"""
Prefetching canonical ensemble Monte Carlo simulation of Lennard Jones particles.
"""
import argparse
import json
from pyfeasst import fstio
from pyfeasst import physical_constants
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./0.9, help='inverse temperature')
parser.add_argument('--num_particles', type=int, default=400, help='number of particles')
parser.add_argument('--cubic_side_length', type=float, default=8,
help='cubic periodic boundary length')
parser.add_argument('--tpc', type=int, default=int(1e4), 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(1e3),
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('--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_node'] = 1
params['procs_per_sim'] = 1
params['num_sims'] = params['num_nodes']*params['procs_per_node']
params['target_acceptance'] = 0.25
if params['fstprt'] == '/feasst/particle/lj_new.txt':
params['potential'] = """Potential Model=LennardJones
Potential VisitModel=LongRangeCorrections"""
params['trials'] = """TrialTranslate tunable_param=2 tunable_target_acceptance={target_acceptance}""".format(**params)
elif params['fstprt'] == '/feasst/particle/spce.txt':
params['cubic_side_length'] = 20
params['num_particles'] = 265
params['beta'] = 1./(300*physical_constants.MolarGasConstant().value()/1e3) # mol/kJ
params['alpha'] = 5.6/params['cubic_side_length']
params['dccb_cut'] = 0.9*3.165
params['dccb_cut'] = params['cubic_side_length']/int(params['cubic_side_length']/params['dccb_cut']) # maximize inside box
params['potential'] = """Potential VisitModel=Ewald alpha={alpha} kmax_squared=38
Potential Model=ModelTwoBodyFactory models=LennardJones,ChargeScreened erfc_table_size=2e4 VisitModel=VisitModelCutoffOuter
#RefPotential Model=HardSphere group=oxygen cutoff={dccb_cut} VisitModel=VisitModelCell min_length={dccb_cut} cell_group=oxygen
Potential Model=ChargeScreenedIntra VisitModel=VisitModelBond
Potential Model=ChargeSelf
Potential VisitModel=LongRangeCorrections""".format(**params)
params['trials'] = """TrialTranslate tunable_param=0.2 tunable_target_acceptance={target_acceptance}
TrialParticlePivot weight=0.5 particle_type=fluid tunable_param=0.5 tunable_target_acceptance={target_acceptance} """.format(**params)
else:
assert False, 'unrecognized fstprt'
return params, args
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("""
Prefetch synchronize=true
#Prefetch synchronize=true trials_per_check=1
RandomMT19937 seed={seed}
Configuration cubic_side_length={cubic_side_length} particle_type=fluid:{fstprt}
{potential}
ThermoParams beta={beta} chemical_potential=-1
Metropolis
{trials}
Checkpoint checkpoint_file={prefix}{sim:03d}_checkpoint.fst num_hours={hours_checkpoint} num_hours_terminate={hours_terminate}
CheckEnergy trials_per_update={tpc} decimal_places=4
# gcmc initialization
TrialAdd particle_type=fluid
Let [write]=trials_per_write={tpc} output_file={prefix}{sim:03d}
Log [write]_init.csv
Tune
Run until_num_particles={num_particles}
Remove name=TrialAdd,Log
# nvt equilibration
ThermoParams beta={beta}
Metropolis trials_per_cycle={tpc} cycles_to_complete={equilibration_cycles}
Log [write]_eq.csv
Movie [write]_eq.xyz
Run until=complete
Remove name=Tune,Log,Movie
# nvt production
Metropolis trials_per_cycle={tpc} cycles_to_complete={production_cycles}
Log [write].csv
Movie [write].xyz
Energy [write]_en.csv append=true
CPUTime [write]_time.txt append=true
Run until=complete
""".format(**params))
def linear_fit(x, b):
return -0.5*x + b
def post_process(params):
""" Compute standard deviation of energy with time """
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
time = pd.read_csv('lj000_time.txt', delim_whitespace=True, header=None)
print(time[1])
en = pd.read_csv('lj000_en.csv', header=None, comment="a")
print(en[0])
equil=500
logt = np.log(time[1][equil:])
logs = np.log(en[2][equil:])
popt, pcov = curve_fit(linear_fit, logt, logs)
plt.scatter(np.log(time[1]), np.log(en[2]))
plt.plot(logt, linear_fit(logt, popt[0]), color='black')
print(popt[0])
#plt.xscale('log')
#plt.yscale('log')
plt.show()
if __name__ == '__main__':
parameters, arguments = parse()
fstio.run_simulations(params=parameters,
sim_node_dependent_params=None,
write_feasst_script=write_feasst_script,
post_process=post_process,
queue_function=fstio.slurm_single_node,
args=arguments)