def test_participation():
W = np.eye(3)
ci = np.array([1, 1, 2])
assert np.allclose(bct.participation_coef(W, ci), [0, 0, 0])
assert np.allclose(bct.participation_coef_sign(W, ci)[0], [0, 0, 0])
W = np.ones((3, 3))
assert np.allclose(bct.participation_coef(W, ci), [
0.44444444, 0.44444444, 0.44444444])
assert np.allclose(bct.participation_coef_sign(W, ci)[0], [
0.44444444, 0.44444444, 0.44444444])
W = np.eye(3)
W[0, 1] = 1
W[0, 2] = 1
assert np.allclose(bct.participation_coef(W, ci), [0.44444444, 0, 0])
assert np.allclose(bct.participation_coef_sign(W, ci)
[0], [0.44444444, 0, 0])
W = np.eye(3)
W[0, 1] = -1
W[0, 2] = -1
W[1, 2] = 1
assert np.allclose(bct.participation_coef_sign(W, ci)[0], [0., 0.5, 0.])
def participation_test():
W = np.eye(3)
ci = np.array([1, 1, 2])
assert np.allclose(bct.participation_coef(W, ci), [0, 0, 0])
assert np.allclose(bct.participation_coef_sign(W, ci)[0], [0, 0, 0])
W = np.ones((3, 3))
assert np.allclose(bct.participation_coef(W, ci), [
0.44444444, 0.44444444, 0.44444444])
assert np.allclose(bct.participation_coef_sign(W, ci)[0], [
0.44444444, 0.44444444, 0.44444444])
W = np.eye(3)
W[0, 1] = 1
W[0, 2] = 1
assert np.allclose(bct.participation_coef(W, ci), [0.44444444, 0, 0])
assert np.allclose(bct.participation_coef_sign(W, ci)
[0], [0.44444444, 0, 0])
W = np.eye(3)
W[0, 1] = -1
W[0, 2] = -1
W[1, 2] = 1
assert np.allclose(bct.participation_coef_sign(W, ci)[0], [0., 0.5, 0.])
def calculate_communities(self, reorder=False, **kwargs):
assert self.community_alg is not None, \
print("Community algorithm has not been set!")
G = self.G
graph_mat = self.graph_mat
# calculate community structure
comm, mod = self.community_alg(graph_mat, **kwargs)
# if there is a reference, relabel communities based on their closest association
if self.ref_community:
comm = self._relabel_community(comm, self.ref_community)
# label vertices of G
G.vs['community'] = comm
G.vs['within_module_degree'] = bct.module_degree_zscore(
graph_mat, comm)
if np.min(graph_mat) < 0:
participation_pos, participation_neg = bct.participation_coef_sign(
graph_mat, comm)
G.vs['part_coef_pos'] = participation_pos
G.vs['part_coef_neg'] = participation_neg
else:
G.vs['part_coef'] = bct.participation_coef(graph_mat, comm)
if reorder:
self.reorder()
# calculate subgraph (within-community) characteristics
self._subgraph_analysis()
return mod
def calculate_communities(self, reorder=False, **kwargs):
assert self.community_alg is not None, \
print("Community algorithm has not been set!")
G = self.G
graph_mat = self.graph_mat
# calculate community structure
comm, mod = self.community_alg(graph_mat, **kwargs)
# if there is a reference, relabel communities based on their closest association
if self.ref_community:
comm = self._relabel_community(comm,self.ref_community)
# label vertices of G
G.vs['community'] = comm
G.vs['within_module_degree'] = bct.module_degree_zscore(graph_mat,comm)
if np.min(graph_mat) < 0:
participation_pos, participation_neg = bct.participation_coef_sign(graph_mat, comm)
G.vs['part_coef_pos'] = participation_pos
G.vs['part_coef_neg'] = participation_neg
else:
G.vs['part_coef'] = bct.participation_coef(graph_mat, comm)
if reorder:
self.reorder()
# calculate subgraph (within-community) characteristics
self._subgraph_analysis()
return mod
ciN = a.assignbctResult(ci[0])
extras.writeResults(QWt, "QWt", ofbT, propDict=propDict, append=appValT)
extras.writeResults(ciN, "ciWt", ofbT, propDict=propDict, append=appValT)
del QWt
nMWt = len(np.unique(ci[0]))
nM[n] = nMWt
extras.writeResults(nMWt, "nMWt", ofbT, propDict=propDict, append=appValT)
del(nMWt)
wmdWt = extras.withinModuleDegree(a.G, ciN, weight='weight')
wmd[:,n] = [wmdWt[v] for v in a.G.nodes()]
extras.writeResults(wmdWt, "wmdWt", ofbT, propDict=propDict, append=appValT)
del wmdWt
pcCentWt = bct.participation_coef_sign(a.bctmat,ci[0])
pcCent[:,n] = pcCentWt
pcCentWt = a.assignbctResult(pcCentWt)
extras.writeResults(pcCentWt, "pcCentWt", ofbT, propDict=propDict, append=appValT)
# # infomap partitioning
# bIM = infomap.nx2infomap(a.G)
# del(bIM)
# f = open("nxdigraph.clu", "r") # recapture results from output file
# modules = mbt.np.array([int(v.strip('\n')) for v in f.readlines()[1:]])
# f.close()
# remove("nxdigraph.clu")
#
# ciNIM = a.assignbctResult(modules)
# QIMWt = community.modularity(ciNIM, a.G)
# QIM[n] = QIMWt
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.in_length_matrix, args.in_conn_matrix])
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
if not args.append_json:
assert_outputs_exist(parser, args, args.out_json)
else:
logging.debug('Using --append_json, make sure to delete {} '
'before re-launching a group analysis.'.format(
args.out_json))
if args.append_json and args.overwrite:
parser.error('Cannot use the append option at the same time as '
'overwrite.\nAmbiguous behavior, consider deleting the '
'output json file first instead.')
conn_matrix = load_matrix_in_any_format(args.in_conn_matrix)
len_matrix = load_matrix_in_any_format(args.in_length_matrix)
if args.filtering_mask:
mask_matrix = load_matrix_in_any_format(args.filtering_mask)
conn_matrix *= mask_matrix
len_matrix *= mask_matrix
N = len_matrix.shape[0]
if args.avg_node_wise:
func_cast = avg_cast
else:
func_cast = list_cast
gtm_dict = {}
betweenness_centrality = bct.betweenness_wei(len_matrix) / ((N - 1) *
(N - 2))
gtm_dict['betweenness_centrality'] = func_cast(betweenness_centrality)
ci, gtm_dict['modularity'] = bct.modularity_louvain_und(conn_matrix,
seed=0)
gtm_dict['assortativity'] = bct.assortativity_wei(conn_matrix, flag=0)
gtm_dict['participation'] = func_cast(
bct.participation_coef_sign(conn_matrix, ci)[0])
gtm_dict['clustering'] = func_cast(bct.clustering_coef_wu(conn_matrix))
gtm_dict['nodal_strength'] = func_cast(bct.strengths_und(conn_matrix))
gtm_dict['local_efficiency'] = func_cast(
bct.efficiency_wei(len_matrix, local=True))
gtm_dict['global_efficiency'] = func_cast(bct.efficiency_wei(len_matrix))
gtm_dict['density'] = func_cast(bct.density_und(conn_matrix)[0])
# Rich club always gives an error for the matrix rank and gives NaN
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tmp_rich_club = bct.rich_club_wu(conn_matrix)
gtm_dict['rich_club'] = func_cast(tmp_rich_club[~np.isnan(tmp_rich_club)])
# Path length gives an infinite distance for unconnected nodes
# All of this is simply to fix that
empty_connections = np.where(np.sum(len_matrix, axis=1) < 0.001)[0]
if len(empty_connections):
len_matrix = np.delete(len_matrix, empty_connections, axis=0)
len_matrix = np.delete(len_matrix, empty_connections, axis=1)
path_length_tuple = bct.distance_wei(len_matrix)
gtm_dict['path_length'] = func_cast(path_length_tuple[0])
gtm_dict['edge_count'] = func_cast(path_length_tuple[1])
if not args.avg_node_wise:
for i in empty_connections:
gtm_dict['path_length'].insert(i, -1)
gtm_dict['edge_count'].insert(i, -1)
if args.small_world:
gtm_dict['omega'], gtm_dict['sigma'] = omega_sigma(len_matrix)
if os.path.isfile(args.out_json) and args.append_json:
with open(args.out_json) as json_data:
out_dict = json.load(json_data)
for key in gtm_dict.keys():
if isinstance(out_dict[key], list):
out_dict[key].append(gtm_dict[key])
else:
out_dict[key] = [out_dict[key], gtm_dict[key]]
else:
out_dict = {}
for key in gtm_dict.keys():
out_dict[key] = [gtm_dict[key]]
with open(args.out_json, 'w') as outfile:
json.dump(out_dict,
outfile,
indent=args.indent,
sort_keys=args.sort_keys)
def calc_graph_vector(filename, thresholds) :
'''
This function calculates graph measures for connectivity matrix loaded from textfile
and save results under the same name with additional superscript +'_GV' (in same dir
filename is located)
Input arguments:
filename(str): name of file containing connectivity matrix (txt extension)
thresholds(list): list containing thresholds of interest #
Kamil Bonna, 14.08.2018
'''
#--- check inputs
import os
if not os.path.exists(filename):
raise Exception('{} does not exist'.format(filename))
if type(thresholds) != list:
raise Exception('thresholds should be a list!')
import numpy as np
import bct
#=== inner variables
N_rep_louvain = 10 # number of Louvain algorithm repetitions
N_measures = 10 # number of graph measures
gamma = 1 # Louvain resolution parameter
#--- load matrix
A_raw = np.loadtxt(filename)
N = A_raw.shape[0] # number of nodes
M_sat = N*(N-1)/2 # max number of connections
#=== calculate output
graph_measures = np.zeros([ len(thresholds), N_measures ]) # create empty output matrix
for thr in range(len(thresholds)) :
#--- thresholding
A = bct.threshold_proportional( A_raw, p=thresholds[thr], copy=True );
A[np.nonzero(A<0)] = 0 # ensure only positive weights
M_act = A[np.nonzero(A>0)].shape[0] / 2 # actual number of nonzero connections
#--- calculate measures
#-- mean connection strenght
S = np.sum(A)/M_act
#-- connection strenght std
Svar = np.std(A[np.nonzero(A)])
#-- modularity
[M,Q] = bct.modularity_louvain_und(A, gamma)
for i in range(N_rep_louvain) :
[Mt,Qt] = bct.modularity_louvain_und(A, gamma)
if Qt > Q :
Q = Qt
M = Mt
#-- participation coefficient
P = np.mean(bct.participation_coef_sign(A, M))
#-- clustering
C = np.mean(bct.clustering_coef_wu(A))
#-- transitivity
T = bct.transitivity_wu(A)
#-- assortativity
Asso = bct.assortativity_wei(A)
#-- global & local efficiency
Eglo = bct.efficiency_wei(A)
Eloc = np.mean(bct.efficiency_wei(A, local=True))
#-- mean eigenvector centralit
Eig = np.mean(bct.eigenvector_centrality_und(A))
#--- write vector to matrix
graph_measures[thr] = [ S, Svar, Q, P, C, T, Asso, Eglo, Eloc, Eig ]
#=== save results to file
np.savetxt( filename[:-4]+'_GV.txt', graph_measures )