def main():
ns_builder = NWBNamespaceBuilder(doc='transcriptions and other speech-related data',
name='speech',
version='0.1.1',
author='Ben Dichter',
contact='*****@*****.**')
Transcription = NWBGroupSpec(neurodata_type_def='Transcription', neurodata_type_inc='NWBDataInterface',
name='transcription',
doc='holds tiers for different levels of transcription (e.g. sentence, word, phoneme)')
Transcription.add_attribute(name='help', dtype='text', value='holds tiers for different levels of transcription',
doc='doc')
Transcription.add_attribute(name='settings', dtype='text', required=False,
doc='text field for entering any algorithms and settings used for automatic transcription')
for feature_type in ('phoneme_features', 'phonemes', 'syllables', 'words', 'sentences'):
intervals = Transcription.add_group(name=feature_type, neurodata_type_inc='TimeIntervals', quantity='?',
doc='label, start, and stop times for ' + feature_type)
intervals.add_attribute(name='help', dtype='text', value='holds ' + feature_type + ' tier times',
doc='doc')
new_data_types = [Transcription]
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('TimeIntervals', namespace='core')
export_spec(ns_builder, new_data_types)
def main():
ns_builder = NWBNamespaceBuilder(
doc='holds metadata relevant for miniscope acquisition',
name='ndx-miniscope',
version='0.2.2',
author='Ben Dichter',
contact='*****@*****.**')
Miniscope = NWBGroupSpec(
neurodata_type_def='Miniscope',
neurodata_type_inc='Device',
doc='extension of Device to hold metadata specific to Miniscopes')
Miniscope.add_attribute(name='excitation',
doc='magnitude of excitation',
dtype='int',
required=False)
Miniscope.add_attribute(name='msCamExposure',
doc='exposure of camera (max=255)',
dtype='int',
required=False)
ns_builder.include_type('Device', namespace='core')
export_spec(ns_builder, [Miniscope])
def main():
# the values for ns_builder are auto-generated from your cookiecutter inputs
ns_builder = NamespaceBuilder(doc='General framework for storing nonrigid motion correction of optical imaging in '
'NWB:N 2.0',
name='ndx-nonrigid-motion-correction',
version='0.1.0',
author='Ben Dichter',
contact='*****@*****.**')
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb for more information
nmc = NWBGroupSpec(
neurodata_type_def='NonrigidMotionCorrection',
neurodata_type_inc='NWBDataInterface',
doc='General framework for storing nonrigid motion correction of optical imaging in NWB:N 2.0',
)
# I think it may make more sense to store pixel_map as an x*y*z array and convert to your form
nmc.add_dataset(name='pixel_map',
dtype='int',
shape=((None, None), (None, None, None)),
dims=(('x', 'y'), ('x', 'y', 'z')),
doc="A mapping function of pixels onto basis variables. To be fully general, this is a sparse "
"matrix (AxBxC) (number of voxels in each of the spatial dimensions). In standard "
"blockwise motion correction, this matrix is a matrix of ones. In CNMF's patches, this matrix "
"is a labels each non-overlapping patch with a unique int")
nmc.add_dataset(name='data',
dtype='float',
shape=(None, None),
dims=('time', 'nblocks * ndims'),
doc="A set of (NxM) basis variables where N is the number of blocks, M is the number of spatial "
"dimensions (1 to 3) each defined for a set of times T (for the number of time-steps). In "
"standard block-wise motion correction, N = 1, in line-by-line motion correction, N = number of"
" lines, in CNMF's non-rigid motion correction, N = number of patches")
nmc.add_attribute(name='interpolation_type',
dtype='text',
doc="the relationship between expected shifts and the estimated movie value. This will frequently"
" be either nearest neighbor (common), 1 pixel local interpolation (most common), or some "
"other interpolation function (least common, such as MATLAB's 3d gridded interpolation)",
default_value='local_interpolation')
nmc.add_attribute(name='spatial_dimensions',
dtype='int',
shape=((2,), (3,)),
dims=('x, y', 'x, y, z'),
doc='number of pixels in each spatial dimension')
nmc.add_attribute(name='nblocks', dtype='int', doc='number of blocks')
new_data_types = [nmc]
ns_builder.include_type('TimeSeries', namespace='core')
export_spec(ns_builder, new_data_types)
# Create a builder for the namespace
ns_builder = NWBNamespaceBuilder(doc='Extension for uobrainflex metadata',
name='uobrainflex_metadata',
version='1.0',
author='Santiago Jaramillo')
# Create extension
LabMetaData_ext = NWBGroupSpec(
doc='Extension for uobrainflex metadata',
neurodata_type_def='LabMetaData_ext',
neurodata_type_inc='LabMetaData',
)
LabMetaData_ext.add_attribute(
name='rig',
doc='Rig where data was collected.',
dtype='text',
shape=None,
)
LabMetaData_ext.add_attribute(
name='session_type',
doc='Type of behavior session.',
dtype='text',
shape=None,
)
LabMetaData_ext.add_attribute(
name='training_stage',
doc='Stage of behavioral training.',
dtype='text',
shape=None,
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='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():
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)
def main():
# the values for ns_builder are auto-generated from your cookiecutter inputs
ns_builder = NWBNamespaceBuilder(doc='An NWB:N extension',
name='ndx-task',
version='0.1.0',
author='Ben Dichter',
contact='*****@*****.**')
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb for more information
task = NWBGroupSpec(neurodata_type_def='Task', neurodata_type_inc='NWBDataInterface', doc='task information')
task.add_attribute(name='description', doc='describe the task', dtype='text', required=True)
task.add_attribute(name='category', doc='free category field', dtype='text', required=False)
task.add_attribute(name='help', doc='help', dtype='text', value='stores data about a specific task')
task.add_attribute(name='auditory', doc='experiment involves auditory stimuli', dtype='bool', required=False)
task.add_attribute(name='visual', doc='experiment involves visual stimuli', dtype='bool', required=False)
task.add_attribute(name='decision', doc='experiment involves decision making', dtype='bool', required=False)
task.add_attribute(name='navigation', doc='experiment involves a navigation task', dtype='bool', required=False)
task.add_attribute(name='rest', doc='experiment involves no task or stimuli', dtype='bool', required=False)
task.add_attribute(name='motor', doc='experiment involves a fine motor task', dtype='bool', required=False)
task.add_attribute(name='olfactory', doc='experiment involves olfactory stimuli', dtype='bool', required=False)
task.add_attribute(name='speech production', doc='experiment involves speaking', dtype='bool', required=False)
task.add_attribute(name='speech perception', doc='experiment involves ', dtype='bool', required=False)
tasks = NWBGroupSpec(neurodata_type_def='Tasks', neurodata_type_inc='LabMetaData', doc='holds task objects')
tasks.add_group(neurodata_type_inc='Tasks', quantity='*', doc='task information')
tasks.add_attribute(name='help', doc='help', dtype='text', value='stores Task objects')
new_data_types = [task, tasks]
ns_builder.include_type('NWBDataInterface', namespace='core')
ns_builder.include_type('LabMetaData', namespace='core')
export_spec(ns_builder, new_data_types)
