##################################################################
# 0) READ THE DATA
currdir = os.getcwd()
dataroot = os.path.join(currdir, 'Data/')
net = loadtxt(dataroot + 'Zachary.txt', dtype=uint8)
net = LoadFromPajek(dataroot + 'Dolphins.net', getlabels=False)
net, labels = LoadFromPajek(dataroot + 'LesMiserables.net', getlabels=True)
# net = loadtxt(dataroot + 'Cat53_cortex.txt', dtype=uint8)
# net = SymmetriseMatrix(net)
N = len(net)
# 1) COMPUTE THE RICH-CLUB OF THE NETWORKS
# 1.1) Rich-club of the empirical network.
# Notice that 'net' is weighted but function RichClub ignores the weights.
rcdens = RichClub(net, rctype='undirected')
# 1.2) Rich-club in an ensemble of rewired networks for comparison
nrealiz = 100
prewire = 10
kmax = len(rcdens)
rewrcdens = zeros((nrealiz, kmax), float)
for re in range(nrealiz):
# Generate a randomly rewired graph (conserving the degrees)
rewnet = RewireNetwork(net, prewire, directed=False)
# Compute the rich club of the rewired graph
rewrcdens[re] = RichClub(rewnet, rctype='undirected')
# Find the average rich-club and deviation for each k value
maxrewphi = rewrcdens.max(axis=0)
from os.path import join
from numpy import *
import matplotlib.pyplot as plt
#from gatools import*
from galib import RichClub
from gamodels import RewireNetwork
# 0) READ THE DATA
datapath = '/yourpath/GAlib/Data/'
net = loadtxt(join(datapath, 'Cat53_cortex.txt'), uint8)
N = len(net)
# 1) COMPUTE THE RICH-CLUB OF THE NETWORK
# 1.1) RichClub of the input degrees
inphi = RichClub(net, weightednet=True, rctype='indegree')
outphi = RichClub(net, weightednet=True, rctype='outdegree')
avphi = RichClub(net, weightednet=True, rctype='average')
# 2) COMPUTE THE RICH-CLUB IN AN ENSEMBLE OF REWIRED NETWORKS FOR COMPARISON
nrealiz = 5
prewire = 10
inkmax = len(inphi)
inrewphi = zeros((nrealiz, inkmax), float)
outkmax = len(outphi)
outrewphi = zeros((nrealiz, outkmax), float)
avkmax = len(avphi)
avrewphi = zeros((nrealiz, avkmax), float)
for re in xrange(nrealiz):
# 2.1) Rewire the network
rewnet = RewireNetwork(net, prewire, directed=True)
from galib import RichClub
import galib.models
from galib.models import RewireNetwork
from galib.tools import SymmetriseMatrix, LoadFromPajek
##################################################################
# 0) READ THE DATA
currdir = os.getcwd()
dataroot = os.path.join(currdir, 'Data/')
net = loadtxt(dataroot + 'Cat53_cortex.txt', dtype=uint8)
N = len(net)
# 1) COMPUTE THE RICH-CLUB OF THE NETWORKS
# 1.1) Empirical network. Three different cases: input, output & average degrees
# Notice that 'net' is weighted but function RichClub ignores the weights.
inphi = RichClub(net, rctype='indegree')
outphi = RichClub(net, rctype='outdegree')
avphi = RichClub(net, rctype='average')
# 1.2) Rich-club in an ensemble of rewired networks for comparison
nrealiz = 100
prewire = 10
inkmax = len(inphi)
rewphi_in = zeros((nrealiz, inkmax), float)
outkmax = len(outphi)
rewphi_out = zeros((nrealiz, outkmax), float)
avkmax = len(avphi)
rewphi_av = zeros((nrealiz, avkmax), float)
for re in range(nrealiz):
# Generate a randomly rewired graph (conserving the in-/out-degrees)
rewnet = RewireNetwork(net, prewire, directed=True)
from os.path import join
from numpy import *
import matplotlib.pyplot as plt
#from gatools import*
from galib import RichClub
from gamodels import RewireNetwork
# 0) READ THE DATA
datapath = '/yourpath/GAlib/Data/'
net = loadtxt(join(datapath, 'Zachary.txt'), uint8)
N = len(net)
# 1) COMPUTE THE RICH-CLUB OF THE NETWORK
# 1.1) RichClub of the input degrees
phidens = RichClub(net, weightednet=True, rctype='undirected')
# 2) COMPUTE THE RICH-CLUB IN AN ENSEMBLE OF REWIRED NETWORKS FOR COMPARISON
nrealiz = 100
prewire = 10
kmax = len(phidens)
rewphidens = zeros((nrealiz, kmax), float)
for re in xrange(nrealiz):
# 2.1) Rewire the network
rewnet = RewireNetwork(net, prewire, directed=False)
# 2.2) Compute the rich club of the rewired network
rewphidens[re] = RichClub(rewnet, rctype='undirected')
# 2.3) Find the average rich-club and deviation for each k value
meanrewphi = rewphidens.mean(axis=0)