Python Graph.num_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 num_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 num_edges [as 别名]

#.........这里部分代码省略.........
		else:
			strName = self.dicProperties["Type"]
			tplUse = ("Nodes", "Edges", "Distribution")
			for key,value in self.dicProperties.items():
				if key in tplUse and (value.__class__ != dict):
					strName += '_' + key[0] + str(value)
				if key == "Clustering":
					strName += '_' + key[0] + str(around(value,4))
			self.dicProperties["Name"] = strName
		print(self.dicProperties["Name"])

	def update_prop(self, lstProp=[]):
		''' update part or all of the graph properties '''
		if lstProp:
			for strPropName in lstProp:
				if strPropName in self.dicGetProp.keys():
					self.dicProperties[strPropName] = self.dicGetProp[strPropName](self.__graph)
				else:
					print("Ignoring unknown property '{}'".format(strPropName))
		else:
			self.dicProperties.update({ strPropName: self.dicGetProp[strPropName](self.__graph) for strPropName in self.dicGetProp.keys() })
			self.bPropToDate = True

	#---------------#
	# Get functions #
	#---------------#

	## basic properties

	def get_name(self):
		return self.dicProperties["Name"]
	
	def num_vertices(self):
		return self.__graph.num_vertices()

	def num_edges(self):
		return self.__graph.num_edges()

	def get_density(self):
		return self.__graph.num_edges()/float(self.__graph.num_vertices()**2)

	def is_weighted(self):
		return self.dicProperties["Weighted"]

	## graph and adjacency matrix
	
	def get_graph(self):
		self.bPropToDate = False
		self.bBetwToDate = False
		self.wBetweeness = False
		return self.__graph

	def get_mat_adjacency(self):
		return adjacency(self.__graph, self.get_weights())

	## complex properties
	
	def get_prop(self, strPropName):
		if strPropName in self.dicProperties.keys():
			if not self.bPropToDate:
				self.dicProperties[strPropName] = self.dicGetProp[strPropName](self.__graph)
			return self.dicProperties[strPropName]
		else:
			print("Ignoring request for unknown property '{}'".format(strPropName))

	def get_dict_properties(self):
		return self.dicProperties

	def get_degrees(self, strType="total", bWeights=True):
		lstValidTypes = ["in", "out", "total"]
		if strType in lstValidTypes:
			return degree_list(self.__graph, strType, bWeights)
		else:
			print("Ignoring invalid degree type '{}'".format(strType))
			return None

	def get_betweenness(self, bWeights=True):
		if bWeights:
			if not self.bWBetwToDate:
				self.wBetweeness = betweenness_list(self.__graph, bWeights)
				self.wBetweeness = True
			return self.wBetweeness
		if not self.bBetwToDate and not bWeights:
			self.betweenness = betweenness_list(self.__graph, bWeights)
			self.bBetwToDate = True
			return self.betweenness

	def get_types(self):
		if "type" in self.graph.edge_properties.keys():
			return self.__graph.edge_properties["type"].a
		else:
			return repeat(1, self.__graph.num_edges())
	
	def get_weights(self):
		if self.dicProperties["Weighted"]:
			epropW = self.__graph.edge_properties["weight"].copy()
			epropW.a = multiply(epropW.a, self.__graph.edge_properties["type"].a)
			return epropW
		else:
			return self.__graph.edge_properties["type"].copy()

# 需要导入模块: from graph_tool import Graph [as 别名]
# 或者: from graph_tool.Graph import num_edges [as 别名]
class BoardGraphGraphtool(BoardGraphBase):

    def __init__(self, number_of_vertices, graph_type):
        super().__init__(number_of_vertices, graph_type)
        # Graph tool creates directed multigraph by default.
        self._graph = Graph()
        self._graph.add_vertex(number_of_vertices)
        self._graph.vertex_properties["cell"] = self._graph.new_vertex_property(
            "object", number_of_vertices * [BoardCell()]
        )
        self._graph.edge_properties["direction"
                                   ] = self._graph.new_edge_property("object")
        self._graph.edge_properties["weight"
                                   ] = self._graph.new_edge_property("int")

    def __getitem__(self, position):
        return self._graph.vp.cell[self._graph.vertex(position)]

    def __setitem__(self, position, board_cell):
        self._graph.vp.cell[self._graph.vertex(position)] = board_cell

    def __contains__(self, position):
        return position in range(0, self.vertices_count())

    def vertices_count(self):
        return self._graph.num_vertices()

    def edges_count(self):
        return self._graph.num_edges()

    def has_edge(self, source_vertice, target_vertice, direction):
        for e in self._graph.vertex(source_vertice).out_edges():
            if (
                int(e.target()) == target_vertice and
                self._graph.ep.direction[e] == direction
            ):
                return True
        return False

    def out_edges_count(self, source_vertice, target_vertice):
        return len([
            1 for e in self._graph.vertex(source_vertice).out_edges()
            if int(e.target()) == target_vertice
        ])

    def reconfigure_edges(self, width, height, tessellation):
        """
        Uses tessellation object to create all edges in graph.
        """
        self._graph.clear_edges()
        for source_vertice in self._graph.vertices():
            for direction in tessellation.legal_directions:
                neighbor_vertice = tessellation.neighbor_position(
                    int(source_vertice),
                    direction,
                    board_width=width,
                    board_height=height
                )
                if neighbor_vertice is not None:
                    e = self._graph.add_edge(
                        source_vertice, neighbor_vertice, add_missing=False
                    )
                    self._graph.ep.direction[e] = direction

    # TODO: Faster version?
    # def reconfigure_edges(self, width, height, tessellation):
    #     """
    #     Uses tessellation object to create all edges in graph.
    #     """
    #     self._graph.clear_edges()
    #     edges_to_add = []
    #     directions_to_add = dict()
    #     for source_vertice in self._graph.vertices():
    #         for direction in tessellation.legal_directions:
    #             neighbor_vertice = tessellation.neighbor_position(
    #                 int(source_vertice), direction,
    #                 board_width=width, board_height=height
    #             )
    #             if neighbor_vertice is not None:
    #                 edge = (int(source_vertice), neighbor_vertice,)

    #                 edges_to_add.append(edge)

    #                 if edge not in directions_to_add:
    #                     directions_to_add[edge] = deque()

    #                 directions_to_add[edge].append(direction)

    #     self._graph.add_edge_list(edges_to_add) if edges_to_add else None

    #     for e in edges_to_add:
    #         e_descriptors = self._graph.edge(
    #             s = self._graph.vertex(e[0]),
    #             t = self._graph.vertex(e[1]),
    #             all_edges = True
    #         )

    #         for e_descriptor in e_descriptors:
    #             if len(directions_to_add[e]) > 0:
    #                 self._graph.ep.direction[e_descriptor] = directions_to_add[e][0]
#.........这里部分代码省略.........

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

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

#.........这里部分代码省略.........
                  function data_processing.get_sa_data().
    sens        : str, optional
                  A string with the name of the sensitivity that you would
                  like to use for the vertices ('ST' or 'S1').
    top         : int, optional
                  An integer specifying the number of vertices to display (
                  the top sensitivity values).
    min_sens    : float, optional
                  A float with the minimum sensitivity to allow in the graph.
    edge_cutoff : float, optional
                  A float specifying the minimum second order sensitivity to
                  show as an edge in the graph.

    Returns
    --------
    g : graph-tool object
        a graph-tool graph object of the network described above.  Each
        vertex has properties 'param', 'sensitivity', and 'confidence'
        corresponding to the name of the parameter, value of the sensitivity
        index, and it's confidence interval.  The only edge property is
        'second_sens', the second order sensitivity index for the
        interaction between the two vertices it connects.
    """

    # get the first/total index dataframe and second order dataframe
    df = df_list[0]
    df2 = df_list[1]

    # Make sure sens is ST or S1
    if sens not in set(['ST', 'S1']):
        raise ValueError('sens must be ST or S1')
    # Make sure that there is a second order index dataframe
    try:
        if not df2:
            raise Exception('Missing second order dataframe!')
    except:
        pass

    # slice the dataframes so the resulting graph will only include the top
    # 'top' values of 'sens' greater than 'min_sens'.
    df = df.sort_values(sens, ascending=False)
    df = df.ix[df[sens] > min_sens, :].head(top)
    df = df.reset_index()

    # initialize a graph
    g = Graph()

    vprop_sens = g.new_vertex_property('double')
    vprop_conf = g.new_vertex_property('double')
    vprop_name = g.new_vertex_property('string')
    eprop_sens = g.new_edge_property('double')

    g.vertex_properties['param'] = vprop_name
    g.vertex_properties['sensitivity'] = vprop_sens
    g.vertex_properties['confidence'] = vprop_conf
    g.edge_properties['second_sens'] = eprop_sens

    # keep a list of all the vertices
    v_list = []

    # Add the vertices to the graph
    for i, param in enumerate(df['Parameter']):
        v = g.add_vertex()
        vprop_sens[v] = df.ix[i, sens]
        vprop_conf[v] = 1 + df.ix[i, '%s_conf' % sens] / df.ix[i, sens]
        vprop_name[v] = param
        v_list.append(v)

    # Make two new columns in second order dataframe that point to the vertices
    # connected on each row.
    df2['vertex1'] = -999
    df2['vertex2'] = -999
    for vertex in v_list:
        param = g.vp.param[vertex]
        df2.ix[df2['Parameter_1'] == param, 'vertex1'] = vertex
        df2.ix[df2['Parameter_2'] == param, 'vertex2'] = vertex

    # Only allow edges for vertices that we've defined
    df_edges = df2[(df2['vertex1'] != -999) & (df2['vertex2'] != -999)]
    # eliminate edges below a certain cutoff value
    pruned = df_edges[df_edges['S2'] > edge_cutoff]
    pruned.reset_index(inplace=True)
    # Add the edges for the graph
    for i, sensitivity in enumerate(pruned['S2']):
        v1 = pruned.ix[i, 'vertex1']
        v2 = pruned.ix[i, 'vertex2']
        e = g.add_edge(v1, v2)
        # multiply by a number to make the lines visible on the plot
        eprop_sens[e] = sensitivity * 150

    # These are ways you can reference properties of vertices or edges
    # g.vp.param[g.vertex(77)]
    # g.vp.param[v_list[0]]

    print ('Created a graph with %s vertices and %s edges.\nVertices are the '
           'top %s %s values greater than %s.\nOnly S2 values (edges) '
           'greater than %s are included.' %
           (g.num_vertices(), g.num_edges(), top, sens, min_sens, edge_cutoff))

    return g