def main(prefix, suffix="pos", unitconv="1.0"):
ipos = []
imode = []
for filename in sorted(glob.glob(prefix + "." + suffix + "*")):
imode.append(filename.split(".")[-1])
ipos.append(open(filename, "r"))
nbeads = len(ipos)
natoms = 0
ifr = 0
while True:
try:
for i in range(nbeads):
ret = read_file(imode[i], ipos[i])
pos = ret["atoms"]
cell = ret["cell"]
if natoms == 0:
natoms = pos.natoms
beads = Beads(natoms, nbeads)
cell.h *= float(unitconv)
beads[i].q = pos.q * float(unitconv)
beads.names = pos.names
except EOFError: # finished reading files
sys.exit(0)
print_file_path("pdb", beads, cell)
ifr += 1
def main(prefix, suffix="pos", unitconv="1.0"):
ipos = []
imode = []
for filename in sorted(glob.glob(prefix + "." + suffix + "*")):
imode.append(filename.split(".")[-1])
ipos.append(open(filename, "r"))
nbeads = len(ipos)
natoms = 0
ifr = 0
while True:
try:
for i in range(nbeads):
ret = read_file(imode[i], ipos[i])
pos = ret["atoms"]
cell = ret["cell"]
if natoms == 0:
natoms = pos.natoms
beads = Beads(natoms, nbeads)
cell.h *= float(unitconv)
beads[i].q = pos.q * float(unitconv)
beads.names = pos.names
except EOFError: # finished reading files
sys.exit(0)
print_file_path("pdb", beads, cell)
ifr += 1
def force(beads, cell, masses, temp, dt, state):
global bead_out_files
nbeads = len(beads.q)
if len(bead_out_files) == 0:
bead_out_files = [
open('CV_' + str(i + 1) + '.out', 'w') for i in range(nbeads)
]
if len(state['modes']['a']) > (nbeads + 1) / 2:
state['modes']['a'] = np.resize(state['modes']['a'], (nbeads + 1) / 2)
cv_set = [cv.get_cv_set(q, state['CV'], masses) for q in beads.q]
if state['ghts'].get('M') is None:
state['ghts']['M'] = np.average(
[bead_cv_set.m for bead_cv_set in cv_set], 0) / AMU
state['ghts']['n'] = np.matmul(state['ghts']['M'], state['ghts']['n'])
params = convert_params(state['params'])
modes = convert_modes(state['modes'])
nmodes = len(modes['a'])
ghts = convert_ghts(state['ghts'])
q = rp.get_q(ghts, cv_set)
r = nm.get_r(q, nmodes)
sigma = nm.get_sigma(modes, r)
d = rp.get_d(ghts, cv_set)
stage = state['stage']
stage['step'] = stage.get('step', 0)
step = stage['step']
print_CV_every = state['output'].get('print_CV_every', 1)
if step % print_CV_every == 0:
restraints = [[
cv.get_cv(bead, restr).value for restr in state['restraints']
] for bead in beads.q]
write_centroid_data(cv_set, sigma, q, d, r, restraints, out_file)
write_bead_data(cv_set, ghts, restraints, bead_out_files)
stage['step'] += 1
if stage['name'] == 'optimize':
optimizer.move(modes, ghts, cv_set, r, sigma, params,
params['K'] / temp, dt)
if stage['name'] == 'sample':
if (stage.get('optimizer_data')
and stage['step'] % stage.get('optimizer_data_step', 1) == 0):
with open(stage['optimizer_data'], 'a+') as f:
write_optimizer_data(cv_set, f)
stage['last_save_step'] = stage.get('last_save_step', stage['step'])
stage['last_saved'] = stage.get('last_saved', (stage['walker'] - 1) *
stage['structures'])
if abs(sigma.value * SQAMU) < stage['q_threshold'] and \
stage['step'] >= stage['last_save_step'] + stage['offset']:
idx = stage['last_saved'] + 1
with open(str(idx) + ".xyz", 'w') as f:
print_file_path('xyz', beads, cell, f, units='angstrom')
stage['last_saved'] = idx
stage['last_save_step'] = stage['step']
if idx == stage['walker'] * stage['structures']:
softexit.trigger('exit ' + str(stage['walker']))
if stage['name'] == 'prepare' and stage['step'] <= stage['prepare_steps']:
params['K'] *= float(stage['step']) / stage['prepare_steps']
params['K_d'] *= float(stage['step']) / stage['prepare_steps']
if stage['step'] == stage['prepare_steps']:
stage['name'] = 'optimize'
stage['step'] = 0
sigma_bias = harmonic_bias(rp.get_sigma(sigma, q), params['K'] * nbeads, 0)
d_bias = side_harmonic_bias(d, params['K_d'] * nbeads, params['d_max'])
restraint_biases = np.zeros(beads.q.shape, beads.q.dtype)
for restraint in state.get('restraints', []):
restraint_biases += np.array(
[restraint_bias(bead, restraint) for bead in beads.q])
if stage['name'] == 'committor':
if abs(sigma.value * SQAMU) > stage['q_threshold']:
softexit.trigger('q_threshold reached')
return sigma_bias * 0
recover_ghts(state, ghts)
recover_modes(state, modes)
return sigma_bias + d_bias + restraint_biases