def launch(self, view_model):
# type: (CSVConnectivityImporterModel) -> ConnectivityIndex
"""
Execute import operations: process the weights and tracts csv files, then use
the reference connectivity passed as input_data for the rest of the attributes.
:raises LaunchException: when the number of nodes in CSV files doesn't match the one in the connectivity
"""
weights_matrix = self._read_csv_file(view_model.weights, view_model.weights_delimiter)
tract_matrix = self._read_csv_file(view_model.tracts, view_model.tracts_delimiter)
self.storage_interface.remove_files([view_model.weights, view_model.tracts])
conn_index = self.load_entity_by_gid(view_model.input_data)
if weights_matrix.shape[0] != conn_index.number_of_regions:
raise LaunchException("The csv files define %s nodes but the connectivity you selected as reference "
"has only %s nodes." % (weights_matrix.shape[0], conn_index.number_of_regions))
input_connectivity = h5.load_from_index(conn_index)
result = Connectivity()
result.centres = input_connectivity.centres
result.region_labels = input_connectivity.region_labels
result.weights = weights_matrix
result.tract_lengths = tract_matrix
result.orientations = input_connectivity.orientations
result.areas = input_connectivity.areas
result.cortical = input_connectivity.cortical
result.hemispheres = input_connectivity.hemispheres
result.configure()
return h5.store_complete(result, self.storage_path)
def launch(self, weights, weights_delimiter, tracts, tracts_delimiter, input_data):
"""
Execute import operations: process the weights and tracts csv files, then use
the reference connectivity passed as input_data for the rest of the attributes.
:param weights: csv file containing the weights measures
:param tracts: csv file containing the tracts measures
:param input_data: a reference connectivity with the additional attributes
:raises LaunchException: when the number of nodes in CSV files doesn't match the one in the connectivity
"""
weights_matrix = self._read_csv_file(weights, weights_delimiter)
tract_matrix = self._read_csv_file(tracts, tracts_delimiter)
FilesHelper.remove_files([weights, tracts])
if weights_matrix.shape[0] != input_data.number_of_regions:
raise LaunchException("The csv files define %s nodes but the connectivity you selected as reference "
"has only %s nodes." % (weights_matrix.shape[0], input_data.number_of_regions))
input_connectivity = h5.load_from_index(input_data)
result = Connectivity()
result.centres = input_connectivity.centres
result.region_labels = input_connectivity.region_labels
result.weights = weights_matrix
result.tract_lengths = tract_matrix
result.orientations = input_connectivity.orientations
result.areas = input_connectivity.areas
result.cortical = input_connectivity.cortical
result.hemispheres = input_connectivity.hemispheres
result.configure()
return h5.store_complete(result, self.storage_path)
def _branch_connectivity(self, original_conn, new_weights, interest_areas,
new_tracts=None):
# type: (Connectivity, numpy.array, numpy.array, numpy.array) -> Connectivity
"""
Generate new Connectivity based on a previous one, by changing weights (e.g. simulate lesion).
The returned connectivity has the same number of nodes. The edges of unselected nodes will have weight 0.
:param original_conn: Original Connectivity, to copy from
:param new_weights: weights matrix for the new connectivity
:param interest_areas: ndarray of the selected node id's
:param new_tracts: tracts matrix for the new connectivity
"""
new_weights, interest_areas, new_tracts = self._reorder_arrays(original_conn, new_weights,
interest_areas, new_tracts)
if new_tracts is None:
new_tracts = original_conn.tract_lengths
for i in range(len(original_conn.weights)):
for j in range(len(original_conn.weights)):
if i not in interest_areas or j not in interest_areas:
new_weights[i][j] = 0
final_conn = Connectivity()
final_conn.parent_connectivity = original_conn.gid.hex
final_conn.saved_selection = interest_areas.tolist()
final_conn.weights = new_weights
final_conn.centres = original_conn.centres
final_conn.region_labels = original_conn.region_labels
final_conn.orientations = original_conn.orientations
final_conn.cortical = original_conn.cortical
final_conn.hemispheres = original_conn.hemispheres
final_conn.areas = original_conn.areas
final_conn.tract_lengths = new_tracts
final_conn.configure()
return final_conn
def parse(self, network):
"""
Populate Connectivity DataType from NetworkX object.
Tested with results from Connectome Mapper Toolkit.
:param network: NetworkX graph
:return: Connectivity object
"""
graph_size = len(network.nodes())
weights_matrix = numpy.zeros((graph_size, graph_size))
tract_matrix = numpy.zeros((graph_size, graph_size))
labels_vector, positions, cortical, hemisphere = [], [], [], []
try:
for node in range(1, graph_size + 1):
node_data = network.nodes[node]
pos = self._find_value(node_data, self.KEY_NODE_COORDINATES)
positions.append(list(pos))
label = self._find_value(node_data, self.KEY_NODE_LABEL)
labels_vector.append(str(label))
if self.REGION_CORTICAL == self._find_value(
node_data, self.KEY_NODE_REGION):
cortical.append(True)
else:
cortical.append(False)
if self.HEMISPHERE_RIGHT == self._find_value(
node_data, self.KEY_NODE_HEMISPHERE):
hemisphere.append(True)
else:
hemisphere.append(False)
# Iterate over edges:
for start, end in network.edges():
weights_matrix[start - 1][end - 1] = self._find_value(
network.adj[start][end], self.KEY_EDGE_WEIGHT)
tract_matrix[start - 1][end - 1] = self._find_value(
network.adj[start][end], self.KEY_EDGE_TRACT)
result = Connectivity()
result.region_labels = numpy.array(labels_vector)
result.centres = numpy.array(positions)
# result.set_metadata({'description': 'Array Columns: labels, X, Y, Z'}, 'centres')
result.hemispheres = numpy.array(hemisphere)
result.cortical = numpy.array(cortical)
result.weights = weights_matrix
result.tract_lengths = tract_matrix
result.configure()
return result
except KeyError as err:
self.logger.exception("Could not parse Connectivity")
raise ParseException(err)
def _cut_connectivity(self,
original_conn,
new_weights,
interest_areas,
new_tracts=None):
# type: (Connectivity, numpy.array, numpy.array, numpy.array) -> Connectivity
"""
Generate new Connectivity object based on current one, by removing nodes (e.g. simulate lesion).
Only the selected nodes will get used in the result. The order of the indices in interest_areas matters.
If indices are not sorted then the nodes will be permuted accordingly.
:param original_conn: Original Connectivity(HasTraits), to cut nodes from
:param new_weights: weights matrix for the new connectivity
:param interest_areas: ndarray with the selected node id's.
:param new_tracts: tracts matrix for the new connectivity
"""
new_weights, interest_areas, new_tracts = self._reorder_arrays(
original_conn, new_weights, interest_areas, new_tracts)
if new_tracts is None:
new_tracts = original_conn.tract_lengths[
interest_areas, :][:, interest_areas]
else:
new_tracts = new_tracts[interest_areas, :][:, interest_areas]
new_weights = new_weights[interest_areas, :][:, interest_areas]
final_conn = Connectivity()
final_conn.parent_connectivity = None
final_conn.weights = new_weights
final_conn.centres = original_conn.centres[interest_areas, :]
final_conn.region_labels = original_conn.region_labels[interest_areas]
if original_conn.orientations is not None and len(
original_conn.orientations):
final_conn.orientations = original_conn.orientations[
interest_areas, :]
if original_conn.cortical is not None and len(original_conn.cortical):
final_conn.cortical = original_conn.cortical[interest_areas]
if original_conn.hemispheres is not None and len(
original_conn.hemispheres):
final_conn.hemispheres = original_conn.hemispheres[interest_areas]
if original_conn.areas is not None and len(original_conn.areas):
final_conn.areas = original_conn.areas[interest_areas]
final_conn.tract_lengths = new_tracts
final_conn.saved_selection = []
final_conn.configure()
return final_conn
def launch(self, view_model):
resolution = view_model.resolution
weighting = view_model.weighting
inj_f_thresh = view_model.inj_f_thresh / 100.
vol_thresh = view_model.vol_thresh
project = dao.get_project_by_id(self.current_project_id)
manifest_file = self.file_handler.get_allen_mouse_cache_folder(
project.name)
manifest_file = os.path.join(manifest_file,
'mouse_connectivity_manifest.json')
cache = MouseConnectivityCache(resolution=resolution,
manifest_file=manifest_file)
# the method creates a dictionary with information about which experiments need to be downloaded
ist2e = dictionary_builder(cache, False)
# the method downloads experiments necessary to build the connectivity
projmaps = download_an_construct_matrix(cache, weighting, ist2e, False)
# the method cleans the file projmaps in 4 steps
projmaps = pms_cleaner(projmaps)
# download from the AllenSDK the annotation volume, the template volume
vol, annot_info = cache.get_annotation_volume()
template, template_info = cache.get_template_volume()
# rotate template in the TVB 3D reference:
template = rotate_reference(template)
# grab the StructureTree instance
structure_tree = cache.get_structure_tree()
# the method includes in the parcellation only brain regions whose volume is greater than vol_thresh
projmaps = areas_volume_threshold(cache, projmaps, vol_thresh,
resolution)
# the method exclude from the experimental dataset
# those exps where the injected fraction of pixel in the injection site is lower than than the inj_f_thr
projmaps = infected_threshold(cache, projmaps, inj_f_thresh)
# the method creates file order and keyword that will be the link between the SC order and the
# id key in the Allen database
[order, key_ord] = create_file_order(projmaps, structure_tree)
# the method builds the Structural Connectivity (SC) matrix
structural_conn = construct_structural_conn(projmaps, order, key_ord)
# the method returns the coordinate of the centres and the name of the brain areas in the selected parcellation
[centres, names] = construct_centres(cache, order, key_ord)
# the method returns the tract lengths between the brain areas in the selected parcellation
tract_lengths = construct_tract_lengths(centres)
# the method associated the parent and the grandparents to the child in the selected parcellation with
# the biggest volume
[unique_parents, unique_grandparents
] = parents_and_grandparents_finder(cache, order, key_ord,
structure_tree)
# the method returns a volume indexed between 0 and N-1, with N=tot brain areas in the parcellation.
# -1=background and areas that are not in the parcellation
vol_parcel = mouse_brain_visualizer(vol, order, key_ord,
unique_parents,
unique_grandparents,
structure_tree, projmaps)
# results: Connectivity, Volume & RegionVolumeMapping
# Connectivity
result_connectivity = Connectivity()
result_connectivity.centres = centres
result_connectivity.region_labels = numpy.array(names)
result_connectivity.weights = structural_conn
result_connectivity.tract_lengths = tract_lengths
result_connectivity.configure()
# Volume
result_volume = Volume()
result_volume.origin = numpy.array([[0.0, 0.0, 0.0]])
result_volume.voxel_size = numpy.array(
[resolution, resolution, resolution])
# result_volume.voxel_unit= micron
# Region Volume Mapping
result_rvm = RegionVolumeMapping()
result_rvm.volume = result_volume
result_rvm.array_data = vol_parcel
result_rvm.connectivity = result_connectivity
result_rvm.title = "Volume mouse brain "
result_rvm.dimensions_labels = ["X", "Y", "Z"]
# Volume template
result_template = StructuralMRI()
result_template.array_data = template
result_template.weighting = 'T1'
result_template.volume = result_volume
connectivity_index = h5.store_complete(result_connectivity,
self.storage_path)
volume_index = h5.store_complete(result_volume, self.storage_path)
rvm_index = h5.store_complete(result_rvm, self.storage_path)
template_index = h5.store_complete(result_template, self.storage_path)
return [connectivity_index, volume_index, rvm_index, template_index]
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)
from tvb.datatypes.connectivity import Connectivity
from tvb.datatypes.cortex import Cortex
from tvb.datatypes.region_mapping import RegionMapping
from tvb.datatypes.sensors import SensorsEEG
from tvb.datatypes.projections import ProjectionMatrix, ProjectionSurfaceEEG
from tvb.simulator import monitors
from tvb.simulator.models import WilsonCowan, Generic2dOscillator, ReducedWongWang, JansenRit, Linear
from tvb.simulator.integrators import VODE, VODEStochastic
from tvb.simulator import simulator
import time
# zipped directory that contains connectivity/tractography info
conn_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/connectivity/DH_20120806_Connectivity/DHconn.zip"
conn = Connectivity(load_file=conn_fname)
conn.configure()
print("connectivity loaded")
# print(conn.summary_info)
plot_connectivity(connectivity=conn)
# pyplot.show()
# triangulated cortical surface mesh
ctx_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/cortex/DH_20120806_Surface_Cortex.zip"
# maps nodes from cortex mesh to brain regions
region_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/DH_20120806_RegionMapping.txt"
rm = RegionMapping(load_file=region_fname)
# matlab matrix that describes how waves propagate to the surface
eeg_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/DH_20120806_ProjectionMatrix.mat"
ctx = Cortex(load_file=ctx_fname, region_mapping_data=rm)
ctx.configure()
