"""
Example single-site Lennard-Jones canonical ensemble Monte Carlo simulation using FEASST.
Run multiple densities using multiple processors/nodes/restarts, and plot results.
Compare with T*=0.9 in https://mmlapps.nist.gov/srs/LJ_PURE/mc.htm.
Note that checkpoints and restarts happen quicker than usual in these tutorial for testing purposes.
Usage: python /path/to/feasst/tutorial/launch.py
Options: python /path/to/feasst/tutorial/launch.py --help
"""
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.fstprt',
help='FEASST particle definition')
parser.add_argument('--beta', type=float, default=1./0.9, help='inverse temperature')
parser.add_argument('--num_particles', type=int, default=500, help='number of particles')
parser.add_argument('--density_lower', type=float, default=0.001, help='lowest number density')
parser.add_argument('--density_upper', type=float, default=0.009, help='highest number density')
parser.add_argument('--tpi', type=int, default=int(1e4),
help='trials per iteration, similar to MC cycles, but not necessary num_particles')
parser.add_argument('--equilibration_iterations', type=int, default=int(1e1),
help='number of iterations for equilibraiton')
parser.add_argument('--production_iterations', type=int, default=int(1e1),
help='number of iterations for production')
parser.add_argument('--hours_checkpoint', type=float, default=0.1, help='hours per checkpoint')
parser.add_argument('--hours_terminate', type=float, default=0.1,
help='hours until termination')
parser.add_argument('--procs_per_node', type=int, default=5, 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['prefix'] = 'lj'
params['script'] = __file__
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['densities'] = np.linspace(params['density_lower'], params['density_upper'],
num=params['num_sims'])
params['cubic_side_lengths'] = np.power(params['num_particles']/
params['densities'], 1./3.).tolist()
params['densities'] = params['densities'].tolist()
return params, args
def sim_node_dependent_params(params):
""" Set parameters that depend upon the sim or node here. """
params['cubic_side_length'] = params['cubic_side_lengths'][params['sim']]
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}
Potential Model LennardJones
#Potential Model LennardJones VisitModel VisitModelCell min_length max_cutoff
Potential VisitModel LongRangeCorrections
ThermoParams beta {beta} chemical_potential -1
Metropolis
TrialTranslate tunable_param 2 tunable_target_acceptance 0.2
Checkpoint checkpoint_file {prefix}{sim}_checkpoint.fst num_hours {hours_checkpoint} num_hours_terminate {hours_terminate}
# grand canonical ensemble initalization
TrialAdd particle_type 0
Run until_num_particles {num_particles}
RemoveTrial name TrialAdd
# canonical ensemble equilibration
Metropolis num_trials_per_iteration {tpi} num_iterations_to_complete {equilibration_iterations}
Tune
CheckEnergy trials_per_update {tpi} decimal_places 8
Log trials_per_write {tpi} output_file {prefix}{sim}_eq.csv
Run until_criteria_complete true
RemoveModify name Tune
RemoveAnalyze name Log
# canonical ensemble production
Metropolis num_trials_per_iteration {tpi} num_iterations_to_complete {production_iterations}
Log trials_per_write {tpi} output_file {prefix}{sim}.csv
Movie trials_per_write {tpi} output_file {prefix}{sim}.xyz
Energy trials_per_write {tpi} output_file {prefix}{sim}_en.csv
CPUTime trials_per_write {tpi} output_file {prefix}{sim}_cpu.txt
GhostTrialVolume trials_per_write {tpi} output_file {prefix}{sim}_pressure.csv trials_per_update {tpi}
ProfileCPU trials_per_write {tpi} output_file {prefix}{sim}_profile.csv
Run until_criteria_complete true
""".format(**params))
def post_process(params):
""" Plot energy and compare with https://mmlapps.nist.gov/srs/LJ_PURE/mc.htm """
ens = np.zeros(shape=(params['num_sims'], 2))
pres = np.zeros(shape=(params['num_sims'], 2))
for sim in range(params['num_sims']):
log = pd.read_csv(params['prefix']+str(sim)+'.csv')
assert int(log['num_particles_of_type0'][0]) == params['num_particles']
energy = pd.read_csv(params['prefix']+str(sim)+'_en.csv')
ens[sim] = np.array([energy['average'][0],
energy['block_stdev'][0]])/params['num_particles']
pressure = pd.read_csv(params['prefix']+str(sim)+'_pressure.csv')
pres[sim] = np.array([pressure['average'][0], pressure['block_stdev'][0]])
# data from https://mmlapps.nist.gov/srs/LJ_PURE/mc.htm
rhos_srsw = [0.001, 0.003, 0.005, 0.007, 0.009]
if len(rhos_srsw) == params['num_sims']: # compare with srsw exactly
en_srsw = [-9.9165E-03, -2.9787E-02, -4.9771E-02, -6.9805E-02, -8.9936E-02]
en_stds_srsw = [1.89E-05, 3.21E-05, 3.80E-05, 7.66E-05, 2.44E-05]
p_srsw = [8.9429E-04, 2.6485E-03, 4.3569E-03, 6.0193E-03, 7.6363E-03]
p_stds_srsw = [2.48E-08, 2.54E-07, 2.19E-07, 1.02E-06, 1.44E-06]
plt.errorbar(rhos_srsw, en_srsw, en_stds_srsw, fmt='+', label='SRSW')
plt.errorbar(params['densities'], ens[:, 0], ens[:, 1], fmt='x', label='FEASST')
plt.xlabel(r'$\rho$', fontsize=16)
plt.ylabel(r'$U/(N\epsilon)$', fontsize=16)
plt.legend(fontsize=16)
#plt.show()
#plt.savefig(params['prefix']+'_energy.png', bbox_inches='tight', transparent='True')
plt.clf()
plt.errorbar(rhos_srsw, p_srsw, p_stds_srsw, fmt='+', label='SRSW')
plt.errorbar(params['densities'], pres[:, 0], pres[:, 1], fmt='x', label='FEASST')
plt.xlabel(r'$\rho$', fontsize=16)
plt.ylabel(r'$P\sigma^3/\epsilon$', fontsize=16)
plt.legend(fontsize=16)
#plt.show()
#plt.savefig(params['prefix']+'_pressure.png', bbox_inches='tight', transparent='True')
for sim in range(params['num_sims']):
diff = ens[sim][0] - en_srsw[sim]
assert np.abs(diff) < 10*np.sqrt(ens[sim][1]**2 + en_stds_srsw[sim]**2)
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)