def spawn_backward(child, current_time, end_time, dt):
"""Propagates the child backwards in time until the current time
is reached."""
nstep = int(round( np.abs((current_time-end_time) / dt) ))
back_time = current_time
for i in range(nstep):
step.step_trajectory(child, back_time, dt)
back_time = back_time + dt
log.print_message('spawn_back', [back_time])
def step_trajectory(traj, init_time, dt):
"""Propagates a single trajectory.
Used to backward/forward propagate a trajectory during spawning.
NOTE: step_wavefunction and step_trajectory could/should probably
be integrated somehow...
"""
current_time = init_time
end_time = init_time + dt
time_step = dt
min_time_step = abs(dt / 2.**5)
while not step_complete(current_time, end_time, time_step):
# save the wavefunction from previous step in case step rejected
traj0 = traj.copy()
# propagate single trajectory
glbl.modules['propagator'].propagate_trajectory(traj, time_step)
# update the couplings for the trajectory
glbl.modules['interface'].evaluate_coupling(traj)
# update current time
proposed_time = current_time + time_step
# check time_step is fine, energy/amplitude conserved
accept = check_step_trajectory(traj0, traj)
# if everything is ok..
if accept:
current_time = proposed_time
else:
# redo time step
# recall -- this time trying to propagate
# to the failed step
time_step *= 0.5
if abs(time_step) < min_time_step:
log.print_message('general',
['minimum time step exceeded -- STOPPING.'])
raise ValueError('Trajectory minimum step exceeded.')
# reset the beginning of the time step and go to beginning of loop
traj = traj0.copy()
def set_initial_coords(wfn):
"""Takes initial position and momentum from geometry specified in input"""
coords = glbl.properties['init_coords']
ndim = coords.shape[-1]
log.print_message('string',[' Initial coordinates taken from input file(s).\n'])
for coord in coords:
itraj = trajectory.Trajectory(glbl.properties['n_states'], ndim,
width=glbl.properties['crd_widths'],
mass=glbl.properties['crd_masses'],
parent=0, kecoef=glbl.modules['integrals'].kecoef)
# set position and momentum
itraj.update_x(np.array(coord[0]))
itraj.update_p(np.array(coord[1]))
# add a single trajectory specified by geometry.dat
wfn.add_trajectory(itraj)
def set_initial_state(wfn):
"""Sets the initial state of the trajectories in the bundle."""
if glbl.properties['init_brightest']:
# initialize to the state with largest transition dipole moment
# set all states to the ground state
for i in range(wfn.n_traj()):
wfn.traj[i].state = 0
# compute transition dipoles
evaluate.update_pes_traj(wfn.traj[i])
# set the initial state to the one with largest t. dip.
for i in range(wfn.n_traj()):
if 'dipole' not in wfn.traj[i].pes.avail_data():
raise KeyError('trajectory ' + str(i) +
': Cannot set state by transition moments - ' +
'dipole not in pes.avail_data()')
tr_dipole = wfn.traj[i].pes.get_data('dipole')
tdip = np.array([
np.linalg.norm(tr_dipole[:, 0, j])
for j in range(1, glbl.properties['n_states'])
])
log.print_message('general',
['Initializing trajectory '+str(i)+
' to state '+str(np.argmax(tdip)+1)+
' | tr. dipople array='+np.array2string(tdip, \
formatter={'float_kind':lambda x: "%.4f" % x})])
wfn.traj[i].state = np.argmax(tdip) + 1
elif len(glbl.properties['init_state']) == wfn.n_traj():
# use "init_state" to set the initial state
for i in range(wfn.n_traj()):
istate = glbl.properties['init_state'][i]
if istate < 0:
wfn.traj[i].state = glbl.properties['n_states'] + istate
else:
wfn.traj[i].state = istate
else:
raise ValueError('Ambiguous initial state assignment.')
def spawn_forward(parent, child_state, initial_time, dt):
"""Propagates the parent forward (into the future) until the coupling
decreases."""
parent_state = parent.state
current_time = initial_time
spawn_time = initial_time
exit_time = initial_time
child_created = False
parent_at_spawn = None
child_at_spawn = None
coup = np.zeros(3)
log.print_message('spawn_start',
[parent.label, parent_state, child_state])
while True:
coup = np.roll(coup,1)
coup[0] = abs(parent.coupling(parent_state, child_state))
child_attempt = parent.copy()
child_attempt.state = child_state
adjust_success = utils.adjust_child(parent, child_attempt,
parent.derivative(parent_state,
child_state))
sij = abs(glbl.modules['integrals'].nuc_overlap(parent, child_attempt))
# if the coupling has already peaked, either we exit with a successful
# spawn from previous step, or we exit with a fail
if np.all(coup[0] < coup[1:]):
sp_str = 'no [decreasing coupling]'
log.print_message('spawn_step',
[current_time, coup[0], sij, sp_str])
if child_created:
log.print_message('spawn_success', [spawn_time])
child_at_spawn.exit_time[parent_state] = current_time
else:
log.print_message('spawn_failure', [current_time])
# exit, we're done trying to spawn
exit_time = current_time
break
# coupling still increasing
else:
# try to set up the child
if not adjust_success:
sp_str = 'no [momentum adjust fail]'
elif sij < glbl.properties['spawn_olap_thresh']:
sp_str = 'no [overlap too small]'
elif not np.all(coup[0] > coup[1:]):
sp_str = 'no [decreasing coupling]'
else:
spawn_time = current_time
child_created = True
parent_at_spawn = parent.copy()
child_at_spawn = child_attempt.copy()
child_at_spawn.last_spawn[parent_state] = spawn_time
child_at_spawn.amplitude = 0j
child_at_spawn.parent = parent.label
child_at_spawn.label = parent.label
sp_str = 'yes'
log.print_message('spawn_step',
[current_time, coup[0], sij, sp_str])
step.step_trajectory(parent, current_time, dt)
current_time = current_time + dt
return child_created, child_at_spawn, parent_at_spawn, spawn_time, exit_time
def spawn(wfn, dt):
"""Propagates to the point of maximum coupling, spawns a new
basis function, then propagates the function to the current time."""
global coup_hist
basis_grown = False
current_time = wfn.time
# list of added trajectories
# we want to know the history of the coupling for each trajectory
# in order to assess spawning criteria -- make sure coup_hist has a slot
# for every trajectory
if len(coup_hist) < wfn.n_traj():
n_add = wfn.n_traj() - len(coup_hist)
for i in range(n_add):
coup_hist.append(np.zeros((glbl.properties['n_states'], 3)))
# iterate over all trajectories in bundle
for i in range(wfn.n_traj()):
# only live trajectories can spawn
if not wfn.traj[i].alive:
continue
for st in range(glbl.properties['n_states']):
# can only spawn to different electronic states
if wfn.traj[i].state == st:
continue
# compute magnitude of coupling to state j
coup = abs(wfn.traj[i].coupling(wfn.traj[i].state, st))
coup_hist[i][st,:] = np.roll(coup_hist[i][st,:],1)
coup_hist[i][st,0] = coup
# if we satisfy spawning conditions, begin spawn process
if spawn_trajectory(wfn, i, st, coup_hist[i][st,:],
current_time):
# we're going to messing with this trajectory -- mess with a copy
parent = wfn.traj[i].copy()
# propagate the parent forward in time until coupling maximized
[success, child, parent_spawn, spawn_time,
exit_time] = spawn_forward(parent, st, current_time, dt)
# set the spawn attempt in wfn, even if spawn failed (avoid repeated fails)
wfn.traj[i].last_spawn[st] = spawn_time
wfn.traj[i].exit_time[st] = exit_time
if success:
# at this point, child is at the spawn point. Propagate
# backwards in time until we reach the current time
child_spawn = child.copy()
# need electronic structure at current geometry -- on correct state
evaluate.update_pes_traj(child)
spawn_backward(child, spawn_time, current_time, -dt)
bundle_overlap = utils.overlap_with_bundle(child, wfn)
if not bundle_overlap:
basis_grown = True
wfn.add_trajectory(child)
child_spawn.label = wfn.traj[-1].label # a little hacky...
utils.write_spawn_log(current_time, spawn_time, exit_time,
parent_spawn, child_spawn)
else:
log.print_message('spawn_bad_step',
['overlap with bundle too large'])
# let caller known if the basis has been changed
return basis_grown
def init_interface():
"""Reads the operator file.
Note that the order of active modes is determined by the init_coords
read from the input file.
"""
global ham
# Read in operator file
ham = VibHam()
ham.mlbl_active = [lbl.lower() for lbl in glbl.properties['crd_labels']]
ham.nmode_active = len(glbl.properties['crd_labels'])
# operator file will always be a separate file
#ham.rdoperfile(glbl.home_path + '/' + glbl.vibronic['opfile'])
ham.rdoperfile(glbl.vibronic['opfile'])
# Ouput some information about the Hamiltonian
log.print_message('string', ['*'*72])
log.print_message('string',
['* Vibronic Coupling Hamiltonian Information'])
log.print_message('string', ['*'*72])
log.print_message('string',
['Operator file: ' + glbl.vibronic['opfile']])
log.print_message('string',
['Number of Hamiltonian terms: ' + str(ham.nterms)])
string = 'Total no. modes: ' + str(ham.nmode_total)
log.print_message('string', [string])
string = 'No. active modes: ' + str(ham.nmode_active)
log.print_message('string', [string])
log.print_message('string', ['Active mode labels:'])
for i in range(ham.nmode_active):
string = str(i+1) + ' ' + ham.mlbl_active[i]
log.print_message('string', [string])
log.print_message('string', ['Active mode frequencies (a.u.):'])
for i in range(ham.nmode_active):
string = str(i+1) + ' ' + str(ham.freq[i])
log.print_message('string', [string])
def set_initial_coords(wfn):
"""Samples a v=0 Wigner distribution."""
# Set the coordinate type: Cartesian or normal mode coordinates
if glbl.methods['interface'] == 'vibronic':
coordtype = 'normal'
ham = glbl.modules['interface'].ham
else:
coordtype = 'cart'
log.print_message('string', [' sampling from v=0 Wigner distribution.\n'])
# if multiple geometries, just take the first one
coords = glbl.properties['init_coords']
ndim = coords.shape[-1]
x_ref = coords[0, 0]
p_ref = coords[0, 1]
w_vec = glbl.properties['crd_widths']
m_vec = glbl.properties['crd_masses']
# create template trajectory basis function
template = trajectory.Trajectory(glbl.properties['n_states'],
ndim,
width=w_vec,
mass=m_vec,
parent=0,
kecoef=glbl.modules['integrals'].kecoef)
template.update_x(x_ref)
template.update_p(p_ref)
# If Cartesian coordinates are being used, then set up the
# mass-weighted Hessian and diagonalise to obtain the normal modes
# and frequencies
if coordtype == 'cart':
iface_dict = glbl.sections[glbl.methods['interface']]
hessian = iface_dict['hessian']
invmass = np.asarray([
1. / np.sqrt(m_vec[i]) if m_vec[i] != 0. else 0
for i in range(len(m_vec))
],
dtype=float)
mw_hess = invmass * hessian * invmass[:, np.newaxis]
evals, evecs = sp_linalg.eigh(mw_hess)
f_cutoff = 0.0001
freq_list = []
mode_list = []
for i in range(len(evals)):
if evals[i] >= 0 and np.sqrt(evals[i]) >= f_cutoff:
freq_list.append(np.sqrt(evals[i]))
mode_list.append(evecs[:, i].tolist())
n_modes = len(freq_list)
freqs = np.asarray(freq_list)
modes = np.asarray(mode_list).transpose()
# confirm that modes * tr(modes) = 1
m_chk = np.dot(modes.T, modes)
if not np.allclose(m_chk, np.eye(len(m_chk))):
raise ValueError('Internal coordinates not orthonormal.')
log.print_message('string', [' -- frequencies from hessian.dat --\n'])
# If normal modes are being used, set the no. modes
# equal to the total number of modes of the model
# Hamiltonian and load only the active frequencies
if coordtype == 'normal':
n_modes = len(w_vec)
# we multiply by 0.5 below -- multiply by 2 here (i.e. default width for
# vibronic hamiltonans is 1/2, assuming frequency weighted coords
freqs = 2. * w_vec
log.print_message(
'string', ['\n -- widths employed in coordinate sampling --\n'])
# write out frequencies
fstr = '\n'.join([
'{0:.5f} au == {1:10.1f} cm^-1'.format(freqs[j],
freqs[j] * constants.au2cm)
for j in range(n_modes)
])
log.print_message('string', [fstr + '\n'])
# loop over the number of initial trajectories
max_try = 1000
ntraj = glbl.properties['n_init_traj']
for i in range(ntraj):
delta_x = np.zeros(n_modes)
delta_p = np.zeros(n_modes)
x_sample = np.zeros(n_modes)
p_sample = np.zeros(n_modes)
for j in range(n_modes):
alpha = 0.5 * freqs[j]
if alpha > constants.fpzero:
sigma_x = (glbl.properties['distrib_compression'] *
np.sqrt(0.25 / alpha))
sigma_p = (glbl.properties['distrib_compression'] *
np.sqrt(alpha))
itry = 0
while itry <= max_try:
dx = np.random.normal(0., sigma_x)
dp = np.random.normal(0., sigma_p)
itry += 1
if mode_overlap(
alpha, dx,
dp) > glbl.properties['init_mode_min_olap']:
break
if mode_overlap(alpha, dx,
dp) < glbl.properties['init_mode_min_olap']:
raise ValueError(
'Cannot get mode overlap > ' +
str(glbl.properties['init_mode_min_olap']) +
' within ' + str(max_try) + ' attempts. Exiting...')
delta_x[j] = dx
delta_p[j] = dp
# If Cartesian coordinates are being used, displace along each
# normal mode to generate the final geometry...
if coordtype == 'cart':
disp_x = np.dot(modes, delta_x) / np.sqrt(m_vec)
disp_p = np.dot(modes, delta_p) * np.sqrt(m_vec)
# ... else if mass- and frequency-scaled normal modes are
# being used, then take the frequency-scaled normal mode
# displacements and momenta as the inital point in phase
# space
elif coordtype == 'normal':
disp_x = delta_x
disp_p = delta_p
#disp_x = delta_x * np.sqrt(freqs)
#disp_p = delta_p * np.sqrt(freqs)
x_sample = x_ref + disp_x
p_sample = p_ref + disp_p
# add new trajectory to the bundle
new_traj = template.copy()
new_traj.update_x(x_sample)
new_traj.update_p(p_sample)
# Add the trajectory to the bundle
wfn.add_trajectory(new_traj)
def step_wavefunction(dt):
"""Propagates the wave packet using a run-time selected propagator."""
# save the wavefunction from previous step in case step rejected
end_time = min(glbl.modules['wfn'].time + dt,
glbl.properties['simulation_time'])
time_step = min(
dt, glbl.properties['simulation_time'] - glbl.modules['wfn'].time)
min_time_step = dt / 2.**5
while not step_complete(glbl.modules['wfn'].time, end_time, dt):
# save the wavefunction from previous step in case step rejected
wfn_start = glbl.modules['wfn'].copy()
# propagate each trajectory in the wavefunction
time_step = min(time_step, end_time - glbl.modules['wfn'].time)
# propagate amplitudes for 1/2 time step using x0
glbl.modules['wfn'].update_amplitudes(0.5 * dt)
# the propagators update the potential energy surface as need be.
glbl.modules['propagator'].propagate_wfn(glbl.modules['wfn'],
time_step)
# update the couplings for all the trajectories
for i in range(glbl.modules['wfn'].n_traj()):
glbl.modules['interface'].evaluate_coupling(
glbl.modules['wfn'].traj[i])
# propagate amplitudes for 1/2 time step using x1
glbl.modules['matrices'].build(glbl.modules['wfn'],
glbl.modules['integrals'])
glbl.modules['wfn'].update_matrices(glbl.modules['matrices'])
glbl.modules['wfn'].update_amplitudes(0.5 * dt)
# Renormalization
if glbl.properties['renorm'] == 1:
glbl.modules['wfn'].renormalize()
# check time_step is fine, energy/amplitude conserved
accept, error_msg = check_step_wfn(wfn_start, glbl.modules['wfn'],
time_step)
# if everything is ok..
if accept:
# update the wavefunction time
glbl.modules['wfn'].time += time_step
# spawn new basis functions if necessary
basis_grown = glbl.modules['adapt'].spawn(glbl.modules['wfn'],
time_step)
# kill the dead trajectories
basis_pruned = utilities.prune(glbl.modules['wfn'])
# if a trajectory has been added, then call update_pes
# to get the electronic structure information at the associated
# centroids. This is necessary in order to propagate the amplitudes
# at the start of the next time step.
if basis_grown and glbl.modules['integrals'].require_centroids:
evaluate.update_pes(glbl.modules['wfn'])
# update the Hamiltonian and associated matrices
if basis_grown or basis_pruned:
glbl.modules['matrices'].build(glbl.modules['wfn'],
glbl.modules['integrals'])
glbl.modules['wfn'].update_matrices(glbl.modules['matrices'])
for i in range(glbl.modules['wfn'].n_traj()):
glbl.modules['interface'].evaluate_coupling(
glbl.modules['wfn'].traj[i])
# re-expression of the basis using the matching pursuit
# algorithm
#if glbl.properties['matching_pursuit'] == 1:
# mp.reexpress_basis(master)
# update the running log
log.print_message('t_step', [
glbl.modules['wfn'].time, time_step, glbl.modules['wfn'].nalive
])
#del wfn_start
else:
# recall -- this time trying to propagate to the failed step
time_step *= 0.5
log.print_message('new_step', [error_msg, time_step])
if time_step < min_time_step:
log.print_message('general',
['minimum time step exceeded -- STOPPING.'])
raise ValueError('Bundle minimum step exceeded.')
# reset the beginning of the time step and go to beginning of loop
#del master
glbl.modules['wfn'] = wfn_start.copy()
def spawn(wfn, dt):
"""Spawns a basis function if the minimum overlap drops below a given
threshold."""
basis_grown = False
current_time = wfn.time
for i in range(wfn.n_traj()):
if not wfn.traj[i].alive:
continue
parent = wfn.traj[i]
for st in range(glbl.properties['n_states']):
# don't check overlap with basis functions on same state
if st == parent.state:
continue
s_array = [
abs(glbl.modules['integrals'].nuc_overlap(parent, wfn.traj[j]))
if wfn.traj[j].state == st and wfn.traj[j].alive else 0.
for j in range(wfn.n_traj())
]
if max(s_array,
key=abs) < glbl.properties['continuous_min_overlap']:
child = parent.copy()
child.amplitude = 0j
child.state = st
child.parent = parent.label
success = utilities.adjust_child(
parent, child, parent.derivative(parent.state,
child.state))
sij = glbl.modules['integrals'].nuc_overlap(parent, child)
# try to set up the child
if not success:
pass
#log.print_message('spawn_bad_step',
# ['cannot adjust kinetic energy of child'])
elif abs(sij) < glbl.properties['spawn_olap_thresh']:
pass
#log.print_message('spawn_bad_step',
# ['child-parent overlap too small'])
else:
child_created = True
spawn_time = current_time
parent.last_spawn[child.state] = spawn_time
child.last_spawn[parent.state] = spawn_time
bundle_overlap = utilities.overlap_with_bundle(child, wfn)
if not bundle_overlap:
basis_grown = True
wfn.add_trajectory(child)
log.print_message('spawn_success',
[current_time, parent.label, st])
utilities.write_spawn_log(current_time, current_time,
current_time, parent,
wfn.traj[-1])
else:
err_msg = ('Traj ' + str(parent.label) +
' from state ' + str(parent.state) +
' to state ' + str(st) + ': ' +
'overlap with bundle too large,' +
' s_max=' +
str(glbl.properties['sij_thresh']))
log.print_message('spawn_bad_step', [err_msg])
return basis_grown
def init_wavefunction():
"""Initializes the trajectories."""
# initialize the interface we'll be using the determine the
# the PES. There are some details here that trajectories
# will want to know about
log.print_message(
'string',
['\n **************\n' + ' initialization\n' + ' **************\n'])
# creat the wave function instance
glbl.modules['wfn'] = wavefunction.Wavefunction()
glbl.modules['interface'] = __import__('nomad.interfaces.' +
glbl.methods['interface'],
fromlist=['NA'])
glbl.modules['interface'].init_interface()
# create glbl modules
glbl.modules['matrices'] = matrices.Matrices()
glbl.modules['init_conds'] = __import__('nomad.initconds.' +
glbl.methods['init_conds'],
fromlist=['NA'])
glbl.modules['adapt'] = __import__('nomad.adapt.' +
glbl.methods['adapt_basis'],
fromlist=['a'])
glbl.modules['propagator'] = __import__('nomad.propagators.' +
glbl.methods['propagator'],
fromlist=['a'])
# WARNING: this should be done more eloquently
# need to determine form of the coefficients on the kinetic energy
# operator, i.e. cartesian vs. normal mode coordinate basis
# this is messy -- should be re-worked mvoing forward...
if glbl.methods['interface'] == 'vibronic':
# if normal modes, read frequencies from vibronic interface
kecoef = 0.5 * glbl.modules['interface'].ham.freq
else:
kecoef = 0.5 / glbl.properties['crd_masses']
glbl.modules['integrals'] = integral.Integral(
kecoef, glbl.methods['ansatz'], glbl.methods['integral_eval'])
# now load the initial trajectories into the bundle
if glbl.properties['restart']:
# print to logfile that we're restarting simulation
log.print_message('string', [
' restarting simulation from checkpoint file: ' +
str(glbl.paths['chkpt_file']) + '\n'
])
# retrieve current wave function, no arguments defaults to most recent simulation
wfn0 = None
ints0 = None
if glbl.mpi['rank'] == 0:
[glbl.modules['wfn'],
glbl.modules['integrals']] = checkpoint.retrieve_simulation(
time=glbl.properties['restart_time'])
[wfn0, ints0] = checkpoint.retrieve_simulation(time=0.)
# only root reads checkpoint file -- then broadcasts contents to other proceses
if glbl.mpi['parallel']:
# synchronize tasks
glbl.mpi['comm'].barrier()
glbl.modules['wfn'] = glbl.mpi['comm'].bcast(glbl.modules['wfn'],
root=0)
wfn0 = glbl.mpi['comm'].bcast(wfn0, root=0)
if glbl.modules['integrals'].require_centroids:
glbl.modules['integrals'].centroid_required = glbl.mpi[
'comm'].bcast(glbl.modules['integrals'].centroid_required,
root=0)
glbl.modules['integrals'].centroids = glbl.mpi['comm'].bcast(
glbl.modules['integrals'].centroids, root=0)
# save copy of t=0 wfn for autocorrelation function
save_initial_wavefunction(wfn0)
# check that we have all the data we need to propagate the first step -- otherwise, we
# need to update the potential
update_surface = False
for i in range(glbl.modules['wfn'].n_traj()):
if glbl.modules['wfn'].traj[i].alive and glbl.modules['wfn'].traj[
i].active:
pes_data = glbl.modules['wfn'].traj[i].pes
if ('potential' not in pes_data.avail_data()
or 'derivative' not in pes_data.avail_data()):
update_suface = True
if glbl.modules['integrals'].require_centroids:
for j in range(i):
pes_data = glbl.modules['integrals'].centroids[i][j].pes
if glbl.modules['integrals'].centroid_required[i][j]:
if ('potential' not in pes_data.avail_data()
and glbl.modules['wfn'].traj[i].state
== glbl.modules['wfn'].traj[j].state):
update_surface = True
if ('derivative' not in pes_data.avail_data()
and glbl.modules['wfn'].traj[i].state !=
glbl.modules['wfn'].traj[j].state):
update_surface = True
if update_surface:
evaluate.update_pes(glbl.modules['wfn'])
# update the couplings for all the trajectories
for i in range(glbl.modules['wfn'].n_traj()):
glbl.modules['interface'].evaluate_coupling(
glbl.modules['wfn'].traj[i])
# build the necessary matrices
glbl.modules['matrices'].build(glbl.modules['wfn'],
glbl.modules['integrals'])
glbl.modules['wfn'].update_matrices(glbl.modules['matrices'])
else:
# first generate the initial nuclear coordinates and momenta
# and add the resulting trajectories to the bundle
glbl.modules['init_conds'].set_initial_coords(glbl.modules['wfn'])
# set the initial state of the trajectories in bundle. This may
# require evaluation of electronic structure
set_initial_state(glbl.modules['wfn'])
# set the initial amplitudes of the basis functions
set_initial_amplitudes(glbl.modules['wfn'])
# add virtual basis functions, if desired
if glbl.properties['virtual_basis']:
virtual_basis(glbl.modules['wfn'])
# update the integrals
glbl.modules['integrals'].update(glbl.modules['wfn'])
# once phase space position and states of the basis functions
# are set, update the potential information
evaluate.update_pes(glbl.modules['wfn'])
# update the couplings for all the trajectories
for i in range(glbl.modules['wfn'].n_traj()):
glbl.modules['interface'].evaluate_coupling(
glbl.modules['wfn'].traj[i])
# compute the hamiltonian matrix...
glbl.modules['matrices'].build(glbl.modules['wfn'],
glbl.modules['integrals'])
glbl.modules['wfn'].update_matrices(glbl.modules['matrices'])
# so that we may appropriately renormalize to unity
glbl.modules['wfn'].renormalize()
# this is the bundle at time t=0. Save in order to compute auto
# correlation function
save_initial_wavefunction(glbl.modules['wfn'])
# write the wavefunction to the archive
if glbl.mpi['rank'] == 0:
checkpoint.archive_simulation(glbl.modules['wfn'],
glbl.modules['integrals'])
log.print_message(
'string', ['\n ***********\n' + ' propagation\n' + ' ***********\n\n'])
log.print_message('t_step', [
glbl.modules['wfn'].time, glbl.properties['default_time_step'],
glbl.modules['wfn'].nalive
])
return glbl.modules['wfn'].time
