From ada845c53110d6ff3390f1f1dd3e47aa23aae295 Mon Sep 17 00:00:00 2001 From: azwphy Date: Sun, 19 May 2024 22:15:39 +0800 Subject: [PATCH 1/5] fix for ... eachindex(...) --- examples/Bayesian_CramerRao_bounds.jl | 6 +- examples/Bayesian_estimation.jl | 4 +- examples/control_optimization_NV.jl | 20 ++-- examples/control_optimization_qubit.jl | 2 +- src/QuanEstimationBase/src/Algorithm/AD.jl | 20 ++-- src/QuanEstimationBase/src/Algorithm/DDPG.jl | 2 +- src/QuanEstimationBase/src/Algorithm/DE.jl | 4 +- src/QuanEstimationBase/src/Algorithm/NM.jl | 2 +- src/QuanEstimationBase/src/Algorithm/PSO.jl | 4 +- .../src/Common/BayesEstimation.jl | 4 +- src/QuanEstimationBase/src/Common/Common.jl | 18 ++-- .../AsymptoticBound/AnalogCramerRao.jl | 2 +- .../AsymptoticBound/AsymptoticBound.jl | 16 ++-- .../AsymptoticBound/CramerRao.jl | 4 +- .../AdaptiveEstimation/AdaptiveEstimation.jl | 2 +- .../AdaptiveEstimation/AdptiveMZI.jl | 34 +++---- .../Lindblad/LindbladDynamics.jl | 94 +++++++++---------- .../Lindblad/LindbladWrapper.jl | 6 +- .../test/adaptive/test_adaptive_estimation.jl | 2 +- .../test/algorithms/test_DDPG.jl | 4 +- .../test/bayesian/test_bayesian_estimation.jl | 2 +- .../test/dynamics/test_lindblad.jl | 2 +- .../test_bayesian_cramer_rao.jl | 4 +- .../objective_functions/test_ziv_zakai.jl | 4 +- 24 files changed, 131 insertions(+), 131 deletions(-) diff --git a/examples/Bayesian_CramerRao_bounds.jl b/examples/Bayesian_CramerRao_bounds.jl index 7e2c211..31e764f 100644 --- a/examples/Bayesian_CramerRao_bounds.jl +++ b/examples/Bayesian_CramerRao_bounds.jl @@ -26,8 +26,8 @@ rho0 = 0.5*ones(2, 2) # prior distribution x = range(-0.5*pi, stop=0.5*pi, length=100) |>Vector mu, eta = 0.0, 0.2 -p_tp = [p_func(x[i], mu, eta) for i in 1:length(x)] -dp_tp = [dp_func(x[i], mu, eta) for i in 1:length(x)] +p_tp = [p_func(x[i], mu, eta) for i in eachindex(x)] +dp_tp = [dp_func(x[i], mu, eta) for i in eachindex(x)] # normalization of the distribution c = trapz(x, p_tp) p = p_tp/c @@ -37,7 +37,7 @@ tspan = range(0., stop=1., length=1000) # dynamics rho = Vector{Matrix{ComplexF64}}(undef, length(x)) drho = Vector{Vector{Matrix{ComplexF64}}}(undef, length(x)) -for i = 1:length(x) +for i in eachindex(x) H0_tp = H0_func(x[i]) dH_tp = dH_func(x[i]) rho_tp, drho_tp = QuanEstimation.expm(tspan, rho0, H0_tp, dH_tp) diff --git a/examples/Bayesian_estimation.jl b/examples/Bayesian_estimation.jl index 08d3c21..d5bf33f 100644 --- a/examples/Bayesian_estimation.jl +++ b/examples/Bayesian_estimation.jl @@ -28,7 +28,7 @@ p = (1.0/(x[end]-x[1]))*ones(length(x)) tspan = range(0., stop=1., length=1000) # dynamics rho = Vector{Matrix{ComplexF64}}(undef, length(x)) -for i = 1:length(x) +for i in eachindex(x) H0_tp = H0_func(x[i]) dH_tp = dH_func(x[i]) rho_tp, drho_tp = QuanEstimation.expm(tspan, rho0, H0_tp, dH_tp) @@ -39,7 +39,7 @@ end Random.seed!(1234) y = [0 for i in 1:500] res_rand = sample(1:length(y), 125, replace=false) -for i in 1:length(res_rand) +for i in eachindex(res_rand) y[res_rand[i]] = 1 end diff --git a/examples/control_optimization_NV.jl b/examples/control_optimization_NV.jl index 12450fa..d8a1885 100755 --- a/examples/control_optimization_NV.jl +++ b/examples/control_optimization_NV.jl @@ -40,16 +40,16 @@ tspan = range(0., 2., length=4000) # guessed control coefficients cnum = 10 rng = MersenneTwister(1234) -ini_1 = [zeros(cnum) for _ in 1:length(Hc)] -ini_2 = 0.2.*[ones(cnum) for _ in 1:length(Hc)] -ini_3 = -0.2.*[ones(cnum) for _ in 1:length(Hc)] -ini_4 = [[range(-0.2, 0.2, length=cnum)...] for _ in 1:length(Hc)] -ini_5 = [[range(-0.2, 0., length=cnum)...] for _ in 1:length(Hc)] -ini_6 = [[range(0., 0.2, length=cnum)...] for _ in 1:length(Hc)] -ini_7 = [-0.2*ones(cnum)+0.01*rand(rng,cnum) for _ in 1:length(Hc)] -ini_8 = [-0.2*ones(cnum)+0.01*rand(rng,cnum) for _ in 1:length(Hc)] -ini_9 = [-0.2*ones(cnum)+0.05*rand(rng,cnum) for _ in 1:length(Hc)] -ini_10 = [-0.2*ones(cnum)+0.05*rand(rng,cnum) for _ in 1:length(Hc)] +ini_1 = [zeros(cnum) for _ in eachindex(Hc)] +ini_2 = 0.2.*[ones(cnum) for _ in eachindex(Hc)] +ini_3 = -0.2.*[ones(cnum) for _ in eachindex(Hc)] +ini_4 = [[range(-0.2, 0.2, length=cnum)...] for _ in eachindex(Hc)] +ini_5 = [[range(-0.2, 0., length=cnum)...] for _ in eachindex(Hc)] +ini_6 = [[range(0., 0.2, length=cnum)...] for _ in eachindex(Hc)] +ini_7 = [-0.2*ones(cnum)+0.01*rand(rng,cnum) for _ in eachindex(Hc)] +ini_8 = [-0.2*ones(cnum)+0.01*rand(rng,cnum) for _ in eachindex(Hc)] +ini_9 = [-0.2*ones(cnum)+0.05*rand(rng,cnum) for _ in eachindex(Hc)] +ini_10 = [-0.2*ones(cnum)+0.05*rand(rng,cnum) for _ in eachindex(Hc)] ctrl0 = [Symbol("ini_", i)|>eval for i in 1:10] # choose the optimization type opt = QuanEstimation.ControlOpt(ctrl=ini_1, ctrl_bound=[-0.2, 0.2], seed=1234) diff --git a/examples/control_optimization_qubit.jl b/examples/control_optimization_qubit.jl index d595fe8..8417bc2 100755 --- a/examples/control_optimization_qubit.jl +++ b/examples/control_optimization_qubit.jl @@ -25,7 +25,7 @@ M = [M1, M2] tspan = range(0., 10., length=2500) # guessed control coefficients cnum = length(tspan)-1 -ctrl = [zeros(cnum) for _ in 1:length(Hc)] +ctrl = [zeros(cnum) for _ in eachindex(Hc)] ctrl_bound = [-2., 2.] # choose the optimization type opt = ControlOpt(ctrl=ctrl, ctrl_bound=ctrl_bound, seed=1234) diff --git a/src/QuanEstimationBase/src/Algorithm/AD.jl b/src/QuanEstimationBase/src/Algorithm/AD.jl index afb9d0f..edc91e9 100644 --- a/src/QuanEstimationBase/src/Algorithm/AD.jl +++ b/src/QuanEstimationBase/src/Algorithm/AD.jl @@ -29,9 +29,9 @@ end function update_ctrl!(alg::autoGRAPE_Adam, obj, dynamics, δ) (; epsilon, beta1, beta2) = alg - for ci in 1:length(δ) + for ci in eachindex(δ) mt, vt = 0.0, 0.0 - for ti in 1:length(δ[1]) + for ti in eachindex(δ[1]) dynamics.data.ctrl[ci][ti], mt, vt = Adam(δ[ci][ti], ti, dynamics.data.ctrl[ci][ti], mt, vt, epsilon, beta1, beta2, obj.eps) end @@ -66,7 +66,7 @@ end function update_state!(alg::AD_Adam, obj, dynamics, δ) (; epsilon, beta1, beta2) = alg mt, vt = 0.0, 0.0 - for ti in 1:length(δ) + for ti in eachindex(δ) dynamics.data.ψ0[ti], mt, vt = Adam(δ[ti], ti, dynamics.data.ψ0[ti], mt, vt, epsilon, beta1, beta2, obj.eps) end end @@ -111,9 +111,9 @@ end function update_M!(opt::Mopt_LinearComb, alg::AD_Adam, obj, δ) (; epsilon, beta1, beta2) = alg - for ci in 1:length(δ) + for ci in eachindex(δ) mt, vt = 0.0, 0.0 - for ti in 1:length(δ[1]) + for ti in eachindex(δ[1]) opt.B[ci][ti], mt, vt = Adam(δ[ci][ti], ti, opt.B[ci][ti], mt, vt, epsilon, beta1, beta2, obj.eps) end end @@ -132,12 +132,12 @@ function update!(opt::Mopt_Rotation, alg::AbstractAD, obj, dynamics, output) suN = suN_generator(dim) opt.Lambda = Matrix{ComplexF64}[] append!(opt.Lambda, [Matrix{ComplexF64}(I,dim,dim)]) - append!(opt.Lambda, [suN[i] for i in 1:length(suN)]) + append!(opt.Lambda, [suN[i] for i in eachindex(suN)]) # if ismissing(Lambda) # opt.Lambda = Matrix{ComplexF64}[] # append!(opt.Lambda, [Matrix{ComplexF64}(I,dim,dim)]) - # append!(opt.Lambda, [suN[i] for i in 1:length(suN)]) + # append!(opt.Lambda, [suN[i] for i in eachindex(suN)]) # end U = rotation_matrix(opt.s, opt.Lambda) @@ -171,7 +171,7 @@ end function update_M!(opt::Mopt_Rotation, alg::AD_Adam, obj, δ) (; epsilon, beta1, beta2) = alg mt, vt = 0.0, 0.0 - for ti in 1:length(δ) + for ti in eachindex(δ) opt.s[ti], mt, vt = Adam(δ[ti], ti, opt.s[ti], mt, vt, epsilon, beta1, beta2, obj.eps) end end @@ -213,9 +213,9 @@ end function update_ctrl!(alg::AD_Adam, obj, dynamics, δ) (; epsilon, beta1, beta2) = alg - for ci in 1:length(δ) + for ci in eachindex(δ) mt, vt = 0.0, 0.0 - for ti in 1:length(δ[1]) + for ti in eachindex(δ[1]) dynamics.data.ctrl[ci][ti], mt, vt = Adam(δ[ci][ti], ti, dynamics.data.ctrl[ci][ti], mt, vt, epsilon, beta1, beta2, obj.eps) end diff --git a/src/QuanEstimationBase/src/Algorithm/DDPG.jl b/src/QuanEstimationBase/src/Algorithm/DDPG.jl index 32558f3..660d552 100644 --- a/src/QuanEstimationBase/src/Algorithm/DDPG.jl +++ b/src/QuanEstimationBase/src/Algorithm/DDPG.jl @@ -204,7 +204,7 @@ function _step!(env::ControlEnv, a) reward_current = log(f_current / env.f_noctrl[env.t]) env.reward = reward_current env.total_reward += reward_current - [append!(env.ctrl_list[i], a[i]) for i = 1:length(a)] + [append!(env.ctrl_list[i], a[i]) for i in eachindex(a)] if env.done set_f!(env.output, f_out) set_buffer!(env.output, env.ctrl_list) diff --git a/src/QuanEstimationBase/src/Algorithm/DE.jl b/src/QuanEstimationBase/src/Algorithm/DE.jl index a6941d7..d2d5e31 100644 --- a/src/QuanEstimationBase/src/Algorithm/DE.jl +++ b/src/QuanEstimationBase/src/Algorithm/DE.jl @@ -317,12 +317,12 @@ function update!(opt::Mopt_Rotation, alg::DE, obj, dynamics, output) suN = suN_generator(dim) Lambda = Matrix{ComplexF64}[] append!(Lambda, [Matrix{ComplexF64}(I,dim,dim)]) - append!(Lambda, [suN[i] for i in 1:length(suN)]) + append!(Lambda, [suN[i] for i in eachindex(suN)]) # if ismissing(Lambda) # Lambda = Matrix{ComplexF64}[] # append!(Lambda, [Matrix{ComplexF64}(I,dim,dim)]) - # append!(Lambda, [suN[i] for i in 1:length(suN)]) + # append!(Lambda, [suN[i] for i in eachindex(suN)]) # end M_num = length(POVM_basis) diff --git a/src/QuanEstimationBase/src/Algorithm/NM.jl b/src/QuanEstimationBase/src/Algorithm/NM.jl index fd5a85f..74db9ee 100644 --- a/src/QuanEstimationBase/src/Algorithm/NM.jl +++ b/src/QuanEstimationBase/src/Algorithm/NM.jl @@ -10,7 +10,7 @@ function update!(opt::StateOpt, alg::NM, obj, dynamics, output) if length(ini_state) > p_num ini_state = [ini_state[i] for i in 1:p_num] end - for pj in 1:length(ini_state) + for pj in eachindex(ini_state) nelder_mead[pj].data.ψ0 = [ini_state[pj][i] for i in 1:dim] end for pj in (length(ini_state)+1):p_num diff --git a/src/QuanEstimationBase/src/Algorithm/PSO.jl b/src/QuanEstimationBase/src/Algorithm/PSO.jl index 4f89d19..1bb2cf5 100644 --- a/src/QuanEstimationBase/src/Algorithm/PSO.jl +++ b/src/QuanEstimationBase/src/Algorithm/PSO.jl @@ -378,12 +378,12 @@ function update!(opt::Mopt_Rotation, alg::PSO, obj, dynamics, output) suN = suN_generator(dim) Lambda = Matrix{ComplexF64}[] append!(Lambda, [Matrix{ComplexF64}(I,dim,dim)]) - append!(Lambda, [suN[i] for i in 1:length(suN)]) + append!(Lambda, [suN[i] for i in eachindex(suN)]) # if ismissing(Lambda) # Lambda = Matrix{ComplexF64}[] # append!(Lambda, [Matrix{ComplexF64}(I,dim,dim)]) - # append!(Lambda, [suN[i] for i in 1:length(suN)]) + # append!(Lambda, [suN[i] for i in eachindex(suN)]) # end particles = repeat(s, p_num) diff --git a/src/QuanEstimationBase/src/Common/BayesEstimation.jl b/src/QuanEstimationBase/src/Common/BayesEstimation.jl index 77cb706..6017e04 100644 --- a/src/QuanEstimationBase/src/Common/BayesEstimation.jl +++ b/src/QuanEstimationBase/src/Common/BayesEstimation.jl @@ -327,7 +327,7 @@ function BayesCost(x, p, xest, rho, M; W=missing, eps=GLOBAL_EPS) end p_num = length(x[1]) - value = [p[i]*sum([tr(rho[i]*M[mi])*(x[1][i]-xest[mi][1])^2 for mi in 1:length(M)]) for i in 1:p_num] + value = [p[i]*sum([tr(rho[i]*M[mi])*(x[1][i]-xest[mi][1])^2 for mi in eachindex(M)]) for i in 1:p_num] return real(trapz(x[1], value)) else # multi-parameter scenario @@ -341,7 +341,7 @@ function BayesCost(x, p, xest, rho, M; W=missing, eps=GLOBAL_EPS) x_list = Iterators.product(x...) p_num = length(x_list) - xCx = [sum([tr(rho_i*M[mi])*([xi...]-xest[mi])'*W*([xi...]-xest[mi]) for mi in 1:length(M)]) for (xi,rho_i) in zip(x_list,rho)] + xCx = [sum([tr(rho_i*M[mi])*([xi...]-xest[mi])'*W*([xi...]-xest[mi]) for mi in eachindex(M)]) for (xi,rho_i) in zip(x_list,rho)] xCx = reshape(xCx, size(p)) value = p.*xCx diff --git a/src/QuanEstimationBase/src/Common/Common.jl b/src/QuanEstimationBase/src/Common/Common.jl index 3d98f39..3dd8a5b 100644 --- a/src/QuanEstimationBase/src/Common/Common.jl +++ b/src/QuanEstimationBase/src/Common/Common.jl @@ -176,15 +176,15 @@ end #### bound control coefficients #### function bound!(ctrl::Vector{Vector{Float64}}, ctrl_bound) - for ck in 1:length(ctrl) - for tk in 1:length(ctrl[1]) + for ck in eachindex(ctrl) + for tk in eachindex(ctrl[1]) ctrl[ck][tk] = (x-> x < ctrl_bound[1] ? ctrl_bound[1] : x > ctrl_bound[2] ? ctrl_bound[2] : x)(ctrl[ck][tk]) end end end function bound!(ctrl::Vector{Float64}, ctrl_bound) - for ck in 1:length(ctrl) + for ck in eachindex(ctrl) ctrl[ck] = (x-> x < ctrl_bound[1] ? ctrl_bound[1] : x > ctrl_bound[2] ? ctrl_bound[2] : x)(ctrl[ck]) end end @@ -259,7 +259,7 @@ end function rotation_matrix(coefficients, Lambda) dim = size(Lambda[1])[1] U = Matrix{ComplexF64}(I,dim,dim) - for i in 1:length(Lambda) + for i in eachindex(Lambda) U = U*exp(1.0im*coefficients[i]*Matrix(Lambda[i])) end U @@ -271,7 +271,7 @@ function initial_state!(psi0, dynamics, p_num, rng) if length(psi0) > p_num psi0 = [psi0[i] for i in 1:p_num] end - for pj in 1:length(psi0) + for pj in eachindex(psi0) dynamics[pj].data.ψ0 = [psi0[pj][i] for i in 1:dim] end for pj in (length(psi0)+1):p_num @@ -289,7 +289,7 @@ function initial_ctrl!(opt, ctrl0, dynamics, p_num, rng) if length(ctrl0) > p_num ctrl0 = [ctrl0[i] for i in 1:p_num] end - for pj in 1:length(ctrl0) + for pj in eachindex(ctrl0) dynamics[pj].data.ctrl = deepcopy(ctrl0[pj]) end if opt.ctrl_bound[1] == -Inf || opt.ctrl_bound[2] == Inf @@ -322,7 +322,7 @@ function initial_M!(measurement0, C_all, dim, p_num, M_num, rng) if length(measurement0) > p_num measurement0 = [measurement0[i] for i in 1:p_num] end - for pj in 1:length(measurement0) + for pj in eachindex(measurement0) C_all[pj] = deepcopy(measurement0[pj]) end for pj in (length(measurement0)+1):p_num @@ -343,7 +343,7 @@ function initial_LinearComb!(measurement0, B_all, basis_num, M_num, p_num, rng) if length(measurement0) > p_num measurement0 = [measurement0[i] for i in 1:p_num] end - for pj in 1:length(measurement0) + for pj in eachindex(measurement0) B_all[pj] = deepcopy(measurement0[pj]) end @@ -357,7 +357,7 @@ function initial_Rotation!(measurement0, s_all, dim, p_num, rng) if length(measurement0) > p_num measurement0 = [measurement0[i] for i in 1:p_num] end - for pj in 1:length(measurement0) + for pj in eachindex(measurement0) s_all[pj] = [measurement0[pj][i] for i in 1:dim*dim] end diff --git a/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AnalogCramerRao.jl b/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AnalogCramerRao.jl index 166d538..828c7d4 100644 --- a/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AnalogCramerRao.jl +++ b/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AnalogCramerRao.jl @@ -50,7 +50,7 @@ function Holevo_bound( para_num = length(dρ) suN = suN_generator(dim) / sqrt(2) Lambda = [Matrix{ComplexF64}(I, dim, dim) / sqrt(2)] - append!(Lambda, [suN[i] for i = 1:length(suN)]) + append!(Lambda, [suN[i] for i in eachindex(suN)]) vec_∂ρ = [[0.0 for i = 1:num] for j = 1:para_num] for pa = 1:para_num diff --git a/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AsymptoticBound.jl b/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AsymptoticBound.jl index 7540ba8..50e49ff 100644 --- a/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AsymptoticBound.jl +++ b/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/AsymptoticBound.jl @@ -285,7 +285,7 @@ end #### objective function for linear combination in Mopt #### function objective(opt::Mopt_LinearComb, obj::CFIM_obj{single_para}, dynamics::Lindblad) (; W, eps) = obj - M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in 1:length(opt.POVM_basis)]) for i in 1:opt.M_num] + M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in eachindex(opt.POVM_basis)]) for i in 1:opt.M_num] ρ, dρ = evolve(dynamics) f = W[1] * CFIM(ρ, dρ[1], M; eps = eps) return f, f @@ -293,7 +293,7 @@ end function objective(opt::Mopt_LinearComb, obj::CFIM_obj{multi_para}, dynamics::Lindblad) (; W, eps) = obj - M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in 1:length(opt.POVM_basis)]) for i in 1:opt.M_num] + M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in eachindex(opt.POVM_basis)]) for i in 1:opt.M_num] ρ, dρ = evolve(dynamics) f = tr(W * pinv(CFIM(ρ, dρ, M; eps = eps))) return f, 1.0 / f @@ -301,7 +301,7 @@ end function objective(opt::Mopt_LinearComb, obj::CFIM_obj{single_para}, dynamics::Kraus) (; W, eps) = obj - M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in 1:length(opt.POVM_basis)]) for i in 1:opt.M_num] + M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in eachindex(opt.POVM_basis)]) for i in 1:opt.M_num] ρ, dρ = evolve(dynamics) f = W[1] * CFIM(ρ, dρ[1], M; eps = eps) return f, f @@ -309,7 +309,7 @@ end function objective(opt::Mopt_LinearComb, obj::CFIM_obj{multi_para}, dynamics::Kraus) (; W, eps) = obj - M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in 1:length(opt.POVM_basis)]) for i in 1:opt.M_num] + M = [sum([opt.B[i][j]*opt.POVM_basis[j] for j in eachindex(opt.POVM_basis)]) for i in 1:opt.M_num] ρ, dρ = evolve(dynamics) f = tr(W * pinv(CFIM(ρ, dρ, M; eps = eps))) return f, 1.0 / f @@ -319,7 +319,7 @@ end function objective(opt::Mopt_Rotation, obj::CFIM_obj{single_para}, dynamics::Lindblad) (; W, eps) = obj U = rotation_matrix(opt.s, opt.Lambda) - M = [U*opt.POVM_basis[i]*U' for i in 1:length(opt.POVM_basis)] + M = [U*opt.POVM_basis[i]*U' for i in eachindex(opt.POVM_basis)] ρ, dρ = evolve(dynamics) f = W[1] * CFIM(ρ, dρ[1], M; eps = eps) return f, f @@ -328,7 +328,7 @@ end function objective(opt::Mopt_Rotation, obj::CFIM_obj{multi_para}, dynamics::Lindblad) (; W, eps) = obj U = rotation_matrix(opt.s, opt.Lambda) - M = [U*opt.POVM_basis[i]*U' for i in 1:length(opt.POVM_basis)] + M = [U*opt.POVM_basis[i]*U' for i in eachindex(opt.POVM_basis)] ρ, dρ = evolve(dynamics) f = tr(W * pinv(CFIM(ρ, dρ, M; eps = eps))) return f, 1.0 / f @@ -337,7 +337,7 @@ end function objective(opt::Mopt_Rotation, obj::CFIM_obj{single_para}, dynamics::Kraus) (; W, eps) = obj U = rotation_matrix(opt.s, opt.Lambda) - M = [U*opt.POVM_basis[i]*U' for i in 1:length(opt.POVM_basis)] + M = [U*opt.POVM_basis[i]*U' for i in eachindex(opt.POVM_basis)] ρ, dρ = evolve(dynamics) f = W[1] * CFIM(ρ, dρ[1], M; eps = eps) return f, f @@ -346,7 +346,7 @@ end function objective(opt::Mopt_Rotation, obj::CFIM_obj{multi_para}, dynamics::Kraus) (; W, eps) = obj U = rotation_matrix(opt.s, opt.Lambda) - M = [U*opt.POVM_basis[i]*U' for i in 1:length(opt.POVM_basis)] + M = [U*opt.POVM_basis[i]*U' for i in eachindex(opt.POVM_basis)] ρ, dρ = evolve(dynamics) f = tr(W * pinv(CFIM(ρ, dρ, M; eps = eps))) return f, 1.0 / f diff --git a/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/CramerRao.jl b/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/CramerRao.jl index b9257ec..eec27d3 100644 --- a/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/CramerRao.jl +++ b/src/QuanEstimationBase/src/ObjectiveFunc/AsymptoticBound/CramerRao.jl @@ -472,7 +472,7 @@ Calculation of the quantum Fisher information (QFI) and quantum Fisher informati """ function QFIM_Kraus(ρ0::AbstractMatrix, K::AbstractVector, dK::AbstractVector; LDtype=:SLD, exportLD::Bool=false, eps=GLOBAL_EPS) para_num = length(dK[1]) - dK = [[dK[i][j] for i in 1:length(K)] for j in 1:para_num] + dK = [[dK[i][j] for i in eachindex(K)] for j in 1:para_num] ρ = [K * ρ0 * K' for K in K] |> sum dρ = [[dK * ρ0 * K' + K * ρ0 * dK' for (K,dK) in zip(K,dK)] |> sum for dK in dK] F = QFIM(ρ, dρ; LDtype=LDtype, exportLD=exportLD, eps=eps) @@ -642,7 +642,7 @@ function FI_Expt(y1, y2, dx; ftype=:norm) p1_pois = pdf.(fit(Poisson, y1), range(0, maximum(y1), step=1)) p2_pois = pdf.(fit(Poisson, y2), range(0, maximum(y2), step=1)) p1_pois, p2_pois = p1_pois/sum(p1_pois), p2_pois/sum(p2_pois) - fidelity = sum([sqrt(p1_pois[i]*p2_pois[i]) for i in 1:length(p1_pois)]) + fidelity = sum([sqrt(p1_pois[i]*p2_pois[i]) for i in eachindex(p1_pois)]) Fc = 8*(1-fidelity)/dx^2 else println("supported values for ftype are 'norm', 'poisson', 'gamma' and 'rayleigh'") diff --git a/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdaptiveEstimation.jl b/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdaptiveEstimation.jl index 5b4a510..b7620d5 100644 --- a/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdaptiveEstimation.jl +++ b/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdaptiveEstimation.jl @@ -167,7 +167,7 @@ function iter_MI(p, p_num, para_num, x, x_list, u, rho_all, M, ei) end value_tp = zeros(size(p)) - for mi in 1:length(M) + for mi in eachindex(M) pyx_list_tp = real.(tr.(rho_u.*[M[mi]])) pyx_tp = reshape(pyx_list, size(p)) mean_tp = trapz(tuple(x...), p.*pyx_tp) diff --git a/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdptiveMZI.jl b/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdptiveMZI.jl index 52ac4f4..c834f92 100644 --- a/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdptiveMZI.jl +++ b/src/QuanEstimationBase/src/Scheme/EstimationStrategy/AdaptiveEstimation/AdptiveMZI.jl @@ -33,7 +33,7 @@ function adaptMZI_online(x, p, rho0, output, target::Symbol) phi_span = range(-pi, stop=pi, length=length(x)) |> collect phi = 0.0 - a_res = [Matrix{ComplexF64}(I, (N+1)^2, (N+1)^2) for i in 1:length(x)] + a_res = [Matrix{ComplexF64}(I, (N+1)^2, (N+1)^2) for i in eachindex(x)] xout, y = [], [] @@ -44,7 +44,7 @@ function adaptMZI_online(x, p, rho0, output, target::Symbol) enter = readline() u = parse(Int64, enter) pyx = zeros(length(x)) |> sparse - for xi in 1:length(x) + for xi in eachindex(x) a_res_tp = a_res[xi]*a_u(a, x[xi], phi, u) pyx[xi] = real(tr(rho0*a_res_tp'*a_res_tp))*(factorial(N-ei)/factorial(N)) a_res[xi] = a_res_tp @@ -66,7 +66,7 @@ function adaptMZI_online(x, p, rho0, output, target::Symbol) u = parse(Int64, enter) pyx = zeros(length(x)) |> sparse - for xi in 1:length(x) + for xi in eachindex(x) a_res_tp = a_res[xi]*a_u(a, x[xi], phi, u) pyx[xi] = real(tr(rho0*a_res_tp'*a_res_tp))*(factorial(N-ei)/factorial(N)) a_res[xi] = a_res_tp @@ -88,11 +88,11 @@ adaptMZI_online(x, p, rho0, output::String, target::String) = adaptMZI_online(x, function calculate_online{sharpness}(x, p, pyx, a_res, a, rho0, N, ei, phi_span, exp_ix) M_res = zeros(length(phi_span)) - for mj in 1:length(phi_span) + for mj in eachindex(phi_span) M1_res = trapz(x, pyx.*p) pyx0, pyx1 = zeros(length(x)), zeros(length(x)) M2_res = 0.0 - for xj in 1:length(x) + for xj in eachindex(x) a_res0 = a_res[xj]*a_u(a, x[xj], phi_span[mj], 0) a_res1 = a_res[xj]*a_u(a, x[xj], phi_span[mj], 1) pyx0[xj] = real(tr(rho0*a_res0'*a_res0))*(factorial(N-(ei+1))/factorial(N)) @@ -108,11 +108,11 @@ end function calculate_online{MI}(x, p, pyx, a_res, a, rho0, N, ei, phi_span, exp_ix) M_res = zeros(length(phi_span)) - for mj in 1:length(phi_span) + for mj in eachindex(phi_span) M1_res = trapz(x, pyx.*p) pyx0, pyx1 = zeros(length(x)), zeros(length(x)) M2_res = 0.0 - for xj in 1:length(x) + for xj in eachindex(x) a_res0 = a_res[xj]*a_u(a, x[xj], phi_span[mj], 0) a_res1 = a_res[xj]*a_u(a, x[xj], phi_span[mj], 1) pyx0[xj] = real(tr(rho0*a_res0'*a_res0))*(factorial(N-(ei+1))/factorial(N)) @@ -174,7 +174,7 @@ function DE_deltaphiOpt(x, p, rho0, comb, p_num, ini_population, c, cr, rng::Abs if length(ini_population) > p_num ini_population = [ini_population[i] for i in 1:p_num] end - for pj in 1:length(ini_population) + for pj in eachindex(ini_population) deltaphi[pj] = [ini_population[pj][i] for i in 1:N] end for pk in (length(ini_population)+1):p_num @@ -245,7 +245,7 @@ function PSO_deltaphiOpt(x, p, rho0, comb, p_num, ini_particle, c0, c1, c2, rng: if length(ini_particle) > p_num ini_particle = [ini_particle[i] for i in 1:p_num] end - for pj in 1:length(ini_particle) + for pj in eachindex(ini_particle) deltaphi[pj] = [ini_particle[pj][i] for i in 1:N] end for pk in (length(ini_particle)+1):p_num @@ -313,20 +313,20 @@ function calculate_offline{sharpness}(delta_phi, x, p, rho0, a, comb, eps) exp_ix = [exp(1.0im*xi) for xi in x] M_res = zeros(length(comb)) - for ui in 1:length(comb) + for ui in eachindex(comb) u = comb[ui] phi = 0.0 - a_res = [Matrix{ComplexF64}(I, (N+1)^2, (N+1)^2) for i in 1:length(x)] + a_res = [Matrix{ComplexF64}(I, (N+1)^2, (N+1)^2) for i in eachindex(x)] for ei in 1:N-1 phi = phi - (-1)^u[ei]*delta_phi[ei] - for xi in 1:length(x) + for xi in eachindex(x) a_res[xi] = a_res[xi]*a_u(a, x[xi], phi, u[ei]) end end pyx = zeros(length(x)) - for xj in 1:length(x) + for xj in eachindex(x) pyx[xj] = real(tr(rho0*a_res[xj]'*a_res[xj]))*(1/factorial(N)) end M_res[ui] = abs(trapz(x, pyx.*p.*exp_ix)) @@ -339,20 +339,20 @@ function calculate_offline{MI}(delta_phi, x, p, rho0, a, comb, eps) exp_ix = [exp(1.0im*xi) for xi in x] M_res = zeros(length(comb)) - for ui in 1:length(comb) + for ui in eachindex(comb) u = comb[ui] phi = 0.0 - a_res = [Matrix{ComplexF64}(I, (N+1)^2, (N+1)^2) for i in 1:length(x)] + a_res = [Matrix{ComplexF64}(I, (N+1)^2, (N+1)^2) for i in eachindex(x)] for ei in 1:N-1 phi = phi - (-1)^u[ei]*delta_phi[ei] - for xi in 1:length(x) + for xi in eachindex(x) a_res[xi] = a_res[xi]*a_u(a, x[xi], phi, u[ei]) end end pyx = zeros(length(x)) - for xj in 1:length(x) + for xj in eachindex(x) pyx[xj] = real(tr(rho0*a_res[xj]'*a_res[xj]))*(1/factorial(N)) end M_res[ui] = trapz(x, pyx.*p.*log.(2, pyx./trapz(x, pyx.*p))) diff --git a/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladDynamics.jl b/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladDynamics.jl index 998ac9b..99cd0ba 100644 --- a/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladDynamics.jl +++ b/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladDynamics.jl @@ -55,7 +55,7 @@ function dissipation( γ::Vector{R}, t::Int = 1, ) where {V<:AbstractVector,R<:Real} - [γ[i] * liouville_dissip(Γ[i]) for i = 1:length(Γ)] |> sum + [γ[i] * liouville_dissip(Γ[i]) for i in eachindex(Γ)] |> sum end function dissipation( @@ -63,7 +63,7 @@ function dissipation( γ::Vector{Vector{R}}, t::Int = 1, ) where {V<:AbstractVector,R<:Real} - [γ[i][t] * liouville_dissip(Γ[i]) for i = 1:length(Γ)] |> sum + [γ[i][t] * liouville_dissip(Γ[i]) for i in eachindex(Γ)] |> sum end function free_evolution(H0) @@ -82,7 +82,7 @@ function liouvillian( end function Htot(H0::T, Hc::V, ctrl) where {T<:AbstractArray, V<:AbstractVector} - [H0] .+ ([ctrl[i] .* [Hc[i]] for i = 1:length(ctrl)] |> sum) + [H0] .+ ([ctrl[i] .* [Hc[i]] for i in eachindex(ctrl)] |> sum) end function Htot( @@ -90,12 +90,12 @@ function Htot( Hc::V, ctrl::Vector{R}, ) where {T<:AbstractArray,V<:AbstractVector, R<:Real} - H0 + ([ctrl[i] * Hc[i] for i = 1:length(ctrl)] |> sum) + H0 + ([ctrl[i] * Hc[i] for i in eachindex(ctrl)] |> sum) end function Htot(H0::V1, Hc::V2, ctrl) where {V1,V2<:AbstractVector} - H0 + ([ctrl[i] * Hc[i] for i = 1:length(ctrl)] |> sum) + H0 + ([ctrl[i] * Hc[i] for i in eachindex(ctrl)] |> sum) end function expL(H, decay_opt, γ, dt, tj = 1) @@ -168,13 +168,13 @@ function expm( Δt = tspan[2] - tspan[1] - ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i = 1:length(tspan)] - ∂ρt_∂x_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i = 1:length(tspan)] + ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i in eachindex(tspan)] + ∂ρt_∂x_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i in eachindex(tspan)] ρt_all[1] = ρ0 |> vec ∂ρt_∂x_all[1] = ρt_all[1] |> zero decay_opt, γ = decay - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) exp_L = expL(H[t-1], decay_opt, γ, Δt, t-1) ρt_all[t] = exp_L * ρt_all[t-1] ∂ρt_∂x_all[t] = -im * Δt * dH_L * ρt_all[t] + exp_L * ∂ρt_∂x_all[t-1] @@ -250,7 +250,7 @@ function expm( Δt = tspan[2] - tspan[1] - ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i = 1:length(tspan)] + ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i in eachindex(tspan)] ∂ρt_∂x_all = [ [Vector{ComplexF64}(undef, (length(H0))^2) for j = 1:param_num] for i = 1:length(tspan) @@ -260,7 +260,7 @@ function expm( ∂ρt_∂x_all[1][pj] = ρt_all[1] |> zero end - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) exp_L = expL(H[t-1], decay_opt, γ, Δt, t-1) ρt_all[t] = exp_L * ρt_all[t-1] for pj = 1:param_num @@ -322,12 +322,12 @@ function expm_py( Δt = tspan[2] - tspan[1] - ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i = 1:length(tspan)] - ∂ρt_∂x_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i = 1:length(tspan)] + ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i in eachindex(tspan)] + ∂ρt_∂x_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i in eachindex(tspan)] ρt_all[1] = ρ0 |> vec ∂ρt_∂x_all[1] = ρt_all[1] |> zero - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) exp_L = expL(H[t-1], decay_opt, γ, Δt, t-1) ρt_all[t] = exp_L * ρt_all[t-1] ∂ρt_∂x_all[t] = -im * Δt * dH_L * ρt_all[t] + exp_L * ∂ρt_∂x_all[t-1] @@ -356,7 +356,7 @@ function expm_py( Δt = tspan[2] - tspan[1] - ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i = 1:length(tspan)] + ρt_all = [Vector{ComplexF64}(undef, (length(H0))^2) for i in eachindex(tspan)] ∂ρt_∂x_all = [ [Vector{ComplexF64}(undef, (length(H0))^2) for j = 1:param_num] for i = 1:length(tspan) @@ -366,7 +366,7 @@ function expm_py( ∂ρt_∂x_all[1][pj] = ρt_all[1] |> zero end - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) exp_L = expL(H[t-1], decay_opt, γ, Δt, t-1) ρt_all[t] = exp_L * ρt_all[t-1] for pj = 1:param_num @@ -407,7 +407,7 @@ function secondorder_derivative( ρt = ρ0 |> vec ∂ρt_∂x = [ρt |> zero for i = 1:param_num] ∂2ρt_∂x = [ρt |> zero for i = 1:param_num] - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced exp_L = expL(H[t-1], decay_opt, γ, Δt, t-1) ρt = exp_L * ρt @@ -485,12 +485,12 @@ function ode( dt = tspan[2] - tspan[1] t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, ctrl, t) = -im * (H(ctrl)[t2Num(t)] * ρ - ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end]), ctrl) ρt = solve(prob_ρ, Tsit5(), saveat=dt).u ∂ρt_func!(∂ρ, ctrl, t) = -im * (dH * ρt[t2Num(t)] - ρt[t2Num(t)] * dH) -im * (H(ctrl)[t2Num(t)] * ∂ρ - ∂ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_∂ρ = ODEProblem(∂ρt_func!, ρ0|>zero, (tspan[1], tspan[end]), ctrl) ∂ρt = solve(prob_∂ρ, Tsit5(), saveat=dt).u @@ -566,19 +566,19 @@ function ode( dt = tspan[2] - tspan[1] t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, ctrl, t) = -im * (H(ctrl)[t2Num(t)] * ρ - ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end]), ctrl) ρt = solve(prob_ρ, Tsit5(), saveat=dt).u ∂ρt_func!(∂ρ, (pa, ctrl,), t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H(ctrl)[t2Num(t)] * ∂ρ - ∂ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) ∂ρt_tp = [] for pa in 1:param_num prob_∂ρ = ODEProblem(∂ρt_func!, ρ0|>zero, (tspan[1], tspan[end]), (pa, ctrl,)) push!(∂ρt_tp, solve(prob_∂ρ, Tsit5(), saveat=dt).u) end - ∂ρt = [[∂ρt_tp[i][j] for i in 1:param_num] for j in 1:length(tspan)] + ∂ρt = [[∂ρt_tp[i][j] for i in 1:param_num] for j in eachindex(tspan)] ρt, ∂ρt end @@ -639,12 +639,12 @@ function ode_py( dt = tspan[2] - tspan[1] t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, ctrl, t) = -im * (H(ctrl)[t2Num(t)] * ρ - ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end]), ctrl) ρt = solve(prob_ρ, Tsit5(), saveat=dt).u ∂ρt_func!(∂ρ, ctrl, t) = -im * (dH * ρt[t2Num(t)] - ρt[t2Num(t)] * dH) -im * (H(ctrl)[t2Num(t)] * ∂ρ - ∂ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_∂ρ = ODEProblem(∂ρt_func!, ρ0|>zero, (tspan[1], tspan[end]), ctrl) ∂ρt = solve(prob_∂ρ, Tsit5(), saveat=dt).u @@ -670,19 +670,19 @@ function ode_py( dt = tspan[2] - tspan[1] t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, ctrl, t) = -im * (H(ctrl)[t2Num(t)] * ρ - ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end]), ctrl) ρt = solve(prob_ρ, Tsit5(), saveat=dt).u ∂ρt_func!(∂ρ, (pa, ctrl,), t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H(ctrl)[t2Num(t)] * ∂ρ - ∂ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) ∂ρt_tp = [] for pa in 1:param_num prob_∂ρ = ODEProblem(∂ρt_func!, ρ0|>zero, (tspan[1], tspan[end]), (pa, ctrl,)) push!(∂ρt_tp, solve(prob_∂ρ, Tsit5(), saveat=dt).u) end - ∂ρt = [[∂ρt_tp[i][j] for i in 1:param_num] for j in 1:length(tspan)] + ∂ρt = [[∂ρt_tp[i][j] for i in 1:param_num] for j in eachindex(tspan)] ρt, ∂ρt end @@ -724,7 +724,7 @@ function evolve(scheme::Scheme{Ket, Lindblad{HT, NonDecay, NonControl, Expm, P}, U = exp(-im * H0 * Δt) ψt = ψ0 ∂ψ∂x = [ψ0 |> zero for i = 1:param_num] - for i = 2:length(tspan) + for i in eachindex(tspan[2:end]) ψt = U * ψt ∂ψ∂x = [-im * Δt * dH[i] * ψt for i = 1:param_num] + [U] .* ∂ψ∂x end @@ -838,12 +838,12 @@ function evolve(scheme::Scheme{Ket, Lindblad{HT, Decay, NonControl, Ode, P}, M, dt = tspan[2] - tspan[1] t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, p, t) = -im * (H0 * ρ - ρ * H0) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end])) ρt = solve(prob_ρ, Tsit5(), saveat=dt).u ∂ρt_func!(∂ρ, pa, t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H0 * ∂ρ - ∂ρ * H0) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) ∂ρt_∂x = typeof(ρ0)[] for pa in 1:param_num @@ -867,7 +867,7 @@ function evolve(scheme::Scheme{DensityMatrix, Lindblad{HT, Decay, NonControl, Ex Δt = tspan[2] - tspan[1] exp_L = expL(H0, decay_opt, γ, Δt, 1) dH_L = [liouville_commu(dH[i]) for i = 1:param_num] - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) ρt = exp_L * ρt ∂ρt_∂x = [-im * Δt * dH_L[i] * ρt for i = 1:param_num] + [exp_L] .* ∂ρt_∂x end @@ -885,12 +885,12 @@ function evolve(scheme::Scheme{DensityMatrix, Lindblad{HT, Decay, NonControl, Od dt = tspan[2] - tspan[1] t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, p, t) = -im * (H0 * ρ - ρ * H0) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end])) ρt = solve(prob_ρ, Tsit5(), saveat=dt).u ∂ρt_func!(∂ρ, pa, t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H0 * ∂ρ - ∂ρ * H0) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) ∂ρt_∂x = typeof(ρ0)[] for pa in 1:param_num @@ -917,7 +917,7 @@ function evolve(scheme::Scheme{DensityMatrix, Lindblad{HT, NonDecay, Control, Ex dH_L = [liouville_commu(dH[i]) for i = 1:param_num] ρt = ρ0 |> vec ∂ρt_∂x = [ρt |> zero for i = 1:param_num] - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced exp_L = expL(H[t-1], Δt) ρt = exp_L * ρt @@ -968,7 +968,7 @@ function evolve(scheme::Scheme{DensityMatrix, Lindblad{HT, Decay, Control, Expm, dH_L = [liouville_commu(dH[i]) for i = 1:param_num] ρt = ρ0 |> vec ∂ρt_∂x = [ρt |> zero for i = 1:param_num] - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced exp_L = expL(H[t-1], decay_opt, γ, Δt, t-1) ρt = exp_L * ρt @@ -995,12 +995,12 @@ function evolve(scheme::Scheme{DensityMatrix, Lindblad{HT, Decay, Control, Ode, t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, ctrl, t) = -im * (H(ctrl)[t2Num(t)] * ρ - ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end]), ctrl) ρt = solve(prob_ρ, Tsit5(), saveat=dt; abstol=abstol, reltol=reltol).u ∂ρt_func!(∂ρ, (pa, ctrl,), t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H(ctrl)[t2Num(t)] * ∂ρ - ∂ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) ∂ρt_∂x = typeof(ρ0)[] for pa in 1:param_num @@ -1025,7 +1025,7 @@ function evolve(scheme::Scheme{Ket, Lindblad{HT, Decay, Control, Expm, P}, M, E} dH_L = [liouville_commu(dH[i]) for i = 1:param_num] ρt = (ψ0 * ψ0') |> vec ∂ρt_∂x = [ρt |> zero for i = 1:param_num] - for t = 2:length(tspan) + for t in eachindex(tspan[2:end]) Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced exp_L = expL(H[t-1], decay_opt, γ, Δt, t-1) ρt = exp_L * ρt @@ -1052,12 +1052,12 @@ function evolve(scheme::Scheme{Ket, Lindblad{HT, Decay, Control, Ode, P}, M, E}) H(ctrl) = Htot(H0, Hc, ctrl) t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 ρt_func!(ρ, ctrl, t) = -im * (H(ctrl)[t2Num(t)] * ρ - ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end]), ctrl) ρt = solve(prob_ρ, Tsit5(), saveat=dt).u ∂ρt_func!(∂ρ, (pa,ctrl,), t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H(ctrl)[t2Num(t)] * ∂ρ - ∂ρ * H(ctrl)[t2Num(t)]) + - ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) + ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) ∂ρt_∂x = typeof(ρ0)[] for pa in 1:param_num @@ -1076,7 +1076,7 @@ end # dH_L = [liouville_commu.(dh) for dh in dH] # ρt = (ψ0 * ψ0') |> vec # ∂ρt_∂x = [ρt |> zero for _ in 1:param_num] -# for t = 2:length(tspan) +# for t in eachindex(tspan[2:end]) # Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced # exp_L = expL(H0[t-1], Δt) # ρt = exp_L * ρt @@ -1117,7 +1117,7 @@ end # dH_L = [liouville_commu(dH[i]) for i = 1:param_num] # ρt = ρ0 |> vec # ∂ρt_∂x = [ρt |> zero for i = 1:param_num] -# for t = 2:length(tspan) +# for t in eachindex(tspan[2:end]) # Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced # exp_L = expL(H0[t-1], Δt) # ρt = exp_L * ρt @@ -1155,7 +1155,7 @@ end # dH_L = [liouville_commu(dH[i]) for i = 1:param_num] # ρt = (ψ0 * ψ0') |> vec # ∂ρt_∂x = [ρt |> zero for i = 1:param_num] -# for t = 2:length(tspan) +# for t in eachindex(tspan[2:end]) # Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced # exp_L = expL(H0[t-1], decay_opt, γ, Δt, t-1) # ρt = exp_L * ρt @@ -1173,12 +1173,12 @@ end # dt = tspan[2] - tspan[1] # t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 # ρt_func!(ρ, p, t) = -im * (H0[t2Num(t)] * ρ - ρ * H0[t2Num(t)]) + -# ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) +# ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) # prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end])) # ρt = solve(prob_ρ, Tsit5(), saveat=dt).u # ∂ρt_func!(∂ρ, pa, t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H0[t2Num(t)] * ∂ρ - ∂ρ * H0[t2Num(t)]) + -# ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) +# ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) # ∂ρt_∂x = typeof(ρ0)[] # for pa in 1:param_num @@ -1197,7 +1197,7 @@ end # dH_L = [liouville_commu(dH[i]) for i = 1:param_num] # ρt = ρ0 |> vec # ∂ρt_∂x = [ρt |> zero for i = 1:param_num] -# for t = 2:length(tspan) +# for t in eachindex(tspan[2:end]) # Δt = tspan[t] - tspan[t-1] # tspan may not be equally spaced # exp_L = expL(H0[t-1], decay_opt, γ, Δt, t-1) # ρt = exp_L * ρt @@ -1214,12 +1214,12 @@ end # dt = tspan[2] - tspan[1] # t2Num(t) = Int(round((t - tspan[1]) / dt)) + 1 # ρt_func!(ρ, p, t) = -im * (H0[t2Num(t)] * ρ - ρ * H0[t2Num(t)]) + -# ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) +# ([γ[i] * (Γ[i] * ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ρ + ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) # prob_ρ = ODEProblem(ρt_func!, ρ0, (tspan[1], tspan[end])) # ρt = solve(prob_ρ, Tsit5(), saveat=dt).u # ∂ρt_func!(∂ρ, pa, t) = -im * (dH[pa] * ρt[t2Num(t)] - ρt[t2Num(t)] * dH[pa]) -im * (H0[t2Num(t)] * ∂ρ - ∂ρ * H0[t2Num(t)]) + -# ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in 1:length(Γ)] |> sum) +# ([γ[i] * (Γ[i] * ∂ρ * Γ[i]' - 0.5*(Γ[i]' * Γ[i] * ∂ρ + ∂ρ * Γ[i]' * Γ[i] )) for i in eachindex(Γ)] |> sum) # ∂ρt_∂x = typeof(ρ0)[] # for pa in 1:param_num diff --git a/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladWrapper.jl b/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladWrapper.jl index 81447be..d24c161 100755 --- a/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladWrapper.jl +++ b/src/QuanEstimationBase/src/Scheme/Parameterization/Lindblad/LindbladWrapper.jl @@ -112,7 +112,7 @@ function Lindblad(opt::StateOpt, tspan, H0, dH; Hc=missing, ctrl=missing, decay= end end - hc = [sum([c[i]*hc for (c,hc) in zip(ctrl,Hc)]) for i in 1:length(ctrl[1]) ] + hc = [sum([c[i]*hc for (c,hc) in zip(ctrl,Hc)]) for i in eachindex(ctrl[1]) ] if typeof(H0) <: AbstractMatrix H0 = [complex(H0+hc) for hc in hc] @@ -249,7 +249,7 @@ function Lindblad(opt::AbstractMopt, tspan, ρ₀, H0, dH; Hc=missing, ctrl=miss end end - hc = [sum([c[i]*hc for (c,hc) in zip(ctrl,Hc)]) for i in 1:length(ctrl[1]) ] + hc = [sum([c[i]*hc for (c,hc) in zip(ctrl,Hc)]) for i in eachindex(ctrl[1]) ] if typeof(H0) <: AbstractMatrix H0 = [complex(H0+hc) for hc in hc] @@ -473,7 +473,7 @@ function Lindblad(opt::StateMeasurementOpt, tspan, H0, dH; Hc=missing, ctrl=miss end end - hc = [sum([c[i]*hc for (c,hc) in zip(ctrl,Hc)]) for i in 1:length(ctrl[1]) ] + hc = [sum([c[i]*hc for (c,hc) in zip(ctrl,Hc)]) for i in eachindex(ctrl[1]) ] if typeof(H0) <: AbstractMatrix H0 = [complex(H0+hc) for hc in hc] diff --git a/src/QuanEstimationBase/test/adaptive/test_adaptive_estimation.jl b/src/QuanEstimationBase/test/adaptive/test_adaptive_estimation.jl index e246edc..6b7153c 100644 --- a/src/QuanEstimationBase/test/adaptive/test_adaptive_estimation.jl +++ b/src/QuanEstimationBase/test/adaptive/test_adaptive_estimation.jl @@ -31,7 +31,7 @@ x = range(-0.25*pi+0.1, stop=3.0*pi/4.0-0.1, length=10) |>Vector p = (1.0/(x[end]-x[1])) * ones(length(x)) # dynamics rho = Vector{Matrix{ComplexF64}}(undef, length(x)) -for i = 1:length(x) +for i in eachindex(x) H0_tp = H0_func(x[i]) dH_tp = dH_func(x[i]) rho_tp, drho_tp = QuanEstimationBase.expm(tspan, rho0, H0_tp, dH_tp) diff --git a/src/QuanEstimationBase/test/algorithms/test_DDPG.jl b/src/QuanEstimationBase/test/algorithms/test_DDPG.jl index a2be7e4..8d44e92 100644 --- a/src/QuanEstimationBase/test/algorithms/test_DDPG.jl +++ b/src/QuanEstimationBase/test/algorithms/test_DDPG.jl @@ -24,7 +24,7 @@ function test_ControlEnv() tspan = range(0.0, 10.0, length = 2500) # guessed control coefficients cnum = length(tspan) - 1 - ctrl = [zeros(cnum) for _ in 1:length(Hc)] + ctrl = [zeros(cnum) for _ in eachindex(Hc)] ctrl_bound = [-2.0, 2.0] obj = QFIM_obj() @@ -101,7 +101,7 @@ function test_StateEnv() tspan = range(0.0, 10.0, length = 10) # guessed control coefficients cnum = length(tspan) - 1 - ctrl = [zeros(cnum) for _ in 1:length(Hc)] + ctrl = [zeros(cnum) for _ in eachindex(Hc)] ctrl_bound = [-2.0, 2.0] obj = QFIM_obj() diff --git a/src/QuanEstimationBase/test/bayesian/test_bayesian_estimation.jl b/src/QuanEstimationBase/test/bayesian/test_bayesian_estimation.jl index 1ce25fd..ec85eb3 100644 --- a/src/QuanEstimationBase/test/bayesian/test_bayesian_estimation.jl +++ b/src/QuanEstimationBase/test/bayesian/test_bayesian_estimation.jl @@ -39,7 +39,7 @@ tspan = range(0., stop=1., length=5) # dynamics rho = Vector{Matrix{ComplexF64}}(undef, length(x)) -for i = 1:length(x) +for i in eachindex(x) H0_tp = H0_func(x[i]) dH_tp = dH_func(x[i]) rho_tp, drho_tp = QuanEstimationBase.expm(tspan, rho0, H0_tp, dH_tp) diff --git a/src/QuanEstimationBase/test/dynamics/test_lindblad.jl b/src/QuanEstimationBase/test/dynamics/test_lindblad.jl index 646239a..02c87ef 100644 --- a/src/QuanEstimationBase/test/dynamics/test_lindblad.jl +++ b/src/QuanEstimationBase/test/dynamics/test_lindblad.jl @@ -25,7 +25,7 @@ M = [M1, M2] tspan = range(0., 10., length=100) # guessed control coefficients cnum = length(tspan)-1 -ctrl = [zeros(cnum) for _ in 1:length(Hc)] +ctrl = [zeros(cnum) for _ in eachindex(Hc)] ctrl_bound = [-2., 2.] # choose the optimization type opt = QuanEstimationBase.ControlOpt(ctrl=ctrl, ctrl_bound=ctrl_bound, seed=1234) diff --git a/src/QuanEstimationBase/test/objective_functions/test_bayesian_cramer_rao.jl b/src/QuanEstimationBase/test/objective_functions/test_bayesian_cramer_rao.jl index 52cdbed..af148d2 100644 --- a/src/QuanEstimationBase/test/objective_functions/test_bayesian_cramer_rao.jl +++ b/src/QuanEstimationBase/test/objective_functions/test_bayesian_cramer_rao.jl @@ -18,8 +18,8 @@ function data_gen() # prior distribution x = range(-0.5 * pi, stop = 0.5 * pi, length = 10) |> Vector mu, eta = 0.0, 0.2 - p_tp = [p_func(x[i], mu, eta) for i in 1:length(x)] - dp_tp = [dp_func(x[i], mu, eta) for i in 1:length(x)] + p_tp = [p_func(x[i], mu, eta) for i in eachindex(x)] + dp_tp = [dp_func(x[i], mu, eta) for i in eachindex(x)] # normalization of the distribution c = trapz(x, p_tp) p = p_tp / c diff --git a/src/QuanEstimationBase/test/objective_functions/test_ziv_zakai.jl b/src/QuanEstimationBase/test/objective_functions/test_ziv_zakai.jl index 2d6a02e..974364e 100644 --- a/src/QuanEstimationBase/test/objective_functions/test_ziv_zakai.jl +++ b/src/QuanEstimationBase/test/objective_functions/test_ziv_zakai.jl @@ -64,8 +64,8 @@ function data_gen() # prior distribution x = range(-0.5 * pi, stop = 0.5 * pi, length = 10) |> Vector mu, eta = 0.0, 0.2 - p_tp = [p_func(x[i], mu, eta) for i in 1:length(x)] - dp_tp = [dp_func(x[i], mu, eta) for i in 1:length(x)] + p_tp = [p_func(x[i], mu, eta) for i in eachindex(x)] + dp_tp = [dp_func(x[i], mu, eta) for i in eachindex(x)] # normalization of the distribution c = trapz(x, p_tp) p = p_tp / c From 94c39768359364b42d2de71f9f291e284b6b9e9f Mon Sep 17 00:00:00 2001 From: azwphy Date: Tue, 21 May 2024 16:52:25 +0800 Subject: [PATCH 2/5] documents reconstruction --- .vscode/settings.json | 3 + Project.toml | 3 - docs/Manifest.toml | 1525 +++++++++++++++++++++++- docs/Project.toml | 2 + docs/make.jl | 50 +- docs/src/api/BaseAPI.md | 9 + docs/src/{api.md => api/GeneralAPI.md} | 2 + docs/src/api/NVMagnetometerAPI.md | 9 + docs/src/index.md | 1 + docs/src/refs.bib | 0 10 files changed, 1562 insertions(+), 42 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 docs/src/api/BaseAPI.md rename docs/src/{api.md => api/GeneralAPI.md} (89%) create mode 100644 docs/src/api/NVMagnetometerAPI.md create mode 100644 docs/src/index.md create mode 100644 docs/src/refs.bib diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..a1bba9c --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "julia.environmentPath": "/home/azw/QuanEstimation.jl/docs" +} \ No newline at end of file diff --git a/Project.toml b/Project.toml index 5295e2b..de3f116 100644 --- a/Project.toml +++ b/Project.toml @@ -10,9 +10,6 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69" [extensions] 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"700de1a5-db45-46bc-99cf-38207098b444" +version = "0.2.5" + +[[libblastrampoline_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" +version = "5.8.0+1" + +[[nghttp2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" +version = "1.52.0+1" + +[[p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+2" diff --git a/docs/Project.toml b/docs/Project.toml index 5f54614..62439cb 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -1,3 +1,5 @@ [deps] Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" +DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244" +DocumenterMarkdown = "997ab1e6-3595-5248-9280-8efb232c3433" QuanEstimation = "088c8dff-a786-4a66-974c-03d3f6773f87" diff --git a/docs/make.jl b/docs/make.jl index 56dd280..b0398c5 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -1,51 +1,27 @@ using QuanEstimation using Documenter using DocumenterMarkdown -import Documenter.Writers.MarkdownWriter: render, renderdoc +using DocumenterCitations -DocMeta.setdocmeta!( - QuanEstimation, - :DocTestSetup, - :(using QuanEstimation); - recursive = true, -) - -# function render(io::IO, mime::MIME"text/plain", anchor::Documenter.Anchors.Anchor, page, doc) -# println(io, "\n", lstrip(Documenter.Anchors.fragment(anchor), '#', " ")) -# if anchor.nth == 1 # add legacy id -# legacy = lstrip(Documenter.Anchors.fragment(anchor), '#', " ") * "-1" -# println(io, "\n", legacy) -# end -# render(io, mime, anchor.object, page, doc) -# end +bib = CitationBibliography("src/refs.bib") -function render(io::IO, mime::MIME"text/plain", node::Documenter.Documents.DocsNode, page, doc) - # Docstring header based on the name of the binding and it's category. - anchor = "## " - header = "**`$(node.object.binding)`** — *$(Documenter.Utilities.doccat(node.object))*." - println(io, anchor, " ", header, "\n\n") - # Body. May contain several concatenated docstrings. - renderdoc(io, mime, node.docstr, page, doc) -end - -makedocs(; - format = Markdown(), +makedocs(bib; root = ".", - source = "docs/src", - build = "docs/build", + source = "src", + build = "build", clean = true, doctest = true, modules = [QuanEstimation], authors = "Hauiming Yu and contributors", repo = "https://github.com/QuanEstimation/QuanEstimation.jl/blob/{commit}{path}#{line}", sitename = "QuanEstimation.jl", - # format = Documenter.HTML(; - # prettyurls = get(ENV, "CI", "false") == "true", - # canonical = "https://HuaimingYuuu.github.io/QuanEstimation.jl", - # assets = String[], - # ), - # pages = [api.md"], - + pages = [ + "API" => [ + "api/GeneralAPI.md", + "api/BaseAPI.md", + "api/NVMagnetometerAPI.md" + ] + ] ) -deploydocs(; repo = "github.com/QuanEstimation/QuanEstimation.jl") +#deploydocs(; repo = "github.com/QuanEstimation/QuanEstimation.jl") diff --git a/docs/src/api/BaseAPI.md b/docs/src/api/BaseAPI.md new file mode 100644 index 0000000..af47110 --- /dev/null +++ b/docs/src/api/BaseAPI.md @@ -0,0 +1,9 @@ +# Base + +```@meta +CurrentModule = QuanEstimationBase +``` + +```@autodocs +Modules = [QuanEstimationBase] +``` diff --git a/docs/src/api.md b/docs/src/api/GeneralAPI.md similarity index 89% rename from docs/src/api.md rename to docs/src/api/GeneralAPI.md index 750b435..4346fab 100644 --- a/docs/src/api.md +++ b/docs/src/api/GeneralAPI.md @@ -1,3 +1,5 @@ +# General + ```@meta CurrentModule = QuanEstimation ``` diff --git a/docs/src/api/NVMagnetometerAPI.md b/docs/src/api/NVMagnetometerAPI.md new file mode 100644 index 0000000..3605d86 --- /dev/null +++ b/docs/src/api/NVMagnetometerAPI.md @@ -0,0 +1,9 @@ +# NVMagnetometer + +```@meta +CurrentModule = NVMagnetometer +``` + +```@autodocs +Modules = [NVMagnetometer] +``` diff --git a/docs/src/index.md b/docs/src/index.md new file mode 100644 index 0000000..d2c7084 --- /dev/null +++ b/docs/src/index.md @@ -0,0 +1 @@ +# QuanEstimation.jl diff --git a/docs/src/refs.bib b/docs/src/refs.bib new file mode 100644 index 0000000..e69de29 From 57c6996db16efeb272c419061a56937276343eb7 Mon Sep 17 00:00:00 2001 From: azwphy Date: Tue, 21 May 2024 16:56:34 +0800 Subject: [PATCH 3/5] documents reconstruction --- .gitignore | 3 ++- .vscode/settings.json | 3 --- 2 files changed, 2 insertions(+), 4 deletions(-) delete mode 100644 .vscode/settings.json diff --git a/.gitignore b/.gitignore index 20d4d64..72437d6 100644 --- a/.gitignore +++ b/.gitignore @@ -6,4 +6,5 @@ /docs/build/ -*.log \ No newline at end of file +*.log +.vscode/ diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index a1bba9c..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "julia.environmentPath": "/home/azw/QuanEstimation.jl/docs" -} \ No newline at end of file From 6a0da6d1e920c7f26d740956a0d19d2e91866c9f Mon Sep 17 00:00:00 2001 From: azwphy Date: Tue, 21 May 2024 17:10:03 +0800 Subject: [PATCH 4/5] gitignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 72437d6..a7f55e7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,7 @@ *.jl.*.cov *.jl.cov *.jl.mem -/Manifest.toml +/**/Manifest.* /deps/build.log /docs/build/ From 4c77d61f1b8b292387f582b363ce62c7110173a0 Mon Sep 17 00:00:00 2001 From: Huai-Ming Yu Date: Tue, 11 Jun 2024 10:15:30 +0800 Subject: [PATCH 5/5] Delete docs/Manifest.toml --- docs/Manifest.toml | 1618 -------------------------------------------- 1 file changed, 1618 deletions(-) delete mode 100644 docs/Manifest.toml diff --git a/docs/Manifest.toml b/docs/Manifest.toml deleted file mode 100644 index 666be9a..0000000 --- a/docs/Manifest.toml +++ /dev/null @@ -1,1618 +0,0 @@ -# This file is machine-generated - 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