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)
def main():
ns_builder = NamespaceBuilder(
doc="""HDMF extensions for storing hierarchical behavioral data""",
name="""ndx-hierarchical-behavioral-data""",
version="""0.1.1""",
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('TimeIntervals', namespace='core')
ns_builder.include_type('DynamicTableRegion', namespace='core')
ns_builder.include_type('VectorData', namespace='core')
behav_table = NWBGroupSpec(
neurodata_type_def='HierarchicalBehavioralTable',
neurodata_type_inc='TimeIntervals',
doc='DynamicTable that holds hierarchical behavioral information.')
behav_table.add_dataset(name='label',
neurodata_type_inc='VectorData',
doc='The label associated with each item',
dtype='text')
next_tier = behav_table.add_dataset(
name='next_tier',
neurodata_type_inc='DynamicTableRegion',
doc='reference to the next tier',
)
next_tier.add_attribute(name='table',
dtype=RefSpec(target_type='TimeIntervals',
reftype='object'),
doc='reference to the next level')
behav_table.add_dataset(
name='next_tier_index',
neurodata_type_inc='VectorIndex',
doc='Index dataset for next tier.',
)
new_data_types = [behav_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 for survey/behavioral data',
name='ndx-survey-data',
version='0.2.0',
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',
shape=(None,),
dims=('num_options',))]
)
survey_data.add_dataset(
neurodata_type_inc='VectorData',
doc='UNIX time of survey response',
name='unix_timestamp',
dtype='int',
shape=(None,),
dims=('num_responses',)
)
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():
ns_builder = NWBNamespaceBuilder(
doc='type for storing time-varying 3D point clouds',
name='ndx-pointcloudseries',
version='0.0.1',
author='Luiz Tauffer and Ben Dichter',
contact='*****@*****.**')
PointCloudSeries = NWBGroupSpec(
doc='type for storing time-varying 3D point clouds',
neurodata_type_def='PointCloudSeries',
neurodata_type_inc='TimeSeries',
)
PointCloudSeries.add_dataset(name='point_cloud',
neurodata_type_inc='VectorData',
doc='datapoints locations over time',
dims=('time', '[x, y, z]'),
shape=(None, 3),
dtype='float',
quantity='?')
PointCloudSeries.add_dataset(name='point_cloud_index',
neurodata_type_inc='VectorIndex',
doc='datapoints indices',
dims=('index', ),
shape=(None),
quantity='?')
PointCloudSeries.add_dataset(name='color',
neurodata_type_inc='VectorData',
doc='datapoints color',
dims=('time', '[r, g, b]'),
shape=(None, 3),
dtype='float',
quantity='?')
PointCloudSeries.add_dataset(name='color_index',
neurodata_type_inc='VectorIndex',
doc='datapoints colors indices',
dims=('index', ),
shape=(None),
quantity='?')
new_data_types = [PointCloudSeries]
ns_builder.include_type('TimeSeries', namespace='core')
ns_builder.include_type('VectorData', namespace='core')
ns_builder.include_type('VectorIndex', namespace='core')
export_spec(ns_builder, new_data_types)
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():
# 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='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 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='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)
