def launch(self, view_model):
# type: (ZIPConnectivityImporterModel) -> [ConnectivityIndex]
"""
Execute import operations: unpack ZIP and build Connectivity object as result.
:raises LaunchException: when `uploaded` is empty or nonexistent
:raises Exception: when
* weights or tracts matrix is invalid (negative values, wrong shape)
* any of the vector orientation, areas, cortical or hemisphere is \
different from the expected number of nodes
"""
if view_model.uploaded is None:
raise LaunchException(
"Please select ZIP file which contains data to import")
files = self.storage_interface.unpack_zip(view_model.uploaded,
self.get_storage_path())
weights_matrix = None
centres = None
labels_vector = None
tract_matrix = None
orientation = None
areas = None
cortical_vector = None
hemisphere_vector = None
for file_name in files:
file_name_low = file_name.lower()
if self.WEIGHT_TOKEN in file_name_low:
weights_matrix = self.read_list_data(file_name)
elif self.CENTRES_TOKEN in file_name_low or self.CENTRES_TOKEN2 in file_name_low:
centres = self.read_list_data(file_name, usecols=[1, 2, 3])
labels_vector = self.read_list_data(file_name,
dtype=numpy.str,
usecols=[0])
elif self.TRACT_TOKEN in file_name_low:
tract_matrix = self.read_list_data(file_name)
elif self.ORIENTATION_TOKEN in file_name_low:
orientation = self.read_list_data(file_name)
elif self.AREA_TOKEN in file_name_low:
areas = self.read_list_data(file_name)
elif self.CORTICAL_INFO in file_name_low:
cortical_vector = self.read_list_data(file_name,
dtype=numpy.bool)
elif self.HEMISPHERE_INFO in file_name_low:
hemisphere_vector = self.read_list_data(file_name,
dtype=numpy.bool)
# Clean remaining text-files.
self.storage_interface.remove_files(files, True)
result = Connectivity()
# Fill positions
if centres is None:
raise Exception(
"Region centres are required for Connectivity Regions! "
"We expect a file that contains *centres* inside the uploaded ZIP."
)
expected_number_of_nodes = len(centres)
if expected_number_of_nodes < 2:
raise Exception("A connectivity with at least 2 nodes is expected")
result.centres = centres
if labels_vector is not None:
result.region_labels = labels_vector
# Fill and check weights
if weights_matrix is not None:
if weights_matrix.shape != (expected_number_of_nodes,
expected_number_of_nodes):
raise Exception(
"Unexpected shape for weights matrix! "
"Should be %d x %d " %
(expected_number_of_nodes, expected_number_of_nodes))
result.weights = weights_matrix
if view_model.normalization:
result.weights = result.scaled_weights(
view_model.normalization)
# Fill and check tracts. Allow empty files for tracts, they will be computed by tvb-library.
if tract_matrix is not None:
if tract_matrix.size != 0:
if numpy.any([x < 0 for x in tract_matrix.flatten()]):
raise Exception(
"Negative values are not accepted in tracts matrix! "
"Please check your file, and use values >= 0")
if tract_matrix.shape != (expected_number_of_nodes,
expected_number_of_nodes):
raise Exception(
"Unexpected shape for tracts matrix! "
"Should be %d x %d " %
(expected_number_of_nodes, expected_number_of_nodes))
result.tract_lengths = tract_matrix
if orientation is not None:
if len(orientation) != expected_number_of_nodes:
raise Exception(
"Invalid size for vector orientation. "
"Expected the same as region-centers number %d" %
expected_number_of_nodes)
result.orientations = orientation
if areas is not None:
if len(areas) != expected_number_of_nodes:
raise Exception(
"Invalid size for vector areas. "
"Expected the same as region-centers number %d" %
expected_number_of_nodes)
result.areas = areas
if cortical_vector is not None:
if len(cortical_vector) != expected_number_of_nodes:
raise Exception(
"Invalid size for vector cortical. "
"Expected the same as region-centers number %d" %
expected_number_of_nodes)
result.cortical = cortical_vector
if hemisphere_vector is not None:
if len(hemisphere_vector) != expected_number_of_nodes:
raise Exception(
"Invalid size for vector hemispheres. "
"Expected the same as region-centers number %d" %
expected_number_of_nodes)
result.hemispheres = hemisphere_vector
result.configure()
return self.store_complete(result)
def launch(self, uploaded, normalization=None):
"""
Execute import operations: unpack ZIP and build Connectivity object as result.
:param uploaded: an archive containing the Connectivity data to be imported
:returns: `Connectivity`
:raises LaunchException: when `uploaded` is empty or nonexistent
:raises Exception: when
* weights or tracts matrix is invalid (negative values, wrong shape)
* any of the vector orientation, areas, cortical or hemisphere is \
different from the expected number of nodes
"""
if uploaded is None:
raise LaunchException("Please select ZIP file which contains data to import")
files = FilesHelper().unpack_zip(uploaded, self.storage_path)
weights_matrix = None
centres = None
labels_vector = None
tract_matrix = None
orientation = None
areas = None
cortical_vector = None
hemisphere_vector = None
for file_name in files:
file_name_low = file_name.lower()
if self.WEIGHT_TOKEN in file_name_low:
weights_matrix = self.read_list_data(file_name)
elif self.CENTRES_TOKEN in file_name_low or self.CENTRES_TOKEN2 in file_name_low:
centres = self.read_list_data(file_name, usecols=[1, 2, 3])
labels_vector = self.read_list_data(file_name, dtype=numpy.str, usecols=[0])
elif self.TRACT_TOKEN in file_name_low:
tract_matrix = self.read_list_data(file_name)
elif self.ORIENTATION_TOKEN in file_name_low:
orientation = self.read_list_data(file_name)
elif self.AREA_TOKEN in file_name_low:
areas = self.read_list_data(file_name)
elif self.CORTICAL_INFO in file_name_low:
cortical_vector = self.read_list_data(file_name, dtype=numpy.bool)
elif self.HEMISPHERE_INFO in file_name_low:
hemisphere_vector = self.read_list_data(file_name, dtype=numpy.bool)
### Clean remaining text-files.
FilesHelper.remove_files(files, True)
result = Connectivity()
result.storage_path = self.storage_path
### Fill positions
if centres is None:
raise Exception("Region centres are required for Connectivity Regions! "
"We expect a file that contains *centres* inside the uploaded ZIP.")
expected_number_of_nodes = len(centres)
if expected_number_of_nodes < 2:
raise Exception("A connectivity with at least 2 nodes is expected")
result.centres = centres
if labels_vector is not None:
result.region_labels = labels_vector
### Fill and check weights
if weights_matrix is not None:
if weights_matrix.shape != (expected_number_of_nodes, expected_number_of_nodes):
raise Exception("Unexpected shape for weights matrix! "
"Should be %d x %d " % (expected_number_of_nodes, expected_number_of_nodes))
result.weights = weights_matrix
if normalization:
result.weights = result.scaled_weights(normalization)
### Fill and check tracts
if tract_matrix is not None:
if numpy.any([x < 0 for x in tract_matrix.flatten()]):
raise Exception("Negative values are not accepted in tracts matrix! "
"Please check your file, and use values >= 0")
if tract_matrix.shape != (expected_number_of_nodes, expected_number_of_nodes):
raise Exception("Unexpected shape for tracts matrix! "
"Should be %d x %d " % (expected_number_of_nodes, expected_number_of_nodes))
result.tract_lengths = tract_matrix
if orientation is not None:
if len(orientation) != expected_number_of_nodes:
raise Exception("Invalid size for vector orientation. "
"Expected the same as region-centers number %d" % expected_number_of_nodes)
result.orientations = orientation
if areas is not None:
if len(areas) != expected_number_of_nodes:
raise Exception("Invalid size for vector areas. "
"Expected the same as region-centers number %d" % expected_number_of_nodes)
result.areas = areas
if cortical_vector is not None:
if len(cortical_vector) != expected_number_of_nodes:
raise Exception("Invalid size for vector cortical. "
"Expected the same as region-centers number %d" % expected_number_of_nodes)
result.cortical = cortical_vector
if hemisphere_vector is not None:
if len(hemisphere_vector) != expected_number_of_nodes:
raise Exception("Invalid size for vector hemispheres. "
"Expected the same as region-centers number %d" % expected_number_of_nodes)
result.hemispheres = hemisphere_vector
return result
