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 xrange(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(1)
else:
cortical.append(0)
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.storage_path = self.storage_path
result.region_labels = labels_vector
result.centres = positions
result.set_metadata(
{'description': 'Array Columns: labels, X, Y, Z'}, 'centres')
result.hemispheres = hemisphere
result.cortical = cortical
result.weights = weights_matrix
result.tract_lengths = tract_matrix
return result
except KeyError as err:
self.logger.exception("Could not parse Connectivity")
raise ParseException(err)
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 network.nodes():
node_data = network.node[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(1)
else:
cortical.append(0)
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.storage_path = self.storage_path
result.region_labels = labels_vector
result.centres = positions
result.set_metadata({'description': 'Array Columns: labels, X, Y, Z'}, 'centres')
result.hemispheres = hemisphere
result.cortical = cortical
result.weights = weights_matrix
result.tract_lengths = tract_matrix
return result
except KeyError, err:
self.logger.exception("Could not parse Connectivity")
raise ParseException(err)
def networkx2connectivity(network_obj, storage_path):
"""
Populate Connectivity DataType from NetworkX object.
"""
network_obj.load()
weights_matrix, tract_matrix, labels_vector = [], [], []
positions, areas, orientation = [], [], []
# Read all nodes
graph_data = network_obj.data
graph_size = len(graph_data.nodes())
for node in graph_data.nodes():
positions.append([
graph_data.node[node][ct.KEY_POS_X],
graph_data.node[node][ct.KEY_POS_Y],
graph_data.node[node][ct.KEY_POS_Z]
])
labels_vector.append(graph_data.node[node][ct.KEY_POS_LABEL])
if ct.KEY_AREA in graph_data.node[node]:
areas.append(graph_data.node[node][ct.KEY_AREA])
if ct.KEY_ORIENTATION_AVG in graph_data.node[node]:
orientation.append(graph_data.node[node][ct.KEY_ORIENTATION_AVG])
weights_matrix.append([0.0] * graph_size)
tract_matrix.append([0.0] * graph_size)
# Read all edges
for edge in network_obj.data.edges():
start = edge[0]
end = edge[1]
weights_matrix[start][end] = graph_data.adj[start][end][ct.KEY_WEIGHT]
tract_matrix[start][end] = graph_data.adj[start][end][ct.KEY_TRACT]
meta = network_obj.get_metadata_as_dict()
result = Connectivity()
result.storage_path = storage_path
result.nose_correction = meta[ct.KEY_NOSE] if ct.KEY_NOSE in meta else None
result.weights = weights_matrix
result.centres = positions
result.region_labels = labels_vector
result.set_metadata({'description': 'Array Columns: labels, X, Y, Z'},
'centres')
result.orientations = orientation
result.areas = areas
result.tract_lengths = tract_matrix
return result, (meta[ct.KEY_UID] if ct.KEY_UID in meta else None)
def networkx2connectivity(network_obj, storage_path):
"""
Populate Connectivity DataType from NetworkX object.
"""
network_obj.load()
weights_matrix, tract_matrix, labels_vector = [], [], []
positions, areas, orientation = [], [], []
# Read all nodes
graph_data = network_obj.data
graph_size = len(graph_data.nodes())
for node in graph_data.nodes():
positions.append([graph_data.node[node][ct.KEY_POS_X],
graph_data.node[node][ct.KEY_POS_Y],
graph_data.node[node][ct.KEY_POS_Z]])
labels_vector.append(graph_data.node[node][ct.KEY_POS_LABEL])
if ct.KEY_AREA in graph_data.node[node]:
areas.append(graph_data.node[node][ct.KEY_AREA])
if ct.KEY_ORIENTATION_AVG in graph_data.node[node]:
orientation.append(graph_data.node[node][ct.KEY_ORIENTATION_AVG])
weights_matrix.append([0.0] * graph_size)
tract_matrix.append([0.0] * graph_size)
# Read all edges
for edge in network_obj.data.edges():
start = edge[0]
end = edge[1]
weights_matrix[start][end] = graph_data.adj[start][end][ct.KEY_WEIGHT]
tract_matrix[start][end] = graph_data.adj[start][end][ct.KEY_TRACT]
meta = network_obj.get_metadata_as_dict()
result = Connectivity()
result.storage_path = storage_path
result.nose_correction = meta[ct.KEY_NOSE] if ct.KEY_NOSE in meta else None
result.weights = weights_matrix
result.centres = positions
result.region_labels = labels_vector
result.set_metadata({'description':'Array Columns: labels, X, Y, Z'},'centres')
result.orientations = orientation
result.areas = areas
result.tract_lengths = tract_matrix
return result, (meta[ct.KEY_UID] if ct.KEY_UID in meta else None)