def test_store_load_region_mapping(session, connectivity_factory,
surface_factory, region_mapping_factory,
sensors_factory):
connectivity = connectivity_factory(2)
conn_idx = ConnectivityIndex()
conn_idx.fill_from_has_traits(connectivity)
session.add(conn_idx)
surface = surface_factory(5)
surf_idx = SurfaceIndex()
surf_idx.fill_from_has_traits(surface)
session.add(surf_idx)
region_mapping = region_mapping_factory(surface, connectivity)
rm_idx = RegionMappingIndex()
rm_idx.fill_from_has_traits(region_mapping)
rm_idx.connectivity = conn_idx
rm_idx.surface = surf_idx
session.add(rm_idx)
sensors = sensors_factory("SEEG", 3)
sensors_seeg_idx = SensorsIndex()
sensors_seeg_idx.fill_from_has_traits(sensors)
session.add(sensors_seeg_idx)
sensors_eeg = sensors_factory("EEG", 3)
sensors_eeg_idx = SensorsIndex()
sensors_eeg_idx.fill_from_has_traits(sensors_eeg)
session.add(sensors_eeg_idx)
time_series = TimeSeries(data=numpy.arange(5))
fcd = Fcd(
array_data=numpy.arange(5),
source=time_series,
)
ts_index = TimeSeriesIndex()
ts_index.fill_from_has_traits(time_series)
session.add(ts_index)
fcd_index = FcdIndex()
fcd_index.fill_from_has_traits(fcd)
fcd_index.source = ts_index
session.add(fcd_index)
session.commit()
res = session.query(ConnectivityIndex)
assert res.count() == 1
assert res[0].number_of_regions == 2
assert res[0].number_of_connections == 4
assert res[0].undirected is True
assert res[0].weights_min == 0
res = session.query(SurfaceIndex)
assert res.count() == 1
res = session.query(RegionMappingIndex)
assert res.count() == 1
def __import_time_series_csv_datatype(self, hrf_folder, connectivity_gid,
patient, user_tag):
path = os.path.join(hrf_folder, self.TIME_SERIES_CSV_FILE)
with open(path) as csv_file:
csv_reader = csv.reader(
csv_file, delimiter=CSVDelimiterOptionsEnum.COMMA.value)
ts = list(csv_reader)
ts_data = np.array(ts, dtype=np.float64).reshape(
(len(ts), 1, len(ts[0]), 1))
ts_time = np.random.rand(ts_data.shape[0], )
project = dao.get_project_by_id(self.current_project_id)
ts_gid = uuid.uuid4()
h5_path = "TimeSeries_{}.h5".format(ts_gid.hex)
operation_folder = self.storage_interface.get_project_folder(
project.name, str(self.operation_id))
h5_path = os.path.join(operation_folder, h5_path)
conn = h5.load_from_gid(connectivity_gid)
ts = TimeSeriesRegion()
ts.data = ts_data
ts.time = ts_time
ts.gid = ts_gid
ts.connectivity = conn
generic_attributes = GenericAttributes()
generic_attributes.user_tag_1 = user_tag
generic_attributes.state = DEFAULTDATASTATE_RAW_DATA
with TimeSeriesRegionH5(h5_path) as ts_h5:
ts_h5.store(ts)
ts_h5.nr_dimensions.store(4)
ts_h5.subject.store(patient)
ts_h5.store_generic_attributes(generic_attributes)
ts_index = TimeSeriesIndex()
ts_index.gid = ts_gid.hex
ts_index.fk_from_operation = self.operation_id
ts_index.time_series_type = "TimeSeriesRegion"
ts_index.data_length_1d = ts_data.shape[0]
ts_index.data_length_2d = ts_data.shape[1]
ts_index.data_length_3d = ts_data.shape[2]
ts_index.data_length_4d = ts_data.shape[3]
ts_index.data_ndim = len(ts_data.shape)
ts_index.sample_period_unit = 'ms'
ts_index.sample_period = TimeSeries.sample_period.default
ts_index.sample_rate = 1024.0
ts_index.subject = patient
ts_index.state = DEFAULTDATASTATE_RAW_DATA
ts_index.labels_ordering = json.dumps(
list(TimeSeries.labels_ordering.default))
ts_index.labels_dimensions = json.dumps(
TimeSeries.labels_dimensions.default)
ts_index.visible = False # we don't want to show these TimeSeries because they are dummy
dao.store_entity(ts_index)
return ts_gid
def make_ts_from_op(session, operation_factory):
# make file stored and indexed time series
two_node_simple_sin_ts = make_ts()
op = operation_factory()
ts_db = TimeSeriesIndex()
ts_db.fk_from_operation = op.id
ts_db.fill_from_has_traits(two_node_simple_sin_ts)
ts_h5_path = h5.path_for_stored_index(ts_db)
with TimeSeriesH5(ts_h5_path) as f:
f.store(two_node_simple_sin_ts)
f.sample_rate.store(two_node_simple_sin_ts.sample_rate)
session.add(ts_db)
session.commit()
return ts_db
def build(data=None, op=None):
ts = time_series_factory(data)
if op is None:
op = operation_factory()
ts_db = TimeSeriesIndex()
ts_db.fk_from_operation = op.id
ts_db.fill_from_has_traits(ts)
ts_h5_path = h5.path_for_stored_index(ts_db)
with TimeSeriesH5(ts_h5_path) as f:
f.store(ts)
f.sample_rate.store(ts.sample_rate)
f.nr_dimensions.store(ts.data.ndim)
ts_db = dao.store_entity(ts_db)
return ts_db
def launch(self, view_model):
# type: (FooDataImporterModel) -> TimeSeriesIndex
array_data = numpy.loadtxt(view_model.array_data)
ts = TimeSeries(data=array_data)
ts.configure()
ts_index = TimeSeriesIndex()
ts_index.fill_from_has_traits(ts)
ts_h5_path = h5.path_for(self.storage_path, TimeSeriesH5, ts_index.gid)
with TimeSeriesH5(ts_h5_path) as ts_h5:
ts_h5.store(ts, scalars_only=True)
ts_h5.store_generic_attributes(GenericAttributes())
ts_h5.write_data_slice(array_data)
return ts_index
def build():
time = numpy.linspace(0, 1000, 4000)
data = numpy.zeros((time.size, 1, 3, 1))
data[:, 0, 0, 0] = numpy.sin(2 * numpy.pi * time / 1000.0 * 40)
data[:, 0, 1, 0] = numpy.sin(2 * numpy.pi * time / 1000.0 * 200)
data[:, 0, 2, 0] = numpy.sin(2 * numpy.pi * time / 1000.0 * 100) + numpy.sin(2 * numpy.pi * time / 1000.0 * 300)
ts = TimeSeries(time=time, data=data, sample_period=1.0 / 4000)
op = operation_factory()
ts_db = TimeSeriesIndex()
ts_db.fk_from_operation = op.id
ts_db.fill_from_has_traits(ts)
ts_h5_path = h5.path_for_stored_index(ts_db)
with TimeSeriesH5(ts_h5_path) as f:
f.store(ts)
session.add(ts_db)
session.commit()
return ts_db