gerador_test.py~

# coding: utf-8
import sys
import math
import random
import matplotlib
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import matplotlib.cm as cmx
from mpl_toolkits.mplot3d import Axes3D

tx = 10
ty = 10
tz = 10
sigmin = 3
sigmax = 6


X = [i for i in range(0,tx)]

Y = [i for i in range(0,ty)]

Z = [i for i in range(0,tz)]

numero_de_clusters = 5

ID = [i for i in range(len(X)*len(Y)*len(Z))]

blocosescolhidos = random.sample(ID, numero_de_clusters)

cluster_mu = []
cluster_sigma = []

for c in range(numero_de_clusters):
	cluster_mu.append([random.uniform(0,tx),random.uniform(0,ty),random.uniform(0,tz)])
	cluster_sigma.append([tx/random.uniform(sigmin,sigmax),ty/random.uniform(sigmin,sigmax),tz/random.uniform(sigmin,sigmax)])

print cluster_mu
print cluster_sigma


# blocos_coord = [(a,b,c) for a in X for b in Y for c in Z]

# dens = [0 for b in range(len(blocos_coord))]
# temp = []
# for c in range(numero_de_clusters):
# 	cov_matrix = np.diag(cluster_sigma[c])
# 	temp.append([dens[b] + st.multivariate_normal.pdf(b,cluster_mu[c],np.diag(cluster_sigma[c])) for b in range(len(blocos_coord))])

blocos_coord = [(a,b,c) for a in X for b in Y for c in Z]
#print blocos_coord

dens = [0 for b in range(len(blocos_coord))]
for c in range(numero_de_clusters):
	cov_matrix = np.diag(cluster_sigma[c])
	for b in range(len(blocos_coord)):
		dens[b] = dens[b] + st.multivariate_normal.pdf(blocos_coord[b],cluster_mu[c],np.diag(cluster_sigma[c]))



def precList(b, Blocos,xmax,ymax,zmax):
	prec = []
	for dx in (-1,0,1):
		if((b[0]+dx>=0) and(b[0]+dx<xmax)):
			for dy in (-1,0,1):
				if((b[1]+dy>=0) and(b[1]+dy<ymax)):
					if(b[2]-1>=0):
						prec.append(Blocos.index((b[0]+dx,b[1]+dy,b[2]-1)))
	return prec


def criaPrecList(blocos_coord,tx,ty,tz):
	PrecList = []
	for b in range(len(blocos_coord)):
		prec = precList(blocos_coord[b], blocos_coord,tx,ty,tz)
		PrecList.append(prec)
	return PrecList

def criaArqPrec(nfile,blocos_coord,tx,ty,tz):
	with open(nfile, 'w') as prec_file:
		for b in range(len(blocos_coord)):
			prec = precList(blocos_coord[b], blocos_coord,tx,ty,tz)
			prec_file.write('{}\t{}\t'.format(b,len(prec)))
			for p in prec:
				prec_file.write('{}\t'.format(p))
			prec_file.write('\n')

def criaAPP(blocos_coord,dens,mxb,mnb,mxc):
	dmin = min(dens)
	dmax = max(dens)
	benef = random.uniform(mnb,mxb)
	benefList = [benef*((dens[b] - dmin)/(dmax-dmin)) for b in range(len(dens))]
	ap_process = [-random.uniform(0,mxc)+benefList[b] for b in range(len(benefList))]
	return ap_process

def criaArqBloco(nfile,blocos_coord,ap_process):
	with open(nfile, 'w') as bfile:
		for b in blocos_coord:
			bid = blocos_coord.index(b) 
			bfile.write('{}\t{}\t{}\t{}\t0\t0\t0\t0\t0\t0\t{}\n'.format(bid,b[0],b[1],b[2],ap_process[bid]))


def UPIT(blocos_coord,value):
	m = Model()
	n = len(blocos_coord) # number of blocks

	# Indicator variable for each block
	x = {}
	for i in range(n):
	   x[i] = m.addVar(vtype=GRB.BINARY, name="x%d" % i)

	m.update()

	# Set objective
	m.setObjective(quicksum(value[i]*x[i] for i in range(n)), GRB.MAXIMIZE)

	# Add constraints
	for b in range(n):
	
    
	   u = edge[0]
	   v = edge[1]
	   print(u,v)
	   m.addConstr(x[u] <= x[v])

	m.optimize()

approc = criaAPP(blocos_coord,dens,20,10,0.5)
criaArqBloco('test.bloc',blocos_coord,approc)
criaArqPrec('test.prec',blocos_coord,tx,ty,tz)

       
 



#print dens


# temp2 = [0 for b in range(len(blocos_coord))]
# for c in range(0, numero_de_clusters):
# 	for b in range(len(blocos_coord)):
# 		if(temp[c][b]!=0):
# 			temp2[b] = temp[c][b]

# print(temp2)

# for i in blocosescolhidos:
# 	for j in range(numero_de_clusters):
# 		for k in range(len(blocos_coord)):
# 			dens[i] = temp[j][k]

# print(blocosescolhidos)
# print(len(dens))
# indice = 0
# indice2 = 0
# for i in range(len(blocosescolhidos)):
# 	indice = blocosescolhidos[i]
# 	for k in range(numero_de_clusters):
# 		for l in range(len(blocos_coord)):
# 			if(temp[k+indice2][l] != 0):
# 				dens[indice] = temp[k][l]
# 				indice = indice + 1
# 			else:
# 				break
# 		indice2 = indice2 + 1
# 		break

# k = 0
# l = 0
# indice = 0
# for i in range(len(blocosescolhidos)):
# 	indice = blocosescolhidos[i]
# 	for v in temp[k]:
# 		if(v != 0):
# 			dens[indice] = dens[indice] + v
# 			indice = indice + 1
# 		else:
# 			k = k + 1
# 			break

# print(dens)

bx = [b[0] for b in blocos_coord]
by = [b[1] for b in blocos_coord]
bz = [b[2] for b in blocos_coord]


# cm = plt.get_cmap("Blues")
# fig = plt.figure()
# ax3d = fig.add_subplot(111, projection = '3d')

# ax3d.scatter(bx,by,bz,c = dens)
# plt.show()


def scatter3d(x,y,z, cs, colorsMap='YlOrRd'):
	cm = plt.get_cmap(colorsMap)
	cNorm = matplotlib.colors.Normalize(vmin=min(cs), vmax=max(cs))
	scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
	fig = plt.figure()
	ax = Axes3D(fig)
	ax.scatter(x, y, z, c=scalarMap.to_rgba(cs),marker = 'o', edgecolors = 'none')
	scalarMap.set_array(cs)
	fig.colorbar(scalarMap)
	plt.show()

scatter3d(bx,by,bz,dens)


# #%% Create Color Map
# colormap = plt.get_cmap("Blues")
# norm = matplotlib.colors.Normalize(vmin=min(dens), vmax=max(dens))

# #%% 3D Plot
# fig = plt.figure()
# ax3D = fig.add_subplot(111, projection='3d')
# ax3D.scatter(bx, by, bz, c=colormap(norm(dens)), marker='o')
# # ax3D.scatter(bx, by, bz, s=10, c=colormap(dens), marker='o')

# plt.show()