# -*- coding: utf-8 -*-

"""

LAST UPDATE: 20 Δεκεμβρίου 2014

@author: Moses Boudourides & Sergios Lenis

"""

import networkx as nx

import matplotlib.pyplot as plt

import random

from networkx.algorithms import bipartite

import os

# try:

# from networkx import graphviz_layout

# layout=nx.graphviz_layout

# except ImportError:

# print("PyGraphviz not found; drawing with spring layout; will be slow.")

# layout=nx.spring_layout

#### ΚΛΙΚΕΣ ΣΕ ΜΗ ΚΑΤΕΥΘΥΝΟΜΕΝΟΥΣ ΓΡΑΦΟΥΣ

# G=nx.Graph()

# G.add_edges_from([(0,1),(1,2),(2,0),(3,4)])

# G.add_node(5)

# pos={0:(0,0),1:(0,1),2:(0.5,1),3:(0.5,0),4:(1,0),5:(0.75,0.5)}

# G = nx.gnp_random_graph(30,0.3)

# G = nx.erdos_renyi_graph(15,0.06)

G = nx.gnm_random_graph(15,10)

# G = nx.gnm_random_graph(35,20)

# G = nx.gnp_random_graph(35,0.1)

# G = nx.erdos_renyi_graph(30,0.024)

# G = nx.barabasi_albert_graph(50,2)

# G = nx.newman_watts_strogatz_graph(20,2,0.9)

# G.add_node(55)

# G.add_edges_from([(50,51),(51,52),(52,50),(53,54)])

# J=nx.complete_graph(10)

# F=nx.complete_graph(5)

# G=nx.disjoint_union(J,F)

# e=(0,14)

# G.add_edge(*e)

#### ΑΝΑΖΗΤΗΣΗ

# number_of_cliques=1

# size_of_cliques=5

# # size_of_cliques1=5

# # size_of_cliques2=4

# # size_of_cliques3=3

# counte=0

# mmin=1000

# while True:

# G = nx.gnm_random_graph(10,20)

# G.remove_nodes_from(nx.isolates(G))

# if counte== mmin:

# print counte,'a'

# mmin+=1000

# testy=nx.find_cliques(G)

# # print testy

# uu=0

# for g in testy:

# if len(g)>=size_of_cliques:

# # if len(g)>=size_of_cliques1 and len(g)==size_of_cliques2 and len(g)==size_of_cliques3:

# # if len(g)>=size_of_cliques1:

# uu+=1

# if uu>=number_of_cliques:

# break

# else:

# counte+=1

# continue

pos=nx.spring_layout(G,k=0.15,iterations=10)

# pos=nx.graphviz_layout(G)

# pos=layout(G)

G.remove_nodes_from(nx.isolates(G))

colors_list=['c','b','g','y','k','m']

colors_to_select=list(colors_list)

graphs=sorted(nx.find_cliques(G), key = len, reverse=True)

cliques_edges=[]

cliques_nodes=[]

nodes_color_alpha=[]

edges_color_alpha=[]

colors_of_edges=[]

edge_width_l=[]

graphs_labels=dict()

graphs_labelsGcc=dict()

graphs_len=dict()

graphs_lists=dict()

for gr in range(len(graphs)):

graph='G' + str(gr+1)

tem='['

ttem=[]

gtem=0

for nd in graphs[gr]:

tem+='%s, ' %nd

ttem.append(nd)

gtem+=1

tem =tem[:-2]+']'

graphs_labels[gr+1]=tem

graphs_labelsGcc[gr]=tem

graphs_len[gr]=gtem

graphs_lists[gr]=ttem

graphh=G.subgraph(graphs[gr])

if len(graphh.nodes()) >1 and len(graphh.edges())>0:

cliques_edges.append(graphh.edges())

cliques_nodes.append(graphh.nodes())

if len(colors_to_select)==0:

colors_to_select=list(colors_list)

color=random.choice(colors_to_select)

colors_to_select.remove(color)

colors_of_edges.append((color))

nodes_color_alpha.append(0.4)

edges_color_alpha.append(0.6)

edge_width_l.append(4.0)

lvl2=[]

for i in range(nx.graph_number_of_cliques(G)):

lvl2.append(graphs_len[i])

print str(" ")

print 'ΚΛΙΚΕΣ ΣΕ ΜΗ ΚΑΤΕΥΘΥΝΟΜΕΝΟΥΣ ΓΡΑΦΟΥΣ'

# print 'CLIQUES IN UNDIRECTED GRAPHS'

print str(" ")

print 'Ο γράφος είναι:'

# print 'The graph is:'

graph_name=str(G.name)+str(lvl2)

print graph_name

print str(" ")

print 'Το σύνολο όλων των κλικών του γράφου G:'

# print 'The set of all maximal cliques in graph G is:'

print sorted(nx.find_cliques(G), key = len, reverse=True)

print 'Το πλήθος όλων των κλικών του G είναι:', nx.graph_number_of_cliques(G)

# print 'The number of maximal cliques of G is:', nx.graph_number_of_cliques(G)

print str(" ")

# colors_list=['c','b','g','y','k','m']

# colors_to_select=list(colors_list)

#

# graphs=sorted(nx.find_cliques(G), key = len, reverse=True)

#

# cliques_edges=[]

# cliques_nodes=[]

# nodes_color_alpha=[]

# edges_color_alpha=[]

# colors_of_edges=[]

# edge_width_l=[]

# graphs_labels=dict()

# graphs_labelsGcc=dict()

# graphs_len=dict()

# graphs_lists=dict()

# for gr in range(len(graphs)):

# graph='G' + str(gr+1)

# tem='['

# ttem=[]

# gtem=0

# for nd in graphs[gr]:

# tem+='%s, ' %nd

# ttem.append(nd)

# gtem+=1

# tem =tem[:-2]+']'

# graphs_labels[gr+1]=tem

# graphs_labelsGcc[gr]=tem

# graphs_len[gr]=gtem

# graphs_lists[gr]=ttem

# graphh=G.subgraph(graphs[gr])

# if len(graphh.nodes()) >1 and len(graphh.edges())>0:

# cliques_edges.append(graphh.edges())

# cliques_nodes.append(graphh.nodes())

# if len(colors_to_select)==0:

# colors_to_select=list(colors_list)

# color=random.choice(colors_to_select)

# colors_to_select.remove(color)

# colors_of_edges.append((color))

# nodes_color_alpha.append(0.4)

# edges_color_alpha.append(0.6)

# edge_width_l.append(4.0)

# lvl2=[]

# for i in range(nx.graph_number_of_cliques(G)):

# lvl2.append(graphs_len[i])

print 'Η λίστα των μεγεθών των κλικών είναι:'

# print 'The list of clique sizes is:'

print lvl2

print str(" ")

print 'Ο αριθμός κλίκας (το μέγεθος της μεγαλύτερης κλίκας) του G είναι:', nx.graph_clique_number(G)

# print 'The clique number (size of the largest clique) for G is:', nx.graph_clique_number(G)

# print sorted(nx.connected_components(G), key = len, reverse=True)

print str(" ")

print 'Το λεξικό των κλικών που περιέχουν κάθε κόμβο είναι:'

# print 'The dictionary of the lists of cliques containing each node:'

print nx.cliques_containing_node(G)

print str(" ")

print 'Το λεξικό του πλήθους κλικών που περιέχουν κάθε κόμβο είναι:'

# print 'The dictionary of the numbers of maximal cliques for each node:'

print nx.number_of_cliques(G)

print str(" ")

print 'Το λεξικό του μεγέθους των μεγαλύτερων κλικών που περιέχουν κάθε κόμβο είναι:'

# print 'The dictionary of the sizes of the largest maximal cliques containing each given node:'

print nx.node_clique_number(G)

print str(" ")

maxclique = [clq for clq in nx.find_cliques(G) if len(clq) == nx.graph_clique_number(G)]

nodes = [n for clq in maxclique for n in clq]

H = G.subgraph(nodes)

# print H.edges()

#### ΣΧΕΔΙΑΣΜΟΣ ΚΛΙΛΩΝ ΜΕΣΑ ΣΕ ΠΕΡΙΒΑΛΛΟΜΕΝΕΣ ΧΡΩΜΑΤΙΣΜΕΝΕΣ ΠΕΡΙΟΧΕΣ

import igraph as ig

file_name=str(G.name)+str(lvl2)+'.graphml'

file_dir='temp'

try:

os.stat(file_dir)

except:

os.mkdir(file_dir)

file_name=os.path.join(file_dir,file_name)

nx.write_graphml(G, file_name)

g = ig.read(file_name, format="graphml")

mcliques=g.maximal_cliques(0,5)

colors_list=["gray","brown","yellow","cyan","green","blue","purple"]

colors_lists=[]

for i in range(len(mcliques)):

if i<len(colors_list):

colors_lists.append(colors_list[i])

else:

color_to_set=(random.random(),random.random(),1)

while color_to_set in colors_lists:

color_to_set=(random.random(),random.random(),1)

colors_lists.append(color_to_set)

group_markers = [(mcliques[i], colors_lists[i]) for i in range(len(mcliques))]

ig.plot(g, mark_groups=group_markers)

colors_list=['c','b','g','y','k','m']

colors_to_select=list(colors_list)

graphs=sorted(nx.find_cliques(G), key = len, reverse=True)

cliques_edges=[]

cliques_nodes=[]

nodes_color_alpha=[]

edges_color_alpha=[]

colors_of_edges=[]

edge_width_l=[]

graphs_labels=dict()

graphs_labelsGcc=dict()

graphs_len=dict()

graphs_lists=dict()

for gr in range(len(graphs)):

graph='G' + str(gr+1)

tem='['

ttem=[]

gtem=0

for nd in graphs[gr]:

tem+='%s, ' %nd

ttem.append(nd)

gtem+=1

tem =tem[:-2]+']'

graphs_labels[gr+1]=tem

graphs_labelsGcc[gr]=tem

graphs_len[gr]=gtem

graphs_lists[gr]=ttem

graphh=G.subgraph(graphs[gr])

if len(graphh.nodes()) >1 and len(graphh.edges())>0:

cliques_edges.append(graphh.edges())

cliques_nodes.append(graphh.nodes())

if len(colors_to_select)==0:

colors_to_select=list(colors_list)

color=random.choice(colors_to_select)

colors_to_select.remove(color)

colors_of_edges.append((color))

nodes_color_alpha.append(0.4)

edges_color_alpha.append(0.6)

edge_width_l.append(4.0)

for i in range(len(cliques_nodes)):

node_list=cliques_nodes[i]

edge_list=cliques_edges[i]

edge_color=colors_of_edges[i]

node_alpha=nodes_color_alpha[i]

edge_alpha=edges_color_alpha[i]

edge_width=edge_width_l[i]

plt.title('Cliques in graph G')

nx.draw(G,pos,nodelist=node_list,with_labels=True,node_size=500,edgelist=edge_list,edge_color=edge_color,width=edge_width,alpha=node_alpha,edgealpha=edge_alpha)

nx.draw_networkx_nodes(G,pos,nodelist=nx.isolates(G),with_labels=True,node_size=500,node_color='w')

plt.figure()

plt.title('The maximal cliques of graph G')

nx.draw(H,with_labels=True)

ClG=nx.make_max_clique_graph(G)

# print 'ClG nodes'

# print ClG.nodes()

# print str(" ")

#

# print 'ClG edges'

# print ClG.edges()

# print str(" ")

#

# print graphs_labels

# print type(graphs_labels)

# graphs=sorted(nx.find_cliques(G), key = len, reverse=True)

# graphs_labels_s=sorted(graphs_labels, key = len, reverse=True)

# ClG_relabeled=nx.relabel_nodes(ClG,graphs_labels,copy=True)

ClG_relabeled=nx.relabel_nodes(ClG,graphs_labels,copy=True)

posC=nx.spring_layout(ClG_relabeled)

# posC=nx.spring_layout(ClG_relabeled,k=0.15,iterations=10)

# print 'ClG_relabeled nodes'

# print ClG_relabeled.nodes()

# print str(" ")

#

# print 'ClG_relabeled edges'

# print ClG_relabeled.edges()

# print str(" ")

lvl2=[]

# print range(nx.graph_number_of_cliques(G)),nx.graph_number_of_cliques(G)

# for i in graphs_lists:

for i in range(nx.graph_number_of_cliques(G)):

lvl2.append(graphs_len[i])

# lvl2.append(len(ClG_relabeled.nodes()[i])) #eval

# lvl2.append(len(eval(ClG_relabeled.nodes()[i]))) #eval

# print 'lvl2'

# print lvl2

# print str(" ")

# plt.figure()

#

# nx.draw(ClG_relabeled,pos=posC,font_size=16,with_labels=False,node_size=[v * 100 for v in lvl2],node_color='g') #node_size=[v * 100 for v in lvl2],

# for p in posC:

# posC[p][1] += 0.04

# nx.draw_networkx_labels(ClG_relabeled,posC)

BcG=nx.make_clique_bipartite(G,fpos=True)

bottom_nodes, top_nodes = bipartite.sets(BcG)

BcG_labels=dict()

Bcg_labels=dict()

for nd in top_nodes:

tem='['

for cc in nx.all_neighbors(BcG,nd):

tem+=str(cc)+', '

tem=tem[:-2]+']'

BcG_labels[nd]=tem

Bcg_labels[nd]=tem

for nd in bottom_nodes:

BcG_labels[nd]=str(nd)

# print bottom_nodes

# print top_nodes

print BcG_labels, 'BcG_labels'

#

# print list(bottom_nodes), 'bottom nodes'

# # print len(list(bottom_nodes))

# print list(top_nodes), 'top nodes'

# print len(list(top_nodes))

BcG_relabel=nx.relabel_nodes(BcG,BcG_labels,copy=True)

# pos=dict(zip(range(len(list(top_nodes))),zip(range(len(list(top_nodes))),[1]*len(list(top_nodes))))) # upper nodes

# pos.update(dict(zip(range(len(list(top_nodes)),len(list(top_nodes))+len(list(bottom_nodes))),zip(range(len(list(bottom_nodes))),[0]*len(list(bottom_nodes)))))) # lower nodes

# print BcG.pos

plt.figure(facecolor='w')

# nx.draw_networkx_edges(BcG,pos=BcG.pos)

plt.title('The bipartite graph of cliques and nodes of G')

nx.draw_networkx_nodes(BcG,pos=BcG.pos,nodelist=list(bottom_nodes),node_color='r')

# nx.draw_networkx(BcG,pos=BcG.pos,nodelist=list(bottom_nodes),with_labels=True,node_color='r') #,node_size=300

# nx.draw_networkx(BcG,pos=BcG.pos,nodelist=list(top_nodes),with_labels=False,node_color='b',node_shape='s') #,node_size=400

nx.draw_networkx_nodes(BcG,pos=BcG.pos,nodelist=list(top_nodes),node_color='b',node_shape='s')

nx.draw_networkx_edges(BcG,pos=BcG.pos)

BcGPos=dict()

for p in BcG.pos: # raise text positions

if p<0:

BcGPos[p]=(BcG.pos[p][0],BcG.pos[p][1]+0.06) #0.045

else:

BcGPos[p]=BcG.pos[p]

# BcGPos[p][1] += 0.045 #offset 0.07

# print pos

nx.draw_networkx_labels(BcG,pos=BcGPos,labels=BcG_labels)

# nx.draw_networkx_labels(BcG,pos=BcG.pos,nodelist=list(bottom_nodes))

plt.axis('off')

plt.axis("tight")

PG=nx.projected_graph(BcG,top_nodes)

posPG=nx.spring_layout(PG,k=0.15,iterations=10)

plt.figure()

plt.title('The graph of cliques of G')

nx.draw(PG,posPG,with_labels=False,node_color='g')

for p in posPG: # raise text positions

posPG[p][1] += 0.045 #offset 0.07

nx.draw_networkx_labels(PG,posPG,labels=Bcg_labels)

plt.show()

# #### ΣΧΕΔΙΑΣΜΟΣ ΚΛΙΛΩΝ ΜΕΣΑ ΣΕ ΠΕΡΙΒΑΛΛΟΜΕΝΕΣ ΧΡΩΜΑΤΙΣΜΕΝΕΣ ΠΕΡΙΟΧΕΣ

# import igraph as ig

# file_name=str(G.name)+str(lvl2)+'.graphml'

# file_dir='temp'

# try:

# os.stat(file_dir)

# except:

# os.mkdir(file_dir)

# nx.write_graphml(G, file_name)

# # print file_name

# g = ig.read(file_name, format="graphml")

# mcliques=g.maximal_cliques(0,5)

# # print mcliques

# colors_list=["gray","brown","yellow","cyan","green","blue","purple"]

# colors_lists=[]

# for i in range(len(mcliques)):

# if i<len(colors_list):

# colors_lists.append(colors_list[i])

# else:

# color_to_set=(random.random(),random.random(),1)

# while color_to_set in colors_lists:

# color_to_set=(random.random(),random.random(),1)

# colors_lists.append(color_to_set)

# group_markers = [(mcliques[i], colors_lists[i]) for i in range(len(mcliques))]

# ig.plot(g, mark_groups=group_markers)