Nv scheme

.gitignore

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

Project.toml

@@ -1,54 +1,14 @@
 name = "QuanEstimation"
 uuid = "088c8dff-a786-4a66-974c-03d3f6773f87"
 authors = ["Jing Liu <liujingphys@hust.edu.cn>", "Huaiming Yu <Huaimingyuuu@gmail.com>", "Mao Zhang <zhangmao2018@hust.edu.cn>"]
-version = "0.1.6"
+version = "0.2.0"
 
 [deps]
-BoundaryValueDiffEq = "764a87c0-6b3e-53db-9096-fe964310641d"
-Convex = "f65535da-76fb-5f13-bab9-19810c17039a"
-DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
-IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
-JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
-SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
-SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
-StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-Trapz = "592b5752-818d-11e9-1e9a-2b8ca4a44cd1"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
-
-[weakdeps]
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
+NVMagnetometer = "28fc4bcf-da03-4841-bbdb-88baaf311b30"
+QuanEstimationBase = "9769ca81-ed10-4016-bab9-e66dc61d4d60"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
 [extensions]
-QuanEstimationPyExt = "PythonCall"
-
-[compat]
-BoundaryValueDiffEq = "2,5"
-Convex = "0.15"
-DelimitedFiles = "1"
-Distributions = "0.25"
-Flux = "0.12, 0.13, 0.14"
-Interpolations = "0.13, 0.14, 0.15"
-IntervalSets = "0.5, 0.7"
-JLD2 = "0.4"
-OrdinaryDiffEq = "6"
-QuadGK = "2"
-Revise = "3"
-SCS = "1, 2"
-StableRNGs = "1"
-StatsBase = "0.33, 0.34"
-Trapz = "2"
-Zygote = "0.6"
-julia = "1.10"
 
 [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/adaptive_estimation_MZI.jl

@@ -4,26 +4,27 @@ using SparseArrays
 # the number of photons
 N = 8
 # probe state
-psi = sum([sin(k*pi/(N+2))*kron(QuanEstimation.basis(N+1,k), 
-      QuanEstimation.basis(N+1, N-k+2)) for k in 1:(N+1)]) |> sparse
+psi = sum([sin(k*pi/(N+2))*kron(basis(N+1,k), 
+      basis(N+1, N-k+2)) for k in 1:(N+1)]) |> sparse
 psi = psi*sqrt(2/(2+N))
 rho0 = psi*psi'
 # prior distribution
 x = range(-pi, pi, length=100)
 p = (1.0/(x[end]-x[1]))*ones(length(x))
-apt = QuanEstimation.Adapt_MZI(x, p, rho0)
+apt =Adapt_MZI(x, p, rho0)
 
 #================online strategy=========================#
-QuanEstimation.online(apt, target=:sharpness, output="phi")
+# online(apt; target=:sharpness, output="phi")
+adapt!(apt; target=:sharpness, output="phi")
 
 #================offline strategy=========================#
-# # algorithm: DE
-# alg = QuanEstimation.DE(p_num=10, ini_population=missing, 
-#                         max_episode=1000, c=1.0, cr=0.5)
-# QuanEstimation.offline(apt, alg, target=:sharpness, seed=1234)
+# algorithm: DE
+alg = DE(p_num=10, ini_population=missing, 
+                        max_episode=1000, c=1.0, cr=0.5)
+offline(apt, alg, target=:sharpness, seed=1234)
 
 # # algorithm: PSO
-# alg = QuanEstimation.PSO(p_num=10, ini_particle=missing,  
+# PSO(p_num=10, ini_particle=missing,  
 #                          max_episode=[1000,100], c0=1.0, 
 #                          c1=2.0, c2=2.0)
-# QuanEstimation.offline(apt, alg, target=:sharpness, seed=1234)
+# offline(apt, alg, target=:sharpness, seed=1234)