"""
Example single-site Lennard-Jones Gibbs ensemble Monte Carlo simulation using FEASST.
"""
import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyfeasst import fstio
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_new.txt',
help='FEASST particle definition')
parser.add_argument('--beta', type=float, default=1., help='inverse temperature')
parser.add_argument('--tpc', type=int, default=int(1e5), help='trials per cycle')
parser.add_argument('--equilibration_cycles', type=int, default=int(1e2),
help='number of cycles for equilibraiton')
parser.add_argument('--production_cycles', type=int, default=int(3e2),
help='number of cycles for production')
parser.add_argument('--hours_checkpoint', type=float, default=1, help='hours per checkpoint')
parser.add_argument('--hours_terminate', type=float, default=1, help='hours until termination')
parser.add_argument('--procs_per_node', type=int, default=1, 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('--queue_flags', type=str, default="", help='extra flags for queue (e.g., for slurm, "-p queue")')
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'] = 'lj'
params['sim_id_file'] = params['prefix']+ '_sim_ids.txt'
params['minutes'] = int(params['hours_terminate']*60) # minutes allocated on queue
params['hours_terminate'] = 0.99*params['hours_terminate'] - 0.0333 # terminate before queue
params['procs_per_sim'] = 1
params['num_sims'] = params['num_nodes']*params['procs_per_node']
params['equil'] = params['equilibration_cycles']*params['tpc']
params['double_equil'] = 2*params['equil']
return params, args
def sim_node_dependent_params(params):
""" Set parameters that depent upon the sim or node here. """
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}
# purposefully start with a bad volume guess to see if equilibration adjusts volume
For [config]:[len]=vapor:16,liquid:8
Configuration name=[config] cubic_side_length=[len] particle_type=fluid:{fstprt}
Potential Model=LennardJones config=[config]
Potential VisitModel=LongRangeCorrections config=[config]
RefPotential VisitModel=DontVisitModel ref=noixn config=[config]
EndFor
ThermoParams beta={beta} chemical_potential=5
Metropolis
For [config]:[param]=vapor:2.0,liquid:0.1
TrialTranslate tunable_param=[param] config=[config]
EndFor
CheckEnergy trials_per_update={tpc} decimal_places=8
Checkpoint checkpoint_file={prefix}{sim:03d}_checkpoint.fst num_hours={hours_checkpoint} num_hours_terminate={hours_terminate}
# fill both boxes with particles
Let [write]=trials_per_write={tpc} output_file={prefix}{sim:03d}
Log [write]_fill.csv
Tune
For [config]:[num]=vapor:112,liquid:400
Movie [write]_[config]_fill.xyz config=[config]
TrialAdd particle_type=fluid config=[config]
Run until_num_particles=[num] config=[config]
Remove name=TrialAdd,Movie
EndFor
# gibbs equilibration cycles: equilibrate, estimate density, adjust, repeat
# start a very long run GibbsInitialize completes once targets are reached
Metropolis trials_per_cycle=1e9 cycles_to_complete=1e9
GibbsInitialize updates_density_equil={equil} updates_per_adjust={double_equil}
TrialGibbsParticleTransfer weight=0.5 particle_type=fluid ref=noixn print_num_accepted=true configs=vapor,liquid
TrialGibbsVolumeTransfer weight=0.01 tunable_param=10. tunable_target_acceptance=0.5 ref=noixn print_num_accepted=true configs=vapor,liquid
Log [write]_eq.csv
For [config]=vapor,liquid
Movie [write]_[config]_eq.xyz config=[config]
EndFor
ProfileCPU [write]_eq_profile.csv
# a new tune is required when new Trials are introduced
# decrease trials per due to infrequency of volume transfer attempts
Tune trials_per_tune=20
Run until=complete
Remove name=GibbsInitialize,Tune,Log,Movie,Movie,ProfileCPU
# gibbs ensemble production
Metropolis trials_per_cycle={tpc} cycles_to_complete={production_cycles}
Log [write].csv
For [analyze]:[file]=Density:_dens.csv,Movie:.xyz,Energy:_en.csv,Volume:_vol.csv,ProfileCPU:_profile.csv,CPUTime:_cpu.csv
For [config]=vapor,liquid
[analyze] [write]_[config][file] config=[config]
EndFor
EndFor
GhostTrialVolume [write]_pressure.csv trials_per_update=1e3
Run until complete
""".format(**params))
def compare(label, average, stdev, params, z_factor=5):
df = pd.read_csv(params['prefix']+"000_"+label+".csv")
df['diff'] = np.abs(df['average']-average)
df['tol'] = np.sqrt(df['block_stdev']**2+stdev**2)
print(label, df)
diverged = df[df['diff'] > z_factor*df['tol']]
if len(diverged) > 0:
print(diverged)
assert len(diverged) == 0
def post_process(params):
z_factor = 3
#fh rhov_rhol_p = [[0.1003, 0.56329, 0.07721], [9.41E-06, 4.51E-05, 5.7E-06]] # T=1.2 srsw fh
rhov_rhol_p = [[2.9556E-02, 7.0094E-01, 2.4950E-02], [3.45E-06, 6.31E-05, 1.67E-06]] # T=1 srsw fh
#fh rhov_rhol_p = [[6.1007E-03, 0.79981, 0.0046465], [5.63E-07, 0.000013, 3.74e-7]] #T=0.8 srsw fh
compare("vapor_dens", rhov_rhol_p[0][0], rhov_rhol_p[1][0], params)
compare("liquid_dens", rhov_rhol_p[0][1], rhov_rhol_p[1][1], params)
compare("pressure", rhov_rhol_p[0][2], rhov_rhol_p[1][2], params)
#if True: # set to true to plot
if False: # set to true to plot
df = pd.read_csv('lj000_eq.csv')
print(df)
#plt.plot(df['volume_vapor'])
label='num_particles_fluid'
#label='volume'
#label='energy'
if label != 'num_particles_fluid':
for config in ['vapor', 'liquid']:
plt.plot(df[label+'_'+config], label=config)
plt.ylabel(label, fontsize=16)
else:
frac_vapor = df[label+'_vapor']/(df[label+'_vapor']+df[label+'_liquid'])
plt.plot(frac_vapor)
plt.ylabel('number fraction in vapor', fontsize=16)
plt.axhline(0.15)
plt.axhline(0.1, linestyle='dashed')
plt.axhline(0.2, linestyle='dashed')
plt.xlabel('trials / 1e5', fontsize=16)
plt.savefig('plot.png', bbox_inches='tight')
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)