speed up a numpy code with huge array

[Alexzive]

Hello Pythonguys!

is there a way to improve the performance of the attached code ? it
takes about 5 h on a dual-core (using only one core) when len(V)
~1MIL. V is an array which is supposed to store all the volumes of
tetrahedral elements of a grid whose coord. are stored in NN (accessed
trough the list of tetraelements --> EL)


Thanks in advance!
Alex

####
print 'start ' + nameodb
#path = '/windows/D/SIM-MM/3D/E_ortho/' + nameodb + '.odb'
path = pt + nameodb + '.odb'
odb = openOdb(path)

N = odb.rootAssembly.instances['PART-1-1'].nodes
if loadV==1:
  pathV=pt+vtet
  V=numpy.loadtxt(pathV)
  VTOT = V[0]
  L3 = V[1]
  print 'using ' + vtet
else:
  NN=[]
  B=[0,0,0,0]
  for i in range(len(N)):
        B[0] = N[i].label
        B[1] = N[i].coordinates[0]
        B[2] = N[i].coordinates[1]
        B[3] = N[i].coordinates[2]
        NN = append(NN,B)

  NN=NN.reshape(-1,4)
  EL = odb.rootAssembly.instances['PART-1-1'].elements

  L1 = max(NN[:,1])-min(NN[:,1])
  L2 = max(NN[:,2])-min(NN[:,2])
  L3 = max(NN[:,3])-min(NN[:,3])
  VTOT=L1*L2*L3
  print 'VTOT: [mm³]' + str(VTOT)

  V = array([])

  print 'calculating new Vtet '
  V = range(len(EL)+2)
  V[0] = VTOT
  V[1] = L3
  for j in range(0,len(EL)):
        Va = EL[j].connectivity[0]
        Vb = EL[j].connectivity[1]
        Vc = EL[j].connectivity[2]
        Vd = EL[j].connectivity[3]
        ix = where(NN[:,0] == Va)
        Xa = NN[ix,1][0][0]
        Ya = NN[ix,2][0][0]
        Za = NN[ix,3][0][0]
        ix = where(NN[:,0] == Vb)
        Xb = NN[ix,1][0][0]
        Yb = NN[ix,2][0][0]
        Zb = NN[ix,3][0][0]
        ix = where(NN[:,0] == Vc)
        Xc = NN[ix,1][0][0]
        Yc = NN[ix,2][0][0]
        Zc = NN[ix,3][0][0]
        ix = where(NN[:,0] == Vd)
        Xd = NN[ix,1][0][0]
        Yd = NN[ix,2][0][0]
        Zd = NN[ix,3][0][0]
        a =  [Xa,Ya,Za]
        b =  [Xb,Yb,Zb]
	c =  [Xc,Yc,Zc]
        d =  [Xd,Yd,Zd]
	aa = numpy.diff([b,a],axis=0)[0]
	bb = numpy.diff([c,b],axis=0)[0]
 	cc = numpy.diff([d,c],axis=0)[0]
        D=array([aa,bb,cc])
        det=numpy.linalg.det(D)
	V[j+2] = abs(det)/6
  pathV = pt + vtet
  savetxt(pathV, V, fmt='%.3e')
###