# import community as comm

# from networkx.algorithms import bipartite as bip

u=0

while True:

G=nx.bipartite_random_graph(n,m,p)

list_of_isolates=nx.isolates(G)

if No_isolates:

G.remove_nodes_from(nx.isolates(G))

partition=comm.best_partition(G)

sel=max(partition.values())

if sel==2 and nx.is_connected(G):

break

u+=1

# print u,sel

ndlss=bipartite.sets(G)

print 'n = %i' %len(ndlss[0])

print 'm = %i' %len(ndlss[1])

print 'Number of edges = %i' %len(G.edges())

ndls=[list(i) for i in ndlss]

slayer1=ndls[0]

slayer2=ndls[1]

layer1=[i for i,v in partition.items() if v==0]

layer2=[i for i,v in partition.items() if v==1]

layer3=[i for i,v in partition.items() if v==2]

print 'Community1 = ', layer1

print 'Community2 = ' ,layer2

print 'Community3 = ' ,layer3

edgeList=[]

for e in G.edges():

if (e[0] in slayer1 and e[1] in slayer2) or (e[0] in slayer2 and e[1] in slayer1):

edgeList.append(e)

fig=plt.figure(num=1,figsize=(16,12))

sets=bipartite.sets(G)

pos=nx.spring_layout(G)

nx.draw_networkx_nodes(G,pos=pos,nodelist=list(sets[0]),node_color='grey',alpha=0.3)

nx.draw_networkx_nodes(G,pos=pos,nodelist=list(sets[1]),node_color='gold')

nx.draw_networkx_labels(G,pos=pos)

nx.draw_networkx_edges(G,pos=pos,alpha=0.2)

plt.axis("off")

fig=plt.figure(num=1,figsize=(16,12))

fig.add_subplot(subp)

sets=bipartite.sets(G)

pos={}

for i,v in enumerate(sets[0]):

pos[v]= (0.,i)

for i,v in enumerate(sets[1]):

pos[v]= (1, i)

rr=nx.attribute_assortativity_coefficient(G,'bipartite')

s_title='Bipartite Graph\nAssortativity_coef(bipartition) = %.2f' %rr

plt.title(s_title)#,{'size': '20'})

nx.draw_networkx_nodes(G,pos=pos,nodelist=list(sets[0]),node_color='grey',alpha=0.3)

nx.draw_networkx_nodes(G,pos=pos,nodelist=list(sets[1]),node_color='gold')

nx.draw_networkx_labels(G,pos=pos)

nx.draw_networkx_edges(G,pos=pos,alpha=0.2)

plt.axis("off")

# plt.show()

for i in layer1:

G.add_node(i,color='r',attr_dict=G.node[i],best_partition_comm='1')

for i in layer2:

G.add_node(i,color='g',attr_dict=G.node[i],best_partition_comm='2')

for i in layer3:

G.add_node(i,color='b',attr_dict=G.node[i],best_partition_comm='3')

fig.add_subplot(subp)

# for i in G.nodes(data=True):

# print i

# rr=nx.attribute_assortativity_coefficient(G,'color')

rra=nx.attribute_assortativity_coefficient(G,'best_partition_comm')

s_title='Community Partition\nAssortativity_coef(3_communities) = %.2f ' %(rra)

plt.title(s_title)#,{'size': '20'})

nodecolor=[i[1]['color'] for i in G.nodes(data=True)]

nx.draw_networkx_nodes(G,pos=pos,node_color=nodecolor,alpha=0.3)

# nx.draw_networkx_nodes(G,pos=pos,nodelist=list(sets[1]),node_color='gold')

nx.draw_networkx_labels(G,pos=pos)

nx.draw_networkx_edges(G,pos=pos,alpha=0.2)

sl11=random.sample(slayer1,len(slayer1)/2)

sl12=list(set(slayer1)-set(sl11))

# print G.nodes(data=True)

for i in sl11:

G.add_node(i,color='y',attr_dict=G.node[i],fattr='1')

for i in sl12:

G.add_node(i,color='grey',attr_dict=G.node[i],fattr='2')

sl21=random.sample(slayer2,len(slayer2)/2)

sl22=list(set(slayer2)-set(sl21))

for i in sl21:

G.add_node(i,color='m',attr_dict=G.node[i],fattr='3')

for i in sl22:

G.add_node(i,color='c',attr_dict=G.node[i],fattr='4')

fig.add_subplot(subp)

# for i in G.nodes():

# if i in la

# for i in G.nodes(data=True):

# print i

rr= nx.attribute_assortativity_coefficient(G,'fattr')

s_title='Discrete vertex attributes\nAssortativity_coef(4_attibutes) = %.2f' %rr

plt.title(s_title)#,{'size': '20'})

nodecolor=[i[1]['color'] for i in G.nodes(data=True)]

nx.draw_networkx_nodes(G,pos=pos,node_color=nodecolor,alpha=0.3)

# nx.draw_networkx_nodes(G,pos=pos,nodelist=list(sets[1]),node_color='gold')

nx.draw_networkx_labels(G,pos=pos)

nx.draw_networkx_edges(G,pos=pos,alpha=0.2)

'''G is a 3-layer graph

'''

fig=plt.figure(figsize=(17,12))

npart={}

for i in G.nodes(data=True):

if i[1]['color'] not in npart:

npart[i[1]['color']]=[i[0]]

else:

npart[i[1]['color']].append(i[0])

npartition=npart.values()#[slayer1,slayer2]

# print npartition

layers={'layer1':layer1,'layer2':layer2,'layer3':layer3}

broken_partition={}

for i,v in enumerate(npartition):

vs=set(v)

for ii,vv in layers.items():

papa=vs.intersection(set(vv))

if len(papa)==len(v):

broken_partition['a_%i_%s_s' %(i,ii)]=v

elif len(papa)>0:

broken_partition['b_%i_%s' %(i,ii)]=list(papa)

vs=vs-set(vv)

broken_graph=nx.Graph()

rbroken_partition=dict()

# colors=[name for name,hex in matplotlib.colors.cnames.iteritems()]

colors=[name[1]['color'] for name in G.nodes(data=True)]

colors=list(set(colors)-set(['red','blue','g']))

cl=dict()

for i,v in broken_partition.items():

name=i.split('_')

for ii in v:

rbroken_partition[ii]=i

if name[-1]=='s':

cl[name[1]]=colors.pop()

elif name[0]=='b' and not cl.has_key(name[1]):

cl[name[1]]=colors.pop()

for i,v in rbroken_partition.items():

name=v.split('_')

broken_graph.add_node(v,color=cl[name[1]])

edg=G[i]

for j in edg:

if j not in broken_partition[v]:

if not broken_graph.has_edge(v,rbroken_partition[j]):

broken_graph.add_edge(v,rbroken_partition[j])

'''G is a 3-layer graph

'''

# layerattri1 = random.sample(G.nodes(),int(len(G.nodes())*attri1))

# layerattri2 = random.sample(set(G.nodes())-set(layerattri1),int(len(G.nodes())*attri2))

# layerattri3 = list(set(G.nodes())-set(layerattri1)-set(layerattri2))

# npartition=[layerattri1,layerattri2,layerattri3]

fig=plt.figure(figsize=(17,12))

npartition=[slayer1,slayer2]

layers={'layer1':layer1,'layer2':layer2,'layer3':layer3}

broken_partition={}

for i,v in enumerate(npartition):

vs=set(v)

for ii,vv in layers.items():

papa=vs.intersection(set(vv))

if len(papa)==len(v):

broken_partition['a_%i_%s_s' %(i,ii)]=v

elif len(papa)>0:

broken_partition['b_%i_%s' %(i,ii)]=list(papa)

vs=vs-set(vv)

broken_graph=nx.Graph()

rbroken_partition=dict()

colors=[name for name,hex in matplotlib.colors.cnames.iteritems()]

colors=list(set(colors)-set(['red','blue','green']))

cl=dict()

for i,v in broken_partition.items():

name=i.split('_')

for ii in v:

rbroken_partition[ii]=i

if name[-1]=='s':

cl[name[1]]=colors.pop()

elif name[0]=='b' and not cl.has_key(name[1]):

cl[name[1]]=colors.pop()

for i,v in rbroken_partition.items():

name=v.split('_')

broken_graph.add_node(v,color=cl[name[1]])

edg=G[i]

for j in edg:

if j not in broken_partition[v]:

if not broken_graph.has_edge(v,rbroken_partition[j]):

broken_graph.add_edge(v,rbroken_partition[j])

if layout:

pos=nx.spring_layout(G)

else:

pos=nx.random_layout(G)

top_set=set()

bottom_set=set()

middle_set=set()

down=[]

right=[]

left=[]

mlayer_part={}

for i in broken_partition:

ii=i.split('_')

if ii[1] not in mlayer_part:

mlayer_part[ii[1]]=set([ii[2]])

else:

mlayer_part[ii[1]].add(ii[2])

layers_m=Counter()

for k,v in mlayer_part.items():

if len(v)==1:

layers_m[1]+=1

elif len(v)==2:

layers_m[2]+=1

elif len(v)==3:

layers_m[3]+=1

else:

print k,v

broken_pos={}

singles=0

for i,v in broken_partition.items():

name=i.split('_')

if name[-1]=='s':

singles+=1

ndnd=random.choice(v)

npos=pos[ndnd]

if ndnd in layer1:

broken_pos[i]=[d2*(npos[0]-d1),d2*(npos[1]+d1)]

top_set.add(i)

left.append(broken_pos[i])

elif ndnd in layer2:

broken_pos[i]=[d2*(npos[0]+d1),d2*(npos[1]+d1)]

bottom_set.add(i)

right.append(broken_pos[i])

else:

broken_pos[i]=[d2*npos[0],d2*(npos[1]-d1)]

middle_set.add(i)

down.append(broken_pos[i])

xleft=[i[0] for i in left]

yleft=[i[1] for i in left]

aleft = [min(xleft)-d1/2.,max(yleft)+d1/2.+d3]

bleft = [max(xleft)+d1/2.,max(yleft)+d1/2.+3*d3]

cleft = [max(xleft)+d1/2.,min(yleft)-d1/2.-3*d3]

dleft = [min(xleft)-d1/2.,min(yleft)-d1/2.-d3]

xright=[i[0] for i in right]

yright=[i[1] for i in right]

aright = [min(xright)-d1/2.,max(yright)+d1/2.+d3]

bright = [max(xright)+d1/2.,max(yright)+d1/2.+3*d3]

cright = [max(xright)+d1/2.,min(yright)-d1/2.-3*d3]

dright = [min(xright)-d1/2.,min(yright)-d1/2.-d3]

xdown=[i[0] for i in down]

ydown=[i[1] for i in down]

adown = [min(xdown)-d1/2.,max(ydown)+d1/2.+d3]

bdown = [max(xdown)+d1/2.,max(ydown)+d1/2.+3*d3]

cdown = [max(xdown)+d1/2.,min(ydown)-d1/2.-3*d3]

ddown = [min(xdown)-d1/2.,min(ydown)-d1/2.-d3]

# fig=plt.figure(figsize=(20,20))

# plt.subplot(1,2,1)

if asso:

ax=fig.add_subplot(121)

else:

ax=fig.add_subplot(111)

ax.add_patch(Polygon([aleft,bleft,cleft,dleft],color='r',alpha=0.1))

plt.plot([aleft[0],bleft[0],cleft[0],dleft[0],aleft[0]],[aleft[1],bleft[1],cleft[1],dleft[1],aleft[1]],'-r')

ax.add_patch(Polygon([aright,bright,cright,dright],color='b',alpha=0.1))

plt.plot([aright[0],bright[0],cright[0],dright[0],aright[0]],[aright[1],bright[1],cright[1],dright[1],aright[1]],'-b')

ax.add_patch(Polygon([adown,bdown,cdown,ddown],color='g',alpha=0.1))

plt.plot([adown[0],bdown[0],cdown[0],ddown[0],adown[0]],[adown[1],bdown[1],cdown[1],ddown[1],adown[1]],'-g')

nodeSize=[nodesize*len(broken_partition[i]) for i in list(top_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(top_set) ]

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(top_set),node_shape='s',node_color=nodeColor,alpha=.9,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(middle_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(middle_set) ]

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(middle_set),node_shape='s',node_color=nodeColor,alpha=.9,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(bottom_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(bottom_set) ]

nx.draw_networkx_nodes(broken_graph,broken_pos,nodelist=list(bottom_set),node_shape='s',node_color=nodeColor,alpha=.9,node_size=nodeSize)

if withlabels:

nx.draw_networkx_labels(G,pos)

lay1_edges=[ed for ed in G.edges() if ed[0] in layer1 and ed[1] in layer1]

lay2_edges=[ed for ed in G.edges() if ed[0] in layer2 and ed[1] in layer2]

lay3_edges=[ed for ed in G.edges() if ed[0] in layer3 and ed[1] in layer3]

nx.draw_networkx_edges(broken_graph,broken_pos,alpha=0.3) #0.15

orr=nx.attribute_assortativity_coefficient(broken_graph,'color')

for i,v in broken_partition.items():

for nd in v:

atrr=G.node[nd]

G.add_node(nd,attr_dict=atrr,asso=i)

# print G.nodes(data=True)

# print len(set(broken_partition))

crr=nx.attribute_assortativity_coefficient(G,'color')

rr=nx.attribute_assortativity_coefficient(G,'asso')

if asso:

# rr=nx.attribute_assortativity_coefficient(broken_graph,'color')

# print 'Bipartition attribute assortativity coefficient wrt community partition(old) = %f' %orr

title_s='Bipartite graph with 3 communities as 3-layers (%i 3-layered, %i 2-layered, %i 1-layered)\n Joint_Assortativity_coef(3_communities,bipartition) = %.2f' %(layers_m[3],layers_m[2],layers_m[1],rr)

else:

# rr=nx.attribute_assortativity_coefficient(broken_graph,'color')

# print 'Discrete attribute assortativity coefficient wrt community partition = %f' %orr

title_s='Bipartite graph with 3 communities as 3-layers (%i 3-layered, %i 2-layered, %i 1-layered)\n Joint_Assortativity_coef(3_communities,%i_attributes) = %.2f' %(layers_m[3],layers_m[2],layers_m[1],len(set(broken_partition)),rr)

# title_s='Bipartite graph with 3 communities as 3-layers (%i 3-layered, %i 2-layered, %i 1-layered)\n Discrete assortativity coefficient of the joint partition for communities and %i attributes = %f' %(layers_m[3],layers_m[2],layers_m[1],len(set(broken_partition)),rr)

# title_s='%i Three vertex attributes (%i 3-layered, %i 2-layered, %i 1-layered)' %(len(npartition),layers_m[3],layers_m[2],layers_m[1])

plt.title(title_s,{'size': '12'})

plt.axis('off')

'''G is a 3-layer graph

'''

# layerattri1 = random.sample(G.nodes(),int(len(G.nodes())*attri1))

# layerattri2 = random.sample(set(G.nodes())-set(layerattri1),int(len(G.nodes())*attri2))

# layerattri3 = list(set(G.nodes())-set(layerattri1)-set(layerattri2))

# npartition=[layerattri1,layerattri2,layerattri3]

npartition=[layer1,layer2,layer3]

layers={'layer1':slayer1,'layer2':slayer2}#,'layer3':layer3}

broken_partition={}

for i,v in enumerate(npartition):

vs=set(v)

for ii,vv in layers.items():

papa=vs.intersection(set(vv))

if len(papa)==len(v):

broken_partition['a_%i_%s_s' %(i,ii)]=v

elif len(papa)>0:

broken_partition['b_%i_%s' %(i,ii)]=list(papa)

vs=vs-set(vv)

broken_graph=nx.Graph()

rbroken_partition=dict()

colors=[name for name,hex in matplotlib.colors.cnames.iteritems()]

# print colors[:2]

# colors=list(set(colors)-set(['red','blue','green']))

if asso:

colors=['r','b','g']

else:

colors=[i[1]['color'] for i in G.nodes(data=True)]

cl=dict()

for i,v in broken_partition.items():

name=i.split('_')

for ii in v:

rbroken_partition[ii]=i

if name[-1]=='s':

cl[name[1]]=colors.pop()

elif name[0]=='b' and not cl.has_key(name[1]):

cl[name[1]]=colors.pop()

for i,v in rbroken_partition.items():

name=v.split('_')

broken_graph.add_node(v,color=cl[name[1]])

edg=G[i]

for j in edg:

if j not in broken_partition[v]:

if not broken_graph.has_edge(v,rbroken_partition[j]):

broken_graph.add_edge(v,rbroken_partition[j])

# print broken_partition,npartition,layers

if layout:

pos=nx.spring_layout(G)

else:

pos=nx.random_layout(G)

top_set=set()

bottom_set=set()

middle_set=set()

down=[]

right=[]

left=[]

mlayer_part={}

for i in broken_partition:

ii=i.split('_')

if ii[1] not in mlayer_part:

mlayer_part[ii[1]]=set([ii[2]])

else:

mlayer_part[ii[1]].add(ii[2])

layers_m=Counter()

for k,v in mlayer_part.items():

if len(v)==1:

layers_m[1]+=1

elif len(v)==2:

layers_m[2]+=1

elif len(v)==3:

layers_m[3]+=1

else:

print k,v

broken_pos={}

singles=0

for i,v in broken_partition.items():

name=i.split('_')

if name[-1]=='s':

singles+=1

ndnd=random.choice(v)

npos=pos[ndnd]

if ndnd in layer1:

broken_pos[i]=[d2*(npos[0]-d1),d2*(npos[1]+d1)]

top_set.add(i)

left.append(broken_pos[i])

elif ndnd in layer2:

broken_pos[i]=[d2*(npos[0]+d1),d2*(npos[1]+d1)]

bottom_set.add(i)

right.append(broken_pos[i])

# else:

# broken_pos[i]=[d2*npos[0],d2*(npos[1]-d1)]

# middle_set.add(i)

# down.append(broken_pos[i])

# print top_set

# print bottom_set

xleft=[i[0] for i in left]

yleft=[i[1] for i in left]

aleft = [min(xleft)-d1/2.,max(yleft)+d1/2.+d3]

bleft = [max(xleft)+d1/2.,max(yleft)+d1/2.+3*d3]

cleft = [max(xleft)+d1/2.,min(yleft)-d1/2.-3*d3]

dleft = [min(xleft)-d1/2.,min(yleft)-d1/2.-d3]

xright=[i[0] for i in right]

yright=[i[1] for i in right]

aright = [min(xright)-d1/2.,max(yright)+d1/2.+d3]

bright = [max(xright)+d1/2.,max(yright)+d1/2.+3*d3]

cright = [max(xright)+d1/2.,min(yright)-d1/2.-3*d3]

dright = [min(xright)-d1/2.,min(yright)-d1/2.-d3]

# xdown=[i[0] for i in down]

# ydown=[i[1] for i in down]

# adown = [min(xdown)-d1/2.,max(ydown)+d1/2.+d3]

# bdown = [max(xdown)+d1/2.,max(ydown)+d1/2.+3*d3]

# cdown = [max(xdown)+d1/2.,min(ydown)-d1/2.-3*d3]

# ddown = [min(xdown)-d1/2.,min(ydown)-d1/2.-d3]

# fig=plt.figure(figsize=(20,20))

# plt.subplot(1,2,1)

ax=fig.add_subplot(122)

ax.add_patch(Polygon([aleft,bleft,cleft,dleft],color='grey',alpha=0.1))

plt.plot([aleft[0],bleft[0],cleft[0],dleft[0],aleft[0]],[aleft[1],bleft[1],cleft[1],dleft[1],aleft[1]],'-',color='grey')

ax.add_patch(Polygon([aright,bright,cright,dright],color='gold',alpha=0.1))

plt.plot([aright[0],bright[0],cright[0],dright[0],aright[0]],[aright[1],bright[1],cright[1],dright[1],aright[1]],'-',color='gold')

# ax.add_patch(Polygon([adown,bdown,cdown,ddown],color='g',alpha=0.1))

# plt.plot([adown[0],bdown[0],cdown[0],ddown[0],adown[0]],[adown[1],bdown[1],cdown[1],ddown[1],adown[1]],'-g')

nodeSize=[nodesize*len(broken_partition[i]) for i in list(top_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(top_set) ]

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(top_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(middle_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(middle_set) ]

nx.draw_networkx_nodes(broken_graph,broken_pos, nodelist=list(middle_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

nodeSize=[nodesize*len(broken_partition[i]) for i in list(bottom_set)]

nodeColor=[broken_graph.node[i]['color'] for i in list(bottom_set) ]

nx.draw_networkx_nodes(broken_graph,broken_pos,nodelist=list(bottom_set),node_shape='s',node_color=nodeColor,alpha=1,node_size=nodeSize)

if withlabels:

nx.draw_networkx_labels(G,pos)

lay1_edges=[ed for ed in G.edges() if ed[0] in layer1 and ed[1] in layer1]

lay2_edges=[ed for ed in G.edges() if ed[0] in layer2 and ed[1] in layer2]

lay3_edges=[ed for ed in G.edges() if ed[0] in layer3 and ed[1] in layer3]

# print G.nodes()

# print npartition

for i,v in enumerate(npartition):

for nd in v:

G.add_node(nd,part=i)

# print 'bbbbbb'

# print G.nodes(data=True)

# print 'aaaaa'

rrd=nx.attribute_assortativity_coefficient(G,'part')

nx.draw_networkx_edges(broken_graph,broken_pos,alpha=0.3) #0.15

orr=nx.attribute_assortativity_coefficient(broken_graph,'color')

for i,v in broken_partition.items():

for nd in v:

atrr=G.node[nd]

G.add_node(nd,attr_dict=atrr,asso=i)

# print G.nodes(data=True)

rr=nx.attribute_assortativity_coefficient(G,'asso')

# print 'Community partition attribute assortativity coefficient wrt bipartition = %f' %orr

# title_s='Bipartite graph with bipartition as 2-layers (%i 2-layered, %i 1-layered)\n Discrete assortativity coefficient of the joint partition of communities and bipartition = %f\n(Community partition attribute assortativity coefficient = %f)' %(layers_m[2],layers_m[1],rr,rrd)

title_s='Bipartite graph with bipartition as 2-layers (%i 2-layered, %i 1-layered)\n Joint_Assortativity_coef(bipartition,3_communities) = %.2f' %(layers_m[2],layers_m[1],rr)

# title_s='%i Three vertex attributes (%i 3-layered, %i 2-layered, %i 1-layered)' %(len(npartition),layers_m[3],layers_m[2],layers_m[1])

plt.title(title_s,{'size': '12'})

plt.axis('off')