def addljtype(root, amber_prmtop, messages):
""" Turns given mask into a new LJ atom type """
# We need a mask, new radius, new epsilon, and for chamber topologies,
# a new radius-1-4 and epsilon-1-4. Don't add the latter ones until we
# know if we have a chamber prmtop or not.
widget_list = [('MaskEntry', 'Atoms to make new LJ Type'),
('Entry', 'New Radius (default old radius)'),
('Entry', 'New Depth (default old depth)')]
# We need 5 string variables, then get the description.
var_list = [StringVar(), StringVar(), StringVar(), StringVar(), StringVar()]
description=('Turns given mask into a new LJ atom type. Uses the radius\n'
'and well depth from the first atom type in <mask> if none\n'
'are provided.')
if amber_prmtop.parm.chamber:
widget_list += [('Entry', 'New Radius for 1-4 Terms'),
('Entry', 'New Depth for 1-4 Terms')]
# Create the window, open it, then wait for it to close
cmd_window = _guiwidgets.ActionWindow('addLJType', amber_prmtop,
widget_list, var_list, description)
cmd_window.wait_window()
# See if we got any variables back
vars_found = True in [bool(v.get()) for v in var_list]
if not vars_found: return
# addljtype expects any _non_specified variables to be None
var_list = [v.get().strip() for v in var_list]
kw_var_list = [var_list[0]]
if var_list[1]: kw_var_list.extend(['radius', var_list[1]])
if var_list[2]: kw_var_list.extend(['epsilon', var_list[2]])
if amber_prmtop.parm.chamber and var_list[3]:
kw_var_list.extend(['radius_14', var_list[3]])
if amber_prmtop.parm.chamber and var_list[4]:
kw_var_list.extend(['epsilon_14', var_list[4]])
try:
action = actions.addLJType(amber_prmtop,ArgumentList(kw_var_list))
except Exception as err:
showerror('Unexpected Error!', '%s: %s' % (type(err).__name__, err))
return
action.execute()
messages.write('%s\n' % action)
def _set_nonbonded_tables(self, nbfixes=None):
"""
Sets the tables of Lennard-Jones nonbonded interaction pairs
"""
from parmed.tools.actions import addLJType
data = self.parm_data
ntypes = data['POINTERS'][NTYPES]
ntypes2 = ntypes * ntypes
# Set up the index lookup tables (not a unique solution)
data['NONBONDED_PARM_INDEX'] = [0 for i in range(ntypes2)]
holder = [0 for i in range(ntypes2)]
idx = 0
for i in range(ntypes):
for j in range(i+1):
idx += 1
holder[ntypes*i+j] = holder[ntypes*j+i] = idx
idx = 0
for i in range(ntypes):
for j in range(ntypes):
data['NONBONDED_PARM_INDEX'][idx] = holder[ntypes*i+j]
idx += 1
nttyp = ntypes * (ntypes + 1) // 2
# Now build the Lennard-Jones arrays
data['LENNARD_JONES_14_ACOEF'] = [0 for i in range(nttyp)]
data['LENNARD_JONES_14_BCOEF'] = [0 for i in range(nttyp)]
data['LENNARD_JONES_ACOEF'] = [0 for i in range(nttyp)]
data['LENNARD_JONES_BCOEF'] = [0 for i in range(nttyp)]
self.recalculate_LJ()
# Now make any NBFIX modifications we had
if nbfixes is not None:
for i, fix in enumerate(nbfixes):
for terms in fix:
j, rmin, eps, rmin14, eps14 = terms
i, j = min(i, j-1), max(i, j-1)
eps = abs(eps)
eps14 = abs(eps14)
idx = data['NONBONDED_PARM_INDEX'][ntypes*i+j] - 1
data['LENNARD_JONES_ACOEF'][idx] = eps * rmin**12
data['LENNARD_JONES_BCOEF'][idx] = 2 * eps * rmin**6
data['LENNARD_JONES_14_ACOEF'][idx] = eps14 * rmin14**12
data['LENNARD_JONES_14_BCOEF'][idx] = 2 * eps14 * rmin14**6
# If we had an explicit set of exceptions, we need to implement all of
# those exclusions. The electrostatic component of that exception is
# already handled (since a scaling factor *can* represent the full
# flexibility of electrostatic exceptions). The vdW component must be
# handled as 1-4 A- and B-coefficients. If vdW types are too compressed
# for this to be handled correctly, use "addLJType" to expand the types
# by 1 (so the tables only get as big as they *need* to get, to make
# sure they continue to fit in CUDA shared memory).
#
# The way we do this is to fill all of the elements with None, then fill
# them in as we walk through the 1-4 exceptions. If we hit a case where
# the target element is not None and *doesn't* equal the computed A- and
# B-coefficients, we have to expand our type list (assume all type
# names will have the same L-J parameters, which has been a fair
# assumption in my experience).
if not self.adjusts: return
for i in range(len(data['LENNARD_JONES_14_ACOEF'])):
data['LENNARD_JONES_14_ACOEF'][i] = None
data['LENNARD_JONES_14_BCOEF'][i] = None
ii = 0
replaced_atoms = set()
while True:
needed_split = False
for pair in self.adjusts:
a1, a2 = pair.atom1, pair.atom2
i, j = sorted([a1.nb_idx - 1, a2.nb_idx - 1])
idx = data['NONBONDED_PARM_INDEX'][ntypes*i+j] - 1
eps = pair.type.epsilon
rmin = pair.type.rmin
rmin6 = rmin * rmin * rmin * rmin * rmin * rmin
acoef = eps * rmin6*rmin6
bcoef = 2 * eps * rmin6
if data['LENNARD_JONES_14_ACOEF'][idx] is not None:
if abs(data['LENNARD_JONES_14_ACOEF'][idx] - acoef) > SMALL:
# Need to split out another type
needed_split = True
assert a1 not in replaced_atoms or a2 not in replaced_atoms
# Only add each atom as a new type ONCE
if a1 in replaced_atoms:
mask = '@%d' % (a2.idx+1)
replaced_atoms.add(a2)
else:
mask = '@%d' % (a1.idx+1)
replaced_atoms.add(a1)
addLJType(self, mask, radius_14=0, epsilon_14=0).execute()
ntypes += 1
# None-out all of the added terms
j = ntypes - 1
for i in range(j):
idx2 = data['NONBONDED_PARM_INDEX'][ntypes*i+j] - 1
data['LENNARD_JONES_14_ACOEF'][idx2] = None
data['LENNARD_JONES_14_BCOEF'][idx2] = None
# We can stop here, since the next loop through the
# explicit exclusions will fill this in
else:
data['LENNARD_JONES_14_ACOEF'][idx] = acoef
data['LENNARD_JONES_14_BCOEF'][idx] = bcoef
ii += 1
if not needed_split:
break
# The following should never happen
assert ii <= len(self.atoms)+1, 'Could not resolve all exceptions. ' \
'Some unexpected problem with the algorithm'
# Now go through and change all None's to 0s, as these terms won't be
# used for any exceptions, anyway
for i, item in enumerate(data['LENNARD_JONES_14_ACOEF']):
if item is None:
assert data['LENNARD_JONES_14_BCOEF'][i] is None, \
'A- and B- coefficients must be in lock-step!'
data['LENNARD_JONES_14_ACOEF'][i] = 0.0
data['LENNARD_JONES_14_BCOEF'][i] = 0.0
def _set_nonbonded_tables(self, nbfixes=None):
"""
Sets the tables of Lennard-Jones nonbonded interaction pairs
"""
from parmed.tools.actions import addLJType
data = self.parm_data
ntypes = data['POINTERS'][NTYPES]
ntypes2 = ntypes * ntypes
# Set up the index lookup tables (not a unique solution)
data['NONBONDED_PARM_INDEX'] = [0 for i in range(ntypes2)]
holder = [0 for i in range(ntypes2)]
idx = 0
for i in range(ntypes):
for j in range(i + 1):
idx += 1
holder[ntypes * i + j] = holder[ntypes * j + i] = idx
idx = 0
for i in range(ntypes):
for j in range(ntypes):
data['NONBONDED_PARM_INDEX'][idx] = \
holder[ntypes*i+j]
idx += 1
nttyp = ntypes * (ntypes + 1) // 2
# Now build the Lennard-Jones arrays
data['LENNARD_JONES_14_ACOEF'] = [0 for i in range(nttyp)]
data['LENNARD_JONES_14_BCOEF'] = [0 for i in range(nttyp)]
data['LENNARD_JONES_ACOEF'] = [0 for i in range(nttyp)]
data['LENNARD_JONES_BCOEF'] = [0 for i in range(nttyp)]
self.recalculate_LJ()
# Now make any NBFIX modifications we had
if nbfixes is not None:
for i, fix in enumerate(nbfixes):
for terms in fix:
j, rmin, eps, rmin14, eps14 = terms
i, j = min(i, j - 1), max(i, j - 1)
eps = abs(eps)
eps14 = abs(eps14)
idx = data['NONBONDED_PARM_INDEX'][ntypes * i + j] - 1
data['LENNARD_JONES_ACOEF'][idx] = eps * rmin**12
data['LENNARD_JONES_BCOEF'][idx] = 2 * eps * rmin**6
data['LENNARD_JONES_14_ACOEF'][idx] = eps14 * rmin14**12
data['LENNARD_JONES_14_BCOEF'][idx] = 2 * eps14 * rmin14**6
# If we had an explicit set of exceptions, we need to implement all of
# those exclusions. The electrostatic component of that exception is
# already handled (since a scaling factor *can* represent the full
# flexibility of electrostatic exceptions). The vdW component must be
# handled as 1-4 A- and B-coefficients. If vdW types are too compressed
# for this to be handled correctly, use "addLJType" to expand the types
# by 1 (so the tables only get as big as they *need* to get, to make
# sure they continue to fit in CUDA shared memory).
#
# The way we do this is to fill all of the elements with None, then fill
# them in as we walk through the 1-4 exceptions. If we hit a case where
# the target element is not None and *doesn't* equal the computed A- and
# B-coefficients, we have to expand our type list (assume all type
# names will have the same L-J parameters, which has been a fair
# assumption in my experience).
if not self.adjusts: return
for i in range(len(data['LENNARD_JONES_14_ACOEF'])):
data['LENNARD_JONES_14_ACOEF'][i] = None
data['LENNARD_JONES_14_BCOEF'][i] = None
ii = 0
replaced_atoms = set()
while True:
needed_split = False
for pair in self.adjusts:
a1, a2 = pair.atom1, pair.atom2
i, j = sorted([a1.nb_idx - 1, a2.nb_idx - 1])
idx = data['NONBONDED_PARM_INDEX'][ntypes * i + j] - 1
eps = pair.type.epsilon
rmin = pair.type.rmin
rmin6 = rmin * rmin * rmin * rmin * rmin * rmin
acoef = eps * rmin6 * rmin6
bcoef = 2 * eps * rmin6
if data['LENNARD_JONES_14_ACOEF'][idx] is not None:
if abs(data['LENNARD_JONES_14_ACOEF'][idx] -
acoef) > SMALL:
# Need to split out another type
needed_split = True
assert (a1 not in replaced_atoms
or a2 not in replaced_atoms)
# Only add each atom as a new type ONCE
if a1 in replaced_atoms:
mask = '@%d' % (a2.idx + 1)
replaced_atoms.add(a2)
else:
mask = '@%d' % (a1.idx + 1)
replaced_atoms.add(a1)
addLJType(self, mask, radius_14=0,
epsilon_14=0).execute()
ntypes += 1
# None-out all of the added terms
j = ntypes - 1
for i in range(j):
_ = data['NONBONDED_PARM_INDEX'][ntypes * i +
j] - 1
data['LENNARD_JONES_14_ACOEF'][_] = None
data['LENNARD_JONES_14_BCOEF'][_] = None
# We can stop here, since the next loop through the
# explicit exclusions will fill this in
else:
data['LENNARD_JONES_14_ACOEF'][idx] = acoef
data['LENNARD_JONES_14_BCOEF'][idx] = bcoef
ii += 1
if not needed_split:
break
# The following should never happen
assert ii <= len(self.atoms)+1, 'Could not resolve all exceptions. ' \
'Some unexpected problem with the algorithm'
# Now go through and change all None's to 0s, as these terms won't be
# used for any exceptions, anyway
for i, item in enumerate(data['LENNARD_JONES_14_ACOEF']):
if item is None:
assert data['LENNARD_JONES_14_BCOEF'][i] is None, \
'A- and B- coefficients must be in lock-step!'
data['LENNARD_JONES_14_ACOEF'][i] = 0.0
data['LENNARD_JONES_14_BCOEF'][i] = 0.0
