class TestNestedBase(TestBase):
def setUp(self):
if type(self) == TestNestedBase:
raise unittest.SkipTest('Abstract Base Class')
super(TestNestedBase, self).setUp()
self.foo_bucket = FooBucket('test_foo_bucket', [
Foo('my_foo1', list(range(10)), 'value1', 10),
Foo('my_foo2', list(range(10, 20)), 'value2', 20)
])
self.foo_builders = {
'my_foo1':
GroupBuilder('my_foo1',
datasets={
'my_data':
DatasetBuilder('my_data',
list(range(10)),
attributes={'attr2': 10})
},
attributes={
'attr1': 'value1',
'namespace': CORE_NAMESPACE,
'data_type': 'Foo'
}),
'my_foo2':
GroupBuilder('my_foo2',
datasets={
'my_data':
DatasetBuilder('my_data',
list(range(10, 20)),
attributes={'attr2': 20})
},
attributes={
'attr1': 'value2',
'namespace': CORE_NAMESPACE,
'data_type': 'Foo'
})
}
self.setUpBucketBuilder()
self.setUpBucketSpec()
self.spec_catalog.register_spec(self.bucket_spec, 'test.yaml')
self.type_map.register_container_type(CORE_NAMESPACE, 'FooBucket',
FooBucket)
self.type_map.register_map(FooBucket, ObjectMapper)
self.manager = BuildManager(self.type_map)
def setUpBucketBuilder(self):
pass
def setUpBucketSpec(self):
pass
def test_build(self):
''' Test default mapping for an Container that has an Container as an attribute value '''
builder = self.manager.build(self.foo_bucket)
self.assertDictEqual(builder, self.bucket_builder)
def test_construct(self):
container = self.manager.construct(self.bucket_builder)
self.assertEqual(container, self.foo_bucket)
def setUp(self):
super(TestNestedBase, self).setUp()
self.foo_bucket = FooBucket('test_foo_bucket', [
Foo('my_foo1', list(range(10)), 'value1', 10),
Foo('my_foo2', list(range(10, 20)), 'value2', 20)])
self.foo_builders = {
'my_foo1': GroupBuilder('my_foo1',
datasets={'my_data': DatasetBuilder(
'my_data',
list(range(10)),
attributes={'attr2': 10})},
attributes={'attr1': 'value1', 'namespace': CORE_NAMESPACE, 'data_type': 'Foo'}),
'my_foo2': GroupBuilder('my_foo2', datasets={'my_data':
DatasetBuilder(
'my_data',
list(range(10, 20)),
attributes={'attr2': 20})},
attributes={'attr1': 'value2', 'namespace': CORE_NAMESPACE, 'data_type': 'Foo'})
}
self.setUpBucketBuilder()
self.setUpBucketSpec()
self.spec_catalog.register_spec(self.bucket_spec, 'test.yaml')
self.type_map.register_container_type(CORE_NAMESPACE, 'FooBucket', FooBucket)
self.type_map.register_map(FooBucket, ObjectMapper)
self.manager = BuildManager(self.type_map)
def setUp(self):
self.bar_spec = GroupSpec(
'A test group specification with a data type',
data_type_def='Bar',
datasets=[
DatasetSpec('an example dataset',
'int',
name='data',
attributes=[
AttributeSpec('attr2',
'an example integer attribute',
'int')
])
],
attributes=[
AttributeSpec('attr1', 'an example string attribute', 'str')
])
self.spec_catalog = SpecCatalog()
self.spec_catalog.register_spec(self.bar_spec, 'test.yaml')
self.namespace = SpecNamespace('a test namespace',
CORE_NAMESPACE, [{
'source': 'test.yaml'
}],
catalog=self.spec_catalog)
self.namespace_catalog = NamespaceCatalog()
self.namespace_catalog.add_namespace(CORE_NAMESPACE, self.namespace)
self.type_map = TypeMap(self.namespace_catalog)
self.type_map.register_container_type(CORE_NAMESPACE, 'Bar', Bar)
self.type_map.register_map(Bar, ObjectMapper)
self.manager = BuildManager(self.type_map)
self.mapper = ObjectMapper(self.bar_spec)
def setUp(self):
type_map = get_type_map()
self.manager = BuildManager(type_map)
self.path = "test_pynwb_io_hdf5.h5"
self.start_time = datetime(1970, 1, 1, 12, 0, 0)
self.create_date = datetime(2017, 4, 15, 12, 0, 0)
self.ts_builder = GroupBuilder('test_timeseries',
attributes={'ancestry': 'TimeSeries',
'source': 'example_source',
'neurodata_type': 'TimeSeries',
'help': 'General purpose 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.ts = TimeSeries('test_timeseries', 'example_source', list(range(100, 200, 10)),
unit='SIunit', resolution=0.1, timestamps=list(range(10)))
self.manager.prebuilt(self.ts, self.ts_builder)
self.builder = GroupBuilder(
'root',
groups={'acquisition':
GroupBuilder('acquisition',
groups={'timeseries':
GroupBuilder('timeseries',
groups={'test_timeseries': self.ts_builder}),
'images': GroupBuilder('images')}),
'analysis': GroupBuilder('analysis'),
'epochs': GroupBuilder('epochs'),
'general': GroupBuilder('general'),
'processing': GroupBuilder('processing'),
'stimulus': GroupBuilder(
'stimulus',
groups={'presentation':
GroupBuilder('presentation'),
'templates': GroupBuilder('templates')})},
datasets={'file_create_date': DatasetBuilder('file_create_date', [str(self.create_date)]),
'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', str(self.start_time))},
attributes={'neurodata_type': 'NWBFile'})
def setUp(self):
self.setUpBazSpec()
self.spec_catalog = SpecCatalog()
self.spec_catalog.register_spec(self.baz_spec, 'test.yaml')
self.namespace = SpecNamespace('a test namespace', CORE_NAMESPACE, [{'source': 'test.yaml'}],
catalog=self.spec_catalog)
self.namespace_catalog = NamespaceCatalog()
self.namespace_catalog.add_namespace(CORE_NAMESPACE, self.namespace)
self.type_map = TypeMap(self.namespace_catalog)
self.type_map.register_container_type(CORE_NAMESPACE, 'Baz', Baz)
self.type_map.register_map(Baz, ObjectMapper)
self.manager = BuildManager(self.type_map)
self.mapper = ObjectMapper(self.baz_spec)
class TestHDF5Writer(GroupBuilderTestCase):
def setUp(self):
type_map = get_type_map()
self.manager = BuildManager(type_map)
self.path = "test_pynwb_io_hdf5.h5"
self.start_time = datetime(1970, 1, 1, 12, 0, 0)
self.create_date = datetime(2017, 4, 15, 12, 0, 0)
self.ts_builder = GroupBuilder('test_timeseries',
attributes={'ancestry': 'TimeSeries',
'source': 'example_source',
'neurodata_type': 'TimeSeries',
'help': 'General purpose 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.ts = TimeSeries('test_timeseries', 'example_source', list(range(100, 200, 10)),
unit='SIunit', resolution=0.1, timestamps=list(range(10)))
self.manager.prebuilt(self.ts, self.ts_builder)
self.builder = GroupBuilder(
'root',
groups={'acquisition':
GroupBuilder('acquisition',
groups={'timeseries':
GroupBuilder('timeseries',
groups={'test_timeseries': self.ts_builder}),
'images': GroupBuilder('images')}),
'analysis': GroupBuilder('analysis'),
'epochs': GroupBuilder('epochs'),
'general': GroupBuilder('general'),
'processing': GroupBuilder('processing'),
'stimulus': GroupBuilder(
'stimulus',
groups={'presentation':
GroupBuilder('presentation'),
'templates': GroupBuilder('templates')})},
datasets={'file_create_date': DatasetBuilder('file_create_date', [str(self.create_date)]),
'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', str(self.start_time))},
attributes={'neurodata_type': 'NWBFile'})
def tearDown(self):
if os.path.exists(self.path):
os.remove(self.path)
def check_fields(self):
f = File(self.path)
self.assertIn('acquisition', f)
self.assertIn('analysis', f)
self.assertIn('epochs', f)
self.assertIn('general', f)
self.assertIn('processing', f)
self.assertIn('file_create_date', f)
self.assertIn('identifier', f)
self.assertIn('session_description', f)
self.assertIn('nwb_version', f)
self.assertIn('session_start_time', f)
acq = f.get('acquisition')
self.assertIn('images', acq)
self.assertIn('timeseries', acq)
ts = acq.get('timeseries')
self.assertIn('test_timeseries', ts)
return f
def test_write_builder(self):
writer = HDF5IO(self.path, self.manager)
writer.write_builder(self.builder)
writer.close()
self.check_fields()
def test_write_attribute_reference_container(self):
writer = HDF5IO(self.path, self.manager)
self.builder.set_attribute('ref_attribute', self.ts)
writer.write_builder(self.builder)
writer.close()
f = self.check_fields()
self.assertIsInstance(f.attrs['ref_attribute'], Reference)
self.assertEqual(f['acquisition/timeseries/test_timeseries'], f[f.attrs['ref_attribute']])
def test_write_attribute_reference_builder(self):
writer = HDF5IO(self.path, self.manager)
self.builder.set_attribute('ref_attribute', self.ts_builder)
writer.write_builder(self.builder)
writer.close()
f = self.check_fields()
self.assertIsInstance(f.attrs['ref_attribute'], Reference)
self.assertEqual(f['acquisition/timeseries/test_timeseries'], f[f.attrs['ref_attribute']])
def test_write_context_manager(self):
with HDF5IO(self.path, self.manager) as writer:
writer.write_builder(self.builder)
self.check_fields()
def test_read_builder(self):
self.maxDiff = None
io = HDF5IO(self.path, self.manager)
io.write_builder(self.builder)
builder = io.read_builder()
self.assertBuilderEqual(builder, self.builder)
io.close()