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#
# Ergodic Dynamics of Ising Model
# R simulation functions, data generation
# (c) 2013, 2014 by Dr.Mehmet Suzen
# GPLv3 or higher
#
source("isingErgodicity.R"); #
# Generate Data
# Parameters
N <- c(32, 64, 128, 256, 512)
ikBT <- c(0.5, 1.0, 1.5, 2.0)
H <- c(-1.0, -0.50, 0.0, 0.50, 1.0)
#
J <- 1.0
nstep <- 500000
for(i in 1:length(N)) {
for(j in 1:length(ikBT)) {
for(k in 1:length(H)) {
print(N[i])
print(ikBT[j])
print(H[k])
runSim(ikBT[j], J, H[k], N[i], nstep, 1) # metropolis
runSim(ikBT[j], J, H[k], N[i], nstep, 2) # glauber
}
}
}
# This is an example analysis
#
# magnetisationMetric <- magnetisationMetricTM1D(1.0, 1.0, 1.0, 50, 500000, 1) # metropolis
# metricTM <- magnetisationMetric[1]/magnetisationMetric # TM metric
# time <- 1:length(metricTM)
# Dt <- 1:length(metricTM)/metricTM # time evolution of the diffusion coefficient
# ff <- lm(metricTM ~ time)
# coeff <- ff$coefficients
# stdErrorsCoefficients <- coef(summary(ff))[, "Std. Error"] # metricTM == D_G t + intercept
# plot(time, metricTM)
# lines(time, ff$fitted.values)
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