update test environment

Manifest.toml

@@ -1,6 +1,6 @@
 # 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 = "1e88ce001819fd9155c699ecf849fef2bc976656"
 
@@ -163,9 +163,9 @@ version = "1.10.0"
 
 [[deps.Preferences]]
 deps = ["TOML"]
-git-tree-sha1 = "9e8fed0505b0c15b4c1295fd59ea47b411c019cf"
+git-tree-sha1 = "9306f6085165d270f7e3db02af26a400d580f5c6"
 uuid = "21216c6a-2e73-6563-6e65-726566657250"
-version = "1.4.2"
+version = "1.4.3"
 
 [[deps.Printf]]
 deps = ["Unicode"]

test/Manifest.toml

@@ -915,7 +915,7 @@ version = "2.3.1"
 
 [[deps.SpinShuttling]]
 deps = ["LinearAlgebra", "QuadGK", "SpecialFunctions", "Statistics"]
-path = "C:\\Users\\ynzhang\\.julia\\dev\\SpinShuttling"
+path = ".."
 uuid = "eff49e45-ae6d-44ae-9170-2124a87971c9"
 version = "0.1.3"
 

test/testfidelity.jl

@@ -4,52 +4,52 @@ using SpinShuttling: characteristicfunction
 figsize=(400, 300)
 visualize=false
 
-# ##
-# @testset begin "test single spin characteristics"
-#     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)
-#     # R=RandomFunction(P , B)
-#     model=OneSpinModel(T,L,M,N,B)
-#     # test customize println
-#     println(model)
+##
+@testset begin "test single spin characteristics"
+    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)
+    # R=RandomFunction(P , B)
+    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
+    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 two spin characteristics"
-#     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 begin "test two spin characteristics"
+    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 = 5000; N=301; L=10; γ=(0.0001,10000); # MHz
     # 0.01 ~ 100 μs
     # v = 0.1 ~ 1000 m/s
-    v=0.1; T=L/v;κₓ=100;
+    v=0.1; T=L/v;κₓ=5;
     B=PinkBrownianField(0,[κₓ],σ, γ)
     model=OneSpinModel(T,L,M,N,B)