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datasets={
'data': DatasetBuilder(
'data', data,
attributes={
'unit': 'image_unit',
'conversion': 1.0,
'resolution': 0.0}
),
'timestamps': DatasetBuilder('timestamps', timestamps,
attributes={'unit': 'Seconds', 'interval': 1}),
'format': DatasetBuilder('format', 'raw'),
'dimension': DatasetBuilder('dimension', [2]),
'field_of_view': DatasetBuilder('field_of_view', [2.0, 2.0, 5.0]),
},
links={
'imaging_plane': LinkBuilder(imgpln_builder, 'imaging_plane')
})
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')
})
'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 get_table_builder(self):
self.device_builder = GroupBuilder('dev1',
attributes={'neurodata_type': 'Device',
'namespace': 'core'})
self.eg_builder = GroupBuilder('tetrode1',
attributes={'neurodata_type': 'ElectrodeGroup',
'namespace': 'core',
'description': 'tetrode description',
'location': 'tetrode location'},
links={
'device': LinkBuilder(self.device_builder, 'device')
})
datasets = [
DatasetBuilder('id', data=[1, 2, 3, 4],
attributes={'neurodata_type': 'ElementIdentifiers', 'namespace': 'core'}),
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',
'namespace': 'core',
'help': 'Metadata about an imaging plane'},
datasets={
'description': DatasetBuilder('description', 'imaging plane description'),
'excitation_lambda': DatasetBuilder('excitation_lambda', 600.),
'imaging_rate': DatasetBuilder('imaging_rate', 300.),
'indicator': DatasetBuilder('indicator', 'GFP'),
'manifold': DatasetBuilder('manifold', (((1., 2., 3.), (4., 5., 6.)),),
attributes={'conversion': 4.0, 'unit': 'manifold unit'}),
'reference_frame': DatasetBuilder('reference_frame', 'A frame to refer to'),
'location': DatasetBuilder('location', 'somewhere in the brain')},
groups={
'optchan1': self.optchan_builder
},
links={
'device': LinkBuilder(device_builder, 'device')
}
)
ts = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
self.is_builder = GroupBuilder('test_iS',
attributes={'namespace': 'core',
'neurodata_type': 'ImageSeries',
'description': 'no description',
'comments': 'no comments',
'help': 'Storage object for time-series 2-D image data'},
datasets={'timestamps': DatasetBuilder('timestamps', ts,
attributes={'unit': 'Seconds',
'interval': 1}),
'external_file': DatasetBuilder('external_file', ['images.tiff'],
attributes={
'starting_frame': [1, 2, 3]}),
'format': DatasetBuilder('format', 'tiff'),
'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,
},
groups={
'reference_images': GroupBuilder('reference_images', groups={'test_iS': self.is_builder}),
},
links={
'imaging_plane': LinkBuilder(self.imgpln_builder, 'imaging_plane')
}
)
return ps_builder
def timestamps_attr(self, container, manager):
ret = container.fields.get('timestamps')
if isinstance(ret, TimeSeries):
owner = ret
curr = owner.fields.get('timestamps')
while isinstance(curr, TimeSeries):
owner = curr
curr = owner.fields.get('timestamps')
ts_builder = manager.build(owner)
tstamps_builder = ts_builder['timestamps']
ret = LinkBuilder(tstamps_builder, 'timestamps')
return ret
def timestamps_carg(self, builder, manager):
tstamps_builder = builder.get('timestamps')
if tstamps_builder is None:
return None
if isinstance(tstamps_builder, LinkBuilder):
# if the parent of our target is available, return the parent object
# Otherwise, return the dataset in the target builder
#
# NOTE: it is not available when data is externally linked
# and we haven't explicitly read that file
if tstamps_builder.builder.parent is not None:
target = tstamps_builder.builder
return manager.construct(target.parent)
else:
return tstamps_builder.builder.data
else:
return tstamps_builder.data