def make_result(self, coords, energy):
from pele.optimize import Result
res = Result()
res.coords = coords
res.energy = energy
res.eigenval = 1.
res.eigenvec = coords.copy()
return res
def make_result(self, coords, energy):
from pele.optimize import Result
res = Result()
res.coords = coords
res.energy = energy
res.eigenval = 1.
res.eigenvec = coords.copy()
return res
def get_result(self):
res = Result()
res.energy = self.energy
res.gradient = self.gradient
res.coords = self.coords
res.nsteps = self.iter_number
res.rms = np.linalg.norm(res.gradient) / np.sqrt(len(res.gradient))
res.nfev = self.transverse_potential.nfev
res.eigenval = self.eigenval
res.eigenvec = self.get_eigenvector()
res.success = self.stop_criterion_satisfied()
return res
def get_result(self):
res = Result()
res.energy = self.energy
res.gradient = self.gradient
res.coords = self.coords
res.nsteps = self.iter_number
res.rms = np.linalg.norm(res.gradient) / np.sqrt(len(res.gradient))
res.nfev = self.transverse_potential.nfev
res.eigenval = self.eigenval
res.eigenvec = self.get_eigenvector()
res.success = self.stop_criterion_satisfied()
return res
def _get_lowest_eigenvector_diagonalization(self, coords, **kwargs):
"""compute the lowest eigenvector by diagonalizing the Hessian
This scales as N**3, so can be very slow for large systems.
"""
if self.verbosity > 3:
print "computing the lowest eigenvector by diagonalizing the Hessian"
hess = self.pot.getHessian(coords)
eigenval, evec = get_smallest_eig(hess)
res = Result()
res.eigenval = eigenval
res.eigenvec = evec
res.nfev = 1
res.success = True
res.rms = 0.
return res
def _get_lowest_eigenvector_diagonalization(self, coords, **kwargs):
"""compute the lowest eigenvector by diagonalizing the Hessian
This scales as N**3, so can be very slow for large systems.
"""
if self.verbosity > 3:
print("computing the lowest eigenvector by diagonalizing the Hessian")
hess = self.pot.getHessian(coords)
eigenval, evec = get_smallest_eig(hess)
res = Result()
res.eigenval = eigenval
res.eigenvec = evec
res.nfev = 1
res.success = True
res.rms = 0.
return res
def get_result(self):
"""return a results object"""
trans_res = self.translator.get_result()
rot_result = self.rotator.get_result()
res = Result()
res.eigenval = rot_result.eigenval
res.eigenvec = rot_result.eigenvec
res.coords = trans_res.coords
res.energy, res.grad = self.get_true_energy_gradient(res.coords)
res.rms = trans_res.rms
res.nfev = rot_result.nfev + trans_res.nfev
res.nsteps = self.iter_number
res.success = self.stop_criterion_satisfied()
if res.eigenval > 0:
res.success = False
return res
def run(self):
"""The main loop of the algorithm"""
coords = np.copy(self.coords)
res = Result() # return object
res.message = []
self._compute_gradients(coords)
iend = 0
for i in xrange(self.nsteps):
iend = i
# get the lowest eigenvalue and eigenvector
self.overlap = self._getLowestEigenVector(coords, i)
overlap = self.overlap
if self.eigenval < 0:
self.negatives_before_check -= 1
# determine whether everything looks OK.
all_ok = self.eigenval < 0 or not self.check_negative
if not all_ok:
if i == 0:
# we need to accept because we haven't saved the state yet
# Also, demand_initial_negative_vec will stop later if needed
all_ok = True
if not all_ok:
if self.negatives_before_check > 0 and not self.demand_initial_negative_vec:
print " positive before check. setting all ok"
all_ok = True
# if everything is OK, then continue, else revert the step
if all_ok:
self._saveState(coords)
self.reduce_step = 0
else:
self.npositive += 1
if self.npositive > self.npositive_max:
logger.warning(
"positive eigenvalue found too many times. ending %s",
self.npositive)
res.message.append(
"positive eigenvalue found too many times %d" %
self.npositive)
break
if self.verbosity > 2:
logger.info(
"the eigenvalue turned positive. %s %s", self.eigenval,
"Resetting last good values and taking smaller steps")
coords = self._resetState()
self.reduce_step += 1
# step uphill along the direction of the lowest eigenvector
coords = self._stepUphill(coords)
# minimize the coordinates in the space perpendicular to the lowest eigenvector
tangent_ret = self._minimizeTangentSpace(
coords, energy=self.get_energy(), gradient=self.get_gradient())
coords = tangent_ret.coords
tangentrms = tangent_ret.rms
# check if we are done and print some stuff
# self._compute_gradients(coords) # this is unnecessary
E = self.get_energy()
grad = self.get_gradient()
rms = np.linalg.norm(grad) * self.rmsnorm
gradpar = np.dot(grad, self.eigenvec) / np.linalg.norm(
self.eigenvec)
if self.iprint > 0:
if (i + 1) % self.iprint == 0:
ostring = "findTS: %3d E %9g rms %8g eigenvalue %9g rms perp %8g grad par %9g overlap %g" % (
i, E, rms, self.eigenval, tangentrms, gradpar, overlap)
extra = " Evec search: %d rms %g" % (
self.leig_result.nfev, self.leig_result.rms)
extra += " Tverse search: %d step %g" % (
self.tangent_result.nfev, self.tangent_move_step)
extra += " Uphill step:%g" % (self.uphill_step_size, )
logger.info("%s %s", ostring, extra)
if callable(self.event):
self.event(energy=E,
coords=coords,
rms=rms,
eigenval=self.eigenval,
stepnum=i)
if rms < self.tol:
break
if self.nfail >= self.nfail_max:
logger.warning(
"stopping findTransitionState. too many failures in eigenvector search %s",
self.nfail)
res.message.append(
"too many failures in eigenvector search %d" % self.nfail)
break
if i == 0 and self.eigenval > 0.:
if self.verbosity > 1:
logger.warning(
"initial eigenvalue is positive - increase NEB spring constant?"
)
if self.demand_initial_negative_vec:
logger.warning(
" aborting transition state search")
res.message.append("initial eigenvalue is positive %f" %
self.eigenval)
break
# done. do one last eigenvector search because coords may have changed
self._getLowestEigenVector(coords, iend)
# print some data
if self.verbosity > 0 or self.iprint > 0:
logger.info("findTransitionState done: %s %s %s %s %s", iend, E,
rms, "eigenvalue", self.eigenval)
success = True
# check if results make sense
if self.eigenval >= 0.:
if self.verbosity > 2:
logger.info(
"warning: transition state is ending with positive eigenvalue %s",
self.eigenval)
success = False
if rms > self.tol:
if self.verbosity > 2:
logger.info(
"warning: transition state search appears to have failed: rms %s",
rms)
success = False
if iend >= self.nsteps:
res.message.append("maximum iterations reached %d" % iend)
# update nfev with the number of calls from the transverse walker
if self._transverse_walker is not None:
twres = self._transverse_walker.get_result()
self.nfev += twres.nfev
res.coords = coords
res.energy = E
res.eigenval = self.eigenval
res.eigenvec = self.eigenvec
res.grad = grad
res.rms = rms
res.nsteps = iend
res.success = success
res.nfev = self.nfev
return res
def run(self):
"""The main loop of the algorithm"""
coords = np.copy(self.coords)
res = Result() # return object
res.message = []
# if starting with positive curvature, disable negative eigenvalue check
# this will be reenabled as soon as the eigenvector becomes negative
negative_before_check = 10
self._compute_gradients(coords)
for i in xrange(self.nsteps):
# get the lowest eigenvalue and eigenvector
self.overlap = self._getLowestEigenVector(coords, i)
overlap = self.overlap
#print self.eigenval
if self.eigenval < 0:
negative_before_check -= 1
# check to make sure the eigenvector is ok
if (i == 0 or self.eigenval <= 0 or not self.check_negative or
(negative_before_check > 0 and not self.demand_initial_negative_vec)):
self._saveState(coords)
self.reduce_step = 0
else:
self.npositive += 1
if self.npositive > self.npositive_max:
logger.warning( "positive eigenvalue found too many times. ending %s", self.npositive)
res.message.append( "positive eigenvalue found too many times %d" % self.npositive )
break
if self.verbosity > 2:
logger.info("the eigenvalue turned positive. %s %s", self.eigenval, "Resetting last good values and taking smaller steps")
coords = self._resetState()
self.reduce_step += 1
# step uphill along the direction of the lowest eigenvector
coords = self._stepUphill(coords)
# minimize the coordinates in the space perpendicular to the lowest eigenvector
tangent_ret = self._minimizeTangentSpace(coords, energy=self.get_energy(), gradient=self.get_gradient())
coords = tangent_ret.coords
tangentrms = tangent_ret.rms
# check if we are done and print some stuff
# self._compute_gradients(coords) # this is unnecessary
E = self.get_energy()
grad = self.get_gradient()
rms = np.linalg.norm(grad) * self.rmsnorm
gradpar = np.dot(grad, self.eigenvec) / np.linalg.norm(self.eigenvec)
if self.iprint > 0:
if (i+1) % self.iprint == 0:
ostring = "findTS: %3d E %9g rms %8g eigenvalue %9g rms perp %8g grad par %9g overlap %g" % (
i, E, rms, self.eigenval, tangentrms, gradpar, overlap)
extra = " Evec search: %d rms %g" % (self.leig_result.nfev, self.leig_result.rms)
extra += " Tverse search: %d step %g" % (self.tangent_result.nfev,
self.tangent_move_step)
extra += " Uphill step:%g" % (self.uphill_step_size,)
logger.info("%s %s", ostring, extra)
if callable(self.event):
self.event(energy=E, coords=coords, rms=rms, eigenval=self.eigenval, stepnum=i)
if rms < self.tol:
break
if self.nfail >= self.nfail_max:
logger.warning("stopping findTransitionState. too many failures in eigenvector search %s", self.nfail)
res.message.append( "too many failures in eigenvector search %d" % self.nfail )
break
if i == 0 and self.eigenval > 0.:
if self.verbosity > 1:
logger.warning("initial eigenvalue is positive - increase NEB spring constant?")
if self.demand_initial_negative_vec:
logger.warning(" aborting transition state search")
res.message.append( "initial eigenvalue is positive %f" % self.eigenval )
break
# done. do one last eigenvector search because coords may have changed
self._getLowestEigenVector(coords, i)
# print some data
if self.verbosity > 0 or self.iprint > 0:
logger.info("findTransitionState done: %s %s %s %s %s", i, E, rms, "eigenvalue", self.eigenval)
success = True
# check if results make sense
if self.eigenval >= 0.:
if self.verbosity > 2:
logger.info( "warning: transition state is ending with positive eigenvalue %s", self.eigenval)
success = False
if rms > self.tol:
if self.verbosity > 2:
logger.info("warning: transition state search appears to have failed: rms %s", rms)
success = False
if i >= self.nsteps:
res.message.append( "maximum iterations reached %d" % i )
# update nfev with the number of calls from the transverse walker
if self._transverse_walker is not None:
twres = self._transverse_walker.get_result()
self.nfev += twres.nfev
#return results
res.coords = coords
res.energy = E
res.eigenval = self.eigenval
res.eigenvec = self.eigenvec
res.grad = grad
res.rms = rms
res.nsteps = i
res.success = success
res.nfev = self.nfev
return res
