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mc_nvt_sc.py
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mc_nvt_sc.py
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#!/usr/bin/env python3
# mc_nvt_sc.py
#------------------------------------------------------------------------------------------------#
# This software was written in 2016/17 #
# by Michael P. Allen <[email protected]>/<[email protected]> #
# and Dominic J. Tildesley <[email protected]> ("the authors"), #
# to accompany the book "Computer Simulation of Liquids", second edition, 2017 ("the text"), #
# published by Oxford University Press ("the publishers"). #
# #
# LICENCE #
# Creative Commons CC0 Public Domain Dedication. #
# To the extent possible under law, the authors have dedicated all copyright and related #
# and neighboring rights to this software to the PUBLIC domain worldwide. #
# This software is distributed without any warranty. #
# You should have received a copy of the CC0 Public Domain Dedication along with this software. #
# If not, see <http://creativecommons.org/publicdomain/zero/1.0/>. #
# #
# DISCLAIMER #
# The authors and publishers make no warranties about the software, and disclaim liability #
# for all uses of the software, to the fullest extent permitted by applicable law. #
# The authors and publishers do not recommend use of this software for any purpose. #
# It is made freely available, solely to clarify points made in the text. When using or citing #
# the software, you should not imply endorsement by the authors or publishers. #
#------------------------------------------------------------------------------------------------#
"""Monte Carlo, NVT ensemble, linear hard molecules."""
def calc_variables ( ):
"""Calculates all variables of interest.
They are collected and returned as a list, for use in the main program.
"""
import numpy as np
import math
from mc_sc_module import n_overlap
from averages_module import VariableType
from maths_module import nematic_order
# Preliminary calculations (m_ratio, eps_box, box are taken from the calling program)
vir = n_overlap ( box/(1.0+eps_box), r, e ) / (3.0*eps_box) # Virial
vol = box**3 # Volume
rho = n / vol # Density
ord = nematic_order ( e ) # Order
# Variables of interest, of class VariableType, containing three attributes:
# .val: the instantaneous value
# .nam: used for headings
# .method: indicating averaging method
# If not set below, .method adopts its default value of avg
# The .nam and some other attributes need only be defined once, at the start of the program,
# but for clarity and readability we assign all the values together below
# Move acceptance ratio
m_r = VariableType ( nam = 'Move ratio', val = m_ratio, instant = False )
# Pressure in units kT/sigma**3
# Ideal gas contribution plus total virial divided by V
p = VariableType ( nam = 'P', val = rho + vir/vol )
# Orientational order parameter
order = VariableType ( nam = 'Nematic order', val = ord )
# Collect together into a list for averaging
return [ m_r, p, order ]
# Takes in a configuration of linear molecules (positions and orientations)
# Cubic periodic boundary conditions
# Conducts Monte Carlo (the temperature is irrelevant)
# Uses no special neighbour lists
# Reads several variables and options from standard input using JSON format
# Leave input empty "{}" to accept supplied defaults
# We take kT=1 throughout defining the unit of energy
# Positions r are divided by box length after reading in
# However, input configuration, output configuration, most calculations, and all results
# are given in simulation units defined by the model
# Despite the program name, there is nothing here specific to spherocylinders
# The model is defined in mc_sc_module
import json
import sys
import numpy as np
import math
from platform import python_version
from config_io_module import read_cnf_mols, write_cnf_mols
from averages_module import run_begin, run_end, blk_begin, blk_end, blk_add
from maths_module import random_translate_vector, random_rotate_vector
from mc_sc_module import introduction, conclusion, overlap, overlap_1
cnf_prefix = 'cnf.'
inp_tag = 'inp'
out_tag = 'out'
sav_tag = 'sav'
print('mc_nvt_sc')
print('Python: '+python_version())
print('NumPy: '+np.__version__)
print()
print('Monte Carlo, constant-NVT ensemble')
# Read parameters in JSON format
try:
nml = json.load(sys.stdin)
except json.JSONDecodeError:
print('Exiting on Invalid JSON format')
sys.exit()
# Set default values, check keys and typecheck values
defaults = {"nblock":10, "nstep":1000, "dr_max":0.05, "de_max":0.05, "eps_box":0.001}
for key, val in nml.items():
if key in defaults:
assert type(val) == type(defaults[key]), key+" has the wrong type"
else:
print('Warning', key, 'not in ',list(defaults.keys()))
# Set parameters to input values or defaults
nblock = nml["nblock"] if "nblock" in nml else defaults["nblock"]
nstep = nml["nstep"] if "nstep" in nml else defaults["nstep"]
dr_max = nml["dr_max"] if "dr_max" in nml else defaults["dr_max"]
de_max = nml["de_max"] if "de_max" in nml else defaults["de_max"]
eps_box = nml["eps_box"] if "eps_box" in nml else defaults["eps_box"]
introduction()
np.random.seed()
# Write out parameters
print( "{:40}{:15d} ".format('Number of blocks', nblock) )
print( "{:40}{:15d} ".format('Number of steps per block', nstep) )
print( "{:40}{:15.6f}".format('Maximum displacement', dr_max) )
print( "{:40}{:15.6f}".format('Maximum rotation', de_max) )
print( "{:40}{:15.6f}".format('Pressure scaling parameter', eps_box) )
# Read in initial configuration
n, box, r, e = read_cnf_mols ( cnf_prefix+inp_tag)
print( "{:40}{:15d} ".format('Number of particles', n) )
print( "{:40}{:15.6f}".format('Box length', box) )
print( "{:40}{:15.6f}".format('Density', n/box**3) )
r = r / box # Convert positions to box units
r = r - np.rint ( r ) # Periodic boundaries
# Initial pressure and overlap check
assert not overlap ( box, r, e ), 'Overlap in initial configuration'
# Initialize arrays for averaging and write column headings
m_ratio = 0.0
run_begin ( calc_variables() )
for blk in range(1,nblock+1): # Loop over blocks
blk_begin()
for stp in range(nstep): # Loop over steps
moves = 0
for i in range(n): # Loop over atoms
ri = random_translate_vector ( dr_max/box, r[i,:] ) # Trial move to new position (in box=1 units)
ri = ri - np.rint ( ri ) # Periodic boundary correction
ei = random_rotate_vector ( de_max, e[i,:] ) # Trial move to new orientation
rj = np.delete(r,i,0) # Array of all the other atoms
ej = np.delete(e,i,0) # Array of all the other atoms
if not overlap_1 ( ri, ei, box, rj, ej ): # Test for non-overlapping configuration
r[i,:] = ri # Update position
e[i,:] = ei # Update position
moves = moves + 1 # Increment move counter
m_ratio = moves / n
blk_add ( calc_variables() )
blk_end(blk) # Output block averages
sav_tag = str(blk).zfill(3) if blk<1000 else 'sav' # Number configuration by block
write_cnf_mols ( cnf_prefix+sav_tag, n, box, r*box, e ) # Save configuration
run_end ( calc_variables() )
assert not overlap ( box, r, e ), 'Overlap in final configuration'
write_cnf_mols ( cnf_prefix+out_tag, n, box, r*box, e ) # Save configuration
conclusion()