def do_check(self, **kwargs):
self.pot.nfev = 0
opt = FindTransitionState(self.x, self.pot, **kwargs)
ret = opt.run()
self.assertEqual(ret.nfev, self.pot.nfev)
self.assertTrue(ret.success)
self.assertGreater(ret.nfev, 0)
def test(): # pragma: no cover
from pele.systems import LJCluster
from pele.transition_states import GeneralizedDimer
from pele.transition_states import FindTransitionState
from pele.utils.xyz import read_xyz
system = LJCluster(13)
x = system.get_random_configuration()
x = read_xyz(open("tests/lj18_ts.xyz", "r")).coords.flatten()
dimer = GeneralizedDimer(x.copy(), system.get_potential(),
rotational_steps=40,
dimer=False,
leig_kwargs=dict(iprint=10, maxstep=2.,
tol=.1,
),
translator_kwargs=dict(iprint=1,
verbosity=10,
),
)
ret = dimer.run()
print(ret)
print("\n\nnow the same with the old version")
searcher = FindTransitionState(x.copy(), system.get_potential(), iprint=1, verbosity=10)
# print ret
ret = searcher.run()
print(ret)
def do_check(self, **kwargs):
self.pot.nfev = 0
opt = FindTransitionState(self.x, self.pot, **kwargs)
ret = opt.run()
self.assertEqual(ret.nfev, self.pot.nfev)
self.assertTrue(ret.success)
self.assertGreater(ret.nfev, 0)
def test1(self):
print("\n\ntesting find ts with harmonic potential")
pot = HarmonicPot()
x0 = np.array([.2, 0])
opt = FindTransitionState(x0, pot, orthogZeroEigs=None,
iprint=1,
verbosity=10, # event=print_event,
hessian_diagonalization=True
)
ret = opt.run()
self.assertFalse(ret.success)
print(ret)
def test_2(self):
self.called = False
def event(**kwargs):
self.called = True
opt = FindTransitionState(self.x0, self.pot, orthogZeroEigs=None,
tangentSpaceQuenchParams=dict(maxstep=1.),
event=event)
ret = opt.run()
self.assertTrue(ret.success)
self.assertTrue(self.called)
def test_2(self):
self.called = False
def event(**kwargs):
self.called = True
opt = FindTransitionState(self.x0, self.pot, orthogZeroEigs=None,
tangentSpaceQuenchParams=dict(maxstep=1.),
event=event)
ret = opt.run()
self.assertTrue(ret.success)
self.assertTrue(self.called)
def test1(self):
print "\n\ntesting find ts with harmonic potential"
pot = HarmonicPot()
x0 = np.array([.2, 0])
opt = FindTransitionState(x0, pot, orthogZeroEigs=None,
iprint=1,
verbosity=10, # event=print_event,
hessian_diagonalization=True
)
ret = opt.run()
self.assertFalse(ret.success)
print ret
def test1(self):
# plot_pot()
opt = FindTransitionState(self.x0, self.pot, orthogZeroEigs=None,
# iprint=1,
# verbosity=10, event=print_event,
# tol=1e-3,
# lowestEigenvectorQuenchParams=dict(iprint=1, events=[print_event])
)
ret = opt.run()
self.assertTrue(ret.success)
assert_arrays_almost_equal(self, ret.coords, self.xts, places=3)
self.assertAlmostEqual(ret.energy, self.ets, delta=1e-3)
self.assertLess(ret.rms, 1e-3)
self.assertEqual(ret.nfev + 1, self.pot.nfev)
def test_from_near_minimum(self):
print "\n\nstarting from a minimum"
x0 = np.array([.6, .1])
opt = FindTransitionState(x0, self.pot, orthogZeroEigs=None,
iprint=1,
verbosity=10, # event=print_event,
# tol=1e-3,
# lowestEigenvectorQuenchParams=dict(iprint=1, events=[print_event])
)
ret = opt.run()
print ret
self.assertTrue(ret.success)
assert_arrays_almost_equal(self, ret.coords, self.xts, places=3)
def test_from_near_minimum(self):
print("\n\nstarting from a minimum")
x0 = np.array([.6, .1])
opt = FindTransitionState(x0, self.pot, orthogZeroEigs=None,
iprint=1,
verbosity=10, # event=print_event,
# tol=1e-3,
# lowestEigenvectorQuenchParams=dict(iprint=1, events=[print_event])
)
ret = opt.run()
print(ret)
self.assertTrue(ret.success)
assert_arrays_almost_equal(self, ret.coords, self.xts, places=3)
def test1(self):
# plot_pot()
opt = FindTransitionState(self.x0, self.pot, orthogZeroEigs=None,
# iprint=1,
# verbosity=10, event=print_event,
# tol=1e-3,
# lowestEigenvectorQuenchParams=dict(iprint=1, events=[print_event])
)
ret = opt.run()
self.assertTrue(ret.success)
assert_arrays_almost_equal(self, ret.coords, self.xts, places=3)
self.assertAlmostEqual(ret.energy, self.ets, delta=1e-3)
self.assertLess(ret.rms, 1e-3)
self.assertEqual(ret.nfev + 1, self.pot.nfev)
def test_from_near_minimum_demand_negative_eigenvalue(self):
print "\n\nstarting from a minimum demand"
# demand that the eigenvalue is negative initially.
# this should fail right away
x0 = np.array([.6, .1])
opt = FindTransitionState(x0, self.pot, orthogZeroEigs=None,
demand_initial_negative_vec=True,
iprint=1,
verbosity=10, # event=print_event,
# tol=1e-3,
# lowestEigenvectorQuenchParams=dict(iprint=1, events=[print_event])
)
ret = opt.run()
print ret
self.assertFalse(ret.success)
self.assertEqual(ret.nsteps, 0)
def test_from_near_minimum_demand_negative_eigenvalue(self):
print("\n\nstarting from a minimum demand")
# demand that the eigenvalue is negative initially.
# this should fail right away
x0 = np.array([.6, .1])
opt = FindTransitionState(x0, self.pot, orthogZeroEigs=None,
demand_initial_negative_vec=True,
iprint=1,
verbosity=10, # event=print_event,
# tol=1e-3,
# lowestEigenvectorQuenchParams=dict(iprint=1, events=[print_event])
)
ret = opt.run()
print(ret)
self.assertFalse(ret.success)
self.assertEqual(ret.nsteps, 0)
def make_dimer(self, x, **kwargs):
return FindTransitionState(
x,
self.pot,
orthogZeroEigs=self.system.get_orthogonalize_to_zero_eigenvectors(
),
invert_gradient=True,
)
def make_dimer(self, x, **kwargs):
return FindTransitionState(
x,
self.pot,
orthogZeroEigs=self.system.get_orthogonalize_to_zero_eigenvectors(
),
**kwargs
# verbosity=10
)
def test(): # pragma: no cover
from pele.systems import LJCluster
from pele.transition_states import GeneralizedDimer
from pele.transition_states import FindTransitionState
from pele.utils.xyz import read_xyz
system = LJCluster(13)
x = system.get_random_configuration()
x = read_xyz(open("tests/lj18_ts.xyz", "r")).coords.flatten()
dimer = GeneralizedDimer(
x.copy(),
system.get_potential(),
rotational_steps=40,
dimer=False,
leig_kwargs=dict(
iprint=10,
maxstep=2.,
tol=.1,
),
translator_kwargs=dict(
iprint=1,
verbosity=10,
),
)
ret = dimer.run()
print(ret)
print("\n\nnow the same with the old version")
searcher = FindTransitionState(x.copy(),
system.get_potential(),
iprint=1,
verbosity=10)
# print ret
ret = searcher.run()
print(ret)
def __init__(self):
self["database"] = BaseParameters()
self["basinhopping"] = BaseParameters()
self["takestep"] = BaseParameters()
self.structural_quench_params = BaseParameters()
self.gui = BaseParameters()
self.double_ended_connect = BaseParameters()
self.double_ended_connect.local_connect_params = BaseParameters()
self.double_ended_connect.local_connect_params.pushoff_params = BaseParameters(
)
self.double_ended_connect.local_connect_params.tsSearchParams = BaseParameters(
FindTransitionState.params())
self.double_ended_connect.local_connect_params.NEBparams = BaseParameters(
NEBDriver.params())
def __init__(self):
self["database"] = BaseParameters()
self["basinhopping"] = BaseParameters()
self["takestep"] = BaseParameters()
self.structural_quench_params = BaseParameters()
self.gui = BaseParameters()
self.double_ended_connect = BaseParameters()
self.double_ended_connect.local_connect_params = BaseParameters()
self.double_ended_connect.local_connect_params.pushoff_params = BaseParameters()
self.double_ended_connect.local_connect_params.tsSearchParams = BaseParameters(FindTransitionState.params())
self.double_ended_connect.local_connect_params.NEBparams = BaseParameters(NEBDriver.params())
def test_params(self):
params = FindTransitionState.params()
def test_params(self):
params = FindTransitionState.params()
