76 lines
1.9 KiB
Julia
76 lines
1.9 KiB
Julia
using SpiDy
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using NPZ
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using DataFrames
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using CSV
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using ProgressMeter
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using Random
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using Statistics
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using LinearAlgebra
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using Plots
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########################
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########################
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Δt = 0.1
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N = 10_000
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tspan = (0., N*Δt)
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saveat = (0:1:N)*Δt
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α = 10.
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ω0 = 7.
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Γ = 5.
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J = LorentzianSD(α, ω0, Γ) # coloring the noise
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matrix = AnisoCoupling([-sin(π/4) 0. 0. # coupling to the environment
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0. 0. 0.
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cos(π/4) 0. 0.]);
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T = 1.
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noise = ClassicalNoise(T);
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navg = 6 # number of stochastic realizations
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nspin = 4 # number of spins
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s0 = zeros(3*nspin)
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for i in 1:nspin
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ϵ = 0.1
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s0tmp = [ϵ*rand(), ϵ*rand(), -1]
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s0[1+(i-1)*3:3+(i-1)*3] = s0tmp./norm(s0tmp)
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end
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J0 = 1.
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JH = Nchain(nspin, J0)
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########################
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########################
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println("Starting...")
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progress = Progress(navg);
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sols = zeros(navg, length(saveat), 3*nspin)
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Threads.@threads for i in 1:navg
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bfields = [bfield(N, Δt, J, noise),
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bfield(N, Δt, J, noise),
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bfield(N, Δt, J, noise)];
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sol = diffeqsolver(s0, tspan, J, bfields, matrix; JH=JH, saveat=saveat);
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sols[i, :, :] = transpose(sol[:, :])
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next!(progress)
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end
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solavg = mean(sols, dims=1)[1, :, :];
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########################
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########################
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### Save data NPZ ###
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npzwrite("./outputs/dynamics.npz", Dict("t" => saveat,
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"S" => solavg))
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### Save data CSV ###
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dataframe = DataFrame(t = saveat,
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Sx = solavg[:, 1],
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Sy = solavg[:, 2],
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Sz = solavg[:, 3])
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CSV.write("./outputs/dynamics.csv", dataframe)
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### Plots ###
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plot(saveat, solavg[:, 1], xlabel="t", ylabel="S_x")
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savefig("./outputs/sx.pdf")
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plot(saveat, solavg[:, 2], xlabel="t", ylabel="S_y")
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savefig("./outputs/sy.pdf")
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plot(saveat, solavg[:, 3], xlabel="t", ylabel="S_z")
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savefig("./outputs/sz.pdf")
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