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forward-ising-problem.jl
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forward-ising-problem.jl
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using Statistics
using NPZ
using Revise
includet("plefka.jl")
function nsf(num, n=4)
"""n-Significant Figures"""
return round(num, sigdigits=n)
end
function main()
sze = 512
R = 1000000 # Repetitions of the simulation
H0 = 0.5 # Uniform distribution of fields parameter
J0 = 1.0 # Average value of couplings
Js = 0.1 # Standard deviation of couplings
B = 21 # Number of values of beta
T = 2^7 # Number of simulation time steps
betas = collect( 1 .+ range(-1, 1, length=B) .* 0.3)
for ib in 1:B
beta_ref = round(betas[ib], digits=3)
EmP_t1_t = zeros(T + 1)
EmP_t = zeros(T + 1)
EmP2_t = zeros(T + 1)
EmP_t1 = zeros(T + 1)
ECP_t1_t = zeros(T + 1)
ECP_t = zeros(T + 1)
ECP2_t = zeros(T + 1)
ECP_t1 = zeros(T + 1)
EDP_t1_t = zeros(T + 1)
EDP_t = zeros(T + 1)
EDP2_t = zeros(T + 1)
EDP_t1 = zeros(T + 1)
mP_t1_t_mean = ones(T + 1)
mP_t_mean = ones(T + 1)
mP2_t_mean = ones(T + 1)
mPexp_mean = ones(T + 1)
mP_t1_mean = ones(T + 1)
mP_t1_t_final = zeros(sze)
mP_t_final = zeros(sze)
mP2_t_final = zeros(sze)
mP_t1_final = zeros(sze)
mPexp_final = zeros(sze)
CP_t1_t_mean = zeros(T + 1)
CP_t_mean = zeros(T + 1)
CP2_t_mean = zeros(T + 1)
CPexp_mean = zeros(T + 1)
CP_t1_mean = zeros(T + 1)
CP_t1_t_final = zeros((sze, sze))
CP_t_final = zeros((sze, sze))
CP2_t_final = zeros((sze, sze))
CP_t1_final = zeros((sze, sze))
CPexp_final = zeros((sze, sze))
DP_t1_t_mean = zeros(T + 1)
DP_t_mean = zeros(T + 1)
DP2_t_mean = zeros(T + 1)
DPexp_mean = zeros(T + 1)
DP_t1_mean = zeros(T + 1)
DP_t1_t_final = zeros((sze, sze))
DP_t_final = zeros((sze, sze))
DP2_t_final = zeros((sze, sze))
DP_t1_final = zeros((sze, sze))
DPexp_final = zeros((sze, sze))
# Load data
filename = "data/data-H0-" * string(H0) * "-J0-" * string(J0) * "-Js-" * string(
Js) * "-N-" * string(sze) * "-R-" * string(R) * "-beta-" * string(beta_ref) * ".npz"
println()
println(filename)
data = npzread(filename)
H = data["H"]
J = data["J"]
s0 = data["s0"]
m_exp = data["m"]
C_exp = data["C"]
D_exp = data["D"]
data = nothing
# Load statistical moments from data
mPexp_mean[1] = 1
for t in 1:T
mPexp_mean[t + 1] = mean(m_exp[:, t])
CPexp_mean[t + 1] = mean(C_exp[:, :, t])
DPexp_mean[t + 1] = mean(D_exp[:, :, t])
# println("Exp",
# " ", string(t) * "/" * string(T),
# " ", mPexp_mean[t + 1],
# " ", CPexp_mean[t + 1],
# " ", DPexp_mean[t + 1])
end
mPexp_final = m_exp[:, T - 1]
CPexp_final = C_exp[:, :, T - 1]
DPexp_final = D_exp[:, :, T - 1]
# run Plefka[t-1,t] order 2
println("Plefka[t-1,t]")
time_P_t1_t = calc_stuff!(CP_t1_t_mean, mP_t1_t_mean, DP_t1_t_mean,
EmP_t1_t, ECP_t1_t, EDP_t1_t,
m_exp, C_exp, D_exp,
mP_t1_t_final, CP_t1_t_final, DP_t1_t_final,
T, beta_ref, IsingPlefka_t1_t{Float64}(),
H, J, s0, sze)
println(time_P_t1_t)
# run Plefka[t] order 2
println("Plefka[t]")
time_P_t = calc_stuff!(CP_t_mean, mP_t_mean, DP_t_mean,
EmP_t, ECP_t, EDP_t,
m_exp, C_exp, D_exp,
mP_t_final, CP_t_final, DP_t_final,
T, beta_ref, IsingPlefka_t{Float64}(),
H, J, s0, sze)
println(time_P_t)
# run Plefka[t-1] order 2
println("Plefka2[t]")
time_P2_t = calc_stuff!(CP2_t_mean, mP2_t_mean, DP2_t_mean,
EmP2_t, ECP2_t, EDP2_t,
m_exp, C_exp, D_exp,
mP2_t_final, CP2_t_final, DP2_t_final,
T, beta_ref, IsingPlefka2_t{Float64}(),
H, J, s0, sze)
println(time_P2_t)
# run Plefka[t-1] order 1
println("Plefka[t-1]")
time_P_t1 = calc_stuff!(CP_t1_mean, mP_t1_mean, DP_t1_mean,
EmP_t1, ECP_t1, EDP_t1,
m_exp, C_exp, D_exp,
mP_t1_final, CP_t1_final, DP_t1_final,
T, beta_ref, IsingPlefka_t1{Float64}(),
H, J, s0, sze)
println(time_P_t1)
# Save results to file
filename = "data/forward/forward_" * string(Int(
round(beta_ref * 100, digits=0))) * "_R_" * string(R) * ".npz"
npzwrite(filename,
m_exp=m_exp[:, T - 1],
C_exp=C_exp[:, :, T - 1],
D_exp=D_exp[:, :, T - 1],
mP_t1_t_mean=mP_t1_t_mean,
mP_t_mean=mP_t_mean,
mP_t1_mean=mP_t1_mean,
mP2_t_mean=mP2_t_mean,
CP_t1_t_mean=CP_t1_t_mean,
CP_t_mean=CP_t_mean,
CP_t1_mean=CP_t1_mean,
CP2_t_mean=CP2_t_mean,
DP_t1_t_mean=DP_t1_t_mean,
DP_t_mean=DP_t_mean,
DP_t1_mean=DP_t1_mean,
DP2_t_mean=DP2_t_mean,
mPexp_mean=mPexp_mean,
CPexp_mean=CPexp_mean,
DPexp_mean=DPexp_mean,
mP_t1_t=mP_t1_t_final,
mP_t=mP_t_final,
mP_t1=mP_t1_final,
mP2_t=mP2_t_final,
CP_t1_t=CP_t1_t_final,
CP_t=CP_t_final,
CP_t1=CP_t1_final,
CP2_t=CP2_t_final,
DP_t1_t=DP_t1_t_final,
DP_t=DP_t_final,
DP_t1=DP_t1_final,
DP2_t=DP2_t_final,
EmP_t1_t=EmP_t1_t,
EmP_t=EmP_t,
EmP_t1=EmP_t1,
EmP2_t=EmP2_t,
ECP_t1_t=ECP_t1_t,
ECP_t=ECP_t,
ECP_t1=ECP_t1,
ECP2_t=ECP2_t,
EDP_t1_t=EDP_t1_t,
EDP_t=EDP_t,
EDP_t1=EDP_t1,
EDP2_t=EDP2_t,
time_P_t1_t=time_P_t1_t,
time_P_t=time_P_t,
time_P_t1=time_P_t1,
time_P2_t=time_P2_t)
end
end
function calc_stuff!(CP_mean, mP_mean, DP_mean,
EmP, ECP, EDP,
m_exp, C_exp, D_exp,
mP_final, CP_final, DP_final,
T, beta_ref, alg,
H, J, s0, sze)
# Initialize kinetic Ising model
tt = Float64
I = Ising(tt, sze)
I.H .= copy(H)
I.J .= copy(J)
MI = MeanIsingModel(tt, sze, alg)
initialize_state!(MI, s0)
time_start = time()
for t in 1:T
update_P!(I, MI)
CP_mean[t + 1] = mean(MI.C)
mP_mean[t + 1] = mean(MI.m)
DP_mean[t + 1] = mean(MI.D)
EmP[t + 1] = mean((MI.m .- m_exp[:, t]).^2)
ECP[t + 1] = mean((MI.C .- C_exp[:, :, t]).^2)
EDP[t + 1] = mean((MI.D .- D_exp[:, :, t]).^2)
# println("beta",
# " ", beta_ref,
# " ", string(alg),
# " ", string(t) * "/" * string(T),
# " ", nsf(mP_mean[t + 1]),
# " ", nsf(CP_mean[t + 1]),
# " ", nsf(DP_mean[t + 1]),
# " ", nsf(EmP[t + 1]),
# " ", nsf(ECP[t + 1]),
# " ", nsf(EDP[t + 1]))
end
println("beta",
" ", beta_ref,
" ", string(alg),
" ", string(T) * "/" * string(T),
" ", nsf(mean(EmP)),
" ", nsf(mean(ECP)),
" ", nsf(mean(EDP)))
mP_final .= MI.m
CP_final .= MI.C
DP_final .= MI.D
time_P = time() - time_start
return time_P
end