fix for ... eachindex(...)

.gitignore

@@ -1,9 +1,10 @@
 *.jl.*.cov
 *.jl.cov
 *.jl.mem
-/Manifest.toml
+/**/Manifest.*
 /deps/build.log
 /docs/build/
 
 
-*.log
+*.log
+.vscode/

Project.toml

@@ -10,9 +10,6 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
 [extensions]
 
-[compat]
-QuanEstimationBase = "0.1.0"
-
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 

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"

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 <Huaimingyuuu@gmail.com> 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")

docs/src/api/BaseAPI.md

@@ -0,0 +1,9 @@
+# Base
+
+```@meta
+CurrentModule = QuanEstimationBase
+```
+
+```@autodocs
+Modules = [QuanEstimationBase]
+```

docs/src/api.md → docs/src/api/GeneralAPI.md

@@ -1,3 +1,5 @@
+# General
+
 ```@meta
 CurrentModule = QuanEstimation
 ```

docs/src/api/NVMagnetometerAPI.md

@@ -0,0 +1,9 @@
+# NVMagnetometer
+
+```@meta
+CurrentModule = NVMagnetometer
+```
+
+```@autodocs
+Modules = [NVMagnetometer]
+```

docs/src/index.md

@@ -0,0 +1 @@
+# QuanEstimation.jl

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)

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
 

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)

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)

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

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)

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)

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