def small_world_wd(W):
'''
An implementation of small worldness. Returned is the coefficient cc/lambda,
the ratio of the clustering coefficient to the characteristic path length.
This ratio is >>1 for small world networks.
inputs: W weighted undirected connectivity matrix
output: s small world coefficient
'''
cc = clustering_coef_wd(W)
_, dists = breadthdist(W)
_lambda, _, _, _, _ = charpath(dists)
return np.mean(cc) / _lambda
def small_world_wd(W):
'''
An implementation of small worldness. Returned is the coefficient cc/lambda,
the ratio of the clustering coefficient to the characteristic path length.
This ratio is >>1 for small world networks.
inputs: W weighted undirected connectivity matrix
output: s small world coefficient
'''
cc = clustering_coef_wd(W)
_, dists = breadthdist(W)
_lambda, _, _, _, _ = charpath(dists)
return np.mean(cc) / _lambda
def test_cluscoef_wd(): x = load_directed_low_modularity_sample(thres=.45) cc = bct.clustering_coef_wd(x) print np.sum(cc) assert np.allclose(np.sum(cc), 289.30817909)
# For each kind, all individual coefficients are stacked in a unique 2D matrix.
print('{0} correlation biomarkers for each subject.'.format(
connectivity_biomarkers['tangent'].shape[1]))
#### Complex graph metrics
eig_cens=[]
clusterings=[]
Node_strengths=[]
for i in range(len(ts_allsites)):
Node_strength=bct.strengths_und(connectivity_biomarkers['tangent'][i])
Node_strengths.append(Node_strength)
eig_cen = bct.centrality.eigenvector_centrality_und(connectivity_biomarkers['tangent'][i])
eig_cens.append(eig_cen)
clustering=bct.clustering_coef_wd(connectivity_biomarkers['tangent'][i])
clusterings.append(clustering)
Node_strengths = np.stack(Node_strengths)
eig_cens=np.stack(eig_cens)
clusterings=np.stack(clusterings)
from nilearn.connectome import sym_matrix_to_vec
mat_connectivity= []
matrix=connectivity_biomarkers['tangent']
for mat in matrix:
def test_cluscoef_wd():
x = load_directed_low_modularity_sample(thres=.45)
cc = bct.clustering_coef_wd(x)
print np.sum(cc)
assert np.allclose(np.sum(cc), 289.30817909)
