import conedy as co N= co.network() co.setRandomSeed(0); node = co.roessler() edge = co.weightedEdge(1.0) N.cycle( 1000, 10, node,edge) print "mean degree before rewiring:" + str( N.meanDegree()) print "should be close to 0.75:" + str( N.meanClustering()) N.rewire(1.0) print "mean degree after rewiring:" + str( N.meanDegree()) print "should be close to 0.0:" + str( N.meanClustering()) #print n.meanDegree()
import conedy as co co.setRandomSeed(10) # set the random seed to 10
import conedy as co co.setRandomSeed(0) N = co.network() N.cycle( 1000, 50, co.node(), co.weightedEdge() ) # Creates a closed chain of 1000 nodes where each is connected to its 50 nearest neighbors to each side. print "should be close to 0.75:" + str(N.meanClustering()) print "should be close to " + str(1000.0 / 2 / 100) + ":" + str( N.meanPathLength()) print "should be 100:" + str(N.meanDegree())
import conedy as co N = co.network() co.setRandomSeed(0) node = co.roessler() edge = co.weightedEdge(1.0) N.cycle(1000, 10, node, edge) print "mean degree before rewiring:" + str(N.meanDegree()) print "should be close to 0.75:" + str(N.meanClustering()) N.rewire(1.0) N.saveAdjacencyList("testPython") print "mean degree after rewiring:" + str(N.meanDegree()) print "should be close to 0.0:" + str(N.meanClustering()) # print n.meanDegree()