def partition(self, force=False):
'''
Calculate the best nodal partition of G using the louvain
algorithm as implemented in :func:`community.best_partition`.
If desired, the node-wise partition can be accessed and manipulated by
the nodal attribute "module" and the module-wise partition as
``G.graph["partition"]``
Parameters
----------
force : bool
pass True to recalculate when a partition exists
Returns
-------
(dict, dict)
Two dictionaries represent the resulting nodal partition of G. The
first maps nodes to modules and the second maps modules to nodes.
See Also
--------
:func:`BrainNetwork.calc_nodal_partition`
'''
if 'partition' not in self.graph:
nodal_partition, module_partition = calc_nodal_partition(self)
nx.set_node_attributes(self, nodal_partition, name='module')
self.graph['partition'] = module_partition
return nx.get_node_attributes(self,
name='module'), self.graph['partition']
def setUpClass(self):
self.karate = nx.karate_club_graph()
self.totalpart = {x: [x] for x in list(self.karate.nodes)}
self.triviallpart = {0: [x for x in list(self.karate.nodes)]}
self.nonpart = {0: [0]}
n, m = gm.calc_nodal_partition(self.karate)
self.bestpart_n = n
self.bestpart_m = m
self.nonbinary = nx.karate_club_graph()
nx.set_edge_attributes(self.nonbinary, '0.5', name='weight')
def throw_out_nonbinary_graph(self):
with self.assertRaises(Exception):
gm.calc_nodal_partition(self.nonbinary)