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='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=
'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():
# 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)
