def metrics(a,
appVal,
degenName,
pcLoss,
edgePCCons = [v for v in range(1,11)],
dVal = "2",
thresholdtype = "local"
):
appValW = appVal
appValT = appVal
# prepare matrices for weighted measures
# pcCent = mbt.np.zeros((len(a.G.nodes()), 10))
betCentT = mbt.np.zeros((len(a.G.nodes()), 10))
# nM = mbt.np.zeros((10))
# wmd = mbt.np.zeros((len(a.G.nodes()), 10))
# pcCentNm = mbt.np.zeros((len(a.G.nodes()), 10))
# nMNm = mbt.np.zeros((10))
# wmdNm = mbt.np.zeros((len(a.G.nodes()), 10))
# QNm = mbt.np.zeros((10))
# list of all nodes present at the beginning in case they're lost later
allNodes = [v for v in a.G.nodes()]
# unweighted measures
for n,e in enumerate(edgePCCons):
ofb = '_'.join(["brain", degenName, thresholdtype, str(e), "d"+dVal+"_"])
a.localThresholding(edgePC=e)
a.removeUnconnectedNodes()
a.makebctmat()
a.weightToDistance()
propDict = {"edgePC":str(a.edgePC),
"pcLoss":pcLoss}
degs = a.G.degree(weight='weight')
extras.writeResults(degs, "degreeWt", ofb, propDict=propDict,
append=appVal)
# weighted betweenness centrality
bcT = mbt.centrality.betweenness_centrality(a.G, weight='distance')
betCentT[:,n] = [bcT[v] if v in a.G.nodes() else mbt.np.nan for v in allNodes]
# # weighted modularity metrics
# ci = bct.modularity_louvain_und_sign(a.bctmat)
# Q = ci[1]
# ciN = a.assignbctResult(ci[0])
# extras.writeResults(Q, "QWt", ofb, propDict=propDict, append=appValT)
# extras.writeResults(ciN, "ciWt", ofb, propDict=propDict, append=appValT)
#
# nMWt = len(mbt.np.unique(ci[0]))
# nM[n] = nMWt
# extras.writeResults(nMWt, "nMWt", ofb, 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", ofb, 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", ofb, propDict=propDict, append=appValT)
#
# # Newman partitioning
# ciNm = bct.modularity_und(a.bctmat)
# QNmWt = ciNm[1]
# QNm[n] = QNmWt
# ciNNm = a.assignbctResult(ciNm[0])
# extras.writeResults(QNmWt, "QNmWt", ofb, propDict=propDict, append=appValT)
# extras.writeResults(ciNNm, "ciNmWt", ofb, propDict=propDict, append=appValT)
#
# nMNmWt = len(mbt.np.unique(ciNm[0]))
# nMNm[n] = nMNmWt
# extras.writeResults(nMNmWt, "nMNmWt", ofb, propDict=propDict, append=appValT)
# del(nMNmWt)
#
# pcCentNmWt = bct.participation_coef_sign(a.bctmat,ciNm[0])
# pcCentNm[:,n] = pcCentNmWt
# pcCentNmWt = a.assignbctResult(pcCentNmWt)
# extras.writeResults(pcCentNmWt, "pcCentNmWt", ofb, propDict=propDict, append=appValT)
#
# wmdNmWt = extras.withinModuleDegree(a.G, ciNNm, weight='weight')
# wmdNm[:,n] = [wmdNmWt[v] for v in a.G.nodes()]
# extras.writeResults(wmdNmWt, "wmdNmWt", ofb, propDict=propDict, append=appValT)
# del wmdNmWt
#
# appValT=True
# now to collect measures in a binary graph
a.binarise()
a.weightToDistance()
a.makebctmat()
#### small worldness metrics ####
degs = mbt.nx.degree(a.G)
extras.writeResults(degs, "degree", ofb, propDict=propDict, append=appVal)
degsNorm = extras.normaliseNodeWise(a.G, mbt.nx.degree, inVal=degs)
extras.writeResults(degsNorm, "degreeNorm", ofb, propDict=propDict, append=appVal)
cc = mbt.nx.clustering(a.G)
extras.writeResults(cc, "cc", ofb, propDict=propDict, append=appVal)
ccNorm = extras.normaliseNodeWise(a.G, mbt.nx.clustering, inVal=cc)
extras.writeResults(ccNorm, "ccNorm", ofb, propDict=propDict, append=appVal)
clustCoeff = mbt.np.mean(cc.values())
extras.writeResults(clustCoeff, "clusterCoeff", ofb, propDict=propDict, append=appVal)
clustCoeffNorm = mbt.np.mean(ccNorm.values())
extras.writeResults(clustCoeffNorm, "clusterCoeffNorm", ofb, propDict=propDict, append=appVal)
del(clustCoeff)
del(clustCoeffNorm)
del(cc)
del(ccNorm)
pl = mbt.nx.average_shortest_path_length(a.G)
extras.writeResults(pl, "pl", ofb, propDict=propDict, append=appVal)
plNorm = extras.normalise(a.G, mbt.nx.average_shortest_path_length, inVal=pl)
extras.writeResults(plNorm, "plNorm", ofb, propDict=propDict, append=appVal)
del(pl)
del(plNorm)
ge = extras.globalefficiency(a.G)
extras.writeResults(ge, "ge", ofb, propDict=propDict, append=appVal)
geNorm = extras.normalise(a.G, extras.globalefficiency, inVal=ge)
extras.writeResults(geNorm, "geNorm", ofb, propDict=propDict, append=appVal)
del(ge)
del(geNorm)
le = extras.localefficiency(a.G)
extras.writeResults(le, "le", ofb, propDict=propDict, append=appVal)
leNorm = extras.normaliseNodeWise(a.G, extras.localefficiency, inVal=le)
extras.writeResults(leNorm, "leNorm", ofb, propDict=propDict, append=appVal)
del(le)
del(leNorm)
# hub metrics
# betCent = mbt.nx.centrality.betweenness_centrality(a.G)
# extras.writeResults(betCent, "betCent", ofb, propDict=propDict, append=appVal)
#
# betCentNorm = extras.normaliseNodeWise(a.G, mbt.nx.centrality.betweenness_centrality, inVal=betCent)
# extras.writeResults(betCentNorm, "betCentNorm", ofb, propDict=propDict, append=appVal)
# del(betCent, betCentNorm)
#
# closeCent = mbt.nx.centrality.closeness_centrality(a.G)
# extras.writeResults(closeCent, "closeCent", ofb, propDict=propDict, append=appVal)
#
# closeCentNorm = extras.normaliseNodeWise(a.G, mbt.nx.centrality.closeness_centrality, inVal=closeCent)
# extras.writeResults(closeCentNorm, "closeCentNorm", ofb, propDict=propDict, append=appVal)
# del(closeCent, closeCentNorm)
try:
eigCent = mbt.nx.centrality.eigenvector_centrality_numpy(a.G)
except:
eigCent = dict(zip(a.G.nodes(), ['NA' for n in a.G.nodes()]))
extras.writeResults(eigCent, "eigCentNP", ofb, propDict=propDict, append=appVal)
try:
eigCentNorm = extras.normaliseNodeWise(a.G, mbt.nx.centrality.eigenvector_centrality_numpy, inVal=eigCent)
except:
eigCentNorm = dict(zip(a.G.nodes(), ['NA' for n in a.G.nodes()]))
extras.writeResults(eigCentNorm, "eigCentNorm", ofb, propDict=propDict, append=appVal)
del(eigCent, eigCentNorm)
eln = extras.edgeLengths(a.G, nodeWise=True)
extras.writeResults(eln, "eln", ofb, propDict=propDict, append=appVal)
elnNorm = {v:[] for v in a.G.nodes()}
elNorm = []
el = extras.edgeLengths(a.G)
meanEL = mbt.np.mean(mbt.np.array((el.values()), dtype=float))
extras.writeResults(meanEL, "meanEL", ofb, propDict=propDict,
append=appVal)
for i in range(500):
rand = mbt.nx.configuration_model(a.G.degree().values())
rand = mbt.nx.Graph(rand) # convert to simple graph from multigraph
mbt.nx.set_node_attributes(rand, 'xyz', {rn:a.G.node[v]['xyz'] for rn,v in enumerate(a.G.nodes())}) # copy across spatial information
res = extras.edgeLengths(rand, nodeWise=True)
elNorm.extend([v for v in extras.edgeLengths(rand).values()])
for x,node in enumerate(elnNorm):
elnNorm[node].append(res[x])
for node in elnNorm:
elnNorm[node] = eln[node]/mbt.np.mean(elnNorm[node])
extras.writeResults(elnNorm, "elnNorm", ofb, propDict=propDict, append=appVal)
del(eln, elnNorm)
meanEL = mbt.np.mean(mbt.np.array((el.values()), dtype=float))
meanELNorm = mbt.np.mean(mbt.np.array((elNorm), dtype=float))
extras.writeResults(meanEL, "meanEL", ofb, propDict=propDict, append=appVal)
extras.writeResults(meanELNorm, "meanELNorm", ofb, propDict=propDict, append=appVal)
medianEL = mbt.np.median(mbt.np.array((el.values()), dtype=float))
medianELNorm = mbt.np.median(mbt.np.array((elNorm), dtype=float))
extras.writeResults(medianEL, "medianEL", ofb, propDict=propDict, append=appVal)
extras.writeResults(medianELNorm, "medianELNorm", ofb, propDict=propDict, append=appVal)
del(el, elNorm, meanEL, meanELNorm, medianEL, medianELNorm)
# # modularity metrics
# ci = bct.modularity_louvain_und(a.bctmat)
# Q = ci[1]
# ciN = a.assignbctResult(ci[0])
# extras.writeResults(Q, "Q", ofb, propDict=propDict, append=appVal)
# extras.writeResults(ciN, "ci", ofb , propDict=propDict, append=appVal)
#
# pcCent = bct.participation_coef(a.bctmat,ci[0])
# pcCent = a.assignbctResult(pcCent)
# extras.writeResults(pcCent, "pcCent", ofb, propDict=propDict,
# append=appVal)
# del pcCent
#
# wmd = extras.withinModuleDegree(a.G, ciN)
# extras.writeResults(wmd, "wmd", ofb, propDict=propDict, append=appVal)
# del wmd
#
# nM = len(mbt.np.unique(ci[0]))
# extras.writeResults(nM, "nM", ofb, propDict=propDict, append=appVal)
# del(nM)
# del(ci, ciN, Q)
#
# # rich club measures
# rc = mbt.nx.rich_club_coefficient(a.G, normalized=False)
# extras.writeResults(rc, "rcCoeff", ofb, propDict=propDict, append=appVal)
# del(rc)
# # robustness
# rbt = a.robustness()
# extras.writeResults(rbt, "robustness", ofb, propDict=propDict,
# append=appVal)
#
# # Newman partitioning
# ciNm = bct.modularity_und(a.bctmat)
# QNm= ciNm[1]
# ciNNm = a.assignbctResult(ciNm[0])
# extras.writeResults(QNm, "QNm", ofb, propDict=propDict, append=appValT)
# extras.writeResults(ciNNm, "ciNm", ofb, propDict=propDict, append=appValT)
# del QNm
#
# nMNm = len(mbt.np.unique(ciNm[0]))
# extras.writeResults(nMNm, "nMNm", ofb, propDict=propDict, append=appValT)
# del nMNm
#
# pcCentNm = bct.participation_coef_sign(a.bctmat,ciNm[0])
# pcCentNm = a.assignbctResult(pcCentNm)
# extras.writeResults(pcCentNm, "pcCentNm", ofb, propDict=propDict, append=appValT)
# del pcCentNm
#
# wmdNm = extras.withinModuleDegree(a.G, ciNNm, weight='weight')
# extras.writeResults(wmdNm, "wmdNm", ofb, propDict=propDict, append=appValT)
# del(wmdNm,ciNNm,ciNm)
# append any further iterations
appVal = True
# propDict = {"pcLoss":pcLoss}
# # weighted measures
#
# a.weightToDistance()
# ofb = '_'.join(["brain", degenName, "d"+dVal+"_"])
# a.makebctmat()
#
# # weighted hub metrics
# degs = a.G.degree(weight='weight')
# extras.writeResults(degs, "degree_wt", ofb, propDict=propDict, append=appValW)
#
# betCent = mbt.centrality.betweenness_centrality(a.G, weight='distance')
# extras.writeResults(betCent, "betCent_wt", ofb, propDict=propDict, append=appValW)
#
# closeCent = mbt.centrality.closeness_centrality(a.G, distance='distance')
# extras.writeResults(closeCent, "closeCent_wt", ofb, propDict=propDict, append=appValW)
#
# eigCent = mbt.centrality.eigenvector_centrality_numpy(a.G)
# extras.writeResults(eigCent, "eigCentNP_wt", ofb, propDict=propDict, append=appValW)
# 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, propDict=propDict, append=appValW)
# extras.writeResults(ciN, "ci_wt", ofb, propDict=propDict, append=appValW)
#
# nM = len(mbt.np.unique(ci[0]))
# extras.writeResults(nM, "nM_wt", ofb, propDict=propDict, append=appValW)
# del(nM)
#
# wmd = extras.withinModuleDegree(a.G, ciN, weight='weight')
# extras.writeResults(wmd, "wmd_wt", ofb, propDict=propDict, append=appValW)
# del wmd
#
#pl = mbt.nx.average_shortest_path_length(a.G, weight="distance")
#extras.writeResults(pl, "pl_wt", ofb, append=appVal)
#del(pl)
#
#ge = extras.globalefficiency(a.G, weight="distance")
#extras.writeResults(ge, "ge_wt", ofb, append=appVal)
#del(ge)
#
#le = extras.localefficiency(a.G, weight="distance")
#extras.writeResults(le, "le_wt", ofb, append=appVal)
#del(le)
#
#pcCent = mbt.np.zeros((len(a.G.nodes()), 10))
#betCentT = mbt.np.zeros((len(a.G.nodes()), 10))
#cc = mbt.np.zeros((len(a.G.nodes()), 10))
#
#nM = mbt.np.zeros((10))
##clustCoeff = mbt.np.zeros((10))
#wmd = mbt.np.zeros((len(a.G.nodes()), 10))
#Q = mbt.np.zeros((10))
#
#pcCentIM = mbt.np.zeros((len(a.G.nodes()), 10))
#nMIM = mbt.np.zeros((10))
#wmdIM = mbt.np.zeros((len(a.G.nodes()), 10))
#QIM = mbt.np.zeros((10))
#
#pcCentNm = mbt.np.zeros((len(a.G.nodes()), 10))
#nMNm = mbt.np.zeros((10))
#wmdNm = mbt.np.zeros((len(a.G.nodes()), 10))
#QNm = mbt.np.zeros((10))
#
#appValT=False
#
#for n,i in enumerate([v for v in range(1,11)]):
# a.localThresholding(edgePC=i)
# a.removeUnconnectedNodes()
# a.makebctmat()
# a.weightToDistance()
# ofbT = '_'.join(["brain", thresholdtype, str(i), "d"+dVal+"_"])
# propDict = {"edgePC":a.edgePC}
#
# # weighted modularity metrics
# ci = bct.modularity_louvain_und_sign(a.bctmat)
# QWt = ci[1]
# Q[n] = QWt
# 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(mbt.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
# extras.writeResults(QIMWt, "QIMWt", ofbT, propDict=propDict,append=appValT)
# extras.writeResults(ciNIM, "ciIMWt", ofbT, propDict=propDict, append=appValT)
# del(QIMWt)
#
# nMIMWt = len(mbt.np.unique(modules))
# nMIM[n] = nMIMWt
# extras.writeResults(nMIMWt, "nMIMWt", ofbT, propDict=propDict, append=appValT)
# del(nMIMWt)
#
# pcCentIMWt = bct.participation_coef_sign(a.bctmat, modules)
# pcCentIM[:,n] = pcCentIMWt
# pcCentIMWt = a.assignbctResult(pcCentIMWt)
# extras.writeResults(pcCentIMWt, "pcCentIMWt", ofbT, propDict=propDict, append=appValT)
# del(pcCentIMWt)
#
# wmdIMWt = extras.withinModuleDegree(a.G, ciNIM, weight='weight')
# wmdIM[:,n] = [wmdIMWt[v] for v in a.G.nodes()]
# extras.writeResults(wmdIMWt, "wmdIMWt", ofbT, propDict=propDict, append=appValT)
# del wmdIMWt
#
# # Newman partitioning
# ciNm = bct.modularity_und(a.bctmat)
# QNmWt = ciNm[1]
# QNm[n] = QNmWt
# ciNNm = a.assignbctResult(ciNm[0])
# extras.writeResults(QNmWt, "QNmWt", ofbT, propDict=propDict, append=appValT)
# extras.writeResults(ciNNm, "ciNmWt", ofbT, propDict=propDict, append=appValT)
#
# nMNmWt = len(mbt.np.unique(ciNm[0]))
# nMNm[n] = nMNmWt
# extras.writeResults(nMNmWt, "nMNmWt", ofbT, propDict=propDict, append=appValT)
# del(nMNmWt)
#
# pcCentNmWt = bct.participation_coef_sign(a.bctmat,ciNm[0])
# pcCentNm[:,n] = pcCentNmWt
# pcCentNmWt = a.assignbctResult(pcCentNmWt)
# extras.writeResults(pcCentNmWt, "pcCentNmWt", ofbT, propDict=propDict, append=appValT)
#
# wmdNmWt = extras.withinModuleDegree(a.G, ciNNm, weight='weight')
# wmdNm[:,n] = [wmdNmWt[v] for v in a.G.nodes()]
# extras.writeResults(wmdNmWt, "wmdNmWt", ofbT, propDict=propDict, append=appValT)
# del wmdNmWt
#
# ccWt = mbt.nx.clustering(a.G, weight="weight")
# cc[:,n] = [ccWt[v] for v in a.G.nodes()]
# extras.writeResults(ccWt, "ccWt", ofbT, propDict=propDict, append=appValT)
#
# clustCoeffWt = mbt.np.average(ccWt.values())
# clustCoeff[n] = clustCoeffWt
# extras.writeResults(clustCoeffWt, "clustCoeffWt", ofbT, propDict=propDict, append=appValT)
# del(clustCoeffWt)
# del(ccWt)
#
# bcT = mbt.nx.centrality.betweenness_centrality(a.G, weight='distance')
# betCentT[:,n] = [bcT[v] for v in a.G.nodes()]
# appValT=True
#
#
#Q = mbt.np.mean(Q)
#extras.writeResults(Q, "QWt_wt", ofb, append=appVal)
#del(Q)
#
#pcCent = a.assignbctResult(mbt.np.mean(pcCent, axis=1))
#extras.writeResults(pcCent, "pcCentWt_wt", ofb, append=appVal)
# del(pcCent,ci)
#
# betCentT = a.assignbctResult(mbt.np.mean(betCentT, axis=1))
# extras.writeResults(betCentT, "betCentWtT_wt", ofb, propDict=propDict, append=appValW)
#
# nM = mbt.np.mean(nM)
# extras.writeResults(nM, "nMWt_wt", ofb, propDict=propDict, append=appValW)
# del(nM)
#
# wmd = a.assignbctResult(mbt.np.mean(wmd, axis=1))
# extras.writeResults(wmd, "wmdWt_wt", ofb, propDict=propDict, append=appValW)
# del(wmd)
#
# # Newman
# QNm = mbt.np.mean(QNm)
# extras.writeResults(QNm, "QNmWt_wt", ofb, append=appValW)
# del(QNm)
#
# pcCentNm = a.assignbctResult(mbt.np.mean(pcCentNm, axis=1))
# extras.writeResults(pcCentNm, "pcCentNmWt_wt", ofb, append=appValW)
# del(pcCentNm)
#
# wmdNm = a.assignbctResult(mbt.np.mean(wmdNm, axis=1))
# extras.writeResults(wmdNm, "wmdNmWt_wt", ofb, append=appValW)
# del(wmdNm)
#
# nMNm = mbt.np.mean(nMNm)
# extras.writeResults(nMNm, "nMNmWt_wt", ofb, append=appValW)
# del(nMNm)
a.applythreshold()
extras.writeResults(ccNorm, "ccNorm", ofb, propDict=propDict, append=appVal)
clustCoeff = np.mean(cc.values())
extras.writeResults(clustCoeff, "clusterCoeff", ofb, propDict=propDict, append=appVal)
clustCoeffNorm = np.mean(ccNorm.values())
extras.writeResults(clustCoeffNorm, "clusterCoeffNorm", ofb, propDict=propDict, append=appVal)
del(clustCoeff)
del(clustCoeffNorm)
del(cc)
del(ccNorm)
pl = mbt.nx.average_shortest_path_length(a.G)
extras.writeResults(pl, "pl", ofb, propDict=propDict, append=appVal)
plNorm = extras.normalise(a.G, mbt.nx.average_shortest_path_length, inVal=pl)
extras.writeResults(plNorm, "plNorm", ofb, propDict=propDict, append=appVal)
del(pl)
del(plNorm)
ge = extras.globalefficiency(a.G)
extras.writeResults(ge, "ge", ofb, propDict=propDict, append=appVal)
geNorm = extras.normalise(a.G, extras.globalefficiency, inVal=ge)
extras.writeResults(geNorm, "geNorm", ofb, propDict=propDict, append=appVal)
del(ge)
del(geNorm)
le = extras.localefficiency(a.G)
extras.writeResults(le, "le", ofb, propDict=propDict, append=appVal)