def launch(self, data_file, dataset_name, connectivity):
"""
Execute import operations:
"""
try:
data = self.read_matlab_data(data_file, dataset_name)
measurement_count, node_count = data.shape
if node_count != connectivity.number_of_regions:
raise LaunchException(
'The measurements are for %s nodes but the selected connectivity'
' contains %s nodes' %
(node_count, connectivity.number_of_regions))
measures = []
for i in xrange(measurement_count):
measure = ConnectivityMeasure(storage_path=self.storage_path,
connectivity=connectivity,
array_data=data[i, :])
measure.user_tag_2 = "nr.-%d" % (i + 1)
measure.user_tag_3 = "conn_%d" % node_count
measures.append(measure)
return measures
except ParseException, excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def launch(self, time_series, sw, sp):
"""
Launch algorithm and build results.
:param time_series: the input time-series for which correlation coefficient should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
:returns: the fcd matrix for the given time-series, with that sw and that sp
:rtype: `Fcd`,`ConnectivityMeasure`
"""
result = [] # where fcd, fcd_segmented (eventually), and connectivity measures will be stored
[fcd, fcd_segmented, eigvect_dict, eigval_dict, Connectivity] = self.algorithm.evaluate()
# Create a Fcd dataType object.
result_fcd = Fcd(storage_path=self.storage_path, source=time_series, sw=sw, sp=sp)
result_fcd.array_data = fcd
result.append(result_fcd)
if np.amax(fcd_segmented)==1.1 :
result_fcd_segmented = Fcd(storage_path=self.storage_path, source=time_series, sw=sw, sp=sp)
result_fcd_segmented.array_data = fcd_segmented
result.append(result_fcd_segmented)
for mode in eigvect_dict.keys():
for var in eigvect_dict[mode].keys():
for ep in eigvect_dict[mode][var].keys():
for eig in range(3):
result_eig = ConnectivityMeasure(storage_path=self.storage_path)
result_eig.connectivity = Connectivity
result_eig.array_data = eigvect_dict[mode][var][ep][eig]
result_eig.title = "Epoch # %d, \n eigenvalue = %s,\n variable = %s,\n mode = %s." % (ep,eigval_dict[mode][var][ep][eig], var, mode)
result.append(result_eig)
return result
def launch(self, view_model):
# type: (ConnectivityMeasureImporterModel) -> [ConnectivityMeasureIndex]
"""
Execute import operations:
"""
try:
data = self.read_matlab_data(view_model.data_file, view_model.dataset_name)
measurement_count, node_count = data.shape
connectivity = self.load_traited_by_gid(view_model.connectivity)
if node_count != connectivity.number_of_regions:
raise LaunchException('The measurements are for %s nodes but the selected connectivity'
' contains %s nodes' % (node_count, connectivity.number_of_regions))
measures = []
self.generic_attributes.user_tag_2 = "conn_%d" % node_count
for i in range(measurement_count):
cm_data = data[i, :]
measure = ConnectivityMeasure()
measure.array_data = cm_data
measure.connectivity = connectivity
measure.title = "Measure %d for Connectivity with %d nodes." % ((i + 1), node_count)
cm_idx = h5.store_complete(measure, self.storage_path)
measures.append(cm_idx)
return measures
except ParseException as excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def launch(self, view_model):
# type: (FCDAdapterModel) -> [FcdIndex, ConnectivityMeasureIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the fcd index for the computed fcd matrix on the given time-series, with that sw and that sp
"""
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
[fcd, fcd_segmented, eigvect_dict,
eigval_dict] = self._compute_fcd_matrix(ts_h5)
connectivity_gid = ts_h5.connectivity.load()
connectivity = self.load_traited_by_gid(connectivity_gid)
result = [
] # list to store: fcd index, fcd_segmented index (eventually), and connectivity measure indexes
# Create an index for the computed fcd.
fcd_index = FcdIndex()
fcd_h5_path = h5.path_for(self.storage_path, FcdH5, fcd_index.gid)
with FcdH5(fcd_h5_path) as fcd_h5:
self._populate_fcd_h5(fcd_h5, fcd, fcd_index.gid,
self.input_time_series_index.gid,
view_model.sw, view_model.sp)
self._populate_fcd_index(fcd_index,
self.input_time_series_index.gid, fcd_h5)
result.append(fcd_index)
if np.amax(fcd_segmented) == 1.1:
result_fcd_segmented_index = FcdIndex()
result_fcd_segmented_h5_path = h5.path_for(
self.storage_path, FcdH5, result_fcd_segmented_index.gid)
with FcdH5(
result_fcd_segmented_h5_path) as result_fcd_segmented_h5:
self._populate_fcd_h5(result_fcd_segmented_h5, fcd_segmented,
result_fcd_segmented_index.gid,
self.input_time_series_index.gid,
view_model.sw, view_model.sp)
self._populate_fcd_index(result_fcd_segmented_index,
self.input_time_series_index.gid,
result_fcd_segmented_h5)
result.append(result_fcd_segmented_index)
for mode in eigvect_dict.keys():
for var in eigvect_dict[mode].keys():
for ep in eigvect_dict[mode][var].keys():
for eig in range(3):
cm_data = eigvect_dict[mode][var][ep][eig]
measure = ConnectivityMeasure()
measure.connectivity = connectivity
measure.array_data = cm_data
measure.title = "Epoch # %d, eigenvalue = %s, variable = %s, " \
"mode = %s." % (ep, eigval_dict[mode][var][ep][eig], var, mode)
cm_index = h5.store_complete(measure,
self.storage_path)
result.append(cm_index)
return result
def create_connectivity_measure(self, connectivity):
"""
:returns: persisted entity ConnectivityMeasure
"""
operation, _, storage_path = self.__create_operation()
conn_measure = ConnectivityMeasure(storage_path=storage_path)
conn_measure.connectivity = connectivity
adapter_instance = StoreAdapter([conn_measure])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return conn_measure
def create_connectivity_measure(self, connectivity):
"""
:returns: persisted entity ConnectivityMeasure
"""
operation, _, storage_path = self.__create_operation()
conn_measure = ConnectivityMeasure(storage_path=storage_path)
conn_measure.connectivity = connectivity
adapter_instance = StoreAdapter([conn_measure])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return conn_measure
def test_happy_flow(self):
self.assertEqual(
0,
TestFactory.get_entity_count(self.test_project,
ConnectivityMeasure()))
self._import('mantini_networks.mat')
self.assertEqual(
6,
TestFactory.get_entity_count(self.test_project,
ConnectivityMeasure()))
def _create_measure(conn, op, op_dir, project_id):
conn_measure = ConnectivityMeasure()
conn_measure.connectivity = h5.load_from_index(conn)
conn_measure.array_data = numpy.array(conn.number_of_regions)
conn_measure_db = h5.store_complete(conn_measure, op_dir)
conn_measure_db.fk_from_operation = op.id
dao.store_entity(conn_measure_db)
count = dao.count_datatypes(project_id, DataTypeMatrix)
return count
def launch(self, time_series, sw, sp):
"""
Launch algorithm and build results.
:param time_series: the input time-series for which correlation coefficient should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
:returns: the fcd matrix for the given time-series, with that sw and that sp
:rtype: `Fcd`,`ConnectivityMeasure`
"""
result = [
] # where fcd, fcd_segmented (eventually), and connectivity measures will be stored
[fcd, fcd_segmented, eigvect_dict, eigval_dict,
Connectivity] = self.algorithm.evaluate()
# Create a Fcd dataType object.
result_fcd = Fcd(storage_path=self.storage_path,
source=time_series,
sw=sw,
sp=sp)
result_fcd.array_data = fcd
result.append(result_fcd)
if np.amax(fcd_segmented) == 1.1:
result_fcd_segmented = Fcd(storage_path=self.storage_path,
source=time_series,
sw=sw,
sp=sp)
result_fcd_segmented.array_data = fcd_segmented
result.append(result_fcd_segmented)
for mode in eigvect_dict.keys():
for var in eigvect_dict[mode].keys():
for ep in eigvect_dict[mode][var].keys():
for eig in range(3):
result_eig = ConnectivityMeasure(
storage_path=self.storage_path)
result_eig.connectivity = Connectivity
result_eig.array_data = eigvect_dict[mode][var][ep][
eig]
result_eig.title = "Epoch # %d, \n " \
"eigenvalue = %s,\n " \
"variable = %s,\n " \
"mode = %s." % (ep, eigval_dict[mode][var][ep][eig], var, mode)
result.append(result_eig)
return result
def build_connectivity_measure(self, result, key, connectivity, title="", label_x="", label_y=""):
measure = ConnectivityMeasure()
measure.array_data = result[key]
measure.connectivity = connectivity
measure.title = title
measure.label_x = label_x
measure.label_y = label_y
return h5.store_complete(measure, self.storage_path)
def build_connectivity_measure(self, result, key, connectivity, title="", label_x="", label_y=""):
measure = ConnectivityMeasure(storage_path=self.storage_path)
measure.array_data = result[key]
measure.connectivity = connectivity
measure.title = title
measure.label_x = label_x
measure.label_y = label_y
return measure
def launch(self, data_file, dataset_name, connectivity):
"""
Execute import operations:
"""
try:
data = self.read_matlab_data(data_file, dataset_name)
measurement_count, node_count = data.shape
if node_count != connectivity.number_of_regions:
raise LaunchException('The measurements are for %s nodes but the selected connectivity'
' contains %s nodes' % (node_count, connectivity.number_of_regions))
measures = []
for i in xrange(measurement_count):
measure = ConnectivityMeasure(storage_path=self.storage_path,
connectivity=connectivity, array_data=data[i, :])
measure.user_tag_2 = "nr.-%d" % (i + 1)
measure.user_tag_3 = "conn_%d" % node_count
measures.append(measure)
return measures
except ParseException, excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def build_connectivity_measure(self,
result,
key,
connectivity,
title="",
label_x="",
label_y=""):
# TODO H5
measure = ConnectivityMeasure()
measure.array_data = result[key]
measure.connectivity = connectivity
measure.title = title
measure.label_x = label_x
measure.label_y = label_y
return measure
def test_happy_flow(self):
assert 0 == TestFactory.get_entity_count(self.test_project,
ConnectivityMeasure())
self._import('mantini_networks.mat')
assert 6 == TestFactory.get_entity_count(self.test_project,
ConnectivityMeasure())
