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 prepare_metadata(algo_category, burst=None, current_ga=GenericAttributes()):
"""
Gather generic_metadata from submitted fields and current to be execute algorithm.
Will populate STATE, GROUP, etc in generic_metadata
"""
generic_metadata = GenericAttributes()
generic_metadata.state = algo_category.defaultdatastate
generic_metadata.parent_burst = burst
generic_metadata.fill_from(current_ga)
return generic_metadata
def _prepare_metadata(algo_category, submit_data, operation_group=None, burst=None):
"""
Gather generic_metadata from submitted fields and current to be execute algorithm.
Will populate STATE, GROUP, etc in generic_metadata
"""
generic_metadata = GenericAttributes()
generic_metadata.state = algo_category.defaultdatastate
generic_metadata.parent_burst = burst
if DataTypeMetaData.KEY_OPERATION_TAG in submit_data:
generic_metadata.operation_tag = submit_data[DataTypeMetaData.KEY_OPERATION_TAG]
if DataTypeMetaData.KEY_TAG_1 in submit_data:
generic_metadata.user_tag_1 = submit_data[DataTypeMetaData.KEY_TAG_1]
if operation_group is not None:
generic_metadata.user_tag_3 = operation_group.name
return generic_metadata
def __import_pearson_coefficients_datatype(self, fc_folder, patient,
user_tag, ts_gid):
path = os.path.join(fc_folder, self.FC_MAT_FILE)
result = ABCUploader.read_matlab_data(path, self.FC_DATASET_NAME)
result = result.reshape((result.shape[0], result.shape[1], 1, 1))
project = dao.get_project_by_id(self.current_project_id)
user = dao.get_user_by_id(project.fk_admin)
pearson_gid = uuid.uuid4()
h5_path = "CorrelationCoefficients_{}.h5".format(pearson_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)
generic_attributes = GenericAttributes()
generic_attributes.user_tag_1 = user_tag
generic_attributes.state = DEFAULTDATASTATE_RAW_DATA
with CorrelationCoefficientsH5(h5_path) as pearson_correlation_h5:
pearson_correlation_h5.array_data.store(result)
pearson_correlation_h5.gid.store(pearson_gid)
pearson_correlation_h5.source.store(ts_gid)
pearson_correlation_h5.labels_ordering.store(
CorrelationCoefficients.labels_ordering.default)
pearson_correlation_h5.subject.store(patient)
pearson_correlation_h5.store_generic_attributes(generic_attributes)
pearson_correlation_index = CorrelationCoefficientsIndex()
pearson_correlation_index.gid = pearson_gid.hex
pearson_correlation_index.fk_from_operation = self.operation_id
pearson_correlation_index.subject = patient
pearson_correlation_index.state = DEFAULTDATASTATE_RAW_DATA
pearson_correlation_index.ndim = 4
pearson_correlation_index.fk_source_gid = ts_gid.hex # we need a random gid here to store the index
pearson_correlation_index.has_valid_time_series = False
dao.store_entity(pearson_correlation_index)