def get_fftable(self):
'''
A method to return a FFTable instance containing all force field
parameters (force constants and rest values).
'''
fftab = FFTable()
for icname in sorted(self.pot.terms.keys()):
ks = []
q0s = []
ms = []
for term in self.pot.terms[icname]:
ks.append(term.k)
q0s.append(term.q0)
if isinstance(term, CosineTerm):
ms.append(term.A)
if isinstance(term, CosineTerm):
fftab.add(icname,
DataArray(data=ks, unit=term.ic.kunit),
DataArray(data=q0s, unit=term.ic.qunit),
m=DataArray(data=ms, unit='au')
)
else:
fftab.add(icname,
DataArray(data=ks, unit=term.ic.kunit),
DataArray(data=q0s, unit=term.ic.qunit)
)
return fftab
def estimate_from_pt(self, trajectories, skip_dihedrals=True, verbose=True):
#SHLL end
'''
Second Step of force field development: calculate harmonic force field
parameters for every internal coordinate separately from perturbation
trajectories.
**Arguments**
trajectories
A dictionairy containing numpy arrays representing perturbation
trajectories for each icname.
**Optional Arguments**
skip_dihedrals
If set to True, the dihedral ff parameters will not
be calculated.
'''
ff = FFTable()
#SHLL 1606
#-- transfer potential info from program to fftable
ff.stretch_pot_kind = self.stretch_pot_kind
ff.bend_pot_kind = self.bend_pot_kind
#SHLL end
maxlength = max([len(icname) for icname in self.model.val.pot.terms.keys()]) + 2
for icname in sorted(self.model.val.pot.terms.keys()):
ics = self.system.ics[icname]
if skip_dihedrals and icname.startswith('dihed'):
continue
ks = DataArray(unit=ics[0].kunit)
# print 'kunit:',ics[0].kunit #GBdebug
q0s = DataArray(unit=ics[0].qunit)
for ic in ics:
#SHLL 1508
#-- estimate spf or harmcos potential params
if icname.startswith('bond') and self.stretch_pot_kind.lower()=='spf':
k, q0 = self.pert_theory.estimate(ic,trajectories[ic.name], pot_kind=self.stretch_pot_kind)
elif icname.startswith('angle') and self.bend_pot_kind.lower()=='harmcos':
k, q0 = self.pert_theory.estimate(ic,trajectories[ic.name], pot_kind=self.bend_pot_kind)
else:
k, q0 = self.pert_theory.estimate(ic,trajectories[ic.name], pot_kind='harmonic')
#original
# k, q0 = self.pert_theory.estimate(ic,trajectories[ic.name])
#SHLL end
ks.append(k)
q0s.append(q0)
ff.add(icname, ks, q0s)
descr = icname + ' '*(maxlength-len(icname))
if verbose:
print ' %s K = %s q0 = %s' % (
descr, ks.string(), q0s.string()
)
self.model.val.update_fftable(ff)
return ff
