class LJTest(unittest.TestCase):
def setUp(self):
self.natoms = 10
self.coords = np.random.uniform(-1, 1., 3 * self.natoms) * self.natoms ** (-1. / 3)
self.pot = LJ()
self.E = self.pot.getEnergy(self.coords)
self.Egrad, self.grad = self.pot.getEnergyGradient(self.coords)
self.ilist = [] # np.zeros([self.natoms*(self.natoms-1)/2, 2])
k = 0
for i in range(self.natoms):
for j in range(i):
k += 1
self.ilist.append([i, j])
self.ilist = np.array(self.ilist)
# print self.ilist
def test_grad_e(self):
self.assertAlmostEqual(self.E, self.Egrad, 7)
def test_lists_e(self):
e = self.pot.getEnergyList(self.coords, self.ilist)
self.assertAlmostEqual(self.E, e, 7)
def test_lists_eg(self):
e, g = self.pot.getEnergyGradientList(self.coords, self.ilist)
self.assertAlmostEqual(self.E, e, 7)
gdiffmax = np.max(np.abs(g - self.grad)) / np.max(np.abs(self.grad))
self.assertLess(gdiffmax, 1e-7)
class LJTest(unittest.TestCase):
def setUp(self):
self.natoms = 10
self.coords = np.random.uniform(-1,1.,3*self.natoms) * self.natoms**(-1./3)
self.pot = LJ()
self.E = self.pot.getEnergy(self.coords)
self.Egrad, self.grad = self.pot.getEnergyGradient(self.coords)
import itertools
self.ilist = [] #np.zeros([self.natoms*(self.natoms-1)/2, 2])
k = 0
for i in range(self.natoms):
for j in range(i):
k += 1
self.ilist.append( [i,j] )
self.ilist = np.array(self.ilist)
#print self.ilist
def test_grad_e(self):
self.assertAlmostEqual(self.E, self.Egrad, 7)
def test_lists_e(self):
e = self.pot.getEnergyList(self.coords, self.ilist)
self.assertAlmostEqual(self.E, e, 7)
def test_lists_eg(self):
e, g = self.pot.getEnergyGradientList(self.coords, self.ilist)
self.assertAlmostEqual(self.E, e, 7)
gdiffmax = np.max(np.abs( g-self.grad )) / np.max(np.abs(self.grad))
self.assertLess(gdiffmax, 1e-7)
class TestLJAfterQuench(unittest.TestCase):
"""do the tests after a short quench so that the energies are not crazy large
"""
def setUp(self):
from pele.optimize import mylbfgs
self.natoms = 10
self.coords = np.random.uniform(-1, 1., 3 * self.natoms) * self.natoms ** (-1. / 3)
self.pot = LJ()
ret = mylbfgs(self.coords, self.pot, tol=2.)
self.coords = ret.coords
self.E = self.pot.getEnergy(self.coords)
self.Egrad, self.grad = self.pot.getEnergyGradient(self.coords)
def test_gradient(self):
e0 = self.pot.getEnergy(self.coords)
e, g, hess = self.pot.getEnergyGradientHessian(self.coords)
gnum = self.pot.NumericalDerivative(self.coords)
maxg = np.max(np.abs(g))
maxgdiff = np.max(np.abs(g - gnum))
self.assertAlmostEqual(e0, e, 5)
self.assertLess(maxgdiff / maxg, 1e-5)
def test_hessian(self):
e, g, hess = self.pot.getEnergyGradientHessian(self.coords)
nhess = self.pot.NumericalHessian(self.coords, eps=1e-8)
# print "hess", hess
# print "numerical", nhess
# diff = hess - nhess
maxhess = np.max(np.abs(hess))
maxdiff = np.max(np.abs(hess - nhess))
# print "diff", hess[:2,:2] - nhess[:2,:2]
# print maxhess, maxdiff, maxdiff / maxhess
# print "diff", (hess[:2,:2] - nhess[:2,:2])/maxhess
# print "diff", (hess - nhess)/maxhess
# print np.max(np.abs((hess-nhess)/nhess))
self.assertLess(maxdiff / maxhess, 1e-5)
natoms = 120 rho = 1.6 boxl = 1. meandiam = boxl / (float(natoms)/rho)**(1./3) print "mean diameter", meandiam #set up potential #diams = np.array([meandiam for i in range(natoms)]) #make them all the same #pot = SoftSphere(diams = diams) pot = SoftSphere() #initial coordinates coords = np.random.uniform(-1,1,[natoms*3]) * (natoms)**(1./3) E = pot.getEnergy(coords) print "initial energy", E printlist = [] #list of coordinates saved for printing printlist.append((coords.copy(), "intial coords")) #test a quench with default lbfgs #from optimize.quench import quench from pele.optimize import lbfgs_ase as quench coords, E, rms, funcalls = quench(coords, pot.getEnergyGradient, iprint=1) printlist.append((coords.copy(), "intial coords")) print "energy post quench", pot.getEnergy(coords)
