How to use the hdmf.build.DatasetBuilder 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 / ui_write / test_ecephys.py View on Github 
elec_builder = DatasetBuilder('electrodes', data=[0, 2],
attributes={'neurodata_type': 'DynamicTableRegion',
'namespace': 'core',
'table': ReferenceBuilder(table_builder),
'description': 'the first and third electrodes'})
es1 = GroupBuilder('test_eS1',
attributes={'namespace': base.CORE_NAMESPACE,
'comments': 'no comments',
'description': 'no description',
'neurodata_type': 'ElectricalSeries'},
datasets={'data': data_builder,
'timestamps': timestamps_builder,
'electrodes': elec_builder})
data = list(zip(reversed(range(10)), reversed(range(10, 20))))
data_builder = DatasetBuilder('data', data,
attributes={'unit': 'volts',
'conversion': 1.0,
'resolution': 0.0})
timestamps_builder = DatasetBuilder('timestamps', timestamps, attributes={'unit': 'seconds', 'interval': 1})
elec_builder = DatasetBuilder('electrodes', data=[1, 3],
attributes={'neurodata_type': 'DynamicTableRegion',
'namespace': 'core',
'table': ReferenceBuilder(table_builder),
'description': 'the second and fourth electrodes'})
es2 = GroupBuilder('test_eS2',
attributes={'namespace': base.CORE_NAMESPACE,
'comments': 'no comments',
'description': 'no description',
'neurodata_type': 'ElectricalSeries'},
datasets={'data': data_builder,
'timestamps': timestamps_builder,general_builder = GroupBuilder('general',
datasets={
'experimenter': DatasetBuilder('experimenter', 'test experimenter'),
'stimulus': DatasetBuilder('stimulus', 'test stimulus notes'),
'experiment_description': DatasetBuilder('experiment_description',
'test experiment description'),
'data_collection': DatasetBuilder('data_collection',
'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',general_builder = GroupBuilder('general',
datasets={
'experimenter': DatasetBuilder('experimenter', 'test experimenter'),
'stimulus': DatasetBuilder('stimulus', 'test stimulus notes'),
'experiment_description': DatasetBuilder('experiment_description',
'test experiment description'),
'data_collection': DatasetBuilder('data_collection',
'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':'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'})'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'})'comments': 'no comments',
'description': 'no description',
'neurodata_type': 'RoiResponseSeries',
'help': ('ROI responses over an imaging plane. Each element on the second dimension of data[] '
'should correspond to the signal from one ROI')},
datasets={
'data': DatasetBuilder(
'data', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
attributes={
'unit': 'lumens',
'conversion': 1.0,
'resolution': 0.0}
),
'timestamps': DatasetBuilder('timestamps', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
attributes={'unit': 'Seconds', 'interval': 1}),
'rois': DatasetBuilder('rois', data=[0],
attributes={'help': 'a subset (i.e. slice or region) of a DynamicTable',
'description': 'the first of two ROIs',
'table': ReferenceBuilder(ps_builder),
'namespace': 'core',
'neurodata_type': 'DynamicTableRegion'}),
})'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'})'help': 'Clustered spike data, whether from automatic clustering tools (eg, klustakwik) or as a result of manual sorting', # noqa: E501
'neurodata_type': 'Clustering',
'namespace': base.CORE_NAMESPACE},
datasets={
'num': DatasetBuilder('num', [0, 1, 2, 0, 1, 2]),
'times': DatasetBuilder('times', list(range(10, 61, 10))),
'peak_over_rms':
DatasetBuilder('peak_over_rms', [100, 101, 102]),
'description':
DatasetBuilder('description',
"A fake Clustering interface")})})
general_builder = GroupBuilder('general',
datasets={
'experimenter': DatasetBuilder('experimenter', 'test experimenter'),
'stimulus': DatasetBuilder('stimulus', 'test stimulus notes'),
'experiment_description': DatasetBuilder('experiment_description',
'test experiment description'),
'data_collection': DatasetBuilder('data_collection',
'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')})
device_builder = GroupBuilder('dev1',
attributes={'neurodata_type': 'Device',
'namespace': 'core',
'help': 'A recording device e.g. amplifier'})
imgpln_builder = 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')
}
)
data = [[[1., 1.] * 2] * 2]
timestamps = list(map(lambda x: x/10, range(10)))
return GroupBuilder(
'test_2ps',
attributes={
'pmt_gain': 1.7,
'scan_line_rate': np.float32(3.4),DatasetBuilder('x', data=[1.0, 1.0, 1.0, 1.0],
attributes={'description': 'the x coordinate of the channel location',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
DatasetBuilder('y', data=[2.0, 2.0, 2.0, 2.0],
attributes={'description': 'the y coordinate of the channel location',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
DatasetBuilder('z', data=[3.0, 3.0, 3.0, 3.0],
attributes={'description': 'the z coordinate of the channel location',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
DatasetBuilder('imp', data=[-1.0, -2.0, -3.0, -4.0],
attributes={'description': 'the impedance of the channel',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
DatasetBuilder('location', data=['CA1', 'CA1', 'CA1', 'CA1'],
attributes={'description': 'the location of channel within the subject e.g. brain region',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
DatasetBuilder('filtering', data=['none', 'none', 'none', 'none'],
attributes={'description': 'description of hardware filtering',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
DatasetBuilder('group', data=[ReferenceBuilder(self.eg_builder),
ReferenceBuilder(self.eg_builder),
ReferenceBuilder(self.eg_builder),
ReferenceBuilder(self.eg_builder)],
attributes={'description': 'a reference to the ElectrodeGroup this electrode is a part of',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
DatasetBuilder('group_name', data=['tetrode1', 'tetrode1', 'tetrode1', 'tetrode1'],
attributes={'description': 'the name of the ElectrodeGroup this electrode is a part of',
'neurodata_type': 'VectorData', 'namespace': 'core'}),
]
return GroupBuilder('electrodes', datasets={d.name: d for d in datasets},
attributes={'colnames': (b'x',
b'y',
b'z',hdmf
A hierarchical data modeling framework for modern science data standards
Package Health Score
84 / 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