Projection Notebook

runs/.ipynb_checkpoints/Projections-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

runs/.ipynb_checkpoints/run_dynamics-checkpoint.jl

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

runs/run_dynamics.jl

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