Python Graph.reindex_edges方法代码示例

# 需要导入模块: from graph_tool import Graph [as 别名]
# 或者: from graph_tool.Graph import reindex_edges [as 别名]
def gen_er(dicProperties):
	np.random.seed()
	# initialize graph
	graphER = Graph()
	nNodes = 0
	nEdges = 0
	rDens = 0.0
	if "Nodes" in dicProperties.keys():
		nNodes = dicProperties["Nodes"]
		graphER.add_vertex(nNodes)
		if "Edges" in dicProperties.keys():
			nEdges = dicProperties["Edges"]
			rDens = nEdges / float(nNodes**2)
			dicProperties["Density"] = rDens
		else:
			rDens = dicProperties["Density"]
			nEdges = int(np.floor(rDens*nNodes**2))
			dicProperties["Edges"] = nEdges
	else:
		nEdges = dicProperties["Edges"]
		rDens = dicProperties["Density"]
		nNodes = int(np.floor(np.sqrt(nEdges/rDens)))
		graphER.add_vertex(nNodes)
		dicProperties["Nodes"] = nNodes
	# generate edges
	numTest,numCurrentEdges = 0,0
	while numCurrentEdges != nEdges and numTest < n_MAXTESTS:
		lstEdges = np.random.randint(0,nNodes,(nEdges-numCurrentEdges,2))
		graphER.add_edge_list(lstEdges)
		# remove loops and duplicate edges
		remove_self_loops(graphER)
		remove_parallel_edges(graphER)
		numCurrentEdges = graphER.num_edges()
		numTest += 1
	graphER.reindex_edges()
	nEdges = graphER.num_edges()
	rDens = nEdges / float(nNodes**2)
	# generate types
	rInhibFrac = dicProperties["InhibFrac"]
	lstTypesGen = np.random.uniform(0,1,nEdges)
	lstTypeLimit = np.full(nEdges,rInhibFrac)
	lstIsExcitatory = np.greater(lstTypesGen,lstTypeLimit)
	nExc = np.count_nonzero(lstIsExcitatory)
	epropType = graphER.new_edge_property("int",np.multiply(2,lstIsExcitatory)-np.repeat(1,nEdges)) # excitatory (True) or inhibitory (False)
	graphER.edge_properties["type"] = epropType
	# and weights
	if dicProperties["Weighted"]:
		lstWeights = dicGenWeights[dicProperties["Distribution"]](graphER,dicProperties,nEdges,nExc) # generate the weights
		epropW = graphER.new_edge_property("double",lstWeights) # crée la propriété pour stocker les poids
		graphER.edge_properties["weight"] = epropW
	return graphER

# 需要导入模块: from graph_tool import Graph [as 别名]
# 或者: from graph_tool.Graph import reindex_edges [as 别名]

#.........这里部分代码省略.........
			dicProperties["Density"] = rDens
		else:
			rDens = dicProperties["Density"]
			nEdges = int(np.floor(rDens*nNodes**2))
			dicProperties["Edges"] = nEdges
	else:
		nEdges = dicProperties["Edges"]
		rDens = dicProperties["Density"]
		nNodes = int(np.floor(np.sqrt(nEdges/rDens)))
		graphFS.add_vertex(nNodes)
		dicProperties["Nodes"] = nNodes
	# on définit le nombre d'arcs à créer
	nArcs = int(np.floor(rDens*nNodes**2)/(1+rFracRecip))
	# on définit les paramètres fonctions de probabilité associées F(x) = A x^{-tau}
	Ai = nArcs*(rInDeg-1)/(nNodes)
	Ao = nArcs*(rOutDeg-1)/(nNodes)
	# on définit les moyennes des distributions de pareto 2 = lomax
	rMi = 1/(rInDeg-2.)
	rMo = 1/(rOutDeg-2.)
	# on définit les trois listes contenant les degrés sortant/entrant/bidirectionnels associés aux noeuds i in range(nNodes)
	lstInDeg = np.random.pareto(rInDeg,nNodes)+1
	lstOutDeg = np.random.pareto(rOutDeg,nNodes)+1
	lstInDeg = np.floor(np.multiply(Ai/np.mean(lstInDeg), lstInDeg)).astype(int)
	lstOutDeg = np.floor(np.multiply(Ao/np.mean(lstOutDeg), lstOutDeg)).astype(int)
	# on génère les stubs qui vont être nécessaires et on les compte
	nInStubs = int(np.sum(lstInDeg))
	nOutStubs = int(np.sum(lstOutDeg))
	lstInStubs = np.zeros(np.sum(lstInDeg))
	lstOutStubs = np.zeros(np.sum(lstOutDeg))
	nStartIn = 0
	nStartOut = 0
	for vert in range(nNodes):
		nInDegVert = lstInDeg[vert]
		nOutDegVert = lstOutDeg[vert]
		for j in range(np.max([nInDegVert,nOutDegVert])):
			if j < nInDegVert:
				lstInStubs[nStartIn+j] += vert
			if j < nOutDegVert:
				lstOutStubs[nStartOut+j] += vert
		nStartOut+=nOutDegVert
		nStartIn+=nInDegVert
	# on vérifie qu'on a à peu près le nombre voulu d'edges
	while nInStubs*(1+rFracRecip)/float(nArcs) < 0.95 :
		vert = np.random.randint(0,nNodes)
		nAddInStubs = int(np.floor(Ai/rMi*(np.random.pareto(rInDeg)+1)))
		lstInStubs = np.append(lstInStubs,np.repeat(vert,nAddInStubs)).astype(int)
		nInStubs+=nAddInStubs
	while nOutStubs*(1+rFracRecip)/float(nArcs) < 0.95 :
		nAddOutStubs = int(np.floor(Ao/rMo*(np.random.pareto(rOutDeg)+1)))
		lstOutStubs = np.append(lstOutStubs,np.repeat(vert,nAddOutStubs)).astype(int)
		nOutStubs+=nAddOutStubs
	# on s'assure d'avoir le même nombre de in et out stubs (1.13 is an experimental correction)
	nMaxStubs = int(1.13*(2.0*nArcs)/(2*(1+rFracRecip)))
	if nInStubs > nMaxStubs and nOutStubs > nMaxStubs:
		np.random.shuffle(lstInStubs)
		np.random.shuffle(lstOutStubs)
		lstOutStubs.resize(nMaxStubs)
		lstInStubs.resize(nMaxStubs)
		nOutStubs = nInStubs = nMaxStubs
	elif nInStubs < nOutStubs:
		np.random.shuffle(lstOutStubs)
		lstOutStubs.resize(nInStubs)
		nOutStubs = nInStubs
	else:
		np.random.shuffle(lstInStubs)
		lstInStubs.resize(nOutStubs)
		nInStubs = nOutStubs
	# on crée le graphe, les noeuds et les stubs
	nRecip = int(np.floor(nInStubs*rFracRecip))
	nEdges = nInStubs + nRecip +1
	# les stubs réciproques
	np.random.shuffle(lstInStubs)
	np.random.shuffle(lstOutStubs)
	lstInRecip = lstInStubs[0:nRecip]
	lstOutRecip = lstOutStubs[0:nRecip]
	lstEdges = np.array([np.concatenate((lstOutStubs,lstInRecip)),np.concatenate((lstInStubs,lstOutRecip))]).astype(int)
	# add edges
	graphFS.add_edge_list(np.transpose(lstEdges))
	remove_self_loops(graphFS)
	remove_parallel_edges(graphFS)
	lstIsolatedVert = find_vertex(graphFS, graphFS.degree_property_map("total"), 0)
	graphFS.remove_vertex(lstIsolatedVert)
	graphFS.reindex_edges()
	nNodes = graphFS.num_vertices()
	nEdges = graphFS.num_edges()
	rDens = nEdges / float(nNodes**2)
	# generate types
	rInhibFrac = dicProperties["InhibFrac"]
	lstTypesGen = np.random.uniform(0,1,nEdges)
	lstTypeLimit = np.full(nEdges,rInhibFrac)
	lstIsExcitatory = np.greater(lstTypesGen,lstTypeLimit)
	nExc = np.count_nonzero(lstIsExcitatory)
	epropType = graphFS.new_edge_property("int",np.multiply(2,lstIsExcitatory)-np.repeat(1,nEdges)) # excitatory (True) or inhibitory (False)
	graphFS.edge_properties["type"] = epropType
	# and weights
	if dicProperties["Weighted"]:
		lstWeights = dicGenWeights[dicProperties["Distribution"]](graphFS,dicProperties,nEdges,nExc) # generate the weights
		epropW = graphFS.new_edge_property("double",lstWeights) # crée la propriété pour stocker les poids
		graphFS.edge_properties["weight"] = epropW
	return graphFS