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add progress bar for test, test the analytics of pink brownian noise
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@EigenSolver
EigenSolver committed Apr 18, 2024
1 parent 38dbcbf commit 86d560a
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Showing 4 changed files with 93 additions and 79 deletions.
10 changes: 8 additions & 2 deletions test/Manifest.toml
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# This file is machine-generated - editing it directly is not advised

julia_version = "1.10.1"
julia_version = "1.10.2"
manifest_format = "2.0"
project_hash = "b7d2887a6e0b6afe3c64da83f9f8503f7d6748ea"
project_hash = "2e8f71a970a5211ff0d3acbb9f0479ce789c8a02"

[[deps.AbstractFFTs]]
deps = ["LinearAlgebra"]
Expand Down Expand Up @@ -458,6 +458,12 @@ version = "1.4.3"
deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"

[[deps.ProgressMeter]]
deps = ["Distributed", "Printf"]
git-tree-sha1 = "763a8ceb07833dd51bb9e3bbca372de32c0605ad"
uuid = "92933f4c-e287-5a05-a399-4b506db050ca"
version = "1.10.0"

[[deps.QuadGK]]
deps = ["DataStructures", "LinearAlgebra"]
git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e"
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1 change: 1 addition & 0 deletions test/Project.toml
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[deps]
FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
LsqFit = "2fda8390-95c7-5789-9bda-21331edee243"
ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
7 changes: 4 additions & 3 deletions test/runtests.jl
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using Test
using SpinShuttling
using UnicodePlots
using ProgressMeter

include("testquadrature.jl")
# include("testquadrature.jl")
include("testfidelity.jl")
include("teststochastics.jl")
include("testspectrum.jl")
# include("teststochastics.jl")
# include("testspectrum.jl")
154 changes: 80 additions & 74 deletions test/testfidelity.jl
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Expand Up @@ -2,85 +2,87 @@
visualize=true

##
@testset begin "test single spin shuttling fidelity"
T=400; L=10; σ = sqrt(2) / 20; M = 20000; N=601; κₜ=1/20;κₓ=1/0.1;
v=L/T;
t=range(0, T, N)
P=hcat(t, v.*t)
B=OrnsteinUhlenbeckField(0,[κₜ,κₓ],σ)
# @testset begin "test single spin shuttling fidelity"
# T=400; L=10; σ = sqrt(2) / 20; M = 20000; N=601; κₜ=1/20;κₓ=1/0.1;
# v=L/T;
# t=range(0, T, N)
# P=hcat(t, v.*t)
# B=OrnsteinUhlenbeckField(0,[κₜ,κₓ],σ)

model=OneSpinModel(T,L,N,B)
# test customize println
println(model)
# model=OneSpinModel(T,L,N,B)
# # test customize println
# println(model)

f1=averagefidelity(model)
f2, f2_err=sampling(model, fidelity, M)
f3=1/2*(1+W(T,L,B))
@test isapprox(f1, f3,rtol=1e-2)
@test isapprox(f2, f3, rtol=1e-2)
println("NI:", f1)
println("MC:", f2)
println("TH:", f3)
end
# f1=averagefidelity(model)
# f2, f2_err=sampling(model, fidelity, M)
# f3=1/2*(1+W(T,L,B))
# @test isapprox(f1, f3,rtol=1e-2)
# @test isapprox(f2, f3, rtol=1e-2)
# println("NI:", f1)
# println("MC:", f2)
# println("TH:", f3)
# end

#
@testset begin "test single spin forth-back shuttling fidelity"
T=200; L=10; σ = sqrt(2) / 20; M = 5000; N=501; κₜ=1/20;κₓ=10;
# exponential should be smaller than 100
B=OrnsteinUhlenbeckField(0,[κₜ,κₓ],σ)
# #
# @testset begin "test single spin forth-back shuttling fidelity"
# T=200; L=10; σ = sqrt(2) / 20; M = 5000; N=501; κₜ=1/20;κₓ=10;
# # exponential should be smaller than 100
# B=OrnsteinUhlenbeckField(0,[κₜ,κₓ],σ)

if visualize
t=range(1e-2*T,T, 10)
f_mc=[sampling(OneSpinForthBackModel(T,L,N,B), fidelity, M)[1] for T in t]
f_ni=[averagefidelity(OneSpinForthBackModel(T,L,N,B)) for T in t]
f_th=[(1+W(T,L,B,path=:forthback))/2 for T in t]
fig=lineplot(t, f_mc,
xlabel="t", ylabel="F", name="monte-carlo sampling",
# ribbon=@. sqrt(f_mc_err/M)
)
lineplot!(fig, t, f_ni, name="numerical integration")
lineplot!(fig, t, f_th, name="theoretical fidelity")
display(fig)
end
# if visualize
# t=range(1e-2*T,T, 10)
# f_mc=[sampling(OneSpinForthBackModel(T,L,N,B), fidelity, M)[1] for T in t]
# f_ni=[averagefidelity(OneSpinForthBackModel(T,L,N,B)) for T in t]
# f_th=[(1+W(T,L,B,path=:forthback))/2 for T in t]
# fig=lineplot(t, f_mc,
# xlabel="t", ylabel="F", name="monte-carlo sampling",
# # ribbon=@. sqrt(f_mc_err/M)
# )
# lineplot!(fig, t, f_ni, name="numerical integration")
# lineplot!(fig, t, f_th, name="theoretical fidelity")
# display(fig)
# end

model=OneSpinForthBackModel(T,L,N,B)
# test customize println
println(model)
# model=OneSpinForthBackModel(T,L,N,B)
# # test customize println
# println(model)

f1=averagefidelity(model)
f2, f2_err=sampling(model, fidelity, M)
f3=1/2*(1+W(T, L, B, path=:forthback))
@test isapprox(f1, f3,rtol=1e-2)
@test isapprox(f2, f3, rtol=1e-2)
println("NI:", f1)
println("MC:", f2)
println("TH:", f3)
end
# f1=averagefidelity(model)
# f2, f2_err=sampling(model, fidelity, M)
# f3=1/2*(1+W(T, L, B, path=:forthback))
# @test isapprox(f1, f3,rtol=1e-2)
# @test isapprox(f2, f3, rtol=1e-2)
# println("NI:", f1)
# println("MC:", f2)
# println("TH:", f3)
# end

##
@testset begin "test two spin sequenced shuttling fidelity"
L=10; σ =sqrt(2)/20; M=20000; N=501; T1=200; T0=25*0.05; κₜ=1/20; κₓ=1/0.1;
B=OrnsteinUhlenbeckField(0,[κₜ,κₓ],σ)
model=TwoSpinModel(T0, T1, L, N, B)
if visualize
display(heatmap(collect(model.R.Σ), title="cross covariance matrix, test fig 4"))
end
f1=averagefidelity(model)
f2, f2_err=sampling(model, fidelity, M)
f3=1/2*(1+W(T0, T1, L,B))
@test isapprox(f1, f3,rtol=1e-2)
@test isapprox(f2, f3, rtol=1e-2)
println("NI:", f1)
println("MC:", f2)
println("TH:", f3)
end
# ##
# @testset begin "test two spin sequenced shuttling fidelity"
# L=10; σ =sqrt(2)/20; M=20000; N=501; T1=200; T0=25*0.05; κₜ=1/20; κₓ=1/0.1;
# B=OrnsteinUhlenbeckField(0,[κₜ,κₓ],σ)
# model=TwoSpinModel(T0, T1, L, N, B)
# if visualize
# display(heatmap(collect(model.R.Σ), title="cross covariance matrix, test fig 4"))
# end
# f1=averagefidelity(model)
# f2, f2_err=sampling(model, fidelity, M)
# f3=1/2*(1+W(T0, T1, L,B))
# @test isapprox(f1, f3,rtol=1e-2)
# @test isapprox(f2, f3, rtol=1e-2)
# println("NI:", f1)
# println("MC:", f2)
# println("TH:", f3)
# end

#
@testset "1/f noise chacacteristics" begin
σ = sqrt(2)/20; M = 4000; N=501; L=10; γ=(1e-3,1e3); # MHz
σ = sqrt(2)/20; M = 400; N=501; L=10; γ=(1e-2,1e2); # MHz
# 0.01 ~ 100 μs
# v = 0.1 ~ 1000 m/s
v=0.1; T=L/v; κₓ=0.01;
v=1; T=L/v; κₓ=10;
# T=10
# 1/T=0.1 N/T=20
B=PinkBrownianField(0,[κₓ],σ, γ)
model=OneSpinModel(T,L,N,B)

Expand All @@ -93,20 +95,24 @@ end
println("MC:", f2)
println("TH:", f3)

t=range(0,T,N)
f_mc, f_mc_err=sampling(model, fidelity, M, vector=true)
t_ni, chi_ni=characteristicfunction(model.R)
f_ni= @. (1+real(chi_ni))/2
k=10
t=range(0,T,k)
f_mc=zeros(k); f_mc_err=zeros(k); f_ni=zeros(k);
@showprogress for i in eachindex(t)
model=OneSpinModel(t[i],L,N,B)
f_mc[i], f_mc_err[i]=sampling(model, fidelity, M)
f_ni[i]=averagefidelity(model)
end
f_th=map(T->(1+W(T,L,B))/2, t)|>collect
if visualize
fig=lineplot(t,f_mc,
xlabel="t", ylabel="F", name="monte-carlo sampling",
# ribbon=sqrt.(f_mc_err/M)
)
lineplot!(fig, t_ni, f_ni, name="numerical integration")
lineplot!(fig, t,f_th, name="theoretical fidelity")
lineplot!(fig, t, f_ni, name="numerical integration")
lineplot!(fig, t, f_th, name="theoretical fidelity")
display(fig)
end

@test all([abs(f_mc[i]-f_th[i]) < sqrt(f_mc_err[i]) for i in 10:N])
@test all([abs(f_mc[i]-f_th[i]) < sqrt(f_mc_err[i]) for i in eachindex(t)])
end

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