import igraph as ig
import numpy as np
class ConverterMixin(object):
"""Answer by FastTurtle https://stackoverflow.com/questions/18020074/convert-a-baseclass-object-into-a-subclass-object-idiomatically"""
@classmethod
def convert_to_class(cls, obj):
obj.__class__ = cls
[docs]class vfGraph(ConverterMixin, ig.Graph):
"""A class for manipulating the graph creating from the transition matrix, built from the (reconstructed) vector
field. This is a derived class from igraph's Graph.
"""
[docs] def __init__(self, *args, **kwds):
super(vfGraph, self).__init__(*args, **kwds)
def build_graph(self, adj_mat):
"""build sparse diffusion graph. The adjacency matrix needs to preserves divergence.
"""
sources, targets = adj_mat.nonzero()
edgelist = list(zip(sources.tolist(), targets.tolist()))
self.__init__(edgelist, edge_attrs={'weight': adj_mat.data.tolist()}, directed=True)
def multimaxflow(self, sources, sinks):
"""Multi-source multi-sink maximum flow. Ported from https://github.com/kazumits/ddhodge/blob/master/R/graphConstr.R
"""
v_num, e_num = self.vcount(), self.ecount()
usrc = v_num # super-source
usink = usrc + 1 # super-sink
new_edges = np.hstack([np.vstack(([usrc] * len(sources), sources)), \
np.vstack((sinks, [usink] * len(sinks)))]).T
self.add_vertices(2)
self.add_edges(new_edges)
w_sum = sum(self.es.get_attribute_values('weight')[:e_num])
self.es['weight'] = [i if i is not None else w_sum for i in self.es['weight']]
mf = self.maxflow(usrc, usink, self.es.get_attribute_values('weight'))
self.es.set_attribute_values('flow', mf.flow)
self.vs.set_attribute_values('pass', self.strength(mode="in", weights=mf.flow))
self.delete_vertices([usrc, usink])
# add gradop, ...