コード例 #1
0
ファイル: nx.py プロジェクト: GaelVaroquaux/nipype
def compute_singlevalued_measures(ntwk, weighted=True, calculate_cliques=False):
    """
    Returns a single value per network
    """
    iflogger.info("Computing single valued measures:")
    measures = {}
    iflogger.info("...Computing degree assortativity (pearson number) ...")
    try:
        measures["degree_pearsonr"] = nx.degree_pearsonr(ntwk)
    except AttributeError:  # For NetworkX 1.6
        measures["degree_pearsonr"] = nx.degree_pearson_correlation_coefficient(ntwk)
    iflogger.info("...Computing degree assortativity...")
    try:
        measures["degree_assortativity"] = nx.degree_assortativity(ntwk)
    except AttributeError:
        measures["degree_assortativity"] = nx.degree_assortativity_coefficient(ntwk)
    iflogger.info("...Computing transitivity...")
    measures["transitivity"] = nx.transitivity(ntwk)
    iflogger.info("...Computing number of connected_components...")
    measures["number_connected_components"] = nx.number_connected_components(ntwk)
    iflogger.info("...Computing average clustering...")
    measures["average_clustering"] = nx.average_clustering(ntwk)
    if nx.is_connected(ntwk):
        iflogger.info("...Calculating average shortest path length...")
        measures["average_shortest_path_length"] = nx.average_shortest_path_length(ntwk, weighted)
    if calculate_cliques:
        iflogger.info("...Computing graph clique number...")
        measures["graph_clique_number"] = nx.graph_clique_number(ntwk)  # out of memory error
    return measures
コード例 #2
0
ファイル: graphutils.py プロジェクト: jwang41/musketeer
def degree_assortativity(G):
#this wrapper helps avoid error due to change in interface name
    if hasattr(nx, 'degree_assortativity_coefficient'):
        return nx.degree_assortativity_coefficient(G)
    elif hasattr(nx, 'degree_assortativity'):
        return nx.degree_assortativity(G)
    else:
        raise ValueError, 'Cannot compute degree assortativity: method not available'
コード例 #3
0
ファイル: nx.py プロジェクト: whiskeyjack-dev/nipype
def compute_singlevalued_measures(ntwk,
                                  weighted=True,
                                  calculate_cliques=False):
    """
    Returns a single value per network
    """
    iflogger.info("Computing single valued measures:")
    measures = {}
    iflogger.info("...Computing degree assortativity (pearson number) ...")
    try:
        measures["degree_pearsonr"] = nx.degree_pearsonr(ntwk)
    except AttributeError:  # For NetworkX 1.6
        measures[
            "degree_pearsonr"] = nx.degree_pearson_correlation_coefficient(
                ntwk)
    iflogger.info("...Computing degree assortativity...")
    try:
        measures["degree_assortativity"] = nx.degree_assortativity(ntwk)
    except AttributeError:
        measures["degree_assortativity"] = nx.degree_assortativity_coefficient(
            ntwk)
    iflogger.info("...Computing transitivity...")
    measures["transitivity"] = nx.transitivity(ntwk)
    iflogger.info("...Computing number of connected_components...")
    measures["number_connected_components"] = nx.number_connected_components(
        ntwk)
    iflogger.info("...Computing graph density...")
    measures["graph_density"] = nx.density(ntwk)
    iflogger.info("...Recording number of edges...")
    measures["number_of_edges"] = nx.number_of_edges(ntwk)
    iflogger.info("...Recording number of nodes...")
    measures["number_of_nodes"] = nx.number_of_nodes(ntwk)
    iflogger.info("...Computing average clustering...")
    measures["average_clustering"] = nx.average_clustering(ntwk)
    if nx.is_connected(ntwk):
        iflogger.info("...Calculating average shortest path length...")
        measures[
            "average_shortest_path_length"] = nx.average_shortest_path_length(
                ntwk, weighted)
    else:
        iflogger.info("...Calculating average shortest path length...")
        measures[
            "average_shortest_path_length"] = nx.average_shortest_path_length(
                nx.connected_component_subgraphs(ntwk)[0], weighted)
    if calculate_cliques:
        iflogger.info("...Computing graph clique number...")
        measures["graph_clique_number"] = nx.graph_clique_number(
            ntwk)  # out of memory error
    return measures
コード例 #4
0
ファイル: nx.py プロジェクト: chrisfilo/nipype
def compute_singlevalued_measures(ntwk, weighted=True,
                                  calculate_cliques=False):
    """
    Returns a single value per network
    """
    iflogger.info('Computing single valued measures:')
    measures = {}
    iflogger.info('...Computing degree assortativity (pearson number) ...')
    try:
        measures['degree_pearsonr'] = nx.degree_pearsonr(ntwk)
    except AttributeError:  # For NetworkX 1.6
        measures[
            'degree_pearsonr'] = nx.degree_pearson_correlation_coefficient(
                ntwk)
    iflogger.info('...Computing degree assortativity...')
    try:
        measures['degree_assortativity'] = nx.degree_assortativity(ntwk)
    except AttributeError:
        measures['degree_assortativity'] = nx.degree_assortativity_coefficient(
            ntwk)
    iflogger.info('...Computing transitivity...')
    measures['transitivity'] = nx.transitivity(ntwk)
    iflogger.info('...Computing number of connected_components...')
    measures['number_connected_components'] = nx.number_connected_components(
        ntwk)
    iflogger.info('...Computing graph density...')
    measures['graph_density'] = nx.density(ntwk)
    iflogger.info('...Recording number of edges...')
    measures['number_of_edges'] = nx.number_of_edges(ntwk)
    iflogger.info('...Recording number of nodes...')
    measures['number_of_nodes'] = nx.number_of_nodes(ntwk)
    iflogger.info('...Computing average clustering...')
    measures['average_clustering'] = nx.average_clustering(ntwk)
    if nx.is_connected(ntwk):
        iflogger.info('...Calculating average shortest path length...')
        measures[
            'average_shortest_path_length'] = nx.average_shortest_path_length(
                ntwk, weighted)
    else:
        iflogger.info('...Calculating average shortest path length...')
        measures[
            'average_shortest_path_length'] = nx.average_shortest_path_length(
                nx.connected_component_subgraphs(ntwk)[0], weighted)
    if calculate_cliques:
        iflogger.info('...Computing graph clique number...')
        measures['graph_clique_number'] = nx.graph_clique_number(
            ntwk)  # out of memory error
    return measures
コード例 #5
0
ファイル: nx.py プロジェクト: ofenlab/nipype
def compute_singlevalued_measures(ntwk,
                                  weighted=True,
                                  calculate_cliques=False):
    """
    Returns a single value per network
    """
    iflogger.info('Computing single valued measures:')
    measures = {}
    iflogger.info('...Computing degree assortativity (pearson number) ...')
    try:
        measures['degree_pearsonr'] = nx.degree_pearsonr(ntwk)
    except AttributeError:  # For NetworkX 1.6
        measures[
            'degree_pearsonr'] = nx.degree_pearson_correlation_coefficient(
                ntwk)
    iflogger.info('...Computing degree assortativity...')
    try:
        measures['degree_assortativity'] = nx.degree_assortativity(ntwk)
    except AttributeError:
        measures['degree_assortativity'] = nx.degree_assortativity_coefficient(
            ntwk)
    iflogger.info('...Computing transitivity...')
    measures['transitivity'] = nx.transitivity(ntwk)
    iflogger.info('...Computing number of connected_components...')
    measures['number_connected_components'] = nx.number_connected_components(
        ntwk)
    iflogger.info('...Computing average clustering...')
    measures['average_clustering'] = nx.average_clustering(ntwk)
    if nx.is_connected(ntwk):
        iflogger.info('...Calculating average shortest path length...')
        measures[
            'average_shortest_path_length'] = nx.average_shortest_path_length(
                ntwk, weighted)
    if calculate_cliques:
        iflogger.info('...Computing graph clique number...')
        measures['graph_clique_number'] = nx.graph_clique_number(
            ntwk)  #out of memory error
    return measures
コード例 #6
0
print G.degree(0)                                   #返回某个节点的度
print G.degree()                                     #返回所有节点的度
print nx.degree_histogram(G)    #返回图中所有节点的度分布序列(从1至最大度的出现频次)
#~~~~~~~~~~~~~~~~~~~~~~draw the degree distribution~~~~~~~~~~~~~~~#
degree =  nx.degree_histogram(G)          #返回图中所有节点的度分布序列
x = range(len(degree))                             #生成x轴序列,从1到最大度
y = [z / float(sum(degree)) for z in degree]  
#将频次转换为频率,这用到Python的一个小技巧:列表内涵,Python的确很方便:)
plt.loglog(x,y,color="blue",linewidth=2)           #在双对数坐标轴上绘制度分布曲线  
plt.show()                                         #显示图表
#~~~~~~~~~~~~~~~~~~~~~~~~~~output the CC AND PATH~~~~~~~~~~~~~~~~~~#
nx.average_clustering(G)#OUTPUT the cluster coefficient
#nx.clustering(G) 则可以计算各个节点的群聚系数。
nx.diameter(G) #返回图G的直径(最长最短路径的长度)
nx.average_shortest_path_length(G)#返回图G所有节点间平均最短路径长度
nx.degree_assortativity(G) #计算一个图的度匹配性
#~~~~~~~~~~~~~Degree centrality measures~~~~~~~~~~~~~~~~~~~~~~~~~~~#
degree_centrality(G)     #Compute the degree centrality for nodes.
in_degree_centrality(G)     #Compute the in-degree centrality for nodes.
out_degree_centrality(G)     #Compute the out-degree centrality for nodes.

closeness_centrality(G[, v, weighted_edges])    # Compute closeness centrality for nodes.

betweenness_centrality(G[, normalized, ...])   #  Compute betweenness centrality for nodes.
edge_betweenness_centrality(G[, normalized, ...])   #  Compute betweenness centrality for edges.

current_flow_closeness_centrality(G[, ...])   #  Compute current-flow closeness centrality for nodes.

current_flow_betweenness_centrality(G[, ...])    # Compute current-flow betweenness centrality for nodes.
edge_current_flow_betweenness_centrality(G)   #  Compute current-flow betweenness centrality for edges.
コード例 #7
0
ファイル: UNICOROutputs.py プロジェクト: mehrdadranaee/UNICOR
def output_graphmetrics(pathadd,paths,file_name,data_dir):
	'''
	output_graphmetrics()
	Calculates graph theory metrics from package NetworkX, stores in file and
	outputs in .csv file.
	''' 
	
	# Graph package
#print outputGraphMetrics
#if outputGraphMetrics:
	try:
		import networkx	as nx	
		
	except ImportError:
		
		raise ImportError, "NetworkX required."

	pathG = nx.Graph()
	
	# Get nrows and columns
	nrows = len(pathadd)
	ncols = len(pathadd[0])
	
	# Now loop through pathadd rows 
	for irow in xrange(nrows):
		
		# Begin loop through pathadd columns
		for icol in xrange(ncols):
			
			# Skip -9999. values
			if pathadd[irow][icol] != -9999.0:
				
				# Get spot node number 
				nodenumber = ncols*irow+icol
								
				# Add node to pathG
				pathG.add_node(nodenumber)
				
				# Check neighbors for edges all 8 conditions
				# Count 0,1,2,3,4,5,6,7 in counter-clockwise
				# around center cell starting 0 in lower
				# left corner
				
				# Left top corner:
				if irow == 0 and icol == 0:
				
					# Get neighbors: spot 1
					if pathadd[irow+1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 2
					if pathadd[irow+1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 3
					if pathadd[irow][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
				
				# Right top corner
				elif irow == 0 and icol == ncols-1:
					
					# Get neighbors: spot 1
					if pathadd[irow+1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 7
					if pathadd[irow][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 0
					if pathadd[irow+1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
				
				# Left bottom corner
				elif irow == nrows-1 and icol == 0:
				
					# Get neighbors: spot 5
					if pathadd[irow-1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 4
					if pathadd[irow-1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 3
					if pathadd[irow][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
				
				# Right bottom corner
				elif irow == nrows-1 and icol == ncols-1:
					
					# Get neighbors: spot 5
					if pathadd[irow-1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
						
					# Get neighbors: spot 7
					if pathadd[irow][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
						
					# Get neighbors: spot 6
					if pathadd[irow-1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
						
				# Top side
				elif irow == 0 and icol != 0 and icol != ncols-1:
				
					# Get neighbors: spot 7
					if pathadd[irow][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 0
					if pathadd[irow+1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 1
					if pathadd[irow+1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 2
					if pathadd[irow+1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 3
					if pathadd[irow][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
				
				# Left side
				elif icol == 0 and irow != 0 and irow != nrows-1:
				
					# Get neighbors -spots 1,2,3,4,5
					# Get neighbors: spot 1
					if pathadd[irow+1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 2
					if pathadd[irow+1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 3
					if pathadd[irow][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 4
					if pathadd[irow-1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 5
					if pathadd[irow-1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
									
				# Right side
				elif icol == ncols-1 and irow != 0 and irow != nrows-1:
				
					# Get neighbors - spots 0,1,5,6,7
					# Get neighbors: spot 0
					if pathadd[irow+1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 1
					if pathadd[irow+1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 5
					if pathadd[irow-1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 6
					if pathadd[irow-1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
										
					# Get neighbors: spot 7
					if pathadd[irow][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
												
				# Bottom side:
				elif irow == nrows-1 and icol != 0 and icol != ncols-1:
				
					# Get neighbors - spots 3,4,5,6,7
					# Get neighbors: spot 3
					if pathadd[irow][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 4
					if pathadd[irow-1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 5
					if pathadd[irow-1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
											
					# Get neighbors: spot 6
					if pathadd[irow-1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
										
					# Get neighbors: spot 7
					if pathadd[irow][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
													
				# Everything else:
				else:
				
					# Get neighbors: spot 0
					if pathadd[irow+1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 1
					if pathadd[irow+1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 2
					if pathadd[irow+1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow+1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 3
					if pathadd[irow][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 4
					if pathadd[irow-1][icol+1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol+1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
					
					# Get neighbors: spot 5
					if pathadd[irow-1][icol] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+icol
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
										
					# Get neighbors: spot 6
					if pathadd[irow-1][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow-1)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
										
					# Get neighbors: spot 7
					if pathadd[irow][icol-1] != -9999.:
						
						# Then get egde number
						edgenumber = ncols*(irow)+(icol-1)
						
						# Then add edge to pathG
						pathG.add_edge(nodenumber,edgenumber)
	
	# Calculate properties from path lengths: min, max, average
	pathlen = []
	for i in xrange(len(paths)):
		pathlen.append(paths[i][2])
	
	# Create file to write info to
	try:
		fout = open(data_dir+file_name, 'w')
	except(IOerror,OSerror) as e:
		print("UNICOROutputs  %s, error%s"(filename,e))
		sys.exit(-1)
		
	# Write header information
	fout.write('Minimum Path Length,')
	fout.write(str(min(pathlen))+'\n')
	fout.write('Maximum Path Length,')
	fout.write(str(max(pathlen))+'\n')
	fout.write('Average Path Length,')
	fout.write(str(sum(pathlen)/len(paths))+'\n')
	fout.write('Density of Graph,')
	fout.write(str(nx.density(pathG))+'\n')
	fout.write('Number of nodes,')
	fout.write(str(nx.number_of_nodes(pathG))+'\n')
	fout.write('Number of edges,')
	fout.write(str(nx.number_of_edges(pathG))+'\n')
	fout.write('Is the graph a bipartite,')
	fout.write(str(nx.is_bipartite(pathG))+'\n')
	fout.write('Size of the largest clique,')
	fout.write(str(nx.graph_clique_number(pathG))+'\n')
	fout.write('Number of maximal cliques,')
	fout.write(str(nx.graph_number_of_cliques(pathG))+'\n')
	fout.write('Transitivity,')
	fout.write(str(nx.transitivity(pathG))+'\n')
	fout.write('Average clustering coefficient,')
	fout.write(str(nx.average_clustering(pathG))+'\n')
	fout.write('Test graph connectivity,')
	fout.write(str(nx.is_connected(pathG))+'\n')
	fout.write('Number of connected components,')
	fout.write(str(nx.number_connected_components(pathG))+'\n')
	fout.write('Consists of a single attracting component,')
	fout.write(str(nx.is_attracting_component(pathG))+'\n')
	if nx.is_attracting_component(pathG) == True:
		fout.write('Number of attracting components,')
		fout.write(str(nx.number_attracting_components(pathG))+'\n')
	if nx.is_connected(pathG):
		fout.write('Center,')
		fout.write(str(nx.center(pathG))+'\n')
		fout.write('Diameter,')
		fout.write(str(nx.diameter(pathG))+'\n')
		#fout.write('Eccentricity,')
		#fout.write(str(nx.eccentricity(pathG))+'\n')
		fout.write('Periphery,')
		fout.write(str(nx.periphery(pathG))+'\n')
		fout.write('Radius,')
		fout.write(str(nx.radius(pathG))+'\n')
	fout.write('Degree assortativity,')
	fout.write(str(nx.degree_assortativity(pathG))+'\n')
	fout.write('Degree assortativity Pearsons r,')
	fout.write(str(nx.degree_pearsonr(pathG))+'\n')
			
	# Close file
	fout.close
	
	del(pathG)
	# End::output_graphmetrics()
		
コード例 #8
0
ファイル: test.py プロジェクト: UncleAnson/my_repo
print(ave(degree))


def p(degree):
    x = list(range(len(degree)))
    y = [i for i in degree]
    plt.bar(x, y, align='center')  #plot、loglog
    plt.ylim(0, 300)
    plt.title('Distribution of Nodes')
    plt.xlabel('Degree')
    plt.ylabel('Number of Nodes')
    for a, b in zip(x, y):
        plt.text(a, b + 2, '%.0f' % b, ha='center')
    #plt.savefig("degree.png")
    plt.show()


p(degree)

# 群聚系数
print(nx.average_clustering(G))  # 平均群聚系数的计算
print(nx.clustering(G))  # 计算各个节点的群聚系数
# 直径和平均距离
print(nx.diameter(G))  # 返回图G的直径(最长的最短路径的长度)
print(nx.average_shortest_path_length(G))  #返回图G所有节点间平均最短路径长度
# 匹配性
print(nx.degree_assortativity(G))  # 图的度匹配性

# 中心性
# .........