def build_ext():
datasets = [
NWBDatasetSpec(doc='list of cell ids',
dtype='uint32',
shape=(None, 1),
name='gid',
quantity='?'),
NWBDatasetSpec(doc='index pointer',
dtype='uint64',
shape=(None, 1),
name='index_pointer'),
NWBDatasetSpec(
doc=
'cell compartment ids corresponding to a given column in the data',
dtype='uint32',
shape=(None, 1),
name='element_id'),
NWBDatasetSpec(
doc='relative position of recording within a given compartment',
dtype='float',
shape=(None, None),
name='element_pos')
]
cont_data = NWBGroupSpec(doc='A spec for storing cell recording variables',
datasets=datasets,
neurodata_type_inc='TimeSeries',
neurodata_type_def='CompartmentSeries')
ns_builder.add_spec(ext_source, cont_data)
ns_builder.export(ns_path)
def test_load_namespace_with_reftype_attribute_check_autoclass_const(self):
ns_builder = NWBNamespaceBuilder('Extension for use in my Lab',
self.prefix)
test_ds_ext = NWBDatasetSpec(
doc='test dataset to add an attr',
name='test_data',
shape=(None, ),
attributes=[
NWBAttributeSpec(name='target_ds',
doc='the target the dataset applies to',
dtype=RefSpec('TimeSeries', 'object'))
],
neurodata_type_def='my_new_type')
ns_builder.add_spec(self.ext_source, test_ds_ext)
ns_builder.export(self.ns_path, outdir=self.tempdir)
type_map = get_type_map(
extensions=os.path.join(self.tempdir, self.ns_path))
my_new_type = type_map.get_container_cls(self.prefix, 'my_new_type')
docval = None
for tmp in get_docval(my_new_type.__init__):
if tmp['name'] == 'target_ds':
docval = tmp
break
self.assertIsNotNone(docval)
self.assertEqual(docval['type'], TimeSeries)
def test_load_namespace_with_reftype_attribute(self):
ns_builder = NWBNamespaceBuilder('Extension for use in my Lab', self.prefix, version='0.1.0')
test_ds_ext = NWBDatasetSpec(doc='test dataset to add an attr',
name='test_data', shape=(None,),
attributes=[NWBAttributeSpec(name='target_ds',
doc='the target the dataset applies to',
dtype=RefSpec('TimeSeries', 'object'))],
neurodata_type_def='my_new_type')
ns_builder.add_spec(self.ext_source, test_ds_ext)
ns_builder.export(self.ns_path, outdir=self.tempdir)
get_type_map(extensions=os.path.join(self.tempdir, self.ns_path))
def main():
ns_builder = NWBNamespaceBuilder(
doc="Detected events from optical physiology ROI fluorescence traces",
name=f"""{NAMESPACE}""",
version="""0.1.0""",
author="""Allen Institute for Brain Science""",
contact="""*****@*****.**"""
)
ns_builder.include_type('RoiResponseSeries', namespace='core')
ns_builder.include_type('DynamicTableRegion', namespace='core')
ns_builder.include_type('TimeSeries', namespace='core')
ns_builder.include_type('NWBDataInterface', namespace='core')
ophys_events_spec = NWBGroupSpec(
neurodata_type_def='OphysEventDetection',
neurodata_type_inc='RoiResponseSeries',
name='event_detection',
doc='Stores event detection output',
datasets=[
NWBDatasetSpec(
name='lambdas',
dtype='float',
doc='calculated regularization weights',
shape=(None,)
),
NWBDatasetSpec(
name='noise_stds',
dtype='float',
doc='calculated noise std deviations',
shape=(None,)
)
]
)
new_data_types = [ophys_events_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 build_dataset(name, d):
kwargs = remap_keys(name, d)
if 'name' in kwargs:
if kwargs['name'] in dataset_ndt:
tmpname = kwargs.pop('name')
kwargs['neurodata_type_def'] = dataset_ndt[tmpname]
#kwargs['neurodata_type_inc'] = 'NWBData'
if 'neurodata_type_def' in kwargs or 'neurodata_type_inc' in kwargs:
kwargs['namespace'] = CORE_NAMESPACE
dset_spec = NWBDatasetSpec(kwargs.pop('doc'), kwargs.pop('dtype'), **kwargs)
if 'attributes' in d:
add_attributes(dset_spec, d['attributes'])
return dset_spec
def _extract_dataset(val):
if val.many:
raise NotImplementedError('many not supported')
if 'values' not in val.schema.fields:
raise ValueError('A dataset must contain an attribute called "values"')
values = val.schema.fields['values']
attributes = _extract_attributes(attributes=val.schema.fields, fields_to_skip=['values'])
return NWBDatasetSpec(
name=val.name,
attributes=attributes,
doc=val.metadata['doc'],
dtype=STYPE_DICT[type(values)],
dims=values.metadata['shape']
)
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="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="""DANDI project extension""",
name="""ndx-dandi""",
version="""0.1.0""",
author=list(map(str.strip, """Yaroslav O Halchenko""".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('Subject', namespace='core')
ns_builder.include_namespace('core')
# TODO: define your new data types
# see https://pynwb.readthedocs.io/en/latest/extensions.html#extending-nwb
# for more information
dandi_subject = NWBGroupSpec(
neurodata_type_def='Subject',
neurodata_type_inc='Subject',
doc="TODO: somehow inherit",
datasets=[
NWBDatasetSpec(
name='subject_id',
quantity=1, # 'zero_or_one',
doc="TODO: somehow inherit")
])
# TODO: add all of your new data types to this list
new_data_types = [dandi_subject]
# 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)
from pynwb.spec import NWBDatasetSpec, NWBNamespaceBuilder, NWBGroupSpec, NWBAttributeSpec, RefSpec
namespace = 'template [CHANGE TO NAME]'
ns_path = namespace + ".namespace.yaml"
ext_source = namespace + ".extensions.yaml"
spec = NWBGroupSpec(
neurodata_type_def='',
neurodata_type_inc='NWBDataInterface',
quantity='?',
doc='',
groups=[],
attributes=[
NWBAttributeSpec(name='', doc='', dtype='', required=False),
NWBAttributeSpec(name='help',
doc='help',
dtype='text',
value='ENTER HELP INFO HERE')
],
datasets=[NWBDatasetSpec(name='', doc='', dtype='', shape=())])
ns_builder = NWBNamespaceBuilder(doc=namespace + ' extensions',
name=namespace,
version='1.0',
author='Ben Dichter',
contact='*****@*****.**')
specs = (spec, )
for spec in specs:
ns_builder.add_spec(ext_source, spec)
ns_builder.export(ns_path)
name = 'ecog'
ns_path = name + ".namespace.yaml"
ext_source = name + ".extensions.yaml"
# Now we define the data structures. We use `NWBDataInterface` as the base type,
# which is the most primitive type you are likely to use as a base. The name of the
# class is `CorticalSurface`, and it requires two matrices, `vertices` and
# `faces`.
surface = NWBGroupSpec(
doc='brain cortical surface',
datasets=[
NWBDatasetSpec(doc='faces for surface, indexes vertices',
shape=(None, 3),
name='faces',
dtype='uint',
dims=('face_number', 'vertex_index')),
NWBDatasetSpec(doc='vertices for surface, points in 3D space',
shape=(None, 3),
name='vertices',
dtype='float',
dims=('vertex_number', 'xyz'))
],
neurodata_type_def='CorticalSurface',
neurodata_type_inc='NWBDataInterface')
# Now we set up the builder and add this object
ns_builder = NWBNamespaceBuilder(name + ' extensions', name, version='0.1.0')
ns_builder.add_spec(ext_source, surface)
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)
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():
# 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="""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():
# 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)
'ElectrodeGroup':
NWBGroupSpec(
'One of possibly many groups, one for each electrode group.',
neurodata_type_def='ElectrodeGroup',
neurodata_type_inc='NWBContainer',
namespace=CORE_NAMESPACE,
attributes=[
AttributeSpec('help',
'str',
"Value is '%s'" % eg_help,
value=eg_help)
],
datasets=[
NWBDatasetSpec('array with description for each channel',
'text',
name='channel_description',
shape=(None, ),
dims=('num_channels', )),
NWBDatasetSpec(
'array with location description for each channel e.g. "CA1"',
'text',
name='channel_location',
shape=(None, ),
dims=('num_channels', )),
NWBDatasetSpec(
'array with description of filtering applied to each channel',
'text',
name='channel_filtering',
shape=(None, ),
dims=('num_channels', )),
NWBDatasetSpec(
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)
from pynwb.spec import NWBDatasetSpec, NWBNamespaceBuilder, NWBGroupSpec
name = 'general'
ns_path = name + '.namespace.yaml'
ext_source = name + '.extensions.yaml'
gid_spec = NWBDatasetSpec(doc='global id for neuron',
shape=(None, 1),
name='cell_index',
dtype='int')
data_val_spec = NWBDatasetSpec(doc='Data values indexed by pointer',
shape=(None, 1),
name='value',
dtype='float')
data_pointer_spec = NWBDatasetSpec(doc='Pointers that index data values',
shape=(None, 1),
name='pointer',
dtype='RefSpec')
gid_pointer_value_spec = [gid_spec, data_val_spec, data_pointer_spec]
cat_cell_info = NWBGroupSpec(
neurodata_type_def='CatCellInfo',
doc='Categorical Cell Info',
datasets=[
gid_spec,
NWBDatasetSpec(name='indices',
doc='indices into values for each gid in order',
shape=(None, 1),
dtype='RefSpec'),
NWBDatasetSpec(name='values',
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():
# 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)
from pynwb.spec import NWBDatasetSpec, NWBNamespaceBuilder, NWBGroupSpec, AttributeSpec
from pynwb import get_class, load_namespaces
## spec for
name = 'simulation_output'
ns_path = name + '.namespace.yaml'
ext_source = name + '.extensions.yaml'
gid_spec = NWBDatasetSpec(doc='global id for neuron',
shape=(None, 1),
name='cell_index', dtype='int', quantity='?')
values = AttributeSpec(shape=(None, 1), name='labels', dtype='str',
required=True, doc='these are the values')
cat_data_spec = NWBDatasetSpec(name='data', shape=(None, 1), dtype='int',
doc='indices into values for each gid in order',
attributes=[values])
cat_cell_info = NWBGroupSpec(neurodata_type_def='CatCellInfo',
doc='Categorical Cell Info',
datasets=[gid_spec, cat_data_spec],
neurodata_type_inc='NWBDataInterface')
# export
ns_builder = NWBNamespaceBuilder(name + ' extensions', name)
for spec in [cat_cell_info]:
ns_builder.add_spec(ext_source, spec)
ns_builder.export(ns_path)
quantity='+',
doc='manipulation',
attributes=[
NWBAttributeSpec(name='brain_region_target',
dtype='text',
doc='Allan Institute Acronym')
])
virus_injection = NWBGroupSpec(
neurodata_type_inc='NWBDataInterface',
neurodata_type_def='VirusInjection',
quantity='+',
doc='notes about surgery that includes virus injection',
datasets=[
NWBDatasetSpec(name='coordinates',
doc='(AP, ML, DV) of virus injection',
dtype='float',
shape=(3, ))
],
attributes=[
NWBAttributeSpec(name='virus', doc='type of virus', dtype='text'),
NWBAttributeSpec(name='volume',
doc='volume of injecting in nL',
dtype='float'),
NWBAttributeSpec(name='rate',
doc='rate of injection (nL/s)',
dtype='float',
required=False),
NWBAttributeSpec(name='scheme',
doc='scheme of injection',
dtype='text',
required=False),
cat_cell_info = NWBGroupSpec(
neurodata_type_def='CatCellInfo',
doc='Categorical Cell Info',
attributes=[
NWBAttributeSpec(
name='help',
doc='help',
dtype='text',
value=
'Categorical information about cells. For most cases the units tables is more appropriate. This '
'structure can be used if you need multiple entries per cell')
],
datasets=[
NWBDatasetSpec(doc='global id for neuron',
shape=(None, ),
name='cell_index',
dtype='int',
quantity='?'),
NWBDatasetSpec(name='indices',
doc='list of indices for values',
shape=(None, ),
dtype='int',
attributes=[values])
],
neurodata_type_inc='NWBDataInterface')
cat_timeseries = NWBGroupSpec(neurodata_type_def='CatTimeSeries',
neurodata_type_inc='TimeSeries',
doc='Categorical data through time',
datasets=[
NWBDatasetSpec(
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 cookie-cutter inputs
ns_builder = NWBNamespaceBuilder(
doc='An NWB extension for storing bipolar schema',
name='ndx-bipolar-scheme',
version='0.4.0',
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', 'ElectricalSeries'):
ns_builder.include_type(type_name, namespace='core')
ndx_bipolar_scheme = NWBGroupSpec(
doc='Group that holds proposed extracellular electrophysiology extensions.',
neurodata_type_def='NdxBipolarScheme',
neurodata_type_inc='LabMetaData',
name='ndx_bipolar_scheme',
groups=[NWBGroupSpec(
neurodata_type_inc='BipolarSchemeTable',
doc='Bipolar referencing scheme used',
quantity='*'
)],
links=[NWBLinkSpec(
name='source',
doc='input to re-referencing scheme',
target_type='ElectricalSeries',
quantity='?',
)]
)
bipolar_scheme_table = NWBGroupSpec(
default_name='bipolar_scheme',
doc='Table that holds information about the bipolar scheme used',
neurodata_type_def='BipolarSchemeTable',
neurodata_type_inc='DynamicTable',
datasets=[
NWBDatasetSpec(
name='anodes',
neurodata_type_inc='DynamicTableRegion',
doc='references the electrodes table',
dims=('num_electrodes',),
shape=(None,),
dtype='int'
),
NWBDatasetSpec(
name='cathodes',
neurodata_type_inc='DynamicTableRegion',
doc='references the electrodes table',
dims=('num_electrodes',),
shape=(None,),
dtype='int'
),
NWBDatasetSpec(
name='anodes_index',
neurodata_type_inc='VectorIndex',
doc='Indices for the anode table',
dims=('num_electrode_grp',),
shape=(None,),
quantity='?',
),
NWBDatasetSpec(
name='cathodes_index',
neurodata_type_inc='VectorIndex',
doc='Indices for the cathode table',
dims=('num_electrode_grp',),
shape=(None,),
quantity='?',
)
]
)
new_data_types = [ndx_bipolar_scheme, bipolar_scheme_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)
name = 'simulation_output'
ns_path = name + '.namespace.yaml'
ext_source = name + '.extensions.yaml'
# Continuous data for cell compartments
Compartments = NWBGroupSpec(
default_name='compartments',
neurodata_type_def='Compartments',
neurodata_type_inc='DynamicTable',
doc='table that holds information about what places are being recorded',
datasets=[
NWBDatasetSpec(
name='number',
neurodata_type_inc='VectorData',
doc=
'cell compartment ids corresponding to a each column in the data',
dtype='int'),
NWBDatasetSpec(name='number_index',
neurodata_type_inc='VectorIndex',
doc='maps cell to compartments',
quantity='?'),
NWBDatasetSpec(
name='position',
neurodata_type_inc='VectorData',
doc=
'position of recording within a compartment. 0 is close to soma, 1 is other end',
dtype='float',
quantity='?'),
NWBDatasetSpec(name='position_index',
neurodata_type_inc='VectorIndex',
ecephys_specimen_ext = NWBGroupSpec(doc="Metadata for ecephys specimen",
attributes=ecephys_specimen_attributes,
neurodata_type_def="EcephysSpecimen",
neurodata_type_inc="Subject")
# Ecephys eye tracking rig metadata extension (inherits from `NWBDataInterface`)
rig_equipment_attr = NWBAttributeSpec(name="equipment",
doc="Description of rig",
dtype="text")
unit_attr = NWBAttributeSpec('unit', 'Unit of measurement for the data', 'text')
rig_monitor_position_dset = NWBDatasetSpec(name="monitor_position",
doc="position of monitor (x, y, z)",
attributes=[unit_attr],
dtype='float32',
dims=(3,))
rig_camera_position_dset = NWBDatasetSpec(name="camera_position",
doc="position of camera (x, y, z)",
attributes=[unit_attr],
dtype='float32',
dims=(3,))
rig_led_position_dset = NWBDatasetSpec(name="led_position",
doc="position of LED (x, y, z)",
attributes=[unit_attr],
dtype='float32',
dims=(3,))
value='settings parameters stored by MWorks')
],
)
sound_play = NWBGroupSpec(
neurodata_type_def='SoundPlay',
neurodata_type_inc='TimeSeries',
doc=
'contains information for sounds played to subject during task. Data represents amplitude',
datasets=[
NWBDatasetSpec(name='data',
dtype='int',
shape=(None, ),
doc='indexes sound_files and sound_names in attributes',
attributes=[
NWBAttributeSpec(name='sound_files',
doc='indexed by data',
dtype='text'),
NWBAttributeSpec(name='sound_names',
doc='indexed by data',
dtype='text')
]),
NWBDatasetSpec(name='amplitude',
dtype='double',
shape=(None, ),
doc='amplitude of sound')
],
)
eye_calibrator = NWBGroupSpec(neurodata_type_def='EyeCalibration',
neurodata_type_inc='NWBDataInterface',
doc='Eye Calibration parameters',
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)
