How to use the hdmf.build.GroupBuilder function in hdmf
To help you get started, we’ve selected a few hdmf examples, based on popular ways it is used in public projects.
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NeurodataWithoutBorders / pynwb / tests / integration / test_io.py View on Github 
ts = TimeSeries('test_timeseries',
list(range(100, 200, 10)), 'SIunit', timestamps=list(range(10)), resolution=0.1)
self.container.add_acquisition(ts)
ts_builder = GroupBuilder('test_timeseries',
attributes={'neurodata_type': 'TimeSeries'},
datasets={'data': DatasetBuilder('data', list(range(100, 200, 10)),
attributes={'unit': 'SIunit',
'conversion': 1.0,
'resolution': 0.1}),
'timestamps': DatasetBuilder('timestamps', list(range(10)),
attributes={'unit': 'seconds',
'interval': 1})})
self.builder = GroupBuilder(
'root', groups={'acquisition': GroupBuilder('acquisition', groups={'test_timeseries': ts_builder}),
'analysis': GroupBuilder('analysis'),
'general': GroupBuilder('general'),
'processing': GroupBuilder('processing'),
'stimulus': GroupBuilder(
'stimulus',
groups={'presentation': GroupBuilder('presentation'),
'templates': GroupBuilder('templates')})},
datasets={'file_create_date': DatasetBuilder('file_create_date', [self.create_date.isoformat()]),
'identifier': DatasetBuilder('identifier', 'TEST123'),
'session_description': DatasetBuilder('session_description', 'a test NWB File'),
'nwb_version': DatasetBuilder('nwb_version', '1.0.6'),
'session_start_time': DatasetBuilder('session_start_time', self.start_time.isoformat())},
attributes={'neurodata_type': 'NWBFile'})self.pxmsk_index_builder = DatasetBuilder('pixel_mask_index', self.pxmsk_index,
attributes={
'namespace': 'core',
'neurodata_type': 'VectorIndex',
'target': ReferenceBuilder(self.pixel_masks_builder),
'help': 'indexes into a list of values for a list of elements'})
self.image_masks_builder = DatasetBuilder('image_mask', self.img_mask,
attributes={
'namespace': 'core',
'neurodata_type': 'VectorData',
'description': 'Image masks for each ROI',
'help': 'Values for a list of elements'})
ps_builder = GroupBuilder(
'test_plane_seg_name',
attributes={
'neurodata_type': 'PlaneSegmentation',
'namespace': 'core',
'description': 'plane segmentation description',
'colnames': (b'image_mask', b'pixel_mask'),
'help': 'Results from segmentation of an imaging plane'},
datasets={
'id': DatasetBuilder('id', data=[0, 1],
attributes={'help': 'unique identifiers for a list of elements',
'namespace': 'core',
'neurodata_type': 'ElementIdentifiers'}),
'pixel_mask': self.pixel_masks_builder,
'pixel_mask_index': self.pxmsk_index_builder,
'image_mask': self.image_masks_builder,
},def setUpBuilder(self):
optchan_builder = GroupBuilder(
'optchan1',
attributes={
'neurodata_type': 'OpticalChannel',
'namespace': 'core',
'help': 'Metadata about an optical channel used to record from an imaging plane'},
datasets={
'description': DatasetBuilder('description', 'a fake OpticalChannel'),
'emission_lambda': DatasetBuilder('emission_lambda', 500.)},
)
device_builder = GroupBuilder('dev1',
attributes={'neurodata_type': 'Device',
'namespace': 'core',
'help': 'A recording device e.g. amplifier'})
return GroupBuilder(
'imgpln1',
attributes={
'neurodata_type': 'ImagingPlane',
'namespace': 'core',
'help': 'Metadata about an imaging plane'},
datasets={
'description': DatasetBuilder('description', 'a fake ImagingPlane'),
'excitation_lambda': DatasetBuilder('excitation_lambda', 600.),
'imaging_rate': DatasetBuilder('imaging_rate', 300.),
'indicator': DatasetBuilder('indicator', 'GFP'),
'location': DatasetBuilder('location', 'somewhere in the brain')},
groups={
'optchan1': optchan_builder
},
links={
'device': LinkBuilder(device_builder, 'device')def setUpBuilder(self):
device_builder = GroupBuilder('dev1',
attributes={'neurodata_type': 'Device',
'namespace': 'core'})
return GroupBuilder('elec1',
attributes={'neurodata_type': 'ElectrodeGroup',
'namespace': 'core',
'description': 'a test ElectrodeGroup',
'location': 'a nonexistent place'},
links={
'device': LinkBuilder(device_builder, 'device')
})self.start_time, file_create_date=self.create_date)
ts = TimeSeries('test_timeseries',
list(range(100, 200, 10)), 'SIunit', timestamps=list(range(10)), resolution=0.1)
self.container.add_acquisition(ts)
ts_builder = GroupBuilder('test_timeseries',
attributes={'neurodata_type': 'TimeSeries'},
datasets={'data': DatasetBuilder('data', list(range(100, 200, 10)),
attributes={'unit': 'SIunit',
'conversion': 1.0,
'resolution': 0.1}),
'timestamps': DatasetBuilder('timestamps', list(range(10)),
attributes={'unit': 'seconds',
'interval': 1})})
self.builder = GroupBuilder(
'root', groups={'acquisition': GroupBuilder('acquisition', groups={'test_timeseries': ts_builder}),
'analysis': GroupBuilder('analysis'),
'general': GroupBuilder('general'),
'processing': GroupBuilder('processing'),
'stimulus': GroupBuilder(
'stimulus',
groups={'presentation': GroupBuilder('presentation'),
'templates': GroupBuilder('templates')})},
datasets={'file_create_date': DatasetBuilder('file_create_date', [self.create_date.isoformat()]),
'identifier': DatasetBuilder('identifier', 'TEST123'),
'session_description': DatasetBuilder('session_description', 'a test NWB File'),
'nwb_version': DatasetBuilder('nwb_version', '1.0.6'),
'session_start_time': DatasetBuilder('session_start_time', self.start_time.isoformat())},
attributes={'neurodata_type': 'NWBFile'})'test data collection notes'),
'institution': DatasetBuilder('institution', 'nomad'),
'lab': DatasetBuilder('lab', 'nolab'),
'notes': DatasetBuilder('notes', 'nonotes'),
'pharmacology': DatasetBuilder('pharmacology', 'nopharmacology'),
'protocol': DatasetBuilder('protocol', 'noprotocol'),
'related_publications': DatasetBuilder('related_publications', 'nopubs'),
'session_id': DatasetBuilder('session_id', '007'),
'slices': DatasetBuilder('slices', 'noslices'),
'source_script': DatasetBuilder('source_script', 'nosources',
attributes={'file_name': 'nofilename'}),
'surgery': DatasetBuilder('surgery', 'nosurgery'),
'virus': DatasetBuilder('virus', 'novirus')}
)
return GroupBuilder('root',
groups={'acquisition': GroupBuilder(
'acquisition',
groups={'test_timeseries': ts_builder}),
'analysis': GroupBuilder('analysis'),
'general': general_builder,
'processing': GroupBuilder('processing', groups={'test_module': module_builder}),
'stimulus': GroupBuilder(
'stimulus',
groups={'presentation':
GroupBuilder('presentation'),
'templates': GroupBuilder('templates')})},
datasets={
'file_create_date':
DatasetBuilder('file_create_date', [self.create_date.isoformat()]),
'identifier': DatasetBuilder('identifier', 'TEST123'),
'session_description': DatasetBuilder('session_description', 'a test NWB File'),def get_plane_segmentation_builder(self):
self.optchan_builder = GroupBuilder(
'test_optical_channel',
attributes={
'neurodata_type': 'OpticalChannel',
'namespace': 'core',
'help': 'Metadata about an optical channel used to record from an imaging plane'},
datasets={
'description': DatasetBuilder('description', 'optical channel description'),
'emission_lambda': DatasetBuilder('emission_lambda', 500.)},
)
device_builder = GroupBuilder('dev1',
attributes={'neurodata_type': 'Device',
'namespace': 'core',
'help': 'A recording device e.g. amplifier'})
self.imgpln_builder = GroupBuilder(
'imgpln1',
attributes={self.container.add_acquisition(ts)
ts_builder = GroupBuilder('test_timeseries',
attributes={'neurodata_type': 'TimeSeries'},
datasets={'data': DatasetBuilder('data', list(range(100, 200, 10)),
attributes={'unit': 'SIunit',
'conversion': 1.0,
'resolution': 0.1}),
'timestamps': DatasetBuilder('timestamps', list(range(10)),
attributes={'unit': 'seconds',
'interval': 1})})
self.builder = GroupBuilder(
'root', groups={'acquisition': GroupBuilder('acquisition', groups={'test_timeseries': ts_builder}),
'analysis': GroupBuilder('analysis'),
'general': GroupBuilder('general'),
'processing': GroupBuilder('processing'),
'stimulus': GroupBuilder(
'stimulus',
groups={'presentation': GroupBuilder('presentation'),
'templates': GroupBuilder('templates')})},
datasets={'file_create_date': DatasetBuilder('file_create_date', [self.create_date.isoformat()]),
'identifier': DatasetBuilder('identifier', 'TEST123'),
'session_description': DatasetBuilder('session_description', 'a test NWB File'),
'nwb_version': DatasetBuilder('nwb_version', '1.0.6'),
'session_start_time': DatasetBuilder('session_start_time', self.start_time.isoformat())},
attributes={'neurodata_type': 'NWBFile'})attributes={'file_name': 'nofilename'}),
'surgery': DatasetBuilder('surgery', 'nosurgery'),
'virus': DatasetBuilder('virus', 'novirus')}
)
return GroupBuilder('root',
groups={'acquisition': GroupBuilder(
'acquisition',
groups={'test_timeseries': ts_builder}),
'analysis': GroupBuilder('analysis'),
'general': general_builder,
'processing': GroupBuilder('processing', groups={'test_module': module_builder}),
'stimulus': GroupBuilder(
'stimulus',
groups={'presentation':
GroupBuilder('presentation'),
'templates': GroupBuilder('templates')})},
datasets={
'file_create_date':
DatasetBuilder('file_create_date', [self.create_date.isoformat()]),
'identifier': DatasetBuilder('identifier', 'TEST123'),
'session_description': DatasetBuilder('session_description', 'a test NWB File'),
'session_start_time': DatasetBuilder('session_start_time', self.start_time.isoformat()),
'timestamps_reference_time': DatasetBuilder('timestamps_reference_time',
self.ref_time.isoformat())
},
attributes={'namespace': base.CORE_NAMESPACE,
'nwb_version': '2.0b',
'neurodata_type': 'NWBFile',
'help': 'an NWB:N file for storing cellular-based neurophysiology data'})hdmf
A hierarchical data modeling framework for modern science data standards
Package Health Score
81 / 100Popular hdmf functions
- hdmf.backends.hdf5.h5_utils.H5DataIO
- hdmf.backends.hdf5.H5DataIO
- hdmf.backends.hdf5.HDF5IO
- hdmf.build.BuildManager
- hdmf.build.DatasetBuilder
- hdmf.build.GroupBuilder
- hdmf.build.LinkBuilder
- hdmf.build.ObjectMapper
- hdmf.build.ObjectMapper.constructor_arg
- hdmf.build.ObjectMapper.object_attr
- hdmf.build.ReferenceBuilder
- hdmf.build.TypeMap
- hdmf.common.DynamicTable
- hdmf.common.DynamicTableRegion
- hdmf.data_utils.DataChunkIterator
- hdmf.utils.call_docval_func
- hdmf.utils.docval
- hdmf.utils.get_docval
- hdmf.utils.getargs
- hdmf.utils.popargs