import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import random
import array
import subprocess
import shlex

def create_files():
    f = open('100k.txt', 'w')
    f.write('#FromNodeId' + '\t'+ 'ToNodeId' + '\t' + 'Weight' + '\n')

    for x in range(1,100001):
        x = random.randint(1,100001)
        y = random.randint(1,100001)
        z = random.randint(1,100)
        z = z / 100
        f.write(str(x) + '\t'+ str(y) + '\t' + str(z) + '\n')
    f.close()

    f = open('20.txt', 'w')
    f.write('#FromNodeId' + '\t'+ 'ToNodeId' + '\t' + 'Weight' + '\n')

    for x in range(1,21):
        x = random.randint(1,21)
        y = random.randint(1,21)
        z = random.randint(1,100)
        z = z / 100
        f.write(str(x) + '\t'+ str(y) + '\t' + str(z) + '\n')
    f.close()
    return 0

def pagerank():			
	G = nx.read_edgelist('./100k.txt',nodetype=int,
		data=(('weight',float),), create_using=nx.DiGraph())
	alpha=0.85
	personalization=None
	max_iter=100
	tol=1.0e-6
	nstart=None
	weight='weight'
	dangling=None
	
	xdd = []
	ydd = []
				
	if len(G) == 0:
		return {}

	if not G.is_directed():
		D = G.to_directed()
	else:
		D = G
	W = nx.stochastic_graph(D, weight=weight)
	N = W.number_of_nodes()
	if nstart is None:
		x = dict.fromkeys(W, 1.0 / N)
	else:
		s = float(sum(nstart.values()))
		x = dict((k, v / s) for k, v in nstart.items())

	if personalization is None:
		p = dict.fromkeys(W, 1.0 / N)
	else:
		missing = set(G) - set(personalization)
		if missing:
			raise NetworkXError('Error 404' % missing)
		s = float(sum(personalization.values()))
		p = dict((k, v / s) for k, v in personalization.items())

	if dangling is None:
		dangling_weights = p
	else:
		missing = set(G) - set(dangling)
		if missing:
			raise NetworkXError('Error 404' % missing)
		s = float(sum(dangling.values()))
		dangling_weights = dict((k, v/s) for k, v in dangling.items())
	dangling_nodes = [n for n in W if W.out_degree(n, weight=weight) == 0.0]
	for zdd in range(max_iter):
		xdd.append(zdd)
		xlast = x
		x = dict.fromkeys(xlast.keys(), 0)
		danglesum = alpha * sum(xlast[n] for n in dangling_nodes)
		for n in x:
			for nbr in W[n]:
				x[nbr] += alpha * xlast[n] * W[n][nbr][weight]
			x[n] += danglesum * dangling_weights[n] + (1.0 - alpha) * p[n]
		err = sum([abs(x[n] - xlast[n]) for n in x])
		ydd.append(err)
		if err < N*tol:
			return print(x)
           
def pagerank_chart():			
	G = nx.read_edgelist('./100k.txt',nodetype=int,
		data=(('weight',float),), create_using=nx.DiGraph())
	alpha=0.85
	personalization=None
	max_iter=100
	tol=1.0e-6
	nstart=None
	weight='weight'
	dangling=None
	
	xdd = []
	ydd = []
				
	if len(G) == 0:
		return {}

	if not G.is_directed():
		D = G.to_directed()
	else:
		D = G
	W = nx.stochastic_graph(D, weight=weight)
	N = W.number_of_nodes()
	if nstart is None:
		x = dict.fromkeys(W, 1.0 / N)
	else:
		s = float(sum(nstart.values()))
		x = dict((k, v / s) for k, v in nstart.items())

	if personalization is None:
		p = dict.fromkeys(W, 1.0 / N)
	else:
		missing = set(G) - set(personalization)
		if missing:
			raise NetworkXError('Error 404' % missing)
		s = float(sum(personalization.values()))
		p = dict((k, v / s) for k, v in personalization.items())

	if dangling is None:
		dangling_weights = p
	else:
		missing = set(G) - set(dangling)
		if missing:
			raise NetworkXError('Error 404' % missing)
		s = float(sum(dangling.values()))
		dangling_weights = dict((k, v/s) for k, v in dangling.items())
	dangling_nodes = [n for n in W if W.out_degree(n, weight=weight) == 0.0]
	for zdd in range(max_iter):
		xdd.append(zdd)
		xlast = x
		x = dict.fromkeys(xlast.keys(), 0)
		danglesum = alpha * sum(xlast[n] for n in dangling_nodes)
		for n in x:
			for nbr in W[n]:
				x[nbr] += alpha * xlast[n] * W[n][nbr][weight]
			x[n] += danglesum * dangling_weights[n] + (1.0 - alpha) * p[n]
		err = sum([abs(x[n] - xlast[n]) for n in x])
		ydd.append(err)
		if err < N*tol:
			xdd1 = np.array(xdd)
			ydd1 = np.array(ydd)
			plt.plot(xdd1, ydd1)
			fname = './test.pdf'
			plt.savefig(fname)
			proc=subprocess.Popen(shlex.split('lpr {f}'.format(f=fname)))
			return 0
            
def main():
    create_files()
    G = nx.read_edgelist('./20.txt',nodetype=int,
  	data=(('weight',float),), create_using=nx.DiGraph())
    print(G.edges(data=True))
    nx.draw(G)
    plt.show()
    
    G = nx.read_edgelist('./100k.txt',nodetype=int,
  	data=(('weight',float),), create_using=nx.DiGraph())
    pr = nx.pagerank(G,0.4)
    #print(pr)
    
    pagerank()
    
    pagerank_chart()

if __name__ == "__main__":
    main()