def _atoms_to_charmm22_psf(system):
result = ['{:8d} !NATOM'.format(system.natom)]
for iatom in xrange(system.natom):
ffatype = system.get_ffatype(iatom)
if len(ffatype) > 4:
log.warning(
'Atom type too long for CHARMM PSF file: {}'.format(ffatype))
result.append(
'{:8d} A 1 MOL {:4} {:4} {:10.6f} {:13.4f} 0'.
format(iatom + 1, ffatype, ffatype, system.charges[iatom],
system.masses[iatom] / amu))
return '\n'.join(result)
def _atoms_to_charmm22_psf(system):
result = ['{:8d} !NATOM'.format(system.natom)]
for iatom in range(system.natom):
ffatype = system.get_ffatype(iatom)
if len(ffatype) > 4:
log.warning('Atom type too long for CHARMM PSF file: {}'.format(ffatype))
if system.charges is None:
charge = 0.0
else:
charge = system.charges[iatom]
result.append('{:8d} A 1 MOL {:4} {:4} {:10.6f} {:13.4f} 0'.format(
iatom+1, ffatype, ffatype, charge, system.masses[iatom]/amu))
return '\n'.join(result)
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])[0]
self.perturbation.generate(trajectory)
self.trajectories.append(trajectory)
def _check_charmm22(valence):
"""Print warnings for all kinds of energy terms not supported by CHARMM22.
**Arguments**
valence
Instance of ValenceFF, which defines the force field.
"""
# First report all types of terms not supported by CHARMM22.
skipped = set([])
supported_kinds = set(['BONDHARM', 'BENDAHARM', 'TORSION', 'TORSCHEBY1', 'TORSCHEBY2', 'TORSCHEBY3', 'TORSCHEBY4', 'TORSCHEBY6'])
for term in valence.iter_terms():
kind = term.basename[:term.basename.find('/')].upper()
if kind not in supported_kinds and kind not in skipped:
log.warning('Not writing {} term to CHARMM22 parameter file.'.format(kind))
skipped.add(kind)
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 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 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)