def test_missing_data_types(self):
"""Test that calling export_spec on a namespace builder without data types raises a warning."""
with self.assertWarnsWith(UserWarning,
'No data types specified. Exiting.'):
export_spec(self.ns_builder, [], '.')
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 test_export(self):
"""Test that export_spec writes the correct files."""
export_spec(self.ns_builder, self.data_types, '.')
self._test_namespace_file()
self._test_extensions_file()
def main():
# these arguments were auto-generated from your cookie-cutter inputs
ns_builder = NamespaceBuilder(
doc='An extension for storing point clouds in an NWB file',
name='ndx-point-cloud-table',
version='0.1.0',
author=list(map(str.strip, 'Ben Dichter'.split(','))),
contact=list(map(str.strip, '*****@*****.**'.split(','))))
ns_builder.include_type('DynamicTable', namespace='hdmf-common')
ns_builder.include_type('VectorData', namespace='hdmf-common')
ns_builder.include_type('VectorIndex', namespace='hdmf-common')
PointCloudTable = GroupSpec(
doc='type for storing time-varying 3D point clouds',
data_type_def='PointCloudTable',
data_type_inc='DynamicTable',
default_name='PointCloudTable')
PointCloudTable.add_dataset(name='timestamps',
data_type_inc='VectorData',
doc='time of each frame in seconds',
dims=('num_frames', ),
shape=(None, ),
dtype='float')
PointCloudTable.add_dataset(
name='point_cloud',
data_type_inc='VectorData',
doc='datapoints locations over time',
dims=('time', '[x, y, z]'),
shape=(None, 3),
dtype='float',
)
PointCloudTable.add_dataset(
name='point_cloud_index',
data_type_inc='VectorIndex',
doc='datapoints indices',
dims=('index', ),
shape=(None, ),
)
PointCloudTable.add_dataset(name='color',
data_type_inc='VectorData',
doc='datapoints color',
dims=('time', '[r, g, b]'),
shape=(None, 3),
dtype='float',
quantity='?')
PointCloudTable.add_dataset(name='color_index',
data_type_inc='VectorIndex',
doc='datapoints colors indices',
dims=('index', ),
shape=(None, ),
quantity='?')
PointCloudTable.add_attribute(
name='colnames',
dims=('num_columns', ),
shape=(None, ),
doc=
'The names of the columns in this table. This should be used to specify '
'an order to the columns.',
default_value=('point_cloud', ),
dtype='text')
new_data_types = [PointCloudTable]
# 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)