"""
Flat-histogram simulation of single-site Lennard Jones particles in the grand canonical ensemble.
Simulate the adsorption of an LJ fluid instead of a rigid porous network of WCA particles,
as described in https://doi.org/10.1021/acs.jpcb.3c00613 .
"""
import argparse
import subprocess
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyfeasst import fstio
from pyfeasst import macrostate_distribution
def parse():
# Parse arguments from command line or change their default values.
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--feasst_install', type=str, default='../../../build/',
help='FEASST install directory (e.g., the path to build)')
parser.add_argument('--fstprt', type=str, default='/feasst/particle/lj.fstprt',
help='FEASST particle definition')
parser.add_argument('--pore', type=str, default='/feasst/plugin/confinement/particle/porel4.fstprt',
help='FEASST particle definition')
parser.add_argument('--beta', type=float, default=1./0.8, help='inverse temperature')
parser.add_argument('--mu', type=float, default=-1, help='chemical potential')
parser.add_argument('--mu_init', type=float, default=10, help='initial chemical potential')
parser.add_argument('--max_particles', type=int, default=64, help='maximum number of particles')
parser.add_argument('--min_particles', type=int, default=0, help='minimum number of particles')
parser.add_argument('--min_sweeps', type=int, default=2,
help='Minimum number of sweeps defined in https://dx.doi.org/10.1063/1.4918557')
parser.add_argument('--cubic_side_length', type=float, default=9,
help='cubic periodic boundary length')
parser.add_argument('--tpc', type=int, default=int(1e5), help='trials per cycle')
parser.add_argument('--equilibration', type=int, default=1e0, help='number of cycles for equilibration')
parser.add_argument('--hours_checkpoint', type=float, default=0.02, help='hours per checkpoint')
parser.add_argument('--hours_terminate', type=float, default=0.2, help='hours until termination')
parser.add_argument('--procs_per_node', type=int, default=32, help='number of processors')
parser.add_argument('--run_type', '-r', type=int, default=0,
help='0: run, 1: submit to queue, 2: post-process')
parser.add_argument('--seed', type=int, default=-1,
help='Random number generator seed. If -1, assign random seed to each sim.')
parser.add_argument('--max_restarts', type=int, default=10, help='Number of restarts in queue')
parser.add_argument('--num_nodes', type=int, default=1, help='Number of nodes in queue')
parser.add_argument('--scratch', type=str, default=None,
help='Optionally write scheduled job to scratch/logname/jobid.')
parser.add_argument('--node', type=int, default=0, help='node ID')
parser.add_argument('--queue_id', type=int, default=-1, help='If != -1, read args from file')
parser.add_argument('--queue_task', type=int, default=0, help='If > 0, restart from checkpoint')
# Convert arguments into a parameter dictionary, and add argument-dependent parameters.
args, unknown_args = parser.parse_known_args()
assert len(unknown_args) == 0, 'An unknown argument was included: '+str(unknown_args)
params = vars(args)
params['script'] = __file__
params['prefix'] = 'pore'
params['sim_id_file'] = params['prefix']+ '_sim_ids.txt'
params['minutes'] = int(params['hours_terminate']*60) # minutes allocated on queue
params['hours_terminate'] = 0.95*params['hours_terminate'] - 0.05 # terminate FEASST before SLURM
params['procs_per_sim'] = 1
params['num_sims'] = params['procs_per_node']*params['num_nodes']
params['wca'] = 2**(1./6.)
params['dccb_cut'] = params['cubic_side_length']/int(params['cubic_side_length']/params['wca']) # maximize inside box
params['min1'] = params['min_particles'] + 2
params['minimums'] = [params['min_particles'], params['min1']]
if params['num_nodes'] == 1:
params['windows'] = macrostate_distribution.window_exponential(
alpha=1.0, minimums=params['minimums'], maximum=params['max_particles'],
number=params['num_sims'], overlap=1, min_size=2)
return params, args
def sim_node_dependent_params(params):
""" Define parameters that are dependent on the sim or node. """
params['min_particles'] = params['windows'][params['sim']][0]
params['max_particles'] = params['windows'][params['sim']][1]
params['sim_start'] = 0
params['sim_end'] = params['num_sims'] - 1
def write_feasst_script(params, script_file):
""" Write fst script for a single simulation with keys of params {} enclosed. """
with open(script_file, 'w', encoding='utf-8') as myfile:
myfile.write("""
MonteCarlo
RandomMT19937 seed {seed}
Configuration cubic_side_length {cubic_side_length} particle_type0 {fstprt} particle_type1 {pore} cutoff0 2.5 cutoff1 {wca} cutoff0_1 {wca} add_particles_of_type1 1 group0 liquid liquid_particle_type 0 group1 pore pore_particle_type 1
NeighborCriteria maximum_distance 1.375 minimum_distance 0.9 site_type0 0 site_type1 0
Potential EnergyMap EnergyMapNeighborCriteria neighbor_index 0 Model LennardJonesForceShift
RefPotential Model LennardJonesForceShift VisitModel VisitModelCell min_length {dccb_cut}
ThermoParams beta {beta} chemical_potential {mu_init}
Metropolis
TrialTranslate weight 1 particle_type 0 tunable_param 0.2 tunable_target_acceptance 0.25
TrialAVB2 weight 0.1 particle_type 0
TrialAVB4 weight 0.1 particle_type 0
CheckEnergy trials_per_update {tpc} tolerance 1e-4
Checkpoint checkpoint_file {prefix}{sim}_checkpoint.fst num_hours {hours_checkpoint} num_hours_terminate {hours_terminate}
# write the pore xyz for visualization
Movie output_file {prefix}n{node}s{sim}_pore.xyz group pore clear_file true
WriteStepper analyze_name Movie
Remove name Movie
# gcmc initialization and nvt equilibration
TrialAdd particle_type 0
Log trials_per_write {tpc} output_file {prefix}n{node}s{sim}_eq.csv
Tune
Run until_num_particles {min_particles} particle_type 0
Remove name TrialAdd
ThermoParams beta {beta} chemical_potential {mu}
Metropolis trials_per_cycle {tpc} cycles_to_complete {equilibration}
Run until complete
Remove name0 Tune name1 Log
# gcmc tm production
FlatHistogram Macrostate MacrostateNumParticles particle_type 0 width 1 max {max_particles} min {min_particles} \
Bias WLTM min_sweeps {min_sweeps} min_flatness 25 collect_flatness 20 min_collect_sweeps 1
TrialTransfer weight 2 particle_type 0 reference_index 0 num_steps 4
TrialTransferAVB weight 0.2 particle_type 0 reference_index 0 num_steps 4
#To print trajectories for each macrostate in separate files, add the following arguments to the "Movie" lines below: multistate true multistate_aggregate false
Log trials_per_write {tpc} output_file {prefix}n{node}s{sim}.csv
Movie trials_per_write {tpc} output_file {prefix}n{node}s{sim}_eq.xyz stop_after_cycle 1 group liquid
Movie trials_per_write {tpc} output_file {prefix}n{node}s{sim}.xyz start_after_cycle 1 group liquid
Tune trials_per_write {tpc} output_file {prefix}n{node}s{sim}_tune.csv multistate true stop_after_cycle 1
Energy trials_per_write {tpc} output_file {prefix}n{node}s{sim}_en.csv multistate true start_after_cycle 1
ProfileCPU trials_per_write {tpc} output_file {prefix}n{node}s{sim}_profile.csv
CriteriaWriter trials_per_write {tpc} output_file {prefix}n{node}s{sim:03d}_crit.csv
CriteriaUpdater trials_per_update 1e5
Run until complete
# continue until all simulations on the node are complete
WriteFileAndCheck sim {sim} sim_start {sim_start} sim_end {sim_end} file_prefix {prefix}n{node}s file_suffix _finished.txt output_file {prefix}n{node}_terminate.txt
Run until_file_exists {prefix}n{node}_terminate.txt trials_per_file_check {tpc}
""".format(**params))
def post_process(params):
import pandas as pd
df=macrostate_distribution.splice_files(prefix=params['prefix']+'n0s', suffix='_crit.csv', shift=False).dataframe()
#print(df['delta_ln_prob'])
delta_ln_prob_1_2_3 = [5.33710, 4.66512, 4.27458]
#print(df['delta_ln_prob_stdev'])
delta_ln_prob_std_1_2_3 = [0.002927, 0.006865, 0.005270]
df = df[1:4]
df['delta_ln_prob_prev'] = [5.33710, 4.66512, 4.27458]
df['delta_ln_prob_std_prev'] = [0.002927, 0.006865, 0.005270]
print(df)
z_factor = 6
diff = df[df.delta_ln_prob - df.delta_ln_prob_prev > z_factor*df.delta_ln_prob_stdev]
print(diff)
assert len(diff) == 0
if __name__ == '__main__':
parameters, arguments = parse()
fstio.run_simulations(params=parameters,
sim_node_dependent_params=sim_node_dependent_params,
write_feasst_script=write_feasst_script,
post_process=post_process,
queue_function=fstio.slurm_single_node,
args=arguments)