NWBAttributeSpec(name='help',
doc='help doc',
dtype='text',
value='help')
])
new_data_types = [
node, edge, point_node, segment_node, polygon_node, environment,
environments
]
# TODO: include the types that are used and their namespaces (where to find them)
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('LabMetaData', namespace='core')
export_spec(ns_builder, new_data_types)
def main():
ns_builder = NWBNamespaceBuilder(
doc='nwb extention for voltage imaging technique called TEMPO',
name=name,
version='0.1.0',
author=list(map(str.strip, 'Saksham Sharda'.split(','))),
contact=list(map(str.strip, '*****@*****.**'.split(',')))
)
ns_builder.include_type('VectorData', namespace='hdmf-common')
ns_builder.include_type('DynamicTable', namespace='hdmf-common')
ns_builder.include_type('Subject', namespace='core')
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('NWBContainer', namespace='core')
ns_builder.include_type('Device', namespace='core')
measurement = NWBDatasetSpec('Flexible vectordataset with a custom unit/conversion/resolution'
' field similar to timeseries.data',
attributes=[
NWBAttributeSpec('unit',
'The base unit of measure used to store data. This should be in the SI unit.'
'COMMENT: This is the SI unit (when appropriate) of the stored data, such as '
'Volts. If the actual data is stored in millivolts, the field ''conversion'' '
'below describes how to convert the data to the specified SI unit.',
'text'),
NWBAttributeSpec('conversion',
'Scalar to multiply each element in '
'data to convert it to the specified unit',
'float32', required=False, default_value=1.0),
NWBAttributeSpec('resolution',
'Smallest meaningful difference between values in data, stored in the specified '
'by unit. COMMENT: E.g., the change in value of the least significant bit, or '
'a larger number if signal noise is known to be present. If unknown, use -1.0',
'float32', required=False, default_value=0.0)
],
neurodata_type_def='Measurement',
neurodata_type_inc='VectorData',
)
# Typedef for laserline
laserline_device = NWBGroupSpec(neurodata_type_def='LaserLine',
neurodata_type_inc='Device',
doc='description of laserline device, part for a TEMPO device',
attributes=[
NWBAttributeSpec('reference',
'reference of the laserline module',
dtype='text',
required=False)
],
quantity='*')
laserline_device.add_dataset(
name='analog_modulation_frequency',
neurodata_type_inc=measurement,
doc='analog_modulation_frequency of the laserline module',
shape=(1,),
dtype='text',
quantity='?'
)
laserline_device.add_dataset(
name='power',
neurodata_type_inc=measurement,
doc='power of the laserline module',
shape=(1,),
dtype='float',
quantity='?'
)
laserline_devices = NWBGroupSpec(neurodata_type_def='LaserLineDevices',
neurodata_type_inc='NWBDataInterface',
name='laserline_devices',
doc='A container for dynamic addition of LaserLine devices',
quantity='?',
groups=[laserline_device])
# Typedef for PhotoDetector
photodetector_device = NWBGroupSpec(neurodata_type_def='PhotoDetector',
neurodata_type_inc='Device',
doc='description of photodetector device, part for a TEMPO device',
attributes=[
NWBAttributeSpec('reference',
'reference of the photodetector module',
dtype='text',
required=False)
],
quantity='*')
photodetector_device.add_dataset(
name='gain',
neurodata_type_inc=measurement,
doc='gain of the photodetector module',
shape=(1,),
dtype='float',
quantity='?'
)
photodetector_device.add_dataset(
name='bandwidth',
neurodata_type_inc=measurement,
doc='bandwidth metadata of the photodetector module',
shape=(1,),
dtype='float',
quantity='?'
)
photodetector_devices = NWBGroupSpec(neurodata_type_def='PhotoDetectorDevices',
neurodata_type_inc='NWBDataInterface',
name='photodetector_devices',
doc='A container for dynamic addition of PhotoDetector devices',
quantity='?',
groups=[photodetector_device])
# Typedef for LockInAmplifier
lockinamp_device = NWBGroupSpec(neurodata_type_def='LockInAmplifier',
neurodata_type_inc='DynamicTable',
doc='description of lock_in_amp device, part for a TEMPO device',
attributes=[
NWBAttributeSpec('demodulation_filter_order',
'demodulation_filter_order of the lockinamp_device module',
dtype='float',
required=False,
default_value=-1),
NWBAttributeSpec('reference',
'reference of the lockinamp_device module',
dtype='text',
required=False)
],
quantity='*')
lockinamp_device.add_dataset(
name='demod_bandwidth',
neurodata_type_inc=measurement,
doc='demod_bandwidth of lock_in_amp',
shape=(1,),
dtype='float',
quantity='?'
)
lockinamp_device.add_dataset(
name='channel_name',
neurodata_type_inc='VectorData',
doc='name of the channel of lock_in_amp',
dims=('no_of_channels',),
shape=(None,),
dtype='text',
quantity='?'
)
lockinamp_device.add_dataset(
name='offset',
neurodata_type_inc=measurement,
doc='offset for channel of lock_in_amp',
dims=('no_of_channels',),
shape=(None,),
dtype='float',
quantity='?'
)
lockinamp_device.add_dataset(
name='gain',
neurodata_type_inc='VectorData',
doc='gain for channel of lock_in_amp',
dims=('no_of_channels',),
shape=(None,),
dtype='float',
quantity='?'
)
lockinamp_devices = NWBGroupSpec(neurodata_type_def='LockInAmplifierDevices',
neurodata_type_inc='NWBDataInterface',
name='lockinamp_devices',
doc='A container for dynamic addition of LockInAmplifier devices',
quantity='?',
groups=[lockinamp_device])
tempo_device = NWBGroupSpec(neurodata_type_def='TEMPO',
neurodata_type_inc='Device',
doc='datatype for a TEMPO device',
attributes=[NWBAttributeSpec(
name='no_of_modules',
doc='the number of electronic modules with this acquisition system',
dtype='int',
required=False,
default_value=3)],
groups=[laserline_devices,
photodetector_devices,
lockinamp_devices]
)
# surgical meta-data specification:
surgery = NWBGroupSpec(neurodata_type_def='Surgery',
neurodata_type_inc='Subject',
doc='Surgery related meta-data of subject',
name='surgery_data',
attributes=[NWBAttributeSpec(
name='surgery_date',
doc='date of surgery',
dtype='text',
required=False),
NWBAttributeSpec(
name='surgery_notes',
doc='surgery notes',
dtype='text',
required=False),
NWBAttributeSpec(
name='surgery_pharmacology',
doc='pharmacology data',
dtype='text',
required=False),
NWBAttributeSpec(
name='surgery_arget_anatomy',
doc='target anatomy of the surgery',
dtype='text',
required=False)],
groups=[
NWBGroupSpec(
name='implantation',
doc='implantation related data',
links=[NWBLinkSpec(name='implantation_device',
doc='device implanted during surgery',
target_type='Device')],
attributes=[NWBAttributeSpec(
name='ophys_implant_name',
doc='optical physiology implant name',
dtype='text',
required=False),
NWBAttributeSpec(
name='ephys_implant_name',
doc='electrophysiology implant name',
dtype='text',
required=False)],
quantity='?'),
NWBGroupSpec(
name='virus_injection',
doc='virus injection related data',
attributes=[NWBAttributeSpec(
name='virus_injection_id',
doc='id of virus injected',
dtype='text',
required=False),
NWBAttributeSpec(
name='virus_injection_opsin',
doc='opsin/protein used',
dtype='text',
required=False),
NWBAttributeSpec(
name='virus_injection_opsin_l_r',
doc='opsin/protein left/right description'
'enter \'L\' or \'R\'',
dtype='text',
required=False,
default_value='L/R'),
NWBAttributeSpec(
name='virus_injection_scheme',
doc='description of injection scheme eg.'
'\'single_bolus\'',
dtype='text',
required=False),
NWBAttributeSpec(
name='virus_injection_tag',
doc='tag for the virus injected',
dtype='text',
required=False),
NWBAttributeSpec(
name='virus_injection_coordinates_description',
doc='description of coordinates'
'\'AP\'/\'ML\'/\'DV\'',
dtype='text',
required=False),
NWBAttributeSpec(
name='virus_injection_volume',
doc='volume of virus injected in ml',
dtype='float',
required=False,
default_value=-1.0)],
datasets=[NWBDatasetSpec(
name='virus_injection_coordinates',
doc='coordinates of virus injection',
dtype='text',
quantity='?')],
quantity='?'),
NWBGroupSpec(
name='ophys_injection',
doc='optical physiology fluorescence injection metadata',
attributes=[NWBAttributeSpec(
name='ophys_injection_date',
doc='date of fluorscent protein injection',
dtype='text',
required=False),
NWBAttributeSpec(
name='ophys_injection_volume',
doc='volume of fluorscent protein injected',
dtype='float',
required=False),
NWBAttributeSpec(
name='ophys_injection_brain_area',
doc='brain area of fluorscent protein injection',
dtype='text',
required=False)
],
datasets=[NWBDatasetSpec(
name='ophys_injection_flr_protein_data',
doc='fluorescence protein name and concentration table',
neurodata_type_inc='DynamicTable')
],
quantity='?')
],
quantity='?')
subject = NWBGroupSpec(
neurodata_type_def='SubjectComplete',
neurodata_type_inc='Surgery',
doc='Mouse metadata used with the TEMPO device',
attributes=[NWBAttributeSpec(
name='sacrificial_date',
doc='sacrificial date of the animal ',
dtype='text',
required=False),
NWBAttributeSpec(
name='strain',
doc='strain of the animal',
dtype='text',
required=False)],
)
new_data_types = [measurement, tempo_device, surgery, subject]
export_spec(ns_builder, new_data_types)
def generate_extended_schema():
# set up silverlab namespace
ns_builder = NWBNamespaceBuilder(
'Extensions for acousto-optic lens data',
'silverlab_extended_schema',
'Silver lab data extension to NWB format for acousto-optic lens experiments',
version='0.4')
ns_builder.include_type('LabMetaData', namespace='core')
ns_builder.include_type('TwoPhotonSeries', namespace='core')
# define attributes Silver lab extension
cycle_time_attr = NWBAttributeSpec(
name='cycle_time',
doc='time in seconds for the microscope to acquire all ROIs once '
'and return to its starting position',
dtype='float')
cycles_per_trial_attr = NWBAttributeSpec(
name='cycles_per_trial',
doc='how many microscope cycles occur in each experimental trial',
dtype="int")
imaging_mode_attr = NWBAttributeSpec(
name='imaging_mode',
doc='the acquisition mode for the experiment; '
'pointing = single-voxel ROIs, '
'miniscan = 2d rectangular ROIs, '
'volume = 3d cuboid ROIs',
dtype='text')
frame_size_attr = NWBAttributeSpec(
name='frame_size',
doc='the 2d imaging frame size in voxels',
shape=(2, ),
dtype='int')
silverlab_api_version_attr = NWBAttributeSpec(
name='silverlab_api_version',
doc='For potential future backwards compatibility, '
'store the \'version\' of this API that created the file.',
dtype='text')
labview_version_attr = NWBAttributeSpec(
name='labview_version',
doc='The version of LabVIEW the data came from',
dtype='text',
required=False)
pockels_column_names_attr = NWBAttributeSpec(
name='columns',
doc='column names for the zplane pockels dataset',
shape=(4, ),
dtype='text')
# define datasets for Silver lab extensions
zplane_pockels_ds = NWBDatasetSpec(
doc='pockels data set, recording calibration data '
'for focusing at different z-planes in four columns: '
'Z offset from focal plane (micrometres), '
'normalised Z, '
'\'Pockels\' i.e. laser power in %, '
'and z offset for drive motors',
name='pockels',
shape=(None, 4),
attributes=[pockels_column_names_attr],
neurodata_type_def='ZplanePockelsDataset')
# define groups for Silver lab extensions
silverlab_optophys_specs = NWBGroupSpec(
doc='A place to store Silver lab specific optophysiology data',
attributes=[
cycle_time_attr, cycles_per_trial_attr, frame_size_attr,
imaging_mode_attr
],
datasets=[zplane_pockels_ds],
neurodata_type_def='SilverLabOptophysiology',
neurodata_type_inc='LabMetaData')
silverlab_metadata_specs = NWBGroupSpec(
doc='A place to store Silver lab specific metadata',
attributes=[silverlab_api_version_attr, labview_version_attr],
neurodata_type_def='SilverLabMetaData',
neurodata_type_inc='LabMetaData',
)
# dimensions ordered as t, x, y [, z], like the TimeSeries data itself
silverlab_pixel_time_offset_data = NWBDatasetSpec(
doc='A datastructure to hold time offsets for pixels. The'
'time offsets are the acquisition time of each pixel '
'relative to a starting time. The starting time is the '
'start of the cycle for pre-2018 LabView versions, '
'and the start of the trial for new versions.',
name='pixel_time_offsets',
shape=[(None, None), (None, None, None), (None, None, None, None)],
neurodata_type_def='PixelTimeOffsets')
silverlab_roi_image_specs = NWBGroupSpec(
doc='An extension to PyNWB\'s TwoPhotonSeries class, designed to hold '
'pixels from an ROI as well as the PixelTimeOffsets for them.',
datasets=[silverlab_pixel_time_offset_data],
neurodata_type_def='ROISeriesWithPixelTimeOffsets',
neurodata_type_inc='TwoPhotonSeries')
# export as schema extension
ext_source = 'silverlab.ophys.yaml'
ns_builder.add_spec(ext_source, silverlab_optophys_specs)
ext_source = 'silverlab.metadata.yaml'
ns_builder.add_spec(ext_source, silverlab_metadata_specs)
ext_source = 'silverlab.roi.yaml'
ns_builder.add_spec(ext_source, silverlab_roi_image_specs)
ns_builder.export('silverlab.namespace.yaml')
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc='NWB extension for survey/behavioral data',
name='ndx-survey-data',
version='0.1.0',
author=list(map(str.strip, 'Ben Dichter, Armin Najarpour Foroushani'.split(','))),
contact=list(map(str.strip, '*****@*****.**'.split(',')))
)
for type_name in ('DynamicTable', 'VectorData'):
ns_builder.include_type(type_name, namespace='core')
survey_data = NWBGroupSpec(
doc='Table that holds information about the survey/behavior',
neurodata_type_def='SurveyTable',
neurodata_type_inc='DynamicTable',
default_name='survey_data'
)
question_response = NWBDatasetSpec(
doc='Column that holds information about a question',
neurodata_type_def='QuestionResponse',
neurodata_type_inc='VectorData',
default_name='question_response',
attributes=[NWBAttributeSpec(name='options',
doc='Response Options',
dtype='text')]
)
survey_data.add_dataset(
name='nrs_pain_intensity_rating',
neurodata_type_inc=question_response,
doc='NRS Pain Intensity Rating',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='nrs_pain_relief_rating',
neurodata_type_inc=question_response,
doc='NRS Pain Relief Rating',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='nrs_relative_pain_intensity_rating',
neurodata_type_inc=question_response,
doc='NRS Relative Pain Intensity Rating',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='nrs_pain_unpleasantness',
neurodata_type_inc=question_response,
doc='NRS Pain Unpleasantness',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='vas_pain_intensity_rating',
neurodata_type_inc=question_response,
doc='VAS Pain Intensity Rating',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='vas_pain_relief_rating',
neurodata_type_inc=question_response,
doc='VAS Pain Relief Rating',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='vas_relative_pain_intensity_rating',
neurodata_type_inc=question_response,
doc='VAS Relative Pain Intensity Rating',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='vas_pain_unpleasantness',
neurodata_type_inc=question_response,
doc='VAS Pain Unpleasantness',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='throbbing',
neurodata_type_inc=question_response,
doc='Throbbing',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='shooting',
neurodata_type_inc=question_response,
doc='Shooting',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='stabbing',
neurodata_type_inc=question_response,
doc='Stabbing',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='sharp',
neurodata_type_inc=question_response,
doc='Sharp',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='cramping',
neurodata_type_inc=question_response,
doc='Cramping',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='gnawing',
neurodata_type_inc=question_response,
doc='Gnawing',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='hot_burning',
neurodata_type_inc=question_response,
doc='Hot-burning',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='aching',
neurodata_type_inc=question_response,
doc='Aching',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='heavy',
neurodata_type_inc=question_response,
doc='Heavy',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='tender',
neurodata_type_inc=question_response,
doc='Tender',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='splitting',
neurodata_type_inc=question_response,
doc='Splitting',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='tiring_exhausting',
neurodata_type_inc=question_response,
doc='Tiring-Exhausting',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='sickening',
neurodata_type_inc=question_response,
doc='Sickening',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='fearful',
neurodata_type_inc=question_response,
doc='Fearful',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
survey_data.add_dataset(
name='cruel_punishing',
neurodata_type_inc=question_response,
doc='Cruel-Punishing',
dims=('num_samples',),
shape=(None,),
dtype='text'
)
new_data_types = [survey_data, question_response]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(doc='Data types for recording data from multiple compartments of multiple '
'neurons in a single TimeSeries.',
name='ndx-simulation-output',
version='0.2.6',
author='Ben Dichter',
contact='*****@*****.**')
types_to_include = ['TimeSeries', 'VectorData', 'VectorIndex', 'DynamicTable', 'LabMetaData']
for ndtype in types_to_include:
ns_builder.include_type(ndtype, namespace='core')
Compartments = NWBGroupSpec(default_name='compartments',
neurodata_type_def='Compartments',
neurodata_type_inc='DynamicTable',
doc='Table that holds information about what places are being recorded.')
Compartments.add_dataset(name='number',
neurodata_type_inc='VectorData',
dtype='int',
doc='Cell compartment ids corresponding to a each column in the data.')
Compartments.add_dataset(name='number_index',
neurodata_type_inc='VectorIndex',
doc='Index that maps cell to compartments.',
quantity='?')
Compartments.add_dataset(name='position',
neurodata_type_inc='VectorData',
dtype='float',
quantity='?',
doc='Position of recording within a compartment. 0 is close to soma, 1 is other end.')
Compartments.add_dataset(name='position_index',
neurodata_type_inc='VectorIndex',
doc='Index for position.',
quantity='?')
Compartments.add_dataset(name='label',
neurodata_type_inc='VectorData',
doc='Labels for compartments.',
dtype='text',
quantity='?')
Compartments.add_dataset(name='label_index',
neurodata_type_inc='VectorIndex',
doc='indexes label',
quantity='?')
CompartmentsSeries = NWBGroupSpec(neurodata_type_def='CompartmentSeries',
neurodata_type_inc='TimeSeries',
doc='Stores continuous data from cell compartments')
CompartmentsSeries.add_link(name='compartments',
target_type='Compartments',
quantity='?',
doc='Metadata about compartments in this CompartmentSeries.')
SimulationMetaData = NWBGroupSpec(name='simulation',
neurodata_type_def='SimulationMetaData',
neurodata_type_inc='LabMetaData',
doc='Group that holds metadata for simulations.')
SimulationMetaData.add_group(name='compartments',
neurodata_type_inc='Compartments',
doc='Table that holds information about what places are being recorded.')
new_data_types = [Compartments, CompartmentsSeries, SimulationMetaData]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc='stores electrical stimulation waveforms',
name='ndx-electrical-stim',
version='0.1.0',
author=list(map(str.strip, 'Jessie R. Liu'.split(','))),
contact=list(map(str.strip, '*****@*****.**'.split(','))))
# TODO: specify the neurodata_types that are used by the extension as well
# as in which namespace they are found
# this is similar to specifying the Python modules that need to be imported
# to use your new data types
ns_builder.include_type('TimeSeries', namespace='core')
ns_builder.include_type('TimeIntervals', namespace='core')
ns_builder.include_type('DynamicTableRegion', namespace='hdmf-common')
ns_builder.include_type('VectorData', namespace='hdmf-common')
# TODO: define your new data types
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb
# for more information
stim_series = NWBGroupSpec(
neurodata_type_def='StimSeries',
neurodata_type_inc='TimeSeries',
doc=('An extension of TimeSeries to include stimulation waveforms '
'used during electrical stimulation.'),
)
stim_series.add_dataset(name='bipolar_electrodes',
neurodata_type_inc='DynamicTableRegion',
doc='DynamicTableRegion pointer to the '
'bipolar electrode pairs corresponding to the '
'stimulation waveforms.')
stim_table = NWBGroupSpec(
neurodata_type_def='StimTable',
neurodata_type_inc='TimeIntervals',
doc=('An extension of TimeIntervals to hold parameters used for '
'various stimulation events.'),
)
stim_table.add_dataset(name='bipolar_pair',
neurodata_type_inc='DynamicTableRegion',
doc='DynamicTableRegion pointer to the '
'bipolar electrode pair used for this '
'stimulation event.')
stim_table.add_dataset(name='frequency',
neurodata_type_inc='VectorData',
doc='Frequency of stimulation waveform, in Hz.')
stim_table.add_dataset(name='amplitude',
neurodata_type_inc='VectorData',
doc='Amplitude of stimulation waveform, in Amps.')
stim_table.add_dataset(name='pulse_width',
neurodata_type_inc='VectorData',
doc='Pulse width of stimulation waveform, '
'in seconds/phase')
new_data_types = [stim_series, stim_table]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc='NWB extension to store pose estimation data',
name='ndx-pose',
version='0.1.1',
author=['Ryan Ly', 'Ben Dichter', 'Alexander Mathis'],
contact=[
'*****@*****.**', '*****@*****.**', '*****@*****.**'
],
)
ns_builder.include_type('SpatialSeries', namespace='core')
ns_builder.include_type('NWBDataInterface', namespace='core')
pose_estimation_series = NWBGroupSpec(
neurodata_type_def='PoseEstimationSeries',
neurodata_type_inc='SpatialSeries',
doc='Estimated position (x, y) or (x, y, z) of a body part over time.',
datasets=[
NWBDatasetSpec(
name='data',
doc='Estimated position (x, y) or (x, y, z).',
dtype='float32',
dims=[['num_frames', 'x, y'], ['num_frames', 'x, y, z']],
shape=[[None, 2], [None, 3]],
attributes=[
NWBAttributeSpec(
name='unit',
dtype='text',
default_value='pixels',
doc=
("Base unit of measurement for working with the data. The default value "
"is 'pixels'. Actual stored values are not necessarily stored in these units. "
"To access the data in these units, multiply 'data' by 'conversion'."
),
required=True,
),
],
),
NWBDatasetSpec(
name='confidence',
doc=
'Confidence or likelihood of the estimated positions, scaled to be between 0 and 1.',
dtype='float32',
dims=['num_frames'],
shape=[None],
attributes=[
NWBAttributeSpec(
name='definition',
dtype='text',
doc=
("Description of how the confidence was computed, e.g., "
"'Softmax output of the deep neural network'."),
required=False,
),
],
),
],
)
pose_estimation = NWBGroupSpec(
neurodata_type_def='PoseEstimation',
neurodata_type_inc='NWBDataInterface',
doc=
('Group that holds estimated position data for multiple body parts, computed from the same video with '
'the same tool/algorithm. The timestamps of each child PoseEstimationSeries type should be the same.'
),
default_name='PoseEstimation',
groups=[
NWBGroupSpec(
neurodata_type_inc='PoseEstimationSeries',
doc='Estimated position data for each body part.',
quantity='*',
),
],
datasets=[
NWBDatasetSpec(
name='description',
doc='Description of the pose estimation procedure and output.',
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='original_videos',
doc=
'Paths to the original video files. The number of files should equal the number of camera devices.',
dtype='text',
dims=['num_files'],
shape=[None],
quantity='?',
),
NWBDatasetSpec(
name='labeled_videos',
doc=
'Paths to the labeled video files. The number of files should equal the number of camera devices.',
dtype='text',
dims=['num_files'],
shape=[None],
quantity='?',
),
NWBDatasetSpec(
name='dimensions',
doc='Dimensions of each labeled video file.',
dtype='uint8',
dims=['num_files', 'width, height'],
shape=[None, 2],
quantity='?',
),
NWBDatasetSpec(
name='scorer',
doc='Name of the scorer / algorithm used.',
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='source_software',
doc=
'Name of the software tool used. Specifying the version attribute is strongly encouraged.',
dtype='text',
quantity='?',
attributes=[
NWBAttributeSpec(
name='version',
doc='Version string of the software tool used.',
dtype='text',
required=False,
),
],
),
NWBDatasetSpec(
name='nodes',
doc=
('Array of body part names corresponding to the names of the PoseEstimationSeries objects within '
'this group.'),
dtype='text',
dims=['num_body_parts'],
shape=[None],
quantity='?',
),
NWBDatasetSpec(
name='edges',
doc=
("Array of pairs of indices corresponding to edges between nodes. Index values correspond to row "
"indices of the 'nodes' dataset. Index values use 0-indexing."
),
dtype='uint8',
dims=['num_edges', 'nodes_index, nodes_index'],
shape=[None, 2],
quantity='?',
),
],
# TODO: collections of multiple links is currently buggy in PyNWB/HDMF
# links=[
# NWBLinkSpec(
# target_type='Device',
# doc='Cameras used to record the videos.',
# quantity='*',
# ),
# ],
)
new_data_types = [pose_estimation_series, pose_estimation]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
print(
'Spec files generated. Please make sure to rerun `pip install .` to load the changes.'
)
def main():
# the values for ns_builder are auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(doc='Implement proposal for hierarchical metadata structure '
'for intracellular electrophysiology data ',
name='ndx-icephys-meta',
version='0.2.0',
author=['Oliver Ruebel',
'Ryan Ly',
'Benjamin Dichter',
'Thomas Braun',
'Andrew Tritt'],
contact=['*****@*****.**',
'*****@*****.**',
'*****@*****.**',
'None',
'*****@*****.**'])
# Create a vector-data column that references a range in a time series. I/e., a VectorData column
# with a compound data type storing the start_index, count, and TimeSeries reference
reference_timeseries_vectordata = NWBDatasetSpec(
neurodata_type_inc='VectorData',
neurodata_type_def='TimeSeriesReferenceVectorData',
doc='Column storing references to a TimeSeries (rows). For each TimeSeries this VectorData '
'column stores the start_index and count to indicate the range in time to be selected '
'as well as an object reference to the TimeSeries.',
dtype=[
NWBDtypeSpec(name='idx_start',
dtype='int32',
doc="Start index into the TimeSeries 'data' and 'timestamp' datasets of the "
"referenced TimeSeries. The first dimension of those arrays is always time."),
NWBDtypeSpec(name='count',
dtype='int32',
doc="Number of data samples available in this time series, during this epoch"),
NWBDtypeSpec(name='timeseries',
dtype=NWBRefSpec(target_type='TimeSeries',
reftype='object'),
doc='The TimeSeries that this index applies to')
]
)
# Create a collection of aligned dynamic tables
aligned_dynamic_tables_spec = NWBGroupSpec(
neurodata_type_inc='DynamicTable',
neurodata_type_def='AlignedDynamicTable',
doc='DynamicTable container that subports storing a collection of subtables. Each sub-table is a '
'DynamicTable itself that is aligned with the main table by row index. I.e., all '
'DynamicTables stored in this group MUST have the same number of rows. This type effectively '
'defines a 2-level table in which the main data is stored in the main table implemented by this type '
'and additional columns of the table are grouped into categories, with each category being '
'represented by a separate DynamicTable stored within the group.',
attributes=[NWBAttributeSpec(name='categories',
dtype='text',
dims=['num_categories'],
doc='The names of the categories in this AlignedDynamicTable. Each '
'category is represented by one DynamicTable stored in the parent group. '
'This attribute should be used to specify an order of categories.',
shape=[None])
],
groups=[NWBGroupSpec(neurodata_type_inc='DynamicTable',
doc='A DynamicTable representing a particular category for columns in the '
'AlignedDynamicTable parent container. The table MUST be aligned '
'with (i.e., have the same number of rows) as all other DynamicTables '
'stored in the AlignedDynamicTable parent container. The name of '
'the category is given by the name of the DynamicTable and its description '
'by the description attribute of the DynamicTable.',
quantity='*')
]
)
electrodes_table_spec = NWBGroupSpec(
neurodata_type_inc='DynamicTable',
neurodata_type_def='IntracellularElectrodesTable',
doc='Table for storing intracellular electrode related metadata.',
attributes=[NWBAttributeSpec(name='description',
dtype='text',
doc='Description of what is in this dynamic table.',
value='Table for storing intracellular electrode related metadata.')],
datasets=[NWBDatasetSpec(
name='electrode',
neurodata_type_inc='VectorData',
doc='Column for storing the reference to the intracellular electrode.',
dtype=NWBRefSpec(target_type='IntracellularElectrode',
reftype='object')
),
]
)
stimuli_table_spec = NWBGroupSpec(
neurodata_type_inc='DynamicTable',
neurodata_type_def='IntracellularStimuliTable',
doc='Table for storing intracellular stimulus related metadata.',
attributes=[NWBAttributeSpec(name='description',
dtype='text',
doc='Description of what is in this dynamic table.',
value='Table for storing intracellular stimulus related metadata.')],
datasets=[NWBDatasetSpec(
name='stimulus',
neurodata_type_inc='TimeSeriesReferenceVectorData',
doc='Column storing the reference to the recorded stimulus for the recording (rows).'),
]
)
responses_table_spec = NWBGroupSpec(
neurodata_type_inc='DynamicTable',
neurodata_type_def='IntracellularResponsesTable',
doc='Table for storing intracellular response related metadata.',
attributes=[NWBAttributeSpec(name='description',
dtype='text',
doc='Description of what is in this dynamic table.',
value='Table for storing intracellular response related metadata.')],
datasets=[NWBDatasetSpec(
name='response',
neurodata_type_inc='TimeSeriesReferenceVectorData',
doc='Column storing the reference to the recorded response for the recording (rows)'),
]
)
# Create our table to group stimulus and response for Intracellular Electrophysiology Recordings
icephys_recordings_table_spec = NWBGroupSpec(
name='intracellular_recordings',
neurodata_type_def='IntracellularRecordingsTable',
neurodata_type_inc='AlignedDynamicTable',
doc='A table to group together a stimulus and response from a single electrode and a single simultaneous '
'recording. Each row in the table represents a single recording consisting typically of a stimulus and a '
'corresponding response. In some cases, however, only a stimulus or a response are recorded as '
'as part of an experiment. In this case both, the stimulus and response will point to the same '
'TimeSeries while the idx_start and count of the invalid column will be set to -1, thus, '
'indicating that no values have been recorded for the stimulus or response, respectively. Note, '
'a recording MUST contain at least a stimulus or a response. Typically the stimulus and response '
'are PatchClampSeries. However, the use of AD/DA channels that are not associated to an electrode '
'is also common in intracellular electrophysiology, in which case other TimeSeries may be used.',
attributes=[NWBAttributeSpec(
name='description',
dtype='text',
doc='Description of the contents of this table. Inherited from AlignedDynamicTable '
'and overwritten here to fix the value of the attribute',
value='A table to group together a stimulus and response from a single electrode '
'and a single simultaneous recording and for storing metadata about the '
'intracellular recording.'),
],
groups=[
NWBGroupSpec(
name='electrodes',
neurodata_type_inc='IntracellularElectrodesTable',
doc='Table for storing intracellular electrode related metadata.',
),
NWBGroupSpec(
name='stimuli',
neurodata_type_inc='IntracellularStimuliTable',
doc='Table for storing intracellular stimulus related metadata.'
),
NWBGroupSpec(
name='responses',
neurodata_type_inc='IntracellularResponsesTable',
doc='Table for storing intracellular response related metadata.'
),
]
)
# Create a SimultaneousRecordingsTable (similar to trials) table to group
# intracellular electrophysiology recording that were
# recorded at the same time and belong together
simultaneous_recordings_table_spec = NWBGroupSpec(
name='simultaneous_recordings',
neurodata_type_def='SimultaneousRecordingsTable',
neurodata_type_inc='DynamicTable',
doc='A table for grouping different intracellular recordings from the '
'IntracellularRecordingsTable table together that were recorded simultaneously '
'from different electrodes',
datasets=[NWBDatasetSpec(name='recordings',
neurodata_type_inc='DynamicTableRegion',
doc='A reference to one or more rows in the IntracellularRecordingsTable table.',
attributes=[
NWBAttributeSpec(
name='table',
dtype=NWBRefSpec(target_type='IntracellularRecordingsTable',
reftype='object'),
doc='Reference to the IntracellularRecordingsTable table that '
'this table region applies to. This specializes the '
'attribute inherited from DynamicTableRegion to fix '
'the type of table that can be referenced here.'
)]),
NWBDatasetSpec(name='recordings_index',
neurodata_type_inc='VectorIndex',
doc='Index dataset for the recordings column.')
]
)
# Create the SequentialRecordingsTable table to group different SimultaneousRecordingsTable together
sequentialrecordings_table_spec = NWBGroupSpec(
name='sequential_recordings',
neurodata_type_def='SequentialRecordingsTable',
neurodata_type_inc='DynamicTable',
doc='A table for grouping different sequential recordings from the '
'SimultaneousRecordingsTable table together. This is typically '
'used to group together sequential recordings where the a sequence '
'of stimuli of the same type with varying parameters '
'have been presented in a sequence.',
datasets=[NWBDatasetSpec(name='simultaneous_recordings',
neurodata_type_inc='DynamicTableRegion',
doc='A reference to one or more rows in the SimultaneousRecordingsTable table.',
attributes=[
NWBAttributeSpec(
name='table',
dtype=NWBRefSpec(target_type='SimultaneousRecordingsTable',
reftype='object'),
doc='Reference to the SimultaneousRecordingsTable table that this table region '
'applies to. This specializes the attribute inherited '
'from DynamicTableRegion to fix the type of table that '
'can be referenced here.'
)
]),
NWBDatasetSpec(name='simultaneous_recordings_index',
neurodata_type_inc='VectorIndex',
doc='Index dataset for the simultaneous_recordings column.'),
NWBDatasetSpec(name='stimulus_type',
neurodata_type_inc='VectorData',
doc='The type of stimulus used for the sequential recording.',
dtype='text')
]
)
# Create the RepetitionsTable table to group different SequentialRecordingsTable together
repetitions_table_spec = NWBGroupSpec(
name='repetitions',
neurodata_type_def='RepetitionsTable',
neurodata_type_inc='DynamicTable',
doc='A table for grouping different sequential intracellular recordings together. '
'With each SequentialRecording typically representing a particular type of stimulus, the '
'RepetitionsTable table is typically used to group sets of stimuli applied in sequence.',
datasets=[NWBDatasetSpec(name='sequential_recordings',
neurodata_type_inc='DynamicTableRegion',
doc='A reference to one or more rows in the SequentialRecordingsTable table.',
attributes=[
NWBAttributeSpec(
name='table',
dtype=NWBRefSpec(target_type='SequentialRecordingsTable',
reftype='object'),
doc='Reference to the SequentialRecordingsTable table that this table region '
'applies to. This specializes the attribute inherited '
'from DynamicTableRegion to fix the type of table that '
'can be referenced here.'
)
]),
NWBDatasetSpec(name='sequential_recordings_index',
neurodata_type_inc='VectorIndex',
doc='Index dataset for the sequential_recordings column.')
]
)
# Create ExperimentalConditionsTable tbale for grouping different RepetitionsTable together
experimental_conditions_table_spec = NWBGroupSpec(
name='experimental_conditions',
neurodata_type_def='ExperimentalConditionsTable',
neurodata_type_inc='DynamicTable',
doc='A table for grouping different intracellular recording repetitions together that '
'belong to the same experimental experimental_conditions.',
datasets=[NWBDatasetSpec(name='repetitions',
neurodata_type_inc='DynamicTableRegion',
doc='A reference to one or more rows in the RepetitionsTable table.',
attributes=[
NWBAttributeSpec(
name='table',
dtype=NWBRefSpec(target_type='RepetitionsTable',
reftype='object'),
doc='Reference to the RepetitionsTable table that this table region '
'applies to. This specializes the attribute inherited '
'from DynamicTableRegion to fix the type of table that '
'can be referenced here.'
)
]),
NWBDatasetSpec(name='repetitions_index',
neurodata_type_inc='VectorIndex',
doc='Index dataset for the repetitions column.')
]
)
# Update NWBFile to modify /general/intracellular_ephys in NWB to support adding the new structure there
# NOTE: If this proposal for extension to NWB gets merged with the core schema the new NWBFile type would
# need to be removed and the NWBFile schema updated instead
icephys_file_spec = NWBGroupSpec(
neurodata_type_inc='NWBFile',
neurodata_type_def='ICEphysFile',
doc='Extension of the NWBFile class to allow placing the new icephys '
'metadata types in /general/intracellular_ephys in the NWBFile '
'NOTE: If this proposal for extension to NWB gets merged with '
'the core schema, then this type would be removed and the '
'NWBFile specification updated instead.',
groups=[NWBGroupSpec(
name='general',
doc='expand definition of general from NWBFile',
groups=[NWBGroupSpec(name='intracellular_ephys',
doc='expand definition from NWBFile',
groups=[NWBGroupSpec(neurodata_type_inc='IntracellularRecordingsTable',
doc=icephys_recordings_table_spec.doc,
name='intracellular_recordings',
quantity='?'),
NWBGroupSpec(neurodata_type_inc='SimultaneousRecordingsTable',
doc=simultaneous_recordings_table_spec.doc,
name='simultaneous_recordings',
quantity='?'),
NWBGroupSpec(neurodata_type_inc='SequentialRecordingsTable',
doc=sequentialrecordings_table_spec.doc,
name='sequential_recordings',
quantity='?'),
NWBGroupSpec(neurodata_type_inc='RepetitionsTable',
doc=repetitions_table_spec.doc,
name='repetitions',
quantity='?'),
NWBGroupSpec(neurodata_type_inc='ExperimentalConditionsTable',
doc=experimental_conditions_table_spec.doc,
name='experimental_conditions',
quantity='?'),
# Update doc on SweepTable to declare it as deprecated
NWBGroupSpec(neurodata_type_inc='SweepTable',
doc='[DEPRACATED] Table used to group different PatchClampSeries.'
'SweepTable is being replaced by IntracellularRecordingsTable '
'and SimultaneousRecordingsTable tabels (and corresponding '
'SequentialRecordingsTable, RepetitionsTable and '
'ExperimentalConditions tables.',
name='sweep_table',
quantity='?')
],
datasets=[NWBDatasetSpec(name='filtering',
doc='[DEPRECATED] Use IntracellularElectrode.filtering instead. '
'Description of filtering used. Includes filtering type '
'and parameters, frequency fall-off, etc. If this changes '
'between TimeSeries, filter description should be stored '
'as a text attribute for each TimeSeries.',
dtype='text',
quantity='?')]
)
]
)
]
)
# Add the type we want to include from core to this list
include_core_types = ['Container',
'DynamicTable',
'DynamicTableRegion',
'VectorData',
'VectorIndex',
'PatchClampSeries',
'IntracellularElectrode',
'NWBFile']
# Include the types that are used by the extension and their namespaces (where to find them)
for type_name in include_core_types:
ns_builder.include_type(type_name, namespace='core')
# Add our new data types to this list
new_data_types = [aligned_dynamic_tables_spec,
reference_timeseries_vectordata,
icephys_recordings_table_spec,
simultaneous_recordings_table_spec,
sequentialrecordings_table_spec,
repetitions_table_spec,
experimental_conditions_table_spec,
icephys_file_spec,
electrodes_table_spec,
stimuli_table_spec,
responses_table_spec]
# Export the spec
project_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
output_dir = os.path.join(project_dir, 'spec')
export_spec(ns_builder=ns_builder,
new_data_types=new_data_types,
output_dir=output_dir)
print("Exported specification to: %s" % output_dir)
def main():
ns_builder = NWBNamespaceBuilder(
doc='NWB extension for hierarchical behavioral data',
name='ndx-hierarchical-behavioral-data',
version='0.1.0',
author=['Ben Dichter', 'Armin Najarpour Foroushani'],
contact=['*****@*****.**'])
# Add the type we want to include from core to this list
include_core_types = ['DynamicTable', 'DynamicTableRegion', 'VectorIndex']
# Include the types that are used by the extension and their namespaces (where to find them)
for type_name in include_core_types:
ns_builder.include_type(type_name, namespace='core')
# Create our table to store phonemes
phonemes_table_spec = NWBGroupSpec(
name='phonemes',
neurodata_type_def='PhonemesTable',
neurodata_type_inc='DynamicTable',
doc='A table to store different phonemes',
attributes=[
NWBAttributeSpec(
name='description',
dtype='text',
doc='Description of what is in this dynamic table.',
value='Table for storing phonemes related data')
],
datasets=[
NWBDatasetSpec(
name='label',
neurodata_type_inc='DynamicTableRegion',
doc=
'Column for storing phonemes. Each row in this DynamicTableRegion is a phoneme.',
dims=('num_phonemes', ),
shape=(None, ),
dtype='text')
])
# Create our table to store syllables
syllables_table_spec = NWBGroupSpec(
name='syllables',
neurodata_type_def='SyllablesTable',
neurodata_type_inc='DynamicTable',
doc='A table to store different syllables',
attributes=[
NWBAttributeSpec(
name='description',
dtype='text',
doc='Description of what is in this dynamic table.',
value='Table for storing syllables related data')
],
datasets=[
NWBDatasetSpec(
name='label',
neurodata_type_inc='DynamicTableRegion',
doc=
'Column for storing syllables. Each row in this DynamicTableRegion is a syllable '
'consisting of phonemes.',
attributes=[
NWBAttributeSpec(
name='table',
dtype=NWBRefSpec(target_type='PhonemesTable',
reftype='object'),
doc='Reference to the PhonemesTable table that '
'this table region applies to. This specializes the '
'attribute inherited from DynamicTableRegion to fix '
'the type of table that can be referenced here.')
],
dims=('num_syllables', ),
shape=(None, ),
dtype='text'),
NWBDatasetSpec(
name='phonemes_index',
neurodata_type_inc='VectorIndex',
doc=
'Column for storing a link to the constituting phonemes (rows)',
dims=('num_syllables', ),
shape=(None, ))
])
# Create our table to store words
words_table_spec = NWBGroupSpec(
name='words',
neurodata_type_def='WordsTable',
neurodata_type_inc='DynamicTable',
doc='A table to store different words',
attributes=[
NWBAttributeSpec(
name='description',
dtype='text',
doc='Description of what is in this dynamic table.',
value='Table for storing word related data')
],
datasets=[
NWBDatasetSpec(
name='label',
neurodata_type_inc='DynamicTableRegion',
doc=
'Column for storing words. Each row in this DynamicTableRegion is a word '
'consisting of syllables.',
attributes=[
NWBAttributeSpec(
name='table',
dtype=NWBRefSpec(target_type='SyllablesTable',
reftype='object'),
doc='Reference to the SyllablesTable table that '
'this table region applies to. This specializes the '
'attribute inherited from DynamicTableRegion to fix '
'the type of table that can be referenced here.')
],
dims=('num_words', ),
shape=(None, ),
dtype='text'),
NWBDatasetSpec(
name='syllables_index',
neurodata_type_inc='VectorIndex',
doc=
'Column for storing a link to the constituting syllables (rows)',
dims=('num_words', ),
shape=(None, ))
])
# Create our table to store sentences
sentences_table_spec = NWBGroupSpec(
name='sentences',
neurodata_type_def='SentencesTable',
neurodata_type_inc='DynamicTable',
doc='A table to store different sentences',
attributes=[
NWBAttributeSpec(
name='description',
dtype='text',
doc='Description of what is in this dynamic table.',
value='Table for storing sentence related data')
],
datasets=[
NWBDatasetSpec(
name='label',
neurodata_type_inc='DynamicTableRegion',
doc=
'Column for storing sentences. Each row in this DynamicTableRegion is a sentence '
'consisting of words.',
attributes=[
NWBAttributeSpec(
name='table',
dtype=NWBRefSpec(target_type='WordsTable',
reftype='object'),
doc='Reference to the WordsTable table that '
'this table region applies to. This specializes the '
'attribute inherited from DynamicTableRegion to fix '
'the type of table that can be referenced here.')
],
dims=('num_sentences', ),
shape=(None, ),
dtype='text'),
NWBDatasetSpec(
name='words_index',
neurodata_type_inc='VectorIndex',
doc=
'Column for storing a link to the constituting words (rows)',
dims=('num_sentences', ),
shape=(None, ))
])
# Create a table to group together all the above groups
transcription_table_spec = NWBGroupSpec(
name='transcription',
neurodata_type_def='TranscriptionTable',
neurodata_type_inc='DynamicTable',
doc=
'This DynamicTable is intended to group together a collection of sub-tables. Each sub-table is a '
'DynamicTable itself. This type effectively defines a 2-level table in which the main data is stored in '
'the main table implemented by this type and additional columns of the table are grouped into categories, '
'with each category being represented by a separate DynamicTable stored within the group.'
'Here, sub-tables are: sentence table which stores different sentences; words table which stores '
'constituting words; syllables table for storing syllables of each word; and phonemes table for storing '
'consisting of phonemes.',
attributes=[
NWBAttributeSpec(
name='categories',
dtype='text',
dims=['num_categories'],
doc=
'The names of the categories in this TranscriptionTable. Each '
'category is represented by one DynamicTable stored in the parent group.'
'This attribute should be used to specify an order of categories.',
shape=[None])
],
groups=[
NWBGroupSpec(
neurodata_type_inc='DynamicTable',
doc=
'A DynamicTable representing a particular category for columns in the '
'TranscriptionTable parent container. The name of the category is given by '
'the name of the DynamicTable and its description by the description attribute '
'of the DynamicTable.',
quantity='*')
])
# Add all of our new data types to this list
new_data_types = [
sentences_table_spec, words_table_spec, syllables_table_spec,
phonemes_table_spec, transcription_table_spec
]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc="""Store the elliptical eye tracking output of DeepLabCut""",
name="""ndx-ellipse-eye-tracking""",
version="""0.1.0""",
author=list(map(str.strip, """Ben Dichter""".split(','))),
contact=list(map(str.strip, """*****@*****.**""".split(','))))
# TODO: specify the neurodata_types that are used by the extension as well
# as in which namespace they are found
# this is similar to specifying the Python modules that need to be imported
# to use your new data types
# as of HDMF 1.6.1, the full ancestry of the neurodata_types that are used by
# the extension should be included, i.e., the neurodata_type and its parent
# type and its parent type and so on. this will be addressed in a future
# release of HDMF.
ns_builder.include_type('SpatialSeries', namespace='core')
ns_builder.include_type('EyeTracking', namespace='core')
ns_builder.include_type('TimeSeries', namespace='core')
# TODO: define your new data types
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb
# for more information
ellipse_series_spec = NWBGroupSpec(
neurodata_type_def='EllipseSeries',
neurodata_type_inc='SpatialSeries',
doc='Information about an ellipse moving over time',
datasets=[
NWBDatasetSpec(
name=
'data', # override SpatialSeries 'data' dataset to be more explicit
dtype='numeric',
doc=
'The (x, y) coordinates of the center of the ellipse at each time point.',
dims=('num_times', 'x, y'),
shape=(None, 2),
),
NWBDatasetSpec(name='area',
dtype='float',
doc='ellipse area',
shape=(None, )),
NWBDatasetSpec(name='width',
dtype='float',
doc='width of ellipse',
shape=(None, )),
NWBDatasetSpec(name='height',
dtype='float',
doc='height of ellipse',
shape=(None, )),
NWBDatasetSpec(name='angle',
dtype='float',
doc='angle that ellipse is rotated by (phi)',
shape=(None, ))
])
ellipse_eye_tracking_spec = NWBGroupSpec(
neurodata_type_def='EllipseEyeTracking',
neurodata_type_inc='EyeTracking',
name=None,
default_name='EyeTracking',
doc='Stores detailed eye tracking information output from DeepLabCut',
groups=[
NWBGroupSpec(neurodata_type_inc=ellipse_series_spec,
name=x,
doc=x.replace('_', ' '))
for x in ('eye_tracking', 'pupil_tracking',
'corneal_reflection_tracking')
] + [
NWBGroupSpec(
neurodata_type_inc='TimeSeries',
name='likely_blink',
doc=
'Indicator of whether there was a probable blink for this frame'
)
],
)
# TODO: add all of your new data types to this list
new_data_types = [ellipse_series_spec, ellipse_eye_tracking_spec]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc="""labels for behavior or neural data""",
name="""ndx-labels""",
version="""0.1.0""",
author=list(map(str.strip, """Akshay Jaggi, Kanishk Jain, Jim Robinson-Bohnslav""".split(','))),
contact=list(map(str.strip, """*****@*****.**""".split(',')))
)
# TODO: specify the neurodata_types that are used by the extension as well
# as in which namespace they are found
# this is similar to specifying the Python modules that need to be imported
# to use your new data types
# as of HDMF 1.6.1, the full ancestry of the neurodata_types that are used by
# the extension should be included, i.e., the neurodata_type and its parent
# type and its parent type and so on. this will be addressed in a future
# release of HDMF.
ns_builder.include_type('ElectricalSeries', namespace='core')
ns_builder.include_type('ImageSeries', namespace='core')
ns_builder.include_type('TimeSeries', namespace='core')
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('NWBContainer', namespace='core')
ns_builder.include_type('DynamicTableRegion', namespace='hdmf-common')
ns_builder.include_type('VectorData', namespace='hdmf-common')
ns_builder.include_type('Data', namespace='hdmf-common')
# TODO: define your new data types
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb
# for more information
representation_series = NWBGroupSpec(
neurodata_type_def='RepresentationSeries',
neurodata_type_inc='TimeSeries',
doc=('Extends TimeSeries to include abstract representations of raw data (e.g. PCs, tSNE)'),
attributes=[
NWBAttributeSpec(
name='method',
doc='a description of the method used to derive the representation',
dtype='text'
),
NWBAttributeSpec(
name='unit',
doc='required unit for RepresentationSeries, default to "a.u."',
default_value="a.u.",
dtype='text',
required=False
),
],
links=[
NWBLinkSpec(
name="video",
target_type="ImageSeries",
doc="ref to video that's being labeled",
quantity="?"
)
],
datasets=[
NWBDatasetSpec(
name='data',
doc='float array of the value of m factors for n time steps',
dtype='float64',
dims=['num_frames', 'num_factors'],
shape=(None, None),
),
]
)
label_series = NWBGroupSpec(
neurodata_type_def='LabelSeries',
neurodata_type_inc='TimeSeries',
doc=('Extends TimeSeries to capture labels encoded'),
attributes=[
NWBAttributeSpec(
name='exclusive',
doc='whether the labels are exclusive or not',
dtype='bool'
),
NWBAttributeSpec(
name='method',
doc='a description of the method used to derive the labels (e.g. DeepEthogram v0.1.0)',
dtype='text'
),
NWBAttributeSpec(
name='unit',
doc='required unit for LabelSeries, default to "label"',
default_value="label",
dtype='text',
required=False
),
],
links=[
NWBLinkSpec(
name="representation",
target_type="RepresentationSeries",
doc="ref to representation series",
quantity="?"
),
NWBLinkSpec(
name="video",
target_type="ImageSeries",
doc="ref to video that's being labeled",
quantity="?"
)
],
datasets=[
NWBDatasetSpec(
name='data',
doc='Binary array of k labels for all n time steps',
dtype='int32',
dims=['num_frames', 'num_labels'],
shape=(None, None),
),
NWBDatasetSpec(
name='scores',
doc='Float array of the probabilities of each of the k labels for all n time steps',
dtype='float64',
dims=['num_frames', 'num_labels'],
shape=(None, None),
quantity="?"
),
NWBDatasetSpec(
name="vocabulary",
doc="list of k labels for the behaviors",
dtype="text",
dims=['num_labels'],
shape=(None,),
quantity="?"
)
]
)
# TODO: add all of your new data types to this list
new_data_types = [label_series, representation_series]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
ns_builder = NWBNamespaceBuilder(
doc=
'Holds structures for recording data from multiple compartments of multiple '
'neurons in a single TimeSeries',
name='ndx-simulation-output',
version='0.2.2',
author='Ben Dichter',
contact='*****@*****.**')
Compartments = NWBGroupSpec(
default_name='compartments',
neurodata_type_def='Compartments',
neurodata_type_inc='DynamicTable',
doc='table that holds information about what places are being recorded'
)
Compartments.add_dataset(
name='number',
neurodata_type_inc='VectorData',
dtype='int',
doc='cell compartment ids corresponding to a each column in the data')
Compartments.add_dataset(name='number_index',
neurodata_type_inc='VectorIndex',
doc='maps cell to compartments',
quantity='?')
Compartments.add_dataset(
name='position',
neurodata_type_inc='VectorData',
dtype='float',
quantity='?',
doc=
'position of recording within a compartment. 0 is close to soma, 1 is other end'
)
Compartments.add_dataset(name='position_index',
neurodata_type_inc='VectorIndex',
doc='indexes position',
quantity='?')
Compartments.add_dataset(name='label',
neurodata_type_inc='VectorData',
doc='labels for compartments',
dtype='text',
quantity='?')
Compartments.add_dataset(name='label_index',
neurodata_type_inc='VectorIndex',
doc='indexes label',
quantity='?')
CompartmentsSeries = NWBGroupSpec(
neurodata_type_def='CompartmentSeries',
neurodata_type_inc='TimeSeries',
doc='Stores continuous data from cell compartments')
CompartmentsSeries.add_link(
name='compartments',
target_type='Compartments',
quantity='?',
doc='meta-data about compartments in this CompartmentSeries')
SimulationMetaData = NWBGroupSpec(
name='simulation',
neurodata_type_def='SimulationMetaData',
neurodata_type_inc='LabMetaData',
doc='group that holds metadata for simulation')
SimulationMetaData.add_group(
name='compartments',
neurodata_type_inc='Compartments',
doc='table that holds information about what places are being recorded'
)
SimulationMetaData.add_attribute(
name='help',
dtype='text',
doc='help',
value='container for simulation meta-data that goes in /general')
new_data_types = [Compartments, CompartmentsSeries, SimulationMetaData]
types_to_include = [
'TimeSeries', 'VectorData', 'VectorIndex', 'DynamicTable',
'LabMetaData'
]
for ndtype in types_to_include:
ns_builder.include_type(ndtype, namespace='core')
export_spec(ns_builder, new_data_types)
#
# The following block of code demonstrates how to create a new namespace, and then add a new `neurodata_type`
# to this namespace. Finally,
# it calls :py:meth:`~hdmf.spec.write.NamespaceBuilder.export` to save the extensions to disk for downstream use.
# sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnails_extensions.png'
from pynwb.spec import NWBNamespaceBuilder, NWBGroupSpec, NWBAttributeSpec
ns_path = "mylab.namespace.yaml"
ext_source = "mylab.extensions.yaml"
ns_builder = NWBNamespaceBuilder('Extension for use in my Lab',
"mylab",
version='0.1.0')
ns_builder.include_type('ElectricalSeries', namespace='core')
ext = NWBGroupSpec(
'A custom ElectricalSeries for my lab',
attributes=[NWBAttributeSpec('trode_id', 'the tetrode id', 'int')],
neurodata_type_inc='ElectricalSeries',
neurodata_type_def='TetrodeSeries')
ns_builder.add_spec(ext_source, ext)
ns_builder.export(ns_path)
####################
# Running this block will produce two YAML files.
#
# The first file, mylab.namespace.yaml, contains the specification of the namespace.
#
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc='An extension to hold metadata about a multi-electrode probe.',
name='ndx-probe',
version='0.1.0',
author=list(map(str.strip, 'Ryan Ly'.split(','))),
contact=list(map(str.strip, '*****@*****.**'.split(',')))
)
ns_builder.include_type('ElectrodeGroup', namespace='core')
stereotrode = NWBGroupSpec(
neurodata_type_def='Stereotrode',
neurodata_type_inc='ElectrodeGroup',
doc=('A subtype of ElectrodeGroup to include metadata about a single stereotrode (group of 2 closely spaced '
'electrodes) on a shank of a probe.'),
attributes=[
NWBAttributeSpec(
name='location',
doc=('Location of the stereotrode in the brain (optional). Specify the area, layer, comments on '
'estimation of area/layer, etc. Use standard atlas names for anatomical regions when '
'possible.'),
dtype='text',
required=False
)
]
)
tetrode = NWBGroupSpec(
neurodata_type_def='Tetrode',
neurodata_type_inc='ElectrodeGroup',
doc=('A subtype of ElectrodeGroup to include metadata about a single tetrode (group of 4 closely spaced '
'electrodes) on a shank of a probe.'),
attributes=[
NWBAttributeSpec(
name='location',
doc=('Location of the tetrode in the brain (optional). Specify the area, layer, comments on '
'estimation of area/layer, etc. Use standard atlas names for anatomical regions when '
'possible.'),
dtype='text',
required=False
)
]
)
shank = NWBGroupSpec(
neurodata_type_def='Shank',
neurodata_type_inc='ElectrodeGroup',
doc=('A subtype of ElectrodeGroup to include metadata about a single shank of a probe.')
)
entry_point_ap_dtype = NWBDtypeSpec(name='ap',
dtype='float',
doc='Anterior-Posterior coordinate, in mm.')
entry_point_lr_dtype = NWBDtypeSpec(name='lr',
dtype='float',
doc='Left-Right coordinate, in mm.')
entry_point_dv_dtype = NWBDtypeSpec(name='dv',
dtype='float',
doc='Dorsal-Ventral coordinate, in mm.')
entry_point = NWBDatasetSpec(
name='entry_point',
doc='The coordinates of the entry point.',
dtype=[entry_point_ap_dtype, entry_point_lr_dtype, entry_point_dv_dtype], # compound dtype
attributes=[
NWBAttributeSpec(
name='reference',
doc=('Description of the reference atlas used for the coordinates, e.g., Allen Institute Common '
'Coordinate Framework v3, or stereotaxic coordinates with zero point at ear-bar zero.'),
dtype='text'
),
NWBAttributeSpec(
name='unit',
doc='Unit of measurement for the coordinates.',
dtype='text',
value='millimeters'
)
],
quantity='?'
)
angle_coronal_dtype = NWBDtypeSpec(
name='coronal',
dtype='float',
doc='Coronal angle, in degrees'
)
angle_sagittal_dtype = NWBDtypeSpec(
name='sagittal',
dtype='float',
doc='Sagittal angle, in degrees'
)
angle_axial_dtype = NWBDtypeSpec(
name='axial',
dtype='float',
doc='Axial angle, in degrees'
)
angle = NWBDatasetSpec(
name='angle',
doc='The angle of the probe.',
dtype=[angle_coronal_dtype, angle_sagittal_dtype, angle_axial_dtype], # compound dtype
attributes=[
NWBAttributeSpec(
name='reference',
doc='Description of the reference frame used for the angles, e.g., which direction is angle zero.',
dtype='text'
),
NWBAttributeSpec(
name='unit',
doc='Unit of measurement for the angles.',
dtype='text',
value='degrees'
)
],
quantity='?'
)
distance_advanced = NWBDatasetSpec(
name='distance_advanced',
doc='The distance that the probe was advanced from the surface of the brain, in mm.',
dtype='float',
attributes=[
NWBAttributeSpec(
name='unit',
doc='Unit of measurement for the distance.',
dtype='text',
value='millimeters'
)
],
quantity='?'
)
probe = NWBGroupSpec(
neurodata_type_def='Probe',
neurodata_type_inc='Device',
doc=('Metadata about a multi-electrode probe (or array), which contains one or many shanks. '
'Each shank should be represented as an ElectrodeGroup. Sub-groupings within a shank '
'should also be represented as ElectrodeGroups.'),
attributes=[
NWBAttributeSpec(
name='description',
doc='Description of the probe as free-form text.',
dtype='text',
),
NWBAttributeSpec(
name='model',
doc='Model name of the probe.',
dtype='text',
),
NWBAttributeSpec(
name='manufacturer',
doc='Manufacturer name of the probe.',
dtype='text',
),
NWBAttributeSpec(
name='id',
doc='Serial number or other unique identifier of the probe.',
dtype='text',
required=False
),
],
datasets=[
entry_point,
angle,
distance_advanced,
]
)
new_data_types = [stereotrode, tetrode, shank, probe]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
LabMetaData_ext.add_attribute(
name='auditory_stim_association',
doc='Descriptor of auditory stimulus-lick port association.',
dtype='text',
shape=None,
)
LabMetaData_ext.add_attribute(
name='visual_stim_association',
doc='Descriptor of auditory stimulus-lick port association.',
dtype='text',
shape=None,
)
LabMetaData_ext.add_attribute(
name='behavior_version',
doc='Version of schema used to translate LabView data to NWB.',
dtype='text',
shape=None,
)
# Add the extension
ext_source = 'uobrainflex.specs.yaml'
ns_builder.include_type('LabMetaData', namespace='core')
ns_builder.add_spec(ext_source, LabMetaData_ext)
# Save the namespace and extensions
ns_path = 'uobrainflex.namespace.yaml'
ns_builder.export(ns_path)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc="""Store the elliptical eye tracking output of DeepLabCut""",
name=f"""{NAMESPACE}""",
version="""0.1.0""",
author=list(map(str.strip, """Ben Dichter""".split(','))),
contact=list(map(str.strip, """*****@*****.**""".split(','))))
ns_builder.include_type('SpatialSeries', namespace='core')
ns_builder.include_type('EyeTracking', namespace='core')
ns_builder.include_type('TimeSeries', namespace='core')
ellipse_series_spec = NWBGroupSpec(
neurodata_type_def='EllipseSeries',
neurodata_type_inc='SpatialSeries',
doc='Information about an ellipse moving over time',
datasets=[
NWBDatasetSpec(
name=
'data', # override SpatialSeries 'data' dataset to be more explicit
dtype='numeric',
doc=
'The (x, y) coordinates of the center of the ellipse at each time point.',
dims=('num_times', 'x, y'),
shape=(None, 2),
),
NWBDatasetSpec(
name='area',
dtype='float',
doc='ellipse area, with nan values in likely blink times',
shape=(None, )),
NWBDatasetSpec(
name='area_raw',
dtype='float',
doc='ellipse area, with no regard to likely blink times',
shape=(None, )),
NWBDatasetSpec(name='width',
dtype='float',
doc='width of ellipse',
shape=(None, )),
NWBDatasetSpec(name='height',
dtype='float',
doc='height of ellipse',
shape=(None, )),
NWBDatasetSpec(name='angle',
dtype='float',
doc='angle that ellipse is rotated by (phi)',
shape=(None, ))
])
ellipse_eye_tracking_spec = NWBGroupSpec(
neurodata_type_def='EllipseEyeTracking',
neurodata_type_inc='EyeTracking',
name=None,
default_name='EyeTracking',
doc='Stores detailed eye tracking information output from DeepLabCut',
groups=[
NWBGroupSpec(neurodata_type_inc=ellipse_series_spec,
name=x,
doc=x.replace('_', ' '))
for x in ('eye_tracking', 'pupil_tracking',
'corneal_reflection_tracking')
] + [
NWBGroupSpec(
neurodata_type_inc='TimeSeries',
name='likely_blink',
doc=
'Indicator of whether there was a probable blink for this frame'
)
])
new_data_types = [ellipse_series_spec, ellipse_eye_tracking_spec]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__)))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc='NWB extension to store single or multi-animal pose tracking.',
name='ndx-pose',
version='0.2.0',
author=['Ryan Ly', 'Ben Dichter', 'Alexander Mathis', 'Talmo Pereira'],
contact=['*****@*****.**', '*****@*****.**', '*****@*****.**', '*****@*****.**'],
)
ns_builder.include_type('SpatialSeries', namespace='core')
ns_builder.include_type('TimeSeries', namespace='core')
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('NWBContainer', namespace='core')
pose_estimation_series = NWBGroupSpec(
neurodata_type_def='PoseEstimationSeries',
neurodata_type_inc='SpatialSeries',
doc='Estimated position (x, y) or (x, y, z) of a body part over time.',
datasets=[
NWBDatasetSpec(
name='data',
doc='Estimated position (x, y) or (x, y, z).',
dtype='float32',
dims=[['num_frames', 'x, y'], ['num_frames', 'x, y, z']],
shape=[[None, 2], [None, 3]],
attributes=[
NWBAttributeSpec(
name='unit',
dtype='text',
default_value='pixels',
doc=("Base unit of measurement for working with the data. The default value "
"is 'pixels'. Actual stored values are not necessarily stored in these units. "
"To access the data in these units, multiply 'data' by 'conversion'."),
required=True,
),
],
),
NWBDatasetSpec(
name='confidence',
doc='Confidence or likelihood of the estimated positions, scaled to be between 0 and 1.',
dtype='float32',
dims=['num_frames'],
shape=[None],
attributes=[
NWBAttributeSpec(
name='definition',
dtype='text',
doc=("Description of how the confidence was computed, e.g., "
"'Softmax output of the deep neural network'."),
required=False,
),
],
),
],
)
pose_grouping_series = NWBGroupSpec(
neurodata_type_def='PoseGroupingSeries',
neurodata_type_inc='TimeSeries',
doc='Instance-level part grouping timeseries for the individual animal. This contains metadata of the part grouping procedure for multi-animal pose trackers.',
datasets=[
NWBDatasetSpec(
name='name',
doc="Description of the type of localization, e.g., 'Centroid' or 'Bounding box'.",
dtype='text'
),
NWBDatasetSpec(
name='data',
doc='Score of the grouping approach that associated all of the keypoints to the same animal within the frame.',
dtype='float32',
dims=['num_frames'],
shape=[None]
),
NWBDatasetSpec(
name='location',
doc='Animal location for two-stage (top-down) multi-animal models, e.g., centroid or bounding box.',
dtype='float32',
dims=[['num_frames', 'x, y'], ['num_frames', 'x, y, z'], ['num_frames', 'x1, y1, x2, y2'], ['num_frames', 'x1, y1, z1, x2, y2, z2']],
shape=[[None, 2], [None, 3], [None, 4], [None, 6]],
quantity="?"
),
],
)
animal_identity_series = NWBGroupSpec(
neurodata_type_def='AnimalIdentitySeries',
neurodata_type_inc='TimeSeries',
doc='Identity of the animal predicted by a tracking or re-ID algorithm in multi-animal experiments.',
datasets=[
NWBDatasetSpec(
name='data',
doc='Score of the identity assignment approach that associated all of the keypoints to the same animal over frames, e.g., MOT tracking score or ID classification probability.',
dtype='float32',
dims=['num_frames'],
shape=[None],
),
NWBDatasetSpec(
name='name',
doc='Unique animal identifier, track label, or class name used to identify this animal in the experiment.',
dtype='text'
),
],
)
pose_estimation = NWBGroupSpec(
neurodata_type_def='PoseEstimation',
neurodata_type_inc='NWBDataInterface',
doc=('Group that holds estimated position data for multiple body parts, computed from the same video with '
'the same tool/algorithm. The timestamps of each child PoseEstimationSeries type should be the same.'),
default_name='PoseEstimation',
groups=[
NWBGroupSpec(
neurodata_type_inc='PoseEstimationSeries',
doc='Estimated position data for each body part.',
quantity='*',
),
NWBGroupSpec(
neurodata_type_inc='PoseGroupingSeries',
doc='Part grouping metadata for the individual in multi-animal experiments.',
quantity='?',
),
NWBGroupSpec(
neurodata_type_inc='AnimalIdentitySeries',
doc='Predicted identity of the individual in multi-animal experiments.',
quantity='?',
),
],
datasets=[
NWBDatasetSpec(
name='description',
doc='Description of the pose estimation procedure and output.',
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='original_videos',
doc='Paths to the original video files. The number of files should equal the number of camera devices.',
dtype='text',
dims=['num_files'],
shape=[None],
quantity='?',
),
NWBDatasetSpec(
name='labeled_videos',
doc='Paths to the labeled video files. The number of files should equal the number of camera devices.',
dtype='text',
dims=['num_files'],
shape=[None],
quantity='?',
),
NWBDatasetSpec(
name='dimensions',
doc='Dimensions of each labeled video file.',
dtype='uint8',
dims=['num_files', 'width, height'],
shape=[None, 2],
quantity='?',
),
NWBDatasetSpec(
name='scorer',
doc='Name of the scorer / algorithm used.',
dtype='text',
quantity='?',
),
NWBDatasetSpec(
name='source_software',
doc='Name of the software tool used. Specifying the version attribute is strongly encouraged.',
dtype='text',
quantity='?',
attributes=[
NWBAttributeSpec(
name='version',
doc='Version string of the software tool used.',
dtype='text',
required=False,
),
],
),
NWBDatasetSpec(
name='nodes',
doc=('Array of body part names corresponding to the names of the SpatialSeries objects within this '
'group.'),
dtype='text',
dims=['num_body_parts'],
shape=[None],
quantity='?',
),
NWBDatasetSpec(
name='edges',
doc=("Array of pairs of indices corresponding to edges between nodes. Index values correspond to row "
"indices of the 'nodes' dataset. Index values use 0-indexing."),
dtype='uint8',
dims=['num_edges', 'nodes_index, nodes_index'],
shape=[None, 2],
quantity='?',
),
],
# TODO: collections of multiple links is currently buggy in PyNWB/HDMF
# links=[
# NWBLinkSpec(
# target_type='Device',
# doc='Camera(s) used to record the videos.',
# quantity='*',
# ),
# ],
)
new_data_types = [pose_estimation_series, pose_estimation, pose_grouping_series, animal_identity_series]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'spec'))
export_spec(ns_builder, new_data_types, output_dir)
def main():
# these arguments were auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(
doc="""extension for fiber photometry data""",
name="""ndx-photometry""",
version="""0.1.0""",
author=list(map(str.strip, """Akshay Jaggi""".split(","))),
contact=list(map(str.strip, """*****@*****.**""".split(","))),
)
# TODO: specify the neurodata_types that are used by the extension as well
# as in which namespace they are found
# this is similar to specifying the Python modules that need to be imported
# to use your new data types
# as of HDMF 1.6.1, the full ancestry of the neurodata_types that are used by
# the extension should be included, i.e., the neurodata_type and its parent
# type and its parent type and so on. this will be addressed in a future
# release of HDMF.
ns_builder.include_type("TimeSeries", namespace="core")
ns_builder.include_type("NWBDataInterface", namespace="core")
ns_builder.include_type("NWBContainer", namespace="core")
ns_builder.include_type("RoiResponseSeries", namespace="core")
ns_builder.include_type("LabMetaData", namespace="core")
ns_builder.include_type("DynamicTable", namespace="hdmf-common")
ns_builder.include_type("DynamicTableRegion", namespace="hdmf-common")
ns_builder.include_type("VectorData", namespace="hdmf-common")
ns_builder.include_type("VectorIndex", namespace="hdmf-common")
ns_builder.include_type("Data", namespace="hdmf-common")
ns_builder.include_type("ElementIdentifiers", namespace="hdmf-common")
# TODO: define your new data types
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb
# for more information
fibers_table = NWBGroupSpec(
neurodata_type_def="FibersTable",
neurodata_type_inc="DynamicTable",
name='fibers',
doc="Extends DynamicTable to hold various Fibers",
datasets=[
NWBDatasetSpec(
name="location",
doc="location of fiber",
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
),
NWBDatasetSpec(
name="excitation_source",
doc="references rows of ExcitationSourcesTable",
dtype="int",
shape=(None,),
neurodata_type_inc="DynamicTableRegion",
),
NWBDatasetSpec(
name="photodetector",
doc="references rows of PhotodetectorsTable",
dtype="int",
shape=(None,),
neurodata_type_inc="DynamicTableRegion",
),
NWBDatasetSpec(
name="fluorophores",
doc="references rows of FluorophoresTable",
shape=(None,),
neurodata_type_inc="DynamicTableRegion",
),
# NWBDatasetSpec(
# name="fluorophores_index",
# doc="indexes fluorophores of FluorophoresTable",
# shape=(None,),
# neurodata_type_inc="VectorIndex",
# ),
NWBDatasetSpec(
name="notes",
doc="description of fiber",
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
),
NWBDatasetSpec(
name="fiber_model_number",
doc="fiber model number",
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
quantity="?",
),
NWBDatasetSpec(
name="dichroic_model_number",
doc="dichroic model number",
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
quantity="?",
),
],
)
photodetectors_table = NWBGroupSpec(
neurodata_type_def="PhotodetectorsTable",
neurodata_type_inc="DynamicTable",
name='photodetectors',
doc="Extends DynamicTable to hold various Photodetectors",
datasets=[
NWBDatasetSpec(
name="peak_wavelength",
doc="peak wavelength of photodetector",
dtype="float",
shape=(None,),
neurodata_type_inc="VectorData",
attributes=[
NWBAttributeSpec(
name="unit", doc="wavelength unit", value="nanometers", dtype="text"
)
],
),
NWBDatasetSpec(
name="type",
doc='"PMT" or "photodiode"',
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
),
NWBDatasetSpec(
name="gain",
doc="gain on the photodetector",
dtype="float",
shape=(None,),
neurodata_type_inc="VectorData",
),
NWBDatasetSpec(
name="model_number",
doc="model number of the photodector",
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
quantity="?",
),
],
)
excitationsources_table = NWBGroupSpec(
neurodata_type_def="ExcitationSourcesTable",
neurodata_type_inc="DynamicTable",
name="excitation_sources",
doc="Extends DynamicTable to hold various Excitation Sources",
datasets=[
NWBDatasetSpec(
name="peak_wavelength",
doc="peak wavelength of the excitation source",
dtype="float",
shape=(None,),
neurodata_type_inc="VectorData",
attributes=[
NWBAttributeSpec(
name="unit", doc="wavelength unit", value="nanometers", dtype="text"
)
],
),
NWBDatasetSpec(
name="source_type",
doc='"LED" or "laser"',
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
),
NWBDatasetSpec(
name="commanded_voltage",
doc="references CommandedVoltageSeries",
dtype=NWBRefSpec(
target_type="CommandedVoltageSeries", reftype="object"
),
shape=(None,),
neurodata_type_inc="VectorData",
),
NWBDatasetSpec(
name="output",
doc="excitation output, references TimeSeries",
dtype="float",
shape=(None,),
neurodata_type_inc="VectorData",
quantity="?",
),
NWBDatasetSpec(
name="model_number",
doc="model number of the excitation source",
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
quantity="?",
),
],
)
commandedvoltage_series = NWBGroupSpec(
neurodata_type_def="CommandedVoltageSeries",
neurodata_type_inc="TimeSeries",
doc="Extends TimeSeries to hold a Commanded Voltage",
datasets=[
NWBDatasetSpec(
name="data",
doc="voltages (length number timesteps) in unit volts",
dtype="float",
shape=(None,),
attributes=[
NWBAttributeSpec(
name="unit", doc="data unit", value="volts", dtype="text"
)
],
),
NWBDatasetSpec(
name="frequency",
doc="voltage frequency in unit hertz",
dtype="float",
attributes=[
NWBAttributeSpec(
name="unit", doc="frequency unit", value="hertz", dtype="text"
)
],
),
NWBDatasetSpec(
name="power",
doc="voltage power in unit volts",
dtype="float",
attributes=[
NWBAttributeSpec(
name="unit", doc="power unit", value="volts", dtype="text"
)
],
),
],
)
multi_commanded_voltage = NWBGroupSpec(
name='commanded_voltages',
neurodata_type_def="MultiCommandedVoltage",
neurodata_type_inc="NWBDataInterface",
doc="holds CommandedVoltageSeries objects",
groups=[
NWBGroupSpec(
neurodata_type_inc="CommandedVoltageSeries",
quantity="*",
doc="commanded voltage series",
)
],
)
deconvolvedroiresponse_series = NWBGroupSpec(
neurodata_type_def="DeconvolvedRoiResponseSeries",
neurodata_type_inc="RoiResponseSeries",
doc="Extends RoiResponseSeries to hold deconvolved data",
links=[
NWBLinkSpec(
name="raw",
target_type="RoiResponseSeries",
doc="ref to roi response series",
)
],
datasets=[
NWBDatasetSpec(
name="deconvolution_filter",
doc="description of deconvolution filter used",
dtype="text",
neurodata_type_inc="VectorData",
quantity="?",
),
NWBDatasetSpec(
name="downsampling_filter",
doc="description of downsampling filter used",
dtype="text",
neurodata_type_inc="VectorData",
quantity="?",
),
],
)
fluorophores_table = NWBGroupSpec(
neurodata_type_def="FluorophoresTable",
neurodata_type_inc="DynamicTable",
name='fluorophores',
doc="Extends DynamicTable to hold various Fluorophores",
datasets=[
NWBDatasetSpec(
name="label",
doc="name of fluorophore",
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
),
NWBDatasetSpec(
name="location",
doc='injection brain region name',
dtype="text",
shape=(None,),
neurodata_type_inc="VectorData",
quantity="?",
),
NWBDatasetSpec(
name="coordinates",
doc="injection taxonomical coordinates in (AP, ML, Z) in mm relative to Bregma",
dtype="float",
shape=(None,3),
neurodata_type_inc="VectorData",
quantity="?",
),
],
)
fiber_photometry = NWBGroupSpec(
neurodata_type_def='FiberPhotometry',
neurodata_type_inc='LabMetaData',
name='fiber_photometry',
doc='all Fiber Photometry metadata',
groups=[
NWBGroupSpec(
name='fibers',
neurodata_type_inc='FibersTable',
doc='table of fibers used'
),
NWBGroupSpec(
name='excitation_sources',
neurodata_type_inc='ExcitationSourcesTable',
doc='table of excitation sources used'
),
NWBGroupSpec(
name='photodetectors',
neurodata_type_inc='PhotodetectorsTable',
doc='table of photodetectors used'
),
NWBGroupSpec(
name='fluorophores',
neurodata_type_inc='FluorophoresTable',
doc='table of fluorophores used'
),
NWBGroupSpec(
name='commanded_voltages',
neurodata_type_inc='MultiCommandedVoltage',
doc='multiple commanded voltage container'
)
]
)
# TODO: add all of your new data types to this list
new_data_types = [
fibers_table,
photodetectors_table,
excitationsources_table,
commandedvoltage_series,
deconvolvedroiresponse_series,
multi_commanded_voltage,
fiber_photometry,
fluorophores_table
]
# export the spec to yaml files in the spec folder
output_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), "..", "..", "spec")
)
export_spec(ns_builder, new_data_types, output_dir)
