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dlma/scripts/setup_simulation_dlca.py
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setup_simulation_dlca.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 4 13:56:09 2021
@author: samarth
"""
import pandas as pd
import os
import sys
from pathlib import Path
from joblib import Parallel, delayed
from matplotlib import pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
from random import random, seed
import subprocess
def create_dirs(phi, N, seed_pct, dim):
global D
D = dim
global params_dir
phi_str = format(phi, '.5f')
params_dir = './'+str(D)+'d/params/phi='+phi_str+'/seed_pct='+str(seed_pct)+'/N='+str(N)+'/'
path = Path(params_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
global results_dir
results_dir = './'+str(D)+'d/results/phi='+phi_str+'/seed_pct='+str(seed_pct)+'/N='+str(N)+'/'
path = Path(results_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
global config_files_dir
config_files_dir = './'+str(D)+'d/config_files/phi='+phi_str+'/seed_pct='+str(seed_pct)+'/N='+str(N)+'/'
path = Path(config_files_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
def run_simulation(phi, N, seed_pct, rng_seed):
lattice=1
x0_bc="periodic"
x1_bc="periodic"
x2_bc="periodic"
alpha=0.5
seedMass=1e12
params_file = params_dir+'params_'+str(rng_seed)+'.csv'
file = open(params_file, "w")
file.write("system=dlma\n")
file.write("lattice="+str(lattice)+"\n")
file.write("aggregation_type=normal\n")
file.write("aggregation_condition=mass\n")
file.write("bind=normal\n")
file.write("movement=brownian\n")
file.write("agg_dist_tolerance=0.0\n")
file.write("D="+str(D)+"\n")
file.write("N="+str(N)+"\n")
file.write("seed_pct="+str(seed_pct)+"\n")
file.write("x0_bc="+str(x0_bc)+"\n")
file.write("x1_bc="+str(x1_bc)+"\n")
file.write("x2_bc="+str(x2_bc)+"\n")
file.write("x3_bc="+str(x2_bc)+"\n")
file.write("phi="+str(phi)+"\n")
file.write("alpha="+str(alpha)+"\n")
file.write("seedMass="+str(seedMass)+"\n")
file.write("rng_seed="+str(rng_seed)+"\n")
file.close()
print("seed_pct = {}, phi = {}, N={},rng_seed={} simulation".format(seed_pct,phi,N,rng_seed))
config_filename=config_files_dir+str(rng_seed)+'.csv'
command = 'simulation '+params_file+' '+config_filename
os.system(command)
"""print("seed_pct = {}, phi = {}, N={},rng_seed={} lbp".format(seed_pct,phi,N,rng_seed))
rog_filename=results_dir+'lbp_'+str(rng_seed)+'.csv'
time_filename=results_dir+'time_'+str(rng_seed)+'.csv'
command = 'lb_bonds_clusterwise_invA ' + config_filename +' ' + rog_filename + ' ' + time_filename
os.system(command)
command = 'sh -c \'echo 1 >/proc/sys/vm/drop_caches\''
#print(command)
os.system(command)
command = 'sh -c \'echo 2 >/proc/sys/vm/drop_caches\''
#print(command)
os.system(command)
command = 'sh -c \'echo 3 >/proc/sys/vm/drop_caches\''
#print(command)
os.system(command)"""
def create_dirs_for_mk_table():
global D
D = dim
global params_dir
params_dir = './mk_table/params/'
path = Path(params_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
global results_dir
results_dir = './mk_table/results/'
path = Path(results_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
global config_files_dir
config_files_dir = './mk_table/config_files/'
path = Path(config_files_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
def run_simulation_for_random_params(phi, N, rng_seed):
lattice=1
x0_bc="periodic"
x1_bc="periodic"
x2_bc="periodic"
alpha=0.5
seedMass=1e12
params_file = params_dir+'params_'+str(rng_seed)+'.csv'
file = open(params_file, "w")
file.write("system=dlma\n")
file.write("lattice="+str(lattice)+"\n")
file.write("aggregation_type=normal\n")
file.write("aggregation_condition=mass\n")
file.write("bind=normal\n")
file.write("movement=brownian\n")
file.write("agg_dist_tolerance=0.0\n")
file.write("D="+str(D)+"\n")
file.write("N="+str(N)+"\n")
file.write("seed_pct="+str(seed_pct)+"\n")
file.write("x0_bc="+str(x0_bc)+"\n")
file.write("x1_bc="+str(x1_bc)+"\n")
file.write("x2_bc="+str(x2_bc)+"\n")
file.write("phi="+str(phi)+"\n")
file.write("alpha="+str(alpha)+"\n")
file.write("seedMass="+str(seedMass)+"\n")
file.write("rng_seed="+str(rng_seed)+"\n")
file.close()
print("phi = {}, N={},rng_seed={} simulation".format(phi,N,rng_seed))
config_filename=config_files_dir+str(rng_seed)+'.csv'
command = 'simulation '+params_file+' '+config_filename
os.system(command)
"""print("phi = {}, N={},rng_seed={} lbp".format(phi,N,rng_seed))
rog_filename=results_dir+'lbp_'+str(rng_seed)+'.csv'
time_filename=results_dir+'time_'+str(rng_seed)+'.csv'
command = 'lb_bonds_clusterwise_invA ' + config_filename +' ' + rog_filename + ' ' + time_filename
os.system(command)"""
seed(18)
seeds=50
seed_pct_range=[100]
dim = 3
#phi_range = [0.125]
phi_range = []
for i in range(5,9):
phi_range.append(0.125 * (1.125**i))
N_range = [64000]
phi_rngs = np.ones(seeds)
N_rngs = np.ones(seeds)
"""for i in range(seeds):
phi_rngs[i] = phi_range[0] + (phi_range[1] - phi_range[0]) * random()
N_rngs[i] = N_range[0] + (N_range[1] - N_range[0]) * random()
N_rngs[i] = int(N_rngs[i])
create_dirs_for_mk_table()
for rng_seed in range(seeds):
run_simulation_for_random_params(phi_rngs[rng_seed], N_rngs[rng_seed], rng_seed)"""
for seed_pct in seed_pct_range:
for phi in phi_range:
for N in N_range:
create_dirs(phi, N, seed_pct, dim)
Parallel(n_jobs=10)(delayed(run_simulation)(phi, N, seed_pct, rng_seed) for rng_seed in range(seeds))
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