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dlma/scripts/setup_simulation.py
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setup_simulation.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
def create_dirs():
global params_dir
params_dir = './params/'
path = Path(params_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
global results_dir
results_dir = './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 = './config_files/'
path = Path(config_files_dir)
try:
path.mkdir(parents=True, exist_ok=True)
except OSError:
pass
else:
pass
def run_simulation(N, rng_seed):
phi=0.01
D=2
L=pow((N/phi),1/D)
lattice=1
N_s=1
x0_periodic=1
x1_periodic=1
x0_L=L
x1_L=L
alpha=-0.5
seedMass=1e12
params_file = params_dir+'params_'+str(rng_seed)+'.csv'
file = open(params_file, "w")
file.write("lattice="+str(lattice)+"\n")
file.write("D="+str(D)+"\n")
file.write("N="+str(N)+"\n")
file.write("x0_periodic="+str(x0_periodic)+"\n")
file.write("x1_periodic="+str(x1_periodic)+"\n")
file.write("x0_L="+str(x0_L)+"\n")
file.write("x1_L="+str(x1_L)+"\n")
file.write("alpha="+str(alpha)+"\n")
file.write("seedMass="+str(seedMass)+"\n")
file.write("N_s="+str(N_s)+"\n")
file.write("rng_seed="+str(rng_seed)+"\n")
file.close()
print("N={}, rng_seed={}".format(N,rng_seed))
config_filename=config_files_dir+str(rng_seed)+'.csv'
command = 'simulation.out '+params_file+' '+config_filename
os.system(command)
rog_filename=results_dir+'rog_'+str(rng_seed)+'.csv'
command = 'radius_of_gyration ' + config_filename +' ' + rog_filename
os.system(command)
def calc_mean_rog(seeds):
rog_df = pd.DataFrame(columns=['rog'])
for rng_seed in range(seeds):
rog_filename=results_dir+'rog_'+str(rng_seed)+'.csv'
temp_df = pd.read_csv(rog_filename)
rog_df.loc[len(rog_df)] = temp_df.iloc[0]['Radius_of_Gyration']
return rog_df['rog'].mean()
create_dirs()
rog_df = pd.DataFrame(columns=(['N', 'Rg']))
seeds=10
for N in range(1000,3501,500):
Parallel(n_jobs=-1)(delayed(run_simulation)(N, rng_seed) for rng_seed in range(seeds))
rog_mean=calc_mean_rog(seeds)
rog_df.loc[len(rog_df)] = [N, rog_mean]
x = np.log(rog_df['N'].values)
x = x.reshape(-1,1)
y = np.log(rog_df['Rg'].values)
model = LinearRegression()
model.fit(x,y)
print(model.coef_[0])
plt.figure(dpi=300)
plt.scatter(rog_df['N'], rog_df['Rg'], color='red')
plt.plot(np.exp(x), np.exp(y))
plt.xscale('log')
plt.yscale('log')
plt.savefig('rg_dla.pdf', format='pdf')
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