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MC_MPI.py
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MC_MPI.py
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#!/usr/bin/env python
from __future__ import print_function
from __future__ import division
from tqdm.auto import tqdm
import numpy as np
from numpy.random import rand
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
#----------------------------------------------------------------------
## BLOCK OF FUNCTIONS USED IN THE MAIN CODE
#----------------------------------------------------------------------
def read_input():
''' a subroutine to get AO and MO number and number of kpoints and otehr information from input.woops'''
dataset={}
file = open('input.MC', "r")
#Default
data = file.readlines()
nt = 18 # number of temperature points
N = 16 # size of the lattice, N x N
eqSteps = 2000 # number of MC sweeps for equilibration
mcSteps = 2000 # number of MC sweeps for calculation
A_data, B_data, C_data, D_data = -15.261, 1.8389, 1.7137, 8.0904
ps = 1.59
T_low = 0.01
T_high = 400
for line in data:
key, value = line.split("=")
dataset[key.strip()] = value.strip()
# Read data
nt = int(dataset["nt"])
N = int(dataset["N"])
eqSteps = int(dataset["eqSteps"])
mcSteps = int(dataset["mcSteps"])
A_data = float(dataset["A_data"])
B_data = float(dataset["B_data"])
C_data = float(dataset["C_data"])
D_data = float(dataset["D_data"])
ps = float(dataset["ps"])
T_low = float(dataset["T_low"])
T_high = float(dataset["T_high"])
return nt, N, eqSteps, mcSteps, A_data, B_data, C_data, D_data, ps, T_low, T_high
def initialstate(N,ps):
''' generates a random spin configuration for initial condition'''
state = ps*np.random.randint(1,2, size=(N,N)) #1.51*(2*np.random.randint(2, size=(N,N))-1) #np.random.uniform(-2.98,2.98, size=(N,N))
return state
def mcmove(config, beta, ps, A_data, B_data, C_data, D_data):
'''Monte Carlo move using Metropolis algorithm '''
for i in range(N*N):
a = np.random.randint(0, N)
b = np.random.randint(0, N)
# Nearest neighbour mean value
nb = (config[(a+1)%N,b] + config[a,(b+1)%N] + config[(a-1)%N,b] + config[a,(b-1)%N])/4.
# site polarization before change
s = config[a, b]
# site polarization after change, note here I constrain the max polarization to be 2*ps.
s1 = np.random.uniform(-2*ps,2*ps)
# unitcell energy before and after
cost_unitcell_orig = A_data/2.*s**2.+B_data/4.*s**4.+C_data/6.*s**6.
cost_unitcell_change = A_data/2.*s1**2.+B_data/4.*s1**4.+C_data/6.*s1**6.
# coupling energy before and after
cost_coupling_orig = 4.*(D_data/2.)*(s-nb)**2.
cost_coupling_change = 4.*(D_data/2.)*(s1-nb)**2.
# Calculate total energy change
cost = (cost_unitcell_change + cost_coupling_change) - (cost_unitcell_orig + cost_coupling_orig)
if cost < 0:
s = s1
elif rand() < np.exp(-cost*beta):
s = s1
config[a, b] = s
return config
def calcMag(config):
'''Magnetization of a given configuration'''
mag = np.abs(np.sum(config))
return mag
#########################################################################
# Here comes the Model parameters
#########################################################################
nt, N, eqSteps, mcSteps, A_data, B_data, C_data, D_data, ps, T_low, T_high = read_input()
#########################################################################
T = np.linspace(T_low, T_high, nt);
E,M,C,X = np.zeros(nt), np.zeros(nt), np.zeros(nt), np.zeros(nt)
E_1,M_1,C_1,X_1 = np.zeros(nt), np.zeros(nt), np.zeros(nt), np.zeros(nt)
n1, n2 = 1.0/(mcSteps*N*N), 1.0/(mcSteps*mcSteps*N*N)
# divide by number of samples, and by system size to get intensive values
#----------------------------------------------------------------------
# MAIN PART OF THE CODE
#----------------------------------------------------------------------
# exit if we have more processes then the number of temperature points
if nt < size-1:
if rank == 0:
print('**ERROR: More processes then the number of temperatrue points.')
exit()
# automatically convert number of temperature steps to be devideable by "size"
if rank == 0:
print(" _ ____ __ __ ____ \n"
" _ __ ___ _ __ (_) _ \ _ _| \/ |/ ___|\n"
" | '_ ` _ \| '_ \| | |_) | | | | |\/| | | \n"
" | | | | | | |_) | | __/| |_| | | | | |___ \n"
" |_| |_| |_| .__/|_|_| \__, |_| |_|\____|\n"
" |_| |___/ \n")
pbar = tqdm(total=nt*(eqSteps+mcSteps),desc="total_% ")
# for tt in tqdm(range(int(nt*rank/size),int(nt*(rank+1)/size)),desc="total_% ",ncols=100):
# E1 = M1 = E2 = M2 = 0
# config = initialstate(N, ps)
# iT=1.0/(T[tt]*0.08617)
#
# for i in range(eqSteps):
# # for i in tqdm(range(eqSteps),desc="Eq_steps",leave=False,ncols=100): # equilibrate
# mcmove(config, iT, ps, A_data, B_data, C_data, D_data) # Monte Carlo moves
#
# for i in range(mcSteps):
# # for i in tqdm(range(mcSteps),desc="MC_steps",leave=False,ncols=100):
# mcmove(config, iT, ps, A_data, B_data, C_data, D_data)
# Mag = calcMag(config) # calculate the magnetisation
#
# M1 = M1 + Mag
# #average Polarization
# M_1[tt] = n1*M1
#
# comm.send(M_1,dest=0,tag=rank)
if rank != 0:
for tt in range(int(nt*(rank-1)/(size-1)),int(nt*((rank-1)+1)/(size-1))):
E1 = M1 = E2 = M2 = 0
config = initialstate(N, ps)
iT=1.0/(T[tt]*0.08617)
for i in range(eqSteps): # equilibrate
mcmove(config, iT, ps, A_data, B_data, C_data, D_data) # Monte Carlo moves
comm.send(None,dest=0,tag=0)
for i in range(mcSteps):
mcmove(config, iT, ps, A_data, B_data, C_data, D_data)
Mag = calcMag(config) # calculate the magnetisation
M1 = M1 + Mag
comm.send(None,dest=0,tag=0)
#average Polarization
M_1[tt] = n1*M1
# print(str(rank)+'fuck',flush=True)
comm.send(M_1,dest=0,tag=rank)
if rank == 0:
# for i in tqdm(range(1,size),desc="total_% sub ",ncols=100):
remaining = size - 1
while remaining > 0:
s = MPI.Status()
comm.Probe(status=s)
if s.tag == 0:
comm.recv(tag=0)
pbar.update()
else:
# collect all data from MPI process
M += comm.recv(source=s.tag,tag=s.tag)
remaining -= 1
pbar.close()
# Write data to Polarization.txt
with open('Polarization.txt', 'w') as f:
for i in range(nt):
print("{0:4d} {1:5f}".format(i,M[i]),file=f)
# Plotting ....
f = plt.figure(figsize=(18, 10)); # plot the calculated values
plt.scatter(T, abs(M), s=50, marker='o', color='RoyalBlue')
plt.xlabel("Temperature (K)", fontsize=20);
plt.ylabel("Magnetization ", fontsize=20); plt.axis('tight');
plt.savefig("MC.png")
print("\n\n Done.")