def mylbfgs(coords, pot, **kwargs):
if not hasattr(pot, "getEnergyGradient"):
# for compatibility with old quenchers.
# assume pot is a getEnergyGradient function
pot = _getEnergyGradientWrapper(pot)
lbfgs = MYLBFGS(coords, pot, **kwargs)
return lbfgs.run()
def mylbfgs(coords, pot, **kwargs):
if not hasattr(pot, "getEnergyGradient"):
# for compatibility with old quenchers.
# assume pot is a getEnergyGradient function
pot = _getEnergyGradientWrapper(pot)
lbfgs = MYLBFGS(coords, pot, **kwargs)
return lbfgs.run()
def _mylbfgs(coords, pot, **kwargs):
lbfgs = MYLBFGS(coords, pot, **kwargs)
ret = lbfgs.run()
coords = ret.coords
e = ret.energy
rms = ret.rms
funcalls = ret.nfev
return coords, e, rms, funcalls, ret
def _mylbfgs(coords, pot, **kwargs):
lbfgs = MYLBFGS(coords, pot, **kwargs)
ret = lbfgs.run()
coords = ret.coords
e = ret.energy
rms = ret.rms
funcalls = ret.nfev
return coords, e, rms, funcalls, ret
def findLowestEigenVector(coords,
pot,
eigenvec0=None,
H0=None,
orthogZeroEigs=0,
dx=1e-3,
**kwargs):
"""
find the eigenvector corresponding to the lowest eigenvalue using
LowestEigPot and the LBFGS minimizer
***orthogZeroEigs is system dependent, don't forget to set it***
Parameters
----------
coords :
the coordinates at which to find the lowest eigenvector
pot :
potential object
eigenvec0 :
the initial guess for the lowest eigenvector (will be random if not
passed)
H0 : float
the initial guess for the diagonal component of the inverse Hermissian
orthogZeroEigs : callable
this function makes a vector orthogonal to the known zero
eigenvectors
orthogZeroEigs=0 : default behavior, assume translational and
rotational symmetry
orthogZeroEigs=None : the vector is unchanged
kwargs :
any additional keyword arguments are passed to the minimizer
See Also
--------
FindTransitionState : uses this class
"""
#combine kwargs with defaults.lowestEigenvectorQuenchParams
kwargs = dict([("iprint", 400), ("tol", 1e-6), ("nsteps", 500)] +
kwargs.items())
if not kwargs.has_key("logger"):
kwargs["logger"] = logging.getLogger(
"pygmin.connect.findTS.leig_quench")
if eigenvec0 is None:
#this random vector should be distributed uniformly on a hypersphere.
eigenvec0 = rotations.vec_random_ndim(coords.shape)
#set up potential for minimization
eigpot = LowestEigPot(coords, pot, orthogZeroEigs=orthogZeroEigs, dx=dx)
#minimize, using the last eigenvector as a starting point
#and starting with H0 from last minimization
quencher = MYLBFGS(eigenvec0, eigpot, rel_energy=True, H0=H0, **kwargs)
res = quencher.run()
#res = Result()
res.eigenval = res.energy
res.eigenvec = res.coords / np.linalg.norm(res.coords)
delattr(res, "energy")
delattr(res, "coords")
res.H0 = quencher.H0
#res.success = res.rms <= tol
return res
def findLowestEigenVector(coords, pot, eigenvec0=None, H0=None, orthogZeroEigs=0, dx=1e-3, **kwargs):
"""
find the eigenvector corresponding to the lowest eigenvalue using
LowestEigPot and the LBFGS minimizer
***orthogZeroEigs is system dependent, don't forget to set it***
Parameters
----------
coords :
the coordinates at which to find the lowest eigenvector
pot :
potential object
eigenvec0 :
the initial guess for the lowest eigenvector (will be random if not
passed)
H0 : float
the initial guess for the diagonal component of the inverse Hermissian
orthogZeroEigs : callable
this function makes a vector orthogonal to the known zero
eigenvectors
orthogZeroEigs=0 : default behavior, assume translational and
rotational symmetry
orthogZeroEigs=None : the vector is unchanged
kwargs :
any additional keyword arguments are passed to the minimizer
See Also
--------
FindTransitionState : uses this class
"""
#combine kwargs with defaults.lowestEigenvectorQuenchParams
kwargs = dict([("iprint",400), ("tol",1e-6), ("nsteps", 500)] +
kwargs.items())
if not kwargs.has_key("logger"):
kwargs["logger"] = logging.getLogger("pygmin.connect.findTS.leig_quench")
if eigenvec0 is None:
#this random vector should be distributed uniformly on a hypersphere.
eigenvec0 = rotations.vec_random_ndim(coords.shape)
#set up potential for minimization
eigpot = LowestEigPot(coords, pot, orthogZeroEigs=orthogZeroEigs, dx=dx)
#minimize, using the last eigenvector as a starting point
#and starting with H0 from last minimization
quencher = MYLBFGS(eigenvec0, eigpot, rel_energy=True, H0=H0,
**kwargs)
res = quencher.run()
#res = Result()
res.eigenval = res.energy
res.eigenvec = res.coords / np.linalg.norm(res.coords)
delattr(res, "energy")
delattr(res, "coords")
res.H0 = quencher.H0
#res.success = res.rms <= tol
return res
