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
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'
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
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
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
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.
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()
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
# .........
