fix test

EigenSolver · Mar 5, 2024 · 83def21 · 83def21
1 parent f693af3
commit 83def21
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diff --git a/test/test.png b/test/test.png
diff --git a/test/testfidelity.jl b/test/testfidelity.jl
@@ -2,29 +2,29 @@ using Plots
 using SpinShuttling: characteristicfunction
 ##
 figsize=(400, 300)
-visualize=true
+visualize=false
 
 ##
-# @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,M,N,B)
-#     # test customize println
-#     println(model)
+@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)
 
-#     f1=averagefidelity(model)
-#     f2, f2_err=sampling(model, fidelity)
-#     f3=1/2*(1+φ(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
+    model=OneSpinModel(T,L,M,N,B)
+    # test customize println
+    println(model)
+
+    f1=averagefidelity(model)
+    f2, f2_err=sampling(model, fidelity)
+    f3=1/2*(1+φ(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"
@@ -60,57 +60,56 @@ visualize=true
     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, M, N, B)
-#     if visualize
-#         display(heatmap(collect(model.R.Σ), title="cross covariance matrix, test fig 4", size=figsize))
-#     end
-#     f1=averagefidelity(model)
-#     f2, f2_err=sampling(model, fidelity)
-#     f3=1/2*(1+φ(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 = 500; N=501; L=10; γ=(1e-7,1e7); # MHz
-#     # 0.01 ~ 100 μs
-#     # v = 0.1 ~ 1000 m/s
-#     v=0.1; T=L/v; κₓ=20;
-#     B=PinkBrownianField(0,[κₓ],σ, γ)
-#     model=OneSpinModel(T,L,M,N,B)
+@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, M, N, B)
+    if visualize
+        display(heatmap(collect(model.R.Σ), title="cross covariance matrix, test fig 4", size=figsize))
+    end
+    f1=averagefidelity(model)
+    f2, f2_err=sampling(model, fidelity)
+    f3=1/2*(1+φ(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
 
-#     f1=averagefidelity(model)
-#     f2, f2_err=sampling(model, fidelity)
-#     f3=1/2*(1+φ(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)
+#
+@testset "1/f noise chacacteristics" begin
+    σ = sqrt(2)/20; M = 500; N=501; L=10; γ=(1e-7,1e7); # MHz
+    # 0.01 ~ 100 μs
+    # v = 0.1 ~ 1000 m/s
+    v=0.1; T=L/v; κₓ=20;
+    B=PinkBrownianField(0,[κₓ],σ, γ)
+    model=OneSpinModel(T,L,M,N,B)
 
-#     t=range(1e-4*T,T,10)
-#     f_mc, f_mc_err=sampling(model, fidelity, vector=true)
-#     t_ni, chi_ni=characteristicfunction(model.R)
-#     f_ni= @. (1+real(chi_ni))/2
-#     f_th=map(T->(1+φ(T,L,B))/2, t)|>collect
-#     if visualize
-#         fig=plot( size=figsize, 
-#             xlabel="t", ylabel="F", label="monte-carlo sampling",
-#             ribbon=sqrt.(f_mc_err/M)
-#             )
-#             plot!(t_ni, f_ni, label="numerical integration")
-#             plot!(t,f_th, label="theoretical fidelity")
-#         savefig(fig, "1fnoise.png")
-#     end
+    f1=averagefidelity(model)
+    f2, f2_err=sampling(model, fidelity)
+    f3=1/2*(1+φ(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)
 
-#     @test all([abs(f_mc[i]-f_th[i]) < sqrt(f_mc_err[i]) for i in 10:N ])
-# end
+    t=range(1e-4*T,T,10)
+    f_mc, f_mc_err=sampling(model, fidelity, vector=true)
+    t_ni, chi_ni=characteristicfunction(model.R)
+    f_ni= @. (1+real(chi_ni))/2
+    f_th=map(T->(1+φ(T,L,B))/2, t)|>collect
+    if visualize
+        fig=plot( size=figsize, 
+            xlabel="t", ylabel="F", label="monte-carlo sampling",
+            ribbon=sqrt.(f_mc_err/M)
+            )
+            plot!(t_ni, f_ni, label="numerical integration")
+            plot!(t,f_th, label="theoretical fidelity")
+        savefig(fig, "1fnoise.png")
+    end
 
+    @test all([abs(f_mc[i]-f_th[i]) < sqrt(f_mc_err[i]) for i in 10:N ])
+end