def build(connectivity, region_mapping, test_user=None, test_project=None):
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 = TimeSeriesRegion(time=time,
data=data,
sample_period=1.0 / 4000,
connectivity=connectivity,
region_mapping=region_mapping)
op = operation_factory(test_user=test_user, test_project=test_project)
ts_db = TimeSeriesRegionIndex()
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 TimeSeriesRegionH5(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 __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 create_region_ts(self, data_shape, connectivity):
if connectivity.number_of_regions != data_shape[1]:
raise LaunchException("Data has %d channels but the connectivity has %d nodes"
% (data_shape[1], connectivity.number_of_regions))
ts_idx = TimeSeriesRegionIndex()
ts_idx.fk_connectivity_gid = connectivity.gid
ts_idx.has_surface_mapping = True
ts_h5_path = h5.path_for(self.storage_path, TimeSeriesRegionH5, ts_idx.gid)
ts_h5 = TimeSeriesRegionH5(ts_h5_path)
ts_h5.connectivity.store(uuid.UUID(connectivity.gid))
return TimeSeriesRegion(), ts_idx, ts_h5
def launch(self, view_model):
# type: (BalloonModelAdapterModel) -> [TimeSeriesRegionIndex]
"""
Launch algorithm and build results.
:param time_series: the input time-series used as neural activation in the Balloon Model
:returns: the simulated BOLD signal
:rtype: `TimeSeries`
"""
input_time_series_h5 = h5.h5_file_for_index(
self.input_time_series_index)
time_line = input_time_series_h5.read_time_page(0, self.input_shape[0])
bold_signal_index = TimeSeriesRegionIndex()
bold_signal_h5_path = h5.path_for(self.storage_path,
TimeSeriesRegionH5,
bold_signal_index.gid)
bold_signal_h5 = TimeSeriesRegionH5(bold_signal_h5_path)
bold_signal_h5.gid.store(uuid.UUID(bold_signal_index.gid))
self._fill_result_h5(bold_signal_h5, input_time_series_h5)
# ---------- Iterate over slices and compose final result ------------##
node_slice = [
slice(self.input_shape[0]),
slice(self.input_shape[1]), None,
slice(self.input_shape[3])
]
small_ts = TimeSeries()
small_ts.sample_period = self.input_time_series_index.sample_period
small_ts.sample_period_unit = self.input_time_series_index.sample_period_unit
small_ts.time = time_line
for node in range(self.input_shape[2]):
node_slice[2] = slice(node, node + 1)
small_ts.data = input_time_series_h5.read_data_slice(
tuple(node_slice))
self.algorithm.time_series = small_ts
partial_bold = self.algorithm.evaluate()
bold_signal_h5.write_data_slice_on_grow_dimension(
partial_bold.data, grow_dimension=2)
bold_signal_h5.write_time_slice(time_line)
bold_signal_shape = bold_signal_h5.data.shape
bold_signal_h5.nr_dimensions.store(len(bold_signal_shape))
bold_signal_h5.close()
input_time_series_h5.close()
self._fill_result_index(bold_signal_index, bold_signal_shape)
return bold_signal_index
def build(connectivity, region_mapping, ts=None, test_user=None, test_project=None, op=None):
if ts is None:
ts = time_series_region_factory(connectivity, region_mapping)
if not op:
op = operation_factory(test_user=test_user, test_project=test_project)
ts_db = TimeSeriesRegionIndex()
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 TimeSeriesRegionH5(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 create_region_ts(self, data_shape, connectivity):
if connectivity.number_of_regions != data_shape[1]:
raise LaunchException(
"Data has %d channels but the connectivity has %d nodes" %
(data_shape[1], connectivity.number_of_regions))
ts_idx = TimeSeriesRegionIndex()
ts_idx.fk_connectivity_gid = connectivity.gid
region_map_indexes = dao.get_generic_entity(RegionMappingIndex,
connectivity.gid,
'fk_connectivity_gid')
ts_idx.has_surface_mapping = False
if len(region_map_indexes) > 0:
ts_idx.fk_region_mapping_gid = region_map_indexes[0].gid
ts_idx.has_surface_mapping = True
ts_h5_path = self.path_for(TimeSeriesRegionH5, ts_idx.gid)
ts_h5 = TimeSeriesRegionH5(ts_h5_path)
ts_h5.connectivity.store(uuid.UUID(connectivity.gid))
return TimeSeriesRegion(), ts_idx, ts_h5
