def cross_modularity(a1, a2):
# There are some problems with my code
ma, _ = bct.modularity_louvain_und_sign(a1)
mb, _ = bct.modularity_louvain_und_sign(a2)
ma, qa = bct.modularity_finetune_und_sign(a1, ci=ma)
mb, qb = bct.modularity_finetune_und_sign(a2, ci=mb)
_, qab = bct.modularity_und_sign(a1, mb)
_, qba = bct.modularity_und_sign(a2, ma)
return (qab + qba) / (qa + qb)
def cross_modularity(a1, a2):
# There are some problems with my code
ma, _ = bct.modularity_louvain_und_sign(a1)
mb, _ = bct.modularity_louvain_und_sign(a2)
ma, qa = bct.modularity_finetune_und_sign(a1, ci=ma)
mb, qb = bct.modularity_finetune_und_sign(a2, ci=mb)
_, qab = bct.modularity_und_sign(a1, mb)
_, qba = bct.modularity_und_sign(a2, ma)
return (qab + qba) / (qa + qb)
def test_modularity_louvain_und_sign_seed():
# performance is same as matlab if randomness is quashed
x = load_signed_sample()
seed = 90772777
_, q = bct.modularity_louvain_und_sign(x, seed=seed)
print(q)
assert np.allclose(q, .46605515)
def test_modularity_louvain_und_sign_seed():
# performance is same as matlab if randomness is quashed
x = load_signed_sample()
seed = 90772777
_, q = bct.modularity_louvain_und_sign(x, seed=seed)
print(q)
assert np.allclose(q, .46605515)
def get_consensus_matrix(network):
import bct
import numpy as np
modules,q = bct.modularity_louvain_und_sign(network, qtype='smp')
module_matrix = np.repeat(modules,repeats=network.shape[0])
module_matrix = np.reshape(module_matrix,newshape=network.shape)
consensus_matrix = module_matrix == module_matrix.transpose()
return (consensus_matrix.astype('float'), modules, q)
def test_modularity_finetune_und_sign_actually_finetune():
x = load_signed_sample()
seed = 34908314
ci, oq = bct.modularity_louvain_und_sign(x, seed=seed)
_, q = bct.modularity_finetune_und_sign(x, seed=seed, ci=ci)
print(q)
assert np.allclose(q, .47282924)
assert q >= oq
seed = 88215881
np.random.seed(seed)
randomized_sample = np.random.random(size=(len(x), len(x)))
randomized_sample = randomized_sample + randomized_sample.T
x[np.where(bct.threshold_proportional(randomized_sample, .2))] = 0
ci, oq = bct.modularity_louvain_und_sign(x, seed=seed)
print(oq)
assert np.allclose(oq, .45254522)
for i in range(100):
_, q = bct.modularity_finetune_und_sign(x, ci=ci)
assert q >= oq
def test_modularity_finetune_und_sign_actually_finetune():
x = load_signed_sample()
seed = 34908314
ci, oq = bct.modularity_louvain_und_sign(x, seed=seed)
_, q = bct.modularity_finetune_und_sign(x, seed=seed, ci=ci)
print(q)
assert np.allclose(q, .47282924)
assert q >= oq
seed = 88215881
np.random.seed(seed)
randomized_sample = np.random.random(size=(len(x), len(x)))
randomized_sample = randomized_sample + randomized_sample.T
x[np.where(bct.threshold_proportional(randomized_sample, .2))] = 0
ci, oq = bct.modularity_louvain_und_sign(x, seed=seed)
print(oq)
assert np.allclose(oq, .45254522)
for i in range(100):
_, q = bct.modularity_finetune_und_sign(x, ci=ci)
assert q >= oq
def cal_dynamic_graph(MTD, impose=False, threshold=False):
'''calculate graph metrics across time(dynamic)'''
#setup outputs
time_points = MTD.shape[0]
ci = np.zeros([MTD.shape[1], MTD.shape[0]])
q = np.zeros([MTD.shape[0]])
WMD = np.zeros([MTD.shape[1], MTD.shape[0]])
PC = np.zeros([MTD.shape[1], MTD.shape[0]])
WW = np.zeros([MTD.shape[1], MTD.shape[0]])
BW = np.zeros([MTD.shape[1], MTD.shape[0]])
#modularity
if impose:
ci = np.tile(
np.loadtxt(
'/home/despoB/kaihwang/Rest/ThaGate/ROIs/Morel_Striatum_Gordon_CI'
), [time_points, 1]).T
for i, t in enumerate(range(0, time_points)):
matrix = MTD[i, :, :]
#need to deal with NANs because of coverage (no signal in some ROIs)
matrix[np.isnan(matrix)] = 0
#threshold here
if threshold:
matrix = bct.threshold_proportional(matrix, threshold)
#modularity
if impose == False:
ci[:, i], q[i] = bct.modularity_louvain_und_sign(matrix)
#PC
# for now, no negative weights
matrix[matrix < 0] = 0
PC[:, i] = bct.participation_coef(matrix, ci[:, i])
#WMD
WMD[:, i] = bct.module_degree_zscore(matrix, ci[:, i])
## within weight
WW[:, i] = cal_within_weight(matrix, ci[:, i])
## between Weight
BW[:, i] = cal_between_weight(matrix, ci[:, i])
# cal q using impsose CI partition
if impose:
q[i] = cal_modularity_w_imposed_community(matrix, ci[:, i])
return ci, q, PC, WMD, WW, BW
def cal_sFC_graph(subject, sequence, roi, impose=False, threshold=1.0):
''' load TS and run static FC'''
ts_path = '/home/despoB/kaihwang/Rest/ThaGate/NotBackedUp/'
fn = ts_path + str(subject) + '_%s_%s_000.netts' % (roi, sequence)
ts = np.loadtxt(fn)
matrix = np.corrcoef(ts)
matrix[np.isnan(matrix)] = 0
matrix = bct.threshold_proportional(matrix, threshold)
num_iter = 200
consensus = np.zeros((num_iter, matrix.shape[0], matrix.shape[1]))
for i in np.arange(0, num_iter):
ci, _ = bct.modularity_louvain_und_sign(matrix, qtype='sta')
consensus[i, :, :] = community_matrix(ci)
mean_matrix = np.nanmean(consensus, axis=0)
mean_matrix[np.isnan(mean_matrix)] = 0
CI, Q = bct.modularity_louvain_und_sign(mean_matrix, qtype='sta')
#no negative weights
matrix[matrix < 0] = 0
PC = bct.participation_coef(matrix, CI)
#WMD
WMD = bct.module_degree_zscore(matrix, CI)
## within weight
WW = cal_within_weight(matrix, CI)
## between Weight
BW = cal_between_weight(matrix, CI)
return CI, Q, PC, WMD, WW, BW
def get_consensus_matrix(network):
"""
#================================
# Helper function for consensus module assignment
#================================
inputs:
network: numpy array of an adjacency matrix
"""
import bct
import numpy as np
modules, q = bct.modularity_louvain_und_sign(network, qtype='smp')
module_matrix = np.repeat(modules, repeats=network.shape[0])
module_matrix = np.reshape(module_matrix, newshape=network.shape)
consensus_matrix = module_matrix == module_matrix.transpose()
return (consensus_matrix.astype('float'), modules, q)
def sample_degenerate_partitions(w, probtune_cap=.10, modularity_cutoff=.95):
ntries = 0
while True:
init_ci, _ = bct.modularity_louvain_und_sign(w)
seed_ci, seed_q = bct.modularity_finetune_und_sign(w, ci=init_ci)
p = (np.random.random() * probtune_cap)
ci, q = bct.modularity_probtune_und_sign(w, ci=seed_ci, p=p)
if q > (seed_q * modularity_cutoff):
print ('found a degenerate partition after %i tries with probtune '
'parameter %.3f: %.5f %.5f' % (ntries, p, q, seed_q))
ntries = 0
yield ci, q
else:
# print 'failed to find degenerate partition, trying again',q,
# seed_q
ntries += 1
def sample_degenerate_partitions(w, probtune_cap=.10, modularity_cutoff=.95):
ntries = 0
while True:
init_ci, _ = bct.modularity_louvain_und_sign(w)
seed_ci, seed_q = bct.modularity_finetune_und_sign(w, ci=init_ci)
p = (np.random.random() * probtune_cap)
ci, q = bct.modularity_probtune_und_sign(w, ci=seed_ci, p=p)
if q > (seed_q * modularity_cutoff):
print(
'found a degenerate partition after %i tries with probtune '
'parameter %.3f: %.5f %.5f' % (ntries, p, q, seed_q))
ntries = 0
yield ci, q
else:
# print 'failed to find degenerate partition, trying again',q,
# seed_q
ntries += 1
def test_modularity_probtune_und_sign(): x = load_signed_sample() seed = 59468096 ci,q = bct.modularity_probtune_und_sign(x, seed=seed) print q assert np.allclose(q, .07885327) seed = 1742447 ci,_ = bct.modularity_louvain_und_sign(x, seed=seed) _,oq = bct.modularity_finetune_und_sign(x, seed=seed, ci=ci) for i in np.arange(.05, .5, .02): fails=0 for j in xrange(100): _,q = bct.modularity_probtune_und_sign(x, ci=ci, p=i) try: assert q < oq except AssertionError: if fails > 5: raise else: fails+=1
def test_modularity_probtune_und_sign():
x = load_signed_sample()
seed = 59468096
ci, q = bct.modularity_probtune_und_sign(x, seed=seed)
print(q)
assert np.allclose(q, .07885327)
seed = 1742447
ci, _ = bct.modularity_louvain_und_sign(x, seed=seed)
_, oq = bct.modularity_finetune_und_sign(x, seed=seed, ci=ci)
for i in np.arange(.05, .5, .02):
fails = 0
for j in range(100):
_, q = bct.modularity_probtune_und_sign(x, ci=ci, p=i)
try:
assert q < oq
except AssertionError:
if fails > 5:
raise
else:
fails += 1
## weighted hub metrics #degs = a.G.degree(weight='weight') #extras.writeResults(degs, "degree_wt", ofb, append=appVal) # #betCent = mbt.nx.centrality.betweenness_centrality(a.G, weight='distance') #extras.writeResults(betCent, "betCent_wt", ofb, append=appVal) # #closeCent = mbt.nx.centrality.closeness_centrality(a.G, distance='distance') #extras.writeResults(closeCent, "closeCent_wt", ofb, append=appVal) # #eigCent = mbt.nx.centrality.eigenvector_centrality_numpy(a.G) #extras.writeResults(eigCent, "eigCentNP_wt", ofb, append=appVal) #del(eigCent) # ## weighted modularity metrics ci = bct.modularity_louvain_und_sign(a.bctmat) Q = ci[1] ciN = a.assignbctResult(ci[0]) extras.writeResults(Q, "Q_wt", ofb, append=appVal) extras.writeResults(ciN, "ci_wt", ofb, append=appVal) nM = len(np.unique(ci[0])) extras.writeResults(nM, "nM_wt", ofb, append=appVal) del(nM) wmd = extras.withinModuleDegree(a.G, ciN, weight='weight') extras.writeResults(wmd, "wmd_wt", ofb, append=appVal) del wmd #pl = mbt.nx.average_shortest_path_length(a.G, weight="distance") #extras.writeResults(pl, "pl_wt", ofb, append=appVal)
left_matrix = matrix[0:200, 0:200]
num_iter = 200
consensus = np.zeros((200, matrix.shape[0], matrix.shape[1]))
left_consensus = np.zeros(
(200, left_matrix.shape[0], left_matrix.shape[1]))
right_consensus = np.zeros(
(200, right_matrix.shape[0], right_matrix.shape[1]))
qs = np.zeros(200)
left_qs = np.zeros(200)
right_qs = np.zeros(200)
for i in np.arange(0, num_iter):
ci, qs[i] = bct.modularity_louvain_und_sign(matrix, qtype='sta')
consensus[i, :, :] = community_matrix(ci)
ci, left_qs[i] = bct.modularity_louvain_und_sign(left_matrix,
qtype='sta')
left_consensus[i, :, :] = community_matrix(ci)
ci, right_qs[i] = bct.modularity_louvain_und_sign(right_matrix,
qtype='sta')
right_consensus[i, :, :] = community_matrix(ci)
mean_matrix = np.mean(consensus, axis=0)
mean_matrix[np.isnan(mean_matrix)] = 0
mean_left_matrix = np.mean(left_consensus, axis=0)
mean_left_matrix[np.isnan(mean_left_matrix)] = 0
matrix = matrix[2:,:] #note, roi 1-200 is left, 201-400 is right right_matrix = matrix[200:,200:] left_matrix = matrix[0:200,0:200] num_iter = 200 consensus = np.zeros((200, matrix.shape[0], matrix.shape[1])) left_consensus = np.zeros((200, left_matrix.shape[0], left_matrix.shape[1])) right_consensus = np.zeros((200, right_matrix.shape[0], right_matrix.shape[1])) qs = np.zeros(200) left_qs = np.zeros(200) right_qs = np.zeros(200) for i in np.arange(0,num_iter): ci, qs[i] =bct.modularity_louvain_und_sign(matrix, qtype='sta') consensus[i, :,:] = community_matrix(ci) ci, left_qs[i] =bct.modularity_louvain_und_sign(left_matrix, qtype='sta') left_consensus[i, :,:] = community_matrix(ci) ci, right_qs[i] =bct.modularity_louvain_und_sign(right_matrix, qtype='sta') right_consensus[i, :,:] = community_matrix(ci) #mean_matrix = np.mean(consensus, axis=0) #mean_matrix[np.isnan(mean_matrix)]=0 #mean_left_matrix = np.mean(left_consensus, axis=0) #mean_left_matrix[np.isnan(mean_left_matrix)]=0
