import networkx as nx
import matplotlib.pyplot as plt

#create a graph with degrees following a power law distribution

#I believe we can eliminate this loop to find s by using the call
#nx.utils.create_degree_sequence(100,powerlaw_sequence) with
#appropriate modification

memory = []

#add word to listners memory
def store_word(node_number, word):
	memory[node_number].append(word)

#check if the word exists in listner's memory
def word_exists(node_number, word):
	if word in node[node_number]:
		return True
	else:
		return False

#check if two memory are connected
def is_peer(node_number1,node_number2):
	if graph[node_number1][node_number2] == 1:
		return True
	else:
		return False

#create memory blocks for each node in the graph
def init_memory(graph):
	for x in range(0,len(graph)):
		memory.append([])

def success(speaker,listner,word):
	memory[speaker][:] = [word]
	memory[listner][:] = [word]

def propagate(speaker,word):
	peers = []
	memory[speaker].append(word)
	for x in range(0,len(graph)):
		if graph[speaker][x] == 1:
			peers.append(x)
			store_word(x,word)

while True:
    s=[]
    while len(s)<1000:
        nextval = int(nx.utils.powerlaw_sequence(1, 2.5)[0]) #100 nodes, power-law exponent 2.5
        if nextval!=0:
            s.append(nextval)
    if sum(s)%2 == 0:
        break
G = nx.configuration_model(s)
G=nx.Graph(G) # remove parallel edges
G.remove_edges_from(G.selfloop_edges())

#draw and show graph
pos = nx.spring_layout(G)
nx.draw_networkx(G, pos)
plt.savefig('test.pdf')