def gradient(self, X):
'''
Compute the gradient of the strain w.r.t. Cartesian coordinates of
the system. For the ic that needs to be constrained, a Lagrange
multiplier is included.
'''
#initialize return value
grad = np.zeros((len(X),))
#compute strain gradient
gstrain = np.zeros(self.coords0.shape)
self.update_pos(self.coords0 + X[:self.ndof].reshape((-1,3)))
self.value = self.compute(gpos=gstrain)
#compute constraint gradient
gconstraint = np.zeros(self.coords0.shape)
self.constraint.update_pos(self.coords0 + X[:self.ndof].reshape((-1,3)))
self.constrain_value = self.constraint.compute(gpos=gconstraint) + 1.0
#construct gradient
grad[:self.ndof] = gstrain.reshape((-1,)) + X[self.ndof]*gconstraint.reshape((-1,))
grad[self.ndof] = self.constrain_value - self.constrain_target
#cartesian penalty, i.e. extra penalty for deviation w.r.t. cartesian equilibrium coords
indices = np.array([[3*i,3*i+1,3*i+2] for i in range(self.ndof//3) if i not in self.cons_ic_atindexes]).ravel()
if len(indices)>0:
grad[indices] += X[indices]/(self.ndof*self.cart_penalty**2)
with log.section('PTGEN', 4, timer='PT Generate'):
log.dump(' Gradient: rms = %.3e max = %.3e cnstr = %.3e' %(np.sqrt((grad[:self.ndof]**2).mean()), max(grad[:self.ndof]), grad[self.ndof]))
return grad
def __init__(self, system, ai, settings, ffrefs=[]):
'''
**Arguments**
system
a Yaff `System` instance defining the system
ai
a `Reference` instance corresponding to the ab initio input data
settings
a `Settings` instance defining all QuickFF settings
**Optional Arguments**
ffrefs
a list of `Reference` instances defining the a-priori force
field contributions.
'''
with log.section('INIT', 1, timer='Initializing'):
log.dump('Initializing program')
self.settings = settings
self.system = system
self.ai = ai
self.ffrefs = ffrefs
self.valence = ValenceFF(system, settings)
self.perturbation = RelaxedStrain(system, self.valence, settings)
self.trajectories = None
self.print_system()
def do_eq_setrv(self, tasks, logger_level=3):
'''
Set the rest values to their respective AI equilibrium values.
'''
with log.section('EQSET', 2, timer='Equil Set RV'):
self.reset_system()
log.dump('Setting rest values to AI equilibrium values for tasks %s' %' '.join(tasks))
for term in self.valence.terms:
vterm = self.valence.vlist.vtab[term.index]
if np.array([task in term.tasks for task in tasks]).any():
if term.kind==3:#cross term
ic0 = self.valence.iclist.ictab[vterm['ic0']]
ic1 = self.valence.iclist.ictab[vterm['ic1']]
self.valence.set_params(term.index, rv0=ic0['value'], rv1=ic1['value'])
elif term.kind==4 and term.ics[0].kind==4:#Cosine of DihedAngle
ic = self.valence.iclist.ictab[vterm['ic0']]
m = self.valence.get_params(term.index, only='m')
rv = ic['value']%(360.0*deg/m)
with log.section('EQSET', 4, timer='Equil Set RV'):
log.dump('Set rest value of %s(%s) (eq=%.3f deg) to %.3f deg' %(
term.basename,
'.'.join([str(at) for at in term.get_atoms()]),
ic['value']/deg, rv/deg
))
self.valence.set_params(term.index, rv0=rv)
else:
rv = self.valence.iclist.ictab[vterm['ic0']]['value']
self.valence.set_params(term.index, rv0=rv)
self.valence.dump_logger(print_level=logger_level)
self.average_pars()
def do_bendclin(self, thresshold=5*deg):
'''
No Harmonic bend can have a rest value equal to are large than
180 deg - thresshold. If a master (or its slaves) has such a rest
value, convert master and all slaves to BendCLin (which corresponds
to a chebychev1 potential with sign=+1):
0.5*K*[1+cos(theta)]
'''
for master in self.valence.iter_masters(label='BendAHarm'):
indices = [master.index]
for slave in master.slaves: indices.append(slave)
found = False
for index in indices:
rv = self.valence.get_params(index, only='rv')
if rv>=180.0*deg-thresshold:
found = True
break
if found:
log.dump('%s has rest value > 180-%.0f deg, converted to BendCheby1' %(master.basename, thresshold/deg))
for index in indices:
term = self.valence.terms[index]
self.valence.modify_term(
index,
Chebychev1, [BendCos(*term.get_atoms())],
term.basename.replace('BendAHarm', 'BendCheby1'),
['HC_FC_DIAG'], ['kjmol', 'au']
)
self.valence.set_params(index, fc=0.0, sign=1.0)
for traj in self.trajectories:
if traj.term.index==index:
traj.rv = None
traj.fc = None
traj.active = False
def do_sqoopdist_to_oopdist(self, thresshold=1e-4*angstrom):
'''
Transform a SqOopdist term with a rest value that has been set to
zero, to a term Oopdist (harmonic in Oopdist instead of square of
Oopdist) with a rest value of 0.0 A.
'''
for master in self.valence.iter_masters(label='SqOopdist'):
indices = [master.index]
for slave in master.slaves: indices.append(slave)
found = False
for index in indices:
rv = self.valence.get_params(index, only='rv')
if rv<=thresshold:
found = True
break
if found:
log.dump('%s has rest value <= %.0f A^2, converted to Oopdist with d0=0' %(master.basename, thresshold/angstrom))
for index in indices:
term = self.valence.terms[index]
self.valence.modify_term(
index,
Harmonic, [OopDist(*term.get_atoms())],
term.basename.replace('SqOopdist', 'Oopdist'),
['HC_FC_DIAG'], ['kjmol/A**2', 'A']
)
self.valence.set_params(index, fc=0.0, rv0=0.0)
def do_pt_estimate(self, do_valence=False, energy_noise=None, logger_level=3):
'''
Estimate force constants and rest values from the perturbation
trajectories
**Optional Arguments**
do_valence
if set to True, the current valence force field will be used to
estimate the contribution of all other valence terms.
'''
with log.section('PTEST', 2, timer='PT Estimate'):
self.reset_system()
message = 'Estimating FF parameters from perturbation trajectories'
if do_valence: message += ' with valence reference'
log.dump(message)
#compute fc and rv from trajectory
only = self.settings.only_traj
for traj in self.trajectories:
if traj is None: continue
if not (only is None or only=='PT_ALL' or only=='pt_all'):
if isinstance(only, str): only = [only]
basename = self.valence.terms[traj.term.master].basename
if basename not in only: continue
self.perturbation.estimate(traj, self.ai, ffrefs=self.ffrefs, do_valence=do_valence, energy_noise=energy_noise)
#set force field parameters to computed fc and rv
for traj in self.trajectories:
if traj is None: continue
if not (only is None or only=='PT_ALL' or only=='pt_all'):
if isinstance(only, str): only = [only]
basename = self.valence.terms[traj.term.master].basename
if basename not in only: continue
self.valence.set_params(traj.term.index, fc=traj.fc, rv0=traj.rv)
#output
self.valence.dump_logger(print_level=logger_level)
def reset_system(self):
'''
routine to reset the system coords to the ai equilbrium
'''
log.dump('Resetting system coordinates to ab initio ref')
self.system.pos = self.ai.coords0.copy()
self.valence.dlist.forward()
self.valence.iclist.forward()
def do_squarebend(self, thresshold=20*deg):
'''
Identify bend patterns in which 4 atoms of type A surround a central
atom of type B with A-B-A angles of 90/180 degrees. A simple
harmonic pattern will not be adequate since a rest value of 90 and
180 degrees is possible for the same A-B-A term. Therefore, a
cosine term with multiplicity of 4 is used (which corresponds to a
chebychev4 potential with sign=-1):
V = K/2*[1-cos(4*theta)]
To identify the patterns, it is assumed that the rest values have
already been estimated from the perturbation trajectories. For each
master and slave of a BENDAHARM term, its rest value is computed and
checked if it lies either the interval [90-thresshold,90+thresshold]
or [180-thresshold,180]. If this is the case, the new cosine term
is used.
**Optional arguments**
thresshold
the (half) the width of the interval around 180 deg (90 degrees)
to check if a square BA4
'''
for master in self.valence.iter_masters(label='BendAHarm'):
rvs = np.zeros([len(master.slaves)+1], float)
rvs[0] = self.valence.get_params(master.index, only='rv')
for i, islave in enumerate(master.slaves):
rvs[1+i] = self.valence.get_params(islave, only='rv')
n90 = 0
n180 = 0
nother = 0
for i, rv in enumerate(rvs):
if 90*deg-thresshold<=rv and rv<=90*deg+thresshold: n90 += 1
elif 180*deg-thresshold<=rv and rv<=180*deg+thresshold: n180 += 1
else: nother += 1
if n90>0 and n180>0:
log.dump('%s has rest values around 90 deg and 180 deg, converted to BendCheby4' %master.basename)
#modify master and slaves
indices = [master.index]
for slave in master.slaves: indices.append(slave)
for index in indices:
term = self.valence.terms[index]
self.valence.modify_term(
index,
Chebychev4, [BendCos(*term.get_atoms())],
term.basename.replace('BendAHarm', 'BendCheby4'),
['HC_FC_DIAG'], ['kjmol', 'au']
)
self.valence.set_params(index, sign=-1)
for traj in self.trajectories:
if traj.term.index==index:
traj.active = False
traj.fc = None
traj.rv = None
def write_trajectories(self):
'''
Write perturbation trajectories to XYZ files.
'''
only = self.settings.only_traj
if not isinstance(only, list): only = [only]
with log.section('XYZ', 3, timer='PT dump XYZ'):
for trajectory in self.trajectories:
if trajectory is None: continue
for pattern in only:
if pattern=='PT_ALL' or pattern in trajectory.term.basename:
log.dump('Writing XYZ trajectory for %s' %trajectory.term.basename)
trajectory.to_xyz()
def plot_trajectories(self, do_valence=False, suffix=''):
'''
Plot energy contributions along perturbation trajectories and
'''
only = self.settings.only_traj
if not isinstance(only, list): only = [only]
with log.section('PLOT', 3, timer='PT plot energy'):
valence = None
if do_valence: valence=self.valence
for trajectory in self.trajectories:
if trajectory is None: continue
for pattern in only:
if pattern=='PT_ALL' or pattern in trajectory.term.basename:
log.dump('Plotting trajectory for %s' %trajectory.term.basename)
trajectory.plot(self.ai, ffrefs=self.ffrefs, valence=valence, suffix=suffix)
def average_pars(self):
'''
Average force field parameters over master and slaves.
'''
log.dump('Averaging force field parameters over master and slaves')
for master in self.valence.iter_masters():
npars = len(self.valence.get_params(master.index))
pars = np.zeros([len(master.slaves)+1, npars], float)
pars[0,:] = np.array(self.valence.get_params(master.index))
for i, islave in enumerate(master.slaves):
pars[1+i,:] = np.array(self.valence.get_params(islave))
if master.kind in [0,2,11,12]:#harmonic,fues,MM3Quartic,MM3Bend
fc, rv = pars.mean(axis=0)
self.valence.set_params(master.index, fc=fc, rv0=rv)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc, rv0=rv)
elif master.kind==1:
a0, a1, a2, a3 = pars.mean(axis=0)
self.valence.set_params(master.index, a0=a0, a1=a1, a2=a2, a3=a3)
for islave in master.slaves:
self.valence.set_params(islave, a0=a0, a1=a1, a2=a2, a3=a3)
elif master.kind==3:#cross
fc, rv0, rv1 = pars.mean(axis=0)
self.valence.set_params(master.index, fc=fc, rv0=rv0, rv1=rv1)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc, rv0=rv0, rv1=rv1)
elif master.kind==4:#cosine
assert pars[:,0].std()<1e-6, 'dihedral multiplicity not unique'
m, fc, rv = pars.mean(axis=0)
self.valence.set_params(master.index, fc=fc, rv0=rv, m=m)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc, rv0=rv, m=m)
elif master.kind in [5, 6, 7, 8, 9]:#chebychev
assert pars.shape[1]==2
fc = pars[:,0].mean()
self.valence.set_params(master.index, fc=fc)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc)
else:
raise NotImplementedError
def do_pt_generate(self):
'''
Generate perturbation trajectories.
'''
with log.section('PTGEN', 2, timer='PT Generate'):
#read if an existing file was specified through fn_traj
fn_traj = self.settings.fn_traj
if fn_traj is not None and os.path.isfile(fn_traj):
self.trajectories = pickle.load(open(fn_traj, 'rb'))
log.dump('Trajectories read from file %s' %fn_traj)
self.update_trajectory_terms()
newname = 'updated_'+fn_traj.split('/')[-1]
pickle.dump(self.trajectories, open(newname, 'wb'))
return
#configure
self.reset_system()
only = self.settings.only_traj
if only is None or only=='PT_ALL' or only=='pt_all':
do_terms = [term for term in self.valence.terms if term.kind in [0,2,11,12]]
else:
if isinstance(only, str): only = [only]
do_terms = []
for pattern in only:
for term in self.valence.iter_terms(pattern):
if term.kind in [0,2,11,12]:
do_terms.append(term)
trajectories = self.perturbation.prepare(do_terms)
#compute
log.dump('Constructing trajectories')
self.trajectories = paracontext.map(self.perturbation.generate, [traj for traj in trajectories if traj.active])
#write the trajectories to the non-existing file fn_traj
if fn_traj is not None:
assert not os.path.isfile(fn_traj)
pickle.dump(self.trajectories, open(fn_traj, 'wb'))
log.dump('Trajectories stored to file %s' %fn_traj)
def update_trajectory_terms(self):
'''
Routine to make ``self.valence.terms`` and the term attribute of each
trajectory in ``self.trajectories`` consistent again. This is usefull
if the trajectory were read from a file and the ``valenceFF`` instance
was modified.
'''
log.dump('Updating terms of trajectories to current valenceFF terms')
with log.section('PTUPD', 3):
#update the terms in the trajectories to match the terms in
#self.valence
for traj in self.trajectories:
found = False
for term in self.valence.iter_terms():
if traj.term.get_atoms()==term.get_atoms():
if found: raise ValueError('Found two terms for trajectory %s with atom indices %s' %(traj.term.basename, str(traj.term.get_atoms())))
traj.term = term
if 'PT_ALL' not in term.tasks:
log.dump('PT_ALL not in tasks of %s-%i, deactivated PT' %(term.basename, term.index))
traj.active = False
found = True
if not found:
log.warning('No term found for trajectory %s with atom indices %s, deactivating trajectory' %(traj.term.basename, str(traj.term.get_atoms())))
traj.active = False
#check if every term with task PT_ALL has a trajectory associated
#with it. It a trajectory is missing, generate it.
for term in self.valence.iter_terms():
if 'PT_ALL' not in term.tasks: continue
found = False
for traj in self.trajectories:
if term.get_atoms()==traj.term.get_atoms():
if found: raise ValueError('Found two trajectories for term %s with atom indices %s' %(term.basename, str(term.get_atoms())))
found =True
if not found:
log.warning('No trajectory found for term %s with atom indices %s. Generating it now.' %(term.basename, str(term.get_atoms())))
trajectory = self.perturbation.prepare([term])[term.index]
self.perturbation.generate(trajectory)
self.trajectories.append(trajectory)
def print_system(self):
'''
dump overview of atoms (and associated parameters) in the system
'''
with log.section('SYS', 3, timer='Initializing'):
log.dump('Atomic configuration of the system:')
log.dump('')
log.dump(' index | x [A] | y [A] | z [A] | ffatype | q | R [A] ')
log.dump('---------------------------------------------------------------------')
for i in range(len(self.system.numbers)):
x, y, z = self.system.pos[i,0], self.system.pos[i,1], self.system.pos[i,2]
if self.system.charges is not None:
q = self.system.charges[i]
else:
q = np.nan
if self.system.radii is not None:
R = self.system.radii[i]
else:
R = np.nan
log.dump(' %4i | % 7.3f | % 7.3f | % 7.3f | %6s | % 7.3f | % 7.3f ' %(
i, x/angstrom, y/angstrom, z/angstrom,
self.system.ffatypes[self.system.ffatype_ids[i]],
q, R/angstrom
))
def print_system(self):
'''
dump overview of atoms (and associated parameters) in the system
'''
with log.section('SYS', 3, timer='Initializing'):
log.dump('Atomic configuration of the system:')
log.dump('')
log.dump(' index | x [A] | y [A] | z [A] | ffatype | q | R [A] ')
log.dump('---------------------------------------------------------------------')
for i in range(len(self.system.numbers)):
x, y, z = self.system.pos[i,0], self.system.pos[i,1], self.system.pos[i,2]
if self.system.charges is not None:
q = self.system.charges[i]
else:
q = np.nan
if self.system.radii is not None:
R = self.system.radii[i]
else:
R = np.nan
log.dump(' %4i | % 7.3f | % 7.3f | % 7.3f | %6s | % 7.3f | % 7.3f ' %(
i, x/angstrom, y/angstrom, z/angstrom,
self.system.ffatypes[self.system.ffatype_ids[i]],
q, R/angstrom
))
def dump_logger(self, print_level=1):
if log.log_level < print_level: return
with log.section('', print_level):
sequence = [
'bondharm', 'bendaharm', 'bendcharm', 'bendcos', 'torsion',
'torsc2harm', 'dihedharm', 'oopdist', 'cross'
]
log.dump('')
for label in sequence:
lines = []
for term in self.iter_masters(label=label):
lines.append(term.to_string(self))
for line in sorted(lines):
log.dump(line)
log.dump('')
def do_pt_generate(self):
'''
Generate perturbation trajectories.
'''
with log.section('PTGEN', 2, timer='PT Generate'):
#read if an existing file was specified through fn_traj
fn_traj = self.settings.fn_traj
if fn_traj is not None and os.path.isfile(fn_traj):
self.trajectories = pickle.load(open(fn_traj, 'rb'))
log.dump('Trajectories read from file %s' % fn_traj)
self.update_trajectory_terms()
newname = 'updated_' + fn_traj.split('/')[-1]
pickle.dump(self.trajectories, open(newname, 'wb'))
return
#configure
self.reset_system()
only = self.settings.only_traj
if only is None or only == 'PT_ALL' or only == 'pt_all':
do_terms = [
term for term in self.valence.terms
if term.kind in [0, 2, 11, 12]
]
else:
if isinstance(only, str): only = [only]
do_terms = []
for pattern in only:
for term in self.valence.iter_terms(pattern):
if term.kind in [0, 2, 11, 12]:
do_terms.append(term)
trajectories = self.perturbation.prepare(do_terms)
#compute
log.dump('Constructing trajectories')
self.trajectories = paracontext.map(
self.perturbation.generate,
[traj for traj in trajectories if traj.active])
#write the trajectories to the non-existing file fn_traj
if fn_traj is not None:
assert not os.path.isfile(fn_traj)
pickle.dump(self.trajectories, open(fn_traj, 'wb'))
log.dump('Trajectories stored to file %s' % fn_traj)
def do_hc_estimatefc(self, tasks, logger_level=3):
'''
Refine force constants using Hessian Cost function.
**Arguments**
tasks
A list of strings identifying which terms should have their
force constant estimated from the hessian cost function. Using
such a flag, one can distinguish between for example force
constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
force constant estimation of the cross terms (flag=HC_FC_CROSS).
If the string 'all' is present in tasks, all fc's will be
estimated.
**Optional Arguments**
logger_level
print level at which the resulting parameters should be dumped to
the logger. By default, the parameters will only be dumped at
the highest log level.
'''
with log.section('HCEST', 2, timer='HC Estimate FC'):
self.reset_system()
log.dump(
'Estimating force constants from Hessian cost for tasks %s' %
' '.join(tasks))
ffrefs = self.kwargs.get('ffrefs', [])
term_indices = []
for index in xrange(self.valence.vlist.nv):
term = self.valence.terms[index]
flagged = False
for flag in tasks:
if flag in term.tasks:
flagged = True
break
if flagged:
#first check if all rest values and multiplicities have been defined
if term.kind == 0: self.valence.check_params(term, ['rv'])
if term.kind == 1:
self.valence.check_params(term,
['a0', 'a1', 'a2', 'a3'])
if term.kind == 3:
self.valence.check_params(term, ['rv0', 'rv1'])
if term.kind == 4:
self.valence.check_params(term, ['rv', 'm'])
if term.is_master():
term_indices.append(index)
else:
#first check if all pars have been defined
if term.kind == 0:
self.valence.check_params(term, ['fc', 'rv'])
if term.kind == 1:
self.valence.check_params(term,
['a0', 'a1', 'a2', 'a3'])
if term.kind == 3:
self.valence.check_params(term, ['fc', 'rv0', 'rv1'])
if term.kind == 4:
self.valence.check_params(term, ['fc', 'rv', 'm'])
cost = HessianFCCost(self.system,
self.ai,
self.valence,
term_indices,
ffrefs=ffrefs)
fcs = cost.estimate()
for index, fc in zip(term_indices, fcs):
master = self.valence.terms[index]
assert master.is_master()
self.valence.set_params(index, fc=fc)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc)
self.valence.dump_logger(print_level=logger_level)
def do_hc_estimatefc(self,
tasks,
logger_level=3,
do_svd=False,
svd_rcond=0.0,
do_mass_weighting=True):
'''
Refine force constants using Hessian Cost function.
**Arguments**
tasks
A list of strings identifying which terms should have their
force constant estimated from the hessian cost function. Using
such a flag, one can distinguish between for example force
constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
force constant estimation of the cross terms (flag=HC_FC_CROSS).
If the string 'all' is present in tasks, all fc's will be
estimated.
**Optional Arguments**
logger_level
print level at which the resulting parameters should be dumped to
the logger. By default, the parameters will only be dumped at
the highest log level.
do_svd
whether or not to do an SVD decomposition before solving the
set of equations and explicitly throw out the degrees of
freedom that correspond to the lowest singular values.
do_mass_weighting
whether or not to apply mass weighing to the ab initio hessian
and the force field contributions before doing the fitting.
'''
with log.section('HCEST', 2, timer='HC Estimate FC'):
self.reset_system()
log.dump(
'Estimating force constants from Hessian cost for tasks %s' %
' '.join(tasks))
term_indices = []
for index in range(self.valence.vlist.nv):
term = self.valence.terms[index]
flagged = False
for flag in tasks:
if flag in term.tasks:
flagged = True
break
if flagged:
#first check if all rest values and multiplicities have been defined
if term.kind == 0: self.valence.check_params(term, ['rv'])
if term.kind == 1:
self.valence.check_params(term,
['a0', 'a1', 'a2', 'a3'])
if term.kind == 3:
self.valence.check_params(term, ['rv0', 'rv1'])
if term.kind == 4:
self.valence.check_params(term, ['rv', 'm'])
if term.is_master():
term_indices.append(index)
else:
#first check if all pars have been defined
if term.kind == 0:
self.valence.check_params(term, ['fc', 'rv'])
if term.kind == 1:
self.valence.check_params(term,
['a0', 'a1', 'a2', 'a3'])
if term.kind == 3:
self.valence.check_params(term, ['fc', 'rv0', 'rv1'])
if term.kind == 4:
self.valence.check_params(term, ['fc', 'rv', 'm'])
if len(term_indices) == 0:
log.dump(
'No terms (with task in %s) found to estimate FC from HC' %
(str(tasks)))
return
# Try to estimate force constants; if the remove_dysfunctional_cross
# keyword is True, a loop is performed which checks whether there
# are cross terms for which corresponding diagonal terms have zero
# force constants. If this is the case, those cross terms are removed
# from the fit and we try again until such cases do no longer occur
max_iter = 100
niter = 0
while niter < max_iter:
cost = HessianFCCost(self.system,
self.ai,
self.valence,
term_indices,
ffrefs=self.ffrefs,
do_mass_weighting=do_mass_weighting)
fcs = cost.estimate(do_svd=do_svd, svd_rcond=svd_rcond)
# No need to continue, if cross terms with corresponding diagonal
# terms with negative force constants are allowed
if self.settings.remove_dysfunctional_cross is False: break
to_remove = []
for index, fc in zip(term_indices, fcs):
term = self.valence.terms[index]
if term.basename.startswith('Cross'):
# Find force constants of corresponding diagonal terms
diag_fcs = np.zeros((2))
for idiag in range(2):
diag_index = term.diag_term_indexes[idiag]
if diag_index in term_indices:
fc_diag = fcs[term_indices.index(diag_index)]
else:
fc_diag = self.valence.get_params(diag_index,
only='fc')
diag_fcs[idiag] = fc_diag
# If a force constant from any corresponding diagonal term is negative,
# we remove the cross term for the next iteration
if np.any(diag_fcs <= 0.0):
to_remove.append(index)
self.valence.set_params(index, fc=0.0)
log.dump(
'WARNING! Dysfunctional cross term %s detected, removing from the hessian fit.'
% term.basename)
if len(to_remove) == 0: break
else:
for index in to_remove:
term_indices.remove(index)
niter += 1
assert niter < max_iter, "Could not remove all dysfunctional cross terms in %d iterations, something is seriously wrong" % max_iter
for index, fc in zip(term_indices, fcs):
master = self.valence.terms[index]
assert master.is_master()
self.valence.set_params(index, fc=fc)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc)
self.valence.dump_logger(print_level=logger_level)
def do_pt_generate(self):
'''
Generate perturbation trajectories.
'''
with log.section('PTGEN', 2, timer='PT Generate'):
#read if an existing file was specified through fn_traj
fn_traj = self.settings.fn_traj
if fn_traj is not None and os.path.isfile(fn_traj):
self.trajectories = pickle.load(open(fn_traj, 'rb'))
log.dump('Trajectories read from file %s' % fn_traj)
self.update_trajectory_terms()
newname = 'updated_' + fn_traj.split('/')[-1]
pickle.dump(self.trajectories, open(newname, 'wb'))
return
#configure
self.reset_system()
only = self.settings.only_traj
dont_traj = self.settings.dont_traj
if sum([only is None, dont_traj is None]) == 0:
raise AssertionError(
'The settings only_traj and dont_traj cannot be specified both'
)
if (only is None or only == 'PT_ALL' or only == 'pt_all'
) and dont_traj is None: # only=None is equivalent to PT_ALL
do_terms = [
term for term in self.valence.terms
if term.kind in [0, 2, 11, 12]
]
elif only is None and dont_traj is not None:
kind2string = {
0: 'bond',
2: 'bend',
11: 'oopdist',
12: 'dihedral'
}
ffatypes = [
self.system.ffatypes[fid]
for fid in self.system.ffatype_ids
]
dont_patterns = dont_traj.split(',') # split patterns
dont_terms = []
for term in self.valence.terms:
if term.kind in [0, 2, 11, 12]:
types = term.basename.split('/')[1].split('.')
option1 = '.'.join(types)
option2 = '.'.join(types[::-1])
for dp in dont_patterns:
pattern = re.compile(dp, re.IGNORECASE)
if pattern.match(option1) or pattern.match(
option2):
dont_terms.append(term)
do_terms = [
term for term in self.valence.terms
if term.kind in [0, 2, 11, 12] and term not in dont_terms
]
with log.section('PTNOT', 3):
for term in dont_terms:
log.dump(
'Taking AI equilibrium rest value instead of generating perturbation trajectory for %s'
% term.basename)
vterm = self.valence.vlist.vtab[term.index]
self.valence.set_params(term.index,
fc=0,
rv0=self.valence.iclist.ictab[
vterm['ic0']]['value'])
else:
if isinstance(only, str): only = [only]
do_terms = []
for pattern in only:
for term in self.valence.iter_terms(pattern):
if term.kind in [0, 2, 11, 12]:
do_terms.append(term)
trajectories = self.perturbation.prepare(do_terms)
#compute
log.dump('Constructing trajectories')
self.trajectories = paracontext.map(
self.perturbation.generate,
[traj for traj in trajectories if traj.active])
#write the trajectories to the non-existing file fn_traj
if fn_traj is not None:
assert not os.path.isfile(fn_traj)
pickle.dump(self.trajectories, open(fn_traj, 'wb'))
log.dump('Trajectories stored to file %s' % fn_traj)
def generate(self, trajectory, remove_com=True):
'''
Method to calculate the perturbation trajectory, i.e. the trajectory
that scans the geometry along the direction of the ic figuring in
the term with the given index (should be a diagonal term). This
method should be implemented in the derived classes.
**Arguments**
trajectory
instance of Trajectory class representing the perturbation
trajectory
**Optional Arguments**
remove_com
if set to True, removes the center of mass translation from the
resulting perturbation trajectories [default=True].
'''
#TODO: find out why system.cell is not parsed correctly when using scoop
#force correct rvecs
self.system0.cell.update_rvecs(self.system_rvecs)
with log.section('PTGEN', 4, timer='PT Generate'):
log.dump(' Generating %s(atoms=%s)' %(trajectory.term.basename, trajectory.term.get_atoms()))
strain = Strain(self.system, trajectory.term, self.valence.terms)
natom = self.system0.natom
q0 = self.valence.iclist.ictab[self.valence.vlist.vtab[trajectory.term.index]['ic0']]['value']
diag = np.array([0.1*angstrom,]*3*natom+[abs(q0-trajectory.targets[0])])
sol = None
for iq, target in enumerate(trajectory.targets):
log.dump(' Frame %i (target=%.3f)' %(iq, target))
strain.constrain_target = target
if abs(target-q0)<1e-6:
sol = np.zeros([strain.ndof+1],float)
#call strain.gradient once to compute/store/log relevant information
strain.gradient(sol)
else:
if sol is not None:
init = sol.copy()
else:
init = np.zeros([3*natom+1], float)
init[-1] = np.sign(q0-target)
sol, infodict, ier, mesg = scipy.optimize.fsolve(strain.gradient, init, xtol=self.settings.pert_traj_tol, full_output=True, diag=diag)
if ier!=1:
#fsolve did not converge, try again after adding small random noise
log.dump(' %s' %mesg.replace('\n', ' '))
log.dump(' Frame %i (target=%.3f) %s(%s) did not converge. Trying again with slightly perturbed initial conditions.' %(
iq, target, trajectory.term.basename, trajectory.term.get_atoms()
))
#try one more time
init = sol.copy()
init[:3*natom] += np.random.normal(0.0, 0.01, [3*natom])*angstrom
sol, infodict, ier, mesg = scipy.optimize.fsolve(strain.gradient, init, xtol=self.settings.pert_traj_tol, full_output=True, diag=diag)
#fsolve did STILL not converge, flag this frame for deletion
if ier!=1:
log.dump(' %s' %mesg.replace('\n', ' '))
log.dump(' Frame %i (target=%.3f) %s(%s) STILL did not converge.' %(
iq, target, trajectory.term.basename, trajectory.term.get_atoms()
))
trajectory.targets[iq] = np.nan
continue
x = self.system0.pos.copy() + sol[:3*natom].reshape((-1,3))
trajectory.values[iq] = strain.constrain_value
log.dump(' Converged (value=%.3f, lagmult=%.3e)' %(strain.constrain_value,sol[3*natom]))
if remove_com:
com = (x.T*self.system0.masses.copy()).sum(axis=1)/self.system0.masses.sum()
for i in range(natom):
x[i,:] -= com
trajectory.coords[iq,:,:] = x
#delete flagged frames
targets = []
values = []
coords = []
for target, value, coord in zip(trajectory.targets, trajectory.values, trajectory.coords):
if not np.isnan(target):
targets.append(target)
values.append(value)
coords.append(coord)
trajectory.targets = np.array(targets)
trajectory.values = np.array(values)
trajectory.coords = np.array(coords)
return trajectory
def project_negative_freqs(hessian, masses, thresshold=0.0):
N = len(masses)
sqrt_mass_matrix = np.diag(
np.sqrt((np.array([masses, masses, masses]).T).ravel()))
isqrt_mass_matrix = np.linalg.inv(sqrt_mass_matrix)
matrix = np.dot(isqrt_mass_matrix,
np.dot(hessian.reshape([3 * N, 3 * N]), isqrt_mass_matrix))
#diagonalize
if ((matrix - matrix.T) < 1e-6 * lightspeed / centimeter).all():
evals, evecs = np.linalg.eigh(matrix)
else:
evals, evecs = np.linalg.eig(matrix)
log.dump('20 lowest frequencies [1/cm] before projection:')
log.dump(str(evals[:4] / (lightspeed / centimeter)))
log.dump(str(evals[4:8] / (lightspeed / centimeter)))
log.dump(str(evals[8:12] / (lightspeed / centimeter)))
log.dump(str(evals[12:16] / (lightspeed / centimeter)))
log.dump(str(evals[16:20] / (lightspeed / centimeter)))
#set negative eigenvalues to zero
evals[evals < thresshold] = 0.0
projected_matrix = np.dot(evecs, np.dot(np.diag(evals), evecs.T))
projected_hessian = np.dot(sqrt_mass_matrix,
np.dot(projected_matrix, sqrt_mass_matrix))
#dump freqs after projection as check
evals, evecs = np.linalg.eigh(projected_matrix)
log.dump('20 lowest frequencies [1/cm] after projection:')
log.dump(str(evals[:4] / (lightspeed / centimeter)))
log.dump(str(evals[4:8] / (lightspeed / centimeter)))
log.dump(str(evals[8:12] / (lightspeed / centimeter)))
log.dump(str(evals[12:16] / (lightspeed / centimeter)))
log.dump(str(evals[16:20] / (lightspeed / centimeter)))
return projected_hessian.reshape([N, 3, N, 3])
def generate(self, trajectory, remove_com=True):
'''
Method to calculate the perturbation trajectory, i.e. the trajectory
that scans the geometry along the direction of the ic figuring in
the term with the given index (should be a diagonal term). This
method should be implemented in the derived classes.
**Arguments**
trajectory
instance of Trajectory class representing the perturbation
trajectory
**Optional Arguments**
remove_com
if set to True, removes the center of mass translation from the
resulting perturbation trajectories [default=True].
'''
index = trajectory.term.index
with log.section('PTGEN', 3, timer='PT Generate'):
log.dump(' Generating %s(atoms=%s)' %
(self.valence.terms[index].basename,
trajectory.term.get_atoms()))
strain = self.strains[index]
natom = self.system.natom
if strain is None:
log.warning(
'Strain for term %i (%s) is not initialized, skipping.' %
(index, self.valence.terms[index].basename))
return
q0 = self.valence.iclist.ictab[self.valence.vlist.vtab[index]
['ic0']]['value']
diag = np.ones([strain.ndof + 1], float)
diag[:strain.ndof] *= 0.1 * angstrom
diag[strain.ndof] *= abs(q0 - trajectory.targets[0])
sol = None
for iq, target in enumerate(trajectory.targets):
log.dump(' Frame %i (target=%.3f)' % (iq, target))
strain.constrain_target = target
if abs(target - q0) < 1e-6:
sol = np.zeros([strain.ndof + 1], float)
strain.gradient(sol)
else:
if sol is not None:
init = sol.copy()
else:
init = np.zeros([strain.ndof + 1], float)
init[-1] = np.sign(q0 - target)
sol, infodict, ier, mesg = scipy.optimize.fsolve(
strain.gradient,
init,
xtol=1e-3,
full_output=True,
diag=diag)
if ier != 1:
#fsolve did not converge, flag this frame for deletion
log.dump(' %s' % mesg.replace('\n', ' '))
log.dump(
' Frame %i (target=%.3f) %s(%s) did not converge. Trying again with slightly perturbed initial conditions.'
% (iq, target, self.valence.terms[index].basename,
trajectory.term.get_atoms()))
#try one more time
init = sol.copy()
init[:3 * natom] += np.random.normal(
0.0, 0.01, [3 * natom]) * angstrom
sol, infodict, ier, mesg = scipy.optimize.fsolve(
strain.gradient,
init,
xtol=1e-3,
full_output=True,
diag=diag)
if ier != 1:
log.dump(' %s' % mesg.replace('\n', ' '))
log.dump(
' Frame %i (target=%.3f) %s(%s) STILL did not converge.'
% (iq, target,
self.valence.terms[index].basename,
trajectory.term.get_atoms()))
trajectory.targets[iq] = np.nan
continue
x = strain.coords0 + sol[:3 * natom].reshape((-1, 3))
trajectory.values[iq] = strain.constrain_value
log.dump(' Converged (value=%.3f, lagmult=%.3e)' %
(strain.constrain_value, sol[3 * natom]))
if remove_com:
com = (x.T * self.system.masses).sum(
axis=1) / self.system.masses.sum()
for i in xrange(natom):
x[i, :] -= com
trajectory.coords[iq, :, :] = x
#delete flagged frames
targets = []
values = []
coords = []
for target, value, coord in zip(trajectory.targets,
trajectory.values,
trajectory.coords):
if not np.isnan(target):
targets.append(target)
values.append(value)
coords.append(coord)
trajectory.targets = np.array(targets)
trajectory.values = np.array(values)
trajectory.coords = np.array(coords)
return trajectory
def generate(self, trajectory, remove_com=True):
'''
Method to calculate the perturbation trajectory, i.e. the trajectory
that scans the geometry along the direction of the ic figuring in
the term with the given index (should be a diagonal term). This
method should be implemented in the derived classes.
**Arguments**
trajectory
instance of Trajectory class representing the perturbation
trajectory
**Optional Arguments**
remove_com
if set to True, removes the center of mass translation from the
resulting perturbation trajectories [default=True].
'''
#TODO: find out why system.cell is not parsed correctly when using scoop
#force correct rvecs
self.system0.cell.update_rvecs(self.system_rvecs)
with log.section('PTGEN', 4, timer='PT Generate'):
log.dump(' Generating %s(atoms=%s)' %(trajectory.term.basename, trajectory.term.get_atoms()))
strain = Strain(self.system, trajectory.term, self.valence.terms)
natom = self.system0.natom
q0 = self.valence.iclist.ictab[self.valence.vlist.vtab[trajectory.term.index]['ic0']]['value']
diag = np.array([0.1*angstrom,]*3*natom+[abs(q0-trajectory.targets[0])])
sol = None
for iq, target in enumerate(trajectory.targets):
log.dump(' Frame %i (target=%.3f)' %(iq, target))
strain.constrain_target = target
if abs(target-q0)<1e-6:
sol = np.zeros([strain.ndof+1],float)
#call strain.gradient once to compute/store/log relevant information
strain.gradient(sol)
else:
if sol is not None:
init = sol.copy()
else:
init = np.zeros([3*natom+1], float)
init[-1] = np.sign(q0-target)
sol, infodict, ier, mesg = scipy.optimize.fsolve(strain.gradient, init, xtol=self.settings.pert_traj_tol, full_output=True, diag=diag)
if ier!=1:
#fsolve did not converge, try again after adding small random noise
log.dump(' %s' %mesg.replace('\n', ' '))
log.dump(' Frame %i (target=%.3f) %s(%s) did not converge. Trying again with slightly perturbed initial conditions.' %(
iq, target, trajectory.term.basename, trajectory.term.get_atoms()
))
#try one more time
init = sol.copy()
init[:3*natom] += np.random.normal(0.0, 0.01, [3*natom])*angstrom
sol, infodict, ier, mesg = scipy.optimize.fsolve(strain.gradient, init, xtol=self.settings.pert_traj_tol, full_output=True, diag=diag)
#fsolve did STILL not converge, flag this frame for deletion
if ier!=1:
log.dump(' %s' %mesg.replace('\n', ' '))
log.dump(' Frame %i (target=%.3f) %s(%s) STILL did not converge.' %(
iq, target, trajectory.term.basename, trajectory.term.get_atoms()
))
trajectory.targets[iq] = np.nan
continue
x = self.system0.pos.copy() + sol[:3*natom].reshape((-1,3))
trajectory.values[iq] = strain.constrain_value
log.dump(' Converged (value=%.3f, lagmult=%.3e)' %(strain.constrain_value,sol[3*natom]))
if remove_com:
com = (x.T*self.system0.masses.copy()).sum(axis=1)/self.system0.masses.sum()
for i in range(natom):
x[i,:] -= com
trajectory.coords[iq,:,:] = x
#delete flagged frames
targets = []
values = []
coords = []
for target, value, coord in zip(trajectory.targets, trajectory.values, trajectory.coords):
if not np.isnan(target):
targets.append(target)
values.append(value)
coords.append(coord)
trajectory.targets = np.array(targets)
trajectory.values = np.array(values)
trajectory.coords = np.array(coords)
return trajectory
def init_oop_terms(self, thresshold_zero=5e-2 * angstrom):
'''
Initialize all out-of-plane terms in the system based on the oops
attribute of the system instance. All oops are given harmonic
potentials.
'''
with log.section('VAL', 3, 'Initializing'):
#get all dihedrals
from molmod.ic import opbend_dist, _opdist_low
ffatypes = [
self.system.ffatypes[fid] for fid in self.system.ffatype_ids
]
opdists = {}
for opdist in self.system.iter_oops():
opdist, types = term_sort_atypes(ffatypes, opdist, 'opdist')
if types in opdists.keys():
opdists[types].append(opdist)
else:
opdists[types] = [opdist]
#loop over all distinct opdist types
nharm = 0
nsq = 0
for types, oops in opdists.iteritems():
d0s = np.zeros(len(oops), float)
for i, oop in enumerate(oops):
if self.system.cell.nvec > 0:
d01 = self.system.pos[oop[1]] - self.system.pos[oop[0]]
d02 = self.system.pos[oop[2]] - self.system.pos[oop[0]]
d03 = self.system.pos[oop[3]] - self.system.pos[oop[0]]
self.system.cell.mic(d01)
self.system.cell.mic(d02)
self.system.cell.mic(d03)
d0s[i] = abs(_opdist_low(d01, d02, d03, 0)[0])
else:
rs = np.array(
[ #mind the order, is(or was) wrongly documented in molmod
self.system.pos[oop[0]],
self.system.pos[oop[1]],
self.system.pos[oop[2]],
self.system.pos[oop[3]],
])
d0s[i] = abs(opbend_dist(rs)[0])
if d0s.mean() < thresshold_zero: #TODO: check this thresshold
#add regular term harmonic in oopdist
for oop in oops:
term = self.add_term(Harmonic, [OopDist(*oop)], types,
['HC_FC_DIAG'],
['kjmol/A**2', 'A'])
self.set_params(term.index, rv0=0.0)
nharm += 1
else:
#add term harmonic in square of oopdist
log.dump(
'Mean absolute value of OopDist %s is %.3e A, used SQOOPDIST'
% ('.'.join(types), d0s.mean() / angstrom))
for oop in oops:
self.add_term(Harmonic, [SqOopDist(*oop)], types,
['PT_ALL', 'HC_FC_DIAG'],
['kjmol/A**4', 'A**2'])
nsq += 1
log.dump(
'Added %i Harmonic and %i SquareHarmonic out-of-plane distance terms'
% (nharm, nsq))
def do_hc_estimatefc(self, tasks, logger_level=3, do_svd=False, svd_rcond=0.0, do_mass_weighting=True):
'''
Refine force constants using Hessian Cost function.
**Arguments**
tasks
A list of strings identifying which terms should have their
force constant estimated from the hessian cost function. Using
such a flag, one can distinguish between for example force
constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
force constant estimation of the cross terms (flag=HC_FC_CROSS).
If the string 'all' is present in tasks, all fc's will be
estimated.
**Optional Arguments**
logger_level
print level at which the resulting parameters should be dumped to
the logger. By default, the parameters will only be dumped at
the highest log level.
do_svd
whether or not to do an SVD decomposition before solving the
set of equations and explicitly throw out the degrees of
freedom that correspond to the lowest singular values.
do_mass_weighting
whether or not to apply mass weighing to the ab initio hessian
and the force field contributions before doing the fitting.
'''
with log.section('HCEST', 2, timer='HC Estimate FC'):
self.reset_system()
log.dump('Estimating force constants from Hessian cost for tasks %s' %' '.join(tasks))
term_indices = []
for index in range(self.valence.vlist.nv):
term = self.valence.terms[index]
flagged = False
for flag in tasks:
if flag in term.tasks:
flagged = True
break
if flagged:
#first check if all rest values and multiplicities have been defined
if term.kind==0: self.valence.check_params(term, ['rv'])
if term.kind==1: self.valence.check_params(term, ['a0', 'a1', 'a2', 'a3'])
if term.kind==3: self.valence.check_params(term, ['rv0','rv1'])
if term.kind==4: self.valence.check_params(term, ['rv', 'm'])
if term.is_master():
term_indices.append(index)
else:
#first check if all pars have been defined
if term.kind==0: self.valence.check_params(term, ['fc', 'rv'])
if term.kind==1: self.valence.check_params(term, ['a0', 'a1', 'a2', 'a3'])
if term.kind==3: self.valence.check_params(term, ['fc', 'rv0','rv1'])
if term.kind==4: self.valence.check_params(term, ['fc', 'rv', 'm'])
if len(term_indices)==0:
log.dump('No terms (with task in %s) found to estimate FC from HC' %(str(tasks)))
return
# Try to estimate force constants; if the remove_dysfunctional_cross
# keyword is True, a loop is performed which checks whether there
# are cross terms for which corresponding diagonal terms have zero
# force constants. If this is the case, those cross terms are removed
# from the fit and we try again until such cases do no longer occur
max_iter = 100
niter = 0
while niter<max_iter:
cost = HessianFCCost(self.system, self.ai, self.valence, term_indices, ffrefs=self.ffrefs, do_mass_weighting=do_mass_weighting)
fcs = cost.estimate(do_svd=do_svd, svd_rcond=svd_rcond)
# No need to continue, if cross terms with corresponding diagonal
# terms with negative force constants are allowed
if self.settings.remove_dysfunctional_cross is False: break
to_remove = []
for index, fc in zip(term_indices, fcs):
term = self.valence.terms[index]
if term.basename.startswith('Cross'):
# Find force constants of corresponding diagonal terms
diag_fcs = np.zeros((2))
for idiag in range(2):
diag_index = term.diag_term_indexes[idiag]
if diag_index in term_indices:
fc_diag = fcs[term_indices.index(diag_index)]
else:
fc_diag = self.valence.get_params(diag_index, only='fc')
diag_fcs[idiag] = fc_diag
# If a force constant from any corresponding diagonal term is negative,
# we remove the cross term for the next iteration
if np.any(diag_fcs<=0.0):
to_remove.append(index)
self.valence.set_params(index, fc=0.0)
log.dump('WARNING! Dysfunctional cross term %s detected, removing from the hessian fit.'%term.basename)
if len(to_remove)==0: break
else:
for index in to_remove:
term_indices.remove(index)
niter += 1
assert niter<max_iter, "Could not remove all dysfunctional cross terms in %d iterations, something is seriously wrong"%max_iter
for index, fc in zip(term_indices, fcs):
master = self.valence.terms[index]
assert master.is_master()
self.valence.set_params(index, fc=fc)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc)
self.valence.dump_logger(print_level=logger_level)
def dump_log(self):
sorted_keys = sorted(self.__dict__.keys())
with log.section('SETT', 3):
for key in sorted_keys:
value = str(self.__dict__[key])
log.dump('%s %s' %(key+' '*(30-len(key)), value))
def do_hc_estimatefc(self,
tasks,
logger_level=3,
do_svd=False,
do_mass_weighting=True):
'''
Refine force constants using Hessian Cost function.
**Arguments**
tasks
A list of strings identifying which terms should have their
force constant estimated from the hessian cost function. Using
such a flag, one can distinguish between for example force
constant refinement (flag=HC_FC_DIAG) of the diagonal terms and
force constant estimation of the cross terms (flag=HC_FC_CROSS).
If the string 'all' is present in tasks, all fc's will be
estimated.
**Optional Arguments**
logger_level
print level at which the resulting parameters should be dumped to
the logger. By default, the parameters will only be dumped at
the highest log level.
do_svd
whether or not to do an SVD decomposition before solving the
set of equations and explicitly throw out the degrees of
freedom that correspond to the lowest singular values.
do_mass_weighting
whether or not to apply mass weighing to the ab initio hessian
and the force field contributions before doing the fitting.
'''
with log.section('HCEST', 2, timer='HC Estimate FC'):
self.reset_system()
log.dump(
'Estimating force constants from Hessian cost for tasks %s' %
' '.join(tasks))
term_indices = []
for index in range(self.valence.vlist.nv):
term = self.valence.terms[index]
flagged = False
for flag in tasks:
if flag in term.tasks:
flagged = True
break
if flagged:
#first check if all rest values and multiplicities have been defined
if term.kind == 0: self.valence.check_params(term, ['rv'])
if term.kind == 1:
self.valence.check_params(term,
['a0', 'a1', 'a2', 'a3'])
if term.kind == 3:
self.valence.check_params(term, ['rv0', 'rv1'])
if term.kind == 4:
self.valence.check_params(term, ['rv', 'm'])
if term.is_master():
term_indices.append(index)
else:
#first check if all pars have been defined
if term.kind == 0:
self.valence.check_params(term, ['fc', 'rv'])
if term.kind == 1:
self.valence.check_params(term,
['a0', 'a1', 'a2', 'a3'])
if term.kind == 3:
self.valence.check_params(term, ['fc', 'rv0', 'rv1'])
if term.kind == 4:
self.valence.check_params(term, ['fc', 'rv', 'm'])
if len(term_indices) == 0:
log.dump(
'No terms (with task in %s) found to estimate FC from HC' %
(str(tasks)))
return
cost = HessianFCCost(self.system,
self.ai,
self.valence,
term_indices,
ffrefs=self.ffrefs,
do_mass_weighting=do_mass_weighting)
fcs = cost.estimate(do_svd=do_svd)
for index, fc in zip(term_indices, fcs):
master = self.valence.terms[index]
assert master.is_master()
self.valence.set_params(index, fc=fc)
for islave in master.slaves:
self.valence.set_params(islave, fc=fc)
self.valence.dump_logger(print_level=logger_level)
def __init__(self, system, ai, **kwargs):
'''
**Arguments**
system
a Yaff `System` object defining the system
ai
a `Reference` instance corresponding to the ab initio input data
**Keyword Arguments**
ffrefs
a list of `Reference` objects corresponding to a priori determined
contributions to the force field (such as eg. electrostatics
or van der Waals contributions)
fn_yaff
the name of the file to write the final parameters to in Yaff
format. The default is `pars.txt`.
fn_charmm22_prm
the name of a CHARMM parameter file. If not given, the file is not written
fn_charmm22_psf
the name of a CHARMM topology file. If not given, the file is not written
fn_sys
the name of the file to write the system to. The default is
`system.chk`.
fn_traj
a cPickle filename to read/write the perturbation trajectories
from/to. If the file exists, the trajectories are read from the
file. If the file does not exist, the trajectories are written
to the file.
only_traj
specifier to determine for which terms a perturbation trajectory
needs to be constructed. If ONLY_TRAJ is a single string, it is
interpreted as a task (only terms that have this task in their
tasks attribute will get a trajectory). If ONLY_TRAJ is a list
of strings, each string is interpreted as the basename of the
term for which a trajectory will be constructed.
plot_traj
if set to True, all energy contributions along each perturbation
trajectory will be plotted using the final force field.
xyz_traj
if set to True, each perturbation trajectory will be written to
an XYZ file.
'''
with log.section('PROG', 2, timer='Initializing'):
log.dump('Initializing program')
self.system = system
self.ai = ai
self.kwargs = kwargs
self.valence = ValenceFF(system)
self.perturbation = RelaxedStrain(system, self.valence)
self.trajectories = None
def dump_log(self):
sorted_keys = sorted(self.__dict__.keys())
with log.section('SETT', 3):
for key in sorted_keys:
value = str(self.__dict__[key])
log.dump('%s %s' %(key+' '*(30-len(key)), value))
def init_dihedral_terms(self, thresshold=20 * deg):
'''
Initialize the dihedral potentials from the local topology. The
dihedral potential will be one of the two following possibilities:
The multiplicity m is determined from the local topology, i.e.
the number of neighbors of the central two atoms in the dihedral
If the equilibrium value of all instances of the torsion are
within `thresshold` of 0 deg or per/2 with per = 180deg/m,
the following potential will be chosen:
0.5*K*(1-cos(m*psi-m*psi0)) with psi0 = 0 or 360/(2*m)
'''
with log.section('VAL', 3, 'Initializing'):
#get all dihedrals
from molmod.ic import dihed_angle, _dihed_angle_low
ffatypes = [
self.system.ffatypes[fid] for fid in self.system.ffatype_ids
]
dihedrals = {}
for dihedral in self.system.iter_dihedrals():
dihedral, types = term_sort_atypes(ffatypes, dihedral,
'dihedral')
if types in dihedrals.keys():
dihedrals[types].append(dihedral)
else:
dihedrals[types] = [dihedral]
#loop over all distinct dihedral types
ncos = 0
for types, diheds in dihedrals.iteritems():
psi0s = np.zeros(len(diheds), float)
ms = np.zeros(len(diheds), float)
for i, dihed in enumerate(diheds):
if self.system.cell.nvec > 0:
d10 = self.system.pos[dihed[0]] - self.system.pos[
dihed[1]]
d12 = self.system.pos[dihed[2]] - self.system.pos[
dihed[1]]
d23 = self.system.pos[dihed[3]] - self.system.pos[
dihed[2]]
self.system.cell.mic(d10)
self.system.cell.mic(d12)
self.system.cell.mic(d23)
psi0s[i] = _dihed_angle_low(d10, d12, d23, 0)[0]
else:
rs = np.array([self.system.pos[j] for j in dihed])
psi0s[i] = dihed_angle(rs)[0]
n1 = len(self.system.neighs1[dihed[1]])
n2 = len(self.system.neighs1[dihed[2]])
ms[i] = get_multiplicity(n1, n2)
nan = False
for m in ms:
if np.isnan(m): nan = True
if nan or None in ms or ms.std() > 1e-3:
ms_string = str(ms)
if nan: ms_string = 'nan'
log.warning('missing dihedral for %s (m is %s)' %
('.'.join(types), ms_string))
continue
m = int(np.round(ms.mean()))
rv = get_restvalue(psi0s, m, thresshold=thresshold, mode=1)
if rv is not None:
#a regular Cosine term is used for the dihedral potential
for dihed in diheds:
term = self.add_term(Cosine, [DihedAngle(*dihed)],
types, ['HC_FC_DIAG'],
['au', 'kjmol', 'deg'])
self.set_params(term.index, rv0=rv, m=m)
ncos += 1
else:
#no dihedral potential could be determine, hence it is ignored
log.warning(
'missing dihedral for %s (could not determine rest value from %s)'
% ('.'.join(types), str(psi0s / deg)))
continue
log.dump('Added %i Cosine dihedral terms' % ncos)
def estimate(self, trajectory, ai, ffrefs=[], do_valence=False, energy_noise=None, Nerrorsteps=100):
'''
Method to estimate the FF parameters for the relevant ic from the
given perturbation trajectory by fitting a harmonic potential to the
covalent energy along the trajectory.
**Arguments**
trajectory
a Trajectory instance representing the perturbation trajectory
ai
an instance of the Reference representing the ab initio input
**Optional Arguments**
ffrefs
a list of Reference instances representing possible a priori
determined contributions to the force field (such as eg.
electrostatics and van der Waals)
do_valence
If set to True, the current valence force field (stored in
self.valence) will be used to compute the valence contribution
energy_noise
If set to a float, the parabolic fitting will be repeated
Nerrorsteps times including normal noise on top of the reference
value. The mean of the noise is 0, while the std equals the
number given by energy_noise. The resulting fits give a
distribution of force constants and rest values instead of
single value, the std is used to identify bad estimates, the
mean is used for the actual FF parametrs. If set to nan, the
parabolic fit is performed only once without any noise.
'''
with log.section('PTEST', 3, timer='PT Estimate'):
term = trajectory.term
index = term.index
basename = term.basename
if 'active' in list(trajectory.__dict__.keys()) and not trajectory.active:
log.dump('Trajectory of %s was deactivated: skipping' %(basename))
return
qs = trajectory.values.copy()
AIs = np.zeros(len(trajectory.coords))
FFs = np.zeros(len(trajectory.coords))
RESs = np.zeros(len(trajectory.coords))
for istep, pos in enumerate(trajectory.coords):
AIs[istep] = ai.energy(pos)
for ref in ffrefs:
FFs[istep] += ref.energy(pos)
if do_valence:
fc = self.valence.get_params(index, only='fc')
rv = self.valence.get_params(index, only='rv')
self.valence.set_params(index, fc=0.0)
self.valence.set_params(index, rv0=0.0)
for istep, pos in enumerate(trajectory.coords):
RESs[istep] += self.valence.calc_energy(pos) #- 0.5*fc*(qs[istep]-rv)**2
self.valence.set_params(index, fc=fc)
self.valence.set_params(index, rv0=rv)
pars = fitpar(qs, AIs-FFs-RESs-min(AIs-FFs-RESs), rcond=-1)
if energy_noise is None:
if pars[0]>0.0: #!=0.0:
trajectory.fc = 2.0*pars[0]
trajectory.rv = -pars[1]/(2.0*pars[0])
else:
trajectory.fc = 0.0
trajectory.rv = qs[len(qs)//2]
log.dump('force constant of %s is not positive: force constant set to zero and rest value set to ab initio equilibrium' %basename)
else:
with log.section('PTEST', 4, timer='PT Estimate'):
log.dump('Performing noise analysis for trajectory of %s' %basename)
As = [pars[0]]
Bs = [pars[1]]
for i in range(Nerrorsteps):
pars = fitpar(qs, AIs-FFs-RESs-min(AIs-FFs-RESs)+np.random.normal(0.0, energy_noise, size=AIs.shape), rcond=-1)
As.append(pars[0])
Bs.append(pars[1])
if 0.0 in As:
log.dump(' force constant of zero detected, removing the relevant runs from analysis')
Bs = np.array([b for a,b in zip(As,Bs) if a!=0.0])
As = np.array([a for a in As if a!=0.0])
ks = As*2.0
q0s = -Bs/(2.0*As)
kunit = trajectory.term.units[0]
qunit = trajectory.term.units[1]
log.dump(' k = %8.3f +- %6.3f (noisefree: %8.3f) %s' %(ks.mean()/parse_unit(kunit), ks.std()/parse_unit(kunit), ks[0]/parse_unit(kunit), kunit))
log.dump(' q0 = %8.3f +- %6.3f (noisefree: %8.3f) %s' %(q0s.mean()/parse_unit(qunit), q0s.std()/parse_unit(qunit), q0s[0]/parse_unit(qunit), qunit))
if q0s.std()/q0s.mean()>0.01:
with log.section('PTEST', 3, timer='PT Estimate'):
fc, rv = self.valence.get_params(trajectory.term.index)
if rv is None:
log.dump('Noise on rest value of %s to high, using ab initio rest value' %basename)
pars = fitpar(qs, AIs-FFs-RESs-min(AIs-FFs-RESs)+np.random.normal(0.0, energy_noise, size=AIs.shape), rcond=-1)
if pars[0]!=0.0:
trajectory.fc = 2.0*pars[0]
trajectory.rv = -pars[1]/(2.0*pars[0])
else:
trajectory.fc = 0.0
trajectory.rv = qs[len(qs)//2]
log.dump('AI force constant of %s is zero: rest value set to middle value' %basename)
else:
log.dump('Noise on rest value of %s to high, using previous value' %basename)
trajectory.fc = fc
trajectory.rv = rv
else:
trajectory.fc = ks.mean()
trajectory.rv = q0s.mean()
#no negative rest values for all ics except dihedrals and bendcos
if term.ics[0].kind not in [1,3,4,11]:
if trajectory.rv<0:
trajectory.rv = 0.0
log.dump('rest value of %s was negative: set to zero' %basename)
def main():
options, fns = parse()
#define logger
if options.silent:
log.set_level('silent')
else:
if options.very_verbose:
log.set_level('highest')
elif options.verbose:
log.set_level('high')
if options.logfile is not None and isinstance(options.logfile, str):
log.write_to_file(options.logfile)
with log.section('QFF', 1, timer='Initializing'):
log.dump('Initializing system')
#read system and ab initio reference
system = None
energy = 0.0
grad = None
hess = None
rvecs = None
for fn in fns:
if fn.endswith('.fchk') or fn.endswith('.xml'):
numbers, coords, energy, grad, hess, masses, rvecs, pbc = read_abinitio(
fn)
if system is None:
system = System(numbers,
coords,
rvecs=rvecs,
charges=None,
radii=None,
masses=masses)
else:
system.pos = coords.copy()
system.cell = Cell(rvecs)
system.numbers = numbers.copy()
if masses is not None: system.masses = masses.copy()
system._init_derived()
elif fn.endswith('.chk'):
sample = load_chk(fn)
if 'energy' in sample.keys(): energy = sample['energy']
if 'grad' in sample.keys(): grad = sample['grad']
elif 'gradient' in sample.keys(): grad = sample['gradient']
if 'hess' in sample.keys(): hess = sample['hess']
elif 'hessian' in sample.keys(): hess = sample['hessian']
if system is None:
system = System.from_file(fn)
else:
if 'pos' in sample.keys(): system.pos = sample['pos']
elif 'coords' in sample.keys():
system.pos = sample['coords']
if 'rvecs' in sample.keys():
system.cell = Cell(sample['rvecs'])
elif 'cell' in sample.keys():
system.cell = Cell(sample['cell'])
if 'bonds' in sample.keys(): system.bonds = sample['bonds']
if 'ffatypes' in sample.keys():
system.ffatypes = sample['ffatypes']
if 'ffatype_ids' in sample.keys():
system.ffatype_ids = sample['ffatype_ids']
system._init_derived()
else:
raise NotImplementedError('File format for %s not supported' %
fn)
assert system is not None, 'No system could be defined from input'
assert grad is not None, 'No ab initio gradient found in input'
assert hess is not None, 'No ab initio hessian found in input'
#complete the system information
if system.bonds is None: system.detect_bonds()
if system.masses is None: system.set_standard_masses()
if system.ffatypes is None:
if options.ffatypes in ['low', 'medium', 'high', 'highest']:
guess_ffatypes(system, options.ffatypes)
elif options.ffatypes is not None:
raise NotImplementedError(
'Guessing atom types from %s not implemented' %
options.ffatypes)
else:
raise AssertionError('No atom types defined')
#construct ab initio reference
ai = SecondOrderTaylor('ai',
coords=system.pos.copy(),
energy=energy,
grad=grad,
hess=hess,
pbc=pbc)
#detect a priori defined contributions to the force field
refs = []
if options.ei is not None:
if rvecs is None:
ff = ForceField.generate(system,
options.ei,
rcut=50 * angstrom)
else:
ff = ForceField.generate(system,
options.ei,
rcut=20 * angstrom,
alpha_scale=3.2,
gcut_scale=1.5,
smooth_ei=True)
refs.append(YaffForceField('EI', ff))
if options.vdw is not None:
ff = ForceField.generate(system, options.vdw, rcut=20 * angstrom)
refs.append(YaffForceField('vdW', ff))
if options.covres is not None:
ff = ForceField.generate(system, options.covres)
refs.append(YaffForceField('Cov res', ff))
#define quickff program
assert options.program_mode in allowed_programs, \
'Given program mode %s not allowed. Choose one of %s' %(
options.program_mode,
', '.join([prog for prog in allowed_programs if not prog=='BaseProgram'])
)
mode = program_modes[options.program_mode]
only_traj = 'PT_ALL'
if options.only_traj is not None: only_traj = options.only_traj.split(',')
program = mode(system,
ai,
ffrefs=refs,
fn_traj=options.fn_traj,
only_traj=only_traj,
plot_traj=options.ener_traj,
xyz_traj=options.xyz_traj,
suffix=options.suffix)
#run program
program.run()
def estimate(self, trajectory, ai, ffrefs=[], do_valence=False):
'''
Method to estimate the FF parameters for the relevant ic from the
given perturbation trajectory by fitting a harmonic potential to the
covalent energy along the trajectory.
**Arguments**
trajectory
a Trajectory instance representing the perturbation trajectory
ai
an instance of the Reference representing the ab initio input
**Optional Arguments**
ffrefs
a list of Reference instances representing possible a priori
determined contributions to the force field (such as eg.
electrostatics and van der Waals)
do_valence
If set to True, the current valence force field (stored in
self.valence) will be used to compute the valence contribution
'''
with log.section('PTEST', 3, timer='PT Estimate'):
term = trajectory.term
index = term.index
basename = term.basename
if 'active' in trajectory.__dict__.keys(
) and not trajectory.active:
log.dump('Trajectory of %s was deactivated: skipping' %
(basename))
return
qs = trajectory.values.copy()
AIs = np.zeros(len(trajectory.coords))
FFs = np.zeros(len(trajectory.coords))
RESs = np.zeros(len(trajectory.coords))
for istep, pos in enumerate(trajectory.coords):
AIs[istep] = ai.energy(pos)
for ref in ffrefs:
FFs[istep] += ref.energy(pos)
if do_valence:
fc = self.valence.get_params(index, only='fc')
rv = self.valence.get_params(index, only='rv')
self.valence.set_params(index, fc=0.0)
self.valence.set_params(index, rv0=0.0)
for istep, pos in enumerate(trajectory.coords):
RESs[istep] += self.valence.calc_energy(
pos) #- 0.5*fc*(qs[istep]-rv)**2
self.valence.set_params(index, fc=fc)
self.valence.set_params(index, rv0=rv)
pars = fitpar(qs,
AIs - FFs - RESs - min(AIs - FFs - RESs),
rcond=-1)
if pars[0] != 0.0:
trajectory.fc = 2.0 * pars[0]
trajectory.rv = -pars[1] / (2.0 * pars[0])
else:
trajectory.fc = 0.0
trajectory.rv = qs[len(qs) / 2]
log.dump(
'force constant of %s is zero: rest value set to middle value'
% basename)
#no negative rest values for all ics except dihedrals and bendcos
if term.ics[0].kind not in [1, 3, 4, 11]:
if trajectory.rv < 0:
trajectory.rv = 0.0
log.dump('rest value of %s was negative: set to zero' %
basename)
def qff(args=None):
if args is None:
args = qff_parse_args()
else:
args = qff_parse_args(args)
#define logger
verbosity = None
if args.silent:
verbosity = 'silent'
else:
if args.very_verbose:
verbosity = 'highest'
elif args.verbose:
verbosity = 'high'
#get settings
kwargs = {
'fn_traj': args.fn_traj,
'only_traj': args.only_traj,
'program_mode': args.program_mode,
'plot_traj': args.plot_traj,
'xyz_traj': args.xyz_traj,
'suffix': args.suffix,
'log_level': verbosity,
'log_file': args.logfile,
'ffatypes': args.ffatypes,
'ei': args.ei,
'ei_rcut': args.ei_rcut,
'vdw': args.vdw,
'vdw_rcut': args.vdw_rcut,
'covres': args.covres,
}
settings = Settings(fn=args.config_file, **kwargs)
with log.section('INIT', 1, timer='Initializing'):
log.dump('Initializing system')
#read system and ab initio reference
system = None
energy = 0.0
grad = None
hess = None
pbc = None
rvecs = None
for fn in args.fn:
if fn.endswith('.fchk') or fn.endswith('.xml'):
numbers, coords, energy, grad, hess, masses, rvecs, pbc = read_abinitio(
fn)
if system is None:
system = System(numbers,
coords,
rvecs=rvecs,
charges=None,
radii=None,
masses=masses)
else:
system.pos = coords.copy()
system.cell = Cell(rvecs)
system.numbers = numbers.copy()
if masses is not None: system.masses = masses.copy()
system._init_derived()
elif fn.endswith('.chk'):
sample = load_chk(fn)
if 'energy' in list(sample.keys()): energy = sample['energy']
if 'grad' in list(sample.keys()): grad = sample['grad']
elif 'gradient' in list(sample.keys()):
grad = sample['gradient']
if 'hess' in list(sample.keys()): hess = sample['hess']
elif 'hessian' in list(sample.keys()): hess = sample['hessian']
if 'rvecs' in list(sample.keys()): pbc = [1, 1, 1]
else: pbc = [0, 0, 0]
if system is None:
system = System.from_file(fn)
else:
if 'pos' in list(sample.keys()): system.pos = sample['pos']
elif 'coords' in list(sample.keys()):
system.pos = sample['coords']
if 'rvecs' in list(sample.keys()):
system.cell = Cell(sample['rvecs'])
elif 'cell' in list(sample.keys()):
system.cell = Cell(sample['cell'])
if 'bonds' in list(sample.keys()):
system.bonds = sample['bonds']
if 'ffatypes' in list(sample.keys()):
system.ffatypes = sample['ffatypes']
if 'ffatype_ids' in list(sample.keys()):
system.ffatype_ids = sample['ffatype_ids']
system._init_derived()
else:
raise NotImplementedError('File format for %s not supported' %
fn)
assert system is not None, 'No system could be defined from input'
assert grad is not None, 'No ab initio gradient found in input'
assert hess is not None, 'No ab initio hessian found in input'
#complete the system information
if system.bonds is None: system.detect_bonds()
if system.masses is None: system.set_standard_masses()
if system.ffatypes is None:
if settings.ffatypes is not None:
set_ffatypes(system, settings.ffatypes)
else:
raise AssertionError('No atom types defined')
if settings.do_hess_negfreq_proj:
log.dump(
'Projecting negative frequencies out of the mass-weighted hessian.'
)
with log.section('SYS', 3, 'Initializing'):
hess = project_negative_freqs(hess, system.masses)
#construct ab initio reference
ai = SecondOrderTaylor('ai',
coords=system.pos.copy(),
energy=energy,
grad=grad,
hess=hess,
pbc=pbc)
#detect a priori defined contributions to the force field
refs = []
if settings.ei is not None:
if rvecs is None:
if settings.ei_rcut is None:
rcut = 50 * angstrom
else:
rcut = settings.ei_rcut
ff = ForceField.generate(system, settings.ei, rcut=rcut)
else:
if settings.ei_rcut is None:
rcut = 20 * angstrom
else:
rcut = settings.ei_rcut
ff = ForceField.generate(system,
settings.ei,
rcut=rcut,
alpha_scale=3.2,
gcut_scale=1.5,
smooth_ei=True)
refs.append(YaffForceField('EI', ff))
if settings.vdw is not None:
ff = ForceField.generate(system,
settings.vdw,
rcut=settings.vdw_rcut)
refs.append(YaffForceField('vdW', ff))
if settings.covres is not None:
ff = ForceField.generate(system, settings.covres)
refs.append(YaffForceField('Cov res', ff))
#define quickff program
assert settings.program_mode in allowed_programs, \
'Given program mode %s not allowed. Choose one of %s' %(
settings.program_mode,
', '.join([prog for prog in allowed_programs if not prog=='BaseProgram'])
)
mode = program_modes[settings.program_mode]
program = mode(system, ai, settings, ffrefs=refs)
#run program
program.run()
return program
def __init__(self, name, ff):
log.dump('Initializing Yaff force field reference for %s' %name)
self.ff = ff
Reference.__init__(self, name)
def estimate(self, trajectory, ai, ffrefs=[], do_valence=False, energy_noise=None, Nerrorsteps=100):
'''
Method to estimate the FF parameters for the relevant ic from the
given perturbation trajectory by fitting a harmonic potential to the
covalent energy along the trajectory.
**Arguments**
trajectory
a Trajectory instance representing the perturbation trajectory
ai
an instance of the Reference representing the ab initio input
**Optional Arguments**
ffrefs
a list of Reference instances representing possible a priori
determined contributions to the force field (such as eg.
electrostatics and van der Waals)
do_valence
If set to True, the current valence force field (stored in
self.valence) will be used to compute the valence contribution
energy_noise
If set to a float, the parabolic fitting will be repeated
Nerrorsteps times including normal noise on top of the reference
value. The mean of the noise is 0, while the std equals the
number given by energy_noise. The resulting fits give a
distribution of force constants and rest values instead of
single value, the std is used to identify bad estimates, the
mean is used for the actual FF parametrs. If set to nan, the
parabolic fit is performed only once without any noise.
'''
with log.section('PTEST', 3, timer='PT Estimate'):
term = trajectory.term
index = term.index
basename = term.basename
if 'active' in list(trajectory.__dict__.keys()) and not trajectory.active:
log.dump('Trajectory of %s was deactivated: skipping' %(basename))
return
qs = trajectory.values.copy()
AIs = np.zeros(len(trajectory.coords))
FFs = np.zeros(len(trajectory.coords))
RESs = np.zeros(len(trajectory.coords))
for istep, pos in enumerate(trajectory.coords):
AIs[istep] = ai.energy(pos)
for ref in ffrefs:
FFs[istep] += ref.energy(pos)
if do_valence:
fc = self.valence.get_params(index, only='fc')
rv = self.valence.get_params(index, only='rv')
self.valence.set_params(index, fc=0.0)
self.valence.set_params(index, rv0=0.0)
for istep, pos in enumerate(trajectory.coords):
RESs[istep] += self.valence.calc_energy(pos) #- 0.5*fc*(qs[istep]-rv)**2
self.valence.set_params(index, fc=fc)
self.valence.set_params(index, rv0=rv)
pars = fitpar(qs, AIs-FFs-RESs-min(AIs-FFs-RESs), rcond=-1)
if energy_noise is None:
if pars[0]!=0.0:
trajectory.fc = 2.0*pars[0]
trajectory.rv = -pars[1]/(2.0*pars[0])
else:
trajectory.fc = 0.0
trajectory.rv = qs[len(qs)//2]
log.dump('force constant of %s is zero: rest value set to middle value' %basename)
else:
with log.section('PTEST', 4, timer='PT Estimate'):
log.dump('Performing noise analysis for trajectory of %s' %basename)
As = [pars[0]]
Bs = [pars[1]]
for i in range(Nerrorsteps):
pars = fitpar(qs, AIs-FFs-RESs-min(AIs-FFs-RESs)+np.random.normal(0.0, energy_noise, size=AIs.shape), rcond=-1)
As.append(pars[0])
Bs.append(pars[1])
if 0.0 in As:
log.dump(' force constant of zero detected, removing the relevant runs from analysis')
Bs = np.array([b for a,b in zip(As,Bs) if a!=0.0])
As = np.array([a for a in As if a!=0.0])
ks = As*2.0
q0s = -Bs/(2.0*As)
kunit = trajectory.term.units[0]
qunit = trajectory.term.units[1]
log.dump(' k = %8.3f +- %6.3f (noisefree: %8.3f) %s' %(ks.mean()/parse_unit(kunit), ks.std()/parse_unit(kunit), ks[0]/parse_unit(kunit), kunit))
log.dump(' q0 = %8.3f +- %6.3f (noisefree: %8.3f) %s' %(q0s.mean()/parse_unit(qunit), q0s.std()/parse_unit(qunit), q0s[0]/parse_unit(qunit), qunit))
if q0s.std()/q0s.mean()>0.01:
with log.section('PTEST', 3, timer='PT Estimate'):
fc, rv = self.valence.get_params(trajectory.term.index)
if rv is None:
log.dump('Noise on rest value of %s to high, using ab initio rest value' %basename)
pars = fitpar(qs, AIs-FFs-RESs-min(AIs-FFs-RESs)+np.random.normal(0.0, energy_noise, size=AIs.shape), rcond=-1)
if pars[0]!=0.0:
trajectory.fc = 2.0*pars[0]
trajectory.rv = -pars[1]/(2.0*pars[0])
else:
trajectory.fc = 0.0
trajectory.rv = qs[len(qs)//2]
log.dump('AI force constant of %s is zero: rest value set to middle value' %basename)
else:
log.dump('Noise on rest value of %s to high, using previous value' %basename)
trajectory.fc = fc
trajectory.rv = rv
else:
trajectory.fc = ks.mean()
trajectory.rv = q0s.mean()
#no negative rest values for all ics except dihedrals and bendcos
if term.ics[0].kind not in [1,3,4,11]:
if trajectory.rv<0:
trajectory.rv = 0.0
log.dump('rest value of %s was negative: set to zero' %basename)
