def launch(self, time_series, dt=None, bold_model=None, RBM=None, neural_input_transformation=None):
"""
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`
"""
time_line = time_series.read_time_page(0, self.input_shape[0])
bold_signal = TimeSeriesRegion(storage_path=self.storage_path,
sample_period=time_series.sample_period,
start_time=time_series.start_time,
connectivity=time_series.connectivity)
##---------- 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(use_storage=False, sample_period=time_series.sample_period, time=time_line)
for node in range(self.input_shape[2]):
node_slice[2] = slice(node, node + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_bold = self.algorithm.evaluate()
bold_signal.write_data_slice(partial_bold.data, grow_dimension=2)
bold_signal.write_time_slice(time_line)
bold_signal.close_file()
return bold_signal
def test_adapter_launch(self):
"""
Test that the adapters launches and successfully generates a datatype measure entry.
"""
meta = {
DataTypeMetaData.KEY_SUBJECT: "John Doe",
DataTypeMetaData.KEY_STATE: "RAW_DATA"
}
algo_group = FlowService().get_algorithm_by_module_and_class(
SIMULATOR_MODULE, SIMULATOR_CLASS)[1]
self.operation = model.Operation(self.test_user.id,
self.test_project.id,
algo_group.id,
json.dumps(''),
meta=json.dumps(meta),
status=model.STATUS_STARTED,
method_name=ABCAdapter.LAUNCH_METHOD)
self.operation = dao.store_entity(self.operation)
storage_path = FilesHelper().get_project_folder(
self.test_project, str(self.operation.id))
dummy_input = numpy.arange(1, 10001).reshape(10, 10, 10, 10)
dummy_time = numpy.arange(1, 11)
# Get connectivity
connectivities = FlowService().get_available_datatypes(
self.test_project.id, "tvb.datatypes.connectivity.Connectivity")[0]
self.assertEqual(2, len(connectivities))
connectivity_gid = connectivities[0][2]
dummy_time_series = TimeSeriesRegion()
dummy_time_series.storage_path = storage_path
dummy_time_series.write_data_slice(dummy_input)
dummy_time_series.write_time_slice(dummy_time)
dummy_time_series.close_file()
dummy_time_series.start_time = 0.0
dummy_time_series.sample_period = 1.0
dummy_time_series.connectivity = connectivity_gid
adapter_instance = StoreAdapter([dummy_time_series])
OperationService().initiate_prelaunch(self.operation, adapter_instance,
{})
dummy_time_series = dao.get_generic_entity(dummy_time_series.__class__,
dummy_time_series.gid,
'gid')[0]
ts_metric_adapter = TimeseriesMetricsAdapter()
resulted_metric = ts_metric_adapter.launch(dummy_time_series)
self.assertTrue(isinstance(resulted_metric, DatatypeMeasure),
"Result should be a datatype measure.")
self.assertTrue(
len(resulted_metric.metrics) >= len(
ts_metric_adapter.available_algorithms.keys()),
"At least a result should have been generated for every metric.")
for metric_value in resulted_metric.metrics.values():
self.assertTrue(isinstance(metric_value, (float, int)))
def test_adapter_launch(self):
"""
Test that the adapters launches and successfully generates a datatype measure entry.
"""
meta = {DataTypeMetaData.KEY_SUBJECT: "John Doe", DataTypeMetaData.KEY_STATE: "RAW_DATA"}
algo_group = FlowService().get_algorithm_by_module_and_class(SIMULATOR_MODULE, SIMULATOR_CLASS)[1]
self.operation = model.Operation(self.test_user.id, self.test_project.id, algo_group.id, json.dumps(''),
meta=json.dumps(meta), status=model.STATUS_STARTED,
method_name=ABCAdapter.LAUNCH_METHOD)
self.operation = dao.store_entity(self.operation)
storage_path = FilesHelper().get_project_folder(self.test_project, str(self.operation.id))
dummy_input = numpy.arange(1, 10001).reshape(10, 10, 10, 10)
dummy_time = numpy.arange(1, 11)
# Get connectivity
connectivities = FlowService().get_available_datatypes(self.test_project.id,
"tvb.datatypes.connectivity.Connectivity")[0]
self.assertEqual(2, len(connectivities))
connectivity_gid = connectivities[0][2]
dummy_time_series = TimeSeriesRegion()
dummy_time_series.storage_path = storage_path
dummy_time_series.write_data_slice(dummy_input)
dummy_time_series.write_time_slice(dummy_time)
dummy_time_series.close_file()
dummy_time_series.start_time = 0.0
dummy_time_series.sample_period = 1.0
dummy_time_series.connectivity = connectivity_gid
adapter_instance = StoreAdapter([dummy_time_series])
OperationService().initiate_prelaunch(self.operation, adapter_instance, {})
dummy_time_series = dao.get_generic_entity(dummy_time_series.__class__, dummy_time_series.gid, 'gid')[0]
ts_metric_adapter = TimeseriesMetricsAdapter()
resulted_metric = ts_metric_adapter.launch(dummy_time_series)
self.assertTrue(isinstance(resulted_metric, DatatypeMeasure), "Result should be a datatype measure.")
self.assertTrue(len(resulted_metric.metrics) >= len(ts_metric_adapter.available_algorithms.keys()),
"At least a result should have been generated for every metric.")
for metric_value in resulted_metric.metrics.values():
self.assertTrue(isinstance(metric_value, (float, int)))