refactor interface

docs/src/guide.md

@@ -51,7 +51,7 @@ f2, f2_err=sampling(model, fidelity, M)
 ```
 For the single spin shuttling at constant velocity, analytical solution is also available. 
 ```julia
-f3=1/2*(1+φ(T,L,B))
+f3=1/2*(1+W(T,L,B))
 ```
 We can compare the results form the three methods.
 ```julia

docs/src/index.md

@@ -50,7 +50,7 @@ averagefidelity
 ```
 
 ```@docs
-φ
+W
 ```
 
 ### Stochastics

src/SpinShuttling.jl

@@ -14,7 +14,7 @@ OneSpinForthBackModel, TwoSpinParallelModel, RandomFunction, CompositeRandomFunc
 OrnsteinUhlenbeckField, PinkBrownianField
 export averagefidelity, fidelity, sampling, characteristicfunction, characteristicvalue
 export covariance, covariancematrix
-export φ
+export W
 
 """
 Spin shuttling model defined by a stochastic field, the realization of the stochastic field is 
@@ -256,16 +256,16 @@ function fidelity(model::ShuttlingModel, randseq::Vector{<:Real}; vector::Bool=f
     else
         Z = missing
     end
-    ϕ = vector ? cumsum(Z)* dt : sum(Z) * dt
-    return (1 .+ cos.(ϕ)) / 2
+    W = vector ? cumsum(Z)* dt : sum(Z) * dt
+    return (1 .+ cos.(W)) / 2
 end
 
 
 
 """
 Analytical average fidelity of a one-spin shuttling model.
 """
-function φ(T::Real,L::Real,B::OrnsteinUhlenbeckField; path=:straight)::Real
+function W(T::Real,L::Real,B::OrnsteinUhlenbeckField; path=:straight)::Real
     κₜ=B.θ[1]
     κₓ=B.θ[2]
     σ =B.σ
@@ -284,7 +284,7 @@ end
 """
 Analytical average fidelity of a sequenced two-spin EPR pair shuttling model.
 """
-function φ(T0::Real,T1::Real,L::Real,B::OrnsteinUhlenbeckField; path=:sequenced)::Real
+function W(T0::Real,T1::Real,L::Real,B::OrnsteinUhlenbeckField; path=:sequenced)::Real
     κₜ=B.θ[1]
     κₓ=B.θ[2]
     σ =B.σ
@@ -304,7 +304,7 @@ end
 """
 Theoretical fidelity of a one-spin shuttling model for a pink-brownian noise.
 """
-function φ(T::Real, L::Real, B::PinkBrownianField)::Real
+function W(T::Real, L::Real, B::PinkBrownianField)::Real
     β= T.*B.γ
     γ= L*B.θ[1]
     return exp(-B.σ^2*T^2*F4(β, γ))

test/testfidelity.jl

@@ -18,7 +18,7 @@ visualize=true
 
     f1=averagefidelity(model)
     f2, f2_err=sampling(model, fidelity, M)
-    f3=1/2*(1+φ(T,L,B))
+    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)
@@ -36,7 +36,7 @@ end
         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+φ(T,L,B,path=:forthback))/2 for T in t]
+        f_th=[(1+W(T,L,B,path=:forthback))/2 for T in t]
         fig=plot(t, f_mc, size=figsize, 
             xlabel="t", ylabel="F", label="monte-carlo sampling",
             # ribbon=@. sqrt(f_mc_err/M)
@@ -52,7 +52,7 @@ end
 
     f1=averagefidelity(model)
     f2, f2_err=sampling(model, fidelity, M)
-    f3=1/2*(1+φ(T, L, B, path=:forthback))
+    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)
@@ -70,7 +70,7 @@ end
     end
     f1=averagefidelity(model)
     f2, f2_err=sampling(model, fidelity, M)
-    f3=1/2*(1+φ(T0, T1, L,B))
+    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)
@@ -89,7 +89,7 @@ end
 
     f1=averagefidelity(model)
     f2, f2_err=sampling(model, fidelity, M)
-    f3=1/2*(1+φ(T,L,B))
+    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)
@@ -100,7 +100,7 @@ end
     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
-    f_th=map(T->(1+φ(T,L,B))/2, t)|>collect
+    f_th=map(T->(1+W(T,L,B))/2, t)|>collect
     if visualize
         fig=plot(t,f_mc, size=figsize, 
             xlabel="t", ylabel="F", label="monte-carlo sampling",

test/teststochastics.jl

@@ -65,7 +65,7 @@ end
         f1=exp(-integrate(R.Σ[:,:], dt, dt, method=:trapezoid)/2) 
         f2=exp(-integrate(R.Σ[:,:], dt, dt, method=:simpson)/2)
         @test isapprox(f1,f2,rtol=1e-2) 
-        f3=φ(T,L,B)
+        f3=W(T,L,B)
         err1[i]=abs(f1-f3)
         err2[i]=abs(f2-f3)
         i+=1
@@ -103,7 +103,7 @@ end
         f1=exp(-integrate(R.Σ[:,:], dt, dt, method=:trapezoid)/2) 
         f1_sym=exp(-integrate(R.Σ, dt)/2)
         f2=exp(-integrate(R.Σ[:,:], dt, dt, method=:simpson)/2)
-        f3=φ(T,v*T,B)
+        f3=W(T,v*T,B)
         err1[i]=abs(f1-f3)/f3
         err1_sym[i]=abs(f1_sym-f3)/f3
         err2[i]=abs(f2-f3)/f3